Despite the remarkable success of foundation models, their task-specific
fine-tuning paradigm makes them inconsistent with the goal of general
perception modeling. The key to eliminating this inconsistency is to use
generalist models for general task modeling. However, existing attempts at
generalist models are inadequate in both versatility and performance. In this
paper, we propose Uni-Perceiver v2, which is the first generalist model capable
of handling major large-scale vision and vision-language tasks with competitive
performance. Specifically, images are encoded as general region proposals,
while texts are encoded via a Transformer-based language model. The encoded
representations are transformed by a task-agnostic decoder. Different tasks are
formulated as a unified maximum likelihood estimation problem. We further
propose an improved optimizer to ensure stable multi-task learning with an
unmixed sampling strategy, which is helpful for tasks requiring large
batch-size training. After being jointly trained on various tasks,
Uni-Perceiver v2 is capable of directly handling downstream tasks without any
task-specific adaptation. Results show that Uni-Perceiver v2 outperforms all
existing generalist models in both versatility and performance. Meanwhile,
compared with the commonly-recognized strong baselines that require
tasks-specific fine-tuning, Uni-Perceiver v2 achieves competitive performance
on a broad range of vision and vision-language tasks.

Monitoring changes in tree cover for rapid assessment of deforestation is
considered the critical component of any climate mitigation policy for reducing
carbon. Here, we map tropical tree cover and deforestation between 2015 and
2022 using 5 m spatial resolution Planet NICFI satellite images over the state
of Mato Grosso (MT) in Brazil and a U-net deep learning model. The tree cover
for the state was 556510.8 km$^2$ in 2015 (58.1 % of the MT State) and was
reduced to 141598.5 km$^2$ (14.8 % of total area) at the end of 2021. After
reaching a minimum deforested area in December 2016 with 6632.05 km$^2$, the
bi-annual deforestation area only showed a slight increase between December
2016 and December 2019. A year after, the areas of deforestation almost doubled
from 9944.5 km$^2$ in December 2019 to 19817.8 km$^2$ in December 2021. The
high-resolution data product showed relatively consistent agreement with the
official deforestation map from Brazil (67.2%) but deviated significantly from
year of forest cover loss estimates from the Global Forest change (GFC)
product, mainly due to large area of fire degradation observed in the GFC data.
High-resolution imagery from Planet NICFI associated with deep learning
technics can significantly improve mapping deforestation extent in tropics.

We explore the relationship between renormalization group (RG) flow and error
correction by constructing quantum algorithms that exactly recognize 1D
symmetry-protected topological (SPT) phases protected by finite internal
Abelian symmetries. For each SPT phase, our algorithm runs a quantum circuit
which emulates RG flow: an arbitrary input ground state wavefunction in the
phase is mapped to a unique minimally-entangled reference state, thereby
allowing for efficient phase identification. This construction is enabled by
viewing a generic input state in the phase as a collection of coherent `errors'
applied to the reference state, and engineering a quantum circuit to
efficiently detect and correct such errors. Importantly, the error correction
threshold is proven to coincide exactly with the phase boundary. We discuss the
implications of our results in the context of condensed matter physics, machine
learning, and near-term quantum algorithms.

Finding Ricci-flat (Calabi--Yau) metrics is a long standing problem in
geometry with deep implications for string theory and phenomenology. A new
attack on this problem uses neural networks to engineer approximations to the
Calabi--Yau metric within a given K\"ahler class. In this paper we investigate
numerical Ricci-flat metrics over smooth and singular K3 surfaces and
Calabi--Yau threefolds. Using these Ricci-flat metric approximations for the
Cefal\'u and Dwork family of quartic twofolds and the Dwork family of quintic
threefolds, we study characteristic forms on these geometries. Using persistent
homology, we show that high curvature regions of the manifolds form clusters
near the singular points, but also elsewhere. For our neural network
approximations, we observe a Bogomolov--Yau type inequality $3c_2 \geq c_1^2$
and observe an identity when our geometries have isolated $A_1$ type
singularities. We sketch a proof that
$\chi(X~\smallsetminus~\mathrm{Sing}\,{X}) + 2~|\mathrm{Sing}\,{X}| = 24$ also
holds for our numerical approximations.

We propose a method for editing images from human instructions: given an
input image and a written instruction that tells the model what to do, our
model follows these instructions to edit the image. To obtain training data for
this problem, we combine the knowledge of two large pretrained models -- a
language model (GPT-3) and a text-to-image model (Stable Diffusion) -- to
generate a large dataset of image editing examples. Our conditional diffusion
model, InstructPix2Pix, is trained on our generated data, and generalizes to
real images and user-written instructions at inference time. Since it performs
edits in the forward pass and does not require per example fine-tuning or
inversion, our model edits images quickly, in a matter of seconds. We show
compelling editing results for a diverse collection of input images and written
instructions.

Masked image modeling (MIM) learns visual representation by masking and
reconstructing image patches. Applying the reconstruction supervision on the
CLIP representation has been proven effective for MIM. However, it is still
under-explored how CLIP supervision in MIM influences performance. To
investigate strategies for refining the CLIP-targeted MIM, we study two
critical elements in MIM, i.e., the supervision position and the mask ratio,
and reveal two interesting perspectives, relying on our developed simple
pipeline, context autodecoder with CLIP target (CAE v2). Firstly, we observe
that the supervision on visible patches achieves remarkable performance, even
better than that on masked patches, where the latter is the standard format in
the existing MIM methods. Secondly, the optimal mask ratio positively
correlates to the model size. That is to say, the smaller the model, the lower
the mask ratio needs to be. Driven by these two discoveries, our simple and
concise approach CAE v2 achieves superior performance on a series of downstream
tasks. For example, a vanilla ViT-Large model achieves 81.7% and 86.7% top-1
accuracy on linear probing and fine-tuning on ImageNet-1K, and 55.9% mIoU on
semantic segmentation on ADE20K with the pre-training for 300 epochs. We hope
our findings can be helpful guidelines for the pre-training in the MIM area,
especially for the small-scale models.

In this paper, we propose MOTRv2, a simple yet effective pipeline to
bootstrap end-to-end multi-object tracking with a pretrained object detector.
Existing end-to-end methods, e.g. MOTR and TrackFormer, are inferior to their
tracking-by-detection counterparts mainly due to their poor detection
performance. We aim to improve MOTR by elegantly incorporating an extra object
detector. We first adopt the anchor formulation of queries and then use an
extra object detector to generate proposals as anchors, providing detection
prior to MOTR. The simple modification greatly eases the conflict between joint
learning detection and association tasks in MOTR. MOTRv2 keeps the end-to-end
feature and scales well on large-scale benchmarks. MOTRv2 ranks the 1st place
(73.4% HOTA on DanceTrack) in the 1st Multiple People Tracking in Group Dance
Challenge. Moreover, MOTRv2 achieves state-of-the-art performance on BDD100K
dataset. We hope this simple and effective pipeline can provide some new
insights to the end-to-end MOT community. Code is available at
\url{https://github.com/megvii-research/MOTRv2}.

Recently, large-scale pre-trained Vision-and-Language (VL) foundation models
have demonstrated remarkable capabilities in many zero-shot downstream tasks,
achieving competitive results for recognizing objects defined by as little as
short text prompts. However, it has also been shown that VL models are still
brittle in Structured VL Concept (SVLC) reasoning, such as the ability to
recognize object attributes, states, and inter-object relations. This leads to
reasoning mistakes, which need to be corrected as they occur by teaching VL
models the missing SVLC skills; often this must be done using private data
where the issue was found, which naturally leads to a data-free continual (no
task-id) VL learning setting. In this work, we introduce the first Continual
Data-Free Structured VL Concepts Learning (ConStruct-VL) benchmark and show it
is challenging for many existing data-free CL strategies. We, therefore,
propose a data-free method comprised of a new approach of Adversarial
Pseudo-Replay (APR) which generates adversarial reminders of past tasks from
past task models. To use this method efficiently, we also propose a continual
parameter-efficient Layered-LoRA (LaLo) neural architecture allowing
no-memory-cost access to all past models at train time. We show this approach
outperforms all data-free methods by as much as ~7% while even matching some
levels of experience-replay (prohibitive for applications where data-privacy
must be preserved).

We propose DiffusionDet, a new framework that formulates object detection as
a denoising diffusion process from noisy boxes to object boxes. During training
stage, object boxes diffuse from ground-truth boxes to random distribution, and
the model learns to reverse this noising process. In inference, the model
refines a set of randomly generated boxes to the output results in a
progressive way. The extensive evaluations on the standard benchmarks,
including MS-COCO and LVIS, show that DiffusionDet achieves favorable
performance compared to previous well-established detectors. Our work brings
two important findings in object detection. First, random boxes, although
drastically different from pre-defined anchors or learned queries, are also
effective object candidates. Second, object detection, one of the
representative perception tasks, can be solved by a generative way. Our code is
available at https://github.com/ShoufaChen/DiffusionDet.

We present a method for performing tasks involving spatial relations between
novel object instances initialized in arbitrary poses directly from point cloud
observations. Our framework provides a scalable way for specifying new tasks
using only 5-10 demonstrations. Object rearrangement is formalized as the
question of finding actions that configure task-relevant parts of the object in
a desired alignment. This formalism is implemented in three steps: assigning a
consistent local coordinate frame to the task-relevant object parts,
determining the location and orientation of this coordinate frame on unseen
object instances, and executing an action that brings these frames into the
desired alignment. We overcome the key technical challenge of determining
task-relevant local coordinate frames from a few demonstrations by developing
an optimization method based on Neural Descriptor Fields (NDFs) and a single
annotated 3D keypoint. An energy-based learning scheme to model the joint
configuration of the objects that satisfies a desired relational task further
improves performance. The method is tested on three multi-object rearrangement
tasks in simulation and on a real robot. Project website, videos, and code:
https://anthonysimeonov.github.io/r-ndf/

The ability to assess the robustness of image classifiers to a diverse set of
manipulations is essential to their deployment in the real world. Recently,
semantic manipulations of real images have been considered for this purpose, as
they may not arise using standard adversarial settings. However, such semantic
manipulations are often limited to style, color or attribute changes. While
expressive, these manipulations do not consider the full capacity of a
pretrained generator to affect adversarial image manipulations. In this work,
we aim at leveraging the full capacity of a pretrained image generator to
generate highly detailed, diverse and photorealistic image manipulations.
Inspired by recent GAN-based image inversion methods, we propose a method
called Adversarial Pivotal Tuning (APT). APT first finds a pivot latent space
input to a pretrained generator that best reconstructs an input image. It then
adjusts the weights of the generator to create small, but semantic,
manipulations which fool a pretrained classifier. Crucially, APT changes both
the input and the weights of the pretrained generator, while preserving its
expressive latent editing capability, thus allowing the use of its full
capacity in creating semantic adversarial manipulations. We demonstrate that
APT generates a variety of semantic image manipulations, which preserve the
input image class, but which fool a variety of pretrained classifiers. We
further demonstrate that classifiers trained to be robust to other robustness
benchmarks, are not robust to our generated manipulations and propose an
approach to improve the robustness towards our generated manipulations. Code
available at: https://captaine.github.io/apt/

Monitoring the performance of machine learning (ML)-based risk prediction
models in healthcare is complicated by the issue of confounding medical
interventions (CMI): when an algorithm predicts a patient to be at high risk
for an adverse event, clinicians are more likely to administer prophylactic
treatment and alter the very target that the algorithm aims to predict.
Ignoring CMI by monitoring only the untreated patients--whose outcomes remain
unaltered--can inflate false alarm rates, because the evolution of both the
model and clinician-ML interactions can induce complex dependencies in the data
that violate standard assumptions. A more sophisticated approach is to
explicitly account for CMI by modeling treatment propensities, but its
time-varying nature makes accurate estimation difficult. Given the many sources
of complexity in the data, it is important to determine situations in which a
simple procedure that ignores CMI provides valid inference. Here we describe
the special case of monitoring model calibration, under either the assumption
of conditional exchangeability or time-constant selection bias. We introduce a
new score-based cumulative sum (CUSUM) chart for monitoring in a frequentist
framework and review an alternative approach using Bayesian inference. Through
simulations, we investigate the benefits of combining model updating with
monitoring and study when over-trust in a prediction model does (or does not)
delay detection. Finally, we simulate monitoring an ML-based postoperative
nausea and vomiting risk calculator during the COVID-19 pandemic.

Many high-level skills that are required for computer vision tasks, such as
parsing questions, comparing and contrasting semantics, and writing
descriptions, are also required in other domains such as natural language
processing. In this paper, we ask whether this makes it possible to learn those
skills from text data and then use them to complete vision tasks without ever
training on visual training data. Key to our approach is exploiting the joint
embedding space of contrastively trained vision and language encoders. In
practice, there can be systematic differences between embedding spaces for
different modalities in contrastive models, and we analyze how these
differences affect our approach and study a variety of strategies to mitigate
this concern. We produce models using only text training data on three tasks:
image captioning, visual entailment and visual question answering, and evaluate
them on standard benchmarks using images. We find that this kind of transfer is
possible and results in only a small drop in performance relative to models
trained on images. We also showcase a variety of stylistic image captioning
models that were trained using no image data and no human-curated language
data, but instead text data from books, the web, or language models.

We derive Cheeger inequalities for directed graphs and hypergraphs using the
reweighted eigenvalue approach that was recently developed for vertex expansion
in undirected graphs [OZ22,KLT22,JPV22]. The goal is to develop a new spectral
theory for directed graphs and an alternative spectral theory for hypergraphs.
  The first main result is a Cheeger inequality relating the vertex expansion
$\vec{\psi}(G)$ of a directed graph $G$ to the vertex-capacitated maximum
reweighted second eigenvalue $\vec{\lambda}_2^{v*}$: \[ \vec{\lambda}_2^{v*}
\lesssim \vec{\psi}(G) \lesssim \sqrt{\vec{\lambda}_2^{v*} \cdot \log
(\Delta/\vec{\lambda}_2^{v*})}. \] This provides a combinatorial
characterization of the fastest mixing time of a directed graph by vertex
expansion, and builds a new connection between reweighted eigenvalued, vertex
expansion, and fastest mixing time for directed graphs.
  The second main result is a stronger Cheeger inequality relating the edge
conductance $\vec{\phi}(G)$ of a directed graph $G$ to the edge-capacitated
maximum reweighted second eigenvalue $\vec{\lambda}_2^{e*}$: \[
\vec{\lambda}_2^{e*} \lesssim \vec{\phi}(G) \lesssim \sqrt{\vec{\lambda}_2^{e*}
\cdot \log (1/\vec{\lambda}_2^{e*})}. \] This provides a certificate for a
directed graph to be an expander and a spectral algorithm to find a sparse cut
in a directed graph, playing a similar role as Cheeger's inequality in
certifying graph expansion and in the spectral partitioning algorithm for
undirected graphs.
  We also use this reweighted eigenvalue approach to derive the improved
Cheeger inequality for directed graphs, and furthermore to derive several
Cheeger inequalities for hypergraphs that match and improve the existing
results in [Lou15,CLTZ18]. These are supporting results that this provides a
unifying approach to lift the spectral theory for undirected graphs to more
general settings.

This article presents the DeepSense 6G dataset, which is a large-scale
dataset based on real-world measurements of co-existing multi-modal sensing and
communication data. The DeepSense 6G dataset is built to advance deep learning
research in a wide range of applications in the intersection of multi-modal
sensing, communication, and positioning. This article provides a detailed
overview of the DeepSense dataset structure, adopted testbeds, data collection
and processing methodology, deployment scenarios, and example applications,
with the objective of facilitating the adoption and reproducibility of
multi-modal sensing and communication datasets.

Transformers achieve unrivalled performance in modelling language, but remain
inefficient in terms of memory and time complexity. A possible remedy is to
reduce the sequence length in the intermediate layers by pooling fixed-length
segments of tokens. Nevertheless, natural units of meaning, such as words or
phrases, display varying sizes. To address this mismatch, we equip language
models with a dynamic-pooling mechanism, which predicts segment boundaries in
an autoregressive fashion. We compare several methods to infer boundaries,
including end-to-end learning through stochastic re-parameterisation,
supervised learning (based on segmentations from subword tokenizers or spikes
in conditional entropy), as well as linguistically motivated boundaries. We
perform character-level evaluation on texts from multiple datasets and
morphologically diverse languages. The results demonstrate that dynamic
pooling, which jointly segments and models language, is often both faster and
more accurate than vanilla Transformers and fixed-length pooling within the
same computational budget.

While deep learning models have replaced hand-designed features across many
domains, these models are still trained with hand-designed optimizers. In this
work, we leverage the same scaling approach behind the success of deep learning
to learn versatile optimizers. We train an optimizer for deep learning which is
itself a small neural network that ingests gradients and outputs parameter
updates. Meta-trained with approximately four thousand TPU-months of compute on
a wide variety of optimization tasks, our optimizer not only exhibits
compelling performance, but optimizes in interesting and unexpected ways. It
requires no hyperparameter tuning, instead automatically adapting to the
specifics of the problem being optimized. We open source our learned optimizer,
meta-training code, the associated train and test data, and an extensive
optimizer benchmark suite with baselines at velo-code.github.io.

Feature selection is a technique in statistical prediction modeling that
identifies features in a record with a strong statistical connection to the
target variable. Excluding features with a weak statistical connection to the
target variable in training not only drops the dimension of the data, which
decreases the time complexity of the algorithm, it also decreases noise within
the data which assists in avoiding overfitting. In all, feature selection
assists in training a robust statistical model that performs well and is
stable. Given the lack of scalability in classical computation, current
techniques only consider the predictive power of the feature and not redundancy
between the features themselves. Recent advancements in feature selection that
leverages quantum annealing (QA) gives a scalable technique that aims to
maximize the predictive power of the features while minimizing redundancy. As a
consequence, it is expected that this algorithm would assist in the
bias/variance trade-off yielding better features for training a statistical
model. This paper tests this intuition against classical methods by utilizing
open-source data sets and evaluate the efficacy of each trained statistical
model well-known prediction algorithms. The numerical results display an
advantage utilizing the features selected from the algorithm that leveraged QA.

Recommender system practitioners are facing increasing pressure to explain
recommendations. We explore how to explain recommendations using counterfactual
logic, i.e. "Had you not interacted with the following items before, it is
likely we would not recommend this item." Compared to traditional explanation
logic, counterfactual explanations are easier to understand and more
technically verifiable. The major challenge of generating such explanations is
the computational cost because it requires repeatedly retraining the models to
obtain the effect on a recommendation caused by removing user (interaction)
history. We propose a learning-based framework to generate counterfactual
explanations. The key idea is to train a surrogate model to learn the effect of
removing a subset of user history on the recommendation. To this end, we first
artificially simulate the counterfactual outcomes on the recommendation after
deleting subsets of history. Then we train surrogate models to learn the
mapping between a history deletion and the change in the recommendation caused
by the deletion. Finally, to generate an explanation, we find the history
subset predicted by the surrogate model that is most likely to remove the
recommendation. Through offline experiments and online user studies, we show
our method, compared to baselines, can generate explanations that are more
counterfactually valid and more satisfactory considered by users.

Cardiovascular diseases are one of the leading cause of death in today's
world and early screening of heart condition plays a crucial role in preventing
them. The heart sound signal is one of the primary indicator of heart condition
and can be used to detect abnormality in the heart. The acquisition of heart
sound signal is non-invasive, cost effective and requires minimum equipment.
But currently the detection of heart abnormality from heart sound signal
depends largely on the expertise and experience of the physician. As such an
automatic detection system for heart abnormality detection from heart sound
signal can be a great asset for the people living in underdeveloped areas. In
this paper we propose a novel deep learning based dual stream network with
attention mechanism that uses both the raw heart sound signal and the MFCC
features to detect abnormality in heart condition of a patient. The deep neural
network has a convolutional stream that uses the raw heart sound signal and a
recurrent stream that uses the MFCC features of the signal. The features from
these two streams are merged together using a novel attention network and
passed through the classification network. The model is trained on the largest
publicly available dataset of PCG signal and achieves an accuracy of 87.11,
sensitivity of 82.41, specificty of 91.8 and a MACC of 87.12.

Next-word predictions from autoregressive neural language models show
remarkable sensitivity to syntax. This work evaluates the extent to which this
behavior arises as a result of a learned ability to maintain implicit
representations of incremental syntactic structures. We extend work in
syntactic probing to the incremental setting and present several probes for
extracting incomplete syntactic structure (operationalized through parse states
from a stack-based parser) from autoregressive language models. We find that
our probes can be used to predict model preferences on ambiguous sentence
prefixes and causally intervene on model representations and steer model
behavior. This suggests implicit incremental syntactic inferences underlie
next-word predictions in autoregressive neural language models.

Variational data assimilation and deep learning share many algorithmic
aspects in common. While the former focuses on system state estimation, the
latter provides great inductive biases to learn complex relationships. We here
design a hybrid architecture learning the assimilation task directly from
partial and noisy observations, using the mechanistic constraint of the 4DVAR
algorithm. Finally, we show in an experiment that the proposed method was able
to learn the desired inversion with interesting regularizing properties and
that it also has computational interests.

One of the challenges in studying the interactions in large graphs is to
learn their diverse pattern and various interaction types. Hence, considering
only one distribution and model to study all nodes and ignoring their diversity
and local features in their neighborhoods, might severely affect the overall
performance. Based on the structural information of the nodes in the graph and
the interactions between them, the main graph can be divided into multiple
sub-graphs. This graph partitioning can tremendously affect the learning
process, however the overall performance is highly dependent on the clustering
method to avoid misleading the model. In this work, we present a new framework
called KD-SGL to effectively learn the sub-graphs, where we define one global
model to learn the overall structure of the graph and multiple local models for
each sub-graph. We assess the performance of the proposed framework and
evaluate it on public datasets. Based on the achieved results, it can improve
the performance of the state-of-the-arts spatiotemporal models with comparable
results compared to ensemble of models with less complexity.

We provide a first finite-particle convergence rate for Stein variational
gradient descent (SVGD). Specifically, whenever the target distribution
satisfies Talagrand's T1 inequality, SVGD with n particles and an appropriate
step size sequence drives the kernel Stein discrepancy to zero at an order
1/sqrt(log log n) rate. We suspect that the dependence on n can be improved,
and we hope that our explicit, non-asymptotic proof strategy will serve as a
template for future refinements.

Midrash collections are complex rabbinic works that consist of text in
multiple languages, which evolved through long processes of unstable oral and
written transmission. Determining the origin of a given passage in such a
compilation is not always straightforward and is often a matter of dispute
among scholars, yet it is essential for scholars' understanding of the passage
and its relationship to other texts in the rabbinic corpus.
  To help solve this problem, we propose a system for classification of
rabbinic literature based on its style, leveraging recently released pretrained
Transformer models for Hebrew. Additionally, we demonstrate how our method can
be applied to uncover lost material from Midrash Tanhuma.

Validation metrics are a key prerequisite for the reliable tracking of
scientific progress and for deciding on the potential clinical translation of
methods. While recent initiatives aim to develop comprehensive theoretical
frameworks for understanding metric-related pitfalls in image analysis
problems, there is a lack of experimental evidence on the concrete effects of
common and rare pitfalls on specific applications. We address this gap in the
literature in the context of colon cancer screening. Our contribution is
twofold. Firstly, we present the winning solution of the Endoscopy computer
vision challenge (EndoCV) on colon cancer detection, conducted in conjunction
with the IEEE International Symposium on Biomedical Imaging (ISBI) 2022.
Secondly, we demonstrate the sensitivity of commonly used metrics to a range of
hyperparameters as well as the consequences of poor metric choices. Based on
comprehensive validation studies performed with patient data from six clinical
centers, we found all commonly applied object detection metrics to be subject
to high inter-center variability. Furthermore, our results clearly demonstrate
that the adaptation of standard hyperparameters used in the computer vision
community does not generally lead to the clinically most plausible results.
Finally, we present localization criteria that correspond well to clinical
relevance. Our work could be a first step towards reconsidering common
validation strategies in automatic colon cancer screening applications.

Diffusion models have experienced a surge of interest as highly expressive
yet efficiently trainable probabilistic models. We show that these models are
an excellent fit for synthesising human motion that co-occurs with audio, for
example co-speech gesticulation, since motion is complex and highly ambiguous
given audio, calling for a probabilistic description. Specifically, we adapt
the DiffWave architecture to model 3D pose sequences, putting Conformers in
place of dilated convolutions for improved accuracy. We also demonstrate
control over motion style, using classifier-free guidance to adjust the
strength of the stylistic expression. Gesture-generation experiments on the
Trinity Speech-Gesture and ZeroEGGS datasets confirm that the proposed method
achieves top-of-the-line motion quality, with distinctive styles whose
expression can be made more or less pronounced. We also synthesise dance motion
and path-driven locomotion using the same model architecture. Finally, we
extend the guidance procedure to perform style interpolation in a manner that
is appealing for synthesis tasks and has connections to product-of-experts
models, a contribution we believe is of independent interest. Video examples
are available at https://www.speech.kth.se/research/listen-denoise-action/

The superior performance of modern deep networks usually comes at the price
of a costly training procedure. In this paper, we present a novel curriculum
learning approach for the efficient training of visual backbones (e.g., vision
Transformers). The proposed method is inspired by the phenomenon that deep
networks mainly learn to recognize some 'easier-to-learn' discriminative
patterns within each example at earlier stages of training, e.g., the
lower-frequency components of images and the original information before data
augmentation. Driven by this observation, we propose a curriculum where the
model always leverages all the training data at each epoch, while the
curriculum starts with only exposing the 'easier-to-learn' patterns of each
example, and introduces gradually more difficult patterns. To implement this
idea, we 1) introduce a cropping operation in the Fourier spectrum of the
inputs, which enables the model to learn from only the lower-frequency
components efficiently, and 2) demonstrate that exposing the features of
original images amounts to adopting weaker data augmentation. Our resulting
algorithm, EfficientTrain, is simple, general, yet surprisingly effective. For
example, it reduces the training time of a wide variety of popular models
(e.g., ConvNeXts, DeiT, PVT, and Swin/CSWin Transformers) by more than
${1.5\times}$ on ImageNet-1K/22K without sacrificing the accuracy. It is
effective for self-supervised learning (i.e., MAE) as well. Code is available
at https://github.com/LeapLabTHU/EfficientTrain.

In this paper we investigate the equilibrium properties of bidirectional
associative memories (BAMs). Introduced by Kosko in 1988 as a generalization of
the Hopfield model to a bipartite structure, the simplest architecture is
defined by two layers of neurons, with synaptic connections only between units
of different layers: even without internal connections within each layer,
information storage and retrieval are still possible through the reverberation
of neural activities passing from one layer to another. We characterize the
computational capabilities of a stochastic extension of this model in the
thermodynamic limit, by applying rigorous techniques from statistical physics.
A detailed picture of the phase diagram at the replica symmetric level is
provided, both at finite temperature and in the noiseless regime. An analytical
and numerical inspection of the transition curves (namely critical lines
splitting the various modes of operation of the machine) is carried out as the
control parameters - noise, load and asymmetry between the two layer sizes -
are tuned. In particular, with a finite asymmetry between the two layers, it is
shown how the BAM can store information more efficiently than the Hopfield
model by requiring less parameters to encode a fixed number of patterns.
Comparisons are made with numerical simulations of neural dynamics. Finally, a
low-load analysis is carried out to explain the retrieval mechanism in the BAM
by analogy with two interacting Hopfield models. A potential equivalence with
two coupled Restricted Boltmzann Machines is also discussed.

It has been argued [Tassieri, \textit{Soft Matter}, 2015, \textbf{11}, 5792]
that linear microrheology with optical tweezers (MOT) of living systems
``\textit{is not an option}'', because of the wide gap between the observation
time required to collect statistically valid data and the mutational times of
the organisms under study. Here, we have taken a first step towards a possible
solution of this problem by exploiting modern machine learning (ML) methods to
reduce the duration of MOT measurements from several tens of minutes down to
one second. This has been achieved by focusing on the analysis of computer
simulated trajectories of an optically trapped particle suspended in a set of
Newtonian fluids having viscosity values spanning three orders of magnitude,
i.e. from $10^{-3}$ to $1$ Pa$\cdot$s. When the particle trajectory is analysed
by means of conventional statistical mechanics principles, we explicate for the
first time in literature the relationship between the required duration of MOT
experiments ($T_m$) and the fluids relative viscosity ($\eta_r$) to achieve an
uncertainty as low as $1\%$; i.e., $T_m\cong 17\eta_r^3$ minutes. This has led
to further evidences explaining why conventional MOT measurements commonly
underestimate the materials' viscoelastic properties, especially in the case of
high viscous fluids or soft-solids such as gels and cells. Finally, we have
developed a ML algorithm to determine the viscosity of Newtonian fluids that
uses feature extraction on raw trajectories acquired at a kHz and for a
duration of only one second, yet capable of returning viscosity values carrying
an error as low as $\sim0.3\%$ at best; hence the opening of a doorway for MOT
in living systems.

Bayesian optimization (BO) algorithms form a class of surrogate-based
heuristics, aimed at efficiently computing high-quality solutions for numerical
black-box optimization problems. The BO pipeline is highly modular, with
different design choices for the initial sampling strategy, the surrogate
model, the acquisition function (AF), the solver used to optimize the AF, etc.
We demonstrate in this work that a dynamic selection of the AF can benefit the
BO design. More precisely, we show that already a na\"ive random forest
regression model, built on top of exploratory landscape analysis features that
are computed from the initial design points, suffices to recommend AFs that
outperform any static choice, when considering performance over the classic
BBOB benchmark suite for derivative-free numerical optimization methods on the
COCO platform. Our work hence paves a way towards AutoML-assisted, on-the-fly
BO designs that adjust their behavior on a run-by-run basis.

The main streams of human activity recognition (HAR) algorithms are developed
based on RGB cameras which are suffered from illumination, fast motion,
privacy-preserving, and large energy consumption. Meanwhile, the biologically
inspired event cameras attracted great interest due to their unique features,
such as high dynamic range, dense temporal but sparse spatial resolution, low
latency, low power, etc. As it is a newly arising sensor, even there is no
realistic large-scale dataset for HAR. Considering its great practical value,
in this paper, we propose a large-scale benchmark dataset to bridge this gap,
termed HARDVS, which contains 300 categories and more than 100K event
sequences. We evaluate and report the performance of multiple popular HAR
algorithms, which provide extensive baselines for future works to compare. More
importantly, we propose a novel spatial-temporal feature learning and fusion
framework, termed ESTF, for event stream based human activity recognition. It
first projects the event streams into spatial and temporal embeddings using
StemNet, then, encodes and fuses the dual-view representations using
Transformer networks. Finally, the dual features are concatenated and fed into
a classification head for activity prediction. Extensive experiments on
multiple datasets fully validated the effectiveness of our model. Both the
dataset and source code will be released on
\url{https://github.com/Event-AHU/HARDVS}.

Localizing objects in 3D scenes based on natural language requires
understanding and reasoning about spatial relations. In particular, it is often
crucial to distinguish similar objects referred by the text, such as "the left
most chair" and "a chair next to the window". In this work we propose a
language-conditioned transformer model for grounding 3D objects and their
spatial relations. To this end, we design a spatial self-attention layer that
accounts for relative distances and orientations between objects in input 3D
point clouds. Training such a layer with visual and language inputs enables to
disambiguate spatial relations and to localize objects referred by the text. To
facilitate the cross-modal learning of relations, we further propose a
teacher-student approach where the teacher model is first trained using
ground-truth object labels, and then helps to train a student model using point
cloud inputs. We perform ablation studies showing advantages of our approach.
We also demonstrate our model to significantly outperform the state of the art
on the challenging Nr3D, Sr3D and ScanRefer 3D object grounding datasets.

When considering post-training quantization, prior work has typically focused
on developing a mixed precision scheme or learning the best way to partition a
network for quantization. In our work, CPT-V, we look at a general way to
improve the accuracy of networks that have already been quantized, simply by
perturbing the quantization scales. Borrowing the idea of contrastive loss from
self-supervised learning, we find a robust way to jointly minimize a loss
function using just 1,000 calibration images. In order to determine the best
performing quantization scale, CPT-V contrasts the features of quantized and
full precision models in a self-supervised fashion.
  Unlike traditional reconstruction-based loss functions, the use of a
contrastive loss function not only rewards similarity between the quantized and
full precision outputs but also helps in distinguishing the quantized output
from other outputs within a given batch. In addition, in contrast to prior
works, CPT-V proposes a block-wise evolutionary search to minimize a global
contrastive loss objective, allowing for accuracy improvement of existing
vision transformer (ViT) quantization schemes. For example, CPT-V improves the
top-1 accuracy of a fully quantized ViT-Base by 10.30%, 0.78%, and 0.15% for
3-bit, 4-bit, and 8-bit weight quantization levels. Extensive experiments on a
variety of other ViT architectures further demonstrate its robustness in
extreme quantization scenarios. Our code is available at <link>.

Chirped pulse amplification (CPA) followed by nonlinear optical (NLO) systems
constitute the backbone of myriad advancements in semiconductor and additive
manufacturing, communication networks, biology and medicine, defense and
national security, and a host of other sectors over the past decades. However,
accurately and efficiently modeling CPA- and NLO-based laser systems is
challenging because of the multitude of coupled linear and nonlinear processes
and high variability in modeling frameworks. The lack of fully-integrated
software CPA+NLO modeling severely hampers further advances to tailor existing
or materialize new CPA+NLO systems and curtails their full potential in
emerging inverse design approaches through data-driven machine learning
methods. Here, we present a modular start-to-end software model, allowing for
the inclusion of an array of amplifier designs and nonlinear optics techniques,
which renders time- and frequency-resolved electromagnetic fields alongside
essential physical characteristics on energy, fluence, and spectral
distribution. To demonstrate its robustness and real-world applicability --
specifically, reverse engineering, system optimization, and inverse design --
we present a case study on the LCLS-II photo-injector laser, representative of
a high-power and spectro-temporally complex CPA+NLO system.

Very large deep learning models trained using gradient descent are remarkably
resistant to memorization given their huge capacity, but are at the same time
capable of fitting large datasets of pure noise. Here methods are introduced by
which models may be trained to memorize datasets that normally are generalized.
We find that memorization is difficult relative to generalization, but that
adding noise makes memorization easier. Increasing the dataset size exaggerates
the characteristics of that dataset: model access to more training samples
makes overfitting easier for random data, but somewhat harder for natural
images. The bias of deep learning towards generalization is explored
theoretically, and we show that generalization results from a model's
parameters being attracted to points of maximal stability with respect to that
model's inputs during gradient descent.

We investigate the sample complexity of bounded two-layer neural networks
using different activation functions.
  In particular, we consider the class
  \[
  \mathcal{H} = \left\{\textbf{x}\mapsto \langle \textbf{v}, \sigma \circ
W\textbf{x} + \textbf{b} \rangle :
  \textbf{b}\in\mathbb{R}^d, W \in \mathbb{R}^{T\times d}, \textbf{v} \in
\mathbb{R}^{T}\right\}
  \]
  where the spectral norm of $W$ and $\textbf{v}$ is bounded by $O(1)$, the
Frobenius norm of $W$ is bounded from its initialization by $R > 0$, and
$\sigma$ is a Lipschitz activation function.
  We prove that if $\sigma$ is element-wise, then the sample complexity of
$\mathcal{H}$ is width independent and that this complexity is tight.
  Moreover, we show that the element-wise property of $\sigma$ is essential for
width-independent bound, in the sense that there exist non-element-wise
activation functions whose sample complexity is provably width-dependent.
  For the upper bound, we use the recent approach for norm-based bounds named
Approximate Description Length (ADL) by arXiv:1910.05697.
  We further develop new techniques and tools for this approach, that will
hopefully inspire future works.

We study a multi-agent mean field type control problem in discrete time where
the agents aim to find a socially optimal strategy and where the state and
action spaces for the agents are assumed to be continuous. We provide several
approximations for this problem: (i) We first show that (building on Bauerle,
N. 2021, arXiv:2106.08755) the problem can be seen as an empirical measure
valued centralized Markov decision process (MDP), where the agents' identities
are not relevant but only their state information is used for the optimal
strategy without loss of optimality. (ii) Secondly, we construct a finite
empirical measure valued MDP by discretizing the state spaces of the agents,
and show that the strategies chosen based on this formulation, which only keep
track of how many agents are in the discretized aggregate sets, are nearly
optimal with increasing discretization rates. (iii) Finally, we look at large
population problems, where the state space of the problem can be taken finite
after discretization, but can be too large for computational efficiency.
Different from the usual approach in the literature, instead of going to the
infinite population limit, which results in a continuous problem and requires
further discretizations again, we perform approximations by choosing a smaller
sample of the large population and find strategies for this small sample and
generalize them to the original large population.
  Furthermore, we provide convergent Q learning iterations for the approximate
models, and show that the learned policies are nearly optimal under standard
regularity assumptions on the cost and dynamic functions of the problem.

Deep neural networks have recently achieved considerable improvements in
learning human behavioral patterns and individual preferences from massive
spatial-temporal trajectories data. However, most of the existing research
concentrates on fusing different semantics underlying sequential trajectories
for mobility pattern learning which, in turn, yields a narrow perspective on
comprehending human intrinsic motions. In addition, the inherent sparsity and
under-explored heterogeneous collaborative items pertaining to human check-ins
hinder the potential exploitation of human diverse periodic regularities as
well as common interests. Motivated by recent advances in disentanglement
learning, in this study we propose a novel disentangled solution called SSDL
for tackling the next POI prediction problem. SSDL primarily seeks to
disentangle the potential time-invariant and time-varying factors into
different latent spaces from massive trajectories data, providing an
interpretable view to understand the intricate semantics underlying human
diverse mobility representations. To address the data sparsity issue, we
present two realistic trajectory augmentation approaches to enhance the
understanding of both the human intrinsic periodicity and constantly-changing
intents. In addition, we devise a POI-centric graph structure to explore
heterogeneous collaborative signals underlying historical check-ins. Extensive
experiments conducted on four real-world datasets demonstrate that our proposed
SSDL significantly outperforms the state-of-the-art approaches -- for example,
it yields up to 8.57% improvements on ACC@1.

Text-video retrieval is an important multi-modal learning task, where the
goal is to retrieve the most relevant video for a given text query. Recently,
pre-trained models, e.g., CLIP, show great potential on this task. However, as
pre-trained models are scaling up, fully fine-tuning them on text-video
retrieval datasets has a high risk of overfitting. Moreover, in practice, it
would be costly to train and store a large model for each task. To overcome the
above issues, we present a novel $\textbf{Cross-Modal Adapter}$ for
parameter-efficient fine-tuning. Inspired by adapter-based methods, we adjust
the pre-trained model with a few parameterization layers. However, there are
two notable differences. First, our method is designed for the multi-modal
domain. Secondly, it allows early cross-modal interactions between CLIP's two
encoders. Although surprisingly simple, our approach has three notable
benefits: (1) reduces $\textbf{99.6}\%$ of fine-tuned parameters, and
alleviates the problem of overfitting, (2) saves approximately 30% of training
time, and (3) allows all the pre-trained parameters to be fixed, enabling the
pre-trained model to be shared across datasets. Extensive experiments
demonstrate that, without bells and whistles, it achieves superior or
comparable performance compared to fully fine-tuned methods on MSR-VTT, MSVD,
VATEX, ActivityNet, and DiDeMo datasets. The code will be available at
\url{https://github.com/LeapLabTHU/Cross-Modal-Adapter}.

Reinforcement learning has recently been used to approach well-known NP-hard
combinatorial problems in graph theory. Among these problems, Hamiltonian cycle
problems are exceptionally difficult to analyze, even when restricted to
individual instances of structurally complex graphs. In this paper, we use
Monte Carlo Tree Search (MCTS), the search algorithm behind many
state-of-the-art reinforcement learning algorithms such as AlphaZero, to create
autonomous agents that learn to play the game of Snake, a game centered on
properties of Hamiltonian cycles on grid graphs. The game of Snake can be
formulated as a single-player discounted Markov Decision Process (MDP) where
the agent must behave optimally in a stochastic environment. Determining the
optimal policy for Snake, defined as the policy that maximizes the probability
of winning - or win rate - with higher priority and minimizes the expected
number of time steps to win with lower priority, is conjectured to be NP-hard.
Performance-wise, compared to prior work in the Snake game, our algorithm is
the first to achieve a win rate over $0.5$ (a uniform random policy achieves a
win rate $< 2.57 \times 10^{-15}$), demonstrating the versatility of AlphaZero
in approaching NP-hard environments.

Traffic flow analysis is revolutionising traffic management. Qualifying
traffic flow data, traffic control bureaus could provide drivers with real-time
alerts, advising the fastest routes and therefore optimising transportation
logistics and reducing congestion. The existing traffic flow datasets have two
major limitations. They feature a limited number of classes, usually limited to
one type of vehicle, and the scarcity of unlabelled data. In this paper, we
introduce a new benchmark traffic flow image dataset called TrafficCAM. Our
dataset distinguishes itself by two major highlights. Firstly, TrafficCAM
provides both pixel-level and instance-level semantic labelling along with a
large range of types of vehicles and pedestrians. It is composed of a large and
diverse set of video sequences recorded in streets from eight Indian cities
with stationary cameras. Secondly, TrafficCAM aims to establish a new benchmark
for developing fully-supervised tasks, and importantly, semi-supervised
learning techniques. It is the first dataset that provides a vast amount of
unlabelled data, helping to better capture traffic flow qualification under a
low cost annotation requirement. More precisely, our dataset has 4,402 image
frames with semantic and instance annotations along with 59,944 unlabelled
image frames. We validate our new dataset through a large and comprehensive
range of experiments on several state-of-the-art approaches under four
different settings: fully-supervised semantic and instance segmentation, and
semi-supervised semantic and instance segmentation tasks. Our benchmark dataset
will be released.

This text presents an introduction to an emerging paradigm in control of
dynamical systems and differentiable reinforcement learning called online
nonstochastic control. The new approach applies techniques from online convex
optimization and convex relaxations to obtain new methods with provable
guarantees for classical settings in optimal and robust control.
  The primary distinction between online nonstochastic control and other
frameworks is the objective. In optimal control, robust control, and other
control methodologies that assume stochastic noise, the goal is to perform
comparably to an offline optimal strategy. In online nonstochastic control,
both the cost functions as well as the perturbations from the assumed dynamical
model are chosen by an adversary. Thus the optimal policy is not defined a
priori. Rather, the target is to attain low regret against the best policy in
hindsight from a benchmark class of policies.
  This objective suggests the use of the decision making framework of online
convex optimization as an algorithmic methodology. The resulting methods are
based on iterative mathematical optimization algorithms, and are accompanied by
finite-time regret and computational complexity guarantees.

We demonstrate an approach for calculating the temperature-dependent
anharmonic properties of complex materials with beyond density-functional
theory accuracy and we apply it to strontium titanate, a propotypical quantum
paraelectric material with strongly anharmonic lattice dynamics. Specifically,
we employ machine-learned potentials in combination with the stochastic
self-consistent harmonic approximation method in order to fully capture quantum
and anharmonic effects. We investigate the cubic to tetragonal transition
characterized by the softening of the antiferrodistortive mode, and we show
that the paraelectric phase is stabilized by anharmonic quantum fluctuations.
We find that a quantitative description of the quantum paraelectric behavior
requires a higher-level treatment of electronic correlation effects via the
random phase approximation. This approach enables detailed studies of emergent
properties in strongly anharmonic materials beyond density-functional theory.

We propose a novel framework to learn how to communicate with intent, i.e.,
to transmit messages over a wireless communication channel based on the
end-goal of the communication. This stays in stark contrast to classical
communication systems where the objective is to reproduce at the receiver side
either exactly or approximately the message sent by the transmitter, regardless
of the end-goal. Our procedure is general enough that can be adapted to any
type of goal or task, so long as the said task is a (almost-everywhere)
differentiable function over which gradients can be propagated. We focus on
supervised learning and reinforcement learning (RL) tasks, and propose
algorithms to learn the communication system and the task jointly in an
end-to-end manner. We then delve deeper into the transmission of images and
propose two systems, one for the classification of images and a second one to
play an Atari game based on RL. The performance is compared with a joint source
and channel coding (JSCC) communication system designed to minimize the
reconstruction error, and results show overall great improvement. Further, for
the RL task, we show that while a JSCC strategy is not better than a random
action selection strategy, with our approach we get close to the upper bound
even for low SNRs.

According to the main international reports, more pervasive industrial and
business-process automation, thanks to machine learning and advanced analytic
tools, will unlock more than 14 trillion USD worldwide annually by 2030. In the
specific case of pricing problems-which constitute the class of problems we
investigate in this paper-, the estimated unlocked value will be about 0.5
trillion USD per year. In particular, this paper focuses on pricing in
e-commerce when the objective function is profit maximization and only
transaction data are available. This setting is one of the most common in
real-world applications. Our work aims to find a pricing strategy that allows
defining optimal prices at different volume thresholds to serve different
classes of users. Furthermore, we face the major challenge, common in
real-world settings, of dealing with limited data available. We design a
two-phase online learning algorithm, namely PVD-B, capable of exploiting the
data incrementally in an online fashion. The algorithm first estimates the
demand curve and retrieves the optimal average price, and subsequently it
offers discounts to differentiate the prices for each volume threshold. We ran
a real-world 4-month-long A/B testing experiment in collaboration with an
Italian e-commerce company, in which our algorithm PVD-B-corresponding to A
configuration-has been compared with human pricing specialists-corresponding to
B configuration. At the end of the experiment, our algorithm produced a total
turnover of about 300 KEuros, outperforming the B configuration performance by
about 55%. The Italian company we collaborated with decided to adopt our
algorithm for more than 1,200 products since January 2022.

Proof Blocks is a software tool that provides students with a scaffolded
proof-writing experience, allowing them to drag and drop prewritten proof lines
into the correct order instead of starting from scratch. In this paper we
describe a randomized controlled trial designed to measure the learning gains
of using Proof Blocks for students learning proof by induction. The study
participants were 332 students recruited after completing the first month of
their discrete mathematics course. Students in the study completed a pretest on
proof writing and a brief (less than 1 hour) learning activity and then
returned one week later to complete the posttest. Depending on the experimental
condition that each student was assigned to, they either completed only Proof
Blocks problems, completed some Proof Blocks problems and some written proofs,
or completed only written proofs for their learning activity. We find that
students in the early phases of learning about proof by induction are able to
learn just as much by using Proof Blocks as by writing proofs from scratch, but
in far less time on task. This finding that Proof Blocks are an effective
learning tool complements previous findings that Proof Blocks are useful exam
questions and are viewed positively by students.

Building on the recent trend of new deep generative models known as
Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now
efficiently accommodate arbitrary complex and high-dimensional data
distributions. The development of appropriate validation methods however has
fallen behind. Indeed, most of the existing metrics either require access to
the true posterior distribution, or fail to provide theoretical guarantees on
the consistency of the inferred approximation beyond the one-dimensional
setting. This work proposes easy to interpret validation diagnostics for
multi-dimensional conditional (posterior) density estimators based on NF. It
also offers theoretical guarantees based on results of local consistency. The
proposed workflow can be used to check, analyse and guarantee consistent
behavior of the estimator. The method is illustrated with a challenging example
that involves tightly coupled parameters in the context of computational
neuroscience. This work should help the design of better specified models or
drive the development of novel SBI-algorithms, hence allowing to build up trust
on their ability to address important questions in experimental science.

With the onset of large-scale astronomical surveys capturing millions of
images, there is an increasing need to develop fast and accurate deconvolution
algorithms that generalize well to different images. A powerful and accessible
deconvolution method would allow for the reconstruction of a cleaner estimation
of the sky. The deconvolved images would be helpful to perform photometric
measurements to help make progress in the fields of galaxy formation and
evolution. We propose a new deconvolution method based on the Learnlet
transform. Eventually, we investigate and compare the performance of different
Unet architectures and Learnlet for image deconvolution in the astrophysical
domain by following a two-step approach: a Tikhonov deconvolution with a
closed-form solution, followed by post-processing with a neural network. To
generate our training dataset, we extract HST cutouts from the CANDELS survey
in the F606W filter (V-band) and corrupt these images to simulate their
blurred-noisy versions. Our numerical results based on these simulations show a
detailed comparison between the considered methods for different noise levels.

As quantum information science and technology (QIST) is becoming more
prevalent and occurring not only in research labs but also in industry, many
educators are considering how best to incorporate learning about quantum
mechanics into various levels of education. Although much of the focus has been
on quantum concepts in non-lab courses, current work in QIST has a substantial
experimental component. Many instructors of undergraduate lab courses want to
provide their students the opportunity to work with quantum experiments. One
common way this is done is through a sequence of quantum optics experiments
often referred to as the ``single-photon experiments.'' These experiments
demonstrate fundamental quantum phenomena with equipment common to research
labs; however, they are resource intensive and cannot be afforded by all
institutions. It is therefore imperative to know what unique affordances these
experiments provide to students. As a starting point, we surveyed and
interviewed instructors who use the single-photon experiments in undergraduate
courses, asking how and why they use the experiments. We describe the most
commonly used experiments in both quantum and beyond-first-year lab courses,
the prevalence of tasks the students perform, and the learning goals, ranging
from conceptual knowledge to lab skills to student affect. Finally, we present
some strategies from these data demonstrating how instructors have addressed
the common challenges of preparing students to work with conceptually and
technically complex experiments and balancing the practice of technical skills
with the completion of the experiments.

Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set
and massive amounts of unlabeled data. To better exploit the unlabeled data the
latest SSL methods use pseudo-labels predicted from a single discriminative
classifier. However, the generated pseudo-labels are inevitably linked to
inherent confirmation bias and noise which greatly affects the model
performance. In this work we introduce a new framework for SSL named NorMatch.
Firstly, we introduce a new uncertainty estimation scheme based on normalizing
flows, as an auxiliary classifier, to enforce highly certain pseudo-labels
yielding a boost of the discriminative classifiers. Secondly, we introduce a
threshold-free sample weighting strategy to exploit better both high and low
confidence pseudo-labels. Furthermore, we utilize normalizing flows to model,
in an unsupervised fashion, the distribution of unlabeled data. This modelling
assumption can further improve the performance of generative classifiers via
unlabeled data, and thus, implicitly contributing to training a better
discriminative classifier. We demonstrate, through numerical and visual
results, that NorMatch achieves state-of-the-art performance on several
datasets.

We investigate the task of missing value estimation in graphs as given by
water distribution systems (WDS) based on sparse signals as a representative
machine learning challenge in the domain of critical infrastructure. The
underlying graphs have a comparably low node degree and high diameter, while
information in the graph is globally relevant, hence graph neural networks face
the challenge of long-term dependencies. We propose a specific architecture
based on message passing which displays excellent results for a number of
benchmark tasks in the WDS domain. Further, we investigate a multi-hop
variation, which requires considerably less resources and opens an avenue
towards big WDS graphs.

Many fascinating systems suffer from a severe (complex action) sign problem
preventing us from calculating them with Markov Chain Monte Carlo simulations.
One promising method to alleviate the sign problem is the transformation of the
integration domain towards Lefschetz Thimbles. Unfortunately, this suffers from
poor scaling originating in numerically integrating of flow equations and
evaluation of an induced Jacobian. In this proceedings we present a new
preliminary Neural Network architecture based on complex-valued affine coupling
layers. This network performs such a transformation efficiently, ultimately
allowing simulation of systems with a severe sign problem. We test this method
within the Hubbard Model at finite chemical potential, modelling strongly
correlated electrons on a spatial lattice of ions.

We know that both the CNN mapping function and the sampling scheme are of
paramount importance for CNN-based image analysis. It is clear that both
functions operate in the same space, with an image axis $\mathcal{I}$ and a
feature axis $\mathcal{F}$. Remarkably, we found that no frameworks existed
that unified the two and kept track of the spatial origin of the data
automatically. Based on our own practical experience, we found the latter to
often result in complex coding and pipelines that are difficult to exchange.
This article introduces our framework for 1, 2 or 3D image classification or
segmentation: DeepVoxNet2 (DVN2). This article serves as an interactive
tutorial, and a pre-compiled version, including the outputs of the code blocks,
can be found online in the public DVN2 repository. This tutorial uses data from
the multimodal Brain Tumor Image Segmentation Benchmark (BRATS) of 2018 to show
an example of a 3D segmentation pipeline.

Transfer adversarial attacks raise critical security concerns in real-world,
black-box scenarios. However, the actual progress of attack methods is
difficult to assess due to two main limitations in existing evaluations. First,
existing evaluations are unsystematic and sometimes unfair since new methods
are often directly added to old ones without complete comparisons to similar
methods. Second, existing evaluations mainly focus on transferability but
overlook another key attack property: stealthiness. In this work, we design
good practices to address these limitations. We first introduce a new attack
categorization, which enables our systematic analyses of similar attacks in
each specific category. Our analyses lead to new findings that complement or
even challenge existing knowledge. Furthermore, we comprehensively evaluate 23
representative attacks against 9 defenses on ImageNet. We pay particular
attention to stealthiness, by adopting diverse imperceptibility metrics and
looking into new, finer-grained characteristics. Our evaluation reveals new
important insights: 1) Transferability is highly contextual, and some white-box
defenses may give a false sense of security since they are actually vulnerable
to (black-box) transfer attacks; 2) All transfer attacks are less stealthy, and
their stealthiness can vary dramatically under the same $L_{\infty}$ bound.

In this article, we look into some essential aspects of convolutional neural
networks (CNNs) with the focus on medical image segmentation. First, we discuss
the CNN architecture, thereby highlighting the spatial origin of the data,
voxel-wise classification and the receptive field. Second, we discuss the
sampling of input-output pairs, thereby highlighting the interaction between
voxel-wise classification, patch size and the receptive field. Finally, we give
a historical overview of crucial changes to CNN architectures for
classification and segmentation, giving insights in the relation between three
pivotal CNN architectures: FCN, U-Net and DeepMedic.

The evaluation of the Human Epidermal growth factor Receptor-2 (HER2)
expression is an important prognostic biomarker for breast cancer treatment
selection. However, HER2 scoring has notoriously high interobserver variability
due to stain variations between centers and the need to estimate visually the
staining intensity in specific percentages of tumor area. In this paper,
focusing on the interpretability of HER2 scoring by a pathologist, we propose a
semi-automatic, two-stage deep learning approach that directly evaluates the
clinical HER2 guidelines defined by the American Society of Clinical Oncology/
College of American Pathologists (ASCO/CAP). In the first stage, we segment the
invasive tumor over the user-indicated Region of Interest (ROI). Then, in the
second stage, we classify the tumor tissue into four HER2 classes. For the
classification stage, we use weakly supervised, constrained optimization to
find a model that classifies cancerous patches such that the tumor surface
percentage meets the guidelines specification of each HER2 class. We end the
second stage by freezing the model and refining its output logits in a
supervised way to all slide labels in the training set. To ensure the quality
of our dataset's labels, we conducted a multi-pathologist HER2 scoring
consensus. For the assessment of doubtful cases where no consensus was found,
our model can help by interpreting its HER2 class percentages output. We
achieve a performance of 0.78 in F1-score on the test set while keeping our
model interpretable for the pathologist, hopefully contributing to
interpretable AI models in digital pathology.

Given their intermittency, distributed energy resources (DERs) have been
commissioned with regulating voltages at fast timescales. Although the IEEE
1547 standard specifies the shape of Volt/VAR control rules, it is not clear
how to optimally customize them per DER. Optimal rule design (ORD) is a
challenging problem as Volt/VAR rules introduce nonlinear dynamics, require
bilinear optimization models, and lurk trade-offs between stability and
steady-state performance. To tackle ORD, we develop a deep neural network (DNN)
that serves as a digital twin of Volt/VAR dynamics. The DNN takes grid
conditions as inputs, uses rule parameters as weights, and computes equilibrium
voltages as outputs. Thanks to this genuine design, ORD is reformulated as a
deep learning task using grid scenarios as training data and aiming at driving
the predicted variables being the equilibrium voltages close to unity. The
learning task is solved by modifying efficient deep-learning routines to
enforce constraints on rule parameters. In the course of DNN-based ORD, we also
review and expand on stability conditions and convergence rates for Volt/VAR
rules on single-/multi-phase feeders. To benchmark the optimality and runtime
of DNN-based ORD, we also devise a novel mixed-integer nonlinear program
formulation. Numerical tests showcase the merits of DNN-based ORD.

Learning discriminative spatiotemporal representation is the key problem of
video understanding. Recently, Vision Transformers (ViTs) have shown their
power in learning long-term video dependency with self-attention.
Unfortunately, they exhibit limitations in tackling local video redundancy, due
to the blind global comparison among tokens. UniFormer has successfully
alleviated this issue, by unifying convolution and self-attention as a relation
aggregator in the transformer format. However, this model has to require a
tiresome and complicated image-pretraining phrase, before being finetuned on
videos. This blocks its wide usage in practice. On the contrary, open-sourced
ViTs are readily available and well-pretrained with rich image supervision.
Based on these observations, we propose a generic paradigm to build a powerful
family of video networks, by arming the pretrained ViTs with efficient
UniFormer designs. We call this family UniFormerV2, since it inherits the
concise style of the UniFormer block. But it contains brand-new local and
global relation aggregators, which allow for preferable accuracy-computation
balance by seamlessly integrating advantages from both ViTs and UniFormer.
Without any bells and whistles, our UniFormerV2 gets the state-of-the-art
recognition performance on 8 popular video benchmarks, including scene-related
Kinetics-400/600/700 and Moments in Time, temporal-related Something-Something
V1/V2, untrimmed ActivityNet and HACS. In particular, it is the first model to
achieve 90% top-1 accuracy on Kinetics-400, to our best knowledge. Code will be
available at https://github.com/OpenGVLab/UniFormerV2.

In the Engineering discipline, predictive maintenance techniques play an
essential role in improving system safety and reliability of industrial
machines. Due to the adoption of crucial and emerging detection techniques and
big data analytics tools, data fusion approaches are gaining popularity. This
article thoroughly reviews the recent progress of data fusion techniques in
predictive maintenance, focusing on their applications in machinery fault
diagnosis. In this review, the primary objective is to classify existing
literature and to report the latest research and directions to help researchers
and professionals to acquire a clear understanding of the thematic area. This
paper first summarizes fundamental data-fusion strategies for fault diagnosis.
Then, a comprehensive investigation of the different levels of data fusion was
conducted on fault diagnosis of industrial machines. In conclusion, a
discussion of data fusion-based fault diagnosis challenges, opportunities, and
future trends are presented.

Process optimization for metal additive manufacturing (AM) is crucial to
ensure repeatability, control microstructure, and minimize defects. Despite
efforts to address this via the traditional design of experiments and
statistical process mapping, there is limited insight on an on-the-fly
optimization framework that can be integrated into a metal AM system.
Additionally, most of these methods, being data-intensive, cannot be supported
by a metal AM alloy or system due to budget restrictions. To tackle this issue,
the article introduces a Reinforcement Learning (RL) methodology transformed
into an optimization problem in the realm of metal AM. An off-policy RL
framework based on Q-learning is proposed to find optimal laser power ($P$) -
scan velocity ($v$) combinations with the objective of maintaining steady-state
melt pool depth. For this, an experimentally validated Eagar-Tsai formulation
is used to emulate the Laser-Directed Energy Deposition environment, where the
laser operates as the agent across the $P-v$ space such that it maximizes
rewards for a melt pool depth closer to the optimum. The culmination of the
training process yields a Q-table where the state ($P,v$) with the highest
Q-value corresponds to the optimized process parameter. The resultant melt pool
depths and the mapping of Q-values to the $P-v$ space show congruence with
experimental observations. The framework, therefore, provides a model-free
approach to learning without any prior.

Exoplanets in protoplanetary disks cause localized deviations from Keplerian
velocity in channel maps of molecular line emission. Current methods of
characterizing these deviations are time consuming, and there is no unified
standard approach. We demonstrate that machine learning can quickly and
accurately detect the presence of planets. We train our model on synthetic
images generated from simulations and apply it to real observations to identify
forming planets in real systems. Machine learning methods, based on computer
vision, are not only capable of correctly identifying the presence of one or
more planets, but they can also correctly constrain the location of those
planets.

Seismic phase picking is at the core of earthquake monitoring. To date, the
advancements in earthquake data collection have led to an exponential increase
in the volume of available seismic waveform data. This highly necessitates a
reliable solution for seismic phase classification. Advanced technologies, such
as convolutional neural networks, have been widely introduced to create
earthquake catalogs from continuous waveforms produced using conventional
methods. However, their performance is restricted by the static convolution
kernels. To cope with this challenge, a dynamic convolutional neural network
based framework, termed DynaPicker, is proposed in this study for detecting
seismic body wave phases that allows a dynamic inference with the deep learning
architecture. We demonstrate the performance of our framework on two tasks,
seismic phase classification and arrival time picking, both tested on four open
source seismic datasets. The results of our experiments show that DynaPicker
can yield a testing accuracy of approximately ninety nine percent in seismic
phase identification. We demonstrate the robustness of the ability of this
method in classifying seismic phases, even when the low magnitude seismic data
is polluted by noise. Moreover, DynaPicker can be extended to deal with input
data of varying lengths for seismic phase detection. Given continuous seismic
data, DynaPicker can correctly identify more seismic events and produce a lower
arrival time picking error compared to the baseline methods.

Neural Stochastic Differential Equations (NSDE) have been trained as both
Variational Autoencoders, and as GANs. However, the resulting Stochastic
Differential Equations can be hard to interpret or analyse due to the generic
nature of the drift and diffusion fields. By restricting our NSDE to be of the
form of Langevin dynamics, and training it as a VAE, we obtain NSDEs that lend
themselves to more elaborate analysis and to a wider range of visualisation
techniques than a generic NSDE. More specifically, we obtain an energy
landscape, the minima of which are in one-to-one correspondence with latent
states underlying the used data. This not only allows us to detect states
underlying the data dynamics in an unsupervised manner, but also to infer the
distribution of time spent in each state according to the learned SDE. More in
general, restricting an NSDE to Langevin dynamics enables the use of a large
set of tools from computational molecular dynamics for the analysis of the
obtained results.

Deep learning based text-to-speech (TTS) systems have been evolving rapidly
with advances in model architectures, training methodologies, and
generalization across speakers and languages. However, these advances have not
been thoroughly investigated for Indian language speech synthesis. Such
investigation is computationally expensive given the number and diversity of
Indian languages, relatively lower resource availability, and the diverse set
of advances in neural TTS that remain untested. In this paper, we evaluate the
choice of acoustic models, vocoders, supplementary loss functions, training
schedules, and speaker and language diversity for Dravidian and Indo-Aryan
languages. Based on this, we identify monolingual models with FastPitch and
HiFi-GAN V1, trained jointly on male and female speakers to perform the best.
With this setup, we train and evaluate TTS models for 13 languages and find our
models to significantly improve upon existing models in all languages as
measured by mean opinion scores. We open-source all models on the Bhashini
platform.

Line-of-sight link blockages represent a key challenge for the reliability
and latency of millimeter wave (mmWave) and terahertz (THz) communication
networks. To address this challenge, this paper leverages mmWave and LiDAR
sensory data to provide awareness about the communication environment and
proactively predict dynamic link blockages before they occur. This allows the
network to make proactive decisions for hand-off/beam switching, enhancing the
network reliability and latency. More specifically, this paper addresses the
following key questions: (i) Can we predict a line-of-sight link blockage,
before it happens, using in-band mmWave/THz signal and LiDAR sensing data? (ii)
Can we also predict when this blockage will occur? (iii) Can we predict the
blockage duration? And (iv) can we predict the direction of the moving
blockage? For that, we develop machine learning solutions that learn special
patterns of the received signal and sensory data, which we call
\textit{pre-blockage signatures}, to infer future blockages. To evaluate the
proposed approaches, we build a large-scale real-world dataset that comprises
co-existing LiDAR and mmWave communication measurements in outdoor vehicular
scenarios. Then, we develop an efficient LiDAR data denoising algorithm that
applies some pre-processing to the LiDAR data. Based on the real-world dataset,
the developed approaches are shown to achieve above 95\% accuracy in predicting
blockages occurring within 100 ms and more than 80\% prediction accuracy for
blockages occurring within one second. Given this future blockage prediction
capability, the paper also shows that the developed solutions can achieve an
order of magnitude saving in network latency, which further highlights the
potential of the developed blockage prediction solutions for wireless networks.

A reinforcement learning approach to design optimised graded metamaterials
for energy harvesting is described. Through the proximal policy optimisation
algorithm, the reinforcement agent is trained to optimally set the lengths and
the spacing of an array of resonators. The design optimisation problem is
formalised in a Markov decision problem by splitting the optimisation procedure
into a discrete number of decisions. The a priori physical knowledge of the
problem has been exploited to constrain the space of possible configurations by
adopting few continuous variables to describe the resonator arrangements. A
preliminary analytical investigation has been performed to characterise the
dispersive properties of the analysed system by treating it as a locally
resonant system. The outcomes of the optimisation procedure confirms the
results of previous investigations, highlighting both the validity of the
proposed approach and the robustness of the systems of graded resonators when
employed for energy harvesting. The role of the resonator spacing is shown to
be secondary with respect to the resonator lengths or, in other words, with
respect to the oscillation frequencies of the resonators. However, it is also
demonstrated that reducing the number of resonators can be advantageous for
energy harvesting. The outcomes related to the joint optimisation of the
resonator lengths and spacing, thanks also to the adaptive control of the
analysis duration, overcome significantly the performance of previously known
systems by working almost uniquely on enlarging the time in which the harvester
oscillations take place without amplifying these oscillations. The proposed
procedure is suitable to be applied to a wide range of design optimisation
problems in which the effect of the design choices can be assessed through
numerical simulations.

Development of air pollution models for large regions is a priority for
population-based epidemiologic studies. The rapid development of big data
information systems and machine learning algorithms have opened new grounds for
refinements of current model frameworks. This commentary overviews recent
contributions and outlines extensions from geostatistics and machine learning
perspectives. For the coming years, expected advances will expand the use of
learning algorithms to model spatial trends and incorporate spatial covariance
models in the learning processes. These extensions will refine existing
modelling frameworks contributing to improve accuracy of air pollution models
for exposure assessment.

Trajectory Representation Learning (TRL) is a powerful tool for
spatial-temporal data analysis and management. TRL aims to convert complicated
raw trajectories into low-dimensional representation vectors, which can be
applied to various downstream tasks, such as trajectory classification,
clustering, and similarity computation. Existing TRL works usually treat
trajectories as ordinary sequence data, while some important spatial-temporal
characteristics, such as temporal regularities and travel semantics, are not
fully exploited. To fill this gap, we propose a novel Self-supervised
trajectory representation learning framework with TemporAl Regularities and
Travel semantics, namely START. The proposed method consists of two stages. The
first stage is a Trajectory Pattern-Enhanced Graph Attention Network (TPE-GAT),
which converts the road network features and travel semantics into
representation vectors of road segments. The second stage is a Time-Aware
Trajectory Encoder (TAT-Enc), which encodes representation vectors of road
segments in the same trajectory as a trajectory representation vector,
meanwhile incorporating temporal regularities with the trajectory
representation. Moreover, we also design two self-supervised tasks, i.e.,
span-masked trajectory recovery and trajectory contrastive learning, to
introduce spatial-temporal characteristics of trajectories into the training
process of our START framework. The effectiveness of the proposed method is
verified by extensive experiments on two large-scale real-world datasets for
three downstream tasks. The experiments also demonstrate that our method can be
transferred across different cities to adapt heterogeneous trajectory datasets.

Insects are an integral part of our ecosystem. These often small and evasive
animals have a big impact on their surroundings, providing a large part of the
present biodiversity and pollination duties, forming the foundation of the food
chain and many biological and ecological processes. Due to factors of human
influence, population numbers and biodiversity have been rapidly declining with
time. Monitoring this decline has become increasingly important for
conservation measures to be effectively implemented. But monitoring methods are
often invasive, time and resource intense, and prone to various biases. Many
insect species produce characteristic mating sounds that can easily be detected
and recorded without large cost or effort. Using deep learning methods, insect
sounds from field recordings could be automatically detected and classified to
monitor biodiversity and species distribution ranges. In this project, I
implement this using existing datasets of insect sounds (Orthoptera and
Cicadidae) and machine learning methods and evaluate their potential for
acoustic insect monitoring. I compare the performance of the conventional
spectrogram-based deep learning method against the new adaptive and
waveform-based approach LEAF. The waveform-based frontend achieved
significantly better classification performance than the Mel-spectrogram
frontend by adapting its feature extraction parameters during training. This
result is encouraging for future implementations of deep learning technology
for automatic insect sound recognition, especially if larger datasets become
available.

Although current neural text-to-speech (TTS) models are able to generate
high-quality speech, intensity controllable emotional TTS is still a
challenging task. Most existing methods need external optimizations for
intensity calculation, leading to suboptimal results or degraded quality. In
this paper, we propose EmoDiff, a diffusion-based TTS model where emotion
intensity can be manipulated by a proposed soft-label guidance technique
derived from classifier guidance. Specifically, instead of being guided with a
one-hot vector for the specified emotion, EmoDiff is guided with a soft label
where the value of the specified emotion and \textit{Neutral} is set to
$\alpha$ and $1-\alpha$ respectively. The $\alpha$ here represents the emotion
intensity and can be chosen from 0 to 1. Our experiments show that EmoDiff can
precisely control the emotion intensity while maintaining high voice quality.
Moreover, diverse speech with specified emotion intensity can be generated by
sampling in the reverse denoising process.

The accurate estimation of photometric redshifts plays a crucial role in
accomplishing science objectives of the large survey projects. The
template-fitting and machine learning are the two main types of methods applied
currently. Based on the training set obtained by cross-correlating the DESI
Legacy Imaging Surveys DR9 galaxy catalogue and SDSS DR16 galaxy catalogue, the
two kinds of methods are used and optimized, such as EAZY for template-fitting
approach and CATBOOST for machine learning. Then the created models are tested
by the cross-matched samples of the DESI Legacy Imaging SurveysDR9 galaxy
catalogue with LAMOST DR7, GAMA DR3 and WiggleZ galaxy catalogues. Moreover
three machine learning methods (CATBOOST, Multi-Layer Perceptron and Random
Forest) are compared, CATBOOST shows its superiority for our case. By feature
selection and optimization of model parameters, CATBOOST can obtain higher
accuracy with optical and infrared photometric information, the best
performance ($MSE=0.0032$, $\sigma_{NMAD}=0.0156$ and $O=0.88$ per cent) with
$g \le 24.0$, $r \le 23.4$ and $z \le 22.5$ is achieved. But EAZY can provide
more accurate photometric redshift estimation for high redshift galaxies,
especially beyond the redhisft range of training sample. Finally, we finish the
redshift estimation of all DESI DR9 galaxies with CATBOOST and EAZY, which will
contribute to the further study of galaxies and their properties.

Archaeology is an intriguing domain for computer vision. It suffers not only
from shortage in (labeled) data, but also from highly-challenging data, which
is often extremely abraded and damaged. This paper proposes a novel
semi-supervised model for classification and retrieval of images of
archaeological artifacts. This model utilizes unique data that exists in the
domain -- manual drawings made by special artists.These are used during
training to implicitly transfer the domain knowledge from the drawings to their
corresponding images, improving their classification results. We show that
while learning how to classify, our model also learns how to generate drawings
of the artifacts, an important documentation task, which is currently performed
manually. Last but not least, we collected a new dataset of stamp-seals of the
Southern Levant.

Power sector decarbonization plays a vital role in the upcoming energy
transition towards a more sustainable future. Decentralized energy resources,
such as Electric Vehicles (EV) and solar photovoltaic systems (PV), are
continuously integrated in residential power systems, increasing the risk of
bottlenecks in power distribution networks. This paper aims to address the
challenge of domestic EV charging while prioritizing clean, solar energy
consumption. Real Time-of-Use tariffs are treated as a price-based Demand
Response (DR) mechanism that can incentivize end-users to optimally shift EV
charging load in hours of high solar PV generation with the use of Deep
Reinforcement Learning (DRL). Historical measurements from the Pecan Street
dataset are analyzed to shape a flexibility potential reward to describe
end-user charging preferences. Experimental results show that the proposed DQN
EV optimal charging policy is able to reduce electricity bills by an average
11.5\% by achieving an average utilization of solar power 88.4

This work aims at providing a new model for time series classification based
on learning from just one example. We assume that time series can be well
characterized as a parametric random process, a sort of Hidden semi-Markov
Model representing a sequence of regression models with variable duration. We
introduce a parametric stochastic model for time series pattern recognition and
provide a maximum-likelihood estimation of its parameters. Particularly, we are
interested in examining two different representations for state duration: i) a
discrete density distribution requiring an estimate for each possible duration;
and ii) a parametric family of continuous density functions, here the Gamma
distribution, with just two parameters to estimate. An application on heartbeat
classification reveals the main strengths and weaknesses of each alternative.

Deep convolutional neural networks (CNNs) have obtained remarkable
performance in single image super-resolution (SISR). However, very deep
networks can suffer from training difficulty and hardly achieve further
performance gain. There are two main trends to solve that problem: improving
the network architecture for better propagation of features through large
number of layers and designing an attention mechanism for selecting most
informative features. Recent SISR solutions propose advanced attention and
self-attention mechanisms. However, constructing a network to use an attention
block in the most efficient way is a challenging problem. To address this
issue, we propose a general recursively defined residual block (RDRB) for
better feature extraction and propagation through network layers. Based on RDRB
we designed recursively defined residual network (RDRN), a novel network
architecture which utilizes attention blocks efficiently. Extensive experiments
show that the proposed model achieves state-of-the-art results on several
popular super-resolution benchmarks and outperforms previous methods by up to
0.43 dB.

Sequential abstractive neural summarizers often do not use the underlying
structure in the input article or dependencies between the input sentences.
This structure is essential to integrate and consolidate information from
different parts of the text. To address this shortcoming, we propose a
hierarchy-aware graph neural network (HierGNN) which captures such dependencies
through three main steps: 1) learning a hierarchical document structure through
a latent structure tree learned by a sparse matrix-tree computation; 2)
propagating sentence information over this structure using a novel
message-passing node propagation mechanism to identify salient information; 3)
using graph-level attention to concentrate the decoder on salient information.
Experiments confirm HierGNN improves strong sequence models such as BART, with
a 0.55 and 0.75 margin in average ROUGE-1/2/L for CNN/DM and XSum. Further
human evaluation demonstrates that summaries produced by our model are more
relevant and less redundant than the baselines, into which HierGNN is
incorporated. We also find HierGNN synthesizes summaries by fusing multiple
source sentences more, rather than compressing a single source sentence, and
that it processes long inputs more effectively.

Analyses based on the body posture are crucial for top-class athletes in many
sports disciplines. If at all, coaches label only the most important keypoints,
since manual annotations are very costly. This paper proposes a method to
detect arbitrary keypoints on the limbs and skis of professional ski jumpers
that requires a few, only partly correct segmentation masks during training.
Our model is based on the Vision Transformer architecture with a special design
for the input tokens to query for the desired keypoints. Since we use
segmentation masks only to generate ground truth labels for the freely
selectable keypoints, partly correct segmentation masks are sufficient for our
training procedure. Hence, there is no need for costly hand-annotated
segmentation masks. We analyze different training techniques for freely
selected and standard keypoints, including pseudo labels, and show in our
experiments that only a few partly correct segmentation masks are sufficient
for learning to detect arbitrary keypoints on limbs and skis.

It is challenging to build a model that can correctly and unifiedly account
for the deconfinement phase transition and thermodynamics of the hot $SU(N)$
pure Yang-Mills (PYM) system, for any $N$. In this article, we slightly
generalize the massive PYM model to the situation with a quasigluon mass
$M_g(T)$ varying with temperature, inspired by the quasigluon model. In such a
framework, we can acquire an effective potential for the temporal gauge field
background by perturbative calculation, rather than adding by hand. The
resulting potential works well to describe the behavior of the hot PYM system
for all $N$, via the single parameter $M_g(T)$. Moreover, under the assumption
of unified eigenvalue distribution, the $M_g(T)$ fitted by machine learning is
found to follow $N$-universality.

Short-term memory in standard, general-purpose, sequence-processing recurrent
neural networks (RNNs) is stored as activations of nodes or "neurons."
Generalising feedforward NNs to such RNNs is mathematically straightforward and
natural, and even historical: already in 1943, McCulloch and Pitts proposed
this as a surrogate to "synaptic modifications" (in effect, generalising the
Lenz-Ising model, the first non-sequence processing RNN architecture of the
1920s). A lesser known alternative approach to storing short-term memory in
"synaptic connections" -- by parameterising and controlling the dynamics of a
context-sensitive time-varying weight matrix through another NN -- yields
another "natural" type of short-term memory in sequence processing NNs: the
Fast Weight Programmers (FWPs) of the early 1990s. FWPs have seen a recent
revival as generic sequence processors, achieving competitive performance
across various tasks. They are formally closely related to the now popular
Transformers. Here we present them in the context of artificial NNs as an
abstraction of biological NNs -- a perspective that has not been stressed
enough in previous FWP work. We first review aspects of FWPs for pedagogical
purposes, then discuss connections to related works motivated by insights from
neuroscience.

While most successful approaches for machine reading comprehension rely on
single training objective, it is assumed that the encoder layer can learn great
representation through the loss function we define in the predict layer, which
is cross entropy in most of time, in the case that we first use neural networks
to encode the question and paragraph, then directly fuse the encoding result of
them. However, due to the distantly loss backpropagating in reading
comprehension, the encoder layer cannot learn effectively and be directly
supervised. Thus, the encoder layer can not learn the representation well at
any time. Base on this, we propose to inject multi granularity information to
the encoding layer. Experiments demonstrate the effect of adding multi
granularity information to the encoding layer can boost the performance of
machine reading comprehension system. Finally, empirical study shows that our
approach can be applied to many existing MRC models.

We propose a simple yet effective image captioning framework that can
determine the quality of an image and notify the user of the reasons for any
flaws in the image. Our framework first determines the quality of images and
then generates captions using only those images that are determined to be of
high quality. The user is notified by the flaws feature to retake if image
quality is low, and this cycle is repeated until the input image is deemed to
be of high quality. As a component of the framework, we trained and evaluated a
low-quality image detection model that simultaneously learns difficulty in
recognizing images and individual flaws, and we demonstrated that our proposal
can explain the reasons for flaws with a sufficient score. We also evaluated a
dataset with low-quality images removed by our framework and found improved
values for all four common metrics (e.g., BLEU-4, METEOR, ROUGE-L, CIDEr),
confirming an improvement in general-purpose image captioning capability. Our
framework would assist the visually impaired, who have difficulty judging image
quality.

Machine learning (ML) techniques increase the effectiveness of software
engineering (SE) lifecycle activities. We systematically collected,
quality-assessed, summarized, and categorized 83 reviews in ML for SE published
between 2009-2022, covering 6,117 primary studies. The SE areas most tackled
with ML are software quality and testing, while human-centered areas appear
more challenging for ML. We propose a number of ML for SE research challenges
and actions including: conducting further empirical validation and industrial
studies on ML; reconsidering deficient SE methods; documenting and automating
data collection and pipeline processes; reexamining how industrial
practitioners distribute their proprietary data; and implementing incremental
ML approaches.

We propose a sim-to-real framework for dexterous manipulation which can
generalize to new objects of the same category in the real world. The key of
our framework is to train the manipulation policy with point cloud inputs and
dexterous hands. We propose two new techniques to enable joint learning on
multiple objects and sim-to-real generalization: (i) using imagined hand point
clouds as augmented inputs; and (ii) designing novel contact-based rewards. We
empirically evaluate our method using an Allegro Hand to grasp novel objects in
both simulation and real world. To the best of our knowledge, this is the first
policy learning-based framework that achieves such generalization results with
dexterous hands. Our project page is available at
https://yzqin.github.io/dexpoint

Federated learning is a distributed learning that allows each client to keep
the original data locally and only upload the parameters of the local model to
the server. Despite federated learning can address data island, it remains
challenging to train with data heterogeneous in a real application. In this
paper, we propose FedSiam-DA, a novel dual-aggregated contrastive federated
learning approach, to personalize both local and global models, under various
settings of data heterogeneity. Firstly, based on the idea of contrastive
learning in the Siamese Network, FedSiam-DA regards the local and global model
as different branches of the Siamese Network during the local training and
controls the update direction of the model by constantly changing model
similarity to personalize the local model. Secondly, FedSiam-DA introduces
dynamic weights based on model similarity for each local model and exercises
the dual-aggregated mechanism to further improve the generalization of the
global model. Moreover, we provide extensive experiments on benchmark datasets,
the results demonstrate that FedSiam-DA achieves outperforming several previous
FL approaches on heterogeneous datasets.

Koopman operator theory is receiving increased attention due to its promise
to linearize nonlinear dynamics. Neural networks that are developed to
represent Koopman operators have shown great success thanks to their ability to
approximate arbitrarily complex functions. However, despite their great
potential, they typically require large training data-sets either from
measurements of a real system or from high-fidelity simulations. In this work,
we propose a novel architecture inspired by physics-informed neural networks,
which leverage automatic differentiation to impose the underlying physical laws
via soft penalty constraints during model training. We demonstrate that it not
only reduces the need of large training data-sets, but also maintains high
effectiveness in approximating Koopman eigenfunctions.

In this paper, a deep learning color image steganography scheme combining
convolutional autoencoders and ResNet architecture is proposed. Traditional
steganography methods suffer from some critical defects such as low capacity,
security, and robustness. In recent decades, image hiding and image extraction
were realized by autoencoder convolutional neural networks to solve the
aforementioned challenges. The contribution of this paper is introducing a new
scheme for color image steganography inspired by ResNet architecture. The
reverse ResNet architecture is utilized to extract the secret image from the
stego image. In the proposed method, all images are passed through the
prepossess model which is a convolutional deep neural network with the aim of
feature extraction. Then, the operational model generates stego and extracted
images. In fact, the operational model is an autoencoder based on ResNet
structure that produces an image from feature maps. The advantage of proposed
structure is identity of models in embedding and extraction phases. The
performance of the proposed method is studied using COCO and CelebA datasets.
For quantitative comparisons with previous related works, peak signal-to-noise
ratio (PSNR), the structural similarity index (SSIM) and hiding capacity are
evaluated. The experimental results verify that the proposed scheme performs
better than traditional and pervious deep steganography methods. The PSNR and
SSIM are more than 40 dB and 0.98, respectively that implies high
imperceptibility of the proposed method. Also, this method can hide a color
image of the same size in another color image, which can be inferred that the
relative capacity of the proposed method is 8 bits per pixel.

Intelligent fault diagnosis is essential to safe operation of machinery.
However, due to scarce fault samples and data heterogeneity in field machinery,
deep learning based diagnosis methods are prone to over-fitting with poor
generalization ability. To solve the problem, this paper proposes a
personalized federated learning framework, enabling multi-task fault diagnosis
method across multiple factories in a privacypreserving manner. Firstly,
rotating machines from different factories with similar vibration feature data
are categorized into machine groups using a federated clustering method. Then,
a multi-task deep learning model based on convolutional neural network is
constructed to diagnose the multiple faults of machinery with heterogeneous
information fusion. Finally, a personalized federated learning framework is
proposed to solve data heterogeneity across different machines using adaptive
hierarchical aggregation strategy. The case study on collected data from real
machines verifies the effectiveness of the proposed framework. The result shows
that the diagnosis accuracy could be improved significantly using the proposed
personalized federated learning, especially for those machines with scarce
fault samples.

We present an algorithm for use in learning mixtures of both Markov chains
(MCs) and Markov decision processes (offline latent MDPs) from trajectories,
with roots dating back to the work of Vempala and Wang. This amounts to
handling Markov chains with optional control input. The method is modular in
nature and amounts to (1) a subspace estimation step, (2) spectral clustering
of trajectories, and (3) a few iterations of the EM algorithm. We provide
end-to-end performance guarantees where we only explicitly require the number
of trajectories to be linear in states and the trajectory length to be linear
in mixing time. Experimental results suggest it outperforms both EM (95.4% on
average) and a previous method by Gupta et al. (54.1%), obtaining 100% permuted
accuracy on an 8x8 gridworld.

Data dimension reduction (DDR) is all about mapping data from high dimensions
to low dimensions, various techniques of DDR are being used for image dimension
reduction like Random Projections, Principal Component Analysis (PCA), the
Variance approach, LSA-Transform, the Combined and Direct approaches, and the
New Random Approach. Auto-encoders (AE) are used to learn end-to-end mapping.
In this paper, we demonstrate that pre-processing not only speeds up the
algorithms but also improves accuracy in both supervised and unsupervised
learning. In pre-processing of DDR, first PCA based DDR is used for supervised
learning, then we explore AE based DDR for unsupervised learning. In PCA based
DDR, we first compare supervised learning algorithms accuracy and time before
and after applying PCA. Similarly, in AE based DDR, we compare unsupervised
learning algorithm accuracy and time before and after AE representation
learning. Supervised learning algorithms including support-vector machines
(SVM), Decision Tree with GINI index, Decision Tree with entropy and Stochastic
Gradient Descent classifier (SGDC) and unsupervised learning algorithm
including K-means clustering, are used for classification purpose. We used two
datasets MNIST and FashionMNIST Our experiment shows that there is massive
improvement in accuracy and time reduction after pre-processing in both
supervised and unsupervised learning.

Tensor Gaussian graphical models (GGMs), interpreting conditional
independence structures within tensor data, have important applications in
numerous areas. Yet, the available tensor data in one single study is often
limited due to high acquisition costs. Although relevant studies can provide
additional data, it remains an open question how to pool such heterogeneous
data. In this paper, we propose a transfer learning framework for tensor GGMs,
which takes full advantage of informative auxiliary domains even when
non-informative auxiliary domains are present, benefiting from the carefully
designed data-adaptive weights. Our theoretical analysis shows substantial
improvement of estimation errors and variable selection consistency on the
target domain under much relaxed conditions, by leveraging information from
auxiliary domains. Extensive numerical experiments are conducted on both
synthetic tensor graphs and a brain functional connectivity network data, which
demonstrates the satisfactory performance of the proposed method.

Document retrieval is a core component of many knowledge-intensive natural
language processing task formulations such as fact verification and question
answering. Sources of textual knowledge, such as Wikipedia articles, condition
the generation of answers from the models. Recent advances in retrieval use
sequence-to-sequence models to incrementally predict the title of the
appropriate Wikipedia page given a query. However, this method requires
supervision in the form of human annotation to label which Wikipedia pages
contain appropriate context. This paper introduces a distant-supervision method
that does not require any annotation to train autoregressive retrievers that
attain competitive R-Precision and Recall in a zero-shot setting. Furthermore
we show that with task-specific supervised fine-tuning, autoregressive
retrieval performance for two Wikipedia-based fact verification tasks can
approach or even exceed full supervision using less than $1/4$ of the annotated
data indicating possible directions for data-efficient autoregressive
retrieval.

Object packing by autonomous robots is an im-portant challenge in warehouses
and logistics industry. Most conventional data-driven packing planning
approaches focus on regular cuboid packing, which are usually heuristic and
limit the practical use in realistic applications with everyday objects. In
this paper, we propose a deep hierarchical reinforcement learning approach to
simultaneously plan packing sequence and placement for irregular object
packing. Specifically, the top manager network infers packing sequence from six
principal view heightmaps of all objects, and then the bottom worker network
receives heightmaps of the next object to predict the placement position and
orientation. The two networks are trained hierarchically in a self-supervised
Q-Learning framework, where the rewards are provided by the packing results
based on the top height , object volume and placement stability in the box. The
framework repeats sequence and placement planning iteratively until all objects
have been packed into the box or no space is remained for unpacked items. We
compare our approach with existing robotic packing methods for irregular
objects in a physics simulator. Experiments show that our approach can pack
more objects with less time cost than the state-of-the-art packing methods of
irregular objects. We also implement our packing plan with a robotic
manipulator to show the generalization ability in the real world.

Many Partial Differential Equations (PDEs) do not have analytical solution,
and can only be solved by numerical methods. In this context, Physics-Informed
Neural Networks (PINN) have become important in the last decades, since it uses
a neural network and physical conditions to approximate any functions. This
paper focuses on hypertuning of a PINN, used to solve a PDE. The behavior of
the approximated solution when we change the learning rate or the activation
function (sigmoid, hyperbolic tangent, GELU, ReLU and ELU) is here analyzed. A
comparative study is done to determine the best characteristics in the problem,
as well as to find a learning rate that allows fast and satisfactory learning.
GELU and hyperbolic tangent activation functions exhibit better performance
than other activation functions. A suitable choice of the learning rate results
in higher accuracy and faster convergence.

Event detection improves when events are captured by two different modalities
rather than just one. But to train detection systems on multiple modalities is
challenging, in particular when there is abundance of unlabelled data but
limited amounts of labeled data. We develop a novel self-supervised learning
technique for multi-modal data that learns (hidden) correlations between
simultaneously recorded microphone (sound) signals and accelerometer (body
vibration) signals. The key objective of this work is to learn useful
embeddings associated with high performance in downstream event detection tasks
when labeled data is scarce and the audio events of interest (songbird
vocalizations) are sparse. We base our approach on deep canonical correlation
analysis (DCCA) that suffers from event sparseness. We overcome the sparseness
of positive labels by first learning a data sampling model from the labelled
data and by applying DCCA on the output it produces. This method that we term
balanced DCCA (b-DCCA) improves the performance of the unsupervised embeddings
on the downstream supervised audio detection task compared to classsical DCCA.
Because data labels are frequently imbalanced, our method might be of broad
utility in low-resource scenarios.

Automatically generating textual descriptions for massive unlabeled images on
the web can greatly benefit realistic web applications, e.g. multimodal
retrieval and recommendation. However, existing models suffer from the problem
of generating ``over-generic'' descriptions, such as their tendency to generate
repetitive sentences with common concepts for different images. These generic
descriptions fail to provide sufficient textual semantics for ever-changing web
images. Inspired by the recent success of Vision-Language Pre-training (VLP)
models that learn diverse image-text concept alignment during pretraining, we
explore leveraging their cross-modal pre-trained knowledge to automatically
enrich the textual semantics of image descriptions. With no need for additional
human annotations, we propose a plug-and-play framework, i.e CapEnrich, to
complement the generic image descriptions with more semantic details.
Specifically, we first propose an automatic data-building strategy to get
desired training sentences, based on which we then adopt prompting strategies,
i.e. learnable and template prompts, to incentivize VLP models to generate more
textual details. For learnable templates, we fix the whole VLP model and only
tune the prompt vectors, which leads to two advantages: 1) the pre-training
knowledge of VLP models can be reserved as much as possible to describe diverse
visual concepts; 2) only lightweight trainable parameters are required, so it
is friendly to low data resources. Extensive experiments show that our method
significantly improves the descriptiveness and diversity of generated sentences
for web images. Our code will be released.

While deep learning-based text-to-speech (TTS) models such as VITS have shown
excellent results, they typically require a sizable set of high-quality <text,
audio> pairs to train, which is expensive to collect. So far, most languages in
the world still lack the training data needed to develop TTS systems. This
paper proposes two improvement methods for the two problems faced by
low-resource Mongolian speech synthesis: a) In view of the lack of high-quality
<text, audio> pairs of data, it is difficult to model the mapping problem from
linguistic features to acoustic features. Improvements are made using
pre-trained VITS model and transfer learning methods. b) In view of the problem
of less labeled information, this paper proposes to use an automatic prosodic
annotation method to label the prosodic information of text and corresponding
speech, thereby improving the naturalness and intelligibility of low-resource
Mongolian language. Through empirical research, the N-MOS of the method
proposed in this paper is 4.195, and the I-MOS is 4.228.

SGD (with momentum) and AdamW are the two most used optimizers for
fine-tuning large neural networks in computer vision. When the two methods
perform the same, SGD is preferable because it uses less memory (12
bytes/parameter) than AdamW (16 bytes/parameter). However, on a suite of
downstream tasks, especially those with distribution shifts, we show that
fine-tuning with AdamW performs substantially better than SGD on modern Vision
Transformer and ConvNeXt models. We find that large gaps in performance between
SGD and AdamW occur when the fine-tuning gradients in the first "embedding"
layer are much larger than in the rest of the model. Our analysis suggests an
easy fix that works consistently across datasets and models: merely freezing
the embedding layer (less than 1\% of the parameters) leads to SGD performing
competitively with AdamW while using less memory. Our insights result in
state-of-the-art accuracies on five popular distribution shift benchmarks:
WILDS-FMoW, WILDS-Camelyon, Living-17, Waterbirds, and DomainNet.

With the development of machine learning, it is difficult for a single server
to process all the data. So machine learning tasks need to be spread across
multiple servers, turning centralized machine learning into a distributed one.
However, privacy remains an unsolved problem in distributed machine learning.
Multi-key homomorphic encryption over torus (MKTFHE) is one of the suitable
candidates to solve the problem. However, there may be security risks in the
decryption of MKTFHE and the most recent result about MKFHE only supports the
Boolean operation and linear operation. So, MKTFHE cannot compute the
non-linear function like Sigmoid directly and it is still hard to perform
common machine learning such as logistic regression and neural networks in high
performance.
  This paper first introduces secret sharing to propose a new distributed
decryption protocol for MKTFHE, then designs an MKTFHE-friendly activation
function, and finally utilizes them to implement logistic regression and neural
network training in MKTFHE. We prove the correctness and security of our
decryption protocol and compare the efficiency and accuracy between using
Taylor polynomials of Sigmoid and our proposed function as an activation
function. The experiments show that the efficiency of our function is 10 times
higher than using 7-order Taylor polynomials straightly and the accuracy of the
training model is similar to that of using a high-order polynomial as an
activation function scheme.

Interest in thermal to visible face recognition has grown significantly over
the last decade due to advancements in thermal infrared cameras and analytics
beyond the visible spectrum. Despite large discrepancies between thermal and
visible spectra, existing approaches bridge domain gaps by either synthesizing
visible faces from thermal faces or by learning the cross-spectrum image
representations. These approaches typically work well with frontal facial
imagery collected at varying ranges and expressions, but exhibit significantly
reduced performance when matching thermal faces with varying poses to frontal
visible faces. We propose a novel Domain and Pose Invariant Framework that
simultaneously learns domain and pose invariant representations. Our proposed
framework is composed of modified networks for extracting the most correlated
intermediate representations from off-pose thermal and frontal visible face
imagery, a sub-network to jointly bridge domain and pose gaps, and a joint-loss
function comprised of cross-spectrum and pose-correction losses. We demonstrate
efficacy and advantages of the proposed method by evaluating on three
thermal-visible datasets: ARL Visible-to-Thermal Face, ARL Multimodal Face, and
Tufts Face. Although DPIF focuses on learning to match off-pose thermal to
frontal visible faces, we also show that DPIF enhances performance when
matching frontal thermal face images to frontal visible face images.

Blockchains with smart contracts are distributed ledger systems which achieve
block state consistency among distributed nodes by only allowing deterministic
operations of smart contracts. However, the power of smart contracts is enabled
by interacting with stochastic off-chain data, which in turn opens the
possibility to undermine the block state consistency. To address this issue, an
oracle smart contract is used to provide a single consistent source of external
data; but, simultaneously this introduces a single point of failure, which is
called the oracle problem. To address the oracle problem, we propose an
adaptive conformal consensus (ACon$^2$) algorithm, which derives consensus from
multiple oracle contracts via the recent advance in online uncertainty
quantification learning. In particular, the proposed algorithm returns a
consensus set, which quantifies the uncertainty of data and achieves a desired
correctness guarantee in the presence of Byzantine adversaries and distribution
shift. We demonstrate the efficacy of the proposed algorithm on two price
datasets and an Ethereum case study. In particular, the Solidity implementation
of the proposed algorithm shows the practicality of the proposed algorithm,
implying that online machine learning algorithms are applicable to address
issues in blockchains.

For the multivariate linear regression model with unknown covariance, the
corrected Akaike information criterion is the minimum variance unbiased
estimator of the expected Kullback--Leibler discrepancy. In this study, based
on the loss estimation framework, we show its inadmissibility as an estimator
of the Kullback--Leibler discrepancy itself, instead of the expected
Kullback--Leibler discrepancy. We provide improved estimators of the
Kullback--Leibler discrepancy that work well in reduced-rank situations and
examine their performance numerically.

How do we imbue robots with the ability to efficiently manipulate unseen
objects and transfer relevant skills based on demonstrations? End-to-end
learning methods often fail to generalize to novel objects or unseen
configurations. Instead, we focus on the task-specific pose relationship
between relevant parts of interacting objects. We conjecture that this
relationship is a generalizable notion of a manipulation task that can transfer
to new objects in the same category; examples include the relationship between
the pose of a pan relative to an oven or the pose of a mug relative to a mug
rack. We call this task-specific pose relationship ``cross-pose" and provide a
mathematical definition of this concept. We propose a vision-based system that
learns to estimate the cross-pose between two objects for a given manipulation
task using learned cross-object correspondences. The estimated cross-pose is
then used to guide a downstream motion planner to manipulate the objects into
the desired pose relationship (placing a pan into the oven or the mug onto the
mug rack). We demonstrate our method's capability to generalize to unseen
objects, in some cases after training on only 10 demonstrations in the real
world. Results show that our system achieves state-of-the-art performance in
both simulated and real-world experiments across a number of tasks.
Supplementary information and videos can be found at
https://sites.google.com/view/tax-pose/home.

Collaborative filtering is one of the most influential recommender system
types. Various methods have been proposed for collaborative filtering, ranging
from matrix factorization to graph convolutional methods. Being inspired by
recent successes of GF-CF and diffusion models, we present a novel concept of
blurring-sharpening process model (BSPM). Diffusion models and BSPMs share the
same processing philosophy in that new information is discovered (e.g., a new
image is generated in the case of diffusion models) while original information
is first perturbed and then recovered to its original form. However, diffusion
models and our BSPMs deal with different types of information, and their
optimal perturbation and recovery processes have a fundamental discrepancy.
Therefore, our BSPMs have different forms from diffusion models. In addition,
our concept not only theoretically subsumes many existing collaborative
filtering models but also outperforms them in terms of Recall and NDCG in the
three benchmark datasets, Gowalla, Yelp2018, and Amazon-book. Our model marks
the best accuracy in them. In addition, the processing time of our method is
one of the shortest cases ever in collaborative filtering. Our proposed concept
has much potential in the future to be enhanced by designing better blurring
(i.e., perturbation) and sharpening (i.e., recovery) processes than what we use
in this paper.

The Zero-Shot Learning (ZSL) task attempts to learn concepts without any
labeled data. Unlike traditional classification/detection tasks, the evaluation
environment is provided unseen classes never encountered during training. As
such, it remains both challenging, and promising on a variety of fronts,
including unsupervised concept learning, domain adaptation, and dataset drift
detection. Recently, there have been a variety of approaches towards solving
ZSL, including improved metric learning methods, transfer learning,
combinations of semantic and image domains using, e.g. word vectors, and
generative models to model the latent space of known classes to classify unseen
classes. We find many approaches require intensive training augmentation with
attributes or features that may be commonly unavailable (attribute-based
learning) or susceptible to adversarial attacks (generative learning). We
propose combining approaches from the related person re-identification task for
ZSL, with key modifications to ensure sufficiently improved performance in the
ZSL setting without the need for feature or training dataset augmentation. We
are able to achieve state-of-the-art performance on the CUB200 and Cars196
datasets in the ZSL setting compared to recent works, with NMI (normalized
mutual inference) of 63.27 and top-1 of 61.04 for CUB200, and NMI 66.03 with
top-1 82.75% in Cars196. We also show state-of-the-art results in the
Generalized Zero-Shot Learning (GZSL) setting, with Harmonic Mean R-1 of 66.14%
on the CUB200 dataset.

Nonlinear dimensionality reduction lacks interpretability due to the absence
of source features in low-dimensional embedding space. We propose an
interpretable method featMAP to preserve source features by tangent space
embedding. The core of our proposal is to utilize local singular value
decomposition (SVD) to approximate the tangent space which is embedded to
low-dimensional space by maintaining the alignment. Based on the embedding
tangent space, featMAP enables the interpretability by locally demonstrating
the source features and feature importance. Furthermore, featMAP embeds the
data points by anisotropic projection to preserve the local similarity and
original density. We apply featMAP to interpreting digit classification, object
detection and MNIST adversarial examples. FeatMAP uses source features to
explicitly distinguish the digits and objects and to explain the
misclassification of adversarial examples. We also compare featMAP with other
state-of-the-art methods on local and global metrics.

Communication costs within Federated learning hinder the system scalability
for reaching more data from more clients. The proposed FL adopts a
hub-and-spoke network topology. All clients communicate through the central
server. Hence, reducing communication overheads via techniques such as data
compression has been proposed to mitigate this issue. Another challenge of
federated learning is unbalanced data distribution, data on each client are not
independent and identically distributed (non-IID) in a typical federated
learning setting. In this paper, we proposed a new compression compensation
scheme called Global Momentum Fusion (GMF) which reduces communication
overheads between FL clients and the server and maintains comparable model
accuracy in the presence of non-IID data. GitHub repository:
https://github.com/tony92151/global-momentum-fusion-fl

Artificial intelligence (AI) continues to transform data analysis in many
domains. Progress in each domain is driven by a growing body of annotated data,
increased computational resources, and technological innovations. In medicine,
the sensitivity of the data, the complexity of the tasks, the potentially high
stakes, and a requirement of accountability give rise to a particular set of
challenges. In this review, we focus on three key methodological approaches
that address some of the particular challenges in AI-driven medical decision
making. (1) Explainable AI aims to produce a human-interpretable justification
for each output. Such models increase confidence if the results appear
plausible and match the clinicians expectations. However, the absence of a
plausible explanation does not imply an inaccurate model. Especially in highly
non-linear, complex models that are tuned to maximize accuracy, such
interpretable representations only reflect a small portion of the
justification. (2) Domain adaptation and transfer learning enable AI models to
be trained and applied across multiple domains. For example, a classification
task based on images acquired on different acquisition hardware. (3) Federated
learning enables learning large-scale models without exposing sensitive
personal health information. Unlike centralized AI learning, where the
centralized learning machine has access to the entire training data, the
federated learning process iteratively updates models across multiple sites by
exchanging only parameter updates, not personal health data. This narrative
review covers the basic concepts, highlights relevant corner-stone and
state-of-the-art research in the field, and discusses perspectives.

Modeling the speaker variability is a key challenge for automatic speech
recognition (ASR) systems. In this paper, the learning hidden unit
contributions (LHUC) based adaptation techniques with compact speaker dependent
(SD) parameters are used to facilitate both speaker adaptive training (SAT) and
unsupervised test-time speaker adaptation for end-to-end (E2E) lattice-free MMI
(LF-MMI) models. An unsupervised model-based adaptation framework is proposed
to estimate the SD parameters in E2E paradigm using LF-MMI and cross entropy
(CE) criterions. Various regularization methods of the standard LHUC
adaptation, e.g., the Bayesian LHUC (BLHUC) adaptation, are systematically
investigated to mitigate the risk of overfitting, on E2E LF-MMI CNN-TDNN and
CNN-TDNN-BLSTM models. Lattice-based confidence score estimation is used for
adaptation data selection to reduce the supervision label uncertainty.
Experiments on the 300-hour Switchboard task suggest that applying BLHUC in the
proposed unsupervised E2E adaptation framework to byte pair encoding (BPE)
based E2E LF-MMI systems consistently outperformed the baseline systems by
relative word error rate (WER) reductions up to 10.5% and 14.7% on the NIST
Hub5'00 and RT03 evaluation sets, and achieved the best performance in WERs of
9.0% and 9.7%, respectively. These results are comparable to the results of
state-of-the-art adapted LF-MMI hybrid systems and adapted Conformer-based E2E
systems.

Correctly recognizing the behaviors of children with Autism Spectrum Disorder
(ASD) is of vital importance for the diagnosis of Autism and timely early
intervention. However, the observation and recording during the treatment from
the parents of autistic children may not be accurate and objective. In such
cases, automatic recognition systems based on computer vision and machine
learning (in particular deep learning) technology can alleviate this issue to a
large extent. Existing human action recognition models can now achieve
persuasive performance on challenging activity datasets, e.g. daily activity,
and sports activity. However, problem behaviors in children with ASD are very
different from these general activities, and recognizing these problem
behaviors via computer vision is less studied. In this paper, we first evaluate
a strong baseline for action recognition, i.e. Video Swin Transformer, on two
autism behaviors datasets (SSBD and ESBD) and show that it can achieve high
accuracy and outperform the previous methods by a large margin, demonstrating
the feasibility of vision-based problem behaviors recognition. Moreover, we
propose language-assisted training to further enhance the action recognition
performance. Specifically, we develop a two-branch multimodal deep learning
framework by incorporating the "freely available" language description for each
type of problem behavior. Experimental results demonstrate that incorporating
additional language supervision can bring an obvious performance boost for the
autism problem behaviors recognition task as compared to using the video
information only (i.e. 3.49% improvement on ESBD and 1.46% on SSBD).

This paper aims to develop resilient transmission mechanisms to suitably
distribute traffic across multiple paths in an arbitrary millimeter-wave
(mmWave) network. The main contributions include: (a) the development of
proactive transmission mechanisms that build resilience against network
disruptions in advance, while achieving a high end-to-end packet rate; (b) the
design of a heuristic path selection algorithm that efficiently selects (in
polynomial time in the network size) multiple proactively resilient paths with
high packet rates; and (c) the development of a hybrid scheduling algorithm
that combines the proposed path selection algorithm with a deep reinforcement
learning (DRL) based online approach for decentralized adaptation to blocked
links and failed paths. To achieve resilience to link failures, a
state-of-the-art Soft Actor-Critic DRL algorithm, which adapts the information
flow through the network, is investigated. The proposed scheduling algorithm
robustly adapts to link failures over different topologies, channel and
blockage realizations while offering a superior performance to alternative
algorithms.

Machining dynamics research lays a solid foundation for machining operations
by providing stable combinations of spindle speed and depth of cut.
Furthermore, machine learning has been applied to predict tool life as a
function of cutting speed. However, the existing research does not consider the
discrete-event dynamics in machine shop, i.e., the machine tool needs to
process a series of parts in queue under various practical production
requirements. This paper addresses the integration of discrete-event dynamics
and machining dynamics to achieve cost savings in machining. We first propose a
learning-based cost function for the studied integrated optimization problem of
machine tool. The proposed cost function utilizes the predicted tool life under
different stable cutting speeds for further optimizing speed selection of
machine tool to deal with the discrete-event dynamics in machine shop. Then,
according to the practical production requirements, we develop several
mathematical optimization models for the related integrated optimization
problems with the consideration of cost, makespan and due date. Numerical
results show the effectiveness of our proposed methods and also the potential
to be used in practice.

The image-based 3D object detection task expects that the predicted 3D
bounding box has a ``tightness'' projection (also referred to as cuboid), which
fits the object contour well on the image while still keeping the geometric
attribute on the 3D space, e.g., physical dimension, pairwise orthogonal, etc.
These requirements bring significant challenges to the annotation. Simply
projecting the Lidar-labeled 3D boxes to the image leads to non-trivial
misalignment, while directly drawing a cuboid on the image cannot access the
original 3D information. In this work, we propose a learning-based 3D box
adaptation approach that automatically adjusts minimum parameters of the
360$^{\circ}$ Lidar 3D bounding box to perfectly fit the image appearance of
panoramic cameras. With only a few 2D boxes annotation as guidance during the
training phase, our network can produce accurate image-level cuboid annotations
with 3D properties from Lidar boxes. We call our method ``you only label
once'', which means labeling on the point cloud once and automatically adapting
to all surrounding cameras. As far as we know, we are the first to focus on
image-level cuboid refinement, which balances the accuracy and efficiency well
and dramatically reduces the labeling effort for accurate cuboid annotation.
Extensive experiments on the public Waymo and NuScenes datasets show that our
method can produce human-level cuboid annotation on the image without needing
manual adjustment.

Coadded astronomical images are created by stacking multiple single-exposure
images. Because coadded images are smaller in terms of data size than the
single-exposure images they summarize, loading and processing them is less
computationally expensive. However, image coaddition introduces additional
dependence among pixels, which complicates principled statistical analysis of
them. We present a principled Bayesian approach for performing light source
parameter inference with coadded astronomical images. Our method implicitly
marginalizes over the single-exposure pixel intensities that contribute to the
coadded images, giving it the computational efficiency necessary to scale to
next-generation astronomical surveys. As a proof of concept, we show that our
method for estimating the locations and fluxes of stars using simulated coadds
outperforms a method trained on single-exposure images.

Federated learning allows multiple clients to collaboratively train a model
without exchanging their data, thus preserving data privacy. Unfortunately, it
suffers significant performance degradation under heterogeneous data at
clients. Common solutions in local training involve designing a specific
auxiliary loss to regularize weight divergence or feature inconsistency.
However, we discover that these approaches fall short of the expected
performance because they ignore the existence of a vicious cycle between
classifier divergence and feature mapping inconsistency across clients, such
that client models are updated in inconsistent feature space with diverged
classifiers. We then propose a simple yet effective framework named Federated
learning with Feature Anchors (FedFA) to align the feature mappings and
calibrate classifier across clients during local training, which allows client
models updating in a shared feature space with consistent classifiers. We
demonstrate that this modification brings similar classifiers and a virtuous
cycle between feature consistency and classifier similarity across clients.
Extensive experiments show that FedFA significantly outperforms the
state-of-the-art federated learning algorithms on various image classification
datasets under label and feature distribution skews.

We propose a decoding-based approach to detect context effects on neural
codes in longitudinal neural recording data. The approach is agnostic to how
information is encoded in neural activity, and can control for a variety of
possible confounding factors present in the data. We demonstrate our approach
by determining whether it is possible to decode location encoding from
prefrontal cortex in the mouse and, further, testing whether the encoding
changes due to task engagement.

Variable selection problem for the nonlinear Cox regression model is
considered. In survival analysis, one main objective is to identify the
covariates that are associated with the risk of experiencing the event of
interest. The Cox proportional hazard model is being used extensively in
survival analysis in studying the relationship between survival times and
covariates, where the model assumes that the covariate has a log-linear effect
on the hazard function. However, this linearity assumption may not be satisfied
in practice. In order to extract a representative subset of features, various
variable selection approaches have been proposed for survival data under the
linear Cox model. However, there exists little literature on variable selection
for the nonlinear Cox model. To break this gap, we extend the recently
developed deep learning-based variable selection model LassoNet to survival
data. Simulations are provided to demonstrate the validity and effectiveness of
the proposed method. Finally, we apply the proposed methodology to analyze a
real data set on diffuse large B-cell lymphoma.

Tabular data synthesis is an emerging approach to circumvent strict
regulations on data privacy while discovering knowledge through big data.
Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN,
CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data,
their training is sensitive to column permutations of input data. In this
paper, we first conduct an extensive empirical study to disclose such a
property of permutation invariance and an in-depth analysis of the existing
synthesizers. We show that changing the input column order worsens the
statistical difference between real and synthetic data by up to 38.67% due to
the encoding of tabular data and the network architectures. To fully unleash
the potential of big synthetic tabular data, we propose two solutions: (i)
AE-GAN, a synthesizer that uses an autoencoder network to represent the tabular
data and GAN networks to synthesize the latent representation, and (ii) a
feature sorting algorithm to find the suitable column order of input data for
CNN-based synthesizers. We evaluate the proposed solutions on five datasets in
terms of the sensitivity to the column permutation, the quality of synthetic
data, and the utility in downstream analyses. Our results show that we enhance
the property of permutation-invariance when training synthesizers and further
improve the quality and utility of synthetic data, up to 22%, compared to the
existing synthesizers.

Active learning has been studied extensively as a method for efficient data
collection. Among the many approaches in literature, Expected Error Reduction
(EER) (Roy and McCallum) has been shown to be an effective method for active
learning: select the candidate sample that, in expectation, maximally decreases
the error on an unlabeled set. However, EER requires the model to be retrained
for every candidate sample and thus has not been widely used for modern deep
neural networks due to this large computational cost. In this paper we
reformulate EER under the lens of Bayesian active learning and derive a
computationally efficient version that can use any Bayesian parameter sampling
method (such as arXiv:1506.02142). We then compare the empirical performance of
our method using Monte Carlo dropout for parameter sampling against state of
the art methods in the deep active learning literature. Experiments are
performed on four standard benchmark datasets and three WILDS datasets
(arXiv:2012.07421). The results indicate that our method outperforms all other
methods except one in the data shift scenario: a model dependent,
non-information theoretic method that requires an order of magnitude higher
computational cost (arXiv:1906.03671).

Emotional Surveillance is an emerging area with wide-reaching privacy
concerns. These concerns are exacerbated by ubiquitous IoT devices with
multiple sensors that can support these surveillance use cases. The work
presented here considers one such use case: the use of a speech emotion
recognition (SER) classifier tied to a smart speaker. This work demonstrates
the ability to evade black-box SER classifiers tied to a smart speaker without
compromising the utility of the smart speaker. This privacy concern is
considered through the lens of adversarial evasion of machine learning. Our
solution, Defeating Acoustic Recognition of Emotion via Genetic Programming
(DARE-GP), uses genetic programming to generate non-invasive additive audio
perturbations (AAPs). By constraining the evolution of these AAPs,
transcription accuracy can be protected while simultaneously degrading SER
classifier performance. The additive nature of these AAPs, along with an
approach that generates these AAPs for a fixed set of users in an utterance and
user location-independent manner, supports real-time, real-world evasion of SER
classifiers. DARE-GP's use of spectral features, which underlay the emotional
content of speech, allows the transferability of AAPs to previously unseen
black-box SER classifiers. Further, DARE-GP outperforms state-of-the-art SER
evasion techniques and is robust against defenses employed by a knowledgeable
adversary. The evaluations in this work culminate with acoustic evaluations
against two off-the-shelf commercial smart speakers, where a single AAP could
evade a black box classifier over 70% of the time. The final evaluation
deployed AAP playback on a small-form-factor system (raspberry pi) integrated
with a wake-word system to evaluate the efficacy of a real-world, real-time
deployment where DARE-GP is automatically invoked with the smart speaker's wake
word.

Matrix completion is a class of machine learning methods that concerns the
prediction of missing entries in a partially observed matrix. This paper
studies matrix completion for mixed data, i.e., data involving mixed types of
variables (e.g., continuous, binary, ordinal). We formulate it as a low-rank
matrix estimation problem under a general family of non-linear factor models
and then propose entrywise consistent estimators for estimating the low-rank
matrix. Tight probabilistic error bounds are derived for the proposed
estimators. The proposed methods are evaluated by simulation studies and
real-data applications for collaborative filtering and large-scale educational
assessment.

Human communication relies on common ground (CG), the mutual knowledge and
beliefs shared by participants, to produce coherent and interesting
conversations. In this paper, we demonstrate that current response generation
(RG) models produce generic and dull responses in dialogues because they act
reflexively, failing to explicitly model CG, both due to the lack of CG in
training data and the standard RG training procedure. We introduce Reflect, a
dataset that annotates dialogues with explicit CG (materialized as inferences
approximating shared knowledge and beliefs) and solicits 9k diverse
human-generated responses each following one common ground. Using Reflect, we
showcase the limitations of current dialogue data and RG models: less than half
of the responses in current data are rated as high quality (sensible, specific,
and interesting) and models trained using this data have even lower quality,
while most Reflect responses are judged high quality. Next, we analyze whether
CG can help models produce better-quality responses by using Reflect CG to
guide RG models. Surprisingly, we find that simply prompting GPT3 to "think"
about CG generates 30% more quality responses, showing promising benefits to
integrating CG into the RG process.

The t-distributed stochastic neighbor embedding (t- SNE) is a method for
interpreting high dimensional (HD) data by mapping each point to a low
dimensional (LD) space (usually two-dimensional). It seeks to retain the
structure of the data. An important component of the t-SNE algorithm is the
initialization procedure, which begins with the random initialization of an LD
vector. Points in this initial vector are then updated to minimize the loss
function (the KL divergence) iteratively using gradient descent. This leads
comparable points to attract one another while pushing dissimilar points apart.
We believe that, by default, these algorithms should employ some form of
informative initialization. Another essential component of the t-SNE is using a
kernel matrix, a similarity matrix comprising the pairwise distances among the
sequences. For t-SNE-based visualization, the Gaussian kernel is employed by
default in the literature. However, we show that kernel selection can also play
a crucial role in the performance of t-SNE. In this work, we assess the
performance of t-SNE with various alternative initialization methods and
kernels, using four different sets, out of which three are biological sequences
(nucleotide, protein, etc.) datasets obtained from various sources, such as the
well-known GISAID database for sequences of the SARS- CoV-2 virus. We perform
subjective and objective assessments of these alternatives. We use the
resulting t-SNE plots and k- ary neighborhood agreement (k-ANA) to evaluate and
compare the proposed methods with the baselines. We show that by using
different techniques, such as informed initialization and kernel matrix
selection, that t-SNE performs significantly better. Moreover, we show that
t-SNE also takes fewer iterations to converge faster with more intelligent
initialization.

Missing data is common in applied data science, particularly for tabular data
sets found in healthcare, social sciences, and natural sciences. Most
supervised learning methods work only on complete data, thus requiring
preprocessing, such as missing value imputation, to work on incomplete data
sets. However, imputation discards potentially useful information encoded by
the pattern of missing values. For data sets with informative missing patterns,
the Missing Indicator Method (MIM), which adds indicator variables to indicate
the missing pattern, can be used in conjunction with imputation to improve
model performance. We show experimentally that MIM improves performance for
informative missing values, and we prove that MIM does not hurt linear models
asymptotically for uninformative missing values. Nonetheless, MIM can increase
variance if many of the added indicators are uninformative, causing harm
particularly for high-dimensional data sets. To address this issue, we
introduce Selective MIM (SMIM), a method that adds missing indicators only for
features that have informative missing patterns. We show empirically that SMIM
performs at least as well as MIM across a range of experimental settings, and
improves MIM for high-dimensional data.

Recent interest in integrated sensing and communications has led to the
design of novel signal processing techniques to recover information from an
overlaid radar-communications signal. Here, we focus on a spectral coexistence
scenario, wherein the channels and transmit signals of both radar and
communications systems are unknown to the common receiver. In this dual-blind
deconvolution (DBD) problem, the receiver admits a multi-carrier wireless
communications signal that is overlaid with the radar signal reflected off
multiple targets. The communications and radar channels are represented by
continuous-valued range-times or delays corresponding to multiple transmission
paths and targets, respectively. Prior works addressed recovery of unknown
channels and signals in this ill-posed DBD problem through atomic norm
minimization but contingent on individual minimum separation conditions for
radar and communications channels. In this paper, we provide an optimal joint
separation condition using extremal functions from the Beurling-Selberg
interpolation theory. Thereafter, we formulate DBD as a low-rank modified
Hankel matrix retrieval and solve it via nuclear norm minimization. We estimate
the unknown target and communications parameters from the recovered low-rank
matrix using multiple signal classification (MUSIC) method. We show that the
joint separation condition also guarantees that the underlying Vandermonde
matrix for MUSIC is well-conditioned. Numerical experiments validate our
theoretical findings.

We present the first Learning-Augmented Binary Search Tree(BST) that attains
Static Optimality and Working-Set Bound given rough predictions. Following the
recent studies in algorithms with predictions and learned index structures,
Lin, Luo, and Woodruff (ICML 2022) introduced the concept of Learning-Augmented
BSTs, which aim to improve BSTs with learned advice. Unfortunately, their
construction gives only static optimality under strong assumptions on the
input.
  In this paper, we present a simple BST maintenance scheme that benefits from
learned advice. With proper predictions, the scheme achieves Static Optimality
and Working-Set Bound, respectively, which are important performance measures
for BSTs. Moreover, the scheme is robust to prediction errors and makes no
assumption on the input.

The study analyzes the impact of constant and alternating magnetic fields and
alternating electric fields on various growth parameters of soy plants: the
germination energy and capacity, plants emergence and number, the Yield(II) of
the fresh mass of seedlings, protein content, and photosynthetic parameters.
Four cultivars were used: MAVKA, MERLIN, VIOLETTA, and ANUSZKA. Moreover, the
advanced Machine Learning processing pipeline was proposed to distinguish the
impact of physical factors on photosynthetic parameters. It is possible to
distinguish exposition on different physical factors for the first three
cultivars; therefore, it indicates that the EM factors have some observable
effect on soy plants. Moreover, some influence of physical factors on growth
parameters was observed. The use of ELM (Electromagnetic) fields had a positive
impact on the germination rate in Merlin plants. The highest values were
recorded for the constant magnetic field (CMF) - Merlin, and the lowest for the
alternating electric field (AEF) - Violetta. An increase in terms of emergence
and number of plants after seed stimulation was observed for the Mavka
cultivar, except for the AEF treatment (number of plants after 30 days) (...)

Nuclear materials are often demanded to function for extended time in extreme
environments, including high radiation fluxes and transmutation, high
temperature and temperature gradients, stresses, and corrosive coolants. They
also have a wide range of microstructural and chemical makeup, with
multifaceted and often out-of-equilibrium interactions. Machine learning (ML)
is increasingly being used to tackle these complex time-dependent interactions
and aid researchers in developing models and making predictions, sometimes with
better accuracy than traditional modeling that focuses on one or two parameters
at a time. Conventional practices of acquiring new experimental data in nuclear
materials research are often slow and expensive, limiting the opportunity for
data-centric ML, but new methods are changing that paradigm. Here we review
high-throughput computational and experimental data approaches, especially
robotic experimentation and active learning that based on Gaussian process and
Bayesian optimization. We show ML examples in structural materials ( e.g.,
reactor pressure vessel (RPV) alloys and radiation detecting scintillating
materials) and highlight new techniques of high-throughput sample preparation
and characterizations, and automated radiation/environmental exposures and
real-time online diagnostics. This review suggests that ML models of material
constitutive relations in plasticity, damage, and even electronic and optical
responses to radiation are likely to become powerful tools as they develop.
Finally, we speculate on how the recent trends in artificial intelligence (AI)
and machine learning will soon make the utilization of ML techniques as
commonplace as the spreadsheet curve-fitting practices of today.

Deep neural networks lack straightforward ways to incorporate domain
knowledge and are notoriously considered black boxes. Prior works attempted to
inject domain knowledge into architectures implicitly through data
augmentation. Building on recent advances on equivariant neural networks, we
propose networks that explicitly encode domain knowledge, specifically
equivariance with respect to rotations. By using unfolded architectures, a rich
framework that originated from sparse coding and has theoretical guarantees, we
present interpretable networks with sparse activations. The equivariant
unfolded networks compete favorably with baselines, with only a fraction of
their parameters, as showcased on (rotated) MNIST and CIFAR-10.

Extensive work has demonstrated that equivariant neural networks can
significantly improve sample efficiency and generalization by enforcing an
inductive bias in the network architecture. These applications typically assume
that the domain symmetry is fully described by explicit transformations of the
model inputs and outputs. However, many real-life applications contain only
latent or partial symmetries which cannot be easily described by simple
transformations of the input. In these cases, it is necessary to learn symmetry
in the environment instead of imposing it mathematically on the network
architecture. We discover, surprisingly, that imposing equivariance constraints
that do not exactly match the domain symmetry is very helpful in learning the
true symmetry in the environment. We differentiate between extrinsic and
incorrect symmetry constraints and show that while imposing incorrect symmetry
can impede the model's performance, imposing extrinsic symmetry can actually
improve performance. We demonstrate that an equivariant model can significantly
outperform non-equivariant methods on domains with latent symmetries both in
supervised learning and in reinforcement learning for robotic manipulation and
control problems.

The progress in modelling time series and, more generally, sequences of
structured-data has recently revamped research in anomaly detection. The task
stands for identifying abnormal behaviours in financial series, IT systems,
aerospace measurements, and the medical domain, where anomaly detection may aid
in isolating cases of depression and attend the elderly. Anomaly detection in
time series is a complex task since anomalies are rare due to highly non-linear
temporal correlations and since the definition of anomalous is sometimes
subjective. Here we propose the novel use of Hyperbolic uncertainty for Anomaly
Detection (HypAD). HypAD learns self-supervisedly to reconstruct the input
signal. We adopt best practices from the state-of-the-art to encode the
sequence by an LSTM, jointly learnt with a decoder to reconstruct the signal,
with the aid of GAN critics. Uncertainty is estimated end-to-end by means of a
hyperbolic neural network. By using uncertainty, HypAD may assess whether it is
certain about the input signal but it fails to reconstruct it because this is
anomalous; or whether the reconstruction error does not necessarily imply
anomaly, as the model is uncertain, e.g. a complex but regular input signal.
The novel key idea is that a detectable anomaly is one where the model is
certain but it predicts wrongly. HypAD outperforms the current state-of-the-art
for univariate anomaly detection on established benchmarks based on data from
NASA, Yahoo, Numenta, Amazon, Twitter. It also yields state-of-the-art
performance on a multivariate dataset of anomaly activities in elderly home
residences, and it outperforms the baseline on SWaT. Overall, HypAD yields the
lowest false alarms at the best performance rate, thanks to successfully
identifying detectable anomalies.

Group lasso is a commonly used regularization method in statistical learning
in which parameters are eliminated from the model according to predefined
groups. However, when the groups overlap, optimizing the group lasso penalized
objective can be time-consuming on large-scale problems because of the
non-separability induced by the overlapping groups. This bottleneck has
seriously limited the application of overlapping group lasso regularization in
many modern problems, such as gene pathway selection and graphical model
estimation. In this paper, we propose a separable penalty as an approximation
of the overlapping group lasso penalty. Thanks to the separability, the
computation of regularization based on our penalty is substantially faster than
that of the overlapping group lasso, especially for large-scale and
high-dimensional problems. We show that the penalty is the tightest separable
relaxation of the overlapping group lasso norm within the family of
$\ell_{q_1}/\ell_{q_2}$ norms. Moreover, we show that the estimator based on
the proposed separable penalty is statistically equivalent to the one based on
the overlapping group lasso penalty with respect to their error bounds and the
rate-optimal performance under the squared loss. We demonstrate the faster
computational time and statistical equivalence of our method compared with the
overlapping group lasso in simulation examples and a classification problem of
cancer tumors based on gene expression and multiple gene pathways.

We present edBB-Demo, a demonstrator of an AI-powered research platform for
student monitoring in remote education. The edBB platform aims to study the
challenges associated to user recognition and behavior understanding in digital
platforms. This platform has been developed for data collection, acquiring
signals from a variety of sensors including keyboard, mouse, webcam,
microphone, smartwatch, and an Electroencephalography band. The information
captured from the sensors during the student sessions is modelled in a
multimodal learning framework. The demonstrator includes: i) Biometric user
authentication in an unsupervised environment; ii) Human action recognition
based on remote video analysis; iii) Heart rate estimation from webcam video;
and iv) Attention level estimation from facial expression analysis.

Online forums that allow for participatory engagement between users have been
transformative for the public discussion of many important issues. However,
such conversations can sometimes escalate into full-blown exchanges of hate and
misinformation. Existing approaches in natural language processing (NLP), such
as deep learning models for classification tasks, use as inputs only a single
comment or a pair of comments depending upon whether the task concerns the
inference of properties of the individual comments or the replies between pairs
of comments, respectively. But in online conversations, comments and replies
may be based on external context beyond the immediately relevant information
that is input to the model. Therefore, being aware of the conversations'
surrounding contexts should improve the model's performance for the inference
task at hand.
  We propose GraphNLI, a novel graph-based deep learning architecture that uses
graph walks to incorporate the wider context of a conversation in a principled
manner. Specifically, a graph walk starts from a given comment and samples
"nearby" comments in the same or parallel conversation threads, which results
in additional embeddings that are aggregated together with the initial
comment's embedding. We then use these enriched embeddings for downstream NLP
prediction tasks that are important for online conversations. We evaluate
GraphNLI on two such tasks - polarity prediction and misogynistic hate speech
detection - and found that our model consistently outperforms all relevant
baselines for both tasks. Specifically, GraphNLI with a biased root-seeking
random walk performs with a macro-F1 score of 3 and 6 percentage points better
than the best-performing BERT-based baselines for the polarity prediction and
hate speech detection tasks, respectively.

Capturing highly appreciated star field images is extremely challenging due
to light pollution, the requirements of specialized hardware, and the high
level of photographic skills needed. Deep learning-based techniques have
achieved remarkable results in low-light image enhancement (LLIE) but have not
been widely applied to star field image enhancement due to the lack of training
data. To address this problem, we construct the first Star Field Image
Enhancement Benchmark (SFIEB) that contains 355 real-shot and 854
semi-synthetic star field images, all having the corresponding reference
images. Using the presented dataset, we propose the first star field image
enhancement approach, namely StarDiffusion, based on conditional denoising
diffusion probabilistic models (DDPM). We introduce dynamic stochastic
corruptions to the inputs of conditional DDPM to improve the performance and
generalization of the network on our small-scale dataset. Experiments show
promising results of our method, which outperforms state-of-the-art low-light
image enhancement algorithms. The dataset and codes will be open-sourced.

This work provides theoretical foundations for kernel methods in the
hyperspherical context. Specifically, we characterise the native spaces
(reproducing kernel Hilbert spaces) and the Sobolev spaces associated with
kernels defined over hyperspheres. Our results have direct consequences for
kernel cubature, determining the rate of convergence of the worst case error,
and expanding the applicability of cubature algorithms based on Stein's method.
We first introduce a suitable characterisation on Sobolev spaces on the
$d$-dimensional hypersphere embedded in $(d+1)$-dimensional Euclidean spaces.
Our characterisation is based on the Fourier--Schoenberg sequences associated
with a given kernel. Such sequences are hard (if not impossible) to compute
analytically on $d$-dimensional spheres, but often feasible over Hilbert
spheres. We circumvent this problem by finding a projection operator that
allows to Fourier mapping from Hilbert into finite dimensional hyperspheres. We
illustrate our findings through some parametric families of kernels.

Comparing Bayesian neural networks (BNNs) with different widths is
challenging because, as the width increases, multiple model properties change
simultaneously, and, inference in the finite-width case is intractable. In this
work, we empirically compare finite- and infinite-width BNNs, and provide
quantitative and qualitative explanations for their performance difference. We
find that when the model is mis-specified, increasing width can hurt BNN
performance. In these cases, we provide evidence that finite-width BNNs
generalize better partially due to the properties of their frequency spectrum
that allows them to adapt under model mismatch.

Tasks critical to enterprise profitability, such as customer churn
prediction, fraudulent account detection or customer lifetime value estimation,
are often tackled by models trained on features engineered from customer data
in tabular format. Application-specific feature engineering adds development,
operationalization and maintenance costs over time. Recent advances in
representation learning present an opportunity to simplify and generalize
feature engineering across applications. When applying these advancements to
tabular data researchers deal with data heterogeneity, variations in customer
engagement history or the sheer volume of enterprise datasets. In this paper,
we propose a novel approach to encode tabular data containing customer
transactions, purchase history and other interactions into a generic
representation of a customer's association with the business. We then evaluate
these embeddings as features to train multiple models spanning a variety of
applications. CASPR, Customer Activity Sequence-based Prediction and
Representation, applies Transformer architecture to encode activity sequences
to improve model performance and avoid bespoke feature engineering across
applications. Our experiments at scale validate CASPR for both small \& large
enterprise applications.

While Emotion Recognition in Conversations (ERC) has seen a tremendous
advancement in the last few years, new applications and implementation
scenarios present novel challenges and opportunities. These range from
leveraging the conversational context, speaker and emotion dynamics modelling,
to interpreting common sense expressions, informal language and sarcasm,
addressing challenges of real time ERC and recognizing emotion causes. This
survey starts by introducing ERC, elaborating on the challenges and
opportunities pertaining to this task. It proceeds with a description of the
main emotion taxonomies and methods to deal with subjectivity in annotations.
It then describes Deep Learning methods relevant for ERC, word embeddings, and
elaborates on the use of performance metrics for the task and methods to deal
with the typically unbalanced ERC datasets. This is followed by a description
and benchmark of key ERC works along with comprehensive tables comparing
several works regarding their methods and performance across different
datasets. The survey highlights the advantage of leveraging techniques to
address unbalanced data, the exploration of mixed emotions and the benefits of
incorporating annotation subjectivity in the learning phase.

In some neural networks, individual neurons correspond to natural
``features'' in the input. Such \emph{monosemantic} neurons are of great help
in interpretability studies, as they can be cleanly understood. In this work we
report preliminary attempts to engineer monosemanticity in toy models. We find
that models can be made more monosemantic without increasing the loss by just
changing which local minimum the training process finds. More monosemantic loss
minima have moderate negative biases, and we are able to use this fact to
engineer highly monosemantic models. We are able to mechanistically interpret
these models, including the residual polysemantic neurons, and uncover a simple
yet surprising algorithm. Finally, we find that providing models with more
neurons per layer makes the models more monosemantic, albeit at increased
computational cost. These findings point to a number of new questions and
avenues for engineering monosemanticity, which we intend to study these in
future work.

This paper focuses on leveraging deep representation learning (DRL) for
speech enhancement (SE). In general, the performance of the deep neural network
(DNN) is heavily dependent on the learning of data representation. However, the
DRL's importance is often ignored in many DNN-based SE algorithms. To obtain a
higher quality enhanced speech, we propose a two-stage DRL-based SE method
through adversarial training. In the first stage, we disentangle different
latent variables because disentangled representations can help DNN generate a
better enhanced speech. Specifically, we use the $\beta$-variational
autoencoder (VAE) algorithm to obtain the speech and noise posterior
estimations and related representations from the observed signal. However,
since the posteriors and representations are intractable and we can only apply
a conditional assumption to estimate them, it is difficult to ensure that these
estimations are always pretty accurate, which may potentially degrade the final
accuracy of the signal estimation. To further improve the quality of enhanced
speech, in the second stage, we introduce adversarial training to reduce the
effect of the inaccurate posterior towards signal reconstruction and improve
the signal estimation accuracy, making our algorithm more robust for the
potentially inaccurate posterior estimations. As a result, better SE
performance can be achieved. The experimental results indicate that the
proposed strategy can help similar DNN-based SE algorithms achieve higher
short-time objective intelligibility (STOI), perceptual evaluation of speech
quality (PESQ), and scale-invariant signal-to-distortion ratio (SI-SDR) scores.
Moreover, the proposed algorithm can also outperform recent competitive SE

In practical applications, multi-view data depicting objectives from assorted
perspectives can facilitate the accuracy increase of learning algorithms.
However, given multi-view data, there is limited work for learning
discriminative node relationships and graph information simultaneously via
graph convolutional network that has drawn the attention from considerable
researchers in recent years. Most of existing methods only consider the
weighted sum of adjacency matrices, yet a joint neural network of both feature
and graph fusion is still under-explored. To cope with these issues, this paper
proposes a joint deep learning framework called Learnable Graph Convolutional
Network and Feature Fusion (LGCN-FF), consisting of two stages: feature fusion
network and learnable graph convolutional network. The former aims to learn an
underlying feature representation from heterogeneous views, while the latter
explores a more discriminative graph fusion via learnable weights and a
parametric activation function dubbed Differentiable Shrinkage Activation (DSA)
function. The proposed LGCN-FF is validated to be superior to various
state-of-the-art methods in multi-view semi-supervised classification.

Motion recognition is a promising direction in computer vision, but the
training of video classification models is much harder than images due to
insufficient data and considerable parameters. To get around this, some works
strive to explore multimodal cues from RGB-D data. Although improving motion
recognition to some extent, these methods still face sub-optimal situations in
the following aspects: (i) Data augmentation, i.e., the scale of the RGB-D
datasets is still limited, and few efforts have been made to explore novel data
augmentation strategies for videos; (ii) Optimization mechanism, i.e., the
tightly space-time-entangled network structure brings more challenges to
spatiotemporal information modeling; And (iii) cross-modal knowledge fusion,
i.e., the high similarity between multimodal representations caused to
insufficient late fusion. To alleviate these drawbacks, we propose to improve
RGB-D-based motion recognition both from data and algorithm perspectives in
this paper. In more detail, firstly, we introduce a novel video data
augmentation method dubbed ShuffleMix, which acts as a supplement to MixUp, to
provide additional temporal regularization for motion recognition. Secondly, a
Unified Multimodal De-coupling and multi-stage Re-coupling framework, termed
UMDR, is proposed for video representation learning. Finally, a novel
cross-modal Complement Feature Catcher (CFCer) is explored to mine potential
commonalities features in multimodal information as the auxiliary fusion
stream, to improve the late fusion results. The seamless combination of these
novel designs forms a robust spatiotemporal representation and achieves better
performance than state-of-the-art methods on four public motion datasets.
Specifically, UMDR achieves unprecedented improvements of +4.5% on the Chalearn
IsoGD dataset.Our code is available at
https://github.com/zhoubenjia/MotionRGBD-PAMI.

Enshrouded in several well-known controversies, dwarf galaxies have been
extensively studied to learn about the underlying cosmology, notwithstanding
that physical processes regulating their properties are poorly understood. To
shed light on these processes, we introduce the Pandora suite of 17
high-resolution (3.5 parsec half-cell side) dwarf galaxy formation cosmological
simulations. Commencing with thermo-turbulent star formation and mechanical
supernova feedback, we gradually increase the complexity of physics
incorporated leading to full-physics models combining magnetism, on-the-fly
radiative transfer and the corresponding stellar photoheating, and
SN-accelerated cosmic rays. We investigate combinations of these processes,
comparing them with observations to constrain what are the main mechanisms
determining dwarf galaxy properties. We find hydrodynamical `SN feedback-only'
simulations struggle to produce realistic dwarf galaxies, leading either to
overquenched or too centrally concentrated, dispersion dominated systems when
compared to observed field dwarfs. Accounting for radiation with cosmic rays
results in extended and rotationally-supported systems. Spatially `distributed'
feedback leads to realistic stellar and HI masses as well as kinematics.
Furthermore, resolved kinematic maps of our full-physics models predict
kinematically distinct clumps and kinematic misalignments of stars, HI and HII
after star formation events. Episodic star formation combined with its
associated feedback induces more core-like dark matter central profiles, which
our `SN feedback-only' models struggle to achieve. Our results demonstrate the
complexity of physical processes required to capture realistic dwarf galaxy
properties, making tangible predictions for integral field unit surveys, radio
synchrotron emission, and for galaxy and multi-phase interstellar medium
properties that JWST will probe.

With the advent of billion-galaxy surveys with complex data, the need of the
hour is to efficiently model galaxy spectral energy distributions (SEDs) with
robust uncertainty quantification. The combination of Simulation-Based
inference (SBI) and amortized Neural Posterior Estimation (NPE) has been
successfully used to analyse simulated and real galaxy photometry both
precisely and efficiently. In this work, we utilise this combination and build
on existing literature to analyse simulated noisy galaxy spectra. Here, we
demonstrate a proof-of-concept study of spectra that is a) an efficient
analysis of galaxy SEDs and inference of galaxy parameters with physically
interpretable uncertainties; and b) amortized calculations of posterior
distributions of said galaxy parameters at the modest cost of a few galaxy fits
with MCMC methods. We utilise the SED generator and inference framework
Prospector to generate simulated spectra, and train a dataset of
2$\times$10$^6$ spectra (corresponding to a 5-parameter SED model) with NPE. We
show that SBI -- with its combination of fast and amortized posterior
estimations -- is capable of inferring accurate galaxy stellar masses and
metallicities. Our uncertainty constraints are comparable to or moderately
weaker than traditional inverse-modeling with Bayesian MCMC methods (e.g., 0.17
and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively).
We also find that our inference framework conducts rapid SED inference
(0.9-1.2$\times$10$^5$ galaxy spectra via SBI/SNPE at the cost of 1 MCMC-based
fit). With this work, we set the stage for further work that focuses of SED
fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs
and the wide-field Roman Space Telescope spectroscopic surveys.

The geometry of 4-string contact interaction of closed string field theory is
characterized using machine learning. We obtain Strebel quadratic differentials
on 4-punctured spheres as a neural network by performing unsupervised learning
with a custom-built loss function. This allows us to solve for local
coordinates and compute their associated mapping radii numerically. We also
train a neural network distinguishing vertex from Feynman region. As a check,
4-tachyon contact term in the tachyon potential is computed and a good
agreement with the results in the literature is observed. We argue that our
algorithm is manifestly independent of number of punctures and scaling it to
characterize the geometry of $n$-string contact interaction is feasible.

Masked Autoencoders (MAEs) learn generalizable representations for image,
text, audio, video, etc., by reconstructing masked input data from tokens of
the visible data. Current MAE approaches for videos rely on random patch, tube,
or frame-based masking strategies to select these tokens. This paper proposes
AdaMAE, an adaptive masking strategy for MAEs that is end-to-end trainable. Our
adaptive masking strategy samples visible tokens based on the semantic context
using an auxiliary sampling network. This network estimates a categorical
distribution over spacetime-patch tokens. The tokens that increase the expected
reconstruction error are rewarded and selected as visible tokens, motivated by
the policy gradient algorithm in reinforcement learning. We show that AdaMAE
samples more tokens from the high spatiotemporal information regions, thereby
allowing us to mask 95% of tokens, resulting in lower memory requirements and
faster pre-training. We conduct ablation studies on the Something-Something v2
(SSv2) dataset to demonstrate the efficacy of our adaptive sampling approach
and report state-of-the-art results of 70.0% and 81.7% in top-1 accuracy on
SSv2 and Kinetics-400 action classification datasets with a ViT-Base backbone
and 800 pre-training epochs.

We propose Token Turing Machines (TTM), a sequential, autoregressive
Transformer model with memory for real-world sequential visual understanding.
Our model is inspired by the seminal Neural Turing Machine, and has an external
memory consisting of a set of tokens which summarise the previous history
(i.e., frames). This memory is efficiently addressed, read and written using a
Transformer as the processing unit/controller at each step. The model's memory
module ensures that a new observation will only be processed with the contents
of the memory (and not the entire history), meaning that it can efficiently
process long sequences with a bounded computational cost at each step. We show
that TTM outperforms other alternatives, such as other Transformer models
designed for long sequences and recurrent neural networks, on two real-world
sequential visual understanding tasks: online temporal activity detection from
videos and vision-based robot action policy learning.

Generative modeling and representation learning are two key tasks in computer
vision. However, these models are typically trained independently, which
ignores the potential for each task to help the other, and leads to training
and model maintenance overheads. In this work, we propose MAsked Generative
Encoder (MAGE), the first framework to unify SOTA image generation and
self-supervised representation learning. Our key insight is that using variable
masking ratios in masked image modeling pre-training can allow generative
training (very high masking ratio) and representation learning (lower masking
ratio) under the same training framework. Inspired by previous generative
models, MAGE uses semantic tokens learned by a vector-quantized GAN at inputs
and outputs, combining this with masking. We can further improve the
representation by adding a contrastive loss to the encoder output. We
extensively evaluate the generation and representation learning capabilities of
MAGE. On ImageNet-1K, a single MAGE ViT-L model obtains 9.10 FID in the task of
class-unconditional image generation and 78.9% top-1 accuracy for linear
probing, achieving state-of-the-art performance in both image generation and
representation learning. Code is available at https://github.com/LTH14/mage.

The ambition of harnessing the quantum for computation is at odds with the
fundamental phenomenon of decoherence. The purpose of quantum error correction
(QEC) is to counteract the natural tendency of a complex system to decohere.
This cooperative process, which requires participation of multiple quantum and
classical components, creates a special type of dissipation that removes the
entropy caused by the errors faster than the rate at which these errors corrupt
the stored quantum information. Previous experimental attempts to engineer such
a process faced an excessive generation of errors that overwhelmed the
error-correcting capability of the process itself. Whether it is practically
possible to utilize QEC for extending quantum coherence thus remains an open
question. We answer it by demonstrating a fully stabilized and error-corrected
logical qubit whose quantum coherence is significantly longer than that of all
the imperfect quantum components involved in the QEC process, beating the best
of them with a coherence gain of $G = 2.27 \pm 0.07$. We achieve this
performance by combining innovations in several domains including the
fabrication of superconducting quantum circuits and model-free reinforcement
learning.

While advances in pre-training have led to dramatic improvements in few-shot
learning of NLP tasks, there is limited understanding of what drives successful
few-shot adaptation in datasets. In particular, given a new dataset and a
pre-trained model, what properties of the dataset make it \emph{few-shot
learnable} and are these properties independent of the specific adaptation
techniques used? We consider an extensive set of recent few-shot learning
methods, and show that their performance across a large number of datasets is
highly correlated, showing that few-shot hardness may be intrinsic to datasets,
for a given pre-trained model. To estimate intrinsic few-shot hardness, we then
propose a simple and lightweight metric called "Spread" that captures the
intuition that few-shot learning is made possible by exploiting feature-space
invariances between training and test samples. Our metric better accounts for
few-shot hardness compared to existing notions of hardness, and is ~8-100x
faster to compute.

Incomplete sky analysis of cosmic microwave background (CMB) polarization
spectra induces a major problem of the leakage between $E$- and $B$-modes of
CMB polarization. In this article, we present a machine learning approach to
remove this $E$-to-$B$ leakage problem using a convolutional neural network
(CNN). For $N_{side}=256$, we develop a CNN system to predict the full sky
spectra with multipole range $2 \leq \ell \leq 384$ from the partial sky
spectra with multipole range $2 \leq \ell \leq 512$ for both $E$- and $B$-modes
simultaneously. In this analysis, we use the theoretical spectrum with
tensor-to-scalar ratio $r=0.001$ to simulate the realization maps of CMB
polarization. We train our CNN system using $10^5$ number of training samples
of full sky spectra obtained from these realization maps and corresponding
partial spectra obtained from the masked realization maps. The full sky $E$-
and $B$-modes spectra predicted by our trained CNN system agree excellently
with the corresponding target full sky spectra as well as the theoretical
spectra of these CMB polarization modes. Interestingly, these predicted full
sky spectra of these polarization modes also preserve the cosmic variances at
each multipole. We note that the significance ratios of these predicted full
sky spectra for each of these polarization modes are approximately within
$3\sigma$. Moreover, our trained CNN system effectively remove the significant
correlations appeared in the partial sky $E$- and $B$-modes spectra. Thus, the
trained CNN system, developed by us, can predict the full sky $E$- and
$B$-modes spectra from the corresponding partial sky spectra preserving the
entire statistical properties and removing the problem of so-called $E$-to-$B$
leakage.

Language models (LMs) are becoming the foundation for almost all major
language technologies, but their capabilities, limitations, and risks are not
well understood. We present Holistic Evaluation of Language Models (HELM) to
improve the transparency of language models. First, we taxonomize the vast
space of potential scenarios (i.e. use cases) and metrics (i.e. desiderata)
that are of interest for LMs. Then we select a broad subset based on coverage
and feasibility, noting what's missing or underrepresented (e.g. question
answering for neglected English dialects, metrics for trustworthiness). Second,
we adopt a multi-metric approach: We measure 7 metrics (accuracy, calibration,
robustness, fairness, bias, toxicity, and efficiency) for each of 16 core
scenarios when possible (87.5% of the time). This ensures metrics beyond
accuracy don't fall to the wayside, and that trade-offs are clearly exposed. We
also perform 7 targeted evaluations, based on 26 targeted scenarios, to analyze
specific aspects (e.g. reasoning, disinformation). Third, we conduct a
large-scale evaluation of 30 prominent language models (spanning open,
limited-access, and closed models) on all 42 scenarios, 21 of which were not
previously used in mainstream LM evaluation. Prior to HELM, models on average
were evaluated on just 17.9% of the core HELM scenarios, with some prominent
models not sharing a single scenario in common. We improve this to 96.0%: now
all 30 models have been densely benchmarked on the same core scenarios and
metrics under standardized conditions. Our evaluation surfaces 25 top-level
findings. For full transparency, we release all raw model prompts and
completions publicly for further analysis, as well as a general modular
toolkit. We intend for HELM to be a living benchmark for the community,
continuously updated with new scenarios, metrics, and models.

Few-shot learning (FSL) is a challenging learning problem in which only a few
samples are available for each class. Decision interpretation is more important
in few-shot classification since there is a greater chance of error than in
traditional classification. However, most of the previous FSL methods are
black-box models. In this paper, we propose an inherently interpretable model
for FSL based on human-friendly attributes. Moreover, we propose an online
attribute selection mechanism that can effectively filter out irrelevant
attributes in each episode. The attribute selection mechanism improves the
accuracy and helps with interpretability by reducing the number of participated
attributes in each episode. We demonstrate that the proposed method achieves
results on par with black-box few-shot-learning models on four widely used
datasets. To further close the performance gap with the black-box models, we
propose a mechanism that trades interpretability for accuracy. It automatically
detects the episodes where the provided human-friendly attributes are not
adequate, and compensates by engaging learned unknown attributes.

In many learning theory problems, a central role is played by a hypothesis
class: we might assume that the data is labeled according to a hypothesis in
the class (usually referred to as the realizable setting), or we might evaluate
the learned model by comparing it with the best hypothesis in the class (the
agnostic setting).
  Taking a step beyond these classic setups that involve only a single
hypothesis class, we introduce comparative learning as a combination of the
realizable and agnostic settings in PAC learning: given two binary hypothesis
classes $S$ and $B$, we assume that the data is labeled according to a
hypothesis in the source class $S$ and require the learned model to achieve an
accuracy comparable to the best hypothesis in the benchmark class $B$. Even
when both $S$ and $B$ have infinite VC dimensions, comparative learning can
still have a small sample complexity. We show that the sample complexity of
comparative learning is characterized by the mutual VC dimension
$\mathsf{VC}(S,B)$ which we define to be the maximum size of a subset shattered
by both $S$ and $B$. We also show a similar result in the online setting, where
we give a regret characterization in terms of the mutual Littlestone dimension
$\mathsf{Ldim}(S,B)$. These results also hold for partial hypotheses.
  We additionally show that the insights necessary to characterize the sample
complexity of comparative learning can be applied to characterize the sample
complexity of realizable multiaccuracy and multicalibration using the mutual
fat-shattering dimension, an analogue of the mutual VC dimension for
real-valued hypotheses. This not only solves an open problem proposed by Hu,
Peale, Reingold (2022), but also leads to independently interesting results
extending classic ones about regression, boosting, and covering number to our
two-hypothesis-class setting.

We consider the problem of global optimization with noisy zeroth order
oracles - a well-motivated problem useful for various applications ranging from
hyper-parameter tuning for deep learning to new material design. Existing work
relies on Gaussian processes or other non-parametric family, which suffers from
the curse of dimensionality. In this paper, we propose a new algorithm GO-UCB
that leverages a parametric family of functions (e.g., neural networks)
instead. Under a realizable assumption and a few other mild geometric
conditions, we show that GO-UCB achieves a cumulative regret of
$\tilde{O}(\sqrt{T})$ where $T$ is the time horizon. At the core of GO-UCB is a
carefully designed uncertainty set over parameters based on gradients that
allows optimistic exploration. Numerical simulation illustrates that GO-UCB
works better than classical Bayesian optimization approaches in high
dimensional cases, even if the model is misspecified.

The vehicle recognition area, including vehicle make-model recognition
(VMMR), re-id, tracking, and parts-detection, has made significant progress in
recent years, driven by several large-scale datasets for each task. These
datasets are often non-overlapping, with different label schemas for each task:
VMMR focuses on make and model, while re-id focuses on vehicle ID. It is
promising to combine these datasets to take advantage of knowledge across
datasets as well as increased training data; however, dataset integration is
challenging due to the domain gap problem. This paper proposes ATEAM, an
annotation team-of-experts to perform cross-dataset labeling and integration of
disjoint annotation schemas. ATEAM uses diverse experts, each trained on
datasets that contain an annotation schema, to transfer knowledge to datasets
without that annotation. Using ATEAM, we integrated several common vehicle
recognition datasets into a Knowledge Integrated Dataset (KID). We evaluate
ATEAM and KID for vehicle recognition problems and show that our integrated
dataset can help off-the-shelf models achieve excellent accuracy on VMMR and
vehicle re-id with no changes to model architectures. We achieve mAP of 0.83 on
VeRi, and accuracy of 0.97 on CompCars. We have released both the dataset and
the ATEAM framework for public use.

Hippocampal place cells can encode spatial locations of an animal in physical
or task-relevant spaces. We simulated place cell populations that encoded
either Euclidean- or graph-based positions of a rat navigating to goal nodes in
a maze with a graph topology, and used manifold learning methods such as UMAP
and Autoencoders (AE) to analyze these neural population activities. The
structure of the latent spaces learned by the AE reflects their true geometric
structure, while PCA fails to do so and UMAP is less robust to noise. Our
results support future applications of AE architectures to decipher the
geometry of spatial encoding in the brain.

Finding similar data in high-dimensional spaces is one of the important tasks
in multimedia applications. Approaches introduced to find exact searching
techniques often use tree-based index structures which are known to suffer from
the curse of the dimensionality problem that limits their performance.
Approximate searching techniques prefer performance over accuracy and they
return good enough results while achieving a better performance. Locality
Sensitive Hashing (LSH) is one of the most popular approximate nearest neighbor
search techniques for high-dimensional spaces. One of the most time-consuming
processes in LSH is to find the neighboring points in the projected spaces. An
improved LSH-based index structure, called radius-optimized Locality Sensitive
Hashing (roLSH) has been proposed to utilize Machine Learning and efficiently
find these neighboring points; thus, further improve the overall performance of
LSH. In this paper, we extend roLSH by experimentally studying the effect of
different types of famous Machine Learning techniques on overall performance.
We compare ten regression techniques on four real-world datasets and show that
Neural Network-based techniques are the best fit to be used in roLSH as their
accuracy and performance trade-off are the best compared to the other
techniques.

Identifying and calibrating quantitative dynamical models for physical
quantum systems is important for a variety of applications. Here we present a
closed-loop Bayesian learning algorithm for estimating multiple unknown
parameters in a dynamical model, using optimised experimental "probe" controls
and measurement. The estimation algorithm is based on a Bayesian particle
filter, and is designed to autonomously choose informationally-optimised probe
experiments with which to compare to model predictions. We demonstrate the
performance of the algorithm in both simulated calibration tasks and in an
experimental single-qubit ion-trap system. Experimentally, we find that with
60x fewer samples, we exceed the precision of conventional calibration methods,
delivering an approximately 93x improvement in efficiency (as quantified by the
reduction of measurements required to achieve a target residual uncertainty and
multiplied by the increase in accuracy). In simulated and experimental
demonstrations, we see that successively longer pulses are selected as the
posterior uncertainty iteratively decreases, leading to an exponential
improvement in the accuracy of model parameters with the number of experimental
queries.

We propose a novel molecular fingerprint-based variational autoencoder
applied for molecular generation on real-world drug molecules. We define more
suitable and pharma-relevant baseline metrics and tests, focusing on the
generation of diverse, drug-like, novel small molecules and scaffolds. When we
apply these molecular generation metrics to our novel model, we observe a
substantial improvement in chemical synthetic accessibility
($\Delta\bar{{SAS}}$ = -0.83) and in computational efficiency up to 5.9x in
comparison to an existing state-of-the-art SMILES-based architecture.

Information overload is a major obstacle to scientific progress. The
explosive growth in scientific literature and data has made it ever harder to
discover useful insights in a large mass of information. Today scientific
knowledge is accessed through search engines, but they are unable to organize
scientific knowledge alone. In this paper we introduce Galactica: a large
language model that can store, combine and reason about scientific knowledge.
We train on a large scientific corpus of papers, reference material, knowledge
bases and many other sources. We outperform existing models on a range of
scientific tasks. On technical knowledge probes such as LaTeX equations,
Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also
performs well on reasoning, outperforming Chinchilla on mathematical MMLU by
41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%. It
also sets a new state-of-the-art on downstream tasks such as PubMedQA and
MedMCQA dev of 77.6% and 52.9%. And despite not being trained on a general
corpus, Galactica outperforms BLOOM and OPT-175B on BIG-bench. We believe these
results demonstrate the potential for language models as a new interface for
science. We open source the model for the benefit of the scientific community.

Systems engineering, in particular in the automotive domain, needs to cope
with the massively increasing numbers of requirements that arise during the
development process. To guarantee a high product quality and make sure that
functional safety standards such as ISO26262 are fulfilled, the exploitation of
potentials of model-driven systems engineering in the form of automatic
analyses, consistency checks, and tracing mechanisms is indispensable. However,
the language in which requirements are written, and the tools needed to operate
on them, are highly individual and require domain-specific tailoring. This
hinders automated processing of requirements as well as the linking of
requirements to models. Introducing formal requirement notations in existing
projects leads to the challenge of translating masses of requirements and
process changes on the one hand and to the necessity of the corresponding
training for the requirements engineers.
  In this paper, based on the analysis of an open-source set of automotive
requirements, we derive domain-specific language constructs helping us to avoid
ambiguities in requirements and increase the level of formality. The main
contribution is the adoption and evaluation of few-shot learning with large
pretrained language models for the automated translation of informal
requirements to structured languages such as a requirement DSL. We show that
support sets of less than ten translation examples can suffice to few-shot
train a language model to incorporate keywords and implement syntactic rules
into informal natural language requirements.

Event cameras such as DAVIS can simultaneously output high temporal
resolution events and low frame-rate intensity images, which own great
potential in capturing scene motion, such as optical flow estimation. Most of
the existing optical flow estimation methods are based on two consecutive image
frames and can only estimate discrete flow at a fixed time interval. Previous
work has shown that continuous flow estimation can be achieved by changing the
quantities or time intervals of events. However, they are difficult to estimate
reliable dense flow , especially in the regions without any triggered events.
In this paper, we propose a novel deep learning-based dense and continuous
optical flow estimation framework from a single image with event streams, which
facilitates the accurate perception of high-speed motion. Specifically, we
first propose an event-image fusion and correlation module to effectively
exploit the internal motion from two different modalities of data. Then we
propose an iterative update network structure with bidirectional training for
optical flow prediction. Therefore, our model can estimate reliable dense flow
as two-frame-based methods, as well as estimate temporal continuous flow as
event-based methods. Extensive experimental results on both synthetic and real
captured datasets demonstrate that our model outperforms existing event-based
state-of-the-art methods and our designed baselines for accurate dense and
continuous optical flow estimation.

Current physics models used to interpret experimental measurements of
particle beams require either simplifying assumptions to be made in order to
ensure analytical tractability, or black box optimization methods to perform
model based inference. This reduces the quantity and quality of information
gained from experimental measurements, in a system where measurements have a
limited availability. However differentiable physics modeling, combined with
machine learning techniques, can overcome these analysis limitations, enabling
accurate, detailed model creation of physical accelerators. Here we examine two
applications of differentiable modeling, first to characterize beam responses
to accelerator elements exhibiting hysteretic behavior, and second to
characterize beam distributions in high dimensional phase spaces.

Recent studies on Next-basket Recommendation (NBR) have achieved much
progress by leveraging Personalized Item Frequency (PIF) as one of the main
features, which measures the frequency of the user's interactions with the
item. However, taking the PIF as an explicit feature incurs bias towards
frequent items. Items that a user purchases frequently are assigned higher
weights in the PIF-based recommender system and appear more frequently in the
personalized recommendation list. As a result, the system will lose the
fairness and balance between items that the user frequently purchases and items
that the user never purchases. We refer to this systematic bias on personalized
recommendation lists as frequency bias, which narrows users' browsing scope and
reduces the system utility. We adopt causal inference theory to address this
issue. Considering the influence of historical purchases on users' future
interests, the user and item representations can be viewed as unobserved
confounders in the causal diagram. In this paper, we propose a deconfounder
model named FENDER (Frequency-aware Deconfounder for Next-basket
Recommendation) to mitigate the frequency bias. With the deconfounder theory
and the causal diagram we propose, FENDER decomposes PIF with a neural tensor
layer to obtain substitute confounders for users and items. Then, FENDER
performs unbiased recommendations considering the effect of these substitute
confounders. Experimental results demonstrate that FENDER has derived diverse
and fair results compared to ten baseline models on three datasets while
achieving competitive performance. Further experiments illustrate how FENDER
balances users' historical purchases and potential interests.

As one of the leading causes of mortality and disability worldwide, Acute
Ischemic Stroke (AIS) occurs when the blood supply to the brain is suddenly
interrupted because of a blocked artery. Within seconds of AIS onset, the brain
cells surrounding the blocked artery die, which leads to the progression of the
lesion. The automated and precise prediction of the existing lesion plays a
vital role in the AIS treatment planning and prevention of further injuries.
The current standard AIS assessment method, which thresholds the 3D measurement
maps extracted from Computed Tomography Perfusion (CTP) images, is not accurate
enough. Due to this fact, in this article, we propose the imbalanced Temporal
Deep Gaussian Process (iTDGP), a probabilistic model that can improve AIS
lesions prediction by using baseline CTP time series. Our proposed model can
effectively extract temporal information from the CTP time series and map it to
the class labels of the brain's voxels. In addition, by using batch training
and voxel-level analysis iTDGP can learn from a few patients and it is robust
against imbalanced classes. Moreover, our model incorporates a post-processor
capable of improving prediction accuracy using spatial information. Our
comprehensive experiments, on the ISLES 2018 and the University of Alberta
Hospital (UAH) datasets, show that iTDGP performs better than state-of-the-art
AIS lesion predictors, obtaining the (cross-validation) Dice score of 71.42%
and 65.37% with a significant p<0.05, respectively.

We propose a platform based on neural networks to solve the image-to-image
translation problem in the context of squeeze flow of micro-droplets. In the
first part of this paper, we present the governing partial differential
equations to lay out the underlying physics of the problem. We also discuss our
developed Python package, sqflow, which can potentially serve as free,
flexible, and scalable standardized benchmarks in the fields of machine
learning and computer vision. In the second part of this paper, we introduce a
residual convolutional neural network to solve the corresponding inverse
problem: to translate a high-resolution (HR) imprint image with a specific
liquid film thickness to a low-resolution (LR) droplet pattern image capable of
producing the given imprint image for an appropriate spread time of droplets.
We propose a neural network architecture that learns to systematically tune the
refinement level of its residual convolutional blocks by using the function
approximators that are trained to map a given input parameter (film thickness)
to an appropriate refinement level indicator. We use multiple stacks of
convolutional layers the output of which is translated according to the
refinement level indicators provided by the directly-connected function
approximators. Together with a non-linear activation function, such a
translation mechanism enables the HR imprint image to be refined sequentially
in multiple steps until the target LR droplet pattern image is revealed. The
proposed platform can be potentially applied to data compression and data
encryption. The developed package and datasets are publicly available on GitHub
at https://github.com/sqflow/sqflow.

Machine learning has made important headway in helping to improve the
treatment of quantum many-body systems. A domain of particular relevance are
correlated inhomogeneous systems. What has been missing so far is a general,
scalable deep-learning approach that would enable the rapid prediction of
spatial densities for strongly correlated systems in arbitrary potentials. In
this work, we present a straightforward scheme, where we learn to predict
densities using convolutional neural networks trained on random potentials.
While we demonstrate this approach in 1D and 2D lattice models using data from
numerical techniques like Quantum Monte Carlo, it is directly applicable as
well to training data obtained from experimental quantum simulators. We train
networks that can predict the densities of multiple observables simultaneously
and that can predict for a whole class of many-body lattice models, for
arbitrary system sizes. We show that our approach can handle well the interplay
of interference and interactions and the behaviour of models with phase
transitions in inhomogeneous situations, and we also illustrate the ability to
solve inverse problems, finding a potential for a desired density.

Deep anomaly detection (AD) aims to provide robust and efficient classifiers
for one-class and unbalanced settings. However current AD models still struggle
on edge-case normal samples and are often unable to keep high performance over
different scales of anomalies. Moreover, there currently does not exist a
unified framework efficiently covering both one-class and unbalanced learnings.
In the light of these limitations, we introduce a new two-stage anomaly
detector which memorizes during training multi-scale normal prototypes to
compute an anomaly deviation score. First, we simultaneously learn
representations and memory modules on multiple scales using a novel
memory-augmented contrastive learning. Then, we train an anomaly distance
detector on the spatial deviation maps between prototypes and observations. Our
model highly improves the state-of-the-art performance on a wide range of
object, style and local anomalies with up to 35\% error relative improvement on
CIFAR-10. It is also the first model to keep high performance across the
one-class and unbalanced settings.

In this paper, we propose a method to partially mimic natural intelligence
for the problem of lifelong learning representations that are compatible. We
take the perspective of a learning agent that is interested in recognizing
object instances in an open dynamic universe in a way in which any update to
its internal feature representation does not render the features in the gallery
unusable for visual search. We refer to this learning problem as Compatible
Lifelong Learning Representations (CL2R) as it considers compatible
representation learning within the lifelong learning paradigm. We identify
stationarity as the property that the feature representation is required to
hold to achieve compatibility and propose a novel training procedure that
encourages local and global stationarity on the learned representation. Due to
stationarity, the statistical properties of the learned features do not change
over time, making them interoperable with previously learned features.
Extensive experiments on standard benchmark datasets show that our CL2R
training procedure outperforms alternative baselines and state-of-the-art
methods. We also provide novel metrics to specifically evaluate compatible
representation learning under catastrophic forgetting in various sequential
learning tasks. Code at
https://github.com/NiccoBiondi/CompatibleLifelongRepresentation.

Self-supervised pre-training, based on the pretext task of instance
discrimination, has fueled the recent advance in label-efficient object
detection. However, existing studies focus on pre-training only a feature
extractor network to learn transferable representations for downstream
detection tasks. This leads to the necessity of training multiple
detection-specific modules from scratch in the fine-tuning phase. We argue that
the region proposal network (RPN), a common detection-specific module, can
additionally be pre-trained towards reducing the localization error of
multi-stage detectors. In this work, we propose a simple pretext task that
provides an effective pre-training for the RPN, towards efficiently improving
downstream object detection performance. We evaluate the efficacy of our
approach on benchmark object detection tasks and additional downstream tasks,
including instance segmentation and few-shot detection. In comparison with
multi-stage detectors without RPN pre-training, our approach is able to
consistently improve downstream task performance, with largest gains found in
label-scarce settings.

Observing a human demonstrator manipulate objects provides a rich, scalable
and inexpensive source of data for learning robotic policies. However,
transferring skills from human videos to a robotic manipulator poses several
challenges, not least a difference in action and observation spaces. In this
work, we use unlabeled videos of humans solving a wide range of manipulation
tasks to learn a task-agnostic reward function for robotic manipulation
policies. Thanks to the diversity of this training data, the learned reward
function sufficiently generalizes to image observations from a previously
unseen robot embodiment and environment to provide a meaningful prior for
directed exploration in reinforcement learning. The learned rewards are based
on distances to a goal in an embedding space learned using a time-contrastive
objective. By conditioning the function on a goal image, we are able to reuse
one model across a variety of tasks. Unlike prior work on leveraging human
videos to teach robots, our method, Human Offline Learned Distances (HOLD)
requires neither a priori data from the robot environment, nor a set of
task-specific human demonstrations, nor a predefined notion of correspondence
across morphologies, yet it is able to accelerate training of several
manipulation tasks on a simulated robot arm compared to using only a sparse
reward obtained from task completion.

The assessment and valuation of real estate requires large datasets with real
estate information. Unfortunately, real estate databases are usually sparse in
practice, i.e., not for each property every important attribute is available.
In this paper, we study the potential of predicting high-level real estate
attributes from visual data, specifically from two visual modalities, namely
indoor (interior) and outdoor (facade) photos. We design three models using
different multimodal fusion strategies and evaluate them for three different
use cases. Thereby, a particular challenge is to handle missing modalities. We
evaluate different fusion strategies, present baselines for the different
prediction tasks, and find that enriching the training data with additional
incomplete samples can lead to an improvement in prediction accuracy.
Furthermore, the fusion of information from indoor and outdoor photos results
in a performance boost of up to 5% in Macro F1-score.

Point clouds are a widely available and canonical data modality which convey
the 3D geometry of a scene. Despite significant progress in classification and
segmentation from point clouds, policy learning from such a modality remains
challenging, and most prior works in imitation learning focus on learning
policies from images or state information. In this paper, we propose a novel
framework for learning policies from point clouds for robotic manipulation with
tools. We use a novel neural network, ToolFlowNet, which predicts dense
per-point flow on the tool that the robot controls, and then uses the flow to
derive the transformation that the robot should execute. We apply this
framework to imitation learning of challenging deformable object manipulation
tasks with continuous movement of tools, including scooping and pouring, and
demonstrate significantly improved performance over baselines which do not use
flow. We perform 50 physical scooping experiments with ToolFlowNet and attain
82% scooping success. See https://tinyurl.com/toolflownet for supplementary
material.

Leveraging on recent advances in random tensor theory, we consider in this
paper a rank-$r$ asymmetric spiked tensor model of the form $\sum_{i=1}^r
\beta_i A_i + W$ where $\beta_i\geq 0$ and the $A_i$'s are rank-one tensors
such that $\langle A_i, A_j \rangle\in [0, 1]$ for $i\neq j$, based on which we
provide an asymptotic study of Hotelling-type tensor deflation in the large
dimensional regime. Specifically, our analysis characterizes the singular
values and alignments at each step of the deflation procedure, for
asymptotically large tensor dimensions. This can be used to construct
consistent estimators of different quantities involved in the underlying
problem, such as the signal-to-noise ratios $\beta_i$ or the alignments between
the different signal components $\langle A_i, A_j \rangle$.

In recent years, the role of artificially intelligent (AI) agents has evolved
from being basic tools to socially intelligent agents working alongside humans
towards common goals. In such scenarios, the ability to predict future behavior
by observing past actions of their human teammates is highly desirable in an AI
agent. Goal-oriented human behavior is complex, hierarchical, and unfolds
across multiple timescales. Despite this observation, relatively little
attention has been paid towards using multi-timescale features to model such
behavior. In this paper, we propose an LSTM network architecture that processes
behavioral information at multiple timescales to predict future behavior. We
demonstrate that our approach for modeling behavior in multiple timescales
substantially improves prediction of future behavior compared to methods that
do not model behavior at multiple timescales. We evaluate our architecture on
data collected in an urban search and rescue scenario simulated in a virtual
Minecraft-based testbed, and compare its performance to that of a number of
valid baselines as well as other methods that do not process inputs at multiple
timescales.

Motivated by the emerging needs of personalized preventative intervention in
many healthcare applications, we consider a multi-stage, dynamic
decision-making problem in the online setting with unknown model parameters. To
deal with the pervasive issue of small sample size in personalized planning, we
develop a novel data-pooling reinforcement learning (RL) algorithm based on a
general perturbed value iteration framework. Our algorithm adaptively pools
historical data, with three main innovations: (i) the weight of pooling ties
directly to the performance of decision (measured by regret) as opposed to
estimation accuracy in conventional methods; (ii) no parametric assumptions are
needed between historical and current data; and (iii) requiring data-sharing
only via aggregate statistics, as opposed to patient-level data. Our
data-pooling algorithm framework applies to a variety of popular RL algorithms,
and we establish a theoretical performance guarantee showing that our pooling
version achieves a regret bound strictly smaller than that of the no-pooling
counterpart. We substantiate the theoretical development with empirically
better performance of our algorithm via a case study in the context of
post-discharge intervention to prevent unplanned readmissions, generating
practical insights for healthcare management. In particular, our algorithm
alleviates privacy concerns about sharing health data, which (i) opens the door
for individual organizations to levering public datasets or published studies
to better manage their own patients; and (ii) provides the basis for public
policy makers to encourage organizations to share aggregate data to improve
population health outcomes for the broader community.

In many real-world sequential decision-making problems, an action does not
immediately reflect on the feedback and spreads its effects over a long time
frame. For instance, in online advertising, investing in a platform produces an
increase of awareness, but the actual reward, i.e., a conversion, might occur
far in the future. Furthermore, whether a conversion takes place depends on:
how fast the awareness grows, its vanishing effects, and the synergy or
interference with other advertising platforms. Previous work has investigated
the Multi-Armed Bandit framework with the possibility of delayed and aggregated
feedback, without a particular structure on how an action propagates in the
future, disregarding possible dynamical effects. In this paper, we introduce a
novel setting, the Dynamical Linear Bandits (DLB), an extension of the linear
bandits characterized by a hidden state. When an action is performed, the
learner observes a noisy reward whose mean is a linear function of the hidden
state and of the action. Then, the hidden state evolves according to a linear
dynamics, affected by the performed action too. We start by introducing the
setting, discussing the notion of optimal policy, and deriving an expected
regret lower bound. Then, we provide an any-time optimistic regret minimization
algorithm, Dynamical Linear Upper Confidence Bound (DynLin-UCB), that suffers
an expected regret of order O(c d sqrt(T)), where c is a constant dependent on
the properties of the linear dynamical evolution, and d is the dimension of the
action vector. Finally, we conduct a numerical validation on a synthetic
environment and on real-world data to show the effectiveness of DynLin-UCB in
comparison with several baselines.

Spillover of economic outcomes often arises over multiple networks, and
distinguishing their separate roles is important in empirical research. For
example, the direction of spillover between two groups (such as banks and
industrial sectors linked in a bipartite graph) has important economic
implications, and a researcher may want to learn which direction is supported
in the data. For this, we need to have an empirical methodology that allows for
both directions of spillover simultaneously. In this paper, we develop a
dynamic linear panel model and asymptotic inference with large $n$ and small
$T$, where both directions of spillover are accommodated through multiple
networks. Using the methodology developed here, we perform an empirical study
of spillovers between bank weakness and zombie-firm congestion in industrial
sectors, using firm-bank matched data from Spain between 2005 and 2012.
Overall, we find that there is positive spillover in both directions between
banks and sectors.

Finding equilibria via gradient play in competitive multi-agent games has
been attracting a growing amount of attention in recent years, with emphasis on
designing efficient strategies where the agents operate in a decentralized and
symmetric manner with guaranteed convergence. While significant efforts have
been made in understanding zero-sum two-player matrix games, the performance in
zero-sum multi-agent games remains inadequately explored, especially in the
presence of delayed feedbacks, leaving the scalability and resiliency of
gradient play open to questions.
  In this paper, we make progress by studying asynchronous gradient plays in
zero-sum polymatrix games under delayed feedbacks. We first establish that the
last iterate of entropy-regularized optimistic multiplicative weight updates
(OMWU) method converges linearly to the quantal response equilibrium (QRE), the
solution concept under bounded rationality, in the absence of delays. While the
linear convergence continues to hold even when the feedbacks are randomly
delayed under mild statistical assumptions, it converges at a noticeably slower
rate due to a smaller tolerable range of learning rates. Moving beyond, we
demonstrate entropy-regularized OMWU -- by adopting two-timescale learning
rates in a delay-aware manner -- enjoys faster last-iterate convergence under
fixed delays, and continues to converge provably even when the delays are
arbitrarily bounded in an average-iterate manner. Our methods also lead to
finite-time guarantees to approximate the Nash equilibrium (NE) by moderating
the amount of regularization. To the best of our knowledge, this work is the
first that aims to understand asynchronous gradient play in zero-sum polymatrix
games under a wide range of delay assumptions, highlighting the role of
learning rates separation.

The need for rapid and reliable robot deployment is on the rise. Imitation
Learning (IL) has become popular for producing motion planning policies from a
set of demonstrations. However, many methods in IL are not guaranteed to
produce stable policies. The generated policy may not converge to the robot
target, reducing reliability, and may collide with its environment, reducing
the safety of the system. Stable Estimator of Dynamic Systems (SEDS) produces
stable policies by constraining the Lyapunov stability criteria during
learning, but the Lyapunov candidate function had to be manually selected. In
this work, we propose a novel method for learning a Lyapunov function and a
policy using a single neural network model. The method can be equipped with an
obstacle avoidance module for convex object pairs to guarantee no collisions.
We demonstrated our method is capable of finding policies in several simulation
environments and transfer to a real-world scenario.

Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as
powerful methods for link prediction (LP). Their performances are less
impressive on community detection (CD), where they are often outperformed by
simpler alternatives such as the Louvain method. It is still unclear to what
extent one can improve CD with GAE and VGAE, especially in the absence of node
features. It is moreover uncertain whether one could do so while simultaneously
preserving good performances on LP in a multi-task setting. In this workshop
paper, summarizing results from our journal publication (Salha-Galvan et al.
2022), we show that jointly addressing these two tasks with high accuracy is
possible. For this purpose, we introduce a community-preserving message passing
scheme, doping our GAE and VGAE encoders by considering both the initial graph
and Louvain-based prior communities when computing embedding spaces. Inspired
by modularity-based clustering, we further propose novel training and
optimization strategies specifically designed for joint LP and CD. We
demonstrate the empirical effectiveness of our approach, referred to as
Modularity-Aware GAE and VGAE, on various real-world graphs.

Imaging Atmospheric Cherenkov Telescopes (IACT) of TAIGA astrophysical
complex allow to observe high energy gamma radiation helping to study many
astrophysical objects and processes. TAIGA-IACT enables us to select gamma
quanta from the total cosmic radiation flux and recover their primary
parameters, such as energy and direction of arrival. The traditional method of
processing the resulting images is an image parameterization - so-called the
Hillas parameters method. At the present time Machine Learning methods, in
particular Deep Learning methods have become actively used for IACT image
processing. This paper presents the analysis of simulated Monte Carlo images by
several Deep Learning methods for a single telescope (mono-mode) and multiple
IACT telescopes (stereo-mode). The estimation of the quality of energy
reconstruction was carried out and their energy spectra were analyzed using
several types of neural networks. Using the developed methods the obtained
results were also compared with the results obtained by traditional methods
based on the Hillas parameters.

We propose and analyse a reduced-rank method for solving least-squares
regression problems with infinite dimensional output. We derive learning bounds
for our method, and study under which setting statistical performance is
improved in comparison to full-rank method. Our analysis extends the interest
of reduced-rank regression beyond the standard low-rank setting to more general
output regularity assumptions. We illustrate our theoretical insights on
synthetic least-squares problems. Then, we propose a surrogate structured
prediction method derived from this reduced-rank method. We assess its benefits
on three different problems: image reconstruction, multi-label classification,
and metabolite identification.

The increasing popularity of Internet of Everything and small-cell devices
has enormously accelerated traffic loads. Consequently, increased bandwidth and
high data rate requirements stimulate the operation at the millimeter wave and
the Tera-Hertz spectrum bands in the fifth generation (5G) and beyond 5G (B5G)
wireless networks. Furthermore, efficient spectrum allocation, maximizing the
spectrum utilization, achieving efficient spectrum sharing (SS), and managing
the spectrum to enhance the system performance remain challenging. To this end,
recent studies have implemented artificial intelligence and machine learning
techniques, enabling intelligent and efficient spectrum leveraging. However,
despite many recent research advances focused on maximizing utilization of the
spectrum bands, achieving efficient sharing, allocation, and management of the
enormous available spectrum remains challenging. Therefore, the current article
acquaints a comprehensive survey on intelligent SS methodologies for 5G and B5G
wireless networks, considering the applications of artificial intelligence for
efficient SS. Specifically, a thorough overview of SS methodologies is
conferred, following which the various spectrum utilization opportunities
arising from the existing SS methodologies in intelligent wireless networks are
discussed. Subsequently, to highlight critical limitations of the existing
methodologies, recent literature on existing SS methodologies is reviewed in
detail, classifying them based on the implemented technology, i.e., cognitive
radio, machine learning, blockchain, and multiple other techniques. Moreover,
the related SS techniques are reviewed to highlight significant challenges in
the B5G intelligent wireless network. Finally, to provide an insight into the
prospective research avenues, the article is concluded by presenting several
potential research directions and proposed solutions.

The goal of this work is to detect and recognize sequences of letters signed
using fingerspelling in British Sign Language (BSL). Previous fingerspelling
recognition methods have not focused on BSL, which has a very different signing
alphabet (e.g., two-handed instead of one-handed) to American Sign Language
(ASL). They also use manual annotations for training. In contrast to previous
methods, our method only uses weak annotations from subtitles for training. We
localize potential instances of fingerspelling using a simple feature
similarity method, then automatically annotate these instances by querying
subtitle words and searching for corresponding mouthing cues from the signer.
We propose a Transformer architecture adapted to this task, with a
multiple-hypothesis CTC loss function to learn from alternative annotation
possibilities. We employ a multi-stage training approach, where we make use of
an initial version of our trained model to extend and enhance our training data
before re-training again to achieve better performance. Through extensive
evaluations, we verify our method for automatic annotation and our model
architecture. Moreover, we provide a human expert annotated test set of 5K
video clips for evaluating BSL fingerspelling recognition methods to support
sign language research.

Stochastic restoration algorithms allow to explore the space of solutions
that correspond to the degraded input. In this paper we reveal additional
fundamental advantages of stochastic methods over deterministic ones, which
further motivate their use. First, we prove that any restoration algorithm that
attains perfect perceptual quality and whose outputs are consistent with the
input must be a posterior sampler, and is thus required to be stochastic.
Second, we illustrate that while deterministic restoration algorithms may
attain high perceptual quality, this can be achieved only by filling up the
space of all possible source images using an extremely sensitive mapping, which
makes them highly vulnerable to adversarial attacks. Indeed, we show that
enforcing deterministic models to be robust to such attacks profoundly hinders
their perceptual quality, while robustifying stochastic models hardly
influences their perceptual quality, and improves their output variability.
These findings provide a motivation to foster progress in stochastic
restoration methods, paving the way to better recovery algorithms.

With increasing interest in explaining machine learning (ML) models, the
first part of this two-part study synthesizes recent research on methods for
explaining global and local aspects of ML models. This study distinguishes
explainability from interpretability, local from global explainability, and
feature importance versus feature relevance. We demonstrate and visualize
different explanation methods, how to interpret them, and provide a complete
Python package (scikit-explain) to allow future researchers to explore these
products. We also highlight the frequent disagreement between explanation
methods for feature rankings and feature effects and provide practical advice
for dealing with these disagreements. We used ML models developed for severe
weather prediction and sub-freezing road surface temperature prediction to
generalize the behavior of the different explanation methods. For feature
rankings, there is substantially more agreement on the set of top features
(e.g., on average, two methods agree on 6 of the top 10 features) than on
specific rankings (on average, two methods only agree on the ranks of 2-3
features in the set of top 10 features). On the other hand, two feature effect
curves from different methods are in high agreement as long as the phase space
is well sampled. Finally, a lesser-known method, tree interpreter, was found
comparable to SHAP for feature effects, and with the widespread use of random
forests in geosciences and computational ease of tree interpreter, we recommend
it be explored in future research.

Machine learning models have shone in a variety of domains and attracted
increasing attention from both the security and the privacy communities. One
important yet worrying question is: will training models under the differential
privacy (DP) constraint unfavorably impact on the adversarial robustness? While
previous works have postulated that privacy comes at the cost of worse
robustness, we give the first theoretical analysis to show that DP models can
indeed be robust and accurate, even sometimes more robust than their
naturally-trained non-private counterparts. We observe three key factors that
influence the privacy-robustness-accuracy tradeoff: (1) hyperparameters for DP
optimizers are critical; (2) pre-training on public data significantly
mitigates the accuracy and robustness drop; (3) choice of DP optimizers makes a
difference. With these factors set properly, we achieve 90\% natural accuracy,
72\% robust accuracy ($+9\%$ than the non-private model) under $l_2(0.5)$
attack, and 69\% robust accuracy ($+16\%$ than the non-private model) with
pre-trained SimCLRv2 model under $l_\infty(4/255)$ attack on CIFAR10 with
$\epsilon=2$. In fact, we show both theoretically and empirically that DP
models are Pareto optimal on the accuracy-robustness tradeoff. Empirically, the
robustness of DP models is consistently observed on MNIST, Fashion MNIST and
CelebA datasets, with ResNet and Vision Transformer. We believe our encouraging
results are a significant step towards training models that are private as well
as robust.

In this paper, we propose the augmented physics-informed neural network
(APINN), which adopts soft and trainable domain decomposition and flexible
parameter sharing to further improve the extended PINN (XPINN) as well as the
vanilla PINN methods. In particular, a trainable gate network is employed to
mimic the hard and discrete decomposition of XPINN, which can be flexibly
fine-tuned for discovering a potentially better partition. It weight-averages
several sub-nets as the output of APINN. APINN does not require complex
interface conditions, and its sub-nets can take advantage of all training
samples rather than just part of the training data in their subdomains. Lastly,
each sub-net shares part of the common parameters to capture the similar
components in each decomposed function. Furthermore, following the PINN
generalization theory in Hu et al. [2021], we show that APINN can improve
generalization by proper gate network initialization and general domain &
function decomposition. Extensive experiments on different types of PDEs
demonstrate how APINN improves the PINN and XPINN methods. Specifically, we
present examples where XPINN performs similarly to or worse than PINN, so that
APINN can significantly improve both. We also show cases where XPINN is already
better than PINN, so APINN can still slightly improve XPINN. Furthermore, we
visualize the optimized gating networks and their optimization trajectories,
and connect them with their performance, which helps discover the possibly
optimal decomposition. Interestingly, if initialized by different
decomposition, the performances of corresponding APINNs can differ drastically.
This, in turn, shows the potential to design an optimal domain decomposition
for the differential equation problem under consideration.

Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from
graph-structured data. A paramount example of such data is the brain, which
operates as a network, from the micro-scale of neurons, to the macro-scale of
regions. This organization deemed GNNs a natural tool of choice to model brain
activity, and have consequently attracted a lot of attention in the
neuroimaging community. Yet, the advantage of adopting these models over
conventional methods has not yet been assessed in a systematic way to gauge if
GNNs are capable of leveraging the underlying structure of the data to improve
learning. In this work, we study and evaluate the performance of five popular
GNN architectures in diagnosing major depression disorder and autism spectrum
disorder in two multi-site clinical datasets, and sex classification on the
UKBioBank, from functional brain scans under a general uniform framework. Our
results show that GNNs fail to outperform kernel-based and structure-agnostic
deep learning models, in which 1D CNNs outperform the other methods in all
scenarios. We highlight that creating optimal graph structures for functional
brain data is a major bottleneck hindering the performance of GNNs, where
existing works use arbitrary measures to define the edges resulting in noisy
graphs. We therefore propose to integrate graph diffusion into existing
architectures and show that it can alleviate this problem and improve their
performance. Our results call for increased moderation and rigorous validation
when evaluating graph methods and advocate for more data-centeric approaches in
developing GNNs for functional neuroimaging applications.

Federated semi-supervised learning (FSSL), facilitates labeled clients and
unlabeled clients jointly training a global model without sharing private data.
Existing FSSL methods mostly focus on pseudo-labeling and consistency
regularization to leverage the knowledge of unlabeled data, which have achieved
substantial success on raw data utilization. However, their training procedures
suffer from the large deviation from local models of labeled clients and
unlabeled clients and the confirmation bias induced by noisy pseudo labels,
which seriously damage the performance of the global model. In this paper, we
propose a novel FSSL method, named Dual Class-aware Contrastive Federated
Semi-Supervised Learning (DCCFSSL), which considers the local class-aware
distribution of individual client's data and the global class-aware
distribution of all clients' data simultaneously in the feature space. By
introducing a dual class-aware contrastive module, DCCFSSL builds a common
training goal for different clients to reduce the large deviation and
introduces contrastive information in the feature space to alleviate the
confirmation bias. Meanwhile, DCCFSSL presents an authentication-reweighted
aggregation method to enhance the robustness of the server's aggregation.
Extensive experiments demonstrate that DCCFSSL not only outperforms
state-of-the-art methods on three benchmarked datasets, but also surpasses the
FedAvg with relabeled unlabeled clients on CIFAR-10 and CIFAR-100 datasets. To
our best knowledge, we are the first to present the FSSL method that utilizes
only 10\% labeled clients of all clients to achieve better performance than the
standard federated supervised learning that uses all clients with labeled data.

Intelligent music generation, one of the most popular subfields of computer
creativity, can lower the creative threshold for non-specialists and increase
the efficiency of music creation. In the last five years, the quality of
algorithm-based automatic music generation has increased significantly,
motivated by the use of modern generative algorithms to learn the patterns
implicit within a piece of music based on rule constraints or a musical corpus,
thus generating music samples in various styles. Some of the available
literature reviews lack a systematic benchmark of generative models and are
traditional and conservative in their perspective, resulting in a vision of the
future development of the field that is not deeply integrated with the current
rapid scientific progress. In this paper, we conduct a comprehensive survey and
analysis of recent intelligent music generation techniques,provide a critical
discussion, explicitly identify their respective characteristics, and present
them in a general table. We first introduce how music as a stream of
information is encoded and the relevant datasets, then compare different types
of generation algorithms, summarize their strengths and weaknesses, and discuss
existing methods for evaluation. Finally, the development of artificial
intelligence in composition is studied, especially by comparing the different
characteristics of music generation techniques in the East and West and
analyzing the development prospects in this field.

Anomaly detection (AD) has attracted considerable attention in both academia
and industry. Due to the lack of anomalous data in many practical cases, AD is
usually solved by first modeling the normal data pattern and then determining
if data fit this model. Generative models (GMs) seem a natural tool to achieve
this purpose, which learn the normal data distribution and estimate it using a
probability density function (PDF). However, some works have observed the ideal
performance of such GM-based AD methods. In this paper, we propose a new
perspective on the ideal performance of GM-based AD methods. We state that in
these methods, the implicit assumption that connects GMs'results to AD's goal
is usually implausible due to normal data's multi-peaked distribution
characteristic, which is quite common in practical cases. We first
qualitatively formulate this perspective, and then focus on the Gaussian
mixture model (GMM) to intuitively illustrate the perspective, which is a
typical GM and has the natural property to approximate multi-peaked
distributions. Based on the proposed perspective, in order to bypass the
implicit assumption in the GMM-based AD method, we suggest integrating the
Discriminative idea to orient GMM to AD tasks (DiGMM). With DiGMM, we establish
a connection of generative and discriminative models, which are two key
paradigms for AD and are usually treated separately before. This connection
provides a possible direction for future works to jointly consider the two
paradigms and incorporate their complementary characteristics for AD.

Self-supervised learning of egomotion and depth has recently attracted great
attentions. These learning models can provide pose and depth maps to support
navigation and perception task for autonomous driving and robots, while they do
not require high-precision ground-truth labels to train the networks. However,
monocular vision based methods suffer from pose scale-ambiguity problem, so
that can not generate physical meaningful trajectory, and thus their
applications are limited in real-world. We propose a novel self-learning deep
neural network framework that can learn to estimate egomotion and depths with
absolute metric scale from monocular images. Coarse depth scale is recovered
via comparing point cloud data against a pretrained model that ensures the
consistency of photometric loss. The scale-ambiguity problem is solved by
introducing a novel two-stages coarse-to-fine scale recovery strategy that
jointly refines coarse poses and depths. Our model successfully produces pose
and depth estimates in global scale-metric, even in low-light condition, i.e.
driving at night. The evaluation on the public datasets demonstrates that our
model outperforms both representative traditional and learning based VOs and
VIOs, e.g. VINS-mono, ORB-SLAM, SC-Learner, and UnVIO.

For bike sharing systems, demand prediction is crucial to ensure the timely
re-balancing of available bikes according to predicted demand. Existing methods
for bike sharing demand prediction are mostly based on its own historical
demand variation, essentially regarding it as a closed system and neglecting
the interaction between different transportation modes. This is particularly
important for bike sharing because it is often used to complement travel
through other modes (e.g., public transit). Despite some recent progress, no
existing method is capable of leveraging spatiotemporal information from
multiple modes and explicitly considers the distribution discrepancy between
them, which can easily lead to negative transfer. To address these challenges,
this study proposes a domain-adversarial multi-relational graph neural network
(DA-MRGNN) for bike sharing demand prediction with multimodal historical data
as input. A temporal adversarial adaptation network is introduced to extract
shareable features from demand patterns of different modes. To capture
correlations between spatial units across modes, we adapt a multi-relational
graph neural network (MRGNN) considering both cross-mode similarity and
difference. In addition, an explainable GNN technique is developed to
understand how our proposed model makes predictions. Extensive experiments are
conducted using real-world bike sharing, subway and ride-hailing data from New
York City. The results demonstrate the superior performance of our proposed
approach compared to existing methods and the effectiveness of different model
components.

In this paper, we perform the convergence analysis of unsupervised
Legendre--Galerkin neural networks (ULGNet), a deep-learning-based numerical
method for solving partial differential equations (PDEs). Unlike existing deep
learning-based numerical methods for PDEs, the ULGNet expresses the solution as
a spectral expansion with respect to the Legendre basis and predicts the
coefficients with deep neural networks by solving a variational residual
minimization problem. Since the corresponding loss function is equivalent to
the residual induced by the linear algebraic system depending on the choice of
basis functions, we prove that the minimizer of the discrete loss function
converges to the weak solution of the PDEs. Numerical evidence will also be
provided to support the theoretical result. Key technical tools include the
variant of the universal approximation theorem for bounded neural networks, the
analysis of the stiffness and mass matrices, and the uniform law of large
numbers in terms of the Rademacher complexity.

Unsupervised domain adaption (UDA) is a transfer learning task where the data
and annotations of the source domain are available but only have access to the
unlabeled target data during training. Most previous methods try to minimise
the domain gap by performing distribution alignment between the source and
target domains, which has a notable limitation, i.e., operating at the domain
level, but neglecting the sample-level differences. To mitigate this weakness,
we propose to improve the unsupervised domain adaptation task with an
inter-domain sample matching scheme. We apply the widely-used and robust
Triplet loss to match the inter-domain samples. To reduce the catastrophic
effect of the inaccurate pseudo-labels generated during training, we propose a
novel uncertainty measurement method to select reliable pseudo-labels
automatically and progressively refine them. We apply the advanced discrete
relaxation Gumbel Softmax technique to realise an adaptive Topk scheme to
fulfil the functionality. In addition, to enable the global ranking
optimisation within one batch for the domain matching, the whole model is
optimised via a novel reinforced attention mechanism with supervision from the
policy gradient algorithm, using the Average Precision (AP) as the reward. Our
model (termed \textbf{\textit{AdaTriplet-RA}}) achieves State-of-the-art
results on several public benchmark datasets, and its effectiveness is
validated via comprehensive ablation studies. Our method improves the accuracy
of the baseline by 9.7\% (ResNet-101) and 6.2\% (ResNet-50) on the VisDa
dataset and 4.22\% (ResNet-50) on the Domainnet dataset. {The source code is
publicly available at \textit{https://github.com/shuxy0120/AdaTriplet-RA}}.

Generating graph-structured data is a challenging problem, which requires
learning the underlying distribution of graphs. Various models such as graph
VAE, graph GANs and graph diffusion models have been proposed to generate
meaningful and reliable graphs, among which the diffusion models have achieved
state-of-the-art performance. In this paper, we argue that running full-rank
diffusion SDEs on the whole space hinders diffusion models from learning graph
topology generation, and hence significantly deteriorates the quality of
generated graph data. To address this limitation, we propose an efficient yet
effective Graph Spectral Diffusion Model (GSDM), which is driven by low-rank
diffusion SDEs on the graph spectrum space. Our spectral diffusion model is
further proven to enjoy a substantially stronger theoretical guarantee than
standard diffusion models. Extensive experiments across various datasets
demonstrate that, our proposed GSDM turns out to be the SOTA model, by
exhibiting either significantly higher generation quality or much less
computational consumption than the baselines.

We present a new streaming algorithm to validate JSON documents against a set
of constraints given as a JSON schema. Among the possible values a JSON
document can hold, objects are unordered collections of key-value pairs while
arrays are ordered collections of values. We prove that there always exists a
visibly pushdown automaton (VPA) that accepts the same set of JSON documents as
a JSON schema. Leveraging this result, our approach relies on learning a VPA
for the provided schema. As the learned VPA assumes a fixed order on the
key-value pairs of the objects, we abstract its transitions in a special kind
of graph, and propose an efficient streaming algorithm using the VPA and its
graph to decide whether a JSON document is valid for the schema. We evaluate
the implementation of our algorithm on a number of random JSON documents, and
compare it to the classical validation algorithm.

Many unsupervised domain adaptation (UDA) methods have been proposed to
bridge the domain gap by utilizing domain invariant information. Most
approaches have chosen depth as such information and achieved remarkable
success. Despite their effectiveness, using depth as domain invariant
information in UDA tasks may lead to multiple issues, such as excessively high
extraction costs and difficulties in achieving a reliable prediction quality.
As a result, we introduce Edge Learning based Domain Adaptation (ELDA), a
framework which incorporates edge information into its training process to
serve as a type of domain invariant information. In our experiments, we
quantitatively and qualitatively demonstrate that the incorporation of edge
information is indeed beneficial and effective and enables ELDA to outperform
the contemporary state-of-the-art methods on two commonly adopted benchmarks
for semantic segmentation based UDA tasks. In addition, we show that ELDA is
able to better separate the feature distributions of different classes. We
further provide an ablation analysis to justify our design decisions.

Masked Autoencoders (MAE) have been prevailing paradigms for large-scale
vision representation pre-training. By reconstructing masked image patches from
a small portion of visible image regions, MAE forces the model to infer
semantic correlation within an image. Recently, some approaches apply
semantic-rich teacher models to extract image features as the reconstruction
target, leading to better performance. However, unlike the low-level features
such as pixel values, we argue the features extracted by powerful teacher
models already encode rich semantic correlation across regions in an intact
image.This raises one question: is reconstruction necessary in Masked Image
Modeling (MIM) with a teacher model? In this paper, we propose an efficient MIM
paradigm named MaskAlign. MaskAlign simply learns the consistency of visible
patch features extracted by the student model and intact image features
extracted by the teacher model. To further advance the performance and tackle
the problem of input inconsistency between the student and teacher model, we
propose a Dynamic Alignment (DA) module to apply learnable alignment. Our
experimental results demonstrate that masked modeling does not lose
effectiveness even without reconstruction on masked regions. Combined with
Dynamic Alignment, MaskAlign can achieve state-of-the-art performance with much
higher efficiency. Code and models will be available at
https://github.com/OpenPerceptionX/maskalign.

Decades of research on the 0-1 knapsack problem led to very efficient
algorithms that are able to quickly solve large problem instances to
optimality. This prompted researchers to also investigate whether relatively
small problem instances exist that are hard for existing solvers and
investigate which features characterize their hardness. Previously the authors
proposed a new class of hard 0-1 knapsack problem instances and demonstrated
that the properties of so-called inclusionwise maximal solutions (IMSs) can be
important hardness indicators for this class. In the current paper, we
formulate several new computationally challenging problems related to the IMSs
of arbitrary 0-1 knapsack problem instances. Based on generalizations of
previous work and new structural results about IMSs, we formulate polynomial
and pseudopolynomial time algorithms for solving these problems. From this we
derive a set of 14 computationally expensive features, which we calculate for
two large datasets on a supercomputer in approximately 540 CPU-hours. We show
that the proposed features contain important information related to the
empirical hardness of a problem instance that was missing in earlier features
from the literature by training machine learning models that can accurately
predict the empirical hardness of a wide variety of 0-1 knapsack problem
instances. Using the instance space analysis methodology, we also show that
hard 0-1 knapsack problem instances are clustered together around a relatively
dense region of the instance space and several features behave differently in
the easy and hard parts of the instance space.

We study the evolution of $L_{*}$ elliptical galaxies in the color-magnitude
diagram in terms of their star-formation history and environment, in an attempt
to learn about their quenching process. We have visually extracted 1109 $L_{*}$
galaxies from a sample of 36500 galaxies that were spectroscopically selected
from Stripe82 of the Sloan Digital Sky Survey. From this sample we have
selected 51 ellipticals based on their surface-brightness profile being
well-fitted by a single S$\acute{e}$rsic profile with S$\acute{e}$rsic indices
$3<n<6$. Our sample consists of 12 blue-cloud $L_{*}$ ellipticals (BLE), 11
green-valley $L_{*}$ ellipticals (GLE), and 28 red-sequence $L_{*}$ ellipticals
(RLE). We find that most of the RLEs and GLEs have been quenched only recently,
or are still forming stars, based on their [{O\sc{iii}}] and H$\alpha$
emission, while the BLEs are forming stars vigorously. The star-formation in
BLEs is found to be extended over the galaxy and not confined to their central
region. In about 40\% of the $L_{*}$ ellipticals (ten BLEs, four GLEs and five
RLEs), star-formation quenching seems to have started only recently, based on
the lower [{O\sc{iii}}] emission compared to the [{O\sc{ii}}] and H$\alpha$
emission, at a given metallicity. We also find that the galaxy color is
correlated with the cosmic-web environment, with the BLEs tending to reside in
lower-density regions, the RLEs preferring denser, clustered regions, and the
GLEs found in either. One possible scenario is that as the star-forming
ellipticals migrate into the clusters, their star formation is suffocated by
the hot intra-cluster medium.

A first causal discovery analysis from observational data of pyroCb (storm
clouds generated from extreme wildfires) is presented. Invariant Causal
Prediction was used to develop tools to understand the causal drivers of pyroCb
formation. This includes a conditional independence test for testing $Y$
conditionally independent of $E$ given $X$ for binary variable $Y$ and
multivariate, continuous variables $X$ and $E$, and a greedy-ICP search
algorithm that relies on fewer conditional independence tests to obtain a
smaller more manageable set of causal predictors. With these tools, we
identified a subset of seven causal predictors which are plausible when
contrasted with domain knowledge: surface sensible heat flux, relative humidity
at $850$ hPa, a component of wind at $250$ hPa, $13.3$\ micro-meters, thermal
emissions, convective available potential energy, and altitude.

Both the temporal dynamics and spatial correlations of Electroencephalogram
(EEG), which contain discriminative emotion information, are essential for the
emotion recognition. However, some redundant information within the EEG signals
would degrade the performance. Specifically,the subjects reach prospective
intense emotions for only a fraction of the stimulus duration. Besides, it is a
challenge to extract discriminative features from the complex spatial
correlations among a number of electrodes. To deal with the problems, we
propose a transformer-based model to robustly capture temporal dynamics and
spatial correlations of EEG. Especially, temporal feature extractors which
share the weight among all the EEG channels are designed to adaptively extract
dynamic context information from raw signals. Furthermore, multi-head
self-attention mechanism within the transformers could adaptively localize the
vital EEG fragments and emphasize the essential brain regions which contribute
to the performance. To verify the effectiveness of the proposed method, we
conduct the experiments on two public datasets, DEAP and MAHNOBHCI. The results
demonstrate that the proposed method achieves outstanding performance on
arousal and valence classification.

We consider the problem of learning a linear operator $\theta$ between two
Hilbert spaces from empirical observations, which we interpret as least squares
regression in infinite dimensions. We show that this goal can be reformulated
as an inverse problem for $\theta$ with the undesirable feature that its
forward operator is generally non-compact (even if $\theta$ is assumed to be
compact or of $p$-Schatten class). However, we prove that, in terms of spectral
properties and regularisation theory, this inverse problem is equivalent to the
known compact inverse problem associated with scalar response regression. Our
framework allows for the elegant derivation of dimension-free rates for generic
learning algorithms under H\"older-type source conditions. The proofs rely on
the combination of techniques from kernel regression with recent results on
concentration of measure for sub-exponential Hilbertian random variables. The
obtained rates hold for a variety of practically-relevant scenarios in
functional regression as well as nonlinear regression with operator-valued
kernels and match those of classical kernel regression with scalar response.

Unsupervised domain adaptation (UDA) aims to improve the prediction
performance in the target domain under distribution shifts from the source
domain. The key principle of UDA is to minimize the divergence between the
source and the target domains. To follow this principle, many methods employ a
domain discriminator to match the feature distributions. Some recent methods
evaluate the discrepancy between two predictions on target samples to detect
those that deviate from the source distribution. However, their performance is
limited because they either match the marginal distributions or measure the
divergence conservatively. In this paper, we present a novel UDA method that
learns domain-invariant features that minimize the domain divergence. We
propose model uncertainty as a measure of the domain divergence. Our UDA method
based on model uncertainty (MUDA) adopts a Bayesian framework and provides an
efficient way to evaluate model uncertainty by means of Monte Carlo dropout
sampling. Empirical results on image recognition tasks show that our method is
superior to existing state-of-the-art methods. We also extend MUDA to
multi-source domain adaptation problems.

Soft-biometric privacy-enhancing techniques represent machine learning
methods that aim to: (i) mitigate privacy concerns associated with face
recognition technology by suppressing selected soft-biometric attributes in
facial images (e.g., gender, age, ethnicity) and (ii) make unsolicited
extraction of sensitive personal information infeasible. Because such
techniques are increasingly used in real-world applications, it is imperative
to understand to what extent the privacy enhancement can be inverted and how
much attribute information can be recovered from privacy-enhanced images. While
these aspects are critical, they have not been investigated in the literature.
We, therefore, study the robustness of several state-of-the-art soft-biometric
privacy-enhancing techniques to attribute recovery attempts. We propose
PrivacyProber, a high-level framework for restoring soft-biometric information
from privacy-enhanced facial images, and apply it for attribute recovery in
comprehensive experiments on three public face datasets, i.e., LFW, MUCT and
Adience. Our experiments show that the proposed framework is able to restore a
considerable amount of suppressed information, regardless of the
privacy-enhancing technique used, but also that there are significant
differences between the considered privacy models. These results point to the
need for novel mechanisms that can improve the robustness of existing
privacy-enhancing techniques and secure them against potential adversaries
trying to restore suppressed information.

Infographics are often an integral component of scientific documents for
reporting qualitative or quantitative findings as they make it much simpler to
comprehend the underlying complex information. However, their interpretation
continues to be a challenge for the blind, low-vision, and other print-impaired
(BLV) individuals. In this paper, we propose ChartParser, a fully automated
pipeline that leverages deep learning, OCR, and image processing techniques to
extract all figures from a research paper, classify them into various chart
categories (bar chart, line chart, etc.) and obtain relevant information from
them, specifically bar charts (including horizontal, vertical, stacked
horizontal and stacked vertical charts) which already have several exciting
challenges. Finally, we present the retrieved content in a tabular format that
is screen-reader friendly and accessible to the BLV users. We present a
thorough evaluation of our approach by applying our pipeline to sample
real-world annotated bar charts from research papers.

Machine learning approaches now achieve impressive generation capabilities in
numerous domains such as image, audio or video. However, most training \&
evaluation frameworks revolve around the idea of strictly modelling the
original data distribution rather than trying to extrapolate from it. This
precludes the ability of such models to diverge from the original distribution
and, hence, exhibit some creative traits. In this paper, we propose various
perspectives on how this complicated goal could ever be achieved, and provide
preliminary results on our novel training objective called \textit{Bounded
Adversarial Divergence} (BAD).

Adversarial attacks against deep learning-based object detectors (ODs) have
been studied extensively in the past few years. These attacks cause the model
to make incorrect predictions by placing a patch containing an adversarial
pattern on the target object or anywhere within the frame. However, none of
prior research proposed a misclassification attack on ODs, in which the patch
is applied on the target object. In this study, we propose a novel, universal,
targeted, label-switch attack against the state-of-the-art object detector,
YOLO. In our attack, we use (i) a tailored projection function to enable the
placement of the adversarial patch on multiple target objects in the image
(e.g., cars), each of which may be located a different distance away from the
camera or have a different view angle relative to the camera, and (ii) a unique
loss function capable of changing the label of the attacked objects. The
proposed universal patch, which is trained in the digital domain, is
transferable to the physical domain. We performed an extensive evaluation using
different types of object detectors, different video streams captured by
different cameras, and various target classes, and evaluated different
configurations of the adversarial patch in the physical domain.

Conveying the linguistic content and maintaining the source speech's speaking
style, such as intonation and emotion, is essential in voice conversion (VC).
However, in a low-resource situation, where only limited utterances from the
target speaker are accessible, existing VC methods are hard to meet this
requirement and capture the target speaker's timber. In this work, a novel VC
model, referred to as MFC-StyleVC, is proposed for the low-resource VC task.
Specifically, speaker timbre constraint generated by clustering method is newly
proposed to guide target speaker timbre learning in different stages.
Meanwhile, to prevent over-fitting to the target speaker's limited data,
perceptual regularization constraints explicitly maintain model performance on
specific aspects, including speaking style, linguistic content, and speech
quality. Besides, a simulation mode is introduced to simulate the inference
process to alleviate the mismatch between training and inference. Extensive
experiments performed on highly expressive speech demonstrate the superiority
of the proposed method in low-resource VC.

The development of generative Machine Learning (ML) models in creative
practices, enabled by the recent improvements in usability and availability of
pre-trained models, is raising more and more interest among artists,
practitioners and performers. Yet, the introduction of such techniques in
artistic domains also revealed multiple limitations that escape current
evaluation methods used by scientists. Notably, most models are still unable to
generate content that lay outside of the domain defined by the training
dataset. In this paper, we propose an alternative prospective framework,
starting from a new general formulation of ML objectives, that we derive to
delineate possible implications and solutions that already exist in the ML
literature (notably for the audio and musical domain). We also discuss existing
relations between generative models and computational creativity and how our
framework could help address the lack of creativity in existing models.

Filters are fundamental in extracting information from data. For time series
and image data that reside on Euclidean domains, filters are the crux of many
signal processing and machine learning techniques, including convolutional
neural networks. Increasingly, modern data also reside on networks and other
irregular domains whose structure is better captured by a graph. To process and
learn from such data, graph filters account for the structure of the underlying
data domain. In this article, we provide a comprehensive overview of graph
filters, including the different filtering categories, design strategies for
each type, and trade-offs between different types of graph filters. We discuss
how to extend graph filters into filter banks and graph neural networks to
enhance the representational power; that is, to model a broader variety of
signal classes, data patterns, and relationships. We also showcase the
fundamental role of graph filters in signal processing and machine learning
applications. Our aim is that this article serves the dual purpose of providing
a unifying framework for both beginner and experienced researchers, as well as
a common understanding that promotes collaborations between signal processing,
machine learning, and application domains.

In this paper, we propose the task of consecutive question generation (CQG),
which generates a set of logically related question-answer pairs to understand
a whole passage, with a comprehensive consideration of the aspects including
accuracy, coverage, and informativeness. To achieve this, we first examine the
four key elements of CQG, i.e., question, answer, rationale, and context
history, and propose a novel dynamic multitask framework with one main task
generating a question-answer pair, and four auxiliary tasks generating other
elements. It directly helps the model generate good questions through both
joint training and self-reranking. At the same time, to fully explore the
worth-asking information in a given passage, we make use of the reranking
losses to sample the rationales and search for the best question series
globally. Finally, we measure our strategy by QA data augmentation and manual
evaluation, as well as a novel application of generated question-answer pairs
on DocNLI. We prove that our strategy can improve question generation
significantly and benefit multiple related NLP tasks.

There has been a growing demand for automated spoken language assessment
systems in recent years. A standard pipeline for this process is to start with
a speech recognition system and derive features, either hand-crafted or based
on deep-learning, that exploit the transcription and audio. Though these
approaches can yield high performance systems, they require speech recognition
systems that can be used for L2 speakers, and preferably tuned to the specific
form of test being deployed. Recently a self-supervised speech representation
based scheme, requiring no speech recognition, was proposed. This work extends
the initial analysis conducted on this approach to a large scale proficiency
test, Linguaskill, that comprises multiple parts, each designed to assess
different attributes of a candidate's speaking proficiency. The performance of
the self-supervised, wav2vec 2.0, system is compared to a high performance
hand-crafted assessment system and a BERT-based text system both of which use
speech transcriptions. Though the wav2vec 2.0 based system is found to be
sensitive to the nature of the response, it can be configured to yield
comparable performance to systems requiring a speech transcription, and yields
gains when appropriately combined with standard approaches.

As an application of Natural Language Processing (NLP) techniques, financial
sentiment analysis (FSA) has become an invaluable tool for investors. Its speed
and accuracy can significantly impact the returns of trading strategies.With
the development of deep learning and Transformer-based pre-trained models like
BERT, the accuracy of FSA has been much improved, but these time-consuming big
models will also slow down the computation. To boost the processing speed of
the FSA system and ensure high precision, we first propose an efficient and
lightweight BERT (ELBERT) along with a novel confidence-window-based (CWB)
early exit mechanism. Based on ELBERT, an innovative method to accelerate text
processing on the GPU platform is developed, solving the difficult problem of
making the early exit mechanism work more effectively with a large input batch
size. Afterward, a fast and high-accuracy FSA system is built. Experimental
results show that the proposed CWB early exit mechanism achieves significantly
higher accuracy than existing early exit methods on BERT under the same
computation cost. Besides, our FSA system can boost the processing speed to
over 1000 texts per second with sufficient accuracy by using this acceleration
method, which is nearly twice as fast as the FastBERT. Hence, this system can
enable modern trading systems to quickly and accurately process financial text
data.

Volumetric magnetic resonance (MR) image segmentation plays an important role
in many clinical applications. Deep learning (DL) has recently achieved
state-of-the-art or even human-level performance on various image segmentation
tasks. Nevertheless, manually annotating volumetric MR images for DL model
training is labor-exhaustive and time-consuming. In this work, we aim to train
a semi-supervised and self-supervised collaborative learning framework for
prostate 3D MR image segmentation while using extremely sparse annotations, for
which the ground truth annotations are provided for just the central slice of
each volumetric MR image. Specifically, semi-supervised learning and
self-supervised learning methods are used to generate two independent sets of
pseudo labels. These pseudo labels are then fused by Boolean operation to
extract a more confident pseudo label set. The images with either manual or
network self-generated labels are then employed to train a segmentation model
for target volume extraction. Experimental results on a publicly available
prostate MR image dataset demonstrate that, while requiring significantly less
annotation effort, our framework generates very encouraging segmentation
results. The proposed framework is very useful in clinical applications when
training data with dense annotations are difficult to obtain.

Recently, handling long videos of complex and occluded sequences has emerged
as a new challenge in the video instance segmentation (VIS) community. However,
existing methods show limitations in addressing the challenge. We argue that
the biggest bottleneck in current approaches is the discrepancy between the
training and the inference. To effectively bridge the gap, we propose a
\textbf{Gen}eralized framework for \textbf{VIS}, namely \textbf{GenVIS}, that
achieves the state-of-the-art performance on challenging benchmarks without
designing complicated architectures or extra post-processing. The key
contribution of GenVIS is the learning strategy. Specifically, we propose a
query-based training pipeline for sequential learning, using a novel target
label assignment strategy. To further fill the remaining gaps, we introduce a
memory that effectively acquires information from previous states. Thanks to
the new perspective, which focuses on building relationships between separate
frames or clips, GenVIS can be flexibly executed in both online and semi-online
manner. We evaluate our methods on popular VIS benchmarks, YouTube-VIS
2019/2021/2022 and Occluded VIS (OVIS), achieving state-of-the-art results.
Notably, we greatly outperform the state-of-the-art on the long VIS benchmark
(OVIS), improving 5.6 AP with ResNet-50 backbone. Code will be available at
https://github.com/miranheo/GenVIS.

Although the UA-Speech and TORGO databases of control and dysarthric speech
are invaluable resources made available to the research community with the
objective of developing robust automatic speech recognition systems, they have
also been used to validate a considerable number of automatic dysarthric speech
classification approaches. Such approaches typically rely on the underlying
assumption that recordings from control and dysarthric speakers are collected
in the same noiseless environment using the same recording setup. In this
paper, we show that this assumption is violated for the UA-Speech and TORGO
databases. Using voice activity detection to extract speech and non-speech
segments, we show that the majority of state-of-the-art dysarthria
classification approaches achieve the same or a considerably better performance
when using the non-speech segments of these databases than when using the
speech segments. These results demonstrate that such approaches trained and
validated on the UA-Speech and TORGO databases are potentially learning
characteristics of the recording environment or setup rather than dysarthric
speech characteristics. We hope that these results raise awareness in the
research community about the importance of the quality of recordings when
developing and evaluating automatic dysarthria classification approaches.

A major challenge in medical image analysis is the automated detection of
biomarkers from neuroimaging data. Traditional approaches, often based on image
registration, are limited in capturing the high variability of cortical
organisation across individuals. Deep learning methods have been shown to be
successful in overcoming this difficulty, and some of them have even
outperformed medical professionals on certain datasets. In this paper, we apply
a deep neural network to analyse the cortical surface data of neonates, derived
from the publicly available Developing Human Connectome Project (dHCP). Our
goal is to identify neurodevelopmental biomarkers and to predict gestational
age at birth based on these biomarkers. Using scans of preterm neonates
acquired around the term-equivalent age, we were able to investigate the impact
of preterm birth on cortical growth and maturation during late gestation.
Besides reaching state-of-the-art prediction accuracy, the proposed model has
much fewer parameters than the baselines, and its error stays low on both
unregistered and registered cortical surfaces.

Multiple Object Tracking (MOT) is widely investigated in computer vision with
many applications. Tracking-By-Detection (TBD) is a popular multiple-object
tracking paradigm. TBD consists of the first step of object detection and the
subsequent of data association, tracklet generation, and update. We propose a
Similarity Learning Module (SLM) motivated from the Siamese network to extract
important object appearance features and a procedure to combine object motion
and appearance features effectively. This design strengthens the modeling of
object motion and appearance features for data association. We design a
Similarity Matching Cascade (SMC) for the data association of our SMILEtrack
tracker. SMILEtrack achieves 81.06 MOTA and 80.5 IDF1 on the MOTChallenge and
the MOT17 test set, respectively.

3D hand-object pose estimation is the key to the success of many computer
vision applications. The main focus of this task is to effectively model the
interaction between the hand and an object. To this end, existing works either
rely on interaction constraints in a computationally-expensive iterative
optimization, or consider only a sparse correlation between sampled hand and
object keypoints. In contrast, we propose a novel dense mutual attention
mechanism that is able to model fine-grained dependencies between the hand and
the object. Specifically, we first construct the hand and object graphs
according to their mesh structures. For each hand node, we aggregate features
from every object node by the learned attention and vice versa for each object
node. Thanks to such dense mutual attention, our method is able to produce
physically plausible poses with high quality and real-time inference speed.
Extensive quantitative and qualitative experiments on large benchmark datasets
show that our method outperforms state-of-the-art methods. The code is
available at https://github.com/rongakowang/DenseMutualAttention.git.

A recent body of literature has investigated the effect of data poisoning
attacks on data-driven control methods. Data poisoning attacks are well-known
to the Machine Learning community, which, however, make use of assumptions,
such as cross-sample independence, that in general do not hold for dynamical
systems. As a consequence, attacks, and detection methods, operate differently
from the i.i.d. setting studied in classical supervised problems. In
particular, data poisoning attacks against data-driven control methods can be
fundamentally seen as changing the behavior of the dynamical system described
by the data. In this work, we study this phenomenon through the lens of
statistical testing, and verify the detectability of different attacks for a
linear dynamical system. On the basis of the arguments hereby presented, we
propose a stealthy data poisoning attack that can escape classical detection
tests, and conclude by showing the efficiency of the proposed attack.

Developing interactive software, such as websites or games, is a particularly
engaging way to learn computer science. However, teaching and giving feedback
on such software is time-consuming -- standard approaches require instructors
to manually grade student-implemented interactive programs. As a result, online
platforms that serve millions, like Code.org, are unable to provide any
feedback on assignments for implementing interactive programs, which critically
hinders students' ability to learn. One approach toward automatic grading is to
learn an agent that interacts with a student's program and explores states
indicative of errors via reinforcement learning. However, existing work on this
approach only provides binary feedback of whether a program is correct or not,
while students require finer-grained feedback on the specific errors in their
programs to understand their mistakes. In this work, we show that exploring to
discover errors can be cast as a meta-exploration problem. This enables us to
construct a principled objective for discovering errors and an algorithm for
optimizing this objective, which provides fine-grained feedback. We evaluate
our approach on a set of over 700K real anonymized student programs from a
Code.org interactive assignment. Our approach provides feedback with 94.3%
accuracy, improving over existing approaches by 17.7% and coming within 1.5% of
human-level accuracy. Project web page: https://ezliu.github.io/dreamgrader.

Deep learning techniques for gravitational-wave parameter estimation have
emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing
results of comparable accuracy. These approaches (e.g., DINGO) enable amortized
inference by training a normalizing flow to represent the Bayesian posterior
conditional on observed data. By conditioning also on the noise power spectral
density (PSD) they can even account for changing detector characteristics.
However, training such networks requires knowing in advance the distribution of
PSDs expected to be observed, and therefore can only take place once all data
to be analyzed have been gathered. Here, we develop a probabilistic model to
forecast future PSDs, greatly increasing the temporal scope of DINGO networks.
Using PSDs from the second LIGO-Virgo observing run (O2) $\unicode{x2013}$ plus
just a single PSD from the beginning of the third (O3) $\unicode{x2013}$ we
show that we can train a DINGO network to perform accurate inference throughout
O3 (on 37 real events). We therefore expect this approach to be a key component
to enable the use of deep learning techniques for low-latency analyses of
gravitational waves.

Recommender systems are considered one of the most rapidly growing branches
of Artificial Intelligence. The demand for finding more efficient techniques to
generate recommendations becomes urgent. However, many recommendations become
useless if there is a delay in generating and showing them to the user.
Therefore, we focus on improving the speed of recommendation systems without
impacting the accuracy. In this paper, we suggest a novel recommender system
based on Factorization Machines and Association Rules (FMAR). We introduce an
approach to generate association rules using two algorithms: (i) apriori and
(ii) frequent pattern (FP) growth. These association rules will be utilized to
reduce the number of items passed to the factorization machines recommendation
model. We show that FMAR has significantly decreased the number of new items
that the recommender system has to predict and hence, decreased the required
time for generating the recommendations. On the other hand, while building the
FMAR tool, we concentrate on making a balance between prediction time and
accuracy of generated recommendations to ensure that the accuracy is not
significantly impacted compared to the accuracy of using factorization machines
without association rules.

Non-differentiable controllers and rule-based policies are widely used for
controlling real systems such as robots and telecommunication networks. In this
paper, we present a practical reinforcement learning method which improves upon
such existing policies with a model-based approach for better sample
efficiency. Our method significantly outperforms state-of-the-art model-based
methods, in terms of sample efficiency, on several widely used robotic
benchmark tasks. We also demonstrate the effectiveness of our approach on a
control problem in the telecommunications domain, where model-based methods
have not previously been explored. Experimental results indicate that a strong
initial performance can be achieved and combined with improved sample
efficiency. We further motivate the design of our algorithm with a theoretical
lower bound on the performance.

Due to the huge amount of parameters, fine-tuning of pretrained language
models (PLMs) is prone to overfitting in the low resource scenarios. In this
work, we present a novel method that operates on the hidden representations of
a PLM to reduce overfitting. During fine-tuning, our method inserts random
autoencoders between the hidden layers of a PLM, which transform activations
from the previous layers into a multi-view compressed representation before
feeding it into the upper layers. The autoencoders are plugged out after
fine-tuning, so our method does not add extra parameters or increase
computation cost during inference. Our method demonstrates promising
performance improvement across a wide range of sequence- and token-level
low-resource NLP tasks.

Recent unsupervised multi-object detection models have shown impressive
performance improvements, largely attributed to novel architectural inductive
biases. Unfortunately, they may produce suboptimal object encodings for
downstream tasks. To overcome this, we propose to exploit object motion and
continuity, i.e., objects do not pop in and out of existence. This is
accomplished through two mechanisms: (i) providing priors on the location of
objects through integration of optical flow, and (ii) a contrastive object
continuity loss across consecutive image frames. Rather than developing an
explicit deep architecture, the resulting Motion and Object Continuity (MOC)
scheme can be instantiated using any baseline object detection model. Our
results show large improvements in the performances of a SOTA model in terms of
object discovery, convergence speed and overall latent object representations,
particularly for playing Atari games. Overall, we show clear benefits of
integrating motion and object continuity for downstream tasks, moving beyond
object representation learning based only on reconstruction.

Tabla is a North Indian percussion instrument used as an accompaniment and an
exclusive instrument for solo performances. Tabla solo is intricate and
elaborate, exhibiting rhythmic evolution through a sequence of homogeneous
sections marked by shared rhythmic characteristics. Each section has a specific
structure and name associated with it. Tabla learning and performance in the
Indian subcontinent is based on stylistic schools called gharana-s. Several
compositions by various composers from different gharana-s are played in each
section. This paper addresses the task of segmenting the tabla solo concert
into musically meaningful sections. We then assign suitable section labels and
recognize gharana-s from the sections. We present a diverse collection of over
38 hours of solo tabla recordings for the task. We motivate the problem and
present different challenges and facets of the tasks. Inspired by the distinct
musical properties of tabla solo, we compute several rhythmic and timbral
features for the segmentation task. This work explores the approach of
automatically locating the significant changes in the rhythmic structure by
analyzing local self-similarity in an unsupervised manner. We also explore
supervised random forest and a convolutional neural network trained on
hand-crafted features. Both supervised and unsupervised approaches are also
tested on a set of held-out recordings. Segmentation of an audio piece into its
structural components and labeling is crucial to many music information
retrieval applications like repetitive structure finding, audio summarization,
and fast music navigation. This work helps us obtain a comprehensive musical
description of the tabla solo concert.

We study minimization problems for deterministic $\omega$-automata in the
presence of don't care words. We prove that the number of priorities in
deterministic parity automata can be efficiently minimized under an arbitrary
set of don't care words. We derive that from a more general result from which
one also obtains an efficient minimization algorithm for deterministic parity
automata with informative right-congruence (without don't care words).
  We then analyze languages of don't care words with a trivial
right-congruence. For such sets of don't care words it is known that weak
deterministic B\"uchi automata (WDBA) have a unique minimal automaton that can
be efficiently computed from a given WDBA (Eisinger, Klaedtke 2006). We give a
congruence-based characterization of the corresponding minimal WDBA, and show
that the don't care minimization results for WDBA do not extend to
deterministic $\omega$-automata with informative right-congruence: for this
class there is no unique minimal automaton for a given don't care set with
trivial right congruence, and the minimization problem is NP-hard. Finally, we
extend an active learning algorithm for WDBA (Maler, Pnueli 1995) to the
setting with an additional set of don't care words with trivial
right-congruence.

Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in
the clinic. Consequently, automatic volume-of-interest segmentation based on
multi-parametric MR imaging is crucial for computer-aided disease diagnosis,
treatment planning, and prognosis monitoring. Despite the extensive studies
conducted in deep learning-based medical image analysis, further investigations
are still required to effectively exploit the information provided by different
imaging parameters. How to fuse the information is a key question in this
field. Here, we propose an uncertainty-aware multi-parametric MR image feature
fusion method to fully exploit the information for enhanced 3D image
segmentation. Uncertainties in the independent predictions of individual
modalities are utilized to guide the fusion of multi-modal image features.
Extensive experiments on two datasets, one for brain tissue segmentation and
the other for abdominal multi-organ segmentation, have been conducted, and our
proposed method achieves better segmentation performance when compared to
existing models.

This technical report describes the CONE approach for Ego4D Natural Language
Queries (NLQ) Challenge in ECCV 2022. We leverage our model CONE, an efficient
window-centric COarse-to-fiNE alignment framework. Specifically, CONE
dynamically slices the long video into candidate windows via a sliding window
approach. Centering at windows, CONE (1) learns the inter-window
(coarse-grained) semantic variance through contrastive learning and speeds up
inference by pre-filtering the candidate windows relevant to the NL query, and
(2) conducts intra-window (fine-grained) candidate moments ranking utilizing
the powerful multi-modal alignment ability of the contrastive vision-text
pre-trained model EgoVLP. On the blind test set, CONE achieves 15.26 and 9.24
for R1@IoU=0.3 and R1@IoU=0.5, respectively.

Optimal Transport (OT) has recently emerged as a central tool in data
sciences to compare in a geometrically faithful way point clouds and more
generally probability distributions. The wide adoption of OT into existing data
analysis and machine learning pipelines is however plagued by several
shortcomings. This includes its lack of robustness to outliers, its high
computational costs, the need for a large number of samples in high dimension
and the difficulty to handle data in distinct spaces. In this review, we detail
several recently proposed approaches to mitigate these issues. We insist in
particular on unbalanced OT, which compares arbitrary positive measures, not
restricted to probability distributions (i.e. their total mass can vary). This
generalization of OT makes it robust to outliers and missing data. The second
workhorse of modern computational OT is entropic regularization, which leads to
scalable algorithms while lowering the sample complexity in high dimension. The
last point presented in this review is the Gromov-Wasserstein (GW) distance,
which extends OT to cope with distributions belonging to different metric
spaces. The main motivation for this review is to explain how unbalanced OT,
entropic regularization and GW can work hand-in-hand to turn OT into efficient
geometric loss functions for data sciences.

Multi-illuminant color constancy is a challenging problem with only a few
existing methods. For example, one prior work used a small set of predefined
white balance settings and spatially blended among them, limiting the solution
to predefined illuminations. Another method proposed a generative adversarial
network and an angular loss, yet the performance is suboptimal due to the lack
of regularization for multi-illumination colors. This paper introduces a
transformer-based multi-task learning method to estimate single and multiple
light colors from a single input image. To help our deep learning model have
better cues of the light colors, achromatic-pixel detection, and edge detection
are used as auxiliary tasks in our multi-task learning setting. By exploiting
extracted content features from the input image as tokens, illuminant color
correlations between pixels are learned by leveraging contextual information in
our transformer. Our transformer approach is further assisted via a contrastive
loss defined between the input, output, and ground truth. We demonstrate that
our proposed model achieves 40.7% improvement compared to a state-of-the-art
multi-illuminant color constancy method on a multi-illuminant dataset (LSMI).
Moreover, our model maintains a robust performance on the single illuminant
dataset (NUS-8) and provides 22.3% improvement on the state-of-the-art single
color constancy method.

We consider the idealized setting of gradient flow on the population risk for
infinitely wide two-layer ReLU neural networks (without bias), and study the
effect of symmetries on the learned parameters and predictors. We first
describe a general class of symmetries which, when satisfied by the target
function $f^*$ and the input distribution, are preserved by the dynamics. We
then study more specific cases. When $f^*$ is odd, we show that the dynamics of
the predictor reduces to that of a (non-linearly parameterized) linear
predictor, and its exponential convergence can be guaranteed. When $f^*$ has a
low-dimensional structure, we prove that the gradient flow PDE reduces to a
lower-dimensional PDE. Furthermore, we present informal and numerical arguments
that suggest that the input neurons align with the lower-dimensional structure
of the problem.

Physics-informed neural networks (PINNs) have emerged as new data-driven PDE
solvers for both forward and inverse problems. While promising, the expensive
computational costs to obtain solutions often restrict their broader
applicability. We demonstrate that the computations in automatic
differentiation (AD) can be significantly reduced by leveraging forward-mode AD
when training PINN. However, a naive application of forward-mode AD to
conventional PINNs results in higher computation, losing its practical benefit.
Therefore, we propose a network architecture, called separable PINN (SPINN),
which can facilitate forward-mode AD for more efficient computation. SPINN
operates on a per-axis basis instead of point-wise processing in conventional
PINNs, decreasing the number of network forward passes. Besides, while the
computation and memory costs of standard PINNs grow exponentially along with
the grid resolution, that of our model is remarkably less susceptible,
mitigating the curse of dimensionality. We demonstrate the effectiveness of our
model in various PDE systems by significantly reducing the training run-time
while achieving comparable accuracy. Project page:
\url{https://jwcho5576.github.io/spinn/}

Physics-informed neural networks (PINNs) have attracted significant attention
for solving partial differential equations (PDEs) in recent years because they
alleviate the curse of dimensionality that appears in traditional methods.
However, the most disadvantage of PINNs is that one neural network corresponds
to one PDE. In practice, we usually need to solve a class of PDEs, not just
one. With the explosive growth of deep learning, many useful techniques in
general deep learning tasks are also suitable for PINNs. Transfer learning
methods may reduce the cost for PINNs in solving a class of PDEs. In this
paper, we proposed a transfer learning method of PINNs via keeping singular
vectors and optimizing singular values (namely SVD-PINNs). Numerical
experiments on high dimensional PDEs (10-d linear parabolic equations and 10-d
Allen-Cahn equations) show that SVD-PINNs work for solving a class of PDEs with
different but close right-hand-side functions.

With the emerge of the Internet of Things (IoT), localization within indoor
environments has become inevitable and has attracted a great deal of attention
in recent years. Several efforts have been made to cope with the challenges of
accurate positioning systems in the presence of signal interference. In this
paper, we propose a novel deep learning approach through Gradient Boosting
Enhanced with Step-Wise Feature Augmentation using Artificial Neural Network
(AugBoost-ANN) for indoor localization applications as it trains over labeled
data. For this purpose, we propose an IoT architecture using a star network
topology to collect the Received Signal Strength Indicator (RSSI) of Bluetooth
Low Energy (BLE) modules by means of a Raspberry Pi as an Access Point (AP) in
an indoor environment. The dataset for the experiments is gathered in the real
world in different periods to match the real environments. Next, we address the
challenges of the AugBoost-ANN training which augments features in each
iteration of making a decision tree using a deep neural network and the
transfer learning technique. Experimental results show more than 8\%
improvement in terms of accuracy in comparison with the existing gradient
boosting and deep learning methods recently proposed in the literature, and our
proposed model acquires a mean location accuracy of 0.77 m.

Semantic communications is considered as a promising technology for reducing
the bandwidth requirements of next-generation communication systems,
particularly targeting human-machine interactions. In contrast to the
source-agnostic approach of conventional wireless communication systems,
semantic communication seeks to ensure that only the relevant information for
the underlying task is communicated to the receiver. A prominent approach to
semantic communications is to model it as a joint source-channel coding (JSCC)
problem. Although JSCC has been a long-standing open problem in communication
and coding theory, remarkable performance gains have been shown recently over
existing separate source and channel coding systems, particularly in
low-latency and low-power scenarios, typically encountered in edge intelligence
applications. Recent progress is thanks to the adoption of deep learning
techniques for JSCC code design, which are shown to outperform the
concatenation of state-of-the-art compression and channel coding schemes, each
of which is a result of decades-long research efforts. In this article, we
present an adaptive deep learning based JSCC (DeepJSCC) architecture for
semantic communications, introduce its design principles, highlight its
benefits, and outline future research challenges that lie ahead.

Auditing machine learning-based (ML) healthcare tools for bias is critical to
preventing patient harm, especially in communities that disproportionately face
health inequities. General frameworks are becoming increasingly available to
measure ML fairness gaps between groups. However, ML for health (ML4H) auditing
principles call for a contextual, patient-centered approach to model
assessment. Therefore, ML auditing tools must be (1) better aligned with ML4H
auditing principles and (2) able to illuminate and characterize communities
vulnerable to the most harm. To address this gap, we propose supplementing ML4H
auditing frameworks with SLOGAN (patient Severity-based LOcal Group biAs
detectioN), an automatic tool for capturing local biases in a clinical
prediction task. SLOGAN adapts an existing tool, LOGAN (LOcal Group biAs
detectioN), by contextualizing group bias detection in patient illness severity
and past medical history. We investigate and compare SLOGAN's bias detection
capabilities to LOGAN and other clustering techniques across patient subgroups
in the MIMIC-III dataset. On average, SLOGAN identifies larger fairness
disparities in over 75% of patient groups than LOGAN while maintaining
clustering quality. Furthermore, in a diabetes case study, health disparity
literature corroborates the characterizations of the most biased clusters
identified by SLOGAN. Our results contribute to the broader discussion of how
machine learning biases may perpetuate existing healthcare disparities.

This paper aims at presenting a new application of information geometry to
reinforcement learning focusing on dynamic treatment resumes. In a standard
framework of reinforcement learning, a Q-function is defined as the conditional
expectation of a reward given a state and an action for a single-stage
situation. We introduce an equivalence relation, called the policy equivalence,
in the space of all the Q-functions. A class of information divergence is
defined in the Q-function space for every stage. The main objective is to
propose an estimator of the optimal policy function by a method of minimum
information divergence based on a dataset of trajectories. In particular, we
discuss the $\gamma$-power divergence that is shown to have an advantageous
property such that the $\gamma$-power divergence between policy-equivalent
Q-functions vanishes. This property essentially works to seek the optimal
policy, which is discussed in a framework of a semiparametric model for the
Q-function. The specific choices of power index $\gamma$ give interesting
relationships of the value function, and the geometric and harmonic means of
the Q-function. A numerical experiment demonstrates the performance of the
minimum $\gamma$-power divergence method in the context of dynamic treatment
regimes.

Linguistic labels are effective means of expressing qualitative assessments
because they account for the uncertain nature of human preferences. However, to
perform computations with linguistic labels, they must first be converted to
numbers using a scale function. Within the context of the Analytic Hierarchy
Process (AHP), the most popular scale used to represent linguistic labels
numerically is the linear 1-9 scale, which was proposed by Saaty. However, this
scale has been criticized by several researchers, and various alternatives are
proposed in the literature. There is a growing interest in scale
individualization rather than relying on a generic fixed scale since the
perceptions of the decision maker regarding these linguistic labels are highly
subjective. The methods proposed in the literature for scale individualization
focus on minimizing the transitivity errors, i.e., consistency. In this
research, we proposed a novel, easy-to-learn, easy-to-implement, and
computationally less demanding scale individualization approach based on
compatibility. We also developed an experimental setup and introduced two new
metrics that can be used by researchers that contribute to the theory of AHP.
To assess the value of scale individualization in general, and the performance
of the proposed novel approach in particular, numerical and two empirical
studies are conducted. The results of the analyses demonstrate that the scale
individualization outperforms the conventional fixed scale approach and
validates the benefit of the proposed novel heuristic.

Quantum computing is a game-changing technology for global academia, research
centers and industries including computational science, mathematics, finance,
pharmaceutical, materials science, chemistry and cryptography. Although it has
seen a major boost in the last decade, we are still a long way from reaching
the maturity of a full-fledged quantum computer. That said, we will be in the
Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens
or even thousands of qubits quantum computing systems. An outstanding
challenge, then, is to come up with an application that can reliably carry out
a nontrivial task of interest on the near-term quantum devices with
non-negligible quantum noise. To address this challenge, several near-term
quantum computing techniques, including variational quantum algorithms, error
mitigation, quantum circuit compilation and benchmarking protocols, have been
proposed to characterize and mitigate errors, and to implement algorithms with
a certain resistance to noise, so as to enhance the capabilities of near-term
quantum devices and explore the boundaries of their ability to realize useful
applications. Besides, the development of near-term quantum devices is
inseparable from the efficient classical simulation, which plays a vital role
in quantum algorithm design and verification, error-tolerant verification and
other applications. This review will provide a thorough introduction of these
near-term quantum computing techniques, report on their progress, and finally
discuss the future prospect of these techniques, which we hope will motivate
researchers to undertake additional studies in this field.

Machine learning-based estimates of poverty and wealth are increasingly being
used to guide the targeting of humanitarian aid and the allocation of social
assistance. However, the ground truth labels used to train these models are
typically borrowed from existing surveys that were designed to produce national
statistics -- not to train machine learning models. Here, we test whether
adaptive sampling strategies for ground truth data collection can improve the
performance of poverty prediction models. Through simulations, we compare the
status quo sampling strategies (uniform at random and stratified random
sampling) to alternatives that prioritize acquiring training data based on
model uncertainty or model performance on sub-populations. Perhaps
surprisingly, we find that none of these active learning methods improve over
uniform-at-random sampling. We discuss how these results can help shape future
efforts to refine machine learning-based estimates of poverty.

Medical images are widely used in clinical practice for diagnosis.
Automatically generating interpretable medical reports can reduce radiologists'
burden and facilitate timely care. However, most existing approaches to
automatic report generation require sufficient labeled data for training. In
addition, the learned model can only generate reports for the training classes,
lacking the ability to adapt to previously unseen novel diseases. To this end,
we propose a lesion guided explainable few weak-shot medical report generation
framework that learns correlation between seen and novel classes through visual
and semantic feature alignment, aiming to generate medical reports for diseases
not observed in training. It integrates a lesion-centric feature extractor and
a Transformer-based report generation module. Concretely, the lesion-centric
feature extractor detects the abnormal regions and learns correlations between
seen and novel classes with multi-view (visual and lexical) embeddings. Then,
features of the detected regions and corresponding embeddings are concatenated
as multi-view input to the report generation module for explainable report
generation, including text descriptions and corresponding abnormal regions
detected in the images. We conduct experiments on FFA-IR, a dataset providing
explainable annotations, showing that our framework outperforms others on
report generation for novel diseases.

Learning with Noisy Labels (LNL) has attracted significant attention from the
research community. Many recent LNL methods rely on the assumption that clean
samples tend to have "small loss". However, this assumption always fails to
generalize to some real-world cases with imbalanced subpopulations, i.e.,
training subpopulations varying in sample size or recognition difficulty.
Therefore, recent LNL methods face the risk of misclassifying those
"informative" samples (e.g., hard samples or samples in the tail
subpopulations) into noisy samples, leading to poor generalization performance.
  To address the above issue, we propose a novel LNL method to simultaneously
deal with noisy labels and imbalanced subpopulations. It first leverages sample
correlation to estimate samples' clean probabilities for label correction and
then utilizes corrected labels for Distributionally Robust Optimization (DRO)
to further improve the robustness. Specifically, in contrast to previous works
using classification loss as the selection criterion, we introduce a
feature-based metric that takes the sample correlation into account for
estimating samples' clean probabilities. Then, we refurbish the noisy labels
using the estimated clean probabilities and the pseudo-labels from the model's
predictions. With refurbished labels, we use DRO to train the model to be
robust to subpopulation imbalance. Extensive experiments on a wide range of
benchmarks demonstrate that our technique can consistently improve current
state-of-the-art robust learning paradigms against noisy labels, especially
when encountering imbalanced subpopulations.

Incorporating various mass shapes and sizes in training deep learning
architectures has made breast mass segmentation challenging. Moreover, manual
segmentation of masses of irregular shapes is time-consuming and error-prone.
Though Deep Neural Network has shown outstanding performance in breast mass
segmentation, it fails in segmenting micro-masses. In this paper, we propose a
novel U-net-shaped transformer-based architecture, called Swin-SFTNet, that
outperforms state-of-the-art architectures in breast mammography-based
micro-mass segmentation. Firstly to capture the global context, we designed a
novel Spatial Feature Expansion and Aggregation Block(SFEA) that transforms
sequential linear patches into a structured spatial feature. Next, we combine
it with the local linear features extracted by the swin transformer block to
improve overall accuracy. We also incorporate a novel embedding loss that
calculates similarities between linear feature embeddings of the encoder and
decoder blocks. With this approach, we achieve higher segmentation dice over
the state-of-the-art by 3.10% on CBIS-DDSM, 3.81% on InBreast, and 3.13% on
CBIS pre-trained model on the InBreast test data set.

Deep generative models for audio synthesis have recently been significantly
improved. However, the task of modeling raw-waveforms remains a difficult
problem, especially for audio waveforms and music signals. Recently, the
realtime audio variational autoencoder (RAVE) method was developed for
high-quality audio waveform synthesis. The RAVE method is based on the
variational autoencoder and utilizes the two-stage training strategy.
Unfortunately, the RAVE model is limited in reproducing wide-pitch polyphonic
music sound. Therefore, to enhance the reconstruction performance, we adopt the
pitch activation data as an auxiliary information to the RAVE model. To handle
the auxiliary information, we propose an enhanced RAVE model with a conditional
variational autoencoder structure and an additional fully-connected layer. To
evaluate the proposed structure, we conducted a listening experiment based on
multiple stimulus tests with hidden references and an anchor (MUSHRA) with the
MAESTRO. The obtained results indicate that the proposed model exhibits a more
significant performance and stability improvement than the conventional RAVE
model.

To align conditional text generation model outputs with desired behaviors,
there has been an increasing focus on training the model using reinforcement
learning (RL) with reward functions learned from human annotations. Under this
framework, we identify three common cases where high rewards are incorrectly
assigned to undesirable patterns: noise-induced spurious correlation, naturally
occurring spurious correlation, and covariate shift. We show that even though
learned metrics achieve high performance on the distribution of the data used
to train the reward function, the undesirable patterns may be amplified during
RL training of the text generation model. While there has been discussion about
reward gaming in the RL or safety community, in this short discussion piece, we
would like to highlight reward gaming in the NLG community using concrete
conditional text generation examples and discuss potential fixes and areas for

Feature matching is an essential step in visual localization, where the
accuracy of camera pose is mainly determined by the established 2D-3D
correspondence. Due to the noise, solving the camera pose accurately requires a
sufficient number of well-distributed 2D-3D correspondences. Existing 2D-3D
feature matching is typically achieved by finding the nearest neighbors in the
feature space, and then removing the outliers by some hand-crafted heuristics.
However, this may lead to a large number of potentially true matches being
missed or the established correct matches being filtered out. In this work, we
introduce a novel 2D-3D matching method, Geometry-Aided Matching (GAM), which
uses both appearance information and geometric context to improve 2D-3D feature
matching. GAM can greatly strengthen the recall of 2D-3D matches while
maintaining high precision. We insert GAM into a hierarchical visual
localization pipeline and show that GAM can effectively improve the robustness
and accuracy of localization. Extensive experiments show that GAM can find more
correct matches than hand-crafted heuristics and learning baselines. Our
proposed localization method achieves state-of-the-art results on multiple
visual localization datasets. Experiments on Cambridge Landmarks dataset show
that our method outperforms the existing state-of-the-art methods and is six
times faster than the top-performed method.

Deep Neural Networks (DNNs) are everywhere, frequently performing a fairly
complex task that used to be unimaginable for machines to carry out. In doing
so, they do a lot of decision making which, depending on the application, may
be disastrous if gone wrong. This necessitates a formal argument that the
underlying neural networks satisfy certain desirable properties. Robustness is
one such key property for DNNs, particularly if they are being deployed in
safety or business critical applications. Informally speaking, a DNN is not
robust if very small changes to its input may affect the output in a
considerable way (e.g. changes the classification for that input). The task of
finding an adversarial example is to demonstrate this lack of robustness,
whenever applicable. While this is doable with the help of constrained
optimization techniques, scalability becomes a challenge due to large-sized
networks. This paper proposes the use of information gathered by preprocessing
the DNN to heavily simplify the optimization problem. Our experiments
substantiate that this is effective, and does significantly better than the
state-of-the-art.

The Metaverse has received much attention recently. Metaverse applications
via mobile augmented reality (MAR) require rapid and accurate object detection
to mix digital data with the real world. Federated learning (FL) is an
intriguing distributed machine learning approach due to its privacy-preserving
characteristics. Due to privacy concerns and the limited computation resources
on mobile devices, we incorporate FL into MAR systems of the Metaverse to train
a model cooperatively. Besides, to balance the trade-off between energy,
execution latency and model accuracy, thereby accommodating different demands
and application scenarios, we formulate an optimization problem to minimize a
weighted combination of total energy consumption, completion time and model
accuracy. Through decomposing the non-convex optimization problem into two
subproblems, we devise a resource allocation algorithm to determine the
bandwidth allocation, transmission power, CPU frequency and video frame
resolution for each participating device. We further present the convergence
analysis and computational complexity of the proposed algorithm. Numerical
results show that our proposed algorithm has better performance (in terms of
energy consumption, completion time and model accuracy) under different weight
parameters compared to existing benchmarks.

Although neural networks have seen tremendous success as predictive models in
a variety of domains, they can be overly confident in their predictions on
out-of-distribution (OOD) data. To be viable for safety-critical applications,
like autonomous vehicles, neural networks must accurately estimate their
epistemic or model uncertainty, achieving a level of system self-awareness.
Techniques for epistemic uncertainty quantification often require OOD data
during training or multiple neural network forward passes during inference.
These approaches may not be suitable for real-time performance on
high-dimensional inputs. Furthermore, existing methods lack interpretability of
the estimated uncertainty, which limits their usefulness both to engineers for
further system development and to downstream modules in the autonomy stack. We
propose the use of evidential deep learning to estimate the epistemic
uncertainty over a low-dimensional, interpretable latent space in a trajectory
prediction setting. We introduce an interpretable paradigm for trajectory
prediction that distributes the uncertainty among the semantic concepts: past
agent behavior, road structure, and social context. We validate our approach on
real-world autonomous driving data, demonstrating superior performance over
state-of-the-art baselines. Our code is available at:
https://github.com/sisl/InterpretableSelfAwarePrediction.

Keyword spotting (KWS) has been widely used in various speech control
scenarios. The training of KWS is usually based on deep neural networks and
requires a large amount of data. Manufacturers often use third-party data to
train KWS. However, deep neural networks are not sufficiently interpretable to
manufacturers, and attackers can manipulate third-party training data to plant
backdoors during the model training. An effective backdoor attack can force the
model to make specified judgments under certain conditions, i.e., triggers. In
this paper, we design a backdoor attack scheme based on Pitch Boosting and
Sound Masking for KWS, called PBSM. Experimental results demonstrated that PBSM
is feasible to achieve an average attack success rate close to 90% in three
victim models when poisoning less than 1% of the training data.

Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for
training 3D detectors, particularly on large-scale lidar data. Surprisingly,
although semantic class labels naturally follow a long-tailed distribution,
contemporary benchmarks focus on only a few common classes (e.g., pedestrian
and car) and neglect many rare classes in-the-tail (e.g., debris and stroller).
However, AVs must still detect rare classes to ensure safe operation. Moreover,
semantic classes are often organized within a hierarchy, e.g., tail classes
such as child and construction-worker are arguably subclasses of pedestrian.
However, such hierarchical relationships are often ignored, which may lead to
misleading estimates of performance and missed opportunities for algorithmic
innovation. We address these challenges by formally studying the problem of
Long-Tailed 3D Detection (LT3D), which evaluates on all classes, including
those in-the-tail. We evaluate and innovate upon popular 3D detection
codebases, such as CenterPoint and PointPillars, adapting them for LT3D. We
develop hierarchical losses that promote feature sharing across common-vs-rare
classes, as well as improved detection metrics that award partial credit to
"reasonable" mistakes respecting the hierarchy (e.g., mistaking a child for an
adult). Finally, we point out that fine-grained tail class accuracy is
particularly improved via multimodal fusion of RGB images with LiDAR; simply
put, small fine-grained classes are challenging to identify from sparse (lidar)
geometry alone, suggesting that multimodal cues are crucial to long-tailed 3D
detection. Our modifications improve accuracy by 5% AP on average for all
classes, and dramatically improve AP for rare classes (e.g., stroller AP
improves from 3.6 to 31.6)!

State-of-the-art deep neural networks (DNNs) are highly effective at tackling
many real-world tasks. However, their wide adoption in mission-critical
contexts is hampered by two major weaknesses - their susceptibility to
adversarial attacks and their opaqueness. The former raises concerns about the
security and generalization of DNNs in real-world conditions, whereas the
latter impedes users' trust in their output. In this research, we (1) examine
the effect of adversarial robustness on interpretability and (2) present a
novel approach for improving the interpretability of DNNs that is based on
regularization of neural activation sensitivity. We evaluate the
interpretability of models trained using our method to that of standard models
and models trained using state-of-the-art adversarial robustness techniques.
Our results show that adversarially robust models are superior to standard
models and that models trained using our proposed method are even better than
adversarially robust models in terms of interpretability.

Early diagnosis of dementia, particularly in the prodromal stage (i.e., mild
cognitive impairment, or MCI), has become a research and clinical priority but
remains challenging. Automated analysis of the drawing process has been studied
as a promising means for screening prodromal and clinical dementia, providing
multifaceted information encompassing features, such as drawing speed, pen
posture, writing pressure, and pauses. We examined the feasibility of using
these features not only for detecting prodromal and clinical dementia but also
for predicting the severity of cognitive impairments assessed using Mini-Mental
State Examination (MMSE) as well as the severity of neuropathological changes
assessed by medial temporal lobe (MTL) atrophy. We collected drawing data with
a digitizing tablet and pen from 145 older adults of cognitively normal (CN),
MCI, and dementia. The nested cross-validation results indicate that the
combination of drawing features could be used to classify CN, MCI, and dementia
with an AUC of 0.909 and 75.1% accuracy (CN vs. MCI: 82.4% accuracy; CN vs.
dementia: 92.2% accuracy; MCI vs. dementia: 80.3% accuracy) and predict MMSE
scores with an $R^2$ of 0.491 and severity of MTL atrophy with an $R^2$ of
0.293. Our findings suggest that automated analysis of the drawing process can
provide information about cognitive impairments and neuropathological changes
due to dementia, which can help identify prodromal and clinical dementia as a
digital biomarker.

Active research is going on to securely transmit a secret message or
so-called steganography by using data-hiding techniques in digital images.
After assessing the state-of-the-art research work, we found, most of the
existing solutions are not promising and are ineffective against machine
learning-based steganalysis. In this paper, a lightweight steganography scheme
is presented through graphical key embedding and obfuscation of data through
encryption. By keeping a mindset of industrial applicability, to show the
effectiveness of the proposed scheme, we emphasized mainly deep learning-based
steganalysis. The proposed steganography algorithm containing two schemes
withstands not only statistical pattern recognizers but also machine learning
steganalysis through feature extraction using a well-known pre-trained deep
learning network Xception. We provided a detailed protocol of the algorithm for
different scenarios and implementation details. Furthermore, different
performance metrics are also evaluated with statistical and machine learning
performance analysis. The results were quite impressive with respect to the
state of the arts. We received 2.55% accuracy through statistical steganalysis
and machine learning steganalysis gave maximum of 49.93~50% correctly
classified instances in good condition.

Real-time multi-model multi-task (MMMT) workloads, a new form of deep
learning inference workloads, are emerging for applications areas like extended
reality (XR) to support metaverse use cases. These workloads combine user
interactivity with computationally complex machine learning (ML) activities.
Compared to standard ML applications, these ML workloads present unique
difficulties and constraints. Real-time MMMT workloads impose heterogeneity and
concurrency requirements on future ML systems and devices, necessitating the
development of new capabilities. This paper begins with a discussion of the
various characteristics of these real-time MMMT ML workloads and presents an
ontology for evaluating the performance of future ML hardware for XR systems.
Next, we present XRBench, a collection of MMMT ML tasks, models, and usage
scenarios that execute these models in three representative ways: cascaded,
concurrent, and cascaded-concurrency for XR use cases. Finally, we emphasize
the need for new metrics that capture the requirements properly. We hope that
our work will stimulate research and lead to the development of a new
generation of ML systems for XR use cases.

Contrastive representation learning is widely employed in visual recognition
for geographic image data (remote-sensing such as satellite imagery or proximal
sensing such as street-view imagery), but because of landscape heterogeneity,
models can show disparate performance across spatial units. In this work, we
consider fairness risks in land-cover semantic segmentation which uses
pre-trained representation in contrastive self-supervised learning. We assess
class distribution shifts and model prediction disparities across selected
sensitive groups: urban and rural scenes for satellite image datasets and city
GDP level for a street view image dataset. We propose a mutual information
training objective for multi-level latent space. The objective improves feature
identification by removing spurious representations of dense local features
which are disparately distributed across groups. The method achieves improved
fairness results and outperforms state-of-the-art methods in terms of
precision-fairness trade-off. In addition, we validate that representations
learnt with the proposed method include lowest sensitive information using a
linear separation evaluation. This work highlights the need for specific
fairness analyses in geographic images, and provides a solution that can be
generalized to different self-supervised learning methods or image data. Our
code is available at: https://anonymous.4open.science/r/FairDCL-1283

Humans tame the complexity of mathematical reasoning by developing
hierarchies of abstractions. With proper abstractions, solutions to hard
problems can be expressed concisely, thus making them more likely to be found.
In this paper, we propose Learning Mathematical Abstractions (LEMMA): an
algorithm that implements this idea for reinforcement learning agents in
mathematical domains. LEMMA augments Expert Iteration with an abstraction step,
where solutions found so far are revisited and rewritten in terms of new
higher-level actions, which then become available to solve new problems. We
evaluate LEMMA on two mathematical reasoning tasks--equation solving and
fraction simplification--in a step-by-step fashion. In these two domains, LEMMA
improves the ability of an existing agent, both solving more problems and
generalizing more effectively to harder problems than those seen during
training.

Multi-objective reinforcement learning (MORL) is a relatively new field which
builds on conventional Reinforcement Learning (RL) to solve multi-objective
problems. One of common algorithm is to extend scalar value Q-learning by using
vector Q values in combination with a utility function, which captures the
user's preference for action selection. This study follows on prior works, and
focuses on what factors influence the frequency with which value-based MORL
Q-learning algorithms learn the optimal policy for an environment with
stochastic state transitions in scenarios where the goal is to maximise the
Scalarised Expected Return (SER) - that is, to maximise the average outcome
over multiple runs rather than the outcome within each individual episode. The
analysis of the interaction between stochastic environment and MORL Q-learning
algorithms run on a simple Multi-objective Markov decision process (MOMDP)
Space Traders problem with different variant versions. The empirical
evaluations show that well designed reward signal can improve the performance
of the original baseline algorithm, however it is still not enough to address
more general environment. A variant of MORL Q-Learning incorporating global
statistics is shown to outperform the baseline method in original Space Traders
problem, but remains below 100 percent effectiveness in finding the find
desired SER-optimal policy at the end of training. On the other hand, Option
learning is guarantied to converge to desired SER-optimal policy but it is not
able to scale up to solve more complex problem in real-life. The main
contribution of this thesis is to identify the extent to which the issue of
noisy Q-value estimates impacts on the ability to learn optimal policies under
the combination of stochastic environments, non-linear utility and a constant
learning rate.

Threshold Autoregressive (TAR) models have been widely used by statisticians
for non-linear time series forecasting during the past few decades, due to
their simplicity and mathematical properties. On the other hand, in the
forecasting community, general-purpose tree-based regression algorithms
(forests, gradient-boosting) have become popular recently due to their ease of
use and accuracy. In this paper, we explore the close connections between TAR
models and regression trees. These enable us to use the rich methodology from
the literature on TAR models to define a hierarchical TAR model as a regression
tree that trains globally across series, which we call SETAR-Tree. In contrast
to the general-purpose tree-based models that do not primarily focus on
forecasting, and calculate averages at the leaf nodes, we introduce a new
forecasting-specific tree algorithm that trains global Pooled Regression (PR)
models in the leaves allowing the models to learn cross-series information and
also uses some time-series-specific splitting and stopping procedures. The
depth of the tree is controlled by conducting a statistical linearity test
commonly employed in TAR models, as well as measuring the error reduction
percentage at each node split. Thus, the proposed tree model requires minimal
external hyperparameter tuning and provides competitive results under its
default configuration. We also use this tree algorithm to develop a forest
where the forecasts provided by a collection of diverse SETAR-Trees are
combined during the forecasting process. In our evaluation on eight publicly
available datasets, the proposed tree and forest models are able to achieve
significantly higher accuracy than a set of state-of-the-art tree-based
algorithms and forecasting benchmarks across four evaluation metrics.

Artificial Neural Networks (ANNs) have been successfully used in various
nuclear engineering applications, such as predicting reactor physics parameters
within reasonable time and with a high level of accuracy. Despite this success,
they cannot provide information about the model prediction uncertainties,
making it difficult to assess ANN prediction credibility, especially in
extrapolated domains. In this study, Deep Neural Networks (DNNs) are used to
predict the assembly axial neutron flux profiles in the SAFARI-1 research
reactor, with quantified uncertainties in the ANN predictions and extrapolation
to cycles not used in the training process. The training dataset consists of
copper-wire activation measurements, the axial measurement locations and the
measured control bank positions obtained from the reactor's historical cycles.
Uncertainty Quantification of the regular DNN models' predictions is performed
using Monte Carlo Dropout (MCD) and Bayesian Neural Networks solved by
Variational Inference (BNN VI). The regular DNNs, DNNs solved with MCD and BNN
VI results agree very well among each other as well as with the new measured
dataset not used in the training process, thus indicating good prediction and
generalization capability. The uncertainty bands produced by MCD and BNN VI
agree very well, and in general, they can fully envelop the noisy measurement
data points. The developed ANNs are useful in supporting the experimental
measurements campaign and neutronics code Verification and Validation (V&V).

Social media has become a platform for people to stand up and raise their
voices against social and criminal acts. Vocalization of such information has
allowed the investigation and identification of criminals. However, revealing
such sensitive information may jeopardize the victim's safety. We propose
#maskUp, a safe method for information communication in a secure fashion to the
relevant authorities, discouraging potential bullying of the victim. This would
ensure security by conserving their privacy through natural language processing
supplemented with selective encryption for sensitive attribute masking. To our
knowledge, this is the first work that aims to protect the privacy of the
victims by masking their private details as well as emboldening them to come
forward to report crimes. The use of masking technology allows only binding
authorities to view/un-mask this data. We construct and evaluate the proposed
methodology on continual learning tasks, allowing practical implementation of
the same in a real-world scenario. #maskUp successfully demonstrates this
integration on sample datasets validating the presented objective.

We present a novel method of using explainability techniques to design
physics-aware neural networks. We demonstrate our approach by developing a
convolutional neural network (CNN) for solving an inverse problem for shallow
subsurface imaging. Although CNNs have gained popularity in recent years across
many fields, the development of CNNs remains an art, as there are no clear
guidelines regarding the selection of hyperparameters that will yield the best
network. While optimization algorithms may be used to select hyperparameters
automatically, these methods focus on developing networks with high predictive
accuracy while disregarding model explainability (descriptive accuracy).
However, the field of Explainable Artificial Intelligence (XAI) addresses the
absence of model explainability by providing tools that allow developers to
evaluate the internal logic of neural networks. In this study, we use the
explainability methods Score-CAM and Deep SHAP to select hyperparameters, such
as kernel sizes and network depth, to develop a physics-aware CNN for shallow
subsurface imaging. We begin with a relatively deep Encoder-Decoder network,
which uses surface wave dispersion images as inputs and generates 2D shear wave
velocity subsurface images as outputs. Through model explanations, we
ultimately find that a shallow CNN using two convolutional layers with an
atypical kernel size of 3x1 yields comparable predictive accuracy but with
increased descriptive accuracy. We also show that explainability methods can be
used to evaluate the network's complexity and decision-making. We believe this
method can be used to develop neural networks with high predictive accuracy
while also providing inherent explainability.

E-commerce platforms provide entrances for customers to enter mini-apps that
can meet their specific shopping requirements. Trigger items displayed on
entrance icons can attract more entering. However, conventional
Click-Through-Rate (CTR) prediction models, which ignore user instant interest
in trigger item, fail to be applied to the new recommendation scenario dubbed
Trigger-Induced Recommendation in Mini-Apps (TIRA). Moreover, due to the high
stickiness of customers to mini-apps, we argue that existing trigger-based
methods that over-emphasize the importance of trigger items, are undesired for
TIRA, since a large portion of customer entries are because of their routine
shopping habits instead of triggers. We identify that the key to TIRA is to
extract customers' personalized entering intention and weigh the impact of
triggers based on this intention. To achieve this goal, we convert CTR
prediction for TIRA into a separate estimation form, and present Deep
Intention-Aware Network (DIAN) with three key elements: 1) Intent Net that
estimates user's entering intention, i.e., whether he/she is affected by the
trigger or by the habits; 2) Trigger-Aware Net and 3) Trigger-Free Net that
estimate CTRs given user's intention is to the trigger-item and the mini-app
respectively. Following a joint learning way, DIAN can both accurately predict
user intention and dynamically balance the results of trigger-free and
trigger-based recommendations based on the estimated intention. Experiments
show that DIAN advances state-of-the-art performance in a large real-world
dataset, and brings a 9.39% lift of online Item Page View and 4.74% CTR for
Juhuasuan, a famous mini-app of Taobao.

The deployment of Multi-Armed Bandits (MAB) has become commonplace in many
economic applications. However, regret guarantees for even state-of-the-art
linear bandit algorithms (such as Optimism in the Face of Uncertainty Linear
bandit (OFUL)) make strong exogeneity assumptions w.r.t. arm covariates. This
assumption is very often violated in many economic contexts and using such
algorithms can lead to sub-optimal decisions. Further, in social science
analysis, it is also important to understand the asymptotic distribution of
estimated parameters. To this end, in this paper, we consider the problem of
online learning in linear stochastic contextual bandit problems with endogenous
covariates. We propose an algorithm we term $\epsilon$-BanditIV, that uses
instrumental variables to correct for this bias, and prove an
$\tilde{\mathcal{O}}(k\sqrt{T})$ upper bound for the expected regret of the
algorithm. Further, we demonstrate the asymptotic consistency and normality of
the $\epsilon$-BanditIV estimator. We carry out extensive Monte Carlo
simulations to demonstrate the performance of our algorithms compared to other
methods. We show that $\epsilon$-BanditIV significantly outperforms other
existing methods in endogenous settings. Finally, we use data from real-time
bidding (RTB) system to demonstrate how $\epsilon$-BanditIV can be used to
estimate the causal impact of advertising in such settings and compare its
performance with other existing methods.

While COVID-19 has impacted humans for a long time, people search the web for
pandemic-related information, causing anxiety. From a theoretic perspective,
previous studies have confirmed that the number of COVID-19 cases can cause
negative emotions, but how statistics of different dimensions, such as the
number of imported cases, the number of local cases, and the number of
government-designated lockdown zones, stimulate people's emotions requires
detailed understanding. In order to obtain the views of people on COVID-19,
this paper first proposes a deep learning model which classifies texts related
to the pandemic from text data with place labels. Next, it conducts a sentiment
analysis based on multi-task learning. Finally, it carries out a fixed-effect
panel regression with outputs of the sentiment analysis. The performance of the
algorithm shows a promising result. The empirical study demonstrates while the
number of local cases is positively associated with risk perception, the number
of imported cases is negatively associated with confidence levels, which
explains why citizens tend to ascribe the protracted pandemic to foreign
factors. Besides, this study finds that previous pandemic hits cities recover
slowly from the suffering, while local governments' spending on healthcare can
improve the situation. Our study illustrates the reasons for risk perception
and confidence based on different sources of statistical information due to
cognitive bias. It complements the knowledge related to epidemic information.
It also contributes to a framework that combines sentiment analysis using
advanced deep learning technology with the empirical regression method.

Understanding of spatial attributes is central to effective 3D radiology
image analysis where crop-based learning is the de facto standard. Given an
image patch, its core spatial properties (e.g., position & orientation) provide
helpful priors on expected object sizes, appearances, and structures through
inherent anatomical consistencies. Spatial correspondences, in particular, can
effectively gauge semantic similarities between inter-image regions, while
their approximate extraction requires no annotations or overbearing
computational costs. However, recent 3D contrastive learning approaches either
neglect correspondences or fail to maximally capitalize on them. To this end,
we propose an extensible 3D contrastive framework (Spade, for Spatial
Debiasing) that leverages extracted correspondences to select more effective
positive & negative samples for representation learning. Our method learns both
globally invariant and locally equivariant representations with downstream
segmentation in mind. We also propose separate selection strategies for global
& local scopes that tailor to their respective representational requirements.
Compared to recent state-of-the-art approaches, Spade shows notable
improvements on three downstream segmentation tasks (CT Abdominal Organ, CT
Heart, MR Heart).

Image harmonization is a critical task in computer vision, which aims to
adjust the fore-ground to make it compatible with the back-ground. Recent works
mainly focus on using global transformation (i.e., normalization and color
curve rendering) to achieve visual consistency. However, these model ignore
local consistency and their model size limit their harmonization ability on
edge devices. Inspired by the dynamic deep networks that adapt the model
structures or parameters conditioned on the inputs, we propose a hierarchical
dynamic network (HDNet) for efficient image harmonization to adapt the model
parameters and features from local to global view for better feature
transformation. Specifically, local dynamics (LD) and mask-aware global
dynamics (MGD) are applied. LD enables features of different channels and
positions to change adaptively and improve the representation ability of
geometric transformation through structural information learning. MGD learns
the representations of fore- and back-ground regions and correlations to global
harmonization. Experiments show that the proposed HDNet reduces more than 80\%
parameters compared with previous methods but still achieves the
state-of-the-art performance on the popular iHarmony4 dataset. Our code is
avaliable in https://github.com/chenhaoxing/HDNet.

Universities have been expanding the data science programs for undergraduate
students. The set of new courses and research opportunities developed in this
context also offer the opportunity to involve graduate students, fostering
their growth as future leaders in data science education. We describe two
programs that (1) provide pathways for graduate students to develop awareness
and skills as teachers and mentors and (2) enhance diversity in the data
science workforce. In the Data Science for Social Good Summer program, during
the course of eight weeks, graduate students mentor a group of students as they
tackle a data science project with social impact. They design technical
training materials, tutor individual students, plan and manage projects and
communication with community partner. While relying on faculty support, they
effectively act as project leaders, in what it perhaps their first experience
in an unsupervised mentoring and research role. The Inclusive Mentoring in Data
Science course provides graduate students with training in effective and
inclusive mentorship strategies. In an experiential learning framework,
enrolled Stanford graduate students are paired with undergraduate students from
non-R1 schools, who they mentor through a weekly one-on-one on-line meeting.
The undergraduate participants are exposed to basic data science topics,
projects and have the opportunity of working on professional skills. These
initiatives offer a prototype of future programs that serve the dual goal of
providing both hands-on mentoring experience for graduate students and research
opportunities for undergraduate students, in a high-touch inclusive and
encouraging environment.

Keypoint annotation in point clouds is an important task for 3D
reconstruction, object tracking and alignment, in particular in deformable or
moving scenes. In the context of agriculture robotics, it is a critical task
for livestock automation to work toward condition assessment or behaviour
recognition. In this work, we propose a novel approach for semantic keypoint
annotation in point clouds, by reformulating the keypoint extraction as a
regression problem of the distance between the keypoints and the rest of the
point cloud. We use the distance on the point cloud manifold mapped into a
radial basis function (RBF), which is then learned using an encoder-decoder
architecture. Special consideration is given to the data augmentation specific
to multi-depth-camera systems by considering noise over the extrinsic
calibration and camera frame dropout. Additionally, we investigate
computationally efficient non-rigid deformation methods that can be applied to
animal point clouds. Our method is tested on data collected in the field, on
moving beef cattle, with a calibrated system of multiple hardware-synchronised
RGB-D cameras.

Most speech enhancement (SE) models learn a point estimate, and do not make
use of uncertainty estimation in the learning process. In this paper, we show
that modeling heteroscedastic uncertainty by minimizing a multivariate Gaussian
negative log-likelihood (NLL) improves SE performance at no extra cost. During
training, our approach augments a model learning complex spectral mapping with
a temporary submodel to predict the covariance of the enhancement error at each
time-frequency bin. Due to unrestricted heteroscedastic uncertainty, the
covariance introduces an undersampling effect, detrimental to SE performance.
To mitigate undersampling, our approach inflates the uncertainty lower bound
and weights each loss component with their uncertainty, effectively
compensating severely undersampled components with more penalties. Our
multivariate setting reveals common covariance assumptions such as scalar and
diagonal matrices. By weakening these assumptions, we show that the NLL
achieves superior performance compared to popular losses including the mean
squared error (MSE), mean absolute error (MAE), and scale-invariant
signal-to-distortion ratio (SI-SDR).

This report considers the problem of resilient distributed optimization and
stochastic learning in a server-based architecture. The system comprises a
server and multiple agents, where each agent has its own local cost function.
The agents collaborate with the server to find a minimum of the aggregate of
the local cost functions. In the context of stochastic learning, the local cost
of an agent is the loss function computed over the data at that agent. In this
report, we consider this problem in a system wherein some of the agents may be
Byzantine faulty and some of the agents may be slow (also called stragglers).
In this setting, we investigate the conditions under which it is possible to
obtain an "approximate" solution to the above problem. In particular, we
introduce the notion of $(f, r; \epsilon)$-resilience to characterize how well
the true solution is approximated in the presence of up to $f$ Byzantine faulty
agents, and up to $r$ slow agents (or stragglers) -- smaller $\epsilon$
represents a better approximation. We also introduce a measure named $(f, r;
\epsilon)$-redundancy to characterize the redundancy in the cost functions of
the agents. Greater redundancy allows for a better approximation when solving
the problem of aggregate cost minimization.
  In this report, we constructively show (both theoretically and empirically)
that $(f, r; \mathcal{O}(\epsilon))$-resilience can indeed be achieved in
practice, given that the local cost functions are sufficiently redundant.

We describe the combinatorics of equilibria and steady states of neurons in
threshold-linear networks that satisfy the Dale's law. The combinatorial code
of a Dale network is characterized in terms of two conditions: (i) a condition
on the network connectivity graph, and (ii) a spectral condition on the
synaptic matrix. We find that in the weak coupling regime the combinatorial
code depends only on the connectivity graph, and not on the particulars of the
synaptic strengths. Moreover, we prove that the combinatorial code of a weakly
coupled network is a sublattice, and we provide a learning rule for encoding a
sublattice in a weakly coupled excitatory network. In the strong coupling
regime we prove that the combinatorial code of a generic Dale network is
intersection-complete and is therefore a convex code, as is common in some
sensory systems in the brain.

With the rapid development of the deep generative models (such as Generative
Adversarial Networks and Auto-encoders), AI-synthesized images of human face
are now of such high qualities that humans can hardly distinguish them from
pristine ones. Although existing detection methods have shown high performance
in specific evaluation settings, e.g., on images from seen models or on images
without real-world post-processings, they tend to suffer serious performance
degradation in real-world scenarios where testing images can be generated by
more powerful generation models or combined with various post-processing
operations. To address this issue, we propose a Global and Local Feature Fusion
(GLFF) to learn rich and discriminative representations by combining
multi-scale global features from the whole image with refined local features
from informative patches for face forgery detection. GLFF fuses information
from two branches: global branch to extract multi-scale semantic features and
local branch to select informative patches for detailed local artifacts
extraction. Due to the lack of face forgery dataset simulating real-world
applications for evaluation, we further create a challenging face forgery
dataset, named DeepFakeFaceForensics (DF$^3$), which contains 6
state-of-the-art generation models and a variety of post-processing techniques
to approach the real-world scenarios. Experimental results demonstrate the
superiority of our method to the state-of-the-art methods on the proposed DF^3
dataset and three other open-source datasets.

Advances in neural networks enable tackling complex computer vision tasks
such as depth estimation of outdoor scenes at unprecedented accuracy. Promising
research has been done on depth estimation. However, current efforts are
computationally resource-intensive and do not consider the resource constraints
of autonomous devices, such as robots and drones. In this work, we present a
fast and battery-efficient approach for depth estimation. Our approach devises
model-agnostic curriculum-based learning for depth estimation. Our experiments
show that the accuracy of our model performs on par with the state-of-the-art
models, while its response time outperforms other models by 71%. All codes are
available online at https://github.com/fatemehkarimii/LightDepth.

The recent advent of play-to-earn (P2E) systems in massively multiplayer
online role-playing games (MMORPGs) has made in-game goods interchangeable with
real-world values more than ever before. The goods in the P2E MMORPGs can be
directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn
via blockchain networks. Unlike traditional in-game goods, once they had been
written to the blockchains, P2E goods cannot be restored by the game operation
teams even with chargeback fraud such as payment fraud, cancellation, or
refund. To tackle the problem, we propose a novel chargeback fraud prediction
method, PU GNN, which leverages graph attention networks with PU loss to
capture both the players' in-game behavior with P2E token transaction patterns.
With the adoption of modified GraphSMOTE, the proposed model handles the
imbalanced distribution of labels in chargeback fraud datasets. The conducted
experiments on two real-world P2E MMORPG datasets demonstrate that PU GNN
achieves superior performances over previously suggested methods.

We present a practical asynchronous data fusion model for networked agents to
perform distributed Bayesian learning without sharing raw data. Our algorithm
uses a gossip-based approach where pairs of randomly selected agents employ
unadjusted Langevin dynamics for parameter sampling. We also introduce an
event-triggered mechanism to further reduce communication between gossiping
agents. These mechanisms drastically reduce communication overhead and help
avoid bottlenecks commonly experienced with distributed algorithms. In
addition, the reduced link utilization by the algorithm is expected to increase
resiliency to occasional link failure. We establish mathematical guarantees for
our algorithm and demonstrate its effectiveness via numerical experiments.

We introduce SketchySGD, a stochastic quasi-Newton method that uses sketching
to approximate the curvature of the loss function. Quasi-Newton methods are
among the most effective algorithms in traditional optimization, where they
converge much faster than first-order methods such as SGD. However, for
contemporary deep learning, quasi-Newton methods are considered inferior to
first-order methods like SGD and Adam owing to higher per-iteration complexity
and fragility due to inexact gradients. SketchySGD circumvents these issues by
a novel combination of subsampling, randomized low-rank approximation, and
dynamic regularization. In the convex case, we show SketchySGD with a fixed
stepsize converges to a small ball around the optimum at a faster rate than
SGD. In the non-convex case, SketchySGD converges linearly under two additional
assumptions, interpolation and the Polyak-Lojaciewicz condition, the latter of
which holds with high probability for wide neural networks. Numerical
experiments on image and tabular data demonstrate the improved reliability and
speed of SketchySGD for deep learning, compared to standard optimizers such as
SGD and Adam and existing quasi-Newton methods.

In this paper, we present the new Orthogonal Polynomials-Quadrature Algorithm
(OPQA), a parallelizable algorithm that estimates both the posterior and the
evidence in a Bayesian analysis in one pass by means of a functional analytic
approach. First, OPQA relates the evidence to an orthogonal projection onto a
special basis of our construct. Second, it lays out a fast and accurate
computational scheme to compute the transform coefficients.
  OPQA can be summarized as follows. First, we consider the $L^2$ space
associated with a measure with exponential weights. Then we constuct a
multivariate orthogonal basis which is dense in this space, such density being
guaranteed by the Riesz's Theorem. As we project the square root of the joint
distribution onto this basis of our choice, the density of the basis allows us
to invoke the Parseval Identity, which equates the evidence with the sum of
squares of the transform coefficients of this orthogonal projection. To compute
those transform coefficients, we propose a computational scheme using
Gauss-Hermite quadrature in higher dimensions. Not only does this approach
avoids the potential high variance problem associated with random sampling
methods, it significantly reduces the complexity of the computation and enables
one to speed up the computational speed by parallelization.
  This new algorithm does not make any assumption about the independence of the
latent variable, nor do we assume any knowledge of the prior. It solves for
both the evidence and the posterior in one pass. An outline of the theoretical
proof of the supporting algorithm will be provided.

Powder X-ray diffraction analysis is a critical component of materials
characterization methodologies. Discerning characteristic Bragg intensity peaks
and assigning them to known crystalline phases is the first qualitative step of
evaluating diffraction spectra. Subsequent to phase identification, Rietveld
refinement may be employed to extract the abundance of quantitative,
material-specific parameters hidden within powder data. These characterization
procedures are yet time-consuming and inhibit efficiency in materials science
workflows. The ever-increasing popularity and propulsion of data science
techniques has provided an obvious solution on the course towards materials
analysis automation. Deep learning has become a prime focus for predicting
crystallographic parameters and features from X-ray spectra. However, the
infeasibility of curating large, well-labelled experimental datasets means that
one must resort to a large number of theoretic simulations for powder data
augmentation to effectively train deep models. Herein, we are interested in
conventional supervised learning algorithms in lieu of deep learning for
multi-label crystalline phase identification and quantitative phase analysis
for a biomedical application. First, models were trained using very limited
experimental data. Further, we incorporated simulated XRD data to assess model
generalizability as well as the efficacy of simulation-based training for
predictive analysis in a real-world X-ray diffraction application.

Few-Shot Text Classification (FSTC) imitates humans to learn a new text
classifier efficiently with only few examples, by leveraging prior knowledge
from historical tasks. However, most prior works assume that all the tasks are
sampled from a single data source, which cannot adapt to real-world scenarios
where tasks are heterogeneous and lie in different distributions. As such,
existing methods may suffer from their globally knowledge-shared mechanisms to
handle the task heterogeneity. On the other hand, inherent task relation are
not explicitly captured, making task knowledge unorganized and hard to transfer
to new tasks. Thus, we explore a new FSTC setting where tasks can come from a
diverse range of data sources. To address the task heterogeneity, we propose a
self-supervised hierarchical task clustering (SS-HTC) method. SS-HTC not only
customizes cluster-specific knowledge by dynamically organizing heterogeneous
tasks into different clusters in hierarchical levels but also disentangles
underlying relations between tasks to improve the interpretability. Extensive
experiments on five public FSTC benchmark datasets demonstrate the
effectiveness of SS-HTC.

The Prophet Inequality and Pandora's Box problems are fundamental stochastic
problem with applications in Mechanism Design, Online Algorithms, Stochastic
Optimization, Optimal Stopping, and Operations Research. A usual assumption in
these works is that the probability distributions of the $n$ underlying random
variables are given as input to the algorithm. Since in practice these
distributions need to be learned, we initiate the study of such stochastic
problems in the Multi-Armed Bandits model.
  In the Multi-Armed Bandits model we interact with $n$ unknown distributions
over $T$ rounds: in round $t$ we play a policy $x^{(t)}$ and receive a partial
(bandit) feedback on the performance of $x^{(t)}$. The goal is to minimize the
regret, which is the difference over $T$ rounds in the total value of the
optimal algorithm that knows the distributions vs. the total value of our
algorithm that learns the distributions from the partial feedback. Our main
results give near-optimal $\tilde{O}(\mathsf{poly}(n)\sqrt{T})$ total regret
algorithms for both Prophet Inequality and Pandora's Box.
  Our proofs proceed by maintaining confidence intervals on the unknown indices
of the optimal policy. The exploration-exploitation tradeoff prevents us from
directly refining these confidence intervals, so the main technique is to
design a regret upper bound that is learnable while playing low-regret Bandit
policies.

Soccer Simulation 2D League is one of the major leagues of RoboCup
competitions. In a Soccer Simulation 2D (SS2D) game, two teams of 11 players
and one coach compete against each other. Several base codes have been released
for the RoboCup soccer simulation 2D (RCSS2D) community that have promoted the
application of multi-agent and AI algorithms in this field. In this paper, we
introduce "Cyrus2D Base", which is derived from the base code of the RCSS2D
2021 champion. We merged Gliders2D base V2.6 with the newest version of the
Helios base. We applied several features of Cyrus2021 to improve the
performance and capabilities of this base alongside a Data Extractor to
facilitate the implementation of machine learning in the field. We have tested
this base code in different teams and scenarios, and the obtained results
demonstrate significant improvements in the defensive and offensive strategy of
the team.

Radiology report summarization is a growing area of research. Given the
Findings and/or Background sections of a radiology report, the goal is to
generate a summary (called an Impression section) that highlights the key
observations and conclusions of the radiology study. Recent efforts have
released systems that achieve promising performance as measured by widely used
summarization metrics such as BLEU and ROUGE. However, the research area of
radiology report summarization currently faces important limitations. First,
most of the results are reported on private datasets. This limitation prevents
the ability to reproduce results and fairly compare different systems and
solutions. Secondly, to the best of our knowledge, most research is carried out
on chest X-rays. Sometimes, studies even omit to mention the concerned modality
and anatomy in the radiology reports used for their experiments. To palliate
these limitations, we propose a new dataset of six different modalities and
anatomies based on the MIMIC-III database. We further release our results and
the data splits used to carry out our experiments. Finally, we propose a simple
report summarization system that outperforms the previous replicable research
on the existing dataset.

Modern deep learning systems are fragile and do not generalize well under
distribution shifts. While much promising work has been accomplished to address
these concerns, a systematic study of the role of optimizers and their
out-of-distribution generalization performance has not been undertaken. In this
study, we examine the performance of popular first-order optimizers for
different classes of distributional shift under empirical risk minimization and
invariant risk minimization. We address the problem settings for image and text
classification using DomainBed, WILDS, and Backgrounds Challenge as
out-of-distribution datasets for the exhaustive study. We search over a wide
range of hyperparameters and examine the classification accuracy
(in-distribution and out-of-distribution) for over 20,000 models. We arrive at
the following findings: i) contrary to conventional wisdom, adaptive optimizers
(e.g., Adam) perform worse than non-adaptive optimizers (e.g., SGD,
momentum-based SGD), ii) in-distribution performance and out-of-distribution
performance exhibit three types of behavior depending on the dataset - linear
returns, increasing returns, and diminishing returns. We believe these findings
can help practitioners choose the right optimizer and know what behavior to
expect.

Given a heterogeneous Gaussian sequence model with mean $\theta \in \mathbb
R^d$ and covariance matrix $\Sigma = \operatorname{diag}(\sigma_1^2,\dots,
\sigma_d^2)$, we study the signal detection problem against sparse
alternatives. Namely, we characterize how large $\epsilon^*>0$ should be, in
order to distinguish with high probability the null hypothesis $\theta=0$ from
the alternative composed of sparse vectors in $\mathbb R^d$, separated from $0$
in $L^t$ norm ($t \geq 1$) by at least~$\epsilon^*$. We find minimax upper and
lower bounds over the minimax separation radius $\epsilon^*$ and prove that
they are always matching. We also derive the corresponding minimax tests
achieving these bounds. Our results reveal new phase transitions regarding the
behavior of $\epsilon^*$ with respect to the level of sparsity, to the $L^t$
metric, and to the heteroscedasticity profile of $\Sigma$. In the case of the
Euclidean (i.e. $L^2$) separation, we bridge the remaining gaps in the
literature.

It remains unclear which physical processes are responsible for the dramatic
increase with height of the temperature in stellar atmospheres, known as the
chromospheric ($\sim$10,000 K) and coronal (several million K) heating
problems. Statistical studies of sun-like stars reveal that chromospheric and
coronal emissions are correlated on a global scale, constraining, in principle,
theoretical models of potential heating mechanisms. However, so far, spatially
resolved observations of the Sun have surprisingly failed to show a similar
correlation on small spatial scales, leaving models poorly constrained. Here we
use unique coordinated high-resolution observations of the chromosphere (from
the Interface Region Imaging Spectrograph or IRIS satellite) and low corona
(from the Hi-C 2.1 sounding rocket), and machine-learning based inversion
techniques to show a strong correlation on spatial scales of a few hundred km
between heating in the chromosphere and low corona for regions with strong
magnetic field ("plage"). These results are compatible with recent advanced 3D
radiative magnetohydrodynamic simulations in which dissipation of current
sheets formed due to the braiding of the magnetic field lines deep in the
atmosphere is responsible for heating the plasma simultaneously to
chromospheric and coronal temperatures. Our results provide deep insight into
the nature of the heating mechanism in solar active regions.

Applying Deep Learning (DL) models to graphical causal learning has brought
outstanding effectiveness and efficiency but is still far from widespread use
in domain sciences. In research of EHR (Electronic Healthcare Records), we
realize that some confounding bias inherently exists in the causally formed
data, which DL cannot automatically adjust. Trace to the source is because the
Acyclic Causal Graph can be Multi-Dimensional, so the bias and causal learning
happen in two subspaces, which makes it unobservable from the learning process.
  This paper initially raises the concept of Dimensionality for causal graphs.
In our case, the 3-Dimensional DAG (Directed Acyclic Graph) space is defined by
the axes of causal variables, the Absolute timeline, and Relative timelines;
This is also the essential difference between Causality and Correlation
problems.
  We propose a novel new framework Causal Representation Learning (CRL), to
realize Graphical Causal Learning in latent space, which aims to provide
general solutions for 1) the inherent bias adjustment and 2) the DL causal
models generalization problem. We will also demonstrate the realization of CRL
with originally designed architecture and experimentally confirm its
feasibility.

Fixed-budget best-arm identification (BAI) is a bandit problem where the
learning agent maximizes the probability of identifying the optimal arm after a
fixed number of observations. In this work, we initiate the study of this
problem in the Bayesian setting. We propose a Bayesian elimination algorithm
and derive an upper bound on the probability that it fails to identify the
optimal arm. The bound reflects the quality of the prior and is the first such
bound in this setting. We prove it using a frequentist-like argument, where we
carry the prior through, and then integrate out the random bandit instance at
the end. Our upper bound asymptotically matches a newly established lower bound
for $2$ arms. Our experimental results show that Bayesian elimination is
superior to frequentist methods and competitive with the state-of-the-art
Bayesian algorithms that have no guarantees in our setting.

Learning to translate images from a source to a target domain with
applications such as converting simple line drawing to oil painting has
attracted significant attention. The quality of translated images is directly
related to two crucial issues. First, the consistency of the output
distribution with that of the target is essential. Second, the generated output
should have a high correlation with the input. Conditional Generative
Adversarial Networks, cGANs, are the most common models for translating images.
The performance of a cGAN drops when we use a limited training dataset. In this
work, we increase the Pix2Pix (a form of cGAN) target distribution modeling
ability with the help of dynamic neural network theory. Our model has two
learning cycles. The model learns the correlation between input and ground
truth in the first cycle. Then, the model's architecture is refined in the
second cycle to learn the target distribution from noise input. These processes
are executed in each iteration of the training procedure. Helping the cGAN
learn the target distribution from noise input results in a better model
generalization during the test time and allows the model to fit almost
perfectly to the target domain distribution. As a result, our model surpasses
the Pix2Pix model in segmenting HC18 and Montgomery's chest x-ray images. Both
qualitative and Dice scores show the superiority of our model. Although our
proposed method does not use thousand of additional data for pretraining, it
produces comparable results for the in and out-domain generalization compared
to the state-of-the-art methods.

Link prediction on dynamic graphs is an important task in graph mining.
Existing approaches based on dynamic graph neural networks (DGNNs) typically
require a significant amount of historical data (interactions over time), which
is not always available in practice. The missing links over time, which is a
common phenomenon in graph data, further aggravates the issue and thus creates
extremely sparse and dynamic graphs. To address this problem, we propose a
novel method based on the neural process, called Graph Sequential Neural ODE
Process (GSNOP). Specifically, GSNOP combines the advantage of the neural
process and neural ordinary differential equation that models the link
prediction on dynamic graphs as a dynamic-changing stochastic process. By
defining a distribution over functions, GSNOP introduces the uncertainty into
the predictions, making it generalize to more situations instead of overfitting
to the sparse data. GSNOP is also agnostic to model structures that can be
integrated with any DGNN to consider the chronological and geometrical
information for link prediction. Extensive experiments on three dynamic graph
datasets show that GSNOP can significantly improve the performance of existing
DGNNs and outperform other neural process variants.

In this article we employ a model open quantum system consisting of two-level
atomic systems coupled to Lorentzian photonic cavities, as an instantiation of
a quantum physical reservoir computer. We then deployed the quantum reservoir
computing approach to an archetypal machine learning problem of image
recognition. We contrast the effectiveness of the quantum physical reservoir
computer against a conventional approach using neural network of the similar
architecture with the quantum physical reservoir computer layer removed.
Remarkably, as the data set size is increased the quantum physical reservoir
computer quickly starts out perform the conventional neural network.
Furthermore, quantum physical reservoir computer provides superior
effectiveness against number of training epochs at a set data set size and
outperformed the neural network approach at every epoch number sampled.
Finally, we have deployed the quantum physical reservoir computer approach to
explore the quantum problem associated with the dynamics of open quantum
systems in which an atomic system ensemble interacts with a structured photonic
reservoir associated with a photonic band gap material. Our results demonstrate
that the quantum physical reservoir computer is equally effective in generating
useful representations for quantum problems, even with limited training data
size.

An understanding of how Social Overhead Capital (SOC) is associated with the
land value of the local community is important for effective urban planning.
However, even within a district, there are multiple sections used for different
purposes; the term for this is spatial heterogeneity. The spatial heterogeneity
issue has to be considered when attempting to comprehend land prices. If there
is spatial heterogeneity within a district, land prices can be managed by
adopting the spatial clustering method. In this study, spatial attributes
including SOC, socio-demographic features, and spatial information in a
specific district are analyzed with Finite Mixture Modeling (FMM) in order to
find (a) the optimal number of clusters and (b) the association among SOCs,
socio-demographic features, and land prices. FMM is a tool used to find
clusters and the attributes' coefficients simultaneously. Using the FMM method,
the results show that four clusters exist in one district and the four clusters
have different associations among SOCs, demographic features, and land prices.
Policymakers and managerial administration need to look for information to make
policy about land prices. The current study finds the consideration of
closeness to SOC to be a significant factor on land prices and suggests the
potential policy direction related to SOC.

Convolutional neural network (CNN) based methods have achieved great
successes in medical image segmentation, but their capability to learn global
representations is still limited due to using small effective receptive fields
of convolution operations. Transformer based methods are capable of modelling
long-range dependencies of information for capturing global representations,
yet their ability to model local context is lacking. Integrating CNN and
Transformer to learn both local and global representations while exploring
multi-scale features is instrumental in further improving medical image
segmentation. In this paper, we propose a hierarchical CNN and Transformer
hybrid architecture, called ConvFormer, for medical image segmentation.
ConvFormer is based on several simple yet effective designs. (1) A feed forward
module of Deformable Transformer (DeTrans) is re-designed to introduce local
information, called Enhanced DeTrans. (2) A residual-shaped hybrid stem based
on a combination of convolutions and Enhanced DeTrans is developed to capture
both local and global representations to enhance representation ability. (3)
Our encoder utilizes the residual-shaped hybrid stem in a hierarchical manner
to generate feature maps in different scales, and an additional Enhanced
DeTrans encoder with residual connections is built to exploit multi-scale
features with feature maps of different scales as input. Experiments on several
datasets show that our ConvFormer, trained from scratch, outperforms various
CNN- or Transformer-based architectures, achieving state-of-the-art

Developing successful artificial intelligence systems in practice depends
both on robust deep learning models as well as large high quality data.
Acquiring and labeling data can become prohibitively expensive and
time-consuming in many real-world applications such as clinical disease models.
Self-supervised learning has demonstrated great potential in increasing model
accuracy and robustness in small data regimes. In addition, many clinical
imaging and disease modeling applications rely heavily on regression of
continuous quantities. However, the applicability of self-supervised learning
for these medical-imaging regression tasks has not been extensively studied. In
this study, we develop a cross-domain self-supervised learning approach for
disease prognostic modeling as a regression problem using 3D images as input.
We demonstrate that self-supervised pre-training can improve the prediction of
Alzheimer's Disease progression from brain MRI. We also show that pre-training
on extended (but not labeled) brain MRI data outperforms pre-training on
natural images. We further observe that the highest performance is achieved
when both natural images and extended brain-MRI data are used for pre-training.

Self-supervised instance discrimination is an effective contrastive pretext
task to learn feature representations and address limited medical image
annotations. The idea is to make features of transformed versions of the same
images similar while forcing all other augmented images' representations to
contrast. However, this instance-based contrastive learning leaves performance
on the table by failing to maximize feature affinity between images with
similar content while counter-productively pushing their representations apart.
Recent improvements on this paradigm (e.g., leveraging multi-modal data,
different images in longitudinal studies, spatial correspondences) either
relied on additional views or made stringent assumptions about data properties,
which can sacrifice generalizability and applicability. To address this
challenge, we propose a new self-supervised contrastive learning method that
uses unsupervised feature clustering to better select positive and negative
image samples. More specifically, we produce pseudo-classes by hierarchically
clustering features obtained by an auto-encoder in an unsupervised manner, and
prevent destructive interference during contrastive learning by avoiding the
selection of negatives from the same pseudo-class. Experiments on 2D skin
dermoscopic image segmentation and 3D multi-class whole heart CT segmentation
demonstrate that our method outperforms state-of-the-art self-supervised
contrastive techniques on these tasks.

Multilingual pre-trained models exhibit zero-shot cross-lingual transfer,
where a model fine-tuned on a source language achieves surprisingly good
performance on a target language. While studies have attempted to understand
transfer, they focus only on MLM, and the large number of differences between
natural languages makes it hard to disentangle the importance of different
properties. In this work, we specifically highlight the importance of word
embedding alignment by proposing a pre-training objective (ALIGN-MLM) whose
auxiliary loss guides similar words in different languages to have similar word
embeddings. ALIGN-MLM either outperforms or matches three widely adopted
objectives (MLM, XLM, DICT-MLM) when we evaluate transfer between pairs of
natural languages and their counterparts created by systematically modifying
specific properties like the script. In particular, ALIGN-MLM outperforms XLM
and MLM by 35 and 30 F1 points on POS-tagging for transfer between languages
that differ both in their script and word order (left-to-right v.s.
right-to-left). We also show a strong correlation between alignment and
transfer for all objectives (e.g., rho=0.727 for XNLI), which together with
ALIGN-MLM's strong performance calls for explicitly aligning word embeddings
for multilingual models.

Hyper-parameters (HPs) are an important part of machine learning (ML) model
development and can greatly influence performance. This paper studies their
behavior for three algorithms: Extreme Gradient Boosting (XGB), Random Forest
(RF), and Feedforward Neural Network (FFNN) with structured data. Our empirical
investigation examines the qualitative behavior of model performance as the HPs
vary, quantifies the importance of each HP for different ML algorithms, and
stability of the performance near the optimal region. Based on the findings, we
propose a set of guidelines for efficient HP tuning by reducing the search
space.

High annotation costs and limited labels for dense 3D medical imaging tasks
have recently motivated an assortment of 3D self-supervised pretraining methods
that improve transfer learning performance. However, these methods commonly
lack spatial awareness despite its centrality in enabling effective 3D image
analysis. More specifically, position, scale, and orientation are not only
informative but also automatically available when generating image crops for
training. Yet, to date, no work has proposed a pretext task that distills all
key spatial features. To fulfill this need, we develop a new self-supervised
method, VectorPOSE, which promotes better spatial understanding with two novel
pretext tasks: Vector Prediction (VP) and Boundary-Focused Reconstruction
(BFR). VP focuses on global spatial concepts (i.e., properties of 3D patches)
while BFR addresses weaknesses of recent reconstruction methods to learn more
effective local representations. We evaluate VectorPOSE on three 3D medical
image segmentation tasks, showing that it often outperforms state-of-the-art
methods, especially in limited annotation settings.

This paper presents an anomaly detection model that combines the strong
statistical foundation of density-estimation-based anomaly detection methods
with the representation-learning ability of deep-learning models. The method
combines an autoencoder, for learning a low-dimensional representation of the
data, with a density-estimation model based on random Fourier features and
density matrices in an end-to-end architecture that can be trained using
gradient-based optimization techniques. The method predicts a degree of
normality for new samples based on the estimated density. A systematic
experimental evaluation was performed on different benchmark datasets. The
experimental results show that the method performs on par with or outperforms
other state-of-the-art methods.

Most of the planetary systems discovered around binary stars are located at
approximately three semi-major axes from the barycentre of their system,
curiously close to low-order mean-motion resonances (MMRs). The formation
mechanism of these circumbinary planets is not yet fully understood. In situ
formation is extremely challenging because of the strong interaction with the
binary. One possible explanation is that, after their formation, the
interactions between these planets and the surrounding protoplanetary disc
cause them to migrate at velocities dependent on the nature of the disc and the
mass of the exoplanet. Although extensive data can be obtained with direct
hydrodynamical simulations, their computational cost remains too high. On the
other hand, the direct n-body simulations approach allows us to model a large
variety of parameters at much lower cost. We analyse the planetary migration
around a wide variety of binary stars using Stokes-like forces that mimic
planetary migration at a constant rate. Our goal is to identify the main
parameters responsible for the ejection of planets at different resonances with
the inner binary. We performed 4200 n-body simulations with Stokes-like forces
and analysed their evolution and outcome as a function of the properties of
each system. For each simulated exoplanet, we applied an ensemble learning
method for classification in order to clarify the relationship between the
inspected parameters and the process of MMR capture. We identify the capture
probability for different N/1 MMRs, 4/1 being the most prone to capture
exoplanets, with 37% probability, followed by MMR 5/1 with $\sim$ 23% of
probability. The eccentricity of the binary is found to be the most important
parameter in determining the MMR capture of each circumbinary exoplanet,
followed by the mass ratio of the binary and the initial eccentricity of the
planet.

Software vulnerabilities, caused by unintentional flaws in source codes, are
the main root cause of cyberattacks. Source code static analysis has been used
extensively to detect the unintentional defects, i.e. vulnerabilities,
introduced into the source codes by software developers. In this paper, we
propose a deep learning approach to detect vulnerabilities from their LLVM IR
representations based on the techniques that have been used in natural language
processing. The proposed approach uses a hierarchical process to first identify
source codes with vulnerabilities, and then it identifies the lines of codes
that contribute to the vulnerability within the detected source codes. This
proposed two-step approach reduces the false alarm of detecting vulnerable
lines. Our extensive experiment on real-world and synthetic codes collected in
NVD and SARD shows high accuracy (about 98\%) in detecting source code
vulnerabilities.

The explosion of scientific publications overloads researchers with
information. This is even more dramatic for interdisciplinary studies, where
several fields need to be explored. A tool to help researchers overcome this is
Natural Language Processing (NLP): a machine-learning (ML) technique that
allows scientists to automatically synthesize information from many articles.
As a practical example, we have used NLP to conduct an interdisciplinary search
for compounds that could be carriers for Diffuse Interstellar Bands (DIBs), a
long-standing open question in astrophysics. We have trained a NLP model on a
corpus of 1.5 million cross-domain articles in open access, and fine-tuned this
model with a corpus of astrophysical publications about DIBs. Our analysis
points us toward several molecules, studied primarily in biology, having
transitions at the wavelengths of several DIBs and composed of abundant
interstellar atoms. Several of these molecules contain chromophores, small
molecular groups responsible for the molecule's colour, that could be promising
candidate carriers. Identifying viable carriers demonstrates the value of using
NLP to tackle open scientific questions, in an interdisciplinary manner.

In recent years, neural network based image denoising approaches have
revolutionized the analysis of biomedical microscopy data. Self-supervised
methods, such as Noise2Void (N2V), are applicable to virtually all noisy
datasets, even without dedicated training data being available. Arguably, this
facilitated the fast and widespread adoption of N2V throughout the life
sciences. Unfortunately, the blind-spot training underlying N2V can lead to
rather visible checkerboard artifacts, thereby reducing the quality of final
predictions considerably. In this work, we present two modifications to the
vanilla N2V setup that both help to reduce the unwanted artifacts considerably.
Firstly, we propose a modified network architecture, i.e., using BlurPool
instead of MaxPool layers throughout the used U-Net, rolling back the residual
U-Net to a non-residual U-Net, and eliminating the skip connections at the
uppermost U-Net level. Additionally, we propose new replacement strategies to
determine the pixel intensity values that fill in the elected blind-spot
pixels. We validate our modifications on a range of microscopy and natural
image data. Based on added synthetic noise from multiple noise types and at
varying amplitudes, we show that both proposed modifications push the current
state-of-the-art for fully self-supervised image denoising.

We study the problem of allocating limited supply of medical resources in
developing countries, in particular, Sierra Leone. We address this problem by
combining machine learning (to predict demand) with optimization (to optimize
allocations). A key challenge is the need to align the loss function used to
train the machine learning model with the decision loss associated with the
downstream optimization problem. Traditional solutions have limited flexibility
in the model architecture and scale poorly to large datasets. We propose a
decision-aware learning algorithm that uses a novel Taylor expansion of the
optimal decision loss to derive the machine learning loss. Importantly, our
approach only requires a simple re-weighting of the training data, ensuring it
is both flexible and scalable, e.g., we incorporate it into a random forest
trained using a multitask learning framework. We apply our framework to
optimize the distribution of essential medicines in collaboration with
policymakers in Sierra Leone; highly uncertain demand and limited budgets
currently result in excessive unmet demand. Out-of-sample results demonstrate
that our end-to-end approach can significantly reduce unmet demand across 1040
health facilities throughout Sierra Leone.

From AlexNet to Inception, autoencoders to diffusion models, the development
of novel and powerful deep learning models and learning algorithms has
proceeded at breakneck speeds. In part, we believe that rapid iteration of
model architecture and learning techniques by a large community of researchers
over a common representation of the underlying entities has resulted in
transferable deep learning knowledge. As a result, model scale, accuracy,
fidelity, and compute performance have dramatically increased in computer
vision and natural language processing. On the other hand, the lack of a common
representation for chemical structure has hampered similar progress. To enable
transferable deep learning, we identify the need for a robust 3-dimensional
representation of materials such as molecules and crystals. The goal is to
enable both materials property prediction and materials generation with 3D
structures. While computationally costly, such representations can model a
large set of chemical structures. We propose $\textit{ParticleGrid}$, a
SIMD-optimized library for 3D structures, that is designed for deep learning
applications and to seamlessly integrate with deep learning frameworks. Our
highly optimized grid generation allows for generating grids on the fly on the
CPU, reducing storage and GPU compute and memory requirements. We show the
efficacy of 3D grids generated via $\textit{ParticleGrid}$ and accurately
predict molecular energy properties using a 3D convolutional neural network.
Our model is able to get 0.006 mean square error and nearly match the values
calculated using computationally costly density functional theory at a fraction
of the time.

We present a novel deep learning method for fully automated detection and
classification of the Cervical Vertebrae Maturation (CVM) stages. The deep
convolutional neural network consists of three parallel networks (TriPodNet)
independently trained with different initialization parameters. They also have
a built-in set of novel directional filters that highlight the Cervical Verte
edges in X-ray images. Outputs of the three parallel networks are combined
using a fully connected layer. 1018 cephalometric radiographs were labeled,
divided by gender, and classified according to the CVM stages. Resulting
images, using different training techniques and patches, were used to train
TripodNet together with a set of tunable directional edge enhancers. Data
augmentation is implemented to avoid overfitting. TripodNet achieves the
state-of-the-art accuracy of 81.18\% in female patients and 75.32\% in male
patients. The proposed TripodNet achieves a higher accuracy in our dataset than
the Swin Transformers and the previous network models that we investigated for
CVM stage estimation.

Cameras are increasingly being deployed in cities, enterprises and roads
world-wide to enable many applications in public safety, intelligent
transportation, retail, healthcare and manufacturing. Often, after initial
deployment of the cameras, the environmental conditions and the scenes around
these cameras change, and our experiments show that these changes can adversely
impact the accuracy of insights from video analytics. This is because the
camera parameter settings, though optimal at deployment time, are not the best
settings for good-quality video capture as the environmental conditions and
scenes around a camera change during operation. Capturing poor-quality video
adversely affects the accuracy of analytics. To mitigate the loss in accuracy
of insights, we propose a novel, reinforcement-learning based system APT that
dynamically, and remotely (over 5G networks), tunes the camera parameters, to
ensure a high-quality video capture, which mitigates any loss in accuracy of
video analytics. As a result, such tuning restores the accuracy of insights
when environmental conditions or scene content change. APT uses reinforcement
learning, with no-reference perceptual quality estimation as the reward
function. We conducted extensive real-world experiments, where we
simultaneously deployed two cameras side-by-side overlooking an enterprise
parking lot (one camera only has manufacturer-suggested default setting, while
the other camera is dynamically tuned by APT during operation). Our experiments
demonstrated that due to dynamic tuning by APT, the analytics insights are
consistently better at all times of the day: the accuracy of object detection
video analytics application was improved on average by ~ 42%. Since our reward
function is independent of any analytics task, APT can be readily used for
different video analytics tasks.

Continuous-time event sequences, i.e., sequences consisting of continuous
time stamps and associated event types ("marks"), are an important type of
sequential data with many applications, e.g., in clinical medicine or user
behavior modeling. Since these data are typically modeled autoregressively
(e.g., using neural Hawkes processes or their classical counterparts), it is
natural to ask questions about future scenarios such as "what kind of event
will occur next" or "will an event of type $A$ occur before one of type $B$".
Unfortunately, some of these queries are notoriously hard to address since
current methods are limited to naive simulation, which can be highly
inefficient. This paper introduces a new typology of query types and a
framework for addressing them using importance sampling. Example queries
include predicting the $n^\text{th}$ event type in a sequence and the hitting
time distribution of one or more event types. We also leverage these findings
further to be applicable for estimating general "$A$ before $B$" type of
queries. We prove theoretically that our estimation method is effectively
always better than naive simulation and show empirically based on three
real-world datasets that it is on average 1,000 times more efficient than
existing approaches.

Spontaneous mouse behavior is composed from repeatedly-used modules of
movement (e.g., rearing, running, grooming) that are flexibly placed into
sequences whose content evolves over time. By identifying behavioral modules
and the order in which they are expressed, researchers can gain insight into
the impact of drugs, genes, context, sensory stimuli and neural activity on
behavior. Here we present a protocol for performing Motion Sequencing (MoSeq),
an ethologically-inspired method that uses 3D machine vision and unsupervised
machine learning to decompose spontaneous mouse behavior in the laboratory into
a series of elemental modules called "syllables". This protocol is based upon a
notebook-based pipeline for MoSeq that includes modules for depth video
acquisition, data pre-processing and modeling, as well as a standardized set of
visualization tools. Users are provided with instructions and code for building
a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse
behavior for submission to the modeling framework; the outputs of this protocol
include syllable labels for each frame of video data as well as summary plots
describing how often each syllable was used and how syllables transitioned from
one to the other over time. This protocol and the accompanying pipeline
significantly lower the bar for adopting this unsupervised, data-driven
approach to characterizing mouse behavior, enabling users without significant
computational ethology experience to gain insight into how the structure of
behavior is altered after experimental manipulations.

We consider the problem of aggregating binary votes from an ensemble of
experts to reveal an underlying binary ground truth where each expert votes
correctly with some independent probability. We focus on settings where the
number of agents is too small for asymptotic results to apply, many experts may
vote correctly with low probability, and there is no central authority who
knows the experts' competences, or their probabilities of voting correctly. Our
approach is to designate a second type of agent -- a judge -- to weight the
experts to improve overall accuracy. The catch is that the judge has imperfect
competence just like the experts. We demonstrate that having a single minimally
competent judge is often better than having none at all. Using an ensemble of
judges to weight the experts can provide a better weighting than any single
judge; even the optimal weighting under the right conditions. As our results
show, the ability of the judge(s) to distinguish between competent and
incompetent experts is paramount. Lastly, given a fixed set of agents with
unknown competences drawn i.i.d. from a common distribution, we show how the
optimal split of the agents between judges and experts depends on the
distribution.

Just like weights, bias terms are the learnable parameters of many popular
machine learning models, including neural networks. Biases are believed to
effectively increase the representational power of neural networks to solve a
wide range of tasks in computer vision. However, we argue that if we consider
the intrinsic distribution of images in the input space as well as some desired
properties a model should have from the first principles, biases can be
completely ignored in addressing many image-related tasks, such as image
classification. Our observation indicates that zero-bias neural networks could
perform comparably to neural networks with bias at least on practical image
classification tasks. In addition, we prove that zero-bias neural networks
possess a nice property called scalar (multiplication) invariance, which has
great potential in learning and understanding images captured under poor
illumination conditions. We then extend scalar invariance to more general cases
that allow us to verify certain convex regions of the input space. Our
experimental results show that zero-bias models could outperform the
state-of-art models by a very large margin (over 60%) when predicting images
under a low illumination condition (multiplying a scalar of 0.01); while
achieving the same-level performance as normal models.

It is shown that various symptoms could remain in the stage of post-acute
sequelae of SARS-CoV-2 infection (PASC), otherwise known as Long COVID. A
number of COVID patients suffer from heterogeneous symptoms, which severely
impact recovery from the pandemic. While scientists are trying to give an
unambiguous definition of Long COVID, efforts in prediction of Long COVID could
play an important role in understanding the characteristic of this new disease.
Vital measurements (e.g. oxygen saturation, heart rate, blood pressure) could
reflect body's most basic functions and are measured regularly during
hospitalization, so among patients diagnosed COVID positive and hospitalized,
we analyze the vital measurements of first 7 days since the hospitalization
start date to study the pattern of the vital measurements and predict Long
COVID with the information from vital measurements.

Military drones are taking an increasingly prominent role in armed conflict,
and the use of multiple drones in a swarm can be useful. Who the drivers of the
research are and what sub-domains exist is analyzed and visually presented in
this research using NLP techniques based on 946 studies. Most research is
conducted in the Western world, led by the United States, the United Kingdom,
and Germany. Through Tf-idf scoring, it is shown that countries have
significant differences in the subdomains studied. Overall, 2019 and 2020 saw
the most works published, with significant interest in military swarm drones as
early as 2008. This study provides a first glimpse into research in this area
and prompts further investigation.

The dynamics of materials failure is one of the most critical phenomena in a
range of scientific and engineering fields, from healthcare to structural
materials to transportation. In this paper we propose a specially designed deep
neural network, DyFraNet, which can predict dynamic fracture behaviors by
identifying a complete history of fracture propagation - from cracking onset,
as a crack grows through the material, modeled as a series of frames evolving
over time and dependent on each other. Furthermore, this model can not only
forecast future fracture processes but also backcast to elucidate the past
fracture history. In this scenario, once provided with the outcome of a
fracture event, the model will elucidate past events that led to this state and
will predict the future evolution of the failure process. By comparing the
predicted results with atomistic-level simulations and theory, we show that
DyFraNet can capture dynamic fracture mechanics by accurately predicting how
cracks develop over time, including measures such as the crack speed, as well
as when cracks become unstable. We use GradCAM to interpret how DyFraNet
perceives the relationship between geometric conditions and fracture dynamics
and we find DyFraNet pays special attention to the areas around crack tips,
which have a critical influence in the early stage of fracture propagation. In
later stages, the model pays increased attention to the existing or newly
formed damage distribution in the material. The proposed approach offers
significant potential to accelerate the exploration of the dynamics in material
design against fracture failures and can be beneficially adapted for all kinds
of dynamical engineering problems.

A search for supersymmetry is presented in events with a single charged
lepton, electron or muon, and multiple hadronic jets. The data correspond to an
integrated luminosity of 138 fb$^{-1}$ of proton-proton collisions at a
center-of-mass energy of 13 TeV, recorded by the CMS experiment at the CERN
LHC. The search targets gluino pair production, where the gluinos decay into
final states with the lightest supersymmetric particle (LSP) and either a top
quark-antiquark ($\mathrm{t\bar{t}}$) pair, or a light-flavor quark-antiquark
($\mathrm{q\bar{q}}$) pair and a virtual or on-shell W boson. The main
backgrounds, $\mathrm{t\bar{t}}$ pair and W+jets production, are suppressed by
requirements on the azimuthal angle between the momenta of the lepton and of
its reconstructed parent W boson candidate, and by top quark and W boson
identification based on a machine-learning technique. The number of observed
events is consistent with the expectations from standard model processes.
Limits are evaluated on supersymmetric particle masses in the context of two
simplified models of gluino pair production. Exclusions for gluino masses reach
up to 2130 (2280) GeV at 95% confidence level for a model with gluino decay to
a $\mathrm{t\bar{t}}$ pair (a $\mathrm{q\bar{q}}$ pair and a W boson) and the
LSP. For the same models, limits on the mass of the LSP reach up to 1270 (1220)
GeV.

Pretrained large generative language models have shown great performance on
many tasks, but exhibit low compositional generalization abilities. Scaling
such models has been shown to improve their performance on various NLP tasks
even just by conditioning them on a few examples to solve the task without any
fine-tuning (also known as in-context learning). In this work, we look at the
gap between the in-distribution (ID) and out-of-distribution (OOD) performance
of such models in semantic parsing tasks with in-context learning. In the ID
settings, the demonstrations are from the same split (test or train) that the
model is being evaluated on, and in the OOD settings, they are from the other
split. We look at how the relative generalization gap of in-context learning
evolves as models are scaled up. We evaluate four model families, OPT, BLOOM,
CodeGen and Codex on three semantic parsing datasets, CFQ, SCAN and GeoQuery
with different number of exemplars, and observe a trend of decreasing relative
generalization gap as models are scaled up.

Tables are everywhere, from scientific journals, papers, websites, and
newspapers all the way to items we buy at the supermarket. Detecting them is
thus of utmost importance to automatically understanding the content of a
document. The performance of table detection has substantially increased thanks
to the rapid development of deep learning networks. The goals of this survey
are to provide a profound comprehension of the major developments in the field
of Table Detection, offer insight into the different methodologies, and provide
a systematic taxonomy of the different approaches. Furthermore, we provide an
analysis of both classic and new applications in the field. Lastly, the
datasets and source code of the existing models are organized to provide the
reader with a compass on this vast literature. Finally, we go over the
architecture of utilizing various object detection and table structure
recognition methods to create an effective and efficient system, as well as a
set of development trends to keep up with state-of-the-art algorithms and
future research. We have also set up a public GitHub repository where we will
be updating the most recent publications, open data, and source code. The
GitHub repository is available at
https://github.com/abdoelsayed2016/table-detection-structure-recognition.

Finding an object of a specific class in an unseen environment remains an
unsolved navigation problem. Hence, we propose a hierarchical learning-based
method for object navigation. The top-level is capable of high-level planning,
and building a memory on a floorplan-level (e.g., which room makes the most
sense for the agent to visit next, where has the agent already been?). While
the lower-level is tasked with efficiently navigating between rooms and looking
for objects in them. Instructions can be provided to the agent using a simple
synthetic language. The top-level intelligently enhances the instructions in
order to make the overall task more tractable. Language grounding, mapping
instructions to visual observations, is performed by utilizing an additional
separate supervised trained goal assessment module. We demonstrate the
effectiveness of our method on a dynamic configurable domestic environment.

Neural Processes (NPs) are popular methods in meta-learning that can estimate
predictive uncertainty on target datapoints by conditioning on a context
dataset. Previous state-of-the-art method Transformer Neural Processes (TNPs)
achieve strong performance but require quadratic computation with respect to
the number of context datapoints, significantly limiting its scalability.
Conversely, existing sub-quadratic NP variants perform significantly worse than
that of TNPs. Tackling this issue, we propose Latent Bottlenecked Attentive
Neural Processes (LBANPs), a new computationally efficient sub-quadratic NP
variant, that has a querying computational complexity independent of the number
of context datapoints. The model encodes the context dataset into a constant
number of latent vectors on which self-attention is performed. When making
predictions, the model retrieves higher-order information from the context
dataset via multiple cross-attention mechanisms on the latent vectors. We
empirically show that LBANPs achieve results competitive with the
state-of-the-art on meta-regression, image completion, and contextual
multi-armed bandits. We demonstrate that LBANPs can trade-off the computational
cost and performance according to the number of latent vectors. Finally, we
show LBANPs can scale beyond existing attention-based NP variants to larger
dataset settings.

Training convolutional neural networks (CNNs) with a strict 1-Lipschitz
constraint under the $l_{2}$ norm is useful for adversarial robustness,
interpretable gradients and stable training. 1-Lipschitz CNNs are usually
designed by enforcing each layer to have an orthogonal Jacobian matrix (for all
inputs) to prevent the gradients from vanishing during backpropagation.
However, their performance often significantly lags behind that of heuristic
methods to enforce Lipschitz constraints where the resulting CNN is not
\textit{provably} 1-Lipschitz. In this work, we reduce this gap by introducing
(a) a procedure to certify robustness of 1-Lipschitz CNNs by replacing the last
linear layer with a 1-hidden layer MLP that significantly improves their
performance for both standard and provably robust accuracy, (b) a method to
significantly reduce the training time per epoch for Skew Orthogonal
Convolution (SOC) layers (>30\% reduction for deeper networks) and (c) a class
of pooling layers using the mathematical property that the $l_{2}$ distance of
an input to a manifold is 1-Lipschitz. Using these methods, we significantly
advance the state-of-the-art for standard and provable robust accuracies on
CIFAR-10 (gains of +1.79\% and +3.82\%) and similarly on CIFAR-100 (+3.78\% and
+4.75\%) across all networks. Code is available at
\url{https://github.com/singlasahil14/improved_l2_robustness}.

The goal of sexism detection is to mitigate negative online content targeting
certain gender groups of people. However, the limited availability of labeled
sexism-related datasets makes it problematic to identify online sexism for
low-resource languages. In this paper, we address the task of automatic sexism
detection in social media for one low-resource language -- Chinese. Rather than
collecting new sexism data or building cross-lingual transfer learning models,
we develop a cross-lingual domain-aware semantic specialisation system in order
to make the most of existing data. Semantic specialisation is a technique for
retrofitting pre-trained distributional word vectors by integrating external
linguistic knowledge (such as lexico-semantic relations) into the specialised
feature space. To do this, we leverage semantic resources for sexism from a
high-resource language (English) to specialise pre-trained word vectors in the
target language (Chinese) to inject domain knowledge. We demonstrate the
benefit of our sexist word embeddings (SexWEs) specialised by our framework via
intrinsic evaluation of word similarity and extrinsic evaluation of sexism
detection. Compared with other specialisation approaches and Chinese baseline
word vectors, our SexWEs shows an average score improvement of 0.033 and 0.064
in both intrinsic and extrinsic evaluations, respectively. The ablative results
and visualisation of SexWEs also prove the effectiveness of our framework on
retrofitting word vectors in low-resource languages. Our code and
sexism-related word vectors will be publicly available.

Neural networks are known to be biased towards learning mechanisms that help
identify $spurious\, attributes$, yielding features that do not generalize well
under distribution shifts. To understand and address this limitation, we study
the geometry of neural network loss landscapes through the lens of $mode\,
connectivity$, the observation that minimizers of neural networks are connected
via simple paths of low loss. Our work addresses two questions: (i) do
minimizers that encode dissimilar mechanisms connect via simple paths of low
loss? (ii) can fine-tuning a pretrained model help switch between such
minimizers? We define a notion of $\textit{mechanistic similarity}$ and
demonstrate that lack of linear connectivity between two minimizers implies the
corresponding models use dissimilar mechanisms for making their predictions.
This property helps us demonstrate that na$\"{i}$ve fine-tuning can fail to
eliminate a model's reliance on spurious attributes. We thus propose a method
for altering a model's mechanisms, named $connectivity$-$based$
$fine$-$tuning$, and validate its usefulness by inducing models invariant to
spurious attributes.

We demonstrate the use of symbolic regression in deriving analytical
formulas, which are needed at various stages of a typical experimental analysis
in collider phenomenology. As a first application, we consider kinematic
variables like the stransverse mass, $M_{T2}$, which are defined
algorithmically through an optimization procedure and not in terms of an
analytical formula. We then train a symbolic regression and obtain the correct
analytical expressions for all known special cases of $M_{T2}$ in the
literature. As a second application, we reproduce the correct analytical
expression for a next-to-leading order (NLO) kinematic distribution from data,
which is simulated with a NLO event generator. Finally, we derive analytical
approximations for the NLO kinematic distributions after detector simulation,
for which no known analytical formulas currently exist.

Emerging artificial intelligence (AI) applications often balance the
preferences and impacts among diverse and contentious stakeholder groups.
Accommodating these stakeholder groups during system design, development, and
deployment requires tools for the elicitation of disparate system interests and
collaboration interfaces supporting negotiation balancing those interests. This
paper introduces interactive visual "participation interfaces" for Markov
Decision Processes (MDPs) and collaborative ranking problems as examples
restoring a human-centered locus of control.

We propose parameterizing the population distribution of the gravitational
wave population modeling framework (Hierarchical Bayesian Analysis) with a
normalizing flow. We first demonstrate the merit of this method on illustrative
experiments and then analyze four parameters of the latest LIGO data release:
primary mass, secondary mass, redshift, and effective spin. Our results show
that despite the small and notoriously noisy dataset, the posterior predictive
distributions (assuming a prior over the parameters of the flow) of the
observed gravitational wave population recover structure that agrees with
robust previous phenomenological modeling results while being less susceptible
to biases introduced by less-flexible distribution models. Therefore, the
method forms a promising flexible, reliable replacement for population
inference distributions, even when data is highly noisy.

With the rapid growth of computing powers and recent advances in deep
learning, we have witnessed impressive demonstrations of novel robot
capabilities in research settings. Nonetheless, these learning systems exhibit
brittle generalization and require excessive training data for practical tasks.
To harness the capabilities of state-of-the-art robot learning models while
embracing their imperfections, we present Sirius, a principled framework for
humans and robots to collaborate through a division of work. In this framework,
partially autonomous robots are tasked with handling a major portion of
decision-making where they work reliably; meanwhile, human operators monitor
the process and intervene in challenging situations. Such a human-robot team
ensures safe deployments in complex tasks. Further, we introduce a new learning
algorithm to improve the policy's performance on the data collected from the
task executions. The core idea is re-weighing training samples with
approximated human trust and optimizing the policies with weighted behavioral
cloning. We evaluate Sirius in simulation and on real hardware, showing that
Sirius consistently outperforms baselines over a collection of contact-rich
manipulation tasks, achieving 8% boost in simulation and 27% on real hardware
than the state-of-the-art methods, with 3 times faster convergence and 15%
memory size. Videos and code are available at
https://ut-austin-rpl.github.io/sirius/

A model-agnostic variable importance method can be used with arbitrary
prediction functions. Here we present some model-free methods that do not
require access to the prediction function. This is useful when that function is
proprietary and not available, or just extremely expensive. It is also useful
when studying residuals from a model. The cohort Shapley (CS) method is
model-free but has exponential cost in the dimension of the input space. A
supervised on-manifold Shapley method from Frye et al. (2020) is also model
free but requires as input a second black box model that has to be trained for
the Shapley value problem. We introduce an integrated gradient version of
cohort Shapley, called IGCS, with cost $\mathcal{O}(nd)$. We show that over the
vast majority of the relevant unit cube that the IGCS value function is close
to a multilinear function for which IGCS matches CS. We use some area under the
curve (AUC) measures to quantify the performance of IGCS. On a problem from
high energy physics we verify that IGCS has nearly the same AUCs as CS. We also
use it on a problem from computational chemistry in 1024 variables. We see
there that IGCS attains much higher AUCs than we get from Monte Carlo sampling.
The code is publicly available at
https://github.com/cohortshapley/cohortintgrad.

In the last decade, Federated Learning (FL) has gained relevance in training
collaborative models without sharing sensitive data. Since its birth,
Centralized FL (CFL) has been the most common approach in the literature, where
a unique entity creates global models. However, using a centralized approach
has the disadvantages of bottleneck at the server node, single point of
failure, and trust needs. Decentralized Federated Learning (DFL) arose to solve
these aspects by embracing the principles of data sharing minimization and
decentralized model aggregation without relying on centralized architectures.
However, despite the work done in DFL, the literature has not (i) studied the
main fundamentals differentiating DFL and CFL; (ii) reviewed application
scenarios and solutions using DFL; and (iii) analyzed DFL frameworks to create
and evaluate new solutions. To this end, this article identifies and analyzes
the main fundamentals of DFL in terms of federation architectures, topologies,
communication mechanisms, security approaches, and key performance indicators.
Additionally, the paper at hand explores existing mechanisms to optimize
critical DFL fundamentals. Then, this work analyzes and compares the most used
DFL application scenarios and solutions according to the fundamentals
previously defined. After that, the most relevant features of the current DFL
frameworks are reviewed and compared. Finally, the evolution of existing DFL
solutions is analyzed to provide a list of trends, lessons learned, and open
challenges.

The internet contains a wealth of knowledge -- from the birthdays of
historical figures to tutorials on how to code -- all of which may be learned
by language models. However, there is a huge variability in the number of times
a given piece of information appears on the web. In this paper, we study the
relationship between the knowledge memorized by large language models and the
information in their pre-training datasets. In particular, we show that a
language model's ability to answer a fact-based question relates to how many
documents associated with that question were seen during pre-training. We
identify these relevant documents by entity linking pre-training datasets and
counting documents that contain the same entities as a given question-answer
pair. Our results demonstrate strong correlational and causal relationships
between accuracy and relevant document count for numerous question answering
datasets (e.g., TriviaQA), pre-training corpora (e.g., ROOTS), and model sizes
(e.g., 176B parameters). Moreover, we find that while larger models are better
at learning long-tail knowledge, we estimate that today's models must be scaled
by many orders of magnitude to reach competitive QA performance on questions
with little support in the pre-training data. Finally, we show that
retrieval-augmentation can reduce the dependence on relevant document count,
presenting a promising approach for capturing the long-tail.

We present DLKoopman -- a software package for Koopman theory that uses deep
learning to learn an encoding of a nonlinear dynamical system into a linear
space, while simultaneously learning the linear dynamics. While several
previous efforts have either restricted the ability to learn encodings, or been
bespoke efforts designed for specific systems, DLKoopman is a generalized tool
that can be applied to data-driven learning and optimization of any dynamical
system. It can either be trained on data from individual states (snapshots) of
a system and used to predict its unknown states, or trained on data from
trajectories of a system and used to predict unknown trajectories for new
initial states. DLKoopman is available on the Python Package Index (PyPI) as
'dlkoopman', and includes extensive documentation and tutorials. Additional
contributions of the package include a novel metric called Average Normalized
Absolute Error for evaluating performance, and a ready-to-use hyperparameter
search module for improving performance.

In this paper we look into the conjecture of Entezari et al.(2021) which
states that if the permutation invariance of neural networks is taken into
account, then there is likely no loss barrier to the linear interpolation
between SGD solutions. First, we observe that neuron alignment methods alone
are insufficient to establish low-barrier linear connectivity between SGD
solutions due to a phenomenon we call variance collapse: interpolated deep
networks suffer a collapse in the variance of their activations, causing poor
performance. Next, we propose REPAIR (REnormalizing Permuted Activations for
Interpolation Repair) which mitigates variance collapse by rescaling the
preactivations of such interpolated networks. We explore the interaction
between our method and the choice of normalization layer, network width, and
depth, and demonstrate that using REPAIR on top of neuron alignment methods
leads to 60%-100% relative barrier reduction across a wide variety of
architecture families and tasks. In particular, we report a 74% barrier
reduction for ResNet50 on ImageNet and 90% barrier reduction for ResNet18 on
CIFAR10.

Recent studies find existing self-supervised speech encoders contain
primarily acoustic rather than semantic information. As a result, pipelined
supervised automatic speech recognition (ASR) to large language model (LLM)
systems achieve state-of-the-art results on semantic spoken language tasks by
utilizing rich semantic representations from the LLM. These systems come at the
cost of labeled audio transcriptions, which is expensive and time-consuming to
obtain. We propose a task-agnostic unsupervised way of incorporating semantic
information from LLMs into self-supervised speech encoders without labeled
audio transcriptions. By introducing semantics, we improve existing speech
encoder spoken language understanding performance by over 10\% on intent
classification, with modest gains in named entity resolution and slot filling,
and spoken question answering FF1 score by over 2\%. Our unsupervised approach
achieves similar performance as supervised methods trained on over 100 hours of
labeled audio transcripts, demonstrating the feasibility of unsupervised
semantic augmentations to existing speech encoders.

Air pollution is a major global environmental health threat, in particular
for people who live or work near pollution sources. Areas adjacent to pollution
sources often have high ambient pollution concentrations, and those areas are
commonly referred to as air pollution hotspots. Detecting and characterizing
pollution hotspots are of great importance for air quality management, but are
challenging due to the high spatial and temporal variability of air pollutants.
In this work, we explore the use of mobile sensing data (i.e., air quality
sensors installed on vehicles) to detect pollution hotspots. One major
challenge with mobile sensing data is uneven sampling, i.e., data collection
can vary by both space and time. To address this challenge, we propose a
two-step approach to detect hotspots from mobile sensing data, which includes
local spike detection and sample-weighted clustering. Essentially, this
approach tackles the uneven sampling issue by weighting samples based on their
spatial frequency and temporal hit rate, so as to identify robust and
persistent hotspots. To contextualize the hotspots and discover potential
pollution source characteristics, we explore a variety of cross-domain urban
data and extract features from them. As a soft-validation of the extracted
features, we build hotspot inference models for cities with and without mobile
sensing data. Evaluation results using real-world mobile sensing air quality
data as well as cross-domain urban data demonstrate the effectiveness of our
approach in detecting and inferring pollution hotspots. Furthermore, the
empirical analysis of hotspots and source features yields useful insights
regarding neighborhood pollution sources.

Direct Loss Minimization (DLM) has been proposed as a pseudo-Bayesian method
motivated as regularized loss minimization. Compared to variational inference,
it replaces the loss term in the evidence lower bound (ELBO) with the
predictive log loss, which is the same loss function used in evaluation. A
number of theoretical and empirical results in prior work suggest that DLM can
significantly improve over ELBO optimization for some models. However, as we
point out in this paper, this is not the case for Bayesian neural networks
(BNNs). The paper explores the practical performance of DLM for BNN, the
reasons for its failure and its relationship to optimizing the ELBO, uncovering
some interesting facts about both algorithms.

We present MargNet, a deep learning-based classifier for identifying stars,
quasars and compact galaxies using photometric parameters and images from the
Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) catalogue. MargNet
consists of a combination of Convolutional Neural Network (CNN) and Artificial
Neural Network (ANN) architectures. Using a carefully curated dataset
consisting of 240,000 compact objects and an additional 150,000 faint objects,
the machine learns classification directly from the data, minimising the need
for human intervention. MargNet is the first classifier focusing exclusively on
compact galaxies and performs better than other methods to classify compact
galaxies from stars and quasars, even at fainter magnitudes. This model and
feature engineering in such deep learning architectures will provide greater
success in identifying objects in the ongoing and upcoming surveys, such as
Dark Energy Survey (DES) and images from the Vera C. Rubin Observatory.

We present CardiacGen, a Deep Learning framework for generating synthetic but
physiologically plausible cardiac signals like ECG. Based on the physiology of
cardiovascular system function, we propose a modular hierarchical generative
model and impose explicit regularizing constraints for training each module
using multi-objective loss functions. The model comprises 2 modules, an HRV
module focused on producing realistic Heart-Rate-Variability characteristics
and a Morphology module focused on generating realistic signal morphologies for
different modalities. We empirically show that in addition to having realistic
physiological features, the synthetic data from CardiacGen can be used for data
augmentation to improve the performance of Deep Learning based classifiers.
CardiacGen code is available at
https://github.com/SENSE-Lab-OSU/cardiac_gen_model.

The vulnerability of the high-performance machine learning models implies a
security risk in applications with real-world consequences. Research on
adversarial attacks is beneficial in guiding the development of machine
learning models on the one hand and finding targeted defenses on the other.
However, most of the adversarial attacks today leverage the gradient or logit
information from the models to generate adversarial perturbation. Works in the
more realistic domain: decision-based attacks, which generate adversarial
perturbation solely based on observing the output label of the targeted model,
are still relatively rare and mostly use gradient-estimation strategies. In
this work, we propose a pixel-wise decision-based attack algorithm that finds a
distribution of adversarial perturbation through a reinforcement learning
algorithm. We call this method Decision-based Black-box Attack with
Reinforcement learning (DBAR). Experiments show that the proposed approach
outperforms state-of-the-art decision-based attacks with a higher attack
success rate and greater transferability.

The performance of approaches to Music Instrument Classification, a popular
task in Music Information Retrieval, is often impacted and limited by the lack
of availability of annotated data for training. We propose to address this
issue with "reprogramming," a technique that utilizes pre-trained deep and
complex neural networks originally targeting a different task by modifying and
mapping both the input and output of the pre-trained model. We demonstrate that
reprogramming can effectively leverage the power of the representation learned
for a different task and that the resulting reprogrammed system can perform on
par or even outperform state-of-the-art systems at a fraction of training
parameters. Our results, therefore, indicate that reprogramming is a promising
technique potentially applicable to other tasks impeded by data scarcity.

The problem of identifying anomalies in dynamic networks is a fundamental
task with a wide range of applications. However, it raises critical challenges
due to the complex nature of anomalies, lack of ground truth knowledge, and
complex and dynamic interactions in the network. Most existing approaches
usually study networks with a single type of connection between vertices, while
in many applications interactions between objects vary, yielding multiplex
networks. We propose ANOMULY, a general, unsupervised edge anomaly detection
framework for multiplex dynamic networks. In each relation type, ANOMULY sees
node embeddings at different GNN layers as hierarchical node states and employs
a GRU cell to capture temporal properties of the network and update node
embeddings over time. We then add an attention mechanism that incorporates
information across different types of relations. Our case study on brain
networks shows how this approach could be employed as a new tool to understand
abnormal brain activity that might reveal a brain disease or disorder.
Extensive experiments on nine real-world datasets demonstrate that ANOMULY
achieves state-of-the-art performance.

People rely heavily on context to enrich meaning beyond what is literally
said, enabling concise but effective communication. To interact successfully
and naturally with people, user-facing artificial intelligence systems will
require similar skills in pragmatics: relying on various types of context --
from shared linguistic goals and conventions, to the visual and embodied world
-- to use language effectively.
  We survey existing grounded settings and pragmatic modeling approaches and
analyze how the task goals, environmental contexts, and communicative
affordances in each work enrich linguistic meaning. We present recommendations
for future grounded task design to naturally elicit pragmatic phenomena, and
suggest directions that focus on a broader range of communicative contexts and
affordances.

We disclose a methodology to determine the participants in discussions and
their contributions in social networks with a local relationship (e.g.,
nationality), providing certain levels of trust and efficiency in the process.
The dynamic is a challenge that has demanded studies and some approximations to
recent solutions. The study addressed the problem of identifying the
nationality of users in the Twitter social network before an opinion request
(of a political nature and social participation). The employed methodology
classifies, via machine learning, the Twitter users' nationality to carry out
opinion studies in three Central American countries. The Random Forests
algorithm is used to generate classification models with small training
samples, using exclusively numerical characteristics based on the number of
times that different interactions among users occur. When averaging the
proportions achieved by inferences of the ratio of nationals of each country,
in the initial data, an average of 77.40% was calculated, compared to 91.60%
averaged after applying the automatic classification model, an average increase
of 14.20%. In conclusion, it can be seen that the suggested set of method
provides a reasonable approach and efficiency in the face of opinion problems.

A popular approach to unveiling the black box of neural NLP models is to
leverage saliency methods, which assign scalar importance scores to each input
component. A common practice for evaluating whether an interpretability method
is \textit{faithful} and \textit{plausible} has been to use
evaluation-by-agreement -- multiple methods agreeing on an explanation
increases its credibility. However, recent work has found that even saliency
methods have weak rank correlations and advocated for the use of alternative
diagnostic methods. In our work, we demonstrate that rank correlation is not a
good fit for evaluating agreement and argue that Pearson-$r$ is a better suited
alternative. We show that regularization techniques that increase faithfulness
of attention explanations also increase agreement between saliency methods.
Through connecting our findings to instance categories based on training
dynamics we show that, surprisingly, easy-to-learn instances exhibit low
agreement in saliency method explanations.

Sampling-based methods are widely adopted solutions for robot motion
planning. The methods are straightforward to implement, effective in practice
for many robotic systems. It is often possible to prove that they have
desirable properties, such as probabilistic completeness and asymptotic
optimality. Nevertheless, they still face challenges as the complexity of the
underlying planning problem increases, especially under tight computation time
constraints, which impact the quality of returned solutions or given inaccurate
models. This has motivated machine learning to improve the computational
efficiency and applicability of Sampling-Based Motion Planners (SBMPs). This
survey reviews such integrative efforts and aims to provide a classification of
the alternative directions that have been explored in the literature. It first
discusses how learning has been used to enhance key components of SBMPs, such
as node sampling, collision detection, distance or nearest neighbor
computation, local planning, and termination conditions. Then, it highlights
planners that use learning to adaptively select between different
implementations of such primitives in response to the underlying problem's
features. It also covers emerging methods, which build complete machine
learning pipelines that reflect the traditional structure of SBMPs. It also
discusses how machine learning has been used to provide data-driven models of
robots, which can then be used by a SBMP. Finally, it provides a comparative
discussion of the advantages and disadvantages of the approaches covered, and
insights on possible future directions of research. An online version of this
survey can be found at: https://prx-kinodynamic.github.io/

Most recent work in visual sound source localization relies on semantic
audio-visual representations learned in a self-supervised manner, and by design
excludes temporal information present in videos. While it proves to be
effective for widely used benchmark datasets, the method falls short for
challenging scenarios like urban traffic. This work introduces temporal context
into the state-of-the-art methods for sound source localization in urban scenes
using optical flow as a means to encode motion information. An analysis of the
strengths and weaknesses of our methods helps us better understand the problem
of visual sound source localization and sheds light on open challenges for
audio-visual scene understanding.

Collaborative Filtering (CF), the most common approach to build Recommender
Systems, became pervasive in our daily lives as consumers of products and
services. However, challenges limit the effectiveness of Collaborative
Filtering approaches when dealing with recommendation data, mainly due to the
diversity and locality of user preferences, structural sparsity of user-item
ratings, subjectivity of rating scales, and increasingly high item
dimensionality and user bases. To answer some of these challenges, some authors
proposed successful approaches combining CF with Biclustering techniques.
  This work assesses the effectiveness of Biclustering approaches for CF,
comparing the impact of algorithmic choices, and identifies principles for
superior Biclustering-based CF. As a result, we propose USBFC, a
Biclustering-based CF approach that creates user-specific models from strongly
coherent and statistically significant rating patterns, corresponding to
subspaces of shared preferences across users. Evaluation on real-world data
reveals that USBCF achieves competitive predictive accuracy against
state-of-the-art CF methods. Moreover, USBFC successfully suppresses the main
shortcomings of the previously proposed state-of-the-art biclustering-based CF
by increasing coverage, and coclustering-based CF by strengthening subspace
homogeneity.

Text classification helps analyse texts for semantic meaning and relevance,
by mapping the words against this hierarchy. An analysis of various types of
texts is invaluable to understanding both their semantic meaning, as well as
their relevance. Text classification is a method of categorising documents. It
combines computer text classification and natural language processing to
analyse text in aggregate. This method provides a descriptive categorization of
the text, with features like content type, object field, lexical
characteristics, and style traits. In this research, the authors aim to use
natural language feature extraction methods in machine learning which are then
used to train some of the basic machine learning models like Naive Bayes,
Logistic Regression, and Support Vector Machine. These models are used to
detect when a teacher must get involved in a discussion when the lines go
off-topic.

Since the COVID-19 outbreak, the use of digital learning or education
platforms has significantly increased. Teachers now digitally distribute
homework and provide exercise questions. In both cases, teachers need to
continuously develop novel and individual questions. This process can be very
time-consuming and should be facilitated and accelerated both through exchange
with other teachers and by using Artificial Intelligence (AI) capabilities. To
address this need, we propose a multilingual Wikimedia framework that allows
for collaborative worldwide teacher knowledge engineering and subsequent
AI-aided question generation, test, and correction. As a proof of concept, we
present >>PhysWikiQuiz<<, a physics question generation and test engine. Our
system (hosted by Wikimedia at https://physwikiquiz.wmflabs.org) retrieves
physics knowledge from the open community-curated database Wikidata. It can
generate questions in different variations and verify answer values and units
using a Computer Algebra System (CAS). We evaluate the performance on a public
benchmark dataset at each stage of the system workflow. For an average formula
with three variables, the system can generate and correct up to 300 questions
for individual students based on a single formula concept name as input by the
teacher.

Few-shot classification in NLP has recently made great strides due to the
availability of large foundation models that, through priming and prompting,
are highly effective few-shot learners. However, this approach has high
variance across different sets of few shots and across different finetuning
runs. For example, we find that validation accuracy on RTE can vary by as much
as 27 points. In this context, we make two contributions for more effective
few-shot learning. First, we propose novel ensembling methods and show that
they substantially reduce variance. Second, since performance depends a lot on
the set of few shots selected, active learning is promising for few-shot
classification. Based on our stable ensembling method, we build on existing
work on active learning and introduce a new criterion: inter-prompt uncertainty
sampling with diversity. We present the first active learning based approach to
select training examples for prompt-based learning and show that it outperforms
prior work on active learning. Finally, we show that our combined method, MEAL
(Multiprompt finetuning and prediction Ensembling with Active Learning),
improves overall performance of prompt-based finetuning by 2.3 absolute points
on five different tasks.

The loss of limb motion arising from damage to the spinal cord is a
disability that could effect people while performing their day-to-day
activities. The restoration of limb movement would enable people with spinal
cord injury to interact with their environment more naturally and this is where
a brain-computer interface (BCI) system could be beneficial. The detection of
limb movement imagination (MI) could be significant for such a BCI, where the
detected MI can guide the computer system. Using MI detection through
electroencephalography (EEG), we can recognize the imagination of movement in a
user and translate this into a physical movement. In this paper, we utilize
pre-trained deep learning (DL) algorithms for the classification of imagined
upper limb movements. We use a publicly available EEG dataset with data
representing seven classes of limb movements. We compute the spectrograms of
the time series EEG signal and use them as an input to the DL model for MI
classification. Our novel approach for the classification of upper limb
movements using pre-trained DL algorithms and spectrograms has achieved
significantly improved results for seven movement classes. When compared with
the recently proposed state-of-the-art methods, our algorithm achieved a
significant average accuracy of 84.9% for classifying seven movements.

This paper proposes a probabilistic deep metric learning (PDML) framework for
hyperspectral image classification, which aims to predict the category of each
pixel for an image captured by hyperspectral sensors. The core problem for
hyperspectral image classification is the spectral variability between
intraclass materials and the spectral similarity between interclass materials,
motivating the further incorporation of spatial information to differentiate a
pixel based on its surrounding patch. However, different pixels and even the
same pixel in one patch might not encode the same material due to the low
spatial resolution of most hyperspectral sensors, leading to an inconsistent
judgment of a specific pixel. To address this issue, we propose a probabilistic
deep metric learning framework to model the categorical uncertainty of the
spectral distribution of an observed pixel. We propose to learn a global
probabilistic distribution for each pixel in the patch and a probabilistic
metric to model the distance between distributions. We treat each pixel in a
patch as a training sample, enabling us to exploit more information from the
patch compared with conventional methods. Our framework can be readily applied
to existing hyperspectral image classification methods with various network
architectures and loss functions. Extensive experiments on four widely used
datasets including IN, UP, KSC, and Houston 2013 datasets demonstrate that our
framework improves the performance of existing methods and further achieves the
state of the art. Code is available at: https://github.com/wzzheng/PDML.

The dynamics of a turbulent flow tend to occupy only a portion of the phase
space at a statistically stationary regime. From a dynamical systems point of
view, this portion is the attractor. The knowledge of the turbulent attractor
is useful for two purposes, at least: (i) We can gain physical insight into
turbulence (what is the shape and geometry of the attractor?), and (ii) it
provides the minimal number of degrees of freedom to accurately describe the
turbulent dynamics. Autoencoders enable the computation of an optimal latent
space, which is a low-order representation of the dynamics. If properly trained
and correctly designed, autoencoders can learn an approximation of the
turbulent attractor, as shown by Doan, Racca and Magri (2022). In this paper,
we theoretically interpret the transformations of an autoencoder. First, we
remark that the latent space is a curved manifold with curvilinear coordinates,
which can be analyzed with simple tools from Riemann geometry. Second, we
characterize the geometrical properties of the latent space. We mathematically
derive the metric tensor, which provides a mathematical description of the
manifold. Third, we propose a method -- proper latent decomposition (PLD) --
that generalizes proper orthogonal decomposition of turbulent flows on the
autoencoder latent space. This decomposition finds the dominant directions in
the curved latent space. This theoretical work opens up computational
opportunities for interpreting autoencoders and creating reduced-order models
of turbulent flows.

The paper investigates the weighted sum-rate maximization (WSRM) problem with
latent interfering sources outside the known network, whose power allocation
policy is hidden from and uncontrollable to optimization. The paper extends the
famous alternate optimization algorithm weighted minimum mean square error
(WMMSE) [1] under a causal inference framework to tackle with WSRM under latent
interference. Namely, with the possibility of power policy shifting in the
hidden network, computing an iterating direction based on the observed
interference inherently implies that counterfactual is ignored in decision
making. A synthetic control (SC) method is used to estimate the counterfactual.
For any link in the known network, SC constructs a convex combination of the
interference on other links and uses it as an estimate. Power iteration is
performed on the estimated rather than the observed interference. The proposed
SC-WMMSE requires no more information than its origin. To our best knowledge,
this is the first paper explores the potential of causal inference to assist
mathematical optimization in addressing classic wireless optimization problems.
Numerical results suggest the superiority of the SC-WMMSE over the original in
both convergence and objective.

We study an important variant of the stochastic multi-armed bandit (MAB)
problem, which takes penalization into consideration. Instead of directly
maximizing cumulative expected reward, we need to balance between the total
reward and fairness level. In this paper, we present some new insights in MAB
and formulate the problem in the penalization framework, where rigorous
penalized regret can be well defined and more sophisticated regret analysis is
possible. Under such a framework, we propose a hard-threshold UCB-like
algorithm, which enjoys many merits including asymptotic fairness, nearly
optimal regret, better tradeoff between reward and fairness. Both gap-dependent
and gap-independent regret bounds have been established. Multiple insightful
comments are given to illustrate the soundness of our theoretical analysis.
Numerous experimental results corroborate the theory and show the superiority
of our method over other existing methods.

Power outages caused by extreme weather events due to climate change have
doubled in the United States in the last two decades. Outages pose severe
health risks to over 4.4 million individuals dependent on in-home medical
devices. Data on the number of such individuals residing in a given area is
limited. This study proposes a load disaggregation model to predict the number
of medical devices behind an electric distribution feeder. This data can be
used to inform planning and response. The proposed solution serves as a measure
for climate change adaptation.

Several recent works show impressive results in mapping language-based human
commands and image scene observations to direct robot executable policies
(e.g., pick and place poses). However, these approaches do not consider the
uncertainty of the trained policy and simply always execute actions suggested
by the current policy as the most probable ones. This makes them vulnerable to
domain shift and inefficient in the number of required demonstrations. We
extend previous works and present the PARTNR algorithm that can detect
ambiguities in the trained policy by analyzing multiple modalities in the pick
and place poses using topological analysis. PARTNR employs an adaptive,
sensitivity-based, gating function that decides if additional user
demonstrations are required. User demonstrations are aggregated to the dataset
and used for subsequent training. In this way, the policy can adapt promptly to
domain shift and it can minimize the number of required demonstrations for a
well-trained policy. The adaptive threshold enables to achieve the
user-acceptable level of ambiguity to execute the policy autonomously and in
turn, increase the trustworthiness of our system. We demonstrate the
performance of PARTNR in a table-top pick and place task.

This paper proposes reverberation as supervision (RAS), a novel unsupervised
loss function for single-channel reverberant speech separation. Prior methods
for unsupervised separation required the synthesis of mixtures of mixtures or
assumed the existence of a teacher model, making them difficult to consider as
potential methods explaining the emergence of separation abilities in an
animal's auditory system. We assume the availability of two-channel mixtures at
training time, and train a neural network to separate the sources given one of
the channels as input such that the other channel may be predicted from the
separated sources. As the relationship between the room impulse responses
(RIRs) of each channel depends on the locations of the sources, which are
unknown to the network, the network cannot rely on learning that relationship.
Instead, our proposed loss function fits each of the separated sources to the
mixture in the target channel via Wiener filtering, and compares the resulting
mixture to the ground-truth one. We show that minimizing the scale-invariant
signal-to-distortion ratio (SI-SDR) of the predicted right-channel mixture with
respect to the ground truth implicitly guides the network towards separating
the left-channel sources. On a semi-supervised reverberant speech separation
task based on the WHAMR! dataset, using training data where just 5% (resp.,
10%) of the mixtures are labeled with associated isolated sources, we achieve
70% (resp., 78%) of the SI-SDR improvement obtained when training with
supervision on the full training set, while a model trained only on the labeled
data obtains 43% (resp., 45%).

In this contribution, the clustering procedure based on K-Means algorithm is
studied as an inverse problem, which is a special case of the illposed
problems. The attempts to improve the quality of the clustering inverse problem
drive to reduce the input data via Principal Component Analysis (PCA). Since
there exists a theorem by Ding and He that links the cardinality of the optimal
clusters found with K-Means and the cardinality of the selected informative PCA
components, the computational experiments tested the theorem between two
quantitative features selection methods: Kaiser criteria (based on imperative
decision) versus Wishart criteria (based on random matrix theory). The results
suggested that PCA reduction with features selection by Wishart criteria leads
to a low matrix condition number and satisfies the relation between clusters
and components predicts by the theorem. The data used for the computations are
from a neuroscientific repository: it regards healthy and young subjects that
performed a task-oriented functional Magnetic Resonance Imaging (fMRI)
paradigm.

We explore the modification of photoluminescence from dye-doped open, half
and full optical microcavities. For each configuration, an analysis of the
reflectivty data indicates the presence of strong coupling. We find that an
open cavity, i.e. a dye-doped dielectric slab placed on a silicon substrate,
shows little if any modification of the photoluminescence spectrum. For the
half-cavity, in which a thin metallic film is placed between the dye-doped
dielectric and the substrate, we find a limited degree of modification of the
photoluminescence. For the fullcavity, for which the dielectric layer is bound
both above and below by a metallic mirror, we f ind very significant
modification to the photoluminescence emission, the photoluminescence clearly
tracking the lower polariton. To learn more about the photo-physics we compare
ref lectivity, absorption and photoluminescence dispersion data, making use
where appropriate of numerical modelling. We discuss our results in the context
of harnessing open cavities to modify molecular photo-physics through molecular
strong coupling.

In this paper, we design an auto encoder based off of Google's FNet
Architecture in order to generate text from a subset of news stories contained
in Google's C4 dataset. We discuss previous attempts and methods to generate
text from autoencoders and non LLM Models. FNET poses multiple advantages to
BERT based encoders in the realm of efficiency which train 80% faster on GPUs
and 70% faster on TPUs. We then compare outputs of how this autencoder perfroms
on different epochs. Finally, we analyze what outputs the encoder produces with
different seed text.

Localization services for wireless devices play an increasingly important
role and a plethora of emerging services and applications already rely on
precise position information. Widely used on-device positioning methods, such
as the global positioning system, enable accurate outdoor positioning and
provide the users with full control over what services are allowed to access
location information. To provide accurate positioning indoors or in cluttered
urban scenarios without line-of-sight satellite connectivity, powerful
off-device positioning systems, which process channel state information (CSI)
with deep neural networks, have emerged recently. Such off-device positioning
systems inherently link a user's data transmission with its localization, since
accurate CSI measurements are necessary for reliable wireless communication --
this not only prevents the users from controlling who can access this
information but also enables virtually everyone in the device's range to
estimate its location, resulting in serious privacy and security concerns. We
propose on-device attacks against off-device wireless positioning systems in
multi-antenna orthogonal frequency-division multiplexing systems while
minimizing the impact on quality-of-service, and we demonstrate their efficacy
using measured datasets for outdoor and indoor scenarios. We also investigate
defenses to counter such attack mechanisms, and we discuss the limitations and
implications on protecting location privacy in future wireless communication
systems.

We present the Cross-stitched Multi-task Unified Dual Recursive Network
(CMUDRN) model targeting the task of unified deraining and desnowing in a
multi-task learning setting. This unified model borrows from the basic Dual
Recursive Network (DRN) architecture developed by Cai et al. The proposed model
makes use of cross-stitch units that enable multi-task learning across two
separate DRN models, each tasked for single image deraining and desnowing,
respectively. By fixing cross-stitch units at several layers of basic
task-specific DRN networks, we perform multi-task learning over the two
separate DRN models. To enable blind image restoration, on top of these
structures we employ a simple neural fusion scheme which merges the output of
each DRN. The separate task-specific DRN models and the fusion scheme are
simultaneously trained by enforcing local and global supervision. Local
supervision is applied on the two DRN submodules, and global supervision is
applied on the data fusion submodule of the proposed model. Consequently, we
both enable feature sharing across task-specific DRN models and control the
image restoration behavior of the DRN submodules. An ablation study shows the
strength of the hypothesized CMUDRN model, and experiments indicate that its
performance is comparable or better than baseline DRN models on the single
image deraining and desnowing tasks. Moreover, CMUDRN enables blind image
restoration for the two underlying image restoration tasks, by unifying
task-specific image restoration pipelines via a naive parametric fusion scheme.
The CMUDRN implementation is available at https://github.com/VCL3D/CMUDRN.

Addiction is a major public health concern characterized by compulsive
reward-seeking behavior. The excitatory glutamatergic signals from the
hippocampus (HIP) to the Nucleus accumbens (NAc) mediate learned behavior in
addiction. Limited comparative studies have investigated the neural pathways
activated by natural and unnatural reward sources. This study has evaluated
neural activities in HIP and NAc associated with food (natural) and morphine
(drug) reward sources using local field potential (LFP). We developed novel
approaches to classify LFP signals into the source of reward and recorded
regions by considering the time-domain feature of these signals. Proposed
methods included a validation step of the LFP signals using autocorrelation,
Lyapunov exponent and Hurst exponent to assess the meaningful stability of
these signals (lack of chaos). By utilizing the probability density function
(PDF) of LFP signals and applying Kullback-Leibler divergence (KLD), data were
classified to the source of the reward. Also, HIP and NAc regions were visually
separated and classified using the symmetrized dot pattern technique, which can
be applied in real-time to ensure the deep brain region of interest is being
targeted accurately during LFP recording. We believe our method provides a
computationally light and fast, real-time signal analysis approach with
real-world implementation.

The great progress of 3D object detectors relies on large-scale data and 3D
annotations. The annotation cost for 3D bounding boxes is extremely expensive
while the 2D ones are easier and cheaper to collect. In this paper, we
introduce a hybrid training framework, enabling us to learn a visual 3D object
detector with massive 2D (pseudo) labels, even without 3D annotations. To break
through the information bottleneck of 2D clues, we explore a new perspective:
Temporal 2D Supervision. We propose a temporal 2D transformation to bridge the
3D predictions with temporal 2D labels. Two steps, including homography wraping
and 2D box deduction, are taken to transform the 3D predictions into 2D ones
for supervision. Experiments conducted on the nuScenes dataset show strong
results (nearly 90% of its fully-supervised performance) with only 25% 3D
annotations. We hope our findings can provide new insights for using a large
number of 2D annotations for 3D perception.

Identifying spurious correlations learned by a trained model is at the core
of refining a trained model and building a trustworthy model. We present a
simple method to identify spurious correlations that have been learned by a
model trained for image classification problems. We apply image-level
perturbations and monitor changes in certainties of predictions made using the
trained model. We demonstrate this approach using an image classification
dataset that contains images with synthetically generated spurious regions and
show that the trained model was overdependent on spurious regions. Moreover, we
remove the learned spurious correlations with an explanation based learning
approach.

In this work, we observe that many existing self-supervised learning
algorithms can be both unified and generalized when seen through the lens of
equivariant representations. Specifically, we introduce a general framework we
call Homomorphic Self-Supervised Learning, and theoretically show how it may
subsume the use of input-augmentations provided an augmentation-homomorphic
feature extractor. We validate this theory experimentally for simple
augmentations, demonstrate how the framework fails when representational
structure is removed, and further empirically explore how the parameters of
this framework relate to those of traditional augmentation-based
self-supervised learning. We conclude with a discussion of the potential
benefits afforded by this new perspective on self-supervised learning.

Disentangling facial movements from other facial characteristics,
particularly from facial identity, remains a challenging task, as facial
movements display great variation between individuals. In this paper, we aim to
characterize individual-specific facial movements. We present a novel training
approach to learn facial movements independently of other facial
characteristics, focusing on each individual separately. We propose
self-supervised Person-Specific Models (PSMs), in which one model per
individual can learn to extract an embedding of the facial movements
independently of the person's identity and other structural facial
characteristics from unlabeled facial video. These models are trained using
encoder-decoder-like architectures. We provide quantitative and qualitative
evidence that a PSM learns a meaningful facial embedding that discovers
fine-grained movements otherwise not characterized by a General Model (GM),
which is trained across individuals and characterizes general patterns of
facial movements. We present quantitative and qualitative evidence that this
approach is easily scalable and generalizable for new individuals: facial
movements knowledge learned on a person can quickly and effectively be
transferred to a new person. Lastly, we propose a novel PSM using curriculum
temporal learning to leverage the temporal contiguity between video frames. Our
code, analysis details, and all pretrained models are available in Github and
Supplementary Materials.

The most recent concern of all people on Earth is the increase in the
concentration of greenhouse gas in the atmosphere. The concentration of these
gases has risen rapidly over the last century and if the trend continues it can
cause many adverse climatic changes. There have been ways implemented to curb
this by the government by limiting processes that emit a higher amount of CO2,
one such greenhouse gas. However, there is mounting evidence that the CO2
numbers supplied by the government do not accurately reflect the performance of
automobiles on the road. Our proposal of using artificial intelligence
techniques to improve a previously rudimentary process takes a radical tack,
but it fits the bill given the situation. To determine which algorithms and
models produce the greatest outcomes, we compared them all and explored a novel
method of ensembling them. Further, this can be used to foretell the rise in
global temperature and to ground crucial policy decisions like the adoption of
electric vehicles. To estimate emissions from vehicles, we used machine
learning, deep learning, and ensemble learning on a massive dataset.

The massive amount of genomic data appearing for SARS-CoV-2 since the
beginning of the COVID-19 pandemic has challenged traditional methods for
studying its dynamics. As a result, new methods such as Pangolin, which can
scale to the millions of samples of SARS-CoV-2 currently available, have
appeared. Such a tool is tailored to take as input assembled, aligned and
curated full-length sequences, such as those found in the GISAID database. As
high-throughput sequencing technologies continue to advance, such assembly,
alignment and curation may become a bottleneck, creating a need for methods
which can process raw sequencing reads directly.
  In this paper, we propose Reads2Vec, an alignment-free embedding approach
that can generate a fixed-length feature vector representation directly from
the raw sequencing reads without requiring assembly. Furthermore, since such an
embedding is a numerical representation, it may be applied to highly optimized
classification and clustering algorithms. Experiments on simulated data show
that our proposed embedding obtains better classification results and better
clustering properties contrary to existing alignment-free baselines. In a study
on real data, we show that alignment-free embeddings have better clustering
properties than the Pangolin tool and that the spike region of the SARS-CoV-2
genome heavily informs the alignment-free clusterings, which is consistent with
current biological knowledge of SARS-CoV-2.

The availability of large, high-quality datasets has been one of the main
drivers of recent progress in question answering (QA). Such annotated datasets
however are difficult and costly to collect, and rarely exist in languages
other than English, rendering QA technology inaccessible to underrepresented
languages. An alternative to building large monolingual training datasets is to
leverage pre-trained language models (PLMs) under a few-shot learning setting.
Our approach, QAmeleon, uses a PLM to automatically generate multilingual data
upon which QA models are trained, thus avoiding costly annotation. Prompt
tuning the PLM for data synthesis with only five examples per language delivers
accuracy superior to translation-based baselines, bridges nearly 60% of the gap
between an English-only baseline and a fully supervised upper bound trained on
almost 50,000 hand labeled examples, and always leads to substantial
improvements compared to fine-tuning a QA model directly on labeled examples in
low resource settings. Experiments on the TyDiQA-GoldP and MLQA benchmarks show
that few-shot prompt tuning for data synthesis scales across languages and is a
viable alternative to large-scale annotation.

Recently, there has been a growing interest for mixed-categorical meta-models
based on Gaussian process (GP) surrogates. In this setting, several existing
approaches use different strategies either by using continuous kernels (e.g.,
continuous relaxation and Gower distance based GP) or by using a direct
estimation of the correlation matrix. In this paper, we present a kernel-based
approach that extends continuous exponential kernels to handle
mixed-categorical variables. The proposed kernel leads to a new GP surrogate
that generalizes both the continuous relaxation and the Gower distance based GP
models. We demonstrate, on both analytical and engineering problems, that our
proposed GP model gives a higher likelihood and a smaller residual error than
the other kernel-based state-of-the-art models. Our method is available in the
open-source software SMT.

State recognition in well-known and customizable environments such as
vehicles enables novel insights into users and potentially their intentions.
Besides safety-relevant insights into, for example, fatigue, user
experience-related assessments become increasingly relevant. As thermal comfort
is vital for overall comfort, we introduce a dataset for its prediction in
vehicles incorporating 31 input signals and self-labeled user ratings based on
a 7-point Likert scale (-3 to +3) by 21 subjects. An importance ranking of such
signals indicates higher impact on prediction for signals like ambient
temperature, ambient humidity, radiation temperature, and skin temperature.
Leveraging modern machine learning architectures enables us to not only
automatically recognize human thermal comfort state but also predict future
states. We provide details on how we train a recurrent network-based classifier
and, thus, perform an initial performance benchmark of our proposed thermal
comfort dataset. Ultimately, we compare our collected dataset to publicly
available datasets.

Due to the domain shift, machine learning systems typically fail to
generalize well to domains different from those of training data, which is the
problem that domain generalization (DG) aims to address. However, most
mainstream DG algorithms lack interpretability and require domain labels, which
are not available in many real-world scenarios. In this work, we propose a
novel DG method, HMOE: Hypernetwork-based Mixture of Experts (MoE), that does
not require domain labels and is more interpretable. We use hypernetworks to
generate the weights of experts, allowing experts to share some useful
meta-knowledge. MoE has proven adept at detecting and identifying heterogeneous
patterns in data. For DG, heterogeneity exactly arises from the domain shift.
We compare HMOE with other DG algorithms under a fair and unified
benchmark-DomainBed. Extensive experiments show that HMOE can perform latent
domain discovery from data of mixed domains and divide it into distinct
clusters that are surprisingly more consistent with human intuition than
original domain labels. Compared to other DG methods, HMOE shows competitive
performance and achieves SOTA results in some cases without using domain
labels.

Video temporal dynamics is conventionally modeled with 3D spatial-temporal
kernel or its factorized version comprised of 2D spatial kernel and 1D temporal
kernel. The modeling power, nevertheless, is limited by the fixed window size
and static weights of a kernel along the temporal dimension. The pre-determined
kernel size severely limits the temporal receptive fields and the fixed weights
treat each spatial location across frames equally, resulting in sub-optimal
solution for long-range temporal modeling in natural scenes. In this paper, we
present a new recipe of temporal feature learning, namely Dynamic Temporal
Filter (DTF), that novelly performs spatial-aware temporal modeling in
frequency domain with large temporal receptive field. Specifically, DTF
dynamically learns a specialized frequency filter for every spatial location to
model its long-range temporal dynamics. Meanwhile, the temporal feature of each
spatial location is also transformed into frequency feature spectrum via 1D
Fast Fourier Transform (FFT). The spectrum is modulated by the learnt frequency
filter, and then transformed back to temporal domain with inverse FFT. In
addition, to facilitate the learning of frequency filter in DTF, we perform
frame-wise aggregation to enhance the primary temporal feature with its
temporal neighbors by inter-frame correlation. It is feasible to plug DTF block
into ConvNets and Transformer, yielding DTF-Net and DTF-Transformer. Extensive
experiments conducted on three datasets demonstrate the superiority of our
proposals. More remarkably, DTF-Transformer achieves an accuracy of 83.5% on
Kinetics-400 dataset. Source code is available at
\url{https://github.com/FuchenUSTC/DTF}.

Offline reinforcement learning (RL) defines a sample-efficient learning
paradigm, where a policy is learned from static and previously collected
datasets without additional interaction with the environment. The major
obstacle to offline RL is the estimation error arising from evaluating the
value of out-of-distribution actions. To tackle this problem, most existing
offline RL methods attempt to acquire a policy both ``close" to the behaviors
contained in the dataset and sufficiently improved over them, which requires a
trade-off between two possibly conflicting targets. In this paper, we propose a
novel approach, which we refer to as adaptive behavior regularization (ABR), to
balance this critical trade-off. By simply utilizing a sample-based
regularization, ABR enables the policy to adaptively adjust its optimization
objective between cloning and improving over the policy used to generate the
dataset. In the evaluation on D4RL datasets, a widely adopted benchmark for
offline reinforcement learning, ABR can achieve improved or competitive
performance compared to existing state-of-the-art algorithms.

The recent advances in deep learning predominantly construct models in their
internal representations, and it is opaque to explain the rationale behind and
decisions to human users. Such explainability is especially essential for
domain adaptation, whose challenges require developing more adaptive models
across different domains. In this paper, we ask the question: how much each
sample in source domain contributes to the network's prediction on the samples
from target domain. To address this, we devise a novel Interpretable Deep
Classifier (IDC) that learns the nearest source samples of a target sample as
evidence upon which the classifier makes the decision. Technically, IDC
maintains a differentiable memory bank for each category and the memory slot
derives a form of key-value pair. The key records the features of
discriminative source samples and the value stores the corresponding
properties, e.g., representative scores of the features for describing the
category. IDC computes the loss between the output of IDC and the labels of
source samples to back-propagate to adjust the representative scores and update
the memory banks. Extensive experiments on Office-Home and VisDA-2017 datasets
demonstrate that our IDC leads to a more explainable model with almost no
accuracy degradation and effectively calibrates classification for optimum
reject options. More remarkably, when taking IDC as a prior interpreter,
capitalizing on 0.1% source training data selected by IDC still yields superior
results than that uses full training set on VisDA-2017 for unsupervised domain
adaptation.

Recent progress on 2D object detection has featured Cascade RCNN, which
capitalizes on a sequence of cascade detectors to progressively improve
proposal quality, towards high-quality object detection. However, there has not
been evidence in support of building such cascade structures for 3D object
detection, a challenging detection scenario with highly sparse LiDAR point
clouds. In this work, we present a simple yet effective cascade architecture,
named 3D Cascade RCNN, that allocates multiple detectors based on the voxelized
point clouds in a cascade paradigm, pursuing higher quality 3D object detector
progressively. Furthermore, we quantitatively define the sparsity level of the
points within 3D bounding box of each object as the point completeness score,
which is exploited as the task weight for each proposal to guide the learning
of each stage detector. The spirit behind is to assign higher weights for
high-quality proposals with relatively complete point distribution, while
down-weight the proposals with extremely sparse points that often incur noise
during training. This design of completeness-aware re-weighting elegantly
upgrades the cascade paradigm to be better applicable for the sparse input
data, without increasing any FLOP budgets. Through extensive experiments on
both the KITTI dataset and Waymo Open Dataset, we validate the superiority of
our proposed 3D Cascade RCNN, when comparing to state-of-the-art 3D object
detection techniques. The source code is publicly available at
\url{https://github.com/caiqi/Cascasde-3D}.

Land cover classification (LCC), and monitoring how land use changes over
time, is an important process in climate change mitigation and adaptation.
Existing approaches that use machine learning with Earth observation data for
LCC rely on fully-annotated and segmented datasets. Creating these datasets
requires a large amount of effort, and a lack of suitable datasets has become
an obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patch
models: an alternative LCC approach utilising Multiple Instance Learning (MIL)
that requires only high-level scene labels. This enables much faster
development of new datasets whilst still providing segmentation through
patch-level predictions, ultimately increasing the accessibility of using LCC
for different scenarios. On the DeepGlobe-LCC dataset, our approach outperforms
non-MIL baselines on both scene- and patch-level prediction. This work provides
the foundation for expanding the use of LCC in climate change mitigation
methods for technology, government, and academia.

Bayesian Networks may be appealing for clinical decision-making due to their
inclusion of causal knowledge, but their practical adoption remains limited as
a result of their inability to deal with unstructured data. While neural
networks do not have this limitation, they are not interpretable and are
inherently unable to deal with causal structure in the input space. Our goal is
to build neural networks that combine the advantages of both approaches.
Motivated by the perspective to inject causal knowledge while training such
neural networks, this work presents initial steps in that direction. We
demonstrate how a neural network can be trained to output conditional
probabilities, providing approximately the same functionality as a Bayesian
Network. Additionally, we propose two training strategies that allow encoding
the independence relations inferred from a given causal structure into the
neural network. We present initial results in a proof-of-concept setting,
showing that the neural model acts as an understudy to its Bayesian Network
counterpart, approximating its probabilistic and causal properties.

Given the fact description text of a legal case, legal judgment prediction
(LJP) aims to predict the case's charge, law article and penalty term. A core
problem of LJP is how to distinguish confusing legal cases, where only subtle
text differences exist. Previous studies fail to distinguish different
classification errors with a standard cross-entropy classification loss, and
ignore the numbers in the fact description for predicting the term of penalty.
To tackle these issues, in this work, first, we propose a moco-based supervised
contrastive learning to learn distinguishable representations, and explore the
best strategy to construct positive example pairs to benefit all three subtasks
of LJP simultaneously. Second, in order to exploit the numbers in legal cases
for predicting the penalty terms of certain cases, we further enhance the
representation of the fact description with extracted crime amounts which are
encoded by a pre-trained numeracy model. Extensive experiments on public
benchmarks show that the proposed method achieves new state-of-the-art results,
especially on confusing legal cases. Ablation studies also demonstrate the
effectiveness of each component.

Contrastive learning (CL) pre-trains general-purpose encoders using an
unlabeled pre-training dataset, which consists of images (called single-modal
CL) or image-text pairs (called multi-modal CL). CL is vulnerable to data
poisoning based backdoor attacks (DPBAs), in which an attacker injects poisoned
inputs into the pre-training dataset so the encoder is backdoored. However,
existing DPBAs achieve limited effectiveness. In this work, we propose new
DPBAs called CorruptEncoder to CL. Our experiments show that CorruptEncoder
substantially outperforms existing DPBAs for both single-modal and multi-modal
CL. CorruptEncoder is the first DPBA that achieves more than 90% attack success
rates on single-modal CL with only a few (3) reference images and a small
poisoning ratio (0.5%). Moreover, we also propose a defense, called localized
cropping, to defend single-modal CL against DPBAs. Our results show that our
defense can reduce the effectiveness of DPBAs, but it sacrifices the utility of
the encoder, highlighting the needs of new defenses.

Choosing a good resource configuration for big data analytics applications
can be challenging, especially in cloud environments. Automated approaches are
desirable as poor decisions can reduce performance and raise costs. The
majority of existing automated approaches either build performance models from
previous workload executions or conduct iterative resource configuration
profiling until a near-optimal solution has been found. In doing so, they only
obtain an implicit understanding of the underlying infrastructure, which is
difficult to transfer to alternative infrastructures and, thus, profiling and
modeling insights are not sustained beyond very specific situations.
  We present Perona, a novel approach to robust infrastructure fingerprinting
for usage in the context of big data analytics. Perona employs common sets and
configurations of benchmarking tools for target resources, so that resulting
benchmark metrics are directly comparable and ranking is enabled. Insignificant
benchmark metrics are discarded by learning a low-dimensional representation of
the input metric vector, and previous benchmark executions are taken into
consideration for context-awareness as well, allowing to detect resource
degradation. We evaluate our approach both on data gathered from our own
experiments as well as within related works for resource configuration
optimization, demonstrating that Perona captures the characteristics from
benchmark runs in a compact manner and produces representations that can be
used directly.

Reflective writing is part of many higher education courses across the globe.
It is often considered a challenging task for students as it requires
self-regulated learning skills to appropriately plan, timely engage and deeply
reflect on learning experiences. Despite an advance in writing analytics and
the pervasiveness of human feedback aimed to support student reflections,
little is known about how to integrate feedback from humans and analytics to
improve students' learning engagement and performance in reflective writing
tasks. This study proposes a personalised behavioural feedback intervention
based on students' writing engagement analytics utilising time-series analysis
of digital traces from a ubiquitous online word processing platform. In a
semester-long experimental study involving 81 postgraduate students, its impact
on learning engagement and performance was studied. The results showed that the
intervention cohort engaged statistically significantly more in their
reflective writing task after receiving the combined feedback compared to the
control cohort which only received human feedback on their reflective writing
content. Further analyses revealed that the intervention cohort reflected more
regularly at the weekly level, the regularity of weekly reflection led to
better performance grades, and the impact on students with low self-regulated
learning skills was higher. This study emphasizes the powerful benefits of
implementing combined feedback approaches in which the strengths of analytics
and human feedback are synthesized to improve student engagement and
performance. Further research should explore the long-term sustainability of
the observed effects and their validity in other contexts.

In this work, we study the hypothesis that speaker identity embeddings
extracted from speech samples may be used for detection and classification of
emotion. In particular, we show that emotions can be effectively identified by
learning speaker identities by use of a 1-D Triplet Convolutional Neural
Network (CNN) & Global Style Token (GST) scheme (e.g., DeepTalk Network) and
reusing the trained speaker recognition model weights to generate features in
the emotion classification domain. The automatic speaker recognition (ASR)
network is trained with VoxCeleb1, VoxCeleb2, and Librispeech datasets with a
triplet training loss function using speaker identity labels. Using an Support
Vector Machine (SVM) classifier, we map speaker identity embeddings into
discrete emotion categories from the CREMA-D, IEMOCAP, and MSP-Podcast
datasets. On the task of speech emotion detection, we obtain 80.8% ACC with
acted emotion samples from CREMA-D, 81.2% ACC with semi-natural emotion samples
in IEMOCAP, and 66.9% ACC with natural emotion samples in MSP-Podcast. We also
propose a novel two-stage hierarchical classifier (HC) approach which
demonstrates +2% ACC improvement on CREMA-D emotion samples. Through this work,
we seek to convey the importance of holistically modeling intra-user variation
within audio samples

Contrastive learning (CL) has shown great power in self-supervised learning
due to its ability to capture insight correlations among large-scale data.
Current CL models are biased to learn only the ability to discriminate positive
and negative pairs due to the discriminative task setting. However, this bias
would lead to ignoring its sufficiency for other downstream tasks, which we
call the discriminative information overfitting problem. In this paper, we
propose to tackle the above problems from the aspect of the Information
Bottleneck (IB) principle, further pushing forward the frontier of CL.
Specifically, we present a new perspective that CL is an instantiation of the
IB principle, including information compression and expression. We
theoretically analyze the optimal information situation and demonstrate that
minimum sufficient augmentation and information-generalized representation are
the optimal requirements for achieving maximum compression and generalizability
to downstream tasks. Therefore, we propose the Masked Reconstruction
Contrastive Learning~(MRCL) model to improve CL models. For implementation in
practice, MRCL utilizes the masking operation for stronger augmentation,
further eliminating redundant and noisy information. In order to alleviate the
discriminative information overfitting problem effectively, we employ the
reconstruction task to regularize the discriminative task. We conduct
comprehensive experiments and show the superiority of the proposed model on
multiple tasks, including image classification, semantic segmentation and
objective detection.

Probabilistic Movement Primitives (ProMPs) are a widely used representation
of movements for human-robot interaction. They also facilitate the
factorization of temporal and spatial structure of movements. In this work we
investigate a method to temporally align observations so that when learning
ProMPs, information in the spatial structure of the observed motion is
maximized while maintaining a smooth phase velocity. We apply the method on
recordings of hand trajectories in a two-dimensional reaching task. A system
for simultaneous recognition of movement and phase is proposed and performance
of movement recognition and movement reproduction is discussed.

When users move in a physical space (e.g., an urban space), they would have
some records called mobility records (e.g., trajectories) generated by devices
such as mobile phones and GPS devices. Naturally, mobility records capture
essential information of how users work, live and entertain in their daily
lives, and therefore, they have been used in a wide range of tasks such as user
profile inference, mobility prediction and traffic management. In this paper,
we expand this line of research by investigating the problem of inferring user
socioeconomic statuses (such as prices of users' living houses as a proxy of
users' socioeconomic statuses) based on their mobility records, which can
potentially be used in real-life applications such as the car loan business.
For this task, we propose a socioeconomic-aware deep model called DeepSEI. The
DeepSEI model incorporates two networks called deep network and recurrent
network, which extract the features of the mobility records from three aspects,
namely spatiality, temporality and activity, one at a coarse level and the
other at a detailed level. We conduct extensive experiments on real mobility
records data, POI data and house prices data. The results verify that the
DeepSEI model achieves superior performance than existing studies. All datasets
used in this paper will be made publicly available.

Railway axle maintenance is critical to avoid catastrophic failures.
Nowadays, condition monitoring techniques are becoming more prominent in the
industry to prevent enormous costs and damage to human lives. This paper
proposes the development of a railway axle condition monitoring system based on
advanced 2D-Convolutional Neural Network (CNN) architectures applied to
time-frequency representations of vibration signals. For this purpose, several
preprocessing steps and different types of Deep Learning (DL) and Machine
Learning (ML) architectures are discussed to design an accurate classification
system. The resultant system converts the railway axle vibration signals into
time-frequency domain representations, i.e., spectrograms, and, thus, trains a
two-dimensional CNN to classify them depending on their cracks. The results
showed that the proposed approach outperforms several alternative methods
tested. The CNN architecture has been tested in 3 different wheelset
assemblies, achieving AUC scores of 0.93, 0.86, and 0.75 outperforming any
other architecture and showing a high level of reliability when classifying 4
different levels of defects.

The soft modes associated with continuous-order phase transitions are
commonly associated with particularly strong anharmonicity. Here, we show that
this can lead to overdamped behavior already far above the actual transition
temperature using molecular dynamics simulations and a machine-learned
potential. While in the overdamped limit the interpretation of lattice
vibrations as phonon quasi-particles with a specified frequency and relaxation
time becomes physically questionable the mathematical description in terms of
damped oscillators still holds. A physically more intuitive picture can be
obtained by considering the relaxation times of the mode coordinate and its
conjugate momentum, which at the instability approach infinity and the inverse
damping factor, respectively. We demonstrate this behavior quantitatively for
the prototypical case of the cubic-to-tetragonal phase transition of the
inorganic halide perovskite CsPbBr3, and show that the overdamped region
extends almost 200 K (or about 60%) above the transition temperature.

Speckle patterns produced by coherent X-ray have a close relationship with
the internal structure of materials but quantitative inversion of the
relationship to determine structure from images is challenging. Here, we
investigate the link between coherent X-ray speckle patterns and sample
structures using a model 2D disk system and explore the ability of machine
learning to learn aspects of the relationship. Specifically, we train a deep
neural network to classify the coherent X-ray speckle pattern images according
to the disk number density in the corresponding structure. It is demonstrated
that the classification system is accurate for both non-disperse and disperse
size distributions.

We initiate an investigation of private sampling from distributions. Given a
dataset with $n$ independent observations from an unknown distribution $P$, a
sampling algorithm must output a single observation from a distribution that is
close in total variation distance to $P$ while satisfying differential privacy.
Sampling abstracts the goal of generating small amounts of realistic-looking
data. We provide tight upper and lower bounds for the dataset size needed for
this task for three natural families of distributions: arbitrary distributions
on $\{1,\ldots ,k\}$, arbitrary product distributions on $\{0,1\}^d$, and
product distributions on $\{0,1\}^d$ with bias in each coordinate bounded away
from 0 and 1. We demonstrate that, in some parameter regimes, private sampling
requires asymptotically fewer observations than learning a description of $P$
nonprivately; in other regimes, however, private sampling proves to be as
difficult as private learning. Notably, for some classes of distributions, the
overhead in the number of observations needed for private learning compared to
non-private learning is completely captured by the number of observations
needed for private sampling.

Large transformer-based language models, e.g. BERT and GPT-3, outperform
previous architectures on most natural language processing tasks. Such language
models are first pre-trained on gigantic corpora of text and later used as
base-model for finetuning on a particular task. Since the pre-training step is
usually not repeated, base models are not up-to-date with the latest
information. In this paper, we update RobBERT, a RoBERTa-based state-of-the-art
Dutch language model, which was trained in 2019. First, the tokenizer of
RobBERT is updated to include new high-frequent tokens present in the latest
Dutch OSCAR corpus, e.g. corona-related words. Then we further pre-train the
RobBERT model using this dataset. To evaluate if our new model is a plug-in
replacement for RobBERT, we introduce two additional criteria based on concept
drift of existing tokens and alignment for novel tokens.We found that for
certain language tasks this update results in a significant performance
increase. These results highlight the benefit of continually updating a
language model to account for evolving language use.

By utilizing the fact that speaker identity and content vary on different
time scales, \acrlong{fhvae} (\acrshort{fhvae}) uses a sequential latent
variable and a segmental latent variable to symbolize these two attributes.
Disentanglement is carried out by assuming the latent variables representing
speaker and content follow sequence-dependent and sequence-independent priors.
For the sequence-dependent prior, \acrshort{fhvae} assumes a Gaussian
distribution with an utterance-scale varying mean and a fixed small variance.
The training process promotes sequential variables getting close to the mean of
its prior with small variance. However, this constraint is relatively weak.
Therefore, we introduce contrastive learning in the \acrshort{fhvae} framework.
The proposed method aims to make the sequential variables clustering when
representing the same speaker, while distancing themselves as far as possible
from those of other speakers. The structure of the framework has not been
changed in the proposed method but only the training process, thus no more cost
is needed during test. Voice conversion has been chosen as the application in
this paper. Latent variable evaluations include speakerincrease verification
and identification for the sequential latent variable, and speech recognition
for the segmental latent variable. Furthermore, assessments of voice conversion
performance are on the grounds of speaker verification and speech recognition
experiments. Experiment results show that the proposed method improves both
sequential and segmental feature extraction compared with \acrshort{fhvae}, and
moderately improved voice conversion performance.

Photonic computing is attracting increasing interest to accelerate
information processing in machine learning applications. The mode-competition
dynamics of multimode semiconductor lasers is useful for solving the
multi-armed bandit problem in reinforcement learning for computing
applications. In this study, we numerically evaluate the chaotic
mode-competition dynamics in a multimode semiconductor laser with optical
feedback and injection. We observe the chaotic mode-competition dynamics among
the longitudinal modes and control them by injecting an external optical signal
into one of the longitudinal modes. We define the dominant mode as the mode
with the maximum intensity; the dominant-mode ratio for the injected mode
increases as the optical injection strength increases. We find that the
characteristics of the dominant mode ratio in terms of the optical injection
strength are different among the modes owing to the different optical feedback
phases. We propose a control technique for the characteristics of the dominant
mode ratio by precisely tuning the initial optical frequency detuning between
the optical injection signal and injected mode. We also evaluate the
relationship between the region for the large dominant mode ratio and injection
locking range. The region for the large dominant mode ratio does not correspond
to the injection-locking range. This discrepancy results from the complex
mode-competition dynamics in multimode semiconductor lasers with both optical
feedback and injection. This control technique of chaotic mode-competition
dynamics in multimode lasers is promising for applications in reinforcement
learning and reservoir computing as photonic artificial intelligence.

Given a set of $n$ points in $d$ dimensions, the Euclidean $k$-means problem
(resp. the Euclidean $k$-median problem) consists of finding $k$ centers such
that the sum of squared distances (resp. sum of distances) from every point to
its closest center is minimized. The arguably most popular way of dealing with
this problem in the big data setting is to first compress the data by computing
a weighted subset known as a coreset and then run any algorithm on this subset.
The guarantee of the coreset is that for any candidate solution, the ratio
between coreset cost and the cost of the original instance is less than a
$(1\pm \varepsilon)$ factor. The current state of the art coreset size is
$\tilde O(\min(k^{2} \cdot \varepsilon^{-2},k\cdot \varepsilon^{-4}))$ for
Euclidean $k$-means and $\tilde O(\min(k^{2} \cdot \varepsilon^{-2},k\cdot
\varepsilon^{-3}))$ for Euclidean $k$-median. The best known lower bound for
both problems is $\Omega(k \varepsilon^{-2})$. In this paper, we improve the
upper bounds $\tilde O(\min(k^{3/2} \cdot \varepsilon^{-2},k\cdot
\varepsilon^{-4}))$ for $k$-means and $\tilde O(\min(k^{4/3} \cdot
\varepsilon^{-2},k\cdot \varepsilon^{-3}))$ for $k$-median. In particular, ours
is the first provable bound that breaks through the $k^2$ barrier while
retaining an optimal dependency on $\varepsilon$.

Transparent objects are common in day-to-day life and hence find many
applications that require robot grasping. Many solutions toward object grasping
exist for non-transparent objects. However, due to the unique visual properties
of transparent objects, standard 3D sensors produce noisy or distorted
measurements. Modern approaches tackle this problem by either refining the
noisy depth measurements or using some intermediate representation of the
depth. Towards this, we study deep learning 6D pose estimation from RGB images
only for transparent object grasping. To train and test the suitability of
RGB-based object pose estimation, we construct a dataset of RGB-only images
with 6D pose annotations. The experiments demonstrate the effectiveness of RGB
image space for grasping transparent objects.

Artificial intelligence (AI) is rapidly emerging as an enabling tool for
solving various complex materials design problems. This paper aims to review
recent advances in AI-driven materials-by-design and their applications to
energetic materials (EM). Trained with data from numerical simulations and/or
physical experiments, AI models can assimilate trends and patterns within the
design parameter space, identify optimal material designs (micro-morphologies,
combinations of materials in composites, etc.), and point to designs with
superior/targeted property and performance metrics. We review approaches
focusing on such capabilities with respect to the three main stages of
materials-by-design, namely representation learning of microstructure
morphology (i.e., shape descriptors), structure-property-performance (S-P-P)
linkage estimation, and optimization/design exploration. We provide a
perspective view of these methods in terms of their potential, practicality,
and efficacy towards the realization of materials-by-design. Specifically,
methods in the literature are evaluated in terms of their capacity to learn
from a small/limited number of data, computational complexity,
generalizability/scalability to other material species and operating
conditions, interpretability of the model predictions, and the burden of
supervision/data annotation. Finally, we suggest a few promising future
research directions for EM materials-by-design, such as meta-learning, active
learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge
the gap between machine learning research and EM research.

Yield forecasting is a critical first step necessary for yield optimisation,
with important consequences for the broader food supply chain, procurement,
price-negotiation, logistics, and supply. However yield forecasting is
notoriously difficult, and oft-inaccurate. Premonition Net is a multi-timeline,
time sequence ingesting approach towards processing the past, the present, and
premonitions of the future. We show how this structure combined with
transformers attains critical yield forecasting proficiency towards improving
food security, lowering prices, and reducing waste. We find data availability
to be a continued difficulty however using our premonition network and our own
collected data we attain yield forecasts 3 weeks ahead with a a testing set
RMSE loss of ~0.08 across our latest season.

Deep learning-based massive MIMO CSI feedback has received a lot of attention
in recent years. Now, there exists a plethora of CSI feedback models that
exploit a wide variety of deep learning models and techniques ranging from
convolutional neural networks (CNNs) to the recent attention-based transformer
networks. Most of the models are based on auto-encoders (AE) architecture with
an encoder network at the user equipment (UE) and a decoder network at the gNB
(base station). However, these models are trained for a single user in a single
channel scenario, making them ineffective in scenarios where a gNB is
addressing various users while each user has different abilities and may employ
a different CSI feedback encoder network and also in scenarios where the users
are employing the same encoder network but are experiencing different channel
conditions. In this work, we address these specific issues by exploiting the
techniques of multi-task learning (MTL) in the context of massive MIMO CSI
feedback.

Question answering over knowledge bases (KBQA) has become a popular approach
to help users extract information from knowledge bases. Although several
systems exist, choosing one suitable for a particular application scenario is
difficult. In this article, we provide a comparative study of six
representative KBQA systems on eight benchmark datasets. In that, we study
various question types, properties, languages, and domains to provide insights
on where existing systems struggle. On top of that, we propose an advanced
mapping algorithm to aid existing models in achieving superior results.
Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages
COVID-19 research and multilingualism for the diversity of future AI. Finally,
we discuss the key findings and their implications as well as performance
guidelines and some future improvements. Our source code is available at
\url{https://github.com/tamlhp/kbqa}.

Knowledge graph completion (KGC) aims to predict the missing links among
knowledge graph (KG) entities. Though various methods have been developed for
KGC, most of them can only deal with the KG entities seen in the training set
and cannot perform well in predicting links concerning novel entities in the
test set. Similar problem exists in temporal knowledge graphs (TKGs), and no
previous temporal knowledge graph completion (TKGC) method is developed for
modeling newly-emerged entities. Compared to KGs, TKGs require temporal
reasoning techniques for modeling, which naturally increases the difficulty in
dealing with novel, yet unseen entities. In this work, we focus on the
inductive learning of unseen entities' representations on TKGs. We propose a
few-shot out-of-graph (OOG) link prediction task for TKGs, where we predict the
missing entities from the links concerning unseen entities by employing a
meta-learning framework and utilizing the meta-information provided by only few
edges associated with each unseen entity. We construct three new datasets for
TKG few-shot OOG link prediction, and we propose a model that mines the
concept-aware information among entities. Experimental results show that our
model achieves superior performance on all three datasets and our concept-aware
modeling component demonstrates a strong effect.

Event detection in power systems aims to identify triggers and event types,
which helps relevant personnel respond to emergencies promptly and facilitates
the optimization of power supply strategies. However, the limited length of
short electrical record texts causes severe information sparsity, and numerous
domain-specific terminologies of power systems makes it difficult to transfer
knowledge from language models pre-trained on general-domain texts. Traditional
event detection approaches primarily focus on the general domain and ignore
these two problems in the power system domain. To address the above issues, we
propose a Multi-Channel graph neural network utilizing Type information for
Event Detection in power systems, named MC-TED, leveraging a semantic channel
and a topological channel to enrich information interaction from short texts.
Concretely, the semantic channel refines textual representations with semantic
similarity, building the semantic information interaction among potential
event-related words. The topological channel generates a relation-type-aware
graph modeling word dependencies, and a word-type-aware graph integrating
part-of-speech tags. To further reduce errors worsened by professional
terminologies in type analysis, a type learning mechanism is designed for
updating the representations of both the word type and relation type in the
topological channel. In this way, the information sparsity and professional
term occurrence problems can be alleviated by enabling interaction between
topological and semantic information. Furthermore, to address the lack of
labeled data in power systems, we built a Chinese event detection dataset based
on electrical Power Event texts, named PoE. In experiments, our model achieves
compelling results not only on the PoE dataset, but on general-domain event
detection datasets including ACE 2005 and MAVEN.

Continual learning refers to a dynamical framework in which a model or agent
receives a stream of non-stationary data over time and must adapt to new data
while preserving previously acquired knowledge. Unfortunately, deep neural
networks fail to meet these two desiderata, incurring the so-called
catastrophic forgetting phenomenon. Whereas a vast array of strategies have
been proposed to attenuate forgetting in the computer vision domain, for
speech-related tasks, on the other hand, there is a dearth of works. In this
paper, we turn our attention toward the joint use of rehearsal and knowledge
distillation (KD) approaches for spoken language understanding under a
class-incremental learning scenario. We report on multiple KD combinations at
different levels in the network, showing that combining feature-level and
predictions-level KDs leads to the best results. Finally, we provide an
ablation study on the effect of the size of the rehearsal memory that
corroborates the appropriateness of our approach for low-resource devices.

Paleoclimatology -- the study of past climate -- is relevant beyond climate
science itself, such as in archaeology and anthropology for understanding past
human dispersal. Information about the Earth's paleoclimate comes from
simulations of physical and biogeochemical processes and from proxy records
found in naturally occurring archives. Climate-field reconstructions (CFRs)
combine these data into a statistical spatial or spatiotemporal model. To date,
there exists no consensus spatiotemporal paleoclimate model that is continuous
in space and time, produces predictions with uncertainty, and can include data
from various sources. A Gaussian process (GP) model would have these desired
properties; however, GPs scale unfavorably with data of the magnitude typical
for building CFRs. We propose to build on recent advances in sparse
spatiotemporal GPs that reduce the computational burden by combining
variational methods based on inducing variables with the state-space
formulation of GPs. We successfully employ such a doubly sparse GP to construct
a probabilistic model of European paleoclimate from the Last Glacial Maximum
(LGM) to the mid-Holocene (MH) that synthesizes paleoclimate simulations and
fossilized pollen proxy data.

This paper addresses a neural network-based surrogate model that provides a
structure-preserving approximation for the fivefold collision integral. The
notion originates from the similarity in structure between the BGK-type
relaxation model and residual neural network (ResNet) when a particle
distribution function is treated as the input to the neural network function.
We extend the ResNet architecture and construct what we call the relaxation
neural network (RelaxNet). Specifically, two feed-forward neural networks with
physics-informed connections and activations are introduced as building blocks
in RelaxNet, which provide bounded and physically realizable approximations of
the equilibrium distribution and velocity-dependent relaxation time
respectively. The evaluation of the collision term is significantly accelerated
since the convolution in the fivefold integral is replaced by tensor
multiplication in the neural network. We fuse the mechanical advection operator
and the RelaxNet-based collision operator into a unified model named the
universal Boltzmann equation (UBE). We prove that UBE preserves the key
structural properties in a many-particle system, i.e., positivity,
conservation, invariance, and H-theorem. These properties promise that RelaxNet
is superior to strategies that naively approximate the right-hand side of the
Boltzmann equation using a machine learning model. The construction of the
RelaxNet-based UBE and its solution algorithm are presented in detail. Several
numerical experiments are investigated. The capability of the current approach
for simulating non-equilibrium flow physics is validated through excellent in-
and out-of-distribution performance.

Liver tumor segmentation in CT images is a critical step in the diagnosis,
surgical planning and postoperative evaluation of liver disease. An automatic
liver and tumor segmentation method can greatly relieve physicians of the heavy
workload of examining CT images and better improve the accuracy of diagnosis.
In the last few decades, many modifications based on U-Net model have been
proposed in the literature. However, there are relatively few improvements for
the advanced UNet++ model. In our paper, we propose an encoding feature
supervised UNet++(ES-UNet++) and apply it to the liver and tumor segmentation.
ES-UNet++ consists of an encoding UNet++ and a segmentation UNet++. The
well-trained encoding UNet++ can extract the encoding features of label map
which are used to additionally supervise the segmentation UNet++. By adding
supervision to the each encoder of segmentation UNet++, U-Nets of different
depths that constitute UNet++ outperform the original version by average 5.7%
in dice score and the overall dice score is thus improved by 2.1%. ES-UNet++ is
evaluated with dataset LiTS, achieving 95.6% for liver segmentation and 67.4%
for tumor segmentation in dice score. In this paper, we also concluded some
valuable properties of ES-UNet++ by conducting comparative anaylsis between
ES-UNet++ and UNet++:(1) encoding feature supervision can accelerate the
convergence of the model.(2) encoding feature supervision enhances the effect
of model pruning by achieving huge speedup while providing pruned models with
fairly good performance.

In this paper, we propose a new paradigm to learn audio features for Music
Structure Analysis (MSA). We train a deep encoder to learn features such that
the Self-Similarity-Matrix (SSM) resulting from those approximates a
ground-truth SSM. This is done by minimizing a loss between both SSMs. Since
this loss is differentiable w.r.t. its input features we can train the encoder
in a straightforward way. We successfully demonstrate the use of this training
paradigm using the Area Under the Curve ROC (AUC) on the RWC-Pop dataset.

Deep learning has achieved great success in learning features from massive
remote sensing images (RSIs). To better understand the connection between
feature learning paradigms (e.g., unsupervised feature learning (USFL),
supervised feature learning (SFL), and self-supervised feature learning
(SSFL)), this paper analyzes and compares them from the perspective of feature
learning signals, and gives a unified feature learning framework. Under this
unified framework, we analyze the advantages of SSFL over the other two
learning paradigms in RSIs understanding tasks and give a comprehensive review
of the existing SSFL work in RS, including the pre-training dataset,
self-supervised feature learning signals, and the evaluation methods. We
further analyze the effect of SSFL signals and pre-training data on the learned
features to provide insights for improving the RSI feature learning. Finally,
we briefly discuss some open problems and possible research directions.

The retinogeniculate pathway (RGVP) is responsible for carrying visual
information from the retina to the lateral geniculate nucleus. Identification
and visualization of the RGVP are important in studying the anatomy of the
visual system and can inform treatment of related brain diseases. Diffusion MRI
(dMRI) tractography is an advanced imaging method that uniquely enables in vivo
mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP
from tractography data relies on expert (manual) selection of tractography
streamlines, which is time-consuming, has high clinical and expert labor costs,
and affected by inter-observer variability. In this paper, we present what we
believe is the first deep learning framework, namely DeepRGVP, to enable fast
and accurate identification of the RGVP from dMRI tractography data. We design
a novel microstructure-informed supervised contrastive learning method that
leverages both streamline label and tissue microstructure information to
determine positive and negative pairs. We propose a simple and successful
streamline-level data augmentation method to address highly imbalanced training
data, where the number of RGVP streamlines is much lower than that of non-RGVP
streamlines. We perform comparisons with several state-of-the-art deep learning
methods that were designed for tractography parcellation, and we show superior
RGVP identification results using DeepRGVP.

Our work investigates out-of-distribution (OOD) detection as a neural network
output explanation problem. We learn a heatmap representation for detecting OOD
images while visualizing in- and out-of-distribution image regions at the same
time. Given a trained and fixed classifier, we train a decoder neural network
to produce heatmaps with zero response for in-distribution samples and high
response heatmaps for OOD samples, based on the classifier features and the
class prediction. Our main innovation lies in the heatmap definition for an OOD
sample, as the normalized difference from the closest in-distribution sample.
The heatmap serves as a margin to distinguish between in- and
out-of-distribution samples. Our approach generates the heatmaps not only for
OOD detection, but also to indicate in- and out-of-distribution regions of the
input image. In our evaluations, our approach mostly outperforms the prior work
on fixed classifiers, trained on CIFAR-10, CIFAR-100 and Tiny ImageNet. The
code is publicly available at: https://github.com/jhornauer/heatmap_ood.

Active Learning (AL) is a powerful tool for learning with less labeled data,
in particular, for specialized domains, like legal documents, where unlabeled
data is abundant, but the annotation requires domain expertise and is thus
expensive. Recent works have shown the effectiveness of AL strategies for
pre-trained language models. However, most AL strategies require a set of
labeled samples to start with, which is expensive to acquire. In addition,
pre-trained language models have been shown unstable during fine-tuning with
small datasets, and their embeddings are not semantically meaningful. In this
work, we propose a pipeline for effectively using active learning with
pre-trained language models in the legal domain. To this end, we leverage the
available unlabeled data in three phases. First, we continue pre-training the
model to adapt it to the downstream task. Second, we use knowledge distillation
to guide the model's embeddings to a semantically meaningful space. Finally, we
propose a simple, yet effective, strategy to find the initial set of labeled
samples with fewer actions compared to existing methods. Our experiments on
Contract-NLI, adapted to the classification task, and LEDGAR benchmarks show
that our approach outperforms standard AL strategies, and is more efficient.
Furthermore, our pipeline reaches comparable results to the fully-supervised
approach with a small performance gap, and dramatically reduced annotation
cost. Code and the adapted data will be made available.

Discussions on causal relations in real life often consider variables for
which the definition of causality is unclear since the notion of interventions
on the respective variables is obscure. Asking 'what qualifies an action for
being an intervention on the variable X' raises the question whether the action
impacted all other variables only through X or directly, which implicitly
refers to a causal model.
  To avoid this known circularity, we instead suggest a notion of
'phenomenological causality' whose basic concept is a set of elementary
actions. Then the causal structure is defined such that elementary actions
change only the causal mechanism at one node (e.g. one of the causal
conditionals in the Markov factorization). This way, the Principle of
Independent Mechanisms becomes the defining property of causal structure in
domains where causality is a more abstract phenomenon rather than being an
objective fact relying on hard-wired causal links between tangible objects. We
describe this phenomenological approach to causality for toy and hypothetical
real-world examples and argue that it is consistent with the causal Markov
condition when the system under consideration interacts with other variables
that control the elementary actions.

Leveraging network information for prediction tasks has become a common
practice in many domains. Being an important part of targeted marketing,
influencer detection can potentially benefit from incorporating dynamic network
representation. In this work, we investigate different dynamic Graph Neural
Networks (GNNs) configurations for influencer detection and evaluate their
prediction performance using a unique corporate data set. We show that using
deep multi-head attention in GNN and encoding temporal attributes significantly
improves performance. Furthermore, our empirical evaluation illustrates that
capturing neighborhood representation is more beneficial that using network
centrality measures.

The linear ensemble based strategy, i.e., averaging ensemble, has been
proposed to improve the performance in unsupervised domain adaptation tasks.
However, a typical UDA task is usually challenged by dynamically changing
factors, such as variable weather, views, and background in the unlabeled
target domain. Most previous ensemble strategies ignore UDA's dynamic and
uncontrollable challenge, facing limited feature representations and
performance bottlenecks. To enhance the model, adaptability between domains and
reduce the computational cost when deploying the ensemble model, we propose a
novel framework, namely Instance aware Model Ensemble With Distillation, IMED,
which fuses multiple UDA component models adaptively according to different
instances and distills these components into a small model. The core idea of
IMED is a dynamic instance aware ensemble strategy, where for each instance, a
nonlinear fusion subnetwork is learned that fuses the extracted features and
predicted labels of multiple component models. The nonlinear fusion method can
help the ensemble model handle dynamically changing factors. After learning a
large capacity ensemble model with good adaptability to different changing
factors, we leverage the ensemble teacher model to guide the learning of a
compact student model by knowledge distillation. Furthermore, we provide the
theoretical analysis of the validity of IMED for UDA. Extensive experiments
conducted on various UDA benchmark datasets, e.g., Office 31, Office Home, and
VisDA 2017, show the superiority of the model based on IMED to the state of the
art methods under the comparable computation cost.

We present DualNER, a simple and effective framework to make full use of both
annotated source language corpus and unlabeled target language text for
zero-shot cross-lingual named entity recognition (NER). In particular, we
combine two complementary learning paradigms of NER, i.e., sequence labeling
and span prediction, into a unified multi-task framework. After obtaining a
sufficient NER model trained on the source data, we further train it on the
target data in a {\it dual-teaching} manner, in which the pseudo-labels for one
task are constructed from the prediction of the other task. Moreover, based on
the span prediction, an entity-aware regularization is proposed to enhance the
intrinsic cross-lingual alignment between the same entities in different
languages. Experiments and analysis demonstrate the effectiveness of our
DualNER. Code is available at https://github.com/lemon0830/dualNER.

Automatic pronunciation assessment is a major component of a
computer-assisted pronunciation training system. To provide in-depth feedback,
scoring pronunciation at various levels of granularity such as phoneme, word,
and utterance, with diverse aspects such as accuracy, fluency, and
completeness, is essential. However, existing multi-aspect multi-granularity
methods simultaneously predict all aspects at all granularity levels;
therefore, they have difficulty in capturing the linguistic hierarchy of
phoneme, word, and utterance. This limitation further leads to neglecting
intimate cross-aspect relations at the same linguistic unit. In this paper, we
propose a Hierarchical Pronunciation Assessment with Multi-aspect Attention
(HiPAMA) model, which hierarchically represents the granularity levels to
directly capture their linguistic structures and introduces multi-aspect
attention that reflects associations across aspects at the same level to create
more connotative representations. By obtaining relational information from both
the granularity- and aspect-side, HiPAMA can take full advantage of multi-task
learning. Remarkable improvements in the experimental results on the
speachocean762 datasets demonstrate the robustness of HiPAMA, particularly in
the difficult-to-assess aspects.

Power-law scaling, a central concept in critical phenomena, is found to be
useful in deep learning, where optimized test errors on handwritten digit
examples converge as a power-law to zero with database size. For rapid decision
making with one training epoch, each example is presented only once to the
trained network, the power-law exponent increased with the number of hidden
layers. For the largest dataset, the obtained test error was estimated to be in
the proximity of state-of-the-art algorithms for large epoch numbers. Power-law
scaling assists with key challenges found in current artificial intelligence
applications and facilitates an a priori dataset size estimation to achieve a
desired test accuracy. It establishes a benchmark for measuring training
complexity and a quantitative hierarchy of machine learning tasks and

In this paper, to unveil interpretable development-specific gene signatures
in human PFC, we propose a novel gene selection method, named Interpretable
Causality Gene Selection (ICGS), which adopts a Bayesian Network (BN) to
represent causality between multiple gene variables and a development variable.
The proposed ICGS method combines the positive instances-based contrastive
learning with a Variational AutoEncoder (VAE) to obtain this optimal BN
structure and use a Markov Blanket (MB) to identify gene signatures causally
related to the development variable. Moreover, the differential expression
genes (DEGs) are used to filter redundant genes before gene selection. In order
to identify gene signatures, we apply the proposed ICGS to the human PFC
single-cell transcriptomics data. The experimental results demonstrate that the
proposed method can effectively identify interpretable development-specific
gene signatures in human PFC. Gene ontology enrichment analysis and ASD-related
gene analysis show that these identified gene signatures reveal the key
biological processes and pathways in human PFC and have more potential for
neurodevelopment disorder cure. These gene signatures are expected to bring
important implications for understanding PFC development heterogeneity and
function in humans.

Recently proposed large-scale text-to-image generative models such as
DALL$\cdot$E 2, Midjourney, and StableDiffusion can generate high-quality and
realistic images from users' prompts. Not limited to the research community,
ordinary Internet users enjoy these generative models, and consequently a
tremendous amount of generated images have been shared on the Internet.
Meanwhile, today's success of deep learning in the computer vision field owes a
lot to images collected from the Internet. These trends lead us to a research
question: "will such generated images impact the quality of future datasets and
the performance of computer vision models positively or negatively?" This paper
empirically answers this question by simulating contamination. Namely, we
generate ImageNet-scale and COCO-scale datasets using a state-of-the-art
generative model and evaluate models trained on ``contaminated'' datasets on
various tasks including image classification and image generation. Throughout
experiments, we conclude that generated images negatively affect downstream
performance, while the significance depends on tasks and the amount of
generated images. The generated datasets are available via
https://github.com/moskomule/dataset-contamination.

Shadow removal from a single image is challenging, particularly with the
presence of soft and self shadows. Unlike hard shadows, soft shadows do not
show any clear boundaries, while self shadows are shadows that cast on the
object itself. Most existing methods require the detection/annotation of binary
shadow masks, without taking into account the ambiguous boundaries of soft and
self shadows. Most deep learning shadow removal methods are GAN-based and
require statistical similarity between shadow and shadow-free domains. In
contrast to these methods, in this paper, we present ShadowDiffusion, the first
diffusion-based shadow removal method. ShadowDiffusion focuses on single-image
shadow removal, even in the presence of soft and self shadows. To guide the
diffusion process to recover semantically meaningful structures during the
reverse diffusion, we introduce a structure preservation loss, where we extract
features from the pre-trained Vision Transformer (DINO-ViT). Moreover, to focus
on the recovery of shadow regions, we inject classifier-driven attention into
the architecture of the diffusion model. To maintain the consistent colors of
the regions where the shadows have been removed, we introduce a chromaticity
consistency loss. Our ShadowDiffusion outperforms state-of-the-art methods on
the SRD, AISTD, LRSS, USR and UIUC datasets, removing hard, soft, and self
shadows robustly. Our method outperforms the SOTA method by 20% of the RMSE of
the whole image on the SRD dataset.

Visual Grounding (VG) in Visual Question Answering (VQA) systems describes
how well a system manages to tie a question and its answer to relevant image
regions. Systems with strong VG are considered intuitively interpretable and
suggest an improved scene understanding. While VQA accuracy performances have
seen impressive gains over the past few years, explicit improvements to VG
performance and evaluation thereof have often taken a back seat on the road to
overall accuracy improvements. A cause of this originates in the predominant
choice of learning paradigm for VQA systems, which consists of training a
discriminative classifier over a predetermined set of answer options.
  In this work, we break with the dominant VQA modeling paradigm of
classification and investigate VQA from the standpoint of an information
retrieval task. As such, the developed system directly ties VG into its core
search procedure. Our system operates over a weighted, directed, acyclic graph,
a.k.a. "lattice", which is derived from the scene graph of a given image in
conjunction with region-referring expressions extracted from the question.
  We give a detailed analysis of our approach and discuss its distinctive
properties and limitations. Our approach achieves the strongest VG performance
among examined systems and exhibits exceptional generalization capabilities in
a number of scenarios.

We are developing a self-learning mechatronic golf robot using combined
data-driven and physics-based methods, to have the robot autonomously learn to
putt the ball from an arbitrary point on the green. Apart from the mechatronic
control design of the robot, this task is accomplished by a camera system with
image recognition and a neural network for predicting the stroke velocity
vector required for a successful hole-in-one. To minimize the number of
time-consuming interactions with the real system, the neural network is
pretrained by evaluating basic physical laws on a model, which approximates the
golf ball dynamics on the green surface in a data-driven manner. Thus, we
demonstrate the synergetic combination of data-driven and physics-based methods
on the golf robot as a mechatronic example system.

Despite the abundance of Electronic Healthcare Records (EHR), its
heterogeneity restricts the utilization of medical data in building predictive
models. To address this challenge, we propose Universal Healthcare Predictive
Framework (UniHPF), which requires no medical domain knowledge and minimal
pre-processing for multiple prediction tasks. Experimental results demonstrate
that UniHPF is capable of building large-scale EHR models that can process any
form of medical data from distinct EHR systems. We believe that our findings
can provide helpful insights for further research on the multi-source learning
of EHRs.

While the emergence of large administrative claims data provides
opportunities for research, their use remains limited by the lack of clinical
annotations relevant to disease outcomes, such as recurrence in breast cancer
(BC). Several challenges arise from the annotation of such endpoints in
administrative claims, including the need to infer both the occurrence and the
date of the recurrence, the right-censoring of data, or the importance of time
intervals between medical visits. Deep learning approaches have been
successfully used to label temporal medical sequences, but no method is
currently able to handle simultaneously right-censoring and visit temporality
to detect survival events in medical sequences. We propose EDEN (Event
DEtection Network), a time-aware Long-Short-Term-Memory network for survival
analyses, and its custom loss function. Our method outperforms several
state-of-the-art approaches on real-world BC datasets. EDEN constitutes a
powerful tool to annotate disease recurrence from administrative claims, thus
paving the way for the massive use of such data in BC research.

World-wide-web, with the website and webpage as the main interface,
facilitates the dissemination of important information. Hence it is crucial to
optimize them for better user interaction, which is primarily done by analyzing
users' behavior, especially users' eye-gaze locations. However, gathering these
data is still considered to be labor and time intensive. In this work, we
enable the development of automatic eye-gaze estimations given a website
screenshots as the input. This is done by the curation of a unified dataset
that consists of website screenshots, eye-gaze heatmap and website's layout
information in the form of image and text masks. Our pre-processed dataset
allows us to propose an effective deep learning-based model that leverages both
image and text spatial location, which is combined through attention mechanism
for effective eye-gaze prediction. In our experiment, we show the benefit of
careful fine-tuning using our unified dataset to improve the accuracy of
eye-gaze predictions. We further observe the capability of our model to focus
on the targeted areas (images and text) to achieve high accuracy. Finally, the
comparison with other alternatives shows the state-of-the-art result of our
model establishing the benchmark for the eye-gaze prediction task.

Pre-trained language models (PLMs) improve the model generalization by
leveraging massive data as the training corpus in the pre-training phase.
However, currently, the out-of-distribution (OOD) generalization becomes a
generally ill-posed problem, even for the large-scale PLMs in natural language
understanding tasks, which prevents the deployment of NLP methods in the real
world. To facilitate the research in this direction, this paper makes the first
attempt to establish a unified benchmark named GLUE-X, highlighting the
importance of OOD robustness and providing insights on how to measure the
robustness of a model and how to improve it. To this end, we collect 13
publicly available datasets as OOD test data, and conduct evaluations on 8
classic NLP tasks over \emph{18} popularly used models. Our findings confirm
that the OOD accuracy in NLP tasks needs to be paid more attention to since the
significant performance decay compared to ID accuracy has been found in all
settings.

Recent studies show that Graph Neural Networks(GNNs) are vulnerable and
easily fooled by small perturbations, which has raised considerable concerns
for adapting GNNs in various safety-critical applications. In this work, we
focus on the emerging but critical attack, namely, Graph Injection Attack(GIA),
in which the adversary poisons the graph by injecting fake nodes instead of
modifying existing structures or node attributes. Inspired by findings that the
adversarial attacks are related to the increased heterophily on perturbed
graphs (the adversary tends to connect dissimilar nodes), we propose a general
defense framework CHAGNN against GIA through cooperative homophilous
augmentation of graph data and model. Specifically, the model generates
pseudo-labels for unlabeled nodes in each round of training to reduce
heterophilous edges of nodes with distinct labels. The cleaner graph is fed
back to the model, producing more informative pseudo-labels. In such an
iterative manner, model robustness is then promisingly enhanced. We present the
theoretical analysis of the effect of homophilous augmentation and provide the
guarantee of the proposal's validity. Experimental results empirically
demonstrate the effectiveness of CHAGNN in comparison with recent
state-of-the-art defense methods on diverse real-world datasets.

Recent advances of data-driven machine learning have revolutionized fields
like computer vision, reinforcement learning, and many scientific and
engineering domains. In many real-world and scientific problems, systems that
generate data are governed by physical laws. Recent work shows that it provides
potential benefits for machine learning models by incorporating the physical
prior and collected data, which makes the intersection of machine learning and
physics become a prevailing paradigm. In this survey, we present this learning
paradigm called Physics-Informed Machine Learning (PIML) which is to build a
model that leverages empirical data and available physical prior knowledge to
improve performance on a set of tasks that involve a physical mechanism. We
systematically review the recent development of physics-informed machine
learning from three perspectives of machine learning tasks, representation of
physical prior, and methods for incorporating physical prior. We also propose
several important open research problems based on the current trends in the
field. We argue that encoding different forms of physical prior into model
architectures, optimizers, inference algorithms, and significant
domain-specific applications like inverse engineering design and robotic
control is far from fully being explored in the field of physics-informed
machine learning. We believe that this study will encourage researchers in the
machine learning community to actively participate in the interdisciplinary
research of physics-informed machine learning.

Quantification, variously called "supervised prevalence estimation" or
"learning to quantify", is the supervised learning task of generating
predictors of the relative frequencies (a.k.a. "prevalence values") of the
classes of interest in unlabelled data samples. While many quantification
methods have been proposed in the past for binary problems and, to a lesser
extent, single-label multiclass problems, the multi-label setting (i.e., the
scenario in which the classes of interest are not mutually exclusive) remains
by and large unexplored. A straightforward solution to the multi-label
quantification problem could simply consist of recasting the problem as a set
of independent binary quantification problems. Such a solution is simple but
na\"ive, since the independence assumption upon which it rests is, in most
cases, not satisfied. In these cases, knowing the relative frequency of one
class could be of help in determining the prevalence of other related classes.
We propose the first truly multi-label quantification methods, i.e., methods
for inferring estimators of class prevalence values that strive to leverage the
stochastic dependencies among the classes of interest in order to predict their
relative frequencies more accurately. We show empirical evidence that natively
multi-label solutions outperform the na\"ive approaches by a large margin. The
code to reproduce all our experiments is available online.

For an autonomous vehicle it is essential to observe the ongoing dynamics of
a scene and consequently predict imminent future scenarios to ensure safety to
itself and others. This can be done using different sensors and modalities. In
this paper we investigate the usage of optical flow for predicting future
semantic segmentations. To do so we propose a model that forecasts flow fields
autoregressively. Such predictions are then used to guide the inference of a
learned warping function that moves instance segmentations on to future frames.
Results on the Cityscapes dataset demonstrate the effectiveness of optical-flow
methods.

The movie and video game industries have adopted photogrammetry as a way to
create digital 3D assets from multiple photographs of a real-world scene. But
photogrammetry algorithms typically output an RGB texture atlas of the scene
that only serves as visual guidance for skilled artists to create material maps
suitable for physically-based rendering. We present a learning-based approach
that automatically produces digital assets ready for physically-based
rendering, by estimating approximate material maps from multi-view captures of
indoor scenes that are used with retopologized geometry. We base our approach
on a material estimation Convolutional Neural Network (CNN) that we execute on
each input image. We leverage the view-dependent visual cues provided by the
multiple observations of the scene by gathering, for each pixel of a given
image, the color of the corresponding point in other images. This image-space
CNN provides us with an ensemble of predictions, which we merge in texture
space as the last step of our approach. Our results demonstrate that the
recovered assets can be directly used for physically-based rendering and
editing of real indoor scenes from any viewpoint and novel lighting. Our method
generates approximate material maps in a fraction of time compared to the
closest previous solutions.

Unsupervised region representation learning aims to extract dense and
effective features from unlabeled urban data. While some efforts have been made
for solving this problem based on multiple views, existing methods are still
insufficient in extracting representations in a view and/or incorporating
representations from different views. Motivated by the success of contrastive
learning for representation learning, we propose to leverage it for multi-view
region representation learning and design a model called ReMVC (Region
Embedding with Multi-View Contrastive Learning) by following two guidelines: i)
comparing a region with others within each view for effective representation
extraction and ii) comparing a region with itself across different views for
cross-view information sharing. We design the intra-view contrastive learning
module which helps to learn distinguished region embeddings and the inter-view
contrastive learning module which serves as a soft co-regularizer to constrain
the embedding parameters and transfer knowledge across multi-views. We exploit
the learned region embeddings in two downstream tasks named land usage
clustering and region popularity prediction. Extensive experiments demonstrate
that our model achieves impressive improvements compared with seven
state-of-the-art baseline methods, and the margins are over 30% in the land
usage clustering task.

Recent years have witnessed the great success of deep learning algorithms in
the geoscience and remote sensing realm. Nevertheless, the security and
robustness of deep learning models deserve special attention when addressing
safety-critical remote sensing tasks. In this paper, we provide a systematic
analysis of backdoor attacks for remote sensing data, where both scene
classification and semantic segmentation tasks are considered. While most of
the existing backdoor attack algorithms rely on visible triggers like squared
patches with well-designed patterns, we propose a novel wavelet transform-based
attack (WABA) method, which can achieve invisible attacks by injecting the
trigger image into the poisoned image in the low-frequency domain. In this way,
the high-frequency information in the trigger image can be filtered out in the
attack, resulting in stealthy data poisoning. Despite its simplicity, the
proposed method can significantly cheat the current state-of-the-art deep
learning models with a high attack success rate. We further analyze how
different trigger images and the hyper-parameters in the wavelet transform
would influence the performance of the proposed method. Extensive experiments
on four benchmark remote sensing datasets demonstrate the effectiveness of the
proposed method for both scene classification and semantic segmentation tasks
and thus highlight the importance of designing advanced backdoor defense
algorithms to address this threat in remote sensing scenarios. The code will be
available online at \url{https://github.com/ndraeger/waba}.

We examine the last-iterate convergence rate of Bregman proximal methods -
from mirror descent to mirror-prox - in constrained variational inequalities.
Our analysis shows that the convergence speed of a given method depends sharply
on the Legendre exponent of the underlying Bregman regularizer (Euclidean,
entropic, or other), a notion that measures the growth rate of said regularizer
near a solution. In particular, we show that boundary solutions exhibit a clear
separation of regimes between methods with a zero and non-zero Legendre
exponent respectively, with linear convergence for the former versus sublinear
for the latter. This dichotomy becomes even more pronounced in linearly
constrained problems where, specifically, Euclidean methods converge along
sharp directions in a finite number of steps, compared to a linear rate for
entropic methods.

When comparing approximate Gaussian process (GP) models, it can be helpful to
be able to generate data from any GP. If we are interested in how approximate
methods perform at scale, we may wish to generate very large synthetic datasets
to evaluate them. Na\"{i}vely doing so would cost \(\mathcal{O}(n^3)\) flops
and \(\mathcal{O}(n^2)\) memory to generate a size \(n\) sample. We demonstrate
how to scale such data generation to large \(n\) whilst still providing
guarantees that, with high probability, the sample is indistinguishable from a
sample from the desired GP.

Emotion recognition is one of the machine learning applications which can be
done using text, speech, or image data gathered from social media spaces.
Detecting emotion can help us in different fields, including opinion mining.
With the spread of social media, different platforms like Twitter have become
data sources, and the language used in these platforms is informal, making the
emotion detection task difficult. EmoPars and ArmanEmo are two new
human-labeled emotion datasets for the Persian language. These datasets,
especially EmoPars, are suffering from inequality between several samples
between two classes. In this paper, we evaluate EmoPars and compare them with
ArmanEmo. Throughout this analysis, we use data augmentation techniques, data
re-sampling, and class-weights with Transformer-based Pretrained Language
Models(PLMs) to handle the imbalance problem of these datasets. Moreover,
feature selection is used to enhance the models' performance by emphasizing the
text's specific features. In addition, we provide a new policy for selecting
data from EmoPars, which selects the high-confidence samples; as a result, the
model does not see samples that do not have specific emotion during training.
Our model reaches a Macro-averaged F1-score of 0.81 and 0.76 on ArmanEmo and
EmoPars, respectively, which are new state-of-the-art results in these
benchmarks.

Federated Learning (FL) is an emerging paradigm that enables distributed
users to collaboratively and iteratively train machine learning models without
sharing their private data. Motivated by the effectiveness and robustness of
self-attention-based architectures, researchers are turning to using
pre-trained Transformers (i.e., foundation models) instead of traditional
convolutional neural networks in FL to leverage their excellent transfer
learning capabilities. Despite recent progress, how pre-trained Transformer
models play a role in FL remains obscure, that is, how to efficiently fine-tune
these pre-trained models in FL and how FL users could benefit from this new
paradigm. In this paper, we explore this issue and demonstrate that the
fine-tuned Transformers achieve extraordinary performance on FL, and that the
lightweight fine-tuning method facilitates a fast convergence rate and low
communication costs. Concretely, we conduct a rigorous empirical study of three
tuning methods (i.e., modifying the input, adding extra modules, and adjusting
the backbone) using two types of pre-trained models (i.e., vision-language
models and vision models) for FL. Our experiments show that 1) Fine-tuning the
bias term of the backbone performs best when relying on a strong pre-trained
model; 2) The vision-language model (e.g., CLIP) outperforms the pure vision
model (e.g., ViT) and is more robust to the few-shot settings; 3) Compared to
pure local training, FL with pre-trained models has a higher accuracy because
it alleviates the problem of over-fitting. We will release our code and
encourage further exploration of pre-trained Transformers and FL.

With the wide and deep adoption of deep learning models in real applications,
there is an increasing need to model and learn the representations of the
neural networks themselves. These models can be used to estimate attributes of
different neural network architectures such as the accuracy and latency,
without running the actual training or inference tasks. In this paper, we
propose a neural architecture representation model that can be used to estimate
these attributes holistically. Specifically, we first propose a simple and
effective tokenizer to encode both the operation and topology information of a
neural network into a single sequence. Then, we design a multi-stage fusion
transformer to build a compact vector representation from the converted
sequence. For efficient model training, we further propose an information flow
consistency augmentation and correspondingly design an architecture consistency
loss, which brings more benefits with less augmentation samples compared with
previous random augmentation strategies. Experiment results on NAS-Bench-101,
NAS-Bench-201, DARTS search space and NNLQP show that our proposed framework
can be used to predict the aforementioned latency and accuracy attributes of
both cell architectures and whole deep neural networks, and achieves promising

The capability of recurrent neural networks to approximate trajectories of a
random dynamical system, with random inputs, on non-compact domains, and over
an indefinite or infinite time horizon is considered. The main result states
that certain random trajectories over an infinite time horizon may be
approximated to any desired accuracy, uniformly in time, by a certain class of
deep recurrent neural networks, with simple feedback structures. The
formulation here contrasts with related literature on this topic, much of which
is restricted to compact state spaces and finite time intervals. The model
conditions required here are natural, mild, and easy to test, and the proof is
very simple.

The pretrain-finetuning paradigm in large-scale sequence models has made
significant progress in natural language processing and computer vision tasks.
However, such a paradigm is still hindered by several challenges in
Reinforcement Learning (RL), including the lack of self-supervised pretraining
algorithms based on offline data and efficient fine-tuning/prompt-tuning over
unseen downstream tasks. In this work, we explore how prompts can improve
sequence modeling-based offline reinforcement learning (offline-RL) algorithms.
Firstly, we propose prompt tuning for offline RL, where a context vector
sequence is concatenated with the input to guide the conditional policy
generation. As such, we can pretrain a model on the offline dataset with
self-supervised loss and learn a prompt to guide the policy towards desired
actions. Secondly, we extend our framework to Meta-RL settings and propose
Contextual Meta Transformer (CMT); CMT leverages the context among different
tasks as the prompt to improve generalization on unseen tasks. We conduct
extensive experiments across three different offline-RL settings: offline
single-agent RL on the D4RL dataset, offline Meta-RL on the MuJoCo benchmark,
and offline MARL on the SMAC benchmark. Superior results validate the strong
performance, and generality of our methods.

Federated learning is a contemporary machine learning paradigm where locally
trained models are distilled into a global model. Due to the intrinsic
permutation invariance of neural networks, Probabilistic Federated Neural
Matching (PFNM) employs a Bayesian nonparametric framework in the generation
process of local neurons, and then creates a linear sum assignment formulation
in each alternative optimization iteration. But according to our theoretical
analysis, the optimization iteration in PFNM omits global information from
existing. In this study, we propose a novel approach that overcomes this flaw
by introducing a Kullback-Leibler divergence penalty at each iteration. The
effectiveness of our approach is demonstrated by experiments on both image
classification and semantic segmentation tasks.

Adversarial attacks can deceive neural networks by adding tiny perturbations
to their input data. Ensemble defenses, which are trained to minimize attack
transferability among sub-models, offer a promising research direction to
improve robustness against such attacks while maintaining a high accuracy on
natural inputs. We discover, however, that recent state-of-the-art (SOTA)
adversarial attack strategies cannot reliably evaluate ensemble defenses,
sizeably overestimating their robustness. This paper identifies the two factors
that contribute to this behavior. First, these defenses form ensembles that are
notably difficult for existing gradient-based method to attack, due to gradient
obfuscation. Second, ensemble defenses diversify sub-model gradients,
presenting a challenge to defeat all sub-models simultaneously, simply summing
their contributions may counteract the overall attack objective; yet, we
observe that ensemble may still be fooled despite most sub-models being
correct. We therefore introduce MORA, a model-reweighing attack to steer
adversarial example synthesis by reweighing the importance of sub-model
gradients. MORA finds that recent ensemble defenses all exhibit varying degrees
of overestimated robustness. Comparing it against recent SOTA white-box
attacks, it can converge orders of magnitude faster while achieving higher
attack success rates across all ensemble models examined with three different
ensemble modes (i.e., ensembling by either softmax, voting or logits). In
particular, most ensemble defenses exhibit near or exactly 0% robustness
against MORA with $\ell^\infty$ perturbation within 0.02 on CIFAR-10, and 0.01
on CIFAR-100. We make MORA open source with reproducible results and
pre-trained models; and provide a leaderboard of ensemble defenses under
various attack strategies.

Existing gait recognition frameworks retrieve an identity in the gallery
based on the distance between a probe sample and the identities in the gallery.
However, existing methods often neglect that the gallery may not contain
identities corresponding to the probes, leading to recognition errors rather
than raising an alarm. In this paper, we introduce a novel uncertainty-aware
gait recognition method that models the uncertainty of identification based on
learned evidence. Specifically, we treat our recognition model as an evidence
collector to gather evidence from input samples and parameterize a Dirichlet
distribution over the evidence. The Dirichlet distribution essentially
represents the density of the probability assigned to the input samples. We
utilize the distribution to evaluate the resultant uncertainty of each probe
sample and then determine whether a probe has a counterpart in the gallery or
not. To the best of our knowledge, our method is the first attempt to tackle
gait recognition with uncertainty modelling. Moreover, our uncertain modeling
significantly improves the robustness against out-of-distribution (OOD)
queries. Extensive experiments demonstrate that our method achieves
state-of-the-art performance on datasets with OOD queries, and can also
generalize well to other identity-retrieval tasks. Importantly, our method
outperforms the state-of-the-art by a large margin of 44.19% when the OOD query
rate is around 50% on OUMVLP.

A chest X-ray is one of the most widely available radiological examinations
for diagnosing and detecting various lung illnesses. The National Institutes of
Health (NIH) provides an extensive database, ChestX-ray8 and ChestXray14, to
help establish a deep learning community for analysing and predicting lung
diseases. ChestX-ray14 consists of 112,120 frontal-view X-ray images of 30,805
distinct patients with text-mined fourteen disease image labels, where each
image has multiple labels and has been utilised in numerous research in the
past. To our current knowledge, no previous study has investigated outliers and
multi-label impact for a single X-ray image during the preprocessing stage. The
effect of outliers is mitigated in this paper by our proposed auto-outlier
fusion technique. The image label is regenerated by concentrating on a
particular factor in one image. The final cleaned dataset will be used to
compare the mechanisms of multi-head self-attention and multi-head attention
with generalised max-pooling.

Predicting the adsorption affinity of a small molecule to a target surface is
of importance to a range of fields, from catalysis to drug delivery and human
safety, but a complex task to perform computationally when taking into account
the effects of the surrounding medium. We present a flexible machine-learning
approach to predict potentials of mean force (PMFs) and adsorption energies for
chemical -- surface pairs from the separate interaction potentials of each
partner with a set of probe atoms. We use a pre-existing library of PMFs
obtained via atomistic molecular dynamics for a variety of inorganic materials
and molecules to train the model. We find good agreement between original and
predicted PMFs in both training and validation groups, confirming the
predictive power of this approach, and demonstrate the flexibility of the model
by producing PMFs for molecules and surfaces outside the training set.

Due to cost benefits, supply chains of integrated circuits (ICs) are largely
outsourced nowadays. However, passing ICs through various third-party providers
gives rise to many threats, like piracy of IC intellectual property or
insertion of hardware Trojans, i.e., malicious circuit modifications.
  In this work, we proactively and systematically harden the physical layouts
of ICs against post-design insertion of Trojans. Toward that end, we propose a
multiplexer-based logic-locking scheme that is (i) devised for layout-level
Trojan prevention, (ii) resilient against state-of-the-art, oracle-less machine
learning attacks, and (iii) fully integrated into a tailored, yet generic,
commercial-grade design flow. Our work provides in-depth security and layout
analysis on a challenging benchmark suite. We show that ours can render layouts
resilient, with reasonable overheads, against Trojan insertion in general and
also against second-order attacks (i.e., adversaries seeking to bypass the
locking defense in an oracle-less setting).
  We release our layout artifacts for independent verification [29] and we will
release our methodology's source code.

The interactions of quarks and gluons are strong at non-perturbative region.
The equation of state (EoS) of a strongly-interacting quantum chromodynamics
(QCD) medium can only be studied using the first-principle lattice QCD
calculations. However, the complicated QCD EoS can be reproduced using simple
statistical formula by treating the medium as a free parton gas whose
fundamental degree of freedoms are dressed quarks and gluons called
quasi-particles, with temperature-dependent masses. We use deep neural network
and auto differentiation to solve this variational problem in which the masses
of quasi gluons, up/down and strange quarks are three unknown functions, whose
forms are represented by deep neural network. We reproduce the QCD EoS using
these machine learned quasi-particle masses, and calculate the shear viscosity
over entropy density ($\eta/s$) as a function of temperature of the hot QCD
matter.

Brain tumor segmentation based on multi-modal magnetic resonance imaging
(MRI) plays a pivotal role in assisting brain cancer diagnosis, treatment, and
postoperative evaluations. Despite the achieved inspiring performance by
existing automatic segmentation methods, multi-modal MRI data are still
unavailable in real-world clinical applications due to quite a few
uncontrollable factors (e.g. different imaging protocols, data corruption, and
patient condition limitations), which lead to a large performance drop during
practical applications. In this work, we propose a Deeply supervIsed knowledGE
tranSfer neTwork (DIGEST), which achieves accurate brain tumor segmentation
under different modality-missing scenarios. Specifically, a knowledge transfer
learning frame is constructed, enabling a student model to learn
modality-shared semantic information from a teacher model pretrained with the
complete multi-modal MRI data. To simulate all the possible modality-missing
conditions under the given multi-modal data, we generate incomplete multi-modal
MRI samples based on Bernoulli sampling. Finally, a deeply supervised knowledge
transfer loss is designed to ensure the consistency of the teacher-student
structure at different decoding stages, which helps the extraction of inherent
and effective modality representations. Experiments on the BraTS 2020 dataset
demonstrate that our method achieves promising results for the incomplete
multi-modal MR image segmentation task.

Reliability is of paramount importance for the physical layer of wireless
systems due to its decisive impact on end-to-end performance. However, the
uncertainty of prevailing deep learning (DL)-based physical layer algorithms is
hard to quantify due to the black-box nature of neural networks. This
limitation is a major obstacle that hinders their practical deployment. In this
paper, we attempt to quantify the uncertainty of an important category of
DL-based channel estimators. An efficient statistical method is proposed to
make blind predictions for the mean squared error of the DL-estimated channel
solely based on received pilots, without knowledge of the ground-truth channel,
the prior distribution of the channel, or the noise statistics. The complexity
of the blind performance prediction is low and scales only linearly with the
number of antennas. Simulation results for ultra-massive multiple-input
multiple-output (UM-MIMO) channel estimation with a mixture of far-field and
near-field paths are provided to verify the accuracy and efficiency of the
proposed method.

The opacity of deep learning models constrains their debugging and
improvement. Augmenting deep models with saliency-based strategies, such as
attention, has been claimed to help get a better understanding of the
decision-making process of black-box models. However, some recent works
challenged saliency's faithfulness in the field of Natural Language Processing
(NLP), questioning attention weights' adherence to the true decision-making
process of the model. We add to this discussion by evaluating the faithfulness
of in-model saliency applied to a video processing task for the first time,
namely, temporal colour constancy. We perform the evaluation by adapting to our
target task two tests for faithfulness from recent NLP literature, whose
methodology we refine as part of our contributions. We show that attention
fails to achieve faithfulness, while confidence, a particular type of in-model
visual saliency, succeeds.

The game of Jenga represents an inspiring benchmark for developing innovative
manipulation solutions for complex tasks. Indeed, it encouraged the study of
novel robotics methods to extract blocks from the tower successfully. A Jenga
game round undoubtedly embeds many traits of complex industrial or surgical
manipulation tasks, requiring a multi-step strategy, the combination of visual
and tactile data, and the highly precise motion of the robotic arm to perform a
single block extraction. In this work, we propose a novel cost-effective
architecture for playing Jenga with e.Do, a 6-DOF anthropomorphic manipulator
manufactured by Comau, a standard depth camera, and an inexpensive
monodirectional force sensor. Our solution focuses on a visual-based control
strategy to accurately align the end-effector with the desired block, enabling
block extraction by pushing. To this aim, we train an instance segmentation
deep learning model on a synthetic custom dataset to segment each piece of the
Jenga tower, allowing visual tracking of the desired block's pose during the
motion of the manipulator. We integrate the visual-based strategy with a 1D
force sensor to detect whether the block can be safely removed by identifying a
force threshold value. Our experimentation shows that our low-cost solution
allows e.DO to precisely reach removable blocks and perform up to 14
consecutive extractions in a row.

By incorporating the graph structural information into Transformers, graph
Transformers have exhibited promising performance for graph representation
learning in recent years. Existing graph Transformers leverage specific
strategies, such as Laplacian eigenvectors and shortest paths of the node
pairs, to preserve the structural features of nodes and feed them into the
vanilla Transformer to learn the representations of nodes. It is hard for such
predefined rules to extract informative graph structural features for arbitrary
graphs whose topology structure varies greatly, limiting the learning capacity
of the models. To this end, we propose an adaptive graph Transformer, termed
Multi-Neighborhood Attention based Graph Transformer (MNA-GT), which captures
the graph structural information for each node from the multi-neighborhood
attention mechanism adaptively. By defining the input to perform scaled-dot
product as an attention kernel, MNA-GT constructs multiple attention kernels
based on different hops of neighborhoods such that each attention kernel can
capture specific graph structural information of the corresponding neighborhood
for each node pair. In this way, MNA-GT can preserve the graph structural
information efficiently by incorporating node representations learned by
different attention kernels. MNA-GT further employs an attention layer to learn
the importance of different attention kernels to enable the model to adaptively
capture the graph structural information for different nodes. Extensive
experiments are conducted on a variety of graph benchmarks, and the empirical
results show that MNA-GT outperforms many strong baselines.

The recent proliferation of the research on multi-agent deep reinforcement
learning (MDRL) offers an encouraging way to coordinate multiple connected and
automated vehicles (CAVs) to pass the intersection. In this paper, we apply a
value decomposition-based MDRL approach (QMIX) to control various CAVs in
mixed-autonomy traffic of different densities to efficiently and safely pass
the non-signalized intersection with fairish fuel consumption. Implementation
tricks including network-level improvements, Q value update by TD ($\lambda$),
and reward clipping operation are added to the pure QMIX framework, which is
expected to improve the convergence speed and the asymptotic performance of the
original version. The efficacy of our approach is demonstrated by several
evaluation metrics: average speed, the number of collisions, and average fuel
consumption per episode. The experimental results show that our approach's
convergence speed and asymptotic performance can exceed that of the original
QMIX and the proximal policy optimization (PPO), a state-of-the-art
reinforcement learning baseline applied to the non-signalized intersection.
Moreover, CAVs under the lower traffic flow controlled by our method can
improve their average speed without collisions and consume the least fuel. The
training is additionally conducted under the doubled traffic density, where the
learning reward converges. Consequently, the model with maximal reward and
minimum crashes can still guarantee low fuel consumption, but slightly reduce
the efficiency of vehicles and induce more collisions than the lower-traffic
counterpart, implying the difficulty of generalizing RL policy to more advanced
scenarios.

Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary
glioma diagnosis plays an important role in the clinic. Contrast-enhanced MRI
sequences (e.g., contrast-enhanced T1-weighted imaging) were utilized in most
of the existing relevant studies, in which remarkable diagnosis results have
been reported. Nevertheless, acquiring contrast-enhanced MRI data is sometimes
not feasible due to the patients physiological limitations. Furthermore, it is
more time-consuming and costly to collect contrast-enhanced MRI data in the
clinic. In this paper, we propose an adaptive PromptNet to address these
issues. Specifically, a PromptNet for glioma grading utilizing only
non-enhanced MRI data has been constructed. PromptNet receives constraints from
features of contrast-enhanced MR data during training through a designed prompt
loss. To further boost the performance, an adaptive strategy is designed to
dynamically weight the prompt loss in a sample-based manner. As a result,
PromptNet is capable of dealing with more difficult samples. The effectiveness
of our method is evaluated on a widely-used BraTS2020 dataset, and competitive
glioma grading performance on NE-MRI data is achieved.

Machine learning (ML) is revolutionizing the world, affecting almost every
field of science and industry. Recent algorithms (in particular, deep networks)
are increasingly data-hungry, requiring large datasets for training. Thus, the
dominant paradigm in ML today involves constructing large, task-specific
datasets.
  However, obtaining quality datasets of such magnitude proves to be a
difficult challenge. A variety of methods have been proposed to address this
data bottleneck problem, but they are scattered across different areas, and it
is hard for a practitioner to keep up with the latest developments. In this
work, we propose a taxonomy of these methods. Our goal is twofold: (1) We wish
to raise the community's awareness of the methods that already exist and
encourage more efficient use of resources, and (2) we hope that such a taxonomy
will contribute to our understanding of the problem, inspiring novel ideas and
strategies to replace current annotation-heavy approaches.

Risk prediction, as a typical time series modeling problem, is usually
achieved by learning trends in markers or historical behavior from sequence
data, and has been widely applied in healthcare and finance. In recent years,
deep learning models, especially Long Short-Term Memory neural networks
(LSTMs), have led to superior performances in such sequence representation
learning tasks. Despite that some attention or self-attention based models with
time-aware or feature-aware enhanced strategies have achieved better
performance compared with other temporal modeling methods, such improvement is
limited due to a lack of guidance from global view. To address this issue, we
propose a novel end-to-end Hierarchical Global View-guided (HGV) sequence
representation learning framework. Specifically, the Global Graph Embedding
(GGE) module is proposed to learn sequential clip-aware representations from
temporal correlation graph at instance level. Furthermore, following the way of
key-query attention, the harmonic $\beta$-attention ($\beta$-Attn) is also
developed for making a global trade-off between time-aware decay and
observation significance at channel level adaptively. Moreover, the
hierarchical representations at both instance level and channel level can be
coordinated by the heterogeneous information aggregation under the guidance of
global view. Experimental results on a benchmark dataset for healthcare risk
prediction, and a real-world industrial scenario for Small and Mid-size
Enterprises (SMEs) credit overdue risk prediction in MYBank, Ant Group, have
illustrated that the proposed model can achieve competitive prediction
performance compared with other known baselines.

Recent advances in neural-based generative modeling have reignited the hopes
of having computer systems capable of conversing with humans and able to
understand natural language. The employment of deep neural architectures has
been largely explored in a multitude of context and tasks to fulfill various
user needs. On one hand, producing textual content that meets specific
requirements is of priority for a model to seamlessly conduct conversations
with different groups of people. On the other hand, latent variable models
(LVM) such as variational auto-encoders (VAEs) as one of the most popular
genres of generative models are designed to characterize the distributional
pattern of textual data. Thus they are inherently capable of learning the
integral textual features that are worth exploring for controllable pursuits.
  \noindent This overview gives an introduction to existing generation schemes,
problems associated with text variational auto-encoders, and a review of
several applications about the controllable generation that are instantiations
of these general formulations,\footnote{A detailed paper list is available at
\url{https://github.com/ImKeTT/CTG-latentAEs}} as well as related datasets,
metrics and discussions for future researches. Hopefully, this overview will
provide an overview of living questions, popular methodologies and raw thoughts
for controllable language generation under the scope of variational
auto-encoder.

As digital music production has become mainstream, the selection of
appropriate virtual instruments plays a crucial role in determining the quality
of music. To search the musical instrument samples or virtual instruments that
make one's desired sound, music producers use their ears to listen and compare
each instrument sample in their collection, which is time-consuming and
inefficient. In this paper, we call this task as Musical Instrument Retrieval
and propose a method for retrieving desired musical instruments using reference
music mixture as a query. The proposed model consists of the Single-Instrument
Encoder and the Multi-Instrument Encoder, both based on convolutional neural
networks. The Single-Instrument Encoder is trained to classify the instruments
used in single-track audio, and we take its penultimate layer's activation as
the instrument embedding. The Multi-Instrument Encoder is trained to estimate
multiple instrument embeddings using the instrument embeddings computed by the
Single-Instrument Encoder as a set of target embeddings. For more generalized
training and realistic evaluation, we also propose a new dataset called Nlakh.
Experimental results showed that the Single-Instrument Encoder was able to
learn the mapping from the audio signal of unseen instruments to the instrument
embedding space and the Multi-Instrument Encoder was able to extract multiple
embeddings from the mixture of music and retrieve the desired instruments
successfully. The code used for the experiment and audio samples are available
at: https://github.com/minju0821/musical_instrument_retrieval

Can we teach natural language understanding models to track their beliefs
through intermediate points in text? We propose a representation learning
framework called breakpoint modeling that allows for learning of this type.
Given any text encoder and data marked with intermediate states (breakpoints)
along with corresponding textual queries viewed as true/false propositions
(i.e., the candidate beliefs of a model, consisting of information changing
through time) our approach trains models in an efficient and end-to-end fashion
to build intermediate representations that facilitate teaching and direct
querying of beliefs at arbitrary points alongside solving other end tasks. To
show the benefit of our approach, we experiment with a diverse set of NLU tasks
including relational reasoning on CLUTRR and narrative understanding on bAbI.
Using novel belief prediction tasks for both tasks, we show the benefit of our
main breakpoint transformer, based on T5, over conventional representation
learning approaches in terms of processing efficiency, prediction accuracy and
prediction consistency, all with minimal to no effect on corresponding QA end
tasks. To show the feasibility of incorporating our belief tracker into more
complex reasoning pipelines, we also obtain SOTA performance on the
three-tiered reasoning challenge for the TRIP benchmark (around 23-32% absolute
improvement on Tasks 2-3).

International Classification of Diseases (ICD) is a global medical
classification system which provides unique codes for diagnoses and procedures
appropriate to a patient's clinical record. However, manual coding by human
coders is expensive and error-prone. Automatic ICD coding has the potential to
solve this problem. With the advancement of deep learning technologies, many
deep learning-based methods for automatic ICD coding are being developed. In
particular, a label attention mechanism is effective for multi-label
classification, i.e., the ICD coding. It effectively obtains the label-specific
representations from the input clinical records. However, because the existing
label attention mechanism finds key tokens in the entire text at once, the
important information dispersed in each paragraph may be omitted from the
attention map. To overcome this, we propose a novel neural network architecture
composed of two parts of encoders and two kinds of label attention layers. The
input text is segmentally encoded in the former encoder and integrated by the
follower. Then, the conventional and partition-based label attention mechanisms
extract important global and local feature representations. Our classifier
effectively integrates them to enhance the ICD coding performance. We verified
the proposed method using the MIMIC-III, a benchmark dataset of the ICD coding.
Our results show that our network improves the ICD coding performance based on
the partition-based mechanism.

Training novice users to operate an excavator for learning different skills
requires the presence of expert teachers. Considering the complexity of the
problem, it is comparatively expensive to find skilled experts as the process
is time-consuming and requires precise focus. Moreover, since humans tend to be
biased, the evaluation process is noisy and will lead to high variance in the
final score of different operators with similar skills. In this work, we
address these issues and propose a novel strategy for the automatic evaluation
of excavator operators. We take into account the internal dynamics of the
excavator and the safety criterion at every time step to evaluate the
performance. To further validate our approach, we use this score prediction
model as a source of reward for a reinforcement learning agent to learn the
task of maneuvering an excavator in a simulated environment that closely
replicates the real-world dynamics. For a policy learned using these external
reward prediction models, our results demonstrate safer solutions following the
required dynamic constraints when compared to policy trained with task-based
reward functions only, making it one step closer to real-life adoption. For
future research, we release our codebase at
https://github.com/pranavAL/InvRL_Auto-Evaluate and video results
https://drive.google.com/file/d/1jR1otOAu8zrY8mkhUOUZW9jkBOAKK71Z/view?usp=share_link .

Network slicing is a well assessed approach enabling virtualization of the
mobile core and radio access network (RAN) in the emerging 5th Generation New
Radio. Slicing is of paramount importance when dealing with the emerging and
diverse vertical applications entailing heterogeneous sets of requirements. 5G
is also envisioning Unmanned Aerial Vehicles (UAVs) to be a key element in the
cellular network standard, aiming at their use as aerial base stations and
exploiting their flexible and quick deployment to enhance the wireless network
performance. This work presents a UAV-assisted 5G network, where the aerial
base stations (UAV-BS) are empowered with network slicing capabilities aiming
at optimizing the Service Level Agreement (SLA) satisfaction ratio of a set of
users. The users belong to three heterogeneous categories of 5G service type,
namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency
communication (URLLC), and massive machine-type communication (mMTC). A first
application of multi-agent and multi-decision deep reinforcement learning for
UAV-BS in a network slicing context is introduced, aiming at the optimization
of the SLA satisfaction ratio of users through the joint allocation of radio
resources to slices and refinement of the UAV-BSs 2-dimensional trajectories.
The performance of the presented strategy have been tested and compared to
benchmark heuristics, highlighting a higher percentage of satisfied users (at
least 27% more) in a variety of scenarios.

In this paper, we revisit and improve the convergence of policy gradient
(PG), natural PG (NPG) methods, and their variance-reduced variants, under
general smooth policy parametrizations. More specifically, with the Fisher
information matrix of the policy being positive definite: i) we show that a
state-of-the-art variance-reduced PG method, which has only been shown to
converge to stationary points, converges to the globally optimal value up to
some inherent function approximation error due to policy parametrization; ii)
we show that NPG enjoys a lower sample complexity; iii) we propose SRVR-NPG,
which incorporates variance-reduction into the NPG update. Our improvements
follow from an observation that the convergence of (variance-reduced) PG and
NPG methods can improve each other: the stationary convergence analysis of PG
can be applied to NPG as well, and the global convergence analysis of NPG can
help to establish the global convergence of (variance-reduced) PG methods. Our
analysis carefully integrates the advantages of these two lines of works.
Thanks to this improvement, we have also made variance-reduction for NPG
possible, with both global convergence and an efficient finite-sample
complexity.

Stressors are related to depression, but this relationship is complex. We
investigate the relationship between open-ended text responses about stressors
and depressive symptoms across gender and racial/ethnic groups. First, we use
topic models and other NLP tools to find thematic and vocabulary differences
when reporting stressors across demographic groups. We train language models
using self-reported stressors to predict depressive symptoms, finding a
relationship between stressors and depression. Finally, we find that
differences in stressors translate to downstream performance differences across
demographic groups.

Federated learning (FL) is a decentralized machine learning technique that
enables multiple clients to collaboratively train models without revealing the
raw data to each other. Although the traditional FL trains a single global
model with average performance among clients, the statistical data
heterogeneity across clients motivates personalized FL (PFL) which learns
personalized models with good performance on each client's data. A key
challenge in PFL is how to promote clients with similar data to collaborate
more in a situation where each client has data from complex distribution and
does not know each other's distribution. In this paper, we propose a new PFL
method, personalized federated learning with multi-branch architecture
(pFedMB), which achieves personalization by splitting each layer of neural
networks into multiple branches and assigning client-specific weights to each
branch. pFedMB is simple but effective to facilitate each client to share the
knowledge with similar clients by adjusting the weights assigned to each
branch. We experimentally show that pFedMB performs better than the
state-of-the-art PFL methods using CIFAR10 dataset.

The existing SSCL of RSI is built based on constructing positive and negative
sample pairs. However, due to the richness of RSI ground objects and the
complexity of the RSI contextual semantics, the same RSI patches have the
coexistence and imbalance of positive and negative samples, which causing the
SSCL pushing negative samples far away while pushing positive samples far away,
and vice versa. We call this the sample confounding issue (SCI). To solve this
problem, we propose a False negAtive sampLes aware contraStive lEarning model
(FALSE) for the semantic segmentation of high-resolution RSIs. Since the SSCL
pretraining is unsupervised, the lack of definable criteria for false negative
sample (FNS) leads to theoretical undecidability, we designed two steps to
implement the FNS approximation determination: coarse determination of FNS and
precise calibration of FNS. We achieve coarse determination of FNS by the FNS
self-determination (FNSD) strategy and achieve calibration of FNS by the FNS
confidence calibration (FNCC) loss function. Experimental results on three RSI
semantic segmentation datasets demonstrated that the FALSE effectively improves
the accuracy of the downstream RSI semantic segmentation task compared with the
current three models, which represent three different types of SSCL models. The
mean Intersection-over-Union on ISPRS Potsdam dataset is improved by 0.7\% on
average; on CVPR DGLC dataset is improved by 12.28\% on average; and on
Xiangtan dataset this is improved by 1.17\% on average. This indicates that the
SSCL model has the ability to self-differentiate FNS and that the FALSE
effectively mitigates the SCI in self-supervised contrastive learning. The
source code is available at https://github.com/GeoX-Lab/FALSE.

Large language models (LLMs) have shown increasing in-context learning
capabilities through scaling up model and data size. Despite this progress,
LLMs are still unable to solve algorithmic reasoning problems. While providing
a rationale with the final answer has led to further improvements in multi-step
reasoning problems, Anil et al. 2022 showed that even simple algorithmic
reasoning tasks such as parity are far from solved. In this work, we identify
and study four key stages for successfully teaching algorithmic reasoning to
LLMs: (1) formulating algorithms as skills, (2) teaching multiple skills
simultaneously (skill accumulation), (3) teaching how to combine skills (skill
composition) and (4) teaching how to use skills as tools. We show that it is
possible to teach algorithmic reasoning to LLMs via in-context learning, which
we refer to as algorithmic prompting. We evaluate our approach on a variety of
arithmetic and quantitative reasoning tasks, and demonstrate significant boosts
in performance over existing prompting techniques. In particular, for long
parity, addition, multiplication and subtraction, we achieve an error reduction
of approximately 10x, 9x, 5x and 2x respectively compared to the best available
baselines.

Backdoor attacks have emerged as one of the major security threats to deep
learning models as they can easily control the model's test-time predictions by
pre-injecting a backdoor trigger into the model at training time. While
backdoor attacks have been extensively studied on images, few works have
investigated the threat of backdoor attacks on time series data. To fill this
gap, in this paper we present a novel generative approach for time series
backdoor attacks against deep learning based time series classifiers. Backdoor
attacks have two main goals: high stealthiness and high attack success rate. We
find that, compared to images, it can be more challenging to achieve the two
goals on time series. This is because time series have fewer input dimensions
and lower degrees of freedom, making it hard to achieve a high attack success
rate without compromising stealthiness. Our generative approach addresses this
challenge by generating trigger patterns that are as realistic as real-time
series patterns while achieving a high attack success rate without causing a
significant drop in clean accuracy. We also show that our proposed attack is
resistant to potential backdoor defenses. Furthermore, we propose a novel
universal generator that can poison any type of time series with a single
generator that allows universal attacks without the need to fine-tune the
generative model for new time series datasets.

When performing real-time learning tasks, the radial basis function neural
network (RBFNN) is expected to make full use of the training samples such that
its learning accuracy and generalization capability are guaranteed. Since the
approximation capability of the RBFNN is finite, training methods with
forgetting mechanisms such as the forgetting factor recursive least squares
(FFRLS) and stochastic gradient descent (SGD) methods are widely used to
maintain the learning ability of the RBFNN to new knowledge. However, with the
forgetting mechanisms, some useful knowledge will get lost simply because they
are learned a long time ago, which we refer to as the passive knowledge
forgetting phenomenon. To address this problem, this paper proposes a real-time
training method named selective memory recursive least squares (SMRLS) in which
the feature space of the RBFNN is evenly discretized into a finite number of
partitions and a synthesized objective function is developed to replace the
original objective function of the ordinary recursive least squares (RLS)
method. SMRLS is featured with a memorization mechanism that synthesizes the
samples within each partition in real-time into representative samples
uniformly distributed over the feature space, and thus overcomes the passive
knowledge forgetting phenomenon and improves the generalization capability of
the learned knowledge. Compared with the SGD or FFRLS methods, SMRLS achieves
improved learning performance (learning speed, accuracy and generalization
capability), which is demonstrated by corresponding simulation results.

We introduce a method, MMD-B-Fair, to learn fair representations of data via
kernel two-sample testing. We find neural features of our data where a maximum
mean discrepancy (MMD) test cannot distinguish between different values of
sensitive attributes, while preserving information about the target. Minimizing
the power of an MMD test is more difficult than maximizing it (as done in
previous work), because the test threshold's complex behavior cannot be simply
ignored. Our method exploits the simple asymptotics of block testing schemes to
efficiently find fair representations without requiring the complex adversarial
optimization or generative modelling schemes widely used by existing work on
fair representation learning. We evaluate our approach on various datasets,
showing its ability to "hide" information about sensitive attributes, and its
effectiveness in downstream transfer tasks.

We describe a neural transducer that maintains the flexibility of standard
sequence-to-sequence (seq2seq) models while incorporating hierarchical phrases
as a source of inductive bias during training and as explicit constraints
during inference. Our approach trains two models: a discriminative parser based
on a bracketing transduction grammar whose derivation tree hierarchically
aligns source and target phrases, and a neural seq2seq model that learns to
translate the aligned phrases one-by-one. We use the same seq2seq model to
translate at all phrase scales, which results in two inference modes: one mode
in which the parser is discarded and only the seq2seq component is used at the
sequence-level, and another in which the parser is combined with the seq2seq
model. Decoding in the latter mode is done with the cube-pruned CKY algorithm,
which is more involved but can make use of new translation rules during
inference. We formalize our model as a source-conditioned synchronous grammar
and develop an efficient variational inference algorithm for training. When
applied on top of both randomly initialized and pretrained seq2seq models, we
find that both inference modes performs well compared to baselines on small
scale machine translation benchmarks.

This paper proposes a new class of heterogeneous causal quantities, named
\textit{outcome conditioned} average structural derivatives (OASD) in a general
nonseparable model. OASD is the average partial effect of a marginal change in
a continuous treatment on the individuals located at different parts of the
outcome distribution, irrespective of individuals' characteristics. OASD
combines both features of ATE and QTE: it is interpreted as straightforwardly
as ATE while at the same time more granular than ATE by breaking the entire
population up according to the rank of the outcome distribution.
  One contribution of this paper is that we establish some close relationships
between the \textit{outcome conditioned average partial effects} and a class of
parameters measuring the effect of counterfactually changing the distribution
of a single covariate on the unconditional outcome quantiles. By exploiting
such relationship, we can obtain root-$n$ consistent estimator and calculate
the semi-parametric efficiency bound for these counterfactual effect
parameters. We illustrate this point by two examples: equivalence between OASD
and the unconditional partial quantile effect (Firpo et al. (2009)), and
equivalence between the marginal partial distribution policy effect (Rothe
(2012)) and a corresponding outcome conditioned parameter.
  Because identification of OASD is attained under a conditional exogeneity
assumption, by controlling for a rich information about covariates, a
researcher may ideally use high-dimensional controls in data. We propose for
OASD a novel automatic debiased machine learning estimator, and present
asymptotic statistical guarantees for it. We prove our estimator is root-$n$
consistent, asymptotically normal, and semiparametrically efficient. We also
prove the validity of the bootstrap procedure for uniform inference on the OASD
process.

We study the problem of rank aggregation where the goal is to obtain a global
ranking by aggregating pair-wise comparisons of voters over a set of items. We
consider an adversarial setting where the voters are partitioned into two sets.
The first set votes in a stochastic manner according to the popular score-based
Bradley-Terry-Luce (BTL) model for pairwise comparisons. The second set
comprises malicious Byzantine voters trying to deteriorate the ranking. We
consider a strongly-adversarial scenario where the Byzantine voters know the
BTL scores, the votes of the good voters, the algorithm, and can collude with
each other. We first show that the popular spectral ranking based
Rank-Centrality algorithm, though optimal for the BTL model, does not perform
well even when a small constant fraction of the voters are Byzantine. We
introduce the Byzantine Spectral Ranking Algorithm (and a faster variant of
it), which produces a reliable ranking when the number of good voters exceeds
the number of Byzantine voters. We show that no algorithm can produce a
satisfactory ranking with probability > 1/2 for all BTL weights when there are
more Byzantine voters than good voters, showing that our algorithm works for
all possible population fractions. We support our theoretical results with
experimental results on synthetic and real datasets to demonstrate the failure
of the Rank-Centrality algorithm under several adversarial scenarios and how
the proposed Byzantine Spectral Ranking algorithm is robust in obtaining good
rankings.

Recent years have witnessed the dramatic growth of Internet video traffic,
where the video bitstreams are often compressed and delivered in low quality to
fit the streamer's uplink bandwidth. To alleviate the quality degradation, it
comes the rise of Neural-enhanced Video Streaming (NVS), which shows great
prospects to recover low-quality videos by mostly deploying neural
super-resolution (SR) on the media server. Despite its benefit, we reveal that
current mainstream works with SR enhancement have not achieved the desired
rate-distortion trade-off between bitrate saving and quality restoration, due
to: (1) overemphasizing the enhancement on the decoder side while omitting the
co-design of encoder, (2) inherent limited restoration capacity to generate
high-fidelity perceptual details, and (3) optimizing the
compression-and-restoration pipeline from the resolution perspective solely,
without considering color bit-depth. Aiming at overcoming these limitations, we
are the first to conduct the encoder-decoder (i.e., codec) synergy by
leveraging the visual-synthesis genius of diffusion models. Specifically, we
present the Codec-aware Diffusion Modeling (CaDM), a novel NVS paradigm to
significantly reduce streaming delivery bitrate while holding pretty higher
restoration capacity over existing methods. First, CaDM improves the encoder's
compression efficiency by simultaneously reducing resolution and color
bit-depth of video frames. Second, CaDM provides the decoder with perfect
quality enhancement by making the denoising diffusion restoration aware of
encoder's resolution-color conditions. Evaluation on public cloud services with
OpenMMLab benchmarks shows that CaDM significantly saves streaming bitrate by a
nearly 100 times reduction over vanilla H.264 and achieves much better recovery
quality (e.g., FID of 0.61) over state-of-the-art neural-enhancing methods.

We consider the problem of time-limited robotic exploration in previously
unseen environments where exploration is limited by a predefined amount of
time. We propose a novel exploration approach using learning-augmented
model-based planning. We generate a set of subgoals associated with frontiers
on the current map and derive a Bellman Equation for exploration with these
subgoals. Visual sensing and advances in semantic mapping of indoor scenes are
exploited for training a deep convolutional neural network to estimate
properties associated with each frontier: the expected unobserved area beyond
the frontier and the expected timesteps (discretized actions) required to
explore it. The proposed model-based planner is guaranteed to explore the whole
scene if time permits. We thoroughly evaluate our approach on a large-scale
pseudo-realistic indoor dataset (Matterport3D) with the Habitat simulator. We
compare our approach with classical and more recent RL-based exploration
methods, demonstrating its clear advantages in several settings.

Federated learning is the process of developing machine learning models over
datasets distributed across data centers such as hospitals, clinical research
labs, and mobile devices while preventing data leakage. This survey examines
previous research and studies on federated learning in the healthcare sector
across a range of use cases and applications. Our survey shows what challenges,
methods, and applications a practitioner should be aware of in the topic of
federated learning. This paper aims to lay out existing research and list the
possibilities of federated learning for healthcare industries.

The hippocampus plays a vital role in the diagnosis and treatment of many
neurological disorders. Recent years, deep learning technology has made great
progress in the field of medical image segmentation, and the performance of
related tasks has been constantly refreshed. In this paper, we focus on the
hippocampus segmentation task and propose a novel hierarchical feedback chain
network. The feedback chain structure unit learns deeper and wider feature
representation of each encoder layer through the hierarchical feature
aggregation feedback chains, and achieves feature selection and feedback
through the feature handover attention module. Then, we embed a global pyramid
attention unit between the feature encoder and the decoder to further modify
the encoder features, including the pair-wise pyramid attention module for
achieving adjacent attention interaction and the global context modeling module
for capturing the long-range knowledge. The proposed approach achieves
state-of-the-art performance on three publicly available datasets, compared
with existing hippocampus segmentation approaches.

Medical datasets often face the problem of data scarcity, as ground truth
labels must be generated by medical professionals. One mitigation strategy is
to pretrain deep learning models on large, unlabelled datasets with
self-supervised learning (SSL). Data augmentations are essential for improving
the generalizability of SSL-trained models, but they are typically handcrafted
and tuned manually. We use an adversarial model to generate masks as
augmentations for 12-lead electrocardiogram (ECG) data, where masks learn to
occlude diagnostically-relevant regions of the ECGs. Compared to random
augmentations, adversarial masking reaches better accuracy when transferring to
to two diverse downstream objectives: arrhythmia classification and gender
classification. Compared to a state-of-art ECG augmentation method 3KG,
adversarial masking performs better in data-scarce regimes, demonstrating the
generalizability of our model.

Recent trends in the machine learning community show that models with
fidelity toward human perceptual measurements perform strongly on vision tasks.
Likewise, human behavioral measurements have been used to regularize model
performance. But can we transfer latent knowledge gained from this across
different learning objectives? In this work, we introduce PERCEP-TL (Perceptual
Transfer Learning), a methodology for improving transfer learning with the
regularization power of psychophysical labels in models. We demonstrate which
models are affected the most by perceptual transfer learning and find that
models with high behavioral fidelity -- including vision transformers --
improve the most from this regularization by as much as 1.9\% Top@1 accuracy
points. These findings suggest that biologically inspired learning agents can
benefit from human behavioral measurements as regularizers and psychophysical
learned representations can be transferred to independent evaluation tasks.

Action advising is a knowledge transfer technique for reinforcement learning
based on the teacher-student paradigm. An expert teacher provides advice to a
student during training in order to improve the student's sample efficiency and
policy performance. Such advice is commonly given in the form of state-action
pairs. However, it makes it difficult for the student to reason with and apply
to novel states. We introduce Explainable Action Advising, in which the teacher
provides action advice as well as associated explanations indicating why the
action was chosen. This allows the student to self-reflect on what it has
learned, enabling advice generalization and leading to improved sample
efficiency and learning performance - even in environments where the teacher is
sub-optimal. We empirically show that our framework is effective in both
single-agent and multi-agent scenarios, yielding improved policy returns and
convergence rates when compared to state-of-the-art methods.

Growing materials data and data-centric informatics tools drastically promote
the discovery and design of materials. While data-driven models, such as
machine learning, have drawn much attention and observed significant progress,
the quality of data resources is equally important but less studied. In this
work, we focus on bias mitigation, an important aspect of materials data
quality. Quantifying the diversity of stability in different crystal systems,
we propose a metric for measuring structure-stability bias in materials data.
To mitigate the bias, we develop an entropy-target active learning (ET-AL)
framework, guiding the acquisition of new data so that diversities of
underrepresented crystal systems are improved, thus mitigating the bias. With
experiments on materials datasets, we demonstrate the capability of ET-AL and
the improvement in machine learning models through bias mitigation. The
approach is applicable to data-centric informatics in other scientific domains.

In this study, we focus on brain tumor sequence registration between
pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain
glioma patients, in the context of Brain Tumor Sequence Registration challenge
(BraTS-Reg 2022). Brain tumor registration is a fundamental requirement in
brain image analysis for quantifying tumor changes. This is a challenging task
due to large deformations and missing correspondences between pre-operative and
follow-up scans. For this task, we adopt our recently proposed Non-Iterative
Coarse-to-finE registration Networks (NICE-Net) - a deep learning-based method
for coarse-to-fine registering images with large deformations. To overcome
missing correspondences, we extend the NICE-Net by introducing dual deep
supervision, where a deep self-supervised loss based on image similarity and a
deep weakly-supervised loss based on manually annotated landmarks are deeply
embedded into the NICE-Net. At the BraTS-Reg 2022, our method achieved a
competitive result on the validation set (mean absolute error: 3.387) and
placed 4th in the final testing phase (Score: 0.3544).

Epilepsy affects millions of people, reducing quality of life and increasing
risk of premature death. One-third of epilepsy cases are drug-resistant and
require surgery for treatment, which necessitates localizing the seizure onset
zone (SOZ) in the brain. Attempts have been made to use cortico-cortical evoked
potentials (CCEPs) to improve SOZ localization but none have been successful
enough for clinical adoption. Here, we compare the performance of ten machine
learning classifiers in localizing SOZ from CCEP data. This preliminary study
validates a novel application of machine learning, and the results establish
our approach as a promising line of research that warrants further
investigation. This work also serves to facilitate discussion and collaboration
with fellow machine learning and/or epilepsy researchers.

Temporal network has become ubiquitous with the rise of online social
platform and e-commerce, but largely under investigated in literature. In this
paper, we propose a statistical framework for temporal network analysis,
leveraging strengths of adaptive network merging, tensor decomposition and
point process. A two-step embedding procedure and a regularized maximum
likelihood estimate based on Poisson point process is developed, where the
initial estimate is based on equal spaced time intervals while the final
estimate on the adaptively merging time intervals. A projected gradient descent
algorithm is proposed to facilitate estimation, where the upper bound of the
tensor estimation error in each iteration is established. Through analysis, it
is shown that the tensor estimation error is significantly reduced by the
proposed method. Extensive numerical experiments also validate this phenomenon,
as well as its advantage over other existing competitors. The proposed method
is also applied to analyze a militarized interstate dispute dataset, where not
only the prediction accuracy increases, but the adaptively merged intervals
also lead to clear interpretation.

Federated learning (FL) allows multiple parties to collaboratively train a
global model without disclosing their data. Existing research often requires
all model parameters to participate in the training procedure. However, with
the advent of powerful pre-trained models, it becomes possible to achieve
higher performance with fewer learnable parameters in FL. In this paper, we
propose a federated adaptive prompt tuning algorithm, FedAPT, for cross-domain
federated image classification scenarios with the vision-language pre-trained
model, CLIP, which gives play to the strong representation ability in FL.
Compared with direct federated prompt tuning, our core idea is to adaptively
unlock specific domain knowledge for each test sample in order to provide them
with personalized prompts. To implement this idea, we design an adaptive prompt
tuning module, which consists of a global prompt, an adaptive network, and some
keys. The server randomly generates a set of keys and assigns a unique key to
each client. Then all clients cooperatively train the global adaptive network
and global prompt with the local datasets and the frozen keys. Ultimately, the
global aggregation model can assign a personalized prompt to CLIP based on the
domain features of each test sample. We perform extensive experiments on two
multi-domain image classification datasets. The results show that FedAPT can
achieve better performance with less than 10\% of the number of parameters of
the fully trained model, and the global model can perform well in different
client domains simultaneously.

The criteria for measuring music similarity are important for developing a
flexible music recommendation system. Some data-driven methods have been
proposed to calculate music similarity from only music signals, such as metric
learning based on a triplet loss using tag information on each musical piece.
However, the resulting music similarity metric usually captures the entire
piece of music, i.e., the mixing of various instrumental sound sources,
limiting the capability of the music recommendation system, e.g., it is
difficult to search for a musical piece containing similar drum sounds. Towards
the development of a more flexible music recommendation system, we propose a
music similarity calculation method that focuses on individual instrumental
sound sources in a musical piece. By fully exploiting the potential of
data-driven methods for our proposed method, we employ weakly supervised metric
learning to individual instrumental sound source signals without using any tag
information, where positive and negative samples in a triplet loss are defined
by whether or not they are from the same musical piece. Furthermore, assuming
that each instrumental sound source is not always available in practice, we
also investigate the effects of using instrumental sound source separation to
obtain each source in the proposed method. Experimental results have shown that
(1) unique similarity metrics can be learned for individual instrumental sound
sources, (2) similarity metrics learned using some instrumental sound sources
are possible to lead to more accurate results than that learned using the
entire musical piece, (3) the performance degraded when learning with the
separated instrumental sounds, and (4) similarity metrics learned by the
proposed method well produced results that correspond to perception by human
senses.

The Stein Variational Gradient Descent (SVGD) algorithm is an deterministic
particle method for sampling. However, a mean-field analysis reveals that the
gradient flow corresponding to the SVGD algorithm (i.e., the Stein Variational
Gradient Flow) only provides a constant-order approximation to the Wasserstein
Gradient Flow corresponding to the KL-divergence minimization. In this work, we
propose the Regularized Stein Variational Gradient Flow which interpolates
between the Stein Variational Gradient Flow and the Wasserstein Gradient Flow.
We establish various theoretical properties of the Regularized Stein
Variational Gradient Flow (and its time-discretization) including convergence
to equilibrium, existence and uniqueness of weak solutions, and stability of
the solutions. We provide preliminary numerical evidence of the improved
performance offered by the regularization.

With the rise of edge computing, various AI services are expected to be
available at a mobile side through the inference based on deep neural network
(DNN) operated at the network edge, called edge inference (EI). On the other
hand, the resulting AI quality (e.g., mean average precision in objective
detection) has been regarded as a given factor, and AI quality control has yet
to be explored despite its importance in addressing the diverse demands of
different users. This work aims at tackling the issue by proposing a feature
hierarchical EI (FHEI), comprising feature network and inference network
deployed at an edge server and corresponding mobile, respectively.
Specifically, feature network is designed based on feature hierarchy, a
one-directional feature dependency with a different scale. A higher scale
feature requires more computation and communication loads while it provides a
better AI quality. The tradeoff enables FHEI to control AI quality gradually
w.r.t. communication and computation loads, leading to deriving a
near-to-optimal solution to maximize multi-user AI quality under the
constraints of uplink \& downlink transmissions and edge server and mobile
computation capabilities. It is verified by extensive simulations that the
proposed joint communication-and-computation control on FHEI architecture
always outperforms several benchmarks by differentiating each user's AI quality
depending on the communication and computation conditions.

Language difference is one of the factors that hinder the acquisition of
second language skills. In this article, we introduce a novel solution that
leverages the strength of deep neural networks to measure the semantic
dissimilarity between languages based on their word distributions in the
embedding space of the multilingual pre-trained language model (e.g.,BERT).
Then, we empirically examine the effectiveness of the proposed semantic
language distance (SLD) in explaining the consistent variation in English
ability of countries, which is proxied by their performance in the
Internet-Based Test of English as Foreign Language (TOEFL iBT). The
experimental results show that the language distance demonstrates negative
influence on a country's average English ability. Interestingly, the effect is
more significant on speaking and writing subskills, which pertain to the
productive aspects of language learning. Besides, we provide specific
recommendations for future research directions.

Quantum computing has gained a lot of attention recently, and scientists have
seen potential applications in this field using quantum computing for
Cryptography and Communication to Machine Learning and Healthcare. Protein
folding has been one of the most interesting areas to study, and it is also one
of the biggest problems of biochemistry. Each protein folds distinctively, and
the difficulty of finding its stable shape rapidly increases with an increase
in the number of amino acids in the chain. A moderate protein has about 100
amino acids, and the number of combinations one needs to verify to find the
stable structure is enormous. At some point, the number of these combinations
will be so vast that classical computers cannot even attempt to solve them. In
this paper, we examine how this problem can be solved with the help of quantum
computing using two different algorithms, Variational Quantum Eigensolver (VQE)
and Quantum Approximate Optimization Algorithm (QAOA), using Qiskit Nature. We
compare the results of different quantum hardware and simulators and check how
error mitigation affects the performance. Further, we make comparisons with
SoTA algorithms and evaluate the reliability of the method.

As robots become increasingly capable of manipulation and long-term autonomy,
long-horizon task and motion planning problems are becoming increasingly
important. A key challenge in such problems is that early actions in the plan
may make future actions infeasible. When reaching a dead-end in the search,
most existing planners use backtracking, which exhaustively reevaluates
motion-level actions, often resulting in inefficient planning, especially when
the search depth is large. In this paper, we propose to learn backjumping
heuristics which identify the culprit action directly using supervised learning
models to guide the task-level search. Based on evaluations on two different
tasks, we find that our method significantly improves planning efficiency
compared to backtracking and also generalizes to problems with novel numbers of
objects.

The decoupled Graph Convolutional Network (GCN), a recent development of GCN
that decouples the neighborhood aggregation and feature transformation in each
convolutional layer, has shown promising performance for graph representation
learning. Existing decoupled GCNs first utilize a simple neural network (e.g.,
MLP) to learn the hidden features of the nodes, then propagate the learned
features on the graph with fixed steps to aggregate the information of
multi-hop neighborhoods. Despite effectiveness, the aggregation operation,
which requires the whole adjacency matrix as the input, is involved in the
model training, causing high training cost that hinders its potential on larger
graphs. On the other hand, due to the independence of node attributes as the
input, the neural networks used in decoupled GCNs are very simple, and advanced
techniques cannot be applied to the modeling. To this end, we further liberate
the aggregation operation from the decoupled GCN and propose a new paradigm of
GCN, termed Neighborhood Convolutional Network (NCN), that utilizes the
neighborhood aggregation result as the input, followed by a special
convolutional neural network tailored for extracting expressive node
representations from the aggregation input. In this way, the model could
inherit the merit of decoupled GCN for aggregating neighborhood information, at
the same time, develop much more powerful feature learning modules. A training
strategy called mask training is incorporated to further boost the model
performance. Extensive results demonstrate the effectiveness of our model for
the node classification task on diverse homophilic graphs and heterophilic
graphs.

Under mild conditions on the network initialization we derive a power series
expansion for the Neural Tangent Kernel (NTK) of arbitrarily deep feedforward
networks in the infinite width limit. We provide expressions for the
coefficients of this power series which depend on both the Hermite coefficients
of the activation function as well as the depth of the network. We observe
faster decay of the Hermite coefficients leads to faster decay in the NTK
coefficients. Using this series, first we relate the effective rank of the NTK
to the effective rank of the input-data Gram. Second, for data drawn uniformly
on the sphere we derive an explicit formula for the eigenvalues of the NTK,
which shows faster decay in the NTK coefficients implies a faster decay in its
spectrum. From this we recover existing results on eigenvalue asymptotics for
ReLU networks and comment on how the activation function influences the RKHS.
Finally, for generic data and activation functions with sufficiently fast
Hermite coefficient decay, we derive an asymptotic upper bound on the spectrum
of the NTK.

Despite the increase in popularity of language models for code generation, it
is still unknown how training on bimodal coding forums affects a model's code
generation performance and reliability. We, therefore, collect a dataset of
over 2.2M StackOverflow questions with answers for finetuning. These fine-tuned
models have average $pass@k$ improvements of 54.64% and 85.35% on the HumanEval
(Chen et al., 2021) and Mostly Basic Program Problems (Austin et al., 2021)
tasks, respectively. This regime further decreases the number of generated
programs with both syntax and runtime errors. However, we find that at higher
temperatures, there are significant decreases to the model's ability to
generate runnable programs despite higher $pass@k$ scores, underscoring the
need for better methods of incorporating such data that mitigate these side
effects. The code can be found
https://github.com/gabeorlanski/bimodalcode-generation

Quantum extreme reservoir computation (QERC) is a versatile quantum neural
network model that combines the concepts of extreme machine learning with
quantum reservoir computation. Key to QERC is the generation of a complex
quantum reservoir (feature space) that does not need to be optimized for
different problem instances. Originally, a periodically-driven system
Hamiltonian dynamics was employed as the quantum feature map. In this work we
capture how the quantum feature map is generated as the number of time-steps of
the dynamics increases by a method to characterize unitary matrices in the form
of weighted networks. Furthermore, to identify the key properties of the
feature map that has sufficiently grown, we evaluate it with various weighted
network models that could be used for the quantum reservoir in image
classification situations. At last, we show how a simple Hamiltonian model
based on a disordered discrete time crystal with its simple implementation
route provides nearly-optimal performance while removing the necessity of
programming of the quantum processor gate by gate.

Most sports visualizations rely on a combination of spatial, highly temporal,
and user-centric data, making sports a challenging target for visualization.
Emerging technologies, such as augmented and mixed reality (AR/XR), have
brought exciting opportunities along with new challenges for sports
visualization. We share our experience working with sports domain experts and
present lessons learned from conducting visualization research in SportsXR. In
our previous work, we have targeted different types of users in sports,
including athletes, game analysts, and fans. Each user group has unique design
constraints and requirements, such as obtaining real-time visual feedback in
training, automating the low-level video analysis workflow, or personalizing
embedded visualizations for live game data analysis. In this paper, we
synthesize our best practices and pitfalls we identified while working on
SportsXR. We highlight lessons learned in working with sports domain experts in
designing and evaluating sports visualizations and in working with emerging
AR/XR technologies. We envision that sports visualization research will benefit
the larger visualization community through its unique challenges and
opportunities for immersive and situated analytics.

Semi-parametric Nearest Neighbor Language Models ($k$NN-LMs) have produced
impressive gains over purely parametric LMs, by leveraging large-scale
neighborhood retrieval over external memory datastores. However, there has been
little investigation into adapting such models for new domains. This work
attempts to fill that gap and suggests the following approaches for adapting
$k$NN-LMs -- 1) adapting the underlying LM (using Adapters), 2) expanding
neighborhood retrieval over an additional adaptation datastore, and 3) adapting
the weights (scores) of retrieved neighbors using a learned Rescorer module. We
study each adaptation strategy separately, as well as the combined performance
improvement through ablation experiments and an extensive set of evaluations
run over seven adaptation domains. Our combined adaptation approach
consistently outperforms purely parametric adaptation and zero-shot ($k$NN-LM)
baselines that construct datastores from the adaptation data. On average, we
see perplexity improvements of 17.1\% and 16\% for these respective baselines,
across domains.

We propose a hierarchically structured variational inference model for
accurately disentangling observable evidence of disease (e.g. brain lesions or
atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially
autoregressive priors, our model (1) addresses the subtle and fine-grained
dependencies that typically exist between anatomical and pathological
generating factors of an MRI to ensure the clinical validity of generated
samples; (2) preserves and disentangles finer pathological details pertaining
to a patient's disease state. Additionally, we experiment with an alternative
training configuration where we provide supervision to a subset of latent
units. It is shown that (1) a partially supervised latent space achieves a
higher degree of disentanglement between evidence of disease and
subject-specific anatomy; (2) when the prior is formulated with an
autoregressive structure, knowledge from the supervision can propagate to the
unsupervised latent units, resulting in more informative latent representations
capable of modelling anatomy-pathology interdependencies.

We are at the cusp of a transition from "learning from data" to "learning
what data to learn from" as a central focus of artificial intelligence (AI)
research. While the first-order learning problem is not completely solved,
large models under unified architectures, such as transformers, have shifted
the learning bottleneck from how to effectively train our models to how to
effectively acquire and use task-relevant data. This problem, which we frame as
exploration, is a universal aspect of learning in open-ended domains, such as
the real world. Although the study of exploration in AI is largely limited to
the field of reinforcement learning, we argue that exploration is essential to
all learning systems, including supervised learning. We propose the problem of
generalized exploration to conceptually unify exploration-driven learning
between supervised learning and reinforcement learning, allowing us to
highlight key similarities across learning settings and open research
challenges. Importantly, generalized exploration serves as a necessary
objective for maintaining open-ended learning processes, which in continually
learning to discover and solve new problems, provides a promising path to more
general intelligence.

Stylized 3D avatars have become increasingly prominent in our modern life.
Creating these avatars manually usually involves laborious selection and
adjustment of continuous and discrete parameters and is time-consuming for
average users. Self-supervised approaches to automatically create 3D avatars
from user selfies promise high quality with little annotation cost but fall
short in application to stylized avatars due to a large style domain gap. We
propose a novel self-supervised learning framework to create high-quality
stylized 3D avatars with a mix of continuous and discrete parameters. Our
cascaded domain bridging framework first leverages a modified portrait
stylization approach to translate input selfies into stylized avatar renderings
as the targets for desired 3D avatars. Next, we find the best parameters of the
avatars to match the stylized avatar renderings through a differentiable
imitator we train to mimic the avatar graphics engine. To ensure we can
effectively optimize the discrete parameters, we adopt a cascaded
relaxation-and-search pipeline. We use a human preference study to evaluate how
well our method preserves user identity compared to previous work as well as
manual creation. Our results achieve much higher preference scores than
previous work and close to those of manual creation. We also provide an
ablation study to justify the design choices in our pipeline.

Motivated by cognitive radios, stochastic Multi-Player Multi-Armed Bandits
has been extensively studied in recent years. In this setting, each player
pulls an arm, and receives a reward corresponding to the arm if there is no
collision, namely the arm was selected by one single player. Otherwise, the
player receives no reward if collision occurs. In this paper, we consider the
presence of malicious players (or attackers) who obstruct the cooperative
players (or defenders) from maximizing their rewards, by deliberately colliding
with them. We provide the first decentralized and robust algorithm RESYNC for
defenders whose performance deteriorates gracefully as $\tilde{O}(C)$ as the
number of collisions $C$ from the attackers increases. We show that this
algorithm is order-optimal by proving a lower bound which scales as
$\Omega(C)$. This algorithm is agnostic to the algorithm used by the attackers
and agnostic to the number of collisions $C$ faced from attackers.

Federated Learning (FL) is a distributed machine learning paradigm where
clients collaboratively train a model using their local (human-generated)
datasets while preserving privacy. While existing studies focus on FL algorithm
development to tackle data heterogeneity across clients, the important issue of
data quality (e.g., label noise) in FL is overlooked. This paper aims to fill
this gap by providing a quantitative study on the impact of label noise on FL.
Theoretically speaking, we derive an upper bound for the generalization error
that is linear in the clients' label noise level. Empirically speaking, we
conduct experiments on MNIST and CIFAR-10 datasets using various FL algorithms.
We show that the global model accuracy linearly decreases as the noise level
increases, which is consistent with our theoretical analysis. We further find
that label noise slows down the convergence of FL training, and the global
model tends to overfit when the noise level is high.

With increasing interest in applying machine learning to develop healthcare
solutions, there is a desire to create interpretable deep learning models for
survival analysis. In this paper, we extend the Neural Additive Model (NAM) by
incorporating pairwise feature interaction networks and equip these models with
loss functions that fit both proportional and non-proportional extensions of
the Cox model. We show that within this extended framework, we can construct
non-proportional hazard models, which we call TimeNAM, that significantly
improve performance over the standard NAM model architecture on benchmark
survival datasets. We apply these model architectures to data from the
Electronic Health Record (EHR) database of Seoul National University Hospital
Gangnam Center (SNUHGC) to build an interpretable neural network survival model
for gastric cancer prediction. We demonstrate that on both benchmark survival
analysis datasets, as well as on our gastric cancer dataset, our model
architectures yield performance that matches, or surpasses, the current
state-of-the-art black-box methods.

We present a Bayesian graph neural network (BGNN) that can estimate the weak
lensing convergence ($\kappa$) from photometric measurements of galaxies along
a given line of sight. The method is of particular interest in strong
gravitational time delay cosmography (TDC), where characterizing the "external
convergence" ($\kappa_{\rm ext}$) from the lens environment and line of sight
is necessary for precise inference of the Hubble constant ($H_0$). Starting
from a large-scale simulation with a $\kappa$ resolution of $\sim$1$'$, we
introduce fluctuations on galaxy-galaxy lensing scales of $\sim$1$''$ and
extract random sightlines to train our BGNN. We then evaluate the model on test
sets with varying degrees of overlap with the training distribution. For each
test set of 1,000 sightlines, the BGNN infers the individual $\kappa$
posteriors, which we combine in a hierarchical Bayesian model to yield
constraints on the hyperparameters governing the population. For a test field
well sampled by the training set, the BGNN recovers the population mean of
$\kappa$ precisely and without bias, resulting in a contribution to the $H_0$
error budget well under 1\%. In the tails of the training set with sparse
samples, the BGNN, which can ingest all available information about each
sightline, extracts more $\kappa$ signal compared to a simplified version of
the traditional method based on matching galaxy number counts, which is limited
by sample variance. Our hierarchical inference pipeline using BGNNs promises to
improve the $\kappa_{\rm ext}$ characterization for precision TDC. The
implementation of our pipeline is available as a public Python package, Node to
Joy.

In many, if not every realistic sequential decision-making task, the
decision-making agent is not able to model the full complexity of the world.
The environment is often much larger and more complex than the agent, a setting
also known as partial observability. In such settings, the agent must leverage
more than just the current sensory inputs; it must construct an agent state
that summarizes previous interactions with the world. Currently, a popular
approach for tackling this problem is to learn the agent-state function via a
recurrent network from the agent's sensory stream as input. Many impressive
reinforcement learning applications have instead relied on environment-specific
functions to aid the agent's inputs for history summarization. These
augmentations are done in multiple ways, from simple approaches like
concatenating observations to more complex ones such as uncertainty estimates.
Although ubiquitous in the field, these additional inputs, which we term
auxiliary inputs, are rarely emphasized, and it is not clear what their role or
impact is. In this work we explore this idea further, and relate these
auxiliary inputs to prior classic approaches to state construction. We present
a series of examples illustrating the different ways of using auxiliary inputs
for reinforcement learning. We show that these auxiliary inputs can be used to
discriminate between observations that would otherwise be aliased, leading to
more expressive features that smoothly interpolate between different states.
Finally, we show that this approach is complementary to state-of-the-art
methods such as recurrent neural networks and truncated back-propagation
through time, and acts as a heuristic that facilitates longer temporal credit
assignment, leading to better performance.

Treatment protocols, disease understanding, and viral characteristics changed
over the course of the COVID-19 pandemic; as a result, the risks associated
with patient comorbidities and biomarkers also changed. We add to the
conversation regarding inflammation, hemostasis and vascular function in
COVID-19 by performing a time-varying observational analysis of over 4000
patients hospitalized for COVID-19 in a New York City hospital system from
March 2020 to August 2021. To perform this analysis, we apply tree-based
generalized additive models with temporal interactions which recover
discontinuous risk changes caused by discrete protocols changes. We find that
the biomarkers of thrombosis increasingly predicted mortality from March 2020
to August 2021, while the association between biomarkers of inflammation and
thrombosis weakened. Beyond COVID-19, this presents a straightforward
methodology to estimate unknown and discontinuous time-varying effects.

Denoising diffusion models, a class of generative models, have garnered
immense interest lately in various deep-learning problems. A diffusion
probabilistic model defines a forward diffusion stage where the input data is
gradually perturbed over several steps by adding Gaussian noise and then learns
to reverse the diffusion process to retrieve the desired noise-free data from
noisy data samples. Diffusion models are widely appreciated for their strong
mode coverage and quality of the generated samples despite their known
computational burdens. Capitalizing on the advances in computer vision, the
field of medical imaging has also observed a growing interest in diffusion
models. To help the researcher navigate this profusion, this survey intends to
provide a comprehensive overview of diffusion models in the discipline of
medical image analysis. Specifically, we introduce the solid theoretical
foundation and fundamental concepts behind diffusion models and the three
generic diffusion modelling frameworks: diffusion probabilistic models,
noise-conditioned score networks, and stochastic differential equations. Then,
we provide a systematic taxonomy of diffusion models in the medical domain and
propose a multi-perspective categorization based on their application, imaging
modality, organ of interest, and algorithms. To this end, we cover extensive
applications of diffusion models in the medical domain. Furthermore, we
emphasize the practical use case of some selected approaches, and then we
discuss the limitations of the diffusion models in the medical domain and
propose several directions to fulfill the demands of this field. Finally, we
gather the overviewed studies with their available open-source implementations
at
https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging.

This paper proposes a novel energy storage price arbitrage algorithm
combining supervised learning with dynamic programming. The proposed approach
uses a neural network to directly predicts the opportunity cost at different
energy storage state-of-charge levels, and then input the predicted opportunity
cost into a model-based arbitrage control algorithm for optimal decisions. We
generate the historical optimal opportunity value function using price data and
a dynamic programming algorithm, then use it as the ground truth and historical
price as predictors to train the opportunity value function prediction model.
Our method achieves 65% to 90% profit compared to perfect foresight in case
studies using different energy storage models and price data from New York
State, which significantly outperforms existing model-based and learning-based
methods. While guaranteeing high profitability, the algorithm is also
light-weighted and can be trained and implemented with minimal computational
cost. Our results also show that the learned prediction model has excellent
transferability. The prediction model trained using price data from one region
also provides good arbitrage results when tested over other regions.

Pseudo-label (PL) filtering forms a crucial part of Self-Training (ST)
methods for unsupervised domain adaptation. Dropout-based Uncertainty-driven
Self-Training (DUST) proceeds by first training a teacher model on source
domain labeled data. Then, the teacher model is used to provide PLs for the
unlabeled target domain data. Finally, we train a student on augmented labeled
and pseudo-labeled data. The process is iterative, where the student becomes
the teacher for the next DUST iteration. A crucial step that precedes the
student model training in each DUST iteration is filtering out noisy PLs that
could lead the student model astray. In DUST, we proposed a simple, effective,
and theoretically sound PL filtering strategy based on the teacher model's
uncertainty about its predictions on unlabeled speech utterances. We estimate
the model's uncertainty by computing disagreement amongst multiple samples
drawn from the teacher model during inference by injecting noise via dropout.
In this work, we show that DUST's PL filtering, as initially used, may fail
under severe source and target domain mismatch. We suggest several approaches
to eliminate or alleviate this issue. Further, we bring insights from the
research in neural network model calibration to DUST and show that a
well-calibrated model correlates strongly with a positive outcome of the DUST
PL filtering step.

Transferring large amount of high resolution images over limited bandwidth is
an important but very challenging task. Compressing images using extremely low
bitrates (<0.1 bpp) has been studied but it often results in low quality images
of heavy artifacts due to the strong constraint in the number of bits available
for the compressed data. It is often said that a picture is worth a thousand
words but on the other hand, language is very powerful in capturing the essence
of an image using short descriptions. With the recent success of diffusion
models for text-to-image generation, we propose a generative image compression
method that demonstrates the potential of saving an image as a short text
embedding which in turn can be used to generate high-fidelity images which is
equivalent to the original one perceptually. For a given image, its
corresponding text embedding is learned using the same optimization process as
the text-to-image diffusion model itself, using a learnable text embedding as
input after bypassing the original transformer. The optimization is applied
together with a learning compression model to achieve extreme compression of
low bitrates <0.1 bpp. Based on our experiments measured by a comprehensive set
of image quality metrics, our method outperforms the other state-of-the-art
deep learning methods in terms of both perceptual quality and diversity.

Neural networks trained on standard image classification data sets are shown
to be less resistant to data set bias. It is necessary to comprehend the
behavior objective function that might correspond to superior performance for
data with biases. However, there is little research on the selection of the
objective function and its representational structure when trained on data set
with biases.
  In this paper, we investigate the performance and internal representational
structure of convolution-based neural networks (e.g., ResNets) trained on
biased data using various objective functions. We specifically study
similarities in representations, using Centered Kernel Alignment (CKA), for
different objective functions (probabilistic and margin-based) and offer a
comprehensive analysis of the chosen ones.
  According to our findings, ResNets representations obtained with Negative Log
Likelihood $(\mathcal{L}_{NLL})$ and Softmax Cross-Entropy
($\mathcal{L}_{SCE}$) as loss functions are equally capable of producing better
performance and fine representations on biased data. We note that without
progressive representational similarities among the layers of a neural network,
the performance is less likely to be robust.

The last decade's research in artificial intelligence had a significant
impact on the advance of autonomous driving. Yet, safety remains a major
concern when it comes to deploying such systems in high-risk environments. The
objective of this thesis is to develop methodological tools which provide
reliable uncertainty estimates for deep neural networks. First, we introduce a
new criterion to reliably estimate model confidence: the true class probability
(TCP). We show that TCP offers better properties for failure prediction than
current uncertainty measures. Since the true class is by essence unknown at
test time, we propose to learn TCP criterion from data with an auxiliary model,
introducing a specific learning scheme adapted to this context. The relevance
of the proposed approach is validated on image classification and semantic
segmentation datasets. Then, we extend our learned confidence approach to the
task of domain adaptation where it improves the selection of pseudo-labels in
self-training methods. Finally, we tackle the challenge of jointly detecting
misclassification and out-of-distributions samples by introducing a new
uncertainty measure based on evidential models and defined on the simplex.

Children's speech recognition is a vital, yet largely overlooked domain when
building inclusive speech technologies. The major challenge impeding progress
in this domain is the lack of adequate child speech corpora; however, recent
advances in self-supervised learning have created a new opportunity for
overcoming this problem of data scarcity. In this paper, we leverage
self-supervised adult speech representations and use three well-known child
speech corpora to build models for children's speech recognition. We assess the
performance of fine-tuning on both native and non-native children's speech,
examine the effect of cross-domain child corpora, and investigate the minimum
amount of child speech required to fine-tune a model which outperforms a
state-of-the-art adult model. We also analyze speech recognition performance
across children's ages. Our results demonstrate that fine-tuning with
cross-domain child corpora leads to relative improvements of up to 46.08% and
45.53% for native and non-native child speech respectively, and absolute
improvements of 14.70% and 31.10%. We also show that with as little as 5 hours
of transcribed children's speech, it is possible to fine-tune a children's
speech recognition system that outperforms a state-of-the-art adult model
fine-tuned on 960 hours of adult speech.

Deep neural state-space models (SSMs) provide a powerful tool for modeling
dynamical systems solely using operational data. Typically, neural SSMs are
trained using data collected from the actual system under consideration,
despite the likely existence of operational data from similar systems which
have previously been deployed in the field. In this paper, we propose the use
of model-agnostic meta-learning (MAML) for constructing deep encoder
network-based SSMs, by leveraging a combination of archived data from similar
systems (used to meta-train offline) and limited data from the actual system
(used for rapid online adaptation). We demonstrate using a numerical example
that meta-learning can result in more accurate neural SSM models than
supervised- or transfer-learning, despite few adaptation steps and limited
online data. Additionally, we show that by carefully partitioning and adapting
the encoder layers while fixing the state-transition operator, we can achieve
comparable performance to MAML while reducing online adaptation complexity.

In real-world decision-making, uncertainty is important yet difficult to
handle. Stochastic dominance provides a theoretically sound approach for
comparing uncertain quantities, but optimization with stochastic dominance
constraints is often computationally expensive, which limits practical
applicability. In this paper, we develop a simple yet efficient approach for
the problem, the Light Stochastic Dominance Solver (light-SD), that leverages
useful properties of the Lagrangian. We recast the inner optimization in the
Lagrangian as a learning problem for surrogate approximation, which bypasses
apparent intractability and leads to tractable updates or even closed-form
solutions for gradient calculations. We prove convergence of the algorithm and
test it empirically. The proposed light-SD demonstrates superior performance on
several representative problems ranging from finance to supply chain
management.

The area of transfer learning comprises supervised machine learning methods
that cope with the issue when the training and testing data have different
input feature spaces or distributions. In this work, we propose a novel
transfer learning algorithm called Renewing Iterative Self-labeling Domain
Adaptation (Re-ISDA). In this work, we propose a novel transfer learning
algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA).

Generative models have demonstrated impressive results on Aspect-based
Sentiment Analysis (ABSA) tasks, particularly for the emerging task of
extracting Aspect-Category-Opinion-Sentiment (ACOS) quadruples. However, these
models struggle with implicit sentiment expressions, which are commonly
observed in opinionated content such as online reviews. In this work, we
introduce GEN-SCL-NAT, which consists of two techniques for improved structured
generation for ACOS quadruple extraction. First, we propose GEN-SCL, a
supervised contrastive learning objective that aids quadruple prediction by
encouraging the model to produce input representations that are discriminable
across key input attributes, such as sentiment polarity and the existence of
implicit opinions and aspects. Second, we introduce GEN-NAT, a new structured
generation format that better adapts autoregressive encoder-decoder models to
extract quadruples in a generative fashion. Experimental results show that
GEN-SCL-NAT achieves top performance across three ACOS datasets, averaging
1.48% F1 improvement, with a maximum 1.73% increase on the LAPTOP-L1 dataset.
Additionally, we see significant gains on implicit aspect and opinion splits
that have been shown as challenging for existing ACOS approaches.

Out-of-distribution detection is crucial to the safe deployment of machine
learning systems. Currently, the state-of-the-art in unsupervised
out-of-distribution detection is dominated by generative-based approaches that
make use of estimates of the likelihood or other measurements from a generative
model. Reconstruction-based methods offer an alternative approach, in which a
measure of reconstruction error is used to determine if a sample is
out-of-distribution. However, reconstruction-based approaches are less
favoured, as they require careful tuning of the model's information bottleneck
- such as the size of the latent dimension - to produce good results. In this
work, we exploit the view of denoising diffusion probabilistic models (DDPM) as
denoising autoencoders where the bottleneck is controlled externally, by means
of the amount of noise applied. We propose to use DDPMs to reconstruct an input
that has been noised to a range of noise levels, and use the resulting
multi-dimensional reconstruction error to classify out-of-distribution inputs.
Our approach outperforms not only reconstruction-based methods, but also
state-of-the-art generative-based approaches.

Emotions lie on a broad continuum and treating emotions as a discrete number
of classes limits the ability of a model to capture the nuances in the
continuum. The challenge is how to describe the nuances of emotions and how to
enable a model to learn the descriptions. In this work, we devise a method to
automatically create a description (or prompt) for a given audio by computing
acoustic properties, such as pitch, loudness, speech rate, and articulation
rate. We pair a prompt with its corresponding audio using 5 different emotion
datasets. We trained a neural network model using these audio-text pairs. Then,
we evaluate the model using one more dataset. We investigate how the model can
learn to associate the audio with the descriptions, resulting in performance
improvement of Speech Emotion Recognition and Speech Audio Retrieval. We expect
our findings to motivate research describing the broad continuum of emotion

Zero-shot transfer learning for document understanding is a crucial yet
under-investigated scenario to help reduce the high cost involved in annotating
document entities. We present a novel query-based framework, QueryForm, that
extracts entity values from form-like documents in a zero-shot fashion.
QueryForm contains a dual prompting mechanism that composes both the document
schema and a specific entity type into a query, which is used to prompt a
Transformer model to perform a single entity extraction task. Furthermore, we
propose to leverage large-scale query-entity pairs generated from form-like
webpages with weak HTML annotations to pre-train QueryForm. By unifying
pre-training and fine-tuning into the same query-based framework, QueryForm
enables models to learn from structured documents containing various entities
and layouts, leading to better generalization to target document types without
the need for target-specific training data. QueryForm sets new state-of-the-art
average F1 score on both the XFUND (+4.6%~10.1%) and the Payment (+3.2%~9.5%)
zero-shot benchmark, with a smaller model size and no additional image input.

Learning analytics (LA) is data collection, analysis, and representation of
data about learners in order to improve their learning and performance.
Furthermore, LA opens the door to opportunities for self-regulated learning in
higher education, a circular process in which learners activate and sustain
behaviours that are oriented toward their personal learning goals. The
potentials of LA and self-regulated learning are huge; however, they are not
yet widely applied in higher education institutions. Slovenian higher education
institutions have lagged behind other European countries in LA adoption. Our
research aims to fill this gap by using a qualitatively and quantitatively led
workflow for building a requirement-oriented LA solution, consisting of
empirically gathering the students' expectations of LA and presenting a
dashboard solution. Translated Student Expectations of Learning Analytics
Questionnaire and focus groups were used to gather expectations from learners.
Based on them, a user interface utilizing LA and grade prediction with an AI
model was implemented for a selected course. The interface includes early grade
prediction, peer comparison, and historical data overview. Grade prediction is
based on a machine learning model built on users' interaction in the virtual
learning environment, demographic data and lab grades. First, classification is
used to determine students at risk of failing - its precision reaches 98% after
the first month of the course. Second, the exact grade is predicted with the
Decision Tree Regressor, reaching a mean absolute error of 11.2grade points (on
a 100 points scale) after the first month. The proposed system's main benefit
is the support for self-regulation of the learning process during the semester,
possibly motivating students to adjust their learning strategies to prevent
failing the course. Initial student evaluation showed positive results.

Logical reasoning is essential in a variety of human activities. A
representative example of a logical task is mathematics. Recent large-scale
models trained on large datasets have been successful in various fields, but
their reasoning ability in arithmetic tasks is limited, which we reproduce
experimentally. Here, we recast this limitation as not unique to mathematics
but common to tasks that require logical operations. We then propose a new set
of tasks, termed logical tasks, which will be the next challenge to address.
This higher point of view helps the development of inductive biases that have
broad impact beyond the solution of individual tasks. We define and
characterize logical tasks and discuss system requirements for their solution.
Furthermore, we discuss the relevance of logical tasks to concepts such as
extrapolation, explainability, and inductive bias. Finally, we provide
directions for solving logical tasks.

One notable weakness of current machine learning algorithms is the poor
ability of models to solve new problems without forgetting previously acquired
knowledge. The Continual Learning paradigm has emerged as a protocol to
systematically investigate settings where the model sequentially observes
samples generated by a series of tasks. In this work, we take a task-agnostic
view of continual learning and develop a hierarchical information-theoretic
optimality principle that facilitates a trade-off between learning and
forgetting. We derive this principle from a Bayesian perspective and show its
connections to previous approaches to continual learning. Based on this
principle, we propose a neural network layer, called the
Mixture-of-Variational-Experts layer, that alleviates forgetting by creating a
set of information processing paths through the network which is governed by a
gating policy. Equipped with a diverse and specialized set of parameters, each
path can be regarded as a distinct sub-network that learns to solve tasks. To
improve expert allocation, we introduce diversity objectives, which we evaluate
in additional ablation studies. Importantly, our approach can operate in a
task-agnostic way, i.e., it does not require task-specific knowledge, as is the
case with many existing continual learning algorithms. Due to the general
formulation based on generic utility functions, we can apply this optimality
principle to a large variety of learning problems, including supervised
learning, reinforcement learning, and generative modeling. We demonstrate the
competitive performance of our method on continual reinforcement learning and
variants of the MNIST, CIFAR-10, and CIFAR-100 datasets.

Finding informative low-dimensional representations that can be computed
efficiently in large datasets is an important problem in data analysis.
Recently, contrastive Principal Component Analysis (cPCA) was proposed as a
more informative generalization of PCA that takes advantage of contrastive
learning. However, the performance of cPCA is sensitive to hyper-parameter
choice and there is currently no online algorithm for implementing cPCA. Here,
we introduce a modified cPCA method, which we denote cPCA*, that is more
interpretable and less sensitive to the choice of hyper-parameter. We derive an
online algorithm for cPCA* and show that it maps onto a neural network with
local learning rules, so it can potentially be implemented in energy efficient
neuromorphic hardware. We evaluate the performance of our online algorithm on
real datasets and highlight the differences and similarities with the original
formulation.

Birds are vital parts of ecosystems across the world and are an excellent
measure of the quality of life on earth. Many bird species are endangered while
others are already extinct. Ecological efforts in understanding and monitoring
bird populations are important to conserve their habitat and species, but this
mostly relies on manual methods in rough terrains. Recent advances in Machine
Learning and Deep Learning have made automatic bird recognition in diverse
environments possible. Birdcall recognition till now has been performed using
convolutional neural networks. In this work, we try and understand how
self-attention can aid in this endeavor. With that we build an pre-trained
Attention-based Spectrogram Transformer baseline for BirdCLEF 2022 and compare
the results against the pre-trained Convolution-based baseline. Our results
show that the transformer models outperformed the convolutional model and we
further validate our results by building baselines and analyzing the results
for the previous year BirdCLEF 2021 challenge. Source code available at
https://github.com/ck090/BirdCLEF-22

Global explanations of a reinforcement learning (RL) agent's expected
behavior can make it safer to deploy. However, such explanations are often
difficult to understand because of the complicated nature of many RL policies.
Effective human explanations are often contrastive, referencing a known
contrast (policy) to reduce redundancy. At the same time, these explanations
also require the additional effort of referencing that contrast when evaluating
an explanation. We conduct a user study to understand whether and when
contrastive explanations might be preferable to complete explanations that do
not require referencing a contrast. We find that complete explanations are
generally more effective when they are the same size or smaller than a
contrastive explanation of the same policy, and no worse when they are larger.
This suggests that contrastive explanations are not sufficient to solve the
problem of effectively explaining reinforcement learning policies, and require
additional careful study for use in this context.

A variational autoencoder (VAE) is a probabilistic machine learning framework
for posterior inference that projects an input set of high-dimensional data to
a lower-dimensional, latent space. The latent space learned with a VAE offers
exciting opportunities to develop new data-driven design processes in creative
disciplines, in particular, to automate the generation of multiple novel
designs that are aesthetically reminiscent of the input data but that were
unseen during training. However, the learned latent space is typically
disorganized and entangled: traversing the latent space along a single
dimension does not result in changes to single visual attributes of the data.
The lack of latent structure impedes designers from deliberately controlling
the visual attributes of new designs generated from the latent space. This
paper presents an experimental study that investigates latent space
disentanglement. We implement three different VAE models from the literature
and train them on a publicly available dataset of 60,000 images of hand-written
digits. We perform a sensitivity analysis to find a small number of latent
dimensions necessary to maximize a lower bound to the log marginal likelihood
of the data. Furthermore, we investigate the trade-offs between the quality of
the reconstruction of the decoded images and the level of disentanglement of
the latent space. We are able to automatically align three latent dimensions
with three interpretable visual properties of the digits: line weight, tilt and
width. Our experiments suggest that i) increasing the contribution of the
Kullback-Leibler divergence between the prior over the latents and the
variational distribution to the evidence lower bound, and ii) conditioning
input image class enhances the learning of a disentangled latent space with a
VAE.

There is a growing body of work that leverages features extracted via
topological data analysis to train machine learning models. While this field,
sometimes known as topological machine learning (TML), has seen some notable
successes, an understanding of how the process of learning from topological
features differs from the process of learning from raw data is still limited.
In this work, we begin to address one component of this larger issue by asking
whether a model trained with topological features learns internal
representations of data that are fundamentally different than those learned by
a model trained with the original raw data. To quantify ``different'', we
exploit two popular metrics that can be used to measure the similarity of the
hidden representations of data within neural networks, neural stitching and
centered kernel alignment. From these we draw a range of conclusions about how
training with topological features does and does not change the representations
that a model learns. Perhaps unsurprisingly, we find that structurally, the
hidden representations of models trained and evaluated on topological features
differ substantially compared to those trained and evaluated on the
corresponding raw data. On the other hand, our experiments show that in some
cases, these representations can be reconciled (at least to the degree required
to solve the corresponding task) using a simple affine transformation. We
conjecture that this means that neural networks trained on raw data may extract
some limited topological features in the process of making predictions.

Automatic Audio Captioning (AAC) is the task that aims to describe an audio
signal using natural language. AAC systems take as input an audio signal and
output a free-form text sentence, called a caption. Evaluating such systems is
not trivial, since there are many ways to express the same idea. For this
reason, several complementary metrics, such as BLEU, CIDEr, SPICE and SPIDEr,
are used to compare a single automatic caption to one or several captions of
reference, produced by a human annotator. Nevertheless, an automatic system can
produce several caption candidates, either using some randomness in the
sentence generation process, or by considering the various competing
hypothesized captions during decoding with beam-search, for instance. If we
consider an end-user of an AAC system, presenting several captions instead of a
single one seems relevant to provide some diversity, similarly to information
retrieval systems. In this work, we explore the possibility to consider several
predicted captions in the evaluation process instead of one. For this purpose,
we propose SPIDEr-max, a metric that takes the maximum SPIDEr value among the
scores of several caption candidates. To advocate for our metric, we report
experiments on Clotho v2.1 and AudioCaps, with a transformed-based system. On
AudioCaps for example, this system reached a SPIDEr-max value (with 5
candidates) close to the SPIDEr human score of reference.

In this paper, we present a comprehensive study on the utility of deep
convolutional neural networks with two state-of-the-art pooling layers which
are placed after convolutional layers and fine-tuned in an end-to-end manner
for visual place recognition task in challenging conditions, including seasonal
and illumination variations. We compared extensively the performance of deep
learned global features with three different loss functions, e.g. triplet,
contrastive and ArcFace, for learning the parameters of the architectures in
terms of fraction of the correct matches during deployment. To verify
effectiveness of our results, we utilized two real world datasets in place
recognition, both indoor and outdoor. Our investigation demonstrates that fine
tuning architectures with ArcFace loss in an end-to-end manner outperforms
other two losses by approximately 1~4% in outdoor and 1~2% in indoor datasets,
given certain thresholds, for the visual place recognition tasks.

Histopathology images are gigapixel-sized and include features and
information at different resolutions. Collecting annotations in histopathology
requires highly specialized pathologists, making it expensive and
time-consuming. Self-training can alleviate annotation constraints by learning
from both labeled and unlabeled data, reducing the amount of annotations
required from pathologists. We study the design of teacher-student
self-training systems for Non-alcoholic Steatohepatitis (NASH) using clinical
histopathology datasets with limited annotations. We evaluate the models on
in-distribution and out-of-distribution test data under clinical data shifts.
We demonstrate that through self-training, the best student model statistically
outperforms the teacher with a $3\%$ absolute difference on the macro F1 score.
The best student model also approaches the performance of a fully supervised
model trained with twice as many annotations.

Recent device fingerprinting approaches rely on deep learning to extract
device-specific features solely from raw RF signals to identify, classify and
authenticate wireless devices. One widely known issue lies in the inability of
these approaches to maintain good performances when the training data and
testing data are collected under varying deployment domains. For example, when
the learning model is trained on data collected from one receiver but tested on
data collected from a different receiver, the performance degrades
substantially compared to when both training and testing data are collected
using the same receiver. The same also happens when considering other varying
domains, like channel condition and protocol configuration. In this paper, we
begin by explaining, through testbed experiments, the challenges these
fingerprinting techniques face when it comes to domain portability. We will
then present some ideas on how to go about addressing these challenges so as to
make deep learning-based device fingerprinting more resilient to domain
variability.

Deep Q-learning based algorithms have been applied successfully in many
decision making problems, while their theoretical foundations are not as well
understood. In this paper, we study a Fitted Q-Iteration with two-layer ReLU
neural network parametrization, and find the sample complexity guarantees for
the algorithm. The approach estimates the Q-function in each iteration using a
convex optimization problem. We show that this approach achieves a sample
complexity of $\tilde{\mathcal{O}}(1/\epsilon^{2})$, which is order-optimal.
This result holds for a countable state-space and does not require any
assumptions such as a linear or low rank structure on the MDP.

Animals are capable of precise and agile locomotion using vision. Replicating
this ability has been a long-standing goal in robotics. The traditional
approach has been to decompose this problem into elevation mapping and foothold
planning phases. The elevation mapping, however, is susceptible to failure and
large noise artifacts, requires specialized hardware, and is biologically
implausible. In this paper, we present the first end-to-end locomotion system
capable of traversing stairs, curbs, stepping stones, and gaps. We show this
result on a medium-sized quadruped robot using a single front-facing depth
camera. The small size of the robot necessitates discovering specialized gait
patterns not seen elsewhere. The egocentric camera requires the policy to
remember past information to estimate the terrain under its hind feet. We train
our policy in simulation. Training has two phases - first, we train a policy
using reinforcement learning with a cheap-to-compute variant of depth image and
then in phase 2 distill it into the final policy that uses depth using
supervised learning. The resulting policy transfers to the real world and is
able to run in real-time on the limited compute of the robot. It can traverse a
large variety of terrain while being robust to perturbations like pushes,
slippery surfaces, and rocky terrain. Videos are at
https://vision-locomotion.github.io

We launch EVA, a vision-centric foundation model to explore the limits of
visual representation at scale using only publicly accessible data. EVA is a
vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision
features conditioned on visible image patches. Via this pretext task, we can
efficiently scale up EVA to one billion parameters, and sets new records on a
broad range of representative vision downstream tasks, such as image
recognition, video action recognition, object detection, instance segmentation
and semantic segmentation without heavy supervised training. Moreover, we
observe quantitative changes in scaling EVA result in qualitative changes in
transfer learning performance that are not present in other models. For
instance, EVA takes a great leap in the challenging large vocabulary instance
segmentation task: our model achieves almost the same state-of-the-art
performance on LVISv1.0 dataset with over a thousand categories and COCO
dataset with only eighty categories. Beyond a pure vision encoder, EVA can also
serve as a vision-centric, multi-modal pivot to connect images and text. We
find initializing the vision tower of a giant CLIP from EVA can greatly
stabilize the training and outperform the training from scratch counterpart
with much fewer samples and less compute, providing a new direction for scaling
up and accelerating the costly training of multi-modal foundation models. To
facilitate future research, we will release all the code and models at
\url{https://github.com/baaivision/EVA}.

Indoor localization systems often fuse inertial odometry with map information
via hand-defined methods to reduce odometry drift, but such methods are
sensitive to noise and struggle to generalize across odometry sources. To
address the robustness problem in map utilization, we propose a data-driven
prior on possible user locations in a map by combining learned spatial map
embeddings and temporal odometry embeddings. Our prior learns to encode which
map regions are feasible locations for a user more accurately than previous
hand-defined methods. This prior leads to a 49% improvement in inertial-only
localization accuracy when used in a particle filter. This result is
significant, as it shows that our relative positioning method can match the
performance of absolute positioning using bluetooth beacons. To show the
generalizability of our method, we also show similar improvements using wheel
encoder odometry.

In open-ended natural-language generation, existing text decoding methods
typically struggle to produce text which is both diverse and high-quality.
Greedy and beam search are known to suffer from text degeneration and
linguistic diversity issues, while temperature, top-k, and nucleus sampling
often yield diverse but low-quality outputs. In this work, we present crowd
sampling, a family of decoding methods based on Bayesian risk minimization, to
address this diversity-quality trade-off. Inspired by the principle of "the
wisdom of the crowd," crowd sampling seeks to select a candidate from a pool of
candidates that has the least expected risk (i.e., highest expected reward)
under a generative model according to a given utility function. Crowd sampling
can be seen as a generalization of numerous existing methods, including
majority voting, and in practice, it can be used as a drop-in replacement for
existing sampling methods. Extensive experiments show that crowd sampling
delivers improvements of 3-7 ROUGE and BLEU points across a wide range of
tasks, including summarization, data-to-text, translation, and textual style
transfer, while achieving new state-of-the-art results on WebNLG and WMT'16.

In existing mobile network systems, the data plane (DP) is mainly considered
a pipeline consisting of network elements end-to-end forwarding user data
traffics. With the rapid maturity of programmable network devices, however,
mobile network infrastructure mutates towards a programmable computing
platform. Therefore, such a programmable DP can provide in-network computing
capability for many application services. In this paper, we target to enhance
the data plane with in-network deep learning (DL) capability. However,
in-network intelligence can be a significant load for network devices. Then,
the paradigm of the functional split is applied so that the deep neural network
(DNN) is decomposed into sub-elements of the data plane for making machine
learning inference jobs more efficient. As a proof-of-concept, we take a Blind
Source Separation (BSS) problem as an example to exhibit the benefits of such
an approach. We implement the proposed enhancement in a full-stack emulator and
we provide a quantitative evaluation with professional datasets. As an initial
trial, our study provides insightful guidelines for the design of the future
mobile network system, employing in-network intelligence (e.g., 6G).

In this work, we focus on intrasentential code-mixing and propose several
different Synthetic Code-Mixing (SCM) data augmentation methods that outperform
the baseline on downstream sentiment analysis tasks across various amounts of
labeled gold data. Most importantly, our proposed methods demonstrate that
strategically replacing parts of sentences in the matrix language with a
constant mask significantly improves classification accuracy, motivating
further linguistic insights into the phenomenon of code-mixing. We test our
data augmentation method in a variety of low-resource and cross-lingual
settings, reaching up to a relative improvement of 7.73% on the extremely
scarce English-Malayalam dataset. We conclude that the code-switch pattern in
code-mixing sentences is also important for the model to learn. Finally, we
propose a language-agnostic SCM algorithm that is cheap yet extremely helpful
for low-resource languages.

State-of-the-art reinforcement learning (RL) algorithms typically use random
sampling (e.g., $\epsilon$-greedy) for exploration, but this method fails in
hard exploration tasks like Montezuma's Revenge. To address the challenge of
exploration, prior works incentivize the agent to visit novel states using an
exploration bonus (also called an intrinsic reward or curiosity). Such methods
can lead to excellent results on hard exploration tasks but can suffer from
intrinsic reward bias and underperform when compared to an agent trained using
only task rewards. This performance decrease occurs when an agent seeks out
intrinsic rewards and performs unnecessary exploration even when sufficient
task reward is available. This inconsistency in performance across tasks
prevents the widespread use of intrinsic rewards with RL algorithms. We propose
a principled constrained policy optimization procedure that automatically tunes
the importance of the intrinsic reward: it suppresses the intrinsic reward when
exploration is unnecessary and increases it when exploration is required. This
results in superior exploration that does not require manual tuning to balance
the intrinsic reward against the task reward. Consistent performance gains
across sixty-one ATARI games validate our claim. The code is available at
https://github.com/Improbable-AI/eipo.

This paper studies the problem of measuring and predicting how memorable an
image is to pattern recognition machines, as a path to explore machine
intelligence. Firstly, we propose a self-supervised machine memory
quantification pipeline, dubbed ``MachineMem measurer'', to collect machine
memorability scores of images. Similar to humans, machines also tend to
memorize certain kinds of images, whereas the types of images that machines and
humans memorialize are different. Through in-depth analysis and comprehensive
visualizations, we gradually unveil that "complex" images are usually more
memorable to machines. We further conduct extensive experiments across 11
different machines (from linear classifiers to modern ViTs) and 9 pre-training
methods to analyze and understand machine memory. This work proposes the
concept of machine memorability and opens a new research direction at the
interface between machine memory and visual data.

The unlabeled sensing problem is to recover an unknown signal from permuted
linear measurements. We propose an alternating minimization algorithm with a
suitable initialization for the widely considered k-sparse permutation model.
Assuming either a Gaussian measurement matrix or a sub-Gaussian signal, we
upper bound the initialization error for the r-local and k-sparse permutation
models in terms of the block size $r$ and the number of shuffles k,
respectively. Our algorithm is computationally scalable and, compared to
baseline methods, achieves superior performance on real and synthetic datasets.

Enabled by the increasing availability of sensor data monitored from
production machinery, condition monitoring and predictive maintenance methods
are key pillars for an efficient and robust manufacturing production cycle in
the Industrial Internet of Things. The employment of machine learning models to
detect and predict deteriorating behavior by analyzing a variety of data
collected across several industrial environments shows promising results in
recent works, yet also often requires transferring the sensor data to
centralized servers located in the cloud. Moreover, although collaborating and
sharing knowledge between industry sites yields large benefits, especially in
the area of condition monitoring, it is often prohibited due to data privacy
issues. To tackle this situation, we propose an Autoencoder-based Federated
Learning method utilizing vibration sensor data from rotating machines, that
allows for a distributed training on edge devices, located on-premise and close
to the monitored machines. Preserving data privacy and at the same time
exonerating possibly unreliable network connections of remote sites, our
approach enables knowledge transfer across organizational boundaries, without
sharing the monitored data. We conducted an evaluation utilizing two real-world
datasets as well as multiple testbeds and the results indicate that our method
enables a competitive performance compared to previous results, while
significantly reducing the resource and network utilization.

New smartphone users have difficulty engaging with it and often use only a
limited set of features like calling and messaging. These users are hesitant to
explore using the smartphone and rely on experienced users to teach them how to
use the phone. However, experienced users are not always around to guide them.
To help new users learn how to use the phone on their own, we propose a natural
language based instruction following agent that operates over the UI and shows
the user how to perform various tasks. Common how-to questions, such as "How to
block calls from unknown numbers?", are documented on support sites with a
sequence of steps in natural language describing what the user should do. We
parse these steps using Large Language Models (LLMs) and generate macros that
can be executed on-device when the user asks a query. To evaluate this agent,
we introduce UGIF-DataSet, a multi-lingual, multi-modal UI grounded dataset for
step-by-step task completion on the smartphone. It contains 523 natural
language instructions with paired sequences of multilingual UI screens and
actions that show how to execute the task in eight languages. We compare the
performance of different large language models including PaLM, GPT3, etc. and
find that the end-to-end task completion success rate is 48% for English UI but
the performance drops to 32% for non-English languages. We analyse the common
failure modes of existing models on this task and point out areas for
improvement.

Modern decision-making systems, from robots to web recommendation engines,
are expected to adapt: to user preferences, changing circumstances or even new
tasks. Yet, it is still uncommon to deploy a dynamically learning agent (rather
than a fixed policy) to a production system, as it's perceived as unsafe. Using
historical data to reason about learning algorithms, similar to offline policy
evaluation (OPE) applied to fixed policies, could help practitioners evaluate
and ultimately deploy such adaptive agents to production. In this work, we
formalize offline learner simulation (OLS) for reinforcement learning (RL) and
propose a novel evaluation protocol that measures both fidelity and efficiency
of the simulation. For environments with complex high-dimensional observations,
we propose a semi-parametric approach that leverages recent advances in latent
state discovery in order to achieve accurate and efficient offline simulations.
In preliminary experiments, we show the advantage of our approach compared to
fully non-parametric baselines. The code to reproduce these experiments will be
made available at https://github.com/microsoft/rl-offline-simulation.

Unsupervised Domain Adaptation (UDA) aims to enhance the generalization of
the learned model to other domains. The domain-invariant knowledge is
transferred from the model trained on labeled source domain, e.g., video game,
to unlabeled target domains, e.g., real-world scenarios, saving annotation
expenses. Existing UDA methods for semantic segmentation usually focus on
minimizing the inter-domain discrepancy of various levels, e.g., pixels,
features, and predictions, for extracting domain-invariant knowledge. However,
the primary intra-domain knowledge, such as context correlation inside an
image, remains underexplored. In an attempt to fill this gap, we propose a
unified pixel- and patch-wise self-supervised learning framework, called PiPa,
for domain adaptive semantic segmentation that facilitates intra-image
pixel-wise correlations and patch-wise semantic consistency against different
contexts. The proposed framework exploits the inherent structures of
intra-domain images, which: (1) explicitly encourages learning the
discriminative pixel-wise features with intra-class compactness and inter-class
separability, and (2) motivates the robust feature learning of the identical
patch against different contexts or fluctuations. Extensive experiments verify
the effectiveness of the proposed method, which obtains competitive accuracy on
the two widely-used UDA benchmarks, i.e., 75.6 mIoU on GTA to Cityscapes and
68.2 mIoU on Synthia to Cityscapes. Moreover, our method is compatible with
other UDA approaches to further improve the performance without introducing
extra parameters.

We describe the SNAD Viewer, a web portal for astronomers which presents a
centralized view of individual objects from the Zwicky Transient Facility's
(ZTF) data releases, including data gathered from multiple publicly available
astronomical archives and data sources. Initially built to enable efficient
expert feedback in the context of adaptive machine learning applications, it
has evolved into a full-fledged community asset that centralizes public
information and provides a multi-dimensional view of ZTF sources. For users, we
provide detailed descriptions of the data sources and choices underlying the
information displayed in the portal. For developers, we describe our
architectural choices and their consequences such that our experience can help
others engaged in similar endeavors or in adapting our publicly released code
to their requirements. The infrastructure we describe here is scalable and
flexible and can be personalized and used by other surveys and for other
science goals. The Viewer has been instrumental in highlighting the crucial
roles domain experts retain in the era of big data in astronomy. Given the
arrival of the upcoming generation of large-scale surveys, we believe similar
systems will be paramount in enabling the materialization of scientific
potential enclosed in current terabyte and future petabyte-scale data sets. The
Viewer is publicly available online at https://ztf.snad.space

Timeline summarisation (TLS) aims to create a time-ordered summary list
concisely describing a series of events with corresponding dates. This differs
from general summarisation tasks because it requires the method to capture
temporal information besides the main idea of the input documents. This paper
proposes a TLS system which can interactively learn from the user's feedback
via reinforcement learning and generate timelines satisfying the user's
interests. We define a compound reward function that can update automatically
according to the received feedback through interaction with the user. The
system utilises the reward function to fine-tune an abstractive summarisation
model via reinforcement learning to guarantee topical coherence, factual
consistency and linguistic fluency of the generated summaries. The proposed
system avoids the need of preference feedback from individual users. The
experiments show that our system outperforms the baseline on the benchmark TLS
dataset and can generate accurate and timeline precises that better satisfy
real users.

Motivated by the entailment property of multi-turn dialogues through
contrastive learning sentence embeddings, we introduce a novel technique,
Curved Contrastive Learning (CCL), for generating semantically meaningful and
conversational graph curved utterance embeddings that can be compared using
cosine similarity. The resulting bi-encoder models can guide transformers as a
response ranking model towards a goal in a zero-shot fashion by projecting the
goal utterance and the corresponding reply candidates into a latent space. Here
the cosine similarity indicates the distance/reachability of a candidate
utterance towards the corresponding goal which we define as curved space.
Furthermore, we explore how these forward-entailing language representations
can be utilized for assessing the likelihood of sequences by the entailment
strength i.e. through the cosine similarity of its individual members (encoded
separately) as an emergent property in the curved space. This allows us to
imagine the likelihood of future patterns in dialogues, specifically by
ordering/identifying future goal utterances that are multiple turns away, given
a dialogue context. As part of our analysis, we investigate characteristics
that make conversations (un)plannable and find strong evidence of planning
capability over multiple turns (in 61.56\% over 3 turns) in conversations from
the DailyDialog dataset. Finally, we will show how we can exploit the curved
property to rank one million utterance & context pairs, in terms of GPU
computation time over 7 million times faster than DialogRPT, while being in
average 2.8\% qualitatively superior for sequences longer than 2 turns.

We present a self-supervised algorithm for several classification tasks
within hematoxylin and eosin (H&E) stained images of breast cancer. Our method
is robust to stain variations inherent to the histology images acquisition
process, which has limited the applicability of automated analysis tools. We
address this problem by imposing constraints a learnt latent space which
leverages stain normalization techniques during training. At every iteration,
we select an image as a normalization target and generate a version of every
image in the batch normalized to that target. We minimize the distance between
the embeddings that correspond to the same image under different staining
variations while maximizing the distance between other samples. We show that
our method not only improves robustness to stain variations across multi-center
data, but also classification performance through extensive experiments on
various normalization targets and methods. Our method achieves the
state-of-the-art performance on several publicly available breast cancer
datasets ranging from tumor classification (CAMELYON17) and subtyping (BRACS)
to HER2 status classification and treatment response prediction.

Besides reproducing tabular data properties of standalone tables, synthetic
relational databases also require modeling the relationships between related
tables. In this paper, we propose the Row Conditional-Tabular Generative
Adversarial Network (RC-TGAN), a novel generative adversarial network (GAN)
model that extends the tabular GAN to support modeling and synthesizing
relational databases. The RC-TGAN models relationship information between
tables by incorporating conditional data of parent rows into the design of the
child table's GAN. We further extend the RC-TGAN to model the influence that
grandparent table rows may have on their grandchild rows, in order to prevent
the loss of this connection when the rows of the parent table fail to transfer
this relationship information. The experimental results, using eight real
relational databases, show significant improvements in the quality of the
synthesized relational databases when compared to the benchmark system,
demonstrating the effectiveness of the RC-TGAN in preserving relationships
between tables of the original database.

The field of deep-learning-based ECG analysis has been largely dominated by
convolutional architectures. This work explores the prospects of applying the
recently introduced structured state space models (SSMs) as a particularly
promising approach due to its ability to capture long-term dependencies in time
series. We demonstrate that this approach leads to significant improvements
over the current state-of-the-art for ECG classification, which we trace back
to individual pathologies. Furthermore, the model's ability to capture
long-term dependencies allows to shed light on long-standing questions in the
literature such as the optimal sampling rate or window size to train
classification models. Interestingly, we find no evidence for using data
sampled at 500Hz as opposed to 100Hz and no advantages from extending the
model's input size beyond 3s. Based on this very promising first assessment,
SSMs could develop into a new modeling paradigm for ECG analysis.

In this article we develop a continuous variable (CV) shadow tomography
scheme with wide ranging applications in quantum optics. Our work is motivated
by the increasing experimental and technological relevance of CV systems in
quantum information, quantum communication, quantum sensing, quantum
simulations, quantum computing and error correction. We introduce two
experimentally realisable schemes for obtaining classical shadows of CV
(possibly non-Gaussian) quantum states using only randomised Gaussian unitaries
and easily implementable Gaussian measurements such as homodyne and heterodyne
detection. For both schemes, we show that
$N=\mathcal{O}\big(\operatorname{poly}\big(\frac{1}{\epsilon},\log\big(\frac{1}{\delta}\big),M_n^{r+\alpha},\log(m)\big)\big)$
samples of an unknown $m$-mode state $\rho$ suffice to learn the expected value
of any $r$-local polynomial in the canonical observables of degree $\alpha$,
both with high probability $1-\delta$ and accuracy $\epsilon$, as long as the
state $\rho$ has moments of order $n>\alpha$ bounded by $M_n$. By
simultaneously truncating states and operators in energy and phase space, we
are able to overcome new mathematical challenges that arise due to the
infinite-dimensionality of CV systems. We also provide a scheme to learn
nonlinear functionals of the state, such as entropies over any small number of
modes, by leveraging recent energy-constrained entropic continuity bounds.
Finally, we provide numerical evidence of the efficiency of our protocols in
the case of CV states of relevance in quantum information theory, including
ground states of quadratic Hamiltonians of many-body systems and cat qubit
states. We expect our scheme to provide good recovery in learning relevant
states of 2D materials and photonic crystals.

We develop a Spatio-TEMporal Mutually Exciting point process with Dynamic
network (STEMMED), i.e., a point process network wherein each node models a
unique community-drug event stream with a dynamic mutually-exciting structure,
accounting for influences from other nodes. We show that STEMMED can be
decomposed node-by-node, suggesting a tractable distributed learning procedure.
Simulation shows that this learning algorithm can accurately recover known
parameters of STEMMED, especially for small networks and long data-horizons.
Next, we turn this node-by-node decomposition into an online cooperative
multi-period forecasting framework, which is asymptotically robust to
operational errors, to facilitate Opioid-related overdose death (OOD) trends
forecasting among neighboring communities. In our numerical study, we
parameterize STEMMED using individual-level OOD data and county-level
demographics in Massachusetts. For any node, we observe that OODs within the
same drug class from nearby locations have the greatest influence on future OOD
trends. Furthermore, the expected proportion of OODs triggered by historical
events varies greatly across counties, ranging between 30%-70%. Finally, in a
practical online forecasting setting, STEMMED-based cooperative framework
reduces prediction error by 60% on average, compared to well-established
forecasting models. Leveraging the growing abundance of public health
surveillance data, STEMMED can provide accurate forecasts of local OOD trends
and highlight complex interactions between OODs across communities and drug
types. Moreover, STEMMED enhances synergies between local and federal
government entities, which is critical to designing impactful policy
interventions.

Millimeter wave (mmWave) and terahertz (THz) drones have the potential to
enable several futuristic applications such as coverage extension, enhanced
security monitoring, and disaster management. However, these drones need to
deploy large antenna arrays and use narrow directive beams to maintain a
sufficient link budget. The large beam training overhead associated with these
arrays makes adjusting these narrow beams challenging for highly-mobile drones.
To address these challenges, this paper proposes a vision-aided machine
learning-based approach that leverages visual data collected from cameras
installed on the drones to enable fast and accurate beam prediction. Further,
to facilitate the evaluation of the proposed solution, we build a synthetic
drone communication dataset consisting of co-existing wireless and visual data.
The proposed vision-aided solution achieves a top-$1$ beam prediction accuracy
of $\approx 91\%$ and close to $100\%$ top-$3$ accuracy. These results
highlight the efficacy of the proposed solution towards enabling highly mobile
mmWave/THz drone communication.

Recent deep metric learning (DML) methods typically leverage solely class
labels to keep positive samples far away from negative ones. However, this type
of method normally ignores the crucial knowledge hidden in the data (e.g.,
intra-class information variation), which is harmful to the generalization of
the trained model. To alleviate this problem, in this paper we propose Online
Batch Diffusion-based Self-Distillation (OBD-SD) for DML. Specifically, we
first propose a simple but effective Progressive Self-Distillation (PSD), which
distills the knowledge progressively from the model itself during training. The
soft distance targets achieved by PSD can present richer relational information
among samples, which is beneficial for the diversity of embedding
representations. Then, we extend PSD with an Online Batch Diffusion Process
(OBDP), which is to capture the local geometric structure of manifolds in each
batch, so that it can reveal the intrinsic relationships among samples in the
batch and produce better soft distance targets. Note that our OBDP is able to
restore the insufficient manifold relationships obtained by the original PSD
and achieve significant performance improvement. Our OBD-SD is a flexible
framework that can be integrated into state-of-the-art (SOTA) DML methods.
Extensive experiments on various benchmarks, namely CUB200, CARS196, and
Stanford Online Products, demonstrate that our OBD-SD consistently improves the
performance of the existing DML methods on multiple datasets with negligible
additional training time, achieving very competitive results. Code:
\url{https://github.com/ZelongZeng/OBD-SD_Pytorch}

Cerebral stroke, the second most substantial cause of death universally, has
been a primary public health concern over the last few years. With the help of
machine learning techniques, early detection of various stroke alerts is
accessible, which can efficiently prevent or diminish the stroke. Medical
dataset, however, are frequently unbalanced in their class label, with a
tendency to poorly predict minority classes. In this paper, the potential risk
factors for stroke are investigated. Moreover, four distinctive approaches are
applied to improve the classification of the minority class in the imbalanced
stroke dataset, which are the ensemble weight voting classifier, the Synthetic
Minority Over-sampling Technique (SMOTE), Principal Component Analysis with
K-Means Clustering (PCA-Kmeans), Focal Loss with the Deep Neural Network (DNN)
and compare their performance. Through the analysis results, SMOTE and
PCA-Kmeans with DNN-Focal Loss work best for the limited size of a large severe
imbalanced dataset,which is 2-4 times outperform Kaggle work.

Reconfigurable intelligent surfaces (RISs) have attracted increasing interest
due to their ability to improve the coverage, reliability, and energy
efficiency of millimeter wave (mmWave) communication systems. However,
designing the RIS beamforming typically requires large channel estimation or
beam training overhead, which degrades the efficiency of these systems. In this
paper, we propose to equip the RIS surfaces with visual sensors (cameras) that
obtain sensing information about the surroundings and user/basestation
locations, guide the RIS beam selection, and reduce the beam training overhead.
We develop a machine learning (ML) framework that leverages this visual sensing
information to efficiently select the optimal RIS reflection beams that reflect
the signals between the basestation and mobile users. To evaluate the developed
approach, we build a high-fidelity synthetic dataset that comprises co-existing
wireless and visual data. Based on this dataset, the results show that the
proposed vision-aided machine learning solution can accurately predict the RIS
beams and achieve near-optimal achievable rate while significantly reducing the
beam training overhead.

Traditional tracking of magnetic markers leads to high computational costs
due to the requirement for iterative optimization procedures. Furthermore, such
approaches rely on the magnetic dipole model for the optimization function,
leading to inaccurate results anytime a non-spherical magnet gets close to a
sensor in the array. We propose to overcome these limitations by using neural
networks to infer the marker's position and orientation. Our method can obtain
the magnet's five degrees of freedom (5 DoF) in a single inference step without
relying on an initial estimation. As a counterpart, the supervised training
phase is data intensive. We solve this by generating synthetic, yet realistic,
data via Finite Element Methods simulations. A fast and accurate inference
largely compensates for the offline training preparation. We evaluate our
system using different cylindrical magnets, tracked with a square array of 16
sensors. We use a portable, neural networks-oriented single-board computer for
the sensors' reading and the position inference, making our setup very compact.
We compared our tracking outputs with vision-based ground truth data. Our
prototype implementation tracks $5$ DoF with an averaged positional error of
4mm and orientation error of 8deg within a 0.2x0.2x0.15m working volume.

We consider channel estimation in systems equipped with a reconfigurable
intelligent surface (RIS). In order to illuminate the additional cascaded
channel as compared to systems without a RIS, a large number of training
sequences has to be transmitted over different phase allocations at the RIS.
However, for a given radio propagation scenario, there exist immanent
characteristics of the environment that imply structural features of the
channels which can be leveraged to reduce the necessary number of phase
allocations. We verify this observation by a study on discrete Fourier
transform (DFT)-based phase allocations where we exhaustively search for the
best combination of DFT columns and show that the solution heavily depends on
the considered scenario. We then propose to learn a neural network (NN) for
joint phase optimization and channel estimation because of the dependency of
the optimal phase allocations on the channel estimator, and vice versa. We
verify the effectiveness of the approach by numerical simulations for different
scenarios where state-of-the-art choices for the phase allocations and the
channel estimator are outperformed.

Understanding patterns of diagnoses, medications, procedures, and laboratory
tests from electronic health records (EHRs) and health insurer claims is
important for understanding disease risk and for efficient clinical
development, which often require rules-based curation in collaboration with
clinicians. We extended an unsupervised phenotyping algorithm, mixEHR, to an
online version allowing us to use it on order of magnitude larger datasets
including a large, US-based claims dataset and a rich regional EHR dataset. In
addition to recapitulating previously observed disease groups, we discovered
clinically meaningful disease subtypes and comorbidities. This work scaled up
an effective unsupervised learning method, reinforced existing clinical
knowledge, and is a promising approach for efficient collaboration with
clinicians.

Driving SMARTS is a regular competition designed to tackle problems caused by
the distribution shift in dynamic interaction contexts that are prevalent in
real-world autonomous driving (AD). The proposed competition supports
methodologically diverse solutions, such as reinforcement learning (RL) and
offline learning methods, trained on a combination of naturalistic AD data and
open-source simulation platform SMARTS. The two-track structure allows focusing
on different aspects of the distribution shift. Track 1 is open to any method
and will give ML researchers with different backgrounds an opportunity to solve
a real-world autonomous driving challenge. Track 2 is designed for strictly
offline learning methods. Therefore, direct comparisons can be made between
different methods with the aim to identify new promising research directions.
The proposed setup consists of 1) realistic traffic generated using real-world
data and micro simulators to ensure fidelity of the scenarios, 2) framework
accommodating diverse methods for solving the problem, and 3) baseline method.
As such it provides a unique opportunity for the principled investigation into
various aspects of autonomous vehicle deployment.

Recent applications of machine-learned normalizing flows to sampling in
lattice field theory suggest that such methods may be able to mitigate critical
slowing down and topological freezing. However, these demonstrations have been
at the scale of toy models, and it remains to be determined whether they can be
applied to state-of-the-art lattice quantum chromodynamics calculations.
Assessing the viability of sampling algorithms for lattice field theory at
scale has traditionally been accomplished using simple cost scaling laws, but
as we discuss in this work, their utility is limited for flow-based approaches.
We conclude that flow-based approaches to sampling are better thought of as a
broad family of algorithms with different scaling properties, and that
scalability must be assessed experimentally.

We present a generalized balancing weight method fully available for
estimating causal effects for an arbitrary mixture of discrete and continuous
interventions. Our weights are trainable through back-propagation, and we give
a method for estimating the weights via neural network algorithms. In addition,
we also provide a method to measure the performance of our weights by
estimating the mutual information for the balanced distribution. Our method is
easy to implement with any present deep learning libraries, and the weights
from it can be used in most state-of-art supervised algorithms.

Human lives are increasingly being affected by the outcomes of automated
decision-making systems and it is essential for the latter to be, not only
accurate, but also fair. The literature of algorithmic fairness has grown
considerably over the last decade, where most of the approaches are evaluated
under the strong assumption that the train and test samples are independently
and identically drawn from the same underlying distribution. However, in
practice, dissimilarity between the training and deployment environments
exists, which compromises the performance of the decision-making algorithm as
well as its fairness guarantees in the deployment data. There is an emergent
research line that studies how to preserve fairness guarantees when the data
generating processes differ between the source (train) and target (test)
domains, which is growing remarkably. With this survey, we aim to provide a
wide and unifying overview on the topic. For such purpose, we propose a
taxonomy of the existing approaches for fair classification under distribution
shift, highlight benchmarking alternatives, point out the relation with other
similar research fields and eventually, identify future venues of research.

Question-answering (QA) that comes naturally to humans is a critical
component in seamless human-computer interaction. It has emerged as one of the
most convenient and natural methods to interact with the web and is especially
desirable in voice-controlled environments. Despite being one of the oldest
research areas, the current QA system faces the critical challenge of handling
multilingual queries. To build an Artificial Intelligent (AI) agent that can
serve multilingual end users, a QA system is required to be language versatile
and tailored to suit the multilingual environment. Recent advances in QA models
have enabled surpassing human performance primarily due to the availability of
a sizable amount of high-quality datasets. However, the majority of such
annotated datasets are expensive to create and are only confined to the English
language, making it challenging to acknowledge progress in foreign languages.
Therefore, to measure a similar improvement in the multilingual QA system, it
is necessary to invest in high-quality multilingual evaluation benchmarks. In
this dissertation, we focus on advancing QA techniques for handling end-user
queries in multilingual environments. This dissertation consists of two parts.
In the first part, we explore multilingualism and a new dimension of
multilingualism referred to as code-mixing. Second, we propose a technique to
solve the task of multi-hop question generation by exploiting multiple
documents. Experiments show our models achieve state-of-the-art performance on
answer extraction, ranking, and generation tasks on multiple domains of MQA,
VQA, and language generation. The proposed techniques are generic and can be
widely used in various domains and languages to advance QA systems.

The way Wikipedia's contributors think can influence how they describe
individuals resulting in a bias based on gender. We use a machine learning
model to prove that there is a difference in how women and men are portrayed on
Wikipedia. Additionally, we use the results of the model to obtain which words
create bias in the overview of the biographies of the English Wikipedia. Using
only adjectives as input to the model, we show that the adjectives used to
portray women have a higher subjectivity than the ones used to describe men.
Extracting topics from the overview using nouns and adjectives as input to the
model, we obtain that women are related to family while men are related to
business and sports.

Graph sparsification is a powerful tool to approximate an arbitrary graph and
has been used in machine learning over homogeneous graphs. In heterogeneous
graphs such as knowledge graphs, however, sparsification has not been
systematically exploited to improve efficiency of learning tasks. In this work,
we initiate the study on heterogeneous graph sparsification and develop
sampling-based algorithms for constructing sparsifiers that are provably sparse
and preserve important information in the original graphs. We have performed
extensive experiments to confirm that the proposed method can improve time and
space complexities of representation learning while achieving comparable, or
even better performance in subsequent graph learning tasks based on the learned
embedding.

The human brain is a complex network comprised of functionally and
anatomically interconnected brain regions. A growing number of studies have
suggested that empirical estimates of brain networks may be useful for
discovery of biomarkers of disease and cognitive state. A prerequisite for
realizing this aim, however, is that brain networks also serve as reliable
markers of an individual. Here, using Human Connectome Project data, we build
upon recent studies examining brain-based fingerprints of individual subjects
and cognitive states based on cognitively-demanding tasks that assess, for
example, working memory, theory of mind, and motor function. Our approach
achieves accuracy of up to 99\% for both identification of the subject of an
fMRI scan, and for classification of the cognitive state of a previously-unseen
subject in a scan. More broadly, we explore the accuracy and reliability of
five different machine learning techniques on subject fingerprinting and
cognitive state decoding objectives, using functional connectivity data from
fMRI scans of a high number of subjects (865) across a number of cognitive
states (8). These results represent an advance on existing techniques for
functional connectivity-based brain fingerprinting and state decoding.
Additionally, 16 different pre-processing pipelines are compared in order to
characterize the effects of different aspects of the production of functional
connectomes (FCs) on the accuracy of subject and task classification, and to
identify possible confounds.

Multimodal relation extraction is an essential task for knowledge graph
construction. In this paper, we take an in-depth empirical analysis that
indicates the inaccurate information in the visual scene graph leads to poor
modal alignment weights, further degrading performance. Moreover, the visual
shuffle experiments illustrate that the current approaches may not take full
advantage of visual information. Based on the above observation, we further
propose a strong baseline with an implicit fine-grained multimodal alignment
based on Transformer for multimodal relation extraction. Experimental results
demonstrate the better performance of our method. Codes are available at
https://github.com/zjunlp/DeepKE/tree/main/example/re/multimodal.

Spatio-temporal Human-Object Interaction (ST-HOI) detection aims at detecting
HOIs from videos, which is crucial for activity understanding. In daily HOIs,
humans often interact with a variety of objects, e.g., holding and touching
dozens of household items in cleaning. However, existing whole body-object
interaction video benchmarks usually provide limited object classes. Here, we
introduce a new benchmark based on AVA: Discovering Interacted Objects (DIO)
including 51 interactions and 1,000+ objects. Accordingly, an ST-HOI learning
task is proposed expecting vision systems to track human actors, detect
interactions and simultaneously discover interacted objects. Even though
today's detectors/trackers excel in object detection/tracking tasks, they
perform unsatisfied to localize diverse/unseen objects in DIO. This profoundly
reveals the limitation of current vision systems and poses a great challenge.
Thus, how to leverage spatio-temporal cues to address object discovery is
explored, and a Hierarchical Probe Network (HPN) is devised to discover
interacted objects utilizing hierarchical spatio-temporal human/context cues.
In extensive experiments, HPN demonstrates impressive performance. Data and
code are available at https://github.com/DirtyHarryLYL/HAKE-AVA.

Keyword extraction has been an important topic for modern natural language
processing. With its applications ranging from ontology generation, fact
verification in summarized text, and recommendation systems. While it has had
significant data-intensive applications, it is often hampered when the data set
is small. Downstream training for keyword extractors is a lengthy process and
requires a significant amount of data. Recently, Few-shot Learning (FSL) and
Zero-Shot Learning (ZSL) have been proposed to tackle this problem. Therefore,
we propose AdaptKeyBERT, a pipeline for training keyword extractors with LLM
bases by incorporating the concept of regularized attention into a pre-training
phase for downstream domain adaptation. As we believe our work has implications
to be utilized in the pipeline of FSL/ZSL and keyword extraction, we
open-source our code as well as provide the fine-tuning library of the same
name AdaptKeyBERT at https://github.com/AmanPriyanshu/AdaptKeyBERT.

With the advances in deep learning, speech enhancement systems benefited from
large neural network architectures and achieved state-of-the-art quality.
However, speaker-agnostic methods are not always desirable, both in terms of
quality and their complexity, when they are to be used in a
resource-constrained environment. One promising way is personalized speech
enhancement (PSE), which is a smaller and easier speech enhancement problem for
small models to solve, because it focuses on a particular test-time user. To
achieve the personalization goal, while dealing with the typical lack of
personal data, we investigate the effect of data augmentation based on neural
speech synthesis (NSS). In the proposed method, we show that the quality of the
NSS system's synthetic data matters, and if they are good enough the augmented
dataset can be used to improve the PSE system that outperforms the
speaker-agnostic baseline. The proposed PSE systems show significant complexity
reduction while preserving the enhancement quality.

We study the problem of estimating 3D shape and pose of an object in terms of
keypoints, from a single 2D image.
  The shape and pose are learned directly from images collected by categories
and their partial 2D keypoint annotations.. In this work, we first propose an
end-to-end training framework for intermediate 2D keypoints extraction and
final 3D shape and pose estimation. The proposed framework is then trained
using only the weak supervision of the intermediate 2D keypoints. Additionally,
we devise a semi-supervised training framework that benefits from both labeled
and unlabeled data. To leverage the unlabeled data, we introduce and exploit
the \emph{piece-wise planar hull} prior of the canonical object shape. These
planar hulls are defined manually once per object category, with the help of
the keypoints. On the one hand, the proposed method learns to segment these
planar hulls from the labeled data. On the other hand, it simultaneously
enforces the consistency between predicted keypoints and the segmented hulls on
the unlabeled data. The enforced consistency allows us to efficiently use the
unlabeled data for the task at hand. The proposed method achieves comparable
results with fully supervised state-of-the-art methods by using only half of
the annotations. Our source code will be made publicly available.

Aims. We present the application of a fully connected neural network for
galaxy merger identification using exclusively photometric information. Our
purpose is not only to test the method's efficiency but also to understand what
merger properties the neural network can learn and what their physical
interpretation is. Methods. We created a class-balanced training dataset of
5860 galaxies split into mergers and non-mergers. The galaxy observations come
from SDSS DR6 and were visually identified in Galaxy Zoo. The 2$\,$930 mergers
were selected from known SDSS mergers and the respective non-mergers are the
closest match in both redshift and $r$ magnitude. The NN architecture was built
by testing a different number of layers with different sizes and variations of
the dropout rate. We compare input spaces constructed using the five SDSS
filters $u$, $g$, $r$, $i$ and $z$; combinations of bands, colours and their
errors; six magnitude types and variations of input normalization. Results. It
was found that the fibre magnitude errors contribute the most to the training
accuracy. Studying the parameters from which they are calculated, we showed
that the input space built from the sky error background in the five SDSS bands
alone leads to 92.64 $\pm$ 0.15 \% training accuracy. We also found that the
input normalization, i.e., how the data are presented to the NN, has a
significant effect on the training performance. Conclusions. We conclude that,
from all the SDSS photometric information, the sky error background is the most
sensitive to merging processes. This finding is supported by an analysis of its
5-band feature space by means of data visualization. Moreover, studying the
plane of the $g$ and $r$ sky error bands shows that a decision boundary line is
enough to achieve a 91.59\% accuracy.

We consider contextual bandits with knapsacks (CBwK), a variant of the
contextual bandit which places global constraints on budget consumption. We
present a new algorithm that is simple, statistically optimal, and
computationally efficient. Our algorithm combines LagrangeBwK (Immorlica et
al., FOCS'19), a Lagrangian-based technique for CBwK, and SquareCB (Foster and
Rakhlin, ICML'20), a regression-based technique for contextual bandits. Our
analysis emphasizes the modularity of both techniques.

This work aims to explore and identify tiny and seemingly unrelated
perturbations of images in object detection that will lead to performance
degradation. While tininess can naturally be defined using $L_p$ norms, we
characterize the degree of "unrelatedness" of an object by the pixel distance
between the occurred perturbation and the object. Triggering errors in
prediction while satisfying two objectives can be formulated as a
multi-objective optimization problem where we utilize genetic algorithms to
guide the search. The result successfully demonstrates that (invisible)
perturbations on the right part of the image can drastically change the outcome
of object detection on the left. An extensive evaluation reaffirms our
conjecture that transformer-based object detection networks are more
susceptible to butterfly effects in comparison to single-stage object detection
networks such as YOLOv5.

Many learning tasks in physics and chemistry involve global spatial
symmetries as well as permutational symmetry between particles. The standard
approach to such problems is equivariant neural networks, which employ tensor
products between various tensors that transform under the spatial group.
However, as the number of different tensors and the complexity of relationships
between them increases, the bookkeeping associated with ensuring parsimony as
well as equivariance quickly becomes nontrivial. In this paper, we propose to
use fusion diagrams, a technique widely used in simulating SU($2$)-symmetric
quantum many-body problems, to design new equivariant components for use in
equivariant neural networks. This yields a diagrammatic approach to
constructing new neural network architectures. We show that when applied to
particles in a given local neighborhood, the resulting components, which we
call fusion blocks, are universal approximators of any continuous equivariant
function defined on the neighborhood. As a practical demonstration, we
incorporate a fusion block into a pre-existing equivariant architecture
(Cormorant) and show that it improves performance on benchmark molecular
learning tasks.

Recent work has suggested post-hoc explainers might be ineffective for
detecting spurious correlations in Deep Neural Networks (DNNs). However, we
show there are serious weaknesses with the existing evaluation frameworks for
this setting. Previously proposed metrics are extremely difficult to interpret
and are not directly comparable between explainer methods. To alleviate these
constraints, we propose a new evaluation methodology, Explainer Divergence
Scores (EDS), grounded in an information theory approach to evaluate
explainers. EDS is easy to interpret and naturally comparable across
explainers. We use our methodology to compare the detection performance of
three different explainers - feature attribution methods, influential examples
and concept extraction, on two different image datasets. We discover post-hoc
explainers often contain substantial information about a DNN's dependence on
spurious artifacts, but in ways often imperceptible to human users. This
suggests the need for new techniques that can use this information to better
detect a DNN's reliance on spurious correlations.

Double-blind peer review is considered a pillar of academic research because
it is perceived to ensure a fair, unbiased, and fact-centered scientific
discussion. Yet, experienced researchers can often correctly guess from which
research group an anonymous submission originates, biasing the peer-review
process. In this work, we present a transformer-based, neural-network
architecture that only uses the text content and the author names in the
bibliography to atttribute an anonymous manuscript to an author. To train and
evaluate our method, we created the largest authorship-identification dataset
to date. It leverages all research papers publicly available on arXiv amounting
to over 2 million manuscripts. In arXiv-subsets with up to 2,000 different
authors, our method achieves an unprecedented authorship attribution accuracy,
where up to 95% of papers are attributed correctly. Thanks to our method, we
are not only able to predict the author of an anonymous work but we also
identify weaknesses of the double-blind review process by finding the key
aspects that make a paper attributable. We believe that this work gives
precious insights into how a submission can remain anonymous in order to
support an unbiased double-blind review process.

Network slicing envisions the 5th generation (5G) mobile network resource
allocation to be based on different requirements for different services, such
as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile
Broadband (eMBB). Open Radio Access Network (O-RAN), proposes an open and
disaggregated concept of RAN by modulizing the functionalities into independent
components. Network slicing for O-RAN can significantly improve performance.
Therefore, an advanced resource allocation solution for network slicing in
O-RAN is proposed in this study by applying Reinforcement Learning (RL). This
research demonstrates an RL compatible simplified edge network simulator with
three components, user equipment(UE), Edge O-Cloud, and Regional O-Cloud. This
simulator is later used to discover how to improve throughput for targeted
network slice(s) by dynamically allocating unused bandwidth from other slices.
Increasing the throughput for certain network slicing can also benefit the end
users with a higher average data rate, peak rate, or shorter transmission time.
The results show that the RL model can provide eMBB traffic with a high peak
rate and shorter transmission time for URLLC compared to balanced and eMBB
focus baselines.

This work presents the results of the machine translation (MT) task from the
Covid-19 MLIA @ Eval initiative, a community effort to improve the generation
of MT systems focused on the current Covid-19 crisis. Nine teams took part in
this event, which was divided in two rounds and involved seven different
language pairs. Two different scenarios were considered: one in which only the
provided data was allowed, and a second one in which the use of external
resources was allowed. Overall, best approaches were based on multilingual
models and transfer learning, with an emphasis on the importance of applying a
cleaning process to the training data.

Recent breakthroughs in computing power have made it feasible to use machine
learning and deep learning to advance scientific computing in many fields, such
as fluid mechanics, solid mechanics, materials science, etc. Neural networks,
in particular, play a central role in this hybridization. Due to their
intrinsic architecture, conventional neural networks cannot be successfully
trained and scoped when data is sparse; a scenario that is true in many
scientific fields. Nonetheless, neural networks offer a strong foundation to
digest physical-driven or knowledge-based constraints during training.
Generally speaking, there are three distinct neural network frameworks to
enforce underlying physics: (i) physics-guided neural networks (PgNN), (ii)
physics-informed neural networks (PiNN) and (iii) physics-encoded neural
networks (PeNN). These approaches offer unique advantages to accelerate the
modeling of complex multiscale multi-physics phenomena. They also come with
unique drawbacks and suffer from unresolved limitations (e.g., stability,
convergence, and generalization) that call for further research. This study
aims to present an in-depth review of the three neural network frameworks
(i.e., PgNN, PiNN, and PeNN) used in scientific computing research. The
state-of-the-art architectures and their applications are reviewed; limitations
are discussed; and future research opportunities in terms of improving
algorithms, considering causalities, expanding applications, and coupling
scientific and deep learning solvers are presented. This critical review
provides a solid starting point for researchers and engineers to comprehend how
to integrate different layers of physics into neural networks.

Large-scale radiance fields are promising mapping tools for smart
transportation applications like autonomous driving or drone delivery. But for
large-scale scenes, compact synchronized RGB-D cameras are not applicable due
to limited sensing range, and using separate RGB and depth sensors inevitably
leads to unsynchronized sequences. Inspired by the recent success of
self-calibrating radiance field training methods that do not require known
intrinsic or extrinsic parameters, we propose the first solution that
self-calibrates the mismatch between RGB and depth frames. We leverage the
important domain-specific fact that RGB and depth frames are actually sampled
from the same trajectory and develop a novel implicit network called the
time-pose function. Combining it with a large-scale radiance field leads to an
architecture that cascades two implicit representation networks. To validate
its effectiveness, we construct a diverse and photorealistic dataset that
covers various RGB-D mismatch scenarios. Through a comprehensive benchmarking
on this dataset, we demonstrate the flexibility of our method in different
scenarios and superior performance over applicable prior counterparts. Codes,
data, and models will be made publicly available.

Video anomaly detection (VAD) has been intensively studied for years because
of its potential applications in intelligent video systems. Existing
unsupervised VAD methods tend to learn normality from training sets consisting
of only normal videos and regard instances deviating from such normality as
anomalies. However, they often consider only local or global normality. Some of
them focus on learning local spatiotemporal representations from consecutive
frames in video clips to enhance the representation for normal events. But
powerful representation allows these methods to represent some anomalies and
causes missed detections. In contrast, the other methods are devoted to
memorizing global prototypical patterns of whole training videos to weaken the
generalization for anomalies, which also restricts them to represent diverse
normal patterns and causes false alarms. To this end, we propose a two-branch
model, Local-Global Normality Network (LGN-Net), to learn local and global
normality simultaneously. Specifically, one branch learns the evolution
regularities of appearance and motion from consecutive frames as local
normality utilizing a spatiotemporal prediction network, while the other branch
memorizes prototype features of the whole videos as global normality by a
memory module. LGN-Net achieves a balance of representing normal and abnormal
instances by fusing local and global normality. The fused normality enables our
model more generalized to various scenes compared to exploiting single
normality. Experiments demonstrate the effectiveness and superior performance
of our method. The code is available online:
https://github.com/Myzhao1999/LGN-Net.

Forecasts of regional electricity net-demand, consumption minus embedded
generation, are an essential input for reliable and economic power system
operation, and energy trading. While such forecasts are typically performed
region by region, operations such as managing power flows require spatially
coherent joint forecasts, which account for cross-regional dependencies. Here
we forecast the joint distribution of net-demand across the 14 regions
constituting Great Britain's electricity network. Joint modelling is
complicated by the fact that the net-demand variability within each region, and
the dependencies between regions, vary with temporal, socio-economical and
weather-related factors. We accommodate for these characteristics by proposing
a multivariate Gaussian model based on a modified Cholesky parametrisation,
which allows us to model each unconstrained parameter via an additive model.
Given that the number of model parameters and covariates is large, we adopt a
semi-automated approach to model selection, based on gradient boosting. In
addition to demonstrating that adopting a covariate-dependent covariance matrix
model leads to substantial forecasting performance improvements, comparable to
those obtained by using a full rather than a diagonal static covariance matrix,
we explore the model output via accumulated local effects and other visual
tools to get insights into how the covariates affect net-demand variability and
dependencies.
  The code for reproducing the results in this paper is available at
https://doi.org/10.5281/zenodo.7315106

We investigate online network topology identification from smooth nodal
observations acquired in a streaming fashion. Different from non-adaptive batch
solutions, our distinctive goal is to track the (possibly) dynamic adjacency
matrix with affordable memory and computational costs by processing signal
snapshots online. To this end, we leverage and truncate dual-based proximal
gradient (DPG) iterations to solve a composite smoothness-regularized,
time-varying inverse problem. Numerical tests with synthetic and real
electrocorticography data showcase the effectiveness of the novel lightweight
iterations when it comes to tracking slowly-varying network connectivity. We
also show that the online DPG algorithm converges faster than a primal-based
baseline of comparable complexity. Aligned with reproducible research
practices, we share the code developed to produce all figures included in this
paper.

In this work, we demonstrate that a major limitation of regression using a
mean-squared error loss is its sensitivity to the scale of its targets. This
makes learning settings consisting of several subtasks with differently-scaled
targets challenging, and causes algorithms to require task-specific learning
rate tuning. A recently-proposed alternative loss function, known as histogram
loss, avoids this issue. However, its computational cost grows linearly with
the number of buckets in the histogram, which renders prediction with
real-valued targets intractable. To address this issue, we propose a novel
approach to training deep learning models on real-valued regression targets,
autoregressive regression, which learns a high-fidelity distribution by
utilizing an autoregressive target decomposition. We demonstrate that this
training objective allows us to solve regression tasks involving multiple
targets with different scales.

Generating engaging content has drawn much recent attention in the NLP
community. Asking questions is a natural way to respond to photos and promote
awareness. However, most answers to questions in traditional question-answering
(QA) datasets are factoids, which reduce individuals' willingness to answer.
Furthermore, traditional visual question generation (VQG) confines the source
data for question generation to single images, resulting in a limited ability
to comprehend time-series information of the underlying event. In this paper,
we propose generating engaging questions from multiple images. We present MVQG,
a new dataset, and establish a series of baselines, including both end-to-end
and dual-stage architectures. Results show that building stories behind the
image sequence enables models to generate engaging questions, which confirms
our assumption that people typically construct a picture of the event in their
minds before asking questions. These results open up an exciting challenge for
visual-and-language models to implicitly construct a story behind a series of
photos to allow for creativity and experience sharing and hence draw attention
to downstream applications.

Leading a healthy lifestyle has become one of the most challenging goals in
today's society due to our sedentary lifestyle and poor eating habits. As a
result, national and international organisms have made numerous efforts to
promote healthier food diets and physical activity habits. However, these
recommendations are sometimes difficult to follow in our daily life and they
are also based on a general population. As a consequence, a new area of
research, personalised nutrition, has been conceived focusing on individual
solutions through smart devices and Artificial Intelligence (AI) methods.
  This study presents the AI4Food-NutritionDB database, the first nutrition
database that considers food images and a nutrition taxonomy based on
recommendations by national and international organisms. In addition, four
different categorisation levels are considered following nutrition experts: 6
nutritional levels, 19 main categories (e.g., "Meat"), 73 subcategories (e.g.,
"White Meat"), and 893 final food products (e.g., "Chicken"). The
AI4Food-NutritionDB opens the doors to new food computing approaches in terms
of food intake frequency, quality, and categorisation. Also, in addition to the
database, we propose a standard experimental protocol and benchmark including
three tasks based on the nutrition taxonomy (i.e., category, subcategory, and
final product) to be used for the research community. Finally, we also release
our Deep Learning models trained with the AI4Food-NutritionDB, which can be
used as pre-trained models, achieving accurate recognition results with
challenging food image databases.

In this article, we give advice on transitioning to a more agile delivery
model for large-scale agile development projects based on experience from the
Parental Benefit Project of the Norwegian Labour and Welfare Administration.
The project modernized a central part of the organizations IT portfolio and
included up to ten development teams working in parallel. The project
successfully changed from using a delivery model which combined traditional
project management elements and agile methods to a more agile delivery model
with autonomous teams and continuous deployment. This transition was completed
in tandem with the project execution. We identify key lessons learned which
will be useful for other organizations considering similar changes and report
how the new delivery model reduced risk and opened up a range of new
possibilities for delivering the benefits of digitalization.

Reformulating the history matching problem from a least-square mathematical
optimization problem into a Markov Decision Process introduces a method in
which reinforcement learning can be utilized to solve the problem. This method
provides a mechanism where an artificial deep neural network agent can interact
with the reservoir simulator and find multiple different solutions to the
problem. Such formulation allows for solving the problem in parallel by
launching multiple concurrent environments enabling the agent to learn
simultaneously from all the environments at once, achieving significant speed
up.

Annotations are necessary to develop computer vision algorithms for
histopathology, but dense annotations at a high resolution are often
time-consuming to make. Deep learning models for segmentation are a way to
alleviate the process, but require large amounts of training data, training
times and computing power. To address these issues, we present seeded iterative
clustering to produce a coarse segmentation densely and at the whole slide
level. The algorithm uses precomputed representations as the clustering space
and a limited amount of sparse interactive annotations as seeds to iteratively
classify image patches. We obtain a fast and effective way of generating dense
annotations for whole slide images and a framework that allows the comparison
of neural network latent representations in the context of transfer learning.

The inference of outcomes in dynamic processes from structural features of
systems is a crucial endeavor in network science. Recent research has suggested
a machine learning-based approach for the interpretation of dynamic patterns
emerging in complex network systems. The hypothesis is applied in this study
towards showing opinions can be classified in the voter model with the use of
topological features from complex networks. An analysis was first performed
with 2 (0 or 1) opinions from a voter model and extended for 3 and then 4
opinions from the same model. A breakdown of the key network topological
features for the estimation is provided and network metrics are ranked in the
order of importance with high accuracy. Our generalized approach is applicable
to dynamical processes running on complex networks. This study is a step
towards the application of machine learning methods to studies of dynamical
patterns from complex network systems.

We study the problem of Reinforcement Learning (RL) with linear function
approximation, i.e. assuming the optimal action-value function is linear in a
known $d$-dimensional feature mapping. Unfortunately, however, based on only
this assumption, the worst case sample complexity has been shown to be
exponential, even under a generative model. Instead of making further
assumptions on the MDP or value functions, we assume that our action space is
such that there always exist playable actions to explore any direction of the
feature space. We formalize this assumption as a ``ball structure'' action
space, and show that being able to freely explore the feature space allows for
efficient RL. In particular, we propose a sample-efficient RL algorithm
(BallRL) that learns an $\epsilon$-optimal policy using only
$\tilde{O}\left(\frac{H^5d^3}{\epsilon^3}\right)$ number of trajectories.

MRSA colonization is a critical public health concern. Decolonization
protocols have been designed for the clearance of MRSA. Successful
decolonization protocols reduce disease incidence; however, multiple protocols
exist, comprising diverse therapies targeting multiple body sites, and the
optimal protocol is unclear. Here, we formulate a machine learning model using
data from a randomized controlled trial (RCT) of MRSA decolonization, which
estimates interactions between body sites, quantifies the contribution of each
therapy to successful decolonization, and enables predictions of the efficacy
of therapy combinations. This work shows how a machine learning model can help
design and improve complex clinical protocols.

The setting of an agent making decisions under uncertainty and under dynamic
constraints is common for the fields of optimal control, reinforcement learning
and recently also for online learning. In the online learning setting, the
quality of an agent's decision is often quantified by the concept of regret,
comparing the performance of the chosen decisions to the best possible ones in
hindsight. While regret is a useful performance measure, when dynamical systems
are concerned, it is important to also assess the stability of the closed-loop
system for a chosen policy. In this work, we show that for linear state
feedback policies and linear systems subject to adversarial disturbances,
linear regret implies asymptotic stability in both time-varying and
time-invariant settings. Conversely, we also show that bounded input bounded
state (BIBS) stability and summability of the state transition matrices imply
linear regret.

Learning anatomical segmentation from heterogeneous labels in multi-center
datasets is a common situation encountered in clinical scenarios, where certain
anatomical structures are only annotated in images coming from particular
medical centers, but not in the full database. Here we first show how
state-of-the-art pixel-level segmentation models fail in naively learning this
task due to domain memorization issues and conflicting labels. We then propose
to adopt HybridGNet, a landmark-based segmentation model which learns the
available anatomical structures using graph-based representations. By analyzing
the latent space learned by both models, we show that HybridGNet naturally
learns more domain-invariant feature representations, and provide empirical
evidence in the context of chest X-ray multiclass segmentation. We hope these
insights will shed light on the training of deep learning models with
heterogeneous labels from public and multi-center datasets.

We investigate composed image retrieval with text feedback. Users gradually
look for the target of interest by moving from coarse to fine-grained feedback.
However, existing methods merely focus on the latter, i.e, fine-grained search,
by harnessing positive and negative pairs during training. This pair-based
paradigm only considers the one-to-one distance between a pair of specific
points, which is not aligned with the one-to-many coarse-grained retrieval
process and compromises the recall rate. In an attempt to fill this gap, we
introduce a unified learning approach to simultaneously modeling the coarse-
and fine-grained retrieval by considering the multi-grained uncertainty. The
key idea underpinning the proposed method is to integrate fine- and
coarse-grained retrieval as matching data points with small and large
fluctuations, respectively. Specifically, our method contains two modules:
uncertainty modeling and uncertainty regularization. (1) The uncertainty
modeling simulates the multi-grained queries by introducing identically
distributed fluctuations in the feature space. (2) Based on the uncertainty
modeling, we further introduce uncertainty regularization to adapt the matching
objective according to the fluctuation range. Compared with existing methods,
the proposed strategy explicitly prevents the model from pushing away potential
candidates in the early stage, and thus improves the recall rate. On the three
public datasets, i.e., FashionIQ, Fashion200k, and Shoes, the proposed method
has achieved +4.03%, + 3.38%, and + 2.40% Recall@50 accuracy over a strong
baseline, respectively.

Type 1 Diabetes (T1D) is a chronic condition where the body produces little
or no insulin, a hormone required for the cells to use blood glucose (BG) for
energy and to regulate BG levels in the body. Finding the right insulin dose
and time remains a complex, challenging and as yet unsolved control task. In
this study, we use the OpenAPS Data Commons dataset, which is an extensive
dataset collected in real-life conditions, to discover temporal patterns in
insulin need driven by well-known factors such as carbohydrates as well as
potentially novel factors. We utilised various time series techniques to spot
such patterns using matrix profile and multi-variate clustering. The better we
understand T1D and the factors impacting insulin needs, the more we can
contribute to building data-driven technology for T1D treatments.

We consider control of dynamical systems through the lens of competitive
analysis. Most prior work in this area focuses on minimizing regret, that is,
the loss relative to an ideal clairvoyant policy that has noncausal access to
past, present, and future disturbances. Motivated by the observation that the
optimal cost only provides coarse information about the ideal closed-loop
behavior, we instead propose directly minimizing the tracking error relative to
the optimal trajectories in hindsight, i.e., imitating the clairvoyant policy.
By embracing a system level perspective, we present an efficient
optimization-based approach for computing follow-the-clairvoyant (FTC) safe
controllers. We prove that these attain minimal regret if no constraints are
imposed on the noncausal benchmark. In addition, we present numerical
experiments to show that our policy retains the hallmark of competitive
algorithms of interpolating between classical $\mathcal{H}_2$ and
$\mathcal{H}_\infty$ control laws - while consistently outperforming regret
minimization methods in constrained scenarios thanks to the superior ability to
chase the clairvoyant.

We consider the problem of contextual multi-armed bandits in the setting of
hypothesis transfer learning. That is, we assume having access to a previously
learned model on an unobserved set of contexts, and we leverage it in order to
accelerate exploration on a new bandit problem. Our transfer strategy is based
on a re-weighting scheme for which we show a reduction in the regret over the
classic Linear UCB when transfer is desired, while recovering the classic
regret rate when the two tasks are unrelated. We further extend this method to
an arbitrary amount of source models, where the algorithm decides which model
is preferred at each time step. Additionally we discuss an approach where a
dynamic convex combination of source models is given in terms of a biased
regularization term in the classic LinUCB algorithm. The algorithms and the
theoretical analysis of our proposed methods substantiated by empirical
evaluations on simulated and real-world data.

Registration of brain scans with pathologies is difficult, yet important
research area. The importance of this task motivated researchers to organize
the BraTS-Reg challenge, jointly with IEEE ISBI 2022 and MICCAI 2022
conferences. The organizers introduced the task of aligning pre-operative to
follow-up magnetic resonance images of glioma. The main difficulties are
connected with the missing data leading to large, nonrigid, and noninvertible
deformations. In this work, we describe our contributions to both the editions
of the BraTS-Reg challenge. The proposed method is based on combined deep
learning and instance optimization approaches. First, the instance optimization
enriches the state-of-the-art LapIRN method to improve the generalizability and
fine-details preservation. Second, an additional objective function weighting
is introduced, based on the inverse consistency. The proposed method is fully
unsupervised and exhibits high registration quality and robustness. The
quantitative results on the external validation set are: (i) IEEE ISBI 2022
edition: 1.85, and 0.86, (ii) MICCAI 2022 edition: 1.71, and 0.86, in terms of
the mean of median absolute error and robustness respectively. The method
scored the 1st place during the IEEE ISBI 2022 version of the challenge and the
3rd place during the MICCAI 2022. Future work could transfer the inverse
consistency-based weighting directly into the deep network training.

In digital pathology, Whole Slide Image (WSI) analysis is usually formulated
as a Multiple Instance Learning (MIL) problem. Although transformer-based
architectures have been used for WSI classification, these methods require
modifications to adapt them to specific challenges of this type of image data.
Despite their power across domains, reference transformer models in classical
Computer Vision (CV) and Natural Language Processing (NLP) tasks are not used
for pathology slide analysis. In this work we demonstrate the use of standard,
frozen, text-pretrained, transformer language models in application to WSI
classification. We propose SeqShort, a multi-head attention-based sequence
reduction input layer to summarize each WSI in a fixed and short size sequence
of instances. This allows us to reduce the computational costs of
self-attention on long sequences, and to include positional information that is
unavailable in other MIL approaches. We demonstrate the effectiveness of our
methods in the task of cancer subtype classification, without the need of
designing a WSI-specific transformer or performing in-domain self-supervised
pretraining, while keeping a reduced compute budget and number of trainable
parameters.

In this paper, we propose a novel strategy for text-independent speaker
identification system: Multi-Label Training (MLT). Instead of the commonly used
one-to-one correspondence between the speech and the speaker label, we divide
all the speeches of each speaker into several subgroups, with each subgroup
assigned a different set of labels. During the identification process, a
specific speaker is identified as long as the predicted label is the same as
one of his/her corresponding labels. We found that this method can force the
model to distinguish the data more accurately, and somehow takes advantages of
ensemble learning, while avoiding the significant increase of computation and
storage burden. In the experiments, we found that not only in clean conditions,
but also in noisy conditions with speech enhancement, Multi-Label Training can
still achieve better identification performance than commom methods. It should
be noted that the proposed strategy can be easily applied to almost all current
text-independent speaker identification models to achieve further improvements.

Over the past years, the main research innovations in face recognition
focused on training deep neural networks on large-scale identity-labeled
datasets using variations of multi-class classification losses. However, many
of these datasets are retreated by their creators due to increased privacy and
ethical concerns. Very recently, privacy-friendly synthetic data has been
proposed as an alternative to privacy-sensitive authentic data to comply with
privacy regulations and to ensure the continuity of face recognition research.
In this paper, we propose an unsupervised face recognition model based on
unlabeled synthetic data (USynthFace). Our proposed USynthFace learns to
maximize the similarity between two augmented images of the same synthetic
instance. We enable this by a large set of geometric and color transformations
in addition to GAN-based augmentation that contributes to the USynthFace model
training. We also conduct numerous empirical studies on different components of
our USynthFace. With the proposed set of augmentation operations, we proved the
effectiveness of our USynthFace in achieving relatively high recognition
accuracies using unlabeled synthetic data.

Census and Household Travel Survey datasets are regularly collected from
households and individuals and provide information on their daily travel
behavior with demographic and economic characteristics. These datasets have
important applications ranging from travel demand estimation to agent-based
modeling. However, they often represent a limited sample of the population due
to privacy concerns or are given aggregated. Synthetic data augmentation is a
promising avenue in addressing these challenges. In this paper, we propose a
framework to generate a synthetic population that includes both socioeconomic
features (e.g., age, sex, industry) and trip chains (i.e., activity locations).
Our model is tested and compared with other recently proposed models on
multiple assessment metrics.

A website browser cookie is a small file created by a web server upon
visitation, which is placed in the user's browser directory to enhance the
user's experience. However, first and third-party cookies have become a
significant threat to users' privacy due to their data collection methods. To
understand the users' perception of the risk of cookies and targeted
advertisements, we conducted a user study through a control versus experimental
group survey. Our goal was to gauge how user knowledge reflected their security
and privacy preferences on the internet; thus, for the experimental group, we
created a learning website and information videos through participatory design
in a workshop with 15 participants. After that, by evaluating the responses of
68 participants through the survey, we analyzed user awareness of cookies,
their privacy implications, and how risk communication can impact user
behavior.

To eliminate the requirement of fully-labeled data for supervised model
training in traditional Federated Learning (FL), extensive attention has been
paid to the application of Self-supervised Learning (SSL) approaches on FL to
tackle the label scarcity problem. Previous works on Federated SSL generally
fall into two categories: parameter-based model aggregation (i.e., FedAvg,
applicable to homogeneous cases) or data-based feature sharing (i.e., knowledge
distillation, applicable to heterogeneous cases) to achieve knowledge transfer
among multiple unlabeled clients. Despite the progress, all of them inevitably
rely on some assumptions, such as homogeneous models or the existence of an
additional public dataset, which hinder the universality of the training
frameworks for more general scenarios. Therefore, in this paper, we propose a
novel and general method named Federated Self-supervised Learning with
Feature-correlation based Aggregation (FedFoA) to tackle the above limitations
in a communication-efficient and privacy-preserving manner. Our insight is to
utilize feature correlation to align the feature mappings and calibrate the
local model updates across clients during their local training process. More
specifically, we design a factorization-based method to extract the
cross-feature relation matrix from the local representations. Then, the
relation matrix can be regarded as a carrier of semantic information to perform
the aggregation phase. We prove that FedFoA is a model-agnostic training
framework and can be easily compatible with state-of-the-art unsupervised FL
methods. Extensive empirical experiments demonstrate that our proposed approach
outperforms the state-of-the-art methods by a significant margin.

A monopoly platform sells either a risky product (with unknown utility) or a
safe product (with known utility) to agents who sequentially arrive and learn
the utility of the risky product by the reporting of previous agents. It is
costly for agents to report utility; hence the platform has to design both the
prices and the reporting bonus to motivate the agents to explore and generate
new information. We characterize the optimal bonus and pricing schemes offered
by the profit-maximizing platform. It turns out that the optimal scheme falls
into one of four types: Full Coverage, Partial Coverage, Immediate Revelation,
and Non-Bonus. In a model of exponential bandit, we find that there is a
dynamical switch of the types along the learning trajectory. Although learning
stops efficiently, information is revealed too slowly compared with the
planner's optimal solution.

In high-energy particle physics, complex Monte Carlo (MC) simulations are
needed to compare theory predictions to measurable quantities. Many and large
MC samples are needed to be generated to take into account all the systematics.
Therefore, the MC statistics (and hence the MC modeling uncertainties) become a
limiting factor for most measurements. Moreover, the significant computational
cost of these programs becomes a bottleneck in most physics analyses.
Therefore, it is extremely important to find a way to reduce the MC samples
generated to decrease the MC statistical uncertainties and lower the
computational cost. In these proceedings, we evaluate an approach called Deep
neural network using Classification for Tuning and Reweighting (DCTR). DCTR is
a method based on a Deep Neural Network (DNN) to reweight simulations to
different models or model parameters and fit simulations, using the full
kinematic information in the event. This reweighting methodology avoids the
need for simulating the detector response multiple times by incorporating the
relevant variations in a single sample. In this way, the MC statistical
uncertainties and the computational cost are both reduced. Moreover, unlike the
standard reweighting, in which the ratio in bins of two histograms at truth
level is performed, multidimensional and unbinned information can be used as
inputs to the DNN. In addition, DCTR can perform tasks that are not possible
with other current existing methods, such as continuous reweighting as a
function of any MC parameter, simultaneous reweighting of more MC parameters
and tuning MC simulations to the data. We test the method on MC simulations of
top quark pair production, which we reweight to different SM parameter values
and to different QCD models.

Transformer-based pre-trained language models have demonstrated superior
performance on various natural language processing tasks. However, it remains
unclear how the skills required to handle these tasks distribute among model
parameters. In this paper, we find that after prompt tuning for specific tasks,
the activations of some neurons within pre-trained Transformers are highly
predictive of the task labels. We dub these neurons skill neurons and confirm
they encode task-specific skills by finding that: (1) Skill neurons are crucial
for handling tasks. Performances of pre-trained Transformers on a task
significantly drop when corresponding skill neurons are perturbed. (2) Skill
neurons are task-specific. Similar tasks tend to have similar distributions of
skill neurons. Furthermore, we demonstrate the skill neurons are most likely
generated in pre-training rather than fine-tuning by showing that the skill
neurons found with prompt tuning are also crucial for other fine-tuning methods
freezing neuron weights, such as the adapter-based tuning and BitFit. We also
explore the applications of skill neurons, including accelerating Transformers
with network pruning and building better transferability indicators. These
findings may promote further research on understanding Transformers. The source
code can be obtained from https://github.com/THU-KEG/Skill-Neuron.

There have been many proposals to reduce constituency parsing to tagging in
the literature. To better understand what these approaches have in common, we
cast several existing proposals into a unifying pipeline consisting of three
steps: linearization, learning, and decoding. In particular, we show how to
reduce tetratagging, a state-of-the-art constituency tagger, to shift--reduce
parsing by performing a right-corner transformation on the grammar and making a
specific independence assumption. Furthermore, we empirically evaluate our
taxonomy of tagging pipelines with different choices of linearizers, learners,
and decoders. Based on the results in English and a set of 8 typologically
diverse languages, we conclude that the linearization of the derivation tree
and its alignment with the input sequence is the most critical factor in
achieving accurate taggers.

We introduce replacing language model (RLM), a sequence-to-sequence language
modeling framework for text style transfer. Our method autoregressively
replaces each token in the original sentence with a text span in the target
style. In contrast, the new span is generated via a non-autoregressive masked
language model. The RLM generation scheme gathers the flexibility of
autoregressive models and the accuracy of non-autoregressive models, which
bridges the gap between sentence-level and word-level style transfer methods.
To further control the style of generated sentences, we conduct a style-content
disentanglement on the hidden representations of RLM. Empirical results on
real-world text style transfer tasks demonstrate the effectiveness of RLM
compared with other baselines.

Speech emotion recognition (SER) plays a vital role in improving the
interactions between humans and machines by inferring human emotion and
affective states from speech signals. Whereas recent works primarily focus on
mining spatiotemporal information from hand-crafted features, we explore how to
model the temporal patterns of speech emotions from dynamic temporal scales.
Towards that goal, we introduce a novel temporal emotional modeling approach
for SER, termed Temporal-aware bI-direction Multi-scale Network (TIM-Net),
which learns multi-scale contextual affective representations from various time
scales. Specifically, TIM-Net first employs temporal-aware blocks to learn
temporal affective representation, then integrates complementary information
from the past and the future to enrich contextual representations, and finally,
fuses multiple time scale features for better adaptation to the emotional
variation. Extensive experimental results on six benchmark SER datasets
demonstrate the superior performance of TIM-Net, gaining 2.34% and 2.61%
improvements of the average UAR and WAR over the second-best on each corpus.
Remarkably, TIM-Net outperforms the latest domain-adaptation method on the
cross-corpus SER tasks, demonstrating strong generalizability.

The diverse relationships among real-world events, including coreference,
temporal, causal, and subevent relations, are fundamental to understanding
natural languages. However, two drawbacks of existing datasets limit event
relation extraction (ERE) tasks: (1) Small scale. Due to the annotation
complexity, the data scale of existing datasets is limited, which cannot well
train and evaluate data-hungry models. (2) Absence of unified annotation.
Different types of event relations naturally interact with each other, but
existing datasets only cover limited relation types at once, which prevents
models from taking full advantage of relation interactions. To address these
issues, we construct a unified large-scale human-annotated ERE dataset
MAVEN-ERE with improved annotation schemes. It contains 103,193 event
coreference chains, 1,216,217 temporal relations, 57,992 causal relations, and
15,841 subevent relations, which is larger than existing datasets of all the
ERE tasks by at least an order of magnitude. Experiments show that ERE on
MAVEN-ERE is quite challenging, and considering relation interactions with
joint learning can improve performances. The dataset and source codes can be
obtained from https://github.com/THU-KEG/MAVEN-ERE.

Gaze estimation methods have significantly matured in recent years, but the
large number of eye images required to train deep learning models poses
significant privacy risks. In addition, the heterogeneous data distribution
across different users can significantly hinder the training process. In this
work, we propose the first federated learning approach for gaze estimation to
preserve the privacy of gaze data. We further employ pseudo-gradient
optimisation to adapt our federated learning approach to the divergent model
updates to address the heterogeneous nature of in-the-wild gaze data in
collaborative setups. We evaluate our approach on a real-world dataset
(MPIIGaze) and show that our work enhances the privacy guarantees of
conventional appearance-based gaze estimation methods, handles the convergence
issues of gaze estimators, and significantly outperforms vanilla federated
learning by 15.8% (from a mean error of 10.63 degrees to 8.95 degrees). As
such, our work paves the way to develop privacy-aware collaborative learning
setups for gaze estimation while maintaining the model's performance.

In this paper, we propose a new architecture to enhance the privacy and
security of networked control systems against malicious adversaries. We
consider an adversary which first learns the system dynamics (privacy) using
system identification techniques, and then performs a data injection attack
(security). In particular, we consider an adversary conducting zero-dynamics
attacks (ZDA) which maximizes the performance cost of the system whilst staying
undetected. However, using the proposed architecture, we show that it is
possible to (i) introduce significant bias in the system estimates of the
adversary: thus providing privacy of the system parameters, and (ii)
efficiently detect attacks when the adversary performs a ZDA using the
identified system: thus providing security. Through numerical simulations, we
illustrate the efficacy of the proposed architecture.

We consider estimation models of the form $Y=X^*+N$, where $X^*$ is some
$m$-dimensional signal we wish to recover, and $N$ is symmetrically distributed
noise that may be unbounded in all but a small $\alpha$ fraction of the
entries. We introduce a family of algorithms that under mild assumptions
recover the signal $X^*$ in all estimation problems for which there exists a
sum-of-squares algorithm that succeeds in recovering the signal $X^*$ when the
noise $N$ is Gaussian. This essentially shows that it is enough to design a
sum-of-squares algorithm for an estimation problem with Gaussian noise in order
to get the algorithm that works with the symmetric noise model. Our framework
extends far beyond previous results on symmetric noise models and is even
robust to adversarial perturbations.
  As concrete examples, we investigate two problems for which no efficient
algorithms were known to work for heavy-tailed noise: tensor PCA and sparse
PCA. For the former, our algorithm recovers the principal component in
polynomial time when the signal-to-noise ratio is at least
$\tilde{O}(n^{p/4}/\alpha)$, that matches (up to logarithmic factors) current
best known algorithmic guarantees for Gaussian noise. For the latter, our
algorithm runs in quasipolynomial time and matches the state-of-the-art
guarantees for quasipolynomial time algorithms in the case of Gaussian noise.
Using a reduction from the planted clique problem, we provide evidence that the
quasipolynomial time is likely to be necessary for sparse PCA with symmetric
noise.
  In our proofs we use bounds on the covering numbers of sets of
pseudo-expectations, which we obtain by certifying in sum-of-squares upper
bounds on the Gaussian complexities of sets of solutions. This approach for
bounding the covering numbers of sets of pseudo-expectations may be interesting
in its own right and may find other application in future works.

In this paper, we provide a new perspective on self-supervised speech models
from how the self-training targets are obtained. We generalize the targets
extractor into Offline Targets Extractor (Off-TE) and Online Targets Extractor
(On-TE), without caring about specific pretext tasks. Based on this, we propose
a new multi-tasking learning framework for self-supervised learning, MT4SSL,
which stands for Boosting Self-Supervised Speech Representation Learning by
Integrating Multiple Targets. MT4SSL refers to two typical models, HuBERT and
data2vec, which use the K-means algorithm as an Off-TE and a teacher network
without gradients as an On-TE, respectively. Our model outperforms previous SSL
methods by nontrivial margins on the LibriSpeech benchmark, and is comparable
to or even better than the best-performing models with no need for that much
data. Furthermore, we find that using both Off-TE and On-TE results in better
convergence in the pre-training phase. With both effectiveness and efficiency,
we think that doing multi-task learning on self-supervised speech models from
our perspective is a promising trend.

When taking photos under an environment with insufficient light, the exposure
time and the sensor gain usually require to be carefully chosen to obtain
images with satisfying visual quality. For example, the images with high ISO
usually have inescapable noise, while the long-exposure ones may be blurry due
to camera shake or object motion. Existing solutions generally suggest to seek
a balance between noise and blur, and learn denoising or deblurring models
under either full- or self-supervision. However, the real-world training pairs
are difficult to collect, and the self-supervised methods merely rely on blurry
or noisy images are limited in performance. In this work, we tackle this
problem by jointly leveraging the short-exposure noisy image and the
long-exposure blurry image for better image restoration. Such setting is
practically feasible due to that short-exposure and long-exposure images can be
either acquired by two individual cameras or synthesized by a long burst of
images. Moreover, the short-exposure images are hardly blurry, and the
long-exposure ones have negligible noise. Their complementarity makes it
feasible to learn restoration model in a self-supervised manner. Specifically,
the noisy images can be used as the supervision information for deblurring,
while the sharp areas in the blurry images can be utilized as the auxiliary
supervision information for self-supervised denoising. By learning in a
collaborative manner, the deblurring and denoising tasks in our method can
benefit each other. Experiments on synthetic and real-world images show the
effectiveness and practicality of the proposed method. Codes are available at
https://github.com/cszhilu1998/SelfIR.

In recent years, research on hyperspectral image (HSI) classification has
continuous progress on introducing deep network models, and recently the graph
convolutional network (GCN) based models have shown impressive performance.
However, these deep learning frameworks based on point estimation suffer from
low generalization and inability to quantify the classification results
uncertainty. On the other hand, simply applying the Bayesian Neural Network
(BNN) based on distribution estimation to classify the HSI is unable to achieve
high classification accuracy due to the large amount of parameters. In this
paper, we design a Bayesian layer with Bayesian idea as an insertion layer into
point estimation based neural networks, and propose a Bayesian Layer Graph
Convolutional Network (BLGCN) model by combining graph convolution operations,
which can effectively extract graph information and estimate the uncertainty of
classification results. Moreover, a Generative Adversarial Network (GAN) is
built to solve the sample imbalance problem of HSI dataset. Finally, we design
a dynamic control training strategy based on the confidence interval of the
classification results, which will terminate the training early when the
confidence interval reaches the preseted threshold. The experimental results
show that our model achieves a balance between high classification accuracy and
strong generalization. In addition, it can quantifies the uncertainty of the
classification results.

Federated learning is a popular distributed and privacy-preserving machine
learning approach. Meanwhile, minimax optimization is an effective hierarchical
model in machine learning. Recently, some federated learning methods have been
proposed to solve the distributed minimax optimization. However, these
federated minimax optimization methods still suffer from high gradient and
communication complexities. To fill this gap, in the paper, we study the
Nonconvex-Strongly-Concave (NSC) minimax optimization, and propose a class of
accelerated federated minimax optimization methods (i.e., FGDA and AdaFGDA) to
solve the distributed minimax problems. Specifically, our methods build on the
momentum-based variance reduced and local-SGD techniques, and our adaptive
algorithm (i.e., AdaFGDA) can flexibly incorporate various adaptive learning
rates by using the unified adaptive matrix. Theoretically, we provide a solid
convergence analysis framework for our algorithms under non-i.i.d. setting.
Moreover, we prove our algorithms obtain lower gradient (i.e., SFO) complexity
of $\tilde{O}(\epsilon^{-3})$ with lower communication complexity of
$\tilde{O}(\epsilon^{-2})$ in finding $\epsilon$-stationary point of NSC
minimax problems. Experimentally, we conduct the distributed fair learning and
robust federated learning tasks to verify efficiency of our methods.

Separation of multiple singing voices into each voice is a rarely studied
area in music source separation research. The absence of a benchmark dataset
has hindered its progress. In this paper, we present an evaluation dataset and
provide baseline studies for multiple singing voices separation. First, we
introduce MedleyVox, an evaluation dataset for multiple singing voices
separation that corresponds to such categories. We specify the problem
definition in this dataset by categorizing the problem into i) duet, ii)
unison, iii)main vs. rest, and iv) N-singing separation. Second, we present a
strategy for construction of multiple singing mixtures using various
single-singing datasets. This can be used to obtain training data. Third, we
propose the improved super-resolution network (iSRNet). Jointly trained with
the Conv-TasNet and the multi-singing mixture construction strategy, the
proposed iSRNet achieved comparable performance to ideal time-frequency masks
on duet and unison subsets of MedleyVox. Audio samples, the dataset, and codes
are available on our GitHub page (https://github.com/jeonchangbin49/MedleyVox).

This paper proposes Sparse View Synthesis. This is a view synthesis problem
where the number of reference views is limited, and the baseline between target
and reference view is significant. Under these conditions, current radiance
field methods fail catastrophically due to inescapable artifacts such 3D
floating blobs, blurring and structural duplication, whenever the number of
reference views is limited, or the target view diverges significantly from the
reference views.
  Advances in network architecture and loss regularisation are unable to
satisfactorily remove these artifacts. The occlusions within the scene ensure
that the true contents of these regions is simply not available to the model.
In this work, we instead focus on hallucinating plausible scene contents within
such regions. To this end we unify radiance field models with adversarial
learning and perceptual losses. The resulting system provides up to 60%
improvement in perceptual accuracy compared to current state-of-the-art
radiance field models on this problem.

Federated learning (FL) is the most practical multi-source learning method
for electronic healthcare records (EHR). Despite its guarantee of privacy
protection, the wide application of FL is restricted by two large challenges:
the heterogeneous EHR systems, and the non-i.i.d. data characteristic. A recent
research proposed a framework that unifies heterogeneous EHRs, named UniHPF. We
attempt to address both the challenges simultaneously by combining UniHPF and
FL. Our study is the first approach to unify heterogeneous EHRs into a single
FL framework. This combination provides an average of 3.4% performance gain
compared to local learning. We believe that our framework is practically
applicable in the real-world FL.

Conditional text-to-image generation has seen countless recent improvements
in terms of quality, diversity and fidelity. Nevertheless, most
state-of-the-art models require numerous inference steps to produce faithful
generations, resulting in performance bottlenecks for end-user applications. In
this paper we introduce Paella, a novel text-to-image model requiring less than
10 steps to sample high-fidelity images, using a speed-optimized architecture
allowing to sample a single image in less than 500 ms, while having 573M
parameters. The model operates on a compressed & quantized latent space, it is
conditioned on CLIP embeddings and uses an improved sampling function over
previous works. Aside from text-conditional image generation, our model is able
to do latent space interpolation and image manipulations such as inpainting,
outpainting, and structural editing. We release all of our code and pretrained
models at https://github.com/dome272/Paella

Story visualization aims to generate a sequence of images to narrate each
sentence in a multi-sentence story, where the images should be realistic and
keep global consistency across dynamic scenes and characters. Current works
face the problem of semantic misalignment because of their fixed architecture
and diversity of input modalities. To address this problem, we explore the
semantic alignment between text and image representations by learning to match
their semantic levels in the GAN-based generative model. More specifically, we
introduce dynamic interactions according to learning to dynamically explore
various semantic depths and fuse the different-modal information at a matched
semantic level, which thus relieves the text-image semantic misalignment
problem. Extensive experiments on different datasets demonstrate the
improvements of our approach, neither using segmentation masks nor auxiliary
captioning networks, on image quality and story consistency, compared with
state-of-the-art methods.

Due to the development of deep learning-based denoisers, the plug-and-play
strategy has achieved great success in image restoration problems. However,
existing plug-and-play image restoration methods are designed for non-blind
Gaussian denoising such as zhang et al (2022), the performance of which visibly
deteriorate for unknown noises. To push the limits of plug-and-play image
restoration, we propose a novel framework with blind Gaussian prior, which can
deal with more complicated image restoration problems in the real world. More
specifically, we build up a new image restoration model by regarding the noise
level as a variable, which is implemented by a two-stage blind Gaussian
denoiser consisting of a noise estimation subnetwork and a denoising
subnetwork, where the noise estimation subnetwork provides the noise level to
the denoising subnetwork for blind noise removal. We also introduce the
curvature map into the encoder-decoder architecture and the supervised
attention module to achieve a highly flexible and effective convolutional
neural network. The experimental results on image denoising, deblurring and
single-image super-resolution are provided to demonstrate the advantages of our
deep curvature denoiser and the resulting plug-and-play blind image restoration
method over the state-of-the-art model-based and learning-based methods. Our
model is shown to be able to recover the fine image details and tiny structures
even when the noise level is unknown for different image restoration tasks. The
source codes are available at https://github.com/Duanlab123/CurvPnP.

Performing simulations with a realistic biophysical auditory nerve fiber
model can be very time consuming, due to the complex nature of the calculations
involved. Here, a surrogate (approximate) model of such an auditory nerve fiber
model was developed using machine learning methods, to perform simulations more
efficiently. Several machine learning models were compared, of which a
Convolutional Neural Network showed the best performance. In fact, the
Convolutional Neural Network was able to emulate the behavior of the auditory
nerve fiber model with extremely high similarity ($R^2 > 0.99$), tested under a
wide range of experimental conditions, whilst reducing the simulation time by
five orders of magnitude. In addition, we introduce a method for randomly
generating charge-balanced waveforms using hyperplane projection. In the second
part of this paper, the Convolutional Neural Network surrogate model was used
by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in
terms energy efficiency. The resulting waveforms resemble a positive
Gaussian-like peak, preceded by an elongated negative phase. When comparing the
energy of the waveforms generated by the Evolutionary Algorithm with the
commonly used square wave, energy decreases of 8% - 45% were observed for
different pulse durations. These results were validated with the original
auditory nerve fiber model, which demonstrates that our proposed surrogate
model can be used as its accurate and efficient replacement.

Recent work has shown that deep vision models tend to be overly dependent on
low-level or "texture" features, leading to poor generalization. Various data
augmentation strategies have been proposed to overcome this so-called texture
bias in DNNs. We propose a simple, lightweight adversarial augmentation
technique that explicitly incentivizes the network to learn holistic shapes for
accurate prediction in an object classification setting. Our augmentations
superpose edgemaps from one image onto another image with shuffled patches,
using a randomly determined mixing proportion, with the image label of the
edgemap image. To classify these augmented images, the model needs to not only
detect and focus on edges but distinguish between relevant and spurious edges.
We show that our augmentations significantly improve classification accuracy
and robustness measures on a range of datasets and neural architectures. As an
example, for ViT-S, We obtain absolute gains on classification accuracy gains
up to 6%. We also obtain gains of up to 28% and 8.5% on natural adversarial and
out-of-distribution datasets like ImageNet-A (for ViT-B) and ImageNet-R (for
ViT-S), respectively. Analysis using a range of probe datasets shows
substantially increased shape sensitivity in our trained models, explaining the
observed improvement in robustness and classification accuracy.

CLIP (Contrastive Language-Image Pre-Training) has shown remarkable zero-shot
transfer capabilities in cross-modal correlation tasks such as visual
classification and image retrieval. However, its performance in cross-modal
generation tasks like zero-shot image captioning remains unsatisfied. In this
work, we discuss that directly employing CLIP for zero-shot image captioning
relies more on the textual modality in context and largely ignores the visual
information, which we call \emph{contextual language prior}. To address this,
we propose Cross-modal Language Models (CLMs) to facilitate unsupervised
cross-modal learning. We further propose Anchor Augment to guide the generative
model's attention to the fine-grained information in the representation of
CLIP. Experiments on MS COCO and Flickr 30K validate the promising performance
of proposed approach in both captioning quality and computational efficiency.

Recently, learned image compression has achieved remarkable performance.
Entropy model, which accurately estimates the distribution of latent
representation, plays an important role in boosting rate distortion
performance. Most entropy models capture correlations in one dimension.
However, there are channel-wise, local and global spatial correlations in
latent representation. To address this issue, we propose multi-reference
entropy models MEM and MEM+ to capture channel, local and global spatial
contexts. We divide latent representation into slices. When decoding current
slice, we use previously decoded slices as contexts and use attention map of
previously decoded slice to predict global correlations in current slice. To
capture local contexts, we propose enhanced checkerboard context capturing to
avoid performance degradation while retaining two-pass decoding. Based on MEM
and MEM+, we propose image compression models MLIC and MLIC+. Extensive
experimental evaluations have shown that our MLIC and MLIC+ achieve
state-of-the-art performance and they reduce BD-rate by 9.77% and 13.09% on
Kodak dataset over VVC when measured in PSNR.

This paper proposes a new algorithm for an automatic variable selection
procedure in High Dimensional Graphical Models. The algorithm selects the
relevant variables for the node of interest on the basis of mutual information.
Several contributions in literature have investigated the use of mutual
information in selecting the appropriate number of relevant features in a large
data-set, but most of them have focused on binary outcomes or required high
computational effort. The algorithm here proposed overcomes these drawbacks as
it is an extension of Chow and Liu's algorithm. Once, the probabilistic
structure of a High Dimensional Graphical Model is determined via the said
algorithm, the best path-step, including variables with the most
explanatory/predictive power for a variable of interest, is determined via the
computation of the entropy coefficient of determination. The latter, being
based on the notion of (symmetric) Kullback-Leibler divergence, turns out to be
closely connected to the mutual information of the involved variables. The
application of the algorithm to a wide range of real-word and publicly
data-sets has highlighted its potential and greater effectiveness compared to
alternative extant methods.

Counterfactuals are central in causal human reasoning and the scientific
discovery process. The uplift, also called conditional average treatment
effect, measures the causal effect of some action, or treatment, on the outcome
of an individual. This paper discusses how it is possible to derive bounds on
the probability of counterfactual statements based on uplift terms. First, we
derive some original bounds on the probability of counterfactuals and we show
that tightness of such bounds depends on the information of the feature set on
the uplift term. Then, we propose a point estimator based on the assumption of
conditional independence between the counterfactual outcomes. The quality of
the bounds and the point estimators are assessed on synthetic data and a large
real-world customer data set provided by a telecom company, showing significant
improvement over the state of the art.

Image-text contrastive learning has proven effective for pretraining medical
image models. When targeting localized downstream tasks like semantic
segmentation or object detection, additional local contrastive losses that
align image regions with sentences have shown promising results. We study how
local contrastive losses are related to global (per-sample) contrastive losses
and which effects they have on localized medical downstream tasks. Based on a
theoretical comparison, we propose to remove some components of local losses
and replace others by a novel distribution prior which enforces uniformity of
representations within each sample. We empirically study this approach on chest
X-ray tasks and find it to be very effective, outperforming methods without
local losses on 12 of 18 tasks.

As music has become more available especially on music streaming platforms,
people have started to have distinct preferences to fit to their varying
listening situations, also known as context. Hence, there has been a growing
interest in considering the user's situation when recommending music to users.
Previous works have proposed user-aware autotaggers to infer situation-related
tags from music content and user's global listening preferences. However, in a
practical music retrieval system, the autotagger could be only used by assuming
that the context class is explicitly provided by the user. In this work, for
designing a fully automatised music retrieval system, we propose to
disambiguate the user's listening information from their stream data. Namely,
we propose a system which can generate a situational playlist for a user at a
certain time 1) by leveraging user-aware music autotaggers, and 2) by
automatically inferring the user's situation from stream data (e.g. device,
network) and user's general profile information (e.g. age). Experiments show
that such a context-aware personalized music retrieval system is feasible, but
the performance decreases in the case of new users, new tracks or when the
number of context classes increases.

Building accurate Deep Learning (DL) models for brain age prediction is a
very relevant topic in neuroimaging, as it could help better understand
neurodegenerative disorders and find new biomarkers. To estimate accurate and
generalizable models, large datasets have been collected, which are often
multi-site and multi-scanner. This large heterogeneity negatively affects the
generalization performance of DL models since they are prone to overfit
site-related noise. Recently, contrastive learning approaches have been shown
to be more robust against noise in data or labels. For this reason, we propose
a novel contrastive learning regression loss for robust brain age prediction
using MRI scans. Our method achieves state-of-the-art performance on the
OpenBHB challenge, yielding the best generalization capability and robustness
to site-related noise.

Federated Learning (FL) is a new decentralized learning used for training
machine learning algorithms where a global model iteratively gathers the
parameters of local models but does not access their local data. A key
challenge in FL is to handle the heterogeneity of local data distribution,
resulting in a drifted global model, which is hard to converge. To cope with
this challenge, current methods adopt different strategies like knowledge
distillation, weighted model aggregation, and multi-task learning, as
regulation. We refer to these approaches as asynchronous FL since they align
user models in either a local or post-hoc manner where model drift has already
happened or has been underestimated. In this paper, we propose an active and
synchronous correlation approach to solve the challenge of user heterogeneity
in FL. Specifically, we aim to approximate FL as the standard deep learning by
actively and synchronously scheduling user learning pace in each round with a
dynamic multi-phase curriculum. A global curriculum ensembles all user
curriculum on its server by the auto-regressive auto-encoder. Then the global
curriculum is divided into multiple phases and broadcast to users to measure
and align the domain-agnostic learning pace. Empirical studies demonstrate that
our approach equips FL with state-of-the-art generalization performance over
existing asynchronous approaches, even facing severe user heterogeneity.

The ROC curve is the major tool for assessing not only the performance but
also the fairness properties of a similarity scoring function in Face
Recognition. In order to draw reliable conclusions based on empirical ROC
analysis, evaluating accurately the uncertainty related to statistical versions
of the ROC curves of interest is necessary. For this purpose, we explain in
this paper that, because the True/False Acceptance Rates are of the form of
U-statistics in the case of similarity scoring, the naive bootstrap approach is
not valid here and that a dedicated recentering technique must be used instead.
This is illustrated on real data of face images, when applied to several
ROC-based metrics such as popular fairness metrics.

Saliency methods attempt to explain deep neural networks by highlighting the
most salient features of a sample. Some widely used methods are based on a
theoretical framework called Deep Taylor Decomposition (DTD), which formalizes
the recursive application of the Taylor Theorem to the network's layers.
However, recent work has found these methods to be independent of the network's
deeper layers and appear to respond only to lower-level image structure. Here,
we investigate the DTD theory to better understand this perplexing behavior and
found that the Deep Taylor Decomposition is equivalent to the basic
gradient$\times$input method when the Taylor root points (an important
parameter of the algorithm chosen by the user) are locally constant. If the
root points are locally input-dependent, then one can justify any explanation.
In this case, the theory is under-constrained. In an empirical evaluation, we
find that DTD roots do not lie in the same linear regions as the input -
contrary to a fundamental assumption of the Taylor theorem. The theoretical
foundations of DTD were cited as a source of reliability for the explanations.
However, our findings urge caution in making such claims.

Federated learning refers to conducting training on multiple distributed
devices and collecting model weights from them to derive a shared
machine-learning model. This allows the model to get benefit from a rich source
of data available from multiple sites. Also, since only model weights are
collected from distributed devices, the privacy of those data is protected. It
is useful in a situation where collaborative training of machine learning
models is necessary while training data are highly sensitive. This study aims
at investigating the implementation of lightweight federated learning to be
deployed on a diverse range of distributed resources, including
resource-constrained edge devices and resourceful cloud servers. As a resource
management framework, the FogBus2 framework, which is a containerized
distributed resource management framework, is selected as the base framework
for the implementation. This research provides an architecture and lightweight
implementation of federated learning in the FogBus2. Moreover, a worker
selection technique is proposed and implemented. The worker selection algorithm
selects an appropriate set of workers to participate in the training to achieve
higher training time efficiency. Besides, this research integrates synchronous
and asynchronous models of federated learning alongside with heuristic-based
worker selection algorithm. It is proven that asynchronous federated learning
is more time efficient compared to synchronous federated learning or sequential
machine learning training. The performance evaluation shows the efficiency of
the federated learning mechanism implemented and integrated with the FogBus2
framework. The worker selection strategy obtains 33.9% less time to reach 80%
accuracy compared to sequential training, while asynchronous further improve
synchronous federated learning training time by 63.3%.

Generative adversarial networks are generative models that are capable of
replicating the implicit probability distribution of the input data with high
accuracy. Traditionally, GANs consist of a Generator and a Discriminator which
interact with each other to produce highly realistic artificial data.
Traditional GANs fall prey to the mode collapse problem, which means that they
are unable to generate the different variations of data present in the input
dataset. Recently, multiple generators have been used to produce more realistic
output by mitigating the mode collapse problem. We use this multiple generator
framework. The novelty in this paper lies in making the generators compete
against each other while interacting with the discriminator simultaneously. We
show that this causes a dramatic reduction in the training time for GANs
without affecting its performance.

Several structure-learning algorithms for staged trees, asymmetric extensions
of Bayesian networks, have been proposed. However, these either do not scale
efficiently as the number of variables considered increases, a priori restrict
the set of models, or they do not find comparable models to existing methods.
Here, we define an alternative algorithm based on a totally ordered hyperstage.
We demonstrate how it can be used to obtain a quadratically-scaling structural
learning algorithm for staged trees that restricts the model space
a-posteriori. Through comparative analysis, we show that through the ordering
provided by the mean posterior distributions, we can outperform existing
methods in both computational time and model score. This method also enables us
to learn more complex relationships than existing model selection techniques by
expanding the model space and illustrates how this can embellish inferences in
a real study.

Differential privacy (DP) provides a formal privacy guarantee that prevents
adversaries with access to machine learning models from extracting information
about individual training points. Differentially private stochastic gradient
descent (DPSGD) is the most popular training method with differential privacy
in image recognition. However, existing DPSGD schemes lead to significant
performance degradation, which prevents the application of differential
privacy. In this paper, we propose a simulated annealing-based differentially
private stochastic gradient descent scheme (SA-DPSGD) which accepts a candidate
update with a probability that depends both on the update quality and on the
number of iterations. Through this random update screening, we make the
differentially private gradient descent proceed in the right direction in each
iteration, and result in a more accurate model finally. In our experiments,
under the same hyperparameters, our scheme achieves test accuracies 98.35%,
87.41% and 60.92% on datasets MNIST, FashionMNIST and CIFAR10, respectively,
compared to the state-of-the-art result of 98.12%, 86.33% and 59.34%. Under the
freely adjusted hyperparameters, our scheme achieves even higher accuracies,
98.89%, 88.50% and 64.17%. We believe that our method has a great contribution
for closing the accuracy gap between private and non-private image
classification.

The reconstruction of gap-free signals from observation data is a critical
challenge for numerous application domains, such as geoscience and space-based
earth observation, when the available sensors or the data collection processes
lead to irregularly-sampled and noisy observations. Optimal interpolation (OI),
also referred to as kriging, provides a theoretical framework to solve
interpolation problems for Gaussian processes (GP). The associated
computational complexity being rapidly intractable for n-dimensional tensors
and increasing numbers of observations, a rich literature has emerged to
address this issue using ensemble methods, sparse schemes or iterative
approaches. Here, we introduce a neural OI scheme. It exploits a variational
formulation with convolutional auto-encoders and a trainable iterative
gradient-based solver. Theoretically equivalent to the OI formulation, the
trainable solver asymptotically converges to the OI solution when dealing with
both stationary and non-stationary linear spatio-temporal GPs. Through a
bi-level optimization formulation, we relate the learning step and the
selection of the training loss to the theoretical properties of the OI, which
is an unbiased estimator with minimal error variance. Numerical experiments for
2D+t synthetic GP datasets demonstrate the relevance of the proposed scheme to
learn computationally-efficient and scalable OI models and solvers from data.
As illustrated for a real-world interpolation problems for satellite-derived
geophysical dynamics, the proposed framework also extends to non-linear and
multimodal interpolation problems and significantly outperforms
state-of-the-art interpolation methods, when dealing with very high missing
data rates.

Meta-Learning aims to accelerate the learning on new tasks by acquiring
useful inductive biases from related data sources. In practice, the number of
tasks available for meta-learning is often small. Yet, most of the existing
approaches rely on an abundance of meta-training tasks, making them prone to
overfitting. How to regularize the meta-learner to ensure generalization to
unseen tasks, is a central question in the literature. We provide a theoretical
analysis using the PAC-Bayesian framework and derive the first bound for
meta-learners with unbounded loss functions. Crucially, our bounds allow us to
derive the PAC-optimal hyper-posterior (PACOH) - the closed-form-solution of
the PAC-Bayesian meta-learning problem, thereby avoiding the reliance on nested
optimization, giving rise to an optimization problem amenable to standard
variational methods that scale well. Our experiments show that, when
instantiating the PACOH with Gaussian processes and Bayesian Neural Networks as
base learners, the resulting methods are more scalable, and yield
state-of-the-art performance, both in terms of predictive accuracy and the
quality of uncertainty estimates. Finally, thanks to the principled treatment
of uncertainty, our meta-learners can also be successfully employed for
sequential decision problems.

Transformer has achieved extraordinary performance in Natural Language
Processing and Computer Vision tasks thanks to its powerful self-attention
mechanism, and its variant Conformer has become a state-of-the-art architecture
in the field of Automatic Speech Recognition (ASR). However, the main-stream
architecture for Automatic Speaker Verification (ASV) is convolutional Neural
Networks, and there is still much room for research on the Conformer based ASV.
In this paper, firstly, we modify the Conformer architecture from ASR to ASV
with very minor changes. Length-Scaled Attention (LSA) method and
Sharpness-Aware Minimizationis (SAM) are adopted to improve model
generalization. Experiments conducted on VoxCeleb and CN-Celeb show that our
Conformer based ASV achieves competitive performance compared with the popular
ECAPA-TDNN. Secondly, inspired by the transfer learning strategy, ASV Conformer
is natural to be initialized from the pretrained ASR model. Via parameter
transferring, self-attention mechanism could better focus on the relationship
between sequence features, brings about 11% relative improvement in EER on test
set of VoxCeleb and CN-Celeb, which reveals the potential of Conformer to unify
ASV and ASR task. Finally, we provide a runtime in ASV-Subtools to evaluate its
inference speed in production scenario. Our code is released at
https://github.com/Snowdar/asv-subtools/tree/master/doc/papers/conformer.md.

Recent deep learning-based methods have achieved promising performance for
computed tomography metal artifact reduction (CTMAR). However, most of them
suffer from two limitations: (i) the domain knowledge is not fully embedded
into the network training; (ii) metal artifacts lack effective representation
models. The aforementioned limitations leave room for further performance
improvement. Against these issues, we propose a novel triple domain
model-driven CTMAR network, termed as TriDoNet, whose network training exploits
triple domain knowledge, i.e., the knowledge of the sinogram, CT image, and
metal artifact domains. Specifically, to explore the non-local repetitive
streaking patterns of metal artifacts, we encode them as an explicit tight
frame sparse representation model with adaptive thresholds. Furthermore, we
design a contrastive regularization (CR) built upon contrastive learning to
exploit clean CT images and metal-affected images as positive and negative
samples, respectively. Experimental results show that our TriDoNet can generate
superior artifact-reduced CT images.

Machine Learning Inter-atomic Potentials (MLIPs) have become a common tool in
use by computational chemists due to their combination of accuracy and speed.
Yet, it is still not clear how well these tools behave at or near transitions
states found in complex molecules. Here we investigate the applicability of
MLIPs in evaluating the transition barrier of two, complex, molecular motor
systems: a 1st generation Feringa motor and the 9c alkene 2nd generation
Feringa motor. We compared paths generated with the Hierarchically Interacting
Particle Neural Network (HIP-NN), the PM3 semi-empirical quantum method (SEQM),
PM3 interfaced with HIP-NN (SEQM+HIP-NN), and Density Functional Theory
calculations. We found that using SEQM+HIP-NN to generate cheap, realistic
pathway guesses then refining the intermediates with DFT allowed us to cheaply
find realistic reaction paths and energy barriers matching experiment,
providing evidence that deep learning can be used for high precision
computational tasks such as transition path sampling while also suggesting
potential application to high throughput screening.

Shallow water and coastal aquatic ecosystems such as coral reefs and seagrass
meadows play a critical role in regulating and understanding Earth's changing
climate and biodiversity. They also play an important role in protecting towns
and cities from erosion and storm surges. Yet technology used for remote
sensing (drones, UAVs, satellites) cannot produce detailed images of these
ecosystems. Fluid lensing effects, the distortions caused by surface waves and
light on underwater objects, are what makes the remote sensing of these
ecosystems a very challenging task. Using machine learning, a proof of concept
model was developed that is able to remove most of these effects and produce a
clearer more stable image.

Galaxy morphology reflects structural properties which contribute to
understand the formation and evolution of galaxies. Deep convolutional networks
have proven to be very successful in learning hidden features that allow for
unprecedented performance on galaxy morphological classification. Such networks
mostly follow the supervised learning paradigm which requires sufficient
labelled data for training. However, it is an expensive and complicated process
of labeling for million galaxies, particularly for the forthcoming survey
projects. In this paper, we present an approach based on contrastive learning
with aim for learning galaxy morphological visual representation using only
unlabeled data. Considering the properties of low semantic information and
contour dominated of galaxy image, the feature extraction layer of the proposed
method incorporates vision transformers and convolutional network to provide
rich semantic representation via the fusion of the multi-hierarchy features. We
train and test our method on 3 classifications of datasets from Galaxy Zoo 2
and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing
accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross
validation demonstrates our model possesses transfer and generalization ability
when applied to the new datasets. The code that reveals our proposed method and
pretrained models are publicly available and can be easily adapted to new
surveys.

Federated Learning (FL) with quantization and deliberately added noise over
wireless networks is a promising approach to preserve the user differential
privacy while reducing the wireless resources. Specifically, an FL learning
process can be fused with quantized Binomial mechanism-based updates
contributed by multiple users to reduce the communication overhead/cost as well
as to protect the privacy of {participating} users. However, the optimization
of wireless transmission and quantization parameters (e.g., transmit power,
bandwidth, and quantization bits) as well as the added noise while guaranteeing
the privacy requirement and the performance of the learned FL model remains an
open and challenging problem. In this paper, we aim to jointly optimize the
level of quantization, parameters of the Binomial mechanism, and devices'
transmit powers to minimize the training time under the constraints of the
wireless networks. The resulting optimization turns out to be a Mixed Integer
Non-linear Programming (MINLP) problem, which is known to be NP-hard. To tackle
it, we transform this MINLP problem into a new problem whose solutions are
proved to be the optimal solutions of the original one. We then propose an
approximate algorithm that can solve the transformed problem with an arbitrary
relative error guarantee. Intensive simulations show that for the same wireless
resources the proposed approach achieves the highest accuracy, close to that of
the standard FL with no quantization and no noise added. This suggests the
faster convergence/training time of the proposed wireless FL framework while
optimally preserving users' privacy.

Machine learning models deployed in healthcare systems face data drawn from
continually evolving environments. However, researchers proposing such models
typically evaluate them in a time-agnostic manner, with train and test splits
sampling patients throughout the entire study period. We introduce the
Evaluation on Medical Datasets Over Time (EMDOT) framework and Python package,
which evaluates the performance of a model class over time. Across five medical
datasets and a variety of models, we compare two training strategies: (1) using
all historical data, and (2) using a window of the most recent data. We note
changes in performance over time, and identify possible explanations for these
shocks.

Recently, powerful Transformer architectures have proven superior in
generating high-quality sentences. Nevertheless, these models tend to produce
dull high-frequency phrases, severely hurting the diversity and novelty of
generated text. In this work, we dig into the intrinsic mechanism of this
problem and found that sparser attention values in Transformer could improve
diversity. To understand such a phenomenon, we first conduct both empirical and
theoretical analysis and then attribute it to representation degeneration
caused by the attentive mixture of the hidden states during training. We term
this process the Trap of Mediocrity. To escape from such a trap, we introduce a
novel attention regularization loss to control the sharpness of the attention
distribution, which is transparent to model structures and can be easily
implemented within 20 lines of python code. We prove that this method could be
mathematically regarded as learning a Bayesian approximation of posterior
attention. Experiments show that our method improved the diversity and novelty
of the generated text while maintaining comparable quality on a variety of
conditional and unconditional generation tasks.

Copyright protection of the Federated Learning (FL) model has become a major
concern since malicious clients in FL can stealthily distribute or sell the FL
model to other parties. In order to prevent such misbehavior, one must be able
to catch the culprit by investigating trace evidence from the model in
question. In this paper, we propose FedTracker, the first FL model protection
framework that, on one hand, employs global watermarks to verify ownerships of
the global model; and on the other hand, embed unique local fingerprints into
respective local models to facilitate tracing the model back to the culprit.
Furthermore, FedTracker introduces the intuition of Continual Learning (CL)
into watermark embedding, and proposes a CL-based watermark mechanism to
improve fidelity. Experimental results show that the proposed FedTracker is
effective in ownership verification, traceability, fidelity, and robustness.

Learning-based image dehazing methods are essential to assist autonomous
systems in enhancing reliability. Due to the domain gap between synthetic and
real domains, the internal information learned from synthesized images is
usually sub-optimal in real domains, leading to severe performance drop of
dehaizing models. Driven by the ability on exploring internal information from
a few unseen-domain samples, meta-learning is commonly adopted to address this
issue via test-time training, which is hyperparameter-sensitive and
time-consuming. In contrast, we present a domain generalization framework based
on meta-learning to dig out representative and discriminative internal
properties of real hazy domains without test-time training. To obtain
representative domain-specific information, we attach two entities termed
adaptation network and distance-aware aggregator to our dehazing network. The
adaptation network assists in distilling domain-relevant information from a few
hazy samples and caching it into a collection of features. The distance-aware
aggregator strives to summarize the generated features and filter out
misleading information for more representative internal properties. To enhance
the discrimination of distilled internal information, we present a novel loss
function called domain-relevant contrastive regularization, which encourages
the internal features generated from the same domain more similar and that from
diverse domains more distinct. The generated representative and discriminative
features are regarded as some external variables of our dehazing network to
regress a particular and powerful function for a given domain. The extensive
experiments on real hazy datasets, such as RTTS and URHI, validate that our
proposed method has superior generalization ability than the state-of-the-art
competitors.

The vision of pervasive machine learning (ML) services can be realized by
training an ML model on time using real-time data collected by internet of
things (IoT) devices. To this end, IoT devices require offloading their data to
an edge server in proximity. On the other hand, high dimensional data with a
heavy volume causes a significant burden to an IoT device with a limited energy
budget. To cope with the limitation, we propose a novel offloading
architecture, called joint data deepening and prefetching (JD2P), which is
feature-by-feature offloading comprising two key techniques. The first one is
data deepening, where each data sample's features are sequentially offloaded in
the order of importance determined by the data embedding technique such as
principle component analysis (PCA). No more features are offloaded when the
features offloaded so far are enough to classify the data, resulting in
reducing the amount of offloaded data. The second one is data prefetching,
where some features potentially required in the future are offloaded in
advance, thus achieving high efficiency via precise prediction and parameter
optimization. To verify the effectiveness of JD2P, we conduct experiments using
the MNIST and fashion-MNIST dataset. Experimental results demonstrate that the
JD2P can significantly reduce the expected energy consumption compared with
several benchmarks without degrading learning accuracy.

Cochlear implantation is currently the most effective treatment for patients
with severe deafness, but mastering cochlear implantation is extremely
challenging because the temporal bone has extremely complex and small
three-dimensional anatomical structures, and it is important to avoid damaging
the corresponding structures when performing surgery. The spatial location of
the relevant anatomical tissues within the target area needs to be determined
using CT prior to the procedure. Considering that the target structures are too
small and complex, the time required for manual segmentation is too long, and
it is extremely challenging to segment the temporal bone and its nearby
anatomical structures quickly and accurately. To overcome this difficulty, we
propose a deep learning-based algorithm, a 3D network model for automatic
segmentation of multi-structural targets in temporal bone CT that can
automatically segment the cochlea, facial nerve, auditory tubercle, vestibule
and semicircular canal. The algorithm combines CNN and Transformer for feature
extraction and takes advantage of spatial attention and channel attention
mechanisms to further improve the segmentation effect, the experimental results
comparing with the results of various existing segmentation algorithms show
that the dice similarity scores, Jaccard coefficients of all targets anatomical
structures are significantly higher while HD95 and ASSD scores are lower,
effectively proving that our method outperforms other advanced methods.

Human values such as honesty, social responsibility, fairness, privacy, and
the like are things considered important by individuals and society. Software
systems, including mobile software applications (apps), may ignore or violate
such values, leading to negative effects in various ways for individuals and
society. While some works have investigated different aspects of human values
in software engineering, this mixed-methods study focuses on honesty as a
critical human value. In particular, we studied (i) how to detect honesty
violations in mobile apps, (ii) the types of honesty violations in mobile apps,
and (iii) the perspectives of app developers on these detected honesty
violations. We first develop and evaluate 7 machine learning (ML) models to
automatically detect violations of the value of honesty in app reviews from an
end user perspective. The most promising was a Deep Neural Network model with
F1 score of 0.921. We then conducted a manual analysis of 401 reviews
containing honesty violations and characterised honest violations in mobile
apps into 10 categories: unfair cancellation and refund policies; false
advertisements; delusive subscriptions; cheating systems; inaccurate
information; unfair fees; no service; deletion of reviews; impersonation; and
fraudulent looking apps. A developer survey and interview study with mobile
developers then identified 7 key causes behind honesty violations in mobile
apps and 8 strategies to avoid or fix such violations. The findings of our
developer study also articulate the negative consequences that honesty
violations might bring for businesses, developers, and users. Finally, the app
developers' feedback shows that our prototype ML-based models can have
promising benefits in practice.

We propose noncanonical domain walls as a new dark energy model inspired by
grand unified theories (GUTs). We investigate the cosmic dynamics and discover
that the domain walls act as either dark energy or dark matter at different
times, depending on the velocity v in the observer's comoving frame. We find a
single stable solution to the dynamics, i.e., only freezing (v = 0)
noncanonical domain walls can enter the phantom zone without having to
experience ghost field instability. This means that the solution has an
equation of state (EoS) w_dw < -1 without having to possess negative kinetic
energy. These domain walls give rise to a late-time cosmic acceleration
starting from z = 0.8, resulting in w_dw = -1.5 and w_eff = -1.03 today. We
learn that the EoS of the noncanonical domain walls is independent of the
potential form. We also investigate the perturbation dynamics following the
model. Our simulations show that compared to LCDM, the amplitude of the dark
matter power spectrum in the noncanonical domain wall model is lower, while the
CMB power spectrum is shifted slighly to lower l multipoles. The proposed model
gives a smaller sigma8 compared to that of LCDM.

Federated learning (FL) allows multiple participants to collaboratively build
deep learning (DL) models without directly sharing data. Consequently, the
issue of copyright protection in FL becomes important since unreliable
participants may gain access to the jointly trained model. Application of
homomorphic encryption (HE) in secure FL framework prevents the central server
from accessing plaintext models. Thus, it is no longer feasible to embed the
watermark at the central server using existing watermarking schemes. In this
paper, we propose a novel client-side FL watermarking scheme to tackle the
copyright protection issue in secure FL with HE. To our best knowledge, it is
the first scheme to embed the watermark to models under the Secure FL
environment. We design a black-box watermarking scheme based on client-side
backdooring to embed a pre-designed trigger set into an FL model by a
gradient-enhanced embedding method. Additionally, we propose a trigger set
construction mechanism to ensure the watermark cannot be forged. Experimental
results demonstrate that our proposed scheme delivers outstanding protection
performance and robustness against various watermark removal attacks and
ambiguity attack.

Video surveillance using drones is both convenient and efficient due to the
ease of deployment and unobstructed movement of drones in many scenarios. An
interesting application of drone-based video surveillance is to estimate crowd
densities (both pedestrians and vehicles) in public places. Deep learning using
convolution neural networks (CNNs) is employed for automatic crowd counting and
density estimation using images and videos. However, the performance and
accuracy of such models typically depend upon the model architecture i.e.,
deeper CNN models improve accuracy at the cost of increased inference time. In
this paper, we propose a novel crowd density estimation model for drones
(DroneNet) using Self-organized Operational Neural Networks (Self-ONN).
Self-ONN provides efficient learning capabilities with lower computational
complexity as compared to CNN-based models. We tested our algorithm on two
drone-view public datasets. Our evaluation shows that the proposed DroneNet
shows superior performance on an equivalent CNN-based model.

Clustering is the task of gathering similar data samples into clusters
without using any predefined labels. It has been widely studied in machine
learning literature, and recent advancements in deep learning have revived
interest in this field. Contrastive clustering (CC) models are a staple of deep
clustering in which positive and negative pairs of each data instance are
generated through data augmentation. CC models aim to learn a feature space
where instance-level and cluster-level representations of positive pairs are
grouped together. Despite improving the SOTA, these algorithms ignore the
cross-instance patterns, which carry essential information for improving
clustering performance. In this paper, we propose a novel contrastive
clustering method, Cross-instance guided Contrastive Clustering (C3), that
considers the cross-sample relationships to increase the number of positive
pairs. In particular, we define a new loss function that identifies similar
instances using the instance-level representation and encourages them to
aggregate together. Extensive experimental evaluations show that our proposed
method can outperform state-of-the-art algorithms on benchmark computer vision
datasets: we improve the clustering accuracy by 6.8%, 2.8%, 4.9%, 1.3% and 0.4%
on CIFAR-10, CIFAR-100, ImageNet-10, ImageNet-Dogs, and Tiny-ImageNet,
respectively.

In the $\ell_p$-subspace sketch problem, we are given an $n\times d$ matrix
$A$ with $n>d$, and asked to build a small memory data structure
$Q(A,\epsilon)$ so that, for any query vector $x\in\mathbb{R}^d$, we can output
a number in $(1\pm\epsilon)\|Ax\|_p^p$ given only $Q(A,\epsilon)$. This problem
is known to require $\tilde{\Omega}(d\epsilon^{-2})$ bits of memory for
$d=\Omega(\log(1/\epsilon))$. However, for $d=o(\log(1/\epsilon))$, no data
structure lower bounds were known.
  We resolve the memory required to solve the $\ell_p$-subspace sketch problem
for any constant $d$ and integer $p$, showing that it is
$\Omega(\epsilon^{-2(d-1)/(d+2p)})$ bits and $\tilde{O}
(\epsilon^{-2(d-1)/(d+2p)})$ words. This shows that one can beat the
$\Omega(\epsilon^{-2})$ lower bound, which holds for $d =
\Omega(\log(1/\epsilon))$, for any constant $d$. We also show how to implement
the upper bound in a single pass stream, with an additional multiplicative
$\operatorname{poly}(\log \log n)$ factor and an additive
$\operatorname{poly}(\log n)$ cost in the memory. Our bounds can be applied to
point queries for SVMs with additive error, yielding an optimal bound of
$\tilde{\Theta}(\epsilon^{-2d/(d+3)})$ for every constant $d$. This is a
near-quadratic improvement over the $\Omega(\epsilon^{-(d+1)/(d+3)})$ lower
bound of (Andoni et al. 2020). Our techniques rely on a novel connection to low
dimensional techniques from geometric functional analysis.

Existing multi-instrumental datasets tend to be biased toward pop and
classical music. In addition, they generally lack high-level annotations such
as emotion tags. In this paper, we propose YM2413-MDB, an 80s FM video game
music dataset with multi-label emotion annotations. It includes 669 audio and
MIDI files of music from Sega and MSX PC games in the 80s using YM2413, a
programmable sound generator based on FM. The collected game music is arranged
with a subset of 15 monophonic instruments and one drum instrument. They were
converted from binary commands of the YM2413 sound chip. Each song was labeled
with 19 emotion tags by two annotators and validated by three verifiers to
obtain refined tags. We provide the baseline models and results for emotion
recognition and emotion-conditioned symbolic music generation using YM2413-MDB.

Smart IoT-based systems often desire continuous execution of multiple
latency-sensitive Deep Learning (DL) applications. The edge servers serve as
the cornerstone of such IoT-based systems, however, their resource limitations
hamper the continuous execution of multiple (multi-tenant) DL applications. The
challenge is that, DL applications function based on bulky "neural network (NN)
models" that cannot be simultaneously maintained in the limited memory space of
the edge. Accordingly, the main contribution of this research is to overcome
the memory contention challenge, thereby, meeting the latency constraints of
the DL applications without compromising their inference accuracy. We propose
an efficient NN model management framework, called Edge-MultiAI, that ushers
the NN models of the DL applications into the edge memory such that the degree
of multi-tenancy and the number of warm-starts are maximized. Edge-MultiAI
leverages NN model compression techniques, such as model quantization, and
dynamically loads NN models for DL applications to stimulate multi-tenancy on
the edge server. We also devise a model management heuristic for Edge-MultiAI,
called iWS-BFE, that functions based on the Bayesian theory to predict the
inference requests for multi-tenant applications, and uses it to choose the
appropriate NN models for loading, hence, increasing the number of warm-start
inferences. We evaluate the efficacy and robustness of Edge-MultiAI under
various configurations. The results reveal that Edge-MultiAI can stimulate the
degree of multi-tenancy on the edge by at least 2X and increase the number of
warm-starts by around 60% without any major loss on the inference accuracy of
the applications.

Brief Hospital Course (BHC) summaries are succinct summaries of an entire
hospital encounter, embedded within discharge summaries, written by senior
clinicians responsible for the overall care of a patient. Methods to
automatically produce summaries from inpatient documentation would be
invaluable in reducing clinician manual burden of summarising documents under
high time-pressure to admit and discharge patients. Automatically producing
these summaries from the inpatient course, is a complex, multi-document
summarisation task, as source notes are written from various perspectives (e.g.
nursing, doctor, radiology), during the course of the hospitalisation. We
demonstrate a range of methods for BHC summarisation demonstrating the
performance of deep learning summarisation models across extractive and
abstractive summarisation scenarios. We also test a novel ensemble extractive
and abstractive summarisation model that incorporates a medical concept
ontology (SNOMED) as a clinical guidance signal and shows superior performance
in 2 real-world clinical data sets.

Machine learning can generate black-box surrogate models which are both
extremely fast and highly accurate. Rigorously verifying the accuracy of these
black-box models, however, is computationally challenging. When it comes to
power systems, learning AC power flow is the cornerstone of any machine
learning surrogate model wishing to drastically accelerate computations,
whether it is for optimization, control, or dynamics. This paper develops for
the first time, to our knowledge, a tractable neural network verification
procedure which incorporates the ground truth of the \emph{non-linear} AC power
flow equations to determine worst-case neural network performance. Our
approach, termed Sequential Targeted Tightening (STT), leverages a loosely
convexified reformulation of the original verification problem, which is a
mixed integer quadratic program (MIQP). Using the sequential addition of
targeted cuts, we iteratively tighten our formulation until either the solution
is sufficiently tight or a satisfactory performance guarantee has been
generated. After learning neural network models of the 14, 57, 118, and 200-bus
PGLib test cases, we compare the performance guarantees generated by our STT
procedure with ones generated by a state-of-the-art MIQP solver, Gurobi 9.5. We
show that STT often generates performance guarantees which are orders of
magnitude tighter than the MIQP upper bound.

State-of-the-art empirical work has shown that visual representations learned
by deep neural networks are robust in nature and capable of performing
classification tasks on diverse datasets. For example, CLIP demonstrated
zero-shot transfer performance on multiple datasets for classification tasks in
a joint embedding space of image and text pairs. However, it showed negative
transfer performance on standard datasets, e.g., BirdsNAP, RESISC45, and MNIST.
In this paper, we propose ContextCLIP, a contextual and contrastive learning
framework for the contextual alignment of image-text pairs by learning robust
visual representations on Conceptual Captions dataset. Our framework was
observed to improve the image-text alignment by aligning text and image
representations contextually in the joint embedding space. ContextCLIP showed
good qualitative performance for text-to-image retrieval tasks and enhanced
classification accuracy. We evaluated our model quantitatively with zero-shot
transfer and fine-tuning experiments on CIFAR-10, CIFAR-100, Birdsnap,
RESISC45, and MNIST datasets for classification task.

Contrastive learning (CL) is a form of self-supervised learning and has been
widely used for various tasks. Different from widely studied instance-level
contrastive learning, pixel-wise contrastive learning mainly helps with
pixel-wise tasks such as medical landmark detection. The counterpart to an
instance in instance-level CL is a pixel, along with its neighboring context,
in pixel-wise CL. Aiming to build better feature representation, there is a
vast literature about designing instance augmentation strategies for
instance-level CL; but there is little similar work on pixel augmentation for
pixel-wise CL with a pixel granularity. In this paper, we attempt to bridge
this gap. We first classify a pixel into three categories, namely low-,
medium-, and high-informative, based on the information quantity the pixel
contains. Inspired by the ``InfoMin" principle, we then design separate
augmentation strategies for each category in terms of augmentation intensity
and sampling ratio. Extensive experiments validate that our information-guided
pixel augmentation strategy succeeds in encoding more discriminative
representations and surpassing other competitive approaches in unsupervised
local feature matching. Furthermore, our pretrained model improves the
performance of both one-shot and fully supervised models. To the best of our
knowledge, we are the first to propose a pixel augmentation method with a pixel
granularity for enhancing unsupervised pixel-wise contrastive learning.

Metric learning aims to build a distance metric typically by learning an
effective embedding function that maps similar objects into nearby points in
its embedding space. Despite recent advances in deep metric learning, it
remains challenging for the learned metric to generalize to unseen classes with
a substantial domain gap. To tackle the issue, we explore a new problem of
few-shot metric learning that aims to adapt the embedding function to the
target domain with only a few annotated data. We introduce three few-shot
metric learning baselines and propose the Channel-Rectifier Meta-Learning
(CRML), which effectively adapts the metric space online by adjusting channels
of intermediate layers. Experimental analyses on miniImageNet, CUB-200-2011,
MPII, as well as a new dataset, miniDeepFashion, demonstrate that our method
consistently improves the learned metric by adapting it to target classes and
achieves a greater gain in image retrieval when the domain gap from the source
classes is larger.

Heavily relying on 3D annotations limits the real-world application of 3D
object detection. In this paper, we propose a method that does not demand any
3D annotation, while being able to predict full-oriented 3D bounding boxes. Our
method, called Recursive Cross-View (RCV), transforms 3D detection into several
2D detection tasks, which only consume some 2D labels, based on the three-view
principle. We propose a recursive paradigm, in which instance segmentation and
3D bounding box generation by Cross-View are implemented recursively until
convergence. Specifically, a frustum is proposed via a 2D detector, followed by
the recursive paradigm that finally outputs a full-oriented 3D box, class, and
score. To justify that our method can be quickly used to new tasks in
real-world scenarios, we do three experiments, namely indoor 3D human
detection, full-oriented 3D hand detection, and real-time detection on a real
3D sensor. RCV achieves decent performance in these experiments. Once trained,
our method can be viewed as a 3D annotation tool. Consequently, we formulate
two 3D labeled dataset, namely '3D_HUMAN' and 'D_HAND', based on RCV, which
could be used to pre-train other 3D detectors. Furthermore, estimated on the
SUN RGB-D benchmark, our method achieves comparable performance with some full
3D supervised learning methods. RCV is the first 3D detection method that does
not consume 3D labels and yields full-oriented 3D boxes on point clouds.

Characterizing the differential excision of mRNA is critical for
understanding the functional complexity of a cell or tissue, from normal
developmental processes to disease pathogenesis. Most transcript reconstruction
methods infer full-length transcripts from high-throughput sequencing data.
However, this is a challenging task due to incomplete annotations and the
differential expression of transcripts across cell-types, tissues, and
experimental conditions. Several recent methods circumvent these difficulties
by considering local splicing events, but these methods lose transcript-level
splicing information and may conflate transcripts. We develop the first
probabilistic model that reconciles the transcript and local splicing
perspectives. First, we formalize the sequence of mRNA excisions (SME)
reconstruction problem, which aims to assemble variable-length sequences of
mRNA excisions from RNA-sequencing data. We then present a novel hierarchical
Bayesian admixture model for the Reconstruction of Excised mRNA (BREM). BREM
interpolates between local splicing events and full-length transcripts and thus
focuses only on SMEs that have high posterior probability. We develop posterior
inference algorithms based on Gibbs sampling and local search of independent
sets and characterize differential SME usage using generalized linear models
based on converged BREM model parameters. We show that BREM achieves higher F1
score for reconstruction tasks and improved accuracy and sensitivity in
differential splicing when compared with four state-of-the-art transcript and
local splicing methods on simulated data. Lastly, we evaluate BREM on both bulk
and scRNA sequencing data based on transcript reconstruction, novelty of
transcripts produced, model sensitivity to hyperparameters, and a functional
analysis of differentially expressed SMEs, demonstrating that BREM captures
relevant biological signal.

Diffusion Monte Carlo (DMC) is one of the most accurate techniques available
for calculating the electronic properties of molecules and materials, yet it
often remains a challenge to economically compute forces using this technique.
As a result, ab initio molecular dynamics simulations and geometry
optimizations that employ Diffusion Monte Carlo forces are often out of reach.
One potential approach for accelerating the computation of "DMC forces" is to
machine learn these forces from DMC energy calculations. In this work, we
employ Behler-Parrinello Neural Networks to learn DMC forces from DMC energy
calculations for geometry optimization and molecular dynamics simulations of
small molecules. We illustrate the unique challenges that stem from learning
forces without explicit force data and from noisy energy data by making
rigorous comparisons of potential energy surface, dynamics, and optimization
predictions among ab initio Density Functional Theory (DFT) simulations and
machine learning models trained on DFT energies with forces, DFT energies
without forces, and DMC energies without forces. We show for three small
molecules - C2, H2O, and CH3Cl - that machine learned DMC dynamics can
reproduce average bond lengths and angles within a few percent of known
experimental results at a 100th of the typical cost. Our work describes a
much-needed means of performing dynamics simulations on high-accuracy, DMC PESs
and for generating DMC-quality molecular geometries given current algorithmic
constraints.

Given an observational study with $n$ independent but heterogeneous units and
one $p$-dimensional sample per unit containing covariates, interventions, and
outcomes, our goal is to learn the counterfactual distribution for each unit.
We consider studies with unobserved confounding which introduces statistical
biases between interventions and outcomes as well as exacerbates the
heterogeneity across units. Modeling the underlying joint distribution as an
exponential family and under suitable conditions, we reduce learning the $n$
unit-level counterfactual distributions to learning $n$ exponential family
distributions with heterogeneous parameters and only one sample per
distribution. We introduce a convex objective that pools all $n$ samples to
jointly learn all $n$ parameters and provide a unit-wise mean squared error
bound that scales linearly with the metric entropy of the parameter space. For
example, when the parameters are $s$-sparse linear combination of $k$ known
vectors, the error is $O(s\log k/p)$. En route, we derive sufficient conditions
for compactly supported distributions to satisfy the logarithmic Sobolev
inequality.

Over the past few years, large knowledge bases have been constructed to store
massive amounts of knowledge. However, these knowledge bases are highly
incomplete. To solve this problem, we propose a web-based question answering
system system with multimodal fusion of unstructured and structured
information, to fill in missing information for knowledge bases. To utilize
unstructured information from the Web for knowledge base completion, we design
a web-based question answering system using multimodal features and question
templates to extract missing facts, which can achieve good performance with
very few questions. To help improve extraction quality, the question answering
system employs structured information from knowledge bases, such as entity
types and entity-to-entity relatedness.

Detecting and classifying diseases using X-Ray images is one of the more
challenging core tasks in the medical and research world. Innovations and
revolutions of Computer Vision with Deep learning methods offer great promise
for fast and accurate diagnosis of screening and detection from chest X-Ray
images (CXR). This work presents rapid detection of diseases in the lung using
the efficient Deep learning pre-trained RepVGG algorithm for deep feature
extraction and classification. We performed automatic classification of X-Ray
images into three categories as Covid-19, Pneumonia, and Normal X-Ray cases.
For evaluation, first, we used a histogram-oriented gradient (HOG) to detect
the shape of the region of interest (ROI). We used the ROI object to improve
the detection accuracy for lung extraction, followed by data pre-processing and
augmentation. Then a pre-trained RepVGG model is used for deep feature
extraction and classification, similar to VGG and ResNet convolutional neural
network for the training-time and inference-time architecture transformed from
the multi to the flat mode by a structural re-parameterization technique. Next,
using the Computer Vision technique, we created a feature map and superimposed
it on the original images. We used this technique for the automatic highlighted
detection of affected areas of people's lungs. Based on the X-Ray images, we
developed an algorithm that classifies X-Ray images with height accuracy and
power faster thanks to the architecture transformation of the model. We compare
deep learning frameworks' accuracy and detection of disease. The study shows
the high power of deep learning methods for X-Ray images based on COVID-19
detection utilizing chest X-Ray. The proposed framework shows better diagnostic
accuracy by comparing popular deep learning models, i.e., VGG, ResNet50,
inceptionV3, DenseNet, and InceptionResnetV2.

Stochastic bilevel optimization, which captures the inherent nested structure
of machine learning problems, is gaining popularity in many recent
applications. Existing works on bilevel optimization mostly consider either
unconstrained problems or constrained upper-level problems. This paper
considers the stochastic bilevel optimization problems with equality
constraints both in the upper and lower levels. By leveraging the special
structure of the equality constraints problem, the paper first presents an
alternating implicit projected SGD approach and establishes the $\tilde{\cal
O}(\epsilon^{-2})$ sample complexity that matches the state-of-the-art
complexity of ALSET \citep{chen2021closing} for unconstrained bilevel problems.
To further save the cost of projection, the paper presents two alternating
implicit projection-efficient SGD approaches, where one algorithm enjoys the
$\tilde{\cal O}(\epsilon^{-2}/T)$ upper-level and ${\cal
O}(\epsilon^{-1.5}/T^{\frac{3}{4}})$ lower-level projection complexity with
${\cal O}(T)$ lower-level batch size, and the other one enjoys $\tilde{\cal
O}(\epsilon^{-1.5})$ upper-level and lower-level projection complexity with
${\cal O}(1)$ batch size. Application to federated bilevel optimization has
been presented to showcase the empirical performance of our algorithms. Our
results demonstrate that equality-constrained bilevel optimization with
strongly-convex lower-level problems can be solved as efficiently as stochastic
single-level optimization problems.

Fully machine translation scarcely guarantees error-free results. Humans
perform post-editing on machine generated translations to correct errors in the
scenario of computer aided translation. In favor of expediting the post-editing
process, recent works have investigated machine translation in an interactive
mode, where machines can automatically refine the rest of translations
constrained on human's edits. In this paper, we utilize the parameterized
objective function of neural machine translation and propose an easy
constrained decoding algorithm to improve the translation quality without
additional training. We demonstrate its capability and time efficiency on a
benchmark dataset, WeTS, where it conditions on humans' guidelines by selecting
spans with potential errors. In the experimental results, our algorithm is
significantly superior to state-of-the-art lexically constrained decoding
method by an increase of 10.37 BLEU in translation quality and a decrease of
63.4% in time cost on average. It even outperforms the benchmark systems
trained with a large amount of annotated data on WeTS in English-German and
German-English.

Controlled Markov chains (CMCs) form the bedrock for model-based
reinforcement learning. In this work, we consider the estimation of the
transition probability matrices of a finite-state finite-control CMC using a
fixed dataset, collected using a so-called logging policy, and develop minimax
sample complexity bounds for nonparametric estimation of these transition
probability matrices. Our results are general, and the statistical bounds
depend on the logging policy through a natural mixing coefficient. We
demonstrate an interesting trade-off between stronger assumptions on mixing
versus requiring more samples to achieve a particular PAC-bound. We demonstrate
the validity of our results under various examples, such as ergodic Markov
chains, weakly ergodic inhomogeneous Markov chains, and controlled Markov
chains with non-stationary Markov, episodic, and greedy controls. Lastly, we
use these sample complexity bounds to establish concomitant ones for offline
evaluation of stationary, Markov policies.

We explore the feasibility of AI assisted hand-gesture recognition using
802.11ad 60GHz (mmWave) technology in smartphones. Range-Doppler information
(RDI) is obtained by using pulse Doppler radar for gesture recognition. We
built a prototype system, where radar sensing and WLAN communication waveform
can coexist by time-division duplex (TDD), to demonstrate the real-time
hand-gesture inference. It can gather sensing data and predict gestures within
100 milliseconds. First, we build the pipeline for the real-time feature
processing, which is robust to occasional frame drops in the data stream. RDI
sequence restoration is implemented to handle the frame dropping in the
continuous data stream, and also applied to data augmentation. Second,
different gestures RDI are analyzed, where finger and hand motions can clearly
show distinctive features. Third, five typical gestures (swipe, palm-holding,
pull-push, finger-sliding and noise) are experimented with, and a
classification framework is explored to segment the different gestures in the
continuous gesture sequence with arbitrary inputs. We evaluate our architecture
on a large multi-person dataset and report > 95% accuracy with one CNN + LSTM
model. Further, a pure CNN model is developed to fit to on-device
implementation, which minimizes the inference latency, power consumption and
computation cost. And the accuracy of this CNN model is more than 93% with only
2.29K parameters.

Multimodal learning (MML) aims to jointly exploit the common priors of
different modalities to compensate for their inherent limitations. However,
existing MML methods often optimize a uniform objective for different
modalities, leading to the notorious "modality imbalance" problem and
counterproductive MML performance. To address the problem, some existing
methods modulate the learning pace based on the fused modality, which is
dominated by the better modality and eventually results in a limited
improvement on the worse modal. To better exploit the features of multimodal,
we propose Prototypical Modality Rebalance (PMR) to perform stimulation on the
particular slow-learning modality without interference from other modalities.
Specifically, we introduce the prototypes that represent general features for
each class, to build the non-parametric classifiers for uni-modal performance
evaluation. Then, we try to accelerate the slow-learning modality by enhancing
its clustering toward prototypes. Furthermore, to alleviate the suppression
from the dominant modality, we introduce a prototype-based entropy
regularization term during the early training stage to prevent premature
convergence. Besides, our method only relies on the representations of each
modality and without restrictions from model structures and fusion methods,
making it with great application potential for various scenarios.

In most medical image processing tasks, the orientation of an image would
affect computing result. However, manually reorienting images wastes time and
effort. In this paper, we study the problem of recognizing orientation in
cardiac MRI and using deep neural network to solve this problem. For multiple
sequences and modalities of MRI, we propose a transfer learning strategy, which
adapts our proposed model from a single modality to multiple modalities. We
also propose a prediction method that uses voting. The results shows that deep
neural network is an effective way in recognition of cardiac MRI orientation
and the voting prediction method could improve accuracy.

Although deep neural networks have shown well-performance in various tasks,
the poor interpretability of the models is always criticized. In the paper, we
propose a new interpretable neural network method, by embedding neurons into
the semantic space to extract their intrinsic global semantics. In contrast to
previous methods that probe latent knowledge inside the model, the proposed
semantic vector externalizes the latent knowledge to static knowledge, which is
easy to exploit. Specifically, we assume that neurons with similar activation
are of similar semantic information. Afterwards, semantic vectors are optimized
by continuously aligning activation similarity and semantic vector similarity
during the training of the neural network. The visualization of semantic
vectors allows for a qualitative explanation of the neural network. Moreover,
we assess the static knowledge quantitatively by knowledge distillation tasks.
Empirical experiments of visualization show that semantic vectors describe
neuron activation semantics well. Without the sample-by-sample guidance from
the teacher model, static knowledge distillation exhibit comparable or even
superior performance with existing relation-based knowledge distillation
methods.

Current 3D object detection methods heavily rely on an enormous amount of
annotations. Semi-supervised learning can be used to alleviate this issue.
Previous semi-supervised 3D object detection methods directly follow the
practice of fully-supervised methods to augment labeled and unlabeled data,
which is sub-optimal. In this paper, we design a data augmentation method for
semi-supervised learning, which we call Semi-Sampling. Specifically, we use
ground truth labels and pseudo labels to crop gt samples and pseudo samples on
labeled frames and unlabeled frames, respectively. Then we can generate a gt
sample database and a pseudo sample database. When training a teacher-student
semi-supervised framework, we randomly select gt samples and pseudo samples to
both labeled frames and unlabeled frames, making a strong data augmentation for
them. Our semi-sampling can be regarded as an extension of gt-sampling to
semi-supervised learning. Our method is simple but effective. We consistently
improve state-of-the-art methods on ScanNet, SUN-RGBD, and KITTI benchmarks by
large margins. For example, when training using only 10% labeled data on
ScanNet, we achieve 3.1 mAP and 6.4 mAP improvement upon 3DIoUMatch in terms of
mAP@0.25 and mAP@0.5. When training using only 1% labeled data on KITTI, we
boost 3DIoUMatch by 3.5 mAP, 6.7 mAP and 14.1 mAP on car, pedestrian and
cyclist classes. Codes will be made publicly available at
https://github.com/LittlePey/Semi-Sampling.

Strong evidence suggests that humans perceive the 3D world by parsing visual
scenes and objects into part-whole hierarchies. Although deep neural networks
have the capability of learning powerful multi-level representations, they can
not explicitly model part-whole hierarchies, which limits their expressiveness
and interpretability in processing 3D vision data such as point clouds. To this
end, we propose an encoder-decoder style latent variable model that explicitly
learns the part-whole hierarchies for the multi-level point cloud segmentation.
Specifically, the encoder takes a point cloud as input and predicts the
per-point latent subpart distribution at the middle level. The decoder takes
the latent variable and the feature from the encoder as an input and predicts
the per-point part distribution at the top level. During training, only
annotated part labels at the top level are provided, thus making the whole
framework weakly supervised. We explore two kinds of approximated inference
algorithms, i.e., most-probable-latent and Monte Carlo methods, and three
stochastic gradient estimations for learning discrete latent variables, i.e.,
straight-through, REINFORCE, and pathwise estimators. Experimental results on
the PartNet dataset show that the proposed method achieves state-of-the-art
performance in not only top-level part segmentation but also middle-level
latent subpart segmentation.

A pair-trading strategy is an approach that utilizes the fluctuations between
prices of a pair of stocks in a short-term time frame, while in the long-term
the pair may exhibit a strong association and co-movement pattern. When the
prices of the stocks exhibit significant divergence, the shares of the stock
that gains in price are sold (a short strategy) while the shares of the other
stock whose price falls are bought (a long strategy). This paper presents a
cointegration-based approach that identifies stocks listed in the five sectors
of the National Stock Exchange (NSE) of India for designing efficient
pair-trading portfolios. Based on the stock prices from Jan 1, 2018, to Dec 31,
2020, the cointegrated stocks are identified and the pairs are formed. The
pair-trading portfolios are evaluated on their annual returns for the year
2021. The results show that the pairs of stocks from the auto and the realty
sectors, in general, yielded the highest returns among the five sectors studied
in the work. However, two among the five pairs from the information technology
(IT) sector are found to have yielded negative returns.

Pretrained language models (PLMs) have been shown to accumulate factual
knowledge during pretrainingng (Petroni et al., 2019). Recent works probe PLMs
for the extent of this knowledge through prompts either in discrete or
continuous forms. However, these methods do not consider symmetry of the task:
object prediction and subject prediction. In this work, we propose Symmetrical
Prompt Enhancement (SPE), a continuous prompt-based method for factual probing
in PLMs that leverages the symmetry of the task by constructing symmetrical
prompts for subject and object prediction. Our results on a popular factual
probing dataset, LAMA, show significant improvement of SPE over previous
probing methods.

Checklists, while being only recently introduced in the medical domain, have
become highly popular in daily clinical practice due to their combined
effectiveness and great interpretability. Checklists are usually designed by
expert clinicians that manually collect and analyze available evidence.
However, the increasing quantity of available medical data is calling for a
partially automated checklist design. Recent works have taken a step in that
direction by learning predictive checklists from categorical data. In this
work, we propose to extend this approach to accomodate learning checklists from
continuous medical data using mixed-integer programming approach. We show that
this extension outperforms a range of explainable machine learning baselines on
the prediction of sepsis from intensive care clinical trajectories.

Quantum neuromorphic computing (QNC) is a subfield of quantum machine
learning (QML) that capitalizes on inherent system dynamics. As a result, QNC
can run on contemporary, noisy quantum hardware and is poised to realize
challenging algorithms in the near term. One key element yet to be added to QNC
is the characterization of the requisite dynamics for universal quantum
neuromorphic computation. We address this issue by proposing a quantum
equivalent to the classical perceptron, a simple mathematical model for a
neuron that is the building block of various machine learning architectures. We
introduce a quantum perceptron (QP) based on the analog dynamics of interacting
qubits with tunable coupling constants. By adding tunable single-qubit
rotations to the QP, we demonstrate that a QP can realize universal quantum
computation, which contrasts sharply with the limited computational complexity
of a single classical perceptron. We show that QPs are analogous to variational
quantum algorithms (VQAs) familiar to the quantum machine learning community.
We derive the quantum neural tangent kernel of a QP and compare the QP's
trainability to the trainability of other VQAs. We discuss the advantages and
drawbacks of kernel formalism. Finally, we demonstrate the effectiveness of QPs
by applying them to numerous QML problems, including calculating the inner
products between quantum states, entanglement witnessing, and quantum
metrology.

Event argument extraction has long been studied as a sequential prediction
problem with extractive-based methods, tackling each argument in isolation.
Although recent work proposes generation-based methods to capture
cross-argument dependency, they require generating and post-processing a
complicated target sequence (template). Motivated by these observations and
recent pretrained language models' capabilities of learning from
demonstrations. We propose a retrieval-augmented generative QA model (R-GQA)
for event argument extraction. It retrieves the most similar QA pair and
augments it as prompt to the current example's context, then decodes the
arguments as answers. Our approach outperforms substantially prior methods
across various settings (i.e. fully supervised, domain transfer, and fewshot
learning). Finally, we propose a clustering-based sampling strategy (JointEnc)
and conduct a thorough analysis of how different strategies influence the
few-shot learning performance. The implementations are available at https://
github.com/xinyadu/RGQA

Nowadays, it is broadly recognized in the power system community that to meet
the ever expanding energy sector's needs, it is no longer possible to rely
solely on physics-based models and that reliable, timely and sustainable
operation of energy systems is impossible without systematic integration of
artificial intelligence (AI) tools. Nevertheless, the adoption of AI in power
systems is still limited, while integration of AI particularly into
distribution grid investment planning is still an uncharted territory. We make
the first step forward to bridge this gap by showing how graph convolutional
networks coupled with the hyperstructures representation learning framework can
be employed for accurate, reliable, and computationally efficient distribution
grid planning with resilience objectives. We further propose a Hyperstructures
Graph Convolutional Neural Networks (Hyper-GCNNs) to capture hidden higher
order representations of distribution networks with attention mechanism. Our
numerical experiments show that the proposed Hyper-GCNNs approach yields
substantial gains in computational efficiency compared to the prevailing
methodology in distribution grid planning and also noticeably outperforms seven
state-of-the-art models from deep learning (DL) community.

Most practical data science problems encounter missing data. A wide variety
of solutions exist, each with strengths and weaknesses that depend upon the
missingness-generating process. Here we develop a theoretical framework for
training and inference using only observed variables enabling modeling of
incomplete datasets without imputation. Using an information and
measure-theoretic argument we construct models with latent space
representations that regularize against the potential bias introduced by
missing data. The theoretical properties of this approach are demonstrated
empirically using a synthetic dataset. The performance of this approach is
tested on 11 benchmarking datasets with missingness and 18 datasets corrupted
across three missingness patterns with comparison against a state-of-the-art
model and industry-standard imputation. We show that our proposed method
overcomes the weaknesses of imputation methods and outperforms the current

Deep neural networks have established themselves as one of the most promising
machine learning techniques. Training such models at large scales is often
parallelized, giving rise to the concept of distributed deep learning.
Distributed techniques are often employed in training large models or large
datasets either out of necessity or simply for speed. Quantum machine learning,
on the other hand, is the interplay between machine learning and quantum
computing. It seeks to understand the advantages of employing quantum devices
in developing new learning algorithms as well as improving the existing ones. A
set of architectures that are heavily explored in quantum machine learning are
quantum neural networks. In this review, we consider ideas from distributed
deep learning as they apply to quantum neural networks. We find that the
distribution of quantum datasets shares more similarities with its classical
counterpart than does the distribution of quantum models, though the unique
aspects of quantum data introduces new vulnerabilities to both approaches. We
review the current state of the art in distributed quantum neural networks,
including recent numerical experiments and the concept of circuit cutting.

Sum-product networks (SPNs) have recently emerged as a novel deep learning
architecture enabling highly efficient probabilistic inference. Since their
introduction, SPNs have been applied to a wide range of data modalities and
extended to time-sequence data. In this paper, we propose a general framework
for modelling sequential treatment decision-making behaviour and treatment
response using recurrent sum-product networks (RSPNs). Models developed using
our framework benefit from the full range of RSPN capabilities, including the
abilities to model the full distribution of the data, to seamlessly handle
latent variables, missing values and categorical data, and to efficiently
perform marginal and conditional inference. Our methodology is complemented by
a novel variant of the expectation-maximization algorithm for RSPNs, enabling
efficient training of our models. We evaluate our approach on a synthetic
dataset as well as real-world data from the MIMIC-IV intensive care unit
medical database. Our evaluation demonstrates that our approach can closely
match the ground-truth data generation process on synthetic data and achieve
results close to neural and probabilistic baselines while using a tractable and
interpretable model.

Traditional evaluation metrics for classification in natural language
processing such as accuracy and area under the curve fail to differentiate
between models with different predictive behaviors despite their similar
performance metrics. We introduce sensitivity score, a metric that scrutinizes
models' behaviors at the vocabulary level to provide insights into disparities
in their decision-making logic. We assess the sensitivity score on a set of
representative words in the test set using two classifiers trained for hospital
readmission classification with similar performance statistics. Our experiments
compare the decision-making logic of clinicians and classifiers based on rank
correlations of sensitivity scores. The results indicate that the language
model's sensitivity score aligns better with the professionals than the xgboost
classifier on tf-idf embeddings, which suggests that xgboost uses some spurious
features. Overall, this metric offers a novel perspective on assessing models'
robustness by quantifying their discrepancy with professional opinions. Our
code is available on GitHub (https://github.com/nyuolab/Model_Sensitivity).

Self-supervised pre-training bears potential to generate expressive
representations without human annotation. Most pre-training in Earth
observation (EO) are based on ImageNet or medium-size, labeled remote sensing
(RS) datasets. We share an unlabeled RS dataset SSL4EO-S12 (Self-Supervised
Learning for Earth Observation - Sentinel-1/2) to assemble a large-scale,
global, multimodal, and multi-seasonal corpus of satellite imagery from the ESA
Sentinel-1 \& -2 satellite missions. For EO applications we demonstrate
SSL4EO-S12 to succeed in self-supervised pre-training for a set of methods:
MoCo-v2, DINO, MAE, and data2vec. Resulting models yield downstream performance
close to, or surpassing accuracy measures of supervised learning. In addition,
pre-training on SSL4EO-S12 excels compared to existing datasets. We make openly
available the dataset, related source code, and pre-trained models at
https://github.com/zhu-xlab/SSL4EO-S12.

Dual-process theories play a central role in both psychology and
neuroscience, figuring prominently in fields ranging from executive control to
reward-based learning to judgment and decision making. In each of these
domains, two mechanisms appear to operate concurrently, one relatively high in
computational complexity, the other relatively simple. Why is neural
information processing organized in this way? We propose an answer to this
question based on the notion of compression. The key insight is that
dual-process structure can enhance adaptive behavior by allowing an agent to
minimize the description length of its own behavior. We apply a single model
based on this observation to findings from research on executive control,
reward-based learning, and judgment and decision making, showing that seemingly
diverse dual-process phenomena can be understood as domain-specific
consequences of a single underlying set of computational principles.

Context: Kubernetes is an open source software that helps in automated
deployment of software and orchestration of containers. With Kubernetes, IT
organizations, such as IBM, Pinterest, and Spotify have experienced an increase
in release frequency. Objective: The goal of this paper is to inform
practitioners and researchers on benefits and challenges of Kubernetes usage by
conducting a multi-vocal literature review of Kubernetes. Methodology: We
conduct a multi-vocal literature review (MLR) where we use 321
Kubernetes-related Internet artifacts to identify benefits and challenges
perceived by practitioners. In our MLR, we also analyze 105 peer-reviewed
publications to identify the research topics addressed by the research
community. Findings: We find 8 benefits that include service level objective
(SLO)-based scalability and self-healing containers. Our identified 15
challenges related to Kubernetes include unavailability of diagnostics and
security tools and attack surface reduction. We observe researchers to address
14 research topics related to Kubernetes, which includes efficient resource
utilization. We also identify 9 challenges that are under-explored in research
publications, which include cultural change, hardware compatibility, learning
curve, maintenance, and testing.

Short-term traffic forecasting is an extensively studied topic in the field
of intelligent transportation system. However, most existing forecasting
systems are limited by the requirement of real-time probe vehicle data because
of their formulation as a time series forecasting problem. Towards this issue,
the NeurIPS 2022 Traffic4cast challenge is dedicated to predicting the citywide
traffic states with publicly available sparse loop count data. This technical
report introduces our second-place winning solution to the extended challenge
of ETA prediction. We present a similarity-based feature extraction method
using multiple nearest neighbor (NN) filters. Similarity-based features, static
features, node flow features and combined features of segments are extracted
for training the gradient boosting decision tree model. Experimental results on
three cities (including London, Madrid and Melbourne) demonstrate the strong
predictive performance of our approach, which outperforms a number of
graph-neural-network-based solutions in the task of travel time estimation. The
source code is available at
\url{https://github.com/c-lyu/Traffic4Cast2022-TSE}.

In human-robot collaboration domains, augmented reality (AR) technologies
have enabled people to visualize the state of robots. Current AR-based
visualization policies are designed manually, which requires a lot of human
efforts and domain knowledge. When too little information is visualized, human
users find the AR interface not useful; when too much information is
visualized, they find it difficult to process the visualized information. In
this paper, we develop a framework, called VARIL, that enables AR agents to
learn visualization policies (what to visualize, when, and how) from
demonstrations. We created a Unity-based platform for simulating warehouse
environments where human-robot teammates collaborate on delivery tasks. We have
collected a dataset that includes demonstrations of visualizing robots' current
and planned behaviors. Results from experiments with real human participants
show that, compared with competitive baselines from the literature, our learned
visualization strategies significantly increase the efficiency of human-robot
teams, while reducing the distraction level of human users. VARIL has been
demonstrated in a built-in-lab mock warehouse.

Recent years have witnessed an increasing interest in end-to-end learned
video compression. Most previous works explore temporal redundancy by detecting
and compressing a motion map to warp the reference frame towards the target
frame. Yet, it failed to adequately take advantage of the historical priors in
the sequential reference frames. In this paper, we propose an Advanced Learned
Video Compression (ALVC) approach with the in-loop frame prediction module,
which is able to effectively predict the target frame from the previously
compressed frames, \textit{without consuming any bit-rate}. The predicted frame
can serve as a better reference than the previously compressed frame, and
therefore it benefits the compression performance. The proposed in-loop
prediction module is a part of the end-to-end video compression and is jointly
optimized in the whole framework. We propose the recurrent and the
bi-directional in-loop prediction modules for compressing P-frames and
B-frames, respectively. The experiments show the state-of-the-art performance
of our ALVC approach in learned video compression. We also outperform the
default hierarchical B mode of x265 in terms of PSNR and beat the slowest mode
of the SSIM-tuned x265 on MS-SSIM. The project page:
https://github.com/RenYang-home/ALVC.

In the correlation clustering problem for complete signed graphs, the input
is a complete signed graph with edges weighted as $+1$ (denote recommendation
to put this pair in the same cluster) or $-1$ (recommending to put this pair of
vertices in separate clusters) and the target is to cluster the set of vertices
such that the number of disagreements with these recommendations is minimized.
  In this paper, we consider the problem of correlation clustering for dynamic
complete signed graphs where (1) a vertex can be added or deleted, and (2) the
sign of an edge can be flipped. In the proposed online scheme, the offline
approximation algorithm in [CALM+21] for correlation clustering is used. Up to
the author's knowledge, this is the first online algorithm for dynamic graphs
which allows a full set of graph editing operations.
  The proposed approach is rigorously analyzed and compared with a baseline
method, which runs the original offline algorithm on each time step. Our
results show that the dynamic operations have local effects on the neighboring
vertices and we employ this locality to reduce the dependency of the running
time in the Baseline to the summation of the degree of all vertices in $G_t$,
the graph after applying the graph edit operation at time step $t$, to the
summation of the degree of the changing vertices (e.g. two endpoints of an
edge) and the number of clusters in the previous time step. Moreover, the
required working memory is reduced to the square of the summation of the degree
of the modified edge endpoints rather than the total number of vertices in the
graph.

We propose an adaptive learning-based framework for uplink massive
multiple-input multiple-output (MIMO) systems with one-bit analog-to-digital
converters. Learning-based detection does not need to estimate channels, which
overcomes a key drawback in one-bit quantized systems. During training,
learning-based detection suffers at high signal-to-noise ratio (SNR) because
observations will be biased to +1 or -1 which leads to many zero-valued
empirical likelihood functions. At low SNR, observations vary frequently in
value but the high noise power makes capturing the effect of the channel
difficult. To address these drawbacks, we propose an adaptive
dithering-and-learning method. During training, received values are mixed with
dithering noise whose statistics are known to the base station, and the
dithering noise power is updated for each antenna element depending on the
observed pattern of the output. We then use the refined probabilities in the
one-bit maximum likelihood detection rule. Simulation results validate the
detection performance of the proposed method vs. our previous method using
fixed dithering noise power as well as zero-forcing and optimal ML detection
both of which assume perfect channel knowledge.

Most state-of-the-art Text-to-Speech systems use the mel-spectrogram as an
intermediate representation, to decompose the task into acoustic modelling and
waveform generation.
  A mel-spectrogram is extracted from the waveform by a simple, fast DSP
operation, but generating a high-quality waveform from a mel-spectrogram
requires computationally expensive machine learning: a neural vocoder. Our
proposed ``autovocoder'' reverses this arrangement. We use machine learning to
obtain a representation that replaces the mel-spectrogram, and that can be
inverted back to a waveform using simple, fast operations including a
differentiable implementation of the inverse STFT.
  The autovocoder generates a waveform 5 times faster than the DSP-based
Griffin-Lim algorithm, and 14 times faster than the neural vocoder HiFi-GAN. We
provide perceptual listening test results to confirm that the speech is of
comparable quality to HiFi-GAN in the copy synthesis task.

Although prior art has demonstrated negligible accuracy drop in sub-byte
quantization -- where weights and/or activations are represented by less than 8
bits -- popular SIMD instructions of CPUs do not natively support these
datatypes. While recent methods, such as ULPPACK, are already using sub-byte
quantization on general-purpose CPUs with vector units, they leave out several
empty bits between the sub-byte values in memory and in vector registers to
avoid overflow to the neighbours during the operations. This results in memory
footprint and bandwidth-usage inefficiencies and suboptimal performance. In
this paper, we present memory layouts for storing, and mechanisms for
processing sub-byte (4-, 2-, or 1-bit) models that utilize all the bits in the
memory as well as in the vector registers for the actual data. We provide
compute kernels for the proposed layout for the GEMV (GEneral Matrix-Vector
multiplication) operations between weights and activations of different
datatypes (e.g., 8-bit activations and 4-bit weights). For evaluation, we
extended the TFLite package and added our methods to it, then ran the models on
the cycle-accurate gem5 simulator to compare detailed memory and CPU cycles of
each method. We compare against nine other methods that are actively used in
production including GEMLOWP, Ruy, XNNPack, and ULPPACK. Furthermore, we
explore the effect of different input and output sizes of deep learning layers
on the performance of our proposed method. Experimental results show 0.96-2.1x
speedup for small sizes and 1.2-6.7x speedup for mid to large sizes. Applying
our proposal to a real-world speech recognition model, Mozilla DeepSpeech, we
proved that our method achieves 1.56-2.11x end-to-end speedup compared to the
state-of-the-art, depending on the bit-width employed.

Entity matching (EM) refers to the problem of identifying pairs of data
records in one or more relational tables that refer to the same entity in the
real world. Supervised machine learning (ML) models currently achieve
state-of-the-art matching performance; however, they require many labeled
examples, which are often expensive or infeasible to obtain. This has inspired
us to approach data labeling for EM using weak supervision. In particular, we
use the labeling function abstraction popularized by Snorkel, where each
labeling function (LF) is a user-provided program that can generate many noisy
match/non-match labels quickly and cheaply. Given a set of user-written LFs,
the quality of data labeling depends on a labeling model to accurately infer
the ground-truth labels. In this work, we first propose a simple but powerful
labeling model for general weak supervision tasks. Then, we tailor the labeling
model specifically to the task of entity matching by considering the
EM-specific transitivity property.
  The general form of our labeling model is simple while substantially
outperforming the best existing method across ten general weak supervision
datasets. To tailor the labeling model for EM, we formulate an approach to
ensure that the final predictions of the labeling model satisfy the
transitivity property required in EM, utilizing an exact solution where
possible and an ML-based approximation in remaining cases. On two single-table
and nine two-table real-world EM datasets, we show that our labeling model
results in a 9% higher F1 score on average than the best existing method. We
also show that a deep learning EM end model (DeepMatcher) trained on labels
generated from our weak supervision approach is comparable to an end model
trained using tens of thousands of ground-truth labels, demonstrating that our
approach can significantly reduce the labeling efforts required in EM.

Neural Ordinary Differential Equations (ODEs) was recently introduced as a
new family of neural network models, which relies on black-box ODE solvers for
inference and training. Some ODE solvers called adaptive can adapt their
evaluation strategy depending on the complexity of the problem at hand, opening
great perspectives in machine learning. However, this paper describes a simple
set of experiments to show why adaptive solvers cannot be seamlessly leveraged
as a black-box for dynamical systems modelling. By taking the Lorenz'63 system
as a showcase, we show that a naive application of the Fehlberg's method does
not yield the expected results. Moreover, a simple workaround is proposed that
assumes a tighter interaction between the solver and the training strategy. The
code is available on github:
https://github.com/Allauzen/adaptive-step-size-neural-ode

Consider a target being tracked by a cognitive radar network. If the target
can intercept some radar network emissions, how can it detect coordination
among the radars? By 'coordination' we mean that the radar emissions satisfy
Pareto optimality with respect to multi-objective optimization over each
radar's utility. This paper provides a novel multi-objective inverse
reinforcement learning approach which allows for both detection of such Pareto
optimal ('coordinating') behavior and subsequent reconstruction of each radar's
utility function, given a finite dataset of radar network emissions. The method
for accomplishing this is derived from the micro-economic setting of Revealed
Preferences, and also applies to more general problems of inverse detection and
learning of multi-objective optimizing systems.

Stability of graph neural networks (GNNs) characterizes how GNNs react to
graph perturbations and provides guarantees for architecture performance in
noisy scenarios. This paper develops a self-regularized graph neural network
(SR-GNN) solution that improves the architecture stability by regularizing the
filter frequency responses in the graph spectral domain. The SR-GNN considers
not only the graph signal as input but also the eigenvectors of the underlying
graph, where the signal is processed to generate task-relevant features and the
eigenvectors to characterize the frequency responses at each layer. We train
the SR-GNN by minimizing the cost function and regularizing the maximal
frequency response close to one. The former improves the architecture
performance, while the latter tightens the perturbation stability and
alleviates the information loss through multi-layer propagation. We further
show the SR-GNN preserves the permutation equivariance, which allows to explore
the internal symmetries of graph signals and to exhibit transference on similar
graph structures. Numerical results with source localization and movie
recommendation corroborate our findings and show the SR-GNN yields a comparable
performance with the vanilla GNN on the unperturbed graph but improves
substantially the stability.

In this work, we propose a novel decoding algorithm for short block codes
based on an edge-weighted graph neural network (EW-GNN). The EW-GNN decoder
operates on the Tanner graph with an iterative message-passing structure, which
algorithmically aligns with the conventional belief propagation (BP) decoding
method. In each iteration, the "weight" on the message passed along each edge
is obtained from a fully connected neural network that has the reliability
information from nodes/edges as its input. Compared to existing
deep-learning-based decoding schemes, the EW-GNN decoder is characterised by
its scalability, meaning that 1) the number of trainable parameters is
independent of the codeword length, and 2) an EW-GNN decoder trained with
shorter/simple codes can be directly used for longer/sophisticated codes of
different code rates. Furthermore, simulation results show that the EW-GNN
decoder outperforms the BP and deep-learning-based BP methods from the
literature in terms of the decoding error rate.

Decoding visual stimuli from brain recordings aims to deepen our
understanding of the human visual system and build a solid foundation for
bridging human and computer vision through the Brain-Computer Interface.
However, reconstructing high-quality images with correct semantics from brain
recordings is a challenging problem due to the complex underlying
representations of brain signals and the scarcity of data annotations. In this
work, we present MinD-Vis: Sparse Masked Brain Modeling with Double-Conditioned
Latent Diffusion Model for Human Vision Decoding. Firstly, we learn an
effective self-supervised representation of fMRI data using mask modeling in a
large latent space inspired by the sparse coding of information in the primary
visual cortex. Then by augmenting a latent diffusion model with
double-conditioning, we show that MinD-Vis can reconstruct highly plausible
images with semantically matching details from brain recordings using very few
paired annotations. We benchmarked our model qualitatively and quantitatively;
the experimental results indicate that our method outperformed state-of-the-art
in both semantic mapping (100-way semantic classification) and generation
quality (FID) by 66% and 41% respectively. An exhaustive ablation study was
also conducted to analyze our framework.

Parkinson's disease is a common neurological disease, entailing a multitude
of motor deficiency symptoms. In this project, we developed a device with an
uploaded edge machine learning algorithm that can detect the onset of freezing
of gait symptoms in a Parkinson's patient. The algorithm achieved an accuracy
of 83.7% in a validation using data from ten patients. The model was deployed
in a microcontroller Arduino Nano 33 BLE Sense Board model and validated in
real-time operation with data streamed to the microcontroller from a computer.

Statistical analysis of tensor-valued data has largely used the
tensor-variate normal (TVN) distribution that may be inadequate when data comes
from distributions with heavier or lighter tails. We study a general family of
elliptically contoured (EC) tensor-variate distributions and derive its
characterizations, moments, marginal and conditional distributions, and the EC
Wishart distribution. We describe procedures for maximum likelihood estimation
from data that are (1) uncorrelated draws from an EC distribution, (2) from a
scale mixture of the TVN distribution, and (3) from an underlying but unknown
EC distribution, where we extend Tyler's robust estimator. A detailed
simulation study highlights the benefits of choosing an EC distribution over
the TVN for heavier-tailed data. We develop tensor-variate classification rules
using discriminant analysis and EC errors and show that they better predict
cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based
rules. A novel tensor-on-tensor regression and tensor-variate analysis of
variance (TANOVA) framework under EC errors is also demonstrated to better
characterize gender, age and ethnic origin than the usual TVN-based TANOVA in
the celebrated Labeled Faces of the Wild dataset.

Recent years have witnessed the booming of various differentiable
optimization algorithms. These algorithms exhibit different execution patterns,
and their execution needs massive computational resources that go beyond a
single CPU and GPU. Existing differentiable optimization libraries, however,
cannot support efficient algorithm development and multi-CPU/GPU execution,
making the development of differentiable optimization algorithms often
cumbersome and expensive. This paper introduces TorchOpt, a PyTorch-based
efficient library for differentiable optimization. TorchOpt provides a unified
and expressive differentiable optimization programming abstraction. This
abstraction allows users to efficiently declare and analyze various
differentiable optimization programs with explicit gradients, implicit
gradients, and zero-order gradients. TorchOpt further provides a
high-performance distributed execution runtime. This runtime can fully
parallelize computation-intensive differentiation operations (e.g. tensor tree
flattening) on CPUs / GPUs and automatically distribute computation to
distributed devices. Experimental results show that TorchOpt achieves
$5.2\times$ training time speedup on an 8-GPU server. TorchOpt is available at:
https://github.com/metaopt/torchopt/.

We propose developing an integrated system to keep autonomous unmanned
aircraft safely separated and behave as expected in conjunction with manned
traffic. The main goal is to achieve safe manned-unmanned vehicle teaming to
improve system performance, have each (robot/human) teammate learn from each
other in various aircraft operations, and reduce the manning needs of manned
aircraft. The proposed system anticipates and reacts to other aircraft using
natural language instructions and can serve as a co-pilot or operate entirely
autonomously. We point out the main technical challenges where improvements on
current state-of-the-art are needed to enable Visual Flight Rules to fully
autonomous aerial operations, bringing insights to these critical areas.
Furthermore, we present an interactive demonstration in a prototypical scenario
with one AI pilot and one human pilot sharing the same terminal airspace,
interacting with each other using language, and landing safely on the same
runway. We also show a demonstration of a vision-only aircraft detection
system.

Optimization and planning methods for tasks involving 3D objects often rely
on prior knowledge and ad-hoc heuristics. In this work, we target
learning-based long-horizon path generation by leveraging recent advances in 3D
deep learning. We present PaintNet, the first dataset for learning robotic
spray painting of free-form 3D objects. PaintNet includes more than 800 object
meshes and the associated painting strokes collected in a real industrial
setting. We then introduce a novel 3D deep learning method to tackle this task
and operate on unstructured input spaces -- point clouds -- and mix-structured
output spaces -- unordered sets of painting strokes. Our extensive experimental
analysis demonstrates the capabilities of our method to predict smooth output
strokes that cover up to 95% of previously unseen object surfaces, with respect
to ground-truth paint coverage. The PaintNet dataset and an implementation of
our proposed approach will be released at
https://gabrieletiboni.github.io/paintnet.

Reinforcement learning has seen increasing applications in real-world
contexts over the past few years. However, physical environments are often
imperfect and policies that perform well in simulation might not achieve the
same performance when applied elsewhere. A common approach to combat this is to
train agents in the presence of an adversary. An adversary acts to destabilise
the agent, which learns a more robust policy and can better handle realistic
conditions. Many real-world applications of reinforcement learning also make
use of goal-conditioning: this is particularly useful in the context of
robotics, as it allows the agent to act differently, depending on which goal is
selected. Here, we focus on the problem of goal-conditioned learning in the
presence of an adversary. We first present DigitFlip and CLEVR-Play, two novel
goal-conditioned environments that support acting against an adversary. Next,
we propose EHER and CHER -- two HER-based algorithms for goal-conditioned
learning -- and evaluate their performance. Finally, we unify the two threads
and introduce IGOAL: a novel framework for goal-conditioned learning in the
presence of an adversary. Experimental results show that combining IGOAL with
EHER allows agents to significantly outperform existing approaches, when acting
against both random and competent adversaries.

Self-attention mechanisms, especially multi-head self-attention (MSA), have
achieved great success in many fields such as computer vision and natural
language processing. However, many existing vision transformer (ViT) works
simply inherent transformer designs from NLP to adapt vision tasks, while
ignoring the fundamental difference between ``how MSA works in image and
language settings''. Language naturally contains highly semantic structures
that are directly interpretable by humans. Its basic unit (word) is discrete
without redundant information, which readily supports interpretable studies on
MSA mechanisms of language transformer. In contrast, visual data exhibits a
fundamentally different structure: Its basic unit (pixel) is a natural
low-level representation with significant redundancies in the neighbourhood,
which poses obvious challenges to the interpretability of MSA mechanism in ViT.
In this paper, we introduce a typical image processing technique, i.e.,
scale-invariant feature transforms (SIFTs), which maps low-level
representations into mid-level spaces, and annotates extensive discrete
keypoints with semantically rich information. Next, we construct a weighted
patch interrelation analysis based on SIFT keypoints to capture the attention
patterns hidden in patches with different semantic concentrations
Interestingly, we find this quantitative analysis is not only an effective
complement to the interpretability of MSA mechanisms in ViT, but can also be
applied to 1) spurious correlation discovery and ``prompting'' during model
inference, 2) and guided model pre-training acceleration. Experimental results
on both applications show significant advantages over baselines, demonstrating
the efficacy of our method.

Chronic obstructive pulmonary disease (COPD) is one of the most common
chronic illnesses in the world and the third leading cause of mortality
worldwide. It is often underdiagnosed or not diagnosed until later in the
disease course. Spirometry tests are the gold standard for diagnosing COPD but
can be difficult to obtain, especially in resource-poor countries. Chest X-rays
(CXRs), however, are readily available and may serve as a screening tool to
identify patients with COPD who should undergo further testing. Currently, no
research applies deep learning (DL) algorithms that use large multi-site and
multi-modal data to detect COPD patients and evaluate fairness across
demographic groups. We use three CXR datasets in our study, CheXpert to
pre-train models, MIMIC-CXR to develop, and Emory-CXR to validate our models.
The CXRs from patients in the early stage of COPD and not on mechanical
ventilation are selected for model training and validation. We visualize the
Grad-CAM heatmaps of the true positive cases on the base model for both
MIMIC-CXR and Emory-CXR test datasets. We further propose two fusion schemes,
(1) model-level fusion, including bagging and stacking methods using MIMIC-CXR,
and (2) data-level fusion, including multi-site data using MIMIC-CXR and
Emory-CXR, and multi-modal using MIMIC-CXRs and MIMIC-IV EHR, to improve the
overall model performance. Fairness analysis is performed to evaluate if the
fusion schemes have a discrepancy in the performance among different
demographic groups. The results demonstrate that DL models can detect COPD
using CXRs, which can facilitate early screening, especially in low-resource
regions where CXRs are more accessible than spirometry. The multi-site data
fusion scheme could improve the model generalizability on the Emory-CXR test
data. Further studies on using CXR or other modalities to predict COPD ought to
be in future work.

Multimodal recommender systems utilizing multimodal features (e.g. images and
textual descriptions) typically show better recommendation accuracy than
general recommendation models based solely on user-item interactions.
Generally, prior work fuses multimodal features into item ID embeddings to
enrich item representations, thus failing to capture the latent semantic
item-item structures. In this context, LATTICE [1] proposes to learn the latent
structure between items explicitly and achieves state-of-the-art performance
for multimodal recommendations. However, we argue the latent graph structure
learning of LATTICE is both inefficient and unnecessary. Experimentally, we
demonstrate that freezing its item-item structure before training can also
achieve competitive performance. Based on this finding, we propose a simple yet
effective model, dubbed as FREEDOM, that FREEzes the item-item graph and
DenOises the user-item interaction graph simultaneously for Multimodal
recommendation. In denoising the user-item interaction graph, we devise a
degree-sensitive edge pruning method, which rejects possibly noisy edges with a
high probability when sampling the graph. We evaluate the proposed model on
three real-world datasets and show that FREEDOM can significantly outperform
the strongest baselines. Compared with LATTICE, FREEDOM achieves an average
improvement of 19.07% in recommendation accuracy while reducing its memory cost
up to 6$\times$ on large graphs. The source code is available at:
https://github.com/enoche/FREEDOM.

Causal structure learning has been extensively studied and widely used in
machine learning and various applications. To achieve an ideal performance,
existing causal structure learning algorithms often need to centralize a large
amount of data from multiple data sources. However, in the privacy-preserving
setting, it is impossible to centralize data from all sources and put them
together as a single dataset. To preserve data privacy, federated learning as a
new learning paradigm has attached much attention in machine learning in recent
years. In this paper, we study a privacy-aware causal structure learning
problem in the federated setting and propose a novel Federated PC (FedPC)
algorithm with two new strategies for preserving data privacy without
centralizing data. Specifically, we first propose a novel layer-wise
aggregation strategy for a seamless adaptation of the PC algorithm into the
federated learning paradigm for federated skeleton learning, then we design an
effective strategy for learning consistent separation sets for federated edge
orientation. The extensive experiments validate that FedPC is effective for
causal structure learning in federated learning setting.

Influenced by the advances in data and computing, the scientific practice
increasingly involves machine learning and artificial intelligence driven
methods which requires specialized capabilities at the system-, science- and
service-level in addition to the conventional large-capacity supercomputing
approaches. The latest distributed architectures built around the composability
of data-centric applications led to the emergence of a new ecosystem for
container coordination and integration. However, there is still a divide
between the application development pipelines of existing supercomputing
environments, and these new dynamic environments that disaggregate fluid
resource pools through accessible, portable and re-programmable interfaces. New
approaches for dynamic composability of heterogeneous systems are needed to
further advance the data-driven scientific practice for the purpose of more
efficient computing and usable tools for specific scientific domains. In this
paper, we present a novel approach for using composable systems in the
intersection between scientific computing, artificial intelligence (AI), and
remote sensing domain. We describe the architecture of a first working example
of a composable infrastructure that federates Expanse, an NSF-funded
supercomputer, with Nautilus, a Kubernetes-based GPU geo-distributed cluster.
We also summarize a case study in wildfire modeling, that demonstrates the
application of this new infrastructure in scientific workflows: a composed
system that bridges the insights from edge sensing, AI and computing
capabilities with a physics-driven simulation.

The edit distance is a metric of dissimilarity between strings, widely
applied in computational biology, speech recognition, and machine learning. Let
$e_k(n)$ denote the average edit distance between random, independent strings
of $n$ characters from an alphabet of size $k$. For $k \geq 2$, it is an open
problem how to efficiently compute the exact value of $\alpha_{k}(n) =
e_k(n)/n$ as well as of $\alpha_{k} = \lim_{n \to \infty} \alpha_{k}(n)$, a
limit known to exist.
  This paper shows that $\alpha_k(n)-Q(n) \leq \alpha_k \leq \alpha_k(n)$, for
a specific $Q(n)=\Theta(\sqrt{\log n / n})$, a result which implies that
$\alpha_k$ is computable. The exact computation of $\alpha_k(n)$ is explored,
leading to an algorithm running in time $T=\mathcal{O}(n^2k\min(3^n,k^n))$, a
complexity that makes it of limited practical use.
  An analysis of statistical estimates is proposed, based on McDiarmid's
inequality, showing how $\alpha_k(n)$ can be evaluated with good accuracy, high
confidence level, and reasonable computation time, for values of $n$ say up to
a quarter million. Correspondingly, 99.9\% confidence intervals of width
approximately $10^{-2}$ are obtained for $\alpha_k$.
  Combinatorial arguments on edit scripts are exploited to analytically
characterize an efficiently computable lower bound $\beta_k^*$ to $\alpha_k$,
such that $ \lim_{k \to \infty} \beta_k^*=1$. In general, $\beta_k^* \leq
\alpha_k \leq 1-1/k$; for $k$ greater than a few dozens, computing $\beta_k^*$
is much faster than generating good statistical estimates with confidence
intervals of width $1-1/k-\beta_k^*$.
  The techniques developed in the paper yield improvements on most previously
published numerical values as well as results for alphabet sizes and string
lengths not reported before.

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure
till date and can lead to severe health complications, such as retinopathy,
limb amputation, cardiovascular diseases, and neuronal disease, if left
untreated.
  Consequently, it becomes crucial to take precautionary measures to
avoid/predict the occurrence of diabetes. Machine learning approaches have been
proposed and evaluated in the literature for diabetes prediction. This paper
proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for
diabetes prediction based on risk factors. The proposed system is underpinned
by the blockchain to obtain a cohesive view of the risk factors data from
patients across different hospitals and to ensure security and privacy of the
user data. Furthermore, we provide a comparative analysis of different medical
sensors, devices, and methods to measure and collect the risk factors values in
the system. Numerical experiments and comparative analysis were carried out
between our proposed system, using the most accurate random forest (RF) model,
and the two most used state-of-the-art machine learning approaches, Logistic
Regression (LR) and Support Vector Machine (SVM), using three real-life
diabetes datasets. The results show that the proposed system using RF predicts
diabetes with 4.57% more accuracy on average compared to LR and SVM, with 2.87
times more execution time. Data balancing without feature selection does not
show significant improvement. The performance is improved by 1.14% and 0.02%
after feature selection for PIMA Indian and Sylhet datasets respectively, while
it reduces by 0.89% for MIMIC III.

In this paper, we propose a digital twin (DT)-assisted cloud-edge
collaborative transcoding scheme to enhance user satisfaction in live
streaming. We first present a DT-assisted transcoding workload estimation (TWE)
model for the cloud-edge collaborative transcoding. Particularly, two DTs are
constructed for emulating the cloud-edge collaborative transcoding process by
analyzing spatial-temporal information of individual videos and transcoding
configurations of transcoding queues, respectively. Two light-weight Bayesian
neural networks are adopted to fit the TWE models in DTs, respectively. We then
formulate a transcoding-path selection problem to maximize long-term user
satisfaction within an average service delay threshold, taking into account the
dynamics of video arrivals and video requests. The problem is transformed into
a standard Markov decision process by using the Lyapunov optimization and
solved by a deep reinforcement learning algorithm. Simulation results based on
the real-world dataset demonstrate that the proposed scheme can effectively
enhance user satisfaction compared with benchmark schemes.

Modern IT system operation demands the integration of system software and
hardware metrics. As a result, it generates a massive amount of data, which can
be potentially used to make data-driven operational decisions. In the basic
form, the decision model needs to monitor a large set of machine data, such as
CPU utilization, allocated memory, disk and network latency, and predicts the
system metrics to prevent performance degradation. Nevertheless, building an
effective prediction model in this scenario is rather challenging as the model
has to accurately capture the long-range coupling dependency in the
Multivariate Time-Series (MTS). Moreover, this model needs to have low
computational complexity and can scale efficiently to the dimension of data
available. In this paper, we propose a highly efficient model named HigeNet to
predict the long-time sequence time series. We have deployed the HigeNet on
production in the D-matrix platform. We also provide offline evaluations on
several publicly available datasets as well as one online dataset to
demonstrate the model's efficacy. The extensive experiments show that training
time, resource usage and accuracy of the model are found to be significantly
better than five state-of-the-art competing models.

Visual semantic segmentation aims at separating a visual sample into diverse
blocks with specific semantic attributes and identifying the category for each
block, and it plays a crucial role in environmental perception. Conventional
learning-based visual semantic segmentation approaches count heavily on
large-scale training data with dense annotations and consistently fail to
estimate accurate semantic labels for unseen categories. This obstruction spurs
a craze for studying visual semantic segmentation with the assistance of
few/zero-shot learning. The emergence and rapid progress of few/zero-shot
visual semantic segmentation make it possible to learn unseen-category from a
few labeled or zero-labeled samples, which advances the extension to practical
applications. Therefore, this paper focuses on the recently published
few/zero-shot visual semantic segmentation methods varying from 2D to 3D space
and explores the commonalities and discrepancies of technical settlements under
different segmentation circumstances. Specifically, the preliminaries on
few/zero-shot visual semantic segmentation, including the problem definitions,
typical datasets, and technical remedies, are briefly reviewed and discussed.
Moreover, three typical instantiations are involved to uncover the interactions
of few/zero-shot learning with visual semantic segmentation, including image
semantic segmentation, video object segmentation, and 3D segmentation. Finally,
the future challenges of few/zero-shot visual semantic segmentation are
discussed.

Automated planetary transit detection has become vital to prioritize
candidates for expert analysis given the scale of modern telescopic surveys.
While current methods for short-period exoplanet detection work effectively due
to periodicity in the light curves, there lacks a robust approach for detecting
single-transit events. However, volunteer-labelled transits recently collected
by the Planet Hunters TESS (PHT) project now provide an unprecedented
opportunity to investigate a data-driven approach to long-period exoplanet
detection. In this work, we train a 1-D convolutional neural network to
classify planetary transits using PHT volunteer scores as training data. We
find using volunteer scores significantly improves performance over synthetic
data, and enables the recovery of known planets at a precision and rate
matching that of the volunteers. Importantly, the model also recovers transits
found by volunteers but missed by current automated methods.

Medical image benchmarks for the segmentation of organs and tumors suffer
from the partially labeling issue due to its intensive cost of labor and
expertise. Current mainstream approaches follow the practice of one network
solving one task. With this pipeline, not only the performance is limited by
the typically small dataset of a single task, but also the computation cost
linearly increases with the number of tasks. To address this, we propose a
Transformer based dynamic on-demand network (TransDoDNet) that learns to
segment organs and tumors on multiple partially labeled datasets. Specifically,
TransDoDNet has a hybrid backbone that is composed of the convolutional neural
network and Transformer. A dynamic head enables the network to accomplish
multiple segmentation tasks flexibly. Unlike existing approaches that fix
kernels after training, the kernels in the dynamic head are generated
adaptively by the Transformer, which employs the self-attention mechanism to
model long-range organ-wise dependencies and decodes the organ embedding that
can represent each organ. We create a large-scale partially labeled Multi-Organ
and Tumor Segmentation benchmark, termed MOTS, and demonstrate the superior
performance of our TransDoDNet over other competitors on seven organ and tumor
segmentation tasks. This study also provides a general 3D medical image
segmentation model, which has been pre-trained on the large-scale MOTS
benchmark and has demonstrated advanced performance over BYOL, the current
predominant self-supervised learning method. Code will be available at
\url{https://git.io/DoDNet}.

We introduce a training rule that enables a network composed of springs and
dashpots to learn precise stress patterns. Our goal is to control the tensions
on a fraction of "target" bonds, which are chosen randomly. The system is
trained by applying stresses to the target bonds, causing the remaining bonds,
which act as the learning degrees of freedom, to evolve. Different criteria for
selecting the target bonds affects whether frustration is present. When there
is at most a single target bond per node the error converges to computer
precision. Additional targets on a single node may lead to slow convergence and
failure. Nonetheless, training is successful even when approaching the limit
predicted by the Maxwell Calladine theorem. We demonstrate the generality of
these ideas by considering dashpots with yield stresses. We show that training
converges, albeit with a slower, power-law decay of the error. Furthermore,
dashpots with yielding stresses prevent the system from relaxing after
training, enabling to encode permanent memories.

Neural HMMs are a type of neural transducer recently proposed for
sequence-to-sequence modelling in text-to-speech. They combine the best
features of classic statistical speech synthesis and modern neural TTS,
requiring less data and fewer training updates, and are less prone to gibberish
output caused by neural attention failures. In this paper, we combine neural
HMM TTS with normalising flows for describing the highly non-Gaussian
distribution of speech acoustics. The result is a powerful, fully probabilistic
model of durations and acoustics that can be trained using exact maximum
likelihood. Compared to dominant flow-based acoustic models, our approach
integrates autoregression for improved modelling of long-range dependences such
as utterance-level prosody. Experiments show that a system based on our
proposal gives more accurate pronunciations and better subjective speech
quality than comparable methods, whilst retaining the original advantages of
neural HMMs. Audio examples and code are available at
https://shivammehta25.github.io/OverFlow/

To acquire a snapshot spectral image, coded aperture snapshot spectral
imaging (CASSI) is proposed. A core problem of the CASSI system is to recover
the reliable and fine underlying 3D spectral cube from the 2D measurement. By
alternately solving a data subproblem and a prior subproblem, deep unfolding
methods achieve good performance. However, in the data subproblem, the used
sensing matrix is ill-suited for the real degradation process due to the device
errors caused by phase aberration, distortion; in the prior subproblem, it is
important to design a suitable model to jointly exploit both spatial and
spectral priors. In this paper, we propose a Residual Degradation Learning
Unfolding Framework (RDLUF), which bridges the gap between the sensing matrix
and the degradation process. Moreover, a Mix$S^2$ Transformer is designed via
mixing priors across spectral and spatial to strengthen the spectral-spatial
representation capability. Finally, plugging the Mix$S^2$ Transformer into the
RDLUF leads to an end-to-end trainable and interpretable neural network
RDLUF-Mix$S^2$. Experimental results establish the superior performance of the
proposed method over existing ones.

It has been considered that urban air mobility (UAM), also known as
drone-taxi or electrical vertical takeoff and landing (eVTOL), will play a key
role in future transportation. By putting UAM into practical future
transportation, several benefits can be realized, i.e., (i) the total travel
time of passengers can be reduced compared to traditional transportation and
(ii) there is no environmental pollution and no special labor costs to operate
the system because electric batteries will be used in UAM system. However,
there are various dynamic and uncertain factors in the flight environment,
i.e., passenger sudden service requests, battery discharge, and collision among
UAMs. Therefore, this paper proposes a novel cooperative MADRL algorithm based
on centralized training and distributed execution (CTDE) concepts for reliable
and efficient passenger delivery in UAM networks. According to the performance
evaluation results, we confirm that the proposed algorithm outperforms other
existing algorithms in terms of the number of serviced passengers increase
(30%) and the waiting time per serviced passenger decrease (26%)

Sleep is known to play a central role in learning and cognition, yet the
mechanisms underlying its role in stabilizing learning and improving energetic
management are still to be clarified. It is characterized by patterns of
cortical activity alternating between the stages of slow wave sleep (NREM) and
rapid eye movement sleep (REM). In this work, we investigate the role of
sleep-like cycles on a biologically-plausible two-area thalamo-cortical spiking
neural network capable of experiencing AWAKE-NREM-REM brain states. We train
the network on a handwritten digits classification task and show the beneficial
effects of sleep on energy consumption and classification performance,
exploiting its effects on internal structure, memory representation and
consolidation. We show the ability of sleep to shape the synaptic distribution,
thus affecting the neuronal firing rate and creating novel multi-area
associations.

Shadows in videos are difficult to detect because of the large shadow
deformation between frames. In this work, we argue that accounting for the
shadow deformation is essential when designing a video shadow detection method.
To this end, we introduce the shadow deformation attention trajectory (SODA), a
new type of video self-attention module, specially designed to handle the large
shadow deformations in videos. Moreover, we present a shadow contrastive
learning mechanism (SCOTCH) which aims at guiding the network to learn a
high-level representation of shadows, unified across different videos. We
demonstrate empirically the effectiveness of our two contributions in an
ablation study. Furthermore, we show that SCOTCH and SODA significantly
outperforms existing techniques for video shadow detection. Code will be
available upon the acceptance of this work.

We investigate the Multi-Armed Bandit problem with Temporally-Partitioned
Rewards (TP-MAB) setting in this paper. In the TP-MAB setting, an agent will
receive subsets of the reward over multiple rounds rather than the entire
reward for the arm all at once. In this paper, we introduce a general
formulation of how an arm's cumulative reward is distributed across several
rounds, called Beta-spread property. Such a generalization is needed to be able
to handle partitioned rewards in which the maximum reward per round is not
distributed uniformly across rounds. We derive a lower bound on the TP-MAB
problem under the assumption that Beta-spread holds. Moreover, we provide an
algorithm TP-UCB-FR-G, which uses the Beta-spread property to improve the
regret upper bound in some scenarios. By generalizing how the cumulative reward
is distributed, this setting is applicable in a broader range of applications.

The reason behind CNNs capability to learn high-dimensional complex features
from the images is the non-linearity introduced by the activation function.
Several advanced activation functions have been discovered to improve the
training process of neural networks, as choosing an activation function is a
crucial step in the modeling. Recent research has proposed using an oscillating
activation function to solve classification problems inspired by the human
brain cortex. This paper explores the performance of one of the CNN
architecture ALexNet on MNIST and CIFAR10 datasets using oscillatory activation
function (GCU) and some other commonly used activation functions like ReLu,
PReLu, and Mish.

In this paper we study circles tangent to conics. We show there are
generically $184$ complex circles tangent to three conics in the plane and we
characterize the real discriminant of the corresponding polynomial system. We
give an explicit example of $3$ conics with $136$ real circles tangent to them.
We conjecture that 136 is the maximal number of real circles. Furthermore, we
implement a hill-climbing algorithm to find instances of conics with many real
circles, and we introduce a machine learning model that, given three real
conics, predicts the number of circles tangent to these three conics.

Student engagement is an important factor in meeting the goals of virtual
learning programs. Automatic measurement of student engagement provides helpful
information for instructors to meet learning program objectives and
individualize program delivery. Many existing approaches solve video-based
engagement measurement using the traditional frameworks of binary
classification (classifying video snippets into engaged or disengaged classes),
multi-class classification (classifying video snippets into multiple classes
corresponding to different levels of engagement), or regression (estimating a
continuous value corresponding to the level of engagement). However, we observe
that while the engagement behaviour is mostly well-defined (e.g., focused, not
distracted), disengagement can be expressed in various ways. In addition, in
some cases, the data for disengaged classes may not be sufficient to train
generalizable binary or multi-class classifiers. To handle this situation, in
this paper, for the first time, we formulate detecting disengagement in virtual
learning as an anomaly detection problem. We design various autoencoders,
including temporal convolutional network autoencoder, long-short-term memory
autoencoder, and feedforward autoencoder using different behavioral and affect
features for video-based student disengagement detection. The result of our
experiments on two publicly available student engagement datasets, DAiSEE and
EmotiW, shows the superiority of the proposed approach for disengagement
detection as an anomaly compared to binary classifiers for classifying videos
into engaged versus disengaged classes (with an average improvement of 9% on
the area under the curve of the receiver operating characteristic curve and 22%
on the area under the curve of the precision-recall curve).

Incremental or continual learning has been extensively studied for image
classification tasks to alleviate catastrophic forgetting, a phenomenon that
earlier learned knowledge is forgotten when learning new concepts. For class
incremental semantic segmentation, such a phenomenon often becomes much worse
due to the background shift, i.e., some concepts learned at previous stages are
assigned to the background class at the current training stage, therefore,
significantly reducing the performance of these old concepts. To address this
issue, we propose a simple yet effective method in this paper, named Mining
unseen Classes via Regional Objectness for Segmentation (MicroSeg). Our
MicroSeg is based on the assumption that background regions with strong
objectness possibly belong to those concepts in the historical or future
stages. Therefore, to avoid forgetting old knowledge at the current training
stage, our MicroSeg first splits the given image into hundreds of segment
proposals with a proposal generator. Those segment proposals with strong
objectness from the background are then clustered and assigned newly-defined
labels during the optimization. In this way, the distribution characterizes of
old concepts in the feature space could be better perceived, relieving the
catastrophic forgetting caused by the background shift accordingly. Extensive
experiments on Pascal VOC and ADE20K datasets show competitive results with
state-of-the-art, well validating the effectiveness of the proposed MicroSeg.

Collaborative Edge Computing (CEC) is an effective method that improves the
performance of Mobile Edge Computing (MEC) systems by offloading computation
tasks from busy edge servers (ESs) to idle ones. However, ESs usually belong to
different MEC service providers so they have no incentive to help others. To
motivate cooperation among them, this paper proposes a cooperative mechanism
where idle ESs can earn extra profits by sharing their spare computational
resources. To achieve the optimal resource allocation, we formulate the social
welfare maximization problem as a Markov Decision Process (MDP) and decompose
it into two stages involving the allocation and execution of offloaded tasks.
The first stage is solved by extending the well-known Deep Deterministic Policy
Gradient (DDPG) algorithm. For the second stage, we first show that we only
need to decide the processing order of tasks and the utilized computational
resources. After that, we propose a dynamic programming and a Deep
Reinforcement Learning (DRL)-based algorithm to solve the two types of
decisions, respectively. Numerical results indicate that our algorithm
significantly improves social welfare under various situations.

This work presents a two-stage framework for progressively developing neural
architectures to adapt/ generalize well on a given training data set. In the
first stage, a manifold-regularized layerwise sparsifying training approach is
adopted where a new layer is added each time and trained independently by
freezing parameters in the previous layers. In order to constrain the functions
that should be learned by each layer, we employ a sparsity regularization term,
manifold regularization term and a physics-informed term. We derive the
necessary conditions for trainability of a newly added layer and analyze the
role of manifold regularization. In the second stage of the Algorithm, a
sequential learning process is adopted where a sequence of small networks is
employed to extract information from the residual produced in stage I and
thereby making robust and more accurate predictions. Numerical investigations
with fully connected network on prototype regression problem, and
classification problem demonstrate that the proposed approach can outperform
adhoc baseline networks. Further, application to physics-informed neural
network problems suggests that the method could be employed for creating
interpretable hidden layers in a deep network while outperforming equivalent
baseline networks.

Learning invariant representations via contrastive learning has seen
state-of-the-art performance in domain generalization (DG). Despite such
success, in this paper, we find that its core learning strategy -- feature
alignment -- could heavily hinder the model generalization. Inspired by the
recent progress in neuron interpretability, we characterize this problem from a
neuron activation view. Specifically, by treating feature elements as neuron
activation states, we show that conventional alignment methods tend to
deteriorate the diversity of learned invariant features, as they
indiscriminately minimize all neuron activation differences. This instead
ignores rich relations among neurons -- many of them often identify the same
visual concepts though they emerge differently. With this finding, we present a
simple yet effective approach, \textit{Concept Contrast} (CoCo), which relaxes
element-wise feature alignments by contrasting high-level concepts encoded in
neurons. This approach is highly flexible and can be integrated into any
contrastive method in DG. Through extensive experiments, we further demonstrate
that our CoCo promotes the diversity of feature representations, and
consistently improves model generalization capability over the DomainBed
benchmark.

Masked autoencoder has demonstrated its effectiveness in self-supervised
point cloud learning. Considering that masking is a kind of corruption, in this
work we explore a more general denoising autoencoder for point cloud learning
(Point-DAE) by investigating more types of corruptions beyond masking.
Specifically, we degrade the point cloud with certain corruptions as input, and
learn an encoder-decoder model to reconstruct the original point cloud from its
corrupted version. Three corruption families (i.e., density/masking, noise, and
affine transformation) and a total of fourteen corruption types are
investigated. Interestingly, the affine transformation-based Point-DAE
generally outperforms others (e.g., the popular masking corruptions),
suggesting a promising direction for self-supervised point cloud learning. More
importantly, we find a statistically significant linear relationship between
task relatedness and model performance on downstream tasks. This finding partly
demystifies the advantage of affine transformation-based Point-DAE, given that
such Point-DAE variants are closely related to the downstream classification
task. Additionally, we reveal that most Point-DAE variants unintentionally
benefit from the manually-annotated canonical poses in the pre-training
dataset. To tackle such an issue, we promote a new dataset setting by
estimating object poses automatically. The codes will be available at
\url{https://github.com/YBZh/Point-DAE.}

Recently, prompt tuning (PT) has gained increasing attention as a
parameter-efficient way of tuning pre-trained language models (PLMs). Despite
extensively reducing the number of tunable parameters and achieving satisfying
performance, PT is training-inefficient due to its slow convergence. To improve
PT's training efficiency, we first make some novel observations about the
prompt transferability of "partial PLMs", which are defined by compressing a
PLM in depth or width. We observe that the soft prompts learned by different
partial PLMs of various sizes are similar in the parameter space, implying that
these soft prompts could potentially be transferred among partial PLMs.
Inspired by these observations, we propose Fast Prompt Tuning (FPT), which
starts by conducting PT using a small-scale partial PLM, and then progressively
expands its depth and width until the full-model size. After each expansion, we
recycle the previously learned soft prompts as initialization for the enlarged
partial PLM and then proceed PT. We demonstrate the feasibility of FPT on 5
tasks and show that FPT could save over 30% training computations while
achieving comparable performance.

Existing imitation learning works mainly assume that the demonstrator who
collects demonstrations shares the same dynamics as the imitator. However, the
assumption limits the usage of imitation learning, especially when collecting
demonstrations for the imitator is difficult. In this paper, we study
out-of-dynamics imitation learning (OOD-IL), which relaxes the assumption to
that the demonstrator and the imitator have the same state spaces but could
have different action spaces and dynamics. OOD-IL enables imitation learning to
utilize demonstrations from a wide range of demonstrators but introduces a new
challenge: some demonstrations cannot be achieved by the imitator due to the
different dynamics. Prior works try to filter out such demonstrations by
feasibility measurements, but ignore the fact that the demonstrations exhibit a
multimodal distribution since the different demonstrators may take different
policies in different dynamics. We develop a better transferability measurement
to tackle this newly-emerged challenge. We firstly design a novel
sequence-based contrastive clustering algorithm to cluster demonstrations from
the same mode to avoid the mutual interference of demonstrations from different
modes, and then learn the transferability of each demonstration with an
adversarial-learning based algorithm in each cluster. Experiment results on
several MuJoCo environments, a driving environment, and a simulated robot
environment show that the proposed transferability measurement more accurately
finds and down-weights non-transferable demonstrations and outperforms prior
works on the final imitation learning performance. We show the videos of our
experiment results on our website.

Normative modeling is an emerging and promising approach to effectively study
disorder heterogeneity in individual participants. In this study, we propose a
novel normative modeling method by combining conditional variational
autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in
Alzheimer's Disease (AD). Specifically, we first train a conditional VAE on the
healthy control (HC) group to create a normative model conditioned on
covariates like age, gender and intracranial volume. Then we incorporate an
adversarial training process to construct a discriminative feature space that
can better generalize to unseen data. Finally, we compute deviations from the
normal criterion at the patient level to determine which brain regions were
associated with AD. Our experiments on OASIS-3 database show that the deviation
maps generated by our model exhibit higher sensitivity to AD compared to other
deep normative models, and are able to better identify differences between the
AD and HC groups.

This paper tackles the problem of how to pre-train a model and make it
generally reusable backbones for downstream task learning. In pre-training, we
propose a method that builds an agent-environment interaction model by learning
domain invariant successor features from the agent's vast experiences covering
various tasks, then discretize them into behavior prototypes which result in an
embodied set structure. To make the model generally reusable for downstream
task learning, we propose (1) embodied feature projection that retains previous
knowledge by projecting the new task's observation-action pair to the embodied
set structure and (2) projected Bellman updates which add learning plasticity
for the new task setting. We provide preliminary results that show downstream
task learning based on a pre-trained embodied set structure can handle unseen
changes in task objectives, environmental dynamics and sensor modalities.

We develop a Synthetic Fusion Pyramid Network (SPF-Net) with a scale-aware
loss function design for accurate crowd counting. Existing crowd-counting
methods assume that the training annotation points were accurate and thus
ignore the fact that noisy annotations can lead to large model-learning bias
and counting error, especially for counting highly dense crowds that appear far
away. To the best of our knowledge, this work is the first to properly handle
such noise at multiple scales in end-to-end loss design and thus push the crowd
counting state-of-the-art. We model the noise of crowd annotation points as a
Gaussian and derive the crowd probability density map from the input image. We
then approximate the joint distribution of crowd density maps with the full
covariance of multiple scales and derive a low-rank approximation for
tractability and efficient implementation. The derived scale-aware loss
function is used to train the SPF-Net. We show that it outperforms various loss
functions on four public datasets: UCF-QNRF, UCF CC 50, NWPU and ShanghaiTech
A-B datasets. The proposed SPF-Net can accurately predict the locations of
people in the crowd, despite training on noisy training annotations.

Conditional Independence (CI) graphs are a type of probabilistic graphical
models that are primarily used to gain insights about feature relationships.
Each edge represents the partial correlation between the connected features
which gives information about their direct dependence. In this survey, we list
out different methods and study the advances in techniques developed to recover
CI graphs. We cover traditional optimization methods as well as recently
developed deep learning architectures along with their recommended
implementations. To facilitate wider adoption, we include preliminaries that
consolidate associated operations, for example techniques to obtain covariance
matrix for mixed datatypes.

This paper addresses the few-shot image classification problem. One notable
limitation of few-shot learning is the variation in describing the same
category, which might result in a significant difference between small labeled
support and large unlabeled query sets. Our approach is to obtain a relation
heatmap between the two sets in order to label the latter one in a transductive
setting manner. This can be solved by using cross-attention with the scaled
dot-product mechanism. However, the magnitude differences between two separate
sets of embedding vectors may cause a significant impact on the output
attention map and affect model performance. We tackle this problem by improving
the attention mechanism with cosine similarity. Specifically, we develop FS-CT
(Few-shot Cosine Transformer), a few-shot image classification method based on
prototypical embedding and transformer-based framework. The proposed Cosine
attention improves FS-CT performances significantly from nearly 5% to over 20%
in accuracy compared to the baseline scaled dot-product attention in various
scenarios on three few-shot datasets mini-ImageNet, CUB-200, and CIFAR-FS.
Additionally, we enhance the prototypical embedding for categorical
representation with learnable weights before feeding them to the attention
module. Our proposed method FS-CT along with the Cosine attention is simple to
implement and can be applied for a wide range of applications. Our codes are
available at https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer

Histological staining is the gold standard for tissue examination in clinical
pathology and life-science research, which visualizes the tissue and cellular
structures using chromatic dyes or fluorescence labels to aid the microscopic
assessment of tissue. However, the current histological staining workflow
requires tedious sample preparation steps, specialized laboratory
infrastructure, and trained histotechnologists, making it expensive,
time-consuming, and not accessible in resource-limited settings. Deep learning
techniques created new opportunities to revolutionize staining methods by
digitally generating histological stains using trained neural networks,
providing rapid, cost-effective, and accurate alternatives to standard chemical
staining methods. These techniques, broadly referred to as virtual staining,
were extensively explored by multiple research groups and demonstrated to be
successful in generating various types of histological stains from label-free
microscopic images of unstained samples; similar approaches were also used for
transforming images of an already stained tissue sample into another type of
stain, performing virtual stain-to-stain transformations. In this Review, we
provide a comprehensive overview of the recent research advances in deep
learning-enabled virtual histological staining techniques. The basic concepts
and the typical workflow of virtual staining are introduced, followed by a
discussion of representative works and their technical innovations. We also
share our perspectives on the future of this emerging field, aiming to inspire
readers from diverse scientific fields to further expand the scope of deep
learning-enabled virtual histological staining techniques and their
applications.

Unsupervised point cloud completion aims to infer the whole geometry of a
partial object observation without requiring partial-complete correspondence.
Differing from existing deterministic approaches, we advocate generative
modeling based unsupervised point cloud completion to explore the missing
correspondence. Specifically, we propose a novel framework that performs
completion by transforming a partial shape encoding into a complete one using a
latent transport module, and it is designed as a latent-space energy-based
model (EBM) in an encoder-decoder architecture, aiming to learn a probability
distribution conditioned on the partial shape encoding. To train the latent
code transport module and the encoder-decoder network jointly, we introduce a
residual sampling strategy, where the residual captures the domain gap between
partial and complete shape latent spaces. As a generative model-based
framework, our method can produce uncertainty maps consistent with human
perception, leading to explainable unsupervised point cloud completion. We
experimentally show that the proposed method produces high-fidelity completion
results, outperforming state-of-the-art models by a significant margin.

Quantization approximates a deep network model with floating-point numbers by
the one with low bit width numbers, in order to accelerate inference and reduce
computation. Quantizing a model without access to the original data, zero-shot
quantization can be accomplished by fitting the real data distribution by data
synthesis. However, zero-shot quantization achieves inferior performance
compared to the post-training quantization with real data. We find it is
because: 1) a normal generator is hard to obtain high diversity of synthetic
data, since it lacks long-range information to allocate attention to global
features; 2) the synthetic images aim to simulate the statistics of real data,
which leads to weak intra-class heterogeneity and limited feature richness. To
overcome these problems, we propose a novel deep network quantizer, dubbed
Long-Range Zero-Shot Generative Deep Network Quantization (LRQ). Technically,
we propose a long-range generator to learn long-range information instead of
simple local features. In order for the synthetic data to contain more global
features, long-range attention using large kernel convolution is incorporated
into the generator. In addition, we also present an Adversarial Margin Add
(AMA) module to force intra-class angular enlargement between feature vector
and class center. As AMA increases the convergence difficulty of the loss
function, which is opposite to the training objective of the original loss
function, it forms an adversarial process. Furthermore, in order to transfer
knowledge from the full-precision network, we also utilize a decoupled
knowledge distillation. Extensive experiments demonstrate that LRQ obtains
better performance than other competitors.

In this study, with the goal of reducing the early detection miss rate of
colorectal cancer (CRC) polyps, we propose utilizing a novel hyper-sensitive
vision-based tactile sensor called HySenSe and a complementary and novel
machine learning (ML) architecture that explores the potentials of utilizing
dilated convolutions, the beneficial features of the ResNet architecture, and
the transfer learning concept applied on a small dataset with the scale of
hundreds of images. The proposed tactile sensor provides high-resolution 3D
textural images of CRC polyps that will be used for their accurate
classification via the proposed dilated residual network. To collect realistic
surface patterns of CRC polyps for training the ML models and evaluating their
performance, we first designed and additively manufactured 160 unique realistic
polyp phantoms consisting of 4 different hardness. Next, the proposed
architecture was compared with the state-of-the-art ML models (e.g., AlexNet
and DenseNet) and proved to be superior in terms of performance and complexity.

Much of the value that IoT (Internet-of-Things) devices bring to ``smart''
homes lies in their ability to automatically trigger other devices' actions:
for example, a smart camera triggering a smart lock to unlock a door. Manually
setting up these rules for smart devices or applications, however, is
time-consuming and inefficient. Rule recommendation systems can automatically
suggest rules for users by learning which rules are popular based on those
previously deployed (e.g., in others' smart homes). Conventional recommendation
formulations require a central server to record the rules used in many users'
homes, which compromises their privacy and leaves them vulnerable to attacks on
the central server's database of rules. Moreover, these solutions typically
leverage generic user-item matrix methods that do not fully exploit the
structure of the rule recommendation problem. In this paper, we propose a new
rule recommendation system, dubbed as FedRule, to address these challenges. One
graph is constructed per user upon the rules s/he is using, and the rule
recommendation is formulated as a link prediction task in these graphs. This
formulation enables us to design a federated training algorithm that is able to
keep users' data private. Extensive experiments corroborate our claims by
demonstrating that FedRule has comparable performance as the centralized
setting and outperforms conventional solutions.

In this paper, we propose a new method for knowledge base completion (KBC):
instance-based learning (IBL). For example, to answer (Jill Biden, lived city,?
), instead of going directly to Washington D.C., our goal is to find Joe Biden,
who has the same lived city as Jill Biden. Through prototype entities, IBL
provides interpretability. We develop theories for modeling prototypes and
combining IBL with translational models. Experiments on various tasks confirmed
the IBL model's effectiveness and interpretability.
  In addition, IBL shed light on the mechanism of rule-based KBC models.
Previous research has generally agreed that rule-based models provide rules
with semantically compatible premises and hypotheses. We challenge this view.
We begin by demonstrating that some logical rules represent {\it instance-based
equivalence} (i.e. prototypes) rather than semantic compatibility. These are
denoted as {\it IBL rules}. Surprisingly, despite occupying only a small
portion of the rule space, IBL rules outperform non-IBL rules in all four
benchmarks. We use a variety of experiments to demonstrate that rule-based
models work because they have the ability to represent instance-based
equivalence via IBL rules. The findings provide new insights of how rule-based
models work and how to interpret their rules.

Data valuation, or the valuation of individual datum contributions, has seen
growing interest in machine learning due to its demonstrable efficacy for tasks
such as noisy label detection. In particular, due to the desirable axiomatic
properties, several Shapley value approximation methods have been proposed. In
these methods, the value function is typically defined as the predictive
accuracy over the entire development set. However, this limits the ability to
differentiate between training instances that are helpful or harmful to their
own classes. Intuitively, instances that harm their own classes may be noisy or
mislabeled and should receive a lower valuation than helpful instances. In this
work, we propose CS-Shapley, a Shapley value with a new value function that
discriminates between training instances' in-class and out-of-class
contributions. Our theoretical analysis shows the proposed value function is
(essentially) the unique function that satisfies two desirable properties for
evaluating data values in classification. Further, our experiments on two
benchmark evaluation tasks (data removal and noisy label detection) and four
classifiers demonstrate the effectiveness of CS-Shapley over existing methods.
Lastly, we evaluate the "transferability" of data values estimated from one
classifier to others, and our results suggest Shapley-based data valuation is
transferable for application across different models.

Tons of images and videos are fed into machines for visual recognition all
the time. Like human vision system (HVS), machine vision system (MVS) is
sensitive to image quality, as quality degradation leads to information loss
and recognition failure. In recent years, MVS-targeted image processing,
particularly image and video compression, has emerged. However, existing
methods only target an individual machine rather than the general machine
community, thus cannot satisfy every type of machine. Moreover, the MVS
characteristics are not well leveraged, which limits compression efficiency. In
this paper, we introduce a new concept, Satisfied Machine Ratio (SMR), to
address these issues. SMR statistically measures the image quality from the
machine's perspective by collecting and combining satisfaction scores from a
large quantity and variety of machine subjects, where such scores are obtained
with MVS characteristics considered properly. We create the first large-scale
SMR dataset that contains over 22 million annotated images for SMR studies.
Furthermore, a deep learning-based model is proposed to predict the SMR for any
given compressed image or video frame. Extensive experiments show that using
the SMR model can significantly improve the performance of machine
recognition-oriented image and video compression. And the SMR model generalizes
well to unseen machines, compression frameworks, and datasets.

As the popularity of graph data increases, there is a growing need to count
the occurrences of subgraph patterns of interest, for a variety of
applications. Many graphs are massive in scale and also fully dynamic (with
insertions and deletions of edges), rendering exact computation of these counts
to be infeasible. Common practice is, instead, to use a small set of edges as a
sample to estimate the counts. Existing sampling algorithms for fully dynamic
graphs sample the edges with uniform probability. In this paper, we show that
we can do much better if we sample edges based on their individual properties.
Specifically, we propose a weighted sampling algorithm called WSD for
estimating the subgraph count in a fully dynamic graph stream, which samples
the edges based on their weights that indicate their importance and reflect
their properties. We determine the weights of edges in a data-driven fashion,
using a novel method based on reinforcement learning. We conduct extensive
experiments to verify that our technique can produce estimates with smaller
errors while often running faster compared with existing algorithms.

The first step in drug discovery is finding drug molecule moieties with
medicinal activity against specific targets. Therefore, it is crucial to
investigate the interaction between drug-target proteins and small chemical
molecules. However, traditional experimental methods for discovering potential
small drug molecules are labor-intensive and time-consuming. There is currently
a lot of interest in building computational models to screen small drug
molecules using drug molecule-related databases. In this paper, we propose a
method for predicting drug-target binding affinity using deep learning models.
This method uses a modified GRU and GNN to extract features from the
drug-target protein sequences and the drug molecule map, respectively, to
obtain their feature vectors. The combined vectors are used as vector
representations of drug-target molecule pairs and then fed into a fully
connected network to predict drug-target binding affinity. This proposed model
demonstrates its accuracy and effectiveness in predicting drug-target binding
affinity on the DAVIS and KIBA datasets.

Data augmentation has been widely used to improve generalization in training
deep neural networks. Recent works show that using worst-case transformations
or adversarial augmentation strategies can significantly improve the accuracy
and robustness. However, due to the non-differentiable properties of image
transformations, searching algorithms such as reinforcement learning or
evolution strategy have to be applied, which are not computationally practical
for large scale problems. In this work, we show that by simply applying
consistency training with random data augmentation, state-of-the-art results on
domain adaptation (DA) and generalization (DG) can be obtained. To further
improve the accuracy and robustness with adversarial examples, we propose a
differentiable adversarial data augmentation method based on spatial
transformer networks (STN). The combined adversarial and random transformations
based method outperforms the state-of-the-art on multiple DA and DG benchmark
datasets. Besides, the proposed method shows desirable robustness to
corruption, which is also validated on commonly used datasets.

Highly accurate simulations of complex phenomena governed by partial
differential equations (PDEs) typically require intrusive methods and entail
expensive computational costs, which might become prohibitive when
approximating steady-state solutions of PDEs for multiple combinations of
control parameters and initial conditions. Therefore, constructing efficient
reduced order models (ROMs) that enable accurate but fast predictions, while
retaining the dynamical characteristics of the physical phenomenon as
parameters vary, is of paramount importance. In this work, a data-driven,
non-intrusive framework which combines ROM construction with reduced dynamics
identification, is presented. Starting from a limited amount of full order
solutions, the proposed approach leverages autoencoder neural networks with
parametric sparse identification of nonlinear dynamics (SINDy) to construct a
low-dimensional dynamical model which can be queried to efficiently compute
full-time solutions at new parameter instances, as well as directly fed to
continuation algorithms. These latter aim at tracking the evolution of periodic
steady-state responses as functions of system parameters, avoiding the
computation of the transient phase, and allowing to detect instabilities and
bifurcations. Featuring an explicit and parametrized modeling of the reduced
dynamics, the proposed data-driven framework presents remarkable capabilities
to generalize both with respect to time and parameters. Applications to
structural mechanics and fluid dynamics problems illustrate the effectiveness
and accuracy of the method.

Machine Learning has become the bedrock of recent advances in text, image,
video, and audio processing and generation. Most production systems deal with
several models during deployment and training, each with a variety of tuned
hyperparameters. Furthermore, data collection and processing aspects of ML
pipelines are receiving increasing interest due to their importance in creating
sustainable high-quality classifiers. We present EdnaML, a framework with a
declarative API for reproducible deep learning. EdnaML provides low-level
building blocks that can be composed manually, as well as a high-level pipeline
orchestration API to automate data collection, data processing, classifier
training, classifier deployment, and model monitoring. Our layered API allows
users to manage ML pipelines at high-level component abstractions, while
providing flexibility to modify any part of it through the building blocks. We
present several examples of ML pipelines with EdnaML, including a large-scale
fake news labeling and classification system with six sub-pipelines managed by
EdnaML.

A successful machine learning (ML) algorithm often relies on a large amount
of high-quality data to train well-performed models. Supervised learning
approaches, such as deep learning techniques, generate high-quality ML
functions for real-life applications, however with large costs and human
efforts to label training data. Recent advancements in federated learning (FL)
allow multiple data owners or organisations to collaboratively train a machine
learning model without sharing raw data. In this light, vertical FL allows
organisations to build a global model when the participating organisations have
vertically partitioned data. Further, in the vertical FL setting the
participating organisation generally requires fewer resources compared to
sharing data directly, enabling lightweight and scalable distributed training
solutions. However, privacy protection in vertical FL is challenging due to the
communication of intermediate outputs and the gradients of model update. This
invites adversary entities to infer other organisations underlying data. Thus,
in this paper, we aim to explore how to protect the privacy of individual
organisation data in a differential privacy (DP) setting. We run experiments
with different real-world datasets and DP budgets. Our experimental results
show that a trade-off point needs to be found to achieve a balance between the
vertical FL performance and privacy protection in terms of the amount of
perturbation noise.

Subspace clustering algorithms are used for understanding the cluster
structure that explains the dataset well. These methods are extensively used
for data-exploration tasks in various areas of Natural Sciences. However, most
of these methods fail to handle unwanted biases in datasets. For datasets where
a data sample represents multiple attributes, naively applying any clustering
approach can result in undesired output. To this end, we propose a novel
framework for jointly removing unwanted attributes (biases) while learning to
cluster data points in individual subspaces. Assuming we have information about
the bias, we regularize the clustering method by adversarially learning to
minimize the mutual information between the data and the unwanted attributes.
Our experimental result on synthetic and real-world datasets demonstrate the
effectiveness of our approach.

Deep learning models have demonstrated superior performance in various
healthcare applications. However, the major limitation of these deep models is
usually the lack of high-quality training data due to the private and sensitive
nature of this field. In this study, we propose a novel textual data
augmentation method to generate artificial clinical notes in patients'
Electronic Health Records (EHRs) that can be used as additional training data
for patient outcomes prediction. Essentially, we fine-tune the generative
language model GPT-2 to synthesize labeled text with the original training
data. More specifically, We propose a teacher-student framework where we first
pre-train a teacher model on the original data, and then train a student model
on the GPT-augmented data under the guidance of the teacher. We evaluate our
method on the most common patient outcome, i.e., the 30-day readmission rate.
The experimental results show that deep models can improve their predictive
performance with the augmented data, indicating the effectiveness of the
proposed architecture.

Barium stars are chemically peculiar stars that exhibit enhancement of
s-process elements. Chemical abundance analysis of barium stars can provide
crucial clues for the study of the chemical evolution of the Galaxy. The Large
Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has released more
than 6 million low-resolution spectra of FGK-type stars by Data Release 9
(DR9), which can significantly increase the sample size of barium stars. In
this paper, we used machine learning algorithms to search for barium stars from
low-resolution spectra of LAMOST. We have applied the Light Gradient Boosting
Machine (LGBM) algorithm to build classifiers of barium stars based on
different features, and build predictors for determining [Ba/Fe] and [Sr/Fe] of
barium candidates. The classification with features in the whole spectrum
performs best: for the sample with strontium enhancement, Precision = 97.81%,
and Recall = 96.05%; for the sample with barium enhancement, Precision = 96.03%
and Recall = 97.70%. In prediction, [Ba/Fe] estimated from BaII line at 4554
\r{A} has smaller dispersion than that from BaII line at 4934 \r{A}: MAE$_{4554
\r{A}}$ = 0.07, $\sigma_{4554 \r{A}}$ = 0.12. [Sr/Fe] estimated from SrII line
at 4077 \r{A} performs better than that from SrII line at 4215 \r{A}:
MAE$_{4077 \r{A}}$ = 0.09, $\sigma_{4077 \r{A}}$ = 0.16. A comparison of the
LGBM and other popular algorithms shows that LGBM is accurate and efficient in
classifying barium stars. This work demonstrated that machine learning can be
used as an effective means to identify chemically peculiar stars and determine
their elemental abundance.

This paper uses a simple state machine to develop a control algorithm for
controlling an infant humanoid in the context of a simple model system. The
algorithm is inspired by a baby who starts learning to stand and walk at 7 to
12 months of age: he or she initially learns to crawl and then, once the lower
limb muscles are strong enough, can learn to walk by coming to support his or
her upper trunk. Ideally, this algorithm-supported locomotion can take the baby
to any desired location: a pile of toys, a tasty snack, or the baby's parents
or relatives. In this paper we analyze the crawling stage, the simple 2d
bipedal model, and the initial walking form from 8 to 18 months of age, and
quantitatively evaluate the ideal kinematics model and simulation results for
these stages.

Rare genetic disorders affect more than 6% of the global population. Reaching
a diagnosis is challenging because rare disorders are very diverse. Many
disorders have recognizable facial features that are hints for clinicians to
diagnose patients. Previous work, such as GestaltMatcher, utilized
representation vectors produced by a DCNN similar to AlexNet to match patients
in high-dimensional feature space to support "unseen" ultra-rare disorders.
However, the architecture and dataset used for transfer learning in
GestaltMatcher have become outdated. Moreover, a way to train the model for
generating better representation vectors for unseen ultra-rare disorders has
not yet been studied. Because of the overall scarcity of patients with
ultra-rare disorders, it is infeasible to directly train a model on them.
Therefore, we first analyzed the influence of replacing GestaltMatcher DCNN
with a state-of-the-art face recognition approach, iResNet with ArcFace.
Additionally, we experimented with different face recognition datasets for
transfer learning. Furthermore, we proposed test-time augmentation, and model
ensembles that mix general face verification models and models specific for
verifying disorders to improve the disorder verification accuracy of unseen
ultra-rare disorders. Our proposed ensemble model achieves state-of-the-art
performance on both seen and unseen disorders.

In machine learning applications, it is common practice to feed as much
information as possible. In most cases, the model can handle large data sets
that allow to predict more accurately. In the presence of data scarcity, a
Few-Shot learning (FSL) approach aims to build more accurate algorithms with
limited training data. We propose a novel end-to-end lightweight architecture
that verifies biometric data by producing competitive results as compared to
state-of-the-art accuracies through Few-Shot learning methods. The dense layers
add to the complexity of state-of-the-art deep learning models which inhibits
them to be used in low-power applications. In presented approach, a shallow
network is coupled with a conventional machine learning technique that exploits
hand-crafted features to verify biometric images from multi-modal sources such
as signatures, periocular region, iris, face, fingerprints etc. We introduce a
self-estimated threshold that strictly monitors False Acceptance Rate (FAR)
while generalizing its results hence eliminating user-defined thresholds from
ROC curves that are likely to be biased on local data distribution. This hybrid
model benefits from few-shot learning to make up for scarcity of data in
biometric use-cases. We have conducted extensive experimentation with commonly
used biometric datasets. The obtained results provided an effective solution
for biometric verification systems.

In molecular and biological sciences, experiments are expensive,
time-consuming, and often subject to ethical constraints. Consequently, one
often faces the challenging task of predicting desirable properties from small
data sets or scarcely-labeled data sets. Although transfer learning can be
advantageous, it requires the existence of a related large data set. This work
introduces three graph-based models incorporating Merriman-Bence-Osher (MBO)
techniques to tackle this challenge. Specifically, graph-based modifications of
the MBO scheme is integrated with state-of-the-art techniques, including a
home-made transformer and an autoencoder, in order to deal with
scarcely-labeled data sets. In addition, a consensus technique is detailed. The
proposed models are validated using five benchmark data sets. We also provide a
thorough comparison to other competing methods, such as support vector
machines, random forests, and gradient boosted decision trees, which are known
for their good performance on small data sets. The performances of various
methods are analyzed using residue-similarity (R-S) scores and R-S indices.
Extensive computational experiments and theoretical analysis show that the new
models perform very well even when as little as 1% of the data set is used as
labeled data.

In this work, we utilize the high-fidelity generation abilities of diffusion
models to solve blind image restoration tasks, using JPEG artifact removal at
high compression levels as an example. We propose an elegant modification of
the forward stochastic differential equation of diffusion models to adapt them
to restoration tasks and name our method DriftRec. Comparing DriftRec against
an $L_2$ regression baseline with the same network architecture and a
state-of-the-art technique for JPEG reconstruction, we show that our approach
can escape both baselines' tendency to generate blurry images, and recovers the
distribution of clean images significantly more faithfully while only requiring
a dataset of clean/corrupted image pairs and no knowledge about the corruption
operation. By utilizing the idea that the distributions of clean and corrupted
images are much closer to each other than to a Gaussian prior, our approach
requires only low levels of added noise, and thus needs comparatively few
sampling steps even without further optimizations.

With only 65% of Indian houses having access to the Internet, digital India
faces a significant Internet divide across gender and city types. Rendering
essential services inaccessible to almost a third of the population
necessitates not only provisioning a fundamental right to Internet access but
taking specific constructive steps to assure its simple, affordable and safe
accessibility. Establishing such a right would also pave way for other
ancillary rights required for data privacy, protection from Internet's possible
harms and the requirement to be treated fairly. We first discuss two arguments
on the universal right to Internet access; from Merten Reglitz, a senior
lecturer on Global Ethics and from Vincent Cerf, one of the founding creators
of the Internet who has had a profound influence on the field. We specifically
argue why Internet access should be treated as a fundamental right. We discuss
the learnings from India, contextualizing them with the global debate and argue
for establishing Internet access as a fundamental right in India and elsewhere
in the form of government legislation to eliminate Internet divide.

Histopathological analysis is crucial in artery characterization for coronary
artery disease (CAD). However, histology requires an invasive and
time-consuming process. In this paper, we propose to generate virtual histology
staining using Optical Coherence Tomography (OCT) images to enable real-time
histological visualization. We develop a deep learning network, namely
Coronary-GAN, to transfer coronary OCT images to virtual histology images. With
a special consideration on the structural constraints in coronary OCT images,
our method achieves better image generation performance than the conventional
GAN-based method. The experimental results indicate that Coronary-GAN generates
virtual histology images that are similar to real histology images, revealing
the human coronary layers.

Human decision-making often involves combining similar states into categories
and reasoning at the level of the categories rather than the actual states.
Guided by this intuition, we propose a novel method for clustering state
features in deep reinforcement learning (RL) methods to improve their
interpretability. Specifically, we propose a plug-and-play framework termed
\emph{vector quantized reinforcement learning} (VQ-RL) that extends classic RL
pipelines with an auxiliary classification task based on vector quantized (VQ)
encoding and aligns with policy training. The VQ encoding method categorizes
features with similar semantics into clusters and results in tighter clusters
with better separation compared to classic deep RL methods, thus enabling
neural models to learn similarities and differences between states better.
Furthermore, we introduce two regularization methods to help increase the
separation between clusters and avoid the risks associated with VQ training. In
simulations, we demonstrate that VQ-RL improves interpretability and
investigate its impact on robustness and generalization of deep RL.

Optical coherence tomography (OCT) has become increasingly essential in
assisting the treatment of coronary artery disease (CAD). Image-guided
solutions such as Percutaneous Coronary Intervention (PCI) are extensively used
during the treatment of CAD. However, unidentified calcified regions within a
narrowed artery could impair the outcome of the PCI. Prior to treatments,
object detection is paramount to automatically procure accurate readings on the
location and thickness of calcifications within the artery. Deep learning-based
object detection methods have been explored in a variety of applications. The
quality of object detection predictions could lead to uncertain results, which
are not desirable in safety-critical scenarios. In this work, we implement an
object detection model, You-Only-Look-Once v5 (YOLO), on a calcification
detection framework within coronary OCT images. We evaluate the uncertainty of
predictions based on the expected calibration errors, thus assessing the
certainty level of detection results. To calibrate confidence scores of
predictions, we implement dependent logistic calibration using each detection
result's confidence and center coordinates. With the calibrated confidence
score of each prediction, we lower the uncertainty of predictions in
calcification detection. Our results show that the YOLO achieves higher
precision and recall in comparison with the other object detection model,
meanwhile producing more reliable results. The calibrated confidence of
prediction results in a confidence error of approximately 0.13, suggesting that
the confidence calibration on calcification detection could provide a more
trustworthy result, indicating a great potential to assist clinical evaluation
of treating the CAD during the imaging-guided procedure.

Vision Transformer (ViT) is a pioneering deep learning framework that can
address real-world computer vision issues, such as image classification and
object recognition. Importantly, ViTs are proven to outperform traditional deep
learning models, such as convolutional neural networks (CNNs). Relatively
recently, a number of ViT mutations have been transplanted into the field of
medical imaging, thereby resolving a variety of critical classification and
segmentation challenges, especially in terms of brain imaging data. In this
work, we provide a novel multimodal deep learning pipeline, MultiCrossViT,
which is capable of analyzing both structural MRI (sMRI) and static functional
network connectivity (sFNC) data for the prediction of schizophrenia disease.
On a dataset with minimal training subjects, our novel model can achieve an AUC
of 0.832. Finally, we visualize multiple brain regions and covariance patterns
most relevant to schizophrenia based on the resulting ViT attention maps by
extracting features from transformer encoders.

When performing complex tasks, humans naturally reason at multiple temporal
and spatial resolutions simultaneously. We contend that for an artificially
intelligent agent to effectively model human teammates, i.e., demonstrate
computational theory of mind (ToM), it should do the same. In this paper, we
present an approach for integrating high and low-resolution spatial and
temporal information to predict human behavior in real time and evaluate it on
data collected from human subjects performing simulated urban search and rescue
(USAR) missions in a Minecraft-based environment. Our model composes neural
networks for high and low-resolution feature extraction with a neural network
for behavior prediction, with all three networks trained simultaneously. The
high-resolution extractor encodes dynamically changing goals robustly by taking
as input the Manhattan distance difference between the humans' Minecraft
avatars and candidate goals in the environment for the latest few actions,
computed from a high-resolution gridworld representation. In contrast, the
low-resolution extractor encodes participants' historical behavior using a
historical state matrix computed from a low-resolution graph representation.
Through supervised learning, our model acquires a robust prior for human
behavior prediction, and can effectively deal with long-term observations. Our
experimental results demonstrate that our method significantly improves
prediction accuracy compared to approaches that only use high-resolution
information.

Purpose: An Electrocardiogram (ECG) is the simplest and fastest bio-medical
test that is used to detect any heart-related disease. ECG signals are
generally stored in paper form, which makes it difficult to store and analyze
the data. While capturing ECG leads from paper ECG records, a lot of background
information is also captured, which results in incorrect data interpretation.
  Methods: We propose a deep learning-based model for individually extracting
all 12 leads from 12-lead ECG images captured using a camera. To simplify the
analysis of the ECG and the calculation of complex parameters, we also propose
a method to convert the paper ECG format into a storable digital format. The
You Only Look Once, Version 3 (YOLOv3) algorithm has been used to extract the
leads present in the image. These leads are then passed on to another deep
learning model which separates the ECG signal and background from the
single-lead image. After that, vertical scanning is performed on the ECG signal
to convert it into a 1-Dimensional (1D) digital form. To perform the task of
digitalization, we used the pix-2-pix deep learning model and binarized the ECG
signals.
  Results: Our proposed method was able to achieve an accuracy of 97.4 %.
  Conclusion: The information on the paper ECG fades away over time. Hence, the
digitized ECG signals make it possible to store the records and access them
anytime. This proves highly beneficial for heart patients who require frequent
ECG reports. The stored data can also be useful for research purposes, as this
data can be used to develop computer algorithms that are capable of analyzing
the data.

We present a novel bipartite graph reasoning Generative Adversarial Network
(BiGraphGAN) for two challenging tasks: person pose and facial image synthesis.
The proposed graph generator consists of two novel blocks that aim to model the
pose-to-pose and pose-to-image relations, respectively. Specifically, the
proposed bipartite graph reasoning (BGR) block aims to reason the long-range
cross relations between the source and target pose in a bipartite graph, which
mitigates some of the challenges caused by pose deformation. Moreover, we
propose a new interaction-and-aggregation (IA) block to effectively update and
enhance the feature representation capability of both a person's shape and
appearance in an interactive way. To further capture the change in pose of each
part more precisely, we propose a novel part-aware bipartite graph reasoning
(PBGR) block to decompose the task of reasoning the global structure
transformation with a bipartite graph into learning different local
transformations for different semantic body/face parts. Experiments on two
challenging generation tasks with three public datasets demonstrate the
effectiveness of the proposed methods in terms of objective quantitative scores
and subjective visual realness. The source code and trained models are
available at https://github.com/Ha0Tang/BiGraphGAN.

Business intelligence (BI) is any knowledge derived from existing data that
may be strategically applied within a business. Data mining is a technique or
method for extracting BI from data using statistical data modeling. Finding
relationships or correlations between the various data items that have been
collected can be used to boost business performance or at the very least better
comprehend what is going on. Root cause analysis (RCA) is discovering the root
causes of problems or events to identify appropriate solutions. RCA can show
why an event occurred and this can help in avoiding occurrences of an issue in
the future. This paper proposes a new clustering + association rule mining
pipeline for getting business insights from data. The results of this pipeline
are in the form of association rules having consequents, antecedents, and
various metrics to evaluate these rules. The results of this pipeline can help
in anchoring important business decisions and can also be used by data
scientists for updating existing models or while developing new ones. The
occurrence of any event is explained by its antecedents in the generated rules.
Hence this output can also help in data-driven root cause analysis.

Mobile cloud offloading is indispensable for inference tasks based on
large-scale deep models. However, transmitting privacy-rich inference data to
the cloud incurs concerns. This paper presents the design of a system called
PriMask, in which the mobile device uses a secret small-scale neural network
called MaskNet to mask the data before transmission. PriMask significantly
weakens the cloud's capability to recover the data or extract certain private
attributes. The MaskNet is em cascadable in that the mobile can opt in to or
out of its use seamlessly without any modifications to the cloud's inference
service. Moreover, the mobiles use different MaskNets, such that the collusion
between the cloud and some mobiles does not weaken the protection for other
mobiles. We devise a {\em split adversarial learning} method to train a neural
network that generates a new MaskNet quickly (within two seconds) at run time.
We apply PriMask to three mobile sensing applications with diverse modalities
and complexities, i.e., human activity recognition, urban environment
crowdsensing, and driver behavior recognition. Results show PriMask's
effectiveness in all three applications.

Predictive models for marine ecosystems are used for a variety of needs. Due
to sparse measurements and limited understanding of the myriad of ocean
processes, there is however uncertainty. There is model uncertainty in the
parameter values, functional forms with diverse parameterizations, level of
complexity needed, and thus in the state fields. We develop a principled
Bayesian model learning methodology that allows interpolation in the space of
candidate models and discovery of new models, all while estimating state fields
and parameter values, as well as the joint probability distributions of all
learned quantities. We address the challenges of high-dimensional and
multidisciplinary dynamics governed by partial differential equations (PDEs) by
using state augmentation and the computationally efficient Gaussian Mixture
Model - Dynamically Orthogonal filter. Our innovations include special
stochastic parameters to unify candidate models into a single general model and
stochastic piecewise function approximations to generate dense candidate model
spaces. They allow handling many candidate models, possibly none of which are
accurate, and learning elusive unknown functional forms in compatible and
embedded models. Our new methodology is generalizable and interpretable and
extrapolates out of the space of models to discover new ones. We perform a
series of twin experiments based on flows past a seamount coupled with
three-to-five component ecosystem models, including flows with chaotic
advection. We quantify learning skills, and evaluate convergence and
sensitivity to hyper-parameters. Our PDE framework successfully discriminates
among model candidates, learns in the absence of prior knowledge by searching
in dense function spaces, and updates joint probabilities while capturing
non-Gaussian statistics. The parameter values and model formulations that best
explain the data are identified.

3D Garment modeling is a critical and challenging topic in the area of
computer vision and graphics, with increasing attention focused on garment
representation learning, garment reconstruction, and controllable garment
manipulation, whereas existing methods were constrained to model garments under
specific categories or with relatively simple topologies. In this paper, we
propose a novel Neural Sewing Machine (NSM), a learning-based framework for
structure-preserving 3D garment modeling, which is capable of learning
representations for garments with diverse shapes and topologies and is
successfully applied to 3D garment reconstruction and controllable
manipulation. To model generic garments, we first obtain sewing pattern
embedding via a unified sewing pattern encoding module, as the sewing pattern
can accurately describe the intrinsic structure and the topology of the 3D
garment. Then we use a 3D garment decoder to decode the sewing pattern
embedding into a 3D garment using the UV-position maps with masks. To preserve
the intrinsic structure of the predicted 3D garment, we introduce an
inner-panel structure-preserving loss, an inter-panel structure-preserving
loss, and a surface-normal loss in the learning process of our framework. We
evaluate NSM on the public 3D garment dataset with sewing patterns with diverse
garment shapes and categories. Extensive experiments demonstrate that the
proposed NSM is capable of representing 3D garments under diverse garment
shapes and topologies, realistically reconstructing 3D garments from 2D images
with the preserved structure, and accurately manipulating the 3D garment
categories, shapes, and topologies, outperforming the state-of-the-art methods
by a clear margin.

Pavlovian reflex is an essential mechanism of nervous systems of living
beings which allows them to learn. Liquid colloid computing devices offer a
high degree of fault-tolerance, reconfigurability and plasticity. As a first
step towards designing and prototyping colloidal neuromorphic computing systems
we decided to evaluate if it is possible to implement Pavlovian reflexes. We
equate an increase of a synaptic weight with decreased resistance. In
laboratory experiments we demonstrated that it is possible to implement
Pavlovian learning using just two volumes of colloid liquid.

Pain monitoring is essential to the quality of care for patients undergoing a
medical procedure with sedation. An automated mechanism for detecting pain
could improve sedation dose titration. Previous studies on facial pain
detection have shown the viability of computer vision methods in detecting pain
in unoccluded faces. However, the faces of patients undergoing procedures are
often partially occluded by medical devices and face masks. A previous
preliminary study on pain detection on artificially occluded faces has shown a
feasible approach to detect pain from a narrow band around the eyes. This study
has collected video data from masked faces of 14 patients undergoing procedures
in an interventional radiology department and has trained a deep learning model
using this dataset. The model was able to detect expressions of pain accurately
and, after causal temporal smoothing, achieved an average precision (AP) of
0.72 and an area under the receiver operating characteristic curve (AUC) of
0.82. These results outperform baseline models and show viability of computer
vision approaches for pain detection of masked faces during procedural
sedation. Cross-dataset performance is also examined when a model is trained on
a publicly available dataset and tested on the sedation videos. The ways in
which pain expressions differ in the two datasets are qualitatively examined.

Although temporal coding through spike-time patterns has long been of
interest in neuroscience, the specific structures that could be useful for
spike-time codes remain highly unclear. Here, we introduce a new analytical
approach, using techniques from discrete mathematics, to study spike-time
codes. We focus on the phenomenon of ``phase precession'' in the rodent
hippocampus. During navigation and learning on a physical track, specific cells
in a rodent's brain form a highly structured pattern relative to the
oscillation of local population activity. Studies of phase precession largely
focus on its well established role in synaptic plasticity and memory formation.
Comparatively less attention has been paid to the fact that phase precession
represents one of the best candidates for a spike-time neural code. The precise
nature of this code remains an open question. Here, we derive an analytical
expression for an operator mapping points in physical space, through individual
spike times, to complex numbers. The properties of this operator highlight a
specific relationship between past and future in hippocampal spike patterns.
Importantly, this approach generalizes beyond the specific phenomenon studied
here, providing a new technique to study the neural codes within spike-time
sequences found during sensory coding and motor behavior. We then introduce a
novel spike-based decoding algorithm, based on this operator, that successfully
decodes a simulated animal's trajectory using only the animal's initial
position and a pattern of spike times. This decoder is robust to noise in spike
times and works on a timescale almost an order of magnitude shorter than
typically used with decoders that work on average firing rate. These results
illustrate the utility of a discrete approach, based on the symmetries in spike
patterns, to provide insight into the structure and function of neural systems.

Implicit neural representation (INR) can describe the target scenes with high
fidelity using a small number of parameters, and is emerging as a promising
data compression technique. However, INR in intrinsically of limited spectrum
coverage, and it is non-trivial to remove redundancy in diverse complex data
effectively. Preliminary studies can only exploit either global or local
correlation in the target data and thus of limited performance. In this paper,
we propose a Tree-structured Implicit Neural Compression (TINC) to conduct
compact representation for local regions and extract the shared features of
these local representations in a hierarchical manner. Specifically, we use MLPs
to fit the partitioned local regions, and these MLPs are organized in tree
structure to share parameters according to the spatial distance. The parameter
sharing scheme not only ensures the continuity between adjacent regions, but
also jointly removes the local and non-local redundancy. Extensive experiments
show that TINC improves the compression fidelity of INR, and has shown
impressive compression capabilities over commercial tools and other deep
learning based methods. Besides, the approach is of high flexibility and can be
tailored for different data and parameter settings. All the reproducible codes
are going to be released on github.

In this study, we propose a technology called the Fashion Intelligence System
based on the visual-semantic embedding (VSE) model to quantify abstract and
complex expressions unique to fashion, such as ''casual,'' ''adult-casual,''
and ''office-casual,'' and to support users' understanding of fashion. However,
the existing VSE model does not support the situations in which the image is
composed of multiple parts such as hair, tops, pants, skirts, and shoes. We
propose partial VSE, which enables sensitive learning for each part of the
fashion coordinates. The proposed model partially learns embedded
representations. This helps retain the various existing practical
functionalities and enables image-retrieval tasks in which changes are made
only to the specified parts and image reordering tasks that focus on the
specified parts. This was not possible with conventional models. Based on both
the qualitative and quantitative evaluation experiments, we show that the
proposed model is superior to conventional models without increasing the
computational complexity.

Contrastive learning has shown remarkable success in the field of multimodal
representation learning. In this paper, we propose a pipeline of contrastive
language-audio pretraining to develop an audio representation by combining
audio data with natural language descriptions. To accomplish this target, we
first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs
from different data sources. Second, we construct a contrastive language-audio
pretraining model by considering different audio encoders and text encoders. We
incorporate the feature fusion mechanism and keyword-to-caption augmentation
into the model design to further enable the model to process audio inputs of
variable lengths and enhance the performance. Third, we perform comprehensive
experiments to evaluate our model across three tasks: text-to-audio retrieval,
zero-shot audio classification, and supervised audio classification. The
results demonstrate that our model achieves superior performance in
text-to-audio retrieval task. In audio classification tasks, the model achieves
state-of-the-art performance in the zero-shot setting and is able to obtain
performance comparable to models' results in the non-zero-shot setting.
LAION-Audio-630K and the proposed model are both available to the public.

Detecting anomalous trajectories has become an important task in many
location-based applications. While many approaches have been proposed for this
task, they suffer from various issues including (1) incapability of detecting
anomalous subtrajectories, which are finer-grained anomalies in trajectory
data, and/or (2) non-data driven, and/or (3) requirement of sufficient
supervision labels which are costly to collect. In this paper, we propose a
novel reinforcement learning based solution called RL4OASD, which avoids all
aforementioned issues of existing approaches. RL4OASD involves two networks,
one responsible for learning features of road networks and trajectories and the
other responsible for detecting anomalous subtrajectories based on the learned
features, and the two networks can be trained iteratively without labeled data.
Extensive experiments are conducted on two real datasets, and the results show
that our solution can significantly outperform the state-of-the-art methods
(with 20-30% improvement) and is efficient for online detection (it takes less
than 0.1ms to process each newly generated data point).

Post-click conversion rate (CVR) prediction is an essential task for
discovering user interests and increasing platform revenues in a range of
industrial applications. One of the most challenging problems of this task is
the existence of severe selection bias caused by the inherent self-selection
behavior of users and the item selection process of systems. Currently, doubly
robust (DR) learning approaches achieve the state-of-the-art performance for
debiasing CVR prediction. However, in this paper, by theoretically analyzing
the bias, variance and generalization bounds of DR methods, we find that
existing DR approaches may have poor generalization caused by inaccurate
estimation of propensity scores and imputation errors, which often occur in
practice. Motivated by such analysis, we propose a generalized learning
framework that not only unifies existing DR methods, but also provides a
valuable opportunity to develop a series of new debiasing techniques to
accommodate different application scenarios. Based on the framework, we propose
two new DR methods, namely DR-BIAS and DR-MSE. DR-BIAS directly controls the
bias of DR loss, while DR-MSE balances the bias and variance flexibly, which
achieves better generalization performance. In addition, we propose a novel
tri-level joint learning optimization method for DR-MSE in CVR prediction, and
an efficient training algorithm correspondingly. We conduct extensive
experiments on both real-world and semi-synthetic datasets, which validate the
effectiveness of our proposed methods.

In this paper, a novel paradigm of mobile edge-quantum computing (MEQC) is
proposed, which brings quantum computing capacities to mobile edge networks
that are closer to mobile users (i.e., edge devices). First, we propose an MEQC
system model where mobile users can offload computational tasks to scalable
quantum computers via edge servers with cryogenic components and fault-tolerant
schemes. Second, we show that it is NP-hard to obtain a centralized solution to
the partial offloading problem in MEQC in terms of the optimal latency and
energy cost of classical and quantum computing. Third, we propose a multi-agent
hybrid discrete-continuous deep reinforcement learning using proximal policy
optimization to learn the long-term sustainable offloading strategy without
prior knowledge. Finally, experimental results demonstrate that the proposed
algorithm can reduce at least 30% of the cost compared with the existing
baseline solutions under different system settings.

In this work, we present a conceptually simple and effective method to train
a strong bilingual multimodal representation model. Starting from the
pretrained multimodal representation model CLIP released by OpenAI, we switched
its text encoder with a pretrained multilingual text encoder XLM-R, and aligned
both languages and image representations by a two-stage training schema
consisting of teacher learning and contrastive learning. We validate our method
through evaluations of a wide range of tasks. We set new state-of-the-art
performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and
COCO-CN. Further, we obtain very close performances with CLIP on almost all
tasks, suggesting that one can simply alter the text encoder in CLIP for
extended capabilities such as multilingual understanding. Our models and code
are available at https://github.com/FlagAI-Open/FlagAI.

Machine learning algorithms designed to learn dynamical systems from data can
be used to forecast, control and interpret the observed dynamics. In this work
we exemplify the use of one of such algorithms, namely Koopman operator
learning, in the context of open quantum system dynamics. We will study the
dynamics of a small spin chain coupled with dephasing gates and show how
Koopman operator learning is an approach to efficiently learn not only the
evolution of the density matrix, but also of every physical observable
associated to the system. Finally, leveraging the spectral decomposition of the
learned Koopman operator, we show how symmetries obeyed by the underlying
dynamics can be inferred directly from data.

Reinforcement Learning (RL) is a popular machine learning paradigm where
intelligent agents interact with the environment to fulfill a long-term goal.
Driven by the resurgence of deep learning, Deep RL (DRL) has witnessed great
success over a wide spectrum of complex control tasks. Despite the encouraging
results achieved, the deep neural network-based backbone is widely deemed as a
black box that impedes practitioners to trust and employ trained agents in
realistic scenarios where high security and reliability are essential. To
alleviate this issue, a large volume of literature devoted to shedding light on
the inner workings of the intelligent agents has been proposed, by constructing
intrinsic interpretability or post-hoc explainability. In this survey, we
provide a comprehensive review of existing works on eXplainable RL (XRL) and
introduce a new taxonomy where prior works are clearly categorized into
model-explaining, reward-explaining, state-explaining, and task-explaining
methods. We also review and highlight RL methods that conversely leverage human
knowledge to promote learning efficiency and final performance of agents while
this kind of method is often ignored in XRL field. Some open challenges and
opportunities in XRL are discussed. This survey intends to provide a high-level
summarization and better understanding of XRL and to motivate future research
on more effective XRL solutions. Corresponding open source codes are collected
and categorized at
https://github.com/Plankson/awesome-explainable-reinforcement-learning.

This paper presents a novel machine learning framework to consistently
detect, localize and rate congenital cleft lip anomalies in human faces. The
goal is to provide a universal, objective measure of facial differences and
reconstructive surgical outcomes that matches human judgments. The proposed
method employs the StyleGAN2 generative adversarial network with model
adaptation to produce normalized transformations of cleft-affected faces in
order to allow for subsequent measurement of deformity using a pixel-wise
subtraction approach. The complete pipeline of the proposed framework consists
of the following steps: image preprocessing, face normalization, color
transformation, morphological erosion, heat-map generation and abnormality
scoring. Heatmaps that finely discern anatomic anomalies are proposed by
exploiting the features of the considered framework. The proposed framework is
validated through computer simulations and surveys containing human ratings.
The anomaly scores yielded by the proposed computer model correlate closely
with the human ratings of facial differences, leading to 0.942 Pearson's r
score.

Given a natural language instruction, and an input and an output scene, our
goal is to train a neuro-symbolic model which can output a manipulation program
that can be executed by the robot on the input scene resulting in the desired
output scene. Prior approaches for this task possess one of the following
limitations: (i) rely on hand-coded symbols for concepts limiting
generalization beyond those seen during training [1] (ii) infer action
sequences from instructions but require dense sub-goal supervision [2] or (iii)
lack semantics required for deeper object-centric reasoning inherent in
interpreting complex instructions [3]. In contrast, our approach is
neuro-symbolic and can handle linguistic as well as perceptual variations, is
end-to-end differentiable requiring no intermediate supervision, and makes use
of symbolic reasoning constructs which operate on a latent neural
object-centric representation, allowing for deeper reasoning over the input
scene. Central to our approach is a modular structure, consisting of a
hierarchical instruction parser, and a manipulation module to learn
disentangled action representations, both trained via RL. Our experiments on a
simulated environment with a 7-DOF manipulator, consisting of instructions with
varying number of steps, as well as scenes with different number of objects,
and objects with unseen attribute combinations, demonstrate that our model is
robust to such variations, and significantly outperforms existing baselines,
particularly in generalization settings.

Considerable research attention has been paid to table detection by
developing not only rule-based approaches reliant on hand-crafted heuristics
but also deep learning approaches. Although recent studies successfully perform
table detection with enhanced results, they often experience performance
degradation when they are used for transferred domains whose table layout
features might differ from the source domain in which the underlying model has
been trained. To overcome this problem, we present DATa, a novel Domain
Adaptation-aided deep Table detection method that guarantees satisfactory
performance in a specific target domain where few trusted labels are available.
To this end, we newly design lexical features and an augmented model used for
re-training. More specifically, after pre-training one of state-of-the-art
vision-based models as our backbone network, we re-train our augmented model,
consisting of the vision-based model and the multilayer perceptron (MLP)
architecture. Using new confidence scores acquired based on the trained MLP
architecture as well as an initial prediction of bounding boxes and their
confidence scores, we calculate each confidence score more accurately. To
validate the superiority of DATa, we perform experimental evaluations by
adopting a real-world benchmark dataset in a source domain and another dataset
in our target domain consisting of materials science articles. Experimental
results demonstrate that the proposed DATa method substantially outperforms
competing methods that only utilize visual representations in the target
domain. Such gains are possible owing to the capability of eliminating high
false positives or false negatives according to the setting of a confidence
score threshold.

Automatic speech quality assessment is essential for audio researchers,
developers, speech and language pathologists, and system quality engineers. The
current state-of-the-art systems are based on framewise speech features
(hand-engineered or learnable) combined with time dependency modeling. This
paper proposes an efficient system with results comparable to the best
performing model in the ConferencingSpeech 2022 challenge. Our proposed system
is characterized by a smaller number of parameters (40-60x), fewer FLOPS
(100x), lower memory consumption (10-15x), and lower latency (30x). Speech
quality practitioners can therefore iterate much faster, deploy the system on
resource-limited hardware, and, overall, the proposed system contributes to
sustainable machine learning. The paper also concludes that framewise
embeddings outperform utterance-level embeddings and that multi-task training
with acoustic conditions modeling does not degrade speech quality prediction
while providing better interpretation.

Soft robotic manipulators provide numerous advantages over conventional rigid
manipulators in fragile environments such as the marine environment. However,
developing analytic inverse models necessary for shape, motion, and force
control of such robots remains a challenging problem. As an alternative to
analytic models, numerical models can be learned using powerful machine learned
methods. In this paper, the Kinematics Transformer is proposed for developing
accurate and precise inverse kinematic models of soft robotic limbs. The
proposed method re-casts the inverse kinematics problem as a sequential
prediction problem and is based on the transformer architecture. Numerical
simulations reveal that the proposed method can effectively be used in
controlling a soft limb. Benchmark studies also reveal that the proposed method
has better accuracy and precision compared to the baseline feed-forward neural
network

The opacity of deep neural networks remains a challenge in deploying
solutions where explanation is as important as precision. We present ConceptX,
a human-in-the-loop framework for interpreting and annotating latent
representational space in pre-trained Language Models (pLMs). We use an
unsupervised method to discover concepts learned in these models and enable a
graphical interface for humans to generate explanations for the concepts. To
facilitate the process, we provide auto-annotations of the concepts (based on
traditional linguistic ontologies). Such annotations enable development of a
linguistic resource that directly represents latent concepts learned within
deep NLP models. These include not just traditional linguistic concepts, but
also task-specific or sensitive concepts (words grouped based on gender or
religious connotation) that helps the annotators to mark bias in the model. The
framework consists of two parts (i) concept discovery and (ii) annotation
platform.

In the field of medical image, deep convolutional neural networks(ConvNets)
have achieved great success in the classification, segmentation, and
registration tasks thanks to their unparalleled capacity to learn image
features. However, these tasks often require large amounts of manually
annotated data and are labor-intensive. Therefore, it is of significant
importance for us to study unsupervised semantic feature learning tasks. In our
work, we propose to learn features in medical images by training ConvNets to
recognize the geometric transformation applied to images and present a simple
self-supervised task that can easily predict the geometric transformation. We
precisely define a set of geometric transformations in mathematical terms and
generalize this model to 3D, taking into account the distinction between
spatial and time dimensions. We evaluated our self-supervised method on CMR
images of different modalities (bSSFP, T2, LGE) and achieved accuracies of
96.4%, 97.5%, and 96.4%, respectively. The code and models of our paper will be
published on: https://github.com/gaoxin492/Geometric_Transformation_CMR

Data-driven Clinical Decision Support Systems (CDSS) have the potential to
improve and standardise care with personalised probabilistic guidance. However,
the size of data required necessitates collaborative learning from analogous
CDSS's, which are often unsharable or imperfectly interoperable (IIO), meaning
their feature sets are not perfectly overlapping. We propose Modular Clinical
Decision Support Networks (MoDN) which allow flexible, privacy-preserving
learning across IIO datasets, while providing interpretable, continuous
predictive feedback to the clinician.
  MoDN is a novel decision tree composed of feature-specific neural network
modules. It creates dynamic personalised representations of patients, and can
make multiple predictions of diagnoses, updatable at each step of a
consultation. The modular design allows it to compartmentalise training updates
to specific features and collaboratively learn between IIO datasets without
sharing any data.

This paper proposes a self-supervised approach to learn universal facial
representations from videos, that can transfer across a variety of facial
analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression
Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS). Our
proposed framework, named MARLIN, is a facial video masked autoencoder, that
learns highly robust and generic facial embeddings from abundantly available
non-annotated web crawled facial videos. As a challenging auxiliary task,
MARLIN reconstructs the spatio-temporal details of the face from the densely
masked facial regions which mainly include eyes, nose, mouth, lips, and skin to
capture local and global aspects that in turn help in encoding generic and
transferable features. Through a variety of experiments on diverse downstream
tasks, we demonstrate MARLIN to be an excellent facial video encoder as well as
feature extractor, that performs consistently well across a variety of
downstream tasks including FAR (1.13% gain over supervised benchmark), FER
(2.64% gain over unsupervised benchmark), DFD (1.86% gain over unsupervised
benchmark), LS (29.36% gain for Frechet Inception Distance), and even in low
data regime. Our codes and pre-trained models will be made public.

This paper presents a novel algorithm for the continuous control of dynamical
systems that combines Trajectory Optimization (TO) and Reinforcement Learning
(RL) in a single framework. The motivations behind this algorithm are the two
main limitations of TO and RL when applied to continuous nonlinear systems to
minimize a non-convex cost function. Specifically, TO can get stuck in poor
local minima when the search is not initialized close to a ``good'' minimum. On
the other hand, when dealing with continuous state and control spaces, the RL
training process may be excessively long and strongly dependent on the
exploration strategy. Thus, our algorithm learns a ``good'' control policy via
TO-guided RL policy search that, when used as initial guess provider for TO,
makes the trajectory optimization process less prone to converge to poor local
optima. Our method is validated on several reaching problems featuring
non-convex obstacle avoidance with different dynamical systems, including a car
model with 6d state, and a 3-joint planar manipulator. Our results show the
great capabilities of CACTO in escaping local minima, while being more
computationally efficient than the DDPG RL algorithm.

Lifelong domain adaptation remains a challenging task in machine learning due
to the differences among the domains and the unavailability of historical data.
The ultimate goal is to learn the distributional shifts while retaining the
previously gained knowledge. Inspired by the Complementary Learning Systems
(CLS) theory, we propose a novel framework called Lifelong Self-Supervised
Domain Adaptation (LLEDA). LLEDA addresses catastrophic forgetting by replaying
hidden representations rather than raw data pixels and domain-agnostic
knowledge transfer using self-supervised learning. LLEDA does not access labels
from the source or the target domain and only has access to a single domain at
any given time. Extensive experiments demonstrate that the proposed method
outperforms several other methods and results in a long-term adaptation, while
being less prone to catastrophic forgetting when transferred to new domains.

Lung cancer is a leading cause of death worldwide. Early-stage detection of
lung cancer is essential for a more favorable prognosis. Radiogenomics is an
emerging discipline that combines medical imaging and genomics features for
modeling patient outcomes non-invasively. This study presents a radiogenomics
pipeline that has: 1) a novel mixed architecture (RA-Seg) to segment lung
cancer through attention and recurrent blocks; and 2) deep feature classifiers
to distinguish Epidermal Growth Factor Receptor (EGFR) mutation status. We
evaluate the proposed algorithm on multiple public datasets to assess its
generalizability and robustness. We demonstrate how the proposed segmentation
and classification methods outperform existing baseline and SOTA approaches
(73.54 Dice and 93 F1 scores).

The empirical risk minimization (ERM) problem with relative entropy
regularization (ERM-RER) is investigated under the assumption that the
reference measure is a~$\sigma$-finite measure instead of a probability
measure. This assumption leads to a generalization of the ERM-RER (g-ERM-RER)
problem that allows for a larger degree of flexibility in the incorporation of
prior knowledge over the set of models. The solution of the g-ERM-RER problem
is shown to be a unique probability measure mutually absolutely continuous with
the reference measure and to exhibit a probably-approximately-correct (PAC)
guarantee for the ERM problem. For a given dataset, the empirical risk is shown
to be a sub-Gaussian random variable when the models are sampled from the
solution to the g-ERM-RER problem. Finally, the sensitivity of the expected
empirical risk to deviations from the solution of the g-ERM-RER problem is
studied. In particular, the expectation of the absolute value of sensitivity is
shown to be upper bounded, up to a constant factor, by the square root of the
lautum information between the models and the datasets.

Graph Neural Networks (GNNs) have become widely-used models for
semi-supervised learning. However, the robustness of GNNs in the presence of
label noise remains a largely under-explored problem. In this paper, we
consider an important yet challenging scenario where labels on nodes of graphs
are not only noisy but also scarce. In this scenario, the performance of GNNs
is prone to degrade due to label noise propagation and insufficient learning.
To address these issues, we propose a novel RTGNN (Robust Training of Graph
Neural Networks via Noise Governance) framework that achieves better robustness
by learning to explicitly govern label noise. More specifically, we introduce
self-reinforcement and consistency regularization as supplemental supervision.
The self-reinforcement supervision is inspired by the memorization effects of
deep neural networks and aims to correct noisy labels. Further, the consistency
regularization prevents GNNs from overfitting to noisy labels via mimicry loss
in both the inter-view and intra-view perspectives. To leverage such
supervisions, we divide labels into clean and noisy types, rectify inaccurate
labels, and further generate pseudo-labels on unlabeled nodes. Supervision for
nodes with different types of labels is then chosen adaptively. This enables
sufficient learning from clean labels while limiting the impact of noisy ones.
We conduct extensive experiments to evaluate the effectiveness of our RTGNN
framework, and the results validate its consistent superior performance over
state-of-the-art methods with two types of label noises and various noise
rates.

We investigate a practical domain adaptation task, called source-free domain
adaptation (SFUDA), where the source-pretrained model is adapted to the target
domain without access to the source data. Existing techniques mainly leverage
self-supervised pseudo labeling to achieve class-wise global alignment [1] or
rely on local structure extraction that encourages feature consistency among
neighborhoods [2]. While impressive progress has been made, both lines of
methods have their own drawbacks - the "global" approach is sensitive to noisy
labels while the "local" counterpart suffers from source bias. In this paper,
we present Divide and Contrast (DaC), a new paradigm for SFUDA that strives to
connect the good ends of both worlds while bypassing their limitations. Based
on the prediction confidence of the source model, DaC divides the target data
into source-like and target-specific samples, where either group of samples is
treated with tailored goals under an adaptive contrastive learning framework.
Specifically, the source-like samples are utilized for learning global class
clustering thanks to their relatively clean labels. The more noisy
target-specific data are harnessed at the instance level for learning the
intrinsic local structures. We further align the source-like domain with the
target-specific samples using a memory bank-based Maximum Mean Discrepancy
(MMD) loss to reduce the distribution mismatch. Extensive experiments on VisDA,
Office-Home, and the more challenging DomainNet have verified the superior
performance of DaC over current state-of-the-art approaches. The code is
available at https://github.com/ZyeZhang/DaC.git.

Network architectures and learning principles are playing key in forming
complex functions in artificial neural networks (ANNs) and spiking neural
networks (SNNs). SNNs are considered the new-generation artificial networks by
incorporating more biological features than ANNs, including dynamic spiking
neurons, functionally specified architectures, and efficient learning
paradigms. Network architectures are also considered embodying the function of
the network. Here, we propose a Motif-topology improved SNN (M-SNN) for the
efficient multi-sensory integration and cognitive phenomenon simulations. The
cognitive phenomenon simulation we simulated includes the cocktail party effect
and McGurk effect, which are discussed by many researchers. Our M-SNN
constituted by the meta operator called network motifs. The source of 3-node
network motifs topology from artificial one pre-learned from the spatial or
temporal dataset. In the single-sensory classification task, the results showed
the accuracy of M-SNN using network motif topologies was higher than the pure
feedforward network topology without using them. In the multi-sensory
integration task, the performance of M-SNN using artificial network motif was
better than the state-of-the-art SNN using BRP (biologically-plausible reward
propagation). Furthermore, the M-SNN could better simulate the cocktail party
effect and McGurk effect with lower computational cost. We think the artificial
network motifs could be considered as some prior knowledge that would
contribute to the multi-sensory integration of SNNs and provide more benefits
for simulating the cognitive phenomenon.

In this paper, we provide a theoretical tool for the interpretation and
analysis of \emph{graph neural networks} (GNNs). We use Markov chains on graphs
to mathematically model the forward propagation processes of GNNs. The graph
neural networks are divided into two classes of operator-consistent and
operator-inconsistent based on whether the Markov chains are time-homogeneous.
Based on this, we study \emph{over-smoothing} which is an important problem in
GNN research. We attribute the over-smoothing problem to the convergence of an
arbitrary initial distribution to a stationary distribution. We prove the
effectiveness of the previous methods for alleviating the over-smoothing
problem. Further, we give the conclusion that operator-consistent GNN cannot
avoid over-smoothing at an exponential rate in the Markovian sense. For
operator-inconsistent GNN, we theoretically give a sufficient condition for
avoiding over-smoothing. Based on this condition, we propose a regularization
term which can be flexibly added to the training of the neural network.
Finally, we design experiments to verify the effectiveness of this condition.
Results show that our proposed sufficient condition not only improves the
performance but also alleviates the over-smoothing phenomenon.

Training neural networks means solving a high-dimensional optimization
problem. Normally the goal is to minimize a loss function that depends on what
is called the network function, or in other words the function that gives the
network output given a certain input. This function depends on a large number
of parameters, also known as weights, that depends on the network architecture.
In general the goal of this optimization problem is to find the global minimum
of the network function. In this paper it is discussed how due to how neural
networks are designed, the neural network function present a very large
symmetry in the parameter space. This work shows how the neural network
function has a number of equivalent minima, in other words minima that give the
same value for the loss function and the same exact output, that grows
factorially with the number of neurons in each layer for feed forward neural
network or with the number of filters in a convolutional neural networks. When
the number of neurons and layers is large, the number of equivalent minima
grows extremely fast. This will have of course consequences for the study of
how neural networks converges to minima during training. This results is known,
but in this paper for the first time a proper mathematical discussion is
presented and an estimate of the number of equivalent minima is derived.

Temporal networks are suitable for modeling complex evolving systems. It has
a wide range of applications, such as social network analysis, recommender
systems, and epidemiology. Recently, modeling such dynamic systems has drawn
great attention in many domains. However, most existing approaches resort to
taking discrete snapshots of the temporal networks and modeling all events with
equal importance. This paper proposes Significant Ties Graph Neural Networks
(STGNN), a novel framework that captures and describes significant ties. To
better model the diversity of interactions, STGNN introduces a novel
aggregation mechanism to organize the most significant historical neighbors'
information and adaptively obtain the significance of node pairs. Experimental
results on four real networks demonstrate the effectiveness of the proposed
framework.

We tackle the task of NeRF inversion for style-based neural radiance fields,
(e.g., StyleNeRF). In the task, we aim to learn an inversion function to
project an input image to the latent space of a NeRF generator and then
synthesize novel views of the original image based on the latent code. Compared
with GAN inversion for 2D generative models, NeRF inversion not only needs to
1) preserve the identity of the input image, but also 2) ensure 3D consistency
in generated novel views. This requires the latent code obtained from the
single-view image to be invariant across multiple views. To address this new
challenge, we propose a two-stage encoder for style-based NeRF inversion. In
the first stage, we introduce a base encoder that converts the input image to a
latent code. To ensure the latent code is view-invariant and is able to
synthesize 3D consistent novel view images, we utilize identity contrastive
learning to train the base encoder. Second, to better preserve the identity of
the input image, we introduce a refining encoder to refine the latent code and
add finer details to the output image. Importantly note that the novelty of
this model lies in the design of its first-stage encoder which produces the
closest latent code lying on the latent manifold and thus the refinement in the
second stage would be close to the NeRF manifold. Through extensive
experiments, we demonstrate that our proposed two-stage encoder qualitatively
and quantitatively exhibits superiority over the existing encoders for
inversion in both image reconstruction and novel-view rendering.

In applications involving sensitive data, such as finance and healthcare, the
necessity for preserving data privacy can be a significant barrier to machine
learning model development. Differential privacy (DP) has emerged as one
canonical standard for provable privacy. However, DP's strong theoretical
guarantees often come at the cost of a large drop in its utility for machine
learning, and DP guarantees themselves can be difficult to interpret. In this
work, we propose a novel privacy notion, membership inference privacy (MIP), to
address these challenges. We give a precise characterization of the
relationship between MIP and DP, and show that MIP can be achieved using less
amount of randomness compared to the amount required for guaranteeing DP,
leading to a smaller drop in utility. MIP guarantees are also easily
interpretable in terms of the success rate of membership inference attacks. Our
theoretical results also give rise to a simple algorithm for guaranteeing MIP
which can be used as a wrapper around any algorithm with a continuous output,
including parametric model training.

Historical Document Image Binarization is a well-known segmentation problem
in image processing. Despite ubiquity, traditional thresholding algorithms
achieved limited success on severely degraded document images. With the advent
of deep learning, several segmentation models were proposed that made
significant progress in the field but were limited by the unavailability of
large training datasets. To mitigate this problem, we have proposed a novel
two-stage framework -- the first of which comprises a generator that generates
degraded samples using variational inference and the second being a CNN-based
binarization network that trains on the generated data. We evaluated our
framework on a range of DIBCO datasets, where it achieved competitive results
against previous state-of-the-art methods.

Vessel segmentation is essential in many medical image applications, such as
the detection of coronary stenoses, retinal vessel diseases and brain
aneurysms. A high pixel-wise accuracy, complete topology structure and
robustness to various contrast variations are three critical aspects of vessel
segmentation. However, most existing methods only focus on achieving part of
them via dedicated designs while few of them can concurrently achieve the three
goals. In this paper, we present a novel affinity feature strengthening network
(AFN) which adopts a contrast-insensitive approach based on multiscale affinity
to jointly model topology and refine pixel-wise segmentation features.
Specifically, for each pixel we derive a multiscale affinity field which
captures the semantic relationships of the pixel with its neighbors on the
predicted mask image. Such a multiscale affinity field can effectively
represent the local topology of a vessel segment of different sizes. Meanwhile,
it does not depend on image intensities and hence is robust to various
illumination and contrast changes. We further learn spatial- and scale-aware
adaptive weights for the corresponding affinity fields to strengthen vessel
features. We evaluate our AFN on four different types of vascular datasets:
X-ray angiography coronary vessel dataset (XCAD), portal vein dataset (PV),
digital subtraction angiography cerebrovascular vessel dataset (DSA) and
retinal vessel dataset (DRIVE). Extensive experimental results on the four
datasets demonstrate that our AFN outperforms the state-of-the-art methods in
terms of both higher accuracy and topological metrics, and meanwhile is more
robust to various contrast changes than existing methods. Codes will be made
public.

Pain is a common occurrence among patients admitted to Intensive Care Units.
Pain assessment in ICU patients still remains a challenge for clinicians and
ICU staff, specifically in cases of non-verbal sedated, mechanically
ventilated, and intubated patients. Current manual observation-based pain
assessment tools are limited by the frequency of pain observations administered
and are subjective to the observer. Facial behavior is a major component in
observation-based tools. Furthermore, previous literature shows the feasibility
of painful facial expression detection using facial action units (AUs).
However, these approaches are limited to controlled or semi-controlled
environments and have never been validated in clinical settings. In this study,
we present our Pain-ICU dataset, the largest dataset available targeting facial
behavior analysis in the dynamic ICU environment. Our dataset comprises 76,388
patient facial image frames annotated with AUs obtained from 49 adult patients
admitted to ICUs at the University of Florida Health Shands hospital. In this
work, we evaluated two vision transformer models, namely ViT and SWIN, for AU
detection on our Pain-ICU dataset and also external datasets. We developed a
completely end-to-end AU detection pipeline with the objective of performing
real-time AU detection in the ICU. The SWIN transformer Base variant achieved
0.88 F1-score and 0.85 accuracy on the held-out test partition of the Pain-ICU
dataset.

This paper introduces RISE, a robust individualized decision learning
framework with sensitive variables, where sensitive variables are collectible
data and important to the intervention decision, but their inclusion in
decision making is prohibited due to reasons such as delayed availability or
fairness concerns. A naive baseline is to ignore these sensitive variables in
learning decision rules, leading to significant uncertainty and bias. To
address this, we propose a decision learning framework to incorporate sensitive
variables during offline training but not include them in the input of the
learned decision rule during model deployment. Specifically, from a causal
perspective, the proposed framework intends to improve the worst-case outcomes
of individuals caused by sensitive variables that are unavailable at the time
of decision. Unlike most existing literature that uses mean-optimal objectives,
we propose a robust learning framework by finding a newly defined quantile- or
infimum-optimal decision rule. The reliable performance of the proposed method
is demonstrated through synthetic experiments and three real-world

This paper leverages machine learned predictions to design online algorithms
for the k-max and k-min search problems. Our algorithms can achieve
performances competitive with the offline algorithm in hindsight when the
predictions are accurate (i.e., consistency) and also provide worst-case
guarantees when the predictions are arbitrarily wrong (i.e., robustness).
Further, we show that our algorithms have attained the Pareto-optimal trade-off
between consistency and robustness, where no other algorithms for k-max or
k-min search can improve on the consistency for a given robustness. To
demonstrate the performance of our algorithms, we evaluate them in experiments
of buying and selling Bitcoin.

To design a drug given a biological molecule by using deep learning methods,
there are many successful models published recently. People commonly used
generative models to design new molecules given certain protein. LiGAN was
regarded as the baseline of deep learning model which was developed on
convolutional neural networks. Recently, GraphBP showed its ability to predict
innovative "real" chemicals that the binding affinity outperformed with
traditional molecular docking methods by using a flow-based generative model
with a graph neural network and multilayer perception. However, all those
methods regarded proteins as rigid bodies and only include a very small part of
proteins related to binding. However, the dynamics of proteins are essential
for drug binding. Based on GraphBP, we proposed to generate more solid work
derived from protein data bank. The results will be evaluated by validity and
binding affinity by using a computational chemistry algorithm.

Scene text removal aims to remove the text and fill the regions with
perceptually plausible background information in natural images. It has
attracted increasing attention due to its various applications in privacy
protection, scene text retrieval, and text editing. With the development of
deep learning, the previous methods have achieved significant improvements.
However, most of the existing methods seem to ignore the large perceptive
fields and global information. The pioneer method can get significant
improvements by only changing training data from the cropped image to the full
image. In this paper, we present a single-stage multi-scale network MSLKANet
for scene text removal in full images. For obtaining large perceptive fields
and global information, we propose multi-scale large kernel attention (MSLKA)
to obtain long-range dependencies between the text regions and the backgrounds
at various granularity levels. Furthermore, we combine the large kernel
decomposition mechanism and atrous spatial pyramid pooling to build a large
kernel spatial pyramid pooling (LKSPP), which can perceive more valid pixels in
the spatial dimension while maintaining large receptive fields and low cost of
computation. Extensive experimental results indicate that the proposed method
achieves state-of-the-art performance on both synthetic and real-world datasets
and the effectiveness of the proposed components MSLKA and LKSPP.

In the light of GPU accelerations, sequential operations such as solving
ordinary differential equations can be bottlenecks for gradient evaluations and
hinder potential speed gains. In this work, we focus on growth functions and
their time derivatives in cosmological particle mesh simulations and show that
these are the majority time cost when using gradient based inference
algorithms. We propose to construct novel conditional B-spline emulators which
directly learn an interpolating function for the growth factor as a function of
time, conditioned on the cosmology. We demonstrate that these emulators are
sufficiently accurate to not bias our results for cosmological inference and
can lead to over an order of magnitude gains in time, especially for small to
intermediate size simulations.

Self-supervised speech representation learning aims to extract meaningful
factors from the speech signal that can later be used across different
downstream tasks, such as speech and/or emotion recognition. Existing models,
such as HuBERT, however, can be fairly large thus may not be suitable for edge
speech applications. Moreover, realistic applications typically involve speech
corrupted by noise and room reverberation, hence models need to provide
representations that are robust to such environmental factors. In this study,
we build on the so-called DistilHuBERT model, which distils HuBERT to a
fraction of its original size, with three modifications, namely: (i) augment
the training data with noise and reverberation, while the student model needs
to distill the clean representations from the teacher model; (ii) introduce a
curriculum learning approach where increasing levels of noise are introduced as
the model trains, thus helping with convergence and with the creation of more
robust representations; and (iii) introduce a multi-task learning approach
where the model also reconstructs the clean waveform jointly with the
distillation task, thus also acting as an enhancement step to ensure additional
environment robustness to the representation. Experiments on three SUPERB tasks
show the advantages of the proposed method not only relative to the original
DistilHuBERT, but also to the original HuBERT, thus showing the advantages of
the proposed method for ``in the wild'' edge speech applications.

We present ThreshNet, a post-processing method to refine the output of neural
networks designed for binary segmentation tasks. ThreshNet uses the confidence
map produced by a base network along with global and local patch information to
significantly improve the performance of even state-of-the-art methods. Binary
segmentation models typically convert confidence maps into predictions by
thresholding the confidence scores at 0.5 (or some other fixed number).
However, we observe that the best threshold is image-dependent and often even
region-specific -- different parts of the image benefit from using different
thresholds. Thus ThreshNet takes a trained segmentation model and learns to
correct its predictions by using a memory-efficient post-processing
architecture that incorporates region-specific thresholds as part of the
training mechanism. Our experiments show that ThreshNet consistently improves
over current the state-of-the-art methods in binary segmentation and saliency
detection, typically by 3 to 5% in mIoU and mBA.

We propose an approach for learning accurately the dynamics of slow
collective variables from atomistic data obtained from ab-initio quantum
mechanical theory, using generalized Langevin equations (GLE). The force
fields, memory kernel, and noise generator are constructed within the
Mori-Zwanzig formalism under the constraint imposed by the
fluctuation-dissipation theorem. Combined with Deep Potential Molecular
Dynamics (DeePMD) and density functional theory, this GLE approach opens the
door to carrying out first-principles multi-scale modeling for a variety of
systems. Here, we demonstrate this capability with a study of the dynamics of
twin domain walls in ferroelectric lead titanate. The importance of memory
effects is illustrated by the fact that while ab-initio GLE agrees well with
molecular dynamics at near-equilibrium conditions, Markovian Langevin dynamics
underestimates the rate of rare events by several orders of magnitude.

Dialogue systems, commonly known as chatbots, have gained escalating
popularity in recent times due to their wide-spread applications in carrying
out chit-chat conversations with users and task-oriented dialogues to
accomplish various user tasks. Existing chatbots are usually trained from
pre-collected and manually-labeled data and/or written with handcrafted rules.
Many also use manually-compiled knowledge bases (KBs). Their ability to
understand natural language is still limited, and they tend to produce many
errors resulting in poor user satisfaction. Typically, they need to be
constantly improved by engineers with more labeled data and more manually
compiled knowledge. This book introduces the new paradigm of lifelong learning
dialogue systems to endow chatbots the ability to learn continually by
themselves through their own self-initiated interactions with their users and
working environments to improve themselves. As the systems chat more and more
with users or learn more and more from external sources, they become more and
more knowledgeable and better and better at conversing. The book presents the
latest developments and techniques for building such continual learning
dialogue systems that continuously learn new language expressions and lexical
and factual knowledge during conversation from users and off conversation from
external sources, acquire new training examples during conversation, and learn
conversational skills. Apart from these general topics, existing works on
continual learning of some specific aspects of dialogue systems are also
surveyed. The book concludes with a discussion of open challenges for future
research.

Human intelligence can remarkably adapt quickly to new tasks and
environments. Starting from a very young age, humans acquire new skills and
learn how to solve new tasks either by imitating the behavior of others or by
following provided natural language instructions. To facilitate research which
can enable similar capabilities in machines, we made the following
contributions (1) formalized the collaborative embodied agent using natural
language task; (2) developed a tool for extensive and scalable data collection;
and (3) collected the first dataset for interactive grounded language
understanding.

Personal data collected at scale promises to improve decision-making and
accelerate innovation. However, sharing and using such data raises serious
privacy concerns. A promising solution is to produce synthetic data, artificial
records to share instead of real data. Since synthetic records are not linked
to real persons, this intuitively prevents classical re-identification attacks.
However, this is insufficient to protect privacy. We here present TAPAS, a
toolbox of attacks to evaluate synthetic data privacy under a wide range of
scenarios. These attacks include generalizations of prior works and novel
attacks. We also introduce a general framework for reasoning about privacy
threats to synthetic data and showcase TAPAS on several examples.

This paper addresses the problem of position estimation in UAVs operating in
a cluttered environment where GPS information is unavailable. A model
learning-based approach is proposed that takes in the rotor RPMs and past state
as input and predicts the one-step-ahead position of the UAV using a novel
spectral-normalized memory neural network (SN-MNN). The spectral normalization
guarantees stable and reliable prediction performance. The predicted position
is transformed to global coordinate frame which is then fused along with the
odometry of other peripheral sensors like IMU, barometer, compass etc., using
the onboard extended Kalman filter to estimate the states of the UAV. The
experimental flight data collected from a motion capture facility using a
micro-UAV is used to train the SN-MNN. The PX4-ECL library is used to replay
the flight data using the proposed algorithm, and the estimated position is
compared with actual ground truth data. The proposed algorithm doesn't require
any additional onboard sensors, and is computationally light. The performance
of the proposed approach is compared with the current state-of-art GPS-denied
algorithms, and it can be seen that the proposed algorithm has the least RMSE
for position estimates.

A reliable voice anti-spoofing countermeasure system needs to robustly
protect automatic speaker verification (ASV) systems in various kinds of
spoofing scenarios. However, the performance of countermeasure systems could be
degraded by channel effects and codecs. In this paper, we show that using the
low-frequency subbands of signals as input can mitigate the negative impact
introduced by codecs on the countermeasure systems. To validate this, two types
of low-pass filters with different cut-off frequencies are applied to
countermeasure systems, and the equal error rate (EER) is reduced by up to 25%
relatively. In addition, we propose a deep learning based bandwidth extension
approach to further improve the detection accuracy. Recent studies show that
the error rate of countermeasure systems increase dramatically when the silence
part is removed by Voice Activity Detection (VAD), our experimental results
show that the filtering and bandwidth extension approaches are also effective
under the codec condition when VAD is applied.

Graph structure learning (GSL), which aims to learn the adjacency matrix for
graph neural networks (GNNs), has shown great potential in boosting the
performance of GNNs. Most existing GSL works apply a joint learning framework
where the estimated adjacency matrix and GNN parameters are optimized for
downstream tasks. However, as GSL is essentially a link prediction task, whose
goal may largely differ from the goal of the downstream task. The inconsistency
of these two goals limits the GSL methods to learn the potential optimal graph
structure. Moreover, the joint learning framework suffers from scalability
issues in terms of time and space during the process of estimation and
optimization of the adjacency matrix. To mitigate these issues, we propose a
graph structure refinement (GSR) framework with a pretrain-finetune pipeline.
Specifically, The pre-training phase aims to comprehensively estimate the
underlying graph structure by a multi-view contrastive learning framework with
both intra- and inter-view link prediction tasks. Then, the graph structure is
refined by adding and removing edges according to the edge probabilities
estimated by the pre-trained model. Finally, the fine-tuning GNN is initialized
by the pre-trained model and optimized toward downstream tasks. With the
refined graph structure remaining static in the fine-tuning space, GSR avoids
estimating and optimizing graph structure in the fine-tuning phase which enjoys
great scalability and efficiency. Moreover, the fine-tuning GNN is boosted by
both migrating knowledge and refining graphs. Extensive experiments are
conducted to evaluate the effectiveness (best performance on six benchmark
datasets), efficiency, and scalability (13.8x faster using 32.8% GPU memory
compared to the best GSL baseline on Cora) of the proposed model.

Dark patterns, which are user interface designs in online services, induce
users to take unintended actions. Recently, dark patterns have been raised as
an issue of privacy and fairness. Thus, a wide range of research on detecting
dark patterns is eagerly awaited. In this work, we constructed a dataset for
dark pattern detection and prepared its baseline detection performance with
state-of-the-art machine learning methods. The original dataset was obtained
from Mathur et al.'s study in 2019, which consists of 1,818 dark pattern texts
from shopping sites. Then, we added negative samples, i.e., non-dark pattern
texts, by retrieving texts from the same websites as Mathur et al.'s dataset.
We also applied state-of-the-art machine learning methods to show the automatic
detection accuracy as baselines, including BERT, RoBERTa, ALBERT, and XLNet. As
a result of 5-fold cross-validation, we achieved the highest accuracy of 0.975
with RoBERTa. The dataset and baseline source codes are available at
https://github.com/yamanalab/ec-darkpattern.

Learning causal structure is useful in many areas of artificial intelligence,
including planning, robotics, and explanation. Constraint-based structure
learning algorithms such as PC use conditional independence (CI) tests to infer
causal structure. Traditionally, constraint-based algorithms perform CI tests
with a preference for smaller-sized conditioning sets, partially because the
statistical power of conventional CI tests declines rapidly as the size of the
conditioning set increases. However, many modern conditional independence tests
are model-based, and these tests use well-regularized models that maintain
statistical power even with very large conditioning sets. This suggests an
intriguing new strategy for constraint-based algorithms which may result in a
reduction of the total number of CI tests performed: Test variable pairs with
large conditioning sets first, as a pre-processing step that finds some
conditional independencies quickly, before moving on to the more conventional
strategy that favors small conditioning sets. We propose such a pre-processing
step for the PC algorithm which relies on performing CI tests on a few randomly
selected large conditioning sets. We perform an empirical analysis on directed
acyclic graphs (DAGs) that correspond to real-world systems and both empirical
and theoretical analyses for Erd\H{o}s-Renyi DAGs. Our results show that
Pre-Processing Plus PC (P3PC) performs far fewer CI tests than the original PC
algorithm, between 0.5% to 36%, and often less than 10%, of the CI tests that
the PC algorithm alone performs. The efficiency gains are particularly
significant for the DAGs corresponding to real-world systems.

We present a unified system to realize one-shot voice conversion (VC) on the
pitch, rhythm, and speaker attributes. Existing works generally ignore the
correlation between prosody and language content, leading to the degradation of
naturalness in converted speech. Additionally, the lack of proper language
features prevents these systems from accurately preserving language content
after conversion. To address these issues, we devise a cascaded modular system
leveraging self-supervised discrete speech units as language representation.
These discrete units provide duration information essential for rhythm
modeling. Our system first extracts utterance-level prosody and speaker
representations from the raw waveform. Given the prosody representation, a
prosody predictor estimates pitch, energy, and duration for each discrete unit
in the utterance. A synthesizer further reconstructs speech based on the
predicted prosody, speaker representation, and discrete units. Experiments show
that our system outperforms previous approaches in naturalness,
intelligibility, speaker transferability, and prosody transferability. Code and
samples are publicly available.

We introduce new differentially private (DP) mechanisms for gradient-based
machine learning (ML) training involving multiple passes (epochs) of a dataset,
substantially improving the achievable privacy-utility-computation tradeoffs.
Our key contribution is an extension of the online matrix factorization DP
mechanism to multiple participations, substantially generalizing the approach
of DMRST2022. We first give conditions under which it is possible to reduce the
problem with per-iteration vector contributions to the simpler one of scalar
contributions. Using this, we formulate the construction of optimal (in total
squared error at each iterate) matrix mechanisms for SGD variants as a convex
program. We propose an efficient optimization algorithm via a closed form
solution to the dual function.
  While tractable, both solving the convex problem offline and computing the
necessary noise masks during training can become prohibitively expensive when
many training steps are necessary. To address this, we design a
Fourier-transform-based mechanism with significantly less computation and only
a minor utility decrease.
  Extensive empirical evaluation on two tasks: example-level DP for image
classification and user-level DP for language modeling, demonstrate substantial
improvements over the previous state-of-the-art. Though our primary application
is to ML, we note our main DP results are applicable to arbitrary linear
queries and hence may have much broader applicability.

Preference-based reinforcement learning (RL) algorithms help avoid the
pitfalls of hand-crafted reward functions by distilling them from human
preference feedback, but they remain impractical due to the burdensome number
of labels required from the human, even for relatively simple tasks. In this
work, we demonstrate that encoding environment dynamics in the reward function
(REED) dramatically reduces the number of preference labels required in
state-of-the-art preference-based RL frameworks. We hypothesize that REED-based
methods better partition the state-action space and facilitate generalization
to state-action pairs not included in the preference dataset. REED iterates
between encoding environment dynamics in a state-action representation via a
self-supervised temporal consistency task, and bootstrapping the
preference-based reward function from the state-action representation. Whereas
prior approaches train only on the preference-labelled trajectory pairs, REED
exposes the state-action representation to all transitions experienced during
policy training. We explore the benefits of REED within the PrefPPO [1] and
PEBBLE [2] preference learning frameworks and demonstrate improvements across
experimental conditions to both the speed of policy learning and the final
policy performance. For example, on quadruped-walk and walker-walk with 50
preference labels, REED-based reward functions recover 83% and 66% of ground
truth reward policy performance and without REED only 38\% and 21\% are
recovered. For some domains, REED-based reward functions result in policies
that outperform policies trained on the ground truth reward.

In this paper, we present a novel data-driven optimization approach for
trajectory based air traffic flow management (ATFM). A key aspect of the
proposed approach is the inclusion of airspace users' trajectory preferences,
which are computed from traffic data by combining clustering and classification
techniques. Machine learning is also used to extract consistent trajectory
options, while optimization is applied to resolve demand-capacity imbalances by
means of a mathematical programming model that judiciously assigns a feasible
4D trajectory and a possible ground delay to each flight. The methodology has
been tested on instances extracted from real air traffic data repositories.
With more than 32,000 flights considered, we solve the largest instances of the
ATFM problem available in the literature in short computational times that are
reasonable from the practical point of view. As a by-product, we highlight the
trade-off between preferences and delays as well as the potential benefits.
Indeed, computing efficient solutions of the problem facilitates a consensus
between network manager and airspace users. In view of the level of accuracy of
the solutions and of the excellent computational performance, we are optimistic
that the proposed approach may provide a significant contribution to the
development of the next generation of air traffic flow management tools.

Mobile health apps provide a unique means of collecting data that can be used
to deliver adaptive interventions.The predicted outcomes considerably influence
the selection of such interventions. Recourse via counterfactuals provides
tangible mechanisms to modify user predictions. By identifying plausible
actions that increase the likelihood of a desired prediction, stakeholders are
afforded agency over their predictions. Furthermore, recourse mechanisms enable
counterfactual reasoning that can help provide insights into candidates for
causal interventional features. We demonstrate the feasibility of GAN-generated
recourse for mobile health applications on ensemble-survival-analysis-based
prediction of medium-term engagement in the Safe Delivery App, a digital
training tool for skilled birth attendants.

In recent years, quantum has been attracted by various fields such as quantum
machine learning, quantum communication, and quantum computers. Among them,
quantum federated learning (QFL) has recently received increasing attention,
where quantum neural networks (QNNs) are integrated into federated learning
(FL). In contrast to the existing QFL methods, we propose quantum split
learning (QSL), which is the extension version of split learning. In classical
computing, split learning has shown many advantages in faster convergence,
communication cost, and even privacy. To fully utilize QSL, we propose
crosschannel pooling which leverages the unique nature of quantum state
tomography that is made by QNN. In numerical results, we corroborate that QSL
achieves not only 1.64% higher top-1 accuracy than QFL but shows
privacy-preserving in the MNIST classification task.

Artificial intelligence (AI) methods including deep neural networks can
provide rapid molecular classification of tumors from routine histology with
accuracy that can match or exceed human pathologists. Discerning how neural
networks make their predictions remains a significant challenge, but
explainability tools can help provide insights into what models have learned
when corresponding histologic features are poorly understood. Conditional
generative adversarial networks (cGANs) are AI models that generate synthetic
images and illustrate subtle differences between image classes. Here, we
describe the use of a cGAN for explaining models trained to classify
molecularly-subtyped tumors, exposing associated histologic features. We
leverage cGANs to create class- and layer-blending visualizations to improve
understanding of subtype morphology. Finally, we demonstrate the potential use
of synthetic histology for augmenting pathology trainee education and show that
clear, intuitive cGAN visualizations can reinforce and improve human
understanding of histologic manifestations of tumor biology

Reinforcement learning from human feedback (RLHF) is a powerful technique for
training agents to perform difficult-to-specify tasks. However, human feedback
can be noisy, particularly when human teachers lack relevant knowledge or
experience. Levels of expertise vary across teachers, and a given teacher may
have differing levels of expertise for different components of a task. RLHF
algorithms that learn from multiple teachers therefore face an expertise
problem: the reliability of a given piece of feedback depends both on the
teacher that it comes from and how specialized that teacher is on relevant
components of the task. Existing state-of-the-art RLHF algorithms assume that
all evaluations come from the same distribution, obscuring this inter- and
intra-human variance, and preventing them from accounting for or taking
advantage of variations in expertise. We formalize this problem, implement it
as an extension of an existing RLHF benchmark, evaluate the performance of a
state-of-the-art RLHF algorithm, and explore techniques to improve query and
teacher selection. Our key contribution is to demonstrate and characterize the
expertise problem, and to provide an open-source implementation for testing
future solutions.

We describe the current content moderation strategy employed by Meta to
remove policy-violating content from its platforms. Meta relies on both
handcrafted and learned risk models to flag potentially violating content for
human review. Our approach aggregates these risk models into a single ranking
score, calibrating them to prioritize more reliable risk models. A key
challenge is that violation trends change over time, affecting which risk
models are most reliable. Our system additionally handles production challenges
such as changing risk models and novel risk models. We use a contextual bandit
to update the calibration in response to such trends. Our approach increases
Meta's top-line metric for measuring the effectiveness of its content
moderation strategy by 13%.

A common challenge in regression is that for many problems, the degrees of
freedom required for a high-quality solution also allows for overfitting.
Regularization is a class of strategies that seek to restrict the range of
possible solutions so as to discourage overfitting while still enabling good
solutions, and different regularization strategies impose different types of
restrictions. In this paper, we present a multilevel regularization strategy
that constructs and trains a hierarchy of neural networks, each of which has
layers that are wider versions of the previous network's layers. We draw
intuition and techniques from the field of Algebraic Multigrid (AMG),
traditionally used for solving linear and nonlinear systems of equations, and
specifically adapt the Full Approximation Scheme (FAS) for nonlinear systems of
equations to the problem of deep learning. Training through V-cycles then
encourage the neural networks to build a hierarchical understanding of the
problem. We refer to this approach as \emph{multilevel-in-width} to distinguish
from prior multilevel works which hierarchically alter the depth of neural
networks. The resulting approach is a highly flexible framework that can be
applied to a variety of layer types, which we demonstrate with both
fully-connected and convolutional layers. We experimentally show with PDE
regression problems that our multilevel training approach is an effective
regularizer, improving the generalize performance of the neural networks
studied.

We introduce an architecture for processing signals supported on hypergraphs
via graph neural networks (GNNs), which we call a Hyper-graph Expansion Neural
Network (HENN), and provide the first bounds on the stability and
transferability error of a hypergraph signal processing model. To do so, we
provide a framework for bounding the stability and transferability error of
GNNs across arbitrary graphs via spectral similarity. By bounding the
difference between two graph shift operators (GSOs) in the positive
semi-definite sense via their eigenvalue spectrum, we show that this error
depends only on the properties of the GNN and the magnitude of spectral
similarity of the GSOs. Moreover, we show that existing transferability results
that assume the graphs are small perturbations of one another, or that the
graphs are random and drawn from the same distribution or sampled from the same
graphon can be recovered using our approach. Thus, both GNNs and our HENNs
(trained using normalized Laplacians as graph shift operators) will be
increasingly stable and transferable as the graphs become larger. Experimental
results illustrate the importance of considering multiple graph representations
in HENN, and show its superior performance when transferability is desired.

Guided cooperation allows intelligent agents with heterogeneous capabilities
to work together by following a leader-follower type of interaction. However,
the associated control problem becomes challenging when the leader agent does
not have complete information about follower agents. There is a need for online
learning and adaptation of cooperation plans. To this end, we develop a
meta-learning-based Stackelberg game-theoretic framework to address the
challenges in the guided cooperative control for linear systems. We first
formulate the guided cooperation between agents as a dynamic Stackelberg game
and use the feedback Stackelberg equilibrium as the agent-wise cooperation
strategy. We further leverage meta-learning to address the incomplete
information of follower agents, where the leader agent learns a meta-response
model from a prescribed set of followers offline and adapts to an unseen
cooperation task with a small amount of online learning data. We use a case
study in robot teaming to corroborate the effectiveness of our framework.
Comparison with other learning approaches shows our learned cooperation
strategy has better transferability for different cooperation tasks.

Predicting the evolution of diseases is challenging, especially when the data
availability is scarce and incomplete. The most popular tools for modelling and
predicting infectious disease epidemics are compartmental models. They stratify
the population into compartments according to health status and model the
dynamics of these compartments using dynamical systems. However, these
predefined systems may not capture the true dynamics of the epidemic due to the
complexity of the disease transmission and human interactions. In order to
overcome this drawback, we propose Sparsity and Delay Embedding based
Forecasting (SPADE4) for predicting epidemics. SPADE4 predicts the future
trajectory of an observable variable without the knowledge of the other
variables or the underlying system. We use random features model with sparse
regression to handle the data scarcity issue and employ Takens' delay embedding
theorem to capture the nature of the underlying system from the observed
variable. We show that our approach outperforms compartmental models when
applied to both simulated and real data.

It has been shown recently that deep learning based models are effective on
speech quality prediction and could outperform traditional metrics in various
perspectives. Although network models have potential to be a surrogate for
complex human hearing perception, they may contain instabilities in
predictions. This work shows that deep speech quality predictors can be
vulnerable to adversarial perturbations, where the prediction can be changed
drastically by unnoticeable perturbations as small as $-30$ dB compared with
speech inputs. In addition to exposing the vulnerability of deep speech quality
predictors, we further explore and confirm the viability of adversarial
training for strengthening robustness of models.

Unpacking and comprehending how deep learning algorithms make decisions has
been a persistent challenge for researchers and end-users. Explaining
time-series predictive models is useful for clinical applications with high
stakes to understand the behavior of prediction models. However, existing
approaches to explain such models are frequently unique to architectures and
data where the features do not have a time-varying component. In this paper, we
introduce WindowSHAP, a model-agnostic framework for explaining time-series
classifiers using Shapley values. We intend for WindowSHAP to mitigate the
computational complexity of calculating Shapley values for long time-series
data as well as improve the quality of explanations. WindowSHAP is based on
partitioning a sequence into time windows. Under this framework, we present
three distinct algorithms of Stationary, Sliding and Dynamic WindowSHAP, each
evaluated against baseline approaches, KernelSHAP and TimeSHAP, using
perturbation and sequence analyses metrics. We applied our framework to
clinical time-series data from both a specialized clinical domain (Traumatic
Brain Injury - TBI) as well as a broad clinical domain (critical care
medicine). The experimental results demonstrate that, based on the two
quantitative metrics, our framework is superior at explaining clinical
time-series classifiers, while also reducing the complexity of computations. We
show that for time-series data with 120 time steps (hours), merging 10 adjacent
time points can reduce the CPU time of WindowSHAP by 80% compared to
KernelSHAP. We also show that our Dynamic WindowSHAP algorithm focuses more on
the most important time steps and provides more understandable explanations. As
a result, WindowSHAP not only accelerates the calculation of Shapley values for
time-series data, but also delivers more understandable explanations with
higher quality.

We investigate the spectral properties of linear-width feed-forward neural
networks, where the sample size is asymptotically proportional to network
width. Empirically, we show that the weight spectra in this high dimensional
regime are invariant when trained by gradient descent for small constant
learning rates and the changes in both operator and Frobenius norm are
$\Theta(1)$ in the limit. This implies the bulk spectra for both the conjugate
and neural tangent kernels are also invariant. We demonstrate similar
characteristics for models trained with mini-batch (stochastic) gradient
descent with small learning rates and provide a theoretical justification for
this special scenario. When the learning rate is large, we show empirically
that an outlier emerges with its corresponding eigenvector aligned to the
training data structure. We also show that after adaptive gradient training,
where we have a lower test error and feature learning emerges, both the weight
and kernel matrices exhibit heavy tail behavior. Different spectral properties
such as invariant bulk, spike, and heavy-tailed distribution correlate to how
far the kernels deviate from initialization. To understand this phenomenon
better, we focus on a toy model, a two-layer network on synthetic data, which
exhibits different spectral properties for different training strategies.
Analogous phenomena also appear when we train conventional neural networks with
real-world data. Our results show that monitoring the evolution of the spectra
during training is an important step toward understanding the training dynamics
and feature learning.

Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMRI)
traditionally relies on the availability of several orthogonal low-resolution
series of 2-dimensional thick slices (volumes). In practice, only a few
low-resolution volumes are acquired. Thus, optimization-based
image-reconstruction methods require strong regularization using hand-crafted
regularizers (e.g., TV). Yet, due to in utero fetal motion and the rapidly
changing fetal brain anatomy, the acquisition of the high-resolution images
that are required to train supervised learning methods is difficult. In this
paper, we sidestep this difficulty by providing a proof of concept of a
self-supervised single-volume superresolution framework for T2-weighted FBMRI
(SAIR). We validate SAIR quantitatively in a motion-free simulated environment.
Our results for different noise levels and resolution ratios suggest that SAIR
is comparable to multiple-volume superresolution reconstruction methods. We
also evaluate SAIR qualitatively on clinical FBMRI data. The results suggest
SAIR could be incorporated into current reconstruction pipelines.

Deep learning models develop successive representations of their input in
sequential layers, the last of which maps the final representation to the
output. Here we investigate the informational content of these representations
by observing the ability of convolutional image classification models to
autoencode the model's input using embeddings existing in various layers. We
find that the deeper the layer, the less accurate that layer's representation
of the input is before training. Inaccurate representation results from
non-uniqueness in which various distinct inputs give approximately the same
embedding. Non-unique representation is a consequence of both exact and
approximate non-invertibility of transformations present in the forward pass.
Learning to classify natural images leads to an increase in representation
clarity for early but not late layers, which instead form abstract images.
Rather than simply selecting for features present in the input necessary for
classification, deep layer representations are found to transform the input so
that it matches representations of the training data such that arbitrary inputs
are mapped to manifolds learned during training. This work provides support for
the theory that the tasks of image recognition and input generation are
inseparable even for models trained exclusively to classify.

Cyberattacks use adversarial techniques to bypass system defenses, persist,
and eventually breach systems. The MITRE ATT\&CK framework catalogs a set of
adversarial techniques and maps between adversaries and their used techniques
and tactics. Understanding how adversaries deploy techniques in conjunction is
pivotal for learning adversary behavior, hunting potential threats, and
formulating a proactive defense. The goal of this research is to aid
cybersecurity practitioners and researchers in choosing detection and
mitigation strategies through co-occurrence analysis of adversarial techniques
reported in MITRE ATT&CK. We collect the adversarial techniques of 115
cybercrime groups and 484 malware from the MITRE ATT\&CK. We apply association
rule mining and network analysis to investigate how adversarial techniques
co-occur. We identify that adversaries pair T1059: Command and scripting
interface and T1105: Ingress tool transfer techniques with a relatively large
number of ATT\&CK techniques. We also identify adversaries using the T1082:
System Information Discovery technique to determine their next course of
action. We observe adversaries deploy the highest number of techniques from the
TA0005: Defense evasion and TA0007: Discovery tactics. Based on our findings on
co-occurrence, we identify six detection, six mitigation strategies, and twelve
adversary behaviors. We urge defenders to prioritize primarily the detection of
TA0007: Discovery and mitigation of TA0005: Defense evasion techniques.
Overall, this study approximates how adversaries leverage techniques based on
publicly reported documents. We advocate organizations investigate adversarial
techniques in their environment and make the findings available for a more
precise and actionable understanding.

We propose and analyze a compact and non-volatile nanomagnetic (all-spin)
non-binary matrix multiplier performing the multiply-and-accumulate (MAC)
operation using two magnetic tunnel junctions - one activated by strain to act
as the multiplier, and the other activated by spin-orbit torque pulses to act
as a domain wall synapse that performs the operation of the accumulator. It has
two advantages over the usual crossbar-based electronic non-binary matrix
multiplier. First, while the crossbar architecture requires N^3 devices to
multiply two matrices, we require only 2N^2 devices. Second, our matrix
multiplier is non-volatile and retains the information about the product matrix
after being powered off. Here, we present an example where each MAC operation
can be performed in ~5 ns and the maximum energy dissipated per operation is
~60N aJ. This provides a very useful hardware accelerator for machine learning
and artificial intelligence tasks which often involve the multiplication of
large matrices. The non-volatility allows the matrix multiplier to be embedded
in powerful non-von-Neumann architectures. It also allows much of the computing
to be done at the edge while reducing the need to access the cloud, thereby
making artificial intelligence more resilient against cyberattacks.

Symmetry-based neural networks often constrain the architecture in order to
achieve invariance or equivariance to a group of transformations. In this
paper, we propose an alternative that avoids this architectural constraint by
learning to produce a canonical representation of the data. These
canonicalization functions can readily be plugged into non-equivariant backbone
architectures. We offer explicit ways to implement them for many groups of
interest. We show that this approach enjoys universality while providing
interpretable insights. Our main hypothesis is that learning a neural network
to perform canonicalization is better than using predefined heuristics. Our
results show that learning the canonicalization function indeed leads to better
results and that the approach achieves excellent performance in practice.

Computer-aided design (CAD) is a promising new area for the application of
artificial intelligence (AI) and machine learning (ML). The current practice of
design of cyber-physical systems uses the digital twin methodology, wherein the
actual physical design is preceded by building detailed models that can be
evaluated by physics simulation models. These physics models are often slow and
the manual design process often relies on exploring near-by variations of
existing designs. AI holds the promise of breaking these design silos and
increasing the diversity and performance of designs by accelerating the
exploration of the design space. In this paper, we focus on the design of
electrical unmanned aerial vehicles (UAVs). The high-density batteries and
purely electrical propulsion systems have disrupted the space of UAV design,
making this domain an ideal target for AI-based design. In this paper, we
develop an AI Designer that synthesizes novel UAV designs. Our approach uses a
deep transformer model with a novel domain-specific encoding such that we can
evaluate the performance of new proposed designs without running expensive
flight dynamics models and CAD tools. We demonstrate that our approach
significantly reduces the overall compute requirements for the design process
and accelerates the design space exploration. Finally, we identify future
research directions to achieve full-scale deployment of AI-assisted CAD for
UAVs.

In this work we propose a novel token-based training strategy that improves
Transformer-Transducer (T-T) based speaker change detection (SCD) performance.
The conventional T-T based SCD model loss optimizes all output tokens equally.
Due to the sparsity of the speaker changes in the training data, the
conventional T-T based SCD model loss leads to sub-optimal detection accuracy.
To mitigate this issue, we use a customized edit-distance algorithm to estimate
the token-level SCD false accept (FA) and false reject (FR) rates during
training and optimize model parameters to minimize a weighted combination of
the FA and FR, focusing the model on accurately predicting speaker changes. We
also propose a set of evaluation metrics that align better with commercial use
cases. Experiments on a group of challenging real-world datasets show that the
proposed training method can significantly improve the overall performance of
the SCD model with the same number of parameters.

In this paper, we present a novel approach to adapt a sequence-to-sequence
Transformer-Transducer ASR system to the keyword spotting (KWS) task. We
achieve this by replacing the keyword in the text transcription with a special
token <kw> and training the system to detect the <kw> token in an audio stream.
At inference time, we create a decision function inspired by conventional KWS
approaches, to make our approach more suitable for the KWS task. Furthermore,
we introduce a specific keyword spotting loss by adapting the
sequence-discriminative Minimum Bayes-Risk training technique. We find that our
approach significantly outperforms ASR based KWS systems. When compared with a
conventional keyword spotting system, our proposal has similar performance
while bringing the advantages and flexibility of sequence-to-sequence training.
Additionally, when combined with the conventional KWS system, our approach can
improve the performance at any operation point.

We are at the beginning of a new age in which artificial entities will
perform significant amounts of high-level cognitive processing rivaling and
even surpassing human thinking. The future belongs to those who can best
collaborate with artificial cognitive entities achieving a high degree of
cognitive augmenta-tion. However, we currently lack theoretically grounded
fundamental metrics able to describe human or artificial cognition much less
augmented and combined cognition. How do we measure thinking, cognition,
information, and knowledge in an implementation-independent way? How can we
tell how much thinking an artificial entity does and how much is done by a
human? How can we measure the combined and possible even emergent effect of
humans working together with intelligent artificial entities? These are some of
the challenges for research-ers in this field. We first define a cognitive
process as the transformation of data, information, knowledge, and wisdom. We
then review several existing and emerging information metrics based on entropy,
processing effort, quantum physics, emergent capacity, and human concept
learning. We then discuss how these fail to answer the above questions and
provide guidelines for future re-search.

Discriminative unsupervised learning methods such as contrastive learning
have demonstrated the ability to learn generalized visual representations on
centralized data. It is nonetheless challenging to adapt such methods to a
distributed system with unlabeled, private, and heterogeneous client data due
to user styles and preferences. Federated learning enables multiple clients to
collectively learn a global model without provoking any privacy breach between
local clients. On the other hand, another direction of federated learning
studies personalized methods to address the local heterogeneity. However, work
on solving both generalization and personalization without labels in a
decentralized setting remains unfamiliar. In this work, we propose a novel
method, FedStyle, to learn a more generalized global model by infusing local
style information with local content information for contrastive learning, and
to learn more personalized local models by inducing local style information for
downstream tasks. The style information is extracted by contrasting original
local data with strongly augmented local data (Sobel filtered images). Through
extensive experiments with linear evaluations in both IID and non-IID settings,
we demonstrate that FedStyle outperforms both the generalization baseline
methods and personalization baseline methods in a stylized decentralized
setting. Through comprehensive ablations, we demonstrate our design of style
infusion and stylized personalization improve performance significantly.

The use of neural network parametrizations to represent the ground state in
variational Monte Carlo (VMC) calculations has generated intense interest in
recent years. However, as we demonstrate in the context of the periodic
Heisenberg spin chain, this approach can produce unreliable wave function
approximations. One of the most obvious signs of failure is the occurrence of
random, persistent spikes in the energy estimate during training. These energy
spikes are caused by regions of configuration space that are over-represented
by the wave function density, which are called ``spurious modes'' in the
machine learning literature. After exploring these spurious modes in detail, we
demonstrate that a collective-variable-based penalization yields a
substantially more robust training procedure, preventing the formation of
spurious modes and improving the accuracy of energy estimates. Because the
penalization scheme is cheap to implement and is not specific to the particular
model studied here, it can be extended to other applications of VMC where a
reasonable choice of collective variable is available.

Imitation learning allows social robots to learn new skills from human
teachers without substantial manual programming, but it is difficult for
robotic imitation learning systems to generalize demonstrated skills as well as
human learners do. Contemporary neurocomputational approaches to imitation
learning achieve limited generalization at the cost of data-intensive training,
and often produce opaque models that are difficult to understand and debug. In
this study, we explore the viability of developing purely-neural controllers
for social robots that learn to imitate by reasoning about the underlying
intentions of demonstrated behaviors. We present NeuroCERIL, a brain-inspired
neurocognitive architecture that uses a novel hypothetico-deductive reasoning
procedure to produce generalizable and human-readable explanations for
demonstrated behavior. This approach combines bottom-up abductive inference
with top-down predictive verification, and captures important aspects of human
causal reasoning that are relevant to a broad range of cognitive domains. Our
empirical results demonstrate that NeuroCERIL can learn various procedural
skills in a simulated robotic imitation learning domain. We also show that its
causal reasoning procedure is computationally efficient, and that its memory
use is dominated by highly transient short-term memories, much like human
working memory. We conclude that NeuroCERIL is a viable neural model of
human-like imitation learning that can improve human-robot collaboration and
contribute to investigations of the neurocomputational basis of human
cognition.

Epistemic uncertainty quantification is a crucial part of drawing credible
conclusions from predictive models, whether concerned about the prediction at a
given point or any downstream evaluation that uses the model as input. When the
predictive model is simple and its evaluation differentiable, this task is
solved by the delta method, where we propagate the asymptotically-normal
uncertainty in the predictive model through the evaluation to compute standard
errors and Wald confidence intervals. However, this becomes difficult when the
model and/or evaluation becomes more complex. Remedies include the bootstrap,
but it can be computationally infeasible when training the model even once is
costly. In this paper, we propose an alternative, the implicit delta method,
which works by infinitesimally regularizing the training loss of the predictive
model to automatically assess downstream uncertainty. We show that the change
in the evaluation due to regularization is consistent for the asymptotic
variance of the evaluation estimator, even when the infinitesimal change is
approximated by a finite difference. This provides both a reliable
quantification of uncertainty in terms of standard errors as well as permits
the construction of calibrated confidence intervals. We discuss connections to
other approaches to uncertainty quantification, both Bayesian and frequentist,
and demonstrate our approach empirically.

The prevalence of abusive language on different online platforms has been a
major concern that raises the need for automated cross-platform abusive
language detection. However, prior works focus on concatenating data from
multiple platforms, inherently adopting Empirical Risk Minimization (ERM)
method. In this work, we address this challenge from the perspective of domain
generalization objective. We design SCL-Fish, a supervised contrastive learning
integrated meta-learning algorithm to detect abusive language on unseen
platforms. Our experimental analysis shows that SCL-Fish achieves better
performance over ERM and the existing state-of-the-art models. We also show
that SCL-Fish is data-efficient and achieves comparable performance with the
large-scale pre-trained models upon finetuning for the abusive language
detection task.

The quality of scene graphs generated by the state-of-the-art (SOTA) models
is compromised due to the long-tail nature of the relationships and their
parent object pairs. Training of the scene graphs is dominated by the majority
relationships of the majority pairs and, therefore, the object-conditional
distributions of relationship in the minority pairs are not preserved after the
training is converged. Consequently, the biased model performs well on more
frequent relationships in the marginal distribution of relationships such as
`on' and `wearing', and performs poorly on the less frequent relationships such
as `eating' or `hanging from'. In this work, we propose virtual evidence
incorporated within-triplet Bayesian Network (BN) to preserve the
object-conditional distribution of the relationship label and to eradicate the
bias created by the marginal probability of the relationships. The insufficient
number of relationships in the minority classes poses a significant problem in
learning the within-triplet Bayesian network. We address this insufficiency by
embedding-based augmentation of triplets where we borrow samples of the
minority triplet classes from its neighborhood triplets in the semantic space.
We perform experiments on two different datasets and achieve a significant
improvement in the mean recall of the relationships. We also achieve better
balance between recall and mean recall performance compared to the SOTA
de-biasing techniques of scene graph models.

Gradient based learning using error back-propagation (``backprop'') is a
well-known contributor to much of the recent progress in AI. A less obvious,
but arguably equally important, ingredient is parameter sharing - most
well-known in the context of convolutional networks. In this essay we relate
parameter sharing (``weight sharing'') to analogy making and the school of
thought of cognitive metaphor. We discuss how recurrent and auto-regressive
models can be thought of as extending analogy making from static features to
dynamic skills and procedures. We also discuss corollaries of this perspective,
for example, how it can challenge the currently entrenched dichotomy between
connectionist and ``classic'' rule-based views of computation.

A significant portion of the effort involved in advanced process control,
process analytics, and machine learning involves acquiring and preparing data.
The published literature often emphasizes increasingly complex modeling
techniques with incremental performance improvements. However, when industrial
case studies are published they often lack important details on data
acquisition and preparation. Although data pre-processing is often unfairly
maligned as trivial and technically uninteresting, in practice it has an
out-sized influence on the success of real-world artificial intelligence
applications. This work describes best practices for acquiring and preparing
operating data to pursue data-driven modelling and control opportunities in
industrial processes. We present practical considerations for pre-processing
industrial time series data to inform the efficient development of reliable
soft sensors that provide valuable process insights.

In this paper, we seek to measure how much information a component in a
neural network could extract from the representations fed into it. Our work
stands in contrast to prior probing work, most of which investigates how much
information a model's representations contain. This shift in perspective leads
us to propose a new principle for probing, the architectural bottleneck
principle: In order to estimate how much information a given component could
extract, a probe should look exactly like the component. Relying on this
principle, we estimate how much syntactic information is available to
transformers through our attentional probe, a probe that exactly resembles a
transformer's self-attention head. Experimentally, we find that, in three
models (BERT, ALBERT, and RoBERTa), a sentence's syntax tree is mostly
extractable by our probe, suggesting these models have access to syntactic
information while composing their contextual representations. Whether this
information is actually used by these models, however, remains an open
question.

Given a symmetric matrix $M$ and a vector $\lambda$, we present new bounds on
the Frobenius-distance utility of the Gaussian mechanism for approximating $M$
by a matrix whose spectrum is $\lambda$, under
$(\varepsilon,\delta)$-differential privacy. Our bounds depend on both
$\lambda$ and the gaps in the eigenvalues of $M$, and hold whenever the top
$k+1$ eigenvalues of $M$ have sufficiently large gaps. When applied to the
problems of private rank-$k$ covariance matrix approximation and subspace
recovery, our bounds yield improvements over previous bounds. Our bounds are
obtained by viewing the addition of Gaussian noise as a continuous-time matrix
Brownian motion. This viewpoint allows us to track the evolution of eigenvalues
and eigenvectors of the matrix, which are governed by stochastic differential
equations discovered by Dyson. These equations allow us to bound the utility as
the square-root of a sum-of-squares of perturbations to the eigenvectors, as
opposed to a sum of perturbation bounds obtained via Davis-Kahan-type theorems.

Kernel methods provide a flexible and theoretically grounded approach to
nonlinear and nonparametric learning. While memory requirements hinder their
applicability to large datasets, many approximate solvers were recently
developed for scaling up kernel methods, such as random Fourier features.
However, these scalable approaches are based on approximations of isotropic
kernels, which are incapable of removing the influence of possibly irrelevant
features. In this work, we design random Fourier features for automatic
relevance determination kernels, widely used for variable selection, and
propose a new method based on joint optimization of the kernel machine
parameters and the kernel relevances. Additionally, we present a new
optimization algorithm that efficiently tackles the resulting objective
function, which is non-convex. Numerical validation on synthetic and real-world
data shows that our approach achieves low prediction error and effectively
identifies relevant predictors. Our solution is modular and uses the PyTorch

In spite of machine learning's rapid growth, its engineering support is
scattered in many forms, and tends to favor certain engineering stages,
stakeholders, and evaluation preferences. We envision a capability-based
framework, which uses fine-grained specifications for ML model behaviors to
unite existing efforts towards better ML engineering. We use concrete scenarios
(model design, debugging, and maintenance) to articulate capabilities' broad
applications across various different dimensions, and their impact on building
safer, more generalizable and more trustworthy models that reflect human needs.
Through preliminary experiments, we show capabilities' potential for reflecting
model generalizability, which can provide guidance for ML engineering process.
We discuss challenges and opportunities for capabilities' integration into ML
engineering.

Neuroimaging-based prediction methods for intelligence and cognitive
abilities have seen a rapid development, while prediction based on functional
connectivity (FC) has shown great promise. The overwhelming majority of
literature has focused on static FC with extremely limited results available on
dynamic FC or region level fMRI time series. Unlike static FC, the latter
features include the temporal variability in the fMRI data. In this project, we
propose a novel bi-LSTM approach that incorporates an $L_0$ regularization for
feature selection. The proposed pipeline is applied to prediction based on
region level fMRI time series as well as dynamic FC and implemented via an
efficient algorithm. We undertake a detailed comparison of prediction
performance for different intelligence measures based on fMRI features acquired
from the Adolescent Brain Cognitive Development (ABCD) study. Our analysis
illustrates that static FC consistently has inferior performance compared to
region level fMRI time series or dynamic FC for unimodal rest and task fMRI
experiments, as well as in almost all cases for multi-task analysis. The
proposed pipeline based on region level time-series identifies several
important brain regions that drive fluctuations in intelligence measures.
Strong test-retest reliability of the selected features is reported, pointing
to reproducible findings. Given the large sample size from ABCD study, our
results provide conclusive evidence that superior intelligence prediction can
be achieved by considering temporal variations in the fMRI data, either at the
region level, or based on dynamic FC, which is one of the first such findings
in literature. These results are particularly noteworthy, given the low
dimensionality of the region level time series, easier interpretability, and
extremely quick computation times, compared to network-based analysis.

Near infrared (NIR) to Visible (VIS) face matching is challenging due to the
significant domain gaps as well as a lack of sufficient data for cross-modality
model training. To overcome this problem, we propose a novel method for paired
NIR-VIS facial image generation. Specifically, we reconstruct 3D face shape and
reflectance from a large 2D facial dataset and introduce a novel method of
transforming the VIS reflectance to NIR reflectance. We then use a
physically-based renderer to generate a vast, high-resolution and
photorealistic dataset consisting of various poses and identities in the NIR
and VIS spectra. Moreover, to facilitate the identity feature learning, we
propose an IDentity-based Maximum Mean Discrepancy (ID-MMD) loss, which not
only reduces the modality gap between NIR and VIS images at the domain level
but encourages the network to focus on the identity features instead of facial
details, such as poses and accessories. Extensive experiments conducted on four
challenging NIR-VIS face recognition benchmarks demonstrate that the proposed
method can achieve comparable performance with the state-of-the-art (SOTA)
methods without requiring any existing NIR-VIS face recognition datasets. With
slightly fine-tuning on the target NIR-VIS face recognition datasets, our
method can significantly surpass the SOTA performance. Code and pretrained
models are released under the insightface
(https://github.com/deepinsight/insightface/tree/master/recognition).

Learning long-horizon tasks such as navigation has presented difficult
challenges for successfully applying reinforcement learning. However, from
another perspective, under a known environment model, methods such as
sampling-based planning can robustly find collision-free paths in environments
without learning. In this work, we propose Control Transformer which models
return-conditioned sequences from low-level policies guided by a sampling-based
Probabilistic Roadmap (PRM) planner. Once trained, we demonstrate that our
framework can solve long-horizon navigation tasks using only local information.
We evaluate our approach on partially-observed maze navigation with MuJoCo
robots, including Ant, Point, and Humanoid, and show that Control Transformer
can successfully navigate large mazes and generalize to new, unknown
environments. Additionally, we apply our method to a differential drive robot
(Turtlebot3) and show zero-shot sim2real transfer under noisy observations.

The use of emojis affords a visual modality to, often private, textual
communication. The task of predicting emojis however provides a challenge for
machine learning as emoji use tends to cluster into the frequently used and the
rarely used emojis. Much of the machine learning research on emoji use has
focused on high resource languages and has conceptualised the task of
predicting emojis around traditional server-side machine learning approaches.
However, traditional machine learning approaches for private communication can
introduce privacy concerns, as these approaches require all data to be
transmitted to a central storage. In this paper, we seek to address the dual
concerns of emphasising high resource languages for emoji prediction and
risking the privacy of people's data. We introduce a new dataset of $118$k
tweets (augmented from $25$k unique tweets) for emoji prediction in Hindi, and
propose a modification to the federated learning algorithm, CausalFedGSD, which
aims to strike a balance between model performance and user privacy. We show
that our approach obtains comparative scores with more complex centralised
models while reducing the amount of data required to optimise the models and
minimising risks to user privacy.

Conventional image signal processing (ISP) frameworks are designed to
reconstruct an RGB image from a single raw measurement. As multi-camera systems
become increasingly popular these days, it is worth exploring improvements in
ISP frameworks by incorporating raw measurements from multiple cameras. This
manuscript is an intermediate progress report of a new ISP framework that is
under development, StereoISP. It employs raw measurements from a stereo camera
pair to generate a demosaicked, denoised RGB image by utilizing disparity
estimated between the two views. We investigate StereoISP by testing the
performance on raw image pairs synthesized from stereo datasets. Our
preliminary results show an improvement in the PSNR of the reconstructed RGB
image by at least 2dB on KITTI 2015 and drivingStereo datasets using ground
truth sparse disparity maps.

Bayesian optimization~(BO) is often used for accelerator tuning due to its
high sample efficiency. However, the computational scalability of training over
large data-set can be problematic and the adoption of historical data in a
computationally efficient way is not trivial. Here, we exploit a neural network
model trained over historical data as a prior mean of BO for FRIB Front-End
tuning.

Session-Based Recommenders (SBRs) aim to predict users' next preferences
regard to their previous interactions in sessions while there is no historical
information about them. Modern SBRs utilize deep neural networks to map users'
current interest(s) during an ongoing session to a latent space so that their
next preference can be predicted. Although state-of-art SBR models achieve
satisfactory results, most focus on studying the sequence of events inside
sessions while ignoring temporal details of those events. In this paper, we
examine the potential of session temporal information in enhancing the
performance of SBRs, conceivably by reflecting the momentary interests of
anonymous users or their mindset shifts during sessions. We propose the STAR
framework, which utilizes the time intervals between events within sessions to
construct more informative representations for items and sessions. Our
mechanism revises session representation by embedding time intervals without
employing discretization. Empirical results on Yoochoose and Diginetica
datasets show that the suggested method outperforms the state-of-the-art
baseline models in Recall and MRR criteria.

The linear matter power spectrum $P(k,z)$ connects theory with large scale
structure observations in cosmology. Its scale dependence is entirely encoded
in the matter transfer function $T(k)$, which can be computed numerically by
Boltzmann solvers, and can also be computed semi-analytically by using fitting
functions such as the well-known Bardeen-Bond-Kaiser-Szalay (BBKS) and
Eisenstein-Hu (EH) formulae. However, both the BBKS and EH formulae have some
significant drawbacks. On the one hand, although BBKS is a simple expression,
it is only accurate up to $10\%$, which is well above the $1\%$ precision goal
of forthcoming surveys. On the other hand, while EH is as accurate as required
by upcoming experiments, it is a rather long and complicated expression. Here,
we use the Genetic Algorithms (GAs), a particular machine learning technique,
to derive simple and accurate fitting formulae for the transfer function
$T(k)$. When the effects of massive neutrinos are also considered, our
expression slightly improves over the EH formula, while being notably shorter
in comparison.

The problem of learning threshold functions is a fundamental one in machine
learning. Classical learning theory implies sample complexity of $O(\xi^{-1}
\log(1/\beta))$ (for generalization error $\xi$ with confidence $1-\beta$). The
private version of the problem, however, is more challenging and in particular,
the sample complexity must depend on the size $|X|$ of the domain. Progress on
quantifying this dependence, via lower and upper bounds, was made in a line of
works over the past decade. In this paper, we finally close the gap for
approximate-DP and provide a nearly tight upper bound of $\tilde{O}(\log^*
|X|)$, which matches a lower bound by Alon et al (that applies even with
improper learning) and improves over a prior upper bound of $\tilde{O}((\log^*
|X|)^{1.5})$ by Kaplan et al. We also provide matching upper and lower bounds
of $\tilde{\Theta}(2^{\log^*|X|})$ for the additive error of private
quasi-concave optimization (a related and more general problem). Our
improvement is achieved via the novel Reorder-Slice-Compute paradigm for
private data analysis which we believe will have further applications.

Training Graph Neural Networks, on graphs containing billions of vertices and
edges, at scale using minibatch sampling poses a key challenge: strong-scaling
graphs and training examples results in lower compute and higher communication
volume and potential performance loss. DistGNN-MB employs a novel Historical
Embedding Cache combined with compute-communication overlap to address this
challenge. On a 32-node (64-socket) cluster of $3^{rd}$ generation Intel Xeon
Scalable Processors with 36 cores per socket, DistGNN-MB trains 3-layer
GraphSAGE and GAT models on OGBN-Papers100M to convergence with epoch times of
2 seconds and 4.9 seconds, respectively, on 32 compute nodes. At this scale,
DistGNN-MB trains GraphSAGE 5.2x faster than the widely-used DistDGL.
DistGNN-MB trains GraphSAGE and GAT 10x and 17.2x faster, respectively, as
compute nodes scale from 2 to 32.

Industry classification schemes provide a taxonomy for segmenting companies
based on their business activities. They are relied upon in industry and
academia as an integral component of many types of financial and economic
analysis. However, even modern classification schemes have failed to embrace
the era of big data and remain a largely subjective undertaking prone to
inconsistency and misclassification. To address this, we propose a multimodal
neural model for training company embeddings, which harnesses the dynamics of
both historical pricing data and financial news to learn objective company
representations that capture nuanced relationships. We explain our approach in
detail and highlight the utility of the embeddings through several case studies
and application to the downstream task of industry classification.

In recent years, advances in deep learning have resulted in a plethora of
successes in the use of reinforcement learning (RL) to solve complex sequential
decision tasks with high-dimensional inputs. However, existing systems lack the
necessary mechanisms to provide humans with a holistic view of their
competence, presenting an impediment to their adoption, particularly in
critical applications where the decisions an agent makes can have significant
consequences. Yet, existing RL-based systems are essentially competency-unaware
in that they lack the necessary interpretation mechanisms to allow human
operators to have an insightful, holistic view of their competency. In this
paper, we extend a recently-proposed framework for explainable RL that is based
on analyses of "interestingness." Our new framework provides various measures
of RL agent competence stemming from interestingness analysis and is applicable
to a wide range of RL algorithms. We also propose novel mechanisms for
assessing RL agents' competencies that: 1) identify agent behavior patterns and
competency-controlling conditions by clustering agent behavior traces solely
using interestingness data; and 2) identify the task elements mostly
responsible for an agent's behavior, as measured through interestingness, by
performing global and local analyses using SHAP values. Overall, our tools
provide insights about RL agent competence, both their capabilities and
limitations, enabling users to make more informed decisions about
interventions, additional training, and other interactions in collaborative
human-machine settings.

This paper is a technical overview of DeepMind and Google's recent work on
reinforcement learning for controlling commercial cooling systems. Building on
expertise that began with cooling Google's data centers more efficiently, we
recently conducted live experiments on two real-world facilities in partnership
with Trane Technologies, a building management system provider. These live
experiments had a variety of challenges in areas such as evaluation, learning
from offline data, and constraint satisfaction. Our paper describes these
challenges in the hope that awareness of them will benefit future applied RL
work. We also describe the way we adapted our RL system to deal with these
challenges, resulting in energy savings of approximately 9% and 13%
respectively at the two live experiment sites.

High resolution simulations of incompressible flows have become routine
across a range of engineering applications. Despite their routine use, due to
the high dimensional parameter space present for most practical applications, a
comprehensive exploration of the available parameter space is often
impractical. In this work, we demonstrate the ability of physics-constrained
deep learning methods to provide an efficient means of exploring
high-dimensional parameter spaces with minimal amounts of data from high
resolution computational fluid dynamic simulations. As a specific application,
we choose the well established problem of a 2D lid driven cavity flow. While
giving an extensive treatment of the classic case of a square cavity, we extend
the analysis to treat an isosceles trapezoid. In so doing, the number of
parameters determining the solution includes not just the Reynolds number, but
also two additional parameters characterizing the geometry of the cavity. Thus,
together with the $\left( x, y \right)$ variation of the flow and pressure in
configuration space, the presence of these three parameters results in the
solution varying in a 5D space. It is shown that in the absence of data,
physics-constrained methods are able to provide an accurate description of the
cavity flow in this 5D space up to intermediate values of the Reynolds number,
but fails to train for sufficiently high Reynolds numbers. In contrast, using a
small quantity of flow data, a single neural network is able to provide an
accurate description for a broad range of Reynolds numbers and cavity
geometries. Once trained, such a model provides a rapid surrogate that can be
used to efficiently explore the 5D space. This 5D surrogate model is
subsequently used to identify critical parameter values for the merger and
splitting of vortices as the Reynolds number and cavity geometry are varied.

Modern AI and machine-learning provide striking performance. However, this
comes with rapidly-spiralling energy costs arising from growing network size
and inefficiencies of the von Neumann architecture. `Reservoir computing'
offers an energy-efficient alternative to large networks, fixing randomised
weights for energetically-cheap training. The massively-parallel processing
underpinning machine-learning is poorly catered for by CMOS, with in materia
neuromorphic computing an attractive solution.
  Nanomagnetic artificial spin-systems are ideal candidates for neuromorphic
hardware. Their passive memory, state-dependent dynamics and nonlinear GHz
spin-wave response provide powerful computation. However, any single physical
reservoir must trade-off between performance metrics including nonlinearity and
memory-capacity, with the compromise typically hard-coded during
nanofabrication. Here, we present three artificial spin-systems: square
artificial spin ice, square artificial spin-vortex ice and a disordered
pinwheel artificial spin-vortex ice. We show how tuning system geometry and
dynamics defines computing performance.
  We engineer networks where each node is a high-dimensional physical
reservoir, implementing parallel, deep and multilayer physical neural network
architectures. This solves the issue of physical reservoir performance
compromise, allowing a small suite of synergistic physical systems to address
diverse tasks and provide a broad range of reprogrammable
computationally-distinct configurations. These networks outperform any single
reservoir across a broad taskset. Crucially, we move beyond reservoir computing
to present a method for reconfigurably programming inter-layer network
connections, enabling on-demand task optimised performance.

To offer accurate and diverse recommendation services, recent methods use
auxiliary information to foster the learning process of user and item
representations. Many SOTA methods fuse different sources of information (user,
item, knowledge graph, tags, etc.) into a graph and use Graph Neural Networks
to introduce the auxiliary information through the message passing paradigm. In
this work, we seek an alternative framework that is light and effective through
self-supervised learning across different sources of information, particularly
for the commonly accessible item tag information. We use a self-supervision
signal to pair users with the auxiliary information associated with the items
they have interacted with before. To achieve the pairing, we create a proxy
training task. For a given item, the model predicts the correct pairing between
the representations obtained from the users that have interacted with this item
and the assigned tags. This design provides an efficient solution, using the
auxiliary information directly to enhance the quality of user and item
embeddings. User behavior in recommendation systems is driven by the complex
interactions of many factors behind the decision-making processes. To make the
pairing process more fine-grained and avoid embedding collapse, we propose an
intent-aware self-supervised pairing process where we split the user embeddings
into multiple sub-embedding vectors. Each sub-embedding vector captures a
specific user intent via self-supervised alignment with a particular cluster of
tags. We integrate our designed framework with various recommendation models,
demonstrating its flexibility and compatibility. Through comparison with
numerous SOTA methods on seven real-world datasets, we show that our method can
achieve better performance while requiring less training time. This indicates
the potential of applying our approach on web-scale datasets.

ASR can be improved by multi-task learning (MTL) with domain enhancing or
domain adversarial training, which are two opposite objectives with the aim to
increase/decrease domain variance towards domain-aware/agnostic ASR,
respectively. In this work, we study how to best apply these two opposite
objectives with speaker labels to improve conformer-based ASR. We also propose
a novel adaptive gradient reversal layer for stable and effective adversarial
training without tuning effort. Detailed analysis and experimental verification
are conducted to show the optimal positions in the ASR neural network (NN) to
apply speaker enhancing and adversarial training. We also explore their
combination for further improvement, achieving the same performance as
i-vectors plus adversarial training. Our best speaker-based MTL achieves 7\%
relative improvement on the Switchboard Hub5'00 set. We also investigate the
effect of such speaker-based MTL w.r.t. cleaner dataset and weaker ASR NN.

With information systems becoming larger scale, recommendation systems are a
topic of growing interest in machine learning research and industry. Even
though progress on improving model design has been rapid in research, we argue
that many advances fail to translate into practice because of two limiting
assumptions. First, most approaches focus on a transductive learning setting
which cannot handle unseen users or items and second, many existing methods are
developed for static settings that cannot incorporate new data as it becomes
available. We argue that these are largely impractical assumptions on
real-world platforms where new user interactions happen in real time. In this
survey paper, we formalize both concepts and contextualize recommender systems
work from the last six years. We then discuss why and how future work should
move towards inductive learning and incremental updates for recommendation
model design and evaluation. In addition, we present best practices and
fundamental open challenges for future research.

We introduce a family of interpretable machine learning models, with two
broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous
logistic link function in General Additive Models (GAMs); and SubscaleHedge, an
expert advice algorithm for combining base models trained on subsets of
features called subscales. LAMs can augment any additive binary classification
model equipped with a sigmoid link function. Moreover, they afford direct
global and local attributions of additive components to the model output in
probability space. We argue that LAMs and SubscaleHedge improve the
interpretability of their base algorithms. Using rigorous null-hypothesis
significance testing on a broad suite of financial modelling data, we show that
our algorithms do not suffer from large performance penalties in terms of
ROC-AUC and calibration.

Linguistic anomalies detectable in spontaneous speech have shown promise for
various clinical applications including screening for dementia and other forms
of cognitive impairment. The feasibility of deploying automated tools that can
classify language samples obtained from speech in large-scale clinical settings
depends on the ability to capture and automatically transcribe the speech for
subsequent analysis. However, the impressive performance of self-supervised
learning (SSL) automatic speech recognition (ASR) models with curated speech
data is not apparent with challenging speech samples from clinical settings.
One of the key questions for successfully applying ASR models for clinical
applications is whether imperfect transcripts they generate provide sufficient
information for downstream tasks to operate at an acceptable level of accuracy.
In this study, we examine the relationship between the errors produced by
several deep learning ASR systems and their impact on the downstream task of
dementia classification. One of our key findings is that, paradoxically, ASR
systems with relatively high error rates can produce transcripts that result in
better downstream classification accuracy than classification based on verbatim
transcripts.

Hierarchical decomposition of control is unavoidable in large dynamical
systems. In reinforcement learning (RL), it is usually solved with subgoals
defined at higher policy levels and achieved at lower policy levels. Reaching
these goals can take a substantial amount of time, during which it is not
verified whether they are still worth pursuing. However, due to the randomness
of the environment, these goals may become obsolete. In this paper, we address
this gap in the state-of-the-art approaches and propose a method in which the
validity of higher-level actions (thus lower-level goals) is constantly
verified at the higher level. If the actions, i.e. lower level goals, become
inadequate, they are replaced by more appropriate ones. This way we combine the
advantages of hierarchical RL, which is fast training, and flat RL, which is
immediate reactivity. We study our approach experimentally on seven benchmark
environments.

Building trustworthy, effective, and responsible machine learning systems
hinges on understanding how differences in training data and modeling decisions
interact to impact predictive performance. In this work, we seek to better
understand how we might characterize, detect, and design for data-model
synergies. We focus on a particular type of data-model inefficiency, in which
adding training data from some sources can actually lower performance evaluated
on key sub-groups of the population, a phenomenon we refer to as negative data
externalities on group performance. Such externalities can arise in standard
learning settings and can manifest differently depending on conditions between
training set size and model size. Data externalities directly imply a lower
bound on feasible model improvements, yet improving models efficiently requires
understanding the underlying data-model tensions. From a broader perspective,
our results indicate that data-efficiency is a key component of both accurate
and trustworthy machine learning.

We propose a clustering procedure to group K populations into subgroups with
the same dependence structure. The method is adapted to paired population and
can be used with panel data. It relies on the differences between orthogonal
projection coefficients of the K density copulas estimated from the K
populations. Each cluster is then constituted by populations having
significantly similar dependence structures. A recent test statistic from
Ngounou-Bakam and Pommeret (2022) is used to construct automatically such
clusters. The procedure is data driven and depends on the asymptotic level of
the test. We illustrate our clustering algorithm via numerical studies and
through two real datasets: a panel of financial datasets and insurance dataset
of losses and allocated loss adjustment expense.

Genome-wide studies leveraging recent high-throughput sequencing technologies
collect high-dimensional data. However, they usually include small cohorts of
patients, and the resulting tabular datasets suffer from the "curse of
dimensionality". Training neural networks on such datasets is typically
unstable, and the models overfit. One problem is that modern weight
initialisation strategies make simplistic assumptions unsuitable for small-size
datasets. We propose Graph-Conditioned MLP, a novel method to introduce priors
on the parameters of an MLP. Instead of randomly initialising the first layer,
we condition it directly on the training data. More specifically, we create a
graph for each feature in the dataset (e.g., a gene), where each node
represents a sample from the same dataset (e.g., a patient). We then use Graph
Neural Networks (GNNs) to learn embeddings from these graphs and use the
embeddings to initialise the MLP's parameters. Our approach opens the prospect
of introducing additional biological knowledge when constructing the graphs. We
present early results on 7 classification tasks from gene expression data and
show that GC-MLP outperforms an MLP.

We introduce a new benchmark dataset, Placenta, for node classification in an
underexplored domain: predicting microanatomical tissue structures from cell
graphs in placenta histology whole slide images. This problem is uniquely
challenging for graph learning for a few reasons. Cell graphs are large (>1
million nodes per image), node features are varied (64-dimensions of 11 types
of cells), class labels are imbalanced (9 classes ranging from 0.21% of the
data to 40.0%), and cellular communities cluster into heterogeneously
distributed tissues of widely varying sizes (from 11 nodes to 44,671 nodes for
a single structure). Here, we release a dataset consisting of two cell graphs
from two placenta histology images totalling 2,395,747 nodes, 799,745 of which
have ground truth labels. We present inductive benchmark results for 7 scalable
models and show how the unique qualities of cell graphs can help drive the
development of novel graph neural network architectures.

We investigate the efficacy of treating all the parameters in a Bayesian
neural network stochastically and find compelling theoretical and empirical
evidence that this standard construction may be unnecessary. To this end, we
prove that expressive predictive distributions require only small amounts of
stochasticity. In particular, partially stochastic networks with only $n$
stochastic biases are universal probabilistic predictors for $n$-dimensional
predictive problems. In empirical investigations, we find no systematic benefit
of full stochasticity across four different inference modalities and eight
datasets; partially stochastic networks can match and sometimes even outperform
fully stochastic networks, despite their reduced memory costs.

Explainable artificial intelligence (XAI) provides explanations for not
interpretable machine learning (ML) models. While many technical approaches
exist, there is a lack of validation of these techniques on real-world
datasets. In this work, we present a use-case of XAI: an ML model which is
trained to estimate electrification rates based on mobile phone data in
Senegal. The data originate from the Data for Development challenge by Orange
in 2014/15. We apply two model-agnostic, local explanation techniques and find
that while the model can be verified, it is biased with respect to the
population density. We conclude our paper by pointing to the two main
challenges we encountered during our work: data processing and model design
that might be restricted by currently available XAI methods, and the importance
of domain knowledge to interpret explanations.

The brain's white matter (WM) undergoes developmental and degenerative
processes during the human lifespan. To investigate the relationship between WM
anatomical regions and age, we study diffusion magnetic resonance imaging
tractography that is finely parcellated into fiber clusters in the deep,
superficial, and cerebellar WM. We propose a deep-learning-based age prediction
model that leverages large convolutional kernels and inverted bottlenecks. We
improve performance using novel discrete multi-faceted mix data augmentation
and a novel prior-knowledge-based loss function that encourages age predictions
in the expected range. We study a dataset of 965 healthy young adults (22-37
years) derived from the Human Connectome Project (HCP). Experimental results
demonstrate that the proposed model achieves a mean absolute error of 2.59
years and outperforms compared methods. We find that the deep WM is the most
informative for age prediction in this cohort, while the superficial WM is the
least informative. Overall, the most predictive WM tracts are the
thalamo-frontal tract from the deep WM and the intracerebellar input and
Purkinje tract from the cerebellar WM.

Economy is severely dependent on the stock market. An uptrend usually
corresponds to prosperity while a downtrend correlates to recession. Predicting
the stock market has thus been a centre of research and experiment for a long
time. Being able to predict short term movements in the market enables
investors to reap greater returns on their investments. Stock prices are
extremely volatile and sensitive to financial market. In this paper we use Deep
Learning networks to predict stock prices, assimilating financial, business and
technology news articles which present information about the market. First, we
create a simple Multilayer Perceptron (MLP) network and then expand into more
complex Recurrent Neural Network (RNN) like Long Short Term Memory (LSTM), and
finally propose FinBERT-LSTM model, which integrates news article sentiments to
predict stock price with greater accuracy by analysing short-term market
information. We then train the model on NASDAQ-100 index stock data and New
York Times news articles to evaluate the performance of MLP, LSTM, FinBERT-LSTM
models using mean absolute error (MAE), mean absolute percentage error (MAPE)
and accuracy metrics.

In modern neuroscience, memory has been postulated to stored in neural
circuits as sequential spike train and Reverberation is one of the specific
example.Former research has made much progress on phenomenon description.
However, the mechanism of reverberation has been unclear yet.
  In this study, combining electrophysiological record and numerical
simulation, we confirmed a formerly unrealized neuron property that is
necessary for the burst generation in reverberation.
  Secondly, we find out the mechanism of sequential pattern generation which
clearly explained by network topology and asynchronous neurotransmitter
release. In addition, we also developed a pipeline that could design the
network fire in manually set order.
  Thirdly, we explored the dynamics of STDP learning and chased down the
effects of STDP Rule in reverberation. With these understandings, we developed
a STDP based learning rule which could drive the network to remember any
presupposed sequence.
  These results indicated that neuron circuit can remember malformation through
STDP rule. Those information are stored in synapse connections. By this way,
animals remember information as spike sequence pattern.

Gaussian process training decomposes into inference of the (approximate)
posterior and learning of the hyperparameters. For non-Gaussian (non-conjugate)
likelihoods, two common choices for approximate inference are Expectation
Propagation (EP) and Variational Inference (VI), which have complementary
strengths and weaknesses. While VI's lower bound to the marginal likelihood is
a suitable objective for inferring the approximate posterior, it does not
automatically imply it is a good learning objective for hyperparameter
optimization. We design a hybrid training procedure where the inference
leverages conjugate-computation VI and the learning uses an EP-like marginal
likelihood approximation. We empirically demonstrate on binary classification
that this provides a good learning objective and generalizes better.

The past decade has witnessed the advances of artificial intelligence with
various applications in engineering. Recently, artificial neural network
empowered inverse design for metasurfaces has been developed that can design
on-demand meta-atoms with diverse shapes and high performance, where the design
process based on artificial intelligence is fast and automatic. However, once
the inverse-designed static meta-atom is fabricated, the function of the
metasurface is fixed. Reconfigurable metasurfaces can realize dynamic
functions, while applying artificial intelligence to design practical
reconfigurable meta-atoms inversely has not been reported yet. Here, we present
a deep-learning-empowered inverse design method for freeform reconfigurable
metasurfaces, which can generate on-demand reconfigurable coding meta-atoms at
self-defined frequency bands. To reduce the scale of dataset, a decoupling
method of the reconfigurable meta-atom based on microwave network theory is
proposed at first, which can convert the inverse design process for
reconfigurable coding meta-atoms to the inverse design for static structures. A
convolutional neural network model is trained to predict the responses of
free-shaped meta-atoms, and the genetic algorithm is applied to generate the
optimal structure patterns rapidly. As a demonstration of concept, several
inverse-designed examples are generated with different self-defined spectrum
responses in microwave band, and an inverse-designed wideband reconfigurable
metasurface prototype is fabricated and measured for beam scanning applications
with broad bandwidth. Our work paves the way for the fast and automatic design
process of high-performance reconfigurable metasurfaces.

Pronoun resolution is a challenging subset of an essential field in natural
language processing called coreference resolution. Coreference resolution is
about finding all entities in the text that refers to the same real-world
entity. This paper presents a hybrid model combining multiple rulebased sieves
with a machine-learning sieve for pronouns. For this purpose, seven
high-precision rule-based sieves are designed for the Persian language. Then, a
random forest classifier links pronouns to the previous partial clusters. The
presented method demonstrates exemplary performance using pipeline design and
combining the advantages of machine learning and rulebased methods. This method
has solved some challenges in end-to-end models. In this paper, the authors
develop a Persian coreference corpus called Mehr in the form of 400 documents.
This corpus fixes some weaknesses of the previous corpora in the Persian
language. Finally, the efficiency of the presented system compared to the
earlier model in Persian is reported by evaluating the proposed method on the
Mehr and Uppsala test sets.

For many years, Evolutionary Algorithms (EAs) have been applied to improve
Neural Networks (NNs) architectures. They have been used for solving different
problems, such as training the networks (adjusting the weights), designing
network topology, optimizing global parameters, and selecting features. Here,
we provide a systematic brief survey about applications of the EAs on the
specific domain of the recurrent NNs named Reservoir Computing (RC). At the
beginning of the 2000s, the RC paradigm appeared as a good option for employing
recurrent NNs without dealing with the inconveniences of the training
algorithms. RC models use a nonlinear dynamic system, with fixed recurrent
neural network named the \textit{reservoir}, and learning process is restricted
to adjusting a linear parametric function. %so the performance of learning is
fast and precise. However, an RC model has several hyper-parameters, therefore
EAs are helpful tools to figure out optimal RC architectures. We provide an
overview of the results on the area, discuss novel advances, and we present our
vision regarding the new trends and still open questions.

Trusting the predictions of deep learning models in safety critical settings
such as the medical domain is still not a viable option. Distentangled
uncertainty quantification in the field of medical imaging has received little
attention. In this paper, we study disentangled uncertainties in image to image
translation tasks in the medical domain. We compare multiple uncertainty
quantification methods, namely Ensembles, Flipout, Dropout, and DropConnect,
while using CycleGAN to convert T1-weighted brain MRI scans to T2-weighted
brain MRI scans. We further evaluate uncertainty behavior in the presence of
out of distribution data (Brain CT and RGB Face Images), showing that epistemic
uncertainty can be used to detect out of distribution inputs, which should
increase reliability of model outputs.

Automated identification of myocardial scar from late gadolinium enhancement
cardiac magnetic resonance images (LGE-CMR) is limited by image noise and
artifacts such as those related to motion and partial volume effect. This paper
presents a novel joint deep learning (JDL) framework that improves such tasks
by utilizing simultaneously learned myocardium segmentations to eliminate
negative effects from non-region-of-interest areas. In contrast to previous
approaches treating scar detection and myocardium segmentation as separate or
parallel tasks, our proposed method introduces a message passing module where
the information of myocardium segmentation is directly passed to guide scar
detectors. This newly designed network will efficiently exploit joint
information from the two related tasks and use all available sources of
myocardium segmentation to benefit scar identification. We demonstrate the
effectiveness of JDL on LGE-CMR images for automated left ventricular (LV) scar
detection, with great potential to improve risk prediction in patients with
both ischemic and non-ischemic heart disease and to improve response rates to
cardiac resynchronization therapy (CRT) for heart failure patients.
Experimental results show that our proposed approach outperforms multiple
state-of-the-art methods, including commonly used two-step
segmentation-classification networks, and multitask learning schemes where
subtasks are indirectly interacted.

We investigated a machine learning method for joint estimation of
polarization and phase for use in a Gaussian modulated CV-QKD system, over an
18 hour period measured on a installed fiber with 5.5 dB attenuation.

Safety-critical applications like autonomous driving use Deep Neural Networks
(DNNs) for object detection and segmentation. The DNNs fail to predict when
they observe an Out-of-Distribution (OOD) input leading to catastrophic
consequences. Existing OOD detection methods were extensively studied for image
inputs but have not been explored much for LiDAR inputs. So in this study, we
proposed two datasets for benchmarking OOD detection in 3D semantic
segmentation. We used Maximum Softmax Probability and Entropy scores generated
using Deep Ensembles and Flipout versions of RandLA-Net as OOD scores. We
observed that Deep Ensembles out perform Flipout model in OOD detection with
greater AUROC scores for both datasets.

Actively monitoring machine learning models during production operations
helps ensure prediction quality and detection and remediation of unexpected or
undesired conditions. Monitoring models already deployed in big data
environments brings the additional challenges of adding monitoring in parallel
to the existing modelling workflow and controlling resource requirements. In
this paper, we describe (1) a framework for monitoring machine learning models;
and, (2) its implementation for a big data supply chain application. We use our
implementation to study drift in model features, predictions, and performance
on three real data sets. We compare hypothesis test and information theoretic
approaches to drift detection in features and predictions using the
Kolmogorov-Smirnov distance and Bhattacharyya coefficient. Results showed that
model performance was stable over the evaluation period. Features and
predictions showed statistically significant drifts; however, these drifts were
not linked to changes in model performance during the time of our study.

The selection of an optimal pacing site, which is ideally scar-free and late
activated, is critical to the response of cardiac resynchronization therapy
(CRT). Despite the success of current approaches formulating the detection of
such late mechanical activation (LMA) regions as a problem of activation time
regression, their accuracy remains unsatisfactory, particularly in cases where
myocardial scar exists. To address this issue, this paper introduces a
multi-task deep learning framework that simultaneously estimates LMA amount and
classify the scar-free LMA regions based on cine displacement encoding with
stimulated echoes (DENSE) magnetic resonance imaging (MRI). With a newly
introduced auxiliary LMA region classification sub-network, our proposed model
shows more robustness to the complex pattern cause by myocardial scar,
significantly eliminates their negative effects in LMA detection, and in turn
improves the performance of scar classification. To evaluate the effectiveness
of our method, we tests our model on real cardiac MR images and compare the
predicted LMA with the state-of-the-art approaches. It shows that our approach
achieves substantially increased accuracy. In addition, we employ the
gradient-weighted class activation mapping (Grad-CAM) to visualize the feature
maps learned by all methods. Experimental results suggest that our proposed
model better recognizes the LMA region pattern.

Advances in reinforcement learning (RL) often rely on massive compute
resources and remain notoriously sample inefficient. In contrast, the human
brain is able to efficiently learn effective control strategies using limited
resources. This raises the question whether insights from neuroscience can be
used to improve current RL methods. Predictive processing is a popular
theoretical framework which maintains that the human brain is actively seeking
to minimize surprise. We show that recurrent neural networks which predict
their own sensory states can be leveraged to minimise surprise, yielding
substantial gains in cumulative reward. Specifically, we present the Predictive
Processing Proximal Policy Optimization (P4O) agent; an actor-critic
reinforcement learning agent that applies predictive processing to a recurrent
variant of the PPO algorithm by integrating a world model in its hidden state.
P4O significantly outperforms a baseline recurrent variant of the PPO algorithm
on multiple Atari games using a single GPU. It also outperforms other
state-of-the-art agents given the same wall-clock time and exceeds human gamer
performance on multiple games including Seaquest, which is a particularly
challenging environment in the Atari domain. Altogether, our work underscores
how insights from the field of neuroscience may support the development of more
capable and efficient artificial agents.

Increasingly high-stakes decisions are made using neural networks in order to
make predictions. Specifically, meteorologists and hedge funds apply these
techniques to time series data. When it comes to prediction, there are certain
limitations for machine learning models (such as lack of expressiveness,
vulnerability of domain shifts and overconfidence) which can be solved using
uncertainty estimation. There is a set of expectations regarding how
uncertainty should ``behave". For instance, a wider prediction horizon should
lead to more uncertainty or the model's confidence should be proportional to
its accuracy. In this paper, different uncertainty estimation methods are
compared to forecast meteorological time series data and evaluate these
expectations. The results show how each uncertainty estimation method performs
on the forecasting task, which partially evaluates the robustness of predicted
uncertainty.

In today's data-driven society, supervised machine learning is rapidly
evolving, and the need for labeled data is increasing. However, the process of
acquiring labels is often expensive and tedious. For this reason, we developed
ALANNO, an open-source annotation system for NLP tasks powered by active
learning. We focus on the practical challenges in deploying active learning
systems and try to find solutions to make active learning effective in
real-world applications. We support the system with a wealth of active learning
methods and underlying machine learning models. In addition, we leave open the
possibility to add new methods, which makes the platform useful for both
high-quality data annotation and research purposes.

Graph Neural Networks (GNNs) are deep learning models designed to process
attributed graphs. GNNs can compute cluster assignments accounting both for the
vertex features and for the graph topology. Existing GNNs for clustering are
trained by optimizing an unsupervised minimum cut objective, which is
approximated by a Spectral Clustering (SC) relaxation. SC offers a closed-form
solution that, however, is not particularly useful for a GNN trained with
gradient descent. Additionally, the SC relaxation is loose and yields overly
smooth cluster assignments, which do not separate well the samples. We propose
a GNN model that optimizes a tighter relaxation of the minimum cut based on
graph total variation (GTV). Our model has two core components: i) a
message-passing layer that minimizes the $\ell_1$ distance in the features of
adjacent vertices, which is key to achieving sharp cluster transitions; ii) a
loss function that minimizes the GTV in the cluster assignments while ensuring
balanced partitions. By optimizing the proposed loss, our model can be
self-trained to perform clustering. In addition, our clustering procedure can
be used to implement graph pooling in deep GNN architectures for graph
classification. Experiments show that our model outperforms other GNN-based
approaches for clustering and graph pooling.

Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data
science competition standpoint, have limited utility in clinical use because of
their narrow focus on diagnosing one specific disease. In real-world clinical
use, multiple diseases need to be considered since they can co-exist in the
same patient. In this work, we demonstrate how federated learning (FL) can be
used to make these toy CXR datasets from Kaggle clinically useful.
Specifically, we train a single FL classification model (`global`) using two
separate CXR datasets -- one annotated for presence of pneumonia and the other
for presence of pneumothorax (two common and life-threatening conditions) --
capable of diagnosing both. We compare the performance of the global FL model
with models trained separately on both datasets (`baseline`) for two different
model architectures. On a standard, naive 3-layer CNN architecture, the global
FL model achieved AUROC of 0.84 and 0.81 for pneumonia and pneumothorax,
respectively, compared to 0.85 and 0.82, respectively, for both baseline models
(p>0.05). Similarly, on a pretrained DenseNet121 architecture, the global FL
model achieved AUROC of 0.88 and 0.91 for pneumonia and pneumothorax,
respectively, compared to 0.89 and 0.91, respectively, for both baseline models
(p>0.05). Our results suggest that FL can be used to create global `meta`
models to make toy datasets from Kaggle clinically useful, a step forward
towards bridging the gap from bench to bedside.

The generation of Chinese fonts has a wide range of applications. The
currently predominated methods are mainly based on deep generative models,
especially the generative adversarial networks (GANs). However, existing
GAN-based models usually suffer from the well-known mode collapse problem. When
mode collapse happens, the kind of GAN-based models will be failure to yield
the correct fonts. To address this issue, we introduce a one-bit stroke
encoding and a few-shot semi-supervised scheme (i.e., using a few paired data
as semi-supervised information) to explore the local and global structure
information of Chinese characters respectively, motivated by the intuition that
strokes and characters directly embody certain local and global modes of
Chinese characters. Based on these ideas, this paper proposes an effective
model called \textit{StrokeGAN+}, which incorporates the stroke encoding and
the few-shot semi-supervised scheme into the CycleGAN model. The effectiveness
of the proposed model is demonstrated by amounts of experiments. Experimental
results show that the mode collapse issue can be effectively alleviated by the
introduced one-bit stroke encoding and few-shot semi-supervised training
scheme, and that the proposed model outperforms the state-of-the-art models in
fourteen font generation tasks in terms of four important evaluation metrics
and the quality of generated characters. Besides CycleGAN, we also show that
the proposed idea can be adapted to other existing models to improve their
performance. The effectiveness of the proposed model for the zero-shot
traditional Chinese font generation is also evaluated in this paper.

In this paper, we first reinvestigate the convergence of vanilla SGD method
under more general learning rates conditions and a more general convex
assumption. Then, by taking advantage of the Lyapunov function technique, we
present the convergence of the momentum SGD and Nesterov accelerated SGD
methods for the convex and non-convex problem under L-smooth assumption,
respectively. The convergence of time averaged SGD was also analyzed.

The fact that we can build models from data, and therefore refine our models
with more data from experiments, is usually given for granted in scientific
inquiry. However, how much information can we extract, and how precise can we
expect our learned model to be, if we have only a finite amount of data at our
disposal? Nuclear physics demands an high degree of precision from models that
are inferred from the limited number of nuclei that can be possibly made in the
laboratories.
  In manuscript I will introduce some concepts of computational science, such
as statistical theory of learning and Hamiltonian complexity, and use them to
contextualise the results concerning the amount of data necessary to
extrapolate a mass model to a given precision.

The dichotomy between the challenging nature of obtaining annotations for
activities, and the more straightforward nature of data collection from
wearables, has resulted in significant interest in the development of
techniques that utilize large quantities of unlabeled data for learning
representations. Contrastive Predictive Coding (CPC) is one such method,
learning effective representations by leveraging properties of time-series data
to setup a contrastive future timestep prediction task. In this work, we
propose enhancements to CPC, by systematically investigating the encoder
architecture, the aggregator network, and the future timestep prediction,
resulting in a fully convolutional architecture, thereby improving
parallelizability. Across sensor positions and activities, our method shows
substantial improvements on four of six target datasets, demonstrating its
ability to empower a wide range of application scenarios. Further, in the
presence of very limited labeled data, our technique significantly outperforms
both supervised and self-supervised baselines, positively impacting situations
where collecting only a few seconds of labeled data may be possible. This is
promising, as CPC does not require specialized data transformations or
reconstructions for learning effective representations.

Humans often speak in a continuous manner which leads to coherent and
consistent prosody properties across neighboring utterances. However, most
state-of-the-art speech synthesis systems only consider the information within
each sentence and ignore the contextual semantic and acoustic features. This
makes it inadequate to generate high-quality paragraph-level speech which
requires high expressiveness and naturalness. To synthesize natural and
expressive speech for a paragraph, a context-aware speech synthesis system
named MaskedSpeech is proposed in this paper, which considers both contextual
semantic and acoustic features. Inspired by the masking strategy in the speech
editing research, the acoustic features of the current sentence are masked out
and concatenated with those of contextual speech, and further used as
additional model input. The phoneme encoder takes the concatenated phoneme
sequence from neighboring sentences as input and learns fine-grained semantic
information from contextual text. Furthermore, cross-utterance coarse-grained
semantic features are employed to improve the prosody generation. The model is
trained to reconstruct the masked acoustic features with the augmentation of
both the contextual semantic and acoustic features. Experimental results
demonstrate that the proposed MaskedSpeech outperformed the baseline system
significantly in terms of naturalness and expressiveness.

Graph neural networks (GNNs) have been successfully applied to early mild
cognitive impairment (EMCI) detection, with the usage of elaborately designed
features constructed from blood oxygen level-dependent (BOLD) time series.
However, few works explored the feasibility of using BOLD signals directly as
features. Meanwhile, existing GNN-based methods primarily rely on hand-crafted
explicit brain topology as the adjacency matrix, which is not optimal and
ignores the implicit topological organization of the brain. In this paper, we
propose a spatial temporal graph convolutional network with a novel graph
structure self-learning mechanism for EMCI detection. The proposed spatial
temporal graph convolution block directly exploits BOLD time series as input
features, which provides an interesting view for rsfMRI-based preclinical AD
diagnosis. Moreover, our model can adaptively learn the optimal topological
structure and refine edge weights with the graph structure self-learning
mechanism. Results on the Alzheimer's Disease Neuroimaging Initiative (ADNI)
database show that our method outperforms state-of-the-art approaches.
Biomarkers consistent with previous studies can be extracted from the model,
proving the reliable interpretability of our method.

With the rapid development of the speech synthesis system, recent
text-to-speech models have reached the level of generating natural speech
similar to what humans say. But there still have limitations in terms of
expressiveness. In particular, the existing emotional speech synthesis models
have shown controllability using interpolated features with scaling parameters
in emotional latent space. However, the emotional latent space generated from
the existing models is difficult to control the continuous emotional intensity
because of the entanglement of features like emotions, speakers, etc. In this
paper, we propose a novel method to control the continuous intensity of
emotions using semi-supervised learning. The model learns emotions of
intermediate intensity using pseudo-labels generated from phoneme-level
sequences of speech information. An embedding space built from the proposed
model satisfies the uniform grid geometry with an emotional basis. In addition,
to improve the naturalness of intermediate emotional speech, a discriminator is
applied to the generation of low-level elements like duration, pitch and
energy. The experimental results showed that the proposed method was superior
in controllability and naturalness. The synthesized speech samples are
available at https://tinyurl.com/34zaehh2

Explainable artificial intelligence is proposed to provide explanations for
reasoning performed by an Artificial Intelligence. There is no consensus on how
to evaluate the quality of these explanations, since even the definition of
explanation itself is not clear in the literature. In particular, for the
widely known Local Linear Explanations, there are qualitative proposals for the
evaluation of explanations, although they suffer from theoretical
inconsistencies. The case of image is even more problematic, where a visual
explanation seems to explain a decision while detecting edges is what it really
does. There are a large number of metrics in the literature specialized in
quantitatively measuring different qualitative aspects so we should be able to
develop metrics capable of measuring in a robust and correct way the desirable
aspects of the explanations. In this paper, we propose a procedure called REVEL
to evaluate different aspects concerning the quality of explanations with a
theoretically coherent development. This procedure has several advances in the
state of the art: it standardizes the concepts of explanation and develops a
series of metrics not only to be able to compare between them but also to
obtain absolute information regarding the explanation itself. The experiments
have been carried out on image four datasets as benchmark where we show REVEL's
descriptive and analytical power.

Human leukocyte antigen (HLA) genes are associated with a variety of
diseases, however direct typing of HLA is time and cost consuming. Thus various
imputation methods using sequential SNPs data have been proposed based on
statistical or deep learning models, e.g. CNN-based model, named DEEP*HLA.
However, imputation efficiency is not sufficient for in frequent alleles and a
large size of reference panel is required. Here, we developed a
Transformer-based model to impute HLA alleles, named "HLA Reliable IMputatioN
by Transformer (HLARIMNT)" to take advantage of sequential nature of SNPs data.
We validated the performance of HLARIMNT using two different reference panels;
Pan-Asian reference panel (n = 530) and Type 1 Diabetes Genetics Consortium
(T1DGC) reference panel (n = 5,225), as well as the mixture of those two panels
(n = 1,060). HLARIMNT achieved higher accuracy than DEEP*HLA by several
indices, especially for infrequent alleles. We also varied the size of data
used for training, and HLARIMNT imputed more accurately among any size of
training data. These results suggest that Transformer-based model may impute
efficiently not only HLA types but also any other gene types from sequential
SNPs data.

Bayesian Optimization is a useful tool for experiment design. Unfortunately,
the classical, sequential setting of Bayesian Optimization does not translate
well into laboratory experiments, for instance battery design, where
measurements may come from different sources and their evaluations may require
significant waiting times. Multi-fidelity Bayesian Optimization addresses the
setting with measurements from different sources. Asynchronous batch Bayesian
Optimization provides a framework to select new experiments before the results
of the prior experiments are revealed. This paper proposes an algorithm
combining multi-fidelity and asynchronous batch methods. We empirically study
the algorithm behavior, and show it can outperform single-fidelity batch
methods and multi-fidelity sequential methods. As an application, we consider
designing electrode materials for optimal performance in pouch cells using
experiments with coin cells to approximate battery performance.

Due to its importance in facial behaviour analysis, facial action unit (AU)
detection has attracted increasing attention from the research community.
Leveraging the online knowledge distillation framework, we propose the
``FANTrans" method for AU detection. Our model consists of a hybrid network of
convolution and transformer blocks to learn per-AU features and to model AU
co-occurrences. The model uses a pre-trained face alignment network as the
feature extractor. After further transformation by a small learnable add-on
convolutional subnet, the per-AU features are fed into transformer blocks to
enhance their representation. As multiple AUs often appear together, we propose
a learnable attention drop mechanism in the transformer block to learn the
correlation between the features for different AUs. We also design a classifier
that predicts AU presence by considering all AUs' features, to explicitly
capture label dependencies. Finally, we make the attempt of adapting online
knowledge distillation in the training stage for this task, further improving
the model's performance. Experiments on the BP4D and DISFA datasets
demonstrating the effectiveness of proposed method.

Bayesian inference has theoretical attractions as a principled framework for
reasoning about beliefs. However, the motivations of Bayesian inference which
claim it to be the only 'rational' kind of reasoning do not apply in practice.
They create a binary split in which all approximate inference is equally
'irrational'. Instead, we should ask ourselves how to define a spectrum of
more- and less-rational reasoning that explains why we might prefer one
Bayesian approximation to another. I explore approximate inference in Bayesian
neural networks and consider the unintended interactions between the
probabilistic model, approximating distribution, optimization algorithm, and
dataset. The complexity of these interactions highlights the difficulty of any
strategy for evaluating Bayesian approximations which focuses entirely on the
method, outside the context of specific datasets and decision-problems. For
given applications, the expected utility of the approximate posterior can
measure inference quality. To assess a model's ability to incorporate different
parts of the Bayesian framework we can identify desirable characteristic
behaviours of Bayesian reasoning and pick decision-problems that make heavy use
of those behaviours. Here, we use continual learning (testing the ability to
update sequentially) and active learning (testing the ability to represent
credence). But existing continual and active learning set-ups pose challenges
that have nothing to do with posterior quality which can distort their ability
to evaluate Bayesian approximations. These unrelated challenges can be removed
or reduced, allowing better evaluation of approximate inference methods.

It is important to guarantee that machine learning algorithms deployed in the
real world do not result in unfairness or unintended social consequences. Fair
ML has largely focused on the protection of single attributes in the simpler
setting where both attributes and target outcomes are binary. However, the
practical application in many a real-world problem entails the simultaneous
protection of multiple sensitive attributes, which are often not simply binary,
but continuous or categorical. To address this more challenging task, we
introduce FairCOCCO, a fairness measure built on cross-covariance operators on
reproducing kernel Hilbert Spaces. This leads to two practical tools: first,
the FairCOCCO Score, a normalised metric that can quantify fairness in settings
with single or multiple sensitive attributes of arbitrary type; and second, a
subsequent regularisation term that can be incorporated into arbitrary learning
objectives to obtain fair predictors. These contributions address crucial gaps
in the algorithmic fairness literature, and we empirically demonstrate
consistent improvements against state-of-the-art techniques in balancing
predictive power and fairness on real-world datasets.

The present paper reviews and discusses work from computer science that
proposes to identify concepts in internal representations (hidden layers) of
DNNs. It is examined, first, how existing methods actually identify concepts
that are supposedly represented in DNNs. Second, it is discussed how conceptual
spaces -- sets of concepts in internal representations -- are shaped by a
tradeoff between predictive accuracy and compression. These issues are
critically examined by drawing on philosophy. While there is evidence that DNNs
able to represent non-trivial inferential relations between concepts, our
ability to identify concepts is severely limited.

The electrification of shared mobility has become popular across the globe.
Many cities have their new shared e-mobility systems deployed, with
continuously expanding coverage from central areas to the city edges. A key
challenge in the operation of these systems is fleet rebalancing, i.e., how EVs
should be repositioned to better satisfy future demand. This is particularly
challenging in the context of expanding systems, because i) the range of the
EVs is limited while charging time is typically long, which constrain the
viable rebalancing operations; and ii) the EV stations in the system are
dynamically changing, i.e., the legitimate targets for rebalancing operations
can vary over time. We tackle these challenges by first investigating rich sets
of data collected from a real-world shared e-mobility system for one year,
analyzing the operation model, usage patterns and expansion dynamics of this
new mobility mode. With the learned knowledge we design a high-fidelity
simulator, which is able to abstract key operation details of EV sharing at
fine granularity. Then we model the rebalancing task for shared e-mobility
systems under continuous expansion as a Multi-Agent Reinforcement Learning
(MARL) problem, which directly takes the range and charging properties of the
EVs into account. We further propose a novel policy optimization approach with
action cascading, which is able to cope with the expansion dynamics and solve
the formulated MARL. We evaluate the proposed approach extensively, and
experimental results show that our approach outperforms the state-of-the-art,
offering significant performance gain in both satisfied demand and net revenue.

Solving real-world sequential manipulation tasks requires robots to have a
repertoire of skills applicable to a wide range of circumstances. To acquire
such skills using data-driven approaches, we need massive and diverse training
data which is often labor-intensive and non-trivial to collect and curate. In
this work, we introduce Active Task Randomization (ATR), an approach that
learns visuomotor skills for sequential manipulation by automatically creating
feasible and novel tasks in simulation. During training, our approach
procedurally generates tasks using a graph-based task parameterization. To
adaptively estimate the feasibility and novelty of sampled tasks, we develop a
relational neural network that maps each task parameter into a compact
embedding. We demonstrate that our approach can automatically create suitable
tasks for efficiently training the skill policies to handle diverse scenarios
with a variety of objects. We evaluate our method on simulated and real-world
sequential manipulation tasks by composing the learned skills using a task
planner. Compared to baseline methods, the skills learned using our approach
consistently achieve better success rates.

Purpose: Human-machine collaboration is a promising strategy to improve
hazard inspection. However, research on the effective integration of opinions
from humans with machines for optimal group decision making is lacking. Hence,
considering the benefits of a brain-computer interface (BCI) to enable
intuitive commutation, this study proposes a novel method to predict human
hazard response choices and decision confidence from brain activities for a
superior confidence-weighted voting strategy. Methodology: First, we developed
a Bayesian inference-based algorithm to ascertain the decision threshold above
which a hazard is reported from human brain signals. This method was tested
empirically with electroencephalogram (EEG) data collected in a laboratory
setting and cross-validated using behavioral indices of the signal detection
theory. Subsequently, based on numerical simulations, the decision criteria for
low-, medium-, and high-confidence level differentiations characterized by
parietal alpha-band EEG power were determined. Findings : The investigated
hazard recognition task was described as a process of probabilistic inference
involving a decision uncertainty evaluation. The results demonstrated the
feasibility of EEG measurements in observing human internal representations of
hazard discrimination. Moreover, the optimal criteria to differentiate between
low-, medium-, and high-confidence levels were obtained by benchmarking against
an optimal Bayesian observer. Originality: This research demonstrates the
potential of a BCI as an effective channel for telecommunication, laying the
foundation for the design of future hazard detection techniques in the
collaborative human-machine systems research field.

Despite the immense success of neural networks in modeling system dynamics
from data, they often remain physics-agnostic black boxes. In the particular
case of physical systems, they might consequently make physically inconsistent
predictions, which makes them unreliable in practice. In this paper, we
leverage the framework of Irreversible port-Hamiltonian Systems (IPHS), which
can describe most multi-physics systems, and rely on Neural Ordinary
Differential Equations (NODEs) to learn their parameters from data. Since IPHS
models are consistent with the first and second principles of thermodynamics by
design, so are the proposed Physically Consistent NODEs (PC-NODEs).
Furthermore, the NODE training procedure allows us to seamlessly incorporate
prior knowledge of the system properties in the learned dynamics. We
demonstrate the effectiveness of the proposed method by learning the
thermodynamics of a building from the real-world measurements and the dynamics
of a simulated gas-piston system. Thanks to the modularity and flexibility of
the IPHS framework, PC-NODEs can be extended to learn physically consistent
models of multi-physics distributed systems.

Universal cross-lingual sentence embeddings map semantically similar
cross-lingual sentences into a shared embedding space. Aligning cross-lingual
sentence embeddings usually requires supervised cross-lingual parallel
sentences. In this work, we propose mSimCSE, which extends SimCSE to
multilingual settings and reveal that contrastive learning on English data can
surprisingly learn high-quality universal cross-lingual sentence embeddings
without any parallel data. In unsupervised and weakly supervised settings,
mSimCSE significantly improves previous sentence embedding methods on
cross-lingual retrieval and multilingual STS tasks. The performance of
unsupervised mSimCSE is comparable to fully supervised methods in retrieving
low-resource languages and multilingual STS. The performance can be further
enhanced when cross-lingual NLI data is available. Our code is publicly
available at https://github.com/yaushian/mSimCSE.

Determination of treatment need of posterior capsular opacification (PCO)--
one of the most common complication of cataract surgery -- is a difficult
process due to its local unavailability and the fact that treatment is provided
only after PCO occurs in the central visual axis. In this paper we propose a
deep learning (DL)-based method to first segment PCO images then classify the
images into \textit{treatment required} and \textit{not yet required} cases in
order to reduce frequent hospital visits. To train the model, we prepare a
training image set with ground truths (GT) obtained from two strategies: (i)
manual and (ii) automated. So, we have two models: (i) Model 1 (trained with
image set containing manual GT) (ii) Model 2 (trained with image set containing
automated GT). Both models when evaluated on validation image set gave Dice
coefficient value greater than 0.8 and intersection-over-union (IoU) score
greater than 0.67 in our experiments. Comparison between gold standard GT and
segmented results from our models gave a Dice coefficient value greater than
0.7 and IoU score greater than 0.6 for both the models showing that automated
ground truths can also result in generation of an efficient model. Comparison
between our classification result and clinical classification shows 0.98
F2-score for outputs from both the models.

We introduce KPI-Check, a novel system that automatically identifies and
cross-checks semantically equivalent key performance indicators (KPIs), e.g.
"revenue" or "total costs", in real-world German financial reports. It combines
a financial named entity and relation extraction module with a BERT-based
filtering and text pair classification component to extract KPIs from
unstructured sentences before linking them to synonymous occurrences in the
balance sheet and profit & loss statement. The tool achieves a high matching
performance of $73.00$% micro F$_1$ on a hold out test set and is currently
being deployed for a globally operating major auditing firm to assist the
auditing procedure of financial statements.

Radar, the only sensor that could provide reliable perception capability in
all weather conditions at an affordable cost, has been widely accepted as a key
supplement to camera and LiDAR in modern advanced driver assistance systems
(ADAS) and autonomous driving systems. Recent state-of-the-art works reveal
that fusion of radar and LiDAR can lead to robust detection in adverse weather,
such as fog. However, these methods still suffer from low accuracy of bounding
box estimations. This paper proposes a bird's-eye view (BEV) fusion learning
for an anchor box-free object detection system, which uses the feature derived
from the radar range-azimuth heatmap and the LiDAR point cloud to estimate the
possible objects. Different label assignment strategies have been designed to
facilitate the consistency between the classification of foreground or
background anchor points and the corresponding bounding box regressions.
Furthermore, the performance of the proposed object detector can be further
enhanced by employing a novel interactive transformer module. We demonstrated
the superior performance of the proposed methods in this paper using the
recently published Oxford Radar RobotCar (ORR) dataset. We showed that the
accuracy of our system significantly outperforms the other state-of-the-art
methods by a large margin.

In the past few years, Artificial Intelligence (AI) has garnered attention
from various industries including financial services (FS). AI has made a
positive impact in financial services by enhancing productivity and improving
risk management. While AI can offer efficient solutions, it has the potential
to bring unintended consequences. One such consequence is the pronounced effect
of AI-related unfairness and attendant fairness-related harms. These
fairness-related harms could involve differential treatment of individuals; for
example, unfairly denying a loan to certain individuals or groups of
individuals. In this paper, we focus on identifying and mitigating individual
unfairness and leveraging some of the recently published techniques in this
domain, especially as applicable to the credit adjudication use case. We also
investigate the extent to which techniques for achieving individual fairness
are effective at achieving group fairness. Our main contribution in this work
is functionalizing a two-step training process which involves learning a fair
similarity metric from a group sense using a small portion of the raw data and
training an individually "fair" classifier using the rest of the data where the
sensitive features are excluded. The key characteristic of this two-step
technique is related to its flexibility, i.e., the fair metric obtained in the
first step can be used with any other individual fairness algorithms in the
second step. Furthermore, we developed a second metric (distinct from the fair
similarity metric) to determine how fairly a model is treating similar
individuals. We use this metric to compare a "fair" model against its baseline
model in terms of their individual fairness value. Finally, some experimental
results corresponding to the individual unfairness mitigation techniques are
presented.

The size of an individual cell type, such as a red blood cell, does not vary
much among humans. We use this knowledge as a prior for classifying and
detecting cells in images with only a few ground truth bounding box
annotations, while most of the cells are annotated with points. This setting
leads to weakly semi-supervised learning. We propose replacing points with
either stochastic (ST) boxes or bounding box predictions during the training
process. The proposed "mean-IOU" ST box maximizes the overlap with all the
boxes belonging to the sample space with a class-specific approximated prior
probability distribution of bounding boxes. Our method trains with both box-
and point-labelled images in conjunction, unlike the existing methods, which
train first with box- and then point-labelled images. In the most challenging
setting, when only 5% images are box-labelled, quantitative experiments on a
urine dataset show that our one-stage method outperforms two-stage methods by
5.56 mAP. Furthermore, we suggest an approach that partially answers "how many
box-labelled annotations are necessary?" before training a machine learning
model.

The new wave of digitization induced by Industry 4.0 calls for ubiquitous and
reliable connectivity to perform and automate industrial operations. 5G
networks can afford the extreme requirements of heterogeneous vertical
applications, but the lack of real data and realistic traffic statistics poses
many challenges for the optimization and configuration of the network for
industrial environments. In this paper, we investigate the network traffic data
generated from a laser cutting machine deployed in a Trumpf factory in Germany.
We analyze the traffic statistics, capture the dependencies between the
internal states of the machine, and model the network traffic as a production
state dependent stochastic process. The two-step model is proposed as follows:
first, we model the production process as a multi-state semi-Markov process,
then we learn the conditional distributions of the production state dependent
packet interarrival time and packet size with generative models. We compare the
performance of various generative models including variational autoencoder
(VAE), conditional variational autoencoder (CVAE), and generative adversarial
network (GAN). The numerical results show a good approximation of the traffic
arrival statistics depending on the production state. Among all generative
models, CVAE provides in general the best performance in terms of the smallest
Kullback-Leibler divergence.

How can a population of reinforcement learning agents autonomously learn a
diversity of cooperative tasks in a shared environment? In the single-agent
paradigm, goal-conditioned policies have been combined with intrinsic
motivation mechanisms to endow agents with the ability to master a wide
diversity of autonomously discovered goals. Transferring this idea to
cooperative multi-agent systems (MAS) entails a challenge: intrinsically
motivated agents that sample goals independently focus on a shared cooperative
goal with low probability, impairing their learning performance. In this work,
we propose a new learning paradigm for modeling such settings, the
Decentralized Intrinsically Motivated Skill Acquisition Problem (Dec-IMSAP),
and employ it to solve cooperative navigation tasks. Agents in a Dec-IMSAP are
trained in a fully decentralized way, which comes in contrast to previous
contributions in multi-goal MAS that consider a centralized goal-selection
mechanism. Our empirical analysis indicates that a sufficient condition for
efficiently learning a diversity of cooperative tasks is to ensure that a group
aligns its goals, i.e., the agents pursue the same cooperative goal and learn
to coordinate their actions through specialization. We introduce the
Goal-coordination game, a fully-decentralized emergent communication algorithm,
where goal alignment emerges from the maximization of individual rewards in
multi-goal cooperative environments and show that it is able to reach equal
performance to a centralized training baseline that guarantees aligned goals.
To our knowledge, this is the first contribution addressing the problem of
intrinsically motivated multi-agent goal exploration in a decentralized
training paradigm.

We consider the random feature ridge regression (RFRR) given by a two-layer
neural network at random initialization. We study the non-asymptotic behaviors
of the training error, cross-validations, and generalization error of RFRR with
nearly orthogonal deterministic input data in the overparameterized regime,
where the number of parameters $N$ is much larger than the sample size $n$. We
respectively establish the concentrations of the training errors,
cross-validations, and generalization errors of RFRR around their corresponding
errors of kernel ridge regression (KRR). This KRR is defined by an expected
kernel from a random feature map. We then approximate the performances of the
KRR by a polynomial kernel matrix, whose degree only depends on the
orthogonality among different input vectors. The degree of this polynomial
kernel essentially determines the asymptotic behavior of RFRR and KRR. Our
results hold for a general class of target functions and input data with weak
approximate orthonormal properties among different data points. Based on these
approximations and nearly orthogonality, we obtain a lower bound for the
generalization error of RFRR.

Recently, non-autoregressive (NAR) neural machine translation models have
received increasing attention due to their efficient parallel decoding.
However, the probabilistic framework of NAR models necessitates conditional
independence assumption on target sequences, falling short of characterizing
human language data. This drawback results in less informative learning signals
for NAR models under conventional MLE training, thereby yielding unsatisfactory
accuracy compared to their autoregressive (AR) counterparts. In this paper, we
propose a simple and model-agnostic multi-task learning framework to provide
more informative learning signals. During training stage, we introduce a set of
sufficiently weak AR decoders that solely rely on the information provided by
NAR decoder to make prediction, forcing the NAR decoder to become stronger or
else it will be unable to support its weak AR partners. Experiments on WMT and
IWSLT datasets show that our approach can consistently improve accuracy of
multiple NAR baselines without adding any additional decoding overhead.

Data estimation is conducted with model-based estimation methods since the
beginning of digital communications. However, motivated by the growing success
of machine learning, current research focuses on replacing model-based data
estimation methods by data-driven approaches, mainly neural networks (NNs). In
this work, we particularly investigate the incorporation of existing model
knowledge into data-driven approaches, which is expected to lead to complexity
reduction and / or performance enhancement. We describe three different
options, namely "model-inspired'' pre-processing, choosing an NN architecture
motivated by the properties of the underlying communication system, and
inferring the layer structure of an NN with the help of model knowledge. Most
of the current publications on NN-based data estimation deal with general
multiple-input multiple-output communication (MIMO) systems. In this work, we
investigate NN-based data estimation for so-called unique word orthogonal
frequency division multiplexing (UW-OFDM) systems. We highlight differences
between UW-OFDM systems and general MIMO systems one has to be aware of when
using NNs for data estimation, and we introduce measures for successful
utilization of NN-based data estimators in UW-OFDM systems. Further, we
investigate the use of NNs for data estimation when channel coded data
transmission is conducted, and we present adaptions to be made, such that
NN-based data estimators provide satisfying performance for this case. We
compare the presented NNs concerning achieved bit error ratio performance and
computational complexity, we show the peculiar distributions of their data
estimates, and we also point out their downsides compared to model-based
equalizers.

In this paper, we tested several sparse optimization algorithms based on the
public dataset of the DREAM5 Gene Regulatory Network Inference Challenge. And
we find that introducing 20% of the regulatory network as a priori known data
can provide a basis for parameter selection of inference algorithms, thus
improving prediction efficiency and accuracy. In addition to testing common
sparse optimization methods, we also developed voting algorithms by bagging
them. Experiments on the DREAM5 dataset show that the sparse optimization-based
inference of the moderation relation works well, achieving better results than
the official DREAM5 results on three datasets. However, the performance of
traditional independent algorithms varies greatly in the face of different
datasets, while our voting algorithm achieves the best results on three of the
four datasets.

Predicting the health risks of patients using Electronic Health Records (EHR)
has attracted considerable attention in recent years, especially with the
development of deep learning techniques. Health risk refers to the probability
of the occurrence of a specific health outcome for a specific patient. The
predicted risks can be used to support decision-making by healthcare
professionals. EHRs are structured patient journey data. Each patient journey
contains a chronological set of clinical events, and within each clinical
event, there is a set of clinical/medical activities. Due to variations of
patient conditions and treatment needs, EHR patient journey data has an
inherently high degree of missingness that contains important information
affecting relationships among variables, including time. Existing deep
learning-based models generate imputed values for missing values when learning
the relationships. However, imputed data in EHR patient journey data may
distort the clinical meaning of the original EHR patient journey data,
resulting in classification bias. This paper proposes a novel end-to-end
approach to modeling EHR patient journey data with Integrated Convolutional and
Recurrent Neural Networks. Our model can capture both long- and short-term
temporal patterns within each patient journey and effectively handle the high
degree of missingness in EHR data without any imputation data generation.
Extensive experimental results using the proposed model on two real-world
datasets demonstrate robust performance as well as superior prediction accuracy
compared to existing state-of-the-art imputation-based prediction methods.

In recent years, self-supervised learning (SSL) has achieved tremendous
success in various speech tasks due to its power to extract representations
from massive unlabeled data. However, compared with tasks such as speech
recognition (ASR), the improvements from SSL representation in speech
separation (SS) and enhancement (SE) are considerably smaller. Based on HuBERT,
this work investigates improving the SSL model for SS and SE. We first update
HuBERT's masked speech prediction (MSP) objective by integrating the separation
and denoising terms, resulting in a multiple pseudo label pre-training scheme,
which significantly improves HuBERT's performance on SS and SE but degrades the
performance on ASR. To maintain its performance gain on ASR, we further propose
an adapter-based architecture for HuBERT's Transformer encoder, where only a
few parameters of each layer are adjusted to the multiple pseudo label MSP
while other parameters remain frozen as default HuBERT. Experimental results
show that our proposed adapter-based multiple pseudo label HuBERT yield
consistent and significant performance improvements on SE, SS, and ASR tasks,
with a faster pre-training speed, at only marginal parameters increase.

Sparse regression has been a popular approach to perform variable selection
and enhance the prediction accuracy and interpretability of the resulting
statistical model. Existing approaches focus on offline regularized regression,
while the online scenario has rarely been studied. In this paper, we propose a
novel online sparse linear regression framework for analyzing streaming data
when data points arrive sequentially. Our proposed method is memory efficient
and requires less stringent restricted strong convexity assumptions.
Theoretically, we show that with a properly chosen regularization parameter,
the $\ell_2$-norm statistical error of our estimator diminishes to zero in the
optimal order of $\tilde{O}({\sqrt{s/t}})$, where $s$ is the sparsity level,
$t$ is the streaming sample size, and $\tilde{O}(\cdot)$ hides logarithmic
terms. Numerical experiments demonstrate the practical efficiency of our
algorithm.

Machine learning has been widely used in healthcare applications to
approximate complex models, for clinical diagnosis, prognosis, and treatment.
As deep learning has the outstanding ability to extract information from time
series, its true capabilities on sparse, irregularly sampled, multivariate, and
imbalanced physiological data are not yet fully explored. In this paper, we
systematically examine the performance of machine learning models for the
clinical prediction task based on the EHR, especially physiological time
series. We choose Physionet 2019 challenge public dataset to predict Sepsis
outcomes in ICU units. Ten baseline machine learning models are compared,
including 3 deep learning methods and 7 non-deep learning methods, commonly
used in the clinical prediction domain. Nine evaluation metrics with specific
clinical implications are used to assess the performance of models. Besides, we
sub-sample training dataset sizes and use learning curve fit to investigate the
impact of the training dataset size on the performance of the machine learning
models. We also propose the general pre-processing method for the physiology
time-series data and use Dice Loss to deal with the dataset imbalanced problem.
The results show that deep learning indeed outperforms non-deep learning, but
with certain conditions: firstly, evaluating with some particular evaluation
metrics (AUROC, AUPRC, Sensitivity, and FNR), but not others; secondly, the
training dataset size is large enough (with an estimation of a magnitude of
thousands).

Semidefinite programming is a fundamental tool in optimization and
theoretical computer science. It has been extensively used as a black-box for
solving many problems, such as embedding, complexity, learning, and
discrepancy.
  One natural setting of semidefinite programming is the small treewidth
setting. The best previous SDP solver under small treewidth setting is due to
Zhang-Lavaei '18, which takes $n^{1.5} \tau^{6.5}$ time. In this work, we show
how to solve a semidefinite programming with $n \times n$ variables, $m$
constraints and $\tau$ treewidth in $n \tau^{2\omega+0.5}$ time, where $\omega
< 2.373$ denotes the exponent of matrix multiplication. We give the first SDP
solver that runs in time in linear in number of variables under this setting.
  In addition, we improve the running time that solves a linear programming
with tau treewidth from $n \tau^2$ (Dong-Lee-Ye '21) to $n
\tau^{(\omega+1)/2}$.

Accurately predicting interactive road agents' future trajectories and
planning a socially compliant and human-like trajectory accordingly are
important for autonomous vehicles. In this paper, we propose a planning-centric
prediction neural network, which takes surrounding agents' historical states
and map context information as input, and outputs the joint multi-modal
prediction trajectories for surrounding agents, as well as a sequence of
control commands for the ego vehicle by imitation learning. An agent-agent
interaction module along the time axis is proposed in our network architecture
to better comprehend the relationship among all the other intelligent agents on
the road. To incorporate the map's topological information, a Dynamic Graph
Convolutional Neural Network (DGCNN) is employed to process the road network
topology. Besides, the whole architecture can serve as a backbone for the
Differentiable Integrated motion Prediction with Planning (DIPP) method by
providing accurate prediction results and initial planning commands.
Experiments are conducted on real-world datasets to demonstrate the
improvements made by our proposed method in both planning and prediction
accuracy compared to the previous state-of-the-art methods.

Recently, flow-based frame interpolation methods have achieved great success
by first modeling optical flow between target and input frames, and then
building synthesis network for target frame generation. However, above cascaded
architecture can lead to large model size and inference delay, hindering them
from mobile and real-time applications. To solve this problem, we propose a
novel Progressive Motion Context Refine Network (PMCRNet) to predict motion
fields and image context jointly for higher efficiency. Different from others
that directly synthesize target frame from deep feature, we explore to simplify
frame interpolation task by borrowing existing texture from adjacent input
frames, which means that decoder in each pyramid level of our PMCRNet only
needs to update easier intermediate optical flow, occlusion merge mask and
image residual. Moreover, we introduce a new annealed multi-scale
reconstruction loss to better guide the learning process of this efficient
PMCRNet. Experiments on multiple benchmarks show that proposed approaches not
only achieve favorable quantitative and qualitative results but also reduces
current model size and running time significantly.

Temporal Action Localization (TAL) methods typically operate on top of
feature sequences from a frozen snippet encoder that is pretrained with the
Trimmed Action Classification (TAC) tasks, resulting in a task discrepancy
problem. While existing TAL methods mitigate this issue either by retraining
the encoder with a pretext task or by end-to-end fine-tuning, they commonly
require an overload of high memory and computation. In this work, we introduce
Soft-Landing (SoLa) strategy, an efficient yet effective framework to bridge
the transferability gap between the pretrained encoder and the downstream tasks
by incorporating a light-weight neural network, i.e., a SoLa module, on top of
the frozen encoder. We also propose an unsupervised training scheme for the
SoLa module; it learns with inter-frame Similarity Matching that uses the frame
interval as its supervisory signal, eliminating the need for temporal
annotations. Experimental evaluation on various benchmarks for downstream TAL
tasks shows that our method effectively alleviates the task discrepancy problem
with remarkable computational efficiency.

Recent works have shown that optical flow can be learned by deep networks
from unlabelled image pairs based on brightness constancy assumption and
smoothness prior. Current approaches additionally impose an augmentation
regularization term for continual self-supervision, which has been proved to be
effective on difficult matching regions. However, this method also amplify the
inevitable mismatch in unsupervised setting, blocking the learning process
towards optimal solution. To break the dilemma, we propose a novel mutual
distillation framework to transfer reliable knowledge back and forth between
the teacher and student networks for alternate improvement. Concretely, taking
estimation of off-the-shelf unsupervised approach as pseudo labels, our insight
locates at defining a confidence selection mechanism to extract relative good
matches, and then add diverse data augmentation for distilling adequate and
reliable knowledge from teacher to student. Thanks to the decouple nature of
our method, we can choose a stronger student architecture for sufficient
learning. Finally, better student prediction is adopted to transfer knowledge
back to the efficient teacher without additional costs in real deployment.
Rather than formulating it as a supervised task, we find that introducing an
extra unsupervised term for multi-target learning achieves best final results.
Extensive experiments show that our approach, termed MDFlow, achieves
state-of-the-art real-time accuracy and generalization ability on challenging
benchmarks. Code is available at https://github.com/ltkong218/MDFlow.

Information Extraction (IE) aims to extract structured information from
heterogeneous sources. IE from natural language texts include sub-tasks such as
Named Entity Recognition (NER), Relation Extraction (RE), and Event Extraction
(EE). Most IE systems require comprehensive understandings of sentence
structure, implied semantics, and domain knowledge to perform well; thus, IE
tasks always need adequate external resources and annotations. However, it
takes time and effort to obtain more human annotations. Low-Resource
Information Extraction (LRIE) strives to use unsupervised data, reducing the
required resources and human annotation. In practice, existing systems either
utilize self-training schemes to generate pseudo labels that will cause the
gradual drift problem, or leverage consistency regularization methods which
inevitably possess confirmation bias. To alleviate confirmation bias due to the
lack of feedback loops in existing LRIE learning paradigms, we develop a
Gradient Imitation Reinforcement Learning (GIRL) method to encourage
pseudo-labeled data to imitate the gradient descent direction on labeled data,
which can force pseudo-labeled data to achieve better optimization capabilities
similar to labeled data. Based on how well the pseudo-labeled data imitates the
instructive gradient descent direction obtained from labeled data, we design a
reward to quantify the imitation process and bootstrap the optimization
capability of pseudo-labeled data through trial and error. In addition to
learning paradigms, GIRL is not limited to specific sub-tasks, and we leverage
GIRL to solve all IE sub-tasks (named entity recognition, relation extraction,
and event extraction) in low-resource settings (semi-supervised IE and few-shot
IE).

Masked image modelling (e.g., Masked AutoEncoder) and contrastive learning
(e.g., Momentum Contrast) have shown impressive performance on unsupervised
visual representation learning. This work presents Masked Contrastive
Representation Learning (MACRL) for self-supervised visual pre-training. In
particular, MACRL leverages the effectiveness of both masked image modelling
and contrastive learning. We adopt an asymmetric setting for the siamese
network (i.e., encoder-decoder structure in both branches), where one branch
with higher mask ratio and stronger data augmentation, while the other adopts
weaker data corruptions. We optimize a contrastive learning objective based on
the learned features from the encoder in both branches. Furthermore, we
minimize the $L_1$ reconstruction loss according to the decoders' outputs. In
our experiments, MACRL presents superior results on various vision benchmarks,
including CIFAR-10, CIFAR-100, Tiny-ImageNet, and two other ImageNet subsets.
Our framework provides unified insights on self-supervised visual pre-training
and future research.

Intelligent mesh generation (IMG) refers to a technique to generate mesh by
machine learning, which is a relatively new and promising research field.
Within its short life span, IMG has greatly expanded the generalizability and
practicality of mesh generation techniques and brought many breakthroughs and
potential possibilities for mesh generation. However, there is a lack of
surveys focusing on IMG methods covering recent works. In this paper, we are
committed to a systematic and comprehensive survey describing the contemporary
IMG landscape. Focusing on 110 preliminary IMG methods, we conducted an
in-depth analysis and evaluation from multiple perspectives, including the core
technique and application scope of the algorithm, agent learning goals, data
types, targeting challenges, advantages and limitations. With the aim of
literature collection and classification based on content extraction, we
propose three different taxonomies from three views of key technique, output
mesh unit element, and applicable input data types. Finally, we highlight some
promising future research directions and challenges in IMG. To maximize the
convenience of readers, a project page of IMG is provided at
\url{https://github.com/xzb030/IMG_Survey}.

Continuous pseudo-labeling (PL) algorithms such as slimIPL have recently
emerged as a powerful strategy for semi-supervised learning in speech
recognition. In contrast with earlier strategies that alternated between
training a model and generating pseudo-labels (PLs) with it, here PLs are
generated in end-to-end manner as training proceeds, improving training speed
and the accuracy of the final model. PL shares a common theme with
teacher-student models such as distillation in that a teacher model generates
targets that need to be mimicked by the student model being trained. However,
interestingly, PL strategies in general use hard-labels, whereas distillation
uses the distribution over labels as the target to mimic. Inspired by
distillation we expect that specifying the whole distribution (aka soft-labels)
over sequences as the target for unlabeled data, instead of a single best pass
pseudo-labeled transcript (hard-labels) should improve PL performance and
convergence. Surprisingly and unexpectedly, we find that soft-labels targets
can lead to training divergence, with the model collapsing to a degenerate
token distribution per frame. We hypothesize that the reason this does not
happen with hard-labels is that training loss on hard-labels imposes
sequence-level consistency that keeps the model from collapsing to the
degenerate solution. In this paper, we show several experiments that support
this hypothesis, and experiment with several regularization approaches that can
ameliorate the degenerate collapse when using soft-labels. These approaches can
bring the accuracy of soft-labels closer to that of hard-labels, and while they
are unable to outperform them yet, they serve as a useful framework for further
improvements.

As a special type of transformer, Vision Transformers (ViTs) are used to
various computer vision applications (CV), such as image recognition. There are
several potential problems with convolutional neural networks (CNNs) that can
be solved with ViTs. For image coding tasks like compression, super-resolution,
segmentation, and denoising, different variants of the ViTs are used. The
purpose of this survey is to present the first application of ViTs in CV. The
survey is the first of its kind on ViTs for CVs to the best of our knowledge.
In the first step, we classify different CV applications where ViTs are
applicable. CV applications include image classification, object detection,
image segmentation, image compression, image super-resolution, image denoising,
and anomaly detection. Our next step is to review the state-of-the-art in each
category and list the available models. Following that, we present a detailed
analysis and comparison of each model and list its pros and cons. After that,
we present our insights and lessons learned for each category. Moreover, we
discuss several open research challenges and future research directions.

We propose LiDAL, a novel active learning method for 3D LiDAR semantic
segmentation by exploiting inter-frame uncertainty among LiDAR frames. Our core
idea is that a well-trained model should generate robust results irrespective
of viewpoints for scene scanning and thus the inconsistencies in model
predictions across frames provide a very reliable measure of uncertainty for
active sample selection. To implement this uncertainty measure, we introduce
new inter-frame divergence and entropy formulations, which serve as the metrics
for active selection. Moreover, we demonstrate additional performance gains by
predicting and incorporating pseudo-labels, which are also selected using the
proposed inter-frame uncertainty measure. Experimental results validate the
effectiveness of LiDAL: we achieve 95% of the performance of fully supervised
learning with less than 5% of annotations on the SemanticKITTI and nuScenes
datasets, outperforming state-of-the-art active learning methods. Code release:
https://github.com/hzykent/LiDAL.

Pre-trained Language Models (PLMs) which are trained on large text corpus via
self-supervised learning method, have yielded promising performance on various
tasks in Natural Language Processing (NLP). However, though PLMs with huge
parameters can effectively possess rich knowledge learned from massive training
text and benefit downstream tasks at the fine-tuning stage, they still have
some limitations such as poor reasoning ability due to the lack of external
knowledge. Research has been dedicated to incorporating knowledge into PLMs to
tackle these issues. In this paper, we present a comprehensive review of
Knowledge-Enhanced Pre-trained Language Models (KE-PLMs) to provide a clear
insight into this thriving field. We introduce appropriate taxonomies
respectively for Natural Language Understanding (NLU) and Natural Language
Generation (NLG) to highlight these two main tasks of NLP. For NLU, we divide
the types of knowledge into four categories: linguistic knowledge, text
knowledge, knowledge graph (KG), and rule knowledge. The KE-PLMs for NLG are
categorized into KG-based and retrieval-based methods. Finally, we point out
some promising future directions of KE-PLMs.

This paper considers the problem of data-driven prediction of partially
observed systems using a recurrent neural network. While neural network based
dynamic predictors perform well with full-state training data, prediction with
partial observation during training phase poses a significant challenge. Here a
predictor for partial observations is developed using an echo-state network
(ESN) and time delay embedding of the partially observed state. The proposed
method is theoretically justified with Taken's embedding theorem and strong
observability of a nonlinear system. The efficacy of the proposed method is
demonstrated on three systems: two synthetic datasets from chaotic dynamical
systems and a set of real-time traffic data.

With the success of the prompt-tuning paradigm in Natural Language Processing
(NLP), various prompt templates have been proposed to further stimulate
specific knowledge for serving downstream tasks, e.g., machine translation,
text generation, relation extraction, and so on. Existing prompt templates are
mainly shared among all training samples with the information of task
description. However, training samples are quite diverse. The sharing task
description is unable to stimulate the unique task-related information in each
training sample, especially for tasks with the finite-label space. To exploit
the unique task-related information, we imitate the human decision process
which aims to find the contrastive attributes between the objective factual and
their potential counterfactuals. Thus, we propose the \textbf{C}ounterfactual
\textbf{C}ontrastive \textbf{Prompt}-Tuning (CCPrompt) approach for many-class
classification, e.g., relation classification, topic classification, and entity
typing. Compared with simple classification tasks, these tasks have more
complex finite-label spaces and are more rigorous for prompts. First of all, we
prune the finite label space to construct fact-counterfactual pairs. Then, we
exploit the contrastive attributes by projecting training instances onto every
fact-counterfactual pair. We further set up global prototypes corresponding
with all contrastive attributes for selecting valid contrastive attributes as
additional tokens in the prompt template. Finally, a simple Siamese
representation learning is employed to enhance the robustness of the model. We
conduct experiments on relation classification, topic classification, and
entity typing tasks in both fully supervised setting and few-shot setting. The
results indicate that our model outperforms former baselines.

Agriculture is at the heart of the solution to achieve sustainability in
feeding the world population, but advancing our understanding on how
agricultural output responds to climatic variability is still needed. Precision
Agriculture (PA), which is a management strategy that uses technology such as
remote sensing, Geographical Information System (GIS), and machine learning for
decision making in the field, has emerged as a promising approach to enhance
crop production, increase yield, and reduce water and nutrient losses and
environmental impacts. In this context, multiple models to predict agricultural
phenotypes, such as crop yield, from genomics (G), environment (E), weather and
soil, and field management practices (M) have been developed. These models have
traditionally been based on mechanistic or statistical approaches. However, AI
approaches are intrinsically well-suited to model complex interactions and have
more recently been developed, outperforming classical methods. Here, we present
a Natural Language Processing (NLP)-based neural network architecture to
process the G, E and M inputs and their interactions. We show that by modeling
DNA as natural language, our approach performs better than previous approaches
when tested for new environments and similarly to other approaches for unseen
seed varieties.

The spread of misinformation is a prominent problem in today's society, and
many researchers in academia and industry are trying to combat it. Due to the
vast amount of misinformation that is created every day, it is unrealistic to
leave this task to human fact-checkers. Data scientists and researchers have
been working on automated misinformation detection for years, and it is still a
challenging problem today. The goal of our research is to add a new level to
automated misinformation detection; classifying segments of text with
persuasive writing techniques in order to produce interpretable reasoning for
why an article can be marked as misinformation. To accomplish this, we present
a novel annotation scheme containing many common persuasive writing tactics,
along with a dataset with human annotations accordingly. For this task, we make
use of a RoBERTa model for text classification, due to its high performance in
NLP. We develop several language model-based baselines and present the results
of our persuasive strategy label predictions as well as the improvements these
intermediate labels make in detecting misinformation and producing
interpretable results.

Domain adaptation is an effective solution to data scarcity in low-resource
scenarios. However, when applied to token-level tasks such as bioNER, domain
adaptation methods often suffer from the challenging linguistic characteristics
that clinical narratives possess, which leads to unsatisfactory performance. In
this paper, we present a simple yet effective hardness-guided domain adaptation
(HGDA) framework for bioNER tasks that can effectively leverage the domain
hardness information to improve the adaptability of the learnt model in
low-resource scenarios. Experimental results on biomedical datasets show that
our model can achieve significant performance improvement over the recently
published state-of-the-art (SOTA) MetaNER model

We present the development of a semi-supervised regression method using
variational autoencoders (VAE), which is customized for use in soft sensing
applications. We motivate the use of semi-supervised learning considering the
fact that process quality variables are not collected at the same frequency as
other process variables leading to many unlabelled records in operational
datasets. These unlabelled records are not possible to use for training quality
variable predictions based on supervised learning methods. Use of VAEs for
unsupervised learning is well established and recently they were used for
regression applications based on variational inference procedures. We extend
this approach of supervised VAEs for regression (SVAER) to make it learn from
unlabelled data leading to semi-supervised VAEs for regression (SSVAER), then
we make further modifications to their architecture using additional
regularization components to make SSVAER well suited for learning from both
labelled and unlabelled process data. The probabilistic regressor resulting
from the variational approach makes it possible to estimate the variance of the
predictions simultaneously, which provides an uncertainty quantification along
with the generated predictions. We provide an extensive comparative study of
SSVAER with other publicly available semi-supervised and supervised learning
methods on two benchmark problems using fixed-size datasets, where we vary the
percentage of labelled data available for training. In these experiments,
SSVAER achieves the lowest test errors in 11 of the 20 studied cases, compared
to other methods where the second best gets 4 lowest test errors out of the 20.

Reconfigurable intelligent surfaces (RIS) is a revolutionary technology to
cost-effectively improve the performance of wireless networks. We first review
the existing framework of channel estimation and passive beamforming (CE & PBF)
in RIS-assisted communication systems. To reduce the excessive pilot signaling
overhead and implementation complexity of the CE & PBF framework, we conceive a
codebook-based framework to strike flexible tradeoffs between communication
performance and signaling overhead. Moreover, we provide useful insights into
the codebook design and learning mechanisms of the RIS reflection pattern.
Finally, we analyze the scalability of the proposed framework by flexibly
adapting the training overhead to the specified quality-of-service requirements
and then elaborate on its appealing advantages over the existing CE & PBF
approaches. It is shown that our novel codebook-based framework can be
beneficially applied to all RIS-assisted scenarios and avoids the curse of
model dependency faced by its existing counterparts, thus constituting a
competitive solution for practical RIS-assisted communication systems.

With the improvement of arithmetic power and algorithm accuracy of personal
devices, biological features are increasingly widely used in personal
identification, and palm vein recognition has rich extractable features and has
been widely studied in recent years. However, traditional recognition methods
are poorly robust and susceptible to environmental influences such as
reflections and noise. In this paper, a convolutional neural network based on
VGG-16 transfer learning fused attention mechanism is used as the feature
extraction network on the infrared palm vein dataset. The palm vein
classification task is first trained using palmprint classification methods,
followed by matching using a similarity function, in which we propose the
multi-task loss function to improve the accuracy of the matching task. In order
to verify the robustness of the model, some experiments were carried out on
datasets from different sources. Then, we used K-means clustering to determine
the adaptive matching threshold and finally achieved an accuracy rate of 98.89%
on prediction set. At the same time, the matching is with high efficiency which
takes an average of 0.13 seconds per palm vein pair, and that means our method
can be adopted in practice.

We consider the stochastic linear contextual bandit problem with
high-dimensional features. We analyze the Thompson sampling (TS) algorithm,
using special classes of sparsity-inducing priors (e.g. spike-and-slab) to
model the unknown parameter, and provide a nearly optimal upper bound on the
expected cumulative regret. To the best of our knowledge, this is the first
work that provides theoretical guarantees of Thompson sampling in high
dimensional and sparse contextual bandits. For faster computation, we use
spike-and-slab prior to model the unknown parameter and variational inference
instead of MCMC to approximate the posterior distribution. Extensive
simulations demonstrate improved performance of our proposed algorithm over
existing ones.

Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a
single neural network for signal reconstruction, and fail to jointly consider
the influences of the sampling operation for reconstruction. In this paper, we
propose unified framework, which jointly considers the sampling and
reconstruction process for image compressive sensing based on well-designed
cascade neural networks. Two sub-networks, which are the sampling sub-network
and the reconstruction sub-network, are included in the proposed framework. In
the sampling sub-network, an adaptive full connected layer instead of the
traditional random matrix is used to mimic the sampling operator. In the
reconstruction sub-network, a cascade network combining stacked denoising
autoencoder (SDA) and convolutional neural network (CNN) is designed to
reconstruct signals. The SDA is used to solve the signal mapping problem and
the signals are initially reconstructed. Furthermore, CNN is used to fully
recover the structure and texture features of the image to obtain better
reconstruction performance. Extensive experiments show that this framework
outperforms many other state-of-the-art methods, especially at low sampling
rates.

Clear identification of bone structures is crucial for ultrasound-guided
lumbar interventions, but it can be challenging due to the complex shapes of
the self-shadowing vertebra anatomy and the extensive background speckle noise
from the surrounding soft tissue structures. Therefore, we propose to use a
patch-like wearable ultrasound solution to capture the reflective bone surfaces
from multiple imaging angles and create 3D bone representations for
interventional guidance. In this work, we will present our method for
estimating the vertebra bone surfaces by using a spatiotemporal U-Net
architecture learning from the B-Mode image and aggregated feature maps of
hand-crafted filters. The methods are evaluated on spine phantom image data
collected by our proposed miniaturized wearable "patch" ultrasound device, and
the results show that a significant improvement on baseline method can be
achieved with promising accuracy. Equipped with this surface estimation
framework, our wearable ultrasound system can potentially provide intuitive and
accurate interventional guidance for clinicians in augmented reality setting.

The state-of-the-art dimensionality reduction approaches largely rely on
complicated optimization procedures. On the other hand, closed-form approaches
requiring merely eigen-decomposition do not have enough sophistication and
nonlinearity. In this paper, we propose a novel nonlinear dimensionality
reduction method -- Inverse Kernel Decomposition (IKD) -- based on an
eigen-decomposition of the sample covariance matrix of data. The method is
inspired by Gaussian process latent variable models (GPLVMs) and has comparable
performance with GPLVMs. To deal with very noisy data with weak correlations,
we propose two solutions -- blockwise and geodesic -- to make use of locally
correlated data points and provide better and numerically more stable latent
estimations. We use synthetic datasets and four real-world datasets to show
that IKD is a better dimensionality reduction method than other
eigen-decomposition-based methods, and achieves comparable performance against
optimization-based methods with faster running speeds. Open-source IKD
implementation in Python can be accessed at this
\url{https://github.com/JerrySoybean/ikd}.

We introduce Breadth-First Pipeline Parallelism, a novel training schedule
which optimizes the combination of pipeline and data parallelism. Breadth-First
Pipeline Parallelism lowers training time, cost and memory usage by combining a
high GPU utilization with a small batch size per GPU, and by making use of
fully sharded data parallelism. Experimentally, we observed increases of up to
53% in training speed.

Collaborative autonomous multi-agent systems covering a specified area have
many potential applications, such as UAV search and rescue, forest fire
fighting, and real-time high-resolution monitoring. Traditional approaches for
such coverage problems involve designing a model-based control policy based on
sensor data. However, designing model-based controllers is challenging, and the
state-of-the-art classical control policy still exhibits a large degree of
suboptimality. In this paper, we present a reinforcement learning (RL) approach
for the multi-agent coverage problem involving agents with second-order
dynamics. Our approach is based on the Multi-Agent Proximal Policy Optimization
Algorithm (MAPPO). To improve the stability of the learning-based policy and
efficiency of exploration, we utilize an imitation loss based on the
state-of-the-art classical control policy. Our trained policy significantly
outperforms the state-of-the-art. Our proposed network architecture includes
incorporation of self attention, which allows a single-shot domain transfer of
the trained policy to a large variety of domain shapes and number of agents. We
demonstrate our proposed method in a variety of simulated experiments.

Advances in deep neural network (DNN) architectures have enabled new
prediction techniques for stock market data. Unlike other multivariate
time-series data, stock markets show two unique characteristics: (i)
\emph{multi-order dynamics}, as stock prices are affected by strong
non-pairwise correlations (e.g., within the same industry); and (ii)
\emph{internal dynamics}, as each individual stock shows some particular
behaviour. Recent DNN-based methods capture multi-order dynamics using
hypergraphs, but rely on the Fourier basis in the convolution, which is both
inefficient and ineffective. In addition, they largely ignore internal dynamics
by adopting the same model for each stock, which implies a severe information
loss.
  In this paper, we propose a framework for stock movement prediction to
overcome the above issues. Specifically, the framework includes temporal
generative filters that implement a memory-based mechanism onto an LSTM network
in an attempt to learn individual patterns per stock. Moreover, we employ
hypergraph attentions to capture the non-pairwise correlations. Here, using the
wavelet basis instead of the Fourier basis, enables us to simplify the message
passing and focus on the localized convolution. Experiments with US market data
over six years show that our framework outperforms state-of-the-art methods in
terms of profit and stability. Our source code and data are available at
\url{https://github.com/thanhtrunghuynh93/estimate}.

In neural architecture search (NAS) methods based on latent space
optimization (LSO), a deep generative model is trained to embed discrete neural
architectures into a continuous latent space. In this case, different
optimization algorithms that operate in the continuous space can be implemented
to search neural architectures. However, the optimization of latent variables
is challenging for gradient-based LSO since the mapping from the latent space
to the architecture performance is generally non-convex. To tackle this
problem, this paper develops a convexity regularized latent space optimization
(CR-LSO) method, which aims to regularize the learning process of latent space
in order to obtain a convex architecture performance mapping. Specifically,
CR-LSO trains a graph variational autoencoder (G-VAE) to learn the continuous
representations of discrete architectures. Simultaneously, the learning process
of latent space is regularized by the guaranteed convexity of input convex
neural networks (ICNNs). In this way, the G-VAE is forced to learn a convex
mapping from the architecture representation to the architecture performance.
Hereafter, the CR-LSO approximates the performance mapping using the ICNN and
leverages the estimated gradient to optimize neural architecture
representations. Experimental results on three popular NAS benchmarks show that
CR-LSO achieves competitive evaluation results in terms of both computational
complexity and architecture performance.

As an efficient way to integrate multiple distributed energy resources and
the user side, a microgrid is mainly faced with the problems of small-scale
volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The
traditional microgrid has a single form and cannot meet the flexible energy
dispatch between the complex demand side and the microgrid. In response to this
problem, the overall environment of wind power, thermostatically controlled
loads, energy storage systems, price-responsive loads and the main grid is
proposed. Secondly, the centralized control of the microgrid operation is
convenient for the control of the reactive power and voltage of the distributed
power supply and the adjustment of the grid frequency. However, there is a
problem in that the flexible loads aggregate and generate peaks during the
electricity price valley. The existing research takes into account the power
constraints of the microgrid and fails to ensure a sufficient supply of
electric energy for a single flexible load. This paper considers the response
priority of each unit component of TCLs and ESSs on the basis of the overall
environment operation of the microgrid so as to ensure the power supply of the
flexible load of the microgrid and save the power input cost to the greatest
extent. Finally, the simulation optimization of the environment can be
expressed as a Markov decision process process. It combines two stages of
offline and online operations in the training process. The addition of multiple
threads with the lack of historical data learning leads to low learning
efficiency. The asynchronous advantage actor-critic with the experience replay
pool memory library is added to solve the data correlation and nonstatic
distribution problems during training.

Gait has been used in clinical and healthcare applications to assess the
physical and cognitive health of older adults. Acoustic based gait detection is
a promising approach to collect gait data of older adults passively and
non-intrusively. However, there has been limited work in developing acoustic
based gait detectors that can operate in noisy polyphonic acoustic scenes of
homes and care homes. We attribute this to the lack of good quality gait
datasets from the real-world to train a gait detector on. In this paper, we put
forward a novel machine learning based filter which can triage gait audio
samples suitable for training machine learning models for gait detection. The
filter achieves this by eliminating noisy samples at an f(1) score of 0.85 and
prioritising gait samples with distinct spectral features and minimal noise. To
demonstrate the effectiveness of the filter, we train and evaluate a deep
learning model on gait datasets collected from older adults with and without
applying the filter. The model registers an increase of 25 points in its f(1)
score on unseen real-word gait data when trained with the filtered gait
samples. The proposed filter will help automate the task of manual annotation
of gait samples for training acoustic based gait detection models for older
adults in indoor environments.

Principal Component Analysis (PCA) and its exponential family extensions have
three components: observations, latents and parameters of a linear
transformation. We consider a generalised setting where the canonical
parameters of the exponential family are a nonlinear transformation of the
latents. We show explicit relationships between particular neural network
architectures and the corresponding statistical models. We find that deep
equilibrium models -- a recently introduced class of implicit neural networks
-- solve maximum a-posteriori (MAP) estimates for the latents and parameters of
the transformation. Our analysis provides a systematic way to relate activation
functions, dropout, and layer structure, to statistical assumptions about the
observations, thus providing foundational principles for unsupervised DEQs. For
hierarchical latents, individual neurons can be interpreted as nodes in a deep
graphical model. Our DEQ feature maps are end-to-end differentiable, enabling
fine-tuning for downstream tasks.

For more clinical applications of deep learning models for medical image
segmentation, high demands on labeled data and computational resources must be
addressed. This study proposes a coarse-to-fine framework with two teacher
models and a student model that combines knowledge distillation and cross
teaching, a consistency regularization based on pseudo-labels, for efficient
semi-supervised learning. The proposed method is demonstrated on the abdominal
multi-organ segmentation task in CT images under the MICCAI FLARE 2022
challenge, with mean Dice scores of 0.8429 and 0.8520 in the validation and
test sets, respectively.

We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym
environments from RDDL declerative description. The discrete time step
evolution of variables in RDDL is described by conditional probability
functions, which fits naturally into the Gym step scheme. Furthermore, since
RDDL is a lifted description, the modification and scaling up of environments
to support multiple entities and different configurations becomes trivial
rather than a tedious process prone to errors. We hope that pyRDDLGym will
serve as a new wind in the reinforcement learning community by enabling easy
and rapid development of benchmarks due to the unique expressive power of RDDL.
By providing explicit access to the model in the RDDL description, pyRDDLGym
can also facilitate research on hybrid approaches for learning from interaction
while leveraging model knowledge. We present the design and built-in examples
of pyRDDLGym, and the additions made to the RDDL language that were
incorporated into the framework.

The viscosity of lead-containing glasses is of fundamental importance for the
manufacturing process, and can be predicted by algorithms such as artificial
neural networks. The SciGlass database was used to provide training, validation
and test data of chemical composition, temperature and viscosity for the
construction of artificial neural networks with node variation in the hidden
layer. The best model built with training data and validation data was compared
with 7 other models from the literature, demonstrating better statistical
evaluations of mean absolute error and coefficient of determination to the test
data, with subsequent sensitivity analysis in agreement with the literature.
Skewness and kurtosis were calculated and there is a good correlation between
the values predicted by the best neural network built with the test data.

Recent research has shown remarkable performance in leveraging multiple
extraneous conditional and non-mutually exclusive semantic concepts for sound
source separation, allowing the flexibility to extract a given target source
based on multiple different queries. In this work, we propose a new optimal
condition training (OCT) method for single-channel target source separation,
based on greedy parameter updates using the highest performing condition among
equivalent conditions associated with a given target source. Our experiments
show that the complementary information carried by the diverse semantic
concepts significantly helps to disentangle and isolate sources of interest
much more efficiently compared to single-conditioned models. Moreover, we
propose a variation of OCT with condition refinement, in which an initial
conditional vector is adapted to the given mixture and transformed to a more
amenable representation for target source extraction. We showcase the
effectiveness of OCT on diverse source separation experiments where it improves
upon permutation invariant models with oracle assignment and obtains
state-of-the-art performance in the more challenging task of text-based source
separation, outperforming even dedicated text-only conditioned models.

Labeling a module defective or non-defective is an expensive task. Hence,
there are often limits on how much-labeled data is available for training.
Semi-supervised classifiers use far fewer labels for training models, but there
are numerous semi-supervised methods, including self-labeling, co-training,
maximal-margin, and graph-based methods, to name a few. Only a handful of these
methods have been tested in SE for (e.g.) predicting defects and even that,
those tests have been on just a handful of projects. This paper takes a wide
range of 55 semi-supervised learners and applies these to over 714 projects. We
find that semi-supervised "co-training methods" work significantly better than
other approaches. However, co-training needs to be used with caution since the
specific choice of co-training methods needs to be carefully selected based on
a user's specific goals. Also, we warn that a commonly-used co-training method
("multi-view"-- where different learners get different sets of columns) does
not improve predictions (while adding too much to the run time costs 11 hours
vs. 1.8 hours). Those cautions stated, we find using these "co-trainers," we
can label just 2.5% of data, then make predictions that are competitive to
those using 100% of the data. It is an open question worthy of future work to
test if these reductions can be seen in other areas of software analytics.
  All the codes used and datasets analyzed during the current study are
available in the https://GitHub.com/Suvodeep90/Semi_Supervised_Methods.

Irregular applications comprise an increasingly important workload domain for
many fields, including bioinformatics, chemistry, physics, social sciences and
machine learning. Therefore, achieving high performance and energy efficiency
in the execution of emerging irregular applications is of vital importance.
This dissertation studies the root causes of inefficiency of irregular
applications in modern computing systems, and fundamentally addresses such
inefficiencies, by proposing low-overhead synchronization techniques among
parallel threads in cooperation with well-crafted data access policies.
  We make four major contributions to accelerating irregular applications in
different contexts including CPU and Near-Data-Processing (NDP) (or
Processing-In-Memory (PIM)) systems. First, we design ColorTM, a novel parallel
graph coloring algorithm for CPU systems that trades off using synchronization
with lower data access costs. Second, we propose SmartPQ, an adaptive priority
queue that achieves high performance under all various contention scenarios in
Non-Uniform Memory Access CPU systems. Third, we introduce SynCron, the first
practical hardware synchronization mechanism tailored for NDP systems. Fourth,
we design SparseP, the first library for high-performance Sparse Matrix Vector
Multiplication on real PIM systems.
  We demonstrate that the execution of irregular applications in CPU and
NDP/PIM architectures can be significantly accelerated by co-designing
lightweight synchronization approaches along with well-crafted data access
policies. This dissertation bridges the gap between processor-centric CPU
systems and memory-centric PIM systems in the critically-important area of
irregular applications. We hope that this dissertation inspires future work in
co-designing software algorithms with cutting-edge computing platforms to
significantly accelerate emerging irregular applications.

The reconstruction of sea surface currents from satellite altimeter data is a
key challenge in spatial oceanography, especially with the upcoming wide-swath
SWOT (Surface Ocean and Water Topography) altimeter mission. Operational
systems however generally fail to retrieve mesoscale dynamics for horizontal
scales below 100km and time-scale below 10 days. Here, we address this
challenge through the 4DVarnet framework, an end-to-end neural scheme backed on
a variational data assimilation formulation. We introduce a parametrization of
the 4DVarNet scheme dedicated to the space-time interpolation of satellite
altimeter data. Within an observing system simulation experiment (NATL60), we
demonstrate the relevance of the proposed approach both for nadir and
nadir+swot altimeter configurations for two contrasted case-study regions in
terms of upper ocean dynamics. We report relative improvement with respect to
the operational optimal interpolation between 30% and 60% in terms of
reconstruction error. Interestingly, for the nadir+swot altimeter
configuration, we reach resolved space-time scales below 70km and 7days. The
code is open-source to enable reproductibility and future collaborative
developments. Beyond its applicability to large-scale domains, we also address
uncertainty quantification issues and generalization properties of the proposed
learning setting. We discuss further future research avenues and extensions to
other ocean data assimilation and space oceanography challenges.

In a space-air-ground integrated network (SAGIN), managing resources for the
growing number of highly-dynamic and heterogeneous radios is a challenging
task. Symbiotic communication (SC) is a novel paradigm, which leverages the
analogy of the natural ecosystem in biology to create a radio ecosystem in
wireless networks that achieves cooperative service exchange and resource
sharing, i.e., service/resource trading, among numerous radios. As a result,
the potential of symbiotic communication can be exploited to enhance resource
management in SAGIN. Despite the fact that different radio resource bottlenecks
can complement each other via symbiotic relationships, unreliable information
sharing among heterogeneous radios and multi-dimensional resources managing
under diverse service requests impose critical challenges on trusted trading
and intelligent decision-making. In this article, we propose a secure and smart
symbiotic SAGIN (S^4) framework by using blockchain for ensuring trusted
trading among heterogeneous radios and machine learning (ML) for guiding
complex service/resource trading. A case study demonstrates that our proposed
S^4 framework provides better service with rational resource management when
compared with existing schemes. Finally, we discuss several potential research
directions for future symbiotic SAGIN.

Visual commonsense understanding requires Vision Language (VL) models to not
only understand image and text but also cross-reference in-between to fully
integrate and achieve comprehension of the visual scene described. Recently,
various approaches have been developed and have achieved high performance on
visual commonsense benchmarks. However, it is unclear whether the models really
understand the visual scene and underlying commonsense knowledge due to limited
evaluation data resources. To provide an in-depth analysis, we present a
Multimodal Evaluation (ME) pipeline to automatically generate question-answer
pairs to test models' understanding of the visual scene, text, and related
knowledge. We then take a step further to show that training with the ME data
boosts the model's performance in standard VCR evaluation. Lastly, our in-depth
analysis and comparison reveal interesting findings: (1) semantically low-level
information can assist the learning of high-level information but not the
opposite; (2) visual information is generally under utilization compared with
text.

Large language models trained for code generation can be applied to speaking
virtual worlds into existence (creating virtual worlds). In this work we show
that prompt-based methods can both accelerate in-VR level editing, as well as
can become part of gameplay rather than just part of game development. As an
example, we present Codex VR Pong which shows non-deterministic game mechanics
using generative processes to not only create static content but also
non-trivial interactions between 3D objects. This demonstration naturally leads
to an integral discussion on how one would evaluate and benchmark experiences
created by generative models - as there are no qualitative or quantitative
metrics that apply in these scenarios. We conclude by discussing impending
challenges of AI-assisted co-creation in VR.

Humans exhibit disagreement during data labeling. We term this disagreement
as human label uncertainty. In this work, we study the ramifications of human
label uncertainty (HLU). Our evaluation of existing uncertainty estimation
algorithms, with the presence of HLU, indicates the limitations of existing
uncertainty metrics and algorithms themselves in response to HLU. Meanwhile, we
observe undue effects in predictive uncertainty and generalizability. To
mitigate the undue effects, we introduce a novel natural scene statistics (NSS)
based label dilution training scheme without requiring massive human labels.
Specifically, we first select a subset of samples with low perceptual quality
ranked by statistical regularities of images. We then assign separate labels to
each sample in this subset to obtain a training set with diluted labels. Our
experiments and analysis demonstrate that training with NSS-based label
dilution alleviates the undue effects caused by HLU.

This work proposes a robust data-driven predictive control approach for
unknown nonlinear systems in the presence of bounded process and measurement
noise. Data-driven reachable sets are employed for the controller design
instead of using an explicit nonlinear system model. Although the process and
measurement noise are bounded, the statistical properties of the noise are not
required to be known. By using the past noisy input-output data in the learning
phase, we propose a novel method to over-approximate reachable sets of an
unknown nonlinear system. Then, we propose a data-driven predictive control
approach to compute safe and robust control policies from noisy online data.
The constraints are guaranteed in the control phase with robust safety margins
through the effective use of the predicted output reachable set obtained in the
learning phase. Finally, a numerical example validates the efficacy of the
proposed approach and demonstrates comparable performance with a model-based
predictive control approach.

Multiple instance learning exhibits a powerful approach for whole slide
image-based diagnosis in the absence of pixel- or patch-level annotations. In
spite of the huge size of hole slide images, the number of individual slides is
often rather small, leading to a small number of labeled samples. To improve
training, we propose and investigate different data augmentation strategies for
multiple instance learning based on the idea of linear interpolations of
feature vectors (known as MixUp). Based on state-of-the-art multiple instance
learning architectures and two thyroid cancer data sets, an exhaustive study is
conducted considering a range of common data augmentation strategies. Whereas a
strategy based on to the original MixUp approach showed decreases in accuracy,
the use of a novel intra-slide interpolation method led to consistent increases
in accuracy.

Nowadays, the PQ flexibility from the distributed energy resources (DERs) in
the high voltage (HV) grids plays a more critical and significant role in grid
congestion management in TSO grids. This work proposed a multi-stage deep
reinforcement learning approach to estimate the PQ flexibility (PQ area) at the
TSO-DSO interfaces and identifies the DER PQ setpoints for each operating point
in a way, that DERs in the meshed HV grid can be coordinated to offer
flexibility for the transmission grid. In the estimation process, we consider
the steady-state grid limits and the robustness in the resulting voltage
profile against uncertainties and the N-1 security criterion regarding thermal
line loading, essential for real-life grid operational planning applications.
Using deep reinforcement learning (DRL) for PQ flexibility estimation is the
first of its kind. Furthermore, our approach of considering N-1 security
criterion for meshed grids and robustness against uncertainty directly in the
optimization tasks offers a new perspective besides the common relaxation
schema in finding a solution with mathematical optimal power flow (OPF).
Finally, significant improvements in the computational efficiency in estimation
PQ area are the highlights of the proposed method.

Keyless entry systems in cars are adopting neural networks for localizing its
operators. Using test-time adversarial defences equip such systems with the
ability to defend against adversarial attacks without prior training on
adversarial samples. We propose a test-time adversarial example detector which
detects the input adversarial example through quantifying the localized
intermediate responses of a pre-trained neural network and confidence scores of
an auxiliary softmax layer. Furthermore, in order to make the network robust,
we extenuate the non-relevant features by non-iterative input sample clipping.
Using our approach, mean performance over 15 levels of adversarial
perturbations is increased by 55.33% for the fast gradient sign method (FGSM)
and 6.3% for both the basic iterative method (BIM) and the projected gradient
method (PGD).

The number of standardized policy documents regarding climate policy and
their publication frequency is significantly increasing. The documents are long
and tedious for manual analysis, especially for policy experts, lawmakers, and
citizens who lack access or domain expertise to utilize data analytics tools.
Potential consequences of such a situation include reduced citizen governance
and involvement in climate policies and an overall surge in analytics costs,
rendering less accessibility for the public. In this work, we use a Latent
Dirichlet Allocation-based pipeline for the automatic summarization and
analysis of 10-years of national energy and climate plans (NECPs) for the
period from 2021 to 2030, established by 27 Member States of the European
Union. We focus on analyzing policy framing, the language used to describe
specific issues, to detect essential nuances in the way governments frame their
climate policies and achieve climate goals. The methods leverage topic modeling
and clustering for the comparative analysis of policy documents across
different countries. It allows for easier integration in potential
user-friendly applications for the development of theories and processes of
climate policy. This would further lead to better citizen governance and
engagement over climate policies and public policy research.

Independent component analysis (ICA) of multi-subject functional magnetic
resonance imaging (fMRI) data has proven useful in providing a fully
multivariate summary that can be used for multiple purposes. ICA can identify
patterns that can discriminate between healthy controls (HC) and patients with
various mental disorders such as schizophrenia (Sz). Temporal functional
network connectivity (tFNC) obtained from ICA can effectively explain the
interactions between brain networks. On the other hand, dictionary learning
(DL) enables the discovery of hidden information in data using learnable basis
signals through the use of sparsity. In this paper, we present a new method
that leverages ICA and DL for the identification of directly interpretable
patterns to discriminate between the HC and Sz groups. We use multi-subject
resting-state fMRI data from $358$ subjects and form subject-specific tFNC
feature vectors from ICA results. Then, we learn sparse representations of the
tFNCs and introduce a new set of sparse features as well as new interpretable
patterns from the learned atoms. Our experimental results show that the new
representation not only leads to effective classification between HC and Sz
groups using sparse features, but can also identify new interpretable patterns
from the learned atoms that can help understand the complexities of mental
diseases such as schizophrenia.

Climate models often require post-processing in order to make accurate
estimates of local climate risk. The most common post-processing applied is
bias-correction and spatial resolution enhancement. However, the statistical
methods typically used for this not only are incapable of capturing
multivariate spatial correlation information but are also reliant on rich
observational data often not available outside of developed countries, limiting
their potential. Here we propose an alternative approach to this challenge
based on a combination of image super resolution (SR) and contrastive learning
generative adversarial networks (GANs). We benchmark performance against NASA's
flagship post-processed CMIP6 climate model product, NEX-GDDP. We find that our
model successfully reaches a spatial resolution double that of NASA's product
while also achieving comparable or improved levels of bias correction in both
daily precipitation and temperature. The resulting higher fidelity simulations
of present and forward-looking climate can enable more local, accurate models
of hazards like flooding, drought, and heatwaves.

Current ground-based cosmological surveys, such as the Dark Energy Survey
(DES), are predicted to discover thousands of galaxy-scale strong lenses, while
future surveys, such as the Vera Rubin Observatory Legacy Survey of Space and
Time (LSST) will increase that number by 1-2 orders of magnitude. The large
number of strong lenses discoverable in future surveys will make strong lensing
a highly competitive and complementary cosmic probe.
  To leverage the increased statistical power of the lenses that will be
discovered through upcoming surveys, automated lens analysis techniques are
necessary. We present two Simulation-Based Inference (SBI) approaches for lens
parameter estimation of galaxy-galaxy lenses. We demonstrate the successful
application of Neural Posterior Estimation (NPE) to automate the inference of a
12-parameter lens mass model for DES-like ground-based imaging data. We compare
our NPE constraints to a Bayesian Neural Network (BNN) and find that it
outperforms the BNN, producing posterior distributions that are for the most
part both more accurate and more precise; in particular, several source-light
model parameters are systematically biased in the BNN implementation.

Standard language model training employs gold human documents or human-human
interaction data, and treats all training data as positive examples. Growing
evidence shows that even with very large amounts of positive training data,
issues remain that can be alleviated with relatively small amounts of negative
data -- examples of what the model should not do. In this work, we propose a
novel procedure to train with such data called the CRINGE loss (ContRastive
Iterative Negative GEneration). We show the effectiveness of this approach
across three different experiments on the tasks of safe generation,
contradiction avoidance, and open-domain dialogue. Our models outperform
multiple strong baselines and are conceptually simple, easy to train and
implement.

As the quantum counterparts to the classical artificial neural networks
underlying widespread machine-learning applications, unitary-based quantum
neural networks are active in various fields of quantum computation. Despite
the potential, their developments have been hampered by the elevated cost of
optimizations and difficulty in realizations. Here, we propose a quantum neural
network in the form of fermion models whose physical properties, such as the
local density of states and conditional conductance, serve as outputs, and
establish an efficient optimization comparable to back-propagation. In addition
to competitive accuracy on challenging classical machine-learning benchmarks,
our fermion quantum neural network performs machine learning on quantum systems
with high precision and without preprocessing. The quantum nature also brings
various other advantages, e.g., quantum correlations entitle networks with more
general and local connectivity facilitating numerical simulations and
experimental realizations, as well as novel perspectives to address the
vanishing gradient problem long plaguing deep networks. We also demonstrate the
applications of our quantum toolbox, such as quantum-entanglement analysis, for
interpretable machine learning, including training dynamics, decision logic
flow, and criteria formulation.

Compared to the great progress of large-scale vision transformers (ViTs) in
recent years, large-scale models based on convolutional neural networks (CNNs)
are still in an early state. This work presents a new large-scale CNN-based
foundation model, termed InternImage, which can obtain the gain from increasing
parameters and training data like ViTs. Different from the recent CNNs that
focus on large dense kernels, InternImage takes deformable convolution as the
core operator, so that our model not only has the large effective receptive
field required for downstream tasks such as detection and segmentation, but
also has the adaptive spatial aggregation conditioned by input and task
information. As a result, the proposed InternImage reduces the strict inductive
bias of traditional CNNs and makes it possible to learn stronger and more
robust patterns with large-scale parameters from massive data like ViTs. The
effectiveness of our model is proven on challenging benchmarks including
ImageNet, COCO, and ADE20K. It is worth mentioning that InternImage-H achieved
a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming
current leading CNNs and ViTs. The code will be released at
https://github.com/OpenGVLab/InternImage.

Cancer is one of the leading causes of death worldwide. It is caused by a
variety of genetic mutations, which makes every instance of the disease unique.
Since chemotherapy can have extremely severe side effects, each patient
requires a personalized treatment plan. Finding the dosages that maximize the
beneficial effects of the drugs and minimize their adverse side effects is
vital. Deep neural networks automate and improve drug selection. However, they
require a lot of data to be trained on. Therefore, there is a need for
machine-learning approaches that require less data. Hybrid quantum neural
networks were shown to provide a potential advantage in problems where training
data availability is limited. We propose a novel hybrid quantum neural network
for drug response prediction, based on a combination of convolutional, graph
convolutional, and deep quantum neural layers of 8 qubits with 363 layers. We
test our model on the reduced Genomics of Drug Sensitivity in Cancer dataset
and show that the hybrid quantum model outperforms its classical analog by 15%
in predicting IC50 drug effectiveness values. The proposed hybrid quantum
machine learning model is a step towards deep quantum data-efficient algorithms
with thousands of quantum gates for solving problems in personalized medicine,
where data collection is a challenge.

In dense image segmentation tasks (e.g., semantic, panoptic), existing
methods can hardly generalize well to unseen image domains, predefined classes,
and image resolution & quality variations. Motivated by these observations, we
construct a large-scale entity segmentation dataset to explore fine-grained
entity segmentation, with a strong focus on open-world and high-quality dense
segmentation. The dataset contains images spanning diverse image domains and
resolutions, along with high-quality mask annotations for training and testing.
Given the high-quality and -resolution nature of the dataset, we propose
CropFormer for high-quality segmentation, which can improve mask prediction
using high-res image crops that provide more fine-grained image details than
the full image. CropFormer is the first query-based Transformer architecture
that can effectively ensemble mask predictions from multiple image crops, by
learning queries that can associate the same entities across the full image and
its crop. With CropFormer, we achieve a significant AP gain of $1.9$ on the
challenging fine-grained entity segmentation task. The dataset and code will be
released at http://luqi.info/entityv2.github.io/.

Image generation has been a long sought-after but challenging task, and
performing the generation task in an efficient manner is similarly difficult.
Often researchers attempt to create a "one size fits all" generator, where
there are few differences in the parameter space for drastically different
datasets. Herein, we present a new transformer-based framework, dubbed
StyleNAT, targeting high-quality image generation with superior efficiency and
flexibility. At the core of our model, is a carefully designed framework that
partitions attention heads to capture local and global information, which is
achieved through using Neighborhood Attention (NA). With different heads able
to pay attention to varying receptive fields, the model is able to better
combine this information, and adapt, in a highly flexible manner, to the data
at hand. StyleNAT attains a new SOTA FID score on FFHQ-256 with 2.046, beating
prior arts with convolutional models such as StyleGAN-XL and transformers such
as HIT and StyleSwin, and a new transformer SOTA on FFHQ-1024 with an FID score
of 4.174. These results show a 6.4% improvement on FFHQ-256 scores when
compared to StyleGAN-XL with a 28% reduction in the number of parameters and
56% improvement in sampling throughput. Code and models will be open-sourced at
https://github.com/SHI-Labs/StyleNAT .

Graph neural networks (GNNs) are susceptible to privacy inference attacks
(PIAs), given their ability to learn joint representation from features and
edges among nodes in graph data. To prevent privacy leakages in GNNs, we
propose a novel heterogeneous randomized response (HeteroRR) mechanism to
protect nodes' features and edges against PIAs under differential privacy (DP)
guarantees without an undue cost of data and model utility in training GNNs.
Our idea is to balance the importance and sensitivity of nodes' features and
edges in redistributing the privacy budgets since some features and edges are
more sensitive or important to the model utility than others. As a result, we
derive significantly better randomization probabilities and tighter error
bounds at both levels of nodes' features and edges departing from existing
approaches, thus enabling us to maintain high data utility for training GNNs.
An extensive theoretical and empirical analysis using benchmark datasets shows
that HeteroRR significantly outperforms various baselines in terms of model
utility under rigorous privacy protection for both nodes' features and edges.
That enables us to defend PIAs in DP-preserving GNNs effectively.

Using 3D CNNs on high resolution medical volumes is very computationally
demanding, especially for large datasets like the UK Biobank which aims to scan
100,000 subjects. Here we demonstrate that using 2D CNNs on a few 2D
projections (representing mean and standard deviation across axial, sagittal
and coronal slices) of the 3D volumes leads to reasonable test accuracy when
predicting the age from brain volumes. Using our approach, one training epoch
with 20,324 subjects takes 40 - 70 seconds using a single GPU, which is almost
100 times faster compared to a small 3D CNN. These results are important for
researchers who do not have access to expensive GPU hardware for 3D CNNs.

Pretrained language models have demonstrated extraordinary capabilities in
language generation. However, real-world tasks often require controlling the
distribution of generated text in order to mitigate bias, promote fairness, and
achieve personalization. Existing techniques for controlling the distribution
of generated text only work with quantified distributions, which require
pre-defined categories, proportions of the distribution, or an existing corpus
following the desired distributions. However, many important distributions,
such as personal preferences, are unquantified. In this work, we tackle the
problem of generating text following arbitrary distributions (quantified and
unquantified) by proposing Nano, a few-shot human-in-the-loop training
algorithm that continuously learns from human feedback. Nano achieves
state-of-the-art results on single topic/attribute as well as quantified
distribution control compared to previous works. We also show that Nano is able
to learn unquantified distributions, achieves personalization, and captures
differences between different individuals' personal preferences with high
sample efficiency.

We present sketched linear discriminant analysis, an iterative randomized
approach to binary-class Gaussian model linear discriminant analysis (LDA) for
very large data. We harness a least squares formulation and mobilize the
stochastic gradient descent framework. Therefore, we obtain a randomized
classifier with performance that is very comparable to that of full data LDA
while requiring access to only one row of the training data at a time. We
present convergence guarantees for the sketched predictions on new data within
a fixed number of iterations. These guarantees account for both the Gaussian
modeling assumptions on the data and algorithmic randomness from the sketching
procedure. Finally, we demonstrate performance with varying step-sizes and
numbers of iterations. Our numerical experiments demonstrate that sketched LDA
can offer a very viable alternative to full data LDA when the data may be too
large for full data analysis.

Federated Learning (FL) is a machine learning paradigm that enables the
training of a shared global model across distributed clients while keeping the
training data local. While most prior work on designing systems for FL has
focused on using stateful always running components, recent work has shown that
components in an FL system can greatly benefit from the usage of serverless
computing and Function-as-a-Service technologies. To this end, distributed
training of models with severless FL systems can be more resource-efficient and
cheaper than conventional FL systems. However, serverless FL systems still
suffer from the presence of stragglers, i.e., slow clients due to their
resource and statistical heterogeneity. While several strategies have been
proposed for mitigating stragglers in FL, most methodologies do not account for
the particular characteristics of serverless environments, i.e., cold-starts,
performance variations, and the ephemeral stateless nature of the function
instances. Towards this, we propose FedLesScan, a novel clustering-based
semi-asynchronous training strategy, specifically tailored for serverless FL.
FedLesScan dynamically adapts to the behaviour of clients and minimizes the
effect of stragglers on the overall system. We implement our strategy by
extending an open-source serverless FL system called FedLess. Moreover, we
comprehensively evaluate our strategy using the 2nd generation Google Cloud
Functions with four datasets and varying percentages of stragglers. Results
from our experiments show that compared to other approaches FedLesScan reduces
training time and cost by an average of 8% and 20% respectively while utilizing
clients better with an average increase in the effective update ratio of
17.75%.

We study the hidden-action principal-agent problem in an online setting. In
each round, the principal posts a contract that specifies the payment to the
agent based on each outcome. The agent then makes a strategic choice of action
that maximizes her own utility, but the action is not directly observable by
the principal. The principal observes the outcome and receives utility from the
agent's choice of action. Based on past observations, the principal dynamically
adjusts the contracts with the goal of maximizing her utility.
  We introduce an online learning algorithm and provide an upper bound on its
Stackelberg regret. We show that when the contract space is $[0,1]^m$, the
Stackelberg regret is upper bounded by $\widetilde O(\sqrt{m} \cdot
T^{1-C/m})$, and lower bounded by $\Omega(T^{1-1/(m+2)})$. This result shows
that exponential-in-$m$ samples are both sufficient and necessary to learn a
near-optimal contract, resolving an open problem on the hardness of online
contract design. When contracts are restricted to some subset $\mathcal{F}
\subset [0,1]^m$, we define an intrinsic dimension of $\mathcal{F}$ that
depends on the covering number of the spherical code in the space and bound the
regret in terms of this intrinsic dimension. When $\mathcal{F}$ is the family
of linear contracts, the Stackelberg regret grows exactly as $\Theta(T^{2/3})$.
  The contract design problem is challenging because the utility function is
discontinuous. Bounding the discretization error in this setting has been an
open problem. In this paper, we identify a limited set of directions in which
the utility function is continuous, allowing us to design a new discretization
method and bound its error. This approach enables the first upper bound with no
restrictions on the contract and action space.

Resistive Random-Access Memory (RRAM) is well-suited to accelerate neural
network (NN) workloads as RRAM-based Processing-in-Memory (PIM) architectures
natively support highly-parallel multiply-accumulate (MAC) operations that form
the backbone of most NN workloads. Unfortunately, NN workloads such as
transformers require support for non-MAC operations (e.g., softmax) that RRAM
cannot provide natively. Consequently, state-of-the-art works either integrate
additional digital logic circuits to support the non-MAC operations or offload
the non-MAC operations to CPU/GPU, resulting in significant performance and
energy efficiency overheads due to data movement.
  In this work, we propose NEON, a novel compiler optimization to enable the
end-to-end execution of the NN workload in RRAM. The key idea of NEON is to
transform each non-MAC operation into a lightweight yet highly-accurate neural
network. Utilizing neural networks to approximate the non-MAC operations
provides two advantages: 1) We can exploit the key strength of RRAM, i.e.,
highly-parallel MAC operation, to flexibly and efficiently execute non-MAC
operations in memory. 2) We can simplify RRAM's microarchitecture by
eliminating the additional digital logic circuits while reducing the data
movement overheads. Acceleration of the non-MAC operations in memory enables
NEON to achieve a 2.28x speedup compared to an idealized digital logic-based
RRAM. We analyze the trade-offs associated with the transformation and
demonstrate feasible use cases for NEON across different substrates.

Sharpness-Aware Minimization (SAM) is a highly effective regularization
technique for improving the generalization of deep neural networks for various
settings. However, the underlying working of SAM remains elusive because of
various intriguing approximations in the theoretical characterizations. SAM
intends to penalize a notion of sharpness of the model but implements a
computationally efficient variant; moreover, a third notion of sharpness was
used for proving generalization guarantees. The subtle differences in these
notions of sharpness can indeed lead to significantly different empirical
results. This paper rigorously nails down the exact sharpness notion that SAM
regularizes and clarifies the underlying mechanism. We also show that the two
steps of approximations in the original motivation of SAM individually lead to
inaccurate local conclusions, but their combination accidentally reveals the
correct effect, when full-batch gradients are applied. Furthermore, we also
prove that the stochastic version of SAM in fact regularizes the third notion
of sharpness mentioned above, which is most likely to be the preferred notion
for practical performance. The key mechanism behind this intriguing phenomenon
is the alignment between the gradient and the top eigenvector of Hessian when
SAM is applied.

Climate change is becoming one of the greatest challenges to the sustainable
development of modern society. Renewable energies with low density greatly
complicate the online optimization and control processes, where modern advanced
computational technologies, specifically quantum computing, have significant
potential to help. In this paper, we discuss applications of quantum computing
algorithms toward state-of-the-art smart grid problems. We suggest potential,
exponential quantum speedup by the use of the Harrow-Hassidim-Lloyd (HHL)
algorithms for sparse matrix inversions in power-flow problems. However,
practical implementations of the algorithm are limited by the noise of quantum
circuits, the hardness of realizations of quantum random access memories
(QRAM), and the depth of the required quantum circuits. We benchmark the
hardware and software requirements from the state-of-the-art power-flow
algorithms, including QRAM requirements from hybrid phonon-transmon systems,
and explicit gate counting used in HHL for explicit realizations. We also
develop near-term algorithms of power flow by variational quantum circuits and
implement real experiments for 6 qubits with a truncated version of power
flows.

We propose a Randomised Subspace Gauss-Newton (R-SGN) algorithm for solving
nonlinear least-squares optimization problems, that uses a sketched Jacobian of
the residual in the variable domain and solves a reduced linear least-squares
on each iteration. A sublinear global rate of convergence result is presented
for a trust-region variant of R-SGN, with high probability, which matches
deterministic counterpart results in the order of the accuracy tolerance.
Promising preliminary numerical results are presented for R-SGN on logistic
regression and on nonlinear regression problems from the CUTEst collection.

Responding with multi-modal content has been recognized as an essential
capability for an intelligent conversational agent. In this paper, we introduce
the MMDialog dataset to better facilitate multi-modal conversation. MMDialog is
composed of a curated set of 1.08 million real-world dialogues with 1.53
million unique images across 4,184 topics. MMDialog has two main and unique
advantages. First, it is the largest multi-modal conversation dataset by the
number of dialogues by 88x. Second, it contains massive topics to generalize
the open-domain. To build engaging dialogue system with this dataset, we
propose and normalize two response producing tasks based on retrieval and
generative scenarios. In addition, we build two baselines for above tasks with
state-of-the-art techniques and report their experimental performance. We also
propose a novel evaluation metric MM-Relevance to measure the multi-modal
responses. Our dataset and scripts are available in
https://github.com/victorsungo/MMDialog.

Privacy-preserving machine learning in data-sharing processes is an
ever-critical task that enables collaborative training of Machine Learning (ML)
models without the need to share the original data sources. It is especially
relevant when an organization must assure that sensitive data remains private
throughout the whole ML pipeline, i.e., training and inference phases. This
paper presents an innovative framework that uses Representation Learning via
autoencoders to generate privacy-preserving embedded data. Thus, organizations
can share the data representation to increase machine learning models'
performance in scenarios with more than one data source for a shared predictive
downstream task.

Li$_{10}$Ge(PS$_6$)$_2$ (LGPS) is a highly concentrated solid electrolyte, in
which Coulombic repulsion between neighboring cations is hypothesized as the
underlying reason for concerted ion hopping, a mechanism common among
superionic conductors such as Li$_7$La$_3$Zr$_2$O$_{12}$ (LLZO) and
Li$_{1.3}$Al$_{0.3}$Ti$_{1.7}$(PO$_4$)$_3$ (LATP). While first principles
simulations using molecular dynamics (MD) provide insight into the Li$^+$
transport mechanism, historically, there has been a gap in the temperature
ranges studied in simulations and experiments. Here, we used a neural network
(NN) potential trained on density functional theory (DFT) simulations, to run
up to 40-nanosecond long MD simulations at DFT-like accuracy to characterize
the ion conduction mechanisms across a range of temperatures that includes
previous simulations and experimental studies. We have confirmed a Li$^+$
sublattice phase transition in LGPS around 400 K, below which the
\textit{ab}-plane diffusivity $D^*_{ab}$ is drastically reduced. Concomitant
with the sublattice phase transition near 400 K, there is less cation-cation
(cross) correlation, as characterized by Haven ratios closer to 1, and the
vibrations in the system are more harmonic at lower temperature. Intuitively,
at high temperature, the collection of vibrational modes may be sufficient to
drive concerted ion hops. However, near room temperature, the vibrational modes
available may be insufficient to overcome electrostatic repulsion, thus
resulting in less correlated ion motion and comparatively slower ion
conduction. Such phenomena of a sublattice phase transition, below which
concerted hopping plays a less significant role, may be extended to other
highly concentrated solid electrolytes such as LLZO and LATP.

Pre-trained protein language models have demonstrated significant
applicability in different protein engineering task. A general usage of these
pre-trained transformer models latent representation is to use a mean pool
across residue positions to reduce the feature dimensions to further downstream
tasks such as predicting bio-physics properties or other functional behaviours.
In this paper we provide a two-fold contribution to machine learning (ML)
driven drug design. Firstly, we demonstrate the power of sparsity by promoting
penalization of pre-trained transformer models to secure more robust and
accurate melting temperature (Tm) prediction of single-chain variable fragments
with a mean absolute error of 0.23C. Secondly, we demonstrate the power of
framing our prediction problem in a probabilistic framework. Specifically, we
advocate for the need of adopting probabilistic frameworks especially in the
context of ML driven drug design.

Accurate prediction of battery health is essential for real-world system
management and lab-based experiment design. However, building a life-prediction
model from different cycling conditions is still a challenge. Large lifetime
variability results from both cycling conditions and initial manufacturing
variability, and this -- along with the limited experimental resources usually
available for each cycling condition -- makes data-driven lifetime prediction
challenging. Here, a hierarchical Bayesian linear model is proposed for battery
life prediction, combining both individual cell features (reflecting
manufacturing variability) with population-wide features (reflecting the impact
of cycling conditions on the population average). The individual features were
collected from the first 100 cycles of data, which is around 5-10% of lifetime.
The model is able to predict end of life with a root mean square error of 3.2
days and mean absolute percentage error of 8.6%, measured through 5-fold
cross-validation, overperforming the baseline (non-hierarchical) model by
around 12-13%.

We consider the problem of learning the structure underlying a Gaussian
graphical model when the variables (or subsets thereof) are corrupted by
independent noise. A recent line of work establishes that even for
tree-structured graphical models, only partial structure recovery is possible
and goes on to devise algorithms to identify the structure up to an
(unavoidable) equivalence class of trees. We extend these results beyond trees
and consider the model selection problem under noise for non tree-structured
graphs, as tree graphs cannot model several real-world scenarios. Although
unidentifiable, we show that, like the tree-structured graphs, the ambiguity is
limited to an equivalence class. This limited ambiguity can help provide
meaningful clustering information (even with noise), which is helpful in
computer and social networks, protein-protein interaction networks, and power
networks. Furthermore, we devise an algorithm based on a novel ancestral
testing method for recovering the equivalence class. We complement these
results with finite sample guarantees for the algorithm in the high-dimensional
regime.

Measuring and monitoring soil organic carbon is critical for agricultural
productivity and for addressing critical environmental problems. Soil organic
carbon not only enriches nutrition in soil, but also has a gamut of co-benefits
such as improving water storage and limiting physical erosion. Despite a litany
of work in soil organic carbon estimation, current approaches do not generalize
well across soil conditions and management practices. We empirically show that
explicit modeling of cause-and-effect relationships among the soil processes
improves the out-of-distribution generalizability of prediction models. We
provide a comparative analysis of soil organic carbon estimation models where
the skeleton is estimated using causal discovery methods. Our framework provide
an average improvement of 81% in test mean squared error and 52% in test mean
absolute error.

The extensive surviving corpus of the ancient scholar Plutarch of Chaeronea
(ca. 45-120 CE) also contains several texts which, according to current
scholarly opinion, did not originate with him and are therefore attributed to
an anonymous author Pseudo-Plutarch. These include, in particular, the work
Placita Philosophorum (Quotations and Opinions of the Ancient Philosophers),
which is extremely important for the history of ancient philosophy. Little is
known about the identity of that anonymous author and its relation to other
authors from the same period. This paper presents a BERT language model for
Ancient Greek. The model discovers previously unknown statistical properties
relevant to these literary, philosophical, and historical problems and can shed
new light on this authorship question. In particular, the Placita
Philosophorum, together with one of the other Pseudo-Plutarch texts, shows
similarities with the texts written by authors from an Alexandrian context
(2nd/3rd century CE).

Community detection is the task of discovering groups of nodes sharing
similar patterns within a network. With recent advancements in deep learning,
methods utilizing graph representation learning and deep clustering have shown
great results in community detection. However, these methods often rely on the
topology of networks (i) ignoring important features such as network
heterogeneity, temporality, multimodality, and other possibly relevant
features. Besides, (ii) the number of communities is not known a priori and is
often left to model selection. In addition, (iii) in multimodal networks all
nodes are assumed to be symmetrical in their features; while true for
homogeneous networks, most of the real-world networks are heterogeneous where
feature availability often varies. In this paper, we propose a novel framework
(named MGTCOM) that overcomes the above challenges (i)--(iii). MGTCOM
identifies communities through multimodal feature learning by leveraging a new
sampling technique for unsupervised learning of temporal embeddings.
Importantly, MGTCOM is an end-to-end framework optimizing network embeddings,
communities, and the number of communities in tandem. In order to assess its
performance, we carried out an extensive evaluation on a number of multimodal
networks. We found out that our method is competitive against state-of-the-art
and performs well in inductive inference.

Interpretability provides a means for humans to verify aspects of machine
learning (ML) models and empower human+ML teaming in situations where the task
cannot be fully automated. Different contexts require explanations with
different properties. For example, the kind of explanation required to
determine if an early cardiac arrest warning system is ready to be integrated
into a care setting is very different from the type of explanation required for
a loan applicant to help determine the actions they might need to take to make
their application successful.
  Unfortunately, there is a lack of standardization when it comes to properties
of explanations: different papers may use the same term to mean different
quantities, and different terms to mean the same quantity. This lack of a
standardized terminology and categorization of the properties of ML
explanations prevents us from both rigorously comparing interpretable machine
learning methods and identifying what properties are needed in what contexts.
  In this work, we survey properties defined in interpretable machine learning
papers, synthesize them based on what they actually measure, and describe the
trade-offs between different formulations of these properties. In doing so, we
enable more informed selection of task-appropriate formulations of explanation
properties as well as standardization for future work in interpretable machine
learning.

Wave propagation through nodes and links of a network forms the basis of
spectral graph theory. Nevertheless, the sound emitted by nodes within the
resonating chamber formed by a network are not well studied. The sound emitted
by vibrations of individual nodes reflects the structure of the overall network
topology but also the location of the node within the network. In this article,
a sound recognition neural network is trained to infer centrality measures from
the nodes' wave-forms. In addition to advancing network representation
learning, sounds emitted by nodes are plausible in most cases. Auralization of
the network topology may open new directions in arts, competing with network
visualization.

Continuous behavioural authentication methods add a unique layer of security
by allowing individuals to verify their unique identity when accessing a
device. Maintaining session authenticity is now feasible by monitoring users'
behaviour while interacting with a mobile or Internet of Things (IoT) device,
making credential theft and session hijacking ineffective. Such a technique is
made possible by integrating the power of artificial intelligence and Machine
Learning (ML). Most of the literature focuses on training machine learning for
the user by transmitting their data to an external server, subject to private
user data exposure to threats. In this paper, we propose a novel Federated
Learning (FL) approach that protects the anonymity of user data and maintains
the security of his data. We present a warmup approach that provides a
significant accuracy increase. In addition, we leverage the transfer learning
technique based on feature extraction to boost the models' performance. Our
extensive experiments based on four datasets: MNIST, FEMNIST, CIFAR-10 and
UMDAA-02-FD, show a significant increase in user authentication accuracy while
maintaining user privacy and data security.

Background: The volume of distribution at steady state (VDss) is a
fundamental pharmacokinetics (PK) property of drugs, which measures how
effectively a drug molecule is distributed throughout the body. Along with the
clearance (CL), it determines the half-life and, therefore, the drug dosing
interval. However, the molecular data size limits the generalizability of the
reported machine learning models. Objective: This study aims to provide a clean
and comprehensive dataset for human VDss as the benchmarking data source,
fostering and benefiting future predictive studies. Moreover, several
predictive models were also built with machine learning regression algorithms.
Methods: The dataset was curated from 13 publicly accessible data sources and
the DrugBank database entirely from intravenous drug administration and then
underwent extensive data cleaning. The molecular descriptors were calculated
with Mordred, and feature selection was conducted for constructing predictive
models. Five machine learning methods were used to build regression models,
grid search was used to optimize hyperparameters, and ten-fold cross-validation
was used to evaluate the model. Results: An enriched dataset of VDss
(https://github.com/da-wen-er/VDss) was constructed with 2440 molecules. Among
the prediction models, the LightGBM model was the most stable and had the best
internal prediction ability with Q2 = 0.837, R2=0.814 and for the other four
models, Q2 was higher than 0.79. Conclusions: To the best of our knowledge,
this is the largest dataset for VDss, which can be used as the benchmark for
computational studies of VDss. Moreover, the regression models reported within
this study can be of use for pharmacokinetic related studies.

Recently, Robey et al. propose a notion of probabilistic robustness, which,
at a high-level, requires a classifier to be robust to most but not all
perturbations. They show that for certain hypothesis classes where proper
learning under worst-case robustness is \textit{not} possible, proper learning
under probabilistic robustness \textit{is} possible with sample complexity
exponentially smaller than in the worst-case robustness setting. This motivates
the question of whether proper learning under probabilistic robustness is
always possible. In this paper, we show that this is \textit{not} the case. We
exhibit examples of hypothesis classes $\mathcal{H}$ with finite VC dimension
that are \textit{not} probabilistically robustly PAC learnable with
\textit{any} proper learning rule. However, if we compare the output of the
learner to the best hypothesis for a slightly \textit{stronger} level of
probabilistic robustness, we show that not only is proper learning
\textit{always} possible, but it is possible via empirical risk minimization.

Question answering models commonly have access to two sources of "knowledge"
during inference time: (1) parametric knowledge - the factual knowledge encoded
in the model weights, and (2) contextual knowledge - external knowledge (e.g.,
a Wikipedia passage) given to the model to generate a grounded answer. Having
these two sources of knowledge entangled together is a core issue for
generative QA models as it is unclear whether the answer stems from the given
non-parametric knowledge or not. This unclarity has implications on issues of
trust, interpretability and factuality. In this work, we propose a new paradigm
in which QA models are trained to disentangle the two sources of knowledge.
Using counterfactual data augmentation, we introduce a model that predicts two
answers for a given question: one based on given contextual knowledge and one
based on parametric knowledge. Our experiments on the Natural Questions dataset
show that this approach improves the performance of QA models by making them
more robust to knowledge conflicts between the two knowledge sources, while
generating useful disentangled answers.

In this note, we introduce a family of "power sum" kernels and the
corresponding Gaussian processes on symmetric groups $\mathrm{S}_n$. Such
processes are bi-invariant: the action of $\mathrm{S}_n$ on itself from both
sides does not change their finite-dimensional distributions. We show that the
values of power sum kernels can be efficiently calculated, and we also propose
a method enabling approximate sampling of the corresponding Gaussian processes
with polynomial computational complexity. By doing this we provide the tools
that are required to use the introduced family of kernels and the respective
processes for statistical modeling and machine learning.

Neural networks can be trained to solve regression problems by using
gradient-based methods to minimize the square loss. However, practitioners
often prefer to reformulate regression as a classification problem, observing
that training on the cross entropy loss results in better performance. By
focusing on two-layer ReLU networks, which can be fully characterized by
measures over their feature space, we explore how the implicit bias induced by
gradient-based optimization could partly explain the above phenomenon. We
provide theoretical evidence that the regression formulation yields a measure
whose support can differ greatly from that for classification, in the case of
one-dimensional data. Our proposed optimal supports correspond directly to the
features learned by the input layer of the network. The different nature of
these supports sheds light on possible optimization difficulties the square
loss could encounter during training, and we present empirical results
illustrating this phenomenon.

This paper presents the equilibrium analysis of a game composed of
heterogeneous electric vehicles (EVs) and a power distribution system operator
(DSO) as players in the game, and charging station operators (CSOs) and a
transportation network operator (TNO) as coordinators. Each EV tries to pick a
charging station as its destination and a route to get there at the same time.
However, the traffic and electrical load congestion on the roads and charging
stations lead to the interdependencies between the optimal decisions of EVs. In
addition, CSOs and the TNO need to apply some tolling to control such
congestion. On the other hand, the pricing in charging stations depends on
real-time distributional locational marginal pricing, which is determined by
the DSO after solving the optimal power flow over the power distribution
network. This paper also takes into account the local and the
coupling/infrastructure constraints of EVs as well as transportation and
distribution networks. This problem is modeled as a generalized aggregative
game, and then a decentralized learning method is proposed to obtain an
equilibrium point of the game, which is known as variational generalized
Wardrop equilibrium. The existence of such an equilibrium point and the
convergence of the proposed algorithm to it are theoretically proven. The
proposed decentralized learning method is scalable and privacy-preserving for
EVs. We undertake numerical studies on the Savannah city model and the IEEE
33-bus distribution network and investigate the impact of various
characteristics on demand and prices.

The characterization of the Hamiltonian parameters defining a quantum walk is
of paramount importance when performing a variety of tasks, from quantum
communication to computation. When dealing with physical implementations of
quantum walks, the parameters themselves may not be directly accessible, thus
it is necessary to find alternative estimation strategies exploiting other
observables. Here, we perform the multiparameter estimation of the Hamiltonian
parameters characterizing a continuous-time quantum walk over a line graph with
$n$-neighbour interactions using a deep neural network model fed with
experimental probabilities at a given evolution time. We compare our results
with the bounds derived from estimation theory and find that the neural network
acts as a nearly optimal estimator both when the estimation of two or three
parameters is performed.

Multilayer Perceptrons struggle to learn certain simple arithmetic tasks.
Specialist neural modules for arithmetic can outperform classical architectures
with gains in extrapolation, interpretability and convergence speeds, but are
highly sensitive to the training range. In this paper, we show that Neural
Multiplication Units (NMUs) are unable to reliably learn tasks as simple as
multiplying two inputs when given different training ranges. Causes of failure
are linked to inductive and input biases which encourage convergence to
solutions in undesirable optima. A solution, the stochastic NMU (sNMU), is
proposed to apply reversible stochasticity, encouraging avoidance of such
optima whilst converging to the true solution. Empirically, we show that
stochasticity provides improved robustness with the potential to improve
learned representations of upstream networks for numerical and image tasks.

Template generation is a crucial step of groupwise image registration which
deforms a group of subjects into a common space. Existing traditional and deep
learning-based methods can generate high-quality template images. However, they
suffer from substantial time costs or limited application scenarios like fixed
group size. In this paper, we propose an efficient groupwise template
generative framework based on variational autoencoder models utilizing the
arithmetic property of latent representation of input images. We acquire the
latent vectors of each input and use the average vector to construct the
template through the decoder. Therefore, the method can be applied to groups of
any scale. Secondly, we explore a siamese training scheme that feeds two images
to the shared-weight twin networks and compares the distances between inputs
and the generated template to prompt the template to be close to the implicit
center. We conduct experiments on 3D brain MRI scans of groups of different
sizes. Results show that our framework can achieve comparable and even better
performance to baselines, with runtime decreased to seconds.

Despite being responsible for state-of-the-art results in several computer
vision and natural language processing tasks, neural networks have faced harsh
criticism due to some of their current shortcomings. One of them is that neural
networks are correlation machines prone to model biases within the data instead
of focusing on actual useful causal relationships. This problem is particularly
serious in application domains affected by aspects such as race, gender, and
age. To prevent models from incurring on unfair decision-making, the AI
community has concentrated efforts in correcting algorithmic biases, giving
rise to the research area now widely known as fairness in AI. In this survey
paper, we provide an in-depth overview of the main debiasing methods for
fairness-aware neural networks in the context of vision and language research.
We propose a novel taxonomy to better organize the literature on debiasing
methods for fairness, and we discuss the current challenges, trends, and
important future work directions for the interested researcher and
practitioner.

Steady-state models which have been learned from historical operational data
may be unfit for model-based optimization unless correlations in the training
data which are introduced by control are accounted for. Using recent results
from work on structural dynamical causal models, we derive a formula for
adjusting for this control confounding, enabling the estimation of a causal
steady-state model from closed-loop steady-state data. The formula assumes that
the available data have been gathered under some fixed control law. It works by
estimating and taking into account the disturbance which the controller is
trying to counteract, and enables learning from data gathered under both
feedforward and feedback control.

The energy sector is facing rapid changes in the transition towards clean
renewable sources. However, the growing share of volatile, fluctuating
renewable generation such as wind or solar energy has already led to an
increase in power grid congestion and network security concerns. Grid operators
mitigate these by modifying either generation or demand (redispatching,
curtailment, flexible loads). Unfortunately, redispatching of fossil generators
leads to excessive grid operation costs and higher emissions, which is in
direct opposition to the decarbonization of the energy sector. In this paper,
we propose an AlphaZero-based grid topology optimization agent as a non-costly,
carbon-free congestion management alternative. Our experimental evaluation
confirms the potential of topology optimization for power grid operation,
achieves a reduction of the average amount of required redispatching by 60%,
and shows the interoperability with traditional congestion management methods.
Our approach also ranked 1st in the WCCI 2022 Learning to Run a Power Network
(L2RPN) competition. Based on our findings, we identify and discuss open
research problems as well as technical challenges for a productive system on a
real power grid.

Autonomous underwater vehicles (AUVs) are becoming standard tools for
underwater exploration and seabed mapping in both scientific and industrial
applications \cite{graham2022rapid, stenius2022system}. Their capacity to dive
untethered allows them to reach areas inaccessible to surface vessels and to
collect data more closely to the seafloor, regardless of the water depth.
However, their navigation autonomy remains bounded by the accuracy of their
dead reckoning (DR) estimate of their global position, severely limited in the
absence of a priori maps of the area and GPS signal. Global localization
systems equivalent to the later exists for the underwater domain, such as LBL
or USBL. However they involve expensive external infrastructure and their
reliability decreases with the distance to the AUV, making them unsuitable for
deep sea surveys.

A paper of Alsinglawi et al was recently accepted and published in Scientific
Reports. In this paper, the authors aim to predict length of stay (LOS),
discretized into either long (> 7 days) or short stays (< 7 days), of lung
cancer patients in an ICU department using various machine learning techniques.
The authors claim to achieve perfect results with an Area Under the Receiver
Operating Characteristic curve (AUROC) of 100% with a Random Forest (RF)
classifier with ADASYN class balancing over sampling technique, which if
accurate could have significant implications for hospital management. However,
we have identified several methodological flaws within the manuscript which
cause the results to be overly optimistic and would have serious consequences
if used in a clinical practice. Moreover, the reporting of the methodology is
unclear and many important details are missing from the manuscript, which makes
reproduction extremely difficult. We highlight the effect these oversights have
had on the result and provide a more believable result of 88.91% AUROC when
these oversights are corrected.

Conversation designers continue to face significant obstacles when creating
production quality task-oriented dialogue systems. The complexity and cost
involved in schema development and data collection is often a major barrier for
such designers, limiting their ability to create natural, user-friendly
experiences. We frame the classification of user intent as the generation of a
canonical form, a lightweight semantic representation using natural language.
We show that canonical forms offer a promising alternative to traditional
methods for intent classification. By tuning soft prompts for a frozen large
language model, we show that canonical forms generalize very well to new,
unseen domains in a zero- or few-shot setting. The method is also
sample-efficient, reducing the complexity and effort of developing new
task-oriented dialogue domains.

Detection of entanglement is an indispensable step to practical quantum
computation and communication. Compared with the conventional entanglement
witness method based on fidelity, we propose a flexible, machine learning
assisted entanglement detection protocol that is robust to different types of
noises and sample efficient. In this protocol, an entanglement classifier for a
generic entangled state is obtained by training a classical machine learning
model with a synthetic dataset. The dataset contains classical features of two
types of states and their labels (either entangled or separable). The classical
features of a state, which are expectation values of a set of k-local Pauli
observables, are estimated sample-efficiently by the classical shadow method.
In the numerical simulation, our classifier can detect the entanglement of
4-qubit GHZ states with coherent noise and W states mixed with large white
noise, with high accuracy.

Model extraction is a major threat for embedded deep neural network models
that leverages an extended attack surface. Indeed, by physically accessing a
device, an adversary may exploit side-channel leakages to extract critical
information of a model (i.e., its architecture or internal parameters).
Different adversarial objectives are possible including a fidelity-based
scenario where the architecture and parameters are precisely extracted (model
cloning). We focus this work on software implementation of deep neural networks
embedded in a high-end 32-bit microcontroller (Cortex-M7) and expose several
challenges related to fidelity-based parameters extraction through side-channel
analysis, from the basic multiplication operation to the feed-forward
connection through the layers. To precisely extract the value of parameters
represented in the single-precision floating point IEEE-754 standard, we
propose an iterative process that is evaluated with both simulations and traces
from a Cortex-M7 target. To our knowledge, this work is the first to target
such an high-end 32-bit platform. Importantly, we raise and discuss the
remaining challenges for the complete extraction of a deep neural network
model, more particularly the critical case of biases.

Many datasets are biased, namely they contain easy-to-learn features that are
highly correlated with the target class only in the dataset but not in the true
underlying distribution of the data. For this reason, learning unbiased models
from biased data has become a very relevant research topic in the last years.
In this work, we tackle the problem of learning representations that are robust
to biases. We first present a margin-based theoretical framework that allows us
to clarify why recent contrastive losses (InfoNCE, SupCon, etc.) can fail when
dealing with biased data. Based on that, we derive a novel formulation of the
supervised contrastive loss (epsilon-SupInfoNCE), providing more accurate
control of the minimal distance between positive and negative samples.
Furthermore, thanks to our theoretical framework, we also propose FairKL, a new
debiasing regularization loss, that works well even with extremely biased data.
We validate the proposed losses on standard vision datasets including CIFAR10,
CIFAR100, and ImageNet, and we assess the debiasing capability of FairKL with
epsilon-SupInfoNCE, reaching state-of-the-art performance on a number of biased
datasets, including real instances of biases in the wild.

X-ray imaging technology has been used for decades in clinical tasks to
reveal the internal condition of different organs, and in recent years, it has
become more common in other areas such as industry, security, and geography.
The recent development of computer vision and machine learning techniques has
also made it easier to automatically process X-ray images and several machine
learning-based object (anomaly) detection, classification, and segmentation
methods have been recently employed in X-ray image analysis. Due to the high
potential of deep learning in related image processing applications, it has
been used in most of the studies. This survey reviews the recent research on
using computer vision and machine learning for X-ray analysis in industrial
production and security applications and covers the applications, techniques,
evaluation metrics, datasets, and performance comparison of those techniques on
publicly available datasets. We also highlight some drawbacks in the published
research and give recommendations for future research in computer vision-based
X-ray analysis.

Self-supervised learning (SSL), which utilizes the input data itself for
representation learning, has achieved state-of-the-art results for various
downstream speech tasks. However, most of the previous studies focused on
offline single-talker applications, with limited investigations in multi-talker
cases, especially for streaming scenarios. In this paper, we investigate SSL
for streaming multi-talker speech recognition, which generates transcriptions
of overlapping speakers in a streaming fashion. We first observe that
conventional SSL techniques do not work well on this task due to the poor
representation of overlapping speech. We then propose a novel SSL training
objective, referred to as bi-label masked speech prediction, which explicitly
preserves representations of all speakers in overlapping speech. We investigate
various aspects of the proposed system including data configuration and
quantizer selection. The proposed SSL setup achieves substantially better word
error rates on the LibriSpeechMix dataset.

Out-of-Domain (OOD) intent detection is important for practical dialog
systems. To alleviate the issue of lacking OOD training samples, some works
propose synthesizing pseudo OOD samples and directly assigning one-hot OOD
labels to these pseudo samples. However, these one-hot labels introduce noises
to the training process because some hard pseudo OOD samples may coincide with
In-Domain (IND) intents. In this paper, we propose an adaptive soft pseudo
labeling (ASoul) method that can estimate soft labels for pseudo OOD samples
when training OOD detectors. Semantic connections between pseudo OOD samples
and IND intents are captured using an embedding graph. A co-training framework
is further introduced to produce resulting soft labels following the smoothness
assumption, i.e., close samples are likely to have similar labels. Extensive
experiments on three benchmark datasets show that ASoul consistently improves
the OOD detection performance and outperforms various competitive baselines.

Physics-informed neural networks (PINNs) [4, 10] are an approach for solving
boundary value problems based on differential equations (PDEs). The key idea of
PINNs is to use a neural network to approximate the solution to the PDE and to
incorporate the residual of the PDE as well as boundary conditions into its
loss function when training it. This provides a simple and mesh-free approach
for solving problems relating to PDEs. However, a key limitation of PINNs is
their lack of accuracy and efficiency when solving problems with larger domains
and more complex, multi-scale solutions. In a more recent approach, finite
basis physics-informed neural networks (FBPINNs) [8] use ideas from domain
decomposition to accelerate the learning process of PINNs and improve their
accuracy. In this work, we show how Schwarz-like additive, multiplicative, and
hybrid iteration methods for training FBPINNs can be developed. We present
numerical experiments on the influence of these different training strategies
on convergence and accuracy. Furthermore, we propose and evaluate a preliminary
implementation of coarse space correction for FBPINNs.

Damage to the inferior frontal gyrus (Broca's area) can cause agrammatic
aphasia wherein patients, although able to comprehend, lack the ability to form
complete sentences. This inability leads to communication gaps which cause
difficulties in their daily lives. The usage of assistive devices can help in
mitigating these issues and enable the patients to communicate effectively.
However, due to lack of large scale studies of linguistic deficits in aphasia,
research on such assistive technology is relatively limited. In this work, we
present two contributions that aim to re-initiate research and development in
this field. Firstly, we propose a model that uses linguistic features from
small scale studies on aphasia patients and generates large scale datasets of
synthetic aphasic utterances from grammatically correct datasets. We show that
the mean length of utterance, the noun/verb ratio, and the simple/complex
sentence ratio of our synthetic datasets correspond to the reported features of
aphasic speech. Further, we demonstrate how the synthetic datasets may be
utilized to develop assistive devices for aphasia patients. The pre-trained T5
transformer is fine-tuned using the generated dataset to suggest 5 corrected
sentences given an aphasic utterance as input. We evaluate the efficacy of the
T5 model using the BLEU and cosine semantic similarity scores. Affirming
results with BLEU score of 0.827/1.00 and semantic similarity of 0.904/1.00
were obtained. These results provide a strong foundation for the concept that a
synthetic dataset based on small scale studies on aphasia can be used to
develop effective assistive technology.

A trade-off between speed and information controls our understanding of
astronomical objects. Fast-to-acquire photometric observations provide global
properties, while costly and time-consuming spectroscopic measurements enable a
better understanding of the physics governing their evolution. Here, we tackle
this problem by generating spectra directly from photometry, through which we
obtain an estimate of their intricacies from easily acquired images. This is
done by using multi-modal conditional diffusion models, where the best out of
the generated spectra is selected with a contrastive network. Initial
experiments on minimally processed SDSS galaxy data show promising results.

Non-IID data distribution across clients and poisoning attacks are two main
challenges in real-world federated learning systems. While both of them have
attracted great research interest with specific strategies developed, no known
solution manages to address them in a unified framework. To jointly overcome
both challenges, we propose SmartFL, a generic approach that optimizes the
server-side aggregation process with a small clean server-collected proxy
dataset (e.g., around one hundred samples, 0.2% of the dataset) via a subspace
training technique. Specifically, the aggregation weight of each participating
client at each round is optimized using the server-collected proxy data, which
is essentially the optimization of the global model in the convex hull spanned
by client models. Since at each round, the number of tunable parameters
optimized on the server side equals the number of participating clients (thus
independent of the model size), we are able to train a global model with
massive parameters using only a small amount of proxy data. We provide
theoretical analyses of the convergence and generalization capacity for
SmartFL. Empirically, SmartFL achieves state-of-the-art performance on both
federated learning with non-IID data distribution and federated learning with
malicious clients. The source code will be released.

In this study, we explore the representation mapping from the domain of
visual arts to the domain of music, with which we can use visual arts as an
effective handle to control music generation. Unlike most studies in multimodal
representation learning that are purely data-driven, we adopt an
analysis-by-synthesis approach that combines deep music representation learning
with user studies. Such an approach enables us to discover
\textit{interpretable} representation mapping without a huge amount of paired
data. In particular, we discover that visual-to-music mapping has a nice
property similar to equivariant. In other words, we can use various image
transformations, say, changing brightness, changing contrast, style transfer,
to control the corresponding transformations in the music domain. In addition,
we released the Vis2Mus system as a controllable interface for symbolic music
generation.

We study the problem of profiling news media on the Web with respect to their
factuality of reporting and bias. This is an important but under-studied
problem related to disinformation and "fake news" detection, but it addresses
the issue at a coarser granularity compared to looking at an individual article
or an individual claim. This is useful as it allows to profile entire media
outlets in advance. Unlike previous work, which has focused primarily on text
(e.g.,~on the text of the articles published by the target website, or on the
textual description in their social media profiles or in Wikipedia), here our
main focus is on modeling the similarity between media outlets based on the
overlap of their audience. This is motivated by homophily considerations,
i.e.,~the tendency of people to have connections to people with similar
interests, which we extend to media, hypothesizing that similar types of media
would be read by similar kinds of users. In particular, we propose GREENER
(GRaph nEural nEtwork for News mEdia pRofiling), a model that builds a graph of
inter-media connections based on their audience overlap, and then uses graph
neural networks to represent each medium. We find that such representations are
quite useful for predicting the factuality and the bias of news media outlets,
yielding improvements over state-of-the-art results reported on two datasets.
When augmented with conventionally used representations obtained from news
articles, Twitter, YouTube, Facebook, and Wikipedia, prediction accuracy is
found to improve by 2.5-27 macro-F1 points for the two tasks.

Drone-camera based human activity recognition (HAR) has received significant
attention from the computer vision research community in the past few years. A
robust and efficient HAR system has a pivotal role in fields like video
surveillance, crowd behavior analysis, sports analysis, and human-computer
interaction. What makes it challenging are the complex poses, understanding
different viewpoints, and the environmental scenarios where the action is
taking place. To address such complexities, in this paper, we propose a novel
Sparse Weighted Temporal Fusion (SWTF) module to utilize sparsely sampled video
frames for obtaining global weighted temporal fusion outcome. The proposed SWTF
is divided into two components. First, a temporal segment network that sparsely
samples a given set of frames. Second, weighted temporal fusion, that
incorporates a fusion of feature maps derived from optical flow, with raw RGB
images. This is followed by base-network, which comprises a convolutional
neural network module along with fully connected layers that provide us with
activity recognition. The SWTF network can be used as a plug-in module to the
existing deep CNN architectures, for optimizing them to learn temporal
information by eliminating the need for a separate temporal stream. It has been
evaluated on three publicly available benchmark datasets, namely Okutama,
MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%,
92.56%, and 78.86% on the respective datasets thereby surpassing the previous
state-of-the-art performances by a significant margin.

Dynamic networks have been extensively explored as they can considerably
improve the model's representation power with acceptable computational cost.
The common practice in implementing dynamic networks is to convert given static
layers into fully dynamic ones where all parameters are dynamic and vary with
the input. Recent studies empirically show the trend that the more dynamic
layers contribute to ever-increasing performance. However, such a fully dynamic
setting 1) may cause redundant parameters and high deployment costs, limiting
the applicability of dynamic networks to a broader range of tasks and models,
and more importantly, 2) contradicts the previous discovery in the human brain
that \textit{when human brains process an attention-demanding task, only
partial neurons in the task-specific areas are activated by the input, while
the rest neurons leave in a baseline state.} Critically, there is no effort to
understand and resolve the above contradictory finding, leaving the primal
question -- to make the computational parameters fully dynamic or not? --
unanswered. The main contributions of our work are challenging the basic
commonsense in dynamic networks, and, proposing and validating the
\textsc{cherry hypothesis} -- \textit{A fully dynamic network contains a subset
of dynamic parameters that when transforming other dynamic parameters into
static ones, can maintain or even exceed the performance of the original
network.} Technically, we propose a brain-inspired partially dynamic network,
namely PAD-Net, to transform the redundant dynamic parameters into static ones.
Also, we further design Iterative Mode Partition to partition the dynamic- and
static-subnet, which alleviates the redundancy in traditional fully dynamic
networks. Our hypothesis and method are comprehensively supported by
large-scale experiments with typical advanced dynamic methods.

Channel state information (CSI) needs to be estimated for reliable and
efficient communication, however, location information is hidden inside and can
be further exploited. This article presents a detailed description of a Massive
Multi-Input Multi-Output (MaMIMO) testbed and provides a set of experimental
location-labelled CSI data. In this article, we focus on the design of the
hardware and software of a MaMIMO testbed for gathering multiple CSI data sets.
We also show this data can be used for learning-based localization and enhanced
communication research. The data set presented in this work is made fully
available to the research community. We show a CSI-based joint communication
and sensing processing pipeline can be evaluated and designed based on the
collected data set. Specifically, the localization output obtained by a
convolutional neural network (CNN) trained on the data sets is used to schedule
users for improving the spectral efficiency (SE) of the communication system.
Finally, we pose promising directions on further exploiting this data set and
creating future data sets.

Current state-of-the-art deep neural networks for image classification are
made up of 10 - 100 million learnable weights and are therefore inherently
prone to overfitting. The complexity of the weight count can be seen as a
function of the number of channels, the spatial extent of the input and the
number of layers of the network. Due to the use of convolutional layers the
scaling of weight complexity is usually linear with regards to the resolution
dimensions, but remains quadratic with respect to the number of channels.
Active research in recent years in terms of using multigrid inspired ideas in
deep neural networks have shown that on one hand a significant number of
weights can be saved by appropriate weight sharing and on the other that a
hierarchical structure in the channel dimension can improve the weight
complexity to linear. In this work, we combine these multigrid ideas to
introduce a joint framework of multigrid inspired architectures, that exploit
multigrid structures in all relevant dimensions to achieve linear weight
complexity scaling and drastically reduced weight counts. Our experiments show
that this structured reduction in weight count is able to reduce overfitting
and thus shows improved performance over state-of-the-art ResNet architectures
on typical image classification benchmarks at lower network complexity.

Adversarial training is widely acknowledged as the most effective defense
against adversarial attacks. However, it is also well established that
achieving both robustness and generalization in adversarially trained models
involves a trade-off. The goal of this work is to provide an in depth
comparison of different approaches for adversarial training in language models.
Specifically, we study the effect of pre-training data augmentation as well as
training time input perturbations vs. embedding space perturbations on the
robustness and generalization of BERT-like language models. Our findings
suggest that better robustness can be achieved by pre-training data
augmentation or by training with input space perturbation. However, training
with embedding space perturbation significantly improves generalization. A
linguistic correlation analysis of neurons of the learned models reveal that
the improved generalization is due to `more specialized' neurons. To the best
of our knowledge, this is the first work to carry out a deep qualitative
analysis of different methods of generating adversarial examples in adversarial
training of language models.

Artificial intelligence is currently powering diverse real-world
applications. These applications have shown promising performance, but raise
complicated ethical issues, i.e. how to embed ethics to make AI applications
behave morally. One way toward moral AI systems is by imitating human prosocial
behavior and encouraging some form of good behavior in systems. However,
learning such normative ethics (especially from images) is challenging mainly
due to a lack of data and labeling complexity. Here, we propose a model that
predicts visual commonsense immorality in a zero-shot manner. We train our
model with an ETHICS dataset (a pair of text and morality annotation) via a
CLIP-based image-text joint embedding. In a testing phase, the immorality of an
unseen image is predicted. We evaluate our model with existing moral/immoral
image datasets and show fair prediction performance consistent with human
intuitions. Further, we create a visual commonsense immorality benchmark with
more general and extensive immoral visual contents. Codes and dataset are
available at
https://github.com/ku-vai/Zero-shot-Visual-Commonsense-Immorality-Prediction.
Note that this paper might contain images and descriptions that are offensive
in nature.

Recently, graph neural networks have been gaining a lot of attention to
simulate dynamical systems due to their inductive nature leading to zero-shot
generalizability. Similarly, physics-informed inductive biases in deep-learning
frameworks have been shown to give superior performance in learning the
dynamics of physical systems. There is a growing volume of literature that
attempts to combine these two approaches. Here, we evaluate the performance of
thirteen different graph neural networks, namely, Hamiltonian and Lagrangian
graph neural networks, graph neural ODE, and their variants with explicit
constraints and different architectures. We briefly explain the theoretical
formulation highlighting the similarities and differences in the inductive
biases and graph architecture of these systems. We evaluate these models on
spring, pendulum, gravitational, and 3D deformable solid systems to compare the
performance in terms of rollout error, conserved quantities such as energy and
momentum, and generalizability to unseen system sizes. Our study demonstrates
that GNNs with additional inductive biases, such as explicit constraints and
decoupling of kinetic and potential energies, exhibit significantly enhanced
performance. Further, all the physics-informed GNNs exhibit zero-shot
generalizability to system sizes an order of magnitude larger than the training
system, thus providing a promising route to simulate large-scale realistic
systems.

In recent years, Web services are becoming more and more intelligent (e.g.,
in understanding user preferences) thanks to the integration of components that
rely on Machine Learning (ML). Before users can interact (inference phase) with
an ML-based service (ML-Service), the underlying ML model must learn (training
phase) from existing data, a process that requires long-lasting batch
computations. The management of these two, diverse phases is complex and
meeting time and quality requirements can hardly be done with manual
approaches.
  This paper highlights some of the major issues in managing ML-services in
both training and inference modes and presents some initial solutions that are
able to meet set requirements with minimum user inputs. A preliminary
evaluation demonstrates that our solutions allow these systems to become more
efficient and predictable with respect to their response time and accuracy.

Dialogue state tracking (DST) aims to convert the dialogue history into
dialogue states which consist of slot-value pairs. As condensed structural
information memorizing all history information, the dialogue state in the last
turn is typically adopted as the input for predicting the current state by DST
models. However, these models tend to keep the predicted slot values unchanged,
which is defined as state momentum in this paper. Specifically, the models
struggle to update slot values that need to be changed and correct wrongly
predicted slot values in the last turn. To this end, we propose MoNET to tackle
state momentum via noise-enhanced training. First, the previous state of each
turn in the training data is noised via replacing some of its slot values.
Then, the noised previous state is used as the input to learn to predict the
current state, improving the model's ability to update and correct slot values.
Furthermore, a contrastive context matching framework is designed to narrow the
representation distance between a state and its corresponding noised variant,
which reduces the impact of noised state and makes the model better understand
the dialogue history. Experimental results on MultiWOZ datasets show that MoNET
outperforms previous DST methods. Ablations and analysis verify the
effectiveness of MoNET in alleviating state momentum and improving anti-noise
ability.

Self-trained autonomous agents developed using machine learning are showing
great promise in a variety of control settings, perhaps most remarkably in
applications involving autonomous vehicles. The main challenge associated with
self-learned agents in the form of deep neural networks, is their black-box
nature: it is impossible for humans to interpret deep neural networks.
Therefore, humans cannot directly interpret the actions of deep neural network
based agents, or foresee their robustness in different scenarios. In this work,
we demonstrate a method for probing which concepts self-learning agents
internalise in the course of their training. For demonstration, we use a chess
playing agent in a fast and light environment developed specifically to be
suitable for research groups without access to enormous computational resources
or machine learning models.

Graph representation of objects and their relations in a scene, known as a
scene graph, provides a precise and discernible interface to manipulate a scene
by modifying the nodes or the edges in the graph. Although existing works have
shown promising results in modifying the placement and pose of objects, scene
manipulation often leads to losing some visual characteristics like the
appearance or identity of objects. In this work, we propose DisPositioNet, a
model that learns a disentangled representation for each object for the task of
image manipulation using scene graphs in a self-supervised manner. Our
framework enables the disentanglement of the variational latent embeddings as
well as the feature representation in the graph. In addition to producing more
realistic images due to the decomposition of features like pose and identity,
our method takes advantage of the probabilistic sampling in the intermediate
features to generate more diverse images in object replacement or addition
tasks. The results of our experiments show that disentangling the feature
representations in the latent manifold of the model outperforms the previous
works qualitatively and quantitatively on two public benchmarks. Project Page:
https://scenegenie.github.io/DispositioNet/

We consider the problem of decision-making under uncertainty in an
environment with safety constraints. Many business and industrial applications
rely on real-time optimization with changing inputs to improve key performance
indicators. In the case of unknown environmental characteristics, real-time
optimization becomes challenging, particularly for the satisfaction of safety
constraints. We propose the ARTEO algorithm, where we cast multi-armed bandits
as a mathematical programming problem subject to safety constraints and learn
the environmental characteristics through changes in optimization inputs and
through exploration. We quantify the uncertainty in unknown characteristics by
using Gaussian processes and incorporate it into the utility function as a
contribution which drives exploration. We adaptively control the size of this
contribution using a heuristic in accordance with the requirements of the
environment. We guarantee the safety of our algorithm with a high probability
through confidence bounds constructed under the regularity assumptions of
Gaussian processes. Compared to existing safe-learning approaches, our
algorithm does not require an exclusive exploration phase and follows the
optimization goals even in the explored points, which makes it suitable for
safety-critical systems. We demonstrate the safety and efficiency of our
approach with two experiments: an industrial process and an online bid
optimization benchmark problem.

In this paper, negatively inclined buoyant jets, which appear during the
discharge of wastewater from processes such as desalination, are observed. To
minimize harmful effects and assess environmental impact, a detailed numerical
investigation is necessary. The selection of appropriate geometry and working
conditions for minimizing such effects often requires numerous experiments and
numerical simulations. For this reason, the application of machine learning
models is proposed. Several models including Support Vector Regression,
Artificial Neural Networks, Random Forests, XGBoost, CatBoost and LightGBM were
trained. The dataset was built with numerous OpenFOAM simulations, which were
validated by experimental data from previous research. The best prediction was
obtained by Artificial Neural Network with an average of R2 0.98 and RMSE 0.28.
In order to understand the working of the machine learning model and the
influence of all parameters on the geometrical characteristics of inclined
buoyant jets, the SHAP feature interpretation method was used.

Image restoration tasks have achieved tremendous performance improvements
with the rapid advancement of deep neural networks. However, most prevalent
deep learning models perform inference statically, ignoring that different
images have varying restoration difficulties and lightly degraded images can be
well restored by slimmer subnetworks. To this end, we propose a new solution
pipeline dubbed ClassPruning that utilizes networks with different capabilities
to process images with varying restoration difficulties. In particular, we use
a lightweight classifier to identify the image restoration difficulty, and then
the sparse subnetworks with different capabilities can be sampled based on
predicted difficulty by performing dynamic N:M fine-grained structured pruning
on base restoration networks. We further propose a novel training strategy
along with two additional loss terms to stabilize training and improve
performance. Experiments demonstrate that ClassPruning can help existing
methods save approximately 40% FLOPs while maintaining performance.

Object re-identification method is made up of backbone network, feature
aggregation, and loss function. However, most backbone networks lack a special
mechanism to handle rich scale variations and mine discriminative feature
representations. In this paper, we firstly design a hierarchical similarity
graph module (HSGM) to reduce the conflict of backbone and re-identification
networks. The designed HSGM builds a rich hierarchical graph to mine the
mapping relationships between global-local and local-local. Secondly, we divide
the feature map along with the spatial and channel directions in each
hierarchical graph. The HSGM applies the spatial features and channel features
extracted from different locations as nodes, respectively, and utilizes the
similarity scores between nodes to construct spatial and channel similarity
graphs. During the learning process of HSGM, we utilize a learnable parameter
to re-optimize the importance of each position, as well as evaluate the
correlation between different nodes. Thirdly, we develop a novel hierarchical
similarity graph network (HSGNet) by embedding the HSGM in the backbone
network. Furthermore, HSGM can be easily embedded into backbone networks of any
depth to improve object re-identification ability. Finally, extensive
experiments on three large-scale object datasets demonstrate that the proposed
HSGNet is superior to state-of-the-art object re-identification approaches.

One of the main problems in applying deep learning techniques to recognize
activities of daily living (ADLs) based on inertial sensors is the lack of
appropriately large labelled datasets to train deep learning-based models. A
large amount of data would be available due to the wide spread of mobile
devices equipped with inertial sensors that can collect data to recognize human
activities. Unfortunately, this data is not labelled. The paper proposes DISC
(Deep Inertial Sensory Clustering), a DL-based clustering architecture that
automatically labels multi-dimensional inertial signals. In particular, the
architecture combines a recurrent AutoEncoder and a clustering criterion to
predict unlabelled human activities-related signals. The proposed architecture
is evaluated on three publicly available HAR datasets and compared with four
well-known end-to-end deep clustering approaches. The experiments demonstrate
the effectiveness of DISC on both clustering accuracy and normalized mutual
information metrics.

Aspect-based sentiment analysis (ABSA) aims at extracting opinionated aspect
terms in review texts and determining their sentiment polarities, which is
widely studied in both academia and industry. As a fine-grained classification
task, the annotation cost is extremely high. Domain adaptation is a popular
solution to alleviate the data deficiency issue in new domains by transferring
common knowledge across domains. Most cross-domain ABSA studies are based on
structure correspondence learning (SCL), and use pivot features to construct
auxiliary tasks for narrowing down the gap between domains. However, their
pivot-based auxiliary tasks can only transfer knowledge of aspect terms but not
sentiment, limiting the performance of existing models. In this work, we
propose a novel Syntax-guided Domain Adaptation Model, named SDAM, for more
effective cross-domain ABSA. SDAM exploits syntactic structure similarities for
building pseudo training instances, during which aspect terms of target domain
are explicitly related to sentiment polarities. Besides, we propose a
syntax-based BERT mask language model for further capturing domain-invariant
features. Finally, to alleviate the sentiment inconsistency issue in multi-gram
aspect terms, we introduce a span-based joint aspect term and sentiment
analysis module into the cross-domain End2End ABSA. Experiments on five
benchmark datasets show that our model consistently outperforms the
state-of-the-art baselines with respect to Micro-F1 metric for the cross-domain
End2End ABSA task.

Learning classifiers using skewed or imbalanced datasets can occasionally
lead to classification issues; this is a serious issue. In some cases, one
class contains the majority of examples while the other, which is frequently
the more important class, is nevertheless represented by a smaller proportion
of examples. Using this kind of data could make many carefully designed
machine-learning systems ineffective. High training fidelity was a term used to
describe biases vs. all other instances of the class. The best approach to all
possible remedies to this issue is typically to gain from the minority class.
The article examines the most widely used methods for addressing the problem of
learning with a class imbalance, including data-level, algorithm-level, hybrid,
cost-sensitive learning, and deep learning, etc. including their advantages and
limitations. The efficiency and performance of the classifier are assessed
using a myriad of evaluation metrics.

Autonomous vehicles currently suffer from a time-inefficient driving style
caused by uncertainty about human behavior in traffic interactions. Accurate
and reliable prediction models enabling more efficient trajectory planning
could make autonomous vehicles more assertive in such interactions. However,
the evaluation of such models is commonly oversimplistic, ignoring the
asymmetric importance of prediction errors and the heterogeneity of the
datasets used for testing. We examine the potential of recasting interactions
between vehicles as gap acceptance scenarios and evaluating models in this
structured environment. To that end, we develop a framework facilitating the
evaluation of any model, by any metric, and in any scenario. We then apply this
framework to state-of-the-art prediction models, which all show themselves to
be unreliable in the most safety-critical situations.

Spiking neural networks (SNNs) are receiving increasing attention due to
their low power consumption and strong bio-plausibility. Optimization of SNNs
is a challenging task. Two main methods, artificial neural network (ANN)-to-SNN
conversion and spike-based backpropagation (BP), both have their advantages and
limitations. For ANN-to-SNN conversion, it requires a long inference time to
approximate the accuracy of ANN, thus diminishing the benefits of SNN. With
spike-based BP, training high-precision SNNs typically consumes dozens of times
more computational resources and time than their ANN counterparts. In this
paper, we propose a novel SNN training approach that combines the benefits of
the two methods. We first train a single-step SNN by approximating the neural
potential distribution with random noise, then convert the single-step SNN to a
multi-step SNN losslessly. The introduction of Gaussian distributed noise leads
to a significant gain in accuracy after conversion. The results show that our
method considerably reduces the training and inference times of SNNs while
maintaining their high accuracy. Compared to the previous two methods, ours can
reduce training time by 65%-75% and achieves more than 100 times faster
inference speed. We also argue that the neuron model augmented with noise makes
it more bio-plausible.

Progress on many Natural Language Processing (NLP) tasks, such as text
classification, is driven by objective, reproducible and scalable evaluation
via publicly available benchmarks. However, these are not always representative
of real-world scenarios where text classifiers are employed, such as sentiment
analysis or misinformation detection. In this position paper, we put forward
two points that aim to alleviate this problem. First, we propose to extend text
classification benchmarks to evaluate the explainability of text classifiers.
We review challenges associated with objectively evaluating the capabilities to
produce valid explanations which leads us to the second main point: We propose
to ground these benchmarks in human-centred applications, for example by using
social media, gamification or to learn explainability metrics from human
judgements.

We analyse the single top production in association with the Higgs at the
Large Hadron Collider (LHC) using Standard Model (SM) effective operators upto
dimension six. We show that the presence of effective operators can
significantly alter the existing bound on the top-Higgs Yukawa coupling. We
analyse events at the LHC with 35.9 and 137(140) fb$^{-1}$ integrated
luminosities using both cut-based and machine learning techniques to probe new
physics (NP) scale and operator coefficients addressing relevant SM background
reduction. The four fermi effective operator(s) that contributes to the signal,
turns out to be crucial and a bound on the operator coefficient is obtained
from the present data and for future sensitivities.

Faced with the threat of identity leakage during voice data publishing, users
are engaged in a privacy-utility dilemma when enjoying convenient voice
services. Existing studies employ direct modification or text-based
re-synthesis to de-identify users' voices, but resulting in inconsistent
audibility in the presence of human participants. In this paper, we propose a
voice de-identification system, which uses adversarial examples to balance the
privacy and utility of voice services. Instead of typical additive examples
inducing perceivable distortions, we design a novel convolutional adversarial
example that modulates perturbations into real-world room impulse responses.
Benefit from this, our system could preserve user identity from exposure by
Automatic Speaker Identification (ASI) while remaining the voice perceptual
quality for non-intrusive de-identification. Moreover, our system learns a
compact speaker distribution through a conditional variational auto-encoder to
sample diverse target embeddings on demand. Combining diverse target generation
and input-specific perturbation construction, our system enables any-to-any
identify transformation for adaptive de-identification. Experimental results
show that our system could achieve 98% and 79% successful de-identification on
mainstream ASIs and commercial systems with an objective Mel cepstral
distortion of 4.31dB and a subjective mean opinion score of 4.48.

In Self-Supervised Learning (SSL), various pretext tasks are designed for
learning feature representations through contrastive loss. However, previous
studies have shown that this loss is less tolerant to semantically similar
samples due to the inherent defect of instance discrimination objectives, which
may harm the quality of learned feature embeddings used in downstream tasks. To
improve the discriminative ability of feature embeddings in SSL, we propose a
new loss function called Angular Contrastive Loss (ACL), a linear combination
of angular margin and contrastive loss. ACL improves contrastive learning by
explicitly adding an angular margin between positive and negative augmented
pairs in SSL. Experimental results show that using ACL for both supervised and
unsupervised learning significantly improves performance. We validated our new
loss function using the FSDnoisy18k dataset, where we achieved 73.6% and 77.1%
accuracy in sound event classification using supervised and self-supervised
learning, respectively.

Clone detection is widely exploited for software vulnerability search. The
approaches based on source code analysis cannot be applied to binary clone
detection because the same source code can produce significantly different
binaries. In this paper, we present BinSeeker, a cross-platform binary seeker
that integrates semantic learning and emulation. With the help of the labeled
semantic flow graph, BinSeeker can quickly identify M candidate functions that
are most similar to the vulnerability from the target binary. The value of M is
relatively large so this semantic learning procedure essentially eliminates
those functions that are very unlikely to have the vulnerability. Then,
semantic emulation is conducted on these M candidates to obtain their dynamic
signature sequences. By comparing signature sequences, BinSeeker produces top-N
functions that exhibit most similar behavior to that of the vulnerability. With
fast filtering of semantic learning and accurate comparison of semantic
emulation, BinSeeker seeks vulnerability precisely with little overhead. The
experiments on six widely used programs with fifteen known CVE vulnerabilities
demonstrate that BinSeeker outperforms three state-of-the-art tools Genius,
Gemini and CACompare. Regarding search accuracy, BinSeeker achieves an MRR
value of 0.65 in the target programs, whereas the MRR values by Genius, Gemini
and CACompare are 0.17, 0.07 and 0.42, respectively. If we consider ranking a
function with the targeted vulnerability in the top-5 as accurate, BinSeeker
achieves the accuracy of 93.33 percent, while the accuracy of the other three
tools is merely 33.33, 13.33 and 53.33 percent, respectively. Such accuracy is
achieved with 0.27s on average to determine whether the target binary function
contains a known vulnerability, and the time for the other three tools are
1.57s, 0.15s and 0.98s, respectively.

Semantic signal processing and communications are poised to play a central
part in developing the next generation of sensor devices and networks. A
crucial component of a semantic system is the extraction of semantic signals
from the raw input signals, which has become increasingly tractable with the
recent advances in machine learning (ML) and artificial intelligence (AI)
techniques. The accurate extraction of semantic signals using the
aforementioned ML and AI methods, and the detection of semantic innovation for
scheduling transmission and/or storage events are critical tasks for reliable
semantic signal processing and communications. In this work, we propose a
reliable semantic information extraction framework based on our previous work
on semantic signal representations in a hierarchical graph-based structure. The
proposed framework includes a time integration method to increase fidelity of
ML outputs in a class-aware manner, a graph-edit-distance based metric to
detect innovation events at the graph-level and filter out sporadic errors, and
a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The
proposed methods within the framework are demonstrated individually and
collectively through simulations and case studies based on real-world computer
vision examples.

Machine learning and deep learning classification models are data-driven, and
the model and the data jointly determine their classification performance. It
is biased to evaluate the model's performance only based on the classifier
accuracy while ignoring the data separability. Sometimes, the model exhibits
excellent accuracy, which might be attributed to its testing on highly
separable data. Most of the current studies on data separability measures are
defined based on the distance between sample points, but this has been
demonstrated to fail in several circumstances. In this paper, we propose a new
separability measure--the rate of separability (RS), which is based on the data
coding rate. We validate its effectiveness as a supplement to the separability
measure by comparing it to four other distance-based measures on synthetic
datasets. Then, we demonstrate the positive correlation between the proposed
measure and recognition accuracy in a multi-task scenario constructed from a
real dataset. Finally, we discuss the methods for evaluating the classification
performance of machine learning and deep learning models considering data
separability.

We consider optimizing a function network in the noise-free grey-box setting
with RKHS function classes, where the exact intermediate results are
observable. We assume that the structure of the network is known (but not the
underlying functions comprising it), and we study three types of structures:
(1) chain: a cascade of scalar-valued functions, (2) multi-output chain: a
cascade of vector-valued functions, and (3) feed-forward network: a fully
connected feed-forward network of scalar-valued functions. We propose a
sequential upper confidence bound based algorithm GPN-UCB along with a general
theoretical upper bound on the cumulative regret. For the Mat\'ern kernel, we
additionally propose a non-adaptive sampling based method along with its
theoretical upper bound on the simple regret. We also provide
algorithm-independent lower bounds on the simple regret and cumulative regret,
showing that GPN-UCB is near-optimal for chains and multi-output chains in
broad cases of interest.

As predictive models are increasingly being employed to make consequential
decisions, there is a growing emphasis on developing techniques that can
provide algorithmic recourse to affected individuals. While such recourses can
be immensely beneficial to affected individuals, potential adversaries could
also exploit these recourses to compromise privacy. In this work, we make the
first attempt at investigating if and how an adversary can leverage recourses
to infer private information about the underlying model's training data. To
this end, we propose a series of novel membership inference attacks which
leverage algorithmic recourse. More specifically, we extend the prior
literature on membership inference attacks to the recourse setting by
leveraging the distances between data instances and their corresponding
counterfactuals output by state-of-the-art recourse methods. Extensive
experimentation with real world and synthetic datasets demonstrates significant
privacy leakage through recourses. Our work establishes unintended privacy
leakage as an important risk in the widespread adoption of recourse methods.

Deep Learning models are easily disturbed by variations in the input images
that were not seen during training, resulting in unpredictable behaviours. Such
Out-of-Distribution (OOD) images represent a significant challenge in the
context of medical image analysis, where the range of possible abnormalities is
extremely wide, including artifacts, unseen pathologies, or different imaging
protocols. In this work, we evaluate various uncertainty frameworks to detect
OOD inputs in the context of Multiple Sclerosis lesions segmentation. By
implementing a comprehensive evaluation scheme including 14 sources of OOD of
various nature and strength, we show that methods relying on the predictive
uncertainty of binary segmentation models often fails in detecting outlying
inputs. On the contrary, learning to segment anatomical labels alongside
lesions highly improves the ability to detect OOD inputs.

We propose a novel hematoxylin and eosin (H&E) stain normalization method
based on a modified U-Net neural network architecture. Unlike previous
deep-learning methods that were often based on generative adversarial networks
(GANs), we take a teacher-student approach and use paired datasets generated by
a trained CycleGAN to train a U-Net to perform the stain normalization task.
Through experiments, we compared our method to two recent competing methods,
CycleGAN and StainNet, a lightweight approach also based on the teacher-student
model. We found that our method is faster and can process larger images with
better quality compared to CycleGAN. We also compared to StainNet and found
that our method delivered quantitatively and qualitatively better results.

State-of-the-art deep-learning-based approaches to Natural Language
Processing (NLP) are credited with various capabilities that involve reasoning
with natural language texts. In this paper we carry out a large-scale empirical
study investigating the detection of formally valid inferences in controlled
fragments of natural language for which the satisfiability problem becomes
increasingly complex. We find that, while transformer-based language models
perform surprisingly well in these scenarios, a deeper analysis re-veals that
they appear to overfit to superficial patterns in the data rather than
acquiring the logical principles governing the reasoning in these fragments.

flowMC is a Python library for accelerated Markov Chain Monte Carlo (MCMC)
leveraging deep generative modeling. It is built on top of the machine learning
libraries JAX and Flax. At its core, flowMC uses a local sampler and a
learnable global sampler in tandem to efficiently sample posterior
distributions. While multiple chains of the local sampler generate samples over
the region of interest in the target parameter space, the package uses these
samples to train a normalizing flow model, then uses it to propose global jumps
across the parameter space. The flowMC sampler can handle non-trivial geometry,
such as multimodal distributions and distributions with local correlations.
  The key features of flowMC are summarized in the following list: * Since
flowMC is built on top of JAX, it supports gradient-based samplers through
automatic differentiation such as MALA and Hamiltonian Monte Carlo (HMC). *
flowMC uses state-of-the-art normalizing flow models such as Rational-Quadratic
Splines to power its global sampler. These models are very efficient in
capturing important features within a relatively short training time. * Use of
accelerators such as GPUs and TPUs are natively supported. The code also
supports the use of multiple accelerators with SIMD parallelism. * By default,
Just-in-time (JIT) compilations are used to further speed up the sampling
process. * We provide a simple black box interface for the users who want to
use flowMC by its default parameters, yet provide at the same time an extensive
guide explaining trade-offs while tuning the sampler parameters. The tight
integration of all the above features makes flowMC a highly performant yet
simple- to-use package for statistical inference.

Several works have proven that finetuning is an applicable approach for
debiasing contextualized word embeddings. Similarly, discrete prompts with
semantic meanings have shown to be effective in debiasing tasks. With unfixed
mathematical representation at the token level, continuous prompts usually
surpass discrete ones at providing a pre-trained language model (PLM) with
additional task-specific information. Despite this, relatively few efforts have
been made to debias PLMs by prompt tuning with continuous prompts compared to
its discrete counterpart. Furthermore, for most debiasing methods that alter a
PLM's original parameters, a major problem is the need to not only decrease the
bias in the PLM but also to ensure that the PLM does not lose its
representation ability. Finetuning methods typically have a hard time
maintaining this balance, as they tend to violently remove meanings of
attribute words. In this paper, we propose ADEPT, a method to debias PLMs using
prompt tuning while maintaining the delicate balance between removing biases
and ensuring representation ability. To achieve this, we propose a new training
criterion inspired by manifold learning and equip it with an explicit debiasing
term to optimize prompt tuning. In addition, we conduct several experiments
with regard to the reliability, quality, and quantity of a previously proposed
attribute training corpus in order to obtain a clearer prototype of a certain
attribute, which indicates the attribute's position and relative distances to
other words on the manifold. We evaluate ADEPT on several widely acknowledged
debiasing benchmarks and downstream tasks, and find that it achieves
competitive results while maintaining (and in some cases even improving) the
PLM's representation ability. We further visualize words' correlation before
and after debiasing a PLM, and give some possible explanations for the visible
effects.

Some physical implementation schemes of quantum computing -- such as those
based on superconducting qubits, quantum dots, and cold atoms -- can apply
two-qubit gates only on certain pairs of qubits. Other schemes -- such as those
based on trapped ions and photonics -- are not subject to such constraints.
These qubit connectivity constraints are commonly viewed as a disadvantage; for
example, compiling an unrestricted n-qubit quantum circuit to one with poor
qubit connectivity, such as a 1D chain, usually results in a blowup of depth by
$O(n^2)$ and size by $O(n)$. It is appealing to conjecture that this overhead
is unavoidable -- a random circuit on n qubits has $\Theta(n)$ 2-qubit gates in
each layer and a constant fraction of them act on qubits separated by distance
$\Theta(n)$.
  While it is known that almost all n-qubit unitaries need quantum circuits of
$\Omega(4^n/n)$ depth and $\Omega(4^n)$ size to realize, in this paper, we show
that all $n$-qubit unitaries can be implemented by circuits of $O(4^n/n)$ depth
and $O(4^n)$ size even under 1D chain qubit connectivity constraints.
  We extend this result and investigate qubit connectivity along three
directions. First, we consider more general connectivity graphs, and show that
the size can always be made $O(4^n)$ as long as the graph is connected. For
depth, we study d-dimensional grids, complete d-ary trees and expander graphs,
and show results similar to the 1D chain. Second, we consider the case when
ancillae are available. We show that, with ancillae, the depth can be made
polynomial, and the space-depth trade-off is not impaired by the qubit
connectivity constraint unless we have exponentially many ancillae. Third, we
consider special unitaries, including diagonal unitaries and quantum state
preparation, the last being a fundamental task used in many quantum algorithms
for machine learning and linear algebra problems.

Spiking neural networks (SNN) are a viable alternative to conventional
artificial neural networks when energy efficiency and computational complexity
are of importance. A major advantage of SNNs is their binary information
transfer through spike trains. The training of SNN has, however, been a
challenge, since neuron models are non-differentiable and traditional
gradient-based backpropagation algorithms cannot be applied directly.
Furthermore, spike-timing-dependent plasticity (STDP), albeit being a
spike-based learning rule, updates weights locally and does not optimize for
the output error of the network. We present desire backpropagation, a method to
derive the desired spike activity of neurons from the output error. The loss
function can then be evaluated locally for every neuron. Incorporating the
desire values into the STDP weight update leads to global error minimization
and increasing classification accuracy. At the same time, the neuron dynamics
and computational efficiency of STDP are maintained, making it a spike-based
supervised learning rule. We trained three-layer networks to classify MNIST and
Fashion-MNIST images and reached an accuracy of 98.41% and 87.56%,
respectively. Furthermore, we show that desire backpropagation is
computationally less complex than backpropagation in traditional neural
networks.

Over the past few years, the field of adversarial attack received numerous
attention from various researchers with the help of successful attack success
rate against well-known deep neural networks that were acknowledged to achieve
high classification ability in various tasks. However, majority of the
experiments were completed under a single model, which we believe it may not be
an ideal case in a real-life situation. In this paper, we introduce a novel
federated adversarial training method for smart home face recognition, named
FLATS, where we observed some interesting findings that may not be easily
noticed in a traditional adversarial attack to federated learning experiments.
By applying different variations to the hyperparameters, we have spotted that
our method can make the global model to be robust given a starving federated
environment. Our code can be found on https://github.com/jcroh0508/FLATS.

Outcome prediction is crucial for head and neck cancer patients as it can
provide prognostic information for early treatment planning. Radiomics methods
have been widely used for outcome prediction from medical images. However,
these methods are limited by their reliance on intractable manual segmentation
of tumor regions. Recently, deep learning methods have been proposed to perform
end-to-end outcome prediction so as to remove the reliance on manual
segmentation. Unfortunately, without segmentation masks, these methods will
take the whole image as input, such that makes them difficult to focus on tumor
regions and potentially unable to fully leverage the prognostic information
within the tumor regions. In this study, we propose a radiomics-enhanced deep
multi-task framework for outcome prediction from PET/CT images, in the context
of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR
2022). In our framework, our novelty is to incorporate radiomics as an
enhancement to our recently proposed Deep Multi-task Survival model (DeepMTS).
The DeepMTS jointly learns to predict the survival risk scores of patients and
the segmentation masks of tumor regions. Radiomics features are extracted from
the predicted tumor regions and combined with the predicted survival risk
scores for final outcome prediction, through which the prognostic information
in tumor regions can be further leveraged. Our method achieved a C-index of
0.681 on the testing set, placing the 2nd on the leaderboard with only 0.00068
lower in C-index than the 1st place.

Quantifying the deviation of a probability distribution is challenging when
the target distribution is defined by a density with an intractable normalizing
constant. The kernel Stein discrepancy (KSD) was proposed to address this
problem and has been applied to various tasks including diagnosing approximate
MCMC samplers and goodness-of-fit testing for unnormalized statistical models.
This article investigates a convergence control property of the diffusion
kernel Stein discrepancy (DKSD), an instance of the KSD proposed by Barp et al.
(2019). We extend the result of Gorham and Mackey (2017), which showed that the
KSD controls the bounded-Lipschitz metric, to functions of polynomial growth.
Specifically, we prove that the DKSD controls the integral probability metric
defined by a class of pseudo-Lipschitz functions, a polynomial generalization
of Lipschitz functions. We also provide practical sufficient conditions on the
reproducing kernel for the stated property to hold. In particular, we show that
the DKSD detects non-convergence in moments with an appropriate kernel.

Underwater automatic target recognition (UATR) has been a challenging
research topic in ocean engineering. Although deep learning brings
opportunities for target recognition on land and in the air, underwater target
recognition techniques based on deep learning have lagged due to sensor
performance and the size of trainable data. This letter proposed a framework
for learning the visual representation of underwater acoustic imageries, which
takes a transformer-based style transfer model as the main body. It could
replace the low-level texture features of optical images with the visual
features of underwater acoustic imageries while preserving their raw high-level
semantic content. The proposed framework could fully use the rich optical image
dataset to generate a pseudo-acoustic image dataset and use it as the initial
sample to train the underwater acoustic target recognition model. The
experiments select the dual-frequency identification sonar (DIDSON) as the
underwater acoustic data source and also take fish, the most common marine
creature, as the research subject. Experimental results show that the proposed
method could generate high-quality and high-fidelity pseudo-acoustic samples,
achieve the purpose of acoustic data enhancement and provide support for the
underwater acoustic-optical images domain transfer research.

We propose GANStrument, a generative adversarial model for instrument sound
synthesis. Given a one-shot sound as input, it is able to generate pitched
instrument sounds that reflect the timbre of the input within an interactive
time. By exploiting instance conditioning, GANStrument achieves better fidelity
and diversity of synthesized sounds and generalization ability to various
inputs. In addition, we introduce an adversarial training scheme for a
pitch-invariant feature extractor that significantly improves the pitch
accuracy and timbre consistency. Experimental results show that GANStrument
outperforms strong baselines that do not use instance conditioning in terms of
generation quality and input editability. Qualitative examples are available
online.

Graph neural networks (GNNs) have been demonstrated to be a powerful
algorithmic model in broad application fields for their effectiveness in
learning over graphs. To scale GNN training up for large-scale and ever-growing
graphs, the most promising solution is distributed training which distributes
the workload of training across multiple computing nodes. However, the
workflows, computational patterns, communication patterns, and optimization
techniques of distributed GNN training remain preliminarily understood. In this
paper, we provide a comprehensive survey of distributed GNN training by
investigating various optimization techniques used in distributed GNN training.
First, distributed GNN training is classified into several categories according
to their workflows. In addition, their computational patterns and communication
patterns, as well as the optimization techniques proposed by recent work are
introduced. Second, the software frameworks and hardware platforms of
distributed GNN training are also introduced for a deeper understanding. Third,
distributed GNN training is compared with distributed training of deep neural
networks, emphasizing the uniqueness of distributed GNN training. Finally,
interesting issues and opportunities in this field are discussed.

Considerable unsupervised video object segmentation algorithms based on deep
learning have the problem of substantive model parameters and computation,
which significantly limits the application of the algorithm in practice. This
paper proposes a video object segmentation network based on motion guidance,
considerably reducing the number of model parameters and computation and
improving the video object segmentation performance. The model comprises a
dual-stream network, motion guidance module, and multi-scale progressive fusion
module. Specifically, RGB images and optical flow estimation are fed into
dual-stream network to extract object appearance features and motion features.
Then, the motion guidance module extracts the semantic information from the
motion features through local attention, which guides the appearance features
to learn rich semantic information. Finally, the multi-scale progressive fusion
module obtains the output features at each stage of the dual-stream network. It
gradually integrates the deep features into the shallow ones yet improves the
edge segmentation effect. In this paper, numerous evaluations are conducted on
three standard datasets, and the experimental results prove the superior
performance of the proposed method.

In this work, we focus on the problem of safe policy transfer in
reinforcement learning: we seek to leverage existing policies when learning a
new task with specified constraints. This problem is important for
safety-critical applications where interactions are costly and unconstrained
policies can lead to undesirable or dangerous outcomes, e.g., with physical
robots that interact with humans. We propose a Constrained Markov Decision
Process (CMDP) formulation that simultaneously enables the transfer of policies
and adherence to safety constraints. Our formulation cleanly separates task
goals from safety considerations and permits the specification of a wide
variety of constraints. Our approach relies on a novel extension of generalized
policy improvement to constrained settings via a Lagrangian formulation. We
devise a dual optimization algorithm that estimates the optimal dual variable
of a target task, thus enabling safe transfer of policies derived from
successor features learned on source tasks. Our experiments in simulated
domains show that our approach is effective; it visits unsafe states less
frequently and outperforms alternative state-of-the-art methods when taking
safety constraints into account.

Scientists continue to develop increasingly complex mechanistic models to
reflect their knowledge more realistically. Statistical inference using these
models can be highly challenging, since the corresponding likelihood function
is often intractable, and model simulation may be computationally burdensome or
infeasible. Fortunately, in many of these situations, it is possible to adopt a
surrogate model or approximate likelihood function. It may be convenient to
base Bayesian inference directly on the surrogate, but this can result in bias
and poor uncertainty quantification. In this paper we propose a new method for
adjusting approximate posterior samples to reduce bias and produce more
accurate uncertainty quantification. We do this by optimising a transform of
the approximate posterior that minimises a scoring rule. Our approach requires
only a (fixed) small number of complex model simulations and is numerically
stable. We demonstrate good performance of the new method on several examples
of increasing complexity.

In this paper, we introduce 3D-CSL, a compact pipeline for Near-Duplicate
Video Retrieval (NDVR), and explore a novel self-supervised learning strategy
for video similarity learning. Most previous methods only extract video spatial
features from frames separately and then design kinds of complex mechanisms to
learn the temporal correlations among frame features. However, parts of
spatiotemporal dependencies have already been lost. To address this, our 3D-CSL
extracts global spatiotemporal dependencies in videos end-to-end with a 3D
transformer and find a good balance between efficiency and effectiveness by
matching on clip-level. Furthermore, we propose a two-stage self-supervised
similarity learning strategy to optimize the entire network. Firstly, we
propose PredMAE to pretrain the 3D transformer with video prediction task;
Secondly, ShotMix, a novel video-specific augmentation, and FCS loss, a novel
triplet loss, are proposed further promote the similarity learning results. The
experiments on FIVR-200K and CC_WEB_VIDEO demonstrate the superiority and
reliability of our method, which achieves the state-of-the-art performance on
clip-level NDVR.

Biomedical knowledge graphs (KG) are heterogenous networks consisting of
biological entities as nodes and relations between them as edges. These
entities and relations are extracted from millions of research papers and
unified in a single resource. The goal of biomedical multi-hop
question-answering over knowledge graph (KGQA) is to help biologist and
scientist to get valuable insights by asking questions in natural language.
Relevant answers can be found by first understanding the question and then
querying the KG for right set of nodes and relationships to arrive at an
answer. To model the question, language models such as RoBERTa and BioBERT are
used to understand context from natural language question. One of the
challenges in KGQA is missing links in the KG. Knowledge graph embeddings (KGE)
help to overcome this problem by encoding nodes and edges in a dense and more
efficient way. In this paper, we use a publicly available KG called Hetionet
which is an integrative network of biomedical knowledge assembled from 29
different databases of genes, compounds, diseases, and more. We have enriched
this KG dataset by creating a multi-hop biomedical question-answering dataset
in natural language for testing the biomedical multi-hop question-answering
system and this dataset will be made available to the research community. The
major contribution of this research is an integrated system that combines
language models with KG embeddings to give highly relevant answers to free-form
questions asked by biologists in an intuitive interface. Biomedical multi-hop
question-answering system is tested on this data and results are highly
encouraging.

Online continual learning (OCL) aims to enable model learning from a
non-stationary data stream to continuously acquire new knowledge as well as
retain the learnt one, under the constraints of having limited system size and
computational cost, in which the main challenge comes from the "catastrophic
forgetting" issue -- the inability to well remember the learnt knowledge while
learning the new ones. With the specific focus on the class-incremental OCL
scenario, i.e. OCL for classification, the recent advance incorporates the
contrastive learning technique for learning more generalised feature
representation to achieve the state-of-the-art performance but is still unable
to fully resolve the catastrophic forgetting. In this paper, we follow the
strategy of adopting contrastive learning but further introduce the
semantically distinct augmentation technique, in which it leverages strong
augmentation to generate more data samples, and we show that considering these
samples semantically different from their original classes (thus being related
to the out-of-distribution samples) in the contrastive learning mechanism
contributes to alleviate forgetting and facilitate model stability. Moreover,
in addition to contrastive learning, the typical classification mechanism and
objective (i.e. softmax classifier and cross-entropy loss) are included in our
model design for faster convergence and utilising the label information, but
particularly equipped with a sampling strategy to tackle the tendency of
favouring the new classes (i.e. model bias towards the recently learnt
classes). Upon conducting extensive experiments on CIFAR-10, CIFAR-100, and
Mini-Imagenet datasets, our proposed method is shown to achieve superior
performance against various baselines.

The exponential growth in demand for digital services drives massive
datacenter energy consumption and negative environmental impacts. Promoting
sustainable solutions to pressing energy and digital infrastructure challenges
is crucial. Several hyperscale cloud providers have announced plans to power
their datacenters using renewable energy. However, integrating renewables to
power the datacenters is challenging because the power generation is
intermittent, necessitating approaches to tackle power supply variability. Hand
engineering domain-specific heuristics-based schedulers to meet specific
objective functions in such complex dynamic green datacenter environments is
time-consuming, expensive, and requires extensive tuning by domain experts. The
green datacenters need smart systems and system software to employ multiple
renewable energy sources (wind and solar) by intelligently adapting computing
to renewable energy generation. We present RARE (Renewable energy Aware
REsource management), a Deep Reinforcement Learning (DRL) job scheduler that
automatically learns effective job scheduling policies while continually
adapting to datacenters' complex dynamic environment. The resulting DRL
scheduler performs better than heuristic scheduling policies with different
workloads and adapts to the intermittent power supply from renewables. We
demonstrate DRL scheduler system design parameters that, when tuned correctly,
produce better performance. Finally, we demonstrate that the DRL scheduler can
learn from and improve upon existing heuristic policies using Offline Learning.

Federated learning is a collaborative method that aims to preserve data
privacy while creating AI models. Current approaches to federated learning tend
to rely heavily on secure aggregation protocols to preserve data privacy.
However, to some degree, such protocols assume that the entity orchestrating
the federated learning process (i.e., the server) is not fully malicious or
dishonest. We investigate vulnerabilities to secure aggregation that could
arise if the server is fully malicious and attempts to obtain access to
private, potentially sensitive data. Furthermore, we provide a method to
further defend against such a malicious server, and demonstrate effectiveness
against known attacks that reconstruct data in a federated learning setting.

Pre-trained Language Model (PLM) has become a representative foundation model
in the natural language processing field. Most PLMs are trained with
linguistic-agnostic pre-training tasks on the surface form of the text, such as
the masked language model (MLM). To further empower the PLMs with richer
linguistic features, in this paper, we aim to propose a simple but effective
way to learn linguistic features for pre-trained language models. We propose
LERT, a pre-trained language model that is trained on three types of linguistic
features along with the original MLM pre-training task, using a
linguistically-informed pre-training (LIP) strategy. We carried out extensive
experiments on ten Chinese NLU tasks, and the experimental results show that
LERT could bring significant improvements over various comparable baselines.
Furthermore, we also conduct analytical experiments in various linguistic
aspects, and the results prove that the design of LERT is valid and effective.
Resources are available at https://github.com/ymcui/LERT

This paper studies a model for online job scheduling in green datacenters. In
green datacenters, resource availability depends on the power supply from the
renewables. Intermittent power supply from renewables leads to intermittent
resource availability, inducing job delays (and associated costs). Green
datacenter operators must intelligently manage their workloads and available
power supply to extract maximum benefits. The scheduler's objective is to
schedule jobs on a set of resources to maximize the total value (revenue) while
minimizing the overall job delay. A trade-off exists between achieving high job
value on the one hand and low expected delays on the other. Hence, the aims of
achieving high rewards and low costs are in opposition. In addition, datacenter
operators often prioritize multiple objectives, including high system
utilization and job completion. To accomplish the opposing goals of maximizing
total job value and minimizing job delays, we apply the
Proportional-Integral-Derivative (PID) Lagrangian methods in Deep Reinforcement
Learning to job scheduling problem in the green datacenter environment.
Lagrangian methods are widely used algorithms for constrained optimization
problems. We adopt a controls perspective to learn the Lagrange multiplier with
proportional, integral, and derivative control, achieving favorable learning
dynamics. Feedback control defines cost terms for the learning agent, monitors
the cost limits during training, and continuously adjusts the learning
parameters to achieve stable performance. Our experiments demonstrate improved
performance compared to scheduling policies without the PID Lagrangian methods.
Experimental results illustrate the effectiveness of the Constraint Controlled
Reinforcement Learning (CoCoRL) scheduler that simultaneously satisfies
multiple objectives.

Spatiotemporal data is readily available due to emerging sensor and data
acquisition technologies that track the positions of moving objects of
interest. Spatiotemporal clustering addresses the need to efficiently discover
patterns and trends in moving object behavior without human supervision. One
application of interest is the discovery of moving clusters, where clusters
have a static identity, but their location and content can change over time. We
propose a two phase spatiotemporal clustering method called spatiotemporal
k-means (STKM) that is able to analyze the multi-scale relationships within
spatiotemporal data. Phase 1 of STKM frames the moving cluster problem as the
minimization of an objective function unified over space and time. It outputs
the short-term associations between objects and is uniquely able to track
dynamic cluster centers with minimal parameter tuning and without
post-processing. Phase 2 outputs the long-term associations and can be applied
to any method that provides a cluster label for each object at every point in
time. We evaluate STKM against baseline methods on a recently developed
benchmark dataset and show that STKM outperforms existing methods, particularly
in the low-data domain, with significant performance improvements demonstrated
for common evaluation metrics on the moving cluster problem.

Harnessing the benefits of drones for urban innovation at scale requires
reliable aerial autonomy. One major barrier to advancing aerial autonomy has
been collecting large-scale aerial datasets for training machine learning
models. Due to costly and time-consuming real-world data collection through
deploying drones, there has been an increasing shift towards using synthetic
data for training models in drone applications. However, to increase
generalizability of trained policies on synthetic data, incorporating domain
randomization into the data generation workflow for addressing the sim-to-real
problem becomes crucial. Current synthetic data generation tools either lack
domain randomization or rely heavily on manual workload or real samples for
configuring and generating diverse realistic simulation scenes. These
dependencies limit scalability of the data generation workflow. Accordingly,
there is a major challenge in balancing generalizability and scalability in
synthetic data generation. To address these gaps, we introduce a modular
scalable data generation workflow tailored to aerial autonomy applications. To
generate realistic configurations of simulation scenes while increasing
diversity, we present an adaptive layered domain randomization approach that
creates a type-agnostic distribution space for assets over the base map of the
environments before pose generation for drone trajectory. We leverage
high-level scene structures to automatically place assets in valid
configurations and then extend the diversity through obstacle generation and
global parameter randomization. We demonstrate the effectiveness of our method
in automatically generating diverse configurations and datasets and show its
potential for downstream performance optimization. Our work contributes to
generating enhanced benchmark datasets for training models that can generalize
better to real-world situations.

We study a novel and important communication pattern in large-scale
model-parallel deep learning (DL), which we call cross-mesh resharding. This
pattern emerges when the two paradigms of model parallelism - intra-operator
and inter-operator parallelism - are combined to support large models on large
clusters. In cross-mesh resharding, a sharded tensor needs to be sent from a
source device mesh to a destination device mesh, on which the tensor may be
distributed with the same or different layouts. We formalize this as a
many-to-many multicast communication problem, and show that existing approaches
either are sub-optimal or do not generalize to different network topologies or
tensor layouts, which result from different model architectures and parallelism
strategies. We then propose two contributions to address cross-mesh resharding:
an efficient broadcast-based communication system, and an
"overlapping-friendly" pipeline schedule. On microbenchmarks, our overall
system outperforms existing ones by up to 10x across various tensor and mesh
layouts. On end-to-end training of two large models, GPT-3 and U-Transformer,
we improve throughput by 10% and 50%, respectively.

A significant level of stigma and inequality exists in mental healthcare,
especially in under-served populations, which spreads through collected data.
When not properly accounted for, machine learning (ML) models learned from data
can reinforce the structural biases already present in society. Here, we
present a systematic study of bias in ML models designed to predict depression
in four different case studies covering different countries and populations. We
find that standard ML approaches show regularly biased behaviors. However, we
show that standard mitigation techniques, and our own post-hoc method, can be
effective in reducing the level of unfair bias. We provide practical
recommendations to develop ML models for depression risk prediction with
increased fairness and trust in the real world. No single best ML model for
depression prediction provides equality of outcomes. This emphasizes the
importance of analyzing fairness during model selection and transparent
reporting about the impact of debiasing interventions.

Metric-based meta-learning is one of the de facto standards in few-shot
learning. It composes of representation learning and metrics calculation
designs. Previous works construct class representations in different ways,
varying from mean output embedding to covariance and distributions. However,
using embeddings in space lacks expressivity and cannot capture class
information robustly, while statistical complex modeling poses difficulty to
metric designs. In this work, we use tensor fields (``areas'') to model classes
from the geometrical perspective for few-shot learning. We present a simple and
effective method, dubbed hypersphere prototypes (HyperProto), where class
information is represented by hyperspheres with dynamic sizes with two sets of
learnable parameters: the hypersphere's center and the radius. Extending from
points to areas, hyperspheres are much more expressive than embeddings.
Moreover, it is more convenient to perform metric-based classification with
hypersphere prototypes than statistical modeling, as we only need to calculate
the distance from a data point to the surface of the hypersphere. Following
this idea, we also develop two variants of prototypes under other measurements.
Extensive experiments and analysis on few-shot learning tasks across NLP and CV
and comparison with 20+ competitive baselines demonstrate the effectiveness of
our approach.

The current paper implements a methodology for automatically detecting
vehicle maneuvers from vehicle telemetry data under naturalistic driving
settings. Previous approaches have treated vehicle maneuver detection as a
classification problem, although both time series segmentation and
classification are required since input telemetry data is continuous. Our
objective is to develop an end-to-end pipeline for frame-by-frame annotation of
naturalistic driving studies videos into various driving events including stop
and lane keeping events, lane changes, left-right turning movements, and
horizontal curve maneuvers. To address the time series segmentation problem,
the study developed an Energy Maximization Algorithm (EMA) capable of
extracting driving events of varying durations and frequencies from continuous
signal data. To reduce overfitting and false alarm rates, heuristic algorithms
were used to classify events with highly variable patterns such as stops and
lane-keeping. To classify segmented driving events, four machine learning
models were implemented, and their accuracy and transferability were assessed
over multiple data sources. The duration of events extracted by EMA were
comparable to actual events, with accuracies ranging from 59.30% (left lane
change) to 85.60% (lane-keeping). Additionally, the overall accuracy of the
1D-convolutional neural network model was 98.99%, followed by the
Long-short-term-memory model at 97.75%, then random forest model at 97.71%, and
the support vector machine model at 97.65%. These model accuracies where
consistent across different data sources. The study concludes that implementing
a segmentation-classification pipeline significantly improves both the accuracy
for driver maneuver detection and transferability of shallow and deep ML models
across diverse datasets.

Selecting subsets of features that differentiate between two conditions is a
key task in a broad range of scientific domains. In many applications, the
features of interest form clusters with similar effects on the data at hand. To
recover such clusters we develop DiSC, a data-driven approach for detecting
groups of features that differentiate between conditions. For each condition,
we construct a graph whose nodes correspond to the features and whose weights
are functions of the similarity between them for that condition. We then apply
a spectral approach to compute subsets of nodes whose connectivity differs
significantly between the condition-specific feature graphs. On the theoretical
front, we analyze our approach with a toy example based on the stochastic block
model. We evaluate DiSC on a variety of datasets, including MNIST,
hyperspectral imaging, simulated scRNA-seq and task fMRI, and demonstrate that
DiSC uncovers features that better differentiate between conditions compared to
competing methods.

Model-free algorithms for reinforcement learning typically require a
condition called Bellman completeness in order to successfully operate
off-policy with function approximation, unless additional conditions are met.
However, Bellman completeness is a requirement that is much stronger than
realizability and that is deemed to be too strong to hold in practice. In this
work, we relax this structural assumption and analyze the statistical
complexity of off-policy reinforcement learning when only realizability holds
for the prescribed function class.
  We establish finite-sample guarantees for off-policy reinforcement learning
that are free of the approximation error term known as inherent Bellman error,
and that depend on the interplay of three factors. The first two are well-know:
they are the metric entropy of the function class and the concentrability
coefficient that represents the cost of learning off-policy. The third factor
is new, and it measures the violation of Bellman completeness, namely the
mis-alignment between the chosen function class and its image through the
Bellman operator.
  In essence, these error bounds establish that off-policy reinforcement
learning remains statistically viable even in absence of Bellman completeness,
and characterize the intermediate situation between the favorable Bellman
complete setting and the worst-case scenario where exponential lower bounds are
in force. Our analysis directly applies to the solution found by temporal
difference algorithms when they converge.

Breast cancer is the second most common type of cancer in women in Canada and
the United States, representing over 25% of all new female cancer cases.
Neoadjuvant chemotherapy treatment has recently risen in usage as it may result
in a patient having a pathologic complete response (pCR), and it can shrink
inoperable breast cancer tumors prior to surgery so that the tumor becomes
operable, but it is difficult to predict a patient's pathologic response to
neoadjuvant chemotherapy. In this paper, we investigate the efficacy of
leveraging learnt volumetric deep features from a newly introduced magnetic
resonance imaging (MRI) modality called synthetic correlated diffusion imaging
(CDI$^s$) for the purpose of pCR prediction. More specifically, we leverage a
volumetric convolutional neural network to learn volumetric deep radiomic
features from a pre-treatment cohort and construct a predictor based on the
learnt features using the post-treatment response. As the first study to
explore the utility of CDI$^s$ within a deep learning perspective for clinical
decision support, we evaluated the proposed approach using the ACRIN-6698 study
against those learnt using gold-standard imaging modalities, and found that the
proposed approach can provide enhanced pCR prediction performance and thus may
be a useful tool to aid oncologists in improving recommendation of treatment of
patients. Subsequently, this approach to leverage volumetric deep radiomic
features (which we name Cancer-Net BCa) can be further extended to other
applications of CDI$^s$ in the cancer domain to further improve prediction

Human skeleton point clouds are commonly used to automatically classify and
predict the behaviour of others. In this paper, we use a contrastive
self-supervised learning method, SimCLR, to learn representations that capture
the semantics of skeleton point clouds. This work focuses on systematically
evaluating the effects that different algorithmic decisions (including
augmentations, dataset partitioning and backbone architecture) have on the
learned skeleton representations. To pre-train the representations, we
normalise six existing datasets to obtain more than 40 million skeleton frames.
We evaluate the quality of the learned representations with three downstream
tasks: skeleton reconstruction, motion prediction, and activity classification.
Our results demonstrate the importance of 1) combining spatial and temporal
augmentations, 2) including additional datasets for encoder training, and 3)
and using a graph neural network as an encoder.

Connected and automated vehicles (CAVs) are viewed as a special kind of
robots that have the potential to significantly improve the safety and
efficiency of traffic. In contrast to many swarm robotics studies that are
demonstrated in labs by employing a small number of robots, CAV studies aims to
achieve cooperative driving of unceasing robot swarm flows. However, how to get
the optimal passing order of such robot swarm flows even for a signal-free
intersection is an NP-hard problem (specifically, enumerating based algorithm
takes days to find the optimal solution to a 20-CAV scenario). Here, we
introduce a novel cooperative driving algorithm (AlphaOrder) that combines
offline deep learning and online tree searching to find a near-optimal passing
order in real-time. AlphaOrder builds a pointer network model from solved
scenarios and generates near-optimal passing orders instantaneously for new
scenarios. Furthermore, our approach provides a general approach to managing
preemptive resource sharing between swarm robotics (e.g., scheduling multiple
automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) at
conflicting areas

Cross-view geo-localization aims to spot images of the same location shot
from two platforms, e.g., the drone platform and the satellite platform.
Existing methods usually focus on optimizing the distance between one embedding
with others in the feature space, while neglecting the redundancy of the
embedding itself. In this paper, we argue that the low redundancy is also of
importance, which motivates the model to mine more diverse patterns. To verify
this point, we introduce a simple yet effective regularization, i.e., Dynamic
Weighted Decorrelation Regularization (DWDR), to explicitly encourage networks
to learn independent embedding channels. As the name implies, DWDR regresses
the embedding correlation coefficient matrix to a sparse matrix, i.e., the
identity matrix, with dynamic weights. The dynamic weights are applied to focus
on still correlated channels during training. Besides, we propose a cross-view
symmetric sampling strategy, which keeps the example balance between different
platforms. Albeit simple, the proposed method has achieved competitive results
on three large-scale benchmarks, i.e., University-1652, CVUSA and CVACT.
Moreover, under the harsh circumstance, e.g., the extremely short feature of 64
dimensions, the proposed method surpasses the baseline model by a clear margin.

Semantic segmentation is a high level computer vision task that assigns a
label for each pixel of an image. It is challengeful to deal with
extremely-imbalanced data in which the ratio of target ixels to background
pixels is lower than 1:1000. Such severe input imbalance leads to output
imbalance for poor model training. This paper considers three issues for
extremely-imbalanced data: inspired by the region based loss, an implicit
measure for the output imbalance is proposed, and an adaptive algorithm is
designed for guiding the output imbalance hyperparameter selection; then it is
generalized to distribution based loss for dealing with output imbalance; and
finally a compound loss with our adaptive hyperparameter selection alogorithm
can keep the consistency of training and inference for harmonizing the output
imbalance. With four popular deep architectures on our private dataset with
three input imbalance scales and three public datasets, extensive experiments
demonstrate the ompetitive/promising performance of the proposed method.

Contrastive learning (CL) benefits the training of sequential recommendation
models with informative self-supervision signals. Existing solutions apply
general sequential data augmentation strategies to generate positive pairs and
encourage their representations to be invariant. However, due to the inherent
properties of user behavior sequences, some augmentation strategies, such as
item substitution, can lead to changes in user intent. Learning
indiscriminately invariant representations for all augmentation strategies
might be sub-optimal. Therefore, we propose Equivariant Contrastive Learning
for Sequential Recommendation (ECL-SR), which endows SR models with great
discriminative power, making the learned user behavior representations
sensitive to invasive augmentations (e.g., item substitution) and insensitive
to mild augmentations (e.g., feature-level dropout masking). In detail, we use
the conditional discriminator to capture differences in behavior due to item
substitution, which encourages the user behavior encoder to be equivariant to
invasive augmentations. Comprehensive experiments on four benchmark datasets
show that the proposed ECL-SR framework achieves competitive performance
compared to state-of-the-art SR models. The source code will be released.

Requirement engineering (RE) is the first and the most important step in
software production and development. The RE is aimed to specify software
requirements. One of the tasks in RE is the categorization of software
requirements as functional and non-functional requirements. The functional
requirements (FR) show the responsibilities of the system while non-functional
requirements represent the quality factors of software. Discrimination between
FR and NFR is a challenging task. Nowadays Deep Learning (DL) has entered all
fields of engineering and has increased accuracy and reduced time in their
implementation process. In this paper, we use deep learning for the
classification of software requirements. Five prominent DL algorithms are
trained for classifying requirements. Also, two voting classification
algorithms are utilized for creating ensemble classifiers based on five DL
methods. The PURE, a repository of Software Requirement Specification (SRS)
documents, is selected for our experiments. We created a dataset from PURE
which contains 4661 requirements where 2617 requirements are functional and the
remaining are non-functional. Our methods are applied to the dataset and their
performance analysis is reported. The results show that the performance of deep
learning models is satisfactory and the voting mechanisms provide better
results.

The last few years have seen a lot of work to address the challenge of
low-latency and high-throughput convolutional neural network inference.
Integrated photonics has the potential to dramatically accelerate neural
networks because of its low-latency nature. Combined with the concept of Joint
Transform Correlator (JTC), the computationally expensive convolution functions
can be computed instantaneously (time of flight of light) with almost no cost.
This 'free' convolution computation provides the theoretical basis of the
proposed PhotoFourier JTC-based CNN accelerator. PhotoFourier addresses a
myriad of challenges posed by on-chip photonic computing in the Fourier domain
including 1D lenses and high-cost optoelectronic conversions. The proposed
PhotoFourier accelerator achieves more than 28X better energy-delay product
compared to state-of-art photonic neural network accelerators.

Suppose we are given an $n$-dimensional order-3 symmetric tensor $T \in
(\mathbb{R}^n)^{\otimes 3}$ that is the sum of $r$ random rank-1 terms. The
problem of recovering the rank-1 components is possible in principle when $r
\lesssim n^2$ but polynomial-time algorithms are only known in the regime $r
\ll n^{3/2}$. Similar "statistical-computational gaps" occur in many
high-dimensional inference tasks, and in recent years there has been a flurry
of work on explaining the apparent computational hardness in these problems by
proving lower bounds against restricted (yet powerful) models of computation
such as statistical queries (SQ), sum-of-squares (SoS), and low-degree
polynomials (LDP). However, no such prior work exists for tensor decomposition,
largely because its hardness does not appear to be explained by a "planted
versus null" testing problem.
  We consider a model for random order-3 tensor decomposition where one
component is slightly larger in norm than the rest (to break symmetry), and the
components are drawn uniformly from the hypercube. We resolve the computational
complexity in the LDP model: $O(\log n)$-degree polynomial functions of the
tensor entries can accurately estimate the largest component when $r \ll
n^{3/2}$ but fail to do so when $r \gg n^{3/2}$. This provides rigorous
evidence suggesting that the best known algorithms for tensor decomposition
cannot be improved, at least by known approaches. A natural extension of the
result holds for tensors of any fixed order $k \ge 3$, in which case the LDP
threshold is $r \sim n^{k/2}$.

Sentiment analysis is the computational study of opinions and emotions
ex-pressed in text. Deep learning is a model that is currently producing
state-of-the-art in various application domains, including sentiment analysis.
Many researchers are using a hybrid approach that combines different deep
learning models and has been shown to improve model performance. In sentiment
analysis, input in text data is first converted into a numerical
representation. The standard method used to obtain a text representation is the
fine-tuned embedding method. However, this method does not pay attention to
each word's context in the sentence. Therefore, the Bidirectional Encoder
Representation from Transformer (BERT) model is used to obtain text
representations based on the context and position of words in sentences. This
research extends the previous hybrid deep learning using BERT representation
for Indonesian sentiment analysis. Our simulation shows that the BERT
representation improves the accuracies of all hybrid architectures. The
BERT-based LSTM-CNN also reaches slightly better accuracies than other
BERT-based hybrid architectures.

Perceiving and manipulating objects in a generalizable way has been actively
studied by the computer vision and robotics communities, where cross-category
generalizable manipulation skills are highly desired yet underexplored. In this
work, we propose to learn such generalizable perception and manipulation via
Generalizable and Actionable Parts (GAParts). By identifying and defining 9
GAPart classes (e.g. buttons, handles, etc), we show that our part-centric
approach allows our method to learn object perception and manipulation skills
from seen object categories and directly generalize to unseen categories.
Following the GAPart definition, we construct a large-scale part-centric
interactive dataset, GAPartNet, where rich, part-level annotations (semantics,
poses) are provided for 1166 objects and 8489 part instances. Based on
GAPartNet, we investigate three cross-category tasks: part segmentation, part
pose estimation, and part-based object manipulation. Given the large domain
gaps between seen and unseen object categories, we propose a strong 3D
segmentation method from the perspective of domain generalization by
integrating adversarial learning techniques. Our method outperforms all
existing methods by a large margin, no matter on seen or unseen categories.
Furthermore, with part segmentation and pose estimation results, we leverage
the GAPart pose definition to design part-based manipulation heuristics that
can generalize well to unseen object categories in both simulation and real
world. The dataset and code will be released.

Segmentation of regions of interest (ROIs) for identifying abnormalities is a
leading problem in medical imaging. Using Machine Learning (ML) for this
problem generally requires manually annotated ground-truth segmentations,
demanding extensive time and resources from radiologists. This work presents a
novel weakly supervised approach that utilizes binary image-level labels, which
are much simpler to acquire, to effectively segment anomalies in medical
Magnetic Resonance (MR) images without ground truth annotations. We train a
binary classifier using these labels and use it to derive seeds indicating
regions likely and unlikely to contain tumors. These seeds are used to train a
generative adversarial network (GAN) that converts cancerous images to healthy
variants, which are then used in conjunction with the seeds to train a ML model
that generates effective segmentations. This method produces segmentations that
achieve Dice coefficients of 0.7903, 0.7868, and 0.7712 on the MICCAI Brain
Tumor Segmentation (BraTS) 2020 dataset for the training, validation, and test
cohorts respectively. We also propose a weakly supervised means of filtering
the segmentations, removing a small subset of poorer segmentations to acquire a
large subset of high quality segmentations. The proposed filtering further
improves the Dice coefficients to up to 0.8374, 0.8232, and 0.8136 for
training, validation, and test, respectively.

Real-time air pollution monitoring is a valuable tool for public health and
environmental surveillance. In recent years, there has been a dramatic increase
in air pollution forecasting and monitoring research using artificial neural
networks (ANNs). Most of the prior work relied on modeling pollutant
concentrations collected from ground-based monitors and meteorological data for
long-term forecasting of outdoor ozone, oxides of nitrogen, and PM2.5. Given
that traditional, highly sophisticated air quality monitors are expensive and
are not universally available, these models cannot adequately serve those not
living near pollutant monitoring sites. Furthermore, because prior models were
built on physical measurement data collected from sensors, they may not be
suitable for predicting public health effects experienced from pollution
exposure. This study aims to develop and validate models to nowcast the
observed pollution levels using Web search data, which is publicly available in
near real-time from major search engines. We developed novel machine
learning-based models using both traditional supervised classification methods
and state-of-the-art deep learning methods to detect elevated air pollution
levels at the US city level, by using generally available meteorological data
and aggregate Web-based search volume data derived from Google Trends. We
validated the performance of these methods by predicting three critical air
pollutants (ozone (O3), nitrogen dioxide (NO2), and fine particulate matter
(PM2.5)), across ten major U.S. metropolitan statistical areas (MSAs) in 2017
and 2018.

Recent work has shown that machine learning (ML) models can be trained to
accurately forecast the dynamics of unknown chaotic dynamical systems. Such ML
models can be used to produce both short-term predictions of the state
evolution and long-term predictions of the statistical patterns of the dynamics
(``climate''). Both of these tasks can be accomplished by employing a feedback
loop, whereby the model is trained to predict forward one time step, then the
trained model is iterated for multiple time steps with its output used as the
input. In the absence of mitigating techniques, however, this technique can
result in artificially rapid error growth, leading to inaccurate predictions
and/or climate instability. In this article, we systematically examine the
technique of adding noise to the ML model input during training as a means to
promote stability and improve prediction accuracy. Furthermore, we introduce
Linearized Multi-Noise Training (LMNT), a regularization technique that
deterministically approximates the effect of many small, independent noise
realizations added to the model input during training. Our case study uses
reservoir computing, a machine-learning method using recurrent neural networks,
to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find
that reservoir computers trained with noise or with LMNT produce climate
predictions that appear to be indefinitely stable and have a climate very
similar to the true system, while reservoir computers trained without
regularization are unstable. Compared with other types of regularization that
yield stability in some cases, we find that both short-term and climate
predictions from reservoir computers trained with noise or with LMNT are
substantially more accurate. Finally, we show that the deterministic aspect of
our LMNT regularization facilitates fast hyperparameter tuning when compared to
training with noise.

In this innovative practice work-in-progress paper, we compare two different
methods to teach machine learning concepts to undergraduate students in
Electrical Engineering. While machine learning is now being offered as a
senior-level elective in several curricula, this does not mean all students are
exposed to it. Exposure to the concepts and practical applications of machine
learning will assist in the creation of a workforce ready to tackle problems
related to machine learning, currently a hot topic in industry. Preliminary
assessments indicate that this approach promotes student learning. While
students prefer the proposed side-by-side teaching approach, numerical
comparisons show that the workshop approach may be more effective for student
learning, indicating that further work in this area is required.

Although query-based systems (QBS) have become one of the main solutions to
share data anonymously, building QBSes that robustly protect the privacy of
individuals contributing to the dataset is a hard problem. Theoretical
solutions relying on differential privacy guarantees are difficult to implement
correctly with reasonable accuracy, while ad-hoc solutions might contain
unknown vulnerabilities. Evaluating the privacy provided by QBSes must thus be
done by evaluating the accuracy of a wide range of privacy attacks. However,
existing attacks require time and expertise to develop, need to be manually
tailored to the specific systems attacked, and are limited in scope. In this
paper, we develop QuerySnout (QS), the first method to automatically discover
vulnerabilities in QBSes. QS takes as input a target record and the QBS as a
black box, analyzes its behavior on one or more datasets, and outputs a
multiset of queries together with a rule to combine answers to them in order to
reveal the sensitive attribute of the target record. QS uses evolutionary
search techniques based on a novel mutation operator to find a multiset of
queries susceptible to lead to an attack, and a machine learning classifier to
infer the sensitive attribute from answers to the queries selected. We showcase
the versatility of QS by applying it to two attack scenarios, three real-world
datasets, and a variety of protection mechanisms. We show the attacks found by
QS to consistently equate or outperform, sometimes by a large margin, the best
attacks from the literature. We finally show how QS can be extended to QBSes
that require a budget, and apply QS to a simple QBS based on the Laplace
mechanism. Taken together, our results show how powerful and accurate attacks
against QBSes can already be found by an automated system, allowing for highly
complex QBSes to be automatically tested "at the pressing of a button".

Time series anomaly detection has applications in a wide range of research
fields and applications, including manufacturing and healthcare. The presence
of anomalies can indicate novel or unexpected events, such as production
faults, system defects, or heart fluttering, and is therefore of particular
interest. The large size and complex patterns of time series have led
researchers to develop specialised deep learning models for detecting anomalous
patterns. This survey focuses on providing structured and comprehensive
state-of-the-art time series anomaly detection models through the use of deep
learning. It providing a taxonomy based on the factors that divide anomaly
detection models into different categories. Aside from describing the basic
anomaly detection technique for each category, the advantages and limitations
are also discussed. Furthermore, this study includes examples of deep anomaly
detection in time series across various application domains in recent years. It
finally summarises open issues in research and challenges faced while adopting
deep anomaly detection models.

Development of navigation algorithms is essential for the successful
deployment of robots in rapidly changing hazardous environments for which prior
knowledge of configuration is often limited or unavailable. Use of traditional
path-planning algorithms, which are based on localization and require detailed
obstacle maps with goal locations, is not possible. In this regard,
vision-based algorithms hold great promise, as visual information can be
readily acquired by a robot's onboard sensors and provides a much richer source
of information from which deep neural networks can extract complex patterns.
Deep reinforcement learning has been used to achieve vision-based robot
navigation. However, the efficacy of these algorithms in environments with
dynamic obstacles and high variation in the configuration space has not been
thoroughly investigated. In this paper, we employ a deep Dyna-Q learning
algorithm for room evacuation and obstacle avoidance in partially observable
environments based on low-resolution raw image data from an onboard camera. We
explore the performance of a robotic agent in environments containing no
obstacles, convex obstacles, and concave obstacles, both static and dynamic.
Obstacles and the exit are initialized in random positions at the start of each
episode of reinforcement learning. Overall, we show that our algorithm and
training approach can generalize learning for collision-free evacuation of
environments with complex obstacle configurations. It is evident that the agent
can navigate to a goal location while avoiding multiple static and dynamic
obstacles, and can escape from a concave obstacle while searching for and
navigating to the exit.

Instrumental aberrations strongly limit high-contrast imaging of exoplanets,
especially when they produce quasistatic speckles in the science images. With
the help of recent advances in deep learning, we have developed in previous
works an approach that applies convolutional neural networks (CNN) to estimate
pupil-plane phase aberrations from point spread functions (PSF). In this work
we take a step further by incorporating into the deep learning architecture the
physical simulation of the optical propagation occurring inside the instrument.
This is achieved with an autoencoder architecture, which uses a differentiable
optical simulator as the decoder. Because this unsupervised learning approach
reconstructs the PSFs, knowing the true phase is not needed to train the
models, making it particularly promising for on-sky applications. We show that
the performance of our method is almost identical to a standard CNN approach,
and that the models are sufficiently stable in terms of training and
robustness. We notably illustrate how we can benefit from the simulator-based
autoencoder architecture by quickly fine-tuning the models on a single test
image, achieving much better performance when the PSFs contain more noise and
aberrations. These early results are very promising and future steps have been
identified to apply the method on real data.

The de facto standard of dynamic histogram binning for radiomic feature
extraction leads to an elevated sensitivity to fluctuations in annotated
regions. This may impact the majority of radiomic studies published recently
and contribute to issues regarding poor reproducibility of radiomic-based
machine learning that has led to significant efforts for data harmonization;
however, we believe the issues highlighted here are comparatively neglected,
but often remedied by choosing static binning.
  The field of radiomics has improved through the development of community
standards and open-source libraries such as PyRadiomics. But differences in
image acquisition, systematic differences between observers' annotations, and
preprocessing steps still pose challenges. These can change the distribution of
voxels altering extracted features and can be exacerbated with dynamic binning.

Representation learning for proteins has primarily focused on the global
understanding of protein sequences regardless of their length. However, shorter
proteins (known as peptides) take on distinct structures and functions compared
to their longer counterparts. Unfortunately, there are not as many naturally
occurring peptides available to be sequenced and therefore less
peptide-specific data to train with. In this paper, we propose a new peptide
data augmentation scheme, where we train peptide language models on
artificially constructed peptides that are small contiguous subsets of longer,
wild-type proteins; we refer to the training peptides as "chopped proteins". We
evaluate the representation potential of models trained with chopped proteins
versus natural peptides and find that training language models with chopped
proteins results in more generalized embeddings for short protein sequences.
These peptide-specific models also retain information about the original
protein they were derived from better than language models trained on
full-length proteins. We compare masked language model training objectives to
three novel peptide-specific training objectives: next-peptide prediction,
contrastive peptide selection and evolution-weighted MLM. We demonstrate
improved zero-shot learning performance for a deep mutational scan peptides

We present RecD (Recommendation Deduplication), a suite of end-to-end
infrastructure optimizations across the Deep Learning Recommendation Model
(DLRM) training pipeline. RecD addresses immense storage, preprocessing, and
training overheads caused by feature duplication inherent in industry-scale
DLRM training datasets. Feature duplication arises because DLRM datasets are
generated from interactions. While each user session can generate multiple
training samples, many features' values do not change across these samples. We
demonstrate how RecD exploits this property, end-to-end, across a deployed
training pipeline. RecD optimizes data generation pipelines to decrease dataset
storage and preprocessing resource demands and to maximize duplication within a
training batch. RecD introduces a new tensor format, InverseKeyedJaggedTensors
(IKJTs), to deduplicate feature values in each batch. We show how DLRM model
architectures can leverage IKJTs to drastically increase training throughput.
RecD improves the training and preprocessing throughput and storage efficiency
by up to 2.49x, 1.79x, and 3.71x, respectively, in an industry-scale DLRM
training system.

The precise control of soft and continuum robots requires knowledge of their
shape. The shape of these robots has, in contrast to classical rigid robots,
infinite degrees of freedom. To partially reconstruct the shape, proprioceptive
techniques use built-in sensors resulting in inaccurate results and increased
fabrication complexity. Exteroceptive methods so far rely on placing reflective
markers on all tracked components and triangulating their position using
multiple motion-tracking cameras. Tracking systems are expensive and infeasible
for deformable robots interacting with the environment due to marker occlusion
and damage. Here, we present a regression approach for 3D shape estimation
using a convolutional neural network. The proposed approach takes advantage of
data-driven supervised learning and is capable of real-time marker-less shape
estimation during inference. Two images of a robotic system are taken
simultaneously at 25 Hz from two different perspectives, and are fed to the
network, which returns for each pair the parameterized shape. The proposed
approach outperforms marker-less state-of-the-art methods by a maximum of 4.4\%
in estimation accuracy while at the same time being more robust and requiring
no prior knowledge of the shape. The approach can be easily implemented due to
only requiring two color cameras without depth and not needing an explicit
calibration of the extrinsic parameters. Evaluations on two types of soft
robotic arms and a soft robotic fish demonstrate our method's accuracy and
versatility on highly deformable systems in real-time. The robust performance
of the approach against different scene modifications (camera alignment and
brightness) suggests its generalizability to a wider range of experimental
setups, which will benefit downstream tasks such as robotic grasping and
manipulation.

We use physics informed neural networks (PINNs) to solve the radiative
transfer equation and calculate a synthetic spectrum for a Type Ia supernova
(SN~Ia) SN 2011fe. The calculation is based on local thermodynamic equilibrium
(LTE) and 9 elements are included. Physical processes included are approximate
radiative equilibrium, bound-bound transitions, and the Doppler effect. A PINN
based gamma-ray scattering approximation is used for radioactive decay energy
deposition. The PINN synthetic spectrum is compared to an observed spectrum, a
synthetic spectrum calculated by the Monte-Carlo radiative transfer program
TARDIS, and the formal solution of the radiative transfer equation. We discuss
the challenges and potential of this deep-learning based radiative transfer
equation solver. In fact, PINNs offer the prospect of simultaneous solution of
the atmosphere problem for both radiation field and thermal state throughout
spacetime. We have made modest steps to realizing that prospect with our
calculations which required many approximations in order to be feasible at this
point in the development of PINN atmosphere solutions.

Image quality assessment (IQA) forms a natural and often straightforward
undertaking for humans, yet effective automation of the task remains highly
challenging. Recent metrics from the deep learning community commonly compare
image pairs during training to improve upon traditional metrics such as PSNR or
SSIM. However, current comparisons ignore the fact that image content affects
quality assessment as comparisons only occur between images of similar content.
This restricts the diversity and number of image pairs that the model is
exposed to during training. In this paper, we strive to enrich these
comparisons with content diversity. Firstly, we relax comparison constraints,
and compare pairs of images with differing content. This increases the variety
of available comparisons. Secondly, we introduce listwise comparisons to
provide a holistic view to the model. By including differentiable regularizers,
derived from correlation coefficients, models can better adjust predicted
scores relative to one another. Evaluation on multiple benchmarks, covering a
wide range of distortions and image content, shows the effectiveness of our
learning scheme for training image quality assessment models.

We explore the downstream task performances for graph neural network (GNN)
self-supervised learning (SSL) methods trained on subgraphs extracted from
relational databases (RDBs). Intuitively, this joint use of SSL and GNNs should
allow to leverage more of the available data, which could translate to better
results. However, we found that naively porting contrastive SSL techniques can
cause ``negative transfer'': linear evaluation on fixed representations from a
pretrained model performs worse than on representations from the
randomly-initialized model. Based on the conjecture that contrastive SSL
conflicts with the message passing layers of the GNN, we propose InfoNode: a
contrastive loss aiming to maximize the mutual information between a node's
initial- and final-layer representation. The primary empirical results support
our conjecture and the effectiveness of InfoNode.

Quantum neural networks (QNNs), an interdisciplinary field of quantum
computing and machine learning, have attracted tremendous research interests
due to the specific quantum advantages. Despite lots of efforts developed in
computer vision domain, one has not fully explored QNNs for the real-world
graph property classification and evaluated them in the quantum device. To
bridge the gap, we propose quantum graph convolutional networks (QuanGCN),
which learns the local message passing among nodes with the sequence of
crossing-gate quantum operations. To mitigate the inherent noises from modern
quantum devices, we apply sparse constraint to sparsify the nodes' connections
and relieve the error rate of quantum gates, and use skip connection to augment
the quantum outputs with original node features to improve robustness. The
experimental results show that our QuanGCN is functionally comparable or even
superior than the classical algorithms on several benchmark graph datasets. The
comprehensive evaluations in both simulator and real quantum machines
demonstrate the applicability of QuanGCN to the future graph analysis problem.

IMPORTANCE: An interpretable machine learning model can provide faithful
explanations of each prediction and yet maintain higher performance than its
black box counterpart.
  OBJECTIVE: To design an interpretable machine learning model which accurately
predicts EEG protopatterns while providing an explanation of its predictions
with assistance of a specialized GUI. To map the cEEG latent features to a 2D
space in order to visualize the ictal-interictal-injury continuum and gain
insight into its high-dimensional structure.
  DESIGN, SETTING, AND PARTICIPANTS: 50,697 50-second cEEG samples from 2,711
ICU patients collected between July 2006 and March 2020 at Massachusetts
General Hospital. Samples were labeled as one of 6 EEG activities by domain
experts, with 124 different experts providing annotations.
  MAIN OUTCOMES AND MEASURES: Our neural network is interpretable because it
uses case-based reasoning: it compares a new EEG reading to a set of learned
prototypical EEG samples from the training dataset. Interpretability was
measured with task-specific neighborhood agreement statistics. Discriminatory
performance was evaluated with AUROC and AUPRC.
  RESULTS: The model achieves AUROCs of 0.87, 0.93, 0.96, 0.92, 0.93, 0.80 for
classes Seizure, LPD, GPD, LRDA, GRDA, Other respectively. This performance is
statistically significantly higher than that of the corresponding
uninterpretable (black box) model with p<0.0001. Videos of the
ictal-interictal-injury continuum are provided.
  CONCLUSION AND RELEVANCE: Our interpretable model and GUI can act as a
reference for practitioners who work with cEEG patterns. We can now better
understand the relationships between different types of cEEG patterns. In the
future, this system may allow for targeted intervention and training in
clinical settings. It could also be used for re-confirming or providing
additional information for diagnostics.

With the fast development of Machine Translation (MT) systems, especially the
new boost from Neural MT (NMT) models, the MT output quality has reached a new
level of accuracy. However, many researchers criticised that the current
popular evaluation metrics such as BLEU can not correctly distinguish the
state-of-the-art NMT systems regarding quality differences. In this short
paper, we describe the design and implementation of a linguistically motivated
human-in-the-loop evaluation metric looking into idiomatic and terminological
Multi-word Expressions (MWEs). MWEs have played a bottleneck in many Natural
Language Processing (NLP) tasks including MT. MWEs can be used as one of the
main factors to distinguish different MT systems by looking into their
capabilities in recognising and translating MWEs in an accurate and meaning
equivalent manner.

Transformers have become central to recent advances in computer vision.
However, training a vision Transformer (ViT) model from scratch can be resource
intensive and time consuming. In this paper, we aim to explore approaches to
reduce the training costs of ViT models. We introduce some algorithmic
improvements to enable training a ViT model from scratch with limited hardware
(1 GPU) and time (24 hours) resources. First, we propose an efficient approach
to add locality to the ViT architecture. Second, we develop a new image size
curriculum learning strategy, which allows to reduce the number of patches
extracted from each image at the beginning of the training. Finally, we propose
a new variant of the popular ImageNet1k benchmark by adding hardware and time
constraints. We evaluate our contributions on this benchmark, and show they can
significantly improve performances given the proposed training budget. We will
share the code in https://github.com/BorealisAI/efficient-vit-training.

Graph Neural Networks (GNNs) as deep learning models working on
graph-structure data have achieved advanced performance in many works. However,
it has been proved repeatedly that, not all edges in a graph are necessary for
the training of machine learning models. In other words, some of the
connections between nodes may bring redundant or even misleading information to
downstream tasks. In this paper, we try to provide a method to drop edges in
order to purify the graph data from a new perspective. Specifically, it is a
framework to purify graphs with the least loss of information, under which the
core problems are how to better evaluate the edges and how to delete the
relatively redundant edges with the least loss of information. To address the
above two problems, we propose several measurements for the evaluation and
different judges and filters for the edge deletion. We also introduce a
residual-iteration strategy and a surrogate model for measurements requiring
unknown information. The experimental results show that our proposed
measurements for KL divergence with constraints to maintain the connectivity of
the graph and delete edges in an iterative way can find out the most edges
while keeping the performance of GNNs. What's more, further experiments show
that this method also achieves the best defense performance against adversarial
attacks.

Recent research in clustering face embeddings has found that unsupervised,
shallow, heuristic-based methods -- including $k$-means and hierarchical
agglomerative clustering -- underperform supervised, deep, inductive methods.
While the reported improvements are indeed impressive, experiments are mostly
limited to face datasets, where the clustered embeddings are highly
discriminative or well-separated by class (Recall@1 above 90% and often nearing
ceiling), and the experimental methodology seemingly favors the deep methods.
We conduct a large-scale empirical study of 17 clustering methods across three
datasets and obtain several robust findings. Notably, deep methods are
surprisingly fragile for embeddings with more uncertainty, where they match or
even perform worse than shallow, heuristic-based methods. When embeddings are
highly discriminative, deep methods do outperform the baselines, consistent
with past results, but the margin between methods is much smaller than
previously reported. We believe our benchmarks broaden the scope of supervised
clustering methods beyond the face domain and can serve as a foundation on
which these methods could be improved. To enable reproducibility, we include
all necessary details in the appendices, and plan to release the code.

Millions of people participate in online peer-to-peer support sessions, yet
there has been little prior research on systematic psychology-based evaluations
of fine-grained peer-counselor behavior in relation to client satisfaction.
This paper seeks to bridge this gap by mapping peer-counselor chat-messages to
motivational interviewing (MI) techniques. We annotate 14,797 utterances from
734 chat conversations using 17 MI techniques and introduce four new
interviewing codes such as chit-chat and inappropriate to account for the
unique conversational patterns observed on online platforms. We automate the
process of labeling peer-counselor responses to MI techniques by fine-tuning
large domain-specific language models and then use these automated measures to
investigate the behavior of the peer counselors via correlational studies.
Specifically, we study the impact of MI techniques on the conversation ratings
to investigate the techniques that predict clients' satisfaction with their
counseling sessions. When counselors use techniques such as reflection and
affirmation, clients are more satisfied. Examining volunteer counselors' change
in usage of techniques suggest that counselors learn to use more introduction
and open questions as they gain experience. This work provides a deeper
understanding of the use of motivational interviewing techniques on
peer-to-peer counselor platforms and sheds light on how to build better
training programs for volunteer counselors on online platforms.

Self-supervised learning (SSL) methods such as WavLM have shown promising
speech separation (SS) results in small-scale simulation-based experiments. In
this work, we extend the exploration of the SSL-based SS by massively scaling
up both the pre-training data (more than 300K hours) and fine-tuning data (10K
hours). We also investigate various techniques to efficiently integrate the
pre-trained model with the SS network under a limited computation budget,
including a low frame rate SSL model training setup and a fine-tuning scheme
using only the part of the pre-trained model. Compared with a supervised
baseline and the WavLM-based SS model using feature embeddings obtained with
the previously released 94K hours trained WavLM, our proposed model obtains
15.9% and 11.2% of relative word error rate (WER) reductions, respectively, for
a simulated far-field speech mixture test set. For conversation transcription
on real meeting recordings using continuous speech separation, the proposed
model achieves 6.8% and 10.6% of relative WER reductions over the purely
supervised baseline on AMI and ICSI evaluation sets, respectively, while
reducing the computational cost by 38%.

Event extraction (EE) is the task of identifying interested event mentions
from text. Conventional efforts mainly focus on the supervised setting.
However, these supervised models cannot generalize to event types out of the
pre-defined ontology. To fill this gap, many efforts have been devoted to the
zero-shot EE problem. This paper follows the trend of modeling event-type
semantics but moves one step further. We argue that using the static embedding
of the event type name might not be enough because a single word could be
ambiguous, and we need a sentence to define the type semantics accurately. To
model the definition semantics, we use two separate transformer models to
project the contextualized event mentions and corresponding definitions into
the same embedding space and then minimize their embedding distance via
contrastive learning. On top of that, we also propose a warming phase to help
the model learn the minor difference between similar definitions. We name our
approach Zero-shot Event extraction with Definition (ZED). Experiments on the
MAVEN dataset show that our model significantly outperforms all previous
zero-shot EE methods with fast inference speed due to the disjoint design.
Further experiments also show that ZED can be easily applied to the few-shot
setting when the annotation is available and consistently outperforms baseline
supervised methods.

Intraoperative assessment of tissue can be guided through fluorescence
imaging which involves systemic dosing with a fluorophore and subsequent
examination of the tissue region of interest with a near-infrared camera. This
typically involves administering indocyanine green (ICG) hours or even days
before surgery and intraoperative visualization at the time predicted for
steady-state signal-to-background status. Here, we describe our efforts to
capture and utilize the information contained in the first few minutes after
ICG administration from the perspective of both signal processing and surgical
practice. We prove a method for characterization of cancerous versus benign
rectal lesions now undergoing further development and validation via
multicenter clinical phase studies.

We present a new convolution layer for deep learning architectures which we
call QuadConv -- an approximation to continuous convolution via quadrature. Our
operator is developed explicitly for use on unstructured data, and accomplishes
this by learning a continuous kernel that can be sampled at arbitrary
locations. In the setting of neural compression, we show that a QuadConv-based
autoencoder, resulting in a Quadrature Convolutional Neural Network (QCNN), can
match the performance of standard discrete convolutions on structured uniform
data, as in CNNs, and maintain this accuracy on unstructured data.

The ATLAS and CMS collaborations at CERN aim to measure the production of
four top quarks in a single LHC collision. The sensitivity to this process is
heavily reliant on the classification accuracy of these events in relation to
the vast background from other Standard Model processes. The four top process
is thus a prime candidate for the implementation and comparison of more
advanced classification techniques based on deep learning, rather than the
commonplace traditional methods. In this paper, we present a data set and
comparison of a variety of methods that have recently emerged. We find that a
transformer architecture (Particle Transformer) and a graph neural network
(Particle Net), with the inclusion of pairwise features, perform best. Compared
to the performance of a baseline boosted decision tree, the improvement in
signal efficiency equates to a gain of $30-50\%$ in integrated luminosity.

Axion-Like Particles (ALPs) are well-motivated extensions of the Standard
Model of Particle Physics and a generic prediction of some string theories.
X-ray observations of bright Active Galactic Nuclei (AGN) hosted by rich
clusters of galaxies are excellent probes of very-light ALPs, with masses
$\mathrm{log}(m_\mathrm{a}/\mathrm{eV})<-12.0$. We evaluate the potential of
future X-ray observatories, particularly $Athena$ and the proposed $AXIS$, to
constrain ALPs via observations of cluster-hosted AGN, taking NGC1275 in the
Perseus cluster as our exemplar. Assuming perfect knowledge of instrument
calibration, we show that a modest exposure (200-ks) of NGC1275 by $Athena$
permits us to exclude all photon-ALP couplings $g_\mathrm{a\gamma}>6.3 \times
10^{-14} \ {\mathrm{GeV}}^{-1}$ at the 95% level, as previously shown by
$Conlon$ et al. (2017), representing a factor of 10 improvement over current
limits. We proceed by assessing the impact of realistic calibration
uncertainties on the $Athena$ projection via a standard $Cash$ likelihood
procedure, showing the projected constraints on $g_\mathrm{a\gamma}$ weaken by
a factor of 10 (back to the current most sensitive constraints). However, we
show how the use of a deep neural network can disentangle the energy-dependent
features induced by instrumental miscalibration and those induced by photon-ALP
mixing, allowing us to recover most of the sensitivity to the ALP physics. In
our explicit demonstration, the machine learning applied allows us to exclude
$g_\mathrm{a\gamma}>1.25 \times 10^{-13} \ {\mathrm{GeV}}^{-1}$, complementing
the projected constraints of next-generation ALP dark matter birefringent
cavity searches for very-light ALPs. Finally, we show that a 200-ks
$AXIS$/on-axis observation of NGC1275 will tighten the current best constraints
on very-light ALPs by a factor of 3.

In this short technical note we propose a baseline for decision-aware
learning for contextual linear optimization, which solves stochastic linear
optimization when cost coefficients can be predicted based on context
information. We propose a decision-aware version of predict-then-optimize. We
reweigh the prediction error by the decision regret incurred by an (unweighted)
pilot estimator of costs to obtain a decision-aware predictor, then optimize
with cost predictions from the decision-aware predictor. This method can be
motivated as a finite-difference, iterate-independent approximation of the
gradients of previously proposed end-to-end learning algorithms; it is also
consistent with previously suggested intuition for end-to-end learning. This
baseline is computationally easy to implement with readily available reweighted
prediction oracles and linear optimization, and can be implemented with convex
optimization so long as the prediction error minimization is convex.
Empirically, we demonstrate that this approach can lead to improvements over a
"predict-then-optimize" framework for settings with misspecified models, and is
competitive with other end-to-end approaches. Therefore, due to its simplicity
and ease of use, we suggest it as a simple baseline for end-to-end and
decision-aware learning.

Large language models (LLMs) have led to a series of breakthroughs in natural
language processing (NLP), owing to their excellent understanding and
generation abilities. Remarkably, what further sets these models apart is the
massive amounts of world knowledge they internalize during pretraining. While
many downstream applications provide the model with an informational context to
aid its performance on the underlying task, how the model's world knowledge
interacts with the factual information presented in the context remains under
explored. As a desirable behavior, an LLM should give precedence to the context
whenever it contains task-relevant information that conflicts with the model's
memorized knowledge. This enables model predictions to be grounded in the
context, which can then be used to update or correct specific model predictions
without frequent retraining. By contrast, when the context is irrelevant to the
task, the model should ignore it and fall back on its internal knowledge. In
this paper, we undertake a first joint study of the aforementioned two
properties, namely controllability and robustness, in the context of LLMs. We
demonstrate that state-of-the-art T5 and PaLM (both pretrained and finetuned)
could exhibit poor controllability and robustness, which do not scale with
increasing model size. As a solution, we propose a novel method - Knowledge
Aware FineTuning (KAFT) - to strengthen both controllability and robustness by
incorporating counterfactual and irrelevant contexts to standard supervised
datasets. Our comprehensive evaluation showcases the utility of KAFT across
model architectures and sizes.

Vision Transformer (ViT) has emerged as a competitive alternative to
convolutional neural networks for various computer vision applications.
Specifically, ViT multi-head attention layers make it possible to embed
information globally across the overall image. Nevertheless, computing and
storing such attention matrices incurs a quadratic cost dependency on the
number of patches, limiting its achievable efficiency and scalability and
prohibiting more extensive real-world ViT applications on resource-constrained
devices. Sparse attention has been shown to be a promising direction for
improving hardware acceleration efficiency for NLP models. However, a
systematic counterpart approach is still missing for accelerating ViT models.
To close the above gap, we propose a first-of-its-kind algorithm-hardware
codesigned framework, dubbed ViTALiTy, for boosting the inference efficiency of
ViTs. Unlike sparsity-based Transformer accelerators for NLP, ViTALiTy unifies
both low-rank and sparse components of the attention in ViTs. At the algorithm
level, we approximate the dot-product softmax operation via first-order Taylor
attention with row-mean centering as the low-rank component to linearize the
cost of attention blocks and further boost the accuracy by incorporating a
sparsity-based regularization. At the hardware level, we develop a dedicated
accelerator to better leverage the resulting workload and pipeline from
ViTALiTy's linear Taylor attention which requires the execution of only the
low-rank component, to further boost the hardware efficiency. Extensive
experiments and ablation studies validate that ViTALiTy offers boosted
end-to-end efficiency (e.g., $3\times$ faster and $3\times$ energy-efficient)
under comparable accuracy, with respect to the state-of-the-art solution.

Text-conditioned image generation models have recently achieved astonishing
results in image quality and text alignment and are consequently employed in a
fast-growing number of applications. Since they are highly data-driven, relying
on billion-sized datasets randomly scraped from the internet, they also suffer,
as we demonstrate, from degenerated and biased human behavior. In turn, they
may even reinforce such biases. To help combat these undesired side effects, we
present safe latent diffusion (SLD). Specifically, to measure the inappropriate
degeneration due to unfiltered and imbalanced training sets, we establish a
novel image generation test bed-inappropriate image prompts (I2P)-containing
dedicated, real-world image-to-text prompts covering concepts such as nudity
and violence. As our exhaustive empirical evaluation demonstrates, the
introduced SLD removes and suppresses inappropriate image parts during the
diffusion process, with no additional training required and no adverse effect
on overall image quality or text alignment.

We study the problem of efficient generative inference for Transformer
models, in one of its most challenging settings: large deep models, with tight
latency targets and long sequence lengths. Better understanding of the
engineering tradeoffs for inference for large Transformer-based models is
important as use cases of these models are growing rapidly throughout
application areas. We develop a simple analytical model for inference
efficiency to select the best multi-dimensional partitioning techniques
optimized for TPU v4 slices based on the application requirements. We combine
these with a suite of low-level optimizations to achieve a new Pareto frontier
on the latency and model FLOPS utilization (MFU) tradeoffs on 500B+ parameter
models that outperforms the FasterTransformer suite of benchmarks. We further
show that with appropriate partitioning, the lower memory requirements of
multiquery attention (i.e. multiple query heads share single key/value head)
enables scaling up to 32x larger context lengths. Finally, we achieve a
low-batch-size latency of 29ms per token during generation (using int8 weight
quantization) and a 76% MFU during large-batch-size processing of input tokens,
while supporting a long 2048-token context length on the PaLM 540B parameter
model.

This paper presents a novel positive and negative set selection strategy for
contrastive learning of medical images based on labels that can be extracted
from clinical data. In the medical field, there exists a variety of labels for
data that serve different purposes at different stages of a diagnostic and
treatment process. Clinical labels and biomarker labels are two examples. In
general, clinical labels are easier to obtain in larger quantities because they
are regularly collected during routine clinical care, while biomarker labels
require expert analysis and interpretation to obtain. Within the field of
ophthalmology, previous work has shown that clinical values exhibit
correlations with biomarker structures that manifest within optical coherence
tomography (OCT) scans. We exploit this relationship between clinical and
biomarker data to improve performance for biomarker classification. This is
accomplished by leveraging the larger amount of clinical data as pseudo-labels
for our data without biomarker labels in order to choose positive and negative
instances for training a backbone network with a supervised contrastive loss.
In this way, a backbone network learns a representation space that aligns with
the clinical data distribution available. Afterwards, we fine-tune the network
trained in this manner with the smaller amount of biomarker labeled data with a
cross-entropy loss in order to classify these key indicators of disease
directly from OCT scans. Our method is shown to outperform state of the art
self-supervised methods by as much as 5% in terms of accuracy on individual
biomarker detection.

Modern statistical learning algorithms are capable of amazing flexibility,
but struggle with interpretability. One possible solution is sparsity: making
inference such that many of the parameters are estimated as being identically
0, which may be imposed through the use of nonsmooth penalties such as the
$\ell_1$ penalty. However, the $\ell_1$ penalty introduces significant bias
when high sparsity is desired. In this article, we retain the $\ell_1$ penalty,
but define learnable penalty weights $\lambda_p$ endowed with hyperpriors. We
start the article by investigating the optimization problem this poses,
developing a proximal operator associated with the $\ell_1$ norm. We then study
the theoretical properties of this variable-coefficient $\ell_1$ penalty in the
context of penalized likelihood. Next, we investigate application of this
penalty to Variational Bayes, developing a model we call the Sparse Bayesian
Lasso which allows for behavior qualitatively like Lasso regression to be
applied to arbitrary variational models. In simulation studies, this gives us
the Uncertainty Quantification and low bias properties of simulation-based
approaches with an order of magnitude less computation. Finally, we apply our
methodology to a Bayesian lagged spatiotemporal regression model of internal
displacement that occurred during the Iraqi Civil War of 2013-2017.

Misinformation spread over social media has become an undeniable infodemic.
However, not all spreading claims are made equal. If propagated, some claims
can be destructive, not only on the individual level, but to organizations and
even countries. Detecting claims that should be prioritized for fact-checking
is considered the first step to fight against spread of fake news. With
training data limited to a handful of languages, developing supervised models
to tackle the problem over lower-resource languages is currently infeasible.
Therefore, our work aims to investigate whether we can use existing datasets to
train models for predicting worthiness of verification of claims in tweets in
other languages. We present a systematic comparative study of six approaches
for cross-lingual check-worthiness estimation across pairs of five diverse
languages with the help of Multilingual BERT (mBERT) model. We run our
experiments using a state-of-the-art multilingual Twitter dataset. Our results
show that for some language pairs, zero-shot cross-lingual transfer is possible
and can perform as good as monolingual models that are trained on the target
language. We also show that in some languages, this approach outperforms (or at
least is comparable to) state-of-the-art models.

This letter explains an algorithm for finding a set of base functions. The
method aims to capture the leading behavior of the dataset in terms of a few
base functions. Implementation of the A-star search will help find these
functions, while the gradient descent optimizes the parameters of the functions
at each search step. We will show the resulting plots to compare the
extrapolation with the unseen data.

This work explores the zero-shot compositional learning ability of large
pre-trained vision-language models(VLMs) within the prompt-based learning
framework and propose a model (\textit{PromptCompVL}) to solve the compositonal
zero-shot learning (CZSL) problem. \textit{PromptCompVL} makes two design
choices: first, it uses a soft-prompting instead of hard-prompting to inject
learnable parameters to reprogram VLMs for compositional learning. Second, to
address the compositional challenge, it uses the soft-embedding layer to learn
primitive concepts in different combinations. By combining both soft-embedding
and soft-prompting, \textit{PromptCompVL} achieves state-of-the-art performance
on the MIT-States dataset. Furthermore, our proposed model achieves consistent
improvement compared to other CLIP-based methods which shows the effectiveness
of the proposed prompting strategies for CZSL.

This paper introduces a new framework for non-parallel emotion conversion in
speech. Our framework is based on two key contributions. First, we propose a
stochastic version of the popular CycleGAN model. Our modified loss function
introduces a Kullback Leibler (KL) divergence term that aligns the source and
target data distributions learned by the generators, thus overcoming the
limitations of sample wise generation. By using a variational approximation to
this stochastic loss function, we show that our KL divergence term can be
implemented via a paired density discriminator. We term this new architecture a
variational CycleGAN (VCGAN). Second, we model the prosodic features of target
emotion as a smooth and learnable deformation of the source prosodic features.
This approach provides implicit regularization that offers key advantages in
terms of better range alignment to unseen and out of distribution speakers. We
conduct rigorous experiments and comparative studies to demonstrate that our
proposed framework is fairly robust with high performance against several
state-of-the-art baselines.

In the procedure of constraining the cosmological parameters with the
observational Hubble data and the type Ia supernova data, the combination of
Masked Autoregressive Flow and Denoising Autoencoder can perform a good result.
The above combination extracts the features from OHD with DAE, and estimates
the posterior distribution of cosmological parameters with MAF. We ask whether
we can find a better tool to compress large data in order to gain better
results while constraining the cosmological parameters. Information maximising
neural networks, a kind of simulation-based machine learning technique, was
proposed at an earlier time. In a series of numerical examples, the results
show that IMNN can find optimal, non-linear summaries robustly. In this work,
we mainly compare the dimensionality reduction capabilities of IMNN and DAE. We
use IMNN and DAE to compress the data into different dimensions and set
different learning rates for MAF to calculate the posterior. Meanwhile, the
training data and mock OHD are generated with a simple Gaussian likelihood, the
spatially flat {\Lambda}CDM model and the real OHD data. To avoid the complex
calculation in comparing the posterior directly, we set different criteria to
compare IMNN and DAE.

Disentanglement of constituent factors of a sensory signal is central to
perception and cognition and hence is a critical task for future artificial
intelligence systems. In this paper, we present a compute engine capable of
efficiently factorizing holographic perceptual representations by exploiting
the computation-in-superposition capability of brain-inspired hyperdimensional
computing and the intrinsic stochasticity associated with analog in-memory
computing based on nanoscale memristive devices. Such an iterative in-memory
factorizer is shown to solve at least five orders of magnitude larger problems
that cannot be solved otherwise, while also significantly lowering the
computational time and space complexity. We present a large-scale experimental
demonstration of the factorizer by employing two in-memory compute chips based
on phase-change memristive devices. The dominant matrix-vector multiply
operations are executed at O(1) thus reducing the computational time complexity
to merely the number of iterations. Moreover, we experimentally demonstrate the
ability to factorize visual perceptual representations reliably and
efficiently.

While there have been a number of remarkable breakthroughs in machine
learning (ML), much of the focus has been placed on model development. However,
to truly realize the potential of machine learning in real-world settings,
additional aspects must be considered across the ML pipeline. Data-centric AI
is emerging as a unifying paradigm that could enable such reliable end-to-end
pipelines. However, this remains a nascent area with no standardized framework
to guide practitioners to the necessary data-centric considerations or to
communicate the design of data-centric driven ML systems. To address this gap,
we propose DC-Check, an actionable checklist-style framework to elicit
data-centric considerations at different stages of the ML pipeline: Data,
Training, Testing, and Deployment. This data-centric lens on development aims
to promote thoughtfulness and transparency prior to system development.
Additionally, we highlight specific data-centric AI challenges and research
opportunities. DC-Check is aimed at both practitioners and researchers to guide
day-to-day development. As such, to easily engage with and use DC-Check and
associated resources, we provide a DC-Check companion website
(https://www.vanderschaar-lab.com/dc-check/). The website will also serve as an
updated resource as methods and tooling evolve over time.

Automated emotion recognition in speech is a long-standing problem. While
early work on emotion recognition relied on hand-crafted features and simple
classifiers, the field has now embraced end-to-end feature learning and
classification using deep neural networks. In parallel to these models,
researchers have proposed several data augmentation techniques to increase the
size and variability of existing labeled datasets. Despite many seminal
contributions in the field, we still have a poor understanding of the interplay
between the network architecture and the choice of data augmentation. Moreover,
only a handful of studies demonstrate the generalizability of a particular
model across multiple datasets, which is a prerequisite for robust real-world
performance. In this paper, we conduct a comprehensive evaluation of popular
deep learning approaches for emotion recognition. To eliminate bias, we fix the
model architectures and optimization hyperparameters using the VESUS dataset
and then use repeated 5-fold cross validation to evaluate the performance on
the IEMOCAP and CREMA-D datasets. Our results demonstrate that long-range
dependencies in the speech signal are critical for emotion recognition and that
speed/rate augmentation offers the most robust performance gain across models.

We introduce a challenging decision-making task that we call active
acquisition for multimodal temporal data (A2MT). In many real-world scenarios,
input features are not readily available at test time and must instead be
acquired at significant cost. With A2MT, we aim to learn agents that actively
select which modalities of an input to acquire, trading off acquisition cost
and predictive performance. A2MT extends a previous task called active feature
acquisition to temporal decision making about high-dimensional inputs. Further,
we propose a method based on the Perceiver IO architecture to address A2MT in
practice. Our agents are able to solve a novel synthetic scenario requiring
practically relevant cross-modal reasoning skills. On two large-scale,
real-world datasets, Kinetics-700 and AudioSet, our agents successfully learn
cost-reactive acquisition behavior. However, an ablation reveals they are
unable to learn to learn adaptive acquisition strategies, emphasizing the
difficulty of the task even for state-of-the-art models. Applications of A2MT
may be impactful in domains like medicine, robotics, or finance, where
modalities differ in acquisition cost and informativeness.

Word embeddings play a significant role in today's Natural Language
Processing tasks and applications. While pre-trained models may be directly
employed and integrated into existing pipelines, they are often fine-tuned to
better fit with specific languages or domains. In this paper, we attempt to
improve available embeddings in the uncovered niche of the Italian medical
domain through the combination of Contrastive Learning (CL) and Knowledge Graph
Embedding (KGE). The main objective is to improve the accuracy of semantic
similarity between medical terms, which is also used as an evaluation task.
Since the Italian language lacks medical texts and controlled vocabularies, we
have developed a specific solution by combining preexisting CL methods
(multi-similarity loss, contextualization, dynamic sampling) and the
integration of KGEs, creating a new variant of the loss. Although without
having outperformed the state-of-the-art, represented by multilingual models,
the obtained results are encouraging, providing a significant leap in
performance compared to the starting model, while using a significantly lower
amount of data.

Reproducing Kernel Hilbert spaces (RKHS) have been a very successful tool in
various areas of machine learning. Recently, Barron spaces have been used to
prove bounds on the generalisation error for neural networks. Unfortunately,
Barron spaces cannot be understood in terms of RKHS due to the strong nonlinear
coupling of the weights. We show that this can be solved by using the more
general Reproducing Kernel Banach spaces (RKBS). This class of integral RKBS
can be understood as an infinite union of RKHS spaces. As the RKBS is not a
Hilbert space, it is not its own dual space. However, we show that its dual
space is again an RKBS where the roles of the data and parameters are
interchanged, forming an adjoint pair of RKBSs including a reproducing property
in the dual space. This allows us to construct the saddle point problem for
neural networks, which can be used in the whole field of primal-dual
optimisation.

To date, the best-performing blind super-resolution (SR) techniques follow
one of two paradigms: A) generate and train a standard SR network on synthetic
low-resolution - high-resolution (LR - HR) pairs or B) attempt to predict the
degradations an LR image has suffered and use these to inform a customised SR
network. Despite significant progress, subscribers to the former miss out on
useful degradation information that could be used to improve the SR process. On
the other hand, followers of the latter rely on weaker SR networks, which are
significantly outperformed by the latest architectural advancements. In this
work, we present a framework for combining any blind SR prediction mechanism
with any deep SR network, using a metadata insertion block to insert prediction
vectors into SR network feature maps. Through comprehensive testing, we prove
that state-of-the-art contrastive and iterative prediction schemes can be
successfully combined with high-performance SR networks such as RCAN and HAN
within our framework. We show that our hybrid models consistently achieve
stronger SR performance than both their non-blind and blind counterparts.
Furthermore, we demonstrate our framework's robustness by predicting
degradations and super-resolving images from a complex pipeline of blurring,
noise and compression.

The first large-scale deployment of private federated learning uses
differentially private counting in the continual release model as a subroutine
(Google AI blog titled "Federated Learning with Formal Differential Privacy
Guarantees"). In this case, a concrete bound on the error is very relevant to
reduce the privacy parameter. The standard mechanism for continual counting is
the binary mechanism. We present a novel mechanism and show that its mean
squared error is both asymptotically optimal and a factor 10 smaller than the
error of the binary mechanism. We also show that the constants in our analysis
are almost tight by giving non-asymptotic lower and upper bounds that differ
only in the constants of lower-order terms. Our algorithm is a matrix mechanism
for the counting matrix and takes constant time per release. We also use our
explicit factorization of the counting matrix to give an upper bound on the
excess risk of the private learning algorithm of Denisov et al. (NeurIPS 2022).
Our lower bound for any continual counting mechanism is the first tight lower
bound on continual counting under approximate differential privacy. It is
achieved using a new lower bound on a certain factorization norm, denoted by
$\gamma_F(\cdot)$, in terms of the singular values of the matrix. In
particular, we show that for any complex matrix, $A \in \mathbb{C}^{m \times
n}$, \[ \gamma_F(A) \geq \frac{1}{\sqrt{m}}\|A\|_1, \] where $\|\cdot \|$
denotes the Schatten-1 norm.
  We believe this technique will be useful in proving lower bounds for a larger
class of linear queries. To illustrate the power of this technique, we show the
first lower bound on the mean squared error for answering parity queries.

We introduce a general statistical learning theory for processes that take as
input a classical random variable and output a quantum state. This is a
practically motivated setting where the learner wants to find an approximate
representation of an unknown quantum process, without having control on the
classical inputs that govern the process itself, e.g., in learning astronomical
processes, studying disordered systems and biological processes not controlled
by the observer. We provide an algorithm for learning with high probability in
this setting with a finite amount of samples, even if the concept class is
infinite. To do this, we review and adapt existing algorithms for shadow
tomography and hypothesis selection, and combine their guarantees with the
uniform convergence on the data of the loss functions of interest. As a
by-product we obtain sufficient conditions for performing shadow tomography of
classical-quantum states with a number of copies which depend only on the
dimension of the quantum register. We give concrete examples of processes that
can be learned in this manner, based on quantum circuits or physically
motivated scenarios like systems subject to Hamiltonians with random
perturbations or data-dependent phase-shifts.

We explore the possibility of using deep learning to generate multifield
images from state-of-the-art hydrodynamic simulations of the CAMELS project. We
use a generative adversarial network to generate images with three different
channels that represent gas density (Mgas), neutral hydrogen density (HI), and
magnetic field amplitudes (B). The quality of each map in each example
generated by the model looks very promising. The GAN considered in this study
is able to generate maps whose mean and standard deviation of the probability
density distribution of the pixels are consistent with those of the maps from
the training data. The mean and standard deviation of the auto power spectra of
the generated maps of each field agree well with those computed from the maps
of IllustrisTNG. Moreover, the cross-correlations between fields in all
instances produced by the emulator are in good agreement with those of the
dataset. This implies that all three maps in each output of the generator
encode the same underlying cosmology and astrophysics.

Current methods for image-to-image translation produce compelling results,
however, the applied transformation is difficult to control, since existing
mechanisms are often limited and non-intuitive. We propose ParGAN, a
generalization of the cycle-consistent GAN framework to learn image
transformations with simple and intuitive controls. The proposed generator
takes as input both an image and a parametrization of the transformation. We
train this network to preserve the content of the input image while ensuring
that the result is consistent with the given parametrization. Our approach does
not require paired data and can learn transformations across several tasks and
datasets. We show how, with disjoint image domains with no annotated
parametrization, our framework can create smooth interpolations as well as
learn multiple transformations simultaneously.

Background: Increased pericardial adipose tissue (PAT) is associated with
many types of cardiovascular disease (CVD). Although cardiac magnetic resonance
images (CMRI) are often acquired in patients with CVD, there are currently no
tools to automatically identify and quantify PAT from CMRI. The aim of this
study was to create a neural network to segment PAT from T2-weighted CMRI and
explore the correlations between PAT volumes (PATV) and CVD outcomes and
mortality. Methods: We trained and tested a deep learning model, PAT-CNN, to
segment PAT on T2-weighted cardiac MR images. Using the segmentations from
PAT-CNN, we automatically calculated PATV on images from 391 patients. We
analysed correlations between PATV and CVD diagnosis and 1-year mortality
post-imaging. Results: PAT-CNN was able to accurately segment PAT with Dice
score/ Hausdorff distances of 0.74 +- 0.03/27.1 +- 10.9~mm, similar to the
values obtained when comparing the segmentations of two independent human
observers ($0.76 +- 0.06/21.2 +- 10.3~mm$). Regression models showed that,
independently of sex and body-mass index, PATV is significantly positively
correlated with a diagnosis of CVD and with 1-year all cause mortality (p-value
< 0.01). Conclusions: PAT-CNN can segment PAT from T2-weighted CMR images
automatically and accurately. Increased PATV as measured automatically from
CMRI is significantly associated with the presence of CVD and can independently
predict 1-year mortality.

Robot assistants are emerging as high-tech solutions to support people in
everyday life. Following and assisting the user in the domestic environment
requires flexible mobility to safely move in cluttered spaces. We introduce a
new approach to person following for assistance and monitoring. Our methodology
exploits an omnidirectional robotic platform to detach the computation of
linear and angular velocities and navigate within the domestic environment
without losing track of the assisted person. While linear velocities are
managed by a conventional Dynamic Window Approach (DWA) local planner, we
trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular
velocities commands and maintain the orientation of the robot towards the user.
We evaluate our navigation system on a real omnidirectional platform in various
indoor scenarios, demonstrating the competitive advantage of our solution
compared to a standard differential steering following.

The task of learning the piano has been a centuries-old challenge for
novices, experts and technologists. Several innovations have been introduced to
support proper posture, movement, and motivation, while sight-reading and
improvisation remain the least-explored areas. In this PhD, we address this gap
by redesigning the piano augmentation as an interactive and adaptive space.
Specifically, we will explore how to support learners with adaptive
visualisations through a two-pronged approach: (1) by designing adaptive
visualisations based on the proficiency of the learner to support regular piano
playing and (2) by assisting them with expert annotations projected on the
piano to encourage improvisation. To this end, we will build a model to
understand the complexities of learners' spatiotemporal data and use these to
support learning. We will then evaluate our approach through user studies
enabling practice and improvisation. Our work contributes to how adaptive
visualisations can push music instrument learning and support multi-target
selection tasks in immersive spaces.

Predicting metro passenger flow precisely is of great importance for dynamic
traffic planning. Deep learning algorithms have been widely applied due to
their robust performance in modelling non-linear systems. However, traditional
deep learning algorithms completely discard the inherent graph structure within
the metro system. Graph-based deep learning algorithms could utilise the graph
structure but raise a few challenges, such as how to determine the weights of
the edges and the shallow receptive field caused by the over-smoothing issue.
To further improve these challenges, this study proposes a model based on
GraphSAGE with an edge weights learner applied. The edge weights learner
utilises socially meaningful features to generate edge weights. Hypergraph and
temporal exploitation modules are also constructed as add-ons for better
performance. A comparison study is conducted on the proposed algorithm and
other state-of-art graph neural networks, where the proposed algorithm could
improve the performance.

Green Security Games with real-time information (GSG-I) add the real-time
information about the agents' movement to the typical GSG formulation. Prior
works on GSG-I have used deep reinforcement learning (DRL) to learn the best
policy for the agent in such an environment without any need to store the huge
number of state representations for GSG-I. However, the decision-making process
of DRL methods is largely opaque, which results in a lack of trust in their
predictions. To tackle this issue, we present an interpretable DRL method for
GSG-I that generates visualization to explain the decisions taken by the DRL
algorithm. We also show that this approach performs better and works well with
a simpler training regimen compared to the existing method.

Recently, a rich polymorphic behavior of ordered ground-state structures of
the asymmetric Wigner bilayer system has been identified. An evolutionary
ground-state search revealed roughly 60.000 possible ground-state structures in
a two-dimensional phase diagram spanned by the plate-to-plate separation
distance and the charge-asymmetry parameter of the system. Partly, the
ground-state phase diagram could be revealed by comparing structures by means
of bond-orientational order parameters. However, large fractions of the phase
diagram remained unclassified. Here, we propose a simple machine learning
approach based on principal component analysis and k-means clustering of order
parameters, to systematically analyze and compare all lattice structures in a
structural database, and we apply our approach to the previously obtained
structural data set of the asymmetric Wigner bilayer system. This allows us to
fill the white spots in the phase diagram and refine the classification of
previously described phases in the system.

Streets networks provide an invaluable source of information about the
different temporal and spatial patterns emerging in our cities. These streets
are often represented as graphs where intersections are modelled as nodes and
streets as links between them. Previous work has shown that raster
representations of the original data can be created through a learning
algorithm on low-dimensional representations of the street networks. In
contrast, models that capture high-level urban network metrics can be trained
through convolutional neural networks. However, the detailed topological data
is lost through the rasterisation of the street network. The models cannot
recover this information from the image alone, failing to capture complex
street network features. This paper proposes a model capable of inferring good
representations directly from the street network. Specifically, we use a
variational autoencoder with graph convolutional layers and a decoder that
outputs a probabilistic fully-connected graph to learn latent representations
that encode both local network structure and the spatial distribution of nodes.
We train the model on thousands of street network segments and use the learnt
representations to generate synthetic street configurations. Finally, we
proposed a possible application to classify the urban morphology of different
network segments by investigating their common characteristics in the learnt
space.

Dyadic and small group collaboration is an evolutionary advantageous
behaviour and the need for such collaboration is a regular occurrence in day to
day life. In this paper we estimate the perceived personality traits of
individuals in dyadic and small groups over thin-slices of interaction on four
multimodal datasets. We find that our transformer based predictive model
performs similarly to human annotators tasked with predicting the perceived
big-five personality traits of participants. Using this model we analyse the
estimated perceived personality traits of individuals performing tasks in small
groups and dyads. Permutation analysis shows that in the case of small groups
undergoing collaborative tasks, the perceived personality of group members
clusters, this is also observed for dyads in a collaborative problem solving
task, but not in dyads under non-collaborative task settings. Additionally, we
find that the group level average perceived personality traits provide a better
predictor of group performance than the group level average self-reported
personality traits.

Purpose: Traffic volume in empty container depots has been highly volatile
due to external factors. Forecasting the expected container truck traffic along
with having a dynamic module to foresee the future workload plays a critical
role in improving the work efficiency. This paper studies the relevant
literature and designs a forecasting model addressing the aforementioned
issues. Methodology: The paper develops a forecasting model to predict hourly
work and traffic volume of container trucks in an empty container depot using a
Bayesian Neural Network based model. Furthermore, the paper experiments with
datasets with different characteristics to assess the model's forecasting range
for various data sources. Findings: The real data of an empty container depot
is utilized to develop a forecasting model and to later verify the capabilities
of the model. The findings show the performance validity of the model and
provide the groundwork to build an effective traffic and workload planning
system for the empty container depot in question. Originality: This paper
proposes a Bayesian deep learning-based forecasting model for traffic and
workload of an empty container depot using real-world data. This designed and
implemented forecasting model offers a solution with which every actor in the
container truck transportation benefits from the optimized workload.

Two central paradigms have emerged in the reinforcement learning (RL)
community: online RL and offline RL. In the online RL setting, the agent has no
prior knowledge of the environment, and must interact with it in order to find
an $\epsilon$-optimal policy. In the offline RL setting, the learner instead
has access to a fixed dataset to learn from, but is unable to otherwise
interact with the environment, and must obtain the best policy it can from this
offline data. Practical scenarios often motivate an intermediate setting: if we
have some set of offline data and, in addition, may also interact with the
environment, how can we best use the offline data to minimize the number of
online interactions necessary to learn an $\epsilon$-optimal policy?
  In this work, we consider this setting, which we call the \textsf{FineTuneRL}
setting, for MDPs with linear structure. We characterize the necessary number
of online samples needed in this setting given access to some offline dataset,
and develop an algorithm, \textsc{FTPedel}, which is provably optimal. We show
through an explicit example that combining offline data with online
interactions can lead to a provable improvement over either purely offline or
purely online RL. Finally, our results illustrate the distinction between
\emph{verifiable} learning, the typical setting considered in online RL, and
\emph{unverifiable} learning, the setting often considered in offline RL, and
show that there is a formal separation between these regimes.

Adversarial perturbation plays a significant role in the field of adversarial
robustness, which solves a maximization problem over the input data. We show
that the backward propagation of such optimization can accelerate $2\times$
(and thus the overall optimization including the forward propagation can
accelerate $1.5\times$), without any utility drop, if we only compute the
output gradient but not the parameter gradient during the backward propagation.

Quantum-enhanced data science, also known as quantum machine learning (QML),
is of growing interest as an application of near-term quantum computers.
Variational QML algorithms have the potential to solve practical problems on
real hardware, particularly when involving quantum data. However, training
these algorithms can be challenging and calls for tailored optimization
procedures. Specifically, QML applications can require a large shot-count
overhead due to the large datasets involved. In this work, we advocate for
simultaneous random sampling over both the dataset as well as the measurement
operators that define the loss function. We consider a highly general loss
function that encompasses many QML applications, and we show how to construct
an unbiased estimator of its gradient. This allows us to propose a shot-frugal
gradient descent optimizer called Refoqus (REsource Frugal Optimizer for
QUantum Stochastic gradient descent). Our numerics indicate that Refoqus can
save several orders of magnitude in shot cost, even relative to optimizers that
sample over measurement operators alone.

An effective aggregation of node features into a graph-level representation
via readout functions is an essential step in numerous learning tasks involving
graph neural networks. Typically, readouts are simple and non-adaptive
functions designed such that the resulting hypothesis space is permutation
invariant. Prior work on deep sets indicates that such readouts might require
complex node embeddings that can be difficult to learn via standard
neighborhood aggregation schemes. Motivated by this, we investigate the
potential of adaptive readouts given by neural networks that do not necessarily
give rise to permutation invariant hypothesis spaces. We argue that in some
problems such as binding affinity prediction where molecules are typically
presented in a canonical form it might be possible to relax the constraints on
permutation invariance of the hypothesis space and learn a more effective model
of the affinity by employing an adaptive readout function. Our empirical
results demonstrate the effectiveness of neural readouts on more than 40
datasets spanning different domains and graph characteristics. Moreover, we
observe a consistent improvement over standard readouts (i.e., sum, max, and
mean) relative to the number of neighborhood aggregation iterations and
different convolutional operators.

A major concern of Machine Learning (ML) models is their opacity. They are
deployed in an increasing number of applications where they often operate as
black boxes that do not provide explanations for their predictions. Among
others, the potential harms associated with the lack of understanding of the
models' rationales include privacy violations, adversarial manipulations, and
unfair discrimination. As a result, the accountability and transparency of ML
models have been posed as critical desiderata by works in policy and law,
philosophy, and computer science.
  In computer science, the decision-making process of ML models has been
studied by developing accountability and transparency methods. Accountability
methods, such as adversarial attacks and diagnostic datasets, expose
vulnerabilities of ML models that could lead to malicious manipulations or
systematic faults in their predictions. Transparency methods explain the
rationales behind models' predictions gaining the trust of relevant
stakeholders and potentially uncovering mistakes and unfairness in models'
decisions. To this end, transparency methods have to meet accountability
requirements as well, e.g., being robust and faithful to the underlying
rationales of a model.
  This thesis presents my research that expands our collective knowledge in the
areas of accountability and transparency of ML models developed for complex
reasoning tasks over text.

Since three decades binary decision diagrams, representing efficiently
Boolean functions, are widely used, in many distinct contexts like model
verification, machine learning. The most famous variant, called reduced ordered
binary decision diagram (ROBDD for short), can be viewed as the result of a
compaction procedure on the full decision tree. In this paper we aim at
computing the exact distribution of the Boolean functions in $k$~variables
according to the ROBDD size, where the ROBDD size is equal to the size of the
underlying directed acyclic graph (DAG) structure. Recall the number of Boolean
functions is equal to $2^{2^k}$, which is of double exponential growth; hence a
combinatorial explosion is to be expected. The maximal size of a ROBDD with $k$
variables is $M_k \sim 2^k / k$ and thus, the support of the ROBDD size
distribution is also of length $M_k$, making $M_k$ a natural complexity unit
for our problem. In this paper, we develop the first polynomial algorithm to
derive the distribution of the Boolean functions with respect to their ROBDD
sizes. The algorithm is essentially quartic in $M_k$ for the time complexity
and quadratic for the space complexity. The main obstacle is to take into
account dependencies inside the DAG structure, and we propose a new
combinatorial counting procedure reminiscent of the inclusion-exclusion
principle. As a by-product, we present an efficient polynomial unranking
algorithm for ROBDDs, which in turn yields a uniform random sampler over the
set of ROBDDs of a given size or of a given profile. This is a great
improvement to the uniform sampler over the set of all Boolean functions in $k$
variables. Indeed, due to the Shannon effect, the uniform distribution over
Boolean functions is heavily biased to extremely complex functions, with near
maximal ROBDD size, thus preventing to sample small ROBDDs

Although deep learning (DL) has received much attention in accelerated
magnetic resonance imaging (MRI), recent studies show that tiny input
perturbations may lead to instabilities of DL-based MRI reconstruction models.
However, the approaches of robustifying these models are underdeveloped.
Compared to image classification, it could be much more challenging to achieve
a robust MRI image reconstruction network considering its regression-based
learning objective, limited amount of training data, and lack of efficient
robustness metrics. To circumvent the above limitations, our work revisits the
problem of DL-based image reconstruction through the lens of robust machine
learning. We find a new instability source of MRI image reconstruction, i.e.,
the lack of reconstruction robustness against spatial transformations of an
input, e.g., rotation and cutout. Inspired by this new robustness metric, we
develop a robustness-aware image reconstruction method that can defend against
both pixel-wise adversarial perturbations as well as spatial transformations.
Extensive experiments are also conducted to demonstrate the effectiveness of
our proposed approaches.

This paper presents miCSE, a mutual information-based Contrastive learning
framework that significantly advances the state-of-the-art in few-shot sentence
embedding. The proposed approach imposes alignment between the attention
pattern of different views during contrastive learning. Learning sentence
embeddings with miCSE entails enforcing the syntactic consistency across
augmented views for every single sentence, making contrastive self-supervised
learning more sample efficient. As a result, the proposed approach shows strong
performance in the few-shot learning domain. While it achieves superior results
compared to state-of-the-art methods on multiple benchmarks in few-shot
learning, it is comparable in the full-shot scenario. The proposed approach is
conceptually simple, easy to implement and optimize, yet empirically powerful.
This study opens up avenues for efficient self-supervised learning methods that
are more robust than current contrastive methods for sentence embedding.

Deep learning-based full-reference image quality assessment (FR-IQA) models
typically rely on the feature distance between the reference and distorted
images. However, the underlying assumption of these models that the distance in
the deep feature domain could quantify the quality degradation does not
scientifically align with the invariant texture perception, especially when the
images are generated artificially by neural networks. In this paper, we bring a
radical shift in inferring the quality with learned features and propose the
Deep Image Dependency (DID) based FR-IQA model. The feature dependency
facilitates the comparisons of deep learning features in a high-order manner
with Brownian distance covariance, which is characterized by the joint
distribution of the features from reference and test images, as well as their
marginal distributions. This enables the quantification of the feature
dependency against nonlinear transformation, which is far beyond the
computation of the numerical errors in the feature space. Experiments on image
quality prediction, texture image similarity, and geometric invariance validate
the superior performance of our proposed measure.

Deep learning techniques have greatly benefited computer-aided diagnostic
systems. However, unlike other fields, in medical imaging, acquiring large
fine-grained annotated datasets such as 3D tumour segmentation is challenging
due to the high cost of manual annotation and privacy regulations. This has
given interest to weakly-supervise methods to utilize the weakly labelled data
for tumour segmentation. In this work, we propose a weakly supervised approach
to obtain regions of interest using binary class labels. Furthermore, we
propose a novel objective function to train the generator model based on a
pretrained binary classification model. Finally, we apply our method to the
brain tumour segmentation problem in MRI.

Predicting the presence of major depressive disorder (MDD) using behavioural
and cognitive signals is a highly non-trivial task. The heterogeneous clinical
profile of MDD means that any given speech, facial expression and/or observed
cognitive pattern may be associated with a unique combination of depressive
symptoms. Conventional discriminative machine learning models potentially lack
the complexity to robustly model this heterogeneity. Bayesian networks,
however, may instead be well-suited to such a scenario. These networks are
probabilistic graphical models that efficiently describe the joint probability
distribution over a set of random variables by explicitly capturing their
conditional dependencies. This framework provides further advantages over
standard discriminative modelling by offering the possibility to incorporate
expert opinion in the graphical structure of the models, generating explainable
model predictions, informing about the uncertainty of predictions, and
naturally handling missing data. In this study, we apply a Bayesian framework
to capture the relationships between depression, depression symptoms, and
features derived from speech, facial expression and cognitive game data
collected at thymia.

A growing number of Machine Learning Frameworks recently made Deep Learning
accessible to a wider audience of engineers, scientists, and practitioners, by
allowing straightforward use of complex neural network architectures and
algorithms. However, since deep learning is rapidly evolving, not only through
theoretical advancements but also with respect to hardware and software
engineering, ML frameworks often lose backward compatibility and introduce
technical debt that can lead to bottlenecks and sub-optimal resource
utilization. Moreover, the focus is in most cases not on deep learning
engineering, but rather on new models and theoretical advancements. In this
work, however, we focus on engineering, more specifically on the data loading
pipeline in the PyTorch Framework. We designed a series of benchmarks that
outline performance issues of certain steps in the data loading process. Our
findings show that for classification tasks that involve loading many files,
like images, the training wall-time can be significantly improved. With our
new, modified ConcurrentDataloader we can reach improvements in GPU utilization
and significantly reduce batch loading time, up to 12X. This allows for the use
of the cloud-based, S3-like object storage for datasets, and have comparable
training time as if datasets are stored on local drives.

Grasp learning has become an exciting and important topic in robotics. Just a
few years ago, the problem of grasping novel objects from unstructured piles of
clutter was considered a serious research challenge. Now, it is a capability
that is quickly becoming incorporated into industrial supply chain automation.
How did that happen? What is the current state of the art in robotic grasp
learning, what are the different methodological approaches, and what machine
learning models are used? This review attempts to give an overview of the
current state of the art of grasp learning research.

User-generated-content (UGC) videos have dominated the Internet during recent
years. While it is well-recognized that the perceptual quality of these videos
can be affected by diverse factors, few existing methods explicitly explore the
effects of different factors in video quality assessment (VQA) for UGC videos,
i.e. the UGC-VQA problem. In this work, we make the first attempt to
disentangle the effects of aesthetic quality issues and technical quality
issues risen by the complicated video generation processes in the UGC-VQA
problem. To overcome the absence of respective supervisions during
disentanglement, we propose the Limited View Biased Supervisions (LVBS) scheme
where two separate evaluators are trained with decomposed views specifically
designed for each issue. Composed of an Aesthetic Quality Evaluator (AQE) and a
Technical Quality Evaluator (TQE) under the LVBS scheme, the proposed
Disentangled Objective Video Quality Evaluator (DOVER) reach excellent
performance (0.91 SRCC for KoNViD-1k, 0.89 SRCC for LSVQ, 0.88 SRCC for
YouTube-UGC) in the UGC-VQA problem. More importantly, our blind subjective
studies prove that the separate evaluators in DOVER can effectively match human
perception on respective disentangled quality issues. Codes and demos are
released in https://github.com/teowu/dover.

In this article, we use artificial intelligence algorithms to show how to
enhance the resolution of the elementary particle track fitting in
inhomogeneous dense detectors, such as plastic scintillators. We use deep
learning to replace more traditional Bayesian filtering methods, drastically
improving the reconstruction of the interacting particle kinematics. We show
that a specific form of neural network, inherited from the field of natural
language processing, is very close to the concept of a Bayesian filter that
adopts a hyper-informative prior. Such a paradigm change can influence the
design of future particle physics experiments and their data exploitation.

Pixel space augmentation has grown in popularity in many Deep Learning areas,
due to its effectiveness, simplicity, and low computational cost. Data
augmentation for videos, however, still remains an under-explored research
topic, as most works have been treating inputs as stacks of static images
rather than temporally linked series of data. Recently, it has been shown that
involving the time dimension when designing augmentations can be superior to
its spatial-only variants for video action recognition. In this paper, we
propose several novel enhancements to these techniques to strengthen the
relationship between the spatial and temporal domains and achieve a deeper
level of perturbations. The video action recognition results of our techniques
outperform their respective variants in Top-1 and Top-5 settings on the UCF-101
and the HMDB-51 datasets.

Prescriptive Process Monitoring systems recommend, during the execution of a
business process, interventions that, if followed, prevent a negative outcome
of the process. Such interventions have to be reliable, that is, they have to
guarantee the achievement of the desired outcome or performance, and they have
to be flexible, that is, they have to avoid overturning the normal process
execution or forcing the execution of a given activity. Most of the existing
Prescriptive Process Monitoring solutions, however, while performing well in
terms of recommendation reliability, provide the users with very specific
(sequences of) activities that have to be executed without caring about the
feasibility of these recommendations. In order to face this issue, we propose a
new Outcome-Oriented Prescriptive Process Monitoring system recommending
temporal relations between activities that have to be guaranteed during the
process execution in order to achieve a desired outcome. This softens the
mandatory execution of an activity at a given point in time, thus leaving more
freedom to the user in deciding the interventions to put in place. Our approach
defines these temporal relations with Linear Temporal Logic over finite traces
patterns that are used as features to describe the historical process data
recorded in an event log by the information systems supporting the execution of
the process. Such encoded log is used to train a Machine Learning classifier to
learn a mapping between the temporal patterns and the outcome of a process
execution. The classifier is then queried at runtime to return as
recommendations the most salient temporal patterns to be satisfied to maximize
the likelihood of a certain outcome for an input ongoing process execution. The
proposed system is assessed using a pool of 22 real-life event logs that have
already been used as a benchmark in the Process Mining community.

Effectively exploring the environment is a key challenge in reinforcement
learning (RL). We address this challenge by defining a novel intrinsic reward
based on a foundation model, such as contrastive language image pretraining
(CLIP), which can encode a wealth of domain-independent semantic
visual-language knowledge about the world. Specifically, our intrinsic reward
is defined based on pre-trained CLIP embeddings without any fine-tuning or
learning on the target RL task. We demonstrate that CLIP-based intrinsic
rewards can drive exploration towards semantically meaningful states and
outperform state-of-the-art methods in challenging sparse-reward
procedurally-generated environments.

General deep learning-based methods for infrared and visible image fusion
rely on the unsupervised mechanism for vital information retention by utilizing
elaborately designed loss functions. However, the unsupervised mechanism
depends on a well designed loss function, which cannot guarantee that all vital
information of source images is sufficiently extracted. In this work, we
propose a novel interactive feature embedding in self-supervised learning
framework for infrared and visible image fusion, attempting to overcome the
issue of vital information degradation. With the help of self-supervised
learning framework, hierarchical representations of source images can be
efficiently extracted. In particular, interactive feature embedding models are
tactfully designed to build a bridge between the self-supervised learning and
infrared and visible image fusion learning, achieving vital information
retention. Qualitative and quantitative evaluations exhibit that the proposed
method performs favorably against state-of-the-art methods.

This work studies the multi-task functional linear regression models where
both the covariates and the unknown regression coefficients (called slope
functions) are curves. For slope function estimation, we employ penalized
splines to balance bias, variance, and computational complexity. The power of
multi-task learning is brought in by imposing additional structures over the
slope functions. We propose a general model with double regularization over the
spline coefficient matrix: i) a matrix manifold constraint, and ii) a composite
penalty as a summation of quadratic terms. Many multi-task learning approaches
can be treated as special cases of this proposed model, such as a reduced-rank
model and a graph Laplacian regularized model. We show the composite penalty
induces a specific norm, which helps to quantify the manifold curvature and
determine the corresponding proper subset in the manifold tangent space. The
complexity of tangent space subset is then bridged to the complexity of
geodesic neighbor via generic chaining. A unified convergence upper bound is
obtained and specifically applied to the reduced-rank model and the graph
Laplacian regularized model. The phase transition behaviors for the estimators
are examined as we vary the configurations of model parameters.

Three-dimensional (3D) freehand ultrasound (US) reconstruction without a
tracker can be advantageous over its two-dimensional or tracked counterparts in
many clinical applications. In this paper, we propose to estimate 3D spatial
transformation between US frames from both past and future 2D images, using
feed-forward and recurrent neural networks (RNNs). With the temporally
available frames, a further multi-task learning algorithm is proposed to
utilise a large number of auxiliary transformation-predicting tasks between
them. Using more than 40,000 US frames acquired from 228 scans on 38 forearms
of 19 volunteers in a volunteer study, the hold-out test performance is
quantified by frame prediction accuracy, volume reconstruction overlap,
accumulated tracking error and final drift, based on ground-truth from an
optical tracker. The results show the importance of modelling the
temporal-spatially correlated input frames as well as output transformations,
with further improvement owing to additional past and/or future frames. The
best performing model was associated with predicting transformation between
moderately-spaced frames, with an interval of less than ten frames at 20 frames
per second (fps). Little benefit was observed by adding frames more than one
second away from the predicted transformation, with or without LSTM-based RNNs.
Interestingly, with the proposed approach, explicit within-sequence loss that
encourages consistency in composing transformations or minimises accumulated
error may no longer be required. The implementation code and volunteer data
will be made publicly available ensuring reproducibility and further research.

Contemporary methods have shown promising results on cardiac image
segmentation, but merely in static learning, i.e., optimizing the network once
for all, ignoring potential needs for model updating. In real-world scenarios,
new data continues to be gathered from multiple institutions over time and new
demands keep growing to pursue more satisfying performance. The desired model
should incrementally learn from each incoming dataset and progressively update
with improved functionality as time goes by. As the datasets sequentially
delivered from multiple sites are normally heterogenous with domain
discrepancy, each updated model should not catastrophically forget previously
learned domains while well generalizing to currently arrived domains or even
unseen domains. In medical scenarios, this is particularly challenging as
accessing or storing past data is commonly not allowed due to data privacy. To
this end, we propose a novel domain-incremental learning framework to recover
past domain inputs first and then regularly replay them during model
optimization. Particularly, we first present a style-oriented replay module to
enable structure-realistic and memory-efficient reproduction of past data, and
then incorporate the replayed past data to jointly optimize the model with
current data to alleviate catastrophic forgetting. During optimization, we
additionally perform domain-sensitive feature whitening to suppress model's
dependency on features that are sensitive to domain changes (e.g.,
domain-distinctive style features) to assist domain-invariant feature
exploration and gradually improve the generalization performance of the
network. We have extensively evaluated our approach with the M&Ms Dataset in
single-domain and compound-domain incremental learning settings with improved
performance over other comparison approaches.

Recent cross-lingual cross-modal works attempt to extend Vision-Language
Pre-training (VLP) models to non-English inputs and achieve impressive
performance. However, these models focus only on understanding tasks utilizing
encoder-only architecture. In this paper, we propose ERNIE-UniX2, a unified
cross-lingual cross-modal pre-training framework for both generation and
understanding tasks. ERNIE-UniX2 integrates multiple pre-training paradigms
(e.g., contrastive learning and language modeling) based on encoder-decoder
architecture and attempts to learn a better joint representation across
languages and modalities. Furthermore, ERNIE-UniX2 can be seamlessly fine-tuned
for varieties of generation and understanding downstream tasks. Pre-trained on
both multilingual text-only and image-text datasets, ERNIE-UniX2 achieves SOTA
results on various cross-lingual cross-modal generation and understanding tasks
such as multimodal machine translation and multilingual visual question
answering.

Computer-vision technologies have emerged to assist security surveillance.
However, automation alert/alarm systems often apply a low-beta threshold to
avoid misses and generates excessive false alarms. This study proposed an
adaptive hazard diagnosis and alarm system with adjustable alert threshold
levels based on environmental scenarios and operator's hazard recognition
performance. We recorded electroencephalogram (EEG) data during hazard
recognition tasks. The linear ballistic accumulator model was used to decompose
the response time into several psychological subcomponents, which were further
estimated by a Markov chain Monte Carlo algorithm and compared among different
types of hazardous scenarios. Participants were most cautious about falling
hazards, followed by electricity hazards, and had the least conservative
attitude toward structural hazards. Participants were classified into three
performance-level subgroups using a latent profile analysis based on task
accuracy. We applied the transfer learning paradigm to classify subgroups based
on their time-frequency representations of EEG data. Additionally, two
continual learning strategies were investigated to ensure a robust adaptation
of the model to predict participants' performance levels in different hazardous
scenarios. These findings can be leveraged in real-world brain-computer
interface applications, which will provide human trust in automation and
promote the successful implementation of alarm technologies.

This article focuses on the control center of each human body: the brain. We
will point out the pivotal role of the cerebral vasculature and how its complex
mechanisms may vary between subjects. We then emphasize a specific acute
pathological state, i.e., acute ischemic stroke, and show how medical imaging
and its analysis can be used to define the treatment. We show how the
core-penumbra concept is used in practice using mismatch criteria and how
machine learning can be used to make predictions of the final infarct, either
via deconvolution or convolutional neural networks.

Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can
significantly impact performance. However, the hyper-parameters are normally
tuned manually, which is often a difficult task. Most recently, effective
automatic hyper-parameter tuning was achieved by using an empirical auto-tuner.
In this work, we address the issue of hyper-parameter auto-tuning using neural
network (NN)-based learning. Inspired by the empirical auto-tuner, we design
and learn a NN-based auto-tuner, and show that considerable improvement in
convergence rate and recovery performance can be achieved.

This paper introduces a Deep Learning approach for signal parameter
estimation in the context of wireless channel modeling. Our work is capable of
multidimensional parameter estimation from a signal containing an unknown
number of paths. The signal parameters are estimated relative to a predefined
grid, providing quasi grid-free, hence, more accurate estimates than previous
grid-limited approaches. It requires no prior knowledge of the number of paths,
giving it an advantage in terms of complexity compared to existing solutions.
Along with the description, we provide an initial performance analysis and a
comparison with State-of-the-Art techniques and discuss future research
directions.

In this paper, we build on advances introduced by the Deep Q-Networks (DQN)
approach to extend the multi-objective tabular Reinforcement Learning (RL)
algorithm W-learning to large state spaces. W-learning algorithm can naturally
solve the competition between multiple single policies in multi-objective
environments. However, the tabular version does not scale well to environments
with large state spaces. To address this issue, we replace underlying Q-tables
with DQN, and propose an addition of W-Networks, as a replacement for tabular
weights (W) representations. We evaluate the resulting Deep W-Networks (DWN)
approach in two widely-accepted multi-objective RL benchmarks: deep sea
treasure and multi-objective mountain car. We show that DWN solves the
competition between multiple policies while outperforming the baseline in the
form of a DQN solution. Additionally, we demonstrate that the proposed
algorithm can find the Pareto front in both tested environments.

Fairness-aware mining of massive data streams is a growing and challenging
concern in the contemporary domain of machine learning. Many stream learning
algorithms are used to replace humans at critical decision-making points e.g.,
hiring staff, assessing credit risk, etc. This calls for handling massive
incoming information with minimum response delay while ensuring fair and high
quality decisions. Recent discrimination-aware learning methods are optimized
based on overall accuracy. However, the overall accuracy is biased in favor of
the majority class; therefore, state-of-the-art methods mainly diminish
discrimination by partially or completely ignoring the minority class. In this
context, we propose a novel adaptation of Na\"ive Bayes to mitigate
discrimination embedded in the streams while maintaining high predictive
performance for both the majority and minority classes. Our proposed algorithm
is simple, fast, and attains multi-objective optimization goals. To handle
class imbalance and concept drifts, a dynamic instance weighting module is
proposed, which gives more importance to recent instances and less importance
to obsolete instances based on their membership in minority or majority class.
We conducted experiments on a range of streaming and static datasets and
deduced that our proposed methodology outperforms existing state-of-the-art
fairness-aware methods in terms of both discrimination score and balanced
accuracy.

Machine-Learned Likelihoods (MLL) is a method that, by combining modern
machine-learning classification techniques with likelihood-based inference
tests, allows to estimate the experimental sensitivity of high-dimensional data
sets. We extend the MLL method by including the exclusion hypothesis tests and
show that the addition of Kernel Density Estimators avoids the need to bin the
classifier output in order to extract the resulting one-dimensional signal and
background probability density functions. We first test our method on toy
models generated with multivariate Gaussian distributions, where the true
probability distribution functions are known. We then apply it to a case of
interest in the search for new physics at the HL-LHC, in which a $Z^\prime$
boson decays into lepton pairs, comparing the performance of our method for
estimating 95\% CL exclusion limits to the results obtained applying a binned
likelihood to the machine-learning classifier output.

Designing a high-efficiency and high-quality expressive network architecture
has always been the most important research topic in the field of deep
learning. Most of today's network design strategies focus on how to integrate
features extracted from different layers, and how to design computing units to
effectively extract these features, thereby enhancing the expressiveness of the
network. This paper proposes a new network design strategy, i.e., to design the
network architecture based on gradient path analysis. On the whole, most of
today's mainstream network design strategies are based on feed forward path,
that is, the network architecture is designed based on the data path. In this
paper, we hope to enhance the expressive ability of the trained model by
improving the network learning ability. Due to the mechanism driving the
network parameter learning is the backward propagation algorithm, we design
network design strategies based on back propagation path. We propose the
gradient path design strategies for the layer-level, the stage-level, and the
network-level, and the design strategies are proved to be superior and feasible
from theoretical analysis and experiments.

The fashion industry is one of the most active and competitive markets in the
world, manufacturing millions of products and reaching large audiences every
year. A plethora of business processes are involved in this large-scale
industry, but due to the generally short life-cycle of clothing items,
supply-chain management and retailing strategies are crucial for good market
performance. Correctly understanding the wants and needs of clients, managing
logistic issues and marketing the correct products are high-level problems with
a lot of uncertainty associated to them given the number of influencing
factors, but most importantly due to the unpredictability often associated with
the future. It is therefore straightforward that forecasting methods, which
generate predictions of the future, are indispensable in order to ameliorate
all the various business processes that deal with the true purpose and meaning
of fashion: having a lot of people wear a particular product or style,
rendering these items, people and consequently brands fashionable. In this
paper, we provide an overview of three concrete forecasting tasks that any
fashion company can apply in order to improve their industrial and market
impact. We underline advances and issues in all three tasks and argue about
their importance and the impact they can have at an industrial level. Finally,
we highlight issues and directions of future work, reflecting on how
learning-based forecasting methods can further aid the fashion industry.

Simulations of Al thin film sputter depositions rely on accurate plasma and
surface interaction models. Establishing the latter commonly requires a higher
level of abstraction and means to dismiss the fundamental atomic fidelity.
Previous works on sputtering processes addressed this issue by establishing
machine learning surrogate models, which include a basic surface state (i.e.,
stoichiometry) as static input. In this work, an evolving surface state and
defect structure are introduced to jointly describe sputtering and growth with
physics-separating artificial neural networks. The data describing the
plasma-surface interactions stem from hybrid reactive molecular
dynamics/time-stamped force bias Monte Carlo simulations of Al neutrals and
Ar$^+$ ions impinging onto Al(001) surfaces. It is demonstrated that the
fundamental processes are comprehensively described by taking the surface state
as well as defect structure into account. Hence, a machine learning
plasma-surface interaction surrogate model is established that resolves the
inherent kinetics with high physical fidelity. The resulting model is not
restricted to input from modeling and simulation, but may similarly be applied
to experimental input data.

We propose a novel deep neural network architecture to learn interpretable
representation for medical image analysis. Our architecture generates a global
attention for region of interest, and then learns bag of words style deep
feature embeddings with local attention. The global, and local feature maps are
combined using a contemporary transformer architecture for highly accurate
Gallbladder Cancer (GBC) detection from Ultrasound (USG) images. Our
experiments indicate that the detection accuracy of our model beats even human
radiologists, and advocates its use as the second reader for GBC diagnosis. Bag
of words embeddings allow our model to be probed for generating interpretable
explanations for GBC detection consistent with the ones reported in medical
literature. We show that the proposed model not only helps understand decisions
of neural network models but also aids in discovery of new visual features
relevant to the diagnosis of GBC. Source-code and model will be available at
https://github.com/sbasu276/RadFormer

Deep Reinforcement Learning has been successfully applied to learn robotic
control. However, the corresponding algorithms struggle when applied to
problems where the agent is only rewarded after achieving a complex task. In
this context, using demonstrations can significantly speed up the learning
process, but demonstrations can be costly to acquire. In this paper, we propose
to leverage a sequential bias to learn control policies for complex robotic
tasks using a single demonstration. To do so, our method learns a
goal-conditioned policy to control a system between successive low-dimensional
goals. This sequential goal-reaching approach raises a problem of compatibility
between successive goals: we need to ensure that the state resulting from
reaching a goal is compatible with the achievement of the following goals. To
tackle this problem, we present a new algorithm called DCIL-II. We show that
DCIL-II can solve with unprecedented sample efficiency some challenging
simulated tasks such as humanoid locomotion and stand-up as well as fast
running with a simulated Cassie robot. Our method leveraging sequentiality is a
step towards the resolution of complex robotic tasks under minimal
specification effort, a key feature for the next generation of autonomous
robots.

National Health and Nutritional Status Survey (NHANSS) is conducted annually
by the Ministry of Health in Negara Brunei Darussalam to assess the population
health and nutritional patterns and characteristics. The main aim of this study
was to discover meaningful patterns (groups) from the obese sample of NHANSS
data by applying data reduction and interpretation techniques. The mixed nature
of the variables (qualitative and quantitative) in the data set added novelty
to the study. Accordingly, the Categorical Principal Component (CATPCA)
technique was chosen to interpret the meaningful results. The relationships
between obesity and the lifestyle factors like demography, socioeconomic
status, physical activity, dietary behavior, history of blood pressure,
diabetes, etc., were determined based on the principal components generated by
CATPCA. The results were validated with the help of the split method technique
to counter verify the authenticity of the generated groups. Based on the
analysis and results, two subgroups were found in the data set, and the salient
features of these subgroups have been reported. These results can be proposed
for the betterment of the healthcare industry.

Explainability has been widely stated as a cornerstone of the responsible and
trustworthy use of machine learning models. With the ubiquitous use of Deep
Neural Network (DNN) models expanding to risk-sensitive and safety-critical
domains, many methods have been proposed to explain the decisions of these
models. Recent years have also seen concerted efforts that have shown how such
explanations can be distorted (attacked) by minor input perturbations. While
there have been many surveys that review explainability methods themselves,
there has been no effort hitherto to assimilate the different methods and
metrics proposed to study the robustness of explanations of DNN models. In this
work, we present a comprehensive survey of methods that study, understand,
attack, and defend explanations of DNN models. We also present a detailed
review of different metrics used to evaluate explanation methods, as well as
describe attributional attack and defense methods. We conclude with lessons and
take-aways for the community towards ensuring robust explanations of DNN model
predictions.

Modern single-particle-tracking techniques produce extensive time-series of
diffusive motion in a wide variety of systems, from single-molecule motion in
living-cells to movement ecology. The quest is to decipher the physical
mechanisms encoded in the data and thus to better understand the probed
systems. We here augment recently proposed machine-learning techniques for
decoding anomalous-diffusion data to include an uncertainty estimate in
addition to the predicted output. To avoid the Black-Box-Problem a
Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is
used to train models for both the classification of the diffusion model and the
regression of the anomalous diffusion exponent of single-particle-trajectories.
Evaluating their performance, we find that these models can achieve a
well-calibrated error estimate while maintaining high prediction accuracies. In
the analysis of the output uncertainty predictions we relate these to
properties of the underlying diffusion models, thus providing insights into the
learning process of the machine and the relevance of the output.

Partial label learning (PLL) is a typical weakly supervised learning, where
each sample is associated with a set of candidate labels. The basic assumption
of PLL is that the ground-truth label must reside in the candidate set.
However, this assumption may not be satisfied due to the unprofessional
judgment of the annotators, thus limiting the practical application of PLL. In
this paper, we relax this assumption and focus on a more general problem, noisy
PLL, where the ground-truth label may not exist in the candidate set. To
address this challenging problem, we further propose a novel framework called
"Automatic Refinement Network (ARNet)". Our method consists of multiple rounds.
In each round, we purify the noisy samples through two key modules, i.e., noisy
sample detection and label correction. To guarantee the performance of these
modules, we start with warm-up training and automatically select the
appropriate correction epoch. Meanwhile, we exploit data augmentation to
further reduce prediction errors in ARNet. Through theoretical analysis, we
prove that our method is able to reduce the noise level of the dataset and
eventually approximate the Bayes optimal classifier. To verify the
effectiveness of ARNet, we conduct experiments on multiple benchmark datasets.
Experimental results demonstrate that our ARNet is superior to existing
state-of-the-art approaches in noisy PLL. Our code will be made public soon.

Scene Graph Generation (SGG) serves a comprehensive representation of the
images for human understanding as well as visual understanding tasks. Due to
the long tail bias problem of the object and predicate labels in the available
annotated data, the scene graph generated from current methodologies can be
biased toward common, non-informative relationship labels. Relationship can
sometimes be non-mutually exclusive, which can be described from multiple
perspectives like geometrical relationships or semantic relationships, making
it even more challenging to predict the most suitable relationship label. In
this work, we proposed the SG-Shuffle pipeline for scene graph generation with
3 components: 1) Parallel Transformer Encoder, which learns to predict object
relationships in a more exclusive manner by grouping relationship labels into
groups of similar purpose; 2) Shuffle Transformer, which learns to select the
final relationship labels from the category-specific feature generated in the
previous step; and 3) Weighted CE loss, used to alleviate the training bias
caused by the imbalanced dataset.

Audio Spectrogram Transformer models rule the field of Audio Tagging,
outrunning previously dominating Convolutional Neural Networks (CNNs). Their
superiority is based on the ability to scale up and exploit large-scale
datasets such as AudioSet. However, Transformers are demanding in terms of
model size and computational requirements compared to CNNs. We propose a
training procedure for efficient CNNs based on offline Knowledge Distillation
(KD) from high-performing yet complex transformers. The proposed training
schema and the efficient CNN design based on MobileNetV3 results in models
outperforming previous solutions in terms of parameter and computational
efficiency and prediction performance. We provide models of different
complexity levels, scaling from low-complexity models up to a new
state-of-the-art performance of .483 mAP on AudioSet. Source Code available at:
https://github.com/fschmid56/EfficientAT

Amorphous red phosphorus (a-P) is one of the remaining puzzling cases in the
structural chemistry of the elements. Here, we elucidate the structure,
stability, and chemical bond-ing in a-P from first principles, combining
machine-learning and density-functional theo-ry (DFT) methods. We show that a-P
structures exist with a range of energies slightly higher than those of
phosphorus nanorods, to which they are closely related, and that the stability
of a-P is linked to the degree of structural relaxation and medium-range order.
We thus complete the stability range of phosphorus allotropes [Angew. Chem.
Int. Ed. 2014, 53, 11629] by now including the previously poorly understood
amorphous phase, and we quantify the covalent and van der Waals interactions in
all main phases of phos-phorus. We also study the electronic densities of
states, including those of hydrogenated a-P. Beyond the present study, our
structural models are expected to enable wider-ranging first-principles
investigations - for example, of a-P-based battery materials.

The upcoming exascale era will provide a new generation of physics
simulations. These simulations will have a high spatiotemporal resolution,
which will impact the training of machine learning models since storing a high
amount of simulation data on disk is nearly impossible. Therefore, we need to
rethink the training of machine learning models for simulations for the
upcoming exascale era. This work presents an approach that trains a neural
network concurrently to a running simulation without storing data on a disk.
The training pipeline accesses the training data by in-memory streaming.
Furthermore, we apply methods from the domain of continual learning to enhance
the generalization of the model. We tested our pipeline on the training of a 3d
autoencoder trained concurrently to laser wakefield acceleration
particle-in-cell simulation. Furthermore, we experimented with various
continual learning methods and their effect on the generalization.

Training facial emotion recognition models requires large sets of data and
costly annotation processes. To alleviate this problem, we developed a gamified
method of acquiring annotated facial emotion data without an explicit labeling
effort by humans. The game, which we named Facegame, challenges the players to
imitate a displayed image of a face that portrays a particular basic emotion.
Every round played by the player creates new data that consists of a set of
facial features and landmarks, already annotated with the emotion label of the
target facial expression. Such an approach effectively creates a robust,
sustainable, and continuous machine learning training process. We evaluated
Facegame with an experiment that revealed several contributions to the field of
affective computing. First, the gamified data collection approach allowed us to
access a rich variation of facial expressions of each basic emotion due to the
natural variations in the players' facial expressions and their expressive
abilities. We report improved accuracy when the collected data were used to
enrich well-known in-the-wild facial emotion datasets and consecutively used
for training facial emotion recognition models. Second, the natural language
prescription method used by the Facegame constitutes a novel approach for
interpretable explainability that can be applied to any facial emotion
recognition model. Finally, we observed significant improvements in the facial
emotion perception and expression skills of the players through repeated game
play.

Federated Learning (FL) is extensively used to train AI/ML models in
distributed and privacy-preserving settings. Participant edge devices in FL
systems typically contain non-independent and identically distributed~(Non-IID)
private data and unevenly distributed computational resources. Preserving user
data privacy while optimizing AI/ML models in a heterogeneous federated network
requires us to address data heterogeneity and system/resource heterogeneity.
Hence, we propose \underline{R}esource-\underline{a}ware \underline{F}ederated
\underline{L}earning~(RaFL) to address these challenges. RaFL allocates
resource-aware models to edge devices using Neural Architecture Search~(NAS)
and allows heterogeneous model architecture deployment by knowledge extraction
and fusion. Integrating NAS into FL enables on-demand customized model
deployment for resource-diverse edge devices. Furthermore, we propose a
multi-model architecture fusion scheme allowing the aggregation of the
distributed learning results. Results demonstrate RaFL's superior resource
efficiency compared to SoTA.

Side-informed steganography has always been among the most secure approaches
in the field. However, a majority of existing methods for JPEG images use the
side information, here the rounding error, in a heuristic way. For the first
time, we show that the usefulness of the rounding error comes from its
covariance with the embedding changes. Unfortunately, this covariance between
continuous and discrete variables is not analytically available. An estimate of
the covariance is proposed, which allows to model steganography as a change in
the variance of DCT coefficients. Since steganalysis today is best performed in
the spatial domain, we derive a likelihood ratio test to preserve a model of a
decompressed JPEG image. The proposed method then bounds the power of this test
by minimizing the Kullback-Leibler divergence between the cover and stego
distributions. We experimentally demonstrate in two popular datasets that it
achieves state-of-the-art performance against deep learning detectors.
Moreover, by considering a different pixel variance estimator for images
compressed with Quality Factor 100, even greater improvements are obtained.

Recognizing useful named entities plays a vital role in medical information
processing, which helps drive the development of medical area research. Deep
learning methods have achieved good results in medical named entity recognition
(NER). However, we find that existing methods face great challenges when
dealing with the nested named entities. In this work, we propose a novel
method, referred to as ASAC, to solve the dilemma caused by the nested
phenomenon, in which the core idea is to model the dependency between different
categories of entity recognition. The proposed method contains two key modules:
the adaptive shared (AS) part and the attentive conditional random field (ACRF)
module. The former part automatically assigns adaptive weights across each task
to achieve optimal recognition accuracy in the multi-layer network. The latter
module employs the attention operation to model the dependency between
different entities. In this way, our model could learn better entity
representations by capturing the implicit distinctions and relationships
between different categories of entities. Extensive experiments on public
datasets verify the effectiveness of our method. Besides, we also perform
ablation analyses to deeply understand our methods.

In the past years, artificial neural networks (ANNs) have become the de-facto
standard to solve tasks in communications engineering that are difficult to
solve with traditional methods. In parallel, the artificial intelligence
community drives its research to biology-inspired, brain-like spiking neural
networks (SNNs), which promise extremely energy-efficient computing. In this
paper, we investigate the use of SNNs in the context of channel equalization
for ultra-low complexity receivers. We propose an SNN-based equalizer with a
feedback structure akin to the decision feedback equalizer (DFE). For
conversion of real-world data into spike signals we introduce a novel ternary
encoding and compare it with traditional log-scale encoding. We show that our
approach clearly outperforms conventional linear equalizers for three different
exemplary channels. We highlight that mainly the conversion of the channel
output to spikes introduces a small performance penalty. The proposed SNN with
a decision feedback structure enables the path to competitive energy-efficient
transceivers.

The continuous increase in global population and the impact of climate change
on crop production are expected to affect the food sector significantly. In
this context, there is need for timely, large-scale and precise mapping of
crops for evidence-based decision making. A key enabler towards this direction
are new satellite missions that freely offer big remote sensing data of high
spatio-temporal resolution and global coverage. During the previous decade and
because of this surge of big Earth observations, deep learning methods have
dominated the remote sensing and crop mapping literature. Nevertheless, deep
learning models require large amounts of annotated data that are scarce and
hard-to-acquire. To address this problem, transfer learning methods can be used
to exploit available annotations and enable crop mapping for other regions,
crop types and years of inspection. In this work, we have developed and trained
a deep learning model for paddy rice detection in South Korea using Sentinel-1
VH time-series. We then fine-tune the model for i) paddy rice detection in
France and Spain and ii) barley detection in the Netherlands. Additionally, we
propose a modification in the pre-trained weights in order to incorporate extra
input features (Sentinel-1 VV). Our approach shows excellent performance when
transferring in different areas for the same crop type and rather promising
results when transferring in a different area and crop type.

We propose an algorithm for learning a conditional generative model of a
molecule given a target. Specifically, given a receptor molecule that one
wishes to bind to, the conditional model generates candidate ligand molecules
that may bind to it. The distribution should be invariant to rigid body
transformations that act $\textit{jointly}$ on the ligand and the receptor; it
should also be invariant to permutations of either the ligand or receptor
atoms. Our learning algorithm is based on a continuous normalizing flow. We
establish semi-equivariance conditions on the flow which guarantee the
aforementioned invariance conditions on the conditional distribution. We
propose a graph neural network architecture which implements this flow, and
which is designed to learn effectively despite the vast differences in size
between the ligand and receptor. We evaluate our method on the CrossDocked2020
dataset, attaining a significant improvement in binding affinity over competing
methods.

We develop Bayesian neural networks (BNNs) that permit to model generic
nonlinearities and time variation for (possibly large sets of) macroeconomic
and financial variables. From a methodological point of view, we allow for a
general specification of networks that can be applied to either dense or sparse
datasets, and combines various activation functions, a possibly very large
number of neurons, and stochastic volatility (SV) for the error term. From a
computational point of view, we develop fast and efficient estimation
algorithms for the general BNNs we introduce. From an empirical point of view,
we show both with simulated data and with a set of common macro and financial
applications that our BNNs can be of practical use, particularly so for
observations in the tails of the cross-sectional or time series distributions
of the target variables.

Federated Learning (FL) enables the training of Deep Learning models without
centrally collecting possibly sensitive raw data. This paves the way for
stronger privacy guarantees when building predictive models. The most used
algorithms for FL are parameter-averaging based schemes (e.g., Federated
Averaging) that, however, have well known limits: (i) Clients must implement
the same model architecture; (ii) Transmitting model weights and model updates
implies high communication cost, which scales up with the number of model
parameters; (iii) In presence of non-IID data distributions,
parameter-averaging aggregation schemes perform poorly due to client model
drifts. Federated adaptations of regular Knowledge Distillation (KD) can solve
and/or mitigate the weaknesses of parameter-averaging FL algorithms while
possibly introducing other trade-offs. In this article, we provide a review of
KD-based algorithms tailored for specific FL issues.

As the advancements in the field of artificial intelligence and nonlinear
optics continues new methods can be used to better describe and determine
nonlinear optical phenomena. In this research we aimed to analyze the
diffraction patterns of an organic material and determine the nonlinear
refraction index of the material in question by utilizing ResNet 152
convolutional neural network architecture in the regions of laser power that
the diffraction rings are not clearly distinguishable. This approach can open
new sights for optical material characterization in situations where the
conventional methods do not apply.

As the Internet grows in popularity, more and more classification jobs, such
as IoT, finance industry and healthcare field, rely on mobile edge computing to
advance machine learning. In the medical industry, however, good diagnostic
accuracy necessitates the combination of large amounts of labeled data to train
the model, which is difficult and expensive to collect and risks jeopardizing
patients' privacy. In this paper, we offer a novel medical diagnostic framework
that employs a federated learning platform to ensure patient data privacy by
transferring classification algorithms acquired in a labeled domain to a domain
with sparse or missing labeled data. Rather than using a generative adversarial
network, our framework uses a discriminative model to build multiple
classification loss functions with the goal of improving diagnostic accuracy.
It also avoids the difficulty of collecting large amounts of labeled data or
the high cost of generating large amount of sample data. Experiments on
real-world image datasets demonstrates that the suggested adversarial federated
transfer learning method is promising for real-world medical diagnosis
applications that use image classification.

Catch-and-effort data are among the primary sources of information for
assessing the status of terrestrial wildlife and fish. In fishery science,
elaborate stock-assessment models are fitted to such data in order to estimate
fish-population sizes and guide management decisions. Given the importance of
catch-and-effort data, we scoured a comprehensive dataset pertaining to
albacore tuna (Thunnus alalunga) in the north Pacific ocean for novel
ecological information content about this commercially valuable species.
Specifically, we used unsupervised learning based on finite mixture modelling
to reveal that the north Pacific albacore-tuna stock can be divided into four
pseudo-cohorts ranging in age from approximately 3 to 12 years old. We
discovered that smaller size pseudo-cohorts inhabit relatively high --
subtropical to temperate -- latitudes, with hotspots off the coast of Japan.
Larger size pseudo-cohorts inhabit lower -- tropical to subtropical --
latitudes, with hotspots in the western and central north Pacific. These
results offer evidence that albacore tuna prefer different habitats depending
on their size and age, and point to long-term migratory routes for the species
that the current tagging technology is unlikely to capture in full. We discuss
the implications of the results for data-driven modelling of albacore tuna in
the north Pacific, as well as the management of the north Pacific albacore-tuna
fishery.

Global localisation from visual data is a challenging problem applicable to
many robotics domains. Prior works have shown that neural networks can be
trained to map images of an environment to absolute camera pose within that
environment, learning an implicit neural mapping in the process. In this work
we evaluate the applicability of such an approach to real-world robotics
scenarios, demonstrating that by constraining the problem to 2-dimensions and
significantly increasing the quantity of training data, a compact model capable
of real-time inference on embedded platforms can be used to achieve
localisation accuracy of several centimetres. We deploy our trained model
onboard a UGV platform, demonstrating its effectiveness in a waypoint
navigation task. Along with this work we will release a novel localisation
dataset comprising simulated and real environments, each with training samples
numbering in the tens of thousands.

We propose a light-weight and highly efficient Joint Detection and Tracking
pipeline for the task of Multi-Object Tracking using a fully-transformer
architecture. It is a modified version of TransTrack, which overcomes the
computational bottleneck associated with its design, and at the same time,
achieves state-of-the-art MOTA score of 73.20%. The model design is driven by a
transformer based backbone instead of CNN, which is highly scalable with the
input resolution. We also propose a drop-in replacement for Feed Forward
Network of transformer encoder layer, by using Butterfly Transform Operation to
perform channel fusion and depth-wise convolution to learn spatial context
within the feature maps, otherwise missing within the attention maps of the
transformer. As a result of our modifications, we reduce the overall model size
of TransTrack by 58.73% and the complexity by 78.72%. Therefore, we expect our
design to provide novel perspectives for architecture optimization in future
research related to multi-object tracking.

Voice conversion for highly expressive speech is challenging. Current
approaches struggle with the balancing between speaker similarity,
intelligibility and expressiveness. To address this problem, we propose
Expressive-VC, a novel end-to-end voice conversion framework that leverages
advantages from both neural bottleneck feature (BNF) approach and information
perturbation approach. Specifically, we use a BNF encoder and a Perturbed-Wav
encoder to form a content extractor to learn linguistic and para-linguistic
features respectively, where BNFs come from a robust pre-trained ASR model and
the perturbed wave becomes speaker-irrelevant after signal perturbation. We
further fuse the linguistic and para-linguistic features through an attention
mechanism, where speaker-dependent prosody features are adopted as the
attention query, which result from a prosody encoder with target speaker
embedding and normalized pitch and energy of source speech as input. Finally
the decoder consumes the integrated features and the speaker-dependent prosody
feature to generate the converted speech. Experiments demonstrate that
Expressive-VC is superior to several state-of-the-art systems, achieving both
high expressiveness captured from the source speech and high speaker similarity
with the target speaker; meanwhile intelligibility is well maintained.

Neural network language model (NNLM) plays an essential role in automatic
speech recognition (ASR) systems, especially in adaptation tasks when text-only
data is available. In practice, an NNLM is typically trained on a combination
of data sampled from multiple corpora. Thus, the data sampling strategy is
important to the adaptation performance. Most existing works focus on designing
static sampling strategies. However, each corpus may show varying impacts at
different NNLM training stages. In this paper, we introduce a novel adaptive
multi-corpora training algorithm that dynamically learns and adjusts the
sampling probability of each corpus along the training process. The algorithm
is robust to corpora sizes and domain relevance. Compared with static sampling
strategy baselines, the proposed approach yields remarkable improvement by
achieving up to relative 7% and 9% word error rate (WER) reductions on
in-domain and out-of-domain adaptation tasks, respectively.

Manual prescription of the field of view (FOV) by MRI technologists is
variable and prolongs the scanning process. Often, the FOV is too large or
crops critical anatomy. We propose a deep-learning framework, trained by
radiologists' supervision, for automating FOV prescription. An intra-stack
shared feature extraction network and an attention network are used to process
a stack of 2D image inputs to generate output scalars defining the location of
a rectangular region of interest (ROI). The attention mechanism is used to make
the model focus on the small number of informative slices in a stack. Then the
smallest FOV that makes the neural network predicted ROI free of aliasing is
calculated by an algebraic operation derived from MR sampling theory. We
retrospectively collected 595 cases between February 2018 and February 2022.
The framework's performance is examined quantitatively with intersection over
union (IoU) and pixel error on position, and qualitatively with a reader study.
We use the t-test for comparing quantitative results from all models and a
radiologist. The proposed model achieves an average IoU of 0.867 and average
ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly
better (P<0.05) than two baseline models and not significantly different from a
radiologist (P>0.12). Finally, the FOV given by the proposed framework achieves
an acceptance rate of 92% from an experienced radiologist.

This paper surveys some recent developments in measures of association
related to a new coefficient of correlation introduced by the author. A
straightforward extension of this coefficient to standard Borel spaces (which
includes all Polish spaces), overlooked in the literature so far, is proposed
at the end of the survey.

This paper is about an extraordinary phenomenon. Suppose we don't use any
low-light images as training data, can we enhance a low-light image by deep
learning? Obviously, current methods cannot do this, since deep neural networks
require to train their scads of parameters using copious amounts of training
data, especially task-related data. In this paper, we show that in the context
of fundamental deep learning, it is possible to enhance a low-light image
without any task-related training data. Technically, we propose a new, magical,
effective and efficient method, termed \underline{Noi}se
\underline{SE}lf-\underline{R}egression (NoiSER), which learns a gray-world
mapping from Gaussian distribution for low-light image enhancement (LLIE).
Specifically, a self-regression model is built as a carrier to learn a
gray-world mapping during training, which is performed by simply iteratively
feeding random noise. During inference, a low-light image is directly fed into
the learned mapping to yield a normal-light one. Extensive experiments show
that our NoiSER is highly competitive to current task-related data based LLIE
models in terms of quantitative and visual results, while outperforming them in
terms of the number of parameters, training time and inference speed. With only
about 1K parameters, NoiSER realizes about 1 minute for training and 1.2 ms for
inference with 600$\times$400 resolution on RTX 2080 Ti. Besides, NoiSER has an
inborn automated exposure suppression capability and can automatically adjust
too bright or too dark, without additional manipulations.

In the scenario of unsupervised extractive summarization, learning
high-quality sentence representations is essential to select salient sentences
from the input document. Previous studies focus more on employing statistical
approaches or pre-trained language models (PLMs) to extract sentence
embeddings, while ignoring the rich information inherent in the heterogeneous
types of interaction between words and sentences. In this paper, we are the
first to propose an unsupervised extractive summarizaiton method with
heterogeneous graph embeddings (HGEs) for Chinese document. A heterogeneous
text graph is constructed to capture different granularities of interactions by
incorporating graph structural information. Moreover, our proposed graph is
general and flexible where additional nodes such as keywords can be easily
integrated. Experimental results demonstrate that our method consistently
outperforms the strong baseline in three summarization datasets.

3D point cloud registration is a fundamental problem in computer vision and
robotics. Recently, learning-based point cloud registration methods have made
great progress. However, these methods are sensitive to outliers, which lead to
more incorrect correspondences. In this paper, we propose a novel deep graph
matching-based framework for point cloud registration. Specifically, we first
transform point clouds into graphs and extract deep features for each point.
Then, we develop a module based on deep graph matching to calculate a soft
correspondence matrix. By using graph matching, not only the local geometry of
each point but also its structure and topology in a larger range are considered
in establishing correspondences, so that more correct correspondences are
found. We train the network with a loss directly defined on the
correspondences, and in the test stage the soft correspondences are transformed
into hard one-to-one correspondences so that registration can be performed by a
correspondence-based solver. Furthermore, we introduce a transformer-based
method to generate edges for graph construction, which further improves the
quality of the correspondences. Extensive experiments on object-level and
scene-level benchmark datasets show that the proposed method achieves
state-of-the-art performance. The code is available at:
\href{https://github.com/fukexue/RGM}{https://github.com/fukexue/RGM}.

Learning an explainable classifier often results in low accuracy model or
ends up with a huge rule set, while learning a deep model is usually more
capable of handling noisy data at scale, but with the cost of hard to explain
the result and weak at generalization. To mitigate this gap, we propose an
end-to-end deep explainable learning approach that combines the advantage of
deep model in noise handling and expert rule-based interpretability.
Specifically, we propose to learn a deep data assessing model which models the
data as a graph to represent the correlations among different observations,
whose output will be used to extract key data features. The key features are
then fed into a rule network constructed following predefined noisy expert
rules with trainable parameters. As these models are correlated, we propose an
end-to-end training framework, utilizing the rule classification loss to
optimize the rule learning model and data assessing model at the same time. As
the rule-based computation is none-differentiable, we propose a gradient
linking search module to carry the gradient information from the rule learning
model to the data assessing model. The proposed method is tested in an industry
production system, showing comparable prediction accuracy, much higher
generalization stability and better interpretability when compared with a
decent deep ensemble baseline, and shows much better fitting power than pure
rule-based approach.

3D inference from monocular vision using neural networks is an important
research area of computer vision. Applications of the research area are various
with many proposed solutions and have shown remarkable performance. Although
many efforts have been invested, there are still unanswered questions, some of
which are fundamental. In this paper, I discuss a problem that I hope will come
to be known as a generalization of the Blind Perspective-n-Point (Blind PnP)
problem for object-driven 3D inference based on 2D representations. The vital
difference between the fundamental problem and the Blind PnP problem is that 3D
inference parameters in the fundamental problem are attached directly to 3D
points and the camera concept will be represented through the sharing of the
parameters of these points. By providing an explainable and robust
gradient-decent solution based on 2D representations for an important special
case of the problem, the paper opens up a new approach for using available
information-based learning methods to solve problems related to 3D object pose
estimation from 2D images.

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method
for applying privacy in the training of deep learning models. This applies
isotropic Gaussian noise to gradients during training, which can perturb these
gradients in any direction, damaging utility. Metric DP, however, can provide
alternative mechanisms based on arbitrary metrics that might be more suitable.
In this paper we apply \textit{directional privacy}, via a mechanism based on
the von Mises-Fisher (VMF) distribution, to perturb gradients in terms of
\textit{angular distance} so that gradient direction is broadly preserved. We
show that this provides $\epsilon d$-privacy for deep learning training, rather
than the $(\epsilon, \delta)$-privacy of the Gaussian mechanism; and that
experimentally, on key datasets, the VMF mechanism can outperform the Gaussian
in the utility-privacy trade-off.

Computer vision applications in intelligent transportation systems (ITS) and
autonomous driving (AD) have gravitated towards deep neural network
architectures in recent years. While performance seems to be improving on
benchmark datasets, many real-world challenges are yet to be adequately
considered in research. This paper conducted an extensive literature review on
the applications of computer vision in ITS and AD, and discusses challenges
related to data, models, and complex urban environments. The data challenges
are associated with the collection and labeling of training data and its
relevance to real world conditions, bias inherent in datasets, the high volume
of data needed to be processed, and privacy concerns. Deep learning (DL) models
are commonly too complex for real-time processing on embedded hardware, lack
explainability and generalizability, and are hard to test in real-world
settings. Complex urban traffic environments have irregular lighting and
occlusions, and surveillance cameras can be mounted at a variety of angles,
gather dirt, shake in the wind, while the traffic conditions are highly
heterogeneous, with violation of rules and complex interactions in crowded
scenarios. Some representative applications that suffer from these problems are
traffic flow estimation, congestion detection, autonomous driving perception,
vehicle interaction, and edge computing for practical deployment. The possible
ways of dealing with the challenges are also explored while prioritizing
practical deployment.

When reading a story, humans can rapidly understand new fictional characters
with a few observations, mainly by drawing analogy to fictional and real people
they met before in their lives. This reflects the few-shot and meta-learning
essence of humans' inference of characters' mental states, i.e., humans'
theory-of-mind (ToM), which is largely ignored in existing research. We fill
this gap with a novel NLP benchmark, TOM-IN-AMC, the first assessment of
models' ability of meta-learning of ToM in a realistic narrative understanding
scenario. Our benchmark consists of $\sim$1,000 parsed movie scripts for this
purpose, each corresponding to a few-shot character understanding task; and
requires models to mimic humans' ability of fast digesting characters with a
few starting scenes in a new movie. Our human study verified that humans can
solve our problem by inferring characters' mental states based on their
previously seen movies; while the state-of-the-art metric-learning and
meta-learning approaches adapted to our task lags 30% behind.

There has been a great deal of recent interest in the development of spatial
prediction algorithms for very large datasets and/or prediction domains. These
methods have primarily been developed in the spatial statistics community, but
there has been growing interest in the machine learning community for such
methods, primarily driven by the success of deep Gaussian process regression
approaches and deep convolutional neural networks. These methods are often
computationally expensive to train and implement and consequently, there has
been a resurgence of interest in random projections and deep learning models
based on random weights -- so called reservoir computing methods. Here, we
combine several of these ideas to develop the Random Ensemble Deep Spatial
(REDS) approach to predict spatial data. The procedure uses random Fourier
features as inputs to an extreme learning machine (a deep neural model with
random weights), and with calibrated ensembles of outputs from this model based
on different random weights, it provides a simple uncertainty quantification.
The REDS method is demonstrated on simulated data and on a classic large
satellite data set.

Classification of cancer cellularity within tissue samples is currently a
manual process performed by pathologists. This process of correctly determining
cancer cellularity can be time intensive. Deep Learning (DL) techniques in
particular have become increasingly more popular for this purpose, due to the
accuracy and performance they exhibit, which can be comparable to the
pathologists. This work investigates the capabilities of two DL approaches to
assess cancer cellularity in whole slide images (WSI) in the SPIE-AAPM-NCI
BreastPathQ challenge dataset. The effects of training on augmented data via
rotations, and combinations of multiple architectures into a single network
were analyzed using a modified Kendall Tau-b prediction probability metric
known as the average prediction probability PK. A deep, transfer learned,
Convolutional Neural Network (CNN) InceptionV3 was used as a baseline,
achieving an average PK value of 0.884, showing improvement from the average PK
value of 0.83 achieved by pathologists. The network was then trained on
additional training datasets which were rotated between 1 and 360 degrees,
which saw a peak increase of PK up to 4.2%. An additional architecture
consisting of the InceptionV3 network and VGG16, a shallow, transfer learned
CNN, was combined in a parallel architecture. This parallel architecture
achieved a baseline average PK value of 0.907, a statistically significantly
improvement over either of the architectures' performances separately (p<0.0001
by unpaired t-test).

It has been widely observed in the machine learning community that a small
perturbation to the input can cause a large change in the prediction of a
trained model, and such phenomena have been intensively studied in the machine
learning community under the name of adversarial attacks. Because graph
algorithms also are widely used for decision making and knowledge discovery, it
is important to design graph algorithms that are robust against adversarial
attacks. In this study, we consider the Lipschitz continuity of algorithms as a
robustness measure and initiate a systematic study of the Lipschitz continuity
of algorithms for (weighted) graph problems.
  Depending on how we embed the output solution to a metric space, we can think
of several Lipschitzness notions. We mainly consider the one that is invariant
under scaling of weights, and we provide Lipschitz continuous algorithms and
lower bounds for the minimum spanning tree problem, the shortest path problem,
and the maximum weight matching problem. In particular, our shortest path
algorithm is obtained by first designing an algorithm for unweighted graphs
that are robust against edge contractions and then applying it to the
unweighted graph constructed from the original weighted graph.
  Then, we consider another Lipschitzness notion induced by a natural mapping
that maps the output solution to its characteristic vector. It turns out that
no Lipschitz continuous algorithm exists for this Lipschitz notion, and we
instead design algorithms with bounded pointwise Lipschitz constants for the
minimum spanning tree problem and the maximum weight bipartite matching
problem. Our algorithm for the latter problem is based on an LP relaxation with
entropy regularization.

Code completion aims to help improve developers' productivity by suggesting
the next code tokens from a given context. Various approaches have been
proposed to incorporate abstract syntax tree (AST) information for model
training, ensuring that code completion is aware of the syntax of the
programming languages. However, existing syntax-aware code completion
approaches are not on-the-fly, as we found that for every two-thirds of
characters that developers type, AST fails to be extracted because it requires
the syntactically correct source code, limiting its practicality in real-world
scenarios. On the other hand, existing on-the-fly code completion does not
consider syntactic information yet. In this paper, we propose PyCoder to
leverage token types, a kind of lightweight syntactic information, which is
readily available and aligns with the natural order of source code. Our PyCoder
is trained in a multi-task training manner so that by learning the supporting
task of predicting token types during the training phase, the models achieve
better performance on predicting tokens and lines of code without the need for
token types in the inference phase. Comprehensive experiments show that PyCoder
achieves the first rank on the CodeXGLUE leaderboard with an accuracy of 77.12%
for the token-level predictions, which is 0.43%-24.25% more accurate than
baselines. In addition, PyCoder achieves an exact match of 43.37% for the
line-level predictions, which is 3.63%-84.73% more accurate than baselines.
These results lead us to conclude that token type information (an alternative
to syntactic information) that is rarely used in the past can greatly improve
the performance of code completion approaches, without requiring the
syntactically correct source code like AST-based approaches do. Our PyCoder is
publicly available on HuggingFace.

Machine learning algorithms typically assume that the training and test
samples come from the same distributions, i.e., in-distribution. However, in
open-world scenarios, streaming big data can be Out-Of-Distribution (OOD),
rendering these algorithms ineffective. Prior solutions to the OOD challenge
seek to identify invariant features across different training domains. The
underlying assumption is that these invariant features should also work
reasonably well in the unlabeled target domain. By contrast, this work is
interested in the domain-specific features that include both invariant features
and features unique to the target domain. We propose a simple yet effective
approach that relies on correlations in general regardless of whether the
features are invariant or not. Our approach uses the most confidently predicted
samples identified by an OOD base model (teacher model) to train a new model
(student model) that effectively adapts to the target domain. Empirical
evaluations on benchmark datasets show that the performance is improved over
the SOTA by ~10-20%

With the continued integration of autonomous vehicles (AVs) into public
roads, a mixed traffic environment with large-scale human-driven vehicles (HVs)
and AVs interactions is imminent. In challenging traffic scenarios, such as
emergency braking, it is crucial to account for the reactive and uncertain
behavior of HVs when developing control strategies for AVs. This paper studies
the safe control of a platoon of AVs interacting with a human-driven vehicle in
longitudinal car-following scenarios. We first propose the use of a model that
combines a first-principles model (nominal model) with a Gaussian process (GP)
learning-based component for predicting behaviors of the human-driven vehicle
when it interacts with AVs. The modeling accuracy of the proposed method shows
a $9\%$ reduction in root mean square error (RMSE) in predicting a HV's
velocity compared to the nominal model. Exploiting the properties of this
model, we design a model predictive control (MPC) strategy for a platoon of AVs
to ensure a safe distance between each vehicle, as well as a (probabilistic)
safety of the human-driven car following the platoon. Compared to a baseline
MPC that uses only a nominal model for HVs, our method achieves better
velocity-tracking performance for the autonomous vehicle platoon and more
robust constraint satisfaction control for a platoon of mixed vehicles system.
Simulation studies demonstrate a $4.2\%$ decrease in the control cost and an
approximate $1m$ increase in the minimum distance between autonomous and
human-driven vehicles to better guarantee safety in challenging traffic

As big data grows ubiquitous across many domains, more and more stakeholders
seek to develop Machine Learning (ML) applications on their data. The success
of an ML application usually depends on the close collaboration of ML experts
and domain experts. However, the shortage of ML engineers remains a fundamental
problem. Low-code Machine learning tools/platforms (aka, AutoML) aim to
democratize ML development to domain experts by automating many repetitive
tasks in the ML pipeline. This research presents an empirical study of around
14k posts (questions + accepted answers) from Stack Overflow (SO) that
contained AutoML-related discussions. We examine how these topics are spread
across the various Machine Learning Life Cycle (MLLC) phases and their
popularity and difficulty. This study offers several interesting findings.
First, we find 13 AutoML topics that we group into four categories. The MLOps
topic category (43% questions) is the largest, followed by Model (28%
questions), Data (27% questions), Documentation (2% questions). Second, Most
questions are asked during Model training (29%) (i.e., implementation phase)
and Data preparation (25%) MLLC phase. Third, AutoML practitioners find the
MLOps topic category most challenging, especially topics related to model
deployment & monitoring and Automated ML pipeline. These findings have
implications for all three AutoML stakeholders: AutoML researchers, AutoML
service vendors, and AutoML developers. Academia and Industry collaboration can
improve different aspects of AutoML, such as better DevOps/deployment support
and tutorial-based documentation.

The extragradient method has recently gained increasing attention, due to its
convergence behavior on smooth games. In $n$-player differentiable games, the
eigenvalues of the Jacobian of the vector field are distributed on the complex
plane, exhibiting more convoluted dynamics compared to classical (i.e., single
player) minimization. In this work, we take a polynomial-based analysis of the
extragradient with momentum for optimizing games with \emph{cross-shaped}
Jacobian spectrum on the complex plane. We show two results. First, based on
the hyperparameter setup, the extragradient with momentum exhibits three
different modes of convergence: when the eigenvalues are distributed $i)$ on
the real line, $ii)$ both on the real line along with complex conjugates, and
$iii)$ only as complex conjugates. Then, we focus on the case $ii)$, i.e., when
the eigenvalues of the Jacobian have \emph{cross-shaped} structure, as observed
in training generative adversarial networks. For this problem class, we derive
the optimal hyperparameters of the momentum extragradient method, and show that
it achieves an accelerated convergence rate.

Domain shift is a well-known problem in the medical imaging community. In
particular, for endoscopic image analysis where the data can have different
modalities the performance of deep learning (DL) methods gets adversely
affected. In other words, methods developed on one modality cannot be used for
a different modality. However, in real clinical settings, endoscopists switch
between modalities for better mucosal visualisation. In this paper, we explore
the domain generalisation technique to enable DL methods to be used in such
scenarios. To this extend, we propose to use super pixels generated with Simple
Linear Iterative Clustering (SLIC) which we refer to as "SUPRA" for SUPeRpixel
Augmented method. SUPRA first generates a preliminary segmentation mask making
use of our new loss "SLICLoss" that encourages both an accurate and
color-consistent segmentation. We demonstrate that SLICLoss when combined with
Binary Cross Entropy loss (BCE) can improve the model's generalisability with
data that presents significant domain shift. We validate this novel compound
loss on a vanilla U-Net using the EndoUDA dataset, which contains images for
Barret's Esophagus and polyps from two modalities. We show that our method
yields an improvement of nearly 25% in the target domain set compared to the
baseline.

One of the important topics in the research field of Chinese classical poetry
is to analyze the poetic style. By examining the relevant works of previous
dynasties, researchers judge a poetic style mostly by their subjective
feelings, and refer to the previous evaluations that have become a certain
conclusion. Although this judgment method is often effective, there may be some
errors. This paper builds the most perfect data set of Chinese classical poetry
at present, trains a BART-poem pre -trained model on this data set, and puts
forward a generally applicable poetry style judgment method based on this
BART-poem model, innovatively introduces in-depth learning into the field of
computational stylistics, and provides a new research method for the study of
classical poetry. This paper attempts to use this method to solve the problem
of poetry style identification in the Tang and Song Dynasties, and takes the
poetry schools that are considered to have a relatively clear and consistent
poetic style, such as the Hongzheng Qizi and Jiajing Qizi, Jiangxi poetic
school and Tongguang poetic school, as the research object, and takes the poems
of their representative poets for testing. Experiments show that the judgment
results of the tested poetry work made by the model are basically consistent
with the conclusions given by critics of previous dynasties, verify some
avant-garde judgments of Mr. Qian Zhongshu, and better solve the task of poetry
style recognition in the Tang and Song dynasties.

Motivated by neural network training in low-bit floating and fixed-point
environments, this work studies the convergence of variants of SGD with
computational error. Considering a general stochastic Lipschitz continuous loss
function, a novel convergence result to a Clarke stationary point is presented
assuming that only an approximation of its stochastic gradient can be computed
as well as error in computing the SGD step itself. Different variants of SGD
are then tested empirically in a variety of low-precision arithmetic
environments, with improved test set accuracy achieved compared to SGD for two
image recognition tasks.

Breast cancer is one of the most common cancer in women around the world. For
diagnosis, pathologists evaluate biomarkers such as HER2 protein using
immunohistochemistry over tissue extracted by a biopsy. Through microscopic
inspection, this assessment estimates the intensity and integrity of the
membrane cells' staining and scores the sample as 0, 1+, 2+, or 3+: a
subjective decision that depends on the interpretation of the pathologist. This
paper presents the preliminary data analysis of the annotations of three
pathologists over the same set of samples obtained using 20x magnification and
including $1,252$ non-overlapping biopsy patches. We evaluate the intra- and
inter-expert variability achieving substantial and moderate agreement,
respectively, according to Fleiss' Kappa coefficient, as a previous stage
towards a generation of a HER2 breast cancer biopsy gold-standard using
supervised learning from multiple pathologist annotations.

This paper proposes a hardware-efficient architecture, Linearized Convolution
Network (LiCo-Net) for keyword spotting. It is optimized specifically for
low-power processor units like microcontrollers. ML operators exhibit
heterogeneous efficiency profiles on power-efficient hardware. Given the exact
theoretical computation cost, int8 operators are more computation-effective
than float operators, and linear layers are often more efficient than other
layers. The proposed LiCo-Net is a dual-phase system that uses the efficient
int8 linear operators at the inference phase and applies streaming convolutions
at the training phase to maintain a high model capacity. The experimental
results show that LiCo-Net outperforms single-value decomposition filter (SVDF)
on hardware efficiency with on-par detection performance. Compared to SVDF,
LiCo-Net reduces cycles by 40% on HiFi4 DSP.

Many real-world systems can be represented as graphs where the different
entities are presented by nodes and their interactions by edges. An important
task in studying large datasets is graph clustering. While there has been a lot
of work on graph clustering using the connectivity between the nodes, many
real-world networks also have node attributes. Clustering attributed graphs
requires joint modeling of graph structure and node attributes. Recent work has
focused on graph convolutional networks and graph convolutional filters to
combine structural and content information. However, these methods are mostly
limited to lowpass filtering and do not explicitly optimize the filters for the
clustering task. In this paper, we introduce a graph signal processing based
approach, where we design polynomial graph filters optimized for clustering.
The proposed approach is formulated as a two-step iterative optimization
problem where graph filters that are interpretable and optimal for the given
data are learned while maximizing the separation between different clusters.
The proposed approach is evaluated on attributed networks and compared to the
state-of-the-art graph convolutional network approaches.

This paper revisits the work of Rauch et al. (1965) and develops a novel
method for recursive maximum likelihood particle filtering for general
state-space models. The new method is based on statistical analysis of
incomplete observations of the systems. Score function and conditional observed
information of the incomplete observations/data are introduced and their
distributional properties are discussed. Some identities concerning the score
function and information matrices of the incomplete data are derived. Maximum
likelihood estimation of state-vector is presented in terms of the score
function and observed information matrices. In particular, to deal with
nonlinear state-space, a sequential Monte Carlo method is developed. It is
given recursively by an EM-gradient-particle filtering which extends the work
of Lange (1995) for state estimation. To derive covariance matrix of
state-estimation errors, an explicit form of observed information matrix is
proposed. It extends Louis (1982) general formula for the same matrix to
state-vector estimation. Under (Neumann) boundary conditions of state
transition probability distribution, the inverse of this matrix coincides with
the Cramer-Rao lower bound on the covariance matrix of estimation errors of
unbiased state-estimator. In the case of linear models, the method shows that
the Kalman filter is a fully efficient state estimator whose covariance matrix
of estimation error coincides with the Cramer-Rao lower bound. Some numerical
examples are discussed to exemplify the main results.

Minimalist Data Wrangling with Python is envisaged as a student's first
introduction to data science, providing a high-level overview as well as
discussing key concepts in detail. We explore methods for cleaning data
gathered from different sources, transforming, selecting, and extracting
features, performing exploratory data analysis and dimensionality reduction,
identifying naturally occurring data clusters, modelling patterns in data,
comparing data between groups, and reporting the results. This textbook is a
non-profit project. Its online and PDF versions are freely available at
https://datawranglingpy.gagolewski.com/.

Seizure type identification is essential for the treatment and management of
epileptic patients. However, it is a difficult process known to be time
consuming and labor intensive. Automated diagnosis systems, with the
advancement of machine learning algorithms, have the potential to accelerate
the classification process, alert patients, and support physicians in making
quick and accurate decisions. In this paper, we present a novel multi-path
seizure-type classification deep learning network (MP-SeizNet), consisting of a
convolutional neural network (CNN) and a bidirectional long short-term memory
neural network (Bi-LSTM) with an attention mechanism. The objective of this
study was to classify specific types of seizures, including complex partial,
simple partial, absence, tonic, and tonic-clonic seizures, using only
electroencephalogram (EEG) data. The EEG data is fed to our proposed model in
two different representations. The CNN was fed with wavelet-based features
extracted from the EEG signals, while the Bi-LSTM was fed with raw EEG signals
to let our MP-SeizNet jointly learns from different representations of seizure
data for more accurate information learning. The proposed MP-SeizNet was
evaluated using the largest available EEG epilepsy database, the Temple
University Hospital EEG Seizure Corpus, TUSZ v1.5.2. We evaluated our proposed
model across different patient data using three-fold cross-validation and
across seizure data using five-fold cross-validation, achieving F1 scores of
87.6% and 98.1%, respectively.

Modern neural networks are over-parameterized and thus rely on strong
regularization such as data augmentation and weight decay to reduce overfitting
and improve generalization. The dominant form of data augmentation applies
invariant transforms, where the learning target of a sample is invariant to the
transform applied to that sample. We draw inspiration from human visual
classification studies and propose generalizing augmentation with invariant
transforms to soft augmentation where the learning target softens non-linearly
as a function of the degree of the transform applied to the sample: e.g., more
aggressive image crop augmentations produce less confident learning targets. We
demonstrate that soft targets allow for more aggressive data augmentation,
offer more robust performance boosts, work with other augmentation policies,
and interestingly, produce better calibrated models (since they are trained to
be less confident on aggressively cropped/occluded examples). Combined with
existing aggressive augmentation strategies, soft target 1) doubles the top-1
accuracy boost across Cifar-10, Cifar-100, ImageNet-1K, and ImageNet-V2, 2)
improves model occlusion performance by up to $4\times$, and 3) halves the
expected calibration error (ECE). Finally, we show that soft augmentation
generalizes to self-supervised classification tasks.

The ability to dynamically adapt neural networks to newly-available data
without performance deterioration would revolutionize deep learning
applications. Streaming learning (i.e., learning from one data example at a
time) has the potential to enable such real-time adaptation, but current
approaches i) freeze a majority of network parameters during streaming and ii)
are dependent upon offline, base initialization procedures over large subsets
of data, which damages performance and limits applicability. To mitigate these
shortcomings, we propose Cold Start Streaming Learning (CSSL), a simple,
end-to-end approach for streaming learning with deep networks that uses a
combination of replay and data augmentation to avoid catastrophic forgetting.
  Because CSSL updates all model parameters during streaming, the algorithm is
capable of beginning streaming from a random initialization, making base
initialization optional. Going further, the algorithm's simplicity allows
theoretical convergence guarantees to be derived using analysis of the Neural
Tangent Random Feature (NTRF). In experiments, we find that CSSL outperforms
existing baselines for streaming learning in experiments on CIFAR100, ImageNet,
and Core50 datasets. Additionally, we propose a novel multi-task streaming
learning setting and show that CSSL performs favorably in this domain. Put
simply, CSSL performs well and demonstrates that the complicated, multi-step
training pipelines adopted by most streaming methodologies can be replaced with
a simple, end-to-end learning approach without sacrificing performance.

Recently, neural network based methods have shown their power in learning
more expressive features on the task of knowledge graph embedding (KGE).
However, the performance of deep methods often falls behind the shallow ones on
simple graphs. One possible reason is that deep models are difficult to train,
while shallow models might suffice for accurately representing the structure of
the simple KGs.
  In this paper, we propose a neural network based model, named DeepE, to
address the problem, which stacks multiple building blocks to predict the tail
entity based on the head entity and the relation. Each building block is an
addition of a linear and a non-linear function. The stacked building blocks are
equivalent to a group of learning functions with different non-linear depth.
Hence, DeepE allows deep functions to learn deep features, and shallow
functions to learn shallow features. Through extensive experiments, we find
DeepE outperforms other state-of-the-art baseline methods. A major advantage of
DeepE is the robustness. DeepE achieves a Mean Rank (MR) score that is 6%, 30%,
65% lower than the best baseline methods on FB15k-237, WN18RR and YAGO3-10. Our
design makes it possible to train much deeper networks on KGE, e.g. 40 layers
on FB15k-237, and without scarifying precision on simple relations.

A flexible method is developed to construct a confidence interval for the
frequency of a queried object in a very large data set, based on a much smaller
sketch of the data. The approach requires no knowledge of the data distribution
or of the details of the sketching algorithm; instead, it constructs provably
valid frequentist confidence intervals for random queries using a conformal
inference approach. After achieving marginal coverage for random queries under
the assumption of data exchangeability, the proposed method is extended to
provide stronger inferences accounting for possibly heterogeneous frequencies
of different random queries, redundant queries, and distribution shifts. While
the presented methods are broadly applicable, this paper focuses on use cases
involving the count-min sketch algorithm and a non-linear variation thereof, to
facilitate comparison to prior work. In particular, the developed methods are
compared empirically to frequentist and Bayesian alternatives, through
simulations and experiments with data sets of SARS-CoV-2 DNA sequences and
classic English literature.

Physics-informed neural networks have been widely applied to learn general
parametric solutions of differential equations. Here, we propose a neural
network to discover parametric eigenvalue and eigenfunction surfaces of quantum
systems. We apply our method to solve the hydrogen molecular ion. This is an
ab-initio deep learning method that solves the Schrodinger equation with the
Coulomb potential yielding realistic wavefunctions that include a cusp at the
ion positions. The neural solutions are continuous and differentiable functions
of the interatomic distance and their derivatives are analytically calculated
by applying automatic differentiation. Such a parametric and analytical form of
the solutions is useful for further calculations such as the determination of
force fields.

Robots operating in human environments must be able to rearrange objects into
semantically-meaningful configurations, even if these objects are previously
unseen. In this work, we focus on the problem of building physically-valid
structures without step-by-step instructions. We propose StructDiffusion, which
combines a diffusion model and an object-centric transformer to construct
structures out of a single RGB-D image based on high-level language goals, such
as "set the table." Our method shows how diffusion models can be used for
complex multi-step 3D planning tasks. StructDiffusion improves success rate on
assembling physically-valid structures out of unseen objects by on average 16%
over an existing multi-modal transformer model, while allowing us to use one
multi-task model to produce a wider range of different structures. We show
experiments on held-out objects in both simulation and on real-world
rearrangement tasks. For videos and additional results, check out our website:
http://weiyuliu.com/StructDiffusion/.

Governments, industry, and academia have undertaken efforts to identify and
mitigate harms in ML-driven systems, with a particular focus on social and
ethical risks of ML components in complex sociotechnical systems. However,
existing approaches are largely disjointed, ad-hoc and of unknown
effectiveness. Systems safety engineering is a well established discipline with
a track record of identifying and managing risks in many complex sociotechnical
domains. We adopt the natural hypothesis that tools from this domain could
serve to enhance risk analyses of ML in its context of use. To test this
hypothesis, we apply a "best of breed" systems safety analysis, Systems
Theoretic Process Analysis (STPA), to a specific high-consequence system with
an important ML-driven component, namely the Prescription Drug Monitoring
Programs (PDMPs) operated by many US States, several of which rely on an
ML-derived risk score. We focus in particular on how this analysis can extend
to identifying social and ethical risks and developing concrete design-level
controls to mitigate them.

The demonstrated success of transfer learning has popularized approaches that
involve pretraining models from massive data sources and subsequent finetuning
towards a specific task. While such approaches have become the norm in fields
such as natural language processing, implementation and evaluation of transfer
learning approaches for chemistry are in the early stages. In this work, we
demonstrate finetuning for downstream tasks on a graph neural network (GNN)
trained over a molecular database containing 2.7 million water clusters. The
use of Graphcore IPUs as an AI accelerator for training molecular GNNs reduces
training time from a reported 2.7 days on 0.5M clusters to 1.2 hours on 2.7M
clusters. Finetuning the pretrained model for downstream tasks of molecular
dynamics and transfer to a different potential energy surface took only 8.3
hours and 28 minutes, respectively, on a single GPU.

X-Ray radiographs are one of the primary results from inertial confinement
fusion (ICF) experiments. Issues such as scarcity of experimental data, high
levels of noise in the data, lack of ground truth data, and low resolution of
data limit the use of machine/deep learning for automated analysis of
radiographs. In this work we combat these roadblocks to the use of machine
learning by creating a synthetic radiograph dataset resembling experimental
radiographs. Accompanying each synthetic radiograph are corresponding contours
of each capsule shell shape, which enables neural networks to train on the
synthetic data for contour extraction and be applied to the experimental
images. Thus, we train an instance of the convolutional neural network U-Net to
segment the shape of the outer shell capsule using the synthetic dataset, and
we apply this instance of U-Net to a set of radiographs taken at the National
Ignition Facility. We show that the network extracted the outer shell shape of
a small number of capsules as an initial demonstration of deep learning for
automatic contour extraction of ICF images. Future work may include extracting
outer shells from all of the dataset, applying different kinds of neural
networks, and extraction of inner shell contours as well.

Text-based games present a unique class of sequential decision making problem
in which agents interact with a partially observable, simulated environment via
actions and observations conveyed through natural language. Such observations
typically include instructions that, in a reinforcement learning (RL) setting,
can directly or indirectly guide a player towards completing reward-worthy
tasks. In this work, we study the ability of RL agents to follow such
instructions. We conduct experiments that show that the performance of
state-of-the-art text-based game agents is largely unaffected by the presence
or absence of such instructions, and that these agents are typically unable to
execute tasks to completion. To further study and address the task of
instruction following, we equip RL agents with an internal structured
representation of natural language instructions in the form of Linear Temporal
Logic (LTL), a formal language that is increasingly used for temporally
extended reward specification in RL. Our framework both supports and highlights
the benefit of understanding the temporal semantics of instructions and in
measuring progress towards achievement of such a temporally extended behaviour.
Experiments with 500+ games in TextWorld demonstrate the superior performance
of our approach.

In this paper, we address the stochastic contextual linear bandit problem,
where a decision maker is provided a context (a random set of actions drawn
from a distribution). The expected reward of each action is specified by the
inner product of the action and an unknown parameter. The goal is to design an
algorithm that learns to play as close as possible to the unknown optimal
policy after a number of action plays. This problem is considered more
challenging than the linear bandit problem, which can be viewed as a contextual
bandit problem with a \emph{fixed} context. Surprisingly, in this paper, we
show that the stochastic contextual problem can be solved as if it is a linear
bandit problem. In particular, we establish a novel reduction framework that
converts every stochastic contextual linear bandit instance to a linear bandit
instance, when the context distribution is known. When the context distribution
is unknown, we establish an algorithm that reduces the stochastic contextual
instance to a sequence of linear bandit instances with small misspecifications
and achieves nearly the same worst-case regret bound as the algorithm that
solves the misspecified linear bandit instances.
  As a consequence, our results imply a $O(d\sqrt{T\log T})$ high-probability
regret bound for contextual linear bandits, making progress in resolving an
open problem in (Li et al., 2019), (Li et al., 2021).
  Our reduction framework opens up a new way to approach stochastic contextual
linear bandit problems, and enables improved regret bounds in a number of
instances including the batch setting, contextual bandits with
misspecifications, contextual bandits with sparse unknown parameters, and
contextual bandits with adversarial corruption.

Artificial neural networks are functions depending on a finite number of
parameters typically encoded as weights and biases. The identification of the
parameters of the network from finite samples of input-output pairs is often
referred to as the \emph{teacher-student model}, and this model has represented
a popular framework for understanding training and generalization. Even if the
problem is NP-complete in the worst case, a rapidly growing literature -- after
adding suitable distributional assumptions -- has established finite sample
identification of two-layer networks with a number of neurons $m=\mathcal
O(D)$, $D$ being the input dimension. For the range $D<m<D^2$ the problem
becomes harder, and truly little is known for networks parametrized by biases
as well. This paper fills the gap by providing constructive methods and
theoretical guarantees of finite sample identification for such wider shallow
networks with biases. Our approach is based on a two-step pipeline: first, we
recover the direction of the weights, by exploiting second order information;
next, we identify the signs by suitable algebraic evaluations, and we recover
the biases by empirical risk minimization via gradient descent. Numerical
results demonstrate the effectiveness of our approach.

In this study, to address the current high earlydetection miss rate of
colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer
learning and machine learning (ML) classifiers to precisely and sensitively
classify the type of CRC polyps. Instead of using the common colonoscopic
images, we applied three different ML algorithms on the 3D textural image
outputs of a unique vision-based surface tactile sensor (VS-TS). To collect
realistic textural images of CRC polyps for training the utilized ML
classifiers and evaluating their performance, we first designed and additively
manufactured 48 types of realistic polyp phantoms with different hardness,
type, and textures. Next, the performance of the used three ML algorithms in
classifying the type of fabricated polyps was quantitatively evaluated using
various statistical metrics.

New technologies and the availability of geospatial data have drawn attention
to spatio-temporal biases present in society. For example: the COVID-19
pandemic highlighted disparities in the availability of broadband service and
its role in the digital divide; the environmental justice movement in the
United States has raised awareness to health implications for minority
populations stemming from historical redlining practices; and studies have
found varying quality and coverage in the collection and sharing of open-source
geospatial data. Despite the extensive literature on machine learning (ML)
fairness, few algorithmic strategies have been proposed to mitigate such
biases. In this paper we highlight the unique challenges for quantifying and
addressing spatio-temporal biases, through the lens of use cases presented in
the scientific literature and media. We envision a roadmap of ML strategies
that need to be developed or adapted to quantify and overcome these challenges
-- including transfer learning, active learning, and reinforcement learning
techniques. Further, we discuss the potential role of ML in providing guidance
to policy makers on issues related to spatial fairness.

Clinical semantic parsing (SP) is an important step toward identifying the
exact information need (as a machine-understandable logical form) from a
natural language query aimed at retrieving information from electronic health
records (EHRs). Current approaches to clinical SP are largely based on
traditional machine learning and require hand-building a lexicon. The recent
advancements in neural SP show a promise for building a robust and flexible
semantic parser without much human effort. Thus, in this paper, we aim to
systematically assess the performance of two such neural SP models for EHR
question answering (QA). We found that the performance of these advanced neural
models on two clinical SP datasets is promising given their ease of application
and generalizability. Our error analysis surfaces the common types of errors
made by these models and has the potential to inform future research into
improving the performance of neural SP models for EHR QA.

Predictive simulations of the shock-to-detonation transition (SDT) in
heterogeneous energetic materials (EM) are vital to the design and control of
their energy release and sensitivity. Due to the complexity of the
thermo-mechanics of EM during the SDT, both macro-scale response and sub-grid
mesoscale energy localization must be captured accurately. This work proposes
an efficient and accurate multiscale framework for SDT simulations of EM. We
employ deep learning to model the mesoscale energy localization of
shock-initiated EM microstructures upon which prediction results are used to
supply reaction progress rate information to the macroscale SDT simulation. The
proposed multiscale modeling framework is divided into two stages. First, a
physics-aware recurrent convolutional neural network (PARC) is used to model
the mesoscale energy localization of shock-initiated heterogeneous EM
microstructures. PARC is trained using direct numerical simulations (DNS) of
hotspot ignition and growth within microstructures of pressed HMX material
subjected to different input shock strengths. After training, PARC is employed
to supply hotspot ignition and growth rates for macroscale SDT simulations. We
show that PARC can play the role of a surrogate model in a multiscale
simulation framework, while drastically reducing the computation cost and
providing improved representations of the sub-grid physics. The proposed
multiscale modeling approach will provide a new tool for material scientists in
designing high-performance and safer energetic materials.

Autonomous driving requires efficient reasoning about the location and
appearance of the different agents in the scene, which aids in downstream tasks
such as object detection, object tracking, and path planning. The past few
years have witnessed a surge in approaches that combine the different taskbased
modules of the classic self-driving stack into an End-toEnd(E2E) trainable
learning system. These approaches replace perception, prediction, and sensor
fusion modules with a single contiguous module with shared latent space
embedding, from which one extracts a human-interpretable representation of the
scene. One of the most popular representations is the Birds-eye View (BEV),
which expresses the location of different traffic participants in the ego
vehicle frame from a top-down view. However, a BEV does not capture the
chromatic appearance information of the participants. To overcome this
limitation, we propose a novel representation that captures various traffic
participants appearance and occupancy information from an array of monocular
cameras covering 360 deg field of view (FOV). We use a learned image embedding
of all camera images to generate a BEV of the scene at any instant that
captures both appearance and occupancy of the scene, which can aid in
downstream tasks such as object tracking and executing language-based commands.
We test the efficacy of our approach on synthetic dataset generated from CARLA.
The code, data set, and results can be found at https://rebrand.ly/APP
OCC-results.

In this paper, we present methods for two types of metacognitive tasks in an
AI system: rapidly expanding a neural classification model to accommodate a new
category of object, and recognizing when a novel object type is observed
instead of misclassifying the observation as a known class. Our methods take
numerical data drawn from an embodied simulation environment, which describes
the motion and properties of objects when interacted with, and we demonstrate
that this type of representation is important for the success of novel type
detection. We present a suite of experiments in rapidly accommodating the
introduction of new categories and concepts and in novel type detection, and an
architecture to integrate the two in an interactive system.

OutlierDetection.jl is an open-source ecosystem for outlier detection in
Julia. It provides a range of high-performance outlier detection algorithms
implemented directly in Julia. In contrast to previous packages, our ecosystem
enables the development highly-scalable outlier detection algorithms using a
high-level programming language. Additionally, it provides a standardized, yet
flexible, interface for future outlier detection algorithms and allows for
model composition unseen in previous packages. Best practices such as unit
testing, continuous integration, and code coverage reporting are enforced
across the ecosystem. The most recent version of OutlierDetection.jl is
available at https://github.com/OutlierDetectionJL/OutlierDetection.jl.

Studying animal movements is essential for effective wildlife conservation
and conflict mitigation. For aerial movements, operational weather radars have
become an indispensable data source in this respect. However, partial
measurements, incomplete spatial coverage, and poor understanding of animal
behaviours make it difficult to reconstruct complete spatio-temporal movement
patterns from available radar data. We tackle this inverse problem by learning
a mapping from high-dimensional radar measurements to low-dimensional latent
representations using a convolutional encoder. Under the assumption that the
latent system dynamics are well approximated by a locally linear Gaussian
transition model, we perform efficient posterior estimation using the classical
Kalman smoother. A convolutional decoder maps the inferred latent system states
back to the physical space in which the known radar observation model can be
applied, enabling fully unsupervised training. To encourage physical
consistency, we additionally introduce a physics-informed loss term that
leverages known mass conservation constraints. Our experiments on synthetic
radar data show promising results in terms of reconstruction quality and
data-efficiency.

We propose a new model-based offline RL framework, called Adversarial Models
for Offline Reinforcement Learning (ARMOR), which can robustly learn policies
to improve upon an arbitrary baseline policy regardless of data coverage. Based
on the concept of relative pessimism, ARMOR is designed to optimize for the
worst-case relative performance when facing uncertainty. In theory, we prove
that the learned policy of ARMOR never degrades the performance of the baseline
policy with any admissible hyperparameter, and can learn to compete with the
best policy within data coverage when the hyperparameter is well tuned, and the
baseline policy is supported by the data. Such a robust policy improvement
property makes ARMOR especially suitable for building real-world learning
systems, because in practice ensuring no performance degradation is imperative
before considering any benefit learning can bring.

Recent video+language datasets cover domains where the interaction is highly
structured, such as instructional videos, or where the interaction is scripted,
such as TV shows. Both of these properties can lead to spurious cues to be
exploited by models rather than learning to ground language. In this paper, we
present GrOunded footbAlL commentaries (GOAL), a novel dataset of football (or
`soccer') highlights videos with transcribed live commentaries in English. As
the course of a game is unpredictable, so are commentaries, which makes them a
unique resource to investigate dynamic language grounding. We also provide
state-of-the-art baselines for the following tasks: frame reordering, moment
retrieval, live commentary retrieval and play-by-play live commentary
generation. Results show that SOTA models perform reasonably well in most
tasks. We discuss the implications of these results and suggest new tasks for
which GOAL can be used. Our codebase is available at:
https://gitlab.com/grounded-sport-convai/goal-baselines.

Wildfires are a common problem in many areas of the world with often
catastrophic consequences. A number of systems have been created to provide
early warnings of wildfires, including those that use satellite data to detect
fires. The increased availability of small satellites, such as CubeSats, allows
the wildfire detection response time to be reduced by deploying constellations
of multiple satellites over regions of interest. By using machine learned
components on-board the satellites, constraints which limit the amount of data
that can be processed and sent back to ground stations can be overcome. There
are hazards associated with wildfire alert systems, such as failing to detect
the presence of a wildfire, or detecting a wildfire in the incorrect location.
It is therefore necessary to be able to create a safety assurance case for the
wildfire alert ML component that demonstrates it is sufficiently safe for use.
This paper describes in detail how a safety assurance case for an ML wildfire
alert system is created. This represents the first fully developed safety case
for an ML component containing explicit argument and evidence as to the safety
of the machine learning.

Visual Inertial Odometry (VIO) is one of the most established state
estimation methods for mobile platforms. However, when visual tracking fails,
VIO algorithms quickly diverge due to rapid error accumulation during inertial
data integration. This error is typically modeled as a combination of additive
Gaussian noise and a slowly changing bias which evolves as a random walk. In
this work, we propose to train a neural network to learn the true bias
evolution. We implement and compare two common sequential deep learning
architectures: LSTMs and Transformers. Our approach follows from recent
learning-based inertial estimators, but, instead of learning a motion model, we
target IMU bias explicitly, which allows us to generalize to locomotion
patterns unseen in training. We show that our proposed method improves state
estimation in visually challenging situations across a wide range of motions by
quadrupedal robots, walking humans, and drones. Our experiments show an average
15% reduction in drift rate, with much larger reductions when there is total
vision failure. Importantly, we also demonstrate that models trained with one
locomotion pattern (human walking) can be applied to another (quadruped robot
trotting) without retraining.

Health sensing for chronic disease management creates immense benefits for
social welfare. Existing health sensing studies primarily focus on the
prediction of physical chronic diseases. Depression, a widespread complication
of chronic diseases, is however understudied. We draw on the medical literature
to support depression prediction using motion sensor data. To connect human
expertise in the decision-making, safeguard trust for this high-stake
prediction, and ensure algorithm transparency, we develop an interpretable deep
learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon
the emergent prototype learning models. To accommodate the temporal
characteristic of sensor data and the progressive property of depression,
TempPNet differs from existing prototype learning models in its capability of
capturing the temporal progression of depression. Extensive empirical analyses
using real-world motion sensor data show that TempPNet outperforms
state-of-the-art benchmarks in depression prediction. Moreover, TempPNet
interprets its predictions by visualizing the temporal progression of
depression and its corresponding symptoms detected from sensor data. We further
conduct a user study to demonstrate its superiority over the benchmarks in
interpretability. This study offers an algorithmic solution for impactful
social good - collaborative care of chronic diseases and depression in health
sensing. Methodologically, it contributes to extant literature with a novel
interpretable deep learning model for depression prediction from sensor data.
Patients, doctors, and caregivers can deploy our model on mobile devices to
monitor patients' depression risks in real-time. Our model's interpretability
also allows human experts to participate in the decision-making by reviewing
the interpretation of prediction outcomes and making informed interventions.

The emergence of variational quantum applications has led to the development
of automatic differentiation techniques in quantum computing. Recently, Zhu et
al. (PLDI 2020) have formulated differentiable quantum programming with bounded
loops, providing a framework for scalable gradient calculation by quantum means
for training quantum variational applications. However, promising parameterized
quantum applications, e.g., quantum walk and unitary implementation, cannot be
trained in the existing framework due to the natural involvement of unbounded
loops. To fill in the gap, we provide the first differentiable quantum
programming framework with unbounded loops, including a newly designed
differentiation rule, code transformation, and their correctness proof.
Technically, we introduce a randomized estimator for derivatives to deal with
the infinite sum in the differentiation of unbounded loops, whose applicability
in classical and probabilistic programming is also discussed. We implement our
framework with Python and Q#, and demonstrate a reasonable sample efficiency.
Through extensive case studies, we showcase an exciting application of our
framework in automatically identifying close-to-optimal parameters for several
parameterized quantum applications.

We consider the problem of query-efficient global max-cut on a weighted
undirected graph in the value oracle model examined by [RSW18]. This model
arises as a natural special case of submodular function maximization: on query
$S \subseteq V$, the oracle returns the total weight of the cut between $S$ and
$V \backslash S$.
  For most constants $c \in (0,1]$, we nail down the query complexity of
achieving a $c$-approximation, for both deterministic and randomized algorithms
(up to logarithmic factors). Analogously to general submodular function
maximization in the same model, we observe a phase transition at $c = 1/2$: we
design a deterministic algorithm for global $c$-approximate max-cut in $O(\log
n)$ queries for any $c < 1/2$, and show that any randomized algorithm requires
$\tilde{\Omega}(n)$ queries to find a $c$-approximate max-cut for any $c >
1/2$. Additionally, we show that any deterministic algorithm requires
$\Omega(n^2)$ queries to find an exact max-cut (enough to learn the entire
graph), and develop a $\tilde{O}(n)$-query randomized $c$-approximation for any
$c < 1$.
  Our approach provides two technical contributions that may be of independent
interest. One is a query-efficient sparsifier for undirected weighted graphs
(prior work of [RSW18] holds only for unweighted graphs). Another is an
extension of the cut dimension to rule out approximation (prior work of
[GPRW20] introducing the cut dimension only rules out exact solutions).

Radio-frequency measurements could satisfy DiVincenzo's readout criterion in
future large-scale solid-state quantum processors, as they allow for high
bandwidths and frequency multiplexing. However, the scalability potential of
this readout technique can only be leveraged if quantum device tuning is
performed using exclusively radio-frequency measurements i.e. without resorting
to current measurements. We demonstrate an algorithm that automatically tunes
double quantum dots using only radio-frequency reflectometry. Exploiting the
high bandwidth of radio-frequency measurements, the tuning was completed within
a few minutes without prior knowledge about the device architecture. Our
results show that it is possible to eliminate the need for transport
measurements for quantum dot tuning, paving the way for more scalable device
architectures.

Deep generative models including generative adversarial networks (GANs) are
powerful unsupervised tools in learning the distributions of data sets.
Building a simple GAN architecture in PyTorch and training on the CANDELS data
set, we generate galaxy images with the Hubble Space Telescope resolution
starting from a noise vector. We proceed by modifying the GAN architecture to
improve the Subaru Hyper Suprime-Cam ground-based images by increasing their
resolution to the HST resolution. We use the super resolution GAN on a large
sample of blended galaxies which we create using CANDELS cutouts. In our
simulated blend sample, $\sim 20 \%$ would unrecognizably be blended even in
the HST resolution cutouts. In the HSC-like cutouts this fraction rises to
$\sim 90\%$. With our modified GAN we can lower this value to $\sim 50\%$. We
quantify the blending fraction in the high, low and GAN resolutions over the
whole manifold of angular separation, flux ratios, sizes and redshift
difference between the two blended objects. The two peaks found by the GAN
deblender result in ten times improvement in the photometry measurement of the
blended objects. Modifying the architecture of the GAN, we also train a
Multi-wavelength GAN with seven band optical+NIR HST cutouts. This
multi-wavelength GAN improves the fraction of detected blends by another $\sim
10\%$ compared to the single-band GAN. This is most beneficial to the current
and future precision cosmology experiments (e.g., LSST, SPHEREx, Euclid,
Roman), specifically those relying on weak gravitational lensing, where
blending is a major source of systematic error.

With a handful of demonstration examples, large-scale language models show
strong capability to perform various tasks by in-context learning from these
examples, without any fine-tuning. We demonstrate that in-context learning
performance can be highly unstable across samples of examples, indicating the
idiosyncrasies of how language models acquire information. We formulate example
selection for in-context learning as a sequential decision problem, and propose
a reinforcement learning algorithm for identifying generalizable policies to
select demonstration examples. For GPT-2, our learned policies demonstrate
strong abilities of generalizing to unseen tasks in training, with a $5.8\%$
improvement on average. Examples selected from our learned policies can even
achieve a small improvement on GPT-3 Ada. However, the improvement diminishes
on larger GPT-3 models, suggesting emerging capabilities of large language
models.

The Vera Rubin Observatory, set to begin observations in mid-2024, will
increase our discovery rate of supernovae to well over one million annually.
There has been a significant push to develop new methodologies to identify,
classify and ultimately understand the millions of supernovae discovered with
the Rubin Observatory. Here, we present the first simulation-based inference
method using normalizing flows, trained to rapidly infer the parameters of toy
supernovae model in multivariate, Rubin-like datastreams. We find that our
method is well-calibrated compared to traditional inference methodologies
(specifically MCMC), requiring only one-ten-thousandth of the CPU hours during
test time.

Current natural language processing (NLP) models such as BERT and RoBERTa
have achieved high overall performance, but they often make systematic errors
due to bias or certain difficult features to learn. Thus research on slice
detection models (SDM) which automatically identifies underperforming groups of
datapoints has gradually caught more attention, which aims at both
understanding model behaviors and providing insights for future model training
and designing. However, there is little systematic research on SDM and
quantitative evaluation of its assessment for NLP models. Our paper fills this
gap by proposing "Discover, Explanation, Improvement" framework that discovers
coherent and underperforming groups of datapoints and unites datapoints of each
slice under human-understandable concepts; it also provides comprehensive
evaluation tasks and the corresponding quantitative metrics, which enable
convenient comparison for future works. Results show that our framework can
accurately select error-prone datapoints with informative semantic features
that summarize error patterns, based on which it directly boosts model
performance by an average of 2.85 points based on trained models without tuning
any parameters across multiple datasets.

Persistent homology, a powerful mathematical tool for data analysis,
summarizes the shape of data through tracking topological features across
changes in different scales. Classical algorithms for persistent homology are
often constrained by running times and memory requirements that grow
exponentially on the number of data points. To surpass this problem, two
quantum algorithms of persistent homology have been developed based on two
different approaches. However, both of these quantum algorithms consider a data
set in the form of a point cloud, which can be restrictive considering that
many data sets come in the form of time series. In this paper, we alleviate
this issue by establishing a quantum Takens's delay embedding algorithm, which
turns a time series into a point cloud by considering a pertinent embedding
into a higher dimensional space. Having this quantum transformation of time
series to point clouds, then one may use a quantum persistent homology
algorithm to extract the topological features from the point cloud associated
with the original times series.

Default implementations of Bayesian Additive Regression Trees (BART)
represent categorical predictors using several binary indicators, one for each
level of each categorical predictor. Regression trees built with these
indicators partition the levels using a ``remove one a time strategy.''
Unfortunately, the vast majority of partitions of the levels cannot be built
with this strategy, severely limiting BART's ability to ``borrow strength''
across groups of levels. We overcome this limitation with a new class of
regression tree and a new decision rule prior that can assign multiple levels
to both the left and right child of a decision node. Motivated by spatial
applications with areal data, we introduce a further decision rule prior that
partitions the areas into spatially contiguous regions by deleting edges from
random spanning trees of a suitably defined network. We implemented our new
regression tree priors in the flexBART package, which, compared to existing
implementations, often yields improved out-of-sample predictive performance
without much additional computational burden. We demonstrate the efficacy of
flexBART using examples from baseball and the spatiotemporal modeling of crime.

We present SLATE, a sequence labeling approach for extracting tasks from
free-form content such as digitally handwritten (or "inked") notes on a virtual
whiteboard. Our approach allows us to create a single, low-latency model to
simultaneously perform sentence segmentation and classification of these
sentences into task/non-task sentences. SLATE greatly outperforms a baseline
two-model (sentence segmentation followed by classification model) approach,
achieving a task F1 score of 84.4\%, a sentence segmentation (boundary
similarity) score of 88.4% and three times lower latency compared to the
baseline. Furthermore, we provide insights into tackling challenges of
performing NLP on the inking domain. We release both our code and dataset for
this novel task.

Quantum kernel methods, i.e., kernel methods with quantum kernels, offer
distinct advantages as a hybrid quantum-classical approach to quantum machine
learning (QML), including applicability to Noisy Intermediate-Scale Quantum
(NISQ) devices and usage for solving all types of machine learning problems.
Kernel methods rely on the notion of similarity between points in a higher
(possibly infinite) dimensional feature space. For machine learning, the notion
of similarity assumes that points close in the feature space should be close in
the machine learning task space. In this paper, we discuss the use of
variational quantum kernels with task-specific quantum metric learning to
generate optimal quantum embeddings (a.k.a. quantum feature encodings) that are
specific to machine learning tasks. Such task-specific optimal quantum
embeddings, implicitly supporting feature selection, are valuable not only to
quantum kernel methods in improving the latter's performance, but they can also
be valuable to non-kernel QML methods based on parameterized quantum circuits
(PQCs) as pretrained embeddings and for transfer learning. This further
demonstrates the quantum utility, and quantum advantage (with
classically-intractable quantum embeddings), of quantum kernel methods.

Although prediction models for delirium, a commonly occurring condition
during general hospitalization or post-surgery, have not gained huge
popularity, their algorithmic bias evaluation is crucial due to the existing
association between social determinants of health and delirium risk. In this
context, using MIMIC-III and another academic hospital dataset, we present some
initial experimental evidence showing how sociodemographic features such as sex
and race can impact the model performance across subgroups. With this work, our
intent is to initiate a discussion about the intersectionality effects of old
age, race and socioeconomic factors on the early-stage detection and prevention
of delirium using ML.

Achieving knowledge sharing within an artificial swarm system could lead to
significant development in autonomous multiagent and robotic systems research
and realize collective intelligence. However, this is difficult to achieve
since there is no generic framework to transfer skills between agents other
than a query-response-based approach. Moreover, natural living systems have a
"forgetfulness" property for everything they learn. Analyzing such ephemeral
nature (temporal memory properties of new knowledge gained) in artificial
systems has never been studied in the literature. We propose a behavior
tree-based framework to realize a query-response mechanism for transferring
skills encoded as the condition-action control sub-flow of that portion of the
knowledge between agents to fill this gap. We simulate a multiagent group with
different initial knowledge on a foraging mission. While performing basic
operations, each robot queries other robots to respond to an unknown condition.
The responding robot shares the control actions by sharing a portion of the
behavior tree that addresses the queries. Specifically, we investigate the
ephemeral nature of the new knowledge gained through such a framework, where
the knowledge gained by the agent is either limited due to memory or is
forgotten over time. Our investigations show that knowledge grows
proportionally with the duration of remembrance, which is trivial. However, we
found minimal impact on knowledge growth due to memory. We compare these cases
against a baseline that involved full knowledge pre-coded on all agents. We
found that knowledge-sharing strived to match the baseline condition by sharing
and achieving knowledge growth as a collective system.

Coreference resolution (CR) is one of the most challenging areas of natural
language processing. This task seeks to identify all textual references to the
same real-world entity. Research in this field is divided into coreference
resolution and anaphora resolution. Due to its application in textual
comprehension and its utility in other tasks such as information extraction
systems, document summarization, and machine translation, this field has
attracted considerable interest. Consequently, it has a significant effect on
the quality of these systems. This article reviews the existing corpora and
evaluation metrics in this field. Then, an overview of the coreference
algorithms, from rule-based methods to the latest deep learning techniques, is
provided. Finally, coreference resolution and pronoun resolution systems in
Persian are investigated.

Word order, an essential property of natural languages, is injected in
Transformer-based neural language models using position encoding. However,
recent experiments have shown that explicit position encoding is not always
useful, since some models without such feature managed to achieve state-of-the
art performance on some tasks. To understand better this phenomenon, we examine
the effect of removing position encodings on the pre-training objective itself
(i.e., masked language modelling), to test whether models can reconstruct
position information from co-occurrences alone. We do so by controlling the
amount of masked tokens in the input sentence, as a proxy to affect the
importance of position information for the task. We find that the necessity of
position information increases with the amount of masking, and that masked
language models without position encodings are not able to reconstruct this
information on the task. These findings point towards a direct relationship
between the amount of masking and the ability of Transformers to capture
order-sensitive aspects of language using position encoding.

A matrix free and a low rank approximation preconditioner are proposed to
accelerate the convergence of stochastic gradient descent (SGD) by exploiting
curvature information sampled from Hessian-vector products or finite
differences of parameters and gradients similar to the BFGS algorithm. Both
preconditioners are fitted with an online updating manner minimizing a
criterion that is free of line search and robust to stochastic gradient noise,
and further constrained to be on certain connected Lie groups to preserve their
corresponding symmetry or invariance, e.g., orientation of coordinates by the
connected general linear group with positive determinants. The Lie group's
equivariance property facilitates preconditioner fitting, and its invariance
property saves any need of damping, which is common in second-order optimizers,
but difficult to tune. The learning rate for parameter updating and step size
for preconditioner fitting are naturally normalized, and their default values
work well in most situations.

With the rising need of interpretable machine learning methods, there is a
necessity for a rise in human effort to provide diverse explanations of the
influencing factors of the model decisions. To improve the trust and
transparency of AI-based systems, the EXplainable Artificial Intelligence (XAI)
field has emerged. The XAI paradigm is bifurcated into two main categories:
feature attribution and counterfactual explanation methods. While feature
attribution methods are based on explaining the reason behind a model decision,
counterfactual explanation methods discover the smallest input changes that
will result in a different decision. In this paper, we aim at building trust
and transparency in time series models by using motifs to generate
counterfactual explanations. We propose Motif-Guided Counterfactual Explanation
(MG-CF), a novel model that generates intuitive post-hoc counterfactual
explanations that make full use of important motifs to provide interpretive
information in decision-making processes. To the best of our knowledge, this is
the first effort that leverages motifs to guide the counterfactual explanation
generation. We validated our model using five real-world time-series datasets
from the UCR repository. Our experimental results show the superiority of MG-CF
in balancing all the desirable counterfactual explanations properties in
comparison with other competing state-of-the-art baselines.

While $\ell_2$ regularization is widely used in training gradient boosted
trees, popular individualized feature attribution methods for trees such as
Saabas and TreeSHAP overlook the training procedure. We propose Prediction
Decomposition Attribution (PreDecomp), a novel individualized feature
attribution for gradient boosted trees when they are trained with $\ell_2$
regularization. Theoretical analysis shows that the inner product between
PreDecomp and labels on in-sample data is essentially the total gain of a tree,
and that it can faithfully recover additive models in the population case when
features are independent. Inspired by the connection between PreDecomp and
total gain, we also propose TreeInner, a family of debiased global feature
attributions defined in terms of the inner product between any individualized
feature attribution and labels on out-sample data for each tree. Numerical
experiments on a simulated dataset and a genomic ChIP dataset show that
TreeInner has state-of-the-art feature selection performance. Code reproducing
experiments is available at https://github.com/nalzok/TreeInner .

While neural networks-based photo processing solutions can provide a better
image quality compared to the traditional ISP systems, their application to
mobile devices is still very limited due to their very high computational
complexity. In this paper, we present a novel MicroISP model designed
specifically for edge devices, taking into account their computational and
memory limitations. The proposed solution is capable of processing up to 32MP
photos on recent smartphones using the standard mobile ML libraries and
requiring less than 1 second to perform the inference, while for FullHD images
it achieves real-time performance. The architecture of the model is flexible,
allowing to adjust its complexity to devices of different computational power.
To evaluate the performance of the model, we collected a novel Fujifilm
UltraISP dataset consisting of thousands of paired photos captured with a
normal mobile camera sensor and a professional 102MP medium-format FujiFilm
GFX100 camera. The experiments demonstrated that, despite its compact size, the
MicroISP model is able to provide comparable or better visual results than the
traditional mobile ISP systems, while outperforming the previously proposed
efficient deep learning based solutions. Finally, this model is also compatible
with the latest mobile AI accelerators, achieving good runtime and low power
consumption on smartphone NPUs and APUs. The code, dataset and pre-trained
models are available on the project website:
https://people.ee.ethz.ch/~ihnatova/microisp.html

We seek to impose linear, equality constraints in feedforward neural
networks. As top layer predictors are usually nonlinear, this is a difficult
task if we seek to deploy standard convex optimization methods and strong
duality. To overcome this, we introduce a new saddle-point Lagrangian with
auxiliary predictor variables on which constraints are imposed. Elimination of
the auxiliary variables leads to a dual minimization problem on the Lagrange
multipliers introduced to satisfy the linear constraints. This minimization
problem is combined with the standard learning problem on the weight matrices.
From this theoretical line of development, we obtain the surprising
interpretation of Lagrange parameters as additional, penultimate layer hidden
units with fixed weights stemming from the constraints. Consequently, standard
minimization approaches can be used despite the inclusion of Lagrange
parameters -- a very satisfying, albeit unexpected, discovery. Examples ranging
from multi-label classification to constrained autoencoders are envisaged in
the future.

Improving model's generalizability against domain shifts is crucial,
especially for safety-critical applications such as autonomous driving.
Real-world domain styles can vary substantially due to environment changes and
sensor noises, but deep models only know the training domain style. Such domain
style gap impedes model generalization on diverse real-world domains. Our
proposed Normalization Perturbation (NP) can effectively overcome this domain
style overfitting problem. We observe that this problem is mainly caused by the
biased distribution of low-level features learned in shallow CNN layers. Thus,
we propose to perturb the channel statistics of source domain features to
synthesize various latent styles, so that the trained deep model can perceive
diverse potential domains and generalizes well even without observations of
target domain data in training. We further explore the style-sensitive channels
for effective style synthesis. Normalization Perturbation only relies on a
single source domain and is surprisingly effective and extremely easy to
implement. Extensive experiments verify the effectiveness of our method for
generalizing models under real-world domain shifts.

The geospace environment is volatile and highly driven. Space weather has
effects on Earth's magnetosphere that cause a dynamic and enigmatic response in
the thermosphere, particularly on the evolution of neutral mass density. Many
models exist that use space weather drivers to produce a density response, but
these models are typically computationally expensive or inaccurate for certain
space weather conditions. In response, this work aims to employ a probabilistic
machine learning (ML) method to create an efficient surrogate for the
Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM), a
physics-based thermosphere model. Our method leverages principal component
analysis to reduce the dimensionality of TIE-GCM and recurrent neural networks
to model the dynamic behavior of the thermosphere much quicker than the
numerical model. The newly developed reduced order probabilistic emulator
(ROPE) uses Long-Short Term Memory neural networks to perform time-series
forecasting in the reduced state and provide distributions for future density.
We show that across the available data, TIE-GCM ROPE has similar error to
previous linear approaches while improving storm-time modeling. We also conduct
a satellite propagation study for the significant November 2003 storm which
shows that TIE-GCM ROPE can capture the position resulting from TIE-GCM density
with < 5 km bias. Simultaneously, linear approaches provide point estimates
that can result in biases of 7 - 18 km.

The increased importance of mobile photography created a need for fast and
performant RAW image processing pipelines capable of producing good visual
results in spite of the mobile camera sensor limitations. While deep
learning-based approaches can efficiently solve this problem, their
computational requirements usually remain too large for high-resolution
on-device image processing. To address this limitation, we propose a novel
PyNET-V2 Mobile CNN architecture designed specifically for edge devices, being
able to process RAW 12MP photos directly on mobile phones under 1.5 second and
producing high perceptual photo quality. To train and to evaluate the
performance of the proposed solution, we use the real-world Fujifilm UltraISP
dataset consisting on thousands of RAW-RGB image pairs captured with a
professional medium-format 102MP Fujifilm camera and a popular Sony mobile
camera sensor. The results demonstrate that the PyNET-V2 Mobile model can
substantially surpass the quality of tradition ISP pipelines, while
outperforming the previously introduced neural network-based solutions designed
for fast image processing. Furthermore, we show that the proposed architecture
is also compatible with the latest mobile AI accelerators such as NPUs or APUs
that can be used to further reduce the latency of the model to as little as 0.5
second. The dataset, code and pre-trained models used in this paper are
available on the project website: https://github.com/gmalivenko/PyNET-v2

Identifying phases and analyzing the stability of dynamic states are
ubiquitous and important problems which appear in various physical systems.
Nonetheless, drawing a phase diagram in high-dimensional and large parameter
spaces has remained challenging. Here, we propose a data-driven method to
derive the phase diagram of lasing modes in topological insulator lasers. The
classification is based on the temporal behaviour of the topological modes
obtained via numerical integration of the rate equation. A semi-supervised
learning method is used and an adaptive library is constructed in order to
distinguish the different topological modes present in the generated parameter
space. The proposed method successfully distinguishes the different topological
phases in the Su-Schrieffer-Heeger (SSH) lattice with saturable gain.This
demonstrates the possibility of classifying the topological phases without
needing for expert knowledge of the system and may give valuable insight into
the fundamental physics of topological insulator lasers via reverse

Estimating treatment effects from observational data is a central problem in
causal inference. Methods to solve this problem exploit inductive biases and
heuristics from causal inference to design multi-head neural network
architectures and regularizers. In this work, we propose to use neurosymbolic
program synthesis, a data-efficient, and interpretable technique, to solve the
treatment effect estimation problem. We theoretically show that neurosymbolic
programming can solve the treatment effect estimation problem. By designing a
Domain Specific Language (DSL) for treatment effect estimation problem based on
the inductive biases used in literature, we argue that neurosymbolic
programming is a better alternative to treatment effect estimation than
traditional methods. Our empirical study reveals that our method, which
implicitly encodes inductive biases in a DSL, achieves better performance on
benchmark datasets than the state-of-the-art methods.

Linear classifier probes are frequently utilized to better understand how
neural networks function. Researchers have approached the problem of
determining unit importance in neural networks by probing their learned,
internal representations. Linear classifier probes identify highly selective
units as the most important for network function. Whether or not a network
actually relies on high selectivity units can be tested by removing them from
the network using ablation. Surprisingly, when highly selective units are
ablated they only produce small performance deficits, and even then only in
some cases. In spite of the absence of ablation effects for selective neurons,
linear decoding methods can be effectively used to interpret network function,
leaving their effectiveness a mystery. To falsify the exclusive role of
selectivity in network function and resolve this contradiction, we
systematically ablate groups of units in subregions of activation space. Here,
we find a weak relationship between neurons identified by probes and those
identified by ablation. More specifically, we find that an interaction between
selectivity and the average activity of the unit better predicts ablation
performance deficits for groups of units in AlexNet, VGG16, MobileNetV2, and
ResNet101. Linear decoders are likely somewhat effective because they overlap
with those units that are causally important for network function.
Interpretability methods could be improved by focusing on causally important
units.

Physically based rendering of complex scenes can be prohibitively costly with
a potentially unbounded and uneven distribution of complexity across the
rendered image. The goal of an ideal level of detail (LoD) method is to make
rendering costs independent of the 3D scene complexity, while preserving the
appearance of the scene. However, current prefiltering LoD methods are limited
in the appearances they can support due to their reliance of approximate models
and other heuristics. We propose the first comprehensive multi-scale LoD
framework for prefiltering 3D environments with complex geometry and materials
(e.g., the Disney BRDF), while maintaining the appearance with respect to the
ray-traced reference. Using a multi-scale hierarchy of the scene, we perform a
data-driven prefiltering step to obtain an appearance phase function and
directional coverage mask at each scale. At the heart of our approach is a
novel neural representation that encodes this information into a compact latent
form that is easy to decode inside a physically based renderer. Once a scene is
baked out, our method requires no original geometry, materials, or textures at
render time. We demonstrate that our approach compares favorably to
state-of-the-art prefiltering methods and achieves considerable savings in
memory for complex scenes.

Analysis of galaxy--galaxy strong lensing systems is strongly dependent on
any prior assumptions made about the appearance of the source. Here we present
a method of imposing a data-driven prior / regularisation for source galaxies
based on denoising diffusion probabilistic models (DDPMs). We use a pre-trained
model for galaxy images, AstroDDPM, and a chain of conditional reconstruction
steps called denoising diffusion reconstruction model (DDRM) to obtain samples
consistent both with the noisy observation and with the distribution of
training data for AstroDDPM. We show that these samples have the qualitative
properties associated with the posterior for the source model: in a
low-to-medium noise scenario they closely resemble the observation, while
reconstructions from uncertain data show greater variability, consistent with
the distribution encoded in the generative model used as prior.

As a result of the ever increasing complexity of configuring and fine-tuning
machine learning models, the field of automated machine learning (AutoML) has
emerged over the past decade. However, software implementations like Auto-WEKA
and Auto-sklearn typically focus on classical machine learning (ML) tasks such
as classification and regression. Our work can be seen as the first attempt at
offering a single AutoML framework for most problem settings that fall under
the umbrella of multi-target prediction, which includes popular ML settings
such as multi-label classification, multivariate regression, multi-task
learning, dyadic prediction, matrix completion, and zero-shot learning.
Automated problem selection and model configuration are achieved by extending
DeepMTP, a general deep learning framework for MTP problem settings, with
popular hyperparameter optimization (HPO) methods. Our extensive benchmarking
across different datasets and MTP problem settings identifies cases where
specific HPO methods outperform others.

Developmental dysplasia of the hip (DDH) is a condition in infants where the
femoral head is incorrectly located in the hip joint. We propose a deep
learning algorithm for segmenting key structures within ultrasound images,
employing this to calculate Femoral Head Coverage (FHC) and provide a screening
diagnosis for DDH. To our knowledge, this is the first study to automate FHC
calculation for DDH screening. Our algorithm outperforms the international
state of the art, agreeing with expert clinicians on 89.8% of our test images.

The aim of this work is to propose an extension of the Deep BSDE solver by
Han, E, Jentzen (2017) to the case of FBSDEs with jumps. As in the
aforementioned solver, starting from a discretized version of the BSDE and
parametrizing the (high dimensional) control processes by means of a family of
ANNs, the BSDE is viewed as model-based reinforcement learning problem and the
ANN parameters are fitted so as to minimize a prescribed loss function. We take
into account both finite and infinite jump activity by introducing, in the
latter case, an approximation with finitely many jumps of the forward process.

In deep learning, transfer learning (TL) has become the de facto approach
when dealing with image related tasks. Visual features learnt for one task have
been shown to be reusable for other tasks, improving performance significantly.
By reusing deep representations, TL enables the use of deep models in domains
with limited data availability, limited computational resources and/or limited
access to human experts. Domains which include the vast majority of real-life
applications. This paper conducts an experimental evaluation of TL, exploring
its trade-offs with respect to performance, environmental footprint, human
hours and computational requirements. Results highlight the cases were a cheap
feature extraction approach is preferable, and the situations where an
expensive fine-tuning effort may be worth the added cost. Finally, a set of
guidelines on the use of TL are proposed.

Life on Earth depends upon the dissolution of ionic salts in water,
particularly NaCl. However, an atomistic scale understanding of the process
remains elusive. Simulations lend themselves conveniently to studying
dissolution since they provide the spatio-temporal resolution that can be
difficult to obtain experimentally. Nevertheless, the complexity of various
inter- and intra-molecular interactions require careful treatment and long time
scale simulations, both of which are typically hindered by computational
expense. Here, we use advances in machine learning potential methodology to
resolve for the first time at an ab initio level of theory the dissolution
mechanism of NaCl in water. The picture that emerges is that of a steady
ion-wise unwrapping of the crystal preceding its rapid disintegration,
reminiscent of crumbling. The onset of crumbling can be explained by a strong
increase in the ratio of the surface to volume of the crystal. Overall,
dissolution is comprised of a series of highly dynamical microscopic
sub-processes, resulting in an inherently stochastic mechanism. These atomistic
level insights now pave the way for a general understanding of dissolution
mechanisms in other crystals, and the methodology is primed for more complex
systems of recent interest such as water/salt interfaces under flow and salt
crystals under confinement.

Most semantic communication systems leverage deep learning models to provide
end-to-end transmission performance surpassing the established source and
channel coding approaches. While, so far, research has mainly focused on
architecture and model improvements, but such a model trained over a full
dataset and ergodic channel responses is unlikely to be optimal for every test
instance. Due to limitations on the model capacity and imperfect optimization
and generalization, such learned models will be suboptimal especially when the
testing data distribution or channel response is different from that in the
training phase, as is likely to be the case in practice. To tackle this, in
this paper, we propose a novel semantic communication paradigm by leveraging
the deep learning model's overfitting property. Our model can for instance be
updated after deployment, which can further lead to substantial gains in terms
of the transmission rate-distortion (RD) performance. This new system is named
adaptive semantic communication (ASC). In our ASC system, the ingredients of
wireless transmitted stream include both the semantic representations of source
data and the adapted decoder model parameters. Specifically, we take the
overfitting concept to the extreme, proposing a series of ingenious methods to
adapt the semantic codec or representations to an individual data or channel
state instance. The whole ASC system design is formulated as an optimization
problem whose goal is to minimize the loss function that is a tripartite
tradeoff among the data rate, model rate, and distortion terms. The experiments
(including user study) verify the effectiveness and efficiency of our ASC
system. Notably, the substantial gain of our overfitted coding paradigm can
catalyze semantic communication upgrading to a new era.

Few-shot named entity recognition (NER) targets generalizing to unseen labels
and/or domains with few labeled examples. Existing metric learning methods
compute token-level similarities between query and support sets, but are not
able to fully incorporate label semantics into modeling. To address this issue,
we propose a simple method to largely improve metric learning for NER: 1)
multiple prompt schemas are designed to enhance label semantics; 2) we propose
a novel architecture to effectively combine multiple prompt-based
representations. Empirically, our method achieves new state-of-the-art (SOTA)
results under 16 of the 18 considered settings, substantially outperforming the
previous SOTA by an average of 8.84% and a maximum of 34.51% in relative gains
of micro F1. Our code is available at https://github.com/AChen-qaq/ProML.

Super-resolution is a machine-learning technique in image processing which
generates high-resolution images from low-resolution images. Inspired by this
approach, we perform a numerical experiment of quantum machine learning, which
takes low-resolution (low plane-wave energy cutoff) one-particle molecular
wavefunctions in plane-wave basis as input and generates high-resolution (high
plane-wave energy cutoff) wavefunctions in fictitious one-dimensional systems,
and study the performance of different learning models. We show that the
trained models can generate wavefunctions having higher fidelity values with
respect to the ground-truth wavefunctions than a simple linear interpolation,
and the results can be improved both qualitatively and quantitatively by
including data-dependent information in the ansatz. On the other hand, the
accuracy of the current approach deteriorates for wavefunctions calculated in
electronic configurations not included in the training dataset. We also discuss
the generalization of this approach to many-body electron wavefunctions.

Diffusion probabilistic models have been recently used in a variety of tasks,
including speech enhancement and synthesis. As a generative approach, diffusion
models have been shown to be especially suitable for imputation problems, where
missing data is generated based on existing data. Phase retrieval is inherently
an imputation problem, where phase information has to be generated based on the
given magnitude. In this work we build upon previous work in the speech domain,
adapting a speech enhancement diffusion model specifically for STFT phase
retrieval. Evaluation using speech quality and intelligibility metrics shows
the diffusion approach is well-suited to the phase retrieval task, with
performance surpassing both classical and modern methods.

To properly assist humans in their needs, human activity recognition (HAR)
systems need the ability to fuse information from multiple modalities. Our
hypothesis is that multimodal sensors, visual and non-visual tend to provide
complementary information, addressing the limitations of other modalities. In
this work, we propose a multi-modal framework that learns to effectively
combine features from RGB Video and IMU sensors, and show its robustness for
MMAct and UTD-MHAD datasets. Our model is trained in two-stage, where in the
first stage, each input encoder learns to effectively extract features, and in
the second stage, learns to combine these individual features. We show
significant improvements of 22% and 11% compared to video only and IMU only
setup on UTD-MHAD dataset, and 20% and 12% on MMAct datasets. Through extensive
experimentation, we show the robustness of our model on zero shot setting, and
limited annotated data setting. We further compare with state-of-the-art
methods that use more input modalities and show that our method outperforms
significantly on the more difficult MMact dataset, and performs comparably in
UTD-MHAD dataset.

Causal structure learning from observational data remains a non-trivial task
due to various factors such as finite sampling, unobserved confounding factors,
and measurement errors. Constraint-based and score-based methods tend to suffer
from high computational complexity due to the combinatorial nature of
estimating the directed acyclic graph (DAG). Motivated by the `Cause-Effect
Pair' NIPS 2013 Workshop on Causality Challenge, in this paper, we take a
different approach and generate a probability distribution over all possible
graphs informed by the cause-effect pair features proposed in response to the
workshop challenge. The goal of the paper is to propose new methods based on
this probabilistic information and compare their performance with traditional
and state-of-the-art approaches. Our experiments, on both synthetic and real
datasets, show that our proposed methods not only have statistically similar or
better performances than some traditional approaches but also are
computationally faster.

End-to-end learning has become a popular method to optimize a constellation
shape of a communication system. When the channel model is differentiable,
end-to-end learning can be applied with conventional backpropagation algorithm
for optimization of the shape. A variety of optimization algorithms have also
been developed for end-to-end learning over a non-differentiable channel model.
In this paper, we compare gradient-free optimization method based on the
cubature Kalman filter, model-free optimization and backpropagation for
end-to-end learning on a fiber-optic channel modeled by the split-step Fourier
method. The results indicate that the gradient-free optimization algorithms
provide a decent replacement to backpropagation in terms of performance at the
expense of computational complexity. Furthermore, the quantization problem of
finite bit resolution of the digital-to-analog and analog-to-digital converters
is addressed and its impact on geometrically shaped constellations is analysed.
Here, the results show that when optimizing a constellation with respect to
mutual information, a minimum number of quantization levels is required to
achieve shaping gain. For generalized mutual information, the gain is
maintained throughout all of the considered quantization levels. Also, the
results implied that the autoencoder can adapt the constellation size to the
given channel conditions.

During the last decades the industry has seen the number of Earth orbiting
satellites rise, mostly due to the need to monitor Earth as well as to
establish global communication networks. Nano, micro, and small satellites have
been a prime tool for answering these needs, with large and mega constellations
planned, leading to a potential launch gap. An effective and commercially
appealing solution is the development of small launchers, as these can
complement the current available launch opportunity offer, serving a large pool
of different types of clients, with a flexible and custom service that large
conventional launchers cannot adequately assure. Rocket Factory Augsburg has
partnered with CEiiA for the development of several structures for the RFA One
rocket. The objective has been the design of solutions that are low-cost,
light, and custom-made, applying design and manufacturing concepts as well as
technologies from other industries, like the aeronautical and automotive, to
the aerospace one. This allows for the implementation of a New Space approach
to the launcher segment, while also building a supply chain and a set of
solutions that enables the industrialisation of such structures for this and
future small launchers. The two main systems under development have been a
versatile Kick-Stage, for payload carrying and orbit insertion, and a sturdy
Payload Fairing. Even though the use of components off-the-shelf have been
widely accepted in the space industry for satellites, these two systems pose
different challenges as they must be: highly reliable during the most extreme
conditions imposed by the launch, so that they can be considered safe to launch
all types of payloads. This paper thus dives deep on the solutions developed in
the last few years, presenting also lessons learned during the manufacturing
and testing of these structures.

Statistical inference of population parameters of astrophysical sources is
challenging. It requires accounting for selection effects, which stem from the
artificial separation between bright detected and dim undetected sources that
is introduced by the analysis pipeline itself. We show that these effects can
be modeled self-consistently in the context of sequential simulation-based
inference. Our approach couples source detection and catalog-based inference in
a principled framework that derives from the truncated marginal neural ratio
estimation (TMNRE) algorithm. It relies on the realization that detection can
be interpreted as prior truncation. We outline the algorithm, and show first
promising results.

Owing to the widespread adoption of the Internet of Things, a vast amount of
sensor information is being acquired in real time. Accordingly, the
communication cost of data from edge devices is increasing. Compressed sensing
(CS), a data compression method that can be used on edge devices, has been
attracting attention as a method to reduce communication costs. In CS,
estimating the appropriate compression ratio is important. There is a method to
adaptively estimate the compression ratio for the acquired data using
reinforcement learning. However, the computational costs associated with
existing reinforcement learning methods that can be utilized on edges are
expensive. In this study, we developed an efficient reinforcement learning
method for edge devices, referred to as the actor--critic online sequential
extreme learning machine (AC-OSELM), and a system to compress data by
estimating an appropriate compression ratio on the edge using AC-OSELM. The
performance of the proposed method in estimating the compression ratio is
evaluated by comparing it with other reinforcement learning methods for edge
devices. The experimental results show that AC-OSELM achieved the same or
better compression performance and faster compression ratio estimation than the
existing methods.

Pre-trained language models (PLMs) have outperformed other NLP models on a
wide range of tasks. Opting for a more thorough understanding of their
capabilities and inner workings, researchers have established the extend to
which they capture lower-level knowledge like grammaticality, and mid-level
semantic knowledge like factual understanding. However, there is still little
understanding of their knowledge of higher-level aspects of language. In
particular, despite the importance of sociodemographic aspects in shaping our
language, the questions of whether, where, and how PLMs encode these aspects,
e.g., gender or age, is still unexplored. We address this research gap by
probing the sociodemographic knowledge of different single-GPU PLMs on multiple
English data sets via traditional classifier probing and information-theoretic
minimum description length probing. Our results show that PLMs do encode these
sociodemographics, and that this knowledge is sometimes spread across the
layers of some of the tested PLMs. We further conduct a multilingual analysis
and investigate the effect of supplementary training to further explore to what
extent, where, and with what amount of pre-training data the knowledge is
encoded. Our overall results indicate that sociodemographic knowledge is still
a major challenge for NLP. PLMs require large amounts of pre-training data to
acquire the knowledge and models that excel in general language understanding
do not seem to own more knowledge about these aspects.

Solving the analytical inverse kinematics (IK) of redundant manipulators in
real time is a difficult problem in robotics since its solution for a given
target pose is not unique. Moreover, choosing the optimal IK solution with
respect to application-specific demands helps to improve the robustness and to
increase the success rate when driving the manipulator from its current
configuration towards a desired pose. This is necessary, especially in
high-dynamic tasks like catching objects in mid-flights. To compute a suitable
target configuration in the joint space for a given target pose in the
trajectory planning context, various factors such as travel time or
manipulability must be considered. However, these factors increase the
complexity of the overall problem which impedes real-time implementation. In
this paper, a real-time framework to compute the analytical inverse kinematics
of a redundant robot is presented. To this end, the analytical IK of the
redundant manipulator is parameterized by so-called redundancy parameters,
which are combined with a target pose to yield a unique IK solution. Most
existing works in the literature either try to approximate the direct mapping
from the desired pose of the manipulator to the solution of the IK or cluster
the entire workspace to find IK solutions. In contrast, the proposed framework
directly learns these redundancy parameters by using a neural network (NN) that
provides the optimal IK solution with respect to the manipulability and the
closeness to the current robot configuration. Monte Carlo simulations show the
effectiveness of the proposed approach which is accurate and real-time capable
($\approx$ \SI{32}{\micro\second}) on the KUKA LBR iiwa 14 R820.

In a spoofing attack, an attacker impersonates a legitimate user to access or
tamper with data intended for or produced by the legitimate user. In wireless
communication systems, these attacks may be detected by relying on features of
the channel and transmitter radios. In this context, a popular approach is to
exploit the dependence of the received signal strength (RSS) at multiple
receivers or access points with respect to the spatial location of the
transmitter. Existing schemes rely on long-term estimates, which makes it
difficult to distinguish spoofing from movement of a legitimate user. This
limitation is here addressed by means of a deep neural network that implicitly
learns the distribution of pairs of short-term RSS vector estimates. The
adopted network architecture imposes the invariance to permutations of the
input (commutativity) that the decision problem exhibits. The merits of the
proposed algorithm are corroborated on a data set that we collected.

Recommender systems are a long-standing research problem in data mining and
machine learning. They are incremental in nature, as new user-item interaction
logs arrive. In real-world applications, we need to periodically train a
collaborative filtering algorithm to extract user/item embedding vectors and
therefore, a time-series of embedding vectors can be naturally defined. We
present a time-series forecasting-based upgrade kit (TimeKit), which works in
the following way: it i) first decides a base collaborative filtering
algorithm, ii) extracts user/item embedding vectors with the base algorithm
from user-item interaction logs incrementally, e.g., every month, iii) trains
our time-series forecasting model with the extracted time-series of embedding
vectors, and then iv) forecasts the future embedding vectors and recommend with
their dot-product scores owing to a recent breakthrough in processing
complicated time-series data, i.e., neural controlled differential equations
(NCDEs). Our experiments with four real-world benchmark datasets show that the
proposed time-series forecasting-based upgrade kit can significantly enhance
existing popular collaborative filtering algorithms.

We propose a Multimodal Machine Learning method for estimating the
Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long
been the subject of many investigations. Our method includes two main models,
i.e. the feature transformation model by multimodal representation learning,
and the photometric redshift estimation model by multimodal transfer learning.
The prediction accuracy of the photometric redshift was significantly improved
owing to the large amount of information offered by the generated spectral
features learned from photometric data via the MML. A total of 415,930 quasars
from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1
and 5, were screened for our experiments. We used |{\Delta}z| =
|(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and
demonstrated a 4.04% increase in accuracy. With the help of the generated
spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach
84.45% of the total test samples, whereas it reaches 80.41% for single-modal
photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown
to decreases from 0.1332 to 0.1235. Our method has the potential to be
generalized to other astronomical data analyses such as galaxy classification
and redshift prediction. The algorithm code can be found at
https://github.com/HongShuxin/PhotoRedshift-MML .

The existing indoor fingerprinting localization methods are rather accurate
after intensive offline calibration for a specific environment, no matter based
on received signal strength (RSS) or channel state information (CSI), but the
well-calibrated localization model (can be a pure statistical one or a
data-driven one) will present poor generalization ability in the highly
variable environments, which results in big loss in knowledge and human effort.
To break the environment-specific localization bottleneck, we propose a
new-fashioned data-driven fingerprinting method for localization based on
model-agnostic meta-learning (MAML), named by MetaLoc. Specifically, MetaLoc is
char acterized by rapldly adapting itself to a new, possibly unseen environment
with very little calibration. The underlying localization model is taken to be
a deep neural network, and we train an optimal set of environment-specific
meta-parameters by leveraging previous data collected from diverse
well-calibrated indoor environments and the maximum mean discrepancy criterion.
We further modify the loss function of vanilla MAML and propose a novel
framework named as MAML-DG, which is able to achieve faster convergence and
better adaptation abilities by forcing the loss on different training domains
to decrease in similar directions. Experiments from simulation and site survey
confirm that the meta-parameters obtained for MetaLoc achieves very rapid
adaptation to new environments, competitive localization accuracy, and high
resistance to significantly reduced reference points (RPs), saving a lot of
calibration effort.

The increasing number of surveillance cameras and security concerns have made
automatic violent activity detection from surveillance footage an active area
for research. Modern deep learning methods have achieved good accuracy in
violence detection and proved to be successful because of their applicability
in intelligent surveillance systems. However, the models are computationally
expensive and large in size because of their inefficient methods for feature
extraction. This work presents a novel architecture for violence detection
called Two-stream Multi-dimensional Convolutional Network (2s-MDCN), which uses
RGB frames and optical flow to detect violence. Our proposed method extracts
temporal and spatial information independently by 1D, 2D, and 3D convolutions.
Despite combining multi-dimensional convolutional networks, our models are
lightweight and efficient due to reduced channel capacity, yet they learn to
extract meaningful spatial and temporal information. Additionally, combining
RGB frames and optical flow yields 2.2% more accuracy than a single RGB stream.
Regardless of having less complexity, our models obtained state-of-the-art
accuracy of 89.7% on the largest violence detection benchmark dataset.

Graph Neural Networks (GNNs) have achieved great successes in many learning
tasks performed on graph structures. Nonetheless, to propagate information GNNs
rely on a message passing scheme which can become prohibitively expensive when
working with industrial-scale graphs. Inspired by the PPRGo model, we propose
the CorePPR model, a scalable solution that utilises a learnable convex
combination of the approximate personalised PageRank and the CoreRank to
diffuse multi-hop neighbourhood information in GNNs. Additionally, we
incorporate a dynamic mechanism to select the most influential neighbours for a
particular node which reduces training time while preserving the performance of
the model. Overall, we demonstrate that CorePPR outperforms PPRGo, particularly
on large graphs where selecting the most influential nodes is particularly
relevant for scalability. Our code is publicly available at:
https://github.com/arielramos97/CorePPR.

Ultra-wideband (UWB) devices are widely used in indoor localization
scenarios. Single-anchor UWB localization shows advantages because of its
simple system setup compared to conventional two-way ranging (TWR) and
trilateration localization methods. In this work, we focus on single-anchor UWB
localization methods that learn statistical features of the channel impulse
response (CIR) in different location areas using a Gaussian mixture model
(GMM). We show that by learning the joint distributions of the amplitudes of
different delay components, we achieve a more accurate location estimate
compared to considering each delay bin independently. Moreover, we develop a
similarity metric between sets of CIRs. With this set-based similarity metric,
we can further improve the estimation performance, compared to treating each
snapshot separately. We showcase the advantages of the proposed methods in
multiple application scenarios.

A major challenge for high dynamic range (HDR) image reconstruction from
multi-exposed low dynamic range (LDR) images, especially with dynamic scenes,
is the extraction and merging of relevant contextual features in order to
suppress any ghosting and blurring artifacts from moving objects. To tackle
this, in this work we propose a novel network for HDR reconstruction with deep
and rich feature extraction layers, including residual attention blocks with
sequential channel and spatial attention. For the compression of the
rich-features to the HDR domain, a residual feature distillation block (RFDB)
based architecture is adopted. In contrast to earlier deep-learning methods for
HDR, the above contributions shift focus from merging/compression to feature
extraction, the added value of which we demonstrate with ablation experiments.
We present qualitative and quantitative comparisons on a public benchmark
dataset, showing that our proposed method outperforms the state-of-the-art.

Can continuous diffusion models bring the same performance breakthrough on
natural language they did for image generation? To circumvent the discrete
nature of text data, we can simply project tokens in a continuous space of
embeddings, as is standard in language modeling. We propose Self-conditioned
Embedding Diffusion, a continuous diffusion mechanism that operates on token
embeddings and allows to learn flexible and scalable diffusion models for both
conditional and unconditional text generation. Through qualitative and
quantitative evaluation, we show that our text diffusion models generate
samples comparable with those produced by standard autoregressive language
models - while being in theory more efficient on accelerator hardware at
inference time. Our work paves the way for scaling up diffusion models for
text, similarly to autoregressive models, and for improving performance with
recent refinements to continuous diffusion.

Robust forecasting of the future anatomical changes inflicted by an ongoing
disease is an extremely challenging task that is out of grasp even for
experienced healthcare professionals. Such a capability, however, is of great
importance since it can improve patient management by providing information on
the speed of disease progression already at the admission stage, or it can
enrich the clinical trials with fast progressors and avoid the need for control
arms by the means of digital twins. In this work, we develop a deep learning
method that models the evolution of age-related disease by processing a single
medical scan and providing a segmentation of the target anatomy at a requested
future point in time. Our method represents a time-invariant physical process
and solves a large-scale problem of modeling temporal pixel-level changes
utilizing NeuralODEs. In addition, we demonstrate the approaches to incorporate
the prior domain-specific constraints into our method and define temporal Dice
loss for learning temporal objectives. To evaluate the applicability of our
approach across different age-related diseases and imaging modalities, we
developed and tested the proposed method on the datasets with 967 retinal OCT
volumes of 100 patients with Geographic Atrophy, and 2823 brain MRI volumes of
633 patients with Alzheimer's Disease. For Geographic Atrophy, the proposed
method outperformed the related baseline models in the atrophy growth
prediction. For Alzheimer's Disease, the proposed method demonstrated
remarkable performance in predicting the brain ventricle changes induced by the
disease, achieving the state-of-the-art result on TADPOLE challenge.

We propose in this work the gradient-enhanced deep neural networks (DNNs)
approach for function approximations and uncertainty quantification. More
precisely, the proposed approach adopts both the function evaluations and the
associated gradient information to yield enhanced approximation accuracy. In
particular, the gradient information is included as a regularization term in
the gradient-enhanced DNNs approach, for which we present similar posterior
estimates (by the two-layer neural networks) as those in the path-norm
regularized DNNs approximations. We also discuss the application of this
approach to gradient-enhanced uncertainty quantification, and present several
numerical experiments to show that the proposed approach can outperform the
traditional DNNs approach in many cases of interests.

Function signature recovery is important for many binary analysis tasks such
as control-flow integrity enforcement, clone detection, and bug finding.
Existing works try to substitute learning-based methods with rule-based methods
to reduce human effort.They made considerable efforts to enhance the system's
performance, which also bring the side effect of higher resource consumption.
However, recovering the function signature is more about providing information
for subsequent tasks, and both efficiency and performance are significant.
  In this paper, we first propose a method called Nimbus for efficient function
signature recovery that furthest reduces the whole-process resource consumption
without performance loss. Thanks to information bias and task relation (i.e.,
the relation between parameter count and parameter type recovery), we utilize
selective inputs and introduce multi-task learning (MTL) structure for function
signature recovery to reduce computational resource consumption, and fully
leverage mutual information. Our experimental results show that, with only
about the one-eighth processing time of the state-of-the-art method, we even
achieve about 1% more prediction accuracy over all function signature recovery
tasks.

This study investigates clustered federated learning (FL), one of the
formulations of FL with non-i.i.d. data, where the devices are partitioned into
clusters and each cluster optimally fits its data with a localized model. We
propose a novel clustered FL framework, which applies a nonconvex penalty to
pairwise differences of parameters. This framework can automatically identify
clusters without a priori knowledge of the number of clusters and the set of
devices in each cluster. To implement the proposed framework, we develop a
novel clustered FL method called FPFC. Advancing from the standard ADMM, our
method is implemented in parallel, updates only a subset of devices at each
communication round, and allows each participating device to perform a variable
amount of work. This greatly reduces the communication cost while
simultaneously preserving privacy, making it practical for FL. We also propose
a new warmup strategy for hyperparameter tuning under FL settings and consider
the asynchronous variant of FPFC (asyncFPFC). Theoretically, we provide
convergence guarantees of FPFC for general nonconvex losses and establish the
statistical convergence rate under a linear model with squared loss. Our
extensive experiments demonstrate the advantages of FPFC over existing methods.

Open Relation Extraction (OpenRE) aims to discover novel relations from open
domains. Previous OpenRE methods mainly suffer from two problems: (1)
Insufficient capacity to discriminate between known and novel relations. When
extending conventional test settings to a more general setting where test data
might also come from seen classes, existing approaches have a significant
performance decline. (2) Secondary labeling must be performed before practical
application. Existing methods cannot label human-readable and meaningful types
for novel relations, which is urgently required by the downstream tasks. To
address these issues, we propose the Active Relation Discovery (ARD) framework,
which utilizes relational outlier detection for discriminating known and novel
relations and involves active learning for labeling novel relations. Extensive
experiments on three real-world datasets show that ARD significantly
outperforms previous state-of-the-art methods on both conventional and our
proposed general OpenRE settings. The source code and datasets will be
available for reproducibility.

It is well known that the performance of any classification model is
effective if the dataset used for the training process and the test process
satisfy some specific requirements. In other words, the more the dataset size
is large, balanced, and representative, the more one can trust the proposed
model's effectiveness and, consequently, the obtained results. Unfortunately,
large-size anonymous datasets are generally not publicly available in
biomedical applications, especially those dealing with pathological human face
images. This concern makes using deep-learning-based approaches challenging to
deploy and difficult to reproduce or verify some published results. In this
paper, we suggest an efficient method to generate a realistic anonymous
synthetic dataset of human faces with the attributes of acne disorders
corresponding to three levels of severity (i.e. Mild, Moderate and Severe).
Therefore, a specific hierarchy StyleGAN-based algorithm trained at distinct
levels is considered. To evaluate the performance of the proposed scheme, we
consider a CNN-based classification system, trained using the generated
synthetic acneic face images and tested using authentic face images.
Consequently, we show that an accuracy of 97,6\% is achieved using
InceptionResNetv2. As a result, this work allows the scientific community to
employ the generated synthetic dataset for any data processing application
without restrictions on legal or ethical concerns. Moreover, this approach can
also be extended to other applications requiring the generation of synthetic
medical images. We can make the code and the generated dataset accessible for
the scientific community.

Most existing deep learning models are trained based on the closed-world
assumption, where the test data is assumed to be drawn i.i.d. from the same
distribution as the training data, known as in-distribution (ID). However, when
models are deployed in an open-world scenario, test samples can be
out-of-distribution (OOD) and therefore should be handled with caution. To
detect such OOD samples drawn from unknown distribution, OOD detection has
received increasing attention lately. However, current endeavors mostly focus
on grid-structured data and its application for graph-structured data remains
under-explored. Considering the fact that data labeling on graphs is commonly
time-expensive and labor-intensive, in this work we study the problem of
unsupervised graph OOD detection, aiming at detecting OOD graphs solely based
on unlabeled ID data. To achieve this goal, we develop a new graph contrastive
learning framework GOOD-D for detecting OOD graphs without using any
ground-truth labels. By performing hierarchical contrastive learning on the
augmented graphs generated by our perturbation-free graph data augmentation
method, GOOD-D is able to capture the latent ID patterns and accurately detect
OOD graphs based on the semantic inconsistency in different granularities
(i.e., node-level, graph-level, and group-level). As a pioneering work in
unsupervised graph-level OOD detection, we build a comprehensive benchmark to
compare our proposed approach with different state-of-the-art methods. The
experiment results demonstrate the superiority of our approach over different
methods on various datasets.

Reference-based image super-resolution (RefSR) is a promising SR branch and
has shown great potential in overcoming the limitations of single image
super-resolution. While previous state-of-the-art RefSR methods mainly focus on
improving the efficacy and robustness of reference feature transfer, it is
generally overlooked that a well reconstructed SR image should enable better SR
reconstruction for its similar LR images when it is referred to as. Therefore,
in this work, we propose a reciprocal learning framework that can appropriately
leverage such a fact to reinforce the learning of a RefSR network. Besides, we
deliberately design a progressive feature alignment and selection module for
further improving the RefSR task. The newly proposed module aligns
reference-input images at multi-scale feature spaces and performs
reference-aware feature selection in a progressive manner, thus more precise
reference features can be transferred into the input features and the network
capability is enhanced. Our reciprocal learning paradigm is model-agnostic and
it can be applied to arbitrary RefSR models. We empirically show that multiple
recent state-of-the-art RefSR models can be consistently improved with our
reciprocal learning paradigm. Furthermore, our proposed model together with the
reciprocal learning strategy sets new state-of-the-art performances on multiple

Thermographic testing, as a non-destructive testing method, is a non-contact
imaging technique for examining specimens by characterizing their thermal
conduction behavior. One of the main limitations of thermography is the
inherent limit to the resolution of the internal structures of any specimen due
to the diffusive nature of heat conduction. Overcoming this limit is the goal
of thermographic super resolution methods. In this work, we present a method
using two-dimensional pixel pattern based active photothermal laser heating in
conjunction with subsequent numerical reconstruction to achieve a
high-resolution reconstruction of internal defect structures. Furthermore, a
forward solution to the underlying inverse problem is proposed along with an
appropriate heuristic to find the regularization parameters necessary for the
inversion in a laboratory setting. This allows the generation of synthetic
measurement data, opening the door for the application of machine learning
based methods for future improvements towards full automation of the method.
The proposed experimental approach is validated using the latest laser coupled
DLP projector technology as a heat source. Finally, the proposed method is
shown to outperform several established conventional thermographic testing
techniques while improving the required measurement times by a factor of 8
compared to currently available photothermal super resolution techniques.

Assigning qualified, unbiased and interested reviewers to paper submissions
is vital for maintaining the integrity and quality of the academic publishing
system and providing valuable reviews to authors. However, matching thousands
of submissions with thousands of potential reviewers within a limited time is a
daunting challenge for a conference program committee. Prior efforts based on
topic modeling have suffered from losing the specific context that help define
the topics in a publication or submission abstract. Moreover, in some cases,
topics identified are difficult to interpret. We propose an approach that
learns from each abstract published by a potential reviewer the topics studied
and the explicit context in which the reviewer studied the topics. Furthermore,
we contribute a new dataset for evaluating reviewer matching systems. Our
experiments show a significant, consistent improvement in precision when
compared with the existing methods. We also use examples to demonstrate why our
recommendations are more explainable. The new approach has been deployed
successfully at top-tier conferences in the last two years.

Most approaches for semantic segmentation use only information from color
cameras to parse the scenes, yet recent advancements show that using depth data
allows to further improve performances. In this work, we focus on
transformer-based deep learning architectures, that have achieved
state-of-the-art performances on the segmentation task, and we propose to
employ depth information by embedding it in the positional encoding.
Effectively, we extend the network to multimodal data without adding any
parameters and in a natural way that makes use of the strength of transformers'
self-attention modules. We also investigate the idea of performing
cross-modality operations inside the attention module, swapping the key inputs
between the depth and color branches. Our approach consistently improves
performances on the Cityscapes benchmark.

The prediction of pancreatic ductal adenocarcinoma therapy response is a
clinically challenging and important task in this high-mortality tumour entity.
The training of neural networks able to tackle this challenge is impeded by a
lack of large datasets and the difficult anatomical localisation of the
pancreas. Here, we propose a hybrid deep neural network pipeline to predict
tumour response to initial chemotherapy which is based on the Response
Evaluation Criteria in Solid Tumors (RECIST) score, a standardised method for
cancer response evaluation by clinicians as well as tumour markers, and
clinical evaluation of the patients. We leverage a combination of
representation transfer from segmentation to classification, as well as
localisation and representation learning. Our approach yields a remarkably
data-efficient method able to predict treatment response with a ROC-AUC of
63.7% using only 477 datasets in total.

In this paper, we introduced the novel concept of advisor network to address
the problem of noisy labels in image classification. Deep neural networks (DNN)
are prone to performance reduction and overfitting problems on training data
with noisy annotations. Weighting loss methods aim to mitigate the influence of
noisy labels during the training, completely removing their contribution. This
discarding process prevents DNNs from learning wrong associations between
images and their correct labels but reduces the amount of data used, especially
when most of the samples have noisy labels. Differently, our method weighs the
feature extracted directly from the classifier without altering the loss value
of each data. The advisor helps to focus only on some part of the information
present in mislabeled examples, allowing the classifier to leverage that data
as well. We trained it with a meta-learning strategy so that it can adapt
throughout the training of the main model. We tested our method on CIFAR10 and
CIFAR100 with synthetic noise, and on Clothing1M which contains real-world
noise, reporting state-of-the-art results.

Federated learning (FL) on deep neural networks facilitates new applications
at the edge, especially for wearable and Internet-of-Thing devices. Such
devices capture a large and diverse amount of data, but they have memory,
compute, power, and connectivity constraints which hinder their participation
in FL. We propose Centaur, a multitier FL framework, enabling ultra-constrained
devices to efficiently participate in FL on large neural nets. Centaur combines
two major ideas: (i) a data selection scheme to choose a portion of samples
that accelerates the learning, and (ii) a partition-based training algorithm
that integrates both constrained and powerful devices owned by the same user.
Evaluations, on four benchmark neural nets and three datasets, show that
Centaur gains ~10% higher accuracy than local training on constrained devices
with ~58% energy saving on average. Our experimental results also demonstrate
the superior efficiency of Centaur when dealing with imbalanced data, client
participation heterogeneity, and various network connection probabilities.

The problem of approximating the Pareto front of a multiobjective
optimization problem can be reformulated as the problem of finding a set that
maximizes the hypervolume indicator. This paper establishes the analytical
expression of the Hessian matrix of the mapping from a (fixed size) collection
of $n$ points in the $d$-dimensional decision space (or $m$ dimensional
objective space) to the scalar hypervolume indicator value. To define the
Hessian matrix, the input set is vectorized, and the matrix is derived by
analytical differentiation of the mapping from a vectorized set to the
hypervolume indicator. The Hessian matrix plays a crucial role in second-order
methods, such as the Newton-Raphson optimization method, and it can be used for
the verification of local optimal sets. So far, the full analytical expression
was only established and analyzed for the relatively simple bi-objective case.
This paper will derive the full expression for arbitrary dimensions ($m\geq2$
objective functions). For the practically important three-dimensional case, we
also provide an asymptotically efficient algorithm with time complexity in
$O(n\log n)$ for the exact computation of the Hessian Matrix' non-zero entries.
We establish a sharp bound of $12m-6$ for the number of non-zero entries. Also,
for the general $m$-dimensional case, a compact recursive analytical expression
is established, and its algorithmic implementation is discussed. Also, for the
general case, some sparsity results can be established; these results are
implied by the recursive expression. To validate and illustrate the
analytically derived algorithms and results, we provide a few numerical
examples using Python and Mathematica implementations. Open-source
implementations of the algorithms and testing data are made available as a
supplement to this paper.

Graph summarization via node grouping is a popular method to build concise
graph representations by grouping nodes from the original graph into supernodes
and encoding edges into superedges such that the loss of adjacency information
is minimized. Such summaries have immense applications in large-scale graph
analytics due to their small size and high query processing efficiency. In this
paper, we reformulate the loss minimization problem for summarization into an
equivalent integer maximization problem. By initially allowing relaxed
(fractional) solutions for integer maximization, we analytically expose the
underlying connections to the spectral properties of the adjacency matrix.
Consequently, we design an algorithm called SpecSumm that consists of two
phases. In the first phase, motivated by spectral graph theory, we apply
k-means clustering on the k largest (in magnitude) eigenvectors of the
adjacency matrix to assign nodes to supernodes. In the second phase, we propose
a greedy heuristic that updates the initial assignment to further improve
summary quality. Finally, via extensive experiments on 11 datasets, we show
that SpecSumm efficiently produces high-quality summaries compared to
state-of-the-art summarization algorithms and scales to graphs with millions of
nodes.

Training robust speaker verification systems without speaker labels has long
been a challenging task. Previous studies observed a large performance gap
between self-supervised and fully supervised methods. In this paper, we apply a
non-contrastive self-supervised learning framework called DIstillation with NO
labels (DINO) and propose two regularization terms applied to embeddings in
DINO. One regularization term guarantees the diversity of the embeddings, while
the other regularization term decorrelates the variables of each embedding. The
effectiveness of various data augmentation techniques are explored, on both
time and frequency domain. A range of experiments conducted on the VoxCeleb
datasets demonstrate the superiority of the regularized DINO framework in
speaker verification. Our method achieves the state-of-the-art speaker
verification performance under a single-stage self-supervised setting on
VoxCeleb. The codes will be made publicly-available.

Sparse representation has attracted great attention because it can greatly
save storage resources and find representative features of data in a
low-dimensional space. As a result, it may be widely applied in engineering
domains including feature extraction, compressed sensing, signal denoising,
picture clustering, and dictionary learning, just to name a few. In this paper,
we propose a spiking sampling network. This network is composed of spiking
neurons, and it can dynamically decide which pixel points should be retained
and which ones need to be masked according to the input. Our experiments
demonstrate that this approach enables better sparse representation of the
original image and facilitates image reconstruction compared to random
sampling. We thus use this approach for compressing massive data from the
dynamic vision sensor, which greatly reduces the storage requirements for event
data.

The clinical interest is often to measure the volume of a structure, which is
typically derived from a segmentation. In order to evaluate and compare
segmentation methods, the similarity between a segmentation and a predefined
ground truth is measured using popular discrete metrics, such as the Dice
score. Recent segmentation methods use a differentiable surrogate metric, such
as soft Dice, as part of the loss function during the learning phase. In this
work, we first briefly describe how to derive volume estimates from a
segmentation that is, potentially, inherently uncertain or ambiguous. This is
followed by a theoretical analysis and an experimental validation linking the
inherent uncertainty to common loss functions for training CNNs, namely
cross-entropy and soft Dice. We find that, even though soft Dice optimization
leads to an improved performance with respect to the Dice score and other
measures, it may introduce a volume bias for tasks with high inherent
uncertainty. These findings indicate some of the method's clinical limitations
and suggest doing a closer ad-hoc volume analysis with an optional
re-calibration step.

This paper analyses the feasibility of including emerging IT-topics into
secondary education (K9-12). We developed a class on Blockchain for K9-12
students which delivers relevant content but is also indented to motivate them
to further engage with the topic. Our course consists of theory material as
well as a hands-on application. Gamification techniques are utilised to
encourage students and improve the learning experience. The assessment,
consisting of 166 students, shows a significant knowledge gain of students
exposed to our materials especially when the theory and the hands-on materials
are combined. Both, i.e., teaching material and chat application, are freely

Planet formation is a multi-scale process in which the coagulation of
$\mathrm{\mu m}$-sized dust grains in protoplanetary disks is strongly
influenced by the hydrodynamic processes on scales of astronomical units
($\approx 1.5\times 10^8 \,\mathrm{km}$). Studies are therefore dependent on
subgrid models to emulate the micro physics of dust coagulation on top of a
large scale hydrodynamic simulation. Numerical simulations which include the
relevant physical effects are complex and computationally expensive. Here, we
present a fast and accurate learned effective model for dust coagulation,
trained on data from high resolution numerical coagulation simulations. Our
model captures details of the dust coagulation process that were so far not
tractable with other dust coagulation prescriptions with similar computational
efficiency.

Mathematical models have been used to understand the spread patterns of
infectious diseases such as Coronavirus Disease 2019 (COVID-19). The
transmission component of the models can be modelled in an age-dependent manner
via introducing contact matrix for the population, which describes the contact
rates between the age groups. Since social contact patterns vary from country
to country, we can compare and group the countries using the corresponding
contact matrices. In this paper, we present a framework for clustering
countries based on their contact matrices with respect to an underlying
epidemic model. Since the pipeline is generic and modular, we demonstrate its
application in a COVID-19 model from R\"ost et. al. which gives a hint about
which countries can be compared in a pandemic situation, when only
non-pharmaceutical interventions are available.

Property inference attacks against machine learning (ML) models aim to infer
properties of the training data that are unrelated to the primary task of the
model, and have so far been formulated as binary decision problems, i.e.,
whether or not the training data have a certain property. However, in
industrial and healthcare applications, the proportion of labels in the
training data is quite often also considered sensitive information. In this
paper we introduce a new type of property inference attack that unlike binary
decision problems in literature, aim at inferring the class label distribution
of the training data from parameters of ML classifier models. We propose a
method based on \emph{shadow training} and a \emph{meta-classifier} trained on
the parameters of the shadow classifiers augmented with the accuracy of the
classifiers on auxiliary data. We evaluate the proposed approach for ML
classifiers with fully connected neural network architectures. We find that the
proposed \emph{meta-classifier} attack provides a maximum relative improvement
of $52\%$ over state of the art.

Federated learning (FL) has emerged as an instance of distributed machine
learning paradigm that avoids the transmission of data generated on the users'
side. Although data are not transmitted, edge devices have to deal with limited
communication bandwidths, data heterogeneity, and straggler effects due to the
limited computational resources of users' devices. A prominent approach to
overcome such difficulties is FedADMM, which is based on the classical
two-operator consensus alternating direction method of multipliers (ADMM). The
common assumption of FL algorithms, including FedADMM, is that they learn a
global model using data only on the users' side and not on the edge server.
However, in edge learning, the server is expected to be near the base station
and have direct access to rich datasets. In this paper, we argue that
leveraging the rich data on the edge server is much more beneficial than
utilizing only user datasets. Specifically, we show that the mere application
of FL with an additional virtual user node representing the data on the edge
server is inefficient. We propose FedTOP-ADMM, which generalizes FedADMM and is
based on a three-operator ADMM-type technique that exploits a smooth cost
function on the edge server to learn a global model parallel to the edge
devices. Our numerical experiments indicate that FedTOP-ADMM has substantial
gain up to 33\% in communication efficiency to reach a desired test accuracy
with respect to FedADMM, including a virtual user on the edge server.

With most technical fields, there exists a delay between fundamental academic
research and practical industrial uptake. Whilst some sciences have robust and
well-established processes for commercialisation, such as the pharmaceutical
practice of regimented drug trials, other fields face transitory periods in
which fundamental academic advancements diffuse gradually into the space of
commerce and industry. For the still relatively young field of
Automated/Autonomous Machine Learning (AutoML/AutonoML), that transitory period
is under way, spurred on by a burgeoning interest from broader society. Yet, to
date, little research has been undertaken to assess the current state of this
dissemination and its uptake. Thus, this review makes two primary contributions
to knowledge around this topic. Firstly, it provides the most up-to-date and
comprehensive survey of existing AutoML tools, both open-source and commercial.
Secondly, it motivates and outlines a framework for assessing whether an AutoML
solution designed for real-world application is 'performant'; this framework
extends beyond the limitations of typical academic criteria, considering a
variety of stakeholder needs and the human-computer interactions required to
service them. Thus, additionally supported by an extensive assessment and
comparison of academic and commercial case-studies, this review evaluates
mainstream engagement with AutoML in the early 2020s, identifying obstacles and
opportunities for accelerating future uptake.

Federated learning (FL) has achieved great success as a privacy-preserving
distributed training paradigm, where many edge devices collaboratively train a
machine learning model by sharing the model updates instead of the raw data
with a server. However, the heterogeneous computational and communication
resources of edge devices give rise to stragglers that significantly decelerate
the training process. To mitigate this issue, we propose a novel FL framework
named stochastic coded federated learning (SCFL) that leverages coded computing
techniques. In SCFL, before the training process starts, each edge device
uploads a privacy-preserving coded dataset to the server, which is generated by
adding Gaussian noise to the projected local dataset. During training, the
server computes gradients on the global coded dataset to compensate for the
missing model updates of the straggling devices. We design a gradient
aggregation scheme to ensure that the aggregated model update is an unbiased
estimate of the desired global update. Moreover, this aggregation scheme
enables periodical model averaging to improve the training efficiency. We
characterize the tradeoff between the convergence performance and privacy
guarantee of SCFL. In particular, a more noisy coded dataset provides stronger
privacy protection for edge devices but results in learning performance
degradation. We further develop a contract-based incentive mechanism to
coordinate such a conflict. The simulation results show that SCFL learns a
better model within the given time and achieves a better privacy-performance
tradeoff than the baseline methods. In addition, the proposed incentive
mechanism grants better training performance than the conventional Stackelberg
game approach.

Despite recent advancements in tabular language model research, real-world
applications are still challenging. In industry, there is an abundance of
tables found in spreadsheets, but acquisition of substantial amounts of labels
is expensive, since only experts can annotate the often highly technical and
domain-specific tables. Active learning could potentially reduce labeling
costs, however, so far there are no works related to active learning in
conjunction with tabular language models. In this paper we investigate
different acquisition functions in a real-world industrial tabular language
model use case for sub-cell named entity recognition. Our results show that
cell-level acquisition functions with built-in diversity can significantly
reduce the labeling effort, while enforced table diversity is detrimental. We
further see open fundamental questions concerning computational efficiency and
the perspective of human annotators.

Pooling publicly-available MRI data from multiple sites allows to assemble
extensive groups of subjects, increase statistical power, and promote data
reuse with machine learning techniques. The harmonization of multicenter data
is necessary to reduce the confounding effect associated with non-biological
sources of variability in the data. However, when applied to the entire dataset
before machine learning, the harmonization leads to data leakage, because
information outside the training set may affect model building, and potentially
falsely overestimate performance. We propose a 1) measurement of the efficacy
of data harmonization; 2) harmonizer transformer, i.e., an implementation of
the ComBat harmonization allowing its encapsulation among the preprocessing
steps of a machine learning pipeline, avoiding data leakage. We tested these
tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at
36 sites. After harmonization, the site effect was removed or reduced, and we
measured the data leakage effect in predicting individual age from MRI data,
highlighting that introducing the harmonizer transformer into a machine
learning pipeline allows for avoiding data leakage.

Removing reverb from reverberant music is a necessary technique to clean up
audio for downstream music manipulations. Reverberation of music contains two
categories, natural reverb, and artificial reverb. Artificial reverb has a
wider diversity than natural reverb due to its various parameter setups and
reverberation types. However, recent supervised dereverberation methods may
fail because they rely on sufficiently diverse and numerous pairs of
reverberant observations and retrieved data for training in order to be
generalizable to unseen observations during inference. To resolve these
problems, we propose an unsupervised method that can remove a general kind of
artificial reverb for music without requiring pairs of data for training. The
proposed method is based on diffusion models, where it initializes the unknown
reverberation operator with a conventional signal processing technique and
simultaneously refines the estimate with the help of diffusion models. We show
through objective and perceptual evaluations that our method outperforms the
current leading vocal dereverberation benchmarks.

Numerical simulations are ubiquitous in science and engineering. Machine
learning for science investigates how artificial neural architectures can learn
from these simulations to speed up scientific discovery and engineering
processes. Most of these architectures are trained in a supervised manner. They
require tremendous amounts of data from simulations that are slow to generate
and memory greedy. In this article, we present our ongoing work to design a
training framework that alleviates those bottlenecks. It generates data in
parallel with the training process. Such simultaneity induces a bias in the
data available during the training. We present a strategy to mitigate this bias
with a memory buffer. We test our framework on the multi-parametric Lorenz's
attractor. We show the benefit of our framework compared to offline training
and the success of our data bias mitigation strategy to capture the complex
chaotic dynamics of the system.

Prompt learning recently become an effective linguistic tool to motivate the
PLMs' knowledge on few-shot-setting tasks. However, studies have shown the lack
of robustness still exists in prompt learning, since suitable initialization of
continuous prompt and expert-first manual prompt are essential in fine-tuning
process. What is more, human also utilize their comparative ability to motivate
their existing knowledge for distinguishing different examples. Motivated by
this, we explore how to use contrastive samples to strengthen prompt learning.
In detail, we first propose our model ConsPrompt combining with prompt encoding
network, contrastive sampling module, and contrastive scoring module.
Subsequently, two sampling strategies, similarity-based and label-based
strategies, are introduced to realize differential contrastive learning. The
effectiveness of proposed ConsPrompt is demonstrated in five different few-shot
learning tasks and shown the similarity-based sampling strategy is more
effective than label-based in combining contrastive learning. Our results also
exhibits the state-of-the-art performance and robustness in different few-shot
settings, which proves that the ConsPrompt could be assumed as a better
knowledge probe to motivate PLMs.

Since the mid-10s, the era of Deep Learning (DL) has continued to this day,
bringing forth new superlatives and innovations each year. Nevertheless, the
speed with which these innovations translate into real applications lags behind
this fast pace. Safety-critical applications, in particular, underlie strict
regulatory and ethical requirements which need to be taken care of and are
still active areas of debate. eXplainable AI (XAI) and privacy-preserving
machine learning (PPML) are both crucial research fields, aiming at mitigating
some of the drawbacks of prevailing data-hungry black-box models in DL. Despite
brisk research activity in the respective fields, no attention has yet been
paid to their interaction. This work is the first to investigate the impact of
private learning techniques on generated explanations for DL-based models. In
an extensive experimental analysis covering various image and time series
datasets from multiple domains, as well as varying privacy techniques, XAI
methods, and model architectures, the effects of private training on generated
explanations are studied. The findings suggest non-negligible changes in
explanations through the introduction of privacy. Apart from reporting
individual effects of PPML on XAI, the paper gives clear recommendations for
the choice of techniques in real applications. By unveiling the
interdependencies of these pivotal technologies, this work is a first step
towards overcoming the remaining hurdles for practically applicable AI in
safety-critical domains.

Multi-view representation learning has developed rapidly over the past
decades and has been applied in many fields. However, most previous works
assumed that each view is complete and aligned. This leads to an inevitable
deterioration in their performance when encountering practical problems such as
missing or unaligned views. To address the challenge of representation learning
on partially aligned multi-view data, we propose a new cross-view graph
contrastive learning framework, which integrates multi-view information to
align data and learn latent representations. Compared with current approaches,
the proposed method has the following merits: (1) our model is an end-to-end
framework that simultaneously performs view-specific representation learning
via view-specific autoencoders and cluster-level data aligning by combining
multi-view information with the cross-view graph contrastive learning; (2) it
is easy to apply our model to explore information from three or more
modalities/sources as the cross-view graph contrastive learning is devised.
Extensive experiments conducted on several real datasets demonstrate the
effectiveness of the proposed method on the clustering and classification
tasks.

Recently, many neural network-based image compression methods have shown
promising results superior to the existing tool-based conventional codecs.
However, most of them are often trained as separate models for different target
bit rates, thus increasing the model complexity. Therefore, several studies
have been conducted for learned compression that supports variable rates with
single models, but they require additional network modules, layers, or inputs
that often lead to complexity overhead, or do not provide sufficient coding
efficiency. In this paper, we firstly propose a selective compression method
that partially encodes the latent representations in a fully generalized manner
for deep learning-based variable-rate image compression. The proposed method
adaptively determines essential representation elements for compression of
different target quality levels. For this, we first generate a 3D importance
map as the nature of input content to represent the underlying importance of
the representation elements. The 3D importance map is then adjusted for
different target quality levels using importance adjustment curves. The
adjusted 3D importance map is finally converted into a 3D binary mask to
determine the essential representation elements for compression. The proposed
method can be easily integrated with the existing compression models with a
negligible amount of overhead increase. Our method can also enable continuously
variable-rate compression via simple interpolation of the importance adjustment
curves among different quality levels. The extensive experimental results show
that the proposed method can achieve comparable compression efficiency as those
of the separately trained reference compression models and can reduce decoding
time owing to the selective compression. The sample codes are publicly
available at https://github.com/JooyoungLeeETRI/SCR.

Subteam replacement is defined as finding the optimal candidate set of people
who can best function as an unavailable subset of members (i.e., subteam) for
certain reasons (e.g., conflicts of interests, employee churn), given a team of
people embedded in a social network working on the same task. Prior
investigations on this problem incorporate graph kernel as the optimal criteria
for measuring the similarity between the new optimized team and the original
team. However, the increasingly abundant social networks reveal fundamental
limitations of existing methods, including (1) the graph kernel-based
approaches are powerless to capture the key intrinsic correlations among node
features, (2) they generally search over the entire network for every member to
be replaced, making it extremely inefficient as the network grows, and (3) the
requirement of equal-sized replacement for the unavailable subteam can be
inapplicable due to limited hiring budget. In this work, we address the
limitations in the state-of-the-art for subteam replacement by (1) proposing
GENIUS, a novel clustering-based graph neural network (GNN) framework that can
capture team network knowledge for flexible subteam replacement, and (2)
equipping the proposed GENIUS with self-supervised positive team contrasting
training scheme to improve the team-level representation learning and
unsupervised node clusters to prune candidates for fast computation. Through
extensive empirical evaluations, we demonstrate the efficacy of the proposed
method (1) effectiveness: being able to select better candidate members that
significantly increase the similarity between the optimized and original teams,
and (2) efficiency: achieving more than 600 times speed-up in average running
time.

Bilevel programming has recently received attention in the literature, due to
a wide range of applications, including reinforcement learning and
hyper-parameter optimization. However, it is widely assumed that the underlying
bilevel optimization problem is solved either by a single machine or in the
case of multiple machines connected in a star-shaped network, i.e., federated
learning setting. The latter approach suffers from a high communication cost on
the central node (e.g., parameter server) and exhibits privacy vulnerabilities.
Hence, it is of interest to develop methods that solve bilevel optimization
problems in a communication-efficient decentralized manner. To that end, this
paper introduces a penalty function based decentralized algorithm with
theoretical guarantees for this class of optimization problems. Specifically, a
distributed alternating gradient-type algorithm for solving consensus bilevel
programming over a decentralized network is developed. A key feature of the
proposed algorithm is to estimate the hyper-gradient of the penalty function
via decentralized computation of matrix-vector products and few vector
communications, which is then integrated within our alternating algorithm to
give the finite-time convergence analysis under different convexity
assumptions. Owing to the generality of this complexity analysis, our result
yields convergence rates for a wide variety of consensus problems including
minimax and compositional optimization. Empirical results on both synthetic and
real datasets demonstrate that the proposed method works well in practice.

Conceptual knowledge is fundamental to human cognition and knowledge bases.
However, existing knowledge probing works only focus on evaluating factual
knowledge of pre-trained language models (PLMs) and ignore conceptual
knowledge. Since conceptual knowledge often appears as implicit commonsense
behind texts, designing probes for conceptual knowledge is hard. Inspired by
knowledge representation schemata, we comprehensively evaluate conceptual
knowledge of PLMs by designing three tasks to probe whether PLMs organize
entities by conceptual similarities, learn conceptual properties, and
conceptualize entities in contexts, respectively. For the tasks, we collect and
annotate 24k data instances covering 393 concepts, which is COPEN, a COnceptual
knowledge Probing bENchmark. Extensive experiments on different sizes and types
of PLMs show that existing PLMs systematically lack conceptual knowledge and
suffer from various spurious correlations. We believe this is a critical
bottleneck for realizing human-like cognition in PLMs. COPEN and our codes are
publicly released at https://github.com/THU-KEG/COPEN.

In pursuit of faster computation, Efficient Transformers demonstrate an
impressive variety of approaches -- models attaining sub-quadratic attention
complexity can utilize a notion of sparsity or a low-rank approximation of
inputs to reduce the number of attended keys; other ways to reduce complexity
include locality-sensitive hashing, key pooling, additional memory to store
information in compacted or hybridization with other architectures, such as
CNN. Often based on a strong mathematical basis, kernelized approaches allow
for the approximation of attention with linear complexity while retaining high
accuracy. Therefore, in the present paper, we aim to expand the idea of
trainable kernel methods to approximate the self-attention mechanism of the
Transformer architecture.

This paper investigates negative sampling for contrastive learning in the
context of audio-text retrieval. The strategy for negative sampling refers to
selecting negatives (either audio clips or textual descriptions) from a pool of
candidates for a positive audio-text pair. We explore sampling strategies via
model-estimated within-modality and cross-modality relevance scores for audio
and text samples. With a constant training setting on the retrieval system from
[1], we study eight sampling strategies, including hard and semi-hard negative
sampling. Experimental results show that retrieval performance varies
dramatically among different strategies. Particularly, by selecting semi-hard
negatives with cross-modality scores, the retrieval system gains improved
performance in both text-to-audio and audio-to-text retrieval. Besides, we show
that feature collapse occurs while sampling hard negatives with cross-modality
scores.

Graph-structured data are widespread in real-world applications, such as
social networks, recommender systems, knowledge graphs, chemical molecules etc.
Despite the success of Euclidean space for graph-related learning tasks, its
ability to model complex patterns is essentially constrained by its
polynomially growing capacity. Recently, hyperbolic spaces have emerged as a
promising alternative for processing graph data with tree-like structure or
power-law distribution, owing to the exponential growth property. Different
from Euclidean space, which expands polynomially, the hyperbolic space grows
exponentially which makes it gains natural advantages in abstracting tree-like
or scale-free graphs with hierarchical organizations.
  In this tutorial, we aim to give an introduction to this emerging field of
graph representation learning with the express purpose of being accessible to
all audiences. We first give a brief introduction to graph representation
learning as well as some preliminary Riemannian and hyperbolic geometry. We
then comprehensively revisit the hyperbolic embedding techniques, including
hyperbolic shallow models and hyperbolic neural networks. In addition, we
introduce the technical details of the current hyperbolic graph neural networks
by unifying them into a general framework and summarizing the variants of each
component. Moreover, we further introduce a series of related applications in a
variety of fields. In the last part, we discuss several advanced topics about
hyperbolic geometry for graph representation learning, which potentially serve
as guidelines for further flourishing the non-Euclidean graph learning
community.

Distribution-to-distribution (D2D) point cloud registration techniques such
as the Normal Distributions Transform (NDT) can align point clouds sampled from
unstructured scenes and provide accurate bounds of their own solution error
covariance -- an important feature for safety-of-life navigation tasks. D2D
methods rely on the assumption of a static scene and are therefore susceptible
to bias from range-shadowing, self-occlusion, moving objects, and distortion
artifacts as the recording device moves between frames. Deep Learning-based
approaches can achieve higher accuracy in dynamic scenes by relaxing these
constraints, however, DNNs produce uninterpretable solutions which can be
problematic from a safety perspective. In this paper, we propose a method of
down-sampling LIDAR point clouds to exclude voxels that violate the assumption
of a static scene and introduce error to the D2D scan matching process. Our
approach uses a solution consistency filter -- identifying and flagging voxels
where D2D contributions disagree with local estimates from a PointNet-based
registration network. Our results show that this technique provides significant
benefits in registration accuracy, and is particularly useful in scenes
containing dense foliage.

Although informal online learning communities have proliferated over the last
two decades, a fundamental question remains: What are the users of these
communities expected to learn? Guided by the work of Etienne Wenger on
communities of practice, we identify three distinct types of learning goals
common to online informal learning communities: the development of domain
skills, the development of identity as a community member, and the development
of community-specific values and practices. Given these goals, what is the best
way to support learning? Drawing from previous research in social computing, we
ask how different types of legitimate peripheral participation by
newcomers-contribution to core tasks, engagement with practice proxies, social
bonding, and feedback exchange-may be associated with these three learning
goals. Using data from the Scratch online community, we conduct a quantitative
analysis to explore these questions. Our study contributes both theoretical
insights and empirical evidence on how different types of learning occur in
informal online environments.

Neural Radiance Fields (NeRFs) learn implicit representations of - typically
static - environments from images. Our paper extends NeRFs to handle dynamic
scenes in an online fashion. We propose ParticleNeRF that adapts to changes in
the geometry of the environment as they occur, learning a new up-to-date
representation every 350 ms. ParticleNeRF can represent the current state of
dynamic environments with much higher fidelity as other NeRF frameworks. To
achieve this, we introduce a new particle-based parametric encoding, which
allows the intermediate NeRF features - now coupled to particles in space - to
move with the dynamic geometry. This is possible by backpropagating the
photometric reconstruction loss into the position of the particles. The
position gradients are interpreted as particle velocities and integrated into
positions using a position-based dynamics (PBS) physics system. Introducing PBS
into the NeRF formulation allows us to add collision constraints to the
particle motion and creates future opportunities to add other movement priors
into the system, such as rigid and deformable body

Fine-grained population maps are needed in several domains, like urban
planning, environmental monitoring, public health, and humanitarian operations.
Unfortunately, in many countries only aggregate census counts over large
spatial units are collected, moreover, these are not always up-to-date. We
present POMELO, a deep learning model that employs coarse census counts and
open geodata to estimate fine-grained population maps with 100m ground sampling
distance. Moreover, the model can also estimate population numbers when no
census counts at all are available, by generalizing across countries. In a
series of experiments for several countries in sub-Saharan Africa, the maps
produced with POMELOare in good agreement with the most detailed available
reference counts: disaggregation of coarse census counts reaches R2 values of
85-89%; unconstrained prediction in the absence of any counts reaches 48-69%.

Subgraph matching is to find all subgraphs in a data graph that are
isomorphic to an existing query graph. Subgraph matching is an NP-hard problem,
yet has found its applications in many areas. Many learning-based methods have
been proposed for graph matching, whereas few have been designed for subgraph
matching. The subgraph matching problem is generally more challenging, mainly
due to the different sizes between the two graphs, resulting in considerable
large space of solutions. Also the extra edges existing in the data graph
connecting to the matched nodes may lead to two matched nodes of two graphs
having different adjacency structures and often being identified as distinct
objects. Due to the extra edges, the existing learning based methods often fail
to generate sufficiently similar node-level embeddings for matched nodes. This
study proposes a novel Adaptive Edge-Deleting Network (AEDNet) for subgraph
matching. The proposed method is trained in an end-to-end fashion. In AEDNet, a
novel sample-wise adaptive edge-deleting mechanism removes extra edges to
ensure consistency of adjacency structure of matched nodes, while a
unidirectional cross-propagation mechanism ensures consistency of features of
matched nodes. We applied the proposed method on six datasets with graph sizes
varying from 20 to 2300. Our evaluations on six open datasets demonstrate that
the proposed AEDNet outperforms six state-of-the-arts and is much faster than
the exact methods on large graphs.

3D convolutional neural networks have revealed superior performance in
processing volumetric data such as video and medical imaging. However, the
competitive performance by leveraging 3D networks results in huge computational
costs, which are far beyond that of 2D networks. In this paper, we propose a
novel Hilbert curve-based cross-dimensionality distillation approach that
facilitates the knowledge of 3D networks to improve the performance of 2D
networks. The proposed Hilbert Distillation (HD) method preserves the
structural information via the Hilbert curve, which maps high-dimensional (>=2)
representations to one-dimensional continuous space-filling curves. Since the
distilled 2D networks are supervised by the curves converted from dimensionally
heterogeneous 3D features, the 2D networks are given an informative view in
terms of learning structural information embedded in well-trained
high-dimensional representations. We further propose a Variable-length Hilbert
Distillation (VHD) method to dynamically shorten the walking stride of the
Hilbert curve in activation feature areas and lengthen the stride in context
feature areas, forcing the 2D networks to pay more attention to learning from
activation features. The proposed algorithm outperforms the current
state-of-the-art distillation techniques adapted to cross-dimensionality
distillation on two classification tasks. Moreover, the distilled 2D networks
by the proposed method achieve competitive performance with the original 3D
networks, indicating the lightweight distilled 2D networks could potentially be
the substitution of cumbersome 3D networks in the real-world scenario.

Recent advances in spatial omics methods enable the molecular composition of
human tumors to be imaged at micron-scale resolution across hundreds of
patients and ten to thousands of molecular imaging channels. Large-scale
molecular imaging datasets offer a new opportunity to understand how the
spatial organization of proteins and cell types within a tumor modulate the
response of a patient to different therapeutic strategies and offer potential
insights into the design of novel therapies to increase patient response.
However, spatial omics datasets require computational analysis methods that can
scale to incorporate hundreds to thousands of imaging channels (ie colors)
while enabling the extraction of molecular patterns that correlate with
treatment responses across large number of patients with potentially
heterogeneous tumors presentations. Here, we have develop a machine learning
strategy for the identification and design of signaling molecule combinations
that predict the degree of immune system engagement with a specific patient
tumors. We specifically train a classifier to predict T cell distribution in
patient tumors using the images from 30-40 molecular imaging channels. Second,
we apply a gradient descent based counterfactual reasoning strategy to the
classifier and discover combinations of signaling molecules predicted to
increase T cell infiltration. Applied to spatial proteomics data of melanoma
tumor, our model predicts that increasing the level of CXCL9, CXCL10, CXCL12,
CCL19 and decreasing the level of CCL8 in melanoma tumor will increase T cell
infiltration by 10-fold across a cohort of 69 patients. The model predicts that
the combination is many fold more effective than single target perturbations.
Our work provides a paradigm for machine learning based prediction and design
of cancer therapeutics based on classification of immune system activity in
spatial omics data.

In this paper, we consider a dynamic sensor placement problem where sensors
can move within a network over time. Sensor placement problem aims to select M
sensor positions from N candidates where M < N. Most existing methods assume
that sensors are static, i.e., they do not move, however, many mobile sensors
like drones, robots, and vehicles can change their positions over time.
Moreover, underlying measurement conditions could also be changed that are
difficult to cover the statically placed sensors. We tackle the problem by
allowing the sensors to change their positions in their neighbors on the
network. Based on a perspective of dictionary learning, we sequentially learn
the dictionary from a pool of observed signals on the network based on graph
sampling theory. Using the learned dictionary, we dynamically determine the
sensor positions such that the non-observed signals on the network can be best
recovered from the observations. Furthermore, sensor positions in each time
slot can be optimized in a decentralized manner to reduce the calculation cost.
In experiments, we validate the effectiveness of the proposed method via the
mean squared error (MSE) of the reconstructed signals. The proposed dynamic
sensor placement outperforms the existing static ones both in synthetic and
real data.

Despite the huge advancement in knowledge discovery and data mining
techniques, the X-ray diffraction (XRD) analysis process has mostly remained
untouched and still involves manual investigation, comparison, and
verification. Due to the large volume of XRD samples from high-throughput XRD
experiments, it has become impossible for domain scientists to process them
manually. Recently, they have started leveraging standard clustering
techniques, to reduce the XRD pattern representations requiring manual efforts
for labeling and verification. Nevertheless, these standard clustering
techniques do not handle problem-specific aspects such as peak shifting,
adjacent peaks, background noise, and mixed phases; hence, resulting in
incorrect composition-phase diagrams that complicate further steps. Here, we
leverage data mining techniques along with domain expertise to handle these
issues. In this paper, we introduce an incremental phase mapping approach based
on binary peak representations using a new threshold based fuzzy dissimilarity
measure. The proposed approach first applies an incremental phase computation
algorithm on discrete binary peak representation of XRD samples, followed by
hierarchical clustering or manual merging of similar pure phases to obtain the
final composition-phase diagram. We evaluate our method on the composition
space of two ternary alloy systems- Co-Ni-Ta and Co-Ti-Ta. Our results are
verified by domain scientists and closely resembles the manually computed
ground-truth composition-phase diagrams. The proposed approach takes us closer
towards achieving the goal of complete end-to-end automated XRD analysis.

Given a dataset of expert agent interactions with an environment of interest,
a viable method to extract an effective agent policy is to estimate the maximum
likelihood policy indicated by this data. This approach is commonly referred to
as behavioral cloning (BC). In this work, we describe a key disadvantage of BC
that arises due to the maximum likelihood objective function; namely that BC is
mean-seeking with respect to the state-conditional expert action distribution
when the learner's policy is represented with a Gaussian. To address this
issue, we introduce a modified version of BC, Adversarial Behavioral Cloning
(ABC), that exhibits mode-seeking behavior by incorporating elements of GAN
(generative adversarial network) training. We evaluate ABC on toy domains and a
domain based on Hopper from the DeepMind Control suite, and show that it
outperforms standard BC by being mode-seeking in nature.

Online Temporal Action Localization (On-TAL) aims to immediately provide
action instances from untrimmed streaming videos. The model is not allowed to
utilize future frames and any processing techniques to modify past predictions,
making On-TAL much more challenging. In this paper, we propose a simple yet
effective framework, termed SimOn, that learns to predict action instances
using the popular Transformer architecture in an end-to-end manner.
Specifically, the model takes the current frame feature as a query and a set of
past context information as keys and values of the Transformer. Different from
the prior work that uses a set of outputs of the model as past contexts, we
leverage the past visual context and the learnable context embedding for the
current query. Experimental results on the THUMOS14 and ActivityNet1.3 datasets
show that our model remarkably outperforms the previous methods, achieving a
new state-of-the-art On-TAL performance. In addition, the evaluation for Online
Detection of Action Start (ODAS) demonstrates the effectiveness and robustness
of our method in the online setting. The code is available at
https://github.com/TuanTNG/SimOn

The high emission and low energy efficiency caused by internal combustion
engines (ICE) have become unacceptable under environmental regulations and the
energy crisis. As a promising alternative solution, multi-power source electric
vehicles (MPS-EVs) introduce different clean energy systems to improve
powertrain efficiency. The energy management strategy (EMS) is a critical
technology for MPS-EVs to maximize efficiency, fuel economy, and range.
Reinforcement learning (RL) has become an effective methodology for the
development of EMS. RL has received continuous attention and research, but
there is still a lack of systematic analysis of the design elements of RL-based
EMS. To this end, this paper presents an in-depth analysis of the current
research on RL-based EMS (RL-EMS) and summarizes the design elements of
RL-based EMS. This paper first summarizes the previous applications of RL in
EMS from five aspects: algorithm, perception scheme, decision scheme, reward
function, and innovative training method. The contribution of advanced
algorithms to the training effect is shown, the perception and control schemes
in the literature are analyzed in detail, different reward function settings
are classified, and innovative training methods with their roles are
elaborated. Finally, by comparing the development routes of RL and RL-EMS, this
paper identifies the gap between advanced RL solutions and existing RL-EMS.
Finally, this paper suggests potential development directions for implementing
advanced artificial intelligence (AI) solutions in EMS.

AI methods are used in societally important settings, ranging from credit to
employment to housing, and it is crucial to provide fairness in regard to
algorithmic decision making. Moreover, many settings are dynamic, with
populations responding to sequential decision policies. We introduce the study
of reinforcement learning (RL) with stepwise fairness constraints, requiring
group fairness at each time step. Our focus is on tabular episodic RL, and we
provide learning algorithms with strong theoretical guarantees in regard to
policy optimality and fairness violation. Our framework provides useful tools
to study the impact of fairness constraints in sequential settings and brings
up new challenges in RL.

Change point detection (CPD) methods aim to detect abrupt changes in
time-series data. Recent CPD methods have demonstrated their potential in
identifying changes in underlying statistical distributions but often fail to
capture complex changes in the correlation structure in time-series data. These
methods also fail to generalize effectively, as even within the same
time-series, different kinds of change points (CPs) may arise that are best
characterized by different types of time-series perturbations. To address this
issue, we propose TiVaCPD, a CPD methodology that uses a time-varying graphical
lasso based method to identify changes in correlation patterns between features
over time, and combines that with an aggregate Kernel Maximum Mean Discrepancy
(MMD) test to identify subtle changes in the underlying statistical
distributions of dynamically established time windows. We evaluate the
performance of TiVaCPD in identifying and characterizing various types of CPs
in time-series and show that our method outperforms current state-of-the-art
CPD methods for all categories of CPs.

Modern retrieval system often requires recomputing the representation of
every piece of data in the gallery when updating to a better representation
model. This process is known as backfilling and can be especially costly in the
real world where the gallery often contains billions of samples. Recently,
researchers have proposed the idea of Backward Compatible Training (BCT) where
the new representation model can be trained with an auxiliary loss to make it
backward compatible with the old representation. In this way, the new
representation can be directly compared with the old representation, in
principle avoiding the need for any backfilling. However, followup work shows
that there is an inherent tradeoff where a backward compatible representation
model cannot simultaneously maintain the performance of the new model itself.
This paper reports our ``not-so-surprising'' finding that adding extra
dimensions to the representation can help here. However, we also found that
naively increasing the dimension of the representation did not work. To deal
with this, we propose Backward-compatible Training with a novel Basis
Transformation ($BT^2$). A basis transformation (BT) is basically a learnable
set of parameters that applies an orthonormal transformation. Such a
transformation possesses an important property whereby the original information
contained in its input is retained in its output. We show in this paper how a
BT can be utilized to add only the necessary amount of additional dimensions.
We empirically verify the advantage of $BT^2$ over other state-of-the-art
methods in a wide range of settings. We then further extend $BT^2$ to other
challenging yet more practical settings, including significant change in model
architecture (CNN to Transformers), modality change, and even a series of
updates in the model architecture mimicking the evolution of deep learning
models.

Data depth, introduced by Tukey (1975), is an important tool in data science,
robust statistics, and computational geometry. One chief barrier to its broader
practical utility is that many common measures of depth are computationally
intensive, requiring on the order of $n^d$ operations to exactly compute the
depth of a single point within a data set of $n$ points in $d$-dimensional
space. Often however, we are not directly interested in the absolute depths of
the points, but rather in their relative ordering. For example, we may want to
find the most central point in a data set (a generalized median), or to
identify and remove all outliers (points on the fringe of the data set with low
depth). With this observation, we develop a novel and instance-adaptive
algorithm for adaptive data depth computation by reducing the problem of
exactly computing $n$ depths to an $n$-armed stochastic multi-armed bandit
problem which we can efficiently solve. We focus our exposition on simplicial
depth, developed by Liu (1990), which has emerged as a promising notion of
depth due to its interpretability and asymptotic properties. We provide general
instance-dependent theoretical guarantees for our proposed algorithms, which
readily extend to many other common measures of data depth including majority
depth, Oja depth, and likelihood depth. When specialized to the case where the
gaps in the data follow a power law distribution with parameter $\alpha<2$, we
show that we can reduce the complexity of identifying the deepest point in the
data set (the simplicial median) from $O(n^d)$ to
$\tilde{O}(n^{d-(d-1)\alpha/2})$, where $\tilde{O}$ suppresses logarithmic
factors. We corroborate our theoretical results with numerical experiments on
synthetic data, showing the practical utility of our proposed methods.

We focus on causal discovery in the presence of measurement error in linear
systems where the mixing matrix, i.e., the matrix indicating the independent
exogenous noise terms pertaining to the observed variables, is identified up to
permutation and scaling of the columns. We demonstrate a somewhat surprising
connection between this problem and causal discovery in the presence of
unobserved parentless causes, in the sense that there is a mapping, given by
the mixing matrix, between the underlying models to be inferred in these
problems. Consequently, any identifiability result based on the mixing matrix
for one model translates to an identifiability result for the other model. We
characterize to what extent the causal models can be identified under a
two-part faithfulness assumption. Under only the first part of the assumption
(corresponding to the conventional definition of faithfulness), the structure
can be learned up to the causal ordering among an ordered grouping of the
variables but not all the edges across the groups can be identified. We further
show that if both parts of the faithfulness assumption are imposed, the
structure can be learned up to a more refined ordered grouping. As a result of
this refinement, for the latent variable model with unobserved parentless
causes, the structure can be identified. Based on our theoretical results, we
propose causal structure learning methods for both models, and evaluate their
performance on synthetic data.

This paper studies reinforcement learning (RL) in doubly inhomogeneous
environments under temporal non-stationarity and subject heterogeneity. In a
number of applications, it is commonplace to encounter datasets generated by
system dynamics that may change over time and population, challenging
high-quality sequential decision making. Nonetheless, most existing RL
solutions require either temporal stationarity or subject homogeneity, which
would result in sub-optimal policies if both assumptions were violated. To
address both challenges simultaneously, we propose an original algorithm to
determine the ``best data chunks" that display similar dynamics over time and
across individuals for policy learning, which alternates between most recent
change point detection and cluster identification. Our method is general, and
works with a wide range of clustering and change point detection algorithms. It
is multiply robust in the sense that it takes multiple initial estimators as
input and only requires one of them to be consistent. Moreover, by borrowing
information over time and population, it allows us to detect weaker signals and
has better convergence properties when compared to applying the clustering
algorithm per time or the change point detection algorithm per subject.
Empirically, we demonstrate the usefulness of our method through extensive
simulations and a real data application.

In this study, we propose a global optimization algorithm based on quantizing
the energy level of an objective function in an NP-hard problem. According to
the white noise hypothesis for a quantization error with a dense and uniform
distribution, we can regard the quantization error as i.i.d. white noise. From
stochastic analysis, the proposed algorithm converges weakly only under
conditions satisfying Lipschitz continuity, instead of local convergence
properties such as the Hessian constraint of the objective function. This shows
that the proposed algorithm ensures global optimization by Laplace's condition.
Numerical experiments show that the proposed algorithm outperforms conventional
learning methods in solving NP-hard optimization problems such as the traveling
salesman problem.

Despite their popularity in deep learning and machine learning in general,
the theoretical properties of adaptive optimizers such as Adagrad, RMSProp,
Adam or AdamW are not yet fully understood. In this paper, we develop a novel
framework to study the stability and generalization of these optimization
methods. Based on this framework, we show provable guarantees about such
properties that depend heavily on a single parameter $\beta_2$. Our empirical
experiments support our claims and provide practical insights into the
stability and generalization properties of adaptive optimization methods.

The use of multilingual language models for tasks in low and high-resource
languages has been a success story in deep learning. In recent times, Arabic
has been receiving widespread attention on account of its dialectal variance.
While prior research studies have tried to adapt these multilingual models for
dialectal variants of Arabic, it still remains a challenging problem owing to
the lack of sufficient monolingual dialectal data and parallel translation data
of such dialectal variants. It remains an open problem on whether the limited
dialectical data can be used to improve the models trained in Arabic on its
dialectal variants. First, we show that multilingual-BERT (mBERT) incrementally
pretrained on Arabic monolingual data takes less training time and yields
comparable accuracy when compared to our custom monolingual Arabic model and
beat existing models (by an avg metric of +$6.41$). We then explore two
continual pre-training methods -- (1) using small amounts of dialectical data
for continual finetuning and (2) parallel Arabic to English data and a
Translation Language Modeling loss function. We show that both approaches help
improve performance on dialectal classification tasks ($+4.64$ avg. gain) when
used on monolingual models.

The $\ell_p$-norm regression problem is a classic problem in optimization
with wide ranging applications in machine learning and theoretical computer
science. The goal is to compute $x^{\star} =\arg\min_{Ax=b}\|x\|_p^p$, where
$x^{\star}\in \mathbb{R}^n, A\in \mathbb{R}^{d\times n},b \in \mathbb{R}^d$ and
$d\leq n$. Efficient high-accuracy algorithms for the problem have been
challenging both in theory and practice and the state of the art algorithms
require $poly(p)\cdot n^{\frac{1}{2}-\frac{1}{p}}$ linear system solves for
$p\geq 2$. In this paper, we provide new algorithms for $\ell_p$-regression
(and a more general formulation of the problem) that obtain a high-accuracy
solution in $O(p n^{\frac{(p-2)}{(3p-2)}})$ linear system solves. We further
propose a new inverse maintenance procedure that speeds-up our algorithm to
$\widetilde{O}(n^{\omega})$ total runtime, where $O(n^{\omega})$ denotes the
running time for multiplying $n \times n$ matrices. Additionally, we give the
first Iteratively Reweighted Least Squares (IRLS) algorithm that is guaranteed
to converge to an optimum in a few iterations. Our IRLS algorithm has shown
exceptional practical performance, beating the currently available
implementations in MATLAB/CVX by 10-50x.

The past few years have seen rapid progress in combining reinforcement
learning (RL) with deep learning. Various breakthroughs ranging from games to
robotics have spurred the interest in designing sophisticated RL algorithms and
systems. However, the prevailing workflow in RL is to learn tabula rasa, which
may incur computational inefficiency. This precludes continuous deployment of
RL algorithms and potentially excludes researchers without large-scale
computing resources. In many other areas of machine learning, the pretraining
paradigm has shown to be effective in acquiring transferable knowledge, which
can be utilized for a variety of downstream tasks. Recently, we saw a surge of
interest in Pretraining for Deep RL with promising results. However, much of
the research has been based on different experimental settings. Due to the
nature of RL, pretraining in this field is faced with unique challenges and
hence requires new design principles. In this survey, we seek to systematically
review existing works in pretraining for deep reinforcement learning, provide a
taxonomy of these methods, discuss each sub-field, and bring attention to open
problems and future directions.

Although numerous algorithms have been proposed to solve the categorical data
clustering problem, how to access the statistical significance of a set of
categorical clusters remains unaddressed. To fulfill this void, we employ the
likelihood ratio test to derive a test statistic that can serve as a
significance-based objective function in categorical data clustering.
Consequently, a new clustering algorithm is proposed in which the
significance-based objective function is optimized via a Monte Carlo search
procedure. As a by-product, we can further calculate an empirical $p$-value to
assess the statistical significance of a set of clusters and develop an
improved gap statistic for estimating the cluster number. Extensive
experimental studies suggest that our method is able to achieve comparable
performance to state-of-the-art categorical data clustering algorithms.
Moreover, the effectiveness of such a significance-based formulation on
statistical cluster validation and cluster number estimation is demonstrated
through comprehensive empirical results.

Sparsity of formal knowledge and roughness of non-ontological construction
make sparsity problem particularly prominent in Open Knowledge Graphs
(OpenKGs). Due to sparse links, learning effective representation for few-shot
entities becomes difficult. We hypothesize that by introducing negative
samples, a contrastive learning (CL) formulation could be beneficial in such
scenarios. However, existing CL methods model KG triplets as binary objects of
entities ignoring the relation-guided ternary propagation patterns and they are
too generic, i.e., they ignore zero-shot, few-shot and synonymity problems that
appear in OpenKGs. To address this, we propose TernaryCL, a CL framework based
on ternary propagation patterns among head, relation and tail. TernaryCL
designs Contrastive Entity and Contrastive Relation to mine ternary
discriminative features with both negative entities and relations, introduces
Contrastive Self to help zero- and few-shot entities learn discriminative
features, Contrastive Synonym to model synonymous entities, and Contrastive
Fusion to aggregate graph features from multiple paths. Extensive experiments
on benchmarks demonstrate the superiority of TernaryCL over state-of-the-art
models.

In decentralized stochastic control (or stochastic team theory) and game
theory, if there is a pre-defined order in a system in which the agents act,
the system is called sequential, otherwise it is non-sequential. Much of the
literature on stochastic control theory, such as studies on the existence
analysis, approximation methods, and on dynamic programming or other analytical
or learning theoretic methods, have focused on sequential systems. The static
reduction method for sequential control problems (via change of measures or
other techniques), in particular, has been shown to be very effective in
arriving at existence, structural, approximation and learning theoretic
results. Many practical systems, however, are non-sequential where the order of
agents acting is random, and dependent on the realization of solution paths and
prior actions taken. The study of such systems is particularly challenging as
tools applicable for sequential models are not directly applicable. In this
paper, we will study static reducibility of non-sequential stochastic control
systems, including by change of measure methods. We revisit the notion of
Causality (a definition due to Witsenhausen and which has been refined by
Andersland and Tekenetzis), and provide an alternative representation using
imaginary agents. Via this representation, we show that Causality, under an
absolute continuity condition, allows for an equivalent static model whose
reduction is policy-independent. This facilitates much of the stochastic
analysis available for sequential systems to also be applicable for
non-sequential systems. We further show that under more relaxed conditions on
the model, such as solvability, such a reduction, when possible at all, is
policy-dependent and thus has limited utility. We will also present a further
reduction method for partially nested causal non-sequential systems.

Mixup is a popular data augmentation technique based on creating new samples
by linear interpolation between two given data samples, to improve both the
generalization and robustness of the trained model. Knowledge distillation
(KD), on the other hand, is widely used for model compression and transfer
learning, which involves using a larger network's implicit knowledge to guide
the learning of a smaller network. At first glance, these two techniques seem
very different, however, we found that "smoothness" is the connecting link
between the two and is also a crucial attribute in understanding KD's interplay
with mixup. Although many mixup variants and distillation methods have been
proposed, much remains to be understood regarding the role of a mixup in
knowledge distillation. In this paper, we present a detailed empirical study on
various important dimensions of compatibility between mixup and knowledge
distillation. We also scrutinize the behavior of the networks trained with a
mixup in the light of knowledge distillation through extensive analysis,
visualizations, and comprehensive experiments on image classification. Finally,
based on our findings, we suggest improved strategies to guide the student
network to enhance its effectiveness. Additionally, the findings of this study
provide insightful suggestions to researchers and practitioners that commonly
use techniques from KD. Our code is available at
https://github.com/hchoi71/MIX-KD.

We consider private federated learning (FL), where a server aggregates
differentially private gradient updates from a large number of clients in order
to train a machine learning model. The main challenge is balancing privacy with
both classification accuracy of the learned model as well as the amount of
communication between the clients and server. In this work, we build on a
recently proposed method for communication-efficient private FL -- the MVU
mechanism -- by introducing a new interpolation mechanism that can accommodate
a more efficient privacy analysis. The result is the new Interpolated MVU
mechanism that provides SOTA results on communication-efficient private FL on a
variety of datasets.

Quantum search algorithms offer a remarkable advantage of quadratic reduction
in query complexity using quantum superposition principle. However, how an
actual architecture may access and handle the database in a quantum superposed
state has been largely unexplored so far; the quantum state of data was simply
assumed to be prepared and accessed by a black-box operation -- so-called
quantum oracle, even though this process, if not appropriately designed, may
adversely diminish the quantum query advantage. Here, we introduce an efficient
quantum data-access process, dubbed as quantum data-access machine (QDAM), and
present a general architecture for quantum search algorithm. We analyze the
runtime of our algorithm in view of the fault-tolerant quantum computation
(FTQC) consisting of logical qubits within an effective quantum error
correction code. Specifically, we introduce a measure involving two
computational complexities, i.e. quantum query and $T$-depth complexities,
which can be critical to assess performance since the logical non-Clifford
gates, such as the $T$ (i.e., $\pi/8$ rotation) gate, are known to be costliest
to implement in FTQC. Our analysis shows that for $N$ searching data, a QDAM
model exhibiting a logarithmic, i.e., $O(\log{N})$, growth of the $T$-depth
complexity can be constructed. Further analysis reveals that our QDAM-embedded
quantum search requires $O(\sqrt{N} \times \log{N})$ runtime cost. Our study
thus demonstrates that the quantum data search algorithm can truly speed up
over classical approaches with the logarithmic $T$-depth QDAM as a key
component.

Many scientific domains gather sufficient labels to train machine algorithms
through human-in-the-loop techniques provided by the Zooniverse.org citizen
science platform. As the range of projects, task types and data rates increase,
acceleration of model training is of paramount concern to focus volunteer
effort where most needed. The application of Transfer Learning (TL) between
Zooniverse projects holds promise as a solution. However, understanding the
effectiveness of TL approaches that pretrain on large-scale generic image sets
vs. images with similar characteristics possibly from similar tasks is an open
challenge. We apply a generative segmentation model on two Zooniverse
project-based data sets: (1) to identify fat droplets in liver cells
(FatChecker; FC) and (2) the identification of kelp beds in satellite images
(Floating Forests; FF) through transfer learning from the first project. We
compare and contrast its performance with a TL model based on the COCO image
set, and subsequently with baseline counterparts. We find that both the FC and
COCO TL models perform better than the baseline cases when using >75% of the
original training sample size. The COCO-based TL model generally performs
better than the FC-based one, likely due to its generalized features. Our
investigations provide important insights into usage of TL approaches on
multi-domain data hosted across different Zooniverse projects, enabling future
projects to accelerate task completion.

Human behavior is the potential and expressive capacity (mental, physical,
and social) of human individuals or groups to respond to internal and external
stimuli. We explore assortative matching as a typical human behavior in virtual
networked communities. We propose a modeling approach based on MAS(Multi-Agent
System) and policy-based reinforcement learning to simulate human behavior
through various environmental parameter settings and agent action strategies.
In our experiment, reinforcement learning serves specific agents who learn from
the environment status and competitor behaviors, then optimize strategy to
achieve better results. This work simulates both the individual and group
level, showing some possible paths for forming relative competitive advantages.
This modeling approach can help further analyze the evolutionary dynamics of
human behavior, communities, and organizations on various socioeconomic topics.

Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance or
Kullback-Leibler divergence between the original data and its low-rank
approximation which often suffers from grossly corruptions or outliers and the
neglect of manifold structures of data. In particular, NTF suffers from
rotational ambiguity, whose solutions with and without rotation transformations
are equally in the sense of yielding the maximum likelihood. In this paper, we
propose three Robust Manifold NTF algorithms to handle outliers by
incorporating structural knowledge about the outliers. They first applies a
half-quadratic optimization algorithm to transform the problem into a general
weighted NTF where the weights are influenced by the outliers. Then, we
introduce the correntropy induced metric, Huber function and Cauchy function
for weights respectively, to handle the outliers. Finally, we introduce a
manifold regularization to overcome the rotational ambiguity of NTF. We have
compared the proposed method with a number of representative references
covering major branches of NTF on a variety of real-world image databases.
Experimental results illustrate the effectiveness of the proposed method under
two evaluation metrics (accuracy and nmi).

Network intrusion detection systems (NIDS) to detect malicious attacks
continues to meet challenges. NIDS are vulnerable to auto-generated port scan
infiltration attempts and NIDS are often developed offline, resulting in a time
lag to prevent the spread of infiltration to other parts of a network. To
address these challenges, we use hypergraphs to capture evolving patterns of
port scan attacks via the set of internet protocol addresses and destination
ports, thereby deriving a set of hypergraph-based metrics to train a robust and
resilient ensemble machine learning (ML) NIDS that effectively monitors and
detects port scanning activities and adversarial intrusions while evolving
intelligently in real-time. Through the combination of (1) intrusion examples,
(2) NIDS update rules, (3) attack threshold choices to trigger NIDS retraining
requests, and (4) production environment with no prior knowledge of the nature
of network traffic 40 scenarios were auto-generated to evaluate the ML ensemble
NIDS comprising three tree-based models. Results show that under the model
settings of an Update-ALL-NIDS rule (namely, retrain and update all the three
models upon the same NIDS retraining request) the proposed ML ensemble NIDS
produced the best results with nearly 100% detection performance throughout the
simulation, exhibiting robustness in the complex dynamics of the simulated
cyber-security scenario.

Motivated by the increasing application of low-resolution LiDAR recently, we
target the problem of low-resolution LiDAR-camera calibration in this work. The
main challenges are two-fold: sparsity and noise in point clouds. To address
the problem, we propose to apply depth interpolation to increase the point
density and supervised contrastive learning to learn noise-resistant features.
The experiments on RELLIS-3D demonstrate that our approach achieves an average
mean absolute rotation/translation errors of 0.15cm/0.33\textdegree on
32-channel LiDAR point cloud data, which significantly outperforms all
reference methods.

Many self-supervised speech models, varying in their pre-training objective,
input modality, and pre-training data, have been proposed in the last few
years. Despite impressive empirical successes on downstream tasks, we still
have a limited understanding of the properties encoded by the models and the
differences across models. In this work, we examine the intermediate
representations for a variety of recent models. Specifically, we measure
acoustic, phonetic, and word-level properties encoded in individual layers,
using a lightweight analysis tool based on canonical correlation analysis
(CCA). We find that these properties evolve across layers differently depending
on the model, and the variations relate to the choice of pre-training
objective. We further investigate the utility of our analyses for downstream
tasks by comparing the property trends with performance on speech recognition
and spoken language understanding tasks. We discover that CCA trends provide
reliable guidance to choose layers of interest for downstream tasks and that
single-layer performance often matches or improves upon using all layers,
suggesting implications for more efficient use of pre-trained models.

Almonds are one of the most lucrative products of California, but are also
among the most sensitive to climate change. In order to better understand the
relationship between climatic factors and almond yield, an automated machine
learning framework is used to build a collection of machine learning models.
The prediction skill is assessed using historical records. Future projections
are derived using 17 downscaled climate outputs. The ensemble mean projection
displays almond yield changes under two different climate scenarios, along with
two technology development scenarios, where the role of technology development
is highlighted. The mean projections and distributions provide insightful
results to stakeholders and can be utilized by policymakers for climate

In this work, we propose a framework relying solely on chat-based customer
support (CS) interactions for predicting the recommendation decision of
individual users. For our case study, we analyzed a total number of 16.4k users
and 48.7k customer support conversations within the financial vertical of a
large e-commerce company in Latin America. Consequently, our main contributions
and objectives are to use Natural Language Processing (NLP) to assess and
predict the recommendation behavior where, in addition to using static
sentiment analysis, we exploit the predictive power of each user's sentiment
dynamics. Our results show that, with respective feature interpretability, it
is possible to predict the likelihood of a user to recommend a product or
service, based solely on the message-wise sentiment evolution of their CS
conversations in a fully automated way.

Multi-object tracking is a cornerstone capability of any robotic system. Most
approaches follow a tracking-by-detection paradigm. However, within this
framework, detectors function in a low precision-high recall regime, ensuring a
low number of false-negatives while producing a high rate of false-positives.
This can negatively affect the tracking component by making data association
and track lifecycle management more challenging. Additionally, false-negative
detections due to difficult scenarios like occlusions can negatively affect
tracking performance. Thus, we propose a method that learns shape and
spatio-temporal affinities between consecutive frames to better distinguish
between true-positive and false-positive detections and tracks, while
compensating for false-negative detections. Our method provides a probabilistic
matching of detections that leads to robust data association and track
lifecycle management. We quantitatively evaluate our method through ablative
experiments and on the nuScenes tracking benchmark where we achieve
state-of-the-art results. Our method not only estimates accurate, high-quality
tracks but also decreases the overall number of false-positive and
false-negative tracks. Please see our project website for source code and demo
videos: sites.google.com/view/shasta-3d-mot/home.

Accelerating the numerical integration of partial differential equations by
learned surrogate model is a promising area of inquiry in the field of air
pollution modeling. Most previous efforts in this field have been made on
learned chemical operators though machine-learned fluid dynamics has been a
more blooming area in machine learning community. Here we show the first trial
on accelerating advection operator in the domain of air quality model using a
realistic wind velocity dataset. We designed a convolutional neural
network-based solver giving coefficients to integrate the advection equation.
We generated a training dataset using a 2nd order Van Leer type scheme with the
10-day east-west components of wind data on 39$^{\circ}$N within North America.
The trained model with coarse-graining showed good accuracy overall, but
instability occurred in a few cases. Our approach achieved up to 12.5$\times$
acceleration. The learned schemes also showed fair results in generalization
tests.

We study non-parametric estimation of the value function of an
infinite-horizon $\gamma$-discounted Markov reward process (MRP) using
observations from a single trajectory. We provide non-asymptotic guarantees for
a general family of kernel-based multi-step temporal difference (TD) estimates,
including canonical $K$-step look-ahead TD for $K = 1, 2, \ldots$ and the
TD$(\lambda)$ family for $\lambda \in [0,1)$ as special cases. Our bounds
capture its dependence on Bellman fluctuations, mixing time of the Markov
chain, any mis-specification in the model, as well as the choice of weight
function defining the estimator itself, and reveal some delicate interactions
between mixing time and model mis-specification. For a given TD method applied
to a well-specified model, its statistical error under trajectory data is
similar to that of i.i.d. sample transition pairs, whereas under
mis-specification, temporal dependence in data inflates the statistical error.
However, any such deterioration can be mitigated by increased look-ahead. We
complement our upper bounds by proving minimax lower bounds that establish
optimality of TD-based methods with appropriately chosen look-ahead and
weighting, and reveal some fundamental differences between value function
estimation and ordinary non-parametric regression.

Since its introduction in 2006, differential privacy has emerged as a
predominant statistical tool for quantifying data privacy in academic works.
Yet despite the plethora of research and open-source utilities that have
accompanied its rise, with limited exceptions, differential privacy has failed
to achieve widespread adoption in the enterprise domain. Our study aims to shed
light on the fundamental causes underlying this academic-industrial utilization
gap through detailed interviews of 24 privacy practitioners across 9 major
companies. We analyze the results of our survey to provide key findings and
suggestions for companies striving to improve privacy protection in their data
workflows and highlight the necessary and missing requirements of existing
differential privacy tools, with the goal of guiding researchers working
towards the broader adoption of differential privacy. Our findings indicate
that analysts suffer from lengthy bureaucratic processes for requesting access
to sensitive data, yet once granted, only scarcely-enforced privacy policies
stand between rogue practitioners and misuse of private information. We thus
argue that differential privacy can significantly improve the processes of
requesting and conducting data exploration across silos, and conclude that with
a few of the improvements suggested herein, the practical use of differential
privacy across the enterprise is within striking distance.

Obtaining photorealistic reconstructions of objects from sparse views is
inherently ambiguous and can only be achieved by learning suitable
reconstruction priors. Earlier works on sparse rigid object reconstruction
successfully learned such priors from large datasets such as CO3D. In this
paper, we extend this approach to dynamic objects. We use cats and dogs as a
representative example and introduce Common Pets in 3D (CoP3D), a collection of
crowd-sourced videos showing around 4,200 distinct pets. CoP3D is one of the
first large-scale datasets for benchmarking non-rigid 3D reconstruction "in the
wild". We also propose Tracker-NeRF, a method for learning 4D reconstruction
from our dataset. At test time, given a small number of video frames of an
unseen object, Tracker-NeRF predicts the trajectories of its 3D points and
generates new views, interpolating viewpoint and time. Results on CoP3D reveal
significantly better non-rigid new-view synthesis performance than existing

As mobile cameras with compact optics are unable to produce a strong bokeh
effect, lots of interest is now devoted to deep learning-based solutions for
this task. In this Mobile AI challenge, the target was to develop an efficient
end-to-end AI-based bokeh effect rendering approach that can run on modern
smartphone GPUs using TensorFlow Lite. The participants were provided with a
large-scale EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field
image pairs captured using the Canon 7D DSLR camera. The runtime of the
resulting models was evaluated on the Kirin 9000's Mali GPU that provides
excellent acceleration results for the majority of common deep learning ops. A
detailed description of all models developed in this challenge is provided in
this paper.

Video super-resolution is one of the most popular tasks on mobile devices,
being widely used for an automatic improvement of low-bitrate and
low-resolution video streams. While numerous solutions have been proposed for
this problem, they are usually quite computationally demanding, demonstrating
low FPS rates and power efficiency on mobile devices. In this Mobile AI
challenge, we address this problem and propose the participants to design an
end-to-end real-time video super-resolution solution for mobile NPUs optimized
for low energy consumption. The participants were provided with the REDS
training dataset containing video sequences for a 4X video upscaling task. The
runtime and power efficiency of all models was evaluated on the powerful
MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of
accelerating floating-point and quantized neural networks. All proposed
solutions are fully compatible with the above NPU, demonstrating an up to 500
FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of
all models developed in the challenge is provided in this paper.

Various depth estimation models are now widely used on many mobile and IoT
devices for image segmentation, bokeh effect rendering, object tracking and
many other mobile tasks. Thus, it is very crucial to have efficient and
accurate depth estimation models that can run fast on low-power mobile
chipsets. In this Mobile AI challenge, the target was to develop deep
learning-based single image depth estimation solutions that can show a
real-time performance on IoT platforms and smartphones. For this, the
participants used a large-scale RGB-to-depth dataset that was collected with
the ZED stereo camera capable to generated depth maps for objects located at up
to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4
platform, where the developed solutions were able to generate VGA resolution
depth maps at up to 27 FPS while achieving high fidelity results. All models
developed in the challenge are also compatible with any Android or Linux-based
mobile devices, their detailed description is provided in this paper.

The role of mobile cameras increased dramatically over the past few years,
leading to more and more research in automatic image quality enhancement and
RAW photo processing. In this Mobile AI challenge, the target was to develop an
efficient end-to-end AI-based image signal processing (ISP) pipeline replacing
the standard mobile ISPs that can run on modern smartphone GPUs using
TensorFlow Lite. The participants were provided with a large-scale Fujifilm
GFX100 camera. The runtime of the resulting models was evaluated on the
Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the
majority of common deep learning ops. The proposed solutions are compatible
with all recent mobile GPUs, being able to process Full HD photos in less than
20-50 milliseconds while achieving high fidelity results. A detailed
description of all models developed in this challenge is provided in this
paper.

We present the Neural Satisfiability Network (NSNet), a general neural
framework that models satisfiability problems as probabilistic inference and
meanwhile exhibits proper explainability. Inspired by the Belief Propagation
(BP), NSNet uses a novel graph neural network (GNN) to parameterize BP in the
latent space, where its hidden representations maintain the same probabilistic
interpretation as BP. NSNet can be flexibly configured to solve both SAT and
#SAT problems by applying different learning objectives. For SAT, instead of
directly predicting a satisfying assignment, NSNet performs marginal inference
among all satisfying solutions, which we empirically find is more feasible for
neural networks to learn. With the estimated marginals, a satisfying assignment
can be efficiently generated by rounding and executing a stochastic local
search. For #SAT, NSNet performs approximate model counting by learning the
Bethe approximation of the partition function. Our evaluations show that NSNet
achieves competitive results in terms of inference accuracy and time efficiency
on multiple SAT and #SAT datasets.

A lack of driver's vigilance is the main cause of most vehicle crashes.
Electroencephalography(EEG) has been reliable and efficient tool for drivers'
drowsiness estimation. Even though previous studies have developed accurate and
robust driver's vigilance detection algorithms, these methods are still facing
challenges on following areas: (a) small sample size training, (b) anomaly
signal detection, and (c) subject-independent classification. In this paper, we
propose a generalized few-shot model, namely EEG-Fest, to improve
aforementioned drawbacks. The EEG-Fest model can (a) classify the query
sample's drowsiness with a few samples, (b) identify whether a query sample is
anomaly signals or not, and (c) achieve subject independent classification. The
proposed algorithm achieves state-of-the-art results on the SEED-VIG dataset
and the SADT dataset. The accuracy of the drowsy class achieves 92% and 94% for
1-shot and 5-shot support samples in the SEED-VIG dataset, and 62% and 78% for
1-shot and 5-shot support samples in the SADT dataset.

This work introduces the novel task of Source-free Multi-target Domain
Adaptation and proposes adaptation framework comprising of \textbf{Co}nsistency
with \textbf{N}uclear-Norm Maximization and \textbf{Mix}Up knowledge
distillation (\textit{CoNMix}) as a solution to this problem.
  The main motive of this work is to solve for Single and Multi target Domain
Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint
where the labeled source data is not available during target adaptation due to
various privacy-related restrictions on data sharing. The source-free approach
leverages target pseudo labels, which can be noisy, to improve the target
adaptation. We introduce consistency between label preserving augmentations and
utilize pseudo label refinement methods to reduce noisy pseudo labels. Further,
we propose novel MixUp Knowledge Distillation (MKD) for better generalization
on multiple target domains using various source-free STDA models.
  We also show that the Vision Transformer (VT) backbone gives better feature
representation with improved domain transferability and class discriminability.
Our proposed framework achieves the state-of-the-art (SOTA) results in various
paradigms of source-free STDA and MTDA settings on popular domain adaptation
datasets like Office-Home, Office-Caltech, and DomainNet. Project Page:
https://sites.google.com/view/conmix-vcl

A classical result in learning theory shows the equivalence of PAC
learnability of binary hypothesis classes and the finiteness of VC dimension.
Extending this to the multiclass setting was an open problem, which was settled
in a recent breakthrough result characterizing multiclass PAC learnability via
the DS dimension introduced earlier by Daniely and Shalev-Shwartz. In this work
we consider list PAC learning where the goal is to output a list of $k$
predictions. List learning algorithms have been developed in several settings
before and indeed, list learning played an important role in the recent
characterization of multiclass learnability. In this work we ask: when is it
possible to $k$-list learn a hypothesis class? We completely characterize
$k$-list learnability in terms of a generalization of DS dimension that we call
the $k$-DS dimension. Generalizing the recent characterization of multiclass
learnability, we show that a hypothesis class is $k$-list learnable if and only
if the $k$-DS dimension is finite.

Detecting change-points in data is challenging because of the range of
possible types of change and types of behaviour of data when there is no
change. Statistically efficient methods for detecting a change will depend on
both of these features, and it can be difficult for a practitioner to develop
an appropriate detection method for their application of interest. We show how
to automatically generate new detection methods based on training a neural
network. Our approach is motivated by many existing tests for the presence of a
change-point being able to be represented by a simple neural network, and thus
a neural network trained with sufficient data should have performance at least
as good as these methods. We present theory that quantifies the error rate for
such an approach, and how it depends on the amount of training data. Empirical
results show that, even with limited training data, its performance is
competitive with the standard CUSUM test for detecting a change in mean when
the noise is independent and Gaussian, and can substantially outperform it in
the presence of auto-correlated or heavy-tailed noise. Our method also shows
strong results in detecting and localising changes in activity based on
accelerometer data.

Deep learning semantic segmentation algorithms have provided improved
frameworks for the automated production of Land-Use and Land-Cover (LULC) maps,
which significantly increases the frequency of map generation as well as
consistency of production quality. In this research, a total of 28 different
model variations were examined to improve the accuracy of LULC maps. The
experiments were carried out using Landsat 5/7 or Landsat 8 satellite images
with the North American Land Change Monitoring System labels. The performance
of various CNNs and extension combinations were assessed, where VGGNet with an
output stride of 4, and modified U-Net architecture provided the best results.
Additional expanded analysis of the generated LULC maps was also provided.
Using a deep neural network, this work achieved 92.4% accuracy for 13 LULC
classes within southern Manitoba representing a 15.8% improvement over
published results for the NALCMS. Based on the large regions of interest,
higher radiometric resolution of Landsat 8 data resulted in better overall
accuracies (88.04%) compare to Landsat 5/7 (80.66%) for 16 LULC classes. This
represents an 11.44% and 4.06% increase in overall accuracy compared to
previously published NALCMS results, including larger land area and higher
number of LULC classes incorporated into the models compared to other published
LULC map automation methods.

In this work, we demonstrate how differentiable stochastic sampling
techniques developed in the context of deep Reinforcement Learning can be used
to perform efficient parameter inference over stochastic, simulation-based,
forward models. As a particular example, we focus on the problem of estimating
parameters of Halo Occupancy Distribution (HOD) models which are used to
connect galaxies with their dark matter halos. Using a combination of
continuous relaxation and gradient parameterization techniques, we can obtain
well-defined gradients with respect to HOD parameters through discrete galaxy
catalogs realizations. Having access to these gradients allows us to leverage
efficient sampling schemes, such as Hamiltonian Monte-Carlo, and greatly speed
up parameter inference. We demonstrate our technique on a mock galaxy catalog
generated from the Bolshoi simulation using the Zheng et al. 2007 HOD model and
find near identical posteriors as standard Markov Chain Monte Carlo techniques
with an increase of ~8x in convergence efficiency. Our differentiable HOD model
also has broad applications in full forward model approaches to cosmic
structure and cosmological analysis.

We present Polite Teacher, a simple yet effective method for the task of
semi-supervised instance segmentation. The proposed architecture relies on the
Teacher-Student mutual learning framework. To filter out noisy pseudo-labels,
we use confidence thresholding for bounding boxes and mask scoring for masks.
The approach has been tested with CenterMask, a single-stage anchor-free
detector. Tested on the COCO 2017 val dataset, our architecture significantly
(approx. +8 pp. in mask AP) outperforms the baseline at different supervision
regimes. To the best of our knowledge, this is one of the first works tackling
the problem of semi-supervised instance segmentation and the first one devoted
to an anchor-free detector.

Causal discovery, the inference of causal relations from data, is a core task
of fundamental importance in all scientific domains, and several new machine
learning methods for addressing the causal discovery problem have been proposed
recently. However, existing machine learning methods for causal discovery
typically require that the data used for inference is pooled and available in a
centralized location. In many domains of high practical importance, such as in
healthcare, data is only available at local data-generating entities (e.g.
hospitals in the healthcare context), and cannot be shared across entities due
to, among others, privacy and regulatory reasons. In this work, we address the
problem of inferring causal structure - in the form of a directed acyclic graph
(DAG) - from a distributed data set that contains both observational and
interventional data in a privacy-preserving manner by exchanging updates
instead of samples. To this end, we introduce a new federated framework,
FED-CD, that enables the discovery of global causal structures both when the
set of intervened covariates is the same across decentralized entities, and
when the set of intervened covariates are potentially disjoint. We perform a
comprehensive experimental evaluation on synthetic data that demonstrates that
FED-CD enables effective aggregation of decentralized data for causal discovery
without direct sample sharing, even when the contributing distributed data sets
cover disjoint sets of interventions. Effective methods for causal discovery in
distributed data sets could significantly advance scientific discovery and
knowledge sharing in important settings, for instance, healthcare, in which
sharing of data across local sites is difficult or prohibited.

Parameter-efficient fine-tuning (PEFT) methods can adapt large language
models to downstream tasks by training a small amount of newly added
parameters. In multi-task settings, PEFT adapters typically train on each task
independently, inhibiting transfer across tasks, or on the concatenation of all
tasks, which can lead to negative interference. To address this, Polytropon
(Ponti et al.) jointly learns an inventory of PEFT adapters and a routing
function to share variable-size sets of adapters across tasks. Subsequently,
adapters can be re-combined and fine-tuned on novel tasks even with limited
data. In this paper, we investigate to what extent the ability to control which
adapters are active for each task leads to sample-efficient generalization.
Thus, we propose less expressive variants where we perform weighted averaging
of the adapters before few-shot adaptation (Poly-mu) instead of learning a
routing function. Moreover, we introduce more expressive variants where
finer-grained task-adapter allocation is learned through a multi-head routing
function (Poly-S). We test these variants on three separate benchmarks for
multi-task learning. We find that Poly-S achieves gains on all three (up to 5.3
points on average) over strong baselines, while incurring a negligible
additional cost in parameter count. In particular, we find that instruction
tuning, where models are fully fine-tuned on natural language instructions for
each task, is inferior to modular methods such as Polytropon and our proposed
variants.

Tensor decomposition serves as a powerful primitive in statistics and machine
learning. In this paper, we focus on using power iteration to decompose an
overcomplete random tensor. Past work studying the properties of tensor power
iteration either requires a non-trivial data-independent initialization, or is
restricted to the undercomplete regime. Moreover, several papers implicitly
suggest that logarithmically many iterations (in terms of the input dimension)
are sufficient for the power method to recover one of the tensor components. In
this paper, we analyze the dynamics of tensor power iteration from random
initialization in the overcomplete regime. Surprisingly, we show that
polynomially many steps are necessary for convergence of tensor power iteration
to any of the true component, which refutes the previous conjecture. On the
other hand, our numerical experiments suggest that tensor power iteration
successfully recovers tensor components for a broad range of parameters,
despite that it takes at least polynomially many steps to converge. To further
complement our empirical evidence, we prove that a popular objective function
for tensor decomposition is strictly increasing along the power iteration path.
Our proof is based on the Gaussian conditioning technique, which has been
applied to analyze the approximate message passing (AMP) algorithm. The major
ingredient of our argument is a conditioning lemma that allows us to generalize
AMP-type analysis to non-proportional limit and polynomially many iterations of
the power method.

Inferring accurate posteriors for high-dimensional representations of the
brightness of gravitationally-lensed sources is a major challenge, in part due
to the difficulties of accurately quantifying the priors. Here, we report the
use of a score-based model to encode the prior for the inference of undistorted
images of background galaxies. This model is trained on a set of
high-resolution images of undistorted galaxies. By adding the likelihood score
to the prior score and using a reverse-time stochastic differential equation
solver, we obtain samples from the posterior. Our method produces independent
posterior samples and models the data almost down to the noise level. We show
how the balance between the likelihood and the prior meet our expectations in
an experiment with out-of-distribution data.

In computer-aided drug discovery (CADD), virtual screening (VS) is used for
identifying the drug candidates that are most likely to bind to a molecular
target in a large library of compounds. Most VS methods to date have focused on
using canonical compound representations (e.g., SMILES strings, Morgan
fingerprints) or generating alternative fingerprints of the compounds by
training progressively more complex variational autoencoders (VAEs) and graph
neural networks (GNNs). Although VAEs and GNNs led to significant improvements
in VS performance, these methods suffer from reduced performance when scaling
to large virtual compound datasets. The performance of these methods has shown
only incremental improvements in the past few years. To address this problem,
we developed a novel method using multiparameter persistence (MP) homology that
produces topological fingerprints of the compounds as multidimensional vectors.
Our primary contribution is framing the VS process as a new topology-based
graph ranking problem by partitioning a compound into chemical substructures
informed by the periodic properties of its atoms and extracting their
persistent homology features at multiple resolution levels. We show that the
margin loss fine-tuning of pretrained Triplet networks attains highly
competitive results in differentiating between compounds in the embedding space
and ranking their likelihood of becoming effective drug candidates. We further
establish theoretical guarantees for the stability properties of our proposed
MP signatures, and demonstrate that our models, enhanced by the MP signatures,
outperform state-of-the-art methods on benchmark datasets by a wide and highly
statistically significant margin (e.g., 93% gain for Cleves-Jain and 54% gain
for DUD-E Diverse dataset).

The Hamiltonian of an isolated quantum mechanical system determines its
dynamics and physical behaviour. This study investigates the possibility of
learning and utilising a system's Hamiltonian and its variational thermal state
estimation for data analysis techniques. For this purpose, we employ the method
of Quantum Hamiltonian-Based Models for the generative modelling of simulated
Large Hadron Collider data and demonstrate the representability of such data as
a mixed state. In a further step, we use the learned Hamiltonian for anomaly
detection, showing that different sample types can form distinct dynamical
behaviours once treated as a quantum many-body system. We exploit these
characteristics to quantify the difference between sample types. Our findings
show that the methodologies designed for field theory computations can be
utilised in machine learning applications to employ theoretical approaches in
data analysis techniques.

In recent years, deep learning has infiltrated every field it has touched,
reducing the need for specialist knowledge and automating the process of
knowledge discovery from data. This review argues that astronomy is no
different, and that we are currently in the midst of a deep learning revolution
that is transforming the way we do astronomy. We trace the history of
astronomical connectionism from the early days of multilayer perceptrons,
through the second wave of convolutional and recurrent neural networks, to the
current third wave of self-supervised and unsupervised deep learning. We then
predict that we will soon enter a fourth wave of astronomical connectionism, in
which finetuned versions of an all-encompassing 'foundation' model will replace
expertly crafted deep learning models. We argue that such a model can only be
brought about through a symbiotic relationship between astronomy and
connectionism, whereby astronomy provides high quality multimodal data to train
the foundation model, and in turn the foundation model is used to advance
astronomical research.

In this work, we show how to learn a visual walking policy that only uses a
monocular RGB camera and proprioception. Since simulating RGB is hard, we
necessarily have to learn vision in the real world. We start with a blind
walking policy trained in simulation. This policy can traverse some terrains in
the real world but often struggles since it lacks knowledge of the upcoming
geometry. This can be resolved with the use of vision. We train a visual module
in the real world to predict the upcoming terrain with our proposed algorithm
Cross-Modal Supervision (CMS). CMS uses time-shifted proprioception to
supervise vision and allows the policy to continually improve with more
real-world experience. We evaluate our vision-based walking policy over a
diverse set of terrains including stairs (up to 19cm high), slippery slopes
(inclination of 35 degrees), curbs and tall steps (up to 20cm), and complex
discrete terrains. We achieve this performance with less than 30 minutes of
real-world data. Finally, we show that our policy can adapt to shifts in the
visual field with a limited amount of real-world experience. Video results and
code at https://antonilo.github.io/vision_locomotion/.

Unsupervised representation learning aims at describing raw data efficiently
to solve various downstream tasks. It has been approached with many techniques,
such as manifold learning, diffusion maps, or more recently self-supervised
learning. Those techniques are arguably all based on the underlying assumption
that target functions, associated with future downstream tasks, have low
variations in densely populated regions of the input space. Unveiling minimal
variations as a guiding principle behind unsupervised representation learning
paves the way to better practical guidelines for self-supervised learning

Objective: Dyspnea is one of the most common symptoms for many pulmonary
diseases including COVID-19. Clinical assessment of dyspnea is mainly performed
by subjective self-report, which has limited accuracy and is challenging for
continuous monitoring. The objective of this research study is to determine if
dyspnea progression in COVID patients can be assessed using a non-invasive
wearable sensor and if the findings are comparable to a learning model of
physiologically induced dyspnea on healthy subjects. Methods: Non-invasive
wearable respiratory sensors were employed to retrieve continuous respiratory
characteristics with user comfort and convenience. Overnight (~16h) respiratory
waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy
subjects with exertion-induced dyspnea were also performed for blind
comparison. The learning model was built from the respiratory features with
self report on 32 healthy subjects under exertion and airway blockage. Results:
High similarity between dyspnea on COVID patients and physiologically induced
dyspnea on healthy subjects was established. COVID patients have consistently
high objective dyspnea scores in comparison with normal breathing of healthy
subjects. We also exhibited continuous dyspnea scoring capability for 12-16
hours on patients. Conclusion: This paper validates the viability to use our
objective dyspnea scoring for clinical dyspnea assessment on COVID patients.
Significance: The proposed system can help the identification of dyspneic
exacerbation in conditions such as COVID, leading to early intervention and
possibly improving their outcome. This approach can be potentially applied to
other pulmonary disorders such as asthma, emphysema, and pneumonia.

The success of AlphaZero (AZ) has demonstrated that neural-network-based Go
AIs can surpass human performance by a large margin. Given that the state space
of Go is extremely large and a human player can play the game from any legal
state, we ask whether adversarial states exist for Go AIs that may lead them to
play surprisingly wrong actions. In this paper, we first extend the concept of
adversarial examples to the game of Go: we generate perturbed states that are
``semantically'' equivalent to the original state by adding meaningless moves
to the game, and an adversarial state is a perturbed state leading to an
undoubtedly inferior action that is obvious even for Go beginners. However,
searching the adversarial state is challenging due to the large, discrete, and
non-differentiable search space. To tackle this challenge, we develop the first
adversarial attack on Go AIs that can efficiently search for adversarial states
by strategically reducing the search space. This method can also be extended to
other board games such as NoGo. Experimentally, we show that the actions taken
by both Policy-Value neural network (PV-NN) and Monte Carlo tree search (MCTS)
can be misled by adding one or two meaningless stones; for example, on 58\% of
the AlphaGo Zero self-play games, our method can make the widely used KataGo
agent with 50 simulations of MCTS plays a losing action by adding two
meaningless stones. We additionally evaluated the adversarial examples found by
our algorithm with amateur human Go players and 90\% of examples indeed lead
the Go agent to play an obviously inferior action. Our code is available at
\url{https://PaperCode.cc/GoAttack}.

Conventional electromyography (EMG) measures the continuous neural activity
during muscle contraction, but lacks explicit quantification of the actual
contraction. Mechanomyography (MMG) and accelerometers only measure body
surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic
snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle
actuation sensing that can be wearable and touchless, capturing both
superficial and deep muscle groups. We verified RMG experimentally by a forearm
wearable sensor for detailed hand gesture recognition. We first converted the
radio sensing outputs to the time-frequency spectrogram, and then employed the
vision transformer (ViT) deep learning network as the classification model,
which can recognize 23 gestures with an average accuracy up to 99% on 8
subjects. By transfer learning, high adaptivity to user difference and sensor
variation were achieved at an average accuracy up to 97%. We further
demonstrated RMG to monitor eye and leg muscles and achieved high accuracy for
eye movement and body postures tracking. RMG can be used with synchronous EMG
to derive stimulation-actuation waveforms for many future applications in
kinesiology, physiotherapy, rehabilitation, and human-machine interface.

We initiate the study of proper losses for evaluating generative models in
the discrete setting. Unlike traditional proper losses, we treat both the
generative model and the target distribution as black-boxes, only assuming
ability to draw i.i.d. samples. We define a loss to be black-box proper if the
generative distribution that minimizes expected loss is equal to the target
distribution. Using techniques from statistical estimation theory, we give a
general construction and characterization of black-box proper losses: they must
take a polynomial form, and the number of draws from the model and target
distribution must exceed the degree of the polynomial. The characterization
rules out a loss whose expectation is the cross-entropy between the target
distribution and the model. By extending the construction to arbitrary sampling
schemes such as Poisson sampling, however, we show that one can construct such
a loss.

Machine learning models are now able to convert user-written text
descriptions into naturalistic images. These models are available to anyone
online and are being used to generate millions of images a day. We investigate
these models and find that they amplify dangerous and complex stereotypes.
Moreover, we find that the amplified stereotypes are difficult to predict and
not easily mitigated by users or model owners. The extent to which these
image-generation models perpetuate and amplify stereotypes and their mass
deployment is cause for serious concern.

We study discrete distribution estimation under user-level local differential
privacy (LDP). In user-level $\varepsilon$-LDP, each user has $m\ge1$ samples
and the privacy of all $m$ samples must be preserved simultaneously. We resolve
the following dilemma: While on the one hand having more samples per user
should provide more information about the underlying distribution, on the other
hand, guaranteeing the privacy of all $m$ samples should make the estimation
task more difficult. We obtain tight bounds for this problem under almost all
parameter regimes. Perhaps surprisingly, we show that in suitable parameter
regimes, having $m$ samples per user is equivalent to having $m$ times more
users, each with only one sample. Our results demonstrate interesting phase
transitions for $m$ and the privacy parameter $\varepsilon$ in the estimation
risk. Finally, connecting with recent results on shuffled DP, we show that
combined with random shuffling, our algorithm leads to optimal error guarantees
(up to logarithmic factors) under the central model of user-level DP in certain
parameter regimes. We provide several simulations to verify our theoretical
findings.

Upcoming astronomical surveys will observe billions of galaxies across cosmic
time, providing a unique opportunity to map the many pathways of galaxy
assembly to an incredibly high resolution. However, the huge amount of data
also poses an immediate computational challenge: current tools for inferring
parameters from the light of galaxies take $\gtrsim 10$ hours per fit. This is
prohibitively expensive. Simulation-based Inference (SBI) is a promising
solution. However, it requires simulated data with identical characteristics to
the observed data, whereas real astronomical surveys are often highly
heterogeneous, with missing observations and variable uncertainties determined
by sky and telescope conditions. Here we present a Monte Carlo technique for
treating out-of-distribution measurement errors and missing data using standard
SBI tools. We show that out-of-distribution measurement errors can be
approximated by using standard SBI evaluations, and that missing data can be
marginalized over using SBI evaluations over nearby data realizations in the
training set. While these techniques slow the inference process from $\sim 1$
sec to $\sim 1.5$ min per object, this is still significantly faster than
standard approaches while also dramatically expanding the applicability of SBI.
This expanded regime has broad implications for future applications to
astronomical surveys.

Predicting emotions expressed in text is a well-studied problem in the NLP
community. Recently there has been active research in extracting the cause of
an emotion expressed in text. Most of the previous work has done causal emotion
entailment in documents. In this work, we propose neural models to extract
emotion cause span and entailment in conversations. For learning such models,
we use RECCON dataset, which is annotated with cause spans at the utterance
level. In particular, we propose MuTEC, an end-to-end Multi-Task learning
framework for extracting emotions, emotion cause, and entailment in
conversations. This is in contrast to existing baseline models that use ground
truth emotions to extract the cause. MuTEC performs better than the baselines
for most of the data folds provided in the dataset.

In this paper, we develop a new algorithm, Annealed Skewed SGD - AskewSGD -
for training deep neural networks (DNNs) with quantized weights. First, we
formulate the training of quantized neural networks (QNNs) as a smoothed
sequence of interval-constrained optimization problems. Then, we propose a new
first-order stochastic method, AskewSGD, to solve each constrained optimization
subproblem. Unlike algorithms with active sets and feasible directions,
AskewSGD avoids projections or optimization under the entire feasible set and
allows iterates that are infeasible. The numerical complexity of AskewSGD is
comparable to existing approaches for training QNNs, such as the
straight-through gradient estimator used in BinaryConnect, or other state of
the art methods (ProxQuant, LUQ). We establish convergence guarantees for
AskewSGD (under general assumptions for the objective function). Experimental
results show that the AskewSGD algorithm performs better than or on par with
state of the art methods in classical benchmarks.

In this paper, we present a data-driven framework for real-time estimation of
reachable sets for control systems where the plant is modeled using neural
networks (NNs). We utilize a running example of a quadrotor model that is
learned using trajectory data via NNs. The NN learned offline, can be excited
online to obtain linear approximations for reachability analysis. We use a
dynamic mode decomposition based approach to obtain linear liftings of the NN
model. The linear models thus obtained can utilize optimal control theory to
obtain polytopic approximations to the reachable sets in real-time. The
polytopic approximations can be tuned to arbitrary degrees of accuracy. The
proposed framework can be extended to other nonlinear models that utilize NNs
to estimate plant dynamics. We demonstrate the effectiveness of the proposed
framework using an illustrative simulation of quadrotor dynamics.

Spelling error correction is the task of identifying and rectifying
misspelled words in texts. It is a potential and active research topic in
Natural Language Processing because of numerous applications in human language
understanding. The phonetically or visually similar yet semantically distinct
characters make it an arduous task in any language. Earlier efforts on spelling
error correction in Bangla and resource-scarce Indic languages focused on
rule-based, statistical, and machine learning-based methods which we found
rather inefficient. In particular, machine learning-based approaches, which
exhibit superior performance to rule-based and statistical methods, are
ineffective as they correct each character regardless of its appropriateness.
In this work, we propose a novel detector-purificator-corrector framework based
on denoising transformers by addressing previous issues. Moreover, we present a
method for large-scale corpus creation from scratch which in turn resolves the
resource limitation problem of any left-to-right scripted language. The
empirical outcomes demonstrate the effectiveness of our approach that
outperforms previous state-of-the-art methods by a significant margin for
Bangla spelling error correction. The models and corpus are publicly available
at https://tinyurl.com/DPCSpell.

Generic motion understanding from video involves not only tracking objects,
but also perceiving how their surfaces deform and move. This information is
useful to make inferences about 3D shape, physical properties and object
interactions. While the problem of tracking arbitrary physical points on
surfaces over longer video clips has received some attention, no dataset or
benchmark for evaluation existed, until now. In this paper, we first formalize
the problem, naming it tracking any point (TAP). We introduce a companion
benchmark, TAP-Vid, which is composed of both real-world videos with accurate
human annotations of point tracks, and synthetic videos with perfect
ground-truth point tracks. Central to the construction of our benchmark is a
novel semi-automatic crowdsourced pipeline which uses optical flow estimates to
compensate for easier, short-term motion like camera shake, allowing annotators
to focus on harder sections of video. We validate our pipeline on synthetic
data and propose a simple end-to-end point tracking model TAP-Net, showing that
it outperforms all prior methods on our benchmark when trained on synthetic
data.

Deep learning has been a popular topic and has achieved success in many
areas. It has drawn the attention of researchers and machine learning
practitioners alike, with developed models deployed to a variety of settings.
Along with its achievements, research has shown that deep learning models are
vulnerable to adversarial attacks. This finding brought about a new direction
in research, whereby algorithms were developed to attack and defend vulnerable
networks. Our interest is in understanding how these attacks effect change on
the intermediate representations of deep learning models. We present a method
for measuring and analyzing the deviations in representations induced by
adversarial attacks, progressively across a selected set of layers. Experiments
are conducted using an assortment of attack algorithms, on the CIFAR-10
dataset, with plots created to visualize the impact of adversarial attacks
across different layers in a network.

Existing graph contrastive learning methods rely on augmentation techniques
based on random perturbations (e.g., randomly adding or dropping edges and
nodes). Nevertheless, altering certain edges or nodes can unexpectedly change
the graph characteristics, and choosing the optimal perturbing ratio for each
dataset requires onerous manual tuning. In this paper, we introduce Implicit
Graph Contrastive Learning (iGCL), which utilizes augmentations in the latent
space learned from a Variational Graph Auto-Encoder by reconstructing graph
topological structure. Importantly, instead of explicitly sampling
augmentations from latent distributions, we further propose an upper bound for
the expected contrastive loss to improve the efficiency of our learning
algorithm. Thus, graph semantics can be preserved within the augmentations in
an intelligent way without arbitrary manual design or prior human knowledge.
Experimental results on both graph-level and node-level tasks show that the
proposed method achieves state-of-the-art performance compared to other
benchmarks, where ablation studies in the end demonstrate the effectiveness of
modules in iGCL.

Today's wireless systems are posing key challenges in terms of quality of
service and quality of physical experience. Metaverse has the potential to
reshape, transform, and add innovations to the existing wireless systems. A
metaverse is a collective virtual open space that can enable wireless systems
using digital twins, digital avatars, and interactive experience technologies.
Machine learning (ML) is indispensable for modeling twins, avatars, and
deploying interactive experience technologies. In this paper, we present the
role of ML in enabling metaverse-based wireless systems. We identify and
discuss a set of key requirements for advancing ML in the metaverse-based
wireless systems. Moreover, we present a case study of distributed split
federated learning for efficiently training meta-space models. Finally, we
discuss the future challenges along with potential solutions.

Image completion with large-scale free-form missing regions is one of the
most challenging tasks for the computer vision community. While researchers
pursue better solutions, drawbacks such as pattern unawareness, blurry
textures, and structure distortion remain noticeable, and thus leave space for
improvement. To overcome these challenges, we propose a new StyleGAN-based
image completion network, Spectral Hint GAN (SH-GAN), inside which a carefully
designed spectral processing module, Spectral Hint Unit, is introduced. We also
propose two novel 2D spectral processing strategies, Heterogeneous Filtering
and Gaussian Split that well-fit modern deep learning models and may further be
extended to other tasks. From our inclusive experiments, we demonstrate that
our model can reach FID scores of 3.4134 and 7.0277 on the benchmark datasets
FFHQ and Places2, and therefore outperforms prior works and reaches a new
state-of-the-art. We also prove the effectiveness of our design via ablation
studies, from which one may notice that the aforementioned challenges, i.e.
pattern unawareness, blurry textures, and structure distortion, can be
noticeably resolved. Our code will be open-sourced at:
https://github.com/SHI-Labs/SH-GAN.

An important issue in model-based control design is that an accurate dynamic
model of the system is generally nonlinear, complex, and costly to obtain. This
limits achievable control performance in practice. Gaussian process (GP) based
estimation of system models is an effective tool to learn unknown dynamics
directly from input/output data. However, conventional GP-based control methods
often ignore the computational cost associated with accumulating data during
the operation of the system and how to handle forgetting in continuous
adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based
model predictive control (MPC) strategy that enables efficient use of online
learning based predictive control without the danger of catastrophic
forgetting. The bio-inspired DGP structure is a combination of a long-term GP
and a short-term GP, where the long-term GP is used to keep the learned
knowledge in memory and the short-term GP is employed to rapidly compensate
unknown dynamics during online operation. Furthermore, a novel recursive online
update strategy for the short-term GP is proposed to successively improve the
learnt model during online operation. Effectiveness of the proposed strategy is
demonstrated via numerical simulations.

The connectivity-aware path design is crucial in the effective deployment of
autonomous Unmanned Aerial Vehicles (UAVs). Recently, Reinforcement Learning
(RL) algorithms have become the popular approach to solving this type of
complex problem, but RL algorithms suffer slow convergence. In this paper, we
propose a Transfer Learning (TL) approach, where we use a teacher policy
previously trained in an old domain to boost the path learning of the agent in
the new domain. As the exploration processes and the training continue, the
agent refines the path design in the new domain based on the subsequent
interactions with the environment. We evaluate our approach considering an old
domain at sub-6 GHz and a new domain at millimeter Wave (mmWave). The teacher
path policy, previously trained at sub-6 GHz path, is the solution to a
connectivity-aware path problem that we formulate as a constrained Markov
Decision Process (CMDP). We employ a Lyapunov-based model-free Deep Q-Network
(DQN) to solve the path design at sub-6 GHz that guarantees connectivity
constraint satisfaction. We empirically demonstrate the effectiveness of our
approach for different urban environment scenarios. The results demonstrate
that our proposed approach is capable of reducing the training time
considerably at mmWave.

Purpose: In laparoscopic liver surgery (LLS), pre-operative information can
be overlaid onto the intra-operative scene by registering a 3D pre-operative
model to the intra-operative partial surface reconstructed from the
laparoscopic video. To assist with this task, we explore the use of
learning-based feature descriptors, which, to our best knowledge, have not been
explored for use in laparoscopic liver registration. Furthermore, a dataset to
train and evaluate the use of learning-based descriptors does not exist.
  Methods: We present the LiverMatch dataset consisting of 16 preoperative
models and their simulated intra-operative 3D surfaces. We also propose the
LiverMatch network designed for this task, which outputs per-point feature
descriptors, visibility scores, and matched points.
  Results: We compare the proposed LiverMatch network with anetwork closest to
LiverMatch, and a histogram-based 3D descriptor on the testing split of the
LiverMatch dataset, which includes two unseen pre-operative models and 1400
intra-operative surfaces. Results suggest that our LiverMatch network can
predict more accurate and dense matches than the other two methods and can be
seamlessly integrated with a RANSAC-ICP-based registration algorithm to achieve
an accurate initial alignment.
  Conclusion: The use of learning-based feature descriptors in LLR is
promising, as it can help achieve an accurate initial rigid alignment, which,
in turn, serves as an initialization for subsequent non-rigid registration. We
will release the dataset and code upon acceptance.

Occluded person re-identification (ReID) is a person retrieval task which
aims at matching occluded person images with holistic ones. For addressing
occluded ReID, part-based methods have been shown beneficial as they offer
fine-grained information and are well suited to represent partially visible
human bodies. However, training a part-based model is a challenging task for
two reasons. Firstly, individual body part appearance is not as discriminative
as global appearance (two distinct IDs might have the same local appearance),
this means standard ReID training objectives using identity labels are not
adapted to local feature learning. Secondly, ReID datasets are not provided
with human topographical annotations. In this work, we propose BPBreID, a body
part-based ReID model for solving the above issues. We first design two modules
for predicting body part attention maps and producing body part-based features
of the ReID target. We then propose GiLt, a novel training scheme for learning
part-based representations that is robust to occlusions and non-discriminative
local appearance. Extensive experiments on popular holistic and occluded
datasets show the effectiveness of our proposed method, which outperforms
state-of-the-art methods by 0.7% mAP and 5.6% rank-1 accuracy on the
challenging Occluded-Duke dataset. Our code is available at
https://github.com/VlSomers/bpbreid.

Given a set of points in the Euclidean space $\mathbb{R}^\ell$ with $\ell>1$,
the pairwise distances between the points are determined by their spatial
location and the metric $d$ that we endow $\mathbb{R}^\ell$ with. Hence, the
distance $d(\mathbf x,\mathbf y)=\delta$ between two points is fixed by the
choice of $\mathbf x$ and $\mathbf y$ and $d$. We study the related problem of
fixing the value $\delta$, and the points $\mathbf x,\mathbf y$, and ask if
there is a topological metric $d$ that computes the desired distance $\delta$.
We demonstrate this problem to be solvable by constructing a metric to
simultaneously give desired pairwise distances between up to $O(\sqrt\ell)$
many points in $\mathbb{R}^\ell$. We then introduce the notion of an
$\varepsilon$-semimetric $\tilde{d}$ to formulate our main result: for all
$\varepsilon>0$, for all $m\geq 1$, for any choice of $m$ points $\mathbf
y_1,\ldots,\mathbf y_m\in\mathbb{R}^\ell$, and all chosen sets of values
$\{\delta_{ij}\geq 0: 1\leq i<j\leq m\}$, there exists an
$\varepsilon$-semimetric $\tilde{\delta}:\mathbb{R}^\ell\times
\mathbb{R}^\ell\to\mathbb{R}$ such that $\tilde{d}(\mathbf y_i,\mathbf
y_j)=\delta_{ij}$, i.e., the desired distances are accomplished, irrespectively
of the topology that the Euclidean or other norms would induce. We showcase our
results by using them to attack unsupervised learning algorithms, specifically
$k$-Means and density-based (DBSCAN) clustering algorithms. These have manifold
applications in artificial intelligence, and letting them run with externally
provided distance measures constructed in the way as shown here, can make
clustering algorithms produce results that are pre-determined and hence
malleable. This demonstrates that the results of clustering algorithms may not
generally be trustworthy, unless there is a standardized and fixed prescription
to use a specific distance function.

Deep neural networks are a promising solution for applications that solve
problems based on learning data sets. DNN accelerators solve the processing
bottleneck as a domain-specific processor. Like other hardware solutions, there
must be exact compatibility between the accelerator and other software
components, especially the compiler. This paper presents a LOCAL (Low
Complexity mapping Algorithm) that is favorable to use at the compiler level to
perform mapping operations in one pass with low computation time and energy
consumption. We first introduce a formal definition of the design space in
order to define the problem's scope, and then we describe the concept of the
LOCAL algorithm. The simulation results show 2x to 38x improvements in
execution time with lower energy consumption compared to previous proposed
dataflow mechanisms.

Graph classification is an important area in both modern research and
industry. Multiple applications, especially in chemistry and novel drug
discovery, encourage rapid development of machine learning models in this area.
To keep up with the pace of new research, proper experimental design, fair
evaluation, and independent benchmarks are essential. Design of strong
baselines is an indispensable element of such works.
  In this thesis, we explore multiple approaches to graph classification. We
focus on Graph Neural Networks (GNNs), which emerged as a de facto standard
deep learning technique for graph representation learning. Classical
approaches, such as graph descriptors and molecular fingerprints, are also
addressed. We design fair evaluation experimental protocol and choose proper
datasets collection. This allows us to perform numerous experiments and
rigorously analyze modern approaches. We arrive to many conclusions, which shed
new light on performance and quality of novel algorithms.
  We investigate application of Jumping Knowledge GNN architecture to graph
classification, which proves to be an efficient tool for improving base graph
neural network architectures. Multiple improvements to baseline models are also
proposed and experimentally verified, which constitutes an important
contribution to the field of fair model comparison.

Reynolds-averaged Navier-Stokes (RANS)-based transition modeling is widely
used in aerospace applications but suffers inaccuracies due to the Boussinesq
turbulent viscosity hypothesis. The eigenspace perturbation method can estimate
the accuracy of a RANS model by injecting perturbations to its predicted
Reynolds stresses. However, there lacks a reliable method for choosing the
strength of the injected perturbation, while existing machine learning models
are often complex and data craving. We examined two light-weighted machine
learning models to help select the strength of the injected perturbation for
estimating the RANS uncertainty of flows undergoing the transition to
turbulence over a Selig-Donovan 7003 airfoil. On the one hand, we examined
polynomial regression to construct a marker function augmented with eigenvalue
perturbations to estimate the uncertainty bound for the predicted skin friction
coefficient. On the other hand, we trained a convolutional neural network (CNN)
to predict high-fidelity turbulence kinetic energy. The trained CNN acts as a
marker function that can be integrated into the eigenspace perturbation method
to quantify the RANS uncertainty. Our findings suggest that the light-weighted
machine learning models are effective in constructing an appropriate marker
function that is promising to enrich the existing eigenspace perturbation
method to quantify the RANS uncertainty more precisely.

In this paper, we are interested in learning a generalizable person
re-identification (re-ID) representation from unlabeled videos. Compared with
1) the popular unsupervised re-ID setting where the training and test sets are
typically under the same domain, and 2) the popular domain generalization (DG)
re-ID setting where the training samples are labeled, our novel scenario
combines their key challenges: the training samples are unlabeled, and
collected form various domains which do no align with the test domain. In other
words, we aim to learn a representation in an unsupervised manner and directly
use the learned representation for re-ID in novel domains. To fulfill this
goal, we make two main contributions: First, we propose Cycle Association
(CycAs), a scalable self-supervised learning method for re-ID with low training
complexity; and second, we construct a large-scale unlabeled re-ID dataset
named LMP-video, tailored for the proposed method. Specifically, CycAs learns
re-ID features by enforcing cycle consistency of instance association between
temporally successive video frame pairs, and the training cost is merely linear
to the data size, making large-scale training possible. On the other hand, the
LMP-video dataset is extremely large, containing 50 million unlabeled person
images cropped from over 10K Youtube videos, therefore is sufficient to serve
as fertile soil for self-supervised learning. Trained on LMP-video, we show
that CycAs learns good generalization towards novel domains. The achieved
results sometimes even outperform supervised domain generalizable models.
Remarkably, CycAs achieves 82.2% Rank-1 on Market-1501 and 49.0% Rank-1 on
MSMT17 with zero human annotation, surpassing state-of-the-art supervised DG
re-ID methods. Moreover, we also demonstrate the superiority of CycAs under the
canonical unsupervised re-ID and the pretrain-and-finetune scenarios.

Double-blind peer review mechanism has become the skeleton of academic
research across multiple disciplines including computer science, yet several
studies have questioned the quality of peer reviews and raised concerns on
potential biases in the process. In this paper, we conduct a thorough and
rigorous study on fairness disparities in peer review with the help of large
language models (LMs). We collect, assemble, and maintain a comprehensive
relational database for the International Conference on Learning
Representations (ICLR) conference from 2017 to date by aggregating data from
OpenReview, Google Scholar, arXiv, and CSRanking, and extracting high-level
features using language models. We postulate and study fairness disparities on
multiple protective attributes of interest, including author gender, geography,
author, and institutional prestige. We observe that the level of disparity
differs and textual features are essential in reducing biases in the predictive
modeling. We distill several insights from our analysis on study the peer
review process with the help of large LMs. Our database also provides avenues
for studying new natural language processing (NLP) methods that facilitate the
understanding of the peer review mechanism. We study a concrete example towards
automatic machine review systems and provide baseline models for the review
generation and scoring tasks such that the database can be used as a benchmark.

We explore space traffic management as an application of collision-free
navigation in multi-agent systems where vehicles have limited observation and
communication ranges. We demonstrate using simulations that GNNs are both
transferable (i.e., they can use initialized weights from analogous
ground-based environments), and scalable with respect to the number of agents.
We find that transfer learning can improve sample efficiency and performance
compared to both the case where the model is trained directly on a space-based
environment, as well as other baseline MARL approaches. Finally, we use our
model to quantify the value of sharing maneuver information between satellite
operators in order to improve decision-making.

Concept bottleneck models perform classification by first predicting which of
a list of human provided concepts are true about a datapoint. Then a downstream
model uses these predicted concept labels to predict the target label. The
predicted concepts act as a rationale for the target prediction. Model trust
issues emerge in this paradigm when soft concept labels are used: it has
previously been observed that extra information about the data distribution
leaks into the concept predictions. In this work we show how Monte-Carlo
Dropout can be used to attain soft concept predictions that do not contain
leaked information.

Single-cell reference atlases are large-scale, cell-level maps that capture
cellular heterogeneity within an organ using single cell genomics. Given their
size and cellular diversity, these atlases serve as high-quality training data
for the transfer of cell type labels to new datasets. Such label transfer,
however, must be robust to domain shifts in gene expression due to measurement
technique, lab specifics and more general batch effects. This requires methods
that provide uncertainty estimates on the cell type predictions to ensure
correct interpretation. Here, for the first time, we introduce uncertainty
quantification methods for cell type classification on single-cell reference
atlases. We benchmark four model classes and show that currently used models
lack calibration, robustness, and actionable uncertainty scores. Furthermore,
we demonstrate how models that quantify uncertainty are better suited to detect
unseen cell types in the setting of atlas-level cell type transfer.

Cross entropy loss has served as the main objective function for
classification-based tasks. Widely deployed for learning neural network
classifiers, it shows both effectiveness and a probabilistic interpretation.
Recently, after the success of self supervised contrastive representation
learning methods, supervised contrastive methods have been proposed to learn
representations and have shown superior and more robust performance, compared
to solely training with cross entropy loss. However, cross entropy loss is
still needed to train the final classification layer. In this work, we
investigate the possibility of learning both the representation and the
classifier using one objective function that combines the robustness of
contrastive learning and the probabilistic interpretation of cross entropy
loss. First, we revisit a previously proposed contrastive-based objective
function that approximates cross entropy loss and present a simple extension to
learn the classifier jointly. Second, we propose a new version of the
supervised contrastive training that learns jointly the parameters of the
classifier and the backbone of the network. We empirically show that our
proposed objective functions show a significant improvement over the standard
cross entropy loss with more training stability and robustness in various
challenging settings.

This paper studies audio-visual suppression for egocentric videos -- where
the speaker is not captured in the video. Instead, potential noise sources are
visible on screen with the camera emulating the off-screen speaker's view of
the outside world. This setting is different from prior work in audio-visual
speech enhancement that relies on lip and facial visuals. In this paper, we
first demonstrate that egocentric visual information is helpful for noise
suppression. We compare object recognition and action classification based
visual feature extractors, and investigate methods to align audio and visual
representations. Then, we examine different fusion strategies for the aligned
features, and locations within the noise suppression model to incorporate
visual information. Experiments demonstrate that visual features are most
helpful when used to generate additive correction masks. Finally, in order to
ensure that the visual features are discriminative with respect to different
noise types, we introduce a multi-task learning framework that jointly
optimizes audio-visual noise suppression and video based acoustic event
detection. This proposed multi-task framework outperforms the audio only
baseline on all metrics, including a 0.16 PESQ improvement. Extensive ablations
reveal the improved performance of the proposed model with multiple active
distractors, over all noise types and across different SNRs.

We propose a new, data-driven approach for efficient pricing of - fixed- and
float-strike - discrete arithmetic Asian and Lookback options when the
underlying process is driven by the Heston model dynamics. The method proposed
in this article constitutes an extension of our previous work, where the
problem of sampling from time-integrated stochastic bridges was addressed. The
model relies on the Seven-League scheme, where artificial neural networks are
employed to "learn" the distribution of the random variable of interest
utilizing stochastic collocation points. The method results in a robust
procedure for Monte Carlo pricing. Furthermore, semi-analytic formulae for
option pricing are provided in a simplified, yet general, framework. The model
guarantees high accuracy and a reduction of the computational time up to
thousands of times compared to classical Monte Carlo pricing schemes.

Commonly used AI networks are very self-confident in their predictions, even
when the evidence for a certain decision is dubious. The investigation of a
deep learning model output is pivotal for understanding its decision processes
and assessing its capabilities and limitations. By analyzing the distributions
of raw network output vectors, it can be observed that each class has its own
decision boundary and, thus, the same raw output value has different support
for different classes. Inspired by this fact, we have developed a new method
for out-of-distribution detection. The method offers an explanatory step beyond
simple thresholding of the softmax output towards understanding and
interpretation of the model learning process and its output. Instead of
assigning the class label of the highest logit to each new sample presented to
the network, it takes the distributions over all classes into consideration. A
probability score interpreter (PSI) is created based on the joint logit values
in relation to their respective correct vs wrong class distributions. The PSI
suggests whether the sample is likely to belong to a specific class, whether
the network is unsure, or whether the sample is likely an outlier or unknown
type for the network. The simple PSI has the benefit of being applicable on
already trained networks. The distributions for correct vs wrong class for each
output node are established by simply running the training examples through the
trained network. We demonstrate our OOD detection method on a challenging
transmission electron microscopy virus image dataset. We simulate a real-world
application in which images of virus types unknown to a trained virus
classifier, yet acquired with the same procedures and instruments, constitute
the OOD samples.

Latent variable models such as the Variational Auto-Encoder (VAE) have become
a go-to tool for analyzing biological data, especially in the field of
single-cell genomics. One remaining challenge is the interpretability of latent
variables as biological processes that define a cell's identity. Outside of
biological applications, this problem is commonly referred to as learning
disentangled representations. Although several disentanglement-promoting
variants of the VAE were introduced, and applied to single-cell genomics data,
this task has been shown to be infeasible from independent and identically
distributed measurements, without additional structure. Instead, recent methods
propose to leverage non-stationary data, as well as the sparse mechanism shift
assumption in order to learn disentangled representations with a causal
semantic. Here, we extend the application of these methodological advances to
the analysis of single-cell genomics data with genetic or chemical
perturbations. More precisely, we propose a deep generative model of
single-cell gene expression data for which each perturbation is treated as a
stochastic intervention targeting an unknown, but sparse, subset of latent
variables. We benchmark these methods on simulated single-cell data to evaluate
their performance at latent units recovery, causal target identification and
out-of-domain generalization. Finally, we apply those approaches to two
real-world large-scale gene perturbation data sets and find that models that
exploit the sparse mechanism shift hypothesis surpass contemporary methods on a
transfer learning task. We implement our new model and benchmarks using the
scvi-tools library, and release it as open-source software at
https://github.com/Genentech/sVAE.

The choice of geometric space for knowledge graph (KG) embeddings can have
significant effects on the performance of KG completion tasks. The hyperbolic
geometry has been shown to capture the hierarchical patterns due to its
tree-like metrics, which addressed the limitations of the Euclidean embedding
models. Recent explorations of the complex hyperbolic geometry further improved
the hyperbolic embeddings for capturing a variety of hierarchical structures.
However, the performance of the hyperbolic KG embedding models for
non-transitive relations is still unpromising, while the complex hyperbolic
embeddings do not deal with multi-relations. This paper aims to utilize the
representation capacity of the complex hyperbolic geometry in multi-relational
KG embeddings. To apply the geometric transformations which account for
different relations and the attention mechanism in the complex hyperbolic
space, we propose to use the fast Fourier transform (FFT) as the conversion
between the real and complex hyperbolic space. Constructing the attention-based
transformations in the complex space is very challenging, while the proposed
Fourier transform-based complex hyperbolic approaches provide a simple and
effective solution. Experimental results show that our methods outperform the
baselines, including the Euclidean and the real hyperbolic embedding models.

With the rapid development of information technologies, centralized data
processing is subject to many limitations, such as computational overheads,
communication delays, and data privacy leakage. Decentralized data processing
over networked terminal nodes becomes an important technology in the era of big
data. Dictionary learning is a powerful representation learning method to
exploit the low-dimensional structure from the high-dimensional data. By
exploiting the low-dimensional structure, the storage and the processing
overhead of data can be effectively reduced. In this paper, we propose a novel
decentralized complete dictionary learning algorithm, which is based on
$\ell^{4}$-norm maximization. Compared with existing decentralized dictionary
learning algorithms, comprehensive numerical experiments show that the novel
algorithm has significant advantages in terms of per-iteration computational
complexity, communication cost, and convergence rate in many scenarios.
Moreover, a rigorous theoretical analysis shows that the dictionaries learned
by the proposed algorithm can converge to the one learned by a centralized
dictionary learning algorithm at a linear rate with high probability under
certain conditions.

The ongoing amalgamation of UAV and ML techniques is creating a significant
synergy and empowering UAVs with unprecedented intelligence and autonomy. This
survey aims to provide a timely and comprehensive overview of ML techniques
used in UAV operations and communications and identify the potential growth
areas and research gaps. We emphasise the four key components of UAV operations
and communications to which ML can significantly contribute, namely, perception
and feature extraction, feature interpretation and regeneration, trajectory and
mission planning, and aerodynamic control and operation. We classify the latest
popular ML tools based on their applications to the four components and conduct
gap analyses. This survey also takes a step forward by pointing out significant
challenges in the upcoming realm of ML-aided automated UAV operations and
communications. It is revealed that different ML techniques dominate the
applications to the four key modules of UAV operations and communications.
While there is an increasing trend of cross-module designs, little effort has
been devoted to an end-to-end ML framework, from perception and feature
extraction to aerodynamic control and operation. It is also unveiled that the
reliability and trust of ML in UAV operations and applications require
significant attention before full automation of UAVs and potential cooperation
between UAVs and humans come to fruition.

Residual minimization is a widely used technique for solving Partial
Differential Equations in variational form. It minimizes the dual norm of the
residual, which naturally yields a saddle-point (min-max) problem over the
so-called trial and test spaces. In the context of neural networks, we can
address this min-max approach by employing one network to seek the trial
minimum, while another network seeks the test maximizers. However, the
resulting method is numerically unstable as we approach the trial solution. To
overcome this, we reformulate the residual minimization as an equivalent
minimization of a Ritz functional fed by optimal test functions computed from
another Ritz functional minimization. We call the resulting scheme the Deep
Double Ritz Method (D$^2$RM), which combines two neural networks for
approximating trial functions and optimal test functions along a nested double
Ritz minimization strategy. Numerical results on several 1D diffusion and
convection problems support the robustness of our method, up to the
approximation properties of the networks and the training capacity of the
optimizers.

Systems for person re-identification (ReID) can achieve a high accuracy when
trained on large fully-labeled image datasets. However, the domain shift
typically associated with diverse operational capture conditions (e.g., camera
viewpoints and lighting) may translate to a significant decline in performance.
This paper focuses on unsupervised domain adaptation (UDA) for video-based ReID
- a relevant scenario that is less explored in the literature. In this
scenario, the ReID model must adapt to a complex target domain defined by a
network of diverse video cameras based on tracklet information. State-of-art
methods cluster unlabeled target data, yet domain shifts across target cameras
(sub-domains) can lead to poor initialization of clustering methods that
propagates noise across epochs, thus preventing the ReID model to accurately
associate samples of same identity. In this paper, an UDA method is introduced
for video person ReID that leverages knowledge on video tracklets, and on the
distribution of frames captured over target cameras to improve the performance
of CNN backbones trained using pseudo-labels. Our method relies on an
adversarial approach, where a camera-discriminator network is introduced to
extract discriminant camera-independent representations, facilitating the
subsequent clustering. In addition, a weighted contrastive loss is proposed to
leverage the confidence of clusters, and mitigate the risk of incorrect
identity associations. Experimental results obtained on three challenging
video-based person ReID datasets - PRID2011, iLIDS-VID, and MARS - indicate
that our proposed method can outperform related state-of-the-art methods. Our
code is available at: \url{https://github.com/dmekhazni/CAWCL-ReID}

Recently, discrete latent variable models have received a surge of interest
in both Natural Language Processing (NLP) and Computer Vision (CV), attributed
to their comparable performance to the continuous counterparts in
representation learning, while being more interpretable in their predictions.
In this paper, we develop a topic-informed discrete latent variable model for
semantic textual similarity, which learns a shared latent space for
sentence-pair representation via vector quantization. Compared with previous
models limited to local semantic contexts, our model can explore richer
semantic information via topic modeling. We further boost the performance of
semantic similarity by injecting the quantized representation into a
transformer-based language model with a well-designed semantic-driven attention
mechanism. We demonstrate, through extensive experiments across various English
language datasets, that our model is able to surpass several strong neural
baselines in semantic textual similarity tasks.

There is a global aging population requiring the need for the right tools
that can enable older adults' greater independence and the ability to age at
home, as well as assist healthcare workers. It is feasible to achieve this
objective by building predictive models that assist healthcare workers in
monitoring and analyzing older adults' behavioral, functional, and
psychological data. To develop such models, a large amount of multimodal sensor
data is typically required. In this paper, we propose MAISON, a scalable
cloud-based platform of commercially available smart devices capable of
collecting desired multimodal sensor data from older adults and patients living
in their own homes. The MAISON platform is novel due to its ability to collect
a greater variety of data modalities than the existing platforms, as well as
its new features that result in seamless data collection and ease of use for
older adults who may not be digitally literate. We demonstrated the feasibility
of the MAISON platform with two older adults discharged home from a large
rehabilitation center. The results indicate that the MAISON platform was able
to collect and store sensor data in a cloud without functional glitches or
performance degradation. This paper will also discuss the challenges faced
during the development of the platform and data collection in the homes of
older adults. MAISON is a novel platform designed to collect multimodal data
and facilitate the development of predictive models for detecting key health
indicators, including social isolation, depression, and functional decline, and
is feasible to use with older adults in the community.

This survey investigates wall modeling in large eddy simulations (LES) using
data-driven machine learning (ML) techniques. To this end, we implement three
ML wall models in an open-source code and compare their performances with the
equilibrium wall model in LES of half-channel flow at eleven friction Reynolds
numbers between $180$ and $10^{10}$. The three models have ''seen'' flows at
only a few Reynolds numbers. We test if these ML wall models can extrapolate to
unseen Reynolds numbers. Among the three models, two are supervised ML models,
and one is a reinforcement learning ML model. The two supervised ML models are
trained against direct numerical simulation (DNS) data, whereas the
reinforcement learning ML model is trained in the context of a wall-modeled LES
with no access to high-fidelity data. The two supervised ML models capture the
law of the wall at both seen and unseen Reynolds numbers--although one model
requires re-training and predicts a smaller von K\'arm\'an constant. The
reinforcement learning model captures the law of the wall reasonably well but
has errors at both low ($Re_\tau<10^3$) and high Reynolds numbers
($Re_\tau>10^6$). In addition to documenting the results, we try to
''understand'' why the ML models behave the way they behave. Analysis shows
that the errors of the supervised ML model is a result of the network design
and the errors in the reinforcement learning model arise due to the present
choice of the ''states'' and the mismatch between the neutral line and the line
separating the action map. In all, we see promises in data-driven machine
learning models.

Federated Learning is a machine learning paradigm where we aim to train
machine learning models in a distributed fashion. Many clients/edge devices
collaborate with each other to train a single model on the central. Clients do
not share their own datasets with each other, decoupling computation and data
on the same device. In this paper, we propose yet another adaptive federated
optimization method and some other ideas in the field of federated learning. We
also perform experiments using these methods and showcase the improvement in
the overall performance of federated learning.

We consider the problem of data classification where the training set
consists of just a few data points. We explore this phenomenon mathematically
and reveal key relationships between the geometry of an AI model's feature
space, the structure of the underlying data distributions, and the model's
generalisation capabilities. The main thrust of our analysis is to reveal the
influence on the model's generalisation capabilities of nonlinear feature
transformations mapping the original data into high, and possibly infinite,
dimensional spaces.

A new research framework is proposed to incorporate machine learning
techniques into the field of experimental chemistry to facilitate
chromatographic enantioseparation. A documentary dataset of chiral molecular
retention times (CMRT dataset) in high-performance liquid chromatography is
established to handle the challenge of data acquisition. Based on the CMRT
dataset, a quantile geometry-enhanced graph neural network is proposed to learn
the molecular structure-retention time relationship, which shows a satisfactory
predictive ability for enantiomers. The domain knowledge of chromatography is
incorporated into the machine learning model to achieve multi-column
prediction, which paves the way for chromatographic enantioseparation
prediction by calculating the separation probability. Experiments confirm that
the proposed research framework works well in retention time prediction and
chromatographic enantioseparation facilitation, which sheds light on the
application of machine learning techniques to the experimental scene and
improves the efficiency of experimenters to speed up scientific discovery.

Denoising diffusions are state-of-the-art generative models which exhibit
remarkable empirical performance and come with theoretical guarantees. The core
idea of these models is to progressively transform the empirical data
distribution into a simple Gaussian distribution by adding noise using a
diffusion. We obtain new samples whose distribution is close to the data
distribution by simulating a "denoising" diffusion approximating the time
reversal of this "noising" diffusion. This denoising diffusion relies on
approximations of the logarithmic derivatives of the noised data densities,
known as scores, obtained using score matching. Such models can be easily
extended to perform approximate posterior simulation in high-dimensional
scenarios where one can only sample from the prior and simulate synthetic
observations from the likelihood. These methods have been primarily developed
for data on $\mathbb{R}^d$ while extensions to more general spaces have been
developed on a case-by-case basis. We propose here a general framework which
not only unifies and generalizes this approach to a wide class of spaces but
also leads to an original extension of score matching. We illustrate the
resulting class of denoising Markov models on various applications.

A real-world application or setting involves interaction between different
modalities (e.g., video, speech, text). In order to process the multimodal
information automatically and use it for an end application, Multimodal
Representation Learning (MRL) has emerged as an active area of research in
recent times. MRL involves learning reliable and robust representations of
information from heterogeneous sources and fusing them. However, in practice,
the data acquired from different sources are typically noisy. In some extreme
cases, a noise of large magnitude can completely alter the semantics of the
data leading to inconsistencies in the parallel multimodal data. In this paper,
we propose a novel method for multimodal representation learning in a noisy
environment via the generalized product of experts technique. In the proposed
method, we train a separate network for each modality to assess the credibility
of information coming from that modality, and subsequently, the contribution
from each modality is dynamically varied while estimating the joint
distribution. We evaluate our method on two challenging benchmarks from two
diverse domains: multimodal 3D hand-pose estimation and multimodal surgical
video segmentation. We attain state-of-the-art performance on both benchmarks.
Our extensive quantitative and qualitative evaluations show the advantages of
our method compared to previous approaches.

We introduce pyGSL, a Python library that provides efficient implementations
of state-of-the-art graph structure learning models along with diverse datasets
to evaluate them on. The implementations are written in GPU-friendly ways,
allowing one to scale to much larger network tasks. A common interface is
introduced for algorithm unrolling methods, unifying implementations of recent
state-of-the-art techniques and allowing new methods to be quickly developed by
avoiding the need to rebuild the underlying unrolling infrastructure.
Implementations of differentiable graph structure learning models are written
in PyTorch, allowing us to leverage the rich software ecosystem that exists
e.g., around logging, hyperparameter search, and GPU-communication. This also
makes it easy to incorporate these models as components in larger gradient
based learning systems where differentiable estimates of graph structure may be
useful, e.g. in latent graph learning. Diverse datasets and performance metrics
allow consistent comparisons across models in this fast growing field. The full
code repository can be found on https://github.com/maxwass/pyGSL.

Tensor program tuning is a non-convex objective optimization problem, to
which search-based approaches have proven to be effective. At the core of the
search-based approaches lies the design of the cost model. Though deep
learning-based cost models perform significantly better than other methods,
they still fall short and suffer from the following problems. First, their
feature extraction heavily relies on expert-level domain knowledge in hardware
architectures. Even so, the extracted features are often unsatisfactory and
require separate considerations for CPUs and GPUs. Second, a cost model trained
on one hardware platform usually performs poorly on another, a problem we call
cross-hardware unavailability.
  In order to address these problems, we propose TLP and MTLTLP. TLP is a deep
learning-based cost model that facilitates tensor program tuning. Instead of
extracting features from the tensor program itself, TLP extracts features from
the schedule primitives. We treat schedule primitives as tensor languages. TLP
is thus a Tensor Language Processing task. In this way, the task of predicting
the tensor program latency through the cost model is transformed into a natural
language processing (NLP) regression task. MTL-TLP combines Multi-Task Learning
and TLP to cope with the cross-hardware unavailability problem.
  We incorporate these techniques into the Ansor framework and conduct detailed
experiments. Results show that TLP can speed up the average search time by 9.1X
and 3.0X on CPU and GPU workloads, respectively, compared to the
state-of-the-art implementation. MTL-TLP can achieve a speed-up of 4.7X and
2.9X on CPU and GPU workloads, respectively, using only 7% of the target
hardware data.

We study the generalization of over-parameterized classifiers where Empirical
Risk Minimization (ERM) for learning leads to zero training error. In these
over-parameterized settings there are many global minima with zero training
error, some of which generalize better than others. We show that under certain
conditions the fraction of "bad" global minima with a true error larger than
{\epsilon} decays to zero exponentially fast with the number of training data
n. The bound depends on the distribution of the true error over the set of
classifier functions used for the given classification problem, and does not
necessarily depend on the size or complexity (e.g. the number of parameters) of
the classifier function set. This might explain the unexpectedly good
generalization even of highly over-parameterized Neural Networks. We support
our mathematical framework with experiments on a synthetic data set and a
subset of MNIST.

Neural networks trained on large datasets by minimizing a loss have become
the state-of-the-art approach for resolving data science problems, particularly
in computer vision, image processing and natural language processing. In spite
of their striking results, our theoretical understanding about how neural
networks operate is limited. In particular, what are the interpolation
capabilities of trained neural networks? In this paper we discuss a theorem of
Domingos stating that "every machine learned by continuous gradient descent is
approximately a kernel machine". According to Domingos, this fact leads to
conclude that all machines trained on data are mere kernel machines. We first
extend Domingo's result in the discrete case and to networks with vector-valued
output. We then study its relevance and significance on simple examples. We
find that in simple cases, the "neural tangent kernel" arising in Domingos'
theorem does provide understanding of the networks' predictions. Furthermore,
when the task given to the network grows in complexity, the interpolation
capability of the network can be effectively explained by Domingos' theorem,
and therefore is limited. We illustrate this fact on a classic perception
theory problem: recovering a shape from its boundary.

To ensure the safety of railroad operations, it is important to monitor and
forecast track geometry irregularities. A higher safety requires forecasting
with a higher spatiotemporal frequency. For forecasting with a high
spatiotemporal frequency, it is necessary to capture spatial correlations.
Additionally, track geometry irregularities are influenced by multiple
exogenous factors. In this study, we propose a method to forecast one type of
track geometry irregularity, vertical alignment, by incorporating spatial and
exogenous factor calculations. The proposed method embeds exogenous factors and
captures spatiotemporal correlations using a convolutional long short-term
memory (ConvLSTM). In the experiment, we compared the proposed method with
other methods in terms of the forecasting performance. Additionally, we
conducted an ablation study on exogenous factors to examine their contribution
to the forecasting performance. The results reveal that spatial calculations
and maintenance record data improve the forecasting of the vertical alignment.

Speech representation learning has improved both speech understanding and
speech synthesis tasks for single language. However, its ability in
cross-lingual scenarios has not been explored. In this paper, we extend the
pretraining method for cross-lingual multi-speaker speech synthesis tasks,
including cross-lingual multi-speaker voice cloning and cross-lingual
multi-speaker speech editing. We propose a speech-text joint pretraining
framework, where we randomly mask the spectrogram and the phonemes given a
speech example and its transcription. By learning to reconstruct the masked
parts of the input in different languages, our model shows great improvements
over speaker-embedding-based multi-speaker TTS methods. Moreover, our framework
is end-to-end for both the training and the inference without any finetuning
effort. In cross-lingual multi-speaker voice cloning and cross-lingual
multi-speaker speech editing tasks, our experiments show that our model
outperforms speaker-embedding-based multi-speaker TTS methods. The code and
model are publicly available at PaddleSpeech.

With the recently increasing capabilities of modern vehicles, novel
approaches for interaction emerged that go beyond traditional touch-based and
voice command approaches. Therefore, hand gestures, head pose, eye gaze, and
speech have been extensively investigated in automotive applications for object
selection and referencing. Despite these significant advances, existing
approaches mostly employ a one-model-fits-all approach unsuitable for varying
user behavior and individual differences. Moreover, current referencing
approaches either consider these modalities separately or focus on a stationary
situation, whereas the situation in a moving vehicle is highly dynamic and
subject to safety-critical constraints. In this paper, I propose a research
plan for a user-centered adaptive multimodal fusion approach for referencing
external objects from a moving vehicle. The proposed plan aims to provide an
open-source framework for user-centered adaptation and personalization using
user observations and heuristics, multimodal fusion, clustering,
transfer-of-learning for model adaptation, and continuous learning, moving
towards trusted human-centered artificial intelligence.

Knowledge graph embedding (KGE) is a increasingly popular technique that aims
to represent entities and relations of knowledge graphs into low-dimensional
semantic spaces for a wide spectrum of applications such as link prediction,
knowledge reasoning and knowledge completion. In this paper, we provide a
systematic review of existing KGE techniques based on representation spaces.
Particularly, we build a fine-grained classification to categorise the models
based on three mathematical perspectives of the representation spaces: (1)
Algebraic perspective, (2) Geometric perspective, and (3) Analytical
perspective. We introduce the rigorous definitions of fundamental mathematical
spaces before diving into KGE models and their mathematical properties. We
further discuss different KGE methods over the three categories, as well as
summarise how spatial advantages work over different embedding needs. By
collating the experimental results from downstream tasks, we also explore the
advantages of mathematical space in different scenarios and the reasons behind
them. We further state some promising research directions from a representation
space perspective, with which we hope to inspire researchers to design their
KGE models as well as their related applications with more consideration of
their mathematical space properties.

Climate change has become one of the biggest challenges of our time. Social
media platforms such as Twitter play an important role in raising public
awareness and spreading knowledge about the dangers of the current climate
crisis. With the increasing number of campaigns and communication about climate
change through social media, the information could create more awareness and
reach the general public and policy makers. However, these Twitter
communications lead to polarization of beliefs, opinion-dominated ideologies,
and often a split into two communities of climate change deniers and believers.
In this paper, we propose a framework that helps identify denier statements on
Twitter and thus classifies the stance of the tweet into one of the two
attitudes towards climate change (denier/believer). The sentimental aspects of
Twitter data on climate change are deeply rooted in general public attitudes
toward climate change. Therefore, our work focuses on learning two closely
related tasks: Stance Detection and Sentiment Analysis of climate change
tweets. We propose a multi-task framework that performs stance detection
(primary task) and sentiment analysis (auxiliary task) simultaneously. The
proposed model incorporates the feature-specific and shared-specific attention
frameworks to fuse multiple features and learn the generalized features for
both tasks. The experimental results show that the proposed framework increases
the performance of the primary task, i.e., stance detection by benefiting from
the auxiliary task, i.e., sentiment analysis compared to its uni-modal and
single-task variants.

Modern Review Helpfulness Prediction systems are dependent upon multiple
modalities, typically texts and images. Unfortunately, those contemporary
approaches pay scarce attention to polish representations of cross-modal
relations and tend to suffer from inferior optimization. This might cause harm
to model's predictions in numerous cases. To overcome the aforementioned
issues, we propose Multimodal Contrastive Learning for Multimodal Review
Helpfulness Prediction (MRHP) problem, concentrating on mutual information
between input modalities to explicitly elaborate cross-modal relations. In
addition, we introduce Adaptive Weighting scheme for our contrastive learning
approach in order to increase flexibility in optimization. Lastly, we propose
Multimodal Interaction module to address the unalignment nature of multimodal
data, thereby assisting the model in producing more reasonable multimodal
representations. Experimental results show that our method outperforms prior
baselines and achieves state-of-the-art results on two publicly available
benchmark datasets for MRHP problem.

Given a particular embodiment, we propose a novel method (C3PO) that learns
policies able to achieve any arbitrary position and pose. Such a policy would
allow for easier control, and would be re-useable as a key building block for
downstream tasks. The method is two-fold: First, we introduce a novel
exploration algorithm that optimizes for uniform coverage, is able to discover
a set of achievable states, and investigates its abilities in attaining both
high coverage, and hard-to-discover states; Second, we leverage this set of
achievable states as training data for a universal goal-achievement policy, a
goal-based SAC variant. We demonstrate the trained policy's performance in
achieving a large number of novel states. Finally, we showcase the influence of
massive unsupervised training of a goal-achievement policy with
state-of-the-art pose-based control of the Hopper, Walker, Halfcheetah,
Humanoid and Ant embodiments.

The advances in language-based Artificial Intelligence (AI) technologies
applied to build educational applications can present AI for social-good
opportunities with a broader positive impact. Across many disciplines,
enhancing the quality of mathematics education is crucial in building critical
thinking and problem-solving skills at younger ages. Conversational AI systems
have started maturing to a point where they could play a significant role in
helping students learn fundamental math concepts. This work presents a
task-oriented Spoken Dialogue System (SDS) built to support play-based learning
of basic math concepts for early childhood education. The system has been
evaluated via real-world deployments at school while the students are
practicing early math concepts with multimodal interactions. We discuss our
efforts to improve the SDS pipeline built for math learning, for which we
explore utilizing MathBERT representations for potential enhancement to the
Natural Language Understanding (NLU) module. We perform an end-to-end
evaluation using real-world deployment outputs from the Automatic Speech
Recognition (ASR), Intent Recognition, and Dialogue Manager (DM) components to
understand how error propagation affects the overall performance in real-world

The advances in deep learning (DL) techniques have the potential to deliver
transformative technological breakthroughs to numerous complex tasks in modern
power systems that suffer from increasing uncertainty and nonlinearity.
However, the vulnerability of DL has yet to be thoroughly explored in power
system tasks under various physical constraints. This work, for the first time,
proposes a novel physics-constrained backdoor poisoning attack, which embeds
the undetectable attack signal into the learned model and only performs the
attack when it encounters the corresponding signal. The paper illustrates the
proposed attack on the real-time fault line localization application.
Furthermore, the simulation results on the 68-bus power system demonstrate that
DL-based fault line localization methods are not robust to our proposed attack,
indicating that backdoor poisoning attacks pose real threats to DL
implementations in power systems. The proposed attack pipeline can be easily
generalized to other power system tasks.

Visually realistic GAN-generated facial images raise obvious concerns on
potential misuse. Many effective forensic algorithms have been developed to
detect such synthetic images in recent years. It is significant to assess the
vulnerability of such forensic detectors against adversarial attacks. In this
paper, we propose a new black-box attack method against GAN-generated image
detectors. A novel contrastive learning strategy is adopted to train the
encoder-decoder network based anti-forensic model under a contrastive loss
function. GAN images and their simulated real counterparts are constructed as
positive and negative samples, respectively. Leveraging on the trained attack
model, imperceptible contrastive perturbation could be applied to input
synthetic images for removing GAN fingerprint to some extent. As such, existing
GAN-generated image detectors are expected to be deceived. Extensive
experimental results verify that the proposed attack effectively reduces the
accuracy of three state-of-the-art detectors on six popular GANs. High visual
quality of the attacked images is also achieved. The source code will be
available at https://github.com/ZXMMD/BAttGAND.

In modern on-driving computing environments, many sensors are used for
context-aware applications. This paper utilizes two deep learning models, U-Net
and EfficientNet, which consist of a convolutional neural network (CNN), to
detect hand gestures and remove noise in the Range Doppler Map image that was
measured through a millimeter-wave (mmWave) radar. To improve the performance
of classification, accurate pre-processing algorithms are essential. Therefore,
a novel pre-processing approach to denoise images before entering the first
deep learning model stage increases the accuracy of classification. Thus, this
paper proposes a deep neural network based high-performance nonlinear
pre-processing method.

We propose Okapi, a simple, efficient, and general method for robust
semi-supervised learning based on online statistical matching. Our method uses
a nearest-neighbours-based matching procedure to generate cross-domain views
for a consistency loss, while eliminating statistical outliers. In order to
perform the online matching in a runtime- and memory-efficient way, we draw
upon the self-supervised literature and combine a memory bank with a
slow-moving momentum encoder. The consistency loss is applied within the
feature space, rather than on the predictive distribution, making the method
agnostic to both the modality and the task in question. We experiment on the
WILDS 2.0 datasets Sagawa et al., which significantly expands the range of
modalities, applications, and shifts available for studying and benchmarking
real-world unsupervised adaptation. Contrary to Sagawa et al., we show that it
is in fact possible to leverage additional unlabelled data to improve upon
empirical risk minimisation (ERM) results with the right method. Our method
outperforms the baseline methods in terms of out-of-distribution (OOD)
generalisation on the iWildCam (a multi-class classification task) and
PovertyMap (a regression task) image datasets as well as the CivilComments (a
binary classification task) text dataset. Furthermore, from a qualitative
perspective, we show the matches obtained from the learned encoder are strongly
semantically related. Code for our paper is publicly available at
https://github.com/wearepal/okapi/.

Fe-B-Si system is a matrix for synthesis of new functional materials with
exceptional magnetic and mechanical properties. Progress in this area is
associated with the search for optimal doping conditions. This theoretical and
experimental study is aimed to address the influence of Ta alloying on the
structure of undercooled (Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts, their
undercoolability and the processes of structure formation during
solidification. Small concentration of Ta complicates standard ab initio and
machine learning investigations. We developed a technique for fast and stable
training of machine learning interatomic potential (MLIP) in this case and
uncovered the structure of the undercooled melts. Molecular dynamic simulations
with MLIP showed that at Ta concentration of 1 at.% there is a sharp change in
the chemical short-range ordering in the melt associated with a change in the
interaction of Ta atoms. This effect leads to a restructuring of the cluster
formation in the system. At the same time, our experimental investigation shows
that melts with a Ta content of 1 at.% have the greatest tendency to
undercoolability. Alloying with Ta promotes the formation of primary crystals
of Fe2B, and at a concentration of more than 1.5 at.% Ta, also of FeTaB.
Herewith, near 1 at.% Ta, the crystallization of the melt proceeds
nontrivially: with the formation of two intermediate metastable phases Fe3B and
Fe2Ta Laves phase. Also, the highest tendency to amorphization under conditions
of quick quenching is exhibited by a melt with a Ta concentration of 1 at.%.
The results not only provide understanding of optimal alloying of Fe-B-Si
materials but also promote a machine learning method for numerical design of
metallic alloys with a small dopant concentration.

The attention mechanism is considered the backbone of the widely-used
Transformer architecture. It contextualizes the input by computing
input-specific attention matrices. We find that this mechanism, while powerful
and elegant, is not as important as typically thought for pretrained language
models. We introduce PAPA, a new probing method that replaces the
input-dependent attention matrices with constant ones -- the average attention
weights over multiple inputs. We use PAPA to analyze several established
pretrained Transformers on six downstream tasks. We find that without any
input-dependent attention, all models achieve competitive performance -- an
average relative drop of only 8% from the probing baseline. Further, little or
no performance drop is observed when replacing half of the input-dependent
attention matrices with constant (input-independent) ones. Interestingly, we
show that better-performing models lose more from applying our method than
weaker models, suggesting that the utilization of the input-dependent attention
mechanism might be a factor in their success. Our results motivate research on
simpler alternatives to input-dependent attention, as well as on methods for
better utilization of this mechanism in the Transformer architecture.

Cryo Focused Ion-Beam Scanning Electron Microscopy (cryo FIB-SEM) enables
three-dimensional and nanoscale imaging of biological specimens via a slice and
view mechanism. The FIB-SEM experiments are, however, limited by a slow
(typically, several hours) acquisition process and the high electron doses
imposed on the beam sensitive specimen can cause damage. In this work, we
present a compressive sensing variant of cryo FIB-SEM capable of reducing the
operational electron dose and increasing speed. We propose two Targeted
Sampling (TS) strategies that leverage the reconstructed image of the previous
sample layer as a prior for designing the next subsampling mask. Our image
recovery is based on a blind Bayesian dictionary learning approach, i.e., Beta
Process Factor Analysis (BPFA). This method is experimentally viable due to our
ultra-fast GPU-based implementation of BPFA. Simulations on artificial
compressive FIB-SEM measurements validate the success of proposed methods: the
operational electron dose can be reduced by up to 20 times. These methods have
large implications for the cryo FIB-SEM community, in which the imaging of beam
sensitive biological materials without beam damage is crucial.

Media has a substantial impact on the public perception of events. A
one-sided or polarizing perspective on any topic is usually described as media
bias. One of the ways how bias in news articles can be introduced is by
altering word choice. Biased word choices are not always obvious, nor do they
exhibit high context-dependency. Hence, detecting bias is often difficult. We
propose a Transformer-based deep learning architecture trained via Multi-Task
Learning using six bias-related data sets to tackle the media bias detection
problem. Our best-performing implementation achieves a macro $F_{1}$ of 0.776,
a performance boost of 3\% compared to our baseline, outperforming existing
methods. Our results indicate Multi-Task Learning as a promising alternative to
improve existing baseline models in identifying slanted reporting.

Wireless ad hoc federated learning (WAFL) is a fully decentralized
collaborative machine learning framework organized by opportunistically
encountered mobile nodes. Compared to conventional federated learning, WAFL
performs model training by weakly synchronizing the model parameters with
others, and this shows great resilience to a poisoned model injected by an
attacker. In this paper, we provide our theoretical analysis of the WAFL's
resilience against model poisoning attacks, by formulating the force balance
between the poisoned model and the legitimate model. According to our
experiments, we confirmed that the nodes directly encountered the attacker has
been somehow compromised to the poisoned model but other nodes have shown great
resilience. More importantly, after the attacker has left the network, all the
nodes have finally found stronger model parameters combined with the poisoned
model. Most of the attack-experienced cases achieved higher accuracy than the
no-attack-experienced cases.

Calculating viscosity in multicompoinent metallic melts is a challenging task
for both classical and \textit{ab~initio} molecular dynamics simulations
methods. The former may not to provide enough accuracy and the latter is too
resources demanding. Machine learning potentials provide optimal balance
between accuracy and computational efficiency and so seem very promising to
solve this problem. Here we address simulating kinematic viscosity in ternary
Al-Cu-Ni melts with using deep neural network potentials (DP) as implemented in
the DeePMD-kit. We calculate both concentration and temperature dependencies of
kinematic viscosity in Al-Cu-Ni and conclude that the developed potential
allows one to simulate viscosity with high accuracy; the deviation from
experimental data does not exceed 9\% and is close to the uncertainty interval
of experimental data. More importantly, our simulations reproduce minimum on
concentration dependency of the viscosity at the eutectic point. Thus, we
conclude that DP-based MD simulations is highly promising way to calculate
viscosity in multicomponent metallic melts.

A large amount of recent research has the far-reaching goal of finding
training methods for deep neural networks that can serve as alternatives to
backpropagation (BP). A prominent example is predictive coding (PC), which is a
neuroscience-inspired method that performs inference on hierarchical Gaussian
generative models. These methods, however, fail to keep up with modern neural
networks, as they are unable to replicate the dynamics of complex layers and
activation functions. In this work, we solve this problem by generalizing PC to
arbitrary probability distributions, enabling the training of architectures,
such as transformers, that are hard to approximate with only Gaussian
assumptions. We perform three experimental analyses. First, we study the gap
between our method and the standard formulation of PC on multiple toy examples.
Second, we test the reconstruction quality on variational autoencoders, where
our method reaches the same reconstruction quality as BP. Third, we show that
our method allows us to train transformer networks and achieve a performance
comparable with BP on conditional language models. More broadly, this method
allows neuroscience-inspired learning to be applied to multiple domains, since
the internal distributions can be flexibly adapted to the data, tasks, and
architectures used.

We study a distributed binary hypothesis testing (HT) problem with
communication and security constraints, involving three parties: a remote
sensor called Alice, a legitimate decision centre called Bob, and an
eavesdropper called Eve, all having their own source observations. In this
system, Alice conveys a rate R description of her observation to Bob, and Bob
performs a binary hypothesis test on the joint distribution underlying his and
Alice's observations. The goal of Alice and Bob is to maximise the exponential
decay of Bob's miss-detection (type II-error) probability under two
constraints: Bob's false alarm-probability (type-I error) probability has to
stay below a given threshold and Eve's uncertainty (equivocation) about Alice's
observations should stay above a given security threshold even when Eve learns
Alice's message. For the special case of testing against independence, we
characterise the largest possible type-II error exponent under the described
type-I error probability and security constraints.

As an alternative but unified and more fundamental description for quantum
physics, Feynman path integrals generalize the classical action principle to a
probabilistic perspective, under which the physical observables' estimation
translates into a weighted sum over all possible paths. The underlying
difficulty is to tackle the whole path manifold from finite samples that can
effectively represent the Feynman propagator dictated probability distribution.
Modern generative models in machine learning can handle learning and
representing probability distribution with high computational efficiency. In
this study, we propose a Fourier-flow generative model to simulate the Feynman
propagator and generate paths for quantum systems. As demonstration, we
validate the path generator on the harmonic and anharmonic oscillators. The
latter is a double-well system without analytic solutions. To preserve the
periodic condition for the system, the Fourier transformation is introduced
into the flow model to approach a Matsubara representation. With this novel
development, the ground-state wave function and low-lying energy levels are
estimated accurately. Our method offers a new avenue to investigate quantum
systems with machine learning assisted Feynman Path integral solving.

Quantum reinforcement learning is an emerging field at the intersection of
quantum computing and machine learning. While we intend to provide a broad
overview of the literature on quantum reinforcement learning (our
interpretation of this term will be clarified below), we put particular
emphasis on recent developments. With a focus on already available noisy
intermediate-scale quantum devices, these include variational quantum circuits
acting as function approximators in an otherwise classical reinforcement
learning setting. In addition, we survey quantum reinforcement learning
algorithms based on future fault-tolerant hardware, some of which come with a
provable quantum advantage. We provide both a birds-eye-view of the field, as
well as summaries and reviews for selected parts of the literature.

We propose a framework for learning a fragment of probabilistic computation
tree logic (pCTL) formulae from a set of states that are labeled as safe or
unsafe. We work in a relational setting and combine ideas from relational
Markov Decision Processes with pCTL model-checking. More specifically, we
assume that there is an unknown relational pCTL target formula that is
satisfied by only safe states, and has a horizon of maximum $k$ steps and a
threshold probability $\alpha$. The task then consists of learning this unknown
formula from states that are labeled as safe or unsafe by a domain expert. We
apply principles of relational learning to induce a pCTL formula that is
satisfied by all safe states and none of the unsafe ones. This formula can then
be used as a safety specification for this domain, so that the system can avoid
getting into dangerous situations in future. Following relational learning
principles, we introduce a candidate formula generation process, as well as a
method for deciding which candidate formula is a satisfactory specification for
the given labeled states. The cases where the expert knows and does not know
the system policy are treated, however, much of the learning process is the
same for both cases. We evaluate our approach on a synthetic relational domain.

The heterogeneity of hardware and data is a well-known and studied problem in
the community of Federated Learning (FL) as running under heterogeneous
settings. Recently, custom-size client models trained with Knowledge
Distillation (KD) has emerged as a viable strategy for tackling the
heterogeneity challenge. However, previous efforts in this direction are aimed
at client model tuning rather than their impact onto the knowledge aggregation
of the global model. Despite performance of global models being the primary
objective of FL systems, under heterogeneous settings client models have
received more attention. Here, we provide more insights into how the chosen
approach for training custom client models has an impact on the global model,
which is essential for any FL application. We show the global model can fully
leverage the strength of KD with heterogeneous data. Driven by empirical
observations, we further propose a new approach that combines KD and Learning
without Forgetting (LwoF) to produce improved personalised models. We bring
heterogeneous FL on pair with the mighty FedAvg of homogeneous FL, in realistic
deployment scenarios with dropping clients.

This work studies the task of device coordination in wireless networks for
over-the-air federated learning (OTA-FL). For conventional metrics of
aggregation error, the task is shown to describe the zero-forcing (ZF) and
minimum mean squared error (MMSE) schemes and reduces to the NP-hard problem of
subset selection. We tackle this problem by studying properties of the optimal
scheme. Our analytical results reveal that this scheme is found by searching
among the leaves of a tree with favorable monotonic features. Invoking these
features, we develop a low-complexity algorithm that approximates the optimal
scheme by tracking a dominant path of the tree sequentially. Our numerical
investigations show that the proposed algorithm closely tracks the optimal
scheme.

Promoting creativity is considered an important goal of education, but
creativity is notoriously hard to measure.In this paper, we make the journey
fromdefining a formal measure of creativity that is efficientlycomputable to
applying the measure in a practical domain. The measure is general and relies
on coretheoretical concepts in creativity theory, namely fluency, flexibility,
and originality, integratingwith prior cognitive science literature. We adapted
the general measure for projects in the popular visual programming language
Scratch.We designed a machine learning model for predicting the creativity of
Scratch projects, trained and evaluated on human expert creativity assessments
in an extensive user study. Our results show that opinions about creativity in
Scratch varied widely across experts. The automatic creativity assessment
aligned with the assessment of the human experts more than the experts agreed
with each other. This is a first step in providing computational models for
measuring creativity that can be applied to educational technologies, and to
scale up the benefit of creativity education in schools.

The heterogeneity gap problem is the main challenge in cross-modal retrieval.
Because cross-modal data (e.g. audiovisual) have different distributions and
representations that cannot be directly compared. To bridge the gap between
audiovisual modalities, we learn a common subspace for them by utilizing the
intrinsic correlation in the natural synchronization of audio-visual data with
the aid of annotated labels. TNN-CCCA is the best audio-visual cross-modal
retrieval (AV-CMR) model so far, but the model training is sensitive to hard
negative samples when learning common subspace by applying triplet loss to
predict the relative distance between inputs. In this paper, to reduce the
interference of hard negative samples in representation learning, we propose a
new AV-CMR model to optimize semantic features by directly predicting labels
and then measuring the intrinsic correlation between audio-visual data using
complete cross-triple loss. In particular, our model projects audio-visual
features into label space by minimizing the distance between predicted label
features after feature projection and ground label representations. Moreover,
we adopt complete cross-triplet loss to optimize the predicted label features
by leveraging the relationship between all possible similarity and
dissimilarity semantic information across modalities. The extensive
experimental results on two audio-visual double-checked datasets have shown an
improvement of approximately 2.1% in terms of average MAP over the current
state-of-the-art method TNN-CCCA for the AV-CMR task, which indicates the
effectiveness of our proposed model.

Fighting online hate speech is a challenge that is usually addressed using
Natural Language Processing via automatic detection and removal of hate
content. Besides this approach, counter narratives have emerged as an effective
tool employed by NGOs to respond to online hate on social media platforms. For
this reason, Natural Language Generation is currently being studied as a way to
automatize counter narrative writing. However, the existing resources necessary
to train NLG models are limited to 2-turn interactions (a hate speech and a
counter narrative as response), while in real life, interactions can consist of
multiple turns. In this paper, we present a hybrid approach for dialogical data
collection, which combines the intervention of human expert annotators over
machine generated dialogues obtained using 19 different configurations. The
result of this work is DIALOCONAN, the first dataset comprising over 3000
fictitious multi-turn dialogues between a hater and an NGO operator, covering 6
targets of hate.

The prevalence of the Internet of things (IoT) and smart meters devices in
smart grids is providing key support for measuring and analyzing the power
consumption patterns. This approach enables end-user to play the role of
prosumers in the market and subsequently contributes to diminish the carbon
footprint and the burden on utility grids. The coordination of trading
surpluses of energy that is generated by house renewable energy resources
(RERs) and the supply of shortages by external networks (main grid) is a
necessity. This paper proposes a hierarchical architecture to manage energy in
multiple smart buildings leveraging federated deep reinforcement learning
(FDRL) with dynamic load in a distributed manner. Within the context of the
developed FDRL-based framework, each agent that is hosted in local building
energy management systems (BEMS) trains a local deep reinforcement learning
(DRL) model and shares its experience in the form of model hyperparameters to
the federation layer in the energy management system (EMS). Simulation studies
are conducted using one EMS and up to twenty smart houses that are equipped
with photovoltaic (PV) systems and batteries. This iterative training approach
enables the proposed discretized soft actor-critic (SAC) agents to aggregate
the collected knowledge to expedite the overall learning procedure and reduce
costs and CO2 emissions, while the federation approach can mitigate privacy
breaches. The numerical results confirm the performance of the proposed
framework under different daytime periods, loads, and temperatures.

Chain event graphs are a family of probabilistic graphical models that
generalise Bayesian networks and have been successfully applied to a wide range
of domains. Unlike Bayesian networks, these models can encode context-specific
conditional independencies as well as asymmetric developments within the
evolution of a process. More recently, new model classes belonging to the chain
event graph family have been developed for modelling time-to-event data to
study the temporal dynamics of a process. However, existing model selection
algorithms for chain event graphs and its variants rely on all parameters
having conjugate priors. This is unrealistic for many real-world applications.
In this paper, we propose a mixture modelling approach to model selection in
chain event graphs that does not rely on conjugacy. Moreover, we also show that
this methodology is more amenable to being robustly scaled than the existing
model selection algorithms used for this family. We demonstrate our techniques
on simulated datasets.

Equivariance of neural networks to transformations helps to improve their
performance and reduce generalization error in computer vision tasks, as they
apply to datasets presenting symmetries (e.g. scalings, rotations,
translations). The method of moving frames is classical for deriving operators
invariant to the action of a Lie group in a manifold.Recently, a rotation and
translation equivariant neural network for image data was proposed based on the
moving frames approach. In this paper we significantly improve that approach by
reducing the computation of moving frames to only one, at the input stage,
instead of repeated computations at each layer. The equivariance of the
resulting architecture is proved theoretically and we build a rotation and
translation equivariant neural network to process volumes, i.e. signals on the
3D space. Our trained model overperforms the benchmarks in the medical volume
classification of most of the tested datasets from MedMNIST3D.

In the field of reinforcement learning, because of the high cost and risk of
policy training in the real world, policies are trained in a simulation
environment and transferred to the corresponding real-world environment.
However, the simulation environment does not perfectly mimic the real-world
environment, lead to model misspecification. Multiple studies report
significant deterioration of policy performance in a real-world environment. In
this study, we focus on scenarios involving a simulation environment with
uncertainty parameters and the set of their possible values, called the
uncertainty parameter set. The aim is to optimize the worst-case performance on
the uncertainty parameter set to guarantee the performance in the corresponding
real-world environment. To obtain a policy for the optimization, we propose an
off-policy actor-critic approach called the Max-Min Twin Delayed Deep
Deterministic Policy Gradient algorithm (M2TD3), which solves a max-min
optimization problem using a simultaneous gradient ascent descent approach.
Experiments in multi-joint dynamics with contact (MuJoCo) environments show
that the proposed method exhibited a worst-case performance superior to several
baseline approaches.

We proposed a machine learning approach to identify and distinguish dusty
stellar sources employing supervised and unsupervised methods and categorizing
point sources, mainly evolved stars, using photometric and spectroscopic data
collected over the IR sky. Spectroscopic data is typically used to identify
specific infrared sources. However, our goal is to determine how well these
sources can be identified using multiwavelength data. Consequently, we
developed a robust training set of spectra of confirmed sources from the Large
and Small Magellanic Clouds derived from SAGE-Spec Spitzer Legacy and SMC-Spec
Spitzer Infrared Spectrograph (IRS) spectral catalogs. Subsequently, we applied
various learning classifiers to distinguish stellar subcategories comprising
young stellar objects (YSOs), C-rich asymptotic giant branch (CAGB), O-rich AGB
stars (OAGB), Red supergiant (RSG), and post-AGB stars. We have classified
around 700 counts of these sources. It should be highlighted that despite
utilizing the limited spectroscopic data we trained, the accuracy and models'
learning curve provided outstanding results for some of the models. Therefore,
the Support Vector Classifier (SVC) is the most accurate classifier for this
limited dataset.

Differentially private data generation techniques have become a promising
solution to the data privacy challenge -- it enables sharing of data while
complying with rigorous privacy guarantees, which is essential for scientific
progress in sensitive domains. Unfortunately, restricted by the inherent
complexity of modeling high-dimensional distributions, existing private
generative models are struggling with the utility of synthetic samples.
  In contrast to existing works that aim at fitting the complete data
distribution, we directly optimize for a small set of samples that are
representative of the distribution under the supervision of discriminative
information from downstream tasks, which is generally an easier task and more
suitable for private training. Our work provides an alternative view for
differentially private generation of high-dimensional data and introduces a
simple yet effective method that greatly improves the sample utility of
state-of-the-art approaches.

Humans demonstrate a variety of interesting behavioral characteristics when
performing tasks, such as selecting between seemingly equivalent optimal
actions, performing recovery actions when deviating from the optimal
trajectory, or moderating actions in response to sensed risks. However,
imitation learning, which attempts to teach robots to perform these same tasks
from observations of human demonstrations, often fails to capture such
behavior. Specifically, commonly used learning algorithms embody inherent
contradictions between the learning assumptions (e.g., single optimal action)
and actual human behavior (e.g., multiple optimal actions), thereby limiting
robot generalizability, applicability, and demonstration feasibility. To
address this, this paper proposes designing imitation learning algorithms with
a focus on utilizing human behavioral characteristics, thereby embodying
principles for capturing and exploiting actual demonstrator behavioral
characteristics. This paper presents the first imitation learning framework,
Bayesian Disturbance Injection (BDI), that typifies human behavioral
characteristics by incorporating model flexibility, robustification, and risk
sensitivity. Bayesian inference is used to learn flexible non-parametric
multi-action policies, while simultaneously robustifying policies by injecting
risk-sensitive disturbances to induce human recovery action and ensuring
demonstration feasibility. Our method is evaluated through risk-sensitive
simulations and real-robot experiments (e.g., table-sweep task, shaft-reach
task and shaft-insertion task) using the UR5e 6-DOF robotic arm, to demonstrate
the improved characterisation of behavior. Results show significant improvement
in task performance, through improved flexibility, robustness as well as
demonstration feasibility.

Deep learning-based recommender systems may lead to over-fitting when lacking
training interaction data. This over-fitting significantly degrades
recommendation performances. To address this data sparsity problem,
cross-domain recommender systems (CDRSs) exploit the data from an auxiliary
source domain to facilitate the recommendation on the sparse target domain.
Most existing CDRSs rely on overlapping users or items to connect domains and
transfer knowledge. However, matching users is an arduous task and may involve
privacy issues when data comes from different companies, resulting in a limited
application for the above CDRSs. Some studies develop CDRSs that require no
overlapping users and items by transferring learned user interaction patterns.
However, they ignore the bias in user interaction patterns between domains and
hence suffer from an inferior performance compared with single-domain
recommender systems. In this paper, based on the above findings, we propose a
novel CDRS, namely semantic clustering enhanced debiasing graph neural
recommender system (SCDGN), that requires no overlapping users and items and
can handle the domain bias. More precisely, SCDGN semantically clusters items
from both domains and constructs a cross-domain bipartite graph generated from
item clusters and users. Then, the knowledge is transferred via this
cross-domain user-cluster graph from the source to the target. Furthermore, we
design a debiasing graph convolutional layer for SCDGN to extract unbiased
structural knowledge from the cross-domain user-cluster graph. Our Experimental
results on three public datasets and a pair of proprietary datasets verify the
effectiveness of SCDGN over state-of-the-art models in terms of cross-domain
recommendations.

Domain generalization (DG) aims to learn a generalizable model from multiple
training domains such that it can perform well on unseen target domains. A
popular strategy is to augment training data to benefit generalization through
methods such as Mixup~\cite{zhang2018mixup}. While the vanilla Mixup can be
directly applied, theoretical and empirical investigations uncover several
shortcomings that limit its performance. Firstly, Mixup cannot effectively
identify the domain and class information that can be used for learning
invariant representations. Secondly, Mixup may introduce synthetic noisy data
points via random interpolation, which lowers its discrimination capability.
Based on the analysis, we propose a simple yet effective enhancement for
Mixup-based DG, namely domain-invariant Feature mIXup (FIX). It learns
domain-invariant representations for Mixup. To further enhance discrimination,
we leverage existing techniques to enlarge margins among classes to further
propose the domain-invariant Feature MIXup with Enhanced Discrimination (FIXED)
approach. We present theoretical insights about guarantees on its
effectiveness. Extensive experiments on seven public datasets across two
modalities including image classification (Digits-DG, PACS, Office-Home) and
time series (DSADS, PAMAP2, UCI-HAR, and USC-HAD) demonstrate that our approach
significantly outperforms nine state-of-the-art related methods, beating the
best performing baseline by 6.5\% on average in terms of test accuracy.

The ultimate goal of continuous sign language recognition(CSLR) is to
facilitate the communication between special people and normal people, which
requires a certain degree of real-time and deploy-ability of the model.
However, in the previous research on CSLR, little attention has been paid to
the real-time and deploy-ability. In order to improve the real-time and
deploy-ability of the model, this paper proposes a zero parameter, zero
computation temporal superposition crossover module(TSCM), and combines it with
2D convolution to form a "TSCM+2D convolution" hybrid convolution, which
enables 2D convolution to have strong spatial-temporal modelling capability
with zero parameter increase and lower deployment cost compared with other
spatial-temporal convolutions. The overall CSLR model based on TSCM is built on
the improved ResBlockT network in this paper. The hybrid convolution of
"TSCM+2D convolution" is applied to the ResBlock of the ResNet network to form
the new ResBlockT, and random gradient stop and multi-level CTC loss are
introduced to train the model, which reduces the final recognition WER while
reducing the training memory usage, and extends the ResNet network from image
classification task to video recognition task. In addition, this study is the
first in CSLR to use only 2D convolution extraction of sign language video
temporal-spatial features for end-to-end learning for recognition. Experiments
on two large-scale continuous sign language datasets demonstrate the
effectiveness of the proposed method and achieve highly competitive results.

Causal learning has attracted much attention in recent years because
causality reveals the essential relationship between things and indicates how
the world progresses. However, there are many problems and bottlenecks in
traditional causal learning methods, such as high-dimensional unstructured
variables, combinatorial optimization problems, unknown intervention,
unobserved confounders, selection bias and estimation bias. Deep causal
learning, that is, causal learning based on deep neural networks, brings new
insights for addressing these problems. While many deep learning-based causal
discovery and causal inference methods have been proposed, there is a lack of
reviews exploring the internal mechanism of deep learning to improve causal
learning. In this article, we comprehensively review how deep learning can
contribute to causal learning by addressing conventional challenges from three
aspects: representation, discovery, and inference. We point out that deep
causal learning is important for the theoretical extension and application
expansion of causal science and is also an indispensable part of general
artificial intelligence. We conclude the article with a summary of open issues
and potential directions for future work.

Deep learning based methods have significantly boosted the study of automatic
building extraction from remote sensing images. However, delineating vectorized
and regular building contours like a human does remains very challenging, due
to the difficulty of the methodology, the diversity of building structures, and
the imperfect imaging conditions. In this paper, we propose the first
end-to-end learnable building contour extraction framework, named BuildMapper,
which can directly and efficiently delineate building polygons just as a human
does. BuildMapper consists of two main components: 1) a contour initialization
module that generates initial building contours; and 2) a contour evolution
module that performs both contour vertex deformation and reduction, which
removes the need for complex empirical post-processing used in existing
methods. In both components, we provide new ideas, including a learnable
contour initialization method to replace the empirical methods, dynamic
predicted and ground truth vertex pairing for the static vertex correspondence
problem, and a lightweight encoder for vertex information extraction and
aggregation, which benefit a general contour-based method; and a well-designed
vertex classification head for building corner vertices detection, which casts
light on direct structured building contour extraction. We also built a
suitable large-scale building dataset, the WHU-Mix (vector) building dataset,
to benefit the study of contour-based building extraction methods. The
extensive experiments conducted on the WHU-Mix (vector) dataset, the WHU
dataset, and the CrowdAI dataset verified that BuildMapper can achieve a
state-of-the-art performance, with a higher mask average precision (AP) and
boundary AP than both segmentation-based and contour-based methods.

Wake-up words (WUW) is a short sentence used to activate a speech recognition
system to receive the user's speech input. WUW utterances include not only the
lexical information for waking up the system but also non-lexical information
such as speaker identity or emotion. In particular, recognizing the user's
emotional state may elaborate the voice communication. However, there is few
dataset where the emotional state of the WUW utterances is labeled. In this
paper, we introduce Hi, KIA, a new WUW dataset which consists of 488 Korean
accent emotional utterances collected from four male and four female speakers
and each of utterances is labeled with four emotional states including anger,
happy, sad, or neutral. We present the step-by-step procedure to build the
dataset, covering scenario selection, post-processing, and human validation for
label agreement. Also, we provide two classification models for WUW speech
emotion recognition using the dataset. One is based on traditional hand-craft
features and the other is a transfer-learning approach using a pre-trained
neural network. These classification models could be used as benchmarks in
further research.

Recent advances in computer vision have shown promising results in image
generation. Diffusion probabilistic models in particular have generated
realistic images from textual input, as demonstrated by DALL-E 2, Imagen and
Stable Diffusion. However, their use in medicine, where image data typically
comprises three-dimensional volumes, has not been systematically evaluated.
Synthetic images may play a crucial role in privacy preserving artificial
intelligence and can also be used to augment small datasets. Here we show that
diffusion probabilistic models can synthesize high quality medical imaging
data, which we show for Magnetic Resonance Images (MRI) and Computed Tomography
(CT) images. We provide quantitative measurements of their performance through
a reader study with two medical experts who rated the quality of the
synthesized images in three categories: Realistic image appearance, anatomical
correctness and consistency between slices. Furthermore, we demonstrate that
synthetic images can be used in a self-supervised pre-training and improve the
performance of breast segmentation models when data is scarce (dice score 0.91
vs. 0.95 without vs. with synthetic data).

Privacy, security, and bandwidth constraints have led to federated learning
(FL) in wireless systems, where training a machine learning (ML) model is
accomplished collaboratively without sharing raw data. Often, such
collaborative FL strategies necessitate model aggregation at a server. On the
other hand, decentralized FL necessitates that participating clients reach a
consensus ML model by exchanging parameter updates. In this work, we propose
the over-the-air clustered wireless FL (CWFL) strategy, which eliminates the
need for a strong central server and yet achieves an accuracy similar to the
server-based strategy while using fewer channel uses as compared to
decentralized FL. We theoretically show that the convergence rate of CWFL per
cluster is O(1/T) while mitigating the impact of noise. Using the MNIST and
CIFAR datasets, we demonstrate the accuracy performance of CWFL for the
different number of clusters across communication rounds.

We introduce CAMRL, the first curriculum-based asymmetric multi-task learning
(AMTL) algorithm for dealing with multiple reinforcement learning (RL) tasks
altogether. To mitigate the negative influence of customizing the one-off
training order in curriculum-based AMTL, CAMRL switches its training mode
between parallel single-task RL and asymmetric multi-task RL (MTRL), according
to an indicator regarding the training time, the overall performance, and the
performance gap among tasks. To leverage the multi-sourced prior knowledge
flexibly and to reduce negative transfer in AMTL, we customize a composite loss
with multiple differentiable ranking functions and optimize the loss through
alternating optimization and the Frank-Wolfe algorithm. The uncertainty-based
automatic adjustment of hyper-parameters is also applied to eliminate the need
of laborious hyper-parameter analysis during optimization. By optimizing the
composite loss, CAMRL predicts the next training task and continuously revisits
the transfer matrix and network weights. We have conducted experiments on a
wide range of benchmarks in multi-task RL, covering Gym-minigrid, Meta-world,
Atari video games, vision-based PyBullet tasks, and RLBench, to show the
improvements of CAMRL over the corresponding single-task RL algorithm and
state-of-the-art MTRL algorithms. The code is available at:
https://github.com/huanghanchi/CAMRL

Various brain functions that are necessary to maintain life activities
materialize through the interaction of countless neurons. Therefore, it is
important to analyze the structure of functional neuronal network. To elucidate
the mechanism of brain function, many studies are being actively conducted on
the structure of functional neuronal ensemble and hub, including all areas of
neuroscience. In addition, recent study suggests that the existence of
functional neuronal ensembles and hubs contributes to the efficiency of
information processing. For these reasons, there is a demand for methods to
infer functional neuronal ensembles from neuronal activity data, and methods
based on Bayesian inference have been proposed. However, there is a problem in
modeling the activity in Bayesian inference. The features of each neuron's
activity have non-stationarity depending on physiological experimental
conditions. As a result, the assumption of stationarity in Bayesian inference
model impedes inference, which leads to destabilization of inference results
and degradation of inference accuracy. In this study, we extend the
expressivity of the model in the previous study and improve it to a soft
clustering method, which can be applied to activity data with non-stationarity.
In addition, for the effectiveness of the method, we apply the developed method
to synthetic data generated by the leaky-integrate-and-fire model, and discuss
the result.

Contrastive learning has become a new paradigm for unsupervised sentence
embeddings. Previous studies focus on instance-wise contrastive learning,
attempting to construct positive pairs with textual data augmentation. In this
paper, we propose a novel Contrastive learning method with Prompt-derived
Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts,
we construct virtual semantic prototypes to each instance, and derive negative
prototypes by using the negative form of the prompts. Using a prototypical
contrastive loss, we enforce the anchor sentence embedding to be close to its
corresponding semantic prototypes, and far apart from the negative prototypes
as well as the prototypes of other sentences. Extensive experimental results on
semantic textual similarity, transfer, and clustering tasks demonstrate the
effectiveness of our proposed model compared to strong baselines. Code is
available at https://github.com/lemon0830/promptCSE.

Transfer Learning is an area of statistics and machine learning research that
seeks answers to the following question: how do we build successful learning
algorithms when the data available for training our model is qualitatively
different from the data we hope the model will perform well on? In this thesis,
we focus on a specific area of Transfer Learning called label shift, also known
as quantification. In quantification, the aforementioned discrepancy is
isolated to a shift in the distribution of the response variable. In such a
setting, accurately inferring the response variable's new distribution is both
an important estimation task in its own right and a crucial step for ensuring
that the learning algorithm can adapt to the new data. We make two
contributions to this field. First, we present a new procedure called SELSE
which estimates the shift in the response variable's distribution. Second, we
prove that SELSE is semiparametric efficient among a large family of
quantification algorithms, i.e., SELSE's normalized error has the smallest
possible asymptotic variance matrix compared to any other algorithm in that
family. This family includes nearly all existing algorithms, including ACC/PACC
quantifiers and maximum likelihood based quantifiers such as EMQ and MLLS.
Empirical experiments reveal that SELSE is competitive with, and in many cases
outperforms, existing state-of-the-art quantification methods, and that this
improvement is especially large when the number of test samples is far greater
than the number of train samples.

As ultra-realistic face forgery techniques emerge, deepfake detection has
attracted increasing attention due to security concerns. Many detectors cannot
achieve accurate results when detecting unseen manipulations despite excellent
performance on known forgeries. In this paper, we are motivated by the
observation that the discrepancies between real and fake videos are extremely
subtle and localized, and inconsistencies or irregularities can exist in some
critical facial regions across various information domains. To this end, we
propose a novel pipeline, Cross-Domain Local Forensics (XDLF), for more general
deepfake video detection. In the proposed pipeline, a specialized framework is
presented to simultaneously exploit local forgery patterns from space,
frequency, and time domains, thus learning cross-domain features to detect
forgeries. Moreover, the framework leverages four high-level forgery-sensitive
local regions of a human face to guide the model to enhance subtle artifacts
and localize potential anomalies. Extensive experiments on several benchmark
datasets demonstrate the impressive performance of our method, and we achieve
superiority over several state-of-the-art methods on cross-dataset
generalization. We also examined the factors that contribute to its performance
through ablations, which suggests that exploiting cross-domain local
characteristics is a noteworthy direction for developing more general deepfake
detectors.

Obtaining large-scale well-annotated is always a daunting challenge,
especially in the medical research domain because of the shortage of domain
expert. Instead of human annotation, in this work, we use the alarm information
generated from bed-side monitor to get the pseudo label for the co-current
photoplethysmography (PPG) signal. Based on this strategy, we end up with over
8 million 30-second PPG segment. To solve the label noise caused by false
alarms, we propose the cluster consistency, which use an unsupervised
auto-encoder (hence not subject to label noise) approach to cluster training
samples into a finite number of clusters. Then the learned cluster membership
is used in the subsequent supervised learning phase to force the distance in
the latent space of samples in the same cluster to be small while that of
samples in different clusters to be big. In the experiment, we compare with the
state-of-the-art algorithms and test on external datasets. The results show the
superiority of our method in both classification performance and efficiency.

Graphons are general and powerful models for generating graphs of varying
size. In this paper, we propose to directly model graphons using neural
networks, obtaining Implicit Graphon Neural Representation (IGNR). Existing
work in modeling and reconstructing graphons often approximates a target
graphon by a fixed resolution piece-wise constant representation. Our IGNR has
the benefit that it can represent graphons up to arbitrary resolutions, and
enables natural and efficient generation of arbitrary sized graphs with desired
structure once the model is learned. Furthermore, we allow the input graph data
to be unaligned and have different sizes by leveraging the Gromov-Wasserstein
distance. We first demonstrate the effectiveness of our model by showing its
superior performance on a graphon learning task. We then propose an extension
of IGNR that can be incorporated into an auto-encoder framework, and
demonstrate its good performance under a more general setting of graphon
learning. We also show that our model is suitable for graph representation
learning and graph generation.

Accent plays a significant role in speech communication, influencing
understanding capabilities and also conveying a person's identity. This paper
introduces a novel and efficient framework for accented Text-to-Speech (TTS)
synthesis based on a Conditional Variational Autoencoder. It has the ability to
synthesize a selected speaker's speech that is converted to any desired target
accent. Our thorough experiments validate the effectiveness of our proposed
framework using both objective and subjective evaluations. The results also
show remarkable performance in terms of the ability to manipulate accents in
the synthesized speech and provide a promising avenue for future accented TTS
research.

Video-and-language pre-training has shown promising results for learning
generalizable representations. Most existing approaches usually model video and
text in an implicit manner, without considering explicit structural
representations of the multi-modal content. We denote such form of
representations as structural knowledge, which express rich semantics of
multiple granularities. There are related works that propose object-aware
approaches to inject similar knowledge as inputs. However, the existing methods
usually fail to effectively utilize such knowledge as regularizations to shape
a superior cross-modal representation space. To this end, we propose a
Cross-modaL knOwledge-enhanced Pre-training (CLOP) method with Knowledge
Regularizations. There are two key designs of ours: 1) a simple yet effective
Structural Knowledge Prediction (SKP) task to pull together the latent
representations of similar videos; and 2) a novel Knowledge-guided sampling
approach for Contrastive Learning (KCL) to push apart cross-modal hard negative
samples. We evaluate our method on four text-video retrieval tasks and one
multi-choice QA task. The experiments show clear improvements, outperforming
prior works by a substantial margin. Besides, we provide ablations and insights
of how our methods affect the latent representation space, demonstrating the
value of incorporating knowledge regularizations into video-and-language
pre-training.

Over the past decade, machine learning model complexity has grown at an
extraordinary rate, as has the scale of the systems training such large models.
However there is an alarmingly low hardware utilization (5-20%) in large scale
AI systems. The low system utilization is a cumulative effect of minor losses
across different layers of the stack, exacerbated by the disconnect between
engineers designing different layers spanning across different industries. We
propose CrossFlow, a novel framework that enables cross-layer analysis all the
way from the technology layer to the algorithmic layer. We also propose
DeepFlow (built on top of CrossFlow using machine learning techniques) to
automate the design space exploration and co-optimization across different
layers of the stack. We have validated CrossFlow accuracy with distributed
training on real commercial hardware and showcase several DeepFlow case studies
demonstrating pitfalls of not optimizing across the
technology-hardware-software stack for what is likely, the most important
workload driving large development investments in all aspects of computing
stack.

Headline generation is a task of generating an appropriate headline for a
given article, which can be further used for machine-aided writing or enhancing
the click-through ratio. Current works only use the article itself in the
generation, but have not taken the writing style of headlines into
consideration. In this paper, we propose a novel Seq2Seq model called CLH3G
(Contrastive Learning enhanced Historical Headlines based Headline Generation)
which can use the historical headlines of the articles that the author wrote in
the past to improve the headline generation of current articles. By taking
historical headlines into account, we can integrate the stylistic features of
the author into our model, and generate a headline not only appropriate for the
article, but also consistent with the author's style. In order to efficiently
learn the stylistic features of the author, we further introduce a contrastive
learning based auxiliary task for the encoder of our model. Besides, we propose
two methods to use the learned stylistic features to guide both the pointer and
the decoder during the generation. Experimental results show that historical
headlines of the same user can improve the headline generation significantly,
and both the contrastive learning module and the two style features fusion
methods can further boost the performance.

Federated Learning (FL), as a rapidly evolving privacy-preserving
collaborative machine learning paradigm, is a promising approach to enable edge
intelligence in the emerging Industrial Metaverse. Even though many successful
use cases have proved the feasibility of FL in theory, in the industrial
practice of Metaverse, the problems of non-independent and identically
distributed (non-i.i.d.) data, learning forgetting caused by streaming
industrial data, and scarce communication bandwidth remain key barriers to
realize practical FL. Facing the above three challenges simultaneously, this
paper presents a high-performance and efficient system named HFEDMS for
incorporating practical FL into Industrial Metaverse. HFEDMS reduces data
heterogeneity through dynamic grouping and training mode conversion (Dynamic
Sequential-to-Parallel Training, STP). Then, it compensates for the forgotten
knowledge by fusing compressed historical data semantics and calibrates
classifier parameters (Semantic Compression and Compensation, SCC). Finally,
the network parameters of the feature extractor and classifier are synchronized
in different frequencies (Layer-wiseAlternative Synchronization Protocol, LASP)
to reduce communication costs. These techniques make FL more adaptable to the
heterogeneous streaming data continuously generated by industrial equipment,
and are also more efficient in communication than traditional methods (e.g.,
Federated Averaging). Extensive experiments have been conducted on the streamed
non-i.i.d. FEMNIST dataset using 368 simulated devices. Numerical results show
that HFEDMS improves the classification accuracy by at least 6.4% compared with
8 benchmarks and saves both the overall runtime and transfer bytes by up to
98%, proving its superiority in precision and efficiency.

This paper presents a learning framework to estimate an agent capability and
task requirement model for multi-agent task allocation. With a set of team
configurations and the corresponding task performances as the training data,
linear task constraints can be learned to be embedded in many existing
optimization-based task allocation frameworks. Comprehensive computational
evaluations are conducted to test the scalability and prediction accuracy of
the learning framework with a limited number of team configurations and
performance pairs. A ROS and Gazebo-based simulation environment is developed
to validate the proposed requirements learning and task allocation framework in
practical multi-agent exploration and manipulation tasks. Results show that the
learning process for scenarios with 40 tasks and 6 types of agents uses around
12 seconds, ending up with prediction errors in the range of 0.5-2%.

For the smart vehicular network, we studied two technologies to realize it.
The first technology is the cooperative scheme which improves capacity by
properly combining the V2V and V2I. The second technology is an online learning
algorithm which can deal with the beam selection problem in mmWave system. Both
are effective and can be used in autonomous driving systems. However,
advancements in the field of IoV have elicited research in different areas
related to the field. This highlights a critical need to address security and
protection challenges as a result of the progression of vehicles and everything
that is being transferred to the internet. In addition, to understand exactly
where research is missing regarding IoV, we found that a survey of current
research in the vulnerabilities and threats to general IoT applications. In
addition to other attacks, we found that DDoS attacks in the form of botnets
are significant threats to the IoT world. Upon researching which threats and
vulnerabilities are leveraged in IoV research, the field was severely lacking
in botnet and DDoS attack research. If developers neglect to address this issue
before interconnected vehicles become a mainstream reality, this discovery can
have severe ramifications for the safety of IoV consumers around the globe.

Mutation-based fuzzing has become one of the most common vulnerability
discovery solutions over the last decade. Fuzzing can be optimized when
targeting specific programs, and given that, some studies have employed online
optimization methods to do it automatically, i.e., tuning fuzzers for any given
program in a program-agnostic manner. However, previous studies have neither
fully explored mutation schemes suitable for online optimization methods, nor
online optimization methods suitable for mutation schemes. In this study, we
propose an optimization framework called SLOPT that encompasses both a
bandit-friendly mutation scheme and mutation-scheme-friendly bandit algorithms.
The advantage of SLOPT is that it can generally be incorporated into existing
fuzzers, such as AFL and Honggfuzz. As a proof of concept, we implemented
SLOPT-AFL++ by integrating SLOPT into AFL++ and showed that the
program-agnostic optimization delivered by SLOPT enabled SLOPT-AFL++ to achieve
higher code coverage than AFL++ in all of ten real-world FuzzBench programs.
Moreover, we ran SLOPT-AFL++ against several real-world programs from OSS-Fuzz
and successfully identified three previously unknown vulnerabilities, even
though these programs have been fuzzed by AFL++ for a considerable number of
CPU days on OSS-Fuzz.

Recent advances in deep learning have led to the development of models
approaching the human level of accuracy. However, healthcare remains an area
lacking in widespread adoption. The safety-critical nature of healthcare
results in a natural reticence to put these black-box deep learning models into
practice. This paper explores interpretable methods for a clinical decision
support system called sleep staging, an essential step in diagnosing sleep
disorders. Clinical sleep staging is an arduous process requiring manual
annotation for each 30s of sleep using physiological signals such as
electroencephalogram (EEG). Recent work has shown that sleep staging using
simple models and an exhaustive set of features can perform nearly as well as
deep learning approaches but only for some specific datasets. Moreover, the
utility of those features from a clinical standpoint is ambiguous. On the other
hand, the proposed framework, NormIntSleep demonstrates exceptional performance
across different datasets by representing deep learning embeddings using
normalized features. NormIntSleep performs 4.5% better than the exhaustive
feature-based approach and 1.5% better than other representation learning
approaches. An empirical comparison between the utility of the interpretations
of these models highlights the improved alignment with clinical expectations
when performance is traded-off slightly. NormIntSleep paired with a clinically
meaningful set of features can best balance this trade-off by providing
reliable, clinically relevant interpretation with robust performance.

Recent advances in reinforcement-learning research have demonstrated
impressive results in building algorithms that can out-perform humans in
complex tasks. Nevertheless, creating reinforcement-learning systems that can
build abstractions of their experience to accelerate learning in new contexts
still remains an active area of research. Previous work showed that
reward-predictive state abstractions fulfill this goal, but have only be
applied to tabular settings. Here, we provide a clustering algorithm that
enables the application of such state abstractions to deep learning settings,
providing compressed representations of an agent's inputs that preserve the
ability to predict sequences of reward. A convergence theorem and simulations
show that the resulting reward-predictive deep network maximally compresses the
agent's inputs, significantly speeding up learning in high dimensional visual
control tasks. Furthermore, we present different generalization experiments and
analyze under which conditions a pre-trained reward-predictive representation
network can be re-used without re-training to accelerate learning -- a form of
systematic out-of-distribution transfer.

This paper focuses on the task of survival time analysis for lung cancer.
Although much progress has been made in this problem in recent years, the
performance of existing methods is still far from satisfactory. Traditional and
some deep learning-based survival time analyses for lung cancer are mostly
based on textual clinical information such as staging, age, histology, etc.
Unlike existing methods that predicting on the single modality, we observe that
a human clinician usually takes multimodal data such as text clinical data and
visual scans to estimate survival time. Motivated by this, in this work, we
contribute a smart cross-modality network for survival analysis network named
Lite-ProSENet that simulates a human's manner of decision making. Extensive
experiments were conducted using data from 422 NSCLC patients from The Cancer
Imaging Archive (TCIA). The results show that our Lite-ProSENet outperforms
favorably again all comparison methods and achieves the new state of the art
with the 89.3% on concordance. The code will be made publicly available.

Vocal entrainment is a social adaptation mechanism in human interaction,
knowledge of which can offer useful insights to an individual's
cognitive-behavioral characteristics. We propose a context-aware approach for
measuring vocal entrainment in dyadic conversations. We use conformers(a
combination of convolutional network and transformer) for capturing both
short-term and long-term conversational context to model entrainment patterns
in interactions across different domains. Specifically we use cross-subject
attention layers to learn intra- as well as inter-personal signals from dyadic
conversations. We first validate the proposed method based on classification
experiments to distinguish between real(consistent) and
fake(inconsistent/shuffled) conversations. Experimental results on interactions
involving individuals with Autism Spectrum Disorder also show evidence of a
statistically-significant association between the introduced entrainment
measure and clinical scores relevant to symptoms, including across gender and
age groups.

Multi-hop reading comprehension requires not only the ability to reason over
raw text but also the ability to combine multiple evidence. We propose a novel
learning approach that helps language models better understand difficult
multi-hop questions and perform "complex, compositional" reasoning. Our model
first learns to decompose each multi-hop question into several sub-questions by
a trainable question decomposer. Instead of answering these sub-questions, we
directly concatenate them with the original question and context, and leverage
a reading comprehension model to predict the answer in a sequence-to-sequence
manner. By using the same language model for these two components, our best
seperate/unified t5-base variants outperform the baseline by 7.2/6.1 absolute
F1 points on a hard subset of DROP dataset.

Incorporating large-scale pre-trained models with the prototypical neural
networks is a de-facto paradigm in few-shot named entity recognition. Existing
methods, unfortunately, are not aware of the fact that embeddings from
pre-trained models contain a prominently large amount of information regarding
word frequencies, biasing prototypical neural networks against learning word
entities. This discrepancy constrains the two models' synergy. Thus, we propose
a one-line-code normalization method to reconcile such a mismatch with
empirical and theoretical grounds. Our experiments based on nine benchmark
datasets show the superiority of our method over the counterpart models and are
comparable to the state-of-the-art methods. In addition to the model
enhancement, our work also provides an analytical viewpoint for addressing the
general problems in few-shot name entity recognition or other tasks that rely
on pre-trained models or prototypical neural networks.

First-principles computations are the driving force behind numerous
discoveries of hydride-based superconductors, mostly at high pressures, during
the last decade. Machine-learning (ML) approaches can further accelerate the
future discoveries if their reliability can be improved. The main challenge of
current ML approaches, typically aiming at predicting the critical temperature
$T_{\rm c}$ of a solid from its chemical composition and target pressure, is
that the correlations to be learned are deeply hidden, indirect, and uncertain.
In this work, we showed that predicting superconductivity at any pressure from
the atomic structure is sustainable and reliable. For a demonstration, we
curated a diverse dataset of 584 atomic structures for which $\lambda$ and
$\omega_{\log}$, two parameters of the electron-phonon interactions, were
computed. We then trained some ML models to predict $\lambda$ and
$\omega_{\log}$, from which $T_{\rm c}$ can be computed in a post-processing
manner. The models were validated and used to identify two possible
superconductors whose $T_{\rm c}\simeq 10-15$K and zero pressure. Going
forward, this strategy will be improved to better contribute to the discoveries
of new superconductors.

In recent years, multilingual pre-trained language models have gained
prominence due to their remarkable performance on numerous downstream Natural
Language Processing tasks (NLP). However, pre-training these large multilingual
language models requires a lot of training data, which is not available for
African Languages. Active learning is a semi-supervised learning algorithm, in
which a model consistently and dynamically learns to identify the most
beneficial samples to train itself on, in order to achieve better optimization
and performance on downstream tasks. Furthermore, active learning effectively
and practically addresses real-world data scarcity. Despite all its benefits,
active learning, in the context of NLP and especially multilingual language
models pretraining, has received little consideration. In this paper, we
present AfroLM, a multilingual language model pretrained from scratch on 23
African languages (the largest effort to date) using our novel self-active
learning framework. Pretrained on a dataset significantly (14x) smaller than
existing baselines, AfroLM outperforms many multilingual pretrained language
models (AfriBERTa, XLMR-base, mBERT) on various NLP downstream tasks (NER, text
classification, and sentiment analysis). Additional out-of-domain sentiment
analysis experiments show that \textbf{AfroLM} is able to generalize well
across various domains. We release the code source, and our datasets used in
our framework at https://github.com/bonaventuredossou/MLM_AL.

We present a dataset generator engine named Web-based Visual Corpus Builder
(Webvicob). Webvicob can readily construct a large-scale visual corpus (i.e.,
images with text annotations) from a raw Wikipedia HTML dump. In this report,
we validate that Webvicob-generated data can cover a wide range of context and
knowledge and helps practitioners to build a powerful Visual Document
Understanding (VDU) backbone. The proposed engine is publicly available at
https://github.com/clovaai/webvicob.

The horseshoe prior is known to possess many desirable properties for
Bayesian estimation of sparse parameter vectors, yet its density function lacks
an analytic form. As such, it is challenging to find a closed-form solution for
the posterior mode. Conventional horseshoe estimators use the posterior mean to
estimate the parameters, but these estimates are not sparse. We propose a novel
expectation-maximisation (EM) procedure for computing the MAP estimates of the
parameters in the case of the standard linear model. A particular strength of
our approach is that the M-step depends only on the form of the prior and it is
independent of the form of the likelihood. We introduce several simple
modifications of this EM procedure that allow for straightforward extension to
generalised linear models. In experiments performed on simulated and real data,
our approach performs comparable, or superior to, state-of-the-art sparse
estimation methods in terms of statistical performance and computational cost.

Neural network-based approaches for solving partial differential equations
(PDEs) have recently received special attention. However, the large majority of
neural PDE solvers only apply to rectilinear domains, and do not systematically
address the imposition of Dirichlet/Neumann boundary conditions over irregular
domain boundaries. In this paper, we present a framework to neurally solve
partial differential equations over domains with irregularly shaped
(non-rectilinear) geometric boundaries. Our network takes in the shape of the
domain as an input (represented using an unstructured point cloud, or any other
parametric representation such as Non-Uniform Rational B-Splines) and is able
to generalize to novel (unseen) irregular domains; the key technical ingredient
to realizing this model is a novel approach for identifying the interior and
exterior of the computational grid in a differentiable manner. We also perform
a careful error analysis which reveals theoretical insights into several
sources of error incurred in the model-building process. Finally, we showcase a
wide variety of applications, along with favorable comparisons with ground
truth solutions.

A fundamental question in any peer-to-peer ride-sharing system is how to,
both effectively and efficiently, meet the request of passengers to balance the
supply and demand in real time. On the passenger side, traditional approaches
focus on pricing strategies by increasing the probability of users' call to
adjust the distribution of demand. However, previous methods do not take into
account the impact of changes in strategy on future supply and demand changes,
which means drivers are repositioned to different destinations due to
passengers' calls, which will affect the driver's income for a period of time
in the future. Motivated by this observation, we make an attempt to optimize
the distribution of demand to handle this problem by learning the long-term
spatio-temporal values as a guideline for pricing strategy. In this study, we
propose an offline deep reinforcement learning based method focusing on the
demand side to improve the utilization of transportation resources and customer
satisfaction. We adopt a spatio-temporal learning method to learn the value of
different time and location, then incentivize the ride requests of passengers
to adjust the distribution of demand to balance the supply and demand in the
system. In particular, we model the problem as a Markov Decision Process (MDP).

Attention-based autoregressive models have achieved state-of-the-art
performance in various sequence-to-sequence tasks, including Text-To-Speech
(TTS) and Neural Machine Translation (NMT), but can be difficult to train. The
standard training approach, teacher forcing, guides a model with the reference
back-history. During inference, the generated back-history must be used. This
mismatch limits the evaluation performance. Attention forcing has been
introduced to address the mismatch, guiding the model with the generated
back-history and reference attention. While successful in tasks with continuous
outputs like TTS, attention forcing faces additional challenges in tasks with
discrete outputs like NMT. This paper introduces the two extensions of
attention forcing to tackle these challenges. (1) Scheduled attention forcing
automatically turns attention forcing on and off, which is essential for tasks
with discrete outputs. (2) Parallel attention forcing makes training parallel,
and is applicable to Transformer-based models. The experiments show that the
proposed approaches improve the performance of models based on RNNs and
Transformers.

Current quantum systems have significant limitations affecting the processing
of large datasets and high dimensionality typical of high energy physics. In
this work, feature and data prototype selection techniques were studied to
tackle this challenge. A grid search was performed and quantum machine learning
models were trained and benchmarked against classical shallow machine learning
methods, trained both in the reduced and the complete datasets. The performance
of the quantum algorithms was found to be comparable to the classical ones,
even when using large datasets.

Recent work shows that the expressive power of Graph Neural Networks (GNNs)
in distinguishing non-isomorphic graphs is exactly the same as that of the
Weisfeiler-Lehman (WL) graph test. In particular, they show that the WL test
can be simulated by GNNs. However, those simulations involve neural networks
for the 'combine' function of size polynomial or even exponential in the number
of graph nodes $n$, as well as feature vectors of length linear in $n$.
  We present an improved simulation of the WL test on GNNs with
\emph{exponentially} lower complexity. In particular, the neural network
implementing the combine function in each node has only a polylogarithmic
number of parameters in $n$, and the feature vectors exchanged by the nodes of
GNN consists of only $O(\log n)$ bits. We also give logarithmic lower bounds
for the feature vector length and the size of the neural networks, showing the
(near)-optimality of our construction.

Spectral methods provide consistent estimators for community detection in
dense graphs. However, their performance deteriorates as the graphs become
sparser. In this work we consider a random graph model that can produce graphs
at different levels of sparsity, and we show that graph neural networks can
outperform spectral methods on sparse graphs. We illustrate the results with
numerical examples in both synthetic and real graphs.

Cardiac resynchronization therapy (CRT) has been established as an important
therapy for heart failure. Mechanical dyssynchrony has the potential to predict
responders to CRT. The aim of this study was to report the development and the
validation of machine learning (ML) models which integrates ECG, gated SPECT
MPI (GMPS) and clinical variables to predict patients' response to CRT. This
analysis included 153 patients who met criteria for CRT from a prospective
cohort study. The variables were used to modeling predictive methods for CRT.
Patients were classified as responders for an increase of LVEF>=5% at
follow-up. In a second analysis, patients were classified super-responders for
increase of LVEF>=15%. For ML, variable selection was applied, and Prediction
Analysis of Microarrays (PAM) approach was used for response modeling while
Naive Bayes (NB) was used for super-response. They were compared to models
obtained with guideline variables. PAM had AUC of 0.80 against 0.71 of logistic
regression with guideline variables (p = 0.47). The sensitivity (0.86) and
specificity (0.75) were better than for guideline alone, sensitivity (0.72) and
specificity (0.22). Neural network with guideline variables outperformed NB
(AUC = 0.87 vs 0.86; p = 0.88). Its sensitivity and specificity (1.0 and 0.75,
respectively) was better than guideline alone (0.40 and 0.06, respectively).
Compared to guideline criteria, ML methods trended towards improved CRT
response and super-response prediction. GMPS had a central role in the
acquisition of most parameters. Further studies are needed to validate the
models.

Within the glassy liquids community, the use of Machine Learning (ML) to
model particles' static structure in order to predict their future dynamics is
currently a hot topic. The actual state of the art consists in Graph Neural
Networks (GNNs) (Bapst 2020) which, beside having a great expressive power, are
heavy models with numerous parameters and lack interpretability. Inspired by
recent advances (Thomas 2018), we build a GNN that learns a robust
representation of the glass' static structure by constraining it to preserve
the roto-translation (SE(3)) equivariance. We show that this constraint not
only significantly improves the predictive power but also allows to reduce the
number of parameters while improving the interpretability. Furthermore, we
relate our learned equivariant features to well-known invariant expert
features, which are easily expressible with a single layer of our network.

We present a data-driven approach to mathematically model physical systems
whose governing partial differential equations are unknown, by learning their
associated Green's function. The subject systems are observed by collecting
input-output pairs of system responses under excitations drawn from a Gaussian
process. Two methods are proposed to learn the Green's function. In the first
method, we use the proper orthogonal decomposition (POD) modes of the system as
a surrogate for the eigenvectors of the Green's function, and subsequently fit
the eigenvalues, using data. In the second, we employ a generalization of the
randomized singular value decomposition (SVD) to operators, in order to
construct a low-rank approximation to the Green's function. Then, we propose a
manifold interpolation scheme, for use in an offline-online setting, where
offline excitation-response data, taken at specific model parameter instances,
are compressed into empirical eigenmodes. These eigenmodes are subsequently
used within a manifold interpolation scheme, to uncover other suitable
eigenmodes at unseen model parameters. The approximation and interpolation
numerical techniques are demonstrated on several examples in one and two
dimensions.

Cement is the most used construction material. The performance of cement
hydrate depends on the constituent phases, viz. alite, belite, aluminate, and
ferrites present in the cement clinker, both qualitatively and quantitatively.
Traditionally, clinker phases are analyzed from optical images relying on a
domain expert and simple image processing techniques. However, the
non-uniformity of the images, variations in the geometry and size of the
phases, and variabilities in the experimental approaches and imaging methods
make it challenging to obtain the phases. Here, we present a machine learning
(ML) approach to detect clinker microstructure phases automatically. To this
extent, we create the first annotated dataset of cement clinker by segmenting
alite and belite particles. Further, we use supervised ML methods to train
models for identifying alite and belite regions. Specifically, we finetune the
image detection and segmentation model Detectron-2 on the cement microstructure
to develop a model for detecting the cement phases, namely, Cementron. We
demonstrate that Cementron, trained only on literature data, works remarkably
well on new images obtained from our experiments, demonstrating its
generalizability. We make Cementron available for public use.

The pervasive application of artificial intelligence and machine learning
algorithms is transforming many industries and aspects of the human experience.
One very important industry trend is the move to convert existing human
dwellings to smart buildings, and to create new smart buildings. Smart
buildings aim to mitigate climate change by reducing energy consumption and
associated carbon emissions. To accomplish this, they leverage artificial
intelligence, big data, and machine learning algorithms to learn and optimize
system performance. These fields of research are currently very rapidly
evolving and advancing, but there has been very little guidance to help
engineers and architects working on smart buildings apply artificial
intelligence algorithms and technologies in a systematic and effective manner.
In this paper we present B-SMART: the first reference architecture for
autonomic smart buildings. B-SMART facilitates the application of artificial
intelligence techniques and technologies to smart buildings by decoupling
conceptually distinct layers of functionality and organizing them into an
autonomic control loop. We also present a case study illustrating how B-SMART
can be applied to accelerate the introduction of artificial intelligence into
an existing smart building.

Deliberation networks are a family of sequence-to-sequence models, which have
achieved state-of-the-art performance in a wide range of tasks such as machine
translation and speech synthesis. A deliberation network consists of multiple
standard sequence-to-sequence models, each one conditioned on the initial input
and the output of the previous model. During training, there are several key
questions: whether to apply Monte Carlo approximation to the gradients or the
loss, whether to train the standard models jointly or separately, whether to
run an intermediate model in teacher forcing or free running mode, whether to
apply task-specific techniques. Previous work on deliberation networks
typically explores one or two training options for a specific task. This work
introduces a unifying framework, covering various training options, and
addresses the above questions. In general, it is simpler to approximate the
gradients. When parallel training is essential, separate training should be
adopted. Regardless of the task, the intermediate model should be in free
running mode. For tasks where the output is continuous, a guided attention loss
can be used to prevent degradation into a standard model.

As the demand for user privacy grows, controlled data removal (machine
unlearning) is becoming an important feature of machine learning models for
data-sensitive Web applications such as social networks and recommender
systems. Nevertheless, at this point it is still largely unknown how to perform
efficient machine unlearning of graph neural networks (GNNs); this is
especially the case when the number of training samples is small, in which case
unlearning can seriously compromise the performance of the model. To address
this issue, we initiate the study of unlearning the Graph Scattering Transform
(GST), a mathematical framework that is efficient, provably stable under
feature or graph topology perturbations, and offers graph classification
performance comparable to that of GNNs. Our main contribution is the first
known nonlinear approximate graph unlearning method based on GSTs. Our second
contribution is a theoretical analysis of the computational complexity of the
proposed unlearning mechanism, which is hard to replicate for deep neural
networks. Our third contribution are extensive simulation results which show
that, compared to complete retraining of GNNs after each removal request, the
new GST-based approach offers, on average, a $10.38$x speed-up and leads to a
$2.6$% increase in test accuracy during unlearning of $90$ out of $100$
training graphs from the IMDB dataset ($10$% training ratio).

Classifying states as entangled or separable is a highly challenging task,
while it is also one of the foundations of quantum information processing
theory. This task is higly nontrivial even for relatively simple cases, such as
two-qutrit Bell-diagonal states, i.e., mixture of nine mutually orthogonal
maximally entangled states. In this article we apply the Gilbert algorithm to
revise previously obtained results for this class. In particular we use
``cartography of entanglement'' to argue that most states left in [Hiesmayr, B.
C. {\em Scientific Reports} {\bf 11}, 19739 (2021)] as unknown to be entangled
or separable are most likely indeed separable, or very weakly entangled. The
presented technique can find endless applications in more general cases.

Deep learning-based pose estimation algorithms can successfully estimate the
pose of objects in an image, especially in the field of color images. 6D Object
pose estimation based on deep learning models for X-ray images often use custom
architectures that employ extensive CAD models and simulated data for training
purposes. Recent RGB-based methods opt to solve pose estimation problems using
small datasets, making them more attractive for the X-ray domain where medical
data is scarcely available. We refine an existing RGB-based model
(SingleShotPose) to estimate the 6D pose of a marked cube from grayscale X-ray
images by creating a generic solution trained on only real X-ray data and
adjusted for X-ray acquisition geometry. The model regresses 2D control points
and calculates the pose through 2D/3D correspondences using
Perspective-n-Point(PnP), allowing a single trained model to be used across all
supporting cone-beam-based X-ray geometries. Since modern X-ray systems
continuously adjust acquisition parameters during a procedure, it is essential
for such a pose estimation network to consider these parameters in order to be
deployed successfully and find a real use case. With a 5-cm/5-degree accuracy
of 93% and an average 3D rotation error of 2.2 degrees, the results of the
proposed approach are comparable with state-of-the-art alternatives, while
requiring significantly less real training examples and being applicable in
real-time applications.

In response to COVID-19, a new project was started to allow students to solve
computerised math drills outside of school. In 9 months it has gone from zero
to one thousand students, in co-operation with ten community libraries in
various slums and low-income regions in Kenya. The program uses the tutor-web
as a study environment and access is provided by donating tablet computers to
participating community libraries. Students are rewarded using the SmileyCoin
cryptocurrency as they progress through the system and the libraries are free
to sell for SMLY small food items, sanitary pads and the tablets themselves.
The reward system is designed to put an emphasis on secondary school
mathematics. Completion of the corresponding collection of drills gives
SmileyCoin awards sufficient to purchase a tablet. Conclusions based on the
first year indicate that the resulting effect on participation and performance
is unprecedented: Eleven libraries with 1301 students opted for voluntary
participation in 2021 causing the program to run at full financial capacity. In
that year, 450 students earned enough SMLY to purchase the tablets, which
involves completing a large collection of drills to a level of excellence.
Status exam questions are dispersed within the drill collection. These
independent measurements show learning which surpasses both rote learning and
mechanical learning and demonstrate an increase in the general ability to
address new mathematical problems not seen before.

Drug development is a wide scientific field that faces many challenges these
days. Among them are extremely high development costs, long development times,
as well as a low number of new drugs that are approved each year. To solve
these problems, new and innovate technologies are needed that make the drug
discovery process of small-molecules more time and cost-efficient, and which
allow to target previously undruggable target classes such as protein-protein
interactions. Structure-based virtual screenings have become a leading
contender in this context. In this review, we give an introduction to the
foundations of structure-based virtual screenings, and survey their progress in
the past few years. We outline key principles, recent success stories, new
methods, available software, and promising future research directions. Virtual
screenings have an enormous potential for the development of new small-molecule
drugs, and are already starting to transform early-stage drug discovery.

With big data becoming increasingly available, IoT hardware becoming widely
adopted, and AI capabilities becoming more powerful, organizations are
continuously investing in sensing. Data coming from sensor networks are
currently combined with sensor fusion and AI algorithms to drive innovation in
fields such as self-driving cars. Data from these sensors can be utilized in
numerous use cases, including alerts in safety systems of urban settings, for
events such as gun shots and explosions. Moreover, diverse types of sensors,
such as sound sensors, can be utilized in low-light conditions or at locations
where a camera is not available. This paper investigates the potential of the
utilization of sound-sensor data in an urban context. Technically, we propose a
novel approach of classifying sound data using the Wigner-Ville distribution
and Convolutional Neural Networks. In this paper, we report on the performance
of the approach on open-source datasets. The concept and work presented is
based on my doctoral thesis, which was performed as part of the Engineering
Doctorate program in Data Science at the University of Eindhoven, in
collaboration with the Dutch National Police. Additional work on real-world
datasets was performed during the thesis, which are not presented here due to
confidentiality.

We propose KGTN-ens, a framework extending the recent Knowledge Graph
Transfer Network (KGTN) in order to incorporate multiple knowledge graph
embeddings at a small cost. We evaluate it with different combinations of
embeddings in a few-shot image classification task. We also construct a new
knowledge source - Wikidata embeddings - and evaluate it with KGTN and
KGTN-ens. Our approach outperforms KGTN in terms of the top-5 accuracy on the
ImageNet-FS dataset for the majority of tested settings.

Gauge Theory plays a crucial role in many areas in science, including high
energy physics, condensed matter physics and quantum information science. In
quantum simulations of lattice gauge theory, an important step is to construct
a wave function that obeys gauge symmetry. In this paper, we have developed
gauge equivariant neural network wave function techniques for simulating
continuous-variable quantum lattice gauge theories in the Hamiltonian
formulation. We have applied the gauge equivariant neural network approach to
find the ground state of 2+1-dimensional lattice gauge theory with U(1) gauge
group using variational Monte Carlo. We have benchmarked our approach against
the state-of-the-art complex Gaussian wave functions, demonstrating improved
performance in the strong coupling regime and comparable results in the weak
coupling regime.

In contrast to the rapid digitalization of several industries, agriculture
suffers from low adoption of climate-smart farming tools. Even though AI-driven
digital agriculture can offer high-performing predictive functionalities, it
lacks tangible quantitative evidence on its benefits to the farmers. Field
experiments can derive such evidence, but are often costly and time consuming.
To this end, we propose an observational causal inference framework for the
empirical evaluation of the impact of digital tools on target farm performance
indicators. This way, we can increase farmers' trust by enhancing the
transparency of the digital agriculture market, and in turn accelerate the
adoption of technologies that aim to increase productivity and secure a
sustainable and resilient agriculture against a changing climate. As a case
study, we perform an empirical evaluation of a recommendation system for
optimal cotton sowing, which was used by a farmers' cooperative during the
growing season of 2021. We leverage agricultural knowledge to develop a causal
graph of the farm system, we use the back-door criterion to identify the impact
of recommendations on the yield and subsequently estimate it using several
methods on observational data. The results show that a field sown according to
our recommendations enjoyed a significant increase in yield (12% to 17%).

We present a method for learning neural representations of flow maps from
time-varying vector field data. The flow map is pervasive within the area of
flow visualization, as it is foundational to numerous visualization techniques,
e.g. integral curve computation for pathlines or streaklines, as well as
computing separation/attraction structures within the flow field. Yet
bottlenecks in flow map computation, namely the numerical integration of vector
fields, can easily inhibit their use within interactive visualization settings.
In response, in our work we seek neural representations of flow maps that are
efficient to evaluate, while remaining scalable to optimize, both in
computation cost and data requirements. A key aspect of our approach is that we
can frame the process of representation learning not in optimizing for samples
of the flow map, but rather, a self-consistency criterion on flow map
derivatives that eliminates the need for flow map samples, and thus numerical
integration, altogether. Central to realizing this is a novel neural network
design for flow maps, coupled with an optimization scheme, wherein our
representation only requires the time-varying vector field for learning,
encoded as instantaneous velocity. We show the benefits of our method over
prior works in terms of accuracy and efficiency across a range of 2D and 3D
time-varying vector fields, while showing how our neural representation of flow
maps can benefit unsteady flow visualization techniques such as streaklines,
and the finite-time Lyapunov exponent.

The pattern of pedestrian crashes varies greatly depending on lighting
circumstances, emphasizing the need of examining pedestrian crashes in various
lighting conditions. Using Louisiana pedestrian fatal and injury crash data
(2010-2019), this study applied Association Rules Mining (ARM) to identify the
hidden pattern of crash risk factors according to three different lighting
conditions (daylight, dark-with-streetlight, and dark-no-streetlight). Based on
the generated rules, the results show that daylight pedestrian crashes are
associated with children (less than 15 years), senior pedestrians (greater than
64 years), older drivers (>64 years), and other driving behaviors such as
failure to yield, inattentive/distracted, illness/fatigue/asleep. Additionally,
young drivers (15-24 years) are involved in severe pedestrian crashes in
daylight conditions. This study also found pedestrian alcohol/drug involvement
as the most frequent item in the dark-with-streetlight condition. This crash
type is particularly associated with pedestrian action (crossing
intersection/midblock), driver age (55-64 years), speed limit (30-35 mph), and
specific area type (business with mixed residential area). Fatal pedestrian
crashes are found to be associated with roadways with high-speed limits (>50
mph) during the dark without streetlight condition. Some other risk factors
linked with high-speed limit related crashes are pedestrians walking
with/against the traffic, presence of pedestrian dark clothing, pedestrian
alcohol/drug involvement. The research findings are expected to provide an
improved understanding of the underlying relationships between pedestrian crash
risk factors and specific lighting conditions. Highway safety experts can
utilize these findings to conduct a decision-making process for selecting
effective countermeasures to reduce pedestrian crashes strategically.

Large pre-trained, zero-shot capable models have shown considerable success
both for standard transfer and adaptation tasks, with particular robustness
towards distribution shifts. In addition, subsequent fine-tuning can
considerably improve performance on a selected downstream task. However,
through naive fine-tuning, these zero-shot models lose their generalizability
and robustness towards distribution shifts. This is a particular problem for
tasks such as Continual Learning (CL), where continuous adaptation has to be
performed as new task distributions are introduced sequentially. In this work,
we showcase that where fine-tuning falls short to adapt such zero-shot capable
models, simple momentum-based weight interpolation can provide consistent
improvements for CL tasks in both memory-free and memory-based settings. In
particular, we find improvements of over $+4\%$ on standard CL benchmarks,
while reducing the error to the upper limit of jointly training on all tasks at
once in parts by more than half, allowing the continual learner to inch closer
to the joint training limits.

A recent popular approach to out-of-distribution (OOD) detection is based on
a self-supervised learning technique referred to as contrastive learning. There
are two main variants of contrastive learning, namely instance and class
discrimination, targeting features that can discriminate between different
instances for the former, and different classes for the latter.
  In this paper, we aim to understand the effectiveness and limitation of
existing contrastive learning methods for OOD detection. We approach this in 3
ways. First, we systematically study the performance difference between the
instance discrimination and supervised contrastive learning variants in
different OOD detection settings. Second, we study which in-distribution (ID)
classes OOD data tend to be classified into. Finally, we study the spectral
decay property of the different contrastive learning approaches and examine how
it correlates with OOD detection performance. In scenarios where the ID and OOD
datasets are sufficiently different from one another, we see that instance
discrimination, in the absence of fine-tuning, is competitive with supervised
approaches in OOD detection. We see that OOD samples tend to be classified into
classes that have a distribution similar to the distribution of the entire
dataset. Furthermore, we show that contrastive learning learns a feature space
that contains singular vectors containing several directions with a high
variance which can be detrimental or beneficial to OOD detection depending on
the inference approach used.

To fight climate change and accommodate the increasing population, global
crop production has to be strengthened. To achieve the "sustainable
intensification" of agriculture, transforming it from carbon emitter to carbon
sink is a priority, and understanding the environmental impact of agricultural
management practices is a fundamental prerequisite to that. At the same time,
the global agricultural landscape is deeply heterogeneous, with differences in
climate, soil, and land use inducing variations in how agricultural systems
respond to farmer actions. The "personalization" of sustainable agriculture
with the provision of locally adapted management advice is thus a necessary
condition for the efficient uplift of green metrics, and an integral
development in imminent policies. Here, we formulate personalized sustainable
agriculture as a Conditional Average Treatment Effect estimation task and use
Causal Machine Learning for tackling it. Leveraging climate data, land use
information and employing Double Machine Learning, we estimate the
heterogeneous effect of sustainable practices on the field-level Soil Organic
Carbon content in Lithuania. We thus provide a data-driven perspective for
targeting sustainable practices and effectively expanding the global carbon
sink.

This paper presents a proof-of-concept method for classifying chemical
compounds directly from NMR data without doing structure elucidation. This can
help to reduce time in finding good structure candidates, as in most cases
matching must be done by a human engineer, or at the very least a process for
matching must be meaningfully interpreted by one. Therefore, for a long time
automation in the area of NMR has been actively sought. The method identified
as suitable for the classification is a convolutional neural network (CNN).
Other methods, including clustering and image registration, have not been found
suitable for the task in a comparative analysis. The result shows that deep
learning can offer solutions to automation problems in cheminformatics.

Dexterous and autonomous robots should be capable of executing elaborated
dynamical motions skillfully. Learning techniques may be leveraged to build
models of such dynamic skills. To accomplish this, the learning model needs to
encode a stable vector field that resembles the desired motion dynamics. This
is challenging as the robot state does not evolve on a Euclidean space, and
therefore the stability guarantees and vector field encoding need to account
for the geometry arising from, for example, the orientation representation. To
tackle this problem, we propose learning Riemannian stable dynamical systems
(RSDS) from demonstrations, allowing us to account for different geometric
constraints resulting from the dynamical system state representation. Our
approach provides Lyapunov-stability guarantees on Riemannian manifolds that
are enforced on the desired motion dynamics via diffeomorphisms built on neural
manifold ODEs. We show that our Riemannian approach makes it possible to learn
stable dynamical systems displaying complicated vector fields on both
illustrative examples and real-world manipulation tasks, where Euclidean
approximations fail.

Deep motion forecasting models have achieved great success when trained on a
massive amount of data. Yet, they often perform poorly when training data is
limited. To address this challenge, we propose a transfer learning approach for
efficiently adapting pre-trained forecasting models to new domains, such as
unseen agent types and scene contexts. Unlike the conventional fine-tuning
approach that updates the whole encoder, our main idea is to reduce the amount
of tunable parameters that can precisely account for the target domain-specific
motion style. To this end, we introduce two components that exploit our prior
knowledge of motion style shifts: (i) a low-rank motion style adapter that
projects and adjusts the style features at a low-dimensional bottleneck; and
(ii) a modular adapter strategy that disentangles the features of scene context
and motion history to facilitate a fine-grained choice of adaptation layers.
Through extensive experimentation, we show that our proposed adapter design,
coined MoSA, outperforms prior methods on several forecasting benchmarks.

While deep reinforcement learning has proven to be successful in solving
control tasks, the "black-box" nature of an agent has received increasing
concerns. We propose a prototype-based post-hoc policy explainer, ProtoX, that
explains a blackbox agent by prototyping the agent's behaviors into scenarios,
each represented by a prototypical state. When learning prototypes, ProtoX
considers both visual similarity and scenario similarity. The latter is unique
to the reinforcement learning context, since it explains why the same action is
taken in visually different states. To teach ProtoX about visual similarity, we
pre-train an encoder using contrastive learning via self-supervised learning to
recognize states as similar if they occur close together in time and receive
the same action from the black-box agent. We then add an isometry layer to
allow ProtoX to adapt scenario similarity to the downstream task. ProtoX is
trained via imitation learning using behavior cloning, and thus requires no
access to the environment or agent. In addition to explanation fidelity, we
design different prototype shaping terms in the objective function to encourage
better interpretability. We conduct various experiments to test ProtoX. Results
show that ProtoX achieved high fidelity to the original black-box agent while
providing meaningful and understandable explanations.

In the orthogonal range reporting problem we must pre-process a set $P$ of
multi-dimensional points, so that for any axis-parallel query rectangle $q$ all
points from $q\cap P$ can be reported efficiently. In this paper we study the
query complexity of multi-dimensional orthogonal range reporting in the pointer
machine model. We present a data structure that answers four-dimensional
orthogonal range reporting queries in almost-optimal time $O(\log n\log\log n +
k)$ and uses $O(n\log^4 n)$ space, where $n$ is the number of points in $P$ and
$k$ is the number of points in $q\cap P$ . This is the first data structure
with nearly-linear space usage that achieves almost-optimal query time in 4d.
This result can be immediately generalized to $d\ge 4$ dimensions: we show that
there is a data structure supporting $d$-dimensional range reporting queries in
time $O(\log^{d-3} n\log\log n+k)$ for any constant $d\ge 4$.

Artificial Intelligence (AI) is one of the most transformative technologies
of the 21st century. The extent and scope of future AI capabilities remain a
key uncertainty, with widespread disagreement on timelines and potential
impacts. As nations and technology companies race toward greater complexity and
autonomy in AI systems, there are concerns over the extent of integration and
oversight of opaque AI decision processes. This is especially true in the
subfield of machine learning (ML), where systems learn to optimize objectives
without human assistance. Objectives can be imperfectly specified or executed
in an unexpected or potentially harmful way. This becomes more concerning as
systems increase in power and autonomy, where an abrupt capability jump could
result in unexpected shifts in power dynamics or even catastrophic failures.
This study presents a hierarchical complex systems framework to model AI risk
and provide a template for alternative futures analysis. Survey data were
collected from domain experts in the public and private sectors to classify AI
impact and likelihood. The results show increased uncertainty over the powerful
AI agent scenario, confidence in multiagent environments, and increased concern
over AI alignment failures and influence-seeking behavior.

Recent advances in NLP are brought by a range of large-scale pretrained
language models (PLMs). These PLMs have brought significant performance gains
for a range of NLP tasks, circumventing the need to customize complex designs
for specific tasks. However, most current work focus on finetuning PLMs on a
domain-specific datasets, ignoring the fact that the domain gap can lead to
overfitting and even performance drop. Therefore, it is practically important
to find an appropriate method to effectively adapt PLMs to a target domain of
interest. Recently, a range of methods have been proposed to achieve this
purpose. Early surveys on domain adaptation are not suitable for PLMs due to
the sophisticated behavior exhibited by PLMs from traditional models trained
from scratch and that domain adaptation of PLMs need to be redesigned to take
effect. This paper aims to provide a survey on these newly proposed methods and
shed light in how to apply traditional machine learning methods to newly
evolved and future technologies. By examining the issues of deploying PLMs for
downstream tasks, we propose a taxonomy of domain adaptation approaches from a
machine learning system view, covering methods for input augmentation, model
optimization and personalization. We discuss and compare those methods and
suggest promising future research directions.

To demonstrate the value of machine learning based smart health technologies,
researchers have to deploy their solutions into complex real-world environments
with real participants. This gives rise to many, oftentimes unexpected,
challenges for creating technology in a lab environment that will work when
deployed in real home environments. In other words, like more mature
disciplines, we need solutions for what can be done at development time to
increase success at deployment time. To illustrate an approach and solutions,
we use an example of an ongoing project that is a pipeline of voice based
machine learning solutions that detects the anger and verbal conflicts of the
participants. For anonymity, we call it the XYZ system. XYZ is a smart health
technology because by notifying the participants of their anger, it encourages
the participants to better manage their emotions. This is important because
being able to recognize one's emotions is the first step to better managing
one's anger. XYZ was deployed in 6 homes for 4 months each and monitors the
emotion of the caregiver of a dementia patient. In this paper we demonstrate
some of the necessary steps to be accomplished during the development stage to
increase deployment time success, and show where continued work is still
necessary. Note that the complex environments arise both from the physical
world and from complex human behavior.

Deep Ensemble Convolutional Neural Networks has become a methodology of
choice for analyzing medical images with a diagnostic performance comparable to
a physician, including the diagnosis of Diabetic Retinopathy. However, commonly
used techniques are deterministic and are therefore unable to provide any
estimate of predictive uncertainty. Quantifying model uncertainty is crucial
for reducing the risk of misdiagnosis. A reliable architecture should be
well-calibrated to avoid over-confident predictions. To address this, we
propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Technique
for 5 Class PIRC Diabetic Retinopathy Classification to produce reliable and
well-calibrated predictions.

In recent years, machine learning has achieved impressive results across
different application areas. However, machine learning algorithms do not
necessarily perform well on a new domain with a different distribution than its
training set. Domain Adaptation (DA) is used to mitigate this problem. One
approach of existing DA algorithms is to find domain invariant features whose
distributions in the source domain are the same as their distribution in the
target domain. In this paper, we propose to let the classifier that performs
the final classification task on the target domain learn implicitly the
invariant features to perform classification. It is achieved via feeding the
classifier during training generated fake samples that are similar to samples
from both the source and target domains. We call these generated samples
domain-agnostic samples. To accomplish this we propose a novel variation of
generative adversarial networks (GAN), called the MiddleGAN, that generates
fake samples that are similar to samples from both the source and target
domains, using two discriminators and one generator. We extend the theory of
GAN to show that there exist optimal solutions for the parameters of the two
discriminators and one generator in MiddleGAN, and empirically show that the
samples generated by the MiddleGAN are similar to both samples from the source
domain and samples from the target domain. We conducted extensive evaluations
using 24 benchmarks; on the 24 benchmarks, we compare MiddleGAN against various
state-of-the-art algorithms and outperform the state-of-the-art by up to 20.1\%
on certain benchmarks.

With the rapid advances of image editing techniques in recent years, image
manipulation detection has attracted considerable attention since the
increasing security risks posed by tampered images. To address these
challenges, a novel multi-scale multi-grained deep network (MSMG-Net) is
proposed to automatically identify manipulated regions. In our MSMG-Net, a
parallel multi-scale feature extraction structure is used to extract
multi-scale features. Then the multi-grained feature learning is utilized to
perceive object-level semantics relation of multi-scale features by introducing
the shunted self-attention. To fuse multi-scale multi-grained features, global
and local feature fusion block are designed for manipulated region segmentation
by a bottom-up approach and multi-level feature aggregation block is designed
for edge artifacts detection by a top-down approach. Thus, MSMG-Net can
effectively perceive the object-level semantics and encode the edge artifact.
Experimental results on five benchmark datasets justify the superior
performance of the proposed method, outperforming state-of-the-art manipulation
detection and localization methods. Extensive ablation experiments and feature
visualization demonstrate the multi-scale multi-grained learning can present
effective visual representations of manipulated regions. In addition, MSMG-Net
shows better robustness when various post-processing methods further manipulate
images.

For data privacy, system reliability, and security, Blockchain technologies
have become more popular in recent years. Despite its usefulness, the
blockchain is vulnerable to cyber assaults; for example, in January 2019 a 51%
attack on Ethereum Classic successfully exposed flaws in the platform's
security. From a statistical point of view, attacks represent a highly unusual
occurrence that deviates significantly from the norm. Blockchain attack
detection may benefit from Deep Learning, a field of study whose aim is to
discover insights, patterns, and anomalies within massive data repositories. In
this work, we define an trusted two way intrusion detection system based on a
Hierarchical weighed fuzzy algorithm and self-organized stacked network (SOSN)
deep learning model, that is trained exploiting aggregate information extracted
by monitoring blockchain activities. Here initially the smart contract handles
the node authentication. The purpose of authenticating the node is to ensure
that only specific nodes can submit and retrieve the information. We implement
Hierarchical weighed fuzzy algorithm to evaluate the trust ability of the
transaction nodes. Then the transaction verification step ensures that all
malicious transactions or activities on the submitted transaction by
self-organized stacked network deep learning model. The whole experimentation
was carried out under matlab environment. Extensive experimental results
confirm that our suggested detection method has better performance over
important indicators such as Precision, Recall, F-Score, overhead.

While reinforcement learning has been used widely in research during the past
few years, it found fewer real-world applications than supervised learning due
to some weaknesses that the RL algorithms suffer from, such as performance
degradation in transitioning from the simulator to the real world. Here, we
argue the design process is a reinforcement learning problem and can
potentially be a proper application for RL algorithms as it is an offline
process and conventionally is done in CAD software - a sort of simulator. This
creates opportunities for using RL methods and, at the same time, raises
challenges. While the design processes are so diverse, here we focus on the
space layout planning (SLP), frame it as an RL problem under the Markov
Decision Process, and use PPO to address the layout design problem. To do so,
we developed an environment named RLDesigner, to simulate the SLP. The
RLDesigner is an OpenAI Gym compatible environment that can be easily
customized to define a diverse range of design scenarios. We publicly share the
environment to encourage both RL and architecture communities to use it for
testing different RL algorithms or in their design practice. The codes are
available in the following GitHub repository https://github.com/
RezaKakooee/rldesigner/tree/Second_Paper

Data-driven identification of differential equations is an interesting but
challenging problem, especially when the given data are corrupted by noise.
When the governing differential equation is a linear combination of various
differential terms, the identification problem can be formulated as solving a
linear system, with the feature matrix consisting of linear and nonlinear terms
multiplied by a coefficient vector. This product is equal to the time
derivative term, and thus generates dynamical behaviors. The goal is to
identify the correct terms that form the equation to capture the dynamics of
the given data. We propose a general and robust framework to recover
differential equations using a weak formulation, for both ordinary and partial
differential equations (ODEs and PDEs). The weak formulation facilitates an
efficient and robust way to handle noise. For a robust recovery against noise
and the choice of hyper-parameters, we introduce two new mechanisms, narrow-fit
and trimming, for the coefficient support and value recovery, respectively. For
each sparsity level, Subspace Pursuit is utilized to find an initial set of
support from the large dictionary. Then, we focus on highly dynamic regions
(rows of the feature matrix), and error normalize the feature matrix in the
narrow-fit step. The support is further updated via trimming of the terms that
contribute the least. Finally, the support set of features with the smallest
Cross-Validation error is chosen as the result. A comprehensive set of
numerical experiments are presented for both systems of ODEs and PDEs with
various noise levels. The proposed method gives a robust recovery of the
coefficients, and a significant denoising effect which can handle up to $100\%$
noise-to-signal ratio for some equations. We compare the proposed method with
several state-of-the-art algorithms for the recovery of differential equations.

A reconstruction attack on a private dataset $D$ takes as input some publicly
accessible information about the dataset and produces a list of candidate
elements of $D$. We introduce a new class of data reconstruction attacks based
on randomized methods for non-convex optimization. We empirically demonstrate
that our attacks can not only reconstruct full rows of $D$ from aggregate query
statistics $Q(D)\in \mathbb{R}^m$, but can do so in a way that reliably ranks
reconstructed rows by their odds of appearing in the private data, providing a
signature that could be used for prioritizing reconstructed rows for further
actions such as identify theft or hate crime. We also design a sequence of
baselines for evaluating reconstruction attacks. Our attacks significantly
outperform those that are based only on access to a public distribution or
population from which the private dataset $D$ was sampled, demonstrating that
they are exploiting information in the aggregate statistics $Q(D)$, and not
simply the overall structure of the distribution. In other words, the queries
$Q(D)$ are permitting reconstruction of elements of this dataset, not the
distribution from which $D$ was drawn. These findings are established both on
2010 U.S. decennial Census data and queries and Census-derived American
Community Survey datasets. Taken together, our methods and experiments
illustrate the risks in releasing numerically precise aggregate statistics of a
large dataset, and provide further motivation for the careful application of
provably private techniques such as differential privacy.

Each student matters, but it is hardly for instructors to observe all the
students during the courses and provide helps to the needed ones immediately.
In this paper, we present StuArt, a novel automatic system designed for the
individualized classroom observation, which empowers instructors to concern the
learning status of each student. StuArt can recognize five representative
student behaviors (hand-raising, standing, sleeping, yawning, and smiling) that
are highly related to the engagement and track their variation trends during
the course. To protect the privacy of students, all the variation trends are
indexed by the seat numbers without any personal identification information.
Furthermore, StuArt adopts various user-friendly visualization designs to help
instructors quickly understand the individual and whole learning status.
Experimental results on real classroom videos have demonstrated the superiority
and robustness of the embedded algorithms. We expect our system promoting the
development of large-scale individualized guidance of students.

A backdoor attack places triggers in victims' deep learning models to enable
a targeted misclassification at testing time. In general, triggers are fixed
artifacts attached to samples, making backdoor attacks easy to spot. Only
recently, a new trigger generation harder to detect has been proposed: the
stylistic triggers that apply stylistic transformations to the input samples
(e.g., a specific writing style). Currently, stylistic backdoor literature
lacks a proper formalization of the attack, which is established in this paper.
Moreover, most studies of stylistic triggers focus on text and images, while
there is no understanding of whether they can work in sound. This work fills
this gap. We propose JingleBack, the first stylistic backdoor attack based on
audio transformations such as chorus and gain. Using 444 models in a speech
classification task, we confirm the feasibility of stylistic triggers in audio,
achieving 96% attack success.

Successful identification of blood vessel blockage is a crucial step for
Alzheimer's disease diagnosis. These blocks can be identified from the spatial
and time-depth variable Two-Photon Excitation Microscopy (TPEF) images of the
brain blood vessels using machine learning methods. In this study, we propose
several preprocessing schemes to improve the performance of these methods. Our
method includes 3D-point cloud data extraction from image modality and their
feature-space fusion to leverage complementary information inherent in
different modalities. We also enforce the learned representation to be
sequence-order invariant by utilizing bi-direction dataflow. Experimental
results on The Clog Loss dataset show that our proposed method consistently
outperforms the state-of-the-art preprocessing methods in stalled and
non-stalled vessel classification.

Finance is a particularly difficult playground for deep reinforcement
learning. However, establishing high-quality market environments and benchmarks
for financial reinforcement learning is challenging due to three major factors,
namely, low signal-to-noise ratio of financial data, survivorship bias of
historical data, and model overfitting in the backtesting stage. In this paper,
we present an openly accessible FinRL-Meta library that has been actively
maintained by the AI4Finance community. First, following a DataOps paradigm, we
will provide hundreds of market environments through an automatic pipeline that
collects dynamic datasets from real-world markets and processes them into
gym-style market environments. Second, we reproduce popular papers as stepping
stones for users to design new trading strategies. We also deploy the library
on cloud platforms so that users can visualize their own results and assess the
relative performance via community-wise competitions. Third, FinRL-Meta
provides tens of Jupyter/Python demos organized into a curriculum and a
documentation website to serve the rapidly growing community. FinRL-Meta is
available at: https://github.com/AI4Finance-Foundation/FinRL-Meta

Deep Neural Network (DNN) based video analytics empowers many computer
vision-based applications to achieve high recognition accuracy. To reduce
inference delay and bandwidth cost for video analytics, the DNN models can be
deployed on the edge nodes, which are proximal to end users. However, the
processing capacity of an edge node is limited, potentially incurring
substantial delay if the inference requests on an edge node is overloaded.
While efforts have been made to enhance video analytics by optimizing the
configurations on a single edge node, we observe that multiple edge nodes can
work collaboratively by utilizing the idle resources on each other to improve
the overall processing capacity and resource utilization. To this end, we
propose a Multiagent Reinforcement Learning (MARL) based approach, named as
EdgeVision, for collaborative video analytics on distributed edges. The edge
nodes can jointly learn the optimal policies for video preprocessing, model
selection, and request dispatching by collaborating with each other to minimize
the overall cost. We design an actor-critic-based MARL algorithm with an
attention mechanism to learn the optimal policies. We build a multi-edge-node
testbed and conduct experiments with real-world datasets to evaluate the
performance of our method. The experimental results show our method can improve
the overall rewards by 33.6%-86.4% compared with the most competitive baseline
methods.

There is an increasing interest in Quantum Machine Learning (QML) models, how
they work and for which applications they could be useful. There have been many
different proposals on how classical data can be encoded and what circuit
ans\"atze and measurement operators should be used to process the encoded data
and measure the output state of an ansatz. The choice of the aforementioned
operators plays a determinant role in the expressive power of the QML model. In
this work we investigate how certain changes in the circuit structure change
this expressivity. We introduce both numerical and analytical tools to explore
the effect that these operators have in the overall performance of the QML
model. These tools are based on previous work on the teacher-student scheme,
the partial Fourier series and the averaged operator size. We focus our
analysis on simple QML models with two and three qubits and observe that
increasing the number of parameterized and entangling gates leads to a more
expressive model for certain circuit structures. Also, on which qubit the
measurement is performed affects the type of functions that QML models could
learn. This work sketches the determinant role that the processing and
measurement operators have on the expressive power of simple quantum circuits.

User-specific future activity prediction in the healthcare domain based on
previous activities can drastically improve the services provided by the
nurses. It is challenging because, unlike other domains, activities in
healthcare involve both nurses and patients, and they also vary from hour to
hour. In this paper, we employ various data processing techniques to organize
and modify the data structure and an LSTM-based multi-label classifier for a
novel 2-stage training approach (user-agnostic pre-training and user-specific
fine-tuning). Our experiment achieves a validation accuracy of 31.58\%,
precision 57.94%, recall 68.31%, and F1 score 60.38%. We concluded that proper
data pre-processing and a 2-stage training process resulted in better
performance. This experiment is a part of the "Fourth Nurse Care Activity
Recognition Challenge" by our team "Not A Fan of Local Minima".

Network slicing enables multiple virtual networks to be instantiated and
customized to meet heterogeneous use case requirements over 5G and beyond
network deployments. However, most of the solutions available today face
scalability issues when considering many slices, due to centralized controllers
requiring a holistic view of the resource availability and consumption over
different networking domains. In order to tackle this challenge, we design a
hierarchical architecture to manage network slices resources in a federated
manner. Driven by the rapid evolution of deep reinforcement learning (DRL)
schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware
local decision agents (DAs) dynamically placed in the radio access network
(RAN). These federated decision entities tailor their resource allocation
policy according to the long-term dynamics of the underlying traffic, defining
specialized clusters that enable faster training and communication overhead
reduction. Indeed, aided by a traffic-aware agent selection algorithm, our
proposed Federated DRL approach provides higher resource efficiency than
benchmark solutions by quickly reacting to end-user mobility patterns and
reducing costly interactions with centralized controllers.

Nanopore sequencing generates noisy electrical signals that need to be
converted into a standard string of DNA nucleotide bases (i.e., A, C, G, T)
using a computational step called basecalling. The accuracy and speed of
basecalling have critical implications for every subsequent step in genome
analysis. Currently, basecallers are developed mainly based on deep learning
techniques to provide high sequencing accuracy without considering the compute
demands of such tools. We observe that state-of-the-art basecallers are slow,
inefficient, and memory-hungry as researchers have adapted deep learning models
from other domains without specialization to the basecalling purpose. Our goal
is to make basecalling highly efficient and fast by building the first
framework for specializing and optimizing machine learning-based basecaller. We
introduce RUBICON, a framework to develop hardware-optimized basecallers.
RUBICON consists of two novel machine-learning techniques that are specifically
designed for basecalling. First, we introduce the first quantization-aware
basecalling neural architecture search (QABAS) framework to specialize the
basecalling neural network architecture for a given hardware acceleration
platform while jointly exploring and finding the best bit-width precision for
each neural network layer. Second, we develop SkipClip, the first technique to
remove the skip connections present in modern basecallers to greatly reduce
resource and storage requirements without any loss in basecalling accuracy. We
demonstrate the benefits of QABAS and SkipClip by developing RUBICALL, the
first hardware-optimized basecaller that performs fast and accurate
basecalling. We show that QABAS and SkipClip can help researchers develop
hardware-optimized basecallers that are superior to expert-designed models.

The application of superconducting materials is becoming more and more
widespread. Traditionally, the discovery of new superconducting materials
relies on the experience of experts and a large number of "trial and error"
experiments, which not only increases the cost of experiments but also prolongs
the period of discovering new superconducting materials. In recent years,
machine learning has been increasingly applied to materials science. Based on
this, this manuscript proposes the use of XGBoost model to identify
superconductors; the first application of deep forest model to predict the
critical temperature of superconductors; the first application of deep forest
to predict the band gap of materials; and application of a new sub-network
model to predict the Fermi energy level of materials. Compared with our known
similar literature, all the above algorithms reach state-of-the-art. Finally,
this manuscript uses the above models to search the COD public dataset and
identify 50 candidate superconducting materials with possible critical
temperature greater than 90 K.

Influence Maximization (IM) is a classical combinatorial optimization
problem, which can be widely used in mobile networks, social computing, and
recommendation systems. It aims at selecting a small number of users such that
maximizing the influence spread across the online social network. Because of
its potential commercial and academic value, there are a lot of researchers
focusing on studying the IM problem from different perspectives. The main
challenge comes from the NP-hardness of the IM problem and \#P-hardness of
estimating the influence spread, thus traditional algorithms for overcoming
them can be categorized into two classes: heuristic algorithms and
approximation algorithms. However, there is no theoretical guarantee for
heuristic algorithms, and the theoretical design is close to the limit.
Therefore, it is almost impossible to further optimize and improve their
performance. With the rapid development of artificial intelligence, the
technology based on Machine Learning (ML) has achieved remarkable achievements
in many fields. In view of this, in recent years, a number of new methods have
emerged to solve combinatorial optimization problems by using ML-based
techniques. These methods have the advantages of fast solving speed and strong
generalization ability to unknown graphs, which provide a brand-new direction
for solving combinatorial optimization problems. Therefore, we abandon the
traditional algorithms based on iterative search and review the recent
development of ML-based methods, especially Deep Reinforcement Learning, to
solve the IM problem and other variants in social networks. We focus on
summarizing the relevant background knowledge, basic principles, common
methods, and applied research. Finally, the challenges that need to be solved
urgently in future IM research are pointed out.

Appearance-based gaze estimation has been very successful with the use of
deep learning. Many following works improved domain generalization for gaze
estimation. However, even though there has been much progress in domain
generalization for gaze estimation, most of the recent work have been focused
on cross-dataset performance -- accounting for different distributions in
illuminations, head pose, and lighting. Although improving gaze estimation in
different distributions of RGB images is important, near-infrared image based
gaze estimation is also critical for gaze estimation in dark settings. Also
there are inherent limitations relying solely on supervised learning for
regression tasks. This paper contributes to solving these problems and proposes
GazeCWL, a novel framework for gaze estimation with near-infrared images using
contrastive learning. This leverages adversarial attack techniques for data
augmentation and a novel contrastive loss function specifically for regression
tasks that effectively clusters the features of different samples in the latent
space. Our model outperforms previous domain generalization models in infrared
image based gaze estimation and outperforms the baseline by 45.6\% while
improving the state-of-the-art by 8.6\%, we demonstrate the efficacy of our
method.

Generally, microscopy image analysis in biology relies on the segmentation of
individual nuclei, using a dedicated stained image, to identify individual
cells. However stained nuclei have drawbacks like the need for sample
preparation, and specific equipment on the microscope but most importantly, and
as it is in most cases, the nuclear stain is not relevant to the biological
questions of interest but is solely used for the segmentation task. In this
study, we used non-stained brightfield images for nuclei segmentation with the
advantage that they can be acquired on any microscope from both live or fixed
samples and do not necessitate specific sample preparation. Nuclei semantic
segmentation from brightfield images was obtained, on four distinct cell lines
with U-Net-based architectures. We tested systematically deep pre-trained
encoders to identify the best performing in combination with the different
neural network architectures used. Additionally, two distinct and effective
strategies were employed for instance segmentation, followed by thorough
instance evaluation. We obtained effective semantic and instance segmentation
of nuclei in brightfield images from standard test sets as well as from very
diverse biological contexts triggered upon treatment with various small
molecule inhibitor. The code used in this study was made public to allow
further use by the community.

Person Search aims to simultaneously localize and recognize a target person
from realistic and uncropped gallery images. One major challenge of person
search comes from the contradictory goals of the two sub-tasks, i.e., person
detection focuses on finding the commonness of all persons so as to distinguish
persons from the background, while person re-identification (re-ID) focuses on
the differences among different persons. In this paper, we propose a novel
Sequential Transformer (SeqTR) for end-to-end person search to deal with this
challenge. Our SeqTR contains a detection transformer and a novel re-ID
transformer that sequentially addresses detection and re-ID tasks. The re-ID
transformer comprises the self-attention layer that utilizes contextual
information and the cross-attention layer that learns local fine-grained
discriminative features of the human body. Moreover, the re-ID transformer is
shared and supervised by multi-scale features to improve the robustness of
learned person representations. Extensive experiments on two widely-used person
search benchmarks, CUHK-SYSU and PRW, show that our proposed SeqTR not only
outperforms all existing person search methods with a 59.3% mAP on PRW but also
achieves comparable performance to the state-of-the-art results with an mAP of
94.8% on CUHK-SYSU.

Deep learning-based physical-layer secret key generation (PKG) has been used
to overcome the imperfect uplink/downlink channel reciprocity in frequency
division duplexing (FDD) orthogonal frequency division multiplexing (OFDM)
systems. However, existing efforts have focused on key generation for users in
a specific environment where the training samples and test samples obey the
same distribution, which is unrealistic for real world applications. This paper
formulates the PKG problem in multiple environments as a learning-based problem
by learning the knowledge such as data and models from known environments to
generate keys quickly and efficiently in multiple new environments.
Specifically, we propose deep transfer learning (DTL) and meta-learning-based
channel feature mapping algorithms for key generation. The two algorithms use
different training methods to pre-train the model in the known environments,
and then quickly adapt and deploy the model to new environments. Simulation
results show that compared with the methods without adaptation, the DTL and
meta-learning algorithms both can improve the performance of generated keys. In
addition, the complexity analysis shows that the meta-learning algorithm can
achieve better performance than the DTL algorithm with less time, lower CPU and
GPU resources.

In collective decision-making, designing algorithms that use only local
information to effect swarm-level behaviour is a non-trivial problem. We used
machine learning techniques to teach swarm members to map their local
perceptions of the environment to an optimal action. A curriculum inspired by
Machine Education approaches was designed to facilitate this learning process
and teach the members the skills required for optimal performance in the
collective perception problem. We extended upon previous approaches by creating
a curriculum that taught agents resilience to malicious influence. The
experimental results show that well-designed rules-based algorithms can produce
effective agents. When performing opinion fusion, we implemented decentralised
resilience by having agents dynamically weight received opinion. We found a
non-significant difference between constant and dynamic weights, suggesting
that momentum-based opinion fusion is perhaps already a resilience mechanism.

Existing deep learning based HDRTV reconstruction methods assume one kind of
tone mapping operators (TMOs) as the degradation procedure to synthesize
SDRTV-HDRTV pairs for supervised training. In this paper, we argue that,
although traditional TMOs exploit efficient dynamic range compression priors,
they have several drawbacks on modeling the realistic degradation: information
over-preservation, color bias and possible artifacts, making the trained
reconstruction networks hard to generalize well to real-world cases. To solve
this problem, we propose a learning-based data synthesis approach to learn the
properties of real-world SDRTVs by integrating several tone mapping priors into
both network structures and loss functions. In specific, we design a
conditioned two-stream network with prior tone mapping results as a guidance to
synthesize SDRTVs by both global and local transformations. To train the data
synthesis network, we form a novel self-supervised content loss to constraint
different aspects of the synthesized SDRTVs at regions with different
brightness distributions and an adversarial loss to emphasize the details to be
more realistic. To validate the effectiveness of our approach, we synthesize
SDRTV-HDRTV pairs with our method and use them to train several HDRTV
reconstruction networks. Then we collect two inference datasets containing both
labeled and unlabeled real-world SDRTVs, respectively. Experimental results
demonstrate that, the networks trained with our synthesized data generalize
significantly better to these two real-world datasets than existing solutions.

With the development of depth sensors in recent years, RGBD object tracking
has received significant attention. Compared with the traditional RGB object
tracking, the addition of the depth modality can effectively solve the target
and background interference. However, some existing RGBD trackers use the two
modalities separately and thus some particularly useful shared information
between them is ignored. On the other hand, some methods attempt to fuse the
two modalities by treating them equally, resulting in the missing of
modality-specific features. To tackle these limitations, we propose a novel
Dual-fused Modality-aware Tracker (termed DMTracker) which aims to learn
informative and discriminative representations of the target objects for robust
RGBD tracking. The first fusion module focuses on extracting the shared
information between modalities based on cross-modal attention. The second aims
at integrating the RGB-specific and depth-specific information to enhance the
fused features. By fusing both the modality-shared and modality-specific
information in a modality-aware scheme, our DMTracker can learn discriminative
representations in complex tracking scenes. Experiments show that our proposed
tracker achieves very promising results on challenging RGBD benchmarks.

Autoencoders are widely used in outlier detection due to their superiority in
handling high-dimensional and nonlinear datasets. The reconstruction of any
dataset by the autoencoder can be considered as a complex regression process.
In regression analysis, outliers can usually be divided into high leverage
points and influential points. Although the autoencoder has shown good results
for the identification of influential points, there are still some problems
when detect high leverage points. Through theoretical derivation, we found that
most outliers are detected in the direction corresponding to the
worst-recovered principal component, but in the direction of the well-recovered
principal components, the anomalies are often ignored. We propose a new loss
function which solve the above deficiencies in outlier detection. The core idea
of our scheme is that in order to better detect high leverage points, we should
suppress the complete reconstruction of the dataset to convert high leverage
points into influential points, and it is also necessary to ensure that the
differences between the eigenvalues of the covariance matrix of the original
dataset and their corresponding reconstructed results in the direction of each
principal component are equal. Besides, we explain the rationality of our
scheme through rigorous theoretical derivation. Finally, our experiments on
multiple datasets confirm that our scheme significantly improves the accuracy
of outlier detection.

Offline reinforcement-learning (RL) algorithms learn to make decisions using
a given, fixed training dataset without the possibility of additional online
data collection. This problem setting is captivating because it holds the
promise of utilizing previously collected datasets without any costly or risky
interaction with the environment. However, this promise also bears the drawback
of this setting. The restricted dataset induces subjective uncertainty because
the agent can encounter unfamiliar sequences of states and actions that the
training data did not cover. Moreover, inherent system stochasticity further
increases uncertainty and aggravates the offline RL problem, preventing the
agent from learning an optimal policy. To mitigate the destructive uncertainty
effects, we need to balance the aspiration to take reward-maximizing actions
with the incurred risk due to incorrect ones. In financial economics, modern
portfolio theory (MPT) is a method that risk-averse investors can use to
construct diversified portfolios that maximize their returns without
unacceptable levels of risk. We integrate MPT into the agent's decision-making
process to present a simple-yet-highly-effective risk-aware planning algorithm
for offline RL. Our algorithm allows us to systematically account for the
\emph{estimated quality} of specific actions and their \emph{estimated risk}
due to the uncertainty. We show that our approach can be coupled with the
Transformer architecture to yield a state-of-the-art planner for offline RL
tasks, maximizing the return while significantly reducing the variance.

Consider a finite sample from an unknown distribution over a countable
alphabet. Unobserved events are alphabet symbols which do not appear in the
sample. Estimating the probabilities of unobserved events is a basic problem in
statistics and related fields, which was extensively studied in the context of
point estimation. In this work we introduce a novel interval estimation scheme
for unobserved events. Our proposed framework applies selective inference, as
we construct confidence intervals (CIs) for the desired set of parameters.
Interestingly, we show that obtained CIs are dimension-free, as they do not
grow with the alphabet size. Further, we show that these CIs are (almost)
tight, in the sense that they cannot be further improved without violating the
prescribed coverage rate. We demonstrate the performance of our proposed scheme
in synthetic and real-world experiments, showing a significant improvement over
the alternatives. Finally, we apply our proposed scheme to large alphabet
modeling. We introduce a novel simultaneous CI scheme for large alphabet
distributions which outperforms currently known methods while maintaining the
prescribed coverage rate.

Zebrafish are a common model organism used to identify new disease
therapeutics. High-throughput drug screens can be performed on larval zebrafish
in multi-well plates by observing changes in behaviour following a treatment.
Analysis of this behaviour can be difficult, however, due to the high
dimensionality of the data obtained. Statistical analysis of individual
statistics (such as the distance travelled) is generally not powerful enough to
detect meaningful differences between treatment groups. Here, we propose a
method for classifying zebrafish models of Parkinson's disease by genotype at 5
days old. Using a set of 2D behavioural features, we train a multi-layer
perceptron neural network. We further show that the use of integrated gradients
can give insight into the impact of each behaviour feature on genotype
classifications by the model. In this way, we provide a novel pipeline for
classifying zebrafish larvae, beginning with feature preparation and ending
with an impact analysis of said features.

Humans and animals excel in combining information from multiple sensory
modalities, controlling their complex bodies, adapting to growth, failures, or
using tools. These capabilities are also highly desirable in robots. They are
displayed by machines to some extent. Yet, the artificial creatures are lagging
behind. The key foundation is an internal representation of the body that the
agent - human, animal, or robot - has developed. The mechanisms of operation of
body models in the brain are largely unknown and even less is known about how
they are constructed from experience after birth. In collaboration with
developmental psychologists, we conducted targeted experiments to understand
how infants acquire first "sensorimotor body knowledge". These experiments
inform our work in which we construct embodied computational models on humanoid
robots that address the mechanisms behind learning, adaptation, and operation
of multimodal body representations. At the same time, we assess which of the
features of the "body in the brain" should be transferred to robots to give
rise to more adaptive and resilient, self-calibrating machines. We extend
traditional robot kinematic calibration focusing on self-contained approaches
where no external metrology is needed: self-contact and self-observation.
Problem formulation allowing to combine several ways of closing the kinematic
chain simultaneously is presented, along with a calibration toolbox and
experimental validation on several robot platforms. Finally, next to models of
the body itself, we study peripersonal space - the space immediately
surrounding the body. Again, embodied computational models are developed and
subsequently, the possibility of turning these biologically inspired
representations into safe human-robot collaboration is studied.

Legal judgment Prediction (LJP), aiming to predict a judgment based on fact
descriptions, serves as legal assistance to mitigate the great work burden of
limited legal practitioners. Most existing methods apply various large-scale
pre-trained language models (PLMs) finetuned in LJP tasks to obtain consistent
improvements. However, we discover the fact that the state-of-the-art (SOTA)
model makes judgment predictions according to wrong (or non-casual)
information, which not only weakens the model's generalization capability but
also results in severe social problems like discrimination. Here, we analyze
the causal mechanism misleading the LJP model to learn the spurious
correlations, and then propose a framework to guide the model to learn the
underlying causality knowledge in the legal texts. Specifically, we first
perform open information extraction (OIE) to refine the text having a high
proportion of causal information, according to which we generate a new set of
data. Then, we design a model learning the weights of the refined data and the
raw data for LJP model training. The extensive experimental results show that
our model is more generalizable and robust than the baselines and achieves a
new SOTA performance on two commonly used legal-specific datasets.

Recent studies have revealed the intriguing few-shot learning ability of
pretrained language models (PLMs): They can quickly adapt to a new task when
fine-tuned on a small amount of labeled data formulated as prompts, without
requiring abundant task-specific annotations. Despite their promising
performance, most existing few-shot approaches that only learn from the small
training set still underperform fully supervised training by nontrivial
margins. In this work, we study few-shot learning with PLMs from a different
perspective: We first tune an autoregressive PLM on the few-shot samples and
then use it as a generator to synthesize a large amount of novel training
samples which augment the original training set. To encourage the generator to
produce label-discriminative samples, we train it via weighted maximum
likelihood where the weight of each token is automatically adjusted based on a
discriminative meta-learning objective. A classification PLM can then be
fine-tuned on both the few-shot and the synthetic samples with regularization
for better generalization and stability. Our approach FewGen achieves an
overall better result across seven classification tasks of the GLUE benchmark
than existing few-shot learning methods, improving no-augmentation methods by
5+ average points, and outperforming augmentation methods by 3+ average points.

Pre-trained Language Models (PLMs) have been applied in NLP tasks and achieve
promising results. Nevertheless, the fine-tuning procedure needs labeled data
of the target domain, making it difficult to learn in low-resource and
non-trivial labeled scenarios. To address these challenges, we propose
Prompt-based Text Entailment (PTE) for low-resource named entity recognition,
which better leverages knowledge in the PLMs. We first reformulate named entity
recognition as the text entailment task. The original sentence with entity
type-specific prompts is fed into PLMs to get entailment scores for each
candidate. The entity type with the top score is then selected as final label.
Then, we inject tagging labels into prompts and treat words as basic units
instead of n-gram spans to reduce time complexity in generating candidates by
n-grams enumeration. Experimental results demonstrate that the proposed method
PTE achieves competitive performance on the CoNLL03 dataset, and better than
fine-tuned counterparts on the MIT Movie and Few-NERD dataset in low-resource
settings.

Background sound is an informative form of art that is helpful in providing a
more immersive experience in real-application voice conversion (VC) scenarios.
However, prior research about VC, mainly focusing on clean voices, pay rare
attention to VC with background sound. The critical problem for preserving
background sound in VC is inevitable speech distortion by the neural separation
model and the cascade mismatch between the source separation model and the VC
model. In this paper, we propose an end-to-end framework via multi-task
learning which sequentially cascades a source separation (SS) module, a
bottleneck feature extraction module and a VC module. Specifically, the source
separation task explicitly considers critical phase information and confines
the distortion caused by the imperfect separation process. The source
separation task, the typical VC task and the unified task shares a uniform
reconstruction loss constrained by joint training to reduce the mismatch
between the SS and VC modules. Experimental results demonstrate that our
proposed framework significantly outperforms the baseline systems while
achieving comparable quality and speaker similarity to the VC models trained
with clean data.

Feature selection is an important and active field of research in machine
learning and data science. Our goal in this paper is to propose a collection of
synthetic datasets that can be used as a common reference point for feature
selection algorithms. Synthetic datasets allow for precise evaluation of
selected features and control of the data parameters for comprehensive
assessment. The proposed datasets are based on applications from electronics in
order to mimic real life scenarios. To illustrate the utility of the proposed
data we employ one of the datasets to test several popular feature selection
algorithms. The datasets are made publicly available on GitHub and can be used
by researchers to evaluate feature selection algorithms.

Traffic state prediction in a transportation network is paramount for
effective traffic operations and management, as well as informed user and
system-level decision-making. However, long-term traffic prediction (beyond 30
minutes into the future) remains challenging in current research. In this work,
we integrate the spatio-temporal dependencies in the transportation network
from network modeling, together with the graph convolutional network (GCN) and
graph attention network (GAT). To further tackle the dramatic computation and
memory cost caused by the giant model size (i.e., number of weights) caused by
multiple cascaded layers, we propose sparse training to mitigate the training
cost, while preserving the prediction accuracy. It is a process of training
using a fixed number of nonzero weights in each layer in each iteration. We
consider the problem of long-term traffic speed forecasting for a real
large-scale transportation network data from the California Department of
Transportation (Caltrans) Performance Measurement System (PeMS). Experimental
results show that the proposed GCN-STGT and GAT-STGT models achieve low
prediction errors on short-, mid- and long-term prediction horizons, of 15, 30
and 45 minutes in duration, respectively. Using our sparse training, we could
train from scratch with high sparsity (e.g., up to 90%), equivalent to 10 times
floating point operations per second (FLOPs) reduction on computational cost
using the same epochs as dense training, and arrive at a model with very small
accuracy loss compared with the original dense training

The study of decentralized learning or independent learning in cooperative
multi-agent reinforcement learning has a history of decades. Recently empirical
studies show that independent PPO (IPPO) can obtain good performance, close to
or even better than the methods of centralized training with decentralized
execution, in several benchmarks. However, decentralized actor-critic with
convergence guarantee is still open. In this paper, we propose
\textit{decentralized policy optimization} (DPO), a decentralized actor-critic
algorithm with monotonic improvement and convergence guarantee. We derive a
novel decentralized surrogate for policy optimization such that the monotonic
improvement of joint policy can be guaranteed by each agent
\textit{independently} optimizing the surrogate. In practice, this
decentralized surrogate can be realized by two adaptive coefficients for policy
optimization at each agent. Empirically, we compare DPO with IPPO in a variety
of cooperative multi-agent tasks, covering discrete and continuous action
spaces, and fully and partially observable environments. The results show DPO
outperforms IPPO in most tasks, which can be the evidence for our theoretical
results.

The conditional survival function of a time-to-event outcome subject to
censoring and truncation is a common target of estimation in survival analysis.
This parameter may be of scientific interest and also often appears as a
nuisance in nonparametric and semiparametric problems. In addition to classical
parametric and semiparametric methods (e.g., based on the Cox proportional
hazards model), flexible machine learning approaches have been developed to
estimate the conditional survival function. However, many of these methods are
either implicitly or explicitly targeted toward risk stratification rather than
overall survival function estimation. Others apply only to discrete-time
settings or require inverse probability of censoring weights, which can be as
difficult to estimate as the outcome survival function itself. Here, we propose
a decomposition of the conditional survival function in terms of observable
regression models in which censoring and truncation play no role. This allows
application of an array of flexible regression and classification methods
rather than only approaches that explicitly handle the complexities inherent to
survival data. We outline estimation procedures based on this decomposition,
assess their performance via numerical simulations, and demonstrate their use
on data from an HIV vaccine trial.

Reinforcement learning (RL) operating on attack graphs leveraging cyber
terrain principles are used to develop reward and state associated with
determination of surveillance detection routes (SDR). This work extends
previous efforts on developing RL methods for path analysis within enterprise
networks. This work focuses on building SDR where the routes focus on exploring
the network services while trying to evade risk. RL is utilized to support the
development of these routes by building a reward mechanism that would help in
realization of these paths. The RL algorithm is modified to have a novel
warm-up phase which decides in the initial exploration which areas of the
network are safe to explore based on the rewards and penalty scale factor.

Modeling of turbulent combustion system requires modeling the underlying
chemistry and the turbulent flow. Solving both systems simultaneously is
computationally prohibitive. Instead, given the difference in scales at which
the two sub-systems evolve, the two sub-systems are typically (re)solved
separately. Popular approaches such as the Flamelet Generated Manifolds (FGM)
use a two-step strategy where the governing reaction kinetics are pre-computed
and mapped to a low-dimensional manifold, characterized by a few reaction
progress variables (model reduction) and the manifold is then ``looked-up''
during the runtime to estimate the high-dimensional system state by the flow
system. While existing works have focused on these two steps independently, in
this work we show that joint learning of the progress variables and the
look--up model, can yield more accurate results. We build on the base
formulation and implementation ChemTab to include the dynamically generated
Themochemical State Variables (Lower Dimensional Dynamic Source Terms). We
discuss the challenges in the implementation of this deep neural network
architecture and experimentally demonstrate it's superior performance.

Graph neural networks (GNNs) have received great attention due to their
success in various graph-related learning tasks. Several GNN frameworks have
then been developed for fast and easy implementation of GNN models. Despite
their popularity, they are not well documented, and their implementations and
system performance have not been well understood. In particular, unlike the
traditional GNNs that are trained based on the entire graph in a full-batch
manner, recent GNNs have been developed with different graph sampling
techniques for mini-batch training of GNNs on large graphs. While they improve
the scalability, their training times still depend on the implementations in
the frameworks as sampling and its associated operations can introduce
non-negligible overhead and computational cost. In addition, it is unknown how
much the frameworks are 'eco-friendly' from a green computing perspective. In
this paper, we provide an in-depth study of two mainstream GNN frameworks along
with three state-of-the-art GNNs to analyze their performance in terms of
runtime and power/energy consumption. We conduct extensive benchmark
experiments at several different levels and present detailed analysis results
and observations, which could be helpful for further improvement and
optimization.

Learning from demonstration (LfD) is a proven technique to teach robots new
skills. Data quality and quantity play a critical role in LfD trained model
performance. In this paper we analyze the effect of enhancing an existing
teleoperation data collection system with real-time haptic feedback; we observe
improvements in the collected data throughput and its quality for model
training. Our experiment testbed was a mobile manipulator robot that opened
doors with latch handles. Evaluation of teleoperated data collection on eight
real world conference room doors found that adding the haptic feedback improved
the data throughput by 6%. We additionally used the collected data to train six
image-based deep imitation learning models, three with haptic feedback and
three without it. These models were used to implement autonomous door-opening
with the same type of robot used during data collection. Our results show that
a policy from a behavior cloning model trained with haptic data performed on
average 11% better than its counterpart with no haptic feedback data,
indicating that haptic feedback resulted in collection of a higher quality
dataset.

Learning to localize the sound source in videos without explicit annotations
is a novel area of audio-visual research. Existing work in this area focuses on
creating attention maps to capture the correlation between the two modalities
to localize the source of the sound. In a video, oftentimes, the objects
exhibiting movement are the ones generating the sound. In this work, we capture
this characteristic by modeling the optical flow in a video as a prior to
better aid in localizing the sound source. We further demonstrate that the
addition of flow-based attention substantially improves visual sound source
localization. Finally, we benchmark our method on standard sound source
localization datasets and achieve state-of-the-art performance on the Soundnet
Flickr and VGG Sound Source datasets. Code:
https://github.com/denfed/heartheflow.

Indoor scenes typically exhibit complex, spatially-varying appearance from
global illumination, making inverse rendering a challenging ill-posed problem.
This work presents an end-to-end, learning-based inverse rendering framework
incorporating differentiable Monte Carlo raytracing with importance sampling.
The framework takes a single image as input to jointly recover the underlying
geometry, spatially-varying lighting, and photorealistic materials.
Specifically, we introduce a physically-based differentiable rendering layer
with screen-space ray tracing, resulting in more realistic specular reflections
that match the input photo. In addition, we create a large-scale,
photorealistic indoor scene dataset with significantly richer details like
complex furniture and dedicated decorations. Further, we design a novel
out-of-view lighting network with uncertainty-aware refinement leveraging
hypernetwork-based neural radiance fields to predict lighting outside the view
of the input photo. Through extensive evaluations on common benchmark datasets,
we demonstrate superior inverse rendering quality of our method compared to
state-of-the-art baselines, enabling various applications such as complex
object insertion and material editing with high fidelity. Code and data will be
made available at \url{https://jingsenzhu.github.io/invrend}.

Zero-click attacks require no user interaction and typically exploit zero-day
(i.e., unpatched) vulnerabilities in instant chat applications (such as
WhatsApp and iMessage) to gain root access to the victim's smartphone and
exfiltrate sensitive data. In this paper, we report our experiences in
attempting to secure smartphones against zero-click attacks. We approached the
problem by first enumerating several properties we believed were necessary to
prevent zero-click attacks against smartphones. Then, we created a security
design that satisfies all the identified properties, and attempted to build it
using off-the-shelf components. Our key idea was to shift the attack surface
from the user's smartphone to a sandboxed virtual smartphone ecosystem where
each chat application runs in isolation. Our performance and usability
evaluations of the system we built highlighted several shortcomings and the
fundamental challenges in securing modern smartphones against zero-click
attacks. In this experience report, we discuss the lessons we learned, and
share insights on the missing components necessary to achieve foolproof
security against zero-click attacks for modern mobile devices.

Recent works on Lottery Ticket Hypothesis have shown that pre-trained
language models (PLMs) contain smaller matching subnetworks(winning tickets)
which are capable of reaching accuracy comparable to the original models.
However, these tickets are proved to be notrobust to adversarial examples, and
even worse than their PLM counterparts. To address this problem, we propose a
novel method based on learning binary weight masks to identify robust tickets
hidden in the original PLMs. Since the loss is not differentiable for the
binary mask, we assign the hard concrete distribution to the masks and
encourage their sparsity using a smoothing approximation of L0
regularization.Furthermore, we design an adversarial loss objective to guide
the search for robust tickets and ensure that the tickets perform well bothin
accuracy and robustness. Experimental results show the significant improvement
of the proposed method over previous work on adversarial robustness evaluation.

Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapproximationmethods,suchas
recurrent neural nets or history-based state abstraction, perform better than
their memory-less counterparts, due to the fact that function approximation in
Markov decision processes (MDP) can be viewed as inducing a Partially
observable MDP. However, there has been little formal analysis of such
history-based algorithms, as most existing frameworks focus exclusively on
memory-less features. In this paper, we introduce a theoretical framework for
studying the behaviour of RL algorithms that learn to control an MDP using
history-based feature abstraction mappings. Furthermore, we use this framework
to design a practical RL algorithm and we numerically evaluate its
effectiveness on a set of continuous control tasks.

Mood inference with mobile sensing data has been studied in ubicomp
literature over the last decade. This inference enables context-aware and
personalized user experiences in general mobile apps and valuable feedback and
interventions in mobile health apps. However, even though model generalization
issues have been highlighted in many studies, the focus has always been on
improving the accuracies of models using different sensing modalities and
machine learning techniques, with datasets collected in homogeneous
populations. In contrast, less attention has been given to studying the
performance of mood inference models to assess whether models generalize to new
countries. In this study, we collected a mobile sensing dataset with 329K
self-reports from 678 participants in eight countries (China, Denmark, India,
Italy, Mexico, Mongolia, Paraguay, UK) to assess the effect of geographical
diversity on mood inference models. We define and evaluate country-specific
(trained and tested within a country), continent-specific (trained and tested
within a continent), country-agnostic (tested on a country not seen on training
data), and multi-country (trained and tested with multiple countries)
approaches trained on sensor data for two mood inference tasks with
population-level (non-personalized) and hybrid (partially personalized) models.
We show that partially personalized country-specific models perform the best
yielding area under the receiver operating characteristic curve (AUROC) scores
of the range 0.78-0.98 for two-class (negative vs. positive valence) and
0.76-0.94 for three-class (negative vs. neutral vs. positive valence)
inference. Overall, we uncover generalization issues of mood inference models
to new countries and how the geographical similarity of countries might impact
mood inference.

Proper functioning of connected and automated vehicles (CAVs) is crucial for
the safety and efficiency of future intelligent transport systems. Meanwhile,
transitioning to fully autonomous driving requires a long period of mixed
autonomy traffic, including both CAVs and human-driven vehicles. Thus,
collaboration decision-making for CAVs is essential to generate appropriate
driving behaviors to enhance the safety and efficiency of mixed autonomy
traffic. In recent years, deep reinforcement learning (DRL) has been widely
used in solving decision-making problems. However, the existing DRL-based
methods have been mainly focused on solving the decision-making of a single
CAV. Using the existing DRL-based methods in mixed autonomy traffic cannot
accurately represent the mutual effects of vehicles and model dynamic traffic
environments. To address these shortcomings, this article proposes a graph
reinforcement learning (GRL) approach for multi-agent decision-making of CAVs
in mixed autonomy traffic. First, a generic and modular GRL framework is
designed. Then, a systematic review of DRL and GRL methods is presented,
focusing on the problems addressed in recent research. Moreover, a comparative
study on different GRL methods is further proposed based on the designed
framework to verify the effectiveness of GRL methods. Results show that the GRL
methods can well optimize the performance of multi-agent decision-making for
CAVs in mixed autonomy traffic compared to the DRL methods. Finally, challenges
and future research directions are summarized. This study can provide a
valuable research reference for solving the multi-agent decision-making
problems of CAVs in mixed autonomy traffic and can promote the implementation
of GRL-based methods into intelligent transportation systems. The source code
of our work can be found at https://github.com/Jacklinkk/Graph_CAVs.

To enable a safe and effective human-robot cooperation, it is crucial to
develop models for the identification of human activities. Egocentric vision
seems to be a viable solution to solve this problem, and therefore many works
provide deep learning solutions to infer human actions from first person
videos. However, although very promising, most of these do not consider the
major challenges that comes with a realistic deployment, such as the
portability of the model, the need for real-time inference, and the robustness
with respect to the novel domains (i.e., new spaces, users, tasks). With this
paper, we set the boundaries that egocentric vision models should consider for
realistic applications, defining a novel setting of egocentric action
recognition in the wild, which encourages researchers to develop novel,
applications-aware solutions. We also present a new model-agnostic technique
that enables the rapid repurposing of existing architectures in this new
context, demonstrating the feasibility to deploy a model on a tiny device
(Jetson Nano) and to perform the task directly on the edge with very low energy
consumption (2.4W on average at 50 fps).

The success of state-of-the-art deep neural networks heavily relies on the
presence of large-scale labelled datasets, which are extremely expensive and
time-consuming to annotate. This paper focuses on tackling semi-supervised part
segmentation tasks by generating high-quality images with a pre-trained GAN and
labelling the generated images with an automatic annotator. In particular, we
formulate the annotator learning as a learning-to-learn problem. Given a
pre-trained GAN, the annotator learns to label object parts in a set of
randomly generated images such that a part segmentation model trained on these
synthetic images with their predicted labels obtains low segmentation error on
a small validation set of manually labelled images. We further reduce this
nested-loop optimization problem to a simple gradient matching problem and
efficiently solve it with an iterative algorithm. We show that our method can
learn annotators from a broad range of labelled images including real images,
generated images, and even analytically rendered images. Our method is
evaluated with semi-supervised part segmentation tasks and significantly
outperforms other semi-supervised competitors when the amount of labelled
examples is extremely limited.

In recent years, generative adversarial networks (GANs) have been an actively
studied topic and shown to successfully produce high-quality realistic images
in various domains. The controllable synthesis ability of GAN generators
suggests that they maintain informative, disentangled, and explainable image
representations, but leveraging and transferring their representations to
downstream tasks is largely unexplored. In this paper, we propose to distill
knowledge from GAN generators by squeezing and spanning their representations.
We squeeze the generator features into representations that are invariant to
semantic-preserving transformations through a network before they are distilled
into the student network. We span the distilled representation of the synthetic
domain to the real domain by also using real training data to remedy the mode
collapse of GANs and boost the student network performance in a real domain.
Experiments justify the efficacy of our method and reveal its great
significance in self-supervised representation learning. Code is available at
https://github.com/yangyu12/squeeze-and-span.

Regulation of multiple reaction modules is quite common in molecular
computation and deep learning networks construction through chemical reactions,
as is always a headache for that sequential execution of modules goes against
the intrinsically parallel nature of chemical reactions. Precisely switching
multiple reaction modules both on and off acts as the core role in programming
chemical reaction systems. Unlike setting up physical compartments or adding
human intervention signals, we adopt the idea of chemical oscillators based on
relaxation oscillation, and assign corresponding clock signal components into
the modules that need to be regulated. This paper mainly demonstrates the
design process of oscillators under the regulation task of three modules, and
provides a suitable approach for automatic termination of the modules cycle. We
provide the simulation results at the level of ordinary differential equation
and ensure that equations can be translated into corresponding chemical
reaction networks.

Time Series Forecasting has been an active area of research due to its many
applications ranging from network usage prediction, resource allocation,
anomaly detection, and predictive maintenance. Numerous publications published
in the last five years have proposed diverse sets of objective loss functions
to address cases such as biased data, long-term forecasting, multicollinear
features, etc. In this paper, we have summarized 14 well-known regression loss
functions commonly used for time series forecasting and listed out the
circumstances where their application can aid in faster and better model
convergence. We have also demonstrated how certain categories of loss functions
perform well across all data sets and can be considered as a baseline objective
function in circumstances where the distribution of the data is unknown. Our
code is available at GitHub:
https://github.com/aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow.

A Differentiable Neural Computer (DNC) is a neural network with an external
memory which allows for iterative content modification via read, write and
delete operations.
  We show that information theoretic properties of the memory contents play an
important role in the performance of such architectures. We introduce a novel
concept of memory demon to DNC architectures which modifies the memory contents
implicitly via additive input encoding. The goal of the memory demon is to
maximize the expected sum of mutual information of the consecutive external
memory contents.

Labelling a large quantity of social media data for the task of supervised
machine learning is not only time-consuming but also difficult and expensive.
On the other hand, the accuracy of supervised machine learning models is
strongly related to the quality of the labelled data on which they train, and
automatic sentiment labelling techniques could reduce the time and cost of
human labelling. We have compared three automatic sentiment labelling
techniques: TextBlob, Vader, and Afinn to assign sentiments to tweets without
any human assistance. We compare three scenarios: one uses training and testing
datasets with existing ground truth labels; the second experiment uses
automatic labels as training and testing datasets; and the third experiment
uses three automatic labelling techniques to label the training dataset and
uses the ground truth labels for testing. The experiments were evaluated on two
Twitter datasets: SemEval-2013 (DS-1) and SemEval-2016 (DS-2). Results show
that the Afinn labelling technique obtains the highest accuracy of 80.17%
(DS-1) and 80.05% (DS-2) using a BiLSTM deep learning model. These findings
imply that automatic text labelling could provide significant benefits, and
suggest a feasible alternative to the time and cost of human labelling efforts.

Ensemble control, an emerging research field focusing on the study of large
populations of dynamical systems, has demonstrated great potential in numerous
scientific and practical applications. Striking examples include pulse design
for exciting spin ensembles in quantum physics, neurostimulation for relieving
neurological disorder symptoms, and path planning for steering robot swarms.
However, the control targets in such applications are generally large-scale
complex and severely underactuated ensemble systems, research into which
stretches the capability of techniques in classical control and dynamical
systems theory to the very limit. This paper then devotes to advancing our
knowledge about controllability of linear ensemble systems by integrating tools
in modern algebra into the technique of separating points developed in our
recent work. In particular, we give an algebraic interpretation of the dynamics
of linear systems in terms of actions of polynomials on vector spaces, and this
leads to the development of the functional canonical form of matrix-valued
functions, which can also be viewed as the generalization of the rational
canonical form of matrices in linear algebra. Then, leveraging the technique of
separating points, we achieve a necessary and sufficient characterization of
uniform ensemble controllability for time-invariant linear ensemble systems as
the ensemble controllability canonical form, in which the system and control
matrices are in the functional canonical and block diagonal form, respectively.
This work successfully launches a new research scheme by adopting and tailoring
finite-dimensional methods to tackle control problems involving
infinite-dimensional ensemble systems, and lays a solid foundation for a more
inclusive ensemble control theory targeting a much broader spectrum of control
and learning problems in both scientific research and practice.

This paper proposes a non-data-driven deep neural network for spectral image
recovery problems such as denoising, single hyperspectral image
super-resolution, and compressive spectral imaging reconstruction. Unlike
previous methods, the proposed approach, dubbed Mixture-Net, implicitly learns
the prior information through the network. Mixture-Net consists of a deep
generative model whose layers are inspired by the linear and non-linear
low-rank mixture models, where the recovered image is composed of a weighted
sum between the linear and non-linear decomposition. Mixture-Net also provides
a low-rank decomposition interpreted as the spectral image abundances and
endmembers, helpful in achieving remote sensing tasks without running
additional routines. The experiments show the MixtureNet effectiveness
outperforming state-of-the-art methods in recovery quality with the advantage
of architecture interpretability.

Machine learning tasks often require a significant amount of training data
for the resultant network to perform suitably for a given problem in any
domain. In agriculture, dataset sizes are further limited by phenotypical
differences between two plants of the same genotype, often as a result of
differing growing conditions. Synthetically-augmented datasets have shown
promise in improving existing models when real data is not available. In this
paper, we employ a contrastive unpaired translation (CUT) generative
adversarial network (GAN) and simple image processing techniques to translate
indoor plant images to appear as field images. While we train our network to
translate an image containing only a single plant, we show that our method is
easily extendable to produce multiple-plant field images. Furthermore, we use
our synthetic multi-plant images to train several YoloV5 nano object detection
models to perform the task of plant detection and measure the accuracy of the
model on real field data images. Including training data generated by the
CUT-GAN leads to better plant detection performance compared to a network
trained solely on real data.

Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) aims
at predicting the relation between a pair of sentences (premise and hypothesis)
as entailment, contradiction or semantic independence. Although deep learning
models have shown promising performance for NLI in recent years, they rely on
large scale expensive human-annotated datasets. Semi-supervised learning (SSL)
is a popular technique for reducing the reliance on human annotation by
leveraging unlabeled data for training. However, despite its substantial
success on single sentence classification tasks where the challenge in making
use of unlabeled data is to assign "good enough" pseudo-labels, for NLI tasks,
the nature of unlabeled data is more complex: one of the sentences in the pair
(usually the hypothesis) along with the class label are missing from the data
and require human annotations, which makes SSL for NLI more challenging. In
this paper, we propose a novel way to incorporate unlabeled data in SSL for NLI
where we use a conditional language model, BART to generate the hypotheses for
the unlabeled sentences (used as premises). Our experiments show that our SSL
framework successfully exploits unlabeled data and substantially improves the
performance of four NLI datasets in low-resource settings. We release our code
at: https://github.com/msadat3/SSL_for_NLI.

This contribution presents a deep learning method for the extraction and
fusion of information relating to kidney stone fragments acquired from
different viewpoints of the endoscope. Surface and section fragment images are
jointly used during the training of the classifier to improve the
discrimination power of the features by adding attention layers at the end of
each convolutional block. This approach is specifically designed to mimic the
morpho-constitutional analysis performed in ex-vivo by biologists to visually
identify kidney stones by inspecting both views. The addition of attention
mechanisms to the backbone improved the results of single view extraction
backbones by 4% on average. Moreover, in comparison to the state-of-the-art,
the fusion of the deep features improved the overall results up to 11% in terms
of kidney stone classification accuracy.

Skeleton-based Motion Capture (MoCap) systems have been widely used in the
game and film industry for mimicking complex human actions for a long time.
MoCap data has also proved its effectiveness in human activity recognition
tasks. However, it is a quite challenging task for smaller datasets. The lack
of such data for industrial activities further adds to the difficulties. In
this work, we have proposed an ensemble-based machine learning methodology that
is targeted to work better on MoCap datasets. The experiments have been
performed on the MoCap data given in the Bento Packaging Activity Recognition
Challenge 2021. Bento is a Japanese word that resembles lunch-box. Upon
processing the raw MoCap data at first, we have achieved an astonishing
accuracy of 98% on 10-fold Cross-Validation and 82% on
Leave-One-Out-Cross-Validation by using the proposed ensemble model.

With the advent and increasing consolidation of e-commerce, digital
advertising has very recently replaced traditional advertising as the main
marketing force in the economy. In the past four years, a particularly
important development in the digital advertising industry is the shift from
second-price auctions to first-price auctions for online display ads. This
shift immediately motivated the intellectually challenging question of how to
bid in first-price auctions, because unlike in second-price auctions, bidding
one's private value truthfully is no longer optimal. Following a series of
recent works in this area, we consider a differentiated setup: we do not make
any assumption about other bidders' maximum bid (i.e. it can be adversarial
over time), and instead assume that we have access to a hint that serves as a
prediction of other bidders' maximum bid, where the prediction is learned
through some blackbox machine learning model. We consider two types of hints:
one where a single point-prediction is available, and the other where a hint
interval (representing a type of confidence region into which others' maximum
bid falls) is available. We establish minimax optimal regret bounds for both
cases and highlight the quantitatively different behavior between the two
settings. We also provide improved regret bounds when the others' maximum bid
exhibits the further structure of sparsity. Finally, we complement the
theoretical results with demonstrations using real bidding data.

Pre-training large transformer models with in-domain data improves domain
adaptation and helps gain performance on the domain-specific downstream tasks.
However, sharing models pre-trained on potentially sensitive data is prone to
adversarial privacy attacks. In this paper, we asked to which extent we can
guarantee privacy of pre-training data and, at the same time, achieve better
downstream performance on legal tasks without the need of additional labeled
data. We extensively experiment with scalable self-supervised learning of
transformer models under the formal paradigm of differential privacy and show
that under specific training configurations we can improve downstream
performance without sacrifying privacy protection for the in-domain data. Our
main contribution is utilizing differential privacy for large-scale
pre-training of transformer language models in the legal NLP domain, which, to
the best of our knowledge, has not been addressed before.

There is an increasing need in our society to achieve faster advances in
Science to tackle urgent problems, such as climate changes, environmental
hazards, sustainable energy systems, pandemics, among others. In certain
domains like chemistry, scientific discovery carries the extra burden of
assessing risks of the proposed novel solutions before moving to the
experimental stage. Despite several recent advances in Machine Learning and AI
to address some of these challenges, there is still a gap in technologies to
support end-to-end discovery applications, integrating the myriad of available
technologies into a coherent, orchestrated, yet flexible discovery process.
Such applications need to handle complex knowledge management at scale,
enabling knowledge consumption and production in a timely and efficient way for
subject matter experts (SMEs). Furthermore, the discovery of novel functional
materials strongly relies on the development of exploration strategies in the
chemical space. For instance, generative models have gained attention within
the scientific community due to their ability to generate enormous volumes of
novel molecules across material domains. These models exhibit extreme
creativity that often translates in low viability of the generated candidates.
In this work, we propose a workbench framework that aims at enabling the
human-AI co-creation to reduce the time until the first discovery and the
opportunity costs involved. This framework relies on a knowledge base with
domain and process knowledge, and user-interaction components to acquire
knowledge and advise the SMEs. Currently,the framework supports four main
activities: generative modeling, dataset triage, molecule adjudication, and
risk assessment.

Many modern computer vision algorithms suffer from two major bottlenecks:
scarcity of data and learning new tasks incrementally. While training the model
with new batches of data the model looses it's ability to classify the previous
data judiciously which is termed as catastrophic forgetting. Conventional
methods have tried to mitigate catastrophic forgetting of the previously
learned data while the training at the current session has been compromised.
The state-of-the-art generative replay based approaches use complicated
structures such as generative adversarial network (GAN) to deal with
catastrophic forgetting. Additionally, training a GAN with few samples may lead
to instability. In this work, we present a novel method to deal with these two
major hurdles. Our method identifies a better embedding space with an improved
contrasting loss to make classification more robust. Moreover, our approach is
able to retain previously acquired knowledge in the embedding space even when
trained with new classes. We update previous session class prototypes while
training in such a way that it is able to represent the true class mean. This
is of prime importance as our classification rule is based on the nearest class
mean classification strategy. We have demonstrated our results by showing that
the embedding space remains intact after training the model with new classes.
We showed that our method preformed better than the existing state-of-the-art
algorithms in terms of accuracy across different sessions.

Personalized speech enhancement (PSE) models achieve promising results
compared with unconditional speech enhancement models due to their ability to
remove interfering speech in addition to background noise. Unlike unconditional
speech enhancement, causal PSE models may occasionally remove the target speech
by mistake. The PSE models also tend to leak interfering speech when the target
speaker is silent for an extended period. We show that existing PSE methods
suffer from a trade-off between speech over-suppression and interference
leakage by addressing one problem at the expense of the other. We propose a new
PSE model training framework using cross-task knowledge distillation to
mitigate this trade-off. Specifically, we utilize a personalized voice activity
detector (pVAD) during training to exclude the non-target speech frames that
are wrongly identified as containing the target speaker with hard or soft
classification. This prevents the PSE model from being too aggressive while
still allowing the model to learn to suppress the input speech when it is
likely to be spoken by interfering speakers. Comprehensive evaluation results
are presented, covering various PSE usage scenarios.

Tuberculosis (TB), an infectious bacterial disease, is a significant cause of
death, especially in low-income countries, with an estimated ten million new
cases reported globally in $2020$. While TB is treatable, non-adherence to the
medication regimen is a significant cause of morbidity and mortality. Thus,
proactively identifying patients at risk of dropping off their medication
regimen enables corrective measures to mitigate adverse outcomes. Using a proxy
measure of extreme non-adherence and a dataset of nearly $700,000$ patients
from four states in India, we formulate and solve the machine learning (ML)
problem of early prediction of non-adherence based on a custom rank-based
metric. We train ML models and evaluate against baselines, achieving a $\sim
100\%$ lift over rule-based baselines and $\sim 214\%$ over a random
classifier, taking into account country-wide large-scale future deployment. We
deal with various issues in the process, including data quality,
high-cardinality categorical data, low target prevalence, distribution shift,
variation across cohorts, algorithmic fairness, and the need for robustness and
explainability. Our findings indicate that risk stratification of non-adherent
patients is a viable, deployable-at-scale ML solution. As the official AI
partner of India's Central TB Division, we are working on multiple city and
state-level pilots with the goal of pan-India deployment.

Supervised deep learning is most commonly applied to difficult problems
defined on large and often extensively curated datasets. Here we demonstrate
the ability of deep representation learning to address problems of
classification and regression from small and poorly formed tabular datasets by
encoding input information as abstracted sequences composed of a fixed number
of characters per input field. We find that small models have sufficient
capacity for approximation of various functions and achieve record
classification benchmark accuracy. Such models are shown to form useful
embeddings of various input features in their hidden layers, even if the
learned task does not explicitly require knowledge of those features. These
models are also amenable to input attribution, allowing for an estimation of
the importance of each input element to the model output as well as of which
inputs features are effectively embedded in the model. We present a
proof-of-concept for the application of small language models to mixed tabular
data without explicit feature engineering, cleaning, or preprocessing, relying
on the model to perform these tasks as part of the representation learning
process.

In massive multiple-input multiple-output (MIMO) systems, the user equipment
(UE) needs to feed the channel state information (CSI) back to the base station
(BS) for the following beamforming. But the large scale of antennas in massive
MIMO systems causes huge feedback overhead. Deep learning (DL) based methods
can compress the CSI at the UE and recover it at the BS, which reduces the
feedback cost significantly. But the compressed CSI must be quantized into bit
streams for transmission. In this paper, we propose an adaptor-assisted
quantization strategy for bit-level DL-based CSI feedback. First, we design a
network-aided adaptor and an advanced training scheme to adaptively improve the
quantization and reconstruction accuracy. Moreover, for easy practical
employment, we introduce the expert knowledge of data distribution and propose
a pluggable and cost-free adaptor scheme. Experiments show that compared with
the state-of-the-art feedback quantization method, this adaptor-aided
quantization strategy can achieve better quantization accuracy and
reconstruction performance with less or no additional cost. The open-source
codes are available at https://github.com/zhang-xd18/QCRNet.

Proteins play a central role in biology from immune recognition to brain
activity. While major advances in machine learning have improved our ability to
predict protein structure from sequence, determining protein function from
structure remains a major challenge. Here, we introduce Holographic
Convolutional Neural Network (H-CNN) for proteins, which is a physically
motivated machine learning approach to model amino acid preferences in protein
structures. H-CNN reflects physical interactions in a protein structure and
recapitulates the functional information stored in evolutionary data. H-CNN
accurately predicts the impact of mutations on protein function, including
stability and binding of protein complexes. Our interpretable computational
model for protein structure-function maps could guide design of novel proteins
with desired function.

High order structures (cavities and cliques) of the gene network of influenza
A virus reveal tight associations among viruses during evolution and are key
signals that indicate viral cross-species infection and cause pandemics. As
indicators for sensing the dynamic changes of viral genes, these higher order
structures have been the focus of attention in the field of virology. However,
the size of the viral gene network is usually huge, and searching these
structures in the networks introduces unacceptable delay. To mitigate this
issue, in this paper, we propose a simple-yet-effective model named HyperSearch
based on deep learning to search cavities in a computable complex network for
influenza virus genetics. Extensive experiments conducted on a public influenza
virus dataset demonstrate the effectiveness of HyperSearch over other advanced
deep-learning methods without any elaborated model crafting. Moreover,
HyperSearch can finish the search works in minutes while 0-1 programming takes
days. Since the proposed method is simple and easy to be transferred to other
complex networks, HyperSearch has the potential to facilitate the monitoring of
dynamic changes in viral genes and help humans keep up with the pace of virus
mutations.

This paper presents a 1-D convolutional graph neural network for fault
detection in microgrids. The combination of 1-D convolutional neural networks
(1D-CNN) and graph convolutional networks (GCN) helps extract both
spatial-temporal correlations from the voltage measurements in microgrids. The
fault detection scheme includes fault event detection, fault type and phase
classification, and fault location. There are five neural network model
training to handle these tasks. Transfer learning and fine-tuning are applied
to reduce training efforts. The combined recurrent graph convolutional neural
networks (1D-CGCN) is compared with the traditional ANN structure on the
Potsdam 13-bus microgrid dataset. The achievable accuracy of 99.27%, 98.1%,
98.75%, and 95.6% for fault detection, fault type classification, fault phase
identification, and fault location respectively.

Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical
architecture to recast problems of high-dimensional linear algebra as ones of
stochastic optimization. Despite the promise of leveraging near- to
intermediate-term quantum resources to accelerate this task, the computational
advantage of VQAs over wholly classical algorithms has not been firmly
established. For instance, while the variational quantum eigensolver (VQE) has
been developed to approximate low-lying eigenmodes of high-dimensional sparse
linear operators, analogous classical optimization algorithms exist in the
variational Monte Carlo (VMC) literature, utilizing neural networks in place of
quantum circuits to represent quantum states. In this paper we ask if classical
stochastic optimization algorithms can be constructed paralleling other VQAs,
focusing on the example of the variational quantum linear solver (VQLS). We
find that such a construction can be applied to the VQLS, yielding a paradigm
that could theoretically extend to other VQAs of similar form.

The US federal government spends more than a trillion dollars per year on
health care, largely provided by private third parties and reimbursed by the
government. A major concern in this system is overbilling, waste and fraud by
providers, who face incentives to misreport on their claims in order to receive
higher payments. In this paper, we develop novel machine learning tools to
identify providers that overbill Medicare, the US federal health insurance
program for elderly adults and the disabled. Using large-scale Medicare claims
data, we identify patterns consistent with fraud or overbilling among inpatient
hospitalizations. Our proposed approach for Medicare fraud detection is fully
unsupervised, not relying on any labeled training data, and is explainable to
end users, providing reasoning and interpretable insights into the potentially
suspicious behavior of the flagged providers. Data from the Department of
Justice on providers facing anti-fraud lawsuits and several case studies
validate our approach and findings both quantitatively and qualitatively.

Synthetic datasets are beneficial for machine learning researchers due to the
possibility of experimenting with new strategies and algorithms in the training
and testing phases. These datasets can easily include more scenarios that might
be costly to research with real data or can complement and, in some cases,
replace real data measurements, depending on the quality of the synthetic data.
They can also solve the unbalanced data problem, avoid overfitting, and can be
used in training while testing can be done with real data. In this paper, we
present, to the best of our knowledge, the first synthetic dataset for Unmanned
Aerial Vehicle (UAV) attacks in 5G and beyond networks based on the following
key observable network parameters that indicate power levels: the Received
Signal Strength Indicator (RSSI) and the Signal to Interference-plus-Noise
Ratio (SINR). The main objective of this data is to enable deep network
development for UAV communication security. Especially, for algorithm
development or the analysis of time-series data applied to UAV attack
recognition. Our proposed dataset provides insights into network functionality
when static or moving UAV attackers target authenticated UAVs in an urban
environment. The dataset also considers the presence and absence of
authenticated terrestrial users in the network, which may decrease the deep
networks ability to identify attacks. Furthermore, the data provides deeper
comprehension of the metrics available in the 5G physical and MAC layers for
machine learning and statistics research. The dataset will available at link
archive-beta.ics.uci.edu

Only increasing accuracy without considering uncertainty may negatively
impact Deep Neural Network (DNN) decision-making and decrease its reliability.
This paper proposes five combined preprocessing and post-processing methods for
time-series binary classification problems that simultaneously increase the
accuracy and reliability of DNN outputs applied in a 5G UAV security dataset.
These techniques use DNN outputs as input parameters and process them in
different ways. Two methods use a well-known Machine Learning (ML) algorithm as
a complement, and the other three use only confidence values that the DNN
estimates. We compare seven different metrics, such as the Expected Calibration
Error (ECE), Maximum Calibration Error (MCE), Mean Confidence (MC), Mean
Accuracy (MA), Normalized Negative Log Likelihood (NLL), Brier Score Loss
(BSL), and Reliability Score (RS) and the tradeoffs between them to evaluate
the proposed hybrid algorithms. First, we show that the eXtreme Gradient
Boosting (XGB) classifier might not be reliable for binary classification under
the conditions this work presents. Second, we demonstrate that at least one of
the potential methods can achieve better results than the classification in the
DNN softmax layer. Finally, we show that the prospective methods may improve
accuracy and reliability with better uncertainty calibration based on the
assumption that the RS determines the difference between MC and MA metrics, and
this difference should be zero to increase reliability. For example, Method 3
presents the best RS of 0.65 even when compared to the XGB classifier, which
achieves RS of 7.22.

There are still many challenges of emotion recognition using physiological
data despite the substantial progress made recently. In this paper, we
attempted to address two major challenges. First, in order to deal with the
sparsely-labeled physiological data, we first decomposed the raw physiological
data using signal spectrum analysis, based on which we extracted both
complexity and energy features. Such a procedure helped reduce noise and
improve feature extraction effectiveness. Second, in order to improve the
explainability of the machine learning models in emotion recognition with
physiological data, we proposed Light Gradient Boosting Machine (LightGBM) and
SHapley Additive exPlanations (SHAP) for emotion prediction and model
explanation, respectively. The LightGBM model outperformed the eXtreme Gradient
Boosting (XGBoost) model on the public Database for Emotion Analysis using
Physiological signals (DEAP) with f1-scores of 0.814, 0.823, and 0.860 for
binary classification of valence, arousal, and liking, respectively, with
cross-subject validation using eight peripheral physiological signals.
Furthermore, the SHAP model was able to identify the most important features in
emotion recognition, and revealed the relationships between the predictor
variables and the response variables in terms of their main effects and
interaction effects. Therefore, the results of the proposed model not only had
good performance using peripheral physiological data, but also gave more
insights into the underlying mechanisms in recognizing emotions.

Predicting discrete events in time and space has many scientific
applications, such as predicting hazardous earthquakes and outbreaks of
infectious diseases. History-dependent spatio-temporal Hawkes processes are
often used to mathematically model these point events. However, previous
approaches have faced numerous challenges, particularly when attempting to
forecast one or multiple future events. In this work, we propose a new neural
architecture for multi-event forecasting of spatio-temporal point processes,
utilizing transformers, augmented with normalizing flows and probabilistic
layers. Our network makes batched predictions of complex history-dependent
spatio-temporal distributions of future discrete events, achieving
state-of-the-art performance on a variety of benchmark datasets including the
South California Earthquakes, Citibike, Covid-19, and Hawkes synthetic pinwheel
datasets. More generally, we illustrate how our network can be applied to any
dataset of discrete events with associated markers, even when no underlying
physics is known.

The class of bigraphical lasso algorithms (and, more broadly,
'tensor'-graphical lasso algorithms) has been used to estimate dependency
structures within matrix and tensor data. However, all current methods to do so
take prohibitively long on modestly sized datasets. We present a novel
tensor-graphical lasso algorithm that analytically estimates the dependency
structure, unlike its iterative predecessors. This provides a speedup of
multiple orders of magnitude, allowing this class of algorithms to be used on
large, real-world datasets.

Epistemic graphs generalize the epistemic approach to probabilistic
argumentation and tackle the uncertainties in and between arguments. A
framework was proposed to generate epistemic constraints from data using a
two-way generalization method in the perspective of only considering the
beliefs of participants without considering the nature of relations represented
in an epistemic graph. The deficiency of original framework is that it is
unable to learn rules using tighter constraints, and the learnt rules might be
counterintuitive. Meanwhile, when dealing with more restricted values, the
filtering computational complexity will increase sharply, and the time
performance would become unreasonable. This paper introduces a filtering-based
approach using a multiple-way generalization step to generate a set of rational
rules based on both the beliefs of each agent on different arguments and the
epistemic graph corresponding to the epistemic constraints. This approach is
able to generated rational rules with multiple restricted values in higher
efficiency. Meanwhile, we have proposed a standard to analyze the rationality
of a dataset based on the postulates of deciding rational rules. We evaluate
the filtering-based approach on two suitable data bases. The empirical results
show that the filtering-based approach performs well with a better efficiency
comparing to the original framework, and rules generated from the improved
approach are ensured to be rational.

Breast cancer is one of the common cancers that endanger the health of women
globally. Accurate target lesion segmentation is essential for early clinical
intervention and postoperative follow-up. Recently, many convolutional neural
networks (CNNs) have been proposed to segment breast tumors from ultrasound
images. However, the complex ultrasound pattern and the variable tumor shape
and size bring challenges to the accurate segmentation of the breast lesion.
Motivated by the selective kernel convolution, we introduce an enhanced
selective kernel convolution for breast tumor segmentation, which integrates
multiple feature map region representations and adaptively recalibrates the
weights of these feature map regions from the channel and spatial dimensions.
This region recalibration strategy enables the network to focus more on
high-contributing region features and mitigate the perturbation of less useful
regions. Finally, the enhanced selective kernel convolution is integrated into
U-net with deep supervision constraints to adaptively capture the robust
representation of breast tumors. Extensive experiments with twelve
state-of-the-art deep learning segmentation methods on three public breast
ultrasound datasets demonstrate that our method has a more competitive
segmentation performance in breast ultrasound images.

Saliency methods compute heat maps that highlight portions of an input that
were most {\em important} for the label assigned to it by a deep net.
Evaluations of saliency methods convert this heat map into a new {\em masked
input} by retaining the $k$ highest-ranked pixels of the original input and
replacing the rest with \textquotedblleft uninformative\textquotedblright\
pixels, and checking if the net's output is mostly unchanged. This is usually
seen as an {\em explanation} of the output, but the current paper highlights
reasons why this inference of causality may be suspect. Inspired by logic
concepts of {\em completeness \& soundness}, it observes that the above type of
evaluation focuses on completeness of the explanation, but ignores soundness.
New evaluation metrics are introduced to capture both notions, while staying in
an {\em intrinsic} framework -- i.e., using the dataset and the net, but no
separately trained nets, human evaluations, etc. A simple saliency method is
described that matches or outperforms prior methods in the evaluations.
Experiments also suggest new intrinsic justifications, based on soundness, for
popular heuristic tricks such as TV regularization and upsampling.

In this paper, we increase the availability and integration of devices in the
learning process to enhance the convergence of federated learning (FL) models.
To address the issue of having all the data in one location, federated
learning, which maintains the ability to learn over decentralized data sets,
combines privacy and technology. Until the model converges, the server combines
the updated weights obtained from each dataset over a number of rounds. The
majority of the literature suggested client selection techniques to accelerate
convergence and boost accuracy. However, none of the existing proposals have
focused on the flexibility to deploy and select clients as needed, wherever and
whenever that may be. Due to the extremely dynamic surroundings, some devices
are actually not available to serve as clients in FL, which affects the
availability of data for learning and the applicability of the existing
solution for client selection. In this paper, we address the aforementioned
limitations by introducing an On-Demand-FL, a client deployment approach for
FL, offering more volume and heterogeneity of data in the learning process. We
make use of the containerization technology such as Docker to build efficient
environments using IoT and mobile devices serving as volunteers. Furthermore,
Kubernetes is used for orchestration. The Genetic algorithm (GA) is used to
solve the multi-objective optimization problem due to its evolutionary
strategy. The performed experiments using the Mobile Data Challenge (MDC)
dataset and the Localfed framework illustrate the relevance of the proposed
approach and the efficiency of the on-the-fly deployment of clients whenever
and wherever needed with less discarded rounds and more available data.

In this work, we propose a family of novel quantum kernels, namely the
Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), for un-attributed
graphs. Different from most existing classical graph kernels, the proposed
HAQJSK kernels can incorporate hierarchical aligned structure information
between graphs and transform graphs of random sizes into fixed-sized aligned
graph structures, i.e., the Hierarchical Transitive Aligned Adjacency Matrix of
vertices and the Hierarchical Transitive Aligned Density Matrix of the
Continuous-Time Quantum Walk (CTQW). For a pair of graphs to hand, the
resulting HAQJSK kernels are defined by measuring the Quantum Jensen-Shannon
Divergence (QJSD) between their transitive aligned graph structures. We show
that the proposed HAQJSK kernels not only reflect richer intrinsic global graph
characteristics in terms of the CTQW, but also address the drawback of
neglecting structural correspondence information arising in most existing
R-convolution kernels. Furthermore, unlike the previous Quantum Jensen-Shannon
Kernels associated with the QJSD and the CTQW, the proposed HAQJSK kernels can
simultaneously guarantee the properties of permutation invariant and positive
definiteness, explaining the theoretical advantages of the HAQJSK kernels.
Experiments indicate the effectiveness of the proposed kernels.

End-to-end singing voice synthesis (SVS) model VISinger can achieve better
performance than the typical two-stage model with fewer parameters. However,
VISinger has several problems: text-to-phase problem, the end-to-end model
learns the meaningless mapping of text-to-phase; glitches problem, the harmonic
components corresponding to the periodic signal of the voiced segment occurs a
sudden change with audible artefacts; low sampling rate, the sampling rate of
24KHz does not meet the application needs of high-fidelity generation with the
full-band rate (44.1KHz or higher). In this paper, we propose VISinger 2 to
address these issues by integrating the digital signal processing (DSP) methods
with VISinger. Specifically, inspired by recent advances in differentiable
digital signal processing (DDSP), we incorporate a DSP synthesizer into the
decoder to solve the above issues. The DSP synthesizer consists of a harmonic
synthesizer and a noise synthesizer to generate periodic and aperiodic signals,
respectively, from the latent representation z in VISinger. It supervises the
posterior encoder to extract the latent representation without phase
information and avoid the prior encoder modelling text-to-phase mapping. To
avoid glitch artefacts, the HiFi-GAN is modified to accept the waveforms
generated by the DSP synthesizer as a condition to produce the singing voice.
Moreover, with the improved waveform decoder, VISinger 2 manages to generate
44.1kHz singing audio with richer expression and better quality. Experiments on
OpenCpop corpus show that VISinger 2 outperforms VISinger, CpopSing and
RefineSinger in both subjective and objective metrics.

Recently, studies on machine learning have focused on methods that use
symmetry implicit in a specific manifold as an inductive bias. In particular,
approaches using Grassmann manifolds have been found to exhibit effective
performance in fields such as point cloud and image set analysis. However,
there is a lack of research on the construction of general learning models to
learn distributions on the Grassmann manifold. In this paper, we lay the
theoretical foundations for learning distributions on the Grassmann manifold
via continuous normalizing flows. Experimental results show that the proposed
method can generate high-quality samples by capturing the data structure.
Further, the proposed method significantly outperformed state-of-the-art
methods in terms of log-likelihood or evidence lower bound. The results
obtained are expected to usher in further research in this field of study.

The task of Compositional Zero-Shot Learning (CZSL) is to recognize images of
novel state-object compositions that are absent during the training stage.
Previous methods of learning compositional embedding have shown effectiveness
in closed-world CZSL. However, in Open-World CZSL (OW-CZSL), their performance
tends to degrade significantly due to the large cardinality of possible
compositions. Some recent works separately predict simple primitives (i.e.,
states and objects) to reduce cardinality. However, they consider simple
primitives as independent probability distributions, ignoring the heavy
dependence between states, objects, and compositions. In this paper, we model
the dependence of compositions via feasibility and contextuality.
Feasibility-dependence refers to the unequal feasibility relations between
simple primitives, e.g., \textit{hairy} is more feasible with \textit{cat} than
with \textit{building} in the real world. Contextuality-dependence represents
the contextual variance in images, e.g., \textit{cat} shows diverse appearances
under the state of \textit{dry} and \textit{wet}. We design Semantic Attention
(SA) and generative Knowledge Disentanglement (KD) to learn the dependence of
feasibility and contextuality, respectively. SA captures semantics in
compositions to alleviate impossible predictions, driven by the visual
similarity between simple primitives. KD disentangles images into unbiased
feature representations, easing contextual bias in predictions. Moreover, we
complement the current compositional probability model with feasibility and
contextuality in a compatible format. Finally, we conduct comprehensive
experiments to analyze and validate the superior or competitive performance of
our model, Semantic Attention and knowledge Disentanglement guided Simple
Primitives (SAD-SP), on three widely-used benchmark OW-CZSL datasets.

Recently, malevolent user hacking has become a huge problem for real-world
companies. In order to learn predictive models for recommender systems,
factorization techniques have been developed to deal with user-item ratings. In
this paper, we suggest a broad architecture of a factorization model with
adversarial training to get over these issues. The effectiveness of our systems
is demonstrated by experimental findings on real-world datasets.

Graph Learning (GL) is at the core of inference and analysis of connections
in data mining and machine learning (ML). By observing a dataset of graph
signals, and considering specific assumptions, Graph Signal Processing (GSP)
tools can provide practical constraints in the GL approach. One applicable
constraint can infer a graph with desired frequency signatures, i.e., spectral
templates. However, a severe computational burden is a challenging barrier,
especially for inference from high-dimensional graph signals. To address this
issue and in the case of the underlying graph having graph product structure,
we propose learning product (high dimensional) graphs from product spectral
templates with significantly reduced complexity rather than learning them
directly from high-dimensional graph signals, which, to the best of our
knowledge, has not been addressed in the related areas. In contrast to the rare
current approaches, our approach can learn all types of product graphs (with
more than two graphs) without knowing the type of graph products and has fewer
parameters. Experimental results on both the synthetic and real-world data,
i.e., brain signal analysis and multi-view object images, illustrate
explainable and meaningful factor graphs supported by expert-related research,
as well as outperforming the rare current restricted approaches.

Online clothing catalogs lack diversity in body shape and garment size.
Brands commonly display their garments on models of one or two sizes, rarely
including plus-size models. In this work, we propose a new method, SizeGAN, for
generating images of garments on different-sized models. To change the garment
and model size while maintaining a photorealistic image, we incorporate image
alignment ideas from the medical imaging literature into the StyleGAN2-ADA
architecture. Our method learns deformation fields at multiple resolutions and
uses a spatial transformer to modify the garment and model size. We evaluate
our approach along three dimensions: realism, garment faithfulness, and size.
To our knowledge, SizeGAN is the first method to focus on this size
under-representation problem for modeling clothing. We provide an analysis
comparing SizeGAN to other plausible approaches and additionally provide the
first clothing dataset with size labels. In a user study comparing SizeGAN and
two recent virtual try-on methods, we show that our method ranks first in each
dimension, and was vastly preferred for realism and garment faithfulness. In
comparison to most previous work, which has focused on generating
photorealistic images of garments, our work shows that it is possible to
generate images that are both photorealistic and cover diverse garment sizes.

Biomolecular binding kinetics including the association (kon) and
dissociation (koff) rates are critical parameters for therapeutic design of
small-molecule drugs, peptides and antibodies. Notably, drug molecule residence
time or dissociation rate has been shown to correlate with their efficacies
better than binding affinities. A wide range of modeling approaches including
quantitative structure-kinetic relationship models, Molecular Dynamics
simulations, enhanced sampling and Machine Learning have been developed to
explore biomolecular binding and dissociation mechanisms and predict binding
kinetic rates. Here, we review recent advances in computational modeling of
biomolecular binding kinetics, with an outlook for future improvements.

Network-based analyses of dynamical systems have become increasingly popular
in climate science. Here we address network construction from a statistical
perspective and highlight the often ignored fact that the calculated
correlation values are only empirical estimates. To measure spurious behaviour
as deviation from a ground truth network, we simulate time-dependent isotropic
random fields on the sphere and apply common network construction techniques.
We find several ways in which the uncertainty stemming from the estimation
procedure has major impact on network characteristics. When the data has
locally coherent correlation structure, spurious link bundle teleconnections
and spurious high-degree clusters have to be expected. Anisotropic estimation
variance can also induce severe biases into empirical networks. We validate our
findings with ERA5 reanalysis data. Moreover we explain why commonly applied
resampling procedures are inappropriate for significance evaluation and propose
a statistically more meaningful ensemble construction framework. By
communicating which difficulties arise in estimation from scarce data and by
presenting which design decisions increase robustness, we hope to contribute to
more reliable climate network construction in the future.

Adversarial reprogramming allows stealing computational resources by
repurposing machine learning models to perform a different task chosen by the
attacker. For example, a model trained to recognize images of animals can be
reprogrammed to recognize medical images by embedding an adversarial program in
the images provided as inputs. This attack can be perpetrated even if the
target model is a black box, supposed that the machine-learning model is
provided as a service and the attacker can query the model and collect its
outputs. So far, no defense has been demonstrated effective in this scenario.
We show for the first time that this attack is detectable using stateful
defenses, which store the queries made to the classifier and detect the
abnormal cases in which they are similar. Once a malicious query is detected,
the account of the user who made it can be blocked. Thus, the attacker must
create many accounts to perpetrate the attack. To decrease this number, the
attacker could create the adversarial program against a surrogate classifier
and then fine-tune it by making few queries to the target model. In this
scenario, the effectiveness of the stateful defense is reduced, but we show
that it is still effective.

Fairness has become a trending topic in natural language processing (NLP),
which addresses biases targeting certain social groups such as genders and
religions. However, regional bias in language models (LMs), a long-standing
global discrimination problem, still remains unexplored. This paper bridges the
gap by analysing the regional bias learned by the pre-trained language models
that are broadly used in NLP tasks. In addition to verifying the existence of
regional bias in LMs, we find that the biases on regional groups can be
strongly influenced by the geographical clustering of the groups. We
accordingly propose a HiErarchical Regional Bias evaluation method (HERB)
utilising the information from the sub-region clusters to quantify the bias in
pre-trained LMs. Experiments show that our hierarchical metric can effectively
evaluate the regional bias with respect to comprehensive topics and measure the
potential regional bias that can be propagated to downstream tasks. Our codes
are available at https://github.com/Bernard-Yang/HERB.

Many real-world problems are usually computationally costly and the objective
functions evolve over time. Data-driven, a.k.a. surrogate-assisted,
evolutionary optimization has been recognized as an effective approach for
tackling expensive black-box optimization problems in a static environment
whereas it has rarely been studied under dynamic environments. This paper
proposes a simple but effective transfer learning framework to empower
data-driven evolutionary optimization to solve dynamic optimization problems.
Specifically, it applies a hierarchical multi-output Gaussian process to
capture the correlation between data collected from different time steps with a
linearly increased number of hyperparameters. Furthermore, an adaptive source
task selection along with a bespoke warm staring initialization mechanisms are
proposed to better leverage the knowledge extracted from previous optimization
exercises. By doing so, the data-driven evolutionary optimization can jump
start the optimization in the new environment with a strictly limited
computational budget. Experiments on synthetic benchmark test problems and a
real-world case study demonstrate the effectiveness of our proposed algorithm
against nine state-of-the-art peer algorithms.

Recent studies on adversarial images have shown that they tend to leave the
underlying low-dimensional data manifold, making them significantly more
challenging for current models to make correct predictions. This so-called
off-manifold conjecture has inspired a novel line of defenses against
adversarial attacks on images. In this study, we find a similar phenomenon
occurs in the contextualized embedding space induced by pretrained language
models, in which adversarial texts tend to have their embeddings diverge from
the manifold of natural ones. Based on this finding, we propose Textual
Manifold-based Defense (TMD), a defense mechanism that projects text embeddings
onto an approximated embedding manifold before classification. It reduces the
complexity of potential adversarial examples, which ultimately enhances the
robustness of the protected model. Through extensive experiments, our method
consistently and significantly outperforms previous defenses under various
attack settings without trading off clean accuracy. To the best of our
knowledge, this is the first NLP defense that leverages the manifold structure
against adversarial attacks. Our code is available at
\url{https://github.com/dangne/tmd}.

Data augmentation is a valuable tool for the design of deep learning systems
to overcome data limitations and stabilize the training process. Especially in
the medical domain, where the collection of large-scale data sets is
challenging and expensive due to limited access to patient data, relevant
environments, as well as strict regulations, community-curated large-scale
public datasets, pretrained models, and advanced data augmentation methods are
the main factors for developing reliable systems to improve patient care.
However, for the development of medical acoustic sensing systems, an emerging
field of research, the community lacks large-scale publicly available data sets
and pretrained models. To address the problem of limited data, we propose a
conditional generative adversarial neural network-based augmentation method
which is able to synthesize mel spectrograms from a learned data distribution
of a source data set. In contrast to previously proposed fully convolutional
models, the proposed model implements residual Squeeze and Excitation modules
in the generator architecture. We show that our method outperforms all
classical audio augmentation techniques and previously published generative
methods in terms of generated sample quality and a performance improvement of
2.84% of Macro F1-Score for a classifier trained on the augmented data set, an
enhancement of $1.14\%$ in relation to previous work. By analyzing the
correlation of intermediate feature spaces, we show that the residual Squeeze
and Excitation modules help the model to reduce redundancy in the latent
features. Therefore, the proposed model advances the state-of-the-art in the
augmentation of clinical audio data and improves the data bottleneck for the
design of clinical acoustic sensing systems.

With climate change predicted to increase the likelihood of landslide events,
there is a growing need for rapid landslide detection technologies that help
inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing
technique that can provide measurements of affected areas independent of
weather or lighting conditions. Usage of SAR, however, is hindered by domain
knowledge that is necessary for the pre-processing steps and its interpretation
requires expert knowledge. We provide simplified, pre-processed,
machine-learning ready SAR datacubes for four globally located landslide events
obtained from several Sentinel-1 satellite passes before and after a landslide
triggering event together with segmentation maps of the landslides. From this
dataset, using the Hokkaido, Japan datacube, we study the feasibility of
SAR-based landslide detection with supervised deep learning (DL). Our results
demonstrate that DL models can be used to detect landslides from SAR data,
achieving an Area under the Precision-Recall curve exceeding 0.7. We find that
additional satellite visits enhance detection performance, but that early
detection is possible when SAR data is combined with terrain information from a
digital elevation model. This can be especially useful for time-critical
emergency interventions. Code is made publicly available at
https://github.com/iprapas/landslide-sar-unet.

Structural alterations have been thoroughly investigated in the brain during
the early onset of schizophrenia (SCZ) with the development of neuroimaging
methods. The objective of the paper is an efficient classification of SCZ in 2
different classes: Cognitive Normal (CN), and SCZ using magnetic resonance
imaging (MRI) images. This paper proposed a lightweight 3D convolutional neural
network (CNN) based framework for SCZ diagnosis using MRI images. In the
proposed model, lightweight 3D CNN is used to extract both spatial and spectral
features simultaneously from 3D volume MRI scans, and classification is done
using an ensemble bagging classifier. Ensemble bagging classifier contributes
to preventing overfitting, reduces variance, and improves the model's accuracy.
The proposed algorithm is tested on datasets taken from three benchmark
databases available as open-source: MCICShare, COBRE, and fBRINPhase-II. These
datasets have undergone preprocessing steps to register all the MRI images to
the standard template and reduce the artifacts. The model achieves the highest
accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall
94.44%, F1-score 92.39% and G-mean 92.19% as compared to the current
state-of-the-art techniques. The performance metrics evidenced the use of this
model to assist the clinicians for automatic accurate diagnosis of SCZ.

Providing accurate estimated time of package delivery on users' purchasing
pages for e-commerce platforms is of great importance to their purchasing
decisions and post-purchase experiences. Although this problem shares some
common issues with the conventional estimated time of arrival (ETA), it is more
challenging with the following aspects: 1) Inductive inference. Models are
required to predict ETA for orders with unseen retailers and addresses; 2)
High-order interaction of order semantic information. Apart from the
spatio-temporal features, the estimated time also varies greatly with other
factors, such as the packaging efficiency of retailers, as well as the
high-order interaction of these factors. In this paper, we propose an inductive
graph transformer (IGT) that leverages raw feature information and structural
graph data to estimate package delivery time. Different from previous graph
transformer architectures, IGT adopts a decoupled pipeline and trains
transformer as a regression function that can capture the multiplex information
from both raw feature and dense embeddings encoded by a graph neural network
(GNN). In addition, we further simplify the GNN structure by removing its
non-linear activation and the learnable linear transformation matrix. The
reduced parameter search space and linear information propagation in the
simplified GNN enable the IGT to be applied in large-scale industrial
scenarios. Experiments on real-world logistics datasets show that our proposed
model can significantly outperform the state-of-the-art methods on estimation
of delivery time. The source code is available at:
https://github.com/enoche/IGT-WSDM23.

Feature selection is of great importance in Machine Learning, where it can be
used to reduce the dimensionality of classification, ranking and prediction
problems. The removal of redundant and noisy features can improve both the
accuracy and scalability of the trained models. However, feature selection is a
computationally expensive task with a solution space that grows
combinatorically. In this work, we consider in particular a quadratic feature
selection problem that can be tackled with the Quantum Approximate Optimization
Algorithm (QAOA), already employed in combinatorial optimization. First we
represent the feature selection problem with the QUBO formulation, which is
then mapped to an Ising spin Hamiltonian. Then we apply QAOA with the goal of
finding the ground state of this Hamiltonian, which corresponds to the optimal
selection of features. In our experiments, we consider seven different
real-world datasets with dimensionality up to 21 and run QAOA on both a quantum
simulator and, for small datasets, the 7-qubit IBM (ibm-perth) quantum
computer. We use the set of selected features to train a classification model
and evaluate its accuracy. Our analysis shows that it is possible to tackle the
feature selection problem with QAOA and that currently available quantum
devices can be used effectively. Future studies could test a wider range of
classification models as well as improve the effectiveness of QAOA by exploring
better performing optimizers for its classical step.

Missing data is a common concern in health datasets, and its impact on good
decision-making processes is well documented. Our study's contribution is a
methodology for tackling missing data problems using a combination of synthetic
dataset generation, missing data imputation and deep learning methods to
resolve missing data challenges. Specifically, we conducted a series of
experiments with these objectives; $a)$ generating a realistic synthetic
dataset, $b)$ simulating data missingness, $c)$ recovering the missing data,
and $d)$ analyzing imputation performance. Our methodology used a gaussian
mixture model whose parameters were learned from a cleaned subset of a real
demographic and health dataset to generate the synthetic data. We simulated
various missingness degrees ranging from $10 \%$, $20 \%$, $30 \%$, and $40\%$
under the missing completely at random scheme MCAR. We used an integrated
performance analysis framework involving clustering, classification and direct
imputation analysis. Our results show that models trained on synthetic and
imputed datasets could make predictions with an accuracy of $83 \%$ and $80 \%$
on $a) $ an unseen real dataset and $b)$ an unseen reserved synthetic test
dataset, respectively. Moreover, the models that used the DAE method for
imputed yielded the lowest log loss an indication of good performance, even
though the accuracy measures were slightly lower. In conclusion, our work
demonstrates that using our methodology, one can reverse engineer a solution to
resolve missingness on an unseen dataset with missingness. Moreover, though we
used a health dataset, our methodology can be utilized in other contexts.

Quantizing floating-point neural network to its fixed-point representation is
crucial for Learned Image Compression (LIC) because it ensures the decoding
consistency for interoperability and reduces space-time complexity for
implementation. Existing solutions often have to retrain the network for model
quantization which is time consuming and impractical. This work suggests the
use of Post-Training Quantization (PTQ) to directly process pretrained,
off-the-shelf LIC models. We theoretically prove that minimizing the mean
squared error (MSE) in PTQ is sub-optimal for compression task and thus develop
a novel Rate-Distortion (R-D) Optimized PTQ (RDO-PTQ) to best retain the
compression performance. Such RDO-PTQ just needs to compress few images (e.g.,
10) to optimize the transformation of weight, bias, and activation of
underlying LIC model from its native 32-bit floating-point (FP32) format to
8-bit fixed-point (INT8) precision for fixed-point inference onwards.
Experiments reveal outstanding efficiency of the proposed method on different
LICs, showing the closest coding performance to their floating-point
counterparts. And, our method is a lightweight and plug-and-play approach
without any need of model retraining which is attractive to practitioners.

Modern supervised learning neural network models require a large amount of
manually labeled data, which makes the construction of domain-specific
knowledge graphs time-consuming and labor-intensive. In parallel, although
there has been much research on named entity recognition and relation
extraction based on distantly supervised learning, constructing a
domain-specific knowledge graph from large collections of textual data without
manual annotations is still an urgent problem to be solved. In response, we
propose an integrated framework for adapting and re-learning knowledge graphs
from one coarse domain (biomedical) to a finer-define domain (oncology). In
this framework, we apply distant-supervision on cross-domain knowledge graph
adaptation. Consequently, no manual data annotation is required to train the
model. We introduce a novel iterative training strategy to facilitate the
discovery of domain-specific named entities and triples. Experimental results
indicate that the proposed framework can perform domain adaptation and
construction of knowledge graph efficiently.

Semiconductor lasers have been rapidly evolving to meet the demands of
next-generation optical networks. This imposes much more stringent requirements
on the laser reliability, which are dominated by degradation mechanisms (e.g.,
sudden degradation) limiting the semiconductor laser lifetime. Physics-based
approaches are often used to characterize the degradation behavior
analytically, yet explicit domain knowledge and accurate mathematical models
are required. Building such models can be very challenging due to a lack of a
full understanding of the complex physical processes inducing the degradation
under various operating conditions. To overcome the aforementioned limitations,
we propose a new data-driven approach, extracting useful insights from the
operational monitored data to predict the degradation trend without requiring
any specific knowledge or using any physical model. The proposed approach is
based on an unsupervised technique, a conditional variational autoencoder, and
validated using vertical-cavity surface-emitting laser (VCSEL) and tunable edge
emitting laser reliability data. The experimental results confirm that our
model (i) achieves a good degradation prediction and generalization performance
by yielding an F1 score of 95.3%, (ii) outperforms several baseline ML based
anomaly detection techniques, and (iii) helps to shorten the aging tests by
early predicting the failed devices before the end of the test and thereby
saving costs

Out-of-distribution (OOD) generalization on graphs is drawing widespread
attention. However, existing efforts mainly focus on the OOD issue of
correlation shift. While another type, covariate shift, remains largely
unexplored but is the focus of this work. From a data generation view, causal
features are stable substructures in data, which play key roles in OOD
generalization. While their complementary parts, environments, are unstable
features that often lead to various distribution shifts. Correlation shift
establishes spurious statistical correlations between environments and labels.
In contrast, covariate shift means that there exist unseen environmental
features in test data. Existing strategies of graph invariant learning and data
augmentation suffer from limited environments or unstable causal features,
which greatly limits their generalization ability on covariate shift. In view
of that, we propose a novel graph augmentation strategy: Adversarial Causal
Augmentation (AdvCA), to alleviate the covariate shift. Specifically, it
adversarially augments the data to explore diverse distributions of the
environments. Meanwhile, it keeps the causal features invariant across diverse
environments. It maintains the environmental diversity while ensuring the
invariance of the causal features, thereby effectively alleviating the
covariate shift. Extensive experimental results with in-depth analyses
demonstrate that AdvCA can outperform 14 baselines on synthetic and real-world
datasets with various covariate shifts.

Semiconductor lasers, one of the key components for optical communication
systems, have been rapidly evolving to meet the requirements of next generation
optical networks with respect to high speed, low power consumption, small form
factor etc. However, these demands have brought severe challenges to the
semiconductor laser reliability. Therefore, a great deal of attention has been
devoted to improving it and thereby ensuring reliable transmission. In this
paper, a predictive maintenance framework using machine learning techniques is
proposed for real-time heath monitoring and prognosis of semiconductor laser
and thus enhancing its reliability. The proposed approach is composed of three
stages: i) real-time performance degradation prediction, ii) degradation
detection, and iii) remaining useful life (RUL) prediction. First of all, an
attention based gated recurrent unit (GRU) model is adopted for real-time
prediction of performance degradation. Then, a convolutional autoencoder is
used to detect the degradation or abnormal behavior of a laser, given the
predicted degradation performance values. Once an abnormal state is detected, a
RUL prediction model based on attention-based deep learning is utilized.
Afterwards, the estimated RUL is input for decision making and maintenance
planning. The proposed framework is validated using experimental data derived
from accelerated aging tests conducted for semiconductor tunable lasers. The
proposed approach achieves a very good degradation performance prediction
capability with a small root mean square error (RMSE) of 0.01, a good anomaly
detection accuracy of 94.24% and a better RUL estimation capability compared to
the existing ML-based laser RUL prediction models.

Fabric manipulation is a long-standing challenge in robotics due to the
enormous state space and complex dynamics. Learning approaches stand out as
promising for this domain as they allow us to learn behaviours directly from
data. Most prior methods however rely heavily on simulation, which is still
limited by the large sim-to-real gap of deformable objects or rely on large
datasets. A promising alternative is to learn fabric manipulation directly from
watching humans perform the task. In this work, we explore how demonstrations
for fabric manipulation tasks can be collected directly by humans, providing an
extremely natural and fast data collection pipeline. Then, using only a handful
of such demonstrations, we show how a pick-and-place policy can be learned and
deployed on a real robot, without any robot data collection at all. We
demonstrate our approach on a fabric folding task, showing that our policy can
reliably reach folded states from crumpled initial configurations. Videos are
available at: https://sites.google.com/view/foldingbyhand

Face Restoration (FR) aims to restore High-Quality (HQ) faces from
Low-Quality (LQ) input images, which is a domain-specific image restoration
problem in the low-level computer vision area. The early face restoration
methods mainly use statistic priors and degradation models, which are difficult
to meet the requirements of real-world applications in practice. In recent
years, face restoration has witnessed great progress after stepping into the
deep learning era. However, there are few works to study deep learning-based
face restoration methods systematically. Thus, this paper comprehensively
surveys recent advances in deep learning techniques for face restoration.
Specifically, we first summarize different problem formulations and analyze the
characteristic of the face image. Second, we discuss the challenges of face
restoration. Concerning these challenges, we present a comprehensive review of
existing FR methods, including prior based methods and deep learning-based
methods. Then, we explore developed techniques in the task of FR covering
network architectures, loss functions, and benchmark datasets. We also conduct
a systematic benchmark evaluation on representative methods. Finally, we
discuss future directions, including network designs, metrics, benchmark
datasets, applications,etc. We also provide an open-source repository for all
the discussed methods, which is available at
https://github.com/TaoWangzj/Awesome-Face-Restoration.

Natural laws are often described through differential equations yet finding a
differential equation that describes the governing law underlying observed data
is a challenging and still mostly manual task. In this paper we make a step
towards the automation of this process: we propose a transformer-based
sequence-to-sequence model that recovers scalar autonomous ordinary
differential equations (ODEs) in symbolic form from time-series data of a
single observed solution of the ODE. Our method is efficiently scalable: after
one-time pretraining on a large set of ODEs, we can infer the governing laws of
a new observed solution in a few forward passes of the model. Then we show that
our model performs better or on par with existing methods in various test cases
in terms of accurate symbolic recovery of the ODE, especially for more complex
expressions.

Deep learning has brought significant breakthroughs in sequential
recommendation (SR) for capturing dynamic user interests. A series of recent
research revealed that models with more parameters usually achieve optimal
performance for SR tasks, inevitably resulting in great challenges for
deploying them in real systems. Following the simple assumption that light
networks might already suffice for certain users, in this work, we propose
CANet, a conceptually simple yet very scalable framework for assigning adaptive
network architecture in an input-dependent manner to reduce unnecessary
computation. The core idea of CANet is to route the input user behaviors with a
light-weighted router module. Specifically, we first construct the routing
space with various submodels parameterized in terms of multiple model
dimensions such as the number of layers, hidden size and embedding size. To
avoid extra storage overhead of the routing space, we employ a weight-slicing
schema to maintain all the submodels in exactly one network. Furthermore, we
leverage several solutions to solve the discrete optimization issues caused by
the router module. Thanks to them, CANet could adaptively adjust its network
architecture for each input in an end-to-end manner, in which the user
preference can be effectively captured. To evaluate our work, we conduct
extensive experiments on benchmark datasets. Experimental results show that
CANet reduces computation by 55 ~ 65% while preserving the accuracy of the
original model. Our codes are available at
https://github.com/icantnamemyself/CANet.

Federated learning (FL) is a promising way to allow multiple data owners
(clients) to collaboratively train machine learning models without compromising
data privacy. Yet, existing FL solutions usually rely on a centralized
aggregator for model weight aggregation, while assuming clients are honest.
Even if data privacy can still be preserved, the problem of single-point
failure and data poisoning attack from malicious clients remains unresolved. To
tackle this challenge, we propose to use distributed ledger technology (DLT) to
achieve FLock, a secure and reliable decentralized Federated Learning system
built on blockchain. To guarantee model quality, we design a novel peer-to-peer
(P2P) review and reward/slash mechanism to detect and deter malicious clients,
powered by on-chain smart contracts. The reward/slash mechanism, in addition,
serves as incentives for participants to honestly upload and review model
parameters in the FLock system. FLock thus improves the performance and the
robustness of FL systems in a fully P2P manner.

In this paper, we present a robust and low complexity deep learning model for
Remote Sensing Image Classification (RSIC), the task of identifying the scene
of a remote sensing image. In particular, we firstly evaluate different low
complexity and benchmark deep neural networks: MobileNetV1, MobileNetV2,
NASNetMobile, and EfficientNetB0, which present the number of trainable
parameters lower than 5 Million (M). After indicating best network
architecture, we further improve the network performance by applying attention
schemes to multiple feature maps extracted from middle layers of the network.
To deal with the issue of increasing the model footprint as using attention
schemes, we apply the quantization technique to satisfies the number trainable
parameter of the model lower than 5 M. By conducting extensive experiments on
the benchmark datasets NWPU-RESISC45, we achieve a robust and low-complexity
model, which is very competitive to the state-of-the-art systems and potential
for real-life applications on edge devices.

In this current technological world, the application of machine learning is
becoming ubiquitous. Incorporating machine learning algorithms on extremely
low-power and inexpensive embedded devices at the edge level is now possible
due to the combination of the Internet of Things (IoT) and edge computing. To
estimate an outcome, traditional machine learning demands vast amounts of
resources. The TinyML concept for embedded machine learning attempts to push
such diversity from usual high-end approaches to low-end applications. TinyML
is a rapidly expanding interdisciplinary topic at the convergence of machine
learning, software, and hardware centered on deploying deep neural network
models on embedded (micro-controller-driven) systems. TinyML will pave the way
for novel edge-level services and applications that survive on distributed edge
inferring and independent decision-making rather than server computation. In
this paper, we explore TinyML's methodology, how TinyML can benefit a few
specific industrial fields, its obstacles, and its future scope.

Entity alignment is to find identical entities in different knowledge graphs
(KGs) that refer to the same real-world object. Embedding-based entity
alignment techniques have been drawing a lot of attention recently because they
can help solve the issue of symbolic heterogeneity in different KGs. However,
in this paper, we show that the progress made in the past was due to biased and
unchallenging evaluation. We highlight two major flaws in existing datasets
that favor embedding-based entity alignment techniques, i.e., the isomorphic
graph structures in relation triples and the weak heterogeneity in attribute
triples. Towards a critical evaluation of embedding-based entity alignment
methods, we construct a new dataset with heterogeneous relations and attributes
based on event-centric KGs. We conduct extensive experiments to evaluate
existing popular methods, and find that they fail to achieve promising
performance. As a new approach to this difficult problem, we propose a
time-aware literal encoder for entity alignment. The dataset and source code
are publicly available to foster future research. Our work calls for more
effective and practical embedding-based solutions to entity alignment.

Fact verification has attracted a lot of research attention recently, e.g.,
in journalism, marketing, and policymaking, as misinformation and
disinformation online can sway one's opinion and affect one's actions. While
fact-checking is a hard task in general, in many cases, false statements can be
easily debunked based on analytics over tables with reliable information.
Hence, table-based fact verification has recently emerged as an important and
growing research area. Yet, progress has been limited due to the lack of
datasets that can be used to pre-train language models (LMs) to be aware of
common table operations, such as aggregating a column or comparing tuples. To
bridge this gap, in this paper we introduce PASTA, a novel state-of-the-art
framework for table-based fact verification via pre-training with synthesized
sentence-table cloze questions. In particular, we design six types of common
sentence-table cloze tasks, including Filter, Aggregation, Superlative,
Comparative, Ordinal, and Unique, based on which we synthesize a large corpus
consisting of 1.2 million sentence-table pairs from WikiTables. PASTA uses a
recent pre-trained LM, DeBERTaV3, and further pretrains it on our corpus. Our
experimental results show that PASTA achieves new state-of-the-art performance
on two table-based fact verification benchmarks: TabFact and SEM-TAB-FACTS. In
particular, on the complex set of TabFact, which contains multiple operations,
PASTA largely outperforms the previous state of the art by 4.7 points (85.6%
vs. 80.9%), and the gap between PASTA and human performance on the small
TabFact test set is narrowed to just 1.5 points (90.6% vs. 92.1%).

Compared with network datasets, multi-dimensional data are much more common
nowadays. If we can model multi-dimensional datasets into networks with
accurate network properties, while, in the meantime, preserving the original
dataset features, we can not only explore the dataset dynamic but also acquire
abundant synthetic network data. This paper proposed a fast scale-free network
model for large-scale multi-dimensional data not limited to the network domain.
The proposed network model is dynamic and able to generate scale-free graphs
within linear time regardless of the scale or field of the modeled dataset. We
further argued that in a dynamic network where edge-generation probability
represents influence, as the network evolves, that influence also decays. We
demonstrated how this influence decay phenomenon is reflected in our model and
provided a case study using the Global Terrorism Database.

Automatic topic classification has been studied extensively to assist
managing and indexing scientific documents in a digital collection. With the
large number of topics being available in recent years, it has become necessary
to arrange them in a hierarchy. Therefore, the automatic classification systems
need to be able to classify the documents hierarchically. In addition, each
paper is often assigned to more than one relevant topic. For example, a paper
can be assigned to several topics in a hierarchy tree. In this paper, we
introduce a new dataset for hierarchical multi-label text classification
(HMLTC) of scientific papers called SciHTC, which contains 186,160 papers and
1,233 categories from the ACM CCS tree. We establish strong baselines for HMLTC
and propose a multi-task learning approach for topic classification with
keyword labeling as an auxiliary task. Our best model achieves a Macro-F1 score
of 34.57% which shows that this dataset provides significant research
opportunities on hierarchical scientific topic classification. We make our
dataset and code available on Github.

Multiview self-supervised representation learning roots in exploring semantic
consistency across data of complex intra-class variation. Such variation is not
directly accessible and therefore simulated by data augmentations. However,
commonly adopted augmentations are handcrafted and limited to simple
geometrical and color changes, which are unable to cover the abundant
intra-class variation. In this paper, we propose to extract the underlying data
variation from datasets and construct a novel augmentation operator, named
local manifold augmentation (LMA). LMA is achieved by training an
instance-conditioned generator to fit the distribution on the local manifold of
data and sampling multiview data using it. LMA shows the ability to create an
infinite number of data views, preserve semantics, and simulate complicated
variations in object pose, viewpoint, lighting condition, background etc.
Experiments show that with LMA integrated, self-supervised learning methods
such as MoCov2 and SimSiam gain consistent improvement on prevalent benchmarks
including CIFAR10, CIFAR100, STL10, ImageNet100, and ImageNet. Furthermore, LMA
leads to representations that obtain more significant invariance to the
viewpoint, object pose, and illumination changes and stronger robustness to
various real distribution shifts reflected by ImageNet-V2, ImageNet-R, ImageNet
Sketch etc.

The generator coordinate method (GCM) is an important tool of choice for
modeling large-amplitude collective motion in atomic nuclei. Recently, it has
attracted increasing interest as it can be exploited to extend ab initio
methods to the description of collective excitations of medium-mass and heavy
deformed nuclei, as well as the nuclear matrix elements (NME) of candidates for
neutrinoless double-beta (NLDBD) decay. The computational complexity of the GCM
increases rapidly with the number of collective coordinates. It imposes a
strong restriction on the applicability of the method. We aim to exploit
machine learning (ML) algorithms to speed up GCM calculations and ultimately
provide a more efficient description of nuclear energy spectra and other
observables such as the NME of NLDBD decay without loss of accuracy. To speed
up GCM calculations, we propose a subspace reduction algorithm that employs
optimized ML models as surrogates for exact quantum-number projection
calculations for norm and Hamiltonian kernels. The model space of the original
GCM is reduced to a subspace relevant for nuclear low energy spectra and the
NME of ground state to ground state $0\nu\beta\beta$ decay based on the
orthogonality condition (OC) and the energy transition-orthogonality procedure
(ENTROP), respectively. For simplicity, a polynomial regression algorithm is
used to learn the norm and Hamiltonian kernels. The efficiency and accuracy of
this algorithm are illustrated for 76Ge and 76Se by comparing results obtained
using the ML models to direct GCM calculations. The results show that the
performance of the GCM+OC/ENTROP+ML is more robust than that of the GCM+ML
alone, and the former can reproduce the results of the original GCM calculation
rather accurately with a significantly reduced computational cost.

Recently, Neural architecture search has achieved great success on
classification tasks for mobile devices. The backbone network for object
detection is usually obtained on the image classification task. However, the
architecture which is searched through the classification task is sub-optimal
because of the gap between the task of image and object detection. As while
work focuses on backbone network architecture search for mobile device object
detection is limited, mainly because the backbone always requires expensive
ImageNet pre-training. Accordingly, it is necessary to study the approach of
network architecture search for mobile device object detection without
expensive pre-training. In this work, we propose a mobile object detection
backbone network architecture search algorithm which is a kind of evolutionary
optimized method based on non-dominated sorting for NAS scenarios. It can
quickly search to obtain the backbone network architecture within certain
constraints. It better solves the problem of suboptimal linear combination
accuracy and computational cost. The proposed approach can search the backbone
networks with different depths, widths, or expansion sizes via a technique of
weight mapping, making it possible to use NAS for mobile devices detection
tasks a lot more efficiently. In our experiments, we verify the effectiveness
of the proposed approach on YoloX-Lite, a lightweight version of the target
detection framework. Under similar computational complexity, the accuracy of
the backbone network architecture we search for is 2.0% mAP higher than
MobileDet. Our improved backbone network can reduce the computational effort
while improving the accuracy of the object detection network. To prove its
effectiveness, a series of ablation studies have been carried out and the
working mechanism has been analyzed in detail.

The increasing reliance on online communities for healthcare information by
patients and caregivers has led to the increase in the spread of
misinformation, or subjective, anecdotal and inaccurate or non-specific
recommendations, which, if acted on, could cause serious harm to the patients.
Hence, there is an urgent need to connect users with accurate and tailored
health information in a timely manner to prevent such harm. This paper proposes
an innovative approach to suggesting reliable information to participants in
online communities as they move through different stages in their disease or
treatment. We hypothesize that patients with similar histories of disease
progression or course of treatment would have similar information needs at
comparable stages. Specifically, we pose the problem of predicting topic tags
or keywords that describe the future information needs of users based on their
profiles, traces of their online interactions within the community (past posts,
replies) and the profiles and traces of online interactions of other users with
similar profiles and similar traces of past interaction with the target users.
The result is a variant of the collaborative information filtering or
recommendation system tailored to the needs of users of online health
communities. We report results of our experiments on an expert curated data set
which demonstrate the superiority of the proposed approach over the state of
the art baselines with respect to accurate and timely prediction of topic tags
(and hence information sources of interest).

In this article, we plan to provide an introduction about some basics about
robots for readers. Several key topics of classic robotics will be introduced,
including robot representation, robot rotational motion, coordinates
transformation and velocity transformation. By now, classic rigid-body robot
analysis is still the main-stream approach in robot controlling and motion
planning. In this article, no data-driven or machine learning based methods
will be introduced. Most of the materials covered in this article are based on
the rigid-body kinematics that the readers probably have learned from the
physics course at high-school or college. Meanwhile, these classic robot
kinematics analyses will serve as the foundation for the latest intelligent
robot control algorithms in modern robotics studies.

Stuttering is a speech disorder where the natural flow of speech is
interrupted by blocks, repetitions or prolongations of syllables, words and
phrases. The majority of existing automatic speech recognition (ASR) interfaces
perform poorly on utterances with stutter, mainly due to lack of matched
training data. Synthesis of speech with stutter thus presents an opportunity to
improve ASR for this type of speech. We describe Stutter-TTS, an end-to-end
neural text-to-speech model capable of synthesizing diverse types of stuttering
utterances. We develop a simple, yet effective prosody-control strategy whereby
additional tokens are introduced into source text during training to represent
specific stuttering characteristics. By choosing the position of the stutter
tokens, Stutter-TTS allows word-level control of where stuttering occurs in the
synthesized utterance. We are able to synthesize stutter events with high
accuracy (F1-scores between 0.63 and 0.84, depending on stutter type). By
fine-tuning an ASR model on synthetic stuttered speech we are able to reduce
word error by 5.7% relative on stuttered utterances, with only minor (<0.2%
relative) degradation for fluent utterances.

In diverse fields ranging from finance to omics, it is increasingly common
that data is distributed and with multiple individual sources (referred to as
``clients'' in some studies). Integrating raw data, although powerful, is often
not feasible, for example, when there are considerations on privacy protection.
Distributed learning techniques have been developed to integrate summary
statistics as opposed to raw data. In many of the existing distributed learning
studies, it is stringently assumed that all the clients have the same model. To
accommodate data heterogeneity, some federated learning methods allow for
client-specific models. In this article, we consider the scenario that clients
form clusters, those in the same cluster have the same model, and different
clusters have different models. Further considering the clustering structure
can lead to a better understanding of the ``interconnections'' among clients
and reduce the number of parameters. To this end, we develop a novel
penalization approach. Specifically, group penalization is imposed for
regularized estimation and selection of important variables, and fusion
penalization is imposed to automatically cluster clients. An effective ADMM
algorithm is developed, and the estimation, selection, and clustering
consistency properties are established under mild conditions. Simulation and
data analysis further demonstrate the practical utility and superiority of the
proposed approach.

In high dimensional variable selection problems, statisticians often seek to
design multiple testing procedures controlling the false discovery rate (FDR)
and simultaneously discovering more relevant variables. Model-X methods, such
as Knockoffs and conditional randomization tests, achieve the first goal of
finite-sample FDR control under the assumption of known covariates
distribution. However, it is not clear whether these methods can concurrently
achieve the second goal of maximizing the number of discoveries. In fact,
designing procedures to discover more relevant variables with finite-sample FDR
control is a largely open question, even in the arguably simplest linear
models.
  In this paper, we derive near-optimal testing procedures in high dimensional
Bayesian linear models with isotropic covariates. We propose a Model-X multiple
testing procedure, PoEdCe, which provably controls the frequentist FDR from
finite samples even under model misspecification, and conjecturally achieves
near-optimal power when the data follow the Bayesian linear model with a known
prior. PoEdCe has three important ingredients: Posterior Expectation, distilled
Conditional randomization test (dCRT), and the Benjamini-Hochberg procedure
with e-values (eBH). The optimality conjecture of PoEdCe is based on a
heuristic calculation of its asymptotic true positive proportion (TPP) and
false discovery proportion (FDP), which is supported by methods from
statistical physics as well as extensive numerical simulations. Furthermore,
when the prior is unknown, we show that an empirical Bayes variant of PoEdCe
still has finite-sample FDR control and achieves near-optimal power.

This article presents differential protection of the distribution line
connecting a wind farm in a microgrid. Machine Learning (ML) based models are
built using differential features extracted from currents at both ends of the
line to assist in relaying decisions. Wavelet coefficients obtained after
feature selection from an extensive list of features are used to train the
classifiers. Internal faults are distinguished from external faults with CT
saturation. The internal faults include the high impedance faults (HIFs) which
have very low currents and test the dependability of the conventional relays.
The faults are simulated in a 5-bus system in PSCAD/EMTDC. The results show
that ML-based models can effectively distinguish faults and other transients
and help maintain security and dependability of the microgrid operation.

Personalized speech enhancement (PSE), a process of estimating a clean target
speech signal in real time by leveraging a speaker embedding vector of the
target talker, has garnered much attention from the research community due to
the recent surge of online meetings across the globe. For practical full duplex
communication, PSE models require an acoustic echo cancellation (AEC)
capability. In this work, we employ a recently proposed causal end-to-end
enhancement network (E3Net) and modify it to obtain a joint PSE-AEC model. We
dedicate the early layers to the AEC task while encouraging later layers for
personalization by adding a bypass connection from the early layers to the mask
prediction layer. This allows us to employ a multi-task learning framework for
joint PSE and AEC training. We provide extensive evaluation test scenarios with
both simulated and real-world recordings. The results show that our joint model
comes close to the expert models for each task and performs significantly
better for the combined PSE-AEC scenario.

Under climate change, the increasing frequency, intensity, and spatial extent
of drought events lead to higher socio-economic costs. However, the
relationships between the hydro-meteorological indicators and drought impacts
are not identified well yet because of the complexity and data scarcity. In
this paper, we proposed a framework based on the extreme gradient model
(XGBoost) for Texas to predict multi-category drought impacts and connected a
typical drought indicator, Standardized Precipitation Index (SPI), to the
text-based impacts from the Drought Impact Reporter (DIR). The preliminary
results of this study showed an outstanding performance of the well-trained
models to assess drought impacts on agriculture, fire, society & public health,
plants & wildlife, as well as relief, response & restrictions in Texas. It also
provided a possibility to appraise drought impacts using hydro-meteorological
indicators with the proposed framework in the United States, which could help
drought risk management by giving additional information and improving the
updating frequency of drought impacts. Our interpretation results using the
Shapley additive explanation (SHAP) interpretability technique revealed that
the rules guiding the predictions of XGBoost comply with domain expertise
knowledge around the role that SPI indicators play around drought impacts.

The link with exponential families has allowed $k$-means clustering to be
generalized to a wide variety of data generating distributions in exponential
families and clustering distortions among Bregman divergences. Getting the
framework to work above exponential families is important to lift roadblocks
like the lack of robustness of some population minimizers carved in their
axiomatization. Current generalisations of exponential families like
$q$-exponential families or even deformed exponential families fail at
achieving the goal. In this paper, we provide a new attempt at getting the
complete framework, grounded in a new generalisation of exponential families
that we introduce, tempered exponential measures (TEM). TEMs keep the maximum
entropy axiomatization framework of $q$-exponential families, but instead of
normalizing the measure, normalize a dual called a co-distribution. Numerous
interesting properties arise for clustering such as improved and controllable
robustness for population minimizers, that keep a simple analytic form.

Bayesian causal structure learning aims to learn a posterior distribution
over directed acyclic graphs (DAGs), and the mechanisms that define the
relationship between parent and child variables. By taking a Bayesian approach,
it is possible to reason about the uncertainty of the causal model. The notion
of modelling the uncertainty over models is particularly crucial for causal
structure learning since the model could be unidentifiable when given only a
finite amount of observational data. In this paper, we introduce a novel method
to jointly learn the structure and mechanisms of the causal model using
Variational Bayes, which we call Variational Bayes-DAG-GFlowNet (VBG). We
extend the method of Bayesian causal structure learning using GFlowNets to
learn not only the posterior distribution over the structure, but also the
parameters of a linear-Gaussian model. Our results on simulated data suggest
that VBG is competitive against several baselines in modelling the posterior
over DAGs and mechanisms, while offering several advantages over existing
methods, including the guarantee to sample acyclic graphs, and the flexibility
to generalize to non-linear causal mechanisms.

In fighting games, individual players of the same skill level often exhibit
distinct strategies from one another through their gameplay. Despite this, the
majority of AI agents for fighting games have only a single strategy for each
"level" of difficulty. To make AI opponents more human-like, we'd ideally like
to see multiple different strategies at each level of difficulty, a concept we
refer to as "multidimensional" difficulty. In this paper, we introduce a
diversity-based deep reinforcement learning approach for generating a set of
agents of similar difficulty that utilize diverse strategies. We find this
approach outperforms a baseline trained with specialized, human-authored reward
functions in both diversity and performance.

Database engines have historically absorbed many of the innovations in data
processing, adding features to process graph data, XML, object oriented, and
text among many others. In this paper, we make the case that it is time to do
the same for AI -- but with a twist! While existing approaches have tried to
achieve this by integrating databases with external ML tools, in this paper we
claim that achieving a truly AI-centric database requires moving the DBMS
engine, at its core, from a relational to a tensor abstraction. This allows us
to: (1) support multi-modal data processing such as images, videos, audio, text
as well as relational; (2) leverage the wellspring of innovation in HW and
runtimes for tensor computation; and (3) exploit automatic differentiation to
enable a novel class of "trainable" queries that can learn to perform a task.
  To support the above scenarios, we introduce TDP: a system that builds upon
our prior work mapping relational queries to tensors. Thanks to a tighter
integration with the tensor runtime, TDP is able to provide a broader coverage
of new emerging scenarios requiring access to multi-modal data and automatic
differentiation.

Quantum machine learning has the potential to provide powerful algorithms for
artificial intelligence. The pursuit of quantum advantage in quantum machine
learning is an active area of research. For current noisy, intermediate-scale
quantum (NISQ) computers, various quantum-classical hybrid algorithms have been
proposed. One such previously proposed hybrid algorithm is a gate-based
variational embedding classifier, which is composed of a classical neural
network and a parameterized gate-based quantum circuit. We propose a quantum
variational embedding classifier based on an analog quantum computer, where
control signals vary continuously in time: our particular focus is
implementation using quantum annealers. In our algorithm, the classical data is
transformed into the parameters of the time-varying Hamiltonian of the analog
quantum computer by a linear transformation. The nonlinearity needed for a
nonlinear classification problem is purely provided by the analog quantum
computer, through the nonlinear dependence of the final quantum state on the
control parameters of the Hamiltonian. We performed numerical simulations that
demonstrate the effectiveness of our algorithm for performing binary and
multi-class classification on linearly inseparable datasets such as concentric
circles and MNIST digits. Our algorithm performs much better than classical
linear classifiers. We find that the performance of our classifier can be
increased by increasing the number of qubits. Our algorithm presents the
possibility to use current quantum annealers for solving practical
machine-learning problems and it could also be useful to explore quantum
advantage in quantum machine learning.

The ability of knowledge graphs to represent complex relationships at scale
has led to their adoption for various needs including knowledge representation,
question-answering, fraud detection, and recommendation systems. Knowledge
graphs are often incomplete in the information they represent, necessitating
the need for knowledge graph completion tasks, such as link and relation
prediction. Pre-trained and fine-tuned language models have shown promise in
these tasks although these models ignore the intrinsic information encoded in
the knowledge graph, namely the entity and relation types. In this work, we
propose the Knowledge Graph Language Model (KGLM) architecture, where we
introduce a new entity/relation embedding layer that learns to differentiate
distinctive entity and relation types, therefore allowing the model to learn
the structure of the knowledge graph. In this work, we show that further
pre-training the language models with this additional embedding layer using the
triples extracted from the knowledge graph, followed by the standard
fine-tuning phase sets a new state-of-the-art performance for the link
prediction task on the benchmark datasets.

The Julia programming language has evolved into a modern alternative to fill
existing gaps in scientific computing and data science applications. Julia
leverages a unified and coordinated single-language and ecosystem paradigm and
has a proven track record of achieving high performance without sacrificing
user productivity. These aspects make Julia a viable alternative to
high-performance computing's (HPC's) existing and increasingly costly many-body
workflow composition strategy in which traditional HPC languages (e.g.,
Fortran, C, C++) are used for simulations, and higher-level languages (e.g.,
Python, R, MATLAB) are used for data analysis and interactive computing.
Julia's rapid growth in language capabilities, package ecosystem, and community
make it a promising universal language for HPC. This paper presents the views
of a multidisciplinary group of researchers from academia, government, and
industry that advocate for an HPC software development paradigm that emphasizes
developer productivity, workflow portability, and low barriers for entry. We
believe that the Julia programming language, its ecosystem, and its community
provide modern and powerful capabilities that enable this group's objectives.
Crucially, we believe that Julia can provide a feasible and less costly
approach to programming scientific applications and workflows that target HPC
facilities. In this work, we examine the current practice and role of Julia as
a common, end-to-end programming model to address major challenges in
scientific reproducibility, data-driven AI/machine learning, co-design and
workflows, scalability and performance portability in heterogeneous computing,
network communication, data management, and community education. As a result,
the diversification of current investments to fulfill the needs of the upcoming
decade is crucial as more supercomputing centers prepare for the exascale era.

Machine learning driven image-based controllers allow robotic systems to take
intelligent actions based on the visual feedback from their environment.
Understanding when these controllers might lead to system safety violations is
important for their integration in safety-critical applications and engineering
corrective safety measures for the system. Existing methods leverage
simulation-based testing (or falsification) to find the failures of
vision-based controllers, i.e., the visual inputs that lead to closed-loop
safety violations. However, these techniques do not scale well to the scenarios
involving high-dimensional and complex visual inputs, such as RGB images. In
this work, we cast the problem of finding closed-loop vision failures as a
Hamilton-Jacobi (HJ) reachability problem. Our approach blends simulation-based
analysis with HJ reachability methods to compute an approximation of the
backward reachable tube (BRT) of the system, i.e., the set of unsafe states for
the system under vision-based controllers. Utilizing the BRT, we can tractably
and systematically find the system states and corresponding visual inputs that
lead to closed-loop failures. These visual inputs can be subsequently analyzed
to find the input characteristics that might have caused the failure. Besides
its scalability to high-dimensional visual inputs, an explicit computation of
BRT allows the proposed approach to capture non-trivial system failures that
are difficult to expose via random simulations. We demonstrate our framework on
two case studies involving an RGB image-based neural network controller for (a)
autonomous indoor navigation, and (b) autonomous aircraft taxiing.

Recent research has demonstrated the capability of behavior signals captured
by smartphones and wearables for longitudinal behavior modeling. However, there
is a lack of a comprehensive public dataset that serves as an open testbed for
fair comparison among algorithms. Moreover, prior studies mainly evaluate
algorithms using data from a single population within a short period, without
measuring the cross-dataset generalizability of these algorithms. We present
the first multi-year passive sensing datasets, containing over 700 user-years
and 497 unique users' data collected from mobile and wearable sensors, together
with a wide range of well-being metrics. Our datasets can support multiple
cross-dataset evaluations of behavior modeling algorithms' generalizability
across different users and years. As a starting point, we provide the benchmark
results of 18 algorithms on the task of depression detection. Our results
indicate that both prior depression detection algorithms and domain
generalization techniques show potential but need further research to achieve
adequate cross-dataset generalizability. We envision our multi-year datasets
can support the ML community in developing generalizable longitudinal behavior
modeling algorithms.

Bayesian optimization (BO) is increasingly employed in critical applications
such as materials design and drug discovery. An increasingly popular strategy
in BO is to forgo the sole reliance on high-fidelity data and instead use an
ensemble of information sources which provide inexpensive low-fidelity data.
The overall premise of this strategy is to reduce the overall sampling costs by
querying inexpensive low-fidelity sources whose data are correlated with
high-fidelity samples. Here, we propose a multi-fidelity cost-aware BO
framework that dramatically outperforms the state-of-the-art technologies in
terms of efficiency, consistency, and robustness. We demonstrate the advantages
of our framework on analytic and engineering problems and argue that these
benefits stem from our two main contributions: (1) we develop a novel
acquisition function for multi-fidelity cost-aware BO that safeguards the
convergence against the biases of low-fidelity data, and (2) we tailor a newly
developed emulator for multi-fidelity BO which enables us to not only
simultaneously learn from an ensemble of multi-fidelity datasets, but also
identify the severely biased low-fidelity sources that should be excluded from
BO.

Both industry and academia have made considerable progress in developing
trustworthy and responsible machine learning (ML) systems. While critical
concepts like fairness and explainability are often addressed, the safety of
systems is typically not sufficiently taken into account. By viewing
data-driven decision systems as socio-technical systems, we draw on the
uncertainty in ML literature to show how fairML systems can also be safeML
systems. We posit that a fair model needs to be an uncertainty-aware model,
e.g. by drawing on distributional regression. For fair decisions, we argue that
a safe fail option should be used for individuals with uncertain
categorization. We introduce semi-structured deep distributional regression as
a modeling framework which addresses multiple concerns brought against standard
ML models and show its use in a real-world example of algorithmic profiling of
job seekers.

The process of screening molecules for desirable properties is a key step in
several applications, ranging from drug discovery to material design. During
the process of drug discovery specifically, protein-ligand docking, or chemical
docking, is a standard in-silico scoring technique that estimates the binding
affinity of molecules with a specific protein target. Recently, however, as the
number of virtual molecules available to test has rapidly grown, these
classical docking algorithms have created a significant computational
bottleneck. We address this problem by introducing Deep Surrogate Docking
(DSD), a framework that applies deep learning-based surrogate modeling to
accelerate the docking process substantially. DSD can be interpreted as a
formalism of several earlier surrogate prefiltering techniques, adding novel
metrics and practical training practices. Specifically, we show that graph
neural networks (GNNs) can serve as fast and accurate estimators of classical
docking algorithms. Additionally, we introduce FiLMv2, a novel GNN architecture
which we show outperforms existing state-of-the-art GNN architectures,
attaining more accurate and stable performance by allowing the model to filter
out irrelevant information from data more efficiently. Through extensive
experimentation and analysis, we show that the DSD workflow combined with the
FiLMv2 architecture provides a 9.496x speedup in molecule screening with a <3%
recall error rate on an example docking task. Our open-source code is available
at https://github.com/ryienh/graph-dock.

Voice anti-spoofing systems are crucial auxiliaries for automatic speaker
verification (ASV) systems. A major challenge is caused by unseen attacks
empowered by advanced speech synthesis technologies. Our previous research on
one-class learning has improved the generalization ability to unseen attacks by
compacting the bona fide speech in the embedding space. However, such
compactness lacks consideration of the diversity of speakers. In this work, we
propose speaker attractor multi-center one-class learning (SAMO), which
clusters bona fide speech around a number of speaker attractors and pushes away
spoofing attacks from all the attractors in a high-dimensional embedding space.
For training, we propose an algorithm for the co-optimization of bona fide
speech clustering and bona fide/spoof classification. For inference, we propose
strategies to enable anti-spoofing for speakers without enrollment. Our
proposed system outperforms existing state-of-the-art single systems with a
relative improvement of 38% on equal error rate (EER) on the ASVspoof2019 LA
evaluation set.

One of the challenges for climbing gyms is to find out popular routes for the
climbers to improve their services and optimally use their infrastructure. This
problem must be addressed preserving both the privacy and convenience of the
climbers and the costs of the gyms. To this aim, a hardware prototype is
developed to collect data using accelerometer sensors attached to a piece of
climbing equipment mounted on the wall, called quickdraw, that connects the
climbing rope to the bolt anchors. The corresponding sensors are configured to
be energy-efficient, hence becoming practical in terms of expenses and time
consumption for replacement when used in large quantities in a climbing gym.
This paper describes hardware specifications, studies data measured by the
sensors in ultra-low power mode, detect patterns in data during climbing
different routes, and develops an unsupervised approach for route clustering.

In recent years, Machine learning (ML) techniques developed for Natural
Language Processing (NLP) have permeated into developing better computer vision
algorithms. In this work, we use such NLP-inspired techniques to improve the
accuracy, robustness and generalizability of ML models for simulating transient
dynamics. We introduce teacher forcing and curriculum learning based training
mechanics to model vortical flows and show an enhancement in accuracy for ML
models, such as FNO and UNet by more than 50%.

Self-supervised pre-training of a speech foundation model, followed by
supervised fine-tuning, has shown impressive quality improvements on automatic
speech recognition (ASR) tasks. Fine-tuning separate foundation models for many
downstream tasks are expensive since the foundation model is usually very big.
Parameter-efficient fine-tuning methods (e.g. adapter, sparse update methods)
offer an alternative paradigm where a small set of parameters are updated to
adapt the foundation model to new tasks. However, these methods still suffer
from a high computational memory cost and slow training speed because they
require backpropagation through the entire neural network at each step. In the
paper, we analyze the performance of features at different layers of a
foundation model on the speech recognition task and propose a novel
hierarchical feature fusion method for resource-efficient transfer learning
from speech foundation models. Experimental results show that the proposed
method can achieve better performance on speech recognition task than existing
algorithms with fewer number of trainable parameters, less computational memory
cost and faster training speed. After combining with Adapters at all layers,
the proposed method can achieve the same performance as fine-tuning the whole
model with $97\%$ fewer trainable encoder parameters and $53\%$ faster training
speed.

A software requirement specification (SRS) document is an essential part of
the software development life cycle which outlines the requirements that a
software program in development must satisfy. This document is often specified
by a diverse group of stakeholders and is subject to continual change, making
the process of maintaining the document and detecting conflicts between
requirements an essential task in software development. Notably, projects that
do not address conflicts in the SRS document early on face considerable
problems later in the development life cycle. These problems incur substantial
costs in terms of time and money, and these costs often become insurmountable
barriers that ultimately result in the termination of a software project
altogether. As a result, early detection of SRS conflicts is critical to
project sustainability. The conflict detection task is approached in numerous
ways, many of which require a significant amount of manual intervention from
developers, or require access to a large amount of labeled, task-specific
training data. In this work, we propose using a prompt-based learning approach
to perform few-shot learning for conflict detection. We compare our results to
supervised learning approaches that use pretrained language models, such as
BERT and its variants. Our results show that prompting with just 32 labeled
examples can achieve a similar level of performance in many key metrics to that
of supervised learning on training sets that are magnitudes larger in size. In
contrast to many other conflict detection approaches, we make no assumptions
about the type of underlying requirements, allowing us to analyze pairings of
both functional and non-functional requirements. This allows us to omit the
potentially expensive task of filtering out non-functional requirements from
our dataset.

We introduce a new method for diverse foreground generation with explicit
control over various factors. Existing image inpainting based foreground
generation methods often struggle to generate diverse results and rarely allow
users to explicitly control specific factors of variation (e.g., varying the
facial identity or expression for face inpainting results). We leverage
contrastive learning with latent codes to generate diverse foreground results
for the same masked input. Specifically, we define two sets of latent codes,
where one controls a pre-defined factor (``known''), and the other controls the
remaining factors (``unknown''). The sampled latent codes from the two sets
jointly bi-modulate the convolution kernels to guide the generator to
synthesize diverse results. Experiments demonstrate the superiority of our
method over state-of-the-arts in result diversity and generation
controllability.

We consider the classic online learning and stochastic multi-armed bandit
(MAB) problems, when at each step, the online policy can probe and find out
which of a small number ($k$) of choices has better reward (or loss) before
making its choice. In this model, we derive algorithms whose regret bounds have
exponentially better dependence on the time horizon compared to the classic
regret bounds. In particular, we show that probing with $k=2$ suffices to
achieve time-independent regret bounds for online linear and convex
optimization. The same number of probes improve the regret bound of stochastic
MAB with independent arms from $O(\sqrt{nT})$ to $O(n^2 \log T)$, where $n$ is
the number of arms and $T$ is the horizon length. For stochastic MAB, we also
consider a stronger model where a probe reveals the reward values of the probed
arms, and show that in this case, $k=3$ probes suffice to achieve
parameter-independent constant regret, $O(n^2)$. Such regret bounds cannot be
achieved even with full feedback after the play, showcasing the power of
limited ``advice'' via probing before making the play. We also present
extensions to the setting where the hints can be imperfect, and to the case of
stochastic MAB where the rewards of the arms can be correlated.

Artificial Intelligence (AI) is having a tremendous impact across most areas
of science. Applications of AI in healthcare have the potential to improve our
ability to detect, diagnose, prognose, and intervene on human disease. For AI
models to be used clinically, they need to be made safe, reproducible and
robust, and the underlying software framework must be aware of the
particularities (e.g. geometry, physiology, physics) of medical data being
processed. This work introduces MONAI, a freely available, community-supported,
and consortium-led PyTorch-based framework for deep learning in healthcare.
MONAI extends PyTorch to support medical data, with a particular focus on
imaging, and provide purpose-specific AI model architectures, transformations
and utilities that streamline the development and deployment of medical AI
models. MONAI follows best practices for software-development, providing an
easy-to-use, robust, well-documented, and well-tested software framework. MONAI
preserves the simple, additive, and compositional approach of its underlying
PyTorch libraries. MONAI is being used by and receiving contributions from
research, clinical and industrial teams from around the world, who are pursuing
applications spanning nearly every aspect of healthcare.

The number of malware is constantly on the rise. Though most new malware are
modifications of existing ones, their sheer number is quite overwhelming. In
this paper, we present a novel system to visualize and map millions of malware
to points in a 2-dimensional (2D) spatial grid. This enables visualizing
relationships within large malware datasets that can be used to develop triage
solutions to screen different malware rapidly and provide situational
awareness. Our approach links two visualizations within an interactive display.
Our first view is a spatial point-based visualization of similarity among the
samples based on a reduced dimensional projection of binary feature
representations of malware. Our second spatial grid-based view provides a
better insight into similarities and differences between selected malware
samples in terms of the binary-based visual representations they share. We also
provide a case study where the effect of packing on the malware data is
correlated with the complexity of the packing algorithm.

Channel Attention reigns supreme as an effective technique in the field of
computer vision. However, the proposed channel attention by SENet suffers from
information loss in feature learning caused by the use of Global Average
Pooling (GAP) to represent channels as scalars. Thus, designing effective
channel attention mechanisms requires finding a solution to enhance features
preservation in modeling channel inter-dependencies. In this work, we utilize
Wavelet transform compression as a solution to the channel representation
problem. We first test wavelet transform as an Auto-Encoder model equipped with
conventional channel attention module. Next, we test wavelet transform as a
standalone channel compression method. We prove that global average pooling is
equivalent to the recursive approximate Haar wavelet transform. With this
proof, we generalize channel attention using Wavelet compression and name it
WaveNet. Implementation of our method can be embedded within existing channel
attention methods with a couple of lines of code. We test our proposed method
using ImageNet dataset for image classification task. Our method outperforms
the baseline SENet, and achieves the state-of-the-art results. Our code
implementation is publicly available at https://github.com/hady1011/WaveNet-C.

In this paper, we take a first step towards answering the question of how to
design fair machine learning algorithms that are robust to adversarial attacks.
Using a minimax framework, we aim to design an adversarially robust fair
regression model that achieves optimal performance in the presence of an
attacker who is able to add a carefully designed adversarial data point to the
dataset or perform a rank-one attack on the dataset. By solving the proposed
nonsmooth nonconvex-nonconcave minimax problem, the optimal adversary as well
as the robust fairness-aware regression model are obtained. For both synthetic
data and real-world datasets, numerical results illustrate that the proposed
adversarially robust fair models have better performance on poisoned datasets
than other fair machine learning models in both prediction accuracy and
group-based fairness measure.

When training early-stage deep neural networks (DNNs), generating
intermediate features via convolution or linear layers occupied most of the
execution time. Accordingly, extensive research has been done to reduce the
computational burden of the convolution or linear layers. In recent
mobile-friendly DNNs, however, the relative number of operations involved in
processing these layers has significantly reduced. As a result, the proportion
of the execution time of other layers, such as batch normalization layers, has
increased. Thus, in this work, we conduct a detailed analysis of the batch
normalization layer to efficiently reduce the runtime overhead in the batch
normalization process. Backed up by the thorough analysis, we present an
extremely efficient batch normalization, named LightNorm, and its associated
hardware module. In more detail, we fuse three approximation techniques that
are i) low bit-precision, ii) range batch normalization, and iii) block
floating point. All these approximate techniques are carefully utilized not
only to maintain the statistics of intermediate feature maps, but also to
minimize the off-chip memory accesses. By using the proposed LightNorm
hardware, we can achieve significant area and energy savings during the DNN
training without hurting the training accuracy. This makes the proposed
hardware a great candidate for the on-device training.

This tutorial is an andragogical guide for students and practitioners seeking
to understand the fundamentals and practice of linear programming. The
exercises demonstrate how to solve classical optimization problems with an
emphasis on spatial analysis in supply chain management and transport
logistics. All exercises display the Python programs and optimization libraries
used to solve them. The first chapter introduces key concepts in linear
programming and contributes a new cognitive framework to help students and
practitioners set up each optimization problem. The cognitive framework
organizes the decision variables, constraints, the objective function, and
variable bounds in a format for direct application to optimization software.
The second chapter introduces two types of mobility optimization problems
(shortest path in a network and minimum cost tour) in the context of delivery
and service planning logistics. The third chapter introduces four types of
spatial optimization problems (neighborhood coverage, flow capturing, zone
heterogeneity, service coverage) and contributes a workflow to visualize the
optimized solutions in maps. The workflow creates decision variables from maps
by using the free geographic information systems (GIS) programs QGIS and GeoDA.
The fourth chapter introduces three types of spatial logistical problems
(spatial distribution, flow maximization, warehouse location optimization) and
demonstrates how to scale the cognitive framework in software to reach
solutions. The final chapter summarizes lessons learned and provides insights
about how students and practitioners can modify the Phyton programs and GIS
workflows to solve their own optimization problem and visualize the results.

In this paper, we investigate the impact of neural networks (NNs) topology on
adversarial robustness. Specifically, we study the graph produced when an input
traverses all the layers of a NN, and show that such graphs are different for
clean and adversarial inputs. We find that graphs from clean inputs are more
centralized around highway edges, whereas those from adversaries are more
diffuse, leveraging under-optimized edges. Through experiments on a variety of
datasets and architectures, we show that these under-optimized edges are a
source of adversarial vulnerability and that they can be used to detect
adversarial inputs.

Standard imitation learning can fail when the expert demonstrators have
different sensory inputs than the imitating agent. This is because partial
observability gives rise to hidden confounders in the causal graph. We break
down the space of confounded imitation learning problems and identify three
settings with different data requirements in which the correct imitation policy
can be identified. We then introduce an algorithm for deconfounded imitation
learning, which trains an inference model jointly with a latent-conditional
policy. At test time, the agent alternates between updating its belief over the
latent and acting under the belief. We show in theory and practice that this
algorithm converges to the correct interventional policy, solves the
confounding issue, and can under certain assumptions achieve an asymptotically
optimal imitation performance.

We formulate grasp learning as a neural field and present Neural Grasp
Distance Fields (NGDF). Here, the input is a 6D pose of a robot end effector
and output is a distance to a continuous manifold of valid grasps for an
object. In contrast to current approaches that predict a set of discrete
candidate grasps, the distance-based NGDF representation is easily interpreted
as a cost, and minimizing this cost produces a successful grasp pose. This
grasp distance cost can be incorporated directly into a trajectory optimizer
for joint optimization with other costs such as trajectory smoothness and
collision avoidance. During optimization, as the various costs are balanced and
minimized, the grasp target is allowed to smoothly vary, as the learned grasp
field is continuous. In simulation benchmarks with a Franka arm, we find that
joint grasping and planning with NGDF outperforms baselines by 63% execution
success while generalizing to unseen query poses and unseen object shapes.
Project page: https://sites.google.com/view/neural-grasp-distance-fields.

As multimodal learning finds applications in a wide variety of high-stakes
societal tasks, investigating their robustness becomes important. Existing work
has focused on understanding the robustness of vision-and-language models to
imperceptible variations on benchmark tasks. In this work, we investigate the
robustness of multimodal classifiers to cross-modal dilutions - a plausible
variation. We develop a model that, given a multimodal (image + text) input,
generates additional dilution text that (a) maintains relevance and topical
coherence with the image and existing text, and (b) when added to the original
text, leads to misclassification of the multimodal input. Via experiments on
Crisis Humanitarianism and Sentiment Detection tasks, we find that the
performance of task-specific fusion-based multimodal classifiers drops by 23.3%
and 22.5%, respectively, in the presence of dilutions generated by our model.
Metric-based comparisons with several baselines and human evaluations indicate
that our dilutions show higher relevance and topical coherence, while
simultaneously being more effective at demonstrating the brittleness of the
multimodal classifiers. Our work aims to highlight and encourage further
research on the robustness of deep multimodal models to realistic variations,
especially in human-facing societal applications. The code and other resources
are available at https://claws-lab.github.io/multimodal-robustness/.

The Transformer architecture is shown to provide a powerful framework as an
end-to-end model for building expression trees from online handwritten gestures
corresponding to glyph strokes. In particular, the attention mechanism was
successfully used to encode, learn and enforce the underlying syntax of
expressions creating latent representations that are correctly decoded to the
exact mathematical expression tree, providing robustness to ablated inputs and
unseen glyphs. For the first time, the encoder is fed with spatio-temporal data
tokens potentially forming an infinitely large vocabulary, which finds
applications beyond that of online gesture recognition. A new supervised
dataset of online handwriting gestures is provided for training models on
generic handwriting recognition tasks and a new metric is proposed for the
evaluation of the syntactic correctness of the output expression trees. A small
Transformer model suitable for edge inference was successfully trained to an
average normalised Levenshtein accuracy of 94%, resulting in valid postfix RPN
tree representation for 94% of predictions.

Continual learning (CL) learns a sequence of tasks incrementally. There are
two popular CL settings, class incremental learning (CIL) and task incremental
learning (TIL). A major challenge of CL is catastrophic forgetting (CF). While
a number of techniques are already available to effectively overcome CF for
TIL, CIL remains to be highly challenging. So far, little theoretical study has
been done to provide a principled guidance on how to solve the CIL problem.
This paper performs such a study. It first shows that probabilistically, the
CIL problem can be decomposed into two sub-problems: Within-task Prediction
(WP) and Task-id Prediction (TP). It further proves that TP is correlated with
out-of-distribution (OOD) detection, which connects CIL and OOD detection. The
key conclusion of this study is that regardless of whether WP and TP or OOD
detection are defined explicitly or implicitly by a CIL algorithm, good WP and
good TP or OOD detection are necessary and sufficient for good CIL
performances. Additionally, TIL is simply WP. Based on the theoretical result,
new CIL methods are also designed, which outperform strong baselines in both
CIL and TIL settings by a large margin.

In this research, we discussed a rising issue for Muslims in today world that
involves a financial and technical innovation, namely: cryptocurrencies. We
found out through a questionnaire that many Muslims are having a hard time
finding the jurisprudence rulings on certain cryptocurrencies. Therefore, the
objective of this research is to investigate and identify features that play a
part in determining the jurisprudence rulings on cryptocurrencies. We have
collected a dataset containing 106 cryptocurrencies classified into 56 Halal
and 50 Haram cryptocurrencies, and used 20 handcrafted features. Moreover,
based on these identified features, we designed an intelligent system that
contains a Machine Learning model for classifying cryptocurrencies into Halal
and Haram.

Recent developments in quantum computing and machine learning have propelled
the interdisciplinary study of quantum machine learning. Sequential modeling is
an important task with high scientific and commercial value. Existing VQC or
QNN-based methods require significant computational resources to perform the
gradient-based optimization of a larger number of quantum circuit parameters.
The major drawback is that such quantum gradient calculation requires a large
amount of circuit evaluation, posing challenges in current near-term quantum
hardware and simulation software. In this work, we approach sequential modeling
by applying a reservoir computing (RC) framework to quantum recurrent neural
networks (QRNN-RC) that are based on classical RNN, LSTM and GRU. The main idea
to this RC approach is that the QRNN with randomly initialized weights is
treated as a dynamical system and only the final classical linear layer is
trained. Our numerical simulations show that the QRNN-RC can reach results
comparable to fully trained QRNN models for several function approximation and
time series prediction tasks. Since the QRNN training complexity is
significantly reduced, the proposed model trains notably faster. In this work
we also compare to corresponding classical RNN-based RC implementations and
show that the quantum version learns faster by requiring fewer training epochs
in most cases. Our results demonstrate a new possibility to utilize quantum
neural network for sequential modeling with greater quantum hardware
efficiency, an important design consideration for noisy intermediate-scale
quantum (NISQ) computers.

This paper computationally demonstrates a sharp improvement in predictive
performance for $k$ nearest neighbors thanks to an efficient forward selection
of the predictor variables. We show both simulated and real-world data that
this novel repeatedly approaches outperformance regression models under
stepwise selection

Temporal data like time series are often observed at irregular intervals
which is a challenging setting for existing machine learning methods. To tackle
this problem, we view such data as samples from some underlying continuous
function. We then define a diffusion-based generative model that adds noise
from a predefined stochastic process while preserving the continuity of the
resulting underlying function. A neural network is trained to reverse this
process which allows us to sample new realizations from the learned
distribution. We define suitable stochastic processes as noise sources and
introduce novel denoising and score-matching models on processes. Further, we
show how to apply this approach to the multivariate probabilistic forecasting
and imputation tasks. Through our extensive experiments, we demonstrate that
our method outperforms previous models on synthetic and real-world datasets.

Exploring the spectrum of novel behaviors a physical system can produce can
be a labor-intensive task. Active learning is a collection of iterative
sampling techniques developed in response to this challenge. However, these
techniques often require a pre-defined metric, such as distance in a space of
known order parameters, in order to guide the search for new behaviors. Order
parameters are rarely known for non-equilibrium systems \textit{a priori},
especially when possible behaviors are also unknown, creating a chicken-and-egg
problem. Here, we combine active and unsupervised learning for automated
exploration of novel behaviors in non-equilibrium systems with unknown order
parameters. We iteratively use active learning based on current order
parameters to expand the library of known behaviors and then relearn order
parameters based on this expanded library. We demonstrate the utility of this
approach in Kuramoto models of coupled oscillators of increasing complexity. In
addition to reproducing known phases, we also reveal previously unknown
behavior and related order parameters.

The scientific literature contains a wealth of cutting-edge knowledge in the
field of materials science, as well as useful data (e.g., numerical data from
experimental results, material properties and structure). These data are
critical for data-driven machine learning (ML) and deep learning (DL) methods
to accelerate material discovery. Due to the large and growing number of
publications, it is difficult for humans to manually retrieve and retain this
knowledge. In this context, we investigate a deep neural network model based on
Bi-LSTM to retrieve knowledge from published scientific articles. The proposed
deep neural network-based model achieves an f-1 score of \~97\% for the
Material Named Entity Recognition (MNER) task. The study addresses motivation,
relevant work, methodology, hyperparameters, and overall performance
evaluation. The analysis provides insight into the results of the experiment
and points to future directions for current research.

We propose Hamiltonian Quantum Generative Adversarial Networks (HQuGANs), to
learn to generate unknown input quantum states using two competing quantum
optimal controls. The game-theoretic framework of the algorithm is inspired by
the success of classical generative adversarial networks in learning
high-dimensional distributions. The quantum optimal control approach not only
makes the algorithm naturally adaptable to the experimental constraints of
near-term hardware, but also has the potential to provide a better convergence
due to overparameterization compared to the circuit model implementations. We
numerically demonstrate the capabilities of the proposed framework to learn
various highly entangled many-body quantum states, using simple two-body
Hamiltonians and under experimentally relevant constraints such as
low-bandwidth controls. We analyze the computational cost of implementing
HQuGANs on quantum computers and show how the framework can be extended to
learn quantum dynamics.

Current metrics for evaluating factuality for abstractive document
summarization have achieved high correlations with human judgment, but they do
not account for the vision modality and thus are not adequate for
vision-and-language summarization. We propose CLIPBERTScore, a simple weighted
combination of CLIPScore and BERTScore to leverage the robustness and strong
factuality detection performance between image-summary and document-summary,
respectively. Next, due to the lack of meta-evaluation benchmarks to evaluate
the quality of multimodal factuality metrics, we collect human judgments of
factuality with respect to documents and images. We show that this simple
combination of two metrics in the zero-shot setting achieves higher
correlations than existing factuality metrics for document summarization,
outperforms an existing multimodal summarization metric, and performs
competitively with strong multimodal factuality metrics specifically fine-tuned
for the task. Our thorough analysis demonstrates the robustness and high
correlation of CLIPBERTScore and its components on four factuality
metric-evaluation benchmarks. Finally, we demonstrate two practical downstream
applications of our CLIPBERTScore metric: for selecting important images to
focus on during training, and as a reward for reinforcement learning to improve
factuality of multimodal summary generation w.r.t automatic and human
evaluation. Our data and code are publicly available at
https://github.com/meetdavidwan/faithful-multimodal-summ

Camera images are ubiquitous in machine learning research. They also play a
central role in the delivery of important services spanning medicine and
environmental surveying. However, the application of machine learning models in
these domains has been limited because of robustness concerns. A primary
failure mode are performance drops due to differences between the training and
deployment data. While there are methods to prospectively validate the
robustness of machine learning models to such dataset drifts, existing
approaches do not account for explicit models of the primary object of
interest: the data. This makes it difficult to create physically faithful drift
test cases or to provide specifications of data models that should be avoided
when deploying a machine learning model. In this study, we demonstrate how
these shortcomings can be overcome by pairing machine learning robustness
validation with physical optics. We examine the role raw sensor data and
differentiable data models can play in controlling performance risks related to
image dataset drift. The findings are distilled into three applications. First,
drift synthesis enables the controlled generation of physically faithful drift
test cases. The experiments presented here show that the average decrease in
model performance is ten to four times less severe than under post-hoc
augmentation testing. Second, the gradient connection between task and data
models allows for drift forensics that can be used to specify
performance-sensitive data models which should be avoided during deployment of
a machine learning model. Third, drift adjustment opens up the possibility for
processing adjustments in the face of drift. This can lead to speed up and
stabilization of classifier training at a margin of up to 20% in validation
accuracy. A guide to access the open code and datasets is available at
https://github.com/aiaudit-org/raw2logit.

Speech quality assessment has been a critical component in many voice
communication related applications such as telephony and online conferencing.
Traditional intrusive speech quality assessment requires the clean reference of
the degraded utterance to provide an accurate quality measurement. This
requirement limits the usability of these methods in real-world scenarios. On
the other hand, non-intrusive subjective measurement is the ``golden standard"
in evaluating speech quality as human listeners can intrinsically evaluate the
quality of any degraded speech with ease. In this paper, we propose a novel
end-to-end model structure called Convolutional Context-Aware Transformer
(CCAT) network to predict the mean opinion score (MOS) of human raters. We
evaluate our model on three MOS-annotated datasets spanning multiple languages
and distortion types and submit our results to the ConferencingSpeech 2022
Challenge. Our experiments show that CCAT provides promising MOS predictions
compared to current state-of-art non-intrusive speech assessment models with
average Pearson correlation coefficient (PCC) increasing from 0.530 to 0.697
and average RMSE decreasing from 0.768 to 0.570 compared to the baseline model
on the challenge evaluation test set.

Deep artificial neural networks (ANNs) used together with deep reinforcement
learning (DRL) are receiving growing attention due to their capabilities to
control complex problems. This technique has been recently used to solve
problems related to flow control. In this work, an ANN trained through a DRL
agent is used to perform active flow control. Two-dimensional simulations of
the flow around a cylinder are conducted and an active control based on two
jets located on the walls of the cylinder is considered. By gathering
information from the flow surrounding the cylinder, the ANN agent is able to
learn effective control strategies for the jets, leading to a significant drag
reduction. In the present work, a Reynolds-number range beyond those previously
considered is studied and compared with results obtained using classical
flow-control methods. Significantly different nature in the control strategies
is identified by the DRL as the Reynolds number Re increases. For Re <= 1000
the classical control strategy based on an opposition control relative to the
wake oscillation is obtained. For Re = 2000 the new strategy consists of an
energisation of the boundary layers and the separation area, which modulate the
flow separation and reduce drag in a fashion similar to that of the drag
crisis, through a high frequency actuation. A cross-application of agents is
performed for a flow at Re = 2000, obtaining similar results in terms of drag
reduction with the agents trained at Re = 1000 and 2000. The fact that two
different strategies yield the same performance make us question whether this
Reynolds number regime (Re = 2000) belongs to a transition towards a
nature-different flow which would only admit a high-frequency actuation
strategy to obtain drag reduction. This finding allows the application of ANNs
trained at lower Reynolds numbers but comparable in nature, saving
computational resources.

Human variation in labeling is often considered noise. Annotation projects
for machine learning (ML) aim at minimizing human label variation, with the
assumption to maximize data quality and in turn optimize and maximize machine
learning metrics. However, this conventional practice assumes that there exists
a ground truth, and neglects that there exists genuine human variation in
labeling due to disagreement, subjectivity in annotation or multiple plausible
answers. In this position paper, we argue that this big open problem of human
label variation persists and critically needs more attention to move our field
forward. This is because human label variation impacts all stages of the ML
pipeline: data, modeling and evaluation. However, few works consider all of
these dimensions jointly; and existing research is fragmented. We reconcile
different previously proposed notions of human label variation, provide a
repository of publicly-available datasets with un-aggregated labels, depict
approaches proposed so far, identify gaps and suggest ways forward. As datasets
are becoming increasingly available, we hope that this synthesized view on the
'problem' will lead to an open discussion on possible strategies to devise
fundamentally new directions.

The library scikit-fda is a Python package for Functional Data Analysis
(FDA). It provides a comprehensive set of tools for representation,
preprocessing, and exploratory analysis of functional data. The library is
built upon and integrated in Python's scientific ecosystem. In particular, it
conforms to the scikit-learn application programming interface so as to take
advantage of the functionality for machine learning provided by this package:
pipelines, model selection, and hyperparameter tuning, among others. The
scikit-fda package has been released as free and open-source software under a
3-Clause BSD license and is open to contributions from the FDA community. The
library's extensive documentation includes step-by-step tutorials and detailed
examples of use.

The emergence of pre-trained language models (PLMs) has shown great success
in many Natural Language Processing (NLP) tasks including text classification.
Due to the minimal to no feature engineering required when using these models,
PLMs are becoming the de facto choice for any NLP task. However, for
domain-specific corpora (e.g., financial, legal, and industrial), fine-tuning a
pre-trained model for a specific task has shown to provide a performance
improvement. In this paper, we compare the performance of four different PLMs
on three public domain-free datasets and a real-world dataset containing
domain-specific words, against a simple SVM linear classifier with TFIDF
vectorized text. The experimental results on the four datasets show that using
PLMs, even fine-tuned, do not provide significant gain over the linear SVM
classifier. Hence, we recommend that for text classification tasks, traditional
SVM along with careful feature engineering can pro-vide a cheaper and superior
performance than PLMs.

This paper proposes a modification to RNN-Transducer (RNN-T) models for
automatic speech recognition (ASR). In standard RNN-T, the emission of a blank
symbol consumes exactly one input frame; in our proposed method, we introduce
additional blank symbols, which consume two or more input frames when emitted.
We refer to the added symbols as big blanks, and the method multi-blank RNN-T.
For training multi-blank RNN-Ts, we propose a novel logit under-normalization
method in order to prioritize emissions of big blanks. With experiments on
multiple languages and datasets, we show that multi-blank RNN-T methods could
bring relative speedups of over +90%/+139% to model inference for English
Librispeech and German Multilingual Librispeech datasets, respectively. The
multi-blank RNN-T method also improves ASR accuracy consistently. We will
release our implementation of the method in the NeMo
(\url{https://github.com/NVIDIA/NeMo}) toolkit.

The brain can efficiently learn a wide range of tasks, motivating the search
for biologically inspired learning rules for improving current artificial
intelligence technology. Most biological models are composed of point neurons,
and cannot achieve the state-of-the-art performances in machine learning.
Recent works have proposed that segregation of dendritic input (neurons receive
sensory information and higher-order feedback in segregated compartments) and
generation of high-frequency bursts of spikes would support error
backpropagation in biological neurons. However, these approaches require
propagating errors with a fine spatio-temporal structure to the neurons, which
is unlikely to be feasible in a biological network.
  To relax this assumption, we suggest that bursts and dendritic input
segregation provide a natural support for biologically plausible target-based
learning, which does not require error propagation. We propose a pyramidal
neuron model composed of three separated compartments. A coincidence mechanism
between the basal and the apical compartments allows for generating
high-frequency bursts of spikes. This architecture allows for a burst-dependent
learning rule, based on the comparison between the target bursting activity
triggered by the teaching signal and the one caused by the recurrent
connections, providing the support for target-based learning. We show that this
framework can be used to efficiently solve spatio-temporal tasks, such as the
store and recall of 3D trajectories.
  Finally, we suggest that this neuronal architecture naturally allows for
orchestrating ``hierarchical imitation learning'', enabling the decomposition
of challenging long-horizon decision-making tasks into simpler subtasks. This
can be implemented in a two-level network, where the high-network acts as a
``manager'' and produces the contextual signal for the low-network, the
``worker''.

The task of motion forecasting is critical for self-driving vehicles (SDVs)
to be able to plan a safe maneuver. Towards this goal, modern approaches reason
about the map, the agents' past trajectories and their interactions in order to
produce accurate forecasts. The predominant approach has been to encode the map
and other agents in the reference frame of each target agent. However, this
approach is computationally expensive for multi-agent prediction as inference
needs to be run for each agent. To tackle the scaling challenge, the solution
thus far has been to encode all agents and the map in a shared coordinate frame
(e.g., the SDV frame). However, this is sample inefficient and vulnerable to
domain shift (e.g., when the SDV visits uncommon states). In contrast, in this
paper, we propose an efficient shared encoding for all agents and the map
without sacrificing accuracy or generalization. Towards this goal, we leverage
pair-wise relative positional encodings to represent geometric relationships
between the agents and the map elements in a heterogeneous spatial graph. This
parameterization allows us to be invariant to scene viewpoint, and save online
computation by re-using map embeddings computed offline. Our decoder is also
viewpoint agnostic, predicting agent goals on the lane graph to enable diverse
and context-aware multimodal prediction. We demonstrate the effectiveness of
our approach on the urban Argoverse 2 benchmark as well as a novel highway
dataset.

Modern speech enhancement (SE) networks typically implement noise suppression
through time-frequency masking, latent representation masking, or
discriminative signal prediction. In contrast, some recent works explore SE via
generative speech synthesis, where the system's output is synthesized by a
neural vocoder after an inherently lossy feature-denoising step. In this paper,
we propose a denoising vocoder (DeVo) approach, where a vocoder accepts noisy
representations and learns to directly synthesize clean speech. We leverage
rich representations from self-supervised learning (SSL) speech models to
discover relevant features. We conduct a candidate search across 15 potential
SSL front-ends and subsequently train our vocoder adversarially with the best
SSL configuration. Additionally, we demonstrate a causal version capable of
running on streaming audio with 10ms latency and minimal performance
degradation. Finally, we conduct both objective evaluations and subjective
listening studies to show our system improves objective metrics and outperforms
an existing state-of-the-art SE model subjectively.

In order to test whether artificial intelligence can create qualified
classical poetry like humans, the author proposes a study of Chinese classical
poetry generation based on a pre-trained model. This paper mainly tries to use
BART and other pre training models, proposes FS2TEXT and RR2TEXT to generate
metrical poetry text and even specific style poetry text, and solves the
problem that the user's writing intention gradually reduces the relevance of
the generated poetry text.
  In order to test the model's results, the authors selected ancient poets, by
combining it with BART's poetic model work, developed a set of AI poetry Turing
problems, it was reviewed by a group of poets and poetry writing researchers.
There were more than 600 participants, and the final results showed that,
high-level poetry lovers can't distinguish between AI activity and human
activity, this indicates that the author's working methods are not
significantly different from human activities. The model of poetry generation
studied by the author generalizes works that cannot be distinguished from those
of advanced scholars.
  The number of modern Chinese poets has reached 5 million. However, many
modern Chinese poets lack language ability and skills as a result of their
childhood learning. However, many modern poets have no creative inspiration,
and the author's model can help them. They can look at this model when they
choose words and phrases and they can write works based on the poems they
already have, and they can write their own poems. The importance of poetry lies
in the author's thoughts and reflections. It doesn't matter how good AI poetry
is. The only thing that matters is for people to see and inspire them.

The BIOSCAN project, led by the International Barcode of Life Consortium,
seeks to study changes in biodiversity on a global scale. One component of the
project is focused on studying the species interaction and dynamics of all
insects. In addition to genetically barcoding insects, over 1.5 million images
per year will be collected, each needing taxonomic classification. With the
immense volume of incoming images, relying solely on expert taxonomists to
label the images would be impossible; however, artificial intelligence and
computer vision technology may offer a viable high-throughput solution.
Additional tasks including manually weighing individual insects to determine
biomass, remain tedious and costly. Here again, computer vision may offer an
efficient and compelling alternative. While the use of computer vision methods
is appealing for addressing these problems, significant challenges resulting
from biological factors present themselves. These challenges are formulated in
the context of machine learning in this paper.

Self-supervised learning via masked prediction pre-training (MPPT) has shown
impressive performance on a range of speech-processing tasks. This paper
proposes a method to bias self-supervised learning towards a specific task. The
core idea is to slightly finetune the model that is used to obtain the target
sequence. This leads to better performance and a substantial increase in
training speed. Furthermore, this paper proposes a variant of MPPT that allows
low-footprint streaming models to be trained effectively by computing the MPPT
loss on masked and unmasked frames. These approaches are evaluated for
automatic speech recognition on the Librispeech corpus, where 100 hours of data
served as the labelled data and 860 hours as the unlabelled data. The biased
training outperforms the unbiased training by 15.5% after 250k updates and
23.8% after 100k updates on test-other. For the streaming models, the
pre-training approach yields a reduction in word error rate of 44.1%.

The substitute-based recommendation is widely used in E-commerce to provide
better alternatives to customers. However, existing research typically uses the
customer behavior signals like co-view and view-but-purchase-another to capture
the substitute relationship. Despite its intuitive soundness, we find that such
an approach might ignore the functionality and characteristics of products. In
this paper, we adapt substitute recommendation into language matching problem
by taking product title description as model input to consider product
functionality. We design a new transformation method to de-noise the signals
derived from production data. In addition, we consider multilingual support
from the engineering point of view. Our proposed end-to-end transformer-based
model achieves both successes from offline and online experiments. The proposed
model has been deployed in a large-scale E-commerce website for 11 marketplaces
in 6 languages. Our proposed model is demonstrated to increase revenue by 19%
based on an online A/B experiment.

Let $\mathcal{D}$ be a dataset of smooth 3D-surfaces, partitioned into
disjoint classes $\mathit{CL}_j$, $j= 1, \ldots, k$. We show how optimized
diffeomorphic registration applied to large numbers of pairs $S,S' \in
\mathcal{D}$ can provide descriptive feature vectors to implement automatic
classification on $\mathcal{D}$, and generate classifiers invariant by rigid
motions in $\mathbb{R}^3$. To enhance accuracy of automatic classification, we
enrich the smallest classes $\mathit{CL}_j$ by diffeomorphic interpolation of
smooth surfaces between pairs $S,S' \in \mathit{CL}_j$. We also implement small
random perturbations of surfaces $S\in \mathit{CL}_j$ by random flows of smooth
diffeomorphisms $F_t:\mathbb{R}^3 \to \mathbb{R}^3$. Finally, we test our
automatic classification methods on a cardiology data base of discretized
mitral valve surfaces.

PROteolysis TArgeting Chimeras (PROTACs) are an emerging therapeutic modality
for degrading a protein of interest (POI) by marking it for degradation by the
proteasome. Recent developments in artificial intelligence (AI) suggest that
deep generative models can assist with the de novo design of molecules with
desired properties, and their application to PROTAC design remains largely
unexplored. We show that a graph-based generative model can be used to propose
novel PROTAC-like structures from empty graphs. Our model can be guided towards
the generation of large molecules (30--140 heavy atoms) predicted to degrade a
POI through policy-gradient reinforcement learning (RL). Rewards during RL are
applied using a boosted tree surrogate model that predicts a molecule's
degradation potential for each POI. Using this approach, we steer the
generative model towards compounds with higher likelihoods of predicted
degradation activity. Despite being trained on sparse public data, the
generative model proposes molecules with substructures found in known
degraders. After fine-tuning, predicted activity against a challenging POI
increases from 50% to >80% with near-perfect chemical validity for sampled
compounds, suggesting this is a promising approach for the optimization of
large, PROTAC-like molecules for targeted protein degradation.

Transformer models have achieved great success across many NLP problems.
However, previous studies in automated ICD coding concluded that these models
fail to outperform some of the earlier solutions such as CNN-based models. In
this paper we challenge this conclusion. We present a simple and scalable
method to process long text with the existing transformer models such as BERT.
We show that this method significantly improves the previous results reported
for transformer models in ICD coding, and is able to outperform one of the
prominent CNN-based methods.

The field of geometric deep learning has had a profound impact on the
development of innovative and powerful graph neural network architectures.
Disciplines such as computer vision and computational biology have benefited
significantly from such methodological advances, which has led to breakthroughs
in scientific domains such as protein structure prediction and design. In this
work, we introduce GCPNet, a new geometry-complete, SE(3)-equivariant graph
neural network designed for 3D graph representation learning. We demonstrate
the state-of-the-art utility and expressiveness of our method on six
independent datasets designed for three distinct geometric tasks:
protein-ligand binding affinity prediction, protein structure ranking, and
Newtonian many-body systems modeling. Our results suggest that GCPNet is a
powerful, general method for capturing complex geometric and physical
interactions within 3D graphs for downstream prediction tasks. The source code,
data, and instructions to train new models or reproduce our results are freely
available on GitHub.

In recent years, graph neural networks (GNNs) have emerged as a promising
tool for solving machine learning problems on graphs. Most GNNs are members of
the family of message passing neural networks (MPNNs). There is a close
connection between these models and the Weisfeiler-Leman (WL) test of
isomorphism, an algorithm that can successfully test isomorphism for a broad
class of graphs. Recently, much research has focused on measuring the
expressive power of GNNs. For instance, it has been shown that standard MPNNs
are at most as powerful as WL in terms of distinguishing non-isomorphic graphs.
However, these studies have largely ignored the distances between the
representations of nodes/graphs which are of paramount importance for learning
tasks. In this paper, we define a distance function between nodes which is
based on the hierarchy produced by the WL algorithm, and propose a model that
learns representations which preserve those distances between nodes. Since the
emerging hierarchy corresponds to a tree, to learn these representations, we
capitalize on recent advances in the field of hyperbolic neural networks. We
empirically evaluate the proposed model on standard node and graph
classification datasets where it achieves competitive performance with
state-of-the-art models.

The free flow of information has been accelerated by the rapid development of
social media technology. There has been a significant social and psychological
impact on the population due to the outbreak of Coronavirus disease (COVID-19).
The COVID-19 pandemic is one of the current events being discussed on social
media platforms. In order to safeguard societies from this pandemic, studying
people's emotions on social media is crucial. As a result of their particular
characteristics, sentiment analysis of texts like tweets remains challenging.
Sentiment analysis is a powerful text analysis tool. It automatically detects
and analyzes opinions and emotions from unstructured data. Texts from a wide
range of sources are examined by a sentiment analysis tool, which extracts
meaning from them, including emails, surveys, reviews, social media posts, and
web articles. To evaluate sentiments, natural language processing (NLP) and
machine learning techniques are used, which assign weights to entities, topics,
themes, and categories in sentences or phrases. Machine learning tools learn
how to detect sentiment without human intervention by examining examples of
emotions in text. In a pandemic situation, analyzing social media texts to
uncover sentimental trends can be very helpful in gaining a better
understanding of society's needs and predicting future trends. We intend to
study society's perception of the COVID-19 pandemic through social media using
state-of-the-art BERT and Deep CNN models. The superiority of BERT models over
other deep models in sentiment analysis is evident and can be concluded from
the comparison of the various research studies mentioned in this article.

Normalizing flows are constructed from a base distribution with a known
density and a diffeomorphism with a tractable Jacobian. The base density of a
normalizing flow can be parameterised by a different normalizing flow, thus
allowing maps to be found between arbitrary distributions. We demonstrate and
explore the utility of this approach and show it is particularly interesting in
the case of conditional normalizing flows and for introducing optimal transport
constraints on maps that are constructed using normalizing flows.

The sensitivity of many physics analyses can be enhanced by constructing
discriminants that preferentially select signal events. Such discriminants
become much more useful if they are uncorrelated with a set of protected
attributes. In this paper we show that a normalizing flow conditioned on the
protected attributes can be used to find a decorrelated representation for any
discriminant. As a normalizing flow is invertible the separation power of the
resulting discriminant will be unchanged at any fixed value of the protected
attributes. We demonstrate the efficacy of our approach by building supervised
jet taggers that produce almost no sculpting in the mass distribution of the
background.

In this article we prove that estimator stability is enough to show that
leave-one-out cross validation is a sound procedure, by providing concentration
bounds in a general framework. In particular, we provide concentration bounds
beyond Lipschitz continuity assumptions on the loss or on the estimator. In
order to obtain our results, we rely on random variables with distribution
satisfying the logarithmic Sobolev inequality, providing us a relatively rich
class of distributions. We illustrate our method by considering several
interesting examples, including linear regression, kernel density estimation,
and stabilized / truncated estimators such as stabilized kernel regression.

The kernel function and its hyperparameters are the central model selection
choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the
hyperparameters of the kernel are chosen by maximising the marginal likelihood,
an approach known as Type-II maximum likelihood (ML-II). However, ML-II does
not account for hyperparameter uncertainty, and it is well-known that this can
lead to severely biased estimates and an underestimation of predictive
uncertainty. While there are several works which employ a fully Bayesian
characterisation of GPs, relatively few propose such approaches for the sparse
GPs paradigm. In this work we propose an algorithm for sparse Gaussian process
regression which leverages MCMC to sample from the hyperparameter posterior
within the variational inducing point framework of Titsias (2009). This work is
closely related to Hensman et al. (2015b) but side-steps the need to sample the
inducing points, thereby significantly improving sampling efficiency in the
Gaussian likelihood case. We compare this scheme against natural baselines in
literature along with stochastic variational GPs (SVGPs) along with an
extensive computational analysis.

Lung segmentation in chest X-rays (CXRs) is an important prerequisite for
improving the specificity of diagnoses of cardiopulmonary diseases in a
clinical decision support system. Current deep learning (DL) models for lung
segmentation are trained and evaluated on CXR datasets in which the
radiographic projections are captured predominantly from the adult population.
However, the shape of the lungs is reported to be significantly different for
pediatrics across the developmental stages from infancy to adulthood. This
might result in age-related data domain shifts that would adversely impact lung
segmentation performance when the models trained on the adult population are
deployed for pediatric lung segmentation. In this work, our goal is to analyze
the generalizability of deep adult lung segmentation models to the pediatric
population and improve performance through a systematic combinatorial approach
consisting of CXR modality-specific weight initializations, stacked
generalization, and an ensemble of the stacked generalization models. Novel
evaluation metrics consisting of Mean Lung Contour Distance and Average Hash
Score are proposed in addition to the Multi-scale Structural Similarity Index
Measure, Intersection of Union, and Dice metrics to evaluate segmentation
performance. We observed a significant improvement (p < 0.05) in cross-domain
generalization through our combinatorial approach. This study could serve as a
paradigm to analyze the cross-domain generalizability of deep segmentation
models for other medical imaging modalities and applications.

In this article we study the connection of stochastic optimal control and
reinforcement learning. Our main motivation is an importance sampling
application to rare events sampling which can be reformulated as an optimal
control problem. By using a parameterized approach the optimal control problem
turns into a stochastic optimization problem which still presents some open
questions regarding how to tackle the scalability to high-dimensional problems
and how to deal with the intrinsic metastability of the system. With the aim to
explore new methods we connect the optimal control problem to reinforcement
learning since both share the same underlying framework namely a Markov
decision process (MDP). We show how the MDP can be formulated for the optimal
control problem. Furthermore, we discuss how the stochastic optimal control
problem can be interpreted in a reinforcement learning framework. At the end of
the article we present the application of two different reinforcement learning
algorithms to the optimal control problem and compare the advantages and
disadvantages of the two algorithms.

Intentionally crafted adversarial samples have effectively exploited
weaknesses in deep neural networks. A standard method in adversarial robustness
assumes a framework to defend against samples crafted by minimally perturbing a
sample such that its corresponding model output changes. These sensitivity
attacks exploit the model's sensitivity toward task-irrelevant features.
Another form of adversarial sample can be crafted via invariance attacks, which
exploit the model underestimating the importance of relevant features. Previous
literature has indicated a tradeoff in defending against both attack types
within a strictly L_p bounded defense. To promote robustness toward both types
of attacks beyond Euclidean distance metrics, we use metric learning to frame
adversarial regularization as an optimal transport problem. Our preliminary
results indicate that regularizing over invariant perturbations in our
framework improves both invariant and sensitivity defense.

In a fissile material, the inherent multiplicity of neutrons born through
induced fissions leads to correlations in their detection statistics. The
correlations between neutrons can be used to trace back some characteristics of
the fissile material. This technique known as neutron noise analysis has
applications in nuclear safeguards or waste identification. It provides a
non-destructive examination method for an unknown fissile material. This is an
example of an inverse problem where the cause is inferred from observations of
the consequences. However, neutron correlation measurements are often noisy
because of the stochastic nature of the underlying processes. This makes the
resolution of the inverse problem more complex since the measurements are
strongly dependent on the material characteristics. A minor change in the
material properties can lead to very different outputs. Such an inverse problem
is said to be ill-posed. For an ill-posed inverse problem the inverse
uncertainty quantification is crucial. Indeed, seemingly low noise in the data
can lead to strong uncertainties in the estimation of the material properties.
Moreover, the analytical framework commonly used to describe neutron
correlations relies on strong physical assumptions and is thus inherently
biased. This paper addresses dual goals. Firstly, surrogate models are used to
improve neutron correlations predictions and quantify the errors on those
predictions. Then, the inverse uncertainty quantification is performed to
include the impact of measurement error alongside the residual model bias.

Following the advent of immersive technologies and the increasing interest in
representing interactive geometrical format, 3D Point Clouds (PC) have emerged
as a promising solution and effective means to display 3D visual information.
In addition to other challenges in immersive applications, objective and
subjective quality assessments of compressed 3D content remain open problems
and an area of research interest. Yet most of the efforts in the research area
ignore the local geometrical structures between points representation. In this
paper, we overcome this limitation by introducing a novel and efficient
objective metric for Point Clouds Quality Assessment, by learning local
intrinsic dependencies using Graph Neural Network (GNN). To evaluate the
performance of our method, two well-known datasets have been used. The results
demonstrate the effectiveness and reliability of our solution compared to
state-of-the-art metrics.

Expressive text-to-speech (TTS) can synthesize a new speaking style by
imiating prosody and timbre from a reference audio, which faces the following
challenges: (1) The highly dynamic prosody information in the reference audio
is difficult to extract, especially, when the reference audio contains
background noise. (2) The TTS systems should have good generalization for
unseen speaking styles. In this paper, we present a
\textbf{no}ise-\textbf{r}obust \textbf{e}xpressive TTS model (NoreSpeech),
which can robustly transfer speaking style in a noisy reference utterance to
synthesized speech. Specifically, our NoreSpeech includes several components:
(1) a novel DiffStyle module, which leverages powerful probabilistic denoising
diffusion models to learn noise-agnostic speaking style features from a teacher
model by knowledge distillation; (2) a VQ-VAE block, which maps the style
features into a controllable quantized latent space for improving the
generalization of style transfer; and (3) a straight-forward but effective
parameter-free text-style alignment module, which enables NoreSpeech to
transfer style to a textual input from a length-mismatched reference utterance.
Experiments demonstrate that NoreSpeech is more effective than previous
expressive TTS models in noise environments. Audio samples and code are
available at:
\href{http://dongchaoyang.top/NoreSpeech\_demo/}{http://dongchaoyang.top/NoreSpeech\_demo/}

This paper presents an accurate, highly efficient, and learning-free method
for large-scale odometry estimation using spinning radar, empirically found to
generalize well across very diverse environments -- outdoors, from urban to
woodland, and indoors in warehouses and mines - without changing parameters.
Our method integrates motion compensation within a sweep with one-to-many scan
registration that minimizes distances between nearby oriented surface points
and mitigates outliers with a robust loss function. Extending our previous
approach CFEAR, we present an in-depth investigation on a wider range of data
sets, quantifying the importance of filtering, resolution, registration cost
and loss functions, keyframe history, and motion compensation. We present a new
solving strategy and configuration that overcomes previous issues with sparsity
and bias, and improves our state-of-the-art by 38%, thus, surprisingly,
outperforming radar SLAM and approaching lidar SLAM. The most accurate
configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the
fastest achieves 1.79% error at 160Hz.

Robotic force-based compliance control is a preferred approach to achieve
high-precision assembly tasks. When the geometric features of assembly objects
are asymmetric or irregular, reinforcement learning (RL) agents are gradually
incorporated into the compliance controller to adapt to complex force-pose
mapping which is hard to model analytically. Since force-pose mapping is
strongly dependent on geometric features, a compliance controller is only
optimal for current geometric features. To reduce the learning cost of assembly
objects with different geometric features, this paper is devoted to answering
how to reconfigure existing controllers for new assembly objects with different
geometric features. In this paper, model-based parameters are first
reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL).
Then the RL agent is transferred based on the proposed Weighted Dimensional
Policy Distillation (WDPD) method. The experiment results demonstrate that the
control reconfiguration method costs less time and achieves better control
performance, which confirms the validity of proposed methods.

We have analyzed a limited sample set of 120 GHz, and 150 GHz time-resolved
millimeter wave (mmW) photoconductive decay (mmPCD) signals of 300 nm thick
air-stable encapsulated perovskite film (methyl-ammonium lead halide) excited
using a pulsed 532-nm laser with fluence 10.6 micro-Joules per cm-2. We
correlated 12 parameters derived directly from acquired mmPCD kinetic-trace
data and its step-response, each with the sample-age based on the date of the
experiment. Five parameters with a high negative correlation with sample age
were finally selected as predictors in the Gaussian Process Regression (GPR)
machine learning model for prediction of the age of the sample. The effects of
aging (between 0 and 40,000 hours after film production) are quantified mainly
in terms of a shift in peak voltage, the response ratio (conductance
parameter), loss-compensated transmission coefficient, and the radiofrequency
(RF) area of the transient itself (flux). Changes in the other step-response
parameters and the decay length of the aging transients are also shown. The GPR
model is found to work well for a forward prediction of the age of the sample
using this method. It is noted that the Matern-5 over 2 GPR kernel for
supervised learning provides the best realistic solution for age prediction
with R squared around 0.97.

Animal groups collaborate with one another throughout their lives to better
comprehend their surroundings. Here, we try to model, using continuous random
walks, how the entire process of birth, reproduction, and death might impact
the searching process. We attempt to simulate an ecosystem where the
post-reproductive foragers leave their colonies to discover where the targets
are while others stay and breed at the base. Actually, a group of foragers
searches for a location from where they access the targets for food supply.
Particularly, we have explored a hypothetical situation in which the relocation
to the new position depends on the agreement level of the species as well as an
additional waiting time due to this agreement level. In this backdrop, detailed
numerical results reveal that searching for an optimal position at an optimal
mean time can be captured for a suitable range of the agreement level. We have
also shown, for a given agreement level, the optimal mean time linearly
increases with the Death-to-Birth ratio.

In a scenario with multiple persons talking simultaneously, the spatial
characteristics of the signals are the most distinct feature for extracting the
target signal. In this work, we develop a deep joint spatial-spectral
non-linear filter that can be steered in an arbitrary target direction. For
this we propose a simple and effective conditioning mechanism, which sets the
initial state of the filter's recurrent layers based on the target direction.
We show that this scheme is more effective than the baseline approach and
increases the flexibility of the filter at no performance cost. The resulting
spatially selective non-linear filters can also be used for speech separation
of an arbitrary number of speakers and enable very accurate multi-speaker
localization as we demonstrate in this paper.

Deep learning methods have contributed substantially to the rapid advancement
of medical image segmentation, the quality of which relies on the suitable
design of loss functions. Popular loss functions, including the cross-entropy
and dice losses, often fall short of boundary detection, thereby limiting
high-resolution downstream applications such as automated diagnoses and
procedures. We developed a novel loss function that is tailored to reflect the
boundary information to enhance the boundary detection. As the contrast between
segmentation and background regions along the classification boundary naturally
induces heterogeneity over the pixels, we propose the piece-wise two-sample
t-test augmented (PTA) loss that is infused with the statistical test for such
heterogeneity. We demonstrate the improved boundary detection power of the PTA
loss compared to benchmark losses without a t-test component.

Besides the complex nature of colonoscopy frames with intrinsic frame
formation artefacts such as light reflections and the diversity of polyp
types/shapes, the publicly available polyp segmentation training datasets are
limited, small and imbalanced. In this case, the automated polyp segmentation
using a deep neural network remains an open challenge due to the overfitting of
training on small datasets. We proposed a simple yet effective polyp
segmentation pipeline that couples the segmentation (FCN) and classification
(CNN) tasks. We find the effectiveness of interactive weight transfer between
dense and coarse vision tasks that mitigates the overfitting in learning. And
It motivates us to design a new training scheme within our segmentation
pipeline. Our method is evaluated on CVC-EndoSceneStill and Kvasir-SEG
datasets. It achieves 4.34% and 5.70% Polyp-IoU improvements compared to the
state-of-the-art methods on the EndoSceneStill and Kvasir-SEG datasets,

Named Entity Recognition and Intent Classification are among the most
important subfields of the field of Natural Language Processing. Recent
research has lead to the development of faster, more sophisticated and
efficient models to tackle the problems posed by those two tasks. In this work
we explore the effectiveness of two separate families of Deep Learning networks
for those tasks: Bidirectional Long Short-Term networks and Transformer-based
networks. The models were trained and tested on the ATIS benchmark dataset for
both English and Greek languages. The purpose of this paper is to present a
comparative study of the two groups of networks for both languages and showcase
the results of our experiments. The models, being the current state-of-the-art,
yielded impressive results and achieved high performance.

The field of emergent communication aims to understand the characteristics of
communication as it emerges from artificial agents solving tasks that require
information exchange. Communication with discrete messages is considered a
desired characteristic, for both scientific and applied reasons. However,
training a multi-agent system with discrete communication is not
straightforward, requiring either reinforcement learning algorithms or relaxing
the discreteness requirement via a continuous approximation such as the
Gumbel-softmax. Both these solutions result in poor performance compared to
fully continuous communication. In this work, we propose an alternative
approach to achieve discrete communication -- quantization of communicated
messages. Using message quantization allows us to train the model end-to-end,
achieving superior performance in multiple setups. Moreover, quantization is a
natural framework that runs the gamut from continuous to discrete
communication. Thus, it sets the ground for a broader view of multi-agent
communication in the deep learning era.

While text-to-image synthesis currently enjoys great popularity among
researchers and the general public, the security of these models has been
neglected so far. Many text-guided image generation models rely on pre-trained
text encoders from external sources, and their users trust that the retrieved
models will behave as promised. Unfortunately, this might not be the case. We
introduce backdoor attacks against text-guided generative models and
demonstrate that their text encoders pose a major tampering risk. Our attacks
only slightly alter an encoder so that no suspicious model behavior is apparent
for image generations with clean prompts. By then inserting a single non-Latin
character into the prompt, the adversary can trigger the model to either
generate images with pre-defined attributes or images following a hidden,
potentially malicious description. We empirically demonstrate the high
effectiveness of our attacks on Stable Diffusion and highlight that the
injection process of a single backdoor takes less than two minutes. Besides
phrasing our approach solely as an attack, it can also force an encoder to
forget phrases related to certain concepts, such as nudity or violence, and
help to make image generation safer.

Explainable information retrieval is an emerging research area aiming to make
transparent and trustworthy information retrieval systems. Given the increasing
use of complex machine learning models in search systems, explainability is
essential in building and auditing responsible information retrieval models.
This survey fills a vital gap in the otherwise topically diverse literature of
explainable information retrieval. It categorizes and discusses recent
explainability methods developed for different application domains in
information retrieval, providing a common framework and unifying perspectives.
In addition, it reflects on the common concern of evaluating explanations and
highlights open challenges and opportunities.

In this paper, we present a new theoretical approach for enabling domain
knowledge acquisition by intelligent systems. We introduce a hybrid model that
starts with minimal input knowledge in the form of an upper ontology of
concepts, stores and reasons over this knowledge through a knowledge graph
database and learns new information through a Logic Neural Network. We study
the behavior of this architecture when handling new data and show that the
final system is capable of enriching its current knowledge as well as extending
it to new domains.

Many clinical and research studies of the human brain require an accurate
structural MRI segmentation. While traditional atlas-based methods can be
applied to volumes from any acquisition site, recent deep learning algorithms
ensure very high accuracy only when tested on data from the same sites
exploited in training (i.e., internal data). The performance degradation
experienced on external data (i.e., unseen volumes from unseen sites) is due to
the inter-site variabilities in intensity distributions induced by different MR
scanner models, acquisition parameters, and unique artefacts. To mitigate this
site-dependency, often referred to as the scanner effect, we propose LOD-Brain,
a 3D convolutional neural network with progressive levels-of-detail (LOD) able
to segment brain data from any site. Coarser network levels are responsible to
learn a robust anatomical prior useful for identifying brain structures and
their locations, while finer levels refine the model to handle site-specific
intensity distributions and anatomical variations. We ensure robustness across
sites by training the model on an unprecedented rich dataset aggregating data
from open repositories: almost 27,000 T1w volumes from around 160 acquisition
sites, at 1.5 - 3T, from a population spanning from 8 to 90 years old.
Extensive tests demonstrate that LOD-Brain produces state-of-the-art results,
with no significant difference in performance between internal and external
sites, and robust to challenging anatomical variations. Its portability opens
the way for large scale application across different healthcare institutions,
patient populations, and imaging technology manufacturers. Code, model, and
demo are available at the project website.

Diffusion-based generative models have had a high impact on the computer
vision and speech processing communities these past years. Besides data
generation tasks, they have also been employed for data restoration tasks like
speech enhancement and dereverberation. While discriminative models have
traditionally been argued to be more powerful e.g. for speech enhancement,
generative diffusion approaches have recently been shown to narrow this
performance gap considerably. In this paper, we systematically compare the
performance of generative diffusion models and discriminative approaches on
different speech restoration tasks. For this, we extend our prior contributions
on diffusion-based speech enhancement in the complex time-frequency domain to
the task of bandwith extension. We then compare it to a discriminatively
trained neural network with the same network architecture on three restoration
tasks, namely speech denoising, dereverberation and bandwidth extension. We
observe that the generative approach performs globally better than its
discriminative counterpart on all tasks, with the strongest benefit for
non-additive distortion models, like in dereverberation and bandwidth
extension. Code and audio examples can be found online at
https://uhh.de/inf-sp-sgmsemultitask

This paper compares the performance of a NN taking the output of a DCT
(Discrete Cosine Transform) of an image patch with leNet for classifying MNIST
hand written digits. The basis functions underlying the DCT bear a passing
resemblance to some of the learned basis function of the Visual Transformer but
are an order of magnitude faster to apply.

Recent advances in coreset methods have shown that a selection of
representative datapoints can replace massive volumes of data for Bayesian
inference, preserving the relevant statistical information and significantly
accelerating subsequent downstream tasks. Existing variational coreset
constructions rely on either selecting subsets of the observed datapoints, or
jointly performing approximate inference and optimizing pseudodata in the
observed space akin to inducing points methods in Gaussian Processes. So far,
both approaches are limited by complexities in evaluating their objectives for
general purpose models, and require generating samples from a typically
intractable posterior over the coreset throughout inference and testing. In
this work, we present a black-box variational inference framework for coresets
that overcomes these constraints and enables principled application of
variational coresets to intractable models, such as Bayesian neural networks.
We apply our techniques to supervised learning problems, and compare them with
existing approaches in the literature for data summarization and inference.

We consider the problem of predictive monitoring (PM), i.e., predicting at
runtime the satisfaction of a desired property from the current system's state.
Due to its relevance for runtime safety assurance and online control, PM
methods need to be efficient to enable timely interventions against predicted
violations, while providing correctness guarantees. We introduce
\textit{quantitative predictive monitoring (QPM)}, the first PM method to
support stochastic processes and rich specifications given in Signal Temporal
Logic (STL). Unlike most of the existing PM techniques that predict whether or
not some property $\phi$ is satisfied, QPM provides a quantitative measure of
satisfaction by predicting the quantitative (aka robust) STL semantics of
$\phi$. QPM derives prediction intervals that are highly efficient to compute
and with probabilistic guarantees, in that the intervals cover with arbitrary
probability the STL robustness values relative to the stochastic evolution of
the system. To do so, we take a machine-learning approach and leverage recent
advances in conformal inference for quantile regression, thereby avoiding
expensive Monte-Carlo simulations at runtime to estimate the intervals. We also
show how our monitors can be combined in a compositional manner to handle
composite formulas, without retraining the predictors nor sacrificing the
guarantees. We demonstrate the effectiveness and scalability of QPM over a
benchmark of four discrete-time stochastic processes with varying degrees of

We investigate the problem of risk averse robot path planning using the deep
reinforcement learning and distributionally robust optimization perspectives.
Our problem formulation involves modelling the robot as a stochastic linear
dynamical system, assuming that a collection of process noise samples is
available. We cast the risk averse motion planning problem as a Markov decision
process and propose a continuous reward function design that explicitly takes
into account the risk of collision with obstacles while encouraging the robot's
motion towards the goal. We learn the risk-averse robot control actions through
Lipschitz approximated Wasserstein distributionally robust deep Q-learning to
hedge against the noise uncertainty. The learned control actions result in a
safe and risk averse trajectory from the source to the goal, avoiding all the
obstacles. Various supporting numerical simulations are presented to
demonstrate our proposed approach.

In recent years, Speech Emotion Recognition (SER) has been investigated
mainly transforming the speech signal into spectrograms that are then
classified using Convolutional Neural Networks pretrained on generic images and
fine tuned with spectrograms. In this paper, we start from the general idea
above and develop a new learning solution for SER, which is based on Compact
Convolutional Transformers (CCTs) combined with a speaker embedding. With CCTs,
the learning power of Vision Transformers (ViT) is combined with a diminished
need for large volume of data as made possible by the convolution. This is
important in SER, where large corpora of data are usually not available. The
speaker embedding allows the network to extract an identity representation of
the speaker, which is then integrated by means of a self-attention mechanism
with the features that the CCT extracts from the spectrogram. Overall, the
solution is capable of operating in real-time showing promising results in a
cross-corpus scenario, where training and test datasets are kept separate.
Experiments have been performed on several benchmarks in a cross-corpus setting
as rarely used in the literature, with results that are comparable or superior
to those obtained with state-of-the-art network architectures. Our code is
available at https://github.com/JabuMlDev/Speaker-VGG-CCT.

Multi-relational databases are the basis of most consolidated data
collections in science and industry today. Most learning and mining algorithms,
however, require data to be represented in a propositional form. While there is
a variety of specialized machine learning algorithms that can operate directly
on multi-relational data sets, propositionalization algorithms transform
multi-relational databases into propositional data sets, thereby allowing the
application of traditional machine learning and data mining algorithms without
their modification. One prominent propositionalization algorithm is RELAGGS by
Krogel and Wrobel, which transforms the data by nested aggregations. We propose
a new neural network based algorithm in the spirit of RELAGGS that employs
trainable composite aggregate functions instead of the static aggregate
functions used in the original approach. In this way, we can jointly train the
propositionalization with the prediction model, or, alternatively, use the
learned aggegrations as embeddings in other algorithms. We demonstrate the
increased predictive performance by comparing N-RELAGGS with RELAGGS and
multiple other state-of-the-art algorithms.

Geospatial Information Systems are used by researchers and Humanitarian
Assistance and Disaster Response (HADR) practitioners to support a wide variety
of important applications. However, collaboration between these actors is
difficult due to the heterogeneous nature of geospatial data modalities (e.g.,
multi-spectral images of various resolutions, timeseries, weather data) and
diversity of tasks (e.g., regression of human activity indicators or detecting
forest fires). In this work, we present a roadmap towards the construction of a
general-purpose neural architecture (GPNA) with a geospatial inductive bias,
pre-trained on large amounts of unlabelled earth observation data in a
self-supervised manner. We envision how such a model may facilitate cooperation
between members of the community. We show preliminary results on the first step
of the roadmap, where we instantiate an architecture that can process a wide
variety of geospatial data modalities and demonstrate that it can achieve
competitive performance with domain-specific architectures on tasks relating to
the U.N.'s Sustainable Development Goals.

The ability to accurately detect onset of dementia is important in the
treatment of the disease. Clinically, the diagnosis of Alzheimer Disease (AD)
and Mild Cognitive Impairment (MCI) patients are based on an integrated
assessment of psychological tests and brain imaging such as positron emission
tomography (PET) and anatomical magnetic resonance imaging (MRI). In this work
using two different datasets, we propose a behavior score-embedded encoder
network (BSEN) that integrates regularly adminstrated psychological tests
information into the encoding procedure of representing subject's restingstate
fMRI data for automatic classification tasks. BSEN is based on a 3D
convolutional autoencoder structure with contrastive loss jointly optimized
using behavior scores from MiniMental State Examination (MMSE) and Clinical
Dementia Rating (CDR). Our proposed classification framework of using BSEN
achieved an overall recognition accuracy of 59.44% (3-class classification: AD,
MCI and Healthy Control), and we further extracted the most discriminative
regions between healthy control (HC) and AD patients.

The sequence length along the time axis is often the dominant factor of the
computational cost of self-supervised speech models. Works have been proposed
to reduce the sequence length for lowering the computational cost. However,
different downstream tasks have different tolerance of sequence compressing, so
a model that produces a fixed compressing rate may not fit all tasks. In this
work, we introduce a once-for-all (OFA) sequence compression framework for
self-supervised speech models that supports a continuous range of compressing
rates. The framework is evaluated on various tasks, showing marginal
degradation compared to the fixed compressing rate variants with a smooth
performance-efficiency trade-off. We further explore adaptive compressing rate
learning, demonstrating the ability to select task-specific preferred frame
periods without needing a grid search.

The existence of the million-degree corona above the cooler photosphere is an
unsolved problem in astrophysics. Detailed study of quiescent corona that
exists regardless of the phase of the solar cycle may provide fruitful hints
towards resolving this conundrum. However, the properties of heating mechanisms
can be obtained only statistically in these regions due to their unresolved
nature. Here, we develop a two-step inversion scheme based on the machine
learning scheme of Upendran & Tripathi (2021a) for the empirical impulsive
heating model of Pauluhn & Solanki (2007), and apply it to disk integrated flux
measurements of the quiet corona as measured by the X-ray solar monitor (XSM)
onboard Chandrayaan - 2. We use data in three energy passbands, viz., 1 - 1.3
keV, 1.3 - 2.3 keV, and 1 - 2.3 keV, and estimate the typical impulsive event
frequencies, timescales, amplitudes, and the distribution of amplitudes. We
find that the impulsive events occur at a frequency of $\approx$25 events per
minute with a typical lifetime of $\approx10$ minutes. They are characterized
by a power law distribution with a slope $\alpha\leq2.0$. The typical
amplitudes of these events lie in an energy range of $10^{21}$ - $10^{24}$
ergs, with a typical radiative loss of about $\approx10^3$ erg cm$^{-2}$
s$^{-1}$ in the energy range of 1 - 2.3 keV. These results provide further
constraints on the properties of sub-pixel impulsive events in maintaining the
quiet solar corona.

Mainstream image caption models are usually two-stage captioners, i.e.,
calculating object features by pre-trained detector, and feeding them into a
language model to generate text descriptions. However, such an operation will
cause a task-based information gap to decrease the performance, since the
object features in detection task are suboptimal representation and cannot
provide all necessary information for subsequent text generation. Besides,
object features are usually represented by the last layer features that lose
the local details of input images. In this paper, we propose a novel One-Stage
Image Captioner (OSIC) with dynamic multi-sight learning, which directly
transforms input image into descriptive sentences in one stage. As a result,
the task-based information gap can be greatly reduced. To obtain rich features,
we use the Swin Transformer to calculate multi-level features, and then feed
them into a novel dynamic multi-sight embedding module to exploit both global
structure and local texture of input images. To enhance the global modeling of
encoder for caption, we propose a new dual-dimensional refining module to
non-locally model the interaction of the embedded features. Finally, OSIC can
obtain rich and useful information to improve the image caption task. Extensive
comparisons on benchmark MS-COCO dataset verified the superior performance of
our method.

Using functional magnetic resonance imaging (fMRI) and deep learning to
explore functional brain networks (FBNs) has attracted many researchers.
However, most of these studies are still based on the temporal correlation
between the sources and voxel signals, and lack of researches on the dynamics
of brain function. Due to the widespread local correlations in the volumes,
FBNs can be generated directly in the spatial domain in a self-supervised
manner by using spatial-wise attention (SA), and the resulting FBNs has a
higher spatial similarity with templates compared to the classical method.
Therefore, we proposed a novel Spatial-Temporal Convolutional Attention (STCA)
model to discover the dynamic FBNs by using the sliding windows. To validate
the performance of the proposed method, we evaluate the approach on HCP-rest
dataset. The results indicate that STCA can be used to discover FBNs in a
dynamic way which provide a novel approach to better understand human brain.

Financial frauds cause billions of losses annually and yet it lacks efficient
approaches in detecting frauds considering user profile and their behaviors
simultaneously in social network . A social network forms a graph structure
whilst Graph neural networks (GNN), a promising research domain in Deep
Learning, can seamlessly process non-Euclidean graph data . In financial fraud
detection, the modus operandi of criminals can be identified by analyzing user
profile and their behaviors such as transaction, loaning etc. as well as their
social connectivity. Currently, most GNNs are incapable of selecting important
neighbors since the neighbors' edge attributes (i.e., behaviors) are ignored.
In this paper, we propose a novel behavior information aggregation network
(BIAN) to combine the user behaviors with other user features. Different from
its close "relatives" such as Graph Attention Networks (GAT) and Graph
Transformer Networks (GTN), it aggregates neighbors based on neighboring edge
attribute distribution, namely, user behaviors in financial social network. The
experimental results on a real-world large-scale financial social network
dataset, DGraph, show that BIAN obtains the 10.2% gain in AUROC comparing with
the State-Of-The-Art models.

The network trained for domain adaptation is prone to bias toward the
easy-to-transfer classes. Since the ground truth label on the target domain is
unavailable during training, the bias problem leads to skewed predictions,
forgetting to predict hard-to-transfer classes. To address this problem, we
propose Cross-domain Moving Object Mixing (CMOM) that cuts several objects,
including hard-to-transfer classes, in the source domain video clip and pastes
them into the target domain video clip. Unlike image-level domain adaptation,
the temporal context should be maintained to mix moving objects in two
different videos. Therefore, we design CMOM to mix with consecutive video
frames, so that unrealistic movements are not occurring. We additionally
propose Feature Alignment with Temporal Context (FATC) to enhance target domain
feature discriminability. FATC exploits the robust source domain features,
which are trained with ground truth labels, to learn discriminative target
domain features in an unsupervised manner by filtering unreliable predictions
with temporal consensus. We demonstrate the effectiveness of the proposed
approaches through extensive experiments. In particular, our model reaches mIoU
of 53.81% on VIPER to Cityscapes-Seq benchmark and mIoU of 56.31% on
SYNTHIA-Seq to Cityscapes-Seq benchmark, surpassing the state-of-the-art
methods by large margins.

Binaural rendering of ambisonic signals is of broad interest to virtual
reality and immersive media. Conventional methods often require manually
measured Head-Related Transfer Functions (HRTFs). To address this issue, we
collect a paired ambisonic-binaural dataset and propose a deep learning
framework in an end-to-end manner. Experimental results show that neural
networks outperform the conventional method in objective metrics and achieve
comparable subjective metrics. To validate the proposed framework, we
experimentally explore different settings of the input features, model
structures, output features, and loss functions. Our proposed system achieves
an SDR of 7.32 and MOSs of 3.83, 3.58, 3.87, 3.58 in quality, timbre,
localization, and immersion dimensions.

Few-shot segmentation aims to devise a generalizing model that segments query
images from unseen classes during training with the guidance of a few support
images whose class tally with the class of the query. There exist two
domain-specific problems mentioned in the previous works, namely spatial
inconsistency and bias towards seen classes. Taking the former problem into
account, our method compares the support feature map with the query feature map
at multi scales to become scale-agnostic. As a solution to the latter problem,
a supervised model, called as base learner, is trained on available classes to
accurately identify pixels belonging to seen classes. Hence, subsequent meta
learner has a chance to discard areas belonging to seen classes with the help
of an ensemble learning model that coordinates meta learner with the base
learner. We simultaneously address these two vital problems for the first time
and achieve state-of-the-art performances on both PASCAL-5i and COCO-20i
datasets.

Recently, machine learning (ML) has become a popular approach to support
self-adaptation. ML has been used to deal with several problems in
self-adaptation, such as maintaining an up-to-date runtime model under
uncertainty and scalable decision-making. Yet, exploiting ML comes with
inherent challenges. In this paper, we focus on a particularly important
challenge for learning-based self-adaptive systems: drift in adaptation spaces.
With adaptation space we refer to the set of adaptation options a self-adaptive
system can select from at a given time to adapt based on the estimated quality
properties of the adaptation options. Drift of adaptation spaces originates
from uncertainties, affecting the quality properties of the adaptation options.
Such drift may imply that eventually no adaptation option can satisfy the
initial set of the adaptation goals, deteriorating the quality of the system,
or adaptation options may emerge that allow enhancing the adaptation goals. In
ML, such shift corresponds to novel class appearance, a type of concept drift
in target data that common ML techniques have problems dealing with. To tackle
this problem, we present a novel approach to self-adaptation that enhances
learning-based self-adaptive systems with a lifelong ML layer. We refer to this
approach as lifelong self-adaptation. The lifelong ML layer tracks the system
and its environment, associates this knowledge with the current tasks,
identifies new tasks based on differences, and updates the learning models of
the self-adaptive system accordingly. A human stakeholder may be involved to
support the learning process and adjust the learning and goal models. We
present a reusable architecture for lifelong self-adaptation and apply it to
the case of drift of adaptation spaces that affects the decision-making in
self-adaptation. We validate the approach for a series of scenarios using the
DeltaIoT exemplar.

Deep learning technology has made great progress in multi-view 3D
reconstruction tasks. At present, most mainstream solutions establish the
mapping between views and shape of an object by assembling the networks of 2D
encoder and 3D decoder as the basic structure while they adopt different
approaches to obtain aggregation of features from several views. Among them,
the methods using attention-based fusion perform better and more stable than
the others, however, they still have an obvious shortcoming -- the strong
independence of each view during predicting the weights for merging leads to a
lack of adaption of the global state. In this paper, we propose a global-aware
attention-based fusion approach that builds the correlation between each branch
and the global to provide a comprehensive foundation for weights inference. In
order to enhance the ability of the network, we introduce a novel loss function
to supervise the shape overall and propose a dynamic two-stage training
strategy that can effectively adapt to all reconstructors with attention-based
fusion. Experiments on ShapeNet verify that our method outperforms existing
SOTA methods while the amount of parameters is far less than the same type of
algorithm, Pix2Vox++. Furthermore, we propose a view-reduction method based on
maximizing diversity and discuss the cost-performance tradeoff of our model to
achieve a better performance when facing heavy input amount and limited
computational cost.

The explosive growth of dynamic and heterogeneous data traffic brings great
challenges for 5G and beyond mobile networks. To enhance the network capacity
and reliability, we propose a learning-based dynamic time-frequency division
duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink
time-frequency resources of base stations (BSs) to meet the asymmetric and
heterogeneous traffic demands while alleviating the inter-cell interference. We
formulate the problem as a decentralized partially observable Markov decision
process (Dec-POMDP) that maximizes the long-term expected sum rate under the
users' packet dropping ratio constraints. In order to jointly optimize the
global resources in a decentralized manner, we propose a federated
reinforcement learning (RL) algorithm named federated Wolpertinger deep
deterministic policy gradient (FWDDPG) algorithm. The BSs decide their local
time-frequency configurations through RL algorithms and achieve global training
via exchanging local RL models with their neighbors under a decentralized
federated learning framework. Specifically, to deal with the large-scale
discrete action space of each BS, we adopt a DDPG-based algorithm to generate
actions in a continuous space, and then utilize Wolpertinger policy to reduce
the mapping errors from continuous action space back to discrete action space.
Simulation results demonstrate the superiority of our proposed algorithm to
benchmark algorithms with respect to system sum rate.

Due to domain shift, deep neural networks (DNNs) usually fail to generalize
well on unknown test data in practice. Domain generalization (DG) aims to
overcome this issue by capturing domain-invariant representations from source
domains. Motivated by the insight that only partial parameters of DNNs are
optimized to extract domain-invariant representations, we expect a general
model that is capable of well perceiving and emphatically updating such
domain-invariant parameters. In this paper, we propose two modules of Domain
Decoupling and Combination (DDC) and Domain-invariance-guided Backpropagation
(DIGB), which can encourage such general model to focus on the parameters that
have a unified optimization direction between pairs of contrastive samples. Our
extensive experiments on two benchmarks have demonstrated that our proposed
method has achieved state-of-the-art performance with strong generalization
capability.

Vascular shunt insertion is a fundamental surgical procedure used to
temporarily restore blood flow to tissues. It is often performed in the field
after major trauma. We formulate a problem of automated vascular shunt
insertion and propose a pipeline to perform Automated Vascular Shunt Insertion
(AVSI) using a da Vinci Research Kit. The pipeline uses a learned visual model
to estimate the locus of the vessel rim, plans a grasp on the rim, and moves to
grasp at that point. The first robot gripper then pulls the rim to stretch open
the vessel with a dilation motion. The second robot gripper then proceeds to
insert a shunt into the vessel phantom (a model of the blood vessel) with a
chamfer tilt followed by a screw motion. Results suggest that AVSI achieves a
high success rate even with tight tolerances and varying vessel orientations up
to 30{\deg}. Supplementary material, dataset, videos, and visualizations can be
found at https://sites.google.com/berkeley.edu/autolab-avsi.

Developing lightweight Deep Convolutional Neural Networks (DCNNs) and Vision
Transformers (ViTs) has become one of the focuses in vision research since the
low computational cost is essential for deploying vision models on edge
devices. Recently, researchers have explored highly computational efficient
Binary Neural Networks (BNNs) by binarizing weights and activations of
Full-precision Neural Networks. However, the binarization process leads to an
enormous accuracy gap between BNN and its full-precision version. One of the
primary reasons is that the Sign function with predefined or learned static
thresholds limits the representation capacity of binarized architectures since
single-threshold binarization fails to utilize activation distributions. To
overcome this issue, we introduce the statistics of channel information into
explicit thresholds learning for the Sign Function dubbed DySign to generate
various thresholds based on input distribution. Our DySign is a straightforward
method to reduce information loss and boost the representative capacity of
BNNs, which can be flexibly applied to both DCNNs and ViTs (i.e., DyBCNN and
DyBinaryCCT) to achieve promising performance improvement. As shown in our
extensive experiments. For DCNNs, DyBCNNs based on two backbones (MobileNetV1
and ResNet18) achieve 71.2% and 67.4% top1-accuracy on ImageNet dataset,
outperforming baselines by a large margin (i.e., 1.8% and 1.5% respectively).
For ViTs, DyBinaryCCT presents the superiority of the convolutional embedding
layer in fully binarized ViTs and achieves 56.1% on the ImageNet dataset, which
is nearly 9% higher than the baseline.

Neural networks trained with ERM (empirical risk minimization) sometimes
learn unintended decision rules, in particular when their training data is
biased, i.e., when training labels are strongly correlated with undesirable
features. To prevent a network from learning such features, recent methods
augment training data such that examples displaying spurious correlations
(i.e., bias-aligned examples) become a minority, whereas the other,
bias-conflicting examples become prevalent. However, these approaches are
sometimes difficult to train and scale to real-world data because they rely on
generative models or disentangled representations. We propose an alternative
based on mixup, a popular augmentation that creates convex combinations of
training examples. Our method, coined SelecMix, applies mixup to contradicting
pairs of examples, defined as showing either (i) the same label but dissimilar
biased features, or (ii) different labels but similar biased features.
Identifying such pairs requires comparing examples with respect to unknown
biased features. For this, we utilize an auxiliary contrastive model with the
popular heuristic that biased features are learned preferentially during
training. Experiments on standard benchmarks demonstrate the effectiveness of
the method, in particular when label noise complicates the identification of
bias-conflicting examples.

Data processing frameworks such as Apache Beam and Apache Spark are used for
a wide range of applications, from logs analysis to data preparation for DNN
training. It is thus unsurprising that there has been a large amount of work on
optimizing these frameworks, including their storage management. The shift to
cloud computing requires optimization across all pipelines concurrently running
across a cluster. In this paper, we look at one specific instance of this
problem: placement of I/O-intensive temporary intermediate data on SSD and HDD.
Efficient data placement is challenging since I/O density is usually unknown at
the time data needs to be placed. Additionally, external factors such as load
variability, job preemption, or job priorities can impact job completion times,
which ultimately affect the I/O density of the temporary files in the workload.
In this paper, we envision that machine learning can be used to solve this
problem. We analyze production logs from Google's data centers for a range of
data processing pipelines. Our analysis shows that I/O density may be
predictable. This suggests that learning-based strategies, if crafted
carefully, could extract predictive features for I/O density of temporary files
involved in various transformations, which could be used to improve the
efficiency of storage management in data processing pipelines.

This paper proposes Mutual Information Regularized Assignment (MIRA), a
pseudo-labeling algorithm for unsupervised representation learning inspired by
information maximization. We formulate online pseudo-labeling as an
optimization problem to find pseudo-labels that maximize the mutual information
between the label and data while being close to a given model probability. We
derive a fixed-point iteration method and prove its convergence to the optimal
solution. In contrast to baselines, MIRA combined with pseudo-label prediction
enables a simple yet effective clustering-based representation learning without
incorporating extra training techniques or artificial constraints such as
sampling strategy, equipartition constraints, etc. With relatively small
training epochs, representation learned by MIRA achieves state-of-the-art
performance on various downstream tasks, including the linear/k-NN evaluation
and transfer learning. Especially, with only 400 epochs, our method applied to
ImageNet dataset with ResNet-50 architecture achieves 75.6% linear evaluation
accuracy.

Deep Forest is a prominent machine learning algorithm known for its high
accuracy in forecasting. Compared with deep neural networks, Deep Forest has
almost no multiplication operations and has better performance on small
datasets. However, due to the deep structure and large forest quantity, it
suffers from large amounts of calculation and memory consumption. In this
paper, an efficient hardware accelerator is proposed for deep forest models,
which is also the first work to implement Deep Forest on FPGA. Firstly, a
delicate node computing unit (NCU) is designed to improve inference speed.
Secondly, based on NCU, an efficient architecture and an adaptive dataflow are
proposed, in order to alleviate the problem of node computing imbalance in the
classification process. Moreover, an optimized storage scheme in this design
also improves hardware utilization and power efficiency. The proposed design is
implemented on an FPGA board, Intel Stratix V, and it is evaluated by two
typical datasets, ADULT and Face Mask Detection. The experimental results show
that the proposed design can achieve around 40x speedup compared to that on a
40 cores high performance x86 CPU.

Solar activity is usually caused by the evolution of solar magnetic fields.
Magnetic field parameters derived from photospheric vector magnetograms of
solar active regions have been used to analyze and forecast eruptive events
such as solar flares and coronal mass ejections. Unfortunately, the most recent
solar cycle 24 was relatively weak with few large flares, though it is the only
solar cycle in which consistent time-sequence vector magnetograms have been
available through the Helioseismic and Magnetic Imager (HMI) on board the Solar
Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look
into another major instrument, namely the Michelson Doppler Imager (MDI) on
board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data
archive of SOHO/MDI covers more active solar cycle 23 with many large flares.
However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a
new deep learning method, named MagNet, to learn from combined LOS
magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations
collected by the Big Bear Solar Observatory (BBSO), and to generate vector
components Bx' and By', which would form vector magnetograms with observed LOS
data. In this way, we can expand the availability of vector magnetograms to the
period from 1996 to present. Experimental results demonstrate the good
performance of the proposed method. To our knowledge, this is the first time
that deep learning has been used to generate photospheric vector magnetograms
of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.

We discuss an application of Generalized Random Forests (GRF) proposed by
Athey et al.(2019) to quantile regression for time series data. We extracted
the theoretical results of the GRF consistency for i.i.d. data to time series
data. In particular, in the main theorem, based only on the general assumptions
for time series data in Davis and Nielsen (2020), and trees in Athey et
al.(2019), we show that the tsQRF (time series Quantile Regression Forests)
estimator is consistent. Davis and Nielsen (2020) also discussed the estimation
problem using Random Forests (RF) for time series data, but the construction
procedure of the RF treated by the GRF is essentially different, and different
ideas are used throughout the theoretical proof. In addition, a simulation and
real data analysis were conducted.In the simulation, the accuracy of the
conditional quantile estimation was evaluated under time series models. In the
real data using the Nikkei Stock Average, our estimator is demonstrated to be
more sensitive than the others in terms of volatility, thus preventing
underestimation of risk.

We show that it is possible to predict which deep network has generated a
given logit vector with accuracy well above chance. We utilize a number of
networks on a dataset, initialized with random weights or pretrained weights,
as well as fine-tuned networks. A classifier is then trained on the logit
vectors of the trained set of this dataset to map the logit vector to the
network index that has generated it. The classifier is then evaluated on the
test set of the dataset. Results are better with randomly initialized networks,
but also generalize to pretrained networks as well as fine-tuned ones.
Classification accuracy is higher using unnormalized logits than normalized
ones. We find that there is little transfer when applying a classifier to the
same networks but with different sets of weights. In addition to help better
understand deep networks and the way they encode uncertainty, we anticipate our
finding to be useful in some applications (e.g. tailoring an adversarial attack
for a certain type of network). Code is available at
https://github.com/aliborji/logits.

Evolution in time-varying environments naturally leads to adaptable
biological systems that can easily switch functionalities. Advances in the
synthesis of environmentally-responsive materials therefore open up the
possibility of creating a wide range of synthetic materials which can also be
trained for adaptability. We consider high-dimensional inverse problems for
materials where any particular functionality can be realized by numerous
equivalent choices of design parameters. By periodically switching targets in a
given design algorithm, we can teach a material to perform incompatible
functionalities with minimal changes in design parameters. We exhibit this
learning strategy for adaptability in two simulated settings: elastic networks
that are designed to switch deformation modes with minimal bond changes; and
heteropolymers whose folding pathway selections are controlled by a minimal set
of monomer affinities. The resulting designs can reveal physical principles,
such as nucleation-controlled folding, that enable adaptability.

Machine learning is the study of computer algorithms that can automatically
improve based on data and experience. Machine learning algorithms build a model
from sample data, called training data, to make predictions or judgments
without being explicitly programmed to do so. A variety of wellknown machine
learning algorithms have been developed for use in the field of computer
science to analyze data. This paper introduced a new machine learning algorithm
called impact learning. Impact learning is a supervised learning algorithm that
can be consolidated in both classification and regression problems. It can
furthermore manifest its superiority in analyzing competitive data. This
algorithm is remarkable for learning from the competitive situation and the
competition comes from the effects of autonomous features. It is prepared by
the impacts of the highlights from the intrinsic rate of natural increase
(RNI). We, moreover, manifest the prevalence of the impact learning over the
conventional machine learning algorithm.

The detection of earthquakes is a fundamental prerequisite for seismology and
contributes to various research areas, such as forecasting earthquakes and
understanding the crust/mantle structure. Recent advances in machine learning
technologies have enabled the automatic detection of earthquakes from waveform
data. In particular, various state-of-the-art deep-learning methods have been
applied to this endeavour. In this study, we proposed and tested a novel phase
detection method employing deep learning, which is based on a standard
convolutional neural network in a new framework. The novelty of the proposed
method is its separate explicit learning strategy for global and local
representations of waveforms, which enhances its robustness and flexibility.
Prior to modelling the proposed method, we identified local representations of
the waveform by the multiple clustering of waveforms, in which the data points
were optimally partitioned. Based on this result, we considered a global
representation and two local representations of the waveform. Subsequently,
different phase detection models were trained for each global and local
representation. For a new waveform, the overall phase probability was evaluated
as a product of the phase probabilities of each model. This additional
information on local representations makes the proposed method robust to noise,
which is demonstrated by its application to the test data. Furthermore, an
application to seismic swarm data demonstrated the robust performance of the
proposed method compared with those of other deep learning methods. Finally, in
an application to low-frequency earthquakes, we demonstrated the flexibility of
the proposed method, which is readily adaptable for the detection of
low-frequency earthquakes by retraining only a local model.

Achieving accurate and automated tumor segmentation plays an important role
in both clinical practice and radiomics research. Segmentation in medicine is
now often performed manually by experts, which is a laborious, expensive and
error-prone task. Manual annotation relies heavily on the experience and
knowledge of these experts. In addition, there is much intra- and interobserver
variation. Therefore, it is of great significance to develop a method that can
automatically segment tumor target regions. In this paper, we propose a deep
learning segmentation method based on multimodal positron emission
tomography-computed tomography (PET-CT), which combines the high sensitivity of
PET and the precise anatomical information of CT. We design an improved spatial
attention network(ISA-Net) to increase the accuracy of PET or CT in detecting
tumors, which uses multi-scale convolution operation to extract feature
information and can highlight the tumor region location information and
suppress the non-tumor region location information. In addition, our network
uses dual-channel inputs in the coding stage and fuses them in the decoding
stage, which can take advantage of the differences and complementarities
between PET and CT. We validated the proposed ISA-Net method on two clinical
datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR)
dataset, and compared with other attention methods for tumor segmentation. The
DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that
ISA-Net method achieves better segmentation performance and has better
generalization. Conclusions: The method proposed in this paper is based on
multi-modal medical image tumor segmentation, which can effectively utilize the
difference and complementarity of different modes. The method can also be
applied to other multi-modal data or single-modal data by proper adjustment.

Various forms of regularization in learning tasks strive for different
notions of simplicity. This paper presents a spectral regularization technique,
which attaches a unique inductive bias to sequence modeling based on an
intuitive concept of simplicity defined in the Chomsky hierarchy. From
fundamental connections between Hankel matrices and regular grammars, we
propose to use the trace norm of the Hankel matrix, the tightest convex
relaxation of its rank, as the spectral regularizer. To cope with the fact that
the Hankel matrix is bi-infinite, we propose an unbiased stochastic estimator
for its trace norm. Ultimately, we demonstrate experimental results on Tomita
grammars, which exhibit the potential benefits of spectral regularization and
validate the proposed stochastic estimator.

Adaptive optimization methods are well known to achieve superior convergence
relative to vanilla gradient methods. The traditional viewpoint in
optimization, particularly in convex optimization, explains this improved
performance by arguing that, unlike vanilla gradient schemes, adaptive
algorithms mimic the behavior of a second-order method by adapting to the
global geometry of the loss function. We argue that in the context of neural
network optimization, this traditional viewpoint is insufficient. Instead, we
advocate for a local trajectory analysis. For iterate trajectories produced by
running a generic optimization algorithm OPT, we introduce
$R^{\text{OPT}}_{\text{med}}$, a statistic that is analogous to the condition
number of the loss Hessian evaluated at the iterates. Through extensive
experiments, we show that adaptive methods such as Adam bias the trajectories
towards regions where $R^{\text{Adam}}_{\text{med}}$ is small, where one might
expect faster convergence. By contrast, vanilla gradient methods like SGD bias
the trajectories towards regions where $R^{\text{SGD}}_{\text{med}}$ is
comparatively large. We complement these empirical observations with a
theoretical result that provably demonstrates this phenomenon in the simplified
setting of a two-layer linear network. We view our findings as evidence for the
need of a new explanation of the success of adaptive methods, one that is
different than the conventional wisdom.

We present the first neural network model to achieve real-time and streaming
target sound extraction. To accomplish this, we propose Waveformer, an
encoder-decoder architecture with a stack of dilated causal convolution layers
as the encoder, and a transformer decoder layer as the decoder. This hybrid
architecture uses dilated causal convolutions for processing large receptive
fields in a computationally efficient manner, while also benefiting from the
performance transformer-based architectures provide. Our evaluations show as
much as 2.2-3.3 dB improvement in SI-SNRi compared to the prior models for this
task while having a 1.2-4x smaller model size and a 1.5-2x lower runtime.
Open-source code and datasets: https://github.com/vb000/Waveformer

We propose an end-to-end music mixing style transfer system that converts the
mixing style of an input multitrack to that of a reference song. This is
achieved with an encoder pre-trained with a contrastive objective to extract
only audio effects related information from a reference music recording. All
our models are trained in a self-supervised manner from an already-processed
wet multitrack dataset with an effective data preprocessing method that
alleviates the data scarcity of obtaining unprocessed dry data. We analyze the
proposed encoder for the disentanglement capability of audio effects and also
validate its performance for mixing style transfer through both objective and
subjective evaluations. From the results, we show the proposed system not only
converts the mixing style of multitrack audio close to a reference but is also
robust with mixture-wise style transfer upon using a music source separation
model.

Named entity recognition models (NER), are widely used for identifying named
entities (e.g., individuals, locations, and other information) in text
documents. Machine learning based NER models are increasingly being applied in
privacy-sensitive applications that need automatic and scalable identification
of sensitive information to redact text for data sharing. In this paper, we
study the setting when NER models are available as a black-box service for
identifying sensitive information in user documents and show that these models
are vulnerable to membership inference on their training datasets. With updated
pre-trained NER models from spaCy, we demonstrate two distinct membership
attacks on these models. Our first attack capitalizes on unintended
memorization in the NER's underlying neural network, a phenomenon NNs are known
to be vulnerable to. Our second attack leverages a timing side-channel to
target NER models that maintain vocabularies constructed from the training
data. We show that different functional paths of words within the training
dataset in contrast to words not previously seen have measurable differences in
execution time. Revealing membership status of training samples has clear
privacy implications, e.g., in text redaction, sensitive words or phrases to be
found and removed, are at risk of being detected in the training dataset. Our
experimental evaluation includes the redaction of both password and health
data, presenting both security risks and privacy/regulatory issues. This is
exacerbated by results that show memorization with only a single phrase. We
achieved 70% AUC in our first attack on a text redaction use-case. We also show
overwhelming success in the timing attack with 99.23% AUC. Finally we discuss
potential mitigation approaches to realize the safe use of NER models in light
of the privacy and security implications of membership inference attacks.

Bimanual activities like coffee stirring, which require coordination of dual
arms, are common in daily life and intractable to learn by robots. Adopting
reinforcement learning to learn these tasks is a promising topic since it
enables the robot to explore how dual arms coordinate together to accomplish
the same task. However, this field has two main challenges: coordination
mechanism and long-horizon task decomposition. Therefore, we propose the
Mixline method to learn sub-tasks separately via the online algorithm and then
compose them together based on the generated data through the offline
algorithm. We constructed a learning environment based on the GPU-accelerated
Isaac Gym. In our work, the bimanual robot successfully learned to grasp, hold
and lift the spoon and cup, insert them together and stir the coffee. The
proposed method has the potential to be extended to other long-horizon bimanual
tasks.

Inferring the quality of network services is the vital basis of optimization
for network operators. However, prevailing real-time video streaming
applications adopt encryption for security, leaving it a problem to extract
Quality of Service (QoS) indicators of real-time video. In this paper, we
propose DaI, a traffic-based real-time video quality estimator. DaI can
partially decrypt the encrypted real-time video data and applies machine
learning methods to estimate key objective Quality of Experience (QoE) metrics
of real-time video. According to the experimental results, DaI can estimate
objective QoE metrics with an average accuracy of 79%.

Asteroids are an indelible part of most astronomical surveys though only a
few surveys are dedicated to their detection. Over the years, high cadence
microlensing surveys have amassed several terabytes of data while scanning
primarily the Galactic Bulge and Magellanic Clouds for microlensing events and
thus provide a treasure trove of opportunities for scientific data mining. In
particular, numerous asteroids have been observed by visual inspection of
selected images. This paper presents novel deep learning-based solutions for
the recovery and discovery of asteroids in the microlensing data gathered by
the MOA project. Asteroid tracklets can be clearly seen by combining all the
observations on a given night and these tracklets inform the structure of the
dataset. Known asteroids were identified within these composite images and used
for creating the labelled datasets required for supervised learning. Several
custom CNN models were developed to identify images with asteroid tracklets.
Model ensembling was then employed to reduce the variance in the predictions as
well as to improve the generalisation error, achieving a recall of 97.67%.
Furthermore, the YOLOv4 object detector was trained to localize asteroid
tracklets, achieving a mean Average Precision (mAP) of 90.97%. These trained
networks will be applied to 16 years of MOA archival data to find both known
and unknown asteroids that have been observed by the survey over the years. The
methodologies developed can be adapted for use by other surveys for asteroid
recovery and discovery.

Uncertainty quantification (UQ) has increasing importance in building robust
high-performance and generalizable materials property prediction models. It can
also be used in active learning to train better models by focusing on getting
new training data from uncertain regions. There are several categories of UQ
methods each considering different types of uncertainty sources. Here we
conduct a comprehensive evaluation on the UQ methods for graph neural network
based materials property prediction and evaluate how they truly reflect the
uncertainty that we want in error bound estimation or active learning. Our
experimental results over four crystal materials datasets (including formation
energy, adsorption energy, total energy, and band gap properties) show that the
popular ensemble methods for uncertainty estimation is NOT the best choice for
UQ in materials property prediction. For the convenience of the community, all
the source code and data sets can be accessed freely at
\url{https://github.com/usccolumbia/materialsUQ}.

Kidney transplantation is the preferred treatment for people suffering from
end-stage renal disease. Successful kidney transplants still fail over time,
known as graft failure; however, the time to graft failure, or graft survival
time, can vary significantly between different recipients. A significant
biological factor affecting graft survival times is the compatibility between
the human leukocyte antigens (HLAs) of the donor and recipient. We propose to
model HLA compatibility using a network, where the nodes denote different HLAs
of the donor and recipient, and edge weights denote compatibilities of the
HLAs, which can be positive or negative. The network is indirectly observed, as
the edge weights are estimated from transplant outcomes rather than directly
observed. We propose a latent space model for such indirectly-observed weighted
and signed networks. We demonstrate that our latent space model can not only
result in more accurate estimates of HLA compatibilities, but can also be
incorporated into survival analysis models to improve accuracy for the
downstream task of predicting graft survival times.

Active learning with strong and weak labelers considers a practical setting
where we have access to both costly but accurate strong labelers and inaccurate
but cheap predictions provided by weak labelers. We study this problem in the
streaming setting, where decisions must be taken \textit{online}. We design a
novel algorithmic template, Weak Labeler Active Cover (WL-AC), that is able to
robustly leverage the lower quality weak labelers to reduce the query
complexity while retaining the desired level of accuracy. Prior active learning
algorithms with access to weak labelers learn a difference classifier which
predicts where the weak labels differ from strong labelers; this requires the
strong assumption of realizability of the difference classifier (Zhang and
Chaudhuri,2015). WL-AC bypasses this \textit{realizability} assumption and thus
is applicable to many real-world scenarios such as random corrupted weak labels
and high dimensional family of difference classifiers (\textit{e.g.,} deep
neural nets). Moreover, WL-AC cleverly trades off evaluating the quality with
full exploitation of weak labelers, which allows to convert any active learning
strategy to one that can leverage weak labelers. We provide an instantiation of
this template that achieves the optimal query complexity for any given weak
labeler, without knowing its accuracy a-priori. Empirically, we propose an
instantiation of the WL-AC template that can be efficiently implemented for
large-scale models (\textit{e.g}., deep neural nets) and show its effectiveness
on the corrupted-MNIST dataset by significantly reducing the number of labels
while keeping the same accuracy as in passive learning.

Directed graphs model asymmetric relationships between nodes and research on
directed graph embedding is of great significance in downstream graph analysis
and inference. Learning source and target embedding of nodes separately to
preserve edge asymmetry has become the dominant approach, but also poses
challenge for learning representations of low or even zero in/out degree nodes
that are ubiquitous in sparse graphs. In this paper, a collaborative
bi-directional aggregation method (COBA) for directed graphs embedding is
proposed by introducing spatial-based graph convolution. Firstly, the source
and target embeddings of the central node are learned by aggregating from the
counterparts of the source and target neighbors, respectively; Secondly, the
source/target embeddings of the zero in/out degree central nodes are enhanced
by aggregating the counterparts of opposite-directional neighbors (i.e.
target/source neighbors); Finally, source and target embeddings of the same
node are correlated to achieve collaborative aggregation. Extensive experiments
on real-world datasets demonstrate that the COBA comprehensively outperforms
state-of-the-art methods on multiple tasks and meanwhile validates the
effectiveness of proposed aggregation strategies.

Skill-based reinforcement learning (RL) has emerged as a promising strategy
to leverage prior knowledge for accelerated robot learning. Skills are
typically extracted from expert demonstrations and are embedded into a latent
space from which they can be sampled as actions by a high-level RL agent.
However, this skill space is expansive, and not all skills are relevant for a
given robot state, making exploration difficult. Furthermore, the downstream RL
agent is limited to learning structurally similar tasks to those used to
construct the skill space. We firstly propose accelerating exploration in the
skill space using state-conditioned generative models to directly bias the
high-level agent towards only sampling skills relevant to a given state based
on prior experience. Next, we propose a low-level residual policy for
fine-grained skill adaptation enabling downstream RL agents to adapt to unseen
task variations. Finally, we validate our approach across four challenging
manipulation tasks that differ from those used to build the skill space,
demonstrating our ability to learn across task variations while significantly
accelerating exploration, outperforming prior works. Code and videos are
available on our project website: https://krishanrana.github.io/reskill.

We study the problem of sequential experimental design to estimate the
parametric component of a partially linear model with a Gaussian process prior.
We consider an active learning setting where an experimenter adaptively decides
which data to collect to achieve their goal efficiently. The experimenter's
goals may vary, such as reducing the classification error probability or
improving the accuracy of estimating the parameters of the data generating
process. This study aims to improve the accuracy of estimating the parametric
component of a partially linear model. Under some assumptions, the parametric
component of a partially linear model can be regarded as a causal parameter,
the average treatment effect (ATE) or the average causal effect (ACE). We
propose a Bayesian sequential experimental design algorithm for a partially
linear model with a Gaussian process prior, which is also considered as a
sequential experimental design tailored to the estimation of ATE or ACE. We
show the effectiveness of the proposed method through numerical experiments
based on synthetic and semi-synthetic data.

The advent of hyper-scale and general-purpose pre-trained models is shifting
the paradigm of building task-specific models for target tasks. In the field of
audio research, task-agnostic pre-trained models with high transferability and
adaptability have achieved state-of-the-art performances through fine-tuning
for downstream tasks. Nevertheless, re-training all the parameters of these
massive models entails an enormous amount of time and cost, along with a huge
carbon footprint. To overcome these limitations, the present study explores and
applies efficient transfer learning methods in the audio domain. We also
propose an integrated parameter-efficient tuning (IPET) framework by
aggregating the embedding prompt (a prompt-based learning approach), and the
adapter (an effective transfer learning method). We demonstrate the efficacy of
the proposed framework using two backbone pre-trained audio models with
different characteristics: the audio spectrogram transformer and wav2vec 2.0.
The proposed IPET framework exhibits remarkable performance compared to
fine-tuning method with fewer trainable parameters in four downstream tasks:
sound event classification, music genre classification, keyword spotting, and
speaker verification. Furthermore, the authors identify and analyze the
shortcomings of the IPET framework, providing lessons and research directions
for parameter efficient tuning in the audio domain.

Spiking neural networks (SNNs) attract great attention due to their low power
consumption, low latency, and biological plausibility. As they are widely
deployed in neuromorphic devices for low-power brain-inspired computing,
security issues become increasingly important. However, compared to deep neural
networks (DNNs), SNNs currently lack specifically designed defense methods
against adversarial attacks. Inspired by neural membrane potential oscillation,
we propose a novel neural model that incorporates the bio-inspired oscillation
mechanism to enhance the security of SNNs. Our experiments show that SNNs with
neural oscillation neurons have better resistance to adversarial attacks than
ordinary SNNs with LIF neurons on kinds of architectures and datasets.
Furthermore, we propose a defense method that changes model's gradients by
replacing the form of oscillation, which hides the original training gradients
and confuses the attacker into using gradients of 'fake' neurons to generate
invalid adversarial samples. Our experiments suggest that the proposed defense
method can effectively resist both single-step and iterative attacks with
comparable defense effectiveness and much less computational costs than
adversarial training methods on DNNs. To the best of our knowledge, this is the
first work that establishes adversarial defense through masking surrogate
gradients on SNNs.

Model-Based Reinforcement Learning (RL) is widely believed to have the
potential to improve sample efficiency by allowing an agent to synthesize large
amounts of imagined experience. Experience Replay (ER) can be considered a
simple kind of model, which has proved extremely effective at improving the
stability and efficiency of deep RL. In principle, a learned parametric model
could improve on ER by generalizing from real experience to augment the dataset
with additional plausible experience. However, owing to the many design choices
involved in empirically successful algorithms, it can be very hard to establish
where the benefits are actually coming from. Here, we provide theoretical and
empirical insight into when, and how, we can expect data generated by a learned
model to be useful. First, we provide a general theorem motivating how learning
a model as an intermediate step can narrow down the set of possible value
functions more than learning a value function directly from data using the
Bellman equation. Second, we provide an illustrative example showing
empirically how a similar effect occurs in a more concrete setting with neural
network function approximation. Finally, we provide extensive experiments
showing the benefit of model-based learning for online RL in environments with
combinatorial complexity, but factored structure that allows a learned model to
generalize. In these experiments, we take care to control for other factors in
order to isolate, insofar as possible, the benefit of using experience
generated by a learned model relative to ER alone.

Pre-trained Vision-Language Models (VLMs) such as CLIP have shown impressive
generalization capability in downstream vision tasks with appropriate text
prompts. Instead of designing prompts manually, Context Optimization (CoOp) has
been recently proposed to learn continuous prompts using task-specific training
data. Despite the performance improvements on downstream tasks, several studies
have reported that CoOp suffers from the overfitting issue in two aspects: (i)
the test accuracy on base classes first gets better and then gets worse during
training; (ii) the test accuracy on novel classes keeps decreasing. However,
none of the existing studies can understand and mitigate such overfitting
problem effectively. In this paper, we first explore the cause of overfitting
by analyzing the gradient flow. Comparative experiments reveal that CoOp favors
generalizable and spurious features in the early and later training stages
respectively, leading to the non-overfitting and overfitting phenomenon. Given
those observations, we propose Subspace Prompt Tuning (SubPT) to project the
gradients in back-propagation onto the low-rank subspace spanned by the
early-stage gradient flow eigenvectors during the entire training process, and
successfully eliminate the overfitting problem. Besides, we equip CoOp with
Novel Feature Learner (NFL) to enhance the generalization ability of the
learned prompts onto novel categories beyond the training set, needless of
image training data. Extensive experiments on 11 classification datasets
demonstrate that SubPT+NFL consistently boost the performance of CoOp and
outperform the state-of-the-art approach CoCoOp. Experiments on more
challenging vision downstream tasks including open-vocabulary object detection
and zero-shot semantic segmentation also verify the effectiveness of the
proposed method. Codes can be found at https://tinyurl.com/mpe64f89.

Real engineering and scientific applications often involve one or more
qualitative inputs. Standard Gaussian processes (GPs), however, cannot directly
accommodate qualitative inputs. The recently introduced latent variable
Gaussian process (LVGP) overcomes this issue by first mapping each qualitative
factor to underlying latent variables (LVs), and then uses any standard GP
covariance function over these LVs. The LVs are estimated similarly to the
other GP hyperparameters through maximum likelihood estimation, and then
plugged into the prediction expressions. However, this plug-in approach will
not account for uncertainty in estimation of the LVs, which can be significant
especially with limited training data. In this work, we develop a fully
Bayesian approach for the LVGP model and for visualizing the effects of the
qualitative inputs via their LVs. We also develop approximations for scaling up
LVGPs and fully Bayesian inference for the LVGP hyperparameters. We conduct
numerical studies comparing plug-in inference against fully Bayesian inference
over a few engineering models and material design applications. In contrast to
previous studies on standard GP modeling that have largely concluded that a
fully Bayesian treatment offers limited improvements, our results show that for
LVGP modeling it offers significant improvements in prediction accuracy and
uncertainty quantification over the plug-in approach.

The paper studies the distributed stochastic compositional optimization
problems over networks, where all the agents' inner-level function is the sum
of each agent's private expectation function. Focusing on the aggregative
structure of the inner-level function, we employ the hybrid variance reduction
method to obtain the information on each agent's private expectation function,
and apply the dynamic consensus mechanism to track the information on each
agent's inner-level function. Then by combining with the standard distributed
stochastic gradient descent method, we propose a distributed aggregative
stochastic compositional gradient descent method. When the objective function
is smooth, the proposed method achieves the optimal convergence rate
$\mathcal{O}\left(K^{-1/2}\right)$. We further combine the proposed method with
the communication compression and propose the communication compressed variant
distributed aggregative stochastic compositional gradient descent method. The
compressed variant of the proposed method maintains the optimal convergence
rate $\mathcal{O}\left(K^{-1/2}\right)$. Simulated experiments on decentralized
reinforcement learning verify the effectiveness of the proposed methods.

We consider distributed linear bandits where $M$ agents learn collaboratively
to minimize the overall cumulative regret incurred by all agents. Information
exchange is facilitated by a central server, and both the uplink and downlink
communications are carried over channels with fixed capacity, which limits the
amount of information that can be transmitted in each use of the channels. We
investigate the regret-communication trade-off by (i) establishing
information-theoretic lower bounds on the required communications (in terms of
bits) for achieving a sublinear regret order; (ii) developing an efficient
algorithm that achieves the minimum sublinear regret order offered by
centralized learning using the minimum order of communications dictated by the
information-theoretic lower bounds. For sparse linear bandits, we show a
variant of the proposed algorithm offers better regret-communication trade-off
by leveraging the sparsity of the problem.

Federated Learning (FL) is a novel machine learning approach that allows the
model trainer to access more data samples, by training the model across
multiple decentralized data sources, while data access constraints are in
place. Such trained models can achieve significantly higher performance beyond
what can be done when trained on a single data source. As part of FL's
promises, none of the training data is ever transmitted to any central
location, ensuring that sensitive data remains local and private. These
characteristics make FL perfectly suited for large-scale applications in
healthcare, where a variety of compliance constraints restrict how data may be
handled, processed, and stored. Despite the apparent benefits of federated
learning, the heterogeneity in the local data distributions pose significant
challenges, and such challenges are even more pronounced in the case of
multilingual data providers. In this paper we present a federated learning
system for training a large-scale multi-lingual model suitable for fine-tuning
on downstream tasks such as medical entity tagging. Our work represents one of
the first such production-scale systems, capable of training across multiple
highly heterogeneous data providers, and achieving levels of accuracy that
could not be otherwise achieved by using central training with public data.
Finally, we show that the global model performance can be further improved by a
training step performed locally.

When humans perform contact-rich manipulation tasks, customized tools are
often necessary and play an important role in simplifying the task. For
instance, in our daily life, we use various utensils for handling food, such as
knives, forks and spoons. Similarly, customized tools for robots may enable
them to more easily perform a variety of tasks. Here, we present an end-to-end
framework to automatically learn tool morphology for contact-rich manipulation
tasks by leveraging differentiable physics simulators. Previous work approached
this problem by introducing manually constructed priors that required detailed
specification of object 3D model, grasp pose and task description to facilitate
the search or optimization. In our approach, we instead only need to define the
objective with respect to the task performance and enable learning a robust
morphology by randomizing the task variations. The optimization is made
tractable by casting this as a continual learning problem. We demonstrate the
effectiveness of our method for designing new tools in several scenarios such
as winding ropes, flipping a box and pushing peas onto a scoop in simulation.
We also validate that the shapes discovered by our method help real robots
succeed in these scenarios.

We introduce efficient deep learning-based methods for legal document
processing including Legal Document Retrieval and Legal Question Answering
tasks in the Automated Legal Question Answering Competition (ALQAC 2022). In
this competition, we achieve 1\textsuperscript{st} place in the first task and
3\textsuperscript{rd} place in the second task. Our method is based on the
XLM-RoBERTa model that is pre-trained from a large amount of unlabeled corpus
before fine-tuning to the specific tasks. The experimental results showed that
our method works well in legal retrieval information tasks with limited labeled
data. Besides, this method can be applied to other information retrieval tasks
in low-resource languages.

The negative demand shock due to the COVID-19 lockdown has reduced net demand
for electricity -- system demand less amount of energy produced by intermittent
renewables, hydroelectric units, and net imports -- that must be served by
controllable generation units. Under normal demand conditions, introducing
additional renewable generation capacity reduces net demand. Consequently, the
lockdown can provide insights about electricity market performance with a large
share of renewables. We find that although the lockdown reduced average
day-ahead prices in Italy by 45%, re-dispatch costs increased by 73%, both
relative to the average of the same magnitude for the same period in previous
years. We estimate a deep-learning model using data from 2017--2019 and find
that predicted re-dispatch costs during the lockdown period are only 26% higher
than the same period in previous years. We argue that the difference between
actual and predicted lockdown period re-dispatch costs is the result of
increased opportunities for suppliers with controllable units to exercise
market power in the re-dispatch market in these persistently low net demand
conditions. Our results imply that without grid investments and other
technologies to manage low net demand conditions, an increased share of
intermittent renewables is likely to increase costs of maintaining a reliable
grid.

Persistent Memory (PMEM), also known as Non-Volatile Memory (NVM), can
deliver higher density and lower cost per bit when compared with DRAM. Its main
drawback is that it is typically slower than DRAM. On the other hand, DRAM has
scalability problems due to its cost and energy consumption. Soon, PMEM will
likely coexist with DRAM in computer systems but the biggest challenge is to
know which data to allocate on each type of memory. This paper describes a
methodology for identifying and characterizing application objects that have
the most influence on the application's performance using Intel Optane DC
Persistent Memory. In the first part of our work, we built a tool that
automates the profiling and analysis of application objects. In the second
part, we build a machine learning model to predict the most critical object
within large-scale graph-based applications. Our results show that using
isolated features does not bring the same benefit compared to using a carefully
chosen set of features. By performing data placement using our predictive
model, we can reduce the execution time degradation by 12\% (average) and 30\%
(max) when compared to the baseline's approach based on LLC misses indicator.

We present a Quality-Diversity benchmark suite for Deep Neuroevolution in
Reinforcement Learning domains for robot control. The suite includes the
definition of tasks, environments, behavioral descriptors, and fitness. We
specify different benchmarks based on the complexity of both the task and the
agent controlled by a deep neural network. The benchmark uses standard
Quality-Diversity metrics, including coverage, QD-score, maximum fitness, and
an archive profile metric to quantify the relation between coverage and
fitness. We also present how to quantify the robustness of the solutions with
respect to environmental stochasticity by introducing corrected versions of the
same metrics. We believe that our benchmark is a valuable tool for the
community to compare and improve their findings. The source code is available
online: https://github.com/adaptive-intelligent-robotics/QDax

In this research work, we have demonstrated the application of Mask-RCNN
(Regional Convolutional Neural Network), a deep-learning algorithm for computer
vision and specifically object detection, to semiconductor defect inspection
domain. Stochastic defect detection and classification during semiconductor
manufacturing has grown to be a challenging task as we continuously shrink
circuit pattern dimensions (e.g., for pitches less than 32 nm). Defect
inspection and analysis by state-of-the-art optical and e-beam inspection tools
is generally driven by some rule-based techniques, which in turn often causes
to misclassification and thereby necessitating human expert intervention. In
this work, we have revisited and extended our previous deep learning-based
defect classification and detection method towards improved defect instance
segmentation in SEM images with precise extent of defect as well as generating
a mask for each defect category/instance. This also enables to extract and
calibrate each segmented mask and quantify the pixels that make up each mask,
which in turn enables us to count each categorical defect instances as well as
to calculate the surface area in terms of pixels. We are aiming at detecting
and segmenting different types of inter-class stochastic defect patterns such
as bridge, break, and line collapse as well as to differentiate accurately
between intra-class multi-categorical defect bridge scenarios (as
thin/single/multi-line/horizontal/non-horizontal) for aggressive pitches as
well as thin resists (High NA applications). Our proposed approach demonstrates
its effectiveness both quantitatively and qualitatively.

For the majority of the machine learning community, the expensive nature of
collecting high-quality human-annotated data and the inability to efficiently
finetune very large state-of-the-art pretrained models on limited compute are
major bottlenecks for building models for new tasks. We propose a zero-shot
simple approach for one such task, Video Moment Retrieval (VMR), that does not
perform any additional finetuning and simply repurposes off-the-shelf models
trained on other tasks. Our three-step approach consists of moment proposal,
moment-query matching and postprocessing, all using only off-the-shelf models.
On the QVHighlights benchmark for VMR, we vastly improve performance of
previous zero-shot approaches by at least 2.5x on all metrics and reduce the
gap between zero-shot and state-of-the-art supervised by over 74%. Further, we
also show that our zero-shot approach beats non-pretrained supervised models on
the Recall metrics and comes very close on mAP metrics; and that it also
performs better than the best pretrained supervised model on shorter moments.
Finally, we ablate and analyze our results and propose interesting future

In this work, we propose MUSTACHE, a new page cache replacement algorithm
whose logic is learned from observed memory access requests rather than fixed
like existing policies. We formulate the page request prediction problem as a
categorical time series forecasting task. Then, our method queries the learned
page request forecaster to obtain the next $k$ predicted page memory references
to better approximate the optimal B\'el\'ady's replacement algorithm. We
implement several forecasting techniques using advanced deep learning
architectures and integrate the best-performing one into an existing
open-source cache simulator. Experiments run on benchmark datasets show that
MUSTACHE outperforms the best page replacement heuristic (i.e., exact LRU),
improving the cache hit ratio by 1.9% and reducing the number of reads/writes
required to handle cache misses by 18.4% and 10.3%.

Restricted Boltzmann Machines (RBMs) are probabilistic generative models that
can be trained by maximum likelihood in principle, but are usually trained by
an approximate algorithm called Contrastive Divergence (CD) in practice. In
general, a CD-k algorithm estimates an average with respect to the model
distribution using a sample obtained from a k-step Markov Chain Monte Carlo
Algorithm (e.g., block Gibbs sampling) starting from some initial
configuration. Choices of k typically vary from 1 to 100. This technical report
explores if it's possible to leverage a simple approximate sampling algorithm
with a modified version of CD in order to train an RBM with k=0. As usual, the
method is illustrated on MNIST.

Gradients of probabilistic model likelihoods with respect to their parameters
are essential for modern computational statistics and machine learning. These
calculations are readily available for arbitrary models via automatic
differentiation implemented in general-purpose machine-learning libraries such
as TensorFlow and PyTorch. Although these libraries are highly optimized, it is
not clear if their general-purpose nature will limit their algorithmic
complexity or implementation speed for the phylogenetic case compared to
phylogenetics-specific code. In this paper, we compare six gradient
implementations of the phylogenetic likelihood functions, in isolation and also
as part of a variational inference procedure. We find that although automatic
differentiation can scale approximately linearly in tree size, it is much
slower than the carefully-implemented gradient calculation for tree likelihood
and ratio transformation operations. We conclude that a mixed approach
combining phylogenetic libraries with machine learning libraries will provide
the optimal combination of speed and model flexibility moving forward.

Spiking Neural Networks (SNNs) are bio-plausible models that hold great
potential for realizing energy-efficient implementations of sequential tasks on
resource-constrained edge devices. However, commercial edge platforms based on
standard GPUs are not optimized to deploy SNNs, resulting in high energy and
latency. While analog In-Memory Computing (IMC) platforms can serve as
energy-efficient inference engines, they are accursed by the immense energy,
latency, and area requirements of high-precision ADCs (HP-ADC), overshadowing
the benefits of in-memory computations. We propose a hardware/software
co-design methodology to deploy SNNs into an ADC-Less IMC architecture using
sense-amplifiers as 1-bit ADCs replacing conventional HP-ADCs and alleviating
the above issues. Our proposed framework incurs minimal accuracy degradation by
performing hardware-aware training and is able to scale beyond simple image
classification tasks to more complex sequential regression tasks. Experiments
on complex tasks of optical flow estimation and gesture recognition show that
progressively increasing the hardware awareness during SNN training allows the
model to adapt and learn the errors due to the non-idealities associated with
ADC-Less IMC. Also, the proposed ADC-Less IMC offers significant energy and
latency improvements, $2-7\times$ and $8.9-24.6\times$, respectively, depending
on the SNN model and the workload, compared to HP-ADC IMC.

Besides accuracy, recent studies on machine learning models have been
addressing the question on how the obtained results can be interpreted. Indeed,
while complex machine learning models are able to provide very good results in
terms of accuracy even in challenging applications, it is difficult to
interpret them. Aiming at providing some interpretability for such models, one
of the most famous methods, called SHAP, borrows the Shapley value concept from
game theory in order to locally explain the predicted outcome of an instance of
interest. As the SHAP values calculation needs previous computations on all
possible coalitions of attributes, its computational cost can be very high.
Therefore, a SHAP-based method called Kernel SHAP adopts an efficient strategy
that approximate such values with less computational effort. In this paper, we
also address local interpretability in machine learning based on Shapley
values. Firstly, we provide a straightforward formulation of a SHAP-based
method for local interpretability by using the Choquet integral, which leads to
both Shapley values and Shapley interaction indices. Moreover, we also adopt
the concept of $k$-additive games from game theory, which contributes to reduce
the computational effort when estimating the SHAP values. The obtained results
attest that our proposal needs less computations on coalitions of attributes to
approximate the SHAP values.

Beamforming is a signal processing technique to steer, shape, and focus an
electromagnetic wave using an array of sensors toward a desired direction. It
has been used in several engineering applications such as radar, sonar,
acoustics, astronomy, seismology, medical imaging, and communications. With the
advances in multi-antenna technologies largely for radar and communications,
there has been a great interest on beamformer design mostly relying on
convex/nonconvex optimization. Recently, machine learning is being leveraged
for obtaining attractive solutions to more complex beamforming problems. This
article captures the evolution of beamforming in the last twenty-five years
from convex-to-nonconvex optimization and optimization-to-learning approaches.
It provides a glimpse of this important signal processing technique into a
variety of transmit-receive architectures, propagation zones, paths, and
conventional/emerging applications.

This paper shows the implementation of reinforcement learning (RL) in
commercial flowsheet simulator software (Aspen Plus V12) for designing and
optimising a distillation sequence. The aim of the SAC agent was to separate a
hydrocarbon mixture in its individual components by utilising distillation.
While doing so it tries to maximise the profit produced by the distillation
sequence. All actions of the agent were set by the SAC agent in Python and
communicated in Aspen Plus via an API. Here the distillation column was
simulated by use of the build-in RADFRAC column. With this a connection was
established for data transfer between Python and Aspen and the agent succeeded
to show learning behaviour, while increasing profit. Although results were
generated, the use of Aspen was slow (190 hours) and Aspen was found unsuitable
for parallelisation. This makes that Aspen is incompatible for solving RL
problems. Code and thesis are available at https://github.com/lollcat/Aspen-RL

We consider a class of Riemannian optimization problems where the objective
is the sum of a smooth function and a nonsmooth function, considered in the
ambient space. This class of problems finds important applications in machine
learning and statistics such as the sparse principal component analysis, sparse
spectral clustering, and orthogonal dictionary learning. We propose a
Riemannian alternating direction method of multipliers (ADMM) to solve this
class of problems. Our algorithm adopts easily computable steps in each
iteration. The iteration complexity of the proposed algorithm for obtaining an
$\epsilon$-stationary point is analyzed under mild assumptions. To the best of
our knowledge, this is the first Riemannian ADMM with provable convergence
guarantee for solving Riemannian optimization problem with nonsmooth objective.
Numerical experiments are conducted to demonstrate the advantage of the

Deep learning-based linkage of records across different databases is becoming
increasingly useful in data integration and mining applications to discover new
insights from multiple sources of data. However, due to privacy and
confidentiality concerns, organisations often are not willing or allowed to
share their sensitive data with any external parties, thus making it
challenging to build/train deep learning models for record linkage across
different organizations' databases. To overcome this limitation, we propose the
first deep learning-based multi-party privacy-preserving record linkage (PPRL)
protocol that can be used to link sensitive databases held by multiple
different organisations. In our approach, each database owner first trains a
local deep learning model, which is then uploaded to a secure environment and
securely aggregated to create a global model. The global model is then used by
a linkage unit to distinguish unlabelled record pairs as matches and
non-matches. We utilise differential privacy to achieve provable privacy
protection against re-identification attacks. We evaluate the linkage quality
and scalability of our approach using several large real-world databases,
showing that it can achieve high linkage quality while providing sufficient
privacy protection against existing attacks.

The goal of algorithmic recourse is to reverse unfavorable decisions (e.g.,
from loan denial to approval) under automated decision making by suggesting
actionable feature changes (e.g., reduce the number of credit cards). To
generate low-cost recourse the majority of methods work under the assumption
that the features are independently manipulable (IMF). To address the feature
dependency issue the recourse problem is usually studied through the causal
recourse paradigm. However, it is well known that strong assumptions, as
encoded in causal models and structural equations, hinder the applicability of
these methods in complex domains where causal dependency structures are
ambiguous. In this work, we develop \texttt{DEAR} (DisEntangling Algorithmic
Recourse), a novel and practical recourse framework that bridges the gap
between the IMF and the strong causal assumptions. \texttt{DEAR} generates
recourses by disentangling the latent representation of co-varying features
from a subset of promising recourse features to capture the main practical
recourse desiderata. Our experiments on real-world data corroborate our
theoretically motivated recourse model and highlight our framework's ability to
provide reliable, low-cost recourse in the presence of feature dependencies.

Models that accurately predict properties based on chemical structure are
valuable tools in drug discovery. However, for many properties, public and
private training sets are typically small, and it is difficult for the models
to generalize well outside of the training data. Recently, large language
models have addressed this problem by using self-supervised pretraining on
large unlabeled datasets, followed by fine-tuning on smaller, labeled datasets.
In this paper, we report MolE, a molecular foundation model that adapts the
DeBERTa architecture to be used on molecular graphs together with a two-step
pretraining strategy. The first step of pretraining is a self-supervised
approach focused on learning chemical structures, and the second step is a
massive multi-task approach to learn biological information. We show that
fine-tuning pretrained MolE achieves state-of-the-art results on 9 of the 22
ADMET tasks included in the Therapeutic Data Commons.

Compared with model-based control and optimization methods, reinforcement
learning (RL) provides a data-driven, learning-based framework to formulate and
solve sequential decision-making problems. The RL framework has become
promising due to largely improved data availability and computing power in the
aviation industry. Many aviation-based applications can be formulated or
treated as sequential decision-making problems. Some of them are offline
planning problems, while others need to be solved online and are
safety-critical. In this survey paper, we first describe standard RL
formulations and solutions. Then we survey the landscape of existing RL-based
applications in aviation. Finally, we summarize the paper, identify the
technical gaps, and suggest future directions of RL research in aviation.

Time series motif discovery has been a fundamental task to identify
meaningful repeated patterns in time series. Recently, time series chains were
introduced as an expansion of time series motifs to identify the continuous
evolving patterns in time series data. Informally, a time series chain (TSC) is
a temporally ordered set of time series subsequences, in which every
subsequence is similar to the one that precedes it, but the last and the first
can be arbitrarily dissimilar. TSCs are shown to be able to reveal latent
continuous evolving trends in the time series, and identify precursors of
unusual events in complex systems. Despite its promising interpretability,
unfortunately, we have observed that existing TSC definitions lack the ability
to accurately cover the evolving part of a time series: the discovered chains
can be easily cut by noise and can include non-evolving patterns, making them
impractical in real-world applications. Inspired by a recent work that tracks
how the nearest neighbor of a time series subsequence changes over time, we
introduce a new TSC definition which is much more robust to noise in the data,
in the sense that they can better locate the evolving patterns while excluding
the non-evolving ones. We further propose two new quality metrics to rank the
discovered chains. With extensive empirical evaluations, we demonstrate that
the proposed TSC definition is significantly more robust to noise than the
state of the art, and the top ranked chains discovered can reveal meaningful
regularities in a variety of real world datasets.

In peer review systems, reviewers are often asked to evaluate various
features of submissions, such as technical quality or novelty. A score is given
to each of the predefined features and based on these the reviewer has to
provide an overall quantitative recommendation. However, reviewers differ in
how much they value different features. It may be assumed that each reviewer
has her own mapping from a set of criteria scores (score vectors) to a
recommendation, and that different reviewers have different mappings in mind.
Recently, Noothigattu, Shah and Procaccia introduced a novel framework for
obtaining an aggregated mapping by means of Empirical Risk Minimization based
on $L(p,q)$ loss functions, and studied its axiomatic properties in the sense
of social choice theory. We provide a body of new results about this framework.
On the one hand we study a trade-off between strategy-proofness and the ability
of the method to properly capture agreements of the majority of reviewers. On
the other hand, we show that dropping a certain unrealistic assumption makes
the previously reported results to be no longer valid. Moreover, in the general
case, strategy-proofness fails dramatically in the sense that a reviewer is
able to make significant changes to the solution in her favor by arbitrarily
small changes to their true beliefs. In particular, no approximate version of
strategy-proofness is possible in this general setting since the method is not
even continuous w.r.t. the data. Finally we propose a modified aggregation
algorithm which is continuous and show that it has good axiomatic properties.

Semi-supervised learning (SSL) leverages both labeled and unlabeled data for
training models when the labeled data is limited and the unlabeled data is
vast. Frequently, the unlabeled data is more widely available than the labeled
data, hence this data is used to improve the level of generalization of a model
when the labeled data is scarce. However, in real-world settings unlabeled data
might depict a different distribution than the labeled dataset distribution.
This is known as distribution mismatch. Such problem generally occurs when the
source of unlabeled data is different from the labeled data. For instance, in
the medical imaging domain, when training a COVID-19 detector using chest X-ray
images, different unlabeled datasets sampled from different hospitals might be
used. In this work, we propose an automatic thresholding method to filter
out-of-distribution data in the unlabeled dataset. We use the Mahalanobis
distance between the labeled and unlabeled datasets using the feature space
built by a pre-trained Image-net Feature Extractor (FE) to score each unlabeled
observation. We test two simple automatic thresholding methods in the context
of training a COVID-19 detector using chest X-ray images. The tested methods
provide an automatic manner to define what unlabeled data to preserve when
training a semi-supervised deep learning architecture.

Existing regulations prohibit model developers from accessing protected
attributes (gender, race, etc.), often resulting in fairness assessments on
populations without knowing their protected groups. In such scenarios,
institutions often adopt a separation between the model developers (who train
models with no access to the protected attributes) and a compliance team (who
may have access to the entire dataset for auditing purpose). However, the model
developers might be allowed to test their models for bias by querying the
compliance team for group fairness metrics. In this paper, we first demonstrate
that simply querying for fairness metrics, such as statistical parity and
equalized odds can leak the protected attributes of individuals to the model
developers. We demonstrate that there always exist strategies by which the
model developers can identify the protected attribute of a targeted individual
in the test dataset from just a single query. In particular, we show that one
can reconstruct the protected attributes of all the individuals from O(Nk log
n/Nk) queries when Nk<<n using techniques from compressed sensing (n: size of
the test dataset, Nk: size of smallest group). Our results pose an interesting
debate in algorithmic fairness: should querying for fairness metrics be viewed
as a neutral-valued solution to ensure compliance with regulations? Or, does it
constitute a violation of regulations and privacy if the number of queries
answered is enough for the model developers to identify the protected
attributes of specific individuals? To address this supposed violation, we also
propose Attribute-Conceal, a novel technique that achieves differential privacy
by calibrating noise to the smooth sensitivity of our bias query, outperforming
naive techniques such as Laplace mechanism. We also include experimental
results on the Adult dataset and synthetic data (broad range of parameters).

Statistical language models conventionally implement representation learning
based on the contextual distribution of words or other formal units, whereas
any information related to the logographic features of written text are often
ignored, assuming they should be retrieved relying on the cooccurence
statistics. On the other hand, as language models become larger and require
more data to learn reliable representations, such assumptions may start to fall
back, especially under conditions of data sparsity. Many languages, including
Chinese and Vietnamese, use logographic writing systems where surface forms are
represented as a visual organization of smaller graphemic units, which often
contain many semantic cues. In this paper, we present a novel study which
explores the benefits of providing language models with logographic information
in learning better semantic representations. We test our hypothesis in the
natural language inference (NLI) task by evaluating the benefit of computing
multi-modal representations that combine contextual information with glyph
information. Our evaluation results in six languages with different typology
and writing systems suggest significant benefits of using multi-modal
embeddings in languages with logograhic systems, especially for words with less
occurence statistics.

The goal of autonomous vehicles is to navigate public roads safely and
comfortably. To enforce safety, traditional planning approaches rely on
handcrafted rules to generate trajectories. Machine learning-based systems, on
the other hand, scale with data and are able to learn more complex behaviors.
However, they often ignore that agents and self-driving vehicle trajectory
distributions can be leveraged to improve safety. In this paper, we propose
modeling a distribution over multiple future trajectories for both the
self-driving vehicle and other road agents, using a unified neural network
architecture for prediction and planning. During inference, we select the
planning trajectory that minimizes a cost taking into account safety and the
predicted probabilities. Our approach does not depend on any rule-based
planners for trajectory generation or optimization, improves with more training
data and is simple to implement. We extensively evaluate our method through a
realistic simulator and show that the predicted trajectory distribution
corresponds to different driving profiles. We also successfully deploy it on a
self-driving vehicle on urban public roads, confirming that it drives safely
without compromising comfort. The code for training and testing our model on a
public prediction dataset and the video of the road test are available at
https://woven.mobi/safepathnet

Molecular shape and geometry dictate key biophysical recognition processes,
yet many graph neural networks disregard 3D information for molecular property
prediction. Here, we propose a new contrastive-learning procedure for graph
neural networks, Molecular Contrastive Learning from Shape Similarity
(MolCLaSS), that implicitly learns a three-dimensional representation. Rather
than directly encoding or targeting three-dimensional poses, MolCLaSS matches a
similarity objective based on Gaussian overlays to learn a meaningful
representation of molecular shape. We demonstrate how this framework naturally
captures key aspects of three-dimensionality that two-dimensional
representations cannot and provides an inductive framework for scaffold
hopping.

We consider the problem of multi-agent navigation and collision avoidance
when observations are limited to the local neighborhood of each agent. We
propose InforMARL, a novel architecture for multi-agent reinforcement learning
(MARL) which uses local information intelligently to compute paths for all the
agents in a decentralized manner. Specifically, InforMARL aggregates
information about the local neighborhood of agents for both the actor and the
critic using a graph neural network and can be used in conjunction with any
standard MARL algorithm. We show that (1) in training, InforMARL has better
sample efficiency and performance than baseline approaches, despite using less
information, and (2) in testing, it scales well to environments with arbitrary
numbers of agents and obstacles.

Machine learning and deep learning-based decision making has become part of
today's software. The goal of this work is to ensure that machine learning and
deep learning-based systems are as trusted as traditional software. Traditional
software is made dependable by following rigorous practice like static
analysis, testing, debugging, verifying, and repairing throughout the
development and maintenance life-cycle. Similarly for machine learning systems,
we need to keep these models up to date so that their performance is not
compromised. For this, current systems rely on scheduled re-training of these
models as new data kicks in. In this work, we propose to measure the data drift
that takes place when new data kicks in so that one can adaptively re-train the
models whenever re-training is actually required irrespective of schedules. In
addition to that, we generate various explanations at sentence level and
dataset level to capture why a given payload text has drifted.

Automatic Arabic handwritten recognition is one of the recently studied
problems in the field of Machine Learning. Unlike Latin languages, Arabic is a
Semitic language that forms a harder challenge, especially with variability of
patterns caused by factors such as writer age. Most of the studies focused on
adults, with only one recent study on children. Moreover, much of the recent
Machine Learning methods focused on using Convolutional Neural Networks, a
powerful class of neural networks that can extract complex features from
images. In this paper we propose a convolutional neural network (CNN) model
that recognizes children handwriting with an accuracy of 91% on the Hijja
dataset, a recent dataset built by collecting images of the Arabic characters
written by children, and 97% on Arabic Handwritten Character Dataset. The
results showed a good improvement over the proposed model from the Hijja
dataset authors, yet it reveals a bigger challenge to solve for children Arabic
handwritten character recognition. Moreover, we proposed a new approach using
multi models instead of single model based on the number of strokes in a
character, and merged Hijja with AHCD which reached an averaged prediction
accuracy of 96%.

Much computer vision research has focused on natural images, but technical
documents typically consist of abstract images, such as charts, drawings,
diagrams, and schematics. How well do general web search engines discover
abstract images? Recent advancements in computer vision and machine learning
have led to the rise of reverse image search engines. Where conventional search
engines accept a text query and return a set of document results, including
images, a reverse image search accepts an image as a query and returns a set of
images as results. This paper evaluates how well common reverse image search
engines discover abstract images. We conducted an experiment leveraging images
from Wikimedia Commons, a website known to be well indexed by Baidu, Bing,
Google, and Yandex. We measure how difficult an image is to find again
(retrievability), what percentage of images returned are relevant (precision),
and the average number of results a visitor must review before finding the
submitted image (mean reciprocal rank). When trying to discover the same image
again among similar images, Yandex performs best. When searching for pages
containing a specific image, Google and Yandex outperform the others when
discovering photographs with precision scores ranging from 0.8191 to 0.8297,
respectively. In both of these cases, Google and Yandex perform better with
natural images than with abstract ones achieving a difference in retrievability
as high as 54\% between images in these categories. These results affect anyone
applying common web search engines to search for technical documents that use
abstract images.

The effective receptive field of a fully convolutional neural network is an
important consideration when designing an architecture, as it defines the
portion of the input visible to each convolutional kernel. We propose a neural
network module, extending traditional skip connections, called the translated
skip connection. Translated skip connections geometrically increase the
receptive field of an architecture with negligible impact on both the size of
the parameter space and computational complexity. By embedding translated skip
connections into a benchmark architecture, we demonstrate that our module
matches or outperforms four other approaches to expanding the effective
receptive fields of fully convolutional neural networks. We confirm this result
across five contemporary image segmentation datasets from disparate domains,
including the detection of COVID-19 infection, segmentation of aerial imagery,
common object segmentation, and segmentation for self-driving cars.

Solar forecasting from ground-based sky images using deep learning models has
shown great promise in reducing the uncertainty in solar power generation. One
of the biggest challenges for training deep learning models is the availability
of labeled datasets. With more and more sky image datasets open sourced in
recent years, the development of accurate and reliable solar forecasting
methods has seen a huge growth in potential. In this study, we explore three
different training strategies for deep-learning-based solar forecasting models
by leveraging three heterogeneous datasets collected around the world with
drastically different climate patterns. Specifically, we compare the
performance of models trained individually based on local datasets (local
models) and models trained jointly based on the fusion of multiple datasets
from different locations (global models), and we further examine the knowledge
transfer from pre-trained solar forecasting models to a new dataset of interest
(transfer learning models). The results suggest that the local models work well
when deployed locally, but significant errors are observed for the scale of the
prediction when applied offsite. The global model can adapt well to individual
locations, while the possible increase in training efforts need to be taken
into account. Pre-training models on a large and diversified source dataset and
transferring to a local target dataset generally achieves superior performance
over the other two training strategies. Transfer learning brings the most
benefits when there are limited local data. With 80% less training data, it can
achieve 1% improvement over the local baseline model trained using the entire
dataset. Therefore, we call on the efforts from the solar forecasting community
to contribute to a global dataset containing a massive amount of imagery and
displaying diversified samples with a range of sky conditions.

In contrast to the fixed parameter analysis (FPA), in the variable parameter
analysis (VPA) the value of the target problem parameter is not fixed, it
rather depends on the structure of a given problem instance and tends to have a
favorable asymptotic behavior when the size of the input increases. While
applying the VPA to an intractable optimization problem with $n$ objects, the
exponential-time dependence in enumeration of the feasible solution set is
attributed solely to the variable parameter $\nu$, $\nu<<n$. As opposed to the
FPA, the VPA does not imply any restriction on some problem parameters, it
rather takes an advantage of a favorable nature of the problem, which permits
to reduce the cost of enumeration of the solution space. Our main technical
contribution is a variable parameter algorithm for a strongly
$\mathsf{NP}$-hard single-machine scheduling problem to minimize maximum job
lateness. The target variable parameter $\nu$ is the number of jobs with some
specific characteristics, the ``emerging'' ones. The solution process is
separated in two phases. At phase 1 a partial solution including $n-\nu$
non-emerging jobs is constructed in a low degree polynomial time. At phase 2
less than $\nu!$ permutations of the $\nu$ emerging jobs are considered. Each
of them are incorporated into the partial schedule of phase 1. Doe to the
results of an earlier conducted experimental study, $\nu/n$ varied from $1/4$
for small problem instances to $1/10$ for the largest tested problem instances,
so that that the ratio becomes closer to 0 for large $n$s.

In this work, we explore combining automatic hyperparameter tuning and
optimization for federated learning (FL) in an online, one-shot procedure. We
apply a principled approach on a method for adaptive client learning rate,
number of local steps, and batch size. In our federated learning applications,
our primary motivations are minimizing communication budget as well as local
computational resources in the training pipeline. Conventionally,
hyperparameter tuning methods involve at least some degree of trial-and-error,
which is known to be sample inefficient. In order to address our motivations,
we propose FATHOM (Federated AuTomatic Hyperparameter OptiMization) as a
one-shot online procedure. We investigate the challenges and solutions of
deriving analytical gradients with respect to the hyperparameters of interest.
Our approach is inspired by the fact that, with the exception of local data, we
have full knowledge of all components involved in our training process, and
this fact can be exploited in our algorithm impactfully. We show that FATHOM is
more communication efficient than Federated Averaging (FedAvg) with optimized,
static valued hyperparameters, and is also more computationally efficient
overall. As a communication efficient, one-shot online procedure, FATHOM solves
the bottleneck of costly communication and limited local computation, by
eliminating a potentially wasteful tuning process, and by optimizing the
hyperparamters adaptively throughout the training procedure without
trial-and-error. We show our numerical results through extensive empirical
experiments with the Federated EMNIST-62 (FEMNIST) and Federated Stack Overflow
(FSO) datasets, using FedJAX as our baseline framework.

We study the convergence of several natural policy gradient (NPG) methods in
infinite-horizon discounted Markov decision processes with regular policy
parametrizations. For a variety of NPGs and reward functions we show that the
trajectories in state-action space are solutions of gradient flows with respect
to Hessian geometries, based on which we obtain global convergence guarantees
and convergence rates. In particular, we show linear convergence for
unregularized and regularized NPG flows with the metrics proposed by Kakade and
Morimura and co-authors by observing that these arise from the Hessian
geometries of conditional entropy and entropy respectively. Further, we obtain
sublinear convergence rates for Hessian geometries arising from other convex
functions like log-barriers. Finally, we interpret the discrete-time NPG
methods with regularized rewards as inexact Newton methods if the NPG is
defined with respect to the Hessian geometry of the regularizer. This yields
local quadratic convergence rates of these methods for step size equal to the
penalization strength.

Analog beamforming is the predominant approach for millimeter wave (mmWave)
communication given its favorable characteristics for limited-resource devices.
In this work, we aim at reducing the spectral efficiency gap between analog and
digital beamforming methods. We propose a method for refined beam selection
based on the estimated raw channel. The channel estimation, an underdetermined
problem, is solved using compressed sensing (CS) methods leveraging angular
domain sparsity of the channel. To reduce the complexity of CS methods, we
propose dictionary learning iterative soft-thresholding algorithm, which
jointly learns the sparsifying dictionary and signal reconstruction. We
evaluate the proposed method on a realistic mmWave setup and show considerable
performance improvement with respect to code-book based analog beamforming

Model-based reinforcement learning (RL) methods are appealing in the offline
setting because they allow an agent to reason about the consequences of actions
without interacting with the environment. Prior methods learn a 1-step dynamics
model, which predicts the next state given the current state and action. These
models do not immediately tell the agent which actions to take, but must be
integrated into a larger RL framework. Can we model the environment dynamics in
a different way, such that the learned model does directly indicate the value
of each action? In this paper, we propose Contrastive Value Learning (CVL),
which learns an implicit, multi-step model of the environment dynamics. This
model can be learned without access to reward functions, but nonetheless can be
used to directly estimate the value of each action, without requiring any TD
learning. Because this model represents the multi-step transitions implicitly,
it avoids having to predict high-dimensional observations and thus scales to
high-dimensional tasks. Our experiments demonstrate that CVL outperforms prior
offline RL methods on complex continuous control benchmarks.

Through their transfer learning abilities, highly-parameterized large
pre-trained language models have dominated the NLP landscape for a multitude of
downstream language tasks. Though linguistically proficient, the inability of
these models to incorporate the learning of non-linguistic entities (numerals
and arithmetic reasoning) limits their usage for tasks that require numeric
comprehension or strict mathematical reasoning. However, as we illustrate in
this paper, building a general purpose language model that also happens to be
proficient in mathematical reasoning is not as straight-forward as training it
on a numeric dataset. In this work, we develop a novel framework that enables
language models to be mathematically proficient while retaining their
linguistic prowess. Specifically, we offer information-theoretic interventions
to overcome the catastrophic forgetting of linguistic skills that occurs while
injecting non-linguistic skills into language models.

Rates of missing data often depend on record-keeping policies and thus may
change across times and locations, even when the underlying features are
comparatively stable. In this paper, we introduce the problem of Domain
Adaptation under Missingness Shift (DAMS). Here, (labeled) source data and
(unlabeled) target data would be exchangeable but for different missing data
mechanisms. We show that when missing data indicators are available, DAMS can
reduce to covariate shift. Focusing on the setting where missing data
indicators are absent, we establish the following theoretical results for
underreporting completely at random: (i) covariate shift is violated
(adaptation is required); (ii) the optimal source predictor can perform worse
on the target domain than a constant one; (iii) the optimal target predictor
can be identified, even when the missingness rates themselves are not; and (iv)
for linear models, a simple analytic adjustment yields consistent estimates of
the optimal target parameters. In experiments on synthetic and semi-synthetic
data, we demonstrate the promise of our methods when assumptions hold. Finally,
we discuss a rich family of future extensions.

The FAIR Guiding Principles aim to improve the findability, accessibility,
interoperability, and reusability of digital content by making them both human
and machine actionable. However, these principles have not yet been broadly
adopted in the domain of machine learning-based program analyses and
optimizations for High-Performance Computing (HPC). In this paper, we design a
methodology to make HPC datasets and machine learning models FAIR after
investigating existing FAIRness assessment and improvement techniques. Our
methodology includes a comprehensive, quantitative assessment for elected data,
followed by concrete, actionable suggestions to improve FAIRness with respect
to common issues related to persistent identifiers, rich metadata descriptions,
license and provenance information. Moreover, we select a representative
training dataset to evaluate our methodology. The experiment shows the
methodology can effectively improve the dataset and model's FAIRness from an
initial score of 19.1% to the final score of 83.0%.

Federated learning provides an effective paradigm to jointly optimize a model
benefited from rich distributed data while protecting data privacy.
Nonetheless, the heterogeneity nature of distributed data makes it challenging
to define and ensure fairness among local agents. For instance, it is
intuitively "unfair" for agents with data of high quality to sacrifice their
performance due to other agents with low quality data. Currently popular
egalitarian and weighted equity-based fairness measures suffer from the
aforementioned pitfall. In this work, we aim to formally represent this problem
and address these fairness issues using concepts from co-operative game theory
and social choice theory. We model the task of learning a shared predictor in
the federated setting as a fair public decision making problem, and then define
the notion of core-stable fairness: Given $N$ agents, there is no subset of
agents $S$ that can benefit significantly by forming a coalition among
themselves based on their utilities $U_N$ and $U_S$ (i.e., $\frac{|S|}{N} U_S
\geq U_N$). Core-stable predictors are robust to low quality local data from
some agents, and additionally they satisfy Proportionality and
Pareto-optimality, two well sought-after fairness and efficiency notions within
social choice. We then propose an efficient federated learning protocol CoreFed
to optimize a core stable predictor. CoreFed determines a core-stable predictor
when the loss functions of the agents are convex. CoreFed also determines
approximate core-stable predictors when the loss functions are not convex, like
smooth neural networks. We further show the existence of core-stable predictors
in more general settings using Kakutani's fixed point theorem. Finally, we
empirically validate our analysis on two real-world datasets, and we show that
CoreFed achieves higher core-stability fairness than FedAvg while having
similar accuracy.

Self-supervised pre-training recently demonstrates success on large-scale
multimodal data, and state-of-the-art contrastive learning methods often
enforce the feature consistency from cross-modality inputs, such as video/audio
or video/text pairs. Despite its convenience to formulate and leverage in
practice, such cross-modality alignment (CMA) is only a weak and noisy
supervision, since two modalities can be semantically misaligned even they are
temporally aligned. For example, even in the commonly adopted instructional
videos, a speaker can sometimes refer to something that is not visually present
in the current frame; and the semantic misalignment would only be more
unpredictable for the raw videos from the internet. We conjecture that might
cause conflicts and biases among modalities, and may hence prohibit CMA from
scaling up to training with larger and more heterogeneous data. This paper
first verifies our conjecture by observing that, even in the latest VATT
pre-training using only instructional videos, there exist strong gradient
conflicts between different CMA losses within the same video, audio, text
triplet, indicating them as the noisy source of supervision. We then propose to
harmonize such gradients, via two techniques: (i) cross-modality gradient
realignment: modifying different CMA loss gradients for each sample triplet, so
that their gradient directions are more aligned; and (ii) gradient-based
curriculum learning: leveraging the gradient conflict information on an
indicator of sample noisiness, to develop a curriculum learning strategy to
prioritize training on less noisy sample triplets. Applying those techniques to
pre-training VATT on the HowTo100M dataset, we consistently improve its
performance on different downstream tasks. Moreover, we are able to scale VATT
pre-training to more complicated non-narrative Youtube8M dataset to further
improve the state-of-the-arts.

As the performance of large language models rapidly improves, benchmarks are
getting larger and more complex as well. We present LMentry, a benchmark that
avoids this "arms race" by focusing on a compact set of tasks that are trivial
to humans, e.g. writing a sentence containing a specific word, identifying
which words in a list belong to a specific category, or choosing which of two
words is longer. LMentry is specifically designed to provide quick and
interpretable insights into the capabilities and robustness of large language
models. Our experiments reveal a wide variety of failure cases that, while
immediately obvious to humans, pose a considerable challenge for large language
models, including OpenAI's latest 175B-parameter instruction-tuned model,
TextDavinci002. LMentry complements contemporary evaluation approaches of large
language models, providing a quick, automatic, and easy-to-run "unit test",
without resorting to large benchmark suites of complex tasks.

We report the discovery of a unique object in the MeerKAT Galaxy Cluster
Legacy Survey (MGCLS) using a machine learning anomaly detection algorithm.
This strange, ring-like source is 30' from the MGCLS field centred on Abell
209, and is not readily explained by simple physical models. With an assumed
host galaxy at redshift 0.55, the luminosity (10^25 W/Hz) is comparable to
powerful radio galaxies. The source consists of a ring of emission 175 kpc
across, quadrilateral enhanced brightness regions bearing resemblance to radio
jets, two ``ears'' separated by 368 kpc, and a diffuse envelope. All of the
structures appear spectrally steep, ranging from -1.0 to -1.5. The ring has
high polarization (25%) except on the bright patches (<10%). We compare this
source to the Odd Radio Circles recently discovered in ASKAP data and discuss
several possible physical models, including a termination shock from starburst
activity, an end-on radio galaxy, and a supermassive black hole merger event.
No simple model can easily explain the observed structure of the source. This
work, as well as other recent discoveries, demonstrates the power of
unsupervised machine learning in mining large datasets for scientifically
interesting sources.

Theoretical interpretations of particle physics data, such as the
determination of the Wilson coefficients of the Standard Model Effective Field
Theory (SMEFT), often involve the inference of multiple parameters from a
global dataset. Optimizing such interpretations requires the identification of
observables that exhibit the highest possible sensitivity to the underlying
theory parameters. In this work we develop a flexible open source framework,
ML4EFT, enabling the integration of unbinned multivariate observables into
global SMEFT fits. As compared to traditional measurements, such observables
enhance the sensitivity to the theory parameters by preventing the information
loss incurred when binning in a subset of final-state kinematic variables. Our
strategy combines machine learning regression and classification techniques to
parameterize high-dimensional likelihood ratios, using the Monte Carlo replica
method to estimate and propagate methodological uncertainties. As a proof of
concept we construct unbinned multivariate observables for top-quark pair and
Higgs+$Z$ production at the LHC, demonstrate their impact on the SMEFT
parameter space as compared to binned measurements, and study the improved
constraints associated to multivariate inputs. Since the number of neural
networks to be trained scales quadratically with the number of parameters and
can be fully parallelized, the ML4EFT framework is well-suited to construct
unbinned multivariate observables which depend on up to tens of EFT
coefficients, as required in global fits.

During image editing, existing deep generative models tend to re-synthesize
the entire output from scratch, including the unedited regions. This leads to a
significant waste of computation, especially for minor editing operations. In
this work, we present Spatially Sparse Inference (SSI), a general-purpose
technique that selectively performs computation for edited regions and
accelerates various generative models, including both conditional GANs and
diffusion models. Our key observation is that users tend to make gradual
changes to the input image. This motivates us to cache and reuse the feature
maps of the original image. Given an edited image, we sparsely apply the
convolutional filters to the edited regions while reusing the cached features
for the unedited regions. Based on our algorithm, we further propose Sparse
Incremental Generative Engine (SIGE) to convert the computation reduction to
latency reduction on off-the-shelf hardware. With 1.2%-area edited regions, our
method reduces the computation of DDIM by 7.5$\times$ and GauGAN by 18$\times$
while preserving the visual fidelity. With SIGE, we accelerate the speed of
DDIM by 3.0x on RTX 3090 and 6.6$\times$ on Apple M1 Pro CPU, and GauGAN by
4.2$\times$ on RTX 3090 and 14$\times$ on Apple M1 Pro CPU.

We present the GPry algorithm for fast Bayesian inference of general
(non-Gaussian) posteriors with a moderate number of parameters. GPry does not
need any pre-training, special hardware such as GPUs, and is intended as a
drop-in replacement for traditional Monte Carlo methods for Bayesian inference.
Our algorithm is based on generating a Gaussian Process surrogate model of the
log-posterior, aided by a Support Vector Machine classifier that excludes
extreme or non-finite values. An active learning scheme allows us to reduce the
number of required posterior evaluations by two orders of magnitude compared to
traditional Monte Carlo inference. Our algorithm allows for parallel
evaluations of the posterior at optimal locations, further reducing wall-clock
times. We significantly improve performance using properties of the posterior
in our active learning scheme and for the definition of the GP prior. In
particular we account for the expected dynamical range of the posterior in
different dimensionalities. We test our model against a number of synthetic and
cosmological examples. GPry outperforms traditional Monte Carlo methods when
the evaluation time of the likelihood (or the calculation of theoretical
observables) is of the order of seconds; for evaluation times of over a minute
it can perform inference in days that would take months using traditional
methods. GPry is distributed as an open source Python package (pip install
gpry) and can also be found at https://github.com/jonaselgammal/GPry.

Frozen pretrained models have become a viable alternative to the
pretraining-then-finetuning paradigm for transfer learning. However, with
frozen models there are relatively few parameters available for adapting to
downstream tasks, which is problematic in computer vision where tasks vary
significantly in input/output format and the type of information that is of
value. In this paper, we present a study of frozen pretrained models when
applied to diverse and representative computer vision tasks, including object
detection, semantic segmentation and video action recognition. From this
empirical analysis, our work answers the questions of what pretraining task
fits best with this frozen setting, how to make the frozen setting more
flexible to various downstream tasks, and the effect of larger model sizes. We
additionally examine the upper bound of performance using a giant frozen
pretrained model with 3 billion parameters (SwinV2-G) and find that it reaches
competitive performance on a varied set of major benchmarks with only one
shared frozen base network: 60.0 box mAP and 52.2 mask mAP on COCO object
detection test-dev, 57.6 val mIoU on ADE20K semantic segmentation, and 81.7
top-1 accuracy on Kinetics-400 action recognition. With this work, we hope to
bring greater attention to this promising path of freezing pretrained image
models.

Testing the significance of a variable or group of variables $X$ for
predicting a response $Y$, given additional covariates $Z$, is a ubiquitous
task in statistics. A simple but common approach is to specify a linear model,
and then test whether the regression coefficient for $X$ is non-zero. However,
when the model is misspecified, the test may have poor power, for example when
$X$ is involved in complex interactions, or lead to many false rejections. In
this work we study the problem of testing the model-free null of conditional
mean independence, i.e. that the conditional mean of $Y$ given $X$ and $Z$ does
not depend on $X$. We propose a simple and general framework that can leverage
flexible nonparametric or machine learning methods, such as additive models or
random forests, to yield both robust error control and high power. The
procedure involves using these methods to perform regressions, first to
estimate a form of projection of $Y$ on $X$ and $Z$ using one half of the data,
and then to estimate the expected conditional covariance between this
projection and $Y$ on the remaining half of the data. While the approach is
general, we show that a version of our procedure using spline regression
achieves what we show is the minimax optimal rate in this nonparametric testing
problem. Numerical experiments demonstrate the effectiveness of our approach
both in terms of maintaining Type I error control, and power, compared to
several existing approaches.

The CERN Large Hadron Collider was built to uncover fundamental particles and
their interactions at the energy frontier. Upon entering its High Luminosity
phase at the end of this decade, the unprecedented interaction rates when
colliding two proton bunches will pose a significant challenge to the
reconstruction algorithms of experiments such as ATLAS and CMS. We propose an
algorithm with a greatly enhanced capability of disentangling individual proton
collisions to obtain a new global event description, considerably improving
over the current state-of-the-art. Employing a metric inspired by optimal
transport problems as the cost function of a graph neural network enhanced with
attention mechanisms, our algorithm is able to compare two particle collections
with different noise levels, thereby learning to correctly flag particles
originating from the main proton interaction amidst products from up to 200
simultaneous pileup collisions. The adoption of such an algorithm will lead to
a quasi-global sensitivity improvement for searches for new physics phenomena
and will enable precision measurements at the percent level in the High
Luminosity era of the Large Hadron Collider.

In offline reinforcement learning (RL), a learner leverages prior logged data
to learn a good policy without interacting with the environment. A major
challenge in applying such methods in practice is the lack of both
theoretically principled and practical tools for model selection and
evaluation. To address this, we study the problem of model selection in offline
RL with value function approximation. The learner is given a nested sequence of
model classes to minimize squared Bellman error and must select among these to
achieve a balance between approximation and estimation error of the classes. We
propose the first model selection algorithm for offline RL that achieves
minimax rate-optimal oracle inequalities up to logarithmic factors. The
algorithm, ModBE, takes as input a collection of candidate model classes and a
generic base offline RL algorithm. By successively eliminating model classes
using a novel one-sided generalization test, ModBE returns a policy with regret
scaling with the complexity of the minimally complete model class. In addition
to its theoretical guarantees, it is conceptually simple and computationally
efficient, amounting to solving a series of square loss regression problems and
then comparing relative square loss between classes. We conclude with several
numerical simulations showing it is capable of reliably selecting a good model
class.

We formulate the problem of performing optimal data compression under the
constraints that compressed data can be used for accurate classification in
machine learning. We show that this translates to a problem of minimizing the
mutual information between data and its compressed version under the constraint
on error probability of classification is small when using the compressed data
for machine learning. We then provide analytical and computational methods to
characterize the optimal trade-off between data compression and classification
error probability. First, we provide an analytical characterization for the
optimal compression strategy for data with binary labels. Second, for data with
multiple labels, we formulate a set of convex optimization problems to
characterize the optimal tradeoff, from which the optimal trade-off between the
classification error and compression efficiency can be obtained by numerically
solving the formulated optimization problems. We further show the improvements
of our formulations over the information-bottleneck methods in classification

In this paper, we present Pangu-Weather, a deep learning based system for
fast and accurate global weather forecast. For this purpose, we establish a
data-driven environment by downloading $43$ years of hourly global weather data
from the 5th generation of ECMWF reanalysis (ERA5) data and train a few deep
neural networks with about $256$ million parameters in total. The spatial
resolution of forecast is $0.25^\circ\times0.25^\circ$, comparable to the ECMWF
Integrated Forecast Systems (IFS). More importantly, for the first time, an
AI-based method outperforms state-of-the-art numerical weather prediction (NWP)
methods in terms of accuracy (latitude-weighted RMSE and ACC) of all factors
(e.g., geopotential, specific humidity, wind speed, temperature, etc.) and in
all time ranges (from one hour to one week). There are two key strategies to
improve the prediction accuracy: (i) designing a 3D Earth Specific Transformer
(3DEST) architecture that formulates the height (pressure level) information
into cubic data, and (ii) applying a hierarchical temporal aggregation
algorithm to alleviate cumulative forecast errors. In deterministic forecast,
Pangu-Weather shows great advantages for short to medium-range forecast (i.e.,
forecast time ranges from one hour to one week). Pangu-Weather supports a wide
range of downstream forecast scenarios, including extreme weather forecast
(e.g., tropical cyclone tracking) and large-member ensemble forecast in
real-time. Pangu-Weather not only ends the debate on whether AI-based methods
can surpass conventional NWP methods, but also reveals novel directions for
improving deep learning weather forecast systems.

Reliable measurement of dependence between variables is essential in many
applications of statistics and machine learning. Current approaches for
dependence estimation, especially density-based approaches, lack in precision,
robustness and/or interpretability (in terms of the type of dependence being
estimated). We propose a two-step approach for dependence quantification
between random variables: 1) We first decompose the probability density
functions (PDF) of the variables involved in terms of multiple local moments of
uncertainty that systematically and precisely identify the different regions of
the PDF (with special emphasis on the tail-regions). 2) We then compute an
optimal transport map to measure the geometric similarity between the
corresponding sets of decomposed local uncertainty moments of the variables.
Dependence is then determined by the degree of one-to-one correspondence
between the respective uncertainty moments of the variables in the optimal
transport map. We utilize a recently introduced Gaussian reproducing kernel
Hilbert space (RKHS) based framework for multi-moment uncertainty decomposition
of the variables. Being based on the Gaussian RKHS, our approach is robust
towards outliers and monotone transformations of data, while the multiple
moments of uncertainty provide high resolution and interpretability of the type
of dependence being quantified. We support these claims through some
preliminary results using simulated data.

Distributing machine learning predictors enables the collection of
large-scale datasets while leaving sensitive raw data at trustworthy sites. We
show that locally training support vector machines (SVMs) and computing their
averages leads to a learning technique that is scalable to a large number of
users, satisfies differential privacy, and is applicable to non-trivial tasks,
such as CIFAR-10. For a large number of participants, communication cost is one
of the main challenges. We achieve a low communication cost by requiring only a
single invocation of an efficient secure multiparty summation protocol. By
relying on state-of-the-art feature extractors (SimCLR), we are able to utilize
differentially private convex learners for non-trivial tasks such as CIFAR-10.
Our experimental results illustrate that for $1{,}000$ users with $50$ data
points each, our scheme outperforms state-of-the-art scalable distributed
learning methods (differentially private federated learning, short DP-FL) while
requiring around $500$ times fewer communication costs: For CIFAR-10, we
achieve a classification accuracy of $79.7\,\%$ for an $\varepsilon = 0.59$
while DP-FL achieves $57.6\,\%$. More generally, we prove learnability
properties for the average of such locally trained models: convergence and
uniform stability. By only requiring strongly convex, smooth, and
Lipschitz-continuous objective functions, locally trained via stochastic
gradient descent (SGD), we achieve a strong utility-privacy tradeoff.

Progress in machine learning (ML) comes with a cost to the environment, given
that training ML models requires significant computational resources, energy
and materials. In the present article, we aim to quantify the carbon footprint
of BLOOM, a 176-billion parameter language model, across its life cycle. We
estimate that BLOOM's final training emitted approximately 24.7 tonnes
of~\carboneq~if we consider only the dynamic power consumption, and 50.5 tonnes
if we account for all processes ranging from equipment manufacturing to
energy-based operational consumption. We also study the energy requirements and
carbon emissions of its deployment for inference via an API endpoint receiving
user queries in real-time. We conclude with a discussion regarding the
difficulty of precisely estimating the carbon footprint of ML models and future
research directions that can contribute towards improving carbon emissions
reporting.

State-of-the-art speaker verification frameworks have typically focused on
speech enhancement techniques with increasingly deeper (more layers) and wider
(number of channels) models to improve their verification performance. Instead,
this paper proposes an approach to increase the model resolution capability
using attention-based dynamic kernels in a convolutional neural network to
adapt the model parameters to be feature-conditioned. The attention weights on
the kernels are further distilled by channel attention and multi-layer feature
aggregation to learn global features from speech. This approach provides an
efficient solution to improving representation capacity with lower data
resources. This is due to the self-adaptation to inputs of the structures of
the model parameters. The proposed dynamic convolutional model achieved 1.62\%
EER and 0.18 miniDCF on the VoxCeleb1 test set and has a 17\% relative
improvement compared to the ECAPA-TDNN.

Deep learning models for semantic segmentation are prone to poor performance
in real-world applications due to the highly challenging nature of the task.
Model uncertainty quantification (UQ) is one way to address this issue of lack
of model trustworthiness by enabling the practitioner to know how much to trust
a segmentation output. Current UQ methods in this application domain are mainly
restricted to Bayesian based methods which are computationally expensive and
are only able to extract central moments of uncertainty thereby limiting the
quality of their uncertainty estimates. We present a simple framework for
high-resolution predictive uncertainty quantification of semantic segmentation
models that leverages a multi-moment functional definition of uncertainty
associated with the model's feature space in the reproducing kernel Hilbert
space (RKHS). The multiple uncertainty functionals extracted from this
framework are defined by the local density dynamics of the model's feature
space and hence automatically align themselves at the tail-regions of the
intrinsic probability density function of the feature space (where uncertainty
is the highest) in such a way that the successively higher order moments
quantify the more uncertain regions. This leads to a significantly more
accurate view of model uncertainty than conventional Bayesian methods.
Moreover, the extraction of such moments is done in a single-shot computation
making it much faster than Bayesian and ensemble approaches (that involve a
high number of forward stochastic passes of the model to quantify its
uncertainty). We demonstrate these advantages through experimental evaluations
of our framework implemented over four different state-of-the-art model
architectures that are trained and evaluated on two benchmark road-scene
segmentation datasets (Camvid and Cityscapes).

The Covid pandemic is a clarion call for increased sensitivity to the
interconnected nature of social problems facing our world today. A
future-oriented education on critical issues, such as those outlined in the
United Nations Sustainable Development Goals (UN SDGs) and designing potential
solutions for such problems is an imperative skill that must be imparted to
children to help them navigate their future in today's unpredictable world.
Towards this goal, we have been conducting 3.5 month-long mentoring programs
for pre-university students in India to participate in a STEAM for Social Good
innovation challenge conducted annually by the Government of India. Using
digital and physical computing skills, we helped children explore creative
solutions for social problems through a constructionist approach to learning,
wherein they ideated and reflected upon the problems in their communities. The
children learnt the Engineering Design Thinking process and worked in online
groups of two or three, from concept to completion. Despite the constraints
posed by the pandemic, they explored creative ways to think about design and
innovation. They completed a variety of tasks by making, tinkering,
engineering, assembling, and programming to grasp the intricate relationship
between software and hardware. Subsequently, the children showcased their
creative abilities through video storytelling to a panel of domain experts. In
this paper, we present the children's perspective of their experiences through
this journey, the evaluation metrics based on IEEE design principles, and our
learnings from conducting this initiative as a university-school partnership
model for 84 middle and high school students. The aspirational intent of this
initiative is to make the children better social problem solvers and help them
perceive social problems as opportunities to enhance life for themselves and
their communities.

We present lilGym, a new benchmark for language-conditioned reinforcement
learning in visual environments. lilGym is based on 2,661 highly-compositional
human-written natural language statements grounded in an interactive visual
environment. We annotate all statements with executable Python programs
representing their meaning to enable exact reward computation in every possible
world state. Each statement is paired with multiple start states and reward
functions to form thousands of distinct Markov Decision Processes of varying
difficulty. We experiment with lilGym with different models and learning
regimes. Our results and analysis show that while existing methods are able to
achieve non-trivial performance, lilGym forms a challenging open problem.
lilGym is available at https://lil.nlp.cornell.edu/lilgym/.

End-to-End automatic speech recognition (ASR) models aim to learn a
generalised speech representation to perform recognition. In this domain there
is little research to analyse internal representation dependencies and their
relationship to modelling approaches. This paper investigates cross-domain
language model dependencies within transformer architectures using SVCCA and
uses these insights to exploit modelling approaches. It was found that specific
neural representations within the transformer layers exhibit correlated
behaviour which impacts recognition performance.
  Altogether, this work provides analysis of the modelling approaches affecting
contextual dependencies and ASR performance, and can be used to create or adapt
better performing End-to-End ASR models and also for downstream tasks.

Reinforcement learning in partially observable domains is challenging due to
the lack of observable state information. Thankfully, learning offline in a
simulator with such state information is often possible. In particular, we
propose a method for partially observable reinforcement learning that uses a
fully observable policy (which we call a state expert) during offline training
to improve online performance. Based on Soft Actor-Critic (SAC), our agent
balances performing actions similar to the state expert and getting high
returns under partial observability. Our approach can leverage the
fully-observable policy for exploration and parts of the domain that are fully
observable while still being able to learn under partial observability. On six
robotics domains, our method outperforms pure imitation, pure reinforcement
learning, the sequential or parallel combination of both types, and a recent
state-of-the-art method in the same setting. A successful policy transfer to a
physical robot in a manipulation task from pixels shows our approach's
practicality in learning interesting policies under partial observability.

This paper presents a framework for jointly grounding objects that follow
certain semantic relationship constraints given in a scene graph. A typical
natural scene contains several objects, often exhibiting visual relationships
of varied complexities between them. These inter-object relationships provide
strong contextual cues toward improving grounding performance compared to a
traditional object query-only-based localization task. A scene graph is an
efficient and structured way to represent all the objects and their semantic
relationships in the image. In an attempt towards bridging these two modalities
representing scenes and utilizing contextual information for improving object
localization, we rigorously study the problem of grounding scene graphs on
natural images. To this end, we propose a novel graph neural network-based
approach referred to as Visio-Lingual Message PAssing Graph Neural Network
(VL-MPAG Net). In VL-MPAG Net, we first construct a directed graph with object
proposals as nodes and an edge between a pair of nodes representing a plausible
relation between them. Then a three-step inter-graph and intra-graph message
passing is performed to learn the context-dependent representation of the
proposals and query objects. These object representations are used to score the
proposals to generate object localization. The proposed method significantly
outperforms the baselines on four public datasets.

The goal of this work is to localize sound sources in visual scenes with a
self-supervised approach. Contrastive learning in the context of sound source
localization leverages the natural correspondence between audio and visual
signals where the audio-visual pairs from the same source are assumed as
positive, while randomly selected pairs are negatives. However, this approach
brings in noisy correspondences; for example, positive audio and visual pair
signals that may be unrelated to each other, or negative pairs that may contain
semantically similar samples to the positive one. Our key contribution in this
work is to show that using a less strict decision boundary in contrastive
learning can alleviate the effect of noisy correspondences in sound source
localization. We propose a simple yet effective approach by slightly modifying
the contrastive loss with a negative margin. Extensive experimental results
show that our approach gives on-par or better performance than the
state-of-the-art methods. Furthermore, we demonstrate that the introduction of
a negative margin to existing methods results in a consistent improvement in

Industrial Internet of Things (IoT) systems increasingly rely on wireless
communication standards. In a common industrial scenario, indoor wireless IoT
devices communicate with access points to deliver data collected from
industrial sensors, robots and factory machines. Due to static or quasi-static
locations of IoT devices and access points, historical observations of IoT
device channel conditions provide a possibility to precisely identify the
device without observing its traditional identifiers (e.g., MAC or IP address).
Such device identification methods based on wireless fingerprinting gained
increased attention lately as an additional cyber-security mechanism for
critical IoT infrastructures. In this paper, we perform a systematic study of a
large class of machine learning algorithms for device identification using
wireless fingerprints for the most popular cellular and Wi-Fi IoT technologies.
We design, implement, deploy, collect relevant data sets, train and test a
multitude of machine learning algorithms, as a part of the complete end-to-end
solution design for device identification via wireless fingerprinting. The
proposed solution is currently being deployed in a real-world industrial IoT
environment as part of H2020 project COLLABS.

We study sample efficient reinforcement learning (RL) under the general
framework of interactive decision making, which includes Markov decision
process (MDP), partially observable Markov decision process (POMDP), and
predictive state representation (PSR) as special cases. Toward finding the
minimum assumption that empowers sample efficient learning, we propose a novel
complexity measure, generalized eluder coefficient (GEC), which characterizes
the fundamental tradeoff between exploration and exploitation in online
interactive decision making. In specific, GEC captures the hardness of
exploration by comparing the error of predicting the performance of the updated
policy with the in-sample training error evaluated on the historical data. We
show that RL problems with low GEC form a remarkably rich class, which subsumes
low Bellman eluder dimension problems, bilinear class, low witness rank
problems, PO-bilinear class, and generalized regular PSR, where generalized
regular PSR, a new tractable PSR class identified by us, includes nearly all
known tractable POMDPs. Furthermore, in terms of algorithm design, we propose a
generic posterior sampling algorithm, which can be implemented in both
model-free and model-based fashion, under both fully observable and partially
observable settings. The proposed algorithm modifies the standard posterior
sampling algorithm in two aspects: (i) we use an optimistic prior distribution
that biases towards hypotheses with higher values and (ii) a loglikelihood
function is set to be the empirical loss evaluated on the historical data,
where the choice of loss function supports both model-free and model-based
learning. We prove that the proposed algorithm is sample efficient by
establishing a sublinear regret upper bound in terms of GEC. In summary, we
provide a new and unified understanding of both fully observable and partially
observable RL.

We study the problem of model selection in causal inference, specifically for
the case of conditional average treatment effect (CATE) estimation under binary
treatments. Unlike model selection in machine learning, we cannot use the
technique of cross-validation here as we do not observe the counterfactual
potential outcome for any data point. Hence, we need to design model selection
techniques that do not explicitly rely on counterfactual data. As an
alternative to cross-validation, there have been a variety of proxy metrics
proposed in the literature, that depend on auxiliary nuisance models also
estimated from the data (propensity score model, outcome regression model).
However, the effectiveness of these metrics has only been studied on synthetic
datasets as we can observe the counterfactual data for them. We conduct an
extensive empirical analysis to judge the performance of these metrics, where
we utilize the latest advances in generative modeling to incorporate multiple
realistic datasets. We evaluate 9 metrics on 144 datasets for selecting between
415 estimators per dataset, including datasets that closely mimic real-world
datasets. Further, we use the latest techniques from AutoML to ensure
consistent hyperparameter selection for nuisance models for a fair comparison
across metrics.

The heat capacity $\mathcal{C}$ of a given probe is a fundamental quantity
that determines, among other properties, the maximum precision in temperature
estimation. In turn, $\mathcal{C}$ is limited by a quadratic scaling with the
number of constituents of the probe, which provides a fundamental limit in
quantum thermometry. Achieving this fundamental bound with realistic probes,
i.e. experimentally amenable, remains an open problem. In this work, we exploit
machine-learning techniques to discover optimal spin-network thermal probes,
restricting ourselves to two-body interactions. This leads to simple
architectures, which we show analytically to approximate the theoretical
maximal value of $\mathcal{C}$ and maintain the optimal scaling for short- and
long-range interactions. Our models can be encoded in currently available
quantum annealers, and find application in other tasks requiring Hamiltonian
engineering, ranging from quantum heat engines to adiabatic Grover's search.

Impact craters are formed due to continuous impacts on the surface of
planetary bodies. Most recent deep learning-based crater detection methods
treat craters as circular shapes, and less attention is paid to extracting the
exact shapes of craters. Extracting precise shapes of the craters can be
helpful for many advanced analyses, such as crater formation. This paper
proposes a combination of unsupervised non-deep learning and semi-supervised
deep learning approach to accurately extract shapes of the craters and detect
missing craters from the existing catalog. In unsupervised non-deep learning,
we have proposed an adaptive rim extraction algorithm to extract craters'
shapes. In this adaptive rim extraction algorithm, we utilized the elevation
profiles of DEMs and applied morphological operation on DEM-derived slopes to
extract craters' shapes. The extracted shapes of the craters are used in
semi-supervised deep learning to get the locations, size, and refined shapes.
Further, the extracted shapes of the craters are utilized to improve the
estimate of the craters' diameter, depth, and other morphological factors. The
craters' shape, estimated diameter, and depth with other morphological factors
will be publicly available.

Given an environment (e.g., a simulator) for evaluating samples in a
specified design space and a set of weighted evaluation metrics -- one can use
Theta-Resonance, a single-step Markov Decision Process (MDP), to train an
intelligent agent producing progressively more optimal samples. In
Theta-Resonance, a neural network consumes a constant input tensor and produces
a policy as a set of conditional probability density functions (PDFs) for
sampling each design dimension. We specialize existing policy gradient
algorithms in deep reinforcement learning (D-RL) in order to use evaluation
feedback (in terms of cost, penalty or reward) to update our policy network
with robust algorithmic stability and minimal design evaluations. We study
multiple neural architectures (for our policy network) within the context of a
simple SoC design space and propose a method of constructing synthetic space
exploration problems to compare and improve design space exploration (DSE)
algorithms. Although we only present categorical design spaces, we also outline
how to use Theta-Resonance in order to explore continuous and mixed
continuous-discrete design spaces.

In this paper, we detail how two types of distributed coordinator election
algorithms can be compared in terms of performance based on an evaluation on
the High Performance Computing (HPC) infrastructure. An experimental approach
based on an MPI (Message Passing Interface) implementation is presented, with
the goal to characterize the relevant evaluation metrics based on statistical
processing of the results. The presented approach can be used to learn master
students of a course on distributed software the basics of algorithms for
coordinator election, and how to conduct an experimental performance evaluation
study. Finally, use cases where distributed coordinator election algorithms are
useful are presented.

Face recognition technology has advanced significantly in recent years due
largely to the availability of large and increasingly complex training datasets
for use in deep learning models. These datasets, however, typically comprise
images scraped from news sites or social media platforms and, therefore, have
limited utility in more advanced security, forensics, and military
applications. These applications require lower resolution, longer ranges, and
elevated viewpoints. To meet these critical needs, we collected and curated the
first and second subsets of a large multi-modal biometric dataset designed for
use in the research and development (R&D) of biometric recognition technologies
under extremely challenging conditions. Thus far, the dataset includes more
than 350,000 still images and over 1,300 hours of video footage of
approximately 1,000 subjects. To collect this data, we used Nikon DSLR cameras,
a variety of commercial surveillance cameras, specialized long-rage R&D
cameras, and Group 1 and Group 2 UAV platforms. The goal is to support the
development of algorithms capable of accurately recognizing people at ranges up
to 1,000 m and from high angles of elevation. These advances will include
improvements to the state of the art in face recognition and will support new
research in the area of whole-body recognition using methods based on gait and
anthropometry. This paper describes methods used to collect and curate the
dataset, and the dataset's characteristics at the current stage.

In this paper, we focus on the theoretical analysis of diffusion-based
generative modeling. Under an $L^2$-accurate score estimator, we provide
convergence guarantees with polynomial complexity for any data distribution
with second-order moment, by either employing an early stopping technique or
assuming smoothness condition on the score function of the data distribution.
Our result does not rely on any log-concavity or functional inequality
assumption and has a logarithmic dependence on the smoothness. In particular,
we show that under only a finite second moment condition, approximating the
following in KL divergence in $\epsilon$-accuracy can be done in $\tilde
O\left(\frac{d^2 \log^2 (1/\delta)}{\epsilon^2}\right)$ steps: 1) the
variance-$\delta$ Gaussian perturbation of any data distribution; 2) data
distributions with $1/\delta$-smooth score functions. Our theoretical analysis
also provides quantitative comparison between different discrete approximations
and may guide the choice of discretization points in practice.

Rule-based classification models described in the language of logic directly
predict boolean values, rather than modeling a probability and translating it
into a prediction as done in statistical models. The vast majority of existing
uncertainty quantification approaches rely on models providing continuous
output not available to rule-based models. In this work, we propose an
uncertainty quantification framework in the form of a meta-model that takes any
binary classifier with binary output as a black box and estimates the
prediction accuracy of that base model at a given input along with a level of
confidence on that estimation. The confidence is based on how well that input
region is explored and is designed to work in any OOD scenario. We demonstrate
the usefulness of this uncertainty model by building an abstaining classifier
powered by it and observing its performance in various scenarios.

Existing federated learning models that follow the standard risk minimization
paradigm of machine learning often fail to generalize in the presence of
spurious correlations in the training data. In many real-world distributed
settings, spurious correlations exist due to biases and data sampling issues on
distributed devices or clients that can erroneously influence models. Current
generalization approaches are designed for centralized training and attempt to
identify features that have an invariant causal relationship with the target,
thereby reducing the effect of spurious features. However, such invariant risk
minimization approaches rely on apriori knowledge of training data
distributions which is hard to obtain in many applications. In this work, we
present a generalizable federated learning framework called FedGen, which
allows clients to identify and distinguish between spurious and invariant
features in a collaborative manner without prior knowledge of training
distributions. We evaluate our approach on real-world datasets from different
domains and show that FedGen results in models that achieve significantly
better generalization than current federated learning approaches.

By conditioning on natural language instructions, large language models
(LLMs) have displayed impressive capabilities as general-purpose computers.
However, task performance depends significantly on the quality of the prompt
used to steer the model, and most effective prompts have been handcrafted by
humans. Inspired by classical program synthesis and the human approach to
prompt engineering, we propose Automatic Prompt Engineer (APE) for automatic
instruction generation and selection. In our method, we treat the instruction
as the "program," optimized by searching over a pool of instruction candidates
proposed by an LLM in order to maximize a chosen score function. To evaluate
the quality of the selected instruction, we evaluate the zero-shot performance
of another LLM following the selected instruction. Experiments on 24 NLP tasks
show that our automatically generated instructions outperform the prior LLM
baseline by a large margin and achieve better or comparable performance to the
instructions generated by human annotators on 19/24 tasks. We conduct extensive
qualitative and quantitative analyses to explore the performance of APE. We
show that APE-engineered prompts can be applied to steer models toward
truthfulness and/or informativeness, as well as to improve few-shot learning
performance by simply prepending them to standard in-context learning prompts.
Please check out our webpage at
https://sites.google.com/view/automatic-prompt-engineer.

We consider unsupervised learning methods for characterizing the disordered
microscopic structure of supercooled liquids and glasses. Specifically, we
perform dimensionality reduction of smooth structural descriptors that describe
radial and bond-orientational correlations, and assess the ability of the
method to grasp the essential structural features of glassy binary mixtures. In
several cases, a few collective variables account for the bulk of the
structural fluctuations within the first coordination shell and also display a
clear connection with the fluctuations of particle mobility. Fine-grained
descriptors that characterize the radial dependence of bond-orientational order
better capture the structural fluctuations relevant for particle mobility, but
are also more difficult to parametrize and to interpret. We also find that
principal component analysis of bond-orientational order parameters provides
identical results to neural network autoencoders, while having the advantage of
being easily interpretable. Overall, our results indicate that glassy binary
mixtures have a broad spectrum of structural features. In the temperature range
we investigate, some mixtures display well-defined locally favored structures,
which are reflected in bimodal distributions of the structural variables
identified by dimensionality reduction.

Regression on observational data can fail to capture a causal relationship in
the presence of unobserved confounding. Confounding strength measures this
mismatch, but estimating it requires itself additional assumptions. A common
assumption is the independence of causal mechanisms, which relies on
concentration phenomena in high dimensions. While high dimensions enable the
estimation of confounding strength, they also necessitate adapted estimators.
In this paper, we derive the asymptotic behavior of the confounding strength
estimator by Janzing and Sch\"olkopf (2018) and show that it is generally not
consistent. We then use tools from random matrix theory to derive an adapted,
consistent estimator.

The availability of Martian atmospheric data provided by several Martian
missions broadened the opportunity to investigate and study the conditions of
the Martian ionosphere. As such, ionospheric models play a crucial part in
improving our understanding of ionospheric behavior in response to different
spatial, temporal, and space weather conditions. This work represents an
initial attempt to construct an electron density prediction model of the
Martian ionosphere using machine learning. The model targets the ionosphere at
solar zenith ranging from 70 to 90 degrees, and as such only utilizes
observations from the Mars Global Surveyor mission. The performance of
different machine learning methods was compared in terms of root mean square
error, coefficient of determination, and mean absolute error. The bagged
regression trees method performed best out of all the evaluated methods.
Furthermore, the optimized bagged regression trees model outperformed other
Martian ionosphere models from the literature (MIRI and NeMars) in finding the
peak electron density value, and the peak density height in terms of
root-mean-square error and mean absolute error.

As a consequence of galaxy clustering, close galaxies observed on the plane
of the sky should be spatially correlated with a probability inversely
proportional to their angular separation. In principle, this information can be
used to improve photometric redshift estimates when spectroscopic redshifts are
available for some of the neighbouring objects. Depending on the depth of the
survey, however, such angular correlation is reduced by chance projections. In
this work, we implement a deep learning model to distinguish between apparent
and real angular neighbours by solving a classification task. We adopt a graph
neural network architecture to tie together the photometry, the spectroscopy
and the spatial information between neighbouring galaxies. We train and
validate the algorithm on the data of the VIPERS galaxy survey, for which
SED-fitting based photometric redshifts are also available. The model yields a
confidence level for a pair of galaxies to be real angular neighbours, enabling
us to disentangle chance superpositions in a probabilistic way. When objects
for which no physical companion can be identified are excluded, all photometric
redshifts' quality metrics improve significantly, confirming that their
estimates were of lower quality. For our typical test configuration, the
algorithm identifies a subset containing ~75% of high-quality photometric
redshifts, for which the dispersion is reduced by as much as 50% (from 0.08 to
0.04), while the fraction of outliers reduces from 3% to 0.8%. Moreover, we
show that the spectroscopic redshift of the angular neighbour with the highest
detection probability provides an excellent estimate of the redshift of the
target galaxy, comparable or even better than the corresponding template
fitting estimate.

Standard classification methods based on handcrafted morphological and
texture features have achieved good performance in breast mass differentiation
in ultrasound (US). In comparison to deep neural networks, commonly perceived
as "black-box" models, classical techniques are based on features that have
well-understood medical and physical interpretation. However, classifiers based
on morphological features commonly underperform in the presence of the
shadowing artifact and ill-defined mass borders, while texture based
classifiers may fail when the US image is too noisy. Therefore, in practice it
would be beneficial to select the classification method based on the appearance
of the particular US image. In this work, we develop a deep meta-network that
can automatically process input breast mass US images and recommend whether to
apply the shape or texture based classifier for the breast mass
differentiation. Our preliminary results demonstrate that meta-learning
techniques can be used to improve the performance of the standard classifiers
based on handcrafted features. With the proposed meta-learning based approach,
we achieved the area under the receiver operating characteristic curve of 0.95
and accuracy of 0.91.

Image segmentation is important in medical imaging, providing valuable,
quantitative information for clinical decision-making in diagnosis, therapy,
and intervention. The state-of-the-art in automated segmentation remains
supervised learning, employing discriminative models such as U-Net. However,
training these models requires access to large amounts of manually labelled
data which is often difficult to obtain in real medical applications. In such
settings, semi-supervised learning (SSL) attempts to leverage the abundance of
unlabelled data to obtain more robust and reliable models. Recently, generative
models have been proposed for semantic segmentation, as they make an attractive
choice for SSL. Their ability to capture the joint distribution over input
images and output label maps provides a natural way to incorporate information
from unlabelled images. This paper analyses whether deep generative models such
as the SemanticGAN are truly viable alternatives to tackle challenging medical
image segmentation problems. To that end, we thoroughly evaluate the
segmentation performance, robustness, and potential subgroup disparities of
discriminative and generative segmentation methods when applied to large-scale,
publicly available chest X-ray datasets.

Training energy-based models (EBMs) with noise-contrastive estimation (NCE)
is theoretically feasible but practically challenging. Effective learning
requires the noise distribution to be approximately similar to the target
distribution, especially in high-dimensional domains. Previous works have
explored modelling the noise distribution as a separate generative model, and
then concurrently training this noise model with the EBM. While this method
allows for more effective noise-contrastive estimation, it comes at the cost of
extra memory and training complexity. Instead, this thesis proposes a
self-adapting NCE algorithm which uses static instances of the EBM along its
training trajectory as the noise distribution. During training, these static
instances progressively converge to the target distribution, thereby
circumventing the need to simultaneously train an auxiliary noise model.
Moreover, we express this self-adapting NCE algorithm in the framework of
Bregman divergences and show that it is a generalization of maximum likelihood
learning for EBMs. The performance of our algorithm is evaluated across a range
of noise update intervals, and experimental results show that shorter update
intervals are conducive to higher synthesis quality.

The evaluation of synthetic micro-structure images is an emerging problem as
machine learning and materials science research have evolved together. Typical
state of the art methods in evaluating synthetic images from generative models
have relied on the Fr\'echet Inception Distance. However, this and other
similar methods, are limited in the materials domain due to both the unique
features that characterize physically accurate micro-structures and limited
dataset sizes. In this study we evaluate a variety of methods on scanning
electron microscope (SEM) images of graphene-reinforced polyurethane foams. The
primary objective of this paper is to report our findings with regards to the
shortcomings of existing methods so as to encourage the machine learning
community to consider enhancements in metrics for assessing quality of
synthetic images in the material science domain.

Composition optimization recently appears in many machine learning
applications such as meta learning and reinforcement learning. Recently many
composition optimization algorithms have been proposed and studied, however,
few adaptive algorithm considers the composition optimization under the
distributed setting. Meanwhile, the existing distributed composition
optimization methods still suffer from high sample and communication
complexities. In the paper, thus, we develop a class of faster momentum-based
federated compositional gradient descent algorithms (i.e., MFCGD and AdaMFCGD)
to solve the nonconvex distributed composition problems, which builds on the
momentum-based variance reduced and local-SGD techniques. In particular, our
adaptive algorithm (i.e., AdaMFCGD) uses a unified adaptive matrix to flexibly
incorporate various adaptive learning rates. Moreover, we provide a solid
theoretical analysis for our algorithms under non-i.i.d. setting, and prove our
algorithms obtain a lower sample and communication complexities simultaneously
than the existing federated compositional algorithms. Specifically, our
algorithms obtain lower sample complexity of $\tilde{O}(\epsilon^{-3})$ with
lower communication complexity of $\tilde{O}(\epsilon^{-2})$ in finding an
$\epsilon$-stationary point. We conduct the experiments on robust federated
learning and distributed meta learning tasks to demonstrate efficiency of our

Convex clustering is a modern method with both hierarchical and $k$-means
clustering characteristics. Although convex clustering can capture the complex
clustering structure hidden in data, the existing convex clustering algorithms
are not scalable to large data sets with sample sizes greater than ten
thousand. Moreover, it is known that convex clustering sometimes fails to
produce hierarchical clustering structures. This undesirable phenomenon is
called cluster split and makes it difficult to interpret clustering results. In
this paper, we propose convex clustering through majorization-minimization
(CCMM) -- an iterative algorithm that uses cluster fusions and sparsity to
enforce a complete cluster hierarchy with reduced memory usage. In the CCMM
algorithm, the diagonal majorization technique makes a highly efficient update
for each iteration. With a current desktop computer, the CCMM algorithm can
solve a single clustering problem featuring over one million objects in
seven-dimensional space within 70 seconds.

Recent studies show that deep neural networks (DNNs) are vulnerable to
backdoor attacks. A backdoor DNN model behaves normally with clean inputs,
whereas outputs attacker's expected behaviors when the inputs contain a
pre-defined pattern called a trigger. However, in some tasks, the attacker
cannot know the exact target that shows his/her expected behavior, because the
task may contain a large number of classes and the attacker does not have full
access to know the semantic details of these classes. Thus, the attacker is
willing to attack multiple suspected targets to achieve his/her purpose. In
light of this, in this paper, we propose the M-to-N backdoor attack, a new
attack paradigm that allows an attacker to launch a fuzzy attack by
simultaneously attacking N suspected targets, and each of the N targets can be
activated by any one of its M triggers. To achieve a better stealthiness, we
randomly select M clean images from the training dataset as our triggers for
each target. Since the triggers used in our attack have the same distribution
as the clean images, the inputs poisoned by the triggers are difficult to be
detected by the input-based defenses, and the backdoor models trained on the
poisoned training dataset are also difficult to be detected by the model-based
defenses. Thus, our attack is stealthier and has a higher probability of
achieving the attack purpose by attacking multiple suspected targets
simultaneously in contrast to prior backdoor attacks. Extensive experiments
show that our attack is effective against different datasets with various
models and achieves high attack success rates (e.g., 99.43% for attacking 2
targets and 98.23% for attacking 4 targets on the CIFAR-10 dataset) when
poisoning only an extremely small portion of the training dataset (e.g., less
than 2%). Besides, it is robust to pre-processing operations and can resist
state-of-the-art defenses.

We develop inductive biases for the machine learning of complex physical
systems based on the port-Hamiltonian formalism. To satisfy by construction the
principles of thermodynamics in the learned physics (conservation of energy,
non-negative entropy production), we modify accordingly the port-Hamiltonian
formalism so as to achieve a port-metriplectic one. We show that the
constructed networks are able to learn the physics of complex systems by parts,
thus alleviating the burden associated to the experimental characterization and
posterior learning process of this kind of systems. Predictions can be done,
however, at the scale of the complete system. Examples are shown on the
performance of the proposed technique.

Deep learning vision systems are widely deployed across applications where
reliability is critical. However, even today's best models can fail to
recognize an object when its pose, lighting, or background varies. While
existing benchmarks surface examples challenging for models, they do not
explain why such mistakes arise. To address this need, we introduce ImageNet-X,
a set of sixteen human annotations of factors such as pose, background, or
lighting the entire ImageNet-1k validation set as well as a random subset of
12k training images. Equipped with ImageNet-X, we investigate 2,200 current
recognition models and study the types of mistakes as a function of model's (1)
architecture, e.g. transformer vs. convolutional, (2) learning paradigm, e.g.
supervised vs. self-supervised, and (3) training procedures, e.g., data
augmentation. Regardless of these choices, we find models have consistent
failure modes across ImageNet-X categories. We also find that while data
augmentation can improve robustness to certain factors, they induce spill-over
effects to other factors. For example, strong random cropping hurts robustness
on smaller objects. Together, these insights suggest to advance the robustness
of modern vision models, future research should focus on collecting additional
data and understanding data augmentation schemes. Along with these insights, we
release a toolkit based on ImageNet-X to spur further study into the mistakes
image recognition systems make.

In this work, we propose a method to encourage safety in a Model Predictive
Control (MPC)-based Reinforcement Learning (RL) agent via Gaussian Process (GP)
regression. This framework consists of 1) a parametric MPC scheme that is
employed as model-based controller with approximate knowledge on the real
system's dynamics, 2) an episodic RL algorithm tasked with adjusting the MPC
parametrization in order to increase its performance, and lastly, 3) GP
regressors used to estimate, directly from data, constraints on the MPC
parameters capable of predicting, up to some probability, whether the
parametrization is likely to yield a safe or unsafe policy. These constraints
are then enforced onto the RL updates in an effort to enhance the learning
method with a probabilistic safety mechanism. Compared to other recent
publications combining safe RL with MPC, our method does not require further
assumptions on, e.g., the prediction model in order to retain computational
tractability. We illustrate the results of our method in a numerical example on
the control of a quadrotor drone in a safety-critical environment.

Graph auto-encoders are widely used to construct graph representations in
Euclidean vector spaces. However, it has already been pointed out empirically
that linear models on many tasks can outperform graph auto-encoders. In our
work, we prove that the solution space induced by graph auto-encoders is a
subset of the solution space of a linear map. This demonstrates that linear
embedding models have at least the representational power of graph
auto-encoders based on graph convolutional networks. So why are we still using
nonlinear graph auto-encoders? One reason could be that actively restricting
the linear solution space might introduce an inductive bias that helps improve
learning and generalization. While many researchers believe that the
nonlinearity of the encoder is the critical ingredient towards this end, we
instead identify the node features of the graph as a more powerful inductive
bias. We give theoretical insights by introducing a corresponding bias in a
linear model and analyzing the change in the solution space. Our experiments
show that the linear encoder can outperform the nonlinear encoder when using
feature information.

Simulations of biophysical systems have provided a huge contribution to our
fundamental understanding of human physiology and remain a central pillar for
developments in medical devices and human machine interfaces. However, despite
their successes, such simulations usually rely on highly computationally
expensive numerical modelling, which is often inefficient to adapt to new
simulation parameters. This limits their use in dynamic models of human
behavior, for example in modelling the electric fields generated by muscles in
a moving arm. We propose the alternative approach to use conditional generative
models, which can learn complex relationships between the underlying generative
conditions whilst remaining inexpensive to sample from. As a demonstration of
this concept, we present BioMime, a hybrid architecture that combines elements
of deep latent variable models and conditional adversarial training to
construct a generative model that can both transform existing data samples to
reflect new modelling assumptions and sample new data from a conditioned
distribution. We demonstrate that BioMime can learn to accurately mimic a
complex numerical model of human muscle biophysics and then use this knowledge
to continuously sample from a dynamically changing system in real-time. We
argue that transfer learning approaches with conditional generative models are
a viable solution for dynamic simulation with any numerical model.

Hyperparameter tuning is a common practice in the application of machine
learning but is a typically ignored aspect in the literature on
privacy-preserving machine learning due to its negative effect on the overall
privacy parameter. In this paper, we aim to tackle this fundamental yet
challenging problem by providing an effective hyperparameter tuning framework
with differential privacy. The proposed method allows us to adopt a broader
hyperparameter search space and even to perform a grid search over the whole
space, since its privacy loss parameter is independent of the number of
hyperparameter candidates. Interestingly, it instead correlates with the
utility gained from hyperparameter searching, revealing an explicit and
mandatory trade-off between privacy and utility. Theoretically, we show that
its additional privacy loss bound incurred by hyperparameter tuning is
upper-bounded by the squared root of the gained utility. However, we note that
the additional privacy loss bound would empirically scale like a squared root
of the logarithm of the utility term, benefiting from the design of doubling
step.

We propose an adaptive variance-reduction method, called AdaSpider, for
minimization of $L$-smooth, non-convex functions with a finite-sum structure.
In essence, AdaSpider combines an AdaGrad-inspired [Duchi et al., 2011, McMahan
& Streeter, 2010], but a fairly distinct, adaptive step-size schedule with the
recursive stochastic path integrated estimator proposed in [Fang et al., 2018].
To our knowledge, Adaspider is the first parameter-free non-convex
variance-reduction method in the sense that it does not require the knowledge
of problem-dependent parameters, such as smoothness constant $L$, target
accuracy $\epsilon$ or any bound on gradient norms. In doing so, we are able to
compute an $\epsilon$-stationary point with $\tilde{O}\left(n +
\sqrt{n}/\epsilon^2\right)$ oracle-calls, which matches the respective lower
bound up to logarithmic factors.

In this work, we consider the use of a model-based decoder in combination
with an unsupervised learning strategy for direction-of-arrival (DoA)
estimation. Relying only on unlabeled training data we show in our analysis
that we can outperform existing unsupervised machine learning methods and
classical methods. This is done by introducing a model-based decoder in an
autoencoder architecture with leads to a meaningful representation of the
statistical model in the latent space. Our numerical simulation show that the
performance of the presented approach is not affected by correlated signals but
rather improves slightly. This is due to the fact, that we propose the
estimation of the correlation parameters simultaneously to the DoA estimation.

The discovery of neural architectures from scratch is the long-standing goal
of Neural Architecture Search (NAS). Searching over a wide spectrum of neural
architectures can facilitate the discovery of previously unconsidered but
well-performing architectures. In this work, we take a large step towards
discovering neural architectures from scratch by expressing architectures
algebraically. This algebraic view leads to a more general method for designing
search spaces, which allows us to compactly represent search spaces that are
100s of orders of magnitude larger than common spaces from the literature.
Further, we propose a Bayesian Optimization strategy to efficiently search over
such huge spaces, and demonstrate empirically that both our search space design
and our search strategy can be superior to existing baselines. We open source
our algebraic NAS approach and provide APIs for PyTorch and TensorFlow.

Data integrity becomes paramount as the number of Internet of Things (IoT)
sensor deployments increases. Sensor data can be altered by benign causes or
malicious actions. Mechanisms that detect drifts and irregularities can prevent
disruptions and data bias in the state of an IoT application. This paper
presents LE3D, an ensemble framework of data drift estimators capable of
detecting abnormal sensor behaviours. Working collaboratively with surrounding
IoT devices, the type of drift (natural/abnormal) can also be identified and
reported to the end-user. The proposed framework is a lightweight and
unsupervised implementation able to run on resource-constrained IoT devices.
Our framework is also generalisable, adapting to new sensor streams and
environments with minimal online reconfiguration. We compare our method against
state-of-the-art ensemble data drift detection frameworks, evaluating both the
real-world detection accuracy as well as the resource utilisation of the
implementation. Experimenting with real-world data and emulated drifts, we show
the effectiveness of our method, which achieves up to 97% of detection accuracy
while requiring minimal resources to run.

Implicit neural representations (INRs) are a rapidly growing research field,
which provides alternative ways to represent multimedia signals. Recent
applications of INRs include image super-resolution, compression of
high-dimensional signals, or 3D rendering. However, these solutions usually
focus on visual data, and adapting them to the audio domain is not trivial.
Moreover, it requires a separately trained model for every data sample. To
address this limitation, we propose HyperSound, a meta-learning method
leveraging hypernetworks to produce INRs for audio signals unseen at training
time. We show that our approach can reconstruct sound waves with quality
comparable to other state-of-the-art models.

Prompts with different control signals (e.g., length, keywords, etc.) can be
used to control text summarization. When control signals are available, they
can control the properties of generated summaries and potentially improve
summarization quality (since more information are given). Unfortunately,
control signals are not already available during inference time. In this paper,
we propose Lotus (shorthand for Latent Prompt Tuning for Summarization), which
is a single model that can be applied in both controlled and uncontrolled
(without control signals) modes. During training, Lotus learns latent prompt
representations from prompts with gold control signals using a contrastive
learning objective. Experiments show Lotus in uncontrolled mode consistently
improves upon strong (uncontrollable) summarization models across four
different summarization datasets. We also demonstrate generated summaries can
be controlled using prompts with user specified control tokens.

Cartesian differential categories come equipped with a differential
combinator that formalizes the directional derivative from multivariable
calculus. Cartesian differential categories provide a categorical semantics of
the differential lambda-calculus and have also found applications in causal
computation, incremental computation, game theory, differentiable programming,
and machine learning. There has recently been a desire to provide a
(coordinate-free) characterization of Jacobians and gradients in Cartesian
differential categories. One's first attempt might be to consider Cartesian
differential categories which are Cartesian closed, such as models of the
differential lambda-calculus, and then take the curry of the derivative.
Unfortunately, this approach excludes numerous important examples of Cartesian
differential categories such as the category of real smooth functions. In this
paper, we introduce linearly closed Cartesian differential categories, which
are Cartesian differential categories that have an internal hom of linear maps,
a bilinear evaluation map, and the ability to curry maps which are linear in
their second argument. As such, the Jacobian of a map is defined as the curry
of its derivative. Many well-known examples of Cartesian differential
categories are linearly closed, such as, in particular, the category of real
smooth functions. We also explain how a Cartesian closed differential category
is linearly closed if and only if a certain linear idempotent on the internal
hom splits. To define the gradient of a map, one must be able to define the
transpose of the Jacobian, which can be done in a Cartesian reverse
differential category. Thus, we define the gradient of a map to be the curry of
its reverse derivative and show this equals the transpose of its Jacobian. We
also explain how a linearly closed Cartesian reverse differential category is
precisely a linearly closed Cartesian differential category with an appropriate
notion of transpose.

Today there exists no shortage of outlier detection algorithms in the
literature, yet the complementary and critical problem of unsupervised outlier
model selection (UOMS) is vastly understudied. In this work we propose ELECT, a
new approach to select an effective candidate model, i.e. an outlier detection
algorithm and its hyperparameter(s), to employ on a new dataset without any
labels. At its core, ELECT is based on meta-learning; transferring prior
knowledge (e.g. model performance) on historical datasets that are similar to
the new one to facilitate UOMS. Uniquely, it employs a dataset similarity
measure that is performance-based, which is more direct and goal-driven than
other measures used in the past. ELECT adaptively searches for similar
historical datasets, as such, it can serve an output on-demand, being able to
accommodate varying time budgets. Extensive experiments show that ELECT
significantly outperforms a wide range of basic UOMS baselines, including no
model selection (always using the same popular model such as iForest) as well
as more recent selection strategies based on meta-features.

This work proposes a universal and adaptive second-order method for
minimizing second-order smooth, convex functions. Our algorithm achieves
$O(\sigma / \sqrt{T})$ convergence when the oracle feedback is stochastic with
variance $\sigma^2$, and improves its convergence to $O( 1 / T^3)$ with
deterministic oracles, where $T$ is the number of iterations. Our method also
interpolates these rates without knowing the nature of the oracle apriori,
which is enabled by a parameter-free adaptive step-size that is oblivious to
the knowledge of smoothness modulus, variance bounds and the diameter of the
constrained set. To our knowledge, this is the first universal algorithm with
such global guarantees within the second-order optimization literature.

This paper presents LE3D; a novel data drift detection framework for
preserving data integrity and confidentiality. LE3D is a generalisable platform
for evaluating novel drift detection mechanisms within the Internet of Things
(IoT) sensor deployments. Our framework operates in a distributed manner,
preserving data privacy while still being adaptable to new sensors with minimal
online reconfiguration. Our framework currently supports multiple drift
estimators for time-series IoT data and can easily be extended to accommodate
new data types and drift detection mechanisms. This demo will illustrate the
functionality of LE3D under a real-world-like scenario.

Lenses encode protocols for synchronising systems. We continue the work begun
by Chollet et al. at the Applied Category Theory Adjoint School in 2020 to
study the properties of the category of small categories and asymmetric delta
lenses. The forgetful functor from the category of lenses to the category of
functors is already known to reflect monos and epis and preserve epis; we show
that it preserves monos, and give a simpler proof that it preserves epis.
Together this gives a complete characterisation of the monic and epic lenses in
terms of elementary properties of their get functors.
  Next, we initiate the study of coequalisers of lenses. We observe that not
all parallel pairs of lenses have coequalisers, and that the forgetful functor
from the category of lenses to the category of functors neither preserves nor
reflects all coequalisers. However, some coequalisers are reflected; we study
when this occurs, and then use what we learned to show that every epic lens is
regular, and that discrete opfibrations have pushouts along monic lenses.
Corollaries include that every monic lens is effective, every monic epic lens
is an isomorphism, and the class of all epic lenses and the class of all monic
lenses form an orthogonal factorisation system.

Mining structural priors in data is a widely recognized technique for
hyperspectral image (HSI) denoising tasks, whose typical ways include
model-based methods and data-based methods. The model-based methods have good
generalization ability, while the runtime cannot meet the fast processing
requirements of the practical situations due to the large size of an HSI data $
\mathbf{X} \in \mathbb{R}^{MN\times B}$. For the data-based methods, they
perform very fast on new test data once they have been trained. However, their
generalization ability is always insufficient. In this paper, we propose a fast
model-based HSI denoising approach. Specifically, we propose a novel
regularizer named Representative Coefficient Total Variation (RCTV) to
simultaneously characterize the low rank and local smooth properties. The RCTV
regularizer is proposed based on the observation that the representative
coefficient matrix $\mathbf{U}\in\mathbb{R}^{MN\times R} (R\ll B)$ obtained by
orthogonally transforming the original HSI $\mathbf{X}$ can inherit the strong
local-smooth prior of $\mathbf{X}$. Since $R/B$ is very small, the HSI
denoising model based on the RCTV regularizer has lower time complexity.
Additionally, we find that the representative coefficient matrix $\mathbf{U}$
is robust to noise, and thus the RCTV regularizer can somewhat promote the
robustness of the HSI denoising model. Extensive experiments on mixed noise
removal demonstrate the superiority of the proposed method both in denoising
performance and denoising speed compared with other state-of-the-art methods.
Remarkably, the denoising speed of our proposed method outperforms all the
model-based techniques and is comparable with the deep learning-based

New emerging technologies powered by Artificial Intelligence (AI) have the
potential to disruptively transform our societies for the better. In
particular, data-driven learning approaches (i.e., Machine Learning (ML)) have
been a true revolution in the advancement of multiple technologies in various
application domains. But at the same time there is growing concerns about
certain intrinsic characteristics of these methodologies that carry potential
risks to both safety and fundamental rights. Although there are mechanisms in
the adoption process to minimize these risks (e.g., safety regulations), these
do not exclude the possibility of harm occurring, and if this happens, victims
should be able to seek compensation. Liability regimes will therefore play a
key role in ensuring basic protection for victims using or interacting with
these systems. However, the same characteristics that make AI systems
inherently risky, such as lack of causality, opacity, unpredictability or their
self and continuous learning capabilities, lead to considerable difficulties
when it comes to proving causation. This paper presents three case studies, as
well as the methodology to reach them, that illustrate these difficulties.
Specifically, we address the cases of cleaning robots, delivery drones and
robots in education. The outcome of the proposed analysis suggests the need to
revise liability regimes to alleviate the burden of proof on victims in cases
involving AI technologies.

In recent years, most of the deep learning solutions are targeted to be
deployed in mobile devices. This makes the need for development of lightweight
models all the more imminent. Another solution is to optimize and prune regular
deep learning models. In this paper, we tackle the problem of CNN model pruning
with the help of Self-Similarity Matrix (SSM) computed from the 2D CNN filters.
We propose two novel algorithms to rank and prune redundant filters which
contribute similar activation maps to the output. One of the key features of
our method is that there is no need of finetuning after training the model.
Both the training and pruning process is completed simultaneously. We benchmark
our method on two of the most popular CNN models - ResNet and VGG and record
their performance on the CIFAR-10 dataset.

Motivated by the prevalence of ``price protection guarantee", which allows a
customer who purchased a product in the past to receive a refund from the
seller during the so-called price protection period (typically defined as a
certain time window after the purchase date) in case the seller decides to
lower the price, we study the impact of such policy on the design of online
learning algorithm for data-driven dynamic pricing with initially unknown
customer demand. We consider a setting where a firm sells a product over a
horizon of $T$ time steps. For this setting, we characterize how the value of
$M$, the length of price protection period, can affect the optimal regret of
the learning process. We show that the optimal regret is
$\tilde{\Theta}(\sqrt{T}+\min\{M,\,T^{2/3}\})$ by first establishing a
fundamental impossible regime with novel regret lower bound instances. Then, we
propose LEAP, a phased exploration type algorithm for \underline{L}earning and
\underline{EA}rning under \underline{P}rice Protection to match this lower
bound up to logarithmic factors or even doubly logarithmic factors (when there
are only two prices available to the seller). Our results reveal the surprising
phase transitions of the optimal regret with respect to $M$. Specifically, when
$M$ is not too large, the optimal regret has no major difference when compared
to that of the classic setting with no price protection guarantee. We also show
that there exists an upper limit on how much the optimal regret can deteriorate
when $M$ grows large. Finally, we conduct extensive numerical experiments to
show the benefit of LEAP over other heuristic methods for this problem.

Joint entity and relation extraction has been a core task in the field of
information extraction. Recent approaches usually consider the extraction of
relational triples from a stereoscopic perspective, either learning a
relation-specific tagger or separate classifiers for each relation type.
However, they still suffer from error propagation, relation redundancy and lack
of high-level connections between triples. To address these issues, we propose
a novel query-based approach to construct instance-level representations for
relational triples. By metric-based comparison between query embeddings and
token embeddings, we can extract all types of triples in one step, thus
eliminating the error propagation problem. In addition, we learn the
instance-level representation of relational triples via contrastive learning.
In this way, relational triples can not only enclose rich class-level semantics
but also access to high-order global connections. Experimental results show
that our proposed method achieves the state of the art on five widely used

Multitask prompted finetuning (MTF) has been shown to help large language
models generalize to new tasks in a zero-shot setting, but so far explorations
of MTF have focused on English data and models. We apply MTF to the pretrained
multilingual BLOOM and mT5 model families to produce finetuned variants called
BLOOMZ and mT0. We find finetuning large multilingual language models on
English tasks with English prompts allows for task generalization to
non-English languages that appear only in the pretraining corpus. Finetuning on
multilingual tasks with English prompts further improves performance on English
and non-English tasks leading to various state-of-the-art zero-shot results. We
also investigate finetuning on multilingual tasks with prompts that have been
machine-translated from English to match the language of each dataset. We find
training on these machine-translated prompts leads to better performance on
human-written prompts in the respective languages. Surprisingly, we find models
are capable of zero-shot generalization to tasks in languages they have never
intentionally seen. We conjecture that the models are learning higher-level
capabilities that are both task- and language-agnostic. In addition, we
introduce xP3, a composite of supervised datasets in 46 languages with English
and machine-translated prompts. Our code, datasets and models are publicly
available at https://github.com/bigscience-workshop/xmtf.

There is limited understanding of the information captured by deep
spatiotemporal models in their intermediate representations. For example, while
evidence suggests that action recognition algorithms are heavily influenced by
visual appearance in single frames, no quantitative methodology exists for
evaluating such static bias in the latent representation compared to bias
toward dynamics. We tackle this challenge by proposing an approach for
quantifying the static and dynamic biases of any spatiotemporal model, and
apply our approach to three tasks, action recognition, automatic video object
segmentation (AVOS) and video instance segmentation (VIS). Our key findings
are: (i) Most examined models are biased toward static information. (ii) Some
datasets that are assumed to be biased toward dynamics are actually biased
toward static information. (iii) Individual channels in an architecture can be
biased toward static, dynamic or a combination of the two. (iv) Most models
converge to their culminating biases in the first half of training. We then
explore how these biases affect performance on dynamically biased datasets. For
action recognition, we propose StaticDropout, a semantically guided dropout
that debiases a model from static information toward dynamics. For AVOS, we
design a better combination of fusion and cross connection layers compared with
previous architectures.

Online damage quantification suffers from insufficient labeled data. In this
context, adopting the domain adaptation on historical labeled data from similar
structures/damages to assist the current diagnosis task would be beneficial.
However, most domain adaptation methods are designed for classification and
cannot efficiently address damage quantification, a regression problem with
continuous real-valued labels. This study first proposes a novel domain
adaptation method, the Online Fuzzy-set-based Joint Distribution Adaptation for
Regression, to address this challenge. By converting the continuous real-valued
labels to fuzzy class labels via fuzzy sets, the conditional distribution
discrepancy is measured, and domain adaptation can simultaneously consider the
marginal and conditional distribution for the regression task. Furthermore, a
framework of online damage quantification integrated with the proposed domain
adaptation method is presented. The method has been verified with an example of
a damaged helicopter panel, in which domain adaptations are conducted across
different damage locations and from simulation to experiment, proving the
accuracy of damage quantification can be improved significantly even in a noisy
environment. It is expected that the proposed approach to be applied to the
fleet-level digital twin considering the individual differences.

We propose to employ the hierarchical coarse-grained structure in the
artificial neural networks explicitly to improve the interpretability without
degrading performance. The idea has been applied in two situations. One is a
neural network called TaylorNet, which aims to approximate the general mapping
from input data to output result in terms of Taylor series directly, without
resorting to any magic nonlinear activations. The other is a new setup for data
distillation, which can perform multi-level abstraction of the input dataset
and generate new data that possesses the relevant features of the original
dataset and can be used as references for classification. In both cases, the
coarse-grained structure plays an important role in simplifying the network and
improving both the interpretability and efficiency. The validity has been
demonstrated on MNIST and CIFAR-10 datasets. Further improvement and some open
questions related are also discussed.

Estimating the pose of a pallet and other logistics objects is crucial for
various use cases, such as automatized material handling or tracking.
Innovations in computer vision, computing power, and machine learning open up
new opportunities for device-free localization based on cameras and neural
networks. Large image datasets with annotated poses are required for training
the network. Manual annotation, especially of 6D poses, is an extremely
labor-intensive process. Hence, newer approaches often leverage synthetic
training data to automatize the process of generating annotated image datasets.
In this work, the generation of synthetic training data for 6D pose estimation
of pallets is presented. The data is then used to train the Deep Object Pose
Estimation (DOPE) algorithm. The experimental validation of the algorithm
proves that the 6D pose estimation of a standardized Euro pallet with a
Red-Green-Blue (RGB) camera is feasible. The comparison of the results from
three varying datasets under different lighting conditions shows the relevance
of an appropriate dataset design to achieve an accurate and robust
localization. The quantitative evaluation shows an average position error of
less than 20 cm for the preferred dataset. The validated training dataset and a
photorealistic model of a Euro pallet are publicly provided.

Graph-based text representation focuses on how text documents are represented
as graphs for exploiting dependency information between tokens and documents
within a corpus. Despite the increasing interest in graph representation
learning, there is limited research in exploring new ways for graph-based text
representation, which is important in downstream natural language processing
tasks. In this paper, we first propose a new heterogeneous word-character text
graph that combines word and character n-gram nodes together with document
nodes, allowing us to better learn dependencies among these entities.
Additionally, we propose two new graph-based neural models, WCTextGCN and
WCTextGAT, for modeling our proposed text graph. Extensive experiments in text
classification and automatic text summarization benchmarks demonstrate that our
proposed models consistently outperform competitive baselines and
state-of-the-art graph-based models.

Stochastic gradient descent-ascent (SGDA) is one of the main workhorses for
solving finite-sum minimax optimization problems. Most practical
implementations of SGDA randomly reshuffle components and sequentially use them
(i.e., without-replacement sampling); however, there are few theoretical
results on this approach for minimax algorithms, especially outside the
easier-to-analyze (strongly-)monotone setups. To narrow this gap, we study the
convergence bounds of SGDA with random reshuffling (SGDA-RR) for smooth
nonconvex-nonconcave objectives with Polyak-{\L}ojasiewicz (P{\L}) geometry. We
analyze both simultaneous and alternating SGDA-RR for nonconvex-P{\L} and
primal-P{\L}-P{\L} objectives, and obtain convergence rates faster than
with-replacement SGDA. Our rates also extend to mini-batch SGDA-RR, recovering
known rates for full-batch gradient descent-ascent (GDA). Lastly, we present a
comprehensive lower bound for two-time-scale GDA, which matches the full-batch
rate for primal-P{\L}-P{\L} case.

Variational inequalities are a broad formalism that encompasses a vast number
of applications. Motivated by applications in machine learning and beyond,
stochastic methods are of great importance. In this paper we consider the
problem of stochastic finite-sum cocoercive variational inequalities. For this
class of problems, we investigate the convergence of the method based on the
SARAH variance reduction technique. We show that for strongly monotone problems
it is possible to achieve linear convergence to a solution using this method.
Experiments confirm the importance and practical applicability of our approach.

We present a human-in-the-loop approach to generate counterfactual (CF)
explanations that preserve global and local feasibility constraints. Global
feasibility constraints refer to the causal constraints that are necessary for
generating actionable CF explanation. Assuming a domain expert with knowledge
on unary and binary causal constraints, our approach efficiently employs this
knowledge to generate CF explanation by rejecting gradient steps that violate
these constraints. Local feasibility constraints encode end-user's constraints
for generating desirable CF explanation. We extract these constraints from the
end-user of the model and exploit them during CF generation via user-defined
distance metric. Through user studies, we demonstrate that incorporating causal
constraints during CF generation results in significantly better explanations
in terms of feasibility and desirability for participants. Adopting local and
global feasibility constraints simultaneously, although improves user
satisfaction, does not significantly improve desirability of the participants
compared to only incorporating global constraints.

Anticipating future actions in a video is useful for many autonomous and
assistive technologies. Prior action anticipation work mostly treats this as a
vision modality problem, where the models learn the task information primarily
from the video features in the target action anticipation datasets. In this
work, we propose a method to make use of the text-modality that is available
during the training, to bring in complementary information that is not present
in the target action anticipation datasets. In particular, we leverage
pre-trained language models to build a text-modality teacher that is able to
predict future actions based on text labels of the past actions extracted from
the input video. To further adapt the teacher to the target domain (cooking),
we also pretrain the teacher on textual instructions from a recipes dataset
(Recipe1M). Then, we distill the knowledge gained by the text-modality teacher
into a vision-modality student to further improve it's performance. We
empirically evaluate this simple cross-modal distillation strategy on two video
datasets EGTEA-GAZE+ and EPIC-KITCHEN 55. Distilling this text-modality
knowledge into a strong vision model (Anticipative Vision Transformer) yields
consistent gains across both datasets, 3.5% relative improvement on top1 class
mean recall for EGTEA-GAZE+, 7.2% on top5 many-shot class mean recall for
EPIC-KITCHEN 55 and achieves new state-of-the-results.

Human electroencephalography (EEG) is a brain monitoring modality that senses
cortical neuroelectrophysiological activity in high-temporal resolution. One of
the greatest challenges posed in applications of EEG is the unstable signal
quality susceptible to inevitable artifacts during recordings. To date, most
existing techniques for EEG artifact removal and reconstruction are applicable
to offline analysis solely, or require individualized training data to
facilitate online reconstruction. We have proposed CLEEGN, a novel
convolutional neural network for plug-and-play automatic EEG reconstruction.
CLEEGN is based on a subject-independent pre-trained model using existing data
and can operate on a new user without any further calibration. The performance
of CLEEGN was validated using multiple evaluations including waveform
observation, reconstruction error assessment, and decoding accuracy on
well-studied labeled datasets. The results of simulated online validation
suggest that, even without any calibration, CLEEGN can largely preserve
inherent brain activity and outperforms leading online/offline artifact removal
methods in the decoding accuracy of reconstructed EEG data. In addition,
visualization of model parameters and latent features exhibit the model
behavior and reveal explainable insights related to existing knowledge of
neuroscience. We foresee pervasive applications of CLEEGN in prospective works
of online plug-and-play EEG decoding and analysis.

The cubic regularization method (CR) and its adaptive version (ARC) are
popular Newton-type methods in solving unconstrained non-convex optimization
problems, due to its global convergence to local minima under mild conditions.
The main aim of this paper is to develop a momentum-accelerated adaptive cubic
regularization method (ARCm) to improve the convergent performance. With the
proper choice of momentum step size, we show the global convergence of ARCm and
the local convergence can also be guaranteed under the \KL property. Such
global and local convergence can also be established when inexact solvers with
low computational costs are employed in the iteration procedure. Numerical
results for non-convex logistic regression and robust linear regression models
are reported to demonstrate that the proposed ARCm significantly outperforms
state-of-the-art cubic regularization methods (e.g., CR, momentum-based CR,
ARC) and the trust region method. In particular, the number of iterations
required by ARCm is less than 10\% to 50\% required by the most competitive
method (ARC) in the experiments.

Global localization plays a critical role in many robot applications.
LiDAR-based global localization draws the community's focus with its robustness
against illumination and seasonal changes. To further improve the localization
under large viewpoint differences, we propose RING++ which has roto-translation
invariant representation for place recognition, and global convergence for both
rotation and translation estimation. With the theoretical guarantee, RING++ is
able to address the large viewpoint difference using a lightweight map with
sparse scans. In addition, we derive sufficient conditions of feature
extractors for the representation preserving the roto-translation invariance,
making RING++ a framework applicable to generic multi-channel features. To the
best of our knowledge, this is the first learning-free framework to address all
subtasks of global localization in the sparse scan map. Validations on
real-world datasets show that our approach demonstrates better performance than
state-of-the-art learning-free methods, and competitive performance with
learning-based methods. Finally, we integrate RING++ into a multi-robot/session
SLAM system, performing its effectiveness in collaborative applications.

MuZero Unplugged presents a promising approach for offline policy learning
from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned
model and leverages Reanalyze algorithm to learn purely from offline data. For
good performance, MCTS requires accurate learned models and a large number of
simulations, thus costing huge computing time. This paper investigates a few
hypotheses where MuZero Unplugged may not work well under the offline RL
settings, including 1) learning with limited data coverage; 2) learning from
offline data of stochastic environments; 3) improperly parameterized models
given the offline data; 4) with a low compute budget. We propose to use a
regularized one-step look-ahead approach to tackle the above issues. Instead of
planning with the expensive MCTS, we use the learned model to construct an
advantage estimation based on a one-step rollout. Policy improvements are
towards the direction that maximizes the estimated advantage with
regularization of the dataset. We conduct extensive empirical studies with
BSuite environments to verify the hypotheses and then run our algorithm on the
RL Unplugged Atari benchmark. Experimental results show that our proposed
approach achieves stable performance even with an inaccurate learned model. On
the large-scale Atari benchmark, the proposed method outperforms MuZero
Unplugged by 43%. Most significantly, it uses only 5.6% wall-clock time (i.e.,
1 hour) compared to MuZero Unplugged (i.e., 17.8 hours) to achieve a 150% IQM
normalized score with the same hardware and software stacks.

Several recently proposed text-to-speech (TTS) models achieved to generate
the speech samples with the human-level quality in the single-speaker and
multi-speaker TTS scenarios with a set of pre-defined speakers. However,
synthesizing a new speaker's voice with a single reference audio, commonly
known as zero-shot multi-speaker text-to-speech (ZSM-TTS), is still a very
challenging task. The main challenge of ZSM-TTS is the speaker domain shift
problem upon the speech generation of a new speaker. To mitigate this problem,
we propose adversarial speaker-consistency learning (ASCL). The proposed method
first generates an additional speech of a query speaker using the external
untranscribed datasets at each training iteration. Then, the model learns to
consistently generate the speech sample of the same speaker as the
corresponding speaker embedding vector by employing an adversarial learning
scheme. The experimental results show that the proposed method is effective
compared to the baseline in terms of the quality and speaker similarity in
ZSM-TTS.

Optimal resource allocation is gaining a renewed interest due its relevance
as a core problem in managing, over time, cloud and high-performance computing
facilities. Semi-Bandit Feedback (SBF) is the reference method for efficiently
solving this problem. In this paper we propose (i) an extension of the optimal
resource allocation to a more general class of problems, specifically with
resources availability changing over time, and (ii) Bayesian Optimization as a
more efficient alternative to SBF. Three algorithms for Bayesian Optimization
for Resource Allocation, namely BORA, are presented, working on allocation
decisions represented as numerical vectors or distributions. The second option
required to consider the Wasserstein distance as a more suitable metric to use
into one of the BORA algorithms. Results on (i) the original SBF case study
proposed in the literature, and (ii) a real-life application (i.e., the
optimization of multi-channel marketing) empirically prove that BORA is a more
efficient and effective learning-and-optimization framework than SBF.

Stochastic human motion prediction aims to forecast multiple plausible future
motions given a single pose sequence from the past. Most previous works focus
on designing elaborate losses to improve the accuracy, while the diversity is
typically characterized by randomly sampling a set of latent variables from the
latent prior, which is then decoded into possible motions. This joint training
of sampling and decoding, however, suffers from posterior collapse as the
learned latent variables tend to be ignored by a strong decoder, leading to
limited diversity. Alternatively, inspired by the diffusion process in
nonequilibrium thermodynamics, we propose MotionDiff, a diffusion probabilistic
model to treat the kinematics of human joints as heated particles, which will
diffuse from original states to a noise distribution. This process offers a
natural way to obtain the "whitened" latents without any trainable parameters,
and human motion prediction can be regarded as the reverse diffusion process
that converts the noise distribution into realistic future motions conditioned
on the observed sequence. Specifically, MotionDiff consists of two parts: a
spatial-temporal transformer-based diffusion network to generate diverse yet
plausible motions, and a graph convolutional network to further refine the
outputs. Experimental results on two datasets demonstrate that our model yields
the competitive performance in terms of both accuracy and diversity.

To work with categorical features, machine learning systems employ embedding
tables. These tables can become exceedingly large in modern recommendation
systems, necessitating the development of new methods for fitting them in
memory, even during training.
  Some of the most successful methods for table compression are Product- and
Residual Vector Quantization (Gray & Neuhoff, 1998). These methods replace
table rows with references to k-means clustered "codewords." Unfortunately,
this means they must first know the table before compressing it, so they can
only save memory during inference, not training. Recent work has used
hashing-based approaches to minimize memory usage during training, but the
compression obtained is inferior to that obtained by "post-training"
quantization.
  We show that the best of both worlds may be obtained by combining techniques
based on hashing and clustering. By first training a hashing-based "sketch",
then clustering it, and then training the clustered quantization, our method
achieves compression ratios close to those of post-training quantization with
the training time memory reductions of hashing-based methods.
  We show experimentally that our method provides better compression and/or
accuracy that previous methods, and we prove that our method always converges
to the optimal embedding table for least-squares training.

In this study, we present meta-sequential prediction (MSP), an unsupervised
framework to learn the symmetry from the time sequence of length at least
three. Our method leverages the stationary property (e.g. constant velocity,
constant acceleration) of the time sequence to learn the underlying equivariant
structure of the dataset by simply training the encoder-decoder model to be
able to predict the future observations. We will demonstrate that, with our
framework, the hidden disentangled structure of the dataset naturally emerges
as a by-product by applying simultaneous block-diagonalization to the
transition operators in the latent space, the procedure which is commonly used
in representation theory to decompose the feature-space based on the type of
response to group actions. We will showcase our method from both empirical and
theoretical perspectives. Our result suggests that finding a simple structured
relation and learning a model with extrapolation capability are two sides of
the same coin. The code is available at
https://github.com/takerum/meta_sequential_prediction.

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial
examples, which can produce erroneous predictions by injecting imperceptible
perturbations. In this work, we study the transferability of adversarial
examples, which is significant due to its threat to real-world applications
where model architecture or parameters are usually unknown. Many existing works
reveal that the adversarial examples are likely to overfit the surrogate model
that they are generated from, limiting its transfer attack performance against
different target models. To mitigate the overfitting of the surrogate model, we
propose a novel attack method, dubbed reverse adversarial perturbation (RAP).
Specifically, instead of minimizing the loss of a single adversarial point, we
advocate seeking adversarial example located at a region with unified low loss
value, by injecting the worst-case perturbation (the reverse adversarial
perturbation) for each step of the optimization procedure. The adversarial
attack with RAP is formulated as a min-max bi-level optimization problem. By
integrating RAP into the iterative process for attacks, our method can find
more stable adversarial examples which are less sensitive to the changes of
decision boundary, mitigating the overfitting of the surrogate model.
Comprehensive experimental comparisons demonstrate that RAP can significantly
boost adversarial transferability. Furthermore, RAP can be naturally combined
with many existing black-box attack techniques, to further boost the
transferability. When attacking a real-world image recognition system, Google
Cloud Vision API, we obtain 22% performance improvement of targeted attacks
over the compared method. Our codes are available at
https://github.com/SCLBD/Transfer_attack_RAP.

In recent years, maximization of DR-submodular continuous functions became an
important research field, with many real-worlds applications in the domains of
machine learning, communication systems, operation research and economics. Most
of the works in this field study maximization subject to down-closed convex set
constraints due to an inapproximability result by Vondr\'ak (2013). However,
Durr et al. (2021) showed that one can bypass this inapproximability by proving
approximation ratios that are functions of $m$, the minimum
$\ell_{\infty}$-norm of any feasible vector. Given this observation, it is
possible to get results for maximizing a DR-submodular function subject to
general convex set constraints, which has led to multiple works on this
problem. The most recent of which is a polynomial time $\tfrac{1}{4}(1 -
m)$-approximation offline algorithm due to Du (2022). However, only a
sub-exponential time $\tfrac{1}{3\sqrt{3}}(1 - m)$-approximation algorithm is
known for the corresponding online problem. In this work, we present a
polynomial time online algorithm matching the $\tfrac{1}{4}(1 -
m)$-approximation of the state-of-the-art offline algorithm. We also present an
inapproximability result showing that our online algorithm and Du's (2022)
offline algorithm are both optimal in a strong sense. Finally, we study the
empirical performance of our algorithm and the algorithm of Du (which was only
theoretically studied previously), and show that they consistently outperform
previously suggested algorithms on revenue maximization, location summarization
and quadratic programming applications.

Dale's principle implies that biological neural networks are composed of
neurons that are either excitatory or inhibitory. While the number of possible
architectures of such Daleian networks is exponentially smaller than
non-Daleian ones, the computational and functional implications of using
Daleian networks by the brain are mostly unknown. Here, we use models of
recurrent spiking neural networks and rate-based networks to show,
surprisingly, that despite the structural limitations on Daleian networks, they
can approximate the computation performed by non-Daleian networks to a very
high degree of accuracy. Moreover, we find that Daleian networks are more
functionally robust to synaptic noise. We then show that unlike non-Daleian
networks, Daleian ones can learn efficiently by tuning single neuron features,
nearly as well as learning by tuning individual synaptic weights - suggesting a
simpler and more biologically plausible learning mechanism. We thus suggest
that in addition to architectural simplicity, Dale's principle confers
computational and learning benefits for biological networks, and offers new
directions for constructing and training biologically-inspired artificial
neural networks

Graph Convolutional Network (GCN) has exhibited strong empirical performance
in many real-world applications. The vast majority of existing works on GCN
primarily focus on the accuracy while ignoring how confident or uncertain a GCN
is with respect to its predictions. Despite being a cornerstone of trustworthy
graph mining, uncertainty quantification on GCN has not been well studied and
the scarce existing efforts either fail to provide deterministic quantification
or have to change the training procedure of GCN by introducing additional
parameters or architectures. In this paper, we propose the first
frequentist-based approach named JuryGCN in quantifying the uncertainty of GCN,
where the key idea is to quantify the uncertainty of a node as the width of
confidence interval by a jackknife estimator. Moreover, we leverage the
influence functions to estimate the change in GCN parameters without
re-training to scale up the computation. The proposed JuryGCN is capable of
quantifying uncertainty deterministically without modifying the GCN
architecture or introducing additional parameters. We perform extensive
experimental evaluation on real-world datasets in the tasks of both active
learning and semi-supervised node classification, which demonstrate the
efficacy of the proposed method.

There still remains an extreme performance gap between Vision Transformers
(ViTs) and Convolutional Neural Networks (CNNs) when training from scratch on
small datasets, which is concluded to the lack of inductive bias. In this
paper, we further consider this problem and point out two weaknesses of ViTs in
inductive biases, that is, the spatial relevance and diverse channel
representation. First, on spatial aspect, objects are locally compact and
relevant, thus fine-grained feature needs to be extracted from a token and its
neighbors. While the lack of data hinders ViTs to attend the spatial relevance.
Second, on channel aspect, representation exhibits diversity on different
channels. But the scarce data can not enable ViTs to learn strong enough
representation for accurate recognition. To this end, we propose Dynamic Hybrid
Vision Transformer (DHVT) as the solution to enhance the two inductive biases.
On spatial aspect, we adopt a hybrid structure, in which convolution is
integrated into patch embedding and multi-layer perceptron module, forcing the
model to capture the token features as well as their neighboring features. On
channel aspect, we introduce a dynamic feature aggregation module in MLP and a
brand new "head token" design in multi-head self-attention module to help
re-calibrate channel representation and make different channel group
representation interacts with each other. The fusion of weak channel
representation forms a strong enough representation for classification. With
this design, we successfully eliminate the performance gap between CNNs and
ViTs, and our DHVT achieves a series of state-of-the-art performance with a
lightweight model, 85.68% on CIFAR-100 with 22.8M parameters, 82.3% on
ImageNet-1K with 24.0M parameters. Code is available at
https://github.com/ArieSeirack/DHVT.

Automated machine learning has been widely explored to reduce human efforts
in designing neural architectures and looking for proper hyperparameters. In
the domain of neural initialization, however, similar automated techniques have
rarely been studied. Most existing initialization methods are handcrafted and
highly dependent on specific architectures. In this paper, we propose a
differentiable quantity, named GradCosine, with theoretical insights to
evaluate the initial state of a neural network. Specifically, GradCosine is the
cosine similarity of sample-wise gradients with respect to the initialized
parameters. By analyzing the sample-wise optimization landscape, we show that
both the training and test performance of a network can be improved by
maximizing GradCosine under gradient norm constraint. Based on this
observation, we further propose the neural initialization optimization (NIO)
algorithm. Generalized from the sample-wise analysis into the real batch
setting, NIO is able to automatically look for a better initialization with
negligible cost compared with the training time. With NIO, we improve the
classification performance of a variety of neural architectures on CIFAR-10,
CIFAR-100, and ImageNet. Moreover, we find that our method can even help to
train large vision Transformer architecture without warmup.

Ordinary Differential Equations (ODEs) have recently gained a lot of
attention in machine learning. However, the theoretical aspects, e.g.,
identifiability and asymptotic properties of statistical estimation are still
obscure. This paper derives a sufficient condition for the identifiability of
homogeneous linear ODE systems from a sequence of equally-spaced error-free
observations sampled from a single trajectory. When observations are disturbed
by measurement noise, we prove that under mild conditions, the parameter
estimator based on the Nonlinear Least Squares (NLS) method is consistent and
asymptotic normal with $n^{-1/2}$ convergence rate. Based on the asymptotic
normality property, we construct confidence sets for the unknown system
parameters and propose a new method to infer the causal structure of the ODE
system, i.e., inferring whether there is a causal link between system
variables. Furthermore, we extend the results to degraded observations,
including aggregated and time-scaled ones. To the best of our knowledge, our
work is the first systematic study of the identifiability and asymptotic
properties in learning linear ODE systems. We also construct simulations with
various system dimensions to illustrate the established theoretical results.

Automatic interpretation on smartphone-captured chest X-ray (CXR) photographs
is challenging due to the geometric distortion (projective transformation)
caused by the non-ideal camera position. In this paper, we proposed an
innovative deep learning-based Projective Transformation Rectification Network
(PTRN) to automatically rectify such distortions by predicting the projective
transformation matrix. PTRN is trained on synthetic data to avoid the expensive
collection of natural data. Therefore, we proposed an innovative synthetic data
framework that accounts for the visual attributes of natural photographs
including screen, background, illuminations, and visual artifacts, and generate
synthetic CXR photographs and projective transformation matrices as the
ground-truth labels for training PTRN. Finally, smartphone-captured CXR
photographs are automatically rectified by trained PTRN and interpreted by a
classifier trained on high-quality digital CXRs to produce final interpretation
results. In the CheXphoto CXR photograph interpretation competition released by
the Stanford University Machine Learning Group, our approach achieves a huge
performance improvement and won first place (ours 0.850, second-best 0.762, in
AUC). A deeper analysis demonstrates that the use of PTRN successfully achieves
the performance on CXR photographs to the same level as on digital CXRs,
indicating PTRN can eliminate all negative impacts of projective transformation
to the interpretation performance. Additionally, there are many real-world
scenarios where distorted photographs have to be used for image classification,
our PTRN can be used to solve those similar problems due to its generality
design.

In this paper, we study the classification problem by estimating the
conditional probability function of the given data. Different from the
traditional expected risk estimation theory on empirical data, we calculate the
probability via Fredholm equation, this leads to estimate the distribution of
the data. Based on the Fredholm equation, a new expected risk estimation theory
by estimating the cumulative distribution function is presented. The main
characteristics of the new expected risk estimation is to measure the risk on
the distribution of the input space. The corresponding empirical risk
estimation is also presented, and an $\varepsilon$-insensitive $L_{1}$
cumulative support vector machines ($\varepsilon$-$L_{1}VSVM$) is proposed by
introducing an insensitive loss. It is worth mentioning that the classification
models and the classification evaluation indicators based on the new mechanism
are different from the traditional one. Experimental results show the
effectiveness of the proposed $\varepsilon$-$L_{1}VSVM$ and the corresponding
cumulative distribution function indicator on validity and interpretability of
small data classification.

The image inpainting task fills missing areas of a corrupted image. Despite
impressive results have been achieved recently, it is still challenging to
restore corrupted images with both vivid textures and reasonable structures.
Some previous methods only tackle regular textures while losing holistic
structures limited by receptive fields of Convolution Neural Networks (CNNs).
To this end, we study learning a Zero-initialized residual addition based
Incremental Transformer on Structural priors (ZITS++), an improved model over
our conference ZITS model. Specifically, given one corrupt image, we present
the Transformer Structure Restorer (TSR) module to restore holistic structural
priors at low image resolution, which are further upsampled by Simple Structure
Upsampler (SSU) module to higher image resolution. Further, to well recover
image texture details, we take the Fourier CNN Texture Restoration (FTR)
module, which has both the Fourier and large-kernel attention convolutions.
Typically, FTR can be independently pre-trained without image structural
priors. Furthermore, to enhance the FTR, the upsampled structural priors from
TSR are further processed by Structure Feature Encoder (SFE), and updating the
FTR by a novel incremental training strategy of Zero-initialized Residual
Addition (ZeroRA). Essentially, a new masking positional encoding is proposed
to encode the large irregular masks. Extensive experiments on various datasets
validate the efficacy of our model compared with other competitors. We also
conduct extensive ablation to compare and verify various priors for image
inpainting tasks.

In the age of big data, the demand for hidden information mining in
technological intellectual property is increasing in discrete countries.
Definitely, a considerable number of graph learning algorithms for
technological intellectual property have been proposed. The goal is to model
the technological intellectual property entities and their relationships
through the graph structure and use the neural network algorithm to extract the
hidden structure information in the graph. However, most of the existing graph
learning algorithms merely focus on the information mining of binary relations
in technological intellectual property, ignoring the higherorder information
hidden in non-binary relations. Therefore, a hypergraph neural network model
based on dual channel convolution is proposed. For the hypergraph constructed
from technological intellectual property data, the hypergraph channel and the
line expanded graph channel of the hypergraph are used to learn the hypergraph,
and the attention mechanism is introduced to adaptively fuse the output
representations of the two channels. The proposed model outperforms the
existing approaches on a variety of datasets.

Lesion appearance is a crucial clue for medical providers to distinguish
referable diabetic retinopathy (rDR) from non-referable DR. Most existing
large-scale DR datasets contain only image-level labels rather than pixel-based
annotations. This motivates us to develop algorithms to classify rDR and
segment lesions via image-level labels. This paper leverages self-supervised
equivariant learning and attention-based multi-instance learning (MIL) to
tackle this problem. MIL is an effective strategy to differentiate positive and
negative instances, helping us discard background regions (negative instances)
while localizing lesion regions (positive ones). However, MIL only provides
coarse lesion localization and cannot distinguish lesions located across
adjacent patches. Conversely, a self-supervised equivariant attention mechanism
(SEAM) generates a segmentation-level class activation map (CAM) that can guide
patch extraction of lesions more accurately. Our work aims at integrating both
methods to improve rDR classification accuracy. We conduct extensive validation
experiments on the Eyepacs dataset, achieving an area under the receiver
operating characteristic curve (AU ROC) of 0.958, outperforming current
state-of-the-art algorithms.

Recently, the ability of self-supervised Vision Transformer (ViT) to
represent pixel-level semantic relationships promotes the development of
unsupervised dense prediction tasks. In this work, we investigate transferring
self-supervised ViT to unsupervised semantic segmentation task. According to
the analysis that the pixel-level representations of self-supervised ViT within
a single image achieve good intra-class compactness and inter-class
discrimination, we propose the Dynamic Clustering Network (DCN) to dynamically
infer the underlying cluster centers for different images. By training with the
proposed modularity loss, the DCN learns to project a set of prototypes to
cluster centers for pixel representations in each image and assign pixels to
different clusters, resulting on dividing each image to class-agnostic regions.
For achieving unsupervised semantic segmentation task, we treat it as a region
classification problem. Based on the regions produced by the DCN, we explore
different ways to extract region-level representations and classify them in an
unsupervised manner. We demonstrate the effectiveness of the proposed method
trough experiments on unsupervised semantic segmentation, and achieve
state-of-the-art performance on PASCAL VOC 2012 unsupervised semantic
segmentation task.

Class-incremental semantic segmentation (CISS) labels each pixel of an image
with a corresponding object/stuff class continually. To this end, it is crucial
to learn novel classes incrementally without forgetting previously learned
knowledge. Current CISS methods typically use a knowledge distillation (KD)
technique for preserving classifier logits, or freeze a feature extractor, to
avoid the forgetting problem. The strong constraints, however, prevent learning
discriminative features for novel classes. We introduce a CISS framework that
alleviates the forgetting problem and facilitates learning novel classes
effectively. We have found that a logit can be decomposed into two terms. They
quantify how likely an input belongs to a particular class or not, providing a
clue for a reasoning process of a model. The KD technique, in this context,
preserves the sum of two terms (i.e., a class logit), suggesting that each
could be changed and thus the KD does not imitate the reasoning process. To
impose constraints on each term explicitly, we propose a new decomposed
knowledge distillation (DKD) technique, improving the rigidity of a model and
addressing the forgetting problem more effectively. We also introduce a novel
initialization method to train new classifiers for novel classes. In CISS, the
number of negative training samples for novel classes is not sufficient to
discriminate old classes. To mitigate this, we propose to transfer knowledge of
negatives to the classifiers successively using an auxiliary classifier,
boosting the performance significantly. Experimental results on standard CISS
benchmarks demonstrate the effectiveness of our framework.

We explore the fairness issue that arises in recommender systems. Biased data
due to inherent stereotypes of particular groups (e.g., male students' average
rating on mathematics is often higher than that on humanities, and vice versa
for females) may yield a limited scope of suggested items to a certain group of
users. Our main contribution lies in the introduction of a novel fairness
notion (that we call equal experience), which can serve to regulate such
unfairness in the presence of biased data. The notion captures the degree of
the equal experience of item recommendations across distinct groups. We propose
an optimization framework that incorporates the fairness notion as a
regularization term, as well as introduce computationally-efficient algorithms
that solve the optimization. Experiments on synthetic and benchmark real
datasets demonstrate that the proposed framework can indeed mitigate such
unfairness while exhibiting a minor degradation of recommendation accuracy.

Multi-task learning (MTL) aims to improve the generalization performance of
multiple tasks by exploiting the shared factors among them. Various metrics
(e.g., F-score, Area Under the ROC Curve) are used to evaluate the performances
of MTL methods. Most existing MTL methods try to minimize either the
misclassified errors for classification or the mean squared errors for
regression. In this paper, we propose a method to directly optimize the
evaluation metrics for a large family of MTL problems. The formulation of MTL
that directly optimizes evaluation metrics is the combination of two parts: (1)
a regularizer defined on the weight matrix over all tasks, in order to capture
the relatedness of these tasks; (2) a sum of multiple structured hinge losses,
each corresponding to a surrogate of some evaluation metric on one task. This
formulation is challenging in optimization because both of its parts are
non-smooth. To tackle this issue, we propose a novel optimization procedure
based on the alternating direction scheme of multipliers, where we decompose
the whole optimization problem into a sub-problem corresponding to the
regularizer and another sub-problem corresponding to the structured hinge
losses. For a large family of MTL problems, the first sub-problem has
closed-form solutions. To solve the second sub-problem, we propose an efficient
primal-dual algorithm via coordinate ascent. Extensive evaluation results
demonstrate that, in a large family of MTL problems, the proposed MTL method of
directly optimization evaluation metrics has superior performance gains against
the corresponding baseline methods.

Design time uncertainty poses an important challenge when developing a
self-adaptive system. As an example, defining how the system should adapt when
facing a new environment state, requires understanding the precise effect of an
adaptation, which may not be known at design time. Online reinforcement
learning, i.e., employing reinforcement learning (RL) at runtime, is an
emerging approach to realizing self-adaptive systems in the presence of design
time uncertainty. By using Online RL, the self-adaptive system can learn from
actual operational data and leverage feedback only available at runtime.
Recently, Deep RL is gaining interest. Deep RL represents learned knowledge as
a neural network whereby it can generalize over unseen inputs, as well as
handle continuous environment states and adaptation actions. A fundamental
problem of Deep RL is that learned knowledge is not explicitly represented. For
a human, it is practically impossible to relate the parametrization of the
neural network to concrete RL decisions and thus Deep RL essentially appears as
a black box. Yet, understanding the decisions made by Deep RL is key to (1)
increasing trust, and (2) facilitating debugging. Such debugging is especially
relevant for self-adaptive systems, because the reward function, which
quantifies the feedback to the RL algorithm, must be defined by developers. The
reward function must be explicitly defined by developers, thus introducing a
potential for human error. To explain Deep RL for self-adaptive systems, we
enhance and combine two existing explainable RL techniques from the machine
learning literature. The combined technique, XRL-DINE, overcomes the respective
limitations of the individual techniques. We present a proof-of-concept
implementation of XRL-DINE, as well as qualitative and quantitative results of
applying XRL-DINE to a self-adaptive system exemplar.

In a backdoor attack, an attacker injects corrupted examples into the
training set. The goal of the attacker is to cause the final trained model to
predict the attacker's desired target label when a predefined trigger is added
to test inputs. Central to these attacks is the trade-off between the success
rate of the attack and the number of corrupted training examples injected. We
pose this attack as a novel bilevel optimization problem: construct strong
poison examples that maximize the attack success rate of the trained model. We
use neural tangent kernels to approximate the training dynamics of the model
being attacked and automatically learn strong poison examples. We experiment on
subclasses of CIFAR-10 and ImageNet with WideResNet-34 and ConvNeXt
architectures on periodic and patch trigger attacks and show that NTBA-designed
poisoned examples achieve, for example, an attack success rate of 90% with ten
times smaller number of poison examples injected compared to the baseline. We
provided an interpretation of the NTBA-designed attacks using the analysis of
kernel linear regression. We further demonstrate a vulnerability in
overparametrized deep neural networks, which is revealed by the shape of the
neural tangent kernel.

Recent studies reveal that a well-trained deep reinforcement learning (RL)
policy can be particularly vulnerable to adversarial perturbations on input
observations. Therefore, it is crucial to train RL agents that are robust
against any attacks with a bounded budget. Existing robust training methods in
deep RL either treat correlated steps separately, ignoring the robustness of
long-term rewards, or train the agents and RL-based attacker together, doubling
the computational burden and sample complexity of the training process. In this
work, we propose a strong and efficient robust training framework for RL, named
Worst-case-aware Robust RL (WocaR-RL) that directly estimates and optimizes the
worst-case reward of a policy under bounded l_p attacks without requiring extra
samples for learning an attacker. Experiments on multiple environments show
that WocaR-RL achieves state-of-the-art performance under various strong
attacks, and obtains significantly higher training efficiency than prior
state-of-the-art robust training methods. The code of this work is available at
https://github.com/umd-huang-lab/WocaR-RL.

In offline model-based reinforcement learning (offline MBRL), we learn a
dynamic model from historically collected data, and subsequently utilize the
learned model and fixed datasets for policy learning, without further
interacting with the environment. Offline MBRL algorithms can improve the
efficiency and stability of policy learning over the model-free algorithms.
However, in most of the existing offline MBRL algorithms, the learning
objectives for the dynamic models and the policies are isolated from each
other. Such an objective mismatch may lead to inferior performance of the
learned agents. In this paper, we address this issue by developing an iterative
offline MBRL framework, where we maximize a lower bound of the true expected
return, by alternating between dynamic-model training and policy learning. With
the proposed unified model-policy learning framework, we achieve competitive
performance on a wide range of continuous-control offline reinforcement
learning datasets. Source code is publicly released.

Graph neural networks (GNNs) have shown remarkable performance on diverse
graph mining tasks. Although different GNNs can be unified as the same message
passing framework, they learn complementary knowledge from the same graph.
Knowledge distillation (KD) is developed to combine the diverse knowledge from
multiple models. It transfers knowledge from high-capacity teachers to a
lightweight student. However, to avoid oversmoothing, GNNs are often shallow,
which deviates from the setting of KD. In this context, we revisit KD by
separating its benefits from model compression and emphasizing its power of
transferring knowledge. To this end, we need to tackle two challenges: how to
transfer knowledge from compact teachers to a student with the same capacity;
and, how to exploit student GNN's own strength to learn knowledge. In this
paper, we propose a novel adaptive KD framework, called BGNN, which
sequentially transfers knowledge from multiple GNNs into a student GNN. We also
introduce an adaptive temperature module and a weight boosting module. These
modules guide the student to the appropriate knowledge for effective learning.
Extensive experiments have demonstrated the effectiveness of BGNN. In
particular, we achieve up to 3.05% improvement for node classification and
7.67% improvement for graph classification over vanilla GNNs.

We study the finite-time behaviour of the popular temporal difference (TD)
learning algorithm when combined with tail-averaging. We derive finite time
bounds on the parameter error of the tail-averaged TD iterate under a step-size
choice that does not require information about the eigenvalues of the matrix
underlying the projected TD fixed point. Our analysis shows that tail-averaged
TD converges at the optimal $O\left(1/t\right)$ rate, both in expectation and
with high probability. In addition, our bounds exhibit a sharper rate of decay
for the initial error (bias), which is an improvement over averaging all
iterates. We also propose and analyse a variant of TD that incorporates
regularisation. From analysis, we conclude that the regularised version of TD
is useful for problems with ill-conditioned features.

Recently, deep learning-based methods have drawn huge attention due to their
simple yet high performance without domain knowledge in sound classification
and localization tasks. However, a lack of gun sounds in existing datasets has
been a major obstacle to implementing a support system to spot criminals from
their gunshots by leveraging deep learning models. Since the occurrence of
gunshot is rare and unpredictable, it is impractical to collect gun sounds in
the real world. As an alternative, gun sounds can be obtained from an FPS game
that is designed to mimic real-world warfare. The recent FPS game offers a
realistic environment where we can safely collect gunshot data while simulating
even dangerous situations. By exploiting the advantage of the game environment,
we construct a gunshot dataset, namely BGG, for the firearm classification and
gunshot localization tasks. The BGG dataset consists of 37 different types of
firearms, distances, and directions between the sound source and a receiver. We
carefully verify that the in-game gunshot data has sufficient information to
identify the location and type of gunshots by training several sound
classification and localization baselines on the BGG dataset. Afterward, we
demonstrate that the accuracy of real-world firearm classification and
localization tasks can be enhanced by utilizing the BGG dataset.

Hateful memes are a growing menace on social media. While the image and its
corresponding text in a meme are related, they do not necessarily convey the
same meaning when viewed individually. Hence, detecting hateful memes requires
careful consideration of both visual and textual information. Multimodal
pre-training can be beneficial for this task because it effectively captures
the relationship between the image and the text by representing them in a
similar feature space. Furthermore, it is essential to model the interactions
between the image and text features through intermediate fusion. Most existing
methods either employ multimodal pre-training or intermediate fusion, but not
both. In this work, we propose the Hate-CLIPper architecture, which explicitly
models the cross-modal interactions between the image and text representations
obtained using Contrastive Language-Image Pre-training (CLIP) encoders via a
feature interaction matrix (FIM). A simple classifier based on the FIM
representation is able to achieve state-of-the-art performance on the Hateful
Memes Challenge (HMC) dataset with an AUROC of 85.8, which even surpasses the
human performance of 82.65. Experiments on other meme datasets such as
Propaganda Memes and TamilMemes also demonstrate the generalizability of the
proposed approach. Finally, we analyze the interpretability of the FIM
representation and show that cross-modal interactions can indeed facilitate the
learning of meaningful concepts. The code for this work is available at
https://github.com/gokulkarthik/hateclipper.

For the video salient object detection (VSOD) task, how to excavate the
information from the appearance modality and the motion modality has always
been a topic of great concern. The two-stream structure, including an RGB
appearance stream and an optical flow motion stream, has been widely used as a
typical pipeline for VSOD tasks, but the existing methods usually only use
motion features to unidirectionally guide appearance features or adaptively but
blindly fuse two modality features. However, these methods underperform in
diverse scenarios due to the uncomprehensive and unspecific learning schemes.
In this paper, following a more secure modeling philosophy, we deeply
investigate the importance of appearance modality and motion modality in a more
comprehensive way and propose a VSOD network with up and down parallel
symmetry, named PSNet. Two parallel branches with different dominant modalities
are set to achieve complete video saliency decoding with the cooperation of the
Gather Diffusion Reinforcement (GDR) module and Cross-modality Refinement and
Complement (CRC) module. Finally, we use the Importance Perception Fusion (IPF)
module to fuse the features from two parallel branches according to their
different importance in different scenarios. Experiments on four dataset
benchmarks demonstrate that our method achieves desirable and competitive

In this paper, we provide a novel strategy for solving Traveling Salesman
Problem, which is a famous combinatorial optimization problem studied intensely
in the TCS community. In particular, we consider the imitation learning
framework, which helps a deterministic algorithm making good choices whenever
it needs to, resulting in a speed up while maintaining the exactness of the
solution without suffering from the unpredictability and a potential large
deviation.
  Furthermore, we demonstrate a strong generalization ability of a graph neural
network trained under the imitation learning framework. Specifically, the model
is capable of solving a large instance of TSP faster than the baseline while
has only seen small TSP instances when training.

As a crucial approach for compact representation learning, hashing has
achieved great success in effectiveness and efficiency. Numerous heuristic
Hamming space metric learning objectives are designed to obtain high-quality
hash codes. Nevertheless, a theoretical analysis of criteria for learning good
hash codes remains largely unexploited. In this paper, we prove that
inter-class distinctiveness and intra-class compactness among hash codes
determine the lower bound of hash codes' performance. Promoting these two
characteristics could lift the bound and improve hash learning. We then propose
a surrogate model to fully exploit the above objective by estimating the
posterior of hash codes and controlling it, which results in a low-bias
optimization. Extensive experiments reveal the effectiveness of the proposed
method. By testing on a series of hash-models, we obtain performance
improvements among all of them, with an up to $26.5\%$ increase in mean Average
Precision and an up to $20.5\%$ increase in accuracy. Our code is publicly
available at https://github.com/VL-Group/LBHash.

Object detection through LiDAR-based point cloud has recently been important
in autonomous driving. Although achieving high accuracy on public benchmarks,
the state-of-the-art detectors may still go wrong and cause a heavy loss due to
the widespread corruptions in the real world like rain, snow, sensor noise,
etc. Nevertheless, there is a lack of a large-scale dataset covering diverse
scenes and realistic corruption types with different severities to develop
practical and robust point cloud detectors, which is challenging due to the
heavy collection costs. To alleviate the challenge and start the first step for
robust point cloud detection, we propose the physical-aware simulation methods
to generate degraded point clouds under different real-world common
corruptions. Then, for the first attempt, we construct a benchmark based on the
physical-aware common corruptions for point cloud detectors, which contains a
total of 1,122,150 examples covering 7,481 scenes, 25 common corruption types,
and 6 severities. With such a novel benchmark, we conduct extensive empirical
studies on 8 state-of-the-art detectors that contain 6 different detection
frameworks. Thus we get several insight observations revealing the
vulnerabilities of the detectors and indicating the enhancement directions.
Moreover, we further study the effectiveness of existing robustness enhancement
methods based on data augmentation and data denoising. The benchmark can
potentially be a new platform for evaluating point cloud detectors, opening a
door for developing novel robustness enhancement methods.

Graph convolution networks (GCN) have been widely used in skeleton-based
action recognition. We note that existing GCN-based approaches primarily rely
on prescribed graphical structures (ie., a manually defined topology of
skeleton joints), which limits their flexibility to capture complicated
correlations between joints. To move beyond this limitation, we propose a new
framework for skeleton-based action recognition, namely Dynamic Group
Spatio-Temporal GCN (DG-STGCN). It consists of two modules, DG-GCN and DG-TCN,
respectively, for spatial and temporal modeling. In particular, DG-GCN uses
learned affinity matrices to capture dynamic graphical structures instead of
relying on a prescribed one, while DG-TCN performs group-wise temporal
convolutions with varying receptive fields and incorporates a dynamic
joint-skeleton fusion module for adaptive multi-level temporal modeling. On a
wide range of benchmarks, including NTURGB+D, Kinetics-Skeleton, BABEL, and
Toyota SmartHome, DG-STGCN consistently outperforms state-of-the-art methods,
often by a notable margin.

We present a deep reinforcement learning method of progressive view
inpainting for colored semantic point cloud scene completion under volume
guidance, achieving high-quality scene reconstruction from only a single RGB-D
image with severe occlusion. Our approach is end-to-end, consisting of three
modules: 3D scene volume reconstruction, 2D RGB-D and segmentation image
inpainting, and multi-view selection for completion. Given a single RGB-D
image, our method first predicts its semantic segmentation map and goes through
the 3D volume branch to obtain a volumetric scene reconstruction as a guide to
the next view inpainting step, which attempts to make up the missing
information; the third step involves projecting the volume under the same view
of the input, concatenating them to complete the current view RGB-D and
segmentation map, and integrating all RGB-D and segmentation maps into the
point cloud. Since the occluded areas are unavailable, we resort to a A3C
network to glance around and pick the next best view for large hole completion
progressively until a scene is adequately reconstructed while guaranteeing
validity. All steps are learned jointly to achieve robust and consistent
results. We perform qualitative and quantitative evaluations with extensive
experiments on the 3D-FUTURE data, obtaining better results than
state-of-the-arts.

Online inference is becoming a key service product for many businesses,
deployed in cloud platforms to meet customer demands. Despite their
revenue-generation capability, these services need to operate under tight
Quality-of-Service (QoS) and cost budget constraints. This paper introduces
KAIROS, a novel runtime framework that maximizes the query throughput while
meeting QoS target and a cost budget. KAIROS designs and implements novel
techniques to build a pool of heterogeneous compute hardware without online
exploration overhead, and distribute inference queries optimally at runtime.
Our evaluation using industry-grade deep learning (DL) models shows that KAIROS
yields up to 2X the throughput of an optimal homogeneous solution, and
outperforms state-of-the-art schemes by up to 70%, despite advantageous
implementations of the competing schemes to ignore their exploration overhead.

Unrecognized patient deterioration can lead to high morbidity and mortality.
Most existing deterioration prediction models require a large number of
clinical information, typically collected in hospital settings, such as medical
images or comprehensive laboratory tests. This is infeasible for telehealth
solutions and highlights a gap in deterioration prediction models that are
based on minimal data, which can be recorded at a large scale in any clinic,
nursing home, or even at the patient's home. In this study, we propose and
develop a prognostic model that predicts if a patient will experience
deterioration in the forthcoming 3-24 hours. The model sequentially processes
routine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c)
temperature. The model is also provided with basic patient information,
including sex, age, vaccination status, vaccination date, and status of
obesity, hypertension, or diabetes. We train and evaluate the model using data
collected from 37,006 COVID-19 patients at NYU Langone Health in New York, USA.
The model achieves an area under the receiver operating characteristic curve
(AUROC) of 0.808-0.880 for 3-24 hour deterioration prediction. We also conduct
occlusion experiments to evaluate the importance of each input feature, where
the results reveal the significance of continuously monitoring the variations
of the vital signs. Our results show the prospect of accurate deterioration
forecast using a minimum feature set that can be relatively easily obtained
using wearable devices and self-reported patient information.

Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative
disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration.
The massive heterogeneity between neurobiological examinations and clinical
assessment is the current biggest challenge in the early diagnosis of
Alzheimer's disease, urging for a comprehensive stratification of the aging
population that is defined by reliable neurobiological biomarkers and closely
associated with clinical outcomes. However, existing statistical inference
approaches in neuroimaging studies of AD subtype identification fail to take
into account the neuropathological domain knowledge, which could lead to
ill-posed results that are sometimes inconsistent with neurological principles.
To fill this knowledge gap, we propose a novel pathology steered stratification
network (PSSN) that integrates mainstream AD pathology with multimodal
longitudinal neuroimaging data to categorize the aging population. By combining
theory-based biological modeling and data-driven deep learning, this
cross-disciplinary approach can not only generate long-term biomarker
prediction consistent with the end-state of individuals but also stratifies
subjects into fine-grained subtypes with distinct neurological underpinnings,
where ag-ing brains within the same subtype share com-mon biological behaviors
that emerge as similar trajectories of cognitive decline. Our stratification
outperforms K-means and SuStaIn in both inter-cluster heterogeneity and
intra-cluster homogeneity of various clinical scores. Importantly, we identify
six subtypes spanning AD spectrum, where each subtype exhibits a distinctive
biomarker pattern that is consistent with its clinical outcome. A disease
evolutionary graph is further provided by quantifying subtype transition
probabilities, which may assist pre-symptomatic diagnosis and guide therapeutic
treatments.

In real-world object recognition, there are numerous object classes to be
recognized. Conventional image recognition based on supervised learning can
only recognize object classes that exist in the training data, and thus has
limited applicability in the real world. On the other hand, humans can
recognize novel objects by asking questions and acquiring knowledge about them.
Inspired by this, we study a framework for acquiring external knowledge through
question generation that would help the model instantly recognize novel
objects. Our pipeline consists of two components: the Object Classifier, which
performs knowledge-based object recognition, and the Question Generator, which
generates knowledge-aware questions to acquire novel knowledge. We also propose
a question generation strategy based on the confidence of the knowledge-aware
prediction of the Object Classifier. To train the Question Generator, we
construct a dataset that contains knowledge-aware questions about objects in
the images. Our experiments show that the proposed pipeline effectively
acquires knowledge about novel objects compared to several baselines.

Soft errors in large VLSI circuits pose dramatic influence on computing- and
memory-intensive neural network (NN) processing. Understanding the influence of
soft errors on NNs is critical to protect against soft errors for reliable NN
processing. Prior work mainly rely on fault simulation to analyze the influence
of soft errors on NN processing. They are accurate but usually specific to
limited configurations of errors and NN models due to the prohibitively slow
simulation speed especially for large NN models and datasets. With the
observation that the influence of soft errors propagates across a large number
of neurons and accumulates as well, we propose to characterize the soft error
induced data disturbance on each neuron with normal distribution model
according to central limit theorem and develop a series of statistical models
to analyze the behavior of NN models under soft errors in general. The
statistical models reveal not only the correlation between soft errors and NN
model accuracy, but also how NN parameters such as quantization and
architecture affect the reliability of NNs. The proposed models are compared
with fault simulation and verified comprehensively. In addition, we observe
that the statistical models that characterize the soft error influence can also
be utilized to predict fault simulation results in many cases and we explore
the use of the proposed statistical models to accelerate fault simulations of
NNs. According to our experiments, the accelerated fault simulation shows
almost two orders of magnitude speedup with negligible simulation accuracy loss
over the baseline fault simulations.

Coded/uncoded content placement in Mobile Edge Caching (MEC) has evolved as
an efficient solution to meet the significant growth of global mobile data
traffic by boosting the content diversity in the storage of caching nodes. To
meet the dynamic nature of the historical request pattern of multimedia
contents, the main focus of recent researches has been shifted to develop
data-driven and real-time caching schemes. In this regard and with the
assumption that users' preferences remain unchanged over a short horizon, the
Top-K popular contents are identified as the output of the learning model. Most
existing datadriven popularity prediction models, however, are not suitable for
the coded/uncoded content placement frameworks. On the one hand, in
coded/uncoded content placement, in addition to classifying contents into two
groups, i.e., popular and nonpopular, the probability of content request is
required to identify which content should be stored partially/completely, where
this information is not provided by existing data-driven popularity prediction
models. On the other hand, the assumption that users' preferences remain
unchanged over a short horizon only works for content with a smooth request
pattern. To tackle these challenges, we develop a Multiple-model (hybrid)
Transformer-based Edge Caching (MTEC) framework with higher generalization
ability, suitable for various types of content with different time-varying
behavior, that can be adapted with coded/uncoded content placement frameworks.
Simulation results corroborate the effectiveness of the proposed MTEC caching
framework in comparison to its counterparts in terms of the cache-hit ratio,
classification accuracy, and the transferred byte volume.

Fashion attribute editing is a task that aims to convert the semantic
attributes of a given fashion image while preserving the irrelevant regions.
Previous works typically employ conditional GANs where the generator explicitly
learns the target attributes and directly execute the conversion. These
approaches, however, are neither scalable nor generic as they operate only with
few limited attributes and a separate generator is required for each dataset or
attribute set. Inspired by the recent advancement of diffusion models, we
explore the classifier-guided diffusion that leverages the off-the-shelf
diffusion model pretrained on general visual semantics such as Imagenet. In
order to achieve a generic editing pipeline, we pose this as multi-attribute
image manipulation task, where the attribute ranges from item category, fabric,
pattern to collar and neckline. We empirically show that conventional methods
fail in our challenging setting, and study efficient adaptation scheme that
involves recently introduced attention-pooling technique to obtain a
multi-attribute classifier guidance. Based on this, we present a mask-free
fashion attribute editing framework that leverages the classifier logits and
the cross-attention map for manipulation. We empirically demonstrate that our
framework achieves convincing sample quality and attribute alignments.

Large-scale point cloud semantic segmentation is an important task in 3D
computer vision, which is widely applied in autonomous driving, robotics, and
virtual reality. Current large-scale point cloud semantic segmentation methods
usually use down-sampling operations to improve computation efficiency and
acquire point clouds with multi-resolution. However, this may cause the problem
of missing local information. Meanwhile, it is difficult for networks to
capture global information in large-scale distributed contexts. To capture
local and global information effectively, we propose an end-to-end deep neural
network called LACV-Net for large-scale point cloud semantic segmentation. The
proposed network contains three main components: 1) a local adaptive feature
augmentation module (LAFA) to adaptively learn the similarity of centroids and
neighboring points to augment the local context; 2) a comprehensive VLAD module
(C-VLAD) that fuses local features with multi-layer, multi-scale, and
multi-resolution to represent a comprehensive global description vector; and 3)
an aggregation loss function to effectively optimize the segmentation
boundaries by constraining the adaptive weight from the LAFA module. Compared
to state-of-the-art networks on several large-scale benchmark datasets,
including S3DIS, Toronto3D, and SensatUrban, we demonstrated the effectiveness
of the proposed network.

Optical phase shifters are essential elements in photonic integrated circuits
(PICs) and function as a direct interface to program the PIC. Non-volatile
phase shifters which can retain the information without a power supply, are
highly desirable for low-power static operations. Here we demonstrate a
non-volatile optical phase shifter by driving a III-V/Si hybrid
metal-oxide-semiconductor (MOS) phase shifter with a ferroelectric field-effect
transistor (FeFET) operating in the source follower mode. Owing to the various
polarization states in the FeFET, we obtained multistate non-volatile phase
shifts up to 1.25${\pi}$ with a CMOS-compatible operation voltage. Furthermore,
we proposed a crossbar array architecture to simplify their controls in
large-scale PICs and verified its feasibility by confirming the selective
write-in operation of a targeted FeFET with a negligible disturbance to the
others. This work paves the way for realizing large-scale non-volatile
programmable PICs for emerging computing applications such as deep learning and
quantum computing.

In this survey, we provide a comprehensive review of more than 200 papers,
technical reports, and GitHub repositories published over the last 10 years on
the recent developments of deep learning techniques for iris recognition,
covering broad topics on algorithm designs, open-source tools, open challenges,
and emerging research. First, we conduct a comprehensive analysis of deep
learning techniques developed for two main sub-tasks in iris biometrics:
segmentation and recognition. Second, we focus on deep learning techniques for
the robustness of iris recognition systems against presentation attacks and via
human-machine pairing. Third, we delve deep into deep learning techniques for
forensic application, especially in post-mortem iris recognition. Fourth, we
review open-source resources and tools in deep learning techniques for iris
recognition. Finally, we highlight the technical challenges, emerging research
trends, and outlook for the future of deep learning in iris recognition.

Understanding dynamics from visual observations is a challenging problem that
requires disentangling individual objects from the scene and learning their
interactions. While recent object-centric models can successfully decompose a
scene into objects, modeling their dynamics effectively still remains a
challenge. We address this problem by introducing SlotFormer -- a
Transformer-based autoregressive model operating on learned object-centric
representations. Given a video clip, our approach reasons over object features
to model spatio-temporal relationships and predicts accurate future object
states. In this paper, we successfully apply SlotFormer to perform video
prediction on datasets with complex object interactions. Moreover, the
unsupervised SlotFormer's dynamics model can be used to improve the performance
on supervised downstream tasks, such as Visual Question Answering (VQA), and
goal-conditioned planning. Compared to past works on dynamics modeling, our
method achieves significantly better long-term synthesis of object dynamics,
while retaining high quality visual generation. Besides, SlotFormer enables VQA
models to reason about the future without object-level labels, even
outperforming counterparts that use ground-truth annotations. Finally, we show
its ability to serve as a world model for model-based planning, which is
competitive with methods designed specifically for such tasks.

Unmanned aerial vehicles (UAVs) are finding use in applications that place
increasing emphasis on robustness to external disturbances including extreme
wind. However, traditional multirotor UAV platforms do not directly sense wind;
conventional flow sensors are too slow, insensitive, or bulky for widespread
integration on UAVs. Instead, drones typically observe the effects of wind
indirectly through accumulated errors in position or trajectory tracking. In
this work, we integrate a novel flow sensor based on micro-electro-mechanical
systems (MEMS) hot-wire technology developed in our prior work onto a
multirotor UAV for wind estimation. These sensors are omnidirectional,
lightweight, fast, and accurate. In order to achieve superior tracking
performance in windy conditions, we train a `wind-aware' residual-based
controller via reinforcement learning using simulated wind gusts and their
aerodynamic effects on the drone. In extensive hardware experiments, we
demonstrate the wind-aware controller outperforming two strong `wind-unaware'
baseline controllers in challenging windy conditions. See:
https://youtu.be/KWqkH9Z-338.

This paper provides a detailed description of the data collection and machine
learning model used in our recent PNAS paper "Flat Teams Drive Scientific
Innovation" Xu et al. [2022a]. Here, we discuss how the features of scientific
publication can be used to estimate the implicit hierarchy in the corresponding
author teams. Besides, we also describe the method of evaluating the impact of
team hierarchy on scientific outputs. More details will be updated in this
article continuously. Raw data and Readme document can be accessed in this
GitHub repository Xu et al. [2022b].

Over the last century, risk scores have been the most popular form of
predictive model used in healthcare and criminal justice. Risk scores are
sparse linear models with integer coefficients; often these models can be
memorized or placed on an index card. Typically, risk scores have been created
either without data or by rounding logistic regression coefficients, but these
methods do not reliably produce high-quality risk scores. Recent work used
mathematical programming, which is computationally slow. We introduce an
approach for efficiently producing a collection of high-quality risk scores
learned from data. Specifically, our approach produces a pool of almost-optimal
sparse continuous solutions, each with a different support set, using a
beam-search algorithm. Each of these continuous solutions is transformed into a
separate risk score through a "star ray" search, where a range of multipliers
are considered before rounding the coefficients sequentially to maintain low
logistic loss. Our algorithm returns all of these high-quality risk scores for
the user to consider. This method completes within minutes and can be valuable
in a broad variety of applications.

Reinforcement learning (RL) algorithms have achieved notable success in
recent years, but still struggle with fundamental issues in long-term credit
assignment. It remains difficult to learn in situations where success is
contingent upon multiple critical steps that are distant in time from each
other and from a sparse reward; as is often the case in real life. Moreover,
how RL algorithms assign credit in these difficult situations is typically not
coded in a way that can rapidly generalize to new situations. Here, we present
an approach using offline contrastive learning, which we call contrastive
introspection (ConSpec), that can be added to any existing RL algorithm and
addresses both issues. In ConSpec, a contrastive loss is used during offline
replay to identify invariances among successful episodes. This takes advantage
of the fact that it is easier to retrospectively identify the small set of
steps that success is contingent upon than it is to prospectively predict
reward at every step taken in the environment. ConSpec stores this knowledge in
a collection of prototypes summarizing the intermediate states required for
success. During training, arrival at any state that matches these prototypes
generates an intrinsic reward that is added to any external rewards. As well,
the reward shaping provided by ConSpec can be made to preserve the optimal
policy of the underlying RL agent. The prototypes in ConSpec provide two key
benefits for credit assignment: (1) They enable rapid identification of all the
critical states. (2) They do so in a readily interpretable manner, enabling out
of distribution generalization when sensory features are altered. In summary,
ConSpec is a modular system that can be added to any existing RL algorithm to
improve its long-term credit assignment.

We explore the capability of plain Vision Transformers (ViTs) for semantic
segmentation and propose the SegVit. Previous ViT-based segmentation networks
usually learn a pixel-level representation from the output of the ViT.
Differently, we make use of the fundamental component -- attention mechanism,
to generate masks for semantic segmentation. Specifically, we propose the
Attention-to-Mask (ATM) module, in which the similarity maps between a set of
learnable class tokens and the spatial feature maps are transferred to the
segmentation masks. Experiments show that our proposed SegVit using the ATM
module outperforms its counterparts using the plain ViT backbone on the ADE20K
dataset and achieves new state-of-the-art performance on COCO-Stuff-10K and
PASCAL-Context datasets. Furthermore, to reduce the computational cost of the
ViT backbone, we propose query-based down-sampling (QD) and query-based
up-sampling (QU) to build a Shrunk structure. With the proposed Shrunk
structure, the model can save up to $40\%$ computations while maintaining
competitive performance.

This paper addresses issues on cough-based COVID-19 detection. We propose a
cross-dataset transfer learning approach to improve the performance of COVID-19
detection by incorporating cough detection, cough segmentation, and data
augmentation. The first aimed at removing non-cough signals and cough signals
with low probability. The second aimed at segregating several coughs in a
waveform into individual coughs. The third aimed at increasing the number of
samples for the deep learning model. These three processing blocks are
important as our finding revealed a large margin of improvement relative to the
baseline methods without these blocks. An ablation study is conducted to
optimize hyperparameters and it was found that alpha mixup is an important
factor among others in improving the model performance via this augmentation
method. A summary of this study with previous studies on the same evaluation
set was given to gain insights into different methods of cough-based COVID-19
detection.

Livestream videos have become a significant part of online learning, where
design, digital marketing, creative painting, and other skills are taught by
experienced experts in the sessions, making them valuable materials. However,
Livestream tutorial videos are usually hours long, recorded, and uploaded to
the Internet directly after the live sessions, making it hard for other people
to catch up quickly. An outline will be a beneficial solution, which requires
the video to be temporally segmented according to topics. In this work, we
introduced a large Livestream video dataset named MultiLive, and formulated the
temporal segmentation of the long Livestream videos (TSLLV) task. We propose
LiveSeg, an unsupervised Livestream video temporal Segmentation solution, which
takes advantage of multimodal features from different domains. Our method
achieved a $16.8\%$ F1-score performance improvement compared with the
state-of-the-art method.

In this paper we show that our Machine Learning (ML) approach, CoMLSim
(Composable Machine Learning Simulator), can simulate PDEs on highly-resolved
grids with higher accuracy and generalization to out-of-distribution source
terms and geometries than traditional ML baselines. Our unique approach
combines key principles of traditional PDE solvers with local-learning and
low-dimensional manifold techniques to iteratively simulate PDEs on large
computational domains. The proposed approach is validated on more than 5
steady-state PDEs across different PDE conditions on highly-resolved grids and
comparisons are made with the commercial solver, Ansys Fluent as well as 4
other state-of-the-art ML methods. The numerical experiments show that our
approach outperforms ML baselines in terms of 1) accuracy across quantitative
metrics and 2) generalization to out-of-distribution conditions as well as
domain sizes. Additionally, we provide results for a large number of ablations
experiments conducted to highlight components of our approach that strongly
influence the results. We conclude that our local-learning and
iterative-inferencing approach reduces the challenge of generalization that
most ML models face.

Contrastive Language-Image Pretraining (CLIP) efficiently learns visual
concepts by pre-training with natural language supervision. CLIP and its visual
encoder have been explored on various vision and language tasks and achieve
strong zero-shot or transfer learning performance. However, the application of
its text encoder solely for text understanding has been less explored. In this
paper, we find that the text encoder of CLIP actually demonstrates strong
ability for phrase understanding, and can even significantly outperform popular
language models such as BERT with a properly designed prompt. Extensive
experiments validate the effectiveness of our method across different datasets
and domains on entity clustering and entity set expansion tasks.

In the experimental sciences, statistical power analyses are often used
before data collection to determine the required sample size. However,
traditional power analyses can be costly when data are difficult or expensive
to collect. We propose synthetic power analyses; a framework for estimating
statistical power at various sample sizes, and empirically explore the
performance of synthetic power analysis for sample size selection in cognitive
neuroscience experiments. To this end, brain imaging data is synthesized using
an implicit generative model conditioned on observed cognitive processes.
Further, we propose a simple procedure to modify the statistical tests which
result in conservative statistics. Our empirical results suggest that synthetic
power analysis could be a low-cost alternative to pilot data collection when
the proposed experiments share cognitive processes with previously conducted
experiments.

Homodyned K (HK) distribution has been widely used to describe the scattering
phenomena arising in various research fields, such as ultrasound imaging or
optics. In this work, we propose a machine learning based approach to the
estimation of the HK distribution parameters. We develop neural networks that
can estimate the HK distribution parameters based on the signal-to-noise ratio,
skewness and kurtosis calculated using fractional-order moments. Compared to
the previous approaches, we consider the orders of the moments as trainable
variables that can be optimized along with the network weights using the
back-propagation algorithm. Networks are trained based on samples generated
from the HK distribution. Obtained results demonstrate that the proposed method
can be used to accurately estimate the HK distribution parameters.

Existing word embedding debiasing methods require social-group-specific word
pairs (e.g., "man"-"woman") for each social attribute (e.g., gender), which
cannot be used to mitigate bias for other social groups, making these methods
impractical or costly to incorporate understudied social groups in debiasing.
We propose that the Stereotype Content Model (SCM), a theoretical framework
developed in social psychology for understanding the content of stereotypes,
which structures stereotype content along two psychological dimensions -
"warmth" and "competence" - can help debiasing efforts to become
social-group-agnostic by capturing the underlying connection between bias and
stereotypes. Using only pairs of terms for warmth (e.g., "genuine"-"fake") and
competence (e.g.,"smart"-"stupid"), we perform debiasing with established
methods and find that, across gender, race, and age, SCM-based debiasing
performs comparably to group-specific debiasing

Image composition aims to blend multiple objects to form a harmonized image.
Existing approaches often assume precisely segmented and intact objects. Such
assumptions, however, are hard to satisfy in unconstrained scenarios. We
present Amodal Instance Composition for compositing imperfect -- potentially
incomplete and/or coarsely segmented -- objects onto a target image. We first
develop object shape prediction and content completion modules to synthesize
the amodal contents. We then propose a neural composition model to blend the
objects seamlessly. Our primary technical novelty lies in using separate
foreground/background representations and blending mask prediction to alleviate
segmentation errors. Our results show state-of-the-art performance on public
COCOA and KINS benchmarks and attain favorable visual results across diverse
scenes. We demonstrate various image composition applications such as object
insertion and de-occlusion.

Data sparsity is one of the main challenges posed by Code-switching (CS),
which is further exacerbated in the case of morphologically rich languages. For
the task of Machine Translation (MT), morphological segmentation has proven
successful in alleviating data sparsity in monolingual contexts; however, it
has not been investigated for CS settings. In this paper, we study the
effectiveness of different segmentation approaches on MT performance, covering
morphology-based and frequency-based segmentation techniques. We experiment on
MT from code-switched Arabic-English to English. We provide detailed analysis,
examining a variety of conditions, such as data size and sentences with
different degrees in CS. Empirical results show that morphology-aware
segmenters perform the best in segmentation tasks but under-perform in MT.
Nevertheless, we find that the choice of the segmentation setup to use for MT
is highly dependent on the data size. For extreme low-resource scenarios, a
combination of frequency and morphology-based segmentations is shown to perform
the best. For more resourced settings, such a combination does not bring
significant improvements over the use of frequency-based segmentation.

Many water-quality monitoring programs aim to measure turbidity to help guide
effective management of waterways and catchments, yet distributing turbidity
sensors throughout networks is typically cost prohibitive. To this end, we
built and compared the ability of dynamic regression (ARIMA), long short-term
memory neural nets (LSTM), and generalized additive models (GAM) to forecast
stream turbidity one step ahead, using surrogate data from relatively low-cost
in-situ sensors and publicly available databases. We iteratively trialled
combinations of four surrogate covariates (rainfall, water level, air
temperature and total global solar exposure) selecting a final model for each
type that minimised the corrected Akaike Information Criterion.
Cross-validation using a rolling time-window indicated that ARIMA, which
included the rainfall and water-level covariates only, produced the most
accurate predictions, followed closely by GAM, which included all four
covariates. We constructed a meta-model, trained on time-series features of
turbidity, to take advantage of the strengths of each model over different time
points and predict the best model (that with the lowest forecast error one-step
prior) for each time step. The meta-model outperformed all other models,
indicating that this methodology can yield high accuracy and may be a viable
alternative to using measurements sourced directly from turbidity-sensors where
costs prohibit their deployment and maintenance, and when predicting turbidity
across the short term. Our findings also indicated that temperature and
light-associated variables, for example underwater illuminance, may hold
promise as cost-effective, high-frequency surrogates of turbidity, especially
when combined with other covariates, like rainfall, that are typically measured
at coarse levels of spatial resolution.

The following work is a preprint collection of formal proofs regarding the
convergence properties of the AdaBoost machine learning algorithm's classifier
and margins. Various math and computer science papers have been written
regarding conjectures and special cases of these convergence properties.
Furthermore, the margins of AdaBoost feature prominently in the research
surrounding the algorithm. At the zenith of this paper we present how
AdaBoost's classifier and margins converge on a value that agrees with decades
of research. After this, we show how various quantities associated with the
combined classifier converge.

Identifying the effects of new interventions from data is a significant
challenge found across a wide range of the empirical sciences. A well-known
strategy for identifying such effects is Pearl's front-door (FD) criterion
(Pearl, 1995). The definition of the FD criterion is declarative, only allowing
one to decide whether a specific set satisfies the criterion. In this paper, we
present algorithms for finding and enumerating possible sets satisfying the FD
criterion in a given causal diagram. These results are useful in facilitating
the practical applications of the FD criterion for causal effects estimation
and helping scientists to select estimands with desired properties, e.g., based
on cost, feasibility of measurement, or statistical power.

One important problem in constructing the reduced dynamics of molecular
systems is the accurate modeling of the non-Markovian behavior arising from the
dynamics of unresolved variables. The main complication emerges from the lack
of scale separations, where the reduced dynamics generally exhibits pronounced
memory and non-white noise terms. We propose a data-driven approach to learn
the reduced model of multi-dimensional resolved variables that faithfully
retains the non-Markovian dynamics. Different from the common approaches based
on the direct construction of the memory function, the present approach seeks a
set of non-Markovian features that encode the history of the resolved
variables, and establishes a joint learning of the extended Markovian dynamics
in terms of both the resolved variables and these features. The training is
based on matching the evolution of the correlation functions of the extended
variables that can be directly obtained from the ones of the resolved
variables. The constructed model essentially approximates the multi-dimensional
generalized Langevin equation and ensures numerical stability without empirical
treatment. We demonstrate the effectiveness of the method by constructing the
reduced models of molecular systems in terms of both one-dimensional and
four-dimensional resolved variables.

Referred to as the third rung of the causal inference ladder, counterfactual
queries typically ask the "What if ?" question retrospectively. The standard
approach to estimate counterfactuals resides in using a structural equation
model that accurately reflects the underlying data generating process. However,
such models are seldom available in practice and one usually wishes to infer
them from observational data alone. Unfortunately, the correct structural
equation model is in general not identifiable from the observed factual
distribution. Nevertheless, in this work, we show that under the assumption
that the main latent contributors to the treatment responses are categorical,
the counterfactuals can be still reliably predicted. Building upon this
assumption, we introduce CounterFactual Query Prediction (CFQP), a novel method
to infer counterfactuals from continuous observations when the background
variables are categorical. We show that our method significantly outperforms
previously available deep-learning-based counterfactual methods, both
theoretically and empirically on time series and image data. Our code is
available at https://github.com/edebrouwer/cfqp.

In recent years, a number of reinforcement learning (RL) methods have been
proposed to explore complex environments which differ across episodes. In this
work, we show that the effectiveness of these methods critically relies on a
count-based episodic term in their exploration bonus. As a result, despite
their success in relatively simple, noise-free settings, these methods fall
short in more realistic scenarios where the state space is vast and prone to
noise. To address this limitation, we introduce Exploration via Elliptical
Episodic Bonuses (E3B), a new method which extends count-based episodic bonuses
to continuous state spaces and encourages an agent to explore states that are
diverse under a learned embedding within each episode. The embedding is learned
using an inverse dynamics model in order to capture controllable aspects of the
environment. Our method sets a new state-of-the-art across 16 challenging tasks
from the MiniHack suite, without requiring task-specific inductive biases. E3B
also matches existing methods on sparse reward, pixel-based VizDoom
environments, and outperforms existing methods in reward-free exploration on
Habitat, demonstrating that it can scale to high-dimensional pixel-based
observations and realistic environments.

Magnetic Resonance Imaging (MRI) is increasingly incorporated into treatment
planning, because of its superior soft tissue contrast used for tumor and soft
tissue delineation versus computed tomography (CT). However, MRI cannot
directly provide mass density or relative stopping power (RSP) maps required
for proton radiotherapy dose calculation. To demonstrate the feasibility of
MRI-only based mass density and RSP estimation using deep learning (DL) for
proton radiotherapy. A DL-based framework was developed to discover underlying
voxel-wise correlation between MR images and mass density and RSP. Five tissue
substitute phantoms including skin, muscle, adipose, 45% hydroxyapatite (HA),
and spongiosa bone were customized for MRI scanning based on material
composition information from ICRP reports. Two animal tissue phantoms made of
pig brain and liver were prepared for DL training. In the phantom study, two DL
models were trained: one containing clinical T1 and T2 MRIs and another
incorporating zero echo time (ZTE) MRIs as input. In the patient application
study, two DL models were trained: one including T1 and T2 MRIs as input, and
one incorporating synthetic dual-energy computed tomography (sDECT) images to
provide bone tissue information. In the phantom study, DL model based on T1 and
T2 MRI demonstrated higher accuracy mass density and RSP estimation in skin,
muscle, adipose, brain, and liver with mean absolute percentage errors (MAPE)
of 0.42%, 0.14%, 0.19%, 0.78% and 0.26% for mass density and 0.30%, 0.11%,
0.16%, 0.61% and 0.23% for RSP, respectively. DL model incorporating ZTE MRI
improved the accuracy of mass density and RSP estimation in 45% HA and
spongiosa bone with MAPE at 0.23% and 0.09% for mass density and 0.19% and
0.07% for RSP, respectively. Results show feasibility of MRI-only based mass
density and RSP estimation for proton therapy treatment planning using DL
method.

Graph Neural Networks (GNNs) have been widely used on graph data and have
shown exceptional performance in the task of link prediction. Despite their
effectiveness, GNNs often suffer from high latency due to non-trivial
neighborhood data dependency in practical deployments. To address this issue,
researchers have proposed methods based on knowledge distillation (KD) to
transfer the knowledge from teacher GNNs to student MLPs, which are known to be
efficient even with industrial scale data, and have shown promising results on
node classification. Nonetheless, using KD to accelerate link prediction is
still unexplored. In this work, we start with exploring two direct analogs of
traditional KD for link prediction, i.e., predicted logit-based matching and
node representation-based matching. Upon observing direct KD analogs do not
perform well for link prediction, we propose a relational KD framework,
Linkless Link Prediction (LLP). Unlike simple KD methods that match independent
link logits or node representations, LLP distills relational knowledge that is
centered around each (anchor) node to the student MLP. Specifically, we propose
two matching strategies that complement each other: rank-based matching and
distribution-based matching. Extensive experiments demonstrate that LLP boosts
the link prediction performance of MLPs with significant margins, and even
outperforms the teacher GNNs on 6 out of 9 benchmarks. LLP also achieves a
776.37x speedup in link prediction inference compared to GNNs on the large
scale OGB-Citation2 dataset.

Team formation is ubiquitous in many sectors: education, labor markets,
sports, etc. A team's success depends on its members' latent types, which are
not directly observable but can be (partially) inferred from past performances.
From the viewpoint of a principal trying to select teams, this leads to a
natural exploration-exploitation trade-off: retain successful teams that are
discovered early, or reassign agents to learn more about their types? We study
a natural model for online team formation, where a principal repeatedly
partitions a group of agents into teams. Agents have binary latent types, each
team comprises two members, and a team's performance is a symmetric function of
its members' types. Over multiple rounds, the principal selects matchings over
agents and incurs regret equal to the deficit in the number of successful teams
versus the optimal matching for the given function. Our work provides a
complete characterization of the regret landscape for all symmetric functions
of two binary inputs. In particular, we develop team-selection policies that,
despite being agnostic of model parameters, achieve optimal or near-optimal
regret against an adaptive adversary.

Recent advances in Transformer architecture have empowered its empirical
success in various tasks across different domains. However, existing works
mainly focus on improving the standard accuracy and computational cost, without
considering the robustness of contaminated samples. Existing work has shown
that the self-attention mechanism, which is the center of the Transformer
architecture, can be viewed as a non-parametric estimator based on the
well-known kernel density estimation (KDE). This motivates us to leverage the
robust kernel density estimation (RKDE) in the self-attention mechanism, to
alleviate the issue of the contamination of data by down-weighting the weight
of bad samples in the estimation process. The modified self-attention mechanism
can be incorporated into different Transformer variants. Empirical results on
language modeling and image classification tasks demonstrate the effectiveness
of this approach.

Knowledge distillation is an effective machine learning technique to transfer
knowledge from a teacher model to a smaller student model, especially with
unlabeled data. In this paper, we focus on knowledge distillation for the RNN-T
model, which is widely used in state-of-the-art (SoTA) automatic speech
recognition (ASR). Specifically, we compared using soft and hard target
distillation to train large-scaleRNN-T models on the LibriSpeech/LibriLight
public dataset (60k hours) and our in-house data (600k hours). We found that
hard tar-gets are more effective when the teacher and student have different
architecture, such as large teacher and small streaming student. On the other
hand, soft target distillation works better in self-training scenario like
iterative large teacher training. For a large model with0.6B weights, we
achieve a new SoTA word error rate (WER) on LibriSpeech (8% relative
improvement on dev-other) using Noisy Student Training with soft target
distillation. It also allows our production teacher to adapt new data domain
continuously.

Most existing methods focus on sentiment analysis of textual data. However,
recently there has been a massive use of images and videos on social platforms,
motivating sentiment analysis from other modalities. Current studies show that
exploring other modalities (e.g., images) increases sentiment analysis
performance. State-of-the-art multimodal models, such as CLIP and VisualBERT,
are pre-trained on datasets with the text paired with images. Although the
results obtained by these models are promising, pre-training and sentiment
analysis fine-tuning tasks of these models are computationally expensive. This
paper introduces a transfer learning approach using joint fine-tuning for
sentiment analysis. Our proposal achieved competitive results using a more
straightforward alternative fine-tuning strategy that leverages different
pre-trained unimodal models and efficiently combines them in a multimodal
space. Moreover, our proposal allows flexibility when incorporating any
pre-trained model for texts and images during the joint fine-tuning stage,
being especially interesting for sentiment classification in low-resource

In this work we consider a variant of adversarial online learning where in
each round one picks $B$ out of $N$ arms and incurs cost equal to the
$\textit{minimum}$ of the costs of each arm chosen. We propose an algorithm
called Follow the Perturbed Multiple Leaders (FPML) for this problem, which we
show (by adapting the techniques of Kalai and Vempala [2005]) achieves expected
regret $\mathcal{O}(T^{\frac{1}{B+1}}\ln(N)^{\frac{B}{B+1}})$ over time horizon
$T$ relative to the $\textit{single}$ best arm in hindsight. This introduces a
trade-off between the budget $B$ and the single-best-arm regret, and we proceed
to investigate several applications of this trade-off. First, we observe that
algorithms which use standard regret minimizers as subroutines can sometimes be
adapted by replacing these subroutines with FPML, and we use this to generalize
existing algorithms for Online Submodular Function Maximization [Streeter and
Golovin, 2008] in both the full feedback and semi-bandit feedback settings.
Next, we empirically evaluate our new algorithms on an online black-box
hyperparameter optimization problem. Finally, we show how FPML can lead to new
algorithms for Linear Programming which require stronger oracles at the benefit
of fewer oracle calls.

This paper introduces REMS, a robotics middleware and control framework that
is designed to introduce the Zen of Python to robotics and to improve robotics
education and development flow. Although existing middleware can serve hardware
abstraction and modularity, setting up environments and learning
middleware-specific syntax and procedures are less viable in education. They
can curb opportunities to understand robotics concepts, theories, and
algorithms. Robotics is a field of integration; students and developers from
various backgrounds will be involved in programming. Establishing Pythonic and
object-oriented robotic framework in a natural way can enhance modular and
abstracted programming for better readability, reusability, and simplicity, but
also supports useful and practical skills generally in coding. REMS is to be a
valuable robot educational medium not just as a tool and to be a platform from
one robot to multi-agent across hardware, simulation, and analytical model
implementations.

Recent object detection models require large amounts of annotated data for
training a new classes of objects. Few-shot object detection (FSOD) aims to
address this problem by learning novel classes given only a few samples. While
competitive results have been achieved using two-stage FSOD detectors,
typically one-stage FSODs underperform compared to them. We make the
observation that the large gap in performance between two-stage and one-stage
FSODs are mainly due to their weak discriminability, which is explained by a
small post-fusion receptive field and a small number of foreground samples in
the loss function. To address these limitations, we propose the Few-shot
RetinaNet (FSRN) that consists of: a multi-way support training strategy to
augment the number of foreground samples for dense meta-detectors, an early
multi-level feature fusion providing a wide receptive field that covers the
whole anchor area and two augmentation techniques on query and source images to
enhance transferability. Extensive experiments show that the proposed approach
addresses the limitations and boosts both discriminability and transferability.
FSRN is almost two times faster than two-stage FSODs while remaining
competitive in accuracy, and it outperforms the state-of-the-art of one-stage
meta-detectors and also some two-stage FSODs on the MS-COCO and PASCAL VOC

Learning energy-based models (EBMs) is known to be difficult especially on
discrete data where gradient-based learning strategies cannot be applied
directly. Although ratio matching is a sound method to learn discrete EBMs, it
suffers from expensive computation and excessive memory requirement, thereby
resulting in difficulties for learning EBMs on high-dimensional data. Motivated
from these limitations, in this study, we propose ratio matching with
gradient-guided importance sampling (RMwGGIS). Particularly, we use the
gradient of the energy function w.r.t. the discrete data space to approximately
construct the provably optimal proposal distribution, which is subsequently
used by importance sampling to efficiently estimate the original ratio matching
objective. We perform experiments on density modeling over synthetic discrete
data, graph generation, and training Ising models to evaluate our proposed
method. The experimental results demonstrate that our method can significantly
alleviate the limitations of ratio matching, perform more effectively in
practice, and scale to high-dimensional problems. Our implementation is
available at {https://github.com/divelab/RMwGGIS.

Improving the generalization of deep networks is an important open challenge,
particularly in domains without plentiful data. The mixup algorithm improves
generalization by linearly interpolating a pair of examples and their
corresponding labels. These interpolated examples augment the original training
set. Mixup has shown promising results in various classification tasks, but
systematic analysis of mixup in regression remains underexplored. Using mixup
directly on regression labels can result in arbitrarily incorrect labels. In
this paper, we propose a simple yet powerful algorithm, C-Mixup, to improve
generalization on regression tasks. In contrast with vanilla mixup, which picks
training examples for mixing with uniform probability, C-Mixup adjusts the
sampling probability based on the similarity of the labels. Our theoretical
analysis confirms that C-Mixup with label similarity obtains a smaller mean
square error in supervised regression and meta-regression than vanilla mixup
and using feature similarity. Another benefit of C-Mixup is that it can improve
out-of-distribution robustness, where the test distribution is different from
the training distribution. By selectively interpolating examples with similar
labels, it mitigates the effects of domain-associated information and yields
domain-invariant representations. We evaluate C-Mixup on eleven datasets,
ranging from tabular to video data. Compared to the best prior approach,
C-Mixup achieves 6.56%, 4.76%, 5.82% improvements in in-distribution
generalization, task generalization, and out-of-distribution robustness,
respectively. Code is released at https://github.com/huaxiuyao/C-Mixup.

Conventional active learning (AL) frameworks aim to reduce the cost of data
annotation by actively requesting the labeling for the most informative data
points. However, introducing AL to data hungry deep learning algorithms has
been a challenge. Some proposed approaches include uncertainty-based
techniques, geometric methods, implicit combination of uncertainty-based and
geometric approaches, and more recently, frameworks based on semi/self
supervised techniques. In this paper, we address two specific problems in this
area. The first is the need for efficient exploitation/exploration trade-off in
sample selection in AL. For this, we present an innovative integration of
recent progress in both uncertainty-based and geometric frameworks to enable an
efficient exploration/exploitation trade-off in sample selection strategy. To
this end, we build on a computationally efficient approximate of Thompson
sampling with key changes as a posterior estimator for uncertainty
representation. Our framework provides two advantages: (1) accurate posterior
estimation, and (2) tune-able trade-off between computational overhead and
higher accuracy. The second problem is the need for improved training protocols
in deep AL. For this, we use ideas from semi/self supervised learning to
propose a general approach that is independent of the specific AL technique
being used. Taken these together, our framework shows a significant improvement
over the state-of-the-art, with results that are comparable to the performance
of supervised-learning under the same setting. We show empirical results of our
framework, and comparative performance with the state-of-the-art on four
datasets, namely, MNIST, CIFAR10, CIFAR100 and ImageNet to establish a new
baseline in two different settings.

The development of state-of-the-art systems in different applied areas of
machine learning (ML) is driven by benchmarks, which have shaped the paradigm
of evaluating generalisation capabilities from multiple perspectives. Although
the paradigm is shifting towards more fine-grained evaluation across diverse
tasks, the delicate question of how to aggregate the performances has received
particular interest in the community. In general, benchmarks follow the
unspoken utilitarian principles, where the systems are ranked based on their
mean average score over task-specific metrics. Such aggregation procedure has
been viewed as a sub-optimal evaluation protocol, which may have created the
illusion of progress. This paper proposes Vote'n'Rank, a framework for ranking
systems in multi-task benchmarks under the principles of the social choice
theory. We demonstrate that our approach can be efficiently utilised to draw
new insights on benchmarking in several ML sub-fields and identify the
best-performing systems in research and development case studies. The
Vote'n'Rank's procedures are more robust than the mean average while being able
to handle missing performance scores and determine conditions under which the
system becomes the winner.

In-hand manipulation is challenging for a multi-finger robotic hand due to
its high degrees of freedom and the complex interaction with the object. To
enable in-hand manipulation, existing deep reinforcement learning based
approaches mainly focus on training a single robot-structure-specific policy
through the centralized learning mechanism, lacking adaptability to changes
like robot malfunction. To solve this limitation, this work treats each finger
as an individual agent and trains multiple agents to control their assigned
fingers to complete the in-hand manipulation task cooperatively. We propose the
Multi-Agent Global-Observation Critic and Local-Observation Actor (MAGCLA)
method, where the critic can observe all agents' actions globally, and the
actor only locally observes its neighbors' actions. Besides, conventional
individual experience replay may cause unstable cooperation due to the
asynchronous performance increment of each agent, which is critical for in-hand
manipulation tasks. To solve this issue, we propose the Synchronized Hindsight
Experience Replay (SHER) method to synchronize and efficiently reuse the
replayed experience across all agents. The methods are evaluated in two in-hand
manipulation tasks on the Shadow dexterous hand. The results show that SHER
helps MAGCLA achieve comparable learning efficiency to a single policy, and the
MAGCLA approach is more generalizable in different tasks. The trained policies
have higher adaptability in the robot malfunction test compared to the baseline
multi-agent and single-agent approaches.

A match cut is a transition between a pair of shots that uses similar
framing, composition, or action to fluidly bring the viewer from one scene to
the next. Match cuts are frequently used in film, television, and advertising.
However, finding shots that work together is a highly manual and time-consuming
process that can take days. We propose a modular and flexible system to
efficiently find high-quality match cut candidates starting from millions of
shot pairs. We annotate and release a dataset of approximately 20k labeled
pairs that we use to evaluate our system, using both classification and metric
learning approaches that leverage a variety of image, video, audio, and
audio-visual feature extractors. In addition, we release code and embeddings
for reproducing our experiments at github.com/netflix/matchcut.

Automatic breast lesion detection and classification is an important task in
computer-aided diagnosis, in which breast ultrasound (BUS) imaging is a common
and frequently used screening tool. Recently, a number of deep learning-based
methods have been proposed for joint localization and classification of breast
lesions using BUS images. In these methods, features extracted by a shared
network trunk are appended by two independent network branches to achieve
classification and localization. Improper information sharing might cause
conflicts in feature optimization in the two branches and leads to performance
degradation. Also, these methods generally require large amounts of pixel-level
annotated data for model training. To overcome these limitations, we proposed a
novel joint localization and classification model based on the attention
mechanism and disentangled semi-supervised learning strategy. The model used in
this study is composed of a classification network and an auxiliary
lesion-aware network. By use of the attention mechanism, the auxiliary
lesion-aware network can optimize multi-scale intermediate feature maps and
extract rich semantic information to improve classification and localization
performance. The disentangled semi-supervised learning strategy only requires
incomplete training datasets for model training. The proposed modularized
framework allows flexible network replacement to be generalized for various
applications. Experimental results on two different breast ultrasound image
datasets demonstrate the effectiveness of the proposed method. The impacts of
various network factors on model performance are also investigated to gain deep
insights into the designed framework.

Presents a differentiated teaching proposal that allows the student to be the
agent in the construction of knowledge, overcoming the difficulties that
Mathematics presents. Aiming to understand how the use of statistical tools can
contribute to the improvement of the teaching-learning process and the
construction of statistical knowledge, studied with students from the
University Campus of Rondonopolis/UFMT. In order to reach the proposed
objective, an analysis was carried out about the didactic activities in the
teaching of basic statistics in which a qualitative-quantitative approach was
chosen, focusing on everyday life, with the use of software, creation and
simulation of models, as well as seeking to establish the frequency of the
students' attitude, through questionnaires and a four-point Likert Scale,
seeking data that would trace the profile of the actors involved that would
help in the understanding of the planned didactic activities. The study is
justified by the lack of methodological theoretical references on the subject
in question. Didactic activities of Statistics were organized, whose
applications took place in alternating classes, being traditional lectures and
practical classes in a Computer Laboratory. Constituting the main focus of
characterization and reflection of this scientific research during the
teaching-learning process of Statistics. The results showed a correct attitude
of the researched about the methodological strategies used, assumed with
reference to the research/action design. This study contributed to motivate,
awaken and answer questions and give meaning and understanding to works with
Mathematical Modeling, as it can promote the improvement of the
teaching-learning process and configures itself as an indispensable tool for
education.

Most existing works on continual learning (CL) focus on overcoming the
catastrophic forgetting (CF) problem, with dynamic models and replay methods
performing exceptionally well. However, since current works tend to assume
exclusivity or dissimilarity among learning tasks, these methods require
constantly accumulating task-specific knowledge in memory for each task. This
results in the eventual prohibitive expansion of the knowledge repository if we
consider learning from a long sequence of tasks. In this work, we introduce a
paradigm where the continual learner gets a sequence of mixed similar and
dissimilar tasks. We propose a new continual learning framework that uses a
task similarity detection function that does not require additional learning,
with which we analyze whether there is a specific task in the past that is
similar to the current task. We can then reuse previous task knowledge to slow
down parameter expansion, ensuring that the CL system expands the knowledge
repository sublinearly to the number of learned tasks. Our experiments show
that the proposed framework performs competitively on widely used computer
vision benchmarks such as CIFAR10, CIFAR100, and EMNIST.

Score-based kernelised Stein discrepancy (KSD) tests have emerged as a
powerful tool for the goodness of fit tests, especially in high dimensions;
however, the test performance may depend on the choice of kernels in an
underlying reproducing kernel Hilbert space (RKHS). Here we assess the effect
of RKHS choice for KSD tests of random networks models, developed for
exponential random graph models (ERGMs) in Xu and Reinert (2021)and for
synthetic graph generators in Xu and Reinert (2022). We investigate the power
performance and the computational runtime of the test in different scenarios,
including both dense and sparse graph regimes. Experimental results on kernel
performance for model assessment tasks are shown and discussed on synthetic and
real-world network applications.

Distributionally Robust Optimization (DRO), as a popular method to train
robust models against distribution shift between training and test sets, has
received tremendous attention in recent years. In this paper, we propose and
analyze stochastic algorithms that apply to both non-convex and convex losses
for solving Kullback Leibler divergence constrained DRO problem. Compared with
existing methods solving this problem, our stochastic algorithms not only enjoy
competitive if not better complexity independent of sample size but also just
require a constant batch size at every iteration, which is more practical for
broad applications. We establish a nearly optimal complexity bound for finding
an $\epsilon$ stationary solution for non-convex losses and an optimal
complexity for finding an $\epsilon$ optimal solution for convex losses.
Empirical studies demonstrate the effectiveness of the proposed algorithms for
solving non-convex and convex constrained DRO problems.

The mixed multinomial logit (MMNL) model assumes constant preference
parameters of a decision-maker throughout different choice situations, which
may be considered too strong for certain choice modelling applications. This
paper proposes an effective approach to model context-dependent
intra-respondent heterogeneity and introduces the idea of Context-aware
Bayesian Mixed Multinomial Logit (C-MMNL) Model, where a neural network maps
contextual information to shifts in the preference parameters of each
individual in each choice occasion. The proposed model offers several key
advantages. First, it supports for both continuous and discrete variables, as
well as complex non-linear interactions between both types of variables.
Secondly, each specification of the context is considered jointly as a whole by
the neural network rather than each variable being considered independently.
Finally, since the parameters of the neural network are shared across all
decision-makers, it can leverage information from other decision-makers and use
it to infer the effect of a particular context. Even though the C-MMNL model
allows for flexible interactions between attributes, there is hardly an
increase in the complexity of the model and the computation time, compared to
the MMNL model. We present two real-world case studies from travel behaviour
domain - a travel mode choice model and a bicycle route choice model. The
bicycle route choice model is based on a large-scale, crowdsourced dataset of
GPS trajectories including 110,083 trips made by 8,555 cyclists.

We present TetGAN, a convolutional neural network designed to generate
tetrahedral meshes. We represent shapes using an irregular tetrahedral grid
which encodes an occupancy and displacement field. Our formulation enables
defining tetrahedral convolution, pooling, and upsampling operations to
synthesize explicit mesh connectivity with variable topological genus. The
proposed neural network layers learn deep features over each tetrahedron and
learn to extract patterns within spatial regions across multiple scales. We
illustrate the capabilities of our technique to encode tetrahedral meshes into
a semantically meaningful latent-space which can be used for shape editing and
synthesis. Our project page is at https://threedle.github.io/tetGAN/.

We present DeepMend, a novel approach to reconstruct restorations to
fractured shapes using learned occupancy functions. Existing shape repair
approaches predict low-resolution voxelized restorations, or require symmetries
or access to a pre-existing complete oracle. We represent the occupancy of a
fractured shape as the conjunction of the occupancy of an underlying complete
shape and the fracture surface, which we model as functions of latent codes
using neural networks. Given occupancy samples from an input fractured shape,
we estimate latent codes using an inference loss augmented with novel penalty
terms that avoid empty or voluminous restorations. We use inferred codes to
reconstruct the restoration shape. We show results with simulated fractures on
synthetic and real-world scanned objects, and with scanned real fractured mugs.
Compared to the existing voxel approach and two baseline methods, our work
shows state-of-the-art results in accuracy and avoiding restoration artifacts
over non-fracture regions of the fractured shape.

Response diversity has become an important criterion for evaluating the
quality of open-domain dialogue generation models. However, current evaluation
metrics for response diversity often fail to capture the semantic diversity of
generated responses, as they mainly consider lexical aspects of the generated
responses. In this paper, we introduce a new automatic evaluation metric to
measure the semantic diversity of generated responses. Through human
evaluation, we demonstrate that our proposed metric captures human judgments on
response diversity better than existing lexical-level diversity metrics.
Furthermore, motivated by analyzing an existing dialogue dataset, we propose a
simple yet effective learning method that improves the semantic diversity of
generated responses. Our learning method weights training samples based on the
semantic distribution of the training set. We show that our learning method
improves response diversity and coherency better than other baseline methods
through automatic and human evaluation.

Various neural network architectures rely on pooling operators to aggregate
information coming from different sources. It is often implicitly assumed in
such contexts that vectors encode epistemic states, i.e. that vectors capture
the evidence that has been obtained about some properties of interest, and that
pooling these vectors yields a vector that combines this evidence. We study,
for a number of standard pooling operators, under what conditions they are
compatible with this idea, which we call the epistemic pooling principle. While
we find that all the considered pooling operators can satisfy the epistemic
pooling principle, this only holds when embeddings are sufficiently
high-dimensional and, for most pooling operators, when the embeddings satisfy
particular constraints (e.g. having non-negative coordinates). We then study
the implications of these constraints, starting from the idea that we should be
able to verify whether an arbitrary propositional formula is satisfied in the
epistemic state encoded by a given vector. We find that when the epistemic
pooling principle is satisfied, in most cases it is impossible to verify the
satisfaction of propositional formulas using linear scoring functions, with two
exceptions: (i) max-pooling with embeddings that are upper-bounded and (ii)
Hadamard pooling with non-negative embeddings. Finally, we also study an
extension of the epistemic pooling principle to weighted epistemic states,
where max-pooling emerges as the most suitable operator.

Annotating large collections of textual data can be time consuming and
expensive. That is why the ability to train models with limited annotation
budgets is of great importance. In this context, it has been shown that under
tight annotation budgets the choice of data representation is key. The goal of
this paper is to better understand why this is so. With this goal in mind, we
propose a metric that measures the extent to which a given representation is
structurally aligned with a task. We conduct experiments on several text
classification datasets testing a variety of models and representations. Using
our proposed metric we show that an efficient representation for a task (i.e.
one that enables learning from few samples) is a representation that induces a
good alignment between latent input structure and class structure.

Grounding language to the visual observations of a navigating agent can be
performed using off-the-shelf visual-language models pretrained on
Internet-scale data (e.g., image captions). While this is useful for matching
images to natural language descriptions of object goals, it remains disjoint
from the process of mapping the environment, so that it lacks the spatial
precision of classic geometric maps. To address this problem, we propose
VLMaps, a spatial map representation that directly fuses pretrained
visual-language features with a 3D reconstruction of the physical world. VLMaps
can be autonomously built from video feed on robots using standard exploration
approaches and enables natural language indexing of the map without additional
labeled data. Specifically, when combined with large language models (LLMs),
VLMaps can be used to (i) translate natural language commands into a sequence
of open-vocabulary navigation goals (which, beyond prior work, can be spatial
by construction, e.g., "in between the sofa and TV" or "three meters to the
right of the chair") directly localized in the map, and (ii) can be shared
among multiple robots with different embodiments to generate new obstacle maps
on-the-fly (by using a list of obstacle categories). Extensive experiments
carried out in simulated and real world environments show that VLMaps enable
navigation according to more complex language instructions than existing
methods. Videos are available at https://vlmaps.github.io.

Multilingual transformer-based models demonstrate remarkable zero and
few-shot transfer across languages by learning and reusing language-agnostic
features. However, as a fixed-size model acquires more languages, its
performance across all languages degrades, a phenomenon termed interference.
Often attributed to limited model capacity, interference is commonly addressed
by adding additional parameters despite evidence that transformer-based models
are overparameterized. In this work, we show that it is possible to reduce
interference by instead identifying and pruning language-specific parameters.
First, we use Shapley Values, a credit allocation metric from coalitional game
theory, to identify attention heads that introduce interference. Then, we show
that removing identified attention heads from a fixed model improves
performance for a target language on both sentence classification and
structural prediction, seeing gains as large as 24.7\%. Finally, we provide
insights on language-agnostic and language-specific attention heads using
attention visualization.

Not all research leads to fruitful results; trying new ways or methods may
surpass the state of the art, but sometimes the hypothesis is not proven or the
improvement is insignificant. In a systems discipline like pervasive computing,
there are many sources of errors, from hardware issues over communication
channels to heterogeneous software environments. However, failure to succeed is
not a failure to progress. It is essential to create platforms for sharing
insights, experiences, and lessons learned when conducting research in
pervasive computing so that the same mistakes are not repeated. And sometimes,
a problem is a symptom of discovering new research challenges. Based on the
collective input of the First International Workshop on Negative Results in
Pervasive Computing (PerFail 2022), co-located with the 20th International
Conference on Pervasive Computing and Communications (PerCom 2022), this paper
presents a comprehensive discussion on perspectives on publishing negative
results and lessons learned in pervasive computing.

Supervised learning algorithms generally assume the availability of enough
memory to store data models during the training and test phases. However, this
assumption is unrealistic when data comes in the form of infinite data streams,
or when learning algorithms are deployed on devices with reduced amounts of
memory. Such memory constraints impact the model behavior and assumptions. In
this paper, we show that under memory constraints, increasing the size of a
tree-based ensemble classifier can worsen its performance. In particular, we
experimentally show the existence of an optimal ensemble size for a
memory-bounded Mondrian forest on data streams and we design an algorithm to
guide the forest toward that optimal number by using an estimation of
overfitting. We tested different variations for this algorithm on a variety of
real and simulated datasets, and we conclude that our method can achieve up to
95% of the performance of an optimally-sized Mondrian forest for stable
datasets, and can even outperform it for datasets with concept drifts. All our
methods are implemented in the OrpailleCC open-source library and are ready to
be used on embedded systems and connected objects.

We combine amortized neural posterior estimation with importance sampling for
fast and accurate gravitational-wave inference. We first generate a rapid
proposal for the Bayesian posterior using neural networks, and then attach
importance weights based on the underlying likelihood and prior. This provides
(1) a corrected posterior free from network inaccuracies, (2) a performance
diagnostic (the sample efficiency) for assessing the proposal and identifying
failure cases, and (3) an unbiased estimate of the Bayesian evidence. By
establishing this independent verification and correction mechanism we address
some of the most frequent criticisms against deep learning for scientific
inference. We carry out a large study analyzing 42 binary black hole mergers
observed by LIGO and Virgo with the SEOBNRv4PHM and IMRPhenomXPHM waveform
models. This shows a median sample efficiency of $\approx 10\%$ (two
orders-of-magnitude better than standard samplers) as well as a ten-fold
reduction in the statistical uncertainty in the log evidence. Given these
advantages, we expect a significant impact on gravitational-wave inference, and
for this approach to serve as a paradigm for harnessing deep learning methods
in scientific applications.

We present a Body Measurement network (BMnet) for estimating 3D
anthropomorphic measurements of the human body shape from silhouette images.
Training of BMnet is performed on data from real human subjects, and augmented
with a novel adversarial body simulator (ABS) that finds and synthesizes
challenging body shapes. ABS is based on the skinned multiperson linear (SMPL)
body model, and aims to maximize BMnet measurement prediction error with
respect to latent SMPL shape parameters. ABS is fully differentiable with
respect to these parameters, and trained end-to-end via backpropagation with
BMnet in the loop. Experiments show that ABS effectively discovers adversarial
examples, such as bodies with extreme body mass indices (BMI), consistent with
the rarity of extreme-BMI bodies in BMnet's training set. Thus ABS is able to
reveal gaps in training data and potential failures in predicting
under-represented body shapes. Results show that training BMnet with ABS
improves measurement prediction accuracy on real bodies by up to 10%, when
compared to no augmentation or random body shape sampling. Furthermore, our
method significantly outperforms SOTA measurement estimation methods by as much
as 3x. Finally, we release BodyM, the first challenging, large-scale dataset of
photo silhouettes and body measurements of real human subjects, to further
promote research in this area. Project website:
https://adversarialbodysim.github.io

We propose CLIP-Fields, an implicit scene model that can be trained with no
direct human supervision. This model learns a mapping from spatial locations to
semantic embedding vectors. The mapping can then be used for a variety of
tasks, such as segmentation, instance identification, semantic search over
space, and view localization. Most importantly, the mapping can be trained with
supervision coming only from web-image and web-text trained models such as
CLIP, Detic, and Sentence-BERT. When compared to baselines like Mask-RCNN, our
method outperforms on few-shot instance identification or semantic segmentation
on the HM3D dataset with only a fraction of the examples. Finally, we show that
using CLIP-Fields as a scene memory, robots can perform semantic navigation in
real-world environments. Our code and demonstrations are available here:
https://mahis.life/clip-fields

Recommender systems are expected to be assistants that help human users find
relevant information in an automatic manner without explicit queries. As
recommender systems evolve, increasingly sophisticated learning techniques are
applied and have achieved better performance in terms of user engagement
metrics such as clicks and browsing time. The increase of the measured
performance, however, can have two possible attributions: a better
understanding of user preferences, and a more proactive ability to utilize
human bounded rationality to seduce user over-consumption. A natural following
question is whether current recommendation algorithms are manipulating user
preferences. If so, can we measure the manipulation level? In this paper, we
present a general framework for benchmarking the degree of manipulations of
recommendation algorithms, in both slate recommendation and sequential
recommendation scenarios. The framework consists of three stages, initial
preference calculation, algorithm training and interaction, and metrics
calculation that involves two proposed metrics, Manipulation Score and
Preference Shift. We benchmark some representative recommendation algorithms in
both synthetic and real-world datasets under the proposed framework. We have
observed that a high online click-through rate does not mean a better
understanding of user initial preference, but ends in prompting users to choose
more documents they initially did not favor. Moreover, we find that the
properties of training data have notable impacts on the manipulation degrees,
and algorithms with more powerful modeling abilities are more sensitive to such
impacts. The experiments also verified the usefulness of the proposed metrics
for measuring the degree of manipulations. We advocate that future
recommendation algorithm studies should be treated as an optimization problem
with constrained user preference manipulations.

We establish strong laws of large numbers and central limit theorems for the
regret of two of the most popular bandit algorithms: Thompson sampling and UCB.
Here, our characterizations of the regret distribution complement the
characterizations of the tail of the regret distribution recently developed by
Fan and Glynn (2021) (arXiv:2109.13595). The tail characterizations there are
associated with atypical bandit behavior on trajectories where the optimal arm
mean is under-estimated, leading to mis-identification of the optimal arm and
large regret. In contrast, our SLLN's and CLT's here describe the typical
behavior and fluctuation of regret on trajectories where the optimal arm mean
is properly estimated. We find that Thompson sampling and UCB satisfy the same
SLLN and CLT, with the asymptotics of both the SLLN and the (mean) centering
sequence in the CLT matching the asymptotics of expected regret. Both the mean
and variance in the CLT grow at $\log(T)$ rates with the time horizon $T$.
Asymptotically as $T \to \infty$, the variability in the number of plays of
each sub-optimal arm depends only on the rewards received for that arm, which
indicates that each sub-optimal arm contributes independently to the overall
CLT variance.

Convolutional neural networks were the standard for solving many computer
vision tasks until recently, when Transformers of MLP-based architectures have
started to show competitive performance. These architectures typically have a
vast number of weights and need to be trained on massive datasets; hence, they
are not suitable for their use in low-data regimes. In this work, we propose a
simple yet effective framework to improve generalization from small amounts of
data. We augment modern CNNs with fully-connected (FC) layers and show the
massive impact this architectural change has in low-data regimes. We further
present an online joint knowledge-distillation method to utilize the extra FC
layers at train time but avoid them during test time. This allows us to improve
the generalization of a CNN-based model without any increase in the number of
weights at test time. We perform classification experiments for a large range
of network backbones and several standard datasets on supervised learning and
active learning. Our experiments significantly outperform the networks without
fully-connected layers, reaching a relative improvement of up to $16\%$
validation accuracy in the supervised setting without adding any extra
parameters during inference.

Refractory multi-principal element alloys exhibiting promising mechanical
properties such as excellent strength retention at elevated temperatures have
been attracting increasing attention. Although their inherent chemical
complexity is considered a defining feature, a challenge arises in predicting
local chemical ordering, particularly in grain boundary regions with enhanced
structural disorder. In this study, we use atomistic simulations of a large
group of bicrystal models to sample a wide variety of interfacial sites (grain
boundary) in NbMoTaW and explore emergent trends in interfacial segregation and
the underlying structural and chemical driving factors. Sampling hundreds of
bicrystals along the [001] symmetric tilt axis and analyzing more than one
hundred and thirty thousand grain boundary sites with a variety of local atomic
environments, we uncover segregation trends in NbMoTaW. While Nb is the
dominant segregant, more notable are the segregation patterns that deviate from
expected behavior and mark situations where local structural and chemical
driving forces lead to interesting segregation events. For example, incomplete
depletion of Ta in low-angle boundaries results from chemical pinning due to
favorable local compositional environments associated with chemical short-range
ordering. Finally, machine learning models capturing and comparing the
structural and chemical features of interfacial sites are developed to weigh
their relative importance and contributions to segregation tendency, revealing
a significant increase in predictive capability when including local chemical
information. Overall, this work, highlighting the complex interplay between
local grain boundary structure and chemical short-range ordering, suggest
tunable segregation and chemical ordering by tailoring grain boundary structure
in multi-principal element alloys.

In safety-critical applications of reinforcement learning such as healthcare
and robotics, it is often desirable to optimize risk-sensitive objectives that
account for tail outcomes rather than expected reward. We prove the first
regret bounds for reinforcement learning under a general class of
risk-sensitive objectives including the popular CVaR objective. Our theory is
based on a novel characterization of the CVaR objective as well as a novel
optimistic MDP construction.

Local models have recently attained astounding performances in Entity
Disambiguation (ED), with generative and extractive formulations being the most
promising research directions. However, previous works limited their studies to
using, as the textual representation of each candidate, only its Wikipedia
title. Although certainly effective, this strategy presents a few critical
issues, especially when titles are not sufficiently informative or
distinguishable from one another. In this paper, we address this limitation and
investigate to what extent more expressive textual representations can mitigate
it. We thoroughly evaluate our approach against standard benchmarks in ED and
find extractive formulations to be particularly well-suited to these
representations: we report a new state of the art on 2 out of 6 benchmarks we
consider and strongly improve the generalization capability over unseen
patterns. We release our code, data and model checkpoints at
https://github.com/SapienzaNLP/extend.

It has become standard to solve NLP tasks by fine-tuning pre-trained language
models (LMs), especially in low-data settings. There is minimal theoretical
understanding of empirical success, e.g., why fine-tuning a model with $10^8$
or more parameters on a couple dozen training points does not result in
overfitting. We investigate whether the Neural Tangent Kernel (NTK) - which
originated as a model to study the gradient descent dynamics of infinitely wide
networks with suitable random initialization - describes fine-tuning of
pre-trained LMs. This study was inspired by the decent performance of NTK for
computer vision tasks (Wei et al., 2022). We also extend the NTK formalism to
fine-tuning with Adam. We present extensive experiments that show that once the
downstream task is formulated as a language modeling problem through prompting,
the NTK lens can often reasonably describe the model updates during fine-tuning
with both SGD and Adam. This kernel view also suggests an explanation for
success of parameter-efficient subspace-based fine-tuning methods. Finally, we
suggest a path toward a formal explanation for our findings via Tensor Programs
(Yang, 2020).

Tremendous progress has been made in reinforcement learning (RL) over the
past decade. Most of these advancements came through the continual development
of new algorithms, which were designed using a combination of mathematical
derivations, intuitions, and experimentation. Such an approach of creating
algorithms manually is limited by human understanding and ingenuity. In
contrast, meta-learning provides a toolkit for automatic machine learning
method optimisation, potentially addressing this flaw. However, black-box
approaches which attempt to discover RL algorithms with minimal prior structure
have thus far not outperformed existing hand-crafted algorithms. Mirror
Learning, which includes RL algorithms, such as PPO, offers a potential
middle-ground starting point: while every method in this framework comes with
theoretical guarantees, components that differentiate them are subject to
design. In this paper we explore the Mirror Learning space by meta-learning a
"drift" function. We refer to the immediate result as Learnt Policy
Optimisation (LPO). By analysing LPO we gain original insights into policy
optimisation which we use to formulate a novel, closed-form RL algorithm,
Discovered Policy Optimisation (DPO). Our experiments in Brax environments
confirm state-of-the-art performance of LPO and DPO, as well as their transfer
to unseen settings.

Processing large point clouds is a challenging task. Therefore, the data is
often downsampled to a smaller size such that it can be stored, transmitted and
processed more efficiently without incurring significant performance
degradation. Traditional task-agnostic sampling methods, such as farthest point
sampling (FPS), do not consider downstream tasks when sampling point clouds,
and thus non-informative points to the tasks are often sampled. This paper
explores a task-oriented sampling for 3D point clouds, and aims to sample a
subset of points that are tailored specifically to a downstream task of
interest. Similar to FPS, we assume that point to be sampled next should depend
heavily on the points that have already been sampled. We thus formulate point
cloud sampling as a sequential generation process, and develop an
attention-based point cloud sampling network (APSNet) to tackle this problem.
At each time step, APSNet attends to all the points in a cloud by utilizing the
history of previously sampled points, and samples the most informative one.
Both supervised learning and knowledge distillation-based self-supervised
learning of APSNet are proposed. Moreover, joint training of APSNet over
multiple sample sizes is investigated, leading to a single APSNet that can
generate arbitrary length of samples with prominent performances. Extensive
experiments demonstrate the superior performance of APSNet against
state-of-the-arts in various downstream tasks, including 3D point cloud
classification, reconstruction, and registration.

We present a robust theoretical framework for the characterisation and
manipulation of optical flow, i.e 2D vector fields, in the context of their use
in motion estimation algorithms and beyond. The definition of two frames of
reference guides the mathematical derivation of flow field application,
inversion, evaluation, and composition operations. This structured approach is
then used as the foundation for an implementation in Python 3, with the fully
differentiable PyTorch version oflibpytorch supporting back-propagation as
required for deep learning. We verify the flow composition method empirically
and provide a working example for its application to optical flow ground truth
in synthetic training data creation. All code is publicly available.

Few-shot named entity recognition (NER) aims at identifying named entities
based on only few labeled instances. Current few-shot NER methods focus on
leveraging existing datasets in the rich-resource domains which might fail in a
training-from-scratch setting where no source-domain data is used. To tackle
training-from-scratch setting, it is crucial to make full use of the annotation
information (the boundaries and entity types). Therefore, in this paper, we
propose a novel multi-task (Seed, Expand and Entail) learning framework,
SEE-Few, for Few-shot NER without using source domain data. The seeding and
expanding modules are responsible for providing as accurate candidate spans as
possible for the entailing module. The entailing module reformulates span
classification as a textual entailment task, leveraging both the contextual
clues and entity type information. All the three modules share the same text
encoder and are jointly learned. Experimental results on four benchmark
datasets under the training-from-scratch setting show that the proposed method
outperformed state-of-the-art few-shot NER methods with a large margin. Our
code is available at https://github.com/unveiled-the-red-hat/SEE-Few.

Optical spectroscopy is indispensable for research and development in
nanoscience and nanotechnology, microelectronics, energy, and advanced
manufacturing. Advanced optical spectroscopy tools often require both
specifically designed high-end instrumentation and intricate data analysis
techniques. Beyond the common analytical tools, deep learning methods are well
suited for interpreting high-dimensional and complicated spectroscopy data.
They offer great opportunities to extract subtle and deep information about
optical properties of materials with simpler optical setups, which would
otherwise require sophisticated instrumentation. In this work, we propose a
computational ellipsometry approach based on a conventional tabletop optical
microscope and a deep learning model called EllipsoNet. Without any prior
knowledge about the multilayer substrates, EllipsoNet can predict the complex
refractive indices of thin films on top of these nontrivial substrates from
experimentally measured optical reflectance spectra with high accuracies. This
task was not feasible previously with traditional reflectometry or ellipsometry
methods. Fundamental physical principles, such as the Kramers-Kronig relations,
are spontaneously learned by the model without any further training. This
approach enables in-operando optical characterization of functional materials
within complex photonic structures or optoelectronic devices.

Recent semantic segmentation models perform well under standard weather
conditions and sufficient illumination but struggle with adverse weather
conditions and nighttime. Collecting and annotating training data under these
conditions is expensive, time-consuming, error-prone, and not always practical.
Usually, synthetic data is used as a feasible data source to increase the
amount of training data. However, just directly using synthetic data may
actually harm the model's performance under normal weather conditions while
getting only small gains in adverse situations. Therefore, we present a novel
architecture specifically designed for using synthetic training data for domain
adaptation. We propose a simple yet powerful addition to DeepLabV3+ by using
weather and time-of-the-day supervisors trained with multi-task learning,
making it both weather and nighttime aware, which improves its mIoU accuracy by
$14$ percentage points on the ACDC dataset while maintaining a score of $75\%$
mIoU on the Cityscapes dataset. Our code is available at
https://github.com/lsmcolab/Semantic-Segmentation-under-Adverse-Conditions.

Single-cell RNA-sequencing (scRNA-seq) has become a routinely used technique
to quantify the gene expression profile of thousands of single cells
simultaneously. Analysis of scRNA-seq data plays an important role in the study
of cell states and phenotypes, and has helped elucidate biological processes,
such as those occurring during development of complex organisms and improved
our understanding of disease states, such as cancer, diabetes, and COVID, among
others. Deep learning, a recent advance of artificial intelligence that has
been used to address many problems involving large datasets, has also emerged
as a promising tool for scRNA-seq data analysis, as it has a capacity to
extract informative, compact features from noisy, heterogeneous, and
high-dimensional scRNA-seq data to improve downstream analysis. The present
review aims at surveying recently developed deep learning techniques in
scRNA-seq data analysis, identifying key steps within the scRNA-seq data
analysis pipeline that have been advanced by deep learning, and explaining the
benefits of deep learning over more conventional analysis tools. Finally, we
summarize the challenges in current deep learning approaches faced within
scRNA-seq data and discuss potential directions for improvements in deep
algorithms for scRNA-seq data analysis.

In this work, we consider a distributed multi-agent stochastic optimization
problem, where each agent holds a local objective function that is smooth and
convex, and that is subject to a stochastic process. The goal is for all agents
to collaborate to find a common solution that optimizes the sum of these local
functions. With the practical assumption that agents can only obtain noisy
numerical function queries at exactly one point at a time, we extend the
distributed stochastic gradient-tracking method to the bandit setting where we
don't have an estimate of the gradient, and we introduce a zero-order (ZO)
one-point estimate (1P-DSGT). We analyze the convergence of this novel
technique for smooth and convex objectives using stochastic approximation
tools, and we prove that it converges almost surely to the optimum. We then
study the convergence rate for when the objectives are additionally strongly
convex. We obtain a rate of $O(\frac{1}{\sqrt{k}})$ after a sufficient number
of iterations $k > K_2$ which is usually optimal for techniques utilizing
one-point estimators. We also provide a regret bound of $O(\sqrt{k})$, which is
exceptionally good compared to the aforementioned techniques. We further
illustrate the usefulness of the proposed technique using numerical

We present Neural Shape Deformation Priors, a novel method for shape
manipulation that predicts mesh deformations of non-rigid objects from
user-provided handle movements. State-of-the-art methods cast this problem as
an optimization task, where the input source mesh is iteratively deformed to
minimize an objective function according to hand-crafted regularizers such as
ARAP. In this work, we learn the deformation behavior based on the underlying
geometric properties of a shape, while leveraging a large-scale dataset
containing a diverse set of non-rigid deformations. Specifically, given a
source mesh and desired target locations of handles that describe the partial
surface deformation, we predict a continuous deformation field that is defined
in 3D space to describe the space deformation. To this end, we introduce
transformer-based deformation networks that represent a shape deformation as a
composition of local surface deformations. It learns a set of local latent
codes anchored in 3D space, from which we can learn a set of continuous
deformation functions for local surfaces. Our method can be applied to
challenging deformations and generalizes well to unseen deformations. We
validate our approach in experiments using the DeformingThing4D dataset, and
compare to both classic optimization-based and recent neural network-based
methods.

In this paper, we present a proof of concept for adversarially attacking the
image-based localization module of an autonomous vehicle. This attack aims to
cause the vehicle to perform a wrong navigational decisions and prevent it from
reaching a desired predefined destination in a simulated urban environment. A
database of rendered images allowed us to train a deep neural network that
performs a localization task and implement, develop and assess the adversarial
pattern. Our tests show that using this adversarial attack we can prevent the
vehicle from turning at a given intersection. This is done by manipulating the
vehicle's navigational module to falsely estimate its current position and thus
fail to initialize the turning procedure until the vehicle misses the last
opportunity to perform a safe turn in a given intersection.

Differential privacy (DP) is one data protection avenue to safeguard user
information used for training deep models by imposing noisy distortion on
privacy data. Such a noise perturbation often results in a severe performance
degradation in automatic speech recognition (ASR) in order to meet a privacy
budget $\varepsilon$. Private aggregation of teacher ensemble (PATE) utilizes
ensemble probabilities to improve ASR accuracy when dealing with the noise
effects controlled by small values of $\varepsilon$. We extend PATE learning to
work with dynamic patterns, namely speech utterances, and perform a first
experimental demonstration that it prevents acoustic data leakage in ASR
training. We evaluate three end-to-end deep models, including LAS, hybrid
CTC/attention, and RNN transducer, on the open-source LibriSpeech and TIMIT
corpora. PATE learning-enhanced ASR models outperform the benchmark DP-SGD
mechanisms, especially under strict DP budgets, giving relative word error rate
reductions between 26.2% and 27.5% for an RNN transducer model evaluated with
LibriSpeech. We also introduce a DP-preserving ASR solution for pretraining on
public speech corpora.

Stochastic optimization algorithms using exponential moving averages of the
past gradients, such as ADAM, RMSProp and AdaGrad, have been having great
successes in many applications, especially in training deep neural networks.
ADAM in particular stands out as efficient and robust. Despite of its
outstanding performance, ADAM has been proved to be divergent for some specific
problems. We revisit the divergent question and provide divergent examples
under stronger conditions such as in expectation or high probability. Under a
variance reduction assumption, we show that an ADAM-type algorithm converges,
which means that it is the variance of gradients that causes the divergence of
original ADAM. To this end, we propose a variance reduced version of ADAM and
provide a convergent analysis of the algorithm. Numerical experiments show that
the proposed algorithm has as good performance as ADAM. Our work suggests a new
direction for fixing the convergence issues.

The superior performance of deep learning relies heavily on a large
collection of sample data, but the data insufficiency problem turns out to be
relatively common in global electricity markets. How to prevent overfitting in
this case becomes a fundamental challenge when training deep learning models in
different market applications. With this in mind, we propose a general
framework, namely Knowledge-Augmented Training (KAT), to improve the sample
efficiency, and the main idea is to incorporate domain knowledge into the
training procedures of deep learning models. Specifically, we propose a novel
data augmentation technique to generate some synthetic data, which are later
processed by an improved training strategy. This KAT methodology follows and
realizes the idea of combining analytical and deep learning models together.
Modern learning theories demonstrate the effectiveness of our method in terms
of effective prediction error feedbacks, a reliable loss function, and rich
gradient noises. At last, we study two popular applications in detail: user
modeling and probabilistic price forecasting. The proposed method outperforms
other competitors in all numerical tests, and the underlying reasons are
explained by further statistical and visualization results.

Raster well-log images are digital representations of well-logs data
generated over the years. Raster digital well logs represent bitmaps of the log
image in a rectangular array of black (zeros) and white dots (ones) called
pixels. Experts study the raster logs manually or with software applications
that still require a tremendous amount of manual input. Besides the loss of
thousands of person-hours, this process is erroneous and tedious. To digitize
these raster logs, one must buy a costly digitizer that is not only manual and
time-consuming but also a hidden technical debt since enterprises stand to lose
more money in additional servicing and consulting charges. We propose a deep
neural network architecture called VeerNet to semantically segment the raster
images from the background grid and classify and digitize the well-log curves.
Raster logs have a substantially greater resolution than images traditionally
consumed by image segmentation pipelines. Since the input has a low
signal-to-resolution ratio, we require rapid downsampling to alleviate
unnecessary computation. We thus employ a modified UNet-inspired architecture
that balances retaining key signals and reducing result dimensionality. We use
attention augmented read-process-write architecture. This architecture
efficiently classifies and digitizes the curves with an overall F1 score of 35%
and IoU of 30%. When compared to the actual las values for Gamma-ray and
derived value of Gamma-ray from VeerNet, a high Pearson coefficient score of
0.62 was achieved.

Barrier function-based inequality constraints are a means to enforce safety
specifications for control systems. When used in conjunction with a convex
optimization program, they provide a computationally efficient method to
enforce safety for the general class of control-affine systems. One of the main
assumptions when taking this approach is the a priori knowledge of the barrier
function itself, i.e., knowledge of the safe set. In the context of navigation
through unknown environments where the locally safe set evolves with time, such
knowledge does not exist. This manuscript focuses on the synthesis of a zeroing
barrier function characterizing the safe set based on safe and unsafe sample
measurements, e.g., from perception data in navigation applications. Prior work
formulated a supervised machine learning algorithm whose solution guaranteed
the construction of a zeroing barrier function with specific level-set
properties. However, it did not explore the geometry of the neural network
design used for the synthesis process. This manuscript describes the specific
geometry of the neural network used for zeroing barrier function synthesis, and
shows how the network provides the necessary representation for splitting the
state space into safe and unsafe regions.

Bias mitigators can improve algorithmic fairness in machine learning models,
but their effect on fairness is often not stable across data splits. A popular
approach to train more stable models is ensemble learning, but unfortunately,
it is unclear how to combine ensembles with mitigators to best navigate
trade-offs between fairness and predictive performance. To that end, we built
an open-source library enabling the modular composition of 8 mitigators, 4
ensembles, and their corresponding hyperparameters, and we empirically explored
the space of configurations on 13 datasets. We distilled our insights from this
exploration in the form of a guidance diagram for practitioners that we
demonstrate is robust and reproducible.

Most existing 3D point cloud object detection approaches heavily rely on
large amounts of labeled training data. However, the labeling process is costly
and time-consuming. This paper considers few-shot 3D point cloud object
detection, where only a few annotated samples of novel classes are needed with
abundant samples of base classes. To this end, we propose Prototypical VoteNet
to recognize and localize novel instances, which incorporates two new modules:
Prototypical Vote Module (PVM) and Prototypical Head Module (PHM).
Specifically, as the 3D basic geometric structures can be shared among
categories, PVM is designed to leverage class-agnostic geometric prototypes,
which are learned from base classes, to refine local features of novel
categories.Then PHM is proposed to utilize class prototypes to enhance the
global feature of each object, facilitating subsequent object localization and
classification, which is trained by the episodic training strategy. To evaluate
the model in this new setting, we contribute two new benchmark datasets,
FS-ScanNet and FS-SUNRGBD. We conduct extensive experiments to demonstrate the
effectiveness of Prototypical VoteNet, and our proposed method shows
significant and consistent improvements compared to baselines on two benchmark
datasets.

In this paper we study the non-stationary stochastic optimization question
with bandit feedback and dynamic regret measures. The seminal work of Besbes et
al. (2015) shows that, when aggregated function changes is known a priori, a
simple re-starting algorithm attains the optimal dynamic regret. In this work,
we designed a stochastic optimization algorithm with fixed step sizes, which
combined together with the multi-scale sampling framework of Wei and Luo (2021)
achieves the optimal dynamic regret in non-stationary stochastic optimization
without requiring prior knowledge of function change budget, thereby closes a
question that has been open for a while. We also establish an additional result
showing that any algorithm achieving good regret against stationary benchmarks
with high probability could be automatically converted to an algorithm that
achieves good regret against dynamic benchmarks, which is applicable to a wide
class of bandit convex optimization algorithms.

Commonly adopted in the manufacturing and aerospace sectors, digital twin
(DT) platforms are increasingly seen as a promising paradigm to control and
monitor software-based, "open", communication systems, which play the role of
the physical twin (PT). In the general framework presented in this work, the DT
builds a Bayesian model of the communication system, which is leveraged to
enable core DT functionalities such as control via multi-agent reinforcement
learning (MARL) and monitoring of the PT for anomaly detection. We specifically
investigate the application of the proposed framework to a simple case-study
system encompassing multiple sensing devices that report to a common receiver.
The Bayesian model trained at the DT has the key advantage of capturing
epistemic uncertainty regarding the communication system, e.g., regarding
current traffic conditions, which arise from limited PT-to-DT data transfer.
Experimental results validate the effectiveness of the proposed Bayesian
framework as compared to standard frequentist model-based solutions.

Designing an incentive-compatible auction mechanism that maximizes the
auctioneer's revenue while minimizes the bidders' ex-post regret is an
important yet intricate problem in economics. Remarkable progress has been
achieved through learning the optimal auction mechanism by neural networks. In
this paper, we consider the popular additive valuation and symmetric valuation
setting; i.e., the valuation for a set of items is defined as the sum of all
items' valuations in the set, and the valuation distribution is invariant when
the bidders and/or the items are permutated. We prove that
permutation-equivariant neural networks have significant advantages: the
permutation-equivariance decreases the expected ex-post regret, improves the
model generalizability, while maintains the expected revenue invariant. This
implies that the permutation-equivariance helps approach the theoretically
optimal dominant strategy incentive compatible condition, and reduces the
required sample complexity for desired generalization. Extensive experiments
fully support our theory. To our best knowledge, this is the first work towards
understanding the benefits of permutation-equivariance in auction mechanisms.

The adversarial vulnerability of neural nets, and subsequent techniques to
create robust models have attracted significant attention; yet we still lack a
full understanding of this phenomenon. Here, we study adversarial examples of
trained neural networks through analytical tools afforded by recent theory
advances connecting neural networks and kernel methods, namely the Neural
Tangent Kernel (NTK), following a growing body of work that leverages the NTK
approximation to successfully analyze important deep learning phenomena and
design algorithms for new applications. We show how NTKs allow to generate
adversarial examples in a ``training-free'' fashion, and demonstrate that they
transfer to fool their finite-width neural net counterparts in the ``lazy''
regime. We leverage this connection to provide an alternative view on robust
and non-robust features, which have been suggested to underlie the adversarial
brittleness of neural nets. Specifically, we define and study features induced
by the eigendecomposition of the kernel to better understand the role of robust
and non-robust features, the reliance on both for standard classification and
the robustness-accuracy trade-off. We find that such features are surprisingly
consistent across architectures, and that robust features tend to correspond to
the largest eigenvalues of the model, and thus are learned early during
training. Our framework allows us to identify and visualize non-robust yet
useful features. Finally, we shed light on the robustness mechanism underlying
adversarial training of neural nets used in practice: quantifying the evolution
of the associated empirical NTK, we demonstrate that its dynamics falls much
earlier into the ``lazy'' regime and manifests a much stronger form of the well
known bias to prioritize learning features within the top eigenspaces of the
kernel, compared to standard training.

The task of Generic Event Boundary Detection (GEBD) aims to detect moments in
videos that are naturally perceived by humans as generic and taxonomy-free
event boundaries. Modeling the dynamically evolving temporal and spatial
changes in a video makes GEBD a difficult problem to solve. Existing approaches
involve very complex and sophisticated pipelines in terms of architectural
design choices, hence creating a need for more straightforward and simplified
approaches. In this work, we address this issue by revisiting a simple and
effective self-supervised method and augment it with a differentiable motion
feature learning module to tackle the spatial and temporal diversities in the
GEBD task. We perform extensive experiments on the challenging Kinetics-GEBD
and TAPOS datasets to demonstrate the efficacy of the proposed approach
compared to the other self-supervised state-of-the-art methods. We also show
that this simple self-supervised approach learns motion features without any
explicit motion-specific pretext task.

Drug recommendation assists doctors in prescribing personalized medications
to patients based on their health conditions. Existing drug recommendation
solutions adopt the supervised multi-label classification setup and only work
with existing drugs with sufficient prescription data from many patients.
However, newly approved drugs do not have much historical prescription data and
cannot leverage existing drug recommendation methods. To address this, we
formulate the new drug recommendation as a few-shot learning problem. Yet,
directly applying existing few-shot learning algorithms faces two challenges:
(1) complex relations among diseases and drugs and (2) numerous false-negative
patients who were eligible but did not yet use the new drugs. To tackle these
challenges, we propose EDGE, which can quickly adapt to the recommendation for
a new drug with limited prescription data from a few support patients. EDGE
maintains a drug-dependent multi-phenotype few-shot learner to bridge the gap
between existing and new drugs. Specifically, EDGE leverages the drug ontology
to link new drugs to existing drugs with similar treatment effects and learns
ontology-based drug representations. Such drug representations are used to
customize the metric space of the phenotype-driven patient representations,
which are composed of a set of phenotypes capturing complex patient health
status. Lastly, EDGE eliminates the false-negative supervision signal using an
external drug-disease knowledge base. We evaluate EDGE on two real-world
datasets: the public EHR data (MIMIC-IV) and private industrial claims data.
Results show that EDGE achieves 7.3% improvement on the ROC-AUC score over the
best baseline.

In this paper, we study phase retrieval under model misspecification and
generative priors. In particular, we aim to estimate an $n$-dimensional signal
$\mathbf{x}$ from $m$ i.i.d.~realizations of the single index model $y =
f(\mathbf{a}^T\mathbf{x})$, where $f$ is an unknown and possibly random
nonlinear link function and $\mathbf{a} \in \mathbb{R}^n$ is a standard
Gaussian vector. We make the assumption
$\mathrm{Cov}[y,(\mathbf{a}^T\mathbf{x})^2] \ne 0$, which corresponds to the
misspecified phase retrieval problem. In addition, the underlying signal
$\mathbf{x}$ is assumed to lie in the range of an $L$-Lipschitz continuous
generative model with bounded $k$-dimensional inputs. We propose a two-step
approach, for which the first step plays the role of spectral initialization
and the second step refines the estimated vector produced by the first step
iteratively. We show that both steps enjoy a statistical rate of order
$\sqrt{(k\log L)\cdot (\log m)/m}$ under suitable conditions. Experiments on
image datasets are performed to demonstrate that our approach performs on par
with or even significantly outperforms several competing methods.

Data in real-world object detection often exhibits the long-tailed
distribution. Existing solutions tackle this problem by mitigating the
competition between the head and tail categories. However, due to the scarcity
of training samples, tail categories are still unable to learn discriminative
representations. Bringing more data into the training may alleviate the
problem, but collecting instance-level annotations is an excruciating task. In
contrast, image-level annotations are easily accessible but not fully
exploited. In this paper, we propose a novel framework CLIS (multi-task
Collaborative Learning with Image-level Supervision), which leverage
image-level supervision to enhance the detection ability in a multi-task
collaborative way. Specifically, there are an object detection task (consisting
of an instance-classification task and a localization task) and an
image-classification task in our framework, responsible for utilizing the two
types of supervision. Different tasks are trained collaboratively by three key
designs: (1) task-specialized sub-networks that learn specific representations
of different tasks without feature entanglement. (2) a siamese sub-network for
the image-classification task that shares its knowledge with the
instance-classification task, resulting in feature enrichment of detectors. (3)
a contrastive learning regularization that maintains representation
consistency, bridging feature gaps of different supervision. Extensive
experiments are conducted on the challenging LVIS dataset. Without
sophisticated loss engineering, CLIS achieves an overall AP of 31.1 with 10.1
point improvement on tail categories, establishing a new state-of-the-art. Code
will be at https://github.com/waveboo/CLIS.

This paper studies semi-supervised video object segmentation through boosting
intra-frame interaction. Recent memory network-based methods focus on
exploiting inter-frame temporal reference while paying little attention to
intra-frame spatial dependency. Specifically, these segmentation model tends to
be susceptible to interference from unrelated nontarget objects in a certain
frame. To this end, we propose Global Spectral Filter Memory network (GSFM),
which improves intra-frame interaction through learning long-term spatial
dependencies in the spectral domain. The key components of GSFM is 2D (inverse)
discrete Fourier transform for spatial information mixing. Besides, we
empirically find low frequency feature should be enhanced in encoder (backbone)
while high frequency for decoder (segmentation head). We attribute this to
semantic information extracting role for encoder and fine-grained details
highlighting role for decoder. Thus, Low (High) Frequency Module is proposed to
fit this circumstance. Extensive experiments on the popular DAVIS and
YouTube-VOS benchmarks demonstrate that GSFM noticeably outperforms the
baseline method and achieves state-of-the-art performance. Besides, extensive
analysis shows that the proposed modules are reasonable and of great
generalization ability. Our source code is available at
https://github.com/workforai/GSFM.

Semantic segmentation is a fundamental topic in computer vision. Several deep
learning methods have been proposed for semantic segmentation with outstanding
results. However, these models require a lot of densely annotated images. To
address this problem, we propose a new algorithm that uses HyperGraph
Convolutional Networks for Weakly-supervised Semantic Segmentation
(HyperGCN-WSS). Our algorithm constructs spatial and k-Nearest Neighbor (k-NN)
graphs from the images in the dataset to generate the hypergraphs. Then, we
train a specialized HyperGraph Convolutional Network (HyperGCN) architecture
using some weak signals. The outputs of the HyperGCN are denominated
pseudo-labels, which are later used to train a DeepLab model for semantic
segmentation. HyperGCN-WSS is evaluated on the PASCAL VOC 2012 dataset for
semantic segmentation, using scribbles or clicks as weak signals. Our algorithm
shows competitive performance against previous methods.

A continual learning (CL) algorithm learns from a non-stationary data stream.
The non-stationarity is modeled by some schedule that determines how data is
presented over time. Most current methods make strong assumptions on the
schedule and have unpredictable performance when such requirements are not met.
A key challenge in CL is thus to design methods robust against arbitrary
schedules over the same underlying data, since in real-world scenarios
schedules are often unknown and dynamic. In this work, we introduce the notion
of schedule-robustness for CL and a novel approach satisfying this desirable
property in the challenging online class-incremental setting. We also present a
new perspective on CL, as the process of learning a schedule-robust predictor,
followed by adapting the predictor using only replay data. Empirically, we
demonstrate that our approach outperforms existing methods on CL benchmarks for
image classification by a large margin.

Diffusion models have achieved unprecedented performance in generative
modeling. The commonly-adopted formulation of the latent code of diffusion
models is a sequence of gradually denoised samples, as opposed to the simpler
(e.g., Gaussian) latent space of GANs, VAEs, and normalizing flows. This paper
provides an alternative, Gaussian formulation of the latent space of various
diffusion models, as well as an invertible DPM-Encoder that maps images into
the latent space. While our formulation is purely based on the definition of
diffusion models, we demonstrate several intriguing consequences. (1)
Empirically, we observe that a common latent space emerges from two diffusion
models trained independently on related domains. In light of this finding, we
propose CycleDiffusion, which uses DPM-Encoder for unpaired image-to-image
translation. Furthermore, applying CycleDiffusion to text-to-image diffusion
models, we show that large-scale text-to-image diffusion models can be used as
zero-shot image-to-image editors. (2) One can guide pre-trained diffusion
models and GANs by controlling the latent codes in a unified, plug-and-play
formulation based on energy-based models. Using the CLIP model and a face
recognition model as guidance, we demonstrate that diffusion models have better
coverage of low-density sub-populations and individuals than GANs.

It is often said that the fundamental problem of causal inference is a
missing data problem -- the comparison of responses to two hypothetical
treatment assignments is made difficult because for every experimental unit
only one potential response is observed. In this paper, we consider the
implications of the converse view: that missing data problems are a form of
causal inference. We make explicit how the missing data problem of recovering
the complete data law from the observed law can be viewed as identification of
a joint distribution over counterfactual variables corresponding to values had
we (possibly contrary to fact) been able to observe them. Drawing analogies
with causal inference, we show how identification assumptions in missing data
can be encoded in terms of graphical models defined over counterfactual and
observed variables. We review recent results in missing data identification
from this viewpoint. In doing so, we note interesting similarities and
differences between missing data and causal identification theories.

The pretrain-finetune paradigm in modern computer vision facilitates the
success of self-supervised learning, which tends to achieve better
transferability than supervised learning. However, with the availability of
massive labeled data, a natural question emerges: how to train a better model
with both self and full supervision signals? In this paper, we propose
Omni-suPErvised Representation leArning with hierarchical supervisions (OPERA)
as a solution. We provide a unified perspective of supervisions from labeled
and unlabeled data and propose a unified framework of fully supervised and
self-supervised learning. We extract a set of hierarchical proxy
representations for each image and impose self and full supervisions on the
corresponding proxy representations. Extensive experiments on both
convolutional neural networks and vision transformers demonstrate the
superiority of OPERA in image classification, segmentation, and object
detection. Code is available at: https://github.com/wangck20/OPERA.

Unsupervised denoising is a crucial challenge in real-world imaging
applications. Unsupervised deep-learning methods have demonstrated impressive
performance on benchmarks based on synthetic noise. However, no metrics are
available to evaluate these methods in an unsupervised fashion. This is highly
problematic for the many practical applications where ground-truth clean images
are not available. In this work, we propose two novel metrics: the unsupervised
mean squared error (MSE) and the unsupervised peak signal-to-noise ratio
(PSNR), which are computed using only noisy data. We provide a theoretical
analysis of these metrics, showing that they are asymptotically consistent
estimators of the supervised MSE and PSNR. Controlled numerical experiments
with synthetic noise confirm that they provide accurate approximations in
practice. We validate our approach on real-world data from two imaging
modalities: videos in raw format and transmission electron microscopy. Our
results demonstrate that the proposed metrics enable unsupervised evaluation of
denoising methods based exclusively on noisy data.

Recent work on applying large language models (LMs) achieves impressive
performance in many NLP applications. Adapting or posttraining an LM using an
unlabeled domain corpus can produce even better performance for end-tasks in
the domain. This paper proposes the problem of continually extending an LM by
incrementally post-train the LM with a sequence of unlabeled domain corpora to
expand its knowledge without forgetting its previous skills. The goal is to
improve the few-shot end-task learning in these domains. The resulting system
is called CPT (Continual PostTraining), which to our knowledge, is the first
continual post-training system. Experimental results verify its effectiveness.

Deep neural network classifiers partition input space into high confidence
regions for each class. The geometry of these class manifolds (CMs) is widely
studied and intimately related to model performance; for example, the margin
depends on CM boundaries. We exploit the notions of Gaussian width and Gordon's
escape theorem to tractably estimate the effective dimension of CMs and their
boundaries through tomographic intersections with random affine subspaces of
varying dimension. We show several connections between the dimension of CMs,
generalization, and robustness. In particular we investigate how CM dimension
depends on 1) the dataset, 2) architecture (including ResNet, WideResNet \&
Vision Transformer), 3) initialization, 4) stage of training, 5) class, 6)
network width, 7) ensemble size, 8) label randomization, 9) training set size,
and 10) robustness to data corruption. Together a picture emerges that higher
performing and more robust models have higher dimensional CMs. Moreover, we
offer a new perspective on ensembling via intersections of CMs. Our code is at
https://github.com/stanislavfort/slice-dice-optimize/

We consider online preemptive scheduling of jobs arriving one by one, to be
assigned to two identical machines, with the goal of makespan minimization. We
study the effect of selecting the best solution out of two independent
solutions constructed in parallel in an online fashion. Two cases are analyzed,
where one case is purely online, and in the other one jobs are presented sorted
by non-increasing sizes. We show that using two solutions rather than one
improves the performance significantly, but that an optimal solution cannot be
obtained for any constant number of solutions constructed in parallel. Our
algorithms have the best possible competitive ratios out of algorithms for each
one of the classes.

There is extensive literature on the estimation of the optimal individualized
treatment regime in a survival context, which dictates treatment to maximize
the expected survival probability. Those methods are based on the key
assumption that we can collect all the confoundings in the observational
studies or in the randomized trials with noncompliance. However, the assumption
sometimes is too restrictive to be applied and the violation would yield bias
on the estimation of the optimal regime. In the article, we propose a method to
learn the optimal regime when some of the confoundings are not observed and a
valid binary instrumental variable is available. Specifically, we establish the
estimator for the potential survival function under any given treatment regime
and for the optimal regime by maximizing the potential survival function under
a prespecified class of regimes. We also propose the doubly robust estimator to
avoid possibly wrong assignment of the nuisance model. Since the estimators of
the potential survival function is jagged, we utilize the kernel smoothed
technique to relieve the burden of the optimization. Asymptotic properties of
the proposed estimators are provided, moreover, simulation results confirm the
finite sample performance when unmeasured confounding exists. Our methods are
also examined and illustrated by a real-world example to dictate personalized
colorectal cancer screening.

Due to the few annotated labels of 3D point clouds, how to learn
discriminative features of point clouds to segment object instances is a
challenging problem. In this paper, we propose a simple yet effective 3D
instance segmentation framework that can achieve good performance by annotating
only one point for each instance. Specifically, to tackle extremely few labels
for instance segmentation, we first oversegment the point cloud into
superpoints in an unsupervised manner and extend the point-level annotations to
the superpoint level. Then, based on the superpoint graph, we propose an
inter-superpoint affinity mining module that considers the semantic and spatial
relations to adaptively learn inter-superpoint affinity to generate
high-quality pseudo labels via semantic-aware random walk. Finally, we propose
a volume-aware instance refinement module to segment high-quality instances by
applying volume constraints of objects in clustering on the superpoint graph.
Extensive experiments on the ScanNet-v2 and S3DIS datasets demonstrate that our
method achieves state-of-the-art performance in the weakly supervised point
cloud instance segmentation task, and even outperforms some fully supervised
methods.

We propose a novel data-driven estimation and control framework for
contact-rich tight tolerance tasks, which estimates the pose of the object
precisely using data-driven methods and compensates for the remaining error via
reinforcement learning (RL). First, the sequential particle filter estimator
updates with the mixture density network (MDN), which is to represent the
general non-injective conditional probability and thus is suitable for finding
out the pose from the measurements including relatively low-dimensional contact
wrench sensing. We further develop the RL-based fastening controller that
adapts to the remaining error by optimizing the admittance gain to complete the
task. The proposed framework is evaluated using an accurate real-time simulator
on the bolting task and successfully transferred to an experimental
environment.

A new and efficient neural-network and finite-difference hybrid method is
developed for solving Poisson equation in a regular domain with jump
discontinuities on embedded irregular interfaces. Since the solution has low
regularity across the interface, when applying finite difference discretization
to this problem, an additional treatment accounting for the jump
discontinuities must be employed. Here, we aim to elevate such an extra effort
to ease our implementation by machine learning methodology. The key idea is to
decompose the solution into singular and regular parts. The neural network
learning machinery incorporating the given jump conditions finds the singular
solution, while the standard finite difference method is used to obtain the
regular solution with associated boundary conditions. Regardless of the
interface geometry, these two tasks only require supervised learning for
function approximation and a fast direct solver for Poisson equation, making
the hybrid method easy to implement and efficient. The two- and
three-dimensional numerical results show that the present hybrid method
preserves second-order accuracy for the solution and its derivatives, and it is
comparable with the traditional immersed interface method in the literature. As
an application, we solve the Stokes equations with singular forces to
demonstrate the robustness of the present method.

Some fifty years ago, in her seminal PhD thesis, Odile Macchi introduced
permanental and determinantal point processes. Her initial motivation was to
provide models for the set of detection times in fundamental bosonic or
fermionic optical experiments, respectively. After two rather quiet decades,
these point processes have quickly become standard examples of point processes
with nontrivial, yet tractable, correlation structures. In particular,
determinantal point processes have been since the 1990s a technical workhorse
in random matrix theory and combinatorics, and a standard model for repulsive
point patterns in machine learning and spatial statistics since the 2010s.
Meanwhile, our ability to experimentally probe the correlations between
detection events in bosonic and fermionic optics has progressed tremendously.
In Part I of this survey, we provide a modern introduction to the concepts in
Macchi's thesis and their physical motivation, under the combined eye of
mathematicians, physicists, and signal processers. Our objective is to provide
a shared basis of knowledge for later cross-disciplinary work on point
processes in quantum optics, and reconnect with the physical roots of
permanental and determinantal point processes.

IVFPQ is a popular index paradigm for time-efficient ad-hoc retrieval.
Instead of traversing the entire database for relevant documents, it
accelerates the retrieval operation by 1) accessing a fraction of the database
guided the activation of latent topics in IVF (inverted file system), and 2)
approximating the exact relevance measurement based on PQ (product
quantization). However, the conventional IVFPQ is limited in retrieval
performance due to the coarse granularity of its latent topics. On the one
hand, it may result in severe loss of retrieval quality when visiting a small
number of topics; on the other hand, it will lead to a huge retrieval cost when
visiting a large number of topics.
  To mitigate the above problem, we propose a novel framework named Bi-Phase
IVFPQ. It jointly uses two types of features: the latent topics and the
explicit terms, to build the inverted file system. Both types of features are
complementary to each other, which helps to achieve better coverage of the
relevant documents. Besides, the documents' memberships to different IVF
entries are learned by distilling knowledge from deep semantic models, which
substantially improves the index quality and retrieval accuracy. We perform
comprehensive empirical studies on popular ad-hoc retrieval benchmarks, whose
results verify the effectiveness and efficiency of our proposed framework.

Humans are remarkably good at understanding and reasoning about complex
visual scenes. The capability to decompose low-level observations into discrete
objects allows us to build a grounded abstract representation and identify the
compositional structure of the world. Accordingly, it is a crucial step for
machine learning models to be capable of inferring objects and their properties
from visual scenes without explicit supervision. However, existing works on
object-centric representation learning either rely on tailor-made neural
network modules or strong probabilistic assumptions in the underlying
generative and inference processes. In this work, we present \ours, a
conceptually simple and general approach to learning object-centric
representations through an energy-based model. By forming a
permutation-invariant energy function using vanilla attention blocks readily
available in Transformers, we can infer object-centric latent variables via
gradient-based MCMC methods where permutation equivariance is automatically
guaranteed. We show that \ours can be easily integrated into existing
architectures and can effectively extract high-quality object-centric
representations, leading to better segmentation accuracy and competitive
downstream task performance. Further, empirical evaluations show that \ours's
learned representations are robust against distribution shift. Finally, we
demonstrate the effectiveness of \ours in systematic compositional
generalization, by re-composing learned energy functions for novel scene
generation and manipulation.

Two-view structure from motion (SfM) is the cornerstone of 3D reconstruction
and visual SLAM (vSLAM). Many existing end-to-end learning-based methods
usually formulate it as a brute regression problem. However, the inadequate
utilization of traditional geometry model makes the model not robust in unseen
environments. To improve the generalization capability and robustness of
end-to-end two-view SfM network, we formulate the two-view SfM problem as a
maximum likelihood estimation (MLE) and solve it with the proposed framework,
denoted as DeepMLE. First, we propose to take the deep multi-scale correlation
maps to depict the visual similarities of 2D image matches decided by
ego-motion. In addition, in order to increase the robustness of our framework,
we formulate the likelihood function of the correlations of 2D image matches as
a Gaussian and Uniform mixture distribution which takes the uncertainty caused
by illumination changes, image noise and moving objects into account.
Meanwhile, an uncertainty prediction module is presented to predict the
pixel-wise distribution parameters. Finally, we iteratively refine the depth
and relative camera pose using the gradient-like information to maximize the
likelihood function of the correlations. Extensive experimental results on
several datasets prove that our method significantly outperforms the
state-of-the-art end-to-end two-view SfM approaches in accuracy and
generalization capability.

We introduce ViFiCon, a self-supervised contrastive learning scheme which
uses synchronized information across vision and wireless modalities to perform
cross-modal association. Specifically, the system uses pedestrian data
collected from RGB-D camera footage as well as WiFi Fine Time Measurements
(FTM) from a user's smartphone device. We represent the temporal sequence by
stacking multi-person depth data spatially within a banded image. Depth data
from RGB-D (vision domain) is inherently linked with an observable pedestrian,
but FTM data (wireless domain) is associated only to a smartphone on the
network. To formulate the cross-modal association problem as self-supervised,
the network learns a scene-wide synchronization of the two modalities as a
pretext task, and then uses that learned representation for the downstream task
of associating individual bounding boxes to specific smartphones, i.e.
associating vision and wireless information. We use a pre-trained region
proposal model on the camera footage and then feed the extrapolated bounding
box information into a dual-branch convolutional neural network along with the
FTM data. We show that compared to fully supervised SoTA models, ViFiCon
achieves high performance vision-to-wireless association, finding which
bounding box corresponds to which smartphone device, without hand-labeled
association examples for training data.

Machine Learning (ML) is changing DBs as many DB components are being
replaced by ML models. One open problem in this setting is how to update such
ML models in the presence of data updates. We start this investigation focusing
on data insertions (dominating updates in analytical DBs). We study how to
update neural network (NN) models when new data follows a different
distribution (a.k.a. it is "out-of-distribution" -- OOD), rendering
previously-trained NNs inaccurate. A requirement in our problem setting is that
learned DB components should ensure high accuracy for tasks on old and new data
(e.g., for approximate query processing (AQP), cardinality estimation (CE),
synthetic data generation (DG), etc.). This paper proposes a novel updatability
framework (DDUp). DDUp can provide updatability for different learned DB system
components, even based on different NNs, without the high costs to retrain the
NNs from scratch. DDUp entails two components: First, a novel, efficient, and
principled statistical-testing approach to detect OOD data. Second, a novel
model updating approach, grounded on the principles of transfer learning with
knowledge distillation, to update learned models efficiently, while still
ensuring high accuracy. We develop and showcase DDUp's applicability for three
different learned DB components, AQP, CE, and DG, each employing a different
type of NN. Detailed experimental evaluation using real and benchmark datasets
for AQP, CE, and DG detail DDUp's performance advantages.

The high effectiveness of neural models of code, such as OpenAI Codex and
AlphaCode, suggests coding capabilities of models that are at least comparable
to those of humans. However, previous work has only used these models for their
raw completion, ignoring how the model reasoning, in the form of attention
weights, can be used for other downstream tasks. Disregarding the attention
weights means discarding a considerable portion of what those models compute
when queried. To profit more from the knowledge embedded in these large
pre-trained models, this work compares multiple approaches to post-process
these valuable attention weights for supporting code exploration. Specifically,
we compare to which extent the transformed attention signal of CodeGen, a large
and publicly available pretrained neural model, agrees with how developers look
at and explore code when each answering the same sense-making questions about
code. At the core of our experimental evaluation, we collect, manually
annotate, and open-source a novel eye-tracking dataset comprising 25 developers
answering sense-making questions on code over 92 sessions. We empirically
evaluate five attention-agnostic heuristics and ten attention-based post
processing approaches of the attention signal against our ground truth of
developers exploring code, including the novel concept of follow-up attention
which exhibits the highest agreement. Beyond the dataset contribution and the
empirical study, we also introduce a novel practical application of the
attention signal of pre-trained models with completely analytical solutions,
going beyond how neural models' attention mechanisms have traditionally been
used.

The prevalence and perniciousness of fake news have been a critical issue on
the Internet, which stimulates the development of automatic fake news detection
in turn. In this paper, we focus on evidence-based fake news detection, where
several evidences are utilized to probe the veracity of news (i.e., a claim).
Most previous methods first employ sequential models to embed the semantic
information and then capture the claim-evidence interaction based on attention
mechanisms. Despite their effectiveness, they still suffer from three
weaknesses. Firstly, sequential models fail to integrate the relevant
information that is scattered far apart in evidences. Secondly, they
underestimate much redundant information in evidences may be useless or
harmful. Thirdly, insufficient data utilization limits the separability and
reliability of representations captured by the model. To solve these problems,
we propose a unified Graph-based sEmantic structure mining framework with
ConTRAstive Learning, namely GETRAL in short. Specifically, we first model
claims and evidences as graph-structured data to capture the long-distance
semantic dependency. Consequently, we reduce information redundancy by
performing graph structure learning. Then the fine-grained semantic
representations are fed into the claim-evidence interaction module for
predictions. Finally, an adversarial contrastive learning module is applied to
make full use of data and strengthen representation learning. Comprehensive
experiments have demonstrated the superiority of GETRAL over the
state-of-the-arts and validated the efficacy of semantic mining with graph
structure and contrastive learning.

The computational complexity of classical numerical methods for solving
Partial Differential Equations (PDE) scales significantly as the resolution
increases. As an important example, climate predictions require fine
spatio-temporal resolutions to resolve all turbulent scales in the fluid
simulations. This makes the task of accurately resolving these scales
computationally out of reach even with modern supercomputers. As a result,
current numerical modelers solve PDEs on grids that are too coarse (3km to
200km on each side), which hinders the accuracy and usefulness of the
predictions. In this paper, we leverage the recent advances in Implicit Neural
Representations (INR) to design a novel architecture that predicts the
spatially continuous solution of a PDE given a spatial position query. By
augmenting coordinate-based architectures with Graph Neural Networks (GNN), we
enable zero-shot generalization to new non-uniform meshes and long-term
predictions up to 250 frames ahead that are physically consistent. Our Mesh
Agnostic Neural PDE Solver (MAgNet) is able to make accurate predictions across
a variety of PDE simulation datasets and compares favorably with existing
baselines. Moreover, MAgNet generalizes well to different meshes and
resolutions up to four times those trained on.

Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) is
challenging due to privacy concerns, which hinders the use of ML for healthcare
applications. Synthetic EHRs generation bypasses the need to share sensitive
real patient records. However, existing methods generate single-modal EHRs by
unconditional generation or by longitudinal inference, which falls short of low
flexibility and makes unrealistic EHRs. In this work, we propose to formulate
EHRs generation as a text-to-text translation task by language models (LMs),
which suffices to highly flexible event imputation during generation. We also
design prompt learning to control the generation conditioned by numerical and
categorical demographic features. We evaluate synthetic EHRs quality by two
perplexity measures accounting for their longitudinal pattern (longitudinal
imputation perplexity, lpl) and the connections cross modalities
(cross-modality imputation perplexity, mpl). Moreover, we utilize two
adversaries: membership and attribute inference attacks for privacy-preserving
evaluation. Experiments on MIMIC-III data demonstrate the superiority of our
methods on realistic EHRs generation (53.1\% decrease of lpl and 45.3\%
decrease of mpl on average compared to the best baselines) with low privacy
risks. Software is available at https://github.com/RyanWangZf/PromptEHR.

No-press Diplomacy is a complex strategy game involving both cooperation and
competition that has served as a benchmark for multi-agent AI research. While
self-play reinforcement learning has resulted in numerous successes in purely
adversarial games like chess, Go, and poker, self-play alone is insufficient
for achieving optimal performance in domains involving cooperation with humans.
We address this shortcoming by first introducing a planning algorithm we call
DiL-piKL that regularizes a reward-maximizing policy toward a human
imitation-learned policy. We prove that this is a no-regret learning algorithm
under a modified utility function. We then show that DiL-piKL can be extended
into a self-play reinforcement learning algorithm we call RL-DiL-piKL that
provides a model of human play while simultaneously training an agent that
responds well to this human model. We used RL-DiL-piKL to train an agent we
name Diplodocus. In a 200-game no-press Diplomacy tournament involving 62 human
participants spanning skill levels from beginner to expert, two Diplodocus
agents both achieved a higher average score than all other participants who
played more than two games, and ranked first and third according to an Elo
ratings model.

The goal of this paper is to introduce pooling strategies for simplicial
convolutional neural networks. Inspired by graph pooling methods, we introduce
a general formulation for a simplicial pooling layer that performs: i) local
aggregation of simplicial signals; ii) principled selection of sampling sets;
iii) downsampling and simplicial topology adaptation. The general layer is then
customized to design four different pooling strategies (i.e., max, top-k,
self-attention, and separated top-k) grounded in the theory of topological
signal processing. Also, we leverage the proposed layers in a hierarchical
architecture that reduce complexity while representing data at different
resolutions. Numerical results on real data benchmarks (i.e., flow and graph
classification) illustrate the advantage of the proposed methods with respect
to the state of the art.

The potential energy surface (PES) of molecules with respect to their nuclear
positions is a primary tool in understanding chemical reactions from first
principles. However, obtaining this information is complicated by the fact that
sampling a large number of ground states over a high-dimensional PES can
require a vast number of state preparations. In this work, we propose using a
generative quantum machine learning model to prepare quantum states at
arbitrary points on the PES. The model is trained using quantum data consisting
of ground-state wavefunctions associated with different classical nuclear
coordinates. Our approach uses a classical neural network to convert the
nuclear coordinates of a molecule into quantum parameters of a variational
quantum circuit. The model is trained using a fidelity loss function to
optimize the neural network parameters. We show that gradient evaluation is
efficient and numerically demonstrate our method's ability to prepare
wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In
all cases, we find that a small number of training points are needed to achieve
very high overlap with the groundstates in practice. From a theoretical
perspective, we further prove limitations on these protocols by showing that if
we were able to learn across an avoided crossing using a small number of
samples, then we would be able to violate Grover's lower bound. Additionally,
we prove lower bounds on the amount of quantum data needed to learn a locally
optimal neural network function using arguments from quantum Fisher
information. This work further identifies that quantum chemistry can be an
important use case for quantum machine learning.

Unlike most neural language models, humans learn language in a rich,
multi-sensory and, often, multi-lingual environment. Current language models
typically fail to fully capture the complexities of multilingual language use.
We train an LSTM language model on images and captions in English and Spanish
from MS-COCO-ES. We find that the visual grounding improves the model's
understanding of semantic similarity both within and across languages and
improves perplexity. However, we find no significant advantage of visual
grounding for abstract words. Our results provide additional evidence of the
advantages of visually grounded language models and point to the need for more
naturalistic language data from multilingual speakers and multilingual datasets
with perceptual grounding.

Incorporating equivariance to symmetry groups as a constraint during neural
network training can improve performance and generalization for tasks
exhibiting those symmetries, but such symmetries are often not perfectly nor
explicitly present. This motivates algorithmically optimizing the architectural
constraints imposed by equivariance. We propose the equivariance relaxation
morphism, which preserves functionality while reparameterizing a group
equivariant layer to operate with equivariance constraints on a subgroup, as
well as the $[G]$-mixed equivariant layer, which mixes layers constrained to
different groups to enable within-layer equivariance optimization. We further
present evolutionary and differentiable neural architecture search (NAS)
algorithms that utilize these mechanisms respectively for equivariance-aware
architectural optimization. Experiments across a variety of datasets show the
benefit of dynamically constrained equivariance to find effective architectures
with approximate equivariance.

We present two versatile methods to generally enhance self-supervised
monocular depth estimation (MDE) models. The high generalizability of our
methods is achieved by solving the fundamental and ubiquitous problems in
photometric loss function. In particular, from the perspective of spatial
frequency, we first propose Ambiguity-Masking to suppress the incorrect
supervision under photometric loss at specific object boundaries, the cause of
which could be traced to pixel-level ambiguity. Second, we present a novel
frequency-adaptive Gaussian low-pass filter, designed to robustify the
photometric loss in high-frequency regions. We are the first to propose
blurring images to improve depth estimators with an interpretable analysis.
Both modules are lightweight, adding no parameters and no need to manually
change the network structures. Experiments show that our methods provide
performance boosts to a large number of existing models, including those who
claimed state-of-the-art, while introducing no extra inference computation at
all.

The increasing popularity of facial manipulation (Deepfakes) and synthetic
face creation raises the need to develop robust forgery detection solutions.
Crucially, most work in this domain assume that the Deepfakes in the test set
come from the same Deepfake algorithms that were used for training the network.
This is not how things work in practice. Instead, we consider the case where
the network is trained on one Deepfake algorithm, and tested on Deepfakes
generated by another algorithm. Typically, supervised techniques follow a
pipeline of visual feature extraction from a deep backbone, followed by a
binary classification head. Instead, our algorithm aggregates features
extracted across all layers of one backbone network to detect a fake. We
evaluate our approach on two domains of interest - Deepfake detection and
Synthetic image detection, and find that we achieve SOTA results.

Denoising diffusion models (DDMs) have emerged as a powerful class of
generative models. A forward diffusion process slowly perturbs the data, while
a deep model learns to gradually denoise. Synthesis amounts to solving a
differential equation (DE) defined by the learnt model. Solving the DE requires
slow iterative solvers for high-quality generation. In this work, we propose
Higher-Order Denoising Diffusion Solvers (GENIE): Based on truncated Taylor
methods, we derive a novel higher-order solver that significantly accelerates
synthesis. Our solver relies on higher-order gradients of the perturbed data
distribution, that is, higher-order score functions. In practice, only
Jacobian-vector products (JVPs) are required and we propose to extract them
from the first-order score network via automatic differentiation. We then
distill the JVPs into a separate neural network that allows us to efficiently
compute the necessary higher-order terms for our novel sampler during
synthesis. We only need to train a small additional head on top of the
first-order score network. We validate GENIE on multiple image generation
benchmarks and demonstrate that GENIE outperforms all previous solvers. Unlike
recent methods that fundamentally alter the generation process in DDMs, our
GENIE solves the true generative DE and still enables applications such as
encoding and guided sampling. Project page and code:
https://nv-tlabs.github.io/GENIE.

The amounts of data that need to be transmitted, processed, and stored by the
modern deep neural networks have reached truly enormous volumes in the last few
years calling for the invention of new paradigms both in hardware and software
development. One of the most promising and rapidly advancing frontiers here is
the creation of new numerical formats. In this work we focus on the family of
block floating point numerical formats due to their combination of wide dynamic
range, numerical accuracy, and efficient hardware implementation of inner
products using simple integer arithmetic. These formats are characterized by a
block of mantissas with a shared scale factor. The basic Block Floating Point
(BFP) format quantizes the block scales into the nearest powers of two on the
right. Its simple modification - Scaled BFP (SBFP) - stores the same scales in
full precision and thus allows higher accuracy. In this paper, we study the
statistical behavior of both these formats rigorously. We develop asymptotic
bounds on the inner product error in SBFP- and BFP-quantized normally
distributed vectors. Next, we refine those asymptotic results to finite
dimensional settings and derive high-dimensional tight bounds for the same
errors. Based on the obtained results we introduce a performance measure
assessing accuracy of any block format. This measure allows us to determine the
optimal parameters, such as the block size, yielding highest accuracy. In
particular, we show that if the precision of the BFP format is fixed at 4 bits,
the optimal block size becomes 64. All theoretical derivations are supported by
numerical experiments and studies on the weights of publicly available
pretrained neural networks.

Combining multi-spectral satellite data and machine learning has been
suggested as a method for monitoring plastic pollutants in the ocean
environment. Recent studies have made theoretical progress regarding the
identification of marine plastic via machine learning. However, no study has
assessed the application of these methods for mapping and monitoring
marine-plastic density. As such, this paper comprised of three main components:
(1) the development of a machine learning model, (2) the construction of the
MAP-Mapper, an automated tool for mapping marine-plastic density, and finally
(3) an evaluation of the whole system for out-of-distribution test locations.
The findings from this paper leverage the fact that machine learning models
need to be high-precision to reduce the impact of false positives on results.
The developed MAP-Mapper architectures provide users choices to reach
high-precision ($\textit{abbv.}$ -HP) or optimum precision-recall
($\textit{abbv.}$ -Opt) values in terms of the training/test data set. Our
MAP-Mapper-HP model greatly increased the precision of plastic detection to
95\%, whilst MAP-Mapper-Opt reaches precision-recall pair of 87\%-88\%. The
MAP-Mapper contributes to the literature with the first tool to exploit
advanced deep/machine learning and multi-spectral imagery to map marine-plastic
density in automated software. The proposed data pipeline has taken a novel
approach to map plastic density in ocean regions. As such, this enables an
initial assessment of the challenges and opportunities of this method to help
guide future work and scientific study.

Deep metric learning for vision is trained by optimizing a representation
network to map (non-)matching image pairs to (non-)similar representations.
During testing, which typically corresponds to image retrieval, both database
and query examples are processed by the same network to obtain the
representation used for similarity estimation and ranking. In this work, we
explore an asymmetric setup by light-weight processing of the query at a small
image resolution to enable fast representation extraction. The goal is to
obtain a network for database examples that is trained to operate on large
resolution images and benefits from fine-grained image details, and a second
network for query examples that operates on small resolution images but
preserves a representation space aligned with that of the database network. We
achieve this with a distillation approach that transfers knowledge from a fixed
teacher network to a student via a loss that operates per image and solely
relies on coupled augmentations without the use of any labels. In contrast to
prior work that explores such asymmetry from the point of view of different
network architectures, this work uses the same architecture but modifies the
image resolution. We conclude that resolution asymmetry is a better way to
optimize the performance/efficiency trade-off than architecture asymmetry.
Evaluation is performed on three standard deep metric learning benchmarks,
namely CUB200, Cars196, and SOP. Code: https://github.com/pavelsuma/raml

The sample compressibility of concept classes plays an important role in
learning theory, as a sufficient condition for PAC learnability, and more
recently as an avenue for robust generalisation in adaptive data analysis.
Whether compression schemes of size $O(d)$ must necessarily exist for all
classes of VC dimension $d$ is unknown, but conjectured to be true by Warmuth.
Recently Chalopin, Chepoi, Moran, and Warmuth (2018) gave a beautiful
unlabelled sample compression scheme of size VC dimension for all maximum
classes: classes that meet the Sauer-Shelah-Perles Lemma with equality. They
also offered a counterexample to compression schemes based on a promising
approach known as corner peeling. In this paper we simplify and extend their
proof technique to deal with so-called extremal classes of VC dimension $d$
which contain maximum classes of VC dimension $d-1$. A criterion is given which
would imply that all extremal classes admit unlabelled compression schemes of
size $d$. We also prove that all intersection-closed classes with VC dimension
$d$ admit unlabelled compression schemes of size at most $11d$.

The empirical studies of Graph Neural Networks (GNNs) broadly take the
original node feature and adjacency relationship as singleview input, ignoring
the rich information of multiple graph views. To circumvent this issue, the
multiview graph analysis framework has been developed to fuse graph information
across views. How to model and integrate shared (i.e. consistency) and
view-specific (i.e. complementarity) information is a key issue in multiview
graph analysis. In this paper, we propose a novel Multiview Variational Graph
Information Bottleneck (MVGIB) principle to maximize the agreement for common
representations and the disagreement for view-specific representations. Under
this principle, we formulate the common and view-specific information
bottleneck objectives across multiviews by using constraints from mutual
information. However, these objectives are hard to directly optimize since the
mutual information is computationally intractable. To tackle this challenge, we
derive variational lower and upper bounds of mutual information terms, and then
instead optimize variational bounds to find the approximate solutions for the
information objectives. Extensive experiments on graph benchmark datasets
demonstrate the superior effectiveness of the proposed method.

In order to deploy current computer vision (CV) models on
resource-constrained low-power devices, recent works have proposed in-sensor
and in-pixel computing approaches that try to partly/fully bypass the image
signal processor (ISP) and yield significant bandwidth reduction between the
image sensor and the CV processing unit by downsampling the activation maps in
the initial convolutional neural network (CNN) layers. However, direct
inference on the raw images degrades the test accuracy due to the difference in
covariance of the raw images captured by the image sensors compared to the
ISP-processed images used for training. Moreover, it is difficult to train deep
CV models on raw images, because most (if not all) large-scale open-source
datasets consist of RGB images. To mitigate this concern, we propose to invert
the ISP pipeline, which can convert the RGB images of any dataset to its raw
counterparts, and enable model training on raw images. We release the raw
version of the COCO dataset, a large-scale benchmark for generic high-level
vision tasks. For ISP-less CV systems, training on these raw images result in a
7.1% increase in test accuracy on the visual wake works (VWW) dataset compared
to relying on training with traditional ISP-processed RGB datasets. To further
improve the accuracy of ISP-less CV models and to increase the energy and
bandwidth benefits obtained by in-sensor/in-pixel computing, we propose an
energy-efficient form of analog in-pixel demosaicing that may be coupled with
in-pixel CNN computations. When evaluated on raw images captured by real
sensors from the PASCALRAW dataset, our approach results in a 8.1% increase in
mAP. Lastly, we demonstrate a further 20.5% increase in mAP by using a novel
application of few-shot learning with thirty shots each for the novel PASCALRAW
dataset, constituting 3 classes.

Open ad hoc teamwork is the problem of training a single agent to efficiently
collaborate with an unknown group of teammates whose composition may change
over time. A variable team composition creates challenges for the agent, such
as the requirement to adapt to new team dynamics and dealing with changing
state vector sizes. These challenges are aggravated in real-world applications
where the controlled agent has no access to the full state of the environment.
In this work, we develop a class of solutions for open ad hoc teamwork under
full and partial observability. We start by developing a solution for the fully
observable case that leverages graph neural network architectures to obtain an
optimal policy based on reinforcement learning. We then extend this solution to
partially observable scenarios by proposing different methodologies that
maintain belief estimates over the latent environment states and team
composition. These belief estimates are combined with our solution for the
fully observable case to compute an agent's optimal policy under partial
observability in open ad hoc teamwork. Empirical results demonstrate that our
approach can learn efficient policies in open ad hoc teamwork in full and
partially observable cases. Further analysis demonstrates that our methods'
success is a result of effectively learning the effects of teammates' actions
while also inferring the inherent state of the environment under partial
observability

The ability to answer causal questions is crucial in many domains, as causal
inference allows one to understand the impact of interventions. In many
applications, only a single intervention is possible at a given time. However,
in some important areas, multiple interventions are concurrently applied.
Disentangling the effects of single interventions from jointly applied
interventions is a challenging task -- especially as simultaneously applied
interventions can interact. This problem is made harder still by unobserved
confounders, which influence both treatments and outcome. We address this
challenge by aiming to learn the effect of a single-intervention from both
observational data and sets of interventions. We prove that this is not
generally possible, but provide identification proofs demonstrating that it can
be achieved under non-linear continuous structural causal models with additive,
multivariate Gaussian noise -- even when unobserved confounders are present.
Importantly, we show how to incorporate observed covariates and learn
heterogeneous treatment effects. Based on the identifiability proofs, we
provide an algorithm that learns the causal model parameters by pooling data
from different regimes and jointly maximizing the combined likelihood. The
effectiveness of our method is empirically demonstrated on both synthetic and
real-world data.

Quantum Machine Learning continues to be a highly active area of interest
within Quantum Computing. Many of these approaches have adapted classical
approaches to the quantum settings, such as QuantumFlow, etc. We push forward
this trend and demonstrate an adaption of the Classical Convolutional Neural
Networks to quantum systems - namely QuCNN. QuCNN is a parameterised
multi-quantum-state based neural network layer computing similarities between
each quantum filter state and each quantum data state. With QuCNN, back
propagation can be achieved through a single-ancilla qubit quantum routine.
QuCNN is validated by applying a convolutional layer with a data state and a
filter state over a small subset of MNIST images, comparing the back propagated
gradients, and training a filter state against an ideal target state.

Volume prediction is one of the fundamental objectives in the Fintech area,
which is helpful for many downstream tasks, e.g., algorithmic trading. Previous
methods mostly learn a universal model for different stocks. However, this kind
of practice omits the specific characteristics of individual stocks by applying
the same set of parameters for different stocks. On the other hand, learning
different models for each stock would face data sparsity or cold start problems
for many stocks with small capitalization. To take advantage of the data scale
and the various characteristics of individual stocks, we propose a dual-process
meta-learning method that treats the prediction of each stock as one task under
the meta-learning framework. Our method can model the common pattern behind
different stocks with a meta-learner, while modeling the specific pattern for
each stock across time spans with stock-dependent parameters. Furthermore, we
propose to mine the pattern of each stock in the form of a latent variable
which is then used for learning the parameters for the prediction module. This
makes the prediction procedure aware of the data pattern. Extensive experiments
on volume predictions show that our method can improve the performance of
various baseline models. Further analyses testify the effectiveness of our
proposed meta-learning framework.

Due to the large accumulation of patients requiring hospitalization, the
COVID-19 pandemic disease caused a high overload of health systems, even in
developed countries. Deep learning techniques based on medical imaging data can
help in the faster detection of COVID-19 cases and monitoring of disease
progression. Regardless of the numerous proposed solutions for lung X-rays,
none of them is a product that can be used in the clinic. Five different
datasets (POLCOVID, AIforCOVID, COVIDx, NIH, and artificially generated data)
were used to construct a representative dataset of 23 799 CXRs for model
training; 1 050 images were used as a hold-out test set, and 44 247 as
independent test set (BIMCV database). A U-Net-based model was developed to
identify a clinically relevant region of the CXR. Each image class (normal,
pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussian
mixture model. A decision tree was used to aggregate predictions from the
InceptionV3 network based on processed CXRs and a dense neural network on
radiomic features. The lung segmentation model gave the Sorensen-Dice
coefficient of 94.86% in the validation dataset, and 93.36% in the testing
dataset. In 5-fold cross-validation, the accuracy for all classes ranged from
91% to 93%, keeping slightly higher specificity than sensitivity and NPV than
PPV. In the hold-out test set, the balanced accuracy ranged between 68% and
100%. The highest performance was obtained for the subtypes N1, P1, and C1. A
similar performance was obtained on the independent dataset for normal and
COVID-19 class subtypes. Seventy-six percent of COVID-19 patients wrongly
classified as normal cases were annotated by radiologists as with no signs of
disease. Finally, we developed the online service (https://circa.aei.polsl.pl)
to provide access to fast diagnosis support tools.

Image segmentation is a historic and significant computer vision task. With
the help of deep learning techniques, image semantic segmentation has made
great progresses. Over recent years, based on guidance of attention mechanism
compared with CNN which overcomes the problems of lacking of interaction
between different channels, and effective capturing and aggregating contextual
information. However, the massive operations generated by the attention
mechanism lead to its extremely high complexity and high demand for GPU memory.
For this purpose, we propose a lightweight and flexible neural network named
Dual Pool Attention Network(DPANet). The most important is that all modules in
DPANet generate \textbf{0} parameters. The first component is spatial pool
attention module, we formulate an easy and powerful method densely to extract
contextual characteristics and reduce the amount of calculation and complexity
dramatically.Meanwhile, it demonstrates the power of even and large kernel
size. The second component is channel pool attention module. It is known that
the computation process of CNN incorporates the information of spatial and
channel dimensions. So, the aim of this module is stripping them out, in order
to construct relationship of all channels and heighten different channels
semantic information selectively. Moreover, we experiments on segmentation
datasets, which shows our method simple and effective with low parameters and
calculation complexity.

The Retinex model is one of the most representative and effective methods for
low-light image enhancement. However, the Retinex model does not explicitly
tackle the noise problem, and shows unsatisfactory enhancing results. In recent
years, due to the excellent performance, deep learning models have been widely
used in low-light image enhancement. However, these methods have two
limitations: i) The desirable performance can only be achieved by deep learning
when a large number of labeled data are available. However, it is not easy to
curate massive low/normal-light paired data; ii) Deep learning is notoriously a
black-box model [1]. It is difficult to explain their inner-working mechanism
and understand their behaviors. In this paper, using a sequential Retinex
decomposition strategy, we design a plug-and-play framework based on the
Retinex theory for simultaneously image enhancement and noise removal.
Meanwhile, we develop a convolutional neural network-based (CNN-based) denoiser
into our proposed plug-and-play framework to generate a reflectance component.
The final enhanced image is produced by integrating the illumination and
reflectance with gamma correction. The proposed plug-and-play framework can
facilitate both post hoc and ad hoc interpretability. Extensive experiments on
different datasets demonstrate that our framework outcompetes the
state-of-the-art methods in both image enhancement and denoising.

A Top Two sampling rule for bandit identification is a method which selects
the next arm to sample from among two candidate arms, a leader and a
challenger. Due to their simplicity and good empirical performance, they have
received increased attention in recent years. For fixed-confidence best arm
identification, theoretical guarantees for Top Two methods have only been
obtained in the asymptotic regime, when the error level vanishes. We derive the
first non-asymptotic upper bound on the expected sample complexity of a Top Two
algorithm holding for any error level. Our analysis highlights sufficient
properties for a regret minimization algorithm to be used as leader. They are
satisfied by the UCB algorithm and our proposed UCB-based Top Two algorithm
enjoys simultaneously non-asymptotic guarantees and competitive empirical

Billions of people across the globe have been using social media platforms in
their local languages to voice their opinions about the various topics related
to the COVID-19 pandemic. Several organizations, including the World Health
Organization, have developed automated social media analysis tools that
classify COVID-19-related tweets into various topics. However, these tools that
help combat the pandemic are limited to very few languages, making several
countries unable to take their benefit. While multi-lingual or low-resource
language-specific tools are being developed, they still need to expand their
coverage, such as for the Nepali language. In this paper, we identify the eight
most common COVID-19 discussion topics among the Twitter community using the
Nepali language, set up an online platform to automatically gather Nepali
tweets containing the COVID-19-related keywords, classify the tweets into the
eight topics, and visualize the results across the period in a web-based
dashboard. We compare the performance of two state-of-the-art multi-lingual
language models for Nepali tweet classification, one generic (mBERT) and the
other Nepali language family-specific model (MuRIL). Our results show that the
models' relative performance depends on the data size, with MuRIL doing better
for a larger dataset. The annotated data, models, and the web-based dashboard
are open-sourced at https://github.com/naamiinepal/covid-tweet-classification.

We introduce a new task, named video corpus visual answer localization
(VCVAL), which aims to locate the visual answer in a large collection of
untrimmed instructional videos using a natural language question. This task
requires a range of skills - the interaction between vision and language, video
retrieval, passage comprehension, and visual answer localization. In this
paper, we propose a cross-modal contrastive global-span (CCGS) method for the
VCVAL, jointly training the video corpus retrieval and visual answer
localization subtasks with the global-span matrix. We have reconstructed a
dataset named MedVidCQA, on which the VCVAL task is benchmarked. Experimental
results show that the proposed method outperforms other competitive methods
both in the video corpus retrieval and visual answer localization subtasks.
Most importantly, we perform detailed analyses on extensive experiments, paving
a new path for understanding the instructional videos, which ushers in further
research.

Collaborative learning is an educational approach that enhances learning
through shared goals and working together. Interaction and regulation are two
essential factors related to the success of collaborative learning. Since the
information from various modalities can reflect the quality of collaboration, a
new multimodal dataset with cognitive and emotional triggers is introduced in
this paper to explore how regulations affect interactions during the
collaborative process. Specifically, a learning task with intentional
interventions is designed and assigned to high school students aged 15 years
old (N=81) in average. Multimodal signals, including video, Kinect, audio, and
physiological data, are collected and exploited to study regulations in
collaborative learning in terms of individual-participant-single-modality,
individual-participant-multiple-modality, and
multiple-participant-multiple-modality. Analysis of annotated emotions, body
gestures, and their interactions indicates that our multimodal dataset with
designed treatments could effectively examine moments of regulation in
collaborative learning. In addition, preliminary experiments based on baseline
models suggest that the dataset provides a challenging in-the-wild scenario,
which could further contribute to the fields of education and affective
computing.

We assess the dependence of Earth's disk-integrated mid-infrared thermal
emission spectrum on observation geometries and investigate which and how
spectral features are impacted by seasonality on Earth. We compiled an
exclusive dataset containing 2690 disk-integrated thermal emission spectra for
four different full-disk observing geometries (North & South Pole centered and
Africa & Pacific centred equatorial views) over four consecutive years. The
spectra were derived from 2378 spectral channels in the wavelength range from
3.75 to 15.4 micron (nominal resolution $\approx$ 1200) and were recorded by
the Atmospheric Infrared Sounder aboard the Aqua satellite. We learned that
there is significant seasonal variability in Earth's thermal emission spectrum,
and the strength of spectral features of bio-indicators, such as N2O, CH4, O3
and CO2 depends strongly on both season and viewing geometry. In addition, we
found a strong spectral degeneracy with respect to the latter two indicating
that multi-epoch measurements and time-dependent signals may be required in
order to fully characterize planetary environments. Even for Earth and
especially for equatorial views, the variations in flux and strength of
absorption features in the disk-integrated data are small and typically $\leq$
10%. Disentangling these variations from the noise in future exoplanet
observations will be a challenge. However, irrespectively of when the planet
will be measured (i.e., day or night or season) the results from mid-infrared
observations will remain the same to the zeroth order which is an advantage
over reflected light observations.

Explainability helps users trust deep learning solutions for time series
classification. However, existing explainability methods for multi-class time
series classifiers focus on one class at a time, ignoring relationships between
the classes. Instead, when a classifier is choosing between many classes, an
effective explanation must show what sets the chosen class apart from the rest.
We now formalize this notion, studying the open problem of class-specific
explainability for deep time series classifiers, a challenging and impactful
problem setting. We design a novel explainability method, DEMUX, which learns
saliency maps for explaining deep multi-class time series classifiers by
adaptively ensuring that its explanation spotlights the regions in an input
time series that a model uses specifically to its predicted class. DEMUX adopts
a gradient-based approach composed of three interdependent modules that combine
to generate consistent, class-specific saliency maps that remain faithful to
the classifier's behavior yet are easily understood by end users. Our
experimental study demonstrates that DEMUX outperforms nine state-of-the-art
alternatives on five popular datasets when explaining two types of deep time
series classifiers. Further, through a case study, we demonstrate that DEMUX's
explanations indeed highlight what separates the predicted class from the
others in the eyes of the classifier. Our code is publicly available at
https://github.com/rameshdoddaiah/DEMUX.

Episodic count has been widely used to design a simple yet effective
intrinsic motivation for reinforcement learning with a sparse reward. However,
the use of episodic count in a high-dimensional state space as well as over a
long episode time requires a thorough state compression and fast hashing, which
hinders rigorous exploitation of it in such hard and complex exploration
environments. Moreover, the interference from task-irrelevant observations in
the episodic count may cause its intrinsic motivation to overlook task-related
important changes of states, and the novelty in an episodic manner can lead to
repeatedly revisit the familiar states across episodes. In order to resolve
these issues, in this paper, we propose a learnable hash-based episodic count,
which we name LECO, that efficiently performs as a task-specific intrinsic
reward in hard exploration problems. In particular, the proposed intrinsic
reward consists of the episodic novelty and the task-specific modulation where
the former employs a vector quantized variational autoencoder to automatically
obtain the discrete state codes for fast counting while the latter regulates
the episodic novelty by learning a modulator to optimize the task-specific
extrinsic reward. The proposed LECO specifically enables the automatic
transition from exploration to exploitation during reinforcement learning. We
experimentally show that in contrast to the previous exploration methods LECO
successfully solves hard exploration problems and also scales to large state
spaces through the most difficult tasks in MiniGrid and DMLab environments.

This paper presents work on novel machine translation (MT) systems between
spoken and signed languages, where signed languages are represented in
SignWriting, a sign language writing system. Our work seeks to address the lack
of out-of-the-box support for signed languages in current MT systems and is
based on the SignBank dataset, which contains pairs of spoken language text and
SignWriting content. We introduce novel methods to parse, factorize, decode,
and evaluate SignWriting, leveraging ideas from neural factored MT. In a
bilingual setup--translating from American Sign Language to (American)
English--our method achieves over 30 BLEU, while in two multilingual
setups--translating in both directions between spoken languages and signed
languages--we achieve over 20 BLEU. We find that common MT techniques used to
improve spoken language translation similarly affect the performance of sign
language translation. These findings validate our use of an intermediate text
representation for signed languages to include them in natural language
processing research.

We investigate efficiency of a gauge-covariant neural network and an
approximation of the Jacobian in optimizing the complexified integration path
toward evading the sign problem in lattice field theories. For the construction
of the complexified integration path, we employ the path optimization method.
The $2$-dimensional $\text{U}(1)$ gauge theory with the complex gauge coupling
constant is used as a laboratory to evaluate the efficiency. It is found that
the gauge-covariant neural network, which is composed of the Stout-like
smearing, can enhance the average phase factor, as the gauge-invariant input
does. For the approximation of the Jacobian, we test the most drastic case in
which we perfectly drop the Jacobian during the learning process. It reduces
the numerical cost of the Jacobian calculation from ${\cal O}(N^3)$ to ${\cal
O}(1)$, where $N$ means the number of degrees of freedom of the theory. The
path optimization using this Jacobian approximation still enhances the average
phase factor at expense of a slight increase of the statistical error.

It has been observed that neural networks perform poorly when the data or
tasks are presented sequentially. Unlike humans, neural networks suffer greatly
from catastrophic forgetting, making it impossible to perform life-long
learning. To address this issue, memory-based continual learning has been
actively studied and stands out as one of the best-performing methods. We
examine memory-based continual learning and identify that large variation in
the representation space is crucial for avoiding catastrophic forgetting.
Motivated by this, we propose to diversify representations by using two types
of perturbations: model-agnostic variation (i.e., the variation is generated
without the knowledge of the learned neural network) and model-based variation
(i.e., the variation is conditioned on the learned neural network). We
demonstrate that enlarging representational variation serves as a general
principle to improve continual learning. Finally, we perform empirical studies
which demonstrate that our method, as a simple plug-and-play component, can
consistently improve a number of memory-based continual learning methods by a
large margin.

We present a computationally-efficient strategy to find the hyperparameters
of a Gaussian process (GP) avoiding the computation of the likelihood function.
The found hyperparameters can then be used directly for regression or passed as
initial conditions to maximum-likelihood (ML) training. Motivated by the fact
that training a GP via ML is equivalent (on average) to minimising the
KL-divergence between the true and learnt model, we set to explore different
metrics/divergences among GPs that are computationally inexpensive and provide
estimates close to those of ML. In particular, we identify the GP
hyperparameters by matching the empirical covariance to a parametric candidate,
proposing and studying various measures of discrepancy. Our proposal extends
the Variogram method developed by the geostatistics literature and thus is
referred to as the Generalised Variogram method (GVM). In addition to the
theoretical presentation of GVM, we provide experimental validation in terms of
accuracy, consistency with ML and computational complexity for different
kernels using synthetic and real-world data.

Given sufficient training data on the source domain, cross-domain few-shot
learning (CD-FSL) aims at recognizing new classes with a small number of
labeled examples on the target domain. The key to addressing CD-FSL is to
narrow the domain gap and transferring knowledge of a network trained on the
source domain to the target domain. To help knowledge transfer, this paper
introduces an intermediate domain generated by mixing images in the source and
the target domain. Specifically, to generate the optimal intermediate domain
for different target data, we propose a novel target guided dynamic mixup
(TGDM) framework that leverages the target data to guide the generation of
mixed images via dynamic mixup. The proposed TGDM framework contains a Mixup-3T
network for learning classifiers and a dynamic ratio generation network (DRGN)
for learning the optimal mix ratio. To better transfer the knowledge, the
proposed Mixup-3T network contains three branches with shared parameters for
classifying classes in the source domain, target domain, and intermediate
domain. To generate the optimal intermediate domain, the DRGN learns to
generate an optimal mix ratio according to the performance on auxiliary target
data. Then, the whole TGDM framework is trained via bi-level meta-learning so
that TGDM can rectify itself to achieve optimal performance on target data.
Extensive experimental results on several benchmark datasets verify the
effectiveness of our method.

To limit global warming to pre-industrial levels, global governments,
industry and academia are taking aggressive efforts to reduce carbon emissions.
The evaluation of anthropogenic carbon dioxide (CO$_2$) emissions, however,
depends on the self-reporting information that is not always reliable. Society
need to develop an objective, independent, and generalized system to meter
CO$_2$ emissions. Satellite CO$_2$ observation from space that reports
column-average regional CO$_2$ dry-air mole fractions has gradually indicated
its potential to build such a system. Nevertheless, estimating anthropogenic
CO$_2$ emissions from CO$_2$ observing satellite is bottlenecked by the
influence of the highly complicated physical characteristics of atmospheric
activities. Here we provide the first method that combines the advanced
artificial intelligence (AI) techniques and the carbon satellite monitor to
quantify anthropogenic CO$_2$ emissions. We propose an integral AI based
pipeline that contains both a data retrieval algorithm and a two-step
data-driven solution. First, the data retrieval algorithm can generate
effective datasets from multi-modal data including carbon satellite, the
information of carbon sources, and several environmental factors. Second, the
two-step data-driven solution that applies the powerful representation of deep
learning techniques to learn to quantify anthropogenic CO$_2$ emissions from
satellite CO$_2$ observation with other factors. Our work unmasks the potential
of quantifying CO$_2$ emissions based on the combination of deep learning
algorithms and the carbon satellite monitor.

There has been a recent surge in single-step adversarial training as it shows
robustness and efficiency. However, a phenomenon referred to as ``catastrophic
overfitting" has been observed, which is prevalent in single-step defenses and
may frustrate attempts to use FGSM adversarial training. To address this issue,
we propose a novel method, Stable and Efficient Adversarial Training (SEAT),
which mitigates catastrophic overfitting by harnessing on local properties that
distinguish a robust model from that of a catastrophic overfitted model. The
proposed SEAT has strong theoretical justifications, in that minimizing the
SEAT loss can be shown to favour smooth empirical risk, thereby leading to
robustness. Experimental results demonstrate that the proposed method
successfully mitigates catastrophic overfitting, yielding superior performance
amongst efficient defenses. Our single-step method can reach 51% robust
accuracy for CIFAR-10 with $l_\infty$ perturbations of radius $8/255$ under a
strong PGD-50 attack, matching the performance of a 10-step iterative
adversarial training at merely 3% computational cost.

The study of bias in Machine Learning is receiving a lot of attention in
recent years, however, few only papers deal explicitly with the problem of race
bias in face anti-spoofing. In this paper, we present a systematic study of
race bias in face anti-spoofing with three key characteristics: the focus is on
analysing potential bias in the bona fide errors, where significant ethical and
legal issues lie; the analysis is not restricted to the final binary outcomes
of the classifier, but also covers the classifier's scalar responses and its
latent space; the threshold determining the operating point of the classifier
is considered a variable. We demonstrate the proposed bias analysis process on
a VQ-VAE based face anti-spoofing algorithm, trained on the Replay Attack and
the Spoof in the Wild (SiW) databases, and analysed for bias on the SiW and
Racial Faces in the Wild (RFW), databases. The results demonstrate that race
bias is not necessarily the result of different mean response values among the
various populations. Instead, it can be better understood as the combined
effect of several possible characteristics of the response distributions:
different means; different variances; bimodal behaviour; existence of outliers.

In this work, we consider the problem of collaborative multi-user
reinforcement learning. In this setting there are multiple users with the same
state-action space and transition probabilities but with different rewards.
Under the assumption that the reward matrix of the $N$ users has a low-rank
structure -- a standard and practically successful assumption in the offline
collaborative filtering setting -- the question is can we design algorithms
with significantly lower sample complexity compared to the ones that learn the
MDP individually for each user. Our main contribution is an algorithm which
explores rewards collaboratively with $N$ user-specific MDPs and can learn
rewards efficiently in two key settings: tabular MDPs and linear MDPs. When $N$
is large and the rank is constant, the sample complexity per MDP depends
logarithmically over the size of the state-space, which represents an
exponential reduction (in the state-space size) when compared to the standard
``non-collaborative'' algorithms.

Before AI and neural nets, the excitement was about iterative learning
control (ILC): the idea to train robots to perform repetitive tasks or train a
system to reject quasi-periodic disturbances. The excitement waned after the
discovery of "learning transients" in systems which satisfy the ILC asymptotic
convergence (AC) stability criteria. The transients may be of long duration,
persisting long after eigenvalues imply they should have decayed, and span
orders of magnitude. They occur both for causal and noncausal learning. The
field recovered with the introduction of tests for "monotonic convergence of
the vector norm"; but no deep and truly satisfying explanation was offered.
Here we explore solutions of the ILC equations that couple the iteration index
to the within-trial sample index. This sheds light on the causal learning - for
which the AC test gives a repeated eigenvalue. Moreover, since 2016, this
author has demonstrated that a new class of solutions, which are soliton-like,
satisfy the recurrence equations of ILC and offer additional insight to
long-term behaviour. A soliton is a wave-like object that emerges in a
dis-persive medium that travels with little or no change of shape at an
identifiable speed. This paper is the first public presentation of the soliton
solutions, which may occur for both causal (i.e. look back) and noncausal (i.e.
look ahead) learning func-tions that have diagonal band structure for their
matrix representation.

In modern communication systems, channel state information is of paramount
importance to achieve capacity. It is then crucial to accurately estimate the
channel. It is possible to perform SISO-OFDM channel estimation using sparse
recovery techniques. However, this approach relies on the use of a physical
wave propagation model to build a dictionary, which requires perfect knowledge
of the system's parameters. In this paper, an unfolded neural network is used
to lighten this constraint. Its architecture, based on a sparse recovery
algorithm, allows SISO-OFDM channel estimation even if the system's parameters
are not perfectly known. Indeed, its unsupervised online learning allows to
learn the system's imperfections in order to enhance the estimation
performance. The practicality of the proposed method is improved with respect
to the state of the art in two aspects: constrained dictionaries are introduced
in order to reduce sample complexity and hierarchical search within
dictionaries is proposed in order to reduce time complexity. Finally, the
performance of the proposed unfolded network is evaluated and compared to
several baselines using realistic channel data, showing the great potential of
the approach.

Regrasping is important for robots to reorient objects in planar manipulation
tasks. Different placements of objects can provide robots with alternative
grasp configurations, which are used in complex planar manipulation tasks that
require multiple pick-rotate-and-place steps due to the constraints of the
environment and robot kinematic constraints. In this work, our goal is to
generate diverse placements of objects on the plane using deep neural networks.
We propose a pipeline with the stages of orientation generation, position
refinement, and placement discrimination to obtain accurate and diverse stable
placements based on the perception of point clouds. A large-scale dataset is
created for training, including simulated placements and contact information
between objects and the plane. The simulation results show that our pipeline
outperforms the start-of-the-art, achieving an accuracy rate of 90% and a
diversity rate of 80% in simulation on generated placements. Our pipeline is
also validated in real-robot experiments. With the generated placements,
sequential pick-rotate-and-place steps are calculated for the robot to reorient
objects to goal poses that are not reachable within one step. Videos and
dataset are available at https://sites.google.com/view/pmvlr2022/.

Traditional Collaborative Filtering (CF) based methods are applied to
understand the personal preferences of users/customers for items or products
from the rating matrix. Usually, the rating matrix is sparse in nature. So
there are some improved variants of the CF method that apply the increasing
amount of side information to handle the sparsity problem. Only linear kernel
or only non-linear kernel is applied in most of the available
recommendation-related work to understand user-item latent feature embeddings
from data. Only linear kernel or only non-linear kernel is not sufficient to
learn complex user-item features from side information of users. Recently, some
researchers have focused on hybrid models that learn some features with
non-linear kernels and some other features with linear kernels. But it is very
difficult to understand which features can be learned accurately with linear
kernels or with non-linear kernels. To overcome this problem, we propose a
novel deep fusion model named FusionDeepMF and the novel attempts of this model
are i) learning user-item rating matrix and side information through linear and
non-linear kernel simultaneously, ii) application of a tuning parameter
determining the trade-off between the dual embeddings that are generated from
linear and non-linear kernels. Extensive experiments on online review datasets
establish that FusionDeepMF can be remarkably futuristic compared to other
baseline approaches. Empirical evidence also shows that FusionDeepMF achieves
better performances compared to the linear kernels of Matrix Factorization (MF)
and the non-linear kernels of Multi-layer Perceptron (MLP).

We showcase important features of the dynamics of the Stochastic Gradient
Descent (SGD) in the training of neural networks. We present empirical
observations that commonly used large step sizes (i) lead the iterates to jump
from one side of a valley to the other causing loss stabilization, and (ii)
this stabilization induces a hidden stochastic dynamics orthogonal to the
bouncing directions that biases it implicitly toward simple predictors.
Furthermore, we show empirically that the longer large step sizes keep SGD high
in the loss landscape valleys, the better the implicit regularization can
operate and find sparse representations. Notably, no explicit regularization is
used so that the regularization effect comes solely from the SGD training
dynamics influenced by the step size schedule. Therefore, these observations
unveil how, through the step size schedules, both gradient and noise drive
together the SGD dynamics through the loss landscape of neural networks. We
justify these findings theoretically through the study of simple neural network
models as well as qualitative arguments inspired from stochastic processes.
Finally, this analysis allows to shed a new light on some common practice and
observed phenomena when training neural networks. The code of our experiments
is available at https://github.com/tml-epfl/sgd-sparse-features.

Multimodal semantic understanding often has to deal with uncertainty, which
means the obtained message tends to refer to multiple targets. Such uncertainty
is problematic for our interpretation, including intra-modal and inter-modal
uncertainty. Little effort studies the modeling of this uncertainty,
particularly in pre-training on unlabeled datasets and fine-tuning in
task-specific downstream tasks. To address this, we project the representations
of all modalities as probabilistic distributions via a Probability Distribution
Encoder (PDE) by utilizing rich multimodal semantic information. Furthermore,
we integrate uncertainty modeling with popular pre-training frameworks and
propose suitable pre-training tasks: Distribution-based Vision-Language
Contrastive learning (D-VLC), Distribution-based Masked Language Modeling
(D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned
models are applied to challenging downstream tasks, including image-text
retrieval, visual question answering, visual reasoning, and visual entailment,
and achieve state-of-the-art results. Code is released at
https://github.com/IIGROUP/MAP.

Facial Expression Recognition (FER) uses images of faces to identify the
emotional state of users, allowing for a closer interaction between humans and
autonomous systems. Unfortunately, as the images naturally integrate some
demographic information, such as apparent age, gender, and race of the subject,
these systems are prone to demographic bias issues. In recent years, machine
learning-based models have become the most popular approach to FER. These
models require training on large datasets of facial expression images, and
their generalization capabilities are strongly related to the characteristics
of the dataset. In publicly available FER datasets, apparent gender
representation is usually mostly balanced, but their representation in the
individual label is not, embedding social stereotypes into the datasets and
generating a potential for harm. Although this type of bias has been overlooked
so far, it is important to understand the impact it may have in the context of
FER. To do so, we use a popular FER dataset, FER+, to generate derivative
datasets with different amounts of stereotypical bias by altering the gender
proportions of certain labels. We then proceed to measure the discrepancy
between the performance of the models trained on these datasets for the
apparent gender groups. We observe a discrepancy in the recognition of certain
emotions between genders of up to $29 \%$ under the worst bias conditions. Our
results also suggest a safety range for stereotypical bias in a dataset that
does not appear to produce stereotypical bias in the resulting model. Our
findings support the need for a thorough bias analysis of public datasets in
problems like FER, where a global balance of demographic representation can
still hide other types of bias that harm certain demographic groups.

Machine learning technologies have been used in a wide range of practical
systems. In practical situations, it is natural to expect the input-output
pairs of a machine learning model to satisfy some requirements. However, it is
difficult to obtain a model that satisfies requirements by just learning from
examples. A simple solution is to add a module that checks whether the
input-output pairs meet the requirements and then modifies the model's outputs.
Such a module, which we call a {\em concurrent verifier} (CV), can give a
certification, although how the generalizability of the machine learning model
changes using a CV is unclear. This paper gives a generalization analysis of
learning with a CV. We analyze how the learnability of a machine learning model
changes with a CV and show a condition where we can obtain a guaranteed
hypothesis using a verifier only in the inference time. We also show that
typical error bounds based on Rademacher complexity will be no larger than that
of the original model when using a CV in multi-class classification and
structured prediction settings.

In learning tasks with label noise, boosting model robustness against
overfitting is a pivotal challenge because the model eventually memorizes
labels including the noisy ones. Identifying the samples with corrupted labels
and preventing the model from learning them is a promising approach to address
this challenge. Per-sample training loss is a previously studied metric that
considers samples with small loss as clean samples on which the model should be
trained. In this work, we first demonstrate the ineffectiveness of this
small-loss trick. Then, we propose a novel discriminator metric called
confidence error and a sieving strategy called CONFES to effectively
differentiate between the clean and noisy samples. We experimentally illustrate
the superior performance of our proposed approach compared to recent studies on
various settings such as synthetic and real-world label noise. Moreover, we
show CONFES can be combined with other approaches such as Co-teaching and
DivideMix to further improve the model performance.

Group interactions are prevalent in a variety of areas. Many of them,
including email exchanges, chemical reactions, and bitcoin transactions, are
directional, and thus they are naturally modeled as directed hypergraphs, where
each hyperarc consists of the set of source nodes and the set of destination
nodes. For directed graphs, which are a special case of directed hypergraphs,
reciprocity has played a key role as a fundamental graph statistic in revealing
organizing principles of graphs and in solving graph learning tasks. For
general directed hypergraphs, however, even no systematic measure of
reciprocity has been developed. In this work, we investigate the reciprocity of
11 real-world hypergraphs. To this end, we first introduce eight axioms that
any reasonable measure of reciprocity should satisfy. Second, we propose
HyperRec, a principled measure of hypergraph reciprocity that satisfies all the
axioms. Third, we develop Ferret, a fast and exact algorithm for computing the
measure, whose search space is up to 10^{147}x smaller than that of naive
computation. Fourth, using them, we examine 11 real-world hypergraphs and
discover patterns that distinguish them from random hypergraphs. Lastly, we
propose ReDi, an intuitive generative model for directed hypergraphs exhibiting
the patterns.

Consider making a prediction over new test data without any opportunity to
learn from a training set of labelled data - instead given access to a set of
expert models and their predictions alongside some limited information about
the dataset used to train them. In scenarios from finance to the medical
sciences, and even consumer practice, stakeholders have developed models on
private data they either cannot, or do not want to, share. Given the value and
legislation surrounding personal information, it is not surprising that only
the models, and not the data, will be released - the pertinent question
becoming: how best to use these models? Previous work has focused on global
model selection or ensembling, with the result of a single final model across
the feature space. Machine learning models perform notoriously poorly on data
outside their training domain however, and so we argue that when ensembling
models the weightings for individual instances must reflect their respective
domains - in other words models that are more likely to have seen information
on that instance should have more attention paid to them. We introduce a method
for such an instance-wise ensembling of models, including a novel
representation learning step for handling sparse high-dimensional domains.
Finally, we demonstrate the need and generalisability of our method on
classical machine learning tasks as well as highlighting a real world use case
in the pharmacological setting of vancomycin precision dosing.

Transformer models achieve state-of-the-art results for image segmentation.
However, achieving long-range attention, necessary to capture global context,
with high-resolution 3D images is a fundamental challenge. This paper
introduces the Full resolutIoN mEmory (FINE) transformer to overcome this
issue. The core idea behind FINE is to learn memory tokens to indirectly model
full range interactions while scaling well in both memory and computational
costs. FINE introduces memory tokens at two levels: the first one allows full
interaction between voxels within local image regions (patches), the second one
allows full interactions between all regions of the 3D volume. Combined, they
allow full attention over high resolution images, e.g. 512 x 512 x 256 voxels
and above. Experiments on the BCV image segmentation dataset shows better
performances than state-of-the-art CNN and transformer baselines, highlighting
the superiority of our full attention mechanism compared to recent transformer
baselines, e.g. CoTr, and nnFormer.

In this paper, we propose a study of the cross-domain few-shot object
detection (CD-FSOD) benchmark, consisting of image data from a diverse data
domain. On the proposed benchmark, we evaluate state-of-art FSOD approaches,
including meta-learning FSOD approaches and fine-tuning FSOD approaches. The
results show that these methods tend to fall, and even underperform the naive
fine-tuning model. We analyze the reasons for their failure and introduce a
strong baseline that uses a mutually-beneficial manner to alleviate the
overfitting problem. Our approach is remarkably superior to existing approaches
by significant margins (2.0\% on average) on the proposed benchmark. Our code
is available at \url{https://github.com/FSOD/CD-FSOD}.

We study the problem of learning controllers for discrete-time non-linear
stochastic dynamical systems with formal reach-avoid guarantees. This work
presents the first method for providing formal reach-avoid guarantees, which
combine and generalize stability and safety guarantees, with a tolerable
probability threshold $p\in[0,1]$ over the infinite time horizon. Our method
leverages advances in machine learning literature and it represents formal
certificates as neural networks. In particular, we learn a certificate in the
form of a reach-avoid supermartingale (RASM), a novel notion that we introduce
in this work. Our RASMs provide reachability and avoidance guarantees by
imposing constraints on what can be viewed as a stochastic extension of level
sets of Lyapunov functions for deterministic systems. Our approach solves
several important problems -- it can be used to learn a control policy from
scratch, to verify a reach-avoid specification for a fixed control policy, or
to fine-tune a pre-trained policy if it does not satisfy the reach-avoid
specification. We validate our approach on $3$ stochastic non-linear
reinforcement learning tasks.

We consider the problem of learning control policies in stochastic systems
which guarantee that the system stabilizes within some specified stabilization
region with probability $1$. Our approach is based on the novel notion of
stabilizing ranking supermartingales (sRSMs) that we introduce in this work.
Our sRSMs overcome the limitation of methods proposed in previous works whose
applicability is restricted to systems in which the stabilizing region cannot
be left once entered under any control policy. We present a learning procedure
that learns a control policy together with an sRSM that formally certifies
probability-$1$ stability, both learned as neural networks. Our experimental
evaluation shows that our learning procedure can successfully learn provably
stabilizing policies in practice.

The concept of dimension is essential to grasp the complexity of data. A
naive approach to determine the dimension of a dataset is based on the number
of attributes. More sophisticated methods derive a notion of intrinsic
dimension (ID) that employs more complex feature functions, e.g., distances
between data points. Yet, many of these approaches are based on empirical
observations, cannot cope with the geometric character of contemporary
datasets, and do lack an axiomatic foundation. A different approach was
proposed by V. Pestov, who links the intrinsic dimension axiomatically to the
mathematical concentration of measure phenomenon. First methods to compute this
and related notions for ID were computationally intractable for large-scale
real-world datasets. In the present work, we derive a computationally feasible
method for determining said axiomatic ID functions. Moreover, we demonstrate
how the geometric properties of complex data are accounted for in our modeling.
In particular, we propose a principle way to incorporate neighborhood
information, as in graph data, into the ID. This allows for new insights into
common graph learning procedures, which we illustrate by experiments on the
Open Graph Benchmark.

The growing dependence on eTextbooks and Massive Open Online Courses (MOOCs)
has led to an increase in the amount of students' learning data. By carefully
analyzing this data, educators can identify difficult exercises, and evaluate
the quality of the exercises when teaching a particular topic. In this study,
an analysis of log data from the use of a semester of the OpenDSA eTextbook was
offered to identify the most difficult data structure course exercises and to
evaluate the quality of the course exercises. Our study is based on analyzing
students' responses to the course exercises. To identify the difficult
exercises, we applied two different approaches, the first of which involved
analyzing student responses to exercises using item response theory (IRT)
analysis and a latent trait model (LTM) technique, and the second involved
determining which exercises were more difficult based on how students
interacted with them. We computed different measures for every exercise such
that difficulty level, trial and error, and hint ratio. We generated an item
characteristics curve, item information curve, and test information function
for each exercise. To evaluate the quality of the exercises, we applied the IRT
analysis to the students' responses to the exercises and, we computed the
difficulty and discrimination index for each exercise. We classified whether
the exercise is good or poor based on these two measures. Our findings showed
that the exercises that related to algorithm analysis topics represented most
of the difficult exercises that students struggle with, and there existing six
exercises out of 56 exercises are classified as poor exercises which could be
rejected or improved. Some of these poor exercises do not differentiate between
students with different abilities; the others give preference to low-ability
students to answer these exercises over high-ability students.

To operate safely, an automated vehicle (AV) must anticipate how the
environment around it will evolve. For that purpose, it is important to know
which prediction models are most appropriate for every situation. Currently,
assessment of prediction models is often performed over a set of trajectories
without distinction of the type of movement they capture, resulting in the
inability to determine the suitability of each model for different situations.
In this work we illustrate how standardized evaluation methods result in wrong
conclusions regarding a model's predictive capabilities, preventing a clear
assessment of prediction models and potentially leading to dangerous on-road
situations. We argue that following evaluation practices in safety assessment
for AVs, assessment of prediction models should be performed in a
scenario-based fashion. To encourage scenario-based assessment of prediction
models and illustrate the dangers of improper assessment, we categorize
trajectories of the Waymo Open Motion dataset according to the type of movement
they capture. Next, three different models are thoroughly evaluated for
different trajectory types and prediction horizons. Results show that common
evaluation methods are insufficient and the assessment should be performed
depending on the application in which the model will operate.

In this paper we examine the use of semantically-aligned speech
representations for end-to-end spoken language understanding (SLU). We employ
the recently-introduced SAMU-XLSR model, which is designed to generate a single
embedding that captures the semantics at the utterance level, semantically
aligned across different languages. This model combines the acoustic
frame-level speech representation learning model (XLS-R) with the Language
Agnostic BERT Sentence Embedding (LaBSE) model. We show that the use of the
SAMU-XLSR model instead of the initial XLS-R model improves significantly the
performance in the framework of end-to-end SLU. Finally, we present the
benefits of using this model towards language portability in SLU.

Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve
context-aware representations via learning from structured relations in
knowledge graphs, and/or linguistic knowledge from syntactic or dependency
analysis. Unlike English, there is a lack of high-performing open-source
Chinese KEPLMs in the natural language processing (NLP) community to support
various language understanding applications. In this paper, we revisit and
advance the development of Chinese natural language understanding with a series
of novel Chinese KEPLMs released in various parameter sizes, namely CKBERT
(Chinese knowledge-enhanced BERT).Specifically, both relational and linguistic
knowledge is effectively injected into CKBERT based on two novel pre-training
tasks, i.e., linguistic-aware masked language modeling and contrastive
multi-hop relation modeling. Based on the above two pre-training paradigms and
our in-house implemented TorchAccelerator, we have pre-trained base (110M),
large (345M) and huge (1.3B) versions of CKBERT efficiently on GPU clusters.
Experiments demonstrate that CKBERT outperforms strong baselines for Chinese
over various benchmark NLP tasks and in terms of different model sizes.

Transformer models can use two fundamentally different kinds of information:
information stored in weights during training, and information provided
``in-context'' at inference time. In this work, we show that transformers
exhibit different inductive biases in how they represent and generalize from
the information in these two sources. In particular, we characterize whether
they generalize via parsimonious rules (rule-based generalization) or via
direct comparison with observed examples (exemplar-based generalization). This
is of important practical consequence, as it informs whether to encode
information in weights or in context, depending on how we want models to use
that information. In transformers trained on controlled stimuli, we find that
generalization from weights is more rule-based whereas generalization from
context is largely exemplar-based. In contrast, we find that in transformers
pre-trained on natural language, in-context learning is significantly
rule-based, with larger models showing more rule-basedness. We hypothesise that
rule-based generalization from in-context information might be an emergent
consequence of large-scale training on language, which has sparse rule-like
structure. Using controlled stimuli, we verify that transformers pretrained on
data containing sparse rule-like structure exhibit more rule-based
generalization.

The area affected by the earthquake is vast and often difficult to entirely
cover, and the earthquake itself is a sudden event that causes multiple defects
simultaneously, that cannot be effectively traced using traditional, manual
methods. This article presents an innovative approach to the problem of
detecting damage after sudden events by using an interconnected set of deep
machine learning models organized in a single pipeline and allowing for easy
modification and swapping models seamlessly. Models in the pipeline were
trained with a synthetic dataset and were adapted to be further evaluated and
used with unmanned aerial vehicles (UAVs) in real-world conditions. Thanks to
the methods presented in the article, it is possible to obtain high accuracy in
detecting buildings defects, segmenting constructions into their components and
estimating their technical condition based on a single drone flight.

Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing
the Few-Shot Learning (FSL) problem across different domains has attracted
rising attention. The core challenge of CD-FSL lies in the domain gap between
the source and novel target datasets. Though many attempts have been made for
CD-FSL without any target data during model training, the huge domain gap makes
it still hard for existing CD-FSL methods to achieve very satisfactory results.
Alternatively, learning CD-FSL models with few labeled target domain data which
is more realistic and promising is advocated in previous
work~\cite{fu2021meta}. Thus, in this paper, we stick to this setting and
technically contribute a novel Multi-Expert Domain Decompositional Network
(ME-D2N). Concretely, to solve the data imbalance problem between the source
data with sufficient examples and the auxiliary target data with limited
examples, we build our model under the umbrella of multi-expert learning. Two
teacher models which can be considered to be experts in their corresponding
domain are first trained on the source and the auxiliary target sets,
respectively. Then, the knowledge distillation technique is introduced to
transfer the knowledge from two teachers to a unified student model. Taking a
step further, to help our student model learn knowledge from different domain
teachers simultaneously, we further present a novel domain decomposition module
that learns to decompose the student model into two domain-related sub parts.
This is achieved by a novel domain-specific gate that learns to assign each
filter to only one specific domain in a learnable way. Extensive experiments
demonstrate the effectiveness of our method. Codes and models are available at
https://github.com/lovelyqian/ME-D2N_for_CDFSL.

$\ell_0$ constrained optimization is prevalent in machine learning,
particularly for high-dimensional problems, because it is a fundamental
approach to achieve sparse learning. Hard-thresholding gradient descent is a
dominant technique to solve this problem. However, first-order gradients of the
objective function may be either unavailable or expensive to calculate in a lot
of real-world problems, where zeroth-order (ZO) gradients could be a good
surrogate. Unfortunately, whether ZO gradients can work with the
hard-thresholding operator is still an unsolved problem. To solve this puzzle,
in this paper, we focus on the $\ell_0$ constrained black-box stochastic
optimization problems, and propose a new stochastic zeroth-order gradient
hard-thresholding (SZOHT) algorithm with a general ZO gradient estimator
powered by a novel random support sampling. We provide the convergence analysis
of SZOHT under standard assumptions. Importantly, we reveal a conflict between
the deviation of ZO estimators and the expansivity of the hard-thresholding
operator, and provide a theoretical minimal value of the number of random
directions in ZO gradients. In addition, we find that the query complexity of
SZOHT is independent or weakly dependent on the dimensionality under different
settings. Finally, we illustrate the utility of our method on a portfolio
optimization problem as well as black-box adversarial attacks.

Multiple Object Tracking (MOT) has rapidly progressed in recent years.
Existing works tend to design a single tracking algorithm to perform both
detection and association. Though ensemble learning has been exploited in many
tasks, i.e, classification and object detection, it hasn't been studied in the
MOT task, which is mainly caused by its complexity and evaluation metrics. In
this paper, we propose a simple but effective ensemble method for MOT, called
EnsembleMOT, which merges multiple tracking results from various trackers with
spatio-temporal constraints. Meanwhile, several post-processing procedures are
applied to filter out abnormal results. Our method is model-independent and
doesn't need the learning procedure. What's more, it can easily work in
conjunction with other algorithms, e.g., tracklets interpolation. Experiments
on the MOT17 dataset demonstrate the effectiveness of the proposed method.
Codes are available at https://github.com/dyhBUPT/EnsembleMOT.

Neural Architecture Search (NAS) algorithms aim at finding efficient Deep
Neural Network (DNN) architectures for a given application under given system
constraints. DNNs are computationally-complex as well as vulnerable to
adversarial attacks. In order to address multiple design objectives, we propose
RoHNAS, a novel NAS framework that jointly optimizes for adversarial-robustness
and hardware-efficiency of DNNs executed on specialized hardware accelerators.
Besides the traditional convolutional DNNs, RoHNAS additionally accounts for
complex types of DNNs such as Capsule Networks. For reducing the exploration
time, RoHNAS analyzes and selects appropriate values of adversarial
perturbation for each dataset to employ in the NAS flow. Extensive evaluations
on multi - Graphics Processing Unit (GPU) - High Performance Computing (HPC)
nodes provide a set of Pareto-optimal solutions, leveraging the tradeoff
between the above-discussed design objectives. For example, a Pareto-optimal
DNN for the CIFAR-10 dataset exhibits 86.07% accuracy, while having an energy
of 38.63 mJ, a memory footprint of 11.85 MiB, and a latency of 4.47 ms.

Fragment-based drug discovery has been an effective paradigm in early-stage
drug development. An open challenge in this area is designing linkers between
disconnected molecular fragments of interest to obtain chemically-relevant
candidate drug molecules. In this work, we propose DiffLinker, an
E(3)-equivariant 3D-conditional diffusion model for molecular linker design.
Given a set of disconnected fragments, our model places missing atoms in
between and designs a molecule incorporating all the initial fragments. Unlike
previous approaches that are only able to connect pairs of molecular fragments,
our method can link an arbitrary number of fragments. Additionally, the model
automatically determines the number of atoms in the linker and its attachment
points to the input fragments. We demonstrate that DiffLinker outperforms other
methods on the standard datasets generating more diverse and
synthetically-accessible molecules. Besides, we experimentally test our method
in real-world applications, showing that it can successfully generate valid
linkers conditioned on target protein pockets.

Natural Gradient Descent (NGD) is a second-order neural network training that
preconditions the gradient descent with the inverse of the Fisher Information
Matrix (FIM). Although NGD provides an efficient preconditioner, it is not
practicable due to the expensive computation required when inverting the FIM.
This paper proposes a new NGD variant algorithm named Component-Wise Natural
Gradient Descent (CW-NGD). CW-NGD is composed of 2 steps. Similar to several
existing works, the first step is to consider the FIM matrix as a
block-diagonal matrix whose diagonal blocks correspond to the FIM of each
layer's weights. In the second step, unique to CW-NGD, we analyze the layer's
structure and further decompose the layer's FIM into smaller segments whose
derivatives are approximately independent. As a result, individual layers' FIMs
are approximated in a block-diagonal form that trivially supports the
inversion. The segment decomposition strategy is varied by layer structure.
Specifically, we analyze the dense and convolutional layers and design their
decomposition strategies appropriately. In an experiment of training a network
containing these 2 types of layers, we empirically prove that CW-NGD requires
fewer iterations to converge compared to the state-of-the-art first-order and
second-order methods.

Recently cross-channel attention, which better leverages multi-channel
signals from microphone array, has shown promising results in the multi-party
meeting scenario. Cross-channel attention focuses on either learning global
correlations between sequences of different channels or exploiting fine-grained
channel-wise information effectively at each time step. Considering the delay
of microphone array receiving sound, we propose a multi-frame cross-channel
attention, which models cross-channel information between adjacent frames to
exploit the complementarity of both frame-wise and channel-wise knowledge.
Besides, we also propose a multi-layer convolutional mechanism to fuse the
multi-channel output and a channel masking strategy to combat the channel
number mismatch problem between training and inference. Experiments on the
AliMeeting, a real-world corpus, reveal that our proposed model outperforms
single-channel model by 31.7\% and 37.0\% CER reduction on Eval and Test sets.
Moreover, with comparable model parameters and training data, our proposed
model achieves a new SOTA performance on the AliMeeting corpus, as compared
with the top ranking systems in the ICASSP2022 M2MeT challenge, a recently held
multi-channel multi-speaker ASR challenge.

We study whether the learning rate $\alpha$, the discount factor $\gamma$ and
the reward signal $r$ have an influence on the overestimation bias of the
Q-Learning algorithm. Our preliminary results in environments which are
stochastic and that require the use of neural networks as function
approximators, show that all three parameters influence overestimation
significantly. By carefully tuning $\alpha$ and $\gamma$, and by using an
exponential moving average of $r$ in Q-Learning's temporal difference target,
we show that the algorithm can learn value estimates that are more accurate
than the ones of several other popular model-free methods that have addressed
its overestimation bias in the past.

Ovarian cancer is one of the most serious cancers that threaten women around
the world. Epithelial ovarian cancer (EOC), as the most commonly seen subtype
of ovarian cancer, has rather high mortality rate and poor prognosis among
various gynecological cancers. Survival analysis outcome is able to provide
treatment advices to doctors. In recent years, with the development of medical
imaging technology, survival prediction approaches based on pathological images
have been proposed. In this study, we designed a deep framework named EOCSA
which analyzes the prognosis of EOC patients based on pathological whole slide
images (WSIs). Specifically, we first randomly extracted patches from WSIs and
grouped them into multiple clusters. Next, we developed a survival prediction
model, named DeepConvAttentionSurv (DCAS), which was able to extract
patch-level features, removed less discriminative clusters and predicted the
EOC survival precisely. Particularly, channel attention, spatial attention, and
neuron attention mechanisms were used to improve the performance of feature
extraction. Then patient-level features were generated from our weight
calculation method and the survival time was finally estimated using LASSO-Cox
model. The proposed EOCSA is efficient and effective in predicting prognosis of
EOC and the DCAS ensures more informative and discriminative features can be
extracted. As far as we know, our work is the first to analyze the survival of
EOC based on WSIs and deep neural network technologies. The experimental
results demonstrate that our proposed framework has achieved state-of-the-art
performance of 0.980 C-index. The implementation of the approach can be found
at https://github.com/RanSuLab/EOCprognosis.

For monitoring crises, political events are extracted from the news. The
large amount of unstructured full-text event descriptions makes a case-by-case
analysis unmanageable, particularly for low-resource humanitarian aid
organizations. This creates a demand to classify events into event types, a
task referred to as event coding. Typically, domain experts craft an event type
ontology, annotators label a large dataset and technical experts develop a
supervised coding system. In this work, we propose PR-ENT, a new event coding
approach that is more flexible and resource-efficient, while maintaining
competitive accuracy: first, we extend an event description such as "Military
injured two civilians'' by a template, e.g. "People were [Z]" and prompt a
pre-trained (cloze) language model to fill the slot Z. Second, we select answer
candidates Z* = {"injured'', "hurt"...} by treating the event description as
premise and the filled templates as hypothesis in a textual entailment task.
This allows domain experts to draft the codebook directly as labeled prompts
and interpretable answer candidates. This human-in-the-loop process is guided
by our interactive codebook design tool. We evaluate PR-ENT in several
robustness checks: perturbing the event description and prompt template,
restricting the vocabulary and removing contextual information.

The Audio Deep Synthesis Detection (ADD) Challenge has been held to detect
generated human-like speech. With our submitted system, this paper provides an
overall assessment of track 1 (Low-quality Fake Audio Detection) and track 2
(Partially Fake Audio Detection). In this paper, spectro-temporal artifacts
were detected using raw temporal signals, spectral features, as well as deep
embedding features. To address track 1, low-quality data augmentation, domain
adaptation via finetuning, and various complementary feature information fusion
were aggregated in our system. Furthermore, we analyzed the clustering
characteristics of subsystems with different features by visualization method
and explained the effectiveness of our proposed greedy fusion strategy. As for
track 2, frame transition and smoothing were detected using self-supervised
learning structure to capture the manipulation of PF attacks in the time
domain. We ranked 4th and 5th in track 1 and track 2, respectively.

Integrated access and backhaul (IAB) is one of the promising techniques for
5G networks and beyond (6G), in which the same node/hardware is used to provide
both backhaul and cellular services in a multi-hop fashion. Due to the
sensitivity of the backhaul links with high rate/reliability demands, proper
network planning is needed to make the IAB network performing appropriately and
as good as possible. In this paper, we study the effect of deployment
optimization on the coverage of IAB networks. We concentrate on the cases
where, due to either geographical or interference management limitations,
unconstrained IAB node placement is not feasible in some areas. To that end, we
propose various millimeter wave (mmWave) blocking-aware constrained deployment
optimization approaches. Our results indicate that, even with limitations on
deployment optimization, network planning boosts the coverage of IAB networks
considerably.

Task-oriented dialogue systems are designed to achieve specific goals while
conversing with humans. In practice, they may have to handle simultaneously
several domains and tasks. The dialogue manager must therefore be able to take
into account domain changes and plan over different domains/tasks in order to
deal with multidomain dialogues. However, learning with reinforcement in such
context becomes difficult because the state-action dimension is larger while
the reward signal remains scarce. Our experimental results suggest that
structured policies based on graph neural networks combined with different
degrees of imitation learning can effectively handle multi-domain dialogues.
The reported experiments underline the benefit of structured policies over
standard policies.

Spurious correlations can cause strong biases in deep neural networks,
impairing generalization ability. While most of existing debiasing methods
require full supervisions on either spurious attributes or target labels,
training a debiased model from a limited amount of both annotations is still an
open issue. To overcome such limitations, we first examined an interesting
phenomenon by the spectral analysis of latent representations: spuriously
correlated, easy-to-learn attributes make neural networks inductively biased
towards encoding lower effective rank representations. We also show that a rank
regularization can amplify this bias in a way that encourages highly correlated
features. Motivated by these observations, we propose a self-supervised
debiasing framework that is potentially compatible with unlabeled samples. We
first pretrain a biased encoder in a self-supervised manner with the rank
regularization, serving as a semantic bottleneck to enforce the encoder to
learn the spuriously correlated attributes. This biased encoder is then used to
discover and upweight bias-conflicting samples in a downstream task, serving as
a boosting to effectively debias the main model. Remarkably, the proposed
debiasing framework significantly improves the generalization performance of
self-supervised learning baselines and, in some cases, even outperforms
state-of-the-art supervised debiasing approaches.

Neural networks are often biased to spuriously correlated features that
provide misleading statistical evidence that does not generalize. This raises a
fundamental question: "Does an optimal unbiased functional subnetwork exist in
a severely biased network? If so, how to extract such subnetwork?" While few
studies have revealed the existence of such optimal subnetworks with the
guidance of ground-truth unbiased samples, the way to discover the optimal
subnetworks with biased training dataset is still unexplored in practice. To
address this, here we first present our theoretical insight that alerts
potential limitations of existing algorithms in exploring unbiased subnetworks
in the presence of strong spurious correlations. We then further elucidate the
importance of bias-conflicting samples on structure learning. Motivated by
these observations, we propose a Debiased Contrastive Weight Pruning (DCWP)
algorithm, which probes unbiased subnetworks without expensive group
annotations. Experimental results demonstrate that our approach significantly
outperforms state-of-the-art debiasing methods despite its considerable
reduction in the number of parameters.

Automatically understanding emotions from visual data is a fundamental task
for human behaviour understanding. While models devised for Facial Expression
Recognition (FER) have demonstrated excellent performances on many datasets,
they often suffer from severe performance degradation when trained and tested
on different datasets due to domain shift. In addition, as face images are
considered highly sensitive data, the accessibility to large-scale datasets for
model training is often denied. In this work, we tackle the above-mentioned
problems by proposing the first Source-Free Unsupervised Domain Adaptation
(SFUDA) method for FER. Our method exploits self-supervised pretraining to
learn good feature representations from the target data and proposes a novel
and robust cluster-level pseudo-labelling strategy that accounts for in-cluster
statistics. We validate the effectiveness of our method in four adaptation
setups, proving that it consistently outperforms existing SFUDA methods when
applied to FER, and is on par with methods addressing FER in the UDA setting.

Deep learning has recently become one of the most compute/data-intensive
methods and is widely used in many research areas and businesses. One of the
critical challenges of deep learning is that it has many parameters that can be
adjusted, and the optimal value may need to be determined for faster operation
and high accuracy. The focus of this paper is the adjustable parameters of the
dataloader. The dataloader in a system mainly groups the data appropriately and
loads it to the main memory for the deep learning model to use. We introduce an
automated framework called Dataloader Parameter Tuner (DPT) that determines the
optimal value for the parameters required for the dataloader. This framework
discovers the optimal values for the number of dataloader's subprocesses (i.e.,
worker) and prefetch factor through grid search to accelerate the data transfer
for machine learning systems.

Technology videos contain rich multi-modal information. In cross-modal
information search, the data features of different modalities cannot be
compared directly, so the semantic gap between different modalities is a key
problem that needs to be solved. To address the above problems, this paper
proposes a novel Feature Fusion based Adversarial Cross-modal Retrieval method
(FFACR) to achieve text-to-video matching, ranking and searching. The proposed
method uses the framework of adversarial learning to construct a video
multimodal feature fusion network and a feature mapping network as generator, a
modality discrimination network as discriminator. Multi-modal features of
videos are obtained by the feature fusion network. The feature mapping network
projects multi-modal features into the same semantic space based on semantics
and similarity. The modality discrimination network is responsible for
determining the original modality of features. Generator and discriminator are
trained alternately based on adversarial learning, so that the data obtained by
the feature mapping network is semantically consistent with the original data
and the modal features are eliminated, and finally the similarity is used to
rank and obtain the search results in the semantic space. Experimental results
demonstrate that the proposed method performs better in text-to-video search
than other existing methods, and validate the effectiveness of the method on
the self-built datasets of technology videos.

Spiking Neural Networks (SNNs), as one of the algorithmic models in
neuromorphic computing, have gained a great deal of research attention owing to
temporal information processing capability, low power consumption, and high
biological plausibility. The potential to efficiently extract spatio-temporal
features makes it suitable for processing the event streams. However, existing
synaptic structures in SNNs are almost full-connections or spatial 2D
convolution, neither of which can extract temporal dependencies adequately. In
this work, we take inspiration from biological synapses and propose a
spatio-temporal synaptic connection SNN (STSC-SNN) model, to enhance the
spatio-temporal receptive fields of synaptic connections, thereby establishing
temporal dependencies across layers. Concretely, we incorporate temporal
convolution and attention mechanisms to implement synaptic filtering and gating
functions. We show that endowing synaptic models with temporal dependencies can
improve the performance of SNNs on classification tasks. In addition, we
investigate the impact of performance vias varied spatial-temporal receptive
fields and reevaluate the temporal modules in SNNs. Our approach is tested on
neuromorphic datasets, including DVS128 Gesture (gesture recognition), N-MNIST,
CIFAR10-DVS (image classification), and SHD (speech digit recognition). The
results show that the proposed model outperforms the state-of-the-art accuracy
on nearly all datasets.

In spite of years of research, the mechanisms that underlie the development
of schizophrenia, as well as its relapse, symptomatology, and treatment,
continue to be a mystery. The absence of appropriate analytic tools to deal
with the variable and complicated nature of schizophrenia may be one of the
factors that contribute to the development of this disorder. Deep learning is a
subfield of artificial intelligence that was inspired by the nervous system. In
recent years, deep learning has made it easier to model and analyse
complicated, high-dimensional, and nonlinear systems. Research on schizophrenia
is one of the many areas of study that has been revolutionised as a result of
the outstanding accuracy that deep learning algorithms have demonstrated in
classification and prediction tasks. Deep learning has the potential to become
a powerful tool for understanding the mechanisms that are at the root of
schizophrenia. In addition, a growing variety of techniques aimed at improving
model interpretability and causal reasoning are contributing to this trend.
Using multi-site fMRI data and a variety of deep learning approaches, this
study seeks to identify different types of schizophrenia. Our proposed method
of temporal aggregation of the 4D fMRI data outperforms existing work. In
addition, this study aims to shed light on the strength of connections between
various brain areas in schizophrenia individuals.

Assembly sequence planning (ASP) is the essential process for modern
manufacturing, proven to be NP-complete thus its effective and efficient
solution has been a challenge for researchers in the field. In this paper, we
present a graph-transformer based framework for the ASP problem which is
trained and demonstrated on a self-collected ASP database. The ASP database
contains a self-collected set of LEGO models. The LEGO model is abstracted to a
heterogeneous graph structure after a thorough analysis of the original
structure and feature extraction. The ground truth assembly sequence is first
generated by brute-force search and then adjusted manually to in line with
human rational habits. Based on this self-collected ASP dataset, we propose a
heterogeneous graph-transformer framework to learn the latent rules for
assembly planning. We evaluated the proposed framework in a series of
experiment. The results show that the similarity of the predicted and ground
truth sequences can reach 0.44, a medium correlation measured by Kendall's
$\tau$. Meanwhile, we compared the different effects of node features and edge
features and generated a feasible and reasonable assembly sequence as a
benchmark for further research. Our data set and code is available on
https://github.com/AIR-DISCOVER/ICRA\_ASP.

Self-supervised video transformer pre-training has recently benefited from
the mask-and-predict pipeline. They have demonstrated outstanding effectiveness
on downstream video tasks and superior data efficiency on small datasets.
However, temporal relation is not fully exploited by these methods. In this
work, we explicitly investigate motion cues in videos as extra prediction
target and propose our Masked Appearance-Motion Modeling (MAM2) framework.
Specifically, we design an encoder-regressor-decoder pipeline for this task.
The regressor separates feature encoding and pretext tasks completion, such
that the feature extraction process is completed adequately by the encoder. In
order to guide the encoder to fully excavate spatial-temporal features, two
separate decoders are used for two pretext tasks of disentangled appearance and
motion prediction. We explore various motion prediction targets and figure out
RGB-difference is simple yet effective. As for appearance prediction, VQGAN
codes are leveraged as prediction target. With our pre-training pipeline,
convergence can be remarkably speed up, e.g., we only require half of epochs
than state-of-the-art VideoMAE (400 v.s. 800) to achieve the competitive
performance. Extensive experimental results prove that our method learns
generalized video representations. Notably, our MAM2 with ViT-B achieves 82.3%
on Kinects-400, 71.3% on Something-Something V2, 91.5% on UCF101, and 62.5% on
HMDB51.

Establishment of point correspondence between camera and object coordinate
systems is a promising way to solve 6D object poses. However, surrogate
objectives of correspondence learning in 3D space are a step away from the true
ones of object pose estimation, making the learning suboptimal for the end
task. In this paper, we address this shortcoming by introducing a new method of
Deep Correspondence Learning Network for direct 6D object pose estimation,
shortened as DCL-Net. Specifically, DCL-Net employs dual newly proposed Feature
Disengagement and Alignment (FDA) modules to establish, in the feature space,
partial-to-partial correspondence and complete-to-complete one for partial
object observation and its complete CAD model, respectively, which result in
aggregated pose and match feature pairs from two coordinate systems; these two
FDA modules thus bring complementary advantages. The match feature pairs are
used to learn confidence scores for measuring the qualities of deep
correspondence, while the pose feature pairs are weighted by confidence scores
for direct object pose regression. A confidence-based pose refinement network
is also proposed to further improve pose precision in an iterative manner.
Extensive experiments show that DCL-Net outperforms existing methods on three
benchmarking datasets, including YCB-Video, LineMOD, and Oclussion-LineMOD;
ablation studies also confirm the efficacy of our novel designs.

Investigating better ways to reuse the released pre-trained language models
(PLMs) can significantly reduce the computational cost and the potential
environmental side-effects. This paper explores a novel PLM reuse paradigm,
Knowledge Integration (KI). Without human annotations available, KI aims to
merge the knowledge from different teacher-PLMs, each of which specializes in a
different classification problem, into a versatile student model. To achieve
this, we first derive the correlation between virtual golden supervision and
teacher predictions. We then design a Model Uncertainty--aware Knowledge
Integration (MUKI) framework to recover the golden supervision for the student.
Specifically, MUKI adopts Monte-Carlo Dropout to estimate model uncertainty for
the supervision integration. An instance-wise re-weighting mechanism based on
the margin of uncertainty scores is further incorporated, to deal with the
potential conflicting supervision from teachers. Experimental results
demonstrate that MUKI achieves substantial improvements over baselines on
benchmark datasets. Further analysis shows that MUKI can generalize well for
merging teacher models with heterogeneous architectures, and even teachers
major in cross-lingual datasets.

In the context of cell-free massive multi-input multi-output (CFmMIMO),
zero-forcing precoding (ZFP) is superior in terms of spectral efficiency.
However, it suffers from channel aging owing to fronthaul and processing
delays. In this paper, we propose a robust scheme coined delay-tolerant
zero-forcing precoding (DT-ZFP), which exploits deep learning-aided channel
prediction to alleviate the effect of outdated channel state information (CSI).
A predictor consisting of a bank of user-specific predictive modules is
specifically designed for such a multi-user scenario. Leveraging the degree of
freedom brought by the prediction horizon, the delivery of CSI and precoded
data through a fronthaul network and the transmission of user data and pilots
over an air interface can be parallelized. Therefore, DT-ZFP not only
effectively combats channel aging but also avoids the inefficient Stop-and-Wait
mechanism of the canonical ZFP in CFmMIMO.

We present a benchmark study of autonomous, chemical agents exhibiting
associative learning of an environmental feature. Associative learning has been
widely studied in cognitive science and artificial intelligence, but are most
commonly implemented in highly complex or carefully engineered systems such as
animal brains, artificial neural networks, DNA computing systems and gene
regulatory networks. The ability to encode environmental correlations and use
them to make predictions is a benchmark of biological resilience, and underpins
a plethora of adaptive responses in the living hierarchy, spanning prey animal
species anticipating the arrival of predators, to epigenetic systems in
microorganisms learning environmental correlations. Given the ubiquitous and
essential presence of learning behaviours in the biosphere, we aimed to explore
whether simple, non-living dissipative structures could also exhibit
associative learning. Inspired by previous modeling of associative learning in
chemical networks, we simulated simple systems composed of long and short term
memory chemical species that could encode the presence or absence of temporal
correlations between two external species. The ability to learn this
association was implemented in Gray-Scott reaction-diffusion spots, emergent
chemical patterns that exhibit self-replication and homeostasis. With the novel
ability of associative learning, we demonstrate that simple chemical patterns
can exhibit a broad repertoire of life-like behaviour, paving the way for in
vitro studies of autonomous chemical learning systems, with potential relevance
to artificial life, origins of life, and systems chemistry. The experimental
realisation of these learning behaviours in protocell systems could advance a
novel research direction in astrobiology, since our system significantly
reduces the lower bound on the required complexity for emergent learning.

Deep neural networks are vulnerable to adversarial attacks, such as backdoor
attacks in which a malicious adversary compromises a model during training such
that specific behaviour can be triggered at test time by attaching a specific
word or phrase to an input. This paper considers the problem of diagnosing
whether a model has been compromised and if so, identifying the backdoor
trigger. We present the first robust defence mechanism that generalizes to
several backdoor attacks against text classification models, without prior
knowledge of the attack type, nor does our method require access to any
(potentially compromised) training resources. Our experiments show that our
technique is highly accurate at defending against state-of-the-art backdoor
attacks, including data poisoning and weight poisoning, across a range of text
classification tasks and model architectures. Our code will be made publicly
available upon acceptance.

In modern smart grids, the proliferation of communication-enabled distributed
energy resource (DER) systems has increased the surface of possible
cyber-physical attacks. Attacks originating from the distributed edge devices
of DER system, such as photovoltaic (PV) system, is often difficult to detect.
An attacker may change the control configurations or various setpoints of the
PV inverters to destabilize the power grid, damage devices, or for the purpose
of economic gain. A more powerful attacker may even manipulate the PV system
metering data transmitted for remote monitoring, so that (s)he can remain
hidden. In this paper, we consider a case where PV systems operating in
different control modes can be simultaneously attacked and the attacker has the
ability to manipulate individual PV bus measurements to avoid detection. We
show that even in such a scenario, with just the aggregated measurements (that
the attacker cannot manipulate), machine learning (ML) techniques are able to
detect the attack in a fast and accurate manner. We use a standard radial
distribution network, together with real smart home electricity consumption
data and solar power data in our experimental setup. We test the performance of
several ML algorithms to detect attacks on the PV system. Our detailed
evaluations show that the proposed intrusion detection system (IDS) is highly
effective and efficient in detecting attacks on PV inverter control modes.

The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer
Interfaces (BCIs) allow the direct communication between humans and machines by
exploiting the neural pathways connected to motor imagination. Therefore, these
systems open the possibility of developing applications that could span from
the medical field to the entertainment industry. In this context, Artificial
Intelligence (AI) approaches become of fundamental importance especially when
wanting to provide a correct and coherent feedback to BCI users. Moreover,
publicly available datasets in the field of MI EEG-based BCIs have been widely
exploited to test new techniques from the AI domain. In this work, AI
approaches applied to datasets collected in different years and with different
devices but with coherent experimental paradigms are investigated with the aim
of providing a concise yet sufficiently comprehensive survey on the evolution
and influence of AI techniques on MI EEG-based BCI data.

In recent years, Artificial Neural Networks (ANNs) have been introduced in
Structural Health Monitoring (SHM) systems. A semi-supervised method with a
data-driven approach allows the ANN training on data acquired from an undamaged
structural condition to detect structural damages. In standard approaches,
after the training stage, a decision rule is manually defined to detect
anomalous data. However, this process could be made automatic using machine
learning methods, whom performances are maximised using hyperparameter
optimization techniques. The paper proposes a semi-supervised method with a
data-driven approach to detect structural anomalies. The methodology consists
of: (i) a Variational Autoencoder (VAE) to approximate undamaged data
distribution and (ii) a One-Class Support Vector Machine (OC-SVM) to
discriminate different health conditions using damage sensitive features
extracted from VAE's signal reconstruction. The method is applied to a scale
steel structure that was tested in nine damage's scenarios by IASC-ASCE
Structural Health Monitoring Task Group.

Recently, graph (network) data is an emerging research area in artificial
intelligence, machine learning and statistics. In this work, we are interested
in whether node's labels (people's responses) are affected by their neighbor's
features (friends' characteristics). We propose a novel latent logistic
regression model to describe the network dependence with binary responses. The
key advantage of our proposed model is that a latent binary indicator is
introduced to indicate whether a node is susceptible to the influence of its
neighbour. A score-type test is proposed to diagnose the existence of network
dependence. In addition, an EM-type algorithm is used to estimate the model
parameters under network dependence. Extensive simulations are conducted to
evaluate the performance of our method. Two public datasets are used to
illustrate the effectiveness of the proposed latent logistic regression model.

Recurrent Neural Networks (RNNs) frequently exhibit complicated dynamics, and
their sensitivity to the initialization process often renders them notoriously
hard to train. Recent works have shed light on such phenomena analyzing when
exploding or vanishing gradients may occur, either of which is detrimental for
training dynamics. In this paper, we point to a formal connection between RNNs
and chaotic dynamical systems and prove a qualitatively stronger phenomenon
about RNNs than what exploding gradients seem to suggest. Our main result
proves that under standard initialization (e.g., He, Xavier etc.), RNNs will
exhibit \textit{Li-Yorke chaos} with \textit{constant} probability
\textit{independent} of the network's width. This explains the experimentally
observed phenomenon of \textit{scrambling}, under which trajectories of nearby
points may appear to be arbitrarily close during some timesteps, yet will be
far away in future timesteps. In stark contrast to their feedforward
counterparts, we show that chaotic behavior in RNNs is preserved under small
perturbations and that their expressive power remains exponential in the number
of feedback iterations. Our technical arguments rely on viewing RNNs as random
walks under non-linear activations, and studying the existence of certain types
of higher-order fixed points called \textit{periodic points} that lead to phase
transitions from order to chaos.

Automatic human matting is highly desired for many real applications. We
investigate recent human matting methods and show that common bad cases happen
when semantic human segmentation fails. This indicates that semantic
understanding is crucial for robust human matting. From this, we develop a fast
yet accurate human matting framework, named Semantic Guided Human Matting
(SGHM). It builds on a semantic human segmentation network and introduces a
light-weight matting module with only marginal computational cost. Unlike
previous works, our framework is data efficient, which requires a small amount
of matting ground-truth to learn to estimate high quality object mattes. Our
experiments show that trained with merely 200 matting images, our method can
generalize well to real-world datasets, and outperform recent methods on
multiple benchmarks, while remaining efficient. Considering the unbearable
labeling cost of matting data and widely available segmentation data, our
method becomes a practical and effective solution for the task of human
matting. Source code is available at
https://github.com/cxgincsu/SemanticGuidedHumanMatting.

Multigraph matching is a recent variant of the graph matching problem. In
this framework, the optimization procedure considers several graphs and
enforces the consistency of the matches along the graphs. This constraint can
be formalized as a cycle consistency across the pairwise permutation matrices,
which implies the definition of a universe of
vertex~\citep{pachauri2013solving}. The label of each vertex is encoded by a
sparse vector and the dimension of this space corresponds to the rank of the
bulk permutation matrix, the matrix built from the aggregation of all the
pairwise permutation matrices. The matching problem can then be formulated as a
non-convex quadratic optimization problem (QAP) under constraints imposed on
the rank and the permutations. In this paper, we introduce a novel kernelized
multigraph matching technique that handles vectors of attributes on both the
vertices and edges of the graphs, while maintaining a low memory usage. We
solve the QAP problem using a projected power optimization approach and propose
several projectors leading to improved stability of the results. We provide
several experiments showing that our method is competitive against other
unsupervised methods.

Retrieving evidences from tabular and textual resources is essential for
open-domain question answering (OpenQA), which provides more comprehensive
information. However, training an effective dense table-text retriever is
difficult due to the challenges of table-text discrepancy and data sparsity
problem. To address the above challenges, we introduce an optimized OpenQA
Table-Text Retriever (OTTeR) to jointly retrieve tabular and textual evidences.
Firstly, we propose to enhance mixed-modality representation learning via two
mechanisms: modality-enhanced representation and mixed-modality negative
sampling strategy. Secondly, to alleviate data sparsity problem and enhance the
general retrieval ability, we conduct retrieval-centric mixed-modality
synthetic pre-training. Experimental results demonstrate that OTTeR
substantially improves the performance of table-and-text retrieval on the
OTT-QA dataset. Comprehensive analyses examine the effectiveness of all the
proposed mechanisms. Besides, equipped with OTTeR, our OpenQA system achieves
the state-of-the-art result on the downstream QA task, with 10.1% absolute
improvement in terms of the exact match over the previous best system.
  All the code and data are available at
https://github.com/Jun-jie-Huang/OTTeR.

News recommendation (NR) is essential for online news services. Existing NR
methods typically adopt a news-user representation learning framework, facing
two potential limitations. First, in news encoder, single candidate news
encoding suffers from an insufficient semantic information problem. Second,
existing graph-based NR methods are promising but lack effective news-user
feature interaction, rendering the graph-based recommendation suboptimal. To
overcome these limitations, we propose dual-interactive graph attention
networks (DIGAT) consisting of news- and user-graph channels. In the news-graph
channel, we enrich the semantics of single candidate news by incorporating the
semantically relevant news information with a semantic-augmented graph (SAG).
In the user-graph channel, multi-level user interests are represented with a
news-topic graph. Most notably, we design a dual-graph interaction process to
perform effective feature interaction between the news and user graphs, which
facilitates accurate news-user representation matching. Experiment results on
the benchmark dataset MIND show that DIGAT outperforms existing news
recommendation methods. Further ablation studies and analyses validate the
effectiveness of (1) semantic-augmented news graph modeling and (2) dual-graph
interaction.

In this paper, we propose a natural language processing architecture that can
handle tasks that previously required two models as one model. With a single
model, we analyze the language patterns and conversational context of
Alzheimer's patients and derive answers from two results: patient
classification and chatbot. If the patient's language characteristics are
identified by chatbots in daily life, doctors can plan more precise diagnosis
and treatment for early diagnosis. The proposed model is used to develop
chatbots that replace questionnaires that required experts. There are two
natural language processing tasks performed by the model. The first is a
'natural language classification' that indicates with probability whether the
patient has an illness, and the second is to generate the next 'answer' of the
chatbot to the patient's answer. In the first half, a context vector, which is
a characteristic of patient utterance, is extracted through a self-attention
neural network. This context vector and chatbot (expert, moderator) questions
are entered together into the encoder to obtain a matrix containing the
characteristics of the interaction between the questioner and the patient. The
vectorized matrix becomes a probability value for classification of patients.
Enter the matrix into the decoder with the next answer from the chatbot (the
moderator) to generate the next utterance. As a result of learning this
structure with DmentiaBank's cookie theft description corpus, it was confirmed
that the value of the loss function of the encoder and decoder was
significantly reduced and converged. This shows that capturing the speech
language pattern of Alzheimer's disease patients can contribute to early
diagnosis and longitudinal studies of the disease in the future.

Non-autoregressive models achieve significant decoding speedup in neural
machine translation but lack the ability to capture sequential dependency.
Directed Acyclic Transformer (DA-Transformer) was recently proposed to model
sequential dependency with a directed acyclic graph. Consequently, it has to
apply a sequential decision process at inference time, which harms the global
translation accuracy. In this paper, we present a Viterbi decoding framework
for DA-Transformer, which guarantees to find the joint optimal solution for the
translation and decoding path under any length constraint. Experimental results
demonstrate that our approach consistently improves the performance of
DA-Transformer while maintaining a similar decoding speedup.

In this manuscript, we show that any neural network with any activation
function can be represented as a decision tree. The representation is
equivalence and not an approximation, thus keeping the accuracy of the neural
network exactly as is. We believe that this work provides better understanding
of neural networks and paves the way to tackle their black-box nature. We share
equivalent trees of some neural networks and show that besides providing
interpretability, tree representation can also achieve some computational
advantages for small networks. The analysis holds both for fully connected and
convolutional networks, which may or may not also include skip connections
and/or normalizations.

Legal case retrieval, which aims to retrieve relevant cases given a query
case, plays an essential role in the legal system. While recent research
efforts improve the performance of traditional ad-hoc retrieval models, legal
case retrieval is still challenging since queries are legal cases, which
contain hundreds of tokens. Legal cases are much longer and more complicated
than keywords queries. Apart from that, the definition of legal relevance is
beyond the general definition. In addition to general topical relevance, the
relevant cases also involve similar situations and legal elements, which can
support the judgment of the current case. In this paper, we propose an
interaction-focused network for legal case retrieval with a multi-view
contrastive learning objective. The contrastive learning views, including
case-view and element-view, aim to overcome the above challenges. The case-view
contrastive learning minimizes the hidden space distance between relevant legal
case representations produced by a pre-trained language model (PLM) encoder.
The element-view builds positive and negative instances by changing legal
elements of cases to help the network better compute legal relevance. To
achieve this, we employ a legal element knowledge-aware indicator to detect
legal elements of cases. We conduct extensive experiments on the benchmark of
relevant case retrieval. Evaluation results indicate our proposed method
obtains significant improvement over the existing methods.

The use of interactive advice in reinforcement learning scenarios allows for
speeding up the learning process for autonomous agents. Current interactive
reinforcement learning research has been limited to real-time interactions that
offer relevant user advice to the current state only. Moreover, the information
provided by each interaction is not retained and instead discarded by the agent
after a single use. In this paper, we present a method for retaining and
reusing provided knowledge, allowing trainers to give general advice relevant
to more than just the current state. Results obtained show that the use of
broad-persistent advice substantially improves the performance of the agent
while reducing the number of interactions required for the trainer.

The idea of using a separately trained target model (or teacher) to improve
the performance of the student model has been increasingly popular in various
machine learning domains, and meta-learning is no exception; a recent discovery
shows that utilizing task-wise target models can significantly boost the
generalization performance. However, obtaining a target model for each task can
be highly expensive, especially when the number of tasks for meta-learning is
large. To tackle this issue, we propose a simple yet effective method, coined
Self-improving Momentum Target (SiMT). SiMT generates the target model by
adapting from the temporal ensemble of the meta-learner, i.e., the momentum
network. This momentum network and its task-specific adaptations enjoy a
favorable generalization performance, enabling self-improving of the
meta-learner through knowledge distillation. Moreover, we found that perturbing
parameters of the meta-learner, e.g., dropout, further stabilize this
self-improving process by preventing fast convergence of the distillation loss
during meta-training. Our experimental results demonstrate that SiMT brings a
significant performance gain when combined with a wide range of meta-learning
methods under various applications, including few-shot regression, few-shot
classification, and meta-reinforcement learning. Code is available at
https://github.com/jihoontack/SiMT.

Deep Neural Networks (DNNs) have been widely applied in Internet of Things
(IoT) systems for various tasks such as image classification and object
detection. However, heavyweight DNN models can hardly be deployed on edge
devices due to limited computational resources. In this paper, an edge-cloud
cooperation framework is proposed to improve inference accuracy while
maintaining low inference latency. To this end, we deploy a lightweight model
on the edge and a heavyweight model on the cloud. A reinforcement learning
(RL)-based DNN compression approach is used to generate the lightweight model
suitable for the edge from the heavyweight model. Moreover, a supervised
learning (SL)-based offloading strategy is applied to determine whether the
sample should be processed on the edge or on the cloud. Our method is
implemented on real hardware and tested on multiple datasets. The experimental
results show that (1) The sizes of the lightweight models obtained by RL-based
DNN compression are up to 87.6% smaller than those obtained by the baseline
method; (2) SL-based offloading strategy makes correct offloading decisions in
most cases; (3) Our method reduces up to 78.8% inference latency and achieves
higher accuracy compared with the cloud-only strategy.

This paper presents a neurosymbolic framework to solve motion planning
problems for mobile robots involving temporal goals. The temporal goals are
described using temporal logic formulas such as Linear Temporal Logic (LTL) to
capture complex tasks. The proposed framework trains Neural Network (NN)-based
planners that enjoy strong correctness guarantees when applying to unseen
tasks, i.e., the exact task (including workspace, LTL formula, and dynamic
constraints of a robot) is unknown during the training of NNs. Our approach to
achieving theoretical guarantees and computational efficiency is based on two
insights. First, we incorporate a symbolic model into the training of NNs such
that the resulting NN-based planner inherits the interpretability and
correctness guarantees of the symbolic model. Moreover, the symbolic model
serves as a discrete "memory", which is necessary for satisfying temporal logic
formulas. Second, we train a library of neural networks offline and combine a
subset of the trained NNs into a single NN-based planner at runtime when a task
is revealed. In particular, we develop a novel constrained NN training
procedure, named formal NN training, to enforce that each neural network in the
library represents a "symbol" in the symbolic model. As a result, our
neurosymbolic framework enjoys the scalability and flexibility benefits of
machine learning and inherits the provable guarantees from control-theoretic
and formal-methods techniques. We demonstrate the effectiveness of our
framework in both simulations and on an actual robotic vehicle, and show that
our framework can generalize to unknown tasks where state-of-the-art
meta-reinforcement learning techniques fail.

Recent progress in deep learning highlights the tremendous potential of
utilizing diverse datasets for achieving effective generalization and makes it
enticing to consider leveraging broad datasets for attaining more robust
generalization in robotic learning as well. However, in practice we likely will
want to learn a new skill in a new environment that is unlikely to be contained
in the prior data. Therefore we ask: how can we leverage existing diverse
offline datasets in combination with small amounts of task-specific data to
solve new tasks, while still enjoying the generalization benefits of training
on large amounts of data? In this paper, we demonstrate that end-to-end offline
RL can be an effective approach for doing this, without the need for any
representation learning or vision-based pre-training. We present pre-training
for robots (PTR), a framework based on offline RL that attempts to effectively
learn new tasks by combining pre-training on existing robotic datasets with
rapid fine-tuning on a new task, with as a few as 10 demonstrations. At its
core, PTR applies an existing offline RL method such as conservative Q-learning
(CQL), but extends it to include several crucial design decisions that enable
PTR to actually work and outperform a variety of prior methods. To the best of
our knowledge, PTR is the first offline RL method that succeeds at learning new
tasks in a new domain on a real WidowX robot with as few as 10 task
demonstrations, by effectively leveraging an existing dataset of diverse
multi-task robot data collected in a variety of toy kitchens. Our
implementation can be found at: https://github.com/Asap7772/PTR.

Deep neural networks often suffer from poor generalization caused by complex
and non-convex loss landscapes. One of the popular solutions is Sharpness-Aware
Minimization (SAM), which smooths the loss landscape via minimizing the
maximized change of training loss when adding a perturbation to the weight.
However, we find the indiscriminate perturbation of SAM on all parameters is
suboptimal, which also results in excessive computation, i.e., double the
overhead of common optimizers like Stochastic Gradient Descent (SGD). In this
paper, we propose an efficient and effective training scheme coined as Sparse
SAM (SSAM), which achieves sparse perturbation by a binary mask. To obtain the
sparse mask, we provide two solutions which are based onFisher information and
dynamic sparse training, respectively. In addition, we theoretically prove that
SSAM can converge at the same rate as SAM, i.e., $O(\log T/\sqrt{T})$. Sparse
SAM not only has the potential for training acceleration but also smooths the
loss landscape effectively. Extensive experimental results on CIFAR10,
CIFAR100, and ImageNet-1K confirm the superior efficiency of our method to SAM,
and the performance is preserved or even better with a perturbation of merely
50% sparsity. Code is availiable at
https://github.com/Mi-Peng/Sparse-Sharpness-Aware-Minimization.

With the development of the convolutional neural network, image style
transfer has drawn increasing attention. However, most existing approaches
adopt a global feature transformation to transfer style patterns into content
images (e.g., AdaIN and WCT). Such a design usually destroys the spatial
information of the input images and fails to transfer fine-grained style
patterns into style transfer results. To solve this problem, we propose a novel
STyle TRansformer (STTR) network which breaks both content and style images
into visual tokens to achieve a fine-grained style transformation.
Specifically, two attention mechanisms are adopted in our STTR. We first
propose to use self-attention to encode content and style tokens such that
similar tokens can be grouped and learned together. We then adopt
cross-attention between content and style tokens that encourages fine-grained
style transformations. To compare STTR with existing approaches, we conduct
user studies on Amazon Mechanical Turk (AMT), which are carried out with 50
human subjects with 1,000 votes in total. Extensive evaluations demonstrate the
effectiveness and efficiency of the proposed STTR in generating visually
pleasing style transfer results.

Large annotated datasets have been a key component in the success of deep
learning. However, annotating medical images is challenging as it requires
expertise and a large budget. In particular, annotating different types of
cells in histopathology suffer from high inter- and intra-rater variability due
to the ambiguity of the task. Under this setting, the relation between
annotators' variability and model performance has received little attention. We
present a large-scale study on the variability of cell annotations among 120
board-certified pathologists and how it affects the performance of a deep
learning model. We propose a method to measure such variability, and by
excluding those annotators with low variability, we verify the trade-off
between the amount of data and its quality. We found that naively increasing
the data size at the expense of inter-rater variability does not necessarily
lead to better-performing models in cell detection. Instead, decreasing the
inter-rater variability with the expense of decreasing dataset size increased
the model performance. Furthermore, models trained from data annotated with
lower inter-labeler variability outperform those from higher inter-labeler
variability. These findings suggest that the evaluation of the annotators may
help tackle the fundamental budget issues in the histopathology domain

Labeling objects with pixel-wise segmentation requires a huge amount of human
labor compared to bounding boxes. Most existing methods for weakly supervised
instance segmentation focus on designing heuristic losses with priors from
bounding boxes. While, we find that box-supervised methods can produce some
fine segmentation masks and we wonder whether the detectors could learn from
these fine masks while ignoring low-quality masks. To answer this question, we
present BoxTeacher, an efficient and end-to-end training framework for
high-performance weakly supervised instance segmentation, which leverages a
sophisticated teacher to generate high-quality masks as pseudo labels.
Considering the massive noisy masks hurt the training, we present a mask-aware
confidence score to estimate the quality of pseudo masks, and propose the
noise-aware pixel loss and noise-reduced affinity loss to adaptively optimize
the student with pseudo masks. Extensive experiments can demonstrate
effectiveness of the proposed BoxTeacher. Without bells and whistles,
BoxTeacher remarkably achieves $34.4$ mask AP and $35.4$ mask AP with ResNet-50
and ResNet-101 respectively on the challenging MS-COCO dataset, which
outperforms the previous state-of-the-art methods by a significant margin. The
code and models are available at https://github.com/hustvl/BoxTeacher.

Artificial Intelligence (AI) increasingly shows its potential to outperform
predicate logic algorithms and human control alike. In automatically deriving a
system model, AI algorithms learn relations in data that are not detectable for
humans. This great strength, however, also makes use of AI methods dubious. The
more complex a model, the more difficult it is for a human to understand the
reasoning for the decisions. As currently, fully automated AI algorithms are
sparse, every algorithm has to provide a reasoning for human operators. For
data engineers, metrics such as accuracy and sensitivity are sufficient.
However, if models are interacting with non-experts, explanations have to be
understandable. This work provides an extensive survey of literature on this
topic, which, to a large part, consists of other surveys. The findings are
mapped to ways of explaining decisions and reasons for explaining decisions. It
shows that the heterogeneity of reasons and methods of and for explainability
lead to individual explanatory frameworks.

New models of the attention-based random forests called LARF (Leaf
Attention-based Random Forest) are proposed. The first idea behind the models
is to introduce a two-level attention, where one of the levels is the "leaf"
attention and the attention mechanism is applied to every leaf of trees. The
second level is the tree attention depending on the "leaf" attention. The
second idea is to replace the softmax operation in the attention with the
weighted sum of the softmax operations with different parameters. It is
implemented by applying a mixture of the Huber's contamination models and can
be regarded as an analog of the multi-head attention with "heads" defined by
selecting a value of the softmax parameter. Attention parameters are simply
trained by solving the quadratic optimization problem. To simplify the tuning
process of the models, it is proposed to make the tuning contamination
parameters to be training and to compute them by solving the quadratic
optimization problem. Many numerical experiments with real datasets are
performed for studying LARFs. The code of proposed algorithms can be found in
https://github.com/andruekonst/leaf-attention-forest.

Melanoma is one of the most serious skin cancers that can occur in any part
of the human skin. Early diagnosing melanoma lesions will significantly
increase their chances of being cured. Improving melanoma segmentation will
help doctors or surgical robots remove the lesion more accurately from body
parts. Recently, the learning-based segmentation methods achieved desired
results in image segmentation compared to traditional algorithms. This study
proposes a new method to improve melanoma skin lesions detection and
segmentation by defining a two-step pipeline based on deep learning models. Our
methods were evaluated on ISIC 2018 (Skin Lesion Analysis Towards Melanoma
Detection Challenge Dataset) well-known dataset. The proposed methods consist
of two main parts for real-time detection of lesion location and segmentation.
In the detection section, the location of the skin lesion is precisely detected
by the fine-tuned You Only Look Once version 3 (F-YOLOv3) and then fed into the
fine-tuned Segmentation Network (F-SegNet). Skin lesion localization helps to
reduce the unnecessary calculation of whole images for segmentation. The
results show that our proposed F-YOLOv3 achieves better performance as 96% in
mAP. Compared to state-of-the-art segmentation approaches, our F-SegNet
achieves higher performance for accuracy, dice coefficient, and Jaccard index
at 95.16%, 92.81%, and 86.2%, respectively.

For many use cases, combining information from different datasets can be of
interest to improve a machine learning model's performance, especially when the
number of samples from at least one of the datasets is small. However, a
potential challenge in such cases is that the features from these datasets are
not identical, even though there are some commonly shared features among the
datasets. To tackle this challenge, we propose a novel framework called Combine
datasets based on Imputation (ComImp). In addition, we propose a variant of
ComImp that uses Principle Component Analysis (PCA), PCA-ComImp in order to
reduce dimension before combining datasets. This is useful when the datasets
have a large number of features that are not shared between them. Furthermore,
our framework can also be utilized for data preprocessing by imputing missing
data, i.e., filling in the missing entries while combining different datasets.
To illustrate the power of the proposed methods and their potential usages, we
conduct experiments for various tasks: regression, classification, and for
different data types: tabular data, time series data, when the datasets to be
combined have missing data. We also investigate how the devised methods can be
used with transfer learning to provide even further model training improvement.
Our results indicate that the proposed methods are somewhat similar to transfer
learning in that the merge can significantly improve the accuracy of a
prediction model on smaller datasets. In addition, the methods can boost
performance by a significant margin when combining small datasets together and
can provide extra improvement when being used with transfer learning.

The advent of large-scale stellar spectroscopic surveys naturally leads to
the implementation of machine learning techniques to isolate, for example,
small sub-samples of potentially interesting stars from the full data set. A
recent example is the application of the t-SNE statistical method to
$\sim$600,000 stellar spectra from the GALAH survey in order to identify a
sample of candidate extremely metal-poor (EMP, [Fe/H] $\leq$ -3) stars. We
report the outcome of low-resolution spectroscopic follow-up of 83 GALAH EMP
candidates that lack any previous metallicity estimates. Overall, the
statistical selection is found to be efficient ($\sim$one-third of the
candidates have [Fe/H] $\leq$ -2.75) with low contamination ($<$10% have [Fe/H]
$>$ -2), and with a metallicity distribution function that is consistent with
previous work. Five stars are found to have [Fe/H] $\leq$ -3.0, one of which is
a main sequence turnoff star. Two other stars are revealed as likely CEMP-$s$
stars and a known carbon star is re-discovered. The results indicate that the
statistical selection approach employed is valid, and therefore it can be
applied to forthcoming even larger stellar spectroscopic surveys with the
expectation of similar positive outcomes.

The goal of offline reinforcement learning (RL) is to learn near-optimal
policies from static logged datasets, thus sidestepping expensive online
interactions. Behavioral cloning (BC) provides a straightforward solution to
offline RL by mimicking offline trajectories via supervised learning. Recent
advances (Chen et al., 2021; Janner et al., 2021; Emmons et al., 2021) have
shown that by conditioning on desired future returns, BC can perform
competitively to their value-based counterparts, while enjoying much more
simplicity and training stability. However, the distribution of returns in the
offline dataset can be arbitrarily skewed and suboptimal, which poses a unique
challenge for conditioning BC on expert returns at test time. We propose
ConserWeightive Behavioral Cloning (CWBC), a simple and effective method for
improving the performance of conditional BC for offline RL with two key
components: trajectory weighting and conservative regularization. Trajectory
weighting addresses the bias-variance tradeoff in conditional BC and provides a
principled mechanism to learn from both low return trajectories (typically
plentiful) and high return trajectories (typically few). Further, we analyze
the notion of conservatism in existing BC methods, and propose a novel
conservative regularize that explicitly encourages the policy to stay close to
the data distribution. The regularizer helps achieve more reliable performance,
and removes the need for ad-hoc tuning of the conditioning value during
evaluation. We instantiate CWBC in the context of Reinforcement Learning via
Supervised Learning (RvS) (Emmons et al., 2021) and Decision Transformer (DT)
(Chen et al., 2021), and empirically show that it significantly boosts the
performance and stability of prior methods on various offline RL benchmarks.
Code is available at https://github.com/tung-nd/cwbc.

Trajectory similarity measures act as query predicates in trajectory
databases, making them the key player in determining the query results. They
also have a heavy impact on the query efficiency. An ideal measure should have
the capability to accurately evaluate the similarity between any two
trajectories in a very short amount of time. However, existing heuristic
measures are mainly based on pointwise comparisons following hand-crafted
rules, thus resulting in either poor quality results or low efficiency in many
cases. Although several deep learning-based measures have recently aimed at
these problems, their improvements are limited by the difficulties to learn the
fine-grained spatial patterns of trajectories.
  To address these issues, we propose a contrastive learning-based trajectory
modelling method named TrajCL, which is robust in application scenarios where
the data set contains low-quality trajectories. Specifically, we present four
trajectory augmentation methods and a novel dual-feature self-attention-based
trajectory backbone encoder. The resultant model can jointly learn both the
spatial and the structural patterns of trajectories. Our model does not involve
any recurrent structures and thus has a high efficiency. Besides, our
pre-trained backbone encoder can be fine-tuned towards other computationally
expensive measures with minimal supervision data. Experimental results show
that TrajCL is consistently and significantly more accurate and faster than the
state-of-the-art trajectory similarity measures. After fine-tuning, i.e., when
being used as an estimator for heuristic measures, TrajCL can even outperform
the state-of-the-art supervised method by up to 32% in the accuracy for
processing trajectory similarity queries.

Batch Normalization (BN) is a core and prevalent technique in accelerating
the training of deep neural networks and improving the generalization on
Computer Vision (CV) tasks. However, it fails to defend its position in Natural
Language Processing (NLP), which is dominated by Layer Normalization (LN). In
this paper, we are trying to answer why BN usually performs worse than LN in
NLP tasks with Transformer models. We find that the inconsistency between
training and inference of BN is the leading cause that results in the failure
of BN in NLP. We define Training Inference Discrepancy (TID) to quantitatively
measure this inconsistency and reveal that TID can indicate BN's performance,
supported by extensive experiments, including image classification, neural
machine translation, language modeling, sequence labeling, and text
classification tasks. We find that BN can obtain much better test performance
than LN when TID keeps small through training. To suppress the explosion of
TID, we propose Regularized BN (RBN) that adds a simple regularization term to
narrow the gap between batch statistics and population statistics of BN. RBN
improves the performance of BN consistently and outperforms or is on par with
LN on 17 out of 20 settings, involving ten datasets and two common variants of
Transformer
  Our code is available at https://github.com/wjxts/RegularizedBN.

Semantic segmentation is a classic computer vision problem dedicated to
labeling each pixel with its corresponding category. As a basic task for
advanced tasks such as industrial quality inspection, remote sensing
information extraction, medical diagnostic aid, and autonomous driving,
semantic segmentation has been developed for a long time in combination with
deep learning, and a lot of works have been accumulated. However, neither the
classic FCN-based works nor the popular Transformer-based works have attained
fine-grained localization of pixel labels, which remains the main challenge in
this field. Recently, with the popularity of autonomous driving, the
segmentation of road scenes has received increasing attention. Based on the
cross-task consistency theory, we incorporate edge priors into semantic
segmentation tasks to obtain better results. The main contribution is that we
provide a model-agnostic method that improves the accuracy of semantic
segmentation models with zero extra inference runtime overhead, verified on the
datasets of road and non-road scenes. From our experimental results, our method
can effectively improve semantic segmentation accuracy.

Hierarchical Reinforcement Learning (HRL) has made notable progress in
complex control tasks by leveraging temporal abstraction. However, previous HRL
algorithms often suffer from serious data inefficiency as environments get
large. The extended components, $i.e.$, goal space and length of episodes,
impose a burden on either one or both high-level and low-level policies since
both levels share the total horizon of the episode. In this paper, we present a
method of Decoupling Horizons Using a Graph in Hierarchical Reinforcement
Learning (DHRL) which can alleviate this problem by decoupling the horizons of
high-level and low-level policies and bridging the gap between the length of
both horizons using a graph. DHRL provides a freely stretchable high-level
action interval, which facilitates longer temporal abstraction and faster
training in complex tasks. Our method outperforms state-of-the-art HRL
algorithms in typical HRL environments. Moreover, DHRL achieves long and
complex locomotion and manipulation tasks.

When large amounts of data continuously arrive in streams, online updating is
an effective way to reduce storage and computational burden. The key idea of
online updating is that the previous estimators are sequentially updated only
using the current data and some summary statistics of historical raw data. In
this article, we develop a renewable learning method for a multiplicative
regression model with streaming data, where the parameter estimator based on a
least product relative error criterion is renewed without revisiting any
historical raw data. Under some regularity conditions, we establish the
consistency and asymptotic normality of the renewable estimator. Moreover, the
theoretical results confirm that the proposed renewable estimator achieves the
same asymptotic distribution as the least product relative error estimator with
the entire dataset. Numerical studies and two real data examples are provided
to evaluate the performance of our proposed method.

In this paper we propose a novel generative approach, DiffRoll, to tackle
automatic music transcription (AMT). Instead of treating AMT as a
discriminative task in which the model is trained to convert spectrograms into
piano rolls, we think of it as a conditional generative task where we train our
model to generate realistic looking piano rolls from pure Gaussian noise
conditioned on spectrograms. This new AMT formulation enables DiffRoll to
transcribe, generate and even inpaint music. Due to the classifier-free nature,
DiffRoll is also able to be trained on unpaired datasets where only piano rolls
are available. Our experiments show that DiffRoll outperforms its
discriminative counterpart by 19 percentage points (ppt.) and our ablation
studies also indicate that it outperforms similar existing methods by 4.8 ppt.
  Source code and demonstration are available https://sony.github.io/DiffRoll/.

Zero-Shot Learning (ZSL) models aim to classify object classes that are not
seen during the training process. However, the problem of class imbalance is
rarely discussed, despite its presence in several ZSL datasets. In this paper,
we propose a Neural Network model that learns a latent feature embedding and a
Gaussian Process (GP) regression model that predicts latent feature prototypes
of unseen classes. A calibrated classifier is then constructed for ZSL and
Generalized ZSL tasks. Our Neural Network model is trained efficiently with a
simple training strategy that mitigates the impact of class-imbalanced training
data. The model has an average training time of 5 minutes and can achieve
state-of-the-art (SOTA) performance on imbalanced ZSL benchmark datasets like
AWA2, AWA1 and APY, while having relatively good performance on the SUN and CUB
datasets.

Building on recent advances in image generation, we present a fully
data-driven approach to rendering markup into images. The approach is based on
diffusion models, which parameterize the distribution of data using a sequence
of denoising operations on top of a Gaussian noise distribution. We view the
diffusion denoising process as a sequential decision making process, and show
that it exhibits compounding errors similar to exposure bias issues in
imitation learning problems. To mitigate these issues, we adapt the scheduled
sampling algorithm to diffusion training. We conduct experiments on four markup
datasets: mathematical formulas (LaTeX), table layouts (HTML), sheet music
(LilyPond), and molecular images (SMILES). These experiments each verify the
effectiveness of the diffusion process and the use of scheduled sampling to fix
generation issues. These results also show that the markup-to-image task
presents a useful controlled compositional setting for diagnosing and analyzing
generative image models.

Few-shot dialogue state tracking (DST) is a realistic problem that trains the
DST model with limited labeled data. Existing few-shot methods mainly transfer
knowledge learned from external labeled dialogue data (e.g., from question
answering, dialogue summarization, machine reading comprehension tasks, etc.)
into DST, whereas collecting a large amount of external labeled data is
laborious, and the external data may not effectively contribute to the
DST-specific task. In this paper, we propose a few-shot DST framework called
CSS, which Combines Self-training and Self-supervised learning methods. The
unlabeled data of the DST task is incorporated into the self-training
iterations, where the pseudo labels are predicted by a DST model trained on
limited labeled data in advance. Besides, a contrastive self-supervised method
is used to learn better representations, where the data is augmented by the
dropout operation to train the model. Experimental results on the MultiWOZ
dataset show that our proposed CSS achieves competitive performance in several
few-shot scenarios.

In this paper, we performs a credit risk analysis, on the data of past loan
applicants of a company named Lending Club. The calculation required the use of
exploratory data analysis and machine learning classification algorithms,
namely, Logistic Regression and Random Forest Algorithm. We further used the
calculated probability of default to design a credit derivative based on the
idea of a Credit Default Swap, to hedge against an event of default. The
results on the test set are presented using various performance measures.

Neural Radiance Fields (NeRFs), despite their outstanding performance on
novel view synthesis, often need dense input views. Many papers train one model
for each scene respectively and few of them explore incorporating multi-modal
data into this problem. In this paper, we focus on a rarely discussed but
important setting: can we train one model that can represent multiple scenes,
with 360$^\circ $ insufficient views and RGB-D images? We refer insufficient
views to few extremely sparse and almost non-overlapping views. To deal with
it, X-NeRF, a fully explicit approach which learns a general scene completion
process instead of a coordinate-based mapping, is proposed. Given a few
insufficient RGB-D input views, X-NeRF first transforms them to a sparse point
cloud tensor and then applies a 3D sparse generative Convolutional Neural
Network (CNN) to complete it to an explicit radiance field whose volumetric
rendering can be conducted fast without running networks during inference. To
avoid overfitting, besides common rendering loss, we apply perceptual loss as
well as view augmentation through random rotation on point clouds. The proposed
methodology significantly out-performs previous implicit methods in our
setting, indicating the great potential of proposed problem and approach. Codes
and data are available at https://github.com/HaoyiZhu/XNeRF.

Human pose estimation (HPE) for 3D skeleton reconstruction in telemedicine
has long received attention. Although the development of deep learning has made
HPE methods in telemedicine simpler and easier to use, addressing low accuracy
and high latency remains a big challenge. In this paper, we propose a novel
multi-view Attention Cube Regression Network (ACRNet), which regresses the 3D
position of joints in real time by aggregating informative attention points on
each cube surface. More specially, a cube whose each surface contains uniformly
distributed attention points with specific coordinate values is first created
to wrap the target from the main view. Then, our network regresses the 3D
position of each joint by summing and averaging the coordinates of attention
points on each surface after being weighted. To verify our method, we first
tested ACRNet on the open-source ITOP dataset; meanwhile, we collected a new
multi-view upper body movement dataset (UBM) on the trunk support trainer
(TruST) to validate the capability of our model in real rehabilitation
scenarios. Experimental results demonstrate the superiority of ACRNet compared
with other state-of-the-art methods. We also validate the efficacy of each
module in ACRNet. Furthermore, Our work analyzes the performance of ACRNet
under the medical monitoring indicator. Because of the high accuracy and
running speed, our model is suitable for real-time telemedicine settings. The
source code is available at https://github.com/BoceHu/ACRNet

Understanding and mapping a new environment are core abilities of any
autonomously navigating agent. While classical robotics usually estimates maps
in a stand-alone manner with SLAM variants, which maintain a topological or
metric representation, end-to-end learning of navigation keeps some form of
memory in a neural network. Networks are typically imbued with inductive
biases, which can range from vectorial representations to birds-eye metric
tensors or topological structures. In this work, we propose to structure neural
networks with two neural implicit representations, which are learned
dynamically during each episode and map the content of the scene: (i) the
Semantic Finder predicts the position of a previously seen queried object; (ii)
the Occupancy and Exploration Implicit Representation encapsulates information
about explored area and obstacles, and is queried with a novel global read
mechanism which directly maps from function space to a usable embedding space.
Both representations are leveraged by an agent trained with Reinforcement
Learning (RL) and learned online during each episode. We evaluate the agent on
Multi-Object Navigation and show the high impact of using neural implicit
representations as a memory source.

In the past decade, deep learning (DL)-based artificial intelligence (AI) has
witnessed unprecedented success and has led to much excitement in medicine.
However, many successful models have not been implemented in the clinic
predominantly due to concerns regarding the lack of interpretability and
generalizability in both spatial and temporal domains. In this work, we used a
DL-based auto segmentation model for intact prostate patients to observe any
temporal performance changes and then correlate them to possible explanatory
variables. We retrospectively simulated the clinical implementation of our DL
model to investigate temporal performance trends. Our cohort included 912
patients with prostate cancer treated with definitive radiotherapy from January
2006 to August 2021 at the University of Texas Southwestern Medical Center
(UTSW). We trained a U-Net-based DL auto segmentation model on the data
collected before 2012 and tested it on data collected from 2012 to 2021 to
simulate the clinical deployment of the trained model starting in 2012. We
visualize the trends using a simple moving average curve and used ANOVA and
t-test to investigate the impact of various clinical factors. The prostate and
rectum contour quality decreased rapidly after 2016-2017. Stereotactic body
radiotherapy (SBRT) and hydrogel spacer use were significantly associated with
prostate contour quality (p=5.6e-12 and 0.002, respectively). SBRT and
physicians' styles are significantly associated with the rectum contour quality
(p=0.0005 and 0.02, respectively). Only the presence of contrast within the
bladder significantly affected the bladder contour quality (p=1.6e-7). We
showed that DL model performance decreased over time in concordance with
changes in clinical practice patterns and changes in clinical personnel.

Instance-dependent label noise is realistic but rather challenging, where the
label-corruption process depends on instances directly. It causes a severe
distribution shift between the distributions of training and test data, which
impairs the generalization of trained models. Prior works put great effort into
tackling the issue. Unfortunately, these works always highly rely on strong
assumptions or remain heuristic without theoretical guarantees. In this paper,
to address the distribution shift in learning with instance-dependent label
noise, a dynamic distribution-calibration strategy is adopted. Specifically, we
hypothesize that, before training data are corrupted by label noise, each class
conforms to a multivariate Gaussian distribution at the feature level. Label
noise produces outliers to shift the Gaussian distribution. During training, to
calibrate the shifted distribution, we propose two methods based on the mean
and covariance of multivariate Gaussian distribution respectively. The
mean-based method works in a recursive dimension-reduction manner for robust
mean estimation, which is theoretically guaranteed to train a high-quality
model against label noise. The covariance-based method works in a distribution
disturbance manner, which is experimentally verified to improve the model
robustness. We demonstrate the utility and effectiveness of our methods on
datasets with synthetic label noise and real-world unknown noise.

We introduce a novel machine learning optimizer called LODO, which online
meta-learns an implicit inverse Hessian of the loss as a subroutine of
quasi-Newton optimization. Our optimizer merges Learning to Optimize (L2O)
techniques with quasi-Newton methods to learn neural representations of
symmetric matrix vector products, which are more flexible than those in other
quasi-Newton methods. Unlike other L2O methods, ours does not require any
meta-training on a training task distribution, and instead learns to optimize
on the fly while optimizing on the test task, adapting to the local
characteristics of the loss landscape while traversing it. Theoretically, we
show that our optimizer approximates the inverse Hessian in noisy loss
landscapes and is capable of representing a wide range of inverse Hessians. We
experimentally verify our algorithm's performance in the presence of noise, and
show that simpler alternatives for representing the inverse Hessians worsen
performance. Lastly, we use our optimizer to train a semi-realistic deep neural
network with 95k parameters, and obtain competitive results against standard
neural network optimizers.

We consider the problem of making AI agents that collaborate well with humans
in partially observable fully cooperative environments given datasets of human
behavior. Inspired by piKL, a human-data-regularized search method that
improves upon a behavioral cloning policy without diverging far away from it,
we develop a three-step algorithm that achieve strong performance in
coordinating with real humans in the Hanabi benchmark. We first use a
regularized search algorithm and behavioral cloning to produce a better human
model that captures diverse skill levels. Then, we integrate the policy
regularization idea into reinforcement learning to train a human-like best
response to the human model. Finally, we apply regularized search on top of the
best response policy at test time to handle out-of-distribution challenges when
playing with humans. We evaluate our method in two large scale experiments with
humans. First, we show that our method outperforms experts when playing with a
group of diverse human players in ad-hoc teams. Second, we show that our method
beats a vanilla best response to behavioral cloning baseline by having experts
play repeatedly with the two agents.

In this paper, we proposed a framework of constructing two types of the
automatic image aesthetics assessment models with different CNN architectures
and improving the performance of the image's aesthetic score prediction by the
ensemble. Moreover, the attention regions of the models to the images are
extracted to analyze the consistency with the subjects in the images. The
experimental results verify that the proposed method is effective for improving
the AS prediction. Moreover, it is found that the AS classification models
trained on XiheAA dataset seem to learn the latent photography principles,
although it can't be said that they learn the aesthetic sense.

The adversarial vulnerability of deep neural networks (DNNs) has been
actively investigated in the past several years. This paper investigates the
scale-variant property of cross-entropy loss, which is the most commonly used
loss function in classification tasks, and its impact on the effective margin
and adversarial robustness of deep neural networks. Since the loss function is
not invariant to logit scaling, increasing the effective weight norm will make
the loss approach zero and its gradient vanish while the effective margin is
not adequately maximized. On typical DNNs, we demonstrate that, if not properly
regularized, the standard training does not learn large effective margins and
leads to adversarial vulnerability. To maximize the effective margins and learn
a robust DNN, we propose to regularize the effective weight norm during
training. Our empirical study on feedforward DNNs demonstrates that the
proposed effective margin regularization (EMR) learns large effective margins
and boosts the adversarial robustness in both standard and adversarial
training. On large-scale models, we show that EMR outperforms basic adversarial
training, TRADES and two regularization baselines with substantial improvement.
Moreover, when combined with several strong adversarial defense methods (MART
and MAIL), our EMR further boosts the robustness.

Medical image super-resolution (SR) is an active research area that has many
potential applications, including reducing scan time, bettering visual
understanding, increasing robustness in downstream tasks, etc. However,
applying deep-learning-based SR approaches for clinical applications often
encounters issues of domain inconsistency, as the test data may be acquired by
different machines or on different organs. In this work, we present a novel
algorithm called domain adaptable volumetric super-resolution (DA-VSR) to
better bridge the domain inconsistency gap. DA-VSR uses a unified feature
extraction backbone and a series of network heads to improve image quality over
different planes. Furthermore, DA-VSR leverages the in-plane and through-plane
resolution differences on the test data to achieve a self-learned domain
adaptation. As such, DA-VSR combines the advantages of a strong feature
generator learned through supervised training and the ability to tune to the
idiosyncrasies of the test volumes through unsupervised learning. Through
experiments, we demonstrate that DA-VSR significantly improves super-resolution
quality across numerous datasets of different domains, thereby taking a further
step toward real clinical applications.

Despite the fast development of multi-agent reinforcement learning (MARL)
methods, there is a lack of commonly-acknowledged baseline implementation and
evaluation platforms. As a result, an urgent need for MARL researchers is to
develop an integrated library suite, similar to the role of RLlib in
single-agent RL, that delivers reliable MARL implementation and replicable
evaluation in various benchmarks. To fill such a research gap, in this paper,
we propose Multi-Agent RLlib (MARLlib), a comprehensive MARL algorithm library
that facilitates RLlib for solving multi-agent problems. With a novel design of
agent-level distributed dataflow, MARLlib manages to unify tens of algorithms,
including different types of independent learning, centralized critic, and
value decomposition methods; this leads to a highly composable integration of
MARL algorithms that are not possible to unify before. Furthermore, MARLlib
goes beyond current work by integrating diverse environment interfaces and
providing flexible parameter sharing strategies; this allows to create
versatile solutions to cooperative, competitive, and mixed tasks with minimal
code modifications for end users. A plethora of experiments are conducted to
substantiate the correctness of our implementation, based on which we further
derive new insights on the relationship between the performance and the design
of algorithmic components. With MARLlib, we expect researchers to be able to
tackle broader real-world multi-agent problems with trustworthy solutions. Our
code\footnote{\url{https://github.com/Replicable-MARL/MARLlib}} and
documentation\footnote{\url{https://marllib.readthedocs.io/}} are released for
reference.

Deep learning model compression is an improving and important field for the
edge deployment of deep learning models. Given the increasing size of the
models and their corresponding power consumption, it is vital to decrease the
model size and compute requirement without a significant drop in the model's
performance. In this paper, we present model compression algorithms for both
non-retraining and retraining conditions. In the first case where retraining of
the model is not feasible due to lack of access to the original data or absence
of necessary compute resources while only having access to off-the-shelf
models, we propose the Bin & Quant algorithm for compression of the deep
learning models using the sensitivity of the network parameters. This results
in 13x compression of the speech command and control model and 7x compression
of the DeepSpeech2 models. In the second case when the models can be retrained
and utmost compression is required for the negligible loss in accuracy, we
propose our novel gradient-weighted k-means clustering algorithm (GWK). This
method uses the gradients in identifying the important weight values in a given
cluster and nudges the centroid towards those values, thereby giving importance
to sensitive weights. Our method effectively combines product quantization with
the EWGS[1] algorithm for sub-1-bit representation of the quantized models. We
test our GWK algorithm on the CIFAR10 dataset across a range of models such as
ResNet20, ResNet56, MobileNetv2 and show 35x compression on quantized models
for less than 2% absolute loss in accuracy compared to the floating-point
models.

Risk and sparsity requirements often need to be enforced simultaneously in
many applications, e.g., in portfolio optimization, assortment planning, and
treatment planning. Properly balancing these potentially conflicting
requirements entails the formulation of functional constrained optimization
with either convex or nonconvex objectives. In this paper, we focus on
projection-free methods that can generate a sparse trajectory for solving these
challenging functional constrained optimization problems. Specifically, for the
convex setting, we propose a Level Conditional Gradient (LCG) method, which
leverages a level-set framework to update the approximation of the optimal
value and an inner conditional gradient oracle (CGO) for solving mini-max
subproblems. We show that the method achieves
$\mathcal{O}\big(\frac{1}{\epsilon^2}\log\frac{1}{\epsilon}\big)$ iteration
complexity for solving both smooth and nonsmooth cases without dependency on a
possibly large size of optimal dual Lagrange multiplier. For the nonconvex
setting, we introduce the Level Inexact Proximal Point (IPP-LCG) method and the
Direct Nonconvex Conditional Gradient (DNCG) method. The first approach taps
into the advantage of LCG by transforming the problem into a series of convex
subproblems and exhibits an
$\mathcal{O}\big(\frac{1}{\epsilon^3}\log\frac{1}{\epsilon}\big)$ iteration
complexity for finding an ($\epsilon,\epsilon$)-KKT point. The DNCG is the
first single-loop projection-free method, with iteration complexity bounded by
$\mathcal{O}\big(1/\epsilon^4\big)$ for computing a so-called $\epsilon$-Wolfe
point. We demonstrate the effectiveness of LCG, IPP-LCG and DNCG by devising
formulations and conducting numerical experiments on two risk averse sparse
optimization applications: a portfolio selection problem with and without
cardinality requirement, and a radiation therapy planning problem in
healthcare.

Usually, lesions are not isolated but are associated with the surrounding
tissues. For example, the growth of a tumour can depend on or infiltrate into
the surrounding tissues. Due to the pathological nature of the lesions, it is
challenging to distinguish their boundaries in medical imaging. However, these
uncertain regions may contain diagnostic information. Therefore, the simple
binarization of lesions by traditional binary segmentation can result in the
loss of diagnostic information. In this work, we introduce the image matting
into the 3D scenes and use the alpha matte, i.e., a soft mask, to describe
lesions in a 3D medical image. The traditional soft mask acted as a training
trick to compensate for the easily mislabelled or under-labelled ambiguous
regions. In contrast, 3D matting uses soft segmentation to characterize the
uncertain regions more finely, which means that it retains more structural
information for subsequent diagnosis and treatment. The current study of image
matting methods in 3D is limited. To address this issue, we conduct a
comprehensive study of 3D matting, including both traditional and
deep-learning-based methods. We adapt four state-of-the-art 2D image matting
algorithms to 3D scenes and further customize the methods for CT images to
calibrate the alpha matte with the radiodensity. Moreover, we propose the first
end-to-end deep 3D matting network and implement a solid 3D medical image
matting benchmark. Its efficient counterparts are also proposed to achieve a
good performance-computation balance. Furthermore, there is no high-quality
annotated dataset related to 3D matting, slowing down the development of
data-driven deep-learning-based methods. To address this issue, we construct
the first 3D medical matting dataset. The validity of the dataset was verified
through clinicians' assessments and downstream experiments.

Compiled software is delivered as executable binary code. Developers write
source code to express the software semantics, but the compiler converts it to
a binary format that the CPU can directly execute. Therefore, binary code
analysis is critical to applications in reverse engineering and computer
security tasks where source code is not available. However, unlike source code
and natural language that contain rich semantic information, binary code is
typically difficult for human engineers to understand and analyze. While
existing work uses AI models to assist source code analysis, few studies have
considered binary code. In this paper, we propose a COntrastive learning Model
for Binary cOde Analysis, or COMBO, that incorporates source code and comment
information into binary code during representation learning. Specifically, we
present three components in COMBO: (1) a primary contrastive learning method
for cold-start pre-training, (2) a simplex interpolation method to incorporate
source code, comments, and binary code, and (3) an intermediate representation
learning algorithm to provide binary code embeddings. Finally, we evaluate the
effectiveness of the pre-trained representations produced by COMBO using three
indicative downstream tasks relating to binary code: algorithmic functionality
classification, binary code similarity, and vulnerability detection. Our
experimental results show that COMBO facilitates representation learning of
binary code visualized by distribution analysis, and improves the performance
on all three downstream tasks by 5.45% on average compared to state-of-the-art
large-scale language representation models. To the best of our knowledge, COMBO
is the first language representation model that incorporates source code,
binary code, and comments into contrastive code representation learning and
unifies multiple tasks for binary code analysis.

The ability to extract high-quality translation dictionaries from monolingual
word embedding spaces depends critically on the geometric similarity of the
spaces -- their degree of "isomorphism." We address the root-cause of faulty
cross-lingual mapping: that word embedding training resulted in the underlying
spaces being non-isomorphic. We incorporate global measures of isomorphism
directly into the Skip-gram loss function, successfully increasing the relative
isomorphism of trained word embedding spaces and improving their ability to be
mapped to a shared cross-lingual space. The result is improved bilingual
lexicon induction in general data conditions, under domain mismatch, and with
training algorithm dissimilarities. We release IsoVec at
https://github.com/kellymarchisio/isovec.

Current lane detection methods are struggling with the invisibility lane
issue caused by heavy shadows, severe road mark degradation, and serious
vehicle occlusion. As a result, discriminative lane features can be barely
learned by the network despite elaborate designs due to the inherent
invisibility of lanes in the wild. In this paper, we target at finding an
enhanced feature space where the lane features are distinctive while
maintaining a similar distribution of lanes in the wild. To achieve this, we
propose a novel Repainting and Imitating Learning (RIL) framework containing a
pair of teacher and student without any extra data or extra laborious labeling.
Specifically, in the repainting step, an enhanced ideal virtual lane dataset is
built in which only the lane regions are repainted while non-lane regions are
kept unchanged, maintaining the similar distribution of lanes in the wild. The
teacher model learns enhanced discriminative representation based on the
virtual data and serves as the guidance for a student model to imitate. In the
imitating learning step, through the scale-fusing distillation module, the
student network is encouraged to generate features that mimic the teacher model
both on the same scale and cross scales. Furthermore, the coupled adversarial
module builds the bridge to connect not only teacher and student models but
also virtual and real data, adjusting the imitating learning process
dynamically. Note that our method introduces no extra time cost during
inference and can be plug-and-play in various cutting-edge lane detection
networks. Experimental results prove the effectiveness of the RIL framework
both on CULane and TuSimple for four modern lane detection methods. The code
and model will be available soon.

Code-switching is a common phenomenon among multilingual speakers, where
alternation between two or more languages occurs within the context of a single
conversation. While multilingual humans can seamlessly switch back and forth
between languages, multilingual neural machine translation (NMT) models are not
robust to such sudden changes in input. This work explores multilingual NMT
models' ability to handle code-switched text. First, we propose checks to
measure switching capability. Second, we investigate simple and effective data
augmentation methods that can enhance an NMT model's ability to support
code-switching. Finally, by using a glass-box analysis of attention modules, we
demonstrate the effectiveness of these methods in improving robustness.

A continuous-time dynamical system with parameter $\varepsilon$ is
nearly-periodic if all its trajectories are periodic with nowhere-vanishing
angular frequency as $\varepsilon$ approaches 0. Nearly-periodic maps are
discrete-time analogues of nearly-periodic systems, defined as
parameter-dependent diffeomorphisms that limit to rotations along a circle
action, and they admit formal $U(1)$ symmetries to all orders when the limiting
rotation is non-resonant. For Hamiltonian nearly-periodic maps on exact
presymplectic manifolds, the formal $U(1)$ symmetry gives rise to a
discrete-time adiabatic invariant. In this paper, we construct a novel
structure-preserving neural network to approximate nearly-periodic symplectic
maps. This neural network architecture, which we call symplectic gyroceptron,
ensures that the resulting surrogate map is nearly-periodic and symplectic, and
that it gives rise to a discrete-time adiabatic invariant and a long-time
stability. This new structure-preserving neural network provides a promising
architecture for surrogate modeling of non-dissipative dynamical systems that
automatically steps over short timescales without introducing spurious
instabilities.

The exploration--exploitation trade-off in reinforcement learning (RL) is a
well-known and much-studied problem that balances greedy action selection with
novel experience, and the study of exploration methods is usually only
considered in the context of learning the optimal policy for a single learning
task. However, in the context of online task transfer, where there is a change
to the task during online operation, we hypothesize that exploration strategies
that anticipate the need to adapt to future tasks can have a pronounced impact
on the efficiency of transfer. As such, we re-examine the
exploration--exploitation trade-off in the context of transfer learning. In
this work, we review reinforcement learning exploration methods, define a
taxonomy with which to organize them, analyze these methods' differences in the
context of task transfer, and suggest avenues for future investigation.

Robotic radiosurgery allows for marker-less lung tumor tracking by detecting
tumor density variations in 2D orthogonal X-ray images. The ability to detect
and track a lung lesion depends on its size, density, and location, and has to
be evaluated on a case-by-case basis. The current method for identifying which
patient can be successfully treated with fiducial-free lung tumor tracking is a
time-consuming process named Lung Optimized Treatment (LOT) simulation. The
process involves CT acquisition, generation of a simulation plan, creation of
the patient breathing model, and execution of the simulation plan on the
treatment delivery platform.
  The aim of the study is to develop a tool to enable binary classification of
trackable and non-trackable lung tumors for automatic selection of optimal
tracking methods for patient undergoing robotic radiosurgery without having to
perform the LOT simulation.
  We developed a deep learning classification model and tested 5 different
network architectures to classify lung cancer lesions from enhanced digitally
reconstructed radiographs (DRRs) generated from planning CTs. This study
included 129 patients with single or multiple lesions, for a total of 144 lung
lesions (n=115 trackable, n=29 untrackable). A total of 271 images were
included in our analysis. We kept 80% of the images for training, 10% for
validation, and the remaining 10% for testing.
  The binary classification accuracy reached 100% after training, both in the
validation and the test set.
  Candidates for fiducial-free lung tumor tracking during robotic lung
radiosurgery can be successfully identified by using a deep learning model
classifying DRR images sourced from simulation CT scans.

WiFi sensing is an important part of the new WiFi 802.11bf standard, which
can detect motion and measure distances. In recent years, some machine learning
methods have been proposed for human activity recognition from WiFi signals.
However, to the best of our knowledge, none of these methods have explored
orientation prediction of the user using WiFi signals. Orientation prediction
is particularly critical for human-machine interaction in an environment with
multiple smart devices. In this paper, we propose a data collection setup and
machine learning models for joint human orientation and activity recognition
using WiFi signals from a single access point (AP) or multiple APs. The results
show feasibility of joint orientation-activity recognition in an indoor
environment with a high accuracy.

The coronavirus pandemic has been going on since the year 2019, and the trend
is still not abating. Therefore, it is particularly important to classify
medical CT scans to assist in medical diagnosis. At present, Supervised Deep
Learning algorithms have made a great success in the classification task of
medical CT scans, but medical image datasets often require professional image
annotation, and many research datasets are not publicly available. To solve
this problem, this paper is inspired by the self-supervised learning algorithm
MAE and uses the MAE model pre-trained on ImageNet to perform transfer learning
on CT Scans dataset. This method improves the generalization performance of the
model and avoids the risk of overfitting on small datasets. Through extensive
experiments on the COVID-CT dataset and the SARS-CoV-2 dataset, we compare the
SSL-based method in this paper with other state-of-the-art supervised
learning-based pretraining methods. Experimental results show that our method
improves the generalization performance of the model more effectively and
avoids the risk of overfitting on small datasets. The model achieved almost the
same accuracy as supervised learning on both test datasets. Finally, ablation
experiments aim to fully demonstrate the effectiveness of our method and how it
works.

We study gravitational pivoting, a constrained version of in-hand
manipulation, where we aim to control the rotation of an object around the grip
point of a parallel gripper. To achieve this, instead of controlling the
gripper to avoid slip, we embrace slip to allow the object to rotate in-hand.
We collect two real-world datasets, a static tracking dataset and a
controller-in-the loop dataset, both annotated with object angle and angular
velocity labels. Both datasets contain force-based tactile information on ten
different household objects. We train an LSTM model to predict the angular
position and velocity of the held object from purely tactile data. We integrate
this model with a controller that opens and closes the gripper allowing the
object to rotate to desired relative angles. We conduct real-world experiments
where the robot is tasked to achieve a relative target angle. We show that our
approach outperforms a sliding-window based MLP in a zero-shot generalization
setting with unseen objects. Furthermore, we show a 16.6% improvement in
performance when the LSTM model is fine-tuned on a small set of data collected
with both the LSTM model and the controller in-the-loop. Code and videos are
available at https://rhys-newbury.github.io/projects/pivoting/

We present Variable Experience Rollout (VER), a technique for efficiently
scaling batched on-policy reinforcement learning in heterogenous environments
(where different environments take vastly different times to generate rollouts)
to many GPUs residing on, potentially, many machines. VER combines the
strengths of and blurs the line between synchronous and asynchronous on-policy
RL methods (SyncOnRL and AsyncOnRL, respectively). VER learns from on-policy
experience (like SyncOnRL) and has no synchronization points (like AsyncOnRL).
  VER leads to significant and consistent speed-ups across a broad range of
embodied navigation and mobile manipulation tasks in photorealistic 3D
simulation environments. Specifically, for PointGoal navigation and ObjectGoal
navigation in Habitat 1.0, VER is 60-100% faster (1.6-2x speedup) than DD-PPO,
the current state of art distributed SyncOnRL, with similar sample efficiency.
For mobile manipulation tasks (open fridge/cabinet, pick/place objects) in
Habitat 2.0 VER is 150% faster (2.5x speedup) on 1 GPU and 170% faster (2.7x
speedup) on 8 GPUs than DD-PPO. Compared to SampleFactory (the current
state-of-the-art AsyncOnRL), VER matches its speed on 1 GPU, and is 70% faster
(1.7x speedup) on 8 GPUs with better sample efficiency.
  We leverage these speed-ups to train chained skills for GeometricGoal
rearrangement tasks in the Home Assistant Benchmark (HAB). We find a surprising
emergence of navigation in skills that do not ostensible require any
navigation. Specifically, the Pick skill involves a robot picking an object
from a table. During training the robot was always spawned close to the table
and never needed to navigate. However, we find that if base movement is part of
the action space, the robot learns to navigate then pick an object in new
environments with 50% success, demonstrating surprisingly high
out-of-distribution generalization.

Many contrastive representation learning methods learn a single global
representation of an entire image. However, dense contrastive representation
learning methods such as DenseCL [19] can learn better representations for
tasks requiring stronger spatial localization of features, such as multi-label
classification, detection, and segmentation. In this work, we study how to
improve the quality of the representations learned by DenseCL by modifying the
training scheme and objective function, and propose DenseCL++. We also conduct
several ablation studies to better understand the effects of: (i) various
techniques to form dense negative pairs among augmentations of different
images, (ii) cross-view dense negative and positive pairs, and (iii) an
auxiliary reconstruction task. Our results show 3.5% and 4% mAP improvement
over SimCLR [3] and DenseCL in COCO multi-label classification. In COCO and VOC
segmentation tasks, we achieve 1.8% and 0.7% mIoU improvements over SimCLR,

Transformers flexibly operate over sets of real-valued vectors representing
task-specific entities and their attributes, where each vector might encode one
word-piece token and its position in a sequence, or some piece of information
that carries no position at all. But as set processors, transformers are at a
disadvantage in reasoning over more general graph-structured data where nodes
represent entities and edges represent relations between entities. To address
this shortcoming, we generalize transformer attention to consider and update
edge vectors in each transformer layer. We evaluate this relational transformer
on a diverse array of graph-structured tasks, including the large and
challenging CLRS Algorithmic Reasoning Benchmark. There, it dramatically
outperforms state-of-the-art graph neural networks expressly designed to reason
over graph-structured data. Our analysis demonstrates that these gains are
attributable to relational attention's inherent ability to leverage the greater
expressivity of graphs over sets.

Streaming anomaly detection refers to the problem of detecting anomalous data
samples in streams of data. This problem poses challenges that classical and
deep anomaly detection methods are not designed to cope with, such as
conceptual drift and continuous learning. State-of-the-art flow anomaly
detection methods rely on fixed memory using hash functions or nearest
neighbors that may not be able to constrain high-frequency values as in a
moving average or remove seamless outliers and cannot be trained in an
end-to-end deep learning architecture. We present a new incremental anomaly
detection method that performs continuous density estimation based on random
Fourier features and the mechanism of quantum measurements and density matrices
that can be viewed as an exponential moving average density. It can process
potentially endless data and its update complexity is constant $O(1)$. A
systematic evaluation against 12 state-of-the-art streaming anomaly detection
algorithms using 12 streaming datasets is presented.

Likelihood-to-evidence ratio estimation is usually cast as either a binary
(NRE-A) or a multiclass (NRE-B) classification task. In contrast to the binary
classification framework, the current formulation of the multiclass version has
an intrinsic and unknown bias term, making otherwise informative diagnostics
unreliable. We propose a multiclass framework free from the bias inherent to
NRE-B at optimum, leaving us in the position to run diagnostics that
practitioners depend on. It also recovers NRE-A in one corner case and NRE-B in
the limiting case. For fair comparison, we benchmark the behavior of all
algorithms in both familiar and novel training regimes: when jointly drawn data
is unlimited, when data is fixed but prior draws are unlimited, and in the
commonplace fixed data and parameters setting. Our investigations reveal that
the highest performing models are distant from the competitors (NRE-A, NRE-B)
in hyperparameter space. We make a recommendation for hyperparameters distinct
from the previous models. We suggest a bound on the mutual information as a
performance metric for simulation-based inference methods, without the need for
posterior samples, and provide experimental results.

Governments have to supervise and inspect social economy enterprises (SEEs).
However, inspecting all SEEs is not possible due to the large number of SEEs
and the low number of inspectors in general. We proposed a prediction model
based on a machine learning approach. The method was trained with the random
forest algorithm with historical data provided by each SEE. Three consecutive
periods of data were concatenated. The proposed method uses these periods as
input data and predicts the risk of each SEE in the fourth period. The model
achieved 76\% overall accuracy. In addition, it obtained good accuracy in
predicting the high risk of a SEE. We found that the legal nature and the
variation of the past-due portfolio are good predictors of the future risk of a
SEE. Thus, the risk of a SEE in a future period can be predicted by a
supervised machine learning method. Predicting the high risk of a SEE improves
the daily work of each inspector by focusing only on high-risk SEEs.

Neurosymbolic Programming (NP) techniques have the potential to accelerate
scientific discovery. These models combine neural and symbolic components to
learn complex patterns and representations from data, using high-level concepts
or known constraints. NP techniques can interface with symbolic domain
knowledge from scientists, such as prior knowledge and experimental context, to
produce interpretable outputs. We identify opportunities and challenges between
current NP models and scientific workflows, with real-world examples from
behavior analysis in science: to enable the use of NP broadly for workflows
across the natural and social sciences.

We explore zero-shot adaptation, where a general-domain model has access to
customer or domain specific parallel data at inference time, but not during
training. We build on the idea of Retrieval Augmented Translation (RAT) where
top-k in-domain fuzzy matches are found for the source sentence, and
target-language translations of those fuzzy-matched sentences are provided to
the translation model at inference time. We propose a novel architecture to
control interactions between a source sentence and the top-k fuzzy
target-language matches, and compare it to architectures from prior work. We
conduct experiments in two language pairs (En-De and En-Fr) by training models
on WMT data and testing them with five and seven multi-domain datasets,
respectively. Our approach consistently outperforms the alternative
architectures, improving BLEU across language pair, domain, and number k of
fuzzy matches.

Amorphous carbon nanotubes (a-CNT) with up to four walls and sizes ranging
from 200 to 3200 atoms have been simulated, starting from initial random
configurations and using the Gaussian Approximation Potential [Phys. Rev. B 95,
094203 (2017)]. The important variables (like density, height, and diameter)
required to successfully simulate a-CNTs, were predicted with a machine
learning random forest technique. The models were validated $\textit{ex post
facto}$ using density functional codes. The a-CNT models ranged from 0.55 nm -
2 nm wide with an average inter-wall spacing of 0.31 nm. The topological
defects in a-CNTs were discussed and new defect configurations were observed.
The electronic density of states and localization in these phases were
discussed and delocalized electrons in the $\pi$ subspace were identified as an
important factor for inter-layer cohesion. Spatial projection of the electronic
conductivity favors axial transport along connecting hexagons, while
non-hexagonal parts of the network either hinder or bifurcate the electronic
transport. A vibrational density of states was calculated and is potentially an
experimentally testable fingerprint of the material and the appearance of a
low-frequency radial breathing mode was discussed. The thermal conductivity at
300 K was calculated using the Green-Kubo formula.

Ensembling BERT models often significantly improves accuracy, but at the cost
of significantly more computation and memory footprint. In this work, we
propose Multi-CLS BERT, a novel ensembling method for CLS-based prediction
tasks that is almost as efficient as a single BERT model. Multi-CLS BERT uses
multiple CLS tokens with a parameterization and objective that encourages their
diversity. Thus instead of fine-tuning each BERT model in an ensemble (and
running them all at test time), we need only fine-tune our single Multi-CLS
BERT model (and run the one model at test time, ensembling just the multiple
final CLS embeddings). To test its effectiveness, we build Multi-CLS BERT on
top of a state-of-the-art pretraining method for BERT (Aroca-Ouellette and
Rudzicz, 2020). In experiments on GLUE and SuperGLUE we show that our Multi-CLS
BERT reliably improves both overall accuracy and confidence estimation. When
only 100 training samples are available in GLUE, the Multi-CLS BERT_Base model
can even outperform the corresponding BERT_Large model. We analyze the behavior
of our Multi-CLS BERT, showing that it has many of the same characteristics and
behavior as a typical BERT 5-way ensemble, but with nearly 4-times less
computation and memory.

Despite recent progress in video and language representation learning, the
weak or sparse correspondence between the two modalities remains a bottleneck
in the area. Most video-language models are trained via pair-level loss to
predict whether a pair of video and text is aligned. However, even in paired
video-text segments, only a subset of the frames are semantically relevant to
the corresponding text, with the remainder representing noise; where the ratio
of noisy frames is higher for longer videos. We propose FineCo (Fine-grained
Contrastive Loss for Frame Sampling), an approach to better learn video and
language representations with a fine-grained contrastive objective operating on
video frames. It helps distil a video by selecting the frames that are
semantically equivalent to the text, improving cross-modal correspondence.
Building on the well established VideoCLIP model as a starting point, FineCo
achieves state-of-the-art performance on YouCookII, a text-video retrieval
benchmark with long videos. FineCo also achieves competitive results on
text-video retrieval (MSR-VTT), and video question answering datasets (MSR-VTT
QA and MSR-VTT MC) with shorter videos.

Automated audio captioning (AAC) aims to describe the content of an audio
clip using simple sentences. Existing AAC methods are developed based on an
encoder-decoder architecture that success is attributed to the use of a
pre-trained CNN10 called PANNs as the encoder to learn rich audio
representations. AAC is a highly challenging task due to its high-dimensional
talent space involves audio of various scenarios. Existing methods only use the
high-dimensional representation of the PANNs as the input of the decoder.
However, the low-dimension representation may retain as much audio information
as the high-dimensional representation may be neglected. In addition, although
the high-dimensional approach may predict the audio captions by learning from
existing audio captions, which lacks robustness and efficiency. To deal with
these challenges, a fusion model which integrates low- and high-dimensional
features AAC framework is proposed. In this paper, a new encoder-decoder
framework is proposed called the Low- and High-Dimensional Feature Fusion
(LHDFF) model for AAC. Moreover, in LHDFF, a new PANNs encoder is proposed
called Residual PANNs (RPANNs) by fusing the low-dimensional feature from the
intermediate convolution layer output and the high-dimensional feature from the
final layer output of PANNs. To fully explore the information of the low- and
high-dimensional fusion feature and high-dimensional feature respectively, we
proposed dual transformer decoder structures to generate the captions in
parallel. Especially, a probabilistic fusion approach is proposed that can
ensure the overall performance of the system is improved by concentrating on
the respective advantages of the two transformer decoders. Experimental results
show that LHDFF achieves the best performance on the Clotho and AudioCaps
datasets compared with other existing models

Is it possible to build a general and automatic natural language generation
(NLG) evaluation metric? Existing learned metrics either perform
unsatisfactorily or are restricted to tasks where large human rating data is
already available. We introduce SESCORE, a model-based metric that is highly
correlated with human judgements without requiring human annotation, by
utilizing a novel, iterative error synthesis and severity scoring pipeline.
This pipeline applies a series of plausible errors to raw text and assigns
severity labels by simulating human judgements with entailment. We evaluate
SESCORE against existing metrics by comparing how their scores correlate with
human ratings. SESCORE outperforms all prior unsupervised metrics on multiple
diverse NLG tasks including machine translation, image captioning, and WebNLG
text generation. For WMT 20/21 En-De and Zh-En, SESCORE improve the average
Kendall correlation with human judgement from 0.154 to 0.195. SESCORE even
achieves comparable performance to the best supervised metric COMET, despite
receiving no human-annotated training data.

Autonomous driving perceives surroundings with line-of-sight sensors that are
compromised under environmental uncertainties. To achieve real time global
information in high definition map, we investigate to share perception
information among connected and automated vehicles. However, it is challenging
to achieve real time perception sharing under varying network dynamics in
automotive edge computing. In this paper, we propose a novel real time dynamic
map, named LiveMap to detect, match, and track objects on the road. We design
the data plane of LiveMap to efficiently process individual vehicle data with
multiple sequential computation components, including detection, projection,
extraction, matching and combination. We design the control plane of LiveMap to
achieve adaptive vehicular offloading with two new algorithms (central and
distributed) to balance the latency and coverage performance based on deep
reinforcement learning techniques. We conduct extensive evaluation through both
realistic experiments on a small-scale physical testbed and network simulations
on an edge network simulator. The results suggest that LiveMap significantly
outperforms existing solutions in terms of latency, coverage, and accuracy.

Multilingual sentence representations from large models can encode semantic
information from two or more languages and can be used for different
cross-lingual information retrieval tasks. In this paper, we integrate
contrastive learning into multilingual representation distillation and use it
for quality estimation of parallel sentences (find semantically similar
sentences that can be used as translations of each other). We validate our
approach with multilingual similarity search and corpus filtering tasks.
Experiments across different low-resource languages show that our method
significantly outperforms previous sentence encoders such as LASER, LASER3, and
LaBSE.

As advancements in technology and medicine are being made, many countries are
still unable to access quality medical care due to cost and lack of qualified
medical personnel. This discrepancy in healthcare has caused many preventable
deaths, either due to lack of detection or lack of care. One of the most
prevalent diseases in the world is pneumonia, an infection of the lungs that
killed 2.56 million people worldwide in 2017. In this same year, the United
States recorded a pneumonia death rate of 15.88 people per 100000 in
population, while much of Sub-Saharan Africa, such as Chad and Guinea,
experienced death rates of over 150 people per 100000. In sub-Saharan Africa,
there is an extreme shortage of doctors and nurses, estimated to be around 2.4
million. The hypothesis being tested is that a deep learning model can receive
input in the form of an x-ray and produce a diagnosis with the equivalent
accuracy of a physician, compared to a prediagnosed image. The model used in
this project is a modified convolutional neural network. The model was trained
on a set of 2000 x-ray images that have predetermined normal and abnormal lung
findings, and then tested on a set of 400 images that contains evenly split
images of pneumonia and healthy lungs. For each computer-run test, data was
collected on a base measurement of accuracy, as well as more specific metrics
such as specificity and sensitivity. Results show that the algorithm tested was
able to accurately identify abnormal lung findings an average of 82.5% of the
time. The model achieved a maximum specificity of 98.5% and a maximum
sensitivity of 90% separately, and the highest simultaneous values of these two
metrics was a sensitivity of 90% and a specificity of 78.5%. This research can
be further improved by testing other deep learning models as well as machine
learning models to improve the metric scores and chance of correct diagnoses.

Data augmentation (DA) is a powerful workhorse for bolstering performance in
modern machine learning. Specific augmentations like translations and scaling
in computer vision are traditionally believed to improve generalization by
generating new (artificial) data from the same distribution. However, this
traditional viewpoint does not explain the success of prevalent augmentations
in modern machine learning (e.g. randomized masking, cutout, mixup), that
greatly alter the training data distribution. In this work, we develop a new
theoretical framework to characterize the impact of a general class of DA on
underparameterized and overparameterized linear model generalization. Our
framework reveals that DA induces implicit spectral regularization through a
combination of two distinct effects: a) manipulating the relative proportion of
eigenvalues of the data covariance matrix in a training-data-dependent manner,
and b) uniformly boosting the entire spectrum of the data covariance matrix
through ridge regression. These effects, when applied to popular augmentations,
give rise to a wide variety of phenomena, including discrepancies in
generalization between over-parameterized and under-parameterized regimes and
differences between regression and classification tasks. Our framework
highlights the nuanced and sometimes surprising impacts of DA on
generalization, and serves as a testbed for novel augmentation design.

Machine learning models are vulnerable to biases that result in unfair
treatment of individuals from different populations. Recent work that aims to
test a model's fairness at the individual level either relies on domain
knowledge to choose metrics, or on input transformations that risk generating
out-of-domain samples. We describe a new approach for testing individual
fairness that does not have either requirement. We propose a novel criterion
for evaluating individual fairness and develop a practical testing method based
on this criterion which we call fAux (pronounced fox). This is based on
comparing the derivatives of the predictions of the model to be tested with
those of an auxiliary model, which predicts the protected variable from the
observed data. We show that the proposed method effectively identifies
discrimination on both synthetic and real-world datasets, and has quantitative
and qualitative advantages over contemporary methods.

Partially observable Markov decision processes (POMDPs) provide a flexible
representation for real-world decision and control problems. However, POMDPs
are notoriously difficult to solve, especially when the state and observation
spaces are continuous or hybrid, which is often the case for physical systems.
While recent online sampling-based POMDP algorithms that plan with observation
likelihood weighting have shown practical effectiveness, a general theory
bounding the approximation error of the particle filtering techniques that
these algorithms use has not previously been proposed. Our main contribution is
to formally justify that optimality guarantees in a finite sample particle
belief MDP (PB-MDP) approximation of a POMDP/belief MDP yields optimality
guarantees in the original POMDP as well. This fundamental bridge between
PB-MDPs and POMDPs allows us to adapt any sampling-based MDP algorithm of
choice to a POMDP by solving the corresponding particle belief MDP
approximation and preserve the convergence guarantees in the POMDP.
Practically, this means additionally assuming access to the observation density
model, and simply swapping out the state transition generative model with a
particle filtering-based model, which only increases the computational
complexity by a factor of $\mathcal{O}(C)$, with $C$ the number of particles in
a particle belief state. In addition to our theoretical contribution, we
perform five numerical experiments on benchmark POMDPs to demonstrate that a
simple MDP algorithm adapted using PB-MDP approximation, Sparse-PFT, achieves
performance competitive with other leading continuous observation POMDP
solvers.

In recent years, Monte Carlo tree search (MCTS) has achieved widespread
adoption within the game community. Its use in conjunction with deep
reinforcement learning has produced success stories in many applications. While
these approaches have been implemented in various games, from simple board
games to more complicated video games such as StarCraft, the use of deep neural
networks requires a substantial training period. In this work, we explore
on-line adaptivity in MCTS without requiring pre-training. We present MCTS-TD,
an adaptive MCTS algorithm improved with temporal difference learning. We
demonstrate our new approach on the game miniXCOM, a simplified version of
XCOM, a popular commercial franchise consisting of several turn-based tactical
games, and show how adaptivity in MCTS-TD allows for improved performances
against opponents.

Transfer learning based approaches have recently achieved promising results
on the few-shot detection task. These approaches however suffer from
``catastrophic forgetting'' issue due to finetuning of base detector, leading
to sub-optimal performance on the base classes. Furthermore, the slow
convergence rate of stochastic gradient descent (SGD) results in high latency
and consequently restricts real-time applications. We tackle the aforementioned
issues in this work. We pose few-shot detection as a hierarchical learning
problem, where the novel classes are treated as the child classes of existing
base classes and the background class. The detection heads for the novel
classes are then trained using a specialized optimization strategy, leading to
significantly lower training times compared to SGD. Our approach obtains
competitive novel class performance on few-shot MS-COCO benchmark, while
completely retaining the performance of the initial model on the base classes.
We further demonstrate the application of our approach to a new class-refined
few-shot detection task.

As the technology industry is moving towards implementing tasks such as
natural language processing, path planning, image classification, and more on
smaller edge computing devices, the demand for more efficient implementations
of algorithms and hardware accelerators has become a significant area of
research. In recent years, several edge deep learning hardware accelerators
have been released that specifically focus on reducing the power and area
consumed by deep neural networks (DNNs). On the other hand, spiking neural
networks (SNNs) which operate on discrete time-series data, have been shown to
achieve substantial power reductions over even the aforementioned edge DNN
accelerators when deployed on specialized neuromorphic event-based/asynchronous
hardware. While neuromorphic hardware has demonstrated great potential for
accelerating deep learning tasks at the edge, the current space of algorithms
and hardware is limited and still in rather early development. Thus, many
hybrid approaches have been proposed which aim to convert pre-trained DNNs into
SNNs. In this work, we provide a general guide to converting pre-trained DNNs
into SNNs while also presenting techniques to improve the deployment of
converted SNNs on neuromorphic hardware with respect to latency, power, and
energy. Our experimental results show that when compared against the Intel
Neural Compute Stick 2, Intel's neuromorphic processor, Loihi, consumes up to
27x less power and 5x less energy in the tested image classification tasks by
using our SNN improvement techniques.

Social media apps have become very promising and omnipresent in daily life.
Most social media apps are used to deliver vital information to those nearby
and far away. As our lives become more hectic, many of us strive to limit our
usage of social media apps because they are too addictive, and the majority of
us have gotten preoccupied with our daily lives. Because of this, we frequently
overlook crucial information, such as invitations to weddings, interviews,
birthday parties, etc., or find ourselves unable to attend the event. In most
cases, this happens because users are more likely to discover the invitation or
information only before the event, giving them little time to prepare. To solve
this issue, in this study, we created a system that will collect social media
chat and filter it using Natural Language Processing (NLP) methods like
Tokenization, Stop Words Removal, Lemmatization, Segmentation, and Named Entity
Recognition (NER). Also, Machine Learning Algorithms such as K-Nearest Neighbor
(KNN) Algorithm are implemented to prioritize the received invitation and to
sort the level of priority. Finally, a customized notification will be
delivered to the users where they acknowledge the upcoming event. So, the
chances of missing the event are less or can be planned.

Three key properties that are desired of trustworthy machine learning models
deployed in high-stakes environments are fairness, explainability, and an
ability to account for various kinds of "drift". While drifts in model
accuracy, for example due to covariate shift, have been widely investigated,
drifts in fairness metrics over time remain largely unexplored. In this paper,
we propose FEAMOE, a novel "mixture-of-experts" inspired framework aimed at
learning fairer, more explainable/interpretable models that can also rapidly
adjust to drifts in both the accuracy and the fairness of a classifier. We
illustrate our framework for three popular fairness measures and demonstrate
how drift can be handled with respect to these fairness constraints.
Experiments on multiple datasets show that our framework as applied to a
mixture of linear experts is able to perform comparably to neural networks in
terms of accuracy while producing fairer models. We then use the large-scale
HMDA dataset and show that while various models trained on HMDA demonstrate
drift with respect to both accuracy and fairness, FEAMOE can ably handle these
drifts with respect to all the considered fairness measures and maintain model
accuracy as well. We also prove that the proposed framework allows for
producing fast Shapley value explanations, which makes computationally
efficient feature attribution based explanations of model decisions readily
available via FEAMOE.

Large-scale linear models are ubiquitous throughout machine learning, with
contemporary application as surrogate models for neural network uncertainty
quantification; that is, the linearised Laplace method. Alas, the computational
cost associated with Bayesian linear models constrains this method's
application to small networks, small output spaces and small datasets. We
address this limitation by introducing a scalable sample-based Bayesian
inference method for conjugate Gaussian multi-output linear models, together
with a matching method for hyperparameter (regularisation) selection.
Furthermore, we use a classic feature normalisation method (the g-prior) to
resolve a previously highlighted pathology of the linearised Laplace method.
Together, these contributions allow us to perform linearised neural network
inference with ResNet-18 on CIFAR100 (11M parameters, 100 output dimensions x
50k datapoints) and with a U-Net on a high-resolution tomographic
reconstruction task (2M parameters, 251k output dimensions).

Lifelong learners must recognize concept vocabularies that evolve over time.
A common yet underexplored scenario is learning with class labels over time
that refine/expand old classes. For example, humans learn to recognize ${\tt
dog}$ before dog breeds. In practical settings, dataset $\textit{versioning}$
often introduces refinement to ontologies, such as autonomous vehicle
benchmarks that refine a previous ${\tt vehicle}$ class into ${\tt school-bus}$
as autonomous operations expand to new cities. This paper formalizes a protocol
for studying the problem of $\textit{Learning with Evolving Class Ontology}$
(LECO). LECO requires learning classifiers in distinct time periods (TPs); each
TP introduces a new ontology of "fine" labels that refines old ontologies of
"coarse" labels (e.g., dog breeds that refine the previous ${\tt dog}$). LECO
explores such questions as whether to annotate new data or relabel the old, how
to leverage coarse labels, and whether to finetune the previous TP's model or
train from scratch. To answer these questions, we leverage insights from
related problems such as class-incremental learning. We validate them under the
LECO protocol through the lens of image classification (CIFAR and iNaturalist)
and semantic segmentation (Mapillary). Our experiments lead to surprising
conclusions; while the current status quo is to relabel existing datasets with
new ontologies (such as COCO-to-LVIS or Mapillary1.2-to-2.0), LECO demonstrates
that a far better strategy is to annotate $\textit{new}$ data with the new
ontology. However, this produces an aggregate dataset with inconsistent
old-vs-new labels, complicating learning. To address this challenge, we adopt
methods from semi-supervised and partial-label learning. Such strategies can
surprisingly be made near-optimal, approaching an "oracle" that learns on the
aggregate dataset exhaustively labeled with the newest ontology.

The ability to accurately predict public transit ridership demand benefits
passengers and transit agencies. Agencies will be able to reallocate buses to
handle under or over-utilized bus routes, improving resource utilization, and
passengers will be able to adjust and plan their schedules to avoid overcrowded
buses and maintain a certain level of comfort. However, accurately predicting
occupancy is a non-trivial task. Various reasons such as heterogeneity,
evolving ridership patterns, exogenous events like weather, and other
stochastic variables, make the task much more challenging. With the progress of
big data, transit authorities now have access to real-time passenger occupancy
information for their vehicles. The amount of data generated is staggering.
While there is no shortage in data, it must still be cleaned, processed,
augmented, and merged before any useful information can be generated. In this
paper, we propose the use and fusion of data from multiple sources, cleaned,
processed, and merged together, for use in training machine learning models to
predict transit ridership. We use data that spans a 2-year period (2020-2022)
incorporating transit, weather, traffic, and calendar data. The resulting data,
which equates to 17 million observations, is used to train separate models for
the trip and stop level prediction. We evaluate our approach on real-world
transit data provided by the public transit agency of Nashville, TN. We
demonstrate that the trip level model based on Xgboost and the stop level model
based on LSTM outperform the baseline statistical model across the entire
transit service day.

Coverage Path Planning involves visiting every unoccupied state in an
environment with obstacles. In this paper, we explore this problem in
environments which are initially unknown to the agent, for purposes of
simulating the task of a vacuum cleaning robot. A survey of prior work reveals
sparse effort in applying learning to solve this problem. In this paper, we
explore modeling a Cover Path Planning problem using Deep Reinforcement
Learning, and compare it with the performance of the built-in algorithm of the
Roomba, a popular vacuum cleaning robot.

In inverse problems we aim to reconstruct some underlying signal of interest
from potentially corrupted and often ill-posed measurements. Classical
optimization-based techniques proceed by optimizing a data consistency metric
together with a regularizer. Current state-of-the-art machine learning
approaches draw inspiration from such techniques by unrolling the iterative
updates for an optimization-based solver and then learning a regularizer from
data. This loop unrolling (LU) method has shown tremendous success, but often
requires a deep model for the best performance leading to high memory costs
during training. Thus, to address the balance between computation cost and
network expressiveness, we propose an LU algorithm with shallow equilibrium
regularizers (LUSER). These implicit models are as expressive as deeper
convolutional networks, but far more memory efficient during training. The
proposed method is evaluated on image deblurring, computed tomography (CT), as
well as single-coil Magnetic Resonance Imaging (MRI) tasks and shows similar,
or even better, performance while requiring up to 8 times less computational
resources during training when compared against a more typical LU architecture
with feedforward convolutional regularizers.

Background: Segmentation and measurement of cardiac chambers is critical in
echocardiography but is also laborious and poorly reproducible. Neural networks
can assist, but supervised approaches require the same laborious manual
annotations, while unsupervised approaches have fared poorly in ultrasound to
date. Objectives: We built a pipeline for self-supervised (no manual labels
required) segmentation of cardiac chambers, combining computer vision, clinical
domain knowledge, and deep learning. Methods: We trained on 450 echocardiograms
(145,000 images) and tested on 8,393 echocardiograms (4,476,266 images; mean
age 61 years, 51% female), using the resulting segmentations to calculate
structural and functional measurements. We also tested our pipeline against
external images from an additional 10,030 patients (20,060 images) with
available manual tracings of the left ventricle. Results: r2 between clinically
measured and pipeline-predicted measurements were similar to reported
inter-clinician variation for LVESV and LVEDV (pipeline vs. clinical r2= 0.74
and r2=0.65, respectively), LVEF and LV mass (r2= 0.46 and r2=0.54), left and
right atrium volumes (r2=0.7 and r2=0.6), and right ventricle area (r2=0.47).
When binarized into normal vs. abnormal categories, average accuracy was 0.81
(range 0.71-0.95). A subset of the test echocardiograms (n=553) had
corresponding cardiac MRI; correlation between pipeline and CMR measurements
was similar to that between clinical echocardiogram and CMR. Finally, in the
external dataset, our pipeline accurately segments the left ventricle with an
average Dice score of 0.83 (95% CI 0.83). Conclusions: Our results demonstrate
a human-label-free, valid, and scalable method for segmentation from
ultrasound, a noisy but globally important imaging modality.

While domain-specific data augmentation can be useful in training neural
networks for medical imaging tasks, such techniques have not been widely used
to date. Here, we test whether domain-specific data augmentation is useful for
medical imaging using a well-benchmarked task: view classification on fetal
ultrasound FETAL-125 and OB-125 datasets. We found that using a
context-preserving cut-paste strategy, we could create valid training data as
measured by performance of the resulting trained model on the benchmark test
dataset. When used in an online fashion, models trained on this data performed
similarly to those trained using traditional data augmentation (FETAL-125
F-score 85.33+/-0.24 vs 86.89+/-0.60, p-value 0.0139; OB-125 F-score
74.60+/-0.11 vs 72.43+/-0.62, p-value 0.0039). Furthermore, the ability to
perform augmentations during training time, as well as the ability to apply
chosen augmentations equally across data classes, are important considerations
in designing a bespoke data augmentation. Finally, we provide open-source code
to facilitate running bespoke data augmentations in an online fashion. Taken
together, this work expands the ability to design and apply domain-guided data
augmentations for medical imaging tasks.

Future wireless networks require high throughput and energy efficiency. This
paper studies using Reinforcement Learning (RL) to do transmission rate and
power control for maximizing a joint reward function consisting of both
throughput and energy consumption. We design the system state to include
factors that reflect packet queue length, interference from other nodes,
quality of the wireless channel, battery status, etc. The reward function is
normalized and does not involve unit conversion. It can be used to train three
different types of agents: throughput-critical, energy-critical, and throughput
and energy balanced. Using the NS-3 network simulation software, we implement
and train these agents in an 802.11ac network with the presence of a jammer. We
then test the agents with two jamming nodes interfering with the packets
received at the receiver. We compare the performance of our RL optimal policies
with the popular Minstrel rate adaptation algorithm: our approach can achieve
(i) higher throughput when using the throughput-critical reward function; (ii)
lower energy consumption when using the energy-critical reward function; and
(iii) higher throughput and slightly higher energy when using the throughput
and energy balanced reward function. Although our discussion is focused on
802.11ac networks, our method is readily applicable to other types of wireless
networks.

We propose a new family of regularized R\'enyi divergences parametrized not
only by the order $\alpha$ but also by a variational function space. These new
objects are defined by taking the infimal convolution of the standard R\'enyi
divergence with the integral probability metric (IPM) associated with the
chosen function space. We derive a novel dual variational representation that
can be used to construct numerically tractable divergence estimators. This
representation avoids risk-sensitive terms and therefore exhibits lower
variance, making it well-behaved when $\alpha>1$; this addresses a notable
weakness of prior approaches. We prove several properties of these new
divergences, showing that they interpolate between the classical R\'enyi
divergences and IPMs. We also study the $\alpha\to\infty$ limit, which leads to
a regularized worst-case-regret and a new variational representation in the
classical case. Moreover, we show that the proposed regularized R\'enyi
divergences inherit features from IPMs such as the ability to compare
distributions that are not absolutely continuous, e.g., empirical measures and
distributions with low-dimensional support. We present numerical results on
both synthetic and real datasets, showing the utility of these new divergences
in both estimation and GAN training applications; in particular, we demonstrate
significantly reduced variance and improved training performance.

As machine learning permeates more industries and models become more
expensive and time consuming to train, the need for efficient automated
hyperparameter optimization (HPO) has never been more pressing. Multi-step
planning based approaches to hyperparameter optimization promise improved
efficiency over myopic alternatives by more effectively balancing out
exploration and exploitation. However, the potential of these approaches has
not been fully realized due to their technical complexity and computational
intensity. In this work, we leverage recent advances in Transformer-based,
natural-language-interfaced hyperparameter optimization to circumvent these
barriers. We build on top of the recently proposed OptFormer which casts both
hyperparameter suggestion and target function approximation as autoregressive
generation thus making planning via rollouts simple and efficient. We conduct
extensive exploration of different strategies for performing multi-step
planning on top of the OptFormer model to highlight its potential for use in
constructing non-myopic HPO strategies.

Continuous-time dynamics models, such as neural ordinary differential
equations, have enabled the modeling of underlying dynamics in time-series data
and accurate forecasting. However, parameterization of dynamics using a neural
network makes it difficult for humans to identify causal structures in the
data. In consequence, this opaqueness hinders the use of these models in the
domains where capturing causal relationships carries the same importance as
accurate predictions, e.g., tsunami forecasting. In this paper, we address this
challenge by proposing a mechanism for mining causal structures from
continuous-time models. We train models to capture the causal structure by
enforcing sparsity in the weights of the input layers of the dynamics models.
We first verify the effectiveness of our method in the scenario where the exact
causal-structures of time-series are known as a priori. We next apply our
method to a real-world problem, namely tsunami forecasting, where the exact
causal-structures are difficult to characterize. Experimental results show that
the proposed method is effective in learning physically-consistent causal
relationships while achieving high forecasting accuracy.

The results of the Anomalous Diffusion Challenge (AnDi Challenge) have shown
that machine learning methods can outperform classical statistical methodology
at the characterization of anomalous diffusion in both the inference of the
anomalous diffusion exponent alpha associated with each trajectory (Task 1),
and the determination of the underlying diffusive regime which produced such
trajectories (Task 2). Furthermore, of the five teams that finished in the top
three across both tasks of the AnDi challenge, three of those teams used
recurrent neural networks (RNNs). While RNNs, like the long short-term memory
(LSTM) network, are effective at learning long-term dependencies in sequential
data, their key disadvantage is that they must be trained sequentially. In
order to facilitate training with larger data sets, by training in parallel, we
propose a new transformer based neural network architecture for the
characterization of anomalous diffusion. Our new architecture, the
Convolutional Transformer (ConvTransformer) uses a bi-layered convolutional
neural network to extract features from our diffusive trajectories that can be
thought of as being words in a sentence. These features are then fed to two
transformer encoding blocks that perform either regression or classification.
To our knowledge, this is the first time transformers have been used for
characterizing anomalous diffusion. Moreover, this may be the first time that a
transformer encoding block has been used with a convolutional neural network
and without the need for a transformer decoding block or positional encoding.
Apart from being able to train in parallel, we show that the ConvTransformer is
able to outperform the previous state of the art at determining the underlying
diffusive regime in short trajectories (length 10-50 steps), which are the most
important for experimental researchers.

Diffusion models (DMs) have recently emerged as SoTA tools for generative
modeling in various domains. Standard DMs can be viewed as an instantiation of
hierarchical variational autoencoders (VAEs) where the latent variables are
inferred from input-centered Gaussian distributions with fixed scales and
variances. Unlike VAEs, this formulation limits DMs from changing the latent
spaces and learning abstract representations. In this work, we propose f-DM, a
generalized family of DMs which allows progressive signal transformation. More
precisely, we extend DMs to incorporate a set of (hand-designed or learned)
transformations, where the transformed input is the mean of each diffusion
step. We propose a generalized formulation and derive the corresponding
de-noising objective with a modified sampling algorithm. As a demonstration, we
apply f-DM in image generation tasks with a range of functions, including
down-sampling, blurring, and learned transformations based on the encoder of
pretrained VAEs. In addition, we identify the importance of adjusting the noise
levels whenever the signal is sub-sampled and propose a simple rescaling
recipe. f-DM can produce high-quality samples on standard image generation
benchmarks like FFHQ, AFHQ, LSUN, and ImageNet with better efficiency and
semantic interpretation.

In real-world applications, the process generating the data might suffer from
nonstationary effects (e.g., due to seasonality, faults affecting sensors or
actuators, and changes in the users' behaviour). These changes, often called
concept drift, might induce severe (potentially catastrophic) impacts on
trained learning models that become obsolete over time, and inadequate to solve
the task at hand. Learning in presence of concept drift aims at designing
machine and deep learning models that are able to track and adapt to concept
drift. Typically, techniques to handle concept drift are either active or
passive, and traditionally, these have been considered to be mutually
exclusive. Active techniques use an explicit drift detection mechanism, and
re-train the learning algorithm when concept drift is detected. Passive
techniques use an implicit method to deal with drift, and continually update
the model using incremental learning. Differently from what present in the
literature, we propose a hybrid alternative which merges the two approaches,
hence, leveraging on their advantages. The proposed method called
Hybrid-Adaptive REBAlancing (HAREBA) significantly outperforms strong baselines
and state-of-the-art methods in terms of learning quality and speed; we
experiment how it is effective under severe class imbalance levels too.

We study the sample complexity of learning an $\epsilon$-optimal policy in
the Stochastic Shortest Path (SSP) problem. We first derive sample complexity
bounds when the learner has access to a generative model. We show that there
exists a worst-case SSP instance with $S$ states, $A$ actions, minimum cost
$c_{\min}$, and maximum expected cost of the optimal policy over all states
$B_{\star}$, where any algorithm requires at least
$\Omega(SAB_{\star}^3/(c_{\min}\epsilon^2))$ samples to return an
$\epsilon$-optimal policy with high probability. Surprisingly, this implies
that whenever $c_{\min}=0$ an SSP problem may not be learnable, thus revealing
that learning in SSPs is strictly harder than in the finite-horizon and
discounted settings. We complement this result with lower bounds when prior
knowledge of the hitting time of the optimal policy is available and when we
restrict optimality by competing against policies with bounded hitting time.
Finally, we design an algorithm with matching upper bounds in these cases. This
settles the sample complexity of learning $\epsilon$-optimal polices in SSP
with generative models.
  We also initiate the study of learning $\epsilon$-optimal policies without
access to a generative model (i.e., the so-called best-policy identification
problem), and show that sample-efficient learning is impossible in general. On
the other hand, efficient learning can be made possible if we assume the agent
can directly reach the goal state from any state by paying a fixed cost. We
then establish the first upper and lower bounds under this assumption.
  Finally, using similar analytic tools, we prove that horizon-free regret is
impossible in SSPs under general costs, resolving an open problem in
(Tarbouriech et al., 2021c).

Low-dose computed tomography (LDCT) reduces the X-ray radiation but
compromises image quality with more noises and artifacts. A plethora of
transformer models have been developed recently to improve LDCT image quality.
However, the success of a transformer model relies on a large amount of paired
noisy and clean data, which is often unavailable in clinical applications. In
computer vision and natural language processing fields, masked autoencoders
(MAE) have been proposed as an effective label-free self-pretraining method for
transformers, due to its excellent feature representation ability. Here, we
redesign the classical encoder-decoder learning model to match the denoising
task and apply it to LDCT denoising problem. The MAE can leverage the unlabeled
data and facilitate structural preservation for the LDCT denoising model when
ground truth data are missing. Experiments on the Mayo dataset validate that
the MAE can boost the transformer's denoising performance and relieve the
dependence on the ground truth data.

One of the central problems in neuroscience is understanding how brain
structure relates to function. Naively one can relate the direct connections of
white matter fiber tracts between brain regions of interest (ROIs) to the
increased co-activation in the same pair of ROIs, but the link between
structural and functional connectomes (SCs and FCs) has proven to be much more
complex. To learn a realistic generative model characterizing population
variation in SCs, FCs, and the SC-FC coupling, we develop a graph auto-encoder
that we refer to as Staf-GATE. We trained Staf-GATE with data from the Human
Connectome Project (HCP) and show state-of-the-art performance in predicting FC
and joint generation of SC and FC. In addition, as a crucial component of the
proposed approach, we provide a masking-based algorithm to extract
interpretable inference about SC-FC coupling. Our interpretation methods
identified important cross-hemisphere and right-hemisphere SC subnetworks for
FC coupling and relating SC and FC with cognitive scores and gender.

Earth observation, aiming at monitoring the state of planet Earth using
remote sensing data, is critical for improving our daily lives and living
environment. With an increasing number of satellites in orbit, more and more
datasets with diverse sensors and research domains are published to facilitate
the research of the remote sensing community. In this paper, for the first
time, we present a comprehensive review of more than 400 publicly published
datasets, including applications like, land use/cover, change/disaster
monitoring, scene understanding, agriculture, climate change and weather
forecasting. We systemically analyze these Earth observation datasets from five
aspects, including the volume, bibliometric analysis, research domains and the
correlation between datasets. Based on the dataset attributes, we propose to
measure, rank and select datasets to build a new benchmark for model
evaluation. Furthermore, a new platform for Earth observation, termed
EarthNets, is released towards a fair and consistent evaluation of deep
learning methods on remote sensing data. EarthNets supports standard dataset
libraries and cutting-edge deep learning models to bridge the gap between
remote sensing and the machine learning community. Based on the EarthNets
platform, extensive deep learning methods are evaluated on the new benchmark.
The insightful results are beneficial to future research. The platform, dataset
collections are publicly available at https://earthnets.nicepage.io.

Micro expression recognition (MER) is a very challenging area of research due
to its intrinsic nature and fine-grained changes. In the literature, the
problem of MER has been solved through handcrafted/descriptor-based techniques.
However, in recent times, deep learning (DL) based techniques have been adopted
to gain higher performance for MER. Also, rich survey articles on MER are
available by summarizing the datasets, experimental settings, conventional and
deep learning methods. In contrast, these studies lack the ability to convey
the impact of network design paradigms and experimental setting strategies for
DL-based MER. Therefore, this paper aims to provide a deep insight into the
DL-based MER frameworks with a perspective on promises in network model
designing, experimental strategies, challenges, and research needs. Also, the
detailed categorization of available MER frameworks is presented in various
aspects of model design and technical characteristics. Moreover, an empirical
analysis of the experimental and validation protocols adopted by MER methods is
presented. The challenges mentioned earlier and network design strategies may
assist the affective computing research community in forging ahead in MER
research. Finally, we point out the future directions, research needs, and draw
our conclusions.

We present a system for applying sim2real approaches to "in the wild" scenes
with realistic visuals, and to policies which rely on active perception using
RGB cameras. Given a short video of a static scene collected using a generic
phone, we learn the scene's contact geometry and a function for novel view
synthesis using a Neural Radiance Field (NeRF). We augment the NeRF rendering
of the static scene by overlaying the rendering of other dynamic objects (e.g.
the robot's own body, a ball). A simulation is then created using the rendering
engine in a physics simulator which computes contact dynamics from the static
scene geometry (estimated from the NeRF volume density) and the dynamic
objects' geometry and physical properties (assumed known). We demonstrate that
we can use this simulation to learn vision-based whole body navigation and ball
pushing policies for a 20 degrees of freedom humanoid robot with an actuated
head-mounted RGB camera, and we successfully transfer these policies to a real
robot. Project video is available at
https://sites.google.com/view/nerf2real/home

In this note, we first derive a one-parameter family of hyperparameter
scaling strategies that interpolates between the neural-tangent scaling and
mean-field/maximal-update scaling. We then calculate the scalings of dynamical
observables -- network outputs, neural tangent kernels, and differentials of
neural tangent kernels -- for wide and deep neural networks. These calculations
in turn reveal a proper way to scale depth with width such that resultant
large-scale models maintain their representation-learning ability. Finally, we
observe that various infinite-width limits examined in the literature
correspond to the distinct corners of the interconnected web spanned by
effective theories for finite-width neural networks, with their training
dynamics ranging from being weakly-coupled to being strongly-coupled.

The objective of this paper is an efficient training method for video tasks.
We make three contributions: (1) We propose Turbo training, a simple and
versatile training paradigm for Transformers on multiple video tasks. (2) We
illustrate the advantages of Turbo training on action classification,
video-language representation learning, and long-video activity classification,
showing that Turbo training can largely maintain competitive performance while
achieving almost 4X speed-up and significantly less memory consumption. (3)
Turbo training enables long-schedule video-language training and end-to-end
long-video training, delivering competitive or superior performance than
previous works, which were infeasible to train under limited resources.

Inverse graphics aims to recover 3D models from 2D observations. Utilizing
differentiable rendering, recent 3D-aware generative models have shown
impressive results of rigid object generation using 2D images. However, it
remains challenging to generate articulated objects, like human bodies, due to
their complexity and diversity in poses and appearances. In this work, we
propose, EVA3D, an unconditional 3D human generative model learned from 2D
image collections only. EVA3D can sample 3D humans with detailed geometry and
render high-quality images (up to 512x256) without bells and whistles (e.g.
super resolution). At the core of EVA3D is a compositional human NeRF
representation, which divides the human body into local parts. Each part is
represented by an individual volume. This compositional representation enables
1) inherent human priors, 2) adaptive allocation of network parameters, 3)
efficient training and rendering. Moreover, to accommodate for the
characteristics of sparse 2D human image collections (e.g. imbalanced pose
distribution), we propose a pose-guided sampling strategy for better GAN
learning. Extensive experiments validate that EVA3D achieves state-of-the-art
3D human generation performance regarding both geometry and texture quality.
Notably, EVA3D demonstrates great potential and scalability to
"inverse-graphics" diverse human bodies with a clean framework.

Generalized in-hand manipulation has long been an unsolved challenge of
robotics. As a small step towards this grand goal, we demonstrate how to design
and learn a simple adaptive controller to achieve in-hand object rotation using
only fingertips. The controller is trained entirely in simulation on only
cylindrical objects, which then - without any fine-tuning - can be directly
deployed to a real robot hand to rotate dozens of objects with diverse sizes,
shapes, and weights over the z-axis. This is achieved via rapid online
adaptation of the controller to the object properties using only proprioception
history. Furthermore, natural and stable finger gaits automatically emerge from
training the control policy via reinforcement learning. Code and more videos
are available at https://haozhi.io/hora

While adversarial training is generally used as a defense mechanism, recent
works show that it can also act as a regularizer. By co-training a neural
network on clean and adversarial inputs, it is possible to improve
classification accuracy on the clean, non-adversarial inputs. We demonstrate
that, contrary to previous findings, it is not necessary to separate batch
statistics when co-training on clean and adversarial inputs, and that it is
sufficient to use adapters with few domain-specific parameters for each type of
input. We establish that using the classification token of a Vision Transformer
(ViT) as an adapter is enough to match the classification performance of dual
normalization layers, while using significantly less additional parameters.
First, we improve upon the top-1 accuracy of a non-adversarially trained
ViT-B16 model by +1.12% on ImageNet (reaching 83.76% top-1 accuracy). Second,
and more importantly, we show that training with adapters enables model soups
through linear combinations of the clean and adversarial tokens. These model
soups, which we call adversarial model soups, allow us to trade-off between
clean and robust accuracy without sacrificing efficiency. Finally, we show that
we can easily adapt the resulting models in the face of distribution shifts.
Our ViT-B16 obtains top-1 accuracies on ImageNet variants that are on average
+4.00% better than those obtained with Masked Autoencoders.

A large body of recent work targets semantically conditioned image
generation. Most such methods focus on the narrower task of pose transfer and
ignore the more challenging task of subject transfer that consists in not only
transferring the pose but also the appearance and background. In this work, we
introduce SCAM (Semantic Cross Attention Modulation), a system that encodes
rich and diverse information in each semantic region of the image (including
foreground and background), thus achieving precise generation with emphasis on
fine details. This is enabled by the Semantic Attention Transformer Encoder
that extracts multiple latent vectors for each semantic region, and the
corresponding generator that exploits these multiple latents by using semantic
cross attention modulation. It is trained only using a reconstruction setup,
while subject transfer is performed at test time. Our analysis shows that our
proposed architecture is successful at encoding the diversity of appearance in
each semantic region. Extensive experiments on the iDesigner and CelebAMask-HD
datasets show that SCAM outperforms SEAN and SPADE; moreover, it sets the new
state of the art on subject transfer.

Deep Ensembles, as a type of Bayesian Neural Networks, can be used to
estimate uncertainty on the prediction of multiple neural networks by
collecting votes from each network and computing the difference in those
predictions. In this paper, we introduce a novel method for uncertainty
estimation called Layer Ensembles that considers a set of independent
categorical distributions for each layer of the network, giving many more
possible samples with overlapped layers, than in the regular Deep Ensembles. We
further introduce Optimized Layer Ensembles with an inference procedure that
reuses common layer outputs, achieving up to 19x speed up and quadratically
reducing memory usage. We also show that Layer Ensembles can be further
improved by ranking samples, resulting in models that require less memory and
time to run while achieving higher uncertainty quality than Deep Ensembles.

Prior work in semantic parsing has shown that conventional seq2seq models
fail at compositional generalization tasks. This limitation led to a resurgence
of methods that model alignments between sentences and their corresponding
meaning representations, either implicitly through latent variables or
explicitly by taking advantage of alignment annotations. We take the second
direction and propose TPol, a two-step approach that first translates input
sentences monotonically and then reorders them to obtain the correct output.
This is achieved with a modular framework comprising a Translator and a
Reorderer component. We test our approach on two popular semantic parsing
datasets. Our experiments show that by means of the monotonic translations,
TPol can learn reliable lexico-logical patterns from aligned data,
significantly improving compositional generalization both over conventional
seq2seq models, as well as over a recently proposed approach that exploits gold
alignments.

Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed
inputs during training -- helps reduce model reliance on spurious correlations
and improves generalization to out-of-distribution (OOD) data. Prior work on
generating counterfactuals only considered restricted classes of perturbations,
limiting their effectiveness. We present COunterfactual Generation via
Retrieval and Editing (CORE), a retrieval-augmented generation framework for
creating diverse counterfactual perturbations for CDA. For each training
example, CORE first performs a dense retrieval over a task-related unlabeled
text corpus using a learned bi-encoder and extracts relevant counterfactual
excerpts. CORE then incorporates these into prompts to a large language model
with few-shot learning capabilities, for counterfactual editing. Conditioning
language model edits on naturally occurring data results in diverse
perturbations. Experiments on natural language inference and sentiment analysis
benchmarks show that CORE counterfactuals are more effective at improving
generalization to OOD data compared to other DA approaches. We also show that
the CORE retrieval framework can be used to encourage diversity in manually
authored perturbations

We introduce Sequential Neural Posterior Score Estimation (SNPSE) and
Sequential Neural Likelihood Score Estimation (SNLSE), two new score-based
methods for Bayesian inference in simulator-based models. Our methods, inspired
by the success of score-based methods in generative modelling, leverage
conditional score-based diffusion models to generate samples from the posterior
distribution of interest. These models can be trained using one of two possible
objective functions, one of which approximates the score of the intractable
likelihood, while the other directly estimates the score of the posterior. We
embed these models into a sequential training procedure, which guides
simulations using the current approximation of the posterior at the observation
of interest, thereby reducing the simulation cost. We validate our methods, as
well as their amortised, non-sequential variants, on several numerical
examples, demonstrating comparable or superior performance to existing
state-of-the-art methods such as Sequential Neural Posterior Estimation (SNPE)
and Sequential Neural Likelihood Estimation (SNLE).

We propose the novel certified training method, SABR, which outperforms
existing methods across perturbation magnitudes on MNIST, CIFAR-10, and
TinyImageNet, in terms of both standard and certifiable accuracies. The key
insight behind SABR is that propagating interval bounds for a small but
carefully selected subset of the adversarial input region is sufficient to
approximate the worst-case loss over the whole region while significantly
reducing approximation errors. SABR does not only establish a new
state-of-the-art in all commonly used benchmarks but more importantly, points
to a new class of certified training methods promising to overcome the
robustness-accuracy trade-off.

Link prediction is the task of inferring missing links between entities in
knowledge graphs. Embedding-based methods have shown effectiveness in
addressing this problem by modeling relational patterns in triples. However,
the link prediction task often requires contextual information in entity
neighborhoods, while most existing embedding-based methods fail to capture it.
Additionally, little attention is paid to the diversity of entity
representations in different contexts, which often leads to false prediction
results. In this situation, we consider that the schema of knowledge graph
contains the specific contextual information, and it is beneficial for
preserving the consistency of entities across contexts. In this paper, we
propose a novel Schema-augmented Multi-level contrastive LEarning framework
(SMiLE) to conduct knowledge graph link prediction. Specifically, we first
exploit network schema as the prior constraint to sample negatives and
pre-train our model by employing a multi-level contrastive learning method to
yield both prior schema and contextual information. Then we fine-tune our model
under the supervision of individual triples to learn subtler representations
for link prediction. Extensive experimental results on four knowledge graph
datasets with thorough analysis of each component demonstrate the effectiveness
of our proposed framework against state-of-the-art baselines. The
implementation of SMiLE is available at https://github.com/GKNL/SMiLE.

A characteristic feature of time-to-event data analysis is possible censoring
of the event time. Most of the statistical learning methods for handling
censored data are limited by the assumption of independent censoring, even if
this can lead to biased predictions when the assumption does not hold. This
paper introduces Clayton-boost, a boosting approach built upon the accelerated
failure time model, which uses a Clayton copula to handle the dependency
between the event and censoring distributions. By taking advantage of a copula,
the independent censoring assumption is not needed any more. During comparisons
with commonly used methods, Clayton-boost shows a strong ability to remove
prediction bias at the presence of dependent censoring and outperforms the
comparing methods either if the dependency strength or percentage censoring are
considerable. The encouraging performance of Clayton-boost shows that there is
indeed reasons to be critical about the independent censoring assumption, and
that real-world data could highly benefit from modelling the potential
dependency.

Monitoring of colonial waterbird nesting islands is essential to tracking
waterbird population trends, which are used for evaluating ecosystem health and
informing conservation management decisions. Recently, unmanned aerial
vehicles, or drones, have emerged as a viable technology to precisely monitor
waterbird colonies. However, manually counting waterbirds from hundreds, or
potentially thousands, of aerial images is both difficult and time-consuming.
In this work, we present a deep learning pipeline that can be used to precisely
detect, count, and monitor waterbirds using aerial imagery collected by a
commercial drone. By utilizing convolutional neural network-based object
detectors, we show that we can detect 16 classes of waterbird species that are
commonly found in colonial nesting islands along the Texas coast. Our
experiments using Faster R-CNN and RetinaNet object detectors give mean
interpolated average precision scores of 67.9% and 63.1% respectively.

Recently, continual learning has received a lot of attention. One of the
significant problems is the occurrence of \emph{concept drift}, which consists
of changing probabilistic characteristics of the incoming data. In the case of
the classification task, this phenomenon destabilizes the model's performance
and negatively affects the achieved prediction quality. Most current methods
apply statistical learning and similarity analysis over the raw data. However,
similarity analysis in streaming data remains a complex problem due to time
limitation, non-precise values, fast decision speed, scalability, etc. This
article introduces a novel method for monitoring changes in the probabilistic
distribution of multi-dimensional data streams. As a measure of the rapidity of
changes, we analyze the popular Kullback-Leibler divergence. During the
experimental study, we show how to use this metric to predict the concept drift
occurrence and understand its nature. The obtained results encourage further
work on the proposed methods and its application in the real tasks where the
prediction of the future appearance of concept drift plays a crucial role, such
as predictive maintenance.

Recent studies of gradient descent with large step sizes have shown that
there is often a regime with an initial increase in the largest eigenvalue of
the loss Hessian (progressive sharpening), followed by a stabilization of the
eigenvalue near the maximum value which allows convergence (edge of stability).
These phenomena are intrinsically non-linear and do not happen for models in
the constant Neural Tangent Kernel (NTK) regime, for which the predictive
function is approximately linear in the parameters. As such, we consider the
next simplest class of predictive models, namely those that are quadratic in
the parameters, which we call second-order regression models. For quadratic
objectives in two dimensions, we prove that this second-order regression model
exhibits progressive sharpening of the NTK eigenvalue towards a value that
differs slightly from the edge of stability, which we explicitly compute. In
higher dimensions, the model generically shows similar behavior, even without
the specific structure of a neural network, suggesting that progressive
sharpening and edge-of-stability behavior aren't unique features of neural
networks, and could be a more general property of discrete learning algorithms
in high-dimensional non-linear models.

Hybrid quantum/molecular mechanics models (QM/MM methods) are widely used in
material and molecular simulations when pure MM models cannot ensure adequate
accuracy but pure QM models are computationally prohibitive. Adaptive QM/MM
coupling methods feature on-the-fly classification of atoms, allowing the QM
and MM subsystems to be updated as needed. The state-of-art "machine-learned
interatomic potentials (MLIPs)" can be applied as the MM models for consistent
QM/MM methods with rigorously justified accuracy. In this work, we propose a
robust adaptive QM/MM method for practical material defect simulation, which is
based on a developed residual-based error estimator. The error estimator
provides both upper and lower bounds for the approximation error, demonstrating
its reliability and efficiency. In particular, we introduce three minor
approximations such that the error estimator can be evaluated efficiently
without losing much accuracy. To update the QM/MM partitions anisotropically, a
novel adaptive algorithm is proposed, where a free interface motion problem
based on the proposed error estimator is solved by employing the fast marching
method. We implement and validate the robustness of the adaptive algorithm on
numerical simulations for various complex crystalline defects.

Machine learning approaches have recently enabled autonomous navigation for
mobile robots in a data-driven manner. Since most existing learning-based
navigation systems are trained with data generated in artificially created
training environments, during real-world deployment at scale, it is inevitable
that robots will encounter unseen scenarios, which are out of the training
distribution and therefore lead to poor real-world performance. On the other
hand, directly training in the real world is generally unsafe and inefficient.
To address this issue, we introduce Self-supervised Environment Synthesis
(SES), in which, after real-world deployment with safety and efficiency
requirements, autonomous mobile robots can utilize experience from the
real-world deployment, reconstruct navigation scenarios, and synthesize
representative training environments in simulation. Training in these
synthesized environments leads to improved future performance in the real
world. The effectiveness of SES at synthesizing representative simulation
environments and improving real-world navigation performance is evaluated via a
large-scale deployment in a high-fidelity, realistic simulator and a
small-scale deployment on a physical robot.

Most stars form in clumpy and sub-structured clusters. These properties also
emerge in hydro-dynamical simulations of star-forming clouds, which provide a
way to generate realistic initial conditions for $N-$body runs of young stellar
clusters. However, producing large sets of initial conditions by
hydro-dynamical simulations is prohibitively expensive in terms of
computational time. We introduce a novel technique for generating new initial
conditions from a given sample of hydro-dynamical simulations, at a tiny
computational cost. In particular, we apply a hierarchical clustering algorithm
to learn a tree representation of the spatial and kinematic relations between
stars, where the leaves represent the single stars and the nodes describe the
structure of the cluster at larger and larger scales. This procedure can be
used as a basis for the random generation of new sets of stars, by simply
modifying the global structure of the stellar cluster, while leaving the
small-scale properties unaltered.

When experience is scarce, models may have insufficient information to adapt
to a new task. In this case, auxiliary information - such as a textual
description of the task - can enable improved task inference and adaptation. In
this work, we propose an extension to the Model-Agnostic Meta-Learning
algorithm (MAML), which allows the model to adapt using auxiliary information
as well as task experience. Our method, Fusion by Meta-Initialization (FuMI),
conditions the model initialization on auxiliary information using a
hypernetwork, rather than learning a single, task-agnostic initialization.
Furthermore, motivated by the shortcomings of existing multi-modal few-shot
learning benchmarks, we constructed iNat-Anim - a large-scale image
classification dataset with succinct and visually pertinent textual class
descriptions. On iNat-Anim, FuMI significantly outperforms uni-modal baselines
such as MAML in the few-shot regime. The code for this project and a dataset
exploration tool for iNat-Anim are publicly available at
https://github.com/s-a-malik/multi-few .

We present Rieoptax, an open source Python library for Riemannian
optimization in JAX. We show that many differential geometric primitives, such
as Riemannian exponential and logarithm maps, are usually faster in Rieoptax
than existing frameworks in Python, both on CPU and GPU. We support various
range of basic and advanced stochastic optimization solvers like Riemannian
stochastic gradient, stochastic variance reduction, and adaptive gradient
methods. A distinguishing feature of the proposed toolbox is that we also
support differentially private optimization on Riemannian manifolds.

Deep reinforcement learning (RL) has brought many successes for autonomous
robot navigation. However, there still exists important limitations that
prevent real-world use of RL-based navigation systems. For example, most
learning approaches lack safety guarantees; and learned navigation systems may
not generalize well to unseen environments. Despite a variety of recent
learning techniques to tackle these challenges in general, a lack of an
open-source benchmark and reproducible learning methods specifically for
autonomous navigation makes it difficult for roboticists to choose what
learning methods to use for their mobile robots and for learning researchers to
identify current shortcomings of general learning methods for autonomous
navigation. In this paper, we identify four major desiderata of applying deep
RL approaches for autonomous navigation: (D1) reasoning under uncertainty, (D2)
safety, (D3) learning from limited trial-and-error data, and (D4)
generalization to diverse and novel environments. Then, we explore four major
classes of learning techniques with the purpose of achieving one or more of the
four desiderata: memory-based neural network architectures (D1), safe RL (D2),
model-based RL (D2, D3), and domain randomization (D4). By deploying these
learning techniques in a new open-source large-scale navigation benchmark and
real-world environments, we perform a comprehensive study aimed at establishing
to what extent can these techniques achieve these desiderata for RL-based
navigation systems.

Teacher-student knowledge distillation is a popular technique for compressing
today's prevailing large language models into manageable sizes that fit
low-latency downstream applications. Both the teacher and the choice of
transfer set used for distillation are crucial ingredients in creating a high
quality student. Yet, the generic corpora used to pretrain the teacher and the
corpora associated with the downstream target domain are often significantly
different, which raises a natural question: should the student be distilled
over the generic corpora, so as to learn from high-quality teacher predictions,
or over the downstream task corpora to align with finetuning? Our study
investigates this trade-off using Domain Classification (DC) and Intent
Classification/Named Entity Recognition (ICNER) as downstream tasks. We distill
several multilingual students from a larger multilingual LM with varying
proportions of generic and task-specific datasets, and report their performance
after finetuning on DC and ICNER. We observe significant improvements across
tasks and test sets when only task-specific corpora is used. We also report on
how the impact of adding task-specific data to the transfer set correlates with
the similarity between generic and task-specific data. Our results clearly
indicate that, while distillation from a generic LM benefits downstream tasks,
students learn better using target domain data even if it comes at the price of
noisier teacher predictions. In other words, target domain data still trumps
teacher knowledge.

Cooperative multi-agent policy gradient (MAPG) algorithms have recently
attracted wide attention and are regarded as a general scheme for the
multi-agent system. Credit assignment plays an important role in MAPG and can
induce cooperation among multiple agents. However, most MAPG algorithms cannot
achieve good credit assignment because of the game-theoretic pathology known as
\textit{centralized-decentralized mismatch}. To address this issue, this paper
presents a novel method, \textit{\underline{M}ulti-\underline{A}gent
\underline{P}olarization \underline{P}olicy \underline{G}radient} (MAPPG).
MAPPG takes a simple but efficient polarization function to transform the
optimal consistency of joint and individual actions into easily realized
constraints, thus enabling efficient credit assignment in MAPG. Theoretically,
we prove that individual policies of MAPPG can converge to the global optimum.
Empirically, we evaluate MAPPG on the well-known matrix game and differential
game, and verify that MAPPG can converge to the global optimum for both
discrete and continuous action spaces. We also evaluate MAPPG on a set of
StarCraft II micromanagement tasks and demonstrate that MAPPG outperforms the
state-of-the-art MAPG algorithms.

Models should have the ability to adapt to unseen data during test-time to
avoid performance drop caused by inevitable distribution shifts in real-world
deployment scenarios. In this work, we tackle the practical yet challenging
test-time adaptation (TTA) problem, where a model adapts to the target domain
without accessing the source data. We propose a simple recipe called
data-efficient prompt tuning (DePT) with two key ingredients. First, DePT plugs
visual prompts into the vision Transformer and only tunes these
source-initialized prompts during adaptation. We find such parameter-efficient
finetuning can efficiently adapt the model representation to the target domain
without overfitting to the noise in the learning objective. Second, DePT
bootstraps the source representation to the target domain by memory bank-based
online pseudo labeling. A hierarchical self-supervised regularization specially
designed for prompts is jointly optimized to alleviate error accumulation
during self-training. With much fewer tunable parameters, DePT demonstrates not
only state-of-the-art performance on major adaptation benchmarks, but also
superior data efficiency, i.e., adaptation with only 1\% or 10\% data without
much performance degradation compared to 100\% data. In addition, DePT is also
versatile to be extended to online or multi-source TTA settings.

Genetic Programming (GP) is an heuristic method that can be applied to many
Machine Learning, Optimization and Engineering problems. In particular, it has
been widely used in Software Engineering for Test-case generation, Program
Synthesis and Improvement of Software (GI).
  Grammar-Guided Genetic Programming (GGGP) approaches allow the user to refine
the domain of valid program solutions. Backus Normal Form is the most popular
interface for describing Context-Free Grammars (CFG) for GGGP. BNF and its
derivatives have the disadvantage of interleaving the grammar language and the
target language of the program.
  We propose to embed the grammar as an internal Domain-Specific Language in
the host language of the framework. This approach has the same expressive power
as BNF and EBNF while using the host language type-system to take advantage of
all the existing tooling: linters, formatters, type-checkers, autocomplete, and
legacy code support. These tools have a practical utility in designing software
in general, and GP systems in particular.
  We also present Meta-Handlers, user-defined overrides of the tree-generation
system. This technique extends our object-oriented encoding with more
practicability and expressive power than existing CFG approaches, achieving the
same expressive power of Attribute Grammars, but without the grammar vs target
language duality.
  Furthermore, we evidence that this approach is feasible, showing an example
Python implementation as proof. We also compare our approach against textual
BNF-representations w.r.t. expressive power and ergonomics. These advantages do
not come at the cost of performance, as shown by our empirical evaluation on 5
benchmarks of our example implementation against PonyGE2. We conclude that our
approach has better ergonomics with the same expressive power and performance
of textual BNF-based grammar encodings.

Crystal nucleation is relevant across the domains of fundamental and applied
sciences. However, in many cases its mechanism remains unclear due to a lack of
temporal or spatial resolution. To gain insights to the molecular details of
nucleation, some form of molecular dynamics simulations are typically
performed, which are, in turn, limited by their ability to run long enough to
sample the nucleation event thoroughly. To overcome the timescale limits in
typical molecular dynamics simulations in a manner free of prior human bias,
here we employ the machine learning augmented molecular dynamics framework
``Reweighted Autoencoded Variational Bayes for enhanced sampling (RAVE)". We
study the two molecular systems, urea and glycine in explicit all-atom water,
due to their enrichment in polymorphic structures and common utility in
commercial applications. From our simulations, we observe back-and-forth
liquid-solid transitions of different polymorphs, correctly ranking polymorph
stabilities as A- $>$ I- $>$ B-urea and $\gamma$- $>$ $\beta$- $\ge$
$\alpha$-glycine. Finally, the machine learning based reaction coordinates
allow for an in-depth analysis of the nucleation mechanism for both molecules,
providing clear evidence of nonclassical two-step nucleation for both urea and
glycine nucleation in water.

High variances in reinforcement learning have shown impeding successful
convergence and hurting task performance. As reward signal plays an important
role in learning behavior, multi-step methods have been considered to mitigate
the problem, and are believed to be more effective than single step methods.
However, there is a lack of comprehensive and systematic study on this
important aspect to demonstrate the effectiveness of multi-step methods in
solving highly complex continuous control problems. In this study, we introduce
a new long $N$-step surrogate stage (LNSS) reward approach to effectively
account for complex environment dynamics while previous methods are usually
feasible for limited number of steps. The LNSS method is simple, low
computational cost, and applicable to value based or policy gradient
reinforcement learning. We systematically evaluate LNSS in OpenAI Gym and
DeepMind Control Suite to address some complex benchmark environments that have
been challenging to obtain good results by DRL in general. We demonstrate
performance improvement in terms of total reward, convergence speed, and
coefficient of variation (CV) by LNSS. We also provide analytical insights on
how LNSS exponentially reduces the upper bound on the variances of Q value from
a respective single step method

Data-driven learning based methods have recently been particularly successful
at learning robust locomotion controllers for a variety of unstructured
terrains. Prior work has shown that incorporating good locomotion priors in the
form of trajectory generators (TGs) is effective at efficiently learning
complex locomotion skills. However, defining a good, single TG as
tasks/environments become increasingly more complex remains a challenging
problem as it requires extensive tuning and risks reducing the effectiveness of
the prior. In this paper, we present Evolved Environmental Trajectory
Generators (EETG), a method that learns a diverse set of specialised locomotion
priors using Quality-Diversity algorithms while maintaining a single policy
within the Policies Modulating TG (PMTG) architecture. The results demonstrate
that EETG enables a quadruped robot to successfully traverse a wide range of
environments, such as slopes, stairs, rough terrain, and balance beams. Our
experiments show that learning a diverse set of specialized TG priors is
significantly (5 times) more efficient than using a single, fixed prior when
dealing with a wide range of environments.

Online learning, in the mistake bound model, is one of the most fundamental
concepts in learning theory. Differential privacy, instead, is the most widely
used statistical concept of privacy in the machine learning community. It is
thus clear that defining learning problems that are online differentially
privately learnable is of great interest. In this paper, we pose the question
on if the two problems are equivalent from a learning perspective, i.e., is
privacy for free in the online learning framework?

Wearing a face mask is one of the adjustments we had to follow to reduce the
spread of the coronavirus. Having our faces covered by masks constantly has
driven the need to understand and investigate how this behavior affects the
recognition capability of face recognition systems. Current face recognition
systems have extremely high accuracy when dealing with unconstrained general
face recognition cases but do not generalize well with occluded masked faces.
In this work, we propose a system for masked face recognition. The proposed
system comprises two Convolutional Neural Network (CNN) models and two
Transformer models. The CNN models have been fine-tuned on FaceNet pre-trained
model. We ensemble the predictions of the four models using the majority voting
technique to identify the person with the mask. The proposed system has been
evaluated on a synthetically masked LFW dataset created in this work. The best
accuracy is obtained using the ensembled models with an accuracy of 92%. This
recognition rate outperformed the accuracy of other models and it shows the
correctness and robustness of the proposed model for recognizing masked faces.
The code and data are available at https://github.com/Hamzah-Luqman/MFR

Simulation-based inference (SBI) solves statistical inverse problems by
repeatedly running a stochastic simulator and inferring posterior distributions
from model-simulations. To improve simulation efficiency, several inference
methods take a sequential approach and iteratively adapt the proposal
distributions from which model simulations are generated. However, many of
these sequential methods are difficult to use in practice, both because the
resulting optimisation problems can be challenging and efficient diagnostic
tools are lacking. To overcome these issues, we present Truncated Sequential
Neural Posterior Estimation (TSNPE). TSNPE performs sequential inference with
truncated proposals, sidestepping the optimisation issues of alternative
approaches. In addition, TSNPE allows to efficiently perform coverage tests
that can scale to complex models with many parameters. We demonstrate that
TSNPE performs on par with previous methods on established benchmark tasks. We
then apply TSNPE to two challenging problems from neuroscience and show that
TSNPE can successfully obtain the posterior distributions, whereas previous
methods fail. Overall, our results demonstrate that TSNPE is an efficient,
accurate, and robust inference method that can scale to challenging scientific
models.

Fog manufacturing can greatly enhance traditional manufacturing systems
through distributed Fog computation units, which are governed by predictive
computational workload offloading methods under different Industrial Internet
architectures. It is known that the predictive offloading methods highly depend
on accurate prediction and uncertainty quantification of runtime performance
metrics, containing multivariate mixed-type responses (i.e., continuous,
counting, binary). In this work, we propose a Bayesian sparse regression for
multivariate mixed responses to enhance the prediction of runtime performance
metrics and to enable the statistical inferences. The proposed method considers
both group and individual variable selection to jointly model the mixed types
of runtime performance metrics. The conditional dependency among multiple
responses is described by a graphical model using the precision matrix, where a
spike-and-slab prior is used to enable the sparse estimation of the graph. The
proposed method not only achieves accurate prediction, but also makes the
predictive model more interpretable with statistical inferences on model
parameters and prediction in the Fog manufacturing. A simulation study and a
real case example in a Fog manufacturing are conducted to demonstrate the
merits of the proposed model.

Gradient-based methods have been widely used for system design and
optimization in diverse application domains. Recently, there has been a renewed
interest in studying theoretical properties of these methods in the context of
control and reinforcement learning. This article surveys some of the recent
developments on policy optimization, a gradient-based iterative approach for
feedback control synthesis, popularized by successes of reinforcement learning.
We take an interdisciplinary perspective in our exposition that connects
control theory, reinforcement learning, and large-scale optimization. We review
a number of recently-developed theoretical results on the optimization
landscape, global convergence, and sample complexity of gradient-based methods
for various continuous control problems such as the linear quadratic regulator
(LQR), $\mathcal{H}_\infty$ control, risk-sensitive control, linear quadratic
Gaussian (LQG) control, and output feedback synthesis. In conjunction with
these optimization results, we also discuss how direct policy optimization
handles stability and robustness concerns in learning-based control, two main
desiderata in control engineering. We conclude the survey by pointing out
several challenges and opportunities at the intersection of learning and
control.

Recently, neural tangent kernel (NTK) has been used to explain the dynamics
of learning parameters of neural networks, at the large width limit.
Quantitative analyses of NTK give rise to network widths that are often
impractical and incur high costs in time and energy in both training and
deployment. Using a matrix factorization technique, we show how to obtain
similar guarantees to those obtained by a prior analysis while reducing
training and inference resource costs. The importance of our result further
increases when the input points' data dimension is in the same order as the
number of input points. More generally, our work suggests how to analyze large
width networks in which dense linear layers are replaced with a low complexity
factorization, thus reducing the heavy dependence on the large width.

Although melanoma occurs more rarely than several other skin cancers,
patients' long term survival rate is extremely low if the diagnosis is missed.
Diagnosis is complicated by a high discordance rate among pathologists when
distinguishing between melanoma and benign melanocytic lesions. A tool that
provides potential concordance information to healthcare providers could help
inform diagnostic, prognostic, and therapeutic decision-making for challenging
melanoma cases. We present a melanoma concordance regression deep learning
model capable of predicting the concordance rate of invasive melanoma or
melanoma in-situ from digitized Whole Slide Images (WSIs). The salient features
corresponding to melanoma concordance were learned in a self-supervised manner
with the contrastive learning method, SimCLR. We trained a SimCLR feature
extractor with 83,356 WSI tiles randomly sampled from 10,895 specimens
originating from four distinct pathology labs. We trained a separate melanoma
concordance regression model on 990 specimens with available concordance ground
truth annotations from three pathology labs and tested the model on 211
specimens. We achieved a Root Mean Squared Error (RMSE) of 0.28 +/- 0.01 on the
test set. We also investigated the performance of using the predicted
concordance rate as a malignancy classifier, and achieved a precision and
recall of 0.85 +/- 0.05 and 0.61 +/- 0.06, respectively, on the test set. These
results are an important first step for building an artificial intelligence
(AI) system capable of predicting the results of consulting a panel of experts
and delivering a score based on the degree to which the experts would agree on
a particular diagnosis. Such a system could be used to suggest additional
testing or other action such as ordering additional stains or genetic tests.

While large code datasets have become available in recent years, acquiring
representative training data with full coverage of general code distribution
remains challenging due to the compositional nature of code and the complexity
of software. This leads to the out-of-distribution (OOD) issues with unexpected
model inference behaviors that have not been systematically studied yet. We
contribute the first systematic approach that simulates various OOD scenarios
along different dimensions of data properties and investigates the model
behaviors in such scenarios. Our extensive studies on six state-of-the-art
models for three code generation tasks expose several failure modes caused by
the out-of-distribution issues. It thereby provides insights and sheds light
for future research in terms of generalization, robustness, and inductive
biases of source code models.

Snow removal aims to locate snow areas and recover clean images without
repairing traces. Unlike the regularity and semitransparency of rain, snow with
various patterns and degradations seriously occludes the background. As a
result, the state-of-the-art snow removal methods usually retains a large
parameter size. In this paper, we propose a lightweight but high-efficient snow
removal network called Laplace Mask Query Transformer (LMQFormer). Firstly, we
present a Laplace-VQVAE to generate a coarse mask as prior knowledge of snow.
Instead of using the mask in dataset, we aim at reducing both the information
entropy of snow and the computational cost of recovery. Secondly, we design a
Mask Query Transformer (MQFormer) to remove snow with the coarse mask, where we
use two parallel encoders and a hybrid decoder to learn extensive snow features
under lightweight requirements. Thirdly, we develop a Duplicated Mask Query
Attention (DMQA) that converts the coarse mask into a specific number of
queries, which constraint the attention areas of MQFormer with reduced
parameters. Experimental results in popular datasets have demonstrated the
efficiency of our proposed model, which achieves the state-of-the-art snow
removal quality with significantly reduced parameters and the lowest running
time.

There are known differences between the physical properties of HII and
diffuse ionized gas (DIG), but most of the studied regions in the literature
are relatively bright. We compiled a faint sample of 390 HII regions with
median $\log_{10}H\alpha$=34.7 in the spiral galaxy NGC300, derived their
physical properties in terms of metallicity, density, extinction, and
kinematics, and performed a comparative analysis of the properties of the DIG.
We used MUSE data of nine fields in NGC300, covering a galactocentric distance
of zero to ~450 arcsec (~4 projected kpc), including spiral arm and inter-arm
regions. We binned the data in dendrogram leaves and extracted all strong
nebular emission lines. We identified HII and DIG regions and compared their
electron densities, metallicity, extinction, and kinematic properties. We also
tested the effectiveness of unsupervised machine-learning algorithms in
distinguishing between the HII and DIG regions. The gas density in the HII and
DIG regions is close to the low-density limit in all fields. The average
velocity dispersion in the DIG is higher than in the HII regions, which can be
explained by the DIG being 1.8 kK hotter than HII gas. The DIG manifests a
lower ionization parameter than HII gas, and the DIG fractions vary between
15-77%, with strong evidence of a contribution by hot low-mass evolved stars
and shocks to the DIG ionization. Most of the DIG is consistent with no
extinction and an oxygen metallicity that is indistinguishable from that of the
HII gas.We observe a flat metallicity profile in the central region, without a
sign of a gradient. The differences between extremely faint HII and DIG regions
follow the same trends and correlations as their much brighter cousins. HII and
DIG are so heterogeneous, however, that the differences within each class are
larger than the differences between the two classes.

State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been
highly successful in leveraging a mix of labeled and unlabeled data by
combining techniques of consistency regularization and pseudo-labeling. During
pseudo-labeling, the model's predictions on unlabeled data are used for
training and thus, model calibration is important in mitigating confirmation
bias. Yet, many SOTA methods are optimized for model performance, with little
focus directed to improve model calibration. In this work, we empirically
demonstrate that model calibration is strongly correlated with model
performance and propose to improve calibration via approximate Bayesian
techniques. We introduce a family of new SSL models that optimizes for
calibration and demonstrate their effectiveness across standard vision
benchmarks of CIFAR-10, CIFAR-100 and ImageNet, giving up to 15.9% improvement
in test accuracy. Furthermore, we also demonstrate their effectiveness in
additional realistic and challenging problems, such as class-imbalanced
datasets and in photonics science.

Upon the significant performance of the supervised deep neural networks,
conventional procedures of developing ML system are \textit{task-centric},
which aims to maximize the task accuracy. However, we scrutinized this
\textit{task-centric} ML system lacks in engineering efficiency when the ML
practitioners solve multiple tasks in their domain. To resolve this problem, we
propose an \textit{efficiency-centric} ML system that concatenates numerous
datasets, classifiers, out-of-distribution detectors, and prediction tables
existing in the practitioners' domain into a single ML pipeline. Under various
image recognition tasks in the real world car-sharing platform, our study
illustrates how we established the proposed system and lessons learned from
this journey as follows. First, the proposed ML system accomplishes supreme
engineering efficiency while achieving a competitive task accuracy. Moreover,
compared to the \textit{task-centric} paradigm, we discovered that the
\textit{efficiency-centric} ML system yields satisfactory prediction results on
multi-labelable samples, which frequently exist in the real world. We analyze
these benefits derived from the representation power, which learned broader
label spaces from the concatenated dataset. Last but not least, our study
elaborated how we deployed this \textit{efficiency-centric} ML system is
deployed in the real world live cloud environment. Based on the proposed
analogies, we highly expect that ML practitioners can utilize our study to
elevate engineering efficiency in their domain.

Advances in neural modeling have achieved state-of-the-art (SOTA) results on
public natural language processing (NLP) benchmarks, at times surpassing human
performance. However, there is a gap between public benchmarks and real-world
applications where noise such as typos or grammatical mistakes is abundant,
resulting in degraded performance. Unfortunately, works that assess the
robustness of neural models on noisy data and suggest improvements are limited
to the English language. Upon analyzing noise in different languages, we
observe that noise types vary across languages and thus require their own
investigation. Thus, to benchmark the performance of pretrained multilingual
models, we construct noisy datasets covering five languages and four NLP tasks.
We see a gap in performance between clean and noisy data. After investigating
ways to boost the zero-shot cross-lingual robustness of multilingual pretrained
models, we propose Robust Contrastive Pretraining (RCP). RCP combines data
augmentation with a contrastive loss term at the pretraining stage and achieves
large improvements on noisy (& original test data) across two sentence-level
classification (+3.2%) and two sequence-labeling (+10 F1-score) multilingual
tasks.

The manual seismic facies annotation relies heavily on the experience of
seismic interpreters, and the distribution of seismic facies in adjacent
locations is very similar, which means that much of the labeling is costly
repetitive work. However, we found that training the model with only a few
evenly sampled labeled slices still suffers from severe classification
confusion, that is, misidentifying one class of seismic facies as another. To
address this issue, we propose a semi-supervised seismic facies identification
method using features from unlabeled data for contrastive learning. We sample
features in regions with high identification confidence, and use an pixel-level
instance discrimination task to narrow the intra-class distance and increase
the inter-class distance. Instance discrimination encourages the latent space
to produce more distinguishable decision boundaries and reduces the bias in the
features of the same class. Our method only needs to extend one branch to
compute the contrastive loss without extensive changes to the network
structure. We have conducted experiments on two public seismic surveys, SEAM AI
and Netherlands F3, and the proposed model achieves an IOU score of more than
90 using only 1% of the annotations in the F3 survey.

Equivariant machine learning methods have shown wide success at 3D learning
applications in recent years. These models explicitly build in the reflection,
translation and rotation symmetries of Euclidean space and have facilitated
large advances in accuracy and data efficiency for a range of applications in
the physical sciences. An outstanding question for equivariant models is why
they achieve such larger-than-expected advances in these applications. To probe
this question, we examine the role of higher order (non-scalar) features in
Euclidean Neural Networks (\texttt{e3nn}). We focus on the previously studied
application of \texttt{e3nn} to the problem of electron density prediction,
which allows for a variety of non-scalar outputs, and examine whether the
nature of the output (scalar $l=0$, vector $l=1$, or higher order $l>1$) is
relevant to the effectiveness of non-scalar hidden features in the network.
Further, we examine the behavior of non-scalar features throughout training,
finding a natural hierarchy of features by $l$, reminiscent of a multipole
expansion. We aim for our work to ultimately inform design principles and
choices of domain applications for {\tt e3nn} networks.

To ensure robust and trustworthy decision-making, it is highly desirable to
enforce constraints over a neural network's parameters and its inputs
automatically by back-propagating output specifications. This way, we can
guarantee that the network makes reliable decisions under perturbations. Here,
we propose a new method for achieving a class of specification guarantees for
neural Ordinary Differentiable Equations (ODEs) by using invariance set
propagation. An invariance of a neural ODE is defined as an output
specification, such as to satisfy mathematical formulae, physical laws, and
system safety. We use control barrier functions to specify the invariance of a
neural ODE on the output layer and propagate it back to the input layer.
Through the invariance backpropagation, we map output specifications onto
constraints on the neural ODE parameters or its input. The satisfaction of the
corresponding constraints implies the satisfaction of output specifications.
This allows us to achieve output specification guarantees by changing the input
or parameters while maximally preserving the model performance. We demonstrate
the invariance propagation on a comprehensive series of representation learning
tasks, including spiral curve regression, autoregressive modeling of joint
physical dynamics, convexity portrait of a function, and safe neural control of
collision avoidance for autonomous vehicles.

Weakly interacting massive particles (WIMPs) can be captured by the Sun and
annihilate in the core, which may result in production of kaons that can decay
at rest into monoenergetic 236 MeV neutrinos. Several studies of detection of
these neutrinos at DUNE have been carried out. It has been shown that if the
WIMP mass is below 4 GeV, then they will evaporate prior to annihilation,
suppressing the signal. Since Jupiter has a cooler core, WIMPs with masses in
the 1-4 GeV range will not evaporate and can thus annihilate into monoenergetic
neutrinos. We calculate the flux of these neutrinos near the surface of Jupiter
and find that it is comparable to the flux at DUNE for masses above 4 GeV and
substantially greater in the 1-4 GeV range. Of course, detecting these
neutrinos would require a neutrino detector near Jupiter. Obviously, it will be
many decades before such a detector can be built, but should direct detection
experiments find a WIMP with a mass in the 1-4 GeV range, it may be one of the
few ways to learn about the annihilation process. A liquid hydrogen time
projection chamber might be able to get precise directional information and
energy of these neutrinos (and hydrogen is plentiful in the vicinity of
Jupiter). We speculate that such a detector could be placed on the far side of
one of the tidally locked Amalthean moons; the moon itself would provide
substantial background shielding and the surface would allow easier deployment
of solar panels for power generation.

We study the decentralized optimization problem where a network of $n$ agents
seeks to minimize the average of a set of heterogeneous non-convex cost
functions distributedly. State-of-the-art decentralized algorithms like Exact
Diffusion~(ED) and Gradient Tracking~(GT) involve communicating every
iteration. However, communication is expensive, resource intensive, and slow.
In this work, we analyze a locally updated GT method (LU-GT), where agents
perform local recursions before interacting with their neighbors. While local
updates have been shown to reduce communication overhead in practice, their
theoretical influence has not been fully characterized. We show LU-GT has the
same communication complexity as the Federated Learning setting but allows
arbitrary network topologies. In addition, we prove that the number of local
updates does not degrade the quality of the solution achieved by LU-GT.
Numerical examples reveal that local updates can lower communication costs in
certain regimes (e.g., well-connected graphs).

Visual Semantic Embedding (VSE) aims to extract the semantics of images and
their descriptions, and embed them into the same latent space for cross-modal
information retrieval. Most existing VSE networks are trained by adopting a
hard negatives loss function which learns an objective margin between the
similarity of relevant and irrelevant image-description embedding pairs.
However, the objective margin in the hard negatives loss function is set as a
fixed hyperparameter that ignores the semantic differences of the irrelevant
image-description pairs. To address the challenge of measuring the optimal
similarities between image-description pairs before obtaining the trained VSE
networks, this paper presents a novel approach that comprises two main parts:
(1) finds the underlying semantics of image descriptions; and (2) proposes a
novel semantically enhanced hard negatives loss function, where the learning
objective is dynamically determined based on the optimal similarity scores
between irrelevant image-description pairs. Extensive experiments were carried
out by integrating the proposed methods into five state-of-the-art VSE networks
that were applied to three benchmark datasets for cross-modal information
retrieval tasks. The results revealed that the proposed methods achieved the
best performance and can also be adopted by existing and future VSE networks.

In disordered photonics, one typically tries to characterize the optically
opaque material in order to be able to deliver light or perform imaging through
it. Among others, multi-mode optical fibers are extensively studied because
they are cheap and easy-to-handle complex devices. Here, instead, we use the
reservoir computing paradigm to turn these optical tools into random projectors
capable of introducing a sufficient amount of interaction to perform non-linear
classification. We show that training a single logistic regression layer on the
data projected by the fiber improves the accuracy with respect to learning it
on the raw images. Surprisingly, the classification accuracy performed with
physical measurements is higher than the one obtained using the standard
transmission matrix model, a widely accepted tool to describe light
transmission through disordered devices. Consistently with the current theory
of deep neural networks, we also reveal that the classifier lives in a flatter
region of the loss landscape when trained on fiber data. These facts suggest
that multi-mode fibers exhibit robust generalization properties, thus making
them promising tools for optically-aided machine learning.

Hyperparameter tuning is a fundamental aspect of machine learning research.
Setting up the infrastructure for systematic optimization of hyperparameters
can take a significant amount of time. Here, we present PyHopper, a black-box
optimization platform designed to streamline the hyperparameter tuning workflow
of machine learning researchers. PyHopper's goal is to integrate with existing
code with minimal effort and run the optimization process with minimal
necessary manual oversight. With simplicity as the primary theme, PyHopper is
powered by a single robust Markov-chain Monte-Carlo optimization algorithm that
scales to millions of dimensions. Compared to existing tuning packages,
focusing on a single algorithm frees the user from having to decide between
several algorithms and makes PyHopper easily customizable. PyHopper is publicly
available under the Apache-2.0 license at https://github.com/PyHopper/PyHopper.

Soft prompts have been recently proposed as a tool for adapting large frozen
language models (LMs) to new tasks. In this work, we repurpose soft prompts to
the task of injecting world knowledge into LMs. We introduce a method to train
soft prompts via self-supervised learning on data from knowledge bases. The
resulting soft knowledge prompts (KPs) are task independent and work as an
external memory of the LMs. We perform qualitative and quantitative experiments
and demonstrate that: (1) KPs can effectively model the structure of the
training data; (2) KPs can be used to improve the performance of LMs in
different knowledge intensive tasks.

Reinforcement Learning (RL) approaches are becoming increasingly popular in
various key disciplines, including robotics and healthcare. However, many of
these systems are complex and non-interpretable, making it challenging for
non-AI experts to understand or intervene. One of the challenges of explaining
RL agent behavior is that, when learning to predict future expected reward,
agents discard contextual information about their experiences when training in
an environment and rely solely on expected utility. We propose a technique,
Experiential Explanations, for generating local counterfactual explanations
that can answer users' why-not questions by explaining qualitatively the
effects of the various environmental rewards on the agent's behavior. We
achieve this by training additional modules alongside the policy. These models,
called influence predictors, model how different reward sources influence the
agent's policy, thus restoring lost contextual information about how the policy
reflects the environment. To generate explanations, we use these models in
addition to the policy to contrast between the agent's intended behavior
trajectory and a counterfactual trajectory suggested by the user.

Pre-trained language models (PLMs) have gained increasing popularity due to
their compelling prediction performance in diverse natural language processing
(NLP) tasks. When formulating a PLM-based prediction pipeline for NLP tasks, it
is also crucial for the pipeline to minimize the calibration error, especially
in safety-critical applications. That is, the pipeline should reliably indicate
when we can trust its predictions. In particular, there are various
considerations behind the pipeline: (1) the choice and (2) the size of PLM, (3)
the choice of uncertainty quantifier, (4) the choice of fine-tuning loss, and
many more. Although prior work has looked into some of these considerations,
they usually draw conclusions based on a limited scope of empirical studies.
There still lacks a holistic analysis on how to compose a well-calibrated
PLM-based prediction pipeline. To fill this void, we compare a wide range of
popular options for each consideration based on three prevalent NLP
classification tasks and the setting of domain shift. In response, we recommend
the following: (1) use ELECTRA for PLM encoding, (2) use larger PLMs if
possible, (3) use Temp Scaling as the uncertainty quantifier, and (4) use Focal
Loss for fine-tuning.

I study the problem of a decision maker choosing a policy to allocate
treatment to a heterogeneous population on the basis of experimental data that
includes only a subset of possible treatment values. The effects of new
treatments are partially identified based on shape restrictions on treatment
response. I propose solving an empirical minimax regret problem to estimate the
policy and show it has a tractable linear- and integer-programming formulation.
I prove the maximum regret of the estimator converges to the lowest possible
maximum regret at the rate at which heterogeneous treatment effects can be
estimated in the experimental data or $N^{-1/2}$, whichever is slower. I apply
my results to design targeted subsidies for electrical grid connections in
rural Kenya, and estimate that $97\%$ of the population should be given a
treatment not implemented in the experiment.

Photon loss is one of the key challenges to overcome in complex photonic
quantum applications. Photon collection efficiencies directly impact the amount
of resources required for measurement-based quantum computation and
communication networks. Promising resources include solid-state quantum light
sources, however, efficiently coupling light from a single quantum emitter to a
guided mode remains demanding. In this work, we eliminate photon losses by
maximizing coupling efficiencies in an emitter-to-fiber interface. We develop a
waveguide-integrated 'Sawfish' photonic crystal cavity and use finite element
simulations to demonstrate that our system transfers, with 97.4% efficiency,
the zero-phonon line emission of a negatively-charged tin vacancy center in
diamond adiabatically to a single-mode fiber. A surrogate model trained by
machine learning provides quantitative estimates of sensitivities to
fabrication tolerances. Our corrugation-based design proves robust under
state-of-the-art nanofabrication parameters, maintaining an emitter-to-fiber
coupling efficiency of 88.6%. To demonstrate its potential in reducing resource
requirements, we apply the Sawfish cavity to a recent one-way quantum repeater
protocol.

With the development and progress of science and technology, the Internet of
Things(IoT) has gradually entered people's lives, bringing great convenience to
our lives and improving people's work efficiency. Specifically, the IoT can
replace humans in jobs that they cannot perform. As a new type of IoT vehicle,
the current status and trend of research on Unmanned Aerial Vehicle(UAV) is
gratifying, and the development prospect is very promising. However, privacy
and communication are still very serious issues in drone applications. This is
because most drones still use centralized cloud-based data processing, which
may lead to leakage of data collected by drones. At the same time, the large
amount of data collected by drones may incur greater communication overhead
when transferred to the cloud. Federated learning as a means of privacy
protection can effectively solve the above two problems. However, federated
learning when applied to UAV networks also needs to consider the heterogeneity
of data, which is caused by regional differences in UAV regulation. In
response, this paper proposes a new algorithm FedBA to optimize the global
model and solves the data heterogeneity problem. In addition, we apply the
algorithm to some real datasets, and the experimental results show that the
algorithm outperforms other algorithms and improves the accuracy of the local
model for UAVs.

We examine LMs' competence of directional predicate entailments by supervised
fine-tuning with prompts. Our analysis shows that contrary to their apparent
success on standard NLI, LMs show limited ability to learn such directional
inference; moreover, existing datasets fail to test directionality, and/or are
infested by artefacts that can be learnt as proxy for entailments, yielding
over-optimistic results. In response, we present BoOQA (Boolean Open QA), a
robust multi-lingual evaluation benchmark for directional predicate
entailments, extrinsic to existing training sets. On BoOQA, we establish
baselines and show evidence of existing LM-prompting models being incompetent
directional entailment learners, in contrast to entailment graphs, however
limited by sparsity.

Visual Question Answering (VQA) models are prone to learn the shortcut
solution formed by dataset biases rather than the intended solution. To
evaluate the VQA models' reasoning ability beyond shortcut learning, the VQA-CP
v2 dataset introduces a distribution shift between the training and test set
given a question type. In this way, the model cannot use the training set
shortcut (from question type to answer) to perform well on the test set.
However, VQA-CP v2 only considers one type of shortcut and thus still cannot
guarantee that the model relies on the intended solution rather than a solution
specific to this shortcut. To overcome this limitation, we propose a new
dataset that considers varying types of shortcuts by constructing different
distribution shifts in multiple OOD test sets. In addition, we overcome the
three troubling practices in the use of VQA-CP v2, e.g., selecting models using
OOD test sets, and further standardize OOD evaluation procedure. Our benchmark
provides a more rigorous and comprehensive testbed for shortcut learning in
VQA. We benchmark recent methods and find that methods specifically designed
for particular shortcuts fail to simultaneously generalize to our varying OOD
test sets. We also systematically study the varying shortcuts and provide
several valuable findings, which may promote the exploration of shortcut
learning in VQA.

As the categories of named entities rapidly increase in real-world
applications, class-incremental learning for NER is in demand, which
continually learns new entity classes while maintaining the old knowledge. Due
to privacy concerns and storage constraints, the model is required to update
without any annotations of the old entity classes. However, in each step on
streaming data, the "O" class in each step might contain unlabeled entities
from the old classes, or potential entities from the incoming classes. In this
work, we first carry out an empirical study to investigate the concealed entity
problem in class-incremental NER. We find that training with "O" leads to
severe confusion of "O" and concealed entity classes, and harms the
separability of potential classes. Based on this discovery, we design a
rehearsal-based representation learning approach for appropriately learning the
"O" class for both old and potential entity classes. Additionally, we provide a
more realistic and challenging benchmark for class-incremental NER which
introduces multiple categories in each step. Experimental results verify our
findings and show the effectiveness of the proposed method on the new

Many natural language processing (NLP) tasks are naturally imbalanced, as
some target categories occur much more frequently than others in the real
world. In such scenarios, current NLP models still tend to perform poorly on
less frequent classes. Addressing class imbalance in NLP is an active research
topic, yet, finding a good approach for a particular task and imbalance
scenario is difficult.
  With this survey, the first overview on class imbalance in deep-learning
based NLP, we provide guidance for NLP researchers and practitioners dealing
with imbalanced data. We first discuss various types of controlled and
real-world class imbalance. Our survey then covers approaches that have been
explicitly proposed for class-imbalanced NLP tasks or, originating in the
computer vision community, have been evaluated on them. We organize the methods
by whether they are based on sampling, data augmentation, choice of loss
function, staged learning, or model design. Finally, we discuss open problems
such as dealing with multi-label scenarios, and propose systematic benchmarking
and reporting in order to move forward on this problem as a community.

Inspired by recent developments regarding the application of self-supervised
learning (SSL), we devise an auxiliary task for trajectory prediction that
takes advantage of map-only information such as graph connectivity with the
intent of improving map comprehension and generalization. We apply this
auxiliary task through two frameworks - multitasking and pretraining. In either
framework we observe significant improvement of our baseline in metrics such as
$\mathrm{minFDE}_6$ (as much as 20.3%) and $\mathrm{MissRate}_6$ (as much as
33.3%), as well as a richer comprehension of map features demonstrated by
different training configurations. The results obtained were consistent in all
three data sets used for experiments: Argoverse, Interaction and NuScenes. We
also submit our new pretrained model's results to the Interaction challenge and
achieve $\textit{1st}$ place with respect to $\mathrm{minFDE}_6$ and
$\mathrm{minADE}_6$.

In a critical software system, the testers have to spend an enormous amount
of time and effort to maintain the software due to the continuous occurrence of
defects. Among such defects, some severe defects may adversely affect the
software. To reduce the time and effort of a tester, many machine learning
models have been proposed in the literature, which use the documented defect
reports to automatically predict the severity of the defective software
modules. In contrast to the traditional approaches, in this work we propose a
metric-based software defect severity prediction (SDSP) model that uses a
self-training semi-supervised learning approach to classify the severity of the
defective software modules. The approach is constructed on a mixture of
unlabelled and labelled defect severity data. The self-training works on the
basis of a decision tree classifier to assign the pseudo-class labels to the
unlabelled instances. The predictions are promising since the self-training
successfully assigns the suitable class labels to the unlabelled instances.
  On the other hand, numerous research studies have covered proposing
prediction approaches as well as the methodological aspects of defect severity
prediction models, the gap in estimating project attributes from the prediction
model remains unresolved. To bridge the gap, we propose five project specific
measures such as the Risk-Factor (RF), the Percent of Saved Budget (PSB), the
Loss in the Saved Budget (LSB), the Remaining Service Time (RST) and Gratuitous
Service Time (GST) to capture project outcomes from the predictions. Similar to
the traditional measures, these measures are also calculated from the observed
confusion matrix. These measures are used to analyse the impact that the
prediction model has on the software project.

Control on spatial location and density of defects in 2D materials can be
achieved using electron beam irradiation. Conversely, ultralow accelerating
voltages (less than or equal to 5kV) are used to measure surface morphology,
with no expected defect creation. We find clear signatures of defect creation
in monolayer (ML) MoS2 at these voltages. Evolution of E' and A1' Raman modes
with electron dose, and appearance of defect activated peaks indicate defect
formation. To simulate Raman spectra of MoS2 at realistic defect distributions,
while retaining density-functional theory accuracy, we combine machine-learning
force fields for phonons and eigenmode projection approach for Raman tensors.
Simulated spectra agree with experiments, with sulphur vacancies as suggested
defects. We decouple defects, doping and carbonaceous contamination using
control (hBN covered and encapsulated MoS2) samples. We observe cryogenic PL
quenching and defect peaks, and find that carbonaceous contamination does not
affect defect creation. These studies have applications in photonics and
quantum emitters.

Baikal-GVD is a large ($\sim$ 1 km$^3$) underwater neutrino telescope
installed in the fresh waters of Lake Baikal. The deep lake water environment
is pervaded by background light, which produces detectable signals in the
Baikal-GVD photosensors. We introduce a neural network for an efficient
separation of these noise hits from the signal ones, stemming from the
propagation of relativistic particles through the detector. The neural network
has a U-net like architecture and employs temporal (causal) structure of
events. On Monte-Carlo simulated data, it reaches 99% signal purity (precision)
and 98% survival efficiency (recall). The benefits of using neural network for
data analysis are discussed, and other possible architectures of neural
networks, including graph based, are examined.

In this paper, we propose a new self-supervised method, which is called
Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers
of images. In DMAE, we corrupt each image by adding Gaussian noises to each
pixel value and randomly masking several patches. A Transformer-based
encoder-decoder model is then trained to reconstruct the original image from
the corrupted one. In this learning paradigm, the encoder will learn to capture
relevant semantics for the downstream tasks, which is also robust to Gaussian
additive noises. We show that the pre-trained encoder can naturally be used as
the base classifier in Gaussian smoothed models, where we can analytically
compute the certified radius for any data point. Although the proposed method
is simple, it yields significant performance improvement in downstream
classification tasks. We show that the DMAE ViT-Base model, which just uses
1/10 parameters of the model developed in recent work arXiv:2206.10550,
achieves competitive or better certified accuracy in various settings. The DMAE
ViT-Large model significantly surpasses all previous results, establishing a
new state-of-the-art on ImageNet dataset. We further demonstrate that the
pre-trained model has good transferability to the CIFAR-10 dataset, suggesting
its wide adaptability. Models and code are available at
https://github.com/quanlin-wu/dmae.

In this paper, we focus on multi-task classification, where related
classification tasks share the same label space and are learned simultaneously.
In particular, we tackle a new setting, which is more realistic than currently
addressed in the literature, where categories shift from training to test data.
Hence, individual tasks do not contain complete training data for the
categories in the test set. To generalize to such test data, it is crucial for
individual tasks to leverage knowledge from related tasks. To this end, we
propose learning an association graph to transfer knowledge among tasks for
missing classes. We construct the association graph with nodes representing
tasks, classes and instances, and encode the relationships among the nodes in
the edges to guide their mutual knowledge transfer. By message passing on the
association graph, our model enhances the categorical information of each
instance, making it more discriminative. To avoid spurious correlations between
task and class nodes in the graph, we introduce an assignment entropy
maximization that encourages each class node to balance its edge weights. This
enables all tasks to fully utilize the categorical information from related
tasks. An extensive evaluation on three general benchmarks and a medical
dataset for skin lesion classification reveals that our method consistently
performs better than representative baselines.

Temporal point processes (TPP) are a natural tool for modeling event-based
data. Among all TPP models, Hawkes processes have proven to be the most widely
used, mainly due to their simplicity and computational ease when considering
exponential or non-parametric kernels. Although non-parametric kernels are an
option, such models require large datasets. While exponential kernels are more
data efficient and relevant for certain applications where events immediately
trigger more events, they are ill-suited for applications where latencies need
to be estimated, such as in neuroscience. This work aims to offer an efficient
solution to TPP inference using general parametric kernels with finite support.
The developed solution consists of a fast L2 gradient-based solver leveraging a
discretized version of the events. After supporting the use of discretization
theoretically, the statistical and computational efficiency of the novel
approach is demonstrated through various numerical experiments. Finally, the
effectiveness of the method is evaluated by modeling the occurrence of
stimuli-induced patterns from brain signals recorded with
magnetoencephalography (MEG). Given the use of general parametric kernels,
results show that the proposed approach leads to a more plausible estimation of
pattern latency compared to the state-of-the-art.

Due to the complex of mixed spectral point spread function within memory
effect range, it is unreliable and slow to use speckle correlation technology
for non-invasive imaging through scattering medium under broadband
illumination. The contrast of the speckles will drastically drop as the light
source's spectrum width increases. Here, we propose a method for producing the
optical transfer function with several speckle frames within memory effect
range to image under broadband illumination. The method can be applied to image
amplitude and color objects under white LED illumination. Compared to other
approaches of imaging under broadband illumination, such as deep learning and
modified phase retrieval, our method can provide more stable results with
faster convergence speed, which can be applied in high speed scattering imaging
under natural light illumination.

The task of designing optical multilayer thin-films regarding a given target
is currently solved using gradient-based optimization in conjunction with
methods that can introduce additional thin-film layers. Recently, Deep Learning
and Reinforcement Learning have been been introduced to the task of designing
thin-films with great success, however a trained network is usually only able
to become proficient for a single target and must be retrained if the optical
targets are varied. In this work, we apply conditional Invertible Neural
Networks (cINN) to inversely designing multilayer thin-films given an optical
target. Since the cINN learns the energy landscape of all thin-film
configurations within the training dataset, we show that cINNs can generate a
stochastic ensemble of proposals for thin-film configurations that that are
reasonably close to the desired target depending only on random variables. By
refining the proposed configurations further by a local optimization, we show
that the generated thin-films reach the target with significantly greater
precision than comparable state-of-the art approaches. Furthermore, we tested
the generative capabilities on samples which are outside the training data
distribution and found that the cINN was able to predict thin-films for
out-of-distribution targets, too. The results suggest that in order to improve
the generative design of thin-films, it is instructive to use established and
new machine learning methods in conjunction in order to obtain the most
favorable results.

AI tools can be useful to address model deficits in the design of
communication systems. However, conventional learning-based AI algorithms yield
poorly calibrated decisions, unabling to quantify their outputs uncertainty.
While Bayesian learning can enhance calibration by capturing epistemic
uncertainty caused by limited data availability, formal calibration guarantees
only hold under strong assumptions about the ground-truth, unknown, data
generation mechanism. We propose to leverage the conformal prediction framework
to obtain data-driven set predictions whose calibration properties hold
irrespective of the data distribution. Specifically, we investigate the design
of baseband demodulators in the presence of hard-to-model nonlinearities such
as hardware imperfections, and propose set-based demodulators based on
conformal prediction. Numerical results confirm the theoretical validity of the
proposed demodulators, and bring insights into their average prediction set
size efficiency.

Federated Learning (FL) is a novel approach enabling several clients holding
sensitive data to collaboratively train machine learning models, without
centralizing data. The cross-silo FL setting corresponds to the case of few
($2$--$50$) reliable clients, each holding medium to large datasets, and is
typically found in applications such as healthcare, finance, or industry. While
previous works have proposed representative datasets for cross-device FL, few
realistic healthcare cross-silo FL datasets exist, thereby slowing algorithmic
research in this critical application. In this work, we propose a novel
cross-silo dataset suite focused on healthcare, FLamby (Federated Learning
AMple Benchmark of Your cross-silo strategies), to bridge the gap between
theory and practice of cross-silo FL. FLamby encompasses 7 healthcare datasets
with natural splits, covering multiple tasks, modalities, and data volumes,
each accompanied with baseline training code. As an illustration, we
additionally benchmark standard FL algorithms on all datasets. Our flexible and
modular suite allows researchers to easily download datasets, reproduce results
and re-use the different components for their research. FLamby is available
at~\url{www.github.com/owkin/flamby}.

Graph Convolution Networks (GCNs), with their efficient ability to capture
high-order connectivity in graphs, have been widely applied in recommender
systems. Stacking multiple neighbor aggregation is the major operation in GCNs.
It implicitly captures popularity features because the number of neighbor nodes
reflects the popularity of a node. However, existing GCN-based methods ignore a
universal problem: users' sensitivity to item popularity is differentiated, but
the neighbor aggregations in GCNs actually fix this sensitivity through Graph
Laplacian Normalization, leading to suboptimal personalization.
  In this work, we propose to model multi-grained popularity features and
jointly learn them together with high-order connectivity, to match the
differentiation of user preferences exhibited in popularity features.
Specifically, we develop a Joint Multi-grained Popularity-aware Graph
Convolution Collaborative Filtering model, short for JMP-GCF, which uses a
popularity-aware embedding generation to construct multi-grained popularity
features, and uses the idea of joint learning to capture the signals within and
between different granularities of popularity features that are relevant for
modeling user preferences. Additionally, we propose a multistage stacked
training strategy to speed up model convergence. We conduct extensive
experiments on three public datasets to show the state-of-the-art performance
of JMP-GCF.

ParaDime is a framework for parametric dimensionality reduction (DR). In
parametric DR, neural networks are trained to embed high-dimensional data items
in a low-dimensional space while minimizing an objective function. ParaDime
builds on the idea that the objective functions of several modern DR techniques
result from transformed inter-item relationships. It provides a common
interface to specify the way these relations and transformations are computed
and how they are used within the losses that govern the training process.
Through this interface, ParaDime unifies parametric versions of DR techniques
such as metric MDS, t-SNE, and UMAP. Furthermore, it allows users to fully
customize each aspect of the DR process. We show how this ease of customization
makes ParaDime suitable for experimenting with interesting techniques, such as
hybrid classification/embedding models or supervised DR, which opens up new
possibilities for visualizing high-dimensional data.

Designing robust algorithms capable of training accurate neural networks on
uncurated datasets from the web has been the subject of much research as it
reduces the need for time consuming human labor. The focus of many previous
research contributions has been on the detection of different types of label
noise; however, this paper proposes to improve the correction accuracy of noisy
samples once they have been detected. In many state-of-the-art contributions, a
two phase approach is adopted where the noisy samples are detected before
guessing a corrected pseudo-label in a semi-supervised fashion. The guessed
pseudo-labels are then used in the supervised objective without ensuring that
the label guess is likely to be correct. This can lead to confirmation bias,
which reduces the noise robustness. Here we propose the pseudo-loss, a simple
metric that we find to be strongly correlated with pseudo-label correctness on
noisy samples. Using the pseudo-loss, we dynamically down weight
under-confident pseudo-labels throughout training to avoid confirmation bias
and improve the network accuracy. We additionally propose to use a confidence
guided contrastive objective that learns robust representation on an
interpolated objective between class bound (supervised) for confidently
corrected samples and unsupervised representation for under-confident label
corrections. Experiments demonstrate the state-of-the-art performance of our
Pseudo-Loss Selection (PLS) algorithm on a variety of benchmark datasets
including curated data synthetically corrupted with in-distribution and
out-of-distribution noise, and two real world web noise datasets. Our
experiments are fully reproducible github.com/PaulAlbert31/SNCF

Timely and effective response to humanitarian crises requires quick and
accurate analysis of large amounts of text data - a process that can highly
benefit from expert-assisted NLP systems trained on validated and annotated
data in the humanitarian response domain. To enable creation of such NLP
systems, we introduce and release HumSet, a novel and rich multilingual dataset
of humanitarian response documents annotated by experts in the humanitarian
response community. The dataset provides documents in three languages (English,
French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021
across the globe. For each document, HUMSET provides selected snippets
(entries) as well as assigned classes to each entry annotated using common
humanitarian information analysis frameworks. HUMSET also provides novel and
challenging entry extraction and multi-label entry classification tasks. In
this paper, we take a first step towards approaching these tasks and conduct a
set of experiments on Pre-trained Language Models (PLM) to establish strong
baselines for future research in this domain. The dataset is available at
https://blog.thedeep.io/humset/.

Modern sensors play a pivotal role in many operating platforms, as they
manage to track the platform dynamics at a relatively low manufacturing costs.
Their widespread use can be found starting from autonomous vehicles, through
tactical platforms, and ending with household appliances in daily use. Upon
leaving the factory, the calibrated sensor starts accumulating different error
sources which slowly wear out its precision and reliability. To that end,
periodic calibration is needed, to restore intrinsic parameters and realign its
readings with the ground truth. While extensive analytic methods exist in the
literature, little is proposed using data-driven techniques and their
unprecedented approximation capabilities. In this study, we show how bias
elimination in low-cost gyroscopes can be performed in considerably shorter
operative time, using a unique convolutional neural network structure. The
strict constraints of traditional methods are replaced by a learning-based
regression which spares the time-consuming averaging time, exhibiting efficient
sifting of background noise from the actual bias.

Face recognition has made tremendous progress in recent years due to the
advances in loss functions and the explosive growth in training sets size. A
properly designed loss is seen as key to extract discriminative features for
classification. Several margin-based losses have been proposed as alternatives
of softmax loss in face recognition. However, two issues remain to consider: 1)
They overlook the importance of hard sample mining for discriminative learning.
2) Label noise ubiquitously exists in large-scale datasets, which can seriously
damage the model's performance. In this paper, starting from the perspective of
decision boundary, we propose a novel mining framework that focuses on the
relationship between a sample's ground truth class center and its nearest
negative class center. Specifically, a closed-set noise label self-correction
module is put forward, making this framework work well on datasets containing a
lot of label noise. The proposed method consistently outperforms SOTA methods
in various face recognition benchmarks. Training code has been released at
https://github.com/SWJTU-3DVision/BoundaryFace.

In this study, we aim to explore efficient tuning methods for speech
self-supervised learning. Recent studies show that self-supervised learning
(SSL) can learn powerful representations for different speech tasks. However,
fine-tuning pre-trained models for each downstream task is
parameter-inefficient since SSL models are notoriously large with millions of
parameters. Adapters are lightweight modules commonly used in NLP to solve this
problem. In downstream tasks, the parameters of SSL models are frozen, and only
the adapters are trained. Given the lack of studies generally exploring the
effectiveness of adapters for self-supervised speech tasks, we intend to fill
this gap by adding various adapter modules in pre-trained speech SSL models. We
show that the performance parity can be achieved with over 90% parameter
reduction, and discussed the pros and cons of efficient tuning techniques. This
is the first comprehensive investigation of various adapter types across speech
tasks.

Models for Visual Question Answering (VQA) often rely on the spurious
correlations, i.e., the language priors, that appear in the biased samples of
training set, which make them brittle against the out-of-distribution (OOD)
test data. Recent methods have achieved promising progress in overcoming this
problem by reducing the impact of biased samples on model training. However,
these models reveal a trade-off that the improvements on OOD data severely
sacrifice the performance on the in-distribution (ID) data (which is dominated
by the biased samples). Therefore, we propose a novel contrastive learning
approach, MMBS, for building robust VQA models by Making the Most of Biased
Samples. Specifically, we construct positive samples for contrastive learning
by eliminating the information related to spurious correlation from the
original training samples and explore several strategies to use the constructed
positive samples for training. Instead of undermining the importance of biased
samples in model training, our approach precisely exploits the biased samples
for unbiased information that contributes to reasoning. The proposed method is
compatible with various VQA backbones. We validate our contributions by
achieving competitive performance on the OOD dataset VQA-CP v2 while preserving
robust performance on the ID dataset VQA v2.

Visual reinforcement learning (RL), which makes decisions directly from
high-dimensional visual inputs, has demonstrated significant potential in
various domains. However, deploying visual RL techniques in the real world
remains challenging due to their low sample efficiency and large generalization
gaps. To tackle these obstacles, data augmentation (DA) has become a widely
used technique in visual RL for acquiring sample-efficient and generalizable
policies by diversifying the training data. This survey aims to provide a
timely and essential review of DA techniques in visual RL in recognition of the
thriving development in this field. In particular, we propose a unified
framework for analyzing visual RL and understanding the role of DA in it. We
then present a principled taxonomy of the existing augmentation techniques used
in visual RL and conduct an in-depth discussion on how to better leverage
augmented data in different scenarios. Moreover, we report a systematic
empirical evaluation of DA-based techniques in visual RL and conclude by
highlighting the directions for future research. As the first comprehensive
survey of DA in visual RL, this work is expected to offer valuable guidance to
this emerging field.

Visual search is of great assistance in reseller commerce, especially for
non-tech savvy users with affinity towards regional languages. It allows
resellers to accurately locate the products that they seek, unlike textual
search which recommends products from head brands. Product attributes available
in e-commerce have a great potential for building better visual search systems
as they capture fine grained relations between data points. In this work, we
design a visual search system for reseller commerce using a multi-task learning
approach. We also highlight and address the challenges like image compression,
cropping, scribbling on the image, etc, faced in reseller commerce. Our model
consists of three different tasks: attribute classification, triplet ranking
and variational autoencoder (VAE). Masking technique is used for designing the
attribute classification. Next, we introduce an offline triplet mining
technique which utilizes information from multiple attributes to capture
relative order within the data. This technique displays a better performance
compared to the traditional triplet mining baseline, which uses single
label/attribute information. We also compare and report incremental gain
achieved by our unified multi-task model over each individual task separately.
The effectiveness of our method is demonstrated using the in-house dataset of
product images from the Lifestyle business-unit of Flipkart, India's largest
e-commerce company. To efficiently retrieve the images in production, we use
the Approximate Nearest Neighbor (ANN) index. Finally, we highlight our
production environment constraints and present the design choices and
experiments conducted to select a suitable ANN index.

Image captioning task has been extensively researched by previous work.
However, limited experiments focus on generating captions based on
non-autoregressive text decoder. Inspired by the recent success of the
denoising diffusion model on image synthesis tasks, we apply denoising
diffusion probabilistic models to text generation in image captioning tasks. We
show that our CLIP-Diffusion-LM is capable of generating image captions using
significantly fewer inference steps than autoregressive models. On the Flickr8k
dataset, the model achieves 0.1876 BLEU-4 score. By training on the combined
Flickr8k and Flickr30k dataset, our model achieves 0.2470 BLEU-4 score. Our
code is available at https://github.com/xu-shitong/diffusion-image-captioning.

In medical settings, Individual Variation (IV) refers to variation that is
due not to population differences or errors, but rather to within-subject
variation, that is the intrinsic and characteristic patterns of variation
pertaining to a given instance or the measurement process. While taking into
account IV has been deemed critical for proper analysis of medical data, this
source of uncertainty and its impact on robustness have so far been neglected
in Machine Learning (ML). To fill this gap, we look at how IV affects ML
performance and generalization and how its impact can be mitigated.
Specifically, we provide a methodological contribution to formalize the problem
of IV in the statistical learning framework and, through an experiment based on
one of the largest real-world laboratory medicine datasets for the problem of
COVID-19 diagnosis, we show that: 1) common state-of-the-art ML models are
severely impacted by the presence of IV in data; and 2) advanced learning
strategies, based on data augmentation and data imprecisiation, and proper
study designs can be effective at improving robustness to IV. Our findings
demonstrate the critical relevance of correctly accounting for IV to enable
safe deployment of ML in clinical settings.

Solar PV yield nowcasting is used to help anticipate peaks and troughs in
demand to support grid integration. This paper compares multiple low-resource
approaches to nowcasting solar PV yield, using a dataset of UK satellite
imagery and solar PV energy readings over a 1 to 4-hour time range. The paper
also estimates the carbon emissions generated and averted by deploying models,
and finds that even small models that could be deployable in low-resource
settings may have a benefit several orders of magnitude greater than its carbon
cost. The paper also examines prediction errors and the activations in a CNN.

Recent efforts to identify non-cognitive predictors of academic achievement
have especially focused on self-constructs, whose measurement is concerned with
a specific domain (e.g., mathematics). However, other important factors, such
as character and motivation, have received less attention. Additionally, the
predictive accuracy of non-cognitive factors lacks evidence from subjects
including English and Science. In this study, we take a person-centered
approach and focus on students' intrinsic motivation, need for cognition, grit,
and growth mindset. We mainly focus on how these factors predict students'
mathematics, English, and science grades between 9th grade and 12th grade.
2,308 samples from high school students in Boston (Female = 1,237; aged from 13
to 17). The research results indicated that: (1) four latent profiles of
students emerged: High in grit students (n = 997, 43.2%, higher scores of
grit); Moderate students (n = 905, 38.3%, moderate in all scores); High in
intrinsic motivation students (n = 252, 11.8%, higher scores of intrinsic
motivation); Low in grit students (n = 154, 6.7%, lower scores of grit); (2)
students' gender, race, maternal education level, and social-economic ranking
predicted the profiles; and (3) four profiles of students had a significant
predictive effect on Mathematics, Science and English scores in both 9th grade
and 12th grade. We discussed the importance of character education for
adolescents and motivation for learning in high school.

A major open problem in neural machine translation (NMT) is the translation
of idiomatic expressions, such as "under the weather". The meaning of these
expressions is not composed by the meaning of their constituent words, and NMT
models tend to translate them literally (i.e., word-by-word), which leads to
confusing and nonsensical translations. Research on idioms in NMT is limited
and obstructed by the absence of automatic methods for quantifying these
errors. In this work, first, we propose a novel metric for automatically
measuring the frequency of literal translation errors without human
involvement. Equipped with this metric, we present controlled translation
experiments with models trained in different conditions (with/without the
test-set idioms) and across a wide range of (global and targeted) metrics and
test sets. We explore the role of monolingual pretraining and find that it
yields substantial targeted improvements, even without observing any
translation examples of the test-set idioms. In our analysis, we probe the role
of idiom context. We find that the randomly initialized models are more local
or "myopic" as they are relatively unaffected by variations of the idiom
context, unlike the pretrained ones.

Vision-based localization in a prior map is of crucial importance for
autonomous vehicles. Given a query image, the goal is to estimate the camera
pose corresponding to the prior map, and the key is the registration problem of
camera images within the map. While autonomous vehicles drive on the road under
occlusion (e.g., car, bus, truck) and changing environment appearance (e.g.,
illumination changes, seasonal variation), existing approaches rely heavily on
dense point descriptors at the feature level to solve the registration problem,
entangling features with appearance and occlusion. As a result, they often fail
to estimate the correct poses. To address these issues, we propose a sparse
semantic map-based monocular localization method, which solves 2D-3D
registration via a well-designed deep neural network. Given a sparse semantic
map that consists of simplified elements (e.g., pole lines, traffic sign
midpoints) with multiple semantic labels, the camera pose is then estimated by
learning the corresponding features between the 2D semantic elements from the
image and the 3D elements from the sparse semantic map. The proposed sparse
semantic map-based localization approach is robust against occlusion and
long-term appearance changes in the environments. Extensive experimental
results show that the proposed method outperforms the state-of-the-art

Accumulated Local Effect (ALE) is a method for accurately estimating feature
effects, overcoming fundamental failure modes of previously-existed methods,
such as Partial Dependence Plots. However, ALE's approximation, i.e. the method
for estimating ALE from the limited samples of the training set, faces two
weaknesses. First, it does not scale well in cases where the input has high
dimensionality, and, second, it is vulnerable to out-of-distribution (OOD)
sampling when the training set is relatively small. In this paper, we propose a
novel ALE approximation, called Differential Accumulated Local Effects (DALE),
which can be used in cases where the ML model is differentiable and an
auto-differentiable framework is accessible. Our proposal has significant
computational advantages, making feature effect estimation applicable to
high-dimensional Machine Learning scenarios with near-zero computational
overhead. Furthermore, DALE does not create artificial points for calculating
the feature effect, resolving misleading estimations due to OOD sampling.
Finally, we formally prove that, under some hypotheses, DALE is an unbiased
estimator of ALE and we present a method for quantifying the standard error of
the explanation. Experiments using both synthetic and real datasets demonstrate
the value of the proposed approach.

With the advancement of technology for artificial intelligence (AI) based
solutions and analytics compute engines, machine learning (ML) models are
getting more complex day by day. Most of these models are generally used as a
black box without user interpretability. Such complex ML models make it more
difficult for people to understand or trust their predictions. There are
variety of frameworks using explainable AI (XAI) methods to demonstrate
explainability and interpretability of ML models to make their predictions more
trustworthy. In this manuscript, we propose a methodology that we define as
Local Interpretable Model Agnostic Shap Explanations (LIMASE). This proposed ML
explanation technique uses Shapley values under the LIME paradigm to achieve
the following (a) explain prediction of any model by using a locally faithful
and interpretable decision tree model on which the Tree Explainer is used to
calculate the shapley values and give visually interpretable explanations. (b)
provide visually interpretable global explanations by plotting local
explanations of several data points. (c) demonstrate solution for the
submodular optimization problem. (d) also bring insight into regional
interpretation e) faster computation compared to use of kernel explainer.

Spiking neural networks (SNNs) are shown to be more biologically plausible
and energy efficient over their predecessors. However, there is a lack of an
efficient and generalized training method for deep SNNs, especially for
deployment on analog computing substrates. In this paper, we put forward a
generalized learning rule, termed Local Tandem Learning (LTL). The LTL rule
follows the teacher-student learning approach by mimicking the intermediate
feature representations of a pre-trained ANN. By decoupling the learning of
network layers and leveraging highly informative supervisor signals, we
demonstrate rapid network convergence within five training epochs on the
CIFAR-10 dataset while having low computational complexity. Our experimental
results have also shown that the SNNs thus trained can achieve comparable
accuracies to their teacher ANNs on CIFAR-10, CIFAR-100, and Tiny ImageNet
datasets. Moreover, the proposed LTL rule is hardware friendly. It can be
easily implemented on-chip to perform fast parameter calibration and provide
robustness against the notorious device non-ideality issues. It, therefore,
opens up a myriad of opportunities for training and deployment of SNN on
ultra-low-power mixed-signal neuromorphic computing chips.10

The infinite horizon setting is widely adopted for problems of reinforcement
learning (RL). These invariably result in stationary policies that are optimal.
In many situations, finite horizon control problems are of interest and for
such problems, the optimal policies are time-varying in general. Another
setting that has become popular in recent times is of Constrained Reinforcement
Learning, where the agent maximizes its rewards while also aims to satisfy
certain constraint criteria. However, this setting has only been studied in the
context of infinite horizon MDPs where stationary policies are optimal. We
present, for the first time, an algorithm for constrained RL in the Finite
Horizon Setting where the horizon terminates after a fixed (finite) time. We
use function approximation in our algorithm which is essential when the state
and action spaces are large or continuous and use the policy gradient method to
find the optimal policy. The optimal policy that we obtain depends on the stage
and so is time-dependent. To the best of our knowledge, our paper presents the
first policy gradient algorithm for the finite horizon setting with
constraints. We show the convergence of our algorithm to an optimal policy. We
further present a sample complexity result for our algorithm in the
unconstrained (i.e., the regular finite horizon MDP) setting. We also compare
and analyze the performance of our algorithm through experiments and show that
our algorithm performs better than other well known algorithms.

The conventional success of textual classification relies on annotated data,
and the new paradigm of pre-trained language models (PLMs) still requires a few
labeled data for downstream tasks. However, in real-world applications, label
noise inevitably exists in training data, damaging the effectiveness,
robustness, and generalization of the models constructed on such data.
Recently, remarkable achievements have been made to mitigate this dilemma in
visual data, while only a few explore textual data. To fill this gap, we
present SelfMix, a simple yet effective method, to handle label noise in text
classification tasks. SelfMix uses the Gaussian Mixture Model to separate
samples and leverages semi-supervised learning. Unlike previous works requiring
multiple models, our method utilizes the dropout mechanism on a single model to
reduce the confirmation bias in self-training and introduces a textual-level
mixup training strategy. Experimental results on three text classification
benchmarks with different types of text show that the performance of our
proposed method outperforms these strong baselines designed for both textual
and visual data under different noise ratios and noise types. Our code is
available at https://github.com/noise-learning/SelfMix.

Few-shot class-incremental learning (FSCIL) is designed to incrementally
recognize novel classes with only few training samples after the (pre-)training
on base classes with sufficient samples, which focuses on both base-class
performance and novel-class generalization. A well known modification to the
base-class training is to apply a margin to the base-class classification.
However, a dilemma exists that we can hardly achieve both good base-class
performance and novel-class generalization simultaneously by applying the
margin during the base-class training, which is still under explored. In this
paper, we study the cause of such dilemma for FSCIL. We first interpret this
dilemma as a class-level overfitting (CO) problem from the aspect of pattern
learning, and then find its cause lies in the easily-satisfied constraint of
learning margin-based patterns. Based on the analysis, we propose a novel
margin-based FSCIL method to mitigate the CO problem by providing the pattern
learning process with extra constraint from the margin-based patterns
themselves. Extensive experiments on CIFAR100, Caltech-USCD Birds-200-2011
(CUB200), and miniImageNet demonstrate that the proposed method effectively
mitigates the CO problem and achieves state-of-the-art performance.

We investigate the joint transmit beamforming and reconfigurable intelligent
surface (RIS) configuration problem to maximize the sum downlink rate of a
RIS-aided cellular multiuser multiple input single output (MU-MISO) system
under imperfect channel state information (CSI) and hardware impairments by
considering a practical phase-dependent RIS amplitude model. To this end, we
present a novel deep reinforcement learning (DRL) framework and compare its
performance against a vanilla DRL agent under two scenarios: the golden
standard where the base station (BS) knows the channel and the phase-dependent
RIS amplitude model perfectly, and the mismatch scenario where the BS has
imperfect CSI and assumes ideal RIS reflections. Our numerical results show
that the introduced framework substantially outperforms the vanilla DRL agent
under mismatch and approaches the golden standard.

How do humans and other animals learn new tasks? A wave of brain recording
studies has investigated how neural representations change during task
learning, with a focus on how tasks can be acquired and coded in ways that
minimise mutual interference. We review recent work that has explored the
geometry and dimensionality of neural task representations in neocortex, and
computational models that have exploited these findings to understand how the
brain may partition knowledge between tasks. We discuss how ideas from machine
learning, including those that combine supervised and unsupervised learning,
are helping neuroscientists understand how natural tasks are learned and coded
in biological brains.

Continual relation extraction (CRE) aims to extract relations towards the
continuous and iterative arrival of new data, of which the major challenge is
the catastrophic forgetting of old tasks. In order to alleviate this critical
problem for enhanced CRE performance, we propose a novel Continual Relation
Extraction framework with Contrastive Learning, namely CRECL, which is built
with a classification network and a prototypical contrastive network to achieve
the incremental-class learning of CRE. Specifically, in the contrastive network
a given instance is contrasted with the prototype of each candidate relations
stored in the memory module. Such contrastive learning scheme ensures the data
distributions of all tasks more distinguishable, so as to alleviate the
catastrophic forgetting further. Our experiment results not only demonstrate
our CRECL's advantage over the state-of-the-art baselines on two public
datasets, but also verify the effectiveness of CRECL's contrastive learning on
improving CRE performance.

With the new generation of satellite technologies, the archives of remote
sensing (RS) images are growing very fast. To make the intrinsic information of
each RS image easily accessible, visual question answering (VQA) has been
introduced in RS. VQA allows a user to formulate a free-form question
concerning the content of RS images to extract generic information. It has been
shown that the fusion of the input modalities (i.e., image and text) is crucial
for the performance of VQA systems. Most of the current fusion approaches use
modality-specific representations in their fusion modules instead of joint
representation learning. However, to discover the underlying relation between
both the image and question modality, the model is required to learn the joint
representation instead of simply combining (e.g., concatenating, adding, or
multiplying) the modality-specific representations. We propose a multi-modal
transformer-based architecture to overcome this issue. Our proposed
architecture consists of three main modules: i) the feature extraction module
for extracting the modality-specific features; ii) the fusion module, which
leverages a user-defined number of multi-modal transformer layers of the
VisualBERT model (VB); and iii) the classification module to obtain the answer.
Experimental results obtained on the RSVQAxBEN and RSVQA-LR datasets (which are
made up of RGB bands of Sentinel-2 images) demonstrate the effectiveness of
VBFusion for VQA tasks in RS. To analyze the importance of using other spectral
bands for the description of the complex content of RS images in the framework
of VQA, we extend the RSVQAxBEN dataset to include all the spectral bands of
Sentinel-2 images with 10m and 20m spatial resolution.

In neural networks, the property of being equivariant to transformations
improves generalization when the corresponding symmetry is present in the data.
In particular, scale-equivariant networks are suited to computer vision tasks
where the same classes of objects appear at different scales, like in most
semantic segmentation tasks. Recently, convolutional layers equivariant to a
semigroup of scalings and translations have been proposed. However, the
equivariance of subsampling and upsampling has never been explicitly studied
even though they are necessary building blocks in some segmentation
architectures. The U-Net is a representative example of such architectures,
which includes the basic elements used for state-of-the-art semantic
segmentation. Therefore, this paper introduces the Scale Equivariant U-Net
(SEU-Net), a U-Net that is made approximately equivariant to a semigroup of
scales and translations through careful application of subsampling and
upsampling layers and the use of aforementioned scale-equivariant layers.
Moreover, a scale-dropout is proposed in order to improve generalization to
different scales in approximately scale-equivariant architectures. The proposed
SEU-Net is trained for semantic segmentation of the Oxford Pet IIIT and the
DIC-C2DH-HeLa dataset for cell segmentation. The generalization metric to
unseen scales is dramatically improved in comparison to the U-Net, even when
the U-Net is trained with scale jittering, and to a scale-equivariant
architecture that does not perform upsampling operators inside the equivariant
pipeline. The scale-dropout induces better generalization on the
scale-equivariant models in the Pet experiment, but not on the cell
segmentation experiment.

Federated learning (FL) has been proposed to protect data privacy and
virtually assemble the isolated data silos by cooperatively training models
among organizations without breaching privacy and security. However, FL faces
heterogeneity from various aspects, including data space, statistical, and
system heterogeneity. For example, collaborative organizations without conflict
of interest often come from different areas and have heterogeneous data from
different feature spaces. Participants may also want to train heterogeneous
personalized local models due to non-IID and imbalanced data distribution and
various resource-constrained devices. Therefore, heterogeneous FL is proposed
to address the problem of heterogeneity in FL. In this survey, we
comprehensively investigate the domain of heterogeneous FL in terms of data
space, statistical, system, and model heterogeneity. We first give an overview
of FL, including its definition and categorization. Then, We propose a precise
taxonomy of heterogeneous FL settings for each type of heterogeneity according
to the problem setting and learning objective. We also investigate the transfer
learning methodologies to tackle the heterogeneity in FL. We further present
the applications of heterogeneous FL. Finally, we highlight the challenges and
opportunities and envision promising future research directions toward new
framework design and trustworthy approaches.

The past few years have witnessed a remarkable advance in deep learning for
EEG-based sleep stage classification (SSC). However, the success of these
models is attributed to possessing a massive amount of labeled data for
training, limiting their applicability in real-world scenarios. In such
scenarios, sleep labs can generate a massive amount of data, but labeling these
data can be expensive and time-consuming. Recently, the self-supervised
learning (SSL) paradigm has shined as one of the most successful techniques to
overcome the scarcity of labeled data. In this paper, we evaluate the efficacy
of SSL to boost the performance of existing SSC models in the few-labels
regime. We conduct a thorough study on three SSC datasets, and we find that
fine-tuning the pretrained SSC models with only 5% of labeled data can achieve
competitive performance to the supervised training with full labels. Moreover,
self-supervised pretraining helps SSC models to be more robust to data
imbalance and domain shift problems. The code is publicly available at
https://github.com/emadeldeen24/eval_ssl_ssc.

Continual relation extraction (CRE) aims to continually learn new relations
from a class-incremental data stream. CRE model usually suffers from
catastrophic forgetting problem, i.e., the performance of old relations
seriously degrades when the model learns new relations. Most previous work
attributes catastrophic forgetting to the corruption of the learned
representations as new relations come, with an implicit assumption that the CRE
models have adequately learned the old relations. In this paper, through
empirical studies we argue that this assumption may not hold, and an important
reason for catastrophic forgetting is that the learned representations do not
have good robustness against the appearance of analogous relations in the
subsequent learning process. To address this issue, we encourage the model to
learn more precise and robust representations through a simple yet effective
adversarial class augmentation mechanism (ACA), which is easy to implement and
model-agnostic. Experimental results show that ACA can consistently improve the
performance of state-of-the-art CRE models on two popular benchmarks.

Warning: this paper contains model outputs exhibiting offensiveness and
biases. Recently pre-trained language models (PLMs) have prospered in various
natural language generation (NLG) tasks due to their ability to generate fairly
fluent text. Nevertheless, these models are observed to capture and reproduce
harmful contents in training corpora, typically toxic language and social
biases, raising severe moral issues. Prior works on ethical NLG tackle
detoxifying and debiasing separately, which is problematic since we find
debiased models still exhibit toxicity while detoxified ones even exacerbate
biases. To address such a challenge, we propose the first unified framework of
detoxifying and debiasing called UDDIA, which jointly formalizes these two
problems as rectifying the output space. We theoretically interpret our
framework as learning a text distribution mixing weighted attributes. Besides,
UDDIA conducts adaptive optimization of only a few parameters during decoding
based on a parameter-efficient tuning schema without any training data. This
leads to minimal generation quality loss and improved rectification performance
with acceptable computational cost. Experimental results demonstrate that
compared to several strong baselines, UDDIA achieves debiasing and detoxifying
simultaneously and better balances efficiency and effectiveness, taking a
further step towards practical ethical NLG.

The problem of algorithmic bias in machine learning has gained a lot of
attention in recent years due to its concrete and potentially hazardous
implications in society. In much the same manner, biases can also alter modern
industrial and safety-critical applications where machine learning are based on
high dimensional inputs such as images. This issue has however been mostly left
out of the spotlight in the machine learning literature. Contrarily to societal
applications where a set of proxy variables can be provided by the common sense
or by regulations to draw the attention on potential risks, industrial and
safety-critical applications are most of the times sailing blind. The variables
related to undesired biases can indeed be indirectly represented in the input
data, or can be unknown, thus making them harder to tackle. This raises serious
and well-founded concerns towards the commercial deployment of AI-based
solutions, especially in a context where new regulations clearly address the
issues opened by undesired biases in AI. Consequently, we propose here to make
an overview of recent advances in this area, firstly by presenting how such
biases can demonstrate themselves, then by exploring different ways to bring
them to light, and by probing different possibilities to mitigate them. We
finally present a practical remote sensing use-case of industrial Fairness.

We study class-incremental learning, a training setup in which new classes of
data are observed over time for the model to learn from. Despite the
straightforward problem formulation, the naive application of classification
models to class-incremental learning results in the "catastrophic forgetting"
of previously seen classes. One of the most successful existing methods has
been the use of a memory of exemplars, which overcomes the issue of
catastrophic forgetting by saving a subset of past data into a memory bank and
utilizing it to prevent forgetting when training future tasks. In our paper, we
propose to enhance the utilization of this memory bank: we not only use it as a
source of additional training data like existing works but also integrate it in
the prediction process explicitly.Our method, the Memory Transformer Network
(MTN), learns how to combine and aggregate the information from the nearest
neighbors in the memory with a transformer to make more accurate predictions.
We conduct extensive experiments and ablations to evaluate our approach. We
show that MTN achieves state-of-the-art performance on the challenging
ImageNet-1k and Google-Landmarks-1k incremental learning benchmarks.

End-to-end trainable models have reached the performance of traditional
handcrafted compression techniques on videos and images. Since the parameters
of these models are learned over large training sets, they are not optimal for
any given image to be compressed. In this paper, we propose an instance-based
fine-tuning of a subset of decoder's bias to improve the reconstruction quality
in exchange for extra encoding time and minor additional signaling cost. The
proposed method is applicable to any end-to-end compression methods, improving
the state-of-the-art neural image compression BD-rate by $3-5\%$.

In April 2022, the Vistamilk SFI Research Centre organized the second edition
of the "International Workshop on Spectroscopy and Chemometrics - Applications
in Food and Agriculture". Within this event, a data challenge was organized
among participants of the workshop. Such data competition aimed at developing a
prediction model to discriminate dairy cows' diet based on milk spectral
information collected in the mid-infrared region. In fact, the development of
an accurate and reliable discriminant model for dairy cows' diet can provide
important authentication tools for dairy processors to guarantee product origin
for dairy food manufacturers from grass-fed animals. Different statistical and
machine learning modelling approaches have been employed during the workshop,
with different pre-processing steps involved and different degree of
complexity. The present paper aims to describe the statistical methods adopted
by participants to develop such classification model.

The self-supervised ultrasound (US) video model pretraining can use a small
amount of labeled data to achieve one of the most promising results on US
diagnosis. However, it does not take full advantage of multi-level knowledge
for learning deep neural networks (DNNs), and thus is difficult to learn
transferable feature representations. This work proposes a hierarchical
contrastive learning (HiCo) method to improve the transferability for the US
video model pretraining. HiCo introduces both peer-level semantic alignment and
cross-level semantic alignment to facilitate the interaction between different
semantic levels, which can effectively accelerate the convergence speed,
leading to better generalization and adaptation of the learned model.
Additionally, a softened objective function is implemented by smoothing the
hard labels, which can alleviate the negative effect caused by local
similarities of images between different classes. Experiments with HiCo on five
datasets demonstrate its favorable results over state-of-the-art approaches.
The source code of this work is publicly available at
https://github.com/983632847/HiCo.

Demonstrations and natural language instructions are two common ways to
specify and teach robots novel tasks. However, for many complex tasks, a
demonstration or language instruction alone contains ambiguities, preventing
tasks from being specified clearly. In such cases, a combination of both a
demonstration and an instruction more concisely and effectively conveys the
task to the robot than either modality alone. To instantiate this problem
setting, we train a single multi-task policy on a few hundred challenging
robotic pick-and-place tasks and propose DeL-TaCo (Joint Demo-Language Task
Conditioning), a method for conditioning a robotic policy on task embeddings
comprised of two components: a visual demonstration and a language instruction.
By allowing these two modalities to mutually disambiguate and clarify each
other during novel task specification, DeL-TaCo (1) substantially decreases the
teacher effort needed to specify a new task and (2) achieves better
generalization performance on novel objects and instructions over previous
task-conditioning methods. To our knowledge, this is the first work to show
that simultaneously conditioning a multi-task robotic manipulation policy on
both demonstration and language embeddings improves sample efficiency and
generalization over conditioning on either modality alone. See additional
materials at https://sites.google.com/view/del-taco-learning

Modern autonomous systems often rely on LiDAR scanners, in particular for
autonomous driving scenarios. In this context, reliable scene understanding is
indispensable. Current learning-based methods typically try to achieve maximum
performance for this task, while neglecting a proper estimation of the
associated uncertainties. In this work, we introduce a novel approach for
solving the task of uncertainty-aware panoptic segmentation using LiDAR point
clouds. Our proposed EvLPSNet network is the first to solve this task
efficiently in a sampling-free manner. It aims to predict per-point semantic
and instance segmentations, together with per-point uncertainty estimates.
Moreover, it incorporates methods for improving the performance by employing
the predicted uncertainties. We provide several strong baselines combining
state-of-the-art panoptic segmentation networks with sampling-free uncertainty
estimation techniques. Extensive evaluations show that we achieve the best
performance on uncertainty-aware panoptic segmentation quality and calibration
compared to these baselines. We make our code available at:
https://github.com/kshitij3112/EvLPSNet

We revisit the standard formulation of tabular actor-critic algorithm as a
two time-scale stochastic approximation with value function computed on a
faster time-scale and policy computed on a slower time-scale. This emulates
policy iteration. We begin by observing that reversal of the time scales will
in fact emulate value iteration and is a legitimate algorithm. We compare the
two empirically with and without function approximation (with both linear and
nonlinear function approximators) and observe that our proposed critic-actor
algorithm performs better empirically though with a marginal increase in the

The study of mortality patterns is a popular research topic in many areas. We
are particularly interested in mortality patterns among main causes of death
associated with age-gender combinations. We use symbolic data analysis (SDA)
and include three dimensions: age, gender, and patterns across main causes of
death. In this study, we present an alternative method to identify clusters of
EU countries with similar mortality patterns in the young population, while
considering comprehensive information on the distribution of deaths among the
main causes of death by different age-gender groups. We explore possible
relationships between mortality patterns in the identified clusters and some
other sociodemographic indicators. We use EU data of crude mortality rates from
2016, as the most recent complete data available.

Past works on multimodal machine translation (MMT) elevate bilingual setup by
incorporating additional aligned vision information. However, an image-must
requirement of the multimodal dataset largely hinders MMT's development --
namely that it demands an aligned form of [image, source text, target text].
This limitation is generally troublesome during the inference phase especially
when the aligned image is not provided as in the normal NMT setup. Thus, in
this work, we introduce IKD-MMT, a novel MMT framework to support the
image-free inference phase via an inversion knowledge distillation scheme. In
particular, a multimodal feature generator is executed with a knowledge
distillation module, which directly generates the multimodal feature from
(only) source texts as the input. While there have been a few prior works
entertaining the possibility to support image-free inference for machine
translation, their performances have yet to rival the image-must translation.
In our experiments, we identify our method as the first image-free approach to
comprehensively rival or even surpass (almost) all image-must frameworks, and
achieved the state-of-the-art result on the often-used Multi30k benchmark. Our
code and data are available at: https://github.com/pengr/IKD-mmt/tree/master..

In this paper, we study the problem of 3D object segmentation from raw point
clouds. Unlike all existing methods which usually require a large amount of
human annotations for full supervision, we propose the first unsupervised
method, called OGC, to simultaneously identify multiple 3D objects in a single
forward pass, without needing any type of human annotations. The key to our
approach is to fully leverage the dynamic motion patterns over sequential point
clouds as supervision signals to automatically discover rigid objects. Our
method consists of three major components, 1) the object segmentation network
to directly estimate multi-object masks from a single point cloud frame, 2) the
auxiliary self-supervised scene flow estimator, and 3) our core object geometry
consistency component. By carefully designing a series of loss functions, we
effectively take into account the multi-object rigid consistency and the object
shape invariance in both temporal and spatial scales. This allows our method to
truly discover the object geometry even in the absence of annotations. We
extensively evaluate our method on five datasets, demonstrating the superior
performance for object part instance segmentation and general object
segmentation in both indoor and the challenging outdoor scenarios.

Prompt tuning learns soft prompts to condition frozen Pre-trained Language
Models (PLMs) for performing downstream tasks in a parameter-efficient manner.
While prompt tuning has gradually reached the performance level of fine-tuning
as the model scale increases, there is still a large performance gap between
prompt tuning and fine-tuning for models of moderate and small scales
(typically less than 11B parameters). In this paper, we empirically show that
the trained prompt tokens can have a negative impact on a downstream task and
thus degrade its performance. To bridge the gap, we propose a novel Prompt
tuning model with an eXtremely small scale (XPrompt) under the regime of
lottery tickets hypothesis. Specifically, XPrompt eliminates the negative
prompt tokens at different granularity levels through a hierarchical structured
pruning, yielding a more parameter-efficient prompt yet with a competitive
performance. Comprehensive experiments are carried out on SuperGLUE tasks, and
the extensive results indicate that XPrompt is able to close the performance
gap at smaller model scales.

As human science pushes the boundaries towards the development of artificial
intelligence (AI), the sweep of progress has caused scholars and policymakers
alike to question the legality of applying or utilising AI in various human
endeavours. For example, debate has raged in international scholarship about
the legitimacy of applying AI to weapon systems to form lethal autonomous
weapon systems (LAWS). Yet the argument holds true even when AI is applied to a
military autonomous system that is not weaponised: how does one hold a machine
accountable for a crime? What about a tort? Can an artificial agent understand
the moral and ethical content of its instructions? These are thorny questions,
and in many cases these questions have been answered in the negative, as
artificial entities lack any contingent moral agency. So what if the AI is not
alone, but linked with or overseen by a human being, with their own moral and
ethical understandings and obligations? Who is responsible for any malfeasance
that may be committed? Does the human bear the legal risks of unethical or
immoral decisions by an AI? These are some of the questions this manuscript
seeks to engage with.

Spatially resolved transcriptomics (SRT) has evolved rapidly through various
technologies, enabling scientists to investigate both morphological contexts
and gene expression profiling at single-cell resolution in parallel. SRT data
are complex and multi-modal, comprising gene expression matrices, spatial
information, and often high-resolution histology images. Because of this
complexity and multi-modality, sophisticated computational algorithms are
required to accurately analyze SRT data. Most efforts in this domain have been
made to utilize conventional machine learning and statistical approaches
exhibiting sub-optimal results due to the complicated nature of SRT datasets.
To address these shortcomings, researchers have recently employed deep learning
algorithms including various state-of-the-art methods mainly in spatial
clustering, spatially variable gene identification, and alignment. In this
paper, we provide an extensive methodological review of these deep learning
methods and discuss their strengths and limitations. We also discuss the new
frontiers, current challenges, limitations, and open questions in this field.
Although researchers have put a great effort into developing deep
learning-based models to analyze SRT data, some modifications are still needed
to have more biologically aware models such as phylogeny-aware clustering, or
treating small patches of histology images together. Collectively, appropriate
strategies are still needed for batch effect removal, transformation and
normalization, overdispersion and zero inflation patterns of gene expression in
the analysis of SRT data with deep learning techniques. Also, we provided a
comprehensive list of all available SRT databases that can be used as an
extensive resource for future studies.

We introduce a general theoretical framework, designed for the study of
gradient optimisation of deep neural networks, that encompasses ubiquitous
architectural choices including batch normalisation, weight normalisation and
skip connections. We use our framework to conduct a global analysis of the
curvature and regularity properties of neural network loss landscapes induced
by normalisation layers and skip connections respectively. We then demonstrate
the utility of this framework in two respects. First, we give the only proof of
which we are presently aware that a class of deep neural networks can be
trained using gradient descent to global optima even when such optima only
exist at infinity, as is the case for the cross-entropy cost. Second, we verify
a prediction made by the theory, that skip connections accelerate training,
with ResNets on MNIST, CIFAR10, CIFAR100 and ImageNet.

While there has been substantial progress in developing systems to automate
fact-checking, they still lack credibility in the eyes of the users. Thus, an
interesting approach has emerged: to perform automatic fact-checking by
verifying whether an input claim has been previously fact-checked by
professional fact-checkers and to return back an article that explains their
decision. This is a sensible approach as people trust manual fact-checking, and
as many claims are repeated multiple times. Yet, a major issue when building
such systems is the small number of known tweet--verifying article pairs
available for training. Here, we aim to bridge this gap by making use of crowd
fact-checking, i.e., mining claims in social media for which users have
responded with a link to a fact-checking article. In particular, we mine a
large-scale collection of 330,000 tweets paired with a corresponding
fact-checking article. We further propose an end-to-end framework to learn from
this noisy data based on modified self-adaptive training, in a distant
supervision scenario. Our experiments on the CLEF'21 CheckThat! test set show
improvements over the state of the art by two points absolute. Our code and
datasets are available at https://github.com/mhardalov/crowdchecked-claims

Embodied dialogue instruction following requires an agent to complete a
complex sequence of tasks from a natural language exchange. The recent
introduction of benchmarks (Padmakumar et al., 2022) raises the question of how
best to train and evaluate models for this multi-turn, multi-agent,
long-horizon task. This paper contributes to that conversation, by arguing that
imitation learning (IL) and related low-level metrics are actually misleading
and do not align with the goals of embodied dialogue research and may hinder
progress. We provide empirical comparisons of metrics, analysis of three
models, and make suggestions for how the field might best progress. First, we
observe that models trained with IL take spurious actions during evaluation.
Second, we find that existing models fail to ground query utterances, which are
essential for task completion. Third, we argue evaluation should focus on
higher-level semantic goals.

Graph Neural Networks (GNNs) have achieved great success in mining
graph-structured data. Despite the superior performance of GNNs in learning
graph representations, serious privacy concerns have been raised for the
trained models which could expose the sensitive information of graphs. We
conduct the first formal study of training GNN models to ensure utility while
satisfying the rigorous node-level differential privacy considering the private
information of both node features and edges. We adopt the training framework
utilizing personalized PageRank to decouple the message-passing process from
feature aggregation during training GNN models and propose differentially
private PageRank algorithms to protect graph topology information formally.
Furthermore, we analyze the privacy degradation caused by the sampling process
dependent on the differentially private PageRank results during model training
and propose a differentially private GNN (DPGNN) algorithm to further protect
node features and achieve rigorous node-level differential privacy. Extensive
experiments on real-world graph datasets demonstrate the effectiveness of the
proposed algorithms for providing node-level differential privacy while
preserving good model utility.

Purpose: To investigate motion compensated, self-supervised, model based deep
learning (MBDL) as a method to reconstruct free breathing, 3D Pulmonary
ultrashort echo time (UTE) acquisitions.
  Theory and Methods: A self-supervised eXtra Dimension MBDL architecture
(XD-MBDL) was developed that combined respiratory states to reconstruct a
single high-quality 3D image. Non-rigid, GPU based motion fields were
incorporated into this architecture by estimating motion fields from a low
resolution motion resolved (XD-GRASP) iterative reconstruction. Motion
Compensated XD-MBDL was evaluated on lung UTE datasets with and without
contrast and was compared to constrained reconstructions and variants of
self-supervised MBDL that do not consider respiratory motion.
  Results: Images reconstructed using XD-MBDL demonstrate improved image
quality as measured by apparent SNR, CNR and visual assessment relative to
self-supervised MBDL approaches that do not account for dynamic respiratory
states, XD-GRASP and a recently proposed motion compensated iterative
reconstruction strategy (iMoCo). Additionally, XD-MBDL reduced reconstruction
time relative to both XD-GRASP and iMoCo.
  Conclusion: A method was developed to allow self-supervised MBDL to combine
multiple respiratory states to reconstruct a single image. This method was
combined with GPU-based image registration to further improve reconstruction
quality. This approach showed promising results reconstructing a user-selected
respiratory phase from free breathing 3D pulmonary UTE acquisitions.

We present a reinforcement learning (RL) framework that enables quadrupedal
robots to perform soccer goalkeeping tasks in the real world. Soccer
goalkeeping using quadrupeds is a challenging problem, that combines highly
dynamic locomotion with precise and fast non-prehensile object (ball)
manipulation. The robot needs to react to and intercept a potentially flying
ball using dynamic locomotion maneuvers in a very short amount of time, usually
less than one second. In this paper, we propose to address this problem using a
hierarchical model-free RL framework. The first component of the framework
contains multiple control policies for distinct locomotion skills, which can be
used to cover different regions of the goal. Each control policy enables the
robot to track random parametric end-effector trajectories while performing one
specific locomotion skill, such as jump, dive, and sidestep. These skills are
then utilized by the second part of the framework which is a high-level planner
to determine a desired skill and end-effector trajectory in order to intercept
a ball flying to different regions of the goal. We deploy the proposed
framework on a Mini Cheetah quadrupedal robot and demonstrate the effectiveness
of our framework for various agile interceptions of a fast-moving ball in the
real world.

The formulation of rheological constitutive equations -- models that relate
internal stresses and deformations in complex fluids -- is a critical step in
the engineering of systems involving soft materials. While data-driven models
provide accessible alternatives to expensive first-principles models and less
accurate empirical models in many engineering disciplines, the development of
similar models for complex fluids has lagged. The diversity of techniques for
characterizing non-Newtonian fluid dynamics creates a challenge for classical
machine learning approaches, which require uniformly structured training data.
Consequently, early machine learning constitutive equations have not been
portable between different deformation protocols or mechanical observables.
Here, we present a data-driven framework that resolves such issues, allowing
rheologists to construct learnable models that incorporate essential physical
information, while remaining agnostic to details regarding particular
experimental protocols or flow kinematics. These scientific machine learning
models incorporate a universal approximator within a materially objective
tensorial constitutive framework. By construction, these models respect
physical constraints, such as frame-invariance and tensor symmetry, required by
continuum mechanics. We demonstrate that this framework facilitates the rapid
discovery of accurate constitutive equations from limited data, and that the
learned models may be used to describe more kinematically complex flows. This
inherent flexibility admits the application of these 'digital fluid twins' to a
range of material systems and engineering problems. We illustrate this
flexibility by deploying a trained model within a multidimensional
computational fluid dynamics simulation -- a task that is not achievable using
any previously developed data-driven rheological equation of state.

With the success of pretraining techniques in representation learning, a
number of continual learning methods based on pretrained models have been
proposed. Some of these methods design continual learning mechanisms on the
pre-trained representations and only allow minimum updates or even no updates
of the backbone models during the training of continual learning. In this
paper, we question whether the complexity of these models is needed to achieve
good performance by comparing them to a simple baseline that we designed. We
argue that the pretrained feature extractor itself can be strong enough to
achieve a competitive or even better continual learning performance on
Split-CIFAR100 and CoRe 50 benchmarks. To validate this, we conduct a very
simple baseline that 1) use the frozen pretrained model to extract image
features for every class encountered during the continual learning stage and
compute their corresponding mean features on training data, and 2) predict the
class of the input based on the nearest neighbor distance between test samples
and mean features of the classes; i.e., Nearest Mean Classifier (NMC). This
baseline is single-headed, exemplar-free, and can be task-free (by updating the
means continually). This baseline achieved 88.53% on 10-Split-CIFAR-100,
surpassing most state-of-the-art continual learning methods that are all
initialized using the same pretrained transformer model. We hope our baseline
may encourage future progress in designing learning systems that can
continually add quality to the learning representations even if they started
from some pretrained weights.

Knowledge Distillation (KD) aims at transferring the knowledge of a
well-performed neural network (the {\it teacher}) to a weaker one (the {\it
student}). A peculiar phenomenon is that a more accurate model doesn't
necessarily teach better, and temperature adjustment can neither alleviate the
mismatched capacity. To explain this, we decompose the efficacy of KD into
three parts: {\it correct guidance}, {\it smooth regularization}, and {\it
class discriminability}. The last term describes the distinctness of {\it wrong
class probabilities} that the teacher provides in KD. Complex teachers tend to
be over-confident and traditional temperature scaling limits the efficacy of
{\it class discriminability}, resulting in less discriminative wrong class
probabilities. Therefore, we propose {\it Asymmetric Temperature Scaling
(ATS)}, which separately applies a higher/lower temperature to the
correct/wrong class. ATS enlarges the variance of wrong class probabilities in
the teacher's label and makes the students grasp the absolute affinities of
wrong classes to the target class as discriminative as possible. Both
theoretical analysis and extensive experimental results demonstrate the
effectiveness of ATS. The demo developed in Mindspore is available at
https://gitee.com/lxcnju/ats-mindspore and will be available at
https://gitee.com/mindspore/models/tree/master/research/cv/ats.

Today, an increasing number of Adaptive Deep Neural Networks (AdNNs) are
being used on resource-constrained embedded devices. We observe that, similar
to traditional software, redundant computation exists in AdNNs, resulting in
considerable performance degradation. The performance degradation is dependent
on the input and is referred to as input-dependent performance bottlenecks
(IDPBs). To ensure an AdNN satisfies the performance requirements of
resource-constrained applications, it is essential to conduct performance
testing to detect IDPBs in the AdNN. Existing neural network testing methods
are primarily concerned with correctness testing, which does not involve
performance testing. To fill this gap, we propose DeepPerform, a scalable
approach to generate test samples to detect the IDPBs in AdNNs. We first
demonstrate how the problem of generating performance test samples detecting
IDPBs can be formulated as an optimization problem. Following that, we
demonstrate how DeepPerform efficiently handles the optimization problem by
learning and estimating the distribution of AdNNs' computational consumption.
We evaluate DeepPerform on three widely used datasets against five popular AdNN
models. The results show that DeepPerform generates test samples that cause
more severe performance degradation (FLOPs: increase up to 552\%). Furthermore,
DeepPerform is substantially more efficient than the baseline methods in
generating test inputs(runtime overhead: only 6-10 milliseconds).

Undoubtedly, social media are brainstormed by a tremendous volume of stories,
feedback, reviews, and reactions expressed in various languages and idioms,
even though some are factually incorrect. These motifs make assessing such data
challenging, time-consuming, and vulnerable to misinterpretation. This paper
describes a classification model for movie reviews founded on deep learning
approaches. Almost 500KB pairs of balanced data from the IMDb movie review
databases are employed to train the model. People's perspectives regarding
movies were classified using both the long short-term memory (LSTM) and
convolutional neural network (CNN) strategies. According to the findings, the
CNN algorithm's prediction accuracy rate would be almost 97.4%. Furthermore,
the model trained by LSTM resulted in accuracies of around and applying 99.2%
within the Keras library. The model is investigated more by modification of
model parameters. According to the outcomes, LTSM outperforms CNN in assessing
IMDb movie reviews and is computationally less costly than LSTM.

Major postoperative complications are devastating to surgical patients. Some
of these complications are potentially preventable via early predictions based
on intraoperative data. However, intraoperative data comprise long and
fine-grained multivariate time series, prohibiting the effective learning of
accurate models. The large gaps associated with clinical events and protocols
are usually ignored. Moreover, deep models generally lack transparency.
Nevertheless, the interpretability is crucial to assist clinicians in planning
for and delivering postoperative care and timely interventions. Towards this
end, we propose a hierarchical model combining the strength of both attention
and recurrent models for intraoperative time series. We further develop an
explanation module for the hierarchical model to interpret the predictions by
providing contributions of intraoperative data in a fine-grained manner.
Experiments on a large dataset of 111,888 surgeries with multiple outcomes and
an external high-resolution ICU dataset show that our model can achieve strong
predictive performance (i.e., high accuracy) and offer robust interpretations
(i.e., high transparency) for predicted outcomes based on intraoperative time
series.

Reference-model-assisted deep reinforcement learning (DRL) for inverter-based
Volt-Var Control (IB-VVC) in active distribution networks is proposed. We
investigate that a large action space increases the learning difficulties of
DRL and degrades the optimization performance in the process of generating data
and training neural networks. To reduce the action space of DRL, we design a
reference-model-assisted DRL approach. We introduce definitions of the
reference model, reference-model-based optimization, and reference actions. The
reference-model-assisted DRL learns the residual actions between the reference
actions and optimal actions, rather than learning the optimal actions directly.
Since the residual actions are considerably smaller than the optimal actions
for a reference model, we can design a smaller action space for the
reference-model-assisted DRL. It reduces the learning difficulties of DRL and
optimises the performance of the reference-model-assisted DRL approach. It is
noteworthy that the reference-model-assisted DRL approach is compatible with
any policy gradient DRL algorithms for continuous action problems. This work
takes the soft actor-critic algorithm as an example and designs a
reference-model-assisted soft actor-critic algorithm. Simulations show that 1)
large action space degrades the performance of DRL in the whole training stage,
and 2) reference-model-assisted DRL requires fewer iteration times and returns
a better optimization performance.

Our project probes the relationship between temperatures and the blossom date
of cherry trees. Through modeling, future flowering will become predictive,
helping the public plan travels and avoid pollen season. To predict the date
when the cherry trees will blossom exactly could be viewed as a multiclass
classification problem, so we applied the multi-class Support Vector Classifier
(SVC) and Recurrent Neural Network (RNN), particularly Long Short-term Memory
(LSTM), to formulate the problem. In the end, we evaluate and compare the
performance of these approaches to find out which one might be more applicable
in reality.

Quantitative Susceptibility Mapping is a parametric imaging technique to
estimate the magnetic susceptibilities of biological tissues from MRI phase
measurements. This problem of estimating the susceptibility map is ill posed.
Regularized recovery approaches exploiting signal properties such as smoothness
and sparsity improve reconstructions, but suffer from over-smoothing artifacts.
Deep learning approaches have shown great potential and generate maps with
reduced artifacts. However, for reasonable reconstructions and network
generalization, they require numerous training datasets resulting in increased
data acquisition time. To overcome this issue, we proposed a subject-specific,
patch-based, unsupervised learning algorithm to estimate the susceptibility
map. We make the problem well-posed by exploiting the redundancies across the
patches of the map using a deep convolutional neural network. We formulated the
recovery of the susceptibility map as a regularized optimization problem and
adopted an alternating minimization strategy to solve it. We tested the
algorithm on a 3D invivo dataset and, qualitatively and quantitatively,
demonstrated improved reconstructions over competing methods.

Graphs emerge in almost every real-world application domain, ranging from
online social networks all the way to health data and movie viewership
patterns. Typically, such real-world graphs are big and dynamic, in the sense
that they evolve over time. Furthermore, graphs usually contain multi-aspect
information i.e. in a social network, we can have the "means of communication"
between nodes, such as who messages whom, who calls whom, and who comments on
whose timeline and so on.
  How can we model and mine useful patterns, such as communities of nodes in
that graph, from such multi-aspect graphs? How can we identify dynamic patterns
in those graphs, and how can we deal with streaming data, when the volume of
data to be processed is very large? In order to answer those questions, in this
thesis, we propose novel tensor-based methods for mining static and dynamic
multi-aspect graphs. In general, a tensor is a higher-order generalization of a
matrix that can represent high-dimensional multi-aspect data such as
time-evolving networks, collaboration networks, and spatio-temporal data like
Electroencephalography (EEG) brain measurements.
  The thesis is organized in two synergistic thrusts: First, we focus on static
multi-aspect graphs, where the goal is to identify coherent communities and
patterns between nodes by leveraging the tensor structure in the data. Second,
as our graphs evolve dynamically, we focus on handling such streaming updates
in the data without having to re-compute the decomposition, but incrementally
update the existing results.

Recent methods for deep metric learning have been focusing on designing
different contrastive loss functions between positive and negative pairs of
samples so that the learned feature embedding is able to pull positive samples
of the same class closer and push negative samples from different classes away
from each other. In this work, we recognize that there is a significant
semantic gap between features at the intermediate feature layer and class
labels at the final output layer. To bridge this gap, we develop a contrastive
Bayesian analysis to characterize and model the posterior probabilities of
image labels conditioned by their features similarity in a contrastive learning
setting. This contrastive Bayesian analysis leads to a new loss function for
deep metric learning. To improve the generalization capability of the proposed
method onto new classes, we further extend the contrastive Bayesian loss with a
metric variance constraint. Our experimental results and ablation studies
demonstrate that the proposed contrastive Bayesian metric learning method
significantly improves the performance of deep metric learning in both
supervised and pseudo-supervised scenarios, outperforming existing methods by a

Distance teaching has become popular these years because of the COVID-19
epidemic. However, both students and teachers face several challenges in
distance teaching, like being easy to distract. We proposed Focus+, a system
designed to detect learners' status with the latest AI technology from their
web camera to solve such challenges. By doing so, teachers can know students'
status, and students can regulate their learning experience. In this research,
we will discuss the expected model's design for training and evaluating the AI
detection model of Focus+.

When logos are increasingly created, logo detection has gradually become a
research hotspot across many domains and tasks. Recent advances in this area
are dominated by deep learning-based solutions, where many datasets, learning
strategies, network architectures, etc. have been employed. This paper reviews
the advance in applying deep learning techniques to logo detection. Firstly, we
discuss a comprehensive account of public datasets designed to facilitate
performance evaluation of logo detection algorithms, which tend to be more
diverse, more challenging, and more reflective of real life. Next, we perform
an in-depth analysis of the existing logo detection strategies and the
strengths and weaknesses of each learning strategy. Subsequently, we summarize
the applications of logo detection in various fields, from intelligent
transportation and brand monitoring to copyright and trademark compliance.
Finally, we analyze the potential challenges and present the future directions
for the development of logo detection to complete this survey.

Probabilistic Circuits (PCs) are a unified framework for tractable
probabilistic models that support efficient computation of various
probabilistic queries (e.g., marginal probabilities). One key challenge is to
scale PCs to model large and high-dimensional real-world datasets: we observe
that as the number of parameters in PCs increases, their performance
immediately plateaus. This phenomenon suggests that the existing optimizers
fail to exploit the full expressive power of large PCs. We propose to overcome
such bottleneck by latent variable distillation: we leverage the less tractable
but more expressive deep generative models to provide extra supervision over
the latent variables of PCs. Specifically, we extract information from
Transformer-based generative models to assign values to latent variables of
PCs, providing guidance to PC optimizers. Experiments on both image and
language modeling benchmarks (e.g., ImageNet and WikiText-2) show that latent
variable distillation substantially boosts the performance of large PCs
compared to their counterparts without latent variable distillation. In
particular, on the image modeling benchmarks, PCs achieve competitive
performance against some of the widely-used deep generative models, including
variational autoencoders and flow-based models, opening up new avenues for
tractable generative modeling.

Polyp segmentation is still known as a difficult problem due to the large
variety of polyp shapes, scanning and labeling modalities. This prevents deep
learning model to generalize well on unseen data. However, Transformer-based
approach recently has achieved some remarkable results on performance with the
ability of extracting global context better than CNN-based architecture and yet
lead to better generalization. To leverage this strength of Transformer, we
propose a new model with encoder-decoder architecture named LAPFormer, which
uses a hierarchical Transformer encoder to better extract global feature and
combine with our novel CNN (Convolutional Neural Network) decoder for capturing
local appearance of the polyps. Our proposed decoder contains a progressive
feature fusion module designed for fusing feature from upper scales and lower
scales and enable multi-scale features to be more correlative. Besides, we also
use feature refinement module and feature selection module for processing
feature. We test our model on five popular benchmark datasets for polyp
segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and
ETIS-Larib

Causal mediation analysis can unpack the black box of causality and is
therefore a powerful tool for disentangling causal pathways in biomedical and
social sciences, and also for evaluating machine learning fairness. To reduce
bias for estimating Natural Direct and Indirect Effects in mediation analysis,
we propose a new method called DeepMed that uses deep neural networks (DNNs) to
cross-fit the infinite-dimensional nuisance functions in the efficient
influence functions. We obtain novel theoretical results that our DeepMed
method (1) can achieve semiparametric efficiency bound without imposing
sparsity constraints on the DNN architecture and (2) can adapt to certain low
dimensional structures of the nuisance functions, significantly advancing the
existing literature on DNN-based semiparametric causal inference. Extensive
synthetic experiments are conducted to support our findings and also expose the
gap between theory and practice. As a proof of concept, we apply DeepMed to
analyze two real datasets on machine learning fairness and reach conclusions
consistent with previous findings.

Parameter-efficient tuning aims at updating only a small subset of parameters
when adapting a pretrained model to downstream tasks. In this work, we
introduce PASTA, in which we only modify the special token representations
(e.g., [SEP] and [CLS] in BERT) before the self-attention module at each layer
in Transformer-based models. PASTA achieves comparable performance to
fine-tuning in natural language understanding tasks including text
classification and NER with up to only 0.029% of total parameters trained. Our
work not only provides a simple yet effective way of parameter-efficient
tuning, which has a wide range of practical applications when deploying
finetuned models for multiple tasks, but also demonstrates the pivotal role of
special tokens in pretrained language models.

Living systems efficiently use chemical fuel to do work, process information,
and assemble patterns despite thermal noise. Whether high efficiency arises
from general principles or specific fine-tuning is unknown. Here, applying a
recent mapping from nonequilibrium systems to battery-resistor circuits, I
derive an analytic expression for the efficiency of any dissipative molecular
machine driven by one or a series of chemical potential differences. This
expression disentangles the chemical potential from the machine's details,
whose effect on the efficiency is fully specified by a constant called the load
resistance. The efficiency passes through a switch-like inflection point if the
balance between chemical potential and load resistance exceeds thermal noise.
Therefore, dissipative chemical engines qualitatively differ from heat engines,
which lack threshold behavior. This explains all-or-none dynein stepping with
increasing ATP concentration observed in single-molecule experiments. These
results indicate that biomolecular energy transduction is efficient not because
of idosyncratic optimization of the biomolecules themselves, but rather because
the concentration of chemical fuel is kept above a threshold level within
cells.

Deep learning-based segmentation methods have been widely employed for
automatic glaucoma diagnosis and prognosis. In practice, fundus images obtained
by different fundus cameras vary significantly in terms of illumination and
intensity. Although recent unsupervised domain adaptation (UDA) methods enhance
the models' generalization ability on the unlabeled target fundus datasets,
they always require sufficient labeled data from the source domain, bringing
auxiliary data acquisition and annotation costs. To further facilitate the data
efficiency of the cross-domain segmentation methods on the fundus images, we
explore UDA optic disc and cup segmentation problems using few labeled source
data in this work. We first design a Searching-based Multi-style Invariant
Mechanism to diversify the source data style as well as increase the data
amount. Next, a prototype consistency mechanism on the foreground objects is
proposed to facilitate the feature alignment for each kind of tissue under
different image styles. Moreover, a cross-style self-supervised learning stage
is further designed to improve the segmentation performance on the target
images. Our method has outperformed several state-of-the-art UDA segmentation
methods under the UDA fundus segmentation with few labeled source data.

The explosion of user-generated videos stimulates a great demand for
no-reference video quality assessment (NR-VQA). Inspired by our observation on
the actions of human annotation, we put forward a Divide and Conquer Video
Quality Estimator (DCVQE) for NR-VQA. Starting from extracting the frame-level
quality embeddings (QE), our proposal splits the whole sequence into a number
of clips and applies Transformers to learn the clip-level QE and update the
frame-level QE simultaneously; another Transformer is introduced to combine the
clip-level QE to generate the video-level QE. We call this hierarchical
combination of Transformers as a Divide and Conquer Transformer (DCTr) layer.
An accurate video quality feature extraction can be achieved by repeating the
process of this DCTr layer several times. Taking the order relationship among
the annotated data into account, we also propose a novel correlation loss term
for model training. Experiments on various datasets confirm the effectiveness
and robustness of our DCVQE model.

This paper addresses the task of conversational question answering (ConvQA)
over knowledge graphs (KGs). The majority of existing ConvQA methods rely on
full supervision signals with a strict assumption of the availability of gold
logical forms of queries to extract answers from the KG. However, creating such
a gold logical form is not viable for each potential question in a real-world
scenario. Hence, in the case of missing gold logical forms, the existing
information retrieval-based approaches use weak supervision via heuristics or
reinforcement learning, formulating ConvQA as a KG path ranking problem.
Despite missing gold logical forms, an abundance of conversational contexts,
such as entire dialog history with fluent responses and domain information, can
be incorporated to effectively reach the correct KG path. This work proposes a
contrastive representation learning-based approach to rank KG paths
effectively. Our approach solves two key challenges. Firstly, it allows weak
supervision-based learning that omits the necessity of gold annotations.
Second, it incorporates the conversational context (entire dialog history and
domain information) to jointly learn its homogeneous representation with KG
paths to improve contrastive representations for effective path ranking. We
evaluate our approach on standard datasets for ConvQA, on which it
significantly outperforms existing baselines on all domains and overall.
Specifically, in some cases, the Mean Reciprocal Rank (MRR) and Hit@5 ranking
metrics improve by absolute 10 and 18 points, respectively, compared to the
state-of-the-art performance.

Recent developments in the methods of explainable AI (xAI) methods allow us
to explore the inner workings of deep neural networks (DNNs), revealing crucial
information about input-output relationships and realizing how data connects
with machine learning models. In this paper we explore interpretability of DNN
models designed for identifying jets coming from top quark decay in the high
energy proton-proton collisions at the Large Hadron Collider (LHC). We review a
subset of existing such top tagger models and explore different quantitative
methods to identify which features play the most important roles in identifying
the top jets. We also investigate how and why feature importance varies across
different xAI metrics, how feature correlations impact their explainability,
and how latent space representations encode information as well as correlate
with physically meaningful quantities. Our studies uncover some major pitfalls
of existing xAI methods and illustrate how they can be overcome to obtain
consistent and meaningful interpretation of these models. We additionally
illustrate the activity of hidden layers as Neural Activation Pattern (NAP)
diagrams and demonstrate how they can be used to understand how DNNs relay
information across the layers and how this understanding can help us to make
such models significantly simpler by allowing effective model reoptimization
and hyperparameter tuning. While the primary focus of this work remains a
detailed study of interpretability of DNN-based top tagger models, it also
features state-of-the art performance obtained from modified implementation of
existing networks.

Naively trained AI models can be heavily biased. This can be particularly
problematic when the biases involve legally or morally protected attributes
such as ethnic background, age or gender. Existing solutions to this problem
come at the cost of extra computation, unstable adversarial optimisation or
have losses on the feature space structure that are disconnected from fairness
measures and only loosely generalise to fairness. In this work we propose a
differentiable approximation of the variance of demographics, a metric that can
be used to measure the bias, or unfairness, in an AI model. Our approximation
can be optimised alongside the regular training objective which eliminates the
need for any extra models during training and directly improves the fairness of
the regularised models. We demonstrate that our approach improves the fairness
of AI models in varied task and dataset scenarios, whilst still maintaining a
high level of classification accuracy. Code is available at
https://bitbucket.org/nelliottrosa/base_fairness.

Should deep learning models be trained to analyze human performance art? To
help answer this question, we explore an application of deep neural networks to
synthesize artistic human motion. Problem tasks in human motion synthesis can
include predicting the motions of humans in-the-wild, as well as generating new
sequences of motions based on said predictions. We will discuss the potential
of a less traditional application, where learning models are applied to
predicting dance movements. There have been notable, recent efforts to analyze
dance movements in a computational light, such as the Everybody Dance Now (EDN)
learning model and a recent Cal Poly master's thesis, Take The Lead (TTL). We
have effectively combined these two works along with our own deep neural
network to produce a new system for dance motion prediction, image-to-image
translation, and video generation.

Modern multi-agent reinforcement learning frameworks rely on centralized
training and reward shaping to perform well. However, centralized training and
dense rewards are not readily available in the real world. Current multi-agent
algorithms struggle to learn in the alternative setup of decentralized training
or sparse rewards. To address these issues, we propose a self-supervised
intrinsic reward ELIGN - expectation alignment - inspired by the
self-organization principle in Zoology. Similar to how animals collaborate in a
decentralized manner with those in their vicinity, agents trained with
expectation alignment learn behaviors that match their neighbors' expectations.
This allows the agents to learn collaborative behaviors without any external
reward or centralized training. We demonstrate the efficacy of our approach
across 6 tasks in the multi-agent particle and the complex Google Research
football environments, comparing ELIGN to sparse and curiosity-based intrinsic
rewards. When the number of agents increases, ELIGN scales well in all
multi-agent tasks except for one where agents have different capabilities. We
show that agent coordination improves through expectation alignment because
agents learn to divide tasks amongst themselves, break coordination symmetries,
and confuse adversaries. These results identify tasks where expectation
alignment is a more useful strategy than curiosity-driven exploration for
multi-agent coordination, enabling agents to do zero-shot coordination.

We measure support with women and migrants in German political debates over
the last 155 years. To do so, we (1) provide a gold standard of 1205 text
snippets in context, annotated for support with our target groups, (2) train a
BERT model on our annotated data, with which (3) we infer large-scale trends.
These show that support with women is stronger than support with migrants, but
both have steadily increased over time. While we hardly find any direct
anti-support with women, there is more polarization when it comes to migrants.
We also discuss the difficulty of annotation as a result of ambiguity in
political discourse and indirectness, i.e., politicians' tendency to relate
stances attributed to political opponents. Overall, our results indicate that
German society, as measured from its political elite, has become fairer over
time.

Sufficient dimension reduction is a powerful tool to extract core information
hidden in the high-dimensional data and has potentially many important
applications in machine learning tasks. However, the existing nonlinear
sufficient dimension reduction methods often lack the scalability necessary for
dealing with large-scale data. We propose a new type of stochastic neural
network under a rigorous probabilistic framework and show that it can be used
for sufficient dimension reduction for large-scale data. The proposed
stochastic neural network is trained using an adaptive stochastic gradient
Markov chain Monte Carlo algorithm, whose convergence is rigorously studied in
the paper as well. Through extensive experiments on real-world classification
and regression problems, we show that the proposed method compares favorably
with the existing state-of-the-art sufficient dimension reduction methods and
is computationally more efficient for large-scale data.

Symmetries built into a neural network have appeared to be very beneficial
for a wide range of tasks as it saves the data to learn them. We depart from
the position that when symmetries are not built into a model a priori, it is
advantageous for robust networks to learn symmetries directly from the data to
fit a task function. In this paper, we present a method to extract symmetries
learned by a neural network and to evaluate the degree to which a network is
invariant to them. With our method, we are able to explicitly retrieve learned
invariances in a form of the generators of corresponding Lie-groups without
prior knowledge of symmetries in the data. We use the proposed method to study
how symmetrical properties depend on a neural network's parameterization and
configuration. We found that the ability of a network to learn symmetries
generalizes over a range of architectures. However, the quality of learned
symmetries depends on the depth and the number of parameters.

We present a method for computing the inverse parameters and the solution
field to inverse parametric PDEs based on randomized neural networks. This
extends the local extreme learning machine technique originally developed for
forward PDEs to inverse problems. We develop three algorithms for training the
neural network to solve the inverse PDE problem. The first algorithm (NLLSQ)
determines the inverse parameters and the trainable network parameters all
together by the nonlinear least squares method with perturbations
(NLLSQ-perturb). The second algorithm (VarPro-F1) eliminates the inverse
parameters from the overall problem by variable projection to attain a reduced
problem about the trainable network parameters only. It solves the reduced
problem first by the NLLSQ-perturb algorithm for the trainable network
parameters, and then computes the inverse parameters by the linear least
squares method. The third algorithm (VarPro-F2) eliminates the trainable
network parameters from the overall problem by variable projection to attain a
reduced problem about the inverse parameters only. It solves the reduced
problem for the inverse parameters first, and then computes the trainable
network parameters afterwards. VarPro-F1 and VarPro-F2 are reciprocal to each
other in a sense. The presented method produces accurate results for inverse
PDE problems, as shown by the numerical examples herein. For noise-free data,
the errors for the inverse parameters and the solution field decrease
exponentially as the number of collocation points or the number of trainable
network parameters increases, and can reach a level close to the machine
accuracy. For noisy data, the accuracy degrades compared with the case of
noise-free data, but the method remains quite accurate. The presented method
has been compared with the physics-informed neural network method.

Recently, there has been an increase in efforts to understand how large
language models (LLMs) propagate and amplify social biases. Several works have
utilized templates for fairness evaluation, which allow researchers to quantify
social biases in the absence of test sets with protected attribute labels.
While template evaluation can be a convenient and helpful diagnostic tool to
understand model deficiencies, it often uses a simplistic and limited set of
templates. In this paper, we study whether bias measurements are sensitive to
the choice of templates used for benchmarking. Specifically, we investigate the
instability of bias measurements by manually modifying templates proposed in
previous works in a semantically-preserving manner and measuring bias across
these modifications. We find that bias values and resulting conclusions vary
considerably across template modifications on four tasks, ranging from an 81%
reduction (NLI) to a 162% increase (MLM) in (task-specific) bias measurements.
Our results indicate that quantifying fairness in LLMs, as done in current
practice, can be brittle and needs to be approached with more care and caution.

This paper designs methods for decentralized multiple hypothesis testing on
graphs that are equipped with provable guarantees on the false discovery rate
(FDR). We consider the setting where distinct agents reside on the nodes of an
undirected graph, and each agent possesses p-values corresponding to one or
more hypotheses local to its node. Each agent must individually decide whether
to reject one or more of its local hypotheses by only communicating with its
neighbors, with the joint aim that the global FDR over the entire graph must be
controlled at a predefined level. We propose a simple decentralized family of
Query-Test-Exchange (QuTE) algorithms and prove that they can control FDR under
independence or positive dependence of the p-values. Our algorithm reduces to
the Benjamini-Hochberg (BH) algorithm when after graph-diameter rounds of
communication, and to the Bonferroni procedure when no communication has
occurred or the graph is empty. To avoid communicating real-valued p-values, we
develop a quantized BH procedure, and extend it to a quantized QuTE procedure.
QuTE works seamlessly in streaming data settings, where anytime-valid p-values
may be continually updated at each node. Last, QuTE is robust to arbitrary
dropping of packets, or a graph that changes at every step, making it
particularly suitable to mobile sensor networks involving drones or other
multi-agent systems. We study the power of our procedure using a simulation
suite of different levels of connectivity and communication on a variety of
graph structures, and also provide an illustrative real-world example.

In light of the smoothness property brought by skip connections in ResNet,
this paper proposed the Skip Logit to introduce the skip connection mechanism
that fits arbitrary DNN dimensions and embraces similar properties to ResNet.
Meta Tanh Normalization (MTN) is designed to learn variance information and
stabilize the training process. With these delicate designs, our Skip Meta
Logit (SML) brought incremental boosts to the performance of extensive SOTA ctr
prediction models on two real-world datasets. In the meantime, we prove that
the optimization landscape of arbitrarily deep skip logit networks has no
spurious local optima. Finally, SML can be easily added to building blocks and
has delivered offline accuracy and online business metrics gains on app ads
learning to rank systems at TikTok.

Data-to-text generation is challenging due to the great variety of the input
data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse
predicates). Recent end-to-end neural methods thus require substantial training
examples to learn to disambiguate and describe the data. Yet, real-world
data-to-text problems often suffer from various data-scarce issues: one may
have access to only a handful of or no training examples, and/or have to rely
on examples in a different domain or schema. To fill this gap, we propose
Any-Shot Data-to-Text (ASDOT), a new approach flexibly applicable to diverse
settings by making efficient use of any given (or no) examples. ASDOT consists
of two steps, data disambiguation and sentence fusion, both of which are
amenable to be solved with off-the-shelf pretrained language models (LMs) with
optional finetuning. In the data disambiguation stage, we employ the prompted
GPT-3 model to understand possibly ambiguous triples from the input data and
convert each into a short sentence with reduced ambiguity. The sentence fusion
stage then uses an LM like T5 to fuse all the resulting sentences into a
coherent paragraph as the final description. We evaluate extensively on various
datasets in different scenarios, including the zero-/few-/full-shot settings,
and generalization to unseen predicates and out-of-domain data. Experimental
results show that ASDOT consistently achieves significant improvement over
baselines, e.g., a 30.81 BLEU gain on the DART dataset under the zero-shot
setting.

Deep learning (DL) has been widely adopted those last years but they are
computing-intensive method. Therefore, scientists proposed diverse optimization
to accelerate their predictions for end-user applications. However, no single
inference framework currently dominates in terms of performance. This paper
takes a holistic approach to conduct an empirical comparison and analysis of
four representative DL inference frameworks. First, given a selection of
CPU-GPU configurations, we show that for a specific DL framework, different
configurations of its settings may have a significant impact on the prediction
speed, memory, and computing power. Second, to the best of our knowledge, this
study is the first to identify the opportunities for accelerating the ensemble
of co-localized models in the same GPU. This measurement study provides an
in-depth empirical comparison and analysis of four representative DL frameworks
and offers practical guidance for service providers to deploy and deliver DL

Adaptive methods are a crucial component widely used for training generative
adversarial networks (GANs). While there has been some work to pinpoint the
"marginal value of adaptive methods" in standard tasks, it remains unclear why
they are still critical for GAN training. In this paper, we formally study how
adaptive methods help train GANs; inspired by the grafting method proposed in
arXiv:2002.11803 [cs.LG], we separate the magnitude and direction components of
the Adam updates, and graft them to the direction and magnitude of SGDA updates
respectively. By considering an update rule with the magnitude of the Adam
update and the normalized direction of SGD, we empirically show that the
adaptive magnitude of Adam is key for GAN training. This motivates us to have a
closer look at the class of normalized stochastic gradient descent ascent
(nSGDA) methods in the context of GAN training. We propose a synthetic
theoretical framework to compare the performance of nSGDA and SGDA for GAN
training with neural networks. We prove that in that setting, GANs trained with
nSGDA recover all the modes of the true distribution, whereas the same networks
trained with SGDA (and any learning rate configuration) suffer from mode
collapse. The critical insight in our analysis is that normalizing the
gradients forces the discriminator and generator to be updated at the same
pace. We also experimentally show that for several datasets, Adam's performance
can be recovered with nSGDA methods.

We develop a novel and simple method to produce prediction intervals (PIs)
for fitting and forecasting exercises. It finds the lower and upper bound of
the intervals by minimising a weighted asymmetric loss function, where the
weight depends on the width of the interval. We give a short mathematical
proof. As a corollary of our proof, we find PIs for values restricted to a
parameterised function and argue why the method works for predicting PIs of
dependent variables. The results of applying the method on a neural network
deployed in a real-world forecasting task prove the validity of its practical
implementation in complex machine learning setups.

The Rasch model is one of the most fundamental models in \emph{item response
theory} and has wide-ranging applications from education testing to
recommendation systems. In a universe with $n$ users and $m$ items, the Rasch
model assumes that the binary response $X_{li} \in \{0,1\}$ of a user $l$ with
parameter $\theta^*_l$ to an item $i$ with parameter $\beta^*_i$ (e.g., a user
likes a movie, a student correctly solves a problem) is distributed as
$\Pr(X_{li}=1) = 1/(1 + \exp{-(\theta^*_l - \beta^*_i)})$. In this paper, we
propose a \emph{new item estimation} algorithm for this celebrated model (i.e.,
to estimate $\beta^*$). The core of our algorithm is the computation of the
stationary distribution of a Markov chain defined on an item-item graph. We
complement our algorithmic contributions with finite-sample error guarantees,
the first of their kind in the literature, showing that our algorithm is
consistent and enjoys favorable optimality properties. We discuss practical
modifications to accelerate and robustify the algorithm that practitioners can
adopt. Experiments on synthetic and real-life datasets, ranging from small
education testing datasets to large recommendation systems datasets show that
our algorithm is scalable, accurate, and competitive with the most commonly
used methods in the literature.

Adversarial pruning compresses models while preserving robustness. Current
methods require access to adversarial examples during pruning. This
significantly hampers training efficiency. Moreover, as new adversarial attacks
and training methods develop at a rapid rate, adversarial pruning methods need
to be modified accordingly to keep up. In this work, we propose a novel
framework to prune a previously trained robust neural network while maintaining
adversarial robustness, without further generating adversarial examples. We
leverage concurrent self-distillation and pruning to preserve knowledge in the
original model as well as regularizing the pruned model via the Hilbert-Schmidt
Information Bottleneck. We comprehensively evaluate our proposed framework and
show its superior performance in terms of both adversarial robustness and
efficiency when pruning architectures trained on the MNIST, CIFAR-10, and
CIFAR-100 datasets against five state-of-the-art attacks. Code is available at
https://github.com/neu-spiral/PwoA/.

Training of Machine Learning (ML) models in real contexts often deals with
big data sets and high-class imbalance samples where the class of interest is
unrepresented (minority class). Practical solutions using classical ML models
address the problem of large data sets using parallel/distributed
implementations of training algorithms, approximate model-based solutions, or
applying instance selection (IS) algorithms to eliminate redundant information.
However, the combined problem of big and high imbalanced datasets has been less
addressed. This work proposes three new methods for IS to be able to deal with
large and imbalanced data sets. The proposed methods use Locality Sensitive
Hashing (LSH) as a base clustering technique, and then three different sampling
methods are applied on top of the clusters (or buckets) generated by LSH. The
algorithms were developed in the Apache Spark framework, guaranteeing their
scalability. The experiments carried out in three different datasets suggest
that the proposed IS methods can improve the performance of a base ML model
between 5% and 19% in terms of the geometric mean.

Federated edge learning (FEL) is a promising paradigm of distributed machine
learning that can preserve data privacy while training the global model
collaboratively. However, FEL is still facing model confidentiality issues due
to eavesdropping risks of exchanging cryptographic keys through traditional
encryption schemes. Therefore, in this paper, we propose a hierarchical
architecture for quantum-secured FEL systems with ideal security based on the
quantum key distribution (QKD) to facilitate public key and model encryption
against eavesdropping attacks. Specifically, we propose a stochastic resource
allocation model for efficient QKD to encrypt FEL keys and models. In FEL
systems, remote FEL workers are connected to cluster heads via quantum-secured
channels to train an aggregated global model collaboratively. However, due to
the unpredictable number of workers at each location, the demand for secret-key
rates to support secure model transmission to the server is unpredictable. The
proposed systems need to efficiently allocate limited QKD resources (i.e.,
wavelengths) such that the total cost is minimized in the presence of
stochastic demand by formulating the optimization problem for the proposed
architecture as a stochastic programming model. To this end, we propose a
federated reinforcement learning-based resource allocation scheme to solve the
proposed model without complete state information. The proposed scheme enables
QKD managers and controllers to train a global QKD resource allocation policy
while keeping their private experiences local. Numerical results demonstrate
that the proposed schemes can successfully achieve the cost-minimizing
objective under uncertain demand while improving the training efficiency by
about 50\% compared to state-of-the-art schemes.

Domain-specific language understanding requires integrating multiple pieces
of relevant contextual information. For example, we see both suicide and
depression-related behavior (multiple contexts) in the text ``I have a gun and
feel pretty bad about my life, and it wouldn't be the worst thing if I didn't
wake up tomorrow''. Domain specificity in self-attention architectures is
handled by fine-tuning on excerpts from relevant domain specific resources
(datasets and external knowledge - medical textbook chapters on mental health
diagnosis related to suicide and depression). We propose a modified
self-attention architecture Knowledge-infused Self Attention Transformer (KSAT)
that achieves the integration of multiple domain-specific contexts through the
use of external knowledge sources. KSAT introduces knowledge-guided biases in
dedicated self-attention layers for each knowledge source to accomplish this.
In addition, KSAT provides mechanics for controlling the trade-off between
learning from data and learning from knowledge. Our quantitative and
qualitative evaluations show that (1) the KSAT architecture provides novel
human-understandable ways to precisely measure and visualize the contributions
of the infused domain contexts, and (2) KSAT performs competitively with other
knowledge-infused baselines and significantly outperforms baselines that use
fine-tuning for domain-specific tasks.

There is an ever-growing zoo of modern neural network models that can
efficiently learn end-to-end control from visual observations. These advanced
deep models, ranging from convolutional to patch-based networks, have been
extensively tested on offline image classification and regression tasks. In
this paper, we study these vision architectures with respect to the open-loop
to closed-loop causality gap, i.e., offline training followed by an online
closed-loop deployment. This causality gap typically emerges in robotics
applications such as autonomous driving, where a network is trained to imitate
the control commands of a human. In this setting, two situations arise: 1)
Closed-loop testing in-distribution, where the test environment shares
properties with those of offline training data. 2) Closed-loop testing under
distribution shifts and out-of-distribution. Contrary to recently reported
results, we show that under proper training guidelines, all vision models
perform indistinguishably well on in-distribution deployment, resolving the
causality gap. In situation 2, We observe that the causality gap disrupts
performance regardless of the choice of the model architecture. Our results
imply that the causality gap can be solved in situation one with our proposed
training guideline with any modern network architecture, whereas achieving
out-of-distribution generalization (situation two) requires further
investigations, for instance, on data diversity rather than the model
architecture.

This paper shows that the optimal policy and value functions of a Markov
Decision Process (MDP), either discounted or not, can be captured by a
finite-horizon undiscounted Optimal Control Problem (OCP), even if based on an
inexact model. This can be achieved by selecting a proper stage cost and
terminal cost for the OCP. A very useful particular case of OCP is a Model
Predictive Control (MPC) scheme where a deterministic (possibly nonlinear)
model is used to limit the computational complexity. In practice, Reinforcement
Learning algorithms can then be used to tune the parameterized MPC scheme. We
verify the proposed theorems analytically in an LQR case and we investigate
some other nonlinear examples in simulations.

Score-based generative models learn a family of noise-conditional score
functions corresponding to the data density perturbed with increasingly large
amounts of noise. These perturbed data densities are tied together by the
Fokker-Planck equation (FPE), a PDE governing the spatial-temporal evolution of
a density undergoing a diffusion process. In this work, we derive a
corresponding equation characterizing the noise-conditional scores of the
perturbed data densities (i.e., their gradients), termed the score FPE.
Surprisingly, despite impressive empirical performance, we observe that scores
learned via denoising score matching (DSM) do not satisfy the underlying score
FPE. We mathematically analyze three implications of satisfying the score FPE
and a potential explanation for why the score FPE is not satisfied in practice.
At last, we propose to regularize the DSM objective to enforce satisfaction of
the score FPE, and show its effectiveness on synthetic data and MNIST.

Human motion synthesis is a long-standing problem with various applications
in digital twins and the Metaverse. However, modern deep learning based motion
synthesis approaches barely consider the physical plausibility of synthesized
motions and consequently they usually produce unrealistic human motions. In
order to solve this problem, we propose a system ``Skeleton2Humanoid'' which
performs physics-oriented motion correction at test time by regularizing
synthesized skeleton motions in a physics simulator. Concretely, our system
consists of three sequential stages: (I) test time motion synthesis network
adaptation, (II) skeleton to humanoid matching and (III) motion imitation based
on reinforcement learning (RL). Stage I introduces a test time adaptation
strategy, which improves the physical plausibility of synthesized human
skeleton motions by optimizing skeleton joint locations. Stage II performs an
analytical inverse kinematics strategy, which converts the optimized human
skeleton motions to humanoid robot motions in a physics simulator, then the
converted humanoid robot motions can be served as reference motions for the RL
policy to imitate. Stage III introduces a curriculum residual force control
policy, which drives the humanoid robot to mimic complex converted reference
motions in accordance with the physical law. We verify our system on a typical
human motion synthesis task, motion-in-betweening. Experiments on the
challenging LaFAN1 dataset show our system can outperform prior methods
significantly in terms of both physical plausibility and accuracy. Code will be
released for research purposes at:
https://github.com/michaelliyunhao/Skeleton2Humanoid

The design of deep learning methods for low light video enhancement remains a
challenging problem owing to the difficulty in capturing low light and ground
truth video pairs. This is particularly hard in the context of dynamic scenes
or moving cameras where a long exposure ground truth cannot be captured. We
approach this problem by training a model on static videos such that the model
can generalize to dynamic videos. Existing methods adopting this approach
operate frame by frame and do not exploit the relationships among neighbouring
frames. We overcome this limitation through a selfcross dilated attention
module that can effectively learn to use information from neighbouring frames
even when dynamics between the frames are different during training and test
times. We validate our approach through experiments on multiple datasets and
show that our method outperforms other state-of-the-art video enhancement
algorithms when trained only on static videos.

Leveraging supervised information can lead to superior retrieval performance
in the image hashing domain but the performance degrades significantly without
enough labeled data. One effective solution to boost the performance is to
employ generative models, such as Generative Adversarial Networks (GANs), to
generate synthetic data in an image hashing model. However, GAN-based methods
are difficult to train and suffer from mode collapse issue, which prevents the
hashing approaches from jointly training the generative models and the hash
functions. This limitation results in sub-optimal retrieval performance. To
overcome this limitation, we propose a novel framework, the generative
cooperative hashing network (CoopHash), which is based on the energy-based
cooperative learning. CoopHash jointly learns a powerful generative
representation of the data and a robust hash function. CoopHash has two
components: a top-down contrastive pair generator that synthesizes contrastive
images and a bottom-up multipurpose descriptor that simultaneously represents
the images from multiple perspectives, including probability density, hash
code, latent code, and category. The two components are jointly learned via a
novel likelihood-based cooperative learning scheme. We conduct experiments on
several real-world datasets and show that the proposed method outperforms the
competing hashing supervised methods, achieving up to 10% relative improvement
over the current state-of-the-art supervised hashing methods, and exhibits a
significantly better performance in out-of-distribution retrieval.

Recent advances in pre-training vision-language models like CLIP have shown
great potential in learning transferable visual representations. Nonetheless,
for downstream inference, CLIP-like models suffer from either 1) degraded
accuracy and robustness in the case of inaccurate text descriptions during
retrieval-based inference (the challenge for zero-shot protocol); or 2)
breaking the well-established vision-language alignment (the challenge for
linear probing). To address them, we propose Decomposed Feature Prompting
(DeFo). DeFo leverages a flexible number of learnable embeddings as textual
input while maintaining the vision-language dual-model architecture, which
enables the model to learn decomposed visual features with the help of
feature-level textual prompts. We further use an additional linear layer to
perform classification, allowing a scalable size of language inputs. Our
empirical study shows DeFo's significance in improving the vision-language
models. For example, DeFo obtains 73.2% test accuracy on ImageNet with a
ResNet-50 backbone without tuning any pretrained weights of both the vision and
language encoder, outperforming zero-shot CLIP by a large margin of 15.0%, and
outperforming state-of-the-art vision-language prompt tuning method by 7.6%.

Quantitative assessment of the abdominal region from clinically acquired CT
scans requires the simultaneous segmentation of abdominal organs. Thanks to the
availability of high-performance computational resources, deep learning-based
methods have resulted in state-of-the-art performance for the segmentation of
3D abdominal CT scans. However, the complex characterization of organs with
fuzzy boundaries prevents the deep learning methods from accurately segmenting
these anatomical organs. Specifically, the voxels on the boundary of organs are
more vulnerable to misprediction due to the highly-varying intensity of
inter-organ boundaries. This paper investigates the possibility of improving
the abdominal image segmentation performance of the existing 3D encoder-decoder
networks by leveraging organ-boundary prediction as a complementary task. To
address the problem of abdominal multi-organ segmentation, we train the 3D
encoder-decoder network to simultaneously segment the abdominal organs and
their corresponding boundaries in CT scans via multi-task learning. The network
is trained end-to-end using a loss function that combines two task-specific
losses, i.e., complete organ segmentation loss and boundary prediction loss. We
explore two different network topologies based on the extent of weights shared
between the two tasks within a unified multi-task framework. To evaluate the
utilization of complementary boundary prediction task in improving the
abdominal multi-organ segmentation, we use three state-of-the-art
encoder-decoder networks: 3D UNet, 3D UNet++, and 3D Attention-UNet. The
effectiveness of utilizing the organs' boundary information for abdominal
multi-organ segmentation is evaluated on two publically available abdominal CT
datasets. A maximum relative improvement of 3.5% and 3.6% is observed in Mean
Dice Score for Pancreas-CT and BTCV datasets, respectively.

Hand-held light field cameras suffer from low spatial resolution due to the
inherent spatio-angular tradeoff. In this paper, we propose a method to improve
the spatial resolution of light field images with the aid of the Texture
Transformer Network (TTSR). The proposed method consists of three modules: the
first module produces an all-in focus high-resolution perspective image which
serves as a reference image for the second module, i.e. TTSR, which in turn
produces a high-resolution light field. The last module refines the spatial
resolution by imposing a light field prior. The results demonstrate around 4 dB
to 6 dB PSNR gain over a bicubically resized light field image

Tactile sensing is essential for a variety of daily tasks. New advances in
event-driven tactile sensors and Spiking Neural Networks (SNNs) spur the
research in related fields. However, SNN-enabled event-driven tactile learning
is still in its infancy due to the limited representation abilities of existing
spiking neurons and high spatio-temporal complexity in the data. In this paper,
to improve the representation capability of existing spiking neurons, we
propose a novel neuron model called "location spiking neuron", which enables us
to extract features of event-based data in a novel way. Specifically, based on
the classical Time Spike Response Model (TSRM), we develop the Location Spike
Response Model (LSRM). In addition, based on the most commonly-used Time Leaky
Integrate-and-Fire (TLIF) model, we develop the Location Leaky
Integrate-and-Fire (LLIF) model. By exploiting the novel location spiking
neurons, we propose several models to capture the complex spatio-temporal
dependencies in the event-driven tactile data. Extensive experiments
demonstrate the significant improvements of our models over other works on
event-driven tactile learning and show the superior energy efficiency of our
models and location spiking neurons, which may unlock their potential on
neuromorphic hardware.

Functionally constrained stochastic optimization problems, where neither the
objective function nor the constraint functions are analytically available,
arise frequently in machine learning applications. In this work, assuming we
only have access to the noisy evaluations of the objective and constraint
functions, we propose and analyze stochastic zeroth-order algorithms for
solving the above class of stochastic optimization problem. When the domain of
the functions is $\mathbb{R}^n$, assuming there are $m$ constraint functions,
we establish oracle complexities of order $\mathcal{O}((m+1)n/\epsilon^2)$ and
$\mathcal{O}((m+1)n/\epsilon^3)$ respectively in the convex and nonconvex
setting, where $\epsilon$ represents the accuracy of the solutions required in
appropriately defined metrics. The established oracle complexities are, to our
knowledge, the first such results in the literature for functionally
constrained stochastic zeroth-order optimization problems. We demonstrate the
applicability of our algorithms by illustrating its superior performance on the
problem of hyperparameter tuning for sampling algorithms and neural network
training.

In recent years, multi-view subspace learning has been garnering increasing
attention. It aims to capture the inner relationships of the data that are
collected from multiple sources by learning a unified representation. In this
way, comprehensive information from multiple views is shared and preserved for
the generalization processes. As a special branch of temporal series
hyperspectral image (HSI) processing, the anomalous change detection task
focuses on detecting very small changes among different temporal images.
However, when the volume of datasets is very large or the classes are
relatively comprehensive, existing methods may fail to find those changes
between the scenes, and end up with terrible detection results. In this paper,
inspired by the sketched representation and multi-view subspace learning, a
sketched multi-view subspace learning (SMSL) model is proposed for HSI
anomalous change detection. The proposed model preserves major information from
the image pairs and improves computational complexity by using a sketched
representation matrix. Furthermore, the differences between scenes are
extracted by utilizing the specific regularizer of the self-representation
matrices. To evaluate the detection effectiveness of the proposed SMSL model,
experiments are conducted on a benchmark hyperspectral remote sensing dataset
and a natural hyperspectral dataset, and compared with other state-of-the art

In this paper, we present a novel model-agnostic machine learning technique
to extract a reduced thermochemical model for reacting hypersonic flows
simulation. A first simulation gathers all relevant thermodynamic states and
the corresponding gas properties via a given model. The states are embedded in
a low-dimensional space and clustered to identify regions with different levels
of thermochemical (non)-equilibrium. Then, a surrogate surface from the reduced
cluster-space to the output space is generated using radial-basis-function
networks. The method is validated and benchmarked on a simulation of a
hypersonic flat-plate boundary layer with finite-rate chemistry. The gas
properties of the reactive air mixture are initially modeled using the
open-source Mutation++ library. Substituting Mutation++ with the light-weight,
machine-learned alternative improves the performance of the solver by 50% while
maintaining overall accuracy.

Acquiring the virtual equivalent of exhibits, such as sculptures, in virtual
reality (VR) museums, can be labour-intensive and sometimes infeasible. Deep
learning based 3D reconstruction approaches allow us to recover 3D shapes from
2D observations, among which single-view-based approaches can reduce the need
for human intervention and specialised equipment in acquiring 3D sculptures for
VR museums. However, there exist two challenges when attempting to use the
well-researched human reconstruction methods: limited data availability and
domain shift. Considering sculptures are usually related to humans, we propose
our unsupervised 3D domain adaptation method for adapting a single-view 3D
implicit reconstruction model from the source (real-world humans) to the target
(sculptures) domain. We have compared the generated shapes with other methods
and conducted ablation studies as well as a user study to demonstrate the
effectiveness of our adaptation method. We also deploy our results in a VR
application.

Wasserstein distributionally robust optimization (\textsf{WDRO}) is a popular
model to enhance the robustness of machine learning with ambiguous data.
However, the complexity of \textsf{WDRO} can be prohibitive in practice since
solving its ``minimax'' formulation requires a great amount of computation.
Recently, several fast \textsf{WDRO} training algorithms for some specific
machine learning tasks (e.g., logistic regression) have been developed.
However, the research on designing efficient algorithms for general large-scale
\textsf{WDRO}s is still quite limited, to the best of our knowledge.
\textit{Coreset} is an important tool for compressing large dataset, and thus
it has been widely applied to reduce the computational complexities for many
optimization problems. In this paper, we introduce a unified framework to
construct the $\epsilon$-coreset for the general \textsf{WDRO} problems. Though
it is challenging to obtain a conventional coreset for \textsf{WDRO} due to the
uncertainty issue of ambiguous data, we show that we can compute a ``dual
coreset'' by using the strong duality property of \textsf{WDRO}. Also, the
error introduced by the dual coreset can be theoretically guaranteed for the
original \textsf{WDRO} objective. To construct the dual coreset, we propose a
novel grid sampling approach that is particularly suitable for the dual
formulation of \textsf{WDRO}. Finally, we implement our coreset approach and
illustrate its effectiveness for several \textsf{WDRO} problems in the

Imbalanced image datasets are commonly available in the domain of biomedical
image analysis. Biomedical images contain diversified features that are
significant in predicting targeted diseases. Generative Adversarial Networks
(GANs) are utilized to address the data limitation problem via the generation
of synthetic images. Training challenges such as mode collapse,
non-convergence, and instability degrade a GAN's performance in synthesizing
diversified and high-quality images. In this work, MSG-SAGAN, an
attention-guided multi-scale gradient GAN architecture is proposed to model the
relationship between long-range dependencies of biomedical image features and
improves the training performance using a flow of multi-scale gradients at
multiple resolutions in the layers of generator and discriminator models. The
intent is to reduce the impact of mode collapse and stabilize the training of
GAN using an attention mechanism with multi-scale gradient learning for
diversified X-ray image synthesis. Multi-scale Structural Similarity Index
Measure (MS-SSIM) and Frechet Inception Distance (FID) are used to identify the
occurrence of mode collapse and evaluate the diversity of synthetic images
generated. The proposed architecture is compared with the multi-scale gradient
GAN (MSG-GAN) to assess the diversity of generated synthetic images. Results
indicate that the MSG-SAGAN outperforms MSG-GAN in synthesizing diversified
images as evidenced by the MS-SSIM and FID scores.

Memory corruption is a serious class of software vulnerabilities, which
requires careful attention to be detected and removed from applications before
getting exploited and harming the system users. Symbolic execution is a
well-known method for analyzing programs and detecting various vulnerabilities,
e.g., memory corruption. Although this method is sound and complete in theory,
it faces some challenges, such as path explosion, when applied to real-world
complex programs. In this paper, we present a method for improving the
efficiency of symbolic execution and detecting four classes of memory
corruption vulnerabilities in executable codes, i.e., heap-based buffer
overflow, stack-based buffer overflow, use-after-free, and double-free. We
perform symbolic execution only on test units rather than the whole program to
avoid path explosion. In our method, test units are considered parts of the
program's code, which might contain vulnerable statements and are statically
identified based on the specifications of memory corruption vulnerabilities.
Then, each test unit is symbolically executed to calculate path and
vulnerability constraints of each statement of the unit, which determine the
conditions on unit input data for executing that statement or activating
vulnerabilities in it, respectively. Solving these constraints gives us input
values for the test unit, which execute the desired statements and reveal
vulnerabilities in them. Finally, we use machine learning to approximate the
correlation between system and unit input data. Thereby, we generate system
inputs that enter the program, reach vulnerable instructions in the desired
test unit, and reveal vulnerabilities in them. This method is implemented as a
plugin for angr framework and evaluated using a group of benchmark programs.
The experiments show its superiority over similar tools in accuracy and

Domain adaptation has been widely adopted to transfer styles across
multi-vendors and multi-centers, as well as to complement the missing
modalities. In this challenge, we proposed an unsupervised domain adaptation
framework for cross-modality vestibular schwannoma (VS) and cochlea
segmentation and Koos grade prediction. We learn the shared representation from
both ceT1 and hrT2 images and recover another modality from the latent
representation, and we also utilize proxy tasks of VS segmentation and brain
parcellation to restrict the consistency of image structures in domain
adaptation. After generating missing modalities, the nnU-Net model is utilized
for VS and cochlea segmentation, while a semi-supervised contrastive learning
pre-train approach is employed to improve the model performance for Koos grade
prediction. On CrossMoDA validation phase Leaderboard, our method received rank
4 in task1 with a mean Dice score of 0.8394 and rank 2 in task2 with
Macro-Average Mean Square Error of 0.3941. Our code is available at
https://github.com/fiy2W/cmda2022.superpolymerization.

We revisit the classical model of Tsitsiklis, Bertsekas and Athans for
distributed stochastic approximation with consensus. The main result is an
analysis of this scheme using the ODE approach to stochastic approximation,
leading to a high probability bound for the tracking error between suitably
interpolated iterates and the limiting differential equation. Several future
directions will also be highlighted.

There are still two problems in SDD causing some inaccurate results: (1) In
the process of feature extraction, with the layer-by-layer acquisition of
semantic information, local information is gradually lost, resulting into less
representative feature maps; (2) During the Non-Maximum Suppression (NMS)
algorithm due to inconsistency in classification and regression tasks, the
classification confidence and predicted detection position cannot accurately
indicate the position of the prediction boxes. Methods: In order to address
these aforementioned issues, we propose a new architecture, a modified version
of Single Shot Multibox Detector (SSD), named Precise Single Stage Detector
(PSSD). Firstly, we improve the features by adding extra layers to SSD.
Secondly, we construct a simple and effective feature enhancement module to
expand the receptive field step by step for each layer and enhance its local
and semantic information. Finally, we design a more efficient loss function to
predict the IOU between the prediction boxes and ground truth boxes, and the
threshold IOU guides classification training and attenuates the scores, which
are used by the NMS algorithm. Main Results: Benefiting from the above
optimization, the proposed model PSSD achieves exciting performance in
real-time. Specifically, with the hardware of Titan Xp and the input size of
320 pix, PSSD achieves 33.8 mAP at 45 FPS speed on MS COCO benchmark and 81.28
mAP at 66 FPS speed on Pascal VOC 2007 outperforming state-of-the-art object
detection models. Besides, the proposed model performs significantly well with
larger input size. Under 512 pix, PSSD can obtain 37.2 mAP with 27 FPS on MS
COCO and 82.82 mAP with 40 FPS on Pascal VOC 2007. The experiment results prove
that the proposed model has a better trade-off between speed and accuracy.

We present state advantage weighting for offline reinforcement learning (RL).
In contrast to action advantage $A(s,a)$ that we commonly adopt in QSA
learning, we leverage state advantage $A(s,s^\prime)$ and QSS learning for
offline RL, hence decoupling the action from values. We expect the agent can
get to the high-reward state and the action is determined by how the agent can
get to that corresponding state. Experiments on D4RL datasets show that our
proposed method can achieve remarkable performance against the common
baselines. Furthermore, our method shows good generalization capability when
transferring from offline to online.

A coreset is a small set that can approximately preserve the structure of the
original input data set. Therefore we can run our algorithm on a coreset so as
to reduce the total computational complexity. Conventional coreset techniques
assume that the input data set is available to process explicitly. However,
this assumption may not hold in real-world scenarios. In this paper, we
consider the problem of coresets construction over relational data. Namely, the
data is decoupled into several relational tables, and it could be very
expensive to directly materialize the data matrix by joining the tables. We
propose a novel approach called ``aggregation tree with pseudo-cube'' that can
build a coreset from bottom to up. Moreover, our approach can neatly circumvent
several troublesome issues of relational learning problems [Khamis et al., PODS
2019]. Under some mild assumptions, we show that our coreset approach can be
applied for the machine learning tasks, such as clustering, logistic regression
and SVM.

Fast and accurate treatment of collisions in the context of modern N-body
planet formation simulations remains a challenging task due to inherently
complex collision processes. We aim to tackle this problem with machine
learning (ML), in particular via residual neural networks. Our model is
motivated by the underlying physical processes of the data-generating process
and allows for flexible prediction of post-collision states. We demonstrate
that our model outperforms commonly used collision handling methods such as
perfect inelastic merging and feed-forward neural networks in both prediction
accuracy and out-of-distribution generalization. Our model outperforms the
current state of the art in 20/24 experiments. We provide a dataset that
consists of 10164 Smooth Particle Hydrodynamics (SPH) simulations of pairwise
planetary collisions. The dataset is specifically suited for ML research to
improve computational aspects for collision treatment and for studying
planetary collisions in general. We formulate the ML task as a multi-task
regression problem, allowing simple, yet efficient training of ML models for
collision treatment in an end-to-end manner. Our models can be easily
integrated into existing N-body frameworks and can be used within our chosen
parameter space of initial conditions, i.e. where similar-sized collisions
during late-stage terrestrial planet formation typically occur.

In this work, we revisit the Transformer-based pre-trained language models
and identify two problems that may limit the expressiveness of the model.
Firstly, existing relative position encoding models (e.g., T5 and DEBERTA)
confuse two heterogeneous information: relative distance and direction. It may
make the model unable to capture the associative semantics of the same
direction or the same distance, which in turn affects the performance of
downstream tasks. Secondly, we notice the pre-trained BERT with Mask Language
Modeling (MLM) pre-training objective outputs similar token representations and
attention weights of different heads, which may impose difficulties in
capturing discriminative semantic representations. Motivated by the above
investigation, we propose two novel techniques to improve pre-trained language
models: Decoupled Directional Relative Position (DDRP) encoding and MTH
pre-training objective. DDRP decouples the relative distance features and the
directional features in classical relative position encoding for better
position information understanding. MTH designs two novel auxiliary losses
besides MLM to enlarge the dissimilarities between (a) last hidden states of
different tokens, and (b) attention weights of different heads, alleviating
homogenization and anisotropic problem in representation learning for better
optimization. Extensive experiments and ablation studies on GLUE benchmark
demonstrate the effectiveness of our proposed methods.

Lensless cameras are characterized by several advantages (e.g.,
miniaturization, ease of manufacture, and low cost) as compared with
conventional cameras. However, they have not been extensively employed due to
their poor image clarity and low image resolution, especially for tasks that
have high requirements on image quality and details such as text detection and
text recognition. To address the problem, a framework of deep-learning-based
pipeline structure was built to recognize text with three steps from raw data
captured by employing lensless cameras. This pipeline structure consisted of
the lensless imaging model U-Net, the text detection model connectionist text
proposal network (CTPN), and the text recognition model convolutional recurrent
neural network (CRNN). Compared with the method focusing only on image
reconstruction, UNet in the pipeline was able to supplement the imaging details
by enhancing factors related to character categories in the reconstruction
process, so the textual information can be more effectively detected and
recognized by CTPN and CRNN with fewer artifacts and high-clarity reconstructed
lensless images. By performing experiments on datasets of different
complexities, the applicability to text detection and recognition on lensless
cameras was verified. This study reasonably demonstrates text detection and
recognition tasks in the lensless camera system,and develops a basic method for
novel applications.

Autoregressive Transformers are strong language models but incur O(T)
complexity during per-token generation due to the self-attention mechanism.
Recent work proposes kernel-based methods to approximate causal self-attention
by replacing it with recurrent formulations with various update rules and
feature maps to achieve O(1) time and memory complexity. We explore these
approaches and find that they are unnecessarily complex, and propose a simple
alternative - decaying fast weights - that runs fast on GPU, outperforms prior
methods, and retains 99% of attention's performance for GPT-2. We also show
competitive performance on WikiText-103 against more complex attention
substitutes.

This work proposes a first-of-its-kind SLAM architecture fusing an
event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for
drone navigation. Each sensor is processed by a bio-inspired Spiking Neural
Network (SNN) with continual Spike-Timing-Dependent Plasticity (STDP) learning,
as observed in the brain. In contrast to most learning-based SLAM systems%,
which a) require the acquisition of a representative dataset of the environment
in which navigation must be performed and b) require an off-line training
phase, our method does not require any offline training phase, but rather the
SNN continuously learns features from the input data on the fly via STDP. At
the same time, the SNN outputs are used as feature descriptors for loop closure
detection and map correction. We conduct numerous experiments to benchmark our
system against state-of-the-art RGB methods and we demonstrate the robustness
of our DVS-Radar SLAM approach under strong lighting variations.

The framework of feedback graphs is a generalization of sequential
decision-making with bandit or full information feedback. In this work, we
study an extension where the directed feedback graph is stochastic, following a
distribution similar to the classical Erd\H{o}s-R\'enyi model. Specifically, in
each round every edge in the graph is either realized or not with a distinct
probability for each edge. We prove nearly optimal regret bounds of order
$\min\bigl\{\min_{\varepsilon} \sqrt{(\alpha_\varepsilon/\varepsilon) T},\,
\min_{\varepsilon} (\delta_\varepsilon/\varepsilon)^{1/3} T^{2/3}\bigr\}$
(ignoring logarithmic factors), where $\alpha_{\varepsilon}$ and
$\delta_{\varepsilon}$ are graph-theoretic quantities measured on the support
of the stochastic feedback graph $\mathcal{G}$ with edge probabilities
thresholded at $\varepsilon$. Our result, which holds without any preliminary
knowledge about $\mathcal{G}$, requires the learner to observe only the
realized out-neighborhood of the chosen action. When the learner is allowed to
observe the realization of the entire graph (but only the losses in the
out-neighborhood of the chosen action), we derive a more efficient algorithm
featuring a dependence on weighted versions of the independence and weak
domination numbers that exhibits improved bounds for some special cases.

The cosmic distance duality relation (DDR) is constrained from the
combination of type-Ia supernovae (SNe Ia) and strong gravitational lensing
(SGL) systems using deep learning method. To make use of the full SGL data, we
reconstruct the luminosity distance from SNe Ia up to the highest redshift of
SGL using deep learning, then it is compared with the angular diameter distance
obtained from SGL. Considering the influence of lens mass profile, we constrain
the possible violation of DDR in three lens mass models. Results show that in
the SIS model and EPL model, DDR is violated at high confidence level, with the
violation parameter $\eta_0=-0.193^{+0.021}_{-0.019}$ and
$\eta_0=-0.247^{+0.014}_{-0.013}$, respectively. In the PL model, however, DDR
is verified within 1$\sigma$ confidence level, with the violation parameter
$\eta_0=-0.014^{+0.053}_{-0.045}$. Our results demonstrate that the constraints
on DDR strongly depend on the lens mass models. Given a specific lens mass
model, DDR can be constrained at a precision of $\textit{O}(10^{-2})$ using
deep learning.

Medical anomaly detection is a crucial yet challenging task aiming at
recognizing abnormal images to assist diagnosis. Due to the high-cost
annotations of abnormal images, most methods utilize only known normal images
during training and identify samples not conforming to the normal profile as
anomalies in the testing phase. A large number of readily available unlabeled
images containing anomalies are thus ignored in the training phase, restricting
their performance. To solve this problem, we propose the Dual-distribution
Discrepancy for Anomaly Detection (DDAD), utilizing both known normal images
and unlabeled images. Two modules are designed to model the normative
distribution of normal images and the unknown distribution of both normal and
unlabeled images, respectively, using ensembles of reconstruction networks.
Subsequently, intra-discrepancy of the normative distribution module, and
inter-discrepancy between the two modules are designed as anomaly scores.
Furthermore, an Anormal Score Refinement Net (ASR-Net) trained via
self-supervised learning is proposed to refine the two anomaly scores. For
evaluation, five medical datasets including chest X-rays, brain MRIs and
retinal fundus images are organized as benchmarks. Experiments on these
benchmarks demonstrate our method achieves significant gains and outperforms
state-of-the-art methods. Code and organized benchmarks will be available at
https://github.com/caiyu6666/DDAD-ASR

Data-driven interatomic potentials have emerged as a powerful class of
surrogate models for {\it ab initio} potential energy surfaces that are able to
reliably predict macroscopic properties with experimental accuracy. In
generating accurate and transferable potentials the most time-consuming and
arguably most important task is generating the training set, which still
requires significant expert user input. To accelerate this process, this work
presents \text{\it hyperactive learning} (HAL), a framework for formulating an
accelerated sampling algorithm specifically for the task of training database
generation. The key idea is to start from a physically motivated sampler (e.g.,
molecular dynamics) and add a biasing term that drives the system towards high
uncertainty and thus to unseen training configurations. Building on this
framework, general protocols for building training databases for alloys and
polymers leveraging the HAL framework will be presented. For alloys, ACE
potentials for AlSi10 are created by fitting to a minimal HAL-generated
database containing 88 configurations (32 atoms each) with fast evaluation
times of <100 microsecond/atom/cpu-core. These potentials are demonstrated to
predict the melting temperature with excellent accuracy. For polymers, a HAL
database is built using ACE, able to determine the density of a long
polyethylene glycol (PEG) polymer formed of 200 monomer units with experimental
accuracy by only fitting to small isolated PEG polymers with sizes ranging from
2 to 32.

The brain effortlessly extracts latent causes of stimuli, but how it does
this at the network level remains unknown. Most prior attempts at this problem
proposed neural networks that implement independent component analysis which
works under the limitation that latent causes are mutually independent. Here,
we relax this limitation and propose a biologically plausible neural network
that extracts correlated latent sources by exploiting information about their
domains. To derive this network, we choose maximum correlative information
transfer from inputs to outputs as the separation objective under the
constraint that the outputs are restricted to their presumed sets. The online
formulation of this optimization problem naturally leads to neural networks
with local learning rules. Our framework incorporates infinitely many source
domain choices and flexibly models complex latent structures. Choices of
simplex or polytopic source domains result in networks with piecewise-linear
activation functions. We provide numerical examples to demonstrate the superior
correlated source separation capability for both synthetic and natural sources.

Floods are large-scale natural disasters that often induce a massive number
of deaths, extensive material damage, and economic turmoil. The effects are
more extensive and longer-lasting in high-population and low-resource
developing countries. Early Warning Systems (EWS) constantly assess water
levels and other factors to forecast floods, to help minimize damage.
Post-disaster, disaster response teams undertake a Post Disaster Needs
Assessment (PDSA) to assess structural damage and determine optimal strategies
to respond to highly affected neighbourhoods. However, even today in developing
countries, EWS and PDSA analysis of large volumes of image and video data is
largely a manual process undertaken by first responders and volunteers. We
propose FloodTransformer, which to the best of our knowledge, is the first
visual transformer-based model to detect and segment flooded areas from aerial
images at disaster sites. We also propose a custom metric, Flood Capacity (FC)
to measure the spatial extent of water coverage and quantify the segmented
flooded area for EWS and PDSA analyses. We use the SWOC Flood segmentation
dataset and achieve 0.93 mIoU, outperforming all other methods. We further show
the robustness of this approach by validating across unseen flood images from
other flood data sources.

NeRF aims to learn a continuous neural scene representation by using a finite
set of input images taken from different viewpoints. The fewer the number of
viewpoints, the higher the likelihood of overfitting on them. This paper
mitigates such limitation by presenting a novel data augmentation approach to
generate geometrically consistent image transitions between viewpoints using
view morphing. View morphing is a highly versatile technique that does not
requires any prior knowledge about the 3D scene because it is based on general
principles of projective geometry. A key novelty of our method is to use the
very same depths predicted by NeRF to generate the image transitions that are
then added to NeRF training. We experimentally show that this procedure enables
NeRF to improve the quality of its synthesised novel views in the case of
datasets with few training viewpoints. We improve PSNR up to 1.8dB and 10.5dB
when eight and four views are used for training, respectively. To the best of
our knowledge, this is the first data augmentation strategy for NeRF that
explicitly synthesises additional new input images to improve the model
generalisation.

Adapting to the changes in transition dynamics is essential in robotic
applications. By learning a conditional policy with a compact context,
context-aware meta-reinforcement learning provides a flexible way to adjust
behavior according to dynamics changes. However, in real-world applications,
the agent may encounter complex dynamics changes. Multiple confounders can
influence the transition dynamics, making it challenging to infer accurate
context for decision-making. This paper addresses such a challenge by
Decomposed Mutual INformation Optimization (DOMINO) for context learning, which
explicitly learns a disentangled context to maximize the mutual information
between the context and historical trajectories, while minimizing the state
transition prediction error. Our theoretical analysis shows that DOMINO can
overcome the underestimation of the mutual information caused by
multi-confounded challenges via learning disentangled context and reduce the
demand for the number of samples collected in various environments. Extensive
experiments show that the context learned by DOMINO benefits both model-based
and model-free reinforcement learning algorithms for dynamics generalization in
terms of sample efficiency and performance in unseen environments.

Although recent point cloud analysis achieves impressive progress, the
paradigm of representation learning from a single modality gradually meets its
bottleneck. In this work, we take a step towards more discriminative 3D point
cloud representation by fully taking advantages of images which inherently
contain richer appearance information, e.g., texture, color, and shade.
Specifically, this paper introduces a simple but effective point cloud
cross-modality training (PointCMT) strategy, which utilizes view-images, i.e.,
rendered or projected 2D images of the 3D object, to boost point cloud
analysis. In practice, to effectively acquire auxiliary knowledge from view
images, we develop a teacher-student framework and formulate the cross modal
learning as a knowledge distillation problem. PointCMT eliminates the
distribution discrepancy between different modalities through novel feature and
classifier enhancement criteria and avoids potential negative transfer
effectively. Note that PointCMT effectively improves the point-only
representation without architecture modification. Sufficient experiments verify
significant gains on various datasets using appealing backbones, i.e., equipped
with PointCMT, PointNet++ and PointMLP achieve state-of-the-art performance on
two benchmarks, i.e., 94.4% and 86.7% accuracy on ModelNet40 and ScanObjectNN,
respectively. Code will be made available at
https://github.com/ZhanHeshen/PointCMT.

Convolutional neural networks (CNNs) have demonstrated gratifying results at
learning discriminative features. However, when applied to unseen domains,
state-of-the-art models are usually prone to errors due to domain shift. After
investigating this issue from the perspective of shortcut learning, we find the
devils lie in the fact that models trained on different domains merely bias to
different domain-specific features yet overlook diverse task-related features.
Under this guidance, a novel Attention Diversification framework is proposed,
in which Intra-Model and Inter-Model Attention Diversification Regularization
are collaborated to reassign appropriate attention to diverse task-related
features. Briefly, Intra-Model Attention Diversification Regularization is
equipped on the high-level feature maps to achieve in-channel discrimination
and cross-channel diversification via forcing different channels to pay their
most salient attention to different spatial locations. Besides, Inter-Model
Attention Diversification Regularization is proposed to further provide
task-related attention diversification and domain-related attention
suppression, which is a paradigm of "simulate, divide and assemble": simulate
domain shift via exploiting multiple domain-specific models, divide attention
maps into task-related and domain-related groups, and assemble them within each
group respectively to execute regularization. Extensive experiments and
analyses are conducted on various benchmarks to demonstrate that our method
achieves state-of-the-art performance over other competing methods. Code is
available at https://github.com/hikvision-research/DomainGeneralization.

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient
models. Recent progress in training methods has enabled successful deep SNNs on
large-scale tasks with low latency. Particularly, backpropagation through time
(BPTT) with surrogate gradients (SG) is popularly used to achieve high
performance in a very small number of time steps. However, it is at the cost of
large memory consumption for training, lack of theoretical clarity for
optimization, and inconsistency with the online property of biological learning
and rules on neuromorphic hardware. Other works connect spike representations
of SNNs with equivalent artificial neural network formulation and train SNNs by
gradients from equivalent mappings to ensure descent directions. But they fail
to achieve low latency and are also not online. In this work, we propose online
training through time (OTTT) for SNNs, which is derived from BPTT to enable
forward-in-time learning by tracking presynaptic activities and leveraging
instantaneous loss and gradients. Meanwhile, we theoretically analyze and prove
that gradients of OTTT can provide a similar descent direction for optimization
as gradients based on spike representations under both feedforward and
recurrent conditions. OTTT only requires constant training memory costs
agnostic to time steps, avoiding the significant memory costs of BPTT for GPU
training. Furthermore, the update rule of OTTT is in the form of three-factor
Hebbian learning, which could pave a path for online on-chip learning. With
OTTT, it is the first time that two mainstream supervised SNN training methods,
BPTT with SG and spike representation-based training, are connected, and
meanwhile in a biologically plausible form. Experiments on CIFAR-10, CIFAR-100,
ImageNet, and CIFAR10-DVS demonstrate the superior performance of our method on
large-scale static and neuromorphic datasets in small time steps.

Using convolutional neural networks, deep learning-based reduced-order models
have demonstrated great potential in accelerating the simulations of coupled
fluid-structure systems for downstream optimization and control tasks. However,
these networks have to operate on a uniform Cartesian grid due to the inherent
restriction of convolutions, leading to difficulties in extracting fine
physical details along a fluid-structure interface without excessive
computational burden. In this work, we present a quasi-monolithic graph neural
network framework for the reduced-order modelling of fluid-structure
interaction systems. With the aid of an arbitrary Lagrangian-Eulerian
formulation, the mesh and fluid states are evolved temporally with two
sub-networks. The movement of the mesh is reduced to the evolution of several
coefficients via proper orthogonal decomposition, and these coefficients are
propagated through time via a multi-layer perceptron. A graph neural network is
employed to predict the evolution of the fluid state based on the state of the
whole system. The structural state is implicitly modelled by the movement of
the mesh on the fluid-structure boundary; hence it makes the proposed
data-driven methodology quasi-monolithic. The effectiveness of the proposed
quasi-monolithic graph neural network architecture is assessed on a
prototypical fluid-structure system of the flow around an elastically-mounted
cylinder. We use the full-order flow snapshots and displacements as target
physical data to learn and infer coupled fluid-structure dynamics. The proposed
framework tracks the interface description and provides the state predictions
during roll-out with acceptable accuracy. We also directly extract the lift and
drag forces from the predicted fluid and mesh states, in contrast to existing
convolution-based architectures.

We motivate and introduce CHARD: Clinical Health-Aware Reasoning across
Dimensions, to investigate the capability of text generation models to act as
implicit clinical knowledge bases and generate free-flow textual explanations
about various health-related conditions across several dimensions. We collect
and present an associated dataset, CHARDat, consisting of explanations about 52
health conditions across three clinical dimensions. We conduct extensive
experiments using BART and T5 along with data augmentation, and perform
automatic, human, and qualitative analyses. We show that while our models can
perform decently, CHARD is very challenging with strong potential for further
exploration.

Image rescaling is a commonly used bidirectional operation, which first
downscales high-resolution images to fit various display screens or to be
storage- and bandwidth-friendly, and afterward upscales the corresponding
low-resolution images to recover the original resolution or the details in the
zoom-in images. However, the non-injective downscaling mapping discards
high-frequency contents, leading to the ill-posed problem for the inverse
restoration task. This can be abstracted as a general image
degradation-restoration problem with information loss. In this work, we propose
a novel invertible framework to handle this general problem, which models the
bidirectional degradation and restoration from a new perspective, i.e.
invertible bijective transformation. The invertibility enables the framework to
model the information loss of pre-degradation in the form of distribution,
which could mitigate the ill-posed problem during post-restoration. To be
specific, we develop invertible models to generate valid degraded images and
meanwhile transform the distribution of lost contents to the fixed distribution
of a latent variable during the forward degradation. Then restoration is made
tractable by applying the inverse transformation on the generated degraded
image together with a randomly-drawn latent variable. We start from image
rescaling and instantiate the model as Invertible Rescaling Network (IRN),
which can be easily extended to the similar decolorization-colorization task.
We further propose to combine the invertible framework with existing
degradation methods such as image compression for wider applications.
Experimental results demonstrate the significant improvement of our model over
existing methods in terms of both quantitative and qualitative evaluations of
upscaling and colorizing reconstruction from downscaled and decolorized images,
and rate-distortion of image compression.

Building dialogue systems requires a large corpus of annotated dialogues.
Such datasets are usually created via crowdsourcing, which is expensive and
time-consuming. In this paper, we propose \textsc{Dialogic}, a novel dialogue
simulation method based on large language model in-context learning to automate
dataset creation. Seeded with a few annotated dialogues, \textsc{Dialogic}
automatically selects in-context examples for demonstration and prompts GPT-3
to generate new dialogues and annotations in a controllable way. Our method can
rapidly expand a small set of dialogue data with minimum or zero \textit{human
involvement} and \textit{parameter update} and is thus much more cost-efficient
and time-saving than crowdsourcing. Experimental results on the MultiWOZ
dataset demonstrate that training a model on the simulated dialogues leads to
even better performance than using the same amount of human-generated dialogues
under the challenging low-resource settings, with as few as 85 dialogues as a
seed. When enough data is available, our method can still serve as an effective
data augmentation method. Human evaluation results also show that our simulated
dialogues have near-human fluency and annotation accuracy. The code and data
are available at \textbf{\url{https://github.com/Leezekun/dialogic}}.

In multiband fusion, an image with a high spatial and low spectral resolution
is combined with an image with a low spatial but high spectral resolution to
produce a single multiband image having high spatial and spectral resolutions.
This comes up in remote sensing applications such as pansharpening~(MS+PAN),
hyperspectral sharpening~(HS+PAN), and HS-MS fusion~(HS+MS). Remote sensing
images are textured and have repetitive structures. Motivated by nonlocal
patch-based methods for image restoration, we propose a convex regularizer that
(i) takes into account long-distance correlations, (ii) penalizes patch
variation, which is more effective than pixel variation for capturing texture
information, and (iii) uses the higher spatial resolution image as a guide
image for weight computation. We come up with an efficient ADMM algorithm for
optimizing the regularizer along with a standard least-squares loss function
derived from the imaging model. The novelty of our algorithm is that by
expressing patch variation as filtering operations and by judiciously splitting
the original variables and introducing latent variables, we are able to solve
the ADMM subproblems efficiently using FFT-based convolution and
soft-thresholding. As far as the reconstruction quality is concerned, our
method is shown to outperform state-of-the-art variational and deep learning

Multimodal representation learning has shown promising improvements on
various vision-language tasks. Most existing methods excel at building
global-level alignment between vision and language while lacking effective
fine-grained image-text interaction. In this paper, we propose a jointly masked
multimodal modeling method to learn fine-grained multimodal representations.
Our method performs joint masking on image-text input and integrates both
implicit and explicit targets for the masked signals to recover. The implicit
target provides a unified and debiased objective for vision and language, where
the model predicts latent multimodal representations of the unmasked input. The
explicit target further enriches the multimodal representations by recovering
high-level and semantically meaningful information: momentum visual features of
image patches and concepts of word tokens. Through such a masked modeling
process, our model not only learns fine-grained multimodal interaction, but
also avoids the semantic gap between high-level representations and low- or
mid-level prediction targets (e.g. image pixels), thus producing semantically
rich multimodal representations that perform well on both zero-shot and
fine-tuned settings. Our pre-trained model (named MAMO) achieves
state-of-the-art performance on various downstream vision-language tasks,
including image-text retrieval, visual question answering, visual reasoning,
and weakly-supervised visual grounding.

Span-based models are one of the most straightforward methods for named
entity recognition (NER). Existing span-based NER systems shallowly aggregate
the token representations to span representations. However, this typically
results in significant ineffectiveness for long-span entities, a coupling
between the representations of overlapping spans, and ultimately a performance
degradation. In this study, we propose DSpERT (Deep Span Encoder
Representations from Transformers), which comprises a standard Transformer and
a span Transformer. The latter uses low-layered span representations as
queries, and aggregates the token representations as keys and values, layer by
layer from bottom to top. Thus, DSpERT produces span representations of deep
semantics.
  With weight initialization from pretrained language models, DSpERT achieves
performance higher than or competitive with recent state-of-the-art systems on
eight NER benchmarks. Experimental results verify the importance of the depth
for span representations, and show that DSpERT performs particularly well on
long-span entities and nested structures. Further, the deep span
representations are well structured and easily separable in the feature space.

A fundamental challenge in deep metric learning is the generalization
capability of the feature embedding network model since the embedding network
learned on training classes need to be evaluated on new test classes. To
address this challenge, in this paper, we introduce a new method called coded
residual transform (CRT) for deep metric learning to significantly improve its
generalization capability. Specifically, we learn a set of diversified
prototype features, project the feature map onto each prototype, and then
encode its features using their projection residuals weighted by their
correlation coefficients with each prototype. The proposed CRT method has the
following two unique characteristics. First, it represents and encodes the
feature map from a set of complimentary perspectives based on projections onto
diversified prototypes. Second, unlike existing transformer-based feature
representation approaches which encode the original values of features based on
global correlation analysis, the proposed coded residual transform encodes the
relative differences between the original features and their projected
prototypes. Embedding space density and spectral decay analysis show that this
multi-perspective projection onto diversified prototypes and coded residual
representation are able to achieve significantly improved generalization
capability in metric learning. Finally, to further enhance the generalization
performance, we propose to enforce the consistency on their feature similarity
matrices between coded residual transforms with different sizes of projection
prototypes and embedding dimensions. Our extensive experimental results and
ablation studies demonstrate that the proposed CRT method outperform the
state-of-the-art deep metric learning methods by large margins and improving
upon the current best method by up to 4.28% on the CUB dataset.

Deep learning models for non-intrusive load monitoring (NILM) tend to require
a large amount of labeled data for training. However, it is difficult to
generalize the trained models to unseen sites due to different load
characteristics and operating patterns of appliances between data sets. For
addressing such problems, self-supervised learning (SSL) is proposed in this
paper, where labeled appliance-level data from the target data set or house is
not required. Initially, only the aggregate power readings from target data set
are required to pre-train a general network via a self-supervised pretext task
to map aggregate power sequences to derived representatives. Then, supervised
downstream tasks are carried out for each appliance category to fine-tune the
pre-trained network, where the features learned in the pretext task are
transferred. Utilizing labeled source data sets enables the downstream tasks to
learn how each load is disaggregated, by mapping the aggregate to labels.
Finally, the fine-tuned network is applied to load disaggregation for the
target sites. For validation, multiple experimental cases are designed based on
three publicly accessible REDD, UK-DALE, and REFIT data sets. Besides,
state-of-the-art neural networks are employed to perform NILM task in the
experiments. Based on the NILM results in various cases, SSL generally
outperforms zero-shot learning in improving load disaggregation performance
without any sub-metering data from the target data sets.

Neural networks (NNs) are increasingly applied in safety-critical systems
such as autonomous vehicles. However, they are fragile and are often
ill-behaved. Consequently, their behaviors should undergo rigorous guarantees
before deployment in practice. In this paper we propose a set-boundary
reachability method to investigate the safety verification problem of NNs from
a topological perspective. Given an NN with an input set and a safe set, the
safety verification problem is to determine whether all outputs of the NN
resulting from the input set fall within the safe set. In our method, the
homeomorphism property of NNs is mainly exploited, which establishes a
relationship mapping boundaries to boundaries. The exploitation of this
property facilitates reachability computations via extracting subsets of the
input set rather than the entire input set, thus controlling the wrapping
effect in reachability analysis and facilitating the reduction of computation
burdens for safety verification. The homeomorphism property exists in some
widely used NNs such as invertible NNs. Notable representations are invertible
residual networks (i-ResNets) and Neural ordinary differential equations
(Neural ODEs). For these NNs, our set-boundary reachability method only needs
to perform reachability analysis on the boundary of the input set. For NNs
which do not feature this property with respect to the input set, we explore
subsets of the input set for establishing the local homeomorphism property, and
then abandon these subsets for reachability computations. Finally, some
examples demonstrate the performance of the proposed method.

Although a number of studies are devoted to novel category discovery, most of
them assume a static setting where both labeled and unlabeled data are given at
once for finding new categories. In this work, we focus on the application
scenarios where unlabeled data are continuously fed into the category discovery
system. We refer to it as the {\bf Continuous Category Discovery} ({\bf CCD})
problem, which is significantly more challenging than the static setting. A
common challenge faced by novel category discovery is that different sets of
features are needed for classification and category discovery: class
discriminative features are preferred for classification, while rich and
diverse features are more suitable for new category mining. This challenge
becomes more severe for dynamic setting as the system is asked to deliver good
performance for known classes over time, and at the same time continuously
discover new classes from unlabeled data. To address this challenge, we develop
a framework of {\bf Grow and Merge} ({\bf GM}) that works by alternating
between a growing phase and a merging phase: in the growing phase, it increases
the diversity of features through a continuous self-supervised learning for
effective category mining, and in the merging phase, it merges the grown model
with a static one to ensure satisfying performance for known classes. Our
extensive studies verify that the proposed GM framework is significantly more
effective than the state-of-the-art approaches for continuous category
discovery.

Steady-state visual evoked potential (SSVEP) is one of the most commonly used
control signal in the brain-computer interface (BCI) systems. However, the
conventional spatial filtering methods for SSVEP classification highly depend
on the subject-specific calibration data. The need for the methods that can
alleviate the demand for the calibration data become urgent. In recent years,
developing the methods that can work in inter-subject classification scenario
has become a promising new direction. As the popular deep learning model
nowadays, Transformer has excellent performance and has been used in EEG signal
classification tasks. Therefore, in this study, we propose a deep learning
model for SSVEP classification based on Transformer structure in inter-subject
classification scenario, termed as SSVEPformer, which is the first application
of the transformer to the classification of SSVEP. Inspired by previous
studies, the model adopts the frequency spectrum of SSVEP data as input, and
explores the spectral and spatial domain information for classification.
Furthermore, to fully utilize the harmonic information, an extended SSVEPformer
based on the filter bank technology (FB-SSVEPformer) is proposed to further
improve the classification performance. Experiments were conducted using two
open datasets (Dataset 1: 10 subjects, 12-class task; Dataset 2: 35 subjects,
40-class task) in the inter-subject classification scenario. The experimental
results show that the proposed models could achieve better results in terms of
classification accuracy and information transfer rate, compared with other
baseline methods. The proposed model validates the feasibility of deep learning
models based on Transformer structure for SSVEP classification task, and could
serve as a potential model to alleviate the calibration procedure in the
practical application of SSVEP-based BCI systems.

The angular momentum of galaxies (galaxy spin) contains rich information
about the initial condition of the Universe, yet it is challenging to
efficiently measure the spin direction for the tremendous amount of galaxies
that are being mapped by the ongoing and forthcoming cosmological surveys. We
present a machine learning based classifier for the Z-wise vs S-wise spirals,
which can help to break the degeneracy in the galaxy spin direction
measurement. The proposed Chirality Equivariant Residual Network (CE-ResNet) is
manifestly equivariant under a reflection of the input image, which guarantees
that there is no inherent asymmetry between the Z-wise and S-wise probability
estimators. We train the model with Sloan Digital Sky Survey (SDSS) images,
with the training labels given by the Galaxy Zoo 1 (GZ1) project. A combination
of data augmentation tricks are used during the training, making the model more
robust to be applied to other surveys. We find a $\sim\!30\%$ increase of both
types of spirals when Dark Energy Spectroscopic Instrument (DESI) images are
used for classification, due to the better imaging quality of DESI. We verify
that the $\sim\!7\sigma$ difference between the numbers of Z-wise and S-wise
spirals is due to human bias, since the discrepancy drops to $<\!1.8\sigma$
with our CE-ResNet classification results. We discuss the potential systematics
that are relevant to the future cosmological applications.

Modern image classifiers achieve high predictive accuracy, but the
predictions typically come without reliable uncertainty estimates. Conformal
prediction algorithms provide uncertainty estimates by predicting a set of
classes based on the probability estimates of the classifier (for example, the
softmax scores). To provide such sets, conformal prediction algorithms often
rely on estimating a cutoff threshold for the probability estimates, and this
threshold is chosen based on a calibration set. Conformal prediction methods
guarantee reliability only when the calibration set is from the same
distribution as the test set. Therefore, the methods need to be recalibrated
for new distributions. However, in practice, labeled data from new
distributions is rarely available, making calibration infeasible. In this work,
we consider the problem of predicting the cutoff threshold for a new
distribution based on unlabeled examples only. While it is impossible in
general to guarantee reliability when calibrating based on unlabeled examples,
we show that our method provides excellent uncertainty estimates under natural
distribution shifts, and provably works for a specific model of a distribution
shift.

Accurate structural response prediction forms a main driver for structural
health monitoring and control applications. This often requires the proposed
model to adequately capture the underlying dynamics of complex structural
systems. In this work, we utilize a learnable Extended Kalman Filter (EKF),
named the Neural Extended Kalman Filter (Neural EKF) throughout this paper, for
learning the latent evolution dynamics of complex physical systems. The Neural
EKF is a generalized version of the conventional EKF, where the modeling of
process dynamics and sensory observations can be parameterized by neural
networks, therefore learned by end-to-end training. The method is implemented
under the variational inference framework with the EKF conducting inference
from sensing measurements. Typically, conventional variational inference models
are parameterized by neural networks independent of the latent dynamics models.
This characteristic makes the inference and reconstruction accuracy weakly
based on the dynamics models and renders the associated training inadequate. We
here show how the structure imposed by the Neural EKF is beneficial to the
learning process. We demonstrate the efficacy of the framework on both
simulated and real-world monitoring datasets, with the results indicating
significant predictive capabilities of the proposed scheme.

Coverage conditions -- which assert that the data logging distribution
adequately covers the state space -- play a fundamental role in determining the
sample complexity of offline reinforcement learning. While such conditions
might seem irrelevant to online reinforcement learning at first glance, we
establish a new connection by showing -- somewhat surprisingly -- that the mere
existence of a data distribution with good coverage can enable sample-efficient
online RL. Concretely, we show that coverability -- that is, existence of a
data distribution that satisfies a ubiquitous coverage condition called
concentrability -- can be viewed as a structural property of the underlying
MDP, and can be exploited by standard algorithms for sample-efficient
exploration, even when the agent does not know said distribution. We complement
this result by proving that several weaker notions of coverage, despite being
sufficient for offline RL, are insufficient for online RL. We also show that
existing complexity measures for online RL, including Bellman rank and
Bellman-Eluder dimension, fail to optimally capture coverability, and propose a
new complexity measure, the sequential extrapolation coefficient, to provide a
unification.

As a recent noticeable topic, domain generalization aims to learn a
generalizable model on multiple source domains, which is expected to perform
well on unseen test domains. Great efforts have been made to learn
domain-invariant features by aligning distributions across domains. However,
existing works are often designed based on some relaxed conditions which are
generally hard to satisfy and fail to realize the desired joint distribution
alignment. In this paper, we propose a novel domain generalization method,
which originates from an intuitive idea that a domain-invariant classifier can
be learned by minimizing the KL-divergence between posterior distributions from
different domains. To enhance the generalizability of the learned classifier,
we formalize the optimization objective as an expectation computed on the
ground-truth marginal distribution. Nevertheless, it also presents two obvious
deficiencies, one of which is the side-effect of entropy increase in
KL-divergence and the other is the unavailability of ground-truth marginal
distributions. For the former, we introduce a term named maximum in-domain
likelihood to maintain the discrimination of the learned domain-invariant
representation space. For the latter, we approximate the ground-truth marginal
distribution with source domains under a reasonable convex hull assumption.
Finally, a Constrained Maximum Cross-domain Likelihood (CMCL) optimization
problem is deduced, by solving which the joint distributions are naturally
aligned. An alternating optimization strategy is carefully designed to
approximately solve this optimization problem. Extensive experiments on four
standard benchmark datasets, i.e., Digits-DG, PACS, Office-Home and
miniDomainNet, highlight the superior performance of our method.

Masked autoencoders (MAEs) have emerged recently as art self-supervised
spatiotemporal representation learners. Inheriting from the image counterparts,
however, existing video MAEs still focus largely on static appearance learning
whilst are limited in learning dynamic temporal information hence less
effective for video downstream tasks. To resolve this drawback, in this work we
present a motion-aware variant -- MotionMAE. Apart from learning to reconstruct
individual masked patches of video frames, our model is designed to
additionally predict the corresponding motion structure information over time.
This motion information is available at the temporal difference of nearby
frames. As a result, our model can extract effectively both static appearance
and dynamic motion spontaneously, leading to superior spatiotemporal
representation learning capability. Extensive experiments show that our
MotionMAE outperforms significantly both supervised learning baseline and
state-of-the-art MAE alternatives, under both domain-specific and
domain-generic pretraining-then-finetuning settings. In particular, when using
ViT-B as the backbone our MotionMAE surpasses the prior art model by a margin
of 1.2% on Something-Something V2 and 3.2% on UCF101 in domain-specific
pretraining setting. Encouragingly, it also surpasses the competing MAEs by a
large margin of over 3% on the challenging video object segmentation task. The
code is available at https://github.com/happy-hsy/MotionMAE.

Prediction interval (PI) is an effective tool to quantify uncertainty and
usually serves as an input to downstream robust optimization. Traditional
approaches focus on improving the quality of PI in the view of statistical
scores and assume the improvement in quality will lead to a higher value in the
power systems operation. However, such an assumption cannot always hold in
practice. In this paper, we propose a value-oriented PI forecasting approach,
which aims at reducing operational costs in downstream operations. For that, it
is required to issue PIs with the guidance of operational costs in robust
optimization, which is addressed within the contextual bandit framework here.
Concretely, the agent is used to select the optimal quantile proportion, while
the environment reveals the costs in operations as rewards to the agent. As
such, the agent can learn the policy of quantile proportion selection for
minimizing the operational cost. The numerical study regarding a two-timescale
operation of a virtual power plant verifies the superiority of the proposed
approach in terms of operational value. And it is especially evident in the
context of extensive penetration of wind power.

Torsades de pointes (TdP) is an irregular heart rhythm as a side effect of
drugs and may cause sudden cardiac death. A machine learning model that can
accurately identify drug TdP risk is necessary. This study uses multinomial
logistic regression models to predict three-class drug TdP risks based on
datasets generated from rabbit ventricular wedge assay experiments. The
training-test split and five-fold cross-validation provide unbiased
measurements for prediction accuracy. We utilize bootstrap to construct a 95%
confidence interval for prediction accuracy. The model interpretation is
further demonstrated by permutation predictor importance. Our study offers an
interpretable modeling method suitable for drug TdP risk prediction. Our method
can be easily generalized to broader applications of drug side effect

Open-vocabulary semantic segmentation aims to segment an image into semantic
regions according to text descriptions, which may not have been seen during
training. Recent two-stage methods first generate class-agnostic mask proposals
and then leverage pre-trained vision-language models, e.g., CLIP, to classify
masked regions. We identify the performance bottleneck of this paradigm to be
the pre-trained CLIP model, since it does not perform well on masked images. To
address this, we propose to finetune CLIP on a collection of masked image
regions and their corresponding text descriptions. We collect training data by
mining an existing image-caption dataset (e.g., COCO Captions), using CLIP to
match masked image regions to nouns in the image captions. Compared with the
more precise and manually annotated segmentation labels with fixed classes
(e.g., COCO-Stuff), we find our noisy but diverse dataset can better retain
CLIP's generalization ability. Along with finetuning the entire model, we
utilize the "blank" areas in masked images using a method we dub mask prompt
tuning. Experiments demonstrate mask prompt tuning brings significant
improvement without modifying any weights of CLIP, and it can further improve a
fully finetuned model. In particular, when trained on COCO and evaluated on
ADE20K-150, our best model achieves 29.6% mIoU, which is +8.5% higher than the
previous state-of-the-art. For the first time, open-vocabulary generalist
models match the performance of supervised specialist models in 2017 without
dataset-specific adaptations.

Recently, a few papers report counter-intuitive observations made from
experiments on recommender system (RecSys). One observation is that users who
spend more time and users who have many interactions with a recommendation
system receive poorer recommendations. Another observation is that models
trained by using only the more recent parts of a dataset show significant
performance improvement. In this opinion paper, we interpret these
counter-intuitive observations from two perspectives. First, the observations
are made with respect to the global timeline of user-item interactions. Second,
the observations are considered counter-intuitive because they contradict our
expectation on a recommender: the more interactions a user has, the higher
chance that the recommender better learns the user preference. For the first
perspective, we discuss the importance of the global timeline by using the
simplest baseline Popularity as a starting point. We answer two questions: (i)
why the simplest model popularity is often ill-defined in academic research?
and (ii) why the popularity baseline is evaluated in this way? The questions
lead to a detailed discussion on the data leakage issue in many offline
evaluations. As the result, model accuracies reported in many academic papers
are less meaningful and incomparable. For the second perspective, we try to
answer two more questions: (i) why models trained by using only the more recent
parts of data demonstrate better performance? and (ii) why more interactions
from users lead to poorer recommendations? The key to both questions is user
preference modeling. We then propose to have a fresh look at RecSys. We discuss
how to conduct more practical offline evaluations and possible ways to
effectively model user preferences. The discussion and opinions in this paper
are on top-N recommendation only, not on rating prediction.

To address practical challenges in establishing and maintaining robust
wireless connectivity such as multi-path effects, low latency, size reduction,
and high data rate, the digital beamformer is performed by the hybrid antenna
array at the frequency of operation of 10 GHz. The proposed digital beamformer,
as a spatial filter, is capable of performing Direction of Arrival (DOA)
estimation and beamforming. The most well-established machine learning
technique of support vector machine (SVM) for the DoA estimation is limited to
problems with linearly-separable datasets.
  To overcome the aforementioned constraint, in the proposed beamformer, the
QS-SVM classifier with a small regularizer has been used for the DoA estimation
in addition to the two beamforming techniques of LCMV and MVDR. The
QS-SVM-based beamformer has been deployed in an FPGA board, as demonstrated in
detail in this work. The implementation results have verified the strong
performance of the QS-SVM-based beamformer in suppressing undesired signals,
deep nulls with powers less than -10 dB in undesired signals, and transferring
desired signals. Furthermore, we have demonstrated that the performance of the
QS-SVM-based beamformer consists of other advantages of average latency time in
the order of milliseconds, performance efficiency of more than 90\%, and
throughput of about 100\%.

Anomaly detection in sequential data has been studied for a long time because
of its potential in various applications, such as detecting abnormal system
behaviors from log data. Although many approaches can achieve good performance
on anomalous sequence detection, how to identify the anomalous entries in
sequences is still challenging due to a lack of information at the entry-level.
In this work, we propose a novel framework called CFDet for fine-grained
anomalous entry detection. CFDet leverages the idea of interpretable machine
learning. Given a sequence that is detected as anomalous, we can consider
anomalous entry detection as an interpretable machine learning task because
identifying anomalous entries in the sequence is to provide an interpretation
to the detection result. We make use of the deep support vector data
description (Deep SVDD) approach to detect anomalous sequences and propose a
novel counterfactual interpretation-based approach to identify anomalous
entries in the sequences. Experimental results on three datasets show that
CFDet can correctly detect anomalous entries.

Few-shot classification aims to learn a classifier to recognize unseen
classes during training, where the learned model can easily become over-fitted
based on the biased distribution formed by only a few training examples. A
recent solution to this problem is calibrating the distribution of these few
sample classes by transferring statistics from the base classes with sufficient
examples, where how to decide the transfer weights from base classes to novel
classes is the key. However, principled approaches for learning the transfer
weights have not been carefully studied. To this end, we propose a novel
distribution calibration method by learning the adaptive weight matrix between
novel samples and base classes, which is built upon a hierarchical Optimal
Transport (H-OT) framework. By minimizing the high-level OT distance between
novel samples and base classes, we can view the learned transport plan as the
adaptive weight information for transferring the statistics of base classes.
The learning of the cost function between a base class and novel class in the
high-level OT leads to the introduction of the low-level OT, which considers
the weights of all the data samples in the base class. Experimental results on
standard benchmarks demonstrate that our proposed plug-and-play model
outperforms competing approaches and owns desired cross-domain generalization
ability, indicating the effectiveness of the learned adaptive weights.

Quantifying the parameters and corresponding uncertainties of hundreds of
strongly lensed quasar systems holds the key to resolving one of the most
important scientific questions: the Hubble constant ($H_{0}$) tension. The
commonly used Markov chain Monte Carlo (MCMC) method has been too
time-consuming to achieve this goal, yet recent work has shown that convolution
neural networks (CNNs) can be an alternative with seven orders of magnitude
improvement in speed. With 31,200 simulated strongly lensed quasar images, we
explore the usage of Vision Transformer (ViT) for simulated strong
gravitational lensing for the first time. We show that ViT could reach
competitive results compared with CNNs, and is specifically good at some
lensing parameters, including the most important mass-related parameters such
as the center of lens $\theta_{1}$ and $\theta_{2}$, the ellipticities $e_1$
and $e_2$, and the radial power-law slope $\gamma'$. With this promising
preliminary result, we believe the ViT (or attention-based) network
architecture can be an important tool for strong lensing science for the next
generation of surveys. The open source of our code and data is in
\url{https://github.com/kuanweih/strong_lensing_vit_resnet}.

Cluster analysis plays an indispensable role in machine learning and data
mining. Learning a good data representation is crucial for clustering
algorithms. Recently, deep clustering, which can learn clustering-friendly
representations using deep neural networks, has been broadly applied in a wide
range of clustering tasks. Existing surveys for deep clustering mainly focus on
the single-view fields and the network architectures, ignoring the complex
application scenarios of clustering. To address this issue, in this paper we
provide a comprehensive survey for deep clustering in views of data sources.
With different data sources and initial conditions, we systematically
distinguish the clustering methods in terms of methodology, prior knowledge,
and architecture. Concretely, deep clustering methods are introduced according
to four categories, i.e., traditional single-view deep clustering,
semi-supervised deep clustering, deep multi-view clustering, and deep transfer
clustering. Finally, we discuss the open challenges and potential future
opportunities in different fields of deep clustering.

In this paper, we consider a challenging but realistic continual learning
(CL) problem, Few-Shot Continual Active Learning (FoCAL), where a CL agent is
provided with unlabeled data for a new or a previously learned task in each
increment and the agent only has limited labeling budget available. Towards
this, we build on the continual learning and active learning literature and
develop a framework that can allow a CL agent to continually learn new object
classes from a few labeled training examples. Our framework represents each
object class using a uniform Gaussian mixture model (GMM) and uses
pseudo-rehearsal to mitigate catastrophic forgetting. The framework also uses
uncertainty measures on the Gaussian representations of the previously learned
classes to find the most informative samples to be labeled in an increment. We
evaluate our approach on the CORe-50 dataset and on a real humanoid robot for
the object classification task. The results show that our approach not only
produces state-of-the-art results on the dataset but also allows a real robot
to continually learn unseen objects in a real environment with limited labeling
supervision provided by its user.

Vision-language pre-training (VLP) has recently proven highly effective for
various uni- and multi-modal downstream applications. However, most existing
end-to-end VLP methods use high-resolution image-text box data to perform well
on fine-grained region-level tasks, such as object detection, segmentation, and
referring expression comprehension. Unfortunately, such high-resolution images
with accurate bounding box annotations are expensive to collect and use for
supervision at scale. In this work, we propose VoLTA (Vision-Language
Transformer with weakly-supervised local-feature Alignment), a new VLP paradigm
that only utilizes image-caption data but achieves fine-grained region-level
image understanding, eliminating the use of expensive box annotations. VoLTA
adopts graph optimal transport-based weakly-supervised alignment on local image
patches and text tokens to germinate an explicit, self-normalized, and
interpretable low-level matching criterion. In addition, VoLTA pushes
multi-modal fusion deep into the uni-modal backbones during pre-training and
removes fusion-specific transformer layers, further reducing memory
requirements. Extensive experiments on a wide range of vision- and
vision-language downstream tasks demonstrate the effectiveness of VoLTA on
fine-grained applications without compromising the coarse-grained downstream
performance, often outperforming methods using significantly more caption and
box annotations.

Multi-modal foundation models are typically trained on millions of pairs of
natural images and text captions, frequently obtained through web-crawling
approaches. Although such models depict excellent generative capabilities, they
do not typically generalize well to specific domains such as medical images
that have fundamentally shifted distributions compared to natural images.
Building generative models for medical images that faithfully depict clinical
context may help alleviate the paucity of healthcare datasets. Thus, in this
study, we seek to research and expand the representational capabilities of
large pretrained foundation models to medical concepts, specifically for
leveraging the Stable Diffusion model to generate domain specific images found
in medical imaging. We explore the sub-components of the Stable Diffusion
pipeline (the variational autoencoder, the U-Net and the text-encoder) to
fine-tune the model to generate medical images. We benchmark the efficacy of
these efforts using quantitative image quality metrics and qualitative
radiologist-driven evaluations that accurately represent the clinical content
of conditional text prompts. Our best-performing model improves upon the stable
diffusion baseline and can be conditioned to insert a realistic-looking
abnormality on a synthetic radiology image, while maintaining a 95% accuracy on
a classifier trained to detect the abnormality.

This study explores the robustness of learning by symmetric loss on private
data. Specifically, we leverage exponential mechanism (EM) on private labels.
First, we theoretically re-discussed properties of EM when it is used for
private learning with symmetric loss. Then, we propose numerical guidance of
privacy budgets corresponding to different data scales and utility guarantees.
Further, we conducted experiments on the CIFAR-10 dataset to present the traits
of symmetric loss. Since EM is a more generic differential privacy (DP)
technique, it being robust has the potential for it to be generalized, and to
make other DP techniques more robust.

Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from
novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it
to dynamic scenes (video) with multiple objects. Nevertheless, computationally
heavy ray marching for every image frame becomes a huge burden. In this paper,
taking advantage of significant redundancy across adjacent frames in videos, we
propose a feature-reusing framework. From the first try of naively reusing the
NSG features, however, we learn that it is crucial to disentangle
object-intrinsic properties consistent across frames from transient ones. Our
proposed method, \textit{Consistency-Field-based NSG (CF-NSG)}, reformulates
neural radiance fields to additionally consider \textit{consistency fields}.
With disentangled representations, CF-NSG takes full advantage of the
feature-reusing scheme and performs an extended degree of scene manipulation in
a more controllable manner. We empirically verify that CF-NSG greatly improves
the inference efficiency by using 85\% less queries than NSG without notable
degradation in rendering quality. Code will be available at:
https://github.com/ldynx/CF-NSG

Extractive summarization for long documents is challenging due to the
extended structured input context. The long-distance sentence dependency
hinders cross-sentence relations modeling, the critical step of extractive
summarization. This paper proposes HEGEL, a hypergraph neural network for long
document summarization by capturing high-order cross-sentence relations. HEGEL
updates and learns effective sentence representations with hypergraph
transformer layers and fuses different types of sentence dependencies,
including latent topics, keywords coreference, and section structure. We
validate HEGEL by conducting extensive experiments on two benchmark datasets,
and experimental results demonstrate the effectiveness and efficiency of HEGEL.

In this work, we provide a theoretical understanding of the framelet-based
graph neural networks through the perspective of energy gradient flow. By
viewing the framelet-based models as discretized gradient flows of some energy,
we show it can induce both low-frequency and high-frequency-dominated dynamics,
via the separate weight matrices for different frequency components. This
substantiates its good empirical performance on both homophilic and
heterophilic graphs. We then propose a generalized energy via framelet
decomposition and show its gradient flow leads to a novel graph neural network,
which includes many existing models as special cases. We then explain how the
proposed model generally leads to more flexible dynamics, thus potentially
enhancing the representation power of graph neural networks.

Recently, deep reinforcement learning (DRL) models have shown promising
results in solving NP-hard Combinatorial Optimization (CO) problems. However,
most DRL solvers can only scale to a few hundreds of nodes for combinatorial
optimization problems on graphs, such as the Traveling Salesman Problem (TSP).
This paper addresses the scalability challenge in large-scale combinatorial
optimization by proposing a novel approach, namely, DIMES. Unlike previous DRL
methods which suffer from costly autoregressive decoding or iterative
refinements of discrete solutions, DIMES introduces a compact continuous space
for parameterizing the underlying distribution of candidate solutions. Such a
continuous space allows stable REINFORCE-based training and fine-tuning via
massively parallel sampling. We further propose a meta-learning framework to
enable the effective initialization of model parameters in the fine-tuning
stage. Extensive experiments show that DIMES outperforms recent DRL-based
methods on large benchmark datasets for Traveling Salesman Problems and Maximal
Independent Set problems.

Obtaining high-quality magnetic and velocity fields through Stokes inversion
is crucial in solar physics. In this paper, we present a new deep learning
method, named Stacked Deep Neural Networks (SDNN), for inferring line-of-sight
(LOS) velocities and Doppler widths from Stokes profiles collected by the Near
InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope
(GST) at the Big Bear Solar Observatory (BBSO). The training data of SDNN is
prepared by a Milne-Eddington (ME) inversion code used by BBSO. We
quantitatively assess SDNN, comparing its inversion results with those obtained
by the ME inversion code and related machine learning (ML) algorithms such as
multiple support vector regression, multilayer perceptrons and a pixel-level
convolutional neural network. Major findings from our experimental study are
summarized as follows. First, the SDNN-inferred LOS velocities are highly
correlated to the ME-calculated ones with the Pearson product-moment
correlation coefficient being close to 0.9 on average. Second, SDNN is faster,
while producing smoother and cleaner LOS velocity and Doppler width maps, than
the ME inversion code. Third, the maps produced by SDNN are closer to ME's maps
than those from the related ML algorithms, demonstrating the better learning
capability of SDNN than the ML algorithms. Finally, comparison between the
inversion results of ME and SDNN based on GST/NIRIS and those from the
Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory in
flare-prolific active region NOAA 12673 is presented. We also discuss
extensions of SDNN for inferring vector magnetic fields with empirical
evaluation.

To achieve desirable performance, current AI systems often require huge
amounts of training data. This is especially problematic in domains where
collecting data is both expensive and time-consuming, e.g., where AI systems
require having numerous interactions with humans, collecting feedback from
them. In this work, we substantiate the idea of $\textit{cognitive models as
simulators}$, which is to have AI systems interact with, and collect feedback
from, cognitive models instead of humans, thereby making their training process
both less costly and faster. Here, we leverage this idea in the context of
moral decision-making, by having reinforcement learning (RL) agents learn about
fairness through interacting with a cognitive model of the Ultimatum Game (UG),
a canonical task in behavioral and brain sciences for studying fairness.
Interestingly, these RL agents learn to rationally adapt their behavior
depending on the emotional state of their simulated UG responder. Our work
suggests that using cognitive models as simulators of humans is an effective
approach for training AI systems, presenting an important way for computational
cognitive science to make contributions to AI.

Image stylization aims at applying a reference style to arbitrary input
images. A common scenario is one-shot stylization, where only one example is
available for each reference style. A successful recent approach for one-shot
face stylization is JoJoGAN, which fine-tunes a pre-trained StyleGAN2 generator
on a single style reference image. However, it cannot generate multiple
stylizations without fine-tuning a new model for each style separately. In this
work, we present a MultiStyleGAN method that is capable of producing multiple
different face stylizations at once by fine-tuning a single generator. The key
component of our method is a learnable Style Transformation module that takes
latent codes as input and learns linear mappings to different regions of the
latent space to produce distinct codes for each style, resulting in a
multistyle space. Our model inherently mitigates overfitting since it is
trained on multiple styles, hence improving the quality of stylizations. Our
method can learn upwards of $12$ image stylizations at once, bringing upto
$8\times$ improvement in training time. We support our results through user
studies that indicate meaningful improvements over existing methods.

The optimal stopping problem is one of the core problems in financial
markets, with broad applications such as pricing American and Bermudan options.
The deep BSDE method [Han, Jentzen and E, PNAS, 115(34):8505-8510, 2018] has
shown great power in solving high-dimensional forward-backward stochastic
differential equations (FBSDEs), and inspired many applications. However, the
method solves backward stochastic differential equations (BSDEs) in a forward
manner, which can not be used for optimal stopping problems that in general
require running BSDE backwardly. To overcome this difficulty, a recent paper
[Wang, Chen, Sudjianto, Liu and Shen, arXiv:1807.06622, 2018] proposed the
backward deep BSDE method to solve the optimal stopping problem. In this paper,
we provide the rigorous theory for the backward deep BSDE method. Specifically,
1. We derive the a posteriori error estimation, i.e., the error of the
numerical solution can be bounded by the training loss function; and; 2. We
give an upper bound of the loss function, which can be sufficiently small
subject to universal approximations. We give two numerical examples, which
present consistent performance with the proved theory.

In recent years, graph representation learning has gained significant
popularity, which aims to generate node embeddings that capture features of
graphs. One of the methods to achieve this is employing a technique called
random walks that captures node sequences in a graph and then learns embeddings
for each node using a natural language processing technique called Word2Vec.
These embeddings are then used for deep learning on graph data for
classification tasks, such as link prediction or node classification. Prior
work operates on pre-collected temporal graph data and is not designed to
handle updates on a graph in real-time. Real world graphs change dynamically
and their entire temporal updates are not available upfront. In this paper, we
propose an end-to-end graph learning pipeline that performs temporal graph
construction, creates low-dimensional node embeddings, and trains multi-layer
neural network models in an online setting. The training of the neural network
models is identified as the main performance bottleneck as it performs repeated
matrix operations on many sequentially connected low-dimensional kernels. We
propose to unlock fine-grain parallelism in these low-dimensional kernels to
boost performance of model training.

Ensemble approaches for deep-learning-based semantic segmentation remain
insufficiently explored despite the proliferation of competitive benchmarks and
downstream applications. In this work, we explore and benchmark the popular
ensembling approach of combining predictions of multiple,
independently-trained, state-of-the-art models at test time on popular
datasets. Furthermore, we propose a novel method inspired by boosting to
sequentially ensemble networks that significantly outperforms the naive
ensemble baseline. Our approach trains a cascade of models conditioned on class
probabilities predicted by the previous model as an additional input. A key
benefit of this approach is that it allows for dynamic computation offloading,
which helps deploy models on mobile devices. Our proposed novel ADaptive
modulatiON (ADON) block allows spatial feature modulation at various layers
using previous-stage probabilities. Our approach does not require sophisticated
sample selection strategies during training and works with multiple neural
architectures. We significantly improve over the naive ensemble baseline on
challenging datasets such as Cityscapes, ADE-20K, COCO-Stuff, and
PASCAL-Context and set a new state-of-the-art.

Deep learning is overwhelmingly dominant in the field of computer vision and
image/video processing for the last decade. However, for image and video
compression, it lags behind the traditional techniques based on discrete cosine
transform (DCT) and linear filters. Built on top of an autoencoder
architecture, learned image compression (LIC) systems have drawn enormous
attention in recent years. Nevertheless, the proposed LIC systems are still
inferior to the state-of-the-art traditional techniques, for example, the
Versatile Video Coding (VVC/H.266) standard, due to either their compression
performance or decoding complexity. Although claimed to outperform the
VVC/H.266 on a limited bit rate range, some proposed LIC systems take over 40
seconds to decode a 2K image on a GPU system. In this paper, we introduce a
powerful and flexible LIC framework with multi-scale progressive (MSP)
probability model and latent representation overfitting (LOF) technique. With
different predefined profiles, the proposed framework can achieve various
balance points between compression efficiency and computational complexity.
Experiments show that the proposed framework achieves 2.5%, 1.0%, and 1.3%
Bjontegaard delta bit rate (BD-rate) reduction over the VVC/H.266 standard on
three benchmark datasets on a wide bit rate range. More importantly, the
decoding complexity is reduced from O(n) to O(1) compared to many other LIC
systems, resulting in over 20 times speedup when decoding 2K images.

The majority of NLG systems have been designed following either a
template-based or a pipeline-based architecture. Recent neural models for
data-to-text generation have been proposed with an end-to-end deep learning
flavor, which handles non-linguistic input in natural language without explicit
intermediary representations. This study compares the most often employed
methods for generating Brazilian Portuguese texts from structured data. Results
suggest that explicit intermediate steps in the generation process produce
better texts than the ones generated by neural end-to-end architectures,
avoiding data hallucination while better generalizing to unseen inputs. Code
and corpus are publicly available.

Purpose: High breast density is associated with reduced efficacy of
mammographic screening and increased risk of developing breast cancer. Accurate
and reliable automated density estimates can be used for direct risk prediction
and passing density related information to further predictive models. Expert
reader assessments of density show a strong relationship to cancer risk but
also inter-reader variation. The effect of label variability on model
performance is important when considering how to utilise automated methods for
both research and clinical purposes. Methods: We utilise subsets of images with
density labels to train a deep transfer learning model which is used to assess
how label variability affects the mapping from representation to prediction. We
then create two end-to-end deep learning models which allow us to investigate
the effect of label variability on the model representation formed. Results: We
show that the trained mappings from representations to labels are altered
considerably by the variability of reader scores. Training on labels with
distribution variation removed causes the Spearman rank correlation
coefficients to rise from $0.751\pm0.002$ to either $0.815\pm0.006$ when
averaging across readers or $0.844\pm0.002$ when averaging across images.
However, when we train different models to investigate the representation
effect we see little difference, with Spearman rank correlation coefficients of
$0.846\pm0.006$ and $0.850\pm0.006$ showing no statistically significant
difference in the quality of the model representation with regard to density
prediction. Conclusions: We show that the mapping between representation and
mammographic density prediction is significantly affected by label variability.
However, the effect of the label variability on the model representation is
limited.

Vision-based segmentation in forested environments is a key functionality for
autonomous forestry operations such as tree felling and forwarding. Deep
learning algorithms demonstrate promising results to perform visual tasks such
as object detection. However, the supervised learning process of these
algorithms requires annotations from a large diversity of images. In this work,
we propose to use simulated forest environments to automatically generate 43 k
realistic synthetic images with pixel-level annotations, and use it to train
deep learning algorithms for tree detection. This allows us to address the
following questions: i) what kind of performance should we expect from deep
learning in harsh synthetic forest environments, ii) which annotations are the
most important for training, and iii) what modality should be used between RGB
and depth. We also report the promising transfer learning capability of
features learned on our synthetic dataset by directly predicting bounding box,
segmentation masks and keypoints on real images. Code available on GitHub
(https://github.com/norlab-ulaval/PercepTreeV1).

Bayesian optimization offers a sample-efficient framework for navigating the
exploration-exploitation trade-off in the vast design space of biological
sequences. Whereas it is possible to optimize the various properties of
interest jointly using a multi-objective acquisition function, such as the
expected hypervolume improvement (EHVI), this approach does not account for
objectives with a hierarchical dependency structure. We consider a common use
case where some regions of the Pareto frontier are prioritized over others
according to a specified $\textit{partial ordering}$ in the objectives. For
instance, when designing antibodies, we would like to maximize the binding
affinity to a target antigen only if it can be expressed in live cell culture
-- modeling the experimental dependency in which affinity can only be measured
for antibodies that can be expressed and thus produced in viable quantities. In
general, we may want to confer a partial ordering to the properties such that
each property is optimized conditioned on its parent properties satisfying some
feasibility condition. To this end, we present PropertyDAG, a framework that
operates on top of the traditional multi-objective BO to impose this desired
ordering on the objectives, e.g. expression $\rightarrow$ affinity. We
demonstrate its performance over multiple simulated active learning iterations
on a penicillin production task, toy numerical problem, and a real-world
antibody design task.

Homophily and heterophily are intrinsic properties of graphs that describe
whether two linked nodes share similar properties. Although many Graph Neural
Network (GNN) models have been proposed, it remains unclear how to design a
model so that it can generalize well to the whole spectrum of homophily. This
work addresses the challenge by identifying three graph features, including the
ego node feature, the aggregated node feature, and the graph structure feature,
that are essential for graph representation learning. It further proposes a new
GNN model called OGNN (Omnipotent Graph Neural Network) that extracts all three
graph features and adaptively fuses them to achieve generalizability across the
whole spectrum of homophily. Extensive experiments on both synthetic and real
datasets demonstrate the superiority (average rank 1.56) of our OGNN compared
with state-of-the-art methods.

Predicting the next visited location of an individual is a key problem in
human mobility analysis, as it is required for the personalization and
optimization of sustainable transport options. Here, we propose a transformer
decoder-based neural network to predict the next location an individual will
visit based on historical locations, time, and travel modes, which are
behaviour dimensions often overlooked in previous work. In particular, the
prediction of the next travel mode is designed as an auxiliary task to help
guide the network's learning. For evaluation, we apply this approach to two
large-scale and long-term GPS tracking datasets involving more than 600
individuals. Our experiments show that the proposed method significantly
outperforms other state-of-the-art next location prediction methods by a large
margin (8.05% and 5.60% relative increase in F1-score for the two datasets,
respectively). We conduct an extensive ablation study that quantifies the
influence of considering temporal features, travel mode information, and the
auxiliary task on the prediction results. Moreover, we experimentally determine
the performance upper bound when including the next mode prediction in our
model. Finally, our analysis indicates that the performance of location
prediction varies significantly with the chosen next travel mode by the
individual. These results show potential for a more systematic consideration of
additional dimensions of travel behaviour in human mobility prediction tasks.
The source code of our model and experiments is available at
https://github.com/mie-lab/location-mode-prediction.

The deployment constraints in practical applications necessitate the pruning
of large-scale deep learning models, i.e., promoting their weight sparsity. As
illustrated by the Lottery Ticket Hypothesis (LTH), pruning also has the
potential of improving their generalization ability. At the core of LTH,
iterative magnitude pruning (IMP) is the predominant pruning method to
successfully find 'winning tickets'. Yet, the computation cost of IMP grows
prohibitively as the targeted pruning ratio increases. To reduce the
computation overhead, various efficient 'one-shot' pruning methods have been
developed, but these schemes are usually unable to find winning tickets as good
as IMP. This raises the question of how to close the gap between pruning
accuracy and pruning efficiency? To tackle it, we pursue the algorithmic
advancement of model pruning. Specifically, we formulate the pruning problem
from a fresh and novel viewpoint, bi-level optimization (BLO). We show that the
BLO interpretation provides a technically-grounded optimization base for an
efficient implementation of the pruning-retraining learning paradigm used in
IMP. We also show that the proposed bi-level optimization-oriented pruning
method (termed BiP) is a special class of BLO problems with a bi-linear problem
structure. By leveraging such bi-linearity, we theoretically show that BiP can
be solved as easily as first-order optimization, thus inheriting the
computation efficiency. Through extensive experiments on both structured and
unstructured pruning with 5 model architectures and 4 data sets, we demonstrate
that BiP can find better winning tickets than IMP in most cases, and is
computationally as efficient as the one-shot pruning schemes, demonstrating 2-7
times speedup over IMP for the same level of model accuracy and sparsity.

Current content filtering and blocking methods are susceptible to various
circumvention techniques and are relatively slow in dealing with new threats.
This is due to these methods using shallow pattern recognition that is based on
regular expression rules found in crowdsourced block lists. We propose a novel
system that aims to remedy the aforementioned issues by examining deep textual
patterns of network-oriented content relating to the domain being interacted
with. Moreover, we propose to use federated learning that allows users to take
advantage of each other's localized knowledge/experience regarding what should
or should not be blocked on a network without compromising privacy. Our
experiments show the promise of our proposed approach in real world settings.
We also provide data-driven recommendations on how to best implement the
proposed system.

Adversarial attacks and defenses disregard the lack of invariance of
convolutional neural networks (CNNs), that is, the inability of CNNs to
classify samples and their symmetric transformations the same. The lack of
invariance of CNNs with respect to symmetry transformations is detrimental when
classifying transformed original samples but not necessarily detrimental when
classifying transformed adversarial samples. For original images, the lack of
invariance means that symmetrically transformed original samples are classified
differently from their correct labels. However, for adversarial images, the
lack of invariance means that symmetrically transformed adversarial images are
classified differently from their incorrect adversarial labels. Might the CNN
lack of invariance revert symmetrically transformed adversarial samples to the
correct classification? This paper answers this question affirmatively for a
threat model that ranges from zero-knowledge adversaries to perfect-knowledge
adversaries. We base our defense against perfect-knowledge adversaries on
devising a Klein four symmetry subgroup that incorporates an additional
artificial symmetry of pixel intensity inversion. The closure property of the
subgroup not only provides a framework for the accuracy evaluation but also
confines the transformations that an adaptive, perfect-knowledge adversary can
apply. We find that by using only symmetry defense, no adversarial samples, and
by changing nothing in the model architecture and parameters, we can defend
against white-box PGD adversarial attacks, surpassing the PGD adversarial
training defense by up to ~50% even against a perfect-knowledge adversary for
ImageNet. The proposed defense also maintains and surpasses the classification
accuracy for non-adversarial samples.

The goal of semantic image synthesis is to generate photo-realistic images
from semantic label maps. It is highly relevant for tasks like content
generation and image editing. Current state-of-the-art approaches, however,
still struggle to generate realistic objects in images at various scales. In
particular, small objects tend to fade away and large objects are often
generated as collages of patches. In order to address this issue, we propose a
Dual Pyramid Generative Adversarial Network (DP-GAN) that learns the
conditioning of spatially-adaptive normalization blocks at all scales jointly,
such that scale information is bi-directionally used, and it unifies
supervision at different scales. Our qualitative and quantitative results show
that the proposed approach generates images where small and large objects look
more realistic compared to images generated by state-of-the-art methods.

Robust machine learning models with accurately calibrated uncertainties are
crucial for safety-critical applications. Probabilistic machine learning and
especially the Bayesian formalism provide a systematic framework to incorporate
robustness through the distributional estimates and reason about uncertainty.
Recent works have shown that approximate inference approaches that take the
weight space uncertainty of neural networks to generate ensemble prediction are
the state-of-the-art. However, architecture choices have mostly been ad hoc,
which essentially ignores the epistemic uncertainty from the architecture
space. To this end, we propose a Unified probabilistic architecture and weight
ensembling Neural Architecture Search (UraeNAS) that leverages advances in
probabilistic neural architecture search and approximate Bayesian inference to
generate ensembles form the joint distribution of neural network architectures
and weights. The proposed approach showed a significant improvement both with
in-distribution (0.86% in accuracy, 42% in ECE) CIFAR-10 and
out-of-distribution (2.43% in accuracy, 30% in ECE) CIFAR-10-C compared to the
baseline deterministic approach.

Can we design a GNN that is accurate and interpretable at the same time?
Could it also be robust to handle the case of homophily, heterophily, or even
noisy edges without network effects? We propose SlenderGNN that has all
desirable properties: (a) accurate, (b) robust, and (c) interpretable. For the
reasons of its success, we had to dig deeper: The result is our GNNLin
framework which highlights the fundamental differences among popular GNN models
(e.g., feature combination, structural normalization, etc.) and thus reveals
the reasons for the success of our SlenderGNN, as well as the reasons for
occasional failures of other GNN variants. Thanks to our careful design,
SlenderGNN passes all the 'sanity checks' we propose, and it achieves the
highest overall accuracy on 9 real-world datasets of both homophily and
heterophily graphs, when compared against 10 recent GNN models. Specifically,
SlenderGNN exceeds the accuracy of linear GNNs and matches or exceeds the
accuracy of nonlinear models with up to 64 times fewer parameters.

This paper presents a novel beverage intake monitoring system that can
accurately recognize beverage kinds and freshness. By mounting carbon
electrodes on the commercial cup, the system measures the electrochemical
impedance spectrum of the fluid in the cup. We studied the frequency
sensitivity of the electrochemical impedance spectrum regarding distinct
beverages and the importance of features like amplitude, phase, and real and
imaginary components for beverage classification. The results show that
features from a low-frequency domain (100 Hz to 1000 Hz) provide more
meaningful information for beverage classification than the higher frequency
domain. Twenty beverages, including carbonated drinks and juices, were
classified with nearly perfect accuracy using a supervised machine learning
approach. The same performance was also observed in the freshness recognition,
where four different kinds of milk and fruit juice were studied.

Unsupervised representation learning leverages large unlabeled datasets and
is competitive with supervised learning. But non-robust encoders may affect
downstream task robustness. Recently, robust representation encoders have
become of interest. Still, all prior work evaluates robustness using a
downstream classification task. Instead, we propose a family of unsupervised
robustness measures, which are model- and task-agnostic and label-free. We
benchmark state-of-the-art representation encoders and show that none dominates
the rest. We offer unsupervised extensions to the FGSM and PGD attacks. When
used in adversarial training, they improve most unsupervised robustness
measures, including certified robustness. We validate our results against a
linear probe and show that, for MOCOv2, adversarial training results in 3 times
higher certified accuracy, a 2-fold decrease in impersonation attack success
rate and considerable improvements in certified robustness.

Recent advancements in dialogue response selection (DRS) are based on the
\textit{task-adaptive pre-training (TAP)} approach, by first initializing their
model with BERT~\cite{devlin-etal-2019-bert}, and adapt to dialogue data with
dialogue-specific or fine-grained pre-training tasks. However, it is uncertain
whether BERT is the best initialization choice, or whether the proposed
dialogue-specific fine-grained learning tasks are actually better than MLM+NSP.
This paper aims to verify assumptions made in previous works and understand the
source of improvements for DRS. We show that initializing with RoBERTa achieve
similar performance as BERT, and MLM+NSP can outperform all previously proposed
TAP tasks, during which we also contribute a new state-of-the-art on the Ubuntu
corpus. Additional analyses shows that the main source of improvements comes
from the TAP step, and that the NSP task is crucial to DRS, different from
common NLU tasks.

Generating a 3D point cloud from a single 2D image is of great importance for
3D scene understanding applications. To reconstruct the whole 3D shape of the
object shown in the image, the existing deep learning based approaches use
either explicit or implicit generative modeling of point clouds, which,
however, suffer from limited quality. In this work, we aim to alleviate this
issue by introducing a hybrid explicit-implicit generative modeling scheme,
which inherits the flow-based explicit generative models for sampling point
clouds with arbitrary resolutions while improving the detailed 3D structures of
point clouds by leveraging the implicit generative adversarial networks (GANs).
We evaluate on the large-scale synthetic dataset ShapeNet, with the
experimental results demonstrating the superior performance of the proposed
method. In addition, the generalization ability of our method is demonstrated
by performing on cross-category synthetic images as well as by testing on real
images from PASCAL3D+ dataset.

Speech is the surface form of a finite set of phonetic units, which can be
represented by discrete codes. We propose the Code BERT (CoBERT) approach for
self-supervised speech representation learning. The idea is to convert an
utterance to a sequence of discrete codes, and perform code representation
learning, where we predict the code representations based on a masked view of
the original speech input. Unlike the prior self-distillation approaches of
which the teacher and the student are of the same modality, our target model
predicts representations from a different modality. CoBERT outperforms the most
recent state-of-the-art performance on the ASR task and brings significant
improvements on the SUPERB speech translation (ST) task.

Consider the problem of improving the estimation of conditional average
treatment effects (CATE) for a target domain of interest by leveraging related
information from a source domain with a different feature space. This
heterogeneous transfer learning problem for CATE estimation is ubiquitous in
areas such as healthcare where we may wish to evaluate the effectiveness of a
treatment for a new patient population for which different clinical covariates
and limited data are available. In this paper, we address this problem by
introducing several building blocks that use representation learning to handle
the heterogeneous feature spaces and a flexible multi-task architecture with
shared and private layers to transfer information between potential outcome
functions across domains. Then, we show how these building blocks can be used
to recover transfer learning equivalents of the standard CATE learners. On a
new semi-synthetic data simulation benchmark for heterogeneous transfer
learning we not only demonstrate performance improvements of our heterogeneous
transfer causal effect learners across datasets, but also provide insights into
the differences between these learners from a transfer perspective.

Multiple-surface segmentation in Optical Coherence Tomography (OCT) images is
a challenge problem, further complicated by the frequent presence of weak image
boundaries. Recently, many deep learning (DL) based methods have been developed
for this task and yield remarkable performance. Unfortunately, due to the
scarcity of training data in medical imaging, it is challenging for DL networks
to learn the global structure of the target surfaces, including surface
smoothness. To bridge this gap, this study proposes to seamlessly unify a U-Net
for feature learning with a constrained differentiable dynamic programming
module to achieve an end-to-end learning for retina OCT surface segmentation to
explicitly enforce surface smoothness. It effectively utilizes the feedback
from the downstream model optimization module to guide feature learning,
yielding a better enforcement of global structures of the target surfaces.
Experiments on Duke AMD (age-related macular degeneration) and JHU MS (multiple
sclerosis) OCT datasets for retinal layer segmentation demonstrated very
promising segmentation accuracy.

Contrastive learning has been extensively studied in sentence embedding
learning, which assumes that the embeddings of different views of the same
sentence are closer. The constraint brought by this assumption is weak, and a
good sentence representation should also be able to reconstruct the original
sentence fragments. Therefore, this paper proposes an information-aggregated
contrastive learning framework for learning unsupervised sentence embeddings,
termed InfoCSE. InfoCSE forces the representation of [CLS] positions to
aggregate denser sentence information by introducing an additional Masked
language model task and a well-designed network. We evaluate the proposed
InfoCSE on several benchmark datasets w.r.t the semantic text similarity (STS)
task. Experimental results show that InfoCSE outperforms SimCSE by an average
Spearman correlation of 2.60% on BERT-base, and 1.77% on BERT-large, achieving
state-of-the-art results among unsupervised sentence representation learning
methods. Our code are available at
https://github.com/caskcsg/sentemb/tree/main/InfoCSE.

Deep learning (DL) methods have been widely applied to anomaly-based network
intrusion detection system (NIDS) to detect malicious traffic. To expand the
usage scenarios of DL-based methods, the federated learning (FL) framework
allows multiple users to train a global model on the basis of respecting
individual data privacy. However, it has not yet been systematically evaluated
how robust FL-based NIDSs are against existing privacy attacks under existing
defenses. To address this issue, we propose two privacy evaluation metrics
designed for FL-based NIDSs, including (1) privacy score that evaluates the
similarity between the original and recovered traffic features using
reconstruction attacks, and (2) evasion rate against NIDSs using Generative
Adversarial Network-based adversarial attack with the reconstructed benign
traffic. We conduct experiments to show that existing defenses provide little
protection that the corresponding adversarial traffic can even evade the SOTA
NIDS Kitsune. To defend against such attacks and build a more robust FL-based
NIDS, we further propose FedDef, a novel optimization-based input perturbation
defense strategy with theoretical guarantee. It achieves both high utility by
minimizing the gradient distance and strong privacy protection by maximizing
the input distance. We experimentally evaluate four existing defenses on four
datasets and show that our defense outperforms all the baselines in terms of
privacy protection with up to 7 times higher privacy score, while maintaining
model accuracy loss within 3% under optimal parameter combination.

In this thesis, we propose an alternative characterization of the notion of
Configuration Space, which we call Visual Configuration Space (VCS). This new
characterization allows an embodied agent (e.g., a robot) to discover its own
body structure and plan obstacle-free motions in its peripersonal space using a
set of its own images in random poses. Here, we do not assume any knowledge of
geometry of the agent, obstacles or the environment. We demonstrate the
usefulness of VCS in (a) building and working with geometry-free models for
robot motion planning, (b) explaining how a human baby might learn to reach
objects in its peripersonal space through motor babbling, and (c) automatically
generating natural looking head motion animations for digital avatars in
virtual environments. This work is based on the formalism of manifolds and
manifold learning using the agent's images and hence we call it Motion Planning
on Visual Manifolds.

Predicting the occurence of rare and extreme events in complex systems is a
well-known problem in non-equilibrium physics. These events can have huge
impacts on human societies. New approaches have emerged in the last ten years,
which better estimate tail distributions. They often use large deviation
concepts without the need to perform heavy direct ensemble simulations. In
particular, a well-known approach is to derive a minimum action principle and
to find its minimizers.
  The analysis of rare reactive events in non-equilibrium systems without
detailed balance is notoriously difficult either theoretically and
computationally. They are described in the limit of small noise by the
Freidlin-Wentzell action. We propose here a new method which minimizes the
geometrical action instead using neural networks: it is called deep gMAM. It
relies on a natural and simple machine-learning formulation of the classical
gMAM approach. We give a detailed description of the method as well as many
examples. These include bimodal switches in complex stochastic (partial)
differential equations, quasi-potential estimates, and extreme events in
Burgers turbulence.

In this work, we establish an asymptotic limit of almost-lossless compression
of a random, finite alphabet tensor which admits a low-rank canonical polyadic
decomposition.

Thanks to the latest advancements in wavefront shaping, optical methods have
proven crucial to achieve imaging and control light in multiply scattering
media, like biological tissues. However, the stability times of living
biological specimens often prevent such methods from gaining insights into
relevant functioning mechanisms in cellular and organ systems. Here we present
a recursive and online optimization routine, borrowed from time series
analysis, to optimally track the transfer matrix of dynamic scattering media
over arbitrarily long timescales. While preserving the advantages of both
optimization-based routines and transfer-matrix measurements, it operates in a
memory-efficient manner. Because it can be readily implemented in existing
wavefront shaping setups, featuring amplitude and/or phase modulation and
phase-resolved or intensity-only acquisition, it paves the way for efficient
optical investigations of living biological specimens.

Extractive models usually formulate text summarization as extracting top-k
important sentences from document as summary. Few work exploited extracting
finer-grained Elementary Discourse Unit (EDU) and there is little analysis and
justification for the extractive unit selection. To fill such a gap, this paper
firstly conducts oracle analysis to compare the upper bound of performance for
models based on EDUs and sentences. The analysis provides evidences from both
theoretical and experimental perspectives to justify that EDUs make more
concise and precise summary than sentences without losing salient information.
Then, considering this merit of EDUs, this paper further proposes EDU-level
extractive model with Varying summary Lengths (EDU-VL) and develops the
corresponding learning algorithm. EDU-VL learns to encode and predict
probabilities of EDUs in document, and encode EDU-level candidate summaries
with different lengths based on various $k$ values and select the best
candidate summary in an end-to-end training manner. Finally, the proposed and
developed approach is experimented on single and multi-document benchmark
datasets and shows the improved performances in comparison with the

When executing a deep neural network (DNN), its model parameters are loaded
into GPU memory before execution, incurring a significant GPU memory burden.
There are studies that reduce GPU memory usage by exploiting CPU memory as a
swap device. However, this approach is not applicable in most embedded systems
with integrated GPUs where CPU and GPU share a common memory. In this regard,
we present Demand Layering, which employs a fast solid-state drive (SSD) as a
co-running partner of a GPU and exploits the layer-by-layer execution of DNNs.
In our approach, a DNN is loaded and executed in a layer-by-layer manner,
minimizing the memory usage to the order of a single layer. Also, we developed
a pipeline architecture that hides most additional delays caused by the
interleaved parameter loadings alongside layer executions. Our implementation
shows a 96.5% memory reduction with just 14.8% delay overhead on average for
representative DNNs. Furthermore, by exploiting the memory-delay tradeoff,
near-zero delay overhead (under 1 ms) can be achieved with a slightly increased
memory usage (still an 88.4% reduction), showing the great potential of Demand
Layering.

Time series datasets are often composed of a variety of sequences from the
same domain, but from different entities, such as individuals, products, or
organizations. We are interested in how time series models can be specialized
to individual sequences (capturing the specific characteristics) while still
retaining statistical power by sharing commonalities across the sequences. This
paper describes the multi-task dynamical system (MTDS); a general methodology
for extending multi-task learning (MTL) to time series models. Our approach
endows dynamical systems with a set of hierarchical latent variables which can
modulate all model parameters. To our knowledge, this is a novel development of
MTL, and applies to time series both with and without control inputs. We apply
the MTDS to motion-capture data of people walking in various styles using a
multi-task recurrent neural network (RNN), and to patient drug-response data
using a multi-task pharmacodynamic model.

Tabular data synthesis is a long-standing research topic in machine learning.
Many different methods have been proposed over the past decades, ranging from
statistical methods to deep generative methods. However, it has not always been
successful due to the complicated nature of real-world tabular data. In this
paper, we present a new model named Score-based Tabular data Synthesis (STaSy)
and its training strategy based on the paradigm of score-based generative
modeling. Despite the fact that score-based generative models have resolved
many issues in generative models, there still exists room for improvement in
tabular data synthesis. Our proposed training strategy includes a self-paced
learning technique and a fine-tuning strategy, which further increases the
sampling quality and diversity by stabilizing the denoising score matching
training. Furthermore, we also conduct rigorous experimental studies in terms
of the generative task trilemma: sampling quality, diversity, and time. In our
experiments with 15 benchmark tabular datasets and 7 baselines, our method
outperforms existing methods in terms of task-dependant evaluations and
diversity.

End-to-end autonomous driving provides a feasible way to automatically
maximize overall driving system performance by directly mapping the raw pixels
from a front-facing camera to control signals. Recent advanced methods
construct a latent world model to map the high dimensional observations into
compact latent space. However, the latent states embedded by the world model
proposed in previous works may contain a large amount of task-irrelevant
information, resulting in low sampling efficiency and poor robustness to input
perturbations. Meanwhile, the training data distribution is usually unbalanced,
and the learned policy is hard to cope with the corner cases during the driving
process. To solve the above challenges, we present a semantic masked recurrent
world model (SEM2), which introduces a latent filter to extract key
task-relevant features and reconstruct a semantic mask via the filtered
features, and is trained with a multi-source data sampler, which aggregates
common data and multiple corner case data in a single batch, to balance the
data distribution. Extensive experiments on CARLA show that our method
outperforms the state-of-the-art approaches in terms of sample efficiency and
robustness to input permutations.

For the current 3D human pose estimation task, a group of methods mainly
learn the rules of 2D-3D projection from spatial and temporal correlation.
However, earlier methods model the global features of the entire body joint in
the time domain, but ignore the motion trajectory of individual joint. The
recent work [29] considers that there are differences in motion between
different joints and deals with the temporal relationship of each joint
separately. However, we found that different joints show the same movement
trends under some specific actions. Therefore, our proposed Fusionformer method
introduces a self-trajectory module and a mutual-trajectory module based on the
spatio-temporal module .After that, the global spatio-temporal features and
local joint trajectory features are fused through a linear network in a
parallel manner. To eliminate the influence of bad 2D poses on 3D projections,
finally we also introduce a pose refinement network to balance the consistency
of 3D projections. In addition, we evaluate the proposed method on two
benchmark datasets (Human3.6M, MPI-INF-3DHP). Comparing our method with the
baseline method poseformer, the results show an improvement of 2.4% MPJPE and
4.3% P-MPJPE on the Human3.6M dataset, respectively.

Brain decoding, aiming to identify the brain states using neural activity, is
important for cognitive neuroscience and neural engineering. However, existing
machine learning methods for fMRI-based brain decoding either suffer from low
classification performance or poor explainability. Here, we address this issue
by proposing a biologically inspired architecture, Spatial Temporal-pyramid
Graph Convolutional Network (STpGCN), to capture the spatial-temporal graph
representation of functional brain activities. By designing multi-scale
spatial-temporal pathways and bottom-up pathways that mimic the information
process and temporal integration in the brain, STpGCN is capable of explicitly
utilizing the multi-scale temporal dependency of brain activities via graph,
thereby achieving high brain decoding performance. Additionally, we propose a
sensitivity analysis method called BrainNetX to better explain the decoding
results by automatically annotating task-related brain regions from the
brain-network standpoint. We conduct extensive experiments on fMRI data under
23 cognitive tasks from Human Connectome Project (HCP) S1200. The results show
that STpGCN significantly improves brain decoding performance compared to
competing baseline models; BrainNetX successfully annotates task-relevant brain
regions. Post hoc analysis based on these regions further validates that the
hierarchical structure in STpGCN significantly contributes to the
explainability, robustness and generalization of the model. Our methods not
only provide insights into information representation in the brain under
multiple cognitive tasks but also indicate a bright future for fMRI-based brain
decoding.

We present a framework that lets a service provider achieve end-to-end
management objectives under varying load. Dynamic control actions are performed
by a reinforcement learning (RL) agent. Our work includes experimentation and
evaluation on a laboratory testbed where we have implemented basic information
services on a service mesh supported by the Istio and Kubernetes platforms. We
investigate different management objectives that include end-to-end delay
bounds on service requests, throughput objectives, and service differentiation.
These objectives are mapped onto reward functions that an RL agent learns to
optimize, by executing control actions, namely, request routing and request
blocking. We compute the control policies not on the testbed, but in a
simulator, which speeds up the learning process by orders of magnitude. In our
approach, the system model is learned on the testbed; it is then used to
instantiate the simulator, which produces near-optimal control policies for
various management objectives. The learned policies are then evaluated on the
testbed using unseen load patterns.

How can we learn from all available data when training machine-learnt climate
models, without incurring any extra cost at simulation time? Typically, the
training data comprises coarse-grained high-resolution data. But only keeping
this coarse-grained data means the rest of the high-resolution data is thrown
out. We use a transfer learning approach, which can be applied to a range of
machine learning models, to leverage all the high-resolution data. We use three
chaotic systems to show it stabilises training, gives improved generalisation
performance and results in better forecasting skill. Our code is at
https://github.com/raghul-parthipan/dont_waste_data

Recently, a new training oaxe loss has proven effective to ameliorate the
effect of multimodality for non-autoregressive translation (NAT), which removes
the penalty of word order errors in the standard cross-entropy loss. Starting
from the intuition that reordering generally occurs between phrases, we extend
oaxe by only allowing reordering between ngram phrases and still requiring a
strict match of word order within the phrases. Extensive experiments on NAT
benchmarks across language pairs and data scales demonstrate the effectiveness
and universality of our approach. %Further analyses show that the proposed
ngram-oaxe alleviates the multimodality problem with a better modeling of
phrase translation. Further analyses show that ngram-oaxe indeed improves the
translation of ngram phrases, and produces more fluent translation with a
better modeling of sentence structure.

In knowledge graph completion (KGC), predicting triples involving emerging
entities and/or relations, which are unseen when the KG embeddings are learned,
has become a critical challenge. Subgraph reasoning with message passing is a
promising and popular solution. Some recent methods have achieved good
performance, but they (i) usually can only predict triples involving unseen
entities alone, failing to address more realistic fully inductive situations
with both unseen entities and unseen relations, and (ii) often conduct message
passing over the entities with the relation patterns not fully utilized. In
this study, we propose a new method named RMPI which uses a novel Relational
Message Passing network for fully Inductive KGC. It passes messages directly
between relations to make full use of the relation patterns for subgraph
reasoning with new techniques on graph transformation, graph pruning,
relation-aware neighborhood attention, addressing empty subgraphs, etc., and
can utilize the relation semantics defined in the ontological schema of KG.
Extensive evaluation on multiple benchmarks has shown the effectiveness of
techniques involved in RMPI and its better performance compared with the
existing methods that support fully inductive KGC. RMPI is also comparable to
the state-of-the-art partially inductive KGC methods with very promising
results achieved. Our codes and data are available at
https://github.com/zjukg/RMPI.

Graph Neural Networks (GNNs) are a large class of relational models for graph
processing. Recent theoretical studies on the expressive power of GNNs have
focused on two issues. On the one hand, it has been proven that GNNs are as
powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish
graphs. Moreover, it has been shown that the equivalence enforced by 1-WL
equals unfolding equivalence. On the other hand, GNNs turned out to be
universal approximators on graphs modulo the constraints enforced by
1-WL/unfolding equivalence. However, these results only apply to Static
Undirected Homogeneous Graphs with node attributes. In contrast, real-life
applications often involve a variety of graph properties, such as, e.g.,
dynamics or node and edge attributes. In this paper, we conduct a theoretical
analysis of the expressive power of GNNs for these two graph types that are
particularly of interest. Dynamic graphs are widely used in modern
applications, and its theoretical analysis requires new approaches. The
attributed type acts as a standard form for all graph types since it has been
shown that all graph types can be transformed without loss to Static Undirected
Homogeneous Graphs with attributes on nodes and edges (SAUHG). The study
considers generic GNN models and proposes appropriate 1-WL tests for those
domains. Then, the results on the expressive power of GNNs are extended by
proving that GNNs have the same capability as the 1-WL test in distinguishing
dynamic and attributed graphs, the 1-WL equivalence equals unfolding
equivalence and that GNNs are universal approximators modulo 1-WL/unfolding
equivalence. Moreover, the proof of the approximation capability holds for
SAUHGs, which include most of those used in practical applications, and it is
constructive in nature allowing to deduce hints on the architecture of GNNs
that can achieve the desired accuracy.

Automatically fixing compilation errors can greatly raise the productivity of
software development, by guiding the novice or AI programmers to write and
debug code. Recently, learning-based program repair has gained extensive
attention and became the state-of-the-art in practice. But it still leaves
plenty of space for improvement. In this paper, we propose an end-to-end
solution TransRepair to locate the error lines and create the correct
substitute for a C program simultaneously. Superior to the counterpart, our
approach takes into account the context of erroneous code and diagnostic
compilation feedback. Then we devise a Transformer-based neural network to
learn the ways of repair from the erroneous code as well as its context and the
diagnostic feedback. To increase the effectiveness of TransRepair, we summarize
5 types and 74 fine-grained sub-types of compilations errors from two
real-world program datasets and the Internet. Then a program corruption
technique is developed to synthesize a large dataset with 1,821,275 erroneous C
programs. Through the extensive experiments, we demonstrate that TransRepair
outperforms the state-of-the-art in both single repair accuracy and full repair
accuracy. Further analysis sheds light on the strengths and weaknesses in the
contemporary solutions for future improvement.

Roboy 3.0 is an open-source tendon-driven humanoid robot that mimics the
musculoskeletal system of the human body. Roboy 3.0 is being developed as a
remote robotic body - or a robotic avatar - for humans to achieve remote
physical presence. Artificial muscles and tendons allow it to closely resemble
human morphology with 3-DoF neck, shoulders and wrists. Roboy 3.0 3-DoF joints
are implemented as ball-and-socket joints. While industry provides a clear
solution for 1-DoF joint pose sensing, it is not the case for the
ball-and-socket joint type. In this paper we present a custom solution to
estimate the pose of a ball-and-socket joint. We embed an array of magnets into
the ball and an array of 3D magnetic sensors into the socket. We then, based on
the changes in the magnetic field as the joint rotates, are able to estimate
the orientation of the joint. We evaluate the performance of two neural network
approaches using the LSTM and Bayesian-filter like DVBF. Results show that in
order to achieve the same mean square error (MSE) DVBFs require significantly
more time training and hyperparameter tuning compared to LSTMs, while DVBF cope
with sensor noise better. Both methods are capable of real-time joint pose
estimation at 37 Hz with MSE of around 0.03 rad for all three degrees of
freedom combined. The LSTM model is deployed and used for joint pose estimation
of Roboy 3.0's shoulder and neck joints. The software implementation and PCB
designs are open-sourced under
https://github.com/Roboy/ball_and_socket_estimator

Continual Learning for Named Entity Recognition (CL-NER) aims to learn a
growing number of entity types over time from a stream of data. However, simply
learning Other-Class in the same way as new entity types amplifies the
catastrophic forgetting and leads to a substantial performance drop. The main
cause behind this is that Other-Class samples usually contain old entity types,
and the old knowledge in these Other-Class samples is not preserved properly.
Thanks to the causal inference, we identify that the forgetting is caused by
the missing causal effect from the old data. To this end, we propose a unified
causal framework to retrieve the causality from both new entity types and
Other-Class. Furthermore, we apply curriculum learning to mitigate the impact
of label noise and introduce a self-adaptive weight for balancing the causal
effects between new entity types and Other-Class. Experimental results on three
benchmark datasets show that our method outperforms the state-of-the-art method
by a large margin. Moreover, our method can be combined with the existing
state-of-the-art methods to improve the performance in CL-NER

The rapid development of point cloud learning has driven point cloud
completion into a new era. However, the information flows of most existing
completion methods are solely feedforward, and high-level information is rarely
reused to improve low-level feature learning. To this end, we propose a novel
Feedback Network (FBNet) for point cloud completion, in which present features
are efficiently refined by rerouting subsequent fine-grained ones. Firstly,
partial inputs are fed to a Hierarchical Graph-based Network (HGNet) to
generate coarse shapes. Then, we cascade several Feedback-Aware Completion
(FBAC) Blocks and unfold them across time recurrently. Feedback connections
between two adjacent time steps exploit fine-grained features to improve
present shape generations. The main challenge of building feedback connections
is the dimension mismatching between present and subsequent features. To
address this, the elaborately designed point Cross Transformer exploits
efficient information from feedback features via cross attention strategy and
then refines present features with the enhanced feedback features. Quantitative
and qualitative experiments on several datasets demonstrate the superiority of
proposed FBNet compared to state-of-the-art methods on point completion task.

For the quantitative monitoring of international relations, political events
are extracted from the news and parsed into "who-did-what-to-whom" patterns.
This has resulted in large data collections which require aggregate statistics
for analysis. The Goldstein Scale is an expert-based measure that ranks
individual events on a one-dimensional scale from conflictual to cooperative.
However, the scale disregards fatality counts as well as perpetrator and victim
types involved in an event. This information is typically considered in
qualitative conflict assessment. To address this limitation, we propose a
probabilistic generative model over the full
subject-predicate-quantifier-object tuples associated with an event. We treat
conflict intensity as an interpretable, ordinal latent variable that correlates
conflictual event types with high fatality counts. Taking a Bayesian approach,
we learn a conflict intensity scale from data and find the optimal number of
intensity classes. We evaluate the model by imputing missing data. Our scale
proves to be more informative than the original Goldstein Scale in
autoregressive forecasting and when compared with global online attention
towards armed conflicts.

Medical text learning has recently emerged as a promising area to improve
healthcare due to the wide adoption of electronic health record (EHR) systems.
The complexity of the medical text such as diverse length, mixed text types,
and full of medical jargon, poses a great challenge for developing effective
deep learning models. BERT has presented state-of-the-art results in many NLP
tasks, such as text classification and question answering. However, the
standalone BERT model cannot deal with the complexity of the medical text,
especially the lengthy clinical notes. Herein, we develop a new model called
KG-MTT-BERT (Knowledge Graph Enhanced Multi-Type Text BERT) by extending the
BERT model for long and multi-type text with the integration of the medical
knowledge graph. Our model can outperform all baselines and other
state-of-the-art models in diagnosis-related group (DRG) classification, which
requires comprehensive medical text for accurate classification. We also
demonstrated that our model can effectively handle multi-type text and the
integration of medical knowledge graph can significantly improve the

Point-of-Interest (POI) recommendation, which benefits from the proliferation
of GPS-enabled devices and location-based social networks (LBSNs), plays an
increasingly important role in recommender systems. It aims to provide users
with the convenience to discover their interested places to visit based on
previous visits and current status. Most existing methods usually merely
leverage recurrent neural networks (RNNs) to explore sequential influences for
recommendation. Despite the effectiveness, these methods not only neglect
topological geographical influences among POIs, but also fail to model
high-order sequential substructures. To tackle the above issues, we propose a
Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which
combines the characteristics of both geographical and sequential influences in
a collaborative way. KBGNN consists of a geographical module and a sequential
module. On the one hand, we construct a geographical graph and leverage a
message passing neural network to capture the topological geographical
influences. On the other hand, we explore high-order sequential substructures
in the user-aware sequential graph using a graph kernel neural network to
capture user preferences. Finally, a consistency learning framework is
introduced to jointly incorporate geographical and sequential information
extracted from two separate graphs. In this way, the two modules effectively
exchange knowledge to mutually enhance each other. Extensive experiments
conducted on two real-world LBSN datasets demonstrate the superior performance
of our proposed method over the state-of-the-arts. Our codes are available at
https://github.com/Fang6ang/KBGNN.

Quasar absorption line analysis is critical for studying gas and dust
components and their physical and chemical properties as well as the evolution
and formation of galaxies in the early universe. Ca II absorbers, which are one
of the dustiest absorbers and are located at lower redshifts than most other
absorbers, are especially valuable when studying physical processes and
conditions in recent galaxies. However, the number of known quasar Ca II
absorbers is relatively low due to the difficulty of detecting them with
traditional methods. In this work, we developed an accurate and quick approach
to search for Ca II absorption lines using deep learning. In our deep learning
model, a convolutional neural network, tuned using simulated data, is used for
the classification task. The simulated training data are generated by inserting
artificial Ca II absorption lines into original quasar spectra from the Sloan
Digital Sky Survey (SDSS) whilst an existing Ca II catalog is adopted as the
test set. The resulting model achieves an accuracy of 96% on the real data in
the test set. Our solution runs thousands of times faster than traditional
methods, taking a fraction of a second to analyze thousands of quasars while
traditional methods may take days to weeks. The trained neural network is
applied to quasar spectra from SDSS's DR7 and DR12 and discovered 399 new
quasar Ca II absorbers. In addition, we confirmed 409 known quasar Ca II
absorbers identified previously by other research groups through traditional
methods.

Multi-label learning has attracted significant attention from both academic
and industry field in recent decades. Although existing multi-label learning
algorithms achieved good performance in various tasks, they implicitly assume
the size of target label space is not huge, which can be restrictive for
real-world scenarios. Moreover, it is infeasible to directly adapt them to
extremely large label space because of the compute and memory overhead.
Therefore, eXtreme Multi-label Learning (XML) is becoming an important task and
many effective approaches are proposed. To fully understand XML, we conduct a
survey study in this paper. We first clarify a formal definition for XML from
the perspective of supervised learning. Then, based on different model
architectures and challenges of the problem, we provide a thorough discussion
of the advantages and disadvantages of each category of methods. For the
benefit of conducting empirical studies, we collect abundant resources
regarding XML, including code implementations, and useful tools. Lastly, we
propose possible research directions in XML, such as new evaluation metrics,
the tail label problem, and weakly supervised XML.

The Partial Area Under the ROC Curve (PAUC), typically including One-way
Partial AUC (OPAUC) and Two-way Partial AUC (TPAUC), measures the average
performance of a binary classifier within a specific false positive rate and/or
true positive rate interval, which is a widely adopted measure when decision
constraints must be considered. Consequently, PAUC optimization has naturally
attracted increasing attention in the machine learning community within the
last few years. Nonetheless, most of the existing methods could only optimize
PAUC approximately, leading to inevitable biases that are not controllable.
Fortunately, a recent work presents an unbiased formulation of the PAUC
optimization problem via distributional robust optimization. However, it is
based on the pair-wise formulation of AUC, which suffers from the limited
scalability w.r.t. sample size and a slow convergence rate, especially for
TPAUC. To address this issue, we present a simpler reformulation of the problem
in an asymptotically unbiased and instance-wise manner. For both OPAUC and
TPAUC, we come to a nonconvex strongly concave minimax regularized problem of
instance-wise functions. On top of this, we employ an efficient solver enjoys a
linear per-iteration computational complexity w.r.t. the sample size and a
time-complexity of $O(\epsilon^{-1/3})$ to reach a $\epsilon$ stationary point.
Furthermore, we find that the minimax reformulation also facilitates the
theoretical analysis of generalization error as a byproduct. Compared with the
existing results, we present new error bounds that are much easier to prove and
could deal with hypotheses with real-valued outputs. Finally, extensive
experiments on several benchmark datasets demonstrate the effectiveness of our
method.

Contrastive learning methods achieve state-of-the-art results in unsupervised
sentence representation learning. Although playing essential roles in
contrastive learning, data augmentation methods applied on sentences have not
been fully explored. Current SOTA method SimCSE utilizes a simple dropout
mechanism as continuous augmentation which outperforms discrete augmentations
such as cropping, word deletion and synonym replacement. To understand the
underlying rationales, we revisit existing approaches and attempt to
hypothesize the desiderata of reasonable data augmentation methods: balance of
semantic consistency and expression diversity. Based on the hypothesis, we
propose three simple yet effective discrete sentence augmentation methods,
i.e., punctuation insertion, affirmative auxiliary and double negation. The
punctuation marks, auxiliaries and negative words act as minimal noises in
lexical level to produce diverse sentence expressions. Unlike traditional
augmentation methods which randomly modify the sentence, our augmentation rules
are well designed for generating semantically consistent and grammatically
correct sentences. We conduct extensive experiments on both English and Chinese
semantic textual similarity datasets. The results show the robustness and
effectiveness of the proposed methods.

In this work, we initiate the study of \emph{Dynamic Tensor Product
Regression}. One has matrices $A_1\in \mathbb{R}^{n_1\times d_1},\ldots,A_q\in
\mathbb{R}^{n_q\times d_q}$ and a label vector $b\in \mathbb{R}^{n_1\ldots
n_q}$, and the goal is to solve the regression problem with the design matrix
$A$ being the tensor product of the matrices $A_1, A_2, \dots, A_q$ i.e.
$\min_{x\in \mathbb{R}^{d_1\ldots d_q}}~\|(A_1\otimes \ldots\otimes
A_q)x-b\|_2$. At each time step, one matrix $A_i$ receives a sparse change, and
the goal is to maintain a sketch of the tensor product $A_1\otimes\ldots
\otimes A_q$ so that the regression solution can be updated quickly.
Recomputing the solution from scratch for each round is very slow and so it is
important to develop algorithms which can quickly update the solution with the
new design matrix. Our main result is a dynamic tree data structure where any
update to a single matrix can be propagated quickly throughout the tree. We
show that our data structure can be used to solve dynamic versions of not only
Tensor Product Regression, but also Tensor Product Spline regression (which is
a generalization of ridge regression) and for maintaining Low Rank
Approximations for the tensor product.

Graph structure learning aims to learn connectivity in a graph from data. It
is particularly important for many computer vision related tasks since no
explicit graph structure is available for images for most cases. A natural way
to construct a graph among images is to treat each image as a node and assign
pairwise image similarities as weights to corresponding edges. It is well known
that pairwise similarities between images are sensitive to the noise in feature
representations, leading to unreliable graph structures. We address this
problem from the viewpoint of statistical tests. By viewing the feature vector
of each node as an independent sample, the decision of whether creating an edge
between two nodes based on their similarity in feature representation can be
thought as a ${\it single}$ statistical test. To improve the robustness in the
decision of creating an edge, multiple samples are drawn and integrated by
${\it multiple}$ statistical tests to generate a more reliable similarity
measure, consequentially more reliable graph structure. The corresponding
elegant matrix form named $\mathcal{B}\textbf{-Attention}$ is designed for
efficiency. The effectiveness of multiple tests for graph structure learning is
verified both theoretically and empirically on multiple clustering and ReID
benchmark datasets. Source codes are available at
https://github.com/Thomas-wyh/B-Attention.

Non-autoregressive translation (NAT) models are typically trained with the
cross-entropy loss, which forces the model outputs to be aligned verbatim with
the target sentence and will highly penalize small shifts in word positions.
Latent alignment models relax the explicit alignment by marginalizing out all
monotonic latent alignments with the CTC loss. However, they cannot handle
non-monotonic alignments, which is non-negligible as there is typically global
word reordering in machine translation. In this work, we explore non-monotonic
latent alignments for NAT. We extend the alignment space to non-monotonic
alignments to allow for the global word reordering and further consider all
alignments that overlap with the target sentence. We non-monotonically match
the alignments to the target sentence and train the latent alignment model to
maximize the F1 score of non-monotonic matching. Extensive experiments on major
WMT benchmarks show that our method substantially improves the translation
performance of CTC-based models. Our best model achieves 30.06 BLEU on WMT14
En-De with only one-iteration decoding, closing the gap between
non-autoregressive and autoregressive models.

Modern day applications, especially information retrieval webapps that
involve "search" as their use cases are gradually moving towards "answering"
modules. Conversational chatbots which have been proved to be more engaging to
users, use Question Answering as their core. Since, precise answering is
computationally expensive, several approaches have been developed to prefetch
the most relevant documents/passages from the database that contain the answer.
We propose a different approach that retrieves the evidence documents
efficiently and accurately, making sure that the relevant document for a given
user query is not missed. We do so by assigning each document (or passage in
our case), a unique identifier and using them to create dense vectors which can
be efficiently indexed. More precisely, we use the identifier to predict
randomly sampled context window words of the relevant question corresponding to
the passage along with the words of passage itself. This naturally embeds the
passage identifier into the vector space in such a way that the embedding is
closer to the question without compromising he information content. This
approach enables efficient creation of real-time query vectors in ~4
milliseconds.

Document-level relation extraction with graph neural networks faces a
fundamental graph construction gap between training and inference - the golden
graph structure only available during training, which causes that most methods
adopt heuristic or syntactic rules to construct a prior graph as a pseudo
proxy. In this paper, we propose $\textbf{ConstGCN}$, a novel graph
convolutional network which performs knowledge-based information propagation
between entities along with all specific relation spaces without any prior
graph construction. Specifically, it updates the entity representation by
aggregating information from all other entities along with each relation space,
thus modeling the relation-aware spatial information. To control the
information flow passing through the indeterminate relation spaces, we propose
to constrain the propagation using transmitting scores learned from the Noise
Contrastive Estimation between fact triples. Experimental results show that our
method outperforms the previous state-of-the-art (SOTA) approaches on the DocRE
dataset.

Large language models (LLMs) have shown exceptional performance on a variety
of natural language tasks. Yet, their capabilities for HTML understanding --
i.e., parsing the raw HTML of a webpage, with applications to automation of
web-based tasks, crawling, and browser-assisted retrieval -- have not been
fully explored. We contribute HTML understanding models (fine-tuned LLMs) and
an in-depth analysis of their capabilities under three tasks: (i) Semantic
Classification of HTML elements, (ii) Description Generation for HTML inputs,
and (iii) Autonomous Web Navigation of HTML pages. While previous work has
developed dedicated architectures and training procedures for HTML
understanding, we show that LLMs pretrained on standard natural language
corpora transfer remarkably well to HTML understanding tasks. For instance,
fine-tuned LLMs are 12% more accurate at semantic classification compared to
models trained exclusively on the task dataset. Moreover, when fine-tuned on
data from the MiniWoB benchmark, LLMs successfully complete 50% more tasks
using 192x less data compared to the previous best supervised model. Out of the
LLMs we evaluate, we show evidence that T5-based models are ideal due to their
bidirectional encoder-decoder architecture. To promote further research on LLMs
for HTML understanding, we create and open-source a large-scale HTML dataset
distilled and auto-labeled from CommonCrawl.

Point cloud upsampling focuses on generating a dense, uniform and
proximity-to-surface point set. Most previous approaches accomplish these
objectives by carefully designing a single-stage network, which makes it still
challenging to generate a high-fidelity point distribution. Instead, upsampling
point cloud in a coarse-to-fine manner is a decent solution. However, existing
coarse-to-fine upsampling methods require extra training strategies, which are
complicated and time-consuming during the training. In this paper, we propose a
simple yet effective cascaded refinement network, consisting of three
generation stages that have the same network architecture but achieve different
objectives. Specifically, the first two upsampling stages generate the dense
but coarse points progressively, while the last refinement stage further adjust
the coarse points to a better position. To mitigate the learning conflicts
between multiple stages and decrease the difficulty of regressing new points,
we encourage each stage to predict the point offsets with respect to the input
shape. In this manner, the proposed cascaded refinement network can be easily
optimized without extra learning strategies. Moreover, we design a
transformer-based feature extraction module to learn the informative global and
local shape context. In inference phase, we can dynamically adjust the model
efficiency and effectiveness, depending on the available computational
resources. Extensive experiments on both synthetic and real-scanned datasets
demonstrate that the proposed approach outperforms the existing

While recent large-scale video-language pre-training made great progress in
video question answering, the design of spatial modeling of video-language
models is less fine-grained than that of image-language models; existing
practices of temporal modeling also suffer from weak and noisy alignment
between modalities. To learn fine-grained visual understanding, we decouple
spatial-temporal modeling and propose a hybrid pipeline, Decoupled
Spatial-Temporal Encoders, integrating an image- and a video-language encoder.
The former encodes spatial semantics from larger but sparsely sampled frames
independently of time, while the latter models temporal dynamics at lower
spatial but higher temporal resolution. To help the video-language model learn
temporal relations for video QA, we propose a novel pre-training objective,
Temporal Referring Modeling, which requires the model to identify temporal
positions of events in video sequences. Extensive experiments demonstrate that
our model outperforms previous work pre-trained on orders of magnitude larger
datasets.

Few-shot object detection (FSOD) is to detect objects with a few examples.
However, existing FSOD methods do not consider hierarchical fine-grained
category structures of objects that exist widely in real life. For example,
animals are taxonomically classified into orders, families, genera and species
etc. In this paper, we propose and solve a new problem called hierarchical
few-shot object detection (Hi-FSOD), which aims to detect objects with
hierarchical categories in the FSOD paradigm. To this end, on the one hand, we
build the first large-scale and high-quality Hi-FSOD benchmark dataset
HiFSOD-Bird, which contains 176,350 wild-bird images falling to 1,432
categories. All the categories are organized into a 4-level taxonomy,
consisting of 32 orders, 132 families, 572 genera and 1,432 species. On the
other hand, we propose the first Hi-FSOD method HiCLPL, where a hierarchical
contrastive learning approach is developed to constrain the feature space so
that the feature distribution of objects is consistent with the hierarchical
taxonomy and the model's generalization power is strengthened. Meanwhile, a
probabilistic loss is designed to enable the child nodes to correct the
classification errors of their parent nodes in the taxonomy. Extensive
experiments on the benchmark dataset HiFSOD-Bird show that our method HiCLPL
outperforms the existing FSOD methods.

Autonomous underwater vehicles (AUV) are commonly used in many underwater
applications. Usually, inertial sensors and Doppler velocity log readings are
used in a nonlinear filter to estimate the AUV navigation solution. The process
noise covariance matrix is tuned according to the inertial sensors'
characteristics. This matrix greatly influences filter accuracy, robustness,
and performance. A common practice is to assume that this matrix is fixed
during the AUV operation. However, it varies over time as the amount of
uncertainty is unknown. Therefore, adaptive tuning of this matrix can lead to a
significant improvement in the filter performance. In this work, we propose a
learning-based adaptive velocity-aided navigation filter. To that end,
handcrafted features are generated and used to tune the momentary system noise
covariance matrix. Once the process noise covariance is learned, it is fed into
the model-based navigation filter. Simulation results show the benefits of our
approach compared to other adaptive approaches.

In this paper we discuss our experience in teaching the Robotic Applications
Programming course at ZHAW combining the use of a Kubernetes (k8s) cluster and
real, heterogeneous, robotic hardware. We discuss the main advantages of our
solutions in terms of seamless simulation-to-real experience for students and
the main shortcomings we encountered with networking and sharing GPUs to
support deep learning workloads. We describe the current and foreseen
alternatives to avoid these drawbacks in future course editions and propose a
more cloud-native approach to deploying multiple robotics applications on a k8s
cluster.

The Transformer architecture has achieved remarkable success in a number of
domains including natural language processing and computer vision. However,
when it comes to graph-structured data, transformers have not achieved
competitive performance, especially on large graphs. In this paper, we identify
the main deficiencies of current graph transformers:(1) Existing node sampling
strategies in Graph Transformers are agnostic to the graph characteristics and
the training process. (2) Most sampling strategies only focus on local
neighbors and neglect the long-range dependencies in the graph. We conduct
experimental investigations on synthetic datasets to show that existing
sampling strategies are sub-optimal. To tackle the aforementioned problems, we
formulate the optimization strategies of node sampling in Graph Transformer as
an adversary bandit problem, where the rewards are related to the attention
weights and can vary in the training procedure. Meanwhile, we propose a
hierarchical attention scheme with graph coarsening to capture the long-range
interactions while reducing computational complexity. Finally, we conduct
extensive experiments on real-world datasets to demonstrate the superiority of
our method over existing graph transformers and popular GNNs.

Understanding human tasks through video observations is an essential
capability of intelligent agents. The challenges of such capability lie in the
difficulty of generating a detailed understanding of situated actions, their
effects on object states (i.e., state changes), and their causal dependencies.
These challenges are further aggravated by the natural parallelism from
multi-tasking and partial observations in multi-agent collaboration. Most prior
works leverage action localization or future prediction as an indirect metric
for evaluating such task understanding from videos. To make a direct
evaluation, we introduce the EgoTaskQA benchmark that provides a single home
for the crucial dimensions of task understanding through question-answering on
real-world egocentric videos. We meticulously design questions that target the
understanding of (1) action dependencies and effects, (2) intents and goals,
and (3) agents' beliefs about others. These questions are divided into four
types, including descriptive (what status?), predictive (what will?),
explanatory (what caused?), and counterfactual (what if?) to provide diagnostic
analyses on spatial, temporal, and causal understandings of goal-oriented
tasks. We evaluate state-of-the-art video reasoning models on our benchmark and
show their significant gaps between humans in understanding complex
goal-oriented egocentric videos. We hope this effort will drive the vision
community to move onward with goal-oriented video understanding and reasoning.

Recent advances in learning aligned multimodal representations have been
primarily driven by training large neural networks on massive, noisy
paired-modality datasets. In this work, we ask whether it is possible to
achieve similar results with substantially less training time and data. We
achieve this by taking advantage of existing pretrained unimodal encoders and
careful curation of alignment data relevant to the downstream task of interest.
We study a natural approach to aligning existing encoders via small auxiliary
functions, and we find that this method is competitive with (or outperforms)
state of the art in many settings while being less prone to overfitting, less
costly to train, and more robust to distribution shift. With a properly chosen
alignment distribution, our method surpasses prior state of the art for
ImageNet zero-shot classification on public data while using two orders of
magnitude less time and data and training 77% fewer parameters.

Recent advances in distilling pretrained language models have discovered
that, besides the expressiveness of knowledge, the student-friendliness should
be taken into consideration to realize a truly knowledgable teacher. Based on a
pilot study, we find that over-parameterized teachers can produce expressive
yet student-unfriendly knowledge and are thus limited in overall
knowledgableness. To remove the parameters that result in
student-unfriendliness, we propose a sparse teacher trick under the guidance of
an overall knowledgable score for each teacher parameter. The knowledgable
score is essentially an interpolation of the expressiveness and
student-friendliness scores. The aim is to ensure that the expressive
parameters are retained while the student-unfriendly ones are removed.
Extensive experiments on the GLUE benchmark show that the proposed sparse
teachers can be dense with knowledge and lead to students with compelling
performance in comparison with a series of competitive baselines.

We highlight the utility of a certain property of model training: instead of
drawing training data from the same distribution as test data, learning a
different training distribution often improves accuracy, especially at small
model sizes. This provides a way to build accurate small models, which are
attractive for interpretability and resource-constrained environments. Here we
empirically show that this principle is both general and effective: it may be
used across tasks/model families, and it can augment prediction accuracy of
traditional models to the extent they are competitive with specialized
techniques. The tasks we consider are explainable clustering and
prototype-based classification. We also look at Random Forests to illustrate
how this principle may be applied to accommodate multiple size constraints,
e.g., number of trees and maximum depth per tree. Results using multiple
datasets are presented and are shown to be statistically significant.

Mislabeled examples are a common issue in real-world data, particularly for
tasks like token classification where many labels must be chosen on a
fine-grained basis. Here we consider the task of finding sentences that contain
label errors in token classification datasets. We study 11 different
straightforward methods that score tokens/sentences based on the predicted
class probabilities output by a (any) token classification model (trained via
any procedure). In precision-recall evaluations based on real-world label
errors in entity recognition data from CoNLL-2003, we identify a simple and
effective method that consistently detects those sentences containing label
errors when applied with different token classification models.

Recently introduced Contrastive Language-Image Pre-Training (CLIP) bridges
images and text by embedding them into a joint latent space. This opens the
door to ample literature that aims to manipulate an input image by providing a
textual explanation. However, due to the discrepancy between image and text
embeddings in the joint space, using text embeddings as the optimization target
often introduces undesired artifacts in the resulting images. Disentanglement,
interpretability, and controllability are also hard to guarantee for
manipulation. To alleviate these problems, we propose to define corpus
subspaces spanned by relevant prompts to capture specific image
characteristics. We introduce CLIP Projection-Augmentation Embedding (PAE) as
an optimization target to improve the performance of text-guided image
manipulation. Our method is a simple and general paradigm that can be easily
computed and adapted, and smoothly incorporated into any CLIP-based image
manipulation algorithm. To demonstrate the effectiveness of our method, we
conduct several theoretical and empirical studies. As a case study, we utilize
the method for text-guided semantic face editing. We quantitatively and
qualitatively demonstrate that PAE facilitates a more disentangled,
interpretable, and controllable image manipulation with state-of-the-art
quality and accuracy.

In this paper, two approximate 3*3 multipliers are proposed and the synthesis
results of the ASAP-7nm process library justify that they can reduce the area
by 31.38% and 36.17%, and the power consumption by 36.73% and 35.66% compared
with the exact multiplier, respectively. They can be aggregated with a 2*2
multiplier to produce an 8*8 multiplier with low error rate based on the
distribution of DNN weights. We propose a hardware-driven software
co-optimization method to improve the DNN accuracy by retraining. Based on the
proposed two approximate 3-bit multipliers, three approximate 8-bit multipliers
with low error-rate are designed for DNNs. Compared with the exact 8-bit
unsigned multiplier, our design can achieve a significant advantage over other
approximate multipliers on the public dataset.

E-commerce query understanding is the process of inferring the shopping
intent of customers by extracting semantic meaning from their search queries.
The recent progress of pre-trained masked language models (MLM) in natural
language processing is extremely attractive for developing effective query
understanding models. Specifically, MLM learns contextual text embedding via
recovering the masked tokens in the sentences. Such a pre-training process
relies on the sufficient contextual information. It is, however, less effective
for search queries, which are usually short text. When applying masking to
short search queries, most contextual information is lost and the intent of the
search queries may be changed. To mitigate the above issues for MLM
pre-training on search queries, we propose a novel pre-training task
specifically designed for short text, called Extended Token Classification
(ETC). Instead of masking the input text, our approach extends the input by
inserting tokens via a generator network, and trains a discriminator to
identify which tokens are inserted in the extended input. We conduct
experiments in an E-commerce store to demonstrate the effectiveness of ETC.

This paper investigates a communication-efficient split learning (SL) over
multiple-input multiple-output (MIMO) communication system. In particular, we
mathematically decompose the inter-layer connection of a neural network (NN) to
a series of linear precoding and combining transformations using over-the-air
computation (OAC), which synergistically form a linear layer in NNs. The
precoding and combining matrices are trainable parameters in such a system,
whereas the MIMO channel is implicit. The proposed system eliminates the
implicit channel estimation through exploiting the channel reciprocity and
properly casting the backpropagation process, significantly saving the system
costs and further improving the overall efficiency. The practical constellation
diagrams are used as the activation function to avoid sending arbitrary analog
signals as in the traditional OAC system. Numerical results are illustrated to
demonstrate the effectiveness of the proposed scheme.

In this paper, we investigate signal detection in
multiple-input-multiple-output (MIMO) communication systems with hardware
impairments, such as power amplifier nonlinearity and in-phase/quadrature
imbalance. To deal with the complex combined effects of hardware imperfections,
neural network (NN) techniques, in particular deep neural networks (DNNs), have
been studied to directly compensate for the impact of hardware impairments.
However, it is difficult to train a DNN with limited pilot signals, hindering
its practical applications. In this work, we investigate how to achieve
efficient Bayesian signal detection in MIMO systems with hardware
imperfections. Characterizing combined hardware imperfections often leads to
complicated signal models, making Bayesian signal detection challenging. To
address this issue, we first train an NN to "model" the MIMO system with
hardware imperfections and then perform Bayesian inference based on the trained
NN. Modelling the MIMO system with NN enables the design of NN architectures
based on the signal flow of the MIMO system, minimizing the number of NN layers
and parameters, which is crucial to achieving efficient training with limited
pilot signals. We then represent the trained NN with a factor graph, and design
an efficient message passing based Bayesian signal detector, leveraging the
unitary approximate message passing (UAMP) algorithm. The implementation of a
turbo receiver with the proposed Bayesian detector is also investigated.
Extensive simulation results demonstrate that the proposed technique delivers
remarkably better performance than state-of-the-art methods.

Graph neural networks (GNNs) have achieved great success in many scenarios
with graph-structured data. However, in many real applications, there are three
issues when applying GNNs: graphs are unknown, nodes have noisy features, and
graphs contain noisy connections. Aiming at solving these problems, we propose
a new graph neural network named as GL-GNN. Our model includes multiple
sub-modules, each sub-module selects important data features and learn the
corresponding key relation graph of data samples when graphs are unknown.
GL-GNN further obtains the network of graphs by learning the network of
sub-modules. The learned graphs are further fused using an aggregation method
over the network of graphs. Our model solves the first issue by simultaneously
learning multiple relation graphs of data samples as well as a relation network
of graphs, and solves the second and the third issue by selecting important
data features as well as important data sample relations. We compare our method
with 14 baseline methods on seven datasets when the graph is unknown and 11
baseline methods on two datasets when the graph is known. The results show that
our method achieves better accuracies than the baseline methods and is capable
of selecting important features and graph edges from the dataset. Our code will
be publicly available at \url{https://github.com/Looomo/GL-GNN}.

The deep learning (DL)-based methods of low-level tasks have many advantages
over the traditional camera in terms of hardware prospects, error accumulation
and imaging effects. Recently, the application of deep learning to replace the
image signal processing (ISP) pipeline has appeared one after another; however,
there is still a long way to go towards real landing. In this paper, we show
the possibility of learning-based method to achieve real-time high-performance
processing in the ISP pipeline. We propose LW-ISP, a novel architecture
designed to implicitly learn the image mapping from RAW data to RGB image.
Based on U-Net architecture, we propose the fine-grained attention module and a
plug-and-play upsampling block suitable for low-level tasks. In particular, we
design a heterogeneous distillation algorithm to distill the implicit features
and reconstruction information of the clean image, so as to guide the learning
of the student model. Our experiments demonstrate that LW-ISP has achieved a
0.38 dB improvement in PSNR compared to the previous best method, while the
model parameters and calculation have been reduced by 23 times and 81 times.
The inference efficiency has been accelerated by at least 15 times. Without
bells and whistles, LW-ISP has achieved quite competitive results in ISP
subtasks including image denoising and enhancement.

Dynamic graph neural network (DGNN) is becoming increasingly popular because
of its widespread use in capturing dynamic features in the real world. A
variety of dynamic graph neural networks designed from algorithmic perspectives
have succeeded in incorporating temporal information into graph processing.
Despite the promising algorithmic performance, deploying DGNNs on hardware
presents additional challenges due to the model complexity, diversity, and the
nature of the time dependency. Meanwhile, the differences between DGNNs and
static graph neural networks make hardware-related optimizations for static
graph neural networks unsuitable for DGNNs. In this paper, we select eight
prevailing DGNNs with different characteristics and profile them on both CPU
and GPU. The profiling results are summarized and analyzed, providing in-depth
insights into the bottlenecks of DGNNs on hardware and identifying potential
optimization opportunities for future DGNN acceleration. Followed by a
comprehensive survey, we provide a detailed analysis of DGNN performance
bottlenecks on hardware, including temporal data dependency, workload
imbalance, data movement, and GPU warm-up. We suggest several optimizations
from both software and hardware perspectives. This paper is the first to
provide an in-depth analysis of the hardware performance of DGNN Code is
available at https://github.com/sharc-lab/DGNN_analysis.

Recent studies have demonstrated that visual recognition models lack
robustness to distribution shift. However, current work mainly considers model
robustness to 2D image transformations, leaving viewpoint changes in the 3D
world less explored. In general, viewpoint changes are prevalent in various
real-world applications (e.g., autonomous driving), making it imperative to
evaluate viewpoint robustness. In this paper, we propose a novel method called
ViewFool to find adversarial viewpoints that mislead visual recognition models.
By encoding real-world objects as neural radiance fields (NeRF), ViewFool
characterizes a distribution of diverse adversarial viewpoints under an
entropic regularizer, which helps to handle the fluctuations of the real camera
pose and mitigate the reality gap between the real objects and their neural
representations. Experiments validate that the common image classifiers are
extremely vulnerable to the generated adversarial viewpoints, which also
exhibit high cross-model transferability. Based on ViewFool, we introduce
ImageNet-V, a new out-of-distribution dataset for benchmarking viewpoint
robustness of image classifiers. Evaluation results on 40 classifiers with
diverse architectures, objective functions, and data augmentations reveal a
significant drop in model performance when tested on ImageNet-V, which provides
a possibility to leverage ViewFool as an effective data augmentation strategy
to improve viewpoint robustness.

Analytical hardware performance models yield swift estimation of desired
hardware performance metrics. However, developing these analytical models for
modern processors with sophisticated microarchitectures is an extremely
laborious task and requires a firm understanding of target microarchitecture's
internal structure. In this paper, we introduce GRANITE, a new machine learning
model that estimates the throughput of basic blocks across different
microarchitectures. GRANITE uses a graph representation of basic blocks that
captures both structural and data dependencies between instructions. This
representation is processed using a graph neural network that takes advantage
of the relational information captured in the graph and learns a rich neural
representation of the basic block that allows more precise throughput
estimation. Our results establish a new state-of-the-art for basic block
performance estimation with an average test error of 6.9% across a wide range
of basic blocks and microarchitectures for the x86-64 target. Compared to
recent work, this reduced the error by 1.7% while improving training and
inference throughput by approximately 3.0x. In addition, we propose the use of
multi-task learning with independent multi-layer feed forward decoder networks.
Our results show that this technique further improves precision of all learned
models while significantly reducing per-microarchitecture training costs. We
perform an extensive set of ablation studies and comparisons with prior work,
concluding a set of methods to achieve high accuracy for basic block
performance estimation.

In this paper, we present a neural network system related to about memory and
recall that consists of one neuron group (the "cue ball") and a one-layer
neural net (the "recall net"). This system realizes the bidirectional
memorization learning between one cue neuron in the cue ball and the neurons in
the recall net. It can memorize many patterns and recall these patterns or
those that are similar at any time. Furthermore, the patterns are recalled at
most the same time. This model's recall situation seems to resemble human
recall of a variety of similar things almost simultaneously when one thing is
recalled. It is also possible for additional learning to occur in the system
without affecting the patterns memorized in advance. Moreover, the memory rate
(the number of memorized patterns / the total number of neurons) is close to
100%; this system's rate is 0.987. Finally, pattern data constraints become an
important aspect of this system.

End-to-end text image translation (TIT), which aims at translating the source
language embedded in images to the target language, has attracted intensive
attention in recent research. However, data sparsity limits the performance of
end-to-end text image translation. Multi-task learning is a non-trivial way to
alleviate this problem via exploring knowledge from complementary related
tasks. In this paper, we propose a novel text translation enhanced text image
translation, which trains the end-to-end model with text translation as an
auxiliary task. By sharing model parameters and multi-task training, our model
is able to take full advantage of easily-available large-scale text parallel
corpus. Extensive experimental results show our proposed method outperforms
existing end-to-end methods, and the joint multi-task learning with both text
translation and recognition tasks achieves better results, proving translation
and recognition auxiliary tasks are complementary.

In this paper, we tackle the problem of domain shift. Most existing methods
perform training on multiple source domains using a single model, and the same
trained model is used on all unseen target domains. Such solutions are
sub-optimal as each target domain exhibits its own speciality, which is not
adapted. Furthermore, expecting the single-model training to learn extensive
knowledge from the multiple source domains is counterintuitive. The model is
more biased toward learning only domain-invariant features and may result in
negative knowledge transfer. In this work, we propose a novel framework for
unsupervised test-time adaptation, which is formulated as a knowledge
distillation process to address domain shift. Specifically, we incorporate
Mixture-of-Experts (MoE) as teachers, where each expert is separately trained
on different source domains to maximize their speciality. Given a test-time
target domain, a small set of unlabeled data is sampled to query the knowledge
from MoE. As the source domains are correlated to the target domains, a
transformer-based aggregator then combines the domain knowledge by examining
the interconnection among them. The output is treated as a supervision signal
to adapt a student prediction network toward the target domain. We further
employ meta-learning to enforce the aggregator to distill positive knowledge
and the student network to achieve fast adaptation. Extensive experiments
demonstrate that the proposed method outperforms the state-of-the-art and
validates the effectiveness of each proposed component. Our code is available
at https://github.com/n3il666/Meta-DMoE.

Large sparse linear algebraic systems can be found in a variety of scientific
and engineering fields, and many scientists strive to solve them in an
efficient and robust manner. In this paper, we propose an interpretable neural
solver, the Fourier Neural Solver (FNS), to address them. FNS is based on deep
learning and Fast Fourier transform. Because the error between the iterative
solution and the ground truth involves a wide range of frequency modes, FNS
combines a stationary iterative method and frequency space correction to
eliminate different components of the error. Local Fourier analysis reveals
that the FNS can pick up on the error components in frequency space that are
challenging to eliminate with stationary methods. Numerical experiments on the
anisotropy diffusion equation, convection-diffusion equation, and Helmholtz
equation show that FNS is more efficient and more robust than the
state-of-the-art neural solver.

Most interpretability research focuses on datasets containing thousands of
images of commonplace objects. However, many high-impact datasets, such as
those in medicine and the geosciences, contain fine-grain objects that require
domain-expert knowledge to recognize and are time-consuming to collect and
annotate. As a result, these datasets contain few annotated images, and current
machine vision models cannot train intensively on them. Thus, adapting
interpretability techniques to maximize the amount of information that models
can learn from small, fine-grain datasets is an important endeavor.
  Using a Mask R-CNN to segment ancient reef fossils in rock sample images, we
present a general paradigm for identifying and mitigating model weaknesses.
Specifically, we apply image perturbations to expose the Mask R-CNN's inability
to distinguish between different classes of fossils and its inconsistency in
segmenting fossils with different textures. To address these shortcomings, we
extend an existing model-editing method for correcting systematic mistakes in
image classification to image segmentation and introduce a novel application of
the technique: encouraging a greater separation between positive and negative
pixels for a given class. Through extensive experiments, we find that editing
the model by perturbing all pixels for a given class in one image is most
effective (compared to using multiple images and/or fewer pixels). Our paradigm
may also generalize to other segmentation models trained on small, fine-grain
datasets.

Multi-task learning (MTL), instruction tuning, and prompting have recently
been shown to improve the generalizability of large language models to new
tasks. However, the benefits of such methods are less well-documented in
smaller language models, with some studies finding contradictory results. In
this work, we explore and isolate the effects of (i) model size, (ii) general
purpose MTL, (iii) in-domain MTL, (iv) instruction tuning, and (v) few-shot
fine-tuning for models with fewer than 500 million parameters. Our experiments
in the zero-shot setting demonstrate that models gain 31% relative improvement,
on average, from general purpose MTL, with an additional 37.6% relative gain
from in-domain MTL. Contradictory to prior works on large models, we find that
instruction tuning provides a modest 2% performance improvement for small
models.

The goal of lifelong learning is to continuously learn from non-stationary
distributions, where the non-stationarity is typically imposed by a sequence of
distinct tasks. Prior works have mostly considered idealistic settings, where
the identity of tasks is known at least at training. In this paper we focus on
a fundamentally harder, so-called task-agnostic setting where the task
identities are not known and the learning machine needs to infer them from the
observations. Our algorithm, which we call TAME (Task-Agnostic continual
learning using Multiple Experts), automatically detects the shift in data
distributions and switches between task expert networks in an online manner. At
training, the strategy for switching between tasks hinges on an extremely
simple observation that for each new coming task there occurs a
statistically-significant deviation in the value of the loss function that
marks the onset of this new task. At inference, the switching between experts
is governed by the selector network that forwards the test sample to its
relevant expert network. The selector network is trained on a small subset of
data drawn uniformly at random. We control the growth of the task expert
networks as well as selector network by employing online pruning. Our
experimental results show the efficacy of our approach on benchmark continual
learning data sets, outperforming the previous task-agnostic methods and even
the techniques that admit task identities at both training and testing, while
at the same time using a comparable model size.

In this paper, we propose an improved linear discriminant analysis, called
spectrally-corrected and regularized linear discriminant analysis (SCRLDA).
This method integrates the design ideas of the sample spectrally-corrected
covariance matrix and the regularized discriminant analysis. The SCRLDA method
is specially designed for classification problems under the assumption that the
covariance matrix follows a spiked model. Through the real and simulated data
analysis, it is shown that our proposed classifier outperforms the classical
R-LDA and can be as competitive as the KNN, SVM classifiers while requiring
lower computational complexity.

There are growing interests in adapting large-scale language models using
parameter-efficient fine-tuning methods. However, accelerating the model itself
and achieving better inference efficiency through model compression has not
been thoroughly explored yet. Model compression could provide the benefits of
reducing memory footprints, enabling low-precision computations, and ultimately
achieving cost-effective inference. To combine parameter-efficient adaptation
and model compression, we propose AlphaTuning consisting of post-training
quantization of the pre-trained language model and fine-tuning only some parts
of quantized parameters for a target task. Specifically, AlphaTuning works by
employing binary-coding quantization, which factorizes the full-precision
parameters into binary parameters and a separate set of scaling factors. During
the adaptation phase, the binary values are frozen for all tasks, while the
scaling factors are fine-tuned for the downstream task. We demonstrate that
AlphaTuning, when applied to GPT-2 and OPT, performs competitively with full
fine-tuning on a variety of downstream tasks while achieving >10x compression
ratio under 4-bit quantization and >1,000x reduction in the number of trainable

The success of most advanced facial expression recognition works relies
heavily on large-scale annotated datasets. However, it poses great challenges
in acquiring clean and consistent annotations for facial expression datasets.
On the other hand, self-supervised contrastive learning has gained great
popularity due to its simple yet effective instance discrimination training
strategy, which can potentially circumvent the annotation issue. Nevertheless,
there remain inherent disadvantages of instance-level discrimination, which are
even more challenging when faced with complicated facial representations. In
this paper, we revisit the use of self-supervised contrastive learning and
explore three core strategies to enforce expression-specific representations
and to minimize the interference from other facial attributes, such as identity
and face styling. Experimental results show that our proposed method
outperforms the current state-of-the-art self-supervised learning methods, in
terms of both categorical and dimensional facial expression recognition tasks.

We study the use of reinforcement learning to learn the optimal leader's
strategy in Stackelberg games. Learning a leader's strategy has an innate
stationarity problem -- when optimizing the leader's strategy, the followers'
strategies might shift. To circumvent this problem, we model the followers via
no-regret dynamics to converge to a Bayesian Coarse-Correlated Equilibrium
(B-CCE) of the game induced by the leader. We then embed the followers'
no-regret dynamics in the leader's learning environment, which allows us to
formulate our learning problem as a standard POMDP. We prove that the optimal
policy of this POMDP achieves the same utility as the optimal leader's strategy
in our Stackelberg game. We solve this POMDP using actor-critic methods, where
the critic is given access to the joint information of all the agents. Finally,
we show that our methods are able to learn optimal leader strategies in a
variety of settings of increasing complexity, including indirect mechanisms
where the leader's strategy is setting up the mechanism's rules.

Artificial neural networks (ANNs) are popular tools for accomplishing many
machine learning tasks, including predicting continuous outcomes. However, the
general lack of confidence measures provided with ANN predictions limit their
applicability. Supplementing point predictions with prediction intervals (PIs)
is common for other learning algorithms, but the complex structure and training
of ANNs renders constructing PIs difficult. This work provides the network
design choices and inferential methods for creating better performing PIs with
ANNs. A two-step experiment is executed across 11 data sets, including an
imaged-based data set. Two distribution-free methods for constructing PIs,
bootstrapping and conformal inference, are considered. The results of the first
experimental step reveal that the choices inherent to building an ANN affect PI
performance. Guidance is provided for optimizing PI performance with respect to
each network feature and PI method. In the second step, 20 algorithms for
constructing PIs, each using the principles of bootstrapping or conformal
inference, are implemented to determine which provides the best performance
while maintaining reasonable computational burden. In general, this trade-off
is optimized when implementing the cross-conformal method, which maintained
interval coverage and efficiency with decreased computational burden.

Training large neural networks with meaningful/usable differential privacy
security guarantees is a demanding challenge. In this paper, we tackle this
problem by revisiting the two key operations in Differentially Private
Stochastic Gradient Descent (DP-SGD): 1) iterative perturbation and 2) gradient
clipping. We propose a generic optimization framework, called {\em ModelMix},
which performs random aggregation of intermediate model states. It strengthens
the composite privacy analysis utilizing the entropy of the training trajectory
and improves the $(\epsilon, \delta)$ DP security parameters by an order of
magnitude.
  We provide rigorous analyses for both the utility guarantees and privacy
amplification of ModelMix. In particular, we present a formal study on the
effect of gradient clipping in DP-SGD, which provides theoretical instruction
on how hyper-parameters should be selected. We also introduce a refined
gradient clipping method, which can further sharpen the privacy loss in private
learning when combined with ModelMix.
  Thorough experiments with significant privacy/utility improvement are
presented to support our theory. We train a Resnet-20 network on CIFAR10 with
$70.4\%$ accuracy via ModelMix given $(\epsilon=8, \delta=10^{-5})$ DP-budget,
compared to the same performance but with $(\epsilon=145.8,\delta=10^{-5})$
using regular DP-SGD; assisted with additional public low-dimensional gradient
embedding, one can further improve the accuracy to $79.1\%$ with
$(\epsilon=6.1, \delta=10^{-5})$ DP-budget, compared to the same performance
but with $(\epsilon=111.2, \delta=10^{-5})$ without ModelMix.

Transformers allow attention between all pairs of tokens, but there is reason
to believe that most of these connections - and their quadratic time and memory
- may not be necessary. But which ones? We evaluate the impact of
sparsification patterns with a series of ablation experiments. First, we
compare masks based on syntax, lexical similarity, and token position to random
connections, and measure which patterns reduce performance the least. We find
that on three common finetuning tasks even using attention that is at least 78%
sparse can have little effect on performance if applied at later transformer
layers, but that applying sparsity throughout the network reduces performance
significantly. Second, we vary the degree of sparsity for three patterns
supported by previous work, and find that connections to neighbouring tokens
are the most significant. Finally, we treat sparsity as an optimizable
parameter, and present an algorithm to learn degrees of neighboring connections
that gives a fine-grained control over the accuracy-sparsity trade-off while
approaching the performance of existing methods.

We present a deep learning approach for learning the joint semantic
embeddings of images and captions in a Euclidean space, such that the semantic
similarity is approximated by the L2 distances in the embedding space. For
that, we introduce a metric learning scheme that utilizes multitask learning to
learn the embedding of identical semantic concepts using a center loss. By
introducing a differentiable quantization scheme into the end-to-end trainable
network, we derive a semantic embedding of semantically similar concepts in
Euclidean space. We also propose a novel metric learning formulation using an
adaptive margin hinge loss, that is refined during the training phase. The
proposed scheme was applied to the MS-COCO, Flicke30K and Flickr8K datasets,
and was shown to compare favorably with contemporary state-of-the-art

Many manipulation tasks require the robot to control the contact between a
grasped compliant tool and the environment, e.g. scraping a frying pan with a
spatula. However, modeling tool-environment interaction is difficult,
especially when the tool is compliant, and the robot cannot be expected to have
the full geometry and physical properties (e.g., mass, stiffness, and friction)
of all the tools it must use. We propose a framework that learns to predict the
effects of a robot's actions on the contact between the tool and the
environment given visuo-tactile perception. Key to our framework is a novel
contact feature representation that consists of a binary contact value, the
line of contact, and an end-effector wrench. We propose a method to learn the
dynamics of these contact features from real world data that does not require
predicting the geometry of the compliant tool. We then propose a controller
that uses this dynamics model for visuo-tactile contact servoing and show that
it is effective at performing scraping tasks with a spatula, even in scenarios
where precise contact needs to be made to avoid obstacles.

Bark beetle outbreaks can result in a devastating impact on forest ecosystem
processes, biodiversity, forest structure and function, and economies. Accurate
and timely detection of bark beetle infestations is crucial to mitigate further
damage, develop proactive forest management activities, and minimize economic
losses. Incorporating remote sensing (RS) data with machine learning (ML) (or
deep learning (DL)) can provide a great alternative to the current approaches
that rely on aerial surveys and field surveys, which are impractical over vast
geographical regions. This paper provides a comprehensive review of past and
current advances in the early detection of bark beetle-induced tree mortality
from three key perspectives: bark beetle & host interactions, RS, and ML/DL. We
parse recent literature according to bark beetle species & attack phases, host
trees, study regions, imagery platforms & sensors, spectral/spatial/temporal
resolutions, spectral signatures, spectral vegetation indices (SVIs), ML
approaches, learning schemes, task categories, models, algorithms,
classes/clusters, features, and DL networks & architectures. This review
focuses on challenging early detection, discussing current challenges and
potential solutions. Our literature survey suggests that the performance of
current ML methods is limited (less than 80%) and depends on various factors,
including imagery sensors & resolutions, acquisition dates, and employed
features & algorithms/networks. A more promising result from DL networks and
then the random forest (RF) algorithm highlighted the potential to detect
subtle changes in visible, thermal, and short-wave infrared (SWIR) spectral
regions.

We show how to develop sampling-based alternating least squares (ALS)
algorithms for decomposition of tensors into any tensor network (TN) format.
Provided the TN format satisfies certain mild assumptions, resulting algorithms
will have input sublinear per-iteration cost. Unlike most previous works on
sampling-based ALS methods for tensor decomposition, the sampling in our
framework is done according to the exact leverage score distribution of the
design matrices in the ALS subproblems. We implement and test two tensor
decomposition algorithms that use our sampling framework in a feature
extraction experiment where we compare them against a number of other
decomposition algorithms.

Several prior works have shown that language models (LMs) can generate text
containing harmful social biases and stereotypes. While decoding algorithms
play a central role in determining properties of LM generated text, their
impact on the fairness of the generations has not been studied. We present a
systematic analysis of the impact of decoding algorithms on LM fairness, and
analyze the trade-off between fairness, diversity and quality. Our experiments
with top-$p$, top-$k$ and temperature decoding algorithms, in open-ended
language generation, show that fairness across demographic groups changes
significantly with change in decoding algorithm's hyper-parameters. Notably,
decoding algorithms that output more diverse text also output more texts with
negative sentiment and regard. We present several findings and provide
recommendations on standardized reporting of decoding details in fairness
evaluations and optimization of decoding algorithms for fairness alongside
quality and diversity.

Given a labeled training set and a collection of unlabeled data, the goal of
active learning (AL) is to identify the best unlabeled points to label. In this
comprehensive study, we analyze the performance of a variety of AL algorithms
on deep neural networks trained on 69 real-world tabular classification
datasets from the OpenML-CC18 benchmark. We consider different data regimes and
the effect of self-supervised model pre-training. Surprisingly, we find that
the classical margin sampling technique matches or outperforms all others,
including current state-of-art, in a wide range of experimental settings. To
researchers, we hope to encourage rigorous benchmarking against margin, and to
practitioners facing tabular data labeling constraints that
hyper-parameter-free margin may often be all they need.

This work presents In-Context Policy Iteration, an algorithm for performing
Reinforcement Learning (RL), in-context, using foundation models. While the
application of foundation models to RL has received considerable attention,
most approaches rely on either (1) the curation of expert demonstrations
(either through manual design or task-specific pretraining) or (2) adaptation
to the task of interest using gradient methods (either fine-tuning or training
of adapter layers). Both of these techniques have drawbacks. Collecting
demonstrations is labor-intensive, and algorithms that rely on them do not
outperform the experts from which the demonstrations were derived. All gradient
techniques are inherently slow, sacrificing the "few-shot" quality that made
in-context learning attractive to begin with. In this work, we present an
algorithm, ICPI, that learns to perform RL tasks without expert demonstrations
or gradients. Instead we present a policy-iteration method in which the prompt
content is the entire locus of learning. ICPI iteratively updates the contents
of the prompt from which it derives its policy through trial-and-error
interaction with an RL environment. In order to eliminate the role of
in-weights learning (on which approaches like Decision Transformer rely
heavily), we demonstrate our algorithm using Codex, a language model with no
prior knowledge of the domains on which we evaluate it.

In this work, we explore the maximum-margin bias of quasi-homogeneous neural
networks trained with gradient flow on an exponential loss and past a point of
separability. We introduce the class of quasi-homogeneous models, which is
expressive enough to describe nearly all neural networks with homogeneous
activations, even those with biases, residual connections, and normalization
layers, while structured enough to enable geometric analysis of its gradient
dynamics. Using this analysis, we generalize the existing results of
maximum-margin bias for homogeneous networks to this richer class of models. We
find that gradient flow implicitly favors a subset of the parameters, unlike in
the case of a homogeneous model where all parameters are treated equally. We
demonstrate through simple examples how this strong favoritism toward
minimizing an asymmetric norm can degrade the robustness of quasi-homogeneous
models. On the other hand, we conjecture that this norm-minimization discards,
when possible, unnecessary higher-order parameters, reducing the model to a
sparser parameterization. Lastly, by applying our theorem to sufficiently
expressive neural networks with normalization layers, we reveal a universal
mechanism behind the empirical phenomenon of Neural Collapse.

Directed Controller Synthesis technique finds solutions for the non-blocking
property in discrete event systems by exploring a reduced portion of the
exponentially big state space, using best-first search. Aiming to minimize the
explored states, it is currently guided by a domain-independent handcrafted
heuristic, with which it reaches state-of-the-art performance. In this work, we
propose a new method for obtaining heuristics based on Reinforcement Learning.
The synthesis algorithm is framed as an RL task with an unbounded action space
and a modified version of DQN is used. With a simple and general set of
features, we show that it is possible to learn heuristics on small versions of
a problem in a way that generalizes to the larger instances. Our agents learn
from scratch and outperform the existing heuristic overall, in instances unseen
during training.

Offline reinforcement learning (RL) addresses the problem of learning a
performant policy from a fixed batch of data collected by following some
behavior policy. Model-based approaches are particularly appealing in the
offline setting since they can extract more learning signals from the logged
dataset by learning a model of the environment. However, the performance of
existing model-based approaches falls short of model-free counterparts, due to
the compounding of estimation errors in the learned model. Driven by this
observation, we argue that it is critical for a model-based method to
understand when to trust the model and when to rely on model-free estimates,
and how to act conservatively w.r.t. both. To this end, we derive an elegant
and simple methodology called conservative Bayesian model-based value expansion
for offline policy optimization (CBOP), that trades off model-free and
model-based estimates during the policy evaluation step according to their
epistemic uncertainties, and facilitates conservatism by taking a lower bound
on the Bayesian posterior value estimate. On the standard D4RL continuous
control tasks, we find that our method significantly outperforms previous
model-based approaches: e.g., MOPO by $116.4$%, MOReL by $23.2$% and COMBO by
$23.7$%. Further, CBOP achieves state-of-the-art performance on $11$ out of
$18$ benchmark datasets while doing on par on the remaining datasets.

This paper targets at improving the generalizability of hypergraph neural
networks in the low-label regime, through applying the contrastive learning
approach from images/graphs (we refer to it as HyperGCL). We focus on the
following question: How to construct contrastive views for hypergraphs via
augmentations? We provide the solutions in two folds. First, guided by domain
knowledge, we fabricate two schemes to augment hyperedges with higher-order
relations encoded, and adopt three vertex augmentation strategies from
graph-structured data. Second, in search of more effective views in a
data-driven manner, we for the first time propose a hypergraph generative model
to generate augmented views, and then an end-to-end differentiable pipeline to
jointly learn hypergraph augmentations and model parameters. Our technical
innovations are reflected in designing both fabricated and generative
augmentations of hypergraphs. The experimental findings include: (i) Among
fabricated augmentations in HyperGCL, augmenting hyperedges provides the most
numerical gains, implying that higher-order information in structures is
usually more downstream-relevant; (ii) Generative augmentations do better in
preserving higher-order information to further benefit generalizability; (iii)
HyperGCL also boosts robustness and fairness in hypergraph representation
learning. Codes are released at https://github.com/weitianxin/HyperGCL.

In this work, we provide a broad comparative analysis of strategies for
pre-training audio understanding models for several tasks in the music domain,
including labelling of genre, era, origin, mood, instrumentation, key, pitch,
vocal characteristics, tempo and sonority. Specifically, we explore how the
domain of pre-training datasets (music or generic audio) and the pre-training
methodology (supervised or unsupervised) affects the adequacy of the resulting
audio embeddings for downstream tasks.
  We show that models trained via supervised learning on large-scale
expert-annotated music datasets achieve state-of-the-art performance in a wide
range of music labelling tasks, each with novel content and vocabularies. This
can be done in an efficient manner with models containing less than 100 million
parameters that require no fine-tuning or reparameterization for downstream
tasks, making this approach practical for industry-scale audio catalogs.
  Within the class of unsupervised learning strategies, we show that the domain
of the training dataset can significantly impact the performance of
representations learned by the model. We find that restricting the domain of
the pre-training dataset to music allows for training with smaller batch sizes
while achieving state-of-the-art in unsupervised learning -- and in some cases,
supervised learning -- for music understanding.
  We also corroborate that, while achieving state-of-the-art performance on
many tasks, supervised learning can cause models to specialize to the
supervised information provided, somewhat compromising a model's generality.

Vision-language models such as CLIP are pretrained on large volumes of
internet sourced image and text pairs, and have been shown to sometimes exhibit
impressive zero- and low-shot image classification performance. However, due to
their size, fine-tuning these models on new datasets can be prohibitively
expensive, both in terms of the supervision and compute required. To combat
this, a series of light-weight adaptation methods have been proposed to
efficiently adapt such models when limited supervision is available. In this
work, we show that while effective on internet-style datasets, even those
remedies under-deliver on classification tasks with images that differ
significantly from those commonly found online. To address this issue, we
present a new approach called SVL-Adapter that combines the complementary
strengths of both vision-language pretraining and self-supervised
representation learning. We report an average classification accuracy
improvement of 10% in the low-shot setting when compared to existing methods,
on a set of challenging visual classification tasks. Further, we present a
fully automatic way of selecting an important blending hyperparameter for our
model that does not require any held-out labeled validation data. Code for our
project is available here: https://github.com/omipan/svl_adapter.

Background: Breast cancer is a multifactorial disease, genetic and
environmental factors will affect its incidence probability. Breast cancer
metastasis is one of the main cause of breast cancer related deaths reported by
the American Cancer Society (ACS). Method: the iMedBot is a web application
that we developed using the python Flask web framework and deployed on Amazon
Web Services. It contains a frontend and a backend. The backend is supported by
a python program we developed using the python Keras and scikit-learn packages,
which can be used to learn deep feedforward neural network (DFNN) models.
Result: the iMedBot can provide two main services: 1. it can predict 5-, 10-,
or 15-year breast cancer metastasis based on a set of clinical information
provided by a user. The prediction is done by using a set of DFNN models that
were pretrained, and 2. It can train DFNN models for a user using user-provided
dataset. The model trained will be evaluated using AUC and both the AUC value
and the AUC ROC curve will be provided. Conclusion: The iMedBot web application
provides a user-friendly interface for user-agent interaction in conducting
personalized prediction and model training. It is an initial attempt to convert
results of deep learning research into an online tool that may stir further
research interests in this direction. Keywords: Deep learning, Breast Cancer,
Web application, Model training.

We introduce a novel interface for large scale collection of human memory and
assistance. Using the 3D Matterport simulator we create a realistic indoor
environments in which we have people perform specific embodied memory tasks
that mimic household daily activities. This interface was then deployed on
Amazon Mechanical Turk allowing us to test and record human memory, navigation
and needs for assistance at a large scale that was previously impossible. Using
the interface we collect the `The Visually Grounded Memory Assistant Dataset'
which is aimed at developing our understanding of (1) the information people
encode during navigation of 3D environments and (2) conditions under which
people ask for memory assistance. Additionally we experiment with with
predicting when people will ask for assistance using models trained on
hand-selected visual and semantic features. This provides an opportunity to
build stronger ties between the machine-learning and cognitive-science
communities through learned models of human perception, memory, and cognition.

Although generative adversarial networks (GANs) have shown promise in medical
imaging, they have four main limitations that impeded their utility:
computational cost, data requirements, reliable evaluation measures, and
training complexity. Our work investigates each of these obstacles in a novel
application of StyleGAN2-ADA to high-resolution medical imaging datasets. Our
dataset is comprised of liver-containing axial slices from non-contrast and
contrast-enhanced computed tomography (CT) scans. Additionally, we utilized
four public datasets composed of various imaging modalities. We trained a
StyleGAN2 network with transfer learning (from the Flickr-Faces-HQ dataset) and
data augmentation (horizontal flipping and adaptive discriminator
augmentation). The network's generative quality was measured quantitatively
with the Fr\'echet Inception Distance (FID) and qualitatively with a visual
Turing test given to seven radiologists and radiation oncologists.
  The StyleGAN2-ADA network achieved a FID of 5.22 ($\pm$ 0.17) on our liver CT
dataset. It also set new record FIDs of 10.78, 3.52, 21.17, and 5.39 on the
publicly available SLIVER07, ChestX-ray14, ACDC, and Medical Segmentation
Decathlon (brain tumors) datasets. In the visual Turing test, the clinicians
rated generated images as real 42% of the time, approaching random guessing.
Our computational ablation study revealed that transfer learning and data
augmentation stabilize training and improve the perceptual quality of the
generated images. We observed the FID to be consistent with human perceptual
evaluation of medical images. Finally, our work found that StyleGAN2-ADA
consistently produces high-quality results without hyperparameter searches or
retraining.

This paper describes LOCL (Learning Object Attribute Composition using
Localization) that generalizes composition zero shot learning to objects in
cluttered and more realistic settings. The problem of unseen Object Attribute
(OA) associations has been well studied in the field, however, the performance
of existing methods is limited in challenging scenes. In this context, our key
contribution is a modular approach to localizing objects and attributes of
interest in a weakly supervised context that generalizes robustly to unseen
configurations. Localization coupled with a composition classifier
significantly outperforms state of the art (SOTA) methods, with an improvement
of about 12% on currently available challenging datasets. Further, the
modularity enables the use of localized feature extractor to be used with
existing OA compositional learning methods to improve their overall

It is often said that a deep learning model is "invariant" to some specific
type of transformation. However, what is meant by this statement strongly
depends on the context in which it is made. In this paper we explore the nature
of invariance and equivariance of deep learning models with the goal of better
understanding the ways in which they actually capture these concepts on a
formal level. We introduce a family of invariance and equivariance metrics that
allows us to quantify these properties in a way that disentangles them from
other metrics such as loss or accuracy. We use our metrics to better understand
the two most popular methods used to build invariance into networks: data
augmentation and equivariant layers. We draw a range of conclusions about
invariance and equivariance in deep learning models, ranging from whether
initializing a model with pretrained weights has an effect on a trained model's
invariance, to the extent to which invariance learned via training can
generalize to out-of-distribution data.

While there have been advancements in autonomous driving control and traffic
simulation, there have been little to no works exploring the unification of
both with deep learning. Works in both areas seem to focus on entirely
different exclusive problems, yet traffic and driving have inherent semantic
relations in the real world. In this paper, we present a generalizable
distillation-style method for traffic-informed imitation learning that directly
optimizes a autonomous driving policy for the overall benefit of faster traffic
flow and lower energy consumption. We capitalize on improving the arbitrarily
defined supervision of speed control in imitation learning systems, as most
driving research focus on perception and steering. Moreover, our method
addresses the lack of co-simulation between traffic and driving simulators and
lays groundwork for directly involving traffic simulation with autonomous
driving in future work. Our results show that, with information from traffic
simulation involved in supervision of imitation learning methods, an autonomous
vehicle can learn how to accelerate in a fashion that is beneficial for traffic
flow and overall energy consumption for all nearby vehicles.

In this paper we have introduced a novel method for gamma hadron separation
in Imaging Atmospheric Cherenkov Telescopes (IACT) using Quantum Machine
Learning. IACTs captures images of Extensive Air Showers (EAS) produced from
very high energy gamma rays. We have used the QML Algorithms, Quantum Support
Vector Classifier (QSVC) and Variational Quantum Classifier (VQC) for binary
classification of the events into signals (Gamma) and background(hadron) using
the image parameters. MAGIC Gamma Telescope dataset is used for this study
which was generated from Monte Carlo Software Coriska. These quantum algorithms
achieve performance comparable to standard multivariate classification
techniques and can be used to solve variety of real-world problems. The
classification accuracy is improved by hyper parameter tuning. We propose a new
architecture for using QSVC efficiently on large datasets and found that
clustering enhance the overall performance.

We report here on the first results of a systematic monitoring of southern
glitching pulsars at the Argentine Institute of Radio astronomy started on the
year 2019. We detected a major glitch in the Vela pulsar (PSR J0835$-$4510) and
two mini-glitches in PSR J1048$-$5832. For each glitch, we present the
measurement of glitch parameters by fitting timing residuals. We then make an
individual pulses study of Vela in observations previous and after the glitch.
We selected 6 days of observations around the major glitch on July 22nd 2021
and study their statistical properties with machine learning techniques. We use
Variational AutoEncoder (VAE) reconstruction of the pulses to separate them
clearly from the noise. We perform a study with Self-Organizing Maps (SOM)
clustering techniques and find an unusual behavior of the clusters two days
prior to the glitch. This behavior is only visible in the the higher amplitude
pulse clusters and if intrinsic to the pulsar could be interpreted as a
precursor of the glitch.

The PI Launchpad attempts to provide an entry level explanation of the
process of space mission development for new Principal Investigators (PIs). In
particular, PI launchpad has a focus on building teams, making partnerships,
and science concept maturity for a space mission concept, not necessarily
technical or engineering practices. Here we briefly summarize the goals of the
PI Launchpad workshops and present some results from the workshops held in 2019
and 2021. The workshop attempts to describe the current process of space
mission development (i.e. space-based telescopes and instrument platforms,
planetary missions of all types, etc.), covering a wide range of topics that a
new PI may need to successfully develop a team and write a proposal. It is not
designed to replace real experience but to provide an easily accessible
resource for potential PIs who seek to learn more about what it takes to submit
a space mission proposal, and what the first steps to take can be. The PI
Launchpad was created in response to the high barrier to entry for early career
or any scientist who is unfamiliar with mission design. These barriers have
been outlined in several recent papers and reports, and are called out in
recent space science Decadal reports.

Translating natural language queries (NLQ) into structured query language
(SQL) in interfaces to relational databases is a challenging task that has been
widely studied by researchers from both the database and natural language
processing communities. Numerous works have been proposed to attack the natural
language interfaces to databases (NLIDB) problem either as a conventional
pipeline-based or an end-to-end deep-learning-based solution. Nevertheless,
regardless of the approach preferred, such solutions exhibit black-box nature,
which makes it difficult for potential users targeted by these systems to
comprehend the decisions made to produce the translated SQL. To this end, we
propose xDBTagger, an explainable hybrid translation pipeline that explains the
decisions made along the way to the user both textually and visually. We also
evaluate xDBTagger quantitatively in three real-world relational databases. The
evaluation results indicate that in addition to being fully interpretable,
xDBTagger is effective in terms of accuracy and translates the queries more
efficiently compared to other state-of-the-art pipeline-based systems up to
10000 times.

Federated learning is a training paradigm that learns from multiple
distributed users without aggregating data on a centralized server. Such a
paradigm promises the ability to deploy machine-learning at-scale to a diverse
population of end-users without first collecting a large, labeled dataset for
all possible tasks. As federated learning typically averages learning updates
across a decentralized population, there is a growing need for personalization
of federated learning systems (i.e conversational agents must be able to
personalize to a specific user's preferences). In this work, we propose a new
direction for personalization research within federated learning, leveraging
both personal embeddings and shared context embeddings. We also present an
approach to predict these ``preference'' embeddings, enabling personalization
without backpropagation. Compared to state-of-the-art personalization
baselines, our approach achieves a 50\% improvement in test-time perplexity
using 0.001\% of the memory required by baseline approaches, and achieving
greater sample- and compute-efficiency.

We consider quantitative analyses of spectral data using laser-induced
breakdown spectroscopy. We address the small size of training data available,
and the validation of the predictions during inference on unknown data. For the
purpose, we build robust calibration models using deep convolutional multitask
learning architectures to predict the concentration of the analyte, alongside
additional spectral information as auxiliary outputs. These secondary
predictions can be used to validate the trustworthiness of the model by taking
advantage of the mutual dependencies of the parameters of the multitask neural
networks. Due to the experimental lack of training samples, we introduce a
simulation-based data augmentation process to synthesise an arbitrary number of
spectra, statistically representative of the experimental data. Given the
nature of the deep learning model, no dimensionality reduction or data
selection processes are required. The procedure is an end-to-end pipeline
including the process of synthetic data augmentation, the construction of a
suitable robust, homoscedastic, deep learning model, and the validation of its
predictions. In the article, we compare the performance of the multitask model
with traditional univariate and multivariate analyses, to highlight the
separate contributions of each element introduced in the process.

Map Space Exploration is the problem of finding optimized mappings of a Deep
Neural Network (DNN) model on an accelerator. It is known to be extremely
computationally expensive, and there has been active research looking at both
heuristics and learning-based methods to make the problem computationally
tractable. However, while there are dozens of mappers out there (all
empirically claiming to find better mappings than others), the research
community lacks systematic insights on how different search techniques navigate
the map-space and how different mapping axes contribute to the accelerator's
performance and efficiency. Such insights are crucial to developing mapping
frameworks for emerging DNNs that are increasingly irregular (due to neural
architecture search) and sparse, making the corresponding map spaces much more
complex. In this work, rather than proposing yet another mapper, we do a
first-of-its-kind apples-to-apples comparison of search techniques leveraged by
different mappers. Next, we extract the learnings from our study and propose
two new techniques that can augment existing mappers -- warm-start and
sparsity-aware -- that demonstrate speedups, scalability, and robustness across
diverse DNN models.

Receiving knowledge, abiding by laws, and being aware of regulations are
common behaviors in human society. Bearing in mind that reinforcement learning
(RL) algorithms benefit from mimicking humanity, in this work, we propose that
an RL agent can act on external guidance in both its learning process and model
deployment, making the agent more socially acceptable. We introduce the
concept, Knowledge-Grounded RL (KGRL), with a formal definition that an agent
learns to follow external guidelines and develop its own policy. Moving towards
the goal of KGRL, we propose a novel actor model with an embedding-based
attention mechanism that can attend to either a learnable internal policy or
external knowledge. The proposed method is orthogonal to training algorithms,
and the external knowledge can be flexibly recomposed, rearranged, and reused
in both training and inference stages. Through experiments on tasks with
discrete and continuous action space, our KGRL agent is shown to be more sample
efficient and generalizable, and it has flexibly rearrangeable knowledge
embeddings and interpretable behaviors.

Deep learning models often tackle the intra-sample structure, such as the
order of words in a sentence and pixels in an image, but have not pay much
attention to the inter-sample relationship. In this paper, we show that
explicitly modeling the inter-sample structure to be more discretized can
potentially help model's expressivity. We propose a novel method, Atom
Modeling, that can discretize a continuous latent space by drawing an analogy
between a data point and an atom, which is naturally spaced away from other
atoms with distances depending on their intra structures. Specifically, we
model each data point as an atom composed of electrons, protons, and neutrons
and minimize the potential energy caused by the interatomic force among data
points. Through experiments with qualitative analysis in our proposed Atom
Modeling on synthetic and real datasets, we find that Atom Modeling can improve
the performance by maintaining the inter-sample relation and can capture an
interpretable intra-sample relation by mapping each component in a data point
to electron/proton/neutron.

Large neural network models are commonly trained through a combination of
advanced parallelism strategies in a single program, multiple data (SPMD)
paradigm. For example, training large transformer models requires combining
data, model, and pipeline partitioning; and optimizer sharding techniques.
However, identifying efficient combinations for many model architectures and
accelerator systems requires significant manual analysis. In this work, we
present an automatic partitioner that identifies these combinations through a
goal-oriented search. Our key findings are that a Monte Carlo Tree Search-based
partitioner leveraging partition-specific compiler analysis directly into the
search and guided goals matches expert-level strategies for various models.

Work on fast weight programmers has demonstrated the effectiveness of
key/value outer product-based learning rules for sequentially generating a
weight matrix (WM) of a neural net (NN) by another NN or itself. However, the
weight generation steps are typically not visually interpretable by humans,
because the contents stored in the WM of an NN are not. Here we apply the same
principle to generate natural images. The resulting fast weight painters (FPAs)
learn to execute sequences of delta learning rules to sequentially generate
images as sums of outer products of self-invented keys and values, one rank at
a time, as if each image was a WM of an NN. We train our FPAs in the generative
adversarial networks framework, and evaluate on various image datasets. We show
how these generic learning rules can generate images with respectable visual
quality without any explicit inductive bias for images. While the performance
largely lags behind the one of specialised state-of-the-art image generators,
our approach allows for visualising how synaptic learning rules iteratively
produce complex connection patterns, yielding human-interpretable meaningful
images. Finally, we also show that an additional convolutional U-Net (now
popular in diffusion models) at the output of an FPA can learn one-step
"denoising" of FPA-generated images to enhance their quality. Our code is
public.

Recent years have seen growing interest among both researchers and
practitioners in user-driven approaches to algorithm auditing, which directly
engage users in detecting problematic behaviors in algorithmic systems.
However, we know little about industry practitioners' current practices and
challenges around user-driven auditing, nor what opportunities exist for them
to better leverage such approaches in practice. To investigate, we conducted a
series of interviews and iterative co-design activities with practitioners who
employ user-driven auditing approaches in their work. Our findings reveal
several challenges practitioners face in appropriately recruiting and
incentivizing user auditors, scaffolding user audits, and deriving actionable
insights from user-driven audit reports. Furthermore, practitioners shared
organizational obstacles to user-driven auditing, surfacing a complex
relationship between practitioners and user auditors. Based on these findings,
we discuss opportunities for future HCI research to help realize the potential
(and mitigate risks) of user-driven auditing in industry practice.

For many applications of probabilistic classifiers it is important that the
predicted confidence vectors reflect true probabilities (one says that the
classifier is calibrated). It has been shown that common models fail to satisfy
this property, making reliable methods for measuring and improving calibration
important tools. Unfortunately, obtaining these is far from trivial for
problems with many classes. We propose two techniques that can be used in
tandem. First, a reduced calibration method transforms the original problem
into a simpler one. We prove for several notions of calibration that solving
the reduced problem minimizes the corresponding notion of miscalibration in the
full problem, allowing the use of non-parametric recalibration methods that
fail in higher dimensions. Second, we propose class-wise calibration methods,
based on intuition building on a phenomenon called neural collapse and the
observation that most of the accurate classifiers found in practice can be
thought of as a union of K different functions which can be recalibrated
separately, one for each class. These typically out-perform their non
class-wise counterparts, especially for classifiers trained on imbalanced data
sets. Applying the two methods together results in class-wise reduced
calibration algorithms, which are powerful tools for reducing the prediction
and per-class calibration errors. We demonstrate our methods on real and
synthetic datasets and release all code as open source at
https://github.com/appliedAI-Initiative

Having access to the parton-level kinematics is important for understanding
the internal dynamics of particle collisions. Here, we present new results
aiming to an efficient reconstruction of parton collisions using
machine-learning techniques. By simulating the collider events, we related
experimentally-accessible quantities with the momentum fractions of the
involved partons. We used photon-hadron production to exploit the cleanliness
of the photon signal, including up to NLO QCD-QED corrections. Neural networks
led to an outstanding reconstruction efficiency, suggesting a powerful strategy
for unveiling the behaviour of the fundamental bricks of matter in high-energy
collisions.

Neural Machine Translation (NMT) systems have received much recent attention
due to their human-level accuracy. While existing works mostly focus on either
improving accuracy or testing accuracy robustness, the computation efficiency
of NMT systems, which is of paramount importance due to often vast translation
demands and real-time requirements, has surprisingly received little attention.
In this paper, we make the first attempt to understand and test potential
computation efficiency robustness in state-of-the-art NMT systems. By analyzing
the working mechanism and implementation of 1455 public-accessible NMT systems,
we observe a fundamental property in NMT systems that could be manipulated in
an adversarial manner to reduce computation efficiency significantly. Our key
motivation is to generate test inputs that could sufficiently delay the
generation of EOS such that NMT systems would have to go through enough
iterations to satisfy the pre-configured threshold. We present NMTSloth, which
develops a gradient-guided technique that searches for a minimal and
unnoticeable perturbation at character-level, token-level, and structure-level,
which sufficiently delays the appearance of EOS and forces these inputs to
reach the naturally-unreachable threshold. To demonstrate the effectiveness of
NMTSloth, we conduct a systematic evaluation on three public-available NMT
systems: Google T5, AllenAI WMT14, and Helsinki-NLP translators. Experimental
results show that NMTSloth can increase NMT systems' response latency and
energy consumption by 85% to 3153% and 86% to 3052%, respectively, by
perturbing just one character or token in the input sentence. Our case study
shows that inputs generated by NMTSloth significantly affect the battery power
in real-world mobile devices (i.e., drain more than 30 times battery power than
normal inputs).

We present a neural point cloud rendering pipeline through a novel
multi-frequency-aware patch adversarial learning framework. The proposed
approach aims to improve the rendering realness by minimizing the spectrum
discrepancy between real and synthesized images, especially on the
high-frequency localized sharpness information which causes image blur
visually. Specifically, a patch multi-discriminator scheme is proposed for the
adversarial learning, which combines both spectral domain (Fourier Transform
and Discrete Wavelet Transform) discriminators as well as the spatial (RGB)
domain discriminator to force the generator to capture global and local
spectral distributions of the real images. The proposed multi-discriminator
scheme not only helps to improve rendering realness, but also enhance the
convergence speed and stability of adversarial learning. Moreover, we introduce
a noise-resistant voxelisation approach by utilizing both the appearance
distance and spatial distance to exclude the spatial outlier points caused by
depth noise. Our entire architecture is fully differentiable and can be learned
in an end-to-end fashion. Extensive experiments show that our method produces
state-of-the-art results for neural point cloud rendering by a significant
margin. Our source code will be made public at a later date.

Anaphora resolution is an important task for information extraction across a
range of languages, text genres, and domains, motivating the need for methods
that do not require large annotated datasets. In-context learning has emerged
as a promising approach, yet there are a number of challenges in applying
in-context learning to resolve anaphora. For example, encoding a single
in-context demonstration that consists of: an anaphor, a paragraph-length
context, and a list of corresponding antecedents, requires conditioning a
language model on a long sequence of tokens, limiting the number of
demonstrations per prompt. In this paper, we present MICE (Mixtures of
In-Context Experts), which we demonstrate is effective for few-shot anaphora
resolution in scientific protocols (Tamari et al., 2021). Given only a handful
of training examples, MICE combines the predictions of hundreds of in-context
experts, yielding a 30% increase in F1 score over a competitive prompt
retrieval baseline. Furthermore, we show MICE can be used to train compact
student models without sacrificing performance. As far as we are aware, this is
the first work to present experimental results demonstrating the effectiveness
of in-context learning on the task of few-shot anaphora resolution in
scientific protocols.

Being different from deep-learning-based (DL-based) image generation methods,
a new image generative model built upon successive subspace learning principle
is proposed and named GenHop (an acronym of Generative PixelHop) in this work.
GenHop consists of three modules: 1) high-to-low dimension reduction, 2) seed
image generation, and 3) low-to-high dimension expansion. In the first module,
it builds a sequence of high-to-low dimensional subspaces through a sequence of
whitening processes, each of which contains samples of joint-spatial-spectral
representation. In the second module, it generates samples in the lowest
dimensional subspace. In the third module, it finds a proper high-dimensional
sample for a seed image by adding details back via locally linear embedding
(LLE) and a sequence of coloring processes. Experiments show that GenHop can
generate visually pleasant images whose FID scores are comparable or even
better than those of DL-based generative models for MNIST, Fashion-MNIST and
CelebA datasets.

Strong static type systems help programmers eliminate many errors without
much burden of supplying type annotations. However, this flexibility makes it
highly non-trivial to diagnose ill-typed programs, especially for novice
programmers. Compared to classic constraint solving and optimization-based
approaches, the data-driven approach has shown great promise in identifying the
root causes of type errors with higher accuracy. Instead of relying on
hand-engineered features, this work explores natural language models for type
error localization, which can be trained in an end-to-end fashion without
requiring any features. We demonstrate that, for novice type error diagnosis,
the language model-based approach significantly outperforms the previous
state-of-the-art data-driven approach. Specifically, our model could predict
type errors correctly 62% of the time, outperforming the state-of-the-art
Nate's data-driven model by 11%, in a more rigorous accuracy metric.
Furthermore, we also apply structural probes to explain the performance
difference between different language models.

Temporal distributional shifts, with underlying dynamics changing over time,
frequently occur in real-world time series, and pose a fundamental challenge
for deep neural networks (DNNs). In this paper, we propose a novel deep
sequence model based on the Koopman theory for time series forecasting: Koopman
Neural Forecaster (KNF) that leverages DNNs to learn the linear Koopman space
and the coefficients of chosen measurement functions. KNF imposes appropriate
inductive biases for improved robustness against distributional shifts,
employing both a global operator to learn shared characteristics, and a local
operator to capture changing dynamics, as well as a specially-designed feedback
loop to continuously update the learnt operators over time for rapidly varying
behaviors. To the best of our knowledge, this is the first time that Koopman
theory is applied to real-world chaotic time series without known governing
laws. We demonstrate that KNF achieves the superior performance compared to the
alternatives, on multiple time series datasets that are shown to suffer from
distribution shifts.

Scheduling plays an important role in automated production. Its impact can be
found in various fields such as the manufacturing industry, the service
industry and the technology industry. A scheduling problem (NP-hard) is a task
of finding a sequence of job assignments on a given set of machines with the
goal of optimizing the objective defined. Methods such as Operation Research,
Dispatching Rules, and Combinatorial Optimization have been applied to
scheduling problems but no solution guarantees to find the optimal solution.
The recent development of Reinforcement Learning has shown success in
sequential decision-making problems. This research presents a Reinforcement
Learning approach for scheduling problems. In particular, this study delivers
an OpenAI gym environment with search-space reduction for Job Shop Scheduling
Problems and provides a heuristic-guided Q-Learning solution with
state-of-the-art performance for Multi-agent Flexible Job Shop Problems.

When analyzing a dataset, it can be useful to assess how smooth the decision
boundaries need to be for a model to better fit the data. This paper addresses
this question by proposing the quantification of how much should the 'rigid'
decision boundaries, produced by an algorithm that naturally finds such
solutions, be relaxed to obtain a performance improvement. The approach we
propose starts with the rigid decision boundaries of a seed Decision Tree (seed
DT), which is used to initialize a Neural DT (NDT). The initial boundaries are
challenged by relaxing them progressively through training the NDT. During this
process, we measure the NDT's performance and decision agreement to its seed
DT. We show how these two measures can help the user in figuring out how
expressive his model should be, before exploring it further via model
selection. The validity of our approach is demonstrated with experiments on
simulated and benchmark datasets.

Quantizing a Deep Neural Network (DNN) model to be used on a custom
accelerator with efficient fixed-point hardware implementations, requires
satisfying many stringent hardware-friendly quantization constraints to train
the model. We evaluate the two main classes of hardware-friendly quantization
methods in the context of weight quantization: the traditional Mean Squared
Quantization Error (MSQE)-based methods and the more recent gradient-based
methods. We study the two methods on MobileNetV1 and MobileNetV2 using multiple
empirical metrics to identify the sources of performance differences between
the two classes, namely, sensitivity to outliers and convergence instability of
the quantizer scaling factor. Using those insights, we propose various
techniques to improve the performance of both quantization methods - they fix
the optimization instability issues present in the MSQE-based methods during
quantization of MobileNet models and allow us to improve validation performance
of the gradient-based methods by 4.0% and 3.3% for MobileNetV1 and MobileNetV2
on ImageNet respectively.

Communication within or between complex systems is commonplace in the natural
sciences and fields such as graph neural networks. The brain is a perfect
example of such a complex system, where communication between brain regions is
constantly being orchestrated. To analyze communication, the brain is often
split up into anatomical regions that each perform certain computations. These
regions must interact and communicate with each other to perform tasks and
support higher-level cognition. On a macroscale, these regions communicate
through signal propagation along the cortex and along white matter tracts over
longer distances. When and what types of signals are communicated over time is
an unsolved problem and is often studied using either functional or structural
data. In this paper, we propose a non-linear generative approach to
communication from functional data. We address three issues with common
connectivity approaches by explicitly modeling the directionality of
communication, finding communication at each timestep, and encouraging
sparsity. To evaluate our model, we simulate temporal data that has sparse
communication between nodes embedded in it and show that our model can uncover
the expected communication dynamics. Subsequently, we apply our model to
temporal neural data from multiple tasks and show that our approach models
communication that is more specific to each task. The specificity of our method
means it can have an impact on the understanding of psychiatric disorders,
which are believed to be related to highly specific communication between brain
regions compared to controls. In sum, we propose a general model for dynamic
communication learning on graphs, and show its applicability to a subfield of
the natural sciences, with potential widespread scientific impact.

Computer-aided pathology diagnosis based on the classification of Whole Slide
Image (WSI) plays an important role in clinical practice, and it is often
formulated as a weakly-supervised Multiple Instance Learning (MIL) problem.
Existing methods solve this problem from either a bag classification or an
instance classification perspective. In this paper, we propose an end-to-end
weakly supervised knowledge distillation framework (WENO) for WSI
classification, which integrates a bag classifier and an instance classifier in
a knowledge distillation framework to mutually improve the performance of both
classifiers. Specifically, an attention-based bag classifier is used as the
teacher network, which is trained with weak bag labels, and an instance
classifier is used as the student network, which is trained using the
normalized attention scores obtained from the teacher network as soft pseudo
labels for the instances in positive bags. An instance feature extractor is
shared between the teacher and the student to further enhance the knowledge
exchange between them. In addition, we propose a hard positive instance mining
strategy based on the output of the student network to force the teacher
network to keep mining hard positive instances. WENO is a plug-and-play
framework that can be easily applied to any existing attention-based bag
classification methods. Extensive experiments on five datasets demonstrate the
efficiency of WENO. Code is available at https://github.com/miccaiif/WENO.

Learning goal conditioned control in the real world is a challenging open
problem in robotics. Reinforcement learning systems have the potential to learn
autonomously via trial-and-error, but in practice the costs of manual reward
design, ensuring safe exploration, and hyperparameter tuning are often enough
to preclude real world deployment. Imitation learning approaches, on the other
hand, offer a simple way to learn control in the real world, but typically
require costly curated demonstration data and lack a mechanism for continuous
improvement. Recently, iterative imitation techniques have been shown to learn
goal directed control from undirected demonstration data, and improve
continuously via self-supervised goal reaching, but results thus far have been
limited to simulated environments. In this work, we present evidence that
iterative imitation learning can scale to goal-directed behavior on a real
robot in a dynamic setting: high speed, precision table tennis (e.g. "land the
ball on this particular target"). We find that this approach offers a
straightforward way to do continuous on-robot learning, without complexities
such as reward design or sim-to-real transfer. It is also scalable -- sample
efficient enough to train on a physical robot in just a few hours. In real
world evaluations, we find that the resulting policy can perform on par or
better than amateur humans (with players sampled randomly from a robotics lab)
at the task of returning the ball to specific targets on the table. Finally, we
analyze the effect of an initial undirected bootstrap dataset size on
performance, finding that a modest amount of unstructured demonstration data
provided up-front drastically speeds up the convergence of a general purpose
goal-reaching policy. See https://sites.google.com/view/goals-eye for videos.

Keeping the power system stable is becoming more challenging with the growing
share of renewable energy sources of low or negligible inertia. Inertia
constants for individual power plants are generally not known and are roughly
estimated by considering the type of power generation technology and the
nameplate capacity of plants. More accurate knowledge of inertia constants of
individual power plants would give greater transparency to decisions of the
transmission system operator (TSO) and to auctions for the procurement of
inertia services. Additionally, a more accurate forecast or estimation of
system inertia would improve the price signals to the power market well in
advance of balancing actions taken by the TSO. We develop methods based on a
combination of mathematical optimisation and machine learning that
reverse-engineer the inertia constants of individual power plants from the
historical values of their power production and from aggregate values of
inertia in a power system. We demonstrate the methods for the power system of
Great Britain (GB), where historical values for aggregate inertia are published
by the TSO. We show that the recovered inertia data is crucial in understanding
certain individual balancing decisions by the TSO. We use the
reverse-engineered inertia constants to predict system inertia which gives
valuable information to the power market.

In this paper, we present a spatio-temporal tendency reasoning (STR) network
for recovering human body pose and shape from videos. Previous approaches have
focused on how to extend 3D human datasets and temporal-based learning to
promote accuracy and temporal smoothing. Different from them, our STR aims to
learn accurate and natural motion sequences in an unconstrained environment
through temporal and spatial tendency and to fully excavate the spatio-temporal
features of existing video data. To this end, our STR learns the representation
of features in the temporal and spatial dimensions respectively, to concentrate
on a more robust representation of spatio-temporal features. More specifically,
for efficient temporal modeling, we first propose a temporal tendency reasoning
(TTR) module. TTR constructs a time-dimensional hierarchical residual
connection representation within a video sequence to effectively reason
temporal sequences' tendencies and retain effective dissemination of human
information. Meanwhile, for enhancing the spatial representation, we design a
spatial tendency enhancing (STE) module to further learns to excite spatially
time-frequency domain sensitive features in human motion information
representations. Finally, we introduce integration strategies to integrate and
refine the spatio-temporal feature representations. Extensive experimental
findings on large-scale publically available datasets reveal that our STR
remains competitive with the state-of-the-art on three datasets. Our code are
available at https://github.com/Changboyang/STR.git.

We propose a novel deep learning (DL) approach to solve one-dimensional
non-linear elliptic, parabolic, and hyperbolic problems on graphs. A system of
physics-informed neural network (PINN) models is used to solve the differential
equations, by assigning each PINN model to a specific edge of the graph.
Kirkhoff-Neumann (KN) nodal conditions are imposed in a weak form by adding a
penalization term to the training loss function. Through the penalization term
that imposes the KN conditions, PINN models associated with edges that share a
node coordinate with each other to ensure continuity of the solution and of its
directional derivatives computed along the respective edges. Using individual
PINN models for each edge of the graph allows our approach to fulfill necessary
requirements for parallelization by enabling different PINN models to be
trained on distributed compute resources. Numerical results show that the
system of PINN models accurately approximate the solutions of the differential
problems across the entire graph for a broad set of graph topologies.

Neural network weights are typically initialized at random from univariate
distributions, controlling just the variance of individual weights even in
highly-structured operations like convolutions. Recent ViT-inspired
convolutional networks such as ConvMixer and ConvNeXt use large-kernel
depthwise convolutions whose learned filters have notable structure; this
presents an opportunity to study their empirical covariances. In this work, we
first observe that such learned filters have highly-structured covariance
matrices, and moreover, we find that covariances calculated from small networks
may be used to effectively initialize a variety of larger networks of different
depths, widths, patch sizes, and kernel sizes, indicating a degree of
model-independence to the covariance structure. Motivated by these findings, we
then propose a learning-free multivariate initialization scheme for
convolutional filters using a simple, closed-form construction of their
covariance. Models using our initialization outperform those using traditional
univariate initializations, and typically meet or exceed the performance of
those initialized from the covariances of learned filters; in some cases, this
improvement can be achieved without training the depthwise convolutional
filters at all.

Ontonotes has served as the most important benchmark for coreference
resolution. However, for ease of annotation, several long documents in
Ontonotes were split into smaller parts. In this work, we build a corpus of
coreference-annotated documents of significantly longer length than what is
currently available. We do so by providing an accurate, manually-curated,
merging of annotations from documents that were split into multiple parts in
the original Ontonotes annotation process. The resulting corpus, which we call
LongtoNotes contains documents in multiple genres of the English language with
varying lengths, the longest of which are up to 8x the length of documents in
Ontonotes, and 2x those in Litbank. We evaluate state-of-the-art neural
coreference systems on this new corpus, analyze the relationships between model
architectures/hyperparameters and document length on performance and efficiency
of the models, and demonstrate areas of improvement in long-document
coreference modeling revealed by our new corpus. Our data and code is available
at: https://github.com/kumar-shridhar/LongtoNotes.

While deep reinforcement learning (RL) agents have showcased strong results
across many domains, a major concern is their inherent opaqueness and the
safety of such systems in real-world use cases. To overcome these issues, we
need agents that can quantify their uncertainty and detect out-of-distribution
(OOD) states. Existing uncertainty estimation techniques, like Monte-Carlo
Dropout or Deep Ensembles, have not seen widespread adoption in on-policy deep
RL. We posit that this is due to two reasons: concepts like uncertainty and OOD
states are not well defined compared to supervised learning, especially for
on-policy RL methods. Secondly, available implementations and comparative
studies for uncertainty estimation methods in RL have been limited. To overcome
the first gap, we propose definitions of uncertainty and OOD for Actor-Critic
RL algorithms, namely, proximal policy optimization (PPO), and present possible
applicable measures. In particular, we discuss the concepts of value and policy
uncertainty. The second point is addressed by implementing different
uncertainty estimation methods and comparing them across a number of
environments. The OOD detection performance is evaluated via a custom
evaluation benchmark of in-distribution (ID) and OOD states for various RL
environments. We identify a trade-off between reward and OOD detection
performance. To overcome this, we formulate a Pareto optimization problem in
which we simultaneously optimize for reward and OOD detection performance. We
show experimentally that the recently proposed method of Masksembles strikes a
favourable balance among the survey methods, enabling high-quality uncertainty
estimation and OOD detection while matching the performance of original RL
agents.

There are complaints about current machine learning techniques such as the
requirement of a huge amount of training data and proficient training skills,
the difficulty of continual learning, the risk of catastrophic forgetting, the
leaking of data privacy/proprietary, etc. Most research efforts have been
focusing on one of those concerned issues separately, paying less attention to
the fact that most issues are entangled in practice. The prevailing big model
paradigm, which has achieved impressive results in natural language processing
and computer vision applications, has not yet addressed those issues, whereas
becoming a serious source of carbon emissions. This article offers an overview
of the learnware paradigm, which attempts to enable users not need to build
machine learning models from scratch, with the hope of reusing small models to
do things even beyond their original purposes, where the key ingredient is the
specification which enables a trained model to be adequately identified to
reuse according to the requirement of future users who know nothing about the
model in advance.

The detection of hazardous conditions near public transit stations is
necessary for ensuring the safety and accessibility of public transit. Smart
city infrastructures aim to facilitate this task among many others through the
use of computer vision. However, most state-of-the-art computer vision models
require thousands of images in order to perform accurate detection, and there
exist few images of hazardous conditions as they are generally rare. In this
paper, we examine the detection of snow-covered sidewalks along bus routes.
Previous work has focused on detecting other vehicles in heavy snowfall or
simply detecting the presence of snow. However, our application has an added
complication of determining if the snow covers areas of importance and can
cause falls or other accidents (e.g. snow covering a sidewalk) or simply covers
some background area (e.g. snow on a neighboring field). This problem involves
localizing the positions of the areas of importance when they are not
necessarily visible.
  We introduce a method that utilizes Structure from Motion (SfM) rather than
additional annotated data to address this issue. Specifically, our method
learns the locations of sidewalks in a given scene by applying a segmentation
model and SfM to images from bus cameras during clear weather. Then, we use the
learned locations to detect if and where the sidewalks become obscured with
snow. After evaluating across various threshold parameters, we identify an
optimal range at which our method consistently classifies different categories
of sidewalk images correctly. Although we demonstrate an application for snow
coverage along bus routes, this method can extend to other hazardous conditions
as well. Code for this project is available at
https://github.com/ind1010/SfM_for_BusEdge.

Cyber-Physical Systems (CPS) are increasingly complex and frequently
integrated into modern societies via critical infrastructure systems, products,
and services. Consequently, there is a need for reliable functionality of these
complex systems under various scenarios, from physical failures due to aging,
through to cyber attacks. Indeed, the development of effective strategies to
restore disrupted infrastructure systems continues to be a major challenge.
Hitherto, there have been an increasing number of papers evaluating
cyber-physical infrastructures, yet a comprehensive review focusing on
mathematical modeling and different optimization methods is still lacking.
Thus, this review paper appraises the literature on optimization techniques for
CPS facing disruption, to synthesize key findings on the current methods in
this domain. A total of 108 relevant research papers are reviewed following an
extensive assessment of all major scientific databases. The main mathematical
modeling practices and optimization methods are identified for both
deterministic and stochastic formulations, categorizing them based on the
solution approach (exact, heuristic, meta-heuristic), objective function, and
network size. We also perform keyword clustering and bibliographic coupling
analyses to summarize the current research trends. Future research needs in
terms of the scalability of optimization algorithms are discussed. Overall,
there is a need to shift towards more scalable optimization solution
algorithms, empowered by data-driven methods and machine learning, to provide
reliable decision-support systems for decision-makers and practitioners.

The application of blockchain technology can be beneficial in the field of
healthcare as well as in the fight against the COVID-19 epidemic. In this work,
the importance of blockchain is analyzed and it is observed that blockchain
technology and the processes associated with it will be utilised in the
healthcare systems of the future for data acquisition from sensors, automatic
patient monitoring, and secure data storage. This technology substantially
simplifies the process of carrying out operations because it can store a
substantial quantity of data in a dispersed and secure manner, as well as
enable access whenever and wherever it is required to do so. With the
assistance of quantum blockchain, the benefits of quantum computing, such as
the capability to acquire thermal imaging based on quantum computing and the
speed with which patients may be located and monitored, can all be exploited to
their full potential. Quantum blockchain is another tool that can be utilised
to maintain the confidentiality, authenticity, and accessibility of data
records. The processing of medical records could potentially benefit from
greater speed and privacy if it combines quantum computing and blockchain
technology. The authors of this paper investigate the possible benefits and
applications of blockchain and quantum technologies in the field of medicine,
pharmacy and healthcare systems. In this context, this work explored and
compared quantum technologies and blockchain-based technologies in conjunction
with other cutting-edge information and communications technologies such as
ratification intelligence, machine learning, drones, and so on.

In split machine learning (ML), different partitions of a neural network (NN)
are executed by different computing nodes, requiring a large amount of
communication cost. To ease communication burden, over-the-air computation
(OAC) can efficiently implement all or part of the computation at the same time
of communication. Based on the proposed system, the system implementation over
wireless network is introduced and we provide the problem formulation. In
particular, we show that the inter-layer connection in a NN of any size can be
mathematically decomposed into a set of linear precoding and combining
transformations over MIMO channels. Therefore, the precoding matrix at the
transmitter and the combining matrix at the receiver of each MIMO link, as well
as the channel matrix itself, can jointly serve as a fully connected layer of
the NN. The generalization of the proposed scheme to the conventional NNs is
also introduced. Finally, we extend the proposed scheme to the widely used
convolutional neural networks and demonstrate its effectiveness under both the
static and quasi-static memory channel conditions with comprehensive
simulations. In such a split ML system, the precoding and combining matrices
are regarded as trainable parameters, while MIMO channel matrix is regarded as
unknown (implicit) parameters.

As robots become more accessible outside of industrial settings, the need for
reliable object grasping and manipulation grows significantly. In such dynamic
environments it is expected that the robot is capable of reliably grasping and
manipulating novel objects in different situations. In this work we present
GraspCaps: a novel architecture based on Capsule Networks for generating
per-point grasp configurations for familiar objects. In our work, the
activation vector of each capsule in the deepest capsule layer corresponds to
one specific class of object. This way, the network is able to extract a rich
feature vector of the objects present in the point cloud input, which is then
used for generating per-point grasp vectors. This approach should allow the
network to learn specific grasping strategies for each of the different object
categories. Along with GraspCaps we present a method for generating a large
object grasping dataset using simulated annealing. The obtained dataset is then
used to train the GraspCaps network. We performed an extensive set of
experiments to assess the performance of the proposed approach regarding
familiar object recognition accuracy and grasp success rate on challenging real
and simulated scenarios.

Pose Guided Human Image Synthesis (PGHIS) is a challenging task of
transforming a human image from the reference pose to a target pose while
preserving its style. Most existing methods encode the texture of the whole
reference human image into a latent space, and then utilize a decoder to
synthesize the image texture of the target pose. However, it is difficult to
recover the detailed texture of the whole human image. To alleviate this
problem, we propose a method by decoupling the human body into several parts
(\eg, hair, face, hands, feet, \etc) and then using each of these parts to
guide the synthesis of a realistic image of the person, which preserves the
detailed information of the generated images. In addition, we design a
multi-head attention-based module for PGHIS. Because most convolutional neural
network-based methods have difficulty in modeling long-range dependency due to
the convolutional operation, the long-range modeling capability of attention
mechanism is more suitable than convolutional neural networks for pose transfer
task, especially for sharp pose deformation. Extensive experiments on
Market-1501 and DeepFashion datasets reveal that our method almost outperforms
other existing state-of-the-art methods in terms of both qualitative and
quantitative metrics.

Capturing the evolving trends of user interest is important for both
recommendation systems and advertising systems, and user behavior sequences
have been successfully used in Click-Through-Rate(CTR) prediction problems.
However, if the user interest is learned on the basis of item-level behaviors,
the performance may be affected by the following two issues. Firstly, some
casual outliers might be included in the behavior sequences as user behaviors
are likely to be diverse. Secondly, the span of time intervals between user
behaviors is random and irregular, for which a RNN-based module employed from
NLP is not perfectly adaptive. To handle these two issues, we propose the
Knowledge aware Adaptive Session multi-Topic network(KAST). It can adaptively
segment user sessions from the whole user behavior sequence, and maintain
similar intents in the same session. Furthermore, in order to improve the
quality of session segmentation and representation, a knowledge-aware module is
introduced so that the structural information from the user-item interaction
can be extracted in an end-to-end manner, and a marginal based loss with these
information is merged into the major loss. Through extensive experiments on
public benchmarks, we demonstrate that KAST can achieve superior performance
than state-of-the-art methods for CTR prediction, and key modules and
hyper-parameters are also evaluated.

In this work of thesis we introduce and study a new family of sorting
devices, which we call pattern-avoiding machines. They consist of two stacks in
series, equipped with a greedy procedure. On both stacks we impose a static
constraint in terms of pattern containment: reading the content from top to
bottom, the first stack is not allowed to contain occurrences of a given
pattern $\sigma$, whereas the second one is not allowed to contain occurrences
of $21$. By analyzing the behavior of pattern-avoiding machines, we aim to gain
a better understanding of the problem of sorting permutations with two
consecutive stacks, which is currently one of the most challenging open
problems in combinatorics.

Recent years have seen advances on principles and guidance relating to
accountable and ethical use of artificial intelligence (AI) spring up around
the globe. Specifically, Data Privacy, Accountability, Interpretability,
Robustness, and Reasoning have been broadly recognized as fundamental
principles of using machine learning (ML) technologies on decision-critical
and/or privacy-sensitive applications. On the other hand, in tremendous
real-world applications, data itself can be well represented as various
structured formalisms, such as graph-structured data (e.g., networks),
grid-structured data (e.g., images), sequential data (e.g., text), etc. By
exploiting the inherently structured knowledge, one can design plausible
approaches to identify and use more relevant variables to make reliable
decisions, thereby facilitating real-world deployments.

Recent advancements in computer vision, particularly by making use of deep
learning, have drastically improved human motion analysis in videos. However,
these improvements have not yet fully translated into improved performance in
clinical in-bed scenarios due to the lack of public datasets representative of
this scenario. To address this issue, we introduce BlanketSet, an RGB-IR-D
action recognition dataset of sequences performed in a hospital bed. This
dataset has the potential to help bridge the improvements attained in general
use cases to these clinical scenarios. The dataset is available at
https://rdm.inesctec.pt/dataset/nis-2022-004

Learning a new task from a handful of examples remains an open challenge in
machine learning. Despite the recent progress in few-shot learning, most
methods rely on supervised pretraining or meta-learning on labeled
meta-training data and cannot be applied to the case where the pretraining data
is unlabeled. In this study, we present an unsupervised few-shot learning
method via deep Laplacian eigenmaps. Our method learns representation from
unlabeled data by grouping similar samples together and can be intuitively
interpreted by random walks on augmented training data. We analytically show
how deep Laplacian eigenmaps avoid collapsed representation in unsupervised
learning without explicit comparison between positive and negative samples. The
proposed method significantly closes the performance gap between supervised and
unsupervised few-shot learning. Our method also achieves comparable performance
to current state-of-the-art self-supervised learning methods under linear
evaluation protocol.

Semi-supervised learning and weakly supervised learning are important
paradigms that aim to reduce the growing demand for labeled data in current
machine learning applications. In this paper, we introduce a novel analysis of
the classical label propagation algorithm (LPA) (Zhu & Ghahramani, 2002) that
moreover takes advantage of useful prior information, specifically
probabilistic hypothesized labels on the unlabeled data. We provide an error
bound that exploits both the local geometric properties of the underlying graph
and the quality of the prior information. We also propose a framework to
incorporate multiple sources of noisy information. In particular, we consider
the setting of weak supervision, where our sources of information are weak
labelers. We demonstrate the ability of our approach on multiple benchmark
weakly supervised classification tasks, showing improvements upon existing
semi-supervised and weakly supervised methods.

The appearance of strong CDCL-based propositional (SAT) solvers has greatly
advanced several areas of automated reasoning (AR). One of the directions in AR
is thus to apply SAT solvers to expressive formalisms such as first-order
logic, for which large corpora of general mathematical problems exist today.
This is possible due to Herbrand's theorem, which allows reduction of
first-order problems to propositional problems by instantiation. The core
challenge is choosing the right instances from the typically infinite Herbrand
universe. In this work, we develop the first machine learning system targeting
this task, addressing its combinatorial and invariance properties. In
particular, we develop a GNN2RNN architecture based on an invariant graph
neural network (GNN) that learns from problems and their solutions
independently of symbol names (addressing the abundance of skolems), combined
with a recurrent neural network (RNN) that proposes for each clause its
instantiations. The architecture is then trained on a corpus of mathematical
problems and their instantiation-based proofs, and its performance is evaluated
in several ways. We show that the trained system achieves high accuracy in
predicting the right instances, and that it is capable of solving many problems
by educated guessing when combined with a ground solver. To our knowledge, this
is the first convincing use of machine learning in synthesizing relevant
elements from arbitrary Herbrand universes.

Novel class discovery (NCD) aims at learning a model that transfers the
common knowledge from a class-disjoint labelled dataset to another unlabelled
dataset and discovers new classes (clusters) within it. Many methods have been
proposed as well as elaborate training pipelines and appropriate objectives and
considerably boosted the performance on NCD tasks. Despite all this, we find
that the existing methods do not sufficiently take advantage of the essence of
the NCD setting. To this end, in this paper, we propose to model both
inter-class and intra-class constraints in NCD based on the symmetric
Kullback-Leibler divergence (sKLD). Specifically, we propose an inter-class
sKLD constraint to effectively exploit the disjoint relationship between
labelled and unlabelled classes, enforcing the separability for different
classes in the embedding space. In addition, we present an intra-class sKLD
constraint to explicitly constrain the intra-relationship between samples and
their augmentations and ensure the stability of the training process at the
same time. We conduct extensive experiments on the popular CIFAR10, CIFAR100
and ImageNet benchmarks and successfully demonstrate that our method can
establish a new state of the art and can achieve significantly performance
improvements, e.g., 3.6\%/3.7\% clustering accuracy improvements on CIFAR100-50
dataset split under the task-aware/-agnostic evaluation protocol, over previous

A desirable objective in self-supervised learning (SSL) is to avoid feature
collapse. Whitening loss guarantees collapse avoidance by minimizing the
distance between embeddings of positive pairs under the conditioning that the
embeddings from different views are whitened. In this paper, we propose a
framework with an informative indicator to analyze whitening loss, which
provides a clue to demystify several interesting phenomena as well as a
pivoting point connecting to other SSL methods. We reveal that batch whitening
(BW) based methods do not impose whitening constraints on the embedding, but
they only require the embedding to be full-rank. This full-rank constraint is
also sufficient to avoid dimensional collapse. Based on our analysis, we
propose channel whitening with random group partition (CW-RGP), which exploits
the advantages of BW-based methods in preventing collapse and avoids their
disadvantages requiring large batch size. Experimental results on ImageNet
classification and COCO object detection reveal that the proposed CW-RGP
possesses a promising potential for learning good representations. The code is
available at https://github.com/winci-ai/CW-RGP.

Sociability is essential for modern robots to increase their acceptability in
human environments. Traditional techniques use manually engineered utility
functions inspired by observing pedestrian behaviors to achieve social
navigation. However, social aspects of navigation are diverse, changing across
different types of environments, societies, and population densities, making it
unrealistic to use hand-crafted techniques in each domain. This paper presents
a data-driven navigation architecture that uses state-of-the-art neural
architectures, namely Conditional Neural Processes, to learn global and local
controllers of the mobile robot from observations. Additionally, we leverage a
state-of-the-art, deep prediction mechanism to detect situations not similar to
the trained ones, where reactive controllers step in to ensure safe navigation.
Our results demonstrate that the proposed framework can successfully carry out
navigation tasks regarding social norms in the data. Further, we showed that
our system produces fewer personal-zone violations, causing less discomfort.

Synthetic voice and splicing audio clips have been generated to spoof
Internet users and artificial intelligence (AI) technologies such as voice
authentication. Existing research work treats spoofing countermeasures as a
binary classification problem: bonafide vs. spoof. This paper extends the
existing Res2Net by involving the recent Conformer block to further exploit the
local patterns on acoustic features. Experimental results on ASVspoof 2019
database show that the proposed SE-Res2Net-Conformer architecture is able to
improve the spoofing countermeasures performance for the logical access
scenario.
  In addition, this paper also proposes to re-formulate the existing audio
splicing detection problem. Instead of identifying the complete splicing
segments, it is more useful to detect the boundaries of the spliced segments.
Moreover, a deep learning approach can be used to solve the problem, which is
different from the previous signal processing techniques.

Compositionality, the phenomenon where the meaning of a phrase can be derived
from its constituent parts, is a hallmark of human language. At the same time,
many phrases are non-compositional, carrying a meaning beyond that of each part
in isolation. Representing both of these types of phrases is critical for
language understanding, but it is an open question whether modern language
models (LMs) learn to do so; in this work we examine this question. We first
formulate a problem of predicting the LM-internal representations of longer
phrases given those of their constituents. We find that the representation of a
parent phrase can be predicted with some accuracy given an affine
transformation of its children. While we would expect the predictive accuracy
to correlate with human judgments of semantic compositionality, we find this is
largely not the case, indicating that LMs may not accurately distinguish
between compositional and non-compositional phrases. We perform a variety of
analyses, shedding light on when different varieties of LMs do and do not
generate compositional representations, and discuss implications for future
modeling work.

Knowledge tracing and dropout prediction are crucial for online education to
estimate students' knowledge states or to prevent dropout rates. While
traditional systems interacting with students suffered from data sparsity and
overfitting, recent sample-level contrastive learning helps to alleviate this
issue. One major limitation of sample-level approaches is that they regard
students' behavior interaction sequences as a bundle, so they often fail to
encode temporal contexts and track their dynamic changes, making it hard to
find optimal representations for knowledge tracing and dropout prediction. To
apply temporal context within the sequence, this study introduces a novel
student modeling framework, SAICL: \textbf{s}tudent modeling with
\textbf{a}uxiliary \textbf{i}nteraction-level \textbf{c}ontrastive
\textbf{l}earning. In detail, SAICL can utilize both proposed
self-supervised/supervised interaction-level contrastive objectives: MilCPC
(\textbf{M}ulti-\textbf{I}nteraction-\textbf{L}evel \textbf{C}ontrastive
\textbf{P}redictive \textbf{C}oding) and SupCPC (\textbf{Sup}ervised
\textbf{C}ontrastive \textbf{P}redictive \textbf{C}oding). While previous
sample-level contrastive methods for student modeling are highly dependent on
data augmentation methods, the SAICL is free of data augmentation while showing
better performance in both self-supervised and supervised settings. By
combining cross-entropy with contrastive objectives, the proposed SAICL
achieved comparable knowledge tracing and dropout prediction performance with
other state-of-art models without compromising inference costs.

Road infrastructure maintenance inspection is typically a labor-intensive and
critical task to ensure the safety of all road users. Existing state-of-the-art
techniques in Artificial Intelligence (AI) for object detection and
segmentation help automate a huge chunk of this task given adequate annotated
data. However, annotating videos from scratch is cost-prohibitive. For
instance, it can take an annotator several days to annotate a 5-minute video
recorded at 30 FPS. Hence, we propose an automated labelling pipeline by
leveraging techniques like few-shot learning and out-of-distribution detection
to generate labels for road damage detection. In addition, our pipeline
includes a risk factor assessment for each damage by instance quantification to
prioritize locations for repairs which can lead to optimal deployment of road
maintenance machinery. We show that the AI models trained with these techniques
can not only generalize better to unseen real-world data with reduced
requirement for human annotation but also provide an estimate of maintenance
urgency, thereby leading to safer roads.

The recent success of large language models for text generation poses a
severe threat to academic integrity, as plagiarists can generate realistic
paraphrases indistinguishable from original work. However, the role of large
autoregressive transformers in generating machine-paraphrased plagiarism and
their detection is still developing in the literature. This work explores T5
and GPT-3 for machine-paraphrase generation on scientific articles from arXiv,
student theses, and Wikipedia. We evaluate the detection performance of six
automated solutions and one commercial plagiarism detection software and
perform a human study with 105 participants regarding their detection
performance and the quality of generated examples. Our results suggest that
large models can rewrite text humans have difficulty identifying as
machine-paraphrased (53% mean acc.). Human experts rate the quality of
paraphrases generated by GPT-3 as high as original texts (clarity 4.0/5,
fluency 4.2/5, coherence 3.8/5). The best-performing detection model (GPT-3)
achieves a 66% F1-score in detecting paraphrases.

In this study, a novel method of data augmentation has been presented for the
segmentation of placental histological images when the labeled data are scarce.
This method generates new realizations of the placenta intervillous morphology
while maintaining the general textures and orientations. As a result, a
diversified artificial dataset of images is generated that can be used for
training deep learning segmentation models. We have observed that on average
the presented method of data augmentation led to a 42% decrease in the binary
cross-entropy loss of the validation dataset compared to the common approach in
the literature. Additionally, the morphology of the intervillous space is
studied under the effect of the proposed image reconstruction technique, and
the diversity of the artificially generated population is quantified. Due to
the high resemblance of the generated images to the real ones, the applications
of the proposed method may not be limited to placental histological images, and
it is recommended that other types of tissues be investigated in future
studies.

Federated Learning is a modern decentralized machine learning technique where
user equipments perform machine learning tasks locally and then upload the
model parameters to a central server. In this paper, we consider a 3-layer
hierarchical federated learning system which involves model parameter exchanges
between the cloud and edge servers, and the edge servers and user equipment. In
a hierarchical federated learning model, delay in communication and computation
of model parameters has a great impact on achieving a predefined global model
accuracy. Therefore, we formulate a joint learning and communication
optimization problem to minimize total model parameter communication and
computation delay, by optimizing local iteration counts and edge iteration
counts. To solve the problem, an iterative algorithm is proposed. After that, a
time-minimized UE-to-edge association algorithm is presented where the maximum
latency of the system is reduced. Simulation results show that the global model
converges faster under optimal edge server and local iteration counts. The
hierarchical federated learning latency is minimized with the proposed
UE-to-edge association strategy.

As powerful tools for representation learning on graphs, graph neural
networks (GNNs) have facilitated various applications from drug discovery to
recommender systems. Nevertheless, the effectiveness of GNNs is immensely
challenged by issues related to data quality, such as distribution shift,
abnormal features and adversarial attacks. Recent efforts have been made on
tackling these issues from a modeling perspective which requires additional
cost of changing model architectures or re-training model parameters. In this
work, we provide a data-centric view to tackle these issues and propose a graph
transformation framework named GTrans which adapts and refines graph data at
test time to achieve better performance. We provide theoretical analysis on the
design of the framework and discuss why adapting graph data works better than
adapting the model. Extensive experiments have demonstrated the effectiveness
of GTrans on three distinct scenarios for eight benchmark datasets where
suboptimal data is presented. Remarkably, GTrans performs the best in most
cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on
three experimental settings.

The detection and localization of possible diseases in crops are usually
automated by resorting to supervised deep learning approaches. In this work, we
tackle these goals with unsupervised models, by applying three different types
of autoencoders to a specific open-source dataset of healthy and unhealthy
pepper and cherry leaf images. CAE, CVAE and VQ-VAE autoencoders are deployed
to screen unlabeled images of such a dataset, and compared in terms of image
reconstruction, anomaly removal, detection and localization. The
vector-quantized variational architecture turns out to be the best performing
one with respect to all these targets.

The sixth-generation (6G) mobile networks are expected to feature the
ubiquitous deployment of machine learning and AI algorithms at the network
edge. With rapid advancements in edge AI, the time has come to realize
intelligence downloading onto edge devices (e.g., smartphones and sensors). To
materialize this version, we propose a novel technology in this article, called
in-situ model downloading, that aims to achieve transparent and real-time
replacement of on-device AI models by downloading from an AI library in the
network. Its distinctive feature is the adaptation of downloading to
time-varying situations (e.g., application, location, and time), devices'
heterogeneous storage-and-computing capacities, and channel states. A key
component of the presented framework is a set of techniques that dynamically
compress a downloaded model at the depth-level, parameter-level, or bit-level
to support adaptive model downloading. We further propose a virtualized 6G
network architecture customized for deploying in-situ model downloading with
the key feature of a three-tier (edge, local, and central) AI library.
Furthermore, experiments are conducted to quantify 6G connectivity requirements
and research opportunities pertaining to the proposed technology are discussed.

In machine learning, meta-learning methods aim for fast adaptability to
unknown tasks using prior knowledge. Model-based meta-reinforcement learning
combines reinforcement learning via world models with Meta Reinforcement
Learning (MRL) for increased sample efficiency. However, adaption to unknown
tasks does not always result in preferable agent behavior. This paper
introduces a new Meta Adaptation Controller (MAC) that employs MRL to apply a
preferred robot behavior from one task to many similar tasks. To do this, MAC
aims to find actions an agent has to take in a new task to reach a similar
outcome as in a learned task. As a result, the agent will adapt quickly to the
change in the dynamic and behave appropriately without the need to construct a
reward function that enforces the preferred behavior.

Pandas is defined as a software library which is used for data analysis in
Python programming language. As pandas is a fast, easy and open source data
analysis tool, it is rapidly used in different software engineering projects
like software development, machine learning, computer vision, natural language
processing, robotics, and others. So a huge interests are shown in software
developers regarding pandas and a huge number of discussions are now becoming
dominant in online developer forums, like Stack Overflow (SO). Such discussions
can help to understand the popularity of pandas library and also can help to
understand the importance, prevalence, difficulties of pandas topics. The main
aim of this research paper is to find the popularity and difficulty of pandas
topics. For this regard, SO posts are collected which are related to pandas
topic discussions. Topic modeling are done on the textual contents of the
posts. We found 26 topics which we further categorized into 5 board categories.
We observed that developers discuss variety of pandas topics in SO related to
error and excepting handling, visualization, External support, dataframe, and
optimization. In addition, a trend chart is generated according to the
discussion of topics in a predefined time series. The finding of this paper can
provide a path to help the developers, educators and learners. For example,
beginner developers can learn most important topics in pandas which are
essential for develop any model. Educators can understand the topics which seem
hard to learners and can build different tutorials which can make that pandas
topic understandable. From this empirical study it is possible to understand
the preferences of developers in pandas topic by processing their SO posts

Brain-machine interfaces (BMIs) are promising for motor rehabilitation and
mobility augmentation. High-accuracy and low-power algorithms are required to
achieve implantable BMI systems. In this paper, we propose a novel spiking
neural network (SNN) decoder for implantable BMI regression tasks. The SNN is
trained with enhanced spatio-temporal backpropagation to fully leverage its
ability in handling temporal problems. The proposed SNN decoder achieves the
same level of correlation coefficient as the state-of-the-art ANN decoder in
offline finger velocity decoding tasks, while it requires only 6.8% of the
computation operations and 9.4% of the memory access.

Monte Carlo methods have become increasingly relevant for control of
non-differentiable systems, approximate dynamics models and learning from data.
These methods scale to high-dimensional spaces and are effective at the
non-convex optimizations often seen in robot learning. We look at sample-based
methods from the perspective of inference-based control, specifically posterior
policy iteration. From this perspective, we highlight how Gaussian noise priors
produce rough control actions that are unsuitable for physical robot
deployment. Considering smoother Gaussian process priors, as used in episodic
reinforcement learning and motion planning, we demonstrate how smoother model
predictive control can be achieved using online sequential inference. This
inference is realized through an efficient factorization of the action
distribution and a novel means of optimizing the likelihood temperature to
improve importance sampling accuracy. We evaluate this approach on several
high-dimensional robot control tasks, matching the sample efficiency of prior
heuristic methods while also ensuring smoothness. Simulation results can be
seen at https://monte-carlo-ppi.github.io/.

We propose a method to accelerate the joint process of physically acquiring
and learning neural Bi-directional Reflectance Distribution Function (BRDF)
models. While BRDF learning alone can be accelerated by meta-learning,
acquisition remains slow as it relies on a mechanical process. We show that
meta-learning can be extended to optimize the physical sampling pattern, too.
After our method has been meta-trained for a set of fully-sampled BRDFs, it is
able to quickly train on new BRDFs with up to five orders of magnitude fewer
physical acquisition samples at similar quality. Our approach also extends to
other linear and non-linear BRDF models, which we show in an extensive

Meta-learning is critical for a variety of practical ML systems -- like
personalized recommendations systems -- that are required to generalize to new
tasks despite a small number of task-specific training points. Existing
meta-learning techniques use two complementary approaches of either learning a
low-dimensional representation of points for all tasks, or task-specific
fine-tuning of a global model trained using all the tasks. In this work, we
propose a novel meta-learning framework that combines both the techniques to
enable handling of a large number of data-starved tasks. Our framework models
network weights as a sum of low-rank and sparse matrices. This allows us to
capture information from multiple domains together in the low-rank part while
still allowing task specific personalization using the sparse part. We
instantiate and study the framework in the linear setting, where the problem
reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse
matrix using a small number of linear measurements. We propose an alternating
minimization method with hard thresholding -- AMHT-LRS -- to learn the low-rank
and sparse part effectively and efficiently. For the realizable, Gaussian data
setting, we show that AMHT-LRS indeed solves the problem efficiently with
nearly optimal samples. We extend AMHT-LRS to ensure that it preserves privacy
of each individual user in the dataset, while still ensuring strong
generalization with nearly optimal number of samples. Finally, on multiple
datasets, we demonstrate that the framework allows personalized models to
obtain superior performance in the data-scarce regime.

As nowadays most web application requests originate from mobile devices,
authentication of mobile users is essential in terms of security
considerations. To this end, recent approaches rely on machine learning
techniques to analyze various aspects of user behavior as a basis for
authentication decisions. These approaches face two challenges: first,
examining behavioral data raises significant privacy concerns, and second,
approaches must scale to support a large number of users. Existing approaches
do not address these challenges sufficiently. We propose mPSAuth, an approach
for continuously tracking various data sources reflecting user behavior (e.g.,
touchscreen interactions, sensor data) and estimating the likelihood of the
current user being legitimate based on machine learning techniques. With
mPSAuth, both the authentication protocol and the machine learning models
operate on homomorphically encrypted data to ensure the users' privacy.
Furthermore, the number of machine learning models used by mPSAuth is
independent of the number of users, thus providing adequate scalability. In an
extensive evaluation based on real-world data from a mobile application, we
illustrate that mPSAuth can provide high accuracy with low encryption and
communication overhead, while the effort for the inference is increased to a
tolerable extent.

Beam parameter optimization in accelerators involves multiple, sometimes
competing objectives. Condensing these multiple objectives into a single
objective unavoidably results in bias towards particular outcomes that do not
necessarily represent the best possible outcome for the operator in terms of
parameter optimization. A more versatile approach is multi-objective
optimization, which establishes the trade-off curve or Pareto front between
objectives. Here we present first results on multi-objective Bayesian
optimization of a simulated laser-plasma accelerator. We find that
multi-objective optimization is equal or even superior in performance to its
single-objective counterparts, and that it is more resilient to different
statistical descriptions of objectives.
  As a second major result of our paper, we significantly reduce the
computational costs of the optimization by choosing the resolution and box size
of the simulations dynamically. This is relevant since even with the use of
Bayesian statistics, performing such optimizations on a multi-dimensional
search space may require hundreds or thousands of simulations. Our algorithm
translates information gained from fast, low-resolution runs with lower
fidelity to high-resolution data, thus requiring fewer actual simulations at
highest computational cost.
  The techniques demonstrated in this paper can be translated to many different
use cases, both computational and experimental.

In this paper we describe the design and the ideas motivating a new Continual
Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems
of object classification and object detection. The benchmark utilises SODA10M,
a recently released large-scale dataset that concerns autonomous driving
related problems. First, we review and discuss existing continual learning
benchmarks, how they are related, and show that most are extreme cases of
continual learning. To this end, we survey the benchmarks used in continual
learning papers at three highly ranked computer vision conferences. Next, we
introduce CLAD-C, an online classification benchmark realised through a
chronological data stream that poses both class and domain incremental
challenges; and CLAD-D, a domain incremental continual object detection
benchmark. We examine the inherent difficulties and challenges posed by the
benchmark, through a survey of the techniques and methods used by the top-3
participants in a CLAD-challenge workshop at ICCV 2021. We conclude with
possible pathways to improve the current continual learning state of the art,
and which directions we deem promising for future research.

Bayesian Optimization (BO) is a common solution to search optimal
hyperparameters based on sample observations of a machine learning model.
Existing BO algorithms could converge slowly even collapse when the potential
observation noise misdirects the optimization. In this paper, we propose a
novel BO algorithm called Neighbor Regularized Bayesian Optimization (NRBO) to
solve the problem. We first propose a neighbor-based regularization to smooth
each sample observation, which could reduce the observation noise efficiently
without any extra training cost. Since the neighbor regularization highly
depends on the sample density of a neighbor area, we further design a
density-based acquisition function to adjust the acquisition reward and obtain
more stable statistics. In addition, we design a adjustment mechanism to ensure
the framework maintains a reasonable regularization strength and density reward
conditioned on remaining computation resources. We conduct experiments on the
bayesmark benchmark and important computer vision benchmarks such as ImageNet
and COCO. Extensive experiments demonstrate the effectiveness of NRBO and it
consistently outperforms other state-of-the-art methods.

Applying reinforcement learning (RL) to combinatorial optimization problems
is attractive as it removes the need for expert knowledge or pre-solved
instances. However, it is unrealistic to expect an agent to solve these (often
NP-)hard problems in a single shot at inference due to their inherent
complexity. Thus, leading approaches often implement additional search
strategies, from stochastic sampling and beam-search to explicit fine-tuning.
In this paper, we argue for the benefits of learning a population of
complementary policies, which can be simultaneously rolled out at inference. To
this end, we introduce Poppy, a simple theoretically grounded training
procedure for populations. Instead of relying on a predefined or hand-crafted
notion of diversity, Poppy induces an unsupervised specialization targeted
solely at maximizing the performance of the population. We show that Poppy
produces a set of complementary policies, and obtains state-of-the-art RL
results on three popular NP-hard problems: the traveling salesman (TSP), the
capacitated vehicle routing (CVRP), and 0-1 knapsack (KP) problems. On TSP
specifically, Poppy outperforms the previous state-of-the-art, dividing the
optimality gap by 5 while reducing the inference time by more than an order of

Price movement prediction has always been one of the traders' concerns in
financial market trading. In order to increase their profit, they can analyze
the historical data and predict the price movement. The large size of the data
and complex relations between them lead us to use algorithmic trading and
artificial intelligence. This paper aims to offer an approach using
Twin-Delayed DDPG (TD3) and the daily close price in order to achieve a trading
strategy in the stock and cryptocurrency markets. Unlike previous studies using
a discrete action space reinforcement learning algorithm, the TD3 is
continuous, offering both position and the number of trading shares. Both the
stock (Amazon) and cryptocurrency (Bitcoin) markets are addressed in this
research to evaluate the performance of the proposed algorithm. The achieved
strategy using the TD3 is compared with some algorithms using technical
analysis, reinforcement learning, stochastic, and deterministic strategies
through two standard metrics, Return and Sharpe ratio. The results indicate
that employing both position and the number of trading shares can improve the
performance of a trading system based on the mentioned metrics.

Training dynamic models, such as neural ODEs, on long trajectories is a hard
problem that requires using various tricks, such as trajectory splitting, to
make model training work in practice. These methods are often heuristics with
poor theoretical justifications, and require iterative manual tuning. We
propose a principled multiple shooting technique for neural ODEs that splits
the trajectories into manageable short segments, which are optimised in
parallel, while ensuring probabilistic control on continuity over consecutive
segments. We derive variational inference for our shooting-based latent neural
ODE models and propose amortized encodings of irregularly sampled trajectories
with a transformer-based recognition network with temporal attention and
relative positional encoding. We demonstrate efficient and stable training, and
state-of-the-art performance on multiple large-scale benchmark datasets.

Given a user's input text, text-matching recommender systems output relevant
items by comparing the input text to available items' description, such as
product-to-product recommendation on e-commerce platforms. As users' interests
and item inventory are expected to change, it is important for a text-matching
system to generalize to data shifts, a task known as out-of-distribution (OOD)
generalization. However, we find that the popular approach of fine-tuning a
large, base language model on paired item relevance data (e.g., user clicks)
can be counter-productive for OOD generalization. For a product recommendation
task, fine-tuning obtains worse accuracy than the base model when recommending
items in a new category or for a future time period. To explain this
generalization failure, we consider an intervention-based importance metric,
which shows that a fine-tuned model captures spurious correlations and fails to
learn the causal features that determine the relevance between any two text
inputs. Moreover, standard methods for causal regularization do not apply in
this setting, because unlike in images, there exist no universally spurious
features in a text-matching task (the same token may be spurious or causal
depending on the text it is being matched to). For OOD generalization on text
inputs, therefore, we highlight a different goal: avoiding high importance
scores for certain features. We do so using an intervention-based regularizer
that constraints the causal effect of any token on the model's relevance score
to be similar to the base model. Results on Amazon product and 3 question
recommendation datasets show that our proposed regularizer improves
generalization for both in-distribution and OOD evaluation, especially in
difficult scenarios when the base model is not accurate.

Artistic style transfer is usually performed between two images, a style
image and a content image. Recently, a model named CLIPstyler demonstrated that
a natural language description of style could replace the necessity of a
reference style image. However, their technique requires a lengthy optimisation
procedure at run-time for each query, requiring multiple forward and backward
passes through a network as well as expensive loss computations. In this work,
we create a generalised text-based style transfer network capable of stylising
images in a single forward pass for an arbitrary text input making the image
stylisation process around 1000 times more efficient than CLIPstyler. We also
demonstrate how our technique eliminates the issue of leakage of unwanted
artefacts into some of the generated images from CLIPstyler, making them
unusable. We also propose an optional fine-tuning step to improve the quality
of the generated image. We qualitatively evaluate the performance of our
framework and show that it can generate images of comparable quality to
state-of-the-art techniques.

Fingerprinting-based positioning significantly improves the indoor
localization performance in non-line-of-sight-dominated areas. However, its
deployment and maintenance is cost-intensive as it needs ground-truth reference
systems for both the initial training and the adaption to environmental
changes. In contrast, channel charting (CC) works without explicit reference
information and only requires the spatial correlations of channel state
information (CSI). While CC has shown promising results in modelling the
geometry of the radio environment, a deeper insight into CC for localization
using multi-anchor large-bandwidth measurements is still pending. We contribute
a novel distance metric for time-synchronized single-input/single-output CSIs
that approaches a linear correlation to the Euclidean distance. This allows to
learn the environment's global geometry without annotations. To efficiently
optimize the global channel chart we approximate the metric with a Siamese
neural network. This enables full CC-assisted fingerprinting and positioning
only using a linear transformation from the chart to the real-world
coordinates. We compare our approach to the state-of-the-art of CC on two
different real-world data sets recorded with a 5G and UWB radio setup. Our
approach outperforms others with localization accuracies of 0.69m for the UWB
and 1.4m for the 5G setup. We show that CC-assisted fingerprinting enables
highly accurate localization and reduces (or eliminates) the need for annotated
training data.

Due to the high performance of multi-channel speech processing, we can use
the outputs from a multi-channel model as teacher labels when training a
single-channel model with knowledge distillation. To the contrary, it is also
known that single-channel speech data can benefit multi-channel models by
mixing it with multi-channel speech data during training or by using it for
model pretraining. This paper focuses on speaker diarization and proposes to
conduct the above bi-directional knowledge transfer alternately. We first
introduce an end-to-end neural diarization model that can handle both single-
and multi-channel inputs. Using this model, we alternately conduct i) knowledge
distillation from a multi-channel model to a single-channel model and ii)
finetuning from the distilled single-channel model to a multi-channel model.
Experimental results on two-speaker data show that the proposed method mutually
improved single- and multi-channel speaker diarization performances.

Human-in-the-loop (HiL) reinforcement learning is gaining traction in domains
with large action and state spaces, and sparse rewards by allowing the agent to
take advice from HiL. Beyond advice accommodation, a sequential decision-making
agent must be able to express the extent to which it was able to utilize the
human advice. Subsequently, the agent should provide a means for the HiL to
inspect parts of advice that it had to reject in favor of the overall
environment objective. We introduce the problem of Advice-Conformance
Verification which requires reinforcement learning (RL) agents to provide
assurances to the human in the loop regarding how much of their advice is being
conformed to. We then propose a Tree-based lingua-franca to support this
communication, called a Preference Tree. We study two cases of good and bad
advice scenarios in MuJoCo's Humanoid environment. Through our experiments, we
show that our method can provide an interpretable means of solving the
Advice-Conformance Verification problem by conveying whether or not the agent
is using the human's advice. Finally, we present a human-user study with 20
participants that validates our method.

Transformer-based pre-trained language models such as BERT have achieved
remarkable results in Semantic Sentence Matching. However, existing models
still suffer from insufficient ability to capture subtle differences. Minor
noise like word addition, deletion, and modification of sentences may cause
flipped predictions. To alleviate this problem, we propose a novel Dual
Attention Enhanced BERT (DABERT) to enhance the ability of BERT to capture
fine-grained differences in sentence pairs. DABERT comprises (1) Dual Attention
module, which measures soft word matches by introducing a new dual channel
alignment mechanism to model affinity and difference attention. (2) Adaptive
Fusion module, this module uses attention to learn the aggregation of
difference and affinity features, and generates a vector describing the
matching details of sentence pairs. We conduct extensive experiments on
well-studied semantic matching and robustness test datasets, and the
experimental results show the effectiveness of our proposed method.

Deep Learning (DL) is dominating the fields of Natural Language Processing
(NLP) and Computer Vision (CV) in the recent times. However, DL commonly relies
on the availability of large data annotations, so other alternative or
complementary pattern-based techniques can help to improve results. In this
paper, we build upon Key Information Extraction (KIE) in purchase documents
using both DL and rule-based corrections. Our system initially trusts on
Optical Character Recognition (OCR) and text understanding based on entity
tagging to identify purchase facts of interest (e.g., product codes,
descriptions, quantities, or prices). These facts are then linked to a same
product group, which is recognized by means of line detection and some grouping
heuristics. Once these DL approaches are processed, we contribute several
mechanisms consisting of rule-based corrections for improving the baseline DL
predictions. We prove the enhancements provided by these rule-based corrections
over the baseline DL results in the presented experiments for purchase
documents from public and NielsenIQ datasets.

Federated learning (FL), which has gained increasing attention recently,
enables distributed devices to train a common machine learning (ML) model for
intelligent inference cooperatively without data sharing.
  However, problems in practical networks, such as
non-independent-and-identically-distributed (non-iid) raw data and limited
bandwidth, give rise to slow and unstable convergence of the FL training
process.
  To address these issues, we propose a new FL method that can significantly
mitigate statistical heterogeneity through the depersonalization mechanism.
  Particularly, we decouple the global and local optimization objectives by
alternating stochastic gradient descent, thus reducing the accumulated variance
in local update phases to accelerate the FL convergence.
  Then we analyze the proposed method detailedly to show the proposed method
converging at a sublinear speed in the general non-convex setting.
  Finally, numerical results are conducted with experiments on public datasets
to verify the effectiveness of our proposed method.

In this work, we propose the novel Prototypical Graph Regression
Self-explainable Trees (ProGReST) model, which combines prototype learning,
soft decision trees, and Graph Neural Networks. In contrast to other works, our
model can be used to address various challenging tasks, including compound
property prediction. In ProGReST, the rationale is obtained along with
prediction due to the model's built-in interpretability. Additionally, we
introduce a new graph prototype projection to accelerate model training.
Finally, we evaluate PRoGReST on a wide range of chemical datasets for
molecular property prediction and perform in-depth analysis with chemical
experts to evaluate obtained interpretations. Our method achieves competitive
results against state-of-the-art methods.

Several machine learning inspired methods for perturbation-based fiber
nonlinearity (PBNLC) compensation have been presented in recent literature. We
critically revisit acclaimed benefits of those over non-learned methods.
Numerical results suggest that learned linear processing of perturbation
triplets of PB-NLC is preferable over feedforward neural-network solutions.

Visual object tracking has focused predominantly on opaque objects, while
transparent object tracking received very little attention. Motivated by the
uniqueness of transparent objects in that their appearance is directly affected
by the background, the first dedicated evaluation dataset has emerged recently.
We contribute to this effort by proposing the first transparent object tracking
training dataset Trans2k that consists of over 2k sequences with 104,343 images
overall, annotated by bounding boxes and segmentation masks. Noting that
transparent objects can be realistically rendered by modern renderers, we
quantify domain-specific attributes and render the dataset containing visual
attributes and tracking situations not covered in the existing object training
datasets. We observe a consistent performance boost (up to 16%) across a
diverse set of modern tracking architectures when trained using Trans2k, and
show insights not previously possible due to the lack of appropriate training
sets. The dataset and the rendering engine will be publicly released to unlock
the power of modern learning-based trackers and foster new designs in
transparent object tracking.

Unsupervised domain adaptation(UDA) has been applied to image semantic
segmentation to solve the problem of domain offset. However, in some difficult
categories with poor recognition accuracy, the segmentation effects are still
not ideal. To this end, in this paper, Intra-subdomain adaptation adversarial
learning segmentation method based on Dynamic Pseudo Labels(IDPL) is proposed.
The whole process consists of 3 steps: Firstly, the instance-level pseudo label
dynamic generation module is proposed, which fuses the class matching
information in global classes and local instances, thus adaptively generating
the optimal threshold for each class, obtaining high-quality pseudo labels.
Secondly, the subdomain classifier module based on instance confidence is
constructed, which can dynamically divide the target domain into easy and
difficult subdomains according to the relative proportion of easy and difficult
instances. Finally, the subdomain adversarial learning module based on
self-attention is proposed. It uses multi-head self-attention to confront the
easy and difficult subdomains at the class level with the help of generated
high-quality pseudo labels, so as to focus on mining the features of difficult
categories in the high-entropy region of target domain images, which promotes
class-level conditional distribution alignment between the subdomains,
improving the segmentation performance of difficult categories. For the
difficult categories, the experimental results show that the performance of
IDPL is significantly improved compared with other latest mainstream methods.

Person search is a challenging problem with various real-world applications,
that aims at joint person detection and re-identification of a query person
from uncropped gallery images. Although, the previous study focuses on rich
feature information learning, it is still hard to retrieve the query person due
to the occurrence of appearance deformations and background distractors. In
this paper, we propose a novel attention-aware relation mixer (ARM) module for
person search, which exploits the global relation between different local
regions within RoI of a person and make it robust against various appearance
deformations and occlusion. The proposed ARM is composed of a relation mixer
block and a spatio-channel attention layer. The relation mixer block introduces
a spatially attended spatial mixing and a channel-wise attended channel mixing
for effectively capturing discriminative relation features within an RoI. These
discriminative relation features are further enriched by introducing a
spatio-channel attention where the foreground and background discriminability
is empowered in a joint spatio-channel space. Our ARM module is generic and it
does not rely on fine-grained supervision or topological assumptions, hence
being easily integrated into any Faster R-CNN based person search methods.
Comprehensive experiments are performed on two challenging benchmark datasets:
CUHKSYSU and PRW. Our PS-ARM achieves state-of-the-art performance on both
datasets. On the challenging PRW dataset, our PS-ARM achieves an absolute gain
of 5 in the mAP score over SeqNet, while operating at a comparable speed.

Reflection high-energy electron diffraction (RHEED) is a powerful tool in
molecular beam epitaxy (MBE), but RHEED images are often difficult to
interpret, requiring experienced operators. We present an approach for
automated surveillance of GaAs substrate deoxidation in MBE using deep learning
based RHEED image-sequence classification. Our approach consists of an
non-supervised auto-encoder (AE) for feature extraction, combined with a
supervised convolutional classifier network. We demonstrate that our
lightweight network model can accurately identify the exact deoxidation moment.
Furthermore we show that the approach is very robust and allows accurate
deoxidation detection during months without requiring re-training. The main
advantage of the approach is that it can be applied to raw RHEED images without
requiring further information such as the rotation angle, temperature, etc.

Few-shot Learning (FSL) methods are being adopted in settings where data is
not abundantly available. This is especially seen in medical domains where the
annotations are expensive to obtain. Deep Neural Networks have been shown to be
vulnerable to adversarial attacks. This is even more severe in the case of FSL
due to the lack of a large number of training examples. In this paper, we
provide a framework to make few-shot segmentation models adversarially robust
in the medical domain where such attacks can severely impact the decisions made
by clinicians who use them. We propose a novel robust few-shot segmentation
framework, Prototypical Neural Ordinary Differential Equation (PNODE), that
provides defense against gradient-based adversarial attacks. We show that our
framework is more robust compared to traditional adversarial defense mechanisms
such as adversarial training. Adversarial training involves increased training
time and shows robustness to limited types of attacks depending on the type of
adversarial examples seen during training. Our proposed framework generalises
well to common adversarial attacks like FGSM, PGD and SMIA while having the
model parameters comparable to the existing few-shot segmentation models. We
show the effectiveness of our proposed approach on three publicly available
multi-organ segmentation datasets in both in-domain and cross-domain settings
by attacking the support and query sets without the need for ad-hoc adversarial
training.

Multimodal sentiment analysis (MSA) is an important way of observing mental
activities with the help of data captured from multiple modalities. However,
due to the recording or transmission error, some modalities may include
incomplete data. Most existing works that address missing modalities usually
assume a particular modality is completely missing and seldom consider a
mixture of missing across multiple modalities. In this paper, we propose a
simple yet effective meta-sampling approach for multimodal sentiment analysis
with missing modalities, namely Missing Modality-based Meta Sampling (M3S). To
be specific, M3S formulates a missing modality sampling strategy into the modal
agnostic meta-learning (MAML) framework. M3S can be treated as an efficient
add-on training component on existing models and significantly improve their
performances on multimodal data with a mixture of missing modalities. We
conduct experiments on IEMOCAP, SIMS and CMU-MOSI datasets, and superior
performance is achieved compared with recent state-of-the-art methods.

Prediction error quantification in machine learning has been left out of most
methodological investigations of neural networks, for both purely data-driven
and physics-informed approaches. Beyond statistical investigations and generic
results on the approximation capabilities of neural networks, we present a
rigorous upper bound on the prediction error of physics-informed neural
networks. This bound can be calculated without the knowledge of the true
solution and only with a priori available information about the characteristics
of the underlying dynamical system governed by a partial differential equation.
We apply this a posteriori error bound exemplarily to four problems: the
transport equation, the heat equation, the Navier-Stokes equation and the
Klein-Gordon equation.

We present SpaceQA, to the best of our knowledge the first open-domain QA
system in Space mission design. SpaceQA is part of an initiative by the
European Space Agency (ESA) to facilitate the access, sharing and reuse of
information about Space mission design within the agency and with the public.
We adopt a state-of-the-art architecture consisting of a dense retriever and a
neural reader and opt for an approach based on transfer learning rather than
fine-tuning due to the lack of domain-specific annotated data. Our evaluation
on a test set produced by ESA is largely consistent with the results originally
reported by the evaluated retrievers and confirms the need of fine tuning for
reading comprehension. As of writing this paper, ESA is piloting SpaceQA
internally.

Nowadays, deep neural networks are widely used in a variety of fields that
have a direct impact on society. Although those models typically show
outstanding performance, they have been used for a long time as black boxes. To
address this, Explainable Artificial Intelligence (XAI) has been developing as
a field that aims to improve the transparency of the model and increase their
trustworthiness. We propose a retraining pipeline that consistently improves
the model predictions starting from XAI and utilizing state-of-the-art
techniques. To do that, we use the XAI results, namely SHapley Additive
exPlanations (SHAP) values, to give specific training weights to the data
samples. This leads to an improved training of the model and, consequently,
better performance. In order to benchmark our method, we evaluate it on both
real-life and public datasets. First, we perform the method on a radar-based
people counting scenario. Afterward, we test it on the CIFAR-10, a public
Computer Vision dataset. Experiments using the SHAP-based retraining approach
achieve a 4% more accuracy w.r.t. the standard equal weight retraining for
people counting tasks. Moreover, on the CIFAR-10, our SHAP-based weighting
strategy ends up with a 3% accuracy rate than the training procedure with equal
weighted samples.

Extracting the reported events from text is one of the key research themes in
natural language processing. This process includes several tasks such as event
detection, argument extraction, role labeling. As one of the most important
topics in natural language processing and natural language understanding, the
applications of event extraction spans across a wide range of domains such as
newswire, biomedical domain, history and humanity, and cyber security. This
report presents a comprehensive survey for event detection from textual
documents. In this report, we provide the task definition, the evaluation
method, as well as the benchmark datasets and a taxonomy of methodologies for
event extraction. We also present our vision of future research direction in
event detection.

PARAGEN is a PyTorch-based NLP toolkit for further development on parallel
generation. PARAGEN provides thirteen types of customizable plugins, helping
users to experiment quickly with novel ideas across model architectures,
optimization, and learning strategies. We implement various features, such as
unlimited data loading and automatic model selection, to enhance its industrial
usage. ParaGen is now deployed to support various research and industry
applications at ByteDance. PARAGEN is available at
https://github.com/bytedance/ParaGen.

Differentially Private methods for training Deep Neural Networks (DNNs) have
progressed recently, in particular with the use of massive batches and
aggregated data augmentations for a large number of steps. These techniques
require much more compute than their non-private counterparts, shifting the
traditional privacy-accuracy trade-off to a privacy-accuracy-compute trade-off
and making hyper-parameter search virtually impossible for realistic scenarios.
In this work, we decouple privacy analysis and experimental behavior of noisy
training to explore the trade-off with minimal computational requirements. We
first use the tools of R\'enyi Differential Privacy (RDP) to show that the
privacy budget, when not overcharged, only depends on the total amount of noise
(TAN) injected throughout training. We then derive scaling laws for training
models with DP-SGD to optimize hyper-parameters with more than a 100 reduction
in computational budget. We apply the proposed method on CIFAR-10 and ImageNet
and, in particular, strongly improve the state-of-the-art on ImageNet with a +9
points gain in accuracy for a privacy budget epsilon=8.

In order to increase the prediction accuracy of the online vehicle velocity
prediction (VVP) strategy, a self-adaptive velocity prediction algorithm fused
with traffic information was presented for the multiple scenarios. Initially,
traffic scenarios were established inside the co-simulation environment. In
addition, the algorithm of a general regressive neural network (GRNN) paired
with datasets of the ego-vehicle, the front vehicle, and traffic lights was
used in traffic scenarios, which increasingly improved the prediction accuracy.
To ameliorate the robustness of the algorithm, then the strategy was optimized
by particle swarm optimization (PSO) and k-fold cross-validation to find the
optimal parameters of the neural network in real-time, which constructed a
self-adaptive online PSO-GRNN VVP strategy with multi-information fusion to
adapt with different operating situations. The self-adaptive online PSO-GRNN
VVP strategy was then deployed to a variety of simulated scenarios to test its
efficacy under various operating situations. Finally, the simulation results
reveal that in urban and highway scenarios, the prediction accuracy is
separately increased by 27.8% and 54.5% when compared to the traditional GRNN
VVP strategy with fixed parameters utilizing only the historical ego-vehicle
velocity dataset.

The prevalent fully-connected tensor network (FCTN) has achieved excellent
success to compress data. However, the FCTN decomposition suffers from slow
computational speed when facing higher-order and large-scale data. Naturally,
there arises an interesting question: can a new model be proposed that
decomposes the tensor into smaller ones and speeds up the computation of the
algorithm? This work gives a positive answer by formulating a novel
higher-order tensor decomposition model that utilizes latent matrices based on
the tensor network structure, which can decompose a tensor into smaller-scale
data than the FCTN decomposition, hence we named it Latent Matrices for Tensor
Network Decomposition (LMTN). Furthermore, three optimization algorithms,
LMTN-PAM, LMTN-SVD and LMTN-AR, have been developed and applied to the
tensor-completion task. In addition, we provide proofs of theoretical
convergence and complexity analysis for these algorithms. Experimental results
show that our algorithm has the effectiveness in both deep learning dataset
compression and higher-order tensor completion, and that our LMTN-SVD algorithm
is 3-6 times faster than the FCTN-PAM algorithm and only a 1.8 points accuracy
drop.

Even if model-driven techniques have been enabled the centrality of the
models in automated development processes, the majority of the industrial
settings does not embrace such a paradigm due to the procedural complexity of
managing model life cycle. This paper proposes a semi-automatic approach for
the completion of high-level models of critical systems. The proposal suggests
a specification guidelines that starts from a partial SysML (Systems Modeling
Language) model of a system and on a set of requirements, expressed in the
well-known Behaviour-Driven Design paradigm. On the base of such requirements,
the approach enables the automatic generation of SysML state machines
fragments. Once completed, the approach also enables the modeller to check the
results improving the quality of the model and avoiding errors both coming from
the mis-interpretation of the tool and from the modeller himself/herself. An
example taken from the railway domain shows the approach.

A growing body of research works has focused on the Offline Reinforcement
Learning (RL) paradigm. Data providers share large pre-collected datasets on
which others can train high-quality agents without interacting with the
environments. Such an offline RL paradigm has demonstrated effectiveness in
many critical tasks, including robot control, autonomous driving, etc. A
well-trained agent can be regarded as a software system. However, less
attention is paid to investigating the security threats to the offline RL
system. In this paper, we focus on a critical security threat: backdoor
attacks. Given normal observations, an agent implanted with backdoors takes
actions leading to high rewards. However, the same agent takes actions that
lead to low rewards if the observations are injected with triggers that can
activate the backdoor. In this paper, we propose Baffle (Backdoor Attack for
Offline Reinforcement Learning) and evaluate how different Offline RL
algorithms react to this attack. Our experiments conducted on four tasks and
four offline RL algorithms expose a disquieting fact: none of the existing
offline RL algorithms is immune to such a backdoor attack. More specifically,
Baffle modifies $10\%$ of the datasets for four tasks (3 robotic controls and 1
autonomous driving). Agents trained on the poisoned datasets perform well in
normal settings. However, when triggers are presented, the agents' performance
decreases drastically by $63.6\%$, $57.8\%$, $60.8\%$ and $44.7\%$ in the four
tasks on average. The backdoor still persists after fine-tuning poisoned agents
on clean datasets. We further show that the inserted backdoor is also hard to
be detected by a popular defensive method. This paper calls attention to
developing more effective protection for the open-source offline RL dataset.

Automated Human Activity Recognition has long been a problem of great
interest in human-centered and ubiquitous computing. In the last years, a
plethora of supervised learning algorithms based on deep neural networks has
been suggested to address this problem using various modalities. While every
modality has its own limitations, there is one common challenge. Namely,
supervised learning requires vast amounts of annotated data which is
practically hard to collect. In this paper, we benefit from the self-supervised
learning paradigm (SSL) that is typically used to learn deep feature
representations from unlabeled data. Moreover, we upgrade a contrastive SSL
framework, namely SimCLR, widely used in various applications by introducing a
temporal feature alignment procedure for Human Activity Recognition.
Specifically, we propose integrating a dynamic time warping (DTW) algorithm in
a latent space to force features to be aligned in a temporal dimension.
Extensive experiments have been conducted for the unimodal scenario with
inertial modality as well as in multimodal settings using inertial and skeleton
data. According to the obtained results, the proposed approach has a great
potential in learning robust feature representations compared to the recent SSL
baselines, and clearly outperforms supervised models in semi-supervised
learning. The code for the unimodal case is available via the following link:
https://github.com/bulatkh/csshar_tfa.

Zero-shot stance detection is challenging because it requires detecting the
stance of previously unseen targets in the inference phase. The ability to
learn transferable target-invariant features is critical for zero-shot stance
detection. In this work, we propose a stance detection approach that can
efficiently adapt to unseen targets, the core of which is to capture
target-invariant syntactic expression patterns as transferable knowledge.
Specifically, we first augment the data by masking the topic words of
sentences, and then feed the augmented data to an unsupervised contrastive
learning module to capture transferable features. Then, to fit a specific
target, we encode the raw texts as target-specific features. Finally, we adopt
an attention mechanism, which combines syntactic expression patterns with
target-specific features to obtain enhanced features for predicting previously
unseen targets. Experiments demonstrate that our model outperforms competitive
baselines on four benchmark datasets.

This paper portrays the method of UAV magnetometry survey data interpolation.
The method accommodates the fact that this kind of data has a spatial
distribution of the samples along a series of straight lines (similar to
maritime tacks), which is a prominent characteristic of many kinds of UAV
surveys. The interpolation relies on the very basic Nearest Neighbours
algorithm, although augmented with a Machine Learning approach. Such an
approach enables the error of less than 5 percent by intelligently adjusting
the Nearest Neighbour algorithm parameters. The method was pilot tested on
geomagnetic data with Borok Geomagnetic Observatory UAV aeromagnetic survey
data.

This paper presents our strategy to address the SemEval-2022 Task 3 PreTENS:
Presupposed Taxonomies Evaluating Neural Network Semantics. The goal of the
task is to identify if a sentence is deemed acceptable or not, depending on the
taxonomic relationship that holds between a noun pair contained in the
sentence. For sub-task 1 -- binary classification -- we propose an effective
way to enhance the robustness and the generalizability of language models for
better classification on this downstream task. We design a two-stage
fine-tuning procedure on the ELECTRA language model using data augmentation
techniques. Rigorous experiments are carried out using multi-task learning and
data-enriched fine-tuning. Experimental results demonstrate that our proposed
model, UU-Tax, is indeed able to generalize well for our downstream task. For
sub-task 2 -- regression -- we propose a simple classifier that trains on
features obtained from Universal Sentence Encoder (USE). In addition to
describing the submitted systems, we discuss other experiments that employ
pre-trained language models and data augmentation techniques. For both
sub-tasks, we perform error analysis to further understand the behaviour of the
proposed models. We achieved a global F1_Binary score of 91.25% in sub-task 1
and a rho score of 0.221 in sub-task 2.

We present CLEAR, a method for learning session-specific causal graphs, in
the possible presence of latent confounders, from attention in pre-trained
attention-based recommenders. These causal graphs describe user behavior,
within the context captured by attention, and can provide a counterfactual
explanation for a recommendation. In essence, these causal graphs allow
answering "why" questions uniquely for any specific session. Using empirical
evaluations we show that, compared to naively using attention weights to
explain input-output relations, counterfactual explanations found by CLEAR are
shorter and an alternative recommendation is ranked higher in the original
top-k recommendations.

Self-supervised pre-training has drawn increasing attention in recent years
due to its superior performance on numerous downstream tasks after fine-tuning.
However, it is well-known that deep learning models lack the robustness to
adversarial examples, which can also invoke security issues to pre-trained
models, despite being less explored. In this paper, we delve into the
robustness of pre-trained models by introducing Pre-trained Adversarial
Perturbations (PAPs), which are universal perturbations crafted for the
pre-trained models to maintain the effectiveness when attacking fine-tuned ones
without any knowledge of the downstream tasks. To this end, we propose a
Low-Level Layer Lifting Attack (L4A) method to generate effective PAPs by
lifting the neuron activations of low-level layers of the pre-trained models.
Equipped with an enhanced noise augmentation strategy, L4A is effective at
generating more transferable PAPs against fine-tuned models. Extensive
experiments on typical pre-trained vision models and ten downstream tasks
demonstrate that our method improves the attack success rate by a large margin
compared with state-of-the-art methods.

Learning provides a powerful tool for vision-based navigation, but the
capabilities of learning-based policies are constrained by limited training
data. If we could combine data from all available sources, including multiple
kinds of robots, we could train more powerful navigation models. In this paper,
we study how a general goal-conditioned model for vision-based navigation can
be trained on data obtained from many distinct but structurally similar robots,
and enable broad generalization across environments and embodiments. We analyze
the necessary design decisions for effective data sharing across robots,
including the use of temporal context and standardized action spaces, and
demonstrate that an omnipolicy trained from heterogeneous datasets outperforms
policies trained on any single dataset. We curate 60 hours of navigation
trajectories from 6 distinct robots, and deploy the trained GNM on a range of
new robots, including an underactuated quadrotor. We find that training on
diverse data leads to robustness against degradation in sensing and actuation.
Using a pre-trained navigation model with broad generalization capabilities can
bootstrap applications on novel robots going forward, and we hope that the GNM
represents a step in that direction. For more information on the datasets,
code, and videos, please check out
http://sites.google.com/view/drive-any-robot.

Generative-adversarial networks (GANs) have been used to produce data closely
resembling example data in a compressed, latent space that is close to
sufficient for reconstruction in the original vector space. The Wasserstein
metric has been used as an alternative to binary cross-entropy, producing more
numerically stable GANs with greater mode covering behavior. Here, a
generalization of the Wasserstein distance, using higher-order moments than the
mean, is derived. Training a GAN with this higher-order Wasserstein metric is
demonstrated to exhibit superior performance, even when adjusted for slightly
higher computational cost. This is illustrated generating synthetic antibody
sequences.

Social media platforms provide a continuous stream of real-time news
regarding crisis events on a global scale. Several machine learning methods
utilize the crowd-sourced data for the automated detection of crises and the
characterization of their precursors and aftermaths. Early detection and
localization of crisis-related events can help save lives and economies. Yet,
the applied automation methods introduce ethical risks worthy of investigation
- especially given their high-stakes societal context. This work identifies and
critically examines ethical risk factors of social media analyses of crisis
events focusing on machine learning methods. We aim to sensitize researchers
and practitioners to the ethical pitfalls and promote fairer and more reliable
designs.

This paper analyzes various types of image misclassification from a
game-theoretic view. Particularly, we consider the misclassification of clean,
adversarial, and corrupted images and characterize it through the distribution
of multi-order interactions. We discover that the distribution of multi-order
interactions varies across the types of misclassification. For example,
misclassified adversarial images have a higher strength of high-order
interactions than correctly classified clean images, which indicates that
adversarial perturbations create spurious features that arise from complex
cooperation between pixels. By contrast, misclassified corrupted images have a
lower strength of low-order interactions than correctly classified clean
images, which indicates that corruptions break the local cooperation between
pixels. We also provide the first analysis of Vision Transformers using
interactions. We found that Vision Transformers show a different tendency in
the distribution of interactions from that in CNNs, and this implies that they
exploit the features that CNNs do not use for the prediction. Our study
demonstrates that the recent game-theoretic analysis of deep learning models
can be broadened to analyze various malfunctions of deep learning models
including Vision Transformers by using the distribution, order, and sign of
interactions.

Visually-situated language is ubiquitous -- sources range from textbooks with
diagrams to web pages with images and tables, to mobile apps with buttons and
forms. Perhaps due to this diversity, previous work has typically relied on
domain-specific recipes with limited sharing of the underlying data, model
architectures, and objectives. We present Pix2Struct, a pretrained
image-to-text model for purely visual language understanding, which can be
finetuned on tasks containing visually-situated language. Pix2Struct is
pretrained by learning to parse masked screenshots of web pages into simplified
HTML. The web, with its richness of visual elements cleanly reflected in the
HTML structure, provides a large source of pretraining data well suited to the
diversity of downstream tasks. Intuitively, this objective subsumes common
pretraining signals such as OCR, language modeling, image captioning. In
addition to the novel pretraining strategy, we introduce a variable-resolution
input representation and a more flexible integration of language and vision
inputs, where language prompts such as questions are rendered directly on top
of the input image. For the first time, we show that a single pretrained model
can achieve state-of-the-art results in six out of nine tasks across four
domains: documents, illustrations, user interfaces, and natural images.

Multi-intent Spoken Language Understanding has great potential for widespread
implementation. Jointly modeling Intent Detection and Slot Filling in it
provides a channel to exploit the correlation between intents and slots.
However, current approaches are apt to formulate these two sub-tasks
differently, which leads to two issues: 1) It hinders models from effective
extraction of shared features. 2) Pretty complicated structures are involved to
enhance expression ability while causing damage to the interpretability of
frameworks. In this work, we describe a Prompt-based Spoken Language
Understanding (PromptSLU) framework, to intuitively unify two sub-tasks into
the same form by offering a common pre-trained Seq2Seq model. In detail, ID and
SF are completed by concisely filling the utterance into task-specific prompt
templates as input, and sharing output formats of key-value pairs sequence.
Furthermore, variable intents are predicted first, then naturally embedded into
prompts to guide slot-value pairs inference from a semantic perspective.
Finally, we are inspired by prevalent multi-task learning to introduce an
auxiliary sub-task, which helps to learn relationships among provided labels.
Experiment results show that our framework outperforms several state-of-the-art
baselines on two public datasets.

Code pre-trained models (CodePTMs) have recently demonstrated significant
success in code intelligence. To interpret these models, some probing methods
have been applied. However, these methods fail to consider the inherent
characteristics of codes. In this paper, to address the problem, we propose a
novel probing method CAT-probing to quantitatively interpret how CodePTMs
attend code structure. We first denoise the input code sequences based on the
token types pre-defined by the compilers to filter those tokens whose attention
scores are too small. After that, we define a new metric CAT-score to measure
the commonality between the token-level attention scores generated in CodePTMs
and the pair-wise distances between corresponding AST nodes. The higher the
CAT-score, the stronger the ability of CodePTMs to capture code structure. We
conduct extensive experiments to integrate CAT-probing with representative
CodePTMs for different programming languages. Experimental results show the
effectiveness of CAT-probing in CodePTM interpretation. Our codes and data are
publicly available at https://github.com/nchen909/CodeAttention.

Glaucoma is the second driving reason for partial or complete blindness among
all the visual deficiencies which mainly occurs because of excessive pressure
in the eye due to anxiety or depression which damages the optic nerve and
creates complications in vision. Traditional glaucoma screening is a
time-consuming process that necessitates the medical professionals' constant
attention, and even so time to time due to the time constrains and pressure
they fail to classify correctly that leads to wrong treatment. Numerous efforts
have been made to automate the entire glaucoma classification procedure
however, these existing models in general have a black box characteristics that
prevents users from understanding the key reasons behind the prediction and
thus medical practitioners generally can not rely on these system. In this
article after comparing with various pre-trained models, we propose a transfer
learning model that is able to classify Glaucoma with 94.71\% accuracy. In
addition, we have utilized Local Interpretable Model-Agnostic
Explanations(LIME) that introduces explainability in our system. This
improvement enables medical professionals obtain important and comprehensive
information that aid them in making judgments. It also lessen the opacity and
fragility of the traditional deep learning models.

Previous literature has proved that Pretrained Language Models (PLMs) can
store factual knowledge. However, we find that facts stored in the PLMs are not
always correct. It motivates us to explore a fundamental question: How do we
calibrate factual knowledge in PLMs without re-training from scratch? In this
work, we propose a simple and lightweight method CaliNet to achieve this goal.
To be specific, we first detect whether PLMs can learn the right facts via a
contrastive score between right and fake facts. If not, we then use a
lightweight method to add and adapt new parameters to specific factual texts.
Experiments on the knowledge probing task show the calibration effectiveness
and efficiency. In addition, through closed-book question answering, we find
that the calibrated PLM possesses knowledge generalization ability after
fine-tuning. Beyond the calibration performance, we further investigate and
visualize the knowledge calibration mechanism.

Deep Q-Networks algorithm (DQN) was the first reinforcement learning
algorithm using deep neural network to successfully surpass human level
performance in a number of Atari learning environments. However, divergent and
unstable behaviour have been long standing issues in DQNs. The unstable
behaviour is often characterised by overestimation in the $Q$-values, commonly
referred to as the overestimation bias. To address the overestimation bias and
the divergent behaviour, a number of heuristic extensions have been proposed.
Notably, multi-step updates have been shown to drastically reduce unstable
behaviour while improving agent's training performance. However, agents are
often highly sensitive to the selection of the multi-step update horizon ($n$),
and our empirical experiments show that a poorly chosen static value for $n$
can in many cases lead to worse performance than single-step DQN. Inspired by
the success of $n$-step DQN and the effects that multi-step updates have on
overestimation bias, this paper proposes a new algorithm that we call `Elastic
Step DQN' (ES-DQN). It dynamically varies the step size horizon in multi-step
updates based on the similarity of states visited. Our empirical evaluation
shows that ES-DQN out-performs $n$-step with fixed $n$ updates, Double DQN and
Average DQN in several OpenAI Gym environments while at the same time
alleviating the overestimation bias.

The challenge that climate change poses to humanity has spurred a rapidly
developing field of artificial intelligence research focused on climate change
applications. The climate change AI (CCAI) community works on a diverse,
challenging set of problems which often involve physics-constrained ML or
heterogeneous spatiotemporal data. It would be desirable to use automated
machine learning (AutoML) techniques to automatically find high-performing
architectures and hyperparameters for a given dataset. In this work, we
benchmark popular AutoML libraries on three high-leverage CCAI applications:
climate modeling, wind power forecasting, and catalyst discovery. We find that
out-of-the-box AutoML libraries currently fail to meaningfully surpass the
performance of human-designed CCAI models. However, we also identify a few key
weaknesses, which stem from the fact that most AutoML techniques are tailored
to computer vision and NLP applications. For example, while dozens of search
spaces have been designed for image and language data, none have been designed
for spatiotemporal data. Addressing these key weaknesses can lead to the
discovery of novel architectures that yield substantial performance gains
across numerous CCAI applications. Therefore, we present a call to action to
the AutoML community, since there are a number of concrete, promising
directions for future work in the space of AutoML for CCAI. We release our code
and a list of resources at
https://github.com/climate-change-automl/climate-change-automl.

Personalized intervention strategies, in particular those that modify
treatment based on a participant's own response, are a core component of
precision medicine approaches. Sequential Multiple Assignment Randomized Trials
(SMARTs) are growing in popularity and are specifically designed to facilitate
the evaluation of sequential adaptive strategies, in particular those embedded
within the SMART. Advances in efficient estimation approaches that are able to
incorporate machine learning while retaining valid inference can allow for more
precise estimates of the effectiveness of these embedded regimes. However, to
the best of our knowledge, such approaches have not yet been applied as the
primary analysis in SMART trials. In this paper, we present a robust and
efficient approach using Targeted Maximum Likelihood Estimation (TMLE) for
estimating and contrasting expected outcomes under the dynamic regimes embedded
in a SMART, together with generating simultaneous confidence intervals for the
resulting estimates. We contrast this method with two alternatives
(G-computation and Inverse Probability Weighting estimators). The precision
gains and robust inference achievable through the use of TMLE to evaluate the
effects of embedded regimes are illustrated using both outcome-blind
simulations and a real data analysis from the Adaptive Strategies for
Preventing and Treating Lapses of Retention in HIV Care (ADAPT-R) trial
(NCT02338739), a SMART with a primary aim of identifying strategies to improve
retention in HIV care among people living with HIV in sub-Saharan Africa.

How can we protect the intellectual property of trained NLP models? Modern
NLP models are prone to stealing by querying and distilling from their publicly
exposed APIs. However, existing protection methods such as watermarking only
work for images but are not applicable to text. We propose
Distillation-Resistant Watermarking (DRW), a novel technique to protect NLP
models from being stolen via distillation. DRW protects a model by injecting
watermarks into the victim's prediction probability corresponding to a secret
key and is able to detect such a key by probing a suspect model. We prove that
a protected model still retains the original accuracy within a certain bound.
We evaluate DRW on a diverse set of NLP tasks including text classification,
part-of-speech tagging, and named entity recognition. Experiments show that DRW
protects the original model and detects stealing suspects at 100% mean average
precision for all four tasks while the prior method fails on two.

Forward gradient learning computes a noisy directional gradient and is a
biologically plausible alternative to backprop for learning deep neural
networks. However, the standard forward gradient algorithm, when applied
naively, suffers from high variance when the number of parameters to be learned
is large. In this paper, we propose a series of architectural and algorithmic
modifications that together make forward gradient learning practical for
standard deep learning benchmark tasks. We show that it is possible to
substantially reduce the variance of the forward gradient estimator by applying
perturbations to activations rather than weights. We further improve the
scalability of forward gradient by introducing a large number of local greedy
loss functions, each of which involves only a small number of learnable
parameters, and a new MLPMixer-inspired architecture, LocalMixer, that is more
suitable for local learning. Our approach matches backprop on MNIST and
CIFAR-10 and significantly outperforms previously proposed backprop-free
algorithms on ImageNet.

The Generative Flow Network is a probabilistic framework where an agent
learns a stochastic policy for object generation, such that the probability of
generating an object is proportional to a given reward function. Its
effectiveness has been shown in discovering high-quality and diverse solutions,
compared to reward-maximizing reinforcement learning-based methods.
Nonetheless, GFlowNets only learn from rewards of the terminal states, which
can limit its applicability. Indeed, intermediate rewards play a critical role
in learning, for example from intrinsic motivation to provide intermediate
feedback even in particularly challenging sparse reward tasks. Inspired by
this, we propose Generative Augmented Flow Networks (GAFlowNets), a novel
learning framework to incorporate intermediate rewards into GFlowNets. We
specify intermediate rewards by intrinsic motivation to tackle the exploration
problem in sparse reward environments. GAFlowNets can leverage edge-based and
state-based intrinsic rewards in a joint way to improve exploration. Based on
extensive experiments on the GridWorld task, we demonstrate the effectiveness
and efficiency of GAFlowNet in terms of convergence, performance, and diversity
of solutions. We further show that GAFlowNet is scalable to a more complex and
large-scale molecule generation domain, where it achieves consistent and
significant performance improvement.

Automatic International Classification of Diseases (ICD) coding aims to
assign multiple ICD codes to a medical note with average length of 3,000+
tokens. This task is challenging due to a high-dimensional space of multi-label
assignment (tens of thousands of ICD codes) and the long-tail challenge: only a
few codes (common diseases) are frequently assigned while most codes (rare
diseases) are infrequently assigned. This study addresses the long-tail
challenge by adapting a prompt-based fine-tuning technique with label
semantics, which has been shown to be effective under few-shot setting. To
further enhance the performance in medical domain, we propose a
knowledge-enhanced longformer by injecting three domain-specific knowledge:
hierarchy, synonym, and abbreviation with additional pretraining using
contrastive learning. Experiments on MIMIC-III-full, a benchmark dataset of
code assignment, show that our proposed method outperforms previous
state-of-the-art method in 14.5% in marco F1 (from 10.3 to 11.8, P<0.001). To
further test our model on few-shot setting, we created a new rare diseases
coding dataset, MIMIC-III-rare50, on which our model improves marco F1 from
17.1 to 30.4 and micro F1 from 17.2 to 32.6 compared to previous method.

A significant number of studies apply acoustic and linguistic characteristics
of human speech as prominent markers of dementia and depression. However,
studies on discriminating depression from dementia are rare. Co-morbid
depression is frequent in dementia and these clinical conditions share many
overlapping symptoms, but the ability to distinguish between depression and
dementia is essential as depression is often curable. In this work, we
investigate the ability of clustering approaches in distinguishing between
depression and dementia from human speech. We introduce a novel aggregated
dataset, which combines narrative speech data from multiple conditions, i.e.,
Alzheimer's disease, mild cognitive impairment, healthy control, and
depression. We compare linear and non-linear clustering approaches and show
that non-linear clustering techniques distinguish better between distinct
disease clusters. Our interpretability analysis shows that the main
differentiating symptoms between dementia and depression are acoustic
abnormality, repetitiveness (or circularity) of speech, word finding
difficulty, coherence impairment, and differences in lexical complexity and
richness.

Neural-network-based approaches recently emerged in the field of data
compression and have already led to significant progress in image compression,
especially in achieving a higher compression ratio. In the lossless image
compression scenario, however, existing methods often struggle to learn a
probability model of full-size high-resolution images due to the limitation of
the computation source. The current strategy is to crop high-resolution images
into multiple non-overlapping patches and process them independently. This
strategy ignores long-term dependencies beyond patches, thus limiting modeling
performance. To address this problem, we propose a hierarchical latent variable
model with a global context to capture the long-term dependencies of
high-resolution images. Besides the latent variable unique to each patch, we
introduce shared latent variables between patches to construct the global
context. The shared latent variables are extracted by a self-supervised
clustering module inside the model's encoder. This clustering module assigns
each patch the confidence that it belongs to any cluster. Later, shared latent
variables are learned according to latent variables of patches and their
confidence, which reflects the similarity of patches in the same cluster and
benefits the global context modeling. Experimental results show that our global
context model improves compression ratio compared to the engineered codecs and
deep learning models on three benchmark high-resolution image datasets, DIV2K,
CLIC.pro, and CLIC.mobile.

Decision-based adversarial attacks construct inputs that fool a
machine-learning model into making targeted mispredictions by making only
hard-label queries. For the most part, these attacks have been applied directly
to isolated neural network models. However, in practice, machine learning
models are just a component of a much larger system. By adding just a single
preprocessor in front of a classifier, we find that state-of-the-art
query-based attacks are as much as seven times less effective at attacking a
prediction pipeline than attacking the machine learning model alone. Hence,
attacks that are unaware of this invariance inevitably waste a large number of
queries to re-discover or overcome it. We, therefore, develop techniques to
first reverse-engineer the preprocessor and then use this extracted information
to attack the end-to-end system. Our extraction method requires only a few
hundred queries to learn the preprocessors used by most publicly available
model pipelines, and our preprocessor-aware attacks recover the same efficacy
as just attacking the model alone. The code can be found at
https://github.com/google-research/preprocessor-aware-black-box-attack.

Scene flow is the collection of each point motion information in the 3D point
clouds. It is a vital tool applied to many tasks, such as autonomous driving
and augmented reality. However, there are always occlusion points between two
consecutive point clouds, whether from the sparsity data sampling or real-world
occlusion. In this paper, we focus on addressing occlusion issues in scene flow
by self-similarity and local consistency of moving objects. We propose a GMA3D
module based on the transformer framework, which utilizes local and global
similarity to infer the motion information of occluded points from the motion
information of local and global non-occluded points respectively, and then uses
an offset generator to aggregate them. Our module is the first to apply the
transformer-based architecture to gauge the scene flow occlusion problem on
point clouds. Experiments show that our GMA3D can solve the occlusion problem
in the scene flow, especially in the real scene. We evaluate the proposed
method on the occluded version datasets and get state-of-the-art results on the
real scene KITTI. To testify that GMA3D is still beneficial for non-occluded
scene flow, we also conducted experiments on non-occluded version datasets and
achieved state-of-the-art results on FlyThings3D and KITTI. The code is
available at https://github.com/O-VIGIA/GMA3D.

Recently, researchers observed that gradient descent for deep neural networks
operates in an ``edge-of-stability'' (EoS) regime: the sharpness (maximum
eigenvalue of the Hessian) is often larger than stability threshold 2/$\eta$
(where $\eta$ is the step size). Despite this, the loss oscillates and
converges in the long run, and the sharpness at the end is just slightly below
$2/\eta$. While many other well-understood nonconvex objectives such as matrix
factorization or two-layer networks can also converge despite large sharpness,
there is often a larger gap between sharpness of the endpoint and $2/\eta$. In
this paper, we study EoS phenomenon by constructing a simple function that has
the same behavior. We give rigorous analysis for its training dynamics in a
large local region and explain why the final converging point has sharpness
close to $2/\eta$. Globally we observe that the training dynamics for our
example has an interesting bifurcating behavior, which was also observed in the
training of neural nets.

The timing of cell divisions in early embryos during the In-Vitro
Fertilization (IVF) process is a key predictor of embryo viability. However,
observing cell divisions in Time-Lapse Monitoring (TLM) is a time-consuming
process and highly depends on experts. In this paper, we propose EmbryosFormer,
a computational model to automatically detect and classify cell divisions from
original time-lapse images. Our proposed network is designed as an
encoder-decoder deformable transformer with collaborative heads. The
transformer contracting path predicts per-image labels and is optimized by a
classification head. The transformer expanding path models the temporal
coherency between embryo images to ensure monotonic non-decreasing constraint
and is optimized by a segmentation head. Both contracting and expanding paths
are synergetically learned by a collaboration head. We have benchmarked our
proposed EmbryosFormer on two datasets: a public dataset with mouse embryos
with 8-cell stage and an in-house dataset with human embryos with 4-cell stage.
Source code: https://github.com/UARK-AICV/Embryos.

Since most scientific literature data are unlabeled, this makes unsupervised
graph-based semantic representation learning crucial. Therefore, an
unsupervised semantic representation learning method of scientific literature
based on graph attention mechanism and maximum mutual information (GAMMI) is
proposed. By introducing a graph attention mechanism, the weighted summation of
nearby node features make the weights of adjacent node features entirely depend
on the node features. Depending on the features of the nearby nodes, different
weights can be applied to each node in the graph. Therefore, the correlations
between vertex features can be better integrated into the model. In addition,
an unsupervised graph contrastive learning strategy is proposed to solve the
problem of being unlabeled and scalable on large-scale graphs. By comparing the
mutual information between the positive and negative local node representations
on the latent space and the global graph representation, the graph neural
network can capture both local and global information. Experimental results
demonstrate competitive performance on various node classification benchmarks,
achieving good results and sometimes even surpassing the performance of
supervised learning.

Academic networks in the real world can usually be portrayed as heterogeneous
information networks (HINs) with multi-type, universally connected nodes and
multi-relationships. Some existing studies for the representation learning of
homogeneous information networks cannot be applicable to heterogeneous
information networks because of the lack of ability to issue heterogeneity. At
the same time, data has become a factor of production, playing an increasingly
important role. Due to the closeness and blocking of businesses among different
enterprises, there is a serious phenomenon of data islands. To solve the above
challenges, aiming at the data information of scientific research teams closely
related to science and technology, we proposed an academic heterogeneous
information network embedding representation learning method based on federated
learning (FedAHE), which utilizes node attention and meta path attention
mechanism to learn low-dimensional, dense and real-valued vector
representations while preserving the rich topological information and
meta-path-based semantic information of nodes in network. Moreover, we combined
federated learning with the representation learning of HINs composed of
scientific research teams and put forward a federal training mechanism based on
dynamic weighted aggregation of parameters (FedDWA) to optimize the node
embeddings of HINs. Through sufficient experiments, the efficiency, accuracy
and feasibility of our proposed framework are demonstrated.

Self-supervised representation learning techniques utilize large datasets
without semantic annotations to learn meaningful, universal features that can
be conveniently transferred to solve a wide variety of downstream supervised
tasks. In this work, we propose a self-supervised method for learning
representations of geographic locations from unlabeled GPS trajectories to
solve downstream geospatial computer vision tasks. Tiles resulting from a
raster representation of the earth's surface are modeled as nodes on a graph or
pixels of an image. GPS trajectories are modeled as allowed Markovian paths on
these nodes. A scalable and distributed algorithm is presented to compute
image-like representations, called reachability summaries, of the spatial
connectivity patterns between tiles and their neighbors implied by the observed
Markovian paths. A convolutional, contractive autoencoder is trained to learn
compressed representations, called reachability embeddings, of reachability
summaries for every tile. Reachability embeddings serve as task-agnostic,
feature representations of geographic locations. Using reachability embeddings
as pixel representations for five different downstream geospatial tasks, cast
as supervised semantic segmentation problems, we quantitatively demonstrate
that reachability embeddings are semantically meaningful representations and
result in 4-23% gain in performance, as measured using area under the
precision-recall curve (AUPRC) metric, when compared to baseline models that
use pixel representations that do not account for the spatial connectivity
between tiles. Reachability embeddings transform sequential, spatiotemporal
mobility data into semantically meaningful tensor representations that can be
combined with other sources of imagery and are designed to facilitate
multimodal learning in geospatial computer vision.

Bayesian optimal experimental design is a sub-field of statistics focused on
developing methods to make efficient use of experimental resources. Any
potential design is evaluated in terms of a utility function, such as the
(theoretically well-justified) expected information gain (EIG); unfortunately
however, under most circumstances the EIG is intractable to evaluate. In this
work we build off of successful variational approaches, which optimize a
parameterized variational model with respect to bounds on the EIG. Past work
focused on learning a new variational model from scratch for each new design
considered. Here we present a novel neural architecture that allows
experimenters to optimize a single variational model that can estimate the EIG
for potentially infinitely many designs. To further improve computational
efficiency, we also propose to train the variational model on a significantly
cheaper-to-evaluate lower bound, and show empirically that the resulting model
provides an excellent guide for more accurate, but expensive to evaluate bounds
on the EIG. We demonstrate the effectiveness of our technique on generalized
linear models, a class of statistical models that is widely used in the
analysis of controlled experiments. Experiments show that our method is able to
greatly improve accuracy over existing approximation strategies, and achieve
these results with far better sample efficiency.

Sets have been used for modeling various types of objects (e.g., a document
as the set of keywords in it and a customer as the set of the items that she
has purchased). Measuring similarity (e.g., Jaccard Index) between sets has
been a key building block of a wide range of applications, including,
plagiarism detection, recommendation, and graph compression. However, as sets
have grown in numbers and sizes, the computational cost and storage required
for set similarity computation have become substantial, and this has led to the
development of hashing and sketching based solutions. In this work, we propose
Set2Box, a learning-based approach for compressed representations of sets from
which various similarity measures can be estimated accurately in constant time.
The key idea is to represent sets as boxes to precisely capture overlaps of
sets. Additionally, based on the proposed box quantization scheme, we design
Set2Box+, which yields more concise but more accurate box representations of
sets. Through extensive experiments on 8 real-world datasets, we show that,
compared to baseline approaches, Set2Box+ is (a) Accurate: achieving up to
40.8X smaller estimation error while requiring 60% fewer bits to encode sets,
(b) Concise: yielding up to 96.8X more concise representations with similar
estimation error, and (c) Versatile: enabling the estimation of four
set-similarity measures from a single representation of each set.

The content quality of shared knowledge in Stack Overflow (SO) is crucial in
supporting software developers with their programming problems. Thus, SO allows
its users to suggest edits to improve the quality of a post (i.e., question and
answer). However, existing research shows that many suggested edits in SO are
rejected due to undesired contents/formats or violating edit guidelines. Such a
scenario frustrates or demotivates users who would like to conduct good-quality
edits. Therefore, our research focuses on assisting SO users by offering them
suggestions on how to improve their editing of posts. First, we manually
investigate 764 (382 questions + 382 answers) rejected edits by rollbacks and
produce a catalog of 19 rejection reasons. Second, we extract 15 texts and
user-based features to capture those rejection reasons. Third, we develop four
machine learning models using those features. Our best-performing model can
predict rejected edits with 69.1% precision, 71.2% recall, 70.1% F1-score, and
69.8% overall accuracy. Fourth, we introduce an online tool named EditEx that
works with the SO edit system. EditEx can assist users while editing posts by
suggesting the potential causes of rejections. We recruit 20 participants to
assess the effectiveness of EditEx. Half of the participants (i.e., treatment
group) use EditEx and another half (i.e., control group) use the SO standard
edit system to edit posts. According to our experiment, EditEx can support SO
standard edit system to prevent 49% of rejected edits, including the commonly
rejected ones. However, it can prevent 12% rejections even in free-form regular
edits. The treatment group finds the potential rejection reasons identified by
EditEx influential. Furthermore, the median workload suggesting edits using
EditEx is half compared to the SO edit system.

Federated learning (FL) is a popular distributed learning framework that can
reduce privacy risks by not explicitly sharing private data. In this work, we
explicitly uncover external covariate shift problem in FL, which is caused by
the independent local training processes on different devices. We demonstrate
that external covariate shifts will lead to the obliteration of some devices'
contributions to the global model. Further, we show that normalization layers
are indispensable in FL since their inherited properties can alleviate the
problem of obliterating some devices' contributions. However, recent works have
shown that batch normalization, which is one of the standard components in many
deep neural networks, will incur accuracy drop of the global model in FL. The
essential reason for the failure of batch normalization in FL is poorly
studied. We unveil that external covariate shift is the key reason why batch
normalization is ineffective in FL. We also show that layer normalization is a
better choice in FL which can mitigate the external covariate shift and improve
the performance of the global model. We conduct experiments on CIFAR10 under
non-IID settings. The results demonstrate that models with layer normalization
converge fastest and achieve the best or comparable accuracy for three
different model architectures.

Out-of-distribution generalization (OODG) is a longstanding challenge for
neural networks, and is quite apparent in tasks with well-defined variables and
rules, where explicit use of the rules can solve problems independently of the
particular values of the variables. Large transformer-based language models
have pushed the boundaries on how well neural networks can generalize to novel
inputs, but their complexity obfuscates they achieve such robustness. As a step
toward understanding how transformer-based systems generalize, we explore the
question of OODG in smaller scale transformers. Using a reasoning task based on
the puzzle Sudoku, we show that OODG can occur on complex problems if the
training set includes examples sampled from the whole distribution of simpler
component tasks.

Explaining deep learning models is of vital importance for understanding
artificial intelligence systems, improving safety, and evaluating fairness. To
better understand and control the CNN model, many methods for
transparency-interpretability have been proposed. However, most of these works
are less intuitive for human understanding and have insufficient human control
over the CNN model. We propose a novel method, Transparent and Controllable
Network Learning (TCNL), to overcome such challenges. Towards the goal of
improving transparency-interpretability, in TCNL, we define some concepts for
specific classification tasks through scientific human-intuition study and
incorporate concept information into the CNN model. In TCNL, the shallow
feature extractor gets preliminary features first. Then several concept feature
extractors are built right after the shallow feature extractor to learn
high-dimensional concept representations. The concept feature extractor is
encouraged to encode information related to the predefined concepts. We also
build the concept mapper to visualize features extracted by the concept
extractor in a human-intuitive way. TCNL provides a generalizable approach to
transparency-interpretability. Researchers can define concepts corresponding to
certain classification tasks and encourage the model to encode specific concept
information, which to a certain extent improves transparency-interpretability
and the controllability of the CNN model. The datasets (with concept sets) for
our experiments will also be released (https://github.com/bupt-ai-cz/TCNL).

The use of options can greatly accelerate exploration in reinforcement
learning, especially when only sparse reward signals are available. While
option discovery methods have been proposed for individual agents, in
multi-agent reinforcement learning settings, discovering collaborative options
that can coordinate the behavior of multiple agents and encourage them to visit
the under-explored regions of their joint state space has not been considered.
In this case, we propose Multi-agent Deep Covering Option Discovery, which
constructs the multi-agent options through minimizing the expected cover time
of the multiple agents' joint state space. Also, we propose a novel framework
to adopt the multi-agent options in the MARL process. In practice, a
multi-agent task can usually be divided into some sub-tasks, each of which can
be completed by a sub-group of the agents. Therefore, our algorithm framework
first leverages an attention mechanism to find collaborative agent sub-groups
that would benefit most from coordinated actions. Then, a hierarchical
algorithm, namely HA-MSAC, is developed to learn the multi-agent options for
each sub-group to complete their sub-tasks first, and then to integrate them
through a high-level policy as the solution of the whole task. This
hierarchical option construction allows our framework to strike a balance
between scalability and effective collaboration among the agents. The
evaluation based on multi-agent collaborative tasks shows that the proposed
algorithm can effectively capture the agent interactions with the attention
mechanism, successfully identify multi-agent options, and significantly
outperforms prior works using single-agent options or no options, in terms of
both faster exploration and higher task rewards.

We consider the parametric data model employed in applications such as line
spectral estimation and direction-of-arrival estimation. We focus on the
stochastic maximum likelihood estimation (MLE) framework and offer approaches
to estimate the parameter of interest in a gridless manner, overcoming the
model complexities of the past. This progress is enabled by the modern trend of
reparameterization of the objective and exploiting the sparse Bayesian learning
(SBL) approach. The latter is shown to be a correlation-aware method, and for
the underlying problem it is identified as a grid-based technique for
recovering a structured covariance matrix of the measurements. For the case
when the structured matrix is expressible as a sampled Toeplitz matrix, such as
when measurements are sampled in time or space at regular intervals, additional
constraints and reparameterization of the SBL objective leads to the proposed
structured matrix recovery technique based on MLE. The proposed optimization
problem is non-convex, and we propose a majorization-minimization based
iterative procedure to estimate the structured matrix; each iteration solves a
semidefinite program. We recover the parameter of interest in a gridless manner
by appealing to the Caratheodory-Fejer result on decomposition of PSD Toeplitz
matrices. For the general case of irregularly spaced time or spatial samples,
we propose an iterative SBL procedure that refines grid points to increase
resolution near potential source locations, while maintaining a low per
iteration complexity. We provide numerical results to evaluate and compare the
performance of the proposed techniques with other gridless techniques, and the
CRB. The proposed correlation-aware approach is more robust to
environmental/system effects such as low number of snapshots, correlated
sources, small separation between source locations and improves sources
identifiability.

Adapting large-scale pretrained models to various downstream tasks via
fine-tuning is a standard method in machine learning. Recently,
parameter-efficient fine-tuning methods show promise in adapting a pretrained
model to different tasks while training only a few parameters. Despite their
success, most existing methods are proposed in Natural Language Processing
tasks with language Transformers, and adaptation to Computer Vision tasks with
Vision Transformers remains under-explored, especially for dense vision tasks.
Further, in multi-task settings, individually fine-tuning and storing separate
models for different tasks is inefficient. In this work, we provide an
extensive multi-task parameter-efficient benchmark and examine existing
parameter-efficient fine-tuning NLP methods for vision tasks. Our results on
four different dense vision tasks showed that existing methods cannot be
efficiently integrated due to the hierarchical nature of the Hierarchical
Vision Transformers. To overcome this issue, we propose Polyhistor and
Polyhistor-Lite, consisting of Decomposed HyperNetworks and Layer-wise Scaling
Kernels, to share information across different tasks with a few trainable
parameters. This leads to favorable performance improvements against existing
parameter-efficient methods while using fewer trainable parameters.
Specifically, Polyhistor achieves competitive accuracy compared to the
state-of-the-art while only using ~10% of their trainable parameters.
Furthermore, our methods show larger performance gains when large networks and
more pretraining data are used.

Research has shown that personality is a key driver to improve engagement and
user experience in conversational systems. Conversational agents should also
maintain a consistent persona to have an engaging conversation with a user.
However, text generation datasets are often crowd sourced and thereby have an
averaging effect where the style of the generation model is an average style of
all the crowd workers that have contributed to the dataset. While one can
collect persona-specific datasets for each task, it would be an expensive and
time consuming annotation effort. In this work, we propose a novel transfer
learning framework which updates only $0.3\%$ of model parameters to learn
style specific attributes for response generation. For the purpose of this
study, we tackle the problem of stylistic story ending generation using the ROC
stories Corpus. We learn style specific attributes from the
PERSONALITY-CAPTIONS dataset. Through extensive experiments and evaluation
metrics we show that our novel training procedure can improve the style
generation by 200 over Encoder-Decoder baselines while maintaining on-par
content relevance metrics with

We show that the ability of a neural network to integrate information from
diverse sources hinges critically on being exposed to properly correlated
signals during the early phases of training. Interfering with the learning
process during this initial stage can permanently impair the development of a
skill, both in artificial and biological systems where the phenomenon is known
as critical learning period. We show that critical periods arise from the
complex and unstable early transient dynamics, which are decisive of final
performance of the trained system and their learned representations. This
evidence challenges the view, engendered by analysis of wide and shallow
networks, that early learning dynamics of neural networks are simple, akin to
those of a linear model. Indeed, we show that even deep linear networks exhibit
critical learning periods for multi-source integration, while shallow networks
do not. To better understand how the internal representations change according
to disturbances or sensory deficits, we introduce a new measure of source
sensitivity, which allows us to track the inhibition and integration of sources
during training. Our analysis of inhibition suggests cross-source
reconstruction as a natural auxiliary training objective, and indeed we show
that architectures trained with cross-sensor reconstruction objectives are
remarkably more resilient to critical periods. Our findings suggest that the
recent success in self-supervised multi-modal training compared to previous
supervised efforts may be in part due to more robust learning dynamics and not
solely due to better architectures and/or more data.

Interpretable machine learning plays a key role in healthcare because it is
challenging in understanding feature importance in deep learning model
predictions. We propose a novel framework that uses deep learning to study
feature sensitivity for model predictions. This work combines sensitivity
analysis with heterogeneous time-series deep learning model prediction, which
corresponds to the interpretations of spatio-temporal features. We forecast
county-level COVID-19 infection using the Temporal Fusion Transformer. We then
use the sensitivity analysis extending Morris Method to see how sensitive the
outputs are with respect to perturbation to our static and dynamic input
features. The significance of the work is grounded in a real-world COVID-19
infection prediction with highly non-stationary, finely granular, and
heterogeneous data. 1) Our model can capture the detailed daily changes of
temporal and spatial model behaviors and achieves high prediction performance
compared to a PyTorch baseline. 2) By analyzing the Morris sensitivity indices
and attention patterns, we decipher the meaning of feature importance with
observational population and dynamic model changes. 3) We have collected 2.5
years of socioeconomic and health features over 3142 US counties, such as
observed cases and deaths, and a number of static (age distribution, health
disparity, and industry) and dynamic features (vaccination, disease spread,
transmissible cases, and social distancing). Using the proposed framework, we
conduct extensive experiments and show our model can learn complex interactions
and perform predictions for daily infection at the county level. Being able to
model the disease infection with a hybrid prediction and description accuracy
measurement with Morris index at the county level is a central idea that sheds
light on individual feature interpretation via sensitivity analysis.

Automatic speech recognition models are often adapted to improve their
accuracy in a new domain. A potential drawback of model adaptation to new
domains is catastrophic forgetting, where the Word Error Rate on the original
domain is significantly degraded. This paper addresses the situation when we
want to simultaneously adapt automatic speech recognition models to a new
domain and limit the degradation of accuracy on the original domain without
access to the original training dataset. We propose several techniques such as
a limited training strategy and regularized adapter modules for the Transducer
encoder, prediction, and joiner network. We apply these methods to the Google
Speech Commands and to the UK and Ireland English Dialect speech data set and
obtain strong results on the new target domain while limiting the degradation
on the original domain.

Internet of Things (IoT) devices are progressively being utilised in a
variety of edge applications to monitor and control home and industry
infrastructure. Due to the limited compute and energy resources, active
security protections are usually minimal in many IoT devices. This has created
a critical security challenge that has attracted researchers' attention in the
field of network security. Despite a large number of proposed Network Intrusion
Detection Systems (NIDSs), there is limited research into practical IoT
implementations, and to the best of our knowledge, no edge-based NIDS has been
demonstrated to operate on common low-power chipsets found in the majority of
IoT devices, such as the ESP8266. This research aims to address this gap by
pushing the boundaries on low-power Machine Learning (ML) based NIDSs. We
propose and develop an efficient and low-power ML-based NIDS, and demonstrate
its applicability for IoT edge applications by running it on a typical smart
light bulb. We also evaluate our system against other proposed edge-based NIDSs
and show that our model has a higher detection performance, and is
significantly faster and smaller, and therefore more applicable to a wider
range of IoT edge devices.

In the real-world application of COVID-19 misinformation detection, a
fundamental challenge is the lack of the labeled COVID data to enable
supervised end-to-end training of the models, especially at the early stage of
the pandemic. To address this challenge, we propose an unsupervised domain
adaptation framework using contrastive learning and adversarial domain mixup to
transfer the knowledge from an existing source data domain to the target
COVID-19 data domain. In particular, to bridge the gap between the source
domain and the target domain, our method reduces a radial basis function (RBF)
based discrepancy between these two domains. Moreover, we leverage the power of
domain adversarial examples to establish an intermediate domain mixup, where
the latent representations of the input text from both domains could be mixed
during the training process. Extensive experiments on multiple real-world
datasets suggest that our method can effectively adapt misinformation detection
systems to the unseen COVID-19 target domain with significant improvements
compared to the state-of-the-art baselines.

Large-scale river models are being refined over coastal regions to improve
the scientific understanding of coastal processes, hazards and responses to
climate change. However, coarse mesh resolutions and approximations in physical
representations of tidal rivers limit the performance of such models at
resolving the complex flow dynamics especially near the river-ocean interface,
resulting in inaccurate simulations of flood inundation. In this research, we
propose a machine learning (ML) framework based on the state-of-the-art
physics-informed neural network (PINN) to simulate the downscaled flow at the
subgrid scale. First, we demonstrate that PINN is able to assimilate
observations of various types and solve the one-dimensional (1-D) Saint-Venant
equations (SVE) directly. We perform the flow simulations over a floodplain and
along an open channel in several synthetic case studies. The PINN performance
is evaluated against analytical solutions and numerical models. Our results
indicate that the PINN solutions of water depth have satisfactory accuracy with
limited observations assimilated. In the case of flood wave propagation induced
by storm surge and tide, a new neural network architecture is proposed based on
Fourier feature embeddings that seamlessly encodes the periodic tidal boundary
condition in the PINN's formulation. Furthermore, we show that the PINN-based
downscaling can produce more reasonable subgrid solutions of the along-channel
water depth by assimilating observational data. The PINN solution outperforms
the simple linear interpolation in resolving the topography and dynamic flow
regimes at the subgrid scale. This study provides a promising path towards
improving emulation capabilities in large-scale models to characterize
fine-scale coastal processes.

We consider the problem of modeling heterogeneous materials where micro-scale
dynamics and interactions affect global behavior. In the presence of
heterogeneities in material microstructure it is often impractical, if not
impossible, to provide quantitative characterization of material response. The
goal of this work is to develop a Bayesian framework for uncertainty
quantification (UQ) in material response prediction when using nonlocal models.
Our approach combines the nonlocal operator regression (NOR) technique and
Bayesian inference. Specifically, we use a Markov chain Monte Carlo (MCMC)
method to sample the posterior probability distribution on parameters involved
in the nonlocal constitutive law, and associated modeling discrepancies
relative to higher fidelity computations. As an application, we consider the
propagation of stress waves through a one-dimensional heterogeneous bar with
randomly generated microstructure. Several numerical tests illustrate the
construction, enabling UQ in nonlocal model predictions. Although nonlocal
models have become popular means for homogenization, their statistical
calibration with respect to high-fidelity models has not been presented before.
This work is a first step towards statistical characterization of nonlocal
model discrepancy in the context of homogenization.

Dominance-based Rough Approach (DRSA) has been proposed as a machine learning
and knowledge discovery methodology to handle Multiple Criteria Decision Aiding
(MCDA). Due to its capacity of asking the decision maker (DM) for simple
preference information and supplying easily understandable and explainable
recommendations, DRSA gained much interest during the years and it is now one
of the most appreciated MCDA approaches. In fact, it has been applied also
beyond MCDA domain, as a general knowledge discovery and data mining
methodology for the analysis of monotonic (and also non-monotonic) data. In
this contribution, we recall the basic principles and the main concepts of
DRSA, with a general overview of its developments and software. We present also
a historical reconstruction of the genesis of the methodology, with a specific
focus on the contribution of Roman S{\l}owi\'nski.

Single image super-resolution (SISR) is the process of obtaining one
high-resolution version of a low-resolution image by increasing the number of
pixels per unit area. This method has been actively investigated by the
research community, due to the wide variety of real-world problems where it can
be applied, from aerial and satellite imaging to compressed image and video
enhancement. Despite the improvements achieved by deep learning in the field,
the vast majority of the used networks are based on traditional convolutions,
with the solutions focusing on going deeper and/or wider, and innovations
coming from jointly employing successful concepts from other fields. In this
work, we decided to step up from the traditional convolutions and adopt the
concept of capsules. Since their overwhelming results both in image
classification and segmentation problems, we question how suitable they are for
SISR. We also verify that different solutions share most of their
configurations, and argue that this trend leads to fewer explorations of
network varieties. During our experiments, we check various strategies to
improve results, ranging from new and different loss functions to changes in
the capsule layers. Our network achieved good results with fewer
convolutional-based layers, showing that capsules might be a concept worth
applying in the image super-resolution problem.

Zero-cost proxies (ZC proxies) are a recent architecture performance
prediction technique aiming to significantly speed up algorithms for neural
architecture search (NAS). Recent work has shown that these techniques show
great promise, but certain aspects, such as evaluating and exploiting their
complementary strengths, are under-studied. In this work, we create
NAS-Bench-Suite: we evaluate 13 ZC proxies across 28 tasks, creating by far the
largest dataset (and unified codebase) for ZC proxies, enabling
orders-of-magnitude faster experiments on ZC proxies, while avoiding
confounding factors stemming from different implementations. To demonstrate the
usefulness of NAS-Bench-Suite, we run a large-scale analysis of ZC proxies,
including a bias analysis, and the first information-theoretic analysis which
concludes that ZC proxies capture substantial complementary information.
Motivated by these findings, we present a procedure to improve the performance
of ZC proxies by reducing biases such as cell size, and we also show that
incorporating all 13 ZC proxies into the surrogate models used by NAS
algorithms can improve their predictive performance by up to 42%. Our code and
datasets are available at https://github.com/automl/naslib/tree/zerocost.

With careful manipulation, malicious agents can reverse engineer private
information encoded in pre-trained language models. Security concerns motivate
the development of quantum pre-training. In this work, we propose a highly
portable quantum language model (PQLM) that can easily transmit information to
downstream tasks on classical machines. The framework consists of a cloud PQLM
built with random Variational Quantum Classifiers (VQC) and local models for
downstream applications. We demonstrate the ad hoc portability of the quantum
model by extracting only the word embeddings and effectively applying them to
downstream tasks on classical machines. Our PQLM exhibits comparable
performance to its classical counterpart on both intrinsic evaluation (loss,
perplexity) and extrinsic evaluation (multilingual sentiment analysis accuracy)
metrics. We also perform ablation studies on the factors affecting PQLM
performance to analyze model stability. Our work establishes a theoretical
foundation for a portable quantum pre-trained language model that could be
trained on private data and made available for public use with privacy
protection guarantees.

We present novel techniques and methodology for unresolved photometric
characterization of low-Earth Orbit (LEO) satellites. With the Pomenis LEO
Satellite Photometric Survey our team has made over 14,000 observations of
Starlink and OneWeb satellites to measure their apparent brightness. From the
apparent brightness of each satellite, we calculate a new metric: the effective
albedo, which quantifies the specularity of the reflecting satellite. Unlike
stellar magnitude units, the effective albedo accounts for apparent range and
phase angle and enables direct comparison of different satellites. Mapping the
effective albedo from multiple observations across the sky produces an all-sky
photometric signature which is distinct for each population of satellites,
including the various sub-models of Starlink satellites. Space Situational
Awareness (SSA) practitioners can use all-sky photometric signatures to
differentiate populations of satellites, compare their reflection
characteristics, identify unknown satellites, and find anomalous members. To
test the efficacy of all-sky signatures for satellite identification, we
applied a machine learning classifier algorithm which correctly identified the
majority of satellites based solely on the effective albedo metric and with as
few as one observation per individual satellite. Our new method of LEO
satellite photometric characterization requires no prior knowledge of the
satellite's properties and is readily scalable to large numbers of satellites
such as those expected with developing communications mega-constellations.

We investigate the resilience of learning-based \textit{Intelligent
Navigation Systems} (INS) to informational flow attacks, which exploit the
vulnerabilities of IT infrastructure and manipulate traffic condition data. To
this end, we propose the notion of \textit{Wardrop Non-Equilibrium Solution}
(WANES), which captures the finite-time behavior of dynamic traffic flow
adaptation under a learning process. The proposed non-equilibrium solution,
characterized by target sets and measurement functions, evaluates the outcome
of learning under a bounded number of rounds of interactions, and it pertains
to and generalizes the concept of approximate equilibrium. Leveraging
finite-time analysis methods, we discover that under the mirror descent (MD)
online-learning framework, the traffic flow trajectory is capable of restoring
to the Wardrop non-equilibrium solution after a bounded INS attack. The
resulting performance loss is of order $\tilde{\mathcal{O}}(T^{\beta})$
($-\frac{1}{2} \leq \beta < 0 )$), with a constant dependent on the size of the
traffic network, indicating the resilience of the MD-based INS. We corroborate
the results using an evacuation case study on a Sioux-Fall transportation
network.

Powered by deep representation learning, reinforcement learning (RL) provides
an end-to-end learning framework capable of solving self-driving (SD) tasks
without manual designs. However, time-varying nonstationary environments cause
proficient but specialized RL policies to fail at execution time. For example,
an RL-based SD policy trained under sunny days does not generalize well to
rainy weather. Even though meta learning enables the RL agent to adapt to new
tasks/environments, its offline operation fails to equip the agent with online
adaptation ability when facing nonstationary environments. This work proposes
an online meta reinforcement learning algorithm based on the \emph{conjectural
online lookahead adaptation} (COLA). COLA determines the online adaptation at
every step by maximizing the agent's conjecture of the future performance in a
lookahead horizon. Experimental results demonstrate that under dynamically
changing weather and lighting conditions, the COLA-based self-adaptive driving
outperforms the baseline policies in terms of online adaptability. A demo
video, source code, and appendixes are available at {\tt
https://github.com/Panshark/COLA}

Depth estimation is critical for any robotic system. In the past years
estimation of depth from monocular images have shown great improvement,
however, in the underwater environment results are still lagging behind due to
appearance changes caused by the medium. So far little effort has been invested
on overcoming this. Moreover, underwater, there are more limitations for using
high resolution depth sensors, this makes generating ground truth for learning
methods another enormous obstacle. So far unsupervised methods that tried to
solve this have achieved very limited success as they relied on domain transfer
from dataset in air. We suggest training using subsequent frames
self-supervised by a reprojection loss, as was demonstrated successfully above
water. We suggest several additions to the self-supervised framework to cope
with the underwater environment and achieve state-of-the-art results on a
challenging forward-looking underwater dataset.

Machine Learning (ML) has achieved enormous success in solving a variety of
problems in computer vision, speech recognition, object detection, to name a
few. The principal reason for this success is the availability of huge datasets
for training deep neural networks (DNNs). However, datasets cannot be publicly
released if they contain sensitive information such as medical records, and
data privacy becomes a major concern. Encryption methods could be a possible
solution, however their deployment on ML applications seriously impacts
classification accuracy and results in substantial computational overhead.
Alternatively, obfuscation techniques could be used, but maintaining a good
trade-off between visual privacy and accuracy is challenging. In this paper, we
propose a method to generate secure synthetic datasets from the original
private datasets. Given a network with Batch Normalization (BN) layers
pretrained on the original dataset, we first record the class-wise BN layer
statistics. Next, we generate the synthetic dataset by optimizing random noise
such that the synthetic data match the layer-wise statistical distribution of
original images. We evaluate our method on image classification datasets
(CIFAR10, ImageNet) and show that synthetic data can be used in place of the
original CIFAR10/ImageNet data for training networks from scratch, producing
comparable classification performance. Further, to analyze visual privacy
provided by our method, we use Image Quality Metrics and show high degree of
visual dissimilarity between the original and synthetic images. Moreover, we
show that our proposed method preserves data-privacy under various
privacy-leakage attacks including Gradient Matching Attack, Model Memorization
Attack, and GAN-based Attack.

Deep neural networks (DNNs) have succeeded in many different perception
tasks, e.g., computer vision, natural language processing, reinforcement
learning, etc. The high-performed DNNs heavily rely on intensive resource
consumption. For example, training a DNN requires high dynamic memory, a
large-scale dataset, and a large number of computations (a long training time);
even inference with a DNN also demands a large amount of static storage,
computations (a long inference time), and energy. Therefore, state-of-the-art
DNNs are often deployed on a cloud server with a large number of
super-computers, a high-bandwidth communication bus, a shared storage
infrastructure, and a high power supplement.
  Recently, some new emerging intelligent applications, e.g., AR/VR, mobile
assistants, Internet of Things, require us to deploy DNNs on
resource-constrained edge devices. Compare to a cloud server, edge devices
often have a rather small amount of resources. To deploy DNNs on edge devices,
we need to reduce the size of DNNs, i.e., we target a better trade-off between
resource consumption and model accuracy.
  In this dissertation, we studied four edge intelligence scenarios, i.e.,
Inference on Edge Devices, Adaptation on Edge Devices, Learning on Edge
Devices, and Edge-Server Systems, and developed different methodologies to
enable deep learning in each scenario. Since current DNNs are often
over-parameterized, our goal is to find and reduce the redundancy of the DNNs
in each scenario.

Many potential applications of reinforcement learning (RL) are stymied by the
large numbers of samples required to learn an effective policy. This is
especially true when applying RL to real-world control tasks, e.g. in the
sciences or robotics, where executing a policy in the environment is costly. In
popular RL algorithms, agents typically explore either by adding stochasticity
to a reward-maximizing policy or by attempting to gather maximal information
about environment dynamics without taking the given task into account. In this
work, we develop a method that allows us to plan for exploration while taking
both the task and the current knowledge about the dynamics into account. The
key insight to our approach is to plan an action sequence that maximizes the
expected information gain about the optimal trajectory for the task at hand. We
demonstrate that our method learns strong policies with 2x fewer samples than
strong exploration baselines and 200x fewer samples than model free methods on
a diverse set of low-to-medium dimensional control tasks in both the open-loop
and closed-loop control settings.

Deep learning is currently the state-of-the-art for automated detection of
referable diabetic retinopathy (DR) from color fundus photographs (CFP). While
the general interest is put on improving results through methodological
innovations, it is not clear how good these approaches perform compared to
standard deep classification models trained with the appropriate settings. In
this paper we propose to model a strong baseline for this task based on a
simple and standard ResNet-18 architecture. To this end, we built on top of
prior art by training the model with a standard preprocessing strategy but
using images from several public sources and an empirically calibrated data
augmentation setting. To evaluate its performance, we covered multiple
clinically relevant perspectives, including image and patient level DR
screening, discriminating responses by input quality and DR grade, assessing
model uncertainties and analyzing its results in a qualitative manner. With no
other methodological innovation than a carefully designed training, our ResNet
model achieved an AUC = 0.955 (0.953 - 0.956) on a combined test set of 61007
test images from different public datasets, which is in line or even better
than what other more complex deep learning models reported in the literature.
Similar AUC values were obtained in 480 images from two separate in-house
databases specially prepared for this study, which emphasize its generalization
ability. This confirms that standard networks can still be strong baselines for
this task if properly trained.

Shape-based virtual screening is widely employed in ligand-based drug design
to search chemical libraries for molecules with similar 3D shapes yet novel 2D
chemical structures compared to known ligands. 3D deep generative models have
the potential to automate this exploration of shape-conditioned 3D chemical
space; however, no existing models can reliably generate valid drug-like
molecules in conformations that adopt a specific shape such as a known binding
pose. We introduce a new multimodal 3D generative model that enables
shape-conditioned 3D molecular design by equivariantly encoding molecular shape
and variationally encoding chemical identity. We ensure local geometric and
chemical validity of generated molecules by using autoregressive fragment-based
generation with heuristic bonding geometries, allowing the model to prioritize
the scoring of rotatable bonds to best align the growing conformational
structure to the target shape. We evaluate our 3D generative model in tasks
relevant to drug design including shape-conditioned generation of chemically
diverse molecular structures and shape-constrained molecular property
optimization, demonstrating its utility over virtual screening of enumerated
libraries.

Controlling False Discovery Rate (FDR) while leveraging the side information
of multiple hypothesis testing is an emerging research topic in modern data
science. Existing methods rely on the test-level covariates while ignoring
metrics about test-level covariates. This strategy may not be optimal for
complex large-scale problems, where indirect relations often exist among
test-level covariates and auxiliary metrics or covariates. We incorporate
auxiliary covariates among test-level covariates in a deep Black-Box framework
controlling FDR (named as NeurT-FDR) which boosts statistical power and
controls FDR for multiple-hypothesis testing. Our method parametrizes the
test-level covariates as a neural network and adjusts the auxiliary covariates
through a regression framework, which enables flexible handling of
high-dimensional features as well as efficient end-to-end optimization. We show
that NeurT-FDR makes substantially more discoveries in three real datasets
compared to competitive baselines.

Although the volume of literature and public attention on machine learning
fairness has been growing significantly, in practice some tasks as basic as
measuring fairness, which is the first step in studying and promoting fairness,
can be challenging. This is because sensitive attributes are often unavailable
due to privacy regulations. The straightforward solution is to use auxiliary
models to predict the missing sensitive attributes. However, our theoretical
analyses show that the estimation error of the directly measured fairness
metrics is proportional to the error rates of auxiliary models' predictions.
Existing works that attempt to reduce the estimation error often require strong
assumptions, e.g. access to the ground-truth sensitive attributes or some form
of conditional independence. In this paper, we drop those assumptions and
propose a framework that uses only off-the-shelf auxiliary models. The main
challenge is how to reduce the negative impact of imperfectly predicted
sensitive attributes on the fairness metrics without knowing the ground-truth
sensitive attributes. Inspired by the noisy label learning literature, we first
derive a closed-form relationship between the directly measured fairness
metrics and their corresponding ground-truth metrics. And then we estimate some
key statistics (most importantly transition matrix in the noisy label
literature), which we use, together with the derived relationship, to calibrate
the fairness metrics. In addition, we theoretically prove the upper bound of
the estimation error in our calibrated metrics and show our method can
substantially decrease the estimation error especially when auxiliary models
are inaccurate or the target model is highly biased. Experiments on COMPAS and
CelebA validate our theoretical analyses and show our method can measure
fairness significantly more accurately than baselines under favorable
circumstances.

Robot grasping is an actively studied area in robotics, mainly focusing on
the quality of generated grasps for object manipulation. However, despite
advancements, these methods do not consider the human-robot collaboration
settings where robots and humans will have to grasp the same objects
concurrently. Therefore, generating robot grasps compatible with human
preferences of simultaneously holding an object becomes necessary to ensure a
safe and natural collaboration experience. In this paper, we propose a novel,
deep neural network-based method called CoGrasp that generates human-aware
robot grasps by contextualizing human preference models of object grasping into
the robot grasp selection process. We validate our approach against existing
state-of-the-art robot grasping methods through simulated and real-robot
experiments and user studies. In real robot experiments, our method achieves
about 88\% success rate in producing stable grasps that also allow humans to
interact and grasp objects simultaneously in a socially compliant manner.
Furthermore, our user study with 10 independent participants indicated our
approach enables a safe, natural, and socially-aware human-robot objects'
co-grasping experience compared to a standard robot grasping technique.

Accurate disease categorization using endoscopic images is a significant
problem in Gastroenterology. This paper describes a technique for assisting
medical diagnosis procedures and identifying gastrointestinal tract disorders
based on the categorization of characteristics taken from endoscopic pictures
using a vision transformer and transfer learning model. Vision transformer has
shown very promising results on difficult image classification tasks. In this
paper, we have suggested a vision transformer based approach to detect
gastrointestianl diseases from wireless capsule endoscopy (WCE) curated images
of colon with an accuracy of 95.63\%. We have compared this transformer based
approach with pretrained convolutional neural network (CNN) model DenseNet201
and demonstrated that vision transformer surpassed DenseNet201 in various
quantitative performance evaluation metrics.

Dynamic benchmarks interweave model fitting and data collection in an attempt
to mitigate the limitations of static benchmarks. In contrast to an extensive
theoretical and empirical study of the static setting, the dynamic counterpart
lags behind due to limited empirical studies and no apparent theoretical
foundation to date. Responding to this deficit, we initiate a theoretical study
of dynamic benchmarking. We examine two realizations, one capturing current
practice and the other modeling more complex settings. In the first model,
where data collection and model fitting alternate sequentially, we prove that
model performance improves initially but can stall after only three rounds.
Label noise arising from, for instance, annotator disagreement leads to even
stronger negative results. Our second model generalizes the first to the case
where data collection and model fitting have a hierarchical dependency
structure. We show that this design guarantees strictly more progress than the
first, albeit at a significant increase in complexity. We support our
theoretical analysis by simulating dynamic benchmarks on two popular datasets.
These results illuminate the benefits and practical limitations of dynamic
benchmarking, providing both a theoretical foundation and a causal explanation
for observed bottlenecks in empirical work.

Optimal transport aligns samples across distributions by minimizing the
transportation cost between them, e.g., the geometric distances. Yet, it
ignores coherence structure in the data such as clusters, does not handle
outliers well, and cannot integrate new data points. To address these
drawbacks, we propose InfoOT, an information-theoretic extension of optimal
transport that maximizes the mutual information between domains while
minimizing geometric distances. The resulting objective can still be formulated
as a (generalized) optimal transport problem, and can be efficiently solved by
projected gradient descent. This formulation yields a new projection method
that is robust to outliers and generalizes to unseen samples. Empirically,
InfoOT improves the quality of alignments across benchmarks in domain
adaptation, cross-domain retrieval, and single-cell alignment.

To improve performance in visual feature representation from photos or videos
for practical applications, we generally require large-scale human-annotated
labeled data while training deep neural networks. However, the cost of
gathering and annotating human-annotated labeled data is expensive. Given that
there is a lot of unlabeled data in the actual world, it is possible to
introduce self-defined pseudo labels as supervisions to prevent this issue.
Self-supervised learning, specifically contrastive learning, is a subset of
unsupervised learning methods that has grown popular in computer vision,
natural language processing, and other domains. The purpose of contrastive
learning is to embed augmented samples from the same sample near to each other
while pushing away those that are not. In the following sections, we will
introduce the regular formulation among different learnings. In the next
sections, we will discuss the regular formulation of various learnings.
Furthermore, we offer some strategies from contrastive learning that have
recently been published and are focused on pretext tasks for visual
representation.

We explore the idea of compressing the prompts used to condition language
models, and show that compressed prompts can retain a substantive amount of
information about the original prompt. For severely compressed prompts, while
fine-grained information is lost, abstract information and general sentiments
can be retained with surprisingly few parameters, which can be useful in the
context of decode-time algorithms for controllability and toxicity reduction.
We explore contrastive conditioning to steer language model generation towards
desirable text and away from undesirable text, and find that some complex
prompts can be effectively compressed into a single token to guide generation.
We also show that compressed prompts are largely compositional, and can be
constructed such that they can be used to control independent aspects of
generated text.

Systems neuroscience relies on two complementary views of neural data,
characterized by single neuron tuning curves and analysis of population
activity. These two perspectives combine elegantly in neural latent variable
models that constrain the relationship between latent variables and neural
activity, modeled by simple tuning curve functions. This has recently been
demonstrated using Gaussian processes, with applications to realistic and
topologically relevant latent manifolds. Those and previous models, however,
missed crucial shared coding properties of neural populations. We propose
feature sharing across neural tuning curves, which significantly improves
performance and leads to better-behaved optimization. We also propose a
solution to the problem of ensemble detection, whereby different groups of
neurons, i.e., ensembles, can be modulated by different latent manifolds. This
is achieved through a soft clustering of neurons during training, thus allowing
for the separation of mixed neural populations in an unsupervised manner. These
innovations lead to more interpretable models of neural population activity
that train well and perform better even on mixtures of complex latent
manifolds. Finally, we apply our method on a recently published grid cell
dataset, recovering distinct ensembles, inferring toroidal latents and
predicting neural tuning curves all in a single integrated modeling framework.

Cardiovascular disease (CVD) is a class of diseases that involve the heart or
blood vessels and according to World Health Organization is the leading cause
of death worldwide. EHR data regarding this case, as well as medical cases in
general, contain missing values very frequently. The percentage of missingness
may vary and is linked with instrument errors, manual data entry procedures,
etc. Even though the missing rate is usually significant, in many cases the
missing value imputation part is handled poorly either with case-deletion or
with simple statistical approaches such as mode and median imputation. These
methods are known to introduce significant bias, since they do not account for
the relationships between the dataset's variables. Within the medical
framework, many datasets consist of lab tests or patient medical tests, where
these relationships are present and strong. To address these limitations, in
this paper we test and modify state-of-the-art missing value imputation methods
based on Generative Adversarial Networks (GANs) and Autoencoders. The
evaluation is accomplished for both the tasks of data imputation and
post-imputation prediction. Regarding the imputation task, we achieve
improvements of 0.20, 7.00% in normalised Root Mean Squared Error (RMSE) and
Area Under the Receiver Operating Characteristic Curve (AUROC) respectively. In
terms of the post-imputation prediction task, our models outperform the
standard approaches by 2.50% in F1-score.

Efforts to utilize growing volumes of clinical imaging data to generate tumor
evaluations continue to require significant manual data wrangling owing to the
data heterogeneity. Here, we propose an artificial intelligence-based solution
for the aggregation and processing of multisequence neuro-oncology MRI data to
extract quantitative tumor measurements. Our end-to-end framework i) classifies
MRI sequences using an ensemble classifier, ii) preprocesses the data in a
reproducible manner, iii) delineates tumor tissue subtypes using convolutional
neural networks, and iv) extracts diverse radiomic features. Moreover, it is
robust to missing sequences and adopts an expert-in-the-loop approach, where
the segmentation results may be manually refined by radiologists. Following the
implementation of the framework in Docker containers, it was applied to two
retrospective glioma datasets collected from the Washington University School
of Medicine (WUSM; n = 384) and the M.D. Anderson Cancer Center (MDA; n = 30)
comprising preoperative MRI scans from patients with pathologically confirmed
gliomas. The scan-type classifier yielded an accuracy of over 99%, correctly
identifying sequences from 380/384 and 30/30 sessions from the WUSM and MDA
datasets, respectively. Segmentation performance was quantified using the Dice
Similarity Coefficient between the predicted and expert-refined tumor masks.
Mean Dice scores were 0.882 ($\pm$0.244) and 0.977 ($\pm$0.04) for whole tumor
segmentation for WUSM and MDA, respectively. This streamlined framework
automatically curated, processed, and segmented raw MRI data of patients with
varying grades of gliomas, enabling the curation of large-scale neuro-oncology
datasets and demonstrating a high potential for integration as an assistive
tool in clinical practice.

Adversarial robustness continues to be a major challenge for deep learning. A
core issue is that robustness to one type of attack often fails to transfer to
other attacks. While prior work establishes a theoretical trade-off in
robustness against different $L_p$ norms, we show that there is potential for
improvement against many commonly used attacks by adopting a domain
generalisation approach. Concretely, we treat each type of attack as a domain,
and apply the Risk Extrapolation method (REx), which promotes similar levels of
robustness against all training attacks. Compared to existing methods, we
obtain similar or superior worst-case adversarial robustness on attacks seen
during training. Moreover, we achieve superior performance on families or
tunings of attacks only encountered at test time. On ensembles of attacks, our
approach improves the accuracy from 3.4% the best existing baseline to 25.9% on
MNIST, and from 16.9% to 23.5% on CIFAR10.

Classifier-free guided diffusion models have recently been shown to be highly
effective at high-resolution image generation, and they have been widely used
in large-scale diffusion frameworks including DALL-E 2, GLIDE and Imagen.
However, a downside of classifier-free guided diffusion models is that they are
computationally expensive at inference time since they require evaluating two
diffusion models, a class-conditional model and an unconditional model,
hundreds of times. To deal with this limitation, we propose an approach to
distilling classifier-free guided diffusion models into models that are fast to
sample from: Given a pre-trained classifier-free guided model, we first learn a
single model to match the output of the combined conditional and unconditional
models, and then progressively distill that model to a diffusion model that
requires much fewer sampling steps. On ImageNet 64x64 and CIFAR-10, our
approach is able to generate images visually comparable to that of the original
model using as few as 4 sampling steps, achieving FID/IS scores comparable to
that of the original model while being up to 256 times faster to sample from.

We present a Deep Reinforcement Learning approach to solving a periodic
review inventory control system with stochastic vendor lead times, lost sales,
correlated demand, and price matching. While this dynamic program has
historically been considered intractable, we show that several policy learning
approaches are competitive with or outperform classical baseline approaches. In
order to train these algorithms, we develop novel techniques to convert
historical data into a simulator. We also present a model-based reinforcement
learning procedure (Direct Backprop) to solve the dynamic periodic review
inventory control problem by constructing a differentiable simulator. Under a
variety of metrics Direct Backprop outperforms model-free RL and newsvendor
baselines, in both simulations and real-world deployments.

Depth perception is pivotal in many fields, such as robotics and autonomous
driving, to name a few. Consequently, depth sensors such as LiDARs rapidly
spread in many applications. The 3D point clouds generated by these sensors
must often be coupled with an RGB camera to understand the framed scene
semantically. Usually, the former is projected over the camera image plane,
leading to a sparse depth map. Unfortunately, this process, coupled with the
intrinsic issues affecting all the depth sensors, yields noise and gross
outliers in the final output. Purposely, in this paper, we propose an effective
unsupervised framework aimed at explicitly addressing this issue by learning to
estimate the confidence of the LiDAR sparse depth map and thus allowing for
filtering out the outliers. Experimental results on the KITTI dataset highlight
that our framework excels for this purpose. Moreover, we demonstrate how this
achievement can improve a wide range of tasks.

Pre-trained vision-language (V-L) models such as CLIP have shown excellent
generalization ability to downstream tasks. However, they are sensitive to the
choice of input text prompts and require careful selection of prompt templates
to perform well. Inspired by the Natural Language Processing (NLP) literature,
recent CLIP adaptation approaches learn prompts as the textual inputs to
fine-tune CLIP for downstream tasks. We note that using prompting to adapt
representations in a single branch of CLIP (language or vision) is sub-optimal
since it does not allow the flexibility to dynamically adjust both
representation spaces on a downstream task. In this work, we propose
Multi-modal Prompt Learning (MaPLe) for both vision and language branches to
improve alignment between the vision and language representations. Our design
promotes strong coupling between the vision-language prompts to ensure mutual
synergy and discourages learning independent uni-modal solutions. Further, we
learn separate prompts across different early stages to progressively model the
stage-wise feature relationships to allow rich context learning. We evaluate
the effectiveness of our approach on three representative tasks of
generalization to novel classes, new target datasets and unseen domain shifts.
Compared with the state-of-the-art method Co-CoOp, MaPLe exhibits favorable
performance and achieves an absolute gain of 3.45% on novel classes and 2.72%
on overall harmonic-mean, averaged over 11 diverse image recognition datasets.
Code: https://tinyurl.com/2dzs8f3w.

The growing proliferation of pretrained generative models has made it
infeasible for a user to be fully cognizant of every model in existence. To
address this need, we introduce the task of content-based model search: given a
query and a large set of generative models, find the models that best match the
query. Because each generative model produces a distribution of images, we
formulate the search problem as an optimization to maximize the probability of
generating a query match given a model. We develop approximations to make this
problem tractable when the query is an image, a sketch, a text description,
another generative model, or a combination of the above. We benchmark our
method in both accuracy and speed over a set of generative models. We
demonstrate that our model search retrieves suitable models for image editing
and reconstruction, few-shot transfer learning, and latent space interpolation.
Finally, we deploy our search algorithm to our online generative model-sharing
platform at https://modelverse.cs.cmu.edu.

From human physiology to environmental evolution, important processes in
nature often exhibit meaningful and strong periodic or quasi-periodic changes.
Due to their inherent label scarcity, learning useful representations for
periodic tasks with limited or no supervision is of great benefit. Yet,
existing self-supervised learning (SSL) methods overlook the intrinsic
periodicity in data, and fail to learn representations that capture periodic or
frequency attributes. In this paper, we present SimPer, a simple contrastive
SSL regime for learning periodic information in data. To exploit the periodic
inductive bias, SimPer introduces customized augmentations, feature similarity
measures, and a generalized contrastive loss for learning efficient and robust
periodic representations. Extensive experiments on common real-world tasks in
human behavior analysis, environmental sensing, and healthcare domains verify
the superior performance of SimPer compared to state-of-the-art SSL methods,
highlighting its intriguing properties including better data efficiency,
robustness to spurious correlations, and generalization to distribution shifts.
Code and data are available at: https://github.com/YyzHarry/SimPer.

The continual learning setting aims to learn new tasks over time without
forgetting the previous ones. The literature reports several significant
efforts to tackle this problem with limited or no access to previous task data.
Among such efforts, typical solutions offer sophisticated techniques involving
memory replay, knowledge distillation, model regularization, and dynamic
network expansion. The resulting methods have a retraining cost at each
learning task, dedicated memory requirements, and setting-specific design
choices. In this work, we show that a frozen CLIP (Contrastive Language-Image
Pretraining) model offers astounding continual learning performance without any
fine-tuning (zero-shot evaluation). We evaluate CLIP under a variety of
settings including class-incremental, domain-incremental and task-agnostic
incremental learning on five popular benchmarks (ImageNet-100 & 1K, CORe50,
CIFAR-100, and TinyImageNet). Without any bells and whistles, the CLIP model
outperforms the state-of-the-art continual learning approaches in the majority
of the settings. We show the effect on the CLIP model's performance by varying
text inputs with simple prompt templates. To the best of our knowledge, this is
the first work to report the CLIP zero-shot performance in a continual setting.
We advocate the use of this strong yet embarrassingly simple baseline for
future comparisons in the continual learning tasks.

Recent studies in Vision-and-Language Navigation (VLN) train RL agents to
execute natural-language navigation instructions in photorealistic
environments, as a step towards intelligent agents or robots that can follow
human instructions. However, given the scarcity of human instruction data and
limited diversity in the training environments, these agents still struggle
with complex language grounding and spatial language understanding.
Pre-training on large text and image-text datasets from the web has been
extensively explored but the improvements are limited. To address the scarcity
of in-domain instruction data, we investigate large-scale augmentation with
synthetic instructions. We take 500+ indoor environments captured in
densely-sampled 360 deg panoramas, construct navigation trajectories through
these panoramas, and generate a visually-grounded instruction for each
trajectory using Marky (Wang et al., 2022), a high-quality multilingual
navigation instruction generator. To further increase the variability of the
trajectories, we also synthesize image observations from novel viewpoints using
an image-to-image GAN. The resulting dataset of 4.2M instruction-trajectory
pairs is two orders of magnitude larger than existing human-annotated datasets,
and contains a wider variety of environments and viewpoints. To efficiently
leverage data at this scale, we train a transformer agent with imitation
learning for over 700M steps of experience. On the challenging Room-across-Room
dataset, our approach outperforms all existing RL agents, improving the
state-of-the-art NDTW from 71.1 to 79.1 in seen environments, and from 64.6 to
66.8 in unseen test environments. Our work points to a new path to improving
instruction-following agents, emphasizing large-scale imitation learning and
the development of synthetic instruction generation capabilities.

Determining the state of a mobile robot is an essential building block of
robot navigation systems. In this paper, we address the problem of estimating
the robots pose in an indoor environment using 2D LiDAR data and investigate
how modern environment models can improve gold standard Monte-Carlo
localization (MCL) systems. We propose a neural occupancy field (NOF) to
implicitly represent the scene using a neural network. With the pretrained
network, we can synthesize 2D LiDAR scans for an arbitrary robot pose through
volume rendering. Based on the implicit representation, we can obtain the
similarity between a synthesized and actual scan as an observation model and
integrate it into an MCL system to perform accurate localization. We evaluate
our approach on five sequences of a self-recorded dataset and three publicly
available datasets. We show that we can accurately and efficiently localize a
robot using our approach surpassing the localization performance of
state-of-the-art methods. The experiments suggest that the presented implicit
representation is able to predict more accurate 2D LiDAR scans leading to an
improved observation model for our particle filter-based localization. The code
of our approach is released at: https://github.com/PRBonn/ir-mcl.

In this work, we explore self-supervised visual pre-training on images from
diverse, in-the-wild videos for real-world robotic tasks. Like prior work, our
visual representations are pre-trained via a masked autoencoder (MAE), frozen,
and then passed into a learnable control module. Unlike prior work, we show
that the pre-trained representations are effective across a range of real-world
robotic tasks and embodiments. We find that our encoder consistently
outperforms CLIP (up to 75%), supervised ImageNet pre-training (up to 81%), and
training from scratch (up to 81%). Finally, we train a 307M parameter vision
transformer on a massive collection of 4.5M images from the Internet and
egocentric videos, and demonstrate clearly the benefits of scaling visual
pre-training for robot learning.

Meta-reinforcement learning algorithms provide a data-driven way to acquire
policies that quickly adapt to many tasks with varying rewards or dynamics
functions. However, learned meta-policies are often effective only on the exact
task distribution on which they were trained and struggle in the presence of
distribution shift of test-time rewards or transition dynamics. In this work,
we develop a framework for meta-RL algorithms that are able to behave
appropriately under test-time distribution shifts in the space of tasks. Our
framework centers on an adaptive approach to distributional robustness that
trains a population of meta-policies to be robust to varying levels of
distribution shift. When evaluated on a potentially shifted test-time
distribution of tasks, this allows us to choose the meta-policy with the most
appropriate level of robustness, and use it to perform fast adaptation. We
formally show how our framework allows for improved regret under distribution
shift, and empirically show its efficacy on simulated robotics problems under a
wide range of distribution shifts.

Modern 3D semantic instance segmentation approaches predominantly rely on
specialized voting mechanisms followed by carefully designed geometric
clustering techniques. Building on the successes of recent Transformer-based
methods for object detection and image segmentation, we propose the first
Transformer-based approach for 3D semantic instance segmentation. We show that
we can leverage generic Transformer building blocks to directly predict
instance masks from 3D point clouds. In our model called Mask3D each object
instance is represented as an instance query. Using Transformer decoders, the
instance queries are learned by iteratively attending to point cloud features
at multiple scales. Combined with point features, the instance queries directly
yield all instance masks in parallel. Mask3D has several advantages over
current state-of-the-art approaches, since it neither relies on (1) voting
schemes which require hand-selected geometric properties (such as centers) nor
(2) geometric grouping mechanisms requiring manually-tuned hyper-parameters
(e.g. radii) and (3) enables a loss that directly optimizes instance masks.
Mask3D sets a new state-of-the-art on ScanNet test (+6.2 mAP), S3DIS 6-fold
(+10.1 mAP), STPLS3D (+11.2 mAP) and ScanNet200 test (+12.4 mAP).

We study first-order methods for constrained min-max optimization. Existing
methods either requires two gradient calls or two projections in each
iteration, which may be costly in applications. In this paper, we first show
that the Optimistic Gradient (OG) method, a single-call single-projection
algorithm, has $O(\frac{1}{\sqrt{T}})$ convergence rate for inclusion problems
with operators that satisfy the weak Minty variation inequality (MVI). Our
second result is the first single-call single-projection algorithm -- the
Accelerated Reflected Gradient (ARG) method that achieves the optimal
$O(\frac{1}{T})$ convergence rate for inclusion problems that satisfy negative
comonotonicity. Both the weak MVI and negative comonotonicity are well-studied
assumptions and capture a rich set of non-convex non-concave min-max
optimization problems. Finally, we show that the Reflected Gradient (RG)
method, another single-call single-projection algorithm, has
$O(\frac{1}{\sqrt{T}})$ last-iterate convergence rate for constrained
convex-concave min-max optimization, answering an open problem of [Hsieh et al,
2019].

Prompt-based learning has emerged as a successful paradigm in natural
language processing, where a single general-purpose language model can be
instructed to perform any task specified by input prompts. Yet task
specification in robotics comes in various forms, such as imitating one-shot
demonstrations, following language instructions, and reaching visual goals.
They are often considered different tasks and tackled by specialized models.
This work shows that we can express a wide spectrum of robot manipulation tasks
with multimodal prompts, interleaving textual and visual tokens. We design a
transformer-based generalist robot agent, VIMA, that processes these prompts
and outputs motor actions autoregressively. To train and evaluate VIMA, we
develop a new simulation benchmark with thousands of procedurally-generated
tabletop tasks with multimodal prompts, 600K+ expert trajectories for imitation
learning, and four levels of evaluation protocol for systematic generalization.
VIMA achieves strong scalability in both model capacity and data size. It
outperforms prior SOTA methods in the hardest zero-shot generalization setting
by up to $2.9\times$ task success rate given the same training data. With
$10\times$ less training data, VIMA still performs $2.7\times$ better than the
top competing approach. We open-source all code, pretrained models, dataset,
and simulation benchmark at https://vimalabs.github.io

The key problem in multivariate time series (MTS) analysis and forecasting
aims to disclose the underlying couplings between variables that drive the
co-movements. Considerable recent successful MTS methods are built with graph
neural networks (GNNs) due to their essential capacity for relational modeling.
However, previous work often used a static graph structure of time-series
variables for modeling MTS failing to capture their ever-changing correlations
over time. To this end, a fully-connected supra-graph connecting any two
variables at any two timestamps is adaptively learned to capture the
high-resolution variable dependencies via an efficient graph convolutional
network. Specifically, we construct the Edge-Varying Fourier Graph Networks
(EV-FGN) equipped with Fourier Graph Shift Operator (FGSO) which efficiently
performs graph convolution in the frequency domain. As a result, a
high-efficiency scale-free parameter learning scheme is derived for MTS
analysis and forecasting according to the convolution theorem. Extensive
experiments show that EV-FGN outperforms state-of-the-art methods on seven
real-world MTS datasets.

To obtain a large amount of training labels inexpensively, researchers have
recently adopted the weak supervision (WS) paradigm, which leverages labeling
rules to synthesize training labels rather than using individual annotations to
achieve competitive results for natural language processing (NLP) tasks.
However, data imbalance is often overlooked in applying the WS paradigm,
despite being a common issue in a variety of NLP tasks. To address this
challenge, we propose Adaptive Ranking-based Sample Selection (ARS2), a
model-agnostic framework to alleviate the data imbalance issue in the WS
paradigm. Specifically, it calculates a probabilistic margin score based on the
output of the current model to measure and rank the cleanliness of each data
point. Then, the ranked data are sampled based on both class-wise and
rule-aware ranking. In particular, the two sample strategies corresponds to our
motivations: (1) to train the model with balanced data batches to reduce the
data imbalance issue and (2) to exploit the expertise of each labeling rule for
collecting clean samples. Experiments on four text classification datasets with
four different imbalance ratios show that ARS2 outperformed the
state-of-the-art imbalanced learning and WS methods, leading to a 2%-57.8%
improvement on their F1-score.

People are regularly confronted with potentially deceptive statements (e.g.,
fake news, misleading product reviews, or lies about activities). Only few
works on automated text-based deception detection have exploited the potential
of deep learning approaches. A critique of deep-learning methods is their lack
of interpretability, preventing us from understanding the underlying
(linguistic) mechanisms involved in deception. However, recent advancements
have made it possible to explain some aspects of such models. This paper
proposes and evaluates six deep-learning models, including combinations of BERT
(and RoBERTa), MultiHead Attention, co-attentions, and transformers. To
understand how the models reach their decisions, we then examine the model's
predictions with LIME. We then zoom in on vocabulary uniqueness and the
correlation of LIWC categories with the outcome class (truthful vs deceptive).
The findings suggest that our transformer-based models can enhance automated
deception detection performances (+2.11% in accuracy) and show significant
differences pertinent to the usage of LIWC features in truthful and deceptive
statements.

Knowledge underpins reasoning. Recent research demonstrates that when
relevant knowledge is provided as additional context to commonsense question
answering (QA), it can substantially enhance the performance even on top of
state-of-the-art. The fundamental challenge is where and how to find such
knowledge that is high quality and on point with respect to the question;
knowledge retrieved from knowledge bases are incomplete and knowledge generated
from language models are inconsistent. We present Rainier, or Reinforced
Knowledge Introspector, that learns to generate contextually relevant knowledge
in response to given questions. Our approach starts by imitating knowledge
generated by GPT-3, then learns to generate its own knowledge via reinforcement
learning where rewards are shaped based on the increased performance on the
resulting question answering. Rainier demonstrates substantial and consistent
performance gains when tested over 9 different commonsense benchmarks:
including 5 datasets that are seen during model training, as well as 4 datasets
that are kept unseen. Our work is the first to report that knowledge generated
by models that are orders of magnitude smaller than GPT-3, even without direct
supervision on the knowledge itself, can exceed the quality of commonsense
knowledge elicited from GPT-3.

Spins in gated semiconductor quantum dots (QDs) are a promising platform for
Hubbard model simulation inaccessible to computation. Precise control of the
tunnel couplings by tuning voltages on metallic gates is vital for a successful
QD-based simulator. However, the number of tunable voltages and the complexity
of the relationships between gate voltages and the parameters of the resulting
Hubbard models quickly increase with the number of quantum dots. As a
consequence, it is not known if and how a particular gate geometry yields a
target Hubbard model. To solve this problem, we propose a hybrid
machine-learning approach using a combination of support vector machines (SVMs)
and Bayesian optimization (BO) to identify combinations of voltages that
realize a desired Hubbard model. SVM constrains the space of voltages by
rejecting voltage combinations producing potentials unsuitable for
tight-binding (TB) approximation. The target voltage combinations are then
identified by BO in the constrained subdomain. For large QD arrays, we propose
a scalable efficient iterative procedure using our SVM-BO approach, which
optimises voltage subsets and utilises a two-QD SVM model for large systems.
Our results use experimental gate lithography images and accurate integrals
calculated with linear combinations of harmonic orbitals to train the machine
learning algorithms.

Machine Translation systems can produce different types of errors, some of
which get characterized as critical or catastrophic due to the specific
negative impact they can have on users. Automatic or human evaluation metrics
do not necessarily differentiate between such critical errors and more
innocuous ones. In this paper we focus on one type of critical error: added
toxicity. We evaluate and analyze added toxicity when translating a large
evaluation dataset (HOLISTICBIAS, over 472k sentences, covering 13 demographic
axes) from English into 164 languages. The toxicity automatic evaluation shows
that added toxicity across languages varies from 0% to 5%. The output languages
with the most added toxicity tend to be low-resource ones, and the demographic
axes with the most added toxicity include sexual orientation, gender and sex,
and ability. We also perform human evaluation on a subset of 8 directions,
confirming the prevalence of true added toxicity.
  We use a measurement of the amount of source contribution to the translation,
where a low source contribution implies hallucination, to interpret what causes
toxicity. We observe that the source contribution is somewhat correlated with
toxicity but that 45.6% of added toxic words have a high source contribution,
suggesting that much of the added toxicity may be due to mistranslations.
Combining the signal of source contribution level with a measurement of
translation robustness allows us to flag 22.3% of added toxicity, suggesting
that added toxicity may be related to both hallucination and the stability of
translations in different contexts. Given these findings, our recommendations
to reduce added toxicity are to curate training data to avoid mistranslations,
mitigate hallucination and check unstable translations.

Weight decay is one of the most widely used forms of regularization in deep
learning, and has been shown to improve generalization and robustness. The
optimization objective driving weight decay is a sum of losses plus a term
proportional to the sum of squared weights. This paper argues that stochastic
gradient descent (SGD) may be an inefficient algorithm for this objective. For
neural networks with ReLU activations, solutions to the weight decay objective
are equivalent to those of a different objective in which the regularization
term is instead a sum of products of $\ell_2$ (not squared) norms of the input
and output weights associated each ReLU. This alternative (and effectively
equivalent) regularization suggests a novel proximal gradient algorithm for
network training. Theory and experiments support the new training approach,
showing that it can converge much faster to the sparse solutions it shares with
standard weight decay training.

In this paper, we present InferES - an original corpus for Natural Language
Inference (NLI) in European Spanish. We propose, implement, and analyze a
variety of corpus-creating strategies utilizing expert linguists and crowd
workers. The objectives behind InferES are to provide high-quality data, and,
at the same time to facilitate the systematic evaluation of automated systems.
Specifically, we focus on measuring and improving the performance of machine
learning systems on negation-based adversarial examples and their ability to
generalize across out-of-distribution topics.
  We train two transformer models on InferES (8,055 gold examples) in a variety
of scenarios. Our best model obtains 72.8% accuracy, leaving a lot of room for
improvement. The "hypothesis-only" baseline performs only 2%-5% higher than
majority, indicating much fewer annotation artifacts than prior work. We find
that models trained on InferES generalize very well across topics (both in- and
out-of-distribution) and perform moderately well on negation-based adversarial
examples.

Conventional frequentist learning is known to yield poorly calibrated models
that fail to reliably quantify the uncertainty of their decisions. Bayesian
learning can improve calibration, but formal guarantees apply only under
restrictive assumptions about correct model specification. Conformal prediction
(CP) offers a general framework for the design of set predictors with
calibration guarantees that hold regardless of the underlying data generation
mechanism. However, when training data are limited, CP tends to produce large,
and hence uninformative, predicted sets. This paper introduces a novel
meta-learning solution that aims at reducing the set prediction size. Unlike
prior work, the proposed meta-learning scheme, referred to as meta-XB, (i)
builds on cross-validation-based CP, rather than the less efficient
validation-based CP; and (ii) preserves formal per-task calibration guarantees,
rather than less stringent task-marginal guarantees. Finally, meta-XB is
extended to adaptive non-conformal scores, which are shown empirically to
further enhance marginal per-input calibration.

Three-dimensional (3D) magnetic reconstruction is vital to the study of novel
magnetic materials for 3D spintronics. Vector field electron tomography (VFET)
is a major in house tool to achieve that. However, conventional VFET
reconstruction exhibits significant artefacts due to the unavoidable presence
of missing wedges. In this article, we propose a deep-learning enhanced VFET
method to address this issue. A magnetic textures library is built by
micromagnetic simulations. MagNet, an U-shaped convolutional neural network, is
trained and tested with dataset generated from the library. We demonstrate that
MagNet outperforms conventional VFET under missing wedge. Quality of
reconstructed magnetic induction fields is significantly improved.

Few existing image defogging or dehazing methods consider dense and
non-uniform particle distributions, which usually happen in smoke, dust and
fog. Dealing with these dense and/or non-uniform distributions can be
intractable, since fog's attenuation and airlight (or veiling effect)
significantly weaken the background scene information in the input image. To
address this problem, we introduce a structure-representation network with
uncertainty feedback learning. Specifically, we extract the feature
representations from a pre-trained Vision Transformer (DINO-ViT) module to
recover the background information. To guide our network to focus on
non-uniform fog areas, and then remove the fog accordingly, we introduce the
uncertainty feedback learning, which produces the uncertainty maps, that have
higher uncertainty in denser fog regions, and can be regarded as an attention
map that represents fog's density and uneven distribution. Based on the
uncertainty map, our feedback network refines our defogged output iteratively.
Moreover, to handle the intractability of estimating the atmospheric light
colors, we exploit the grayscale version of our input image, since it is less
affected by varying light colors that are possibly present in the input image.
The experimental results demonstrate the effectiveness of our method both
quantitatively and qualitatively compared to the state-of-the-art methods in
handling dense and non-uniform fog or smoke.

We evaluate the reasoning abilities of large language models in multilingual
settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by
manually translating 250 grade-school math problems from the GSM8K dataset
(Cobbe et al., 2021) into ten typologically diverse languages. We find that the
ability to solve MGSM problems via chain-of-thought prompting emerges with
increasing model scale, and that models have strikingly strong multilingual
reasoning abilities, even in underrepresented languages such as Bengali and
Swahili. Finally, we show that the multilingual reasoning abilities of language
models extend to other tasks such as commonsense reasoning and word-in-context
semantic judgment. The MGSM benchmark is publicly available at
https://github.com/google-research/url-nlp.

We present 3DiM, a diffusion model for 3D novel view synthesis, which is able
to translate a single input view into consistent and sharp completions across
many views. The core component of 3DiM is a pose-conditional image-to-image
diffusion model, which takes a source view and its pose as inputs, and
generates a novel view for a target pose as output. 3DiM can generate multiple
views that are 3D consistent using a novel technique called stochastic
conditioning. The output views are generated autoregressively, and during the
generation of each novel view, one selects a random conditioning view from the
set of available views at each denoising step. We demonstrate that stochastic
conditioning significantly improves the 3D consistency of a naive sampler for
an image-to-image diffusion model, which involves conditioning on a single
fixed view. We compare 3DiM to prior work on the SRN ShapeNet dataset,
demonstrating that 3DiM's generated completions from a single view achieve much
higher fidelity, while being approximately 3D consistent. We also introduce a
new evaluation methodology, 3D consistency scoring, to measure the 3D
consistency of a generated object by training a neural field on the model's
output views. 3DiM is geometry free, does not rely on hyper-networks or
test-time optimization for novel view synthesis, and allows a single model to
easily scale to a large number of scenes.

Applying Reinforcement learning (RL) following maximum likelihood estimation
(MLE) pre-training is a versatile method for enhancing neural machine
translation (NMT) performance. However, recent work has argued that the gains
produced by RL for NMT are mostly due to promoting tokens that have already
received a fairly high probability in pre-training. We hypothesize that the
large action space is a main obstacle to RL's effectiveness in MT, and conduct
two sets of experiments that lend support to our hypothesis. First, we find
that reducing the size of the vocabulary improves RL's effectiveness. Second,
we find that effectively reducing the dimension of the action space without
changing the vocabulary also yields notable improvement as evaluated by BLEU,
semantic similarity, and human evaluation. Indeed, by initializing the
network's final fully connected layer (that maps the network's internal
dimension to the vocabulary dimension), with a layer that generalizes over
similar actions, we obtain a substantial improvement in RL performance: 1.5
BLEU points on average.

Transformer is the cornerstone model of Natural Language Processing (NLP)
over the past decade. Despite its great success in Deep Learning (DL)
applications, the increasingly growing parameter space required by transformer
models boosts the demand on accelerating the performance of transformer models.
In addition, NLP problems can commonly be faced with variable-length sequences
since their word numbers can vary among sentences. Existing DL frameworks need
to pad variable-length sequences to the maximal length, which, however, leads
to significant memory and computational overhead. In this paper, we present
ByteTransformer, a high-performance transformer boosted for variable-length
inputs. We propose a zero padding algorithm that enables the whole transformer
to be free from redundant computations on useless padded tokens. Besides the
algorithmic level optimization, we provide architectural-aware optimizations
for transformer functioning modules, especially the performance-critical
algorithm, multi-head attention (MHA). Experimental results on an NVIDIA A100
GPU with variable-length sequence inputs validate that our fused MHA (FMHA)
outperforms the standard PyTorch MHA by 6.13X. The end-to-end performance of
ByteTransformer for a standard BERT transformer model surpasses the
state-of-the-art Transformer frameworks, such as PyTorch JIT, TensorFlow XLA,
Tencent TurboTransformer and NVIDIA FasterTransformer, by 87\%, 131\%, 138\%
and 46\%, respectively.

Despite the popularity of feature importance measures in interpretable
machine learning, the statistical adequacy of these methods is rarely
discussed. From a statistical perspective, a major distinction is between
analyzing a variable's importance before and after adjusting for covariates -
i.e., between marginal and conditional measures. Our work draws attention to
this rarely acknowledged, yet crucial distinction and showcases its
implications. Further, we reveal that for testing conditional feature
importance (CFI), only few methods are available and practitioners have
hitherto been severely restricted in method application due to mismatching data
requirements. Most real-world data exhibits complex feature dependencies and
incorporates both continuous and categorical data (mixed data). Both properties
are oftentimes neglected by CFI measures. To fill this gap, we propose to
combine the conditional predictive impact (CPI) framework (arXiv:1901.09917)
with sequential knockoff sampling (arXiv:2010.14026). The CPI enables CFI
measurement that controls for any feature dependencies by sampling valid
knockoffs - hence, generating synthetic data with similar statistical
properties - for the data to be analyzed. Sequential knockoffs were
deliberately designed to handle mixed data and thus allow us to extend the CPI
approach to such datasets. We demonstrate through numerous simulations and a
real-world example that our proposed workflow controls type I error, achieves
high power and is in line with results given by other CFI measures, whereas
marginal feature importance metrics result in misleading interpretations. Our
findings highlight the necessity of developing statistically adequate,
specialized methods for mixed data.

We develop a backward-in-time machine learning algorithm that uses a sequence
of neural networks to solve optimal switching problems in energy production,
where electricity and fossil fuel prices are subject to stochastic jumps. We
then apply this algorithm to a variety of energy scheduling problems, including
novel high-dimensional energy production problems. Our experimental results
demonstrate that the algorithm performs with accuracy and experiences linear to
sub-linear slowdowns as dimension increases, demonstrating the value of the
algorithm for solving high-dimensional switching problems.

Modern deep learning involves training costly, highly overparameterized
networks, thus motivating the search for sparser networks that can still be
trained to the same accuracy as the full network (i.e. matching). Iterative
magnitude pruning (IMP) is a state of the art algorithm that can find such
highly sparse matching subnetworks, known as winning tickets. IMP operates by
iterative cycles of training, masking smallest magnitude weights, rewinding
back to an early training point, and repeating. Despite its simplicity, the
underlying principles for when and how IMP finds winning tickets remain
elusive. In particular, what useful information does an IMP mask found at the
end of training convey to a rewound network near the beginning of training? How
does SGD allow the network to extract this information? And why is iterative
pruning needed? We develop answers in terms of the geometry of the error
landscape. First, we find that$\unicode{x2014}$at higher
sparsities$\unicode{x2014}$pairs of pruned networks at successive pruning
iterations are connected by a linear path with zero error barrier if and only
if they are matching. This indicates that masks found at the end of training
convey the identity of an axial subspace that intersects a desired linearly
connected mode of a matching sublevel set. Second, we show SGD can exploit this
information due to a strong form of robustness: it can return to this mode
despite strong perturbations early in training. Third, we show how the flatness
of the error landscape at the end of training determines a limit on the
fraction of weights that can be pruned at each iteration of IMP. Finally, we
show that the role of retraining in IMP is to find a network with new small
weights to prune. Overall, these results make progress toward demystifying the
existence of winning tickets by revealing the fundamental role of error
landscape geometry.

General scene understanding for robotics requires flexible semantic
representation, so that novel objects and structures which may not have been
known at training time can be identified, segmented and grouped. We present an
algorithm which fuses general learned features from a standard pre-trained
network into a highly efficient 3D geometric neural field representation during
real-time SLAM. The fused 3D feature maps inherit the coherence of the neural
field's geometry representation. This means that tiny amounts of human
labelling interacting at runtime enable objects or even parts of objects to be
robustly and accurately segmented in an open set manner.

A single rolling-shutter (RS) image may be viewed as a row-wise combination
of a sequence of global-shutter (GS) images captured by a (virtual) moving GS
camera within the exposure duration. Although RS cameras are widely used, the
RS effect causes obvious image distortion especially in the presence of fast
camera motion, hindering downstream computer vision tasks. In this paper, we
propose to invert the RS image capture mechanism, i.e., recovering a continuous
high framerate GS video from two time-consecutive RS frames. We call this task
the RS temporal super-resolution (RSSR) problem. The RSSR is a very challenging
task, and to our knowledge, no practical solution exists to date. This paper
presents a novel deep-learning based solution. By leveraging the multi-view
geometry relationship of the RS imaging process, our learning-based framework
successfully achieves high framerate GS generation. Specifically, three novel
contributions can be identified: (i) novel formulations for bidirectional RS
undistortion flows under constant velocity as well as constant acceleration
motion model. (ii) a simple linear scaling operation, which bridges the RS
undistortion flow and regular optical flow. (iii) a new mutual conversion
scheme between varying RS undistortion flows that correspond to different
scanlines. Our method also exploits the underlying spatial-temporal geometric
relationships within a deep learning framework, where no additional supervision
is required beyond the necessary middle-scanline GS image. Building upon these
contributions, we represent the very first rolling-shutter temporal
super-resolution deep-network that is able to recover high framerate GS videos
from just two RS frames. Extensive experimental results on both synthetic and
real data show that our proposed method can produce high-quality GS image
sequences with rich details, outperforming the state-of-the-art methods.

Learning a many-body Hamiltonian from its dynamics is a fundamental problem
in physics. In this work, we propose the first algorithm to achieve the
Heisenberg limit for learning an interacting $N$-qubit local Hamiltonian. After
a total evolution time of $\mathcal{O}(\epsilon^{-1})$, the proposed algorithm
can efficiently estimate any parameter in the $N$-qubit Hamiltonian to
$\epsilon$-error with high probability. The proposed algorithm is robust
against state preparation and measurement error, does not require eigenstates
or thermal states, and only uses $\mathrm{polylog}(\epsilon^{-1})$ experiments.
In contrast, the best previous algorithms, such as recent works using
gradient-based optimization or polynomial interpolation, require a total
evolution time of $\mathcal{O}(\epsilon^{-2})$ and $\mathcal{O}(\epsilon^{-2})$
experiments. Our algorithm uses ideas from quantum simulation to decouple the
unknown $N$-qubit Hamiltonian $H$ into noninteracting patches, and learns $H$
using a quantum-enhanced divide-and-conquer approach. We prove a matching lower
bound to establish the asymptotic optimality of our algorithm.

Digital human recommendation system has been developed to help customers find
their favorite products and is playing an active role in various recommendation
contexts. How to timely catch and learn the dynamics of the preferences of the
customers, while meeting their exact requirements, becomes crucial in the
digital human recommendation domain. We design a novel practical digital human
interactive recommendation agent framework based on Reinforcement Learning(RL)
to improve the efficiency of the interactive recommendation decision-making by
leveraging both the digital human features and the superior flexibility of RL.
Our proposed framework learns through real-time interactions between the
digital human and customers dynamically through the state-of-art RL algorithms,
combined with multimodal embedding and graph embedding, to improve the accuracy
of personalization and thus enable the digital human agent to timely catch the
attention of the customer. Experiments on real business data demonstrate that
our framework can provide better personalized customer engagement and better
customer experiences.

Recent studies show that graph convolutional network (GCN) often performs
worse for low-degree nodes, exhibiting the so-called structural unfairness for
graphs with long-tailed degree distributions prevalent in the real world. Graph
contrastive learning (GCL), which marries the power of GCN and contrastive
learning, has emerged as a promising self-supervised approach for learning node
representations. How does GCL behave in terms of structural fairness?
Surprisingly, we find that representations obtained by GCL methods are already
fairer to degree bias than those learned by GCN. We theoretically show that
this fairness stems from intra-community concentration and inter-community
scatter properties of GCL, resulting in a much clear community structure to
drive low-degree nodes away from the community boundary. Based on our
theoretical analysis, we further devise a novel graph augmentation method,
called GRAph contrastive learning for DEgree bias (GRADE), which applies
different strategies to low- and high-degree nodes. Extensive experiments on
various benchmarks and evaluation protocols validate the effectiveness of the

We explore using neural operators, or neural network representations of
nonlinear maps between function spaces, to accelerate infinite-dimensional
Bayesian inverse problems (BIPs) with models governed by nonlinear parametric
partial differential equations (PDEs). Neural operators have gained significant
attention in recent years for their ability to approximate the
parameter-to-solution maps defined by PDEs using as training data solutions of
PDEs at a limited number of parameter samples. The computational cost of BIPs
can be drastically reduced if the large number of PDE solves required for
posterior characterization are replaced with evaluations of trained neural
operators. However, reducing error in the resulting BIP solutions via reducing
the approximation error of the neural operators in training can be challenging
and unreliable. We provide an a priori error bound result that implies certain
BIPs can be ill-conditioned to the approximation error of neural operators,
thus leading to inaccessible accuracy requirements in training. To reliably
deploy neural operators in BIPs, we consider a strategy for enhancing the
performance of neural operators, which is to correct the prediction of a
trained neural operator by solving a linear variational problem based on the
PDE residual. We show that a trained neural operator with error correction can
achieve a quadratic reduction of its approximation error, all while retaining
substantial computational speedups of posterior sampling when models are
governed by highly nonlinear PDEs. The strategy is applied to two numerical
examples of BIPs based on a nonlinear reaction--diffusion problem and
deformation of hyperelastic materials. We demonstrate that posterior
representations of the two BIPs produced using trained neural operators are
greatly and consistently enhanced by error correction.

Generative models for 2D images has recently seen tremendous progress in
quality, resolution and speed as a result of the efficiency of 2D convolutional
architectures. However it is difficult to extend this progress into the 3D
domain since most current 3D representations rely on custom network components.
This paper addresses a central question: Is it possible to directly leverage 2D
image generative models to generate 3D shapes instead? To answer this, we
propose XDGAN, an effective and fast method for applying 2D image GAN
architectures to the generation of 3D object geometry combined with additional
surface attributes, like color textures and normals. Specifically, we propose a
novel method to convert 3D shapes into compact 1-channel geometry images and
leverage StyleGAN3 and image-to-image translation networks to generate 3D
objects in 2D space. The generated geometry images are quick to convert to 3D
meshes, enabling real-time 3D object synthesis, visualization and interactive
editing. Moreover, the use of standard 2D architectures can help bring more 2D
advances into the 3D realm. We show both quantitatively and qualitatively that
our method is highly effective at various tasks such as 3D shape generation,
single view reconstruction and shape manipulation, while being significantly
faster and more flexible compared to recent 3D generative models.

Despite achieving state-of-the-art results in nearly all Natural Language
Processing applications, fine-tuning Transformer-based language models still
requires a significant amount of labeled data to work. A well known technique
to reduce the amount of human effort in acquiring a labeled dataset is
\textit{Active Learning} (AL): an iterative process in which only the minimal
amount of samples is labeled. AL strategies require access to a quantified
confidence measure of the model predictions. A common choice is the softmax
activation function for the final layer. As the softmax function provides
misleading probabilities, this paper compares eight alternatives on seven
datasets. Our almost paradoxical finding is that most of the methods are too
good at identifying the true most uncertain samples (outliers), and that
labeling therefore exclusively outliers results in worse performance. As a
heuristic we propose to systematically ignore samples, which results in
improvements of various methods compared to the softmax function.

Computer-aided disease diagnosis and prognosis based on medical images is a
rapidly emerging field. Many Convolutional Neural Network (CNN) architectures
have been developed by researchers for disease classification and localization
from chest X-ray images. It is known that different thoracic disease lesions
are more likely to occur in specific anatomical regions compared to others.
Based on this knowledge, we first estimate a disease-dependent spatial
probability, i.e., an anatomical prior, that indicates the probability of
occurrence of a disease in a specific region in a chest X-ray image. Next, we
develop a novel attention-based classification model that combines information
from the estimated anatomical prior and automatically extracted chest region of
interest (ROI) masks to provide attention to the feature maps generated from a
deep convolution network. Unlike previous works that utilize various
self-attention mechanisms, the proposed method leverages the extracted chest
ROI masks along with the probabilistic anatomical prior information, which
selects the region of interest for different diseases to provide attention. The
proposed method shows superior performance in disease classification on the NIH
ChestX-ray14 dataset compared to existing state-of-the-art methods while
reaching an area under the ROC curve (AUC) of 0.8427. Regarding disease
localization, the proposed method shows competitive performance compared to
state-of-the-art methods, achieving an accuracy of 61% with an Intersection
over Union (IoU) threshold of 0.3. The proposed method can also be generalized
to other medical image-based disease classification and localization tasks
where the probability of occurrence of the lesion is dependent on specific
anatomical sites.

Deploying graph neural networks (GNNs) on whole-graph classification or
regression tasks is known to be challenging: it often requires computing node
features that are mindful of both local interactions in their neighbourhood and
the global context of the graph structure. GNN architectures that navigate this
space need to avoid pathological behaviours, such as bottlenecks and
oversquashing, while ideally having linear time and space complexity
requirements. In this work, we propose an elegant approach based on propagating
information over expander graphs. We provide an efficient method for
constructing expander graphs of a given size, and use this insight to propose
the EGP model. We show that EGP is able to address all of the above concerns,
while requiring minimal effort to set up, and provide evidence of its empirical
utility on relevant datasets and baselines in the Open Graph Benchmark.
Importantly, using expander graphs as a template for message passing
necessarily gives rise to negative curvature. While this appears to be
counterintuitive in light of recent related work on oversquashing, we
theoretically demonstrate that negatively curved edges are likely to be
required to obtain scalable message passing without bottlenecks. To the best of
our knowledge, this is a previously unstudied result in the context of graph
representation learning, and we believe our analysis paves the way to a novel
class of scalable methods to counter oversquashing in GNNs.

Striking progress has recently been made in understanding human cognition by
analyzing how its neuronal underpinnings are engaged in different modes of
information processing. Specifically, neural information can be decomposed into
synergistic, redundant, and unique features, with synergistic components being
particularly aligned with complex cognition. However, two fundamental questions
remain unanswered: (a) precisely how and why a cognitive system can become
highly synergistic; and (b) how these informational states map onto artificial
neural networks in various learning modes. To address these questions, here we
employ an information-decomposition framework to investigate the information
processing strategies adopted by simple artificial neural networks performing a
variety of cognitive tasks in both supervised and reinforcement learning
settings. Our results show that synergy increases as neural networks learn
multiple diverse tasks. Furthermore, performance in tasks requiring integration
of multiple information sources critically relies on synergistic neurons.
Finally, randomly turning off neurons during training through dropout increases
network redundancy, corresponding to an increase in robustness. Overall, our
results suggest that while redundant information is required for robustness to
perturbations in the learning process, synergistic information is used to
combine information from multiple modalities -- and more generally for flexible
and efficient learning. These findings open the door to new ways of
investigating how and why learning systems employ specific
information-processing strategies, and support the principle that the capacity
for general-purpose learning critically relies in the system's information
dynamics.

Purpose The main purpose in this study is to propose a pipeline for COVID-19
detection from a big and challenging database of Computed Tomography (CT)
images. The proposed pipeline includes a segmentation part, a region of
interest extraction part, and a classifier part. Methods The methodology used
in the segmentation part is traditional segmentation methods as well as UNet
based segmentation. In the classification part a Convolutional Neural Network
(CNN) was used to take the final diagnosis decisions. Results In the
segmentation part, the proposed segmentation methods show high dice scores on a
publicly vailable dataset. In the classification part, the results show high
accuracy on the validation partition of COV19-CT-DB dataset as well as higher
precision, recall, and macro F1 score. The classification results were compared
to our previous works other studies as well as on the same dataset. Conclusions
The improved work in this paper proposes efficient pipeline with a potential of
having clinical usage for COVID-19 detection and diagnosis via CT images.
  The code is on github at https://github.com/IDU-CVLab/COV19D_3rd

Recent efforts have been very successful in accurately mapping welfare in
datasparse regions of the world using satellite imagery and other
non-traditional data sources. However, the literature to date has focused on
predicting a particular class of welfare measures, asset indices, which are
relatively insensitive to short term fluctuations in well-being. We suggest
that predicting more volatile welfare measures, such as consumption
expenditure, substantially benefits from the incorporation of data sources with
high temporal resolution. By incorporating daily weather data into training and
prediction, we improve consumption prediction accuracy significantly compared
to models that only utilize satellite imagery.

Recent success in fine-tuning large models, that are pretrained on broad data
at scale, on downstream tasks has led to a significant paradigm shift in deep
learning, from task-centric model design to task-agnostic representation
learning and task-specific fine-tuning. As the representations of pretrained
models are used as a foundation for different downstream tasks, this paper
proposes a new task-agnostic framework, \textit{SynBench}, to measure the
quality of pretrained representations using synthetic data. We set up a
reference by a theoretically-derived robustness-accuracy tradeoff of the class
conditional Gaussian mixture. Given a pretrained model, the representations of
data synthesized from the Gaussian mixture are used to compare with our
reference to infer the quality. By comparing the ratio of area-under-curve
between the raw data and their representations, SynBench offers a quantifiable
score for robustness-accuracy performance benchmarking. Our framework applies
to a wide range of pretrained models taking continuous data inputs and is
independent of the downstream tasks and datasets. Evaluated with several
pretrained vision transformer models, the experimental results show that our
SynBench score well matches the actual linear probing performance of the
pre-trained model when fine-tuned on downstream tasks. Moreover, our framework
can be used to inform the design of robust linear probing on pretrained
representations to mitigate the robustness-accuracy tradeoff in downstream
tasks.

Equivariance guarantees that a model's predictions capture key symmetries in
data. When an image is translated or rotated, an equivariant model's
representation of that image will translate or rotate accordingly. The success
of convolutional neural networks has historically been tied to translation
equivariance directly encoded in their architecture. The rising success of
vision transformers, which have no explicit architectural bias towards
equivariance, challenges this narrative and suggests that augmentations and
training data might also play a significant role in their performance. In order
to better understand the role of equivariance in recent vision models, we
introduce the Lie derivative, a method for measuring equivariance with strong
mathematical foundations and minimal hyperparameters. Using the Lie derivative,
we study the equivariance properties of hundreds of pretrained models, spanning
CNNs, transformers, and Mixer architectures. The scale of our analysis allows
us to separate the impact of architecture from other factors like model size or
training method. Surprisingly, we find that many violations of equivariance can
be linked to spatial aliasing in ubiquitous network layers, such as pointwise
non-linearities, and that as models get larger and more accurate they tend to
display more equivariance, regardless of architecture. For example,
transformers can be more equivariant than convolutional neural networks after
training.

Employing large antenna arrays and utilizing large bandwidth have the
potential of bringing very high data rates to future wireless communication
systems. To achieve that, however, new challenges associated with these systems
need to be addressed. First, the large array aperture brings the communications
to the near-field region, where the far-field assumptions no longer hold.
Second, the analog-only (phase shifter based) beamforming architectures result
in performance degradation in wideband systems due to their frequency
unawareness. To address these problems, this paper proposes a low-complexity
frequency-aware near-field beamforming framework for hybrid time-delay (TD) and
phase-shifter (PS) based RF architectures. Specifically, a \textit{signal model
inspired online learning} framework is proposed to learn the phase shifts of
the quantized analog phase-shifters. Thanks to the model-inspired design, the
proposed learning approach has fast convergence performance. Further, a
low-complexity \textit{geometry-assisted} method is developed to configure the
delay settings of the TD units. Simulation results highlight the efficacy of
the proposed solution in achieving robust near-field beamforming performance
for wideband large antenna array systems.

Artificial Intelligence (AI) is one of the approaches that has been proposed
to analyze the collected data (e.g., vibration signals) providing a diagnosis
of the asset's operating condition. It is known that models trained with
labeled data (supervised) achieve excellent results, but two main problems make
their application in production processes difficult: (i) impossibility or long
time to obtain a sample of all operational conditions (since faults seldom
happen) and (ii) high cost of experts to label all acquired data. Another
limitating factor for the applicability of AI approaches in this context is the
lack of interpretability of the models (black-boxes), which reduces the
confidence of the diagnosis and trust/adoption from users. To overcome these
problems, a new generic and interpretable approach for classifying faults in
rotating machinery based on transfer learning from augmented synthetic data to
real rotating machinery is here proposed, namelly FaultD-XAI (Fault Diagnosis
using eXplainable AI). To provide scalability using transfer learning,
synthetic vibration signals are created mimicking the characteristic behavior
of failures in operation. The application of Gradient-weighted Class Activation
Mapping (Grad-CAM) with 1D Convolutional Neural Network (1D CNN) allows the
interpretation of results, supporting the user in decision making and
increasing diagnostic confidence. The proposed approach not only obtained
promising diagnostic performance, but was also able to learn characteristics
used by experts to identify conditions in a source domain and apply them in
another target domain. The experimental results suggest a promising approach on
exploiting transfer learning, synthetic data and explainable artificial
intelligence for fault diagnosis. Lastly, to guarantee reproducibility and
foster research in the field, the developed dataset is made publicly available.

Meta-training, which fine-tunes the language model (LM) on various downstream
tasks by maximizing the likelihood of the target label given the task
instruction and input instance, has improved the zero-shot task generalization
performance. However, meta-trained LMs still struggle to generalize to
challenging tasks containing novel labels unseen during meta-training. In this
paper, we propose Flipped Learning, an alternative method of meta-training
which trains the LM to generate the task instruction given the input instance
and label. During inference, the LM trained with Flipped Learning, referred to
as Flipped, selects the label option that is most likely to generate the task
instruction. On 14 tasks of the BIG-bench benchmark, the 11B-sized Flipped
outperforms zero-shot T0-11B and even a 16 times larger 3-shot GPT-3 (175B) on
average by 8.4% and 9.7% points, respectively. Flipped gives particularly large
improvements on unseen labels, outperforming T0-11B by up to +20% average F1
score. This indicates that the strong task generalization of Flipped comes from
improved generalization to novel labels. We release our code at
https://github.com/seonghyeonye/Flipped-Learning.

Clinical adoption of personalized virtual heart simulations faces challenges
in model personalization and expensive computation. While an ideal solution is
an efficient neural surrogate that at the same time is personalized to an
individual subject, the state-of-the-art is either concerned with personalizing
an expensive simulation model, or learning an efficient yet generic surrogate.
This paper presents a completely new concept to achieve personalized neural
surrogates in a single coherent framework of meta-learning (metaPNS). Instead
of learning a single neural surrogate, we pursue the process of learning a
personalized neural surrogate using a small amount of context data from a
subject, in a novel formulation of few-shot generative modeling underpinned by:
1) a set-conditioned neural surrogate for cardiac simulation that, conditioned
on subject-specific context data, learns to generate query simulations not
included in the context set, and 2) a meta-model of amortized variational
inference that learns to condition the neural surrogate via simple feed-forward
embedding of context data. As test time, metaPNS delivers a personalized neural
surrogate by fast feed-forward embedding of a small and flexible number of data
available from an individual, achieving -- for the first time --
personalization and surrogate construction for expensive simulations in one
end-to-end learning framework. Synthetic and real-data experiments demonstrated
that metaPNS was able to improve personalization and predictive accuracy in
comparison to conventionally-optimized cardiac simulation models, at a fraction
of computation.

Purpose: Real-life applications using quadrotors introduce a number of
disturbances and time-varying properties that pose a challenge to flight
controllers. We observed that, when a quadrotor is tasked with picking up and
dropping a payload, traditional PID and RL-based controllers found in
literature struggle to maintain flight after the vehicle changes its dynamics
due to interaction with this external object.
  Methods: In this work, we introduce domain randomization during the training
phase of a low-level waypoint guidance controller based on Soft Actor-Critic.
The resulting controller is evaluated on the proposed payload pick up and drop
task with added disturbances that emulate real-life operation of the vehicle.
  Results & Conclusion: We show that, by introducing a certain degree of
uncertainty in quadrotor dynamics during training, we can obtain a controller
that is capable to perform the proposed task using a larger variation of
quadrotor parameters. Additionally, the RL-based controller outperforms a
traditional positional PID controller with optimized gains in this task, while
remaining agnostic to different simulation parameters.

Automating the research for the best neural network model is a task that has
gained more and more relevance in the last few years. In this context, Neural
Architecture Search (NAS) represents the most effective technique whose results
rival the state of the art hand-crafted architectures. However, this approach
requires a lot of computational capabilities as well as research time, which
makes prohibitive its usage in many real-world scenarios. With its sequential
model-based optimization strategy, Progressive Neural Architecture Search
(PNAS) represents a possible step forward to face this resources issue. Despite
the quality of the found network architectures, this technique is still limited
in research time. A significant step in this direction has been done by
Pareto-Optimal Progressive Neural Architecture Search (POPNAS), which expands
PNAS with a time predictor to enable a trade-off between search time and
accuracy, considering a multi-objective optimization problem. This paper
proposes a new version of the Pareto-Optimal Progressive Neural Architecture
Search, called POPNASv2. Our approach enhances its first version and improves
its performance. We expanded the search space by adding new operators and
improved the quality of both predictors to build more accurate Pareto fronts.
Moreover, we introduced cell equivalence checks and enriched the search
strategy with an adaptive greedy exploration step. Our efforts allow POPNASv2
to achieve PNAS-like performance with an average 4x factor search time
speed-up.

Collusive practices of firms continue to be a major threat to competition and
consumer welfare. Academic research on this topic aims at understanding the
economic drivers and behavioral patterns of cartels, among others, to guide
competition authorities on how to tackle them. Utilizing topical machine
learning techniques in the domain of natural language processing enables me to
analyze the publications on this issue over more than 20 years in a novel way.
Coming from a stylized oligopoly-game theory focus, researchers recently turned
toward empirical case studies of bygone cartels. Uni- and multivariate time
series analyses reveal that the latter did not supersede the former but filled
a gap the decline in rule-based reasoning has left. Together with a tendency
towards monocultures in topics covered and an endogenous constriction of the
topic variety, the course of cartel research has changed notably: The variety
of subjects included has grown, but the pluralism in economic questions
addressed is in descent. It remains to be seen whether this will benefit or
harm the cartel detection capabilities of authorities in the future.

Word or word-fragment based Language Models (LM) are typically preferred over
character-based ones in many downstream applications. This may not be
surprising as words seem more linguistically relevant units than characters.
Words provide at least two kinds of relevant information: boundary information
and meaningful units. However, word boundary information may be absent or
unreliable in the case of speech input (word boundaries are not marked
explicitly in the speech stream). Here, we systematically compare LSTMs as a
function of the input unit (character, phoneme, word, word part), with or
without gold boundary information. We probe linguistic knowledge in the
networks at the lexical, syntactic and semantic levels using three
speech-adapted black box NLP psycholinguistically-inpired benchmarks (pWUGGY,
pBLIMP, pSIMI). We find that the absence of boundaries costs between 2\% and
28\% in relative performance depending on the task. We show that gold
boundaries can be replaced by automatically found ones obtained with an
unsupervised segmentation algorithm, and that even modest segmentation
performance gives a gain in performance on two of the three tasks compared to
basic character/phone based models without boundary information.

Prompt tuning, or the conditioning of a frozen pretrained language model
(PLM) with soft prompts learned from data, has demonstrated impressive
performance on a wide range of NLP tasks. However, prompt tuning requires a
large training dataset to be effective and is outperformed by finetuning the
entire PLM in data-scarce regimes. Previous work (Gu et al., 2022, Vu et al.,
2022) proposed to transfer soft prompts pretrained on the source domain to the
target domain. In this paper, we explore domain adaptation for prompt tuning, a
problem setting where unlabeled data from the target domain are available
during pretraining. We propose bOosting Prompt TunIng with doMain Adaptation
(OPTIMA), which regularizes the decision boundary to be smooth around regions
where source and target data distributions are similar. Extensive experiments
demonstrate that OPTIMA significantly enhances the transferability and
sample-efficiency of prompt tuning compared to strong baselines. Moreover, in
few-shot settings, OPTIMA exceeds full-model tuning by a large margin.

Graph neural networks (GNNs) have recently been popular in natural language
and programming language processing, particularly in text and source code
classification. Graph pooling which processes node representation into the
entire graph representation, which can be used for multiple downstream tasks,
e.g., graph classification, is a crucial component of GNNs. Recently, to
enhance graph learning, Manifold Mixup, a data augmentation strategy that mixes
the graph data vector after the pooling layer, has been introduced. However,
since there are a series of graph pooling methods, how they affect the
effectiveness of such a Mixup approach is unclear. In this paper, we take the
first step to explore the influence of graph pooling methods on the
effectiveness of the Mixup-based data augmentation approach. Specifically, 9
types of hybrid pooling methods are considered in the study, e.g.,
$\mathcal{M}_{sum}(\mathcal{P}_{att},\mathcal{P}_{max})$. The experimental
results on both natural language datasets (Gossipcop, Politifact) and
programming language datasets (Java250, Python800) demonstrate that hybrid
pooling methods are more suitable for Mixup than the standard max pooling and
the state-of-the-art graph multiset transformer (GMT) pooling, in terms of
metric accuracy and robustness.

News representation and user-oriented modeling are both essential for news
recommendation. Most existing methods are based on textual information but
ignore the visual information and users' dynamic interests. However, compared
to textual only content, multimodal semantics is beneficial for enhancing the
comprehension of users' temporal and long-lasting interests. In our work, we
propose a vision-linguistics coordinate time sequence news recommendation.
Firstly, a pretrained multimodal encoder is applied to embed images and texts
into the same feature space. Then the self-attention network is used to learn
the chronological sequence. Additionally, an attentional GRU network is
proposed to model user preference in terms of time adequately. Finally, the
click history and user representation are embedded to calculate the ranking
scores for candidate news. Furthermore, we also construct a large scale
multimodal news recommendation dataset V-MIND. Experimental results show that
our model outperforms baselines and achieves SOTA on our independently
constructed dataset.

The optimal pivoting path of the simplex method is crucial for solving linear
programming problems. This study proposes a new method to provide the optimal
pivot and all the corresponding best pivoting paths using the Monte Carlo tree
search (MCTS). First, we propose SimplexPseudoTree to transfer the simplex
method into tree search mode while avoiding repeated basis variables. Based on
the SimplexPseudoTree, we propose four reinforcement learning (RL) models with
two actions and two rewards to make the Monte Carlo tree search suitable for
the simplex method. In addition, we set a new action selection criterion to
ameliorate the inaccurate evaluation in the initial exploration. We
experimentally validated that the proposed schedule can avoid unnecessary
searches and provide the optimal pivot. Additionally, we theoretically proved
that our method determines the minimum number of pivot iterations and all
corresponding optimal pivoting paths. Furthermore, this method provides the
best labels for the pivot rules based on supervised learning.

This paper presents a deep learning framework for image classification aimed
at increasing predictive performance for Cytotoxic Edema (CE) diagnosis in
infants and children. The proposed framework includes two 3D network
architectures optimized to learn from two types of clinical MRI data , a trace
Diffusion Weighted Image (DWI) and the calculated Apparent Diffusion
Coefficient map (ADC). This work proposes a robust and novel solution based on
volumetric analysis of 3D images (using pixels from time slices) and 3D
convolutional neural network (CNN) models. While simple in architecture, the
proposed framework shows significant quantitative results on the domain
problem. We use a dataset curated from a Childrens Hospital Colorado (CHCO)
patient registry to report a predictive performance F1 score of 0.91 at
distinguishing CE patients from children with severe neurologic injury without
CE. In addition, we perform analysis of our systems output to determine the
association of CE with Abusive Head Trauma (AHT) , a type of traumatic brain
injury (TBI) associated with abuse , and overall functional outcome and in
hospital mortality of infants and young children. We used two clinical
variables, AHT diagnosis and Functional Status Scale (FSS) score, to arrive at
the conclusion that CE is highly correlated with overall outcome and that
further study is needed to determine whether CE is a biomarker of AHT. With
that, this paper introduces a simple yet powerful deep learning based solution
for automated CE classification. This solution also enables an indepth analysis
of progression of CE and its correlation to AHT and overall neurologic outcome,
which in turn has the potential to empower experts to diagnose and mitigate AHT
during early stages of a childs life.

Federated learning (FL) is a distributed method to train a global model over
a set of local clients while keeping data localized. It reduces the risks of
privacy and security but faces important challenges including expensive
communication costs and client drift issues. To address these issues, we
propose FedElasticNet, a communication-efficient and drift-robust FL framework
leveraging the elastic net. It repurposes two types of the elastic net
regularizers (i.e., $\ell_1$ and $\ell_2$ penalties on the local model
updates): (1) the $\ell_1$-norm regularizer sparsifies the local updates to
reduce the communication costs and (2) the $\ell_2$-norm regularizer resolves
the client drift problem by limiting the impact of drifting local updates due
to data heterogeneity. FedElasticNet is a general framework for FL; hence,
without additional costs, it can be integrated into prior FL techniques, e.g.,
FedAvg, FedProx, SCAFFOLD, and FedDyn. We show that our framework effectively
resolves the communication cost and client drift problems simultaneously.

We study the relationship between task-agnostic intrinsic and task-specific
extrinsic social bias evaluation measures for Masked Language Models (MLMs),
and find that there exists only a weak correlation between these two types of
evaluation measures. Moreover, we find that MLMs debiased using different
methods still re-learn social biases during fine-tuning on downstream tasks. We
identify the social biases in both training instances as well as their assigned
labels as reasons for the discrepancy between intrinsic and extrinsic bias
evaluation measurements. Overall, our findings highlight the limitations of
existing MLM bias evaluation measures and raise concerns on the deployment of
MLMs in downstream applications using those measures.

In the field of deep learning based computer vision, the development of deep
object detection has led to unique paradigms (e.g., two-stage or set-based) and
architectures (e.g., Faster-RCNN or DETR) which enable outstanding performance
on challenging benchmark datasets. Despite this, the trained object detectors
typically do not reliably assess uncertainty regarding their own knowledge, and
the quality of their probabilistic predictions is usually poor. As these are
often used to make subsequent decisions, such inaccurate probabilistic
predictions must be avoided. In this work, we investigate the uncertainty
calibration properties of different pretrained object detection architectures
in a multi-class setting. We propose a framework to ensure a fair, unbiased,
and repeatable evaluation and conduct detailed analyses assessing the
calibration under distributional changes (e.g., distributional shift and
application to out-of-distribution data). Furthermore, by investigating the
influence of different detector paradigms, post-processing steps, and suitable
choices of metrics, we deliver novel insights into why poor detector
calibration emerges. Based on these insights, we are able to improve the
calibration of a detector by simply finetuning its last layer.

A common thread of open-domain question answering (QA) models employs a
retriever-reader pipeline that first retrieves a handful of relevant passages
from Wikipedia and then peruses the passages to produce an answer. However,
even state-of-the-art readers fail to capture the complex relationships between
entities appearing in questions and retrieved passages, leading to answers that
contradict the facts. In light of this, we propose a novel knowledge Graph
enhanced passage reader, namely Grape, to improve the reader performance for
open-domain QA. Specifically, for each pair of question and retrieved passage,
we first construct a localized bipartite graph, attributed to entity embeddings
extracted from the intermediate layer of the reader model. Then, a graph neural
network learns relational knowledge while fusing graph and contextual
representations into the hidden states of the reader model. Experiments on
three open-domain QA benchmarks show Grape can improve the state-of-the-art
performance by up to 2.2 exact match score with a negligible overhead increase,
with the same retriever and retrieved passages. Our code is publicly available
at https://github.com/jumxglhf/GRAPE.

Speech is the fundamental mode of human communication, and its synthesis has
long been a core priority in human-computer interaction research. In recent
years, machines have managed to master the art of generating speech that is
understandable by humans. But the linguistic content of an utterance
encompasses only a part of its meaning. Affect, or expressivity, has the
capacity to turn speech into a medium capable of conveying intimate thoughts,
feelings, and emotions -- aspects that are essential for engaging and
naturalistic interpersonal communication. While the goal of imparting
expressivity to synthesised utterances has so far remained elusive, following
recent advances in text-to-speech synthesis, a paradigm shift is well under way
in the fields of affective speech synthesis and conversion as well. Deep
learning, as the technology which underlies most of the recent advances in
artificial intelligence, is spearheading these efforts. In the present
overview, we outline ongoing trends and summarise state-of-the-art approaches
in an attempt to provide a comprehensive overview of this exciting field.

Entity typing aims to assign types to the entity mentions in given texts. The
traditional classification-based entity typing paradigm has two unignorable
drawbacks: 1) it fails to assign an entity to the types beyond the predefined
type set, and 2) it can hardly handle few-shot and zero-shot situations where
many long-tail types only have few or even no training instances. To overcome
these drawbacks, we propose a novel generative entity typing (GET) paradigm:
given a text with an entity mention, the multiple types for the role that the
entity plays in the text are generated with a pre-trained language model (PLM).
However, PLMs tend to generate coarse-grained types after fine-tuning upon the
entity typing dataset. Besides, we only have heterogeneous training data
consisting of a small portion of human-annotated data and a large portion of
auto-generated but low-quality data. To tackle these problems, we employ
curriculum learning (CL) to train our GET model upon the heterogeneous data,
where the curriculum could be self-adjusted with the self-paced learning
according to its comprehension of the type granularity and data heterogeneity.
Our extensive experiments upon the datasets of different languages and
downstream tasks justify the superiority of our GET model over the
state-of-the-art entity typing models. The code has been released on
https://github.com/siyuyuan/GET.

Reservoir computing is a machine learning paradigm that transforms the
transient dynamics of high-dimensional nonlinear systems for processing
time-series data. Although reservoir computing was initially proposed to model
information processing in the mammalian cortex, it remains unclear how the
non-random network architecture, such as the modular architecture, in the
cortex integrates with the biophysics of living neurons to characterize the
function of biological neuronal networks (BNNs). Here, we used optogenetics and
fluorescent calcium imaging to record the multicellular responses of cultured
BNNs and employed the reservoir computing framework to decode their
computational capabilities. Micropatterned substrates were used to embed the
modular architecture in the BNNs. We first show that modular BNNs can be used
to classify static input patterns with a linear decoder and that the modularity
of the BNNs positively correlates with the classification accuracy. We then
used a timer task to verify that BNNs possess a short-term memory of ~1 s and
finally show that this property can be exploited for spoken digit
classification. Interestingly, BNN-based reservoirs allow transfer learning,
wherein a network trained on one dataset can be used to classify separate
datasets of the same category. Such classification was not possible when the
input patterns were directly decoded by a linear decoder, suggesting that BNNs
act as a generalization filter to improve reservoir computing performance. Our
findings pave the way toward a mechanistic understanding of information
processing within BNNs and, simultaneously, build future expectations toward
the realization of physical reservoir computing systems based on BNNs.

Federated Learning (FL) is a setting for training machine learning models in
distributed environments where the clients do not share their raw data but
instead send model updates to a server. However, model updates can be subject
to attacks and leak private information. Differential Privacy (DP) is a leading
mitigation strategy which involves adding noise to clipped model updates,
trading off performance for strong theoretical privacy guarantees. Previous
work has shown that the threat model of DP is conservative and that the
obtained guarantees may be vacuous or may not directly translate to information
leakage in practice. In this paper, we aim to achieve a tighter measurement of
the model exposure by considering a realistic threat model. We propose a novel
method, CANIFE, that uses canaries - carefully crafted samples by a strong
adversary to evaluate the empirical privacy of a training round. We apply this
attack to vision models trained on CIFAR-10 and CelebA and to language models
trained on Sent140 and Shakespeare. In particular, in realistic FL scenarios,
we demonstrate that the empirical epsilon obtained with CANIFE is 2-7x lower
than the theoretical bound.

There is great demand for scalable, secure, and efficient privacy-preserving
machine learning models that can be trained over distributed data. While deep
learning models typically achieve the best results in a centralized non-secure
setting, different models can excel when privacy and communication constraints
are imposed. Instead, tree-based approaches such as XGBoost have attracted much
attention for their high performance and ease of use; in particular, they often
achieve state-of-the-art results on tabular data. Consequently, several recent
works have focused on translating Gradient Boosted Decision Tree (GBDT) models
like XGBoost into federated settings, via cryptographic mechanisms such as
Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC). However,
these do not always provide formal privacy guarantees, or consider the full
range of hyperparameters and implementation settings. In this work, we
implement the GBDT model under Differential Privacy (DP). We propose a general
framework that captures and extends existing approaches for differentially
private decision trees. Our framework of methods is tailored to the federated
setting, and we show that with a careful choice of techniques it is possible to
achieve very high utility while maintaining strong levels of privacy.

Many real-world problems can be phrased as a multi-objective optimization
problem, where the goal is to identify the best set of compromises between the
competing objectives. Multi-objective Bayesian optimization (BO) is a sample
efficient strategy that can be deployed to solve these vector-valued
optimization problems where access is limited to a number of noisy objective
function evaluations. In this paper, we propose a novel information-theoretic
acquisition function for BO called Joint Entropy Search (JES), which considers
the joint information gain for the optimal set of inputs and outputs. We
present several analytical approximations to the JES acquisition function and
also introduce an extension to the batch setting. We showcase the effectiveness
of this new approach on a range of synthetic and real-world problems in terms
of the hypervolume and its weighted variants.

Multi-aspect controllable text generation is a more challenging and practical
task than single-aspect control. Existing methods achieve complex multi-aspect
control by fusing multiple controllers learned from single-aspect, but suffer
from attribute degeneration caused by the mutual interference of these
controllers. To address this, we provide observations on attribute fusion from
a distributional perspective and propose to directly search for the
intersection areas of multiple attribute distributions as their combination for
generation. Our method first estimates the attribute space with an autoencoder
structure. Afterward, we iteratively approach the intersections by jointly
minimizing distances to points representing different attributes. Finally, we
map them to attribute-relevant sentences with a prefix-tuning-based decoder.
Experiments on the three-aspect control task, including sentiment, topic, and
detoxification aspects, reveal that our method outperforms several strong
baselines on attribute relevance and text quality and achieves the SOTA.
Further analysis also supplies some explanatory support for the effectiveness

Stochastic gradient descent (SGD) is a widely adopted iterative method for
optimizing differentiable objective functions. In this paper, we propose and
discuss a novel approach to scale up SGD in applications involving non-convex
functions and large datasets. We address the bottleneck problem arising when
using both shared and distributed memory. Typically, the former is bounded by
limited computation resources and bandwidth whereas the latter suffers from
communication overheads. We propose a unified distributed and parallel
implementation of SGD (named DPSGD) that relies on both asynchronous
distribution and lock-free parallelism. By combining two strategies into a
unified framework, DPSGD is able to strike a better trade-off between local
computation and communication. The convergence properties of DPSGD are studied
for non-convex problems such as those arising in statistical modelling and
machine learning. Our theoretical analysis shows that DPSGD leads to speed-up
with respect to the number of cores and number of workers while guaranteeing an
asymptotic convergence rate of $O(1/\sqrt{T})$ given that the number of cores
is bounded by $T^{1/4}$ and the number of workers is bounded by $T^{1/2}$ where
$T$ is the number of iterations. The potential gains that can be achieved by
DPSGD are demonstrated empirically on a stochastic variational inference
problem (Latent Dirichlet Allocation) and on a deep reinforcement learning
(DRL) problem (advantage actor critic - A2C) resulting in two algorithms: DPSVI
and HSA2C. Empirical results validate our theoretical findings. Comparative
studies are conducted to show the performance of the proposed DPSGD against the
state-of-the-art DRL algorithms.

Spiking neural networks, also often referred to as the third generation of
neural networks, carry the potential for a massive reduction in memory and
energy consumption over traditional, second-generation neural networks.
Inspired by the undisputed efficiency of the human brain, they introduce
temporal and neuronal sparsity, which can be exploited by next-generation
neuromorphic hardware. To open the pathway toward engineering applications, we
introduce this exciting technology in the context of continuum mechanics.
However, the nature of spiking neural networks poses a challenge for regression
problems, which frequently arise in the modeling of engineering sciences. To
overcome this problem, a framework for regression using spiking neural networks
is proposed. In particular, a network topology for decoding binary spike trains
to real numbers is introduced, utilizing the membrane potential of spiking
neurons. As the aim of this contribution is a concise introduction to this new
methodology, several different spiking neural architectures, ranging from
simple spiking feed-forward to complex spiking long short-term memory neural
networks, are derived. Several numerical experiments directed towards
regression of linear and nonlinear, history-dependent material models are
carried out. A direct comparison with counterparts of traditional neural
networks shows that the proposed framework is much more efficient while
retaining precision and generalizability. All code has been made publicly
available in the interest of reproducibility and to promote continued
enhancement in this new domain.

Existing works on valuing digital assets on the Internet typically focus on a
single asset class. To promote the development of automated valuation
techniques, preferably those that are generally applicable to multiple asset
classes, we construct DASH, the first Digital Asset Sales History dataset that
features multiple digital asset classes spanning from classical to
blockchain-based ones. Consisting of 280K transactions of domain names
(DASH_DN), email addresses (DASH_EA), and non-fungible token (NFT)-based
identifiers (DASH_NFT), such as Ethereum Name Service names, DASH advances the
field in several aspects: the subsets DASH_DN, DASH_EA, and DASH_NFT are the
largest freely accessible domain name transaction dataset, the only publicly
available email address transaction dataset, and the first NFT transaction
dataset that focuses on identifiers, respectively.
  We build strong conventional feature-based models as the baselines for DASH.
We next explore deep learning models based on fine-tuning pre-trained language
models, which have not yet been explored for digital asset valuation in the
previous literature. We find that the vanilla fine-tuned model already performs
reasonably well, outperforming all but the best-performing baselines. We
further propose improvements to make the model more aware of the time
sensitivity of transactions and the popularity of assets. Experimental results
show that our improved model consistently outperforms all the other models
across all asset classes on DASH.

Complementary to finding good general word embeddings, an important question
for representation learning is to find dynamic word embeddings, e.g., across
time or domain. Current methods do not offer a way to use or predict
information on structure between sub-corpora, time or domain and dynamic
embeddings can only be compared after post-alignment. We propose novel word
embedding methods that provide general word representations for the whole
corpus, domain-specific representations for each sub-corpus, sub-corpus
structure, and embedding alignment simultaneously. We present an empirical
evaluation on New York Times articles and two English Wikipedia datasets with
articles on science and philosophy. Our method, called Word2Vec with Structure
Prediction (W2VPred), provides better performance than baselines in terms of
the general analogy tests, domain-specific analogy tests, and multiple specific
word embedding evaluations as well as structure prediction performance when no
structure is given a priori. As a use case in the field of Digital Humanities
we demonstrate how to raise novel research questions for high literature from
the German Text Archive.

Feature engineering is required to obtain better results for time series
forecasting, and decomposition is a crucial one. One decomposition approach
often cannot be used for numerous forecasting tasks since the standard time
series decomposition lacks flexibility and robustness. Traditional feature
selection relies heavily on preexisting domain knowledge, has no generic
methodology, and requires a lot of labor. However, most time series prediction
models based on deep learning typically suffer from interpretability issue, so
the "black box" results lead to a lack of confidence. To deal with the above
issues forms the motivation of the thesis. In the paper we propose TSDFNet as a
neural network with self-decomposition mechanism and an attentive feature
fusion mechanism, It abandons feature engineering as a preprocessing convention
and creatively integrates it as an internal module with the deep model. The
self-decomposition mechanism empowers TSDFNet with extensible and adaptive
decomposition capabilities for any time series, users can choose their own
basis functions to decompose the sequence into temporal and generalized spatial
dimensions. Attentive feature fusion mechanism has the ability to capture the
importance of external variables and the causality with target variables. It
can automatically suppress the unimportant features while enhancing the
effective ones, so that users do not have to struggle with feature selection.
Moreover, TSDFNet is easy to look into the "black box" of the deep neural
network by feature visualization and analyze the prediction results. We
demonstrate performance improvements over existing widely accepted models on
more than a dozen datasets, and three experiments showcase the interpretability
of TSDFNet.

Aiming to ensure chatbot quality by predicting chatbot failure and enabling
human-agent collaboration, Machine-Human Chatting Handoff (MHCH) has attracted
lots of attention from both industry and academia in recent years. However,
most existing methods mainly focus on the dialogue context or assist with
global satisfaction prediction based on multi-task learning, which ignore the
grounded relationships among the causal variables, like the user state and
labor cost. These variables are significantly associated with handoff
decisions, resulting in prediction bias and cost increasement. Therefore, we
propose Causal-Enhance Module (CEM) by establishing the causal graph of MHCH
based on these two variables, which is a simple yet effective module and can be
easy to plug into the existing MHCH methods. For the impact of users, we use
the user state to correct the prediction bias according to the causal
relationship of multi-task. For the labor cost, we train an auxiliary cost
simulator to calculate unbiased labor cost through counterfactual learning so
that a model becomes cost-aware. Extensive experiments conducted on four
real-world benchmarks demonstrate the effectiveness of CEM in generally
improving the performance of existing MHCH methods without any elaborated model
crafting.

Anomaly detection is a branch of machine learning and data analysis which
aims at identifying observations that exhibit abnormal behaviour. Be it
measurement errors, disease development, severe weather, production quality
default(s) (items) or failed equipment, financial frauds or crisis events,
their on-time identification, isolation and explanation constitute an important
task in almost any branch of industry and science. By providing a robust
ordering, data depth -- statistical function that measures belongingness of any
point of the space to a data set -- becomes a particularly useful tool for
detection of anomalies. Already known for its theoretical properties, data
depth has undergone substantial computational developments in the last decade
and particularly recent years, which has made it applicable for
contemporary-sized problems of data analysis and machine learning.
  In this article, data depth is studied as an efficient anomaly detection
tool, assigning abnormality labels to observations with lower depth values, in
a multivariate setting. Practical questions of necessity and reasonability of
invariances and shape of the depth function, its robustness and computational
complexity, choice of the threshold are discussed. Illustrations include
use-cases that underline advantageous behaviour of data depth in various
settings.

This paper focuses on drawing inference on the causal impact of an
intervention at a specific time point, as manifested in an outcome variable
over time. We operate on the interrupted time series framework and expand on
approaches such as the synthetic control (Abadie 2003) and Bayesian structural
time series (Brodersen et al 2015), by replacing the underlying dynamic linear
regression model with a non-parametric formulation based on Gaussian Processes.
The developed models possess a high degree of flexibility posing very little
limitations on the functional form and allow to incorporate uncertainty,
stemming from its estimation, under the Bayesian framework. We introduce two
families of non-parametric structural time series models either operating on
the trajectory of the outcome variable alone, or in a multivariate setting
using multiple output Gaussian processes. The paper engages closely with a case
study focusing on the impact of the accelerated UK vaccination schedule, as
contrasted with the rest of Europe, to illustrate the methodology and present
the implementation procedure.

Botnet detectors based on machine learning are potential targets for
adversarial evasion attacks. Several research works employ adversarial training
with samples generated from generative adversarial nets (GANs) to make the
botnet detectors adept at recognising adversarial evasions. However, the
synthetic evasions may not follow the original semantics of the input samples.
This paper proposes a novel GAN model leveraged with deep reinforcement
learning (DRL) to explore semantic aware samples and simultaneously harden its
detection. A DRL agent is used to attack the discriminator of the GAN that acts
as a botnet detector. The discriminator is trained on the crafted perturbations
by the agent during the GAN training, which helps the GAN generator converge
earlier than the case without DRL. We name this model RELEVAGAN, i.e. ["relive
a GAN" or deep REinforcement Learning-based Evasion Generative Adversarial
Network] because, with the help of DRL, it minimises the GAN's job by letting
its generator explore the evasion samples within the semantic limits. During
the GAN training, the attacks are conducted to adjust the discriminator weights
for learning crafted perturbations by the agent. RELEVAGAN does not require
adversarial training for the ML classifiers since it can act as an adversarial
semantic-aware botnet detection model. Code will be available at
https://github.com/rhr407/RELEVAGAN.

The estimation of heterogeneous treatment effects (HTEs) has attracted
considerable interest in many disciplines, most prominently in medicine and
economics. Contemporary research has so far primarily focused on continuous and
binary responses where HTEs are traditionally estimated by a linear model,
which allows the estimation of constant or heterogeneous effects even under
certain model misspecifications. More complex models for survival, count, or
ordinal outcomes require stricter assumptions to reliably estimate the
treatment effect. Most importantly, the noncollapsibility issue necessitates
the joint estimation of treatment and prognostic effects. Model-based forests
allow simultaneous estimation of covariate-dependent treatment and prognostic
effects, but only for randomized trials. In this paper, we propose
modifications to model-based forests to address the confounding issue in
observational data. In particular, we evaluate an orthogonalization strategy
originally proposed by Robinson (1988, Econometrica) in the context of
model-based forests targeting HTE estimation in generalized linear models and
transformation models. We found that this strategy reduces confounding effects
in a simulated study with various outcome distributions. We demonstrate the
practical aspects of HTE estimation for survival and ordinal outcomes by an
assessment of the potentially heterogeneous effect of Riluzole on the progress
of Amyotrophic Lateral Sclerosis.

We present an analysis of large-scale pretrained deep learning models used
for cross-modal (text-to-audio) retrieval. We use embeddings extracted by these
models in a metric learning framework to connect matching pairs of audio and
text. Shallow neural networks map the embeddings to a common dimensionality.
Our system, which is an extension of our submission to the Language-based Audio
Retrieval Task of the DCASE Challenge 2022, employs the RoBERTa foundation
model as the text embedding extractor. A pretrained PANNs model extracts the
audio embeddings. To improve the generalisation of our model, we investigate
how pretraining with audio and associated noisy text collected from the online
platform Freesound improves the performance of our method. Furthermore, our
ablation study reveals that the proper choice of the loss function and
fine-tuning the pretrained models are essential in training a competitive
retrieval system.

This paper proposes a novel Transformer-based model for music score
infilling, to generate a music passage that fills in the gap between given past
and future contexts. While existing infilling approaches can generate a passage
that connects smoothly locally with the given contexts, they do not take into
account the musical form or structure of the music and may therefore generate
overly smooth results. To address this issue, we propose a structure-aware
conditioning approach that employs a novel attention-selecting module to supply
user-provided structure-related information to the Transformer for infilling.
With both objective and subjective evaluations, we show that the proposed model
can harness the structural information effectively and generate melodies in the
style of pop of higher quality than the two existing structure-agnostic
infilling models.

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm for
training neural policies to solve complex control tasks. However, these
policies tend to be overfit to the exact specifications of the task and
environment they were trained on, and thus do not perform well when conditions
deviate slightly or when composed hierarchically to solve even more complex
tasks. Recent work has shown that training a mixture of policies, as opposed to
a single one, that are driven to explore different regions of the state-action
space can address this shortcoming by generating a diverse set of behaviors,
referred to as skills, that can be collectively used to great effect in
adaptation tasks or for hierarchical planning. This is typically realized by
including a diversity term - often derived from information theory - in the
objective function optimized by RL. However these approaches often require
careful hyperparameter tuning to be effective. In this work, we demonstrate
that less widely-used neuroevolution methods, specifically Quality Diversity
(QD), are a competitive alternative to information-theory-augmented RL for
skill discovery. Through an extensive empirical evaluation comparing eight
state-of-the-art methods on the basis of (i) metrics directly evaluating the
skills' diversity, (ii) the skills' performance on adaptation tasks, and (iii)
the skills' performance when used as primitives for hierarchical planning; QD
methods are found to provide equal, and sometimes improved, performance whilst
being less sensitive to hyperparameters and more scalable. As no single method
is found to provide near-optimal performance across all environments, there is
a rich scope for further research which we support by proposing future
directions and providing optimized open-source implementations.

Despite the impressive progress of self-supervised learning (SSL), its
applicability to low-compute networks has received limited attention. Reported
performance has trailed behind standard supervised pre-training by a large
margin, barring self-supervised learning from making an impact on models that
are deployed on device. Most prior works attribute this poor performance to the
capacity bottleneck of the low-compute networks and opt to bypass the problem
through the use of knowledge distillation (KD). In this work, we revisit SSL
for efficient neural networks, taking a closer at what are the detrimental
factors causing the practical limitations, and whether they are intrinsic to
the self-supervised low-compute setting. We find that, contrary to accepted
knowledge, there is no intrinsic architectural bottleneck, we diagnose that the
performance bottleneck is related to the model complexity vs regularization
strength trade-off. In particular, we start by empirically observing that the
use of local views can have a dramatic impact on the effectiveness of the SSL
methods. This hints at view sampling being one of the performance bottlenecks
for SSL on low-capacity networks. We hypothesize that the view sampling
strategy for large neural networks, which requires matching views in very
diverse spatial scales and contexts, is too demanding for low-capacity
architectures. We systematize the design of the view sampling mechanism,
leading to a new training methodology that consistently improves the
performance across different SSL methods (e.g. MoCo-v2, SwAV, DINO), different
low-size networks (e.g. MobileNetV2, ResNet18, ResNet34, ViT-Ti), and different
tasks (linear probe, object detection, instance segmentation and
semi-supervised learning). Our best models establish a new state-of-the-art for
SSL methods on low-compute networks despite not using a KD loss term.

Unsupervised learning on 3D point clouds has undergone a rapid evolution,
especially thanks to data augmentation-based contrastive methods. However, data
augmentation is not ideal as it requires a careful selection of the type of
augmentations to perform, which in turn can affect the geometric and semantic
information learned by the network during self-training. To overcome this
issue, we propose an augmentation-free unsupervised approach for point clouds
to learn transferable point-level features via soft clustering, named SoftClu.
SoftClu assumes that the points belonging to a cluster should be close to each
other in both geometric and feature spaces. This differs from typical
contrastive learning, which builds similar representations for a whole point
cloud and its augmented versions. We exploit the affiliation of points to their
clusters as a proxy to enable self-training through a pseudo-label prediction
task. Under the constraint that these pseudo-labels induce the equipartition of
the point cloud, we cast SoftClu as an optimal transport problem. We formulate
an unsupervised loss to minimize the standard cross-entropy between
pseudo-labels and predicted labels. Experiments on downstream applications,
such as 3D object classification, part segmentation, and semantic segmentation,
show the effectiveness of our framework in outperforming state-of-the-art

The main goal of Few-Shot learning algorithms is to enable learning from
small amounts of data. One of the most popular and elegant Few-Shot learning
approaches is Model-Agnostic Meta-Learning (MAML). The main idea behind this
method is to learn shared universal weights of a meta-model, which then are
adapted for specific tasks. However, due to limited data size, the method
suffers from over-fitting and poorly quantifies uncertainty. Bayesian
approaches could, in principle, alleviate these shortcomings by learning weight
distributions in place of point-wise weights. Unfortunately, previous Bayesian
modifications of MAML are limited in a way similar to the classic MAML, e.g.,
task-specific adaptations must share the same structure and can not diverge
much from the universal meta-model. Additionally, task-specific distributions
are considered as posteriors to the universal distributions working as priors,
and optimizing them jointly with gradients is hard and poses a risk of getting
stuck in local optima.
  In this paper, we propose BayesianHyperShot, a novel generalization of
Bayesian MAML, which employs Bayesian principles along with Hypernetworks for
MAML. We achieve better convergence than the previous methods by classically
learning universal weights. Furthermore, Bayesian treatment of the specific
tasks enables uncertainty quantification, and high flexibility of task
adaptations is achieved using Hypernetworks instead of gradient-based updates.
Consequently, the proposed approach not only improves over the previous
methods, both classic and Bayesian MAML in several standard Few-Shot learning
benchmarks but also benefits from the properties of the Bayesian framework.

In recent years, a large number of XAI (eXplainable Artificial Intelligence)
solutions have been proposed to explain existing ML (Machine Learning) models
or to create interpretable ML models. Evaluation measures have recently been
proposed and it is now possible to compare these XAI solutions. However,
selecting the most relevant XAI solution among all this diversity is still a
tedious task, especially when meeting specific needs and constraints. In this
paper, we propose AutoXAI, a framework that recommends the best XAI solution
and its hyperparameters according to specific XAI evaluation metrics while
considering the user's context (dataset, ML model, XAI needs and constraints).
It adapts approaches from context-aware recommender systems and strategies of
optimization and evaluation from AutoML (Automated Machine Learning). We apply
AutoXAI to two use cases, and show that it recommends XAI solutions adapted to
the user's needs with the best hyperparameters matching the user's constraints.

High-accuracy per-pixel depth is vital for computational photography, so
smartphones now have multimodal camera systems with time-of-flight (ToF) depth
sensors and multiple color cameras. However, producing accurate high-resolution
depth is still challenging due to the low resolution and limited active
illumination power of ToF sensors. Fusing RGB stereo and ToF information is a
promising direction to overcome these issues, but a key problem remains: to
provide high-quality 2D RGB images, the main color sensor's lens is optically
stabilized, resulting in an unknown pose for the floating lens that breaks the
geometric relationships between the multimodal image sensors. Leveraging ToF
depth estimates and a wide-angle RGB camera, we design an automatic calibration
technique based on dense 2D/3D matching that can estimate camera extrinsic,
intrinsic, and distortion parameters of a stabilized main RGB sensor from a
single snapshot. This lets us fuse stereo and ToF cues via a correlation
volume. For fusion, we apply deep learning via a real-world training dataset
with depth supervision estimated by a neural reconstruction method. For
evaluation, we acquire a test dataset using a commercial high-power depth
camera and show that our approach achieves higher accuracy than existing

Paging is a prototypical problem in the area of online algorithms. It has
also played a central role in the development of learning-augmented algorithms
-- a recent line of research that aims to ameliorate the shortcomings of
classical worst-case analysis by giving algorithms access to predictions. Such
predictions can typically be generated using a machine learning approach, but
they are inherently imperfect. Previous work on learning-augmented paging has
investigated predictions on (i) when the current page will be requested again
(reoccurrence predictions), (ii) the current state of the cache in an optimal
algorithm (state predictions), (iii) all requests until the current page gets
requested again, and (iv) the relative order in which pages are requested.
  We study learning-augmented paging from the new perspective of requiring the
least possible amount of predicted information. More specifically, the
predictions obtained alongside each page request are limited to one bit only.
We consider two natural such setups: (i) discard predictions, in which the
predicted bit denotes whether or not it is ``safe'' to evict this page, and
(ii) phase predictions, where the bit denotes whether the current page will be
requested in the next phase (for an appropriate partitioning of the input into
phases). We develop algorithms for each of the two setups that satisfy all
three desirable properties of learning-augmented algorithms -- that is, they
are consistent, robust and smooth -- despite being limited to a one-bit
prediction per request. We also present lower bounds establishing that our
algorithms are essentially best possible.

Grasping the commonsense properties of everyday concepts is an important
prerequisite to language understanding. While contextualised language models
are reportedly capable of predicting such commonsense properties with
human-level accuracy, we argue that such results have been inflated because of
the high similarity between training and test concepts. This means that models
which capture concept similarity can perform well, even if they do not capture
any knowledge of the commonsense properties themselves. In settings where there
is no overlap between the properties that are considered during training and
testing, we find that the empirical performance of standard language models
drops dramatically. To address this, we study the possibility of fine-tuning
language models to explicitly model concepts and their properties. In
particular, we train separate concept and property encoders on two types of
readily available data: extracted hyponym-hypernym pairs and generic sentences.
Our experimental results show that the resulting encoders allow us to predict
commonsense properties with much higher accuracy than is possible by directly
fine-tuning language models. We also present experimental results for the
related task of unsupervised hypernym discovery.

Although virtue ethics has repeatedly been proposed as a suitable framework
for the development of artificial moral agents (AMAs), it has been proven
difficult to approach from a computational perspective. In this work, we
present the first technical implementation of artificial virtuous agents (AVAs)
in moral simulations. First, we review previous conceptual and technical work
in artificial virtue ethics and describe a functionalistic path to AVAs based
on dispositional virtues, bottom-up learning, and top-down eudaimonic reward.
We then provide the details of a technical implementation in a moral simulation
based on a tragedy of the commons scenario. The experimental results show how
the AVAs learn to tackle cooperation problems while exhibiting core features of
their theoretical counterpart, including moral character, dispositional
virtues, learning from experience, and the pursuit of eudaimonia. Ultimately,
we argue that virtue ethics provides a compelling path toward morally excellent
machines and that our work provides an important starting point for such
endeavors.

Logical rules, both transferable and explainable, are widely used as weakly
supervised signals for many downstream tasks such as named entity tagging. To
reduce the human effort of writing rules, previous researchers adopt an
iterative approach to automatically learn logical rules from several seed
rules. However, obtaining more seed rules can only be accomplished by extra
human annotation with heavy costs. Limited by the size and quality of the seed
rules, the model performance of previous systems is bounded. In this paper, we
develop a novel framework STREAM to distill task-specific logical rules from
large pre-trained models. Specifically, we borrow recent prompt-based language
models as the knowledge expert to yield initial seed rules, and based on the
formed high-quality instance pool that acts as an intermediary role, we keep
teaching the expert to fit our task and learning task-specific logical rules.
Experiments on three public named entity tagging benchmarks demonstrate the
effectiveness of our proposed framework. With several predefined prompt
templates, our system has gained significant improvements over previous

GBSVM (Granular-ball Support Vector Machine) is an important attempt to use
the coarse granularity of a granular-ball as the input to construct a
classifier instead of a data point. It is the first classifier whose input
contains no points, i.e., $x_i$, in the history of machine learning. However,
on the one hand, its dual model is not derived, and the algorithm has not been
implemented and can not be applied. On the other hand, there are some errors in
its existing model. To address these problems, this paper has fixed the errors
of the original model of GBSVM, and derived its dual model. Furthermore, an
algorithm is designed using particle swarm optimization algorithm to solve the
dual model. The experimental results on the UCI benchmark datasets demonstrate
that GBSVM has good robustness and efficiency.

Accurate segmentation of mandibular canals in lower jaws is important in
dental implantology. Medical experts determine the implant position and
dimensions manually from 3D CT images to avoid damaging the mandibular nerve
inside the canal. In this paper, we propose a novel dual-stage deep
learning-based scheme for the automatic segmentation of the mandibular canal.
Particularly, we first enhance the CBCT scans by employing the novel
histogram-based dynamic windowing scheme, which improves the visibility of
mandibular canals. After enhancement, we design 3D deeply supervised attention
U-Net architecture for localizing the volumes of interest (VOIs), which contain
the mandibular canals (i.e., left and right canals). Finally, we employed the
multi-scale input residual U-Net architecture (MS-R-UNet) to segment the
mandibular canals using VOIs accurately. The proposed method has been
rigorously evaluated on 500 scans. The results demonstrate that our technique
outperforms the current state-of-the-art segmentation performance and
robustness methods.

While the ability to build quantum computers is improving dramatically,
developing quantum algorithms is limited and relies on human insight and
ingenuity. Although a number of quantum programming languages have been
developed, it is challenging for software developers who are not familiar with
quantum computing to learn and use these languages. It is, therefore, necessary
to develop tools to support developing new quantum algorithms and programs
automatically. This paper proposes AutoQC, an approach to automatically
synthesizing quantum circuits using the neural network from input and output
pairs. We consider a quantum circuit a sequence of quantum gates and synthesize
a quantum circuit probabilistically by prioritizing with a neural network at
each step. The experimental results highlight the ability of AutoQC to
synthesize some essential quantum circuits at a lower cost.

Optimization problem, which is aimed at finding the global minimal value of a
given cost function, is one of the central problem in science and engineering.
Various numerical methods have been proposed to solve this problem, among which
the Gradient Descent (GD) method is the most popular one due to its simplicity
and efficiency. However, the GD method suffers from two main issues: the local
minima and the slow convergence especially near the minima point. The Natural
Gradient Descent(NGD), which has been proved as one of the most powerful method
for various optimization problems in machine learning, tensor network,
variational quantum algorithms and so on, supplies an efficient way to
accelerate the convergence. Here, we give a unified method to extend the NGD
method to a more general situation which keeps the fast convergence by looking
for a more suitable metric through introducing a 'proper' reference Riemannian
manifold. Our method generalizes the NDG, and may give more insight of the
optimization methods.

Evaluating the safety of an autonomous vehicle (AV) depends on the behavior
of surrounding agents which can be heavily influenced by factors such as
environmental context and informally-defined driving etiquette. A key challenge
is in determining a minimum set of assumptions on what constitutes reasonable
foreseeable behaviors of other road users for the development of AV safety
models and techniques. In this paper, we propose a data-driven AV safety design
methodology that first learns ``reasonable'' behavioral assumptions from data,
and then synthesizes an AV safety concept using these learned behavioral
assumptions. We borrow techniques from control theory, namely high order
control barrier functions and Hamilton-Jacobi reachability, to provide
inductive bias to aid interpretability, verifiability, and tractability of our
approach. In our experiments, we learn an AV safety concept using
demonstrations collected from a highway traffic-weaving scenario, compare our
learned concept to existing baselines, and showcase its efficacy in evaluating
real-world driving logs.

Most of the smart applications, such as smart energy metering devices, demand
strong privacy preservation to strengthen data privacy. However, it is
difficult to protect the privacy of the smart device data, especially on the
client side. It is mainly because payment for billing is computed by the server
deployed at the client's side, and it is highly challenging to prevent the
leakage of client's information to unauthorised users. Various researchers have
discussed this problem and have proposed different privacy preservation
techniques. Conventional techniques suffer from the problem of high
computational and communication overload on the client side. In addition, the
performance of these techniques deteriorates due to computational complexity
and their inability to handle the security of large-scale data. Due to these
limitations, it becomes easy for the attackers to introduce malicious attacks
on the server, posing a significant threat to data security. In this context,
this proposal intends to design novel privacy preservation and secure billing
framework using deep learning techniques to ensure data security in smart
energy metering devices. This research aims to overcome the limitations of the
existing techniques to achieve robust privacy preservation in smart devices and
increase the cyber resilience of these devices.

Medical imaging quantitative features had once disputable usefulness in
clinical studies. Nowadays, advancements in analysis techniques, for instance
through machine learning, have enabled quantitative features to be
progressively useful in diagnosis and research. Tissue characterisation is
improved via the 'radiomics' features, whose extraction can be automated.
Despite the advances, stability of quantitative features remains an important
open problem. As features can be highly sensitive to variations of acquisition
details, it is not trivial to quantify stability and efficiently select stable
features. In this work, we develop and validate a Computed Tomography (CT)
simulator environment based on the publicly available ASTRA toolbox
(www.astra-toolbox.com). We show that the variability, stability and
discriminative power of the radiomics features extracted from the virtual
phantom images generated by the simulator are similar to those observed in a
tandem phantom study. Additionally, we show that the variability is matched
between a multi-center phantom study and simulated results. Consequently, we
demonstrate that the simulator can be utilised to assess radiomics features'
stability and discriminative power.

Surface reconstruction for point clouds is an important task in 3D computer
vision. Most of the latest methods resolve this problem by learning signed
distance functions (SDF) from point clouds, which are limited to reconstructing
shapes or scenes with closed surfaces. Some other methods tried to represent
shapes or scenes with open surfaces using unsigned distance functions (UDF)
which are learned from large scale ground truth unsigned distances. However,
the learned UDF is hard to provide smooth distance fields near the surface due
to the noncontinuous character of point clouds. In this paper, we propose a
novel method to learn consistency-aware unsigned distance functions directly
from raw point clouds. We achieve this by learning to move 3D queries to reach
the surface with a field consistency constraint, where we also enable to
progressively estimate a more accurate surface. Specifically, we train a neural
network to gradually infer the relationship between 3D queries and the
approximated surface by searching for the moving target of queries in a dynamic
way, which results in a consistent field around the surface. Meanwhile, we
introduce a polygonization algorithm to extract surfaces directly from the
gradient field of the learned UDF. The experimental results in surface
reconstruction for synthetic and real scan data show significant improvements
over the state-of-the-art under the widely used benchmarks.

The process of robot design is a complex task and the majority of design
decisions are still based on human intuition or tedious manual tuning. A more
informed way of facing this task is computational design methods where design
parameters are concurrently optimized with corresponding controllers. Existing
approaches, however, are strongly influenced by predefined control rules or
motion templates and cannot provide end-to-end solutions. In this paper, we
present a design optimization framework using model-free meta reinforcement
learning, and its application to the optimizing kinematics and actuator
parameters of quadrupedal robots. We use meta reinforcement learning to train a
locomotion policy that can quickly adapt to different designs. This policy is
used to evaluate each design instance during the design optimization. We
demonstrate that the policy can control robots of different designs to track
random velocity commands over various rough terrains. With controlled
experiments, we show that the meta policy achieves close-to-optimal performance
for each design instance after adaptation. Lastly, we compare our results
against a model-based baseline and show that our approach allows higher
performance while not being constrained by predefined motions or gait patterns.

Low-thrust trajectory design and in-flight control remain two of the most
challenging topics for new-generation satellite operations. Most of the
solutions currently implemented are based on reference trajectories and lead to
sub-optimal fuel usage. Other solutions are based on simple guidance laws that
need to be updated periodically, increasing the cost of operations. Whereas
some optimization strategies leverage Artificial Intelligence methods, all of
the approaches studied so far need either previously generated data or a strong
a priori knowledge of the satellite dynamics. This study uses model-free
Reinforcement Learning to train an agent on a constrained pericenter raising
scenario for a low-thrust medium-Earth-orbit satellite. The agent does not have
any prior knowledge of the environment dynamics, which makes it unbiased from
classical trajectory optimization patterns. The trained agent is then used to
design a trajectory and to autonomously control the satellite during the
cruise. Simulations show that a dynamics-agnostic agent is able to learn a
quasi-optimal guidance law and responds well to uncertainties in the
environment dynamics. The results obtained open the door to the usage of
Reinforcement Learning on more complex scenarios, multi-satellite problems, or
to explore trajectories in environments where a reference solution is not known

Convolutional neural networks (CNNs) have achieved superhuman performance in
multiple vision tasks, especially image classification. However, unlike humans,
CNNs leverage spurious features, such as background information to make
decisions. This tendency creates different problems in terms of robustness or
weak generalization performance. Through our work, we introduce a contrastive
learning-based approach (CLAD) to mitigate the background bias in CNNs. CLAD
encourages semantic focus on object foregrounds and penalizes learning features
from irrelavant backgrounds. Our method also introduces an efficient way of
sampling negative samples. We achieve state-of-the-art results on the
Background Challenge dataset, outperforming the previous benchmark with a
margin of 4.1\%. Our paper shows how CLAD serves as a proof of concept for
debiasing of spurious features, such as background and texture (in
supplementary material).

We introduce a new paradigm for generative modeling built on Continuous
Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale.
Specifically, we present the notion of Flow Matching (FM), a simulation-free
approach for training CNFs based on regressing vector fields of fixed
conditional probability paths. Flow Matching is compatible with a general
family of Gaussian probability paths for transforming between noise and data
samples -- which subsumes existing diffusion paths as specific instances.
Interestingly, we find that employing FM with diffusion paths results in a more
robust and stable alternative for training diffusion models. Furthermore, Flow
Matching opens the door to training CNFs with other, non-diffusion probability
paths. An instance of particular interest is using Optimal Transport (OT)
displacement interpolation to define the conditional probability paths. These
paths are more efficient than diffusion paths, provide faster training and
sampling, and result in better generalization. Training CNFs using Flow
Matching on ImageNet leads to state-of-the-art performance in terms of both
likelihood and sample quality, and allows fast and reliable sample generation
using off-the-shelf numerical ODE solvers.

The recent integration of generative neural strategies and audio processing
techniques have fostered the widespread of synthetic speech synthesis or
transformation algorithms. This capability proves to be harmful in many legal
and informative processes (news, biometric authentication, audio evidence in
courts, etc.). Thus, the development of efficient detection algorithms is both
crucial and challenging due to the heterogeneity of forgery techniques.
  This work investigates the discriminative role of silenced parts in synthetic
speech detection and shows how first digit statistics extracted from MFCC
coefficients can efficiently enable a robust detection. The proposed procedure
is computationally-lightweight and effective on many different algorithms since
it does not rely on large neural detection architecture and obtains an accuracy
above 90\% in most of the classes of the ASVSpoof dataset.

Synchronous local stochastic gradient descent (local SGD) suffers from some
workers being idle and random delays due to slow and straggling workers, as it
waits for the workers to complete the same amount of local updates. In this
paper, to mitigate stragglers and improve communication efficiency, a novel
local SGD strategy, named STSyn, is developed. The key point is to wait for the
$K$ fastest workers, while keeping all the workers computing continually at
each synchronization round, and making full use of any effective (completed)
local update of each worker regardless of stragglers. An analysis of the
average wall-clock time, average number of local updates and average number of
uploading workers per round is provided to gauge the performance of STSyn. The
convergence of STSyn is also rigorously established even when the objective
function is nonconvex. Experimental results show the superiority of the
proposed STSyn against state-of-the-art schemes through utilization of the
straggler-tolerant technique and additional effective local updates at each
worker, and the influence of system parameters is studied. By waiting for
faster workers and allowing heterogeneous synchronization with different
numbers of local updates across workers, STSyn provides substantial
improvements both in time and communication efficiency.

As an important part of intelligent transportation systems, traffic
forecasting has attracted tremendous attention from academia and industry.
Despite a lot of methods being proposed for traffic forecasting, it is still
difficult to model complex spatial-temporal dependency. Temporal dependency
includes short-term dependency and long-term dependency, and the latter is
often overlooked. Spatial dependency can be divided into two parts:
distance-based spatial dependency and hidden spatial dependency. To model
complex spatial-temporal dependency, we propose a novel framework for traffic
forecasting, named Spatial-Temporal Graph Convolutional Gated Recurrent Network
(STGCGRN). We design an attention module to capture long-term dependency by
mining periodic information in traffic data. We propose a Double Graph
Convolution Gated Recurrent Unit (DGCGRU) to capture spatial dependency, which
integrates graph convolutional network and GRU. The graph convolution part
models distance-based spatial dependency with the distance-based predefined
adjacency matrix and hidden spatial dependency with the self-adaptive adjacency
matrix, respectively. Specially, we employ the multi-head mechanism to capture
multiple hidden dependencies. In addition, the periodic pattern of each
prediction node may be different, which is often ignored, resulting in mutual
interference of periodic information among nodes when modeling spatial
dependency. For this, we explore the architecture of model and improve the
performance. Experiments on four datasets demonstrate the superior performance
of our model.

With the increasingly strengthened data privacy act and the difficult data
centralization, Federated Learning (FL) has become an effective solution to
collaboratively train the model while preserving each client's privacy. FedAvg
is a standard aggregation algorithm that makes the proportion of dataset size
of each client as aggregation weight. However, it can't deal with
non-independent and identically distributed (non-i.i.d) data well because of
its fixed aggregation weights and the neglect of data distribution. In this
paper, we propose an aggregation strategy that can effectively deal with
non-i.i.d dataset, namely FedGraph, which can adjust the aggregation weights
adaptively according to the training condition of local models in whole
training process. The FedGraph takes three factors into account from coarse to
fine: the proportion of each local dataset size, the topology factor of model
graphs, and the model weights. We calculate the gravitational force between
local models by transforming the local models into topology graphs. The
FedGraph can explore the internal correlation between local models better
through the weighted combination of the proportion each local dataset, topology
structure, and model weights. The proposed FedGraph has been applied to the
MICCAI Federated Tumor Segmentation Challenge 2021 (FeTS) datasets, and the
validation results show that our method surpasses the previous state-of-the-art
by 2.76 mean Dice Similarity Score. The source code will be available at
Github.

For training a few-shot keyword spotting (FS-KWS) model, a large labeled
dataset containing massive target keywords has known to be essential to
generalize to arbitrary target keywords with only a few enrollment samples. To
alleviate the expensive data collection with labeling, in this paper, we
propose a novel FS-KWS system trained only on synthetic data. The proposed
system is based on metric learning enabling target keywords to be detected
using distance metrics. Exploiting the speech synthesis model that generates
speech with pseudo phonemes instead of texts, we easily obtain a large
collection of multi-view samples with the same semantics. These samples are
sufficient for training, considering metric learning does not intrinsically
necessitate labeled data. All of the components in our framework do not require
any supervision, making our method unsupervised. Experimental results on real
datasets show our proposed method is competitive even without any labeled and
real datasets.

Pitch scaling algorithms have a significant impact on the security of
Automatic Speaker Verification (ASV) systems. Although numerous anti-spoofing
algorithms have been proposed to identify the pitch-shifted voice and even
restore it to the original version, they either have poor performance or
require the original voice as a reference, limiting the prospects of
applications. In this paper, we propose a no-reference approach termed
PSVRF$^1$ for high-quality restoration of pitch-shifted voice. Experiments on
AISHELL-1 and AISHELL-3 demonstrate that PSVRF can restore the voice disguised
by various pitch-scaling techniques, which obviously enhances the robustness of
ASV systems to pitch-scaling attacks. Furthermore, the performance of PSVRF
even surpasses that of the state-of-the-art reference-based approach.

Developing neural architectures that are capable of logical reasoning has
become increasingly important for a wide range of applications (e.g., natural
language processing). Towards this grand objective, we propose a symbolic
reasoning architecture that chains many join operators together to model output
logical expressions. In particular, we demonstrate that such an ensemble of
join-chains can express a broad subset of ''tree-structured'' first-order
logical expressions, named FOET, which is particularly useful for modeling
natural languages. To endow it with differentiable learning capability, we
closely examine various neural operators for approximating the symbolic
join-chains. Interestingly, we find that the widely used multi-head
self-attention module in transformer can be understood as a special neural
operator that implements the union bound of the join operator in probabilistic
predicate space. Our analysis not only provides a new perspective on the
mechanism of the pretrained models such as BERT for natural language
understanding but also suggests several important future improvement

Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to
synthesize training labels efficiently. The core component of PWS is the label
model, which infers true labels by aggregating the outputs of multiple noisy
supervision sources abstracted as labeling functions (LFs). Existing
statistical label models typically rely only on the outputs of LF, ignoring the
instance features when modeling the underlying generative process. In this
paper, we attempt to incorporate the instance features into a statistical label
model via the proposed FABLE. In particular, it is built on a mixture of
Bayesian label models, each corresponding to a global pattern of correlation,
and the coefficients of the mixture components are predicted by a Gaussian
Process classifier based on instance features. We adopt an auxiliary
variable-based variational inference algorithm to tackle the non-conjugate
issue between the Gaussian Process and Bayesian label models. Extensive
empirical comparison on eleven benchmark datasets sees FABLE achieving the
highest averaged performance across nine baselines.

Gradient regularization (GR) is a method that penalizes the gradient norm of
the training loss during training. Although some studies have reported that GR
improves generalization performance in deep learning, little attention has been
paid to it from the algorithmic perspective, that is, the algorithms of GR that
efficiently improve performance. In this study, we first reveal that a specific
finite-difference computation, composed of both gradient ascent and descent
steps, reduces the computational cost for GR. In addition, this computation
empirically achieves better generalization performance. Next, we theoretically
analyze a solvable model, a diagonal linear network, and clarify that GR has a
desirable implicit bias in a certain problem. In particular, learning with the
finite-difference GR chooses better minima as the ascent step size becomes
larger. Finally, we demonstrate that finite-difference GR is closely related to
some other algorithms based on iterative ascent and descent steps for exploring
flat minima: sharpness-aware minimization and the flooding method. We reveal
that flooding performs finite-difference GR in an implicit way. Thus, this work
broadens our understanding of GR in both practice and theory.

Continuous diagnosis and prognosis are essential for intensive care patients.
It can provide more opportunities for timely treatment and rational resource
allocation, especially for sepsis, a main cause of death in ICU, and COVID-19,
a new worldwide epidemic. Although deep learning methods have shown their great
superiority in many medical tasks, they tend to catastrophically forget, over
fit, and get results too late when performing diagnosis and prognosis in the
continuous mode. In this work, we summarized the three requirements of this
task, proposed a new concept, continuous classification of time series (CCTS),
and designed a novel model training method, restricted update strategy of
neural networks (RU). In the context of continuous prognosis, our method
outperformed all baselines and achieved the average accuracy of 90%, 97%, and
85% on sepsis prognosis, COVID-19 mortality prediction, and eight diseases
classification. Superiorly, our method can also endow deep learning with
interpretability, having the potential to explore disease mechanisms and
provide a new horizon for medical research. We have achieved disease staging
for sepsis and COVID-19, discovering four stages and three stages with their
typical biomarkers respectively. Further, our method is a data-agnostic and
model-agnostic plug-in, it can be used to continuously prognose other diseases
with staging and even implement CCTS in other fields.

We design general-purpose algorithms for addressing fairness issues and mode
collapse in generative modeling. More precisely, to design fair algorithms for
as many sensitive variables as possible, including variables we might not be
aware of, we assume no prior knowledge of sensitive variables: our algorithms
use unsupervised fairness only, meaning no information related to the sensitive
variables is used for our fairness-improving methods. All images of faces (even
generated ones) have been removed to mitigate legal risks.

Consider the task of learning a hypothesis class $\mathcal{H}$ in the
presence of an adversary that can replace up to an $\eta$ fraction of the
examples in the training set with arbitrary adversarial examples. The adversary
aims to fail the learner on a particular target test point $x$ which is known
to the adversary but not to the learner. In this work we aim to characterize
the smallest achievable error $\epsilon=\epsilon(\eta)$ by the learner in the
presence of such an adversary in both realizable and agnostic settings. We
fully achieve this in the realizable setting, proving that
$\epsilon=\Theta(\mathtt{VC}(\mathcal{H})\cdot \eta)$, where
$\mathtt{VC}(\mathcal{H})$ is the VC dimension of $\mathcal{H}$. Remarkably, we
show that the upper bound can be attained by a deterministic learner. In the
agnostic setting we reveal a more elaborate landscape: we devise a
deterministic learner with a multiplicative regret guarantee of $\epsilon \leq
C\cdot\mathtt{OPT} + O(\mathtt{VC}(\mathcal{H})\cdot \eta)$, where $C > 1$ is a
universal numerical constant. We complement this by showing that for any
deterministic learner there is an attack which worsens its error to at least
$2\cdot \mathtt{OPT}$. This implies that a multiplicative deterioration in the
regret is unavoidable in this case. Finally, the algorithms we develop for
achieving the optimal rates are inherently improper. Nevertheless, we show that
for a variety of natural concept classes, such as linear classifiers, it is
possible to retain the dependence $\epsilon=\Theta_{\mathcal{H}}(\eta)$ by a
proper algorithm in the realizable setting. Here $\Theta_{\mathcal{H}}$
conceals a polynomial dependence on $\mathtt{VC}(\mathcal{H})$.

Hyperbox-based machine learning algorithms are an important and popular
branch of machine learning in the construction of classifiers using fuzzy sets
and logic theory and neural network architectures. This type of learning is
characterised by many strong points of modern predictors such as a high
scalability, explainability, online adaptation, effective learning from a small
amount of data, native ability to deal with missing data and accommodating new
classes. Nevertheless, there is no comprehensive existing package for
hyperbox-based machine learning which can serve as a benchmark for research and
allow non-expert users to apply these algorithms easily. hyperbox-brain is an
open-source Python library implementing the leading hyperbox-based machine
learning algorithms. This library exposes a unified API which closely follows
and is compatible with the renowned scikit-learn and numpy toolboxes. The
library may be installed from Python Package Index (PyPI) and the conda package
manager and is distributed under the GPL-3 license. The source code,
documentation, detailed tutorials, and the full descriptions of the API are
available at https://uts-caslab.github.io/hyperbox-brain.

Answering complex questions that require multi-step multi-type reasoning over
raw text is challenging, especially when conducting numerical reasoning. Neural
Module Networks(NMNs), follow the programmer-interpreter framework and design
trainable modules to learn different reasoning skills. However, NMNs only have
limited reasoning abilities, and lack numerical reasoning capability. We
up-grade NMNs by: (a) bridging the gap between its interpreter and the complex
questions; (b) introducing addition and subtraction modules that perform
numerical reasoning over numbers. On a subset of DROP, experimental results
show that our proposed methods enhance NMNs' numerical reasoning skills by
17.7% improvement of F1 score and significantly outperform previous

We propose a generalized framework for block-structured nonconvex
optimization, which can be applied to structured subgraph detection in
interdependent networks, such as multi-layer networks, temporal networks,
networks of networks, and many others. Specifically, we design an effective,
efficient, and parallelizable projection algorithm, namely Graph
Block-structured Gradient Projection (GBGP), to optimize a general non-linear
function subject to graph-structured constraints. We prove that our algorithm:
1) runs in nearly-linear time on the network size; 2) enjoys a theoretical
approximation guarantee. Moreover, we demonstrate how our framework can be
applied to two very practical applications and conduct comprehensive
experiments to show the effectiveness and efficiency of our proposed algorithm.

Knowledge Tracing (KT), which aims to model student knowledge level and
predict their performance, is one of the most important applications of user
modeling. Modern KT approaches model and maintain an up-to-date state of
student knowledge over a set of course concepts according to students'
historical performance in attempting the problems. However, KT approaches were
designed to model knowledge by observing relatively small problem-solving steps
in Intelligent Tutoring Systems. While these approaches were applied
successfully to model student knowledge by observing student solutions for
simple problems, they do not perform well for modeling complex problem solving
in students.M ost importantly, current models assume that all problem attempts
are equally valuable in quantifying current student knowledge.However, for
complex problems that involve many concepts at the same time, this assumption
is deficient. In this paper, we argue that not all attempts are equivalently
important in discovering students' knowledge state, and some attempts can be
summarized together to better represent student performance. We propose a novel
student knowledge tracing approach, Granular RAnk based TEnsor factorization
(GRATE), that dynamically selects student attempts that can be aggregated while
predicting students' performance in problems and discovering the concepts
presented in them. Our experiments on three real-world datasets demonstrate the
improved performance of GRATE, compared to the state-of-the-art baselines, in
the task of student performance prediction. Our further analysis shows that
attempt aggregation eliminates the unnecessary fluctuations from students'
discovered knowledge states and helps in discovering complex latent concepts in
the problems.

We present a unified hard-constraint framework for solving geometrically
complex PDEs with neural networks, where the most commonly used Dirichlet,
Neumann, and Robin boundary conditions (BCs) are considered. Specifically, we
first introduce the "extra fields" from the mixed finite element method to
reformulate the PDEs so as to equivalently transform the three types of BCs
into linear forms. Based on the reformulation, we derive the general solutions
of the BCs analytically, which are employed to construct an ansatz that
automatically satisfies the BCs. With such a framework, we can train the neural
networks without adding extra loss terms and thus efficiently handle
geometrically complex PDEs, alleviating the unbalanced competition between the
loss terms corresponding to the BCs and PDEs. We theoretically demonstrate that
the "extra fields" can stabilize the training process. Experimental results on
real-world geometrically complex PDEs showcase the effectiveness of our method
compared with state-of-the-art baselines.

We explore the probabilistic partition of unity network (PPOU-Net) model in
the context of high-dimensional regression problems. With the PPOU-Nets, the
target function for any given input is approximated by a mixture of experts
model, where each cluster is associated with a fixed-degree polynomial. The
weights of the clusters are determined by a DNN that defines a partition of
unity. The weighted average of the polynomials approximates the target function
and produces uncertainty quantification naturally. Our training strategy
leverages automatic differentiation and the expectation maximization (EM)
algorithm. During the training, we (i) apply gradient descent to update the DNN
coefficients; (ii) update the polynomial coefficients using weighted
least-squares solves; and (iii) compute the variance of each cluster according
to a closed-form formula derived from the EM algorithm. The PPOU-Nets
consistently outperform the baseline fully-connected neural networks of
comparable sizes in numerical experiments of various data dimensions. We also
explore the proposed model in applications of quantum computing, where the
PPOU-Nets act as surrogate models for cost landscapes associated with
variational quantum circuits.

Despite great progress achieved by transformer in various vision tasks, it is
still underexplored for skeleton-based action recognition with only a few
attempts. Besides, these methods directly calculate the pair-wise global
self-attention equally for all the joints in both the spatial and temporal
dimensions, undervaluing the effect of discriminative local joints and the
short-range temporal dynamics. In this work, we propose a novel Focal and
Global Spatial-Temporal Transformer network (FG-STFormer), that is equipped
with two key components: (1) FG-SFormer: focal joints and global parts coupling
spatial transformer. It forces the network to focus on modelling correlations
for both the learned discriminative spatial joints and human body parts
respectively. The selective focal joints eliminate the negative effect of
non-informative ones during accumulating the correlations. Meanwhile, the
interactions between the focal joints and body parts are incorporated to
enhance the spatial dependencies via mutual cross-attention. (2) FG-TFormer:
focal and global temporal transformer. Dilated temporal convolution is
integrated into the global self-attention mechanism to explicitly capture the
local temporal motion patterns of joints or body parts, which is found to be
vital important to make temporal transformer work. Extensive experimental
results on three benchmarks, namely NTU-60, NTU-120 and NW-UCLA, show our
FG-STFormer surpasses all existing transformer-based methods, and compares
favourably with state-of-the art GCN-based methods.

The cryptocurrency market is highly volatile compared to traditional
financial markets. Hence, forecasting its volatility is crucial for risk
management. In this paper, we investigate CryptoQuant data (e.g. on-chain
analytics, exchange and miner data) and whale-alert tweets, and explore their
relationship to Bitcoin's next-day volatility, with a focus on extreme
volatility spikes. We propose a deep learning Synthesizer Transformer model for
forecasting volatility. Our results show that the model outperforms existing
state-of-the-art models when forecasting extreme volatility spikes for Bitcoin
using CryptoQuant data as well as whale-alert tweets. We analysed our model
with the Captum XAI library to investigate which features are most important.
We also backtested our prediction results with different baseline trading
strategies and the results show that we are able to minimize drawdown while
keeping steady profits. Our findings underscore that the proposed method is a
useful tool for forecasting extreme volatility movements in the Bitcoin market.

Existing pipelines of semantic correspondence commonly include extracting
high-level semantic features for the invariance against intra-class variations
and background clutters. This architecture, however, inevitably results in a
low-resolution matching field that additionally requires an ad-hoc
interpolation process as a post-processing for converting it into a
high-resolution one, certainly limiting the overall performance of matching
results. To overcome this, inspired by recent success of implicit neural
representation, we present a novel method for semantic correspondence, called
Neural Matching Field (NeMF). However, complicacy and high-dimensionality of a
4D matching field are the major hindrances, which we propose a cost embedding
network to process a coarse cost volume to use as a guidance for establishing
high-precision matching field through the following fully-connected network.
Nevertheless, learning a high-dimensional matching field remains challenging
mainly due to computational complexity, since a naive exhaustive inference
would require querying from all pixels in the 4D space to infer pixel-wise
correspondences. To overcome this, we propose adequate training and inference
procedures, which in the training phase, we randomly sample matching candidates
and in the inference phase, we iteratively performs PatchMatch-based inference
and coordinate optimization at test time. With these combined, competitive
results are attained on several standard benchmarks for semantic
correspondence. Code and pre-trained weights are available at
https://ku-cvlab.github.io/NeMF/.

The advancement in wireless communication technologies is becoming more
demanding and pervasive. One of the fundamental parameters that limit the
efficiency of the network are the security challenges. The communication
network is vulnerable to security attacks such as spoofing attacks and signal
strength attacks. Intrusion detection signifies a central approach to ensuring
the security of the communication network. In this paper, an Intrusion
Detection System based on the framework of graph theory is proposed. A
Layerwise Graph Theory-Based Intrusion Detection System (LGTBIDS) algorithm is
designed to detect the attacked node. The algorithm performs the layer-wise
analysis to extract the vulnerable nodes and ultimately the attacked node(s).
For each layer, every node is scanned for the possibility of susceptible
node(s). The strategy of the IDS is based on the analysis of energy efficiency
and secrecy rate. The nodes with the energy efficiency and secrecy rate beyond
the range of upper and lower thresholds are detected as the nodes under attack.
Further, detected node(s) are transmitted with a random sequence of bits
followed by the process of re-authentication. The obtained results validate the
better performance, low time computations, and low complexity. Finally, the
proposed approach is compared with the conventional solution of intrusion

Recent 3D-based manipulation methods either directly predict the grasp pose
using 3D neural networks, or solve the grasp pose using similar objects
retrieved from shape databases. However, the former faces generalizability
challenges when testing with new robot arms or unseen objects; and the latter
assumes that similar objects exist in the databases. We hypothesize that recent
3D modeling methods provides a path towards building digital replica of the
evaluation scene that affords physical simulation and supports robust
manipulation algorithm learning. We propose to reconstruct high-quality meshes
from real-world point clouds using state-of-the-art neural surface
reconstruction method (the Real2Sim step). Because most simulators take meshes
for fast simulation, the reconstructed meshes enable grasp pose labels
generation without human efforts. The generated labels can train grasp network
that performs robustly in the real evaluation scene (the Sim2Real step). In
synthetic and real experiments, we show that the Real2Sim2Real pipeline
performs better than baseline grasp networks trained with a large dataset and a
grasp sampling method with retrieval-based reconstruction. The benefit of the
Real2Sim2Real pipeline comes from 1) decoupling scene modeling and grasp
sampling into sub-problems, and 2) both sub-problems can be solved with
sufficiently high quality using recent 3D learning algorithms and mesh-based
physical simulation techniques.

The activity of the grid cell population in the medial entorhinal cortex
(MEC) of the mammalian brain forms a vector representation of the self-position
of the animal. Recurrent neural networks have been proposed to explain the
properties of the grid cells by updating the neural activity vector based on
the velocity input of the animal. In doing so, the grid cell system effectively
performs path integration. In this paper, we investigate the algebraic,
geometric, and topological properties of grid cells using recurrent network
models. Algebraically, we study the Lie group and Lie algebra of the recurrent
transformation as a representation of self-motion. Geometrically, we study the
conformal isometry of the Lie group representation where the local displacement
of the activity vector in the neural space is proportional to the local
displacement of the agent in the 2D physical space. Topologically, the compact
abelian Lie group representation automatically leads to the torus topology
commonly assumed and observed in neuroscience. We then focus on a simple
non-linear recurrent model that underlies the continuous attractor neural
networks of grid cells. Our numerical experiments show that conformal isometry
leads to hexagon periodic patterns in the grid cell responses and our model is
capable of accurate path integration. Code is available at
\url{https://github.com/DehongXu/grid-cell-rnn}.

In this modern technological era, categorization and ranking of research
journals is gaining popularity among researchers and scientists. It plays a
significant role for publication of their research findings in a quality
journal. Although, many research works exist on journal categorization and
ranking, however, there is a lack of research works to categorize and predict
the journals using suitable machine learning techniques. This work aims to
categorize and predict various academic research journals. This work suggests a
hybrid predictive model comprising of five steps. The first step is to prepare
the dataset with twenty features. The second step is to pre-process the
dataset. The third step is to apply an appropriate clustering algorithm for
categorization. The fourth step is to apply appropriate feature selection
techniques to get an effective subset of features. The fifth step involves some
ensemble plus non ensemble methods to train the model. The model is trained on
a full set of features, and a selected subset of features is obtained by
applying three feature selection techniques. After model training, the
prediction results are evaluated in terms of precision, recall, and accuracy.
The results can help the researchers and the practitioners in predicting the
journal category.

Federated learning (FL) strives to enable collaborative training of machine
learning models without centrally collecting clients' private data. Different
from centralized training, the local datasets across clients in FL are
non-independent and identically distributed (non-IID). In addition, the
data-owning clients may drop out of the training process arbitrarily. These
characteristics will significantly degrade the training performance. This paper
proposes a Dropout-Resilient Secure Federated Learning (DReS-FL) framework
based on Lagrange coded computing (LCC) to tackle both the non-IID and dropout
problems. The key idea is to utilize Lagrange coding to secretly share the
private datasets among clients so that each client receives an encoded version
of the global dataset, and the local gradient computation over this dataset is
unbiased. To correctly decode the gradient at the server, the gradient function
has to be a polynomial in a finite field, and thus we construct polynomial
integer neural networks (PINNs) to enable our framework. Theoretical analysis
shows that DReS-FL is resilient to client dropouts and provides privacy
protection for the local datasets. Furthermore, we experimentally demonstrate
that DReS-FL consistently leads to significant performance gains over baseline
methods.

Network security has become the biggest concern in the area of cyber security
because of the exponential growth in computer networks and applications.
Intrusion detection plays an important role in the security of information
systems or networks devices. The purpose of an intrusion detection system (IDS)
is to detect malicious activities and then generate an alarm against these
activities. Having a large amount of data is one of the key problems in
detecting attacks. Most of the intrusion detection systems use all features of
datasets to evaluate the models and result in is, low detection rate, high
computational time and uses of many computer resources. For fast attacks
detection IDS needs a lightweight data. A feature selection method plays a key
role to select best features to achieve maximum accuracy. This research work
conduct experiments by considering on two updated attacks datasets, UNSW-NB15
and CICDDoS2019. This work suggests a wrapper based Genetic Algorithm (GA)
features selection method with ensemble classifiers. GA select the best feature
subsets and achieve high accuracy, detection rate (DR) and low false alarm rate
(FAR) compared to existing approaches. This research focuses on multi-class
classification. Implements two ensemble methods: stacking and bagging to detect
different types of attacks. The results show that GA improve the accuracy
significantly with stacking ensemble classifier.

Uncertainty estimation is essential to make neural networks trustworthy in
real-world applications. Extensive research efforts have been made to quantify
and reduce predictive uncertainty. However, most existing works are designed
for unimodal data, whereas multi-view uncertainty estimation has not been
sufficiently investigated. Therefore, we propose a new multi-view
classification framework for better uncertainty estimation and out-of-domain
sample detection, where we associate each view with an uncertainty-aware
classifier and combine the predictions of all the views in a principled way.
The experimental results with real-world datasets demonstrate that our proposed
approach is an accurate, reliable, and well-calibrated classifier, which
predominantly outperforms the multi-view baselines tested in terms of expected
calibration error, robustness to noise, and accuracy for the in-domain sample
classification and the out-of-domain sample detection tasks.

With 84.75 million Filipinos online, the ability for models to process online
text is crucial for developing Filipino NLP applications. To this end, spelling
correction is a crucial preprocessing step for downstream processing. However,
the lack of data prevents the use of language models for this task. In this
paper, we propose an N-Gram + Damerau Levenshtein distance model with automatic
rule extraction. We train the model on 300 samples, and show that despite
limited training data, it achieves good performance and outperforms other deep
learning approaches in terms of accuracy and edit distance. Moreover, the model
(1) requires little compute power, (2) trains in little time, thus allowing for
retraining, and (3) is easily interpretable, allowing for direct
troubleshooting, highlighting the success of traditional approaches over more
complex deep learning models in settings where data is unavailable.

One-class classification is a fundamental problem in pattern recognition with
a wide range of applications. This work presents a semi-supervised quantum
machine learning algorithm for such a problem, which we call a variational
quantum one-class classifier (VQOCC). The algorithm is suitable for noisy
intermediate-scale quantum computing because the VQOCC trains a
fully-parameterized quantum autoencoder with a normal dataset and does not
require decoding. The performance of the VQOCC is compared with that of the
one-class support vector machine (OC-SVM), the kernel principal component
analysis (PCA), and the deep convolutional autoencoder (DCAE) using handwritten
digit and Fashion-MNIST datasets. The numerical experiment examined various
structures of VQOCC by varying data encoding, the number of parameterized
quantum circuit layers, and the size of the latent feature space. The benchmark
shows that the classification performance of VQOCC is comparable to that of
OC-SVM and PCA, although the number of model parameters grows only
logarithmically with the data size. The quantum algorithm outperformed DCAE in
most cases under similar training conditions. Therefore, our algorithm
constitutes an extremely compact and effective machine learning model for
one-class classification.

In this paper, we introduce a new methodology to solve the orthogonal
non-negative matrix factorization (ONMF) problem, where the objective is to
approximate an input data matrix by the product of two non-negative matrices,
the features matrix and the mixing matrix, while one of them is orthogonal. We
show how the ONMF can be interpreted as a specific facility-location problem
(FLP), and adapt a maximum-entropy-principle based solution for FLP to the ONMF
problem. The proposed approach guarantees orthogonality of the features or the
mixing matrix, while ensuring that both of the matrix factors are non-negative.
Also, the features (mixing) matrix has exactly one non-zero element across each
row (column), providing the maximum sparsity of the orthogonal factor. This
enables a semantic interpretation of the underlying data matrix using
non-overlapping features. The experiments on synthetic data and a standard
microarray dataset demonstrate significant improvements in terms of sparsity
and orthogonality scores of features (mixing) matrices, while achieving
approximately the same or better (up to 3%) reconstruction errors.

Characterizing the implicit structure of the computation within neural
networks is a foundational problem in the area of deep learning
interpretability. Can their inner decision process be captured symbolically in
some familiar logic? We show that any transformer neural network can be
translated into an equivalent fixed-size first-order logic formula which may
also use majority quantifiers. The idea is to simulate transformers with highly
uniform threshold circuits and leverage known theoretical connections between
circuits and logic. Our findings also reveal the surprising fact that the
entire transformer computation can be reduced merely to the division of two
(large) integers. While our results are most pertinent for transformers, they
apply equally to a broader class of neural network architectures, namely those
with a fixed-depth uniform computation graph made up of standard neural net
components, which includes feedforward and convolutional networks.

The unparalleled success of artificial intelligence (AI) in the technology
sector has catalyzed an enormous amount of research in the scientific
community. It has proven to be a powerful tool, but as with any rapidly
developing field, the deluge of information can be overwhelming, confusing and
sometimes misleading. This can make it easy to become lost in the same hype
cycles that have historically ended in the periods of scarce funding and
depleted expectations known as AI Winters. Furthermore, while the importance of
innovative, high-risk research cannot be overstated, it is also imperative to
understand the fundamental limits of available techniques, especially in young
fields where the rules appear to be constantly rewritten and as the likelihood
of application to high-stakes scenarios increases. In this perspective, we
highlight the guiding principles of data-driven modeling, how these principles
imbue models with almost magical predictive power, and how they also impose
limitations on the scope of problems they can address. Particularly,
understanding when not to use data-driven techniques, such as machine learning,
is not something commonly explored, but is just as important as knowing how to
apply the techniques properly. We hope that the discussion to follow provides
researchers throughout the sciences with a better understanding of when said
techniques are appropriate, the pitfalls to watch for, and most importantly,
the confidence to leverage the power they can provide.

Improving the accessibility and automation capabilities of mobile devices can
have a significant positive impact on the daily lives of countless users. To
stimulate research in this direction, we release a human-annotated dataset with
approximately 500k unique annotations aimed at increasing the understanding of
the functionality of UI elements. This dataset augments images and view
hierarchies from RICO, a large dataset of mobile UIs, with annotations for
icons based on their shapes and semantics, and associations between different
elements and their corresponding text labels, resulting in a significant
increase in the number of UI elements and the categories assigned to them. We
also release models using image-only and multimodal inputs; we experiment with
various architectures and study the benefits of using multimodal inputs on the
new dataset. Our models demonstrate strong performance on an evaluation set of
unseen apps, indicating their generalizability to newer screens. These models,
combined with the new dataset, can enable innovative functionalities like
referring to UI elements by their labels, improved coverage and better
semantics for icons etc., which would go a long way in making UIs more usable
for everyone.

Continual learning in neural networks suffers from a phenomenon called
catastrophic forgetting, in which a network quickly forgets what was learned in
a previous task. The human brain, however, is able to continually learn new
tasks and accumulate knowledge throughout life. Neuroscience findings suggest
that continual learning success in the human brain is potentially associated
with its modular structure and memory consolidation mechanisms. In this paper
we propose a novel topological regularization that penalizes cycle structure in
a neural network during training using principled theory from persistent
homology and optimal transport. The penalty encourages the network to learn
modular structure during training. The penalization is based on the closed-form
expressions of the Wasserstein distance and barycenter for the topological
features of a 1-skeleton representation for the network. Our topological
continual learning method combines the proposed regularization with a tiny
episodic memory to mitigate forgetting. We demonstrate that our method is
effective in both shallow and deep network architectures for multiple image
classification datasets.

To proactively offer social media users a safe online experience, there is a
need for systems that can detect harmful posts and promptly alert platform
moderators. In order to guarantee the enforcement of a consistent policy,
moderators are provided with detailed guidelines. In contrast, most
state-of-the-art models learn what abuse is from labelled examples and as a
result base their predictions on spurious cues, such as the presence of group
identifiers, which can be unreliable. In this work we introduce the concept of
policy-aware abuse detection, abandoning the unrealistic expectation that
systems can reliably learn which phenomena constitute abuse from inspecting the
data alone. We propose a machine-friendly representation of the policy that
moderators wish to enforce, by breaking it down into a collection of intents
and slots. We collect and annotate a dataset of 3,535 English posts with such
slots, and show how architectures for intent classification and slot filling
can be used for abuse detection, while providing a rationale for model
decisions.

Medical conversations between patients and medical professionals have
implicit functional sections, such as "history taking", "summarization",
"education", and "care plan." In this work, we are interested in learning to
automatically extract these sections. A direct approach would require
collecting large amounts of expert annotations for this task, which is
inherently costly due to the contextual inter-and-intra variability between
these sections. This paper presents an approach that tackles the problem of
learning to classify medical dialogue into functional sections without
requiring a large number of annotations. Our approach combines pseudo-labeling
and human-in-the-loop. First, we bootstrap using weak supervision with
pseudo-labeling to generate dialogue turn-level pseudo-labels and train a
transformer-based model, which is then applied to individual sentences to
create noisy sentence-level labels. Second, we iteratively refine
sentence-level labels using a cluster-based human-in-the-loop approach. Each
iteration requires only a few dozen annotator decisions. We evaluate the
results on an expert-annotated dataset of 100 dialogues and find that while our
models start with 69.5% accuracy, we can iteratively improve it to 82.5%. The
code used to perform all experiments described in this paper can be found here:
https://github.com/curai/curai-research/tree/main/functional-sections.

Edge caching plays an increasingly important role in boosting user content
retrieval performance while reducing redundant network traffic. The
effectiveness of caching ultimately hinges on the accuracy of predicting
content popularity in the near future. However, at the network edge, content
popularity can be extremely dynamic due to diverse user content retrieval
behaviors and the low-degree of user multiplexing. It's challenging for the
traditional reactive caching systems to keep up with the dynamic content
popularity patterns. In this paper, we propose a novel Predictive Edge Caching
(PEC) system that predicts the future content popularity using fine-grained
learning models that mine sequential patterns in user content retrieval
behaviors, and opportunistically prefetches contents predicted to be popular in
the near future using idle network bandwidth. Through extensive experiments
driven by real content retrieval traces, we demonstrate that PEC can adapt to
highly dynamic content popularity, and significantly improve cache hit ratio
and reduce user content retrieval latency over the state-of-art caching
policies. More broadly, our study demonstrates that edge caching performance
can be boosted by deep mining of user content retrieval behaviors.

Open-source is frequently described as a driver for unprecedented
communication and collaboration, and the process works best when projects
support teamwork. Yet, open-source cooperation processes in no way protect
project contributors from considerations of trust, power, and influence.
Indeed, achieving the level of trust necessary to contribute to a project and
thus influence its direction is a constant process of change, and developers
take many different routes over many communication channels to achieve it. We
refer to this process of influence-seeking and trust-building as trust
ascendancy.
  This paper describes a methodology for understanding the notion of trust
ascendancy and introduces the capabilities that are needed to localize trust
ascendancy operations happening over open-source projects. Much of the prior
work in understanding trust in open-source software development has focused on
a static view of the problem using different forms of quantity measures.
However, trust ascendancy is not static, but rather adapts to changes in the
open-source ecosystem in response to new input. This paper is the first attempt
to articulate and study these signals from a dynamic view of the problem. In
that respect, we identify related work that may help illuminate research
challenges, implementation tradeoffs, and complementary solutions. Our
preliminary results show the effectiveness of our method at capturing the trust
ascendancy developed by individuals involved in a well-documented 2020 social
engineering attack. Our future plans highlight research challenges and
encourage cross-disciplinary collaboration to create more automated, accurate,
and efficient ways to model and then track trust ascendancy in open-source
projects.

Domain Generalization (DG) aims to learn a model that can generalize well to
unseen target domains from a set of source domains. With the idea of invariant
causal mechanism, a lot of efforts have been put into learning robust causal
effects which are determined by the object yet insensitive to the domain
changes. Despite the invariance of causal effects, they are difficult to be
quantified and optimized. Inspired by the ability that humans adapt to new
environments by prior knowledge, We develop a novel Contrastive Causal Model
(CCM) to transfer unseen images to taught knowledge which are the features of
seen images, and quantify the causal effects based on taught knowledge.
Considering the transfer is affected by domain shifts in DG, we propose a more
inclusive causal graph to describe DG task. Based on this causal graph, CCM
controls the domain factor to cut off excess causal paths and uses the
remaining part to calculate the causal effects of images to labels via the
front-door criterion. Specifically, CCM is composed of three components: (i)
domain-conditioned supervised learning which teaches CCM the correlation
between images and labels, (ii) causal effect learning which helps CCM measure
the true causal effects of images to labels, (iii) contrastive similarity
learning which clusters the features of images that belong to the same class
and provides the quantification of similarity. Finally, we test the performance
of CCM on multiple datasets including PACS, OfficeHome, and TerraIncognita. The
extensive experiments demonstrate that CCM surpasses the previous DG methods
with clear margins.

In this thesis, we research learning algorithms for optimal decision making
in two different contexts, Reinforcement Learning in Part I and Auction Design
in Part II.
  Reinforcement learning (RL) is an area of machine learning that is concerned
with how an agent should act in an environment in order to maximize its
cumulative reward over time. In Chapter 2, inspired by statistical physics, we
develop a novel approach to Reinforcement Learning (RL) that not only learns
optimal policies with enhanced desirable properties but also sheds new light on
maximum entropy RL. In Chapter 3, we tackle the generalization problem in RL
using a Bayesian perspective. We show that imperfect knowledge of the
environments dynamics effectively turn a fully-observed Markov Decision Process
(MDP) into a Partially Observed MDP (POMDP) that we call the Epistemic POMDP.
Informed by this observation, we develop a new policy learning algorithm LEEP
which has improved generalization properties.
  Designing an incentive compatible, individually rational auction that
maximizes revenue is a challenging and intractable problem. Recently, deep
learning based approaches have been proposed to learn optimal auctions from
data. While successful, this approach suffers from a few limitations, including
sample inefficiency, lack of generalization to new auctions, and training
difficulties. In Chapter 4, we construct a symmetry preserving neural network
architecture, EquivariantNet, suitable for anonymous auctions. EquivariantNet
is not only more sample efficient but is also able to learn auction rules that
generalize well to other settings. In Chapter 5, we propose a novel formulation
of the auction learning problem as a two player game. The resulting learning
algorithm, ALGNet, is easier to train, more reliable and better suited for non
stationary settings.

Static bug detection tools help developers detect problems in the code,
including bad programming practices and potential defects. However, it is known
that static bug detectors remain underutilized due to various reasons. Recent
advances to incorporate static bug detectors in modern software development
workflows, such as in code review and continuous integration, are shown capable
of better motivating developers to fix the reported warnings on the fly.
Moreover, tracking the static code warnings will benefit many downstream
software engineering tasks, such as learning the fix patterns for automated
program repair and learning which warnings are of more interest, so they can be
prioritized automatically. Hence, precisely tracking the warnings by static bug
detectors is critical to improve the utilization of static bug detectors
further.
  In this paper, we study the effectiveness of the state-of-the-art (SOA)
solution in tracking the warnings by static bug detectors and propose a better
solution based on our analysis of the insufficiencies of the SOA solution. In
particular, we examined over 2000 commits in four large-scale open-source
systems (i.e., JClouds, Kafka, Spring-boot, and Guava) and crafted a dataset of
3,452 static code warnings by two static bug detectors (i.e., Spotbugs and
PMD). We manually uncover the ground-truth evolution status of the static
warnings: persistent, resolved, or newly-introduced. Moreover, upon manual
analysis, we identified the main reasons behind the insufficiencies of the SOA
solution. Finally, we propose a better approach to improving the tracking of
static warnings over software development history. Our evaluation shows that
our proposed approach provides a significant improvement in terms of the
precision of the tracking, i.e., from 66.9% to 90.0%.

Consumers increasingly bring IoT devices into their living spaces without
understanding how their data is collected, processed, and used. We present
PrivacyCube, a novel tangible device designed to explore the extent to which
privacy awareness in smart homes can be elevated. PrivacyCube visualises IoT
devices' data consumption displaying privacy-related notices. PrivacyCube aims
at assisting families to (i) understand key privacy aspects better and (ii)
have conversations around data management practices of IoT devices. Thus,
families can learn and make informed privacy decisions collectively.

By using low-cost microcontrollers and TinyML, we investigate the feasibility
of detecting potential early warning signs of domestic violence and other
anti-social behaviors within the home. We created a machine learning model to
determine if a door was closed aggressively by analyzing audio data and feeding
this into a convolutional neural network to classify the sample. Under test
conditions, with no background noise, accuracy of 88.89\% was achieved,
declining to 87.50\% when assorted background noises were mixed in at a
relative volume of 0.5 times that of the sample. The model is then deployed on
an Arduino Nano BLE 33 Sense attached to the door, and only begins sampling
once an acceleration greater than a predefined threshold acceleration is
detected. The predictions made by the model can then be sent via BLE to another
device, such as a smartphone of Raspberry Pi.

Weight and activation binarization can efficiently compress deep neural
networks and accelerate model inference, but cause severe accuracy degradation.
Existing optimization methods for binary neural networks (BNNs) focus on
fitting full-precision networks to reduce quantization errors, and suffer from
the trade-off between accuracy and computational complexity. In contrast,
considering the limited learning ability and information loss caused by the
limited representational capability of BNNs, we propose IR$^2$Net to stimulate
the potential of BNNs and improve the network accuracy by restricting the input
information and recovering the feature information, including: 1) information
restriction: for a BNN, by evaluating the learning ability on the input
information, discarding some of the information it cannot focus on, and
limiting the amount of input information to match its learning ability; 2)
information recovery: due to the information loss in forward propagation, the
output feature information of the network is not enough to support accurate
classification. By selecting some shallow feature maps with richer information,
and fusing them with the final feature maps to recover the feature information.
In addition, the computational cost is reduced by streamlining the information
recovery method to strike a better trade-off between accuracy and efficiency.
Experimental results demonstrate that our approach still achieves comparable
accuracy even with $ \sim $10x floating-point operations (FLOPs) reduction for
ResNet-18. The models and code are available at
https://github.com/pingxue-hfut/IR2Net.

Recently, Graph Neural Networks (GNNs) have been applied to graph learning
tasks and achieved state-of-the-art results. However, many competitive methods
employ preprocessing on the target nodes, such as subgraph extraction and
customized labeling, to capture some information that is hard to be learned by
normal GNNs. Such operations are time-consuming and do not scale to large
graphs. In this paper, we propose an efficient GNN framework called Geodesic
GNN (GDGNN). It injects conditional relationships between nodes into the model
without labeling. Specifically, we view the shortest paths between two nodes as
the spatial graph context of the neighborhood around them. The GNN embeddings
of nodes on the shortest paths are used to generate geodesic representations.
Conditioned on the geodesic representations, GDGNN is able to generate node,
link, and graph representations that carry much richer structural information
than plain GNNs. We theoretically prove that GDGNN is more powerful than plain
GNNs, and present experimental results to show that GDGNN achieves highly
competitive performance with state-of-the-art GNN models on link prediction and
graph classification tasks while taking significantly less time.

Context. Photospheric bright points (BPs), as the smallest magnetic element
of the photosphere and the footpoint tracer of the magnetic flux tube, are of
great significance to the study of BPs. Compared with the study of the
characteristics and evolution of a few specific BPs, the study of BPs groups
can provide us with a better understanding of the characteristics and overall
activities of BPs groups. Aims. We aim to find out the evolution
characteristics of the brightness and number of BPs groups at different
brightness levels, and how these characteristics differ between quiet and
active regions. Methods. We propose a hybrid BPs detection model (HBD Model)
combining traditional technology and neural network. The Model is used to
detect and calculate the BPs brightness characteristics of each frame of
continuous high resolution image sequences of active and quiet regions in
TiO-band of a pair of BBSO. Using machine learning clustering method, the PBs
of each frame was divided into four levels groups (level1-level4) according to
the brightness from low to high. Finally, Fourier transform and inverse Fourier
transform are used to analyze the evolution of BPs brightness and quantity in
these four levels groups. Results. The activities of BPs groups are not random
and disorderly. In different levels of brightness, their quantity and
brightness evolution show complex changes. Among the four levels of brightness,
BPs in the active region were more active and intense than those in the quiet
region. However, the quantity and brightness evolution of BPs groups in the
quiet region showed the characteristics of large periodic changes and small
periodic changes in the medium and high brightness levels (level3 and level4).
The brightness evolution of PBs group in the quiet region has obvious periodic
changes, but the active region is in a completely random and violent
fluctuation state.

We demonstrate the adaption of three established methods to the field of
surrogate machine learning model development. These methods are data
augmentation, custom loss functions and transfer learning. Each of these
methods have seen widespread use in the field of machine learning, however,
here we apply them specifically to surrogate machine learning model
development. The machine learning model that forms the basis behind this work
was intended to surrogate a traditional engineering model used in the UK
nuclear industry. Previous performance of this model has been hampered by poor
performance due to limited training data. Here, we demonstrate that through a
combination of additional techniques, model performance can be significantly
improved. We show that each of the aforementioned techniques have utility in
their own right and in combination with one another. However, we see them best
applied as part of a transfer learning operation. Five pre-trained surrogate
models produced prior to this research were further trained with an augmented
dataset and with our custom loss function. Through the combination of all three
techniques, we see an improvement of at least $38\%$ in performance across the
five models.

Leveraging artificial intelligence for automatic retrosynthesis speeds up
organic pathway planning in digital laboratories. However, existing deep
learning approaches are unexplainable, like "black box" with few insights,
notably limiting their applications in real retrosynthesis scenarios. Here, we
propose MechRetro, a chemical-mechanism-driven graph learning framework for
interpretable retrosynthetic prediction and pathway planning, which learns
several retrosynthetic actions to simulate a reverse reaction via elaborate
self-adaptive joint learning. By integrating chemical knowledge as prior
information, we design a novel Graph Transformer architecture to adaptively
learn discriminative and chemically meaningful molecule representations,
highlighting the strong capacity in molecule feature representation learning.
We demonstrate that MechRetro outperforms the state-of-the-art approaches for
retrosynthetic prediction with a large margin on large-scale benchmark
datasets. Extending MechRetro to the multi-step retrosynthesis analysis, we
identify efficient synthetic routes via an interpretable reasoning mechanism,
leading to a better understanding in the realm of knowledgeable synthetic
chemists. We also showcase that MechRetro discovers a novel pathway for
protokylol, along with energy scores for uncertainty assessment, broadening the
applicability for practical scenarios. Overall, we expect MechRetro to provide
meaningful insights for high-throughput automated organic synthesis in drug

Extracting relevant urban patterns from multiple data sources can be
difficult using classical clustering algorithms since we have to make a
suitable setup of the hyperparameters of the algorithms and deal with outliers.
It should be addressed correctly to help urban planners in the decision-making
process for the further development of a big city. For instance, experts' main
interest in criminology is comprehending the relationship between crimes and
the socio-economic characteristics at specific georeferenced locations. In
addition, the classical clustering algorithms take little notice of the
intricate spatial correlations in georeferenced data sources. This paper
presents a new approach to detecting the most relevant urban patterns from
multiple data sources based on tensor decomposition. Compared to classical
methods, the proposed approach's performance is attested to validate the
identified patterns' quality. The result indicates that the approach can
effectively identify functional patterns to characterize the data set for
further analysis in achieving good clustering quality. Furthermore, we
developed a generic framework named TensorAnalyzer, where the effectiveness and
usefulness of the proposed methodology are tested by a set of experiments and a
real-world case study showing the relationship between the crime events around
schools and students performance and other variables involved in the analysis.

Pre-training a diverse set of robot controllers in simulation has enabled
robots to adapt online to damage in robot locomotion tasks. However, finding
diverse, high-performing controllers requires specialized hardware and
extensive tuning of a large number of hyperparameters. On the other hand, the
Covariance Matrix Adaptation MAP-Annealing algorithm, an evolution strategies
(ES)-based quality diversity algorithm, does not have these limitations and has
been shown to achieve state-of-the-art performance in standard benchmark
domains. However, CMA-MAE cannot scale to modern neural network controllers due
to its quadratic complexity. We leverage efficient approximation methods in ES
to propose three new CMA-MAE variants that scale to very high dimensions. Our
experiments show that the variants outperform ES-based baselines in benchmark
robotic locomotion tasks, while being comparable with state-of-the-art deep
reinforcement learning-based quality diversity algorithms. Source code and
videos are available at https://scalingcmamae.github.io

While large language models (LLMs) have demonstrated impressive capabilities
across tasks in language understanding and interactive decision making, their
abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g.
action plan generation) have primarily been studied as separate topics. In this
paper, we explore the use of LLMs to generate both reasoning traces and
task-specific actions in an interleaved manner, allowing for greater synergy
between the two: reasoning traces help the model induce, track, and update
action plans as well as handle exceptions, while actions allow it to interface
with external sources, such as knowledge bases or environments, to gather
additional information. We apply our approach, named ReAct, to a diverse set of
language and decision making tasks and demonstrate its effectiveness over
state-of-the-art baselines, as well as improved human interpretability and
trustworthiness over methods without reasoning or acting components.
Concretely, on question answering (HotpotQA) and fact verification (Fever),
ReAct overcomes issues of hallucination and error propagation prevalent in
chain-of-thought reasoning by interacting with a simple Wikipedia API, and
generates human-like task-solving trajectories that are more interpretable than
baselines without reasoning traces. On two interactive decision making
benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and
reinforcement learning methods by an absolute success rate of 34% and 10%
respectively, while being prompted with only one or two in-context examples.

More tasks in Machine Reading Comprehension(MRC) require, in addition to
answer prediction, the extraction of evidence sentences that support the
answer. However, the annotation of supporting evidence sentences is usually
time-consuming and labor-intensive. In this paper, to address this issue and
considering that most of the existing extraction methods are semi-supervised,
we propose an unsupervised evidence extraction method (U3E). U3E takes the
changes after sentence-level feature erasure in the document as input,
simulating the decline in problem-solving ability caused by human memory
decline. In order to make selections on the basis of fully understanding the
semantics of the original text, we also propose metrics to quickly select the
optimal memory model for this input changes. To compare U3E with typical
evidence extraction methods and investigate its effectiveness in evidence
extraction, we conduct experiments on different datasets. Experimental results
show that U3E is simple but effective, not only extracting evidence more
accurately, but also significantly improving model performance.

With the growing workload of inference tasks on mobile devices,
state-of-the-art neural architectures (NAs) are typically designed through
Neural Architecture Search (NAS) to identify NAs with good tradeoffs between
accuracy and efficiency (e.g., latency). Since measuring the latency of a huge
set of candidate architectures during NAS is not scalable, approaches are
needed for predicting end-to-end inference latency on mobile devices. Such
predictions are challenging due to hardware heterogeneity, optimizations
applied by ML frameworks, and the diversity of neural architectures. Motivated
by these challenges, in this paper, we first quantitatively assess
characteristics of neural architectures and mobile devices that have
significant effects on inference latency. Based on this assessment, we propose
a latency prediction framework which addresses these challenges by developing
operation-wise latency predictors, under a variety of settings and a number of
hardware devices, with multi-core CPUs and GPUs, achieving high accuracy in
end-to-end latency prediction, as shown by our comprehensive evaluations. To
illustrate that our approach does not require expensive data collection, we
also show that accurate predictions can be achieved on real-world NAs using
only small amounts of profiling data.

Many modern high-performing machine learning models such as GPT-3 primarily
rely on scaling up models, e.g., transformer networks. Simultaneously, a
parallel line of work aims to improve the model performance by augmenting an
input instance with other (labeled) instances during inference. Examples of
such augmentations include task-specific prompts and similar examples retrieved
from the training data by a nonparametric component. Remarkably,
retrieval-based methods have enjoyed success on a wide range of problems,
ranging from standard natural language processing and vision tasks to protein
folding, as demonstrated by many recent efforts, including WebGPT and
AlphaFold. Despite growing literature showcasing the promise of these models,
the theoretical underpinning for such models remains underexplored. In this
paper, we present a formal treatment of retrieval-based models to characterize
their generalization ability. In particular, we focus on two classes of
retrieval-based classification approaches: First, we analyze a local learning
framework that employs an explicit local empirical risk minimization based on
retrieved examples for each input instance. Interestingly, we show that
breaking down the underlying learning task into local sub-tasks enables the
model to employ a low complexity parametric component to ensure good overall
accuracy. The second class of retrieval-based approaches we explore learns a
global model using kernel methods to directly map an input instance and
retrieved examples to a prediction, without explicitly solving a local learning
task.

In this paper, we design a resource management scheme to support stateful
applications, which will be prevalent in 6G networks. Different from stateless
applications, stateful applications require context data while executing
computing tasks from user terminals (UTs). Using a multi-tier computing
paradigm with servers deployed at the core network, gateways, and base stations
to support stateful applications, we aim to optimize long-term resource
reservation by jointly minimizing the usage of computing, storage, and
communication resources and the cost from reconfiguring resource reservation.
The coupling among different resources and the impact of UT mobility create
challenges in resource management. To address the challenges, we develop
digital twin (DT) empowered network planning with two elements, i.e.,
multi-resource reservation and resource reservation reconfiguration. First, DTs
are designed for collecting UT status data, based on which UTs are grouped
according to their mobility patterns. Second, an algorithm is proposed to
customize resource reservation for different groups to satisfy their different
resource demands. Last, a Meta-learning-based approach is developed to
reconfigure resource reservation for balancing the network resource usage and
the reconfiguration cost. Simulation results demonstrate that the proposed
DT-empowered network planning outperforms benchmark frameworks by using less
resources and incurring lower reconfiguration costs.

Large language models have recently shown promising progress in mathematical
reasoning when fine-tuned with human-generated sequences walking through a
sequence of solution steps. However, the solution sequences are not formally
structured and the resulting model-generated sequences may not reflect the kind
of systematic reasoning we might expect an expert human to produce. In this
paper, we study how to build stronger reasoning capability in language models
using the idea of relational abstractions. We introduce new types of sequences
that more explicitly provide an abstract characterization of the transitions
through intermediate solution steps to the goal state. We find that models that
are supplied with such sequences as prompts can solve tasks with a
significantly higher accuracy, and models that are trained to produce such
sequences solve problems better than those that are trained with previously
used human-generated sequences and other baselines. Our work thus takes several
steps toward elucidating and improving how language models perform on tasks
requiring multi-step mathematical reasoning.

Federated Learning (FL) has emerged as a means of performing distributed
learning using local data stored at clients and a coordinating server. But,
recent studies showed that FL can suffer from poor performance when client
training data are not independently and identically distributed (non-IID). This
paper proposes a new complementary approach to mitigating this performance
degradation when the server has access to a small dataset, on which it can
perform auxiliary learning. Our analysis and experiments show that
incorporating server learning with FL in an incremental fashion can provide
significant benefits when the distribution of server data is similar to that of
the aggregate samples of all clients, even when the server dataset is small,
and improve the convergence rate considerably at the beginning of the learning
process.

This research studies the network traffic signal control problem. It uses the
Lyapunov control function to derive the back pressure method, which is equal to
differential queue lengths weighted by intersection lane flows. Lyapunov
control theory is a platform that unifies several current theories for
intersection signal control. We further use the theorem to derive the
flow-based and other pressure-based signal control algorithms. For example, the
Dynamic, Optimal, Real-time Algorithm for Signals (DORAS) algorithm may be
derived by defining the Lyapunov function as the sum of queue length. The study
then utilizes the back pressure as a reward in the reinforcement learning (RL)
based network signal control, whose agent is trained with double Deep Q-Network
(Double-DQN). The proposed algorithm is compared with several traditional and
RL-based methods under passenger traffic flow and mixed flow with freight
traffic, respectively. The numerical tests are conducted on a single corridor
and on a local grid network under three traffic demand scenarios of low,
medium, and heavy traffic, respectively. The numerical simulation demonstrates
that the proposed algorithm outperforms the others in terms of the average
vehicle waiting time on the network.

Data streams are often defined as large amounts of data flowing continuously
at high speed. Moreover, these data are likely subject to changes in data
distribution, known as concept drift. Given all the reasons mentioned above,
learning from streams is often online and under restrictions of memory
consumption and run-time. Although many classification algorithms exist, most
of the works published in the area use Naive Bayes (NB) and Hoeffding Trees
(HT) as base learners in their experiments. This article proposes an in-depth
evaluation of k-Nearest Neighbors (k-NN) as a candidate for classifying data
streams subjected to concept drift. It also analyses the complexity in time and
the two main parameters of k-NN, i.e., the number of nearest neighbors used for
predictions (k), and window size (w). We compare different parameter values for
k-NN and contrast it to NB and HT both with and without a drift detector (RDDM)
in many datasets. We formulated and answered 10 research questions which led to
the conclusion that k-NN is a worthy candidate for data stream classification,
especially when the run-time constraint is not too restrictive.

Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases,
but may also occur in other non-cardiovascular conditions such as mental,
neurological, metabolic and infectious conditions. However, most of the recent
success of deep learning (DL) based diagnostic predictions in selected patient
cohorts have been limited to a small set of cardiac diseases. In this study, we
use a population-based dataset of >250,000 patients with >1000 medical
conditions and >2 million ECGs to identify a wide range of diseases that could
be accurately diagnosed from the patient's first in-hospital ECG. Our DL models
uncovered 128 diseases and 68 disease categories with strong discriminative

Inappropriate design and deployment of machine learning (ML) systems leads to
negative downstream social and ethical impact -- described here as social and
ethical risks -- for users, society and the environment. Despite the growing
need to regulate ML systems, current processes for assessing and mitigating
risks are disjointed and inconsistent. We interviewed 30 industry practitioners
on their current social and ethical risk management practices, and collected
their first reactions on adapting safety engineering frameworks into their
practice -- namely, System Theoretic Process Analysis (STPA) and Failure Mode
and Effects Analysis (FMEA). Our findings suggest STPA/FMEA can provide
appropriate structure toward social and ethical risk assessment and mitigation
processes. However, we also find nontrivial challenges in integrating such
frameworks in the fast-paced culture of the ML industry. We call on the ML
research community to strengthen existing frameworks and assess their efficacy,
ensuring that ML systems are safer for all people.

We articulate the design imperatives for machine-learning based digital twins
for nonlinear dynamical systems subject to external driving, which can be used
to monitor the ``health'' of the target system and anticipate its future
collapse. We demonstrate that, with single or parallel reservoir computing
configurations, the digital twins are capable of challenging forecasting and
monitoring tasks. Employing prototypical systems from climate, optics and
ecology, we show that the digital twins can extrapolate the dynamics of the
target system to certain parameter regimes never experienced before, make
continual forecasting/monitoring with sparse real-time updates under
non-stationary external driving, infer hidden variables and accurately predict
their dynamical evolution, adapt to different forms of external driving, and
extrapolate the global bifurcation behaviors to systems of some different
sizes. These features make our digital twins appealing in significant
applications such as monitoring the health of critical systems and forecasting
their potential collapse induced by environmental changes.

Data-driven machine learning models are being increasingly employed in
several important inference problems in biology, chemistry, and physics which
require learning over combinatorial spaces. Recent empirical evidence (see,
e.g., [1], [2], [3]) suggests that regularizing the spectral representation of
such models improves their generalization power when labeled data is scarce.
However, despite these empirical studies, the theoretical underpinning of when
and how spectral regularization enables improved generalization is poorly
understood. In this paper, we focus on learning pseudo-Boolean functions and
demonstrate that regularizing the empirical mean squared error by the L_1 norm
of the spectral transform of the learned function reshapes the loss landscape
and allows for data-frugal learning, under a restricted secant condition on the
learner's empirical error measured against the ground truth function. Under a
weaker quadratic growth condition, we show that stationary points which also
approximately interpolate the training data points achieve statistically
optimal generalization performance. Complementing our theory, we empirically
demonstrate that running gradient descent on the regularized loss results in a
better generalization performance compared to baseline algorithms in several
data-scarce real-world problems.

The cyberthreat landscape is continuously evolving. Hence, continuous
monitoring and sharing of threat intelligence have become a priority for
organizations. Threat reports, published by cybersecurity vendors, contain
detailed descriptions of attack Tactics, Techniques, and Procedures (TTP)
written in an unstructured text format. Extracting TTP from these reports aids
cybersecurity practitioners and researchers learn and adapt to evolving attacks
and in planning threat mitigation. Researchers have proposed TTP extraction
methods in the literature, however, not all of these proposed methods are
compared to one another or to a baseline. \textit{The goal of this study is to
aid cybersecurity researchers and practitioners choose attack technique
extraction methods for monitoring and sharing threat intelligence by comparing
the underlying methods from the TTP extraction studies in the literature.} In
this work, we identify ten existing TTP extraction studies from the literature
and implement five methods from the ten studies. We find two methods, based on
Term Frequency-Inverse Document Frequency(TFIDF) and Latent Semantic Indexing
(LSI), outperform the other three methods with a F1 score of 84\% and 83\%,
respectively. We observe the performance of all methods in F1 score drops in
the case of increasing the class labels exponentially. We also implement and
evaluate an oversampling strategy to mitigate class imbalance issues.
Furthermore, oversampling improves the classification performance of TTP
extraction. We provide recommendations from our findings for future
cybersecurity researchers, such as the construction of a benchmark dataset from
a large corpus; and the selection of textual features of TTP. Our work, along
with the dataset and implementation source code, can work as a baseline for
cybersecurity researchers to test and compare the performance of future TTP
extraction methods.

Astrobot swarms are used to capture astronomical signals to generate the map
of the observable universe for the purpose of dark energy studies. The
convergence of each swarm in the course of its coordination has to surpass a
particular threshold to yield a satisfactory map. The current coordination
methods do not always reach desired convergence rates. Moreover, these methods
are so complicated that one cannot formally verify their results without
resource-demanding simulations. Thus, we use support vector machines to train a
model which can predict the convergence of a swarm based on the data of
previous coordination of that swarm. Given a fixed parity, i.e., the rotation
direction of the outer arm of an astrobot, corresponding to a swarm, our
algorithm reaches a better predictive performance compared to the state of the
art. Additionally, we revise our algorithm to solve a more generalized
convergence prediction problem according to which the parities of astrobots may
differ. We present the prediction results of a generalized scenario, associated
with a 487-astrobot swarm, which are interestingly efficient and collision-free
given the excessive complexity of this scenario compared to the constrained
one.

Traditional communication system design has always been based on the paradigm
of first establishing a mathematical model of the communication channel, then
designing and optimizing the system according to the model. The advent of
modern machine learning techniques, specifically deep neural networks, has
opened up opportunities for data-driven system design and optimization. This
article draws examples from the optimization of reconfigurable intelligent
surface, distributed channel estimation and feedback for multiuser beamforming,
and active sensing for millimeter wave (mmWave) initial alignment to illustrate
that a data-driven design that bypasses explicit channel modelling can often
discover excellent solutions to communication system design and optimization
problems that are otherwise computationally difficult to solve. We show that by
performing an end-to-end training of a deep neural network using a large number
of channel samples, a machine learning based approach can potentially provide
significant system-level improvements as compared to the traditional
model-based approach for solving optimization problems. The key to the
successful applications of machine learning techniques is in choosing the
appropriate neural network architecture to match the underlying problem
structure.

We consider a multi-armed bandit problem with $M$ latent contexts, where an
agent interacts with the environment for an episode of $H$ time steps.
Depending on the length of the episode, the learner may not be able to estimate
accurately the latent context. The resulting partial observation of the
environment makes the learning task significantly more challenging. Without any
additional structural assumptions, existing techniques to tackle partially
observed settings imply the decision maker can learn a near-optimal policy with
$O(A)^H$ episodes, but do not promise more. In this work, we show that learning
with {\em polynomial} samples in $A$ is possible. We achieve this by using
techniques from experiment design. Then, through a method-of-moments approach,
we design a procedure that provably learns a near-optimal policy with
$O(\texttt{poly}(A) + \texttt{poly}(M,H)^{\min(M,H)})$ interactions. In
practice, we show that we can formulate the moment-matching via maximum
likelihood estimation. In our experiments, this significantly outperforms the
worst-case guarantees, as well as existing practical methods.

We consider episodic reinforcement learning in reward-mixing Markov decision
processes (RMMDPs): at the beginning of every episode nature randomly picks a
latent reward model among $M$ candidates and an agent interacts with the MDP
throughout the episode for $H$ time steps. Our goal is to learn a near-optimal
policy that nearly maximizes the $H$ time-step cumulative rewards in such a
model. Previous work established an upper bound for RMMDPs for $M=2$. In this
work, we resolve several open questions remained for the RMMDP model. For an
arbitrary $M\ge2$, we provide a sample-efficient
algorithm--$\texttt{EM}^2$--that outputs an $\epsilon$-optimal policy using
$\tilde{O} \left(\epsilon^{-2} \cdot S^d A^d \cdot \texttt{poly}(H, Z)^d
\right)$ episodes, where $S, A$ are the number of states and actions
respectively, $H$ is the time-horizon, $Z$ is the support size of reward
distributions and $d=\min(2M-1,H)$. Our technique is a higher-order extension
of the method-of-moments based approach, nevertheless, the design and analysis
of the \algname algorithm requires several new ideas beyond existing
techniques. We also provide a lower bound of $(SA)^{\Omega(\sqrt{M})} /
\epsilon^{2}$ for a general instance of RMMDP, supporting that super-polynomial
sample complexity in $M$ is necessary.

While Self-Supervised Learning has helped reap the benefit of the scale from
the available unlabeled data, the learning paradigms are continuously being
bettered. We present a new pre-training strategy named ccc-wav2vec 2.0, which
uses clustering and an augmentation-based cross-contrastive loss as its
self-supervised objective. Through the clustering module, we scale down the
influence of those negative examples that are highly similar to the positive.
The Cross-Contrastive loss is computed between the encoder output of the
original sample and the quantizer output of its augmentation and vice-versa,
bringing robustness to the pre-training strategy. ccc-wav2vec 2.0 achieves up
to 15.6% and 12.7% relative WER improvement over the baseline wav2vec 2.0 on
the test-clean and test-other sets, respectively, of LibriSpeech, without the
use of any language model. The proposed method also achieves up to 14.9%
relative WER improvement over the baseline wav2vec 2.0 when fine-tuned on
Switchboard data. We make all our codes publicly available on GitHub.

Previous matching component analysis (MCA) techniques map two data domains to
a common domain for further processing in data fusion and transfer learning
contexts. In this paper, we extend these techniques to the star-graph
multimodal (SGM) case in which one particular data domain is connected to $m$
others via an objective function. We provide a particular feasible point for
the resulting trace maximization problem in closed form and algorithms for its
computation and iterative improvement, leading to our main result, the SGM
maps. We also provide numerical examples demonstrating that SGM is capable of
encoding into its maps more information than MCA when few training points are
available. In addition, we develop a further generalization of the MCA
covariance constraint, eliminating a previous feasibility condition and
allowing larger values of the rank of the prescribed covariance matrix.

Curricula for goal-conditioned reinforcement learning agents typically rely
on poor estimates of the agent's epistemic uncertainty or fail to consider the
agents' epistemic uncertainty altogether, resulting in poor sample efficiency.
We propose a novel algorithm, Query The Agent (QTA), which significantly
improves sample efficiency by estimating the agent's epistemic uncertainty
throughout the state space and setting goals in highly uncertain areas.
Encouraging the agent to collect data in highly uncertain states allows the
agent to improve its estimation of the value function rapidly. QTA utilizes a
novel technique for estimating epistemic uncertainty, Predictive Uncertainty
Networks (PUN), to allow QTA to assess the agent's uncertainty in all
previously observed states. We demonstrate that QTA offers decisive sample
efficiency improvements over preexisting methods.

Deep model-based architectures (DMBAs) integrating physical measurement
models and learned image regularizers are widely used in parallel magnetic
resonance imaging (PMRI). Traditional DMBAs for PMRI rely on pre-estimated coil
sensitivity maps (CSMs) as a component of the measurement model. However,
estimation of accurate CSMs is a challenging problem when measurements are
highly undersampled. Additionally, traditional training of DMBAs requires
high-quality groundtruth images, limiting their use in applications where
groundtruth is difficult to obtain. This paper addresses these issues by
presenting SPICE as a new method that integrates self-supervised learning and
automatic coil sensitivity estimation. Instead of using pre-estimated CSMs,
SPICE simultaneously reconstructs accurate MR images and estimates high-quality
CSMs. SPICE also enables learning from undersampled noisy measurements without
any groundtruth. We validate SPICE on experimentally collected data, showing
that it can achieve state-of-the-art performance in highly accelerated data
acquisition settings (up to 10x).

Peak detection is a problem in sequential data analysis that involves
differentiating regions with higher counts (peaks) from regions with lower
counts (background noise).
  It is crucial to correctly predict areas that deviate from the background
noise, in both the train and test sets of labels.
  Dynamic programming changepoint algorithms have been proposed to solve the
peak detection problem by constraining the mean to alternatively increase and
then decrease.
  The current constrained changepoint algorithms only create predictions on the
test set, while completely ignoring the train set.
  Changepoint algorithms that are both accurate when fitting the train set, and
make predictions on the test set, have been proposed but not in the context of
peak detection models.
  We propose to resolve these issues by creating a new dynamic programming
algorithm, FLOPART, that has zero train label errors, and is able to provide
highly accurate predictions on the test set.
  We provide an empirical analysis that shows FLOPART has a similar time
complexity while being more accurate than the existing algorithms in terms of
train and test label errors.

In adversarial machine learning, the popular $\ell_\infty$ threat model has
been the focus of much previous work. While this mathematical definition of
imperceptibility successfully captures an infinite set of additive image
transformations that a model should be robust to, this is only a subset of all
transformations which leave the semantic label of an image unchanged. Indeed,
previous work also considered robustness to spatial attacks as well as other
semantic transformations; however, designing defense methods against the
composition of spatial and $\ell_{\infty}$ perturbations remains relatively
underexplored. In the following, we improve the understanding of this seldom
investigated compositional setting. We prove theoretically that no linear
classifier can achieve more than trivial accuracy against a composite adversary
in a simple statistical setting, illustrating its difficulty. We then
investigate how state-of-the-art $\ell_{\infty}$ defenses can be adapted to
this novel threat model and study their performance against compositional
attacks. We find that our newly proposed TRADES$_{\text{All}}$ strategy
performs the strongest of all. Analyzing its logit's Lipschitz constant for RT
transformations of different sizes, we find that TRADES$_{\text{All}}$ remains
stable over a wide range of RT transformations with and without $\ell_\infty$
perturbations.

Learning physical systems on unstructured meshes by flat Graph neural
networks (GNNs) faces the challenge of modeling the long-range interactions due
to the scaling complexity w.r.t. the number of nodes, limiting the
generalization under mesh refinement. On regular grids, the convolutional
neural networks (CNNs) with a U-net structure can resolve this challenge by
efficient stride, pooling, and upsampling operations. Nonetheless, these tools
are much less developed for graph neural networks (GNNs), especially when GNNs
are employed for learning large-scale mesh-based physics. The challenges arise
from the highly irregular meshes and the lack of effective ways to construct
the multi-level structure without losing connectivity. Inspired by the
bipartite graph determination algorithm, we introduce Bi-Stride Multi-Scale
Graph Neural Network (BSMS-GNN) by proposing \textit{bi-stride} as a simple
pooling strategy for building the multi-level GNN. \textit{Bi-stride} pools
nodes by striding every other BFS frontier; it 1) works robustly on any
challenging mesh in the wild, 2) avoids using a mesh generator at coarser
levels, 3) avoids the spatial proximity for building coarser levels, and 4)
uses non-parametrized aggregating/returning instead of MLPs during pooling and
unpooling. Experiments show that our framework significantly outperforms the
state-of-the-art method's computational efficiency in representative
physics-based simulation cases.

We report the development of a multileaf collimator (MLC) for charged
particle beams, based on independently actuated tungsten strips which can
selectively scatter unwanted particles. The MLC is used in conjunction with an
emittance exchange beamline to rapidly generate highly variable longitudinal
bunch profiles. The developed MLC consists of 40 independent leaves that are 2
mm wide and can move up to 10 mm, and operates in an ultra high vacuum
environment, enabled by novel features such as magnetically coupled actuation.
An experiment at the Argonne Wakefield Accelerator, which previously used
inflexible, laser-cut masks for beam shaping before an emittance exchange
beamline, was conducted to test functionality. The experiment demonstrated
myriad transverse mask silhouettes, as measured on a scintillator downstream of
the MLC and the corresponding longitudinal profiles after emittance exchange,
as measured using a transverse deflecting cavity. Rapidly changing between mask
shapes enables expeditious execution of various experiments without the
downtime associated with traditional methods. The many degrees of freedom of
the MLC can enable optimization of experimental figures of merit using
feed-forward control and advanced machine learning methods.

Large neural networks are often overparameterised and prone to overfitting,
Dropout is a widely used regularization technique to combat overfitting and
improve model generalization. However, unstructured Dropout is not always
effective for specific network architectures and this has led to the formation
of multiple structured Dropout approaches to improve model performance and,
sometimes, reduce the computational resources required for inference. In this
work, we revisit structured Dropout comparing different Dropout approaches to
natural language processing and computer vision tasks for multiple
state-of-the-art networks. Additionally, we devise an approach to structured
Dropout we call \textbf{\emph{ProbDropBlock}} which drops contiguous blocks
from feature maps with a probability given by the normalized feature salience
values. We find that with a simple scheduling strategy the proposed approach to
structured Dropout consistently improved model performance compared to
baselines and other Dropout approaches on a diverse range of tasks and models.
In particular, we show \textbf{\emph{ProbDropBlock}} improves RoBERTa
finetuning on MNLI by $0.22\%$, and training of ResNet50 on ImageNet by
$0.28\%$.

A new modification of Isolation Forest called Attention-Based Isolation
Forest (ABIForest) for solving the anomaly detection problem is proposed. It
incorporates the attention mechanism in the form of the Nadaraya-Watson
regression into the Isolation Forest for improving solution of the anomaly
detection problem. The main idea underlying the modification is to assign
attention weights to each path of trees with learnable parameters depending on
instances and trees themselves. The Huber's contamination model is proposed to
be used for defining the attention weights and their parameters. As a result,
the attention weights are linearly depend on the learnable attention parameters
which are trained by solving the standard linear or quadratic optimization
problem. ABIForest can be viewed as the first modification of Isolation Forest,
which incorporates the attention mechanism in a simple way without applying
gradient-based algorithms. Numerical experiments with synthetic and real
datasets illustrate outperforming results of ABIForest. The code of proposed
algorithms is available.

This work concerns controlling the false discovery rate (FDR) in networks
under communication constraints. We present sample-and-forward, a flexible and
communication-efficient version of the Benjamini-Hochberg (BH) procedure for
multihop networks with general topologies. Our method evidences that the nodes
in a network do not need to communicate p-values to each other to achieve a
decent statistical power under the global FDR control constraint. Consider a
network with a total of $m$ p-values, our method consists of first sampling the
(empirical) CDF of the p-values at each node and then forwarding
$\mathcal{O}(\log m)$ bits to its neighbors. Under the same assumptions as for
the original BH procedure, our method has both the provable finite-sample FDR
control as well as competitive empirical detection power, even with a few
samples at each node. We provide an asymptotic analysis of power under a
mixture model assumption on the p-values.

Antibodies comprise the most versatile class of binding molecules, with
numerous applications in biomedicine. Therapeutic antibody development requires
designing novel and diverse sequences with improved properties, while
maintaining the structural consistency. Recently, deep language models and
graph neural networks have shown impressive success in antibody sequence
generation. Since only a limited number of antibody structures are known,
training a model using this limited data can lead to degraded performance,
particularly lacking diversity in the generated samples. To address such
issues, we leverage the method of Model Reprogramming (MR), which focuses on
repurposing pretrained machine learning models for target domain tasks with
scarce data, where it may be difficult to train a high-performing model from
scratch. We introduce Reprogramming for Protein Sequence Infilling, a framework
in which pretrained natural language models are repurposed for protein sequence
infilling via reprogramming, to infill protein sequence templates as a method
of novel protein generation. For variable CDR sequence design, we formulate the
task as text infilling that uses the constant region of an antibody as the
sequence template. Results on antibody design benchmarks show that our
reprogrammed model on low resourced antibody sequence dataset provides highly
diverse CDR sequences, up to more than a two-fold increase of diversity over
the baselines, without losing structural integrity and naturalness. The
performance benefit of the reprogrammed model learning only from antibody
sequences is more evident for longer CDR design or for multiple loop infilling
at once, compared to existing graph-based models that require additional
structural information. The generated sequences also demonstrate enhanced
antigen binding specificity or virus neutralization ability.

Mechanical ventilation is a key form of life support for patients with
pulmonary impairment. Healthcare workers are required to continuously adjust
ventilator settings for each patient, a challenging and time consuming task.
Hence, it would be beneficial to develop an automated decision support tool to
optimize ventilation treatment. We present DeepVent, a Conservative Q-Learning
(CQL) based offline Deep Reinforcement Learning (DRL) agent that learns to
predict the optimal ventilator parameters for a patient to promote 90 day
survival. We design a clinically relevant intermediate reward that encourages
continuous improvement of the patient vitals as well as addresses the challenge
of sparse reward in RL. We find that DeepVent recommends ventilation parameters
within safe ranges, as outlined in recent clinical trials. The CQL algorithm
offers additional safety by mitigating the overestimation of the value
estimates of out-of-distribution states/actions. We evaluate our agent using
Fitted Q Evaluation (FQE) and demonstrate that it outperforms physicians from
the MIMIC-III dataset.

In brain signal processing, deep learning (DL) models have become commonly
used. However, the performance gain from using end-to-end DL models compared to
conventional ML approaches is usually significant but moderate, typically at
the cost of increased computational load and deteriorated explainability. The
core idea behind deep learning approaches is scaling the performance with
bigger datasets. However, brain signals are temporal data with a low
signal-to-noise ratio, uncertain labels, and nonstationary data in time. Those
factors may influence the training process and slow down the models'
performance improvement. These factors' influence may differ for end-to-end DL
model and one using hand-crafted features. As not studied before, this paper
compares models that use raw ECoG signal and time-frequency features for BCI
motor imagery decoding. We investigate whether the current dataset size is a
stronger limitation for any models. Finally, obtained filters were compared to
identify differences between hand-crafted features and optimized with
backpropagation. To compare the effectiveness of both strategies, we used a
multilayer perceptron and a mix of convolutional and LSTM layers that were
already proved effective in this task. The analysis was performed on the
long-term clinical trial database (almost 600 minutes of recordings) of a
tetraplegic patient executing motor imagery tasks for 3D hand translation. For
a given dataset, the results showed that end-to-end training might not be
significantly better than the hand-crafted features-based model. The
performance gap is reduced with bigger datasets, but considering the increased
computational load, end-to-end training may not be profitable for this

The advent of modern, high-speed electron detectors has made the collection
of multidimensional hyperspectral transmission electron microscopy datasets,
such as 4D-STEM, a routine. However, many microscopists find such experiments
daunting since such datasets' analysis, collection, long-term storage, and
networking remain challenging. Some common issues are the large and unwieldy
size of the said datasets, often running into several gigabytes,
non-standardized data analysis routines, and a lack of clarity about the
computing and network resources needed to utilize the electron microscope
fully. However, the existing computing and networking bottlenecks introduce
significant penalties in each step of these experiments, and thus, real-time
analysis-driven automated experimentation for multidimensional TEM is
exceptionally challenging. One solution is integrating microscopy with edge
computing, where moderately powerful computational hardware performs the
preliminary analysis before handing off the heavier computation to HPC systems.
In this perspective, we trace the roots of computation in modern electron
microscopy, demonstrate deep learning experiments running on an edge system,
and discuss the networking requirements for tying together microscopes, edge
computers, and HPC systems.

As virtual personal assistants have now penetrated the consumer market, with
products such as Siri and Alexa, the research community has produced several
works on task-oriented dialogue tasks such as hotel booking, restaurant
booking, and movie recommendation. Assisting users to cook is one of these
tasks that are expected to be solved by intelligent assistants, where
ingredients and their corresponding attributes, such as name, unit, and
quantity, should be provided to users precisely and promptly. However, existing
ingredient information scraped from the cooking website is in the unstructured
form with huge variation in the lexical structure, for example, '1 garlic
clove, crushed', and '1 (8 ounce) package cream cheese, softened', making it
difficult to extract information exactly. To provide an engaged and successful
conversational service to users for cooking tasks, we propose a new ingredient
parsing model that can parse an ingredient phrase of recipes into the structure
form with its corresponding attributes with over 0.93 F1-score. Experimental
results show that our model achieves state-of-the-art performance on AllRecipes
and Food.com datasets.

To help improve the safety and accessibility of indoor spaces, researchers
and health professionals have created assessment instruments that enable
homeowners and trained experts to audit and improve homes. With advances in
computer vision, augmented reality (AR), and mobile sensors, new approaches are
now possible. We introduce RASSAR (Room Accessibility and Safety Scanning in
Augmented Reality), a new proof-of-concept prototype for semi-automatically
identifying, categorizing, and localizing indoor accessibility and safety
issues using LiDAR + camera data, machine learning, and AR. We present an
overview of the current RASSAR prototype and a preliminary evaluation in a
single home.

Psychomotor retardation associated with depression has been linked with
tangible differences in vowel production. This paper investigates a
knowledge-driven machine learning (ML) method that integrates spectrotemporal
information of speech at the vowel-level to identify the depression. Low-level
speech descriptors are learned by a convolutional neural network (CNN) that is
trained for vowel classification. The temporal evolution of those low-level
descriptors is modeled at the high-level within and across utterances via a
long short-term memory (LSTM) model that takes the final depression decision. A
modified version of the Local Interpretable Model-agnostic Explanations (LIME)
is further used to identify the impact of the low-level spectrotemporal vowel
variation on the decisions and observe the high-level temporal change of the
depression likelihood. The proposed method outperforms baselines that model the
spectrotemporal information in speech without integrating the vowel-based
information, as well as ML models trained with conventional prosodic and
spectrotemporal features. The conducted explainability analysis indicates that
spectrotemporal information corresponding to non-vowel segments less important
than the vowel-based information. Explainability of the high-level information
capturing the segment-by-segment decisions is further inspected for
participants with and without depression. The findings from this work can
provide the foundation toward knowledge-driven interpretable decision-support
systems that can assist clinicians to better understand fine-grain temporal
changes in speech data, ultimately augmenting mental health diagnosis and care.

Magnetic resonance imaging (MRI) is one of the most commonly applied tests in
neurology and neurosurgery. However, the utility of MRI is largely limited by
its long acquisition time, which might induce many problems including patient
discomfort and motion artifacts. Acquiring fewer k-space sampling is a
potential solution to reducing the total scanning time. However, it can lead to
severe aliasing reconstruction artifacts and thus affect the clinical
diagnosis. Nowadays, deep learning has provided new insights into the sparse
reconstruction of MRI. In this paper, we present a new approach to this problem
that iteratively fuses the information of k-space and MRI images using novel
dual Squeeze-Excitation Networks and Cross-Iteration Residual Connections. This
study included 720 clinical multi-coil brain MRI cases adopted from the
open-source deidentified fastMRI Dataset. 8-folder downsampling rate was
applied to generate the sparse k-space. Results showed that the average
reconstruction error over 120 testing cases by our proposed method was 2.28%,
which outperformed the existing image-domain prediction (6.03%, p<0.001),
k-space synthesis (6.12%, p<0.001), and dual-domain feature fusion (4.05%,
p<0.001).

Inverse protein folding, i.e., designing sequences that fold into a given
three-dimensional structure, is one of the fundamental design challenges in
bio-engineering and drug discovery. Traditionally, inverse folding mainly
involves learning from sequences that have an experimentally resolved
structure. However, the known structures cover only a tiny space of the protein
sequences, imposing limitations on the model learning. Recently proposed
forward folding models, e.g., AlphaFold, offer unprecedented opportunity for
accurate estimation of the structure given a protein sequence. Naturally,
incorporating a forward folding model as a component of an inverse folding
approach offers the potential of significantly improving the inverse folding,
as the folding model can provide a feedback on any generated sequence in the
form of the predicted protein structure or a structural confidence metric.
However, at present, these forward folding models are still prohibitively slow
to be a part of the model optimization loop during training. In this work, we
propose to perform knowledge distillation on the folding model's confidence
metrics, e.g., pTM or pLDDT scores, to obtain a smaller, faster and end-to-end
differentiable distilled model, which then can be included as part of the
structure consistency regularized inverse folding model training. Moreover, our
regularization technique is general enough and can be applied in other design
tasks, e.g., sequence-based protein infilling. Extensive experiments show a
clear benefit of our method over the non-regularized baselines. For example, in
inverse folding design problems we observe up to 3% improvement in sequence
recovery and up to 45% improvement in protein diversity, while still preserving
structural consistency of the generated sequences.

A hallmark of biological intelligence and control is combinatorial
generalization: animals are able to learn various things, then piece them
together in new combinations to produce appropriate outputs for new tasks.
Inspired by the ability of primates to readily imitate seen movement sequences,
we present a model of motor control using a realistic model of arm dynamics,
tasked with imitating a guide that makes arbitrary two-segment drawings. We
hypothesize that modular organization is one of the keys to such flexible and
generalizable control. We construct a modular control model consisting of
separate encoding and motor RNNs and a scheduler, which we train end-to-end on
the task. We show that the modular structure allows the model to generalize not
only to unseen two-segment trajectories, but to new drawings consisting of many
more segments than it was trained on, and also allows for rapid adaptation to
perturbations. Finally, our model recapitulates experimental observations of
the preparatory and execution-related processes unfolding during motor control,
providing a normative explanation for functional segregation of preparatory and
execution-related activity within the motor cortex.

Credit is an essential component of financial wellbeing in America, and
unequal access to it is a large factor in the economic disparities between
demographic groups that exist today. Today, machine learning algorithms,
sometimes trained on alternative data, are increasingly being used to determine
access to credit, yet research has shown that machine learning can encode many
different versions of "unfairness," thus raising the concern that banks and
other financial institutions could -- potentially unwittingly -- engage in
illegal discrimination through the use of this technology. In the US, there are
laws in place to make sure discrimination does not happen in lending and
agencies charged with enforcing them. However, conversations around fair credit
models in computer science and in policy are often misaligned: fair machine
learning research often lacks legal and practical considerations specific to
existing fair lending policy, and regulators have yet to issue new guidance on
how, if at all, credit risk models should be utilizing practices and techniques
from the research community. This paper aims to better align these sides of the
conversation. We describe the current state of credit discrimination regulation
in the United States, contextualize results from fair ML research to identify
the specific fairness concerns raised by the use of machine learning in
lending, and discuss regulatory opportunities to address these concerns.

Language modelling and machine translation tasks mostly use subword or
character inputs, but syllables are seldom used. Syllables provide shorter
sequences than characters, require less-specialised extracting rules than
morphemes, and their segmentation is not impacted by the corpus size. In this
study, we first explore the potential of syllables for open-vocabulary language
modelling in 21 languages. We use rule-based syllabification methods for six
languages and address the rest with hyphenation, which works as a
syllabification proxy. With a comparable perplexity, we show that syllables
outperform characters and other subwords. Moreover, we study the importance of
syllables on neural machine translation for a non-related and low-resource
language-pair (Spanish--Shipibo-Konibo). In pairwise and multilingual systems,
syllables outperform unsupervised subwords, and further morphological
segmentation methods, when translating into a highly synthetic language with a
transparent orthography (Shipibo-Konibo). Finally, we perform some human
evaluation, and discuss limitations and opportunities.

In this paper, we propose a new technique to uniquely classify and identify
multiple users accessing a single application using keystroke dynamics. This
problem is usually encountered when multiple users have legitimate access to
shared computers and accounts, where, at times, one user can inadvertently be
logged in on another user's account. Since the login processes are usually
bypassed at this stage, we rely on keystroke dynamics in order to tell users
apart. Our algorithm uses the quantile transform and techniques from
localization to classify and identify users. Specifically, we use an algorithm
known as ordinal Unfolding based Localization (UNLOC), which uses only ordinal
data obtained from comparing distance proxies, by "locating" users in a reduced
PCA/Kernel-PCA/t-SNE space based on their typing patterns. Our results are
validated with the help of benchmark keystroke datasets and show that our
algorithm outperforms other methods.

Recent approaches employ deep learning-based solutions for the recovery of a
sharp image from its blurry observation. This paper introduces adversarial
attacks against deep learning-based image deblurring methods and evaluates the
robustness of these neural networks to untargeted and targeted attacks. We
demonstrate that imperceptible distortion can significantly degrade the
performance of state-of-the-art deblurring networks, even producing drastically
different content in the output, indicating the strong need to include
adversarially robust training not only in classification but also for image
recovery.

Explainable question answering systems should produce not only accurate
answers but also rationales that justify their reasoning and allow humans to
check their work. But what sorts of rationales are useful and how can we train
systems to produce them? We propose a new style of rationale for open-book
question answering, called \emph{markup-and-mask}, which combines aspects of
extractive and free-text explanations. In the markup phase, the passage is
augmented with free-text markup that enables each sentence to stand on its own
outside the discourse context. In the masking phase, a sub-span of the
marked-up passage is selected. To train a system to produce markup-and-mask
rationales without annotations, we leverage in-context learning. Specifically,
we generate silver annotated data by sending a series of prompts to a frozen
pretrained language model, which acts as a teacher. We then fine-tune a smaller
student model by training on the subset of rationales that led to correct
answers. The student is "honest" in the sense that it is a pipeline: the
rationale acts as a bottleneck between the passage and the answer, while the
"untrusted" teacher operates under no such constraints. Thus, we offer a new
way to build trustworthy pipeline systems from a combination of end-task
annotations and frozen pretrained language models.

A key contributor to recent progress in 3D detection from single images is
monocular depth estimation. Existing methods focus on how to leverage depth
explicitly, by generating pseudo-pointclouds or providing attention cues for
image features. More recent works leverage depth prediction as a pretraining
task and fine-tune the depth representation while training it for 3D detection.
However, the adaptation is insufficient and is limited in scale by manual
labels. In this work, we propose to further align depth representation with the
target domain in unsupervised fashions. Our methods leverage commonly available
LiDAR or RGB videos during training time to fine-tune the depth representation,
which leads to improved 3D detectors. Especially when using RGB videos, we show
that our two-stage training by first generating pseudo-depth labels is critical
because of the inconsistency in loss distribution between the two tasks. With
either type of reference data, our multi-task learning approach improves over
the state of the art on both KITTI and NuScenes, while matching the test-time
complexity of its single task sub-network.

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an
emerging medical condition that has been observed in several patients with a
positive diagnosis for COVID-19. Historical Electronic Health Records (EHR)
like diagnosis codes, lab results and clinical notes have been analyzed using
deep learning and have been used to predict future clinical events. In this
paper, we propose an interpretable deep learning approach to analyze historical
diagnosis code data from the National COVID Cohort Collective (N3C) to find the
risk factors contributing to developing Long COVID. Using our deep learning
approach, we are able to predict if a patient is suffering from Long COVID from
a temporally ordered list of diagnosis codes up to 45 days post the first COVID
positive test or diagnosis for each patient, with an accuracy of 70.48\%. We
are then able to examine the trained model using Gradient-weighted Class
Activation Mapping (GradCAM) to give each input diagnoses a score. The highest
scored diagnosis were deemed to be the most important for making the correct
prediction for a patient. We also propose a way to summarize these top
diagnoses for each patient in our cohort and look at their temporal trends to
determine which codes contribute towards a positive Long COVID diagnosis.

We prove two lower bounds for the complexity of non-log-concave sampling
within the framework of Balasubramanian et al. (2022), who introduced the use
of Fisher information (FI) bounds as a notion of approximate first-order
stationarity in sampling. Our first lower bound shows that averaged LMC is
optimal for the regime of large FI by reducing the problem of finding
stationary points in non-convex optimization to sampling. Our second lower
bound shows that in the regime of small FI, obtaining a FI of at most
$\varepsilon^2$ from the target distribution requires
$\text{poly}(1/\varepsilon)$ queries, which is surprising as it rules out the
existence of high-accuracy algorithms (e.g., algorithms using
Metropolis-Hastings filters) in this context.

Few shot classification aims to learn to recognize novel categories using
only limited samples per category. Most current few shot methods use a base
dataset rich in labeled examples to train an encoder that is used for obtaining
representations of support instances for novel classes. Since the test
instances are from a distribution different to the base distribution, their
feature representations are of poor quality, degrading performance. In this
paper we propose to make use of the well-trained feature representations of the
base dataset that are closest to each support instance to improve its
representation during meta-test time. To this end, we propose BaseTransformers,
that attends to the most relevant regions of the base dataset feature space and
improves support instance representations. Experiments on three benchmark data
sets show that our method works well for several backbones and achieves
state-of-the-art results in the inductive one shot setting. Code is available
at github.com/mayug/BaseTransformers

Fast radio bursts (FRBs) are one of the most mysterious astronomical
transients. Observationally, they can be classified into repeaters and
apparently non-repeaters. However, due to the lack of continuous observations,
some apparently repeaters may have been incorrectly recognized as
non-repeaters. In a series of two papers, we intend to solve such problem with
machine learning. In this second paper of the series, we focus on an array of
unsupervised machine learning methods. We apply multiple unsupervised machine
learning algorithms to the first CHIME/FRB catalog to learn their features and
classify FRBs into different clusters without any premise about the FRBs being
repeaters or non-repeaters. These clusters reveal the differences between
repeaters and non-repeaters. Then, by comparing with the identities of the FRBs
in the observed classes, we evaluate the performance of various algorithms and
analyze the physical meaning behind the results. Finally, we recommend a list
of most credible repeater candidates as targets for future observing campaigns
to search for repeated bursts in combination of the results presented in Paper
I using supervised machine learning methods.

Observationally, the mysterious fast radio bursts (FRBs) are classified as
repeating ones and apparently non-repeating ones. While repeating FRBs cannot
be classified into the non-repeating group, it is unknown whether the
apparently non-repeating FRBs are actually repeating FRBs whose repetitions are
yet to be discovered, or whether they belong to another physically distinct
type from the repeating ones. In a series of two papers, we attempt to
disentangle this mystery with machine learning methods. In this first paper, we
focus on an array of supervised machine learning methods. We train the machine
learning algorithms with a fraction of the observed FRBs in the first CHIME/FRB
catalog, telling them which ones are apparently non-repeating and which ones
are repeating. We then let the trained models predict the repetitiveness of the
rest of the FRB data with the observed parameters, and we compare the
predictions with the observed repetitiveness. We find that the models can
predict most FRBs correctly, hinting towards distinct mechanisms behind
repeating and non-repeating FRBs. We also find that the two most important
distinguishing factors between non-repeating and repeating FRBs are brightness
temperature and rest-frame frequency bandwidth. By applying the trained models
back to the entire first CHIME catalog, we further identify some potentially
repeating FRBs currently reported as non-repeating. We recommend a list of
these bursts as targets for future observing campaigns to search for repeated
bursts in a combination with the results presented in Paper II using
unsupervised machine learning methods.

While recent camera-only 3D detection methods leverage multiple timesteps,
the limited history they use significantly hampers the extent to which temporal
fusion can improve object perception. Observing that existing works' fusion of
multi-frame images are instances of temporal stereo matching, we find that
performance is hindered by the interplay between 1) the low granularity of
matching resolution and 2) the sub-optimal multi-view setup produced by limited
history usage. Our theoretical and empirical analysis demonstrates that the
optimal temporal difference between views varies significantly for different
pixels and depths, making it necessary to fuse many timesteps over long-term
history. Building on our investigation, we propose to generate a cost volume
from a long history of image observations, compensating for the coarse but
efficient matching resolution with a more optimal multi-view matching setup.
Further, we augment the per-frame monocular depth predictions used for
long-term, coarse matching with short-term, fine-grained matching and find that
long and short term temporal fusion are highly complementary. While maintaining
high efficiency, our framework sets new state-of-the-art on nuScenes, achieving
first place on the test set and outperforming previous best art by 5.2% mAP and
3.7% NDS on the validation set. Code will be released
$\href{https://github.com/Divadi/SOLOFusion}{here.}$

Active learning promises to improve annotation efficiency by iteratively
selecting the most important data to be annotated first. However, we uncover a
striking contradiction to this promise: active learning fails to select data as
efficiently as random selection at the first few choices. We identify this as
the cold start problem in vision active learning, caused by a biased and
outlier initial query. This paper seeks to address the cold start problem by
exploiting the three advantages of contrastive learning: (1) no annotation is
required; (2) label diversity is ensured by pseudo-labels to mitigate bias; (3)
typical data is determined by contrastive features to reduce outliers.
Experiments are conducted on CIFAR-10-LT and three medical imaging datasets
(i.e. Colon Pathology, Abdominal CT, and Blood Cell Microscope). Our initial
query not only significantly outperforms existing active querying strategies
but also surpasses random selection by a large margin. We foresee our solution
to the cold start problem as a simple yet strong baseline to choose the initial
query for vision active learning. Code is available:
https://github.com/c-liangyu/CSVAL

We introduce the first work to explore web-scale diffusion models for
robotics. DALL-E-Bot enables a robot to rearrange objects in a scene, by first
inferring a text description of those objects, then generating an image
representing a natural, human-like arrangement of those objects, and finally
physically arranging the objects according to that image. The significance is
that we achieve this zero-shot using DALL-E, without needing any further data
collection or training. Encouraging real-world results with human studies show
that this is an exciting direction for the future of web-scale robot learning
algorithms. We also propose a list of recommendations to the text-to-image
community, to align further developments of these models with applications to
robotics. Videos are available at: https://www.robot-learning.uk/dall-e-bot

Image copy detection and retrieval from large databases leverage two
components. First, a neural network maps an image to a vector representation,
that is relatively robust to various transformations of the image. Second, an
efficient but approximate similarity search algorithm trades scalability (size
and speed) against quality of the search, thereby introducing a source of
error. This paper improves the robustness of image copy detection with active
indexing, that optimizes the interplay of these two components. We reduce the
quantization loss of a given image representation by making imperceptible
changes to the image before its release. The loss is back-propagated through
the deep neural network back to the image, under perceptual constraints. These
modifications make the image more retrievable. Our experiments show that the
retrieval and copy detection of activated images is significantly improved. For
instance, activation improves by $+40\%$ the Recall1@1 on various image
transformations, and for several popular indexing structures based on product
quantization and locality sensitivity hashing.

Defect identification at commit check-in time prevents the introduction of
defects into software. Current defect identification approaches either rely on
manually crafted features such as change metrics or involve training expensive
machine learning or deep learning models. By relying on a complex underlying
model, these approaches are not often explainable, which means the models'
predictions cannot be understood by the developers. An approach that is not
explainable might not be adopted in real-life development environments because
of developers' lack of trust in its results. Furthermore, because of an
extensive training process, these approaches cannot readily learn from new
examples when they arrive, making them unsuitable for fast online prediction.
To address these limitations, we propose an approach called IRJIT that employs
information retrieval on source code, and labels new commits as buggy or clean
based on their similarity to past buggy or clean commits. Our approach is
online and explainable as it can learn from new data without retraining, and
developers can see the documents that support a prediction. Through an
evaluation of 8 open-source projects, we show that IRJIT achieves AUC and F1
score close to the state-of-the-art machine learning approach JITLine, without
considerable re-training.

We present a new flow-based formulation for identical parallel machine
scheduling with a regular objective function and without idle time. The
formulation is constructed with the help of a decision diagram that represents
all job sequences that respect specific ordering rules. These rules rely on a
partition of the planning horizon into, generally non-uniform, periods and do
not exclude all optimal solutions, but they constrain solutions to adhere to a
canonical form. The new formulation has numerous variables and constraints, and
hence we apply a Dantzig-Wolfe decomposition in order to compute the linear
programming relaxation in reasonable time; the resulting lower bound is
stronger than the bound from the classical time-indexed formulation. We develop
a branch-and-price framework that solves several instances from the literature
for the first time. We compare the new formulation with the time-indexed and
arc-time-indexed formulation by means of a series of computational experiments.

Post-hoc explanation methods have become increasingly depended upon for
understanding black-box classifiers in high-stakes applications, precipitating
a need for reliable explanations. While numerous explanation methods have been
proposed, recent works have shown that many existing methods can be
inconsistent or unstable. In addition, high-performing classifiers are often
highly nonlinear and can exhibit complex behavior around the decision boundary,
leading to brittle or misleading local explanations. Therefore, there is an
impending need to quantify the uncertainty of such explanation methods in order
to understand when explanations are trustworthy. We introduce a novel
uncertainty quantification method parameterized by a Gaussian Process model,
which combines the uncertainty approximation of existing methods with a novel
geodesic-based similarity which captures the complexity of the target black-box
decision boundary. The proposed framework is highly flexible; it can be used
with any black-box classifier and feature attribution method to amortize
uncertainty estimates for explanations. We show theoretically that our proposed
geodesic-based kernel similarity increases with the complexity of the decision
boundary. Empirical results on multiple tabular and image datasets show that
our decision boundary-aware uncertainty estimate improves understanding of
explanations as compared to existing methods.

Segmentation of brain arterio-venous malformations (bAVMs) in 3D rotational
angiographies (3DRA) is still an open problem in the literature, with high
relevance for clinical practice. While deep learning models have been applied
for segmenting the brain vasculature in these images, they have never been used
in cases with bAVMs. This is likely caused by the difficulty to obtain
sufficiently annotated data to train these approaches. In this paper we
introduce a first deep learning model for blood vessel segmentation in 3DRA
images of patients with bAVMs. To this end, we densely annotated 5 3DRA volumes
of bAVM cases and used these to train two alternative 3DUNet-based
architectures with different segmentation objectives. Our results show that the
networks reach a comprehensive coverage of relevant structures for bAVM
analysis, much better than what is obtained using standard methods. This is
promising for achieving a better topological and morphological characterisation
of the bAVM structures of interest. Furthermore, the models have the ability to
segment venous structures even when missing in the ground truth labelling,
which is relevant for planning interventional treatments. Ultimately, these
results could be used as more reliable first initial guesses, alleviating the
cumbersome task of creating manual labels.

We revisit the problem of learning mixtures of spherical Gaussians. Given
samples from mixture $\frac{1}{k}\sum_{j=1}^{k}\mathcal{N}(\mu_j, I_d)$, the
goal is to estimate the means $\mu_1, \mu_2, \ldots, \mu_k \in \mathbb{R}^d$ up
to a small error. The hardness of this learning problem can be measured by the
separation $\Delta$ defined as the minimum distance between all pairs of means.
Regev and Vijayaraghavan (2017) showed that with $\Delta = \Omega(\sqrt{\log
k})$ separation, the means can be learned using $\mathrm{poly}(k, d)$ samples,
whereas super-polynomially many samples are required if $\Delta = o(\sqrt{\log
k})$ and $d = \Omega(\log k)$. This leaves open the low-dimensional regime
where $d = o(\log k)$.
  In this work, we give an algorithm that efficiently learns the means in $d =
O(\log k/\log\log k)$ dimensions under separation $d/\sqrt{\log k}$ (modulo
doubly logarithmic factors). This separation is strictly smaller than
$\sqrt{\log k}$, and is also shown to be necessary. Along with the results of
Regev and Vijayaraghavan (2017), our work almost pins down the critical
separation threshold at which efficient parameter learning becomes possible for
spherical Gaussian mixtures. More generally, our algorithm runs in time
$\mathrm{poly}(k)\cdot f(d, \Delta, \epsilon)$, and is thus fixed-parameter
tractable in parameters $d$, $\Delta$ and $\epsilon$.
  Our approach is based on estimating the Fourier transform of the mixture at
carefully chosen frequencies, and both the algorithm and its analysis are
simple and elementary. Our positive results can be easily extended to learning
mixtures of non-Gaussian distributions, under a mild condition on the Fourier
spectrum of the distribution.

Machine learning (ML) enabled classification models are becoming increasingly
popular for tackling the sheer volume and speed of online misinformation. In
building these models, data scientists need to take a stance on the legitimacy,
authoritativeness and objectivity of the sources of `truth' used for model
training and testing. This has political, ethical and epistemic implications
which are rarely addressed in technical papers. Despite (and due to) their
reported high performance, ML-driven moderation systems have the potential to
shape online public debate and create downstream negative impacts such as undue
censorship and reinforcing false beliefs. This article reports on a responsible
innovation (RI) inflected collaboration at the intersection of social studies
of science and data science. We identify a series of algorithmic
contingencies--key moments during model development which could lead to
different future outcomes, uncertainty and harmful effects. We conclude by
offering an agenda of reflexivity and responsible development of ML tools for
combating misinformation.

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language
model with 130 billion parameters. It is an attempt to open-source a 100B-scale
model at least as good as GPT-3 and unveil how models of such a scale can be
successfully pre-trained. Over the course of this effort, we face numerous
unexpected technical and engineering challenges, particularly on loss spikes
and disconvergence. In this paper, we introduce the training process of
GLM-130B including its design choices, training strategies for both efficiency
and stability, and engineering efforts. The resultant GLM-130B model offers
significant outperformance over GPT-3 175B on a wide range of popular English
benchmarks while the performance advantage is not observed in OPT-175B and
BLOOM-176B. It also consistently and significantly outperforms ERNIE TITAN 3.0
260B -- the largest Chinese language model -- across related benchmarks.
Finally, we leverage a unique scaling property of GLM-130B to reach INT4
quantization, without quantization aware training and with almost no
performance loss, making it the first among 100B-scale models. More
importantly, the property allows its effective inference on 4$\times$RTX 3090
(24G) or 8$\times$RTX 2080 Ti (11G) GPUs, the most ever affordable GPUs
required for using 100B-scale models. The GLM-130B model weights are publicly
accessible and its code, training logs, related toolkit, and lessons learned
are open-sourced at https://github.com/THUDM/GLM-130B .

Random masks define surprisingly effective sparse neural network models, as
has been shown empirically. The resulting Erd\"os-R\'enyi (ER) random graphs
can often compete with dense architectures and state-of-the-art lottery ticket
pruning algorithms struggle to outperform them, even though the random
baselines do not rely on computationally expensive pruning-training iterations
but can be drawn initially without significant computational overhead. We offer
a theoretical explanation of how such ER masks can approximate arbitrary target
networks if they are wider by a logarithmic factor in the inverse sparsity $1 /
\log(1/\text{sparsity})$. While we are the first to show theoretically and
experimentally that random ER source networks contain strong lottery tickets,
we also prove the existence of weak lottery tickets that require a lower degree
of overparametrization than strong lottery tickets. These unusual results are
based on the observation that ER masks are well trainable in practice, which we
verify in experiments with varied choices of random masks. Some of these
data-free choices outperform previously proposed random approaches on standard
image classification benchmark datasets.

The training success, training speed and generalization ability of neural
networks rely crucially on the choice of random parameter initialization. It
has been shown for multiple architectures that initial dynamical isometry is
particularly advantageous. Known initialization schemes for residual blocks,
however, miss this property and suffer from degrading separability of different
inputs for increasing depth and instability without Batch Normalization or lack
feature diversity. We propose a random initialization scheme, RISOTTO, that
achieves perfect dynamical isometry for residual networks with ReLU activation
functions even for finite depth and width. It balances the contributions of the
residual and skip branches unlike other schemes, which initially bias towards
the skip connections. In experiments, we demonstrate that in most cases our
approach outperforms initialization schemes proposed to make Batch
Normalization obsolete, including Fixup and SkipInit, and facilitates stable
training. Also in combination with Batch Normalization, we find that RISOTTO
often achieves the overall best result.

Diversity is an important criterion for many areas of machine learning (ML),
including generative modeling and dataset curation. Yet little work has gone
into understanding, formalizing, and measuring diversity in ML. In this paper,
we address the diversity evaluation problem by proposing the Vendi Score, which
connects and extends ideas from ecology and quantum statistical mechanics to
ML. The Vendi Score is defined as the exponential of the Shannon entropy of the
eigenvalues of a similarity matrix. This matrix is induced by a user-defined
similarity function applied to the sample to be evaluated for diversity. In
taking a similarity function as input, the Vendi Score enables its user to
specify any desired form of diversity. Importantly, unlike many existing
metrics in ML, the Vendi Score doesn't require a reference dataset or
distribution over samples or labels, it is therefore general and applicable to
any generative model, decoding algorithm, and dataset from any domain where
similarity can be defined. We showcased the Vendi Score on molecular generative
modeling, a domain where diversity plays an important role in enabling the
discovery of novel molecules. We found that the Vendi Score addresses
shortcomings of the current diversity metric of choice in that domain. We also
applied the Vendi Score to generative models of images and decoding algorithms
of text and found it confirms known results about diversity in those domains.
Furthermore, we used the Vendi Score to measure mode collapse, a known
limitation of generative adversarial networks (GANs). In particular, the Vendi
Score revealed that even GANs that capture all the modes of a labeled dataset
can be less diverse than the original dataset. Finally, the interpretability of
the Vendi Score allowed us to diagnose several benchmark ML datasets for
diversity, opening the door for diversity-informed data augmentation.

This article aims to provide a theoretical account and corresponding paradigm
for analysing how explainable artificial intelligence (XAI) influences people's
behaviour and cognition. It uses insights from research on behaviour change.
Two notable frameworks for thinking about behaviour change techniques are
nudges - aimed at influencing behaviour - and boosts - aimed at fostering
capability. It proposes that local and concept-based explanations are more
adjacent to nudges, while global and counterfactual explanations are more
adjacent to boosts. It outlines a method for measuring XAI influence and argues
for the benefits of understanding it for optimal, safe and ethical human-AI
collaboration.

Few-shot prompting is a surprisingly powerful way to use Large Language
Models (LLMs) to solve various tasks. However, this approach struggles as the
task complexity increases or when the individual reasoning steps of the task
themselves are hard to learn, especially when embedded in more complex tasks.
To address this, we propose Decomposed Prompting, a new approach to solve
complex tasks by decomposing them (via prompting) into simpler sub-tasks that
can be delegated to a library of prompting-based LLMs dedicated to these
sub-tasks. This modular structure allows each prompt to be optimized for its
specific sub-task, further decomposed if necessary, and even easily replaced
with more effective prompts, trained models, or symbolic functions if desired.
We show that the flexibility and modularity of Decomposed Prompting allows it
to outperform prior work on few-shot prompting using GPT3. On symbolic
reasoning tasks, we can further decompose sub-tasks that are hard for LLMs into
even simpler solvable sub-tasks. When the complexity comes from the input
length, we can recursively decompose the task into the same task but with
smaller inputs. We also evaluate our approach on textual multi-step reasoning
tasks: on long-context multi-hop QA task, we can more effectively teach the
sub-tasks via our separate sub-tasks prompts; and on open-domain multi-hop QA,
we can incorporate a symbolic information retrieval within our decomposition
framework, leading to improved performance on both tasks.

Conditionality has become a core component for Generative Adversarial
Networks (GANs) for generating synthetic images. GANs are usually using latent
conditionality to control the generation process. However, tabular data only
contains manifest variables. Thus, latent conditionality either restricts the
generated data or does not produce sufficiently good results. Therefore, we
propose a new methodology to include conditionality in tabular GANs inspired by
image completion methods. This article presents ciDATGAN, an evolution of the
Directed Acyclic Tabular GAN (DATGAN) that has already been shown to outperform
state-of-the-art tabular GAN models. First, we show that the addition of
conditional inputs does hinder the model's performance compared to its
predecessor. Then, we demonstrate that ciDATGAN can be used to unbias datasets
with the help of well-chosen conditional inputs. Finally, it shows that
ciDATGAN can learn the logic behind the data and, thus, be used to complete
large synthetic datasets using data from a smaller feeder dataset.

This paper presents a web-based demonstration of Emotion Twenty Questions
(EMO20Q), a dialog game whose purpose is to study how people describe emotions.
EMO20Q can also be used to develop artificially intelligent dialog agents that
can play the game. In previous work, an EMO20Q agent used a sequential Bayesian
machine learning model and could play the question-asking role. Newer
transformer-based neural machine learning models have made it possible to
develop an agent for the question-answering role.
  This demo paper describes the recent developments in the question-answering
role of the EMO20Q game, which requires the agent to respond to more open-ended
inputs. Furthermore, we also describe the design of the system, including the
web-based front-end, agent architecture and programming, and updates to earlier
software used.
  The demo system will be available to collect pilot data during the ACII
conference and this data will be used to inform future experiments and system
design.

We present Phenaki, a model capable of realistic video synthesis, given a
sequence of textual prompts. Generating videos from text is particularly
challenging due to the computational cost, limited quantities of high quality
text-video data and variable length of videos. To address these issues, we
introduce a new model for learning video representation which compresses the
video to a small representation of discrete tokens. This tokenizer uses causal
attention in time, which allows it to work with variable-length videos. To
generate video tokens from text we are using a bidirectional masked transformer
conditioned on pre-computed text tokens. The generated video tokens are
subsequently de-tokenized to create the actual video. To address data issues,
we demonstrate how joint training on a large corpus of image-text pairs as well
as a smaller number of video-text examples can result in generalization beyond
what is available in the video datasets. Compared to the previous video
generation methods, Phenaki can generate arbitrary long videos conditioned on a
sequence of prompts (i.e. time variable text or a story) in open domain. To the
best of our knowledge, this is the first time a paper studies generating videos
from time variable prompts. In addition, compared to the per-frame baselines,
the proposed video encoder-decoder computes fewer tokens per video but results
in better spatio-temporal consistency.

Generating long, temporally consistent video remains an open challenge in
video generation. Primarily due to computational limitations, most prior
methods limit themselves to training on a small subset of frames that are then
extended to generate longer videos through a sliding window fashion. Although
these techniques may produce sharp videos, they have difficulty retaining
long-term temporal consistency due to their limited context length. In this
work, we present Temporally Consistent Video Transformer (TECO), a
vector-quantized latent dynamics video prediction model that learns compressed
representations to efficiently condition on long videos of hundreds of frames
during both training and generation. We use a MaskGit prior for dynamics
prediction which enables both sharper and faster generations compared to prior
work. Our experiments show that TECO outperforms SOTA baselines in a variety of
video prediction benchmarks ranging from simple mazes in DMLab, large 3D worlds
in Minecraft, and complex real-world videos from Kinetics-600. In addition, to
better understand the capabilities of video prediction models in modeling
temporal consistency, we introduce several challenging video prediction tasks
consisting of agents randomly traversing 3D scenes of varying difficulty. This
presents a challenging benchmark for video prediction in partially observable
environments where a model must understand what parts of the scenes to
re-create versus invent depending on its past observations or generations.
Generated videos are available at https://wilson1yan.github.io/teco

Knowledge tracing (KT) is a fundamental task in educational data mining that
mainly focuses on students' dynamic cognitive states of skills. The
question-answering process of students can be regarded as a thinking process
that considers the following two problems. One problem is which skills are
needed to answer the question, and the other is how to use these skills in
order. If a student wants to answer a question correctly, the student should
not only master the set of skills involved in the question but also think and
obtain the associative path on the skills graph. The nodes in the associative
path refer to the skills needed and the path shows the order of using them. The
associative path is referred to as the skill mode. Thus, obtaining the skill
modes is the key to answering questions successfully. However, most existing KT
models only focus on a set of skills, without considering the skill modes. We
propose a KT model, called APGKT, that exploits skill modes. Specifically, we
extract the subgraph topology of the skills involved in the question and
combine the difficulty level of the skills to obtain the skill modes via
encoding; then, through multi-layer recurrent neural networks, we obtain a
student's higher-order cognitive states of skills, which is used to predict the
student's future answering performance. Experiments on five benchmark datasets
validate the effectiveness of the proposed model.

Face animation aims at creating photo-realistic portrait videos with animated
poses and expressions. A common practice is to generate displacement fields
that are used to warp pixels and features from source to target. However, prior
attempts often produce sub-optimal displacements. In this work, we present a
geometry driven model and propose two geometric patterns as guidance: 3D face
rendered displacement maps and posed neural codes. The model can optionally use
one of the patterns as guidance for displacement estimation. To model
displacements at locations not covered by the face model (e.g., hair), we
resort to source image features for contextual information and propose a
progressive warping module that alternates between feature warping and
displacement estimation at increasing resolutions. We show that the proposed
model can synthesize portrait videos with high fidelity and achieve the new
state-of-the-art results on the VoxCeleb1 and VoxCeleb2 datasets for both cross
identity and same identity reconstruction.

Prompt tuning provides an efficient mechanism to adapt large vision-language
models to downstream tasks by treating part of the input language prompts as
learnable parameters while freezing the rest of the model. Existing works for
prompt tuning are however prone to damaging the generalization capabilities of
the foundation models, because the learned prompts lack the capacity of
covering certain concepts within the language model. To avoid such limitation,
we propose a probabilistic modeling of the underlying distribution of prompts,
allowing prompts within the support of an associated concept to be derived
through stochastic sampling. This results in a more complete and richer
transfer of the information captured by the language model, providing better
generalization capabilities for downstream tasks. The resulting algorithm
relies on a simple yet powerful variational framework that can be directly
integrated with other developments. We show our approach is seamlessly
integrated into both standard and conditional prompt learning frameworks,
improving the performance on both cases considerably, especially with regards
to preserving the generalization capability of the original model. Our method
provides the current state-of-the-art for prompt learning, surpassing CoCoOp by
1.6% average Top-1 accuracy on the standard benchmark. Remarkably, it even
surpasses the original CLIP model in terms of generalization to new classes.
Implementation code will be released.

Data-driven techniques for machine vision heavily depend on the training data
to sufficiently resemble the data occurring during test and application.
However, in practice unknown distortion can lead to a domain gap between
training and test data, impeding the performance of a machine vision system.
With our proposed approach this domain gap can be closed by unpaired learning
of the pristine-to-distortion mapping function of the unknown distortion. This
learned mapping function may then be used to emulate the unknown distortion in
the training data. Employing a fixed setup, our approach is independent from
prior knowledge of the distortion. Within this work, we show that we can
effectively learn unknown distortions at arbitrary strengths. When applying our
approach to instance segmentation in an autonomous driving scenario, we achieve
results comparable to an oracle with knowledge of the distortion. An average
gain in mean Average Precision (mAP) of up to 0.19 can be achieved.

Proportional-integral-derivative (PID) controllers have been widely used in
the process industry. However, the satisfactory control performance of a PID
controller depends strongly on the tuning parameters. Conventional PID tuning
methods require extensive knowledge of the system model, which is not always
known especially in the case of complex dynamical systems. In contrast,
reinforcement learning-based PID tuning has gained popularity since it can
treat PID tuning as a black-box problem and deliver the optimal PID parameters
without requiring explicit process models. In this paper, we present a novel
entropy-maximizing twin-delayed deep deterministic policy gradient (EMTD3)
method for automating the PID tuning. In the proposed method, an
entropy-maximizing stochastic actor is employed at the beginning to encourage
the exploration of the action space. Then a deterministic actor is deployed to
focus on local exploitation and discover the optimal solution. The
incorporation of the entropy-maximizing term can significantly improve the
sample efficiency and assist in fast convergence to the global solution. Our
proposed method is applied to the PID tuning of a second-order system to verify
its effectiveness in improving the sample efficiency and discovering the
optimal PID parameters compared to traditional TD3.

In automated planning, recognising the goal of an agent from a trace of
observations is an important task with many applications. The state-of-the-art
approaches to goal recognition rely on the application of planning techniques,
which requires a model of the domain actions and of the initial domain state
(written, e.g., in PDDL). We study an alternative approach where goal
recognition is formulated as a classification task addressed by machine
learning. Our approach, called GRNet, is primarily aimed at making goal
recognition more accurate as well as faster by learning how to solve it in a
given domain. Given a planning domain specified by a set of propositions and a
set of action names, the goal classification instances in the domain are solved
by a Recurrent Neural Network (RNN). A run of the RNN processes a trace of
observed actions to compute how likely it is that each domain proposition is
part of the agent's goal, for the problem instance under considerations. These
predictions are then aggregated to choose one of the candidate goals. The only
information required as input of the trained RNN is a trace of action labels,
each one indicating just the name of an observed action. An experimental
analysis confirms that \our achieves good performance in terms of both goal
classification accuracy and runtime, obtaining better performance w.r.t. a
state-of-the-art goal recognition system over the considered benchmarks.

Axon is a language that enables shape and rank inference for tensors in a
Deep Learning graphs. It aims to make shapes implicit and inferred, in a
similar manner to how types are implicit and inferred in many functional
programming languages. Tensor dimensions are represented by expressions
consisting of symbolic variables, constants, and arithmetic operators. Tensor
shapes can be expressed as either a sequence of these dimension expressions, as
a symbolic variable, or as an appending of other shapes. This allows complex
constraints on shapes to be expressed. Axon is functional in style, with a type
system similar in to Standard ML, extended to include shape information. It
provides a suite of built in operators over tensors, including pointwise
arithmetic operators, maps, reduction, loops and user defined functions. We
describe a shape inference algorithm based on constraint solving which infers
information about shapes, from both shape information provided by the
programmer and the structure of the program. This allows fully automatic
inference of the shapes of tensors for complex Deep Learning graphs. This
approach reduces programmer effort when specifying graphs, as tensor shapes are
not explicit, allows composition of Deep Learning graphs while maintaining
input and output tensor shape compatibility, and aids in automated error
detection by identifying shape mismatches at runtime.

Neural networks have gained much interest because of their effectiveness in
many applications. However, their mathematical properties are generally not
well understood. If there is some underlying geometric structure inherent to
the data or to the function to approximate, it is often desirable to take this
into account in the design of the neural network. In this work, we start with a
non-autonomous ODE and build neural networks using a suitable,
structure-preserving, numerical time-discretisation. The structure of the
neural network is then inferred from the properties of the ODE vector field.
Besides injecting more structure into the network architectures, this modelling
procedure allows a better theoretical understanding of their behaviour. We
present two universal approximation results and demonstrate how to impose some
particular properties on the neural networks. A particular focus is on
1-Lipschitz architectures including layers that are not 1-Lipschitz. These
networks are expressive and robust against adversarial attacks, as shown for
the CIFAR-10 dataset.

This paper presents the novel idea of generating object proposals by
leveraging temporal information for video object detection. The feature
aggregation in modern region-based video object detectors heavily relies on
learned proposals generated from a single-frame RPN. This imminently introduces
additional components like NMS and produces unreliable proposals on low-quality
frames. To tackle these restrictions, we present SparseVOD, a novel video
object detection pipeline that employs Sparse R-CNN to exploit temporal
information. In particular, we introduce two modules in the dynamic head of
Sparse R-CNN. First, the Temporal Feature Extraction module based on the
Temporal RoI Align operation is added to extract the RoI proposal features.
Second, motivated by sequence-level semantic aggregation, we incorporate the
attention-guided Semantic Proposal Feature Aggregation module to enhance object
feature representation before detection. The proposed SparseVOD effectively
alleviates the overhead of complicated post-processing methods and makes the
overall pipeline end-to-end trainable. Extensive experiments show that our
method significantly improves the single-frame Sparse RCNN by 8%-9% in mAP.
Furthermore, besides achieving state-of-the-art 80.3% mAP on the ImageNet VID
dataset with ResNet-50 backbone, our SparseVOD outperforms existing
proposal-based methods by a significant margin on increasing IoU thresholds
(IoU > 0.5).

In surveys, it is typically up to the individuals to decide if they want to
participate or not, which leads to participation bias: the individuals willing
to share their data might not be representative of the entire population.
Similarly, there are cases where one does not have direct access to any data of
the target population and has to resort to publicly available proxy data
sampled from a different distribution. In this paper, we present Differentially
Private Propensity Scores for Bias Correction (DiPPS), a method for
approximating the true data distribution of interest in both of the above
settings. We assume that the data analyst has access to a dataset $\tilde{D}$
that was sampled from the distribution of interest in a biased way. As
individuals may be more willing to share their data when given a privacy
guarantee, we further assume that the analyst is allowed locally differentially
private access to a set of samples $D$ from the true, unbiased distribution.
Each data point from the private, unbiased dataset $D$ is mapped to a
probability distribution over clusters (learned from the biased dataset
$\tilde{D}$), from which a single cluster is sampled via the exponential
mechanism and shared with the data analyst. This way, the analyst gathers a
distribution over clusters, which they use to compute propensity scores for the
points in the biased $\tilde{D}$, which are in turn used to reweight the points
in $\tilde{D}$ to approximate the true data distribution. It is now possible to
compute any function on the resulting reweighted dataset without further access
to the private $D$. In experiments on datasets from various domains, we show
that DiPPS successfully brings the distribution of the available dataset closer
to the distribution of interest in terms of Wasserstein distance. We further
show that this results in improved estimates for different statistics.

Self-supervised monocular depth estimation is a salient task for 3D scene
understanding. Learned jointly with monocular ego-motion estimation, several
methods have been proposed to predict accurate pixel-wise depth without using
labeled data. Nevertheless, these methods focus on improving performance under
ideal conditions without natural or digital corruptions. A general absence of
occlusions is assumed even for object-specific depth estimation. These methods
are also vulnerable to adversarial attacks, which is a pertinent concern for
their reliable deployment on robots and autonomous driving systems. We propose
MIMDepth, a method that adapts masked image modeling (MIM) for self-supervised
monocular depth estimation. While MIM has been used to learn generalizable
features during pre-training, we show how it could be adapted for direct
training of monocular depth estimation. Our experiments show that MIMDepth is
more robust to noise, blur, weather conditions, digital artifacts, occlusions,
as well as untargeted and targeted adversarial attacks.

The emergence of Quantum Machine Learning (QML) to enhance traditional
classical learning methods has seen various limitations to its realisation.
There is therefore an imperative to develop quantum models with unique model
hypotheses to attain expressional and computational advantage. In this work we
extend the linear quantum support vector machine (QSVM) with kernel function
computed through quantum kernel estimation (QKE), to form a decision tree
classifier constructed from a decision directed acyclic graph of QSVM nodes -
the ensemble of which we term the quantum random forest (QRF). To limit
overfitting, we further extend the model to employ a low-rank Nystr\"{o}m
approximation to the kernel matrix. We provide generalisation error bounds on
the model and theoretical guarantees to limit errors due to finite sampling on
the Nystr\"{o}m-QKE strategy. In doing so, we show that we can achieve lower
sampling complexity when compared to QKE. We numerically illustrate the effect
of varying model hyperparameters and finally demonstrate that the QRF is able
obtain superior performance over QSVMs, while also requiring fewer kernel
estimations.

Machine learning is a powerful approach for fitting microstructural models to
diffusion MRI data. Early machine learning microstructure imaging
implementations trained regressors to estimate model parameters in a supervised
way, using synthetic training data with known ground truth. However, a drawback
of this approach is that the choice of training data impacts fitted parameter
values. Self-supervised learning is emerging as an attractive alternative to
supervised learning in this context. Thus far, both supervised and
self-supervised learning have typically been applied to isotropic models, such
as intravoxel incoherent motion (IVIM), as opposed to models where the
directionality of anisotropic structures is also estimated. In this paper, we
demonstrate self-supervised machine learning model fitting for a directional
microstructural model. In particular, we fit a combined T1-ball-stick model to
the multidimensional diffusion (MUDI) challenge diffusion-relaxation dataset.
Our self-supervised approach shows clear improvements in parameter estimation
and computational time, for both simulated and in-vivo brain data, compared to
standard non-linear least squares fitting. Code for the artificial neural net
constructed for this study is available for public use from the following
GitHub repository: https://github.com/jplte/deep-T1-ball-stick

We consider how to most efficiently leverage teleoperator time to collect
data for learning robust image-based value functions and policies for sparse
reward robotic tasks. To accomplish this goal, we modify the process of data
collection to include more than just successful demonstrations of the desired
task. Instead we develop a novel protocol that we call Visual Backtracking
Teleoperation (VBT), which deliberately collects a dataset of visually similar
failures, recoveries, and successes. VBT data collection is particularly useful
for efficiently learning accurate value functions from small datasets of
image-based observations. We demonstrate VBT on a real robot to perform
continuous control from image observations for the deformable manipulation task
of T-shirt grasping. We find that by adjusting the data collection process we
improve the quality of both the learned value functions and policies over a
variety of baseline methods for data collection. Specifically, we find that
offline reinforcement learning on VBT data outperforms standard behavior
cloning on successful demonstration data by 13% when both methods are given
equal-sized datasets of 60 minutes of data from the real robot.

We demonstrate the utility of deep learning for modeling the clustering of
particles that are aerodynamically coupled to turbulent fluids. Using a
Lagrangian particle module within the ATHENA++ hydrodynamics code, we simulate
the dynamics of particles in the Epstein drag regime within a periodic domain
of isotropic forced hydrodynamic turbulence. This setup is an idealized model
relevant to the collisional growth of micron to mmsized dust particles in early
stage planet formation. The simulation data is used to train a U-Net deep
learning model to predict gridded three-dimensional representations of the
particle density and velocity fields, given as input the corresponding fluid
fields. The trained model qualitatively captures the filamentary structure of
clustered particles in a highly non-linear regime. We assess model fidelity by
calculating metrics of the density structure (the radial distribution function)
and of the velocity field (the relative velocity and the relative radial
velocity between particles). Although trained only on the spatial fields, the
model predicts these statistical quantities with errors that are typically <
10%. Our results suggest that, given appropriately expanded training data, deep
learning could be used to accelerate calculations of particle clustering and
collision outcomes both in protoplanetary disks, and in related two-fluid
turbulence problems that arise in other disciplines.

Artistic pieces can be studied from several perspectives, one example being
their reception among readers over time. In the present work, we approach this
interesting topic from the standpoint of literary works, particularly assessing
the task of predicting whether a book will become a best seller. Dissimilarly
from previous approaches, we focused on the full content of books and
considered visualization and classification tasks. We employed visualization
for the preliminary exploration of the data structure and properties, involving
SemAxis and linear discriminant analyses. Then, to obtain quantitative and more
objective results, we employed various classifiers. Such approaches were used
along with a dataset containing (i) books published from 1895 to 1924 and
consecrated as best sellers by the \emph{Publishers Weekly Bestseller Lists}
and (ii) literary works published in the same period but not being mentioned in
that list. Our comparison of methods revealed that the best-achieved result -
combining a bag-of-words representation with a logistic regression classifier -
led to an average accuracy of 0.75 both for the leave-one-out and 10-fold
cross-validations. Such an outcome suggests that it is unfeasible to predict
the success of books with high accuracy using only the full content of the
texts. Nevertheless, our findings provide insights into the factors leading to
the relative success of a literary work.

Graph Contrastive Learning (GCL), learning the node representations by
augmenting graphs, has attracted considerable attentions. Despite the
proliferation of various graph augmentation strategies, some fundamental
questions still remain unclear: what information is essentially encoded into
the learned representations by GCL? Are there some general graph augmentation
rules behind different augmentations? If so, what are they and what insights
can they bring? In this paper, we answer these questions by establishing the
connection between GCL and graph spectrum. By an experimental investigation in
spectral domain, we firstly find the General grAph augMEntation (GAME) rule for
GCL, i.e., the difference of the high-frequency parts between two augmented
graphs should be larger than that of low-frequency parts. This rule reveals the
fundamental principle to revisit the current graph augmentations and design new
effective graph augmentations. Then we theoretically prove that GCL is able to
learn the invariance information by contrastive invariance theorem, together
with our GAME rule, for the first time, we uncover that the learned
representations by GCL essentially encode the low-frequency information, which
explains why GCL works. Guided by this rule, we propose a spectral graph
contrastive learning module (SpCo), which is a general and GCL-friendly
plug-in. We combine it with different existing GCL models, and extensive
experiments well demonstrate that it can further improve the performances of a
wide variety of different GCL methods.

Federated Learning (FL) has recently emerged as a possible way to tackle the
domain shift in real-world Semantic Segmentation (SS) without compromising the
private nature of the collected data. However, most of the existing works on FL
unrealistically assume labeled data in the remote clients. Here we propose a
novel task (FFREEDA) in which the clients' data is unlabeled and the server
accesses a source labeled dataset for pre-training only. To solve FFREEDA, we
propose LADD, which leverages the knowledge of the pre-trained model by
employing self-supervision with ad-hoc regularization techniques for local
training and introducing a novel federated clustered aggregation scheme based
on the clients' style. Our experiments show that our algorithm is able to
efficiently tackle the new task outperforming existing approaches. The code is
available at https://github.com/Erosinho13/LADD.

In this paper, we study the problem of unsupervised object segmentation from
single images. We do not introduce a new algorithm, but systematically
investigate the effectiveness of existing unsupervised models on challenging
real-world images. We firstly introduce four complexity factors to
quantitatively measure the distributions of object- and scene-level biases in
appearance and geometry for datasets with human annotations. With the aid of
these factors, we empirically find that, not surprisingly, existing
unsupervised models catastrophically fail to segment generic objects in
real-world images, although they can easily achieve excellent performance on
numerous simple synthetic datasets, due to the vast gap in objectness biases
between synthetic and real images. By conducting extensive experiments on
multiple groups of ablated real-world datasets, we ultimately find that the key
factors underlying the colossal failure of existing unsupervised models on
real-world images are the challenging distributions of object- and scene-level
biases in appearance and geometry. Because of this, the inductive biases
introduced in existing unsupervised models can hardly capture the diverse
object distributions. Our research results suggest that future work should
exploit more explicit objectness biases in the network design.

With the proliferation of distributed generations, traditional passive
consumers in distribution networks are evolving into "prosumers", which can
both produce and consume energy. Energy trading with the main grid or between
prosumers is inevitable if the energy surplus and shortage exist. To this end,
this paper investigates the peer-to-peer (P2P) energy trading market, which is
formulated as a generalized Nash game. We first prove the existence and
uniqueness of the generalized Nash equilibrium (GNE). Then, an distributed
online algorithm is proposed to track the GNE in the time-varying environment.
Its regret is proved to be bounded by a sublinear function of learning time,
which indicates that the online algorithm has an acceptable accuracy in
practice. Finally, numerical results with six microgrids validate the
performance of the algorithm.

Real-time learning is crucial for robotic agents adapting to ever-changing,
non-stationary environments. A common setup for a robotic agent is to have two
different computers simultaneously: a resource-limited local computer tethered
to the robot and a powerful remote computer connected wirelessly. Given such a
setup, it is unclear to what extent the performance of a learning system can be
affected by resource limitations and how to efficiently use the wirelessly
connected powerful computer to compensate for any performance loss. In this
paper, we implement a real-time learning system called the Remote-Local
Distributed (ReLoD) system to distribute computations of two deep reinforcement
learning (RL) algorithms, Soft Actor-Critic (SAC) and Proximal Policy
Optimization (PPO), between a local and a remote computer. The performance of
the system is evaluated on two vision-based control tasks developed using a
robotic arm and a mobile robot. Our results show that SAC's performance
degrades heavily on a resource-limited local computer. Strikingly, when all
computations of the learning system are deployed on a remote workstation, SAC
fails to compensate for the performance loss, indicating that, without careful
consideration, using a powerful remote computer may not result in performance
improvement. However, a carefully chosen distribution of computations of SAC
consistently and substantially improves its performance on both tasks. On the
other hand, the performance of PPO remains largely unaffected by the
distribution of computations. In addition, when all computations happen solely
on a powerful tethered computer, the performance of our system remains on par
with an existing system that is well-tuned for using a single machine. ReLoD is
the only publicly available system for real-time RL that applies to multiple
robots for vision-based tasks.

Classifying pill categories from real-world images is crucial for various
smart healthcare applications. Although existing approaches in image
classification might achieve a good performance on fixed pill categories, they
fail to handle novel instances of pill categories that are frequently presented
to the learning algorithm. To this end, a trivial solution is to train the
model with novel classes. However, this may result in a phenomenon known as
catastrophic forgetting, in which the system forgets what it learned in
previous classes. In this paper, we address this challenge by introducing the
class incremental learning (CIL) ability to traditional pill image
classification systems. Specifically, we propose a novel incremental
multi-stream intermediate fusion framework enabling incorporation of an
additional guidance information stream that best matches the domain of the
problem into various state-of-the-art CIL methods. From this framework, we
consider color-specific information of pill images as a guidance stream and
devise an approach, namely "Color Guidance with Multi-stream intermediate
fusion"(CG-IMIF) for solving CIL pill image classification task. We conduct
comprehensive experiments on real-world incremental pill image classification
dataset, namely VAIPE-PCIL, and find that the CG-IMIF consistently outperforms
several state-of-the-art methods by a large margin in different task settings.
Our code, data, and trained model are available at
https://github.com/vinuni-vishc/CG-IMIF.

Traditional visual navigation methods of micro aerial vehicle (MAV) usually
calculate a passable path that satisfies the constraints depending on a prior
map. However, these methods have issues such as high demand for computing
resources and poor robustness in face of unfamiliar environments. Aiming to
solve the above problems, we propose a neuromorphic reinforcement learning
method (Neuro-Planner) that combines spiking neural network (SNN) and deep
reinforcement learning (DRL) to realize MAV 3D visual navigation with depth
camera. Specifically, we design spiking actor network based on two-state LIF
(TS-LIF) neurons and its encoding-decoding schemes for efficient inference.
Then our improved hybrid deep deterministic policy gradient (HDDPG) and
TS-LIF-based spatio-temporal back propagation (STBP) algorithms are used as the
training framework for actor-critic network architecture. To verify the
effectiveness of the proposed Neuro-Planner, we carry out detailed comparison
experiments with various SNN training algorithm (STBP, BPTT and SLAYER) in the
software-in-the-loop (SITL) simulation framework. The navigation success rate
of our HDDPG-STBP is 4.3\% and 5.3\% higher than that of the original DDPG in
the two evaluation environments. To the best of our knowledge, this is the
first work combining neuromorphic computing and deep reinforcement learning for
MAV 3D visual navigation task.

We present Imagen Video, a text-conditional video generation system based on
a cascade of video diffusion models. Given a text prompt, Imagen Video
generates high definition videos using a base video generation model and a
sequence of interleaved spatial and temporal video super-resolution models. We
describe how we scale up the system as a high definition text-to-video model
including design decisions such as the choice of fully-convolutional temporal
and spatial super-resolution models at certain resolutions, and the choice of
the v-parameterization of diffusion models. In addition, we confirm and
transfer findings from previous work on diffusion-based image generation to the
video generation setting. Finally, we apply progressive distillation to our
video models with classifier-free guidance for fast, high quality sampling. We
find Imagen Video not only capable of generating videos of high fidelity, but
also having a high degree of controllability and world knowledge, including the
ability to generate diverse videos and text animations in various artistic
styles and with 3D object understanding. See
https://imagen.research.google/video/ for samples.

Given the current point-to-point navigation capabilities of autonomous
vehicles, researchers are looking into complex service requests that require
the vehicles to visit multiple points of interest. In this paper, we develop a
layered planning framework, called GLAD, for complex service requests in
autonomous urban driving. There are three layers for service-level,
behavior-level, and motion-level planning. The layered framework is unique in
its tight coupling, where the different layers communicate user preferences,
safety estimates, and motion costs for system optimization. GLAD is visually
grounded by perceptual learning from a dataset of 13.8k instances collected
from driving behaviors. GLAD enables autonomous vehicles to efficiently and
safely fulfill complex service requests. Experimental results from abstract and
full simulation show that our system outperforms a few competitive baselines
from the literature.

Communication technologies enable coordination among connected and autonomous
vehicles (CAVs). However, it remains unclear how to utilize shared information
to improve the safety and efficiency of the CAV system. In this work, we
propose a framework of constrained multi-agent reinforcement learning (MARL)
with a parallel safety shield for CAVs in challenging driving scenarios. The
coordination mechanisms of the proposed MARL include information sharing and
cooperative policy learning, with Graph Convolutional Network (GCN)-Transformer
as a spatial-temporal encoder that enhances the agent's environment awareness.
The safety shield module with Control Barrier Functions (CBF)-based safety
checking protects the agents from taking unsafe actions. We design a
constrained multi-agent advantage actor-critic (CMAA2C) algorithm to train safe
and cooperative policies for CAVs. With the experiment deployed in the CARLA
simulator, we verify the effectiveness of the safety checking, spatial-temporal
encoder, and coordination mechanisms designed in our method by comparative
experiments in several challenging scenarios with the defined hazard vehicles
(HAZV). Results show that our proposed methodology significantly increases
system safety and efficiency in challenging scenarios.

Accurate mapping of large-scale environments is an essential building block
of most outdoor autonomous systems. Challenges of traditional mapping methods
include the balance between memory consumption and mapping accuracy. This paper
addresses the problems of achieving large-scale 3D reconstructions with
implicit representations using 3D LiDAR measurements. We learn and store
implicit features through an octree-based hierarchical structure, which is
sparse and extensible. The features can be turned into signed distance values
through a shallow neural network. We leverage binary cross entropy loss to
optimize the local features with the 3D measurements as supervision. Based on
our implicit representation, we design an incremental mapping system with
regularization to tackle the issue of catastrophic forgetting in continual
learning. Our experiments show that our 3D reconstructions are more accurate,
complete, and memory-efficient than current state-of-the-art 3D mapping
methods.

In this paper we study the problem of multiclass classification with a
bounded number of different labels $k$, in the realizable setting. We extend
the traditional PAC model to a) distribution-dependent learning rates, and b)
learning rates under data-dependent assumptions. First, we consider the
universal learning setting (Bousquet, Hanneke, Moran, van Handel and
Yehudayoff, STOC '21), for which we provide a complete characterization of the
achievable learning rates that holds for every fixed distribution. In
particular, we show the following trichotomy: for any concept class, the
optimal learning rate is either exponential, linear or arbitrarily slow.
Additionally, we provide complexity measures of the underlying hypothesis class
that characterize when these rates occur. Second, we consider the problem of
multiclass classification with structured data (such as data lying on a low
dimensional manifold or satisfying margin conditions), a setting which is
captured by partial concept classes (Alon, Hanneke, Holzman and Moran, FOCS
'21). Partial concepts are functions that can be undefined in certain parts of
the input space. We extend the traditional PAC learnability of total concept
classes to partial concept classes in the multiclass setting and investigate
differences between partial and total concepts.

Convolution neural networks (CNNs) have good performance in low-complexity
classification tasks such as acoustic scene classifications (ASCs). However,
there are few studies on the relationship between the length of target speech
and the size of the convolution kernels. In this paper, we combine Selective
Kernel Network with Temporal-Convolution (TC-SKNet) to adjust the receptive
field of convolution kernels to solve the problem of variable length of target
voice while keeping low-complexity. GridMask is a data augmentation strategy by
masking part of the raw data or feature area. It can enhance the generalization
of the model as the role of dropout. In our experiments, the performance gain
brought by GridMask is stronger than spectrum augmentation in ASCs. Finally, we
adopt AutoML to search best structure of TC-SKNet and hyperparameters of
GridMask for improving the classification performance. As a result, a peak
accuracy of 59.87% TC-SKNet is equivalent to that of SOTA, but the parameters
only use 20.9 K.

Hierarchical time series are common in several applied fields. Forecasts are
required to be coherent, that is, to satisfy the constraints given by the
hierarchy. The most popular technique to enforce coherence is called
reconciliation, which adjusts the base forecasts computed for each time series.
However, recent works on probabilistic reconciliation present several
limitations. In this paper, we propose a new approach based on conditioning to
reconcile any type of forecast distribution. We then introduce a new algorithm,
called Bottom-Up Importance Sampling, to efficiently sample from the reconciled
distribution. It can be used for any base forecast distribution: discrete,
continuous, or in the form of samples, providing a major speedup compared to
the current methods. Experiments on several temporal hierarchies show a
significant improvement over base probabilistic forecasts.

Conformal prediction is a widely used method to quantify uncertainty in
settings where the data is independent and identically distributed (IID), or
more generally, exchangeable. Conformal prediction takes in a pre-trained
classifier, a calibration dataset and a confidence level as inputs, and returns
a function which maps feature vectors to subsets of classes. The output of the
returned function for a new feature vector (i.e., a test data point) is
guaranteed to contain the true class with the pre-specified confidence. Despite
its success and usefulness in IID settings, extending conformal prediction to
non-exchangeable (e.g., Markovian) data in a manner that provably preserves all
desirable theoretical properties has largely remained an open problem. As a
solution, we extend conformal prediction to the setting of a Hidden Markov
Model (HMM) with unknown parameters. The key idea behind the proposed method is
to partition the non-exchangeable Markovian data from the HMM into exchangeable
blocks by exploiting the de Finetti's Theorem for Markov Chains discovered by
Diaconis and Freedman (1980). The permutations of the exchangeable blocks are
then viewed as randomizations of the observed Markovian data from the HMM. The
proposed method provably retains all desirable theoretical guarantees offered
by the classical conformal prediction framework.

Semantic segmentation is an important and prevalent task, but severely
suffers from the high cost of pixel-level annotations when extending to more
classes in wider applications. To this end, we focus on the problem named
weak-shot semantic segmentation, where the novel classes are learnt from
cheaper image-level labels with the support of base classes having
off-the-shelf pixel-level labels. To tackle this problem, we propose SimFormer,
which performs dual similarity transfer upon MaskFormer. Specifically,
MaskFormer disentangles the semantic segmentation task into two sub-tasks:
proposal classification and proposal segmentation for each proposal. Proposal
segmentation allows proposal-pixel similarity transfer from base classes to
novel classes, which enables the mask learning of novel classes. We also learn
pixel-pixel similarity from base classes and distill such class-agnostic
semantic similarity to the semantic masks of novel classes, which regularizes
the segmentation model with pixel-level semantic relationship across images. In
addition, we propose a complementary loss to facilitate the learning of novel
classes. Comprehensive experiments on the challenging COCO-Stuff-10K and ADE20K
datasets demonstrate the effectiveness of our method. Codes are available at
https://github.com/bcmi/SimFormer-Weak-Shot-Semantic-Segmentation.

Therapeutic antibody development has become an increasingly popular approach
for drug development. To date, antibody therapeutics are largely developed
using large scale experimental screens of antibody libraries containing
hundreds of millions of antibody sequences. The high cost and difficulty of
developing therapeutic antibodies create a pressing need for computational
methods to predict antibody properties and create bespoke designs. However, the
relationship between antibody sequence and activity is a complex physical
process and traditional iterative design approaches rely on large scale assays
and random mutagenesis. Deep learning methods have emerged as a promising way
to learn antibody property predictors, but predicting antibody properties and
target-specific activities depends critically on the choice of antibody
representations and data linking sequences to properties is often limited.
Existing works have not yet investigated the value, limitations and
opportunities of these methods in application to antibody-based drug discovery.
In this paper, we present results on a novel SARS-CoV-2 antibody binding
dataset and an additional benchmark dataset. We compare three classes of
models: conventional statistical sequence models, supervised learning on each
dataset independently, and fine-tuning an antibody specific pre-trained
language model. Experimental results suggest that self-supervised pretraining
of feature representation consistently offers significant improvement in over
previous approaches. We also investigate the impact of data size on the model
performance, and discuss challenges and opportunities that the machine learning
community can address to advance in silico engineering and design of
therapeutic antibodies.

Reliability measures associated to machine learning model predictions are
critical to reinforcing user confidence in artificial intelligence. Therefore,
those models that are able to provide not only predictions, but also
reliability enjoy greater popularity. In the field of recommender systems,
reliability is crucial, since users tend to prefer those recommendations that
are sure to interest them, i.e.\ high predictions with high reliabilities. In
this paper we present ResBeMF, a new recommender system based on collaborative
filtering that provides reliabilities associated with its predictions.
Experimental results show that ResBeMF offers greater customization than other
models, allowing to adjust the balance between prediction quality and
prediction reliability.

The Agents, Interaction and Complexity research group at the University of
Southampton has a long track record of research in multiagent systems (MAS). We
have made substantial scientific contributions across learning in MAS,
game-theoretic techniques for coordinating agent systems, and formal methods
for representation and reasoning. We highlight key results achieved by the
group and elaborate on recent work and open research challenges in developing
trustworthy autonomous systems and deploying human-centred AI systems that aim
to support societal good.

Multi-agent active search requires autonomous agents to choose sensing
actions that efficiently locate targets. In a realistic setting, agents also
must consider the costs that their decisions incur. Previously proposed active
search algorithms simplify the problem by ignoring uncertainty in the agent's
environment, using myopic decision making, and/or overlooking costs. In this
paper, we introduce an online active search algorithm to detect targets in an
unknown environment by making adaptive cost-aware decisions regarding the
agent's actions. Our algorithm combines principles from Thompson Sampling (for
search space exploration and decentralized multi-agent decision making), Monte
Carlo Tree Search (for long horizon planning) and pareto-optimal confidence
bounds (for multi-objective optimization in an unknown environment) to propose
an online lookahead planner that removes all the simplifications. We analyze
the algorithm's performance in simulation to show its efficacy in cost aware
active search.

Epilepsy is a neurological brain disorder which life threatening and gives
rise to recurrent seizures that are unprovoked. It occurs due to the abnormal
chemical changes in our brain. Over the course of many years, studies have been
conducted to support automatic diagnosis of epileptic seizures for the ease of
clinicians. For that, several studies entail the use of machine learning
methods for the early prediction of epileptic seizures. Mainly, feature
extraction methods have been used to extract the right features from the EEG
data generated by the EEG machine and then various machine learning classifiers
are used for the classification process. This study provides a systematic
literature review of feature selection process as well as the classification
performance. This study was limited to the finding of most used feature
extraction methods and the classifiers used for accurate classification of
normal to epileptic seizures. The existing literature was examined from
well-known repositories such as MPDI, IEEEXplore, Wiley, Elsevier, ACM,
Springerlink and others. Furthermore, a taxonomy was created that recapitulates
the state-of-the-art used solutions for this problem. We also studied the
nature of different benchmark and unbiased datasets and gave a rigorous
analysis of the working of classifiers. Finally, we concluded the research by
presenting the gaps, challenges and opportunities which can further help
researchers in prediction of epileptic seizure

Data hiding with deep neural networks (DNNs) has experienced impressive
successes in recent years. A prevailing scheme is to train an autoencoder,
consisting of an encoding network to embed (or transform) secret messages in
(or into) a carrier, and a decoding network to extract the hidden messages.
This scheme may suffer from several limitations regarding practicability,
security, and embedding capacity. In this work, we describe a different
computational framework to hide images in deep probabilistic models.
Specifically, we use a DNN to model the probability density of cover images,
and hide a secret image in one particular location of the learned distribution.
As an instantiation, we adopt a SinGAN, a pyramid of generative adversarial
networks (GANs), to learn the patch distribution of one cover image. We hide
the secret image by fitting a deterministic mapping from a fixed set of noise
maps (generated by an embedding key) to the secret image during patch
distribution learning. The stego SinGAN, behaving as the original SinGAN, is
publicly communicated; only the receiver with the embedding key is able to
extract the secret image. We demonstrate the feasibility of our SinGAN approach
in terms of extraction accuracy and model security. Moreover, we show the
flexibility of the proposed method in terms of hiding multiple images for
different receivers and obfuscating the secret image.

We investigate the problem of cumulative regret minimization for individual
sequence prediction with respect to the best expert in a finite family of size
K under limited access to information. We assume that in each round, the
learner can predict using a convex combination of at most p experts for
prediction, then they can observe a posteriori the losses of at most m experts.
We assume that the loss function is range-bounded and exp-concave. In the
standard multi-armed bandits setting, when the learner is allowed to play only
one expert per round and observe only its feedback, known optimal regret bounds
are of the order O($\sqrt$ KT). We show that allowing the learner to play one
additional expert per round and observe one additional feedback improves
substantially the guarantees on regret. We provide a strategy combining only p
= 2 experts per round for prediction and observing m $\ge$ 2 experts' losses.
Its randomized regret (wrt. internal randomization of the learners' strategy)
is of order O (K/m) log(K$\delta$ --1) with probability 1 -- $\delta$, i.e., is
independent of the horizon T ("constant" or "fast rate" regret) if (p $\ge$ 2
and m $\ge$ 3). We prove that this rate is optimal up to a logarithmic factor
in K. In the case p = m = 2, we provide an upper bound of order O(K 2
log(K$\delta$ --1)), with probability 1 -- $\delta$. Our strategies do not
require any prior knowledge of the horizon T nor of the confidence parameter
$\delta$. Finally, we show that if the learner is constrained to observe only
one expert feedback per round, the worst-case regret is the "slow rate"
$\Omega$($\sqrt$ KT), suggesting that synchronous observation of at least two
experts per round is necessary to have a constant regret.

Strong image search models can be learned for a specific domain, ie. set of
labels, provided that some labeled images of that domain are available. A
practical visual search model, however, should be versatile enough to solve
multiple retrieval tasks simultaneously, even if those cover very different
specialized domains. Additionally, it should be able to benefit from even
unlabeled images from these various retrieval tasks. This is the more practical
scenario that we consider in this paper. We address it with the proposed
Grappa, an approach that starts from a strong pretrained model, and adapts it
to tackle multiple retrieval tasks concurrently, using only unlabeled images
from the different task domains. We extend the pretrained model with multiple
independently trained sets of adaptors that use pseudo-label sets of different
sizes, effectively mimicking different pseudo-granularities. We reconcile all
adaptor sets into a single unified model suited for all retrieval tasks by
learning fusion layers that we guide by propagating pseudo-granularity
attentions across neighbors in the feature space. Results on a benchmark
composed of six heterogeneous retrieval tasks show that the unsupervised Grappa
model improves the zero-shot performance of a state-of-the-art self-supervised
learning model, and in some places reaches or improves over a task label-aware
oracle that selects the most fitting pseudo-granularity per task.

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a pivotal
role for reliable communications between UAV and ground terminal. This paper
proposes a three-dimensional (3D) non-stationary hybrid model including both
large-scale and small-scale fading for U2G multiple-input-multiple-output
(MIMO) channels. Distinctive channel characteristics under U2G scenarios, i.e.,
3D trajectory and posture of UAV, fuselage scattering effect (FSE), and posture
variation fading (PVF), are incorporated into the proposed model. The channel
parameters, i.e., path loss (PL), shadow fading (SF), path delay, and path
angle, are generated incorporating machine learning (ML) and ray tracing (RT)
techniques to capture the structure-related characteristics. In order to
guarantee the physical continuity of channel parameters such as Doppler phase
and path power, the time evolution methods of inter- and intra- stationary
intervals are proposed. Key statistical properties , i.e., temporal
autocorrection function (ACF), power delay profile (PDP), level crossing rate
(LCR), average fading duration (AFD), and stationary interval (SI) are given,
and the impact of the change of fuselage and posture variation is analyzed. It
is demonstrated that both posture variation and fuselage scattering have
crucial effects on channel characteristics. The validity and practicability of
the proposed model are verified by comparing the simulation results with the
measured ones.

Advances in data-driven deep learning for chest X-ray image analysis
underscore the need for explainability, privacy, large datasets and significant
computational resources. We frame privacy and explainability as a lossy
single-image compression problem to reduce both computational and data
requirements without training. For Cardiomegaly detection in chest X-ray
images, we propose HeartSpot and four spatial bias priors. HeartSpot priors
define how to sample pixels based on domain knowledge from medical literature
and from machines. HeartSpot privatizes chest X-ray images by discarding up to
97% of pixels, such as those that reveal the shape of the thoracic cage, bones,
small lesions and other sensitive features. HeartSpot priors are ante-hoc
explainable and give a human-interpretable image of the preserved spatial
features that clearly outlines the heart. HeartSpot offers strong compression,
with up to 32x fewer pixels and 11x smaller filesize. Cardiomegaly detectors
using HeartSpot are up to 9x faster to train or at least as accurate (up to
+.01 AUC ROC) when compared to a baseline DenseNet121. HeartSpot is post-hoc
explainable by re-using existing attribution methods without requiring access
to the original non-privatized image. In summary, HeartSpot improves speed and
accuracy, reduces image size, improves privacy and ensures explainability.
  Source code: https://www.github.com/adgaudio/HeartSpot

Although transfer learning is considered to be a milestone in deep
reinforcement learning, the mechanisms behind it are still poorly understood.
In particular, predicting if knowledge can be transferred between two given
tasks is still an unresolved problem. In this work, we explore the use of
network distillation as a feature extraction method to better understand the
context in which transfer can occur. Notably, we show that distillation does
not prevent knowledge transfer, including when transferring from multiple tasks
to a new one, and we compare these results with transfer without prior
distillation. We focus our work on the Atari benchmark due to the variability
between different games, but also to their similarities in terms of visual
features.

Context: Automated software defect prediction (SDP) methods are increasingly
applied, often with the use of machine learning (ML) techniques. Yet, the
existing ML-based approaches require manually extracted features, which are
cumbersome, time consuming and hardly capture the semantic information reported
in bug reporting tools. Deep learning (DL) techniques provide practitioners
with the opportunities to automatically extract and learn from more complex and
high-dimensional data. Objective: The purpose of this study is to
systematically identify, analyze, summarize, and synthesize the current state
of the utilization of DL algorithms for SDP in the literature. Method: We
systematically selected a pool of 102 peer-reviewed studies and then conducted
a quantitative and qualitative analysis using the data extracted from these
studies. Results: Main highlights include: (1) most studies applied supervised
DL; (2) two third of the studies used metrics as an input to DL algorithms; (3)
Convolutional Neural Network is the most frequently used DL algorithm.
Conclusion: Based on our findings, we propose to (1) develop more comprehensive
DL approaches that automatically capture the needed features; (2) use diverse
software artifacts other than source code; (3) adopt data augmentation
techniques to tackle the class imbalance problem; (4) publish replication
packages.

We are perceiving and communicating with the world in a multisensory manner,
where different information sources are sophisticatedly processed and
interpreted by separate parts of the human brain to constitute a complex, yet
harmonious and unified sensing system. To endow the machines with true
intelligence, the multimodal machine learning that incorporates data from
various modalities has become an increasingly popular research area with
emerging technical advances in recent years. In this paper, we present a survey
on multimodal machine learning from a novel perspective considering not only
the purely technical aspects but also the nature of different data modalities.
We analyze the commonness and uniqueness of each data format ranging from
vision, audio, text and others, and then present the technical development
categorized by the combination of Vision+X, where the vision data play a
fundamental role in most multimodal learning works. We investigate the existing
literature on multimodal learning from both the representation learning and
downstream application levels, and provide an additional comparison in the
light of their technical connections with the data nature, e.g., the semantic
consistency between image objects and textual descriptions, or the rhythm
correspondence between video dance moves and musical beats. The exploitation of
the alignment, as well as the existing gap between the intrinsic nature of data
modality and the technical designs, will benefit future research studies to
better address and solve a specific challenge related to the concrete
multimodal task, and to prompt a unified multimodal machine learning framework
closer to a real human intelligence system.

In this paper, we consider privacy aspects of wireless federated learning
(FL) with Over-the-Air (OtA) transmission of gradient updates from multiple
users/agents to an edge server. By exploiting the waveform superposition
property of multiple access channels, OtA FL enables the users to transmit
their updates simultaneously with linear processing techniques, which improves
resource efficiency. However, this setting is vulnerable to privacy leakage
since an adversary node can hear directly the uncoded message. Traditional
perturbation-based methods provide privacy protection while sacrificing the
training accuracy due to the reduced signal-to-noise ratio. In this work, we
aim at minimizing privacy leakage to the adversary and the degradation of model
accuracy at the edge server at the same time. More explicitly, spatially
correlated perturbations are added to the gradient vectors at the users before
transmission. Using the zero-sum property of the correlated perturbations, the
side effect of the added perturbation on the aggregated gradients at the edge
server can be minimized. In the meanwhile, the added perturbation will not be
canceled out at the adversary, which prevents privacy leakage. Theoretical
analysis of the perturbation covariance matrix, differential privacy, and model
convergence is provided, based on which an optimization problem is formulated
to jointly design the covariance matrix and the power scaling factor to balance
between privacy protection and convergence performance. Simulation results
validate the correlated perturbation approach can provide strong defense
ability while guaranteeing high learning accuracy.

To date, there has been no systematic investigation of thermal profiles of
keyboards, and thus no efforts have been made to secure them. This serves as
our main motivation for constructing a means for password harvesting from
keyboard thermal emanations. Specifically, we introduce Thermanator: a new
post-factum insider attack based on heat transfer caused by a user typing a
password on a typical external (plastic) keyboard.
  We conduct and describe a user study that collected thermal residues from 30
users entering 10 unique passwords (both weak and strong) on 4 popular
commodity keyboards. Results show that entire sets of key-presses can be
recovered by non-expert users as late as 30 seconds after initial password
entry, while partial sets can be recovered as late as 1 minute after entry.
However, the thermal residue side-channel lacks information about password
length, duplicate key-presses, and key-press ordering. To overcome these
limitations, we leverage keyboard acoustic emanations and combine the two to
yield AcuTherm, the first hybrid side-channel attack on keyboards. AcuTherm
significantly reduces password search without the need for any training on the
victim's typing. We report results gathered for many representative passwords
based on a user study involving 19 subjects.
  The takeaway of this work is three-fold: (1) using plastic keyboards to enter
secrets (such as passwords and PINs) is even less secure than previously
recognized, (2) post-factum thermal imaging attacks are realistic, and (3)
hybrid (multiple side-channel) attacks are both realistic and effective.

We extend the use of Classification Without Labels for anomaly detection with
a hypothesis test designed to exclude the background-only hypothesis. By
testing for statistical independence of the two discriminating dataset regions,
we are able exclude the background-only hypothesis without relying on fixed
anomaly score cuts or extrapolations of background estimates between regions.
The method relies on the assumption of conditional independence of anomaly
score features and dataset regions, which can be ensured using existing
decorrelation techniques. As a benchmark example, we consider the LHC Olympics
dataset where we show that mutual information represents a suitable test for
statistical independence and our method exhibits excellent and robust
performance at different signal fractions even in presence of realistic feature
correlations.

What role does the break from realism play in the potential for generative
artificial intelligence as a creative tool? Through exploration of glitch, we
examine the prospective value of these artefacts in creative practice. This
paper describes findings from an exploration of AI-generated "mistakes" when
using movement produced by a generative deep learning model as an inspiration
source in dance composition.

Learning a good state representation is a critical skill when dealing with
multiple tasks in Reinforcement Learning as it allows for transfer and better
generalization between tasks. However, defining what constitute a useful
representation is far from simple and there is so far no standard method to
find such an encoding. In this paper, we argue that distillation -- a process
that aims at imitating a set of given policies with a single neural network --
can be used to learn a state representation displaying favorable
characteristics. In this regard, we define three criteria that measure
desirable features of a state encoding: the ability to select important
variables in the input space, the ability to efficiently separate states
according to their corresponding optimal action, and the robustness of the
state encoding on new tasks. We first evaluate these criteria and verify the
contribution of distillation on state representation on a toy environment based
on the standard inverted pendulum problem, before extending our analysis on
more complex visual tasks from the Atari and Procgen benchmarks.

Producing statistics that respect the privacy of the samples while still
maintaining their accuracy is an important topic of research. We study minimax
lower bounds when the class of estimators is restricted to the differentially
private ones. In particular, we show that characterizing the power of a
distributional test under differential privacy can be done by solving a
transport problem. With specific coupling constructions, this observation
allows us to derivate Le Cam-type and Fano-type inequalities for both regular
definitions of differential privacy and for divergence-based ones (based on
Renyi divergence). We then proceed to illustrate our results on three simple,
fully worked out examples. In particular, we show that the problem class has a
huge importance on the provable degradation of utility due to privacy. For some
problems, privacy leads to a provable degradation only when the rate of the
privacy parameters is small enough whereas for other problem, the degradation
systematically occurs under much looser hypotheses on the privacy parametters.
Finally, we show that the known privacy guarantees of DP-SGLD, a private convex
solver, when used to perform maximum likelihood, leads to an algorithm that is
near-minimax optimal in both the sample size and the privacy tuning parameters
of the problem for a broad class of parametric estimation procedures that
includes exponential families.

Visual-Semantic Embedding (VSE) aims to learn an embedding space where
related visual and semantic instances are close to each other. Recent VSE
models tend to design complex structures to pool visual and semantic features
into fixed-length vectors and use hard triplet loss for optimization. However,
we find that: (1) combining simple pooling methods is no worse than these
sophisticated methods; and (2) only considering the most
difficult-to-distinguish negative sample leads to slow convergence and poor
Recall@K improvement. To this end, we propose an adaptive pooling strategy that
allows the model to learn how to aggregate features through a combination of
simple pooling methods. We also introduce a strategy to dynamically select a
group of negative samples to make the optimization converge faster and perform
better. Experimental results on Flickr30K and MS-COCO demonstrate that a
standard VSE using our pooling and optimization strategies outperforms current
state-of-the-art systems (at least 1.0% on the metrics of recall) in
image-to-text and text-to-image retrieval. Source code of our experiments is
available at https://github.com/96-Zachary/vse_2ad.

Rating strategies in a game is an important area of research in game theory
and artificial intelligence, and can be applied to any real-world competitive
or cooperative setting. Traditionally, only transitive dependencies between
strategies have been used to rate strategies (e.g. Elo), however recent work
has expanded ratings to utilize game theoretic solutions to better rate
strategies in non-transitive games. This work generalizes these ideas and
proposes novel algorithms suitable for N-player, general-sum rating of
strategies in normal-form games according to the payoff rating system. This
enables well-established solution concepts, such as equilibria, to be leveraged
to efficiently rate strategies in games with complex strategic interactions,
which arise in multiagent training and real-world interactions between many
agents. We empirically validate our methods on real world normal-form data
(Premier League) and multiagent reinforcement learning agent evaluation.

This paper proposes a distributed Gaussian process regression (GPR) with
over-the-air computation, termed AirComp GPR, for communication- and
computation-efficient data analysis over wireless networks. GPR is a
non-parametric regression method that can model the target flexibly. However,
its computational complexity and communication efficiency tend to be
significant as the number of data increases. AirComp GPR focuses on that
product-of-experts-based GPR approximates the exact GPR by a sum of values
reported from distributed nodes. We introduce AirComp for the training and
prediction steps to allow the nodes to transmit their local computation results
simultaneously; the communication strategies are presented, including
distributed training based on perfect and statistical channel state information
cases. Applying to a radio map construction task, we demonstrate that AirComp
GPR speeds up the computation time while maintaining the communication cost in
training constant regardless of the numbers of data and nodes.

Motivated by image-on-scalar regression with data aggregated across multiple
sites, we consider a setting in which multiple independent studies each collect
multiple dependent vector outcomes, with potential mean model parameter
homogeneity between studies and outcome vectors. To determine the validity of
jointly analyzing these data sources, we must learn which of these data sources
share mean model parameters. We propose a new model fusion approach that
delivers improved flexibility, statistical performance and computational speed
over existing methods. Our proposed approach specifies a quadratic inference
function within each data source and fuses mean model parameter vectors in
their entirety based on a new formulation of a pairwise fusion penalty. We
establish theoretical properties of our estimator and propose an asymptotically
equivalent weighted oracle meta-estimator that is more computationally
efficient. Simulations and application to the ABIDE neuroimaging consortium
highlight the flexibility of the proposed approach. An R package is provided
for ease of implementation.

Time series analysis is of immense importance in extensive applications, such
as weather forecasting, anomaly detection, and action recognition. This paper
focuses on temporal variation modeling, which is the common key problem of
extensive analysis tasks. Previous methods attempt to accomplish this directly
from the 1D time series, which is extremely challenging due to the intricate
temporal patterns. Based on the observation of multi-periodicity in time
series, we ravel out the complex temporal variations into the multiple
intraperiod- and interperiod-variations. To tackle the limitations of 1D time
series in representation capability, we extend the analysis of temporal
variations into the 2D space by transforming the 1D time series into a set of
2D tensors based on multiple periods. This transformation can embed the
intraperiod- and interperiod-variations into the columns and rows of the 2D
tensors respectively, making the 2D-variations to be easily modeled by 2D
kernels. Technically, we propose the TimesNet with TimesBlock as a task-general
backbone for time series analysis. TimesBlock can discover the
multi-periodicity adaptively and extract the complex temporal variations from
transformed 2D tensors by a parameter-efficient inception block. Our proposed
TimesNet achieves consistent state-of-the-art in five mainstream time series
analysis tasks, including short- and long-term forecasting, imputation,
classification, and anomaly detection.

We present that by predicting the spectrum in discrete space from the phase
shift in continuous space, the neural network can remarkably reproduce the
numerical L\"uscher's formula to a high precision. The model-independent
property of the L\"uscher's formula is naturally realized by the
generalizability of the neural network. This exhibits the great potential of
the neural network to extract model-independent relation between
model-dependent quantities, and this data-driven approach could greatly
facilitate the discovery of the physical principles underneath the intricate
data.

Joint-Embedding Self Supervised Learning (JE-SSL) has seen a rapid
development, with the emergence of many method variations and few principled
guidelines that would help practitioners to successfully deploy those methods.
The main reason for that pitfall actually comes from JE-SSL's core principle of
not employing any input reconstruction. Without any visual clue, it becomes
extremely cryptic to judge the quality of a learned representation without
having access to a labelled dataset. We hope to correct those limitations by
providing a single -- theoretically motivated -- criterion that reflects the
quality of learned JE-SSL representations: their effective rank. Albeit simple
and computationally friendly, this method -- coined RankMe -- allows one to
assess the performance of JE-SSL representations, even on different downstream
datasets, without requiring any labels, training or parameters to tune. Through
thorough empirical experiments involving hundreds of repeated training
episodes, we demonstrate how RankMe can be used for hyperparameter selection
with nearly no loss in final performance compared to the current selection
method that involve dataset labels. We hope that RankMe will facilitate the use
of JE-SSL in domains with little or no labeled data.

Optimizing multiple competing objectives is a common problem across science
and industry. The inherent inextricable trade-off between those objectives
leads one to the task of exploring their Pareto front. A meaningful quantity
for the purpose of the latter is the hypervolume indicator, which is used in
Bayesian Optimization (BO) and Evolutionary Algorithms (EAs). However, the
computational complexity for the calculation of the hypervolume scales
unfavorably with increasing number of objectives and data points, which
restricts its use in those common multi-objective optimization frameworks. To
overcome these restrictions we propose to approximate the hypervolume function
with a deep neural network, which we call DeepHV. For better sample efficiency
and generalization, we exploit the fact that the hypervolume is
scale-equivariant in each of the objectives as well as permutation invariant
w.r.t. both the objectives and the samples, by using a deep neural network that
is equivariant w.r.t. the combined group of scalings and permutations. We
evaluate our method against exact, and approximate hypervolume methods in terms
of accuracy, computation time, and generalization. We also apply and compare
our methods to state-of-the-art multi-objective BO methods and EAs on a range
of synthetic benchmark test cases. The results show that our methods are
promising for such multi-objective optimization tasks.

Feature importance techniques have enjoyed widespread attention in the
explainable AI literature as a means of determining how trained machine
learning models make their predictions. We consider Shapley value based
approaches to feature importance, applied in the context of time series data.
We present closed form solutions for the SHAP values of a number of time series
models, including VARMAX. We also show how KernelSHAP can be applied to time
series tasks, and how the feature importances that come from this technique can
be combined to perform "event detection". Finally, we explore the use of Time
Consistent Shapley values for feature importance.

We describe several features of parallel or distributed asynchronous
iterative algorithms such as unbounded delays, possible out of order messages
or flexible communication. We concentrate on the concept of macroiteration
sequence which was introduced in order to study the convergence or termination
of asynchronous iterations. A survey of asynchronous iterations for convex
optimization problems is also presented. Finally, a new result of convergence
for parallel or distributed asynchronous iterative algorithms with flexible
communication for convex optimization problems and machine learning is
proposed.

The goal of this work is to develop deep learning numerical methods for
solving option XVA pricing problems given by non-linear PDE models. A novel
strategy for the treatment of the boundary conditions is proposed, which allows
to get rid of the heuristic choice of the weights for the different addends
that appear in the loss function related to the training process. It is based
on defining the losses associated to the boundaries by means of the PDEs that
arise from substituting the related conditions into the model equation itself.
Further, automatic differentiation is employed to obtain accurate approximation
of the partial derivatives.

The imitation learning of self-driving vehicle policies through behavioral
cloning is often carried out in an open-loop fashion, ignoring the effect of
actions to future states. Training such policies purely with Empirical Risk
Minimization (ERM) can be detrimental to real-world performance, as it biases
policy networks towards matching only open-loop behavior, showing poor results
when evaluated in closed-loop. In this work, we develop an efficient and
simple-to-implement principle called Closed-loop Weighted Empirical Risk
Minimization (CW-ERM), in which a closed-loop evaluation procedure is first
used to identify training data samples that are important for practical driving
performance and then we these samples to help debias the policy network. We
evaluate CW-ERM in a challenging urban driving dataset and show that this
procedure yields a significant reduction in collisions as well as other
non-differentiable closed-loop metrics.

Reconfigurable meta-surfaces are emerging as a novel and revolutionizing
technology to enable intelligent wireless environments. Due to the low cost,
improved efficiency, and passive nature of reflecting elements, it is becoming
possible to program and control the wireless environment. Since wireless
physical layer technologies can generally adapt to the wireless environment,
their combination with reconfigurable surfaces and deep learning approaches can
open new avenues for achieving secure 6G vehicular aided heterogeneous networks
(HetNets). Motivated by these appealing advantages, this work provides an
intelligent and secure radio environment (ISRE) paradigm for 6G vehicular aided
HetNets. We present an overview of enabling technologies for ISRE-based 6G
vehicular aided HetNets. We discuss features, design goals, and applications of
such networks. Next, we outline new opportunities provided by ISRE-based 6G
vehicular HetNets and we present a case study using the contextual bandit
approach in terms of best IRS for secure communications. Finally, we discuss
some future research opportunities.

In engineering design, one often wishes to calculate the probability that the
performance of a system is satisfactory under uncertainty. State of the art
algorithms exist to solve this problem using active learning with Gaussian
process models. However, these algorithms cannot be applied to problems which
often occur in the autonomous vehicle domain where the performance of a system
may be undefined under certain circumstances. To solve this problem, we
introduce a hierarchical model for the system performance, where undefined
performance is classified before the performance is regressed. This enables
active learning Gaussian process methods to be applied to problems where the
performance of the system is sometimes undefined, and we demonstrate the
effectiveness of our approach by testing our methodology on synthetic numerical
examples for the autonomous driving domain.

We propose topology-aware feature partitioning into $k$ disjoint partitions
for given scene features as a method for object-centric representation
learning. To this end, we propose to use minimum $s$-$t$ graph cuts as a
partitioning method which is represented as a linear program. The method is
topologically aware since it explicitly encodes neighborhood relationships in
the image graph. To solve the graph cuts our solution relies on an efficient,
scalable, and differentiable quadratic programming approximation. Optimizations
specific to cut problems allow us to solve the quadratic programs and compute
their gradients significantly more efficiently compared with the general
quadratic programming approach. Our results show that our approach is scalable
and outperforms existing methods on object discovery tasks with textured scenes
and objects.

It is unclear how changing the learning rule of a deep neural network alters
its learning dynamics and representations. To gain insight into the
relationship between learned features, function approximation, and the learning
rule, we analyze infinite-width deep networks trained with gradient descent
(GD) and biologically-plausible alternatives including feedback alignment (FA),
direct feedback alignment (DFA), and error modulated Hebbian learning (Hebb),
as well as gated linear networks (GLN). We show that, for each of these
learning rules, the evolution of the output function at infinite width is
governed by a time varying effective neural tangent kernel (eNTK). In the lazy
training limit, this eNTK is static and does not evolve, while in the rich
mean-field regime this kernel's evolution can be determined self-consistently
with dynamical mean field theory (DMFT). This DMFT enables comparisons of the
feature and prediction dynamics induced by each of these learning rules. In the
lazy limit, we find that DFA and Hebb can only learn using the last layer
features, while full FA can utilize earlier layers with a scale determined by
the initial correlation between feedforward and feedback weight matrices. In
the rich regime, DFA and FA utilize a temporally evolving and depth-dependent
NTK. Counterintuitively, we find that FA networks trained in the rich regime
exhibit more feature learning if initialized with smaller correlation between
the forward and backward pass weights. GLNs admit a very simple formula for
their lazy limit kernel and preserve conditional Gaussianity of their
preactivations under gating functions. Error modulated Hebb rules show very
small task-relevant alignment of their kernels and perform most task relevant
learning in the last layer.

Models need to be trained with privacy-preserving learning algorithms to
prevent leakage of possibly sensitive information contained in their training
data. However, canonical algorithms like differentially private stochastic
gradient descent (DP-SGD) do not benefit from model scale in the same way as
non-private learning. This manifests itself in the form of unappealing
tradeoffs between privacy and utility (accuracy) when using DP-SGD on complex
tasks. To remediate this tension, a paradigm is emerging: fine-tuning with
differential privacy from a model pretrained on public (i.e., non-sensitive)
  In this work, we identify an oversight of existing approaches for
differentially private fine tuning. They do not tailor the fine-tuning approach
to the specifics of learning with privacy. Our main result is to show how
carefully selecting the layers being fine-tuned in the pretrained neural
network allows us to establish new state-of-the-art tradeoffs between privacy
and accuracy. For instance, we achieve 77.9% accuracy for $(\varepsilon,
\delta)=(2, 10^{-5})$ on CIFAR-100 for a model pretrained on ImageNet. Our work
calls for additional hyperparameter search to configure the differentially
private fine-tuning procedure itself.

Estimating treatment effects is one of the most challenging and important
tasks of data analysts. Traditional statistical methods aim to estimate average
treatment effects over a population. While being highly useful, such average
treatment effects do not help to decide which individuals profit most by the
treatment. This is where uplift modeling becomes important. Uplift models help
to select the right individuals for treatment, to maximize the overall
treatment effect (uplift). A challenging problem in uplift modeling is to
evaluate the models. Previous literature suggests methods like the Qini curve
and the transformed outcome mean squared error. However, these metrics suffer
from variance: Their evaluations are strongly affected by random noise in the
data, which makes these evaluations to a certain degree arbitrary. Recently,
authors suggested the concept of doubly-robust estimation to improve the
evaluation of uplift models. However, to justify a change of current
state-of-art uplift model evaluation procedures, a comprehensive theoretical
analysis as well as empirical evidence is missing. In this paper, we
theoretically analyze the variance of uplift evaluation metrics and derive
possible methods of variance reduction of which one corresponds to the
suggested doubly-robust procedure. We derive simple conditions under which the
variance reduction methods improve the uplift evaluation metrics and
empirically demonstrate their benefits on simulated data as well as on
real-world data. Our paper provides strong evidence to change the current
state-of-art uplift evaluation routine on RCT data by using the suggested
variance reduction procedures.

Fine-grained categories that largely share the same set of parts cannot be
discriminated based on part information alone, as they mostly differ in the way
the local parts relate to the overall global structure of the object. We
propose Relational Proxies, a novel approach that leverages the relational
information between the global and local views of an object for encoding its
semantic label. Starting with a rigorous formalization of the notion of
distinguishability between fine-grained categories, we prove the necessary and
sufficient conditions that a model must satisfy in order to learn the
underlying decision boundaries in the fine-grained setting. We design
Relational Proxies based on our theoretical findings and evaluate it on seven
challenging fine-grained benchmark datasets and achieve state-of-the-art
results on all of them, surpassing the performance of all existing works with a
margin exceeding 4% in some cases. We also experimentally validate our theory
on fine-grained distinguishability and obtain consistent results across
multiple benchmarks. Implementation is available at
https://github.com/abhrac/relational-proxies.

This paper presents an adaptive leading cruise control strategy for the
connected and automated vehicle (CAV) and first considers its impact on the
following human-driven vehicle (HDV) with diverse driving characteristics in
the unified optimization framework for improved holistic energy efficiency. The
car-following behaviors of HDV are statistically calibrated using the Next
Generation Simulation dataset. In a typical single-lane car-following scenario
where CAVs and HDVs share the road, the longitudinal speed control of CAVs can
substantially reduce the energy consumption of the following HDV by avoiding
unnecessary acceleration and braking. Moreover, apart from the objectives
including car-following safety and traffic efficiency, the energy efficiencies
of both CAV and HDV are incorporated into the reward function of reinforcement
learning. The specific driving pattern of the following HDV is learned in
real-time from historical speed information to predict its acceleration and
power consumption in the optimization horizon. A comprehensive simulation is
conducted to statistically verify the positive impacts of CAV on the holistic
energy efficiency of the mixed traffic flow with uncertain and diverse human
driving behaviors. Simulation results indicate that the holistic energy
efficiency is improved by 4.38% on average.

Machine learning (ML) applications continue to revolutionize many domains. In
recent years, there has been considerable research interest in building novel
ML applications for a variety of Internet of Things (IoT) domains, such as
precision agriculture, smart cities, and smart manufacturing. IoT domains are
characterized by continuous streams of data originating from diverse,
geographically distributed sensors, and they often require a real-time or
semi-real-time response. IoT characteristics pose several fundamental
challenges to designing and implementing effective ML applications.
Sensor/network failures that result in data stream interruptions is one such
challenge. Unfortunately, the performance of many ML applications quickly
degrades when faced with data incompleteness. Current techniques to handle data
incompleteness are based upon data imputation ( i.e., they try to fill-in
missing data). Unfortunately, these techniques may fail, especially when
multiple sensors' data streams become concurrently unavailable (due to
simultaneous sensor failures). With the aim of building robust IoT-coupled ML
applications, this paper proposes SECOE, a unique, proactive approach for
alleviating potentially simultaneous sensor failures. The fundamental idea
behind SECOE is to create a carefully chosen ensemble of ML models in which
each model is trained assuming a set of failed sensors (i.e., the training set
omits corresponding values). SECOE includes a novel technique to minimize the
number of models in the ensemble by harnessing the correlations among sensors.
We demonstrate the efficacy of the SECOE approach through a series of
experiments involving three distinct datasets. The experimental findings reveal
that SECOE effectively preserves prediction accuracy in the presence of sensor
failures.

The number of newly published vulnerabilities is constantly increasing. Until
now, the information available when a new vulnerability is published is
manually assessed by experts using a Common Vulnerability Scoring System (CVSS)
vector and score. This assessment is time consuming and requires expertise.
Various works already try to predict CVSS vectors or scores using machine
learning based on the textual descriptions of the vulnerability to enable
faster assessment. However, for this purpose, previous works only use the texts
available in databases such as National Vulnerability Database. With this work,
the publicly available web pages referenced in the National Vulnerability
Database are analyzed and made available as sources of texts through web
scraping. A Deep Learning based method for predicting the CVSS vector is
implemented and evaluated. The present work provides a classification of the
National Vulnerability Database's reference texts based on the suitability and
crawlability of their texts. While we identified the overall influence of the
additional texts is negligible, we outperformed the state-of-the-art with our
Deep Learning prediction models.

Autonomous systems with uncertainties are prevalent in robotics. However,
ensuring the safety of those systems is challenging due to sophisticated
dynamics and the hardness to predict future states. Usually, a classical motion
planning method considering all possible states will not find a feasible path
in crowded environments. To overcome this conservativeness, we propose a
density-based method. The proposed method uses a neural network and the
Liouville equation to learn the density evolution, and by applying a
gradient-based optimization procedure, we can plan for feasible and probably
safe trajectories to minimize the collision risk. We conduct experiments on
simulated environments and environments generated from real-world data and
outperform baseline methods such as model predictive control (MPC) and
nonlinear programming (NLP). While our method requires planning time in
advance, the online computational complexity is very low when compared to other
methods.

While personalization in distributed learning has been extensively studied,
existing approaches employ dedicated algorithms to optimize their specific type
of parameters (e.g., client clusters or model interpolation weights), making it
difficult to simultaneously optimize different types of parameters to yield
better performance. Moreover, their algorithms require centralized or static
undirected communication networks, which can be vulnerable to center-point
failures or deadlocks. This study proposes optimizing various types of
parameters using a single algorithm that runs on more practical communication
environments. First, we propose a gradient-based bilevel optimization that
reduces most personalization approaches to the optimization of client-wise
hyperparameters. Second, we propose a decentralized algorithm to estimate
gradients with respect to the hyperparameters, which can run even on stochastic
and directed communication networks. Our empirical results demonstrated that
the gradient-based bilevel optimization enabled combining existing
personalization approaches which led to state-of-the-art performance,
confirming it can perform on multiple simulated communication environments
including a stochastic and directed network.

Volatility clustering is a crucial property that has a substantial impact on
stock market patterns. Nonetheless, developing robust models for accurately
predicting future stock price volatility is a difficult research topic. For
predicting the volatility of three equities listed on India's national stock
market (NSE), we propose multiple volatility models depending on the
generalized autoregressive conditional heteroscedasticity (GARCH),
Glosten-Jagannathan-GARCH (GJR-GARCH), Exponential general autoregressive
conditional heteroskedastic (EGARCH), and LSTM framework. Sector-wise stocks
have been chosen in our study. The sectors which have been considered are
banking, information technology (IT), and pharma. yahoo finance has been used
to obtain stock price data from Jan 2017 to Dec 2021. Among the pulled-out
records, the data from Jan 2017 to Dec 2020 have been taken for training, and
data from 2021 have been chosen for testing our models. The performance of
predicting the volatility of stocks of three sectors has been evaluated by
implementing three different types of GARCH models as well as by the LSTM model
are compared. It has been observed the LSTM performed better in predicting
volatility in pharma over banking and IT sectors. In tandem, it was also
observed that E-GARCH performed better in the case of the banking sector and
for IT and pharma, GJR-GARCH performed better.

This work describes the development and validation of a fully automated deep
learning model, iDAScore v2.0, for the evaluation of embryos incubated for 2,
3, and 5 or more days. The model is trained and evaluated on an extensive and
diverse dataset including 181,428 embryos from 22 IVF clinics across the world.
For discriminating transferred embryos with known outcome (KID), we show AUCs
ranging from 0.621 to 0.708 depending on the day of transfer. Predictive
performance increased over time and showed a strong correlation with
morphokinetic parameters. The model has equivalent performance to KIDScore D3
on day 3 embryos while significantly surpassing the performance of KIDScore D5
v3 on day 5+ embryos. This model provides an analysis of time-lapse sequences
without the need for user input, and provides a reliable method for ranking
embryos for likelihood to implant, at both cleavage and blastocyst stages. This
greatly improves embryo grading consistency and saves time compared to
traditional embryo evaluation methods.

As a promising integrated computation and communication learning paradigm,
federated learning (FL) carries a periodic sharing from distributed clients.
Due to the non-i.i.d. data distribution on clients, FL model suffers from the
gradient diversity, poor performance, bad convergence, etc. In this work, we
aim to tackle this key issue by adopting data-driven importance sampling (IS)
for local training. We propose a trustworthy framework, named importance
sampling federated learning (ISFL), which is especially compatible with neural
network (NN) models. The framework is evaluated both theoretically and
experimentally. Firstly, we derive the parameter deviation bound between ISFL
and the centralized full-data training to identify the main factors of the
non-i.i.d. dilemmas. We will then formulate the selection of optimal IS weights
as an optimization problem and obtain theoretical solutions. We also employ
water-filling methods to calculate the IS weights and develop the complete ISFL
algorithms. The experimental results on CIFAR-10 fit our proposed theories well
and prove that ISFL reaps higher performance, as well as better convergence on
non-i.i.d. data. To the best of our knowledge, ISFL is the first non-i.i.d. FL
solution from the local sampling aspect which exhibits theoretical NN
compatibility. Furthermore, as a local sampling approach, ISFL can be easily
migrated into emerging FL frameworks.

Solving constrained nonlinear optimization problems (CNLPs) is a longstanding
problem that arises in various fields, e.g., economics, computer science, and
engineering. We propose optimization-informed neural networks (OINN), a deep
learning approach to solve CNLPs. By neurodynamic optimization methods, a CNLP
is first reformulated as an initial value problem (IVP) involving an ordinary
differential equation (ODE) system. A neural network model is then used as an
approximate solution for this IVP, with the endpoint being the prediction to
the CNLP. We propose a novel training algorithm that directs the model to hold
the best prediction during training. In a nutshell, OINN transforms a CNLP into
a neural network training problem. By doing so, we can solve CNLPs based on
deep learning infrastructure only, without using standard optimization solvers
or numerical integration solvers. The effectiveness of the proposed approach is
demonstrated through a collection of classical problems, e.g., variational
inequalities, nonlinear complementary problems, and standard CNLPs.

Aggregating a dataset, then injecting some noise, is a simple and common way
to release differentially private data.However, aggregated data -- even without
noise -- is not an appropriate input for machine learning classifiers.In this
work, we show how a new model, similar to a logistic regression, may be learned
from aggregated data only by approximating the unobserved feature distribution
with a maximum entropy hypothesis. The resulting model is a Markov Random Field
(MRF), and we detail how to apply, modify and scale a MRF training algorithm to
our setting. Finally we present empirical evidence on several public datasets
that the model learned this way can achieve performances comparable to those of
a logistic model trained with the full unaggregated data.

Self-supervised learning on graphs has recently achieved remarkable success
in graph representation learning. With hundreds of self-supervised pretext
tasks proposed over the past few years, the research community has greatly
developed, and the key is no longer to design more powerful but complex pretext
tasks, but to make more effective use of those already on hand. This paper
studies the problem of how to automatically, adaptively, and dynamically learn
instance-level self-supervised learning strategies for each node from a given
pool of pretext tasks. In this paper, we propose a novel multi-teacher
knowledge distillation framework for Automated Graph Self-Supervised Learning
(AGSSL), which consists of two main branches: (i) Knowledge Extraction:
training multiple teachers with different pretext tasks, so as to extract
different levels of knowledge with different inductive biases; (ii) Knowledge
Integration: integrating different levels of knowledge and distilling them into
the student model. Without simply treating different teachers as equally
important, we provide a provable theoretical guideline for how to integrate the
knowledge of different teachers, i.e., the integrated teacher probability
should be close to the true Bayesian class-probability. To approach the
theoretical optimum in practice, two adaptive knowledge integration strategies
are proposed to construct a relatively "good" integrated teacher. Extensive
experiments on eight datasets show that AGSSL can benefit from multiple pretext
tasks, outperforming the corresponding individual tasks; by combining a few
simple but classical pretext tasks, the resulting performance is comparable to
other leading counterparts.

Recent years have witnessed great success in handling graph-related tasks
with Graph Neural Networks (GNNs). Despite their great academic success,
Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical
industrial applications. One reason for this academic-industrial gap is the
neighborhood-fetching latency incurred by data dependency in GNNs, which make
it hard to deploy for latency-sensitive applications that require fast
inference. Conversely, without involving any feature aggregation, MLPs have no
data dependency and infer much faster than GNNs, but their performance is less
competitive. Motivated by these complementary strengths and weaknesses, we
propose a Graph Self-Distillation on Neighborhood (GSDN) framework to reduce
the gap between GNNs and MLPs. Specifically, the GSDN framework is based purely
on MLPs, where structural information is only implicitly used as prior to guide
knowledge self-distillation between the neighborhood and the target,
substituting the explicit neighborhood information propagation as in GNNs. As a
result, GSDN enjoys the benefits of graph topology-awareness in training but
has no data dependency in inference. Extensive experiments have shown that the
performance of vanilla MLPs can be greatly improved with self-distillation,
e.g., GSDN improves over stand-alone MLPs by 15.54\% on average and outperforms
the state-of-the-art GNNs on six datasets. Regarding inference speed, GSDN
infers 75X-89X faster than existing GNNs and 16X-25X faster than other
inference acceleration methods.

While showing impressive performance on various kinds of learning tasks, it
is yet unclear whether deep learning models have the ability to robustly tackle
reasoning tasks. than by learning the underlying reasoning process that is
actually required to solve the tasks. Measuring the robustness of reasoning in
machine learning models is challenging as one needs to provide a task that
cannot be easily shortcut by exploiting spurious statistical correlations in
the data, while operating on complex objects and constraints. reasoning task.
To address this issue, we propose ChemAlgebra, a benchmark for measuring the
reasoning capabilities of deep learning models through the prediction of
stoichiometrically-balanced chemical reactions. ChemAlgebra requires
manipulating sets of complex discrete objects -- molecules represented as
formulas or graphs -- under algebraic constraints such as the mass preservation
principle. We believe that ChemAlgebra can serve as a useful test bed for the
next generation of machine reasoning models and as a promoter of their
development.

Visual feature pyramid has shown its superiority in both effectiveness and
efficiency in a wide range of applications. However, the existing methods
exorbitantly concentrate on the inter-layer feature interactions but ignore the
intra-layer feature regulations, which are empirically proved beneficial.
Although some methods try to learn a compact intra-layer feature representation
with the help of the attention mechanism or the vision transformer, they ignore
the neglected corner regions that are important for dense prediction tasks. To
address this problem, in this paper, we propose a Centralized Feature Pyramid
(CFP) for object detection, which is based on a globally explicit centralized
feature regulation. Specifically, we first propose a spatial explicit visual
center scheme, where a lightweight MLP is used to capture the globally
long-range dependencies and a parallel learnable visual center mechanism is
used to capture the local corner regions of the input images. Based on this, we
then propose a globally centralized regulation for the commonly-used feature
pyramid in a top-down fashion, where the explicit visual center information
obtained from the deepest intra-layer feature is used to regulate frontal
shallow features. Compared to the existing feature pyramids, CFP not only has
the ability to capture the global long-range dependencies, but also efficiently
obtain an all-round yet discriminative feature representation. Experimental
results on the challenging MS-COCO validate that our proposed CFP can achieve
the consistent performance gains on the state-of-the-art YOLOv5 and YOLOX
object detection baselines.

We study the mixing properties of an important optimization algorithm of
machine learning: the stochastic gradient Langevin dynamics (SGLD) with a fixed
step size. The data stream is not assumed to be independent hence the SGLD is
not a Markov chain, merely a \emph{Markov chain in a random environment}, which
complicates the mathematical treatment considerably. We derive a strong law of
large numbers and a functional central limit theorem for SGLD.

Asynchronous time series are often observed in several applications such as
health care, astronomy, and climate science, and pose a significant challenge
to the standard deep learning architectures. Interpolation of asynchronous time
series is vital for many real-world tasks like root cause analysis, and medical
diagnosis. In this paper, we propose a novel encoder-decoder architecture
called Tripletformer, which works on the set of observations where each set
element is a triple of time, channel, and value, for the probabilistic
interpolation of the asynchronous time series. Both the encoder and the decoder
of the Tripletformer are modeled using attention layers and fully connected
layers and are invariant to the order in which set elements are presented. The
proposed Tripletformer is compared with a range of baselines over multiple
real-world and synthetic asynchronous time series datasets, and the
experimental results attest that it produces more accurate and certain
interpolations. We observe an improvement in negative loglikelihood error up to
33% over real and 800% over synthetic asynchronous time series datasets
compared to the state-of-the-art model using the Tripletformer.

Conditional generation of time-dependent data is a task that has much
interest, whether for data augmentation, scenario simulation, completing
missing data, or other purposes. Recent works proposed a Transformer-based Time
series generative adversarial network (TTS-GAN) to address the limitations of
recurrent neural networks. However, this model assumes a unimodal distribution
and tries to generate samples around the expectation of the real data
distribution. One of its limitations is that it may generate a random
multivariate time series; it may fail to generate samples in the presence of
multiple sub-components within an overall distribution. One could train models
to fit each sub-component separately to overcome this limitation. Our work
extends the TTS-GAN by conditioning its generated output on a particular
encoded context allowing the use of one model to fit a mixture distribution
with multiple sub-components. Technically, it is a conditional generative
adversarial network that models realistic multivariate time series under
different types of conditions, such as categorical variables or multivariate
time series. We evaluate our model on UniMiB Dataset, which contains
acceleration data following the XYZ axes of human activities collected using
Smartphones. We use qualitative evaluations and quantitative metrics such as
Principal Component Analysis (PCA), and we introduce a modified version of the
Frechet inception distance (FID) to measure the performance of our model and
the statistical similarities between the generated and the real data
distributions. We show that this transformer-based CGAN can generate realistic
high-dimensional and long data sequences under different kinds of conditions.

We study the problem of episodic reinforcement learning in continuous
state-action spaces with unknown rewards and transitions. Specifically, we
consider the setting where the rewards and transitions are modeled using
parametric bilinear exponential families. We propose an algorithm, BEF-RLSVI,
that a) uses penalized maximum likelihood estimators to learn the unknown
parameters, b) injects a calibrated Gaussian noise in the parameter of rewards
to ensure exploration, and c) leverages linearity of the exponential family
with respect to an underlying RKHS to perform tractable planning. We further
provide a frequentist regret analysis of BEF-RLSVI that yields an upper bound
of $\tilde{\mathcal{O}}(\sqrt{d^3H^3K})$, where $d$ is the dimension of the
parameters, $H$ is the episode length, and $K$ is the number of episodes. Our
analysis improves the existing bounds for the bilinear exponential family of
MDPs by $\sqrt{H}$ and removes the handcrafted clipping deployed in existing
\RLSVI-type algorithms. Our regret bound is order-optimal with respect to $H$
and $K$.

Active yaw control (AYC) of wind turbines has been widely applied to increase
the annual energy production (AEP) of a wind farm. AYC efficiency depends on
the wind direction and the wind farm layout because an AYC method utilizes wake
deflection by yawing wind turbines. Conventional optimization of a wind farm
layout assumed that the swept areas of all wind turbines are aligned
perpendicular to the wind direction, thereby allowing non-optimal utilization
of an AYC method. Higher AEP can be obtained by joint optimization which
considers an AYC method in the layout design stage. Joint optimization of the
farm layout and AYC has been difficult due to the non-convexity of the problem
and the computational inefficiency. In the present study, a particle swarm
optimization based method is developed for joint optimization. The layout is
optimized with simultaneous consideration for yaw angles for all wind
velocities to obtain a globally optimal layout. A number of random initial
particles consisting of the layout and yaw angles of wind turbines reduce the
initial layout dependency on the optimized layout. To deal with the challenge
of large-scale optimization, the adaptive granularity learning distributed
particle swarm optimization algorithm is implemented. The improvement in AEP
when using a jointly optimized layout compared to a conventionally optimized
layout in a real wind farm is demonstrated using the present method.

Independent component analysis (ICA) is a blind source separation method for
linear disentanglement of independent latent sources from observed data. We
investigate the special setting of noisy linear ICA where the observations are
split among different views, each receiving a mixture of shared and individual
sources. We prove that the corresponding linear structure is identifiable, and
the shared sources can be recovered, provided that sufficiently many diverse
views and data points are available. To computationally estimate the sources,
we optimize a constrained form of the joint log-likelihood of the observed data
among all views. We show empirically that our objective recovers the sources in
high dimensional settings, also in the case when the measurements are corrupted
by noise. Finally, we apply the proposed model in a challenging real-life
application, where the estimated shared sources from two large transcriptome
datasets (observed data) provided by two different labs (two different views)
lead to a more plausible representation of the underlying graph structure than
existing baselines.

Deep neural networks have demonstrated superior performance on
appearance-based gaze estimation tasks. However, due to variations in person,
illuminations, and background, performance degrades dramatically when applying
the model to a new domain. In this paper, we discover an interesting gaze
jitter phenomenon in cross-domain gaze estimation, i.e., the gaze predictions
of two similar images can be severely deviated in target domain. This is
closely related to cross-domain gaze estimation tasks, but surprisingly, it has
not been noticed yet previously. Therefore, we innovatively propose to utilize
the gaze jitter to analyze and optimize the gaze domain adaptation task. We
find that the high-frequency component (HFC) is an important factor that leads
to jitter. Based on this discovery, we add high-frequency components to input
images using the adversarial attack and employ contrastive learning to
encourage the model to obtain similar representations between original and
perturbed data, which reduces the impacts of HFC. We evaluate the proposed
method on four cross-domain gaze estimation tasks, and experimental results
demonstrate that it significantly reduces the gaze jitter and improves the gaze
estimation performance in target domains.

Electrocardiogram (ECG), a technique for medical monitoring of cardiac
activity, is an important method for identifying cardiovascular disease.
However, analyzing the increasing quantity of ECG data consumes a lot of
medical resources. This paper explores an effective algorithm for automatic
classifications of multi-classes of heartbeat types based on ECG. Most neural
network based methods target the individual heartbeats, ignoring the secrets
embedded in the temporal sequence. And the ECG signal has temporal variation
and unique individual characteristics, which means that the same type of ECG
signal varies among patients under different physical conditions. A two-stream
architecture is used in this paper and presents an enhanced version of ECG
recognition based on this. The architecture achieves classification of holistic
ECG signal and individual heartbeat and incorporates identified and temporal
stream networks. Identified networks are used to extract features of individual
heartbeats, while temporal networks aim to extract temporal correlations
between heartbeats. Results on the MIT-BIH Arrhythmia Database demonstrate that
the proposed algorithm performs an accuracy of 99.38\%. In addition, the
proposed algorithm reaches an 88.07\% positive accuracy on massive data in real
life, showing that the proposed algorithm can efficiently categorize different
classes of heartbeat with high diagnostic performance.

Masked image modeling (MIM) has become a popular strategy for self-supervised
learning~(SSL) of visual representations with Vision Transformers. A
representative MIM model, the masked auto-encoder (MAE), randomly masks a
subset of image patches and reconstructs the masked patches given the unmasked
patches. Concurrently, many recent works in self-supervised learning utilize
the student/teacher paradigm which provides the student with an additional
target based on the output of a teacher composed of an exponential moving
average (EMA) of previous students. Although common, relatively little is known
about the dynamics of the interaction between the student and teacher. Through
analysis on a simple linear model, we find that the teacher conditionally
removes previous gradient directions based on feature similarities which
effectively acts as a conditional momentum regularizer. From this analysis, we
present a simple SSL method, the Reconstruction-Consistent Masked Auto-Encoder
(RC-MAE) by adding an EMA teacher to MAE. We find that RC-MAE converges faster
and requires less memory usage than state-of-the-art self-distillation methods
during pre-training, which may provide a way to enhance the practicality of
prohibitively expensive self-supervised learning of Vision Transformer models.
Additionally, we show that RC-MAE achieves more robustness and better
performance compared to MAE on downstream tasks such as ImageNet-1K
classification, object detection, and instance segmentation.

Is dynamics prediction indispensable for physical reasoning? If so, what kind
of roles do the dynamics prediction modules play during the physical reasoning
process? Most studies focus on designing dynamics prediction networks and
treating physical reasoning as a downstream task without investigating the
questions above, taking for granted that the designed dynamics prediction would
undoubtedly help the reasoning process. In this work, we take a closer look at
this assumption, exploring this fundamental hypothesis by comparing two
learning mechanisms: Learning from Dynamics (LfD) and Learning from Intuition
(LfI). In the first experiment, we directly examine and compare these two
mechanisms. Results show a surprising finding: Simple LfI is better than or on
par with state-of-the-art LfD. This observation leads to the second experiment
with Ground-truth Dynamics, the ideal case of LfD wherein dynamics are obtained
directly from a simulator. Results show that dynamics, if directly given
instead of approximated, would achieve much higher performance than LfI alone
on physical reasoning; this essentially serves as the performance upper bound.
Yet practically, LfD mechanism can only predict Approximate Dynamics using
dynamics learning modules that mimic the physical laws, making the following
downstream physical reasoning modules degenerate into the LfI paradigm; see the
third experiment. We note that this issue is hard to mitigate, as dynamics
prediction errors inevitably accumulate in the long horizon. Finally, in the
fourth experiment, we note that LfI, the extremely simpler strategy when done
right, is more effective in learning to solve physical reasoning problems.
Taken together, the results on the challenging benchmark of PHYRE show that LfI
is, if not better, as good as LfD for dynamics prediction. However, the
potential improvement from LfD, though challenging, remains lucrative.

Subsurface tile drainage pipes provide agronomic, economic and environmental
benefits. By lowering the water table of wet soils, they improve the aeration
of plant roots and ultimately increase the productivity of farmland. They do
however also provide an entryway of agrochemicals into subsurface water bodies
and increase nutrition loss in soils. For maintenance and infrastructural
development, accurate maps of tile drainage pipe locations and drained
agricultural land are needed. However, these maps are often outdated or not
present. Different remote sensing (RS) image processing techniques have been
applied over the years with varying degrees of success to overcome these
restrictions. Recent developments in deep learning (DL) techniques improve upon
the conventional techniques with machine learning segmentation models. In this
study, we introduce two DL-based models: i) improved U-Net architecture; and
ii) Visual Transformer-based encoder-decoder in the framework of tile drainage
pipe detection. Experimental results confirm the effectiveness of both models
in terms of detection accuracy when compared to a basic U-Net architecture. Our
code and models are publicly available at
https://git.tu-berlin.de/rsim/drainage-pipes-detection.

One of the most popular training algorithms for deep neural networks is the
Adaptive Moment Estimation (Adam) introduced by Kingma and Ba. Despite its
success in many applications there is no satisfactory convergence analysis:
only local convergence can be shown for batch mode under some restrictions on
the hyperparameters, counterexamples exist for incremental mode. Recent results
show that for simple quadratic objective functions limit cycles of period 2
exist in batch mode, but only for atypical hyperparameters, and only for the
algorithm without bias correction. %More general there are several more
adaptive gradient methods which try to estimate a fitting learning rate and /
or search direction from the training data to improve the learning process
compared to pure gradient descent with fixed learningrate. We extend the
convergence analysis for Adam in the batch mode with bias correction and show
that even for quadratic objective functions as the simplest case of convex
functions 2-limit-cycles exist, for all choices of the hyperparameters. We
analyze the stability of these limit cycles and relate our analysis to other
results where approximate convergence was shown, but under the additional
assumption of bounded gradients which does not apply to quadratic functions.
The investigation heavily relies on the use of computer algebra due to the
complexity of the equations.

Blue straggler stars (BSS) are peculiar objects which normally appear as a
single broad sequence along the extension of the main sequence. Only four
globular clusters (GCs) have been observed to have two distinct and parallel
BSS sequences. For the first time for any open cluster (OC), we report double
BSS sequences in Berkeley 17. Using the machine-learning based membership
algorithm ML-MOC on Gaia EDR3 data, we identify 627 cluster members, including
21 BSS candidates out to 15 arcmin from the cluster center. Both the BSS
sequences are almost equally populated and parallel to one another in Gaia as
well as in Pan-STARRS colour-magnitude diagram (CMD). We statistically confirm
their presence and report that both BSS sequences are highly segregated
compared to the reference population out to $\sim$5.5 arcmin and not segregated
thereafter. The lower densities of OCs make BSS formation impossible via the
collisional channel. Therefore, mass transfer seems to be the only viable
channel for forming candidates of both sequences. The gap between the red and
blue BSS sequences, on the other hand, is significant and presents a great
opportunity to understand the connection between BSS formation and internal as
well as external dynamics of the parent clusters.

Spectral graph convolutional neural networks (GCNNs) have been producing
encouraging results in graph classification tasks. However, most spectral GCNNs
utilize fixed graphs when aggregating node features, while omitting edge
feature learning and failing to get an optimal graph structure. Moreover, many
existing graph datasets do not provide initialized edge features, further
restraining the ability of learning edge features via spectral GCNNs. In this
paper, we try to address this issue by designing an edge feature scheme and an
add-on layer between every two stacked graph convolution layers in GCNN. Both
are lightweight while effective in filling the gap between edge feature
learning and performance enhancement of graph classification. The edge feature
scheme makes edge features adapt to node representations at different graph
convolution layers. The add-on layers help adjust the edge features to an
optimal graph structure. To test the effectiveness of our method, we take
Euclidean positions as initial node features and extract graphs with semantic
information from point cloud objects. The node features of our extracted graphs
are more scalable for edge feature learning than most existing graph datasets
(in one-hot encoded label format). Three new graph datasets are constructed
based on ModelNet40, ModelNet10 and ShapeNet Part datasets. Experimental
results show that our method outperforms state-of-the-art graph classification
methods on the new datasets by reaching 96.56% overall accuracy on
Graph-ModelNet40, 98.79% on Graph-ModelNet10 and 97.91% on Graph-ShapeNet Part.
The constructed graph datasets will be released to the community.

Research on diagnosing diseases based on voice signals currently are rapidly
increasing, including cough-related diseases. When training the cough sound
signals into deep learning models, it is necessary to have a standard input by
segmenting several cough signals into individual cough signals. Previous
research has been developed to segment cough signals from non-cough signals.
This research evaluates the segmentation methods of several cough signals from
a single audio file into several single-cough signals. We evaluate three
different methods employing manual segmentation as a baseline and automatic
segmentation. The results by two automatic segmentation methods obtained
precisions of 73% and 70% compared to 49% by manual segmentation. The
agreements of listening tests to count the number of correct single-cough
segmentations show fair and moderate correlations for automatic segmentation
methods and are comparable with manual segmentation.

In this work we present a framework which may transform research and praxis
in epidemic planning. Introduced in the context of the ongoing COVID-19
pandemic, we provide a concrete demonstration of the way algorithms may learn
from epidemiological models to scale their value for epidemic preparedness. Our
contributions in this work are two fold: 1) a novel platform which makes it
easy for decision making stakeholders to interact with epidemiological models
and algorithms developed within the Machine learning community, and 2) the
release of this work under the Apache-2.0 License. The objective of this paper
is not to look closely at any particular models or algorithms, but instead to
highlight how they can be coupled and shared to empower evidence-based decision
making.

The full acceptance of Deep Learning (DL) models in the clinical field is
rather low with respect to the quantity of high-performing solutions reported
in the literature. Particularly, end users are reluctant to rely on the rough
predictions of DL models. Uncertainty quantification methods have been proposed
in the literature as a potential response to reduce the rough decision provided
by the DL black box and thus increase the interpretability and the
acceptability of the result by the final user. In this review, we propose an
overview of the existing methods to quantify uncertainty associated to DL
predictions. We focus on applications to medical image analysis, which present
specific challenges due to the high dimensionality of images and their quality
variability, as well as constraints associated to real-life clinical routine.
We then discuss the evaluation protocols to validate the relevance of
uncertainty estimates. Finally, we highlight the open challenges of uncertainty
quantification in the medical field.

In federated learning (FL), classifiers (e.g., deep networks) are trained on
datasets from multiple centers without exchanging data across them, and thus
improves sample efficiency. In the classical setting of FL, the same labeling
criterion is usually employed across all centers being involved in training.
This constraint greatly limits the applicability of FL. For example, standards
used for disease diagnosis are more likely to be different across clinical
centers, which mismatches the classical FL setting. In this paper, we consider
an important yet under-explored setting of FL, namely FL with mixed-type labels
where different labeling criteria can be employed by various centers, leading
to inter-center label space differences and challenging existing FL methods
designed for the classical setting. To effectively and efficiently train models
with mixed-type labels, we propose a theory-guided and model-agnostic approach
that can make use of the underlying correspondence between those label spaces
and can be easily combined with various FL methods such as FedAvg. We present
convergence analysis based on over-parameterized ReLU networks. We show that
the proposed method can achieve linear convergence in label projection, and
demonstrate the impact of the parameters of our new setting on the convergence
rate. The proposed method is evaluated and the theoretical findings are
validated on benchmark and medical datasets.

Time series synthesis is an important research topic in the field of deep
learning, which can be used for data augmentation. Time series data types can
be broadly classified into regular or irregular. However, there are no existing
generative models that show good performance for both types without any model
changes. Therefore, we present a general purpose model capable of synthesizing
regular and irregular time series data. To our knowledge, we are the first
designing a general purpose time series synthesis model, which is one of the
most challenging settings for time series synthesis. To this end, we design a
generative adversarial network-based method, where many related techniques are
carefully integrated into a single framework, ranging from neural
ordinary/controlled differential equations to continuous time-flow processes.
Our method outperforms all existing methods.

Human eye contact is a form of non-verbal communication and can have a great
influence on social behavior. Since the location and size of the eye contact
targets vary across different videos, learning a generic video-independent eye
contact detector is still a challenging task. In this work, we address the task
of one-way eye contact detection for videos in the wild. Our goal is to build a
unified model that can identify when a person is looking at his gaze targets in
an arbitrary input video. Considering that this requires time-series relative
eye movement information, we propose to formulate the task as a temporal
segmentation. Due to the scarcity of labeled training data, we further propose
a gaze target discovery method to generate pseudo-labels for unlabeled videos,
which allows us to train a generic eye contact segmentation model in an
unsupervised way using in-the-wild videos. To evaluate our proposed approach,
we manually annotated a test dataset consisting of 52 videos of human
conversations. Experimental results show that our eye contact segmentation
model outperforms the previous video-dependent eye contact detector and can
achieve 71.88% framewise accuracy on our annotated test set. Our code and
evaluation dataset are available at
https://github.com/ut-vision/Video-Independent-ECS.

We study embedding table placement for distributed recommender systems, which
aims to partition and place the tables on multiple hardware devices (e.g.,
GPUs) to balance the computation and communication costs. Although prior work
has explored learning-based approaches for the device placement of
computational graphs, embedding table placement remains to be a challenging
problem because of 1) the operation fusion of embedding tables, and 2) the
generalizability requirement on unseen placement tasks with different numbers
of tables and/or devices. To this end, we present DreamShard, a reinforcement
learning (RL) approach for embedding table placement. DreamShard achieves the
reasoning of operation fusion and generalizability with 1) a cost network to
directly predict the costs of the fused operation, and 2) a policy network that
is efficiently trained on an estimated Markov decision process (MDP) without
real GPU execution, where the states and the rewards are estimated with the
cost network. Equipped with sum and max representation reductions, the two
networks can directly generalize to any unseen tasks with different numbers of
tables and/or devices without fine-tuning. Extensive experiments show that
DreamShard substantially outperforms the existing human expert and RNN-based
strategies with up to 19% speedup over the strongest baseline on large-scale
synthetic tables and our production tables. The code is available at
https://github.com/daochenzha/dreamshard

Recently, few-shot object detection~(FSOD) has received much attention from
the community, and many methods are proposed to address this problem from a
knowledge transfer perspective. Though promising results have been achieved,
these methods fail to achieve shot-stable:~methods that excel in low-shot
regimes are likely to struggle in high-shot regimes, and vice versa. We believe
this is because the primary challenge of FSOD changes when the number of shots
varies. In the low-shot regime, the primary challenge is the lack of
inner-class variation. In the high-shot regime, as the variance approaches the
real one, the main hindrance to the performance comes from misalignment between
learned and true distributions. However, these two distinct issues remain
unsolved in most existing FSOD methods. In this paper, we propose to overcome
these challenges by exploiting rich knowledge the model has learned and
effectively transferring them to the novel classes. For the low-shot regime, we
propose a distribution calibration method to deal with the lack of inner-class
variation problem. Meanwhile, a shift compensation method is proposed to
compensate for possible distribution shift during fine-tuning. For the
high-shot regime, we propose to use the knowledge learned from ImageNet as
guidance for the feature learning in the fine-tuning stage, which will
implicitly align the distributions of the novel classes. Although targeted
toward different regimes, these two strategies can work together to further
improve the FSOD performance. Experiments on both the VOC and COCO benchmarks
show that our proposed method can significantly outperform the baseline method
and produce competitive results in both low-shot settings (shot<5) and
high-shot settings (shot>=5). Code is available at
https://github.com/JulioZhao97/EffTrans_Fsdet.git.

The Arcade Learning Environment (ALE) has become an essential benchmark for
assessing the performance of reinforcement learning algorithms. However, the
computational cost of generating results on the entire 57-game dataset limits
ALE's use and makes the reproducibility of many results infeasible. We propose
a novel solution to this problem in the form of a principled methodology for
selecting small but representative subsets of environments within a benchmark
suite. We applied our method to identify a subset of five ALE games, called
Atari-5, which produces 57-game median score estimates within 10% of their true
values. Extending the subset to 10-games recovers 80% of the variance for
log-scores for all games within the 57-game set. We show this level of
compression is possible due to a high degree of correlation between many of the
games in ALE.

Developing efficient time series anomaly detection techniques is important to
maintain service quality and provide early alarms. Generative neural network
methods are one class of the unsupervised approaches that are achieving
increasing attention in recent years. In this paper, we have proposed an
unsupervised Generative Adversarial Network (GAN)-based anomaly detection
framework, DEGAN. It relies solely on normal time series data as input to train
a well-configured discriminator (D) into a standalone anomaly predictor. In
this framework, time series data is processed by the sliding window method.
Expected normal patterns in data are leveraged to develop a generator (G)
capable of generating normal data patterns. Normal data is also utilized in
hyperparameter tuning and D model selection steps. Validated D models are then
extracted and applied to evaluate unseen (testing) time series and identify
patterns that have anomalous characteristics. Kernel density estimation (KDE)
is applied to data points that are likely to be anomalous to generate
probability density functions on the testing time series. The segments with the
highest relative probabilities are detected as anomalies. To evaluate the
performance, we tested on univariate acceleration time series for five miles of
a Class I railroad track. We implemented the framework to detect the real
anomalous observations identified by operators. The results show that
leveraging the framework with a CNN D architecture results in average best
recall and precision of 80% and 86%, respectively, which demonstrates that a
well-trained standalone D model has the potential to be a reliable anomaly
detector. Moreover, the influence of GAN hyperparameters, GAN architectures,
sliding window sizes, clustering of time series, and model validation with
labeled/unlabeled data were also investigated.

Electrical energy is essential in today's society. Accurate electrical load
forecasting is beneficial for better scheduling of electricity generation and
saving electrical energy. In this paper, we propose theory-guided deep-learning
load forecasting 2.0 (TgDLF2.0) to solve this issue, which is an improved
version of the theory-guided deep-learning framework for load forecasting via
ensemble long short-term memory (TgDLF). TgDLF2.0 introduces the deep-learning
model Transformer and transfer learning on the basis of dividing the electrical
load into dimensionless trends and local fluctuations, which realizes the
utilization of domain knowledge, captures the long-term dependency of the load
series, and is more appropriate for realistic scenarios with scarce samples.
Cross-validation experiments on different districts show that TgDLF2.0 is
approximately 16% more accurate than TgDLF and saves more than half of the
training time. TgDLF2.0 with 50% weather noise has the same accuracy as TgDLF
without noise, which proves its robustness. We also preliminarily mine the
interpretability of Transformer in TgDLF2.0, which may provide future potential
for better theory guidance. Furthermore, experiments demonstrate that transfer
learning can accelerate convergence of the model in half the number of training
epochs and achieve better performance.

Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted
increasing attention from the graph machine learning community in recent years,
owing to its capability to learn performant node embeddings without costly
label information. One weakness of conventional SSL frameworks for GNNs is that
they learn through a single philosophy, such as mutual information maximization
or generative reconstruction. When applied to various downstream tasks, these
frameworks rarely perform equally well for every task, because one philosophy
may not span the extensive knowledge required for all tasks. In light of this,
we introduce ParetoGNN, a multi-task SSL framework for node representation
learning over graphs. Specifically, ParetoGNN is self-supervised by manifold
pretext tasks observing multiple philosophies. To reconcile different
philosophies, we explore a multiple-gradient descent algorithm, such that
ParetoGNN actively learns from every pretext task while minimizing potential
conflicts. We conduct comprehensive experiments over four downstream tasks
(i.e., node classification, node clustering, link prediction, and partition
prediction), and our proposal achieves the best overall performance across
tasks on 11 widely adopted benchmark datasets. Besides, we observe that
learning from multiple philosophies enhances not only the task generalization
but also the single task performance, demonstrating that ParetoGNN achieves
better task generalization via the disjoint yet complementary knowledge learned
from different philosophies.

Algorithmic fairness has received increased attention in socially sensitive
domains. While rich literature on mean fairness has been established, research
on quantile fairness remains sparse but vital. To fulfill great needs and
advocate the significance of quantile fairness, we propose a novel framework to
learn a real-valued quantile function under the fairness requirement of
Demographic Parity with respect to sensitive attributes, such as race or
gender, and thereby derive a reliable fair prediction interval. Using optimal
transport and functional synchronization techniques, we establish theoretical
guarantees of distribution-free coverage and exact fairness for the induced
prediction interval constructed by fair quantiles. A hands-on pipeline is
provided to incorporate flexible quantile regressions with an efficient
fairness adjustment post-processing algorithm. We demonstrate the superior
empirical performance of this approach on several benchmark datasets. Our
results show the model's ability to uncover the mechanism underlying the
fairness-accuracy trade-off in a wide range of societal and medical

This paper introduces a Factor Augmented Sparse Throughput (FAST) model that
utilizes both latent factors and sparse idiosyncratic components for
nonparametric regression. The FAST model bridges factor models on one end and
sparse nonparametric models on the other end. It encompasses structured
nonparametric models such as factor augmented additive models and sparse
low-dimensional nonparametric interaction models and covers the cases where the
covariates do not admit factor structures. Via diversified projections as
estimation of latent factor space, we employ truncated deep ReLU networks to
nonparametric factor regression without regularization and to a more general
FAST model using nonconvex regularization, resulting in factor augmented
regression using neural network (FAR-NN) and FAST-NN estimators respectively.
We show that FAR-NN and FAST-NN estimators adapt to the unknown low-dimensional
structure using hierarchical composition models in nonasymptotic minimax rates.
We also study statistical learning for the factor augmented sparse additive
model using a more specific neural network architecture. Our results are
applicable to the weak dependent cases without factor structures. In proving
the main technical result for FAST-NN, we establish a new deep ReLU network
approximation result that contributes to the foundation of neural network
theory. Our theory and methods are further supported by simulation studies and
an application to macroeconomic data.

We propose Waveformer that learns attention mechanism in the wavelet
coefficient space, requires only linear time complexity, and enjoys universal
approximating power. Specifically, we first apply forward wavelet transform to
project the input sequences to multi-resolution orthogonal wavelet bases, then
conduct nonlinear transformations (in this case, a random feature kernel) in
the wavelet coefficient space, and finally reconstruct the representation in
input space via backward wavelet transform. We note that other non-linear
transformations may be used, hence we name the learning paradigm Wavelet
transformatIon for Sequence lEarning (WISE). We emphasize the importance of
backward reconstruction in the WISE paradigm -- without it, one would be mixing
information from both the input space and coefficient space through skip
connections, which shall not be considered as mathematically sound. Compared
with Fourier transform in recent works, wavelet transform is more efficient in
time complexity and better captures local and positional information; we
further support this through our ablation studies. Extensive experiments on
seven long-range understanding datasets from the Long Range Arena benchmark and
code understanding tasks demonstrate that (1) Waveformer achieves competitive
and even better accuracy than a number of state-of-the-art Transformer variants
and (2) WISE can boost accuracies of various attention approximation methods
without increasing the time complexity. These together showcase the superiority
of learning attention in a wavelet coefficient space over the input space.

The overhead of non-linear functions dominates the performance of the secure
multiparty computation (MPC) based privacy-preserving machine learning (PPML).
This work introduces two sets of novel secure three-party computation (3PC)
protocols, using additive and replicated secret sharing schemes respectively.
We name the whole family of protocols as Bicoptor, its basis is a new sign
determination protocol, which relies on a clever use of the truncation protocol
proposed in SecureML (S&P 2017). Our 3PC sign determination protocol only
requires two communication rounds, and does not involve any preprocessing. Such
sign determination protocol is well-suited for computing non-linear functions
in PPML, e.g. the activation function ReLU, Maxpool, and their variants. We
develop suitable protocols for these non-linear functions, which form a family
of GPU-friendly protocols, Bicoptor. All Bicoptor protocols only require two
communication rounds without preprocessing. We evaluate the protocols using
additive secret sharing under a 3-party LAN network over a public cloud, and
achieve 90,000 DReLU/ReLU or 3,200 Maxpool (find the maximum value of nine
inputs) operations per second. Under the same settings and environment, our
ReLU protocol has a one or even two order(s) of magnitude improvement to the
state-of-the-art works, Edabits (CRYPTO 2020) or Falcon (PETS 2021),
respectively without batch processing.

This work proposes a new method to sequentially train a deep neural network
on multiple tasks without suffering catastrophic forgetting, while endowing it
with the capability to quickly adapt to unseen tasks. Starting from existing
work on network masking (Wortsman et al., 2020), we show that simply learning a
linear combination of a small number of task-specific masks (impressions) on a
randomly initialized backbone network is sufficient to both retain accuracy on
previously learned tasks, as well as achieve high accuracy on new tasks. In
contrast to previous methods, we do not require to generate dedicated masks or
contexts for each new task, instead leveraging transfer learning to keep
per-task parameter overhead small. Our work illustrates the power of linearly
combining individual impressions, each of which fares poorly in isolation, to
achieve performance comparable to a dedicated mask. Moreover, even repeated
impressions from the same task (homogeneous masks), when combined can approach
the performance of heterogeneous combinations if sufficiently many impressions
are used. Our approach scales more efficiently than existing methods, often
requiring orders of magnitude fewer parameters and can function without
modification even when task identity is missing. In addition, in the setting
where task labels are not given at inference, our algorithm gives an often
favorable alternative to the entropy based task-inference methods proposed in
(Wortsman et al., 2020). We evaluate our method on a number of well known image
classification data sets and architectures.

Recognition of electroencephalographic (EEG) signals highly affect the
efficiency of non-invasive brain-computer interfaces (BCIs). While recent
advances of deep-learning (DL)-based EEG decoders offer improved performances,
the development of geometric learning (GL) has attracted much attention for
offering exceptional robustness in decoding noisy EEG data. However, there is a
lack of studies on the merged use of deep neural networks (DNNs) and geometric
learning for EEG decoding. We herein propose a manifold attention network
(mAtt), a novel geometric deep learning (GDL)-based model, featuring a manifold
attention mechanism that characterizes spatiotemporal representations of EEG
data fully on a Riemannian symmetric positive definite (SPD) manifold. The
evaluation of the proposed MAtt on both time-synchronous and -asyncronous EEG
datasets suggests its superiority over other leading DL methods for general EEG
decoding. Furthermore, analysis of model interpretation reveals the capability
of MAtt in capturing informative EEG features and handling the non-stationarity
of brain dynamics.

Machine learning based traffic forecasting models leverage sophisticated
spatiotemporal auto-correlations to provide accurate predictions of city-wide
traffic states. However, existing methods assume a reliable and unbiased
forecasting environment, which is not always available in the wild. In this
work, we investigate the vulnerability of spatiotemporal traffic forecasting
models and propose a practical adversarial spatiotemporal attack framework.
Specifically, instead of simultaneously attacking all geo-distributed data
sources, an iterative gradient-guided node saliency method is proposed to
identify the time-dependent set of victim nodes. Furthermore, we devise a
spatiotemporal gradient descent based scheme to generate real-valued
adversarial traffic states under a perturbation constraint. Meanwhile, we
theoretically demonstrate the worst performance bound of adversarial traffic
forecasting attacks. Extensive experiments on two real-world datasets show that
the proposed two-step framework achieves up to $67.8\%$ performance degradation
on various advanced spatiotemporal forecasting models. Remarkably, we also show
that adversarial training with our proposed attacks can significantly improve
the robustness of spatiotemporal traffic forecasting models. Our code is
available in \url{https://github.com/luckyfan-cs/ASTFA}.

Industry 5.0 aims at maximizing the collaboration between humans and
machines. Machines are capable of automating repetitive jobs, while humans
handle creative tasks. As a critical component of Industrial Internet of Things
(IIoT) systems for service delivery, network data stream analytics often
encounter concept drift issues due to dynamic IIoT environments, causing
performance degradation and automation difficulties. In this paper, we propose
a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept
drift adaptation in IIoT systems, consisting of dynamic data pre-processing,
the proposed Drift-based Dynamic Feature Selection (DD-FS) method, dynamic
model learning & selection, and the proposed Window-based Performance Weighted
Probability Averaging Ensemble (W-PWPAE) model. It is a complete automated data
stream analytics framework that enables automatic, effective, and efficient
data analytics for IIoT systems in Industry 5.0. Experimental results on two
public IoT datasets demonstrate that the proposed framework outperforms
state-of-the-art methods for IIoT data stream analytics.

Cloud removal is an essential task in remote sensing data analysis. As the
image sensors are distant from the earth ground, it is likely that part of the
area of interests is covered by cloud. Moreover, the atmosphere in between
creates a constant haze layer upon the acquired images. To recover the ground
image, we propose to use scattering model for temporal sequence of images of
any scene in the framework of low rank and sparse models. We further develop
its variant, which is much faster and yet more accurate. To measure the
performance of different methods {\em objectively}, we develop a semi-realistic
simulation method to produce cloud cover so that various methods can be
quantitatively analysed, which enables detailed study of many aspects of cloud
removal algorithms, including verifying the effectiveness of proposed models in
comparison with the state-of-the-arts, including deep learning models, and
addressing the long standing problem of the determination of regularisation
parameters. The latter is companioned with theoretic analysis on the range of
the sparsity regularisation parameter and verified numerically.

We present methods for estimating loss-based measures of the performance of a
prediction model in a target population that differs from the source population
in which the model was developed, in settings where outcome and covariate data
are available from the source population but only covariate data are available
on a simple random sample from the target population. Prior work adjusting for
differences between the two populations has used various weighting estimators
with inverse odds or density ratio weights. Here, we develop more robust
estimators for the target population risk (expected loss) that can be used with
data-adaptive (e.g., machine learning-based) estimation of nuisance parameters.
We examine the large-sample properties of the estimators and evaluate finite
sample performance in simulations. Last, we apply the methods to data from lung
cancer screening using nationally representative data from the National Health
and Nutrition Examination Survey (NHANES) and extend our methods to account for
the complex survey design of the NHANES.

Graph Neural Networks (GNNs) have shown great ability in modeling
graph-structured data for various domains. However, GNNs are known as black-box
models that lack interpretability. Without understanding their inner working,
we cannot fully trust them, which largely limits their adoption in high-stake
scenarios. Though some initial efforts have been taken to interpret the
predictions of GNNs, they mainly focus on providing post-hoc explanations using
an additional explainer, which could misrepresent the true inner working
mechanism of the target GNN. The works on self-explainable GNNs are rather
limited. Therefore, we study a novel problem of learning prototype-based
self-explainable GNNs that can simultaneously give accurate predictions and
prototype-based explanations on predictions. We design a framework which can
learn prototype graphs that capture representative patterns of each class as
class-level explanations. The learned prototypes are also used to
simultaneously make prediction for for a test instance and provide
instance-level explanation. Extensive experiments on real-world and synthetic
datasets show the effectiveness of the proposed framework for both prediction
accuracy and explanation quality.

Ensemble of machine learning models yields improved performance as well as
robustness. However, their memory requirements and inference costs can be
prohibitively high. Knowledge distillation is an approach that allows a single
model to efficiently capture the approximate performance of an ensemble while
showing poor scalability as demand for re-training when introducing new teacher
models. In this paper, we study if we can utilize the meta-learning strategy to
directly predict the parameters of a single model with comparable performance
of an ensemble. Hereto, we introduce WeightFormer, a Transformer-based model
that can predict student network weights layer by layer in a forward pass,
according to the teacher model parameters. The proprieties of WeightFormer are
investigated on the CIFAR-10, CIFAR-100, and ImageNet datasets for model
structures of VGGNet-11, ResNet-50, and ViT-B/32, where it demonstrates that
our method can achieve approximate classification performance of an ensemble
and outperforms both the single network and standard knowledge distillation.
More encouragingly, we show that WeightFormer results can further exceeds
average ensemble with minor fine-tuning. Importantly, our task along with the
model and results can potentially lead to a new, more efficient, and scalable
paradigm of ensemble networks parameter learning.

Hierarchical Imitation Learning (HIL) has been proposed to recover
highly-complex behaviors in long-horizontal tasks from expert demonstrations by
modeling the task hierarchy with the option framework. Existing methods either
overlook the causal relationship between the subtask and its corresponding
policy or fail to learn the policy in an end-to-end fashion, which leads to
suboptimality. In this work, we develop a novel HIL algorithm based on
Adversarial Inverse Reinforcement Learning and adapt it with the
Expectation-Maximization algorithm in order to directly recover a hierarchical
policy from the unannotated demonstrations. Further, we introduce a directed
information term to the objective function to enhance the causality and propose
a Variational Autoencoder framework for learning with our objectives in an
end-to-end fashion. Theoretical justifications and evaluations on challenging
robotic control tasks are provided to show the superiority of our algorithm.
The codes are available at https://github.com/LucasCJYSDL/HierAIRL.

Calibration is a fundamental property of a good predictive model: it requires
that the model predicts correctly in proportion to its confidence. Modern
neural networks, however, provide no strong guarantees on their calibration --
and can be either poorly calibrated or well-calibrated depending on the
setting. It is currently unclear which factors contribute to good calibration
(architecture, data augmentation, overparameterization, etc), though various
claims exist in the literature.
  We propose a systematic way to study the calibration error: by decomposing it
into (1) calibration error on the train set, and (2) the calibration
generalization gap. This mirrors the fundamental decomposition of
generalization. We then investigate each of these terms, and give empirical
evidence that (1) DNNs are typically always calibrated on their train set, and
(2) the calibration generalization gap is upper-bounded by the standard
generalization gap. Taken together, this implies that models with small
generalization gap (|Test Error - Train Error|) are well-calibrated. This
perspective unifies many results in the literature, and suggests that
interventions which reduce the generalization gap (such as adding data, using
heavy augmentation, or smaller model size) also improve calibration. We thus
hope our initial study lays the groundwork for a more systematic and
comprehensive understanding of the relation between calibration,
generalization, and optimization.

Nowadays, AI companies improve service quality by aggressively collecting
users' data generated by edge devices, which jeopardizes data privacy. To
prevent this, Federated Learning is proposed as a private learning scheme,
using which users can locally train the model without collecting users' raw
data to servers. However, for machine-learning applications on edge devices
that have hard memory constraints, implementing a large model using FL is
infeasible. To meet the memory requirement, a recent collaborative learning
scheme named split federal learning is a potential solution since it keeps a
small model on the device and keeps the rest of the model on the server. In
this work, we implement a simply SFL framework on the Arduino board and verify
its correctness on the Chinese digits audio dataset for keyword spotting
application with over 90% accuracy. Furthermore, on the English digits audio
dataset, our SFL implementation achieves 13.89% higher accuracy compared to a
state-of-the-art FL implementation.

Businesses generate thousands of documents that communicate their strategic
vision and provide details of key products, services, entities, and processes.
Knowledge workers then face the laborious task of reading these documents to
identify, extract, and synthesize information relevant to their organizational
goals. To automate information gathering, question answering (QA) offers a
flexible framework where human-posed questions can be adapted to extract
diverse knowledge. Finetuning QA systems requires access to labeled data
(tuples of context, question, and answer). However, data curation for document
QA is uniquely challenging because the context (i.e., answer evidence passage)
needs to be retrieved from potentially long, ill-formatted documents. Existing
QA datasets sidestep this challenge by providing short, well-defined contexts
that are unrealistic in real-world applications. We present a three-stage
document QA approach: (1) text extraction from PDF; (2) evidence retrieval from
extracted texts to form well-posed contexts; (3) QA to extract knowledge from
contexts to return high-quality answers - extractive, abstractive, or Boolean.
Using QASPER as a surrogate to our proprietary data, our
detect-retrieve-comprehend (DRC) system achieves a +6.25 improvement in
Answer-F1 over existing baselines while delivering superior context selection.
Our results demonstrate that DRC holds tremendous promise as a flexible
framework for practical document QA.

Deep learning (DL) can fail when there are data mismatches between training
and testing data. Due to its operator-dependent nature, acquisition-related
data mismatches, caused by different scanner settings, can occur in ultrasound
imaging. Therefore, mitigating effects of such data mismatches is essential for
wider clinical adoption of DL powered ultrasound imaging. To mitigate the
effects, ideally we need to collect a large training set at each scanner
setting. However, acquiring such training sets is expensive. Another approach
could be training on a subset of imaging settings, which makes the data
generation less expensive. However, there will still be generalization issues.
As an alternative approach that is inexpensive and generalizable, we propose to
collect a large training set at a single setting and a small calibration set at
each scanner setting. Then, the calibration set will be used to calibrate data
mismatches by using a signals and systems perspective. We tested the proposed
solution to classify two phantoms. To investigate generalizability of the
proposed solution, we calibrated three types of data mismatches: pulse
frequency, focus and output power mismatches. To calibrate the setting
mismatches, we calculated the setting transfer functions. The CNN trained with
no calibration resulted in mean classification accuracies of 55.3%, 64.4% and
70.3% for pulse frequency, focus and output power mismatches, respectively. By
using the setting transfer functions, which allowed a matching of the training
and testing domains, we obtained mean accuracies of 95.3%, 92.99% and 99.32%,
respectively. Therefore, the incorporation of the setting transfer functions
between scanner settings can provide an economical means of generalizing a DL
model for specific classification tasks where scanner settings are not fixed by
the operator.

In many real-world problems, the learning agent needs to learn a problem's
abstractions and solution simultaneously. However, most such abstractions need
to be designed and refined by hand for different problems and domains of
application. This paper presents a novel top-down approach for constructing
state abstractions while carrying out reinforcement learning. Starting with
state variables and a simulator, it presents a novel domain-independent
approach for dynamically computing an abstraction based on the dispersion of
Q-values in abstract states as the agent continues acting and learning.
Extensive empirical evaluation on multiple domains and problems shows that this
approach automatically learns abstractions that are finely-tuned to the
problem, yield powerful sample efficiency, and result in the RL agent
significantly outperforming existing approaches.

Clustering algorithms are widely used in many societal resource allocation
applications, such as loan approvals and candidate recruitment, among others,
and hence, biased or unfair model outputs can adversely impact individuals that
rely on these applications. To this end, many fair clustering approaches have
been recently proposed to counteract this issue. Due to the potential for
significant harm, it is essential to ensure that fair clustering algorithms
provide consistently fair outputs even under adversarial influence. However,
fair clustering algorithms have not been studied from an adversarial attack
perspective. In contrast to previous research, we seek to bridge this gap and
conduct a robustness analysis against fair clustering by proposing a novel
black-box fairness attack. Through comprehensive experiments, we find that
state-of-the-art models are highly susceptible to our attack as it can reduce
their fairness performance significantly. Finally, we propose Consensus Fair
Clustering (CFC), the first robust fair clustering approach that transforms
consensus clustering into a fair graph partitioning problem, and iteratively
learns to generate fair cluster outputs. Experimentally, we observe that CFC is
highly robust to the proposed attack and is thus a truly robust fair clustering
alternative.

In this work, we explore the emotional reactions that real-world images tend
to induce by using natural language as the medium to express the rationale
behind an affective response to a given visual stimulus. To embark on this
journey, we introduce and share with the research community a large-scale
dataset that contains emotional reactions and free-form textual explanations
for 85,007 publicly available images, analyzed by 6,283 annotators who were
asked to indicate and explain how and why they felt in a particular way when
observing a specific image, producing a total of 526,749 responses. Even though
emotional reactions are subjective and sensitive to context (personal mood,
social status, past experiences) - we show that there is significant common
ground to capture potentially plausible emotional responses with a large
support in the subject population. In light of this crucial observation, we ask
the following questions: i) Can we develop multi-modal neural networks that
provide reasonable affective responses to real-world visual data, explained
with language? ii) Can we steer such methods towards producing explanations
with varying degrees of pragmatic language or justifying different emotional
reactions while adapting to the underlying visual stimulus? Finally, iii) How
can we evaluate the performance of such methods for this novel task? With this
work, we take the first steps in addressing all of these questions, thus paving
the way for richer, more human-centric, and emotionally-aware image analysis
systems. Our introduced dataset and all developed methods are available on
https://affective-explanations.org

Recently there has been increasing interest in developing and deploying deep
graph learning algorithms for many graph analysis tasks such as node and edge
classification, link prediction, and clustering with numerous practical
applications such as fraud detection, drug discovery, or recommender systems.
Allbeit there is a limited number of publicly available graph-structured
datasets, most of which are tiny compared to production-sized applications with
trillions of edges and billions of nodes. Further, new algorithms and models
are benchmarked across similar datasets with similar properties. In this work,
we tackle this shortcoming by proposing a scalable synthetic graph generation
tool that can mimic the original data distribution of real-world graphs and
scale them to arbitrary sizes. This tool can be used then to learn a set of
parametric models from proprietary datasets that can subsequently be released
to researchers to study various graph methods on the synthetic data increasing
prototype development and novel applications. Finally, the performance of the
graph learning algorithms depends not only on the size but also on the
dataset's structure. We show how our framework generalizes across a set of
datasets, mimicking both structural and feature distributions as well as its
scalability across varying dataset sizes.

News recommendation is one of the most challenging tasks in recommender
systems, mainly due to the ephemeral relevance of news to users. As social
media, and particularly microblogging applications like Twitter or Weibo, gains
popularity as platforms for news dissemination, personalized news
recommendation in this context becomes a significant challenge. We revisit news
recommendation in the microblogging scenario, by taking into consideration
social interactions and observations tracing how the information that is up for
recommendation spreads in an underlying network. We propose a deep-learning
based approach that is diffusion and influence-aware, called Influence-Graph
News Recommender (IGNiteR). It is a content-based deep recommendation model
that jointly exploits all the data facets that may impact adoption decisions,
namely semantics, diffusion-related features pertaining to local and global
influence among users, temporal attractiveness, and timeliness, as well as
dynamic user preferences. To represent the news, a multi-level attention-based
encoder is used to reveal the different interests of users. This news encoder
relies on a CNN for the news content and on an attentive LSTM for the diffusion
traces. For the latter, by exploiting previously observed news diffusions
(cascades) in the microblogging medium, users are mapped to a latent space that
captures potential influence on others or susceptibility of being influenced
for news adoptions. Similarly, a time-sensitive user encoder enables us to
capture the dynamic preferences of users with an attention-based bidirectional
LSTM. We perform extensive experiments on two real-world datasets, showing that
IGNiteR outperforms the state-of-the-art deep-learning based news
recommendation methods.

$k$-subset sampling is ubiquitous in machine learning, enabling
regularization and interpretability through sparsity. The challenge lies in
rendering $k$-subset sampling amenable to end-to-end learning. This has
typically involved relaxing the reparameterized samples to allow for
backpropagation, with the risk of introducing high bias and high variance. In
this work, we fall back to discrete $k$-subset sampling on the forward pass.
This is coupled with using the gradient with respect to the exact marginals,
computed efficiently, as a proxy for the true gradient. We show that our
gradient estimator, SIMPLE, exhibits lower bias and variance compared to
state-of-the-art estimators, including the straight-through Gumbel estimator
when $k = 1$. Empirical results show improved performance on learning to
explain and sparse linear regression. We provide an algorithm for computing the
exact ELBO for the $k$-subset distribution, obtaining significantly lower loss
compared to SOTA.

Clustering models constitute a class of unsupervised machine learning methods
which are used in a number of application pipelines, and play a vital role in
modern data science. With recent advancements in deep learning -- deep
clustering models have emerged as the current state-of-the-art over traditional
clustering approaches, especially for high-dimensional image datasets. While
traditional clustering approaches have been analyzed from a robustness
perspective, no prior work has investigated adversarial attacks and robustness
for deep clustering models in a principled manner. To bridge this gap, we
propose a blackbox attack using Generative Adversarial Networks (GANs) where
the adversary does not know which deep clustering model is being used, but can
query it for outputs. We analyze our attack against multiple state-of-the-art
deep clustering models and real-world datasets, and find that it is highly
successful. We then employ some natural unsupervised defense approaches, but
find that these are unable to mitigate our attack. Finally, we attack Face++, a
production-level face clustering API service, and find that we can
significantly reduce its performance as well. Through this work, we thus aim to
motivate the need for truly robust deep clustering models.

Making use of strong correlations between closely separated multiple or
double sources and photometric and astrometric metadata in Gaia EDR3, we
generate a catalog of candidate double and multiply imaged lensed quasars and
AGNs, comprising 3140 systems. It includes two partially overlapping parts, a
sample of distant (redshifts mostly greater than 1) sources with perturbed
data, and systems resolved into separate components by Gaia at separations less
than $2\arcsec$. For the first part, which is roughly one third of the
published catalog, we synthesized 0.617 million redshifts by multiple machine
learning prediction and classification methods, using independent photometric
and astrometric data from Gaia EDR3 and WISE with accurate spectroscopic
redshifts from SDSS as a training set. Using these synthetic redshifts, we
estimate a rate of 4.9\% of interlopers with spectroscopic redshift below 1 in
this part of the catalog. Unresolved candidate double and dual AGNs and quasars
are selected as sources with marginally high BP/RP excess factor
(phot_bp_rp_excess_factor), which is sensitive to source extent, limiting our
search to high-redshift quasars. For the second part of the catalog, additional
filters on measured parallax and near-neighbor statistics are applied to
diminish the propagation of remaining stellar contaminants. The estimated rate
of positives (double or multiple sources) is 98\%, and the estimated rate of
dual (physically related quasars) is greater than 54\%. A few dozen
serendipitously found objects of interest are discussed in more detail,
including known and new lensed images, planetary nebulae and young infrared
stars of peculiar morphology, and quasars with catastrophic redshift errors in
SDSS.

Despite the success of large vision and language models (VLMs) in many
downstream applications, it is unclear how well they encode compositional
information. Here, we create the Attribution, Relation, and Order (ARO)
benchmark to systematically evaluate the ability of VLMs to understand
different types of relationships, attributes, and order. ARO consists of Visual
Genome Attribution, to test the understanding of objects' properties; Visual
Genome Relation, to test for relational understanding; and COCO &
Flickr30k-Order, to test for order sensitivity. ARO is orders of magnitude
larger than previous benchmarks of compositionality, with more than 50,000 test
cases. We show where state-of-the-art VLMs have poor relational understanding,
can blunder when linking objects to their attributes, and demonstrate a severe
lack of order sensitivity. VLMs are predominantly trained and evaluated on
large datasets with rich compositional structure in the images and captions.
Yet, training on these datasets has not been enough to address the lack of
compositional understanding, and evaluating on these datasets has failed to
surface this deficiency. To understand why these limitations emerge and are not
represented in the standard tests, we zoom into the evaluation and training
procedures. We demonstrate that it is possible to perform well on retrieval
over existing datasets without using the composition and order information.
Given that contrastive pretraining optimizes for retrieval on datasets with
similar shortcuts, we hypothesize that this can explain why the models do not
need to learn to represent compositional information. This finding suggests a
natural solution: composition-aware hard negative mining. We show that a
simple-to-implement modification of contrastive learning significantly improves
the performance on tasks requiring understanding of order and compositionality.

We propose a new metric learning paradigm, Regression-based Elastic Metric
Learning (REML), which optimizes the elastic metric for manifold regression on
the manifold of discrete curves. Our method recognizes that the "ideal" metric
is trajectory-dependent and thus creates an opportunity for improved regression
fit on trajectories of curves. When tested on cell shape trajectories, REML's
learned metric generates a better regression fit than the conventionally used
square-root-velocity SRV metric.

Deploying radio frequency (RF) localisation systems invariably entails
non-trivial effort, particularly for the latest learning-based breeds. There
has been little prior work on characterising and comparing how learnt localiser
networks can be deployed in the field under real-world RF distribution shifts.
In this paper, we present RadioBench: a suite of 8 learnt localiser nets from
the state-of-the-art to study and benchmark their real-world deployability,
utilising five novel industry-grade datasets. We train 10k models to analyse
the inner workings of these learnt localiser nets and uncover their differing
behaviours across three performance axes: (i) learning, (ii) proneness to
distribution shift, and (iii) localisation. We use insights gained from this
analysis to recommend best practices for the deployability of learning-based RF
localisation under practical constraints.

Dataset discovery from data lakes is essential in many real application
scenarios. In this paper, we propose Starmie, an end-to-end framework for
dataset discovery from data lakes (with table union search as the main use
case). Our proposed framework features a contrastive learning method to train
column encoders from pre-trained language models in a fully unsupervised
manner. The column encoder of Starmie captures the rich contextual semantic
information within tables by leveraging a contrastive multi-column pre-training
strategy. We utilize the cosine similarity between column embedding vectors as
the column unionability score and propose a filter-and-verification framework
that allows exploring a variety of design choices to compute the unionability
score between two tables accordingly. Empirical evaluation results on real
table benchmark datasets show that Starmie outperforms the best-known solutions
in the effectiveness of table union search by 6.8 in MAP and recall. Moreover,
Starmie is the first to employ the HNSW (Hierarchical Navigable Small World)
index for accelerate query processing of table union search which provides a
3,000X performance gain over the linear scan baseline and a 400X performance
gain over an LSH index (the state-of-the-art solution for data lake indexing).

We consider estimating a compact set from finite data by approximating the
support function of that set via sublinear regression. Support functions
uniquely characterize a compact set up to closure of convexification, and are
sublinear (convex as well as positive homogeneous of degree one). Conversely,
any sublinear function is the support function of a compact set. We leverage
this property to transcribe the task of learning a compact set to that of
learning its support function. We propose two algorithms to perform the
sublinear regression, one via convex and another via nonconvex programming. The
convex programming approach involves solving a quadratic program (QP) followed
by a linear program (LP), and is referred to as QP-LP. The nonconvex
programming approach involves training a input sublinear neural network. We
illustrate the proposed methods via numerical examples on learning the reach
sets of controlled dynamics subject to set-valued input uncertainties from
trajectory data.

We consider probabilistic time-series models for systems that gradually
transition among a finite number of states. We are particularly motivated by
applications such as human activity analysis where the observed time-series
contains segments representing distinct activities such as running or walking
as well as segments characterized by continuous transition among these states.
Accordingly, the dynamical Wasserstein barycenter (DWB) model introduced in
Cheng et al. in 2021 [1] associates with each state, which we call a pure
state, its own probability distribution, and models these continuous
transitions with the dynamics of the barycentric weights that combine the pure
state distributions via the Wasserstein barycenter. Here, focusing on the
univariate case where Wasserstein distances and barycenters can be computed in
closed form, we extend [1] by discussing two challenges associated with
learning a DWB model and two improvements. First, we highlight the issue of
uniqueness in identifying the model parameters. Secondly, we discuss the
challenge of estimating a dynamically evolving distribution given a limited
number of samples. The uncertainty associated with this estimation may cause a
model's learned dynamics to not reflect the gradual transitions characteristic
of the system. The first improvement introduces a regularization framework that
addresses this uncertainty by imposing temporal smoothness on the dynamics of
the barycentric weights while leveraging the understanding of the
non-uniqueness of the problem. This is done without defining an entire
stochastic model for the dynamics of the system as in [1]. Our second
improvement lifts the Gaussian assumption on the pure states distributions in
[1] by proposing a quantile-based non-parametric representation. We pose model
estimation in a variational framework and propose a finite approximation to the
infinite dimensional problem.

Motion planning for safe autonomous driving requires learning how the
environment around an ego-vehicle evolves with time. Ego-centric perception of
driveable regions in a scene not only changes with the motion of actors in the
environment, but also with the movement of the ego-vehicle itself.
Self-supervised representations proposed for large-scale planning, such as
ego-centric freespace, confound these two motions, making the representation
difficult to use for downstream motion planners. In this paper, we use
geometric occupancy as a natural alternative to view-dependent representations
such as freespace. Occupancy maps naturally disentangle the motion of the
environment from the motion of the ego-vehicle. However, one cannot directly
observe the full 3D occupancy of a scene (due to occlusion), making it
difficult to use as a signal for learning. Our key insight is to use
differentiable raycasting to "render" future occupancy predictions into future
LiDAR sweep predictions, which can be compared with ground-truth sweeps for
self-supervised learning. The use of differentiable raycasting allows occupancy
to emerge as an internal representation within the forecasting network. In the
absence of groundtruth occupancy, we quantitatively evaluate the forecasting of
raycasted LiDAR sweeps and show improvements of upto 15 F1 points. For
downstream motion planners, where emergent occupancy can be directly used to
guide non-driveable regions, this representation relatively reduces the number
of collisions with objects by up to 17% as compared to freespace-centric motion
planners.

Recent works have shown that Large Language Models (LLMs) can be applied to
ground natural language to a wide variety of robot skills. However, in
practice, learning multi-task, language-conditioned robotic skills typically
requires large-scale data collection and frequent human intervention to reset
the environment or help correcting the current policies. In this work, we
propose a novel approach to efficiently learn general-purpose
language-conditioned robot skills from unstructured, offline and reset-free
data in the real world by exploiting a self-supervised visuo-lingual affordance
model, which requires annotating as little as 1% of the total data with
language. We evaluate our method in extensive experiments both in simulated and
real-world robotic tasks, achieving state-of-the-art performance on the
challenging CALVIN benchmark and learning over 25 distinct visuomotor
manipulation tasks with a single policy in the real world. We find that when
paired with LLMs to break down abstract natural language instructions into
subgoals via few-shot prompting, our method is capable of completing
long-horizon, multi-tier tasks in the real world, while requiring an order of
magnitude less data than previous approaches. Code and videos are available at
http://hulc2.cs.uni-freiburg.de

Machine learning techniques using neural networks have achieved promising
success for time-series data classification. However, the models that they
produce are challenging to verify and interpret. In this paper, we propose an
explainable neural-symbolic framework for the classification of time-series
behaviors. In particular, we use an expressive formal language, namely Signal
Temporal Logic (STL), to constrain the search of the computation graph for a
neural network. We design a novel time function and sparse softmax function to
improve the soundness and precision of the neural-STL framework. As a result,
we can efficiently learn a compact STL formula for the classification of
time-series data through off-the-shelf gradient-based tools. We demonstrate the
computational efficiency, compactness, and interpretability of the proposed
method through driving scenarios and naval surveillance case studies, compared
with state-of-the-art baselines.

Existing deep metric learning approaches fall into three general categories:
contrastive learning, average precision (AP) maximization, and classification.
We propose a novel alternative approach, \emph{contextual similarity
optimization}, inspired by work in unsupervised metric learning. Contextual
similarity is a discrete similarity measure based on relationships between
neighborhood sets, and is widely used in the unsupervised setting as
pseudo-supervision. Inspired by this success, we propose a framework which
optimizes \emph{a combination of contextual and cosine similarities}.
Contextual similarity calculation involves several non-differentiable
operations, including the heaviside function and intersection of sets. We show
how to circumvent non-differentiability to explicitly optimize contextual
similarity, and we further incorporate appropriate similarity regularization to
yield our novel metric learning loss. The resulting loss function achieves
state-of-the-art Recall @ 1 accuracy on standard supervised image retrieval
benchmarks when combined with the standard contrastive loss. Code is released
here:
\url{https://github.com/Chris210634/metric-learning-using-contextual-similarity}

Existing studies on provably efficient algorithms for Markov games (MGs)
almost exclusively build on the "optimism in the face of uncertainty" (OFU)
principle. This work focuses on a different approach of posterior sampling,
which is celebrated in many bandits and reinforcement learning settings but
remains under-explored for MGs. Specifically, for episodic two-player zero-sum
MGs, a novel posterior sampling algorithm is developed with general function
approximation. Theoretical analysis demonstrates that the posterior sampling
algorithm admits a $\sqrt{T}$-regret bound for problems with a low multi-agent
decoupling coefficient, which is a new complexity measure for MGs, where $T$
denotes the number of episodes. When specialized to linear MGs, the obtained
regret bound matches the state-of-the-art results. To the best of our
knowledge, this is the first provably efficient posterior sampling algorithm
for MGs with frequentist regret guarantees, which enriches the toolbox for MGs
and promotes the broad applicability of posterior sampling.

Understanding generalization and robustness of machine learning models
fundamentally relies on assuming an appropriate metric on the data space.
Identifying such a metric is particularly challenging for non-Euclidean data
such as graphs. Here, we propose a pseudometric for attributed graphs, the Tree
Mover's Distance (TMD), and study its relation to generalization. Via a
hierarchical optimal transport problem, TMD reflects the local distribution of
node attributes as well as the distribution of local computation trees, which
are known to be decisive for the learning behavior of graph neural networks
(GNNs). First, we show that TMD captures properties relevant to graph
classification: a simple TMD-SVM performs competitively with standard GNNs.
Second, we relate TMD to generalization of GNNs under distribution shifts, and
show that it correlates well with performance drop under such shifts.

We propose polar encoding, a representation of categorical and numerical
$[0,1]$-valued attributes with missing values that preserves the information
encoded in the distribution of the missing values. Unlike the existing
missing-indicator approach, this does not require imputation. We support our
proposal with three different arguments. Firstly, polar encoding ensures that
missing values become equidistant from all non-missing values by mapping the
latter onto the unit circle. Secondly, polar encoding lets decision trees
choose how missing values should be split, providing a practical realisation of
the missingness incorporated in attributes (MIA) proposal. And lastly, polar
encoding corresponds to the normalised representation of categorical and
$[0,1]$-valued attributes when viewed as barycentric attributes, a new concept
based on traditional barycentric coordinates. In particular, we show that
barycentric attributes are fuzzified categorical attributes, that their
normalised representation generalises one-hot encoding, and that the polar
encoding of $[0, 1]$-valued attributes is analogous to the one-hot encoding of
binary attributes. With an experiment based on twenty real-life datasets with
missing values, we show that polar encoding performs about as well or better
than the missing-indicator approach in terms of the resulting classification

In this paper, we introduce the notion of reproducible policies in the
context of stochastic bandits, one of the canonical problems in interactive
learning. A policy in the bandit environment is called reproducible if it
pulls, with high probability, the \emph{exact} same sequence of arms in two
different and independent executions (i.e., under independent reward
realizations). We show that not only do reproducible policies exist, but also
they achieve almost the same optimal (non-reproducible) regret bounds in terms
of the time horizon. More specifically, in the stochastic multi-armed bandits
setting, we develop a policy with an optimal problem-dependent regret bound
whose dependence on the reproducibility parameter is also optimal. Similarly,
for stochastic linear bandits (with finitely and infinitely many arms) we
develop reproducible policies that achieve the best-known problem-independent
regret bounds with an optimal dependency on the reproducibility parameter. Our
results show that even though randomization is crucial for the
exploration-exploitation trade-off, an optimal balance can still be achieved
while pulling the exact same arms in two different rounds of executions.

Crystallization represents a fundamental process engendering solidification
of a material and determines its microstructure. Driven by complex phenomena at
the atomic scale, its understanding for alloys still remains elusive. The
present work proposes a large scale molecular dynamics simulation study of the
homogeneous crystal nucleation pathways of prototypical undercooled Al-Ni
binary alloys. An unsupervised topological learning analysis shows that the
nucleation sets in first from a chemical ordering, followed by a
bond-orientational ordering of the underlying crystal phase. Our results
indicate also a different polymorph selection that depends on composition.
While the nucleation pathway of Al50 Ni50 displays a single step with the
emergence of B2 short-range order, a step-wise nucleation toward the L12 phase
is seen for Al25 Ni75 . The influence of the nucleation of pure Al and Ni
counterparts is further discussed.

Individual neurons in neural networks often represent a mixture of unrelated
features. This phenomenon, called polysemanticity, can make interpreting neural
networks more difficult and so we aim to understand its causes. We propose
doing so through the lens of feature \emph{capacity}, which is the fractional
dimension each feature consumes in the embedding space. We show that in a toy
model the optimal capacity allocation tends to monosemantically represent the
most important features, polysemantically represent less important features (in
proportion to their impact on the loss), and entirely ignore the least
important features. Polysemanticity is more prevalent when the inputs have
higher kurtosis or sparsity and more prevalent in some architectures than
others. Given an optimal allocation of capacity, we go on to study the geometry
of the embedding space. We find a block-semi-orthogonal structure, with
differing block sizes in different models, highlighting the impact of model
architecture on the interpretability of its neurons.

Sample reweighting is an effective strategy for learning from training data
coming from a mixture of subpopulations. In volumetric medical image
segmentation, the data inputs are similarly distributed, but the associated
data labels fall into two subpopulations -- "label-sparse" and "label-dense" --
depending on whether the data image occurs near the beginning/end of the
volumetric scan or the middle. Existing reweighting algorithms have focused on
hard- and soft- thresholding of the label-sparse data, which results in loss of
information and reduced sample efficiency by discarding valuable data input.
For this setting, we propose AdaWAC as an adaptive weighting algorithm that
introduces a set of trainable weights which, at the saddle point of the
underlying objective, assigns label-dense samples to supervised cross-entropy
loss and label-sparse samples to unsupervised consistency regularization. We
provide a convergence guarantee for AdaWAC by recasting the optimization as
online mirror descent on a saddle point problem. Moreover, we empirically
demonstrate that AdaWAC not only enhances segmentation performance and sample
efficiency but also improves robustness to the subpopulation shift in labels.

Fairness considerations have motivated new clustering problems and algorithms
in recent years. In this paper we consider the Priority Matroid Median problem
which generalizes the Priority $k$-Median problem that has recently been
studied. The input consists of a set of facilities $\mathcal{F}$ and a set of
clients $\mathcal{C}$ that lie in a metric space $(\mathcal{F} \cup
\mathcal{C},d)$, and a matroid $\mathcal{M}=(\mathcal{F},\mathcal{I})$ over the
facilities. In addition each client $j$ has a specified radius $r_j \ge 0$ and
each facility $i \in \mathcal{F}$ has an opening cost $f_i$. The goal is to
choose a subset $S \subseteq \mathcal{F}$ of facilities to minimize the
$\sum_{i \in \mathcal{F}} f_i + \sum_{j \in \mathcal{C}} d(j,S)$ subject to two
constraints: (i) $S$ is an independent set in $\mathcal{M}$ (that is $S \in
\mathcal{I}$) and (ii) for each client $j$, its distance to an open facility is
at most $r_j$ (that is, $d(j,S) \le r_j$). For this problem we describe the
first bicriteria $(c_1,c_2)$ approximations for fixed constants $c_1,c_2$: the
radius constraints of the clients are violated by at most a factor of $c_1$ and
the objective cost is at most $c_2$ times the optimum cost. We also improve the
previously known bicriteria approximation for the uniform radius setting ($r_j
:= L$ $\forall j \in \mathcal{C}$).

The ability to endow maps of indoor scenes with semantic information is an
integral part of robotic agents which perform different tasks such as target
driven navigation, object search or object rearrangement. The state-of-the-art
methods use Deep Convolutional Neural Networks (DCNNs) for predicting semantic
segmentation of an image as useful representation for these tasks. The accuracy
of semantic segmentation depends on the availability and the amount of labeled
data from the target environment or the ability to bridge the domain gap
between test and training environment. We propose RegConsist, a method for
self-supervised pre-training of a semantic segmentation model, exploiting the
ability of the agent to move and register multiple views in the novel
environment. Given the spatial and temporal consistency cues used for pixel
level data association, we use a variant of contrastive learning to train a
DCNN model for predicting semantic segmentation from RGB views in the target
environment. The proposed method outperforms models pre-trained on ImageNet and
achieves competitive performance when using models that are trained for exactly
the same task but on a different dataset. We also perform various ablation
studies to analyze and demonstrate the efficacy of our proposed method.

This paper will try to delineate the making of a Humanoid prototype intended
to assist people with disability (PWD). The assistance that this prototype will
offer is rather rudimentary. However, our key focus is to make the prototype
cost-friendly while pertaining to its humanoid-like functionalities.
Considering growing needs of Robots, facilities for further installment of
features have been made available in this project. The prototype will be of
humanoid shape harnessing the power of Artificial Neural Network (ANN) to
converse with the users. The prototype uses a raspberry pi and as the
computational capability of a raspberry pi is minimal, we cut corners to
squeeze the last drop of performance and make it as efficient as possible.

Contrastive learning is a powerful framework for learning self-supervised
representations that generalize well to downstream supervised tasks. We show
that multiple existing contrastive learning methods can be reinterpreted as
learning kernel functions that approximate a fixed positive-pair kernel. We
then prove that a simple representation obtained by combining this kernel with
PCA provably minimizes the worst-case approximation error of linear predictors,
under a straightforward assumption that positive pairs have similar labels. Our
analysis is based on a decomposition of the target function in terms of the
eigenfunctions of a positive-pair Markov chain, and a surprising equivalence
between these eigenfunctions and the output of Kernel PCA. We give
generalization bounds for downstream linear prediction using our Kernel PCA
representation, and show empirically on a set of synthetic tasks that applying
Kernel PCA to contrastive learning models can indeed approximately recover the
Markov chain eigenfunctions, although the accuracy depends on the kernel
parameterization as well as on the augmentation strength.

The COVID-19 crisis has emphasized the need for scientific methods such as
machine learning to speed up the discovery of solutions to the pandemic.
Harnessing machine learning techniques requires quality data, skilled personnel
and advanced compute infrastructure. In Africa, however, machine learning
competencies and compute infrastructures are limited. This paper demonstrates a
cross-border collaborative capacity building approach to the application of
machine learning techniques in discovering answers to COVID-19 questions.

Meta-learning or learning to learn is a popular approach for learning new
tasks with limited data (i.e., few-shot learning) by leveraging the
commonalities among different tasks. However, meta-learned models can perform
poorly when context data is limited, or when data is drawn from an
out-of-distribution (OoD) task. Especially in safety-critical settings, this
necessitates an uncertainty-aware approach to meta-learning. In addition, the
often multimodal nature of task distributions can pose unique challenges to
meta-learning methods. In this work, we present UnLiMiTD (uncertainty-aware
meta-learning for multimodal task distributions), a novel method for
meta-learning that (1) makes probabilistic predictions on in-distribution tasks
efficiently, (2) is capable of detecting OoD context data at test time, and (3)
performs on heterogeneous, multimodal task distributions. To achieve this goal,
we take a probabilistic perspective and train a parametric, tuneable
distribution over tasks on the meta-dataset. We construct this distribution by
performing Bayesian inference on a linearized neural network, leveraging
Gaussian process theory. We demonstrate that UnLiMiTD's predictions compare
favorably to, and outperform in most cases, the standard baselines, especially
in the low-data regime. Furthermore, we show that UnLiMiTD is effective in
detecting data from OoD tasks. Finally, we confirm that both of these findings
continue to hold in the multimodal task-distribution setting.

Research on video frame interpolation has made significant progress in recent
years. However, existing methods mostly use off-the-shelf metrics to measure
the quality of interpolation results with the exception of a few methods that
employ user studies, which is time-consuming. As video frame interpolation
results often exhibit unique artifacts, existing quality metrics sometimes are
not consistent with human perception when measuring the interpolation results.
Some recent deep learning-based perceptual quality metrics are shown more
consistent with human judgments, but their performance on videos is compromised
since they do not consider temporal information. In this paper, we present a
dedicated perceptual quality metric for measuring video frame interpolation
results. Our method learns perceptual features directly from videos instead of
individual frames. It compares pyramid features extracted from video frames and
employs Swin Transformer blocks-based spatio-temporal modules to extract
spatio-temporal information. To train our metric, we collected a new video
frame interpolation quality assessment dataset. Our experiments show that our
dedicated quality metric outperforms state-of-the-art methods when measuring
video frame interpolation results. Our code and model are made publicly
available at \url{https://github.com/hqqxyy/VFIPS}.

All state-of-the-art (SOTA) differentially private machine learning (DP ML)
methods are iterative in nature, and their privacy analyses allow publicly
releasing the intermediate training checkpoints. However, DP ML benchmarks, and
even practical deployments, typically use only the final training checkpoint to
make predictions. In this work, for the first time, we comprehensively explore
various methods that aggregate intermediate checkpoints to improve the utility
of DP training. Empirically, we demonstrate that checkpoint aggregations
provide significant gains in the prediction accuracy over the existing SOTA for
CIFAR10 and StackOverflow datasets, and that these gains get magnified in
settings with periodically varying training data distributions. For instance,
we improve SOTA StackOverflow accuracies to 22.7% (+0.43% absolute) for
$\epsilon=8.2$, and 23.84% (+0.43%) for $\epsilon=18.9$. Theoretically, we show
that uniform tail averaging of checkpoints improves the empirical risk
minimization bound compared to the last checkpoint of DP-SGD. Lastly, we
initiate an exploration into estimating the uncertainty that DP noise adds in
the predictions of DP ML models. We prove that, under standard assumptions on
the loss function, the sample variance from last few checkpoints provides a
good approximation of the variance of the final model of a DP run. Empirically,
we show that the last few checkpoints can provide a reasonable lower bound for
the variance of a converged DP model.

Federated learning (FL) can help promote data privacy by training a shared
model in a de-centralized manner on the physical devices of clients. In the
presence of highly heterogeneous distributions of local data, personalized FL
strategy seeks to mitigate the potential client drift. In this paper, we
present the group personalization approach for applications of FL in which
there exist inherent partitions among clients that are significantly distinct.
In our method, the global FL model is fine-tuned through another FL training
process over each homogeneous group of clients, after which each group-specific
FL model is further adapted and personalized for any client. The proposed
method can be well interpreted from a Bayesian hierarchical modeling
perspective. With experiments on two real-world datasets, we demonstrate this
approach can achieve superior personalization performance than other FL
counterparts.

In this work, we propose a multi-armed bandit-based framework for identifying
a compact set of informative data instances (i.e., the prototypes) from a
source dataset $\mathcal{S}$ that best represents a given target set
$\mathcal{T}$. Prototypical examples of a given dataset offer interpretable
insights into the underlying data distribution and assist in example-based
reasoning, thereby influencing every sphere of human decision-making. Current
state-of-the-art prototype selection approaches require
$O(|\mathcal{S}||\mathcal{T}|)$ similarity comparisons between source and
target data points, which becomes prohibitively expensive for large-scale
settings. We propose to mitigate this limitation by employing stochastic greedy
search in the space of prototypical examples and multi-armed bandits for
reducing the number of similarity comparisons. Our randomized algorithm,
ProtoBandit, identifies a set of $k$ prototypes incurring $O(k|\mathcal{S}|)$
similarity comparisons, which is independent of the size of the target set. An
interesting outcome of our analysis is for the $k$-medoids clustering problem
($\mathcal{T} = \mathcal{S}$ setting) in which we show that our algorithm
ProtoBandit approximates the BUILD step solution of the partitioning around
medoids (PAM) method in $O(k|\mathcal{S}|)$ complexity. Empirically, we observe
that ProtoBandit reduces the number of similarity computation calls by several
orders of magnitudes ($100-1000$ times) while obtaining solutions similar in
quality to those from state-of-the-art approaches.

Non-functional bugs (e.g., performance- or accuracy-related bugs) in Deep
Learning (DL) frameworks can lead to some of the most devastating consequences.
Reporting those bugs on a repository such as GitHub is a standard route to fix
them. Yet, given the growing number of new GitHub reports for DL frameworks, it
is intrinsically difficult for developers to distinguish those that reveal
non-functional bugs among the others, and assign them to the right contributor
for investigation in a timely manner. In this paper, we propose MHNurf - an
end-to-end tool for automatically identifying non-functional bug related
reports in DL frameworks. The core of MHNurf is a Multifaceted Hierarchical
Attention Network (MHAN) that tackles three unaddressed challenges: (1)
learning the semantic knowledge, but doing so by (2) considering the hierarchy
(e.g., words/tokens in sentences/statements) and focusing on the important
parts (i.e., words, tokens, sentences, and statements) of a GitHub report,
while (3) independently extracting information from different types of
features, i.e., content, comment, code, command, and label.
  To evaluate MHNurf, we leverage 3,721 GitHub reports from five DL frameworks
for conducting experiments. The results show that MHNurf works the best with a
combination of content, comment, and code, which considerably outperforms the
classic HAN where only the content is used. MHNurf also produces significantly
more accurate results than nine other state-of-the-art classifiers with strong
statistical significance, i.e., up to 71% AUC improvement and has the best
Scott-Knott rank on four frameworks while 2nd on the remaining one. To
facilitate reproduction and promote future research, we have made our dataset,
code, and detailed supplementary results publicly available at:
https://github.com/ideas-labo/APSEC2022-MHNurf.

Large language models (LLMs) have displayed an impressive ability to harness
natural language to perform complex tasks. In this work, we explore whether we
can leverage this learned ability to find and explain patterns in data.
Specifically, given a pre-trained LLM and data examples, we introduce
interpretable autoprompting (iPrompt), an algorithm that generates a
natural-language string explaining the data. iPrompt iteratively alternates
between generating explanations with an LLM and reranking them based on their
performance when used as a prompt. Experiments on a wide range of datasets,
from synthetic mathematics to natural-language understanding, show that iPrompt
can yield meaningful insights by accurately finding groundtruth dataset
descriptions. Moreover, the prompts produced by iPrompt are simultaneously
human-interpretable and highly effective for generalization: on real-world
sentiment classification datasets, iPrompt produces prompts that match or even
improve upon human-written prompts for GPT-3. Finally, experiments with an fMRI
dataset show the potential for iPrompt to aid in scientific discovery. All code
for using the methods and data here is made available on Github.

Recently, neural control policies have outperformed existing model-based
planning-and-control methods for autonomously navigating quadrotors through
cluttered environments in minimum time. However, they are not perception aware,
a crucial requirement in vision-based navigation due to the camera's limited
field of view and the underactuated nature of a quadrotor. We propose a method
to learn neural network policies that achieve perception-aware, minimum-time
flight in cluttered environments. Our method combines imitation learning and
reinforcement learning (RL) by leveraging a privileged learning-by-cheating
framework. Using RL, we first train a perception-aware teacher policy with
full-state information to fly in minimum time through cluttered environments.
Then, we use imitation learning to distill its knowledge into a vision-based
student policy that only perceives the environment via a camera. Our approach
tightly couples perception and control, showing a significant advantage in
computation speed (10x faster) and success rate. We demonstrate the closed-loop
control performance using a physical quadrotor and hardware-in-the-loop
simulation at speeds up to 50km/h.

In cooperative multi-agent reinforcement learning, a team of agents works
together to achieve a common goal. Different environments or tasks may require
varying degrees of coordination among agents in order to achieve the goal in an
optimal way. The nature of coordination will depend on properties of the
environment -- its spatial layout, distribution of obstacles, dynamics, etc. We
term this variation of properties within an environment as heterogeneity.
Existing literature has not sufficiently addressed the fact that different
environments may have different levels of heterogeneity. We formalize the
notions of coordination level and heterogeneity level of an environment and
present HECOGrid, a suite of multi-agent RL environments that facilitates
empirical evaluation of different MARL approaches across different levels of
coordination and environmental heterogeneity by providing a quantitative
control over coordination and heterogeneity levels of the environment. Further,
we propose a Centralized Training Decentralized Execution learning approach
called Stateful Active Facilitator (SAF) that enables agents to work
efficiently in high-coordination and high-heterogeneity environments through a
differentiable and shared knowledge source used during training and dynamic
selection from a shared pool of policies. We evaluate SAF and compare its
performance against baselines IPPO and MAPPO on HECOGrid. Our results show that
SAF consistently outperforms the baselines across different tasks and different
heterogeneity and coordination levels.

Detecting anomalies at the time of happening is vital in environments like
buildings and homes to identify potential cyber-attacks. This paper discussed
the various mechanisms to detect anomalies as soon as they occur. We shed light
on crucial considerations when building machine learning models. We constructed
and gathered data from multiple self-build (DIY) IoT devices with different
in-situ sensors and found effective ways to find the point, contextual and
combine anomalies. We also discussed several challenges and potential solutions
when dealing with sensing devices that produce data at different sampling rates
and how we need to pre-process them in machine learning models. This paper also
looks at the pros and cons of extracting sub-datasets based on environmental
conditions.

Federated learning is gaining popularity as it enables training of
high-utility models across several clients without directly sharing their
private data. As a downside, the federated setting makes the model vulnerable
to various adversarial attacks in the presence of malicious clients.
Specifically, an adversary can perform backdoor attacks to control model
predictions via poisoning the training dataset with a trigger. In this work, we
propose a mitigation for backdoor attacks in a federated learning setup. Our
solution forces the model optimization trajectory to focus on the invariant
directions that are generally useful for utility and avoid selecting directions
that favor few and possibly malicious clients. Concretely, we consider the sign
consistency of the pseudo-gradient (the client update) as an estimation of the
invariance. Following this, our approach performs dimension-wise filtering to
remove pseudo-gradient elements with low sign consistency. Then, a robust mean
estimator eliminates outliers among the remaining dimensions. Our theoretical
analysis further shows the necessity of the defense combination and illustrates
how our proposed solution defends the federated learning model. Empirical
results on three datasets with different modalities and varying number of
clients show that our approach mitigates backdoor attacks with a negligible
cost on the model utility.

Comparison of echelle spectra to synthetic models has become a computational
statistics challenge, with over ten thousand individual spectral lines
affecting a typical cool star echelle spectrum. Telluric artifacts, imperfect
line lists, inexact continuum placement, and inflexible models frustrate the
scientific promise of these information-rich datasets. Here we debut an
interpretable machine-learning framework "blas\'e" that addresses these and
other challenges. The semi-empirical approach can be viewed as "transfer
learning" -- first pre-training models on noise-free precomputed synthetic
spectral models, then learning the corrections to line depths and widths from
whole-spectrum fitting to an observed spectrum. The auto-differentiable model
employs back-propagation, the fundamental algorithm empowering modern Deep
Learning and Neural Networks. Here, however, the 40,000+ parameters symbolize
physically interpretable line profile properties such as amplitude, width,
location, and shape, plus radial velocity and rotational broadening. This
hybrid data-/model- driven framework allows joint modeling of stellar and
telluric lines simultaneously, a potentially transformative step forwards for
mitigating the deleterious telluric contamination in the near-infrared. The
blas\'e approach acts as both a deconvolution tool and semi-empirical model.
The general purpose scaffolding may be extensible to many scientific
applications, including precision radial velocities, Doppler imaging, chemical
abundances, and remote sensing. Its sparse-matrix architecture and
GPU-acceleration make blas\'e fast. The open-source PyTorch-based code includes
tutorials, Application Programming Interface (API) documentation, and more. We
show how the tool fits into the existing Python spectroscopy ecosystem,
demonstrate a range of astrophysical applications, and discuss limitations and
future extensions.

Developing the final summative assessment of a course at the start of
curriculum development is an implementation of "backward design," whereby
learning objectives are identified first and the curriculum is engineered
end-to-beginning to achieve them. We trained in backward design through the
Professional Development Program (PDP) and adapted PDP assessment ideas for
evaluation of curriculum designs and teaching efficacy. A pre-/post-quiz is an
assessment administered the first and last day of a course; a learner's scores
are used to measure normalized gain: the ratio of what a student learned during
a course relative to what they knew entering it. The intentional process of
developing a pre-/post-quiz for every course focuses the educator on the
essential understanding desired of the learners exiting the course. The
normalized-gain statistics for the course can then be used to evaluate the
course's efficacy, and improvements to the curriculum can be monitored by
tracking the normalized gains over time, using the same pre-/post-quiz.
Moreover, an individual instructor may self-evaluate their teaching efficacy by
tracking normalized gains from all courses over time. Here we discuss applying
the practice of backward curriculum design starting with a custom
pre-/post-quiz and utilizing it for immediate and longitudinal evaluation,
focusing primarily on designing an entire undergraduate science course.

Classification is a general tool of science; it is used to sort and
categorize biological organisms, chemical elements, astronomical objects, and
many other things. In scientific classification, taxonomy often reflects shared
physical properties that, in turn, may indicate shared origins and/or
evolution. A "hands-on" galaxy-classification activity developed and
implemented by Professional Development Program (PDP) participants, for a
high-school summer STEM enrichment program, has been adopted for various age
groups and venues, from young (K-3) to college students. We detail the basic
tools required, outline the general activity, and describe the modifications to
the activity based on learners' ages and learning objectives. We describe the
facilitation strategies learned through PDP training and used when implementing
the activity, including prompts to motivate the students. We also discuss how
we connected the classification process to astronomy and science more broadly
during the concluding remarks.

The amount and complexity of data delivered by modern galaxy surveys has been
steadily increasing over the past years. Extracting coherent scientific
information from these large and multi-modal data sets remains an open issue
and data driven approaches such as deep learning have rapidly emerged as a
potentially powerful solution to some long lasting challenges. This enthusiasm
is reflected in an unprecedented exponential growth of publications using
neural networks. Half a decade after the first published work in astronomy
mentioning deep learning, we believe it is timely to review what has been the
real impact of this new technology in the field and its potential to solve key
challenges raised by the size and complexity of the new datasets. In this
review we first aim at summarizing the main applications of deep learning for
galaxy surveys that have emerged so far. We then extract the major achievements
and lessons learned and highlight key open questions and limitations. Overall,
state-of-the art deep learning methods are rapidly adopted by the astronomical
community, reflecting a democratization of these methods. We show that the
majority of works using deep learning up to date are oriented to computer
vision tasks. This is also the domain of application where deep learning has
brought the most important breakthroughs so far. We report that the
applications are becoming more diverse and deep learning is used for estimating
galaxy properties, identifying outliers or constraining the cosmological model.
Most of these works remain at the exploratory level. Some common challenges
will most likely need to be addressed before moving to the next phase of
deployment of deep learning in the processing of future surveys; e.g.
uncertainty quantification, interpretability, data labeling and domain shift
issues from training with simulations, which constitutes a common practice in
astronomy.

Ark 564 is an extreme high-Eddington Narrow-line Seyfert 1 galaxy, known for
being one of the brightest, most rapidly variable soft X-ray AGN, and for
having one of the lowest temperature coronae. Here we present a 410-ks NuSTAR
observation and two 115-ks XMM-Newton observations of this unique source, which
reveal a very strong, relativistically broadened iron line. We compute the
Fourier-resolved time lags by first using Gaussian processes to interpolate the
NuSTAR gaps, implementing the first employment of multi-task learning for
application in AGN timing. By fitting simultaneously the time lags and the flux
spectra with the relativistic reverberation model RELTRANS, we constrain the
mass at $2.3^{+2.6}_{-1.3} \times 10^6M_\odot$, although additional components
are required to describe the prominent soft excess in this source. These
results motivate future combinations of machine learning, Fourier-resolved
timing, and the development of reverberation models.

Accumulation of corporate data in the cloud has attracted more enterprise
applications to the cloud creating data gravity. As a consequence, network
traffic has become more cloud centric. This increase in cloud centric traffic
poses new challenges in designing learning systems for streaming data due to
class imbalance. The number of classes plays a vital role in the accuracy of
the classifiers built from the data streams. In this paper, we present a vector
quantization-based sampling method, which substantially reduces the class
imbalance in data streams. We demonstrate its effectiveness by conducting
experiments on network traffic and anomaly dataset with commonly used ML model
building methods; Multilayered Perceptron on TensorFlow backend, Support Vector
Machines, K-Nearest Neighbour, and Random Forests. We built models using
parallel processing, batch processing, and randomly selecting samples. We show
that the accuracy of classification models improves when the data streams are
pre-processed with our method. We used out of the box hyper-parameters of these
classifiers and auto sklearn for hyperparameter optimization.

The field of AI alignment is concerned with AI systems that pursue unintended
goals. One commonly studied mechanism by which an unintended goal might arise
is specification gaming, in which the designer-provided specification is flawed
in a way that the designers did not foresee. However, an AI system may pursue
an undesired goal even when the specification is correct, in the case of goal
misgeneralization. Goal misgeneralization is a specific form of robustness
failure for learning algorithms in which the learned program competently
pursues an undesired goal that leads to good performance in training situations
but bad performance in novel test situations. We demonstrate that goal
misgeneralization can occur in practical systems by providing several examples
in deep learning systems across a variety of domains. Extrapolating forward to
more capable systems, we provide hypotheticals that illustrate how goal
misgeneralization could lead to catastrophic risk. We suggest several research
directions that could reduce the risk of goal misgeneralization for future
systems.

Owing to the advantages of increased accuracy and the potential to detect
unseen patterns, provided by data mining techniques they have been widely
incorporated for standard classification problems. They have often been used
for high precision disease prediction in the medical field, and several hybrid
prediction models capable of achieving high accuracies have been proposed.
Though this stands true most of the previous models fail to efficiently address
the recurring issue of bad data quality which plagues most high dimensional
data, and especially proves troublesome in the highly sensitive medical data.
This work proposes a robust self healing (RSH) hybrid prediction model which
functions by using the data in its entirety by removing errors and
inconsistencies from it rather than discarding any data. Initial processing
involves data preparation followed by cleansing or scrubbing through
context-dependent attribute correction, which ensures that there is no
significant loss of relevant information before the feature selection and
prediction phases. An ensemble of heterogeneous classifiers, subjected to local
boosting, is utilized to build the prediction model and genetic algorithm based
wrapper feature selection technique wrapped on the respective classifiers is
employed to select the corresponding optimal set of features, which warrant
higher accuracy. The proposed method is compared with some of the existing high
performing models and the results are analyzed.

Designing neural networks with bounded Lipschitz constant is a promising way
to obtain certifiably robust classifiers against adversarial examples. However,
the relevant progress for the important $\ell_\infty$ perturbation setting is
rather limited, and a principled understanding of how to design expressive
$\ell_\infty$ Lipschitz networks is still lacking. In this paper, we bridge the
gap by studying certified $\ell_\infty$ robustness from a novel perspective of
representing Boolean functions. We derive two fundamental impossibility results
that hold for any standard Lipschitz network: one for robust classification on
finite datasets, and the other for Lipschitz function approximation. These
results identify that networks built upon norm-bounded affine layers and
Lipschitz activations intrinsically lose expressive power even in the
two-dimensional case, and shed light on how recently proposed Lipschitz
networks (e.g., GroupSort and $\ell_\infty$-distance nets) bypass these
impossibilities by leveraging order statistic functions. Finally, based on
these insights, we develop a unified Lipschitz network that generalizes prior
works, and design a practical version that can be efficiently trained (making
certified robust training free). Extensive experiments show that our approach
is scalable, efficient, and consistently yields better certified robustness
across multiple datasets and perturbation radii than prior Lipschitz networks.
Our code is available at https://github.com/zbh2047/SortNet.

While federated learning (FL) promises to preserve privacy in distributed
training of deep learning models, recent work in the image and NLP domains
showed that training updates leak private data of participating clients. At the
same time, most high-stakes applications of FL (e.g., legal and financial) use
tabular data. Compared to the NLP and image domains, reconstruction of tabular
data poses several unique challenges: (i) categorical features introduce a
significantly more difficult mixed discrete-continuous optimization problem,
(ii) the mix of categorical and continuous features causes high variance in the
final reconstructions, and (iii) structured data makes it difficult for the
adversary to judge reconstruction quality. In this work, we tackle these
challenges and propose the first comprehensive reconstruction attack on tabular
data, called TabLeak. TabLeak is based on three key ingredients: (i) a softmax
structural prior, implicitly converting the mixed discrete-continuous
optimization problem into an easier fully continuous one, (ii) a way to reduce
the variance of our reconstructions through a pooled ensembling scheme
exploiting the structure of tabular data, and (iii) an entropy measure which
can successfully assess reconstruction quality. Our experimental evaluation
demonstrates the effectiveness of TabLeak, reaching a state-of-the-art on four
popular tabular datasets. For instance, on the Adult dataset, we improve attack
accuracy by 10% compared to the baseline on the practically relevant batch size
of 32 and further obtain non-trivial reconstructions for batch sizes as large
as 128. Our findings are important as they show that performing FL on tabular
data, which often poses high privacy risks, is highly vulnerable.

Annotation of large-scale 3D data is notoriously cumbersome and costly. As an
alternative, weakly-supervised learning alleviates such a need by reducing the
annotation by several order of magnitudes. We propose COARSE3D, a novel
architecture-agnostic contrastive learning strategy for 3D segmentation. Since
contrastive learning requires rich and diverse examples as keys and anchors, we
leverage a prototype memory bank capturing class-wise global dataset
information efficiently into a small number of prototypes acting as keys. An
entropy-driven sampling technique then allows us to select good pixels from
predictions as anchors. Experiments on three projection-based backbones show we
outperform baselines on three challenging real-world outdoor datasets, working
with as low as 0.001% annotations.

Predicting the binding structure of a small molecule ligand to a protein -- a
task known as molecular docking -- is critical to drug design. Recent deep
learning methods that treat docking as a regression problem have decreased
runtime compared to traditional search-based methods but have yet to offer
substantial improvements in accuracy. We instead frame molecular docking as a
generative modeling problem and develop DiffDock, a diffusion generative model
over the non-Euclidean manifold of ligand poses. To do so, we map this manifold
to the product space of the degrees of freedom (translational, rotational, and
torsional) involved in docking and develop an efficient diffusion process on
this space. Empirically, DiffDock obtains a 38% top-1 success rate (RMSD<2A) on
PDBBind, significantly outperforming the previous state-of-the-art of
traditional docking (23%) and deep learning (20%) methods. Moreover, DiffDock
has fast inference times and provides confidence estimates with high selective
accuracy.

The rapid development in ubiquitous computing has enabled the use of
microcontrollers as edge devices. These devices are used to develop truly
distributed IoT-based mechanisms where machine learning (ML) models are
utilized. However, integrating ML models to edge devices requires an
understanding of various software tools such as programming languages and
domain-specific knowledge. Anomaly detection is one of the domains where a high
level of expertise is required to achieve promising results. In this work, we
present AnoML which is an end-to-end data science pipeline that allows the
integration of multiple wireless communication protocols, anomaly detection
algorithms, deployment to the edge, fog, and cloud platforms with minimal user
interaction. We facilitate the development of IoT anomaly detection mechanisms
by reducing the barriers that are formed due to the heterogeneity of an IoT
environment. The proposed pipeline supports four main phases: (i) data
ingestion, (ii) model training, (iii) model deployment, (iv) inference and
maintaining. We evaluate the pipeline with two anomaly detection datasets while
comparing the efficiency of several machine learning algorithms within
different nodes. We also provide the source code
(https://gitlab.com/IOTGarage/anoml-iot-analytics) of the developed tools which
are the main components of the pipeline.

Unlike vision and language data which usually has a unique format, molecules
can naturally be characterized using different chemical formulations. One can
view a molecule as a 2D graph or define it as a collection of atoms located in
a 3D space. For molecular representation learning, most previous works designed
neural networks only for a particular data format, making the learned models
likely to fail for other data formats. We believe a general-purpose neural
network model for chemistry should be able to handle molecular tasks across
data modalities. To achieve this goal, in this work, we develop a novel
Transformer-based Molecular model called Transformer-M, which can take
molecular data of 2D or 3D formats as input and generate meaningful semantic
representations. Using the standard Transformer as the backbone architecture,
Transformer-M develops two separated channels to encode 2D and 3D structural
information and incorporate them with the atom features in the network modules.
When the input data is in a particular format, the corresponding channel will
be activated, and the other will be disabled. By training on 2D and 3D
molecular data with properly designed supervised signals, Transformer-M
automatically learns to leverage knowledge from different data modalities and
correctly capture the representations. We conducted extensive experiments for
Transformer-M. All empirical results show that Transformer-M can simultaneously
achieve strong performance on 2D and 3D tasks, suggesting its broad
applicability. The code and models will be made publicly available at
https://github.com/lsj2408/Transformer-M.

Efficiently discovering relevant Web services with respect to a specific user
query has become a growing challenge owing to the incredible growth in the
field of web technologies. In previous works, different clustering models have
been used to address these issues. But, most of the traditional clustering
techniques are computationally intensive and fail to address all the problems
involved. Also, the current standards fail to incorporate the semantic
relatedness of Web services during clustering and retrieval resulting in
decreased performance. In this paper, we propose a framework for web services
retrieval that uses a bottom-up, decentralized and self organising approach to
cluster available services. It also provides online, dynamic computation of
clusters thus overcoming the drawbacks of traditional clustering methods. We
also use the semantic similarity between Web services for the clustering
process to enhance the precision and lower the recall.

Probabilistic generative models provide a flexible and systematic framework
for learning the underlying geometry of data. However, model selection in this
setting is challenging, particularly when selecting for ill-defined qualities
such as disentanglement or interpretability. In this work, we address this gap
by introducing a method for ranking generative models based on the training
dynamics exhibited during learning. Inspired by recent theoretical
characterizations of disentanglement, our method does not require supervision
of the underlying latent factors. We evaluate our approach by demonstrating the
need for disentanglement metrics which do not require labels\textemdash the
underlying generative factors. We additionally demonstrate that our approach
correlates with baseline supervised methods for evaluating disentanglement.
Finally, we show that our method can be used as an unsupervised indicator for
downstream performance on reinforcement learning and fairness-classification
problems.

This paper presents a search for generic short-duration gravitational-wave
(GW) transients (or GW bursts) in the data from the third observing run of
Advanced LIGO and Advanced Virgo. We use coherent WaveBurst (cWB) pipeline
enhanced with a decision-tree classification algorithm for more efficient
separation of GW signals from noise transients. The machine-learning (ML)
algorithm is trained on a representative set of noise events and a set of
simulated stochastic signals that are not correlated with any known signal
model. This training procedure preserves the model-independent nature of the
search. We demonstrate that the ML-enhanced cWB pipeline can detect GW signals
at a larger distance than previous model-independent searches. The sensitivity
improvements are achieved across the broad spectrum of simulated signals, with
the goal of testing the robustness of this model-agnostic search. At a
false-alarm rate of one event per century, the detectable signal amplitudes are
reduced up to almost an order of magnitude, most notably for the single-cycle
signal morphologies. This ML-enhanced pipeline also improves the detection
efficiency of compact binary mergers in a wide range of masses, from stellar
mass to intermediate-mass black holes, both with circular and elliptical
orbits. After excluding previously detected compact binaries, no new
gravitational-wave signals are observed for the two-fold Hanford-Livingston and
the three-fold Hanford-Livingston-Virgo detector networks. With the improved
sensitivity of the all-sky search, we obtain the most stringent constraints on
the isotropic emission of gravitational-wave energy from short-duration burst
sources.

In this paper, we tackle the important yet under-investigated problem of
making long-horizon prediction of event sequences. Existing state-of-the-art
models do not perform well at this task due to their autoregressive structure.
We propose HYPRO, a hybridly normalized probabilistic model that naturally fits
this task: its first part is an autoregressive base model that learns to
propose predictions; its second part is an energy function that learns to
reweight the proposals such that more realistic predictions end up with higher
probabilities. We also propose efficient training and inference algorithms for
this model. Experiments on multiple real-world datasets demonstrate that our
proposed HYPRO model can significantly outperform previous models at making
long-horizon predictions of future events. We also conduct a range of ablation
studies to investigate the effectiveness of each component of our proposed
methods.

Inverse reinforcement learning (IRL) aims to recover the reward function and
the associated optimal policy that best fits observed sequences of states and
actions implemented by an expert. Many algorithms for IRL have an inherently
nested structure: the inner loop finds the optimal policy given parametrized
rewards while the outer loop updates the estimates towards optimizing a measure
of fit. For high dimensional environments such nested-loop structure entails a
significant computational burden. To reduce the computational burden of a
nested loop, novel methods such as SQIL [1] and IQ-Learn [2] emphasize policy
estimation at the expense of reward estimation accuracy. However, without
accurate estimated rewards, it is not possible to do counterfactual analysis
such as predicting the optimal policy under different environment dynamics
and/or learning new tasks. In this paper we develop a novel single-loop
algorithm for IRL that does not compromise reward estimation accuracy. In the
proposed algorithm, each policy improvement step is followed by a stochastic
gradient step for likelihood maximization. We show that the proposed algorithm
provably converges to a stationary solution with a finite-time guarantee. If
the reward is parameterized linearly, we show the identified solution
corresponds to the solution of the maximum entropy IRL problem. Finally, by
using robotics control problems in MuJoCo and their transfer settings, we show
that the proposed algorithm achieves superior performance compared with other
IRL and imitation learning benchmarks.

In this work we present a Mixture of Task-Aware Experts Network for Machine
Reading Comprehension on a relatively small dataset. We particularly focus on
the issue of common-sense learning, enforcing the common ground knowledge by
specifically training different expert networks to capture different kinds of
relationships between each passage, question and choice triplet. Moreover, we
take inspi ration on the recent advancements of multitask and transfer learning
by training each network a relevant focused task. By making the
mixture-of-networks aware of a specific goal by enforcing a task and a
relationship, we achieve state-of-the-art results and reduce over-fitting.

Physical Unclonable Functions (PUFs) are promising security primitives for
resource-constrained IoT devices. And the XOR Arbiter PUF (XOR-PUF) is one of
the most studied PUFs, out of an effort to improve the resistance against
machine learning attacks of probably the most lightweight delay-based PUFs -
the Arbiter PUFs. However, recent attack studies reveal that even XOR-PUFs with
large XOR sizes are still not safe against machine learning attacks. Increasing
PUF stages or components and using different challenges for different
components are two ways to improve the security of APUF-based PUFs, but more
stages or components lead to more hardware cost and higher operation power, and
different challenges for different components require the transmission of more
bits during operations, which also leads to higher power consumption. In this
paper, we present a strategy that combines the choice of XOR Arbiter PUF
(XOR-PUF) architecture parameters with the way XOR-PUFs are used to achieve
lightweights in hardware cost and energy consumption as well as security
against machine learning attacks. Experimental evaluations show that with the
proposed strategy, highly lightweight component-differentially challenged
XOR-PUFs can withstand the most powerful machine learning attacks developed so
far and maintain excellent intra-device and inter-device performance, rendering
this strategy a potential blueprint for the fabrication and use of XOR-PUFs for
resource-constrained IoT applications.

We study the complexity of finding the global solution to stochastic
nonconvex optimization when the objective function satisfies global
Kurdyka-Lojasiewicz (KL) inequality and the queries from stochastic gradient
oracles satisfy mild expected smoothness assumption. We first introduce a
general framework to analyze Stochastic Gradient Descent (SGD) and its
associated nonlinear dynamics under the setting. As a byproduct of our
analysis, we obtain a sample complexity of
$\mathcal{O}(\epsilon^{-(4-\alpha)/\alpha})$ for SGD when the objective
satisfies the so called $\alpha$-PL condition, where $\alpha$ is the degree of
gradient domination. Furthermore, we show that a modified SGD with variance
reduction and restarting (PAGER) achieves an improved sample complexity of
$\mathcal{O}(\epsilon^{-2/\alpha})$ when the objective satisfies the average
smoothness assumption. This leads to the first optimal algorithm for the
important case of $\alpha=1$ which appears in applications such as policy
optimization in reinforcement learning.

In recent years, imitation learning (IL) has been widely used in industry as
the core of autonomous vehicle (AV) planning modules. However, previous work on
IL planners shows sample inefficiency and low generalisation in safety-critical
scenarios, on which they are rarely tested. As a result, IL planners can reach
a performance plateau where adding more training data ceases to improve the
learnt policy. First, our work presents an IL model using the spline
coefficient parameterisation and offline expert queries to enhance safety and
training efficiency. Then, we expose the weakness of the learnt IL policy by
synthetically generating critical scenarios through optimisation of parameters
of the driver's risk field (DRF), a parametric human driving behaviour model
implemented in a multi-agent traffic simulator based on the Lyft Prediction
Dataset. To continuously improve the learnt policy, we retrain the IL model
with augmented data. Thanks to the expressivity and interpretability of the
DRF, the desired driving behaviours can be encoded and aggregated to the
original training data. Our work constitutes a full development cycle that can
efficiently and continuously improve the learnt IL policies in closed-loop.
Finally, we show that our IL planner developed with 30 times less training
resource still has superior performance compared to the previous

Decision-makers often act in response to data-driven predictions, with the
goal of achieving favorable outcomes. In such settings, predictions don't
passively forecast the future; instead, predictions actively shape the
distribution of outcomes they are meant to predict. This performative
prediction setting raises new challenges for learning "optimal" decision rules.
In particular, existing solution concepts do not address the apparent tension
between the goals of forecasting outcomes accurately and steering individuals
to achieve desirable outcomes.
  To contend with this concern, we introduce a new optimality concept --
performative omniprediction -- adapted from the supervised (non-performative)
learning setting. A performative omnipredictor is a single predictor that
simultaneously encodes the optimal decision rule with respect to many
possibly-competing objectives. Our main result demonstrates that efficient
performative omnipredictors exist, under a natural restriction of performative
prediction, which we call outcome performativity. On a technical level, our
results follow by carefully generalizing the notion of outcome
indistinguishability to the outcome performative setting. From an appropriate
notion of Performative OI, we recover many consequences known to hold in the
supervised setting, such as omniprediction and universal adaptability.

Handling out-of-distribution (OOD) samples has become a major stake in the
real-world deployment of machine learning systems. This work explores the
application of self-supervised contrastive learning to the simultaneous
detection of two types of OOD samples: unseen classes and adversarial
perturbations. Since in practice the distribution of such samples is not known
in advance, we do not assume access to OOD examples. We show that similarity
functions trained with contrastive learning can be leveraged with the maximum
mean discrepancy (MMD) two-sample test to verify whether two independent sets
of samples are drawn from the same distribution. Inspired by this approach, we
introduce CADet (Contrastive Anomaly Detection), a method based on image
augmentations to perform anomaly detection on single samples. CADet compares
favorably to adversarial detection methods to detect adversarially perturbed
samples on ImageNet. Simultaneously, it achieves comparable performance to
unseen label detection methods on two challenging benchmarks: ImageNet-O and
iNaturalist. CADet is fully self-supervised and requires neither labels for
in-distribution samples nor access to OOD examples.

We investigate the parameterization of deep neural networks that by design
satisfy the continuity equation, a fundamental conservation law. This is
enabled by the observation that any solution of the continuity equation can be
represented as a divergence-free vector field. We hence propose building
divergence-free neural networks through the concept of differential forms, and
with the aid of automatic differentiation, realize two practical constructions.
As a result, we can parameterize pairs of densities and vector fields that
always exactly satisfy the continuity equation, foregoing the need for extra
penalty methods or expensive numerical simulation. Furthermore, we prove these
models are universal and so can be used to represent any divergence-free vector
field. Finally, we experimentally validate our approaches by computing neural
network-based solutions to fluid equations, solving for the Hodge
decomposition, and learning dynamical optimal transport maps.

Aligning the visual and language spaces requires to train deep neural
networks from scratch on giant multimodal datasets; CLIP trains both an image
and a text encoder, while LiT manages to train just the latter by taking
advantage of a pretrained vision network. In this paper, we show that sparse
relative representations are sufficient to align text and images without
training any network. Our method relies on readily available single-domain
encoders (trained with or without supervision) and a modest (in comparison)
number of image-text pairs. ASIF redefines what constitutes a multimodal model
by explicitly disentangling memory from processing: here the model is defined
by the embedded pairs of all the entries in the multimodal dataset, in addition
to the parameters of the two encoders. Experiments on standard zero-shot visual
benchmarks demonstrate the typical transfer ability of image-text models.
Overall, our method represents a simple yet surprisingly strong baseline for
foundation multimodal models, raising important questions on their data
efficiency and on the role of retrieval in machine learning.

In this work, we apply information theory inspired methods to quantify
changes in daily activity patterns. We use in-home movement monitoring data and
show how they can help indicate the occurrence of healthcare-related events.
Three different types of entropy measures namely Shannon's entropy, entropy
rates for Markov chains, and entropy production rate have been utilised. The
measures are evaluated on a large-scale in-home monitoring dataset that has
been collected within our dementia care clinical study. The study uses Internet
of Things (IoT) enabled solutions for continuous monitoring of in-home
activity, sleep, and physiology to develop care and early intervention
solutions to support people living with dementia (PLWD) in their own homes. Our
main goal is to show the applicability of the entropy measures to time-series
activity data analysis and to use the extracted measures as new engineered
features that can be fed into inference and analysis models. The results of our
experiments show that in most cases the combination of these measures can
indicate the occurrence of healthcare-related events. We also find that
different participants with the same events may have different measures based
on one entropy measure. So using a combination of these measures in an
inference model will be more effective than any of the single measures.

In NLP, models are usually evaluated by reporting single-number performance
scores on a number of readily available benchmarks, without much deeper
analysis. Here, we argue that - especially given the well-known fact that
benchmarks often contain biases, artefacts, and spurious correlations - deeper
results analysis should become the de-facto standard when presenting new models
or benchmarks. We present a tool that researchers can use to study properties
of the dataset and the influence of those properties on their models'
behaviour. Our Text Characterization Toolkit includes both an easy-to-use
annotation tool, as well as off-the-shelf scripts that can be used for specific
analyses. We also present use-cases from three different domains: we use the
tool to predict what are difficult examples for given well-known trained models
and identify (potentially harmful) biases and heuristics that are present in a
dataset.

In this paper we employ deep learning techniques to detect financial asset
bubbles by using observed call option prices. The proposed algorithm is widely
applicable and model-independent. We test the accuracy of our methodology in
numerical experiments within a wide range of models and apply it to market data
of tech stocks in order to assess if asset price bubbles are present. In
addition, we provide a theoretical foundation of our approach in the framework
of local volatility models. To this purpose, we give a new necessary and
sufficient condition for a process with time-dependent local volatility
function to be a strict local martingale.

A multitude of work has shown that machine learning-based medical diagnosis
systems can be biased against certain subgroups of people. This has motivated a
growing number of bias mitigation algorithms that aim to address fairness
issues in machine learning. However, it is difficult to compare their
effectiveness in medical imaging for two reasons. First, there is little
consensus on the criteria to assess fairness. Second, existing bias mitigation
algorithms are developed under different settings, e.g., datasets, model
selection strategies, backbones, and fairness metrics, making a direct
comparison and evaluation based on existing results impossible. In this work,
we introduce MEDFAIR, a framework to benchmark the fairness of machine learning
models for medical imaging. MEDFAIR covers eleven algorithms from various
categories, nine datasets from different imaging modalities, and three model
selection criteria. Through extensive experiments, we find that the
under-studied issue of model selection criterion can have a significant impact
on fairness outcomes; while in contrast, state-of-the-art bias mitigation
algorithms do not significantly improve fairness outcomes over empirical risk
minimization (ERM) in both in-distribution and out-of-distribution settings. We
evaluate fairness from various perspectives and make recommendations for
different medical application scenarios that require different ethical
principles. Our framework provides a reproducible and easy-to-use entry point
for the development and evaluation of future bias mitigation algorithms in deep
learning. Code is available at https://github.com/ys-zong/MEDFAIR.

Labeling articulated objects in unconstrained settings have a wide variety of
applications including entertainment, neuroscience, psychology, ethology, and
many fields of medicine. Large offline labeled datasets do not exist for all
but the most common articulated object categories (e.g., humans). Hand labeling
these landmarks within a video sequence is a laborious task. Learned landmark
detectors can help, but can be error-prone when trained from only a few
examples. Multi-camera systems that train fine-grained detectors have shown
significant promise in detecting such errors, allowing for self-supervised
solutions that only need a small percentage of the video sequence to be
hand-labeled. The approach, however, is based on calibrated cameras and rigid
geometry, making it expensive, difficult to manage, and impractical in
real-world scenarios. In this paper, we address these bottlenecks by combining
a non-rigid 3D neural prior with deep flow to obtain high-fidelity landmark
estimates from videos with only two or three uncalibrated, handheld cameras.
With just a few annotations (representing 1-2% of the frames), we are able to
produce 2D results comparable to state-of-the-art fully supervised methods,
along with 3D reconstructions that are impossible with other existing
approaches. Our Multi-view Bootstrapping in the Wild (MBW) approach
demonstrates impressive results on standard human datasets, as well as tigers,
cheetahs, fish, colobus monkeys, chimpanzees, and flamingos from videos
captured casually in a zoo. We release the codebase for MBW as well as this
challenging zoo dataset consisting image frames of tail-end distribution
categories with their corresponding 2D, 3D labels generated from minimal human
intervention.

We present a convolutional neural network, designed in the auto-encoder
configuration that can detect and denoise astrophysical gravitational waves
from merging black hole binaries, orders of magnitude faster than the
conventional matched-filtering based detection that is currently employed at
advanced LIGO (aLIGO). The Neural-Net architecture is such that it learns from
the sparse representation of data in the time-frequency domain and constructs a
non-linear mapping function that maps this representation into two separate
masks for signal and noise, facilitating the separation of the two, from raw
data. This approach is the first of its kind to apply machine learning based
gravitational wave detection/denoising in the 2D representation of
gravitational wave data. We applied our formalism to the first gravitational
wave event detected, GW150914, successfully recovering the signal at all three
phases of coalescence at both detectors. This method is further tested on the
gravitational wave data from the second observing run ($O2$) of aLIGO,
reproducing all binary black hole mergers detected in $O2$ at both the aLIGO
detectors. The Neural-Net seems to have uncovered a pattern of 'ringing' after
the ringdown phase of the coalescence, which is not a feature that is present
in the conventional binary merger templates. This method can also interpolate
and extrapolate between modeled templates and explore gravitational waves that
are unmodeled and hence not present in the template bank of signals used in the
matched-filtering detection pipelines. Faster and efficient detection schemes,
such as this method, will be instrumental as ground based detectors reach their
design sensitivity, likely to result in several hundreds of potential
detections in a few months of observing runs.

The audio spectrogram is a time-frequency representation that has been widely
used for audio classification. The temporal resolution of a spectrogram depends
on hop size. Previous works generally assume the hop size should be a constant
value such as ten milliseconds. However, a fixed hop size or resolution is not
always optimal for different types of sound. This paper proposes a novel
method, DiffRes, that enables differentiable temporal resolution learning to
improve the performance of audio classification models. Given a spectrogram
calculated with a fixed hop size, DiffRes merges non-essential time frames
while preserving important frames. DiffRes acts as a "drop-in" module between
an audio spectrogram and a classifier, and can be end-to-end optimized. We
evaluate DiffRes on the mel-spectrogram, followed by state-of-the-art
classifier backbones, and apply it to five different subtasks. Compared with
using the fixed-resolution mel-spectrogram, the DiffRes-based method can
achieve the same or better classification accuracy with at least 25% fewer
temporal dimensions on the feature level, which alleviates the computational
cost at the same time. Starting from a high-temporal-resolution spectrogram
such as one-millisecond hop size, we show that DiffRes can improve
classification accuracy with the same computational complexity.

Anatomical structures such as blood vessels in contrast-enhanced CT (ceCT)
images can be challenging to segment due to the variability in contrast medium
diffusion. The combined use of ceCT and contrast-free (CT) CT images can
improve the segmentation performances, but at the cost of a double radiation
exposure. To limit the radiation dose, generative models could be used to
synthesize one modality, instead of acquiring it. The CycleGAN approach has
recently attracted particular attention because it alleviates the need for
paired data that are difficult to obtain. Despite the great performances
demonstrated in the literature, limitations still remain when dealing with 3D
volumes generated slice by slice from unpaired datasets with different fields
of view. We present an extension of CycleGAN to generate high fidelity images,
with good structural consistency, in this context. We leverage anatomical
constraints and automatic region of interest selection by adapting the
Self-Supervised Body Regressor. These constraints enforce anatomical
consistency and allow feeding anatomically-paired input images to the
algorithm. Results show qualitative and quantitative improvements, compared to
stateof-the-art methods, on the translation task between ceCT and CT images
(and vice versa).

Federated learning (FL) has emerged as a promising paradigm for enabling the
collaborative training of models without centralized access to the raw data on
local devices. In the typical FL paradigm (e.g., FedAvg), model weights are
sent to and from the server each round to participating clients. However, this
can quickly put a massive communication burden on the system, especially if
more capable models beyond very small MLPs are employed. Recently, the use of
pre-trained models has been shown effective in federated learning optimization
and improving convergence. This opens the door for new research questions. Can
we adjust the weight-sharing paradigm in federated learning, leveraging strong
and readily-available pre-trained models, to significantly reduce the
communication burden while simultaneously achieving excellent performance? To
this end, we investigate the use of parameter-efficient fine-tuning in
federated learning. Specifically, we systemically evaluate the performance of
several parameter-efficient fine-tuning methods across a variety of client
stability, data distribution, and differential privacy settings. By only
locally tuning and globally sharing a small portion of the model weights,
significant reductions in the total communication overhead can be achieved
while maintaining competitive performance in a wide range of federated learning
scenarios, providing insight into a new paradigm for practical and effective
federated systems.

Detecting anomalies over real-world datasets remains a challenging task. Data
annotation is an intensive human labor problem, particularly in sequential
datasets, where the start and end time of anomalies are not known. As a result,
data collected from sequential real-world processes can be largely unlabeled or
contain inaccurate labels. These characteristics challenge the application of
anomaly detection techniques based on supervised learning. In contrast,
Multiple Instance Learning (MIL) has been shown effective on problems with
incomplete knowledge of labels in the training dataset, mainly due to the
notion of bags. While largely under-leveraged for anomaly detection, MIL
provides an appealing formulation for anomaly detection over real-world
datasets, and it is the primary contribution of this paper. In this paper, we
propose an MIL-based formulation and various algorithmic instantiations of this
framework based on different design decisions for key components of the
framework. We evaluate the resulting algorithms over four datasets that capture
different physical processes along different modalities. The experimental
evaluation draws out several observations. The MIL-based formulation performs
no worse than single instance learning on easy to moderate datasets and
outperforms single-instance learning on more challenging datasets. Altogether,
the results show that the framework generalizes well over diverse datasets
resulting from different real-world application domains.

In recent years, the development of accurate deep keyword spotting (KWS)
models has resulted in KWS technology being embedded in a number of
technologies such as voice assistants. Many of these models rely on large
amounts of labelled data to achieve good performance. As a result, their use is
restricted to applications for which a large labelled speech data set can be
obtained. Self-supervised learning seeks to mitigate the need for large
labelled data sets by leveraging unlabelled data, which is easier to obtain in
large amounts. However, most self-supervised methods have only been
investigated for very large models, whereas KWS models are desired to be small.
In this paper, we investigate the use of self-supervised pretraining for the
smaller KWS models in a label-deficient scenario. We pretrain the Keyword
Transformer model using the self-supervised framework Data2Vec and carry out
experiments on a label-deficient setup of the Google Speech Commands data set.
It is found that the pretrained models greatly outperform the models without
pretraining, showing that Data2Vec pretraining can increase the performance of
KWS models in label-deficient scenarios. The source code is made publicly

Most existing methods for Magnetic Resonance Imaging (MRI) reconstruction
with deep learning assume that a high signal-to-noise ratio (SNR), fully
sampled sampled dataset exists and use fully supervised training. In many
circumstances, however, such a dataset does not exist and may be highly
impractical to acquire. Recently, a number of self-supervised methods for MR
reconstruction have been proposed, which require a training dataset with
sub-sampled k-space data only. However, existing methods do not denoise sampled
data, so are only applicable in the high SNR regime.
  In this work, we propose a method based on Noisier2Noise and Self-Supervised
Learning via Data Undersampling (SSDU) that trains a network to reconstruct
clean images from sub-sampled, noisy training data. To our knowledge, our
approach is the first that simultaneously denoises and reconstructs images in
an entirely self-supervised manner. Our method is applicable to any network
architecture, has a strong mathematical basis, and is straight-forward to
implement. We evaluate our method on the multi-coil fastMRI brain dataset and
find that it performs competitively with a network trained on clean, fully
sampled data and substantially improves over methods that do not explicitly
remove measurement noise.

The working mechanisms of complex natural systems tend to abide by concise
and profound partial differential equations (PDEs). Methods that directly mine
equations from data are called PDE discovery, which reveals consistent physical
laws and facilitates our interaction with the natural world. In this paper, an
enhanced deep reinforcement-learning framework is proposed to uncover symbolic
open-form PDEs with little prior knowledge. Specifically, (1) we first build a
symbol library and define that a PDE can be represented as a tree structure.
Then, (2) we design a structure-aware recurrent neural network agent by
combining structured inputs and monotonic attention to generate the pre-order
traversal of PDE expression trees. The expression trees are then split into
function terms, and their coefficients can be calculated by the sparse
regression method. (3) All of the generated PDE candidates are first filtered
by some physical and mathematical constraints, and then evaluated by a
meticulously designed reward function considering the fitness to data and the
parsimony of the equation. (4) We adopt the risk-seeking policy gradient to
iteratively update the agent to improve the best-case performance. The
experiment demonstrates that our framework is capable of mining the governing
equations of several canonical systems with great efficiency and scalability.

Bayesian interpretations of neural processing require that biological
mechanisms represent and operate upon probability distributions in accordance
with Bayes' theorem. Many have speculated that synaptic failure constitutes a
mechanism of variational, i.e., approximate, Bayesian inference in the brain.
Whereas models have previously used synaptic failure to sample over uncertainty
in model parameters, we demonstrate that by adapting transmission probabilities
to learned network weights, synaptic failure can sample not only over model
uncertainty, but complete posterior predictive distributions as well. Our
results potentially explain the brain's ability to perform probabilistic
searches and to approximate complex integrals. These operations are involved in
numerous calculations, including likelihood evaluation and state value
estimation for complex planning.

Simulating facial appearance change following bony movement is a critical
step in orthognathic surgical planning for patients with jaw deformities.
Conventional biomechanics-based methods such as the finite-element method (FEM)
are labor intensive and computationally inefficient. Deep learning-based
approaches can be promising alternatives due to their high computational
efficiency and strong modeling capability. However, the existing deep
learning-based method ignores the physical correspondence between facial soft
tissue and bony segments and thus is significantly less accurate compared to
FEM. In this work, we propose an Attentive Correspondence assisted Movement
Transformation network (ACMT-Net) to estimate the facial appearance by
transforming the bony movement to facial soft tissue through a point-to-point
attentive correspondence matrix. Experimental results on patients with jaw
deformity show that our proposed method can achieve comparable facial change
prediction accuracy compared with the state-of-the-art FEM-based approach with
significantly improved computational efficiency.

A vast literature shows that the learning-based visual perception model is
sensitive to adversarial noises, but few works consider the robustness of
robotic perception models under widely-existing camera motion perturbations. To
this end, we study the robustness of the visual perception model under camera
motion perturbations to investigate the influence of camera motion on robotic
perception. Specifically, we propose a motion smoothing technique for arbitrary
image classification models, whose robustness under camera motion perturbations
could be certified. The proposed robustness certification framework based on
camera motion smoothing provides tight and scalable robustness guarantees for
visual perception modules so that they are applicable to wide robotic
applications. As far as we are aware, this is the first work to provide
robustness certification for the deep perception module against camera motions,
which improves the trustworthiness of robotic perception. A realistic indoor
robotic dataset with a dense point cloud map for the entire room, MetaRoom, is
introduced for the challenging certifiable robust perception task. We conduct
extensive experiments to validate the certification approach via motion
smoothing against camera motion perturbations. Our framework guarantees the
certified accuracy of 81.7% against camera translation perturbation along depth
direction within -0.1m ~ 0.1m. We also validate the effectiveness of our method
on the real-world robot by conducting hardware experiments on the robotic arm
with an eye-in-hand camera. The code is available at
https://github.com/HanjiangHu/camera-motion-smoothing.

Conventional forgery localizing methods usually rely on different forgery
footprints such as JPEG artifacts, edge inconsistency, camera noise, etc., with
cross-entropy loss to locate manipulated regions. However, these methods have
the disadvantage of over-fitting and focusing on only a few specific forgery
footprints. On the other hand, real-life manipulated images are generated via a
wide variety of forgery operations and thus, leave behind a wide variety of
forgery footprints. Therefore, we need a more general approach for image
forgery localization that can work well on a variety of forgery conditions. A
key assumption in underlying forged region localization is that there remains a
difference of feature distribution between untampered and manipulated regions
in each forged image sample, irrespective of the forgery type. In this paper,
we aim to leverage this difference of feature distribution to aid in image
forgery localization. Specifically, we use contrastive loss to learn mapping
into a feature space where the features between untampered and manipulated
regions are well-separated for each image. Also, our method has the advantage
of localizing manipulated region without requiring any prior knowledge or
assumption about the forgery type. We demonstrate that our work outperforms
several existing methods on three benchmark image manipulation datasets. Code
is available at https://github.com/niloy193/CFLNet.

For the best human-robot interaction experience, the robot's navigation
policy should take into account personal preferences of the user. In this
paper, we present a learning framework complemented by a perception pipeline to
train a depth vision-based, personalized navigation controller from user
demonstrations. Our refined virtual reality interface enables the demonstration
of robot navigation trajectories under motion of the user for dynamic
interaction scenarios. In a detailed analysis, we evaluate different
configurations of the perception pipeline. As the experiments demonstrate, our
new pipeline compresses the perceived depth images to a latent state
representation and, thus, enables efficient reasoning about the robot's dynamic
environment to the learning. We discuss the robot's navigation performance in
various virtual scenes by enrolling a variational autoencoder in combination
with a motion predictor and demonstrate the first personalized robot navigation
controller that solely relies on depth images.

We propose an intuitive, machine-learning approach to multiparameter
inference, dubbed the InferoStatic Networks (ISN) method, to model the score
and likelihood ratio estimators in cases when the probability density can be
sampled but not computed directly. The ISN uses a backend neural network that
models a scalar function called the inferostatic potential $\varphi$. In
addition, we introduce new strategies, respectively called Kernel Score
Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the
score and the likelihood ratio functions from simulated data. We illustrate the
new techniques with some toy examples and compare to existing approaches in the
literature. We mention en passant some new loss functions that optimally
incorporate latent information from simulations into the training procedure.

Motivated by theoretical advancements in dimensionality reduction techniques
we use a recent model, called Block Markov Chains, to conduct a practical study
of clustering in real-world sequential data. Clustering algorithms for Block
Markov Chains possess theoretical optimality guarantees and can be deployed in
sparse data regimes. Despite these favorable theoretical properties, a thorough
evaluation of these algorithms in realistic settings has been lacking.
  We address this issue and investigate the suitability of these clustering
algorithms in exploratory data analysis of real-world sequential data. In
particular, our sequential data is derived from human DNA, written text, animal
movement data and financial markets. In order to evaluate the determined
clusters, and the associated Block Markov Chain model, we further develop a set
of evaluation tools. These tools include benchmarking, spectral noise analysis
and statistical model selection tools. An efficient implementation of the
clustering algorithm and the new evaluation tools is made available together
with this paper.
  Practical challenges associated to real-world data are encountered and
discussed. It is ultimately found that the Block Markov Chain model assumption,
together with the tools developed here, can indeed produce meaningful insights
in exploratory data analyses despite the complexity and sparsity of real-world
data.

Robotic taxonomies have appeared as high-level hierarchical abstractions that
classify how humans move and interact with their environment. They have proven
useful to analyse grasps, manipulation skills, and whole-body support poses.
Despite the efforts devoted to design their hierarchy and underlying
categories, their use in application fields remains scarce. This may be
attributed to the lack of computational models that fill the gap between the
discrete hierarchical structure of the taxonomy and the high-dimensional
heterogeneous data associated to its categories. To overcome this problem, we
propose to model taxonomy data via hyperbolic embeddings that capture the
associated hierarchical structure. To do so, we formulate a Gaussian process
hyperbolic latent variable model and enforce the taxonomy structure through
graph-based priors on the latent space and distance-preserving back
constraints. We test our model on the whole-body support pose taxonomy to learn
hyperbolic embeddings that comply with the original graph structure. We show
that our model properly encodes unseen poses from existing or new taxonomy
categories, it can be used to generate trajectories between the embeddings, and
it outperforms its Euclidean counterparts.

Due to the immense potential of quantum computers and the significant
computing overhead required in machine learning applications, the variational
quantum classifier (VQC) has received a lot of interest recently for image
classification. The performance of VQC is jeopardized by the noise in Noisy
Intermediate-Scale Quantum (NISQ) computers, which is a significant hurdle. It
is crucial to remember that large error rates occur in quantum algorithms due
to quantum decoherence and imprecision of quantum gates. Previous studies have
looked towards using ensemble learning in conventional computing to reduce
quantum noise. We also point out that the simple average aggregation in
classical ensemble learning may not work well for NISQ computers due to the
unbalanced confidence distribution in VQC. Therefore, in this study, we suggest
that ensemble quantum classifiers be optimized with plurality voting. On the
MNIST dataset and IBM quantum computers, experiments are carried out. The
results show that the suggested method can outperform state-of-the-art on two-
and four-class classifications by up to 16.0% and 6.1% , respectively.

Arrival/Travel times for public transit exhibit variability on account of
factors like seasonality, dwell times at bus stops, traffic signals, travel
demand fluctuation etc. The developing world in particular is plagued by
additional factors like lack of lane discipline, excess vehicles, diverse modes
of transport and so on. This renders the bus arrival time prediction (BATP) to
be a challenging problem especially in the developing world. A novel
data-driven model based on recurrent neural networks (RNNs) is proposed for
BATP (in real-time) in the current work. The model intelligently incorporates
both spatial and temporal correlations in a unique (non-linear) fashion
distinct from existing approaches. In particular, we propose a Gated Recurrent
Unit (GRU) based Encoder-Decoder(ED) OR Seq2Seq RNN model (originally
introduced for language translation) for BATP. The geometry of the dynamic real
time BATP problem enables a nice fit with the Encoder-Decoder based RNN
structure. We feed relevant additional synchronized inputs (from previous
trips) at each step of the decoder (a feature classically unexplored in machine
translation applications). Further motivated from accurately modelling
congestion influences on travel time prediction, we additionally propose to use
a bidirectional layer at the decoder (something unexplored in other time-series
based ED application contexts). The effectiveness of the proposed algorithms is
demonstrated on real field data collected from challenging traffic conditions.
Our experiments indicate that the proposed method outperforms diverse existing
state-of-art data-driven approaches proposed for the same problem.

Mobile applications play an important role in the economy today and there is
an increasing trend for app enablement on multiple platforms. However,
creating, distributing, and maintaining an application remain expert tasks.
Even for software developers, the process can be error-prone and
resource-consuming, especially when targeting different platforms
simultaneously. Researchers have proposed several frameworks to facilitate
cross-platform app development, but little attention has been paid to
non-technical users. In this paper, we described the Flow framework, which
takes the advantage of domain-specific languages to enable no-code
specification for app modeling. The cloud-native coordination mechanism further
supports non-technical users to execute, monitor, and maintain apps for any
target platforms. User evaluations were conducted to assess the usability and
user experience with the system. The results indicated that users can develop
apps in Flow with ease, but the prototype could be optimized to reduce learning
time and workload.

We address the unsolved task of robotic bin packing with irregular objects,
such as groceries, where the underlying constraints on object placement and
manipulation, and the diverse objects' physical properties make preprogrammed
strategies unfeasible. Our approach is to learn directly from expert
demonstrations in order to extract implicit task knowledge and strategies to
achieve an efficient space usage, safe object positioning and to generate
human-like behaviors that enhance human-robot trust. We collect and make
available a novel and diverse dataset, BoxED, of box packing demonstrations by
humans in virtual reality. In total, 263 boxes were packed with
supermarket-like objects by 43 participants, yielding 4644 object
manipulations. We use the BoxED dataset to learn a Markov chain to predict the
object packing sequence for a given set of objects and compare it with human
performance. Our experimental results show that the model surpasses human
performance by generating sequence predictions that humans classify as
human-like more frequently than human-generated sequences.

Test-time training provides a new approach solving the problem of domain
shift. In its framework, a test-time training phase is inserted between
training phase and test phase. During test-time training phase, usually parts
of the model are updated with test sample(s). Then the updated model will be
used in the test phase. However, utilizing test samples for test-time training
has some limitations. Firstly, it will lead to overfitting to the test-time
procedure thus hurt the performance on the main task. Besides, updating part of
the model without changing other parts will induce a mismatch problem. Thus it
is hard to perform better on the main task. To relieve above problems, we
propose to use mixup in test-time training (MixTTT) which controls the change
of model's parameters as well as completing the test-time procedure. We
theoretically show its contribution in alleviating the mismatch problem of
updated part and static part for the main task as a specific regularization
effect for test-time training. MixTTT can be used as an add-on module in
general test-time training based methods to further improve their performance.
Experimental results show the effectiveness of our method.

The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature
unidimensional transformation often used to Gaussianize features in machine
learning. In this paper, we investigate the problem of applying the YJ
transformation in a cross-silo Federated Learning setting under privacy
constraints. For the first time, we prove that the YJ negative log-likelihood
is in fact convex, which allows us to optimize it with exponential search. We
numerically show that the resulting algorithm is more stable than the
state-of-the-art approach based on the Brent minimization method. Building on
this simple algorithm and Secure Multiparty Computation routines, we propose
SecureFedYJ, a federated algorithm that performs a pooled-equivalent YJ
transformation without leaking more information than the final fitted
parameters do. Quantitative experiments on real data demonstrate that, in
addition to being secure, our approach reliably normalizes features across
silos as well as if data were pooled, making it a viable approach for safe
federated feature Gaussianization.

Intelligent systems that use Machine Learning classification algorithms are
increasingly common in everyday society. However, many systems use black-box
models that do not have characteristics that allow for self-explanation of
their predictions. This situation leads researchers in the field and society to
the following question: How can I trust the prediction of a model I cannot
understand? In this sense, XAI emerges as a field of AI that aims to create
techniques capable of explaining the decisions of the classifier to the
end-user. As a result, several techniques have emerged, such as
Explanation-by-Example, which has a few initiatives consolidated by the
community currently working with XAI. This research explores the Item Response
Theory (IRT) as a tool to explaining the models and measuring the level of
reliability of the Explanation-by-Example approach. To this end, four datasets
with different levels of complexity were used, and the Random Forest model was
used as a hypothesis test. From the test set, 83.8% of the errors are from
instances in which the IRT points out the model as unreliable.

Humans can generate reasonable answers to novel queries (Schulz, 2012): if I
asked you what kind of food you want to eat for lunch, you would respond with a
food, not a time. The thought that one would respond "After 4pm" to "What would
you like to eat" is either a joke or a mistake, and seriously entertaining it
as a lunch option would likely never happen in the first place. While
understanding how people come up with new ideas, thoughts, explanations, and
hypotheses that obey the basic constraints of a novel search space is of
central importance to cognitive science, there is no agreed-on formal model for
this kind of reasoning. We propose that a core component of any such reasoning
system is a type theory: a formal imposition of structure on the kinds of
computations an agent can perform, and how they're performed. We motivate this
proposal with three empirical observations: adaptive constraints on learning
and inference (i.e. generating reasonable hypotheses), how people draw
distinctions between improbability and impossibility, and people's ability to
reason about things at varying levels of abstraction.

Gaussian processes (GPs) produce good probabilistic models of functions, but
most GP kernels require $O((n+m)n^2)$ time, where $n$ is the number of data
points and $m$ the number of predictive locations. We present a new kernel that
allows for Gaussian process regression in $O((n+m)\log(n+m))$ time. Our "binary
tree" kernel places all data points on the leaves of a binary tree, with the
kernel depending only on the depth of the deepest common ancestor. We can store
the resulting kernel matrix in $O(n)$ space in $O(n \log n)$ time, as a sum of
sparse rank-one matrices, and approximately invert the kernel matrix in $O(n)$
time. Sparse GP methods also offer linear run time, but they predict less well
than higher dimensional kernels. On a classic suite of regression tasks, we
compare our kernel against Mat\'ern, sparse, and sparse variational kernels.
The binary tree GP assigns the highest likelihood to the test data on a
plurality of datasets, usually achieves lower mean squared error than the
sparse methods, and often ties or beats the Mat\'ern GP. On large datasets, the
binary tree GP is fastest, and much faster than a Mat\'ern GP.

Masked image modeling (MIM) revolutionizes self-supervised learning (SSL) for
image pre-training. In contrast to previous dominating self-supervised methods,
i.e., contrastive learning, MIM attains state-of-the-art performance by masking
and reconstructing random patches of the input image. However, the associated
security and privacy risks of this novel generative method are unexplored. In
this paper, we perform the first security risk quantification of MIM through
the lens of backdoor attacks. Different from previous work, we are the first to
systematically threat modeling on SSL in every phase of the model supply chain,
i.e., pre-training, release, and downstream phases. Our evaluation shows that
models built with MIM are vulnerable to existing backdoor attacks in release
and downstream phases and are compromised by our proposed method in
pre-training phase. For instance, on CIFAR10, the attack success rate can reach
99.62%, 96.48%, and 98.89% in the downstream phase, release phase, and
pre-training phase, respectively. We also take the first step to investigate
the success factors of backdoor attacks in the pre-training phase and find the
trigger number and trigger pattern play key roles in the success of backdoor
attacks while trigger location has only tiny effects. In the end, our empirical
study of the defense mechanisms across three detection-level on model supply
chain phases indicates that different defenses are suitable for backdoor
attacks in different phases. However, backdoor attacks in the release phase
cannot be detected by all three detection-level methods, calling for more
effective defenses in future research.

Learning how to generalize the model to unseen domains is an important area
of research. In this paper, we propose TripleE, and the main idea is to
encourage the network to focus on training on subsets (learning with replay)
and enlarge the data space in learning on subsets. Learning with replay
contains two core designs, EReplayB and EReplayD, which conduct the replay
schema on batch and dataset, respectively. Through this, the network can focus
on learning with subsets instead of visiting the global set at a glance,
enlarging the model diversity in ensembling. To enlarge the data space in
learning on subsets, we verify that an exhaustive and singular augmentation
(ESAug) performs surprisingly well on expanding the data space in subsets
during replays. Our model dubbed TripleE is frustratingly easy, based on simple
augmentation and ensembling. Without bells and whistles, our TripleE method
surpasses prior arts on six domain generalization benchmarks, showing that this
approach could serve as a stepping stone for future research in domain

Traffic congestion is a major urban issue due to its adverse effects on
health and the environment, so much so that reducing it has become a priority
for urban decision-makers. In this work, we investigate whether a high amount
of data on traffic flow throughout a city and the knowledge of the road city
network allows an Artificial Intelligence to predict the traffic flux far
enough in advance in order to enable emission reduction measures such as those
linked to the Low Emission Zone policies. To build a predictive model, we use
the city of Valencia traffic sensor system, one of the densest in the world,
with nearly 3500 sensors distributed throughout the city. In this work we train
and characterize an LSTM (Long Short-Term Memory) Neural Network to predict
temporal patterns of traffic in the city using historical data from the years
2016 and 2017. We show that the LSTM is capable of predicting future evolution
of the traffic flux in real-time, by extracting patterns out of the measured
data.

Bayesian optimization (BO) is a class of popular methods for expensive
black-box optimization, and has been widely applied to many scenarios. However,
BO suffers from the curse of dimensionality, and scaling it to high-dimensional
problems is still a challenge. In this paper, we propose a variable selection
method MCTS-VS based on Monte Carlo tree search (MCTS), to iteratively select
and optimize a subset of variables. That is, MCTS-VS constructs a
low-dimensional subspace via MCTS and optimizes in the subspace with any BO
algorithm. We give a theoretical analysis of the general variable selection
method to reveal how it can work. Experiments on high-dimensional synthetic
functions and real-world problems (i.e., NAS-bench problems and MuJoCo
locomotion tasks) show that MCTS-VS equipped with a proper BO optimizer can
achieve state-of-the-art performance.

People segment complex, ever-changing and continuous experience into basic,
stable and discrete spatio-temporal experience units, called events. Event
segmentation literature investigates the mechanisms that allow people to
extract events. Event segmentation theory points out that people predict
ongoing activities and observe prediction error signals to find event
boundaries that keep events apart. In this study, we investigated the mechanism
giving rise to this ability by a computational model and accompanying
psychological experiments. Inspired from event segmentation theory and
predictive processing, we introduced a self-supervised model of event
segmentation. This model consists of neural networks that predict the sensory
signal in the next time-step to represent different events, and a cognitive
model that regulates these networks on the basis of their prediction errors. In
order to verify the ability of our model in segmenting events, learning them
during passive observation, and representing them in its internal
representational space, we prepared a video that depicts human behaviors
represented by point-light displays. We compared event segmentation behaviors
of participants and our model with this video in two hierarchical event
segmentation levels. By using point-biserial correlation technique, we
demonstrated that event segmentation decisions of our model correlated with the
responses of participants. Moreover, by approximating representation space of
participants by a similarity-based technique, we showed that our model formed a
similar representation space with those of participants. The result suggests
that our model that tracks the prediction error signals can produce human-like
event boundaries and event representations. Finally, we discussed our
contribution to the literature of event cognition and our understanding of how
event segmentation is implemented in the brain.

Recently, there has been a trend of shifting the execution of deep learning
inference tasks toward the edge of the network, closer to the user, to reduce
latency and preserve data privacy. At the same time, growing interest is being
devoted to the energetic sustainability of machine learning. At the
intersection of these trends, we hence find the energetic characterization of
machine learning at the edge, which is attracting increasing attention.
Unfortunately, calculating the energy consumption of a given neural network
during inference is complicated by the heterogeneity of the possible underlying
hardware implementation. In this work, we hence aim at profiling the energetic
consumption of inference tasks for some modern edge nodes and deriving simple
but realistic models. To this end, we performed a large number of experiments
to collect the energy consumption of convolutional and fully connected layers
on two well-known edge boards by NVIDIA, namely Jetson TX2 and Xavier. From the
measurements, we have then distilled a simple, practical model that can provide
an estimate of the energy consumption of a certain inference task on the
considered boards. We believe that this model can be used in many contexts as,
for instance, to guide the search for efficient architectures in Neural
Architecture Search, as a heuristic in Neural Network pruning, or to find
energy-efficient offloading strategies in a Split computing context, or simply
to evaluate the energetic performance of Deep Neural Network architectures.

We review the current state of research on electromagnetic probes in the
context of heavy-ion collisions. The focus is on thermal photons and dileptons
which provide unique insights into the properties of the created hot and dense
matter. This review is intended to provide an introductory overview of the
topic as well as a discussion of recent theoretical and experimental results.
In particular, we discuss the role of vector-meson spectral functions in the
calculation of photon and dilepton rates and present recent results obtained
from different frameworks. Furthermore, we will highlight the special role of
photons and dileptons to provide information on observables such as the
temperature, the lifetime, the polarization and the electrical conductivity of
the produced medium as well as their use to learn about chiral symmetry
restoration and phase transitions.

Sharpness-aware minimization (SAM) and related adversarial deep-learning
methods can drastically improve generalization, but their underlying mechanisms
are not yet fully understood. Here, we establish SAM as a relaxation of the
Bayes objective where the expected negative-loss is replaced by the optimal
convex lower bound, obtained by using the so-called Fenchel biconjugate. The
connection enables a new Adam-like extension of SAM to automatically obtain
reasonable uncertainty estimates, while sometimes also improving its accuracy.
By connecting adversarial and Bayesian methods, our work opens a new path to
robustness.

Renewable Energies (RE) have gained more attention in recent years since they
offer clean and sustainable energy. One of the major sustainable development
goals (SDG-7) set by the United Nations (UN) is to achieve affordable and clean
energy for everyone. Among the world's all renewable resources, solar energy is
considered as the most abundant and can certainly fulfill the target of SDGs.
Solar energy is converted into electrical energy through Photovoltaic (PV)
panels with no greenhouse gas emissions. However, power generated by PV panels
is highly dependent on solar radiation received at a particular location over a
given time period. Therefore, it is challenging to forecast the amount of PV
output power. Predicting the output power of PV systems is essential since
several public or private institutes generate such green energy, and need to
maintain the balance between demand and supply. This research aims to forecast
PV system output power based on weather and derived features using different
machine learning models. The objective is to obtain the best-fitting model to
precisely predict output power by inspecting the data. Moreover, different
performance metrics are used to compare and evaluate the accuracy under
different machine learning models such as random forest, XGBoost, KNN, etc.

Deep Metric Learning trains a neural network to map input images to a
lower-dimensional embedding space such that similar images are closer together
than dissimilar images. When used for item retrieval, a query image is embedded
using the trained model and the closest items from a database storing their
respective embeddings are returned as the most similar items for the query.
Especially in product retrieval, where a user searches for a certain product by
taking a photo of it, the image background is usually not important and thus
should not influence the embedding process. Ideally, the retrieval process
always returns fitting items for the photographed object, regardless of the
environment the photo was taken in. In this paper, we analyze the influence of
the image background on Deep Metric Learning models by utilizing five common
loss functions and three common datasets. We find that Deep Metric Learning
networks are prone to so-called background bias, which can lead to a severe
decrease in retrieval performance when changing the image background during
inference. We also show that replacing the background of images during training
with random background images alleviates this issue. Since we use an automatic
background removal method to do this background replacement, no additional
manual labeling work and model changes are required while inference time stays
the same. Qualitative and quantitative analyses, for which we introduce a new
evaluation metric, confirm that models trained with replaced backgrounds attend
more to the main object in the image, benefitting item retrieval systems.

Self-supervised monocular depth estimation refers to training a monocular
depth estimation (MDE) network using only RGB images to overcome the difficulty
of collecting dense ground truth depth. Many previous works addressed this
problem using depth classification or depth regression. However, depth
classification tends to fall into local minima due to the bilinear
interpolation search on the target view. Depth classification overcomes this
problem using pre-divided depth bins, but those depth candidates lead to
discontinuities in the final depth result, and using the same probability for
weighted summation of color and depth is ambiguous. To overcome these
limitations, we use some predefined planes that are parallel to the ground,
allowing us to automatically segment the ground and predict continuous depth
for it. We further model depth as a mixture Laplace distribution, which
provides a more certain objective for optimization. Previous works have shown
that MDE networks only use the vertical image position of objects to estimate
the depth and ignore relative sizes. We address this problem for the first time
in both stereo and monocular training using resize cropping data augmentation.
Based on our analysis of resize cropping, we combine it with our plane
definition and improve our training strategy so that the network could learn
the relationship between depth and both the vertical image position and
relative size of objects. We further combine the self-distillation stage with
post-processing to provide more accurate supervision and save extra time in
post-processing. We conduct extensive experiments to demonstrate the
effectiveness of our analysis and improvements.

Fostered by novel analytical techniques, digitalization and automation,
modern bioprocess development provides high amounts of heterogeneous
experimental data, containing valuable process information. In this context,
data-driven methods like machine learning (ML) approaches have a high potential
to rationally explore large design spaces while exploiting experimental
facilities most efficiently. The aim of this review is to demonstrate how ML
methods have been applied so far in bioprocess development, especially in
strain engineering and selection, bioprocess optimization, scale-up, monitoring
and control of bioprocesses. For each topic, we will highlight successful
application cases, current challenges and point out domains that can
potentially benefit from technology transfer and further progress in the field
of ML.

Generating virtual populations of anatomy that capture sufficient variability
while remaining plausible is essential for conducting in-silico trials of
medical devices. However, not all anatomical shapes of interest are always
available for each individual in a population. Hence,
missing/partially-overlapping anatomical information is often available across
individuals in a population. We introduce a generative shape model for complex
anatomical structures, learnable from datasets of unpaired datasets. The
proposed generative model can synthesise complete whole complex shape
assemblies coined virtual chimaeras, as opposed to natural human chimaeras. We
applied this framework to build virtual chimaeras from databases of whole-heart
shape assemblies that each contribute samples for heart substructures.
Specifically, we propose a generative shape compositional framework which
comprises two components - a part-aware generative shape model which captures
the variability in shape observed for each structure of interest in the
training population; and a spatial composition network which assembles/composes
the structures synthesised by the former into multi-part shape assemblies (viz.
virtual chimaeras). We also propose a novel self supervised learning scheme
that enables the spatial composition network to be trained with partially
overlapping data and weak labels. We trained and validated our approach using
shapes of cardiac structures derived from cardiac magnetic resonance images
available in the UK Biobank. Our approach significantly outperforms a PCA-based
shape model (trained with complete data) in terms of generalisability and
specificity. This demonstrates the superiority of the proposed approach as the
synthesised cardiac virtual populations are more plausible and capture a
greater degree of variability in shape than those generated by the PCA-based
shape model.

Despite the tremendous success, existing machine learning models still fall
short of human-like systematic generalization -- learning compositional rules
from limited data and applying them to unseen combinations in various domains.
We propose Neural-Symbolic Recursive Machine (NSR) to tackle this deficiency.
The core representation of NSR is a Grounded Symbol System (GSS) with
combinatorial syntax and semantics, which entirely emerges from training data.
Akin to the neuroscience studies suggesting separate brain systems for
perceptual, syntactic, and semantic processing, NSR implements analogous
separate modules of neural perception, syntactic parsing, and semantic
reasoning, which are jointly learned by a deduction-abduction algorithm. We
prove that NSR is expressive enough to model various sequence-to-sequence
tasks. Superior systematic generalization is achieved via the inductive biases
of equivariance and recursiveness embedded in NSR. In experiments, NSR achieves
state-of-the-art performance in three benchmarks from different domains: SCAN
for semantic parsing, PCFG for string manipulation, and HINT for arithmetic
reasoning. Specifically, NSR achieves 100% generalization accuracy on SCAN and
PCFG and outperforms state-of-the-art models on HINT by about 23%. Our NSR
demonstrates stronger generalization than pure neural networks due to its
symbolic representation and inductive biases. NSR also demonstrates better
transferability than existing neural-symbolic approaches due to less
domain-specific knowledge required.

Lifelong object re-identification incrementally learns from a stream of
re-identification tasks. The objective is to learn a representation that can be
applied to all tasks and that generalizes to previously unseen
re-identification tasks. The main challenge is that at inference time the
representation must generalize to previously unseen identities. To address this
problem, we apply continual meta metric learning to lifelong object
re-identification. To prevent forgetting of previous tasks, we use knowledge
distillation and explore the roles of positive and negative pairs. Based on our
observation that the distillation and metric losses are antagonistic, we
propose to remove positive pairs from distillation to robustify model updates.
Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on
extensive intra-domain experiments on person and vehicle re-identification
datasets, as well as inter-domain experiments on the LReID benchmark. Our
experiments demonstrate that DwoPP significantly outperforms the
state-of-the-art. The code is here: https://github.com/wangkai930418/DwoPP_code

Neural networks have proven to be very powerful at computer vision tasks.
However, they often exhibit unexpected behaviours, violating known requirements
expressing background knowledge. This calls for models (i) able to learn from
the requirements, and (ii) guaranteed to be compliant with the requirements
themselves. Unfortunately, the development of such models is hampered by the
lack of datasets equipped with formally specified requirements. In this paper,
we introduce the ROad event Awareness Dataset with logical Requirements
(ROAD-R), the first publicly available dataset for autonomous driving with
requirements expressed as logical constraints. Given ROAD-R, we show that
current state-of-the-art models often violate its logical constraints, and that
it is possible to exploit them to create models that (i) have a better
performance, and (ii) are guaranteed to be compliant with the requirements
themselves.

In this work we present FreDSNet, a deep learning solution which obtains
semantic 3D understanding of indoor environments from single panoramas.
Omnidirectional images reveal task-specific advantages when addressing scene
understanding problems due to the 360-degree contextual information about the
entire environment they provide. However, the inherent characteristics of the
omnidirectional images add additional problems to obtain an accurate detection
and segmentation of objects or a good depth estimation. To overcome these
problems, we exploit convolutions in the frequential domain obtaining a wider
receptive field in each convolutional layer. These convolutions allow to
leverage the whole context information from omnidirectional images. FreDSNet is
the first network that jointly provides monocular depth estimation and semantic
segmentation from a single panoramic image exploiting fast Fourier
convolutions. Our experiments show that FreDSNet has similar performance as
specific state of the art methods for semantic segmentation and depth
estimation. FreDSNet code is publicly available in
https://github.com/Sbrunoberenguel/FreDSNet

Identification of regions affected by floods is a crucial piece of
information required for better planning and management of post-disaster relief
and rescue efforts. Traditionally, remote sensing images are analysed to
identify the extent of damage caused by flooding. The data acquired from
sensors onboard earth observation satellites are analyzed to detect the flooded
regions, which can be affected by low spatial and temporal resolution. However,
in recent years, the images acquired from Unmanned Aerial Vehicles (UAVs) have
also been utilized to assess post-disaster damage. Indeed, a UAV based platform
can be rapidly deployed with a customized flight plan and minimum dependence on
the ground infrastructure. This work proposes two approaches for identifying
flooded regions in UAV aerial images. The first approach utilizes texture-based
unsupervised segmentation to detect flooded areas, while the second uses an
artificial neural network on the texture features to classify images as flooded
and non-flooded. Unlike the existing works where the models are trained and
tested on images of the same geographical regions, this work studies the
performance of the proposed model in identifying flooded regions across
geographical regions. An F1-score of 0.89 is obtained using the proposed
segmentation-based approach which is higher than existing classifiers. The
robustness of the proposed approach demonstrates that it can be utilized to
identify flooded regions of any region with minimum or no user intervention.

Compared to visible-to-visible (V2V) person re-identification (ReID), the
visible-to-infrared (V2I) person ReID task is more challenging due to the lack
of sufficient training samples and the large cross-modality discrepancy.
  To this end, we propose Flow2Flow, a unified framework that could jointly
achieve training sample expansion and cross-modality image generation for V2I
person ReID.
  Specifically, Flow2Flow learns bijective transformations from both the
visible image domain and the infrared domain to a shared isotropic Gaussian
domain with an invertible visible flow-based generator and an infrared one,
  With Flow2Flow, we are able to generate pseudo training samples by the
transformation from latent Gaussian noises to visible or infrared images, and
generate cross-modality images by transformations from existing-modality images
to latent Gaussian noises to missing-modality images.
  For the purpose of identity alignment and modality alignment of generated
images, we develop adversarial training strategies to train Flow2Flow.
  Specifically, we design an image encoder and a modality discriminator for
each modality.
  The image encoder encourages the generated images to be similar to real
images of the same identity via identity adversarial training, and the modality
discriminator makes the generated images modal-indistinguishable from real
images via modality adversarial training.
  Experimental results on SYSU-MM01 and RegDB demonstrate that both training
sample expansion and cross-modality image generation can significantly improve
V2I ReID accuracy.

Most recent self-supervised methods for learning image representations focus
on either producing a global feature with invariance properties, or producing a
set of local features. The former works best for classification tasks while the
latter is best for detection and segmentation tasks. This paper explores the
fundamental trade-off between learning local and global features. A new method
called VICRegL is proposed that learns good global and local features
simultaneously, yielding excellent performance on detection and segmentation
tasks while maintaining good performance on classification tasks. Concretely,
two identical branches of a standard convolutional net architecture are fed two
differently distorted versions of the same image. The VICReg criterion is
applied to pairs of global feature vectors. Simultaneously, the VICReg
criterion is applied to pairs of local feature vectors occurring before the
last pooling layer. Two local feature vectors are attracted to each other if
their l2-distance is below a threshold or if their relative locations are
consistent with a known geometric transformation between the two input images.
We demonstrate strong performance on linear classification and segmentation
transfer tasks. Code and pretrained models are publicly available at:
https://github.com/facebookresearch/VICRegL

Denoising diffusion probabilistic models and score matching models have
proven to be very powerful for generative tasks. While these approaches have
also been applied to the generation of discrete graphs, they have, so far,
relied on continuous Gaussian perturbations. Instead, in this work, we suggest
using discrete noise for the forward Markov process. This ensures that in every
intermediate step the graph remains discrete. Compared to the previous
approach, our experimental results on four datasets and multiple architectures
show that using a discrete noising process results in higher quality generated
samples indicated with an average MMDs reduced by a factor of 1.5. Furthermore,
the number of denoising steps is reduced from 1000 to 32 steps leading to a 30
times faster sampling procedure.

Grazing-Incidence Small-Angle X-ray Scattering (GISAXS) is a modern imaging
technique used in material research to study nanoscale materials.
Reconstruction of the parameters of an imaged object imposes an ill-posed
inverse problem that is further complicated when only an in-plane GISAXS signal
is available. Traditionally used inference algorithms such as Approximate
Bayesian Computation (ABC) rely on computationally expensive scattering
simulation software, rendering analysis highly time-consuming. We propose a
simulation-based framework that combines variational auto-encoders and
normalizing flows to estimate the posterior distribution of object parameters
given its GISAXS data. We apply the inference pipeline to experimental data and
demonstrate that our method reduces the inference cost by orders of magnitude
while producing consistent results with ABC.

We propose a new class of deep reinforcement learning (RL) algorithms that
model latent representations in hyperbolic space. Sequential decision-making
requires reasoning about the possible future consequences of current behavior.
Consequently, capturing the relationship between key evolving features for a
given task is conducive to recovering effective policies. To this end,
hyperbolic geometry provides deep RL models with a natural basis to precisely
encode this inherently hierarchical information. However, applying existing
methodologies from the hyperbolic deep learning literature leads to fatal
optimization instabilities due to the non-stationarity and variance
characterizing RL gradient estimators. Hence, we design a new general method
that counteracts such optimization challenges and enables stable end-to-end
learning with deep hyperbolic representations. We empirically validate our
framework by applying it to popular on-policy and off-policy RL algorithms on
the Procgen and Atari 100K benchmarks, attaining near universal performance and
generalization benefits. Given its natural fit, we hope future RL research will
consider hyperbolic representations as a standard tool.

As the intention is to reduce the amount of thermal generation and to
increase the share of clean energy, power systems are increasingly becoming
susceptible to frequency instability after outages due to reduced levels of
inertia. To address this issue frequency constraints are being included in the
scheduling process, which ensure a tolerable frequency deviation in case of any
contingencies. In this paper, a method is proposed to integrate the non-linear
frequency nadir constraint into the unit commitment problem, using machine
learning. First a synthetic training dataset is generated. Then two of the
available classic machine learning methods, namely logistic regression and
support vector machine, are proposed to predict the frequency nadir. To be able
to compare the machine learning methods to traditional frequency constrained
unit commitment approaches, simulations on the power system of La Palma island
are carried out for both proposed methods as well as an analytical linearized
formulation of the frequency nadir. Our results show that the unit commitment
problem with a machine learning based frequency nadir constraint is solved
considerably faster than with the analytical formulation, while still achieving
an acceptable frequency response quality after outages.

The automated analysis of microscopy images is a challenge in the context of
single-cell tracking and quantification. This work has as goals the study of
the performance of deep learning for segmenting microscopy images and the
improvement of the previously available pipeline for tracking single cells.
Deep learning techniques, mainly convolutional neural networks, have been
applied to cell segmentation problems and have shown high accuracy and fast
performance. To perform the image segmentation, an analysis of hyperparameters
was done in order to implement a convolutional neural network with U-Net
architecture. Furthermore, different models were built in order to optimize the
size of the network and the number of learnable parameters. The trained network
is then used in the pipeline that localizes the traps in a microfluidic device,
performs the image segmentation on trap images, and evaluates the fluorescence
intensity and the area of single cells over time. The tracking of the cells
during an experiment is performed by image processing algorithms, such as
centroid estimation and watershed. Finally, with all improvements in the neural
network to segment single cells and in the pipeline, quasi-real-time image
analysis was enabled, where 6.20GB of data was processed in 4 minutes.

Markov chain Monte Carlo (MCMC) is an established approach for uncertainty
quantification and propagation in scientific applications. A key challenge in
applying MCMC to scientific domains is computation: the target density of
interest is often a function of expensive computations, such as a high-fidelity
physical simulation, an intractable integral, or a slowly-converging iterative
algorithm. Thus, using an MCMC algorithms with an expensive target density
becomes impractical, as these expensive computations need to be evaluated at
each iteration of the algorithm. In practice, these computations often
approximated via a cheaper, low-fidelity computation, leading to bias in the
resulting target density. Multi-fidelity MCMC algorithms combine models of
varying fidelities in order to obtain an approximate target density with lower
computational cost. In this paper, we describe a class of asymptotically exact
multi-fidelity MCMC algorithms for the setting where a sequence of models of
increasing fidelity can be computed that approximates the expensive target
density of interest. We take a pseudo-marginal MCMC approach for multi-fidelity
inference that utilizes a cheaper, randomized-fidelity unbiased estimator of
the target fidelity constructed via random truncation of a telescoping series
of the low-fidelity sequence of models. Finally, we discuss and evaluate the
proposed multi-fidelity MCMC approach on several applications, including
log-Gaussian Cox process modeling, Bayesian ODE system identification,
PDE-constrained optimization, and Gaussian process regression parameter
inference.

Multi-label classification is becoming increasingly ubiquitous, but not much
attention has been paid to interpretability. In this paper, we develop a
multi-label classifier that can be represented as a concise set of simple
"if-then" rules, and thus, it offers better interpretability compared to
black-box models. Notably, our method is able to find a small set of relevant
patterns that lead to accurate multi-label classification, while existing
rule-based classifiers are myopic and wasteful in searching rules,requiring a
large number of rules to achieve high accuracy. In particular, we formulate the
problem of choosing multi-label rules to maximize a target function, which
considers not only discrimination ability with respect to labels, but also
diversity. Accounting for diversity helps to avoid redundancy, and thus, to
control the number of rules in the solution set. To tackle the said
maximization problem we propose a 2-approximation algorithm, which relies on a
novel technique to sample high-quality rules. In addition to our theoretical
analysis, we provide a thorough experimental evaluation, which indicates that
our approach offers a trade-off between predictive performance and
interpretability that is unmatched in previous work.

Movement Primitives (MPs) are a well-known concept to represent and generate
modular trajectories. MPs can be broadly categorized into two types: (a)
dynamics-based approaches that generate smooth trajectories from any initial
state, e. g., Dynamic Movement Primitives (DMPs), and (b) probabilistic
approaches that capture higher-order statistics of the motion, e. g.,
Probabilistic Movement Primitives (ProMPs). To date, however, there is no
method that unifies both, i. e. that can generate smooth trajectories from an
arbitrary initial state while capturing higher-order statistics. In this paper,
we introduce a unified perspective of both approaches by solving the ODE
underlying the DMPs. We convert expensive online numerical integration of DMPs
into basis functions that can be computed offline. These basis functions can be
used to represent trajectories or trajectory distributions similar to ProMPs
while maintaining all the properties of dynamical systems. Since we inherit the
properties of both methodologies, we call our proposed model Probabilistic
Dynamic Movement Primitives (ProDMPs). Additionally, we embed ProDMPs in deep
neural network architecture and propose a new cost function for efficient
end-to-end learning of higher-order trajectory statistics. To this end, we
leverage Bayesian Aggregation for non-linear iterative conditioning on sensory
inputs. Our proposed model achieves smooth trajectory generation,
goal-attractor convergence, correlation analysis, non-linear conditioning, and
online re-planing in one framework.

Using on-demand processing pipelines to generate virtual geospatial products
is beneficial to optimizing resource management and decreasing processing
requirements and data storage space. Additionally, pre-processed products
improve data quality for data-driven analytical algorithms, such as machine
learning or deep learning models. This paper proposes a method to integrate
virtual products based on integrating open-source processing pipelines. In
order to validate and evaluate the functioning of this approach, we have
integrated it into a geo-imagery management framework based on Open Data Cube
(ODC). To validate the methodology, we have performed three experiments
developing on-demand processing pipelines using multi-sensor remote sensing
data, for instance, Sentinel-1 and Sentinel-2. These pipelines are integrated
using open-source processing frameworks.

Avoiding out-of-distribution (OOD) data is critical for training supervised
machine learning models in the medical imaging domain. Furthermore, obtaining
labeled medical data is difficult and expensive since it requires expert
annotators like doctors, radiologists, etc. Active learning (AL) is a
well-known method to mitigate labeling costs by selecting the most diverse or
uncertain samples. However, current AL methods do not work well in the medical
imaging domain with OOD data. We propose Diagnose (avoiDing out-of-dIstribution
dAta usinG submodular iNfOrmation meaSurEs), a novel active learning framework
that can jointly model similarity and dissimilarity, which is crucial in mining
in-distribution data and avoiding OOD data at the same time. Particularly, we
use a small number of data points as exemplars that represent a query set of
in-distribution data points and a private set of OOD data points. We illustrate
the generalizability of our framework by evaluating it on a wide variety of
real-world OOD scenarios. Our experiments verify the superiority of Diagnose
over the state-of-the-art AL methods across multiple domains of medical
imaging.

Reinforcement learning (RL) has recently proven great success in various
domains. Yet, the design of the reward function requires detailed domain
expertise and tedious fine-tuning to ensure that agents are able to learn the
desired behaviour. Using a sparse reward conveniently mitigates these
challenges. However, the sparse reward represents a challenge on its own, often
resulting in unsuccessful training of the agent. In this paper, we therefore
address the sparse reward problem in RL. Our goal is to find an effective
alternative to reward shaping, without using costly human demonstrations, that
would also be applicable to a wide range of domains. Hence, we propose to use
model predictive control~(MPC) as an experience source for training RL agents
in sparse reward environments. Without the need for reward shaping, we
successfully apply our approach in the field of mobile robot navigation both in
simulation and real-world experiments with a Kuboki Turtlebot 2. We furthermore
demonstrate great improvement over pure RL algorithms in terms of success rate
as well as number of collisions and timeouts. Our experiments show that MPC as
an experience source improves the agent's learning process for a given task in
the case of sparse rewards.

Training deep learning models on medical datasets that perform well for all
classes is a challenging task. It is often the case that a suboptimal
performance is obtained on some classes due to the natural class imbalance
issue that comes with medical data. An effective way to tackle this problem is
by using targeted active learning, where we iteratively add data points to the
training data that belong to the rare classes. However, existing active
learning methods are ineffective in targeting rare classes in medical datasets.
In this work, we propose Clinical (targeted aCtive Learning for ImbalaNced
medICal imAge cLassification) a framework that uses submodular mutual
information functions as acquisition functions to mine critical data points
from rare classes. We apply our framework to a wide-array of medical imaging
datasets on a variety of real-world class imbalance scenarios - namely, binary
imbalance and long-tail imbalance. We show that Clinical outperforms the
state-of-the-art active learning methods by acquiring a diverse set of data
points that belong to the rare classes.

Internet based businesses and products (e.g. e-commerce, music streaming) are
becoming more and more sophisticated every day with a lot of focus on improving
customer satisfaction. A core way they achieve this is by providing customers
with an easy access to their products by structuring them in catalogues using
navigation bars and providing recommendations. We refer to these catalogues as
product concepts, e.g. product categories on e-commerce websites, public
playlists on music streaming platforms. These product concepts typically
contain products that are linked with each other through some common features
(e.g. a playlist of songs by the same artist). How they are defined in the
backend of the system can be different for different products. In this work, we
represent product concepts using database queries and tackle two learning
problems. First, given sets of products that all belong to the same unknown
product concept, we learn a database query that is a representation of this
product concept. Second, we learn product concepts and their corresponding
queries when the given sets of products are associated with multiple product
concepts. To achieve these goals, we propose two approaches that combine the
concepts of PU learning with Decision Trees and Clustering. Our experiments
demonstrate, via a simulated setup for a music streaming service, that our
approach is effective in solving these problems.

We consider Sharpness-Aware Minimization (SAM), a gradient-based optimization
method for deep networks that has exhibited performance improvements on image
and language prediction problems. We show that when SAM is applied with a
convex quadratic objective, for most random initializations it converges to a
cycle that oscillates between either side of the minimum in the direction with
the largest curvature, and we provide bounds on the rate of convergence.
  In the non-quadratic case, we show that such oscillations effectively perform
gradient descent, with a smaller step-size, on the spectral norm of the
Hessian. In such cases, SAM's update may be regarded as a third derivative --
the derivative of the Hessian in the leading eigenvector direction -- that
encourages drift toward wider minima.

We propose a) a Language Agnostic end-to-end Speech Translation model (LAST),
and b) a data augmentation strategy to increase code-switching (CS)
performance. With increasing globalization, multiple languages are increasingly
used interchangeably during fluent speech. Such CS complicates traditional
speech recognition and translation, as we must recognize which language was
spoken first and then apply a language-dependent recognizer and subsequent
translation component to generate the desired target language output. Such a
pipeline introduces latency and errors. In this paper, we eliminate the need
for that, by treating speech recognition and translation as one unified
end-to-end speech translation problem. By training LAST with both input
languages, we decode speech into one target language, regardless of the input
language. LAST delivers comparable recognition and speech translation accuracy
in monolingual usage, while reducing latency and error rate considerably when
CS is observed.

A central question of machine learning is how deep nets manage to learn tasks
in high dimensions. An appealing hypothesis is that they achieve this feat by
building a representation of the data where information irrelevant to the task
is lost. For image datasets, this view is supported by the observation that
after (and not before) training, the neural representation becomes less and
less sensitive to diffeomorphisms acting on images as the signal propagates
through the net. This loss of sensitivity correlates with performance, and
surprisingly correlates with a gain of sensitivity to white noise acquired
during training. These facts are unexplained, and as we demonstrate still hold
when white noise is added to the images of the training set. Here, we (i) show
empirically for various architectures that stability to image diffeomorphisms
is achieved by spatial pooling in the first half of the net, and by channel
pooling in the second half, (ii) introduce a scale-detection task for a simple
model of data where pooling is learned during training, which captures all
empirical observations above and (iii) compute in this model how stability to
diffeomorphisms and noise scale with depth. The scalings are found to depend on
the presence of strides in the net architecture. We find that the increased
sensitivity to noise is due to the perturbing noise piling up during pooling,
after being rectified by ReLU units.

Predictive learning uses a known state to generate a future state over a
period of time. It is a challenging task to predict spatiotemporal sequence
because the spatiotemporal sequence varies both in time and space. The
mainstream method is to model spatial and temporal structures at the same time
using RNN-based or transformer-based architecture, and then generates future
data by using learned experience in the way of auto-regressive. The method of
learning spatial and temporal features simultaneously brings a lot of
parameters to the model, which makes the model difficult to be convergent. In
this paper, a modular design is proposed, which decomposes spatiotemporal
sequence model into two modules: a spatial encoder-decoder and a predictor.
These two modules can extract spatial features and predict future data
respectively. The spatial encoder-decoder maps the data into a latent embedding
space and generates data from the latent space while the predictor forecasts
future embedding from past. By applying the design to the current research and
performing experiments on KTH-Action and MovingMNIST datasets, we both improve
computational performance and obtain state-of-the-art results.

We consider escaping saddle points of nonconvex problems where only the
function evaluations can be accessed. Although a variety of works have been
proposed, the majority of them require either second or first-order
information, and only a few of them have exploited zeroth-order methods,
particularly the technique of negative curvature finding with zeroth-order
methods which has been proven to be the most efficient method for escaping
saddle points. To fill this gap, in this paper, we propose two zeroth-order
negative curvature finding frameworks that can replace Hessian-vector product
computations without increasing the iteration complexity. We apply the proposed
frameworks to ZO-GD, ZO-SGD, ZO-SCSG, ZO-SPIDER and prove that these ZO
algorithms can converge to $(\epsilon,\delta)$-approximate second-order
stationary points with less query complexity compared with prior zeroth-order
works for finding local minima.

The science of cities is a relatively new and interdisciplinary topic. It
borrows techniques from agent-based modeling, stochastic processes, and partial
differential equations. However, how the cities rise and fall, how they evolve,
and the mechanisms responsible for these phenomena are still open questions.
Scientists have only recently started to develop forecasting tools, despite
their importance in urban planning, transportation planning, and epidemic
spreading modeling. Here, we build a fully interpretable statistical model
that, incorporating only the minimum number of constraints, can predict
different phenomena arising in the city. Using data on the movements of
car-sharing vehicles in different Italian cities, we infer a model using the
Maximum Entropy (MaxEnt) principle. With it, we describe the activity in
different city zones and apply it to activity forecasting and anomaly detection
(e.g., strikes, and bad weather conditions). We compare our method with
different models explicitly made for forecasting: SARIMA models and Deep
Learning Models. We find that MaxEnt models are highly predictive,
outperforming SARIMAs and having similar results as a Neural Network. These
results show how relevant statistical inference can be in building a robust and
general model describing urban systems phenomena. This article identifies the
significant observables for processes happening in the city, with the
perspective of a deeper understanding of the fundamental forces driving its
dynamics.

We consider core-periphery structured graphs, which are graphs with a group
of densely and sparsely connected nodes, respectively, referred to as core and
periphery nodes. The so-called core score of a node is related to the
likelihood of it being a core node. In this paper, we focus on learning the
core scores of a graph from its node attributes and connectivity structure. To
this end, we propose two classes of probabilistic graphical models: affine and
nonlinear. First, we describe affine generative models to model the dependence
of node attributes on its core scores, which determine the graph structure.
Next, we discuss nonlinear generative models in which the partial correlations
of node attributes influence the graph structure through latent core scores. We
develop algorithms for inferring the model parameters and core scores of a
graph when both the graph structure and node attributes are available. When
only the node attributes of graphs are available, we jointly learn a
core-periphery structured graph and its core scores. We provide results from
numerical experiments on several synthetic and real-world datasets to
demonstrate the efficacy of the developed models and algorithms.

Anthropomorphic robot avatars present a conceptually novel approach to remote
affective communication, allowing people across the world a wider specter of
emotional and social exchanges over traditional 2D and 3D image data. However,
there are several limitations of current telepresence robots, such as the high
weight, complexity of the system that prevents its fast deployment, and the
limited workspace of the avatars mounted on either static or wheeled mobile
platforms.
  In this paper, we present a novel concept of telecommunication through a
robot avatar based on an anthropomorphic swarm of drones; SwarMan. The
developed system consists of nine nanocopters controlled remotely by the
operator through a gesture recognition interface. SwarMan allows operators to
communicate by directly following their motions and by recognizing one of the
prerecorded emotional patterns, thus rendering the captured emotion as
illumination on the drones. The LSTM MediaPipe network was trained on a
collected dataset of 600 short videos with five emotional gestures. The
accuracy of achieved emotion recognition was 97% on the test dataset.
  As communication through the swarm avatar significantly changes the visual
appearance of the operator, we investigated the ability of the users to
recognize and respond to emotions performed by the swarm of drones. The
experimental results revealed a high consistency between the users in rating
emotions. Additionally, users indicated low physical demand (2.25 on the Likert
scale) and were satisfied with their performance (1.38 on the Likert scale)
when communicating by the SwarMan interface.

In 3D medical image segmentation, small targets segmentation is crucial for
diagnosis but still faces challenges. In this paper, we propose the Axis
Projection Attention UNet, named APAUNet, for 3D medical image segmentation,
especially for small targets. Considering the large proportion of the
background in the 3D feature space, we introduce a projection strategy to
project the 3D features into three orthogonal 2D planes to capture the
contextual attention from different views. In this way, we can filter out the
redundant feature information and mitigate the loss of critical information for
small lesions in 3D scans. Then we utilize a dimension hybridization strategy
to fuse the 3D features with attention from different axes and merge them by a
weighted summation to adaptively learn the importance of different
perspectives. Finally, in the APA Decoder, we concatenate both high and low
resolution features in the 2D projection process, thereby obtaining more
precise multi-scale information, which is vital for small lesion segmentation.
Quantitative and qualitative experimental results on two public datasets (BTCV
and MSD) demonstrate that our proposed APAUNet outperforms the other methods.
Concretely, our APAUNet achieves an average dice score of 87.84 on BTCV, 84.48
on MSD-Liver and 69.13 on MSD-Pancreas, and significantly surpass the previous
SOTA methods on small targets.

Timely recognition of plant pests from field images is significant to avoid
potential losses of crop yields. Traditional convolutional neural network-based
deep learning models demand high computational capability and require large
labelled samples for each pest type for training. On the other hand, the
existing lightweight network-based approaches suffer in correctly classifying
the pests because of common characteristics and high similarity between
multiple plant pests. In this work, a novel double attention-based lightweight
deep learning architecture is proposed to automatically recognize different
plant pests. The lightweight network facilitates faster and small data training
while the double attention module increases performance by focusing on the most
pertinent information. The proposed approach achieves 96.61%, 99.08% and 91.60%
on three variants of two publicly available datasets with 5869, 545 and 500
samples, respectively. Moreover, the comparison results reveal that the
proposed approach outperforms existing approaches on both small and large
datasets consistently.

In tasks like question answering or text summarisation, it is essential to
have background knowledge about the relevant entities. The information about
entities - in particular, about long-tail or emerging entities - in publicly
available knowledge graphs like DBpedia or CaLiGraph is far from complete. In
this paper, we present an approach that exploits the semi-structured nature of
listings (like enumerations and tables) to identify the main entities of the
listing items (i.e., of entries and rows). These entities, which we call
subject entities, can be used to increase the coverage of knowledge graphs. Our
approach uses a transformer network to identify subject entities at the
token-level and surpasses an existing approach in terms of performance while
being bound by fewer limitations. Due to a flexible input format, it is
applicable to any kind of listing and is, unlike prior work, not dependent on
entity boundaries as input. We demonstrate our approach by applying it to the
complete Wikipedia corpus and extracting 40 million mentions of subject
entities with an estimated precision of 71% and recall of 77%. The results are
incorporated in the most recent version of CaLiGraph.

AI systems are becoming increasingly intertwined with human life. In order to
effectively collaborate with humans and ensure safety, AI systems need to be
able to understand, interpret and predict human moral judgments and decisions.
Human moral judgments are often guided by rules, but not always. A central
challenge for AI safety is capturing the flexibility of the human moral mind --
the ability to determine when a rule should be broken, especially in novel or
unusual situations. In this paper, we present a novel challenge set consisting
of rule-breaking question answering (RBQA) of cases that involve potentially
permissible rule-breaking -- inspired by recent moral psychology studies. Using
a state-of-the-art large language model (LLM) as a basis, we propose a novel
moral chain of thought (MORALCOT) prompting strategy that combines the
strengths of LLMs with theories of moral reasoning developed in cognitive
science to predict human moral judgments. MORALCOT outperforms seven existing
LLMs by 6.2% F1, suggesting that modeling human reasoning might be necessary to
capture the flexibility of the human moral mind. We also conduct a detailed
error analysis to suggest directions for future work to improve AI safety using
RBQA. Our data is open-sourced at
https://huggingface.co/datasets/feradauto/MoralExceptQA and code at
https://github.com/feradauto/MoralCoT

The design of Luenberger observers for nonlinear systems involves state
transformation to another coordinate system where the dynamics are
asymptotically stable and linear up to output injection. The observer then
provides a state estimate in the original coordinates by inverting the
transformation map. For general nonlinear systems, however, the main challenge
is to find such a transformation and to ensure that it is injective. This paper
addresses this challenge by proposing a learning method that employs supervised
physics-informed neural networks to approximate both the transformation and its
inverse. It is shown that the proposed method exhibits better generalization
capabilities than other contemporary methods. Moreover, the observer is shown
to be robust under the neural network's approximation error and the system
uncertainties.

With the recent developments in neuroscience and engineering, it is now
possible to record brain signals and decode them. Also, a growing number of
stimulation methods have emerged to modulate and influence brain activity.
Current brain-computer interface (BCI) technology is mainly on therapeutic
outcomes, it already demonstrated its efficiency as assistive and
rehabilitative technology for patients with severe motor impairments. Recently,
artificial intelligence (AI) and machine learning (ML) technologies have been
used to decode brain signals. Beyond this progress, combining AI with advanced
BCIs in the form of implantable neurotechnologies grants new possibilities for
the diagnosis, prediction, and treatment of neurological and psychiatric
disorders. In this context, we envision the development of closed loop,
intelligent, low-power, and miniaturized neural interfaces that will use brain
inspired AI techniques with neuromorphic hardware to process the data from the
brain. This will be referred to as Brain Inspired Brain Computer Interfaces
(BI-BCIs). Such neural interfaces would offer access to deeper brain regions
and better understanding for brain's functions and working mechanism, which
improves BCIs operative stability and system's efficiency. On one hand, brain
inspired AI algorithms represented by spiking neural networks (SNNs) would be
used to interpret the multimodal neural signals in the BCI system. On the other
hand, due to the ability of SNNs to capture rich dynamics of biological neurons
and to represent and integrate different information dimensions such as time,
frequency, and phase, it would be used to model and encode complex information
processing in the brain and to provide feedback to the users. This paper
provides an overview of the different methods to interface with the brain,
presents future applications and discusses the merger of AI and BCIs.

We address the well-known wearable activity recognition problem of having to
work with sensors that are non-optimal in terms of information they provide but
have to be used due to wearability/usability concerns (e.g. the need to work
with wrist-worn IMUs because they are embedded in most smart watches). To
mitigate this problem we propose a method that facilitates the use of
information from sensors that are only present during the training process and
are unavailable during the later use of the system. The method transfers
information from the source sensors to the latent representation of the target
sensor data through contrastive loss that is combined with the classification
loss during joint training. We evaluate the method on the well-known PAMAP2 and
Opportunity benchmarks for different combinations of source and target sensors
showing average (over all activities) F1 score improvements of between 5% and
13% with the improvement on individual activities, particularly well suited to
benefit from the additional information going up to between 20% and 40%.

With the recent advances in mobile energy storage technologies, electric
vehicles (EVs) have become a crucial part of smart grids. When EVs participate
in the demand response program, the charging cost can be significantly reduced
by taking full advantage of the real-time pricing signals. However, many
stochastic factors exist in the dynamic environment, bringing significant
challenges to design an optimal charging/discharging control strategy. This
paper develops an optimal EV charging/discharging control strategy for
different EV users under dynamic environments to maximize EV users' benefits.
We first formulate this problem as a Markov decision process (MDP). Then we
consider EV users with different behaviors as agents in different environments.
Furthermore, a horizontal federated reinforcement learning (HFRL)-based method
is proposed to fit various users' behaviors and dynamic environments. This
approach can learn an optimal charging/discharging control strategy without
sharing users' profiles. Simulation results illustrate that the proposed
real-time EV charging/discharging control strategy can perform well among
various stochastic factors.

Data protection regulations like the GDPR or the California Consumer Privacy
Act give users more control over the data that is collected about them.
Deleting the collected data is often insufficient to guarantee data privacy
since it is often used to train machine learning models, which can expose
information about the training data. Thus, a guarantee that a trained model
does not expose information about its training data is additionally needed. In
this paper, we present UnlearnSPN -- an algorithm that removes the influence of
single data points from a trained sum-product network and thereby allows
fulfilling data privacy requirements on demand.

Light field display caters to the viewer's immersive experience by providing
binocular depth sensation and motion parallax. Glasses-free tensor light field
display is becoming a prominent area of research in auto-stereoscopic display
technology. Stacking light attenuating layers is one of the approaches to
implement a light field display with a good depth of field, wide viewing angles
and high resolution. This paper presents a compact and efficient representation
of light field data based on scalable compression of the binary represented
image layers suitable for additive layered display using a Deep Belief Network
(DBN). The proposed scheme learns and optimizes the additive layer patterns
using a convolutional neural network (CNN). Weighted binary images represent
the optimized patterns, reducing the file size and introducing scalable
encoding. The DBN further compresses the weighted binary patterns into a latent
space representation followed by encoding the latent data using an h.254 codec.
The proposed scheme is compared with benchmark codecs such as h.264 and h.265
and achieved competitive performance on light field data.

We report the possibility of using a simple neural network for effortless
restoration of low-light images inspired by the retina model, which mimics the
neurophysiological principles and dynamics of various types of optical neurons.
The proposed neural network model saves the cost of computational overhead in
contrast with traditional signal-processing models, and generates results
comparable with complicated deep learning models from the subjective perceptual
perspective. This work shows that to directly simulate the functionalities of
retinal neurons using neural networks not only avoids the manually seeking for
the optimal parameters, but also paves the way to build corresponding
artificial versions for certain neurobiological organizations.

The Atacama Large Millimeter/submillimeter Array with the planned electronic
upgrades will deliver an unprecedented amount of deep and high resolution
observations. Wider fields of view are possible with the consequential cost of
image reconstruction. Alternatives to commonly used applications in image
processing have to be sought and tested. Advanced image reconstruction methods
are critical to meet the data requirements needed for operational purposes.
Astrostatistics and astroinformatics techniques are employed. Evidence is given
that these interdisciplinary fields of study applied to synthesis imaging meet
the Big Data challenges and have the potentials to enable new scientific
discoveries in radio astronomy and astrophysics.

Estimation of a regression function from independent and identically
distributed random variables is considered. The $L_2$ error with integration
with respect to the design measure is used as an error criterion.
Over-parametrized deep neural network estimates are defined where all the
weights are learned by the gradient descent. It is shown that the expected
$L_2$ error of these estimates converges to zero with the rate close to
$n^{-1/(1+d)}$ in case that the regression function is H\"older smooth with
H\"older exponent $p \in [1/2,1]$. In case of an interaction model where the
regression function is assumed to be a sum of H\"older smooth functions where
each of the functions depends only on $d^*$ many of $d$ components of the
design variable, it is shown that these estimates achieve the corresponding
$d^*$-dimensional rate of convergence.

Few-shot fine-grained recognition (FS-FGR) aims to recognize novel
fine-grained categories with the help of limited available samples.
Undoubtedly, this task inherits the main challenges from both few-shot learning
and fine-grained recognition. First, the lack of labeled samples makes the
learned model easy to overfit. Second, it also suffers from high intra-class
variance and low inter-class difference in the datasets. To address this
challenging task, we propose a two-stage background suppression and foreground
alignment framework, which is composed of a background activation suppression
(BAS) module, a foreground object alignment (FOA) module, and a local to local
(L2L) similarity metric. Specifically, the BAS is introduced to generate a
foreground mask for localization to weaken background disturbance and enhance
dominative foreground objects. What's more, considering the lack of labeled
samples, we compute the pairwise similarity of feature maps using both the raw
image and the refined image. The FOA then reconstructs the feature map of each
support sample according to its correction to the query ones, which addresses
the problem of misalignment between support-query image pairs. To enable the
proposed method to have the ability to capture subtle differences in confused
samples, we present a novel L2L similarity metric to further measure the local
similarity between a pair of aligned spatial features in the embedding space.
Extensive experiments conducted on multiple popular fine-grained benchmarks
demonstrate that our method outperforms the existing state-of-the-art by a

A network (CC-Net) based on complementary consistency training is proposed
for semi-supervised left atrial image segmentation in this paper. From the
perspective of complementary information, CC-Net efficiently utilizes unlabeled
data and resolves the problem that semi-supervised segmentation algorithms
currently in use have limited capacity to extract information from unlabeled
data. A primary model and two complementary auxiliary models are part of the
complementary symmetric structure of the CC-Net. The inter-model perturbation
is formed between the main model and the auxiliary model to form complementary
consistency training. The complementary information between the two auxiliary
models helps the main model to focus on the fuzzy region effectively.
Additionally, forcing consistency between the main model and the auxiliary
models makes it easier to obtain decision boundaries with low uncertainty.
CC-Net was validated in the benchmark dataset of the 2018 Atrial Segmentation
Challenge. The Dice reached of 89.82% with 10% labeled data training and 91.27%
with 20% labeled data training. By comparing with current state-of-the-art
algorithms, CC-Net has the best segmentation performance and robustness. Our
code is publicly available at https://github.com/Cuthbert-Huang/CC-Net.

Federated learning is a newly emerging distributed learning framework that
facilitates the collaborative training of a shared global model among
distributed participants with their privacy preserved. However, federated
learning systems are vulnerable to Byzantine attacks from malicious
participants, who can upload carefully crafted local model updates to degrade
the quality of the global model and even leave a backdoor. While this problem
has received significant attention recently, current defensive schemes heavily
rely on various assumptions, such as a fixed Byzantine model, availability of
participants' local data, minority attackers, IID data distribution, etc.
  To relax those constraints, this paper presents Robust-FL, the first
prediction-based Byzantine-robust federated learning scheme where none of the
assumptions is leveraged. The core idea of the Robust-FL is exploiting
historical global model to construct an estimator based on which the local
models will be filtered through similarity detection. We then cluster local
models to adaptively adjust the acceptable differences between the local models
and the estimator such that Byzantine users can be identified. Extensive
experiments over different datasets show that our approach achieves the
following advantages simultaneously: (i) independence of participants' local
data, (ii) tolerance of majority attackers, (iii) generalization to variable
Byzantine model.

We propose a non-learning depth completion method for a sparse depth map
captured using a light detection and ranging (LiDAR) sensor guided by a pair of
stereo images. Generally, conventional stereo-aided depth completion methods
have two limiations. (i) They assume the given sparse depth map is accurately
aligned to the input image, whereas the alignment is difficult to achieve in
practice. (ii) They have limited accuracy in the long range because the depth
is estimated by pixel disparity. To solve the abovementioned limitations, we
propose selective stereo matching (SSM) that searches the most appropriate
depth value for each image pixel from its neighborly projected LiDAR points
based on an energy minimization framework. This depth selection approach can
handle any type of mis-projection. Moreover, SSM has an advantage in terms of
long-range depth accuracy because it directly uses the LiDAR measurement rather
than the depth acquired from the stereo. SSM is a discrete process; thus, we
apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to
generate a continuous depth map while preserving depth discontinuity across
object boundaries. Experimentally, compared with the previous state-of-the-art
stereo-aided depth completion, the proposed method reduced the mean absolute
error (MAE) of the depth estimation to 0.65 times and demonstrated
approximately twice more accurate estimation in the long range. Moreover, under
various LiDAR-camera calibration errors, the proposed method reduced the depth
estimation MAE to 0.34-0.93 times from previous depth completion methods.

Accurately and robustly estimating the state of deformable linear objects
(DLOs), such as ropes and wires, is crucial for DLO manipulation and other
applications. However, it remains a challenging open issue due to the high
dimensionality of the state space, frequent occlusion, and noises. This paper
focuses on learning to robustly estimate the states of DLOs from single-frame
point clouds in the presence of occlusions using a data-driven method. We
propose a novel two-branch network architecture to exploit global and local
information of input point cloud respectively and design a fusion module to
effectively leverage both the advantages. Simulation and real-world
experimental results demonstrate that our method can generate globally smooth
and locally precise DLO state estimation results even with heavily occluded
point clouds, which can be directly applied to real-world robotic manipulation
of DLOs in 3-D space.

Wide-field time domain facilities detect transient events in large numbers
through difference imaging. For example, Zwicky Transient Facility produces
alerts for hundreds of thousands of transient events per night, a rate set to
be dwarfed by the upcoming Vera Rubin Observatory. The automation provided by
Machine Learning (ML) is, therefore, necessary to classify these events and
select the most interesting sources for follow-up observations. Cataclysmic
Variables (CVs) are a transient class that are numerous, bright, and nearby,
providing excellent laboratories for the study of accretion and binary
evolution. Here we focus on our use of ML to identify CVs from photometric data
of transient sources published by the Gaia Science Alerts program (GSA) - a
large, easily accessible resource, not fully explored with ML. The use of light
curve feature extraction techniques and source metadata from the Gaia survey
resulted in a Random Forest model capable of distinguishing CVs from
supernovae, Active Galactic Nuclei, and Young Stellar Objects with a 92\%
precision score and an 85\% hit rate. Of 13,280 sources within GSA without an
assigned transient classification our model predicts the CV class for
$\sim$2800. Spectroscopic observations are underway to classify a statistically
significant sample of these targets to validate the performance of the model.
This work puts us on a path towards the classification of rare CV subtypes from
future wide-field surveys such as the Legacy Survey of Space and Time.

In many complex sequential decision making tasks, online planning is crucial
for high-performance. For efficient online planning, Monte Carlo Tree Search
(MCTS) employs a principled mechanism for trading off between exploration and
exploitation. MCTS outperforms comparison methods in various discrete decision
making domains such as Go, Chess, and Shogi. Following, extensions of MCTS to
continuous domains have been proposed. However, the inherent high branching
factor and the resulting explosion of search tree size is limiting existing
methods. To solve this problem, this paper proposes Continuous Monte Carlo
Graph Search (CMCGS), a novel extension of MCTS to online planning in
environments with continuous state and action spaces. CMCGS takes advantage of
the insight that, during planning, sharing the same action policy between
several states can yield high performance. To implement this idea, at each time
step CMCGS clusters similar states into a limited number of stochastic action
bandit nodes, which produce a layered graph instead of an MCTS search tree.
Experimental evaluation with limited sample budgets shows that CMCGS
outperforms comparison methods in several complex continuous DeepMind Control
Suite benchmarks and a 2D navigation task.

We study the problem of routing and scheduling of real-time flows over a
multi-hop millimeter wave (mmWave) mesh. We develop a model-free deep
reinforcement learning algorithm that determines which subset of the mmWave
links should be activated during each time slot and using what power level. The
proposed algorithm, called Adaptive Activator RL (AARL), can handle a variety
of network topologies, network loads, and interference models, as well as adapt
to different workloads. We demonstrate the operation of AARL on several
topologies: a small topology with 10 links, a moderately-sized mesh with 48
links, and a large topology with 96 links. For each topology, the results of
AARL are compared to those of a greedy scheduling algorithm. AARL is shown to
outperform the greedy algorithm in two aspects. First, its schedule obtains
higher goodput. Second, and even more importantly, while the run time of the
greedy algorithm renders it impractical for real-time scheduling, the run time
of AARL is suitable for meeting the time constraints of typical 5G networks.

Real-world deployment of machine learning models is challenging when data
evolves over time. And data does evolve over time. While no model can work when
data evolves in an arbitrary fashion, if there is some pattern to these
changes, we might be able to design methods to address it. This paper addresses
situations when data evolves gradually. We introduce a novel time-varying
importance weight estimator that can detect gradual shifts in the distribution
of data. Such an importance weight estimator allows the training method to
selectively sample past data -- not just similar data from the past like a
standard importance weight estimator would but also data that evolved in a
similar fashion in the past. Our time-varying importance weight is quite
general. We demonstrate different ways of implementing it that exploit some
known structure in the evolution of data. We demonstrate and evaluate this
approach on a variety of problems ranging from supervised learning tasks
(multiple image classification datasets) where the data undergoes a sequence of
gradual shifts of our design to reinforcement learning tasks (robotic
manipulation and continuous control) where data undergoes a shift organically
as the policy or the task changes.

We study the identification of a linear time-invariant dynamical system
affected by large-and-sparse disturbances modeling adversarial attacks or
faults. Under the assumption that the states are measurable, we develop
necessary and sufficient conditions for the recovery of the system matrices by
solving a constrained lasso-type optimization problem. In addition, we provide
an upper bound on the estimation error whenever the disturbance sequence is a
combination of small noise values and large adversarial values. Our results
depend on the null space property that has been widely used in the lasso
literature, and we investigate under what conditions this property holds for
linear time-invariant dynamical systems. Lastly, we further study the
conditions for a specific probabilistic model and support the results with
numerical experiments.

Distributionally robust optimization has been shown to offer a principled way
to regularize learning models. In this paper, we find that Tikhonov
regularization is distributionally robust in an optimal transport sense (i.e.,
if an adversary chooses distributions in a suitable optimal transport
neighborhood of the empirical measure), provided that suitable martingale
constraints are also imposed. Further, we introduce a relaxation of the
martingale constraints which not only provides a unified viewpoint to a class
of existing robust methods but also leads to new regularization tools. To
realize these novel tools, tractable computational algorithms are proposed. As
a byproduct, the strong duality theorem proved in this paper can be potentially
applied to other problems of independent interest.

We present SLOT-V, a novel supervised learning framework that learns observer
models (human preferences) from robot motion trajectories in a legibility
context. Legibility measures how easily a (human) observer can infer the
robot's goal from a robot motion trajectory. When generating such trajectories,
existing planners often rely on an observer model that estimates the quality of
trajectory candidates. These observer models are frequently hand-crafted or,
occasionally, learned from demonstrations. Here, we propose to learn them in a
supervised manner using the same data format that is frequently used during the
evaluation of aforementioned approaches. We then demonstrate the generality of
SLOT-V using a Franka Emika in a simulated manipulation environment. For this,
we show that it can learn to closely predict various hand-crafted observer
models, i.e., that SLOT-V's hypothesis space encompasses existing handcrafted
models. Next, we showcase SLOT-V's ability to generalize by showing that a
trained model continues to perform well in environments with unseen goal
configurations and/or goal counts. Finally, we benchmark SLOT-V's sample
efficiency (and performance) against an existing IRL approach and show that
SLOT-V learns better observer models with less data. Combined, these results
suggest that SLOT-V can learn viable observer models. Better observer models
imply more legible trajectories, which may - in turn - lead to better and more
transparent human-robot interaction.

The rapid growth of data generated from Internet of Things (IoTs) such as
smart phones and smart home devices presents new challenges to cloud computing
in transferring, storing, and processing the data. With increasingly more
powerful edge devices, edge computing, on the other hand, has the potential to
better responsiveness, privacy, and cost efficiency. However, resources across
the cloud and edge are highly distributed and highly diverse. To address these
challenges, this paper proposes EdgeFaaS, a Function-as-a-Service (FaaS) based
computing framework that supports the flexible, convenient, and optimized use
of distributed and heterogeneous resources across IoT, edge, and cloud systems.
EdgeFaaS allows cluster resources and individual devices to be managed under
the same framework and provide computational and storage resources for
functions. It provides virtual function and virtual storage interfaces for
consistent function management and storage management across heterogeneous
compute and storage resources. It automatically optimizes the scheduling of
functions and placement of data according to their performance and privacy
requirements. EdgeFaaS is evaluated based on two edge workflows: video
analytics workflow and federated learning workflow, both of which are
representative edge applications and involve large amounts of input data
generated from edge devices.

Decision making or scientific discovery pipelines such as job hiring and drug
discovery often involve multiple stages: before any resource-intensive step,
there is often an initial screening that uses predictions from a machine
learning model to shortlist a few candidates from a large pool. We study
screening procedures that aim to select candidates whose unobserved outcomes
exceed user-specified values. We develop a method that wraps around any
prediction model to produce a subset of candidates while controlling the
proportion of falsely selected units. Building upon the conformal inference
framework, our method first constructs p-values that quantify the statistical
evidence for large outcomes; it then determines the shortlist by comparing the
p-values to a threshold introduced in the multiple testing literature. In many
cases, the procedure selects candidates whose predictions are above a
data-dependent threshold. Our theoretical guarantee holds under mild
exchangeability conditions on the samples, generalizing existing results on
multiple conformal p-values. We demonstrate the empirical performance of our
method via simulations, and apply it to job hiring and drug discovery datasets.

Conceptually, Neural Ordinary Differential Equations (NeuralODEs) pose an
attractive way to extract dynamical laws from time series data, as they are
natural extensions of the traditional differential equation-based modeling
paradigm of the physical sciences. In practice, NeuralODEs display long
training times and suboptimal results, especially for longer duration data
where they may fail to fit the data altogether. While methods have been
proposed to stabilize NeuralODE training, many of these involve placing a
strong constraint on the functional form the trained NeuralODE can take that
the actual underlying governing equation does not guarantee satisfaction. In
this work, we present a novel NeuralODE training algorithm that leverages tools
from the chaos and mathematical optimization communities - synchronization and
homotopy optimization - for a breakthrough in tackling the NeuralODE training
obstacle. We demonstrate architectural changes are unnecessary for effective
NeuralODE training. Compared to the conventional training methods, our
algorithm achieves drastically lower loss values without any changes to the
model architectures. Experiments on both simulated and real systems with
complex temporal behaviors demonstrate NeuralODEs trained with our algorithm
are able to accurately capture true long term behaviors and correctly
extrapolate into the future.

We consider infinite-horizon discounted Markov decision processes and study
the convergence rates of the natural policy gradient (NPG) and the Q-NPG
methods with the log-linear policy class. Using the compatible function
approximation framework, both methods with log-linear policies can be written
as approximate versions of the policy mirror descent (PMD) method. We show that
both methods attain linear convergence rates and $\mathcal{O}(1/\epsilon^2)$
sample complexities using a simple, non-adaptive geometrically increasing step
size, without resorting to entropy or other strongly convex regularization.
Lastly, as a byproduct, we obtain sublinear convergence rates for both methods
with arbitrary constant step size.

Learning high-performance deep neural networks for dynamic modeling of high
Degree-Of-Freedom (DOF) robots remains challenging due to the sampling
complexity. Typical unknown system disturbance caused by unmodeled dynamics
(such as internal compliance, cables) further exacerbates the problem. In this
paper, a novel framework characterized by both high data efficiency and
disturbance-adapting capability is proposed to address the problem of modeling
gravitational dynamics using deep nets in feedforward gravity compensation
control for high-DOF master manipulators with unknown disturbance. In
particular, Feedforward Deep Neural Networks (FDNNs) are learned from both
prior knowledge of an existing analytical model and observation of the robot
system by Knowledge Distillation (KD). Through extensive experiments in
high-DOF master manipulators with significant disturbance, we show that our
method surpasses a standard Learning-from-Scratch (LfS) approach in terms of
data efficiency and disturbance adaptation. Our initial feasibility study has
demonstrated the potential of outperforming the analytical teacher model as the
training data increases.

3D object detection is a crucial research topic in computer vision, which
usually uses 3D point clouds as input in conventional setups. Recently, there
is a trend of leveraging multiple sources of input data, such as complementing
the 3D point cloud with 2D images that often have richer color and fewer
noises. However, due to the heterogeneous geometrics of the 2D and 3D
representations, it prevents us from applying off-the-shelf neural networks to
achieve multimodal fusion. To that end, we propose Bridged Transformer (BrT),
an end-to-end architecture for 3D object detection. BrT is simple and
effective, which learns to identify 3D and 2D object bounding boxes from both
points and image patches. A key element of BrT lies in the utilization of
object queries for bridging 3D and 2D spaces, which unifies different sources
of data representations in Transformer. We adopt a form of feature aggregation
realized by point-to-patch projections which further strengthen the
correlations between images and points. Moreover, BrT works seamlessly for
fusing the point cloud with multi-view images. We experimentally show that BrT
surpasses state-of-the-art methods on SUN RGB-D and ScanNetV2 datasets.

Determining whether multiple instructions can access the same memory location
is a critical task in binary analysis. It is challenging as statically
computing precise alias information is undecidable in theory. The problem
aggravates at the binary level due to the presence of compiler optimizations
and the absence of symbols and types. Existing approaches either produce
significant spurious dependencies due to conservative analysis or scale poorly
to complex binaries.
  We present a new machine-learning-based approach to predict memory
dependencies by exploiting the model's learned knowledge about how binary
programs execute. Our approach features (i) a self-supervised procedure that
pretrains a neural net to reason over binary code and its dynamic value flows
through memory addresses, followed by (ii) supervised finetuning to infer the
memory dependencies statically. To facilitate efficient learning, we develop
dedicated neural architectures to encode the heterogeneous inputs (i.e., code,
data values, and memory addresses from traces) with specific modules and fuse
them with a composition learning strategy.
  We implement our approach in NeuDep and evaluate it on 41 popular software
projects compiled by 2 compilers, 4 optimizations, and 4 obfuscation passes. We
demonstrate that NeuDep is more precise (1.5x) and faster (3.5x) than the
current state-of-the-art. Extensive probing studies on security-critical
reverse engineering tasks suggest that NeuDep understands memory access
patterns, learns function signatures, and is able to match indirect calls. All
these tasks either assist or benefit from inferring memory dependencies.
Notably, NeuDep also outperforms the current state-of-the-art on these tasks.

In this work, we propose a novel and scalable solution to address the
challenges of developing efficient dense predictions on edge platforms. Our
first key insight is that MultiTask Learning (MTL) and hardware-aware Neural
Architecture Search (NAS) can work in synergy to greatly benefit on-device
Dense Predictions (DP). Empirical results reveal that the joint learning of the
two paradigms is surprisingly effective at improving DP accuracy, achieving
superior performance over both the transfer learning of single-task NAS and
prior state-of-the-art approaches in MTL, all with just 1/10th of the
computation. To the best of our knowledge, our framework, named EDNAS, is the
first to successfully leverage the synergistic relationship of NAS and MTL for
DP. Our second key insight is that the standard depth training for multi-task
DP can cause significant instability and noise to MTL evaluation. Instead, we
propose JAReD, an improved, easy-to-adopt Joint Absolute-Relative Depth loss,
that reduces up to 88% of the undesired noise while simultaneously boosting
accuracy. We conduct extensive evaluations on standard datasets, benchmark
against strong baselines and state-of-the-art approaches, as well as provide an
analysis of the discovered optimal architectures.

Bayesian optimization (BO) is a popular method for efficiently inferring
optima of an expensive black-box function via a sequence of queries. Existing
information-theoretic BO procedures aim to make queries that most reduce the
uncertainty about optima, where the uncertainty is captured by Shannon entropy.
However, an optimal measure of uncertainty would, ideally, factor in how we
intend to use the inferred quantity in some downstream procedure. In this
paper, we instead consider a generalization of Shannon entropy from work in
statistical decision theory (DeGroot 1962, Rao 1984), which contains a broad
class of uncertainty measures parameterized by a problem-specific loss function
corresponding to a downstream task. We first show that special cases of this
entropy lead to popular acquisition functions used in BO procedures such as
knowledge gradient, expected improvement, and entropy search. We then show how
alternative choices for the loss yield a flexible family of acquisition
functions that can be customized for use in novel optimization settings.
Additionally, we develop gradient-based methods to efficiently optimize our
proposed family of acquisition functions, and demonstrate strong empirical
performance on a diverse set of sequential decision making tasks, including
variants of top-$k$ optimization, multi-level set estimation, and sequence
search.

Optimizing strategic decisions (a.k.a. computing equilibrium) is key to the
success of many non-cooperative multi-agent applications. However, in many
real-world situations, we may face the exact opposite of this game-theoretic
problem -- instead of prescribing equilibrium of a given game, we may directly
observe the agents' equilibrium behaviors but want to infer the underlying
parameters of an unknown game. This research question, also known as inverse
game theory, has been studied in multiple recent works in the context of
Stackelberg games. Unfortunately, existing works exhibit quite negative
results, showing statistical hardness and computational hardness, assuming
follower's perfectly rational behaviors. Our work relaxes the perfect
rationality agent assumption to the classic quantal response model, a more
realistic behavior model of bounded rationality. Interestingly, we show that
the smooth property brought by such bounded rationality model actually leads to
provably more efficient learning of the follower utility parameters in general
Stackelberg games. Systematic empirical experiments on synthesized games
confirm our theoretical results and further suggest its robustness beyond the
strict quantal response model.

Visual imitation learning provides an effective framework to learn skills
from demonstrations. However, the quality of the provided demonstrations
usually significantly affects the ability of an agent to acquire desired
skills. Therefore, the standard visual imitation learning assumes near-optimal
demonstrations, which are expensive or sometimes prohibitive to collect.
Previous works propose to learn from noisy demonstrations; however, the noise
is usually assumed to follow a context-independent distribution such as a
uniform or gaussian distribution. In this paper, we consider another crucial
yet underexplored setting -- imitation learning with task-irrelevant yet
locally consistent segments in the demonstrations (e.g., wiping sweat while
cutting potatoes in a cooking tutorial). We argue that such noise is common in
real world data and term them "extraneous" segments. To tackle this problem, we
introduce Extraneousness-Aware Imitation Learning (EIL), a self-supervised
approach that learns visuomotor policies from third-person demonstrations with
extraneous subsequences. EIL learns action-conditioned observation embeddings
in a self-supervised manner and retrieves task-relevant observations across
visual demonstrations while excluding the extraneous ones. Experimental results
show that EIL outperforms strong baselines and achieves comparable policies to
those trained with perfect demonstration on both simulated and real-world robot
control tasks. The project page can be found at
https://sites.google.com/view/eil-website.

Studying the physical properties of materials under high pressure and
temperature through experiments is difficult. Theoretical simulations can
compensate for this deficiency. Currently, large-scale simulations using
machine learning force fields are gaining popularity. As an important nuclear
energy material, the evolution of the physical properties of uranium under
extreme conditions is still unclear. Herein, we trained an accurate machine
learning force field on alpha-U and predicted the lattice dynamics and elastic
properties at high pressures and temperatures. The force field agrees well with
the ab initio molecular dynamics (AIMD) and experimental results, and it
exhibits higher accuracy than classical potentials. Based on the
high-temperature lattice dynamics study, we first present the
temperature-pressure range in which the Kohn anomalous behavior of the
${\Sigma}$4 optical mode exists. Phonon spectral function analysis showed that
the phonon anharmonicity of alpha-U is very weak. We predict that the
single-crystal elastic constants C44, C55, C66, polycrystalline modulus (E,G),
and polycrystalline sound velocity ($C_L$,$C_S$) have strong heating-induced
softening. All the elastic moduli exhibited compression-induced hardening
behavior. The Poisson's ratio shows that it is difficult to compress alpha-U at
high pressures and temperatures. Moreover, we observed that the material
becomes substantially more anisotropic at high pressures and temperatures. The
accurate predictions of alpha-U demonstrate the reliability of the method. This
versatile method facilitates the study of other complex metallic materials.

We study high-probability regret bounds for adversarial $K$-armed bandits
with time-varying feedback graphs over $T$ rounds. For general strongly
observable graphs, we develop an algorithm that achieves the optimal regret
$\widetilde{\mathcal{O}}((\sum_{t=1}^T\alpha_t)^{1/2}+\max_{t\in[T]}\alpha_t)$
with high probability, where $\alpha_t$ is the independence number of the
feedback graph at round $t$. Compared to the best existing result [Neu, 2015]
which only considers graphs with self-loops for all nodes, our result not only
holds more generally, but importantly also removes any $\text{poly}(K)$
dependence that can be prohibitively large for applications such as contextual
bandits. Furthermore, we also develop the first algorithm that achieves the
optimal high-probability regret bound for weakly observable graphs, which even
improves the best expected regret bound of [Alon et al., 2015] by removing the
$\mathcal{O}(\sqrt{KT})$ term with a refined analysis. Our algorithms are based
on the online mirror descent framework, but importantly with an innovative
combination of several techniques. Notably, while earlier works use optimistic
biased loss estimators for achieving high-probability bounds, we find it
important to use a pessimistic one for nodes without self-loop in a strongly
observable graph.

Attack-resilience is essential to maintain continuous service availability in
Internet of Vehicles (IoV) where critical tasks are carried out. In this paper,
we address the problem of service outage due to attacks on the edge network and
propose an attack-resilient mapping of vehicles to edge nodes that host
different types of service instances considering resource efficiency and delay.
The distribution of service requests (of an attack-affected edge node) to
multiple attack-free edge nodes is performed with an optimal vehicle-to-edge
(V2E) mapping. The optimal mapping aims to improve the user experience with
minimal delay while considering fair usage of edge capacities and balanced load
upon a failure over different edge nodes. The proposed mapping solution is used
within a deep reinforcement learning (DRL) based framework to effectively deal
with the dynamism in service requests and vehicle mobility. We demonstrate the
effectiveness of the proposed mapping approach through extensive simulation
results using real-world vehicle mobility datasets from three cities.

The problem of safely learning and controlling a dynamical system - i.e., of
stabilizing an originally (partially) unknown system while ensuring that it
does not leave a prescribed 'safe set' - has recently received tremendous
attention in the controls community. Further complexities arise, however, when
the structure of the safe set itself depends on the unknown part of the
system's dynamics. In particular, a popular approach based on control Lyapunov
functions (CLF), control barrier functions (CBF) and Gaussian processes (to
build confidence set around the unknown term), which has proved successful in
the known-safe set setting, becomes inefficient as-is, due to the introduction
of higher-order terms to be estimated and bounded with high probability using
only system state measurements. In this paper, we build on the recent
literature on GPs and reproducing kernels to perform this latter task, and show
how to correspondingly modify the CLF-CBF-based approach to obtain safety
guarantees. Namely, we derive exponential CLF and second relative order
exponential CBF constraints whose satisfaction guarantees stability and forward
in-variance of the partially unknown safe set with high probability. To
overcome the intractability of verification of these conditions on the
continuous domain, we apply discretization of the state space and use Lipschitz
continuity properties of dynamics to derive equivalent CLF and CBF certificates
in discrete state space. Finally, we present an algorithm for the control
design aim using the derived certificates.

Existing hybrid retrievers which integrate sparse and dense retrievers, are
indexing-heavy, limiting their applicability in real-world on-devices settings.
We ask the question "Is it possible to reduce the indexing memory of hybrid
retrievers without sacrificing performance?" Driven by this question, we
leverage an indexing-efficient dense retriever (i.e. DrBoost) to obtain a light
hybrid retriever. Moreover, to further reduce the memory, we introduce a
lighter dense retriever (LITE) which is jointly trained on contrastive learning
and knowledge distillation from DrBoost. Compared to previous heavy hybrid
retrievers, our Hybrid-LITE retriever saves 13 memory while maintaining 98.0
  In addition, we study the generalization of light hybrid retrievers along two
dimensions, out-of-domain (OOD) generalization and robustness against
adversarial attacks. We evaluate models on two existing OOD benchmarks and
create six adversarial attack sets for robustness evaluation. Experiments show
that our light hybrid retrievers achieve better robustness performance than
both sparse and dense retrievers. Nevertheless there is a large room to improve
the robustness of retrievers, and our datasets can aid future research.

There are two de facto standard architectures in recent computer vision:
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Strong
inductive biases of convolutions help the model learn sample effectively, but
such strong biases also limit the upper bound of CNNs when sufficient data are
available. On the contrary, ViT is inferior to CNNs for small data but superior
for sufficient data. Recent approaches attempt to combine the strengths of
these two architectures. However, we show these approaches overlook that the
optimal inductive bias also changes according to the target data scale changes
by comparing various models' accuracy on subsets of sampled ImageNet at
different ratios. In addition, through Fourier analysis of feature maps, the
model's response patterns according to signal frequency changes, we observe
which inductive bias is advantageous for each data scale. The more
convolution-like inductive bias is included in the model, the smaller the data
scale is required where the ViT-like model outperforms the ResNet performance.
To obtain a model with flexible inductive bias on the data scale, we show
reparameterization can interpolate inductive bias between convolution and
self-attention. By adjusting the number of epochs the model stays in the
convolution, we show that reparameterization from convolution to self-attention
interpolates the Fourier analysis pattern between CNNs and ViTs. Adapting these
findings, we propose Progressive Reparameterization Scheduling (PRS), in which
reparameterization adjusts the required amount of convolution-like or
self-attention-like inductive bias per layer. For small-scale datasets, our PRS
performs reparameterization from convolution to self-attention linearly faster
at the late stage layer. PRS outperformed previous studies on the small-scale
dataset, e.g., CIFAR-100.

Artificial intelligence (AI)-enabled everyday technologies could help address
age-related challenges like physical impairments and cognitive decline. While
recent research studied older adults' experiences with specific AI-enabled
products (e.g., conversational agents and assistive robots), it remains unknown
how older adults perceive and experience current AI-enabled everyday
technologies in general, which could impact their adoption of future AI-enabled
products. We conducted a survey study (N=41) and semi-structured interviews
(N=15) with older adults to understand their experiences and perceptions of AI.
We found that older adults were enthusiastic about learning and using
AI-enabled products, but they lacked learning avenues. Additionally, they
worried when AI-enabled products outwitted their expectations, intruded on
their privacy, or impacted their decision-making skills. Therefore, they held
mixed views towards AI-enabled products such as AI, an aid, or an adversary. We
conclude with design recommendations that make older adults feel inclusive,
secure, and in control of their interactions with AI-enabled products.

Robust planning in interactive scenarios requires predicting the uncertain
future to make risk-aware decisions. Unfortunately, due to long-tail
safety-critical events, the risk is often under-estimated by finite-sampling
approximations of probabilistic motion forecasts. This can lead to
overconfident and unsafe robot behavior, even with robust planners. Instead of
assuming full prediction coverage that robust planners require, we propose to
make prediction itself risk-aware. We introduce a new prediction objective to
learn a risk-biased distribution over trajectories, so that risk evaluation
simplifies to an expected cost estimation under this biased distribution. This
reduces the sample complexity of the risk estimation during online planning,
which is needed for safe real-time performance. Evaluation results in a
didactic simulation environment and on a real-world dataset demonstrate the

Purpose: To answer the long-standing question of whether a model trained from
a single clinical site can be generalized to external sites.
  Materials and Methods: 17,537 chest x-ray radiographs (CXRs) from 3,264
COVID-19-positive patients and 4,802 COVID-19-negative patients were collected
from a single site for AI model development. The generalizability of the
trained model was retrospectively evaluated using four different real-world
clinical datasets with a total of 26,633 CXRs from 15,097 patients (3,277
COVID-19-positive patients). The area under the receiver operating
characteristic curve (AUC) was used to assess diagnostic performance.
  Results: The AI model trained using a single-source clinical dataset achieved
an AUC of 0.82 (95% CI: 0.80, 0.84) when applied to the internal temporal test
set. When applied to datasets from two external clinical sites, an AUC of 0.81
(95% CI: 0.80, 0.82) and 0.82 (95% CI: 0.80, 0.84) were achieved. An AUC of
0.79 (95% CI: 0.77, 0.81) was achieved when applied to a multi-institutional
COVID-19 dataset collected by the Medical Imaging and Data Resource Center
(MIDRC). A power-law dependence, N^(k )(k is empirically found to be -0.21 to
-0.25), indicates a relatively weak performance dependence on the training data
sizes.
  Conclusion: COVID-19 classification AI model trained using well-curated data
from a single clinical site is generalizable to external clinical sites without
a significant drop in performance.

Surrogate safety measures can provide fast and pro-active safety analysis and
give insights on the pre-crash process and crash failure mechanism by studying
near misses. However, validating surrogate safety measures by connecting them
to crashes is still an open question. This paper proposed a method to connect
surrogate safety measures to crash probability using probabilistic time series
prediction. The method used sequences of speed, acceleration and
time-to-collision to estimate the probability density functions of those
variables with transformer masked autoregressive flow (transformer-MAF). The
autoregressive structure mimicked the causal relationship between condition,
action and crash outcome and the probability density functions are used to
calculate the conditional action probability, crash probability and conditional
crash probability. The predicted sequence is accurate and the estimated
probability is reasonable under both traffic conflict context and normal
interaction context and the conditional crash probability shows the
effectiveness of evasive action to avoid crashes in a counterfactual
experiment.

Knowledge distillation has recently become popular as a method of model
aggregation on the server for federated learning. It is generally assumed that
there are abundant public unlabeled data on the server. However, in reality,
there exists a domain discrepancy between the datasets of the server domain and
a client domain, which limits the performance of knowledge distillation. How to
improve the aggregation under such a domain discrepancy setting is still an
open problem. In this paper, we first analyze the generalization bound of the
aggregation model produced from knowledge distillation for the client domains,
and then describe two challenges, server-to-client discrepancy and
client-to-client discrepancy, brought to the aggregation model by the domain
discrepancies. Following our analysis, we propose an adaptive knowledge
aggregation algorithm FedD3A based on domain discrepancy aware distillation to
lower the bound. FedD3A performs adaptive weighting at the sample level in each
round of FL. For each sample in the server domain, only the client models of
its similar domains will be selected for playing the teacher role. To achieve
this, we show that the discrepancy between the server-side sample and the
client domain can be approximately measured using a subspace projection matrix
calculated on each client without accessing its raw data. The server can thus
leverage the projection matrices from multiple clients to assign weights to the
corresponding teacher models for each server-side sample. We validate FedD3A on
two popular cross-domain datasets and show that it outperforms the compared
competitors in both cross-silo and cross-device FL settings.

Deep Neural Networks are known to be brittle to even minor distribution
shifts compared to the training distribution. While one line of work has
demonstrated that Simplicity Bias (SB) of DNNs - bias towards learning only the
simplest features - is a key reason for this brittleness, another recent line
of work has surprisingly found that diverse/ complex features are indeed
learned by the backbone, and their brittleness is due to the linear
classification head relying primarily on the simplest features. To bridge the
gap between these two lines of work, we first hypothesize and verify that while
SB may not altogether preclude learning complex features, it amplifies simpler
features over complex ones. Namely, simple features are replicated several
times in the learned representations while complex features might not be
replicated. This phenomenon, we term Feature Replication Hypothesis, coupled
with the Implicit Bias of SGD to converge to maximum margin solutions in the
feature space, leads the models to rely mostly on the simple features for
classification. To mitigate this bias, we propose Feature Reconstruction
Regularizer (FRR) to ensure that the learned features can be reconstructed back
from the logits. The use of {\em FRR} in linear layer training (FRR-L)
encourages the use of more diverse features for classification. We further
propose to finetune the full network by freezing the weights of the linear
layer trained using FRR-L, to refine the learned features, making them more
suitable for classification. Using this simple solution, we demonstrate up to
15% gains in OOD accuracy on the recently introduced semi-synthetic datasets
with extreme distribution shifts. Moreover, we demonstrate noteworthy gains
over existing SOTA methods on the standard OOD benchmark DomainBed as well.

Audio-visual embodied navigation, as a hot research topic, aims training a
robot to reach an audio target using egocentric visual (from the sensors
mounted on the robot) and audio (emitted from the target) input. The
audio-visual information fusion strategy is naturally important to the
navigation performance, but the state-of-the-art methods still simply
concatenate the visual and audio features, potentially ignoring the direct
impact of context. Moreover, the existing approaches requires either phase-wise
training or additional aid (e.g. topology graph and sound semantics). Up till
this date, the work that deals with the more challenging setup with moving
target(s) is still rare. As a result, we propose an end-to-end framework FSAAVN
(feature self-attention audio-visual navigation) to learn chasing after a
moving audio target using a context-aware audio-visual fusion strategy
implemented as a self-attention module. Our thorough experiments validate the
superior performance (both quantitatively and qualitatively) of FSAAVN in
comparison with the state-of-the-arts, and also provide unique insights about
the choice of visual modalities, visual/audio encoder backbones and fusion
patterns.

Layer-wise distillation is a powerful tool to compress large models (i.e.
teacher models) into small ones (i.e., student models). The student distills
knowledge from the teacher by mimicking the hidden representations of the
teacher at every intermediate layer. However, layer-wise distillation is
difficult. Since the student has a smaller model capacity than the teacher, it
is often under-fitted. Furthermore, the hidden representations of the teacher
contain redundant information that the student does not necessarily need for
the target task's learning. To address these challenges, we propose a novel
Task-aware layEr-wise Distillation (TED). TED designs task-aware filters to
align the hidden representations of the student and the teacher at each layer.
The filters select the knowledge that is useful for the target task from the
hidden representations. As such, TED reduces the knowledge gap between the two
models and helps the student to fit better on the target task. We evaluate TED
in two scenarios: continual pre-training and fine-tuning. TED demonstrates
significant and consistent improvements over existing distillation methods in
both scenarios.

Gate functions in recurrent models, such as an LSTM and GRU, play a central
role in learning various time scales in modeling time series data by using a
bounded activation function. However, it is difficult to train gates to capture
extremely long time scales due to gradient vanishing of the bounded function
for large inputs, which is known as the saturation problem. We closely analyze
the relation between saturation of the gate function and efficiency of the
training. We prove that the gradient vanishing of the gate function can be
mitigated by accelerating the convergence of the saturating function, i.e.,
making the output of the function converge to 0 or 1 faster. Based on the
analysis results, we propose a gate function called fast gate that has a doubly
exponential convergence rate with respect to inputs by simple function
composition. We empirically show that our method outperforms previous methods
in accuracy and computational efficiency on benchmark tasks involving extremely
long time scales.

Data-driven decision making is becoming an integral part of manufacturing
companies. Data is collected and commonly used to improve efficiency and
produce high quality items for the customers. IoT-based and other forms of
object tracking are an emerging tool for collecting movement data of
objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over
space and time. Movement data can provide valuable insights like process
bottlenecks, resource utilization, effective working time etc. that can be used
for decision making and improving efficiency.
  Turning movement data into valuable information for industrial management and
decision making requires analysis methods. We refer to this process as movement
analytics. The purpose of this document is to review the current state of work
for movement analytics both in manufacturing and more broadly.
  We survey relevant work from both a theoretical perspective and an
application perspective. From the theoretical perspective, we put an emphasis
on useful methods from two research areas: machine learning, and logic-based
knowledge representation. We also review their combinations in view of movement
analytics, and we discuss promising areas for future development and
application. Furthermore, we touch on constraint optimization.
  From an application perspective, we review applications of these methods to
movement analytics in a general sense and across various industries. We also
describe currently available commercial off-the-shelf products for tracking in
manufacturing, and we overview main concepts of digital twins and their

Traditional recurrent neural networks (RNNs) have a fixed, finite number of
memory cells. In theory (assuming bounded range and precision), this limits
their formal language recognition power to regular languages, and in practice,
RNNs have been shown to be unable to learn many context-free languages (CFLs).
In order to expand the class of languages RNNs recognize, prior work has
augmented RNNs with a nondeterministic stack data structure, putting them on
par with pushdown automata and increasing their language recognition power to
CFLs. Nondeterminism is needed for recognizing all CFLs (not just deterministic
CFLs), but in this paper, we show that nondeterminism and the neural controller
interact to produce two more unexpected abilities. First, the nondeterministic
stack RNN can recognize not only CFLs, but also many non-context-free
languages. Second, it can recognize languages with much larger alphabet sizes
than one might expect given the size of its stack alphabet. Finally, to
increase the information capacity in the stack and allow it to solve more
complicated tasks with large alphabet sizes, we propose a new version of the
nondeterministic stack that simulates stacks of vectors rather than discrete
symbols. We demonstrate perplexity improvements with this new model on the Penn
Treebank language modeling benchmark.

Estimating heterogeneous treatment effects is a well-studied topic in the
statistics literature. More recently, it has regained attention due to an
increasing need for precision medicine as well as the increased use of
state-of-art machine learning methods in the estimation. Furthermore,
estimating heterogeneous treatment effects is directly related to building an
individualized treatment rule, which is a decision rule of treatment according
to patient characteristics. This paper examines the connection and
disconnection between these two research problems. Notably, a better estimation
of the heterogeneous treatment effects may or may not lead to a better
individualized treatment rule. We provide theoretical frameworks to explain the
connection and disconnection and demonstrate two different scenarios through
simulations. Our conclusion sheds light on a practical guide that under certain
circumstances, there is no need to enhance estimation of the treatment effects,
as it does not alter the treatment decision.

Industrial insertion tasks are often performed repetitively with parts that
are subject to tight tolerances and prone to breakage. In this paper, we
present a safe method to learn a visuo-tactile insertion policy that is robust
against grasp pose variations while minimizing human inputs and collision
between the robot and the environment. We achieve this by dividing the
insertion task into two phases. In the first align phase, we learn a
tactile-based grasp pose estimation model to align the insertion part with the
receptacle. In the second insert phase, we learn a vision-based policy to guide
the part into the receptacle. Using force-torque sensing, we also develop a
safe self-supervised data collection pipeline that limits collision between the
part and the surrounding environment. Physical experiments on the USB insertion
task from the NIST Assembly Taskboard suggest that our approach can achieve
45/45 insertion successes on 45 different initial grasp poses, improving on two
baselines: (1) a behavior cloning agent trained on 50 human insertion
demonstrations (1/45) and (2) an online RL policy (TD3) trained in real (0/45).

Humans tend to decompose a sentence into different parts like \textsc{sth do
sth at someplace} and then fill each part with certain content. Inspired by
this, we follow the \textit{principle of modular design} to propose a novel
image captioner: learning to Collocate Visual-Linguistic Neural Modules
(CVLNM). Unlike the \re{widely used} neural module networks in VQA, where the
language (\ie, question) is fully observable, \re{the task of collocating
visual-linguistic modules is more challenging.} This is because the language is
only partially observable, for which we need to dynamically collocate the
modules during the process of image captioning. To sum up, we make the
following technical contributions to design and train our CVLNM: 1)
\textit{distinguishable module design} -- \re{four modules in the encoder}
including one linguistic module for function words and three visual modules for
different content words (\ie, noun, adjective, and verb) and another linguistic
one in the decoder for commonsense reasoning, 2) a self-attention based
\textit{module controller} for robustifying the visual reasoning, 3) a
part-of-speech based \textit{syntax loss} imposed on the module controller for
further regularizing the training of our CVLNM. Extensive experiments on the
MS-COCO dataset show that our CVLNM is more effective, \eg, achieving a new
state-of-the-art 129.5 CIDEr-D, and more robust, \eg, being less likely to
overfit to dataset bias and suffering less when fewer training samples are
available. Codes are available at \url{https://github.com/GCYZSL/CVLMN}

Large-scale self-supervised pre-training Transformer architecture have
significantly boosted the performance for various tasks in natural language
processing (NLP) and computer vision (CV). However, there is a lack of
researches on processing multivariate time-series by pre-trained Transformer,
and especially, current study on masking time-series for self-supervised
learning is still a gap. Different from language and image processing, the
information density of time-series increases the difficulty of research. The
challenge goes further with the invalidity of the previous patch embedding and
mask methods. In this paper, according to the data characteristics of
multivariate time-series, a patch embedding method is proposed, and we present
an self-supervised pre-training approach based on Masked Autoencoders (MAE),
called MTSMAE, which can improve the performance significantly over supervised
learning without pre-training. Evaluating our method on several common
multivariate time-series datasets from different fields and with different
characteristics, experiment results demonstrate that the performance of our
method is significantly better than the best method currently available.

Due to the privacy protection or the difficulty of data collection, we cannot
observe individual outputs for each instance, but we can observe aggregated
outputs that are summed over multiple instances in a set in some real-world
applications. To reduce the labeling cost for training regression models for
such aggregated data, we propose an active learning method that sequentially
selects sets to be labeled to improve the predictive performance with fewer
labeled sets. For the selection measurement, the proposed method uses the
mutual information, which quantifies the reduction of the uncertainty of the
model parameters by observing the aggregated output. With Bayesian linear basis
functions for modeling outputs given an input, which include approximated
Gaussian processes and neural networks, we can efficiently calculate the mutual
information in a closed form. With the experiments using various datasets, we
demonstrate that the proposed method achieves better predictive performance
with fewer labeled sets than existing methods.

Telemedicine and mobile health applications, especially during the quarantine
imposed by the covid-19 pandemic, led to an increase on the need of
transferring health monitor readings from patients to specialists. Considering
that most home medical devices use seven-segment displays, an automatic display
reading algorithm should provide a more reliable tool for remote health care.
This work proposes an end-to-end method for detection and reading seven-segment
displays from medical devices based on deep learning object detection models.
Two state of the art model families, EfficientDet and EfficientDet-lite,
previously trained with the MS-COCO dataset, were fine-tuned on a dataset
comprised by medical devices photos taken with mobile digital cameras, to
simulate real case applications. Evaluation of the trained model show high
efficiency, where all models achieved more than 98% of detection precision and
more than 98% classification accuracy, with model EfficientDet-lite1 showing
100% detection precision and 100% correct digit classification for a test set
of 104 images and 438 digits.

We introduce a representation learning framework for spatial trajectories. We
represent partial observations of trajectories as probability distributions in
a learned latent space, which characterize the uncertainty about unobserved
parts of the trajectory. Our framework allows us to obtain samples from a
trajectory for any continuous point in time, both interpolating and
extrapolating. Our flexible approach supports directly modifying specific
attributes of a trajectory, such as its pace, as well as combining different
partial observations into single representations. Experiments show our method's
advantage over baselines in prediction tasks.

Vertical Federated Learning (FL) is a new paradigm that enables users with
non-overlapping attributes of the same data samples to jointly train a model
without directly sharing the raw data. Nevertheless, recent works show that
it's still not sufficient to prevent privacy leakage from the training process
or the trained model. This paper focuses on studying the privacy-preserving
tree boosting algorithms under the vertical FL. The existing solutions based on
cryptography involve heavy computation and communication overhead and are
vulnerable to inference attacks. Although the solution based on Local
Differential Privacy (LDP) addresses the above problems, it leads to the low
accuracy of the trained model.
  This paper explores to improve the accuracy of the widely deployed tree
boosting algorithms satisfying differential privacy under vertical FL.
Specifically, we introduce a framework called OpBoost. Three order-preserving
desensitization algorithms satisfying a variant of LDP called distance-based
LDP (dLDP) are designed to desensitize the training data. In particular, we
optimize the dLDP definition and study efficient sampling distributions to
further improve the accuracy and efficiency of the proposed algorithms. The
proposed algorithms provide a trade-off between the privacy of pairs with large
distance and the utility of desensitized values. Comprehensive evaluations show
that OpBoost has a better performance on prediction accuracy of trained models
compared with existing LDP approaches on reasonable settings. Our code is open
source.

We propose a neural network-based approach to topology optimization that aims
to reduce the use of support structures in additive manufacturing. Our approach
uses a network architecture that allows the simultaneous determination of an
optimized: (1) part segmentation, (2) the topology of each part, and (3) the
build direction of each part that collectively minimize the amount of support
structure. Through training, the network learns a material density and segment
classification in the continuous 3D space. Given a problem domain with
prescribed load and displacement boundary conditions, the neural network takes
as input 3D coordinates of the voxelized domain as training samples and outputs
a continuous density field. Since the neural network for topology optimization
learns the density distribution field, analytical solutions to the density
gradient can be obtained from the input-output relationship of the neural
network. We demonstrate our approach on several compliance minimization
problems with volume fraction constraints, where support volume minimization is
added as an additional criterion to the objective function. We show that
simultaneous optimization of part segmentation along with the topology and
print angle optimization further reduces the support structure, compared to a
combined print angle and topology optimization without segmentation.

Deploying ultra-dense networks that operate on millimeter wave (mmWave) band
is a promising way to address the tremendous growth on mobile data traffic.
However, one key challenge of ultra-dense mmWave network (UDmmN) is beam
management due to the high propagation delay, limited beam coverage as well as
numerous beams and users. In this paper, a novel systematic beam control scheme
is presented to tackle the beam management problem which is difficult due to
the nonconvex objective function. We employ double deep Q-network (DDQN) under
a federated learning (FL) framework to address the above optimization problem,
and thereby fulfilling adaptive and intelligent beam management in UDmmN. In
the proposed beam management scheme based on FL (BMFL), the non-rawdata
aggregation can theoretically protect user privacy while reducing handoff cost.
Moreover, we propose to adopt a data cleaning technique in the local model
training for BMFL, with the aim to further strengthen the privacy protection of
users while improving the learning convergence speed. Simulation results
demonstrate the performance gain of our proposed scheme.

There exist features that are related to the label in the same way across
different settings for that task; these are semantic features or semantics.
Features with varying relationships to the label are nuisances. For example, in
detecting cows from natural images, the shape of the head is a semantic and
because images of cows often have grass backgrounds but only in certain
settings, the background is a nuisance. Relationships between a nuisance and
the label are unstable across settings and, consequently, models that exploit
nuisance-label relationships face performance degradation when these
relationships change. Direct knowledge of a nuisance helps build models that
are robust to such changes, but knowledge of a nuisance requires extra
annotations beyond the label and the covariates. In this paper, we develop an
alternative way to produce robust models by data augmentation. These data
augmentations corrupt semantic information to produce models that identify and
adjust for where nuisances drive predictions. We study semantic corruptions in
powering different robust-modeling methods for multiple out-of distribution
(OOD) tasks like classifying waterbirds, natural language inference, and
detecting Cardiomegaly in chest X-rays.

In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model.
This model generalizes graph diffusion in different feature spaces, and uses
the inception module to avoid the large amount of computations caused by
complex network structures. We evaluate GIDN model on Open Graph Benchmark(OGB)
datasets, reached an 11% higher performance than AGDN on ogbl-collab dataset.

Latent variable models are crucial in scientific research, where a key
variable, such as effort, ability, and belief, is unobserved in the sample but
needs to be identified. This paper proposes a novel method for estimating
realizations of a latent variable $X^*$ in a random sample that contains its
multiple measurements. With the key assumption that the measurements are
independent conditional on $X^*$, we provide sufficient conditions under which
realizations of $X^*$ in the sample are locally unique in a class of
deviations, which allows us to identify realizations of $X^*$. To the best of
our knowledge, this paper is the first to provide such identification in
observation. We then use the Kullback-Leibler distance between the two
probability densities with and without the conditional independence as the loss
function to train a Generative Element Extraction Networks (GEEN) that maps
from the observed measurements to realizations of $X^*$ in the sample. The
simulation results imply that this proposed estimator works quite well and the
estimated values are highly correlated with realizations of $X^*$. Our
estimator can be applied to a large class of latent variable models and we
expect it will change how people deal with latent variables.

Keypoint detection serves as the basis for many computer vision and robotics
applications. Despite the fact that colored point clouds can be readily
obtained, most existing keypoint detectors extract only geometry-salient
keypoints, which can impede the overall performance of systems that intend to
(or have the potential to) leverage color information. To promote advances in
such systems, we propose an efficient multi-modal keypoint detector that can
extract both geometry-salient and color-salient keypoints in colored point
clouds. The proposed CEntroid Distance (CED) keypoint detector comprises an
intuitive and effective saliency measure, the centroid distance, that can be
used in both 3D space and color space, and a multi-modal non-maximum
suppression algorithm that can select keypoints with high saliency in two or
more modalities. The proposed saliency measure leverages directly the
distribution of points in a local neighborhood and does not require normal
estimation or eigenvalue decomposition. We evaluate the proposed method in
terms of repeatability and computational efficiency (i.e. running time) against
state-of-the-art keypoint detectors on both synthetic and real-world datasets.
Results demonstrate that our proposed CED keypoint detector requires minimal
computational time while attaining high repeatability. To showcase one of the
potential applications of the proposed method, we further investigate the task
of colored point cloud registration. Results suggest that our proposed CED
detector outperforms state-of-the-art handcrafted and learning-based keypoint
detectors in the evaluated scenes. The C++ implementation of the proposed
method is made publicly available at
https://github.com/UCR-Robotics/CED_Detector.

In this paper, we consider a bandit problem in which there are a number of
groups each consisting of infinitely many arms. Whenever a new arm is requested
from a given group, its mean reward is drawn from an unknown reservoir
distribution (different for each group), and the uncertainty in the arm's mean
reward can only be reduced via subsequent pulls of the arm. The goal is to
identify the infinite-arm group whose reservoir distribution has the highest
$(1-\alpha)$-quantile (e.g., median if $\alpha = \frac{1}{2}$), using as few
total arm pulls as possible. We introduce a two-step algorithm that first
requests a fixed number of arms from each group and then runs a finite-arm
grouped max-quantile bandit algorithm. We characterize both the
instance-dependent and worst-case regret, and provide a matching lower bound
for the latter, while discussing various strengths, weaknesses, algorithmic
improvements, and potential lower bounds associated with our instance-dependent
upper bounds.

While cross entropy (CE) is the most commonly used loss to train deep neural
networks for classification tasks, many alternative losses have been developed
to obtain better empirical performance. Among them, which one is the best to
use is still a mystery, because there seem to be multiple factors affecting the
answer, such as properties of the dataset, the choice of network architecture,
and so on. This paper studies the choice of loss function by examining the
last-layer features of deep networks, drawing inspiration from a recent line
work showing that the global optimal solution of CE and mean-square-error (MSE)
losses exhibits a Neural Collapse phenomenon. That is, for sufficiently large
networks trained until convergence, (i) all features of the same class collapse
to the corresponding class mean and (ii) the means associated with different
classes are in a configuration where their pairwise distances are all equal and
maximized. We extend such results and show through global solution and
landscape analyses that a broad family of loss functions including commonly
used label smoothing (LS) and focal loss (FL) exhibits Neural Collapse. Hence,
all relevant losses(i.e., CE, LS, FL, MSE) produce equivalent features on
training data. Based on the unconstrained feature model assumption, we provide
either the global landscape analysis for LS loss or the local landscape
analysis for FL loss and show that the (only!) global minimizers are neural
collapse solutions, while all other critical points are strict saddles whose
Hessian exhibit negative curvature directions either in the global scope for LS
loss or in the local scope for FL loss near the optimal solution. The
experiments further show that Neural Collapse features obtained from all
relevant losses lead to largely identical performance on test data as well,
provided that the network is sufficiently large and trained until convergence.

Large language models (LLMs) have a substantial capacity for high-level
analogical reasoning: reproducing patterns in linear text that occur in their
training data (zero-shot evaluation) or in the provided context (few-shot
in-context learning). However, recent studies show that even the largest LLMs
fail in scenarios that require reasoning over multiple objects or facts or
making sequences of logical deductions. We propose a two-stage probabilistic
inference paradigm, ThinkSum, that reasons over sets of objects or facts in a
structured manner. In the first stage (Think -- 'fast' retrieval of
associations), a LLM is queried in parallel over a set of phrases extracted
from the prompt or an auxiliary model call. In the second stage (Sum -- 'slow'
probabilistic inference or reasoning), the results of these queries are
aggregated to make the final prediction. We demonstrate the advantages of
ThinkSum on the BIG-bench suite of evaluation tasks, achieving improvements
over the state of the art using GPT-family models on ten difficult tasks, often
with far smaller model variants. We compare and contrast ThinkSum with other
proposed modifications to direct prompting of LLMs, such as variants of
chain-of-thought prompting. We argue that because the probabilistic inference
in ThinkSum is performed outside of calls to the LLM, ThinkSum is less
sensitive to prompt design, yields more interpretable predictions, and can be
flexibly combined with latent variable models to extract structured knowledge
from LLMs.

This paper proposes an integration of surrogate modeling and topology to
significantly reduce the amount of data required to describe the underlying
global dynamics of robot controllers, including closed-box ones. A Gaussian
Process (GP), trained with randomized short trajectories over the state-space,
acts as a surrogate model for the underlying dynamical system. Then, a
combinatorial representation is built and used to describe the dynamics in the
form of a directed acyclic graph, known as {\it Morse graph}. The Morse graph
is able to describe the system's attractors and their corresponding regions of
attraction (\roa). Furthermore, a pointwise confidence level of the global
dynamics estimation over the entire state space is provided. In contrast to
alternatives, the framework does not require estimation of Lyapunov functions,
alleviating the need for high prediction accuracy of the GP. The framework is
suitable for data-driven controllers that do not expose an analytical model as
long as Lipschitz-continuity is satisfied. The method is compared against
established analytical and recent machine learning alternatives for estimating
\roa s, outperforming them in data efficiency without sacrificing accuracy.
Link to code: https://go.rutgers.edu/49hy35en

Adversarial training (AT) is proved to reliably improve network's robustness
against adversarial data. However, current AT with a pre-specified perturbation
budget has limitations in learning a robust network. Firstly, applying a
pre-specified perturbation budget on networks of various model capacities will
yield divergent degree of robustness disparity between natural and robust
accuracies, which deviates from robust network's desideratum. Secondly, the
attack strength of adversarial training data constrained by the pre-specified
perturbation budget fails to upgrade as the growth of network robustness, which
leads to robust overfitting and further degrades the adversarial robustness. To
overcome these limitations, we propose \emph{Strength-Adaptive Adversarial
Training} (SAAT). Specifically, the adversary employs an adversarial loss
constraint to generate adversarial training data. Under this constraint, the
perturbation budget will be adaptively adjusted according to the training state
of adversarial data, which can effectively avoid robust overfitting. Besides,
SAAT explicitly constrains the attack strength of training data through the
adversarial loss, which manipulates model capacity scheduling during training,
and thereby can flexibly control the degree of robustness disparity and adjust
the tradeoff between natural accuracy and robustness. Extensive experiments
show that our proposal boosts the robustness of adversarial training.

We consider the task of estimating a structural model of dynamic decisions by
a human agent based upon the observable history of implemented actions and
visited states. This problem has an inherent nested structure: in the inner
problem, an optimal policy for a given reward function is identified while in
the outer problem, a measure of fit is maximized. Several approaches have been
proposed to alleviate the computational burden of this nested-loop structure,
but these methods still suffer from high complexity when the state space is
either discrete with large cardinality or continuous in high dimensions. Other
approaches in the inverse reinforcement learning (IRL) literature emphasize
policy estimation at the expense of reduced reward estimation accuracy. In this
paper we propose a single-loop estimation algorithm with finite time guarantees
that is equipped to deal with high-dimensional state spaces without
compromising reward estimation accuracy. In the proposed algorithm, each policy
improvement step is followed by a stochastic gradient step for likelihood
maximization. We show that the proposed algorithm converges to a stationary
solution with a finite-time guarantee. Further, if the reward is parameterized
linearly, we show that the algorithm approximates the maximum likelihood
estimator sublinearly. Finally, by using robotics control problems in MuJoCo
and their transfer settings, we show that the proposed algorithm achieves
superior performance compared with other IRL and imitation learning benchmarks.

Continuous neural representations have recently emerged as a powerful and
flexible alternative to classical discretized representations of signals.
However, training them to capture fine details in multi-scale signals is
difficult and computationally expensive. Here we propose random weight
factorization as a simple drop-in replacement for parameterizing and
initializing conventional linear layers in coordinate-based multi-layer
perceptrons (MLPs) that significantly accelerates and improves their training.
We show how this factorization alters the underlying loss landscape and
effectively enables each neuron in the network to learn using its own
self-adaptive learning rate. This not only helps with mitigating spectral bias,
but also allows networks to quickly recover from poor initializations and reach
better local minima. We demonstrate how random weight factorization can be
leveraged to improve the training of neural representations on a variety of
tasks, including image regression, shape representation, computed tomography,
inverse rendering, solving partial differential equations, and learning
operators between function spaces.

In recent years, self-supervised learning paradigm has received extensive
attention due to its great success in various down-stream tasks. However, the
fine-tuning strategies for adapting those pre-trained models to speaker
verification task have yet to be fully explored. In this paper, we analyze
several feature extraction approaches built on top of a pre-trained model, as
well as regularization and learning rate schedule to stabilize the fine-tuning
process and further boost performance: multi-head factorized attentive pooling
is proposed to factorize the comparison of speaker representations into
multiple phonetic clusters. We regularize towards the parameters of the
pre-trained model and we set different learning rates for each layer of the
pre-trained model during fine-tuning. The experimental results show our method
can significantly shorten the training time to 4 hours and achieve SOTA
performance: 0.59%, 0.79% and 1.77% EER on Vox1-O, Vox1-E and Vox1-H,

Agricultural research is essential for increasing food production to meet the
requirements of an increasing population in the coming decades. Recently,
satellite technology has been improving rapidly and deep learning has seen much
success in generic computer vision tasks and many application areas which
presents an important opportunity to improve analysis of agricultural land.
Here we present a systematic review of 150 studies to find the current uses of
deep learning on satellite imagery for agricultural research. Although we
identify 5 categories of agricultural monitoring tasks, the majority of the
research interest is in crop segmentation and yield prediction. We found that,
when used, modern deep learning methods consistently outperformed traditional
machine learning across most tasks; the only exception was that Long Short-Term
Memory (LSTM) Recurrent Neural Networks did not consistently outperform Random
Forests (RF) for yield prediction. The reviewed studies have largely adopted
methodologies from generic computer vision, except for one major omission:
benchmark datasets are not utilised to evaluate models across studies, making
it difficult to compare results. Additionally, some studies have specifically
utilised the extra spectral resolution available in satellite imagery, but
other divergent properties of satellite images - such as the hugely different
scales of spatial patterns - are not being taken advantage of in the reviewed
studies.

In many applications with real-world consequences, it is crucial to develop
reliable uncertainty estimation for the predictions made by the AI decision
systems. Targeting at the goal of estimating uncertainty, various deep neural
network (DNN) based uncertainty estimation algorithms have been proposed.
However, the robustness of the uncertainty returned by these algorithms has not
been systematically explored. In this work, to raise the awareness of the
research community on robust uncertainty estimation, we show that
state-of-the-art uncertainty estimation algorithms could fail catastrophically
under our proposed adversarial attack despite their impressive performance on
uncertainty estimation. In particular, we aim at attacking the out-domain
uncertainty estimation: under our attack, the uncertainty model would be fooled
to make high-confident predictions for the out-domain data, which they
originally would have rejected. Extensive experimental results on various
benchmark image datasets show that the uncertainty estimated by
state-of-the-art methods could be easily corrupted by our attack.

Understanding a complex system of relationships between courses is of great
importance for the university's educational mission. This paper is dedicated to
the study of course-prerequisite networks that model interactions between
courses, represent the flow of knowledge in academic curricula, and serve as a
key tool for visualizing, analyzing, and optimizing complex curricula. We show
how course-prerequisite networks can be used by students, faculty, and
administrators for detecting important courses, improving existing and creating
new courses, navigating complex curricula, allocating teaching resources,
increasing interdisciplinary interactions between departments, revamping
curricula, and enhancing the overall students' learning experience. The
proposed methodology is illustrated with a network of courses taught at the
California Institute of Technology.

We study learning on social media with an equilibrium model of users
interacting with shared news stories. Rational users arrive sequentially and
each observes an original story (i.e., a private signal) and a sample of
predecessors' stories in a news feed, then decides which stories to share. The
observed sample of stories depends on what predecessors share as well as the
sampling algorithm, which represents a design choice of the platform. We focus
on how much the algorithm relies on virality (how many times a story has been
previously shared) when generating news feeds. Showing users more viral stories
can increase information aggregation, but it can also generate steady states
where most shared stories are wrong. Such misleading steady states
self-perpetuate, as users who observe these wrong stories develop wrong
beliefs, and thus rationally continue to share them. We find that these bad
steady states appear discontinuously, and even a benevolent platform designer
either accepts these misleading steady states or induces fragile learning
outcomes in the optimal design.

Imagine experiencing a crash as the passenger of an autonomous vehicle.
Wouldn't you want to know why it happened? Current end-to-end optimizable deep
neural networks (DNNs) in 3D detection, multi-object tracking, and motion
forecasting provide little to no explanations about how they make their
decisions. To help bridge this gap, we design an end-to-end optimizable
multi-object tracking architecture and training protocol inspired by the
recently proposed method of interchange intervention training (IIT). By
enumerating different tracking decisions and associated reasoning procedures,
we can train individual networks to reason about the possible decisions via
IIT. Each network's decisions can be explained by the high-level structural
causal model (SCM) it is trained in alignment with. Moreover, our proposed
model learns to rank these outcomes, leveraging the promise of deep learning in
end-to-end training, while being inherently interpretable.

Acquiring commonsense knowledge and reasoning is an important goal in modern
NLP research. Despite much progress, there is still a lack of understanding
(especially at scale) of the nature of commonsense knowledge itself. A
potential source of structured commonsense knowledge that could be used to
derive insights is ConceptNet. In particular, ConceptNet contains several
coarse-grained relations, including HasContext, FormOf and SymbolOf, which can
prove invaluable in understanding broad, but critically important, commonsense
notions such as 'context'. In this article, we present a methodology based on
unsupervised knowledge graph representation learning and clustering to reveal
and study substructures in three heavily used commonsense relations in
ConceptNet. Our results show that, despite having an 'official' definition in
ConceptNet, many of these commonsense relations exhibit considerable
sub-structure. In the future, therefore, such relations could be sub-divided
into other relations with more refined definitions. We also supplement our core
study with visualizations and qualitative analyses.

Security incidents and data breaches are increasing rapidly, and only a
fraction of them is being reported. Public vulnerability databases, e.g.,
national vulnerability database (NVD) and common vulnerability and exposure
(CVE), have been leading the effort in documenting vulnerabilities and sharing
them to aid defenses. Both are known for many issues, including brief
vulnerability descriptions. Those descriptions play an important role in
communicating the vulnerability information to security analysts in order to
develop the appropriate countermeasure. Many resources provide additional
information about vulnerabilities, however, they are not utilized to boost
public repositories. In this paper, we devise a pipeline to augment
vulnerability description through third party reference (hyperlink) scrapping.
To normalize the description, we build a natural language summarization
pipeline utilizing a pretrained language model that is fine-tuned using labeled
instances and evaluate its performance against both human evaluation (golden
standard) and computational metrics, showing initial promising results in terms
of summary fluency, completeness, correctness, and understanding.

It is widely acknowledged that trained convolutional neural networks (CNNs)
have different levels of sensitivity to signals of different frequency. In
particular, a number of empirical studies have documented CNNs sensitivity to
low-frequency signals. In this work we show with theory and experiments that
this observed sensitivity is a consequence of the frequency distribution of
natural images, which is known to have most of its power concentrated in
low-to-mid frequencies. Our theoretical analysis relies on representations of
the layers of a CNN in frequency space, an idea that has previously been used
to accelerate computations and study implicit bias of network training
algorithms, but to the best of our knowledge has not been applied in the domain
of model robustness.

Version identification (VI) has seen substantial progress over the past few
years. On the one hand, the introduction of the metric learning paradigm has
favored the emergence of scalable yet accurate VI systems. On the other hand,
using features focusing on specific aspects of musical pieces, such as melody,
harmony, or lyrics, yielded interpretable and promising performances. In this
work, we build upon these recent advances and propose a metric learning-based
system systematically leveraging four dimensions commonly admitted to convey
musical similarity between versions: melodic line, harmonic structure, rhythmic
patterns, and lyrics. We describe our deliberately simple model architecture,
and we show in particular that an approximated representation of the lyrics is
an efficient proxy to discriminate between versions and non-versions. We then
describe how these features complement each other and yield new
state-of-the-art performances on two publicly available datasets. We finally
suggest that a VI system using a combination of melodic, harmonic, rhythmic and
lyrics features could theoretically reach the optimal performances obtainable
on these datasets.

With the increasing attention to large vision-language models such as CLIP,
there has been a significant amount of effort dedicated to building efficient
prompts. Unlike conventional methods of only learning one single prompt, we
propose to learn multiple comprehensive prompts to describe diverse
characteristics of categories such as intrinsic attributes or extrinsic
contexts. However, directly matching each prompt to the same visual feature is
problematic, as it pushes the prompts to converge to one point. To solve this
problem, we propose to apply optimal transport to match the vision and text
modalities. Specifically, we first model images and the categories with visual
and textual feature sets. Then, we apply a two-stage optimization strategy to
learn the prompts. In the inner loop, we optimize the optimal transport
distance to align visual features and prompts by the Sinkhorn algorithm, while
in the outer loop, we learn the prompts by this distance from the supervised
data. Extensive experiments are conducted on the few-shot recognition task and
the improvement demonstrates the superiority of our method.

Environment prediction frameworks are essential for autonomous vehicles to
facilitate safe maneuvers in a dynamic environment. Previous approaches have
used occupancy grid maps as a bird's eye-view representation of the scene and
optimized the prediction architectures directly in pixel space. Although these
methods have had some success in spatiotemporal prediction, they are, at times,
hindered by unrealistic and incorrect predictions. We postulate that the
quality and realism of the forecasted occupancy grids can be improved with the
use of generative models. We propose a framework that decomposes occupancy grid
prediction into task-independent low-dimensional representation learning and
task-dependent prediction in the latent space. We demonstrate that our approach
achieves state-of-the-art performance on the real-world autonomous driving
dataset, NuScenes.

Reinforcement Learning (RL) has seen many recent successes for quadruped
robot control. The imitation of reference motions provides a simple and
powerful prior for guiding solutions towards desired solutions without the need
for meticulous reward design. While much work uses motion capture data or
hand-crafted trajectories as the reference motion, relatively little work has
explored the use of reference motions coming from model-based trajectory
optimization. In this work, we investigate several design considerations that
arise with such a framework, as demonstrated through four dynamic behaviours:
trot, front hop, 180 backflip, and biped stepping. These are trained in
simulation and transferred to a physical Solo 8 quadruped robot without further
adaptation. In particular, we explore the space of feed-forward designs
afforded by the trajectory optimizer to understand its impact on RL learning
efficiency and sim-to-real transfer. These findings contribute to the long
standing goal of producing robot controllers that combine the interpretability
and precision of model-based optimization with the robustness that model-free
RL-based controllers offer.

Since the recognition in the early nineties of the vanishing/exploding (V/E)
gradient issue plaguing the training of neural networks (NNs), significant
efforts have been exerted to overcome this obstacle. However, a clear solution
to the V/E issue remained elusive so far. In this manuscript a new architecture
of NN is proposed, designed to mathematically prevent the V/E issue to occur.
The pursuit of approximate dynamical isometry, i.e. parameter configurations
where the singular values of the input-output Jacobian are tightly distributed
around 1, leads to the derivation of a NN's architecture that shares common
traits with the popular Residual Network model. Instead of skipping connections
between layers, the idea is to filter the previous activations orthogonally and
add them to the nonlinear activations of the next layer, realising a convex
combination between them. Remarkably, the impossibility for the gradient
updates to either vanish or explode is demonstrated with analytical bounds that
hold even in the infinite depth case. The effectiveness of this method is
empirically proved by means of training via backpropagation an extremely deep
multilayer perceptron of 50k layers, and an Elman NN to learn long-term
dependencies in the input of 10k time steps in the past. Compared with other
architectures specifically devised to deal with the V/E problem, e.g. LSTMs for
recurrent NNs, the proposed model is way simpler yet more effective.
Surprisingly, a single layer vanilla RNN can be enhanced to reach state of the
art performance, while converging super fast; for instance on the psMNIST task,
it is possible to get test accuracy of over 94% in the first epoch, and over
98% after just 10 epochs.

Prior works on event-based optical flow estimation have investigated several
gradient-based learning methods to train neural networks for predicting optical
flow. However, they do not utilize the fast data rate of event data streams and
rely on a spatio-temporal representation constructed from a collection of
events over a fixed period of time (often between two grayscale frames). As a
result, optical flow is only evaluated at a frequency much lower than the rate
data is produced by an event-based camera, leading to a temporally sparse
optical flow estimation. To predict temporally dense optical flow, we cast the
problem as a sequential learning task and propose a training methodology to
train sequential networks for continuous prediction on an event stream. We
propose two types of networks: one focused on performance and another focused
on compute efficiency. We first train long-short term memory networks (LSTMs)
on the DSEC dataset and demonstrated 10x temporally dense optical flow
estimation over existing flow estimation approaches. The additional benefit of
having a memory to draw long temporal correlations back in time results in a
19.7% improvement in flow prediction accuracy of LSTMs over similar networks
with no memory elements. We subsequently show that the inherent recurrence of
spiking neural networks (SNNs) enables them to learn and estimate temporally
dense optical flow with 31.8% lesser parameters than LSTM, but with a slightly
increased error. This demonstrates potential for energy-efficient
implementation of fast optical flow prediction using SNNs.

In this paper, we explore the ability of a robot arm to learn the underlying
operation space defined by the positions (x, y, z) that the arm's end-effector
can reach, including disturbances, by deploying and thoroughly evaluating a
Spiking Neural Network SNN-based adaptive control algorithm. While traditional
control algorithms for robotics have limitations in both adapting to new and
dynamic environments, we show that the robot arm can learn the operational
space and complete tasks faster over time. We also demonstrate that the
adaptive robot control algorithm based on SNNs enables a fast response while
maintaining energy efficiency. We obtained these results by performing an
extensive search of the adaptive algorithm parameter space, and evaluating
algorithm performance for different SNN network sizes, learning rates, dynamic
robot arm trajectories, and response times. We show that the robot arm learns
to complete tasks 15% faster in specific experiment scenarios such as scenarios
with six or nine random target points.

We tackle the problem of aligning pre-trained large language models (LMs)
with human preferences. If we view text generation as a sequential
decision-making problem, reinforcement learning (RL) appears to be a natural
conceptual framework. However, using RL for LM-based generation faces empirical
challenges, including training instability due to the combinatorial action
space, as well as a lack of open-source libraries and benchmarks customized for
LM alignment. Thus, a question rises in the research community: is RL a
practical paradigm for NLP?
  To help answer this, we first introduce an open-source modular library,
RL4LMs (Reinforcement Learning for Language Models), for optimizing language
generators with RL. The library consists of on-policy RL algorithms that can be
used to train any encoder or encoder-decoder LM in the HuggingFace library
(Wolf et al. 2020) with an arbitrary reward function. Next, we present the GRUE
(General Reinforced-language Understanding Evaluation) benchmark, a set of 6
language generation tasks which are supervised not by target strings, but by
reward functions which capture automated measures of human preference.GRUE is
the first leaderboard-style evaluation of RL algorithms for NLP tasks. Finally,
we introduce an easy-to-use, performant RL algorithm, NLPO (Natural Language
Policy Optimization)} that learns to effectively reduce the combinatorial
action space in language generation. We show 1) that RL techniques are
generally better than supervised methods at aligning LMs to human preferences;
and 2) that NLPO exhibits greater stability and performance than previous
policy gradient methods (e.g., PPO (Schulman et al. 2017)), based on both
automatic and human evaluation.

We study the paradigmatic spiked matrix model of principal components
analysis, where the rank-one signal is corrupted by additive noise. While the
noise is typically taken from a Wigner matrix with independent entries, here
the potential acting on the eigenvalues has a quadratic plus a quartic
component. The quartic term induces strong correlations between the matrix
elements, which makes the setting relevant for applications but analytically
challenging. Our work provides the first characterization of the Bayes-optimal
limits for inference in this model with structured noise. If the signal prior
is rotational-invariant, then we show that a spectral estimator is optimal. In
contrast, for more general priors, the existing approximate message passing
algorithm (AMP) falls short of achieving the information-theoretic limits, and
we provide a justification for this sub-optimality. Finally, by generalizing
the theory of Thouless-Anderson-Palmer equations, we cure the issue by
proposing a novel AMP which matches the theoretical limits. Our
information-theoretic analysis is based on the replica method, a powerful
heuristic from statistical mechanics; instead, the novel AMP comes with a
rigorous state evolution analysis tracking its performance in the
high-dimensional limit. Even if we focus on a specific noise distribution, our
methodology can be generalized to a wide class of trace ensembles, at the cost
of more involved expressions.

This paper addresses the dire need for a platform that efficiently provides a
framework for running reinforcement learning (RL) experiments. We propose the
CaiRL Environment Toolkit as an efficient, compatible, and more sustainable
alternative for training learning agents and propose methods to develop more
efficient environment simulations.
  There is an increasing focus on developing sustainable artificial
intelligence. However, little effort has been made to improve the efficiency of
running environment simulations. The most popular development toolkit for
reinforcement learning, OpenAI Gym, is built using Python, a powerful but slow
programming language. We propose a toolkit written in C++ with the same
flexibility level but works orders of magnitude faster to make up for Python's
inefficiency. This would drastically cut climate emissions.
  CaiRL also presents the first reinforcement learning toolkit with a built-in
JVM and Flash support for running legacy flash games for reinforcement learning
research. We demonstrate the effectiveness of CaiRL in the classic control
benchmark, comparing the execution speed to OpenAI Gym. Furthermore, we
illustrate that CaiRL can act as a drop-in replacement for OpenAI Gym to
leverage significantly faster training speeds because of the reduced
environment computation time.

Modern deep learning systems require huge data sets to achieve impressive
performance, but there is little guidance on how much or what kind of data to
collect. Over-collecting data incurs unnecessary present costs, while
under-collecting may incur future costs and delay workflows. We propose a new
paradigm for modeling the data collection workflow as a formal optimal data
collection problem that allows designers to specify performance targets,
collection costs, a time horizon, and penalties for failing to meet the
targets. Additionally, this formulation generalizes to tasks requiring multiple
data sources, such as labeled and unlabeled data used in semi-supervised
learning. To solve our problem, we develop Learn-Optimize-Collect (LOC), which
minimizes expected future collection costs. Finally, we numerically compare our
framework to the conventional baseline of estimating data requirements by
extrapolating from neural scaling laws. We significantly reduce the risks of
failing to meet desired performance targets on several classification,
segmentation, and detection tasks, while maintaining low total collection
costs.

Reinforcement Learning (RL) is a general framework concerned with an agent
that seeks to maximize rewards in an environment. The learning typically
happens through trial and error using explorative methods, such as
epsilon-greedy. There are two approaches, model-based and model-free
reinforcement learning, that show concrete results in several disciplines.
Model-based RL learns a model of the environment for learning the policy while
model-free approaches are fully explorative and exploitative without
considering the underlying environment dynamics. Model-free RL works
conceptually well in simulated environments, and empirical evidence suggests
that trial and error lead to a near-optimal behavior with enough training. On
the other hand, model-based RL aims to be sample efficient, and studies show
that it requires far less training in the real environment for learning a good
policy.
  A significant challenge with RL is that it relies on a well-defined reward
function to work well for complex environments and such a reward function is
challenging to define. Goal-Directed RL is an alternative method that learns an
intrinsic reward function with emphasis on a few explored trajectories that
reveals the path to the goal state.
  This paper introduces a novel reinforcement learning algorithm for predicting
the distance between two states in a Markov Decision Process. The learned
distance function works as an intrinsic reward that fuels the agent's learning.
Using the distance-metric as a reward, we show that the algorithm performs
comparably to model-free RL while having significantly better
sample-efficiently in several test environments.

Deep Reinforcement Learning (RL) is unquestionably a robust framework to
train autonomous agents in a wide variety of disciplines. However, traditional
deep and shallow model-free RL algorithms suffer from low sample efficiency and
inadequate generalization for sparse state spaces. The options framework with
temporal abstractions is perhaps the most promising method to solve these
problems, but it still has noticeable shortcomings. It only guarantees local
convergence, and it is challenging to automate initiation and termination
conditions, which in practice are commonly hand-crafted.
  Our proposal, the Deep Variational Q-Network (DVQN), combines deep
generative- and reinforcement learning. The algorithm finds good policies from
a Gaussian distributed latent-space, which is especially useful for defining
options. The DVQN algorithm uses MSE with KL-divergence as regularization,
combined with traditional Q-Learning updates. The algorithm learns a
latent-space that represents good policies with state clusters for options. We
show that the DVQN algorithm is a promising approach for identifying initiation
and termination conditions for option-based reinforcement learning. Experiments
show that the DVQN algorithm, with automatic initiation and termination, has
comparable performance to Rainbow and can maintain stability when trained for
extended periods after convergence.

This paper introduces a novel evaluation methodology for entity resolution
algorithms. It is motivated by PatentsView.org, a U.S. Patents and Trademarks
Office patent data exploration tool that disambiguates patent inventors using
an entity resolution algorithm. We provide a data collection methodology and
tailored performance estimators that account for sampling biases. Our approach
is simple, practical and principled -- key characteristics that allow us to
paint the first representative picture of PatentsView's disambiguation
performance. This approach is used to inform PatentsView's users of the
reliability of the data and to allow the comparison of competing disambiguation

Epidemics and outbreaks present arduous challenges requiring both individual
and communal efforts. Social media offer significant amounts of data that can
be leveraged for bio-surveillance. They also provide a platform to quickly and
efficiently reach a sizeable percentage of the population, hence their
potential impact on various aspects of epidemic mitigation. The general
objective of this systematic literature review is to provide a methodical
overview of the integration of social media in different epidemic-related
contexts. Three research questions were conceptualized for this review,
resulting in over 10000 publications collected in the first PRISMA stage, 129
of which were selected for inclusion. A thematic method-oriented synthesis was
undertaken and identified 5 main themes related to social media enabled
epidemic surveillance, misinformation management, and mental health. Findings
uncover a need for more robust applications of the lessons learned from
epidemic post-mortem documentation. A vast gap exists between retrospective
analysis of epidemic management and result integration in prospective studies.
Harnessing the full potential of social media in epidemic related tasks
requires streamlining the results of epidemic forecasting, public opinion
understanding and misinformation propagation, all while keeping abreast of
potential mental health implications. Pro-active prevention has thus become
vital for epidemic curtailment and containment.

While tremendous advances in visual and auditory realism have been made for
virtual and augmented reality (VR/AR), introducing a plausible sense of
physicality into the virtual world remains challenging. Closing the gap between
real-world physicality and immersive virtual experience requires a closed
interaction loop: applying user-exerted physical forces to the virtual
environment and generating haptic sensations back to the users. However,
existing VR/AR solutions either completely ignore the force inputs from the
users or rely on obtrusive sensing devices that compromise user experience.
  By identifying users' muscle activation patterns while engaging in VR/AR, we
design a learning-based neural interface for natural and intuitive force
inputs. Specifically, we show that lightweight electromyography sensors,
resting non-invasively on users' forearm skin, inform and establish a robust
understanding of their complex hand activities. Fuelled by a
neural-network-based model, our interface can decode finger-wise forces in
real-time with 3.3% mean error, and generalize to new users with little
calibration. Through an interactive psychophysical study, we show that human
perception of virtual objects' physical properties, such as stiffness, can be
significantly enhanced by our interface. We further demonstrate that our
interface enables ubiquitous control via finger tapping. Ultimately, we
envision our findings to push forward research towards more realistic
physicality in future VR/AR.

Photo retouching is a difficult task for novice users as it requires expert
knowledge and advanced tools. Photographers often spend a great deal of time
generating high-quality retouched photos with intricate details. In this paper,
we introduce a one-shot learning based technique to automatically retouch
details of an input image based on just a single pair of before and after
example images. Our approach provides accurate and generalizable detail edit
transfer to new images. We achieve these by proposing a new representation for
image to image maps. Specifically, we propose neural field based transformation
blending in the patch space for defining patch to patch transformations for
each frequency band. This parametrization of the map with anchor
transformations and associated weights, and spatio-spectral localized patches,
allows us to capture details well while staying generalizable. We evaluate our
technique both on known ground truth filtes and artist retouching edits. Our
method accurately transfers complex detail retouching edits.

Distilling knowledge from a large teacher model to a lightweight one is a
widely successful approach for generating compact, powerful models in the
semi-supervised learning setting where a limited amount of labeled data is
available. In large-scale applications, however, the teacher tends to provide a
large number of incorrect soft-labels that impairs student performance. The
sheer size of the teacher additionally constrains the number of soft-labels
that can be queried due to prohibitive computational and/or financial costs.
The difficulty in achieving simultaneous \emph{efficiency} (i.e., minimizing
soft-label queries) and \emph{robustness} (i.e., avoiding student inaccuracies
due to incorrect labels) hurts the widespread application of knowledge
distillation to many modern tasks. In this paper, we present a parameter-free
approach with provable guarantees to query the soft-labels of points that are
simultaneously informative and correctly labeled by the teacher. At the core of
our work lies a game-theoretic formulation that explicitly considers the
inherent trade-off between the informativeness and correctness of input
instances. We establish bounds on the expected performance of our approach that
hold even in worst-case distillation instances. We present empirical
evaluations on popular benchmarks that demonstrate the improved distillation
performance enabled by our work relative to that of state-of-the-art active
learning and active distillation methods.

We identify and prove a general principle: $L_1$ sparsity can be achieved
using a redundant parametrization plus $L_2$ penalty. Our results lead to a
simple algorithm, \textit{spred}, that seamlessly integrates $L_1$
regularization into any modern deep learning framework. Practically, we
demonstrate (1) the efficiency of \textit{spred} in optimizing conventional
tasks such as lasso and sparse coding, (2) benchmark our method for nonlinear
feature selection of six gene selection tasks, and (3) illustrate the usage of
the method for achieving structured and unstructured sparsity in deep learning
in an end-to-end manner. Conceptually, our result bridges the gap in
understanding the inductive bias of the redundant parametrization common in
deep learning and conventional statistical learning.

Unsupervised Domain Adaptation (UDA) aims at classifying unlabeled target
images leveraging source labeled ones. In this work, we consider the Partial
Domain Adaptation (PDA) variant, where we have extra source classes not present
in the target domain. Most successful algorithms use model selection strategies
that rely on target labels to find the best hyper-parameters and/or models
along training. However, these strategies violate the main assumption in PDA:
only unlabeled target domain samples are available. Moreover, there are also
inconsistencies in the experimental settings - architecture, hyper-parameter
tuning, number of runs - yielding unfair comparisons. The main goal of this
work is to provide a realistic evaluation of PDA methods with the different
model selection strategies under a consistent evaluation protocol. We evaluate
7 representative PDA algorithms on 2 different real-world datasets using 7
different model selection strategies. Our two main findings are: (i) without
target labels for model selection, the accuracy of the methods decreases up to
30 percentage points; (ii) only one method and model selection pair performs
well on both datasets. Experiments were performed with our PyTorch framework,
BenchmarkPDA, which we open source.

(1) Background: The success of physiotherapy depends on the regular and
correct performance of movement exercises. A system that automatically
evaluates these could support the therapy. Previous approaches in this area
rarely rely on Deep Learning methods and do not yet fully use their potential.
(2) Methods: Using a measurement system consisting of 17 IMUs, a dataset of
four Functional Movement Screening (FMS) exercises is recorded. Exercise
execution is evaluated by physiotherapists using the FMS criteria. This dataset
is used to train a neural network that assigns the correct FMS score to an
exercise repetition. We use an architecture consisting of CNN, LSTM and Dense
layers. Based on this framework, we apply various methods to optimize the
performance of the network. For the optimization, we perform a extensive
hyperparameter optimization. In addition, we are comparing different CNN
structures that have been specifically adapted for use with IMU data. Finally,
the developed network is trained with the data of different FMS exercises and
the performance is compared. (3) Results: The evaluation shows that the
presented approach achieves a convincing performance in the classification of
unknown repetitions of already known subjects. However, the trained network is
yet unable to achieve consistent performance on the data of a previously
unknown subjects. Additionally, it can be seen that the performance of the
network differs significantly depending on the exercise it is trained for.

Spiking neural networks (SNNs) have received substantial attention in recent
years due to their sparse and asynchronous communication nature, and thus can
be deployed in neuromorphic hardware and achieve extremely high energy
efficiency. However, SNNs currently can hardly realize a comparable performance
to that of artificial neural networks (ANNs) because their limited scalability
does not allow for large-scale networks. Especially for Transformer, as a model
of ANNs that has accomplished remarkable performance in various machine
learning tasks, its implementation in SNNs by conventional methods requires a
large number of neurons, notably in the self-attention module. Inspired by the
mechanisms in the nervous system, we propose an efficient spiking Transformer
(EST) framework enabled by partial information to address the above problem. In
this model, we not only implemented the self-attention module with a reasonable
number of neurons, but also introduced partial-information self-attention
(PSA), which utilizes only partial input signals, further reducing
computational resources compared to conventional methods. The experimental
results show that our EST can outperform the state-of-the-art SNN model in
terms of accuracy and the number of time steps on both Cifar-10/100 and
ImageNet datasets. In particular, the proposed EST model achieves 78.48% top-1
accuracy on the ImageNet dataset with only 16 time steps. In addition, our
proposed PSA reduces flops by 49.8% with negligible performance loss compared
to a self-attention module with full information.

Background and Objective: Parkinson's disease (PD) is the second most common
progressive neurological condition after Alzheimer's, characterized by motor
and non-motor symptoms. Developing a method to diagnose the condition in its
beginning phases is essential because of the significant number of individuals
afflicting with this illness. PD is typically identified using motor symptoms
or other Neuroimaging techniques, such as DATSCAN and SPECT. These methods are
expensive, time-consuming, and unavailable to the general public; furthermore,
they are not very accurate. These constraints encouraged us to develop a novel
technique using SHAP and Hard Voting Ensemble Method based on voice signals.
Methods: In this article, we used Pearson Correlation Coefficients to
understand the relationship between input features and the output, and finally,
input features with high correlation were selected. These selected features
were classified by the Extreme Gradient Boosting (XGBoost), Light Gradient
Boosting Machine (LightGBM), Gradient Boosting, and Bagging. Moreover, the Hard
Voting Ensemble Method was determined based on the performance of the four
classifiers. At the final stage, we proposed Shapley Additive exPlanations
(SHAP) to rank the features according to their significance in diagnosing
Parkinson's disease. Results and Conclusion: The proposed method achieved
85.42% accuracy, 84.94% F1-score, 86.77% precision, 87.62% specificity, and
83.20% sensitivity. The study's findings demonstrated that the proposed method
outperformed state-of-the-art approaches and can assist physicians in
diagnosing Parkinson's cases.

We present a 3D generative model for general natural scenes. Lacking
necessary volumes of 3D data characterizing the target scene, we propose to
learn from a single scene. Our key insight is that a natural scene often
contains multiple constituents whose geometry, texture, and spatial
arrangements follow some clear patterns, but still exhibit rich variations over
different regions within the same scene. This suggests localizing the learning
of a generative model on substantial local regions. Hence, we exploit a
multi-scale convolutional network, which possesses the spatial locality bias in
nature, to learn from the statistics of local regions at multiple scales within
a single scene. In contrast to existing methods, our learning setup bypasses
the need to collect data from many homogeneous 3D scenes for learning common
features. We coin our method SinGRAV, for learning a Generative RAdiance Volume
from a Single natural scene. We demonstrate the ability of SinGRAV in
generating plausible and diverse variations from a single scene, the merits of
SinGRAV over state-of-the-art generative neural scene methods, as well as the
versatility of SinGRAV by its use in a variety of applications, spanning 3D
scene editing, composition, and animation. Code and data will be released to
facilitate further research.

Robots such as autonomous vehicles and assistive manipulators are
increasingly operating in dynamic environments and close physical proximity to
people. In such scenarios, the robot can leverage a human motion predictor to
predict their future states and plan safe and efficient trajectories. However,
no model is ever perfect -- when the observed human behavior deviates from the
model predictions, the robot might plan unsafe maneuvers. Recent works have
explored maintaining a confidence parameter in the human model to overcome this
challenge, wherein the predicted human actions are tempered online based on the
likelihood of the observed human action under the prediction model. This has
opened up a new research challenge, i.e., \textit{how to compute the future
human states online as the confidence parameter changes?} In this work, we
propose a Hamilton-Jacobi (HJ) reachability-based approach to overcome this
challenge. Treating the confidence parameter as a virtual state in the system,
we compute a parameter-conditioned forward reachable tube (FRT) that provides
the future human states as a function of the confidence parameter. Online, as
the confidence parameter changes, we can simply query the corresponding FRT,
and use it to update the robot plan. Computing parameter-conditioned FRT
corresponds to an (offline) high-dimensional reachability problem, which we
solve by leveraging recent advances in data-driven reachability analysis.
Overall, our framework enables online maintenance and updates of safety
assurances in human-robot interaction scenarios, even when the human prediction
model is incorrect. We demonstrate our approach in several safety-critical
autonomous driving scenarios, involving a state-of-the-art deep learning-based
prediction model.

We propose a novel deep architecture for the task of reasoning about social
interactions in videos. We leverage the multi-step reasoning capabilities of
Compositional Attention Networks (MAC), and propose a multimodal extension
(MAC-X). MAC-X is based on a recurrent cell that performs iterative mid-level
fusion of input modalities (visual, auditory, text) over multiple reasoning
steps, by use of a temporal attention mechanism. We then combine MAC-X with
LSTMs for temporal input processing in an end-to-end architecture. Our ablation
studies show that the proposed MAC-X architecture can effectively leverage
multimodal input cues using mid-level fusion mechanisms. We apply MAC-X to the
task of Social Video Question Answering in the Social IQ dataset and obtain a
2.5% absolute improvement in terms of binary accuracy over the current

Deep regression models typically learn in an end-to-end fashion and do not
explicitly try to learn a regression-aware representation. Their
representations tend to be fragmented and fail to capture the continuous nature
of regression tasks. In this paper, we propose Supervised Contrastive
Regression (SupCR), a framework that learns a regression-aware representation
by contrasting samples against each other based on their target distance. SupCR
is orthogonal to existing regression models, and can be used in combination
with such models to improve performance. Extensive experiments using five
real-world regression datasets that span computer vision, human-computer
interaction, and healthcare show that using SupCR achieves the state-of-the-art
performance and consistently improves prior regression baselines on all
datasets, tasks, and input modalities. SupCR also improves robustness to data
corruptions, resilience to reduced training data, performance on transfer
learning, and generalization to unseen targets.

Despite exciting progress in large-scale language generation, the
expressiveness of its representations is severely limited by the
\textit{anisotropy} issue where the hidden representations are distributed into
a narrow cone in the vector space. To address this issue, we present ContraGen,
a novel contrastive learning framework to improve the representation with
better uniformity and discrimination. We assess ContraGen on a wide range of
downstream tasks in natural and programming languages. We show that ContraGen
can effectively enhance both uniformity and discrimination of the
representations and lead to the desired improvement on various language
understanding tasks where discriminative representations are crucial for
attaining good performance. Specifically, we attain $44\%$ relative improvement
on the Semantic Textual Similarity tasks and $34\%$ on Code-to-Code Search
tasks. Furthermore, by improving the expressiveness of the representations,
ContraGen also boosts the source code generation capability with $9\%$ relative
improvement on execution accuracy on the HumanEval benchmark.

In the collider phenomenology of extensions of the Standard Model with
partner particles, cascade decays occur generically, and they can be
challenging to discover when the spectrum of new particles is compressed and
the signal cross section is low. Achieving discovery-level significance and
measuring the properties of the new particles appearing as intermediate states
in the cascade decays is a longstanding problem, with analysis techniques for
some decay topologies already optimized. We focus our attention on a benchmark
decay topology with four final state particles where there is room for
improvement, and where multidimensional analysis techniques have been shown to
be effective in the past. Using machine learning techniques, we identify the
optimal kinematic observables for discovery, spin determination and mass
measurement. In agreement with past work, we confirm that the kinematic
observable $\Delta_4$ is highly effective. We quantify the achievable accuracy
for spin determination and for the precision for mass measurements as a
function of the signal size.

Many high-performance classification models utilize complex CNN-based
architectures for Alzheimer's Disease classification. We aim to investigate two
relevant questions regarding classification of Alzheimer's Disease using MRI:
"Do Vision Transformer-based models perform better than CNN-based models?" and
"Is it possible to use a shallow 3D CNN-based model to obtain satisfying
results?" To achieve these goals, we propose two models that can take in and
process 3D MRI scans: Convolutional Voxel Vision Transformer (CVVT)
architecture, and ConvNet3D-4, a shallow 4-block 3D CNN-based model. Our
results indicate that the shallow 3D CNN-based models are sufficient to achieve
good classification results for Alzheimer's Disease using MRI scans.

We study the personalized federated learning problem under asynchronous
updates. In this problem, each client seeks to obtain a personalized model that
simultaneously outperforms local and global models. We consider two
optimization-based frameworks for personalization: (i) Model-Agnostic
Meta-Learning (MAML) and (ii) Moreau Envelope (ME). MAML involves learning a
joint model adapted for each client through fine-tuning, whereas ME requires a
bi-level optimization problem with implicit gradients to enforce
personalization via regularized losses. We focus on improving the scalability
of personalized federated learning by removing the synchronous communication
assumption. Moreover, we extend the studied function class by removing
boundedness assumptions on the gradient norm. Our main technical contribution
is a unified proof for asynchronous federated learning with bounded staleness
that we apply to MAML and ME personalization frameworks. For the smooth and
non-convex functions class, we show the convergence of our method to a
first-order stationary point. We illustrate the performance of our method and
its tolerance to staleness through experiments for classification tasks over
heterogeneous datasets.

Temporal graph is an abstraction for modeling dynamic systems that consist of
evolving interaction elements. In this paper, we aim to solve an important yet
neglected problem -- how to learn information from high-order neighbors in
temporal graphs? -- to enhance the informativeness and discriminativeness for
the learned node representations. We argue that when learning high-order
information from temporal graphs, we encounter two challenges, i.e.,
computational inefficiency and over-smoothing, that cannot be solved by
conventional techniques applied on static graphs. To remedy these deficiencies,
we propose a temporal propagation-based graph neural network, namely TPGNN. To
be specific, the model consists of two distinct components, i.e., propagator
and node-wise encoder. The propagator is leveraged to propagate messages from
the anchor node to its temporal neighbors within $k$-hop, and then
simultaneously update the state of neighborhoods, which enables efficient
computation, especially for a deep model. In addition, to prevent
over-smoothing, the model compels the messages from $n$-hop neighbors to update
the $n$-hop memory vector preserved on the anchor. The node-wise encoder adopts
transformer architecture to learn node representations by explicitly learning
the importance of memory vectors preserved on the node itself, that is,
implicitly modeling the importance of messages from neighbors at different
layers, thus mitigating the over-smoothing. Since the encoding process will not
query temporal neighbors, we can dramatically save time consumption in
inference. Extensive experiments on temporal link prediction and node
classification demonstrate the superiority of TPGNN over state-of-the-art
baselines in efficiency and robustness.

This paper presents a deep-learning framework, Multi-load Generative
Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in
one shot. The main contribution of MultiLoad-GAN is the capture of
spatial-temporal correlations among a group of loads to enable the generation
of realistic synthetic load profiles in large quantity for meeting the emerging
need in distribution system planning. The novelty and uniqueness of the
MultiLoad-GAN framework are three-fold. First, it generates a group of load
profiles bearing realistic spatial-temporal correlations in one shot. Second,
two complementary metrics for evaluating realisticness of generated load
profiles are developed: statistics metrics based on domain knowledge and a
deep-learning classifier for comparing high-level features. Third, to tackle
data scarcity, a novel iterative data augmentation mechanism is developed to
generate training samples for enhancing the training of both the classifier and
the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms
state-of-the-art approaches in realisticness, computational efficiency, and
robustness. With little finetuning, the MultiLoad-GAN approach can be readily
extended to generate a group of load or PV profiles for a feeder, a substation,
or a service area.

This paper explores continuous-time control synthesis for target-driven
navigation to satisfy complex high-level tasks expressed as linear temporal
logic (LTL). We propose a model-free framework using deep reinforcement
learning (DRL) where the underlying dynamic system is unknown (an opaque box).
Unlike prior work, this paper considers scenarios where the given LTL
specification might be infeasible and therefore cannot be accomplished
globally. Instead of modifying the given LTL formula, we provide a general
DRL-based approach to satisfy it with minimal violation. To do this, we
transform a previously multi-objective DRL problem, which requires simultaneous
automata satisfaction and minimum violation cost, into a single objective. By
guiding the DRL agent with a sampling-based path planning algorithm for the
potentially infeasible LTL task, the proposed approach mitigates the myopic
tendencies of DRL, which are often an issue when learning general LTL tasks
that can have long or infinite horizons. This is achieved by decomposing an
infeasible LTL formula into several reach-avoid sub-tasks with shorter
horizons, which can be trained in a modular DRL architecture. Furthermore, we
overcome the challenge of the exploration process for DRL in complex and
cluttered environments by using path planners to design rewards that are dense
in the configuration space. The benefits of the presented approach are
demonstrated through testing on various complex nonlinear systems and compared
with state-of-the-art baselines. The Video demonstration can be found on
YouTube Channel:https://youtu.be/jBhx6Nv224E.

Synchronous updates may compromise the efficiency of cross-device federated
learning once the number of active clients increases. The \textit{FedBuff}
algorithm (Nguyen et al., 2022) alleviates this problem by allowing
asynchronous updates (staleness), which enhances the scalability of training
while preserving privacy via secure aggregation. We revisit the
\textit{FedBuff} algorithm for asynchronous federated learning and extend the
existing analysis by removing the boundedness assumptions from the gradient
norm. This paper presents a theoretical analysis of the convergence rate of
this algorithm when heterogeneity in data, batch size, and delay are
considered.

Transfer learning (TL) techniques, which leverage prior knowledge gained from
data with different distributions to achieve higher performance and reduced
training time, are often used in computer vision (CV) and natural language
processing (NLP), but have yet to be fully utilized in the field of radio
frequency machine learning (RFML). This work systematically evaluates how radio
frequency (RF) TL behavior by examining how the training domain and task,
characterized by the transmitter/receiver hardware and channel environment,
impact RF TL performance for an example automatic modulation classification
(AMC) use-case. Through exhaustive experimentation using carefully curated
synthetic datasets with varying signal types, signal-to-noise ratios (SNRs),
and frequency offsets (FOs), generalized conclusions are drawn regarding how
best to use RF TL techniques for domain adaptation and sequential learning.
Consistent with trends identified in other modalities, results show that RF TL
performance is highly dependent on the similarity between the source and target
domains/tasks. Results also discuss the impacts of channel environment,
hardware variations, and domain/task difficulty on RF TL performance, and
compare RF TL performance using head re-training and model fine-tuning methods.

Grokking, the unusual phenomenon for algorithmic datasets where
generalization happens long after overfitting the training data, has remained
elusive. We aim to understand grokking by analyzing the loss landscapes of
neural networks, identifying the mismatch between training and test losses as
the cause for grokking. We refer to this as the "LU mechanism" because training
and test losses (against model weight norm) typically resemble "L" and "U",
respectively. This simple mechanism can nicely explain many aspects of
grokking: data size dependence, weight decay dependence, the emergence of
representations, etc. Guided by the intuitive picture, we are able to induce
grokking on tasks involving images, language and molecules. In the reverse
direction, we are able to eliminate grokking for algorithmic datasets. We
attribute the dramatic nature of grokking for algorithmic datasets to
representation learning.

Learning to produce contact-rich, dynamic behaviors from raw sensory data has
been a longstanding challenge in robotics. Prominent approaches primarily focus
on using visual or tactile sensing, where unfortunately one fails to capture
high-frequency interaction, while the other can be too delicate for large-scale
data collection. In this work, we propose a data-centric approach to dynamic
manipulation that uses an often ignored source of information: sound. We first
collect a dataset of 25k interaction-sound pairs across five dynamic tasks
using commodity contact microphones. Then, given this data, we leverage
self-supervised learning to accelerate behavior prediction from sound. Our
experiments indicate that this self-supervised 'pretraining' is crucial to
achieving high performance, with a 34.5% lower MSE than plain supervised
learning and a 54.3% lower MSE over visual training. Importantly, we find that
when asked to generate desired sound profiles, online rollouts of our models on
a UR10 robot can produce dynamic behavior that achieves an average of 11.5%
improvement over supervised learning on audio similarity metrics.

This paper is on soft prompt learning for Vision \& Language (V&L) models.
Similarly to their NLP counterparts, V\&L models can be adapted to a downstream
task by learning soft continuous prompts using a few training examples. Current
methods learn the soft prompts by minimizing a cross-entropy loss using as
class weights the features obtained by passing the prompts plus the class names
through the text encoder. Such methods, however, significantly overfit the
training data suffering from large accuracy degradation when tested on unseen
classes from the same domain. Our main contribution, in this paper, is a
surprisingly simple approach to alleviate this problem: we use a second cross
entropy loss to minimize the distance between the learned soft prompts and a
set of hand-engineered manual prompts (obtained by prompt engineering). The
proposed loss can be interpreted in multiple ways including as a regularizer,
as a means for language-based augmentation, and as a way of learning more
discriminative class centroids. Importantly, our formulation is inherently
amenable to including, during training, virtual classes, i.e. class names for
which no visual samples are available, further increasing the robustness of the
learned prompts. Through extensive evaluations on 11 datasets, we show that our
approach (a) significantly outperforms all prior works on soft prompting, and
(b) matches and surpasses, for the first time, the accuracy on novel classes
obtained by hand-crafted prompts and CLIP for the majority of the test
datasets. Code will be made available.

Multi-label classification, which predicts a set of labels for an input, has
many applications. However, multiple recent studies showed that multi-label
classification is vulnerable to adversarial examples. In particular, an
attacker can manipulate the labels predicted by a multi-label classifier for an
input via adding carefully crafted, human-imperceptible perturbation to it.
Existing provable defenses for multi-class classification achieve sub-optimal
provable robustness guarantees when generalized to multi-label classification.
In this work, we propose MultiGuard, the first provably robust defense against
adversarial examples to multi-label classification. Our MultiGuard leverages
randomized smoothing, which is the state-of-the-art technique to build provably
robust classifiers. Specifically, given an arbitrary multi-label classifier,
our MultiGuard builds a smoothed multi-label classifier via adding random noise
to the input. We consider isotropic Gaussian noise in this work. Our major
theoretical contribution is that we show a certain number of ground truth
labels of an input are provably in the set of labels predicted by our
MultiGuard when the $\ell_2$-norm of the adversarial perturbation added to the
input is bounded. Moreover, we design an algorithm to compute our provable
robustness guarantees. Empirically, we evaluate our MultiGuard on VOC 2007,
MS-COCO, and NUS-WIDE benchmark datasets. Our code is available at:
\url{https://github.com/quwenjie/MultiGuard}

We propose MINT, a new Multilingual INTimacy analysis dataset covering 13,384
tweets in 10 languages including English, French, Spanish, Italian, Portuguese,
Korean, Dutch, Chinese, Hindi, and Arabic. We benchmarked a list of popular
multilingual pre-trained language models. The dataset is released along with
the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis
(https://sites.google.com/umich.edu/semeval-2023-tweet-intimacy).

Transfer learning from large language models (LLMs) has emerged as a powerful
technique to enable knowledge-based fine-tuning for a number of tasks,
adaptation of models for different domains and even languages. However, it
remains an open question, if and when transfer learning will work, i.e. leading
to positive or negative transfer. In this paper, we analyze the knowledge
transfer across three natural language processing (NLP) tasks - text
classification, sentimental analysis, and sentence similarity, using three LLMs
- BERT, RoBERTa, and XLNet - and analyzing their performance, by fine-tuning on
target datasets for domain and cross-lingual adaptation tasks, with and without
an intermediate task training on a larger dataset. Our experiments showed that
fine-tuning without an intermediate task training can lead to a better
performance for most tasks, while more generalized tasks might necessitate a
preceding intermediate task training step. We hope that this work will act as a
guide on transfer learning to NLP practitioners.

We have witnessed in recent years an ever-growing volume of information
becoming available in a streaming manner in various application areas. As a
result, there is an emerging need for online learning methods that train
predictive models on-the-fly. A series of open challenges, however, hinder
their deployment in practice. These are, learning as data arrive in real-time
one-by-one, learning from data with limited ground truth information, learning
from nonstationary data, and learning from severely imbalanced data, while
occupying a limited amount of memory for data storage. We propose the
ActiSiamese algorithm, which addresses these challenges by combining online
active learning, siamese networks, and a multi-queue memory. It develops a new
density-based active learning strategy which considers similarity in the latent
(rather than the input) space. We conduct an extensive study that compares the
role of different active learning budgets and strategies, the performance
with/without memory, the performance with/without ensembling, in both synthetic
and real-world datasets, under different data nonstationarity characteristics
and class imbalance levels. ActiSiamese outperforms baseline and
state-of-the-art algorithms, and is effective under severe imbalance, even only
when a fraction of the arriving instances' labels is available. We publicly
release our code to the community.

Deep learning has achieved remarkable success in many machine learning tasks
such as image classification, speech recognition, and game playing. However,
these breakthroughs are often difficult to translate into real-world
engineering systems because deep learning models require a massive number of
training samples, which are costly to obtain in practice. To address labeled
data scarcity, few-shot meta-learning optimizes learning algorithms that can
efficiently adapt to new tasks quickly. While meta-learning is gaining
significant interest in the machine learning literature, its working principles
and theoretic fundamentals are not as well understood in the engineering
  This review monograph provides an introduction to meta-learning by covering
principles, algorithms, theory, and engineering applications. After introducing
meta-learning in comparison with conventional and joint learning, we describe
the main meta-learning algorithms, as well as a general bilevel optimization
framework for the definition of meta-learning techniques. Then, we summarize
known results on the generalization capabilities of meta-learning from a
statistical learning viewpoint. Applications to communication systems,
including decoding and power allocation, are discussed next, followed by an
introduction to aspects related to the integration of meta-learning with
emerging computing technologies, namely neuromorphic and quantum computing. The
monograph is concluded with an overview of open research challenges.

The framework of Language Games studies the emergence of languages in
populations of agents. Recent contributions relying on deep learning methods
focused on agents communicating via an idealized communication channel, where
utterances produced by a speaker are directly perceived by a listener. This
comes in contrast with human communication, which instead relies on a
sensory-motor channel, where motor commands produced by the speaker (e.g. vocal
or gestural articulators) result in sensory effects perceived by the listener
(e.g. audio or visual). Here, we investigate if agents can evolve a shared
language when they are equipped with a continuous sensory-motor system to
produce and perceive signs, e.g. drawings. To this end, we introduce the
Graphical Referential Game (GREG) where a speaker must produce a graphical
utterance to name a visual referent object consisting of combinations of MNIST
digits while a listener has to select the corresponding object among distractor
referents, given the produced message. The utterances are drawing images
produced using dynamical motor primitives combined with a sketching library. To
tackle GREG we present CURVES: a multimodal contrastive deep learning mechanism
that represents the energy (alignment) between named referents and utterances
generated through gradient ascent on the learned energy landscape. We, then,
present a set of experiments and metrics based on a systematic compositional
dataset to evaluate the resulting language. We show that our method allows the
emergence of a shared, graphical language with compositional properties.

In the Machine Learning (ML) literature, a well-known problem is the Dataset
Shift problem where, differently from the ML standard hypothesis, the data in
the training and test sets can follow different probability distributions,
leading ML systems toward poor generalisation performances. This problem is
intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals
as Electroencephalographic (EEG) are often used. In fact, EEG signals are
highly non-stationary both over time and between different subjects. To
overcome this problem, several proposed solutions are based on recent transfer
learning approaches such as Domain Adaption (DA). In several cases, however,
the actual causes of the improvements remain ambiguous. This paper focuses on
the impact of data normalisation, or standardisation strategies applied
together with DA methods. In particular, using \textit{SEED}, \textit{DEAP},
and \textit{BCI Competition IV 2a} EEG datasets, we experimentally evaluated
the impact of different normalization strategies applied with and without
several well-known DA methods, comparing the obtained performances. It results
that the choice of the normalisation strategy plays a key role on the
classifier performances in DA scenarios, and interestingly, in several cases,
the use of only an appropriate normalisation schema outperforms the DA
technique.

Anomaly detection in time-series has a wide range of practical applications.
While numerous anomaly detection methods have been proposed in the literature,
a recent survey concluded that no single method is the most accurate across
various datasets. To make matters worse, anomaly labels are scarce and rarely
available in practice. The practical problem of selecting the most accurate
model for a given dataset without labels has received little attention in the
literature. This paper answers this question i.e. Given an unlabeled dataset
and a set of candidate anomaly detectors, how can we select the most accurate
model? To this end, we identify three classes of surrogate (unsupervised)
metrics, namely, prediction error, model centrality, and performance on
injected synthetic anomalies, and show that some metrics are highly correlated
with standard supervised anomaly detection performance metrics such as the
$F_1$ score, but to varying degrees. We formulate metric combination with
multiple imperfect surrogate metrics as a robust rank aggregation problem. We
then provide theoretical justification behind the proposed approach.
Large-scale experiments on multiple real-world datasets demonstrate that our
proposed unsupervised approach is as effective as selecting the most accurate
model based on partially labeled data.

Deep learning techniques have become prominent in modern fault diagnosis for
complex processes. In particular, convolutional neural networks (CNNs) have
shown an appealing capacity to deal with multivariate time-series data by
converting them into images. However, existing CNN techniques mainly focus on
capturing local or multi-scale features from input images. A deep CNN is often
required to indirectly extract global features, which are critical to describe
the images converted from multivariate dynamical data. This paper proposes a
novel local-global CNN (LG-CNN) architecture that directly accounts for both
local and global features for fault diagnosis. Specifically, the local features
are acquired by traditional local kernels whereas global features are extracted
by using 1D tall and fat kernels that span the entire height and width of the
image. Both local and global features are then merged for classification using
fully-connected layers. The proposed LG-CNN is validated on the benchmark
Tennessee Eastman process (TEP) dataset. Comparison with traditional CNN shows
that the proposed LG-CNN can greatly improve the fault diagnosis performance
without significantly increasing the model complexity. This is attributed to
the much wider local receptive field created by the LG-CNN than that by CNN.
The proposed LG-CNN architecture can be easily extended to other image
processing and computer vision tasks.

Due to their widespread use on heterogeneous hardware devices, deep learning
(DL) models are compiled into executables by DL compilers to fully leverage
low-level hardware primitives. This approach allows DL computations to be
undertaken at low cost across a variety of computing platforms, including CPUs,
GPUs, and various hardware accelerators.
  We present BTD (Bin to DNN), a decompiler for deep neural network (DNN)
executables. BTD takes DNN executables and outputs full model specifications,
including types of DNN operators, network topology, dimensions, and parameters
that are (nearly) identical to those of the input models. BTD delivers a
practical framework to process DNN executables compiled by different DL
compilers and with full optimizations enabled on x86 platforms. It employs
learning-based techniques to infer DNN operators, dynamic analysis to reveal
network architectures, and symbolic execution to facilitate inferring
dimensions and parameters of DNN operators.
  Our evaluation reveals that BTD enables accurate recovery of full
specifications of complex DNNs with millions of parameters (e.g., ResNet). The
recovered DNN specifications can be re-compiled into a new DNN executable
exhibiting identical behavior to the input executable. We show that BTD can
boost two representative attacks, adversarial example generation and knowledge
stealing, against DNN executables. We also demonstrate cross-architecture
legacy code reuse using BTD, and envision BTD being used for other critical
downstream tasks like DNN security hardening and patching.

A large class of hyperbolic and advection-dominated PDEs can have solutions
with discontinuities. This paper investigates, both theoretically and
empirically, the operator learning of PDEs with discontinuous solutions. We
rigorously prove, in terms of lower approximation bounds, that methods which
entail a linear reconstruction step (e.g. DeepONet or PCA-Net) fail to
efficiently approximate the solution operator of such PDEs. In contrast, we
show that certain methods employing a non-linear reconstruction mechanism can
overcome these fundamental lower bounds and approximate the underlying operator
efficiently. The latter class includes Fourier Neural Operators and a novel
extension of DeepONet termed shift-DeepONet. Our theoretical findings are
confirmed by empirical results for advection equation, inviscid Burgers'
equation and compressible Euler equations of aerodynamics.

Influence functions estimate effect of individual data points on predictions
of the model on test data and were adapted to deep learning in Koh and Liang
[2017]. They have been used for detecting data poisoning, detecting helpful and
harmful examples, influence of groups of datapoints, etc. Recently, Ilyas et
al. [2022] introduced a linear regression method they termed datamodels to
predict the effect of training points on outputs on test data. The current
paper seeks to provide a better theoretical understanding of such interesting
empirical phenomena. The primary tool is harmonic analysis and the idea of
noise stability. Contributions include: (a) Exact characterization of the
learnt datamodel in terms of Fourier coefficients. (b) An efficient method to
estimate the residual error and quality of the optimum linear datamodel without
having to train the datamodel. (c) New insights into when influences of groups
of datapoints may or may not add up linearly.

Bayesian optimization (BO) is one of the most effective methods for
closed-loop experimental design and black-box optimization. However, a key
limitation of BO is that it is an inherently sequential algorithm (one
experiment is proposed per round) and thus cannot directly exploit
high-throughput (parallel) experiments. Diverse modifications to the BO
framework have been proposed in the literature to enable exploitation of
parallel experiments but such approaches are limited in the degree of
parallelization that they can achieve and can lead to redundant experiments
(thus wasting resources and potentially compromising performance). In this
work, we present new parallel BO paradigms that exploit the structure of the
system to partition the design space. Specifically, we propose an approach that
partitions the design space by following the level sets of the performance
function and an approach that exploits partially-separable structures of the
performance function found. We conduct extensive numerical experiments using a
reactor case study to benchmark the effectiveness of these approaches against a
variety of state-of-the-art parallel algorithms reported in the literature. Our
computational results show that our approaches significantly reduce the
required search time and increase the probability of finding a global (rather
than local) solution.

A prevalent feature of high-dimensional data is the dependence among
covariates, and model selection is known to be challenging when covariates are
highly correlated. To perform model selection for the high-dimensional Cox
proportional hazards model in presence of correlated covariates with factor
structure, we propose a new model, Factor-Augmented Regularized Model for
Hazard Regression (FarmHazard), which builds upon latent factors that drive
covariate dependence and extends Cox's model. This new model generates
procedures that operate in two steps by learning factors and idiosyncratic
components from high-dimensional covariate vectors and then using them as new
predictors. Cox's model is a widely used semi-parametric model for survival
analysis, where censored data and time-dependent covariates bring additional
technical challenges. We prove model selection consistency and estimation
consistency under mild conditions. We also develop a factor-augmented variable
screening procedure to deal with strong correlations in ultra-high dimensional
problems. Extensive simulations and real data experiments demonstrate that our
procedures enjoy good performance and achieve better results on model
selection, out-of-sample C-index and screening than alternative methods.

(Stochastic) bilevel optimization is a frequently encountered problem in
machine learning with a wide range of applications such as meta-learning,
hyper-parameter optimization, and reinforcement learning. Most of the existing
studies on this problem only focused on analyzing the convergence or improving
the convergence rate, while little effort has been devoted to understanding its
generalization behaviors. In this paper, we conduct a thorough analysis on the
generalization of first-order (gradient-based) methods for the bilevel
optimization problem. We first establish a fundamental connection between
algorithmic stability and generalization error in different forms and give a
high probability generalization bound which improves the previous best one from
$\bigO(\sqrt{n})$ to $\bigO(\log n)$, where $n$ is the sample size. We then
provide the first stability bounds for the general case where both inner and
outer level parameters are subject to continuous update, while existing work
allows only the outer level parameter to be updated. Our analysis can be
applied in various standard settings such as strongly-convex-strongly-convex
(SC-SC), convex-convex (C-C), and nonconvex-nonconvex (NC-NC). Our analysis for
the NC-NC setting can also be extended to a particular
nonconvex-strongly-convex (NC-SC) setting that is commonly encountered in
practice. Finally, we corroborate our theoretical analysis and demonstrate how
iterations can affect the generalization error by experiments on meta-learning
and hyper-parameter optimization.

Monitoring of industrial processes is a critical capability in industry and
in government to ensure reliability of production cycles, quick emergency
response, and national security. Process monitoring allows users to gauge the
involvement of an organization in an industrial process or predict the
degradation or aging of machine parts in processes taking place at a remote
location. Similar to many data science applications, we usually only have
access to limited raw data, such as satellite imagery, short video clips, some
event logs, and signatures captured by a small set of sensors. To combat data
scarcity, we leverage the knowledge of subject matter experts (SMEs) who are
familiar with the process. Various process mining techniques have been
developed for this type of analysis; typically such approaches combine
theoretical process models built based on domain expert insights with ad-hoc
integration of available pieces of raw data. Here, we introduce a novel
mathematically sound method that integrates theoretical process models (as
proposed by SMEs) with interrelated minimal Hidden Markov Models (HMM), built
via non-negative tensor factorization and discrete model simulations. Our
method consolidates: (a) Theoretical process models development, (b) Discrete
model simulations (c) HMM, (d) Joint Non-negative Matrix Factorization (NMF)
and Non-negative Tensor Factorization (NTF), and (e) Custom model selection. To
demonstrate our methodology and its abilities, we apply it on simple synthetic
and real world process models.

Uncloneable encryption, first introduced by Broadbent and Lord (TQC 2020), is
a form of encryption producing a quantum ciphertext with the property that if
the ciphertext is distributed between two non-communicating parties, they
cannot both learn the underlying plaintext even after receiving the decryption
key. In this work, we introduce a variant of uncloneable encryption in which
several possible decryption keys can decrypt a particular encryption, and the
security requirement is that two parties who receive independently generated
decryption keys cannot both learn the underlying ciphertext. We show that this
variant of uncloneable encryption can be achieved device-independently, i.e.,
without trusting the quantum states and measurements used in the scheme.
Moreover, we show that this variant of uncloneable encryption works just as
well as the original definition in constructing private-key quantum money, and
that uncloneable bits can be achieved in this variant without using the quantum
random oracle model.

Corpora that contain tabular data such as WebTables are a vital resource for
the academic community. Essentially, they are the backbone of any modern
research in information management. They are used for various tasks of data
extraction, knowledge base construction, question answering, column semantic
type detection and many other. Such corpora are useful not only as a source of
data, but also as a base for building test datasets. So far, there were no such
corpora for the Russian language and this seriously hindered research in the
aforementioned areas.
  In this paper, we present the first corpus of Web tables created specifically
out of Russian language material. It was built via a special toolkit we have
developed to crawl the Russian Wikipedia. Both the corpus and the toolkit are
open-source and publicly available. Finally, we present a short study that
describes Russian Wikipedia tables and their statistics.

Pre-training across 3D vision and language remains under development because
of limited training data. Recent works attempt to transfer vision-language
pre-training models to 3D vision. PointCLIP converts point cloud data to
multi-view depth maps, adopting CLIP for shape classification. However, its
performance is restricted by the domain gap between rendered depth maps and
images, as well as the diversity of depth distributions. To address this issue,
we propose CLIP2Point, an image-depth pre-training method by contrastive
learning to transfer CLIP to the 3D domain, and adapt it to point cloud
classification. We introduce a new depth rendering setting that forms a better
visual effect, and then render 52,460 pairs of images and depth maps from
ShapeNet for pre-training. The pre-training scheme of CLIP2Point combines
cross-modality learning to enforce the depth features for capturing expressive
visual and textual features and intra-modality learning to enhance the
invariance of depth aggregation. Additionally, we propose a novel Dual-Path
Adapter (DPA) module, i.e., a dual-path structure with simplified adapters for
few-shot learning. The dual-path structure allows the joint use of CLIP and
CLIP2Point, and the simplified adapter can well fit few-shot tasks without
post-search. Experimental results show that CLIP2Point is effective in
transferring CLIP knowledge to 3D vision. Our CLIP2Point outperforms PointCLIP
and other self-supervised 3D networks, achieving state-of-the-art results on
zero-shot and few-shot classification.

Quantum reservoir computing is a class of quantum machine learning algorithms
involving a reservoir of an echo state network based on a register of qubits,
but the dependence of its memory capacity on the hyperparameters is still
rather unclear. In order to maximize its accuracy in time--series predictive
tasks, we investigate the relation between the memory of the network and the
reset rate of the evolution of the quantum reservoir. We benchmark the network
performance by three non--linear maps with fading memory on IBM quantum
hardware. The memory capacity of the quantum reservoir is maximized for central
values of the memory reset rate in the interval [0,1]. As expected, the memory
capacity increases approximately linearly with the number of qubits. After
optimization of the memory reset rate, the mean squared errors of the predicted
outputs in the tasks may decrease by a factor ~1/5 with respect to previous

The minimum cost flow problem is one of the most studied network optimization
problems and appears in numerous applications. Some efficient algorithms exist
for this problem, which are freely available in the form of libraries or
software packages. It is noticeable that none of these solvers is better than
the other solution methods on all instances. Thus, the question arises whether
the fastest algorithm can be selected for a given instance based on the
characteristics of the instance. To this end, we train several machine learning
classifiers to predict the fastest among a given set of solvers. We accomplish
this by creating a representative data set of 81,000 instances and
characterizing each of these instances by a vector of relevant features. To
achieve better performance, we conduct a grid search to optimize the
hyperparameters of the classifiers. Finally, we evaluate the different
classifiers by means of accuracy. It is shown that tree-based models appear to
adapt and exploit the relevant structures of the minimum-cost flow problem
particularly well on a large number of instances, predicting the fastest solver
with an accuracy of more than 90%.

Multi-Agent Reinforcement Learning (MARL) -- where multiple agents learn to
interact in a shared dynamic environment -- permeates across a wide range of
critical applications. While there has been substantial progress on
understanding the global convergence of policy optimization methods in
single-agent RL, designing and analysis of efficient policy optimization
algorithms in the MARL setting present significant challenges, which
unfortunately, remain highly inadequately addressed by existing theory. In this
paper, we focus on the most basic setting of competitive multi-agent RL, namely
two-player zero-sum Markov games, and study equilibrium finding algorithms in
both the infinite-horizon discounted setting and the finite-horizon episodic
setting. We propose a single-loop policy optimization method with symmetric
updates from both agents, where the policy is updated via the
entropy-regularized optimistic multiplicative weights update (OMWU) method and
the value is updated on a slower timescale. We show that, in the
full-information tabular setting, the proposed method achieves a finite-time
last-iterate linear convergence to the quantal response equilibrium of the
regularized problem, which translates to a sublinear last-iterate convergence
to the Nash equilibrium by controlling the amount of regularization. Our
convergence results improve upon the best known iteration complexities, and
lead to a better understanding of policy optimization in competitive Markov
games.

Safety is one of the biggest concerns to applying reinforcement learning (RL)
to the physical world. In its core part, it is challenging to ensure RL agents
persistently satisfy a hard state constraint without white-box or black-box
dynamics models. This paper presents an integrated model learning and safe
control framework to safeguard any agent, where its dynamics are learned as
Gaussian processes. The proposed theory provides (i) a novel method to
construct an offline dataset for model learning that best achieves safety
requirements; (ii) a parameterization rule for safety index to ensure the
existence of safe control; (iii) a safety guarantee in terms of probabilistic
forward invariance when the model is learned using the aforementioned dataset.
Simulation results show that our framework guarantees almost zero safety
violation on various continuous control tasks.

Vision transformers have recently achieved competitive results across various
vision tasks but still suffer from heavy computation costs when processing a
large number of tokens. Many advanced approaches have been developed to reduce
the total number of tokens in large-scale vision transformers, especially for
image classification tasks. Typically, they select a small group of essential
tokens according to their relevance with the class token, then fine-tune the
weights of the vision transformer. Such fine-tuning is less practical for dense
prediction due to the much heavier computation and GPU memory cost than image
classification. In this paper, we focus on a more challenging problem, i.e.,
accelerating large-scale vision transformers for dense prediction without any
additional re-training or fine-tuning. In response to the fact that
high-resolution representations are necessary for dense prediction, we present
two non-parametric operators, a token clustering layer to decrease the number
of tokens and a token reconstruction layer to increase the number of tokens.
The following steps are performed to achieve this: (i) we use the token
clustering layer to cluster the neighboring tokens together, resulting in
low-resolution representations that maintain the spatial structures; (ii) we
apply the following transformer layers only to these low-resolution
representations or clustered tokens; and (iii) we use the token reconstruction
layer to re-create the high-resolution representations from the refined
low-resolution representations. The results obtained by our method are
promising on five dense prediction tasks, including object detection, semantic
segmentation, panoptic segmentation, instance segmentation, and depth
estimation.

For long-tailed classification, most works often pretrain a big model on a
large-scale dataset, and then fine-tune the whole model for adapting to
long-tailed data. Though promising, fine-tuning the whole pretrained model
tends to suffer from high cost in computation and deployment of different
models for different tasks, as well as weakened generalization ability for
overfitting to certain features of long-tailed data. To alleviate these issues,
we propose an effective Long-tailed Prompt Tuning method for long-tailed
classification. LPT introduces several trainable prompts into a frozen
pretrained model to adapt it to long-tailed data. For better effectiveness, we
divide prompts into two groups: 1) a shared prompt for the whole long-tailed
dataset to learn general features and to adapt a pretrained model into target
domain; and 2) group-specific prompts to gather group-specific features for the
samples which have similar features and also to empower the pretrained model
with discrimination ability. Then we design a two-phase training paradigm to
learn these prompts. In phase 1, we train the shared prompt via supervised
prompt tuning to adapt a pretrained model to the desired long-tailed domain. In
phase 2, we use the learnt shared prompt as query to select a small best
matched set for a group of similar samples from the group-specific prompt set
to dig the common features of these similar samples, then optimize these
prompts with dual sampling strategy and asymmetric GCL loss. By only
fine-tuning a few prompts while fixing the pretrained model, LPT can reduce
training and deployment cost by storing a few prompts, and enjoys a strong
generalization ability of the pretrained model. Experiments show that on
various long-tailed benchmarks, with only ~1.1% extra parameters, LPT achieves
comparable performance than previous whole model fine-tuning methods, and is
more robust to domain-shift.

Hip fracture risk assessment is an important but challenging task.
Quantitative CT-based patient specific finite element analysis (FEA) computes
the force (fracture load) to break the proximal femur in a particular loading
condition. It provides different structural information about the proximal
femur that can influence a subject overall fracture risk. To obtain a more
robust measure of fracture risk, we used principal component analysis (PCA) to
develop a global FEA computed fracture risk index that incorporates the
FEA-computed yield and ultimate failure loads and energies to failure in four
loading conditions (single-limb stance and impact from a fall onto the
posterior, posterolateral, and lateral aspects of the greater trochanter) of
110 hip fracture subjects and 235 age and sex matched control subjects from the
AGES-Reykjavik study. We found that the first PC (PC1) of the FE parameters was
the only significant predictor of hip fracture. Using a logistic regression
model, we determined if prediction performance for hip fracture using PC1
differed from that using FE parameters combined by stratified random resampling
with respect to hip fracture status. The results showed that the average of the
area under the receive operating characteristic curve (AUC) using PC1 was
always higher than that using all FE parameters combined in the male subjects.
The AUC of PC1 and AUC of the FE parameters combined were not significantly
different than that in the female subjects or in all subjects

Denoising diffusion probabilistic models (DDPMs) and generative adversarial
networks (GANs) are popular generative models for neural vocoders. The DDPMs
and GANs can be characterized by the iterative denoising framework and
adversarial training, respectively. This study proposes a fast and high-quality
neural vocoder called \textit{WaveFit}, which integrates the essence of GANs
into a DDPM-like iterative framework based on fixed-point iteration. WaveFit
iteratively denoises an input signal, and trains a deep neural network (DNN)
for minimizing an adversarial loss calculated from intermediate outputs at all
iterations. Subjective (side-by-side) listening tests showed no statistically
significant differences in naturalness between human natural speech and those
synthesized by WaveFit with five iterations. Furthermore, the inference speed
of WaveFit was more than 240 times faster than WaveRNN. Audio demos are
available at \url{google.github.io/df-conformer/wavefit/}.

Context has been an important topic in recommender systems over the past two
decades. A standard representational approach to context assumes that
contextual variables and their structures are known in an application. Most of
the prior CARS papers following representational approach manually selected and
considered only a few crucial contextual variables in an application, such as
time, location, and company of a person. This prior work demonstrated
significant recommendation performance improvements when various CARS-based
methods have been deployed in numerous applications. However, some recommender
systems applications deal with a much bigger and broader types of contexts, and
manually identifying and capturing a few contextual variables is not sufficient
in such cases. In this paper, we study such ``context-rich'' applications
dealing with a large variety of different types of contexts. We demonstrate
that supporting only a few most important contextual variables, although
useful, is not sufficient. In our study, we focus on the application that
recommends various banking products to commercial customers within the context
of dialogues initiated by customer service representatives. In this
application, we managed to identify over two hundred types of contextual
variables. Sorting those variables by their importance forms the Long Tail of
Context (LTC). In this paper, we empirically demonstrate that LTC matters and
using all these contextual variables from the Long Tail leads to significant
improvements in recommendation performance.

We address the problem of generating a sequence of LEGO brick assembly with
high-fidelity structures, satisfying physical constraints between bricks. The
assembly problem is challenging since the number of possible structures
increases exponentially with the number of available bricks, complicating the
physical constraints to satisfy across bricks. To tackle this problem, our
method performs a brick structure assessment to predict the next brick position
and its confidence by employing a U-shaped sparse 3D convolutional network. The
convolution filter efficiently validates physical constraints in a
parallelizable and scalable manner, allowing to process of different brick
types. To generate a novel structure, we devise a sampling strategy to
determine the next brick position by considering attachable positions under
physical constraints. Instead of using handcrafted brick assembly datasets, our
model is trained with a large number of 3D objects that allow to create a new
high-fidelity structure. We demonstrate that our method successfully generates
diverse brick structures while handling two different brick types and
outperforms existing methods based on Bayesian optimization, graph generative
model, and reinforcement learning, all of which are limited to a single brick
type.

Sound data analysis is essential to retrieve meaningful biological
information from single-cell proteomics experiments. This analysis is carried
out by computational methods that are assembled into workflows, and their
implementations influence the conclusions that can be drawn from the data. In
this work, we explore and compare the computational workflows that have been
used over the last four years and identify a profound lack of consensus on how
to analyze single-cell proteomics data. We highlight the need for benchmarking
of computational workflows, standardization of computational tools and data, as
well as carefully designed experiments. Finally, we cover the current
standardization efforts that aim to fill the gap and list the remaining missing
pieces, and conclude with lessons learned from the replication of published
single-cell proteomics analyses.

Monotonic linear interpolation (MLI) - on the line connecting a random
initialization with the minimizer it converges to, the loss and accuracy are
monotonic - is a phenomenon that is commonly observed in the training of neural
networks. Such a phenomenon may seem to suggest that optimization of neural
networks is easy. In this paper, we show that the MLI property is not
necessarily related to the hardness of optimization problems, and empirical
observations on MLI for deep neural networks depend heavily on biases. In
particular, we show that interpolating both weights and biases linearly leads
to very different influences on the final output, and when different classes
have different last-layer biases on a deep network, there will be a long
plateau in both the loss and accuracy interpolation (which existing theory of
MLI cannot explain). We also show how the last-layer biases for different
classes can be different even on a perfectly balanced dataset using a simple
model. Empirically we demonstrate that similar intuitions hold on practical
networks and realistic datasets.

Recent efforts to learn reward functions from human feedback have tended to
use deep neural networks, whose lack of transparency hampers our ability to
explain agent behaviour or verify alignment. We explore the merits of learning
intrinsically interpretable tree models instead. We develop a recently proposed
method for learning reward trees from preference labels, and show it to be
broadly competitive with neural networks on challenging high-dimensional tasks,
with good robustness to limited or corrupted data. Having found that reward
tree learning can be done effectively in complex settings, we then consider why
it should be used, demonstrating that the interpretable reward structure gives
significant scope for traceability, verification and explanation.

Chemical and mineral compositions of asteroids reflect the formation and
history of our Solar System. This knowledge is also important for planetary
defence and in-space resource utilisation. We aim to develop a fast and robust
neural-network-based method for deriving the mineral modal and chemical
compositions of silicate materials from their visible and near-infrared
spectra. The method should be able to process raw spectra without significant
pre-processing. We designed a convolutional neural network with two hidden
layers for the analysis of the spectra, and trained it using labelled
reflectance spectra. For the training, we used a dataset that consisted of
reflectance spectra of real silicate samples stored in the RELAB and C-Tape
databases, namely olivine, orthopyroxene, clinopyroxene, their mixtures, and
olivine-pyroxene-rich meteorites. We used the model on two datasets. First, we
evaluated the model reliability on a test dataset where we compared the model
classification with known compositional reference values. The individual
classification results are mostly within 10 percentage-point intervals around
the correct values. Second, we classified the reflectance spectra of S-complex
(Q-type and V-type, also including A-type) asteroids with known Bus-DeMeo
taxonomy classes. The predicted mineral chemical composition of S-type and
Q-type asteroids agree with the chemical composition of ordinary chondrites.
The modal abundances of V-type and A-type asteroids show a dominant
contribution of orthopyroxene and olivine, respectively. Additionally, our
predictions of the mineral modal composition of S-type and Q-type asteroids
show an apparent depletion of olivine related to the attenuation of its
diagnostic absorptions with space weathering. This trend is consistent with
previous results of the slower pyroxene response to space weathering relative
to olivine.

The graph neural network (GNN) models have presented impressive achievements
in numerous machine learning tasks. However, many existing GNN models are shown
to be vulnerable to adversarial attacks, which creates a stringent need to
build robust GNN architectures. In this work, we propose a novel interpretable
message passing scheme with adaptive structure (ASMP) to defend against
adversarial attacks on graph structure. Layers in ASMP are derived based on
optimization steps that minimize an objective function that learns the node
feature and the graph structure simultaneously. ASMP is adaptive in the sense
that the message passing process in different layers is able to be carried out
over dynamically adjusted graphs. Such property allows more fine-grained
handling of the noisy (or perturbed) graph structure and hence improves the
robustness. Convergence properties of the ASMP scheme are theoretically
established. Integrating ASMP with neural networks can lead to a new family of
GNN models with adaptive structure (ASGNN). Extensive experiments on
semi-supervised node classification tasks demonstrate that the proposed ASGNN
outperforms the state-of-the-art GNN architectures in terms of classification
performance under various adversarial attacks.

Deep learning systems have been reported to acheive state-of-the-art
performances in many applications, and one of the keys for achieving this is
the existence of well trained classifiers on benchmark datasets which can be
used as backbone feature extractors in downstream tasks. As a main-stream loss
function for training deep neural network (DNN) classifiers, the cross entropy
loss can easily lead us to find models which demonstrate severe overfitting
behavior when no other techniques are used for alleviating it such as data
augmentation. In this paper, we prove that the existing cross entropy loss
minimization for training DNN classifiers essentially learns the conditional
entropy of the underlying data distribution of the dataset, i.e., the
information or uncertainty remained in the labels after revealing the input. In
this paper, we propose a mutual information learning framework where we train
DNN classifiers via learning the mutual information between the label and
input. Theoretically, we give the population error probability lower bound in
terms of the mutual information. In addition, we derive the mutual information
lower and upper bounds for a concrete binary classification data model in
$\mbR^n$, and also the error probability lower bound in this scenario. Besides,
we establish the sample complexity for accurately learning the mutual
information from empirical data samples drawn from the underlying data
distribution. Empirically, we conduct extensive experiments on several
benchmark datasets to support our theory. Without whistles and bells, the
proposed mutual information learned classifiers (MILCs) acheive far better
generalization performances than the state-of-the-art classifiers with an
improvement which can exceed more than 10\% in testing accuracy.

While the maximum entropy (MaxEnt) reinforcement learning (RL) framework --
often touted for its exploration and robustness capabilities -- is usually
motivated from a probabilistic perspective, the use of deep probabilistic
models has not gained much traction in practice due to their inherent
complexity. In this work, we propose the adoption of latent variable policies
within the MaxEnt framework, which we show can provably approximate any policy
distribution, and additionally, naturally emerges under the use of world models
with a latent belief state. We discuss why latent variable policies are
difficult to train, how naive approaches can fail, then subsequently introduce
a series of improvements centered around low-cost marginalization of the latent
state, allowing us to make full use of the latent state at minimal additional
cost. We instantiate our method under the actor-critic framework, marginalizing
both the actor and critic. The resulting algorithm, referred to as Stochastic
Marginal Actor-Critic (SMAC), is simple yet effective. We experimentally
validate our method on continuous control tasks, showing that effective
marginalization can lead to better exploration and more robust training.

Consider an online convex optimization problem where the loss functions are
self-concordant barriers, smooth relative to a convex function $h$, and
possibly non-Lipschitz. We analyze the regret of online mirror descent with
$h$. Then, based on the result, we prove the following in a unified manner.
Denote by $T$ the time horizon and $d$ the parameter dimension. 1. For online
portfolio selection, the regret of $\widetilde{\text{EG}}$, a variant of
exponentiated gradient due to Helmbold et al., is $\tilde{O} ( T^{2/3} d^{1/3}
)$ when $T > 4 d / \log d$. This improves on the original $\tilde{O} ( T^{3/4}
d^{1/2} )$ regret bound for $\widetilde{\text{EG}}$. 2. For online portfolio
selection, the regret of online mirror descent with the logarithmic barrier is
$\tilde{O}(\sqrt{T d})$. The regret bound is the same as that of Soft-Bayes due
to Orseau et al. up to logarithmic terms. 3. For online learning quantum states
with the logarithmic loss, the regret of online mirror descent with the
log-determinant function is also $\tilde{O} ( \sqrt{T d} )$. Its per-iteration
time is shorter than all existing algorithms we know.

Bayesian neural networks (BNNs) have received an increased interest in the
last years. In BNNs, a complete posterior distribution of the unknown weight
and bias parameters of the network is produced during the training stage. This
probabilistic estimation offers several advantages with respect to point-wise
estimates, in particular, the ability to provide uncertainty quantification
when predicting new data. This feature inherent to the Bayesian paradigm, is
useful in countless machine learning applications. It is particularly appealing
in areas where decision-making has a crucial impact, such as medical healthcare
or autonomous driving. The main challenge of BNNs is the computational cost of
the training procedure since Bayesian techniques often face a severe curse of
dimensionality. Adaptive importance sampling (AIS) is one of the most prominent
Monte Carlo methodologies benefiting from sounded convergence guarantees and
ease for adaptation. This work aims to show that AIS constitutes a successful
approach for designing BNNs. More precisely, we propose a novel algorithm
PMCnet that includes an efficient adaptation mechanism, exploiting geometric
information on the complex (often multimodal) posterior distribution. Numerical
results illustrate the excellent performance and the improved exploration
capabilities of the proposed method for both shallow and deep neural networks.

In Reinforcement Learning (RL), the goal of agents is to discover an optimal
policy that maximizes the expected cumulative rewards. This objective may also
be viewed as finding a policy that optimizes a linear function of its
state-action occupancy measure, hereafter referred as Linear RL. However, many
supervised and unsupervised RL problems are not covered in the Linear RL
framework, such as apprenticeship learning, pure exploration and variational
intrinsic control, where the objectives are non-linear functions of the
occupancy measures. RL with non-linear utilities looks unwieldy, as methods
like Bellman equation, value iteration, policy gradient, dynamic programming
that had tremendous success in Linear RL, fail to trivially generalize. In this
paper, we derive the policy gradient theorem for RL with general utilities. The
policy gradient theorem proves to be a cornerstone in Linear RL due to its
elegance and ease of implementability. Our policy gradient theorem for RL with
general utilities shares the same elegance and ease of implementability. Based
on the policy gradient theorem derived, we also present a simple sample-based
algorithm. We believe our results will be of interest to the community and
offer inspiration to future works in this generalized setting.

Transferring knowledge from an image synthesis model trained on a large
dataset is a promising direction for learning generative image models from
various domains efficiently. While previous works have studied GAN models, we
present a recipe for learning vision transformers by generative knowledge
transfer. We base our framework on state-of-the-art generative vision
transformers that represent an image as a sequence of visual tokens to the
autoregressive or non-autoregressive transformers. To adapt to a new domain, we
employ prompt tuning, which prepends learnable tokens called prompt to the
image token sequence, and introduce a new prompt design for our task. We study
on a variety of visual domains, including visual task adaptation
benchmark~\cite{zhai2019large}, with varying amount of training images, and
show effectiveness of knowledge transfer and a significantly better image
generation quality over existing works.

Data is the fuel powering AI and creates tremendous value for many domains.
However, collecting datasets for AI is a time-consuming, expensive, and
complicated endeavor. For practitioners, data investment remains to be a leap
of faith in practice. In this work, we study the data budgeting problem and
formulate it as two sub-problems: predicting (1) what is the saturating
performance if given enough data, and (2) how many data points are needed to
reach near the saturating performance. Different from traditional
dataset-independent methods like PowerLaw, we proposed a learning method to
solve data budgeting problems. To support and systematically evaluate the
learning-based method for data budgeting, we curate a large collection of 383
tabular ML datasets, along with their data vs performance curves. Our empirical
evaluation shows that it is possible to perform data budgeting given a small
pilot study dataset with as few as $50$ data points.

Portfolio optimization has been an area of research that has attracted a lot
of attention from researchers and financial analysts. Designing an optimum
portfolio is a complex task since it not only involves accurate forecasting of
future stock returns and risks but also needs to optimize them. This paper
presents a systematic approach to portfolio optimization using two approaches,
the hierarchical risk parity algorithm and the Eigen portfolio on seven sectors
of the Indian stock market. The portfolios are built following the two
approaches to historical stock prices from Jan 1, 2016, to Dec 31, 2020. The
portfolio performances are evaluated on the test data from Jan 1, 2021, to Nov
1, 2021. The backtesting results of the portfolios indicate that the
performance of the HRP portfolio is superior to that of its Eigen counterpart
on both training and test data for the majority of the sectors studied.

Robust imitation learning seeks to mimic expert controller behavior while
ensuring stability, but current methods require accurate plant models. Here,
robust imitation learning is addressed for stabilizing poorly modeled plants
with linear dynamic output feedback. Open-loop input-output properties are used
to characterize an uncertain plant, and the feedback matrix of the dynamic
controller is learned while enforcing stability through the controller's
open-loop QSR-dissipativity properties. The imitation learning method is
applied to two systems with parametric uncertainty.

The problem of identifying the best arm among a collection of items having
Gaussian rewards distribution is well understood when the variances are known.
Despite its practical relevance for many applications, few works studied it for
unknown variances. In this paper we introduce and analyze two approaches to
deal with unknown variances, either by plugging in the empirical variance or by
adapting the transportation costs. In order to calibrate our two stopping
rules, we derive new time-uniform concentration inequalities, which are of
independent interest. Then, we illustrate the theoretical and empirical
performances of our two sampling rule wrappers on Track-and-Stop and on a Top
Two algorithm. Moreover, by quantifying the impact on the sample complexity of
not knowing the variances, we reveal that it is rather small.

Imposing explicit constraints is relatively new but increasingly pressing in
deep learning, stimulated by, e.g., trustworthy AI that performs robust
optimization over complicated perturbation sets and scientific applications
that need to respect physical laws and constraints. However, it can be hard to
reliably solve constrained deep learning problems without optimization
expertise. The existing deep learning frameworks do not admit constraints.
General-purpose optimization packages can handle constraints but do not perform
auto-differentiation and have trouble dealing with nonsmoothness. In this
paper, we introduce a new software package called NCVX, whose initial release
contains the solver PyGRANSO, a PyTorch-enabled general-purpose optimization
package for constrained machine/deep learning problems, the first of its kind.
NCVX inherits auto-differentiation, GPU acceleration, and tensor variables from
PyTorch, and is built on freely available and widely used open-source
frameworks. NCVX is available at https://ncvx.org, with detailed documentation
and numerous examples from machine/deep learning and other fields.

Proton FLASH therapy leverages ultra-high dose-rate radiation to enhance the
sparing of organs at risk without compromising tumor control probability. To
prepare for the delivery of high doses to targets, we aim to develop a deep
learning-based image-guide framework to enable fast volumetric image
reconstruction for accurate target localization before FLSAH beam delivery. The
proposed framework comprises four modules, including orthogonal kV x-ray
projection acquisition, DL-based volumetric image generation, image quality
analyses, and water equivalent thickness evaluation. We investigated volumetric
image reconstruction using four kV projection pairs with different source
angles. Thirty lung patients were identified from the institutional database,
and each patient contains a four-dimensional computed tomography dataset with
ten respiratory phases. The retrospective patient study indicated that the
proposed framework could reconstruct patient volumetric anatomy, including
tumors and organs at risk from orthogonal x-ray projections. Considering all
evaluation metrics, the kV projections with source angles of 135 and 225
degrees yielded the optimal volumetric images. The proposed framework has been
demonstrated to reconstruct volumetric images with accurate lesion locations
from two orthogonal x-ray projections. The embedded WET module can be used to
detect potential proton beam-specific patient anatomy variations. The framework
can deliver fast volumetric image generation and can potentially guide
treatment delivery systems for proton FLASH therapy.

Text-to-image generation models have recently attracted unprecedented
attention as they unlatch imaginative applications in all areas of life.
However, developing such models requires huge amounts of data that might
contain privacy-sensitive information, e.g., face identity. While privacy risks
have been extensively demonstrated in the image classification and GAN
generation domains, privacy risks in the text-to-image generation domain are
largely unexplored. In this paper, we perform the first privacy analysis of
text-to-image generation models through the lens of membership inference.
Specifically, we propose three key intuitions about membership information and
design four attack methodologies accordingly. We conduct comprehensive
evaluations on two mainstream text-to-image generation models including
sequence-to-sequence modeling and diffusion-based modeling. The empirical
results show that all of the proposed attacks can achieve significant
performance, in some cases even close to an accuracy of 1, and thus the
corresponding risk is much more severe than that shown by existing membership
inference attacks. We further conduct an extensive ablation study to analyze
the factors that may affect the attack performance, which can guide developers
and researchers to be alert to vulnerabilities in text-to-image generation
models. All these findings indicate that our proposed attacks pose a realistic
privacy threat to the text-to-image generation models.

Rapid advances in artificial intelligence (AI) in the last decade have
largely been built upon the wide applications of deep learning (DL). However,
the high carbon footprint yielded by larger and larger DL networks becomes a
concern for sustainability. Furthermore, DL decision mechanism is somewhat
obsecure and can only be verified by test data. Green learning (GL) has been
proposed as an alternative paradigm to address these concerns. GL is
characterized by low carbon footprints, small model sizes, low computational
complexity, and logical transparency. It offers energy-effective solutions in
cloud centers as well as mobile/edge devices. GL also provides a clear and
logical decision-making process to gain people's trust. Several statistical
tools have been developed to achieve this goal in recent years. They include
subspace approximation, unsupervised and supervised representation learning,
supervised discriminant feature selection, and feature space partitioning. We
have seen a few successful GL examples with performance comparable with
state-of-the-art DL solutions. This paper offers an introduction to GL, its
demonstrated applications, and future outlook.

In adversarial machine learning, deep neural networks can fit the adversarial
examples on the training dataset but have poor generalization ability on the
test set. This phenomenon is called robust overfitting, and it can be observed
when adversarially training neural nets on common datasets, including SVHN,
CIFAR-10, CIFAR-100, and ImageNet. In this paper, we study the robust
overfitting issue of adversarial training by using tools from uniform
stability. One major challenge is that the outer function (as a maximization of
the inner function) is nonsmooth, so the standard technique (e.g., hardt et
al., 2016) cannot be applied. Our approach is to consider $\eta$-approximate
smoothness: we show that the outer function satisfies this modified smoothness
assumption with $\eta$ being a constant related to the adversarial perturbation
$\epsilon$. Based on this, we derive stability-based generalization bounds for
stochastic gradient descent (SGD) on the general class of $\eta$-approximate
smooth functions, which covers the adversarial loss. Our results suggest that
robust test accuracy decreases in $\epsilon$ when $T$ is large, with a speed
between $\Omega(\epsilon\sqrt{T})$ and $\mathcal{O}(\epsilon T)$. This
phenomenon is also observed in practice. Additionally, we show that a few
popular techniques for adversarial training (e.g., early stopping, cyclic
learning rate, and stochastic weight averaging) are stability-promoting in
theory.

Deepfakes pose severe threats of visual misinformation to our society. One
representative deepfake application is face manipulation that modifies a
victim's facial attributes in an image, e.g., changing her age or hair color.
The state-of-the-art face manipulation techniques rely on Generative
Adversarial Networks (GANs). In this paper, we propose the first defense
system, namely UnGANable, against GAN-inversion-based face manipulation. In
specific, UnGANable focuses on defending GAN inversion, an essential step for
face manipulation. Its core technique is to search for alternative images
(called cloaked images) around the original images (called target images) in
image space. When posted online, these cloaked images can jeopardize the GAN
inversion process. We consider two state-of-the-art inversion techniques
including optimization-based inversion and hybrid inversion, and design five
different defenses under five scenarios depending on the defender's background
knowledge. Extensive experiments on four popular GAN models trained on two
benchmark face datasets show that UnGANable achieves remarkable effectiveness
and utility performance, and outperforms multiple baseline methods. We further
investigate four adaptive adversaries to bypass UnGANable and show that some of
them are slightly effective.

We introduce a machine learning-powered course allocation mechanism.
Concretely, we extend the state-of-the-art Course Match mechanism with a
machine learning-based preference elicitation module. In an iterative,
asynchronous manner, this module generates pairwise comparison queries that are
tailored to each individual student. Regarding incentives, our machine
learning-powered course match (MLCM) mechanism retains the attractive
strategyproofness in the large property of Course Match. Regarding welfare, we
perform computational experiments using a simulator that was fitted to
real-world data. We find that, compared to Course Match, MLCM is able to
increase average student utility by 4%-9% and minimum student utility by
10%-21%, even with only ten comparison queries.

We consider Linear Stochastic Approximation (LSA) with a constant stepsize
and Markovian data. Viewing the joint process of the data and LSA iterate as a
time-homogeneous Markov chain, we prove its convergence to a unique limiting
and stationary distribution in Wasserstein distance and establish
non-asymptotic, geometric convergence rates. Furthermore, we show that the bias
vector of this limit admits an infinite series expansion with respect to the
stepsize. Consequently, the bias is proportional to the stepsize up to higher
order terms. This result stands in contrast with LSA under i.i.d. data, for
which the bias vanishes. In the reversible chain setting, we provide a general
characterization of the relationship between the bias and the mixing time of
the Markovian data, establishing that they are roughly proportional to each
other.
  While Polyak-Ruppert tail-averaging reduces the variance of the LSA iterates,
it does not affect the bias. The above characterization allows us to show that
the bias can be reduced using Richardson-Romberg extrapolation with $m \ge 2$
stepsizes, which eliminates the $m - 1$ leading terms in the bias expansion.
This extrapolation scheme leads to an exponentially smaller bias and an
improved mean squared error, both in theory and empirically. Our results
immediately apply to the Temporal Difference learning algorithm with linear
function approximation, Markovian data and constant stepsizes.

Most of the vision-based sign language research to date has focused on
Isolated Sign Language Recognition (ISLR), where the objective is to predict a
single sign class given a short video clip. Although there has been significant
progress in ISLR, its real-life applications are limited. In this paper, we
focus on the challenging task of Sign Spotting instead, where the goal is to
simultaneously identify and localise signs in continuous co-articulated sign
videos. To address the limitations of current ISLR-based models, we propose a
hierarchical sign spotting approach which learns coarse-to-fine spatio-temporal
sign features to take advantage of representations at various temporal levels
and provide more precise sign localisation. Specifically, we develop
Hierarchical Sign I3D model (HS-I3D) which consists of a hierarchical network
head that is attached to the existing spatio-temporal I3D model to exploit
features at different layers of the network. We evaluate HS-I3D on the ChaLearn
2022 Sign Spotting Challenge - MSSL track and achieve a state-of-the-art 0.607
F1 score, which was the top-1 winning solution of the competition.

CRRA utility where the risk aversion coefficient is a constant is commonly
seen in various economics models. But wealth-driven risk aversion rarely shows
up in investor's investment problems. This paper mainly focus on numerical
solutions to the optimal consumption-investment choices under wealth-driven
aversion done by neural network. A jump-diffusion model is used to simulate the
artificial data that is needed for the neural network training. The WDRA Model
is set up for describing the investment problem and there are two parameters
that require to be optimized, which are the investment rate of the wealth on
the risky assets and the consumption during the investment time horizon. Under
this model, neural network LSTM with one objective function is implemented and
shows promising results.

Stable Diffusion is a recent open-source image generation model comparable to
proprietary models such as DALLE, Imagen, or Parti. Stable Diffusion comes with
a safety filter that aims to prevent generating explicit images. Unfortunately,
the filter is obfuscated and poorly documented. This makes it hard for users to
prevent misuse in their applications, and to understand the filter's
limitations and improve it. We first show that it is easy to generate
disturbing content that bypasses the safety filter. We then reverse-engineer
the filter and find that while it aims to prevent sexual content, it ignores
violence, gore, and other similarly disturbing content. Based on our analysis,
we argue safety measures in future model releases should strive to be fully
open and properly documented to stimulate security contributions from the

Greedy layer-wise or module-wise training of neural networks is compelling in
constrained and on-device settings, as it circumvents a number of problems of
end-to-end back-propagation. However, it suffers from a stagnation problem,
whereby early layers overfit and deeper layers stop increasing the test
accuracy after a certain depth. We propose to solve this issue by introducing a
simple module-wise regularization inspired by the minimizing movement scheme
for gradient flows in distribution space. The method, which we call TRGL for
Transport Regularized Greedy Learning, is particularly well-adapted to residual
networks. We study it theoretically, proving that it leads to greedy modules
that are regular and that successively solve the task. Experimentally, we show
improved accuracy of module-wise trained networks when our regularization is
added.

This paper addresses a novel multi-agent deep reinforcement learning
(MADRL)-based multiple unmanned aerial vehicles (UAV) positioning algorithm for
reliable mobile access services (i.e., UAVs work as mobile base stations),
where the MADRL is designed by the concept of centralized training and
distributed execution (CTDE) for multi-agent cooperation and coordination. The
reliable mobile access services can be achieved in following two ways, i.e.,
(i) energy-efficient UAV operation and (ii) reliable wireless communication
services. For energy-efficient UAV operation, the reward of our proposed MADRL
algorithm contains the features for UAV energy consumption models in order to
realize efficient operations. Furthermore, for reliable wireless communication
services, the quality of service (QoS) requirements of individual users are
considered as a part of rewards and 60GHz mmWave radio is used for mobile
access. This paper considers the 60GHz mmWave access for utilizing the benefits
of (i) ultra-wide-bandwidth for multi-Gbps high-speed communications and (ii)
high-directional communications for spatial reuse that is obviously good for
densely deployed users. Lastly, the performance of our proposed MADRL-based
multi-UAV positioning algorithm is evaluated; and it can be confirmed that the
proposed algorithm outperforms the other existing algorithms.

Self-supervised learning has been widely applied to train high-quality vision
transformers. Unleashing their excellent performance on memory and compute
constraint devices is therefore an important research topic. However, how to
distill knowledge from one self-supervised ViT to another has not yet been
explored. Moreover, the existing self-supervised knowledge distillation (SSKD)
methods focus on ConvNet based architectures are suboptimal for ViT knowledge
distillation. In this paper, we study knowledge distillation of self-supervised
vision transformers (ViT-SSKD). We show that directly distilling information
from the crucial attention mechanism from teacher to student can significantly
narrow the performance gap between both. In experiments on ImageNet-Subset and
ImageNet-1K, we show that our method AttnDistill outperforms existing
self-supervised knowledge distillation (SSKD) methods and achieves
state-of-the-art k-NN accuracy compared with self-supervised learning (SSL)
methods learning from scratch (with the ViT-S model). We are also the first to
apply the tiny ViT-T model on self-supervised learning. Moreover, AttnDistill
is independent of self-supervised learning algorithms, it can be adapted to ViT
based SSL methods to improve the performance in future research. The code is
here: https://github.com/wangkai930418/attndistill

Unsupervised multimodal change detection is a practical and challenging topic
that can play an important role in time-sensitive emergency applications. To
address the challenge that multimodal remote sensing images cannot be directly
compared due to their modal heterogeneity, we take advantage of two types of
modality-independent structural relationships in multimodal images. In
particular, we present a structural relationship graph representation learning
framework for measuring the similarity of the two structural relationships.
Firstly, structural graphs are generated from preprocessed multimodal image
pairs by means of an object-based image analysis approach. Then, a structural
relationship graph convolutional autoencoder (SR-GCAE) is proposed to learn
robust and representative features from graphs. Two loss functions aiming at
reconstructing vertex information and edge information are presented to make
the learned representations applicable for structural relationship similarity
measurement. Subsequently, the similarity levels of two structural
relationships are calculated from learned graph representations and two
difference images are generated based on the similarity levels. After obtaining
the difference images, an adaptive fusion strategy is presented to fuse the two
difference images. Finally, a morphological filtering-based postprocessing
approach is employed to refine the detection results. Experimental results on
five datasets with different modal combinations demonstrate the effectiveness
of the proposed method.

Lifelong language learning seeks to have models continuously learn multiple
tasks in a sequential order without suffering from catastrophic forgetting.
State-of-the-art approaches rely on sparse experience replay as the primary
approach to prevent forgetting. Experience replay usually adopts sampling
methods for the memory population; however, the effect of the chosen sampling
strategy on model performance has not yet been studied. In this paper, we
investigate how relevant the selective memory population is in the lifelong
learning process of text classification and question-answering tasks. We found
that methods that randomly store a uniform number of samples from the entire
data stream lead to high performances, especially for low memory size, which is
consistent with computer vision studies.

Following generative adversarial networks (GANs), a de facto standard model
for image generation, denoising diffusion models (DDMs) have been actively
researched and attracted strong attention due to their capability to generate
images with high quality and diversity. However, the way the internal
self-attention mechanism works inside the UNet of DDMs is under-explored. To
unveil them, in this paper, we first investigate the self-attention operations
within the black-boxed diffusion models and build hypotheses. Next, we verify
the hypotheses about the self-attention map by conducting frequency analysis
and testing the relationships with the generated objects. In consequence, we
find out that the attention map is closely related to the quality of generated
images. On the other hand, diffusion guidance methods based on additional
information such as labels are proposed to improve the quality of generated
images. Inspired by these methods, we present label-free guidance based on the
intermediate self-attention map that can guide existing pretrained diffusion
models to generate images with higher fidelity. In addition to the enhanced
sample quality when used alone, we show that the results are further improved
by combining our method with classifier guidance on ImageNet 128x128.

Group equivariant convolutional neural networks (G-CNNs) have been
successfully applied in geometric deep learning. Typically, G-CNNs have the
advantage over CNNs that they do not waste network capacity on training
symmetries that should have been hard-coded in the network. The recently
introduced framework of PDE-based G-CNNs (PDE-G-CNNs) generalises G-CNNs.
PDE-G-CNNs have the core advantages that they simultaneously 1) reduce network
complexity, 2) increase classification performance, and 3) provide geometric
interpretability. Their implementations primarily consist of linear and
morphological convolutions with kernels.
  In this paper we show that the previously suggested approximative
morphological kernels do not always accurately approximate the exact kernels
accurately. More specifically, depending on the spatial anisotropy of the
Riemannian metric, we argue that one must resort to sub-Riemannian
approximations. We solve this problem by providing a new approximative kernel
that works regardless of the anisotropy. We provide new theorems with better
error estimates of the approximative kernels, and prove that they all carry the
same reflectional symmetries as the exact ones.
  We test the effectiveness of multiple approximative kernels within the
PDE-G-CNN framework on two datasets, and observe an improvement with the new
approximative kernels. We report that the PDE-G-CNNs again allow for a
considerable reduction of network complexity while having comparable or better
performance than G-CNNs and CNNs on the two datasets. Moreover, PDE-G-CNNs have
the advantage of better geometric interpretability over G-CNNs, as the
morphological kernels are related to association fields from neurogeometry.

No-reference image quality assessment (NR-IQA) aims to quantify how humans
perceive visual distortions of digital images without access to their
undistorted references. NR-IQA models are extensively studied in computational
vision, and are widely used for performance evaluation and perceptual
optimization of man-made vision systems. Here we make one of the first attempts
to examine the perceptual robustness of NR-IQA models. Under a Lagrangian
formulation, we identify insightful connections of the proposed perceptual
attack to previous beautiful ideas in computer vision and machine learning. We
test one knowledge-driven and three data-driven NR-IQA methods under four
full-reference IQA models (as approximations to human perception of
just-noticeable differences). Through carefully designed psychophysical
experiments, we find that all four NR-IQA models are vulnerable to the proposed
perceptual attack. More interestingly, we observe that the generated
counterexamples are not transferable, manifesting themselves as distinct design
flows of respective NR-IQA methods.

While PAC-Bayes is now an established learning framework for bounded losses,
its extension to the case of unbounded losses (as simple as the squared loss on
an unbounded space) remains largely uncharted and has attracted a growing
interest in recent years. We contribute to this line of work by developing an
extention of Markov's inequality for supermartingales, which we use to
establish a novel PAC-Bayesian generalisation bound holding for unbounded
losses. We show that this bound extends, unifies and even improves on existing
PAC-Bayesian bounds.

In many automation tasks involving manipulation of rigid objects, the poses
of the objects must be acquired. Vision-based pose estimation using a single
RGB or RGB-D sensor is especially popular due to its broad applicability.
However, single-view pose estimation is inherently limited by depth ambiguity
and ambiguities imposed by various phenomena like occlusion, self-occlusion,
reflections, etc. Aggregation of information from multiple views can
potentially resolve these ambiguities, but the current state-of-the-art
multi-view pose estimation method only uses multiple views to aggregate
single-view pose estimates, and thus rely on obtaining good single-view
estimates. We present a multi-view pose estimation method which aggregates
learned 2D-3D distributions from multiple views for both the initial estimate
and optional refinement. Our method performs probabilistic sampling of 3D-3D
correspondences under epipolar constraints using learned 2D-3D correspondence
distributions which are implicitly trained to respect visual ambiguities such
as symmetry. Evaluation on the T-LESS dataset shows that our method reduces
pose estimation errors by 80-91% compared to the best single-view method, and
we present state-of-the-art results on T-LESS with four views, even compared
with methods using five and eight views.

Self-attention is of vital importance in semantic segmentation as it enables
modeling of long-range context, which translates into improved performance. We
argue that it is equally important to model short-range context, especially to
tackle cases where not only the regions of interest are small and ambiguous,
but also when there exists an imbalance between the semantic classes. To this
end, we propose Masked Supervised Learning (MaskSup), an effective single-stage
learning paradigm that models both short- and long-range context, capturing the
contextual relationships between pixels via random masking. Experimental
results demonstrate the competitive performance of MaskSup against strong
baselines in both binary and multi-class segmentation tasks on three standard
benchmark datasets, particularly at handling ambiguous regions and retaining
better segmentation of minority classes with no added inference cost. In
addition to segmenting target regions even when large portions of the input are
masked, MaskSup is also generic and can be easily integrated into a variety of
semantic segmentation methods. We also show that the proposed method is
computationally efficient, yielding an improved performance by 10\% on the mean
intersection-over-union (mIoU) while requiring $3\times$ less learnable

Scene Graphs are widely applied in computer vision as a graphical
representation of relationships between objects shown in images. However, these
applications have not yet reached a practical stage of development owing to
biased training caused by long-tailed predicate distributions. In recent years,
many studies have tackled this problem. In contrast, relatively few works have
considered predicate similarities as a unique dataset feature which also leads
to the biased prediction. Due to the feature, infrequent predicates (e.g.,
parked on, covered in) are easily misclassified as closely-related frequent
predicates (e.g., on, in). Utilizing predicate similarities, we propose a new
classification scheme that branches the process to several fine-grained
classifiers for similar predicate groups. The classifiers aim to capture the
differences among similar predicates in detail. We also introduce the idea of
transfer learning to enhance the features for the predicates which lack
sufficient training samples to learn the descriptive representations. The
results of extensive experiments on the Visual Genome dataset show that the
combination of our method and an existing debiasing approach greatly improves
performance on tail predicates in challenging SGCls/SGDet tasks. Nonetheless,
the overall performance of the proposed approach does not reach that of the
current state of the art, so further analysis remains necessary as future work.

Previous virtual try-on methods usually focus on aligning a clothing item
with a person, limiting their ability to exploit the complex pose, shape and
skin color of the person, as well as the overall structure of the clothing,
which is vital to photo-realistic virtual try-on. To address this potential
weakness, we propose a fill in fabrics (FIFA) model, a self-supervised
conditional generative adversarial network based framework comprised of a
Fabricator and a unified virtual try-on pipeline with a Segmenter, Warper and
Fuser. The Fabricator aims to reconstruct the clothing image when provided with
a masked clothing as input, and learns the overall structure of the clothing by
filling in fabrics. A virtual try-on pipeline is then trained by transferring
the learned representations from the Fabricator to Warper in an effort to warp
and refine the target clothing. We also propose to use a multi-scale structural
constraint to enforce global context at multiple scales while warping the
target clothing to better fit the pose and shape of the person. Extensive
experiments demonstrate that our FIFA model achieves state-of-the-art results
on the standard VITON dataset for virtual try-on of clothing items, and is
shown to be effective at handling complex poses and retaining the texture and
embroidery of the clothing.

Domain generalization (DG) has been a hot topic in image recognition, with a
goal to train a general model that can perform well on unseen domains.
Recently, federated learning (FL), an emerging machine learning paradigm to
train a global model from multiple decentralized clients without compromising
data privacy, brings new challenges, also new possibilities, to DG. In the FL
scenario, many existing state-of-the-art (SOTA) DG methods become ineffective,
because they require the centralization of data from different domains during
training. In this paper, we propose a novel domain generalization method for
image recognition under federated learning through cross-client style transfer
(CCST) without exchanging data samples. Our CCST method can lead to more
uniform distributions of source clients, and thus make each local model learn
to fit the image styles of all the clients to avoid the different model biases.
Two types of style (single image style and overall domain style) with
corresponding mechanisms are proposed to be chosen according to different
scenarios. Our style representation is exceptionally lightweight and can hardly
be used for the reconstruction of the dataset. The level of diversity is also
flexible to be controlled with a hyper-parameter. Our method outperforms recent
SOTA DG methods on two DG benchmarks (PACS, OfficeHome) and a large-scale
medical image dataset (Camelyon17) in the FL setting. Last but not least, our
method is orthogonal to many classic DG methods, achieving additive performance
by combined utilization.

Classification and identification of wild animals for tracking and protection
purposes has become increasingly important with the deterioration of the
environment, and technology is the agent of change which augments this process
with novel solutions. Computer vision is one such technology which uses the
abilities of artificial intelligence and machine learning models on visual
inputs. Convolution neural networks (CNNs) have multiple layers which have
different weights for the purpose of prediction of a particular input. The
precedent for classification, however, is set by the image processing
techniques which provide nearly ideal input images that produce optimal
results. Image segmentation is one such widely used image processing method
which provides a clear demarcation of the areas of interest in the image, be it
regions or objects. The Efficiency of CNN can be related to the preprocessing
done before training. Further, it is a well-established fact that heterogeneity
in image sources is detrimental to the performance of CNNs. Thus, the added
functionality of heterogeneity elimination is performed by the image processing
techniques, introducing a level of consistency that sets the tone for the
excellent feature extraction and eventually in classification.

Prevalent state-of-the-art instance segmentation methods fall into a
query-based scheme, in which instance masks are derived by querying the image
feature using a set of instance-aware embeddings. In this work, we devise a new
training framework that boosts query-based models through discriminative query
embedding learning. It explores two essential properties, namely dataset-level
uniqueness and transformation equivariance, of the relation between queries and
instances. First, our algorithm uses the queries to retrieve the corresponding
instances from the whole training dataset, instead of only searching within
individual scenes. As querying instances across scenes is more challenging, the
segmenters are forced to learn more discriminative queries for effective
instance separation. Second, our algorithm encourages both image (instance)
representations and queries to be equivariant against geometric
transformations, leading to more robust, instance-query matching. On top of
four famous, query-based models ($i.e.,$ CondInst, SOLOv2, SOTR, and
Mask2Former), our training algorithm provides significant performance gains
($e.g.,$ +1.6 - 3.2 AP) on COCO dataset. In addition, our algorithm promotes
the performance of SOLOv2 by 2.7 AP, on LVISv1 dataset.

Identification of tree species plays a key role in forestry related tasks
like forest conservation, disease diagnosis and plant production. There had
been a debate regarding the part of the tree to be used for differentiation,
whether it should be leaves, fruits, flowers or bark. Studies have proven that
bark is of utmost importance as it will be present despite seasonal variations
and provides a characteristic identity to a tree by variations in the
structure. In this paper, a deep learning based approach is presented by
leveraging the method of computer vision to classify 50 tree species, on the
basis of bark texture using the BarkVN-50 dataset. This is the maximum number
of trees being considered for bark classification till now. A convolutional
neural network(CNN), ResNet101 has been implemented using transfer-learning
based technique of fine tuning to maximise the model performance. The model
produced an overall accuracy of >94% during the evaluation. The performance
validation has been done using K-Fold Cross Validation and by testing on unseen
data collected from the Internet, this proved the model's generalization
capability for real-world uses.

The impact performance of the wheel during wheel development must be ensured
through a wheel impact test for vehicle safety. However, manufacturing and
testing a real wheel take a significant amount of time and money because
developing an optimal wheel design requires numerous iterative processes of
modifying the wheel design and verifying the safety performance. Accordingly,
the actual wheel impact test has been replaced by computer simulations, such as
Finite Element Analysis (FEA), but it still requires high computational costs
for modeling and analysis. Moreover, FEA experts are needed. This study
presents an aluminum road wheel impact performance prediction model based on
deep learning that replaces the computationally expensive and time-consuming 3D
FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and
barrier mass value used for wheel impact test are utilized as the inputs to
predict the magnitude of maximum von Mises stress, corresponding location, and
the stress distribution of 2D disk-view. The wheel impact performance
prediction model can replace the impact test in the early wheel development
stage by predicting the impact performance in real time and can be used without
domain knowledge. The time required for the wheel development process can be
shortened through this mechanism.

Autonomous recording units and passive acoustic monitoring present minimally
intrusive methods of collecting bioacoustics data. Combining this data with
species agnostic bird activity detection systems enables the monitoring of
activity levels of bird populations. Unfortunately, variability in ambient
noise levels and subject distance contribute to difficulties in accurately
detecting bird activity in recordings. The choice of acoustic frontend directly
affects the impact these issues have on system performance. In this paper, we
benchmark traditional fixed-parameter acoustic frontends against the new
generation of learnable frontends on a wide-ranging bird audio detection task
using data from the DCASE2018 BAD Challenge. We observe that Per-Channel Energy
Normalization is the best overall performer, achieving an accuracy of 89.9%,
and that in general learnable frontends significantly outperform traditional
methods. We also identify challenges in learning filterbanks for bird audio.

Human health is closely associated with their daily behavior and environment.
However, keeping a healthy lifestyle is still challenging for most people as it
is difficult to recognize their living behaviors and identify their surrounding
situations to take appropriate action. Human activity recognition is a
promising approach to building a behavior model of users, by which users can
get feedback about their habits and be encouraged to develop a healthier
lifestyle. In this paper, we present a smart light wearable badge with six
kinds of sensors, including an infrared array sensor MLX90640 offering
privacy-preserving, low-cost, and non-invasive features, to recognize daily
activities in a realistic unmodified kitchen environment. A multi-channel
convolutional neural network (MC-CNN) based on data and feature fusion methods
is applied to classify 14 human activities associated with potentially
unhealthy habits. Meanwhile, we evaluate the impact of the infrared array
sensor on the recognition accuracy of these activities. We demonstrate the
performance of the proposed work to detect the 14 activities performed by ten
volunteers with an average accuracy of 92.44 % and an F1 score of 88.27 %.

Nowadays, user privacy is becoming an issue that cannot be bypassed for
system developers, especially for that of web applications where data can be
easily transferred through internet. Thankfully, federated learning proposes an
innovative method to train models with distributed devices while data are kept
in local storage. However, unlike general neural networks, although graph-based
networks have achieved great success in classification tasks and advanced
recommendation system, its high performance relies on the rich context provided
by a graph structure, which is vulnerable when data attributes are incomplete.
Therefore, the latter becomes a realistic problem when implementing federated
learning for graph-based networks. Knowing that data embedding is a
representation in a different space, we propose our Federated Graph-based
Networks with Shared Embedding (Feras), which uses shared embedding data to
train the network and avoids the direct sharing of original data. A solid
theoretical proof of the convergence of Feras is given in this work.
Experiments on different datasets (PPI, Flickr, Reddit) are conducted to show
the efficiency of Feras for centralized learning. Finally, Feras enables the
training of current graph-based models in the federated learning framework for
privacy concern.

The $k$ nearest neighbor algorithm ($k$NN) is one of the most popular
nonparametric methods used for various purposes, such as treatment effect
estimation, missing value imputation, classification, and clustering. The main
advantage of $k$NN is its simplicity of hyperparameter optimization. It often
produces favorable results with minimal effort. This paper proposes a generic
semiparametric (or nonparametric if required) approach named Local Resampler
(LR). LR utilizes $k$NN to create subsamples from the original sample and then
generates synthetic values that are drawn from locally estimated distributions.
LR can accurately create synthetic samples, even if the original sample has a
non-convex distribution. Moreover, LR shows better or similar performance to
other popular synthetic data methods with minimal model optimization with
parametric distributional assumptions.

Reinforcement learning (RL) trains many agents, which is resource-intensive
and must scale to large GPU clusters. Different RL training algorithms offer
different opportunities for distributing and parallelising the computation.
Yet, current distributed RL systems tie the definition of RL algorithms to
their distributed execution: they hard-code particular distribution strategies
and only accelerate specific parts of the computation (e.g. policy network
updates) on GPU workers. Fundamentally, current systems lack abstractions that
decouple RL algorithms from their execution.
  We describe MindSpore Reinforcement Learning (MSRL), a distributed RL
training system that supports distribution policies that govern how RL training
computation is parallelised and distributed on cluster resources, without
requiring changes to the algorithm implementation. MSRL introduces the new
abstraction of a fragmented dataflow graph, which maps Python functions from an
RL algorithm's training loop to parallel computational fragments. Fragments are
executed on different devices by translating them to low-level dataflow
representations, e.g. computational graphs as supported by deep learning
engines, CUDA implementations or multi-threaded CPU processes. We show that
MSRL subsumes the distribution strategies of existing systems, while scaling RL
training to 64 GPUs.

Today, many industrial processes are undergoing digital transformation, which
often requires the integration of well-understood domain models and
state-of-the-art machine learning technology in business processes. However,
requirements elicitation and design decision making about when, where and how
to embed various domain models and end-to-end machine learning techniques
properly into a given business workflow requires further exploration. This
paper aims to provide an overview of the requirements engineering process for
machine learning applications in terms of cross domain collaborations. We first
review the literature on requirements engineering for machine learning, and
then go through the collaborative requirements analysis process step-by-step.
An example case of industrial data-driven intelligence applications is also
discussed in relation to the aforementioned steps.

Functional data clustering is to identify heterogeneous morphological
patterns in the continuous functions underlying the discrete
measurements/observations. Application of functional data clustering has
appeared in many publications across various fields of sciences, including but
not limited to biology, (bio)chemistry, engineering, environmental science,
medical science, psychology, social science, etc. The phenomenal growth of the
application of functional data clustering indicates the urgent need for a
systematic approach to develop efficient clustering methods and scalable
algorithmic implementations. On the other hand, there is abundant literature on
the cluster analysis of time series, trajectory data, spatio-temporal data,
etc., which are all related to functional data. Therefore, an overarching
structure of existing functional data clustering methods will enable the
cross-pollination of ideas across various research fields. We here conduct a
comprehensive review of original clustering methods for functional data. We
propose a systematic taxonomy that explores the connections and differences
among the existing functional data clustering methods and relates them to the
conventional multivariate clustering methods. The structure of the taxonomy is
built on three main attributes of a functional data clustering method and
therefore is more reliable than existing categorizations. The review aims to
bridge the gap between the functional data analysis community and the
clustering community and to generate new principles for functional data
clustering.

The Vision Transformer (ViT) architecture has recently established its place
in the computer vision literature, with multiple architectures for recognition
of image data or other visual modalities. However, training ViTs for RGB-D
object recognition remains an understudied topic, viewed in recent literature
only through the lens of multi-task pretraining in multiple modalities. Such
approaches are often computationally intensive and have not yet been applied
for challenging object-level classification tasks. In this work, we propose a
simple yet strong recipe for transferring pretrained ViTs in RGB-D domains for
single-view 3D object recognition, focusing on fusing RGB and depth
representations encoded jointly by the ViT. Compared to previous works in
multimodal Transformers, the key challenge here is to use the atested
flexibility of ViTs to capture cross-modal interactions at the downstream and
not the pretraining stage. We explore which depth representation is better in
terms of resulting accuracy and compare two methods for injecting RGB-D fusion
within the ViT architecture (i.e., early vs. late fusion). Our results in the
Washington RGB-D Objects dataset demonstrates that in such RGB $\rightarrow$
RGB-D scenarios, late fusion techniques work better than most popularly
employed early fusion. With our transfer baseline, adapted ViTs score up to
95.1\% top-1 accuracy in Washington, achieving new state-of-the-art results in
this benchmark. We additionally evaluate our approach with an open-ended
lifelong learning protocol, where we show that our adapted RGB-D encoder leads
to features that outperform unimodal encoders, even without explicit
fine-tuning. We further integrate our method with a robot framework and
demonstrate how it can serve as a perception utility in an interactive robot
learning scenario, both in simulation and with a real robot.

Machine learning on electromyography (EMG) has recently achieved remarkable
success on a variety of tasks, while such success relies heavily on the
assumption that the training and future data must be of the same data
distribution. However, this assumption may not hold in many real-world
applications. Model calibration is required via data re-collection and label
annotation, which is generally very expensive and time-consuming. To address
this problem, transfer learning (TL), which aims to improve target learners'
performance by transferring the knowledge from related source domains, is
emerging as a new paradigm to reduce the amount of calibration effort. In this
survey, we assess the eligibility of more than fifty published peer-reviewed
representative transfer learning approaches for EMG applications. Unlike
previous surveys on purely transfer learning or EMG-based machine learning,
this survey aims to provide an insight into the biological foundations of
existing transfer learning methods on EMG-related analysis. In specific, we
first introduce the physiological structure of the muscles and the EMG
generating mechanism, and the recording of EMG to provide biological insights
behind existing transfer learning approaches. Further, we categorize existing
research endeavors into data based, model based, training scheme based, and
adversarial based. This survey systematically summarizes and categorizes
existing transfer learning approaches for EMG related machine learning
applications. In addition, we discuss possible drawbacks of existing works and
point out the future direction of better EMG transfer learning algorithms to
enhance practicality for real-world applications.

Multi-domain image-to-image (I2I) translations can transform a source image
according to the style of a target domain. One important, desired
characteristic of these transformations, is their graduality, which corresponds
to a smooth change between the source and the target image when their
respective latent-space representations are linearly interpolated. However,
state-of-the-art methods usually perform poorly when evaluated using
inter-domain interpolations, often producing abrupt changes in the appearance
or non-realistic intermediate images. In this paper, we argue that one of the
main reasons behind this problem is the lack of sufficient inter-domain
training data and we propose two different regularization methods to alleviate
this issue: a new shrinkage loss, which compacts the latent space, and a Mixup
data-augmentation strategy, which flattens the style representations between
domains. We also propose a new metric to quantitatively evaluate the degree of
the interpolation smoothness, an aspect which is not sufficiently covered by
the existing I2I translation metrics. Using both our proposed metric and
standard evaluation protocols, we show that our regularization techniques can
improve the state-of-the-art multi-domain I2I translations by a large margin.
Our code will be made publicly available upon the acceptance of this article.

Low-overhead visual place recognition (VPR) is a highly active research
topic. Mobile robotics applications often operate under low-end hardware, and
even more hardware capable systems can still benefit from freeing up onboard
system resources for other navigation tasks. This work addresses lightweight
VPR by proposing a novel system based on the combination of binary-weighted
classifier networks with a one-dimensional convolutional network, dubbed
merger. Recent work in fusing multiple VPR techniques has mainly focused on
increasing VPR performance, with computational efficiency not being highly
prioritized. In contrast, we design our technique prioritizing low inference
times, taking inspiration from the machine learning literature where the
efficient combination of classifiers is a heavily researched topic. Our
experiments show that the merger achieves inference times as low as 1
millisecond, being significantly faster than other well-established lightweight
VPR techniques, while achieving comparable or superior VPR performance on
several visual changes such as seasonal variations and viewpoint lateral
shifts.

We study reinforcement learning for continuous-time Markov decision processes
(MDPs) in the finite-horizon episodic setting. We present a learning algorithm
based on the methods of value iteration and upper confidence bound. We derive
an upper bound on the worst-case expected regret for the proposed algorithm,
and establish a worst-case lower bound, both bounds are of the order of
square-root on the number of episodes. Finally, we conduct simulation
experiments to illustrate the performance of our algorithm.

The idea of embedding optimization problems into deep neural networks as
optimization layers to encode constraints and inductive priors has taken hold
in recent years. Most existing methods focus on implicitly differentiating
Karush-Kuhn-Tucker (KKT) conditions in a way that requires expensive
computations on the Jacobian matrix, which can be slow and memory-intensive. In
this paper, we developed a new framework, named Alternating Differentiation
(Alt-Diff), that differentiates optimization problems (here, specifically in
the form of convex optimization problems with polyhedral constraints) in a fast
and recursive way. Alt-Diff decouples the differentiation procedure into a
primal update and a dual update in an alternating way. Accordingly, Alt-Diff
substantially decreases the dimensions of the Jacobian matrix and thus
significantly increases the computational speed of implicit differentiation.
Further, we present the computational complexity of the forward and backward
pass of Alt-Diff and show that Alt-Diff enjoys quadratic computational
complexity in the backward pass. Another notable difference between Alt-Diff
and state-of-the-arts is that Alt-Diff can be truncated for the optimization
layer. We theoretically show that: 1) Alt-Diff can converge to consistent
gradients obtained by differentiating KKT conditions; 2) the error between the
gradient obtained by the truncated Alt-Diff and by differentiating KKT
conditions is upper bounded by the same order of variables' truncation error.
Therefore, Alt-Diff can be truncated to further increases computational speed
without sacrificing much accuracy. A series of comprehensive experiments
demonstrate that Alt-Diff yields results comparable to the state-of-the-arts in
far less time.

Accurately predicting road networks from satellite images requires a global
understanding of the network topology. We propose to capture such high-level
information by introducing a graph-based framework that simulates the addition
of sequences of graph edges using a reinforcement learning (RL) approach. In
particular, given a partially generated graph associated with a satellite
image, an RL agent nominates modifications that maximize a cumulative reward.
As opposed to standard supervised techniques that tend to be more restricted to
commonly used surrogate losses, these rewards can be based on various complex,
potentially non-continuous, metrics of interest. This yields more power and
flexibility to encode problem-dependent knowledge. Empirical results on several
benchmark datasets demonstrate enhanced performance and increased high-level
reasoning about the graph topology when using a tree-based search. We further
highlight the superiority of our approach under substantial occlusions by
introducing a new synthetic benchmark dataset for this task.

A platoon-based driving is a technology allowing vehicles to follow each
other at close distances to, e.g., save fuel. However, it requires reliable
wireless communications to adjust their speeds. Recent studies have shown that
the frequency band dedicated for vehicle-to-vehicle communications can be too
busy for intra-platoon communications. Thus it is reasonable to use additional
spectrum resources, of low occupancy, i.e., secondary spectrum channels. The
challenge is to model the interference in those channels to enable proper
channel selection. In this paper, we propose a two-layered Radio Environment
Map (REM) that aims at providing platoons with accurate location-dependent
interference models by using the Federated Learning approach. Each platoon is
equipped with a Local REM that is updated on the basis of raw interference
samples and previous interference model stored in the Global REM. The model in
global REM is obtained by merging models reported by platoons. The nodes
exchange only parameters of interference models, reducing the required control
channel capacity. Moreover, in the proposed architecture platoon can utilize
Local REM to predict channel occupancy, even when the connection to the Global
REM is temporarily unavailable. The proposed system is validated via computer
simulations considering non-trivial interference patterns.

We have gained access to vast amounts of multi-omics data thanks to Next
Generation Sequencing. However, it is challenging to analyse this data due to
its high dimensionality and much of it not being annotated. Lack of annotated
data is a significant problem in machine learning, and Self-Supervised Learning
(SSL) methods are typically used to deal with limited labelled data. However,
there is a lack of studies that use SSL methods to exploit inter-omics
relationships on unlabelled multi-omics data. In this work, we develop a novel
and efficient pre-training paradigm that consists of various SSL components,
including but not limited to contrastive alignment, data recovery from
corrupted samples, and using one type of omics data to recover other omic
types. Our pre-training paradigm improves performance on downstream tasks with
limited labelled data. We show that our approach outperforms the
state-of-the-art method in cancer type classification on the TCGA pan-cancer
dataset in semi-supervised setting. Moreover, we show that the encoders that
are pre-trained using our approach can be used as powerful feature extractors
even without fine-tuning. Our ablation study shows that the method is not
overly dependent on any pretext task component. The network architectures in
our approach are designed to handle missing omic types and multiple datasets
for pre-training and downstream training. Our pre-training paradigm can be
extended to perform zero-shot classification of rare cancers.

Over the years, the paradigm of medical image analysis has shifted from
manual expertise to automated systems, often using deep learning (DL) systems.
The performance of deep learning algorithms is highly dependent on data
quality. Particularly for the medical domain, it is an important aspect as
medical data is very sensitive to quality and poor quality can lead to
misdiagnosis. To improve the diagnostic performance, research has been done
both in complex DL architectures and in improving data quality using dataset
dependent static hyperparameters. However, the performance is still constrained
due to data quality and overfitting of hyperparameters to a specific dataset.
To overcome these issues, this paper proposes random data augmentation based
enhancement. The main objective is to develop a generalized, data-independent
and computationally efficient enhancement approach to improve medical data
quality for DL. The quality is enhanced by improving the brightness and
contrast of images. In contrast to the existing methods, our method generates
enhancement hyperparameters randomly within a defined range, which makes it
robust and prevents overfitting to a specific dataset. To evaluate the
generalization of the proposed method, we use four medical datasets and compare
its performance with state-of-the-art methods for both classification and
segmentation tasks. For grayscale imagery, experiments have been performed
with: COVID-19 chest X-ray, KiTS19, and for RGB imagery with: LC25000 datasets.
Experimental results demonstrate that with the proposed enhancement
methodology, DL architectures outperform other existing methods. Our code is
publicly available at:
https://github.com/aleemsidra/Augmentation-Based-Generalized-Enhancement

We consider the problem of estimating the marginal independence structure of
a Bayesian network from observational data in the form of an undirected graph
called the unconditional dependence graph. We show that unconditional
dependence graphs correspond to the graphs having equal independence and
intersection numbers. Using this observation, a Gr\"obner basis for a toric
ideal associated to unconditional dependence graphs is given and then extended
by additional binomial relations to connect the space of unconditional
dependence graphs. An MCMC method, called GrUES (Gr\"obner-based Unconditional
Equivalence Search), is implemented based on the resulting moves and applied to
synthetic Gaussian data. GrUES recovers the true marginal independence
structure via a BIC-optimal or MAP estimate at a higher rate than simple
independence tests while also yielding an estimate of the posterior, for which
the $20\%$ HPD credible sets include the true structure at a high rate for
graphs with density at least $0.5$.

The PADME apparatus was built at the Frascati National Laboratory of INFN to
search for a dark photon ($A'$) produced via the process $e^+ e^- \rightarrow
A' \gamma$. The central component of the PADME detector is an electromagnetic
calorimeter composed of 616 BGO crystals dedicated to the measurement of the
energy and position of the final state photons. The high beam particle
multiplicity over a short bunch duration requires reliable identification and
measurement of overlapping signals. A regression machine-learning-based
algorithm has been developed to disentangle with high efficiency close-in-time
events and precisely reconstruct the amplitude of the hits and the time with
sub-nanosecond resolution. The performance of the algorithm and the sequence of
improvements leading to the achieved results are presented and discussed.

Object detection algorithms allow to enable many interesting applications
which can be implemented in different devices, such as smartphones and wearable
devices. In the context of a cultural site, implementing these algorithms in a
wearable device, such as a pair of smart glasses, allow to enable the use of
augmented reality (AR) to show extra information about the artworks and enrich
the visitors' experience during their tour. However, object detection
algorithms require to be trained on many well annotated examples to achieve
reasonable results. This brings a major limitation since the annotation process
requires human supervision which makes it expensive in terms of time and costs.
A possible solution to reduce these costs consist in exploiting tools to
automatically generate synthetic labeled images from a 3D model of the site.
However, models trained with synthetic data do not generalize on real images
acquired in the target scenario in which they are supposed to be used.
Furthermore, object detectors should be able to work with different wearable
devices or different mobile devices, which makes generalization even harder. In
this paper, we present a new dataset collected in a cultural site to study the
problem of domain adaptation for object detection in the presence of multiple
unlabeled target domains corresponding to different cameras and a labeled
source domain obtained considering synthetic images for training purposes. We
present a new domain adaptation method which outperforms current
state-of-the-art approaches combining the benefits of aligning the domains at
the feature and pixel level with a self-training process. We release the
dataset at the following link https://iplab.dmi.unict.it/OBJ-MDA/ and the code
of the proposed architecture at https://github.com/fpv-iplab/STMDA-RetinaNet.

We study the training of deep neural networks by gradient descent where
floating-point arithmetic is used to compute the gradients. In this framework
and under realistic assumptions, we demonstrate that it is highly unlikely to
find ReLU neural networks that maintain, in the course of training with
gradient descent, superlinearly many affine pieces with respect to their number
of layers. In virtually all approximation theoretical arguments which yield
high order polynomial rates of approximation, sequences of ReLU neural networks
with exponentially many affine pieces compared to their numbers of layers are
used. As a consequence, we conclude that approximating sequences of ReLU neural
networks resulting from gradient descent in practice differ substantially from
theoretically constructed sequences. The assumptions and the theoretical
results are compared to a numerical study, which yields concurring results.

Reaching tasks with random targets and obstacles can still be challenging
when the robotic arm is operating in unstructured environments. In contrast to
traditional model-based methods, model-free reinforcement learning methods do
not require complex inverse kinematics or dynamics equations to be calculated.
In this paper, we train a deep neural network via an improved Proximal Policy
Optimization (PPO) algorithm, which aims to map from task space to joint space
for a 6-DoF manipulator. In particular, we modify the original PPO and design
an effective representation for environmental inputs and outputs to train the
robot faster in a larger workspace. Firstly, a type of action ensemble is
adopted to improve output efficiency. Secondly, the policy is designed to join
in value function updates directly. Finally, the distance between obstacles and
links of the manipulator is calculated based on a geometry method as part of
the representation of states. Since training such a task in real-robot is
time-consuming and strenuous, we develop a simulation environment to train the
model. We choose Gazebo as our first simulation environment since it often
produces a smaller Sim-to-Real gap than other simulators. However, the training
process in Gazebo is time-consuming and takes a long time. Therefore, to
address this limitation, we propose a Sim-to-Sim method to reduce the training
time significantly. The trained model is finally used in a real-robot setup
without fine-tuning. Experimental results showed that using our method, the
robot was capable of tracking a single target or reaching multiple targets in
unstructured environments.

Background: Drug synergy occurs when the combined effect of two drugs is
greater than the sum of the individual drugs' effect. While cell line data
measuring the effect of single drugs are readily available, there is relatively
less comparable data on drug synergy given the vast amount of possible drug
combinations. Thus, there is interest to use computational approaches to
predict drug synergy for untested pairs of drugs.
  Methods: We introduce a Graph Neural Network (GNN) based model for drug
synergy prediction, which utilizes drug chemical structures and cell line gene
expression data. We use information from the largest drug combination database
available (DrugComb), combining drug synergy scores in order to construct high
confidence benchmark datasets.
  Results: Our proposed solution for drug synergy predictions offers a number
of benefits: 1) It utilizes a combination of 34 distinct drug synergy datasets
to learn on a wide variety of drugs and cell lines representations. 2) It is
trained on constructed high confidence benchmark datasets. 3) It learns
task-specific drug representations, instead of relying on generalized and
pre-computed chemical drug features. 4) It achieves similar or better
prediction performance (AUPR scores ranging from 0.777 to 0.964) compared to
state-of-the-art baseline models when tested on various benchmark datasets.
  Conclusions: We demonstrate that a GNN based model can provide
state-of-the-art drug synergy predictions by learning task-specific
representations of drugs.

Distributed data storage services tailored to specific applications have
grown popular in the high-performance computing (HPC) community as a way to
address I/O and storage challenges. These services offer a variety of specific
interfaces, semantics, and data representations. They also expose many tuning
parameters, making it difficult for their users to find the best configuration
for a given workload and platform.
  To address this issue, we develop a novel variational-autoencoder-guided
asynchronous Bayesian optimization method to tune HPC storage service
parameters. Our approach uses transfer learning to leverage prior tuning
results and use a dynamically updated surrogate model to explore the large
parameter search space in a systematic way.
  We implement our approach within the DeepHyper open-source framework, and
apply it to the autotuning of a high-energy physics workflow on Argonne's Theta
supercomputer. We show that our transfer-learning approach enables a more than
$40\times$ search speedup over random search, compared with a $2.5\times$ to
$10\times$ speedup when not using transfer learning. Additionally, we show that
our approach is on par with state-of-the-art autotuning frameworks in speed and
outperforms them in resource utilization and parallelization capabilities.

End-to-end reinforcement learning techniques are among the most successful
methods for robotic manipulation tasks. However, the training time required to
find a good policy capable of solving complex tasks is prohibitively large.
Therefore, depending on the computing resources available, it might not be
feasible to use such techniques. The use of domain knowledge to decompose
manipulation tasks into primitive skills, to be performed in sequence, could
reduce the overall complexity of the learning problem, and hence reduce the
amount of training required to achieve dexterity. In this paper, we propose the
use of Davenport chained rotations to decompose complex 3D rotation goals into
a concatenation of a smaller set of more simple rotation skills.
State-of-the-art reinforcement-learning-based methods can then be trained using
less overall simulated experience. We compare its performance with the popular
Hindsight Experience Replay method, trained in an end-to-end fashion using the
same amount of experience in a simulated robotic hand environment. Despite a
general decrease in performance of the primitive skills when being sequentially
executed, we find that decomposing arbitrary 3D rotations into elementary
rotations is beneficial when computing resources are limited, obtaining
increases of success rates of approximately 10% on the most complex 3D
rotations with respect to the success rates obtained by HER trained in an
end-to-end fashion, and increases of success rates between 20% and 40% on the
most simple rotations.

Since the release of various large-scale natural language processing (NLP)
pre-trained models, parameter efficient transfer learning (PETL) has become a
popular paradigm capable of achieving impressive performance on various
downstream tasks. PETL aims at making good use of the representation knowledge
in the pre-trained large models by fine-tuning a small number of parameters.
Recently, it has also attracted increasing attention to developing various PETL
techniques for vision tasks. Popular PETL techniques such as Prompt-tuning and
Adapter have been proposed for high-level visual downstream tasks such as image
classification and video recognition. However, Prefix-tuning remains
under-explored for vision tasks. In this work, we intend to adapt large
video-based models to downstream tasks with a good parameter-accuracy
trade-off. Towards this goal, we propose a framework with a unified view called
visual-PETL (V-PETL) to investigate the different aspects affecting the
trade-off. Specifically, we analyze the positional importance of trainable
parameters and differences between NLP and vision tasks in terms of data
structures and pre-training mechanisms while implementing various PETL
techniques, especially for the under-explored prefix-tuning technique. Based on
a comprehensive understanding of differences between NLP and video data, we
propose a new variation of prefix-tuning module called parallel attention
(PATT) for video-based downstream tasks. An extensive empirical analysis on two
video datasets via different frozen backbones has been carried and the findings
show that the proposed PATT can effectively contribute to other PETL
techniques. An effective scheme Swin-BAPAT derived from the proposed V-PETL
framework achieves significantly better performance than the state-of-the-art
AdaptFormer-Swin with slightly more parameters and outperforms full-tuning with
far less parameters.

Quantum reservoir computers (QRC) and quantum extreme learning machines
(QELM) aim to efficiently post-process the outcome of fixed -- generally
uncalibrated -- quantum devices to solve tasks such as the estimation of the
properties of quantum states. The characterisation of their potential and
limitations, which is currently lacking, will enable the full deployment of
such approaches to problems of system identification, device performance
optimization, and state or process reconstruction. We present a framework to
model QRCs and QELMs, showing that they can be concisely described via single
effective measurements, and provide an explicit characterisation of the
information exactly retrievable with such protocols. We furthermore find a
close analogy between the training process of QELMs and that of reconstructing
the effective measurement characterising the given device. Our analysis paves
the way to a more thorough understanding of the capabilities and limitations of
both QELMs and QRCs, and has the potential to become a powerful measurement
paradigm for quantum state estimation that is more resilient to noise and
imperfections.

We propose a Proportional Integral Derivative (PID) controller-based coaching
scheme to expedite reinforcement learning (RL).

Prototype learning and decoder construction are the keys for few-shot
segmentation. However, existing methods use only a single prototype generation
mode, which can not cope with the intractable problem of objects with various
scales. Moreover, the one-way forward propagation adopted by previous methods
may cause information dilution from registered features during the decoding
process. In this research, we propose a rich prototype generation module (RPGM)
and a recurrent prediction enhancement module (RPEM) to reinforce the prototype
learning paradigm and build a unified memory-augmented decoder for few-shot
segmentation, respectively. Specifically, the RPGM combines superpixel and
K-means clustering to generate rich prototype features with complementary scale
relationships and adapt the scale gap between support and query images. The
RPEM utilizes the recurrent mechanism to design a round-way propagation
decoder. In this way, registered features can provide object-aware information
continuously. Experiments show that our method consistently outperforms other
competitors on two popular benchmarks PASCAL-${{5}^{i}}$ and COCO-${{20}^{i}}$.

Program synthesis approaches struggle to learn programs with numerical
values. An especially difficult problem is learning continuous values over
multiple examples, such as intervals. To overcome this limitation, we introduce
an inductive logic programming approach which combines relational learning with
numerical reasoning. Our approach, which we call NUMSYNTH, uses satisfiability
modulo theories solvers to efficiently learn programs with numerical values.
Our approach can identify numerical values in linear arithmetic fragments, such
as real difference logic, and from infinite domains, such as real numbers or
integers. Our experiments on four diverse domains, including game playing and
program synthesis, show that our approach can (i) learn programs with numerical
values from linear arithmetical reasoning, and (ii) outperform existing
approaches in terms of predictive accuracies and learning times.

In robotics, optimizing controller parameters under safety constraints is an
important challenge. Safe Bayesian optimization (BO) quantifies uncertainty in
the objective and constraints to safely guide exploration in such settings.
Hand-designing a suitable probabilistic model can be challenging, however. In
the presence of unknown safety constraints, it is crucial to choose reliable
model hyper-parameters to avoid safety violations. Here, we propose a
data-driven approach to this problem by meta-learning priors for safe BO from
offline data. We build on a meta-learning algorithm, F-PACOH, capable of
providing reliable uncertainty quantification in settings of data scarcity. As
core contribution, we develop a novel framework for choosing safety-compliant
priors in a data-riven manner via empirical uncertainty metrics and a frontier
search algorithm. On benchmark functions and a high-precision motion system, we
demonstrate that our meta-learned priors accelerate the convergence of safe BO
approaches while maintaining safety.

Recently, change detection (CD) of remote sensing images have achieved great
progress with the advances of deep learning. However, current methods generally
deliver incomplete CD regions and irregular CD boundaries due to the limited
representation ability of the extracted visual features. To relieve these
issues, in this work we propose a novel learning framework named Fully
Transformer Network (FTN) for remote sensing image CD, which improves the
feature extraction from a global view and combines multi-level visual features
in a pyramid manner. More specifically, the proposed framework first utilizes
the advantages of Transformers in long-range dependency modeling. It can help
to learn more discriminative global-level features and obtain complete CD
regions. Then, we introduce a pyramid structure to aggregate multi-level visual
features from Transformers for feature enhancement. The pyramid structure
grafted with a Progressive Attention Module (PAM) can improve the feature
representation ability with additional interdependencies through channel
attentions. Finally, to better train the framework, we utilize the
deeply-supervised learning with multiple boundaryaware loss functions.
Extensive experiments demonstrate that our proposed method achieves a new
state-of-the-art performance on four public CD benchmarks. For model
reproduction, the source code is released at https://github.com/AI-Zhpp/FTN.

Perceiving the surrounding environment is essential for enabling autonomous
or assisted driving functionalities. Common tasks in this domain include
detecting road users, as well as determining lane boundaries and classifying
driving conditions. Over the last few years, a large variety of powerful Deep
Learning models have been proposed to address individual tasks of camera-based
automotive perception with astonishing performances. However, the limited
capabilities of in-vehicle embedded computing platforms cannot cope with the
computational effort required to run a heavy model for each individual task. In
this work, we present CERBERUS (CEnteR Based End-to-end peRception Using a
Single model), a lightweight model that leverages a multitask-learning approach
to enable the execution of multiple perception tasks at the cost of a single
inference. The code will be made publicly available at
https://github.com/cscribano/CERBERUS

Small target detection is an essential yet challenging task in defense
applications, since differentiating low-contrast targets from natural textured
and noisy environment remains difficult. To better take into account the
contextual information, we propose to explore deep learning approaches based on
attention mechanisms. Specifically, we propose a customized version of
TransUnet including channel attention, which has shown a significant
improvement in performance. Moreover, the lack of annotated data induces weak
detection precision, leading to many false alarms. We thus explore a contrario
methods in order to select meaningful potential targets detected by a weak deep
learning training.
  --
  La d\'etection de petites cibles est une probl\'ematique d\'elicate mais
essentielle dans le domaine de la d\'efense, notamment lorsqu'il s'agit de
diff\'erencier ces cibles d'un fond bruit\'e ou textur\'e, ou lorsqu'elles sont
de faible contraste. Pour mieux prendre en compte les informations
contextuelles, nous proposons d'explorer diff\'erentes approches de
segmentation par apprentissage profond, dont certaines bas\'ees sur les
m\'ecanismes d'attention. Nous proposons \'egalement d'inclure un module
d'attention par canal au TransUnet, r\'eseau \`a l'\'etat de l'art, ce qui
permet d'am\'eliorer significativement les performances. Par ailleurs, le
manque de donn\'ees annot\'ees induit une perte en pr\'ecision lors des
d\'etections, conduisant \`a de nombreuses fausses alarmes non pertinentes.
Nous explorons donc des m\'ethodes a contrario afin de s\'electionner les
cibles les plus significatives d\'etect\'ees par un r\'eseau entra\^in\'e avec
peu de donn\'ees.

Current breakthroughs in natural language processing have benefited
dramatically from neural language models, through which distributional
semantics can leverage neural data representations to facilitate downstream
applications. Since neural embeddings use context prediction on word
co-occurrences to yield dense vectors, they are inevitably prone to capture
more semantic association than semantic similarity. To improve vector space
models in deriving semantic similarity, we post-process neural word embeddings
through deep metric learning, through which we can inject lexical-semantic
relations, including syn/antonymy and hypo/hypernymy, into a distributional
space. We introduce hierarchy-fitting, a novel semantic specialization approach
to modelling semantic similarity nuances inherently stored in the IS-A
hierarchies. Hierarchy-fitting attains state-of-the-art results on the common-
and rare-word benchmark datasets for deriving semantic similarity from neural
word embeddings. It also incorporates an asymmetric distance function to
specialize hypernymy's directionality explicitly, through which it
significantly improves vanilla embeddings in multiple evaluation tasks of
detecting hypernymy and directionality without negative impacts on semantic
similarity judgement. The results demonstrate the efficacy of hierarchy-fitting
in specializing neural embeddings with semantic relations in late fusion,
potentially expanding its applicability to aggregating heterogeneous data and
various knowledge resources for learning multimodal semantic spaces.

Audio-visual active speaker detection (AVASD) is well-developed, and now is
an indispensable front-end for several multi-modal applications. However, to
the best of our knowledge, the adversarial robustness of AVASD models hasn't
been investigated, not to mention the effective defense against such attacks.
In this paper, we are the first to reveal the vulnerability of AVASD models
under audio-only, visual-only, and audio-visual adversarial attacks through
extensive experiments. What's more, we also propose a novel audio-visual
interaction loss (AVIL) for making attackers difficult to find feasible
adversarial examples under an allocated attack budget. The loss aims at pushing
the inter-class embeddings to be dispersed, namely non-speech and speech
clusters, sufficiently disentangled, and pulling the intra-class embeddings as
close as possible to keep them compact. Experimental results show the AVIL
outperforms the adversarial training by 33.14 mAP (%) under multi-modal
attacks.

How to design proper training pairs is critical for super-resolving
real-world low-quality (LQ) images, which suffers from the difficulties in
either acquiring paired ground-truth high-quality (HQ) images or synthesizing
photo-realistic degraded LQ observations. Recent works mainly focus on modeling
the degradation with handcrafted or estimated degradation parameters, which are
however incapable to model complicated real-world degradation types, resulting
in limited quality improvement. Notably, LQ face images, which may have the
same degradation process as natural images, can be robustly restored with
photo-realistic textures by exploiting their strong structural priors. This
motivates us to use the real-world LQ face images and their restored HQ
counterparts to model the complex real-world degradation (namely ReDegNet), and
then transfer it to HQ natural images to synthesize their realistic LQ
counterparts. By taking these paired HQ-LQ face images as inputs to explicitly
predict the degradation-aware and content-independent representations, we could
control the degraded image generation, and subsequently transfer these
degradation representations from face to natural images to synthesize the
degraded LQ natural images. Experiments show that our ReDegNet can well learn
the real degradation process from face images. The restoration network trained
with our synthetic pairs performs favorably against SOTAs. More importantly,
our method provides a new way to handle the real-world complex scenarios by
learning their degradation representations from the facial portions, which can
be used to significantly improve the quality of non-facial areas. The source
code is available at https://github.com/csxmli2016/ReDegNet.

Offline reinforcement learning, which aims at optimizing sequential
decision-making strategies with historical data, has been extensively applied
in real-life applications. State-Of-The-Art algorithms usually leverage
powerful function approximators (e.g. neural networks) to alleviate the sample
complexity hurdle for better empirical performances. Despite the successes, a
more systematic understanding of the statistical complexity for function
approximation remains lacking. Towards bridging the gap, we take a step by
considering offline reinforcement learning with differentiable function class
approximation (DFA). This function class naturally incorporates a wide range of
models with nonlinear/nonconvex structures. Most importantly, we show offline
RL with differentiable function approximation is provably efficient by
analyzing the pessimistic fitted Q-learning (PFQL) algorithm, and our results
provide the theoretical basis for understanding a variety of practical
heuristics that rely on Fitted Q-Iteration style design. In addition, we
further improve our guarantee with a tighter instance-dependent
characterization. We hope our work could draw interest in studying
reinforcement learning with differentiable function approximation beyond the
scope of current research.

Capitalise on deep learning models, offering Natural Language Processing
(NLP) solutions as a part of the Machine Learning as a Service (MLaaS) has
generated handsome revenues. At the same time, it is known that the creation of
these lucrative deep models is non-trivial. Therefore, protecting these
inventions intellectual property rights (IPR) from being abused, stolen and
plagiarized is vital. This paper proposes a practical approach for the IPR
protection on recurrent neural networks (RNN) without all the bells and
whistles of existing IPR solutions. Particularly, we introduce the Gatekeeper
concept that resembles the recurrent nature in RNN architecture to embed keys.
Also, we design the model training scheme in a way such that the protected RNN
model will retain its original performance iff a genuine key is presented.
Extensive experiments showed that our protection scheme is robust and effective
against ambiguity and removal attacks in both white-box and black-box
protection schemes on different RNN variants. Code is available at
https://github.com/zhiqin1998/RecurrentIPR

Federated Learning (FL) effectively protects client data privacy. However,
client absence or leaving during training can seriously degrade model
performances, particularly for unbalanced and non-IID client data. We address
this issue by generating data digests from the raw data and using them to guide
training at the FL moderator. The proposed FL framework, called FedDig, can
tolerate unexpected client absence in cross-silo scenarios while preserving
client data privacy because the digests de-identify the raw data by mixing
encoded features in the features space. We evaluate FedDig using EMNIST,
CIFAR-10, and CIFAR-100; the results consistently outperform against three
baseline algorithms (FedAvg, FedProx, and FedNova) by large margins in various
client absence scenarios.

Infinitesimal gradient boosting is defined as the vanishing-learning-rate
limit of the popular tree-based gradient boosting algorithm from machine
learning (Dombry and Duchamps, 2021). It is characterized as the solution of a
nonlinear ordinary differential equation in a infinite-dimensional function
space where the infinitesimal boosting operator driving the dynamics depends on
the training sample. We consider the asymptotic behavior of the model in the
large sample limit and prove its convergence to a deterministic process. This
infinite population limit is again characterized by a differential equation
that depends on the population distribution. We explore some properties of this
population limit: we prove that the dynamics makes the test error decrease and
we consider its long time behavior.

The notion of smart cities is being adapted globally to provide a better
quality of living. A smart city's smart mobility component focuses on providing
smooth and safe commuting for its residents and promotes eco-friendly and
sustainable alternatives such as public transit (bus). Among several smart
applications, a system that provides up-to-the-minute information like bus
arrival, travel duration, schedule, etc., improves the reliability of public
transit services. Still, this application needs live information on traffic
flow, accidents, events, and the location of the buses. Most cities lack the
infrastructure to provide these data. In this context, a bus arrival prediction
model is proposed for forecasting the arrival time using limited data sets. The
location data of public transit buses and spatial characteristics are used for
the study. One of the routes of Tumakuru city service, Tumakuru, India, is
selected and divided into two spatial patterns: sections with intersections and
sections without intersections. The machine learning model XGBoost is modeled
for both spatial patterns individually. A model to dynamically predict bus
arrival time is developed using the preceding trip information and the machine
learning model to estimate the arrival time at a downstream bus stop. The
performance of models is compared based on the R-squared values of the
predictions made, and the proposed model established superior results. It is
suggested to predict bus arrival in the study area. The proposed model can also
be extended to other similar cities with limited traffic-related
infrastructure.

In this paper, we look at the impact of Environment, Social and Governance
related news articles and social media data on the stock market performance. We
pick four stocks of companies which are widely known in their domain to
understand the complete effect of ESG as the newly opted investment style
remains restricted to only the stocks with widespread information. We summarise
live data of both twitter tweets and newspaper articles and create a sentiment
index using a dictionary technique based on online information for the month of
July, 2022. We look at the stock price data for all the four companies and
calculate the percentage change in each of them. We also compare the overall
sentiment of the company to its percentage change over a specific historical
period.

Spatial autocorrelation and spatial heterogeneity widely exist in spatial
data, which make the traditional machine learning model perform badly. Spatial
domain generalization is a spatial extension of domain generalization, which
can generalize to unseen spatial domains in continuous 2D space. Specifically,
it learns a model under varying data distributions that generalizes to unseen
domains. Although tremendous success has been achieved in domain
generalization, there exist very few works on spatial domain generalization.
The advancement of this area is challenged by: 1) Difficulty in characterizing
spatial heterogeneity, and 2) Difficulty in obtaining predictive models for
unseen locations without training data. To address these challenges, this paper
proposes a generic framework for spatial domain generalization. Specifically,
We develop the spatial interpolation graph neural network that handles spatial
data as a graph and learns the spatial embedding on each node and their
relationships. The spatial interpolation graph neural network infers the
spatial embedding of an unseen location during the test phase. Then the spatial
embedding of the target location is used to decode the parameters of the
downstream-task model directly on the target location. Finally, extensive
experiments on thirteen real-world datasets demonstrate the proposed method's
strength.

Graph Convolutional Neural Networks (GCNs) has been generally accepted to be
an effective tool for node representations learning. An interesting way to
understand GCNs is to think of them as a message passing mechanism where each
node updates its representation by accepting information from its neighbours
(also known as positive samples). However, beyond these neighbouring nodes,
graphs have a large, dark, all-but forgotten world in which we find the
non-neighbouring nodes (negative samples). In this paper, we show that this
great dark world holds a substantial amount of information that might be useful
for representation learning. Most specifically, it can provide negative
information about the node representations. Our overall idea is to select
appropriate negative samples for each node and incorporate the negative
information contained in these samples into the representation updates.
Moreover, we show that the process of selecting the negative samples is not
trivial. Our theme therefore begins by describing the criteria for a good
negative sample, followed by a determinantal point process algorithm for
efficiently obtaining such samples. A GCN, boosted by diverse negative samples,
then jointly considers the positive and negative information when passing
messages. Experimental evaluations show that this idea not only improves the
overall performance of standard representation learning but also significantly
alleviates over-smoothing problems.

Automated machine vision pipelines do not need the exact visual content to
perform their tasks. Therefore, there is a potential to remove private
information from the data without significantly affecting the machine vision
accuracy. We present a novel method to create a privacy-preserving latent
representation of an image that could be used by a downstream machine vision
model. This latent representation is constructed using adversarial training to
prevent accurate reconstruction of the input while preserving the task
accuracy. Specifically, we split a Deep Neural Network (DNN) model and insert
an autoencoder whose purpose is to both reduce the dimensionality as well as
remove information relevant to input reconstruction while minimizing the impact
on task accuracy. Our results show that input reconstruction ability can be
reduced by about 0.8 dB at the equivalent task accuracy, with degradation
concentrated near the edges, which is important for privacy. At the same time,
30% bit savings are achieved compared to coding the features directly.

Deep generative models parametrized up to a normalizing constant (e.g.
energy-based models) are difficult to train by maximizing the likelihood of the
data because the likelihood and/or gradients thereof cannot be explicitly or
efficiently written down. Score matching is a training method, whereby instead
of fitting the likelihood $\log p(x)$ for the training data, we instead fit the
score function $\nabla_x \log p(x)$ -- obviating the need to evaluate the
partition function. Though this estimator is known to be consistent, its
unclear whether (and when) its statistical efficiency is comparable to that of
maximum likelihood -- which is known to be (asymptotically) optimal. We
initiate this line of inquiry in this paper, and show a tight connection
between statistical efficiency of score matching and the isoperimetric
properties of the distribution being estimated -- i.e. the Poincar\'e,
log-Sobolev and isoperimetric constant -- quantities which govern the mixing
time of Markov processes like Langevin dynamics. Roughly, we show that the
score matching estimator is statistically comparable to the maximum likelihood
when the distribution has a small isoperimetric constant. Conversely, if the
distribution has a large isoperimetric constant -- even for simple families of
distributions like exponential families with rich enough sufficient statistics
-- score matching will be substantially less efficient than maximum likelihood.
We suitably formalize these results both in the finite sample regime, and in
the asymptotic regime. Finally, we identify a direct parallel in the discrete
setting, where we connect the statistical properties of pseudolikelihood
estimation with approximate tensorization of entropy and the Glauber dynamics.

We propose a new framework to improve automatic speech recognition (ASR)
systems in resource-scarce environments using a generative adversarial network
(GAN) operating on acoustic input features. The GAN is used to enhance the
features of mismatched data prior to decoding, or can optionally be used to
fine-tune the acoustic model. We achieve improvements that are comparable to
multi-style training (MTR), but at a lower computational cost. With less than
one hour of data, an ASR system trained on good quality data, and evaluated on
mismatched audio is improved by between 11.5% and 19.7% relative word error
rate (WER). Experiments demonstrate that the framework can be very useful in
under-resourced environments where training data and computational resources
are limited. The GAN does not require parallel training data, because it
utilises a baseline acoustic model to provide an additional loss term that
guides the generator to create acoustic features that are better classified by
the baseline.

We study the task of prompting large-scale language models to perform
multi-step reasoning. Existing work shows that when prompted with a chain of
thoughts (CoT), sequences of short sentences describing intermediate reasoning
steps towards a final answer, large language models can generate new reasoning
chains and predict answers for new inputs. A central question is which
reasoning examples make the most effective prompts. In this work, we propose
complexity-based prompting, a simple and effective example selection scheme for
multi-step reasoning. We show that prompts with higher reasoning complexity,
i.e., chains with more reasoning steps, achieve substantially better
performance on math word reasoning tasks over strong baselines. We further
extend our complexity-based criteria from prompting (selecting inputs) to
decoding (selecting outputs), where we sample multiple reasoning chains from
the model, then choose the majority of generated answers from complex reasoning
chains (over simple chains). When used to prompt GPT-3, our approach
substantially improves multi-step reasoning accuracy, with an 8.6% absolute
improvement on GSM8K, and 6.4% on MathQA. Compared with existing example
selection schemes like manual tuning or retrieval-based selection, selection
based on reasoning complexity is intuitive, easy to implement, and
annotation-efficient. Further results demonstrate the robustness of our methods
under format perturbation and distribution shift.

Although the terms mood and emotion are closely related and often used
interchangeably, they are distinguished based on their duration, intensity and
attribution. To date, hardly any computational models have (a) examined mood
recognition, and (b) modelled the interplay between mood and emotional state in
their analysis. In this paper, as a first step towards mood prediction, we
propose a framework that utilises both dominant emotion (or mood) labels, and
emotional change labels on the AFEW-VA database. Experiments evaluating
unimodal (trained only using mood labels) and multimodal (trained with both
mood and emotion change labels) convolutional neural networks confirm that
incorporating emotional change information in the network training process can
significantly improve the mood prediction performance, thus highlighting the
importance of modelling emotion and mood simultaneously for improved
performance in affective state recognition.

Camera physiological measurement is a fast growing field of computer vision.
Remote photoplethysmography (rPPG) uses video cameras (imagers) to measure the
peripheral blood volume pulse (BVP). Simply, this enables heart rate
measurement via webcams, smartphone cameras and many other imaging devices. The
current state-of-the-art methods are supervised deep neural architectures that
have large numbers of parameters and a signal number of hyperparameters.
Replication of results and benchmarking of new models is critical for
scientific progress. However, as with many other applications of deep learning,
reliable codebases are not easy to find. We present a comprehensive toolbox,
rPPG-Toolbox, containing code for training and evaluating unsupervised and
supervised rPPG models: https://github.com/ubicomplab/rPPG-Toolbox

In the era of deep learning, data is the critical determining factor in the
performance of neural network models. Generating large datasets suffers from
various difficulties such as scalability, cost efficiency and photorealism. To
avoid expensive and strenuous dataset collection and annotations, researchers
have inclined towards computer-generated datasets. Although, a lack of
photorealism and a limited amount of computer-aided data, has bounded the
accuracy of network predictions.
  To this end, we present WorldGen -- an open source framework to autonomously
generate countless structured and unstructured 3D photorealistic scenes such as
city view, object collection, and object fragmentation along with its rich
ground truth annotation data. WorldGen being a generative model gives the user
full access and control to features such as texture, object structure, motion,
camera and lens properties for better generalizability by diminishing the data
bias in the network. We demonstrate the effectiveness of WorldGen by presenting
an evaluation on deep optical flow. We hope such a tool can open doors for
future research in a myriad of domains related to robotics and computer vision
by reducing manual labor and the cost of acquiring rich and high-quality data.

Continual learning aims to alleviate catastrophic forgetting when handling
consecutive tasks under non-stationary distributions. Gradient-based
meta-learning algorithms have shown the capability to implicitly solve the
transfer-interference trade-off problem between different examples. However,
they still suffer from the catastrophic forgetting problem in the setting of
continual learning, since the past data of previous tasks are no longer
available. In this work, we propose a novel efficient meta-learning algorithm
for solving the online continual learning problem, where the regularization
terms and learning rates are adapted to the Taylor approximation of the
parameter's importance to mitigate forgetting. The proposed method expresses
the gradient of the meta-loss in closed-form and thus avoid computing
second-order derivative which is computationally inhibitable. We also use
Proximal Gradient Descent to further improve computational efficiency and
accuracy. Experiments on diverse benchmarks show that our method achieves
better or on-par performance and much higher efficiency compared to the

Document denoising is considered one of the most challenging tasks in
computer vision. There exist millions of documents that are still to be
digitized, but problems like document degradation due to natural and man-made
factors make this task very difficult. This paper introduces a supervised
approach for cleaning dirty documents using a new fully convolutional
auto-encoder architecture. This paper focuses on restoring documents with
discrepancies like deformities caused due to aging of a document, creases left
on the pages that were xeroxed, random black patches, lightly visible text,
etc., and also improving the quality of the image for better optical character
recognition system (OCR) performance. Removing noise from scanned documents is
a very important step before the documents as this noise can severely affect
the performance of an OCR system. The experiments in this paper have shown
promising results as the model is able to learn a variety of ordinary as well
as unusual noises and rectify them efficiently.

Qualitative research is an approach to understanding social phenomenon based
around human interpretation of data, particularly text. Probabilistic topic
modelling is a machine learning approach that is also based around the analysis
of text and often is used to in order to understand social phenomena. Both of
these approaches aim to extract important themes or topics in a textual corpus
and therefore we may see them as analogous to each other. However there are
also considerable differences in how the two approaches function. One is a
highly human interpretive process, the other is automated and statistical. In
this paper we use this analogy as the basis for our Theme and Topic system, a
tool for qualitative researchers to conduct textual research that integrates
topic modelling into an accessible interface. This is an example of a more
general approach to the design of interactive machine learning systems in which
existing human professional processes can be used as the model for processes
involving machine learning. This has the particular benefit of providing a
familiar approach to existing professionals, that may can make machine learning
seem less alien and easier to learn. Our design approach has two elements. We
first investigate the steps professionals go through when performing tasks and
design a workflow for Theme and Topic that integrates machine learning. We then
designed interfaces for topic modelling in which familiar concepts from
qualitative research are mapped onto machine learning concepts. This makes
these the machine learning concepts more familiar and easier to learn for
qualitative researchers.

This paper uses information-theoretic tools to analyze the generalization
error in unsupervised domain adaptation (UDA). We present novel upper bounds
for two notions of generalization errors. The first notion measures the gap
between the population risk in the target domain and that in the source domain,
and the second measures the gap between the population risk in the target
domain and the empirical risk in the source domain. While our bounds for the
first kind of error are in line with the traditional analysis and give similar
insights, our bounds on the second kind of error are algorithm-dependent, which
also provide insights into algorithm designs. Specifically, we present two
simple techniques for improving generalization in UDA and validate them
experimentally.

The continuous expansion of the urban construction scale has recently
contributed to the demand for the dynamics of traffic intersections that are
managed, making adaptive modellings become a hot topic. Existing deep learning
methods are powerful to fit complex heterogeneous graphs. However, they still
have drawbacks, which can be roughly classified into two categories, 1)
spatiotemporal async-modelling approaches separately consider temporal and
spatial dependencies, resulting in weak generalization and large instability
while aggregating; 2) spatiotemporal sync-modelling is hard to capture
long-term temporal dependencies because of the local receptive field. In order
to overcome above challenges, a \textbf{C}ombined \textbf{D}ynamic
\textbf{V}irtual spatiotemporal \textbf{G}raph \textbf{M}apping
\textbf{(CDVGM)} is proposed in this work. The contributions are the following:
1) a dynamic virtual graph Laplacian ($DVGL$) is designed, which considers both
the spatial signal passing and the temporal features simultaneously; 2) the
Long-term Temporal Strengthen model ($LT^2S$) for improving the stability of
time series forecasting; Extensive experiments demonstrate that CDVGM has
excellent performances of fast convergence speed and low resource consumption
and achieves the current SOTA effect in terms of both accuracy and
generalization. The code is available at
\hyperlink{https://github.com/Dandelionym/CDVGM.}{https://github.com/Dandelionym/CDVGM.}

We study the problem of deployment efficient reinforcement learning (RL) with
linear function approximation under the \emph{reward-free} exploration setting.
This is a well-motivated problem because deploying new policies is costly in
real-life RL applications. Under the linear MDP setting with feature dimension
$d$ and planning horizon $H$, we propose a new algorithm that collects at most
$\widetilde{O}(\frac{d^2H^5}{\epsilon^2})$ trajectories within $H$ deployments
to identify $\epsilon$-optimal policy for any (possibly data-dependent) choice
of reward functions. To the best of our knowledge, our approach is the first to
achieve optimal deployment complexity and optimal $d$ dependence in sample
complexity at the same time, even if the reward is known ahead of time. Our
novel techniques include an exploration-preserving policy discretization and a
generalized G-optimal experiment design, which could be of independent
interest. Lastly, we analyze the related problem of regret minimization in
low-adaptive RL and provide information-theoretic lower bounds for switching
cost and batch complexity.

In this paper, we develop an optimal weight adaptation strategy of model
predictive control (MPC) for connected and automated vehicles (CAVs) in mixed
traffic. Built upon our recent paper, we model the interaction between a CAV
and a human-driven vehicle (HDV) as a simultaneous game and formulate a
game-theoretic MPC problem to find a Nash equilibrium of the game. In the MPC
problem, the weights in the HDV's objective function can be learned online
using moving horizon inverse reinforcement learning. Using Bayesian
optimization, we propose a strategy to optimally adapt the weights in the CAV's
objective function given the HDV's objective weights so that the expected true
cost when using MPC in simulations can be minimized. We validate the
effectiveness of the derived optimal strategy by numerical simulations of a
vehicle crossing example at an unsignalized intersection.

Current NAS-based semantic segmentation methods focus on accuracy
improvements rather than light-weight design. In this paper, we proposed a
two-stage framework to design our NAS-based RSPNet model for light-weight
semantic segmentation. The first architecture search determines the inner cell
structure, and the second architecture search considers exponentially growing
paths to finalize the outer structure of the network. It was shown in the
literature that the fusion of high- and low-resolution feature maps produces
stronger representations. To find the expected macro structure without manual
design, we adopt a new path-attention mechanism to efficiently search for
suitable paths to fuse useful information for better segmentation. Our search
for repeatable micro-structures from cells leads to a superior network
architecture in semantic segmentation. In addition, we propose an RSP
(recursive Stage Partial) architecture to search a light-weight design for
NAS-based semantic segmentation. The proposed architecture is very efficient,
simple, and effective that both the macro- and micro- structure searches can be
completed in five days of computation on two V100 GPUs. The light-weight NAS
architecture with only 1/4 parameter size of SoTA architectures can achieve
SoTA performance on semantic segmentation on the Cityscapes dataset without
using any backbones.

The photon-counting detector (PCD) based spectral computed tomography
attracts much more attentions since it has the capability to provide more
accurate identification and quantitative analysis for biomedical materials. The
limited number of photons within narrow energy-bin leads to low signal-noise
ratio data. The existing supervised deep reconstruction networks for CT
reconstruction are difficult to address these challenges. In this paper, we
propose an iterative deep reconstruction network to synergize model and data
priors into a unified framework, named as Spectral2Spectral. Our
Spectral2Spectral employs an unsupervised deep training strategy to obtain
high-quality images from noisy data with an end-to-end fashion. The structural
similarity prior within image-spectral domain is refined as a regularization
term to further constrain the network training. The weights of neural network
are automatically updated to capture image features and structures with
iterative process. Three large-scale preclinical datasets experiments
demonstrate that the Spectral2spectral reconstruct better image quality than

Convolutional neural networks (CNNs) have been employed along with
Variational Monte Carlo methods for finding the ground state of quantum
many-body spin systems with great success. In order to do so, however, a CNN
with only linearly many variational parameters has to circumvent the ``curse of
dimensionality'' and successfully approximate a wavefunction on an
exponentially large Hilbert space. In our work, we provide a theoretical and
experimental analysis of how the CNN optimizes learning for spin systems, and
investigate the CNN's low dimensional approximation. We first quantify the role
played by physical symmetries of the underlying spin system during training. We
incorporate our insights into a new training algorithm and demonstrate its
improved efficiency, accuracy and robustness. We then further investigate the
CNN's ability to approximate wavefunctions by looking at the entanglement
spectrum captured by the size of the convolutional filter. Our insights reveal
the CNN to be an ansatz fundamentally centered around the occurrence statistics
of $K$-motifs of the input strings. We use this motivation to provide the
shallow CNN ansatz with a unifying theoretical interpretation in terms of other
well-known statistical and physical ansatzes such as the maximum entropy
(MaxEnt) and entangled plaquette correlator product states (EP-CPS). Using
regression analysis, we find further relationships between the CNN's
approximations of the different motifs' expectation values. Our results allow
us to gain a comprehensive, improved understanding of how CNNs successfully
approximate quantum spin Hamiltonians and to use that understanding to improve
CNN performance.

A key assumption in most existing works on FL algorithms' convergence
analysis is that the noise in stochastic first-order information has a finite
variance. Although this assumption covers all light-tailed (i.e.,
sub-exponential) and some heavy-tailed noise distributions (e.g., log-normal,
Weibull, and some Pareto distributions), it fails for many fat-tailed noise
distributions (i.e., ``heavier-tailed'' with potentially infinite variance)
that have been empirically observed in the FL literature. To date, it remains
unclear whether one can design convergent algorithms for FL systems that
experience fat-tailed noise. This motivates us to fill this gap in this paper
by proposing an algorithmic framework called FAT-Clipping (\ul{f}ederated
\ul{a}veraging with \ul{t}wo-sided learning rates and \ul{clipping}), which
contains two variants: FAT-Clipping per-round (FAT-Clipping-PR) and
FAT-Clipping per-iteration (FAT-Clipping-PI). Specifically, for the largest
$\alpha \in (1,2]$ such that the fat-tailed noise in FL still has a bounded
$\alpha$-moment, we show that both variants achieve
$\mathcal{O}((mT)^{\frac{2-\alpha}{\alpha}})$ and
$\mathcal{O}((mT)^{\frac{1-\alpha}{3\alpha-2}})$ convergence rates in the
strongly-convex and general non-convex settings, respectively, where $m$ and
$T$ are the numbers of clients and communication rounds. Moreover, at the
expense of more clipping operations compared to FAT-Clipping-PR,
FAT-Clipping-PI further enjoys a linear speedup effect with respect to the
number of local updates at each client and being lower-bound-matching (i.e.,
order-optimal). Collectively, our results advance the understanding of
designing efficient algorithms for FL systems that exhibit fat-tailed
first-order oracle information.

In this paper, we study the infinite-depth limit of finite-width residual
neural networks with random Gaussian weights. With proper scaling, we show that
by fixing the width and taking the depth to infinity, the pre-activations
converge in distribution to a zero-drift diffusion process. Unlike the
infinite-width limit where the pre-activation converge weakly to a Gaussian
random variable, we show that the infinite-depth limit yields different
distributions depending on the choice of the activation function. We document
two cases where these distributions have closed-form (different) expressions.
We further show an intriguing change of regime phenomenon of the
post-activation norms when the width increases from 3 to 4. Lastly, we study
the sequential limit infinite-depth-then-infinite-width and compare it with the
more commonly studied infinite-width-then-infinite-depth limit.

In this work, we describe a generic approach to show convergence with high
probability for stochastic convex optimization. In previous works, either the
convergence is only in expectation or the bound depends on the diameter of the
domain. Instead, we show high probability convergence with bounds depending on
the initial distance to the optimal solution as opposed to the domain diameter.
The algorithms use step sizes analogous to the standard settings and are
universal to Lipschitz functions, smooth functions, and their linear
combinations.

Background and aim: Hip fracture can be devastating. The proximal femoral
strength can be computed by subject-specific finite element (FE) analysis (FEA)
using quantitative CT images. The aim of this paper is to design a deep
learning-based model for hip fracture prediction with multi-view information
fusion. Method: We developed a multi-view variational autoencoder (MMVAE) for
feature representation learning and designed the product of expert model (PoE)
for multi-view information fusion.We performed genome-wide association studies
(GWAS) to select the most relevant genetic features with proximal femoral
strengths and integrated genetic features with DXA-derived imaging features and
clinical variables for proximal femoral strength prediction. Results: The
designed model achieved the mean absolute percentage error of 0.2050,0.0739 and
0.0852 for linear fall, nonlinear fall and nonlinear stance fracture load
prediction, respectively. For linear fall and nonlinear stance fracture load
prediction, integrating genetic and DXA-derived imaging features were
beneficial; while for nonlinear fall fracture load prediction, integrating
genetic features, DXA-derived imaging features as well as clinical variables,
the model achieved the best performance. Conclusion: The proposed model is
capable of predicting proximal femoral strengths using genetic features,
DXA-derived imaging features as well as clinical variables. Compared to
performing FEA using QCT images to calculate proximal femoral strengths, the
presented method is time-efficient and cost effective, and radiation dosage is
limited. From the technique perspective, the final models can be applied to
other multi-view information integration tasks.

Wireless networked control system (WNCS) connecting sensors, controllers, and
actuators via wireless communications is a key enabling technology for highly
scalable and low-cost deployment of control systems in the Industry 4.0 era.
Despite the tight interaction of control and communications in WNCSs, most
existing works adopt separative design approaches. This is mainly because the
co-design of control-communication policies requires large and hybrid state and
action spaces, making the optimal problem mathematically intractable and
difficult to be solved effectively by classic algorithms. In this paper, we
systematically investigate deep learning (DL)-based estimator-control-scheduler
co-design for a model-unknown nonlinear WNCS over wireless fading channels. In
particular, we propose a co-design framework with the awareness of the sensor's
age-of-information (AoI) states and dynamic channel states. We propose a novel
deep reinforcement learning (DRL)-based algorithm for controller and scheduler
optimization utilizing both model-free and model-based data. An AoI-based
importance sampling algorithm that takes into account the data accuracy is
proposed for enhancing learning efficiency. We also develop novel schemes for
enhancing the stability of joint training. Extensive experiments demonstrate
that the proposed joint training algorithm can effectively solve the
estimation-control-scheduling co-design problem in various scenarios and
provide significant performance gain compared to separative design and some
benchmark policies.

Federated Learning (FL) is a nascent privacy-preserving learning framework
under which the local data of participating clients is kept locally throughout
model training. Scarce communication resources and data heterogeneity are two
defining characteristics of FL. Besides, a FL system is often implemented in a
harsh environment -- leaving the clients vulnerable to Byzantine attacks. To
the best of our knowledge, no gradient compressors simultaneously achieve
quantitative Byzantine resilience and privacy preservation. In this paper, we
fill this gap via revisiting the stochastic sign SGD \cite{jin 2020}. We
propose $\beta$-stochastic sign SGD, which contains a gradient compressor that
encodes a client's gradient information in sign bits subject to the privacy
budget $\beta>0$. We show that as long as $\beta>0$, $\beta$-stochastic sign
SGD converges in the presence of partial client participation and mobile
Byzantine faults, showing that it achieves quantifiable Byzantine-resilience
and differential privacy simultaneously. In sharp contrast, when $\beta=0$, the
compressor is not differentially private. Notably, for the special case when
each of the stochastic gradients involved is bounded with known bounds, our
gradient compressor with $\beta=0$ coincides with the compressor proposed in
\cite{jin 2020}. As a byproduct, we show that when the clients report sign
messages, the popular information aggregation rules simple mean, trimmed mean,
median and majority vote are identical in terms of the output signs. Our
theories are corroborated by experiments on MNIST and CIFAR-10 datasets.

Neural networks are configured by choosing an architecture and hyperparameter
values; doing so often involves expert intuition and hand-tuning to find a
configuration that extrapolates well without overfitting. This paper considers
automatic methods for configuring a neural network that extrapolates in time
for the domain of visible and near-infrared (VNIR) spectroscopy. In particular,
we study the effect of (a) selecting samples for validating configurations and
(b) using ensembles.
  Most of the time, models are built of the past to predict the future. To
encourage the neural network model to extrapolate, we consider validating model
configurations on samples that are shifted in time similar to the test set. We
experiment with three validation set choices: (1) a random sample of 1/3 of
non-test data (the technique used in previous work), (2) using the latest 1/3
(sorted by time), and (3) using a semantically meaningful subset of the data.
Hyperparameter optimization relies on the validation set to estimate test-set
error, but neural network variance obfuscates the true error value. Ensemble
averaging - computing the average across many neural networks - can reduce the
variance of prediction errors.
  To test these methods, we do a comprehensive study of a held-out 2018 harvest
season of mango fruit given VNIR spectra from 3 prior years. We find that
ensembling improves the state-of-the-art model's variance and accuracy.
Furthermore, hyperparameter optimization experiments - with and without
ensemble averaging and with each validation set choice - show that when
ensembling is combined with using the latest 1/3 of samples as the validation
set, a neural network configuration is found automatically that is on par with
the state-of-the-art.

Infant pose monitoring during sleep has multiple applications in both
healthcare and home settings. In a healthcare setting, pose detection can be
used for region of interest detection and movement detection for noncontact
based monitoring systems. In a home setting, pose detection can be used to
detect sleep positions which has shown to have a strong influence on multiple
health factors. However, pose monitoring during sleep is challenging due to
heavy occlusions from blanket coverings and low lighting. To address this, we
present a novel dataset, Simultaneously-collected multimodal Mannequin Lying
pose (SMaL) dataset, for under the cover infant pose estimation. We collect
depth and pressure imagery of an infant mannequin in different poses under
various cover conditions. We successfully infer full body pose under the cover
by training state-of-art pose estimation methods and leveraging existing
multimodal adult pose datasets for transfer learning. We demonstrate a
hierarchical pretraining strategy for transformer-based models to significantly
improve performance on our dataset. Our best performing model was able to
detect joints under the cover within 25mm 86% of the time with an overall mean
error of 16.9mm. Data, code and models publicly available at
https://github.com/DanielKyr/SMaL

Recent large-scale neural autoregressive sequence models have shown
impressive performances on a variety of natural language generation tasks.
However, their generated sequences often exhibit degenerate properties such as
non-termination, undesirable repetition, and premature termination, when
generated with decoding algorithms such as greedy search, beam search, top-$k$
sampling, and nucleus sampling. In this paper, we focus on the problem of
non-terminating sequences resulting from an incomplete decoding algorithm. We
first define an incomplete probable decoding algorithm which includes greedy
search, top-$k$ sampling, and nucleus sampling, beyond the incomplete decoding
algorithm originally put forward by Welleck et al. (2020). We then propose a
non-monotonic self-terminating language model, which significantly relaxes the
constraint of monotonically increasing termination probability in the
originally proposed self-terminating language model by Welleck et al. (2020),
to address the issue of non-terminating sequences when using incomplete
probable decoding algorithms. We prove that our proposed model prevents
non-terminating sequences when using not only incomplete probable decoding
algorithms but also beam search. We empirically validate our model on sequence
completion tasks with various architectures.

We present intrinsic neural radiance fields, dubbed IntrinsicNeRF, that
introduce intrinsic decomposition into the NeRF-based~\cite{mildenhall2020nerf}
neural rendering method and can perform editable novel view synthesis in
room-scale scenes while existing inverse rendering combined with neural
rendering methods~\cite{zhang2021physg, zhang2022modeling} can only work on
object-specific scenes. Given that intrinsic decomposition is a fundamentally
ambiguous and under-constrained inverse problem, we propose a novel
distance-aware point sampling and adaptive reflectance iterative clustering
optimization method that enables IntrinsicNeRF with traditional intrinsic
decomposition constraints to be trained in an unsupervised manner, resulting in
temporally consistent intrinsic decomposition results. To cope with the problem
of different adjacent instances of similar reflectance in a scene being
incorrectly clustered together, we further propose a hierarchical clustering
method with coarse-to-fine optimization to obtain a fast hierarchical indexing
representation. It enables compelling real-time augmented reality applications
such as scene recoloring, material editing, and illumination variation.
Extensive experiments on Blender Object and Replica Scene demonstrate that we
can obtain high-quality, consistent intrinsic decomposition results and
high-fidelity novel view synthesis even for challenging sequences. Code and
data are available on the project webpage:
https://zju3dv.github.io/intrinsic_nerf/.

We developed a novel SSL approach to capture global consistency and
pixel-level local consistencies between differently augmented views of the same
images to accommodate downstream discriminative and dense predictive tasks. We
adopted the teacher-student architecture used in previous contrastive SSL
methods. In our method, the global consistency is enforced by aggregating the
compressed representations of augmented views of the same image. The
pixel-level consistency is enforced by pursuing similar representations for the
same pixel in differently augmented views. Importantly, we introduced an
uncertainty-aware context stabilizer to adaptively preserve the context gap
created by the two views from different augmentations. Moreover, we used Monte
Carlo dropout in the stabilizer to measure uncertainty and adaptively balance
the discrepancy between the representations of the same pixels in different
views.

Graph contrastive learning (GCL), as an emerging self-supervised learning
technique on graphs, aims to learn representations via instance discrimination.
Its performance heavily relies on graph augmentation to reflect invariant
patterns that are robust to small perturbations; yet it still remains unclear
about what graph invariance GCL should capture. Recent studies mainly perform
topology augmentations in a uniformly random manner in the spatial domain,
ignoring its influence on the intrinsic structural properties embedded in the
spectral domain. In this work, we aim to find a principled way for topology
augmentations by exploring the invariance of graphs from the spectral
perspective. We develop spectral augmentation which guides topology
augmentations by maximizing the spectral change. Extensive experiments on both
graph and node classification tasks demonstrate the effectiveness of our method
in self-supervised representation learning. The proposed method also brings
promising generalization capability in transfer learning, and is equipped with
intriguing robustness property under adversarial attacks. Our study sheds light
on a general principle for graph topology augmentation.

Given the wide and ever growing range of different efficient Transformer
attention mechanisms, it is important to identify which attention is most
effective when given a task. In this work, we are also interested in combining
different attention types to build heterogeneous Transformers. We first propose
a DARTS-like Neural Architecture Search (NAS) method to find the best attention
for a given task, in this setup, all heads use the same attention (homogeneous
models). Our results suggest that NAS is highly effective on this task, and it
identifies the best attention mechanisms for IMDb byte level text
classification and Listops. We then extend our framework to search for and
build Transformers with multiple different attention types, and call them
heterogeneous Transformers. We show that whilst these heterogeneous
Transformers are better than the average homogeneous models, they cannot
outperform the best. We explore the reasons why heterogeneous attention makes
sense, and why it ultimately fails.

The Transformer is an extremely powerful and prominent deep learning
architecture. In this work, we challenge the commonly held belief in deep
learning that going deeper is better, and show an alternative design approach
that is building wider attention Transformers. We demonstrate that wide single
layer Transformer models can compete with or outperform deeper ones in a
variety of Natural Language Processing (NLP) tasks when both are trained from
scratch. The impact of changing the model aspect ratio on Transformers is then
studied systematically. This ratio balances the number of layers and the number
of attention heads per layer while keeping the total number of attention heads
and all other hyperparameters constant. On average, across 4 NLP tasks and 10
attention types, single layer wide models perform 0.3% better than their deep
counterparts. We show an in-depth evaluation and demonstrate how wide models
require a far smaller memory footprint and can run faster on commodity
hardware, in addition, these wider models are also more interpretable. For
example, a single layer Transformer on the IMDb byte level text classification
has 3.1x faster inference latency on a CPU than its equally accurate deeper
counterpart, and is half the size. We therefore put forward wider and shallower
models as a viable and desirable alternative for small models on NLP tasks, and
as an important area of research for domains beyond this.

Prevention of complete and dimensional collapse of representations has
recently become a design principle for self-supervised learning (SSL). However,
questions remain in our theoretical understanding: When do those collapses
occur? What are the mechanisms and causes? We provide answers to these
questions by thoroughly analyzing SSL loss landscapes for a linear model. We
derive an analytically tractable theory of SSL landscape and show that it
accurately captures an array of collapse phenomena and identifies their causes.
Finally, we leverage the interpretability afforded by the analytical theory to
understand how dimensional collapse can be beneficial and what affects the
robustness of SSL against data imbalance.

The success of modern machine learning algorithms depends crucially on
efficient data representation and compression through dimensionality reduction.
This practice seemingly contradicts the conventional intuition suggesting that
data processing always leads to information loss. We prove that this intuition
is wrong. For any non-convex problem, there exists an optimal, benign
auto-encoder (BAE) extracting a lower-dimensional data representation that is
strictly beneficial: Compressing model inputs improves model performance. We
prove that BAE projects data onto a manifold whose dimension is the
compressibility dimension of the learning model. We develop and implement an
efficient algorithm for computing BAE and show that BAE improves model
performance in every dataset we consider. Furthermore, by compressing
"malignant" data dimensions, BAE makes learning more stable and robust.

Developing simple, sample-efficient learning algorithms for robust
classification is a pressing issue in today's tech-dominated world, and current
theoretical techniques requiring exponential sample complexity and complicated
improper learning rules fall far from answering the need. In this work we study
the fundamental paradigm of (robust) $\textit{empirical risk minimization}$
(RERM), a simple process in which the learner outputs any hypothesis minimizing
its training error. RERM famously fails to robustly learn VC classes (Montasser
et al., 2019a), a bound we show extends even to `nice' settings such as
(bounded) halfspaces. As such, we study a recent relaxation of the robust model
called $\textit{tolerant}$ robust learning (Ashtiani et al., 2022) where the
output classifier is compared to the best achievable error over slightly larger
perturbation sets. We show that under geometric niceness conditions, a natural
tolerant variant of RERM is indeed sufficient for $\gamma$-tolerant robust
learning VC classes over $\mathbb{R}^d$, and requires only $\tilde{O}\left(
\frac{VC(H)d\log \frac{D}{\gamma\delta}}{\epsilon^2}\right)$ samples for
robustness regions of (maximum) diameter $D$.

The performance of high-contrast imaging instruments is limited by wavefront
errors, in particular by non-common path aberrations (NCPA). Focal-plane
wavefront sensing (FPWFS) is appropriate to handle NCPA because it measures the
aberration where it matters the most, i.e., at the science focal plane. Phase
retrieval from focal-plane images results nonetheless in a sign ambiguity for
even modes of the pupil-plane phase. The phase diversity methods currently used
to solve the sign ambiguity tend to reduce the science duty cycle, i.e., the
fraction of observing time dedicated to science. In this work, we explore how
we can combine the phase diversity provided by a vortex coronagraph with modern
deep learning techniques to perform efficient FPWFS without losing observing
time. We apply the state-of-the-art convolutional neural network
EfficientNet-B4 to infer phase aberrations from simulated focal-plane images.
The two cases of scalar and vector vortex coronagraphs (SVC and VVC) are
considered, respectively using a single post-coronagraphic PSF or two PSFs
obtained by splitting the circular polarization states. The sign ambiguity is
properly lifted in both cases even at low signal-to-noise ratios (S/N). Using
either the SVC or the VVC, we reach very similar performance compared to using
phase diversity with a defocused PSF, except for high levels of aberrations
where the SVC slightly underperforms compared to the other approaches. The
models finally show great robustness when trained on data with a wide range of
wavefront errors and noise levels. The proposed FPWFS technique provides a 100%
science duty cycle for instruments using a vortex coronagraph and does not
require any additional hardware in the case of the SVC.

In Nature Machine Intelligence 4, 367 (2022), Schuetz et al provide a scheme
to employ graph neural networks (GNN) as a heuristic to solve a variety of
classical, NP-hard combinatorial optimization problems. It describes how the
network is trained on sample instances and the resulting GNN heuristic is
evaluated applying widely used techniques to determine its ability to succeed.
Clearly, the idea of harnessing the powerful abilities of such networks to
``learn'' the intricacies of complex, multimodal energy landscapes in such a
hands-off approach seems enticing. And based on the observed performance, the
heuristic promises to be highly scalable, with a computational cost linear in
the input size $n$, although there is likely a significant overhead in the
pre-factor due to the GNN itself. However, closer inspection shows that the
reported results for this GNN are only minutely better than those for gradient
descent and get outperformed by a greedy algorithm, for example, for Max-Cut.
The discussion also highlights what I believe are some common misconceptions in
the evaluations of heuristics.

Empirical evaluation of deep learning models against adversarial attacks
entails solving nontrivial constrained optimization problems. Popular
algorithms for solving these constrained problems rely on projected gradient
descent (PGD) and require careful tuning of multiple hyperparameters. Moreover,
PGD can only handle $\ell_1$, $\ell_2$, and $\ell_\infty$ attack models due to
the use of analytical projectors. In this paper, we introduce a novel
algorithmic framework that blends a general-purpose constrained-optimization
solver PyGRANSO, With Constraint-Folding (PWCF), to add reliability and
generality to robustness evaluation. PWCF 1) finds good-quality solutions
without the need of delicate hyperparameter tuning, and 2) can handle general
attack models, e.g., general $\ell_p$ ($p \geq 0$) and perceptual attacks,
which are inaccessible to PGD-based algorithms.

The success of deep learning in natural language processing raises intriguing
questions about the nature of linguistic meaning and ways in which it can be
processed by natural and artificial systems. One such question has to do with
subword segmentation algorithms widely employed in language modeling, machine
translation, and other tasks since 2016. These algorithms often cut words into
semantically opaque pieces, such as 'period', 'on', 't', and 'ist' in
'period|on|t|ist'. The system then represents the resulting segments in a dense
vector space, which is expected to model grammatical relations among them. This
representation may in turn be used to map 'period|on|t|ist' (English) to
'par|od|ont|iste' (French). Thus, instead of being modeled at the lexical
level, translation is reformulated more generally as the task of learning the
best bilingual mapping between the sequences of subword segments of two
languages; and sometimes even between pure character sequences:
'p|e|r|i|o|d|o|n|t|i|s|t' $\rightarrow$ 'p|a|r|o|d|o|n|t|i|s|t|e'. Such subword
segmentations and alignments are at work in highly efficient end-to-end machine
translation systems, despite their allegedly opaque nature. The computational
value of such processes is unquestionable. But do they have any linguistic or
philosophical plausibility? I attempt to cast light on this question by
reviewing the relevant details of the subword segmentation algorithms and by
relating them to important philosophical and linguistic debates, in the spirit
of making artificial intelligence more transparent and explainable.

In recent years, there has been a growing interest in using machine learning
to overcome the high cost of numerical simulation, with some learned models
achieving impressive speed-ups over classical solvers whilst maintaining
accuracy. However, these methods are usually tested at low-resolution settings,
and it remains to be seen whether they can scale to the costly high-resolution
simulations that we ultimately want to tackle.
  In this work, we propose two complementary approaches to improve the
framework from MeshGraphNets, which demonstrated accurate predictions in a
broad range of physical systems. MeshGraphNets relies on a message passing
graph neural network to propagate information, and this structure becomes a
limiting factor for high-resolution simulations, as equally distant points in
space become further apart in graph space. First, we demonstrate that it is
possible to learn accurate surrogate dynamics of a high-resolution system on a
much coarser mesh, both removing the message passing bottleneck and improving
performance; and second, we introduce a hierarchical approach (MultiScale
MeshGraphNets) which passes messages on two different resolutions (fine and
coarse), significantly improving the accuracy of MeshGraphNets while requiring
less computational resources.

To lower the communication complexity of federated min-max learning, a
natural approach is to utilize the idea of infrequent communications (through
multiple local updates) same as in conventional federated learning. However,
due to the more complicated inter-outer problem structure in federated min-max
learning, theoretical understandings of communication complexity for federated
min-max learning with infrequent communications remain very limited in the
literature. This is particularly true for settings with non-i.i.d. datasets and
partial client participation. To address this challenge, in this paper, we
propose a new algorithmic framework called stochastic sampling averaging
gradient descent ascent (SAGDA), which i) assembles stochastic gradient
estimators from randomly sampled clients as control variates and ii) leverages
two learning rates on both server and client sides. We show that SAGDA achieves
a linear speedup in terms of both the number of clients and local update steps,
which yields an $\mathcal{O}(\epsilon^{-2})$ communication complexity that is
orders of magnitude lower than the state of the art. Interestingly, by noting
that the standard federated stochastic gradient descent ascent (FSGDA) is in
fact a control-variate-free special version of SAGDA, we immediately arrive at
an $\mathcal{O}(\epsilon^{-2})$ communication complexity result for FSGDA.
Therefore, through the lens of SAGDA, we also advance the current understanding
on communication complexity of the standard FSGDA method for federated min-max
learning.

The two most important algorithms in artificial intelligence are
backpropagation and belief propagation. In spite of their importance, the
connection between them is poorly characterized. We show that when an input to
backpropagation is converted into an input to belief propagation so that
(loopy) belief propagation can be run on it, then the result of belief
propagation encodes the result of backpropagation; thus backpropagation is
recovered as a special case of belief propagation. In other words, we prove for
apparently the first time that belief propagation generalizes backpropagation.
Our analysis is a theoretical contribution, which we motivate with the
expectation that it might reconcile our understandings of each of these
algorithms, and serve as a guide to engineering researchers seeking to improve
the behavior of systems that use one or the other.

We address the problem of safe reinforcement learning from pixel
observations. Inherent challenges in such settings are (1) a trade-off between
reward optimization and adhering to safety constraints, (2) partial
observability, and (3) high-dimensional observations. We formalize the problem
in a constrained, partially observable Markov decision process framework, where
an agent obtains distinct reward and safety signals. To address the curse of
dimensionality, we employ a novel safety critic using the stochastic latent
actor-critic (SLAC) approach. The latent variable model predicts rewards and
safety violations, and we use the safety critic to train safe policies. Using
well-known benchmark environments, we demonstrate competitive performance over
existing approaches with respects to computational requirements, final reward
return, and satisfying the safety constraints.

The capabilities of a robot will be increased significantly by exploiting
throwing behavior. In particular, throwing will enable robots to rapidly place
the object into the target basket, located outside its feasible kinematic
space, without traveling to the desired location. In previous approaches, the
robot often learned a parameterized throwing kernel through analytical
approaches, imitation learning, or hand-coding. There are many situations in
which such approaches do not work/generalize well due to various object shapes,
heterogeneous mass distribution, and also obstacles that might be presented in
the environment. It is obvious that a method is needed to modulate the throwing
kernel through its meta parameters. In this paper, we tackle object throwing
problem through a deep reinforcement learning approach that enables robots to
precisely throw objects into moving baskets while there are obstacles
obstructing the path. To the best of our knowledge, we are the first group that
addresses throwing objects with obstacle avoidance. Such a throwing skill not
only increases the physical reachability of a robot arm but also improves the
execution time. In particular, the robot detects the pose of the target object,
basket, and obstacle at each time step, predicts the proper grasp configuration
for the target object, and then infers appropriate parameters to throw the
object into the basket. Due to safety constraints, we develop a simulation
environment in Gazebo to train the robot and then use the learned policy in
real-robot directly. To assess the performers of the proposed approach, we
perform extensive sets of experiments in both simulation and real robots in
three scenarios. Experimental results showed that the robot could precisely
throw a target object into the basket outside its kinematic range and
generalize well to new locations and objects without colliding with obstacles.

As more practical and scalable quantum computers emerge, much attention has
been focused on realizing quantum supremacy in machine learning. Existing
quantum ML methods either (1) embed a classical model into a target Hamiltonian
to enable quantum optimization or (2) represent a quantum model using
variational quantum circuits and apply classical gradient-based optimization.
The former method leverages the power of quantum optimization but only supports
simple ML models, while the latter provides flexibility in model design but
relies on gradient calculation, resulting in barren plateau (i.e., gradient
vanishing) and frequent classical-quantum interactions. To address the
limitations of existing quantum ML methods, we introduce Quark, a gradient-free
quantum learning framework that optimizes quantum ML models using quantum
optimization. Quark does not rely on gradient computation and therefore avoids
barren plateau and frequent classical-quantum interactions. In addition, Quark
can support more general ML models than prior quantum ML methods and achieves a
dataset-size-independent optimization complexity. Theoretically, we prove that
Quark can outperform classical gradient-based methods by reducing model query
complexity for highly non-convex problems; empirically, evaluations on the Edge
Detection and Tiny-MNIST tasks show that Quark can support complex ML models
and significantly reduce the number of measurements needed for discovering
near-optimal weights for these tasks.

With the growing adoption of deep learning models in different real-world
domains, including computational biology, it is often necessary to understand
which data features are essential for the model's decision. Despite extensive
recent efforts to define different feature importance metrics for deep learning
models, we identified that inherent stochasticity in the design and training of
deep learning models makes commonly used feature importance scores unstable.
This results in varied explanations or selections of different features across
different runs of the model. We demonstrate how the signal strength of features
and correlation among features directly contribute to this instability. To
address this instability, we explore the ensembling of feature importance
scores of models across different epochs and find that this simple approach can
substantially address this issue. For example, we consider knockoff inference
as they allow feature selection with statistical guarantees. We discover
considerable variability in selected features in different epochs of deep
learning training, and the best selection of features doesn't necessarily occur
at the lowest validation loss, the conventional approach to determine the best
model. As such, we present a framework to combine the feature importance of
trained models across different hyperparameter settings and epochs, and instead
of selecting features from one best model, we perform an ensemble of feature
importance scores from numerous good models. Across the range of experiments in
simulated and various real-world datasets, we demonstrate that the proposed
framework consistently improves the power of feature selection.

The reconstruction of charged particle trajectories in tracking detectors is
a key problem in the analysis of experimental data for high-energy and nuclear
physics. The amount of data in modern experiments is so large that classical
tracking methods, such as the Kalman filter, cannot process them fast enough.
To solve this problem, we developed two neural network track recognition
algorithms based on deep learning architectures for local (track by track) and
global (all tracks in an event) tracking in pixel and strip-based particle
detectors. These algorithms were tested using the GEM tracker of the BM@N
experiment at JINR (Dubna) and the cylindrical GEM inner tracker of the BESIII
experiment. The RDGraphNet neural network model for global track finding, based
on a reverse directed graph, showed encouraging results: 98% recall and 86%
precision for track finding. The TrackNETv2 local neural network model was
supplemented by an additional neuro-classifier was introduced to filter out
false tracks. Preliminary tests demonstrated a recall value of 99% at the first
stage. After applying the neuro-classifier, the precision was 77% with a slight
decrease in the recall to 94%. This result can be improved after further model

This chapter is meant to be part of the book "Differential Privacy for
Artificial Intelligence Applications." We give an introduction to the most
important property of differential privacy -- composition: running multiple
independent analyses on the data of a set of people will still be
differentially private as long as each of the analyses is private on its own --
as well as the related topic of privacy amplification by subsampling. This
chapter introduces the basic concepts and gives proofs of the key results
needed to apply these tools in practice.

This paper considers the problem of learning safe policies in the context of
reinforcement learning (RL). In particular, a safe policy or controller is one
that, with high probability, maintains the trajectory of the agent in a given
safe set. We relate this notion of safety to the notion of average safety often
considered in the literature by providing theoretical bounds in terms of their
safety and performance. The challenge of working with the probabilistic notion
of safety considered in this work is the lack of expressions for their
gradients. Indeed, policy optimization algorithms rely on gradients of the
objective function and the constraints. To the best of our knowledge, this work
is the first one providing such explicit gradient expressions for probabilistic
constraints. It is worth noting that such probabilistic gradients are naturally
algorithm independent, which provides possibilities for them to be applied to
various policy-based algorithms. In addition, we consider a continuous
navigation problem to empirically illustrate the advantages (in terms of safety
and performance) of working with probabilistic constraints as compared to
average constraints.

A post-pandemic world resulted in economic upheaval, particularly for the
cities' communities. While significant work in NLP4PI focuses on national and
international events, there is a gap in bringing such state-of-the-art methods
into the community development field. In order to help with community
development, we must learn about the communities we develop. To that end, we
propose the task of community learning as a computational task of extracting
natural language data about the community, transforming and loading it into a
suitable knowledge graph structure for further downstream applications. We
study two particular cases of homelessness and education in showing the
visualization capabilities of a knowledge graph, and also discuss other
usefulness such a model can provide.

Prediction uncertainty estimation has clinical significance as it can
potentially quantify prediction reliability. Clinicians may trust 'blackbox'
models more if robust reliability information is available, which may lead to
more models being adopted into clinical practice. There are several deep
learning-inspired uncertainty estimation techniques, but few are implemented on
medical datasets -- fewer on single institutional datasets/models. We sought to
compare dropout variational inference (DO), test-time augmentation (TTA),
conformal predictions, and single deterministic methods for estimating
uncertainty using our model trained to predict feeding tube placement for 271
head and neck cancer patients treated with radiation. We compared the area
under the curve (AUC), sensitivity, specificity, positive predictive value
(PPV), and negative predictive value (NPV) trends for each method at various
cutoffs that sought to stratify patients into 'certain' and 'uncertain'
cohorts. These cutoffs were obtained by calculating the percentile
"uncertainty" within the validation cohort and applied to the testing cohort.
Broadly, the AUC, sensitivity, and NPV increased as the predictions were more
'certain' -- i.e., lower uncertainty estimates. However, when a majority vote
(implementing 2/3 criteria: DO, TTA, conformal predictions) or a stricter
approach (3/3 criteria) were used, AUC, sensitivity, and NPV improved without a
notable loss in specificity or PPV. Especially for smaller, single
institutional datasets, it may be important to evaluate multiple estimations
techniques before incorporating a model into clinical practice.

Due to its distributed nature, federated learning is vulnerable to poisoning
attacks, in which malicious clients poison the training process via
manipulating their local training data and/or local model updates sent to the
cloud server, such that the poisoned global model misclassifies many
indiscriminate test inputs or attacker-chosen ones. Existing defenses mainly
leverage Byzantine-robust federated learning methods or detect malicious
clients. However, these defenses do not have provable security guarantees
against poisoning attacks and may be vulnerable to more advanced attacks. In
this work, we aim to bridge the gap by proposing FLCert, an ensemble federated
learning framework, that is provably secure against poisoning attacks with a
bounded number of malicious clients. Our key idea is to divide the clients into
groups, learn a global model for each group of clients using any existing
federated learning method, and take a majority vote among the global models to
classify a test input. Specifically, we consider two methods to group the
clients and propose two variants of FLCert correspondingly, i.e., FLCert-P that
randomly samples clients in each group, and FLCert-D that divides clients to
disjoint groups deterministically. Our extensive experiments on multiple
datasets show that the label predicted by our FLCert for a test input is
provably unaffected by a bounded number of malicious clients, no matter what
poisoning attacks they use.

Deep neural networks have incredible capacity and expressibility, and can
seemingly memorize any training set. This introduces a problem when training in
the presence of noisy labels, as the noisy examples cannot be distinguished
from clean examples by the end of training. Recent research has dealt with this
challenge by utilizing the fact that deep networks seem to memorize clean
examples much earlier than noisy examples. Here we report a new empirical
result: for each example, when looking at the time it has been memorized by
each model in an ensemble of networks, the diversity seen in noisy examples is
much larger than the clean examples. We use this observation to develop a new
method for noisy labels filtration. The method is based on a statistics of the
data, which captures the differences in ensemble learning dynamics between
clean and noisy data. We test our method on three tasks: (i) noise amount
estimation; (ii) noise filtration; (iii) supervised classification. We show
that our method improves over existing baselines in all three tasks using a
variety of datasets, noise models, and noise levels. Aside from its improved
performance, our method has two other advantages. (i) Simplicity, which implies
that no additional hyperparameters are introduced. (ii) Our method is modular:
it does not work in an end-to-end fashion, and can therefore be used to clean a
dataset for any other future usage.

This paper builds bridges between two families of probabilistic algorithms:
(hierarchical) variational inference (VI), which is typically used to model
distributions over continuous spaces, and generative flow networks (GFlowNets),
which have been used for distributions over discrete structures such as graphs.
We demonstrate that, in certain cases, VI algorithms are equivalent to special
cases of GFlowNets in the sense of equality of expected gradients of their
learning objectives. We then point out the differences between the two families
and show how these differences emerge experimentally. Notably, GFlowNets, which
borrow ideas from reinforcement learning, are more amenable than VI to
off-policy training without the cost of high gradient variance induced by
importance sampling. We argue that this property of GFlowNets can provide
advantages for capturing diversity in multimodal target distributions.

Crystal-graph attention networks have emerged recently as remarkable tools
for the prediction of thermodynamic stability and materials properties from
unrelaxed crystal structures. Previous networks trained on two million
materials exhibited, however, strong biases originating from underrepresented
chemical elements and structural prototypes in the available data. We tackled
this issue computing additional data to provide better balance across both
chemical and crystal-symmetry space. Crystal-graph networks trained with this
new data show unprecedented generalization accuracy, and allow for reliable,
accelerated exploration of the whole space of inorganic compounds. We applied
this universal network to perform machine-learning assisted high-throughput
materials searches including 2500 binary and ternary structure prototypes and
spanning about 1 billion compounds. After validation using density-functional
theory, we uncover in total 19512 additional materials on the convex hull of
thermodynamic stability and ~150000 compounds with a distance of less than 50
meV/atom from the hull. Combining again machine learning and ab-initio methods,
we finally evaluate the discovered materials for applications as
superconductors, superhard materials, and we look for candidates with large gap
deformation potentials, finding several compounds with extreme values of these
properties.

How can we design neural networks that allow for stable universal
approximation of maps between topologically interesting manifolds? The answer
is with a coordinate projection. Neural networks based on topological data
analysis (TDA) use tools such as persistent homology to learn topological
signatures of data and stabilize training but may not be universal
approximators or have stable inverses. Other architectures universally
approximate data distributions on submanifolds but only when the latter are
given by a single chart, making them unable to learn maps that change topology.
By exploiting the topological parallels between locally bilipschitz maps,
covering spaces, and local homeomorphisms, and by using universal approximation
arguments from machine learning, we find that a novel network of the form
$\mathcal{T} \circ p \circ \mathcal{E}$, where $\mathcal{E}$ is an injective
network, $p$ a fixed coordinate projection, and $\mathcal{T}$ a bijective
network, is a universal approximator of local diffeomorphisms between compact
smooth submanifolds embedded in $\mathbb{R}^n$. We emphasize the case when the
target map changes topology. Further, we find that by constraining the
projection $p$, multivalued inversions of our networks can be computed without
sacrificing universality. As an application, we show that learning a group
invariant function with unknown group action naturally reduces to the question
of learning local diffeomorphisms for finite groups. Our theory permits us to
recover orbits of the group action. We also outline possible extensions of our
architecture to address molecular imaging of molecules with symmetries.
Finally, our analysis informs the choice of topologically expressive starting
spaces in generative problems.

In social science, formal and quantitative models, such as ones describing
economic growth and collective action, are used to formulate mechanistic
explanations, provide predictions, and uncover questions about observed
phenomena. Here, we demonstrate the use of a machine learning system to aid the
discovery of symbolic models that capture nonlinear and dynamical relationships
in social science datasets. By extending neuro-symbolic methods to find compact
functions and differential equations in noisy and longitudinal data, we show
that our system can be used to discover interpretable models from real-world
data in economics and sociology. Augmenting existing workflows with symbolic
regression can help uncover novel relationships and explore counterfactual
models during the scientific process. We propose that this AI-assisted
framework can bridge parametric and non-parametric models commonly employed in
social science research by systematically exploring the space of nonlinear
models and enabling fine-grained control over expressivity and
interpretability.

In recent decades, neuromorphic computing aiming to imitate brains' behaviors
has been developed in various fields of computer science. The Artificial Neural
Network (ANN) is an important concept in Artificial Intelligence (AI). It is
utilized in recognition and classification. To explore a better way to simulate
obtained brain behaviors, which is fast and energy-efficient, on hardware,
researchers need an advanced method such as neuromorphic computing. In this
case, Spiking Neural Network (SNN) becomes an optimal choice in hardware
implementation. Recent works are focusing on accelerating SNN computing.
However, most accelerator solutions are based on CPU-accelerator architecture
which is energy-inefficient due to the complex control flows in this structure.
This paper proposes Wenquxing 22A, a low-power neuromorphic processor that
combines general-purpose CPU functions and SNN to efficiently compute it with
RISC-V SNN extension instructions. The main idea of Wenquxing 22A is to
integrate the SNN calculation unit into the pipeline of a general-purpose CPU
to achieve low-power computing with customized RISC-V SNN instructions version
1.0 (RV-SNN V1.0), Streamlined Leaky Integrate-and-Fire (LIF) model, and the
binary stochastic Spike-timing-dependent-plasticity (STDP). The source code of
Wenquxing 22A is released online on Gitee and GitHub. We apply Wenquxing 22A to
the recognition of the MNIST dataset to make a comparison with other SNN
systems. Our experiment results show that Wenquxing 22A improves the energy
expenses by 5.13 times over the accelerator solution, ODIN, with approximately
classification accuracy, 85.00% for 3-bit ODIN online learning, and 91.91% for
1-bit Wenquxing 22A.

Adversarial Training (AT) has been demonstrated as one of the most effective
methods against adversarial examples. While most existing works focus on AT
with a single type of perturbation e.g., the $\ell_\infty$ attacks), DNNs are
facing threats from different types of adversarial examples. Therefore,
adversarial training for multiple perturbations (ATMP) is proposed to
generalize the adversarial robustness over different perturbation types (in
$\ell_1$, $\ell_2$, and $\ell_\infty$ norm-bounded perturbations). However, the
resulting model exhibits trade-off between different attacks. Meanwhile, there
is no theoretical analysis of ATMP, limiting its further development. In this
paper, we first provide the smoothness analysis of ATMP and show that $\ell_1$,
$\ell_2$, and $\ell_\infty$ adversaries give different contributions to the
smoothness of the loss function of ATMP. Based on this, we develop the
stability-based excess risk bounds and propose adaptive smoothness-weighted
adversarial training for multiple perturbations. Theoretically, our algorithm
yields better bounds. Empirically, our experiments on CIFAR10 and CIFAR100
achieve the state-of-the-art performance against the mixture of multiple
perturbations attacks.

Autonomous navigation in crowded spaces poses a challenge for mobile robots
due to the highly dynamic, partially observable environment. Occlusions are
highly prevalent in such settings due to a limited sensor field of view and
obstructing human agents. Previous work has shown that observed interactive
behaviors of human agents can be used to estimate potential obstacles despite
occlusions. We propose integrating such social inference techniques into the
planning pipeline. We use a variational autoencoder with a specially designed
loss function to learn representations that are meaningful for occlusion
inference. This work adopts a deep reinforcement learning approach to
incorporate the learned representation for occlusion-aware planning. In
simulation, our occlusion-aware policy achieves comparable collision avoidance
performance to fully observable navigation by estimating agents in occluded
spaces. We demonstrate successful policy transfer from simulation to the
real-world Turtlebot 2i. To the best of our knowledge, this work is the first
to use social occlusion inference for crowd navigation.

Distributed optimization enables networked agents to cooperatively solve a
global optimization problem even with each participating agent only having
access to a local partial view of the objective function. Despite making
significant inroads, most existing results on distributed optimization rely on
noise-free information sharing among the agents, which is problematic when
communication channels are noisy, messages are coarsely quantized, or shared
information are obscured by additive noise for the purpose of achieving
differential privacy. The problem of information-sharing noise is particularly
pronounced in the state-of-the-art gradient-tracking based distributed
optimization algorithms, in that information-sharing noise will accumulate with
iterations on the gradient-tracking estimate of these algorithms, and the
ensuing variance will even grow unbounded when the noise is persistent. This
paper proposes a new gradient-tracking based distributed optimization approach
that can avoid information-sharing noise from accumulating in the gradient
estimation. The approach is applicable even when the {inter-agent interaction
is} time-varying, which is key to enable the incorporation of a decaying factor
in inter-agent interaction to gradually eliminate the influence of
information-sharing noise. In fact, we rigorously prove that the proposed
approach can ensure the almost sure convergence of all agents to the same
optimal solution even in the presence of persistent information-sharing noise.
The approach is applicable to general directed graphs. It is also capable of
ensuring the almost sure convergence of all agents to an optimal solution when
the gradients are noisy, which is common in machine learning applications.
Numerical simulations confirm the effectiveness of the proposed approach.

In the past decade, many architectures of convolution neural networks were
designed by handcraft, such as Vgg16, ResNet, DenseNet, etc. They all achieve
state-of-the-art level on different tasks in their time. However, it still
relies on human intuition and experience, and it also takes so much time
consumption for trial and error. Neural Architecture Search (NAS) focused on
this issue. In recent works, the Neural Predictor has significantly improved
with few training architectures as training samples. However, the sampling
efficiency is already considerable. In this paper, our proposed
Siamese-Predictor is inspired by past works of predictor-based NAS. It is
constructed with the proposed Estimation Code, which is the prior knowledge
about the training procedure. The proposed Siamese-Predictor gets significant
benefits from this idea. This idea causes it to surpass the current SOTA
predictor on NASBench-201. In order to explore the impact of the Estimation
Code, we analyze the relationship between it and accuracy. We also propose the
search space Tiny-NanoBench for lightweight CNN architecture. This
well-designed search space is easier to find better architecture with few FLOPs
than NASBench-201. In summary, the proposed Siamese-Predictor is a
predictor-based NAS. It achieves the SOTA level, especially with limited
computation budgets. It applied to the proposed Tiny-NanoBench can just use a
few trained samples to find extremely lightweight CNN architecture.

Recently, pre-trained transformer-based models have achieved great success in
the task of definition generation (DG). However, previous encoder-decoder
models lack effective representation learning to contain full semantic
components of the given word, which leads to generating under-specific
definitions. To address this problem, we propose a novel contrastive learning
method, encouraging the model to capture more detailed semantic representations
from the definition sequence encoding. According to both automatic and manual
evaluation, the experimental results on three mainstream benchmarks demonstrate
that the proposed method could generate more specific and high-quality
definitions compared with several state-of-the-art models.

Social networks are considered to be heterogeneous graph neural networks
(HGNNs) with deep learning technological advances. HGNNs, compared to
homogeneous data, absorb various aspects of information about individuals in
the training stage. That means more information has been covered in the
learning result, especially sensitive information. However, the
privacy-preserving methods on homogeneous graphs only preserve the same type of
node attributes or relationships, which cannot effectively work on
heterogeneous graphs due to the complexity. To address this issue, we propose a
novel heterogeneous graph neural network privacy-preserving method based on a
differential privacy mechanism named HeteDP, which provides a double guarantee
on graph features and topology. In particular, we first define a new attack
scheme to reveal privacy leakage in the heterogeneous graphs. Specifically, we
design a two-stage pipeline framework, which includes the privacy-preserving
feature encoder and the heterogeneous link reconstructor with gradients
perturbation based on differential privacy to tolerate data diversity and
against the attack. To better control the noise and promote model performance,
we utilize a bi-level optimization pattern to allocate a suitable privacy
budget for the above two modules. Our experiments on four public benchmarks
show that the HeteDP method is equipped to resist heterogeneous graph privacy
leakage with admirable model generalization.

Reliable and robust user identification and authentication are important and
often necessary requirements for many digital services. It becomes paramount in
social virtual reality (VR) to ensure trust, specifically in digital encounters
with lifelike realistic-looking avatars as faithful replications of real
persons. Recent research has shown that the movements of users in extended
reality (XR) systems carry user-specific information and can thus be used to
verify their identities. This article compares three different potential
encodings of the motion data from head and hands (scene-relative,
body-relative, and body-relative velocities), and the performances of five
different machine learning architectures (random forest, multi-layer
perceptron, fully recurrent neural network, long-short term memory, gated
recurrent unit). We use the publicly available dataset "Talking with Hands" and
publish all code to allow reproducibility and to provide baselines for future
work. After hyperparameter optimization, the combination of a long-short term
memory architecture and body-relative data outperformed competing combinations:
the model correctly identifies any of the 34 subjects with an accuracy of 100%
within 150 seconds. Altogether, our approach provides an effective foundation
for behaviometric-based identification and authentication to guide researchers
and practitioners. Data and code are published under
https://go.uniwue.de/58w1r.

Simulators driven by deep learning are gaining popularity as a tool for
efficiently emulating accurate but expensive numerical simulators. Successful
applications of such neural simulators can be found in the domains of physics,
chemistry, and structural biology, amongst others. Likewise, a neural simulator
for cellular dynamics can augment lab experiments and traditional computational
methods to enhance our understanding of a cell's interaction with its physical
environment. In this work, we propose an autoregressive probabilistic model
that can reproduce spatiotemporal dynamics of single cell migration,
traditionally simulated with the Cellular Potts model. We observe that standard
single-step training methods do not only lead to inconsistent rollout
stability, but also fail to accurately capture the stochastic aspects of the
dynamics, and we propose training strategies to mitigate these issues. Our
evaluation on two proof-of-concept experimental scenarios shows that neural
methods have the potential to faithfully simulate stochastic cellular dynamics
at least an order of magnitude faster than a state-of-the-art implementation of
the Cellular Potts model.

Air quality monitoring is becoming an essential task with rising awareness
about air quality. Low cost air quality sensors are easy to deploy but are not
as reliable as the costly and bulky reference monitors. The low quality sensors
can be calibrated against the reference monitors with the help of deep
learning. In this paper, we translate the task of sensor calibration into a
semi-supervised domain adaptation problem and propose a novel solution for the
same. The problem is challenging because it is a regression problem with
covariate shift and label gap. We use histogram loss instead of mean squared or
mean absolute error, which is commonly used for regression, and find it useful
against covariate shift. To handle the label gap, we propose weighting of
samples for adversarial entropy optimization. In experimental evaluations, the
proposed scheme outperforms many competitive baselines, which are based on
semi-supervised and supervised domain adaptation, in terms of R2 score and mean
absolute error. Ablation studies show the relevance of each proposed component
in the entire scheme.

Time-dependent Partial Differential Equations with given initial conditions
are considered in this paper. New differentiation techniques of the unknown
solution with respect to time variable are proposed. It is shown that the
proposed techniques allow to generate accurate higher order derivatives
simultaneously for a set of spatial points. The calculated derivatives can then
be used for data-driven solution in different ways. An application for Physics
Informed Neural Networks by the well-known DeepXDE software solution in Python
under Tensorflow background framework has been presented for three real-life
PDEs: Burgers', Allen-Cahn and Schrodinger equations.

Representing 3D objects and scenes with neural radiance fields has become
very popular over the last years. Recently, surface-based representations have
been proposed, that allow to reconstruct 3D objects from simple photographs.
However, most current techniques require an accurate camera calibration, i.e.
camera parameters corresponding to each image, which is often a difficult task
to do in real-life situations. To this end, we propose a method for learning 3D
surfaces from noisy camera parameters. We show that we can learn camera
parameters together with learning the surface representation, and demonstrate
good quality 3D surface reconstruction even with noisy camera observations.

The ultra-wide optical coherence tomography angiography (OCTA) has become an
important imaging modality in diabetic retinopathy (DR) diagnosis. However,
there are few researches focusing on automatic DR analysis using ultra-wide
OCTA. In this paper, we present novel and practical deep-learning solutions
based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge
(DRAC). In the segmentation of DR lesions task, we utilize UNet and UNet++ to
segment three lesions with strong data augmentation and model ensemble. In the
image quality assessment task, we create an ensemble of InceptionV3,
SE-ResNeXt, and Vision Transformer models. Pre-training on the large dataset as
well as the hybrid MixUp and CutMix strategy are both adopted to boost the
generalization ability of our model. In the DR grading task, we build a Vision
Transformer (ViT) and fnd that the ViT model pre-trained on color fundus images
serves as a useful substrate for OCTA images. Our proposed methods ranked 4th,
3rd, and 5th on the three leaderboards of DRAC, respectively. The source code
will be made available at https://github.com/FDU-VTS/DRAC.

We present Gradient Gating (G$^2$), a novel framework for improving the
performance of Graph Neural Networks (GNNs). Our framework is based on gating
the output of GNN layers with a mechanism for multi-rate flow of message
passing information across nodes of the underlying graph. Local gradients are
harnessed to further modulate message passing updates. Our framework flexibly
allows one to use any basic GNN layer as a wrapper around which the multi-rate
gradient gating mechanism is built. We rigorously prove that G$^2$ alleviates
the oversmoothing problem and allows the design of deep GNNs. Empirical results
are presented to demonstrate that the proposed framework achieves
state-of-the-art performance on a variety of graph learning tasks, including on
large-scale heterophilic graphs.

Technological advancements have spurred the usage of machine learning based
applications in sports science. Physiotherapists, sports coaches and athletes
actively look to incorporate the latest technologies in order to further
improve performance and avoid injuries. While wearable sensors are very
popular, their use is hindered by constraints on battery power and sensor
calibration, especially for use cases which require multiple sensors to be
placed on the body. Hence, there is renewed interest in video-based data
capture and analysis for sports science. In this paper, we present the
application of classifying S\&C exercises using video. We focus on the popular
Military Press exercise, where the execution is captured with a video-camera
using a mobile device, such as a mobile phone, and the goal is to classify the
execution into different types. Since video recordings need a lot of storage
and computation, this use case requires data reduction, while preserving the
classification accuracy and enabling fast prediction. To this end, we propose
an approach named BodyMTS to turn video into time series by employing body pose
tracking, followed by training and prediction using multivariate time series
classifiers. We analyze the accuracy and robustness of BodyMTS and show that it
is robust to different types of noise caused by either video quality or pose
estimation factors. We compare BodyMTS to state-of-the-art deep learning
methods which classify human activity directly from videos and show that
BodyMTS achieves similar accuracy, but with reduced running time and model
engineering effort. Finally, we discuss some of the practical aspects of
employing BodyMTS in this application in terms of accuracy and robustness under
reduced data quality and size. We show that BodyMTS achieves an average
accuracy of 87\%, which is significantly higher than the accuracy of human
domain experts.

Content-based image retrieval (CBIR) systems are an emerging technology that
supports reading and interpreting medical images. Since 3D brain MR images are
high dimensional, dimensionality reduction is necessary for CBIR using machine
learning techniques. In addition, for a reliable CBIR system, each dimension in
the resulting low-dimensional representation must be associated with a
neurologically interpretable region. We propose a localized variational
autoencoder (Loc-VAE) that provides neuroanatomically interpretable
low-dimensional representation from 3D brain MR images for clinical CBIR.
Loc-VAE is based on $\beta$-VAE with the additional constraint that each
dimension of the low-dimensional representation corresponds to a local region
of the brain. The proposed Loc-VAE is capable of acquiring representation that
preserves disease features and is highly localized, even under high-dimensional
compression ratios (4096:1). The low-dimensional representation obtained by
Loc-VAE improved the locality measure of each dimension by 4.61 points compared
to naive $\beta$-VAE, while maintaining comparable brain reconstruction
capability and information about the diagnosis of Alzheimer's disease.

Handwritten text recognition for historical documents is an important task
but it remains difficult due to a lack of sufficient training data in
combination with a large variability of writing styles and degradation of
historical documents. While recurrent neural network architectures are commonly
used for handwritten text recognition, they are often computationally expensive
to train and the benefit of recurrence drastically differs by task. For these
reasons, it is important to consider non-recurrent architectures. In the
context of handwritten date recognition, we propose an architecture based on
the EfficientNetV2 class of models that is fast to train, robust to parameter
choices, and accurately transcribes handwritten dates from a number of sources.
For training, we introduce a database containing almost 10 million tokens,
originating from more than 2.2 million handwritten dates which are segmented
from different historical documents. As dates are some of the most common
information on historical documents, and with historical archives containing
millions of such documents, the efficient and automatic transcription of dates
has the potential to lead to significant cost-savings over manual
transcription. We show that training on handwritten text with high variability
in writing styles result in robust models for general handwritten text
recognition and that transfer learning from the DARE system increases
transcription accuracy substantially, allowing one to obtain high accuracy even
when using a relatively small training sample.

Unsupervised reinforcement learning (URL) poses a promising paradigm to learn
useful behaviors in a task-agnostic environment without the guidance of
extrinsic rewards to facilitate the fast adaptation of various downstream
tasks. Previous works focused on the pre-training in a model-free manner while
lacking the study of transition dynamics modeling that leaves a large space for
the improvement of sample efficiency in downstream tasks. To this end, we
propose an Efficient Unsupervised Reinforcement Learning Framework with
Multi-choice Dynamics model (EUCLID), which introduces a novel model-fused
paradigm to jointly pre-train the dynamics model and unsupervised exploration
policy in the pre-training phase, thus better leveraging the environmental
samples and improving the downstream task sampling efficiency. However,
constructing a generalizable model which captures the local dynamics under
different behaviors remains a challenging problem. We introduce the
multi-choice dynamics model that covers different local dynamics under
different behaviors concurrently, which uses different heads to learn the state
transition under different behaviors during unsupervised pre-training and
selects the most appropriate head for prediction in the downstream task.
Experimental results in the manipulation and locomotion domains demonstrate
that EUCLID achieves state-of-the-art performance with high sample efficiency,
basically solving the state-based URLB benchmark and reaching a mean normalized
score of 104.0$\pm$1.2$\%$ in downstream tasks with 100k fine-tuning steps,
which is equivalent to DDPG's performance at 2M interactive steps with 20x more
data.

Approximate computing (AxC) has been long accepted as a design alternative
for efficient system implementation at the cost of relaxed accuracy
requirements. Despite the AxC research activities in various application
domains, AxC thrived the past decade when it was applied in Machine Learning
(ML). The by definition approximate notion of ML models but also the increased
computational overheads associated with ML applications-that were effectively
mitigated by corresponding approximations-led to a perfect matching and a
fruitful synergy. AxC for AI/ML has transcended beyond academic prototypes. In
this work, we enlighten the synergistic nature of AxC and ML and elucidate the
impact of AxC in designing efficient ML systems. To that end, we present an
overview and taxonomy of AxC for ML and use two descriptive application
scenarios to demonstrate how AxC boosts the efficiency of ML systems.

In order to perform machine learning among multiple parties while protecting
the privacy of raw data, privacy-preserving machine learning based on secure
multi-party computation (MPL for short) has been a hot spot in recent. The
configuration of MPL usually follows the peer-to-peer architecture, where each
party has the same chance to reveal the output result. However, typical
business scenarios often follow a hierarchical architecture where a powerful,
usually privileged party, leads the tasks of machine learning. Only the
privileged party can reveal the final model even if other assistant parties
collude with each other. It is even required to avoid the abort of machine
learning to ensure the scheduled deadlines and/or save used computing resources
when part of assistant parties drop out.
  Motivated by the above scenarios, we propose pMPL, a robust MPL framework
with a privileged part}. pMPL supports three-party training in the semi-honest
setting. By setting alternate shares for the privileged party, pMPL is robust
to tolerate one of the rest two parties dropping out during the training. With
the above settings, we design a series of efficient protocols based on vector
space secret sharing for pMPL to bridge the gap between vector space secret
sharing and machine learning. Finally, the experimental results show that the
performance of pMPL is promising when we compare it with the state-of-the-art
MPL frameworks. Especially, in the LAN setting, pMPL is around $16\times$ and
$5\times$ faster than TF-encrypted (with ABY3 as the back-end framework) for
the linear regression, and logistic regression, respectively. Besides, the
accuracy of trained models of linear regression, logistic regression, and BP
neural networks can reach around 97%, 99%, and 96% on MNIST dataset

Generalization error boundaries are essential for comprehending how well
machine learning models work. In this work, we suggest a creative method, i.e.,
the Auxiliary Distribution Method, that derives new upper bounds on
generalization errors that are appropriate for supervised learning scenarios.
We show that our general upper bounds can be specialized under some conditions
to new bounds involving the generalized $\alpha$-Jensen-Shannon,
$\alpha$-R\'enyi ($0< \alpha < 1$) information between random variable modeling
the set of training samples and another random variable modeling the set of
hypotheses. Our upper bounds based on generalized $\alpha$-Jensen-Shannon
information are also finite. Additionally, we demonstrate how our auxiliary
distribution method can be used to derive the upper bounds on generalization
error under the distribution mismatch scenario in supervised learning
algorithms, where the distributional mismatch is modeled as
$\alpha$-Jensen-Shannon or $\alpha$-R\'enyi ($0< \alpha < 1$) between the
distribution of test and training data samples. We also outline the
circumstances in which our proposed upper bounds might be tighter than other
earlier upper bounds.

Deep learning models struggle with compositional generalization, i.e. the
ability to recognize or generate novel combinations of observed elementary
concepts. In hopes of enabling compositional generalization, various
unsupervised learning algorithms have been proposed with inductive biases that
aim to induce compositional structure in learned representations (e.g.
disentangled representation and emergent language learning). In this work, we
evaluate these unsupervised learning algorithms in terms of how well they
enable compositional generalization. Specifically, our evaluation protocol
focuses on whether or not it is easy to train a simple model on top of the
learned representation that generalizes to new combinations of compositional
factors. We systematically study three unsupervised representation learning
algorithms - $\beta$-VAE, $\beta$-TCVAE, and emergent language (EL)
autoencoders - on two datasets that allow directly testing compositional
generalization. We find that directly using the bottleneck representation with
simple models and few labels may lead to worse generalization than using
representations from layers before or after the learned representation itself.
In addition, we find that the previously proposed metrics for evaluating the
levels of compositionality are not correlated with actual compositional
generalization in our framework. Surprisingly, we find that increasing pressure
to produce a disentangled representation produces representations with worse
generalization, while representations from EL models show strong compositional
generalization. Taken together, our results shed new light on the compositional
generalization behavior of different unsupervised learning algorithms with a
new setting to rigorously test this behavior, and suggest the potential
benefits of delevoping EL learning algorithms for more generalizable
representations.

In this paper, we address the problem of unsupervised Domain Adaptation. The
need for such an adaptation arises when the distribution of the target data
differs from that which is used to develop the model and the ground truth
information of the target data is unknown. We propose an algorithm that uses
optimal transport theory with a verifiably efficient and implementable solution
to learn the best latent feature representation. This is achieved by minimizing
the cost of transporting the samples from the target domain to the distribution
of the source domain.

Glaucoma is the leading cause of irreversible blindness in people over the
age of 60, accounting for 6.6 to 8% of all blindness in 2010, but there is
still much to be learned about the genetic origins of the eye disease. With the
modern development of Next-Generation Sequencing (NGS) technologies, scientists
are starting to learn more about the genetic origins of Glaucoma. This research
uses differential expression (DE) and gene ontology (GO) analyses to study the
genetic differences between mice with severe Glaucoma and multiple control
groups. Optical nerve head (ONH) and retina data samples of genome-wide RNA
expression from NCBI (NIH) are used for pairwise comparison experimentation. In
addition, principal component analysis (PCA) and dispersion visualization
methods are employed to perform quality control tests of the sequenced data.
Genes with skewed gene counts are also identified, as they may be marker genes
for a particular severity of Glaucoma. The gene ontologies found in this
experiment support existing knowledge of Glaucoma genesis, providing confidence
that the results were valid. Future researchers can thoroughly study the gene
lists generated by the DE and GO analyses to find potential activator or
protector genes for Glaucoma in mice to develop drug treatments or gene
therapies to slow or stop the progression of the disease. The overall goal is
that in the future, such treatments can be made for humans as well to improve
the quality of life for human patients with Glaucoma and reduce Glaucoma
blindness rates.

In Bayesian optimization (BO) for expensive black-box optimization tasks,
acquisition function (AF) guides sequential sampling and plays a pivotal role
for efficient convergence to better optima. Prevailing AFs usually rely on
artificial experiences in terms of preferences for exploration or exploitation,
which runs a risk of a computational waste or traps in local optima and
resultant re-optimization. To address the crux, the idea of data-driven AF
selection is proposed, and the sequential AF selection task is further
formalized as a Markov decision process (MDP) and resort to powerful
reinforcement learning (RL) technologies. Appropriate selection policy for AFs
is learned from superior BO trajectories to balance between exploration and
exploitation in real time, which is called reinforcement-learning-assisted
Bayesian optimization (RLABO). Competitive and robust BO evaluations on five
benchmark problems demonstrate RL's recognition of the implicit AF selection
pattern and imply the proposal's potential practicality for intelligent AF
selection as well as efficient optimization in expensive black-box problems.

Quadruped locomotion now has acquired the skill to traverse or even sprint on
uneven terrains in remote uncontrolled environment. However, surviving in the
wild requires not only the maneuverability, but also the ability to handle
unexpected hardware failures. We present the first deep reinforcement learning
based methodology to train fault-tolerant controllers, which can bring an
injured quadruped back home safely and speedily. We adopt the teacher-student
framework to train the controller with close-to-reality joint-locking failure
in the simulation, which can be zero-shot transferred to the physical robot
without any fine-tuning. Extensive simulation and real-world experiments
demonstrate that our fault-tolerant controller can efficiently lead a quadruped
stably when it faces joint failure during locomotion.

Semantic communication has become a popular research area due its high
spectrum efficiency and error-correction performance. Some studies use deep
learning to extract semantic features, which usually form end-to-end semantic
communication systems and are hard to address the varying wireless
environments. Therefore, the novel semantic-based coding methods and
performance metrics have been investigated and the designed semantic systems
consist of various modules as in the conventional communications but with
improved functions. This article discusses recent achievements in the
state-of-art semantic communications exploiting the conventional modules in
wireless systems. We demonstrate through two examples that the traditional
hybrid automatic repeat request and modulation methods can be redesigned for
novel semantic coding and metrics to further improve the performance of
wireless semantic communications. At the end of this article, some open issues
are identified.

As a decentralized training approach, federated learning enables multiple
organizations to jointly train a model without exposing their private data.
This work investigates vertical federated learning (VFL) to address scenarios
where collaborating organizations have the same set of users but with different
features, and only one party holds the labels. While VFL shows good
performance, practitioners often face uncertainty when preparing
non-transparent, internal/external features and samples for the VFL training
phase. Moreover, to balance the prediction accuracy and the resource
consumption of model inference, practitioners require to know which subset of
prediction instances is genuinely needed to invoke the VFL model for inference.
To this end, we co-design the VFL modeling process by proposing an interactive
real-time visualization system, VFLens, to help practitioners with feature
engineering, sample selection, and inference. A usage scenario, a quantitative
experiment, and expert feedback suggest that VFLens helps practitioners boost
VFL efficiency at a lower cost with sufficient confidence.

We present a dynamic model in which the weights are conditioned on an input
sample x and are learned to match those that would be obtained by finetuning a
base model on x and its label y. This mapping between an input sample and
network weights is shown to be approximated by a linear transformation of the
sample distribution, which suggests that a denoising diffusion model can be
suitable for this task. The diffusion model we therefore employ focuses on
modifying a single layer of the base model and is conditioned on the input,
activations, and output of this layer. Our experiments demonstrate the wide
applicability of the method for image classification, 3D reconstruction,
tabular data, speech separation, and few-shot classification problems in NLP.
Our code is available at https://github.com/ShaharLutatiPersonal/OCD.

Stein Variational Gradient Descent (SVGD) is a popular sampling algorithm
used in various machine learning tasks. It is well known that SVGD arises from
a discretization of the kernelized gradient flow of the Kullback-Leibler
divergence $D_{KL}\left(\cdot\mid\pi\right)$, where $\pi$ is the target
distribution. In this work, we propose to enhance SVGD via the introduction of
importance weights, which leads to a new method for which we coin the name
$\beta$-SVGD. In the continuous time and infinite particles regime, the time
for this flow to converge to the equilibrium distribution $\pi$, quantified by
the Stein Fisher information, depends on $\rho_0$ and $\pi$ very weakly. This
is very different from the kernelized gradient flow of Kullback-Leibler
divergence, whose time complexity depends on
$D_{KL}\left(\rho_0\mid\pi\right)$. Under certain assumptions, we provide a
descent lemma for the population limit $\beta$-SVGD, which covers the descent
lemma for the population limit SVGD when $\beta\to 0$. We also illustrate the
advantages of $\beta$-SVGD over SVGD by simple experiments.

While point cloud semantic segmentation is a significant task in 3D scene
understanding, this task demands a time-consuming process of fully annotating
labels. To address this problem, recent studies adopt a weakly supervised
learning approach under the sparse annotation. Different from the existing
studies, this study aims to reduce the epistemic uncertainty measured by the
entropy for a precise semantic segmentation. We propose the graphical
information gain based attention network called GaIA, which alleviates the
entropy of each point based on the reliable information. The graphical
information gain discriminates the reliable point by employing relative entropy
between target point and its neighborhoods. We further introduce anchor-based
additive angular margin loss, ArcPoint. The ArcPoint optimizes the unlabeled
points containing high entropy towards semantically similar classes of the
labeled points on hypersphere space. Experimental results on S3DIS and
ScanNet-v2 datasets demonstrate our framework outperforms the existing weakly
supervised methods. We have released GaIA at https://github.com/Karel911/GaIA.

Graphs are ubiquitous and are often used to understand the dynamics of a
system. Probabilistic Graphical Models comprising Bayesian and Markov networks,
and Conditional Independence graphs are some of the popular graph
representation techniques. They can model relationships between features
(nodes) together with the underlying distribution. Although theoretically these
models can represent very complex dependency functions, in practice often
simplifying assumptions are made due to computational limitations associated
with graph operations. This work introduces Neural Graphical Models (NGMs)
which attempt to represent complex feature dependencies with reasonable
computational costs. Specifically, given a graph of feature relationships and
corresponding samples, we capture the dependency structure between the features
along with their complex function representations by using neural networks as a
multi-task learning framework. We provide efficient learning, inference and
sampling algorithms for NGMs. Moreover, NGMs can fit generic graph structures
including directed, undirected and mixed-edge graphs as well as support mixed
input data types. We present empirical studies that show NGMs' capability to
represent Gaussian graphical models, inference analysis of a lung cancer data
and extract insights from a real world infant mortality data provided by CDC.

Predicting the impact of publications in science and technology has become an
important research area, which is useful in various real world scenarios such
as technology investment, research direction selection, and technology
policymaking. Citation trajectory prediction is one of the most popular tasks
in this area. Existing approaches mainly rely on mining temporal and graph data
from academic articles. Some recent methods are capable of handling cold-start
prediction by aggregating metadata features of new publications. However, the
implicit factors causing citations and the richer information from handling
temporal and attribute features still need to be explored. In this paper, we
propose CTPIR, a new citation trajectory prediction framework that is able to
represent the influence (the momentum of citation) of either new or existing
publications using the history information of all their attributes. Our
framework is composed of three modules: difference-preserved graph embedding,
fine-grained influence representation, and learning-based trajectory
calculation. To test the effectiveness of our framework in more situations, we
collect and construct a new temporal knowledge graph dataset from the real
world, named AIPatent, which stems from global patents in the field of
artificial intelligence. Experiments are conducted on both the APS academic
dataset and our contributed AIPatent dataset. The results demonstrate the
strengths of our approach in the citation trajectory prediction task.

In this paper, we first extend the recent Masked Auto-Encoder (MAE) model
from a single modality to audio-visual multi-modalities. Subsequently, we
propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining
contrastive learning and masked data modeling, two major self-supervised
learning frameworks, to learn a joint and coordinated audio-visual
representation. Our experiments show that the contrastive audio-visual
correspondence learning objective not only enables the model to perform
audio-visual retrieval tasks, but also helps the model learn a better joint
representation. As a result, our fully self-supervised pretrained CAV-MAE
achieves a new SOTA accuracy of 65.9% on VGGSound, and is comparable with the
previous best supervised pretrained model on AudioSet in the audio-visual event
classification task.

Review comments play an important role in the evolution of documents. For a
large document, the number of review comments may become large, making it
difficult for the authors to quickly grasp what the comments are about. It is
important to identify the nature of the comments to identify which comments
require some action on the part of document authors, along with identifying the
types of these comments. In this paper, we introduce an annotated review
comment dataset ReAct. The review comments are sourced from OpenReview site. We
crowd-source annotations for these reviews for actionability and type of
comments. We analyze the properties of the dataset and validate the quality of
annotations. We release the dataset (https://github.com/gtmdotme/ReAct) to the
research community as a major contribution. We also benchmark our data with
standard baselines for classification tasks and analyze their performance.

Time-series data analysis is important because numerous real-world tasks such
as forecasting weather, electricity consumption, and stock market involve
predicting data that vary over time. Time-series data are generally recorded
over a long period of observation with long sequences owing to their periodic
characteristics and long-range dependencies over time. Thus, capturing
long-range dependency is an important factor in time-series data forecasting.
To solve these problems, we proposed two novel modules, Grouped Self-Attention
(GSA) and Compressed Cross-Attention (CCA). With both modules, we achieved a
computational space and time complexity of order $O(l)$ with a sequence length
$l$ under small hyperparameter limitations, and can capture locality while
considering global information. The results of experiments conducted on
time-series datasets show that our proposed model efficiently exhibited reduced
computational complexity and performance comparable to or better than existing
methods.

Graph coarsening is a widely used dimensionality reduction technique for
approaching large-scale graph machine learning problems. Given a large graph,
graph coarsening aims to learn a smaller-tractable graph while preserving the
properties of the originally given graph. Graph data consist of node features
and graph matrix (e.g., adjacency and Laplacian). The existing graph coarsening
methods ignore the node features and rely solely on a graph matrix to simplify
graphs. In this paper, we introduce a novel optimization-based framework for
graph dimensionality reduction. The proposed framework lies in the unification
of graph learning and dimensionality reduction. It takes both the graph matrix
and the node features as the input and learns the coarsen graph matrix and the
coarsen feature matrix jointly while ensuring desired properties. The proposed
optimization formulation is a multi-block non-convex optimization problem,
which is solved efficiently by leveraging block majorization-minimization,
$\log$ determinant, Dirichlet energy, and regularization frameworks. The
proposed algorithms are provably convergent and practically amenable to
numerous tasks. It is also established that the learned coarsened graph is
$\epsilon\in(0,1)$ similar to the original graph. Extensive experiments
elucidate the efficacy of the proposed framework for real-world applications.

In this paper, we consider a novel research problem, music-to-text
synaesthesia. Different from the classical music tagging problem that
classifies a music recording into pre-defined categories, the music-to-text
synaesthesia aims to generate descriptive texts from music recordings for
further understanding. Although this is a new and interesting application to
the machine learning community, to our best knowledge, the existing
music-related datasets do not contain the semantic descriptions on music
recordings and cannot serve the music-to-text synaesthesia task. In light of
this, we collect a new dataset that contains 1,955 aligned pairs of classical
music recordings and text descriptions. Based on this, we build a computational
model to generate sentences that can describe the content of the music
recording. To tackle the highly non-discriminative classical music, we design a
group topology-preservation loss in our computational model, which considers
more samples as a group reference and preserves the relative topology among
different samples. Extensive experimental results qualitatively and
quantitatively demonstrate the effectiveness of our proposed model over five
heuristics or pre-trained competitive methods and their variants on our
collected dataset.

Deep neural networks (DNNs) are shown to be vulnerable to adversarial
examples. A well-trained model can be easily attacked by adding small
perturbations to the original data. One of the hypotheses of the existence of
the adversarial examples is the off-manifold assumption: adversarial examples
lie off the data manifold. However, recent research showed that on-manifold
adversarial examples also exist. In this paper, we revisit the off-manifold
assumption and want to study a question: at what level is the poor performance
of neural networks against adversarial attacks due to on-manifold adversarial
examples? Since the true data manifold is unknown in practice, we consider two
approximated on-manifold adversarial examples on both real and synthesis
datasets. On real datasets, we show that on-manifold adversarial examples have
greater attack rates than off-manifold adversarial examples on both
standard-trained and adversarially-trained models. On synthetic datasets,
theoretically, We prove that on-manifold adversarial examples are powerful, yet
adversarial training focuses on off-manifold directions and ignores the
on-manifold adversarial examples. Furthermore, we provide analysis to show that
the properties derived theoretically can also be observed in practice. Our
analysis suggests that on-manifold adversarial examples are important, and we
should pay more attention to on-manifold adversarial examples for training
robust models.

We improve the theoretical and empirical performance of
neural-network(NN)-based active learning algorithms for the non-parametric
streaming setting. In particular, we introduce two regret metrics by minimizing
the population loss that are more suitable in active learning than the one used
in state-of-the-art (SOTA) related work. Then, the proposed algorithm leverages
the powerful representation of NNs for both exploitation and exploration, has
the query decision-maker tailored for $k$-class classification problems with
the performance guarantee, utilizes the full feedback, and updates parameters
in a more practical and efficient manner. These careful designs lead to a
better regret upper bound, improving by a multiplicative factor $O(\log T)$ and
removing the curse of input dimensionality. Furthermore, we show that the
algorithm can achieve the same performance as the Bayes-optimal classifier in
the long run under the hard-margin setting in classification problems. In the
end, we use extensive experiments to evaluate the proposed algorithm and SOTA
baselines, to show the improved empirical performance.

We consider stochastic gradient descents on the space of large symmetric
matrices of suitable functions that are invariant under permuting the rows and
columns using the same permutation. We establish deterministic limits of these
random curves as the dimensions of the matrices go to infinity while the
entries remain bounded. Under a "small noise" assumption the limit is shown to
be the gradient flow of functions on graphons whose existence was established
in arXiv:2111.09459. We also consider limits of stochastic gradient descents
with added properly scaled reflected Brownian noise. The limiting curve of
graphons is characterized by a family of stochastic differential equations with
reflections and can be thought of as an extension of the classical
McKean-Vlasov limit for interacting diffusions. The proofs introduce a family
of infinite-dimensional exchangeable arrays of reflected diffusions and a novel
notion of propagation of chaos for large matrices of interacting diffusions.

The selection of most informative and discriminative features from
high-dimensional data has been noticed as an important topic in machine
learning and data engineering. Using matrix factorization-based techniques such
as nonnegative matrix factorization for feature selection has emerged as a hot
topic in feature selection. The main goal of feature selection using matrix
factorization is to extract a subspace which approximates the original space
but in a lower dimension. In this study, rank revealing QR (RRQR)
factorization, which is computationally cheaper than singular value
decomposition (SVD), is leveraged in obtaining the most informative features as
a novel unsupervised feature selection technique. This technique uses the
permutation matrix of QR for feature selection which is a unique property to
this factorization method. Moreover, QR factorization is embedded into
non-negative matrix factorization (NMF) objective function as a new
unsupervised feature selection method. Lastly, a hybrid feature selection
algorithm is proposed by coupling RRQR, as a filter-based technique, and a
Genetic algorithm as a wrapper-based technique. In this method, redundant
features are removed using RRQR factorization and the most discriminative
subset of features are selected using the Genetic algorithm. The proposed
algorithm shows to be dependable and robust when compared against
state-of-the-art feature selection algorithms in supervised, unsupervised, and
semi-supervised settings. All methods are tested on seven available microarray
datasets using KNN, SVM and C4.5 classifiers. In terms of evaluation metrics,
the experimental results shows that the proposed method is comparable with the
state-of-the-art feature selection.

Malicious actors may seek to use different voice-spoofing attacks to fool ASV
systems and even use them for spreading misinformation. Various countermeasures
have been proposed to detect these spoofing attacks. Due to the extensive work
done on spoofing detection in automated speaker verification (ASV) systems in
the last 6-7 years, there is a need to classify the research and perform
qualitative and quantitative comparisons on state-of-the-art countermeasures.
Additionally, no existing survey paper has reviewed integrated solutions to
voice spoofing evaluation and speaker verification, adversarial/antiforensics
attacks on spoofing countermeasures, and ASV itself, or unified solutions to
detect multiple attacks using a single model. Further, no work has been done to
provide an apples-to-apples comparison of published countermeasures in order to
assess their generalizability by evaluating them across corpora. In this work,
we conduct a review of the literature on spoofing detection using hand-crafted
features, deep learning, end-to-end, and universal spoofing countermeasure
solutions to detect speech synthesis (SS), voice conversion (VC), and replay
attacks. Additionally, we also review integrated solutions to voice spoofing
evaluation and speaker verification, adversarial and anti-forensics attacks on
voice countermeasures, and ASV. The limitations and challenges of the existing
spoofing countermeasures are also presented. We report the performance of these
countermeasures on several datasets and evaluate them across corpora. For the
experiments, we employ the ASVspoof2019 and VSDC datasets along with GMM, SVM,
CNN, and CNN-GRU classifiers. (For reproduceability of the results, the code of
the test bed can be found in our GitHub Repository.

Graph embedding provides a feasible methodology to conduct pattern
classification for graph-structured data by mapping each data into the
vectorial space. Various pioneering works are essentially coding method that
concentrates on a vectorial representation about the inner properties of a
graph in terms of the topological constitution, node attributions, link
relations, etc. However, the classification for each targeted data is a
qualitative issue based on understanding the overall discrepancies within the
dataset scale. From the statistical point of view, these discrepancies manifest
a metric distribution over the dataset scale if the distance metric is adopted
to measure the pairwise similarity or dissimilarity. Therefore, we present a
novel embedding strategy named $\mathbf{MetricDistribution2vec}$ to extract
such distribution characteristics into the vectorial representation for each
data. We demonstrate the application and effectiveness of our representation
method in the supervised prediction tasks on extensive real-world structural
graph datasets. The results have gained some unexpected increases compared with
a surge of baselines on all the datasets, even if we take the lightweight
models as classifiers. Moreover, the proposed methods also conducted
experiments in Few-Shot classification scenarios, and the results still show
attractive discrimination in rare training samples based inference.

The firing dynamics of biological neurons in mathematical models is often
determined by the model's parameters, representing the neurons' underlying
properties. The parameter estimation problem seeks to recover those parameters
of a single neuron or a neuron population from their responses to external
stimuli and interactions between themselves. Most common methods for tackling
this problem in the literature use some mechanistic models in conjunction with
either a simulation-based or solution-based optimization scheme. In this paper,
we study an automatic approach of learning the parameters of neuron populations
from a training set consisting of pairs of spiking series and parameter labels
via supervised learning. Unlike previous work, this automatic learning does not
require additional simulations at inference time nor expert knowledge in
deriving an analytical solution or in constructing some approximate models. We
simulate many neuronal populations with different parameter settings using a
stochastic neuron model. Using that data, we train a variety of supervised
machine learning models, including convolutional and deep neural networks,
random forest, and support vector regression. We then compare their performance
against classical approaches including a genetic search, Bayesian sequential
estimation, and a random walk approximate model. The supervised models almost
always outperform the classical methods in parameter estimation and spike
reconstruction errors, and computation expense. Convolutional neural network,
in particular, is the best among all models across all metrics. The supervised
models can also generalize to out-of-distribution data to a certain extent.

PointGoal navigation in indoor environment is a fundamental task for personal
robots to navigate to a specified point.
  Recent studies solved this PointGoal navigation task with near-perfect
success rate in photo-realistically simulated environments,
  under the assumptions with noiseless actuation and most importantly, perfect
localization with GPS and compass sensors.
  However, accurate GPS signal can not be obtained in real indoor environment.
  To improve the pointgoal navigation accuracy in real indoor,
  we proposed novel vision and vision-motion calibration strategies to train
visual and motion path integration in unsupervised manner.
  Sepecifically, visual calibration computes the relative pose of the agent
from the re-projection error of two adjacent frames, and then
  replaces the accurate GPS signal with the path integration.
  This pseudo position is also used to calibrate self-motion integration
  which assists agent to update their internal perception of location and helps
improve the success rate of navigation.
  The training and inference process only use RGB, depth, collision as well as
self-action information.
  The experiments show that the proposed system achieves satisfactory results
and
  outperforms the partially supervised learning algorithms on the popular
Gibson dataset.

Self-supervised monocular depth estimation (MDE) models universally suffer
from the notorious edge-fattening issue. Triplet loss, popular for metric
learning, has made a great success in many computer vision tasks. In this
paper, we redesign the patch-based triplet loss in MDE to alleviate the
ubiquitous edge-fattening issue. We show two drawbacks of the raw triplet loss
in MDE and demonstrate our problem-driven redesigns. First, we present a min.
operator based strategy applied to all negative samples, to prevent
well-performing negatives sheltering the error of edge-fattening negatives.
Second, we split the anchor-positive distance and anchor-negative distance from
within the original triplet, which directly optimizes the positives without any
mutual effect with the negatives. Extensive experiments show the combination of
these two small redesigns can achieve unprecedented results: Our powerful and
versatile triplet loss not only makes our model outperform all previous SoTA by
a large margin, but also provides substantial performance boosts to a large
number of existing models, while introducing no extra inference computation at
all.

Polycystic Ovary Syndrome (PCOS) is an endrocrinological dysfunction
prevalent among women of reproductive age. PCOS is a combination of syndromes
caused by an excess of androgens - a group of sex hormones - in women.
Syndromes including acne, alopecia, hirsutism, hyperandrogenaemia,
oligo-ovulation, etc. are caused by PCOS. It is also a major cause of female
infertility. An estimated 15% of reproductive-aged women are affected by PCOS
globally. The necessity of detecting PCOS early due to the severity of its
deleterious effects cannot be overstated. In this paper, we have developed
PCONet - a Convolutional Neural Network (CNN) - to detect polycistic ovary from
ovarian ultrasound images. We have also fine tuned InceptionV3 - a pretrained
convolutional neural network of 45 layers - by utilizing the transfer learning
method to classify polcystic ovarian ultrasound images. We have compared these
two models on various quantitative performance evaluation parameters and
demonstrated that PCONet is the superior one among these two with an accuracy
of 98.12%, whereas the fine tuned InceptionV3 showcased an accuracy of 96.56%
on test images.

Conventional in-memory computing (IMC) architectures consist of analog
memristive crossbars to accelerate matrix-vector multiplication (MVM), and
digital functional units to realize nonlinear vector (NLV) operations in deep
neural networks (DNNs). These designs, however, require energy-hungry signal
conversion units which can dissipate more than 95% of the total power of the
system. In-Memory Analog Computing (IMAC) circuits, on the other hand, remove
the need for signal converters by realizing both MVM and NLV operations in the
analog domain leading to significant energy savings. However, they are more
susceptible to reliability challenges such as interconnect parasitic and noise.
Here, we introduce a practical approach to deploy large matrices in DNNs onto
multiple smaller IMAC subarrays to alleviate the impacts of noise and
parasitics while keeping the computation in the analog domain.

Density based representations of atomic environments that are invariant under
Euclidean symmetries have become a widely used tool in the machine learning of
interatomic potentials, broader data-driven atomistic modelling and the
visualisation and analysis of materials datasets.The standard mechanism used to
incorporate chemical element information is to create separate densities for
each element and form tensor products between them. This leads to a steep
scaling in the size of the representation as the number of elements increases.
Graph neural networks, which do not explicitly use density representations,
escape this scaling by mapping the chemical element information into a fixed
dimensional space in a learnable way. We recast this approach as tensor
factorisation by exploiting the tensor structure of standard neighbour density
based descriptors. In doing so, we form compact tensor-reduced representations
whose size does not depend on the number of chemical elements, but remain
systematically convergeable and are therefore applicable to a wide range of
data analysis and regression tasks.

Transformer networks have seen great success in natural language processing
and machine vision, where task objectives such as next word prediction and
image classification benefit from nuanced context sensitivity across
high-dimensional inputs. However, there is an ongoing debate about how and when
transformers can acquire highly structured behavior and achieve systematic
generalization. Here, we explore how well a causal transformer can perform a
set of algorithmic tasks, including copying, sorting, and hierarchical
compositions of these operations. We demonstrate strong generalization to
sequences longer than those used in training by replacing the standard
positional encoding typically used in transformers with labels arbitrarily
paired with items in the sequence. By finding the layer and head configuration
sufficient to solve the task, then performing ablation experiments and
representation analysis, we show that two-layer transformers learn
generalizable solutions to multi-level problems and develop signs of systematic
task decomposition. They also exploit shared computation across related tasks.
These results provide key insights into how transformer models may be capable
of decomposing complex decisions into reusable, multi-level policies in tasks
requiring structured behavior.

Signature-based malware detectors have proven to be insufficient as even a
small change in malignant executable code can bypass these signature-based
detectors. Many machine learning-based models have been proposed to efficiently
detect a wide variety of malware. Many of these models are found to be
susceptible to adversarial attacks - attacks that work by generating
intentionally designed inputs that can force these models to misclassify. Our
work aims to explore vulnerabilities in the current state of the art malware
detectors to adversarial attacks. We train a Transformers-based malware
detector, carry out adversarial attacks resulting in a misclassification rate
of 23.9% and propose defenses that reduce this misclassification rate to half.
An implementation of our work can be found at
https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers.

Recent developments in deep representation models through counterfactual
balancing have led to a promising framework for estimating Individual Treatment
Effects (ITEs) that are essential to causal inference in the Neyman-Rubin
potential outcomes framework. While Randomized Control Trials are vital to
understanding causal effects, they are sometimes infeasible, costly, or
unethical to conduct. Motivated by these potential obstacles to data
acquisition, we focus on transferring the causal knowledge acquired in prior
experiments to new scenarios for which only limited data is available. To this
end, we first observe that the absolute values of ITEs are invariant under the
action of the symmetric group on the labels of treatments. Given this
invariance, we propose a symmetrized task distance for calculating the
similarity of a target scenario with those encountered before. The
aforementioned task distance is then used to transfer causal knowledge from the
closest of all the available previously learned tasks to the target scenario.
We provide upper bounds on the counterfactual loss and ITE error of the target
task indicating the transferability of causal knowledge. Empirical studies are
provided for various real-world, semi-synthetic, and synthetic datasets
demonstrating that the proposed symmetrized task distance is strongly related
to the estimation of the counterfactual loss. Numerical results indicate that
transferring causal knowledge reduces the amount of required data by up to 95%
when compared to training from scratch. These results reveal the promise of our
method when applied to important albeit challenging real-world scenarios such
as transferring the knowledge of treatment effects (e.g., medicine, social
policy, personal training, etc.) studied on a population to other groups absent
in the study.

In this paper we discuss the motivation, design, and analysis of ambisonic
decoders for systems where the vertical order is less than the horizontal
order, known as mixed-order Ambisonic systems. This can be due to the use of
microphone arrays that emphasize horizontal spatial resolution or speaker
arrays that provide sparser coverage vertically. First, we review Ambisonic
reproduction criteria, as defined by Gerzon, and summarize recent results on
the relative perceptual importance of the various criteria. Then we show that
using full-order decoders with mixed-order program material results in poorer
performance than with a properly designed mixed-order decoder. We then
introduce a new implementation of a decoder optimizer that draws upon
techniques from machine learning for quick and robust convergence, discuss the
construction of the objective function, and apply it to the problem of
designing two-band decoders for mixed-order signal sets and non-uniform
loudspeaker layouts. Results of informal listening tests are summarized and
future directions discussed.

Geometric deep learning has gained much attention in recent years due to more
available data acquired from non-Euclidean domains. Some examples include point
clouds for 3D models and wireless sensor networks in communications. Graphs are
common models to connect these discrete data points and capture the underlying
geometric structure. With the large amount of these geometric data, graphs with
arbitrarily large size tend to converge to a limit model -- the manifold. Deep
neural network architectures have been proved as a powerful technique to solve
problems based on these data residing on the manifold. In this paper, we
propose a manifold neural network (MNN) composed of a bank of manifold
convolutional filters and point-wise nonlinearities. We define a manifold
convolution operation which is consistent with the discrete graph convolution
by discretizing in both space and time domains. To sum up, we focus on the
manifold model as the limit of large graphs and construct MNNs, while we can
still bring back graph neural networks by the discretization of MNNs. We carry
out experiments based on point-cloud dataset to showcase the performance of our
proposed MNNs.

In this study, we propose parameter-varying neural ordinary differential
equations (NODEs) where the evolution of model parameters is represented by
partition-of-unity networks (POUNets), a mixture of experts architecture. The
proposed variant of NODEs, synthesized with POUNets, learn a meshfree partition
of space and represent the evolution of ODE parameters using sets of
polynomials associated to each partition. We demonstrate the effectiveness of
the proposed method for three important tasks: data-driven dynamics modeling of
(1) hybrid systems, (2) switching linear dynamical systems, and (3) latent
dynamics for dynamical systems with varying external forcing.

We discuss differences and similarities between variational Monte Carlo
approaches that use conventional and artificial neural network
parameterizations of the ground-state wave function for systems of fermions. We
focus on a relatively shallow neural-network architectures, the so called
restricted Boltzmann machine, and discuss unsupervised learning algorithms that
are suitable to model complicated many-body correlations. We analyze the
strengths and weaknesses of conventional and neural-network wave functions by
solving various circular quantum-dots systems. Results for up to 90 electrons
are presented and particular emphasis is placed on how to efficiently implement
these methods on homogeneous and heterogeneous high-performance computing
facilities.

Anomaly detection is a ubiquitous and challenging task relevant across many
disciplines. With the vital role communication networks play in our daily
lives, the security of these networks is imperative for smooth functioning of
society. To this end, we propose a novel self-supervised deep learning
framework CAAD for anomaly detection in wireless communication systems.
Specifically, CAAD employs contrastive learning in an adversarial setup to
learn effective representations of normal and anomalous behavior in wireless
networks. We conduct rigorous performance comparisons of CAAD with several
state-of-the-art anomaly detection techniques and verify that CAAD yields a
mean performance improvement of 92.84%. Additionally, we also augment CAAD
enabling it to systematically incorporate expert feedback through a novel
contrastive learning feedback loop to improve the learned representations and
thereby reduce prediction uncertainty (CAAD-EF). We view CAAD-EF as a novel,
holistic and widely applicable solution to anomaly detection.

In representation learning, a common approach is to seek representations
which disentangle the underlying factors of variation. Eastwood & Williams
(2018) proposed three metrics for quantifying the quality of such disentangled
representations: disentanglement (D), completeness (C) and informativeness (I).
In this work, we first connect this DCI framework to two common notions of
linear and nonlinear identifiability, thus establishing a formal link between
disentanglement and the closely-related field of independent component
analysis. We then propose an extended DCI-ES framework with two new measures of
representation quality - explicitness (E) and size (S) - and point out how D
and C can be computed for black-box predictors. Our main idea is that the
functional capacity required to use a representation is an important but
thus-far neglected aspect of representation quality, which we quantify using
explicitness or ease-of-use (E). We illustrate the relevance of our extensions
on the MPI3D and Cars3D datasets.

In order to infer the strategy of an intelligent attacker, it is desired for
the defender to cognitively sense the attacker's state. In this context, we aim
to learn the information that an adversary has gathered about us from a
Bayesian perspective. Prior works employ linear Gaussian state-space models and
solve this inverse cognition problem through the design of inverse stochastic
filters. In practice, these counter-adversarial settings are highly nonlinear
systems. We address this by formulating the inverse cognition as a nonlinear
Gaussian state-space model, wherein the adversary employs an unscented Kalman
filter (UKF) to estimate our state with reduced linearization errors. To
estimate the adversary's estimate of us, we propose and develop an inverse UKF
(IUKF), wherein the system model is known to both the adversary and the
defender. We also derive the conditions for the stochastic stability of IUKF in
the mean-squared boundedness sense. Numerical experiments for multiple
practical system models show that the estimation error of IUKF converges and
closely follows the recursive Cram\'{e}r-Rao lower bound.

Potential societal and environmental effects such as the rapidly increasing
resource use and the associated environmental impact, reproducibility issues,
and exclusivity, the privatization of ML research leading to a public research
brain-drain, a narrowing of the research effort caused by a focus on deep
learning, and the introduction of biases through a lack of sociodemographic
diversity in data and personnel caused by recent developments in machine
learning are a current topic of discussion and scientific publications.
However, these discussions and publications focus mainly on computer
science-adjacent fields, including computer vision and natural language
processing or basic ML research. Using bibliometric analysis of the complete
and full-text analysis of the open-access literature, we show that the same
observations can be made for applied machine learning in chemistry and biology.
These developments can potentially affect basic and applied research, such as
drug discovery and development, beyond the known issue of biased data sets.

This paper develops a model-free sequential test for conditional
independence. The proposed test allows researchers to analyze an incoming
i.i.d. data stream with any arbitrary dependency structure, and safely conclude
whether a feature is conditionally associated with the response under study. We
allow the processing of data points online as soon as they arrive and stop data
acquisition once significant results are detected while rigorously controlling
the type-I error rate. Our test can work with any sophisticated machine
learning algorithm to enhance data efficiency to the extent possible. The
developed method is inspired by two statistical frameworks. The first is the
model-X conditional randomization test, a test for conditional independence
that is valid in offline settings where the sample size is fixed in advance.
The second is testing by betting, a "game-theoretic" approach for sequential
hypothesis testing. We conduct synthetic experiments to demonstrate the
advantage of our test over out-of-the-box sequential tests that account for the
multiplicity of tests in the time horizon, and demonstrate the practicality of
our proposal by applying it to real-world tasks.

Humans are capable of abstracting various tasks as different combinations of
multiple attributes. This perspective of compositionality is vital for human
rapid learning and adaption since previous experiences from related tasks can
be combined to generalize across novel compositional settings. In this work, we
aim to achieve zero-shot policy generalization of Reinforcement Learning (RL)
agents by leveraging the task compositionality. Our proposed method is a meta-
RL algorithm with disentangled task representation, explicitly encoding
different aspects of the tasks. Policy generalization is then performed by
inferring unseen compositional task representations via the obtained
disentanglement without extra exploration. The evaluation is conducted on three
simulated tasks and a challenging real-world robotic insertion task.
Experimental results demonstrate that our proposed method achieves policy
generalization to unseen compositional tasks in a zero-shot manner.

This work analyzes the solution trajectory of gradient-based algorithms via a
novel basis function decomposition. We show that, although solution
trajectories of gradient-based algorithms may vary depending on the learning
task, they behave almost monotonically when projected onto an appropriate
orthonormal function basis. Such projection gives rise to a basis function
decomposition of the solution trajectory. Theoretically, we use our proposed
basis function decomposition to establish the convergence of gradient descent
(GD) on several representative learning tasks. In particular, we improve the
convergence of GD on symmetric matrix factorization and provide a completely
new convergence result for the orthogonal symmetric tensor decomposition.
Empirically, we illustrate the promise of our proposed framework on realistic
deep neural networks (DNNs) across different architectures, gradient-based
solvers, and datasets. Our key finding is that gradient-based algorithms
monotonically learn the coefficients of a particular orthonormal function basis
of DNNs defined as the eigenvectors of the conjugate kernel after training. Our
code is available at https://github.com/jianhaoma/function-basis-decomposition.

We analyze 14,651 HIV1 reverse transcriptase (HIV RT) sequences from the
Stanford HIV Drug Resistance Database labeled with treatment regimen in order
to study the evolution this enzyme under drug selection in the clinic. Our goal
is to identify distinct sectors of HIV RT's sequence space that are undergoing
evolution as a way to identify individual selections and/or evolutionary
solutions. We utilize Uniform Manifold Approximation and Projection (UMAP), a
graph-based dimensionality reduction technique uniquely suited for the
detection of non-linear dependencies and visualize the results using an
unsupervised clustering algorithm based on density analysis. Our analysis
produced 21 distinct clusters of sequences. Supporting the biological
significance of these clusters, they tend to represent phylogenetically related
sequences with strong correspondence to distinct treatment regimens. Thus, this
method for visualization of areas of HIV RT undergoing evolution can help infer
information about selective pressures, although it is correlative. The mutation
signatures associated with each cluster may represent the higher-order
epistatic context facilitating these evolutionary pathways, information that is
generally not accessible by other types of mutational co-dependence analyses.

Multi-armed bandit (MAB) algorithms are efficient approaches to reduce the
opportunity cost of online experimentation and are used by companies to find
the best product from periodically refreshed product catalogs. However, these
algorithms face the so-called cold-start at the onset of the experiment due to
a lack of knowledge of customer preferences for new products, requiring an
initial data collection phase known as the burn-in period. During this period,
MAB algorithms operate like randomized experiments, incurring large burn-in
costs which scale with the large number of products. We attempt to reduce the
burn-in by identifying that many products can be cast into two-sided products,
and then naturally model the rewards of the products with a matrix, whose rows
and columns represent the two sides respectively. Next, we design two-phase
bandit algorithms that first use subsampling and low-rank matrix estimation to
obtain a substantially smaller targeted set of products and then apply a UCB
procedure on the target products to find the best one. We theoretically show
that the proposed algorithms lower costs and expedite the experiment in cases
when there is limited experimentation time along with a large product set. Our
analysis also reveals three regimes of long, short, and ultra-short horizon
experiments, depending on dimensions of the matrix. Empirical evidence from
both synthetic data and a real-world dataset on music streaming services
validates this superior performance.

As machine learning (ML) methods continue to be applied to a broad scope of
problems in the physical sciences, uncertainty quantification is becoming
correspondingly more important for their robust application. Uncertainty aware
machine learning methods have been used in select applications, but largely for
scalar properties. In this work, we showcase an exemplary study in which neural
network ensembles are used to predict the X-ray absorption spectra of small
molecules, as well as their point-wise uncertainty, from local atomic
environments. The performance of the resulting surrogate clearly demonstrates
quantitative correlation between errors relative to ground truth and the
predicted uncertainty estimates. Significantly, the model provides an upper
bound on the expected error. Specifically, an important quality of this
uncertainty-aware model is that it can indicate when the model is predicting on
out-of-sample data. This allows for its integration with large scale sampling
of structures together with active learning or other techniques for structure
refinement. Additionally, our models can be generalized to larger molecules
than those used for training, and also successfully track uncertainty due to
random distortions in test molecules. While we demonstrate this workflow on a
specific example, ensemble learning is completely general. We believe it could
have significant impact on ML-enabled forward modeling of a broad array of
molecular and materials properties.

When the cornea becomes hydrated above its physiologic level it begins to
significantly scatter light, loosing transparency and thus impairing eyesight.
This condition, known as corneal edema, can be associated with different
causes, such as corneal scarring, corneal infection, corneal inflammation, and
others, making it difficult to diagnose and quantify. Previous works have shown
that Second Harmonic Generation Microscopy (SHG) represents a valuable
non-linear optical imaging tool to non-invasively identify and monitor changes
in the collagen architecture of the cornea, potentially playing a pivotal role
in future in-vivo cornea diagnostic methods. However, the interpretation of SHG
data can pose significant problems when transferring such approaches to
clinical settings, given the low availability of public data sets, and training
resources. In this work we explore the use of three Deep Learning models, the
highly popular InceptionV3 and ResNet50, alongside FLIMBA, a custom developed
architecture, requiring no pre-training, to automatically detect corneal edema
in SHG images of porcine cornea. We discuss and evaluate data augmentation
strategies tuned to the specifics of the herein addressed application and
observe that Deep Learning models building on different architectures provide
complementary results. Importantly, we observe that the combined use of such
complementary models boosts the overall classification performance in the case
of differentiating edematous and healthy corneal tissues, up to an AU-ROC=0.98.
These results have potential to be extrapolated to other diagnostics scenarios,
such as differentiation of corneal edema in different stages, automated
extraction of hydration level of cornea, or automated identification of corneal
edema causes, and thus pave the way for novel methods for cornea diagnostics
with Deep-Learning assisted non-linear optical imaging.

Reimplementing solutions to previously solved software engineering problems
is not only inefficient but also introduces inadequate and error-prone code.
Many existing methods achieve impressive performance on this issue by using
autoregressive text-generation models trained on code. However, these methods
are not without their flaws. The generated code from these models can be buggy,
lack documentation, and introduce vulnerabilities that may go unnoticed by
developers. An alternative to code generation -- neural code search -- is a
field of machine learning where a model takes natural language queries as input
and, in turn, relevant code samples from a database are returned. Due to the
nature of this pre-existing database, code samples can be documented, tested,
licensed, and checked for vulnerabilities before being used by developers in
production. In this work, we present CodeDSI, an end-to-end unified approach to
code search. CodeDSI is trained to directly map natural language queries to
their respective code samples, which can be retrieved later. In an effort to
improve the performance of code search, we have investigated docid
representation strategies, impact of tokenization on docid structure, and
dataset sizes on overall code search performance. Our results demonstrate
CodeDSI strong performance, exceeding conventional robust baselines by 2-6%
across varying dataset sizes.

Guided exploration with expert demonstrations improves data efficiency for
reinforcement learning, but current algorithms often overuse expert
information. We propose a novel algorithm to speed up Q-learning with the help
of a limited amount of imperfect expert demonstrations. The algorithm avoids
excessive reliance on expert data by relaxing the optimal expert assumption and
gradually reducing the usage of uninformative expert data. Experimentally, we
evaluate our approach on a sparse-reward chain environment and six more
complicated Atari games with delayed rewards. With the proposed methods, we can
achieve better results than Deep Q-learning from Demonstrations (Hester et al.,
2017) in most environments.

In this paper, we study the problem of coverage of an environment with an
energy-constrained robot in the presence of multiple charging stations. As the
robot's on-board power supply is limited, it might not have enough energy to
cover all the points in the environment with a single charge. Instead, it will
need to stop at one or more charging stations to recharge its battery
intermittently. The robot cannot violate the energy constraint, i.e., visit a
location with negative available energy. To solve this problem, we propose a
deep Q-learning framework that produces a policy to maximize the coverage and
minimize the budget violations. Our proposed framework also leverages the
memory of a recurrent neural network (RNN) to better suit this multi-objective
optimization problem. We have tested the presented framework within a 16 x 16
grid environment having charging stations and various obstacle configurations.
Results show that our proposed method finds feasible solutions and outperforms
a comparable existing technique.

Establishing how a set of learners can provide privacy-preserving federated
learning in a fully decentralized (peer-to-peer, no coordinator) manner is an
open problem. We propose the first privacy-preserving consensus-based algorithm
for the distributed learners to achieve decentralized global model aggregation
in an environment of high mobility, where the communication graph between the
learners may vary between successive rounds of model aggregation. In
particular, in each round of global model aggregation, the Metropolis-Hastings
method is applied to update the weighted adjacency matrix based on the current
communication topology. In addition, the Shamir's secret sharing scheme is
integrated to facilitate privacy in reaching consensus of the global model. The
paper establishes the correctness and privacy properties of the proposed
algorithm. The computational efficiency is evaluated by a simulation built on a
federated learning framework with a real-word dataset.

Large pre-trained language models have brought remarkable progress in NLP.
Pre-training and Fine-tuning have given state-of-art performance across tasks
in text processing. Data Augmentation techniques have also helped build
state-of-art models on low or zero resource tasks. Many works in the past have
attempted at learning a single massively-multilingual machine translation model
for zero-shot translation. Although those translation models are producing
correct translations, the main challenge is those models are producing the
wrong languages for zero-shot translation. This work and its results indicate
that prompt conditioned large models do not suffer from off-target language
errors i.e. errors arising due to translation to wrong languages. We
empirically demonstrate the effectiveness of self-supervised pre-training and
data augmentation for zero-shot multi-lingual machine translation.

Explainability is becoming an increasingly important topic for deep neural
networks. Though the operation in convolutional layers is easier to understand,
processing becomes opaque in fully-connected layers. The basic idea in our work
is that each instance, as it flows through the layers, causes a different
activation pattern in the hidden layers and in our Paths methodology, we
cluster these activation vectors for each hidden layer and then see how the
clusters in successive layers connect to one another as activation flows from
the input layer to the output. We find that instances of the same class follow
a small number of cluster sequences over the layers, which we name ``decision
paths." Such paths explain how classification decisions are typically made, and
also help us determine outliers that follow unusual paths. We also propose
using the Sankey diagram to visualize such pathways. We validate our method
with experiments on two feed-forward networks trained on MNIST and CELEB data
sets, and one recurrent network trained on PenDigits.

To address three significant challenges of massive wireless communications
including propagation loss, long-distance transmission, and channel fading, we
aim at establishing the hybrid antenna array with bowtie elements in a compact
size for beamforming applications. In this work we rigorously demonstrate that
bowtie elements allow for a significant improvement in the beamforming
performance of the hybrid antenna array compared to not only other available
antenna arrays, but also its geometrical counterpart with dipole elements. We
have achieved a greater than 15 dB increase in SINR values, a greater than 20%
improvement in the antenna efficiency, a significant enhancement in the DoA
estimation, and 20 increments in the directivity for the hybrid antenna array
with bowtie elements, compared to its geometrical counterpart, by performing a
three-class classification LS-SVM (LeastSquares Support Vector Machine)
optimization method. The proposed hybrid antenna array has shown a 3D uniform
directivity, which is accompanied by its superior performance in the 3D uniform
beam-scanning capability. The directivities remain almost constant at 40.83 dBi
with the variation of angle {\theta}, and 41.21 dBi with the variation of angle
{\phi}. The unrivaled functionality and performance of the hybrid antenna array
with bowtie elements makes it a potential candidate for beamforming
applications in massive wireless communications.

Recognizing an image and segmenting it into coherent regions are often
treated as separate tasks. Human vision, however, has a general sense of
segmentation hierarchy before recognition occurs. We are thus inspired to learn
image recognition with hierarchical image segmentation based entirely on
unlabeled images. Our insight is to learn fine-to-coarse features concurrently
at superpixels, segments, and full image levels, enforcing consistency and
goodness of feature induced segmentations while maximizing discrimination among
image instances.
  Our model innovates vision transformers on three aspects. 1) We use adaptive
segment tokens instead of fixed-shape patch tokens. 2) We create a token
hierarchy by inserting graph pooling between transformer blocks, naturally
producing consistent multi-scale segmentations while increasing the segment
size and reducing the number of tokens. 3) We produce hierarchical image
segmentation for free while training for recognition by maximizing image-wise
discrimination.
  Our work delivers the first concurrent recognition and hierarchical
segmentation model without any supervision. Validated on ImageNet and PASCAL
VOC, it achieves better recognition and segmentation with higher computational

Polar codes are widely used state-of-the-art codes for reliable communication
that have recently been included in the 5th generation wireless standards (5G).
However, there remains room for the design of polar decoders that are both
efficient and reliable in the short blocklength regime. Motivated by recent
successes of data-driven channel decoders, we introduce a novel
$\textbf{C}$ur$\textbf{RI}$culum based $\textbf{S}$equential neural decoder for
$\textbf{P}$olar codes (CRISP). We design a principled curriculum, guided by
information-theoretic insights, to train CRISP and show that it outperforms the
successive-cancellation (SC) decoder and attains near-optimal reliability
performance on the Polar(16,32) and Polar(22, 64) codes. The choice of the
proposed curriculum is critical in achieving the accuracy gains of CRISP, as we
show by comparing against other curricula. More notably, CRISP can be readily
extended to Polarization-Adjusted-Convolutional (PAC) codes, where existing SC
decoders are significantly less reliable. To the best of our knowledge, CRISP
constructs the first data-driven decoder for PAC codes and attains near-optimal
performance on the PAC(16, 32) code.

Clustering based on the random walk operator has been proven effective for
undirected graphs, but its generalization to directed graphs (digraphs) is much
more challenging. Although the random walk operator is well-defined for
digraphs, in most cases such graphs are not strongly connected, and hence the
associated random walks are not irreducible, which is a crucial property for
clustering that exists naturally in the undirected setting. To remedy this, the
usual workaround is to either naively symmetrize the adjacency matrix or to
replace the natural random walk operator by the teleporting random walk
operator, but this can lead to the loss of valuable information carried by edge
directionality. In this paper, we introduce a new clustering framework, the
Parametrized Random Walk Diffusion Kernel Clustering (P-RWDKC), which is
suitable for handling both directed and undirected graphs. Our framework is
based on the diffusion geometry and the generalized spectral clustering
framework. Accordingly, we propose an algorithm that automatically reveals the
cluster structure at a given scale, by considering the random walk dynamics
associated with a parametrized kernel operator, and by estimating its critical
diffusion time. Experiments on $K$-NN graphs constructed from real-world
datasets and real-world graphs show that our clustering approach performs well
in all tested cases, and outperforms existing approaches in most of them.

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph
structure and text-rich entity/relation information. KG representation models
should consider graph structures and text semantics, but no comprehensive
open-sourced framework is mainly designed for KG regarding informative text
description. In this paper, we present PromptKG, a prompt learning framework
for KG representation learning and application that equips the cutting-edge
text-based methods, integrates a new prompt learning model and supports various
tasks (e.g., knowledge graph completion, question answering, recommendation,
and knowledge probing). PromptKG is publicly open-sourced at
https://github.com/zjunlp/PromptKG with long-term technical support.

Smoothness and low dimensional structures play central roles in improving
generalization and stability in learning and statistics. The combination of
these properties has led to many advances in semi-supervised learning,
generative modeling, and control of dynamical systems. However, learning smooth
functions is generally challenging, except in simple cases such as learning
linear or kernel models. Typical methods are either too conservative, relying
on crude upper bounds such as spectral normalization, too lax, penalizing
smoothness on average, or too computationally intensive, requiring the solution
of large-scale semi-definite programs. These issues are only exacerbated when
trying to simultaneously exploit low dimensionality using, e.g., manifolds.
This work proposes to overcome these obstacles by combining techniques from
semi-infinite constrained learning and manifold regularization. To do so, it
shows that, under typical conditions, the problem of learning a Lipschitz
continuous function on a manifold is equivalent to a dynamically weighted
manifold regularization problem. This observation leads to a practical
algorithm based on a weighted Laplacian penalty whose weights are adapted using
stochastic gradient techniques. We prove that, under mild conditions, this
method estimates the Lipschitz constant of the solution, learning a globally
smooth solution as a byproduct. Numerical examples illustrate the advantages of
using this method to impose global smoothness on manifolds as opposed to
imposing smoothness on average.

We propose a federated methodology to learn low-dimensional representations
from a dataset that is distributed among several clients. In particular, we
move away from the commonly-used cross-entropy loss in federated learning, and
seek to learn shared low-dimensional representations of the data in a
decentralized manner via the principle of maximal coding rate reduction (MCR2).
Our proposed method, which we refer to as FLOW, utilizes MCR2 as the objective
of choice, hence resulting in representations that are both between-class
discriminative and within-class compressible. We theoretically show that our
distributed algorithm achieves a first-order stationary point. Moreover, we
demonstrate, via numerical experiments, the utility of the learned
low-dimensional representations.

Apple diseases, if not diagnosed early, can lead to massive resource loss and
pose a serious threat to humans and animals who consume the infected apples.
Hence, it is critical to diagnose these diseases early in order to manage plant
health and minimize the risks associated with them. However, the conventional
approach of monitoring plant diseases entails manual scouting and analyzing the
features, texture, color, and shape of the plant leaves, resulting in delayed
diagnosis and misjudgments. Our work proposes an ensembled system of Xception,
InceptionResNet, and MobileNet architectures to detect 5 different types of
apple plant diseases. The model has been trained on the publicly available
Plant Pathology 2021 dataset and can classify multiple diseases in a given
plant leaf. The system has achieved outstanding results in multi-class and
multi-label classification and can be used in a real-time setting to monitor
large apple plantations to aid the farmers manage their yields effectively.

Deep learning techniques have recently been utilized for model-free
age-associated gait feature extraction. However, acquiring model-free gait
demands accurate pre-processing such as background subtraction, which is
non-trivial in unconstrained environments. On the other hand, model-based gait
can be obtained without background subtraction and is less affected by
covariates. For model-based gait-based age group classification problems,
present works rely solely on handcrafted features, where feature extraction is
tedious and requires domain expertise. This paper proposes a deep learning
approach to extract age-associated features from model-based gait for age group
classification. Specifically, we first develop an unconstrained gait dataset
called Multimedia University Gait Age and Gender dataset (MMU GAG). Next, the
body joint coordinates are determined via pose estimation algorithms and
represented as compact gait graphs via a novel part aggregation scheme. Then, a
Part-AdaptIve Residual Graph Convolutional Neural Network (PairGCN) is designed
for age-associated feature learning. Experiments suggest that PairGCN features
are far more informative than handcrafted features, yielding up to 99% accuracy
for classifying subjects as a child, adult, or senior in the MMU GAG dataset.

In continuous control, exploration is often performed through undirected
strategies in which parameters of the networks or selected actions are
perturbed by random noise. Although the deep setting of undirected exploration
has been shown to improve the performance of on-policy methods, they introduce
an excessive computational complexity and are known to fail in the off-policy
setting. The intrinsically motivated exploration is an effective alternative to
the undirected strategies, but they are usually studied for discrete action
domains. In this paper, we investigate how intrinsic motivation can effectively
be combined with deep reinforcement learning in the control of continuous
systems to obtain a directed exploratory behavior. We adapt the existing
theories on animal motivational systems into the reinforcement learning
paradigm and introduce a novel and scalable directed exploration strategy. The
introduced approach, motivated by the maximization of the value function's
error, can benefit from a collected set of experiences by extracting useful
information and unify the intrinsic exploration motivations in the literature
under a single exploration objective. An extensive set of empirical studies
demonstrate that our framework extends to larger and more diverse state spaces,
dramatically improves the baselines, and outperforms the undirected strategies
significantly.

Deep neural networks and other machine learning systems, despite being
extremely powerful and able to make predictions with high accuracy, are
vulnerable to adversarial attacks. We proposed the DeltaBound attack: a novel,
powerful attack in the hard-label setting with $\ell_2$ norm bounded
perturbations. In this scenario, the attacker has only access to the top-1
predicted label of the model and can be therefore applied to real-world
settings such as remote API. This is a complex problem since the attacker has
very little information about the model. Consequently, most of the other
techniques present in the literature require a massive amount of queries for
attacking a single example. Oppositely, this work mainly focuses on the
evaluation of attack's power in the low queries regime $\leq 1000$ queries)
with $\ell_2$ norm in the hard-label settings. We find that the DeltaBound
attack performs as well and sometimes better than current state-of-the-art
attacks while remaining competitive across different kinds of models. Moreover,
we evaluate our method against not only deep neural networks, but also non-deep
learning models, such as Gradient Boosting Decision Trees and Multinomial Naive
Bayes.

Freestanding Silicon nitride (SiN) devices are central to the field of
nanomechanical resonators and for other technology applications such as
transmission electron imaging and nanopore bioassays. The nanofabrication
techniques used for fabricating these devices often lack flexibility. While
photolithography requires printing of an expensive photomask for each new
design iteration, electron-beam lithography is extremely slow and commands high
equipment cost. Here we demonstrate maskless rapid prototyping of freestanding
SiN nanomechanical resonators fabricated by femtosecond laser ablation of plain
SiN membrane in ambient air. We fabricate microbeams with different widths from
7 to 100 um, and we characterize their resonance frequency and mechanical
quality (Q) factors. We find that membrane cracking can be avoided during
fabrication by carefully engineering the etch pattern, and that laser etching
has a negligible effect on built-in tensile stress. For each beam, Q-factors
are measured for several eigenmodes and are found to remain high after laser
etching. All beams show quality factors greater than 105, while unetched plain
membranes have Q > 106. Possible causes for Q-factor reduction are identified,
along with future process improvement directions.

Evolutionary algorithms (EAs) provide unique advantages for optimizing neural
networks in complex search spaces. This paper introduces a new web platform,
NeuroEvo (neuroevo.io), that allows users to interactively design and train
neural network classifiers using evolutionary and particle swarm algorithms.
The classification problem and training data are provided by the user and, upon
completion of the training process, the best classifier is made available to
download and implement in Python, Java, and JavaScript. NeuroEvo is a
cloud-based application that leverages GPU parallelization to improve the speed
with which the independent evolutionary steps, such as mutation, crossover, and
fitness evaluation, are executed across the population. This paper outlines the
training algorithms and opportunities for users to specify design decisions and
hyperparameter settings. The algorithms described in this paper are also made
available as a Python package, neuroevo (PyPI:
https://pypi.org/project/neuroevo/).

We provide a framework for probabilistic reasoning in Vadalog-based Knowledge
Graphs (KGs), satisfying the requirements of ontological reasoning: full
recursion, powerful existential quantification, expression of inductive
definitions. Vadalog is a Knowledge Representation and Reasoning (KRR) language
based on Warded Datalog+/-, a logical core language of existential rules, with
a good balance between computational complexity and expressive power. Handling
uncertainty is essential for reasoning with KGs. Yet Vadalog and Warded
Datalog+/- are not covered by the existing probabilistic logic programming and
statistical relational learning approaches for several reasons, including
insufficient support for recursion with existential quantification, and the
impossibility to express inductive definitions. In this work, we introduce Soft
Vadalog, a probabilistic extension to Vadalog, satisfying these desiderata. A
Soft Vadalog program induces what we call a Probabilistic Knowledge Graph
(PKG), which consists of a probability distribution on a network of chase
instances, structures obtained by grounding the rules over a database using the
chase procedure. We exploit PKGs for probabilistic marginal inference. We
discuss the theory and present MCMC-chase, a Monte Carlo method to use Soft
Vadalog in practice. We apply our framework to solve data management and
industrial problems, and experimentally evaluate it in the Vadalog system.
  Under consideration in Theory and Practice of Logic Programming (TPLP).

Sentence-final particles serve an essential role in spoken Japanese because
they express the speaker's mental attitudes toward a proposition and/or an
interlocutor. They are acquired at early ages and occur very frequently in
everyday conversation. However, there has been little proposal for a
computational model of acquiring sentence-final particles. This paper proposes
Subjective BERT, a self-attention model that takes various subjective senses in
addition to language and images as input and learns the relationship between
words and subjective senses. An evaluation experiment revealed that the model
understands the usage of "yo", which expresses the speaker's intention to
communicate new information, and that of "ne", which denotes the speaker's
desire to confirm that some information is shared.

Physics-informed neural networks (PINNs) have emerged as an effective
technique for solving PDEs in a wide range of domains. It is noticed, however,
the performance of PINNs can vary dramatically with different sampling
procedures. For instance, a fixed set of (prior chosen) training points may
fail to capture the effective solution region (especially for problems with
singularities). To overcome this issue, we present in this work an adaptive
strategy, termed the failure-informed PINNs (FI-PINNs), which is inspired by
the viewpoint of reliability analysis. The key idea is to define an effective
failure probability based on the residual, and then, with the aim of placing
more samples in the failure region, the FI-PINNs employs a failure-informed
enrichment technique to adaptively add new collocation points to the training
set, such that the numerical accuracy is dramatically improved. In short,
similar as adaptive finite element methods, the proposed FI-PINNs adopts the
failure probability as the posterior error indicator to generate new training
points. We prove rigorous error bounds of FI-PINNs and illustrate its
performance through several problems.

Few-shot learning is an important, but challenging problem of machine
learning aimed at learning from only fewer labeled training examples. It has
become an active area of research due to deep learning requiring huge amounts
of labeled dataset, which is not feasible in the real world. Learning from a
few examples is also an important attempt towards learning like humans.
Few-shot learning has proven a very good promise in different areas of machine
learning applications, particularly in image classification. As it is a recent
technique, most researchers focus on understanding and solving the issues
related to its concept by focusing only on common image datasets like
Mini-ImageNet and Omniglot. Few-shot learning also opens an opportunity to
address low resource languages like Amharic. In this study, offline handwritten
Amharic character recognition using few-shot learning is addressed.
Particularly, prototypical networks, the popular and simpler type of few-shot
learning, is implemented as a baseline. Using the opportunities explored in the
nature of Amharic alphabet having row-wise and column-wise similarities, a
novel way of augmenting the training episodes is proposed. The experimental
results show that the proposed method outperformed the baseline method. This
study has implemented few-shot learning for Amharic characters for the first
time. More importantly, the findings of the study open new ways of examining
the influence of training episodes in few-shot learning, which is one of the
important issues that needs exploration. The datasets used for this study are
collected from native Amharic language writers using an Android App developed
as a part of this study.

Many real-world dynamical systems are associated with first integrals (a.k.a.
invariant quantities), which are quantities that remain unchanged over time.
The discovery and understanding of first integrals are fundamental and
important topics both in the natural sciences and in industrial applications.
First integrals arise from the conservation laws of system energy, momentum,
and mass, and from constraints on states; these are typically related to
specific geometric structures of the governing equations. Existing neural
networks designed to ensure such first integrals have shown excellent accuracy
in modeling from data. However, these models incorporate the underlying
structures, and in most situations where neural networks learn unknown systems,
these structures are also unknown. This limitation needs to be overcome for
scientific discovery and modeling of unknown systems. To this end, we propose
first integral-preserving neural differential equation (FINDE). By leveraging
the projection method and the discrete gradient method, FINDE finds and
preserves first integrals from data, even in the absence of prior knowledge
about underlying structures. Experimental results demonstrate that FINDE can
predict future states of target systems much longer and find various quantities
consistent with well-known first integrals in a unified manner.

For decades, the determination of an objects location has been implemented
utilizing different technologies. Despite GPS (Global Positioning System)
provides a scalable efficient and cost effective location services however the
satellite emitted signals cannot be exploited indoor to effectively determine
the location. In contrast to GPS which is a cost effective localization
technology for outdoor locations several technologies have been studied for
indoor localization. These include Wireless Fidelity (Wi-Fi) Bluetooth Low
Energy (BLE) and Received Signal Strength Indicator (RSSI) etc. This paper
presents an enhanced method of using RSSI as a mean to determine an objects
location by applying some Machine Learning (ML) concepts. The binary
classification is defined by considering the adjacency of the coordinates
denoting objects locations. The proposed features were tested empirically via
multiple classifiers that achieved a maximum of 96 percent accuracy.

Accurate and reliable prediction of Photovoltaic (PV) power output is
critical to electricity grid stability and power dispatching capabilities.
However, Photovoltaic (PV) power generation is highly volatile and unstable due
to different reasons. The Wavelet Transform (WT) has been utilised in time
series applications, such as Photovoltaic (PV) power prediction, to model the
stochastic volatility and reduce prediction errors. Yet the existing Wavelet
Transform (WT) approach has a limitation in terms of time complexity. It
requires reconstructing the decomposed components and modelling them separately
and thus needs more time for reconstruction, model configuration and training.
The aim of this study is to improve the efficiency of applying Wavelet
Transform (WT) by proposing a new method that uses a single simplified model.
Given a time series and its Wavelet Transform (WT) coefficients, it trains one
model with the coefficients as features and the original time series as labels.
This eliminates the need for component reconstruction and training numerous
models. This work contributes to the day-ahead aggregated solar Photovoltaic
(PV) power time series prediction problem by proposing and comprehensively
evaluating a new approach of employing WT. The proposed approach is evaluated
using 17 months of aggregated solar Photovoltaic (PV) power data from two
real-world datasets. The evaluation includes the use of a variety of prediction
models, including Linear Regression, Random Forest, Support Vector Regression,
and Convolutional Neural Networks. The results indicate that using a
coefficients-based strategy can give predictions that are comparable to those
obtained using the components-based approach while requiring fewer models and
less computational time.

Benefiting from the fusion of communication and intelligent technologies,
network-enabled robots have become important to support future machine-assisted
and unmanned applications. To provide high-quality services for robots in wide
areas, hybrid satellite-terrestrial networks are a key technology. Through
hybrid networks, computation-intensive and latency-sensitive tasks can be
offloaded to mobile edge computing (MEC) servers. However, due to the mobility
of mobile robots and unreliable wireless network environments, excessive local
computations and frequent service migrations may significantly increase the
service delay. To address this issue, this paper aims to minimize the average
task completion time for MEC-based offloading initiated by
satellite-terrestrial-network-enabled robots. Different from conventional
mobility-aware schemes, the proposed scheme makes the offloading decision by
jointly considering the mobility control of robots. A joint optimization
problem of task offloading and velocity control is formulated. Using Lyapunov
optimization, the original optimization is decomposed into a velocity control
subproblem and a task offloading subproblem. Then, based on the Markov decision
process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed.
Theoretical analysis proves the convergence of the improved RL algorithm, and
the simulation results show that the proposed scheme can effectively reduce the
offloading delay.

In class incremental learning (CIL) a model must learn new classes in a
sequential manner without forgetting old ones. However, conventional CIL
methods consider a balanced distribution for each new task, which ignores the
prevalence of long-tailed distributions in the real world. In this work we
propose two long-tailed CIL scenarios, which we term ordered and shuffled
LT-CIL. Ordered LT-CIL considers the scenario where we learn from head classes
collected with more samples than tail classes which have few. Shuffled LT-CIL,
on the other hand, assumes a completely random long-tailed distribution for
each task. We systematically evaluate existing methods in both LT-CIL scenarios
and demonstrate very different behaviors compared to conventional CIL
scenarios. Additionally, we propose a two-stage learning baseline with a
learnable weight scaling layer for reducing the bias caused by long-tailed
distribution in LT-CIL and which in turn also improves the performance of
conventional CIL due to the limited exemplars. Our results demonstrate the
superior performance (up to 6.44 points in average incremental accuracy) of our
approach on CIFAR-100 and ImageNet-Subset. The code is available at
https://github.com/xialeiliu/Long-Tailed-CIL

The ACII Affective Vocal Bursts (A-VB) competition introduces a new topic in
affective computing, which is understanding emotional expression using the
non-verbal sound of humans. We are familiar with emotion recognition via verbal
vocal or facial expression. However, the vocal bursts such as laughs, cries,
and signs, are not exploited even though they are very informative for behavior
analysis. The A-VB competition comprises four tasks that explore non-verbal
information in different spaces. This technical report describes the method and
the result of SclabCNU Team for the tasks of the challenge. We achieved
promising results compared to the baseline model provided by the organizers.

We develop a regression based primal-dual martingale approach for solving
finite time horizon MDPs with general state and action space. As a result, our
method allows for the construction of tight upper and lower biased
approximations of the value functions, and, provides tight approximations to
the optimal policy. In particular, we prove tight error bounds for the
estimated duality gap featuring polynomial dependence on the time horizon, and
sublinear dependence on the cardinality/dimension of the possibly infinite
state and action space.From a computational point of view the proposed method
is efficient since, in contrast to usual duality-based methods for optimal
control problems in the literature, the Monte Carlo procedures here involved do
not require nested simulations.

Accurate medical classification requires a large number of multi-modal data,
and in many cases, in different formats. Previous studies have shown promising
results when using multi-modal data, outperforming single-modality models on
when classifying disease such as AD. However, those models are usually not
flexible enough to handle missing modalities. Currently, the most common
workaround is excluding samples with missing modalities which leads to
considerable data under-utilisation. Adding to the fact that labelled medical
images are already scarce, the performance of data-driven methods like deep
learning is severely hampered. Therefore, a multi-modal method that can
gracefully handle missing data in various clinical settings is highly
desirable. In this paper, we present the Multi-Modal Mixing Transformer (3MT),
a novel Transformer for disease classification based on multi-modal data. In
this work, we test it for \ac{AD} or \ac{CN} classification using neuroimaging
data, gender, age and MMSE scores. The model uses a novel Cascaded Modality
Transformers architecture with cross-attention to incorporate multi-modal
information for more informed predictions. Auxiliary outputs and a novel
modality dropout mechanism were incorporated to ensure an unprecedented level
of modality independence and robustness. The result is a versatile network that
enables the mixing of an unlimited number of modalities with different formats
and full data utilization. 3MT was first tested on the ADNI dataset and
achieved state-of-the-art test accuracy of $0.987\pm0.0006$. To test its
generalisability, 3MT was directly applied to the AIBL after training on the
ADNI dataset, and achieved a test accuracy of $0.925\pm0.0004$ without
fine-tuning. Finally, we show that Grad-CAM visualizations are also possible
with our model for explainable results.

Electroencephalograms (EEGs) are brain dynamics measured outside the brain,
which have been widely utilized in non-invasive brain-computer interface
applications. Recently, various neural network approaches have been proposed to
improve the accuracy of EEG signal recognition. However, these approaches
severely rely on manually designed network structures for different tasks which
generally are not sharing the same empirical design cross-task-wise. In this
paper, we propose a cross-task neural architecture search (CTNAS-EEG) framework
for EEG signal recognition, which can automatically design the network
structure across tasks and improve the recognition accuracy of EEG signals.
Specifically, a compatible search space for cross-task searching and an
efficient constrained searching method is proposed to overcome challenges
brought by EEG signals. By unifying structure search on different EEG tasks,
this work is the first to explore and analyze the searched structure difference
cross-task-wise. Moreover, by introducing architecture search, this work is the
first to analyze model performance by customizing model structure for each
human subject. Detailed experimental results suggest that the proposed
CTNAS-EEG could reach state-of-the-art performance on different EEG tasks, such
as Motor Imagery (MI) and Emotion recognition. Extensive experiments and
detailed analysis are provided as a good reference for follow-up researchers.

Inspired by the success of contrastive learning (CL) in computer vision and
natural language processing, graph contrastive learning (GCL) has been
developed to learn discriminative node representations on graph datasets.
However, the development of GCL on Heterogeneous Information Networks (HINs) is
still in the infant stage. For example, it is unclear how to augment the HINs
without substantially altering the underlying semantics, and how to design the
contrastive objective to fully capture the rich semantics. Moreover, early
investigations demonstrate that CL suffers from sampling bias, whereas
conventional debiasing techniques are empirically shown to be inadequate for
GCL. How to mitigate the sampling bias for heterogeneous GCL is another
important problem. To address the aforementioned challenges, we propose a novel
Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. In
particular, we use metapaths as the augmentation to generate multiple subgraphs
as multi-views, and propose a contrastive objective to maximize the mutual
information between any pairs of metapath-induced views. To alleviate the
sampling bias, we further propose a positive sampling strategy to explicitly
select positives for each node via jointly considering semantic and structural
information preserved on each metapath view. Extensive experiments demonstrate
HGCML consistently outperforms state-of-the-art baselines on five real-world
benchmark datasets.

The method of random Fourier features (RFF), proposed in a seminal paper by
Rahimi and Recht (NIPS'07), is a powerful technique to find approximate
low-dimensional representations of points in (high-dimensional) kernel space,
for shift-invariant kernels. While RFF has been analyzed under various notions
of error guarantee, the ability to preserve the kernel distance with
\emph{relative} error is less understood. We show that for a significant range
of kernels, including the well-known Laplacian kernels, RFF cannot approximate
the kernel distance with small relative error using low dimensions. We
complement this by showing as long as the shift-invariant kernel is analytic,
RFF with $\mathrm{poly}(\epsilon^{-1} \log n)$ dimensions achieves
$\epsilon$-relative error for pairwise kernel distance of $n$ points, and the
dimension bound is improved to $\mathrm{poly}(\epsilon^{-1}\log k)$ for the
specific application of kernel $k$-means. Finally, going beyond RFF, we make
the first step towards data-oblivious dimension-reduction for general
shift-invariant kernels, and we obtain a similar $\mathrm{poly}(\epsilon^{-1}
\log n)$ dimension bound for Laplacian kernels. We also validate the
dimension-error tradeoff of our methods on simulated datasets, and they
demonstrate superior performance compared with other popular methods including
random-projection and Nystr\"{o}m methods.

Few-shot class-incremental learning (FSCIL) faces challenges of memorizing
old class distributions and estimating new class distributions given few
training samples. In this study, we propose a learnable distribution
calibration (LDC) approach, with the aim to systematically solve these two
challenges using a unified framework. LDC is built upon a parameterized
calibration unit (PCU), which initializes biased distributions for all classes
based on classifier vectors (memory-free) and a single covariance matrix. The
covariance matrix is shared by all classes, so that the memory costs are fixed.
During base training, PCU is endowed with the ability to calibrate biased
distributions by recurrently updating sampled features under the supervision of
real distributions. During incremental learning, PCU recovers distributions for
old classes to avoid `forgetting', as well as estimating distributions and
augmenting samples for new classes to alleviate `over-fitting' caused by the
biased distributions of few-shot samples. LDC is theoretically plausible by
formatting a variational inference procedure. It improves FSCIL's flexibility
as the training procedure requires no class similarity priori. Experiments on
CUB200, CIFAR100, and mini-ImageNet datasets show that LDC outperforms the
state-of-the-arts by 4.64%, 1.98%, and 3.97%, respectively. LDC's effectiveness
is also validated on few-shot learning scenarios.

The problem of generating textual descriptions for the visual data has gained
research attention in the recent years. In contrast to that the problem of
generating visual data from textual descriptions is still very challenging,
because it requires the combination of both Natural Language Processing (NLP)
and Computer Vision techniques. The existing methods utilize the Generative
Adversarial Networks (GANs) and generate the uncompressed images from textual
description. However, in practice, most of the visual data are processed and
transmitted in the compressed representation. Hence, the proposed work attempts
to generate the visual data directly in the compressed representation form
using Deep Convolutional GANs (DCGANs) to achieve the storage and computational
efficiency. We propose GAN models for compressed image generation from text.
The first model is directly trained with JPEG compressed DCT images (compressed
domain) to generate the compressed images from text descriptions. The second
model is trained with RGB images (pixel domain) to generate JPEG compressed DCT
representation from text descriptions. The proposed models are tested on an
open source benchmark dataset Oxford-102 Flower images using both RGB and JPEG
compressed versions, and accomplished the state-of-the-art performance in the
JPEG compressed domain. The code will be publicly released at GitHub after
acceptance of paper.

It is a challenge to segment the location and size of rectal cancer tumours
through deep learning. In this paper, in order to improve the ability of
extracting suffi-cient feature information in rectal tumour segmentation,
attention enlarged ConvNeXt UNet (AACN-UNet), is proposed. The network mainly
includes two improvements: 1) the encoder stage of UNet is changed to ConvNeXt
structure for encoding operation, which can not only integrate multi-scale
semantic information on a large scale, but al-so reduce information loss and
extract more feature information from CT images; 2) CBAM attention mechanism is
added to improve the connection of each feature in channel and space, which is
conducive to extracting the effective feature of the target and improving the
segmentation accuracy.The experiment with UNet and its variant network shows
that AACN-UNet is 0.9% ,1.1% and 1.4% higher than the current best results in
P, F1 and Miou.Compared with the training time, the number of parameters in
UNet network is less. This shows that our proposed AACN-UNet has achieved
ex-cellent results in CT image segmentation of rectal cancer.

Federated learning aims to train models collaboratively across different
clients without the sharing of data for privacy considerations. However, one
major challenge for this learning paradigm is the {\em data heterogeneity}
problem, which refers to the discrepancies between the local data distributions
among various clients. To tackle this problem, we first study how data
heterogeneity affects the representations of the globally aggregated models.
Interestingly, we find that heterogeneous data results in the global model
suffering from severe {\em dimensional collapse}, in which representations tend
to reside in a lower-dimensional space instead of the ambient space. Moreover,
we observe a similar phenomenon on models locally trained on each client and
deduce that the dimensional collapse on the global model is inherited from
local models. In addition, we theoretically analyze the gradient flow dynamics
to shed light on how data heterogeneity result in dimensional collapse for
local models. To remedy this problem caused by the data heterogeneity, we
propose {\sc FedDecorr}, a novel method that can effectively mitigate
dimensional collapse in federated learning. Specifically, {\sc FedDecorr}
applies a regularization term during local training that encourages different
dimensions of representations to be uncorrelated. {\sc FedDecorr}, which is
implementation-friendly and computationally-efficient, yields consistent
improvements over baselines on standard benchmark datasets. Code will be
released.

With increasing demands for high-quality semantic segmentation in the
industry, hard-distinguishing semantic boundaries have posed a significant
threat to existing solutions. Inspired by real-life experience, i.e., combining
varied observations contributes to higher visual recognition confidence, we
present the equipotential learning (EPL) method. This novel module transfers
the predicted/ground-truth semantic labels to a self-defined potential domain
to learn and infer decision boundaries along customized directions. The
conversion to the potential domain is implemented via a lightweight
differentiable anisotropic convolution without incurring any parameter
overhead. Besides, the designed two loss functions, the point loss and the
equipotential line loss implement anisotropic field regression and
category-level contour learning, respectively, enhancing prediction
consistencies in the inter/intra-class boundary areas. More importantly, EPL is
agnostic to network architectures, and thus it can be plugged into most
existing segmentation models. This paper is the first attempt to address the
boundary segmentation problem with field regression and contour learning.
Meaningful performance improvements on Pascal Voc 2012 and Cityscapes
demonstrate that the proposed EPL module can benefit the off-the-shelf fully
convolutional network models when recognizing semantic boundary areas. Besides,
intensive comparisons and analysis show the favorable merits of EPL for
distinguishing semantically-similar and irregular-shaped categories.

As a fundamental design tool in many engineering disciplines, multi-body
dynamics (MBD) models a complex structure with a differential equation group
containing multiple physical quantities. Engineers must constantly adjust
structures at the design stage, which requires a highly efficient solver. The
rise of deep learning technologies has offered new perspectives on MBD.
Unfortunately, existing black-box models suffer from poor accuracy and
robustness, while the advanced methodologies of single-output operator
regression cannot deal with multiple quantities simultaneously. To address
these challenges, we propose PINO-MBD, a deep learning framework for solving
practical MBD problems based on the theory of physics-informed neural operator
(PINO). PINO-MBD uses a single network for all quantities in a multi-body
system, instead of training dozens, or even hundreds of networks as in the
existing literature. We demonstrate the flexibility and feasibility of PINO-MBD
for one toy example and two practical applications: vehicle-track coupled
dynamics (VTCD) and reliability analysis of a four-storey building. The
performance of VTCD indicates that our framework outperforms existing software
and machine learning-based methods in terms of efficiency and precision,
respectively. For the reliability analysis, PINO-MBD can provide
higher-resolution results in less than a quarter of the time incurred when
using the probability density evolution method (PDEM). This framework
integrates mechanics and deep learning technologies and may reveal a new
concept for MBD and probabilistic engineering.

Research in medical visual question answering (MVQA) can contribute to the
development of computeraided diagnosis. MVQA is a task that aims to predict
accurate and convincing answers based on given medical images and associated
natural language questions. This task requires extracting medical
knowledge-rich feature content and making fine-grained understandings of them.
Therefore, constructing an effective feature extraction and understanding
scheme are keys to modeling. Existing MVQA question extraction schemes mainly
focus on word information, ignoring medical information in the text. Meanwhile,
some visual and textual feature understanding schemes cannot effectively
capture the correlation between regions and keywords for reasonable visual
reasoning. In this study, a dual-attention learning network with word and
sentence embedding (WSDAN) is proposed. We design a module, transformer with
sentence embedding (TSE), to extract a double embedding representation of
questions containing keywords and medical information. A dualattention learning
(DAL) module consisting of self-attention and guided attention is proposed to
model intensive intramodal and intermodal interactions. With multiple DAL
modules (DALs), learning visual and textual co-attention can increase the
granularity of understanding and improve visual reasoning. Experimental results
on the ImageCLEF 2019 VQA-MED (VQA-MED 2019) and VQA-RAD datasets demonstrate
that our proposed method outperforms previous state-of-the-art methods.
According to the ablation studies and Grad-CAM maps, WSDAN can extract rich
textual information and has strong visual reasoning ability.

In many real-world scenarios, such as gene knockout experiments, targeted
interventions are often accompanied by unknown interventions at off-target
sites. Moreover, different units can get randomly exposed to different unknown
interventions, thereby creating a mixture of interventions. Identifying
different components of this mixture can be very valuable in some applications.
Motivated by such situations, in this work, we study the problem of identifying
all components present in a mixture of interventions on a given causal Bayesian
Network. We construct an example to show that, in general, the components are
not identifiable from the mixture distribution. Next, assuming that the given
network satisfies a positivity condition, we show that, if the set of mixture
components satisfy a mild exclusion assumption, then they can be uniquely
identified. Our proof gives an efficient algorithm to recover these targets
from the exponentially large search space of possible targets. In the more
realistic scenario, where distributions are given via finitely many samples, we
conduct a simulation study to analyze the performance of an algorithm derived
from our identifiability proof.

Grounding spatial relations in natural language for object placing could have
ambiguity and compositionality issues. To address the issues, we introduce
ParaGon, a PARsing And visual GrOuNding framework for language-conditioned
object placement. It parses language instructions into relations between
objects and grounds those objects in visual scenes. A particle-based GNN then
conducts relational reasoning between grounded objects for placement
generation. ParaGon encodes all of those procedures into neural networks for
end-to-end training, which avoids annotating parsing and object reference
grounding labels. Our approach inherently integrates parsing-based methods into
a probabilistic, data-driven framework. It is data-efficient and generalizable
for learning compositional instructions, robust to noisy language inputs, and
adapts to the uncertainty of ambiguous instructions.

Temporal point process serves as an essential tool for modeling time-to-event
data in continuous time space. Despite having massive amounts of event sequence
data from various domains like social media, healthcare etc., real world
application of temporal point process faces two major challenges: 1) it is not
generalizable to predict events from unseen sequences in dynamic environment 2)
they are not capable of thriving in continually evolving environment with
minimal supervision while retaining previously learnt knowledge. To tackle
these issues, we propose \textit{HyperHawkes}, a hypernetwork based temporal
point process framework which is capable of modeling time of occurrence of
events for unseen sequences. Thereby, we solve the problem of zero-shot
learning for time-to-event modeling. We also develop a hypernetwork based
continually learning temporal point process for continuous modeling of
time-to-event sequences with minimal forgetting. In this way,
\textit{HyperHawkes} augments the temporal point process with zero-shot
modeling and continual learning capabilities. We demonstrate the application of
the proposed framework through our experiments on two real-world datasets. Our
results show the efficacy of the proposed approach in terms of predicting
future events under zero-shot regime for unseen event sequences. We also show
that the proposed model is able to predict sequences continually while
retaining information from previous event sequences, hence mitigating
catastrophic forgetting for time-to-event data.

The agnostic setting is the hardest generalization of the PAC model since it
is akin to learning with adversarial noise. We study an open question on the
existence of efficient quantum boosting algorithms in this setting. We answer
this question in the affirmative by providing a quantum version of the
Kalai-Kanade potential boosting algorithm. This algorithm shows the standard
quadratic speedup in the VC dimension of the weak learner compared to the
classical case.
  Using our boosting algorithm as a subroutine, we give a quantum algorithm for
agnostically learning decision trees in polynomial running time without using
membership queries. To the best of our knowledge, this is the first algorithm
(quantum or classical) to do so. Learning decision trees without membership
queries is hard (and an open problem) in the standard classical realizable
setting. In general, even coming up with weak learners in the agnostic setting
is a challenging task. We show how to construct a quantum agnostic weak learner
using standard quantum algorithms, which is of independent interest for
designing ensemble learning setups.

Actor-critic (AC) algorithms are a class of model-free deep reinforcement
learning algorithms, which have proven their efficacy in diverse domains,
especially in solving continuous control problems. Improvement of exploration
(action entropy) and exploitation (expected return) using more efficient
samples is a critical issue in AC algorithms. A basic strategy of a learning
algorithm is to facilitate indiscriminately exploring all of the environment
state space, as well as to encourage exploring rarely visited states rather
than frequently visited one. Under this strategy, we propose a new method to
boost exploration through an intrinsic reward, based on measurement of a
state's novelty and the associated benefit of exploring the state (with regards
to policy optimization), altogether called plausible novelty. With incentivized
exploration of plausible novel states, an AC algorithm is able to improve its
sample efficiency and hence training performance. The new method is verified by
extensive simulations of continuous control tasks of MuJoCo environments on a
variety of prominent off-policy AC algorithms.

This paper studies the adaptive optimal control problem for a class of linear
time-delay systems described by delay differential equations (DDEs). A crucial
strategy is to take advantage of recent developments in reinforcement learning
and adaptive dynamic programming and develop novel methods to learn adaptive
optimal controllers from finite samples of input and state data. In this paper,
the data-driven policy iteration (PI) is proposed to solve the
infinite-dimensional algebraic Riccati equation (ARE) iteratively in the
absence of exact model knowledge. Interestingly, the proposed recursive PI
algorithm is new in the present context of continuous-time time-delay systems,
even when the model knowledge is assumed known. The efficacy of the proposed
learning-based control methods is validated by means of practical applications
arising from metal cutting and autonomous driving.

COVID-19 has a spectrum of disease severity, ranging from asymptomatic to
requiring hospitalization. Providing appropriate medical care to severe
patients is crucial to reduce mortality risks. Hence, in classifying patients
into severity categories, the more important classification errors are
"under-diagnosis", in which patients are misclassified into less severe
categories and thus receive insufficient medical care. The Neyman-Pearson (NP)
classification paradigm has been developed to prioritize the designated type of
error. However, current NP procedures are either for binary classification or
do not provide high probability controls on the prioritized errors in
multi-class classification. Here, we propose a hierarchical NP (H-NP) framework
and an umbrella algorithm that generally adapts to popular classification
methods and controls the under-diagnosis errors with high probability. On an
integrated collection of single-cell RNA-seq (scRNA-seq) datasets for 740
patients, we explore ways of featurization and demonstrate the efficacy of the
H-NP algorithm in controlling the under-diagnosis errors regardless of
featurization. Beyond COVID-19 severity classification, the H-NP algorithm
generally applies to multi-class classification problems, where classes have a
priority order.

Bus bunching is a natural-occurring phenomenon that undermines the efficiency
and stability of the public transportation system. The mainstream solutions
control the bus to intentionally stay longer at certain stations. Existing
control methods include conventional methods that provide a formula to
calculate the control time and reinforcement learning (RL) methods that
determine the control policy through repeated interactions with the system. In
this paper, we propose an integrated proximal policy optimization model with
dual-headway (IPPO-DH). IPPO-DH integrates the conventional headway control
with reinforcement learning, so that it acquires the advantages of both
algorithms -- it is more efficient in normal environments and more stable in
harsh ones. To demonstrate such an advantage, we design a bus simulation
environment and compare IPPO-DH with RL and several conventional methods. The
results show that the proposed model maintains the application value of the
conventional method by avoiding the instability of the RL method in certain
environments, and improves the efficiency compared with the conventional
control, shedding new light on real-world bus transit system optimization.

Accurate long series forecasting of traffic information is critical for the
development of intelligent traffic systems. We may benefit from the rapid
growth of neural network analysis technology to better understand the
underlying functioning patterns of traffic networks as a result of this
progress. Due to the fact that traffic data and facility utilization
circumstances are sequentially dependent on past and present situations,
several related neural network techniques based on temporal dependency
extraction models have been developed to solve the problem. The complicated
topological road structure, on the other hand, amplifies the effect of spatial
interdependence, which cannot be captured by pure temporal extraction
approaches. Additionally, the typical Deep Recurrent Neural Network (RNN)
topology has a constraint on global information extraction, which is required
for comprehensive long-term prediction. This study proposes a new
spatial-temporal neural network architecture, called Spatial-Temporal
Graph-Informer (STGIN), to handle the long-term traffic parameters forecasting
issue by merging the Informer and Graph Attention Network (GAT) layers for
spatial and temporal relationships extraction. The attention mechanism
potentially guarantees long-term prediction performance without significant
information loss from distant inputs. On two real-world traffic datasets with
varying horizons, experimental findings validate the long sequence prediction
abilities, and further interpretation is provided.

We present FRMT, a new dataset and evaluation benchmark for Few-shot
Region-aware Machine Translation, a type of style-targeted translation. The
dataset consists of professional translations from English into two regional
variants each of Portuguese and Mandarin Chinese. Source documents are selected
to enable detailed analysis of phenomena of interest, including lexically
distinct terms and distractor terms. We explore automatic evaluation metrics
for FRMT and validate their correlation with expert human evaluation across
both region-matched and mismatched rating scenarios. Finally, we present a
number of baseline models for this task, and offer guidelines for how
researchers can train, evaluate, and compare their own models. Our dataset and
evaluation code are publicly available: https://bit.ly/frmt-task

Semi-supervised learning has the potential to improve the data-efficiency of
training data-hungry deep neural networks, which is especially important for
medical image analysis tasks where labeled data is scarce. In this work, we
present a simple semi-supervised learning method for lesion segmentation tasks
based on the ideas of cut-paste augmentation and consistency regularization. By
exploiting the mask information available in the labeled data, we synthesize
partially labeled samples from the unlabeled images so that the usual
supervised learning objective (e.g., binary cross entropy) can be applied.
Additionally, we introduce a background consistency term to regularize the
training on the unlabeled background regions of the synthetic images. We
empirically verify the effectiveness of the proposed method on two public
lesion segmentation datasets, including an eye fundus photograph dataset and a
brain CT scan dataset. The experiment results indicate that our method achieves
consistent and superior performance over other self-training and
consistency-based methods without introducing sophisticated network components.

Noise Contrastive Estimation (NCE) is a popular approach for learning
probability density functions parameterized up to a constant of
proportionality. The main idea is to design a classification problem for
distinguishing training data from samples from an easy-to-sample noise
distribution $q$, in a manner that avoids having to calculate a partition
function. It is well-known that the choice of $q$ can severely impact the
computational and statistical efficiency of NCE. In practice, a common choice
for $q$ is a Gaussian which matches the mean and covariance of the data.
  In this paper, we show that such a choice can result in an exponentially bad
(in the ambient dimension) conditioning of the Hessian of the loss, even for
very simple data distributions. As a consequence, both the statistical and
algorithmic complexity for such a choice of $q$ will be problematic in
practice, suggesting that more complex noise distributions are essential to the
success of NCE.

Things are becoming more advanced as technology advances, and machines now
perform the majority of the manual work. The most often used home appliance is
the washing machine for cloths. Modification and research in this field is
essential since it pertains to the amount of time, water, and electricity
required for washing. In this work, a Fuzzy Logic Controller has been developed
for smart washing machines. The objective of this paper is to optimize the
consumption of electricity, water, and detergent for washing machines. The type
of dirt, volume of clothes, and type of cloth play a vital role in saving
water, electricity, and detergent. However, none of the work on the Fuzzy Logic
Controller provided a design procedure endowed with the specified inputs and
outputs implemented in Python. In this paper, we used the Mamdani approach and
created an algorithm based on multi-input multi-output. The algorithm is
implemented in Python. The results of this simulation show that the washing
machine provides better execution at a low computation cost.

A new interpoint distance-based measure is proposed to identify the optimal
number of clusters present in a data set. Designed in nonparametric approach,
it is independent of the distribution of given data. Interpoint distances
between the data members make our cluster validity index applicable to
univariate and multivariate data measured on arbitrary scales, or having
observations in any dimensional space where the number of study variables can
be even larger than the sample size. Our proposed criterion is compatible with
any clustering algorithm, and can be used to determine the unknown number of
clusters or to assess the quality of the resulting clusters for a data set.
Demonstration through synthetic and real-life data establishes its superiority
over the well-known clustering accuracy measures of the literature.

Although large language models have achieved impressive zero-shot ability,
the huge model size generally incurs high cost. Recently, semi-parametric
language models, which augment a smaller language model with an external
retriever, have demonstrated promising language modeling capabilities. However,
it remains unclear whether such semi-parametric language models can perform
competitively well as their fully-parametric counterparts on zero-shot
generalization to downstream tasks. In this work, we introduce $\text{Zemi}$, a
zero-shot semi-parametric language model. To our best knowledge, this is the
first semi-parametric language model that can demonstrate strong zero-shot
performance on a wide range of held-out unseen tasks. We train $\text{Zemi}$
with a novel semi-parametric multitask prompted training paradigm, which shows
significant improvement compared with the parametric multitask training as
proposed by T0. Specifically, we augment the multitask training and zero-shot
evaluation with retrieval from a large-scale task-agnostic unlabeled corpus. In
order to incorporate multiple potentially noisy retrieved augmentations, we
further propose a novel $\text{augmentation fusion}$ module leveraging
perceiver resampler and gated cross-attention. Notably, our proposed
$\text{Zemi}_\text{LARGE}$ outperforms T0-3B by 16% on all seven evaluation
tasks while being 3.9x smaller in model size.

Most causal discovery procedures assume that there are no latent confounders
in the system, which is often violated in real-world problems. In this paper,
we consider a challenging scenario for causal structure identification, where
some variables are latent and they form a hierarchical graph structure to
generate the measured variables; the children of latent variables may still be
latent and only leaf nodes are measured, and moreover, there can be multiple
paths between every pair of variables (i.e., it is beyond tree structure). We
propose an estimation procedure that can efficiently locate latent variables,
determine their cardinalities, and identify the latent hierarchical structure,
by leveraging rank deficiency constraints over the measured variables. We show
that the proposed algorithm can find the correct Markov equivalence class of
the whole graph asymptotically under proper restrictions on the graph

There has been a rapid development and interest in adversarial training and
defenses in the machine learning community in the recent years. One line of
research focuses on improving the performance and efficiency of adversarial
robustness certificates for neural networks \cite{gowal:19, wong_zico:18,
raghunathan:18, WengTowardsFC:18, wong:scalable:18, singh:convex_barrier:19,
Huang_etal:19, single-neuron-relax:20, Zhang2020TowardsSA}. While each
providing a certification to lower (or upper) bound the true distortion under
adversarial attacks via relaxation, less studied was the tightness of
relaxation. In this paper, we analyze a family of linear outer approximation
based certificate methods via a meta algorithm, IBP-Lin. The aforementioned
works often lack quantitative analysis to answer questions such as how does the
performance of the certificate method depend on the network configuration and
the choice of approximation parameters. Under our framework, we make a first
attempt at answering these questions, which reveals that the tightness of
linear approximation based certification can depend heavily on the
configuration of the trained networks.

The NP-hard problem of optimizing a shallow ReLU network can be characterized
as a combinatorial search over each training example's activation pattern
followed by a constrained convex problem given a fixed set of activation
patterns. We explore the implications of this combinatorial aspect of ReLU
optimization in this work. We show that it can be naturally modeled via a
geometric and combinatoric object known as a zonotope with its vertex set
isomorphic to the set of feasible activation patterns. This assists in analysis
and provides a foundation for further research. We demonstrate its usefulness
when we explore the sensitivity of the optimal loss to perturbations of the
training data. Later we discuss methods of zonotope vertex selection and its
relevance to optimization. Overparameterization assists in training by making a
randomly chosen vertex more likely to contain a good solution. We then
introduce a novel polynomial-time vertex selection procedure that provably
picks a vertex containing the global optimum using only double the minimum
number of parameters required to fit the data. We further introduce a local
greedy search heuristic over zonotope vertices and demonstrate that it
outperforms gradient descent on underparameterized problems.

Internet of Things (IoT) is a distributed communication technology system
that offers the possibility for physical devices (e.g. vehicles home appliances
sensors actuators etc.) known as Things to connect and exchange data more
importantly without human interaction. Since IoT plays a significant role in
our daily lives we must secure the IoT environment to work effectively. Among
the various security requirements authentication to the IoT devices is
essential as it is the first step in preventing any negative impact of possible
attackers. Using the current IEEE 802.11 infrastructure this paper implements
an IoT devices authentication scheme based on something that is in the IoT
devices environment (i.e. ambient access points). Data from the broadcast
messages (i.e. beacon frame characteristics) are utilized to implement the
authentication factor that confirms proximity between two devices in an ad hoc
IoT network.

In this work, we present the winning solution for ORBIT Few-Shot Video Object
Recognition Challenge 2022. Built upon the ProtoNet baseline, the performance
of our method is improved with three effective techniques. These techniques
include the embedding adaptation, the uniform video clip sampler and the
invalid frame detection. In addition, we re-factor and re-implement the
official codebase to encourage modularity, compatibility and improved
performance. Our implementation accelerates the data loading in both training
and testing.

Conformal prediction is a distribution-free technique for establishing valid
prediction intervals. Although conventionally people conduct conformal
prediction in the output space, this is not the only possibility. In this
paper, we propose feature conformal prediction, which extends the scope of
conformal prediction to semantic feature spaces by leveraging the inductive
bias of deep representation learning. From a theoretical perspective, we
demonstrate that feature conformal prediction provably outperforms regular
conformal prediction under mild assumptions. Our approach could be combined
with not only vanilla conformal prediction, but also other adaptive conformal
prediction methods. Experiments on various predictive inference tasks
corroborate the efficacy of our method.

The smaller memory bandwidth in smart devices prompts development of smaller
Automatic Speech Recognition (ASR) models. To obtain a smaller model, one can
employ the model compression techniques. Knowledge distillation (KD) is a
popular model compression approach that has shown to achieve smaller model size
with relatively lesser degradation in the model performance. In this approach,
knowledge is distilled from a trained large size teacher model to a smaller
size student model. Also, the transducer based models have recently shown to
perform well for on-device streaming ASR task, while the conformer models are
efficient in handling long term dependencies. Hence in this work we employ a
streaming transducer architecture with conformer as the encoder. We propose a
multi-stage progressive approach to compress the conformer transducer model
using KD. We progressively update our teacher model with the distilled student
model in a multi-stage setup. On standard LibriSpeech dataset, our experimental
results have successfully achieved compression rates greater than 60% without
significant degradation in the performance compared to the larger teacher
model.

To analyze this characteristic of vulnerability, we developed an automated
deep learning method for detecting microvessels in intravascular optical
coherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from
85 lesions and 37 normal segments were analyzed. Manual annotation was done
using a dedicated software (OCTOPUS) previously developed by our group. Data
augmentation in the polar (r,{\theta}) domain was applied to raw IVOCT images
to ensure that microvessels appear at all possible angles. Pre-processing
methods included guidewire/shadow detection, lumen segmentation, pixel
shifting, and noise reduction. DeepLab v3+ was used to segment microvessel
candidates. A bounding box on each candidate was classified as either
microvessel or non-microvessel using a shallow convolutional neural network.
For better classification, we used data augmentation (i.e., angle rotation) on
bounding boxes with a microvessel during network training. Data augmentation
and pre-processing steps improved microvessel segmentation performance
significantly, yielding a method with Dice of 0.71+/-0.10 and pixel-wise
sensitivity/specificity of 87.7+/-6.6%/99.8+/-0.1%. The network for classifying
microvessels from candidates performed exceptionally well, with sensitivity of
99.5+/-0.3%, specificity of 98.8+/-1.0%, and accuracy of 99.1+/-0.5%. The
classification step eliminated the majority of residual false positives, and
the Dice coefficient increased from 0.71 to 0.73. In addition, our method
produced 698 image frames with microvessels present, compared to 730 from
manual analysis, representing a 4.4% difference. When compared to the manual
method, the automated method improved microvessel continuity, implying improved
segmentation performance. The method will be useful for research purposes as
well as potential future treatment planning.

Even pruned by the state-of-the-art network compression methods, Graph Neural
Networks (GNNs) training upon non-Euclidean graph data often encounters
relatively higher time costs, due to its irregular and nasty density
properties, compared with data in the regular Euclidean space. Another natural
property concomitantly with graph is class-imbalance which cannot be alleviated
by the massive graph data while hindering GNNs' generalization. To fully tackle
these unpleasant properties, (i) theoretically, we introduce a hypothesis about
what extent a subset of the training data can approximate the full dataset's
learning effectiveness. The effectiveness is further guaranteed and proved by
the gradients' distance between the subset and the full set; (ii) empirically,
we discover that during the learning process of a GNN, some samples in the
training dataset are informative for providing gradients to update model
parameters. Moreover, the informative subset is not fixed during training
process. Samples that are informative in the current training epoch may not be
so in the next one. We also notice that sparse subnets pruned from a
well-trained GNN sometimes forget the information provided by the informative
subset, reflected in their poor performances upon the subset. Based on these
findings, we develop a unified data-model dynamic sparsity framework named
Graph Decantation (GraphDec) to address challenges brought by training upon a
massive class-imbalanced graph data. The key idea of GraphDec is to identify
the informative subset dynamically during the training process by adopting
sparse graph contrastive learning. Extensive experiments on benchmark datasets
demonstrate that GraphDec outperforms baselines for graph and node tasks, with
respect to classification accuracy and data usage efficiency.

It is ten years since neural networks made their spectacular comeback.
Prompted by this anniversary, we take a holistic perspective on Artificial
Intelligence (AI). Supervised Learning for cognitive tasks is effectively
solved - provided we have enough high-quality labeled data. However, deep
neural network models are not easily interpretable, and thus the debate between
blackbox and whitebox modeling has come to the fore. The rise of attention
networks, self-supervised learning, generative modeling, and graph neural
networks has widened the application space of AI. Deep Learning has also
propelled the return of reinforcement learning as a core building block of
autonomous decision making systems. The possible harms made possible by new AI
technologies have raised socio-technical issues such as transparency, fairness,
and accountability. The dominance of AI by Big-Tech who control talent,
computing resources, and most importantly, data may lead to an extreme AI
divide. Failure to meet high expectations in high profile, and much heralded
flagship projects like self-driving vehicles could trigger another AI winter.

Recent years have witnessed a growing interest in using machine learning to
predict and identify phase transitions in various systems. Here we adopt
convolutional neural networks (CNNs) to study the phase transitions of Vicsek
model, solving the problem that traditional order parameters are insufficiently
able to do. Within the large-scale simulations, there are four phases, and we
confirm that all the phase transitions between two neighboring phases are
first-order. We have successfully classified the phase by using CNNs with a
high accuracy and identified the phase transition points, while traditional
approaches using various order parameters fail to obtain. These results
indicate that the great potential of machine learning approach in understanding
the complexities in collective behaviors, and in related complex systems in
general.

In nuclear reaction experiments, the measured decay energy spectra can give
insights into the shell structure of decaying systems. However, extracting the
underlying physics from the measurements is challenging due to detector
resolution and acceptance effects. The Richardson-Lucy (RL) algorithm, a
deblurring method that is commonly used in optics and has proven to be a
successful technique for restoring images, was applied to our experimental
nuclear physics data. The only inputs to the method are the observed energy
spectrum and the detector's response matrix also known as the transfer matrix.
We demonstrate that the technique can help access information about the shell
structure of particle-unbound systems from the measured decay energy spectrum
that isn't immediately accessible via traditional approaches such as chi-square
fitting. For a similar purpose, we developed a machine learning model that uses
a deep neural network (DNN) classifier to identify resonance states from the
measured decay energy spectrum. We tested the performance of both methods on
simulated data and experimental measurements. Then, we applied both algorithms
to the decay energy spectrum of $^{26}\mathrm{O} \rightarrow ^{24}\mathrm{O}$ +
n + n measured via invariant mass spectroscopy. The resonance states restored
using the RL algorithm to deblur the measured decay energy spectrum agree with
those found by the DNN classifier. Both deblurring and DNN approaches suggest
that the raw decay energy spectrum of $^{26}\mathrm{O}$ exhibits three peaks at
approximately 0.15~MeV, 1.50~MeV, and 5.00~MeV, with half-widths of 0.29~MeV,
0.80~MeV, and 1.85~MeV, respectively.

Developing deep generative models has been an emerging field due to the
ability to model and generate complex data for various purposes, such as image
synthesis and molecular design. However, the advancement of deep generative
models is limited by challenges to generate objects that possess multiple
desired properties: 1) the existence of complex correlation among real-world
properties is common but hard to identify; 2) controlling individual property
enforces an implicit partially control of its correlated properties, which is
difficult to model; 3) controlling multiple properties under various manners
simultaneously is hard and under-explored. We address these challenges by
proposing a novel deep generative framework that recovers semantics and the
correlation of properties through disentangled latent vectors. The correlation
is handled via an explainable mask pooling layer, and properties are precisely
retained by generated objects via the mutual dependence between latent vectors
and properties. Our generative model preserves properties of interest while
handling correlation and conflicts of properties under a multi-objective
optimization framework. The experiments demonstrate our model's superior
performance in generating data with desired properties.

A key challenge in off-road navigation is that even visually similar or
semantically identical terrain may have substantially different traction
properties. Existing work typically assumes a nominal or expected robot
dynamical model for planning, which can lead to degraded performance if the
assumed models are not realizable given the terrain properties. In contrast,
this work introduces a new probabilistic representation of traversability as a
distribution of parameters in the robot's dynamical model that are conditioned
on the terrain characteristics. This model is learned in a self-supervised
manner by fitting a probability distribution over the parameters identified
online, encoded as a neural network that takes terrain features as input. This
work then presents two risk-aware planning algorithms that leverage the learned
traversability model to plan risk-aware trajectories. Finally, a method for
detecting unfamiliar terrain with respect to the training data is introduced
based on a Gaussian Mixture Model fit to the latent space of the trained model.
Experiments demonstrate that the proposed approach outperforms existing work
that assumes nominal or expected robot dynamics in both success rate and
completion time for representative navigation tasks. Furthermore, when the
proposed approach is deployed in an unseen environment, excluding unfamiliar
terrains during planning leads to improved success rate.

Neural control of memory-constrained, agile robots requires small, yet highly
performant models. We leverage graph hyper networks to learn graph hyper
policies trained with off-policy reinforcement learning resulting in networks
that are two orders of magnitude smaller than commonly used networks yet encode
policies comparable to those encoded by much larger networks trained on the
same task. We show that our method can be appended to any off-policy
reinforcement learning algorithm, without any change in hyperparameters, by
showing results across locomotion and manipulation tasks. Further, we obtain an
array of working policies, with differing numbers of parameters, allowing us to
pick an optimal network for the memory constraints of a system. Training
multiple policies with our method is as sample efficient as training a single
policy. Finally, we provide a method to select the best architecture, given a
constraint on the number of parameters. Project website:
https://sites.google.com/usc.edu/graphhyperpolicy

We investigate the impact of the Covid-19 pandemic on the transition between
high school and college in Brazil. Using microdata from the universe of
students that applied to a selective university, we document how the Covid-19
shock increased enrollment for students in the top 10% high-quality public and
private high schools. This increase comes at the expense of graduates from
relatively lower-quality schools. Furthermore, this effect is entirely driven
by applicants who were at high school during the Covid pandemic. The effect is
large and completely offsets the gains in student background diversity achieved
by a bold quota policy implemented years before Covid. These results suggest
that not only students from underprivileged backgrounds endured larger negative
effects on learning during the pandemic, but they also experienced a stall in
their educational paths.

Class imbalance is a ubiquitous phenomenon occurring in real world data
distributions. To overcome its detrimental effect on training accurate
classifiers, existing work follows three major directions: class re-balancing,
information transfer, and representation learning. In this paper, we propose a
new and complementary direction for improving performance on long tailed
datasets - optimizing the backbone architecture through neural architecture
search (NAS). We find that an architecture's accuracy obtained on a balanced
dataset is not indicative of good performance on imbalanced ones. This poses
the need for a full NAS run on long tailed datasets which can quickly become
prohibitively compute intensive. To alleviate this compute burden, we aim to
efficiently adapt a NAS super-network from a balanced source dataset to an
imbalanced target one. Among several adaptation strategies, we find that the
most effective one is to retrain the linear classification head with reweighted
loss, while freezing the backbone NAS super-network trained on a balanced
source dataset. We perform extensive experiments on multiple datasets and
provide concrete insights to optimize architectures for long tailed datasets.

When humans socially interact with another agent (e.g., human, pet, or robot)
through touch, they do so by applying varying amounts of force with different
directions, locations, contact areas, and durations. While previous work on
touch gesture recognition has focused on the spatio-temporal distribution of
normal forces, we hypothesize that the addition of shear forces will permit
more reliable classification. We present a soft, flexible skin with an array of
tri-axial tactile sensors for the arm of a person or robot. We use it to
collect data on 13 touch gesture classes through user studies and train a
Convolutional Neural Network (CNN) to learn spatio-temporal features from the
recorded data. The network achieved a recognition accuracy of 74% with normal
and shear data, compared to 66% using only normal force data. Adding
distributed shear data improved classification accuracy for 11 out of 13 touch
gesture classes.

Most existing failure detection algorithms rely on statistical methods, and
very few use machine learning (ML). This paper explores the viability of ML in
the field of failure detection: is it possible to implement an ML-based
detector that achieves a satisfactory quality of service? We implement a
prototype that uses a basic long short-term memory neural network algorithm,
and study its behavior with real traces. Although ML model has comparatively
longer computing time, our prototype performs well in terms of accuracy and
detection time.

Temporal modeling is crucial for various video learning tasks. Most recent
approaches employ either factorized (2D+1D) or joint (3D) spatial-temporal
operations to extract temporal contexts from the input frames. While the former
is more efficient in computation, the latter often obtains better performance.
In this paper, we attribute this to a dilemma between the sufficiency and the
efficiency of interactions among various positions in different frames. These
interactions affect the extraction of task-relevant information shared among
frames. To resolve this issue, we prove that frame-by-frame alignments have the
potential to increase the mutual information between frame representations,
thereby including more task-relevant information to boost effectiveness. Then
we propose Alignment-guided Temporal Attention (ATA) to extend 1-dimensional
temporal attention with parameter-free patch-level alignments between
neighboring frames. It can act as a general plug-in for image backbones to
conduct the action recognition task without any model-specific design.
Extensive experiments on multiple benchmarks demonstrate the superiority and
generality of our module.

In this paper we motivate the causal mechanisms behind sample selection
induced collider bias (selection collider bias) that can cause Large Language
Models (LLMs) to learn unconditional dependence between entities that are
unconditionally independent in the real world. We show that selection collider
bias can become amplified in underspecified learning tasks, and although
difficult to overcome, we describe a method to exploit the resulting spurious
correlations for determination of when a model may be uncertain about its
prediction. We demonstrate an uncertainty metric that matches human uncertainty
in tasks with gender pronoun underspecification on an extended version of the
Winogender Schemas evaluation set, and we provide online demos where users can
evaluate spurious correlations and apply our uncertainty metric to their own
texts and models. Finally, we generalize our approach to address a wider range
of prediction tasks.

We introduce Midas, a robotics simulation framework based on the Incremental
Potential Contact (IPC) model. Our simulator guarantees intersection-free,
stable, and accurate resolution of frictional contact. We demonstrate the
efficacy of our framework with experimental validations on high-precision tasks
and through comparisons with Bullet physics. A reinforcement learning pipeline
using Midas is also developed and tested to perform intersection-free
peg-in-hole tasks.

In this paper, we provide an in-depth study of Stochastic Backpropagation
(SBP) when training deep neural networks for standard image classification and
object detection tasks. During backward propagation, SBP calculates the
gradients by only using a subset of feature maps to save the GPU memory and
computational cost. We interpret SBP as an efficient way to implement
stochastic gradient decent by performing backpropagation dropout, which leads
to considerable memory saving and training process speedup, with a minimal
impact on the overall model accuracy. We offer some good practices to apply SBP
in training image recognition models, which can be adopted in learning a wide
range of deep neural networks. Experiments on image classification and object
detection show that SBP can save up to 40% of GPU memory with less than 1%
accuracy degradation.

Person re-identification (Re-ID) has become increasingly important as it
supports a wide range of security applications. Traditional person Re-ID mainly
relies on optical camera-based systems, which incur several limitations due to
the changes in the appearance of people, occlusions, and human poses. In this
work, we propose a WiFi vision-based system, 3D-ID, for person Re-ID in 3D
space. Our system leverages the advances of WiFi and deep learning to help WiFi
devices see, identify, and recognize people. In particular, we leverage
multiple antennas on next-generation WiFi devices and 2D AoA estimation of the
signal reflections to enable WiFi to visualize a person in the physical
environment. We then leverage deep learning to digitize the visualization of
the person into 3D body representation and extract both the static body shape
and dynamic walking patterns for person Re-ID. Our evaluation results under
various indoor environments show that the 3D-ID system achieves an overall
rank-1 accuracy of 85.3%. Results also show that our system is resistant to
various attacks. The proposed 3D-ID is thus very promising as it could augment
or complement camera-based systems.

Numerically solving partial differential equations (PDEs) often entails
spatial and temporal discretizations. Traditional methods (e.g., finite
difference, finite element, smoothed-particle hydrodynamics) frequently adopt
explicit spatial discretizations, such as grids, meshes, and point clouds,
where each degree-of-freedom corresponds to a location in space. While these
explicit spatial correspondences are intuitive to model and understand, these
representations are not necessarily optimal for accuracy, memory-usage, or
adaptivity. In this work, we explore implicit neural representation as an
alternative spatial discretization, where spatial information is implicitly
stored in the neural network weights. With implicit neural spatial
representation, PDE-constrained time-stepping translates into updating neural
network weights, which naturally integrates with commonly adopted optimization
time integrators. We validate our approach on a variety of classic PDEs with
examples involving large elastic deformations, turbulent fluids, and multiscale
phenomena. While slower to compute than traditional representations, our
approach exhibits higher accuracy, lower memory consumption, and dynamically
adaptive allocation of degrees of freedom without complex remeshing.

Knowledge graphs represent factual knowledge about the world as relationships
between concepts and are critical for intelligent decision making in enterprise
applications. New knowledge is inferred from the existing facts in the
knowledge graphs by encoding the concepts and relations into low-dimensional
feature vector representations. The most effective representations for this
task, called Knowledge Graph Embeddings (KGE), are learned through neural
network architectures. Due to their impressive predictive performance, they are
increasingly used in high-impact domains like healthcare, finance and
education. However, are the black-box KGE models adversarially robust for use
in domains with high stakes? This thesis argues that state-of-the-art KGE
models are vulnerable to data poisoning attacks, that is, their predictive
performance can be degraded by systematically crafted perturbations to the
training knowledge graph. To support this argument, two novel data poisoning
attacks are proposed that craft input deletions or additions at training time
to subvert the learned model's performance at inference time. These adversarial
attacks target the task of predicting the missing facts in knowledge graphs
using KGE models, and the evaluation shows that the simpler attacks are
competitive with or outperform the computationally expensive ones. The thesis
contributions not only highlight and provide an opportunity to fix the security
vulnerabilities of KGE models, but also help to understand the black-box
predictive behaviour of KGE models.

Learning representations in the joint domain of vision and touch can improve
manipulation dexterity, robustness, and sample-complexity by exploiting mutual
information and complementary cues. Here, we present Visuo-Tactile Transformers
(VTTs), a novel multimodal representation learning approach suited for
model-based reinforcement learning and planning. Our approach extends the
Visual Transformer \cite{dosovitskiy2021image} to handle visuo-tactile
feedback. Specifically, VTT uses tactile feedback together with self and
cross-modal attention to build latent heatmap representations that focus
attention on important task features in the visual domain. We demonstrate the
efficacy of VTT for representation learning with a comparative evaluation
against baselines on four simulated robot tasks and one real world block
pushing task. We conduct an ablation study over the components of VTT to
highlight the importance of cross-modality in representation learning.

Neural Motion Planners (NMPs) have emerged as a promising tool for solving
robot navigation tasks in complex environments. However, these methods often
require expert data for learning, which limits their application to scenarios
where data generation is time-consuming. Recent developments have also led to
physics-informed deep neural models capable of representing complex dynamical
Partial Differential Equations (PDEs). Inspired by these developments, we
propose Neural Time Fields (NTFields) for robot motion planning in cluttered
scenarios. Our framework represents a wave propagation model generating
continuous arrival time to find path solutions informed by a nonlinear
first-order PDE called Eikonal Equation. We evaluate our method in various
cluttered 3D environments, including the Gibson dataset, and demonstrate its
ability to solve motion planning problems for 4-DOF and 6-DOF robot
manipulators where the traditional grid-based Eikonal planners often face the
curse of dimensionality. Furthermore, the results show that our method exhibits
high success rates and significantly lower computational times than the
state-of-the-art methods, including NMPs that require training data from
classical planners.

We present a method to remove unknown convolutive noise introduced to speech
by reverberations of recording environments, utilizing some amount of training
speech data from the reverberant environment, and any available non-reverberant
speech data. Using Fourier transform computed over long temporal windows, which
ideally cover the entire room impulse response, we convert room induced
convolution to additions in the log spectral domain. Next, we compute a
spectral normalization vector from statistics gathered over reverberated as
well as over clean speech in the log spectral domain. During operation, this
normalization vectors are used to alleviate reverberations from complex speech
spectra recorded under the same reverberant conditions . Such dereverberated
complex speech spectra are used to compute complex FDLP-spectrograms for use in
automatic speech recognition.

Predicting the responses of a cell under perturbations may bring important
benefits to drug discovery and personalized therapeutics. In this work, we
propose a novel graph variational Bayesian causal inference framework to
predict a cell's gene expressions under counterfactual perturbations
(perturbations that this cell did not factually receive), leveraging
information representing biological knowledge in the form of gene regulatory
networks (GRNs) to aid individualized cellular response predictions. Aiming at
a data-adaptive GRN, we also developed an adjacency matrix updating technique
for graph convolutional networks and used it to refine GRNs during
pre-training, which generated more insights on gene relations and enhanced
model performance. Additionally, we propose a robust estimator within our
framework for the asymptotically efficient estimation of marginal perturbation
effect, which is yet to be carried out in previous works. With extensive
experiments, we exhibited the advantage of our approach over state-of-the-art
deep learning models for individual response prediction.

A foundational set of findable, accessible, interoperable, and reusable
(FAIR) principles were proposed in 2016 as prerequisites for proper data
management and stewardship, with the goal of enabling the reusability of
scholarly data. The principles were also meant to apply to other digital
assets, at a high level, and over time, the FAIR guiding principles have been
re-interpreted or extended to include the software, tools, algorithms, and
workflows that produce data. FAIR principles are now being adapted in the
context of AI models and datasets. Here, we present the perspectives, vision,
and experiences of researchers from different countries, disciplines, and
backgrounds who are leading the definition and adoption of FAIR principles in
their communities of practice, and discuss outcomes that may result from
pursuing and incentivizing FAIR AI research. The material for this report
builds on the FAIR for AI Workshop held at Argonne National Laboratory on June
7, 2022.

Early backdoor attacks against machine learning set off an arms race in
attack and defence development. Defences have since appeared demonstrating some
ability to detect backdoors in models or even remove them. These defences work
by inspecting the training data, the model, or the integrity of the training
procedure. In this work, we show that backdoors can be added during
compilation, circumventing any safeguards in the data preparation and model
training stages. As an illustration, the attacker can insert weight-based
backdoors during the hardware compilation step that will not be detected by any
training or data-preparation process. Next, we demonstrate that some backdoors,
such as ImpNet, can only be reliably detected at the stage where they are
inserted and removing them anywhere else presents a significant challenge. We
conclude that machine-learning model security requires assurance of provenance
along the entire technical pipeline, including the data, model architecture,
compiler, and hardware specification.

Despite the widespread use of unsupervised models, very few methods are
designed to explain them. Most explanation methods explain a scalar model
output. However, unsupervised models output representation vectors, the
elements of which are not good candidates to explain because they lack semantic
meaning. To bridge this gap, recent works defined a scalar explanation output:
a dot product-based similarity in the representation space to the sample being
explained (i.e., an explicand). Although this enabled explanations of
unsupervised models, the interpretation of this approach can still be opaque
because similarity to the explicand's representation may not be meaningful to
humans. To address this, we propose contrastive corpus similarity, a novel and
semantically meaningful scalar explanation output based on a reference corpus
and a contrasting foil set of samples. We demonstrate that contrastive corpus
similarity is compatible with many post-hoc feature attribution methods to
generate COntrastive COrpus Attributions (COCOA) and quantitatively verify that
features important to the corpus are identified. We showcase the utility of
COCOA in two ways: (i) we draw insights by explaining augmentations of the same
image in a contrastive learning setting (SimCLR); and (ii) we perform zero-shot
object localization by explaining the similarity of image representations to
jointly learned text representations (CLIP).

Training graph neural networks (GNNs) on large graphs is complex and
extremely time consuming. This is attributed to overheads caused by sparse
matrix multiplication, which are sidestepped when training multi-layer
perceptrons (MLPs) with only node features. MLPs, by ignoring graph context,
are simple and faster for graph data, however they usually sacrifice prediction
accuracy, limiting their applications for graph data. We observe that for most
message passing-based GNNs, we can trivially derive an analog MLP (we call this
a PeerMLP) whose weights can be made identical, making us curious about how do
GNNs using weights from a fully trained PeerMLP perform? Surprisingly, we find
that GNNs initialized with such weights significantly outperform their PeerMLPs
for graph data, motivating us to use PeerMLP training as a precursor,
initialization step to GNN training. To this end, we propose an embarrassingly
simple, yet hugely effective initialization method for GNN training
acceleration, called MLPInit. Our extensive experiments on multiple large-scale
graph datasets with diverse GNN architectures validate that MLPInit can
accelerate the training of GNNs (up to 33X speedup on OGB-products) and often
improve prediction performance (e.g., up to 7.97% improvement for GraphSAGE
across 7 datasets for node classification, and up to 17.81% improvement across
4 datasets for link prediction on metric Hits@10). Most importantly, MLPInit is
extremely simple to implement and can be flexibly used as a plug-and-play
initialization method for message passing-based GNNs.

We present a framework for airfoil shape optimization to reduce the trailing
edge noise for the design of wind turbine blades. Far-field noise is evaluated
using Amiet's theory coupled with the TNO-Blake model to calculate the wall
pressure spectrum and fast turn-around XFOIL simulations to evaluate the
boundary layer parameters. The computational framework is first validated using
a NACA0012 airfoil at zero angle of attack. Particle swarm optimization is used
to find the optimized airfoil configuration. The multi-objective optimization
minimizes the A-weighted overall sound pressure level at various angles of
attack, while ensuring enough lift and minimum drag. We compare classic
parametrization methods to define the airfoil geometry (i.e., CST) to a machine
learning method (i.e., a variational autoencoder). We observe that variational
autoencoders can represent a wide variety of geometries, with only four encoded
variables, leading to efficient optimizations, which result in improved optimal
shapes. When compared to the baseline geometry, a NACA0012, the
autoencoder-based optimized airfoil reduces by 3% (1.75 dBA) the overall sound
pressure level (with decreased noise across the entire frequency range), while
maintaining favorable aerodynamic properties in terms of lift and drag.

In this paper, we introduce an approach for detecting modifications in
assembled printed circuit boards based on photographs taken without tight
control over perspective and illumination conditions. One instance of this
problem is the visual inspection of gas pumps PCBs, which can be modified by
fraudsters wishing to deceive costumers or evade taxes. Given the uncontrolled
environment and the huge number of possible modifications, we address the
problem as a case of anomaly detection, proposing an approach that is directed
towards the characteristics of that scenario, while being well-suited for other
similar applications. The proposed approach employs a deep convolutional
autoencoder trained to reconstruct images of an unmodified board, but which
remains unable to do the same for images showing modifications. By comparing
the input image with its reconstruction, it is possible to segment anomalies
and modifications in a pixel-wise manner. Experiments performed on a dataset
built to represent real-world situations (and which we will make publicly
available) show that our approach outperforms other state-of-the-art approaches
for anomaly segmentation in the considered scenario, while producing comparable
results on the popular MVTec-AD dataset for a more general object anomaly

Modern systems are designed to operate in increasingly variable and uncertain
environments. Not only are these environments complex, in the sense that they
contain a tremendous number of variables, but they also change over time.
Systems must be able to adjust their behaviour at run-time to manage these
uncertainties. These self-adaptive systems have been studied extensively. This
paper proposes a definition of a safety-critical self-adaptive system and then
describes a taxonomy for classifying adaptations into different types based on
their impact on the system's safety and the system's safety case. The taxonomy
expresses criteria for classification and then describes specific criteria that
the safety case for a self-adaptive system must satisfy, depending on the type
of adaptations performed. Each type in the taxonomy is illustrated using the
example of a safety-critical self-adaptive water heating system.

We introduce adaptive weight decay, which automatically tunes the
hyper-parameter for weight decay during each training iteration. For
classification problems, we propose changing the value of the weight decay
hyper-parameter on the fly based on the strength of updates from the
classification loss (i.e., gradient of cross-entropy), and the regularization
loss (i.e., $\ell_2$-norm of the weights). We show that this simple
modification can result in large improvements in adversarial robustness -- an
area which suffers from robust overfitting -- without requiring extra data.
Specifically, our reformulation results in 20% relative robustness improvement
for CIFAR-100, and 10% relative robustness improvement on CIFAR-10 comparing to
traditional weight decay. In addition, this method has other desirable
properties, such as less sensitivity to learning rate, and smaller weight
norms, which the latter contributes to robustness to overfitting to label
noise, and pruning.

Dynamic adaptation has become an essential technique in accelerating
distributed machine learning (ML) training. Recent studies have shown that
dynamically adjusting model structure (e.g., lottery ticket hypothesis) or
hyperparameters (e.g., batch size) can significantly accelerate training
without sacrificing accuracy. However, existing ML cluster schedulers are not
designed to handle dynamic adaptation. We show that existing schemes fail to
provide fairness and degrade system efficiency when the training throughput
changes over time under dynamic adaptation. We design Shockwave, a scheduler
with future planning that builds on two key ideas. First, Shockwave extends
classic market theory from static settings to dynamic settings to co-optimize
efficiency and fairness. Second, Shockwave utilizes stochastic dynamic
programming to handle dynamic changes. We build a system for Shockwave and
validate its performance with both trace-driven simulation and cluster
experiments. Results show that for traces of ML jobs with dynamic adaptation,
Shockwave improves makespan by 1.3X and fairness by 2X when compared with
existing fair scheduling schemes.

Dual encoding models that encode a pair of inputs are widely used for
representation learning. Many approaches train dual encoding models by
maximizing agreement between pairs of encodings on centralized training data.
However, in many scenarios, datasets are inherently decentralized across many
clients (user devices or organizations) due to privacy concerns, motivating
federated learning. In this work, we focus on federated training of dual
encoding models on decentralized data composed of many small, non-IID
(independent and identically distributed) client datasets. We show that
existing approaches that work well in centralized settings perform poorly when
naively adapted to this setting using federated averaging. We observe that, we
can simulate large-batch loss computation on individual clients for loss
functions that are based on encoding statistics. Based on this insight, we
propose a novel federated training approach, Distributed Cross Correlation
Optimization (DCCO), which trains dual encoding models using encoding
statistics aggregated across clients, without sharing individual data samples.
Our experimental results on two datasets demonstrate that the proposed DCCO
approach outperforms federated variants of existing approaches by a large
margin.

Modern data science applications often involve complex relational data with
dynamic structures. An abrupt change in such dynamic relational data is
typically observed in systems that undergo regime changes due to interventions.
In such a case, we consider a factorized fusion shrinkage model in which all
decomposed factors are dynamically shrunk towards group-wise fusion structures,
where the shrinkage is obtained by applying global-local shrinkage priors to
the successive differences of the row vectors of the factorized matrices. The
proposed priors enjoy many favorable properties in comparison and clustering of
the estimated dynamic latent factors. Comparing estimated latent factors
involves both adjacent and long-term comparisons, with the time range of
comparison considered as a variable. Under certain conditions, we demonstrate
that the posterior distribution attains the minimax optimal rate up to
logarithmic factors. In terms of computation, we present a structured
mean-field variational inference framework that balances optimal posterior
inference with computational scalability, exploiting both the dependence among
components and across time. The framework can accommodate a wide variety of
models, including dynamic matrix factorization, latent space models for
networks and low-rank tensors. The effectiveness of our methodology is
demonstrated through extensive simulations and real-world data analysis.

Incorporating the Hamiltonian structure of physical dynamics into deep
learning models provides a powerful way to improve the interpretability and
prediction accuracy. While previous works are mostly limited to the Euclidean
spaces, their extension to the Lie group manifold is needed when rotations form
a key component of the dynamics, such as the higher-order physics beyond simple
point-mass dynamics for N-body celestial interactions. Moreover, the multiscale
nature of these processes presents a challenge to existing methods as a long
time horizon is required. By leveraging a symplectic Lie-group manifold
preserving integrator, we present a method for data-driven discovery of
non-Newtonian astronomy. Preliminary results show the importance of both these
properties in training stability and prediction accuracy.

Numerous real-world problems from a diverse set of application areas exist
that exhibit temporal dependencies. We focus on a specific type of time series
classification which we refer to as aggregated time series classification. We
consider an aggregated sequence of a multi-variate time series, and propose a
methodology to make predictions based solely on the aggregated information. As
a case study, we apply our methodology to the challenging problem of household
water end-use dissagregation when using non-intrusive water monitoring. Our
methodology does not require a-priori identification of events, and to our
knowledge, it is considered for the first time. We conduct an extensive
experimental study using a residential water-use simulator, involving different
machine learning classifiers, multi-label classification methods, and
successfully demonstrate the effectiveness of our methodology.

We consider statistical learning question for $\psi$-weakly dependent
processes, that unifies a large class of weak dependence conditions such as
mixing, association,$\cdots$ The consistency of the empirical risk minimization
algorithm is established. We derive the generalization bounds and provide the
learning rate, which, on some H{\"o}lder class of hypothesis, is close to the
usual $O(n^{-1/2})$ obtained in the {\it i.i.d.} case. Application to time
series prediction is carried out with an example of causal models with
exogenous covariates.

Prevailing deep graph learning models often suffer from label sparsity issue.
Although many graph few-shot learning (GFL) methods have been developed to
avoid performance degradation in face of limited annotated data, they
excessively rely on labeled data, where the distribution shift in the test
phase might result in impaired generalization ability. Additionally, they lack
a general purpose as their designs are coupled with task or data-specific
characteristics. To this end, we propose a general and effective Contrastive
Graph Few-shot Learning framework (CGFL). CGFL leverages a self-distilled
contrastive learning procedure to boost GFL. Specifically, our model firstly
pre-trains a graph encoder with contrastive learning using unlabeled data.
Later, the trained encoder is frozen as a teacher model to distill a student
model with a contrastive loss. The distilled model is finally fed to GFL. CGFL
learns data representation in a self-supervised manner, thus mitigating the
distribution shift impact for better generalization and making model task and
data-independent for a general graph mining purpose. Furthermore, we introduce
an information-based method to quantitatively measure the capability of CGFL.
Comprehensive experiments demonstrate that CGFL outperforms state-of-the-art
baselines on several graph mining tasks in the few-shot scenario. We also
provide quantitative measurement of CGFL's success.

Consider the problem of estimating the causal effect of some attribute of a
text document; for example: what effect does writing a polite vs. rude email
have on response time? To estimate a causal effect from observational data, we
need to adjust for confounding aspects of the text that affect both the
treatment and outcome -- e.g., the topic or writing level of the text. These
confounding aspects are unknown a priori, so it seems natural to adjust for the
entirety of the text (e.g., using a transformer). However, causal
identification and estimation procedures rely on the assumption of overlap: for
all levels of the adjustment variables, there is randomness leftover so that
every unit could have (not) received treatment. Since the treatment here is
itself an attribute of the text, it is perfectly determined, and overlap is
apparently violated. The purpose of this paper is to show how to handle causal
identification and obtain robust causal estimation in the presence of apparent
overlap violations. In brief, the idea is to use supervised representation
learning to produce a data representation that preserves confounding
information while eliminating information that is only predictive of the
treatment. This representation then suffices for adjustment and can satisfy
overlap. Adapting results on non-parametric estimation, we find that this
procedure is robust to conditional outcome misestimation, yielding a low-bias
estimator with valid uncertainty quantification under weak conditions.
Empirical results show strong improvements in bias and uncertainty
quantification relative to the natural baseline.

Conformer, combining convolution and self-attention sequentially to capture
both local and global information, has shown remarkable performance and is
currently regarded as the state-of-the-art for automatic speech recognition
(ASR). Several other studies have explored integrating convolution and
self-attention but they have not managed to match Conformer's performance. The
recently introduced Branchformer achieves comparable performance to Conformer
by using dedicated branches of convolution and self-attention and merging local
and global context from each branch. In this paper, we propose E-Branchformer,
which enhances Branchformer by applying an effective merging method and
stacking additional point-wise modules. E-Branchformer sets new
state-of-the-art word error rates (WERs) 1.81% and 3.65% on LibriSpeech
test-clean and test-other sets without using any external training data.

Industry 4.0 targets the conversion of the traditional industries into
intelligent ones through technological revolution. This revolution is only
possible through innovation, optimization, interconnection, and rapid
decision-making capability. Numerical models are believed to be the key
components of Industry 4.0, facilitating quick decision-making through
simulations instead of costly experiments. However, numerical investigation of
precise, high-fidelity models for optimization or decision-making is usually
time-consuming and computationally expensive. In such instances, data-driven
surrogate models are excellent substitutes for fast computational analysis and
the probabilistic prediction of the output parameter for new input parameters.
The emergence of Internet of Things (IoT) and Machine Learning (ML) has made
the concept of surrogate modeling even more viable. However, these surrogate
models contain intrinsic uncertainties, originate from modeling defects, or
both. These uncertainties, if not quantified and minimized, can produce a
skewed result. Therefore, proper implementation of uncertainty quantification
techniques is crucial during optimization, cost reduction, or safety
enhancement processes analysis. This chapter begins with a brief overview of
the concept of surrogate modeling, transfer learning, IoT and digital twins.
After that, a detailed overview of uncertainties, uncertainty quantification
frameworks, and specifics of uncertainty quantification methodologies for a
surrogate model linked to a digital twin is presented. Finally, the use of
uncertainty quantification approaches in the nuclear industry has been
addressed.

Surrogate modeling has brought about a revolution in computation in the
branches of science and engineering. Backed by Artificial Intelligence, a
surrogate model can present highly accurate results with a significant
reduction in computation time than computer simulation of actual models.
Surrogate modeling techniques have found their use in numerous branches of
science and engineering, energy system modeling being one of them. Since the
idea of hybrid and sustainable energy systems is spreading rapidly in the
modern world for the paradigm of the smart energy shift, researchers are
exploring the future application of artificial intelligence-based surrogate
modeling in analyzing and optimizing hybrid energy systems. One of the
promising technologies for assessing applicability for the energy system is the
digital twin, which can leverage surrogate modeling. This work presents a
comprehensive framework/review on Artificial Intelligence-driven surrogate
modeling and its applications with a focus on the digital twin framework and
energy systems. The role of machine learning and artificial intelligence in
constructing an effective surrogate model is explained. After that, different
surrogate models developed for different sustainable energy sources are
presented. Finally, digital twin surrogate models and associated uncertainties
are described.

To deploy deep learning algorithms on resource-limited scenarios, an emerging
device-resistive random access memory (ReRAM) has been regarded as promising
via analog computing. However, the practicability of ReRAM is primarily limited
due to the weight drifting of ReRAM neural networks due to multi-factor
reasons, including manufacturing, thermal noises, and etc. In this paper, we
propose a novel Bayesian optimization method for fault tolerant neural network
architecture (BayesFT). For neural architecture search space design, instead of
conducting neural architecture search on the whole feasible neural architecture
search space, we first systematically explore the weight drifting tolerance of
different neural network components, such as dropout, normalization, number of
layers, and activation functions in which dropout is found to be able to
improve the neural network robustness to weight drifting. Based on our
analysis, we propose an efficient search space by only searching for dropout
rates for each layer. Then, we use Bayesian optimization to search for the
optimal neural architecture robust to weight drifting. Empirical experiments
demonstrate that our algorithmic framework has outperformed the
state-of-the-art methods by up to 10 times on various tasks, such as image
classification and object detection.

The manifold hypothesis, which assumes that data lie on or close to an
unknown manifold of low intrinsic dimensionality, is a staple of modern machine
learning research. However, recent work has shown that real-world data exhibit
distinct non-manifold structures, which result in singularities that can lead
to erroneous conclusions about the data. Detecting such singularities is
therefore crucial as a precursor to interpolation and inference tasks. We
address detecting singularities by developing (i) persistent local homology, a
new topology-driven framework for quantifying the intrinsic dimension of a data
set locally, and (ii) Euclidicity, a topology-based multi-scale measure for
assessing the 'manifoldness' of individual points. We show that our approach
can reliably identify singularities of complex spaces, while also capturing
singular structures in real-world data sets.

This paper addresses the problem of reliably and efficiently solving broad
classes of long-horizon stochastic path planning problems. Starting with a
vanilla RL formulation with a stochastic dynamics simulator and an occupancy
matrix of the environment, our approach computes useful options with policies
as well as high-level paths that compose the discovered options.
  Our main contributions are (1) data-driven methods for creating abstract
states that serve as endpoints for helpful options, (2) methods for computing
option policies using auto-generated option guides in the form of dense
pseudo-reward functions, and (3) an overarching algorithm for composing the
computed options. We show that this approach yields strong guarantees of
executability and solvability: under fairly general conditions, the computed
option guides lead to composable option policies and consequently ensure
downward refinability. Empirical evaluation on a range of robots, environments,
and tasks shows that this approach effectively transfers knowledge across
related tasks and that it outperforms existing approaches by a significant
margin.

Recent work has shown that augmenting environments with language descriptions
improves policy learning. However, for environments with complex language
abstractions, learning how to ground language to observations is difficult due
to sparse, delayed rewards. We propose Language Dynamics Distillation (LDD),
which pretrains a model to predict environment dynamics given demonstrations
with language descriptions, and then fine-tunes these language-aware pretrained
representations via reinforcement learning (RL). In this way, the model is
trained to both maximize expected reward and retain knowledge about how
language relates to environment dynamics. On SILG, a benchmark of five tasks
with language descriptions that evaluate distinct generalization challenges on
unseen environments (NetHack, ALFWorld, RTFM, Messenger, and Touchdown), LDD
outperforms tabula-rasa RL, VAE pretraining, and methods that learn from
unlabeled demonstrations in inverse RL and reward shaping with pretrained
experts. In our analyses, we show that language descriptions in demonstrations
improve sample-efficiency and generalization across environments, and that
dynamics modelling with expert demonstrations is more effective than with
non-experts.

This paper presents a methodology for combining programming and mathematics
to optimize elevator wait times. Based on simulated user data generated
according to the canonical three-peak model of elevator traffic, we first
develop a naive model from an intuitive understanding of the logic behind
elevators. We take into consideration a general array of features including
capacity, acceleration, and maximum wait time thresholds to adequately model
realistic circumstances. Using the same evaluation framework, we proceed to
develop a Deep Q Learning model in an attempt to match the hard-coded naive
approach for elevator control. Throughout the majority of the paper, we work
under a Markov Decision Process (MDP) schema, but later explore how the
assumption fails to characterize the highly stochastic overall Elevator Group
Control System (EGCS).

Evaluating clustering quality with reliable evaluation metrics like
normalized mutual information (NMI) requires labeled data that can be expensive
to annotate. We focus on the underexplored problem of estimating clustering
quality with limited labels. We adapt existing approaches from the few-sample
model evaluation literature to actively sub-sample, with a learned surrogate
model, the most informative data points for annotation to estimate the
evaluation metric. However, we find that their estimation can be biased and
only relies on the labeled data. To that end, we introduce CEREAL, a
comprehensive framework for few-sample clustering evaluation that extends
active sampling approaches in three key ways. First, we propose novel NMI-based
acquisition functions that account for the distinctive properties of clustering
and uncertainties from a learned surrogate model. Next, we use ideas from
semi-supervised learning and train the surrogate model with both the labeled
and unlabeled data. Finally, we pseudo-label the unlabeled data with the
surrogate model. We run experiments to estimate NMI in an active sampling
pipeline on three datasets across vision and language. Our results show that
CEREAL reduces the area under the absolute error curve by up to 57% compared to
the best sampling baseline. We perform an extensive ablation study to show that
our framework is agnostic to the choice of clustering algorithm and evaluation
metric. We also extend CEREAL from clusterwise annotations to pairwise
annotations. Overall, CEREAL can efficiently evaluate clustering with limited
human annotations.

Question answering over knowledge bases (KBs) aims to answer natural language
questions with factual information such as entities and relations in KBs.
Previous methods either generate logical forms that can be executed over KBs to
obtain final answers or predict answers directly. Empirical results show that
the former often produces more accurate answers, but it suffers from
non-execution issues due to potential syntactic and semantic errors in the
generated logical forms. In this work, we propose a novel framework DecAF that
jointly generates both logical forms and direct answers, and then combines the
merits of them to get the final answers. Moreover, different from most of the
previous methods, DecAF is based on simple free-text retrieval without relying
on any entity linking tools -- this simplification eases its adaptation to
different datasets. DecAF achieves new state-of-the-art accuracy on WebQSP,
FreebaseQA, and GrailQA benchmarks, while getting competitive results on the
ComplexWebQuestions benchmark.

Model ensembles have long been used in machine learning to reduce the
variance in individual model predictions, making them more robust to input
perturbations. Pseudo-ensemble methods like dropout have also been commonly
used in deep learning models to improve generalization. However, the
application of these techniques to improve neural networks' robustness against
input perturbations remains underexplored. We introduce Kernel Average Pool
(KAP), a new neural network building block that applies the mean filter along
the kernel dimension of the layer activation tensor. We show that ensembles of
kernels with similar functionality naturally emerge in convolutional neural
networks equipped with KAP and trained with backpropagation. Moreover, we show
that when combined with activation noise, KAP models are remarkably robust
against various forms of adversarial attacks. Empirical evaluations on CIFAR10,
CIFAR100, TinyImagenet, and Imagenet datasets show substantial improvements in
robustness against strong adversarial attacks such as AutoAttack that are on
par with adversarially trained networks but are importantly obtained without
training on any adversarial examples.

Smart energy performance monitoring and optimisation at the supplier and
consumer levels is essential to realising smart cities. In order to implement a
more sustainable energy management plan, it is crucial to conduct a better
energy forecast. The next-generation smart meters can also be used to measure,
record, and report energy consumption data, which can be used to train machine
learning (ML) models for predicting energy needs. However, sharing fine-grained
energy data and performing centralised learning may compromise users' privacy
and leave them vulnerable to several attacks. This study addresses this issue
by utilising federated learning (FL), an emerging technique that performs ML
model training at the user level, where data resides. We introduce FedTrees, a
new, lightweight FL framework that benefits from the outstanding features of
ensemble learning. Furthermore, we developed a delta-based early stopping
algorithm to monitor FL training and stop it when it does not need to continue.
The simulation results demonstrate that FedTrees outperforms the most popular
federated averaging (FedAvg) framework and the baseline Persistence model for
providing accurate energy forecasting patterns while taking only 2% of the
computation time and 13% of the communication rounds compared to FedAvg, saving
considerable amounts of computation and communication resources.

A fundamental challenge of over-parameterized deep learning models is
learning meaningful data representations that yield good performance on a
downstream task without over-fitting spurious input features. This work
proposes MaskTune, a masking strategy that prevents over-reliance on spurious
(or a limited number of) features. MaskTune forces the trained model to explore
new features during a single epoch finetuning by masking previously discovered
features. MaskTune, unlike earlier approaches for mitigating shortcut learning,
does not require any supervision, such as annotating spurious features or
labels for subgroup samples in a dataset. Our empirical results on biased
MNIST, CelebA, Waterbirds, and ImagenNet-9L datasets show that MaskTune is
effective on tasks that often suffer from the existence of spurious
correlations. Finally, we show that MaskTune outperforms or achieves similar
performance to the competing methods when applied to the selective
classification (classification with rejection option) task. Code for MaskTune
is available at https://github.com/aliasgharkhani/Masktune.

Normalization is an important but understudied challenge in privacy-related
application domains such as federated learning (FL) and differential privacy
(DP). While the unsuitability of batch normalization for FL and DP has already
been shown, the impact of the other normalization methods on the performance of
federated or differentially private models is not well-known. To address this,
we draw a performance comparison among layer normalization (LayerNorm), group
normalization (GroupNorm), and the recently proposed kernel normalization
(KernelNorm) in FL and DP settings. Our results indicate LayerNorm and
GroupNorm provide no performance gain compared to the baseline (i.e. no
normalization) for shallow models, but they considerably enhance performance of
deeper models. KernelNorm, on the other hand, significantly outperforms its
competitors in terms of accuracy and convergence rate (or communication
efficiency) for both shallow and deeper models. Given these key observations,
we propose a kernel normalized ResNet architecture called KNResNet-13 for
differentially private learning environments. Using the proposed architecture,
we provide new state-of-the-art accuracy values on the CIFAR-10 and Imagenette
datasets.

Robotic manipulation can benefit from wrist-mounted force/torque (F/T)
sensors, but conventional F/T sensors can be expensive, difficult to install,
and damaged by high loads. We present Visual Force/Torque Sensing (VFTS), a
method that visually estimates the 6-axis F/T measurement that would be
reported by a conventional F/T sensor. In contrast to approaches that sense
loads using internal cameras placed behind soft exterior surfaces, our approach
uses an external camera with a fisheye lens that observes a soft gripper. VFTS
includes a deep learning model that takes a single RGB image as input and
outputs a 6-axis F/T estimate. We trained the model with sensor data collected
while teleoperating a robot (Stretch RE1 from Hello Robot Inc.) to perform
manipulation tasks. VFTS outperformed F/T estimates based on motor currents,
generalized to a novel home environment, and supported three autonomous tasks
relevant to healthcare: grasping a blanket, pulling a blanket over a manikin,
and cleaning a manikin's limbs. VFTS also performed well with a manually
operated pneumatic gripper. Overall, our results suggest that an external
camera observing a soft gripper can perform useful visual force/torque sensing
for a variety of manipulation tasks.

Continual learning aims to train a model incrementally on a sequence of tasks
without forgetting previous knowledge. Although continual learning has been
widely studied in computer vision, its application to Vision+Language tasks is
not that straightforward, as settings can be parameterized in multiple ways
according to their input modalities. In this paper, we present a detailed study
of how different settings affect performance for Visual Question Answering. We
first propose three plausible task formulations and demonstrate their impact on
the performance of continual learning algorithms. We break down several factors
of task similarity, showing that performance and sensitivity to task order
highly depend on the shift of the output distribution. We also investigate the
potential of pretrained models and compare the robustness of transformer models
with different visual embeddings. Finally, we provide an analysis interpreting
model representations and their impact on forgetting. Our results highlight the
importance of stabilizing visual representations in deeper layers.

Federated Learning (FL) is a machine learning technique that addresses the
privacy challenges in terms of access rights of local datasets by enabling the
training of a model across nodes holding their data samples locally. To achieve
decentralized federated learning, blockchain-based FL was proposed as a
distributed FL architecture. In decentralized FL, the chief is eliminated from
the learning process as workers collaborate between each other to train the
global model. Decentralized FL applications need to account for the additional
delay incurred by blockchain-based FL deployments. Particularly in this
setting, to detect targeted/untargeted poisoning attacks, we investigate the
end-to-end learning completion latency of a realistic decentralized FL process
protected against poisoning attacks. We propose a technique which consists in
decoupling the monitoring phase from the detection phase in defenses against
poisoning attacks in a decentralized federated learning deployment that aim at
monitoring the behavior of the workers. We demonstrate that our proposed
blockchain-based monitoring improved network scalability, robustness and time
efficiency. The parallelization of operations results in minimized latency over
the end-to-end communication, computation, and consensus delays incurred during
the FL and blockchain operations.

A search for new heavy resonances decaying to WW, WZ, ZZ, WH, or ZH boson
pairs in the all-jets final state is presented. The analysis is based on
proton-proton collision data recorded by the CMS detector in 2016-2018 at a
centre-of-mass energy of 13 TeV at the CERN LHC, corresponding to an integrated
luminosity of 138 fb$^{-1}$. The search is sensitive to resonances with masses
above 1.3 TeV, decaying to bosons that are highly Lorentz-boosted such that
each of the bosons forms a single large-radius jet. Machine learning techniques
are employed to identify such jets. No significant excess over the estimated
standard model background is observed. A maximum local significance of 3.6
standard deviations, corresponding to a global significance of 2.3 standard
deviations, is observed at masses of 2.1 and 2.9 TeV. In a heavy vector triplet
model, spin-1 Z' and W' resonances with masses below 4.8 TeV are excluded at
the 95% confidence level (CL). These limits are the most stringent to date. In
a bulk graviton model, spin-2 gravitons and spin-0 radions with masses below
1.4 and 2.7 TeV, respectively, are excluded at 95% CL. Production of heavy
resonances through vector boson fusion is constrained with upper cross section
limits at 95% CL as low as 0.1 fb.

Petrophysical inversion is an important aspect of reservoir modeling. However
due to the lack of a unique and straightforward relationship between seismic
traces and rock properties, predicting petrophysical properties directly from
seismic data is a complex task. Many studies have attempted to identify the
direct end-to-end link using supervised machine learning techniques, but face
different challenges such as a lack of large petrophysical training dataset or
estimates that may not conform with physics or depositional history of the
rocks. We present a rock and wave physics informed neural network (RW-PINN)
model that can estimate porosity directly from seismic image traces with no or
limited number of wells, with predictions that are consistent with rock physics
and geologic knowledge of deposition. As an example, we use the uncemented sand
rock physics model and normal-incidence wave physics to guide the learning of
RW-PINN to eventually get good estimates of porosities from normal-incidence
seismic traces and limited well data. Training RW-PINN with few wells (weakly
supervised) helps in tackling the problem of non-uniqueness as different
porosity logs can give similar seismic traces. We use weighted normalized root
mean square error loss function to train the weakly supervised network and
demonstrate the impact of different weights on porosity predictions. The
RW-PINN estimated porosities and seismic traces are compared to predictions
from a completely supervised model, which gives slightly better porosity
estimates but poorly matches the seismic traces, in addition to requiring a
large amount of labeled training data. In this paper, we demonstrate the
complete workflow for executing petrophysical inversion of seismic data using
self-supervised or weakly supervised rock physics informed neural networks.

Unmanned Aerial Vehicles (UAVs) are increasingly deployed to provide wireless
connectivity to static and mobile ground users in situations of increased
network demand or points-of-failure in existing terrestrial cellular
infrastructure. However, UAVs are energy-constrained and may experience
interference from nearby UAV cells sharing the same frequency spectrum, thereby
impacting the system's energy efficiency (EE). We aim to address research gaps
that focus on optimising the system's EE using a 2D trajectory optimisation of
UAVs serving only static ground users, and neglect the impact of interference
from nearby UAV cells. Unlike previous work that assume global spatial
knowledge of ground users' location via a central controller that periodically
scans the network perimeter and provides real-time updates to the UAVs for
decision making, we focus on a realistic decentralised approach suitable in
emergencies. Thus, we apply a decentralised Multi-Agent Reinforcement Learning
(MARL) approach that maximizes the system's EE by jointly optimising each UAV's
3D trajectory, number of connected static and mobile users, and the energy
consumed, while taking into account the impact of interference and the UAVs'
coordination on the system's EE in a dynamic network environment. To address
this, we propose a direct collaborative Communication-enabled Multi-Agent
Decentralised Double Deep Q-Network (CMAD-DDQN) approach. The CMAD-DDQN is a
collaborative algorithm that allows UAVs to explicitly share knowledge by
communicating with its nearest neighbours based on existing 3GPP guidelines.
Our approach is able to maximise the system's EE without degrading the coverage
performance in the network. Simulation results show that the proposed approach
outperforms existing baselines in term of maximising the systems' EE by about
15% - 85%.

Electrocardiogram (ECG) signal is one of the most effective sources of
information mainly employed for the diagnosis and prediction of cardiovascular
diseases (CVDs) connected with the abnormalities in heart rhythm. Clearly,
single modality ECG (i.e. time series) cannot convey its complete
characteristics, thus, exploiting both time and time-frequency modalities in
the form of time-series data and spectrogram is needed. Leveraging the
cutting-edge self-supervised learning (SSL) technique on unlabeled data, we
propose SSL-based multimodality ECG classification. Our proposed network
follows SSL learning paradigm and consists of two modules corresponding to
pre-stream task, and down-stream task, respectively. In the SSL-pre-stream
task, we utilize self-knowledge distillation (KD) techniques with no labeled
data, on various transformations and in both time and frequency domains. In the
down-stream task, which is trained on labeled data, we propose a gate fusion
mechanism to fuse information from multimodality.To evaluate the effectiveness
of our approach, ten-fold cross validation on the 12-lead PhysioNet 2020
dataset has been conducted.

Differentially private (DP) optimization is the standard paradigm to learn
large neural networks that are accurate and privacy-preserving. The
computational cost for DP deep learning, however, is notoriously heavy due to
the per-sample gradient clipping. Existing DP implementations are
$2-1000\times$ more costly in time and space complexity than the standard
(non-private) training. In this work, we develop a novel Book-Keeping (BK)
technique that implements existing DP optimizers (thus achieving the same
accuracy), with a substantial improvement on the computational cost.
Specifically, BK enables DP training on large models and high dimensional data
to be roughly as efficient as the standard training, whereas previous DP
algorithms can be inefficient or incapable of training due to memory error. The
computational advantage of BK is supported by the complexity analysis as well
as extensive experiments on vision and language tasks. Our implementation
achieves state-of-the-art (SOTA) accuracy with very small extra cost: on GPT2
and at the same memory cost, BK has 1.0$\times$ the time complexity of the
standard training (0.75$\times$ training speed in practice), and 0.6$\times$
the time complexity of the most efficient DP implementation (1.24$\times$
training speed in practice). We will open-source the codebase for the BK

Evaluation is a key part of machine learning (ML), yet there is a lack of
support and tooling to enable its informed and systematic practice. We
introduce Evaluate and Evaluation on the Hub --a set of tools to facilitate the
evaluation of models and datasets in ML. Evaluate is a library to support best
practices for measurements, metrics, and comparisons of data and models. Its
goal is to support reproducibility of evaluation, centralize and document the
evaluation process, and broaden evaluation to cover more facets of model
performance. It includes over 50 efficient canonical implementations for a
variety of domains and scenarios, interactive documentation, and the ability to
easily share implementations and outcomes. The library is available at
https://github.com/huggingface/evaluate. In addition, we introduce Evaluation
on the Hub, a platform that enables the large-scale evaluation of over 75,000
models and 11,000 datasets on the Hugging Face Hub, for free, at the click of a
button. Evaluation on the Hub is available at
https://huggingface.co/autoevaluate.

We study the problem of differentially private (DP) fine-tuning of large
pre-trained models -- a recent privacy-preserving approach suitable for solving
downstream tasks with sensitive data. Existing work has demonstrated that high
accuracy is possible under strong privacy constraint, yet requires significant
computational overhead or modifications to the network architecture.
  We propose differentially private bias-term fine-tuning (DP-BiTFiT), which
matches the state-of-the-art accuracy for DP algorithms and the efficiency of
the standard BiTFiT. DP-BiTFiT is model agnostic (not modifying the network
architecture), parameter efficient (only training about $0.1\%$ of the
parameters), and computation efficient (almost removing the overhead caused by
DP, in both the time and space complexity). On a wide range of tasks, DP-BiTFiT
is $2\sim 30\times$ faster and uses $2\sim 8\times$ less memory than DP full
fine-tuning, even faster than the standard full fine-tuning. This amazing
efficiency enables us to conduct DP fine-tuning on language and vision tasks
with long-sequence texts and high-resolution images, which were computationally
difficult using existing methods.

Evaluating hypothetical statements about how the world would be had a
different course of action been taken is arguably one key capability expected
from modern AI systems. Counterfactual reasoning underpins discussions in
fairness, the determination of blame and responsibility, credit assignment, and
regret. In this paper, we study the evaluation of counterfactual statements
through neural models. Specifically, we tackle two causal problems required to
make such evaluations, i.e., counterfactual identification and estimation from
an arbitrary combination of observational and experimental data. First, we show
that neural causal models (NCMs) are expressive enough and encode the
structural constraints necessary for performing counterfactual reasoning.
Second, we develop an algorithm for simultaneously identifying and estimating
counterfactual distributions. We show that this algorithm is sound and complete
for deciding counterfactual identification in general settings. Third,
considering the practical implications of these results, we introduce a new
strategy for modeling NCMs using generative adversarial networks. Simulations
corroborate with the proposed methodology.

Temporal networks model a variety of important phenomena involving timed
interactions between entities. Existing methods for machine learning on
temporal networks generally exhibit at least one of two limitations. First,
time is assumed to be discretized, so if the time data is continuous, the user
must determine the discretization and discard precise time information. Second,
edge representations can only be calculated indirectly from the nodes, which
may be suboptimal for tasks like edge classification. We present a simple
method that avoids both shortcomings: construct the line graph of the network,
which includes a node for each interaction, and weigh the edges of this graph
based on the difference in time between interactions. From this derived graph,
edge representations for the original network can be computed with efficient
classical methods. The simplicity of this approach facilitates explicit
theoretical analysis: we can constructively show the effectiveness of our
method's representations for a natural synthetic model of temporal networks.
Empirical results on real-world networks demonstrate our method's efficacy and
efficiency on both edge classification and temporal link prediction.

Reward and representation learning are two long-standing challenges for
learning an expanding set of robot manipulation skills from sensory
observations. Given the inherent cost and scarcity of in-domain, task-specific
robot data, learning from large, diverse, offline human videos has emerged as a
promising path towards acquiring a generally useful visual representation for
control; however, how these human videos can be used for general-purpose reward
learning remains an open question. We introduce
$\textbf{V}$alue-$\textbf{I}$mplicit $\textbf{P}$re-training (VIP), a
self-supervised pre-trained visual representation capable of generating dense
and smooth reward functions for unseen robotic tasks. VIP casts representation
learning from human videos as an offline goal-conditioned reinforcement
learning problem and derives a self-supervised dual goal-conditioned
value-function objective that does not depend on actions, enabling pre-training
on unlabeled human videos. Theoretically, VIP can be understood as a novel
implicit time contrastive objective that generates a temporally smooth
embedding, enabling the value function to be implicitly defined via the
embedding distance, which can then be used to construct the reward for any
goal-image specified downstream task. Trained on large-scale Ego4D human videos
and without any fine-tuning on in-domain, task-specific data, VIP's frozen
representation can provide dense visual reward for an extensive set of
simulated and $\textbf{real-robot}$ tasks, enabling diverse reward-based visual
control methods and significantly outperforming all prior pre-trained
representations. Notably, VIP can enable simple, $\textbf{few-shot}$ offline RL
on a suite of real-world robot tasks with as few as 20 trajectories.

While standard bandit algorithms sometimes incur high regret, their
performance can be greatly improved by "warm starting" with historical data.
Unfortunately, how best to incorporate historical data is unclear: naively
initializing reward estimates using all historical samples can suffer from
spurious data and imbalanced data coverage, leading to computational and
storage issues - particularly in continuous action spaces. We address these two
challenges by proposing Artificial Replay, a meta-algorithm for incorporating
historical data into any arbitrary base bandit algorithm. Artificial Replay
uses only a subset of the historical data as needed to reduce computation and
storage. We show that for a broad class of base algorithms that satisfy
independence of irrelevant data (IIData), a novel property that we introduce,
our method achieves equal regret as a full warm-start approach while
potentially using only a fraction of the historical data. We complement these
theoretical results with a case study of $K$-armed and continuous combinatorial
bandit algorithms, including on a green security domain using real poaching
data, to show the practical benefits of Artificial Replay in achieving optimal
regret alongside low computational and storage costs.

3D-aware image synthesis aims at learning a generative model that can render
photo-realistic 2D images while capturing decent underlying 3D shapes. A
popular solution is to adopt the generative adversarial network (GAN) and
replace the generator with a 3D renderer, where volume rendering with neural
radiance field (NeRF) is commonly used. Despite the advancement of synthesis
quality, existing methods fail to obtain moderate 3D shapes. We argue that,
considering the two-player game in the formulation of GANs, only making the
generator 3D-aware is not enough. In other words, displacing the generative
mechanism only offers the capability, but not the guarantee, of producing
3D-aware images, because the supervision of the generator primarily comes from
the discriminator. To address this issue, we propose GeoD through learning a
geometry-aware discriminator to improve 3D-aware GANs. Concretely, besides
differentiating real and fake samples from the 2D image space, the
discriminator is additionally asked to derive the geometry information from the
inputs, which is then applied as the guidance of the generator. Such a simple
yet effective design facilitates learning substantially more accurate 3D
shapes. Extensive experiments on various generator architectures and training
datasets verify the superiority of GeoD over state-of-the-art alternatives.
Moreover, our approach is registered as a general framework such that a more
capable discriminator (i.e., with a third task of novel view synthesis beyond
domain classification and geometry extraction) can further assist the generator
with a better multi-view consistency.

As an emerging secure learning paradigm in leveraging cross-silo private
data, vertical federated learning (VFL) is expected to improve advertising
models by enabling the joint learning of complementary user attributes
privately owned by the advertiser and the publisher. However, the 1) restricted
applicable scope to overlapped samples and 2) high system challenge of
real-time federated serving have limited its application to advertising
systems.
  In this paper, we advocate new learning setting Semi-VFL (Vertical
Semi-Federated Learning) as a lightweight solution to utilize all available
data (both the overlapped and non-overlapped data) that is free from federated
serving. Semi-VFL is expected to perform better than single-party models and
maintain a low inference cost. It's notably important to i) alleviate the
absence of the passive party's feature and ii) adapt to the whole sample space
to implement a good solution for Semi-VFL. Thus, we propose a carefully
designed joint privileged learning framework (JPL) as an efficient
implementation of Semi-VFL. Specifically, we build an inference-efficient
single-party student model applicable to the whole sample space and meanwhile
maintain the advantage of the federated feature extension. Novel feature
imitation and ranking consistency restriction methods are proposed to extract
cross-party feature correlations and maintain cross-sample-space consistency
for both the overlapped and non-overlapped data.
  We conducted extensive experiments on real-world advertising datasets. The
results show that our method achieves the best performance over baseline
methods and validate its effectiveness in maintaining cross-view feature
correlation.

With the increasing need for handling large state and action spaces, general
function approximation has become a key technique in reinforcement learning
(RL). In this paper, we propose a general framework that unifies model-based
and model-free RL, and an Admissible Bellman Characterization (ABC) class that
subsumes nearly all Markov Decision Process (MDP) models in the literature for
tractable RL. We propose a novel estimation function with decomposable
structural properties for optimization-based exploration and the functional
eluder dimension as a complexity measure of the ABC class. Under our framework,
a new sample-efficient algorithm namely OPtimization-based ExploRation with
Approximation (OPERA) is proposed, achieving regret bounds that match or
improve over the best-known results for a variety of MDP models. In particular,
for MDPs with low Witness rank, under a slightly stronger assumption, OPERA
improves the state-of-the-art sample complexity results by a factor of $dH$.
Our framework provides a generic interface to design and analyze new RL models
and algorithms.

Sketch-and-extrude is a common and intuitive modeling process in computer
aided design. This paper studies the problem of learning the shape given in the
form of point clouds by inverse sketch-and-extrude. We present ExtrudeNet, an
unsupervised end-to-end network for discovering sketch and extrude from point
clouds. Behind ExtrudeNet are two new technical components: 1) an effective
representation for sketch and extrude, which can model extrusion with freeform
sketches and conventional cylinder and box primitives as well; and 2) a
numerical method for computing the signed distance field which is used in the
network learning. This is the first attempt that uses machine learning to
reverse engineer the sketch-and-extrude modeling process of a shape in an
unsupervised fashion. ExtrudeNet not only outputs a compact, editable and
interpretable representation of the shape that can be seamlessly integrated
into modern CAD software, but also aligns with the standard CAD modeling
process facilitating various editing applications, which distinguishes our work
from existing shape parsing research. Code is released at
https://github.com/kimren227/ExtrudeNet.

Batch reinforcement learning (BRL) is an emerging research area in the RL
community. It learns exclusively from static datasets (i.e. replay buffers)
without interaction with the environment. In the offline settings, existing
replay experiences are used as prior knowledge for BRL models to find the
optimal policy. Thus, generating replay buffers is crucial for BRL model
benchmark. In our B2RL (Building Batch RL) dataset, we collected real-world
data from our building management systems, as well as buffers generated by
several behavioral policies in simulation environments. We believe it could
help building experts on BRL research. To the best of our knowledge, we are the
first to open-source building datasets for the purpose of BRL learning.

A novel neural architecture was recently developed that enforces an exact
upper bound on the Lipschitz constant of the model by constraining the norm of
its weights in a minimal way, resulting in higher expressiveness compared to
other techniques. We present a new and interesting direction for this
architecture: estimation of the Wasserstein metric (Earth Mover's Distance) in
optimal transport by employing the Kantorovich-Rubinstein duality to enable its
use in geometric fitting applications. Specifically, we focus on the field of
high-energy particle physics, where it has been shown that a metric for the
space of particle-collider events can be defined based on the Wasserstein
metric, referred to as the Energy Mover's Distance (EMD). This metrization has
the potential to revolutionize data-driven collider phenomenology. The work
presented here represents a major step towards realizing this goal by providing
a differentiable way of directly calculating the EMD. We show how the
flexibility that our approach enables can be used to develop novel clustering

Comparing unpaired samples of a distribution or population taken at different
points in time is a fundamental task in many application domains where
measuring populations is destructive and cannot be done repeatedly on the same
sample, such as in single-cell biology. Optimal transport (OT) can solve this
challenge by learning an optimal coupling of samples across distributions from
unpaired data. However, the usual formulation of OT assumes conservation of
mass, which is violated in unbalanced scenarios in which the population size
changes (e.g., cell proliferation or death) between measurements. In this work,
we introduce NubOT, a neural unbalanced OT formulation that relies on the
formalism of semi-couplings to account for creation and destruction of mass. To
estimate such semi-couplings and generalize out-of-sample, we derive an
efficient parameterization based on neural optimal transport maps and propose a
novel algorithmic scheme through a cycle-consistent training procedure. We
apply our method to the challenging task of forecasting heterogeneous responses
of multiple cancer cell lines to various drugs, where we observe that by
accurately modeling cell proliferation and death, our method yields notable
improvements over previous neural optimal transport methods.

Meta-training agents with memory has been shown to culminate in Bayes-optimal
agents, which casts Bayes-optimality as the implicit solution to a numerical
optimization problem rather than an explicit modeling assumption. Bayes-optimal
agents are risk-neutral, since they solely attune to the expected return, and
ambiguity-neutral, since they act in new situations as if the uncertainty were
known. This is in contrast to risk-sensitive agents, which additionally exploit
the higher-order moments of the return, and ambiguity-sensitive agents, which
act differently when recognizing situations in which they lack knowledge.
Humans are also known to be averse to ambiguity and sensitive to risk in ways
that aren't Bayes-optimal, indicating that such sensitivity can confer
advantages, especially in safety-critical situations. How can we extend the
meta-learning protocol to generate risk- and ambiguity-sensitive agents? The
goal of this work is to fill this gap in the literature by showing that risk-
and ambiguity-sensitivity also emerge as the result of an optimization problem
using modified meta-training algorithms, which manipulate the
experience-generation process of the learner. We empirically test our proposed
meta-training algorithms on agents exposed to foundational classes of
decision-making experiments and demonstrate that they become sensitive to risk
and ambiguity.

Partial differential equations (PDEs) are central to describing complex
physical system simulations. Their expensive solution techniques have led to an
increased interest in deep neural network based surrogates. However, the
practical utility of training such surrogates is contingent on their ability to
model complex multi-scale spatio-temporal phenomena. Various neural network
architectures have been proposed to target such phenomena, most notably Fourier
Neural Operators (FNOs), which give a natural handle over local & global
spatial information via parameterization of different Fourier modes, and U-Nets
which treat local and global information via downsampling and upsampling paths.
However, generalizing across different equation parameters or time-scales still
remains a challenge. In this work, we make a comprehensive comparison between
various FNO, ResNet, and U-Net like approaches to fluid mechanics problems in
both vorticity-stream and velocity function form. For U-Nets, we transfer
recent architectural improvements from computer vision, most notably from
object segmentation and generative modeling. We further analyze the design
considerations for using FNO layers to improve performance of U-Net
architectures without major degradation of computational cost. Finally, we show
promising results on generalization to different PDE parameters and time-scales
with a single surrogate model. Source code for our PyTorch benchmark framework
is available at https://github.com/microsoft/pdearena.

In this paper, we introduce a neural-augmented decoder for Turbo codes called
TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP
algorithm but has much better reliability than the max-log-MAP baseline and
performs close to the MAP algorithm. We show that TINYTURBO exhibits strong
robustness on a variety of practical channels of interest, such as EPA and EVA
channels, which are included in the LTE standards. We also show that TINYTURBO
strongly generalizes across different rate, blocklengths, and trellises. We
verify the reliability and efficiency of TINYTURBO via over-the-air

Incorporating unstructured data into physical models is a challenging problem
that is emerging in data assimilation. Traditional approaches focus on
well-defined observation operators whose functional forms are typically assumed
to be known. This prevents these methods from achieving a consistent model-data
synthesis in configurations where the mapping from data-space to model-space is
unknown. To address these shortcomings, in this paper we develop a
physics-informed dynamical variational autoencoder ($\Phi$-DVAE) for embedding
diverse data streams into time-evolving physical systems described by
differential equations. Our approach combines a standard (possibly nonlinear)
filter for the latent state-space model and a VAE, to embed the unstructured
data stream into the latent dynamical system. A variational Bayesian framework
is used for the joint estimation of the embedding, latent states, and unknown
system parameters. To demonstrate the method, we look at three examples: video
datasets generated by the advection and Korteweg-de Vries partial differential
equations, and a velocity field generated by the Lorenz-63 system. Comparisons
with relevant baselines show that the $\Phi$-DVAE provides a data efficient
dynamics encoding methodology that is competitive with standard approaches,
with the added benefit of incorporating a physically interpretable latent
space.

The shuffled linear regression problem aims to recover linear relationships
in datasets where the correspondence between input and output is unknown. This
problem arises in a wide range of applications including survey data, in which
one needs to decide whether the anonymity of the responses can be preserved
while uncovering significant statistical connections. In this work, we propose
a novel optimization algorithm for shuffled linear regression based on a
posterior-maximizing objective function assuming Gaussian noise prior. We
compare and contrast our approach with existing methods on synthetic and real
data. We show that our approach performs competitively while achieving
empirical running-time improvements. Furthermore, we demonstrate that our
algorithm is able to utilize the side information in the form of seeds, which
recently came to prominence in related problems.

Prior work has shown that Visual Recognition datasets frequently
underrepresent bias groups $B$ (e.g. Female) within class labels $Y$ (e.g.
Programmers). This dataset bias can lead to models that learn spurious
correlations between class labels and bias groups such as age, gender, or race.
Most recent methods that address this problem require significant architectural
changes or additional loss functions requiring more hyper-parameter tuning.
Alternatively, data sampling baselines from the class imbalance literature
(e.g. Undersampling, Upweighting), which can often be implemented in a single
line of code and often have no hyperparameters, offer a cheaper and more
efficient solution. However, these methods suffer from significant
shortcomings. For example, Undersampling drops a significant part of the input
distribution while Oversampling repeats samples, causing overfitting. To
address these shortcomings, we introduce a new class conditioned sampling
method: Bias Mimicking. The method is based on the observation that if a class
$c$ bias distribution, i.e. $P_D(B|Y=c)$ is mimicked across every
$c^{\prime}\neq c$, then $P_D(Y|B) = P_D(Y)$. Using this notion, BM, through a
novel training procedure, ensures that the model is exposed to the entire
distribution without repeating samples. Consequently, Bias Mimicking improves
underrepresented groups average accuracy of sampling methods by 3\% over four
benchmarks while maintaining and sometimes improving performance compared to
non sampling methods.

Knowledge graph embedding aims to predict the missing relations between
entities in knowledge graphs. Tensor-decomposition-based models, such as
ComplEx, provide a good trade-off between efficiency and expressiveness, that
is crucial because of the large size of real world knowledge graphs. The recent
multi-partition embedding interaction (MEI) model subsumes these models by
using the block term tensor format and provides a systematic solution for the
trade-off. However, MEI has several drawbacks, some of which carried from its
subsumed tensor-decomposition-based models. In this paper, we address these
drawbacks and introduce the Multi-partition Embedding Interaction iMproved
beyond block term format (MEIM) model, with independent core tensor for
ensemble effects and soft orthogonality for max-rank mapping, in addition to
multi-partition embedding. MEIM improves expressiveness while still being
highly efficient, helping it to outperform strong baselines and achieve
state-of-the-art results on difficult link prediction benchmarks using fairly
small embedding sizes. The source code is released at
https://github.com/tranhungnghiep/MEIM-KGE.

Individual privacy accounting enables bounding differential privacy (DP) loss
individually for each participant involved in the analysis. This can be
informative as often the individual privacy losses are considerably smaller
than those indicated by the DP bounds that are based on considering worst-case
bounds at each data access. In order to account for the individual privacy
losses in a principled manner, we need a privacy accountant for adaptive
compositions of randomised mechanisms, where the loss incurred at a given data
access is allowed to be smaller than the worst-case loss. This kind of analysis
has been carried out for the R\'enyi differential privacy (RDP) by Feldman and
Zrnic (2021), however not yet for the so-called optimal privacy accountants. We
make first steps in this direction by providing a careful analysis using the
Gaussian differential privacy which gives optimal bounds for the Gaussian
mechanism, one of the most versatile DP mechanisms. This approach is based on
determining a certain supermartingale for the hockey-stick divergence and on
extending the R\'enyi divergence-based fully adaptive composition results by
Feldman and Zrnic (2021). We also consider measuring the individual
$(\varepsilon,\delta)$-privacy losses using the so-called privacy loss
distributions. With the help of the Blackwell theorem, we can then make use of
the RDP analysis to construct an approximative individual
$(\varepsilon,\delta)$-accountant.

Though successful, federated learning presents new challenges for machine
learning, especially when the issue of data heterogeneity, also known as
Non-IID data, arises. To cope with the statistical heterogeneity, previous
works incorporated a proximal term in local optimization or modified the model
aggregation scheme at the server side or advocated clustered federated learning
approaches where the central server groups agent population into clusters with
jointly trainable data distributions to take the advantage of a certain level
of personalization. While effective, they lack a deep elaboration on what kind
of data heterogeneity and how the data heterogeneity impacts the accuracy
performance of the participating clients. In contrast to many of the prior
federated learning approaches, we demonstrate not only the issue of data
heterogeneity in current setups is not necessarily a problem but also in fact
it can be beneficial for the FL participants. Our observations are intuitive:
(1) Dissimilar labels of clients (label skew) are not necessarily considered
data heterogeneity, and (2) the principal angle between the agents' data
subspaces spanned by their corresponding principal vectors of data is a better
estimate of the data heterogeneity. Our code is available at
https://github.com/MMorafah/FL-SC-NIID.

Traditional analyses of gradient descent show that when the largest
eigenvalue of the Hessian, also known as the sharpness $S(\theta)$, is bounded
by $2/\eta$, training is "stable" and the training loss decreases
monotonically. Recent works, however, have observed that this assumption does
not hold when training modern neural networks with full batch or large batch
gradient descent. Most recently, Cohen et al. (2021) observed two important
phenomena. The first, dubbed progressive sharpening, is that the sharpness
steadily increases throughout training until it reaches the instability cutoff
$2/\eta$. The second, dubbed edge of stability, is that the sharpness hovers at
$2/\eta$ for the remainder of training while the loss continues decreasing,
albeit non-monotonically.
  We demonstrate that, far from being chaotic, the dynamics of gradient descent
at the edge of stability can be captured by a cubic Taylor expansion: as the
iterates diverge in direction of the top eigenvector of the Hessian due to
instability, the cubic term in the local Taylor expansion of the loss function
causes the curvature to decrease until stability is restored. This property,
which we call self-stabilization, is a general property of gradient descent and
explains its behavior at the edge of stability. A key consequence of
self-stabilization is that gradient descent at the edge of stability implicitly
follows projected gradient descent (PGD) under the constraint $S(\theta) \le
2/\eta$. Our analysis provides precise predictions for the loss, sharpness, and
deviation from the PGD trajectory throughout training, which we verify both
empirically in a number of standard settings and theoretically under mild
conditions. Our analysis uncovers the mechanism for gradient descent's implicit
bias towards stability.

Self-supervised methods have achieved remarkable success in transfer
learning, often achieving the same or better accuracy than supervised
pre-training. Most prior work has done so by increasing pre-training
computation by adding complex data augmentation, multiple views, or lengthy
training schedules. In this work, we investigate a related, but orthogonal
question: given a fixed FLOP budget, what are the best datasets, models, and
(self-)supervised training methods for obtaining high accuracy on
representative visual tasks? Given the availability of large datasets, this
setting is often more relevant for both academic and industry labs alike. We
examine five large-scale datasets (JFT-300M, ALIGN, ImageNet-1K, ImageNet-21K,
and COCO) and six pre-training methods (CLIP, DINO, SimCLR, BYOL, Masked
Autoencoding, and supervised). In a like-for-like fashion, we characterize
their FLOP and CO$_2$ footprints, relative to their accuracy when transferred
to a canonical image segmentation task. Our analysis reveals strong disparities
in the computational efficiency of pre-training methods and their dependence on
dataset quality. In particular, our results call into question the
commonly-held assumption that self-supervised methods inherently scale to
large, uncurated data. We therefore advocate for (1) paying closer attention to
dataset curation and (2) reporting of accuracies in context of the total

The application of machine learning to physics problems is widely found in
the scientific literature. Both regression and classification problems are
addressed by a large array of techniques that involve learning algorithms.
Unfortunately, the measurement errors of the data used to train machine
learning models are almost always neglected. This leads to estimations of the
performance of the models (and thus their generalisation power) that is too
optimistic since it is always assumed that the target variables (what one wants
to predict) are correct. In physics, this is a dramatic deficiency as it can
lead to the belief that theories or patterns exist where, in reality, they do
not. This paper addresses this deficiency by deriving formulas for commonly
used metrics (both for regression and classification problems) that take into
account measurement errors of target variables. The new formulas give an
estimation of the metrics which is always more pessimistic than what is
obtained with the classical ones, not taking into account measurement errors.
The formulas given here are of general validity, completely model-independent,
and can be applied without limitations. Thus, with statistical confidence, one
can analyze the existence of relationships when dealing with measurements with
errors of any kind. The formulas have wide applicability outside physics and
can be used in all problems where measurement errors are relevant to the
conclusions of studies.

We present a framework for cloud characterization that leverages modern
unsupervised deep learning technologies. While previous neural network-based
cloud classification models have used supervised learning methods, unsupervised
learning allows us to avoid restricting the model to artificial categories
based on historical cloud classification schemes and enables the discovery of
novel, more detailed classifications. Our framework learns cloud features
directly from radiance data produced by NASA's Moderate Resolution Imaging
Spectroradiometer (MODIS) satellite instrument, deriving cloud characteristics
from millions of images without relying on pre-defined cloud types during the
training process. We present preliminary results showing that our method
extracts physically relevant information from radiance data and produces
meaningful cloud classes.

Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of
the electrostatic potential of biological macromolecules, including proteins.
At sufficient resolution, the cryo-EM maps, along with some knowledge about the
imaged molecules, allow de novo atomic modelling. Typically, this is done
through a laborious manual process. Recent advances in machine learning
applications to protein structure prediction show potential for automating this
process. Taking inspiration from these techniques, we have built ModelAngelo
for automated model building of proteins in cryo-EM maps. ModelAngelo first
uses a residual convolutional neural network (CNN) to initialize a graph
representation with nodes assigned to individual amino acids of the proteins in
the map and edges representing the protein chain. The graph is then refined
with a graph neural network (GNN) that combines the cryo-EM data, the amino
acid sequence data and prior knowledge about protein geometries. The GNN
refines the geometry of the protein chain and classifies the amino acids for
each of its nodes. The final graph is post-processed with a hidden Markov model
(HMM) search to map each protein chain to entries in a user provided sequence
file. Application to 28 test cases shows that ModelAngelo outperforms the
state-of-the-art and approximates manual building for cryo-EM maps with
resolutions better than 3.5 \r{A}.

A wind turbines' power curve is easily accessible damage sensitive data, and
as such is a key part of structural health monitoring in wind turbines. Power
curve models can be constructed in a number of ways, but the authors argue that
probabilistic methods carry inherent benefits in this use case, such as
uncertainty quantification and allowing uncertainty propagation analysis. Many
probabilistic power curve models have a key limitation in that they are not
physically meaningful - they return mean and uncertainty predictions outside of
what is physically possible (the maximum and minimum power outputs of the wind
turbine). This paper investigates the use of two bounded Gaussian Processes in
order to produce physically meaningful probabilistic power curve models. The
first model investigated was a warped heteroscedastic Gaussian process, and was
found to be ineffective due to specific shortcomings of the Gaussian Process in
relation to the warping function. The second model - an approximated Gaussian
Process with a Beta likelihood was highly successful and demonstrated that a
working bounded probabilistic model results in better predictive uncertainty
than a corresponding unbounded one without meaningful loss in predictive
accuracy. Such a bounded model thus offers increased accuracy for performance
monitoring and increased operator confidence in the model due to guaranteed
physical plausibility.

Self-supervised learning (SSL) has proven vital in speech and audio-related
applications. The paradigm trains a general model on unlabeled data that can
later be used to solve specific downstream tasks. This type of model is costly
to train as it requires manipulating long input sequences that can only be
handled by powerful centralised servers. Surprisingly, despite many attempts to
increase training efficiency through model compression, the effects of
truncating input sequence lengths to reduce computation have not been studied.
In this paper, we provide the first empirical study of SSL pre-training for
different specified sequence lengths and link this to various downstream tasks.
We find that training on short sequences can dramatically reduce resource costs
while retaining a satisfactory performance for all tasks. This simple one-line
change would promote the migration of SSL training from data centres to
user-end edge devices for more realistic and personalised applications.

Online stores and service providers rely heavily on recommendation softwares
to guide users through the vast amount of available products. Consequently, the
field of recommender systems has attracted increased attention from the
industry and academia alike, but despite this joint effort, the field still
faces several challenges. For instance, most existing work models the
recommendation problem as a matrix completion problem to predict the user
preference for an item. This abstraction prevents the system from utilizing the
rich information from the ordered sequence of user actions logged in online
sessions. To address this limitation, researchers have recently developed a
promising new breed of algorithms called sequence-aware recommender systems to
predict the user's next action by utilizing the time series composed of the
sequence of actions in an ongoing user session. This paper proposes a novel
sequence-aware recommendation approach based on a complex network generated by
the hidden metric space model, which combines node similarity and popularity to
generate links. We build a network model from data and then use it to predict
the user's subsequent actions. The network model provides an additional source
of information that improves the accuracy of the recommendations. The proposed
method is implemented and tested experimentally on a large dataset. The results
prove that the proposed approach performs better than state-of-the-art

In this paper, we give an in-depth error analysis for surrogate models
generated by a variant of the Sparse Identification of Nonlinear Dynamics
(SINDy) method. We start with an overview of a variety of non-linear system
identification techniques, namely, SINDy, weak-SINDy, and the occupation kernel
method. Under the assumption that the dynamics are a finite linear combination
of a set of basis functions, these methods establish a matrix equation to
recover coefficients. We illuminate the structural similarities between these
techniques and establish a projection property for the weak-SINDy technique.
Following the overview, we analyze the error of surrogate models generated by a
simplified version of weak-SINDy. In particular, under the assumption of
boundedness of a composition operator given by the solution, we show that (i)
the surrogate dynamics converges towards the true dynamics and (ii) the
solution of the surrogate model is reasonably close to the true solution.
Finally, as an application, we discuss the use of a combination of weak-SINDy
surrogate modeling and proper orthogonal decomposition (POD) to build a
surrogate model for partial differential equations (PDEs).

A simple generative model based on a continuous-time normalizing flow between
any pair of base and target probability densities is proposed. The velocity
field of this flow is inferred from the probability current of a time-dependent
density that interpolates between the base and the target in finite time.
Unlike conventional normalizing flow inference methods based the maximum
likelihood principle, which require costly backpropagation through ODE solvers,
our interpolant approach leads to a simple quadratic loss for the velocity
itself which is expressed in terms of expectations that are readily amenable to
empirical estimation. The flow can be used to generate samples from either the
base or target, and to estimate the likelihood at any time along the
interpolant. In addition, the flow can be optimized to minimize the path length
of the interpolant density, thereby paving the way for building optimal
transport maps. The approach is also contextualized in its relation to
diffusions. In particular, in situations where the base is a Gaussian density,
we show that the velocity of our normalizing flow can also be used to construct
a diffusion model to sample the target as well as estimating its score. This
allows one to map methods based on stochastic differential equations to those
using ordinary differential equations, simplifying the mechanics of the model,
but capturing equivalent dynamics. Benchmarking on density estimation tasks
illustrates that the learned flow can match and surpass maximum likelihood
continuous flows at a fraction of the conventional ODE training costs.

Early fault detection and fault prognosis are crucial to ensure efficient and
safe operations of complex engineering systems such as the Spallation Neutron
Source (SNS) and its power electronics (high voltage converter modulators).
Following an advanced experimental facility setup that mimics SNS operating
conditions, the authors successfully conducted 21 fault prognosis experiments,
where fault precursors are introduced in the system to a degree enough to cause
degradation in the waveform signals, but not enough to reach a real fault. Nine
different machine learning techniques based on ensemble trees, convolutional
neural networks, support vector machines, and hierarchical voting ensembles are
proposed to detect the fault precursors. Although all 9 models have shown a
perfect and identical performance during the training and testing phase, the
performance of most models has decreased in the prognosis phase once they got
exposed to real-world data from the 21 experiments. The hierarchical voting
ensemble, which features multiple layers of diverse models, maintains a
distinguished performance in early detection of the fault precursors with 95%
success rate (20/21 tests), followed by adaboost and extremely randomized trees
with 52% and 48% success rates, respectively. The support vector machine models
were the worst with only 24% success rate (5/21 tests). The study concluded
that a successful implementation of machine learning in the SNS or particle
accelerator power systems would require a major upgrade in the controller and
the data acquisition system to facilitate streaming and handling big data for
the machine learning models. In addition, this study shows that the best
performing models were diverse and based on the ensemble concept to reduce the
bias and hyperparameter sensitivity of individual models.

Group-equivariant neural networks have emerged as a data-efficient approach
to solve classification and regression tasks, while respecting the relevant
symmetries of the data. However, little work has been done to extend this
paradigm to the unsupervised and generative domains. Here, we present
Holographic-(V)AE (H-(V)AE), a fully end-to-end SO(3)-equivariant (variational)
autoencoder in Fourier space, suitable for unsupervised learning and generation
of data distributed around a specified origin. H-(V)AE is trained to
reconstruct the spherical Fourier encoding of data, learning in the process a
latent space with a maximally informative invariant embedding alongside an
equivariant frame describing the orientation of the data. We extensively test
the performance of H-(V)AE on diverse datasets and show that its latent space
efficiently encodes the categorical features of spherical images and structural
features of protein atomic environments. Our work can further be seen as a case
study for equivariant modeling of a data distribution by reconstructing its
Fourier encoding.

Implicit neural networks have become increasingly attractive in the machine
learning community since they can achieve competitive performance but use much
less computational resources. Recently, a line of theoretical works established
the global convergences for first-order methods such as gradient descent if the
implicit networks are over-parameterized. However, as they train all layers
together, their analyses are equivalent to only studying the evolution of the
output layer. It is unclear how the implicit layer contributes to the training.
Thus, in this paper, we restrict ourselves to only training the implicit layer.
We show that global convergence is guaranteed, even if only the implicit layer
is trained. On the other hand, the theoretical understanding of when and how
the training performance of an implicit neural network can be generalized to
unseen data is still under-explored. Although this problem has been studied in
standard feed-forward networks, the case of implicit neural networks is still
intriguing since implicit networks theoretically have infinitely many layers.
Therefore, this paper investigates the generalization error for implicit neural
networks. Specifically, we study the generalization of an implicit network
activated by the ReLU function over random initialization. We provide a
generalization bound that is initialization sensitive. As a result, we show
that gradient flow with proper random initialization can train a sufficient
over-parameterized implicit network to achieve arbitrarily small generalization
errors.

Automated feature extraction capability and significant performance of Deep
Neural Networks (DNN) make them suitable for Internet of Things (IoT)
applications. However, deploying DNN on edge devices becomes prohibitive due to
the colossal computation, energy, and storage requirements. This paper presents
a novel approach for designing and training lightweight DNN using large-size
DNN. The approach considers the available storage, processing speed, and
maximum allowable processing time to execute the task on edge devices. We
present a knowledge distillation based training procedure to train the
lightweight DNN to achieve adequate accuracy. During the training of
lightweight DNN, we introduce a novel early halting technique, which preserves
network resources; thus, speedups the training procedure. Finally, we present
the empirically and real-world evaluations to verify the effectiveness of the
proposed approach under different constraints using various edge devices.

Machine learning algorithms typically assume independent and identically
distributed samples in training and at test time. Much work has shown that
high-performing ML classifiers can degrade significantly and provide
overly-confident, wrong classification predictions, particularly for
out-of-distribution (OOD) inputs. Conditional language models (CLMs) are
predominantly trained to classify the next token in an output sequence, and may
suffer even worse degradation on OOD inputs as the prediction is done
auto-regressively over many steps. Furthermore, the space of potential
low-quality outputs is larger as arbitrary text can be generated and it is
important to know when to trust the generated output. We present a highly
accurate and lightweight OOD detection method for CLMs, and demonstrate its
effectiveness on abstractive summarization and translation. We also show how
our method can be used under the common and realistic setting of distribution
shift for selective generation (analogous to selective prediction for
classification) of high-quality outputs, while automatically abstaining from
low-quality ones, enabling safer deployment of generative language models.

This paper aims at bringing some light and understanding to the field of deep
learning for dynamic point cloud processing. Specifically, we focus on the
hierarchical features learning aspect, with the ultimate goal of understanding
which features are learned at the different stages of the process and what
their meaning is. Last, we bring clarity on how hierarchical components of the
network affect the learned features and their importance for a successful
learning model. This study is conducted for point cloud prediction tasks,
useful for predicting coding applications.

Knowledge transfer between artificial neural networks has become an important
topic in deep learning. Among the open questions are what kind of knowledge
needs to be preserved for the transfer, and how it can be effectively achieved.
Several recent work have shown good performance of distillation methods using
relation-based knowledge. These algorithms are extremely attractive in that
they are based on simple inter-sample similarities. Nevertheless, a proper
metric of affinity and use of it in this context is far from well understood.
In this paper, by explicitly modularising knowledge distillation into a
framework of three components, i.e. affinity, normalisation, and loss, we give
a unified treatment of these algorithms as well as study a number of unexplored
combinations of the modules. With this framework we perform extensive
evaluations of numerous distillation objectives for image classification, and
obtain a few useful insights for effective design choices while demonstrating
how relation-based knowledge distillation could achieve comparable performance
to the state of the art in spite of the simplicity.

Automated driving is one of the most active research areas in computer
science. Deep learning methods have made remarkable breakthroughs in machine
learning in general and in automated driving (AD)in particular. However, there
are still unsolved problems to guarantee reliability and safety of automated
systems, especially to effectively incorporate all available information and
knowledge in the driving task. Knowledge graphs (KG) have recently gained
significant attention from both industry and academia for applications that
benefit by exploiting structured, dynamic, and relational data. The complexity
of graph-structured data with complex relationships and inter-dependencies
between objects has posed significant challenges to existing machine learning
algorithms. However, recent progress in knowledge graph embeddings and graph
neural networks allows to applying machine learning to graph-structured data.
Therefore, we motivate and discuss the potential benefit of KGs applied to the
main tasks of AD including 1) ontologies 2) perception, 3) scene understanding,
4) motion planning, and 5) validation. Then, we survey, analyze and categorize
ontologies and KG-based approaches for AD. We discuss current research
challenges and propose promising future research directions for KG-based
solutions for AD.

We consider the application of machine learning to the evaluation of
geothermal resource potential. A supervised learning problem is defined where
maps of 10 geological and geophysical features within the state of Nevada, USA
are used to define geothermal potential across a broad region. We have
available a relatively small set of positive training sites (known resources or
active power plants) and negative training sites (known drill sites with
unsuitable geothermal conditions) and use these to constrain and optimize
artificial neural networks for this classification task. The main objective is
to predict the geothermal resource potential at unknown sites within a large
geographic area where the defining features are known. These predictions could
be used to target promising areas for further detailed investigations. We
describe the evolution of our work from defining a specific neural network
architecture to training and optimization trials. Upon analysis we expose the
inevitable problems of model variability and resulting prediction uncertainty.
Finally, to address these problems we apply the concept of Bayesian neural
networks, a heuristic approach to regularization in network training, and make
use of the practical interpretation of the formal uncertainty measures they
provide.

Riemannian geometry is a mathematical field which has been the cornerstone of
revolutionary scientific discoveries such as the theory of general relativity.
Despite early uses in robot design and recent applications for exploiting data
with specific geometries, it mostly remains overlooked in robotics. With this
blue sky paper, we argue that Riemannian geometry provides the most suitable
tools to analyze and generate well-coordinated, energy-efficient motions of
robots with many degrees of freedom. Via preliminary solutions and novel
research directions, we discuss how Riemannian geometry may be leveraged to
design and combine physically-meaningful synergies for robotics, and how this
theory also opens the door to coupling motion synergies with perceptual inputs.

Understanding how biological neural networks carry out learning using
spike-based local plasticity mechanisms can lead to the development of
powerful, energy-efficient, and adaptive neuromorphic processing systems. A
large number of spike-based learning models have recently been proposed
following different approaches. However, it is difficult to assess if and how
they could be mapped onto neuromorphic hardware, and to compare their features
and ease of implementation. To this end, in this survey, we provide a
comprehensive overview of representative brain-inspired synaptic plasticity
models and mixed-signal CMOS neuromorphic circuits within a unified framework.
We review historical, bottom-up, and top-down approaches to modeling synaptic
plasticity, and we identify computational primitives that can support
low-latency and low-power hardware implementations of spike-based learning
rules. We provide a common definition of a locality principle based on pre- and
post-synaptic neuron information, which we propose as a fundamental requirement
for physical implementations of synaptic plasticity. Based on this principle,
we compare the properties of these models within the same framework, and
describe the mixed-signal electronic circuits that implement their computing
primitives, pointing out how these building blocks enable efficient on-chip and
online learning in neuromorphic processing systems.

Nanodiamonds have a wide range of applications including catalysis, sensing,
tribology and biomedicine. To leverage nanodiamond design via machine learning,
we introduce the new dataset ND5k, consisting of 5,089 diamondoid and
nanodiamond structures and their frontier orbital energies. ND5k structures are
optimized via tight-binding density functional theory (DFTB) and their frontier
orbital energies are computed using density functional theory (DFT) with the
PBE0 hybrid functional. We also compare recent machine learning models for
predicting frontier orbital energies for similar structures as they have been
trained on (interpolation on ND5k), and we test their abilities to extrapolate
predictions to larger structures. For both the interpolation and extrapolation
task, we find best performance using the equivariant graph neural network
PaiNN. The second best results are achieved with a message passing neural
network using a tailored set of atomic descriptors proposed here.

Learning neural fields has been an active topic in deep learning research,
focusing, among other issues, on finding more compact and easy-to-fit
representations. In this paper, we introduce a novel low-rank representation
termed Tensor Train Neural Fields (TT-NF) for learning neural fields on dense
regular grids and efficient methods for sampling from them. Our representation
is a TT parameterization of the neural field, trained with backpropagation to
minimize a non-convex objective. We analyze the effect of low-rank compression
on the downstream task quality metrics in two settings. First, we demonstrate
the efficiency of our method in a sandbox task of tensor denoising, which
admits comparison with SVD-based schemes designed to minimize reconstruction
error. Furthermore, we apply the proposed approach to Neural Radiance Fields,
where the low-rank structure of the field corresponding to the best quality can
be discovered only through learning.

Self-supervised learning makes great progress in large model pre-training but
suffers in training small models. Previous solutions to this problem mainly
rely on knowledge distillation and indeed have a two-stage learning procedure:
first train a large teacher model, then distill it to improve the
generalization ability of small ones. In this work, we present a new one-stage
solution to obtain pre-trained small models without extra teachers: slimmable
networks for contrastive self-supervised learning (\emph{SlimCLR}). A slimmable
network contains a full network and several weight-sharing sub-networks. We can
pre-train for only one time and obtain various networks including small ones
with low computation costs. However, in self-supervised cases, the interference
between weight-sharing networks leads to severe performance degradation. One
evidence of the interference is \emph{gradient imbalance}: a small proportion
of parameters produces dominant gradients during backpropagation, and the main
parameters may not be fully optimized. The divergence in gradient directions of
various networks may also cause interference between networks. To overcome
these problems, we make the main parameters produce dominant gradients and
provide consistent guidance for sub-networks via three techniques: slow start
training of sub-networks, online distillation, and loss re-weighting according
to model sizes. Besides, a switchable linear probe layer is applied during
linear evaluation to avoid the interference of weight-sharing linear layers. We
instantiate SlimCLR with typical contrastive learning frameworks and achieve
better performance than previous arts with fewer parameters and FLOPs.

Mixture models in variational inference (VI) is an active field of research.
Recent works have established their connection to multiple importance sampling
(MIS) through the MISELBO and advanced the use of ensemble approximations for
large-scale problems. However, as we show here, an independent learning of the
ensemble components can lead to suboptimal diversity. Hence, we study the
effect of instead using MISELBO as an objective function for learning mixtures,
and we propose the first ever mixture of variational approximations for a
normalizing flow-based hierarchical variational autoencoder (VAE) with
VampPrior and a PixelCNN decoder network. Two major insights led to the
construction of this novel composite model. First, mixture models have
potential to be off-the-shelf tools for practitioners to obtain more flexible
posterior approximations in VAEs. Therefore, we make them more accessible by
demonstrating how to apply them to four popular architectures. Second, the
mixture components cooperate in order to cover the target distribution while
trying to maximize their diversity when MISELBO is the objective function. We
explain this cooperative behavior by drawing a novel connection between VI and
adaptive importance sampling. Finally, we demonstrate the superiority of the
Mixture VAEs' learned feature representations on both image and single-cell
transcriptome data, and obtain state-of-the-art results among VAE architectures
in terms of negative log-likelihood on the MNIST and FashionMNIST datasets.
Code available here: \url{https://github.com/Lagergren-Lab/MixtureVAEs}.

SGD with momentum acceleration is one of the key components for improving the
performance of neural networks. For decentralized learning, a straightforward
approach using momentum acceleration is Distributed SGD (DSGD) with momentum
acceleration (DSGDm). However, DSGDm performs worse than DSGD when the data
distributions are statistically heterogeneous. Recently, several studies have
addressed this issue and proposed methods with momentum acceleration that are
more robust to data heterogeneity than DSGDm, although their convergence rates
remain dependent on data heterogeneity and decrease when the data distributions
are heterogeneous. In this study, we propose Momentum Tracking, which is a
method with momentum acceleration whose convergence rate is proven to be
independent of data heterogeneity. More specifically, we analyze the
convergence rate of Momentum Tracking in the standard deep learning setting,
where the objective function is non-convex and the stochastic gradient is used.
Then, we identify that it is independent of data heterogeneity for any momentum
coefficient $\beta\in [0, 1)$. Through image classification tasks, we
demonstrate that Momentum Tracking is more robust to data heterogeneity than
the existing decentralized learning methods with momentum acceleration and can
consistently outperform these existing methods when the data distributions are
heterogeneous.

Training recurrent neural networks is predominantly achieved via
backpropagation through time (BPTT). However, this algorithm is not an optimal
solution from both a biological and computational perspective. A more efficient
and biologically plausible alternative for BPTT is e-prop. We investigate the
applicability of e-prop to long short-term memorys (LSTMs), for both supervised
and reinforcement learning (RL) tasks. We show that e-prop is a suitable
optimization algorithm for LSTMs by comparing it to BPTT on two benchmarks for
supervised learning. This proves that e-prop can achieve learning even for
problems with long sequences of several hundred timesteps. We introduce
extensions that improve the performance of e-prop, which can partially be
applied to other network architectures. With the help of these extensions we
show that, under certain conditions, e-prop can outperform BPTT for one of the
two benchmarks for supervised learning. Finally, we deliver a proof of concept
for the integration of e-prop to RL in the domain of deep recurrent Q-learning.

Motivation: The rapid growth of metagenomics sequencing data makes
metagenomics increasingly dependent on computational and statistical methods
for fast and efficient analysis. Consequently, novel analysis tools for
big-data metagenomics are constantly emerging. One of the biggest challenges
for researchers occurs in the analysis planning stage: selecting the most
suitable metagenomics software tool to gain valuable insights from sequencing
data. The building process of data analysis pipelines is often laborious and
time-consuming since it requires a deep and critical understanding of how to
apply a particular tool to complete a specified metagenomics task.
  Results: We have addressed this challenge by using machine learning methods
to develop a classification system of metagenomics software tools into 13
classes (11 semantic annotations of EDAM and two virus-specific classes) based
on the descriptions of the tools. We trained three classifiers (Naive Bayes,
Logistic Regression, and Random Forest) using 15 text feature extraction
techniques (TF-IDF, GloVe, BERT-based models, and others). The manually curated
dataset includes 224 software tools and contains text from the abstract and the
methods section of the tools' publications. The best classification
performance, with an Area Under the Precision-Recall Curve score of 0.85, is
achieved using Logistic regression, BioBERT for text embedding, and text from
abstracts only. The proposed system provides accurate and unified
identification of metagenomics data analysis tools and tasks, which is a
crucial step in the construction of metagenomics data analysis pipelines.

Understanding the steps required to perform a task is an important skill for
AI systems. Learning these steps from instructional videos involves two
subproblems: (i) identifying the temporal boundary of sequentially occurring
segments and (ii) summarizing these steps in natural language. We refer to this
task as Procedure Segmentation and Summarization (PSS). In this paper, we take
a closer look at PSS and propose three fundamental improvements over current
methods. The segmentation task is critical, as generating a correct summary
requires each step of the procedure to be correctly identified. However,
current segmentation metrics often overestimate the segmentation quality
because they do not consider the temporal order of segments. In our first
contribution, we propose a new segmentation metric that takes into account the
order of segments, giving a more reliable measure of the accuracy of a given
predicted segmentation. Current PSS methods are typically trained by proposing
segments, matching them with the ground truth and computing a loss. However,
much like segmentation metrics, existing matching algorithms do not consider
the temporal order of the mapping between candidate segments and the ground
truth. In our second contribution, we propose a matching algorithm that
constrains the temporal order of segment mapping, and is also differentiable.
Lastly, we introduce multi-modal feature training for PSS, which further
improves segmentation. We evaluate our approach on two instructional video
datasets (YouCook2 and Tasty) and observe an improvement over the
state-of-the-art of $\sim7\%$ and $\sim2.5\%$ for procedure segmentation and
summarization, respectively.

This article sets forth a review of knowledge distillation techniques with a
focus on their applicability to retail banking contexts. Predictive machine
learning algorithms used in banking environments, especially in risk and
control functions, are generally subject to regulatory and technical
constraints limiting their complexity. Knowledge distillation gives the
opportunity to improve the performances of simple models without burdening
their application, using the results of other - generally more complex and
better-performing - models. Parsing recent advances in this field, we highlight
three main approaches: Soft Targets, Sample Selection and Data Augmentation. We
assess the relevance of a subset of such techniques by applying them to open
source datasets, before putting them to the test on the use cases of BPCE, a
major French institution in the retail banking sector. As such, we demonstrate
the potential of knowledge distillation to improve the performance of these
models without altering their form and simplicity.

We prove that a family $\mathcal{T}$ of distinct triangles on $n$ given
vertices that does not have a rainbow triangle (that is, three edges, each
taken from a different triangle in $\mathcal{T}$, that form together a
triangle) must be of size at most $\frac{n^2}{8}$. We also show that this
result is sharp and characterize the extremal case. In addition, we discuss a
version of this problem in which the triangles are not necessarily distinct,
and show that in this case, the same bound holds asymptotically. After posting
the original arXiv version of this paper, we learned that the sharp upper bound
of $\frac{n^2}{8}$ was proved much earlier by Gy\H{o}ri (2006) and
independently by Frankl, F\"uredi and Simonyi (unpublished). Gy\H{o}ri also
obtained a stronger version of our result for the case when repetitions are
allowed.

Neurons in the brain are often finely tuned for specific task variables.
Moreover, such disentangled representations are highly sought after in machine
learning. Here we mathematically prove that simple biological constraints on
neurons, namely nonnegativity and energy efficiency in both activity and
weights, promote such sought after disentangled representations by enforcing
neurons to become selective for single factors of task variation. We
demonstrate these constraints lead to disentangling in a variety of tasks and
architectures, including variational autoencoders. We also use this theory to
explain why the brain partitions its cells into distinct cell types such as
grid and object-vector cells, and also explain when the brain instead entangles
representations in response to entangled task factors. Overall, this work
provides a mathematical understanding of why, when, and how neurons represent
factors in both brains and machines, and is a first step towards understanding
of how task demands structure neural representations.

In this work, we propose a novel method to improve the generalization ability
of CNN-based face forgery detectors. Our method considers the feature anomalies
of forged faces caused by the prevalent blending operations in face forgery
algorithms. Specifically, we propose a weakly supervised Second Order Local
Anomaly (SOLA) learning module to mine anomalies in local regions using deep
feature maps. SOLA first decomposes the neighborhood of local features by
different directions and distances and then calculates the first and second
order local anomaly maps which provide more general forgery traces for the
classifier. We also propose a Local Enhancement Module (LEM) to improve the
discrimination between local features of real and forged regions, so as to
ensure accuracy in calculating anomalies. Besides, an improved Adaptive Spatial
Rich Model (ASRM) is introduced to help mine subtle noise features via
learnable high pass filters. With neither pixel level annotations nor external
synthetic data, our method using a simple ResNet18 backbone achieves
competitive performances compared with state-of-the-art works when evaluated on
unseen forgeries.

Deep learning models are nowadays broadly deployed to solve an incredibly
large variety of tasks. Commonly, leveraging over the availability of "big
data", deep neural networks are trained as black-boxes, minimizing an objective
function at its output. This however does not allow control over the
propagation of some specific features through the model, like gender or race,
for solving some an uncorrelated task. This raises issues either in the privacy
domain (considering the propagation of unwanted information) and of bias
(considering that these features are potentially used to solve the given task).
  In this work we propose IRENE, a method to achieve information removal at the
bottleneck of deep neural networks, which explicitly minimizes the estimated
mutual information between the features to be kept ``private'' and the target.
Experiments on a synthetic dataset and on CelebA validate the effectiveness of
the proposed approach, and open the road towards the development of approaches
guaranteeing information removal in deep neural networks.

Many Graph Neural Networks (GNNs) perform poorly compared to simple
heuristics on Link Prediction (LP) tasks. This is due to limitations in
expressive power such as the inability to count triangles (the backbone of most
LP heuristics) and because they can not distinguish automorphic nodes (those
having identical structural roles). Both expressiveness issues can be
alleviated by learning link (rather than node) representations and
incorporating structural features such as triangle counts. Since explicit link
representations are often prohibitively expensive, recent works resorted to
subgraph-based methods, which have achieved state-of-the-art performance for
LP, but suffer from poor efficiency due to high levels of redundancy between
subgraphs. We analyze the components of subgraph GNN (SGNN) methods for link
prediction. Based on our analysis, we propose a novel full-graph GNN called
ELPH (Efficient Link Prediction with Hashing) that passes subgraph sketches as
messages to approximate the key components of SGNNs without explicit subgraph
construction. ELPH is provably more expressive than Message Passing GNNs
(MPNNs). It outperforms existing SGNN models on many standard LP benchmarks
while being orders of magnitude faster. However, it shares the common GNN
limitation that it is only efficient when the dataset fits in GPU memory.
Accordingly, we develop a highly scalable model, called BUDDY, which uses
feature precomputation to circumvent this limitation without sacrificing
predictive performance. Our experiments show that BUDDY also outperforms SGNNs
on standard LP benchmarks while being highly scalable and faster than ELPH.

Self-supervised representations have been extensively studied for
discriminative and generative tasks. However, their robustness capabilities
have not been extensively investigated. This work focuses on self-supervised
representations for spoken generative language models. First, we empirically
demonstrate how current state-of-the-art speech representation models lack
robustness to basic signal variations that do not alter the spoken information.
To overcome this, we propose an effective and efficient method to learn robust
self-supervised speech representation for generative spoken language modeling.
The proposed approach is based on applying a set of signal transformations to
the speech signal and optimizing the model using an iterative pseudo-labeling
scheme. Our method significantly improves over the evaluated baselines when
considering encoding metrics. We additionally evaluate our method on the
speech-to-speech translation task. We consider Spanish-English and
French-English conversions and empirically demonstrate the benefits of
following the proposed approach.

Iris-based identification systems are among the most popular approaches for
person identification. Such systems require good-quality segmentation modules
that ideally identify the regions for different eye components. This paper
introduces the new two-headed architecture, where the eye components and
eyelashes are segmented using two separate decoding modules. Moreover, we
investigate various training scenarios by adopting different training losses.
Thanks to the two-headed approach, we were also able to examine the quality of
the model with the convex prior, which enforces the convexity of the segmented
shapes. We conducted an extensive evaluation of various learning scenarios on
real-life conditions high-resolution near-infrared iris images.

Learning to search is the task of building artificial agents that learn to
autonomously use a search box to find information. So far, it has been shown
that current language models can learn symbolic query reformulation policies,
in combination with traditional term-based retrieval, but fall short of
outperforming neural retrievers. We extend the previous learning to search
setup to a hybrid environment, which accepts discrete query refinement
operations, after a first-pass retrieval step performed by a dual encoder.
Experiments on the BEIR task show that search agents, trained via behavioral
cloning, outperform the underlying search system based on a combined dual
encoder retriever and cross encoder reranker. Furthermore, we find that simple
heuristic Hybrid Retrieval Environments (HRE) can improve baseline performance
by several nDCG points. The search agent based on HRE (HARE) produces
state-of-the-art performance on both zero-shot and in-domain evaluations. We
carry out an extensive qualitative analysis to shed light on the agents
policies.

Regularized optimal transport (OT) is now increasingly used as a loss or as a
matching layer in neural networks. Entropy-regularized OT can be computed using
the Sinkhorn algorithm but it leads to fully-dense transportation plans,
meaning that all sources are (fractionally) matched with all targets. To
address this issue, several works have investigated quadratic regularization
instead. This regularization preserves sparsity and leads to unconstrained and
smooth (semi) dual objectives, that can be solved with off-the-shelf gradient
methods. Unfortunately, quadratic regularization does not give direct control
over the cardinality (number of nonzeros) of the transportation plan. We
propose in this paper a new approach for OT with explicit cardinality
constraints on the transportation plan. Our work is motivated by an application
to sparse mixture of experts, where OT can be used to match input tokens such
as image patches with expert models such as neural networks. Cardinality
constraints ensure that at most $k$ tokens are matched with an expert, which is
crucial for computational performance reasons. Despite the nonconvexity of
cardinality constraints, we show that the corresponding (semi) dual problems
are tractable and can be solved with first-order gradient methods. Our method
can be thought as a middle ground between unregularized OT (recovered in the
limit case $k=1$) and quadratically-regularized OT (recovered when $k$ is large
enough). The smoothness of the objectives increases as $k$ increases, giving
rise to a trade-off between convergence speed and sparsity of the optimal plan.

Melanoma skin cancer is one of the most dangerous and life-threatening
cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which
causes melanoma skin cancer. However, it is difficult to detect and classify
melanoma and nevus mole at the immature stages. In this work, an automatic deep
learning system is developed based on the intensity value estimation with a
convolutional neural network model (CNN) to detect and classify melanoma and
nevus mole more accurately. Since intensity levels are the most distinctive
features for object or region of interest identification, the high-intensity
pixel values are selected from the extracted lesion images. Incorporating those
high-intensity features into the CNN improves the overall performance of the
proposed model than the state-of-the-art methods for detecting melanoma skin
cancer. To evaluate the system, we used 5-fold cross-validation. Experimental
results show that a superior percentage of accuracy (92.58%), sensitivity
(93.76%), specificity (91.56%), and precision (90.68%) are achieved.

Graph Neural Networks (GNNs) are a promising deep learning approach for
circumventing many real-world problems on graph-structured data. However, these
models usually have at least one of four fundamental limitations:
over-smoothing, over-fitting, difficult to train, and strong homophily
assumption. For example, Simple Graph Convolution (SGC) is known to suffer from
the first and fourth limitations. To tackle these limitations, we identify a
set of key designs including (D1) dilated convolution, (D2) multi-channel
learning, (D3) self-attention score, and (D4) sign factor to boost learning
from different types (i.e. homophily and heterophily) and scales (i.e. small,
medium, and large) of networks, and combine them into a graph neural network,
GPNet, a simple and efficient one-layer model. We theoretically analyze the
model and show that it can approximate various graph filters by adjusting the
self-attention score and sign factor. Experiments show that GPNet consistently
outperforms baselines in terms of average rank, average accuracy, complexity,
and parameters on semi-supervised and full-supervised tasks, and achieves
competitive performance compared to state-of-the-art model with inductive
learning task.

Recent rapid developments in reinforcement learning algorithms have been
giving us novel possibilities in many fields. However, due to their exploring
property, we have to take the risk into consideration when we apply those
algorithms to safety-critical problems especially in real environments. In this
study, we deal with a safe exploration problem in reinforcement learning under
the existence of disturbance. We define the safety during learning as
satisfaction of the constraint conditions explicitly defined in terms of the
state and propose a safe exploration method that uses partial prior knowledge
of a controlled object and disturbance. The proposed method assures the
satisfaction of the explicit state constraints with a pre-specified probability
even if the controlled object is exposed to a stochastic disturbance following
a normal distribution. As theoretical results, we introduce sufficient
conditions to construct conservative inputs not containing an exploring aspect
used in the proposed method and prove that the safety in the above explained
sense is guaranteed with the proposed method. Furthermore, we illustrate the
validity and effectiveness of the proposed method through numerical simulations
of an inverted pendulum and a four-bar parallel link robot manipulator.

Developing a deep learning method for medical segmentation tasks heavily
relies on a large amount of labeled data. However, the annotations require
professional knowledge and are limited in number. Recently, semi-supervised
learning has demonstrated great potential in medical segmentation tasks. Most
existing methods related to cardiac magnetic resonance images only focus on
regular images with similar domains and high image quality. A semi-supervised
domain generalization method was developed in [2], which enhances the quality
of pseudo labels on varied datasets. In this paper, we follow the strategy in
[2] and present a domain generalization method for semi-supervised medical
segmentation. Our main goal is to improve the quality of pseudo labels under
extreme MRI Analysis with various domains. We perform Fourier transformation on
input images to learn low-level statistics and cross-domain information. Then
we feed the augmented images as input to the double cross pseudo supervision
networks to calculate the variance among pseudo labels. We evaluate our method
on the CMRxMotion dataset [1]. With only partially labeled data and without
domain labels, our approach consistently generates accurate segmentation
results of cardiac magnetic resonance images with different respiratory
motions. Code will be available after the conference.

Interpreting critical variables involved in complex biological processes
related to survival time can help understand prediction from survival models,
evaluate treatment efficacy, and develop new therapies for patients. Currently,
the predictive results of deep learning (DL)-based models are better than or as
good as standard survival methods, they are often disregarded because of their
lack of transparency and little interpretability, which is crucial to their
adoption in clinical applications. In this paper, we introduce a novel, easily
deployable approach, called EXplainable CEnsored Learning (EXCEL), to
iteratively exploit critical variables and simultaneously implement (DL) model
training based on these variables. First, on a toy dataset, we illustrate the
principle of EXCEL; then, we mathematically analyze our proposed method, and we
derive and prove tight generalization error bounds; next, on two semi-synthetic
datasets, we show that EXCEL has good anti-noise ability and stability;
finally, we apply EXCEL to a variety of real-world survival datasets including
clinical data and genetic data, demonstrating that EXCEL can effectively
identify critical features and achieve performance on par with or better than
the original models. It is worth pointing out that EXCEL is flexibly deployed
in existing or emerging models for explainable survival data in the presence of
right censoring.

To train machine learning algorithms to predict emotional expressions in
terms of arousal and valence, annotated datasets are needed. However, as
different people perceive others' emotional expressions differently, their
annotations are per se subjective. For this, annotations are typically
collected from multiple annotators and averaged to obtain ground-truth labels.
However, when exclusively trained on this averaged ground-truth, the trained
network is agnostic to the inherent subjectivity in emotional expressions. In
this work, we therefore propose an end-to-end Bayesian neural network capable
of being trained on a distribution of labels to also capture the
subjectivity-based label uncertainty. Instead of a Gaussian, we model the label
distribution using Student's t-distribution, which also accounts for the number
of annotations. We derive the corresponding Kullback-Leibler divergence loss
and use it to train an estimator for the distribution of labels, from which the
mean and uncertainty can be inferred. We validate the proposed method using two
in-the-wild datasets. We show that the proposed t-distribution based approach
achieves state-of-the-art uncertainty modeling results in speech emotion
recognition, and also consistent results in cross-corpora evaluations.
Furthermore, analyses reveal that the advantage of a t-distribution over a
Gaussian grows with increasing inter-annotator correlation and a decreasing
number of annotators.

Within the context of topological data analysis, the problems of identifying
topological significance and matching signals across datasets are important and
useful inferential tasks in many applications. The limitation of existing
solutions to these problems, however, is computational speed. In this paper, we
harness the state-of-the-art for persistent homology computation by studying
the problem of determining topological prevalence and cycle matching using a
cohomological approach, which increases their feasibility and applicability to
a wider variety of applications and contexts. We demonstrate this on a wide
range of real-life, large-scale, and complex datasets. We extend existing
notions of topological prevalence and cycle matching to include general
non-Morse filtrations. This provides the most general and flexible
state-of-the-art adaptation of topological signal identification and persistent
cycle matching, which performs comparisons of orders of ten for thousands of
sampled points in a matter of minutes on standard institutional HPC CPU

Identifying and removing microplastics (MPs) from the environment is a global
challenge. This study explores how the colloidal fraction of common MPs behave
at aqueous interfaces of liquid crystal (LC) films. We observed polyethylene
(PE) and polystyrene (PS) microparticles to be captured at the LC interface and
exhibit distinct two-dimensional aggregation patterns. The addition of low
concentrations of surfactant (sodium dodecylsulfate (SDS)) was found to further
amplify the differences in PS/PE aggregation patterns, with PS changing from a
linear chain-like morphology to a singly dispersed state with increasing SDS
concentration and PE forming dense clusters at all SDS concentrations.
Statistical characterization of assembly patterns using fractal geometric
theory-based machine learning and a deep learning image recognition model
yielded highly accurate classification of PE vs PS (>99%). Additionally, by
performing feature importance analysis on our deep learning model, dense,
multi-branched assemblies were confirmed to be unique features of PE relative
to PS. To obtain additional insight into the origin of these key features, we
performed microscopic characterization of LC ordering at the microparticle
surfaces. These observations led us to predict that both microparticle types
should generate LC-mediated interactions (due to elastic strain) with a dipolar
symmetry, a prediction consistent with the observed interfacial organization of
PS but not PE. We conclude that the non-equilibrium organization of the PE
microparticles arises from their polycrystalline nature, which leads to rough
particle surfaces and weakened LC elastic interactions and enhanced capillary
forces. Overall, our results highlight the potential utility of LC interfaces
for surface-sensitive characterization of colloidal MPs.

This paper reviews the entire engineering process of trustworthy Machine
Learning (ML) algorithms designed to equip critical systems with advanced
analytics and decision functions. We start from the fundamental principles of
ML and describe the core elements conditioning its trust, particularly through
its design: namely domain specification, data engineering, design of the ML
algorithms, their implementation, evaluation and deployment. The latter
components are organized in an unique framework for the design of trusted ML
systems.

Neural networks embed the geometric structure of a data manifold lying in a
high-dimensional space into latent representations. Ideally, the distribution
of the data points in the latent space should depend only on the task, the
data, the loss, and other architecture-specific constraints. However, factors
such as the random weights initialization, training hyperparameters, or other
sources of randomness in the training phase may induce incoherent latent spaces
that hinder any form of reuse. Nevertheless, we empirically observe that, under
the same data and modeling choices, distinct latent spaces typically differ by
an unknown quasi-isometric transformation: that is, in each space, the
distances between the encodings do not change. In this work, we propose to
adopt pairwise similarities as an alternative data representation, that can be
used to enforce the desired invariance without any additional training. We show
how neural architectures can leverage these relative representations to
guarantee, in practice, latent isometry invariance, effectively enabling latent
space communication: from zero-shot model stitching to latent space comparison
between diverse settings. We extensively validate the generalization capability
of our approach on different datasets, spanning various modalities (images,
text, graphs), tasks (e.g., classification, reconstruction) and architectures
(e.g., CNNs, GCNs, transformers).

Deep learning has had remarkable success in robotic perception, but its
data-centric nature suffers when it comes to generalizing to ever-changing
environments. By contrast, physics-based optimization generalizes better, but
it does not perform as well in complicated tasks due to the lack of high-level
semantic information and the reliance on manual parametric tuning. To take
advantage of these two complementary worlds, we present PyPose: a
robotics-oriented, PyTorch-based library that combines deep perceptual models
with physics-based optimization techniques. Our design goal for PyPose is to
make it user-friendly, efficient, and interpretable with a tidy and
well-organized architecture. Using an imperative style interface, it can be
easily integrated into real-world robotic applications. Besides, it supports
parallel computing of any order gradients of Lie groups and Lie algebras and
$2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments
show that PyPose achieves 3-20$\times$ speedup in computation compared to
state-of-the-art libraries. To boost future research, we provide concrete
examples across several fields of robotics, including SLAM, inertial
navigation, planning, and control.

Denoising diffusion probabilistic models are currently becoming the leading
paradigm of generative modeling for many important data modalities. Being the
most prevalent in the computer vision community, diffusion models have also
recently gained some attention in other domains, including speech, NLP, and
graph-like data. In this work, we investigate if the framework of diffusion
models can be advantageous for general tabular problems, where datapoints are
typically represented by vectors of heterogeneous features. The inherent
heterogeneity of tabular data makes it quite challenging for accurate modeling,
since the individual features can be of completely different nature, i.e., some
of them can be continuous and some of them can be discrete. To address such
data types, we introduce TabDDPM -- a diffusion model that can be universally
applied to any tabular dataset and handles any type of feature. We extensively
evaluate TabDDPM on a wide set of benchmarks and demonstrate its superiority
over existing GAN/VAE alternatives, which is consistent with the advantage of
diffusion models in other fields. Additionally, we show that TabDDPM is
eligible for privacy-oriented setups, where the original datapoints cannot be
publicly shared.

Black-box models, such as deep neural networks, exhibit superior predictive
performances, but understanding their behavior is notoriously difficult. Many
explainable artificial intelligence methods have been proposed to reveal the
decision-making processes of black box models. However, their applications in
high-stakes domains remain limited. Recently proposed neural additive models
(NAM) have achieved state-of-the-art interpretable machine learning. NAM can
provide straightforward interpretations with slight performance sacrifices
compared with multi-layer perceptron. However, NAM can only model
1$^{\text{st}}$-order feature interactions; thus, it cannot capture the
co-relationships between input features. To overcome this problem, we propose a
novel interpretable machine learning method called higher-order neural additive
models (HONAM) and a feature interaction method for high interpretability.
HONAM can model arbitrary orders of feature interactions. Therefore, it can
provide the high predictive performance and interpretability that high-stakes
domains need. In addition, we propose a novel hidden unit to effectively learn
sharp-shape functions. We conducted experiments using various real-world
datasets to examine the effectiveness of HONAM. Furthermore, we demonstrate
that HONAM can achieve fair AI with a slight performance sacrifice. The source
code for HONAM is publicly available.

Inverse molecular design is critical in material science and drug discovery,
where the generated molecules should satisfy certain desirable properties. In
this paper, we propose equivariant energy-guided stochastic differential
equations (EEGSDE), a flexible framework for controllable 3D molecule
generation under the guidance of an energy function in diffusion models.
Formally, we show that EEGSDE naturally exploits the geometric symmetry in 3D
molecular conformation, as long as the energy function is invariant to
orthogonal transformations. Empirically, under the guidance of designed energy
functions, EEGSDE significantly improves the baseline on QM9, in inverse
molecular design targeted to quantum properties and molecular structures.
Furthermore, EEGSDE is able to generate molecules with multiple target
properties by combining the corresponding energy functions linearly.

Extracting informative representations from videos is fundamental for the
effective learning of various downstream tasks. Inspired by classical works on
saliency, we present a novel information-theoretic approach to discover
meaningful representations from videos in an unsupervised fashion. We argue
that local entropy of pixel neighborhoods and its evolution in a video stream
is a valuable intrinsic supervisory signal for learning to attend to salient
features. We, thus, abstract visual features into a concise representation of
keypoints that serve as dynamic information transporters. We discover in an
unsupervised fashion spatio-temporally consistent keypoint representations that
carry the prominent information across video frames, thanks to two original
information-theoretic losses. First, a loss that maximizes the information
covered by the keypoints in a frame. Second, a loss that encourages optimized
keypoint transportation over time, thus, imposing consistency of the
information flow. We evaluate our keypoint-based representation compared to
state-of-the-art baselines in different downstream tasks such as learning
object dynamics. To evaluate the expressivity and consistency of the keypoints,
we propose a new set of metrics. Our empirical results showcase the superior
performance of our information-driven keypoints that resolve challenges like
attendance to both static and dynamic objects, and to objects abruptly entering
and leaving the scene.

Due to the subjective crowdsourcing annotations and the inherent inter-class
similarity of facial expressions, the real-world Facial Expression Recognition
(FER) datasets usually exhibit ambiguous annotation. To simplify the learning
paradigm, most previous methods convert ambiguous annotation results into
precise one-hot annotations and train FER models in an end-to-end supervised
manner. In this paper, we rethink the existing training paradigm and propose
that it is better to use weakly supervised strategies to train FER models with
original ambiguous annotation.

Multi-view clustering methods are essential for the stratification of
patients into sub-groups of similar molecular characteristics. In recent years,
a wide range of methods has been developed for this purpose. However, due to
the high diversity of cancer-related data, a single method may not perform
sufficiently well in all cases. We present Parea, a multi-view hierarchical
ensemble clustering approach for disease subtype discovery. We demonstrate its
performance on several machine learning benchmark datasets. We apply and
validate our methodology on real-world multi-view cancer patient data. Parea
outperforms the current state-of-the-art on six out of seven analysed cancer
types. We have integrated the Parea method into our developed Python package
Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and
flexible design of ensemble workflows while incorporating a wide range of
fusion and clustering algorithms.

Deep learning has shown superb performance in detecting objects and
classifying images, ensuring a great promise for analyzing medical imaging.
Translating the success of deep learning to medical imaging, in which doctors
need to understand the underlying process, requires the capability to interpret
and explain the prediction of neural networks. Interpretability of deep neural
networks often relies on estimating the importance of input features (e.g.,
pixels) with respect to the outcome (e.g., class probability). However, a
number of importance estimators (also known as saliency maps) have been
developed and it is unclear which ones are more relevant for medical imaging
applications. In the present work, we investigated the performance of several
importance estimators in explaining the classification of computed tomography
(CT) images by a convolutional deep network, using three distinct evaluation
metrics. First, the model-centric fidelity measures a decrease in the model
accuracy when certain inputs are perturbed. Second, concordance between
importance scores and the expert-defined segmentation masks is measured on a
pixel level by a receiver operating characteristic (ROC) curves. Third, we
measure a region-wise overlap between a XRAI-based map and the segmentation
mask by Dice Similarity Coefficients (DSC). Overall, two versions of SmoothGrad
topped the fidelity and ROC rankings, whereas both Integrated Gradients and
SmoothGrad excelled in DSC evaluation. Interestingly, there was a critical
discrepancy between model-centric (fidelity) and human-centric (ROC and DSC)
evaluation. Expert expectation and intuition embedded in segmentation maps does
not necessarily align with how the model arrived at its prediction.
Understanding this difference in interpretability would help harnessing the
power of deep learning in medicine.

Powered by acoustics, existing therapeutic and diagnostic procedures will
become less invasive and new methods will become available that have never been
available before. Acoustically driven microrobot navigation based on
microbubbles is a promising approach for targeted drug delivery. Previous
studies have used acoustic techniques to manipulate microbubbles in vitro and
in vivo for the delivery of drugs using minimally invasive procedures. Even
though many advanced capabilities and sophisticated control have been achieved
for acoustically powered microrobots, there remain many challenges that remain
to be solved. In order to develop the next generation of intelligent
micro/nanorobots, it is highly desirable to conduct accurate identification of
the micro-nanorobots and to control their dynamic motion autonomously. Here we
use reinforcement learning control strategies to learn the microrobot dynamics
and manipulate them through acoustic forces. The result demonstrated for the
first time autonomous acoustic navigation of microbubbles in a microfluidic
environment. Taking advantage of the benefit of the second radiation force,
microbubbles swarm to form a large swarm, which is then driven along the
desired trajectory. More than 100 thousand images were used for the training to
study the unexpected dynamics of microbubbles. As a result of this work, the
microrobots are validated to be controlled, illustrating a good level of
robustness and providing computational intelligence to the microrobots, which
enables them to navigate independently in an unstructured environment without
requiring outside assistance.

The performance of existing single-view 3D reconstruction methods heavily
relies on large-scale 3D annotations. However, such annotations are tedious and
expensive to collect. Semi-supervised learning serves as an alternative way to
mitigate the need for manual labels, but remains unexplored in 3D
reconstruction. Inspired by the recent success of semi-supervised image
classification tasks, we propose SSP3D, a semi-supervised framework for 3D
reconstruction. In particular, we introduce an attention-guided prototype shape
prior module for guiding realistic object reconstruction. We further introduce
a discriminator-guided module to incentivize better shape generation, as well
as a regularizer to tolerate noisy training samples. On the ShapeNet benchmark,
the proposed approach outperforms previous supervised methods by clear margins
under various labeling ratios, (i.e., 1%, 5% , 10% and 20%). Moreover, our
approach also performs well when transferring to real-world Pix3D datasets
under labeling ratios of 10%. We also demonstrate our method could transfer to
novel categories with few novel supervised data. Experiments on the popular
ShapeNet dataset show that our method outperforms the zero-shot baseline by
over 12% and we also perform rigorous ablations and analysis to validate our
approach.

We analyze the convergence rate of the unregularized natural policy gradient
algorithm with log-linear policy parametrizations in infinite-horizon
discounted Markov decision processes. In the deterministic case, when the
Q-value is known and can be approximated by a linear combination of a known
feature function up to a bias error, we show that a geometrically-increasing
step size yields a linear convergence rate towards an optimal policy. We then
consider the sample-based case, when the best representation of the Q- value
function among linear combinations of a known feature function is known up to
an estimation error. In this setting, we show that the algorithm enjoys the
same linear guarantees as in the deterministic case up to an error term that
depends on the estimation error, the bias error, and the condition number of
the feature covariance matrix. Our results build upon the general framework of
policy mirror descent and extend previous findings for the softmax tabular
parametrization to the log-linear policy class.

Research around Spiking Neural Networks has ignited during the last years due
to their advantages when compared to traditional neural networks, including
their efficient processing and inherent ability to model complex temporal
dynamics. Despite these differences, Spiking Neural Networks face similar
issues than other neural computation counterparts when deployed in real-world
settings. This work addresses one of the practical circumstances that can
hinder the trustworthiness of this family of models: the possibility of
querying a trained model with samples far from the distribution of its training
data (also referred to as Out-of-Distribution or OoD data). Specifically, this
work presents a novel OoD detector that can identify whether test examples
input to a Spiking Neural Network belong to the distribution of the data over
which it was trained. For this purpose, we characterize the internal
activations of the hidden layers of the network in the form of spike count
patterns, which lay a basis for determining when the activations induced by a
test instance is atypical. Furthermore, a local explanation method is devised
to produce attribution maps revealing which parts of the input instance push
most towards the detection of an example as an OoD sample. Experimental results
are performed over several image classification datasets to compare the
proposed detector to other OoD detection schemes from the literature. As the
obtained results clearly show, the proposed detector performs competitively
against such alternative schemes, and produces relevance attribution maps that
conform to expectations for synthetically created OoD instances.

In supervised learning -- for instance in image classification -- modern
massive datasets are commonly labeled by a crowd of workers. The obtained
labels in this crowdsourcing setting are then aggregated for training. The
aggregation step generally leverages a per worker trust score. Yet, such
worker-centric approaches discard each task ambiguity. Some intrinsically
ambiguous tasks might even fool expert workers, which could eventually be
harmful for the learning step. In a standard supervised learning setting --
with one label per task and balanced classes -- the Area Under the Margin (AUM)
statistic is tailored to identify mislabeled data. We adapt the AUM to identify
ambiguous tasks in crowdsourced learning scenarios, introducing the Weighted
AUM (WAUM). The WAUM is an average of AUMs weighted by worker and task
dependent scores. We show that the WAUM can help discarding ambiguous tasks
from the training set, leading to better generalization or calibration
performance. We report improvements with respect to feature-blind aggregation
strategies both for simulated settings and for the CIFAR-10H crowdsourced
dataset.

We present a model for non-blind image deconvolution that incorporates the
classic iterative method into a deep learning application. Instead of using
large over-parameterised generative networks to create sharp picture
representations, we build our network based on the iterative Landweber
deconvolution algorithm, which is integrated with trainable convolutional
layers to enhance the recovered image structures and details. Additional to the
data fidelity term, we also add Hessian and sparse constraints as
regularization terms to improve the image reconstruction quality. Our proposed
model is \textit{self-supervised} and converges to a solution based purely on
the input blurred image and respective blur kernel without the requirement of
any pre-training. We evaluate our technique using standard computer vision
benchmarking datasets as well as real microscope images obtained by our
enhanced depth-of-field (EDOF) underwater microscope, demonstrating the
capabilities of our model in a real-world application. The quantitative results
demonstrate that our approach is competitive with state-of-the-art non-blind
image deblurring methods despite having a fraction of the parameters and not
being pre-trained, demonstrating the efficiency and efficacy of embedding a
classic deconvolution approach inside a deep network.

Authors writing documents imprint identifying information within their texts:
vocabulary, registry, punctuation, misspellings, or even emoji usage. Finding
these details is very relevant to profile authors, relating back to their
gender, occupation, age, and so on. But most importantly, repeating writing
patterns can help attributing authorship to a text. Previous works use
hand-crafted features or classification tasks to train their authorship models,
leading to poor performance on out-of-domain authors. A better approach to this
task is to learn stylometric representations, but this by itself is an open
research challenge. In this paper, we propose PART: a contrastively trained
model fit to learn \textbf{authorship embeddings} instead of semantics. By
comparing pairs of documents written by the same author, we are able to
determine the proprietary of a text by evaluating the cosine similarity of the
evaluated documents, a zero-shot generalization to authorship identification.
To this end, a pre-trained Transformer with an LSTM head is trained with the
contrastive training method. We train our model on a diverse set of authors,
from literature, anonymous blog posters and corporate emails; a heterogeneous
set with distinct and identifiable writing styles. The model is evaluated on
these datasets, achieving zero-shot 72.39\% and 86.73\% accuracy and top-5
accuracy respectively on the joint evaluation dataset when determining
authorship from a set of 250 different authors. We qualitatively assess the
representations with different data visualizations on the available datasets,
profiling features such as book types, gender, age, or occupation of the
author.

We lay the foundations of a non-parametric theory of best-arm identification
in multi-armed bandits with a fixed budget T. We consider general, possibly
non-parametric, models D for distributions over the arms; an overarching
example is the model D = P(0,1) of all probability distributions over [0,1]. We
propose upper bounds on the average log-probability of misidentifying the
optimal arm based on information-theoretic quantities that correspond to infima
over Kullback-Leibler divergences between some distributions in D and a given
distribution. This is made possible by a refined analysis of the
successive-rejects strategy of Audibert, Bubeck, and Munos (2010). We finally
provide lower bounds on the same average log-probability, also in terms of the
same new information-theoretic quantities; these lower bounds are larger when
the (natural) assumptions on the considered strategies are stronger. All these
new upper and lower bounds generalize existing bounds based, e.g., on gaps
between distributions.

Bayesian optimization is a powerful collection of methods for optimizing
stochastic expensive black box functions. One key component of a Bayesian
optimization algorithm is the acquisition function that determines which
solution should be evaluated in every iteration. A popular and very effective
choice is the Knowledge Gradient acquisition function, however there is no
analytical way to compute it. Several different implementations make different
approximations. In this paper, we review and compare the spectrum of Knowledge
Gradient implementations and propose One-shot Hybrid KG, a new approach that
combines several of the previously proposed ideas and is cheap to compute as
well as powerful and efficient. We prove the new method preserves theoretical
properties of previous methods and empirically show the drastically reduced
computational overhead with equal or improved performance. All experiments are
implemented in BOTorch and code is available on github.

Handwritten text recognition has been widely studied in the last decades for
its numerous applications. Nowadays, the state-of-the-art approach consists in
a three-step process. The document is segmented into text lines, which are then
ordered and recognized. However, this three-step approach has many drawbacks.
The three steps are treated independently whereas they are closely related.
Errors accumulate from one step to the other. The ordering step is based on
heuristic rules which prevent its use for documents with a complex layouts or
for heterogeneous documents. The need for additional physical segmentation
annotations for training the segmentation stage is inherent to this approach.
In this thesis, we propose to tackle these issues by performing the handwritten
text recognition of whole document in an end-to-end way. To this aim, we
gradually increase the difficulty of the recognition task, moving from isolated
lines to paragraphs, and then to whole documents. We proposed an approach at
the line level, based on a fully convolutional network, in order to design a
first generic feature extraction step for the handwriting recognition task.
Based on this preliminary work, we studied two different approaches to
recognize handwritten paragraphs. We reached state-of-the-art results at
paragraph level on the RIMES 2011, IAM and READ 2016 datasets and outperformed
the line-level state of the art on these datasets. We finally proposed the
first end-to-end approach dedicated to the recognition of both text and layout,
at document level. Characters and layout tokens are sequentially predicted
following a learned reading order. We proposed two new metrics we used to
evaluate this task on the RIMES 2009 and READ 2016 dataset, at page level and
double-page level.

We tackle the problem of generating audio samples conditioned on descriptive
text captions. In this work, we propose AaudioGen, an auto-regressive
generative model that generates audio samples conditioned on text inputs.
AudioGen operates on a learnt discrete audio representation. The task of
text-to-audio generation poses multiple challenges. Due to the way audio
travels through a medium, differentiating ``objects'' can be a difficult task
(e.g., separating multiple people simultaneously speaking). This is further
complicated by real-world recording conditions (e.g., background noise,
reverberation, etc.). Scarce text annotations impose another constraint,
limiting the ability to scale models. Finally, modeling high-fidelity audio
requires encoding audio at high sampling rate, leading to extremely long
sequences. To alleviate the aforementioned challenges we propose an
augmentation technique that mixes different audio samples, driving the model to
internally learn to separate multiple sources. We curated 10 datasets
containing different types of audio and text annotations to handle the scarcity
of text-audio data points. For faster inference, we explore the use of
multi-stream modeling, allowing the use of shorter sequences while maintaining
a similar bitrate and perceptual quality. We apply classifier-free guidance to
improve adherence to text. Comparing to the evaluated baselines, AudioGen
outperforms over both objective and subjective metrics. Finally, we explore the
ability of the proposed method to generate audio continuation conditionally and
unconditionally. Samples: https://tinyurl.com/audiogen-text2audio

We present a novel $Q$-learning algorithm to solve distributionally robust
Markov decision problems, where the corresponding ambiguity set of transition
probabilities for the underlying Markov decision process is a Wasserstein ball
around a (possibly estimated) reference measure. We prove convergence of the
presented algorithm and provide several examples also using real data to
illustrate both the tractability of our algorithm as well as the benefits of
considering distributional robustness when solving stochastic optimal control
problems, in particular when the estimated distributions turn out to be
misspecified in practice.

Lewis signaling games are a class of simple communication games for
simulating the emergence of language. In these games, two agents must agree on
a communication protocol in order to solve a cooperative task. Previous work
has shown that agents trained to play this game with reinforcement learning
tend to develop languages that display undesirable properties from a linguistic
point of view (lack of generalization, lack of compositionality, etc). In this
paper, we aim to provide better understanding of this phenomenon by
analytically studying the learning problem in Lewis games. As a core
contribution, we demonstrate that the standard objective in Lewis games can be
decomposed in two components: a co-adaptation loss and an information loss.
This decomposition enables us to surface two potential sources of overfitting,
which we show may undermine the emergence of a structured communication
protocol. In particular, when we control for overfitting on the co-adaptation
loss, we recover desired properties in the emergent languages: they are more
compositional and generalize better.

Channel state information (CSI) plays a critical role in achieving the
potential benefits of massive multiple input multiple output (MIMO) systems. In
frequency division duplex (FDD) massive MIMO systems, the base station (BS)
relies on sustained and accurate CSI feedback from the users. However, due to
the large number of antennas and users being served in massive MIMO systems,
feedback overhead can become a bottleneck. In this paper, we propose a
model-driven deep learning method for CSI feedback, called learnable
optimization and regularization algorithm (LORA). Instead of using l1-norm as
the regularization term, a learnable regularization module is introduced in
LORA to automatically adapt to the characteristics of CSI. We unfold the
conventional iterative shrinkage-thresholding algorithm (ISTA) to a neural
network and learn both the optimization process and regularization term by
end-toend training. We show that LORA improves the CSI feedback accuracy and
speed. Besides, a novel learnable quantization method and the corresponding
training scheme are proposed, and it is shown that LORA can operate
successfully at different bit rates, providing flexibility in terms of the CSI
feedback overhead. Various realistic scenarios are considered to demonstrate
the effectiveness and robustness of LORA through numerical simulations.

We propose a nonnegative tensor decomposition with focusing on the
relationship between the modes of tensors. Traditional decomposition methods
assume low-rankness in the representation, resulting in difficulties in global
optimization and target rank selection. To address these problems, we present
an alternative way to decompose tensors, a many-body approximation for tensors,
based on an information geometric formulation. A tensor is treated via an
energy-based model, where the tensor and its mode correspond to a probability
distribution and a random variable, respectively, and many-body approximation
is performed on it by taking the interaction between variables into account.
Our model can be globally optimized in polynomial time in terms of the KL
divergence minimization, which is empirically faster than low-rank
approximations keeping comparable reconstruction error. Furthermore, we
visualize interactions between modes as tensor networks and reveal a nontrivial
relationship between many-body approximation and low-rank approximation.

Cross-Technology Communication (CTC) is an emerging technology to support
direct communication between wireless devices that follow different standards.
In spite of the many different proposals from the community to enable CTC, the
performance aspect of CTC is an equally important problem but has seldom been
studied before. We find this problem is extremely challenging, due to the
following reasons: on one hand, a link for CTC is essentially different from a
conventional wireless link. The conventional link indicators like RSSI
(received signal strength indicator) and SNR (signal to noise ratio) cannot be
used to directly characterize a CTC link. On the other hand, the indirect
indicators like PER (packet error rate), which is adopted by many existing CTC
proposals, cannot capture the short-term link behavior. As a result, the
existing CTC proposals fail to keep reliable performance under dynamic channel
conditions. In order to address the above challenge, we in this paper propose
AdaComm, a generic framework to achieve self-adaptive CTC in dynamic channels.
Instead of reactively adjusting the CTC sender, AdaComm adopts online learning
mechanism to adaptively adjust the decoding model at the CTC receiver. The
self-adaptive decoding model automatically learns the effective features
directly from the raw received signals that are embedded with the current
channel state. With the lossless channel information, AdaComm further adopts
the fine tuning and full training modes to cope with the continuous and abrupt
channel dynamics. We implement AdaComm and integrate it with two existing CTC
approaches that respectively employ CSI (channel state information) and RSSI as
the information carrier. The evaluation results demonstrate that AdaComm can
significantly reduce the SER (symbol error rate) by 72.9% and 49.2%,
respectively, compared with the existing approaches.

While Unmanned Aerial Vehicles (UAVs) are increasingly deployed in several
missions, their inability of reliable and consistent autonomous landing poses a
major setback for deploying such systems truly autonomously. In this paper we
present an autonomous UAV landing system for landing on a moving platform. In
contrast to existing attempts, the proposed system relies only on the camera
sensor, and has been designed as lightweight as possible. The proposed system
can be deployed on a low power platform as part of the drone payload, whilst
being indifferent to any external communication or any other sensors. The
system relies on a Neural Network (NN) based controller, for which a target and
environment agnostic simulator was created, used in training and testing of the
proposed system, via Reinforcement Learning (RL) and Proximal Policy
optimization (PPO) to optimally control and steer the drone towards landing on
the target. Through real-world testing, the system was evaluated with an
average deviation of 15cm from the center of the target, for 40 landing
attempts.

One main challenge in federated learning is the large communication cost of
exchanging weight updates from clients to the server at each round. While prior
work has made great progress in compressing the weight updates through gradient
compression methods, we propose a radically different approach that does not
update the weights at all. Instead, our method freezes the weights at their
initial \emph{random} values and learns how to sparsify the random network for
the best performance. To this end, the clients collaborate in training a
\emph{stochastic} binary mask to find the optimal sparse random network within
the original one. At the end of the training, the final model is a sparse
network with random weights -- or a subnetwork inside the dense random network.
We show improvements in accuracy, communication (less than $1$ bit per
parameter (bpp)), convergence speed, and final model size (less than $1$ bpp)
over relevant baselines on MNIST, EMNIST, CIFAR-10, and CIFAR-100 datasets, in
the low bitrate regime under various system configurations.

Recent advances in image captioning have focused on scaling the data and
model size, substantially increasing the cost of pre-training and finetuning.
As an alternative to large models, we present SmallCap, which generates a
caption conditioned on an input image and related captions retrieved from a
datastore. Our model is lightweight and fast to train as the only learned
parameters are in newly introduced cross-attention layers between a pre-trained
CLIP encoder and GPT-2 decoder. SmallCap can transfer to new domains without
additional finetuning and exploit large-scale data in a training-free fashion
because the contents of the datastore can be readily replaced. Our experiments
show that SmallCap, trained only on COCO, has competitive performance on this
benchmark, and also transfers to other domains without retraining, solely
through retrieval from target-domain data. Further improvement is achieved
through the training-free exploitation of diverse human-labeled and web data,
which proves effective for other domains, including the nocaps image captioning
benchmark, designed to test generalization to unseen visual concepts.

Missing data persists as a major barrier to data analysis across numerous
applications. Recently, deep generative models have been used for imputation of
missing data, motivated by their ability to capture highly non-linear and
complex relationships in the data. In this work, we investigate the ability of
deep models, namely variational autoencoders (VAEs), to account for uncertainty
in missing data through multiple imputation strategies. We find that VAEs
provide poor empirical coverage of missing data, with underestimation and
overconfident imputations, particularly for more extreme missing data values.
To overcome this, we employ $\beta$-VAEs, which viewed from a generalized Bayes
framework, provide robustness to model misspecification. Assigning a good value
of $\beta$ is critical for uncertainty calibration and we demonstrate how this
can be achieved using cross-validation. In downstream tasks, we show how
multiple imputation with $\beta$-VAEs can avoid false discoveries that arise as
artefacts of imputation.

Policy robustness in Reinforcement Learning (RL) may not be desirable at any
price; the alterations caused by robustness requirements from otherwise optimal
policies should be explainable and quantifiable. Policy gradient algorithms
that have strong convergence guarantees are usually modified to obtain robust
policies in ways that do not preserve algorithm guarantees, which defeats the
purpose of formal robustness requirements. In this work we study a notion of
robustness in partially observable MDPs where state observations are perturbed
by a noise-induced stochastic kernel. We characterise the set of policies that
are maximally robust by analysing how the policies are altered by this kernel.
We then establish a connection between such robust policies and certain
properties of the noise kernel, as well as with structural properties of the
underlying MDPs, constructing sufficient conditions for policy robustness. We
use these notions to propose a robustness-inducing scheme, applicable to any
policy gradient algorithm, to formally trade off the reward achieved by a
policy with its robustness level through lexicographic optimisation, which
preserves convergence properties of the original algorithm. We test the the
proposed approach through numerical experiments on safety-critical RL
environments, and show how the proposed method helps achieve high robustness
when state errors are introduced in the policy roll-out.

Retrosynthetic planning plays a critical role in drug discovery and organic
chemistry. Starting from a target molecule as the root node, it aims to find a
complete reaction tree subject to the constraint that all leaf nodes belong to
a set of starting materials. The multi-step reactions are crucial because they
determine the flow chart in the production of the Organic Chemical Industry.
However, existing datasets lack curation of tree-structured multi-step
reactions, and fail to provide such reaction trees, limiting models'
understanding of organic molecule transformations. In this work, we first
develop a benchmark curated for the retrosynthetic planning task, which
consists of 124,869 reaction trees retrieved from the public USPTO-full
dataset. On top of that, we propose Metro: Memory-Enhanced Transformer for
RetrOsynthetic planning. Specifically, the dependency among molecules in the
reaction tree is captured as context information for multi-step retrosynthesis
predictions through transformers with a memory module. Extensive experiments
show that Metro dramatically outperforms existing single-step retrosynthesis
models by at least 10.7% in top-1 accuracy. The experiments demonstrate the
superiority of exploiting context information in the retrosynthetic planning
task. Moreover, the proposed model can be directly used for synthetic
accessibility analysis, as it is trained on reaction trees with the shortest
depths. Our work is the first step towards a brand new formulation for
retrosynthetic planning in the aspects of data construction, model design, and
evaluation. Code is available at https://github.com/SongtaoLiu0823/metro.

As deep networks require large amounts of accurately labeled training data, a
strategy to collect sufficiently large and accurate annotations is as important
as innovations in recognition methods. This is especially true for building
Computer Aided Detection (CAD) systems for chest X-rays where domain expertise
of radiologists is required to annotate the presence and location of
abnormalities on X-ray images. However, there lacks concrete evidence that
provides guidance on how much resource to allocate for data annotation such
that the resulting CAD system reaches desired performance. Without this
knowledge, practitioners often fall back to the strategy of collecting as much
detail as possible on as much data as possible which is cost inefficient. In
this work, we investigate how the cost of data annotation ultimately impacts
the CAD model performance on classification and segmentation of chest
abnormalities in frontal-view X-ray images. We define the cost of annotation
with respect to the following three dimensions: quantity, quality and
granularity of labels. Throughout this study, we isolate the impact of each
dimension on the resulting CAD model performance on detecting 10 chest
abnormalities in X-rays. On a large scale training data with over 120K X-ray
images with gold-standard annotations, we find that cost-efficient annotations
provide great value when collected in large amounts and lead to competitive
performance when compared to models trained with only gold-standard
annotations. We also find that combining large amounts of cost efficient
annotations with only small amounts of expensive labels leads to competitive
CAD models at a much lower cost.

Spin polarization and entanglement are utilized by the BESIII experiment to
learn more about the production and decay properties of hyperon-antihyperon
pairs in a series of recent analyses of its unprecedented datasets at the
$J/\psi$ and $\psi'$ resonances. This has lead to the observation of
significant transverse polarisation in decays of $J/\psi$ or $\psi'$ into
$\Lambda \bar{\Lambda}$, $\Sigma \bar{\Sigma}$, $\Xi \bar{\Xi}$, and $\Omega
\bar{\Omega}$. Because of the non-zero polarization, the decay parameters for
the most common hadronic weak decays of both hyperons and antihyperons could be
determined independently for the first time. Comparing these to each other
yields precise tests of direct, $\Delta S = 1$ CP-violation that complement
measurements of kaon decays.

Early stopping techniques can be utilized to decrease the time cost, however
currently the ultimate goal of early stopping techniques is closely related to
the accuracy upgrade or the ability of the neural network to generalize better
on unseen data without being large or complex in structure and not directly
with its efficiency. Time efficiency is a critical factor in neural networks,
especially when dealing with the segmentation of 3D point cloud data, not only
because a neural network itself is computationally expensive, but also because
point clouds are large and noisy data, making learning processes even more
costly. In this paper, we propose a new early stopping technique based on
fundamental mathematics aiming to upgrade the trade-off between the learning
efficiency and accuracy of neural networks dealing with 3D point clouds. Our
results show that by employing our early stopping technique in four distinct
and highly utilized neural networks in segmenting 3D point clouds, the training
time efficiency of the models is greatly improved, with efficiency gain values
reaching up to 94\%, while the models achieving in just a few epochs
approximately similar segmentation accuracy metric values like the ones that
are obtained in the training of the neural networks in 200 epochs. Also, our
proposal outperforms four conventional early stopping approaches in
segmentation accuracy, implying a promising innovative early stopping technique
in point cloud segmentation.

We design online algorithms for the fair allocation of public goods to a set
of $N$ agents over a sequence of $T$ rounds and focus on improving their
performance using predictions. In the basic model, a public good arrives in
each round, the algorithm learns every agent's value for the good, and must
irrevocably decide the amount of investment in the good without exceeding a
total budget of $B$ across all rounds. The algorithm can utilize (potentially
inaccurate) predictions of each agent's total value for all the goods to
arrive. We measure the performance of the algorithm using a proportional
fairness objective, which informally demands that every group of agents be
rewarded in proportion to its size and the cohesiveness of its preferences.
  In the special case of binary agent preferences and a unit budget, we show
that $O(\log N)$ proportional fairness can be achieved without using any
predictions, and that this is optimal even if perfectly accurate predictions
were available. However, for general preferences and budget no algorithm can
achieve better than $\Theta(T/B)$ proportional fairness without predictions. We
show that algorithms with (reasonably accurate) predictions can do much better,
achieving $\Theta(\log (T/B))$ proportional fairness. We also extend this
result to a general model in which a batch of $L$ public goods arrive in each
round and achieve $O(\log (\min(N,L) \cdot T/B))$ proportional fairness. Our
exact bounds are parametrized as a function of the error in the predictions and
the performance degrades gracefully with increasing errors.

MXenes represent one of the largest class of 2D materials with promising
applications in many fields and their properties tunable by the surface group
composition. Raman spectroscopy is expected to yield rich information about the
surface composition, but the interpretation of measured spectra has proven
challenging. The interpretation is usually done via comparison to simulated
spectra, but there are large discrepancies between the experimental and earlier
simulated spectra. In this work, we develop a computational approach to
simulate Raman spectra of complex materials that combines machine-learning
force-field molecular dynamics and reconstruction of Raman tensors via
projection to pristine system modes. The approach can account for the effects
of finite temperature, mixed surfaces, and disorder. We apply our approach to
simulate Raman spectra of titanium carbide MXene and show that all these
effects must be included in order to properly reproduce the experimental
spectra, in particular the broad features. We discuss the origin of the peaks
and how they evolve with surface composition, which can then be used to
interpret experimental results.

Manual approvals are still used by banks and other NGOs to approve loans. It
takes time and is prone to mistakes because it is controlled by a bank
employee. Several fields of machine learning mining technologies have been
utilized to enhance various areas of credit rating forecast. A major goal of
this research is to look at current sentiment analysis techniques that are
being used to generate creditworthiness.

Collaborative Metric Learning (CML) has recently emerged as a popular method
in recommendation systems (RS), closing the gap between metric learning and
Collaborative Filtering. Following the convention of RS, existing methods
exploit unique user representation in their model design. This paper focuses on
a challenging scenario where a user has multiple categories of interests. Under
this setting, we argue that the unique user representation might induce
preference bias, especially when the item category distribution is imbalanced.
To address this issue, we propose a novel method called
\textit{Diversity-Promoting Collaborative Metric Learning} (DPCML), with the
hope of considering the commonly ignored minority interest of the user. The key
idea behind DPCML is to include a multiple set of representations for each user
in the system. Based on this embedding paradigm, user preference toward an item
is aggregated from different embeddings by taking the minimum item-user
distance among the user embedding set. Furthermore, we observe that the
diversity of the embeddings for the same user also plays an essential role in
the model. To this end, we propose a \textit{diversity control regularization}
term to accommodate the multi-vector representation strategy better.
Theoretically, we show that DPCML could generalize well to unseen test data by
tackling the challenge of the annoying operation that comes from the minimum
value. Experiments over a range of benchmark datasets speak to the efficacy of
DPCML.

The report describes the digital architecture developed for the West
Cambridge Digital Twin, particularly focussed on real-time sensor data
collection and analysis with a privacy framework allowing occupants of the
buildings to be first-class participants in the system. The implementation has
some notable characteristics. In particular 'push' technology is used
throughout such that information streams from the incoming asynchronous
individual sensor events through to the end-user web pages with the absolute
minimum latency, including real-time generated simple and complex events
derived from the the underlying sensor data and the updating of visualisations
such as an in-building heatmap. We believe the ability of the entire system to
respond in the timescale of individual sensor messages to be unique. JSON
structures are used to represent all data types including sensor readings,
sensor types, building objects, organisations and people, with the idea that
JSON-LD may represent a more suitable way than XML/RDF for managing relations
between those objects (such as the 'occupies' relationship of people to
offices, or the 'type' relationship of sensors to sensor types).

With the recent advance in neural machine translation demonstrating its
importance, research on quality estimation (QE) has been steadily progressing.
QE aims to automatically predict the quality of machine translation (MT) output
without reference sentences. Despite its high utility in the real world, there
remain several limitations concerning manual QE data creation: inevitably
incurred non-trivial costs due to the need for translation experts, and issues
with data scaling and language expansion. To tackle these limitations, we
present QUAK, a Korean-English synthetic QE dataset generated in a fully
automatic manner. This consists of three sub-QUAK datasets QUAK-M, QUAK-P, and
QUAK-H, produced through three strategies that are relatively free from
language constraints. Since each strategy requires no human effort, which
facilitates scalability, we scale our data up to 1.58M for QUAK-P, H and 6.58M
for QUAK-M. As an experiment, we quantitatively analyze word-level QE results
in various ways while performing statistical analysis. Moreover, we show that
datasets scaled in an efficient way also contribute to performance improvements
by observing meaningful performance gains in QUAK-M, P when adding data up to
1.58M.

Motivated by the design of fast reinforcement learning algorithms, we study
the diffusive limit of a class of pure jump ergodic stochastic control
problems. We show that, whenever the intensity of jumps is large enough, the
approximation error is governed by the H{\"o}lder continuity of the Hessian
matrix of the solution to the limit ergodic partial differential equation. This
extends to this context the results of [1] obtained for finite horizon
problems. We also explain how to construct a first order error correction term
under appropriate smoothness assumptions. Finally, we quantify the error
induced by the use of the Markov control policy constructed from the numerical
finite difference scheme associated to the limit diffusive problem, this seems
to be new in the literature and of its own interest. This approach permits to
reduce very significantly the numerical resolution cost.

Dedicated neural network (NN) architectures have been designed to handle
specific data types (such as CNN for images or RNN for text), which ranks them
among state-of-the-art methods for dealing with these data. Unfortunately, no
architecture has been found for dealing with tabular data yet, for which tree
ensemble methods (tree boosting, random forests) usually show the best
predictive performances. In this work, we propose a new sparse initialization
technique for (potentially deep) multilayer perceptrons (MLP): we first train a
tree-based procedure to detect feature interactions and use the resulting
information to initialize the network, which is subsequently trained via
standard stochastic gradient strategies. Numerical experiments on several
tabular data sets show that this new, simple and easy-to-use method is a solid
concurrent, both in terms of generalization capacity and computation time, to
default MLP initialization and even to existing complex deep learning
solutions. In fact, this wise MLP initialization raises the resulting NN
methods to the level of a valid competitor to gradient boosting when dealing
with tabular data. Besides, such initializations are able to preserve the
sparsity of weights introduced in the first layers of the network through
training. This fact suggests that this new initializer operates an implicit
regularization during the NN training, and emphasizes that the first layers act
as a sparse feature extractor (as for convolutional layers in CNN).

Pre-training on large-scale video data has become a common recipe for
learning transferable spatiotemporal representations in recent years. Despite
some progress, existing methods are mostly limited to highly curated datasets
(e.g., K400) and exhibit unsatisfactory out-of-the-box representations. We
argue that it is due to the fact that they only capture pixel-level knowledge
rather than spatiotemporal commonsense, which is far away from cognition-level
video understanding. Inspired by the great success of image-text pre-training
(e.g., CLIP), we take the first step to exploit language semantics to boost
transferable spatiotemporal representation learning. We introduce a new pretext
task, Turning to Video for Transcript Sorting (TVTS), which sorts shuffled ASR
scripts by attending to learned video representations. We do not rely on
descriptive captions and learn purely from video, i.e., leveraging the natural
transcribed speech knowledge to provide noisy but useful semantics over time.
Furthermore, rather than the simple concept learning in vision-caption
contrast, we encourage cognition-level temporal commonsense reasoning via
narrative reorganization. The advantages enable our model to contextualize what
is happening like human beings and seamlessly apply to large-scale uncurated
video data in the real world. Note that our method differs from ones designed
for video-text alignment (e.g., Frozen) and multimodal representation learning
(e.g., Merlot). Our method demonstrates strong out-of-the-box spatiotemporal
representations on diverse video benchmarks, e.g., +13.6% gains over VideoMAE
on SSV2 via linear probing.

Over past few years afterward the birth of ResNet, skip connection has become
the defacto standard for the design of modern architectures due to its
widespread adoption, easy optimization and proven performance. Prior work has
explained the effectiveness of the skip connection mechanism from different
perspectives. In this work, we deep dive into the model's behaviors with skip
connections which can be formulated as a learnable Markov chain. An efficient
Markov chain is preferred as it always maps the input data to the target domain
in a better way. However, while a model is explained as a Markov chain, it is
not guaranteed to be optimized following an efficient Markov chain by existing
SGD-based optimizers which are prone to get trapped in local optimal points. In
order to towards a more efficient Markov chain, we propose a simple routine of
penal connection to make any residual-like model become a learnable Markov
chain. Aside from that, the penal connection can also be viewed as a particular
model regularization and can be easily implemented with one line of code in the
most popular deep learning frameworks~\footnote{Source code:
\url{https://github.com/densechen/penal-connection}}. The encouraging
experimental results in multi-modal translation and image recognition
empirically confirm our conjecture of the learnable Markov chain view and
demonstrate the superiority of the proposed penal connection.

Machine learning models (mainly neural networks) are used more and more in
real life. Users feed their data to the model for training. But these processes
are often one-way. Once trained, the model remembers the data. Even when data
is removed from the dataset, the effects of these data persist in the model.
With more and more laws and regulations around the world protecting data
privacy, it becomes even more important to make models forget this data
completely through machine unlearning.
  This paper adopts the projection residual method based on Newton iteration
method. The main purpose is to implement machine unlearning tasks in the
context of linear regression models and neural network models. This method
mainly uses the iterative weighting method to completely forget the data and
its corresponding influence, and its computational cost is linear in the
feature dimension of the data. This method can improve the current machine
learning method. At the same time, it is independent of the size of the
training set. Results were evaluated by feature injection testing (FIT).
Experiments show that this method is more thorough in deleting data, which is
close to model retraining.

Recent Vision-Language Pre-trained (VLP) models based on dual encoder have
attracted extensive attention from academia and industry due to their superior
performance on various cross-modal tasks and high computational efficiency.
They attempt to learn cross-modal representation using contrastive learning on
image-text pairs, however, the built inter-modal correlations only rely on a
single view for each modality. Actually, an image or a text contains various
potential views, just as humans could capture a real-world scene via diverse
descriptions or photos. In this paper, we propose ERNIE-ViL 2.0, a Multi-View
Contrastive learning framework to build intra-modal and inter-modal
correlations between diverse views simultaneously, aiming at learning a more
robust cross-modal representation. Specifically, we construct multiple views
within each modality to learn the intra-modal correlation for enhancing the
single-modal representation. Besides the inherent visual/textual views, we
construct sequences of object tags as a special textual view to narrow the
cross-modal semantic gap on noisy image-text pairs. Pre-trained with 29M
publicly available datasets, ERNIE-ViL 2.0 achieves competitive results on
English cross-modal retrieval. Additionally, to generalize our method to
Chinese cross-modal tasks, we train ERNIE-ViL 2.0 through scaling up the
pre-training datasets to 1.5B Chinese image-text pairs, resulting in
significant improvements compared to previous SOTA results on Chinese
cross-modal retrieval. We release our pre-trained models in
https://github.com/PaddlePaddle/ERNIE.

Traditional machine learning (ML) models usually rely on large-scale labeled
datasets to achieve strong performance. However, such labeled datasets are
often challenging and expensive to obtain. Also, the predefined categories
limit the model's ability to generalize to other visual concepts as additional
labeled data is required. On the contrary, the newly emerged multimodal model,
which contains both visual and linguistic modalities, learns the concept of
images from the raw text. It is a promising way to solve the above problems as
it can use easy-to-collect image-text pairs to construct the training dataset
and the raw texts contain almost unlimited categories according to their
semantics. However, learning from a large-scale unlabeled dataset also exposes
the model to the risk of potential poisoning attacks, whereby the adversary
aims to perturb the model's training dataset to trigger malicious behaviors in
it. Previous work mainly focuses on the visual modality. In this paper, we
instead focus on answering two questions: (1) Is the linguistic modality also
vulnerable to poisoning attacks? and (2) Which modality is most vulnerable? To
answer the two questions, we conduct three types of poisoning attacks against
CLIP, the most representative multimodal contrastive learning framework.
Extensive evaluations on different datasets and model architectures show that
all three attacks can perform well on the linguistic modality with only a
relatively low poisoning rate and limited epochs. Also, we observe that the
poisoning effect differs between different modalities, i.e., with lower MinRank
in the visual modality and with higher Hit@K when K is small in the linguistic
modality. To mitigate the attacks, we propose both pre-training and
post-training defenses. We empirically show that both defenses can
significantly reduce the attack performance while preserving the model's
utility.

The practice of deep learning has shown that neural networks generalize
remarkably well even with an extreme number of learned parameters. This appears
to contradict traditional statistical wisdom, in which a trade-off between
model complexity and fit to the data is essential. We set out to resolve this
discrepancy from a convex optimization and sparse recovery perspective. We
consider the training and generalization properties of two-layer ReLU networks
with standard weight decay regularization. Under certain regularity assumptions
on the data, we show that ReLU networks with an arbitrary number of parameters
learn only simple models that explain the data. This is analogous to the
recovery of the sparsest linear model in compressed sensing. For ReLU networks
and their variants with skip connections or normalization layers, we present
isometry conditions that ensure the exact recovery of planted neurons. For
randomly generated data, we show the existence of a phase transition in
recovering planted neural network models. The situation is simple: whenever the
ratio between the number of samples and the dimension exceeds a numerical
threshold, the recovery succeeds with high probability; otherwise, it fails
with high probability. Surprisingly, ReLU networks learn simple and sparse
models even when the labels are noisy. The phase transition phenomenon is
confirmed through numerical experiments.

Diffusion-based image translation guided by semantic texts or a single target
image has enabled flexible style transfer which is not limited to the specific
domains. Unfortunately, due to the stochastic nature of diffusion models, it is
often difficult to maintain the original content of the image during the
reverse diffusion. To address this, here we present a novel diffusion-based
unsupervised image translation method using disentangled style and content
  Specifically, inspired by the splicing Vision Transformer, we extract
intermediate keys of multihead self attention layer from ViT model and used
them as the content preservation loss. Then, an image guided style transfer is
performed by matching the [CLS] classification token from the denoised samples
and target image, whereas additional CLIP loss is used for the text-driven
style transfer. To further accelerate the semantic change during the reverse
diffusion, we also propose a novel semantic divergence loss and resampling
strategy. Our experimental results show that the proposed method outperforms
state-of-the-art baseline models in both text-guided and image-guided
translation tasks.

We describe a minimalistic and interpretable method for unsupervised
learning, without resorting to data augmentation, hyperparameter tuning, or
other engineering designs, that achieves performance close to the SOTA SSL
methods. Our approach leverages the sparse manifold transform, which unifies
sparse coding, manifold learning, and slow feature analysis. With a one-layer
deterministic sparse manifold transform, one can achieve 99.3% KNN top-1
accuracy on MNIST, 81.1% KNN top-1 accuracy on CIFAR-10 and 53.2% on CIFAR-100.
With a simple gray-scale augmentation, the model gets 83.2% KNN top-1 accuracy
on CIFAR-10 and 57% on CIFAR-100. These results significantly close the gap
between simplistic ``white-box'' methods and the SOTA methods. Additionally, we
provide visualization to explain how an unsupervised representation transform
is formed. The proposed method is closely connected to latent-embedding
self-supervised methods and can be treated as the simplest form of VICReg.
Though there remains a small performance gap between our simple constructive
model and SOTA methods, the evidence points to this as a promising direction
for achieving a principled and white-box approach to unsupervised learning.

Large machine learning models, or so-called foundation models, aim to serve
as base-models for application-oriented machine learning. Although these models
showcase impressive performance, they have been empirically found to pose
serious security and privacy issues. We may however wonder if this is a
limitation of the current models, or if these issues stem from a fundamental
intrinsic impossibility of the foundation model learning problem itself. This
paper aims to systematize our knowledge supporting the latter. More precisely,
we identify several key features of today's foundation model learning problem
which, given the current understanding in adversarial machine learning, suggest
incompatibility of high accuracy with both security and privacy. We begin by
observing that high accuracy seems to require (1) very high-dimensional models
and (2) huge amounts of data that can only be procured through user-generated
datasets. Moreover, such data is fundamentally heterogeneous, as users
generally have very specific (easily identifiable) data-generating habits. More
importantly, users' data is filled with highly sensitive information, and maybe
heavily polluted by fake users. We then survey lower bounds on accuracy in
privacy-preserving and Byzantine-resilient heterogeneous learning that, we
argue, constitute a compelling case against the possibility of designing a
secure and privacy-preserving high-accuracy foundation model. We further stress
that our analysis also applies to other high-stake machine learning
applications, including content recommendation. We conclude by calling for
measures to prioritize security and privacy, and to slow down the race for ever
larger models.

We present TransLPC, a novel detection model for large point clouds that is
based on a transformer architecture. While object detection with transformers
has been an active field of research, it has proved difficult to apply such
models to point clouds that span a large area, e.g. those that are common in
autonomous driving, with lidar or radar data. TransLPC is able to remedy these
issues: The structure of the transformer model is modified to allow for larger
input sequence lengths, which are sufficient for large point clouds. Besides
this, we propose a novel query refinement technique to improve detection
accuracy, while retaining a memory-friendly number of transformer decoder
queries. The queries are repositioned between layers, moving them closer to the
bounding box they are estimating, in an efficient manner. This simple technique
has a significant effect on detection accuracy, which is evaluated on the
challenging nuScenes dataset on real-world lidar data. Besides this, the
proposed method is compatible with existing transformer-based solutions that
require object detection, e.g. for joint multi-object tracking and detection,
and enables them to be used in conjunction with large point clouds.

Deep neural networks virtually dominate the domain of most modern vision
systems, providing high performance at a cost of increased computational
complexity.Since for those systems it is often required to operate both in
real-time and with minimal energy consumption (e.g., for wearable devices or
autonomous vehicles, edge Internet of Things (IoT), sensor networks), various
network optimisation techniques are used, e.g., quantisation, pruning, or
dedicated lightweight architectures. Due to the logarithmic distribution of
weights in neural network layers, a method providing high performance with
significant reduction in computational precision (for 4-bit weights and less)
is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic
distribution). This method introduces additional possibilities of replacing the
typical for neural networks Multiply and ACcumulate (MAC -- performing, e.g.,
convolution operations) units, with more energy-efficient Bitshift and
ACcumulate (BAC). In this paper, we show that a hardware neural network
accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104
SoC FPGA can be at least $1.4x$ more energy efficient than the uniform
quantisation version. To further reduce the actual power requirement by
omitting part of the computation for zero weights, we also propose a new
pruning method adapted to logarithmic quantisation.

Understanding how the brain encodes external stimuli and how these stimuli
can be decoded from the measured brain activities are long-standing and
challenging questions in neuroscience. In this paper, we focus on
reconstructing the complex image stimuli from fMRI (functional magnetic
resonance imaging) signals. Unlike previous works that reconstruct images with
single objects or simple shapes, our work aims to reconstruct image stimuli
that are rich in semantics, closer to everyday scenes, and can reveal more
perspectives. However, data scarcity of fMRI datasets is the main obstacle to
applying state-of-the-art deep learning models to this problem. We find that
incorporating an additional text modality is beneficial for the reconstruction
problem compared to directly translating brain signals to images. Therefore,
the modalities involved in our method are: (i) voxel-level fMRI signals, (ii)
observed images that trigger the brain signals, and (iii) textual description
of the images. To further address data scarcity, we leverage an aligned
vision-language latent space pre-trained on massive datasets. Instead of
training models from scratch to find a latent space shared by the three
modalities, we encode fMRI signals into this pre-aligned latent space. Then,
conditioned on embeddings in this space, we reconstruct images with a
generative model. The reconstructed images from our pipeline balance both
naturalness and fidelity: they are photo-realistic and capture the ground truth
image contents well.

Offline reinforcement learning (Offline RL) suffers from the innate
distributional shift as it cannot interact with the physical environment during
training. To alleviate such limitation, state-based offline RL leverages a
learned dynamics model from the logged experience and augments the predicted
state transition to extend the data distribution. For exploiting such benefit
also on the image-based RL, we firstly propose a generative model, S2P
(State2Pixel), which synthesizes the raw pixel of the agent from its
corresponding state. It enables bridging the gap between the state and the
image domain in RL algorithms, and virtually exploring unseen image
distribution via model-based transition in the state space. Through
experiments, we confirm that our S2P-based image synthesis not only improves
the image-based offline RL performance but also shows powerful generalization
capability on unseen tasks.

Post-silicon validation is one of the most critical processes in modern
semiconductor manufacturing. Specifically, correct and deep understanding in
test cases of manufactured devices is key to enable post-silicon tuning and
debugging. This analysis is typically performed by experienced human experts.
However, with the fast development in semiconductor industry, test cases can
contain hundreds of variables. The resulting high-dimensionality poses enormous
challenges to experts. Thereby, some recent prior works have introduced
data-driven variable selection algorithms to tackle these problems and achieved
notable success. Nevertheless, for these methods, experts are not involved in
training and inference phases, which may lead to bias and inaccuracy due to the
lack of prior knowledge. Hence, this work for the first time aims to design a
novel conditional variable selection approach while keeping experts in the
loop. In this way, we expect that our algorithm can be more efficiently and
effectively trained to identify the most critical variables under certain
expert knowledge. Extensive experiments on both synthetic and real-world
datasets from industry have been conducted and shown the effectiveness of our
method.

This work intends to lay the foundations for identifying the prevailing
forest types and the delineation of forest units within private forest
inventories in the Autonomous Province of Trento (PAT), using currently
available remote sensing solutions. In particular, data from LiDAR and
hyperspectral surveys of 2014 made available by PAT were acquired and
processed. Such studies are very important in the context of forest management
scenarios. The method includes defining tree species ground-truth by outlining
single tree crowns with polygons and labeling them. Successively two supervised
machine learning classifiers, K-Nearest Neighborhood and Support Vector Machine
(SVM) were used. The results show that, by setting specific hyperparameters,
the SVM methodology gave the best results in classification of tree species.
Biomass was estimated using canopy parameters and the Jucker equation for the
above ground biomass (AGB) and that of Scrinzi for the tariff volume. Predicted
values were compared with 11 field plots of fixed radius where volume and
biomass were field-estimated in 2017. Results show significant coefficients of
correlation: 0.94 for stem volume and 0.90 for total aboveground tree biomass.

Out-of-distribution (OOD) detection has recently gained substantial attention
due to the importance of identifying out-of-domain samples in reliability and
safety. Although OOD detection methods have advanced by a great deal, they are
still susceptible to adversarial examples, which is a violation of their
purpose. To mitigate this issue, several defenses have recently been proposed.
Nevertheless, these efforts remained ineffective, as their evaluations are
based on either small perturbation sizes, or weak attacks. In this work, we
re-examine these defenses against an end-to-end PGD attack on in/out data with
larger perturbation sizes, e.g. up to commonly used $\epsilon=8/255$ for the
CIFAR-10 dataset. Surprisingly, almost all of these defenses perform worse than
a random detection under the adversarial setting. Next, we aim to provide a
robust OOD detection method. In an ideal defense, the training should expose
the model to almost all possible adversarial perturbations, which can be
achieved through adversarial training. That is, such training perturbations
should based on both in- and out-of-distribution samples. Therefore, unlike OOD
detection in the standard setting, access to OOD, as well as in-distribution,
samples sounds necessary in the adversarial training setup. These tips lead us
to adopt generative OOD detection methods, such as OpenGAN, as a baseline. We
subsequently propose the Adversarially Trained Discriminator (ATD), which
utilizes a pre-trained robust model to extract robust features, and a generator
model to create OOD samples. Using ATD with CIFAR-10 and CIFAR-100 as the
in-distribution data, we could significantly outperform all previous methods in
the robust AUROC while maintaining high standard AUROC and classification
accuracy. The code repository is available at https://github.com/rohban-lab/ATD .

Due to limited communication capacities of edge devices, most existing
federated learning (FL) methods randomly select only a subset of devices to
participate in training for each communication round. Compared with engaging
all the available clients, the random-selection mechanism can lead to
significant performance degradation on non-IID (independent and identically
distributed) data. In this paper, we show our key observation that the
essential reason resulting in such performance degradation is the
class-imbalance of the grouped data from randomly selected clients. Based on
our key observation, we design an efficient heterogeneity-aware client sampling
mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS), which can
effectively reduce class-imbalance of the group dataset from the intentionally
selected clients. In particular, we propose a measure of class-imbalance and
then employ homomorphic encryption to derive this measure in a
privacy-preserving way. Based on this measure, we also design a
computation-efficient client sampling strategy, such that the actively selected
clients will generate a more class-balanced grouped dataset with theoretical
guarantees. Extensive experimental results demonstrate Fed-CBS outperforms the
status quo approaches. Furthermore, it achieves comparable or even better
performance than the ideal setting where all the available clients participate
in the FL training.

In recent years, prompt tuning has set off a research boom in the adaptation
of pre-trained models. In this paper, we propose Graph Prompt as an efficient
and effective alternative to full fine-tuning for adapting the pre-trianed GNN
models to downstream tasks. To the best of our knowledge, we are the first to
explore the effectiveness of prompt tuning on existing pre-trained GNN models.
Specifically, without tuning the parameters of the pre-trained GNN model, we
train a task-specific graph prompt that provides graph-level transformations on
the downstream graphs during the adaptation stage. Then, we introduce a
concrete implementation of the graph prompt, called GP-Feature (GPF), which
adds learnable perturbations to the feature space of the downstream graph. GPF
has a strong expressive ability that it can modify both the node features and
the graph structure implicitly. Accordingly, we demonstrate that GPF can
achieve the approximately equivalent effect of any graph-level transformations
under most existing pre-trained GNN models. We validate the effectiveness of
GPF on numerous pre-trained GNN models, and the experimental results show that
with a small amount (about 0.1% of that for fine-tuning ) of tunable
parameters, GPF can achieve comparable performances as fine-tuning, and even
obtain significant performance gains in some cases.

We showcase a novel solution to a recommendation system problem where we face
a perpetual soft item cold start issue. Our system aims to recommend demanded
products to prospective sellers for listing in Amazon stores. These products
always have only few interactions thereby giving rise to a perpetual soft item
cold start situation. Modern collaborative filtering methods solve cold start
using content attributes and exploit the existing implicit signals from warm
start items. This approach fails in our use-case since our entire item set
faces cold start issue always. Our Product Graph has over 500 Million nodes and
over 5 Billion edges which makes training and inference using modern graph
algorithms very compute intensive. To overcome these challenges we propose a
system which reduces the dataset size and employs an improved modelling
technique to reduce storage and compute without loss in performance.
Particularly, we reduce our graph size using a filtering technique and then
exploit this reduced product graph using Weighted Averaging of Messages over
Layers (WAML) algorithm. WAML simplifies training on large graphs and improves
over previous methods by reducing compute time to 1/7 of LightGCN and 1/26 of
Graph Attention Network (GAT) and increasing recall$@100$ by 66% over LightGCN
and 2.3x over GAT.

Massively multilingual models pretrained on abundant corpora with
self-supervision achieve state-of-the-art results in a wide range of natural
language processing tasks. In machine translation, multilingual pretrained
models are often fine-tuned on parallel data from one or multiple language
pairs. Multilingual fine-tuning improves performance on medium- and
low-resource languages but requires modifying the entire model and can be
prohibitively expensive. Training a new set of adapters on each language pair
or training a single set of adapters on all language pairs while keeping the
pretrained model's parameters frozen has been proposed as a parameter-efficient
alternative. However, the former do not permit any sharing between languages,
while the latter share parameters for all languages and have to deal with
negative interference. In this paper, we propose training language-family
adapters on top of a pretrained multilingual model to facilitate cross-lingual
transfer. Our model consistently outperforms other adapter-based approaches. We
also demonstrate that language-family adapters provide an effective method to
translate to languages unseen during pretraining.

In this paper we examine the relationship between the flow of the replicator
dynamic, the continuum limit of Multiplicative Weights Update, and a game's
response graph. We settle an open problem establishing that under the
replicator, sink chain components -- a topological notion of long-run outcome
of a dynamical system -- always exist and are approximated by the sink
connected components of the game's response graph. More specifically, each sink
chain component contains a sink connected component of the response graph, as
well as all mixed strategy profiles whose support consists of pure profiles in
the same connected component, a set we call the content of the connected
component. As a corollary, all profiles are chain recurrent in games with
strongly connected response graphs. In any two-player game sharing a response
graph with a zero-sum game, the sink chain component is unique. In two-player
zero-sum and potential games the sink chain components and sink connected
components are in a one-to-one correspondence, and we conjecture that this
holds in all games.

Unsupervised learning has been widely used in many real-world applications.
One of the simplest and most important unsupervised learning models is the
Gaussian mixture model (GMM). In this work, we study the multi-task learning
problem on GMMs, which aims to leverage potentially similar GMM parameter
structures among tasks to obtain improved learning performance compared to
single-task learning. We propose a multi-task GMM learning procedure based on
the EM algorithm that not only can effectively utilize unknown similarity
between related tasks but is also robust against a fraction of outlier tasks
from arbitrary sources. The proposed procedure is shown to achieve minimax
optimal rate of convergence for both parameter estimation error and the excess
mis-clustering error, in a wide range of regimes. Moreover, we generalize our
approach to tackle the problem of transfer learning for GMMs, where similar
theoretical results are derived. Finally, we demonstrate the effectiveness of
our methods through simulations and a real data analysis. To the best of our
knowledge, this is the first work studying multi-task and transfer learning on
GMMs with theoretical guarantees.

Feed recommendation allows users to constantly browse items until feel
uninterested and leave the session, which differs from traditional
recommendation scenarios. Within a session, user's decision to continue
browsing or not substantially affects occurrences of later clicks. However,
such type of exposure bias is generally ignored or not explicitly modeled in
most feed recommendation studies. In this paper, we model this effect as part
of intra-session context, and propose a novel intra-session Context-aware Feed
Recommendation (INSCAFER) framework to maximize the total views and total
clicks simultaneously. User click and browsing decisions are jointly learned by
a multi-task setting, and the intra-session context is encoded by the
session-wise exposed item sequence. We deploy our model on Alipay with all key
business benchmarks improved. Our method sheds some lights on feed
recommendation studies which aim to optimize session-level click and view
metrics.

We consider the problem of minimizing the number of matrix-vector queries
needed for accurate trace estimation in the dynamic setting where our
underlying matrix is changing slowly, such as during an optimization process.
Specifically, for any $m$ matrices $A_1,...,A_m$ with consecutive differences
bounded in Schatten-$1$ norm by $\alpha$, we provide a novel binary tree
summation procedure that simultaneously estimates all $m$ traces up to
$\epsilon$ error with $\delta$ failure probability with an optimal query
complexity of $\widetilde{O}\left(m \alpha\sqrt{\log(1/\delta)}/\epsilon +
m\log(1/\delta)\right)$, improving the dependence on both $\alpha$ and $\delta$
from Dharangutte and Musco (NeurIPS, 2021). Our procedure works without
additional norm bounds on $A_i$ and can be generalized to a bound for the
$p$-th Schatten norm for $p \in [1,2]$, giving a complexity of
$\widetilde{O}\left(m \alpha\left(\sqrt{\log(1/\delta)}/\epsilon\right)^p +m
\log(1/\delta)\right)$.
  By using novel reductions to communication complexity and
information-theoretic analyses of Gaussian matrices, we provide matching lower
bounds for static and dynamic trace estimation in all relevant parameters,
including the failure probability. Our lower bounds (1) give the first tight
bounds for Hutchinson's estimator in the matrix-vector product model with
Frobenius norm error even in the static setting, and (2) are the first
unconditional lower bounds for dynamic trace estimation, resolving open
questions of prior work.

We propose a Gaussian manifold variational auto-encoder (GM-VAE) whose latent
space consists of a set of diagonal Gaussian distributions. It is known that
the set of the diagonal Gaussian distributions with the Fisher information
metric forms a product hyperbolic space, which we call a Gaussian manifold. To
learn the VAE endowed with the Gaussian manifold, we first propose a pseudo
Gaussian manifold normal distribution based on the Kullback-Leibler divergence,
a local approximation of the squared Fisher-Rao distance, to define a density
over the latent space. With the newly proposed distribution, we introduce
geometric transformations at the last and the first of the encoder and the
decoder of VAE, respectively to help the transition between the Euclidean and
Gaussian manifolds. Through the empirical experiments, we show competitive
generalization performance of GM-VAE against other variants of hyperbolic- and
Euclidean-VAEs. Our model achieves strong numerical stability, which is a
common limitation reported with previous hyperbolic-VAEs.

This paper analyzes the simulation to reality gap in reinforcement learning
(RL) cyber-physical systems with fractional delays (i.e. delays that are
non-integer multiple of the sampling period). The consideration of fractional
delay has important implications on the nature of the cyber-physical system
considered. Systems with delays are non-Markovian, and the system state vector
needs to be extended to make the system Markovian. We show that this is not
possible when the delay is in the output, and the problem would always be
non-Markovian. Based on this analysis, a sampling scheme is proposed that
results in efficient RL training and agents that perform well in realistic
multirotor unmanned aerial vehicle simulations. We demonstrate that the
resultant agents do not produce excessive oscillations, which is not the case
with RL agents that do not consider time delay in the model.

This paper is concerned with forecast error, particularly in relation to loss
reserving. This is generally regarded as consisting of three components, namely
parameter, process and model errors. The first two of these components, and
their estimation, are well understood, but less so model error. Model error
itself is considered in two parts: one part that is capable of estimation from
past data (internal model error), and another part that is not (external model
error). Attention is focused here on internal model error. Estimation of this
error component is approached by means of Bayesian model averaging, using the
Bayesian interpretation of the LASSO. This is used to generate a set of
admissible models, each with its prior probability and the likelihood of
observed data. A posterior on the model set, conditional on the data, results,
and an estimate of model error (contained in a loss reserve) is obtained as the
variance of the loss reserve according to this posterior. The population of
models entering materially into the support of the posterior may turn out to be
thinner than desired, and bootstrapping of the LASSO is used to gain bulk. This
provides the bonus of an estimate of parameter error also. It turns out that
the estimates of parameter and model errors are entangled, and dissociation of
them is at least difficult, and possibly not even meaningful. These matters are
discussed. The majority of the discussion applies to forecasting generally, but
numerical illustration of the concepts is given in relation to insurance data
and the problem of insurance loss reserving.

Most existing methods for multi-source unsupervised domain adaptation (UDA)
rely on a common feature encoder to extract domain-invariant features. However,
learning such an encoder involves updating the parameters of the entire
network, which makes the optimization computationally expensive, particularly
when coupled with min-max objectives. Inspired by recent advances in prompt
learning that adapts high-capacity deep models for downstream tasks in a
computationally economic way, we introduce Multi-Prompt Alignment (MPA), a
simple yet efficient two-stage framework for multi-source UDA. Given a source
and target domain pair, MPA first trains an individual prompt to minimize the
domain gap through a contrastive loss, while tuning only a small set of
parameters. Then, MPA derives a low-dimensional latent space through an
auto-encoding process that maximizes the agreement of multiple learned prompts.
The resulting embedding further facilitates generalization to unseen domains.
Extensive experiments show that our method achieves state-of-the-art results on
popular benchmark datasets while requiring substantially fewer tunable
parameters. To the best of our knowledge, we are the first to apply prompt
learning to the multi-source UDA problem and our method achieves the highest
reported average accuracy of 54.1% on DomainNet, the most challenging UDA
dataset to date, with only 15.9M parameters trained. More importantly, we
demonstrate that the learned embedding space can be easily adapted to novel
unseen domains.

Explaining generalizations and preventing over-confident predictions are
central goals of studies on the loss landscape of neural networks. Flatness,
defined as loss invariability on perturbations of a pre-trained solution, is
widely accepted as a predictor of generalization in this context. However, the
problem that flatness and generalization bounds can be changed arbitrarily
according to the scale of a parameter was pointed out, and previous studies
partially solved the problem with restrictions: Counter-intuitively, their
generalization bounds were still variant for the function-preserving parameter
scaling transformation or limited only to an impractical network structure. As
a more fundamental solution, we propose new prior and posterior distributions
invariant to scaling transformations by \textit{decomposing} the scale and
connectivity of parameters, thereby allowing the resulting generalization bound
to describe the generalizability of a broad class of networks with the more
practical class of transformations such as weight decay with batch
normalization. We also show that the above issue adversely affects the
uncertainty calibration of Laplace approximation and propose a solution using
our invariant posterior. We empirically demonstrate our posterior provides
effective flatness and calibration measures with low complexity in such a
practical parameter transformation case, supporting its practical effectiveness
in line with our rationale.

Multilevel data are prevalent in many real-world applications. However, it
remains an open research problem to identify and justify a class of models that
flexibly capture a wide range of multilevel data. Motivated by the versatility
of the mixture of experts (MoE) models in fitting regression data, in this
article we extend upon the MoE and study a class of mixed MoE (MMoE) models for
multilevel data. Under some regularity conditions, we prove that the MMoE is
dense in the space of any continuous mixed effects models in the sense of weak
convergence. As a result, the MMoE has a potential to accurately resemble
almost all characteristics inherited in multilevel data, including the marginal
distributions, dependence structures, regression links, random intercepts and
random slopes. In a particular case where the multilevel data is hierarchical,
we further show that a nested version of the MMoE universally approximates a
broad range of dependence structures of the random effects among different
factor levels.

In this paper, we propose a theoretical framework to explain the efficacy of
prompt learning in zero/few-shot scenarios. First, we prove that conventional
pre-training and fine-tuning paradigm fails in few-shot scenarios due to
overfitting the unrepresentative labelled data. We then detail the assumption
that prompt learning is more effective because it empowers pre-trained language
model that is built upon massive text corpora, as well as domain-related human
knowledge to participate more in prediction and thereby reduces the impact of
limited label information provided by the small training set. We further
hypothesize that language discrepancy can measure the quality of prompting.
Comprehensive experiments are performed to verify our assumptions. More
remarkably, inspired by the theoretical framework, we propose an
annotation-agnostic template selection method based on perplexity, which
enables us to ``forecast'' the prompting performance in advance. This approach
is especially encouraging because existing work still relies on development set
to post-hoc evaluate templates. Experiments show that this method leads to
significant prediction benefits compared to state-of-the-art zero-shot methods.

We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that
leverages prior experience to accelerate reinforcement learning. Unlike
existing methods that learn a single skill prior from a large and diverse
dataset, our framework learns a library of different distinction skill priors
(i.e., behavior priors) from a collection of specialized datasets, and learns
how to combine them to solve a new task. This formulation allows the algorithm
to acquire a set of specialized skill priors that are more reusable for
downstream tasks; however, it also brings up additional challenges of how to
effectively combine these unstructured sets of skill priors to form a new prior
for new tasks. Specifically, it requires the agent not only to identify which
skill prior(s) to use but also how to combine them (either sequentially or
concurrently) to form a new prior. To achieve this goal, ASPiRe includes
Adaptive Weight Module (AWM) that learns to infer an adaptive weight assignment
between different skill priors and uses them to guide policy learning for
downstream tasks via weighted Kullback-Leibler divergences. Our experiments
demonstrate that ASPiRe can significantly accelerate the learning of new
downstream tasks in the presence of multiple priors and show improvement on
competitive baselines.

Training large neural networks is time consuming. To speed up the process,
distributed training is often used. One of the largest bottlenecks in
distributed training is communicating gradients across different nodes.
Different gradient compression techniques have been proposed to alleviate the
communication bottleneck, including topK gradient sparsification, which
truncates the gradient to the largest K components before sending it to other
nodes. While some authors have investigated topK gradient sparsification in the
parameter-server framework by applying topK compression in both the
worker-to-server (uplink) and server-to-worker (downlink) direction, the
currently accepted belief says that adding extra compression degrades the
convergence of the model. We demonstrate, on the contrary, that adding downlink
compression can potentially improve the performance of topK sparsification: not
only does it reduce the amount of communication per step, but also,
counter-intuitively, can improve the upper bound in the convergence analysis.
To show this, we revisit non-convex convergence analysis of topK stochastic
gradient descent (SGD) and extend it from the unidirectional to the
bidirectional setting. We also remove a restriction of the previous analysis
that requires unrealistically large values of K. We experimentally evaluate
bidirectional topK SGD against unidirectional topK SGD and show that models
trained with bidirectional topK SGD will perform as well as models trained with
unidirectional topK SGD while yielding significant communication benefits for
large numbers of workers.

Deep neural networks have shown excellent prospects in speech separation
tasks. However, obtaining good results while keeping a low model complexity
remains challenging in real-world applications. In this paper, we provide a
bio-inspired efficient encoder-decoder architecture by mimicking the brain's
top-down attention, called TDANet, with decreased model complexity without
sacrificing performance. The top-down attention in TDANet is extracted by the
global attention (GA) module and the cascaded local attention (LA) layers. The
GA module takes multi-scale acoustic features as input to extract global
attention signal, which then modulates features of different scales by direct
top-down connections. The LA layers use features of adjacent layers as input to
extract the local attention signal, which is used to modulate the lateral input
in a top-down manner. On three benchmark datasets, TDANet consistently achieved
competitive separation performance to previous state-of-the-art (SOTA) methods
with higher efficiency. Specifically, TDANet's multiply-accumulate operations
(MACs) are only 5\% of Sepformer, one of the previous SOTA models, and CPU
inference time is only 10\% of Sepformer. In addition, a large-size version of
TDANet obtained SOTA results on three datasets, with MACs still only 10\% of
Sepformer and the CPU inference time only 24\% of Sepformer. Our study suggests
that top-down attention can be a more efficient strategy for speech separation.

Modelling semantic similarity plays a fundamental role in lexical semantic
applications. A natural way of calculating semantic similarity is to access
handcrafted semantic networks, but similarity prediction can also be
anticipated in a distributional vector space. Similarity calculation continues
to be a challenging task, even with the latest breakthroughs in deep neural
language models. We first examined popular methodologies in measuring taxonomic
similarity, including edge-counting that solely employs semantic relations in a
taxonomy, as well as the complex methods that estimate concept specificity. We
further extrapolated three weighting factors in modelling taxonomic similarity.
To study the distinct mechanisms between taxonomic and distributional
similarity measures, we ran head-to-head comparisons of each measure with human
similarity judgements from the perspectives of word frequency, polysemy degree
and similarity intensity. Our findings suggest that without fine-tuning the
uniform distance, taxonomic similarity measures can depend on the shortest path
length as a prime factor to predict semantic similarity; in contrast to
distributional semantics, edge-counting is free from sense distribution bias in
use and can measure word similarity both literally and metaphorically; the
synergy of retrofitting neural embeddings with concept relations in similarity
prediction may indicate a new trend to leverage knowledge bases on transfer
learning. It appears that a large gap still exists on computing semantic
similarity among different ranges of word frequency, polysemous degree and
similarity intensity.

Integral equations (IEs) are functional equations defined through integral
operators, where the unknown function is integrated over a possibly
multidimensional space. Important applications of IEs have been found
throughout theoretical and applied sciences, including in physics, chemistry,
biology, and engineering; often in the form of inverse problems. IEs are
especially useful since differential equations, e.g. ordinary differential
equations (ODEs), and partial differential equations (PDEs) can be formulated
in an integral version which is often more convenient to solve. Moreover,
unlike ODEs and PDEs, IEs can model inherently non-local dynamical systems,
such as ones with long distance spatiotemporal relations. While efficient
algorithms exist for solving given IEs, no method exists that can learn an
integral equation and its associated dynamics from data alone. In this article,
we introduce Neural Integral Equations (NIE), a method that learns an unknown
integral operator from data through a solver. We also introduce an attentional
version of NIE, called Attentional Neural Integral Equations (ANIE), where the
integral is replaced by self-attention, which improves scalability and provides
interpretability. We show that learning dynamics via integral equations is
faster than doing so via other continuous methods, such as Neural ODEs.
Finally, we show that ANIE outperforms other methods on several benchmark tasks
in ODE, PDE, and IE systems of synthetic and real-world data.

Language models significantly benefit from context tokens, such as prompts or
scratchpads. They perform better when prompted with informative instructions,
and they acquire new reasoning capabilities by generating a scratch-pad before
predicting the final answers. However, they do not \textit{internalize} these
performance gains, which disappear when the context tokens are gone. Our work
proposes to apply context distillation so that a language model can improve
itself by internalizing these gains. Concretely, given a synthetic unlabeled
input for the target task, we condition the model on ``[instructions] +
[task-input]'' to predict ``[scratch-pad] + [final answer]''; then we fine-tune
the same model to predict its own ``[final answer]'' conditioned on the
``[task-input]'', without seeing the ``[instructions]'' or using the
``[scratch-pad]''.
  We show that context distillation is a general method to train language
models, and it can effectively internalize 3 types of training signals. First,
it can internalize abstract task instructions and explanations, so we can
iteratively update the model parameters with new instructions and overwrite old
ones. Second, it can internalize step-by-step reasoning for complex tasks
(e.g., 8-digit addition), and such a newly acquired capability proves to be
useful for other downstream tasks. Finally, it can internalize concrete
training examples, and it outperforms directly learning with gradient descent
by 9\% on the SPIDER Text-to-SQL dataset; furthermore, combining context
distillation operations can internalize more training examples than the context
window size allows.

Brain-inspired computing - leveraging neuroscientific principles underpinning
the unparalleled efficiency of the brain in solving cognitive tasks - is
emerging to be a promising pathway to solve several algorithmic and
computational challenges faced by deep learning today. Nonetheless, current
research in neuromorphic computing is driven by our well-developed notions of
running deep learning algorithms on computing platforms that perform
deterministic operations. In this article, we argue that taking a different
route of performing temporal information encoding in probabilistic neuromorphic
systems may help solve some of the current challenges in the field. The article
considers superparamagnetic tunnel junctions as a potential pathway to enable a
new generation of brain-inspired computing that combines the facets and
associated advantages of two complementary insights from computational
neuroscience -- how information is encoded and how computing occurs in the
brain. Hardware-algorithm co-design analysis demonstrates $97.41\%$ accuracy of
a state-compressed 3-layer spintronics enabled stochastic spiking network on
the MNIST dataset with high spiking sparsity due to temporal information
encoding.

Pre-training a large transformer model on a massive amount of unlabeled data
and fine-tuning it on labeled datasets for diverse downstream tasks has proven
to be a successful strategy, for a variety of vision and natural language
processing tasks. However, direct fine-tuning of the pre-trained model may be
suboptimal if there exist large discrepancies across data domains for
pre-training and fine-tuning. To tackle this issue, several previous studies
have proposed further pre-training strategies, where we continue to pre-train
the model on the target unlabeled dataset before fine-tuning. However, all of
them solely focus on language models and we empirically find that a Vision
Transformer is vulnerable to overfitting as we continue to pretrain the model
on target unlabeled data. In order to tackle this limitation, we propose
self-distillation as a regularization for a further pre-training stage.
Specifically, we first further pre-train the initial pre-trained model on the
target unlabeled data and then consider it as a teacher for self-distillation.
Then we take the same initial pre-trained model as a student and enforce its
hidden representations to be close to those of the teacher while optimizing the
student with a masked auto-encoding objective. We empirically validate the
efficacy of self-distillation on a variety of benchmark datasets for image and
text classification tasks. Experimentally, we show that our proposed method
outperforms all the relevant baselines. Theoretically, we analyze the proposed
method with a simplified model to understand how self-distillation for further
pre-training can potentially help improve the performance of the downstream
tasks.

Deep learning methods, in particular convolutional neural networks, have
emerged as a powerful tool in medical image computing tasks. While these
complex models provide excellent performance, their black-box nature may hinder
real-world adoption in high-stakes decision-making. In this paper, we propose
an interactive system to take advantage of state-of-the-art interpretability
techniques to assist radiologists with breast cancer screening. Our system
integrates a deep learning model into the radiologists' workflow and provides
novel interactions to promote understanding of the model's decision-making
process. Moreover, we demonstrate that our system can take advantage of user
interactions progressively to provide finer-grained explainability reports with
little labeling overhead. Due to the generic nature of the adopted
interpretability technique, our system is domain-agnostic and can be used for
many different medical image computing tasks, presenting a novel perspective on
how we can leverage visual analytics to transform originally static
interpretability techniques to augment human decision making and promote the
adoption of medical AI.

The extraction of sequence patterns from a collection of functionally linked
unlabeled DNA sequences is known as DNA motif discovery, and it is a key task
in computational biology. Several deep learning-based techniques have recently
been introduced to address this issue. However, these algorithms can not be
used in real-world situations because of the need for labeled data. Here, we
presented RL-MD, a novel reinforcement learning based approach for DNA motif
discovery task. RL-MD takes unlabelled data as input, employs a relative
information-based method to evaluate each proposed motif, and utilizes these
continuous evaluation results as the reward. The experiments show that RL-MD
can identify high-quality motifs in real-world data.

Machine learning models rely on various assumptions to attain high accuracy.
One of the preliminary assumptions of these models is the independent and
identical distribution, which suggests that the train and test data are sampled
from the same distribution. However, this assumption seldom holds in the real
world due to distribution shifts. As a result models that rely on this
assumption exhibit poor generalization capabilities. Over the recent years,
dedicated efforts have been made to improve the generalization capabilities of
these models collectively known as -- \textit{domain generalization methods}.
The primary idea behind these methods is to identify stable features or
mechanisms that remain invariant across the different distributions. Many
generalization approaches employ causal theories to describe invariance since
causality and invariance are inextricably intertwined. However, current surveys
deal with the causality-aware domain generalization methods on a very
high-level. Furthermore, we argue that it is possible to categorize the methods
based on how causality is leveraged in that method and in which part of the
model pipeline is it used. To this end, we categorize the causal domain
generalization methods into three categories, namely, (i) Invariance via Causal
Data Augmentation methods which are applied during the data pre-processing
stage, (ii) Invariance via Causal representation learning methods that are
utilized during the representation learning stage, and (iii) Invariance via
Transferring Causal mechanisms methods that are applied during the
classification stage of the pipeline. Furthermore, this survey includes
in-depth insights into benchmark datasets and code repositories for domain
generalization methods. We conclude the survey with insights and discussions on
future directions.

The Transformer architecture model, based on self-attention and multi-head
attention, has achieved remarkable success in offline end-to-end Automatic
Speech Recognition (ASR). However, self-attention and multi-head attention
cannot be easily applied for streaming or online ASR. For self-attention in
Transformer ASR, the softmax normalization function-based attention mechanism
makes it impossible to highlight important speech information. For multi-head
attention in Transformer ASR, it is not easy to model monotonic alignments in
different heads. To overcome these two limits, we integrate sparse attention
and monotonic attention into Transformer-based ASR. The sparse mechanism
introduces a learned sparsity scheme to enable each self-attention structure to
fit the corresponding head better. The monotonic attention deploys
regularization to prune redundant heads for the multi-head attention structure.
The experiments show that our method can effectively improve the attention
mechanism on widely used benchmarks of speech recognition.

The performance of music source separation (MSS) models has been greatly
improved in recent years thanks to the development of novel neural network
architectures and training pipelines. However, recent model designs for MSS
were mainly motivated by other audio processing tasks or other research fields,
while the intrinsic characteristics and patterns of the music signals were not
fully discovered. In this paper, we propose band-split RNN (BSRNN), a
frequency-domain model that explictly splits the spectrogram of the mixture
into subbands and perform interleaved band-level and sequence-level modeling.
The choices of the bandwidths of the subbands can be determined by a priori
knowledge or expert knowledge on the characteristics of the target source in
order to optimize the performance on a certain type of target musical
instrument. To better make use of unlabeled data, we also describe a
semi-supervised model finetuning pipeline that can further improve the
performance of the model. Experiment results show that BSRNN trained only on
MUSDB18-HQ dataset significantly outperforms several top-ranking models in
Music Demixing (MDX) Challenge 2021, and the semi-supervised finetuning stage
further improves the performance on all four instrument tracks.

We explore the task of text to 3D object generation using CLIP. Specifically,
we use CLIP for guidance without access to any datasets, a setting we refer to
as pure CLIP guidance. While prior work has adopted this setting, there is no
systematic study of mechanics for preventing adversarial generations within
CLIP. We illustrate how different image-based augmentations prevent the
adversarial generation problem, and how the generated results are impacted. We
test different CLIP model architectures and show that ensembling different
models for guidance can prevent adversarial generations within bigger models
and generate sharper results. Furthermore, we implement an implicit voxel grid
model to show how neural networks provide an additional layer of
regularization, resulting in better geometrical structure and coherency of
generated objects. Compared to prior work, we achieve more coherent results
with higher memory efficiency and faster training speeds.

Molecular complexes formed by proteins and small-molecule ligands are
ubiquitous, and predicting their 3D structures can facilitate both biological
discoveries and the design of novel enzymes or drug molecules. Here we propose
NeuralPLexer, a deep generative model framework to rapidly predict
protein-ligand complex structures and their fluctuations using protein backbone
template and molecular graph inputs. NeuralPLexer jointly samples protein and
small-molecule 3D coordinates at an atomistic resolution through a generative
model that incorporates biophysical constraints and inferred proximity
information into a time-truncated diffusion process. The reverse-time
generative diffusion process is learned by a novel stereochemistry-aware
equivariant graph transformer that enables efficient, concurrent gradient field
prediction for all heavy atoms in the protein-ligand complex. NeuralPLexer
outperforms existing physics-based and learning-based methods on benchmarking
problems including fixed-backbone blind protein-ligand docking and
ligand-coupled binding site repacking. Moreover, we identify preliminary
evidence that NeuralPLexer enriches bound-state-like protein structures when
applied to systems where protein folding landscapes are significantly altered
by the presence of ligands. Our results reveal that a data-driven approach can
capture the structural cooperativity among protein and small-molecule entities,
showing promise for the computational identification of novel drug targets and
the end-to-end differentiable design of functional small-molecules and
ligand-binding proteins.

Language Models pretrained on large textual data have been shown to encode
different types of knowledge simultaneously. Traditionally, only the features
from the last layer are used when adapting to new tasks or data. We put forward
that, when using or finetuning deep pretrained models, intermediate layer
features that may be relevant to the downstream task are buried too deep to be
used efficiently in terms of needed samples or steps. To test this, we propose
a new layer fusion method: Depth-Wise Attention (DWAtt), to help re-surface
signals from non-final layers. We compare DWAtt to a basic concatenation-based
layer fusion method (Concat), and compare both to a deeper model baseline --
all kept within a similar parameter budget. Our findings show that DWAtt and
Concat are more step- and sample-efficient than the baseline, especially in the
few-shot setting. DWAtt outperforms Concat on larger data sizes. On CoNLL-03
NER, layer fusion shows 3.68-9.73% F1 gain at different few-shot sizes. The
layer fusion models presented significantly outperform the baseline in various
training scenarios with different data sizes, architectures, and training

We study the usability of pre-trained weakly supervised audio tagging (AT)
models as feature extractors for general audio representations. We mainly
analyze the feasibility of transferring those embeddings to other tasks within
the speech and sound domains. Specifically, we benchmark weakly supervised
pre-trained models (MobileNetV2 and EfficientNet-B0) against modern
self-supervised learning methods (BYOL-A) as feature extractors. Fourteen
downstream tasks are used for evaluation ranging from music instrument
classification to language classification. Our results indicate that AT
pre-trained models are an excellent transfer learning choice for music, event,
and emotion recognition tasks. Further, finetuning AT models can also benefit
speech-related tasks such as keyword spotting and intent classification.

How might we design Reinforcement Learning (RL)-based recommenders that
encourage aligning user trajectories with the underlying user satisfaction?
Three research questions are key: (1) measuring user satisfaction, (2)
combatting sparsity of satisfaction signals, and (3) adapting the training of
the recommender agent to maximize satisfaction. For measurement, it has been
found that surveys explicitly asking users to rate their experience with
consumed items can provide valuable orthogonal information to the
engagement/interaction data, acting as a proxy to the underlying user
satisfaction. For sparsity, i.e, only being able to observe how satisfied users
are with a tiny fraction of user-item interactions, imputation models can be
useful in predicting satisfaction level for all items users have consumed. For
learning satisfying recommender policies, we postulate that reward shaping in
RL recommender agents is powerful for driving satisfying user experiences.
Putting everything together, we propose to jointly learn a policy network and a
satisfaction imputation network: The role of the imputation network is to learn
which actions are satisfying to the user; while the policy network, built on
top of REINFORCE, decides which items to recommend, with the reward utilizing
the imputed satisfaction. We use both offline analysis and live experiments in
an industrial large-scale recommendation platform to demonstrate the promise of
our approach for satisfying user experiences.

Many image enhancement or editing operations, such as forward and inverse
tone mapping or color grading, do not have a unique solution, but instead a
range of solutions, each representing a different style. Despite this, existing
learning-based methods attempt to learn a unique mapping, disregarding this
style. In this work, we show that information about the style can be distilled
from collections of image pairs and encoded into a 2- or 3-dimensional vector.
This gives us not only an efficient representation but also an interpretable
latent space for editing the image style. We represent the global color mapping
between a pair of images as a custom normalizing flow, conditioned on a
polynomial basis of the pixel color. We show that such a network is more
effective than PCA or VAE at encoding image style in low-dimensional space and
lets us obtain an accuracy close to 40 dB, which is about 7-10 dB improvement
over the state-of-the-art methods.

Buddhism is an influential religion with a long-standing history and profound
philosophy. Nowadays, more and more people worldwide aspire to learn the
essence of Buddhism, attaching importance to Buddhism dissemination. However,
Buddhist scriptures written in classical Chinese are obscure to most people and
machine translation applications. For instance, general Chinese-English neural
machine translation (NMT) fails in this domain. In this paper, we proposed a
novel approach to building a practical NMT model for Buddhist scriptures. The
performance of our translation pipeline acquired highly promising results in
ablation experiments under three criteria.

The extent to which text-only language models (LMs) learn to represent the
physical, non-linguistic world is an open question. Prior work has shown that
pretrained LMs can be taught to ``understand'' visual inputs when the models'
parameters are updated on image captioning tasks. We test a stronger
hypothesis: that the conceptual representations learned by text-only models are
functionally equivalent (up to a linear transformation) to those learned by
models trained on vision tasks. Specifically, we show that the image
representations from vision models can be transferred as continuous prompts to
frozen LMs by training only a single linear projection. Using these to prompt
the LM achieves competitive performance on captioning and visual question
answering tasks compared to models that tune both the image encoder and text
decoder (such as the MAGMA model). We compare three image encoders with
increasing amounts of linguistic supervision seen during pretraining: BEIT (no
linguistic information), NF-ResNET (lexical category information), and CLIP
(full natural language descriptions). We find that all three encoders perform
equally well at transferring visual property information to the language model
(e.g., whether an animal is large or small), but that image encoders pretrained
with linguistic supervision more saliently encode category information (e.g.,
distinguishing hippo vs.\ elephant) and thus perform significantly better on
benchmark language-and-vision tasks. Our results indicate that LMs encode
conceptual information structurally similarly to vision-based models, even
those that are solely trained on images.

MobileViT (MobileViTv1) combines convolutional neural networks (CNNs) and
vision transformers (ViTs) to create light-weight models for mobile vision
tasks. Though the main MobileViTv1-block helps to achieve competitive
state-of-the-art results, the fusion block inside MobileViTv1-block, creates
scaling challenges and has a complex learning task. We propose changes to the
fusion block that are simple and effective to create MobileViTv3-block, which
addresses the scaling and simplifies the learning task. Our proposed
MobileViTv3-block used to create MobileViTv3-XXS, XS and S models outperform
MobileViTv1 on ImageNet-1k, ADE20K, COCO and PascalVOC2012 datasets. On
ImageNet-1K, MobileViTv3-XXS and MobileViTv3-XS surpasses MobileViTv1-XXS and
MobileViTv1-XS by 2% and 1.9% respectively. Recently published MobileViTv2
architecture removes fusion block and uses linear complexity transformers to
perform better than MobileViTv1. We add our proposed fusion block to
MobileViTv2 to create MobileViTv3-0.5, 0.75 and 1.0 models. These new models
give better accuracy numbers on ImageNet-1k, ADE20K, COCO and PascalVOC2012
datasets as compared to MobileViTv2. MobileViTv3-0.5 and MobileViTv3-0.75
outperforms MobileViTv2-0.5 and MobileViTv2-0.75 by 2.1% and 1.0% respectively
on ImageNet-1K dataset. For segmentation task, MobileViTv3-1.0 achieves 2.07%
and 1.1% better mIOU compared to MobileViTv2-1.0 on ADE20K dataset and
PascalVOC2012 dataset respectively. Our code and the trained models are
available at: https://github.com/micronDLA/MobileViTv3

In this paper, we argue that the way we have been training and evaluating ML
models has largely forgotten the fact that they are applied in an organization
or societal context as they provide value to people. We show that with this
perspective we fundamentally change how we evaluate, select and deploy ML
models - and to some extent even what it means to learn. Specifically, we
stress that the notion of value plays a central role in learning and
evaluating, and different models may require different learning practices and
provide different values based on the application context they are applied. We
also show that this concretely impacts how we select and embed models into
human workflows based on experimental datasets. Nothing of what is presented
here is hard: to a large extent is a series of fairly trivial observations with
massive practical implications.

The deployment of machine learning classifiers in high-stakes domains
requires well-calibrated confidence scores for model predictions. In this paper
we introduce the notion of variable-based calibration to characterize
calibration properties of a model with respect to a variable of interest,
generalizing traditional score-based calibration and metrics such as expected
calibration error (ECE). In particular, we find that models with near-perfect
ECE can exhibit significant variable-based calibration error as a function of
features of the data. We demonstrate this phenomenon both theoretically and in
practice on multiple well-known datasets, and show that it can persist after
the application of existing recalibration methods. To mitigate this issue, we
propose strategies for detection, visualization, and quantification of
variable-based calibration error. We then examine the limitations of current
score-based recalibration methods and explore potential modifications. Finally,
we discuss the implications of these findings, emphasizing that an
understanding of calibration beyond simple aggregate measures is crucial for
endeavors such as fairness and model interpretability.

High-fidelity 3D scene reconstruction from monocular videos continues to be
challenging, especially for complete and fine-grained geometry reconstruction.
The previous 3D reconstruction approaches with neural implicit representations
have shown a promising ability for complete scene reconstruction, while their
results are often over-smooth and lack enough geometric details. This paper
introduces a novel neural implicit scene representation with volume rendering
for high-fidelity online 3D scene reconstruction from monocular videos. For
fine-grained reconstruction, our key insight is to incorporate geometric priors
into both the neural implicit scene representation and neural volume rendering,
thus leading to an effective geometry learning mechanism based on volume
rendering optimization. Benefiting from this, we present MonoNeuralFusion to
perform the online neural 3D reconstruction from monocular videos, by which the
3D scene geometry is efficiently generated and optimized during the on-the-fly
3D monocular scanning. The extensive comparisons with state-of-the-art
approaches show that our MonoNeuralFusion consistently generates much better
complete and fine-grained reconstruction results, both quantitatively and
qualitatively.

Drowsiness on the road is a widespread problem with fatal consequences; thus,
a multitude of solutions implementing machine learning techniques have been
proposed by researchers. Among existing methods, Ghoddoosian et al.'s
drowsiness detection method utilizes temporal blinking patterns to detect early
signs of drowsiness. Although the method reported promising results,
Ghoddoosian et al.'s algorithm was developed and tested only on a powerful
desktop computer, which is not practical to apply in a moving vehicle setting.
In this paper, we propose an embedded system that can process Ghoddoosian's
drowsiness detection algorithm on a small minicomputer and interact with the
user by phone; combined, the devices are powerful enough to run a web server
and our drowsiness detection server. We used the AioRTC protocol on GitHub to
conduct real-time transmission of video frames from the client to the server
and evaluated the communication speed and processing times of the program on
various platforms. Based on our results, we found that a Mini PC was most
suitable for our proposed system. Furthermore, we proposed an algorithm that
considers the importance of sensitivity over specificity, specifically
regarding drowsiness detection algorithms. Our algorithm optimizes the
threshold to adjust the false positive and false negative rates of the
drowsiness detection models. We anticipate our proposed platform can help many
researchers to advance their research on drowsiness detection solutions in
embedded system settings.

Introduction. We investigate the generalization ability of models built on
datasets containing a small number of subjects, recorded in single study
protocols. Next, we propose and evaluate methods combining these datasets into
a single, large dataset. Finally, we propose and evaluate the use of ensemble
techniques by combining gradient boosting with an artificial neural network to
measure predictive power on new, unseen data.
  Methods. Sensor biomarker data from six public datasets were utilized in this
study. To test model generalization, we developed a gradient boosting model
trained on one dataset (SWELL), and tested its predictive power on two datasets
previously used in other studies (WESAD, NEURO). Next, we merged four small
datasets, i.e. (SWELL, NEURO, WESAD, UBFC-Phys), to provide a combined total of
99 subjects,. In addition, we utilized random sampling combined with another
dataset (EXAM) to build a larger training dataset consisting of 200 synthesized
subjects,. Finally, we developed an ensemble model that combines our gradient
boosting model with an artificial neural network, and tested it on two
additional, unseen publicly available stress datasets (WESAD and Toadstool).
  Results. Our method delivers a robust stress measurement system capable of
achieving 85% predictive accuracy on new, unseen validation data, achieving a
25% performance improvement over single models trained on small datasets.
  Conclusion. Models trained on small, single study protocol datasets do not
generalize well for use on new, unseen data and lack statistical power.
Ma-chine learning models trained on a dataset containing a larger number of
varied study subjects capture physiological variance better, resulting in more
robust stress detection.

We develop fast distribution-free conformal prediction algorithms for
obtaining multivalid coverage on exchangeable data in the batch setting.
Multivalid coverage guarantees are stronger than marginal coverage guarantees
in two ways: (1) They hold even conditional on group membership -- that is, the
target coverage level $1-\alpha$ holds conditionally on membership in each of
an arbitrary (potentially intersecting) group in a finite collection
$\mathcal{G}$ of regions in the feature space. (2) They hold even conditional
on the value of the threshold used to produce the prediction set on a given
example. In fact multivalid coverage guarantees hold even when conditioning on
group membership and threshold value simultaneously.
  We give two algorithms: both take as input an arbitrary non-conformity score
and an arbitrary collection of possibly intersecting groups $\mathcal{G}$, and
then can equip arbitrary black-box predictors with prediction sets. Our first
algorithm (BatchGCP) is a direct extension of quantile regression, needs to
solve only a single convex minimization problem, and produces an estimator
which has group-conditional guarantees for each group in $\mathcal{G}$. Our
second algorithm (BatchMVP) is iterative, and gives the full guarantees of
multivalid conformal prediction: prediction sets that are valid conditionally
both on group membership and non-conformity threshold. We evaluate the
performance of both of our algorithms in an extensive set of experiments. Code
to replicate all of our experiments can be found at
https://github.com/ProgBelarus/BatchMultivalidConformal

Recent years have witnessed a growing academic interest in multi-view
subspace clustering. In this paper, we propose a novel Double Graphs
Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to
harness both global and local structural information of multi-view data in a
unified framework. Specifically, DGRMSC firstly learns a latent representation
to exploit the global complementary information of multiple views. Based on the
learned latent representation, we learn a self-representation to explore its
global cluster structure. Further, Double Graphs Regularization (DGR) is
performed on both latent representation and self-representation to take
advantage of their local manifold structures simultaneously. Then, we design an
iterative algorithm to solve the optimization problem effectively. Extensive
experimental results on real-world datasets demonstrate the effectiveness of
the proposed method.

We show two average-reward off-policy control algorithms, Differential
Q-learning (Wan, Naik, & Sutton 2021a) and RVI Q-learning (Abounadi Bertsekas &
Borkar 2001), converge in weakly communicating MDPs. Weakly communicating MDPs
are the most general MDPs that can be solved by a learning algorithm with a
single stream of experience. The original convergence proofs of the two
algorithms require that the solution set of the average-reward optimality
equation only has one degree of freedom, which is not necessarily true for
weakly communicating MDPs. To the best of our knowledge, our results are the
first showing average-reward off-policy control algorithms converge in weakly
communicating MDPs. As a direct extension, we show that average-reward options
algorithms for temporal abstraction introduced by Wan, Naik, & Sutton (2021b)
converge if the Semi-MDP induced by options is weakly communicating.

Data augmentation is used extensively to improve model generalisation.
However, reliance on external libraries to implement augmentation methods
introduces a vulnerability into the machine learning pipeline. It is well known
that backdoors can be inserted into machine learning models through serving a
modified dataset to train on. Augmentation therefore presents a perfect
opportunity to perform this modification without requiring an initially
backdoored dataset. In this paper we present three backdoor attacks that can be
covertly inserted into data augmentation. Our attacks each insert a backdoor
using a different type of computer vision augmentation transform, covering
simple image transforms, GAN-based augmentation, and composition-based
augmentation. By inserting the backdoor using these augmentation transforms, we
make our backdoors difficult to detect, while still supporting arbitrary
backdoor functionality. We evaluate our attacks on a range of computer vision
benchmarks and demonstrate that an attacker is able to introduce backdoors
through just a malicious augmentation routine.

Introduction. The stress response has both subjective, psychological and
objectively measurable, biological components. Both of them can be expressed
differently from person to person, complicating the development of a generic
stress measurement model. This is further compounded by the lack of large,
labeled datasets that can be utilized to build machine learning models for
accurately detecting periods and levels of stress. The aim of this review is to
provide an overview of the current state of stress detection and monitoring
using wearable devices, and where applicable, machine learning techniques
utilized.
  Methods. This study reviewed published works contributing and/or using
datasets designed for detecting stress and their associated machine learning
methods, with a systematic review and meta-analysis of those that utilized
wearable sensor data as stress biomarkers. The electronic databases of Google
Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a
total of 24 articles were identified and included in the final analysis. The
reviewed works were synthesized into three categories of publicly available
stress datasets, machine learning, and future research directions.
  Results. A wide variety of study-specific test and measurement protocols were
noted in the literature. A number of public datasets were identified that are
labeled for stress detection. In addition, we discuss that previous works show
shortcomings in areas such as their labeling protocols, lack of statistical
power, validity of stress biomarkers, and generalization ability.
  Conclusion. Generalization of existing machine learning models still require
further study, and research in this area will continue to provide improvements
as newer and more substantial datasets become available for study.

Low-dose computed tomography (LDCT) is an important topic in the field of
radiology over the past decades. LDCT reduces ionizing radiation-induced
patient health risks but it also results in a low signal-to-noise ratio (SNR)
and a potential compromise in the diagnostic performance. In this paper, to
improve the LDCT denoising performance, we introduce the conditional denoising
diffusion probabilistic model (DDPM) and show encouraging results with a high
computational efficiency. Specifically, given the high sampling cost of the
original DDPM model, we adapt the fast ordinary differential equation (ODE)
solver for a much-improved sampling efficiency. The experiments show that the
accelerated DDPM can achieve 20x speedup without compromising image quality.

Although haptic sensing has recently been used for legged robot localization
in extreme environments where a camera or LiDAR might fail, the problem of
efficiently representing the haptic signatures in a learned prior map is still
open. This paper introduces an approach to terrain representation for haptic
localization inspired by recent trends in machine learning. It combines this
approach with the proven Monte Carlo algorithm to obtain an accurate,
computation-efficient, and practical method for localizing legged robots under
adversarial environmental conditions. We apply the triplet loss concept to
learn highly descriptive embeddings in a transformer-based neural network. As
the training haptic data are not labeled, the positive and negative examples
are discriminated by their geometric locations discovered while training. We
demonstrate experimentally that the proposed approach outperforms by a large
margin the previous solutions to haptic localization of legged robots
concerning the accuracy, inference time, and the amount of data stored in the
map. As far as we know, this is the first approach that completely removes the
need to use a dense terrain map for accurate haptic localization, thus paving
the way to practical applications.

Lane changes are complex driving behaviors and frequently involve
safety-critical situations. This study aims to develop a lane-change-related
evasive behavior model, which can facilitate the development of safety-aware
traffic simulations and predictive collision avoidance systems. Large-scale
connected vehicle data from the Safety Pilot Model Deployment (SPMD) program
were used for this study. A new surrogate safety measure, two-dimensional
time-to-collision (2D-TTC), was proposed to identify the safety-critical
situations during lane changes. The validity of 2D-TTC was confirmed by showing
a high correlation between the detected conflict risks and the archived
crashes. A deep deterministic policy gradient (DDPG) algorithm, which could
learn the sequential decision-making process over continuous action spaces, was
used to model the evasive behaviors in the identified safety-critical
situations. The results showed the superiority of the proposed model in
replicating both the longitudinal and lateral evasive behaviors.

Enabling robots to perform complex dynamic tasks such as picking up an object
in one sweeping motion or pushing off a wall to quickly turn a corner is a
challenging problem. The dynamic interactions implicit in these tasks are
critical towards the successful execution of such tasks. Graph neural networks
(GNNs) provide a principled way of learning the dynamics of interactive systems
but can suffer from scaling issues as the number of interactions increases.
Furthermore, the problem of using learned GNN-based models for optimal control
is insufficiently explored. In this work, we present a method for efficiently
learning the dynamics of interacting systems by simultaneously learning a
dynamic graph structure and a stable and locally linear forward model of the
system. The dynamic graph structure encodes evolving contact modes along a
trajectory by making probabilistic predictions over the edges of the graph.
Additionally, we introduce a temporal dependence in the learned graph structure
which allows us to incorporate contact measurement updates during execution
thus enabling more accurate forward predictions. The learned stable and locally
linear dynamics enable the use of optimal control algorithms such as iLQR for
long-horizon planning and control for complex interactive tasks. Through
experiments in simulation and in the real world, we evaluate the performance of
our method by using the learned interaction dynamics for control and
demonstrate generalization to more objects and interactions not seen during
training. We introduce a control scheme that takes advantage of contact
measurement updates and hence is robust to prediction inaccuracies during
execution.

We study a general matrix optimization problem with a fixed-rank positive
semidefinite (PSD) constraint. We perform the Burer-Monteiro factorization and
consider a particular Riemannian quotient geometry in a search space that has a
total space equipped with the Euclidean metric. When the original objective f
satisfies standard restricted strong convexity and smoothness properties, we
characterize the global landscape of the factorized objective under the
Riemannian quotient geometry. We show the entire search space can be divided
into three regions: (R1) the region near the target parameter of interest,
where the factorized objective is geodesically strongly convex and smooth; (R2)
the region containing neighborhoods of all strict saddle points; (R3) the
remaining regions, where the factorized objective has a large gradient. To our
best knowledge, this is the first global landscape analysis of the
Burer-Monteiro factorized objective under the Riemannian quotient geometry. Our
results provide a fully geometric explanation for the superior performance of
vanilla gradient descent under the Burer-Monteiro factorization. When f
satisfies a weaker restricted strict convexity property, we show there exists a
neighborhood near local minimizers such that the factorized objective is
geodesically convex. To prove our results we provide a comprehensive landscape
analysis of a matrix factorization problem with a least squares objective,
which serves as a critical bridge. Our conclusions are also based on a result
of independent interest stating that the geodesic ball centered at Y with a
radius 1/3 of the least singular value of Y is a geodesically convex set under
the Riemannian quotient geometry, which as a corollary, also implies a
quantitative bound of the convexity radius in the Bures-Wasserstein space. The
convexity radius obtained is sharp up to constants.

Shapley values are ubiquitous in interpretable Machine Learning due to their
strong theoretical background and efficient implementation in the SHAP library.
Computing these values used to induce an exponential cost with respect to the
number of input features of an opaque model. Now, with efficient
implementations such as Interventional TreeSHAP, this exponential burden is
alleviated assuming one is explaining ensembles of decision trees. Although
Interventional TreeSHAP has risen in popularity, it still lacks a formal proof
of how/why it works. We provide such proof with the aim of not only increasing
the transparency of the algorithm but also to encourage further development of
these ideas. Notably, our proof for Interventional TreeSHAP is easily adapted
to Shapley-Taylor indices.

We consider a latent space model for dynamic networks, where our objective is
to estimate the pairwise inner products of the latent positions. To balance
posterior inference and computational scalability, we present a structured
mean-field variational inference framework, where the time-dependent properties
of the dynamic networks are exploited to facilitate computation and inference.
Additionally, an easy-to-implement block coordinate ascent algorithm is
developed with message-passing type updates in each block, whereas the
complexity per iteration is linear with the number of nodes and time points. To
facilitate learning of the pairwise latent distances, we adopt a Gamma prior
for the transition variance different from the literature. To certify the
optimality, we demonstrate that the variational risk of the proposed
variational inference approach attains the minimax optimal rate under certain
conditions. En route, we derive the minimax lower bound, which might be of
independent interest. To best of our knowledge, this is the first such exercise
for dynamic latent space models. Simulations and real data analysis demonstrate
the efficacy of our methodology and the efficiency of our algorithm. Finally,
our proposed methodology can be readily extended to the case where the scales
of the latent nodes are learned in a nodewise manner.

Machine-learning based classifiers have become indispensable in the field of
astrophysics, allowing separation of astronomical sources into various classes,
with computational efficiency suitable for application to the enormous data
volumes that wide-area surveys now typically produce. In the standard
supervised classification paradigm, a model is typically trained and validated
using data from relatively small areas of sky, before being used to classify
sources in other areas of the sky. However, population shifts between the
training examples and the sources to be classified can lead to `silent'
degradation in model performance, which can be challenging to identify when the
ground-truth is not available. In this Letter, we present a novel methodology
using the NannyML Confidence-Based Performance Estimation (CBPE) method to
predict classifier F1-score in the presence of population shifts, but without
ground-truth labels. We apply CBPE to the selection of quasars with
decision-tree ensemble models, using broad-band photometry, and show that the
F1-scores are predicted remarkably well (MAPE ~ 10%; R^2 = 0.74-0.92). We
discuss potential use-cases in the domain of astronomy, including
machine-learning model and/or hyperparameter selection, and evaluation of the
suitability of training datasets for a particular classification problem.

The pre-trained conversational models still fail to capture the implicit
commonsense (CS) knowledge hidden in the dialogue interaction, even though they
were pre-trained with an enormous dataset. In order to build a dialogue agent
with CS capability, we firstly inject external knowledge into a pre-trained
conversational model to establish basic commonsense through efficient Adapter
tuning (Section 4). Secondly, we propose the ``two-way learning'' method to
enable the bidirectional relationship between CS knowledge and sentence pairs
so that the model can generate a sentence given the CS triplets, also generate
the underlying CS knowledge given a sentence (Section 5). Finally, we leverage
this integrated CS capability to improve open-domain dialogue response
generation so that the dialogue agent is capable of understanding the CS
knowledge hidden in dialogue history on top of inferring related other
knowledge to further guide response generation (Section 6). The experiment
results demonstrate that CS\_Adapter fusion helps DialoGPT to be able to
generate series of CS knowledge. And the DialoGPT+CS\_Adapter response model
adapted from CommonGen training can generate underlying CS triplets that fits
better to dialogue context.

Being able to train Named Entity Recognition (NER) models for emerging topics
is crucial for many real-world applications especially in the medical domain
where new topics are continuously evolving out of the scope of existing models
and datasets. For a realistic evaluation setup, we introduce a novel COVID-19
news NER dataset (COVIDNEWS-NER) and release 3000 entries of hand annotated
strongly labelled sentences and 13000 auto-generated weakly labelled sentences.
Besides the dataset, we propose CONTROSTER, a recipe to strategically combine
weak and strong labels in improving NER in an emerging topic through transfer
learning. We show the effectiveness of CONTROSTER on COVIDNEWS-NER while
providing analysis on combining weak and strong labels for training. Our key
findings are: (1) Using weak data to formulate an initial backbone before
tuning on strong data outperforms methods trained on only strong or weak data.
(2) A combination of out-of-domain and in-domain weak label training is crucial
and can overcome saturation when being training on weak labels from a single
source.

We consider the problem of optimization of deep learning models with smooth
activation functions. While there exist influential results on the problem from
the ``near initialization'' perspective, we shed considerable new light on the
problem. In particular, we make two key technical contributions for such models
with $L$ layers, $m$ width, and $\sigma_0^2$ initialization variance. First,
for suitable $\sigma_0^2$, we establish a $O(\frac{\text{poly}(L)}{\sqrt{m}})$
upper bound on the spectral norm of the Hessian of such models, considerably
sharpening prior results. Second, we introduce a new analysis of optimization
based on Restricted Strong Convexity (RSC) which holds as long as the squared
norm of the average gradient of predictors is
$\Omega(\frac{\text{poly}(L)}{\sqrt{m}})$ for the square loss. We also present
results for more general losses. The RSC based analysis does not need the
``near initialization" perspective and guarantees geometric convergence for
gradient descent (GD). To the best of our knowledge, ours is the first result
on establishing geometric convergence of GD based on RSC for deep learning
models, thus becoming an alternative sufficient condition for convergence that
does not depend on the widely-used Neural Tangent Kernel (NTK). We share
preliminary experimental results supporting our theoretical advances.

Recent machine learning approaches have been effective in Artificial
Intelligence (AI) applications. They produce robust results with a high level
of accuracy. However, most of these techniques do not provide
human-understandable explanations for supporting their results and decisions.
They usually act as black boxes, and it is not easy to understand how decisions
have been made. Explainable Artificial Intelligence (XAI), which has received
much interest recently, tries to provide human-understandable explanations for
decision-making and trained AI models. For instance, in digital agriculture,
related domains often present peculiar or input features with no link to
background knowledge. The application of the data mining process on
agricultural data leads to results (knowledge), which are difficult to explain.
In this paper, we propose a knowledge map model and an ontology design as an
XAI framework (OAK4XAI) to deal with this issue. The framework does not only
consider the data analysis part of the process, but it takes into account the
semantics aspect of the domain knowledge via an ontology and a knowledge map
model, provided as modules of the framework. Many ongoing XAI studies aim to
provide accurate and verbalizable accounts for how given feature values
contribute to model decisions. The proposed approach, however, focuses on
providing consistent information and definitions of concepts, algorithms, and
values involved in the data mining models. We built an Agriculture Computing
Ontology (AgriComO) to explain the knowledge mined in agriculture. AgriComO has
a well-designed structure and includes a wide range of concepts and
transformations suitable for agriculture and computing domains.

Molecular pretraining, which learns molecular representations over massive
unlabeled data, has become a prominent paradigm to solve a variety of tasks in
computational chemistry and drug discovery. Recently, prosperous progress has
been made in molecular pretraining with different molecular featurizations,
including 1D SMILES strings, 2D graphs, and 3D geometries. However, the role of
molecular featurizations with their corresponding neural architectures in
molecular pretraining remains largely unexamined. In this paper, through two
case studies -- chirality classification and aromatic ring counting -- we first
demonstrate that different featurization techniques convey chemical information
differently. In light of this observation, we propose a simple and effective
MOlecular pretraining framework with COmplementary featurizations (MOCO). MOCO
comprehensively leverages multiple featurizations that complement each other
and outperforms existing state-of-the-art models that solely relies on one or
two featurizations on a wide range of molecular property prediction tasks.

Clustering is a widely deployed unsupervised learning tool. Model-based
clustering is a flexible framework to tackle data heterogeneity when the
clusters have different shapes. Likelihood-based inference for mixture
distributions often involves non-convex and high-dimensional objective
functions, imposing difficult computational and statistical challenges. The
classic expectation-maximization (EM) algorithm is a computationally thrifty
iterative method that maximizes a surrogate function minorizing the
log-likelihood of observed data in each iteration, which however suffers from
bad local maxima even in the special case of the standard Gaussian mixture
model with common isotropic covariance matrices. On the other hand, recent
studies reveal that the unique global solution of a semidefinite programming
(SDP) relaxed $K$-means achieves the information-theoretically sharp threshold
for perfectly recovering the cluster labels under the standard Gaussian mixture
model. In this paper, we extend the SDP approach to a general setting by
integrating cluster labels as model parameters and propose an iterative
likelihood adjusted SDP (iLA-SDP) method that directly maximizes the
\emph{exact} observed likelihood in the presence of data heterogeneity. By
lifting the cluster assignment to group-specific membership matrices, iLA-SDP
avoids centroids estimation -- a key feature that allows exact recovery under
well-separateness of centroids without being trapped by their adversarial
configurations. Thus iLA-SDP is less sensitive than EM to initialization and
more stable on high-dimensional data. Our numeric experiments demonstrate that
iLA-SDP can achieve lower mis-clustering errors over several widely used
clustering methods including $K$-means, SDP and EM algorithms.

Airway segmentation in computed tomography images can be used to analyze
pulmonary diseases, however, manual segmentation is labor intensive and relies
on expert knowledge. This manuscript details our contribution to MICCAI's 2022
Airway Tree Modelling challenge, a competition of fully automated methods for
airway segmentation. We employed a previously developed deep learning
architecture based on a modified EfficientDet (MEDSeg), training from scratch
for binary airway segmentation using the provided annotations. Our method
achieved 90.72 Dice in internal validation, 95.52 Dice on external validation,
and 93.49 Dice in the final test phase, while not being specifically designed
or tuned for airway segmentation. Open source code and a pip package for
predictions with our model and trained weights are in
https://github.com/MICLab-Unicamp/medseg.

Generative Flow Networks (GFlowNets) are recently proposed models for
learning stochastic policies that generate compositional objects by sequences
of actions with the probability proportional to a given reward function. The
central problem of GFlowNets is to improve their exploration and
generalization. In this work, we propose a novel path regularization method
based on optimal transport theory that places prior constraints on the
underlying structure of the GFlowNets. The prior is designed to help the
GFlowNets better discover the latent structure of the target distribution or
enhance its ability to explore the environment in the context of active
learning. The path regularization controls the flow in GFlowNets to generate
more diverse and novel candidates via maximizing the optimal transport
distances between two forward policies or to improve the generalization via
minimizing the optimal transport distances. In addition, we derive an efficient
implementation of the regularization by finding its closed form solutions in
specific cases and a meaningful upper bound that can be used as an
approximation to minimize the regularization term. We empirically demonstrate
the advantage of our path regularization on a wide range of tasks, including
synthetic hypergrid environment modeling, discrete probabilistic modeling, and
biological sequence design.

It is quite challenging to ensure the safety of reinforcement learning (RL)
agents in an unknown and stochastic environment under hard constraints that
require the system state not to reach certain specified unsafe regions. Many
popular safe RL methods such as those based on the Constrained Markov Decision
Process (CMDP) paradigm formulate safety violations in a cost function and try
to constrain the expectation of cumulative cost under a threshold. However, it
is often difficult to effectively capture and enforce hard reachability-based
safety constraints indirectly with such constraints on safety violation costs.
In this work, we leverage the notion of barrier function to explicitly encode
the hard safety constraints, and given that the environment is unknown, relax
them to our design of \emph{generative-model-based soft barrier functions}.
Based on such soft barriers, we propose a safe RL approach that can jointly
learn the environment and optimize the control policy, while effectively
avoiding unsafe regions with safety probability optimization. Experiments on a
set of examples demonstrate that our approach can effectively enforce hard
safety constraints and significantly outperform CMDP-based baseline methods in
system safe rate measured via simulations.

We develop an approach for improving the trustworthiness and overall accuracy
of program synthesizers based on large language models for source code. Given a
natural language description of a programming problem, our method samples both
candidate programs as well as candidate predicates specifying how the program
should behave. We learn to analyze the agreement between programs and
predicates to judge both which program is most likely to be correct, and also
judge whether the language model is able to solve the programming problem in
the first place. This latter capacity allows favoring high precision over broad
recall: fostering trust by only proposing a program when the system is certain
that it is correct.

Human reasoning is grounded in an ability to identify highly abstract
commonalities governing superficially dissimilar visual inputs. Recent efforts
to develop algorithms with this capacity have largely focused on approaches
that require extensive direct training on visual reasoning tasks, and yield
limited generalization to problems with novel content. In contrast, a long
tradition of research in cognitive science has focused on elucidating the
computational principles underlying human analogical reasoning; however, this
work has generally relied on manually constructed representations. Here we
present visiPAM (visual Probabilistic Analogical Mapping), a model of visual
reasoning that synthesizes these two approaches. VisiPAM employs learned
representations derived directly from naturalistic visual inputs, coupled with
a similarity-based mapping operation derived from cognitive theories of human
reasoning. We show that without any direct training, visiPAM outperforms a
state-of-the-art deep learning model on an analogical mapping task. In
addition, visiPAM closely matches the pattern of human performance on a novel
task involving mapping of 3D objects across disparate categories.

Cardiotocography (CTG) is a key element when it comes to monitoring fetal
well-being. Obstetricians use it to observe the fetal heart rate (FHR) and the
uterine contraction (UC). The goal is to determine how the fetus reacts to the
contraction and whether it is receiving adequate oxygen. If a problem occurs,
the physician can then respond with an intervention. Unfortunately, the
interpretation of CTGs is highly subjective and there is a low inter- and
intra-observer agreement rate among practitioners. This can lead to unnecessary
medical intervention that represents a risk for both the mother and the fetus.
Recently, computer-assisted diagnosis techniques, especially based on
artificial intelligence models (mostly supervised), have been proposed in the
literature. But, many of these models lack generalization to unseen/test data
samples due to overfitting. Moreover, the unsupervised models were applied to a
very small portion of the CTG samples where the normal and abnormal classes are
highly separable. In this work, deep unsupervised learning approaches, trained
in a semi-supervised manner, are proposed for anomaly detection in CTG signals.
The GANomaly framework, modified to capture the underlying distribution of data
samples, is used as our main model and is applied to the CTU-UHB dataset.
Unlike the recent studies, all the CTG data samples, without any specific
preferences, are used in our work. The experimental results show that our
modified GANomaly model outperforms state-of-the-arts. This study admit the
superiority of the deep unsupervised models over the supervised ones in CTG
abnormality detection.

One of the promising applications of satellite images is building
construction monitoring. It allows to control the construction progress around
the world even in the locations that are hard to reach. One of the main hurdles
of this approach is the interpretation of the image data. In this paper, we
have employed several novel deep learning techniques to tackle the problem.
Various image segmentation and object detection networks were combined into a
unified pipeline, which was then used to determine the building construction
progress.

Reinforcement learning is a promising paradigm for learning robot control,
allowing complex control policies to be learned without requiring a dynamics
model. However, even state of the art algorithms can be difficult to tune for
optimum performance. We propose employing an ensemble of multiple reinforcement
learning agents, each with a different set of hyperparameters, along with a
mechanism for choosing the best performing set(s) on-line. In the literature,
the ensemble technique is used to improve performance in general, but the
current work specifically addresses decreasing the hyperparameter tuning
effort. Furthermore, our approach targets on-line learning on a single robotic
system, and does not require running multiple simulators in parallel. Although
the idea is generic, the Deep Deterministic Policy Gradient was the model
chosen, being a representative deep learning actor-critic method with good
performance in continuous action settings but known high variance. We compare
our online weighted q-ensemble approach to q-average ensemble strategies
addressed in literature using alternate policy training, as well as online
training, demonstrating the advantage of the new approach in eliminating
hyperparameter tuning. The applicability to real-world systems was validated in
common robotic benchmark environments: the bipedal robot half cheetah and the
swimmer. Online Weighted Q-Ensemble presented overall lower variance and
superior results when compared with q-average ensembles using randomized
parameterizations.

Vision transformers (ViTs) have quickly superseded convolutional networks
(ConvNets) as the current state-of-the-art (SOTA) models for medical image
segmentation. Hierarchical transformers (e.g., Swin Transformers) reintroduced
several ConvNet priors and further enhanced the practical viability of adapting
volumetric segmentation in 3D medical datasets. The effectiveness of hybrid
approaches is largely credited to the large receptive field for non-local
self-attention and the large number of model parameters. In this work, we
propose a lightweight volumetric ConvNet, termed 3D UX-Net, which adapts the
hierarchical transformer using ConvNet modules for robust volumetric
segmentation. Specifically, we revisit volumetric depth-wise convolutions with
large kernel size (e.g. starting from $7\times7\times7$) to enable the larger
global receptive fields, inspired by Swin Transformer. We further substitute
the multi-layer perceptron (MLP) in Swin Transformer blocks with pointwise
depth convolutions and enhance model performances with fewer normalization and
activation layers, thus reducing the number of model parameters. 3D UX-Net
competes favorably with current SOTA transformers (e.g. SwinUNETR) using three
challenging public datasets on volumetric brain and abdominal imaging: 1)
MICCAI Challenge 2021 FLARE, 2) MICCAI Challenge 2021 FeTA, and 3) MICCAI
Challenge 2022 AMOS. 3D UX-Net consistently outperforms SwinUNETR with
improvement from 0.929 to 0.938 Dice (FLARE2021) and 0.867 to 0.874 Dice
(Feta2021). We further evaluate the transfer learning capability of 3D UX-Net
with AMOS2022 and demonstrates another improvement of $2.27\%$ Dice (from 0.880
to 0.900). The source code with our proposed model are available at
https://github.com/MASILab/3DUX-Net.

Autonomous racing with scaled race cars has gained increasing attention as an
effective approach for developing perception, planning and control algorithms
for safe autonomous driving at the limits of the vehicle's handling. To train
agile control policies for autonomous racing, learning-based approaches largely
utilize reinforcement learning, albeit with mixed results. In this study, we
benchmark a variety of imitation learning policies for racing vehicles that are
applied directly or for bootstrapping reinforcement learning both in simulation
and on scaled real-world environments. We show that interactive imitation
learning techniques outperform traditional imitation learning methods and can
greatly improve the performance of reinforcement learning policies by
bootstrapping thanks to its better sample efficiency. Our benchmarks provide a
foundation for future research on autonomous racing using Imitation Learning
and Reinforcement Learning.

In this paper, we explore how to utilize pre-trained language model to
perform few-shot text classification where only a few annotated examples are
given for each class. Since using traditional cross-entropy loss to fine-tune
language model under this scenario causes serious overfitting and leads to
sub-optimal generalization of model, we adopt supervised contrastive learning
on few labeled data and consistency-regularization on vast unlabeled data.
Moreover, we propose a novel contrastive consistency to further boost model
performance and refine sentence representation. After conducting extensive
experiments on four datasets, we demonstrate that our model (FTCC) can
outperform state-of-the-art methods and has better robustness.

The recent observation of neural power-law scaling relations has made a
significant impact in the field of deep learning. A substantial amount of
attention has been dedicated as a consequence to the description of scaling
laws, although mostly for supervised learning and only to a reduced extent for
reinforcement learning frameworks. In this paper we present an extensive study
of performance scaling for a cornerstone reinforcement learning algorithm,
AlphaZero. On the basis of a relationship between Elo rating, playing strength
and power-law scaling, we train AlphaZero agents on the games Connect Four and
Pentago and analyze their performance. We find that player strength scales as a
power law in neural network parameter count when not bottlenecked by available
compute, and as a power of compute when training optimally sized agents. We
observe nearly identical scaling exponents for both games. Combining the two
observed scaling laws we obtain a power law relating optimal size to compute
similar to the ones observed for language models. We find that the predicted
scaling of optimal neural network size fits our data for both games. This
scaling law implies that previously published state-of-the-art game-playing
models are significantly smaller than their optimal size, given the respective
compute budgets. We also show that large AlphaZero models are more sample
efficient, performing better than smaller models with the same amount of

Temporal graph networks (TGNs) have gained prominence as models for embedding
dynamic interactions, but little is known about their theoretical
underpinnings. We establish fundamental results about the representational
power and limits of the two main categories of TGNs: those that aggregate
temporal walks (WA-TGNs), and those that augment local message passing with
recurrent memory modules (MP-TGNs). Specifically, novel constructions reveal
the inadequacy of MP-TGNs and WA-TGNs, proving that neither category subsumes
the other. We extend the 1-WL (Weisfeiler-Leman) test to temporal graphs, and
show that the most powerful MP-TGNs should use injective updates, as in this
case they become as expressive as the temporal WL. Also, we show that
sufficiently deep MP-TGNs cannot benefit from memory, and MP/WA-TGNs fail to
compute graph properties such as girth.
  These theoretical insights lead us to PINT -- a novel architecture that
leverages injective temporal message passing and relative positional features.
Importantly, PINT is provably more expressive than both MP-TGNs and WA-TGNs.
PINT significantly outperforms existing TGNs on several real-world benchmarks.

We devise a graph attention network-based approach for learning a scene
triangle mesh representation in order to estimate an image camera position in a
dynamic environment. Previous approaches built a scene-dependent model that
explicitly or implicitly embeds the structure of the scene. They use
convolution neural networks or decision trees to establish 2D/3D-3D
correspondences. Such a mapping overfits the target scene and does not
generalize well to dynamic changes in the environment. Our work introduces a
novel approach to solve the camera relocalization problem by using the
available triangle mesh. Our 3D-3D matching framework consists of three blocks:
(1) a graph neural network to compute the embedding of mesh vertices, (2) a
convolution neural network to compute the embedding of grid cells defined on
the RGB-D image, and (3) a neural network model to establish the correspondence
between the two embeddings. These three components are trained end-to-end. To
predict the final pose, we run the RANSAC algorithm to generate camera pose
hypotheses, and we refine the prediction using the point-cloud representation.
Our approach significantly improves the camera pose accuracy of the
state-of-the-art method from $0.358$ to $0.506$ on the RIO10 benchmark for
dynamic indoor camera relocalization.

We show that the representation cost of fully connected neural networks with
homogeneous nonlinearities - which describes the implicit bias in function
space of networks with $L_2$-regularization or with losses such as the
cross-entropy - converges as the depth of the network goes to infinity to a
notion of rank over nonlinear functions. We then inquire under which conditions
the global minima of the loss recover the `true' rank of the data: we show that
for too large depths the global minimum will be approximately rank 1
(underestimating the rank); we then argue that there is a range of depths which
grows with the number of datapoints where the true rank is recovered. Finally,
we discuss the effect of the rank of a classifier on the topology of the
resulting class boundaries and show that autoencoders with optimal nonlinear
rank are naturally denoising.

A procedural level generator is a tool that generates levels from noise. One
approach to build generators is using machine learning, but given the training
data rarity, multiple methods have been proposed to train generators from
nothing. However, level generation tasks tend to have sparse feedback, which is
commonly mitigated using game-specific supplemental rewards. This paper
proposes a novel approach to train generators from nothing by learning at
multiple level sizes starting from a small size up to the desired sizes. This
approach employs the observed phenomenon that feedback is denser at smaller
sizes to avoid supplemental rewards. It also presents the benefit of training
generators to output levels at various sizes. We apply this approach to train
controllable generators using generative flow networks. We also modify
diversity sampling to be compatible with generative flow networks and to expand
the expressive range. The results show that our methods can generate
high-quality diverse levels for Sokoban, Zelda and Danger Dave for a variety of
sizes, after only 3h 29min up to 6h 11min (depending on the game) of training
on a single commodity machine. Also, the results show that our generators can
output levels for sizes that were unavailable during training.

Ionic Liquids (ILs) provide a promising solution for CO$_2$ capture and
storage to mitigate global warming. However, identifying and designing the
high-capacity IL from the giant chemical space requires expensive, and
exhaustive simulations and experiments. Machine learning (ML) can accelerate
the process of searching for desirable ionic molecules through accurate and
efficient property predictions in a data-driven manner. But existing
descriptors and ML models for the ionic molecule suffer from the inefficient
adaptation of molecular graph structure. Besides, few works have investigated
the explainability of ML models to help understand the learned features that
can guide the design of efficient ionic molecules. In this work, we develop
both fingerprint-based ML models and Graph Neural Networks (GNNs) to predict
the CO$_2$ absorption in ILs. Fingerprint works on graph structure at the
feature extraction stage, while GNNs directly handle molecule structure in both
the feature extraction and model prediction stage. We show that our method
outperforms previous ML models by reaching a high accuracy (MAE of 0.0137,
$R^2$ of 0.9884). Furthermore, we take the advantage of GNNs feature
representation and develop a substructure-based explanation method that
provides insight into how each chemical fragments within IL molecules
contribute to the CO$_2$ absorption prediction of ML models. We also show that
our explanation result agrees with some ground truth from the theoretical
reaction mechanism of CO$_2$ absorption in ILs, which can advise on the design
of novel and efficient functional ILs in the future.

Recent progress in empirical and certified robustness promises to deliver
reliable and deployable Deep Neural Networks (DNNs). Despite that success, most
existing evaluations of DNN robustness have been done on images sampled from
the same distribution that the model was trained on. Yet, in the real world,
DNNs may be deployed in dynamic environments that exhibit significant
distribution shifts. In this work, we take a first step towards thoroughly
investigating the interplay between empirical and certified adversarial
robustness on one hand and domain generalization on another. To do so, we train
robust models on multiple domains and evaluate their accuracy and robustness on
an unseen domain. We observe that: (1) both empirical and certified robustness
generalize to unseen domains, and (2) the level of generalizability does not
correlate well with input visual similarity, measured by the FID between source
and target domains. We also extend our study to cover a real-world medical
application, in which adversarial augmentation enhances both the robustness and
generalization accuracy in unseen domains.

We investigate the cross-calibration of the Hinode/SOT-SP and SDO/HMI
instrument meta-data, specifically the correspondence of the scaling and
pointing information. Accurate calibration of these datasets gives the
correspondence needed by inter-instrument studies and learning-based
magnetogram systems, and is required for physically-meaningful photospheric
magnetic field vectors. We approach the problem by robustly fitting geometric
models on correspondences between images from each instrument's pipeline. This
technique is common in computer vision, but several critical details are
required when using scanning slit spectrograph data like Hinode/SOT-SP. We
apply this technique to data spanning a decade of the Hinode mission. Our
results suggest corrections to the published Level 2 Hinode/SOT-SP data. First,
an analysis on approximately 2,700 scans suggests that the reported pixel size
in Hinode/SOT-SP Level 2 data is incorrect by around 1%. Second, analysis of
over 12,000 scans show that the pointing information is often incorrect by
dozens of arcseconds with a strong bias. Regression of these corrections
indicates that thermal effects have caused secular and cyclic drift in
Hinode/SOT-SP pointing data over its mission. We offer two solutions. First,
direct co-alignment with SDO/HMI data via our procedure can improve alignments
for many Hinode/SOT-SP scans. Second, since the pointing errors are
predictable, simple post-hoc corrections can substantially improve the
pointing. We conclude by illustrating the impact of this updated calibration on
derived physical data products needed for research and interpretation. Among
other things, our results suggest that the pointing errors induce a hemispheric
bias in estimates of radial current density.

Spaceborne synthetic aperture radar (SAR) can provide accurate images of the
ocean surface roughness day-or-night in nearly all weather conditions, being an
unique asset for many geophysical applications. Considering the huge amount of
data daily acquired by satellites, automated techniques for physical features
extraction are needed. Even if supervised deep learning methods attain
state-of-the-art results, they require great amount of labeled data, which are
difficult and excessively expensive to acquire for ocean SAR imagery. To this
end, we use the subaperture decomposition (SD) algorithm to enhance the
unsupervised learning retrieval on the ocean surface, empowering ocean
researchers to search into large ocean databases. We empirically prove that SD
improve the retrieval precision with over 20% for an unsupervised transformer
auto-encoder network. Moreover, we show that SD brings important performance
boost when Doppler centroid images are used as input data, leading the way to
new unsupervised physics guided retrieval algorithms.

Underlying data structures, such as symmetries or invariances to
transformations, are often exploited to improve the solution of learning tasks.
However, embedding these properties in models or learning algorithms can be
challenging and computationally intensive. Data augmentation, on the other
hand, induces these symmetries during training by applying multiple
transformations to the input data. Despite its ubiquity, its effectiveness
depends on the choices of which transformations to apply, when to do so, and
how often. In fact, there is both empirical and theoretical evidence that the
indiscriminate use of data augmentation can introduce biases that outweigh its
benefits. This work tackles these issues by automatically adapting the data
augmentation while solving the learning task. To do so, it formulates data
augmentation as an invariance-constrained learning problem and leverages Monte
Carlo Markov Chain (MCMC) sampling to solve it. The result is a practical
algorithm that not only does away with a priori searches for augmentation
distributions, but also dynamically controls if and when data augmentation is
applied. Our experiments illustrate the performance of this method, which
achieves state-of-the-art results in automatic data augmentation benchmarks for
CIFAR datasets. Furthermore, this approach can be used to gather insights on
the actual symmetries underlying a learning task.

Contrastive methods have led a recent surge in the performance of
self-supervised representation learning (SSL). Recent methods like BYOL or
SimSiam purportedly distill these contrastive methods down to their essence,
removing bells and whistles, including the negative examples, that do not
contribute to downstream performance. These "non-contrastive" methods work
surprisingly well without using negatives even though the global minimum lies
at trivial collapse. We empirically analyze these non-contrastive methods and
find that SimSiam is extraordinarily sensitive to dataset and model size. In
particular, SimSiam representations undergo partial dimensional collapse if the
model is too small relative to the dataset size. We propose a metric to measure
the degree of this collapse and show that it can be used to forecast the
downstream task performance without any fine-tuning or labels. We further
analyze architectural design choices and their effect on the downstream
performance. Finally, we demonstrate that shifting to a continual learning
setting acts as a regularizer and prevents collapse, and a hybrid between
continual and multi-epoch training can improve linear probe accuracy by as many
as 18 percentage points using ResNet-18 on ImageNet. Our project page is at
https://alexanderli.com/noncontrastive-ssl/.

Transformers are quickly becoming one of the most heavily applied deep
learning architectures across modalities, domains, and tasks. In vision, on top
of ongoing efforts into plain transformers, hierarchical transformers have also
gained significant attention, thanks to their performance and easy integration
into existing frameworks. These models typically employ localized attention
mechanisms, such as the sliding-window Neighborhood Attention (NA) or Swin
Transformer's Shifted Window Self Attention. While effective at reducing self
attention's quadratic complexity, local attention weakens two of the most
desirable properties of self attention: long range inter-dependency modeling,
and global receptive field. In this paper, we introduce Dilated Neighborhood
Attention (DiNA), a natural, flexible and efficient extension to NA that can
capture more global context and expand receptive fields exponentially at no
additional cost. NA's local attention and DiNA's sparse global attention
complement each other, and therefore we introduce Dilated Neighborhood
Attention Transformer (DiNAT), a new hierarchical vision transformer built upon
both. DiNAT variants enjoy significant improvements over strong baselines such
as NAT, Swin, and ConvNeXt. Our large model is faster and ahead of its Swin
counterpart by 1.5% box AP in COCO object detection, 1.3% mask AP in COCO
instance segmentation, and 1.1% mIoU in ADE20K semantic segmentation. Paired
with new frameworks, our large variant is the new state of the art panoptic
segmentation model on COCO (58.2 PQ) and ADE20K (48.5 PQ), and instance
segmentation model on Cityscapes (44.5 AP) and ADE20K (35.4 AP) (no extra
data). It also matches the state of the art specialized semantic segmentation
models on ADE20K (58.2 mIoU), and ranks second on Cityscapes (84.5 mIoU) (no
extra data). We open-source our project.

This work is on training a generative action/video recognition model whose
output is a free-form action-specific caption describing the video (rather than
an action class label). A generative approach has practical advantages like
producing more fine-grained and human-readable output, and being naturally
open-world. To this end, we propose to adapt a pre-trained generative Vision &
Language (V&L) Foundation Model for video/action recognition. While recently
there have been a few attempts to adapt V&L models trained with contrastive
learning (e.g. CLIP) for video/action, to the best of our knowledge, we propose
the very first method that sets outs to accomplish this goal for a generative
model. We firstly show that direct fine-tuning of a generative model to produce
action classes suffers from severe overfitting. To alleviate this, we introduce
REST, a training framework consisting of two key components: an unsupervised
method for adapting the generative model to action/video by means of
pseudo-caption generation and Self-training, i.e. without using any
action-specific labels; (b) a Retrieval approach based on CLIP for discovering
a diverse set of pseudo-captions for each video to train the model.
Importantly, we show that both components are necessary to obtain high
accuracy. We evaluate REST on the problem of zero-shot action recognition where
we show that our approach is very competitive when compared to contrastive
learning-based methods. Code will be made available.

This paper introduces a simple efficient learning algorithms for general
sequential decision making. The algorithm combines Optimism for exploration
with Maximum Likelihood Estimation for model estimation, which is thus named
OMLE. We prove that OMLE learns the near-optimal policies of an enormously rich
class of sequential decision making problems in a polynomial number of samples.
This rich class includes not only a majority of known tractable model-based
Reinforcement Learning (RL) problems (such as tabular MDPs, factored MDPs, low
witness rank problems, tabular weakly-revealing/observable POMDPs and
multi-step decodable POMDPs), but also many new challenging RL problems
especially in the partially observable setting that were not previously known
to be tractable.
  Notably, the new problems addressed by this paper include (1) observable
POMDPs with continuous observation and function approximation, where we achieve
the first sample complexity that is completely independent of the size of
observation space; (2) well-conditioned low-rank sequential decision making
problems (also known as Predictive State Representations (PSRs)), which include
and generalize all known tractable POMDP examples under a more intrinsic
representation; (3) general sequential decision making problems under SAIL
condition, which unifies our existing understandings of model-based RL in both
fully observable and partially observable settings. SAIL condition is
identified by this paper, which can be viewed as a natural generalization of
Bellman/witness rank to address partial observability. This paper also presents
a reward-free variant of OMLE algorithm, which learns approximate dynamic
models that enable the computation of near-optimal policies for all reward
functions simultaneously.

Adapting model parameters to incoming streams of data is a crucial factor to
deep learning scalability. Interestingly, prior continual learning strategies
in online settings inadvertently anchor their updated parameters to a local
parameter subspace to remember old tasks, else drift away from the subspace and
forget. From this observation, we formulate a trade-off between constructing
multiple parameter modes and allocating tasks per mode. Mode-Optimized Task
Allocation (MOTA), our contributed adaptation strategy, trains multiple modes
in parallel, then optimizes task allocation per mode. We empirically
demonstrate improvements over baseline continual learning strategies and across
varying distribution shifts, namely sub-population, domain, and task shift.

In equivariant machine learning the idea is to restrict the learning to a
hypothesis class where all the functions are equivariant with respect to some
group action. Irreducible representations or invariant theory are typically
used to parameterize the space of such functions. In this note, we explicate a
general procedure, attributed to Malgrange, to express all polynomial maps
between linear spaces that are equivariant with respect to the action of a
group $G$, given a characterization of the invariant polynomials on a bigger
space. The method also parametrizes smooth equivariant maps in the case that
$G$ is a compact Lie group.

Partial Observability -- where agents can only observe partial information
about the true underlying state of the system -- is ubiquitous in real-world
applications of Reinforcement Learning (RL). Theoretically, learning a
near-optimal policy under partial observability is known to be hard in the
worst case due to an exponential sample complexity lower bound. Recent work has
identified several tractable subclasses that are learnable with polynomial
samples, such as Partially Observable Markov Decision Processes (POMDPs) with
certain revealing or decodability conditions. However, this line of research is
still in its infancy, where (1) unified structural conditions enabling
sample-efficient learning are lacking; (2) existing sample complexities for
known tractable subclasses are far from sharp; and (3) fewer sample-efficient
algorithms are available than in fully observable RL.
  This paper advances all three aspects above for Partially Observable RL in
the general setting of Predictive State Representations (PSRs). First, we
propose a natural and unified structural condition for PSRs called
\emph{B-stability}. B-stable PSRs encompasses the vast majority of known
tractable subclasses such as weakly revealing POMDPs, low-rank
future-sufficient POMDPs, decodable POMDPs, and regular PSRs. Next, we show
that any B-stable PSR can be learned with polynomial samples in relevant
problem parameters. When instantiated in the aforementioned subclasses, our
sample complexities improve substantially over the current best ones. Finally,
our results are achieved by three algorithms simultaneously: Optimistic Maximum
Likelihood Estimation, Estimation-to-Decisions, and Model-Based Optimistic
Posterior Sampling. The latter two algorithms are new for sample-efficient
learning of POMDPs/PSRs.

Recent breakthroughs in text-to-image synthesis have been driven by diffusion
models trained on billions of image-text pairs. Adapting this approach to 3D
synthesis would require large-scale datasets of labeled 3D data and efficient
architectures for denoising 3D data, neither of which currently exist. In this
work, we circumvent these limitations by using a pretrained 2D text-to-image
diffusion model to perform text-to-3D synthesis. We introduce a loss based on
probability density distillation that enables the use of a 2D diffusion model
as a prior for optimization of a parametric image generator. Using this loss in
a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a
Neural Radiance Field, or NeRF) via gradient descent such that its 2D
renderings from random angles achieve a low loss. The resulting 3D model of the
given text can be viewed from any angle, relit by arbitrary illumination, or
composited into any 3D environment. Our approach requires no 3D training data
and no modifications to the image diffusion model, demonstrating the
effectiveness of pretrained image diffusion models as priors.

New methods designed to preserve data privacy require careful scrutiny.
Failure to preserve privacy is hard to detect, and yet can lead to catastrophic
results when a system implementing a ``privacy-preserving'' method is attacked.
A recent work selected for an Outstanding Paper Award at ICML 2022 (Dong et
al., 2022) claims that dataset condensation (DC) significantly improves data
privacy when training machine learning models. This claim is supported by
theoretical analysis of a specific dataset condensation technique and an
empirical evaluation of resistance to some existing membership inference
attacks.
  In this note we examine the claims in the work of Dong et al. (2022) and
describe major flaws in the empirical evaluation of the method and its
theoretical analysis. These flaws imply that their work does not provide
statistically significant evidence that DC improves the privacy of training ML
models over a naive baseline. Moreover, previously published results show that
DP-SGD, the standard approach to privacy preserving ML, simultaneously gives
better accuracy and achieves a (provably) lower membership attack success rate.

We first show that the discounted cost, cost up to an exit time, and ergodic
cost involving controlled non-degenerate diffusions are continuous on the space
of stationary control policies when the policies are given a topology
introduced by Borkar [V. S. Borkar, A topology for Markov controls, Applied
Mathematics and Optimization 20 (1989), 55-62]. The same applies for finite
horizon problems when the control policies are markov and the topology is
revised to include time also as a parameter. We then establish that finite
action/piecewise constant stationary policies are dense in the space of
stationary Markov policies under this topology. Using the above mentioned
continuity and denseness results we establish that finite action/piecewise
constant policies approximate optimal stationary policies with arbitrary
precision. This gives rise to the applicability of many numerical methods such
as policy iteration and stochastic learning methods for discounted cost, cost
up to an exit time, and ergodic cost optimal control problems in
continuous-time. For the finite-horizon setup, we establish additionally near
optimality of time-discretized policies by an analogous argument. We thus
present a unified and concise approach for approximations directly applicable
under several commonly adopted cost criteria.

Training vision or language models on large datasets can take days, if not
weeks. We show that averaging the weights of the k latest checkpoints, each
collected at the end of an epoch, can speed up the training progression in
terms of loss and accuracy by dozens of epochs, corresponding to time savings
up to ~68 and ~30 GPU hours when training a ResNet50 on ImageNet and
RoBERTa-Base model on WikiText-103, respectively. We also provide the code and
model checkpoint trajectory to reproduce the results and facilitate research on
reusing historical weights for faster convergence.

We investigate the combinatorics of max-pooling layers, which are functions
that downsample input arrays by taking the maximum over shifted windows of
input coordinates, and which are commonly used in convolutional neural
networks. We obtain results on the number of linearity regions of these
functions by equivalently counting the number of vertices of certain Minkowski
sums of simplices. We characterize the faces of such polytopes and obtain
generating functions and closed formulas for the number of vertices and facets
in a 1D max-pooling layer depending on the size of the pooling windows and
stride, and for the number of vertices in a special case of 2D max-pooling.

A Transformer-based deep direct sampling method is proposed for solving a
class of boundary value inverse problem. A real-time reconstruction is achieved
by evaluating the learned inverse operator between carefully designed data and
the reconstructed images. An effort is made to give a case study for a
fundamental and critical question: whether and how one can benefit from the
theoretical structure of a mathematical problem to develop task-oriented and
structure-conforming deep neural network? Inspired by direct sampling methods
for inverse problems, the 1D boundary data are preprocessed by a partial
differential equation-based feature map to yield 2D harmonic extensions in
different frequency input channels. Then, by introducing learnable non-local
kernel, the approximation of direct sampling is recast to a modified attention
mechanism. The proposed method is then applied to electrical impedance
tomography, a well-known severely ill-posed nonlinear inverse problem. The new
method achieves superior accuracy over its predecessors and contemporary
operator learners, as well as shows robustness with respect to noise. This
research shall strengthen the insights that the attention mechanism, despite
being invented for natural language processing tasks, offers great flexibility
to be modified in conformity with the a priori mathematical knowledge, which
ultimately leads to the design of more physics-compatible neural architectures.

Causal inference and model interpretability research are gaining increasing
attention, especially in the domains of healthcare and bioinformatics. Despite
recent successes in this field, decorrelating features under nonlinear
environments with human interpretable representations has not been adequately
investigated. To address this issue, we introduce a novel method with a
variable decorrelation regularizer to handle both linear and nonlinear
confounding. Moreover, we employ association rules as new representations using
association rule mining based on the original features to further proximate
human decision patterns to increase model interpretability. Extensive
experiments are conducted on four healthcare datasets (one synthetically
generated and three real-world collections on different diseases). Quantitative
results in comparison to baseline approaches on parameter estimation and
causality computation indicate the model's superior performance. Furthermore,
expert evaluation given by healthcare professionals validates the effectiveness
and interpretability of the proposed model.

We report a search for cosmic-ray boosted dark matter with protons using the
0.37 megaton$\times$years data collected at Super-Kamiokande experiment during
the 1996-2018 period (SKI-IV phase). We searched for an excess of proton
recoils above the atmospheric neutrino background from the vicinity of the
Galactic Center. No such excess is observed, and limits are calculated for two
reference models of dark matter with either a constant interaction
cross-section or through a scalar mediator. This is the first experimental
search for boosted dark matter with hadrons using directional information. The
results present the most stringent limits on cosmic-ray boosted dark matter and
exclude the dark matter-nucleon elastic scattering cross-section between
$10^{-33}\text{ cm}^{-2}$ and $10^{-27}\text{ cm}^{-2}$ for dark matter mass
from 10 MeV/$c^2$ to 1 GeV/$c^2$.

Inverse problems are paramount in Science and Engineering. In this paper, we
consider the setup of Statistical Inverse Problem (SIP) and demonstrate how
Stochastic Gradient Descent (SGD) algorithms can be used in the linear SIP
setting. We provide consistency and finite sample bounds for the excess risk.
We also propose a modification for the SGD algorithm where we leverage machine
learning methods to smooth the stochastic gradients and improve empirical
performance. We exemplify the algorithm in a setting of great interest
nowadays: the Functional Linear Regression model. In this case we consider a
synthetic data example and examples with a real data classification problem.

Making user interaction with laboratory equipment more convenient and
intuitive should promote experimental work and help researchers to complete
their tasks efficiently. The most common form of interaction in current
instrumentation is either direct tactile, with buttons and knobs, or interfaced
through a computer, using a mouse and keyboard. Scripting is another function
typical of smart and automated laboratory equipment, yet users are currently
required to learn bespoke programming languages and libraries for individual
pieces of equipment. In this paper we present two open-source, novel and
intuitive ways of interacting with and scripting laboratory equipment. We
choose the OpenFlexure family of microscopes as our exemplar, due to their
open-source nature and smart control system. Firstly, we demonstrate
"OpenFlexure Voice Control" to enable users to control the microscope
hands-free. Secondly, we present "OpenFlexure Blockly" which uses the Blockly
Visual Programming Language to enable users to easily create scripts for the
microscope, using a drag and drop web interface. We explain the design choices
when developing these tools, and discuss more typical use cases and more
general applications.

This paper develops a new EiHi net to solve the out-of-distribution (OoD)
generalization problem in deep learning. EiHi net is a model learning paradigm
that can be blessed on any visual backbone. This paradigm can change the
previous learning method of the deep model, namely find out correlations
between inductive sample features and corresponding categories, which suffers
from pseudo correlations between indecisive features and labels. We fuse SimCLR
and VIC-Reg via explicitly and dynamically establishing the original - positive
- negative sample pair as a minimal learning element, the deep model
iteratively establishes a relationship close to the causal one between features
and labels, while suppressing pseudo correlations. To further validate the
proposed model, and strengthen the established causal relationships, we develop
a human-in-the-loop strategy, with few guidance samples, to prune the
representation space directly. Finally, it is shown that the developed EiHi net
makes significant improvements in the most difficult and typical OoD dataset
Nico, compared with the current SOTA results, without any domain ($e.g.$
background, irrelevant features) information.

3D visual grounding aims to find the objects within point clouds mentioned by
free-form natural language descriptions with rich semantic components. However,
existing methods either extract the sentence-level features coupling all words,
or focus more on object names, which would lose the word-level information or
neglect other attributes. To alleviate this issue, we present EDA that
Explicitly Decouples the textual attributes in a sentence and conducts Dense
Alignment between such fine-grained language and point cloud objects.
Specifically, we first propose a text decoupling module to produce textual
features for every semantic component. Then, we design two losses to supervise
the dense matching between two modalities: the textual position alignment and
object semantic alignment. On top of that, we further introduce two new visual
grounding tasks, locating objects without object names and locating auxiliary
objects referenced in the descriptions, both of which can thoroughly evaluate
the model's dense alignment capacity. Through experiments, we achieve
state-of-the-art performance on two widely-adopted visual grounding datasets ,
ScanRefer and SR3D/NR3D, and obtain absolute leadership on our two
newly-proposed tasks. The code will be available at
https://github.com/yanmin-wu/EDA.

The desire to make applications and machines more intelligent and the
aspiration to enable their operation without human interaction have been
driving innovations in neural networks, deep learning, and other machine
learning techniques. Although reinforcement learning has been primarily used in
video games, recent advancements and the development of diverse and powerful
reinforcement algorithms have enabled the reinforcement learning community to
move from playing video games to solving complex real-life problems in
autonomous systems such as self-driving cars, delivery drones, and automated
robotics. Understanding the environment of an application and the algorithms'
limitations plays a vital role in selecting the appropriate reinforcement
learning algorithm that successfully solves the problem on hand in an efficient
manner. Consequently, in this study, we identify three main environment types
and classify reinforcement learning algorithms according to those environment
types. Moreover, within each category, we identify relationships between
algorithms. The overview of each algorithm provides insight into the
algorithms' foundations and reviews similarities and differences among
algorithms. This study provides a perspective on the field and helps
practitioners and researchers to select the appropriate algorithm for their use
case.

Classical machine learning models such as deep neural networks are usually
trained by using Stochastic Gradient Descent-based (SGD) algorithms. The
classical SGD can be interpreted as a discretization of the stochastic gradient
flow. In this paper we propose a novel, robust and accelerated stochastic
optimizer that relies on two key elements: (1) an accelerated Nesterov-like
Stochastic Differential Equation (SDE) and (2) its semi-implicit Gauss-Seidel
type discretization. The convergence and stability of the obtained method,
referred to as NAG-GS, are first studied extensively in the case of the
minimization of a quadratic function. This analysis allows us to come up with
an optimal step size (or learning rate) in terms of rate of convergence while
ensuring the stability of NAG-GS. This is achieved by the careful analysis of
the spectral radius of the iteration matrix and the covariance matrix at
stationarity with respect to all hyperparameters of our method. We show that
NAG-GS is competitive with state-of-the-art methods such as momentum SGD with
weight decay and AdamW for the training of machine learning models such as the
logistic regression model, the residual networks models on standard computer
vision datasets, and Transformers in the frame of the GLUE benchmark.

A zero-shot RL agent is an agent that can solve any RL task in a given
environment, instantly with no additional planning or learning, after an
initial reward-free learning phase. This marks a shift from the reward-centric
RL paradigm towards "controllable" agents that can follow arbitrary
instructions in an environment. Current RL agents can solve families of related
tasks at best, or require planning anew for each task. Strategies for
approximate zero-shot RL ave been suggested using successor features (SFs)
[BBQ+ 18] or forward-backward (FB) representations [TO21], but testing has been
limited.
  After clarifying the relationships between these schemes, we introduce
improved losses and new SF models, and test the viability of zero-shot RL
schemes systematically on tasks from the Unsupervised RL benchmark [LYL+21]. To
disentangle universal representation learning from exploration, we work in an
offline setting and repeat the tests on several existing replay buffers.
  SFs appear to suffer from the choice of the elementary state features. SFs
with Laplacian eigenfunctions do well, while SFs based on auto-encoders,
inverse curiosity, transition models, low-rank transition matrix, contrastive
learning, or diversity (APS), perform unconsistently. In contrast, FB
representations jointly learn the elementary and successor features from a
single, principled criterion. They perform best and consistently across the
board, reaching 85% of supervised RL performance with a good replay buffer, in
a zero-shot manner.

Normalizing flows are powerful non-parametric statistical models that
function as a hybrid between density estimators and generative models. Current
learning algorithms for normalizing flows assume that data points are sampled
independently, an assumption that is frequently violated in practice, which may
lead to erroneous density estimation and data generation. We propose a
likelihood objective of normalizing flows incorporating dependencies between
the data points, for which we derive a flexible and efficient learning
algorithm suitable for different dependency structures. We show that respecting
dependencies between observations can improve empirical results on both
synthetic and real-world data.

In this paper we present a world model, which learns causal features using
the invariance principle. In particular, we use contrastive unsupervised
learning to learn the invariant causal features, which enforces invariance
across augmentations of irrelevant parts or styles of the observation. The
world-model-based reinforcement learning methods independently optimize
representation learning and the policy. Thus naive contrastive loss
implementation collapses due to a lack of supervisory signals to the
representation learning module. We propose an intervention invariant auxiliary
task to mitigate this issue. Specifically, we utilize depth prediction to
explicitly enforce the invariance and use data augmentation as style
intervention on the RGB observation space. Our design leverages unsupervised
representation learning to learn the world model with invariant causal
features. Our proposed method significantly outperforms current
state-of-the-art model-based and model-free reinforcement learning methods on
out-of-distribution point navigation tasks on the iGibson dataset. Moreover,
our proposed model excels at the sim-to-real transfer of our perception
learning module. Finally, we evaluate our approach on the DeepMind control
suite and enforce invariance only implicitly since depth is not available.
Nevertheless, our proposed model performs on par with the state-of-the-art
counterpart.

Graphs are used widely to model complex systems, and detecting anomalies in a
graph is an important task in the analysis of complex systems. Graph anomalies
are patterns in a graph that do not conform to normal patterns expected of the
attributes and/or structures of the graph. In recent years, graph neural
networks (GNNs) have been studied extensively and have successfully performed
difficult machine learning tasks in node classification, link prediction, and
graph classification thanks to the highly expressive capability via message
passing in effectively learning graph representations. To solve the graph
anomaly detection problem, GNN-based methods leverage information about the
graph attributes (or features) and/or structures to learn to score anomalies
appropriately. In this survey, we review the recent advances made in detecting
graph anomalies using GNN models. Specifically, we summarize GNN-based methods
according to the graph type (i.e., static and dynamic), the anomaly type (i.e.,
node, edge, subgraph, and whole graph), and the network architecture (e.g.,
graph autoencoder, graph convolutional network). To the best of our knowledge,
this survey is the first comprehensive review of graph anomaly detection
methods based on GNNs.

Mobile UI understanding is important for enabling various interaction tasks
such as UI automation and accessibility. Previous mobile UI modeling often
depends on the view hierarchy information of a screen, which directly provides
the structural data of the UI, with the hope to bypass challenging tasks of
visual modeling from screen pixels. However, view hierarchy is not always
available, and is often corrupted with missing object descriptions or
misaligned bounding box positions. As a result, although using view hierarchy
offers some short-term gains, it may ultimately hinder the applicability and
performance of the model. In this paper, we propose Spotlight, a vision-only
approach for mobile UI understanding. Specifically, we enhance a
vision-language model that only takes the screenshot of the UI and a region of
interest on the screen -- the focus -- as the input. This general architecture
is easily scalable and capable of performing a range of UI modeling tasks. Our
experiments show that our model obtains SoTA results on several representative
UI tasks and outperforms previous methods that use both screenshots and view
hierarchies as input. Furthermore, we explore the multi-task learning and
few-shot prompting capacity of the proposed models, demonstrating promising
results in the multi-task learning direction.

Detecting objects under adverse weather and lighting conditions is crucial
for the safe and continuous operation of an autonomous vehicle, and remains an
unsolved problem. We present a Gated Differentiable Image Processing (GDIP)
block, a domain-agnostic network architecture, which can be plugged into
existing object detection networks (e.g., Yolo) and trained end-to-end with
adverse condition images such as those captured under fog and low lighting. Our
proposed GDIP block learns to enhance images directly through the downstream
object detection loss. This is achieved by learning parameters of multiple
image pre-processing (IP) techniques that operate concurrently, with their
outputs combined using weights learned through a novel gating mechanism. We
further improve GDIP through a multi-stage guidance procedure for progressive
image enhancement. Finally, trading off accuracy for speed, we propose a
variant of GDIP that can be used as a regularizer for training Yolo, which
eliminates the need for GDIP-based image enhancement during inference,
resulting in higher throughput and plausible real-world deployment. We
demonstrate significant improvement in detection performance over several
state-of-the-art methods through quantitative and qualitative studies on
synthetic datasets such as PascalVOC, and real-world foggy (RTTS) and
low-lighting (ExDark) datasets.

We present IvySyn: the first fully-automated framework for vulnerability
discovery in Deep Learning (DL) frameworks. IvySyn leverages the
statically-typed nature of native APIs in order to automatically perform
type-aware mutation-based fuzzing on low-level kernel APIs. Given a set of
offending inputs that trigger memory safety and fatal runtime errors in
low-level, native DL (C/C++) code, IvySyn automatically synthesizes code
snippets in high-level languages (e.g., in Python), which propagate offending
inputs via high(er)-level APIs. Such code snippets essentially act as Proof of
Vulnerability, as they demonstrate the existence of bugs in native code that
attackers can target through various high-level APIs. Our experimental
evaluation shows that IvySyn significantly outperforms past approaches, both in
terms of efficiency and effectiveness, in finding real vulnerabilities in
popular DL frameworks. Specifically, we used IvySyn to test TensorFlow and
PyTorch: although still an early research prototype, IvySyn has already helped
the corresponding TensorFlow and PyTorch framework developers to identify and
fix 58 previously-unknown security vulnerabilities, and assign 36 unique CVEs.

Enabling artificial neural networks (ANNs) to have temporal understanding in
visual tasks is an essential requirement in order to achieve complete
perception of video sequences. A wide range of benchmark datasets is available
to allow for the evaluation of such capabilities when using conventional
frame-based video sequences. In contrast, evaluating them for systems targeting
neuromorphic data is still a challenge due to the lack of appropriate datasets.
In this work we define a new benchmark task for action recognition in
event-based video sequences, DVS-Gesture-Chain (DVS-GC), which is based on the
temporal combination of multiple gestures from the widely used DVS-Gesture
dataset. This methodology allows to create datasets that are arbitrarily
complex in the temporal dimension. Using our newly defined task, we evaluate
the spatio-temporal understanding of different feed-forward convolutional ANNs
and convolutional Spiking Neural Networks (SNNs). Our study proves how the
original DVS Gesture benchmark could be solved by networks without temporal
understanding, unlike the new DVS-GC which demands an understanding of the
ordering of events. From there, we provide a study showing how certain elements
such as spiking neurons or time-dependent weights allow for temporal
understanding in feed-forward networks without the need for recurrent
connections. Code available at:
https://github.com/VicenteAlex/DVS-Gesture-Chain

This research presents an examination of categorizing the severity states of
patients based on their electronic health records during a certain time range
using multiple machine learning and deep learning approaches. The suggested
method uses an EHR dataset collected from an open-source platform to categorize
severity. Some tools were used in this research, such as openRefine was used to
pre-process, RapidMiner was used for implementing three algorithms (Fast Large
Margin, Generalized Linear Model, Multi-layer Feed-forward Neural Network) and
Tableau was used to visualize the data, for implementation of algorithms we
used Google Colab. Here we implemented several supervised and unsupervised
algorithms along with semi-supervised and deep learning algorithms. The
experimental results reveal that hyperparameter-tuned Random Forest
outperformed all the other supervised machine learning algorithms with 76%
accuracy as well as Generalized Linear algorithm achieved the highest precision
score 78%, whereas the hyperparameter-tuned Hierarchical Clustering with 86%
precision score and Gaussian Mixture Model with 61% accuracy outperformed other
unsupervised approaches. Dimensionality Reduction improved results a lot for
most unsupervised techniques. For implementing Deep Learning we employed a
feed-forward neural network (multi-layer) and the Fast Large Margin approach
for semi-supervised learning. The Fast Large Margin performed really well with
a recall score of 84% and an F1 score of 78%. Finally, the Multi-layer
Feed-forward Neural Network performed admirably with 75% accuracy, 75%
precision, 87% recall, 81% F1 score.

Self-Supervised Learning (SSL) methods such as VICReg, Barlow Twins or W-MSE
avoid collapse of their joint embedding architectures by constraining or
regularizing the covariance matrix of their projector's output. This study
highlights important properties of such strategy, which we coin
Variance-Covariance regularization (VCReg). More precisely, we show that VCReg
enforces pairwise independence between the features of the learned
representation. This result emerges by bridging VCReg applied on the
projector's output to kernel independence criteria applied on the projector's
input. This provides the first theoretical motivations and explanations of
VCReg. We empirically validate our findings where (i) we observe that SSL
methods employing VCReg learn visual representations with greater pairwise
independence than other methods, (i) we put in evidence which projector's
characteristics favor pairwise independence, and show it to emerge
independently from learning the projector, (ii) we use these findings to obtain
nontrivial performance gains for VICReg, (iii) we demonstrate that the scope of
VCReg goes beyond SSL by using it to solve Independent Component Analysis. We
hope that our findings will support the adoption of VCReg in SSL and beyond.

Federated Learning (FL) is an intriguing distributed machine learning
approach due to its privacy-preserving characteristics. To balance the
trade-off between energy and execution latency, and thus accommodate different
demands and application scenarios, we formulate an optimization problem to
minimize a weighted sum of total energy consumption and completion time through
two weight parameters. The optimization variables include bandwidth,
transmission power and CPU frequency of each device in the FL system, where all
devices are linked to a base station and train a global model collaboratively.
Through decomposing the non-convex optimization problem into two subproblems,
we devise a resource allocation algorithm to determine the bandwidth
allocation, transmission power, and CPU frequency for each participating
device. We further present the convergence analysis and computational
complexity of the proposed algorithm. Numerical results show that our proposed
algorithm not only has better performance at different weight parameters (i.e.,
different demands) but also outperforms the state of the art.

We introduce super reinforcement learning in the batch setting, which takes
the observed action as input for enhanced policy learning. In the presence of
unmeasured confounders, the recommendations from human experts recorded in the
observed data allow us to recover certain unobserved information. Including
this information in the policy search, the proposed super reinforcement
learning will yield a super-policy that is guaranteed to outperform both the
standard optimal policy and the behavior one (e.g., the expert's
recommendation). Furthermore, to address the issue of unmeasured confounding in
finding super-policies, a number of non-parametric identification results are
established. Finally, we develop two super-policy learning algorithms and
derive their corresponding finite-sample regret guarantees.

This paper addresses the issue of actuator selection for active flow control
by proposing a novel method built on top of a reinforcement learning agent.
Starting from a pre-trained agent using numerous actuators, the algorithm
estimates the impact of a potential actuator removal on the value function,
indicating the agent's performance. It is applied to two test cases, the
one-dimensional Kuramoto-Sivashinsky equation and a laminar bi-dimensional flow
around an airfoil at Re=1000 for different angles of attack ranging from 12 to
20 degrees, to demonstrate its capabilities and limits. The proposed
actuator-sparsification method relies on a sequential elimination of the least
relevant action components, starting from a fully developed layout. The
relevancy of each component is evaluated using metrics based on the value
function. Results show that, while still being limited by this intrinsic
elimination paradigm (i.e. the sequential elimination), actuator patterns and
obtained policies demonstrate relevant performances and allow to draw an
accurate approximation of the Pareto front of performances versus actuator
budget.

Neural network representation learning for spatial data is a common need for
geographic artificial intelligence (GeoAI) problems. In recent years, many
advancements have been made in representation learning for points, polylines,
and networks, whereas little progress has been made for polygons, especially
complex polygonal geometries. In this work, we focus on developing a
general-purpose polygon encoding model, which can encode a polygonal geometry
(with or without holes, single or multipolygons) into an embedding space. The
result embeddings can be leveraged directly (or finetuned) for downstream tasks
such as shape classification, spatial relation prediction, and so on. To
achieve model generalizability guarantees, we identify a few desirable
properties: loop origin invariance, trivial vertex invariance, part permutation
invariance, and topology awareness. We explore two different designs for the
encoder: one derives all representations in the spatial domain; the other
leverages spectral domain representations. For the spatial domain approach, we
propose ResNet1D, a 1D CNN-based polygon encoder, which uses circular padding
to achieve loop origin invariance on simple polygons. For the spectral domain
approach, we develop NUFTspec based on Non-Uniform Fourier Transformation
(NUFT), which naturally satisfies all the desired properties. We conduct
experiments on two tasks: 1) shape classification based on MNIST; 2) spatial
relation prediction based on two new datasets - DBSR-46K and DBSR-cplx46K. Our
results show that NUFTspec and ResNet1D outperform multiple existing baselines
with significant margins. While ResNet1D suffers from model performance
degradation after shape-invariance geometry modifications, NUFTspec is very
robust to these modifications due to the nature of the NUFT.

As a special case of common object removal, image person removal is playing
an increasingly important role in social media and criminal investigation
domains. Due to the integrity of person area and the complexity of human
posture, person removal has its own dilemmas. In this paper, we propose a novel
idea to tackle these problems from the perspective of data synthesis.
Concerning the lack of dedicated dataset for image person removal, two dataset
production methods are proposed to automatically generate images, masks and
ground truths respectively. Then, a learning framework similar to local image
degradation is proposed so that the masks can be used to guide the feature
extraction process and more texture information can be gathered for final
prediction. A coarse-to-fine training strategy is further applied to refine the
details. The data synthesis and learning framework combine well with each
other. Experimental results verify the effectiveness of our method
quantitatively and qualitatively, and the trained network proves to have good
generalization ability either on real or synthetic images.

Robotic locomotion is often approached with the goal of maximizing robustness
and reactivity by increasing motion control frequency. We challenge this
intuitive notion by demonstrating robust and dynamic locomotion with a learned
motion controller executing at as low as 8 Hz on a real ANYmal C quadruped. The
robot is able to robustly and repeatably achieve a high heading velocity of 1.5
m/s, traverse uneven terrain, and resist unexpected external perturbations. We
further present a comparative analysis of deep reinforcement learning (RL)
based motion control policies trained and executed at frequencies ranging from
5 Hz to 200 Hz. We show that low-frequency policies are less sensitive to
actuation latencies and variations in system dynamics. This is to the extent
that a successful sim-to-real transfer can be performed even without any
dynamics randomization or actuation modeling. We support this claim through a
set of rigorous empirical evaluations. Moreover, to assist reproducibility, we
provide the training and deployment code along with an extended analysis at
https://ori-drs.github.io/lfmc/.

The fundamental goal of self-supervised learning (SSL) is to produce useful
representations of data without access to any labels for classifying the data.
Modern methods in SSL, which form representations based on known or constructed
relationships between samples, have been particularly effective at this task.
Here, we aim to extend this framework to incorporate algorithms based on kernel
methods where embeddings are constructed by linear maps acting on the feature
space of a kernel. In this kernel regime, we derive methods to find the optimal
form of the output representations for contrastive and non-contrastive loss
functions. This procedure produces a new representation space with an inner
product denoted as the induced kernel which generally correlates points which
are related by an augmentation in kernel space and de-correlates points
otherwise. We analyze our kernel model on small datasets to identify common
features of self-supervised learning algorithms and gain theoretical insights
into their performance on downstream tasks.

Feature selection is the problem of selecting a subset of features for a
machine learning model that maximizes model quality subject to a resource
budget constraint. For neural networks, prior methods, including those based on
$\ell_1$ regularization, attention, and stochastic gates, typically select all
of the features in one evaluation round, ignoring the residual value of the
features during selection (i.e., the marginal contribution of a feature
conditioned on the previously selected features). We propose a feature
selection algorithm called Sequential Attention that achieves state-of-the-art
empirical results for neural networks. This algorithm is based on an efficient
implementation of greedy forward selection and uses attention weights at each
step as a proxy for marginal feature importance. We provide theoretical
insights into our Sequential Attention algorithm for linear regression models
by showing that an adaptation to this setting is equivalent to the classical
Orthogonal Matching Pursuit algorithm [PRK1993], and thus inherits all of its
provable guarantees. Lastly, our theoretical and empirical analyses provide new
explanations towards the effectiveness of attention and its connections to
overparameterization, which might be of independent interest.

Students often make mistakes on their introductory programming assignments as
part of their learning process. Unfortunately, providing custom repairs for
these mistakes can require a substantial amount of time and effort from class
instructors. Automated program repair (APR) techniques can be used to
synthesize such fixes. Prior work has explored the use of symbolic and neural
techniques for APR in the education domain. Both types of approaches require
either substantial engineering efforts or large amounts of data and training.
We propose to use a large language model trained on code, such as Codex, to
build an APR system -- MMAPR -- for introductory Python programming
assignments. Our system can fix both syntactic and semantic mistakes by
combining multi-modal prompts, iterative querying, test-case-based selection of
few-shots, and program chunking. We evaluate MMAPR on 286 real student programs
and compare to a baseline built by combining a state-of-the-art Python syntax
repair engine, BIFI, and state-of-the-art Python semantic repair engine for
student assignments, Refactory. We find that MMAPR can fix more programs and
produce smaller patches on average.

The potential use of robotics for laboratory experiments offers an attractive
route to alleviate scientists from tedious tasks while accelerating the process
of obtaining new materials, where topical issues such as climate change and
disease risks worldwide would greatly benefit. While some experimental
workflows can already benefit from automation, it is common that sample
preparation is still carried out manually due to the high level of motor
function required when dealing with heterogeneous systems, e.g., different
tools, chemicals, and glassware. A fundamental workflow in chemical fields is
crystallisation, where one application is polymorph screening, i.e., obtaining
a three dimensional molecular structure from a crystal. For this process, it is
of utmost importance to recover as much of the sample as possible since
synthesising molecules is both costly in time and money. To this aim, chemists
have to scrape vials to retrieve sample contents prior to imaging plate
transfer. Automating this process is challenging as it goes beyond robotic
insertion tasks due to a fundamental requirement of having to execute
fine-granular movements within a constrained environment that is the sample
vial. Motivated by how human chemists carry out this process of scraping powder
from vials, our work proposes a model-free reinforcement learning method for
learning a scraping policy, leading to a fully autonomous sample scraping
procedure. To realise that, we first create a simulation environment with a
Panda Franka Emika robot using a laboratory scraper which is inserted into a
simulated vial, to demonstrate how a scraping policy can be learned
successfully. We then evaluate our method on a real robotic manipulator in
laboratory settings, and show that our method can autonomously scrape powder
across various setups.

In this paper three customised Artificial Intelligence (AI) frameworks,
considering Deep Learning (convolutional neural networks), Machine Learning
algorithms and data reduction techniques are proposed, for a problem of
long-term summer air temperature prediction. Specifically, the prediction of
average air temperature in the first and second August fortnights, using input
data from previous months, at two different locations, Paris (France) and
C\'ordoba (Spain), is considered. The target variable, mainly in the first
August fortnight, can contain signals of extreme events such as heatwaves, like
the mega-heatwave of 2003, which affected France and the Iberian Peninsula.
Thus, an accurate prediction of long-term air temperature may be valuable also
for different problems related to climate change, such as attribution of
extreme events, and in other problems related to renewable energy. The analysis
carried out this work is based on Reanalysis data, which are first processed by
a correlation analysis among different prediction variables and the target
(average air temperature in August first and second fortnights). An area with
the largest correlation is located, and the variables within, after a feature
selection process, are the input of different deep learning and ML algorithms.
The experiments carried out show a very good prediction skill in the three
proposed AI frameworks, both in Paris and C\'ordoba regions.

Global machine learning force fields (MLFFs), that have the capacity to
capture collective many-atom interactions in molecular systems, currently only
scale up to a few dozen atoms due a considerable growth of the model complexity
with system size. For larger molecules, locality assumptions are typically
introduced, with the consequence that non-local interactions are poorly or not
at all described, even if those interactions are contained within the reference
ab initio data. Here, we approach this challenge and develop an exact iterative
parameter-free approach to train global symmetric gradient domain machine
learning (sGDML) force fields for systems with up to several hundred atoms,
without resorting to any localization of atomic interactions or other
potentially uncontrolled approximations. This means that all atomic degrees of
freedom remain fully correlated in the global sGDML FF, allowing the accurate
description of complex molecules and materials that present phenomena with
far-reaching characteristic correlation lengths. We assess the accuracy and
efficiency of our MLFFs on a newly developed MD22 benchmark dataset containing
molecules from 42 to 370 atoms. The robustness of our approach is demonstrated
in nanosecond long path-integral molecular dynamics simulations for the
supramolecular complexes in the MD22 dataset.

We study the problem of training a two-layer neural network (NN) of arbitrary
width using stochastic gradient descent (SGD) where the input
$\boldsymbol{x}\in \mathbb{R}^d$ is Gaussian and the target $y \in \mathbb{R}$
follows a multiple-index model, i.e.,
$y=g(\langle\boldsymbol{u_1},\boldsymbol{x}\rangle,...,\langle\boldsymbol{u_k},\boldsymbol{x}\rangle)$
with a noisy link function $g$. We prove that the first-layer weights of the NN
converge to the $k$-dimensional principal subspace spanned by the vectors
$\boldsymbol{u_1},...,\boldsymbol{u_k}$ of the true model, when online SGD with
weight decay is used for training. This phenomenon has several important
consequences when $k \ll d$. First, by employing uniform convergence on this
smaller subspace, we establish a generalization error bound of
$\mathcal{O}(\sqrt{{kd}/{T}})$ after $T$ iterations of SGD, which is
independent of the width of the NN. We further demonstrate that, SGD-trained
ReLU NNs can learn a single-index target of the form
$y=f(\langle\boldsymbol{u},\boldsymbol{x}\rangle) + \epsilon$ by recovering the
principal direction, with a sample complexity linear in $d$ (up to log
factors), where $f$ is a monotonic function with at most polynomial growth, and
$\epsilon$ is the noise. This is in contrast to the known $d^{\Omega(p)}$
sample requirement to learn any degree $p$ polynomial in the kernel regime, and
it shows that NNs trained with SGD can outperform the neural tangent kernel at
initialization. Finally, we also provide compressibility guarantees for NNs
using the approximate low-rank structure produced by SGD.

Humans naturally decompose their environment into entities at the appropriate
level of abstraction to act in the world. Allowing machine learning algorithms
to derive this decomposition in an unsupervised way has become an important
line of research. However, current methods are restricted to simulated data or
require additional information in the form of motion or depth in order to
successfully discover objects. In this work, we overcome this limitation by
showing that reconstructing features from models trained in a self-supervised
manner is a sufficient training signal for object-centric representations to
arise in a fully unsupervised way. Our approach, DINOSAUR, significantly
out-performs existing object-centric learning models on simulated data and is
the first unsupervised object-centric model that scales to real world-datasets
such as COCO and PASCAL VOC. DINOSAUR is conceptually simple and shows
competitive performance compared to more involved pipelines from the computer
vision literature.

In this work, we present a new paradigm, called 4D-StOP, to tackle the task
of 4D Panoptic LiDAR Segmentation. 4D-StOP first generates spatio-temporal
proposals using voting-based center predictions, where each point in the 4D
volume votes for a corresponding center. These tracklet proposals are further
aggregated using learned geometric features. The tracklet aggregation method
effectively generates a video-level 4D scene representation over the entire
space-time volume. This is in contrast to existing end-to-end trainable
state-of-the-art approaches which use spatio-temporal embeddings that are
represented by Gaussian probability distributions. Our voting-based tracklet
generation method followed by geometric feature-based aggregation generates
significantly improved panoptic LiDAR segmentation quality when compared to
modeling the entire 4D volume using Gaussian probability distributions. 4D-StOP
achieves a new state-of-the-art when applied to the SemanticKITTI test dataset
with a score of 63.9 LSTQ, which is a large (+7%) improvement compared to
current best-performing end-to-end trainable methods. The code and pre-trained
models are available at: https://github.com/LarsKreuzberg/4D-StOP.

Effective data-driven PDE forecasting methods often rely on fixed spatial and
/ or temporal discretizations. This raises limitations in real-world
applications like weather prediction where flexible extrapolation at arbitrary
spatiotemporal locations is required. We address this problem by introducing a
new data-driven approach, DINo, that models a PDE's flow with continuous-time
dynamics of spatially continuous functions. This is achieved by embedding
spatial observations independently of their discretization via Implicit Neural
Representations in a small latent space temporally driven by a learned ODE.
This separate and flexible treatment of time and space makes DINo the first
data-driven model to combine the following advantages. It extrapolates at
arbitrary spatial and temporal locations; it can learn from sparse irregular
grids or manifolds; at test time, it generalizes to new grids or resolutions.
DINo outperforms alternative neural PDE forecasters in a variety of challenging
generalization scenarios on representative PDE systems.

We study the application of variance reduction (VR) techniques to general
non-convex stochastic optimization problems. In this setting, the recent work
STORM [Cutkosky-Orabona '19] overcomes the drawback of having to compute
gradients of "mega-batches" that earlier VR methods rely on. There, STORM
utilizes recursive momentum to achieve the VR effect and is then later made
fully adaptive in STORM+ [Levy et al., '21], where full-adaptivity removes the
requirement for obtaining certain problem-specific parameters such as the
smoothness of the objective and bounds on the variance and norm of the
stochastic gradients in order to set the step size. However, STORM+ crucially
relies on the assumption that the function values are bounded, excluding a
large class of useful functions. In this work, we propose META-STORM, a
generalized framework of STORM+ that removes this bounded function values
assumption while still attaining the optimal convergence rate for non-convex
optimization. META-STORM not only maintains full-adaptivity, removing the need
to obtain problem specific parameters, but also improves the convergence rate's
dependency on the problem parameters. Furthermore, META-STORM can utilize a
large range of parameter settings that subsumes previous methods allowing for
more flexibility in a wider range of settings. Finally, we demonstrate the
effectiveness of META-STORM through experiments across common deep learning
tasks. Our algorithm improves upon the previous work STORM+ and is competitive
with widely used algorithms after the addition of per-coordinate update and
exponential moving average heuristics.

Existing federated learning paradigms usually extensively exchange
distributed models at a central solver to achieve a more powerful model.
However, this would incur severe communication burden between a server and
multiple clients especially when data distributions are heterogeneous. As a
result, current federated learning methods often require a large number of
communication rounds in training. Unlike existing paradigms, we introduce an
alternative perspective to significantly decrease the communication cost in
federate learning. In this work, we first introduce a meta knowledge
representation method that extracts meta knowledge from distributed clients.
The extracted meta knowledge encodes essential information that can be used to
improve the current model. As the training progresses, the contributions of
training samples to a federated model also vary. Thus, we introduce a dynamic
weight assignment mechanism that enables samples to contribute adaptively to
the current model update. Then, informative meta knowledge from all active
clients is sent to the server for model update. Training a model on the
combined meta knowledge without exposing original data among different clients
can significantly mitigate the heterogeneity issues. Moreover, to further
ameliorate data heterogeneity, we also exchange meta knowledge among clients as
conditional initialization for local meta knowledge extraction. Extensive
experiments demonstrate the effectiveness and efficiency of our proposed
method. Remarkably, our method outperforms the state-of-the-art by a large
margin (from $74.07\%$ to $92.95\%$) on MNIST with a restricted communication
budget (i.e. 10 rounds).

Although deep federated learning has received much attention in recent years,
progress has been made mainly in the context of natural images and barely for
computational pathology. However, deep federated learning is an opportunity to
create datasets that reflect the data diversity of many laboratories. Further,
the effort of dataset construction can be divided among many. Unfortunately,
existing algorithms cannot be easily applied to computational pathology since
previous work presupposes that data distributions of laboratories must be
similar. This is an unlikely assumption, mainly since different laboratories
have different staining styles. As a solution, we propose BottleGAN, a
generative model that can computationally align the staining styles of many
laboratories and can be trained in a privacy-preserving manner to foster
federated learning in computational pathology. We construct a heterogenic
multi-institutional dataset based on the PESO segmentation dataset and improve
the IOU by 42\% compared to existing federated learning algorithms. An
implementation of BottleGAN is available at
https://github.com/MECLabTUDA/BottleGAN

In the era of big data, it is prevailing of high-dimensional matrix-variate
observations that may be independent or dependent. Unsupervised learning of
matrix objects through low-rank approximation has benefited discovery of the
hidden pattern and structure whilst concomitant statistical inference is known
challenging and yet in infancy by the fact that, there is limited work and all
focus on a class of bilinear form matrix factor models. In this paper, we
propose a novel class of hierarchical CP product matrix factor models which
model the rank-1 components of the low-rank CP decomposition of a matrix object
by the tool of high-dimensional vector factor models. The induced CP
tensor-decomposition based matrix factor model (TeDFaM) are apparently more
informative in that it naturally incorporates the row-wise and column-wise
interrelated information. Furthermore, the inner separable covariance structure
yields efficient moment estimators of the loading matrices and thus approximate
least squares estimators for the factor scores. The proposed TeDFaM model and
estimation procedure make the signal part achieves better peak signal to noise
ratio, evidenced in both theory and numerical analytics compared to bilinear
form matrix factor models and existing methods. We establish an inferential
theory for TeDFaM estimation including consistency, rates of convergence, and
the limiting distributions under regular conditions. In applications, the
proposed model and estimation procedure are superior in terms of matrix
reconstruction for both independent two-dimensional image data and serial
correlated matrix time series. The algorithm is fast and can be implemented
expediently through an accompanied R package TeDFaM.

In this paper, we propose an access control method with a secret key for
object detection models for the first time so that unauthorized users without a
secret key cannot benefit from the performance of trained models. The method
enables us not only to provide a high detection performance to authorized users
but to also degrade the performance for unauthorized users. The use of
transformed images was proposed for the access control of image classification
models, but these images cannot be used for object detection models due to
performance degradation. Accordingly, in this paper, selected feature maps are
encrypted with a secret key for training and testing models, instead of input
images. In an experiment, the protected models allowed authorized users to
obtain almost the same performance as that of non-protected models but also
with robustness against unauthorized access without a key.

Monocular depth estimation is an important task that can be applied to many
robotic applications. Existing methods focus on improving depth estimation
accuracy via training increasingly deeper and wider networks, however these
suffer from large computational complexity. Recent studies found that edge
information are important cues for convolutional neural networks (CNNs) to
estimate depth. Inspired by the above observations, we present a novel
lightweight Edge Guided Depth Estimation Network (EGD-Net) in this study. In
particular, we start out with a lightweight encoder-decoder architecture and
embed an edge guidance branch which takes as input image gradients and
multi-scale feature maps from the backbone to learn the edge attention
features. In order to aggregate the context information and edge attention
features, we design a transformer-based feature aggregation module (TRFA). TRFA
captures the long-range dependencies between the context information and edge
attention features through cross-attention mechanism. We perform extensive
experiments on the NYU depth v2 dataset. Experimental results show that the
proposed method runs about 96 fps on a Nvidia GTX 1080 GPU whilst achieving the
state-of-the-art performance in terms of accuracy.

Generating realistic motions for digital humans is time-consuming for many
graphics applications. Data-driven motion synthesis approaches have seen solid
progress in recent years through deep generative models. These results offer
high-quality motions but typically suffer in motion style diversity. For the
first time, we propose a framework using the denoising diffusion probabilistic
model (DDPM) to synthesize styled human motions, integrating two tasks into one
pipeline with increased style diversity compared with traditional motion
synthesis methods. Experimental results show that our system can generate
high-quality and diverse walking motions.

Existing analysis of AdaGrad and other adaptive methods for smooth convex
optimization is typically for functions with bounded domain diameter. In
unconstrained problems, previous works guarantee an asymptotic convergence rate
without an explicit constant factor that holds true for the entire function
class. Furthermore, in the stochastic setting, only a modified version of
AdaGrad, different from the one commonly used in practice, in which the latest
gradient is not used to update the stepsize, has been analyzed. Our paper aims
at bridging these gaps and developing a deeper understanding of AdaGrad and its
variants in the standard setting of smooth convex functions as well as the more
general setting of quasar convex functions. First, we demonstrate new
techniques to explicitly bound the convergence rate of the vanilla AdaGrad for
unconstrained problems in both deterministic and stochastic settings. Second,
we propose a variant of AdaGrad for which we can show the convergence of the
last iterate, instead of the average iterate. Finally, we give new accelerated
adaptive algorithms and their convergence guarantee in the deterministic
setting with explicit dependency on the problem parameters, improving upon the
asymptotic rate shown in previous works.

Black-box attacks can generate adversarial examples without accessing the
parameters of target model, largely exacerbating the threats of deployed deep
neural networks (DNNs). However, previous works state that black-box attacks
fail to mislead target models when their training data and outputs are
inaccessible. In this work, we argue that black-box attacks can pose practical
attacks in this extremely restrictive scenario where only several test samples
are available. Specifically, we find that attacking the shallow layers of DNNs
trained on a few test samples can generate powerful adversarial examples. As
only a few samples are required, we refer to these attacks as lightweight
black-box attacks. The main challenge to promoting lightweight attacks is to
mitigate the adverse impact caused by the approximation error of shallow
layers. As it is hard to mitigate the approximation error with few available
samples, we propose Error TransFormer (ETF) for lightweight attacks. Namely,
ETF transforms the approximation error in the parameter space into a
perturbation in the feature space and alleviates the error by disturbing
features. In experiments, lightweight black-box attacks with the proposed ETF
achieve surprising results. For example, even if only 1 sample per category
available, the attack success rate in lightweight black-box attacks is only
about 3% lower than that of the black-box attacks with complete training data.

Many network applications can be formulated as NP-hard combinatorial
optimization problems of community detection (CD). Due to the NP-hardness, to
balance the CD quality and efficiency remains a challenge. Most existing CD
methods are transductive, which are independently optimized only for the CD on
a single graph. Some of these methods use advanced machine learning techniques
to obtain high-quality CD results but usually have high complexity. Other
approaches use fast heuristic approximation to ensure low runtime but may
suffer from quality degradation. In contrast to these transductive methods, we
propose an alternative inductive community detection (ICD) method across graphs
of a system or scenario to alleviate the NP-hard challenge. ICD first conducts
the offline training of an adversarial dual GNN on historical graphs to capture
key properties of the system. The trained model is then directly generalized to
new unseen graphs for online CD without additional optimization, where a better
trade-off between quality and efficiency can be achieved. ICD can also capture
the permutation invariant community labels in the offline training and tackle
the online CD on new graphs with non-fixed number of nodes and communities.
Experiments on a set of benchmarks demonstrate that ICD can achieve a
significant trade-off between quality and efficiency over various baselines.

Diffusion models are a class of generative models that learn to synthesize
samples by inverting a diffusion process that gradually maps data into noise.
While these models have enjoyed great success recently, a full theoretical
understanding of their observed properties is still lacking, in particular,
their weak sensitivity to the choice of noise family and the role of adequate
scheduling of noise levels for good synthesis. By identifying a correspondence
between diffusion models and a well-known paradigm in cognitive science known
as serial reproduction, whereby human agents iteratively observe and reproduce
stimuli from memory, we show how the aforementioned properties of diffusion
models can be explained as a natural consequence of this correspondence. We
then complement our theoretical analysis with simulations that exhibit these
key features. Our work highlights how classic paradigms in cognitive science
can shed light on state-of-the-art machine learning problems.

We study the problem of novel view synthesis of objects from a single image.
Existing methods have demonstrated the potential in single-view view synthesis.
However, they still fail to recover the fine appearance details, especially in
self-occluded areas. This is because a single view only provides limited
information. We observe that manmade objects usually exhibit symmetric
appearances, which introduce additional prior knowledge. Motivated by this, we
investigate the potential performance gains of explicitly embedding symmetry
into the scene representation. In this paper, we propose SymmNeRF, a neural
radiance field (NeRF) based framework that combines local and global
conditioning under the introduction of symmetry priors. In particular, SymmNeRF
takes the pixel-aligned image features and the corresponding symmetric features
as extra inputs to the NeRF, whose parameters are generated by a hypernetwork.
As the parameters are conditioned on the image-encoded latent codes, SymmNeRF
is thus scene-independent and can generalize to new scenes. Experiments on
synthetic and realworld datasets show that SymmNeRF synthesizes novel views
with more details regardless of the pose transformation, and demonstrates good
generalization when applied to unseen objects. Code is available at:
https://github.com/xingyi-li/SymmNeRF.

Many material response functions depend strongly on microstructure, such as
inhomogeneities in phase or orientation. Homogenization presents the task of
predicting the mean response of a sample of the microstructure to external
loading for use in subgrid models and structure-property explorations. Although
many microstructural fields have obvious segmentations, learning directly from
the graph induced by the segmentation can be difficult because this
representation does not encode all the information of the full field. We
develop a means of deep learning of hidden features on the reduced graph given
the native discretization and a segmentation of the initial input field. The
features are associated with regions represented as nodes on the reduced graph.
This reduced representation is then the basis for the subsequent
multi-level/scale graph convolutional network model. There are a number of
advantages of reducing the graph before fully processing with convolutional
layers it, such as interpretable features and efficiency on large meshes. We
demonstrate the performance of the proposed network relative to convolutional
neural networks operating directly on the native discretization of the data
using three physical exemplars.

Tensor train (TT) format is a common approach for computationally efficient
work with multidimensional arrays, vectors, matrices, and discretized functions
in a wide range of applications, including computational mathematics and
machine learning. In this work, we propose a new algorithm for TT-tensor
optimization, which leads to very accurate approximations for the minimum and
maximum tensor element. The method consists in sequential tensor
multiplications of the TT-cores with an intelligent selection of candidates for
the optimum. We propose the probabilistic interpretation of the method, and
make estimates on its complexity and convergence. We perform extensive
numerical experiments with random tensors and various multivariable benchmark
functions with the number of input dimensions up to $100$. Our approach
generates a solution close to the exact optimum for all model problems, while
the running time is no more than $50$ seconds on a regular laptop.

We consider two biologically plausible structures, the Spiking Neural Network
(SNN) and the self-attention mechanism. The former offers an energy-efficient
and event-driven paradigm for deep learning, while the latter has the ability
to capture feature dependencies, enabling Transformer to achieve good
performance. It is intuitively promising to explore the marriage between them.
In this paper, we consider leveraging both self-attention capability and
biological properties of SNNs, and propose a novel Spiking Self Attention (SSA)
as well as a powerful framework, named Spiking Transformer (Spikformer). The
SSA mechanism in Spikformer models the sparse visual feature by using
spike-form Query, Key, and Value without softmax. Since its computation is
sparse and avoids multiplication, SSA is efficient and has low computational
energy consumption. It is shown that Spikformer with SSA can outperform the
state-of-the-art SNNs-like frameworks in image classification on both
neuromorphic and static datasets. Spikformer (66.3M parameters) with comparable
size to SEW-ResNet-152 (60.2M,69.26%) can achieve 74.81% top1 accuracy on
ImageNet using 4 time steps, which is the state-of-the-art in directly trained
SNNs models.

Polymers are a vital part of everyday life. Their chemical universe is so
large that it presents unprecedented opportunities as well as significant
challenges to identify suitable application-specific candidates. We present a
complete end-to-end machine-driven polymer informatics pipeline that can search
this space for suitable candidates at unprecedented speed and accuracy. This
pipeline includes a polymer chemical fingerprinting capability called polyBERT
(inspired by Natural Language Processing concepts), and a multitask learning
approach that maps the polyBERT fingerprints to a host of properties. polyBERT
is a chemical linguist that treats the chemical structure of polymers as a
chemical language. The present approach outstrips the best presently available
concepts for polymer property prediction based on handcrafted fingerprint
schemes in speed by two orders of magnitude while preserving accuracy, thus
making it a strong candidate for deployment in scalable architectures including
cloud infrastructures.

We propose Make-A-Video -- an approach for directly translating the
tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video
(T2V). Our intuition is simple: learn what the world looks like and how it is
described from paired text-image data, and learn how the world moves from
unsupervised video footage. Make-A-Video has three advantages: (1) it
accelerates training of the T2V model (it does not need to learn visual and
multimodal representations from scratch), (2) it does not require paired
text-video data, and (3) the generated videos inherit the vastness (diversity
in aesthetic, fantastical depictions, etc.) of today's image generation models.
We design a simple yet effective way to build on T2I models with novel and
effective spatial-temporal modules. First, we decompose the full temporal U-Net
and attention tensors and approximate them in space and time. Second, we design
a spatial temporal pipeline to generate high resolution and frame rate videos
with a video decoder, interpolation model and two super resolution models that
can enable various applications besides T2V. In all aspects, spatial and
temporal resolution, faithfulness to text, and quality, Make-A-Video sets the
new state-of-the-art in text-to-video generation, as determined by both
qualitative and quantitative measures.

Sparse Principal Component Analysis is a cardinal technique for obtaining
combinations of features, or principal components (PCs), that explain the
variance of high-dimensional datasets in an interpretable manner. At its heart,
this involves solving a sparsity and orthogonality constrained convex
maximization problem, which is extremely computationally challenging. Most
existing work address sparse PCA via heuristics such as iteratively computing
one sparse PC and deflating the covariance matrix, which does not guarantee the
orthogonality, let alone the optimality, of the resulting solution. We
challenge this status by reformulating the orthogonality conditions as rank
constraints and optimizing over the sparsity and rank constraints
simultaneously. We design tight semidefinite relaxations and propose tractable
second-order cone versions of these relaxations which supply high-quality upper
bounds. We also design valid second-order cone inequalities which hold when
each PC's individual sparsity is specified, and demonstrate that these
inequalities tighten our relaxations significantly. Moreover, we propose exact
methods and rounding mechanisms that exploit these relaxations' tightness to
obtain solutions with a bound gap on the order of 1%-5% for real-world datasets
with p = 100s or 1000s of features and r \in {2, 3} components. We investigate
the performance of our methods in spiked covariance settings and demonstrate
that simultaneously considering the orthogonality and sparsity constraints
leads to improvements in the Area Under the ROC curve of 2%-8% compared to
state-of-the-art deflation methods. All in all, our approach solves sparse PCA
problems with multiple components to certifiable (near) optimality in a
practically tractable fashion.

Context. Activity on the far side of the Sun is routinely studied through the
analysis of the seismic oscillations detected on the near side using
helioseismic techniques such as phase shift sensitive holography. Recently, the
neural network FarNet was developed to improve these detections. Aims. We aim
to create a new machine learning tool, FarNet II, which further increases the
scope of FarNet, and to evaluate its performance in comparison to FarNet and
the standard helioseismic method for detecting far side activity. Methods. We
developed FarNet II, a neural network that retains some of the general
characteristics of FarNet but improves the detections in general, as well as
the temporal coherence among successive predictions. The main novelties are the
implementation of attention and convolutional long short term memory (ConvLSTM)
modules. A cross validation approach, training the network 37 times with a
different validation set for each run, was employed to leverage the limited
amount of data available. We evaluate the performance of FarNet II using three
years of extreme ultraviolet observations of the far side of the Sun acquired
with the Solar Terrestrial Relations Observatory (STEREO) as a proxy of
activity. The results from FarNet II were compared with those obtained from
FarNet and the standard helioseismic method using the Dice coefficient as a
metric. Results. FarNet II achieves a Dice coefficient that improves that of
FarNet by over 0.2 points for every output position on the sequences from the
evaluation dates. Its improvement over FarNet is higher than that of FarNet
over the standard method. Conclusions. The new network is a very promising tool
for improving the detection of activity on the far side of the Sun given by
pure helioseismic techniques. Space weather forecasts can potentially benefit
from the higher sensitivity provided by this novel method.

The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a
variational autoencoder (VAE) often play antagonistic roles, and tuning the
weight of the KLD loss in $\beta$-VAE to achieve a balance between the two
losses is a tricky and dataset-specific task. As a result, current practices in
VAE training often result in a trade-off between the reconstruction fidelity
and the continuity$/$disentanglement of the latent space, if the weight $\beta$
is not carefully tuned. In this paper, we present intuitions and a careful
analysis of the antagonistic mechanism of the two losses, and propose, based on
the insights, a simple yet effective two-stage method for training a VAE.
Specifically, the method aggregates a learned Gaussian posterior $z \sim
q_{\theta} (z|x)$ with a decoder decoupled from the KLD loss, which is trained
to learn a new conditional distribution $p_{\phi} (x|z)$ of the input data $x$.
Experimentally, we show that the aggregated VAE maximally satisfies the
Gaussian assumption about the latent space, while still achieves a
reconstruction error comparable to when the latent space is only loosely
regularized by $\mathcal{N}(\mathbf{0},I)$. The proposed approach does not
require hyperparameter (i.e., the KLD weight $\beta$) tuning given a specific
dataset as required in common VAE training practices. We evaluate the method
using a medical dataset intended for 3D skull reconstruction and shape
completion, and the results indicate promising generative capabilities of the
VAE trained using the proposed method. Besides, through guided manipulation of
the latent variables, we establish a connection between existing autoencoder
(AE)-based approaches and generative approaches, such as VAE, for the shape
completion problem. Codes and pre-trained weights are available at
https://github.com/Jianningli/skullVAE

The majority of real-world processes are spatiotemporal, and the data
generated by them exhibits both spatial and temporal evolution. Weather is one
of the most important processes that fall under this domain, and forecasting it
has become a crucial part of our daily routine. Weather data analysis is
considered the most complex and challenging task. Although numerical weather
prediction models are currently state-of-the-art, they are resource intensive
and time-consuming. Numerous studies have proposed time-series-based models as
a viable alternative to numerical forecasts. Recent research has primarily
focused on forecasting weather at a specific location. Therefore, models can
only capture temporal correlations. This self-contained paper explores various
methods for regional data-driven weather forecasting, i.e., forecasting over
multiple latitude-longitude points to capture spatiotemporal correlations. The
results showed that spatiotemporal prediction models reduced computational cost
while improving accuracy; in particular, the proposed tensor train dynamic mode
decomposition-based forecasting model has comparable accuracy to ConvLSTM
without the need for training. We use the NASA POWER meteorological dataset to
evaluate the models and compare them with the current state of the art.

Humans are skillful navigators: We aptly maneuver through new places, realize
when we are back at a location we have seen before, and can even conceive of
shortcuts that go through parts of our environments we have never visited.
Current methods in model-based reinforcement learning on the other hand
struggle with generalizing about environment dynamics out of the training
distribution. We argue that two principles can help bridge this gap: latent
learning and parsimonious dynamics. Humans tend to think about environment
dynamics in simple terms -- we reason about trajectories not in reference to
what we expect to see along a path, but rather in an abstract latent space,
containing information about the places' spatial coordinates. Moreover, we
assume that moving around in novel parts of our environment works the same way
as in parts we are familiar with. These two principles work together in tandem:
it is in the latent space that the dynamics show parsimonious characteristics.
We develop a model that learns such parsimonious dynamics. Using a variational
objective, our model is trained to reconstruct experienced transitions in a
latent space using locally linear transformations, while encouraged to invoke
as few distinct transformations as possible. Using our framework, we
demonstrate the utility of learning parsimonious latent dynamics models in a
range of policy learning and planning tasks.

Argument Unit Recognition and Classification aims at identifying argument
units from text and classifying them as pro or against. One of the design
choices that need to be made when developing systems for this task is what the
unit of classification should be: segments of tokens or full sentences.
Previous research suggests that fine-tuning language models on the token-level
yields more robust results for classifying sentences compared to training on
sentences directly. We reproduce the study that originally made this claim and
further investigate what exactly token-based systems learned better compared to
sentence-based ones. We develop systematic tests for analysing the behavioural
differences between the token-based and the sentence-based system. Our results
show that token-based models are generally more robust than sentence-based
models both on manually perturbed examples and on specific subpopulations of
the data.

A critically important, ubiquitous, and yet poorly understood ingredient in
modern deep networks (DNs) is batch normalization (BN), which centers and
normalizes the feature maps. To date, only limited progress has been made
understanding why BN boosts DN learning and inference performance; work has
focused exclusively on showing that BN smooths a DN's loss landscape. In this
paper, we study BN theoretically from the perspective of function
approximation; we exploit the fact that most of today's state-of-the-art DNs
are continuous piecewise affine (CPA) splines that fit a predictor to the
training data via affine mappings defined over a partition of the input space
(the so-called "linear regions"). {\em We demonstrate that BN is an
unsupervised learning technique that -- independent of the DN's weights or
gradient-based learning -- adapts the geometry of a DN's spline partition to
match the data.} BN provides a "smart initialization" that boosts the
performance of DN learning, because it adapts even a DN initialized with random
weights to align its spline partition with the data. We also show that the
variation of BN statistics between mini-batches introduces a dropout-like
random perturbation to the partition boundaries and hence the decision boundary
for classification problems. This per mini-batch perturbation reduces
overfitting and improves generalization by increasing the margin between the
training samples and the decision boundary.

Neuromorphic Systems-on-Chip (NSoCs) are becoming heterogeneous by
integrating general-purpose processors (GPPs) and neural processing units
(NPUs) on the same SoC. For embedded systems, an NSoC may need to execute user
applications built using a variety of machine learning models. We propose a
real-time scheduler, called PRISM, which can schedule machine learning models
on a heterogeneous NSoC either individually or concurrently to improve their
system performance. PRISM consists of the following four key steps. First, it
constructs an interprocessor communication (IPC) graph of a machine learning
model from a mapping and a self-timed schedule. Second, it creates a
transaction order for the communication actors and embeds this order into the
IPC graph. Third, it schedules the graph on an NSoC by overlapping
communication with the computation. Finally, it uses a Hill Climbing heuristic
to explore the design space of mapping operations on GPPs and NPUs to improve
the performance. Unlike existing schedulers which use only the NPUs of an NSoC,
PRISM improves performance by enabling batch, pipeline, and operation
parallelism via exploiting a platform's heterogeneity. For use-cases with
concurrent applications, PRISM uses a heuristic resource sharing strategy and a
non-preemptive scheduling to reduce the expected wait time before concurrent
operations can be scheduled on contending resources. Our extensive evaluations
with 20 machine learning workloads show that PRISM significantly improves the
performance per watt for both individual applications and use-cases when
compared to state-of-the-art schedulers.

Convolutional neural networks show remarkable results in classification but
struggle with learning new things on the fly. We present a novel rehearsal-free
approach, where a deep neural network is continually learning new unseen object
categories without saving any data of prior sequences. Our approach is called
RECALL, as the network recalls categories by calculating logits for old
categories before training new ones. These are then used during training to
avoid changing the old categories. For each new sequence, a new head is added
to accommodate the new categories. To mitigate forgetting, we present a
regularization strategy where we replace the classification with a regression.
Moreover, for the known categories, we propose a Mahalanobis loss that includes
the variances to account for the changing densities between known and unknown
categories. Finally, we present a novel dataset for continual learning,
especially suited for object recognition on a mobile robot (HOWS-CL-25),
including 150,795 synthetic images of 25 household object categories. Our
approach RECALL outperforms the current state of the art on CORe50 and
iCIFAR-100 and reaches the best performance on HOWS-CL-25.

A key challenge for Industry 4.0 applications is to develop control systems
for automated manufacturing services that are capable of addressing both data
integration and semantic interoperability issues, as well as monitoring and
decision making tasks. To address such an issue in advanced manufacturing
systems, principled knowledge representation approaches based on formal
ontologies have been proposed as a foundation to information management and
maintenance in presence of heterogeneous data sources. In addition, ontologies
provide reasoning and querying capabilities to aid domain experts and end users
in the context of constraint validation and decision making. Finally,
ontology-based approaches to advanced manufacturing services can support the
explainability and interpretability of the behaviour of monitoring, control,
and simulation systems that are based on black-box machine learning algorithms.
In this work, we provide a novel ontology for the classification of
process-induced defects known from the metal additive manufacturing literature.
Together with a formal representation of the characterising features and
sources of defects, we integrate our knowledge base with state-of-the-art
ontologies in the field. Our knowledge base aims at enhancing the modelling
capabilities of additive manufacturing ontologies by adding further defect
analysis terminology and diagnostic inference features.

Restoration of poor quality images with a blended set of artifacts plays a
vital role for a reliable diagnosis. Existing studies have focused on specific
restoration problems such as image deblurring, denoising, and exposure
correction where there is usually a strong assumption on the artifact type and
severity. As a pioneer study in blind X-ray restoration, we propose a joint
model for generic image restoration and classification: Restore-to-Classify
Generative Adversarial Networks (R2C-GANs). Such a jointly optimized model
keeps any disease intact after the restoration. Therefore, this will naturally
lead to a higher diagnosis performance thanks to the improved X-ray image
quality. To accomplish this crucial objective, we define the restoration task
as an Image-to-Image translation problem from poor quality having noisy,
blurry, or over/under-exposed images to high quality image domain. The proposed
R2C-GAN model is able to learn forward and inverse transforms between the two
domains using unpaired training samples. Simultaneously, the joint
classification preserves the disease label during restoration. Moreover, the
R2C-GANs are equipped with operational layers/neurons reducing the network
depth and further boosting both restoration and classification performances.
The proposed joint model is extensively evaluated over the QaTa-COV19 dataset
for Coronavirus Disease 2019 (COVID-19) classification. The proposed
restoration approach achieves over 90% F1-Score which is significantly higher
than the performance of any deep model. Moreover, in the qualitative analysis,
the restoration performance of R2C-GANs is approved by a group of medical
doctors. We share the software implementation at
https://github.com/meteahishali/R2C-GAN.

In the last years, neural networks (NN) have evolved from laboratory
environments to the state-of-the-art for many real-world problems. It was shown
that NN models (i.e., their weights and biases) evolve on unique trajectories
in weight space during training. Following, a population of such neural network
models (referred to as model zoo) would form structures in weight space. We
think that the geometry, curvature and smoothness of these structures contain
information about the state of training and can reveal latent properties of
individual models. With such model zoos, one could investigate novel approaches
for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn
rich representations of such populations, or (iv) exploit the model zoos for
generative modelling of NN weights and biases. Unfortunately, the lack of
standardized model zoos and available benchmarks significantly increases the
friction for further research about populations of NNs. With this work, we
publish a novel dataset of model zoos containing systematically generated and
diverse populations of NN models for further research. In total the proposed
model zoo dataset is based on eight image datasets, consists of 27 model zoos
trained with varying hyperparameter combinations and includes 50'360 unique NN
models as well as their sparsified twins, resulting in over 3'844'360 collected
model states. Additionally, to the model zoo data we provide an in-depth
analysis of the zoos and provide benchmarks for multiple downstream tasks. The
dataset can be found at www.modelzoos.cc.

Copy detection patterns (CDP) are recent technologies for protecting products
from counterfeiting. However, in contrast to traditional copy fakes, deep
learning-based fakes have shown to be hardly distinguishable from originals by
traditional authentication systems. Systems based on classical supervised
learning and digital templates assume knowledge of fake CDP at training time
and cannot generalize to unseen types of fakes. Authentication based on printed
copies of originals is an alternative that yields better results even for
unseen fakes and simple authentication metrics but comes at the impractical
cost of acquisition and storage of printed copies. In this work, to overcome
these shortcomings, we design a machine learning (ML) based authentication
system that only requires digital templates and printed original CDP for
training, whereas authentication is based solely on digital templates, which
are used to estimate original printed codes. The obtained results show that the
proposed system can efficiently authenticate original and detect fake CDP by
accurately locating the anomalies in the fake CDP. The empirical evaluation of
the authentication system under investigation is performed on the original and
ML-based fakes CDP printed on two industrial printers.

Tradition ML development methodology does not enable a large number of
contributors, each with distinct objectives, to work collectively on the
creation and extension of a shared intelligent system. Enabling such a
collaborative methodology can accelerate the rate of innovation, increase ML
technologies accessibility and enable the emergence of novel capabilities. We
believe that this can be achieved through the definition of abstraction
boundaries and a modularized representation of ML models and methods. We
present a multi-agent framework for collaborative and asynchronous extension of
dynamic large-scale multitask intelligent systems.

Dataset complexity assessment aims to predict classification performance on a
dataset with complexity calculation before training a classifier, which can
also be used for classifier selection and dataset reduction. The training
process of deep convolutional neural networks (DCNNs) is iterative and
time-consuming because of hyperparameter uncertainty and the domain shift
introduced by different datasets. Hence, it is meaningful to predict
classification performance by assessing the complexity of datasets effectively
before training DCNN models. This paper proposes a novel method called
cumulative maximum scaled Area Under Laplacian Spectrum (cmsAULS), which can
achieve state-of-the-art complexity assessment performance on six datasets.

To address the distribution shifts between training and test data, domain
generalization (DG) leverages multiple source domains to learn a model that
generalizes well to unseen domains. However, existing DG methods generally
suffer from overfitting to the source domains, partly due to the limited
coverage of the expected region in feature space. Motivated by this, we propose
to perform mixup with data interpolation and extrapolation to cover the
potential unseen regions. To prevent the detrimental effects of unconstrained
extrapolation, we carefully design a policy to generate the instance weights,
named Flatness-aware Gradient-based Mixup (FGMix). The policy employs a
gradient-based similarity to assign greater weights to instances that carry
more invariant information, and learns the similarity function towards flatter
minima for better generalization. On the DomainBed benchmark, we validate the
efficacy of various designs of FGMix and demonstrate its superiority over other
DG algorithms.

This work introduces DiGress, a discrete denoising diffusion model for
generating graphs with categorical node and edge attributes. Our model defines
a diffusion process that progressively edits a graph with noise (adding or
removing edges, changing the categories), and a graph transformer network that
learns to revert this process. With these two ingredients in place, we reduce
distribution learning over graphs to a simple sequence of classification tasks.
We further improve sample quality by proposing a new Markovian noise model that
preserves the marginal distribution of node and edge types during diffusion,
and by adding auxiliary graph-theoretic features derived from the noisy graph
at each diffusion step. Finally, we propose a guidance procedure for
conditioning the generation on graph-level features. Overall, DiGress achieves
state-of-the-art performance on both molecular and non-molecular datasets, with
up to 3x validity improvement on a dataset of planar graphs. In particular, it
is the first model that scales to the large GuacaMol dataset containing 1.3M
drug-like molecules without using a molecule-specific representation such as
SMILES or fragments.

Learning representations of neural network weights given a model zoo is an
emerging and challenging area with many potential applications from model
inspection, to neural architecture search or knowledge distillation. Recently,
an autoencoder trained on a model zoo was able to learn a hyper-representation,
which captures intrinsic and extrinsic properties of the models in the zoo. In
this work, we extend hyper-representations for generative use to sample new
model weights. We propose layer-wise loss normalization which we demonstrate is
key to generate high-performing models and several sampling methods based on
the topology of hyper-representations. The models generated using our methods
are diverse, performant and capable to outperform strong baselines as evaluated
on several downstream tasks: initialization, ensemble sampling and transfer
learning. Our results indicate the potential of knowledge aggregation from
model zoos to new models via hyper-representations thereby paving the avenue
for novel research directions.

New Attacks are increasingly used by attackers everyday but many of them are
not detected by Intrusion Detection Systems as most IDS ignore raw packet
information and only care about some basic statistical information extracted
from PCAP files. Using networking programs to extract fixed statistical
features from packets is good, but may not enough to detect nowadays
challenges. We think that it is time to utilize big data and deep learning for
automatic dynamic feature extraction from packets. It is time to get inspired
by deep learning pre-trained models in computer vision and natural language
processing, so security deep learning solutions will have its pre-trained
models on big datasets to be used in future researches. In this paper, we
proposed a new approach for embedding packets based on character-level
embeddings, inspired by FastText success on text data. We called this approach
FastPacket. Results are measured on subsets of CIC-IDS-2017 dataset, but we
expect promising results on big data pre-trained models. We suggest building
pre-trained FastPacket on MAWI big dataset and make it available to community,
similar to FastText. To be able to outperform currently used NIDS, to start a
new era of packet-level NIDS that can better detect complex attacks.

The growing use of supervised machine learning in research and industry has
increased the need for labeled datasets. Crowdsourcing has emerged as a popular
method to create data labels. However, working on large batches of tasks leads
to worker fatigue, negatively impacting labeling quality. To address this, we
present TruEyes, a collaborative crowdsourcing system, enabling the
distribution of micro-tasks to mobile app users. TruEyes allows machine
learning practitioners to publish labeling tasks, mobile app developers to
integrate task ads for monetization, and users to label data instead of
watching advertisements. To evaluate the system, we conducted an experiment
with N=296 participants. Our results show that the quality of the labeled data
is comparable to traditional crowdsourcing approaches and most users prefer
task ads over traditional ads. We discuss extensions to the system and address
how mobile advertisement space can be used as a productive resource in the
future.

This research aims to make metaverse characters more realistic by adding lip
animations learnt from videos in the wild. To achieve this, our approach is to
extend Tacotron 2 text-to-speech synthesizer to generate lip movements together
with mel spectrogram in one pass. The encoder and gate layer weights are
pre-trained on LJ Speech 1.1 data set while the decoder is retrained on 93
clips of TED talk videos extracted from LRS 3 data set. Our novel decoder
predicts displacement in 20 lip landmark positions across time, using labels
automatically extracted by OpenFace 2.0 landmark predictor. Training converged
in 7 hours using less than 5 minutes of video. We conducted ablation study for
Pre/Post-Net and pre-trained encoder weights to demonstrate the effectiveness
of transfer learning between audio and visual speech data.

Artistic painting has achieved significant progress during recent years.
Using an autoencoder to connect the original images with compressed latent
spaces and a cross attention enhanced U-Net as the backbone of diffusion,
latent diffusion models (LDMs) have achieved stable and high fertility image
generation. In this paper, we focus on enhancing the creative painting ability
of current LDMs in two directions, textual condition extension and model
retraining with Wikiart dataset. Through textual condition extension, users'
input prompts are expanded with rich contextual knowledge for deeper
understanding and explaining the prompts. Wikiart dataset contains 80K famous
artworks drawn during recent 400 years by more than 1,000 famous artists in
rich styles and genres. Through the retraining, we are able to ask these
artists to draw novel and creative painting on modern topics. Direct
comparisons with the original model show that the creativity and artistry are
enriched.

Sequential labeling is a fundamental NLP task, forming the backbone of many
applications. Supervised learning of Seq2Seq models (like T5) has shown great
success on these problems. However there remains a significant disconnect
between the training objectives of these models vs the metrics and desiderata
we care about in practical applications. For example, a practical sequence
tagging application may want to optimize for a certain precision-recall
trade-off (of the top-k predictions) which is quite different from the standard
objective of maximizing the likelihood of the gold labeled sequence. Thus to
bridge this gap, we propose GROOT -- a simple yet effective framework for
Generative Reward Optimization Of Text sequences. GROOT works by training a
generative sequential labeling model to match the decoder output distribution
with that of the (black-box) reward function. Using an iterative training
regime, we first generate prediction candidates, then correct errors in them,
and finally contrast those candidates (based on their reward values). As
demonstrated via extensive experiments on four public benchmarks, GROOT
significantly improves all reward metrics. Furthermore, GROOT also leads to
improvements of the overall decoder distribution as evidenced by the quality
gains of the top-$k$ candidates.

Zero-shot learning on 3D point cloud data is a related underexplored problem
compared to its 2D image counterpart. 3D data brings new challenges for ZSL due
to the unavailability of robust pre-trained feature extraction models. To
address this problem, we propose a prompt-guided 3D scene generation and
supervision method that augments 3D data to learn the network better, exploring
the complex interplay of seen and unseen objects. First, we merge point clouds
of two 3D models in certain ways described by a prompt. The prompt acts like
the annotation describing each 3D scene. Later, we perform contrastive learning
to train our proposed architecture in an end-to-end manner. We argue that 3D
scenes can relate objects more efficiently than single objects because popular
language models (like BERT) can achieve high performance when objects appear in
a context. Our proposed prompt-guided scene generation method encapsulates data
augmentation and prompt-based annotation/captioning to improve 3D ZSL
performance. We have achieved state-of-the-art ZSL and generalized ZSL
performance on synthetic (ModelNet40, ModelNet10) and real-scanned
(ScanOjbectNN) 3D object datasets.

Diffusion models have been recently studied as powerful generative inverse
problem solvers, owing to their high quality reconstructions and the ease of
combining existing iterative solvers. However, most works focus on solving
simple linear inverse problems in noiseless settings, which significantly
under-represents the complexity of real-world problems. In this work, we extend
diffusion solvers to efficiently handle general noisy (non)linear inverse
problems via approximation of the posterior sampling. Interestingly, the
resulting posterior sampling scheme is a blended version of diffusion sampling
with the manifold constrained gradient without a strict measurement consistency
projection step, yielding a more desirable generative path in noisy settings
compared to the previous studies. Our method demonstrates that diffusion models
can incorporate various measurement noise statistics such as Gaussian and
Poisson, and also efficiently handle noisy nonlinear inverse problems such as
Fourier phase retrieval and non-uniform deblurring.

Data driven optimization and machine learning based performance diagnostics
of radio access networks entails significant challenges arising not only from
the nature of underlying data sources but also due to complex spatio-temporal
relationships and interdependencies between cells due to user mobility and
varying traffic patterns. We discuss how to study these configuration and
performance management data sets and identify relationships between cells in
terms of key performance indicators using multivariate analysis. To this end,
we leverage a novel framework based on canonical correlation analysis (CCA),
which is a highly effective method for not only dimensionality reduction but
also for analyzing relationships across different sets of multivariate data. As
a case study, we discuss energy saving use-case based on cell shutdown in
commercial cellular networks, where we apply CCA to analyze the impact of
capacity cell shutdown on the KPIs of coverage cell in the same sector. Data
from LTE Network is used to analyzed example case. We conclude that CCA is a
viable approach for identifying key relationships not only between network
planning and configuration data, but also dynamic performance data, paving the
way for endeavors such as dimensionality reduction, performance analysis, and
root cause analysis for performance diagnostics.

Recommender systems have become an essential tool for providers and users of
online services and goods, especially with the increased use of the Internet to
access information and purchase products and services. This work proposes a
novel recommendation method based on complex networks generated by a
similarity-popularity model to predict ones. We first construct a model of a
network having users and items as nodes from observed ratings and then use it
to predict unseen ratings. The prospect of producing accurate rating
predictions using a similarity-popularity model with hidden metric spaces and
dot-product similarity is explored. The proposed approach is implemented and
experimentally compared against baseline and state-of-the-art recommendation
methods on 21 datasets from various domains. The experimental results
demonstrate that the proposed method produces accurate predictions and
outperforms existing methods. We also show that the proposed approach produces
superior results in low dimensions, proving its effectiveness for data
visualization and exploration.

Randomized, skewed caches (RSCs) such as CEASER-S have recently received much
attention to defend against contention-based cache side channels. By
randomizing and regularly changing the mapping(s) of addresses to cache sets,
these techniques are designed to obfuscate the leakage of memory access
patterns. However, new attack techniques, e.g., Prime+Prune+Probe, soon
demonstrated the limits of RSCs as they allow attackers to more quickly learn
which addresses contend in the cache and use this information to circumvent the
randomization. To yet maintain side-channel resilience, RSCs must change the
random mapping(s) more frequently with adverse effects on performance and
implementation complexity. This work aims to make randomization-based
approaches more robust to allow for reduced re-keying rates and presents
Chameleon Cache. Chameleon Cache extends RSCs with a victim cache (VC) to
decouple contention in the RSC from evictions observed by the user. The VC
allows Chameleon Cache to make additional use of the multiple mappings RSCs
provide to translate addresses to cache set indices: when a cache line is
evicted from the RSC to the VC under one of its mappings, the VC automatically
reinserts this evicted line back into the RSC by using a different mapping. As
a result, the effects of previous RSC set contention are hidden and Chameleon
Cache exhibits side-channel resistance and eviction patterns similar to fully
associative caches with random replacement. We show that Chameleon Cache has
performance overheads of < 1% and stress that VCs are more generically helpful
to increase side-channel resistance and re-keying intervals of randomized
caches.

We present a simple but efficient and robust reconstruction algorithm for
Fourier ptychographic microscopy, termed error-laxity Fourier ptychographic
iterative engine (Elfpie), that is simultaneously robust to (1) noise signal
(including Gaussian, Poisson, and salt & pepper noises), (2) problematic
background illumination problem, (3) vignetting effects, and (4) misaligning of
LED positions, without the need of calibrating or recovering of these system
errors. In Elfpie, we embedded the inverse problem of FPM under the framework
of feature extraction/recovering and proposed a new data fidelity cost function
regularized by the global second-order total-variation regularization (Hessian
regularization). The closed-form complex gradient for the cost function is
derived and is back-propagated using the AdaBelief optimizer with an adaptive
learning rate to update the entire Fourier spectrum of the sample and system
pupil function. The Elfpie is tested on both simulation data and experimental
data and is compared against the state-of-the-art (SOTA) algorithm. Results
show the superiority of the Elfpie among other SOTA methods, in both
reconstruction quality under different degeneration issues, and implementation
efficiency. In general, compared against SOTA methods, the Elfpie is robust to
Gaussian noise with 100 times larger noise strength, salt & pepper noise with
1000 times larger noise strength, and Poisson noise with 10 times noise
strength. The Elfpie is able to reconstruct high-fidelity sample field under
large LED position misalignments up to 2 mm. It can also bypass the vignetting
effect in which all SOTA methods fail to reconstruct the sample pattern. With
parallel computation, the Elfpie is able to be K times faster than traditional
FPM, where K is the number of used LEDs.

The literature for fairness-aware machine learning knows a plethora of
different fairness notions. It is however wellknown, that it is impossible to
satisfy all of them, as certain notions contradict each other. In this paper,
we take a closer look at academic performance prediction (APP) systems and try
to distil which fairness notions suit this task most. For this, we scan recent
literature proposing guidelines as to which fairness notion to use and apply
these guidelines onto APP. Our findings suggest equalised odds as most suitable
notion for APP, based on APP's WYSIWYG worldview as well as potential long-term
improvements for the population.

Existing self-supervised learning strategies are constrained to either a
limited set of objectives or generic downstream tasks that predominantly target
uni-modal applications. This has isolated progress for imperative multi-modal
applications that are diverse in terms of complexity and domain-affinity, such
as meme analysis. Here, we introduce two self-supervised pre-training methods,
namely Ext-PIE-Net and MM-SimCLR that (i) employ off-the-shelf multi-modal
hate-speech data during pre-training and (ii) perform self-supervised learning
by incorporating multiple specialized pretext tasks, effectively catering to
the required complex multi-modal representation learning for meme analysis. We
experiment with different self-supervision strategies, including potential
variants that could help learn rich cross-modality representations and evaluate
using popular linear probing on the Hateful Memes task. The proposed solutions
strongly compete with the fully supervised baseline via label-efficient
training while distinctly outperforming them on all three tasks of the Memotion
challenge with 0.18%, 23.64%, and 0.93% performance gain, respectively.
Further, we demonstrate the generalizability of the proposed solutions by
reporting competitive performance on the HarMeme task. Finally, we empirically
establish the quality of the learned representations by analyzing task-specific
learning, using fewer labeled training samples, and arguing that the complexity
of the self-supervision strategy and downstream task at hand are correlated.
Our efforts highlight the requirement of better multi-modal self-supervision
methods involving specialized pretext tasks for efficient fine-tuning and
generalizable performance.

To discover new drugs is to seek and to prove causality. As an emerging
approach leveraging human knowledge and creativity, data, and machine
intelligence, causal inference holds the promise of reducing cognitive bias and
improving decision making in drug discovery. While it has been applied across
the value chain, the concepts and practice of causal inference remain obscure
to many practitioners. This article offers a non-technical introduction to
causal inference, reviews its recent applications, and discusses opportunities
and challenges of adopting the causal language in drug discovery and

Renal cell carcinoma (RCC) is a common cancer that varies in clinical
behavior. Indolent RCC is often low-grade without necrosis and can be monitored
without treatment. Aggressive RCC is often high-grade and can cause metastasis
and death if not promptly detected and treated. While most kidney cancers are
detected on CT scans, grading is based on histology from invasive biopsy or
surgery. Determining aggressiveness on CT images is clinically important as it
facilitates risk stratification and treatment planning. This study aims to use
machine learning methods to identify radiology features that correlate with
features on pathology to facilitate assessment of cancer aggressiveness on CT
images instead of histology. This paper presents a novel automated method,
Correlated Feature Aggregation By Region (CorrFABR), for classifying
aggressiveness of clear cell RCC by leveraging correlations between radiology
and corresponding unaligned pathology images. CorrFABR consists of three main
steps: (1) Feature Aggregation where region-level features are extracted from
radiology and pathology images, (2) Fusion where radiology features correlated
with pathology features are learned on a region level, and (3) Prediction where
the learned correlated features are used to distinguish aggressive from
indolent clear cell RCC using CT alone as input. Thus, during training,
CorrFABR learns from both radiology and pathology images, but during inference,
CorrFABR will distinguish aggressive from indolent clear cell RCC using CT
alone, in the absence of pathology images. CorrFABR improved classification
performance over radiology features alone, with an increase in binary
classification F1-score from 0.68 (0.04) to 0.73 (0.03). This demonstrates the
potential of incorporating pathology disease characteristics for improved
classification of aggressiveness of clear cell RCC on CT images.

In recent times there is a growing development of video based applications
for surgical purposes. Part of these applications can work offline after the
end of the procedure, other applications must react immediately. However, there
are cases where the response should be done during the procedure but some delay
is acceptable. In the literature, the online-offline performance gap is known.
Our goal in this study was to learn the performance-delay trade-off and design
an MS-TCN++-based algorithm that can utilize this trade-off. To this aim, we
used our open surgery simulation data-set containing 96 videos of 24
participants that perform a suturing task on a variable tissue simulator. In
this study, we used video data captured from the side view. The Networks were
trained to identify the performed surgical gestures. The naive approach is to
reduce the MS-TCN++ depth, as a result, the receptive field is reduced, and
also the number of required future frames is also reduced. We showed that this
method is sub-optimal, mainly in the small delay cases. The second method was
to limit the accessible future in each temporal convolution. This way, we have
flexibility in the network design and as a result, we achieve significantly
better performance than in the naive approach.

A dominant approach for addressing unsupervised domain adaptation is to map
data points for the source and the target domains into an embedding space which
is modeled as the output-space of a shared deep encoder. The encoder is trained
to make the embedding space domain-agnostic to make a source-trained classifier
generalizable on the target domain. A secondary mechanism to improve UDA
performance further is to make the source domain distribution more compact to
improve model generalizability. We demonstrate that increasing the interclass
margins in the embedding space can help to develop a UDA algorithm with
improved performance. We estimate the internally learned multi-modal
distribution for the source domain, learned as a result of pretraining, and use
it to increase the interclass class separation in the source domain to reduce
the effect of domain shift. We demonstrate that using our approach leads to
improved model generalizability on four standard benchmark UDA image
classification datasets and compares favorably against exiting methods.

Low-rank approximation in data streams is a fundamental and significant task
in computing science, machine learning and statistics. Multiple streaming
algorithms have emerged over years and most of them are inspired by randomized
algorithms, more specifically, sketching methods. However, many algorithms are
not able to leverage information of data streams and consequently suffer from
low accuracy. Existing data-driven methods improve accuracy but the training
cost is expensive in practice. In this paper, from a subspace perspective, we
propose a tensor-based sketching method for low-rank approximation of data
streams. The proposed algorithm fully exploits the structure of data streams
and obtains quasi-optimal sketching matrices by performing tensor decomposition
on training data. A series of experiments are carried out and show that the
proposed tensor-based method can be more accurate and much faster than the
previous work.

Background and objective: Sharing of medical data is required to enable the
cross-agency flow of healthcare information and construct high-accuracy
computer-aided diagnosis systems. However, the large sizes of medical datasets,
the massive amount of memory of saved deep convolutional neural network (DCNN)
models, and patients' privacy protection are problems that can lead to
inefficient medical data sharing. Therefore, this study proposes a novel
soft-label dataset distillation method for medical data sharing.
  Methods: The proposed method distills valid information of medical image data
and generates several compressed images with different data distributions for
anonymous medical data sharing. Furthermore, our method can extract essential
weights of DCNN models to reduce the memory required to save trained models for
efficient medical data sharing.
  Results: The proposed method can compress tens of thousands of images into
several soft-label images and reduce the size of a trained model to a few
hundredths of its original size. The compressed images obtained after
distillation have been visually anonymized; therefore, they do not contain the
private information of the patients. Furthermore, we can realize high-detection
performance with a small number of compressed images.
  Conclusions: The experimental results show that the proposed method can
improve the efficiency and security of medical data sharing.

Deep learning has become a useful data analysis method, however mainstream
adaption in distributed computer software and embedded devices has been low so
far. Often, adding deep learning inference in mainstream applications and
devices requires new hardware with signal processors suited for convolutional
neural networks. This work adds new data types (quantized 16-bit and 8-bit
integer, 16-bit floating point) to Caffe in order to save memory and increase
inference speed on existing commodity graphics processors with OpenCL, common
in everyday devices. Existing models can be executed effortlessly in
mixed-precision mode. Additionally, we propose a variation of
mixture-of-experts to increase inference speed on AlexNet for image
classification. We managed to decrease memory usage up to 3.29x while
increasing inference speed up to 3.01x on certain devices. We demonstrate with
five simple examples how the presented techniques can easily be applied to
different machine learning problems. The whole pipeline, consisting of models,
example python scripts and modified Caffe library, is available as Open Source
software.

We extend techniques and learnings about the stochastic properties of
nonlinear responses from finance to medicine, particularly oncology where it
can inform dosing and intervention. We define antifragility. We propose uses of
risk analysis to medical problems, through the properties of nonlinear
responses (convex or concave). We 1) link the convexity/concavity of the
dose-response function to the statistical properties of the results; 2) define
"antifragility" as a mathematical property for local beneficial convex
responses and the generalization of "fragility" as its opposite, locally
concave in the tails of the statistical distribution; 3) propose mathematically
tractable relations between dosage, severity of conditions, and iatrogenics. In
short we propose a framework to integrate the necessary consequences of
nonlinearities in evidence-based oncology and more general clinical risk

Open-domain dialogue systems aim to interact with humans through natural
language texts in an open-ended fashion. However, the widely successful neural
networks may not work well for dialogue systems, as they tend to generate
generic responses. In this work, we propose an Equal-size Hard
Expectation--Maximization (EqHard-EM) algorithm to train a multi-decoder model
for diverse dialogue generation. Our algorithm assigns a sample to a decoder in
a hard manner and additionally imposes an equal-assignment constraint to ensure
that all decoders are well-trained. We provide detailed theoretical analysis to
justify our approach. Further, experiments on two large-scale, open-domain
dialogue datasets verify that our EqHard-EM algorithm generates high-quality
diverse responses.

Pruning neural networks has become popular in the last decade when it was
shown that a large number of weights can be safely removed from modern neural
networks without compromising accuracy. Numerous pruning methods have been
proposed since then, each claiming to be better than the previous. Many
state-of-the-art (SOTA) techniques today rely on complex pruning methodologies
utilizing importance scores, getting feedback through back-propagation or
having heuristics-based pruning rules amongst others. We question this pattern
of introducing complexity in order to achieve better pruning results. We
benchmark these SOTA techniques against Global Magnitude Pruning (Global MP), a
naive pruning baseline, to evaluate whether complexity is really needed to
achieve higher performance. Global MP ranks weights in order of their
magnitudes and prunes the smallest ones. Hence, in its vanilla form, it is one
of the simplest pruning techniques. Surprisingly, we find that vanilla Global
MP outperforms all the other SOTA techniques and achieves a new SOTA result. It
also achieves good performance on FLOPs sparsification, which we find is
enhanced, when pruning is conducted in a gradual fashion. We also find that
Global MP is generalizable across tasks, datasets and models with superior
performance. Moreover, a common issue that many pruning algorithms run into at
high sparsity rates, namely, layer-collapse, can be easily fixed in Global MP
by setting a minimum threshold of weights to be retained in each layer. Lastly,
unlike many other SOTA techniques, Global MP does not require any additional
algorithm specific hyper-parameters and is very straightforward to tune and
implement. We showcase our findings on various models (WRN-28-8, ResNet-32,
ResNet-50, MobileNet-V1 and FastGRNN) and multiple datasets (CIFAR-10, ImageNet
and HAR-2). Code is available at https://github.com/manasgupta-1/GlobalMP.

It is common to evaluate the performance of a machine learning model by
measuring its predictive power on a test dataset. This approach favors
complicated models that can smoothly fit complex functions and generalize well
from training data points. Although essential components of intelligence, speed
and data efficiency of this learning process are rarely reported or compared
between different candidate models. In this paper, we introduce a benchmark of
increasingly difficult tasks together with a data efficiency metric to measure
how quickly machine learning models learn from training data. We compare the
learning speed of some established sequential supervised models, such as RNNs,
LSTMs, or Transformers, with relatively less known alternative models based on
reservoir computing. The proposed tasks require a wide range of computational
primitives, such as memory or the ability to compute Boolean functions, to be
effectively solved. Surprisingly, we observe that reservoir computing systems
that rely on dynamically evolving feature maps learn faster than fully
supervised methods trained with stochastic gradient optimization while
achieving comparable accuracy scores. The code, benchmark, trained models, and
results to reproduce our experiments are available at
https://github.com/hugcis/benchmark_learning_efficiency/ .

Multicalibration is a desirable fairness criteria that constrains calibration
error among flexibly-defined groups in the data while maintaining overall
calibration. However, when outcome probabilities are correlated with group
membership, multicalibrated models can exhibit a higher percent calibration
error among groups with lower base rates than groups with higher base rates. As
a result, it remains possible for a decision-maker to learn to trust or
distrust model predictions for specific groups. To alleviate this, we propose
proportional multicalibration, a criteria that constrains the percent
calibration error among groups and within prediction bins. We prove that
satisfying proportional multicalibration bounds a model's multicalibration as
well its differential calibration, a stronger fairness criteria inspired by the
fairness notion of sufficiency. We provide an efficient algorithm for
post-processing risk prediction models for proportional multicalibration and
evaluate it empirically. We conduct simulation studies and investigate a
real-world application of PMC-postprocessing to prediction of emergency
department patient admissions. We observe that proportional multicalibration is
a promising criteria for controlling simultenous measures of calibration
fairness of a model over intersectional groups with virtually no cost in terms
of classification performance.

Mathematical reasoning, a core ability of human intelligence, presents unique
challenges for machines in abstract thinking and logical reasoning. Recent
large pre-trained language models such as GPT-3 have achieved remarkable
progress on mathematical reasoning tasks written in text form, such as math
word problems (MWP). However, it is unknown if the models can handle more
complex problems that involve math reasoning over heterogeneous information,
such as tabular data. To fill the gap, we present Tabular Math Word Problems
(TabMWP), a new dataset containing 38,431 open-domain grade-level problems that
require mathematical reasoning on both textual and tabular data. Each question
in TabMWP is aligned with a tabular context, which is presented as an image,
semi-structured text, and a structured table. There are two types of questions:
free-text and multi-choice, and each problem is annotated with gold solutions
to reveal the multi-step reasoning process. We evaluate different pre-trained
models on TabMWP, including the GPT-3 model in a few-shot setting. As earlier
studies suggest, since few-shot GPT-3 relies on the selection of in-context
examples, its performance is unstable and can degrade to near chance. The
unstable issue is more severe when handling complex problems like TabMWP. To
mitigate this, we further propose a novel approach, PromptPG, which utilizes
policy gradient to learn to select in-context examples from a small amount of
training data and then constructs the corresponding prompt for the test
example. Experimental results show that our method outperforms the best
baseline by 5.31% on the accuracy metric and reduces the prediction variance
significantly compared to random selection, which verifies its effectiveness in
the selection of in-context examples.

The acquisition of advanced models relies on large datasets in many fields,
which makes storing datasets and training models expensive. As a solution,
dataset distillation can synthesize a small dataset that preserves most
information of the original large dataset. The recently proposed dataset
distillation method by matching network parameters has been proven effective
for several datasets. However, the dimension of network parameters is usually
large. And we found that a few parameters in the distillation process are
difficult to match, which harms the distillation performance. Based on this
observation, this paper proposes a new method to solve the problem using
parameter pruning. The proposed method can synthesize more robust distilled
datasets and improve the distillation performance by pruning difficult-to-match
parameters in the distillation process. Experimental results on three datasets
show that the proposed method outperformed other state-of-the-art dataset
distillation methods.

Spherical image processing has been widely applied in many important fields,
such as omnidirectional vision for autonomous cars, global climate modelling,
and medical imaging. It is non-trivial to extend an algorithm developed for
flat images to the spherical ones. In this work, we focus on the challenging
task of spherical image inpainting with deep learning-based regularizer.
Instead of a naive application of existing models for planar images, we employ
a fast directional spherical Haar framelet transform and develop a novel
optimization framework based on a sparsity assumption of the framelet
transform. Furthermore, by employing progressive encoder-decoder architecture,
a new and better-performed deep CNN denoiser is carefully designed and works as
an implicit regularizer. Finally, we use a plug-and-play method to handle the
proposed optimization model, which can be implemented efficiently by training
the CNN denoiser prior. Numerical experiments are conducted and show that the
proposed algorithms can greatly recover damaged spherical images and achieve
the best performance over purely using deep learning denoiser and plug-and-play
model.

Sharing medical datasets between hospitals is challenging because of the
privacy-protection problem and the massive cost of transmitting and storing
many high-resolution medical images. However, dataset distillation can
synthesize a small dataset such that models trained on it achieve comparable
performance with the original large dataset, which shows potential for solving
the existing medical sharing problems. Hence, this paper proposes a novel
dataset distillation-based method for medical dataset sharing. Experimental
results on a COVID-19 chest X-ray image dataset show that our method can
achieve high detection performance even using scarce anonymized chest X-ray
images.

Dense prediction tasks are common for 3D point clouds, but the inherent
uncertainties in massive points and their embeddings have long been ignored. In
this work, we present CUE, a novel uncertainty estimation method for dense
prediction tasks of 3D point clouds. Inspired by metric learning, the key idea
of CUE is to explore cross-point embeddings upon a conventional dense
prediction pipeline. Specifically, CUE involves building a probabilistic
embedding model and then enforcing metric alignments of massive points in the
embedding space. We demonstrate that CUE is a generic and effective tool for
dense uncertainty estimation of 3D point clouds in two different tasks: (1) in
3D geometric feature learning we for the first time obtain well-calibrated
dense uncertainty, and (2) in semantic segmentation we reduce uncertainty`s
Expected Calibration Error of the state-of-the-arts by 43.8%. All uncertainties
are estimated without compromising predictive performance.

In drug discovery, aqueous solubility is an important pharmacokinetic
property which affects absorption and assay availability of drug. Thus, in
silico prediction of solubility has been studied for its utility in virtual
screening and lead optimization. Recently, machine learning (ML) methods using
experimental data has been popular because physics-based methods like quantum
mechanics and molecular dynamics are not suitable for high-throughput tasks due
to its computational costs. However, ML method can exhibit over-fitting problem
in a data-deficient condition, and this is the case for most chemical property
datasets. In addition, ML methods are regarded as a black box function in that
it is difficult to interpret contribution of hidden features to outputs,
hindering analysis and modification of structure-activity relationship. To deal
with mentioned issues, we developed Bayesian graph neural networks (GNNs) with
the self-attention readout layer. Unlike most GNNs using self-attention in node
updates, self-attention applied at readout layer enabled a model to improve
prediction performance as well as to identify atom-wise importance, which can
help lead optimization as exemplified for three FDA-approved drugs. Also,
Bayesian inference enables us to separate more or less accurate results
according to uncertainty in solubility prediction task We expect that our
accurate, reliable and interpretable model can be used for more careful
decision-making and various applications in the development of drugs.

Semantic segmentation is an essential task in developing medical image
diagnosis systems. However, building an annotated medical dataset is expensive.
Thus, semi-supervised methods are significant in this circumstance. In
semi-supervised learning, the quality of labels plays a crucial role in model
performance. In this work, we present a new pseudo labeling strategy that
enhances the quality of pseudo labels used for training student networks. We
follow the multi-stage semi-supervised training approach, which trains a
teacher model on a labeled dataset and then uses the trained teacher to render
pseudo labels for student training. By doing so, the pseudo labels will be
updated and more precise as training progress. The key difference between
previous and our methods is that we update the teacher model during the student
training process. So the quality of pseudo labels is improved during the
student training process. We also propose a simple but effective strategy to
enhance the quality of pseudo labels using a momentum model -- a slow copy
version of the original model during training. By applying the momentum model
combined with re-rendering pseudo labels during student training, we achieved
an average of 84.1% Dice Score on five datasets (i.e., Kvarsir, CVC-ClinicDB,
ETIS-LaribPolypDB, CVC-ColonDB, and CVC-300) with only 20% of the dataset used
as labeled data. Our results surpass common practice by 3% and even approach
fully-supervised results on some datasets. Our source code and pre-trained
models are available at https://github.com/sun-asterisk-research/online
learning ssl

Hyperparameter optimization is the process of identifying the appropriate
hyperparameter configuration of a given machine learning model with regard to a
given learning task. For smaller data sets, an exhaustive search is possible;
However, when the data size and model complexity increase, the number of
configuration evaluations becomes the main computational bottleneck. A
promising paradigm for tackling this type of problem is surrogate-based
optimization. The main idea underlying this paradigm considers an incrementally
updated model of the relation between the hyperparameter space and the output
(target) space; the data for this model are obtained by evaluating the main
learning engine, which is, for example, a factorization machine-based model. By
learning to approximate the hyperparameter-target relation, the surrogate
(machine learning) model can be used to score large amounts of hyperparameter
configurations, exploring parts of the configuration space beyond the reach of
direct machine learning engine evaluation. Commonly, a surrogate is selected
prior to optimization initialization and remains the same during the search. We
investigated whether dynamic switching of surrogates during the optimization
itself is a sensible idea of practical relevance for selecting the most
appropriate factorization machine-based models for large-scale online
recommendation. We conducted benchmarks on data sets containing hundreds of
millions of instances against established baselines such as Random Forest- and
Gaussian process-based surrogates. The results indicate that surrogate
switching can offer good performance while considering fewer learning engine
evaluations.

The stochastic reaction network is widely used to model stochastic processes
in physics, chemistry and biology. However, the size of the state space
increases exponentially with the number of species, making it challenging to
investigate the time evolution of the chemical master equation for the reaction
network. Here, we propose a machine-learning approach using the variational
autoregressive network to solve the chemical master equation. The approach is
based on the reinforcement learning framework and does not require any data
simulated in prior by another method. Different from simulating single
trajectories, the proposed approach tracks the time evolution of the joint
probability distribution in the state space of species counts, and supports
direct sampling on configurations and computing their normalized joint
probabilities. We apply the approach to various systems in physics and biology,
and demonstrate that it accurately generates the probability distribution over
time in the genetic toggle switch, the early life self-replicator, the epidemic
model and the intracellular signaling cascade. The variational autoregressive
network exhibits a plasticity in representing the multi-modal distribution by
feedback regulations, cooperates with the conservation law, enables
time-dependent reaction rates, and is efficient for high-dimensional reaction
networks with allowing a flexible upper count limit. The results suggest a
general approach to investigate stochastic reaction networks based on modern
machine learning.

In order to achieve a virtual certification process and robust designs for
turbomachinery, the uncertainty bounds for computational fluid dynamics have to
be known. The formulation of turbulence closure models implies a major source
of the overall uncertainty of Reynold-averaged Navier Stokes simulations. We
discuss the common practice of applying a physics constrained eigenspace
perturbation of the Reynolds stress tensor in order to account for the model
form uncertainty of turbulence models. Since the basic methodology often leads
to generous uncertainty estimates, we extend a recent approach of adding a
machine learning strategy. The application of a data-driven method is motivated
by striving for the detection of flow regions, which are prone to suffer from a
lack of turbulence model prediction accuracy. In this way any user input
related to choosing the degree of uncertainty is supposed to become obsolete.
This work especially investigates an approach, which tries to determine an a
priori estimation of prediction confidence, when there is no accurate data
available to judge the prediction. The flow around the NACA 4412 airfoil at
near-stall conditions serves to demonstrate the successful application of the
data-driven eigenspace perturbation framework. We especially highlight the
objectives and limitations of the underlying methodology finally.

Probabilistic predictions from neural networks which account for predictive
uncertainty during classification is crucial in many real-world and high-impact
decision making settings. However, in practice most datasets are trained on
non-probabilistic neural networks which by default do not capture this inherent
uncertainty. This well-known problem has led to the development of post-hoc
calibration procedures, such as Platt scaling (logistic), isotonic and beta
calibration, which transforms the scores into well calibrated empirical
probabilities. A plausible alternative to the calibration approach is to use
Bayesian neural networks, which directly models a predictive distribution.
Although they have been applied to images and text datasets, they have seen
limited adoption in the tabular and small data regime. In this paper, we
demonstrate that Bayesian neural networks yields competitive performance when
compared to calibrated neural networks and conduct experiments across a wide
array of datasets.

Diffusion models are powerful generative models that simulate the reverse of
diffusion processes using score functions to synthesize data from noise. The
sampling process of diffusion models can be interpreted as solving the reverse
stochastic differential equation (SDE) or the ordinary differential equation
(ODE) of the diffusion process, which often requires up to thousands of
discretization steps to generate a single image. This has sparked a great
interest in developing efficient integration techniques for reverse-S/ODEs.
Here, we propose an orthogonal approach to accelerating score-based sampling:
Denoising MCMC (DMCMC). DMCMC first uses MCMC to produce samples in the product
space of data and variance (or diffusion time). Then, a reverse-S/ODE
integrator is used to denoise the MCMC samples. Since MCMC traverses close to
the data manifold, the computation cost of producing a clean sample for DMCMC
is much less than that of producing a clean sample from noise. To verify the
proposed concept, we show that Denoising Langevin Gibbs (DLG), an instance of
DMCMC, successfully accelerates all six reverse-S/ODE integrators considered in
this work on the tasks of CIFAR10 and CelebA-HQ-256 image generation. Notably,
combined with integrators of Karras et al. (2022) and pre-trained score models
of Song et al. (2021b), DLG achieves SOTA results. In the limited number of
score function evaluation (NFE) settings on CIFAR10, we have $3.86$ FID with
$\approx 10$ NFE and $2.63$ FID with $\approx 20$ NFE. On CelebA-HQ-256, we
have $6.99$ FID with $\approx 160$ NFE, which beats the current best record of
Kim et al. (2022) among score-based models, $7.16$ FID with $4000$ NFE. Code:
https://github.com/1202kbs/DMCMC

Bangladeshi Sign Language (BdSL) - like other sign languages - is tough to
learn for general people, especially when it comes to expressing letters. In
this poster, we propose PerSign, a system that can reproduce a person's image
by introducing sign gestures in it. We make this operation personalized, which
means the generated image keeps the person's initial image profile - face, skin
tone, attire, background - unchanged while altering the hand, palm, and finger
positions appropriately. We use an image-to-image translation technique and
build a corresponding unique dataset to accomplish the task. We believe the
translated image can reduce the communication gap between signers (person who
uses sign language) and non-signers without having prior knowledge of BdSL.

In recent years, there has been widespread attention drawn to convolutional
neural network (CNN) based blind image quality assessment (IQA). A large number
of works start by extracting deep features from CNN. Then, those features are
processed through spatial average pooling (SAP) and fully connected layers to
predict quality. Inspired by full reference IQA and texture features, in this
paper, we extend SAP ($1^{st}$ moment) into spatial moment pooling (SMP) by
incorporating higher order moments (such as variance, skewness). Moreover, we
provide learning friendly normalization to circumvent numerical issue when
computing gradients of higher moments. Experimental results suggest that simply
upgrading SAP to SMP significantly enhances CNN-based blind IQA methods and
achieves state of the art performance.

We present a flow-based approach to the optimal transport (OT) problem
between two continuous distributions $\pi_0,\pi_1$ on $\mathbb{R}^d$, of
minimizing a transport cost $\mathbb{E}[c(X_1-X_0)]$ in the set of couplings
$(X_0,X_1)$ whose marginal distributions on $X_0,X_1$ equals $\pi_0,\pi_1$,
respectively, where $c$ is a cost function. Our method iteratively constructs a
sequence of neural ordinary differentiable equations (ODE), each learned by
solving a simple unconstrained regression problem, which monotonically reduce
the transport cost while automatically preserving the marginal constraints.
This yields a monotonic interior approach that traverses inside the set of
valid couplings to decrease the transport cost, which distinguishes itself from
most existing approaches that enforce the coupling constraints from the
outside. The main idea of the method draws from rectified flow, a recent
approach that simultaneously decreases the whole family of transport costs
induced by convex functions $c$ (and is hence multi-objective in nature), but
is not tailored to minimize a specific transport cost. Our method is a
single-object variant of rectified flow that guarantees to solve the OT problem
for a fixed, user-specified convex cost function $c$.

Traditional training paradigms for extractive and abstractive summarization
systems always only use token-level or sentence-level training objectives.
However, the output summary is always evaluated from summary-level which leads
to the inconsistency in training and evaluation. In this paper, we propose a
Contrastive Learning based re-ranking framework for one-stage summarization
called COLO. By modeling a contrastive objective, we show that the
summarization model is able to directly generate summaries according to the
summary-level score without additional modules and parameters. Extensive
experiments demonstrate that COLO boosts the extractive and abstractive results
of one-stage systems on CNN/DailyMail benchmark to 44.58 and 46.33 ROUGE-1
score while preserving the parameter efficiency and inference efficiency.
Compared with state-of-the-art multi-stage systems, we save more than 100 GPU
training hours and obtaining 3~8 speed-up ratio during inference while
maintaining comparable results.

Among the challenges not yet resolved for Counterfactual Explanations (CE),
there are stability, synthesis of the various CE and the lack of
plausibility/sparsity guarantees. From a more practical point of view, recent
studies show that the prescribed counterfactual recourses are often not
implemented exactly by the individuals and demonstrate that most
state-of-the-art CE algorithms are very likely to fail in this noisy
environment. To address these issues, we propose a probabilistic framework that
gives a sparse local counterfactual rule for each observation: we provide rules
that give a range of values that can change the decision with a given high
probability instead of giving diverse CE. In addition, the recourses derived
from these rules are robust by construction. These local rules are aggregated
into a regional counterfactual rule to ensure the stability of the
counterfactual explanations across observations. Our local and regional rules
guarantee that the recourses are faithful to the data distribution because our
rules use a consistent estimator of the probabilities of changing the decision
based on a Random Forest. In addition, these probabilities give interpretable
and sparse rules as we select the smallest set of variables having a given
probability of changing the decision. Codes for computing our counterfactual
rules are available, and we compare their relevancy with standard CE and recent
similar attempts.

Vessel segmentation in medical images is one of the important tasks in the
diagnosis of vascular diseases and therapy planning. Although learning-based
segmentation approaches have been extensively studied, a large amount of
ground-truth labels are required in supervised methods and confusing background
structures make neural networks hard to segment vessels in an unsupervised
manner. To address this, here we introduce a novel diffusion adversarial
representation learning (DARL) model that leverages a denoising diffusion
probabilistic model with adversarial learning, and apply it for vessel
segmentation. In particular, for self-supervised vessel segmentation, DARL
learns background image distribution using a diffusion module, which lets a
generation module effectively provide vessel representations. Also, by
adversarial learning based on the proposed switchable spatially-adaptive
denormalization, our model estimates synthetic fake vessel images as well as
vessel segmentation masks, which further makes the model capture
vessel-relevant semantic information. Once the proposed model is trained, the
model generates segmentation masks by one step and can be applied to general
vascular structure segmentation of coronary angiography and retinal images.
Experimental results on various datasets show that our method significantly
outperforms existing unsupervised and self-supervised methods in vessel
segmentation.

The detection of entanglement provides a definitive proof of quantumness. Its
ascertainment might be challenging for hot or macroscopic objects, where
entanglement is typically weak, but nevertheless present. Here we propose a
platform for measuring entanglement by connecting the objects of interest to an
uncontrolled quantum network, whose emission (readout) is trained to learn and
sense the entanglement of the former. First, we demonstrate the platform and
its features with generic quantum systems. As the network effectively learns to
recognise quantum states, it is possible to sense the amount of entanglement
after training with only non-entangled states. Furthermore, by taking into
account measurement errors, we demonstrate entanglement sensing with precision
that scales beyond the standard quantum limit and outperforms measurements
performed directly on the objects. Finally, we utilise our platform for sensing
gravity-induced entanglement between two masses and predict an improvement of
two orders of magnitude in the precision of entanglement estimation compared to
existing techniques.

Optimizing machine learning algorithms that are used to solve the objective
function has been of great interest. Several approaches to optimize common
algorithms, such as gradient descent and stochastic gradient descent, were
explored. One of these approaches is reducing the gradient variance through
adaptive sampling to solve large-scale optimization's empirical risk
minimization (ERM) problems. In this paper, we will explore how starting with a
small sample and then geometrically increasing it and using the solution of the
previous sample ERM to compute the new ERM. This will solve ERM problems with
first-order optimization algorithms of sublinear convergence but with lower
computational complexity. This paper starts with theoretical proof of the
approach, followed by two experiments comparing the gradient descent with the
adaptive sampling of the gradient descent and ADAM with adaptive sampling ADAM
on different datasets.

It is well-known that a deep neural network has a strong fitting capability
and can easily achieve a low training error even with randomly assigned class
labels. When the number of training samples is small, or the class labels are
noisy, networks tend to memorize patterns specific to individual instances to
minimize the training error. This leads to the issue of overfitting and poor
generalisation performance. This paper explores a remedy by suppressing the
network's tendency to rely on instance-specific patterns for empirical error
minimisation. The proposed method is based on an adversarial training
framework. It suppresses features that can be utilized to identify individual
instances among samples within each class. This leads to classifiers only using
features that are both discriminative across classes and common within each
class. We call our method Adversarial Suppression of Identity Features (ASIF),
and demonstrate the usefulness of this technique in boosting generalisation
accuracy when faced with small datasets or noisy labels. Our source code is

Topological phase classifications have been intensively studied via
machine-learning techniques where different forms of the training data are
proposed in order to maximize the information extracted from the systems of
interests. Due to the complexity in quantum physics, advanced mathematical
architecture should be considered in designing machines. In this work, we
incorporate quaternion algebras into data analysis either in the frame of
supervised and unsupervised learning to classify two-dimensional Chern
insulators. For the unsupervised-learning aspect, we apply the principal
component analysis (PCA) on the quaternion-transformed eigenstates to
distinguish topological phases. For the supervised-learning aspect, we
construct our machine by adding one quaternion convolutional layer on top of a
conventional convolutional neural networks. The machine takes
quaternion-transformed configurations as inputs and successfully classify all
distinct topological phases, even for those structures that are not seen by the
machine during the training process. Our work demonstrates the power of
quaternion algebras on extracting crucial features from the targeted data and
the advantages of quaternion-based neural networks than conventional ones in
the tasks of topological phase classifications.

In offline reinforcement learning, weighted regression is a common method to
ensure the learned policy stays close to the behavior policy and to prevent
selecting out-of-sample actions. In this work, we show that due to the limited
distributional expressivity of policy models, previous methods might still
select unseen actions during training, which deviates from their initial
motivation. To address this problem, we adopt a generative approach by
decoupling the learned policy into two parts: an expressive generative behavior
model and an action evaluation model. The key insight is that such decoupling
avoids learning an explicitly parameterized policy model with a closed-form
expression. Directly learning the behavior policy allows us to leverage
existing advances in generative modeling, such as diffusion-based methods, to
model diverse behaviors. As for action evaluation, we combine our method with
an in-sample planning technique to further avoid selecting out-of-sample
actions and increase computational efficiency. Experimental results on D4RL
datasets show that our proposed method achieves competitive or superior
performance compared with state-of-the-art offline RL methods, especially in
complex tasks such as AntMaze. We also empirically demonstrate that our method
can successfully learn from a heterogeneous dataset containing multiple
distinctive but similarly successful strategies, whereas previous unimodal
policies fail.

Smart meter measurements, though critical for accurate demand forecasting,
face several drawbacks including consumers' privacy, data breach issues, to
name a few. Recent literature has explored Federated Learning (FL) as a
promising privacy-preserving machine learning alternative which enables
collaborative learning of a model without exposing private raw data for short
term load forecasting. Despite its virtue, standard FL is still vulnerable to
an intractable cyber threat known as Byzantine attack carried out by faulty
and/or malicious clients. Therefore, to improve the robustness of federated
short-term load forecasting against Byzantine threats, we develop a
state-of-the-art differentially private secured FL-based framework that ensures
the privacy of the individual smart meter's data while protect the security of
FL models and architecture. Our proposed framework leverages the idea of
gradient quantization through the Sign Stochastic Gradient Descent (SignSGD)
algorithm, where the clients only transmit the `sign' of the gradient to the
control centre after local model training. As we highlight through our
experiments involving benchmark neural networks with a set of Byzantine attack
models, our proposed approach mitigates such threats quite effectively and thus
outperforms conventional Fed-SGD models.

Core-collapse supernovae (CCSNe) emit powerful gravitational waves (GWs).
Since GWs emitted by a source contain information about the source, observing
GWs from CCSNe may allow us to learn more about CCSNs. We study if it is
possible to infer the iron core mass from the bounce and early ring-down GW
signal. We generate GW signals for a range of stellar models using numerical
simulations and apply machine learning to train and classify the signals. We
consider an idealized favourable scenario. First, we use rapidly rotating
models, which produce stronger GWs than slowly rotating models. Second, we
limit ourselves to models with four different masses, which simplifies the
selection process. We show that the classification accuracy does not exceed
~70%, signifying that even in this optimistic scenario, the information
contained in the bounce and early ring-down GW signal is not sufficient to
precisely probe the iron core mass. This suggests that it may be necessary to
incorporate additional information such as the GWs from later post-bounce
evolution and neutrino observations to accurately measure the iron core mass.

This paper proposes a novel and fast self-supervised solution for sparse-view
CBCT reconstruction (Cone Beam Computed Tomography) that requires no external
training data. Specifically, the desired attenuation coefficients are
represented as a continuous function of 3D spatial coordinates, parameterized
by a fully-connected deep neural network. We synthesize projections discretely
and train the network by minimizing the error between real and synthesized
projections. A learning-based encoder entailing hash coding is adopted to help
the network capture high-frequency details. This encoder outperforms the
commonly used frequency-domain encoder in terms of having higher performance
and efficiency, because it exploits the smoothness and sparsity of human
organs. Experiments have been conducted on both human organ and phantom
datasets. The proposed method achieves state-of-the-art accuracy and spends
reasonably short computation time.

The Iterative Hard Thresholding (IHT) algorithm has been considered
extensively as an effective deterministic algorithm for solving sparse
optimizations. The IHT algorithm benefits from the information of the batch
(full) gradient at each point and this information is a crucial key for the
convergence analysis of the generated sequence. However, this strength becomes
a weakness when it comes to machine learning and high dimensional statistical
applications because calculating the batch gradient at each iteration is
computationally expensive or impractical. Fortunately, in these applications
the objective function has a summation structure that can be taken advantage of
to approximate the batch gradient by the stochastic mini-batch gradient. In
this paper, we study the mini-batch Stochastic IHT (SIHT) algorithm for solving
the sparse optimizations. As opposed to previous works where increasing and
variable mini-batch size is necessary for derivation, we fix the mini-batch
size according to a lower bound that we derive and show our work. To prove
stochastic convergence of the objective value function we first establish a
critical sparse stochastic gradient descent property. Using this stochastic
gradient descent property we show that the sequence generated by the stochastic
mini-batch SIHT is a supermartingale sequence and converges with probability
one. Unlike previous work we do not assume the function to be a restricted
strongly convex. To the best of our knowledge, in the regime of sparse
optimization, this is the first time in the literature that it is shown that
the sequence of the stochastic function values converges with probability one
by fixing the mini-batch size for all steps.

In this paper, we propose a probabilistic model for computing an
interpolative decomposition (ID) in which each column of the observed matrix
has its own priority or importance, so that the end result of the decomposition
finds a set of features that are representative of the entire set of features,
and the selected features also have higher priority than others. This approach
is commonly used for low-rank approximation, feature selection, and extracting
hidden patterns in data, where the matrix factors are latent variables
associated with each data dimension. Gibbs sampling for Bayesian inference is
applied to carry out the optimization. We evaluate the proposed models on
real-world datasets, including ten Chinese A-share stocks, and demonstrate that
the proposed Bayesian ID algorithm with intervention (IID) produces comparable
reconstructive errors to existing Bayesian ID algorithms while selecting
features with higher scores or priority.

We show that quantum states with "low stabilizer complexity" can be
efficiently distinguished from Haar-random. Specifically, given an $n$-qubit
pure state $|\psi\rangle$, we give an efficient algorithm that distinguishes
whether $|\psi\rangle$ is (i) Haar-random or (ii) a state with stabilizer
fidelity at least $\frac{1}{k}$ (i.e., has fidelity at least $\frac{1}{k}$ with
some stabilizer state), promised that one of these is the case. With black-box
access to $|\psi\rangle$, our algorithm uses $O\!\left( k^{12}
\log(1/\delta)\right)$ copies of $|\psi\rangle$ and $O\!\left(n k^{12}
\log(1/\delta)\right)$ time to succeed with probability at least $1-\delta$,
and, with access to a state preparation unitary for $|\psi\rangle$ (and its
inverse), $O\!\left( k^{3} \log(1/\delta)\right)$ queries and $O\!\left(n k^{3}
\log(1/\delta)\right)$ time suffice.
  As a corollary, we prove that $\omega(\log(n))$ $T$-gates are necessary for
any Clifford+$T$ circuit to prepare computationally pseudorandom quantum
states, a first-of-its-kind lower bound.

Motion transfer aims to transfer the motion of a driving video to a source
image. When there are considerable differences between object in the driving
video and that in the source image, traditional single domain motion transfer
approaches often produce notable artifacts; for example, the synthesized image
may fail to preserve the human shape of the source image (cf . Fig. 1 (a)). To
address this issue, in this work, we propose a Motion and Appearance Adaptation
(MAA) approach for cross-domain motion transfer, in which we regularize the
object in the synthesized image to capture the motion of the object in the
driving frame, while still preserving the shape and appearance of the object in
the source image. On one hand, considering the object shapes of the synthesized
image and the driving frame might be different, we design a shape-invariant
motion adaptation module that enforces the consistency of the angles of object
parts in two images to capture the motion information. On the other hand, we
introduce a structure-guided appearance consistency module designed to
regularize the similarity between the corresponding patches of the synthesized
image and the source image without affecting the learned motion in the
synthesized image. Our proposed MAA model can be trained in an end-to-end
manner with a cyclic reconstruction loss, and ultimately produces a
satisfactory motion transfer result (cf . Fig. 1 (b)). We conduct extensive
experiments on human dancing dataset Mixamo-Video to Fashion-Video and human
face dataset Vox-Celeb to Cufs; on both of these, our MAA model outperforms
existing methods both quantitatively and qualitatively.

With an increasing focus on data privacy, there have been pilot studies on
recommender systems in a federated learning (FL) framework, where multiple
parties collaboratively train a model without sharing their data. Most of these
studies assume that the conventional FL framework can fully protect user
privacy. However, there are serious privacy risks in matrix factorization in
federated recommender systems based on our study. This paper first provides a
rigorous theoretical analysis of the server reconstruction attack in four
scenarios in federated recommender systems, followed by comprehensive
experiments. The empirical results demonstrate that the FL server could infer
users' information with accuracy >80% based on the uploaded gradients from FL
nodes. The robustness analysis suggests that our reconstruction attack analysis
outperforms the random guess by >30% under Laplace noises with b no larger than
0.5 for all scenarios. Then, the paper proposes a new privacy-preserving
framework based on homomorphic encryption, Privacy-Preserving Multi-View Matrix
Factorization (PrivMVMF), to enhance user data privacy protection in federated
recommender systems. The proposed PrivMVMF is successfully implemented and
tested thoroughly with the MovieLens dataset.

Guaranteeing real-time and accurate object detection simultaneously is
paramount in autonomous driving environments. However, the existing object
detection neural network systems are characterized by a tradeoff between
computation time and accuracy, making it essential to optimize such a tradeoff.
Fortunately, in many autonomous driving environments, images come in a
continuous form, providing an opportunity to use optical flow. In this paper,
we improve the performance of an object detection neural network utilizing
optical flow estimation. In addition, we propose a Lyapunov optimization
framework for time-average performance maximization subject to stability. It
adaptively determines whether to use optical flow to suit the dynamic vehicle
environment, thereby ensuring the vehicle's queue stability and the
time-average maximum performance simultaneously. To verify the key ideas, we
conduct numerical experiments with various object detection neural networks and
optical flow estimation networks. In addition, we demonstrate the
self-configurable stabilized detection with YOLOv3-tiny and FlowNet2-S, which
are the real-time object detection network and an optical flow estimation
network, respectively. In the demonstration, our proposed framework improves
the accuracy by 3.02%, the number of detected objects by 59.6%, and the queue
stability for computing capabilities.

In real-world applications, Federated Learning (FL) meets two challenges: (1)
scalability, especially when applied to massive IoT networks; and (2) how to be
robust against an environment with heterogeneous data. Realizing the first
problem, we aim to design a novel FL framework named Full-stack FL (F2L). More
specifically, F2L utilizes a hierarchical network architecture, making
extending the FL network accessible without reconstructing the whole network
system. Moreover, leveraging the advantages of hierarchical network design, we
propose a new label-driven knowledge distillation (LKD) technique at the global
server to address the second problem. As opposed to current knowledge
distillation techniques, LKD is capable of training a student model, which
consists of good knowledge from all teachers' models. Therefore, our proposed
algorithm can effectively extract the knowledge of the regions' data
distribution (i.e., the regional aggregated models) to reduce the divergence
between clients' models when operating under the FL system with non-independent
identically distributed data. Extensive experiment results reveal that: (i) our
F2L method can significantly improve the overall FL efficiency in all global
distillations, and (ii) F2L rapidly achieves convergence as global distillation
stages occur instead of increasing on each communication cycle.

Graph Neural Networks (GNNs), which aggregate features from neighbors, are
widely used for graph-structured data processing due to their powerful
representation learning capabilities. It is generally believed that GNNs can
implicitly remove the non-predictive noises. However, the analysis of implicit
denoising effect in graph neural networks remains open. In this work, we
conduct a comprehensive theoretical study and analyze when and why the implicit
denoising happens in GNNs. Specifically, we study the convergence properties of
noise matrix. Our theoretical analysis suggests that the implicit denoising
largely depends on the connectivity, the graph size, and GNN architectures.
Moreover, we formally define and propose the adversarial graph signal denoising
(AGSD) problem by extending graph signal denoising problem. By solving such a
problem, we derive a robust graph convolution, where the smoothness of the node
representations and the implicit denoising effect can be enhanced. Extensive
empirical evaluations verify our theoretical analyses and the effectiveness of
our proposed model.

We consider the problem of learning a set of probability distributions from
the Bellman dynamics in distributional reinforcement learning~(RL) that learns
the whole return distribution compared with only its expectation in classical
RL. Despite its success to obtain superior performance, we still have a poor
understanding of how the value distribution in distributional RL works. In this
study, we analyze the optimization benefits of distributional RL by leverage of
additional value distribution information over classical RL in the Neural
Fitted Z-Iteration~(Neural FZI) framework. To begin with, we demonstrate that
the distribution loss of distributional RL has desirable smoothness
characteristics and hence enjoys stable gradients, which is in line with its
tendency to promote optimization stability. Furthermore, the acceleration
effect of distributional RL is revealed by decomposing the return distribution.
It turns out that distributional RL can perform favorably if the value
distribution approximation is appropriate, measured by the variance of gradient
estimates in each environment for any specific distributional RL algorithm.
Rigorous experiments validate the stable optimization behaviors of
distributional RL, contributing to its acceleration effects compared to
classical RL. The findings of our research illuminate how the value
distribution in distributional RL algorithms helps the optimization.

In this paper, we introduce an emerging quantum machine learning (QML)
framework to assist classical deep learning methods for biosignal processing
applications. Specifically, we propose a hybrid quantum-classical neural
network model that integrates a variational quantum circuit (VQC) into a deep
neural network (DNN) for electroencephalogram (EEG), electromyogram (EMG), and
electrocorticogram (ECoG) analysis. We demonstrate that the proposed quantum
neural network (QNN) achieves state-of-the-art performance while the number of
trainable parameters is kept small for VQC.

Classical algorithms are often not effective for solving nonconvex
optimization problems where local minima are separated by high barriers. In
this paper, we explore possible quantum speedups for nonconvex optimization by
leveraging the global effect of quantum tunneling. Specifically, we introduce a
quantum algorithm termed the quantum tunneling walk (QTW) and apply it to
nonconvex problems where local minima are approximately global minima. We show
that QTW achieves quantum speedup over classical stochastic gradient descents
(SGD) when the barriers between different local minima are high but thin and
the minima are flat. Based on this observation, we construct a specific
double-well landscape, where classical algorithms cannot efficiently hit one
target well knowing the other well but QTW can when given proper initial states
near the known well. Finally, we corroborate our findings with numerical

Large language models such as GPT-3 (Brown et al., 2020) can perform
arbitrary tasks without undergoing fine-tuning after being prompted with only a
few labeled examples. An arbitrary task can be reformulated as a natural
language prompt, and a language model can be asked to generate the completion,
indirectly performing the task in a paradigm known as prompt-based learning. To
date, emergent prompt-based learning capabilities have mainly been demonstrated
for unidirectional language models. However, bidirectional language models
pre-trained on denoising objectives such as masked language modeling produce
stronger learned representations for transfer learning. This motivates the
possibility of prompting bidirectional models, but their pre-training
objectives have made them largely incompatible with the existing prompting
paradigm. We present SAP (Sequential Autoregressive Prompting), a technique
that enables the prompting of bidirectional models. Utilizing the machine
translation task as a case study, we prompt the bidirectional mT5 model (Xue et
al., 2021) with SAP and demonstrate its few-shot and zero-shot translations
outperform the few-shot translations of unidirectional models like GPT-3 and
XGLM (Lin et al., 2021), despite mT5's approximately 50% fewer parameters. We
further show SAP is effective on question answering and summarization. For the
first time, our results demonstrate prompt-based learning is an emergent
property of a broader class of language models, rather than only unidirectional
models.

Detecting obstacles is crucial for safe and efficient autonomous driving. To
this end, we present NVRadarNet, a deep neural network (DNN) that detects
dynamic obstacles and drivable free space using automotive RADAR sensors. The
network utilizes temporally accumulated data from multiple RADAR sensors to
detect dynamic obstacles and compute their orientation in a top-down bird's-eye
view (BEV). The network also regresses drivable free space to detect
unclassified obstacles. Our DNN is the first of its kind to utilize sparse
RADAR signals in order to perform obstacle and free space detection in real
time from RADAR data only. The network has been successfully used for
perception on our autonomous vehicles in real self-driving scenarios. The
network runs faster than real time on an embedded GPU and shows good
generalization across geographic regions.

Deep learning has achieved outstanding performance for face recognition
benchmarks, but performance reduces significantly for low resolution (LR)
images. We propose an attention similarity knowledge distillation approach,
which transfers attention maps obtained from a high resolution (HR) network as
a teacher into an LR network as a student to boost LR recognition performance.
Inspired by humans being able to approximate an object's region from an LR
image based on prior knowledge obtained from HR images, we designed the
knowledge distillation loss using the cosine similarity to make the student
network's attention resemble the teacher network's attention. Experiments on
various LR face related benchmarks confirmed the proposed method generally
improved recognition performances on LR settings, outperforming
state-of-the-art results by simply transferring well-constructed attention
maps. The code and pretrained models are publicly available in the
https://github.com/gist-ailab/teaching-where-to-look.

Research on text-to-image generation has witnessed significant progress in
generating diverse and photo-realistic images, driven by diffusion and
auto-regressive models trained on large-scale image-text data. Though
state-of-the-art models can generate high-quality images of common entities,
they often have difficulty generating images of uncommon entities, such as
`Chortai (dog)' or `Picarones (food)'. To tackle this issue, we present the
Retrieval-Augmented Text-to-Image Generator (Re-Imagen), a generative model
that uses retrieved information to produce high-fidelity and faithful images,
even for rare or unseen entities. Given a text prompt, Re-Imagen accesses an
external multi-modal knowledge base to retrieve relevant (image, text) pairs,
and uses them as references to generate the image. With this retrieval step,
Re-Imagen is augmented with the knowledge of high-level semantics and low-level
visual details of the mentioned entities, and thus improves its accuracy in
generating the entities' visual appearances. We train Re-Imagen on a
constructed dataset containing (image, text, retrieval) triples to teach the
model to ground on both text prompt and retrieval. Furthermore, we develop a
new sampling strategy to interleave the classifier-free guidance for text and
retrieval condition to balance the text and retrieval alignment. Re-Imagen
achieves new SoTA FID results on two image generation benchmarks, such as COCO
(ie, FID = 5.25) and WikiImage (ie, FID = 5.82) without fine-tuning. To further
evaluate the capabilities of the model, we introduce EntityDrawBench, a new
benchmark that evaluates image generation for diverse entities, from frequent
to rare, across multiple visual domains. Human evaluation on EntityDrawBench
shows that Re-Imagen performs on par with the best prior models in
photo-realism, but with significantly better faithfulness, especially on less
frequent entities.

Semi-inclusive deep-inelastic scattering (SIDIS) at the Electron-Ion Collider
will allow for precise mapping of the 3D momentum and spin structure of
nucleons and nuclei over a large kinematic region. In this contribution, we
demonstrate methods utilizing the hadronic final state and scattered electron,
as well as machine learning, to more reliably reconstruct the virtual photon
four momentum and SIDIS kinematics across the inclusive DIS coverage at the
EIC.

Many actor-critic deep reinforcement learning (DRL) algorithms have achieved
cutting-edge performance in tackling various challenging reinforcement learning
(RL) problems, including complex control tasks with high-dimensional continuous
state and action spaces. Despite of widely reported success, existing DRL
algorithms often suffer from the ineffective exploration issue, resulting in
limited learning stability and performance. To address this limitation, several
ensemble DRL algorithms have been proposed recently to boost exploration and
stabilize the learning process. However, many existing ensemble algorithms are
designed to train each base learner individually without controlling explicitly
the collaboration among the trained base learners. In this paper, we propose a
new technique to train an ensemble of base learners based on the multi-step
integration methods. The new multi-step training technique enables us to
develop a new hierarchical training algorithm for ensemble DRL that promotes
inter-learner collaboration through explicit inter-learner parameter sharing.
The design of our new algorithm is verified theoretically. The algorithm is
also shown empirically to outperform several cutting-edge DRL algorithms on
multiple benchmark RL problems.

Object removal and image inpainting in facial images is a task in which
objects that occlude a facial image are specifically targeted, removed, and
replaced by a properly reconstructed facial image. Two different approaches
utilizing U-net and modulated generator respectively have been widely endorsed
for this task for their unique advantages but notwithstanding each method's
innate disadvantages. U-net, a conventional approach for conditional GANs,
retains fine details of unmasked regions but the style of the reconstructed
image is inconsistent with the rest of the original image and only works
robustly when the size of the occluding object is small enough. In contrast,
the modulated generative approach can deal with a larger occluded area in an
image and provides {a} more consistent style, yet it usually misses out on most
of the detailed features. This trade-off between these two models necessitates
an invention of a model that can be applied to any size of mask while
maintaining a consistent style and preserving minute details of facial
features. Here, we propose Semantics-Guided Inpainting Network (SGIN) which
itself is a modification of the modulated generator, aiming to take advantage
of its advanced generative capability and preserve the high-fidelity details of
the original image. By using the guidance of a semantic map, our model is
capable of manipulating facial features which grants direction to the
one-to-many problem for further practicability.

Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance
in many data mining and machine learning tasks, including dimensionality
reduction, outlier detection, similarity search and subspace clustering.
However, since their convergence generally requires sample sizes (that is,
neighborhood sizes) on the order of hundreds of points, existing ID estimation
methods may have only limited usefulness for applications in which the data
consists of many natural groups of small size. In this paper, we propose a
local ID estimation strategy stable even for `tight' localities consisting of
as few as 20 sample points. The estimator applies MLE techniques over all
available pairwise distances among the members of the sample, based on a recent
extreme-value-theoretic model of intrinsic dimensionality, the Local Intrinsic
Dimension (LID). Our experimental results show that our proposed estimation
technique can achieve notably smaller variance, while maintaining comparable
levels of bias, at much smaller sample sizes than state-of-the-art estimators.

Synthetic data generated by generative models can enhance the performance and
capabilities of data-hungry deep learning models in medical imaging. However,
there is (1) limited availability of (synthetic) datasets and (2) generative
models are complex to train, which hinders their adoption in research and
clinical applications. To reduce this entry barrier, we propose medigan, a
one-stop shop for pretrained generative models implemented as an open-source
framework-agnostic Python library. medigan allows researchers and developers to
create, increase, and domain-adapt their training data in just a few lines of
code. Guided by design decisions based on gathered end-user requirements, we
implement medigan based on modular components for generative model (i)
execution, (ii) visualisation, (iii) search & ranking, and (iv) contribution.
The library's scalability and design is demonstrated by its growing number of
integrated and readily-usable pretrained generative models consisting of 21
models utilising 9 different Generative Adversarial Network architectures
trained on 11 datasets from 4 domains, namely, mammography, endoscopy, x-ray,
and MRI. Furthermore, 3 applications of medigan are analysed in this work,
which include (a) enabling community-wide sharing of restricted data, (b)
investigating generative model evaluation metrics, and (c) improving clinical
downstream tasks. In (b), extending on common medical image synthesis
assessment and reporting standards, we show Fr\'echet Inception Distance
variability based on image normalisation and radiology-specific feature
extraction.

Reasoning is a fundamental problem for computers and deeply studied in
Artificial Intelligence. In this paper, we specifically focus on answering
multi-hop logical queries on Knowledge Graphs (KGs). This is a complicated task
because, in real-world scenarios, the graphs tend to be large and incomplete.
Most previous works have been unable to create models that accept full
First-Order Logical (FOL) queries, which include negative queries, and have
only been able to process a limited set of query structures. Additionally, most
methods present logic operators that can only perform the logical operation
they are made for. We introduce a set of models that use Neural Networks to
create one-point vector embeddings to answer the queries. The versatility of
neural networks allows the framework to handle FOL queries with Conjunction
($\wedge$), Disjunction ($\vee$) and Negation ($\neg$) operators. We
demonstrate experimentally the performance of our model through extensive
experimentation on well-known benchmarking datasets. Besides having more
versatile operators, the models achieve a 10\% relative increase over the best
performing state of the art and more than 30\% over the original method based
on single-point vector embeddings.

Dynamic movement primitives are widely used for learning skills which can be
demonstrated to a robot by a skilled human or controller. While their
generalization capabilities and simple formulation make them very appealing to
use, they possess no strong guarantees to satisfy operational safety
constraints for a task. In this paper, we present constrained dynamic movement
primitives (CDMP) which can allow for constraint satisfaction in the robot
workspace. We present a formulation of a non-linear optimization to perturb the
DMP forcing weights regressed by locally-weighted regression to admit a Zeroing
Barrier Function (ZBF), which certifies workspace constraint satisfaction. We
demonstrate the proposed CDMP under different constraints on the end-effector
movement such as obstacle avoidance and workspace constraints on a physical
robot. A video showing the implementation of the proposed algorithm using
different manipulators in different environments could be found here
https://youtu.be/hJegJJkJfys.

Data is the lifeblood of modern machine learning systems, including for those
in Music Information Retrieval (MIR). However, MIR has long been mired by small
datasets and unreliable labels. In this work, we propose to break this
bottleneck using generative modeling. By pipelining a generative model of notes
(Coconet trained on Bach Chorales) with a structured synthesis model of chamber
ensembles (MIDI-DDSP trained on URMP), we demonstrate a system capable of
producing unlimited amounts of realistic chorale music with rich annotations
including mixes, stems, MIDI, note-level performance attributes (staccato,
vibrato, etc.), and even fine-grained synthesis parameters (pitch, amplitude,
etc.). We call this system the Chamber Ensemble Generator (CEG), and use it to
generate a large dataset of chorales from four different chamber ensembles
(CocoChorales). We demonstrate that data generated using our approach improves
state-of-the-art models for music transcription and source separation, and we
release both the system and the dataset as an open-source foundation for future
work in the MIR community.

Marker-based Optical Motion Capture (OMC) systems and the associated
musculoskeletal (MSK) modeling predictions have offered the ability to gain
insights into in vivo joint and muscle loading non-invasively as well as aid
clinical decision-making. However, an OMC system is lab-based, expensive, and
requires a line of sight. A widely used alternative is the Inertial Motion
Capture (IMC) system, which is portable, user-friendly, and relatively low
cost, although it is not as accurate as an OMC system. Irrespective of the
choice of motion capture technique, one needs to use an MSK model to obtain the
kinematic and kinetic outputs, which is a computationally expensive tool
increasingly well approximated by machine learning (ML) methods. Here, we
present an ML approach to map IMC data to the human upper-extremity MSK outputs
computed from OMC input data. Essentially, we attempt to predict high-quality
MSK outputs from the relatively easier-to-obtain IMC data. We use OMC and IMC
data simultaneously collected for the same subjects to train an ML
(feed-forward multi-layer perceptron) model that predicts OMC-based MSK outputs
from IMC measurements. We demonstrate that our ML predictions have a high
degree of agreement with the desired OMC-based MSK estimates. Thus, this
approach will be instrumental in getting the technology from 'lab to field'
where OMC-based systems are infeasible.

Convolutional neural networks (CNNs) are one of the most popular models of
Artificial Neural Networks (ANN)s in Computer Vision (CV). A variety of
CNN-based structures were developed by researchers to solve problems like image
classification, object detection, and image similarity measurement. Although
CNNs have shown their value in most cases, they still have a downside: they
easily overfit when there are not enough samples in the dataset. Most medical
image datasets are examples of such a dataset. Additionally, many datasets also
contain both designed features and images, but CNNs can only deal with images
directly. This represents a missed opportunity to leverage additional
information. For this reason, we propose a new structure of CNN-based model:
CompNet, a composite convolutional neural network. This is a specially designed
neural network that accepts combinations of images and designed features as
input in order to leverage all available information. The novelty of this
structure is that it uses learned features from images to weight designed
features in order to gain all information from both images and designed
features. With the use of this structure on classification tasks, the results
indicate that our approach has the capability to significantly reduce
overfitting. Furthermore, we also found several similar approaches proposed by
other researchers that can combine images and designed features. To make
comparison, we first applied those similar approaches on LIDC and compared the
results with the CompNet results, then we applied our CompNet on the datasets
that those similar approaches originally used in their works and compared the
results with the results they proposed in their papers. All these comparison
results showed that our model outperformed those similar approaches on
classification tasks either on LIDC dataset or on their proposed datasets.

Quantum machine learning (QML) is the use of quantum computing for the
computation of machine learning algorithms. With the prevalence and importance
of classical data, a hybrid quantum-classical approach to QML is called for.
Parameterized Quantum Circuits (PQCs), and particularly Quantum Kernel PQCs,
are generally used in the hybrid approach to QML. In this paper we discuss some
important aspects of PQCs with quantum kernels including PQCs, quantum kernels,
quantum kernels with quantum advantage, and the trainability of quantum
kernels. We conclude that quantum kernels with hybrid kernel methods, a.k.a.
quantum kernel methods, offer distinct advantages as a hybrid approach to QML.
Not only do they apply to Noisy Intermediate-Scale Quantum (NISQ) devices, but
they also can be used to solve all types of machine learning problems including
regression, classification, clustering, and dimension reduction. Furthermore,
beyond quantum utility, quantum advantage can be attained if the quantum
kernels, i.e., the quantum feature encodings, are classically intractable.

An insufficient number of training samples is a common problem in neural
network applications. While data augmentation methods require at least a
minimum number of samples, we propose a novel, rendering-based pipeline for
synthesizing annotated data sets. Our method does not modify existing samples
but synthesizes entirely new samples. The proposed rendering-based pipeline is
capable of generating and annotating synthetic and partly-real image and video
data in a fully automatic procedure. Moreover, the pipeline can aid the
acquisition of real data. The proposed pipeline is based on a rendering
process. This process generates synthetic data. Partly-real data bring the
synthetic sequences closer to reality by incorporating real cameras during the
acquisition process. The benefits of the proposed data generation pipeline,
especially for machine learning scenarios with limited available training data,
are demonstrated by an extensive experimental validation in the context of
automatic license plate recognition. The experiments demonstrate a significant
reduction of the character error rate and miss rate from 73.74% and 100% to
14.11% and 41.27% respectively, compared to an OCR algorithm trained on a real
data set solely. These improvements are achieved by training the algorithm on
synthesized data solely. When additionally incorporating real data, the error
rates can be decreased further. Thereby, the character error rate and miss rate
can be reduced to 11.90% and 39.88% respectively. All data used during the
experiments as well as the proposed rendering-based pipeline for the automated
data generation is made publicly available under (URL will be revealed upon
publication).

Teaching physics in real-life contexts continues to be a challenge for
teachers at different educational levels. In this article, three Context-Rich
Problems are proposed to be implemented in the classroom for higher education,
using the explosion that occurred in Beirut as a case study. These problems
require the search and analysis of real data, integrating technologies as tools
to learn physics. In particular, the analysis of images, videos, maps and audio
recordings is suggested. The proposed activities are designed to promote active
learning of classical mechanics subjects and the development of collaborative
skills. The results of each problem allow a discussion based on scientific
evidence in the classroom.

Optimal transport (OT) offers a versatile framework to compare complex data
distributions in a geometrically meaningful way. Traditional methods for
computing the Wasserstein distance and geodesic between probability measures
require mesh-dependent domain discretization and suffer from the
curse-of-dimensionality. We present GeONet, a mesh-invariant deep neural
operator network that learns the non-linear mapping from the input pair of
initial and terminal distributions to the Wasserstein geodesic connecting the
two endpoint distributions. In the offline training stage, GeONet learns the
saddle point optimality conditions for the dynamic formulation of the OT
problem in the primal and dual spaces that are characterized by a coupled PDE
system. The subsequent inference stage is instantaneous and can be deployed for
real-time predictions in the online learning setting. We demonstrate that
GeONet achieves comparable testing accuracy to the standard OT solvers on a
simulation example and the CIFAR-10 dataset with considerably reduced
inference-stage computational cost by orders of magnitude.

Methods such as Layer Normalization (LN) and Batch Normalization (BN) have
proven to be effective in improving the training of Recurrent Neural Networks
(RNNs). However, existing methods normalize using only the instantaneous
information at one particular time step, and the result of the normalization is
a preactivation state with a time-independent distribution. This implementation
fails to account for certain temporal differences inherent in the inputs and
the architecture of RNNs. Since these networks share weights across time steps,
it may also be desirable to account for the connections between time steps in
the normalization scheme. In this paper, we propose a normalization method
called Assorted-Time Normalization (ATN), which preserves information from
multiple consecutive time steps and normalizes using them. This setup allows us
to introduce longer time dependencies into the traditional normalization
methods without introducing any new trainable parameters. We present
theoretical derivations for the gradient propagation and prove the weight
scaling invariance property. Our experiments applying ATN to LN demonstrate
consistent improvement on various tasks, such as Adding, Copying, and Denoise
Problems and Language Modeling Problems.

3D object detection is an essential part of automated driving, and deep
neural networks (DNNs) have achieved state-of-the-art performance for this
task. However, deep models are notorious for assigning high confidence scores
to out-of-distribution (OOD) inputs, that is, inputs that are not drawn from
the training distribution. Detecting OOD inputs is challenging and essential
for the safe deployment of models. OOD detection has been studied extensively
for the classification task, but it has not received enough attention for the
object detection task, specifically LiDAR-based 3D object detection. In this
paper, we focus on the detection of OOD inputs for LiDAR-based 3D object
detection. We formulate what OOD inputs mean for object detection and propose
to adapt several OOD detection methods for object detection. We accomplish this
by our proposed feature extraction method. To evaluate OOD detection methods,
we develop a simple but effective technique of generating OOD objects for a
given object detection model. Our evaluation based on the KITTI dataset shows
that different OOD detection methods have biases toward detecting specific OOD
objects. It emphasizes the importance of combined OOD detection methods and
more research in this direction.

High-performance deep learning methods typically rely on large annotated
training datasets, which are difficult to obtain in many clinical applications
due to the high cost of medical image labeling. Existing data assessment
methods commonly require knowing the labels in advance, which are not feasible
to achieve our goal of 'knowing which data to label.' To this end, we formulate
and propose a novel and efficient data assessment strategy, EXponentiAl
Marginal sINgular valuE (EXAMINE) score, to rank the quality of unlabeled
medical image data based on their useful latent representations extracted via
Self-supervised Learning (SSL) networks. Motivated by theoretical implication
of SSL embedding space, we leverage a Masked Autoencoder for feature
extraction. Furthermore, we evaluate data quality based on the marginal change
of the largest singular value after excluding the data point in the dataset. We
conduct extensive experiments on a pathology dataset. Our results indicate the
effectiveness and efficiency of our proposed methods for selecting the most
valuable data to label.

In recent years, hybrid design strategies combining machine learning (ML)
with electromagnetic optimization algorithms have emerged as a new paradigm for
the inverse design of photonic structures and devices. While a trained,
data-driven neural network can rapidly identify solutions near the global
optimum with a given dataset's design space, an iterative optimization
algorithm can further refine the solution and overcome dataset limitations.
Furthermore, such hybrid ML-optimization methodologies can reduce computational
costs and expedite the discovery of novel electromagnetic components. However,
existing hybrid ML-optimization methods have yet to optimize across both
materials and geometries in a single integrated and user-friendly environment.
In addition, due to the challenge of acquiring large datasets for ML, as well
as the exponential growth of isolated models being trained for photonics
design, there is a need to standardize the ML-optimization workflow while
making the pre-trained models easily accessible. Motivated by these challenges,
here we introduce DeepAdjoint, a general-purpose, open-source, and
multi-objective "all-in-one" global photonics inverse design application
framework which integrates pre-trained deep generative networks with
state-of-the-art electromagnetic optimization algorithms such as the adjoint
variables method. DeepAdjoint allows a designer to specify an arbitrary optical
design target, then obtain a photonic structure that is robust to fabrication
tolerances and possesses the desired optical properties - all within a single
user-guided application interface. Our framework thus paves a path towards the
systematic unification of ML and optimization algorithms for photonic inverse
design.

Neural networks are widely used for almost any task of recognizing image
content. Even though much effort has been put into investigating efficient
network architectures, optimizers, and training strategies, the influence of
image interpolation on the performance of neural networks is not well studied.
Furthermore, research has shown that neural networks are often sensitive to
minor changes in the input image leading to drastic drops of their performance.
Therefore, we propose the use of keypoint agnostic frequency selective
mesh-to-grid resampling (FSMR) for the processing of input data for neural
networks in this paper. This model-based interpolation method already showed
that it is capable of outperforming common interpolation methods in terms of
PSNR. Using an extensive experimental evaluation we show that depending on the
network architecture and classification task the application of FSMR during
training aids the learning process. Furthermore, we show that the usage of FSMR
in the application phase is beneficial. The classification accuracy can be
increased by up to 4.31 percentage points for ResNet50 and the Oxflower17
dataset.

Learning mappings between infinite-dimensional function spaces has achieved
empirical success in many disciplines of machine learning, including generative
modeling, functional data analysis, causal inference, and multi-agent
reinforcement learning. In this paper, we study the statistical limit of
learning a Hilbert-Schmidt operator between two infinite-dimensional Sobolev
reproducing kernel Hilbert spaces. We establish the information-theoretic lower
bound in terms of the Sobolev Hilbert-Schmidt norm and show that a
regularization that learns the spectral components below the bias contour and
ignores the ones that are above the variance contour can achieve the optimal
learning rate. At the same time, the spectral components between the bias and
variance contours give us flexibility in designing computationally feasible
machine learning algorithms. Based on this observation, we develop a multilevel
kernel operator learning algorithm that is optimal when learning linear
operators between infinite-dimensional function spaces.

We consider reinforcement learning in an environment modeled by an episodic,
finite, stage-dependent Markov decision process of horizon $H$ with $S$ states,
and $A$ actions. The performance of an agent is measured by the regret after
interacting with the environment for $T$ episodes. We propose an optimistic
posterior sampling algorithm for reinforcement learning (OPSRL), a simple
variant of posterior sampling that only needs a number of posterior samples
logarithmic in $H$, $S$, $A$, and $T$ per state-action pair. For OPSRL we
guarantee a high-probability regret bound of order at most
$\widetilde{\mathcal{O}}(\sqrt{H^3SAT})$ ignoring $\text{poly}\log(HSAT)$
terms. The key novel technical ingredient is a new sharp anti-concentration
inequality for linear forms which may be of independent interest. Specifically,
we extend the normal approximation-based lower bound for Beta distributions by
Alfers and Dinges [1984] to Dirichlet distributions. Our bound matches the
lower bound of order $\Omega(\sqrt{H^3SAT})$, thereby answering the open
problems raised by Agrawal and Jia [2017b] for the episodic setting.

In this paper, we introduce Masked Multi-Step Multivariate Forecasting
(MMMF), a novel and general self-supervised learning framework for time series
forecasting with known future information. In many real-world forecasting
scenarios, some future information is known, e.g., the weather information when
making a short-to-mid-term electricity demand forecast, or the oil price
forecasts when making an airplane departure forecast. Existing machine learning
forecasting frameworks can be categorized into (1) sample-based approaches
where each forecast is made independently, and (2) time series regression
approaches where the future information is not fully incorporated. To overcome
the limitations of existing approaches, we propose MMMF, a framework to train
any neural network model capable of generating a sequence of outputs, that
combines both the temporal information from the past and the known information
about the future to make better predictions. Experiments are performed on two
real-world datasets for (1) mid-term electricity demand forecasting, and (2)
two-month ahead flight departures forecasting. They show that the proposed MMMF
framework outperforms not only sample-based methods but also existing time
series forecasting models with the exact same base models. Furthermore, once a
neural network model is trained with MMMF, its inference speed is similar to
that of the same model trained with traditional regression formulations, thus
making MMMF a better alternative to existing regression-trained time series
forecasting models if there is some available future information.

VQ (Vendor Qualification) and IOQ (Installation and Operation Qualification)
audits are implemented in warehouses to ensure all equipment being turned over
in the fulfillment network meets the quality standards. Audit checks are likely
to be skipped if there are many checks to be performed in a short time. In
addition, exploratory data analysis reveals several instances of similar checks
being performed on the same assets and thus, duplicating the effort. In this
work, Natural Language Processing and Machine Learning are applied to trim a
large checklist dataset for a network of warehouses by identifying similarities
and duplicates, and predict the non-critical ones with a high passing rate. The
study proposes ML classifiers to identify checks which have a high passing
probability of IOQ and VQ and assign priorities to checks to be prioritized
when the time is not available to perform all checks. This research proposes
using NLP-based BlazingText classifier to throttle the checklists with a high
passing rate, which can reduce 10%-37% of the checks and achieve significant
cost reduction. The applied algorithm over performs Random Forest and Neural
Network classifiers and achieves an area under the curve of 90%. Because of
imbalanced data, down-sampling and upweighting have shown a positive impact on
the models' accuracy using F1 score, which improve from 8% to 75%. In addition,
the proposed duplicate detection process identifies 17% possible redundant
checks to be trimmed.

When applied to autonomous vehicle settings, action recognition can help
enrich an environment model's understanding of the world and improve plans for
future action. Towards these improvements in autonomous vehicle
decision-making, we propose in this work a novel two-stage online action
recognition system, termed RADACS. RADACS formulates the problem of active
agent detection and adapts ideas about actor-context relations from human
activity recognition in a straightforward two-stage pipeline for action
detection and classification. We show that our proposed scheme can outperform
the baseline on the ICCV2021 Road Challenge dataset and by deploying it on a
real vehicle platform, we demonstrate how a higher-order understanding of agent
actions in an environment can improve decisions on a real autonomous vehicle.

Grand efforts in neuroscience are working toward mapping the connectomes of
many new species, including the near completion of the Drosophila melanogaster.
It is important to ask whether these models could benefit artificial
intelligence. In this work we ask two fundamental questions: (1) where and when
biological connectomes can provide use in machine learning, (2) which design
principles are necessary for extracting a good representation of the
connectome. Toward this end, we translate the motor circuit of the C. Elegans
nematode into artificial neural networks at varying levels of biophysical
realism and evaluate the outcome of training these networks on motor and
non-motor behavioral tasks. We demonstrate that biophysical realism need not be
upheld to attain the advantages of using biological circuits. We also establish
that, even if the exact wiring diagram is not retained, the architectural
statistics provide a valuable prior. Finally, we show that while the C. Elegans
locomotion circuit provides a powerful inductive bias on locomotion problems,
its structure may hinder performance on tasks unrelated to locomotion such as
visual classification problems.

The performance of nuclear reactors and other nuclear systems depends on a
precise understanding of the neutron interaction cross sections for materials
used in these systems. These cross sections exhibit resonant structure whose
shape is determined in part by the angular momentum quantum numbers of the
resonances. The correct assignment of the quantum numbers of neutron resonances
is, therefore, paramount. In this project, we apply machine learning to
automate the quantum number assignments using only the resonances' energies and
widths and not relying on detailed transmission or capture measurements. The
classifier used for quantum number assignment is trained using stochastically
generated resonance sequences whose distributions mimic those of real data. We
explore the use of several physics-motivated features for training our
classifier. These features amount to out-of-distribution tests of a given
resonance's widths and resonance-pair spacings. We pay special attention to
situations where either capture widths cannot be trusted for classification
purposes or where there is insufficient information to classify resonances by
the total spin $J$. We demonstrate the efficacy of our classification approach
using simulated and actual $^{52}$Cr resonance data.

Graph Neural Networks (GNNs) are a popular class of machine learning models.
Inspired by the learning to explain (L2X) paradigm, we propose L2XGNN, a
framework for explainable GNNs which provides faithful explanations by design.
L2XGNN learns a mechanism for selecting explanatory subgraphs (motifs) which
are exclusively used in the GNNs message-passing operations. L2XGNN is able to
select, for each input graph, a subgraph with specific properties such as being
sparse and connected. Imposing such constraints on the motifs often leads to
more interpretable and effective explanations. Experiments on several datasets
suggest that L2XGNN achieves the same classification accuracy as baseline
methods using the entire input graph while ensuring that only the provided
explanations are used to make predictions. Moreover, we show that L2XGNN is
able to identify motifs responsible for the graph's properties it is intended
to predict.

In edge computing, users' service profiles must be migrated in response to
user mobility. Reinforcement learning (RL) frameworks have been proposed to do
so. Nevertheless, these frameworks do not consider occasional server failures,
which although rare, can prevent the smooth and safe functioning of edge
computing users' latency sensitive applications such as autonomous driving and
real-time obstacle detection, because users' computing jobs can no longer be
completed. As these failures occur at a low probability, it is difficult for RL
algorithms, which are inherently data-driven, to learn an optimal service
migration solution for both the typical and rare event scenarios. Therefore, we
introduce a rare events adaptive resilience framework FIRE, which integrates
importance sampling into reinforcement learning to place backup services. We
sample rare events at a rate proportional to their contribution to the value
function, to learn an optimal policy. Our framework balances service migration
trade-offs between delay and migration costs, with the costs of failure and the
costs of backup placement and migration. We propose an importance sampling
based Q-learning algorithm, and prove its boundedness and convergence to
optimality. Following which we propose novel eligibility traces, linear
function approximation and deep Q-learning versions of our algorithm to ensure
it scales to real-world scenarios. We extend our framework to cater to users
with different risk tolerances towards failure. Finally, we use trace driven
experiments to show that our algorithm gives cost reductions in the event of
failures.

We address object tracking by radar and the robustness of the current
state-of-the-art methods to process outliers. The standard tracking algorithms
extract detections from radar image space to use it in the filtering stage.
Filtering is performed by a Kalman filter, which assumes Gaussian distributed
noise. However, this assumption does not account for large modeling errors and
results in poor tracking performance during abrupt motions. We take the
Gaussian Sum Filter (single-object variant of the Multi Hypothesis Tracker) as
our baseline and propose a modification by modelling process noise with a
distribution that has heavier tails than a Gaussian. Variational Bayes provides
a fast, computationally cheap inference algorithm. Our simulations show that -
in the presence of process outliers - the robust tracker outperforms the
Gaussian Sum filter when tracking single objects.

Decentralized learning algorithms enable the training of deep learning models
over large distributed datasets generated at different devices and locations,
without the need for a central server. In practical scenarios, the distributed
datasets can have significantly different data distributions across the agents.
The current state-of-the-art decentralized algorithms mostly assume the data
distributions to be Independent and Identically Distributed (IID). This paper
focuses on improving decentralized learning over non-IID data distributions
with minimal compute and memory overheads. We propose Neighborhood Gradient
Clustering (NGC), a novel decentralized learning algorithm that modifies the
local gradients of each agent using self- and cross-gradient information. In
particular, the proposed method replaces the local gradients of the model with
the weighted mean of the self-gradients, model-variant cross-gradients
(derivatives of the received neighbors' model parameters with respect to the
local dataset), and data-variant cross-gradients (derivatives of the local
model with respect to its neighbors' datasets). Further, we present CompNGC, a
compressed version of NGC that reduces the communication overhead by $32
\times$ by compressing the cross-gradients. We demonstrate the empirical
convergence and efficiency of the proposed technique over non-IID data
distributions sampled from the CIFAR-10 dataset on various model architectures
and graph topologies. Our experiments demonstrate that NGC and CompNGC
outperform the existing state-of-the-art (SoTA) decentralized learning
algorithm over non-IID data by $1-5\%$ with significantly less compute and
memory requirements. Further, we also show that the proposed NGC method
outperforms the baseline by $5-40\%$ with no additional communication.

For most natural language processing tasks, the dominant practice is to
finetune large pretrained transformer models (e.g., BERT) using smaller
downstream datasets. Despite the success of this approach, it remains unclear
to what extent these gains are attributable to the massive background corpora
employed for pretraining versus to the pretraining objectives themselves. This
paper introduces a large-scale study of self-pretraining, where the same
(downstream) training data is used for both pretraining and finetuning. In
experiments addressing both ELECTRA and RoBERTa models and 10 distinct
downstream datasets, we observe that self-pretraining rivals standard
pretraining on the BookWiki corpus (despite using around
$10\times$--$500\times$ less data), outperforming the latter on $7$ and $5$
datasets, respectively. Surprisingly, these task-specific pretrained models
often perform well on other tasks, including the GLUE benchmark. Our results
suggest that in many scenarios, performance gains attributable to pretraining
are driven primarily by the pretraining objective itself and are not always
attributable to the incorporation of massive datasets. These findings are
especially relevant in light of concerns about intellectual property and
offensive content in web-scale pretraining data.

Assuming unknown classes could be present during classification, the open set
recognition (OSR) task aims to classify an instance into a known class or
reject it as unknown. In this paper, we use a two-stage training strategy for
the OSR problems. In the first stage, we introduce a self-supervised feature
decoupling method that finds the content features of the input samples from the
known classes. Specifically, our feature decoupling approach learns a
representation that can be split into content features and transformation
features. In the second stage, we fine-tune the content features with the class
labels. The fine-tuned content features are then used for the OSR problems.
Moreover, we consider an unsupervised OSR scenario, where we cluster the
content features learned from the first stage. To measure representation
quality, we introduce intra-inter ratio (IIR). Our experimental results
indicate that our proposed self-supervised approach outperforms others in image
and malware OSR problems. Also, our analyses indicate that IIR is correlated
with OSR performance.

Automated optic disc (OD) and optic cup (OC) segmentation in fundus images is
relevant to efficiently measure the vertical cup-to-disc ratio (vCDR), a
biomarker commonly used in ophthalmology to determine the degree of
glaucomatous optic neuropathy. In general this is solved using coarse-to-fine
deep learning algorithms in which a first stage approximates the OD and a
second one uses a crop of this area to predict OD/OC masks. While this approach
is widely applied in the literature, there are no studies analyzing its real
contribution to the results. In this paper we present a comprehensive analysis
of different coarse-to-fine designs for OD/OC segmentation using 5 public
databases, both from a standard segmentation perspective and for estimating the
vCDR for glaucoma assessment. Our analysis shows that these algorithms not
necessarily outperfom standard multi-class single-stage models, especially when
these are learned from sufficiently large and diverse training sets.
Furthermore, we noticed that the coarse stage achieves better OD segmentation
results than the fine one, and that providing OD supervision to the second
stage is essential to ensure accurate OC masks. Moreover, both the single-stage
and two-stage models trained on a multi-dataset setting showed results in pair
or even better than other state-of-the-art alternatives, while ranking first in
REFUGE for OD/OC segmentation. Finally, we evaluated the models for vCDR
prediction in comparison with six ophthalmologists on a subset of AIROGS
images, to understand them in the context of inter-observer variability. We
noticed that vCDR estimates recovered both from single-stage and coarse-to-fine
models can obtain good glaucoma detection results even when they are not highly
correlated with manual measurements from experts.

Transformer-based models, capable of learning better global dependencies,
have recently demonstrated exceptional representation learning capabilities in
computer vision and medical image analysis. Transformer reformats the image
into separate patches and realize global communication via the self-attention
mechanism. However, positional information between patches is hard to preserve
in such 1D sequences, and loss of it can lead to sub-optimal performance when
dealing with large amounts of heterogeneous tissues of various sizes in 3D
medical image segmentation. Additionally, current methods are not robust and
efficient for heavy-duty medical segmentation tasks such as predicting a large
number of tissue classes or modeling globally inter-connected tissues
structures. Inspired by the nested hierarchical structures in vision
transformer, we proposed a novel 3D medical image segmentation method (UNesT),
employing a simplified and faster-converging transformer encoder design that
achieves local communication among spatially adjacent patch sequences by
aggregating them hierarchically. We extensively validate our method on multiple
challenging datasets, consisting anatomies of 133 structures in brain, 14
organs in abdomen, 4 hierarchical components in kidney, and inter-connected
kidney tumors). We show that UNesT consistently achieves state-of-the-art
performance and evaluate its generalizability and data efficiency.
Particularly, the model achieves whole brain segmentation task complete ROI
with 133 tissue classes in single network, outperforms prior state-of-the-art
method SLANT27 ensembled with 27 network tiles, our model performance increases
the mean DSC score of the publicly available Colin and CANDI dataset from
0.7264 to 0.7444 and from 0.6968 to 0.7025, respectively.

We present Sparrow, an information-seeking dialogue agent trained to be more
helpful, correct, and harmless compared to prompted language model baselines.
We use reinforcement learning from human feedback to train our models with two
new additions to help human raters judge agent behaviour. First, to make our
agent more helpful and harmless, we break down the requirements for good
dialogue into natural language rules the agent should follow, and ask raters
about each rule separately. We demonstrate that this breakdown enables us to
collect more targeted human judgements of agent behaviour and allows for more
efficient rule-conditional reward models. Second, our agent provides evidence
from sources supporting factual claims when collecting preference judgements
over model statements. For factual questions, evidence provided by Sparrow
supports the sampled response 78% of the time. Sparrow is preferred more often
than baselines while being more resilient to adversarial probing by humans,
violating our rules only 8% of the time when probed. Finally, we conduct
extensive analyses showing that though our model learns to follow our rules it
can exhibit distributional biases.

In recent years, the geospatial industry has been developing at a steady
pace. This growth implies the addition of satellite constellations that produce
a copious supply of satellite imagery and other Remote Sensing data on a daily
basis. Sometimes, this information, even if in some cases we are referring to
publicly available data, it sits unaccounted for due to the sheer size of it.
Processing such large amounts of data with the help of human labour or by using
traditional automation methods is not always a viable solution from the
standpoint of both time and other resources.
  Within the present work, we propose an approach for creating a multi-modal
and spatio-temporal dataset comprised of publicly available Remote Sensing data
and testing for feasibility using state of the art Machine Learning (ML)
techniques. Precisely, the usage of Convolutional Neural Networks (CNN) models
that are capable of separating different classes of vegetation that are present
in the proposed dataset. Popularity and success of similar methods in the
context of Geographical Information Systems (GIS) and Computer Vision (CV) more
generally indicate that methods alike should be taken in consideration and
further analysed and developed.

As one of the most extensive social networking services, Twitter has more
than 300 million active users as of 2022. Among its many functions, Twitter is
now one of the go-to platforms for consumers to share their opinions about
products or experiences, including flight services provided by commercial
airlines. This study aims to measure customer satisfaction by analyzing
sentiments of Tweets that mention airlines using a machine learning approach.
Relevant Tweets are retrieved from Twitter's API and processed through
tokenization and vectorization. After that, these processed vectors are passed
into a pre-trained machine learning classifier to predict the sentiments. In
addition to sentiment analysis, we also perform lexical analysis on the
collected Tweets to model keywords' frequencies, which provide meaningful
contexts to facilitate the interpretation of sentiments. We then apply time
series methods such as Bollinger Bands to detect abnormalities in sentiment
data. Using historical records from January to July 2022, our approach is
proven to be capable of capturing sudden and significant changes in passengers'
sentiment. This study has the potential to be developed into an application
that can help airlines, along with several other customer-facing businesses,
efficiently detect abrupt changes in customers' sentiments and take adequate
measures to counteract them.

Kernel Regularized Least Squares (KRLS) is a popular method for flexibly
estimating models that may have complex relationships between variables.
However, its usefulness to many researchers is limited for two reasons. First,
existing approaches are inflexible and do not allow KRLS to be combined with
theoretically-motivated extensions such as fixed effects or non-linear
outcomes. Second, estimation is extremely computationally intensive for even
modestly sized datasets.
  Our paper addresses both concerns by introducing generalized KRLS (gKRLS). We
note that KRLS can be re-formulated as a hierarchical model thereby allowing
easy inference and modular model construction. Computationally, we also
implement random sketching to dramatically accelerate estimation while
incurring a limited penalty in estimation quality. We demonstrate that gKRLS
can be fit on datasets with tens of thousands of observations in under one
minute. Further, state-of-the-art techniques that require fitting the model
over a dozen times (e.g. meta-learners) can be estimated quickly.

Quantum neural networks have been widely studied in recent years, given their
potential practical utility and recent results regarding their ability to
efficiently express certain classical data. However, analytic results to date
rely on assumptions and arguments from complexity theory. Due to this, there is
little intuition as to the source of the expressive power of quantum neural
networks or for which classes of classical data any advantage can be reasonably
expected to hold. Here, we study the relative expressive power between a broad
class of neural network sequence models and a class of recurrent models based
on Gaussian operations with non-Gaussian measurements. We explicitly show that
quantum contextuality is the source of an unconditional memory separation in
the expressivity of the two model classes. Additionally, as we are able to
pinpoint quantum contextuality as the source of this separation, we use this
intuition to study the relative performance of our introduced model on a
standard translation data set exhibiting linguistic contextuality. In doing so,
we demonstrate that our introduced quantum models are able to outperform state
of the art classical models even in practice.

The Barlow Twins self-supervised learning objective requires neither negative
samples or asymmetric learning updates, achieving results on a par with the
current state-of-the-art within Computer Vision. As such, we present Audio
Barlow Twins, a novel self-supervised audio representation learning approach,
adapting Barlow Twins to the audio domain. We pre-train on the large-scale
audio dataset AudioSet, and evaluate the quality of the learnt representations
on 18 tasks from the HEAR 2021 Challenge, achieving results which outperform,
or otherwise are on a par with, the current state-of-the-art for instance
discrimination self-supervised learning approaches to audio representation
learning. Code at https://github.com/jonahanton/SSL_audio.

Equilibrium selection in multi-agent games refers to the problem of selecting
a Pareto-optimal equilibrium. It has been shown that many state-of-the-art
multi-agent reinforcement learning (MARL) algorithms are prone to converging to
Pareto-dominated equilibria due to the uncertainty each agent has about the
policy of the other agents during training. To address suboptimal equilibrium
selection, we propose Pareto-AC (PAC), an actor-critic algorithm that utilises
a simple principle of no-conflict games (a superset of cooperative games with
identical rewards): each agent can assume the others will choose actions that
will lead to a Pareto-optimal equilibrium. We evaluate PAC in a diverse set of
multi-agent games and show that it converges to higher episodic returns
compared to alternative MARL algorithms, as well as successfully converging to
a Pareto-optimal equilibrium in a range of matrix games. Finally, we propose a
graph neural network extension which is shown to efficiently scale in games
with up to 15 agents.

The physics of a closed quantum mechanical system is governed by its
Hamiltonian. However, in most practical situations, this Hamiltonian is not
precisely known, and ultimately all there is are data obtained from
measurements on the system. In this work, we introduce a highly scalable,
data-driven approach to learning families of interacting many-body Hamiltonians
from dynamical data, by bringing together techniques from gradient-based
optimization from machine learning with efficient quantum state representations
in terms of tensor networks. Our approach is highly practical, experimentally
friendly, and intrinsically scalable to allow for system sizes of above 100
spins. In particular, we demonstrate on synthetic data that the algorithm works
even if one is restricted to one simple initial state, a small number of
single-qubit observables, and time evolution up to relatively short times. For
the concrete example of the one-dimensional Heisenberg model our algorithm
exhibits an error constant in the system size and scaling as the inverse square
root of the size of the data set.

An essential aspect of topological phases of matter is the existence of
Wilson loop operators that keep the ground state subspace invariant. Here we
present and implement an \it unbiased \rm numerical optimization scheme to
systematically find the Wilson loop operators given a single ground state wave
function of a gapped Hamiltonian on a disk. We then show how these Wilson loop
operators can be cut and glued through further optimization to give operators
that can create, move, and annihilate anyon excitations. We subsequently use
these operators to determine the braiding statistics and topological twists of
the anyons, yielding a way to fully extract topological order from a single
wave function. We apply our method to the ground state of the perturbed toric
code and doubled semion models with a magnetic field that is up to a half of
the critical value. From a contemporary perspective, this can be thought of as
a machine learning approach to discover emergent 1-form symmetries of a ground
state wave function. From an application perspective, our approach can be
relevant to find Wilson loop operators in current quantum simulators.

We study the robustness of conformal prediction, a powerful tool for
uncertainty quantification, to label noise. Our analysis tackles both
regression and classification problems, characterizing when and how it is
possible to construct uncertainty sets that correctly cover the unobserved
noiseless ground truth labels. Through stylized theoretical examples and
practical experiments, we argue that naive conformal prediction covers the
noiseless ground truth label unless the noise distribution is adversarially
designed. This leads us to believe that correcting for label noise is
unnecessary except for pathological data distributions or noise sources. In
such cases, we can also correct for noise of bounded size in the conformal
prediction algorithm in order to ensure correct coverage of the ground truth
labels without score or data regularity.

Teaching a multi-fingered dexterous robot to grasp objects in the real world
has been a challenging problem due to its high dimensional state and action
space. We propose a robot-learning system that can take a small number of human
demonstrations and learn to grasp unseen object poses given partially occluded
observations. Our system leverages a small motion capture dataset and generates
a large dataset with diverse and successful trajectories for a multi-fingered
robot gripper. By adding domain randomization, we show that our dataset
provides robust grasping trajectories that can be transferred to a policy
learner. We train a dexterous grasping policy that takes the point clouds of
the object as input and predicts continuous actions to grasp objects from
different initial robot states. We evaluate the effectiveness of our system on
a 22-DoF floating Allegro Hand in simulation and a 23-DoF Allegro robot hand
with a KUKA arm in real world. The policy learned from our dataset can
generalize well on unseen object poses in both simulation and the real world

Multilingual search can be achieved with subword tokenization. The accuracy
of traditional TF-IDF approaches depend on manually curated tokenization, stop
words and stemming rules, whereas subword TF-IDF (STF-IDF) can offer higher
accuracy without such heuristics. Moreover, multilingual support can be
incorporated inherently as part of the subword tokenization model training.
XQuAD evaluation demonstrates the advantages of STF-IDF: superior information
retrieval accuracy of 85.4% for English and over 80% for 10 other languages
without any heuristics-based preprocessing. The software to reproduce these
results are open-sourced as a part of Text2Text:
https://github.com/artitw/text2text

Although the remote sensing (RS) community has begun to pretrain transformers
(intended to be fine-tuned on RS tasks), it is unclear how these models perform
under distribution shifts. Here, we pretrain a new RS transformer--called
SatViT-V2--on 1.3 million satellite-derived RS images, then fine-tune it (along
with five other models) to investigate how it performs on distributions not
seen during training. We split an expertly labeled land cover dataset into 14
datasets based on source biome. We train each model on each biome separately
and test them on all other biomes. In all, this amounts to 1638 biome transfer
experiments. After fine-tuning, we find that SatViT-V2 outperforms SatViT-V1 by
3.1% on in-distribution (matching biomes) and 2.8% on out-of-distribution
(mismatching biomes) data. Additionally, we find that initializing fine-tuning
from the linear probed solution (i.e., leveraging LPFT [1]) improves
SatViT-V2's performance by another 1.2% on in-distribution and 2.4% on
out-of-distribution data. Next, we find that pretrained RS transformers are
better calibrated under distribution shifts than non-pretrained models and
leveraging LPFT results in further improvements in model calibration. Lastly,
we find that five measures of distribution shift are moderately correlated with
biome transfer performance. We share code and pretrained model weights.
(https://github.com/antofuller/SatViT)

Purpose: This study aimed to use deep learning-based dose prediction to
assess head and neck (HN) plan quality and identify sub-optimal plans.
  Methods: A total of 245 VMAT HN plans were created using RapidPlan
knowledge-based planning (KBP). A subset of 112 high-quality plans was selected
under the supervision of an HN radiation oncologist. We trained a 3D Dense
Dilated U-Net architecture to predict 3D dose distributions using three-fold
cross-validation on 90 plans. Model inputs included CT images, target
prescriptions, and contours for targets and organs at risk (OARs). The model's
performance was assessed on the remaining 22 test plans. We then tested the
application of the dose prediction model for automated review of plan quality.
Dose distributions were predicted on 14 clinical plans. The predicted versus
clinical OAR dose metrics were compared to flag OARs with sub-optimal normal
tissue sparing using a 2Gy dose difference or 3% dose-volume threshold. OAR
flags were compared to manual flags by three HN radiation oncologists.
  Results: The predicted dose distributions were of comparable quality to the
KBP plans. The differences between the predicted and KBP-planned D1%, D95%, and
D99% across the targets were within -2.53%(SD=1.34%), -0.42%(SD=1.27%), and
-0.12%(SD=1.97%), respectively, while the OAR mean and maximum doses were
within -0.33Gy(SD=1.40Gy) and -0.96Gy(SD=2.08Gy). For the plan quality
assessment study, radiation oncologists flagged 47 OARs for possible plan
improvement. There was high inter-physician variability; 83% of
physician-flagged OARs were flagged by only one of three physicians. The
comparative dose prediction model flagged 63 OARs, including 30/47
physician-flagged OARs.
  Conclusion: Deep learning can predict high-quality dose distributions, which
can be used as comparative dose distributions for automated, individualized
assessment of HN plan quality.

Audio-Text retrieval takes a natural language query to retrieve relevant
audio files in a database. Conversely, Text-Audio retrieval takes an audio file
as a query to retrieve relevant natural language descriptions. Most of the
literature train retrieval systems with one audio captioning dataset, but
evaluating the benefit of training with multiple datasets is underexplored.
Moreover, retrieval systems have to learn the alignment between elaborated
sentences describing audio content of variable length ranging from a few
seconds to several minutes. In this work, we propose a new collection of web
audio-text pairs and a new framework for retrieval. First, we provide a new
collection of about five thousand web audio-text pairs that we refer to as
WavText5K. When used to train our retrieval system, WavText5K improved
performance more than other audio captioning datasets. Second, our framework
learns to connect language and audio content by using a text encoder, two audio
encoders, and a contrastive learning objective. Combining both audio encoders
helps to process variable length audio. The two contributions beat state of the
art performance for AudioCaps and Clotho on Text-Audio retrieval by a relative
2% and 16%, and Audio-Text retrieval by 6% and 23%.

Humour is a substantial element of human affect and cognition. Its automatic
understanding can facilitate a more naturalistic human-device interaction and
the humanisation of artificial intelligence. Current methods of humour
detection are solely based on staged data making them inadequate for
'real-world' applications. We address this deficiency by introducing the novel
Passau-Spontaneous Football Coach Humour (Passau-SFCH) dataset, comprising of
about 11 hours of recordings. The Passau-SFCH dataset is annotated for the
presence of humour and its dimensions (sentiment and direction) as proposed in
Martin's Humor Style Questionnaire. We conduct a series of experiments,
employing pretrained Transformers, convolutional neural networks, and
expert-designed features. The performance of each modality (text, audio, video)
for spontaneous humour recognition is analysed and their complementarity is
investigated. Our findings suggest that for the automatic analysis of humour
and its sentiment, facial expressions are most promising, while humour
direction can be best modelled via text-based features. The results reveal
considerable differences among various subjects, highlighting the individuality
of humour usage and style. Further, we observe that a decision-level fusion
yields the best recognition result. Finally, we make our code publicly
available at https://www.github.com/EIHW/passau-sfch. The Passau-SFCH dataset
is available upon request.

In this paper, we study the application of DRL algorithms in the context of
local navigation problems, in which a robot moves towards a goal location in
unknown and cluttered workspaces equipped only with limited-range exteroceptive
sensors, such as LiDAR. Collision avoidance policies based on DRL present some
advantages, but they are quite susceptible to local minima, once their capacity
to learn suitable actions is limited to the sensor range. Since most robots
perform tasks in unstructured environments, it is of great interest to seek
generalized local navigation policies capable of avoiding local minima,
especially in untrained scenarios. To do so, we propose a novel reward function
that incorporates map information gained in the training stage, increasing the
agent's capacity to deliberate about the best course of action. Also, we use
the SAC algorithm for training our ANN, which shows to be more effective than
others in the state-of-the-art literature. A set of sim-to-sim and sim-to-real
experiments illustrate that our proposed reward combined with the SAC
outperforms the compared methods in terms of local minima and collision
avoidance.

The statistical supervised learning framework assumes an input-output set
with a joint probability distribution that is reliably represented by the
training dataset. The learner is then required to output a prediction rule
learned from the training dataset's input-output pairs. In this work, we
provide meaningful insights into the asymptotic equipartition property (AEP)
\citep{Shannon:1948} in the context of machine learning, and illuminate some of
its potential ramifications for few-shot learning. We provide theoretical
guarantees for reliable learning under the information-theoretic AEP, and for
the generalization error with respect to the sample size. We then focus on a
highly efficient recurrent neural net (RNN) framework and propose a
reduced-entropy algorithm for few-shot learning. We also propose a mathematical
intuition for the RNN as an approximation of a sparse coding solver. We verify
the applicability, robustness, and computational efficiency of the proposed
approach with image deblurring and optical coherence tomography (OCT) speckle
suppression. Our experimental results demonstrate significant potential for
improving learning models' sample efficiency, generalization, and time
complexity, that can therefore be leveraged for practical real-time

We present a method to synthesize novel views from a single $360^\circ$
panorama image based on the neural radiance field (NeRF). Prior studies in a
similar setting rely on the neighborhood interpolation capability of
multi-layer perceptions to complete missing regions caused by occlusion, which
leads to artifacts in their predictions. We propose 360FusionNeRF, a
semi-supervised learning framework where we introduce geometric supervision and
semantic consistency to guide the progressive training process. Firstly, the
input image is re-projected to $360^\circ$ images, and auxiliary depth maps are
extracted at other camera positions. The depth supervision, in addition to the
NeRF color guidance, improves the geometry of the synthesized views.
Additionally, we introduce a semantic consistency loss that encourages
realistic renderings of novel views. We extract these semantic features using a
pre-trained visual encoder such as CLIP, a Vision Transformer trained on
hundreds of millions of diverse 2D photographs mined from the web with natural
language supervision. Experiments indicate that our proposed method can produce
plausible completions of unobserved regions while preserving the features of
the scene. When trained across various scenes, 360FusionNeRF consistently
achieves the state-of-the-art performance when transferring to synthetic
Structured3D dataset (PSNR~5%, SSIM~3% LPIPS~13%), real-world Matterport3D
dataset (PSNR~3%, SSIM~3% LPIPS~9%) and Replica360 dataset (PSNR~8%, SSIM~2%
LPIPS~18%).

Persistence diagrams (PDs), often characterized as sets of death and birth of
homology class, have been known for providing a topological representation of a
graph structure, which is often useful in machine learning tasks. Prior works
rely on a single graph signature to construct PDs. In this paper, we explore
the use of a family of multi-scale graph signatures to enhance the robustness
of topological features. We propose a deep learning architecture to handle this
set input. Experiments on benchmark graph classification datasets demonstrate
that our proposed architecture outperforms other persistent homology-based
methods and achieves competitive performance compared to state-of-the-art
methods using graph neural networks. In addition, our approach can be easily
applied to large size of input graphs as it does not suffer from limited
scalability which can be an issue for graph kernel methods.

In the past decade, deep learning has dramatically changed the traditional
hand-craft feature manner with strong feature learning capability, resulting in
tremendous improvement of conventional tasks. However, deep neural networks
have recently been demonstrated vulnerable to adversarial examples, a kind of
malicious samples crafted by small elaborately designed noise, which mislead
the DNNs to make the wrong decisions while remaining imperceptible to humans.
Adversarial examples can be divided into digital adversarial attacks and
physical adversarial attacks. The digital adversarial attack is mostly
performed in lab environments, focusing on improving the performance of
adversarial attack algorithms. In contrast, the physical adversarial attack
focus on attacking the physical world deployed DNN systems, which is a more
challenging task due to the complex physical environment (i.e., brightness,
occlusion, and so on). Although the discrepancy between digital adversarial and
physical adversarial examples is small, the physical adversarial examples have
a specific design to overcome the effect of the complex physical environment.
In this paper, we review the development of physical adversarial attacks in
DNN-based computer vision tasks, including image recognition tasks, object
detection tasks, and semantic segmentation. For the sake of completeness of the
algorithm evolution, we will briefly introduce the works that do not involve
the physical adversarial attack. We first present a categorization scheme to
summarize the current physical adversarial attacks. Then discuss the advantages
and disadvantages of the existing physical adversarial attacks and focus on the
technique used to maintain the adversarial when applied into physical
environment. Finally, we point out the issues of the current physical
adversarial attacks to be solved and provide promising research directions.

In order to efficiently learn a dynamics model for a task in a new
environment, one can adapt a model learned in a similar source environment.
However, existing adaptation methods can fail when the target dataset contains
transitions where the dynamics are very different from the source environment.
For example, the source environment dynamics could be of a rope manipulated in
free-space, whereas the target dynamics could involve collisions and
deformation on obstacles. Our key insight is to improve data efficiency by
focusing model adaptation on only the regions where the source and target
dynamics are similar. In the rope example, adapting the free-space dynamics
requires significantly fewer data than adapting the free-space dynamics while
also learning collision dynamics. We propose a new method for adaptation that
is effective in adapting to regions of similar dynamics. Additionally, we
combine this adaptation method with prior work on planning with unreliable
dynamics to make a method for data-efficient online adaptation, called FOCUS.
We first demonstrate that the proposed adaptation method achieves statistically
significantly lower prediction error in regions of similar dynamics on
simulated rope manipulation and plant watering tasks. We then show on a
bimanual rope manipulation task that FOCUS achieves data-efficient online
learning, in simulation and in the real world.

When a business sells to another business (B2B), the buying business is
represented by a group of individuals, termed account, who collectively decide
whether to buy. The seller advertises to each individual and interacts with
them, mostly by digital means. The sales cycle is long, most often over a few
months. There is heterogeneity among individuals belonging to an account in
seeking information and hence the seller needs to score the interest of each
individual over a long horizon to decide which individuals must be reached and
when. Moreover, the buy decision rests with the account and must be scored to
project the likelihood of purchase, a decision that is subject to change all
the way up to the actual decision, emblematic of group decision making. We
score decision of the account and its individuals in a dynamic manner. Dynamic
scoring allows opportunity to influence different individual members at
different time points over the long horizon. The dataset contains behavior logs
of each individual's communication activities with the seller; but, there are
no data on consultations among individuals which result in the decision. Using
neural network architecture, we propose several ways to aggregate information
from individual members' activities, to predict the group's collective
decision. Multiple evaluations find strong model performance.

Neural Posterior Estimation methods for simulation-based inference can be
ill-suited for dealing with posterior distributions obtained by conditioning on
multiple observations, as they may require a large number of simulator calls to
yield accurate approximations. Neural Likelihood Estimation methods can
naturally handle multiple observations, but require a separate inference step,
which may affect their efficiency and performance. We introduce a new method
for simulation-based inference that enjoys the benefits of both approaches. We
propose to model the scores for the posterior distributions induced by
individual observations, and introduce a sampling algorithm that combines the
learned scores to approximately sample from the target efficiently.

Deep neural network models are massively deployed on a wide variety of
hardware platforms. This results in the appearance of new attack vectors that
significantly extend the standard attack surface, extensively studied by the
adversarial machine learning community. One of the first attack that aims at
drastically dropping the performance of a model, by targeting its parameters
(weights) stored in memory, is the Bit-Flip Attack (BFA). In this work, we
point out several evaluation challenges related to the BFA. First of all, the
lack of an adversary's budget in the standard threat model is problematic,
especially when dealing with physical attacks. Moreover, since the BFA presents
critical variability, we discuss the influence of some training parameters and
the importance of the model architecture. This work is the first to present the
impact of the BFA against fully-connected architectures that present different
behaviors compared to convolutional neural networks. These results highlight
the importance of defining robust and sound evaluation methodologies to
properly evaluate the dangers of parameter-based attacks as well as measure the
real level of robustness offered by a defense.

Collision avoidance in the presence of dynamic obstacles in unknown
environments is one of the most critical challenges for unmanned systems. In
this paper, we present a method that identifies obstacles in terms of
ellipsoids to estimate linear and angular obstacle velocities. Our proposed
method is based on the idea of any object can be approximately expressed by
ellipsoids. To achieve this, we propose a method based on variational Bayesian
estimation of Gaussian mixture model, the Kyachiyan algorithm, and a refinement
algorithm. Our proposed method does not require knowledge of the number of
clusters and can operate in real-time, unlike existing optimization-based
methods. In addition, we define an ellipsoid-based feature vector to match
obstacles given two timely close point frames. Our method can be applied to any
environment with static and dynamic obstacles, including the ones with rotating
obstacles. We compare our algorithm with other clustering methods and show that
when coupled with a trajectory planner, the overall system can efficiently
traverse unknown environments in the presence of dynamic obstacles.

Despite deep-learning being state-of-the-art for data-driven model
predictions, it has not yet found frequent application in ecology. Given the
low sample size typical in many environmental research fields, the default
choice for the modelling of ecosystems and its functions remain process-based
models. The process understanding coded in these models complements the sparse
data and neural networks can detect hidden dynamics even in noisy data.
Embedding the process model in the neural network adds information to learn
from, improving interpretability and predictive performance of the combined
model towards the data-only neural networks and the mechanism-only process
model. At the example of carbon fluxes in forest ecosystems, we compare
different approaches of guiding a neural network towards process model theory.
Evaluation of the results under four classical prediction scenarios supports
decision-making on the appropriate choice of a process-guided neural network.

Radio loud active galactic nuclei (RLAGNs) are often morphologically complex
objects that can consist of multiple, spatially separated, components.
Astronomers often rely on visual inspection to resolve radio component
association. However, applying visual inspection to all the hundreds of
thousands of well-resolved RLAGNs that appear in the images from the Low
Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) at $144$ MHz, is a
daunting, time-consuming process, even with extensive manpower.
  Using a machine learning approach, we aim to automate the radio component
association of large ($> 15$ arcsec) radio components.
  We turned the association problem into a classification problem and trained
an adapted Fast region-based convolutional neural network to mimic the expert
annotations from the first LoTSS data release. We implemented a rotation data
augmentation to reduce overfitting and simplify the component association by
removing unresolved radio sources that are likely unrelated to the large and
bright radio components that we consider using predictions from an existing
gradient boosting classifier.
  For large ($> 15$ arcsec) and bright ($> 10$ mJy) radio components in the
LoTSS first data release, our model provides the same associations for
$85.3\%\pm0.6$ of the cases as those derived when astronomers perform the
association manually. When the association is done through public crowd-sourced
efforts, a result similar to that of our model is attained.
  Our method is able to efficiently carry out manual radio-component
association for huge radio surveys and can serve as a basis for either
automated radio morphology classification or automated optical host
identification. This opens up an avenue to study the completeness and
reliability of samples of radio sources with extended, complex morphologies.

Road rutting is a severe road distress that can cause premature failure of
road incurring early and costly maintenance costs. Research on road damage
detection using image processing techniques and deep learning are being
actively conducted in the past few years. However, these researches are mostly
focused on detection of cracks, potholes, and their variants. Very few research
has been done on the detection of road rutting. This paper proposes a novel
road rutting dataset comprising of 949 images and provides both object level
and pixel level annotations. Object detection models and semantic segmentation
models were deployed to detect road rutting on the proposed dataset, and
quantitative and qualitative analysis of model predictions were done to
evaluate model performance and identify challenges faced in the detection of
road rutting using the proposed method. Object detection model YOLOX-s achieves
mAP@IoU=0.5 of 61.6% and semantic segmentation model PSPNet (Resnet-50)
achieves IoU of 54.69 and accuracy of 72.67, thus providing a benchmark
accuracy for similar work in future. The proposed road rutting dataset and the
results of our research study will help accelerate the research on detection of
road rutting using deep learning.

We consider the problem of sequential sparse subset selections in an online
learning setup. Assume that the set $[N]$ consists of $N$ distinct elements. On
the $t^{\text{th}}$ round, a monotone reward function $f_t: 2^{[N]} \to
\mathbb{R}_+,$ which assigns a non-negative reward to each subset of $[N],$ is
revealed to a learner. The learner selects (perhaps randomly) a subset $S_t
\subseteq [N]$ of $k$ elements before the reward function $f_t$ for that round
is revealed $(k \leq N)$. As a consequence of its choice, the learner receives
a reward of $f_t(S_t)$ on the $t^{\text{th}}$ round. The learner's goal is to
design an online subset selection policy to maximize its expected cumulative
reward accrued over a given time horizon. In this connection, we propose an
online learning policy called SCore (Subset Selection with Core) that solves
the problem for a large class of reward functions. The proposed SCore policy is
based on a new concept of $\alpha$-Core, which is a generalization of the
notion of Core from the cooperative game theory literature. We establish a
learning guarantee for the SCore policy in terms of a new performance metric
called $\alpha$-augmented regret. In this new metric, the power of the offline
benchmark is suitably augmented compared to the online policy. We give several
illustrative examples to show that a broad class of reward functions, including
submodular, can be efficiently learned with the SCore policy. We also outline
how the SCore policy can be used under a semi-bandit feedback model and
conclude the paper with a number of open problems.

Biological and artificial agents need to deal with constant changes in the
real world. We study this problem in four classical continuous control
environments, augmented with morphological perturbations. Learning to locomote
when the length and the thickness of different body parts vary is challenging,
as the control policy is required to adapt to the morphology to successfully
balance and advance the agent. We show that a control policy based on the
proprioceptive state performs poorly with highly variable body configurations,
while an (oracle) agent with access to a learned encoding of the perturbation
performs significantly better. We introduce DMAP, a biologically-inspired,
attention-based policy network architecture. DMAP combines independent
proprioceptive processing, a distributed policy with individual controllers for
each joint, and an attention mechanism, to dynamically gate sensory information
from different body parts to different controllers. Despite not having access
to the (hidden) morphology information, DMAP can be trained end-to-end in all
the considered environments, overall matching or surpassing the performance of
an oracle agent. Thus DMAP, implementing principles from biological motor
control, provides a strong inductive bias for learning challenging sensorimotor
tasks. Overall, our work corroborates the power of these principles in
challenging locomotion tasks.

Metabolic health is increasingly implicated as a risk factor across
conditions from cardiology to neurology, and efficiency assessment of body
composition is critical to quantitatively characterizing these relationships.
2D low dose single slice computed tomography (CT) provides a high resolution,
quantitative tissue map, albeit with a limited field of view. Although numerous
potential analyses have been proposed in quantifying image context, there has
been no comprehensive study for low-dose single slice CT longitudinal
variability with automated segmentation. We studied a total of 1816 slices from
1469 subjects of Baltimore Longitudinal Study on Aging (BLSA) abdominal dataset
using supervised deep learning-based segmentation and unsupervised clustering
method. 300 out of 1469 subjects that have two year gap in their first two
scans were pick out to evaluate longitudinal variability with measurements
including intraclass correlation coefficient (ICC) and coefficient of variation
(CV) in terms of tissues/organs size and mean intensity. We showed that our
segmentation methods are stable in longitudinal settings with Dice ranged from
0.821 to 0.962 for thirteen target abdominal tissues structures. We observed
high variability in most organ with ICC<0.5, low variability in the area of
muscle, abdominal wall, fat and body mask with average ICC>0.8. We found that
the variability in organ is highly related to the cross-sectional position of
the 2D slice. Our efforts pave quantitative exploration and quality control to
reduce uncertainties in longitudinal analysis.

Flow analysis carried out using phase contrast cardiac magnetic resonance
imaging (PC-CMR) enables the quantification of important parameters that are
used in the assessment of cardiovascular function. An essential part of this
analysis is the identification of the correct CMR views and quality control
(QC) to detect artefacts that could affect the flow quantification. We propose
a novel deep learning based framework for the fully-automated analysis of flow
from full CMR scans that first carries out these view selection and QC steps
using two sequential convolutional neural networks, followed by automatic aorta
and pulmonary artery segmentation to enable the quantification of key flow
parameters. Accuracy values of 0.958 and 0.914 were obtained for view
classification and QC, respectively. For segmentation, Dice scores were
$>$0.969 and the Bland-Altman plots indicated excellent agreement between
manual and automatic peak flow values. In addition, we tested our pipeline on
an external validation data set, with results indicating good robustness of the
pipeline. This work was carried out using multivendor clinical data consisting
of 986 cases, indicating the potential for the use of this pipeline in a
clinical setting.

Open-set semi-supervised learning (OSSL) has attracted growing interest,
which investigates a more practical scenario where out-of-distribution (OOD)
samples are only contained in unlabeled data. Existing OSSL methods like
OpenMatch learn an OOD detector to identify outliers, which often update all
modal parameters (i.e., full fine-tuning) to propagate class information from
labeled data to unlabeled ones. Currently, prompt learning has been developed
to bridge gaps between pre-training and fine-tuning, which shows higher
computational efficiency in several downstream tasks. In this paper, we propose
a prompt-driven efficient OSSL framework, called OpenPrompt, which can
propagate class information from labeled to unlabeled data with only a small
number of trainable parameters. We propose a prompt-driven joint space learning
mechanism to detect OOD data by maximizing the distribution gap between ID and
OOD samples in unlabeled data, thereby our method enables the outliers to be
detected in a new way. The experimental results on three public datasets show
that OpenPrompt outperforms state-of-the-art methods with less than 1% of
trainable parameters. More importantly, OpenPrompt achieves a 4% improvement in
terms of AUROC on outlier detection over a fully supervised model on CIFAR10.

3D scenes are dominated by a large number of background points, which is
redundant for the detection task that mainly needs to focus on foreground
objects. In this paper, we analyze major components of existing sparse 3D CNNs
and find that 3D CNNs ignore the redundancy of data and further amplify it in
the down-sampling process, which brings a huge amount of extra and unnecessary
computational overhead. Inspired by this, we propose a new convolution operator
named spatial pruned sparse convolution (SPS-Conv), which includes two
variants, spatial pruned submanifold sparse convolution (SPSS-Conv) and spatial
pruned regular sparse convolution (SPRS-Conv), both of which are based on the
idea of dynamically determining crucial areas for redundancy reduction. We
validate that the magnitude can serve as important cues to determine crucial
areas which get rid of the extra computations of learning-based methods. The
proposed modules can easily be incorporated into existing sparse 3D CNNs
without extra architectural modifications. Extensive experiments on the KITTI,
Waymo and nuScenes datasets demonstrate that our method can achieve more than
50% reduction in GFLOPs without compromising the performance.

The paucity of labeled data is a typical challenge in the automotive
industry. Annotating time-series measurements requires solid domain knowledge
and in-depth exploratory data analysis, which implies a high labeling effort.
Conventional Active Learning (AL) addresses this issue by actively querying the
most informative instances based on the estimated classification probability
and retraining the model iteratively. However, the learning efficiency strongly
relies on the initial model, resulting in the trade-off between the size of the
initial dataset and the query number. This paper proposes a novel Few-Shot
Learning (FSL)-based AL framework, which addresses the trade-off problem by
incorporating a Prototypical Network (ProtoNet) in the AL iterations. The
results show an improvement, on the one hand, in the robustness to the initial
model and, on the other hand, in the learning efficiency of the ProtoNet
through the active selection of the support set in each iteration. This
framework was validated on UCI HAR/HAPT dataset and a real-world braking
maneuver dataset. The learning performance significantly surpasses traditional
AL algorithms on both datasets, achieving 90% classification accuracy with 10%
and 5% labeling effort, respectively.

Complementary-label learning (CLL) is a common application in the scenario of
weak supervision. However, in real-world datasets, CLL encounters
class-imbalanced training samples, where the quantity of samples of one class
is significantly lower than those of other classes. Unfortunately, existing CLL
approaches have yet to explore the problem of class-imbalanced samples, which
reduces the prediction accuracy, especially in imbalanced classes. In this
paper, we propose a novel problem setting to allow learning from
class-imbalanced complementarily labeled samples for multi-class
classification. Accordingly, to deal with this novel problem, we propose a new
CLL approach, called Weighted Complementary-Label Learning (WCLL). The proposed
method models a weighted empirical risk minimization loss by utilizing the
class-imbalanced complementarily labeled information, which is also applicable
to multi-class imbalanced training samples. Furthermore, the estimation error
bound of the proposed method was derived to provide a theoretical guarantee.
Finally, we do extensive experiments on widely-used benchmark datasets to
validate the superiority of our method by comparing it with existing

We consider Legendre-Bregman projections defined on the Hermitian matrix
space and design iterative optimization algorithms based on them. A general
duality theorem is established for Bregman divergences on Hermitian matrices,
and it plays a crucial role in proving the convergence of the iterative
algorithms. We study both exact and approximate Bregman projection algorithms.
In the particular case of Kullback-Leibler divergence, our approximate
iterative algorithm gives rise to the non-commutative versions of both the
generalized iterative scaling (GIS) algorithm for maximum entropy inference and
the AdaBoost algorithm in machine learning as special cases. As the
Legendre-Bregman projections are simple matrix functions on Hermitian matrices,
quantum algorithmic techniques are applicable to achieve potential speedups in
each iteration of the algorithm. We discuss several quantum algorithmic design
techniques applicable in our setting, including the smooth function evaluation
technique, two-phase quantum minimum finding, and NISQ Gibbs state preparation.

This report presents an automatic evaluation of the general machine
translation task of the Seventh Conference on Machine Translation (WMT22). It
evaluates a total of 185 systems for 21 translation directions including
high-resource to low-resource language pairs and from closely related to
distant languages. This large-scale automatic evaluation highlights some of the
current limits of state-of-the-art machine translation systems. It also shows
how automatic metrics, namely chrF, BLEU, and COMET, can complement themselves
to mitigate their own limits in terms of interpretability and accuracy.

5G and beyond mobile networks will support heterogeneous use cases at an
unprecedented scale, thus demanding automated control and optimization of
network functionalities customized to the needs of individual users. Such
fine-grained control of the Radio Access Network (RAN) is not possible with the
current cellular architecture. To fill this gap, the Open RAN paradigm and its
specification introduce an open architecture with abstractions that enable
closed-loop control and provide data-driven, and intelligent optimization of
the RAN at the user level. This is obtained through custom RAN control
applications (i.e., xApps) deployed on near-real-time RAN Intelligent
Controller (near-RT RIC) at the edge of the network. Despite these premises, as
of today the research community lacks a sandbox to build data-driven xApps, and
create large-scale datasets for effective AI training. In this paper, we
address this by introducing ns-O-RAN, a software framework that integrates a
real-world, production-grade near-RT RIC with a 3GPP-based simulated
environment on ns-3, enabling the development of xApps and automated
large-scale data collection and testing of Deep Reinforcement Learning-driven
control policies for the optimization at the user-level. In addition, we
propose the first user-specific O-RAN Traffic Steering (TS) intelligent
handover framework. It uses Random Ensemble Mixture, combined with a
state-of-the-art Convolutional Neural Network architecture, to optimally assign
a serving base station to each user in the network. Our TS xApp, trained with
more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC
and controls its base stations. We evaluate the performance on a large-scale
deployment, showing that the xApp-based handover improves throughput and
spectral efficiency by an average of 50% over traditional handover heuristics,
with less mobility overhead.

Entity Linking is the task of matching a mention to an entity in a given
knowledge base (KB). It contributes to annotating a massive amount of documents
existing on the Web to harness new facts about their matched entities. However,
existing Entity Linking systems focus on developing models that are typically
domain-dependent and robust only to a particular knowledge base on which they
have been trained. The performance is not as adequate when being evaluated on
documents and knowledge bases from different domains.
  Approaches based on pre-trained language models, such as Wu et al. (2020),
attempt to solve the problem using a zero-shot setup, illustrating some
potential when evaluated on a general-domain KB. Nevertheless, the performance
is not equivalent when evaluated on a domain-specific KB. To allow for more
accurate Entity Linking across different domains, we propose our framework:
Cross-Domain Neural Entity Linking (CDNEL). Our objective is to have a single
system that enables simultaneous linking to both the general-domain KB and the
domain-specific KB. CDNEL works by learning a joint representation space for
these knowledge bases from different domains. It is evaluated using the
external Entity Linking dataset (Zeshel) constructed by Logeswaran et al.
(2019) and the Reddit dataset collected by Botzer et al. (2021), to compare our
proposed method with the state-of-the-art results. The proposed framework uses
different types of datasets for fine-tuning, resulting in different model
variants of CDNEL. When evaluated on four domains included in the Zeshel
dataset, these variants achieve an average precision gain of 9%.

Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual
representations with great transferability, which achieves promising accuracy
for zero-shot classification. To further improve its downstream performance,
existing works propose additional learnable modules upon CLIP and fine-tune
them by few-shot training sets. However, the resulting extra training cost and
data requirement severely hinder the efficiency for model deployment and
knowledge transfer. In this paper, we introduce a free-lunch enhancement
method, CALIP, to boost CLIP's zero-shot performance via a parameter-free
Attention module. Specifically, we guide visual and textual representations to
interact with each other and explore cross-modal informative features via
attention. As the pre-training has largely reduced the embedding distances
between two modalities, we discard all learnable parameters in the attention
and bidirectionally update the multi-modal features, enabling the whole process
to be parameter-free and training-free. In this way, the images are blended
with textual-aware signals and the text representations become visual-guided
for better adaptive zero-shot alignment. We evaluate CALIP on various
benchmarks of 14 datasets for both 2D image and 3D point cloud few-shot
classification, showing consistent zero-shot performance improvement over CLIP.
Based on that, we further insert a small number of linear layers in CALIP's
attention module and verify our robustness under the few-shot settings, which
also achieves leading performance compared to existing methods. Those extensive
experiments demonstrate the superiority of our approach for efficient
enhancement of CLIP.

The problem of sparse multichannel blind deconvolution (S-MBD) arises
frequently in many engineering applications such as radar/sonar/ultrasound
imaging. To reduce its computational and implementation cost, we propose a
compression method that enables blind recovery from much fewer measurements
with respect to the full received signal in time. The proposed compression
measures the signal through a filter followed by a subsampling, allowing for a
significant reduction in implementation cost. We derive theoretical guarantees
for the identifiability and recovery of a sparse filter from compressed
measurements. Our results allow for the design of a wide class of compression
filters. We, then, propose a data-driven unrolled learning framework to learn
the compression filter and solve the S-MBD problem. The encoder is a recurrent
inference network that maps compressed measurements into an estimate of sparse
filters. We demonstrate that our unrolled learning method is more robust to
choices of source shapes and has better recovery performance compared to
optimization-based methods. Finally, in applications with limited data (fewshot
learning), we highlight the superior generalization capability of unrolled
learning compared to conventional deep learning.

The last two decades have seen tremendous growth in data collections because
of the realization of recent technologies, including the internet of things
(IoT), E-Health, industrial IoT 4.0, autonomous vehicles, etc. The challenge of
data transmission and storage can be handled by utilizing state-of-the-art data
compression methods. Recent data compression methods are proposed using deep
learning methods, which perform better than conventional methods. However,
these methods require a lot of data and resources for training. Furthermore, it
is difficult to materialize these deep learning-based solutions on IoT devices
due to the resource-constrained nature of IoT devices. In this paper, we
propose lightweight data compression methods based on data statistics and
deviation. The proposed method performs better than the deep learning method in
terms of compression ratio (CR). We simulate and compare the proposed data
compression methods for various time series signals, e.g., accelerometer, gas
sensor, gyroscope, electrical power consumption, etc. In particular, it is
observed that the proposed method achieves 250.8\%, 94.3\%, and 205\% higher CR
than the deep learning method for the GYS, Gactive, and ACM datasets,
respectively. The code and data are available at
https://github.com/vidhi0206/data-compression .

Recently, Supervised Contrastive Learning (SCL) has been shown to achieve
excellent performance in most classification tasks. In SCL, a neural network is
trained to optimize two objectives: pull an anchor and positive samples
together in the embedding space, and push the anchor apart from the negatives.
However, these two different objectives may conflict, requiring trade-offs
between them during optimization. In this work, we formulate the SCL problem as
a Multi-Objective Optimization problem for the fine-tuning phase of RoBERTa
language model. Two methods are utilized to solve the optimization problem: (i)
the linear scalarization (LS) method, which minimizes a weighted linear
combination of pertask losses; and (ii) the Exact Pareto Optimal (EPO) method
which finds the intersection of the Pareto front with a given preference
vector. We evaluate our approach on several GLUE benchmark tasks, without using
data augmentations, memory banks, or generating adversarial examples. The
empirical results show that the proposed learning strategy significantly
outperforms a strong competitive contrastive learning baseline

In this work, we present the Textless Vision-Language Transformer (TVLT),
where homogeneous transformer blocks take raw visual and audio inputs for
vision-and-language representation learning with minimal modality-specific
design, and do not use text-specific modules such as tokenization or automatic
speech recognition (ASR). TVLT is trained by reconstructing masked patches of
continuous video frames and audio spectrograms (masked autoencoding) and
contrastive modeling to align video and audio. TVLT attains performance
comparable to its text-based counterpart on various multimodal tasks, such as
visual question answering, image retrieval, video retrieval, and multimodal
sentiment analysis, with 28x faster inference speed and only 1/3 of the
parameters. Our findings suggest the possibility of learning compact and
efficient visual-linguistic representations from low-level visual and audio
signals without assuming the prior existence of text. Our code and checkpoints
are available at: https://github.com/zinengtang/TVLT

Source code is essential for researchers to reproduce the methods and
replicate the results of artificial intelligence (AI) papers. Some
organizations and researchers manually collect AI papers with available source
code to contribute to the AI community. However, manual collection is a
labor-intensive and time-consuming task. To address this issue, we propose a
method to automatically identify papers with available source code and extract
their source code repository URLs. With this method, we find that 20.5% of
regular papers of 10 top AI conferences published from 2010 to 2019 are
identified as papers with available source code and that 8.1% of these source
code repositories are no longer accessible. We also create the XMU NLP Lab
README Dataset, the largest dataset of labeled README files for source code
document research. Through this dataset, we have discovered that quite a few
README files have no installation instructions or usage tutorials provided.
Further, a large-scale comprehensive statistical analysis is made for a general
picture of the source code of AI conference papers. The proposed solution can
also go beyond AI conference papers to analyze other scientific papers from
both journals and conferences to shed light on more domains.

Convex optimization finds many real-life applications, where - optimized on
real data - optimization results may expose private data attributes (e.g.,
individual health records, commercial information, etc.), thus leading to
privacy breaches. To avoid these breaches and formally guarantee privacy to
optimization data owners, we develop a new privacy-preserving perturbation
strategy for convex optimization programs by combining stochastic
(chance-constrained) programming and differential privacy. Unlike standard
noise-additive strategies, which perturb either optimization data or
optimization results, we express the optimization variables as functions of the
random perturbation using linear decision rules; we then optimize these rules
to accommodate the perturbation within the problem's feasible region by
enforcing chance constraints. This way, the perturbation is feasible and makes
different, yet adjacent in the sense of a given distance function, optimization
datasets statistically similar in randomized optimization results, thereby
enabling probabilistic differential privacy guarantees. The chance-constrained
optimization additionally internalizes the conditional value-at-risk measure to
model the tolerance towards the worst-case realizations of the optimality loss
with respect to the non-private solution. We demonstrate the privacy properties
of our perturbation strategy analytically and through optimization and machine
learning applications.

With the development of deep learning, neural network-based speech
enhancement (SE) models have shown excellent performance. Meanwhile, it was
shown that the development of self-supervised pre-trained models can be applied
to various downstream tasks. In this paper, we will consider the application of
the pre-trained model to the real-time SE problem. Specifically, the encoder
and bottleneck layer of the DEMUCS model are initialized using the
self-supervised pretrained WavLM model, the convolution in the encoder is
replaced by causal convolution, and the transformer encoder in the bottleneck
layer is based on causal attention mask. In addition, as discretizing the noisy
speech representations is more beneficial for denoising, we utilize a
quantization module to discretize the representation output from the bottleneck
layer, which is then fed into the decoder to reconstruct the clean speech
waveform. Experimental results on the Valentini dataset and an internal dataset
show that the pre-trained model based initialization can improve the SE
performance and the discretization operation suppresses the noise component in
the representations to some extent, which can further improve the performance.

In reinforcement learning for safety-critical settings, it is often desirable
for the agent to obey safety constraints at all points in time, including
during training. We present a novel neurosymbolic approach called SPICE to
solve this safe exploration problem. SPICE uses an online shielding layer based
on symbolic weakest preconditions to achieve a more precise safety analysis
than existing tools without unduly impacting the training process. We evaluate
the approach on a suite of continuous control benchmarks and show that it can
achieve comparable performance to existing safe learning techniques while
incurring fewer safety violations. Additionally, we present theoretical results
showing that SPICE converges to the optimal safe policy under reasonable
assumptions.

With the advances of the Internet of Things (IoT) and 5G/6G wireless
communications, the paradigms of mobile computing have developed dramatically
in recent years, from centralized mobile cloud computing to distributed fog
computing and mobile edge computing (MEC). MEC pushes compute-intensive
assignments to the edge of the network and brings resources as close to the
endpoints as possible, addressing the shortcomings of mobile devices with
regard to storage space, resource optimisation, computational performance and
efficiency. Compared to cloud computing, as the distributed and closer
infrastructure, the convergence of MEC with other emerging technologies,
including the Metaverse, 6G wireless communications, artificial intelligence
(AI), and blockchain, also solves the problems of network resource allocation,
more network load as well as latency requirements. Accordingly, this paper
investigates the computational paradigms used to meet the stringent
requirements of modern applications. The application scenarios of MEC in mobile
augmented reality (MAR) are provided. Furthermore, this survey presents the
motivation of MEC-based Metaverse and introduces the applications of MEC to the
Metaverse. Particular emphasis is given on a set of technical fusions mentioned
above, e.g., 6G with MEC paradigm, MEC strengthened by blockchain, etc.

In this paper, we examine the development and usage of six low-resource
machine translation systems translating between the Ukrainian language and each
of the official languages of the Baltic states. We developed these systems in
reaction to the escalating Ukrainian refugee crisis caused by the Russian
military aggression in Ukraine in the hope that they might be helpful for
refugees and public administrations. Now, two months after MT systems were made
public, we analyze their usage patterns and statistics. Our findings show that
the Latvian-Ukrainian and Lithuanian-Ukrainian systems are integrated into the
public services of Baltic states, leading to more than 127 million translated
sentences for the Lithuanian-Ukrainian system. Motivated by these findings, we
further enhance our MT systems by better Ukrainian toponym translation and
publish an improved version of the Lithuanian-Ukrainian system.

Mobile networks are becoming energy hungry, and this trend is expected to
continue due to a surge in communication and computation demand. Multi-access
Edge Computing (MEC), will entail energy-consuming services and applications,
with non-negligible impact in terms of ecological sustainability. In this
paper, we provide a comprehensive review of existing approaches to make edge
computing networks greener, including but not limited to the exploitation of
renewable energy resources, and context-awareness. We hence provide an updated
account of recent developments on MEC from an energetic sustainability
perspective, addressing the initial deployment of computing resources, their
dynamic (re)allocation, as well as distributed and federated learning designs.
In doing so, we highlight the energy aspects of these algorithms, advocating
the need for energy-sustainable edge computing systems that are aligned with
Sustainable Development Goals (SDGs) and the Paris agreement. To the best of
our knowledge, this is the first work providing a systematic literature review
on the efficient deployment and management of energy harvesting MEC, with
special focus on the deployment, provisioning, and scheduling of computing
tasks, including federated learning for distributed edge intelligence, toward
making edge networks greener and more sustainable. At the end of the paper,
open research avenues and challenges are identified for all the surveyed
topics.

In this paper we consider algorithm unfolding for the Multiple Measurement
Vector (MMV) problem in the case where only few training samples are available.
Algorithm unfolding has been shown to empirically speed-up in a data-driven way
the convergence of various classical iterative algorithms but for supervised
learning it is important to achieve this with minimal training data. For this
we consider learned block iterative shrinkage thresholding algorithm (LBISTA)
under different training strategies. To approach almost data-free optimization
at minimal training overhead the number of trainable parameters for algorithm
unfolding has to be substantially reduced. We therefore explicitly propose a
reduced-size network architecture based on the Kronecker structure imposed by
the MMV observation model and present the corresponding theory in this context.
To ensure proper generalization, we then extend the analytic weight approach by
Lui et al to LBISTA and the MMV setting. Rigorous theoretical guarantees and
convergence results are stated for this case. We show that the network weights
can be computed by solving an explicit equation at the reduced MMV dimensions
which also admits a closed-form solution. Towards more practical problems, we
then consider convolutional observation models and show that the proposed
architecture and the analytical weight computation can be further simplified
and thus open new directions for convolutional neural networks. Finally, we
evaluate the unfolded algorithms in numerical experiments and discuss
connections to other sparse recovering algorithms.

Recent work on Path-Dependent Partial Differential Equations (PPDEs) has
shown that PPDE solutions can be approximated by a probabilistic
representation, implemented in the literature by the estimation of conditional
expectations using regression. However, a limitation of this approach is to
require the selection of a basis in a function space. In this paper, we
overcome this limitation by the use of deep learning methods, and we show that
this setting allows for the derivation of error bounds on the approximation of
conditional expectations. Numerical examples based on a two-person zero-sum
game, as well as on Asian and barrier option pricing, are presented. In
comparison with other deep learning approaches, our algorithm appears to be
more accurate, especially in large dimensions.

Video surveillance systems are increasingly used in different fields, from
the domestic to the commercial environment. Current systems are being improved
and complemented with new elements and functionalities. This paper proposes the
design of a video surveillance robot based on Raspberry Pi with the abilities
to perform tasks of motion detection, send video on real time, fire detection
and also, the possibility of control it remotely from the Internet. In order to
check the information received from the robot, as well as the video sent, a
client application has been developed to any device with an Internet
connection. In addition to this, in order to protect the information obtained
by the robot, a secure system is proposed, which uses different security
mechanisms to achieve this goal.

Time-Series Forecasting is a powerful data modeling discipline that analyzes
historical observations to predict future values of a time-series. It has been
utilized in numerous applications, including but not limited to economics,
meteorology, and health. In this paper, we use time-series forecasting
techniques to model and predict the future incidence of chickenpox. To achieve
this, we implement and simulate multiple models and data preprocessing
techniques on a Hungary-collected dataset. We demonstrate that the LSTM model
outperforms all other models in the vast majority of the experiments in terms
of county-level forecasting, whereas the SARIMAX model performs best at the
national level. We also demonstrate that the performance of the traditional
data preprocessing method is inferior to that of the data preprocessing method
that we have proposed.

Score-based generative modelling (SGM) has proven to be a very effective
method for modelling densities on finite-dimensional spaces. In this work we
propose to extend this methodology to learn generative models over functional
spaces. To do so, we represent functional data in spectral space to dissociate
the stochastic part of the processes from their space-time part. Using
dimensionality reduction techniques we then sample from their stochastic
component using finite dimensional SGM. We demonstrate our method's
effectiveness for modelling various multimodal datasets.

Reinforcement Learning (RL) has witnessed great strides for quadruped
locomotion, with continued progress in the reliable sim-to-real transfer of
policies. However, it remains a challenge to reuse a policy on another robot,
which could save time for retraining. In this work, we present a framework for
zero-shot policy retargeting wherein diverse motor skills can be transferred
between robots of different shapes and sizes. The new framework centers on a
planning-and-control pipeline that systematically integrates RL and Model
Predictive Control (MPC). The planning stage employs RL to generate a
dynamically plausible trajectory as well as the contact schedule, avoiding the
combinatorial complexity of contact sequence optimization. This information is
then used to seed the MPC to stabilize and robustify the policy roll-out via a
new Hybrid Kinodynamic (HKD) model that implicitly optimizes the foothold
locations. Hardware results show an ability to transfer policies from both the
A1 and Laikago robots to the MIT Mini Cheetah robot without requiring any
policy re-tuning.

We propose a deep learning method for the numerical solution of partial
differential equations that arise as gradient flows. The method relies on the
Brezis--Ekeland principle, which naturally defines an objective function to be
minimized, and so is ideally suited for a machine learning approach using deep
neural networks. We describe our approach in a general framework and illustrate
the method with the help of an example implementation for the heat equation in
space dimensions two to seven.

The DAMA/LIBRA observation of an annual modulation in the detection rate
compatible with that expected for dark matter particles from the galactic halo
has accumulated evidence for more than twenty years. It is the only hint of a
direct detection of the elusive dark matter, but it is in strong tension with
the negative results of other very sensitive experiments, requiring ad-hoc
scenarios to reconcile all the present experimental results. Testing the
DAMA/LIBRA result using the same target material, NaI(Tl), removes the
dependence on the particle and halo models and is the goal of the ANAIS-112
experiment, taking data at the Canfranc Underground Laboratory in Spain since
August 2017 with 112.5 kg of NaI(Tl). At very low energies, the detection rate
is dominated by non-bulk scintillation events and careful event selection is
mandatory. This article summarizes the efforts devoted to better characterize
and filter this contribution in ANAIS-112 data using a boosted decision tree
(BDT), trained for this goal with high efficiency. We report on the selection
of the training populations, the procedure to determine the optimal cut on the
BDT parameter, the estimate of the efficiencies for the selection of bulk
scintillation in the region of interest (ROI), and the evaluation of the
performance of this analysis with respect to the previous filtering. The
improvement achieved in background rejection in the ROI, but moreover, the
increase in detection efficiency, push the ANAIS-112 sensitivity to test the
DAMA/LIBRA annual modulation result beyond 3$\sigma$ with three-year exposure,
being possible to reach 5$\sigma$ by extending the data taking for a few more
years than the scheduled 5 years which were due in August 2022.

In the literature on game-theoretic equilibrium finding, focus has mainly
been on solving a single game in isolation. In practice, however, strategic
interactions -- ranging from routing problems to online advertising auctions --
evolve dynamically, thereby leading to many similar games to be solved. To
address this gap, we introduce meta-learning for equilibrium finding and
learning to play games. We establish the first meta-learning guarantees for a
variety of fundamental and well-studied classes of games, including two-player
zero-sum games, general-sum games, and Stackelberg games. In particular, we
obtain rates of convergence to different game-theoretic equilibria that depend
on natural notions of similarity between the sequence of games encountered,
while at the same time recovering the known single-game guarantees when the
sequence of games is arbitrary. Along the way, we prove a number of new results
in the single-game regime through a simple and unified framework, which may be
of independent interest. Finally, we evaluate our meta-learning algorithms on
endgames faced by the poker agent Libratus against top human professionals. The
experiments show that games with varying stack sizes can be solved
significantly faster using our meta-learning techniques than by solving them
separately, often by an order of magnitude.

Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by
learning the correlation between the input graphs and labels. However, by
presenting a graph classification investigation on the training graphs with
severe bias, surprisingly, we discover that GNNs always tend to explore the
spurious correlations to make decision, even if the causal correlation always
exists. This implies that existing GNNs trained on such biased datasets will
suffer from poor generalization capability. By analyzing this problem in a
causal view, we find that disentangling and decorrelating the causal and bias
latent variables from the biased graphs are both crucial for debiasing.
Inspiring by this, we propose a general disentangled GNN framework to learn the
causal substructure and bias substructure, respectively. Particularly, we
design a parameterized edge mask generator to explicitly split the input graph
into causal and bias subgraphs. Then two GNN modules supervised by
causal/bias-aware loss functions respectively are trained to encode causal and
bias subgraphs into their corresponding representations. With the disentangled
representations, we synthesize the counterfactual unbiased training samples to
further decorrelate causal and bias variables. Moreover, to better benchmark
the severe bias problem, we construct three new graph datasets, which have
controllable bias degrees and are easier to visualize and explain. Experimental
results well demonstrate that our approach achieves superior generalization
performance over existing baselines. Furthermore, owing to the learned edge
mask, the proposed model has appealing interpretability and transferability.
Code and data are available at: https://github.com/googlebaba/DisC.

In intelligent manufacturing, the quality of machine translation engineering
drawings will directly affect its manufacturing accuracy. Currently, most of
the work is manually translated, greatly reducing production efficiency. This
paper proposes an automatic translation method for welded structural
engineering drawings based on Cyclic Generative Adversarial Networks
(CycleGAN). The CycleGAN network model of unpaired transfer learning is used to
learn the feature mapping of real welding engineering drawings to realize
automatic translation of engineering drawings. U-Net and PatchGAN are the main
network for the generator and discriminator, respectively. Based on removing
the identity mapping function, a high-dimensional sparse network is proposed to
replace the traditional dense network for the Cyclegan generator to improve
noise robustness. Increase the residual block hidden layer to increase the
resolution of the generated graph. The improved and fine-tuned network models
are experimentally validated, computing the gap between real and generated
data. It meets the welding engineering precision standard and solves the main
problem of low drawing recognition efficiency in the welding manufacturing
process. The results show. After training with our model, the PSNR, SSIM and
MSE of welding engineering drawings reach about 44.89%, 99.58% and 2.11,
respectively, which are superior to traditional networks in both training speed
and accuracy.

Adversarial training has been demonstrated to be one of the most effective
remedies for defending adversarial examples, yet it often suffers from the huge
robustness generalization gap on unseen testing adversaries, deemed as the
\emph{adversarially robust generalization problem}. Despite the preliminary
understandings devoted on adversarially robust generalization, little is known
from the architectural perspective. Thus, this paper tries to bridge the gap by
systematically examining the most representative architectures (e.g., Vision
Transformers and CNNs). In particular, we first comprehensively evaluated
\emph{20} adversarially trained architectures on ImageNette and CIFAR-10
datasets towards several adversaries (multiple $\ell_p$-norm adversarial
attacks), and found that Vision Transformers (e.g., PVT, CoAtNet) often yield
better adversarially robust generalization. To further understand what
architectural ingredients favor adversarially robust generalization, we delve
into several key building blocks and revealed the fact via the lens of
Rademacher complexity that the higher weight sparsity contributes significantly
towards the better adversarially robust generalization of Vision Transformers,
which can be often achieved by attention layers. Our extensive studies
discovered the close relationship between architectural design and
adversarially robust generalization, and instantiated several important
insights. We hope our findings could help to better understand the mechanism
towards designing robust deep learning architectures.

Heavy equipment manufacturing splits specific contours in drawings and cuts
sheet metal to scale for welding. Currently, most of the segmentation and
extraction of weld map contours is achieved manually. Its efficiency is greatly
reduced. Therefore, we propose a U-net-based contour segmentation and
extraction method for welding engineering drawings. The contours of the parts
required for engineering drawings can be automatically divided and blanked,
which significantly improves manufacturing efficiency. U-net includes an
encoder-decoder, which implements end-to-end mapping through semantic
differences and spatial location feature information between the encoder and
decoder. While U-net excels at segmenting medical images, our extensive
experiments on the Welding Structural Diagram dataset show that the classic
U-Net architecture falls short in segmenting welding engineering drawings.
Therefore, we design a novel Channel Spatial Sequence Attention Module (CSSAM)
and improve on the classic U-net. At the same time, vertical max pooling and
average horizontal pooling are proposed. Pass the pooling operation through two
equal convolutions into the CSSAM module. The output and the features before
pooling are fused by semantic clustering, which replaces the traditional jump
structure and effectively narrows the semantic gap between the encoder and the
decoder, thereby improving the segmentation performance of welding engineering
drawings. We use vgg16 as the backbone network. Compared with the classic
U-net, our network has good performance in engineering drawing dataset

We present a first-principles study of the electronic and magnetic properties
of epitaxial interfaces between the Heusler compounds Ti$_2$MnIn and Ni$_2$MnIn
and the III-V semiconductors, InSb and InAs, respectively. We use density
functional theory (DFT) with a machine-learned Hubbard $U$ correction
determined by Bayesian optimization. We evaluate these interfaces for
prospective applications in Majorana-based quantum computing and spintronics.
In both interfaces, states from the Heusler penetrate into the gap of the
semiconductor, decaying within a few atomic layers. The magnetic interactions
at the interface are weak and local in space and energy. Magnetic moments of
less than 0.1 $\mu_B$ are induced in the two atomic layers closest to the
interface. The induced spin polarization around the Fermi level of the
semiconductor also decays within a few atomic layers. The decisive factor for
the induced spin polarization around the Fermi level of the semiconductor is
the spin polarization around the Fermi level in the Heusler, rather than the
overall magnetic moment. As a result, the ferrimagnetic narrow-gap
semiconductor Ti$_2$MnIn induces a more significant spin polarization in the
InSb than the ferromagnetic metal Ni$_2$MnIn induces in the InAs. This is
explained by the position of the transition metal $d$ states in the Heusler
with respect to the Fermi level. Based on our results, these interfaces are
unlikely to be useful for Majorana devices but could be of interest for
spintronics.

The development of unsupervised hashing is advanced by the recent popular
contrastive learning paradigm. However, previous contrastive learning-based
works have been hampered by (1) insufficient data similarity mining based on
global-only image representations, and (2) the hash code semantic loss caused
by the data augmentation. In this paper, we propose a novel method, namely
Weighted Contrative Hashing (WCH), to take a step towards solving these two
problems. We introduce a novel mutual attention module to alleviate the problem
of information asymmetry in network features caused by the missing image
structure during contrative augmentation. Furthermore, we explore the
fine-grained semantic relations between images, i.e., we divide the images into
multiple patches and calculate similarities between patches. The aggregated
weighted similarities, which reflect the deep image relations, are distilled to
facilitate the hash codes learning with a distillation loss, so as to obtain
better retrieval performance. Extensive experiments show that the proposed WCH
significantly outperforms existing unsupervised hashing methods on three

Thanks to recent advances in deep learning, sophisticated generation tools
exist, nowadays, that produce extremely realistic synthetic speech. However,
malicious uses of such tools are possible and likely, posing a serious threat
to our society. Hence, synthetic voice detection has become a pressing research
topic, and a large variety of detection methods have been recently proposed.
Unfortunately, they hardly generalize to synthetic audios generated by tools
never seen in the training phase, which makes them unfit to face real-world
scenarios. In this work, we aim at overcoming this issue by proposing a new
detection approach that leverages only the biometric characteristics of the
speaker, with no reference to specific manipulations. Since the detector is
trained only on real data, generalization is automatically ensured. The
proposed approach can be implemented based on off-the-shelf speaker
verification tools. We test several such solutions on three popular test sets,
obtaining good performance, high generalization ability, and high robustness to
audio impairment.

Medical image classification is one of the most critical problems in the
image recognition area. One of the major challenges in this field is the
scarcity of labelled training data. Additionally, there is often class
imbalance in datasets as some cases are very rare to happen. As a result,
accuracy in classification task is normally low. Deep Learning models, in
particular, show promising results on image segmentation and classification
problems, but they require very large datasets for training. Therefore, there
is a need to generate more of synthetic samples from the same distribution.
Previous work has shown that feature generation is more efficient and leads to
better performance than corresponding image generation. We apply this idea in
the Medical Imaging domain. We use transfer learning to train a segmentation
model for the small dataset for which gold-standard class annotations are
available. We extracted the learnt features and use them to generate synthetic
features conditioned on class labels, using Auxiliary Classifier GAN (ACGAN).
We test the quality of the generated features in a downstream classification
task for brain tumors according to their severity level. Experimental results
show a promising result regarding the validity of these generated features and
their overall contribution to balancing the data and improving the
classification class-wise accuracy.

In the era of a data-driven society with the ubiquity of Internet of Things
(IoT) devices storing large amounts of data localized at different places,
distributed learning has gained a lot of traction, however, assuming
independent and identically distributed data (iid) across the devices. While
relaxing this assumption that anyway does not hold in reality due to the
heterogeneous nature of devices, federated learning (FL) has emerged as a
privacy-preserving solution to train a collaborative model over non-iid data
distributed across a massive number of devices. However, the appearance of
malicious devices (attackers), who intend to corrupt the FL model, is
inevitable due to unrestricted participation. In this work, we aim to identify
such attackers and mitigate their impact on the model, essentially under a
setting of bidirectional label flipping attacks with collusion. We propose two
graph theoretic algorithms, based on Minimum Spanning Tree and k-Densest graph,
by leveraging correlations between local models. Our FL model can nullify the
influence of attackers even when they are up to 70% of all the clients whereas
prior works could not afford more than 50% of clients as attackers. The
effectiveness of our algorithms is ascertained through experiments on two
benchmark datasets, namely MNIST and Fashion-MNIST, with overwhelming
attackers. We establish the superiority of our algorithms over the existing
ones using accuracy, attack success rate, and early detection round.

Detection of offensive language in social media is one of the key challenges
for social media. Researchers have proposed many advanced methods to accomplish
this task. In this report, we try to use the learnings from their approach and
incorporate our ideas to improve upon them. We have successfully achieved an
accuracy of 74% in classifying offensive tweets. We also list upcoming
challenges in the abusive content detection in the social media world.

We present a framework to integrate tensor network (TN) methods with
reinforcement learning (RL) for solving dynamical optimisation tasks. We
consider the RL actor-critic method, a model-free approach for solving RL
problems, and introduce TNs as the approximators for its policy and value
functions. Our "actor-critic with tensor networks" (ACTeN) method is especially
well suited to problems with large and factorisable state and action spaces. As
an illustration of the applicability of ACTeN we solve the exponentially hard
task of sampling rare trajectories in two paradigmatic stochastic models, the
East model of glasses and the asymmetric simple exclusion process (ASEP), the
latter being particularly challenging to other methods due to the absence of
detailed balance. With substantial potential for further integration with the
vast array of existing RL methods, the approach introduced here is promising
both for applications in physics and to multi-agent RL problems more generally.

Nowadays, the development of information technology is growing rapidly. In
the big data era, the privacy of personal information has been more pronounced.
The major challenge is to find a way to guarantee that sensitive personal
information is not disclosed while data is published and analyzed. Centralized
differential privacy is established on the assumption of a trusted third-party
data curator. However, this assumption is not always true in reality. As a new
privacy preservation model, local differential privacy has relatively strong
privacy guarantees. Although federated learning has relatively been a
privacy-preserving approach for distributed learning, it still introduces
various privacy concerns. To avoid privacy threats and reduce communication
costs, in this article, we propose integrating federated learning and local
differential privacy with momentum gradient descent to improve the performance
of machine learning models.

POST-traumatic stress disorder (PTSD) is a chronic and debilitating mental
condition that is developed in response to catastrophic life events, such as
military combat, sexual assault, and natural disasters. PTSD is characterized
by flashbacks of past traumatic events, intrusive thoughts, nightmares,
hypervigilance, and sleep disturbance, all of which affect a person's life and
lead to considerable social, occupational, and interpersonal dysfunction. The
diagnosis of PTSD is done by medical professionals using self-assessment
questionnaire of PTSD symptoms as defined in the Diagnostic and Statistical
Manual of Mental Disorders (DSM). In this paper, and for the first time, we
collected, annotated, and prepared for public distribution a new video database
for automatic PTSD diagnosis, called PTSD in the wild dataset. The database
exhibits "natural" and big variability in acquisition conditions with different
pose, facial expression, lighting, focus, resolution, age, gender, race,
occlusions and background. In addition to describing the details of the dataset
collection, we provide a benchmark for evaluating computer vision and machine
learning based approaches on PTSD in the wild dataset. In addition, we propose
and we evaluate a deep learning based approach for PTSD detection in respect to
the given benchmark. The proposed approach shows very promising results.
Interested researcher can download a copy of PTSD-in-the wild dataset from:
http://www.lissi.fr/PTSD-Dataset/

Tensor Robust Principal Component Analysis (TRPCA), which aims to recover a
low-rank tensor corrupted by sparse noise, has attracted much attention in many
real applications. This paper develops a new Global Weighted TRPCA method
(GWTRPCA), which is the first approach simultaneously considers the
significance of intra-frontal slice and inter-frontal slice singular values in
the Fourier domain. Exploiting this global information, GWTRPCA penalizes the
larger singular values less and assigns smaller weights to them. Hence, our
method can recover the low-tubal-rank components more exactly. Moreover, we
propose an effective adaptive weight learning strategy by a Modified Cauchy
Estimator (MCE) since the weight setting plays a crucial role in the success of
GWTRPCA. To implement the GWTRPCA method, we devise an optimization algorithm
using an Alternating Direction Method of Multipliers (ADMM) method. Experiments
on real-world datasets validate the effectiveness of our proposed method.

The similarity learning problem in the oil \& gas industry aims to construct
a model that estimates similarity between interval measurements for logging
data. Previous attempts are mostly based on empirical rules, so our goal is to
automate this process and exclude expensive and time-consuming expert
labelling.
  One of the approaches for similarity learning is self-supervised learning
(SSL). In contrast to the supervised paradigm, this one requires little or no
labels for the data. Thus, we can learn such models even if the data labelling
is absent or scarce. Nowadays, most SSL approaches are contrastive and
non-contrastive. However, due to possible wrong labelling of positive and
negative samples, contrastive methods don't scale well with the number of
objects. Non-contrastive methods don't rely on negative samples. Such
approaches are actively used in the computer vision.
  We introduce non-contrastive SSL for time series data. In particular, we
build on top of BYOL and Barlow Twins methods that avoid using negative pairs
and focus only on matching positive pairs. The crucial part of these methods is
an augmentation strategy. Different augmentations of time series exist, while
their effect on the performance can be both positive and negative. Our
augmentation strategies and adaption for BYOL and Barlow Twins together allow
us to achieve a higher quality (ARI $= 0.49$) than other self-supervised
methods (ARI $= 0.34$ only), proving usefulness of the proposed non-contrastive
self-supervised approach for the interval similarity problem and time series
representation learning in general.

A recent trend in speech processing is the use of embeddings created through
machine learning models trained on a specific task with large datasets. By
leveraging the knowledge already acquired, these models can be reused in new
tasks where the amount of available data is small. This paper proposes a
pipeline to create a new model, called Mel and Wave Embeddings for Human Voice
Tasks (MeWEHV), capable of generating robust embeddings for speech processing.
MeWEHV combines the embeddings generated by a pre-trained raw audio waveform
encoder model, and deep features extracted from Mel Frequency Cepstral
Coefficients (MFCCs) using Convolutional Neural Networks (CNNs). We evaluate
the performance of MeWEHV on three tasks: speaker, language, and accent
identification. For the first one, we use the VoxCeleb1 dataset and present
YouSpeakers204, a new and publicly available dataset for English speaker
identification that contains 19607 audio clips from 204 persons speaking in six
different accents, allowing other researchers to work with a very balanced
dataset, and to create new models that are robust to multiple accents. For
evaluating the language identification task, we use the VoxForge and Common
Language datasets. Finally, for accent identification, we use the Latin
American Spanish Corpora (LASC) and Common Voice datasets. Our approach allows
a significant increase in the performance of state-of-the-art models on all the
tested datasets, with a low additional computational cost.

The increasing prevalence of neural networks (NNs) in safety-critical
applications calls for methods to certify safe behavior. This paper presents a
backward reachability approach for safety verification of neural feedback loops
(NFLs), i.e., closed-loop systems with NN control policies. While recent works
have focused on forward reachability as a strategy for safety certification of
NFLs, backward reachability offers advantages over the forward strategy,
particularly in obstacle avoidance scenarios. Prior works have developed
techniques for backward reachability analysis for systems without NNs, but the
presence of NNs in the feedback loop presents a unique set of problems due to
the nonlinearities in their activation functions and because NN models are
generally not invertible. To overcome these challenges, we use existing forward
NN analysis tools to efficiently find an over-approximation of the
backprojection (BP) set, i.e., the set of states for which the NN control
policy will drive the system to a given target set. We present frameworks for
calculating BP over-approximations for both linear and nonlinear systems with
control policies represented by feedforward NNs and propose computationally
efficient strategies. We use numerical results from a variety of models to
showcase the proposed algorithms, including a demonstration of safety
certification for a 6D system.

Convolutional neural network (CNN) becomes one of the most popular and
prominent deep learning architectures for computer vision, but its black box
feature hides the internal prediction process. For this reason, AI
practitioners have shed light on explainable AI to provide the interpretability
of the model behavior. In particular, class activation map (CAM) and Grad-CAM
based methods have shown promise results, but they have architectural
limitation or gradient computing burden. To resolve these, Score-CAM has been
suggested as a gradient-free method, however, it requires more execution time
compared to CAM or Grad-CAM based methods. Therefore, we propose a lightweight
architecture and gradient free Reciprocal CAM (Recipro-CAM) by spatially
masking the extracted feature maps to exploit the correlation between
activation maps and network outputs. With the proposed method, we achieved the
gains of 1:78 - 3:72% in the ResNet family compared to Score-CAM in Average
Drop- Coherence-Complexity (ADCC) metric, excluding the VGG-16 (1:39% drop). In
addition, Recipro-CAM exhibits a saliency map generation rate similar to
Grad-CAM and approximately 148 times faster than Score-CAM.

One of the vital breakthroughs in the history of machine translation is the
development of the Transformer model. Not only it is revolutionary for various
translation tasks, but also for a majority of other NLP tasks. In this paper,
we aim at a Transformer-based system that is able to translate a source
sentence in German to its counterpart target sentence in English. We perform
the experiments on the news commentary German-English parallel sentences from
the WMT'13 dataset. In addition, we investigate the effect of the inclusion of
additional general-domain data in training from the IWSLT'16 dataset to improve
the Transformer model performance. We find that including the IWSLT'16 dataset
in training helps achieve a gain of 2 BLEU score points on the test set of the
WMT'13 dataset. Qualitative analysis is introduced to analyze how the usage of
general-domain data helps improve the quality of the produced translation
sentences.

Information has exploded on the Internet and mobile with the advent of the
big data era. In particular, recommendation systems are widely used to help
consumers who struggle to select the best products among such a large amount of
information. However, recommendation systems are vulnerable to malicious user
biases, such as fake reviews to promote or demote specific products, as well as
attacks that steal personal information. Such biases and attacks compromise the
fairness of the recommendation model and infringe the privacy of users and
systems by distorting data.Recently, deep-learning collaborative filtering
recommendation systems have shown to be more vulnerable to this bias. In this
position paper, we examine the effects of bias that cause various ethical and
social issues, and discuss the need for designing the robust recommendation
system for fairness and stability.

Contrastive learning has recently achieved remarkable success in many domains
including graphs. However contrastive loss, especially for graphs, requires a
large number of negative samples which is unscalable and computationally
prohibitive with a quadratic time complexity. Sub-sampling is not optimal and
incorrect negative sampling leads to sampling bias. In this work, we propose a
meta-node based approximation technique that can (a) proxy all negative
combinations (b) in quadratic cluster size time complexity, (c) at graph level,
not node level, and (d) exploit graph sparsity. By replacing node-pairs with
additive cluster-pairs, we compute the negatives in cluster-time at graph
level. The resulting Proxy approximated meta-node Contrastive (PamC) loss,
based on simple optimized GPU operations, captures the full set of negatives,
yet is efficient with a linear time complexity. By avoiding sampling, we
effectively eliminate sample bias. We meet the criterion for larger number of
samples, thus achieving block-contrastiveness, which is proven to outperform
pair-wise losses. We use learnt soft cluster assignments for the meta-node
constriction, and avoid possible heterophily and noise added during edge
creation. Theoretically, we show that real world graphs easily satisfy
conditions necessary for our approximation. Empirically, we show promising
accuracy gains over state-of-the-art graph clustering on 6 benchmarks.
Importantly, we gain substantially in efficiency; up to 3x in training time,
1.8x in inference time and over 5x in GPU memory reduction.

This work proposes a novel reconfigurable architecture for low latency Graph
Neural Network (GNN) design specifically for particle detectors. Adopting
FPGA-based GNNs for particle detectors is challenging since it requires
sub-microsecond latency to deploy the networks for online event selection in
the Level-1 triggers for the CERN Large Hadron Collider experiments. This paper
proposes a custom code transformation with strength reduction for the matrix
multiplication operations in the interaction-network based GNNs with fully
connected graphs, which avoids the costly multiplication. It exploits sparsity
patterns as well as binary adjacency matrices, and avoids irregular memory
access, leading to a reduction in latency and improvement in hardware
efficiency. In addition, we introduce an outer-product based matrix
multiplication approach which is enhanced by the strength reduction for low
latency design. Also, a fusion step is introduced to further reduce the design
latency. Furthermore, an GNN-specific algorithm-hardware co-design approach is
presented which not only finds a design with a much better latency but also
finds a high accuracy design under a given latency constraint. Finally, a
customizable template for this low latency GNN hardware architecture has been
designed and open-sourced, which enables the generation of low-latency FPGA
designs with efficient resource utilization using a high-level synthesis tool.
Evaluation results show that our FPGA implementation is up to 24 times faster
and consumes up to 45 times less power than a GPU implementation. Compared to
our previous FPGA implementations, this work achieves 6.51 to 16.7 times lower
latency. Moreover, the latency of our FPGA design is sufficiently low to enable
deployment of GNNs in a sub-microsecond, real-time collider trigger system,
enabling it to benefit from improved accuracy.

Context: Cross-project defect prediction (CPDP) models are being developed to
optimize the testing resources. Objectives: Proposing an ensemble
classification framework for CPDP as many existing models are lacking with
better performances and analysing the main objectives of CPDP from the outcomes
of the proposed classification framework. Method: For the classification task,
we propose a bootstrap aggregation based hybrid-inducer ensemble learning
(HIEL) technique that uses probabilistic weighted majority voting (PWMV)
strategy. To know the impact of HIEL on the software project, we propose three
project-specific performance measures such as percent of perfect cleans (PPC),
percent of non-perfect cleans (PNPC), and false omission rate (FOR) from the
predictions to calculate the amount of saved cost, remaining service time, and
percent of the failures in the target project. Results: On many target projects
from PROMISE, NASA, and AEEEM repositories, the proposed model outperformed
recent works such as TDS, TCA+, HYDRA, TPTL, and CODEP in terms of F-measure.
In terms of AUC, the TCA+ and HYDRA models stand as strong competitors to the
HIEL model. Conclusion: For better predictions, we recommend ensemble learning
approaches for the CPDP models. And, to estimate the benefits from the CPDP
models, we recommend the above project-specific performance measures.

As a challenging task, text-to-image generation aims to generate
photo-realistic and semantically consistent images according to the given text
descriptions. Existing methods mainly extract the text information from only
one sentence to represent an image and the text representation effects the
quality of the generated image well. However, directly utilizing the limited
information in one sentence misses some key attribute descriptions, which are
the crucial factors to describe an image accurately. To alleviate the above
problem, we propose an effective text representation method with the
complements of attribute information. Firstly, we construct an attribute memory
to jointly control the text-to-image generation with sentence input. Secondly,
we explore two update mechanisms, sample-aware and sample-joint mechanisms, to
dynamically optimize a generalized attribute memory. Furthermore, we design an
attribute-sentence-joint conditional generator learning scheme to align the
feature embeddings among multiple representations, which promotes the
cross-modal network training. Experimental results illustrate that the proposed
method obtains substantial performance improvements on both the CUB (FID from
14.81 to 8.57) and COCO (FID from 21.42 to 12.39) datasets.

This work considers the scheduling problem for Urban Air Mobility (UAM)
vehicles travelling between origin-destination pairs with both hard and soft
trip deadlines. Each route is described by a discrete probability distribution
over trip completion times (or delay) and over inter-arrival times of requests
(or demand) for the route along with a fixed hard or soft deadline. Soft
deadlines carry a cost that is incurred when the deadline is missed. An online,
safe scheduler is developed that ensures that hard deadlines are never missed,
and that average cost of missing soft deadlines is minimized. The system is
modelled as a Markov Decision Process (MDP) and safe model-based learning is
used to find the probabilistic distributions over route delays and demand.
Monte Carlo Tree Search (MCTS) Earliest Deadline First (EDF) is used to safely
explore the learned models in an online fashion and develop a near-optimal
non-preemptive scheduling policy. These results are compared with Value
Iteration (VI) and MCTS (Random) scheduling solutions.

Active automata learning became a popular tool for the behavioral analysis of
communication protocols. The main advantage is that no manual modeling effort
is required since a behavioral model is automatically inferred from a black-box
system. However, several real-world applications of this technique show that
the overhead for the establishment of an active interface might hamper the
practical applicability. Our recent work on the active learning of Bluetooth
Low Energy (BLE) protocol found that the active interaction creates a
bottleneck during learning. Considering the automata learning toolset, passive
learning techniques appear as a promising solution since they do not require an
active interface to the system under learning. Instead, models are learned
based on a given data set. In this paper, we evaluate passive learning for two
network protocols: BLE and Message Queuing Telemetry Transport (MQTT). Our
results show that passive techniques can correctly learn with less data than
required by active learning. However, a general random data generation for
passive learning is more expensive compared to the costs of active learning.

Image animation aims to animate a source image by using motion learned from a
driving video. Current state-of-the-art methods typically use convolutional
neural networks (CNNs) to predict motion information, such as motion keypoints
and corresponding local transformations. However, these CNN based methods do
not explicitly model the interactions between motions; as a result, the
important underlying motion relationship may be neglected, which can
potentially lead to noticeable artifacts being produced in the generated
animation video. To this end, we propose a new method, the motion transformer,
which is the first attempt to build a motion estimator based on a vision
transformer. More specifically, we introduce two types of tokens in our
proposed method: i) image tokens formed from patch features and corresponding
position encoding; and ii) motion tokens encoded with motion information. Both
types of tokens are sent into vision transformers to promote underlying
interactions between them through multi-head self attention blocks. By adopting
this process, the motion information can be better learned to boost the model
performance. The final embedded motion tokens are then used to predict the
corresponding motion keypoints and local transformations. Extensive experiments
on benchmark datasets show that our proposed method achieves promising results
to the state-of-the-art baselines. Our source code will be public available.

Text detection in natural scene images has applications for autonomous
driving, navigation help for elderly and blind people. However, the research on
Urdu text detection is usually hindered by lack of data resources. We have
developed a dataset of scene images with Urdu text. We present the use of
machine learning methods to perform detection of Urdu text from the scene
images. We extract text regions using channel enhanced Maximally Stable
Extremal Region (MSER) method. First, we classify text and noise based on their
geometric properties. Next, we use a support vector machine for early
discarding of non-text regions. To further remove the non-text regions, we use
histogram of oriented gradients (HoG) features obtained and train a second SVM
classifier. This improves the overall performance on text region detection
within the scene images. To support research on Urdu text, We aim to make the
data freely available for research use. We also aim to highlight the challenges
and the research gap for Urdu text detection.

Visual oddity task was conceived as a universal ethnic-independent analytic
intelligence test for humans. Advancements in artificial intelligence led to
important breakthroughs, yet competing with humans on such analytic
intelligence tasks remains challenging and typically resorts to
non-biologically-plausible architectures. We present a biologically realistic
system that receives inputs from synthetic eye movements - saccades, and
processes them with neurons incorporating dynamics of neocortical neurons. We
introduce a procedurally generated visual oddity dataset to train an
architecture extending conventional relational networks and our proposed
system. Both approaches surpass the human accuracy, and we uncover that both
share the same essential underlying mechanism of reasoning. Finally, we show
that the biologically inspired network achieves superior accuracy, learns
faster and requires fewer parameters than the conventional network.

This paper aims to synthesize a reachability controller for an unknown
dynamical system. We first learn the unknown system using Gaussian processes
and the (probabilistic) guarantee on the learned model. Then we use the
funnel-based controller synthesis approach using this approximated dynamical
system to design the controller for a reachability specification. Finally, the
merits of the proposed method are shown using a numerical example.

Machine learning is becoming ubiquitous. From financial to medicine, machine
learning models are boosting decision-making processes and even outperforming
humans in some tasks. This huge progress in terms of prediction quality does
not however find a counterpart in the security of such models and corresponding
predictions, where perturbations of fractions of the training set (poisoning)
can seriously undermine the model accuracy. Research on poisoning attacks and
defenses even predates the introduction of deep neural networks, leading to
several promising solutions. Among them, ensemble-based defenses, where
different models are trained on portions of the training set and their
predictions are then aggregated, are getting significant attention, due to
their relative simplicity and theoretical and practical guarantees. The work in
this paper designs and implements a hash-based ensemble approach for ML
robustness and evaluates its applicability and performance on random forests, a
machine learning model proved to be more resistant to poisoning attempts on
tabular datasets. An extensive experimental evaluation is carried out to
evaluate the robustness of our approach against a variety of attacks, and
compare it with a traditional monolithic model based on random forests.

We define a novel neuro-symbolic framework, argumentative reward learning,
which combines preference-based argumentation with existing approaches to
reinforcement learning from human feedback. Our method improves prior work by
generalising human preferences, reducing the burden on the user and increasing
the robustness of the reward model. We demonstrate this with a number of

Coronary Heart Disease (CHD) is a leading cause of death in the modern world.
The development of modern analytical tools for diagnostics and treatment of CHD
is receiving substantial attention from the scientific community. Deep
learning-based algorithms, such as segmentation networks and detectors, play an
important role in assisting medical professionals by providing timely analysis
of a patient's angiograms. This paper focuses on X-Ray Coronary Angiography
(XCA), which is considered to be a "gold standard" in the diagnosis and
treatment of CHD. First, we describe publicly available datasets of XCA images.
Then, classical and modern techniques of image preprocessing are reviewed. In
addition, common frame selection techniques are discussed, which are an
important factor of input quality and thus model performance. In the following
two chapters we discuss modern vessel segmentation and stenosis detection
networks and, finally, open problems and current limitations of the current

Physics-Informed Neural Networks (PINN) emerged as a powerful tool for
solving scientific computing problems, ranging from the solution of Partial
Differential Equations to data assimilation tasks. One of the advantages of
using PINN is to leverage the usage of Machine Learning computational
frameworks relying on the combined usage of CPUs and co-processors, such as
accelerators, to achieve maximum performance. This work investigates the
design, implementation, and performance of PINNs, using the Quantum Processing
Unit (QPU) co-processor. We design a simple Quantum PINN to solve the
one-dimensional Poisson problem using a Continuous Variable (CV) quantum
computing framework. We discuss the impact of different optimizers, PINN
residual formulation, and quantum neural network depth on the quantum PINN
accuracy. We show that the optimizer exploration of the training landscape in
the case of quantum PINN is not as effective as in classical PINN, and basic
Stochastic Gradient Descent (SGD) optimizers outperform adaptive and high-order
optimizers. Finally, we highlight the difference in methods and algorithms
between quantum and classical PINNs and outline future research challenges for
quantum PINN development.

We introduce a fully quantum generative adversarial network intended for use
with binary data. The architecture incorporates several features found in other
classical and quantum machine learning models, which up to this point had not
been used in conjunction. In particular, we incorporate noise reuploading in
the generator, auxiliary qubits in the discriminator to enhance expressivity,
and a direct connection between the generator and discriminator circuits,
obviating the need to access the generator's probability distribution. We show
that, as separate components, the generator and discriminator perform as
desired. We empirically demonstrate the expressive power of our model on both
synthetic data as well as low energy states of an Ising model. Our
demonstrations suggest that the model is not only capable of reproducing
discrete training data, but also of potentially generalizing from it.

We present a study on the development of Finnish first-year physics majors'
attitudes towards physics, as measured by the Colorado Learning Attitudes about
Science Survey, before, during and after the period of mandatory remote
learning due to the coronavirus pandemic. We find that in the years before the
pandemic, these attitudes did not change, but the period of extended remote
learning due to the pandemic had a negative effect on the students' expert-like
views. Similarly, the students who experienced the remote learning period in
high school displayed a lower level of expert-like thinking as they entered
university. As contact teaching resumed, moderate positive gains were seen,
bridging some but not all of the gap in student attitudes left by the pandemic.

The exceptional progress in the field of machine learning (ML) in recent
years has attracted a lot of interest in using this technology in aviation.
Possible airborne applications of ML include safety-critical functions, which
must be developed in compliance with rigorous certification standards of the
aviation industry. Current certification standards for the aviation industry
were developed prior to the ML renaissance without taking specifics of ML
technology into account. There are some fundamental incompatibilities between
traditional design assurance approaches and certain aspects of ML-based
systems. In this paper, we analyze the current airborne certification standards
and show that all objectives of the standards can be achieved for a
low-criticality ML-based system if certain assumptions about ML development
workflow are applied.

Incorporating knowledge graphs (KGs) as side information in recommendation
has recently attracted considerable attention. Despite the success in general
recommendation scenarios, prior methods may fall short of performance
satisfaction for the cold-start problem in which users are associated with very
limited interactive information. Since the conventional methods rely on
exploring the interaction topology, they may however fail to capture sufficient
information in cold-start scenarios. To mitigate the problem, we propose a
novel Knowledge-aware Neural Networks with Personalized Feature Referencing
Mechanism, namely KPER. Different from most prior methods which simply enrich
the targets' semantics from KGs, e.g., product attributes, KPER utilizes the
KGs as a "semantic bridge" to extract feature references for cold-start users
or items. Specifically, given cold-start targets, KPER first probes
semantically relevant but not necessarily structurally close users or items as
adaptive seeds for referencing features. Then a Gated Information Aggregation
module is introduced to learn the combinatorial latent features for cold-start
users and items. Our extensive experiments over four real-world datasets show
that, KPER consistently outperforms all competing methods in cold-start
scenarios, whilst maintaining superiority in general scenarios without
compromising overall performance, e.g., by achieving 0.81%-16.08% and
1.01%-14.49% performance improvement across all datasets in Top-10
recommendation.

In recent years, implicit surface representations through neural networks
that encode the signed distance have gained popularity and have achieved
state-of-the-art results in various tasks (e.g. shape representation, shape
reconstruction, and learning shape priors). However, in contrast to
conventional shape representations such as polygon meshes, the implicit
representations cannot be easily edited and existing works that attempt to
address this problem are extremely limited. In this work, we propose the first
method for efficient interactive editing of signed distance functions expressed
through neural networks, allowing free-form editing. Inspired by 3D sculpting
software for meshes, we use a brush-based framework that is intuitive and can
in the future be used by sculptors and digital artists. In order to localize
the desired surface deformations, we regulate the network by using a copy of it
to sample the previously expressed surface. We introduce a novel framework for
simulating sculpting-style surface edits, in conjunction with interactive
surface sampling and efficient adaptation of network weights. We qualitatively
and quantitatively evaluate our method in various different 3D objects and
under many different edits. The reported results clearly show that our method
yields high accuracy, in terms of achieving the desired edits, while at the
same time preserving the geometry outside the interaction areas.

Policy-gradient methods are widely used for learning control policies. They
can be easily distributed to multiple workers and reach state-of-the-art
results in many domains. Unfortunately, they exhibit large variance and
subsequently suffer from high-sample complexity since they aggregate gradients
over entire trajectories. At the other extreme, planning methods, like tree
search, optimize the policy using single-step transitions that consider future
lookahead. These approaches have been mainly considered for value-based
algorithms. Planning-based algorithms require a forward model and are
computationally intensive at each step, but are more sample efficient. In this
work, we introduce SoftTreeMax, the first approach that integrates tree-search
into policy gradient. Traditionally, gradients are computed for single
state-action pairs. Instead, our tree-based policy structure leverages all
gradients at the tree leaves in each environment step. This allows us to reduce
the variance of gradients by three orders of magnitude and to benefit from
better sample complexity compared with standard policy gradient. On Atari,
SoftTreeMax demonstrates up to 5x better performance in faster run-time
compared with distributed PPO.

Anomaly-based Intrusion Detection System (IDS) has been a hot research topic
because of its ability to detect new threats rather than only memorized
signatures threats of signature-based IDS. Especially after the availability of
advanced technologies that increase the number of hacking tools and increase
the risk impact of an attack. The problem of any anomaly-based model is its
high false-positive rate. The high false-positive rate is the reason why
anomaly IDS is not commonly applied in practice. Because anomaly-based models
classify an unseen pattern as a threat where it may be normal but not included
in the training dataset. This type of problem is called overfitting where the
model is not able to generalize. Optimizing Anomaly-based models by having a
big training dataset that includes all possible normal cases may be an optimal
solution but could not be applied in practice. Although we can increase the
number of training samples to include much more normal cases, still we need a
model that has more ability to generalize. In this research paper, we propose
applying deep model instead of traditional models because it has more ability
to generalize. Thus, we will obtain less false-positive by using big data and
deep model. We made a comparison between machine learning and deep learning
algorithms in the optimization of anomaly-based IDS by decreasing the
false-positive rate. We did an experiment on the NSL-KDD benchmark and compared
our results with one of the best used classifiers in traditional learning in
IDS optimization. The experiment shows 10% lower false-positive by using deep
learning instead of traditional learning.

Graph Contrastive Learning (GCL) has been an emerging solution for graph
self-supervised learning. Existing GCL methods always adopt the
binary-contrastive setting: making binary decisions (positive/negative pairs)
on the generated views, pulling positive (similar) pairs close and pushing
negative (dissimilar) pairs far away. Despite promising performances, two
critical issues arise: (i) the validity of view construction cannot be
guaranteed: graph perturbation may produce invalid views against semantics and
intrinsic topology of graph data; (ii) the binary-contrastive setting is
unreliable: for non-euclidean graph data, positive (similar) pairs and negative
(dissimilar) pairs are very difficult to be decided. The two problems further
raise the research question: Is the binary-contrastive setting really necessary
for graph contrastive learning? In this paper, we investigate this research
question and introduce a novel GCL paradigm, namely Graph Soft-Contrastive
Learning (GSCL), which can conduct graph self-supervise learning via
neighborhood ranking without relying on the binary-contrastive setting.
Specifically, GSCL is built upon the basic assumption of graph homophily that
connected neighbors are more similar than far-distant nodes. Then, under the
GSCL paradigm, we develop pair-wise and list-wise Gated Ranking infoNCE Loss
functions to preserve the relative ranking relationship in the neighborhood.
Moreover, as the neighborhood size exponentially expands with more hops
considered, we propose neighborhood sampling strategies to improve learning
efficiency. Extensive experimental results show that GSCL can achieve
competitive or even superior performance compared with current state-of-the-art
GCL methods. We expect our work can stimulate more research efforts to jump out
of the traditional binary-contrastive setting and conform to the inherent
characteristics and properties of graphs.

We propose using a two-layered deployment of machine learning models to
prevent adversarial attacks. The first layer determines whether the data was
tampered, while the second layer solves a domain-specific problem. We explore
three sets of features and three dataset variations to train machine learning
models. Our results show clustering algorithms achieved promising results. In
particular, we consider the best results were obtained by applying the DBSCAN
algorithm to the structured structural similarity index measure computed
between the images and a white reference image.

With the growing use of information technology in all life domains, hacking
has become more negatively effective than ever before. Also with developing
technologies, attacks numbers are growing exponentially every few months and
become more sophisticated so that traditional IDS becomes inefficient detecting
them. This paper proposes a solution to detect not only new threats with higher
detection rate and lower false positive than already used IDS, but also it
could detect collective and contextual security attacks. We achieve those
results by using Networking Chatbot, a deep recurrent neural network: Long
Short Term Memory (LSTM) on top of Apache Spark Framework that has an input of
flow traffic and traffic aggregation and the output is a language of two words,
normal or abnormal. We propose merging the concepts of language processing,
contextual analysis, distributed deep learning, big data, anomaly detection of
flow analysis. We propose a model that describes the network abstract normal
behavior from a sequence of millions of packets within their context and
analyzes them in near real-time to detect point, collective and contextual
anomalies. Experiments are done on MAWI dataset, and it shows better detection
rate not only than signature IDS, but also better than traditional anomaly IDS.
The experiment shows lower false positive, higher detection rate and better
point anomalies detection. As for prove of contextual and collective anomalies
detection, we discuss our claim and the reason behind our hypothesis. But the
experiment is done on random small subsets of the dataset because of hardware
limitations, so we share experiment and our future vision thoughts as we wish
that full prove will be done in future by other interested researchers who have
better hardware infrastructure than ours.

In this research, we develop machine learning models to predict future sensor
readings of a waste-to-fuel plant, which would enable proactive control of the
plant's operations. We developed models that predict sensor readings for 30 and
60 minutes into the future. The models were trained using historical data, and
predictions were made based on sensor readings taken at a specific time. We
compare three types of models: (a) a n\"aive prediction that considers only the
last predicted value, (b) neural networks that make predictions based on past
sensor data (we consider different time window sizes for making a prediction),
and (c) a gradient boosted tree regressor created with a set of features that
we developed. We developed and tested our models on a real-world use case at a
waste-to-fuel plant in Canada. We found that approach (c) provided the best
results, while approach (b) provided mixed results and was not able to
outperform the n\"aive consistently.

Natural Language Inference (NLI) is a hot topic research in natural language
processing, contradiction detection between sentences is a special case of NLI.
This is considered a difficult NLP task which has a big influence when added as
a component in many NLP applications, such as Question Answering Systems, text
Summarization. Arabic Language is one of the most challenging low-resources
languages in detecting contradictions due to its rich lexical, semantics
ambiguity. We have created a data set of more than 12k sentences and named
ArNLI, that will be publicly available. Moreover, we have applied a new model
inspired by Stanford contradiction detection proposed solutions on English
language. We proposed an approach to detect contradictions between pairs of
sentences in Arabic language using contradiction vector combined with language
model vector as an input to machine learning model. We analyzed results of
different traditional machine learning classifiers and compared their results
on our created data set (ArNLI) and on an automatic translation of both PHEME,
SICK English data sets. Best results achieved using Random Forest classifier
with an accuracy of 99%, 60%, 75% on PHEME, SICK and ArNLI respectively.

While copper-graphene (Cu/Gr) composites have been promising materials due to
their theoretically high strength and conductivity, their design has been
hampered by the large number of variables affecting their properties. We
applied four different Machine Learning (ML) models to manually collected
datasets compiling the yield strength and ultimate tensile strength of
graphene-reinforced copper composites processed with powder metallurgy
techniques. Our results indicate that ML models can predict the mechanical
properties of Cu/Gr composites with satisfactory accuracy. Feature analysis
provided new insights into the most important factors that affect these

Physical reservoir computing is a computational paradigm that enables
spatio-temporal pattern recognition to be performed directly in matter. The use
of physical matter leads the way towards energy-efficient devices capable of
solving machine learning problems without having to build a system of millions
of interconnected neurons. We propose a high performance "skyrmion mixture
reservoir" that implements the reservoir computing model with multi-dimensional
inputs. We show that our implementation solves spoken digit classification
tasks at the nanosecond timescale, with an overall model accuracy of 97.4% and
a less that 1% word error rate; the best performance ever reported for
in-materio reservoir computers. Due to the quality of the results and the low
power properties of magnetic texture reservoirs, we argue that skyrmion fabrics
are a compelling candidate for reservoir computing.

Multilingual machine translation has been proven an effective strategy to
support translation between multiple languages with a single model. However,
most studies focus on multilingual sentence translation without considering
generating long documents across different languages, which requires an
understanding of multilingual context dependency and is typically harder. In
this paper, we first spot that naively incorporating auxiliary multilingual
data either auxiliary-target or source-auxiliary brings no improvement to the
source-target language pair in our interest. Motivated by this observation, we
propose a novel framework called Multilingual Transitivity (MTrans) to find an
implicit optimal route via source-auxiliary-target within the multilingual
model. To encourage MTrans, we propose a novel method called Triplet Parallel
Data (TPD), which uses parallel triplets that contain (source-auxiliary,
auxiliary-target, and source-target) for training. The auxiliary language then
serves as a pivot and automatically facilitates the implicit information
transition flow which is easier to translate. We further propose a novel
framework called Bidirectional Multilingual Agreement (Bi-MAgree) that
encourages the bidirectional agreement between different languages. To
encourage Bi-MAgree, we propose a novel method called Multilingual
Kullback-Leibler Divergence (MKL) that forces the output distribution of the
inputs with the same meaning but in different languages to be consistent with
each other. The experimental results indicate that our methods bring consistent
improvements over strong baselines on three document translation tasks:
IWSLT2015 Zh-En, De-En, and Vi-En. Our analysis validates the usefulness and
existence of MTrans and Bi-MAgree, and our frameworks and methods are effective
on synthetic auxiliary data.

Exploring the expansion history of the universe, understanding its
evolutionary stages, and predicting its future evolution are important goals in
astrophysics. Today, machine learning tools are used to help achieving these
goals by analyzing transient sources, which are modeled as uncertain time
series. Although black-box methods achieve appreciable performance, existing
interpretable time series methods failed to obtain acceptable performance for
this type of data. Furthermore, data uncertainty is rarely taken into account
in these methods. In this work, we propose an uncertaintyaware subsequence
based model which achieves a classification comparable to that of
state-of-the-art methods. Unlike conformal learning which estimates model
uncertainty on predictions, our method takes data uncertainty as additional
input. Moreover, our approach is explainable-by-design, giving domain experts
the ability to inspect the model and explain its predictions. The
explainability of the proposed method has also the potential to inspire new
developments in theoretical astrophysics modeling by suggesting important
subsequences which depict details of light curve shapes. The dataset, the
source code of our experiment, and the results are made available on a public
repository.

Groundwater level prediction is an applied time series forecasting task with
important social impacts to optimize water management as well as preventing
some natural disasters: for instance, floods or severe droughts. Machine
learning methods have been reported in the literature to achieve this task, but
they are only focused on the forecast of the groundwater level at a single
location. A global forecasting method aims at exploiting the groundwater level
time series from a wide range of locations to produce predictions at a single
place or at several places at a time. Given the recent success of global
forecasting methods in prestigious competitions, it is meaningful to assess
them on groundwater level prediction and see how they are compared to local
methods. In this work, we created a dataset of 1026 groundwater level time
series. Each time series is made of daily measurements of groundwater levels
and two exogenous variables, rainfall and evapotranspiration. This dataset is
made available to the communities for reproducibility and further evaluation.
To identify the best configuration to effectively predict groundwater level for
the complete set of time series, we compared different predictors including
local and global time series forecasting methods. We assessed the impact of
exogenous variables. Our result analysis shows that the best predictions are
obtained by training a global method on past groundwater levels and rainfall
data.

This paper proposes a novel video inpainting method. We make three main
contributions: First, we extended previous Transformers with patch alignment by
introducing Deformed Patch-based Homography (DePtH), which improves patch-level
feature alignments without additional supervision and benefits challenging
scenes with various deformation. Second, we introduce Mask Pruning-based Patch
Attention (MPPA) to improve patch-wised feature matching by pruning out less
essential features and using saliency map. MPPA enhances matching accuracy
between warped tokens with invalid pixels. Third, we introduce a
Spatial-Temporal weighting Adaptor (STA) module to obtain accurate attention to
spatial-temporal tokens under the guidance of the Deformation Factor learned
from DePtH, especially for videos with agile motions. Experimental results
demonstrate that our method outperforms recent methods qualitatively and
quantitatively and achieves a new state-of-the-art.

Movement-sound interactive systems are at the interface of different artistic
and educational practices. Within this multiplicity of uses, we examine common
denominators in terms of learning, appropriation and relationship to
technological systems. While these topics have been previously reported at
NIME, we wanted to investigate how practitioners, coming from different
perspectives, relate to these questions. We conducted interviews with 6 artists
who are engaged in movementsound interactions: 1 performer, 1
performer/composer, 1 composer, 1 teacher/composer, 1 dancer/teacher, 1 dancer.
Through a thematic analysis of the transcripts we identified three main themes
related to (1) the mediating role of technological tools (2) usability and
normativity, and (3) learning and practice. These results provide ground for
discussion about the design and study of movement-sound interactive systems.

Online continual learning (OCL) aims to train neural networks incrementally
from a non-stationary data stream with a single pass through data.
Rehearsal-based methods attempt to approximate the observed input distributions
over time with a small memory and revisit them later to avoid forgetting.
Despite its strong empirical performance, rehearsal methods still suffer from a
poor approximation of the loss landscape of past data with memory samples. This
paper revisits the rehearsal dynamics in online settings. We provide
theoretical insights on the inherent memory overfitting risk from the viewpoint
of biased and dynamic empirical risk minimization, and examine the merits and
limits of repeated rehearsal. Inspired by our analysis, a simple and intuitive
baseline, Repeated Augmented Rehearsal (RAR), is designed to address the
underfitting-overfitting dilemma of online rehearsal. Surprisingly, across four
rather different OCL benchmarks, this simple baseline outperforms vanilla
rehearsal by 9%-17% and also significantly improves state-of-the-art
rehearsal-based methods MIR, ASER, and SCR. We also demonstrate that RAR
successfully achieves an accurate approximation of the loss landscape of past
data and high-loss ridge aversion in its learning trajectory. Extensive
ablation studies are conducted to study the interplay between repeated and
augmented rehearsal and reinforcement learning (RL) is applied to dynamically
adjust the hyperparameters of RAR to balance the stability-plasticity trade-off
online. Code is available at
https://github.com/YaqianZhang/RepeatedAugmentedRehearsal

Affective speech analysis is an ongoing topic of research. A relatively new
problem in this field is the analysis of vocal bursts, which are nonverbal
vocalisations such as laughs or sighs. Current state-of-the-art approaches to
address affective vocal burst analysis are mostly based on wav2vec2 or HuBERT
features. In this paper, we investigate the use of the wav2vec successor
data2vec in combination with a multitask learning pipeline to tackle different
analysis problems at once. To assess the performance of our efficient multitask
learning architecture, we participate in the 2022 ACII Affective Vocal Burst
Challenge, showing that our approach substantially outperforms the baseline
established there in three different subtasks.

Funding agencies are largely relied on a topic matching between domain
experts and research proposals to assign proposal reviewers. As proposals are
increasingly interdisciplinary, it is challenging to profile the
interdisciplinary nature of a proposal, and, thereafter, find expert reviewers
with an appropriate set of expertise. An essential step in solving this
challenge is to accurately model and classify the interdisciplinary labels of a
proposal. Existing methodological and application-related literature, such as
textual classification and proposal classification, are insufficient in jointly
addressing the three key unique issues introduced by interdisciplinary proposal
data: 1) the hierarchical structure of discipline labels of a proposal from
coarse-grain to fine-grain, e.g., from information science to AI to
fundamentals of AI. 2) the heterogeneous semantics of various main textual
parts that play different roles in a proposal; 3) the number of proposals is
imbalanced between non-interdisciplinary and interdisciplinary research. Can we
simultaneously address the three issues in understanding the proposal's
interdisciplinary nature? In response to this question, we propose a
hierarchical mixup multiple-label classification framework, which we called
H-MixUp. H-MixUp leverages a transformer-based semantic information extractor
and a GCN-based interdisciplinary knowledge extractor for the first and second
issues. H-MixUp develops a fused training method of Wold-level MixUp,
Word-level CutMix, Manifold MixUp, and Document-level MixUp to address the
third issue.

Markerless human motion capture (mocap) from multiple RGB cameras is a widely
studied problem. Existing methods either need calibrated cameras or calibrate
them relative to a static camera, which acts as the reference frame for the
mocap system. The calibration step has to be done a priori for every capture
session, which is a tedious process, and re-calibration is required whenever
cameras are intentionally or accidentally moved. In this paper, we propose a
mocap method which uses multiple static and moving extrinsically uncalibrated
RGB cameras. The key components of our method are as follows. First, since the
cameras and the subject can move freely, we select the ground plane as a common
reference to represent both the body and the camera motions unlike existing
methods which represent bodies in the camera coordinate. Second, we learn a
probability distribution of short human motion sequences ($\sim$1sec) relative
to the ground plane and leverage it to disambiguate between the camera and
human motion. Third, we use this distribution as a motion prior in a novel
multi-stage optimization approach to fit the SMPL human body model and the
camera poses to the human body keypoints on the images. Finally, we show that
our method can work on a variety of datasets ranging from aerial cameras to
smartphones. It also gives more accurate results compared to the
state-of-the-art on the task of monocular human mocap with a static camera. Our
code is available for research purposes on
https://github.com/robot-perception-group/SmartMocap.

The ability of continual learning systems to transfer knowledge from
previously seen tasks in order to maximize performance on new tasks is a
significant challenge for the field, limiting the applicability of continual
learning solutions to realistic scenarios. Consequently, this study aims to
broaden our understanding of transfer and its driving forces in the specific
case of continual reinforcement learning. We adopt SAC as the underlying RL
algorithm and Continual World as a suite of continuous control tasks. We
systematically study how different components of SAC (the actor and the critic,
exploration, and data) affect transfer efficacy, and we provide recommendations
regarding various modeling options. The best set of choices, dubbed ClonEx-SAC,
is evaluated on the recent Continual World benchmark. ClonEx-SAC achieves 87%
final success rate compared to 80% of PackNet, the best method in the
benchmark. Moreover, the transfer grows from 0.18 to 0.54 according to the
metric provided by Continual World.

The cost efficiency of model inference is critical to real-world machine
learning (ML) applications, especially for delay-sensitive tasks and
resource-limited devices. A typical dilemma is: in order to provide complex
intelligent services (e.g. smart city), we need inference results of multiple
ML models, but the cost budget (e.g. GPU memory) is not enough to run all of
them. In this work, we study underlying relationships among black-box ML models
and propose a novel learning task: model linking, which aims to bridge the
knowledge of different black-box models by learning mappings (dubbed model
links) between their output spaces. We propose the design of model links which
supports linking heterogeneous black-box ML models. Also, in order to address
the distribution discrepancy challenge, we present adaptation and aggregation
methods of model links. Based on our proposed model links, we developed a
scheduling algorithm, named MLink. Through collaborative multi-model inference
enabled by model links, MLink can improve the accuracy of obtained inference
results under the cost budget. We evaluated MLink on a multi-modal dataset with
seven different ML models and two real-world video analytics systems with six
ML models and 3,264 hours of video. Experimental results show that our proposed
model links can be effectively built among various black-box models. Under the
budget of GPU memory, MLink can save 66.7% inference computations while
preserving 94% inference accuracy, which outperforms multi-task learning, deep
reinforcement learning-based scheduler and frame filtering baselines.

Freighter airlines need to recover both aircraft and cargo schedules when
disruptions happen. This process is usually divided into three sequential
decisions to recovery flights, aircraft, and cargoes. This study focuses on the
integrated recovery problem that makes aircraft and cargo recovery decisions
simultaneously. We formulate two integrated models based on the flight
connection network, one is the arc-based model, and the other is the
string-based model. The arc-based model makes the flight delay decisions by
duplicating flight copies, and is solved directly by commercial solvers such as
Cplex. The string-based model makes the flight delay decisions in the variable
generation process. The main difficulty of the string-based model is that the
number of constraints grows with the newly generated flight delay decisions.
Therefore, the traditional column generation method can not be applied
directly. To tackle this challenge, we propose a machine learning based
column-and-row generation approach. The machine learning method is used to
uncover the critical delay decisions of short through connections in each
column-and-row generation iteration by eliminating the poor flight delay
decisions. We also propose a set of valid inequality constraints which can
greatly improve the objective of LP relaxation solution and reduce the integral
gap. The effectiveness and efficiency of our model is tested by simulated
scenarios based on real operational data from the largest Chinese freighter
airlines. The computational results show that a significant cost reduction can
be achieved with the proposed string-based model in reasonable time.

We introduce YATO, an open-source toolkit for text analysis with deep
learning. It focuses on fundamental sequence labeling and sequence
classification tasks on text. Designed in a hierarchical structure, YATO
supports free combinations of three types of features including 1) traditional
neural networks (CNN, RNN, etc.); 2) pre-trained language models (BERT,
RoBERTa, ELECTRA, etc.); and 3) user-customed neural features via a simple
configurable file. Benefiting from the advantages of flexibility and ease of
use, YATO can facilitate reproducing and refinement of state-of-the-art NLP
models, and promote the cross-disciplinary applications of NLP techniques.
Source code, examples, and documentation are publicly available at
https://github.com/jiesutd/YATO.

Mobile-centric AI applications have high requirements for resource-efficiency
of model inference. Input filtering is a promising approach to eliminate the
redundancy so as to reduce the cost of inference. Previous efforts have
tailored effective solutions for many applications, but left two essential
questions unanswered: (1) theoretical filterability of an inference workload to
guide the application of input filtering techniques, thereby avoiding the
trial-and-error cost for resource-constrained mobile applications; (2) robust
discriminability of feature embedding to allow input filtering to be widely
effective for diverse inference tasks and input content. To answer them, we
first formalize the input filtering problem and theoretically compare the
hypothesis complexity of inference models and input filters to understand the
optimization potential. Then we propose the first end-to-end learnable input
filtering framework that covers most state-of-the-art methods and surpasses
them in feature embedding with robust discriminability. We design and implement
InFi that supports six input modalities and multiple mobile-centric
deployments. Comprehensive evaluations confirm our theoretical results and show
that InFi outperforms strong baselines in applicability, accuracy, and
efficiency. InFi achieve 8.5x throughput and save 95% bandwidth, while keeping
over 90% accuracy, for a video analytics application on mobile platforms.

There are two popular loss functions used for vision-language retrieval,
i.e., triplet loss and contrastive learning loss, both of them essentially
minimize the difference between the similarities of negative pairs and positive
pairs. More specifically, Triplet loss with Hard Negative mining (Triplet-HN),
which is widely used in existing retrieval models to improve the discriminative
ability, is easy to fall into local minima in training. On the other hand,
Vision-Language Contrastive learning loss (VLC), which is widely used in the
vision-language pre-training, has been shown to achieve significant performance
gains on vision-language retrieval, but the performance of fine-tuning with VLC
on small datasets is not satisfactory. This paper proposes a unified loss of
pair similarity optimization for vision-language retrieval, providing a
powerful tool for understanding existing loss functions. Our unified loss
includes the hard sample mining strategy of VLC and introduces the margin used
by the triplet loss for better similarity separation. It is shown that both
Triplet-HN and VLC are special forms of our unified loss. Compared with the
Triplet-HN, our unified loss has a fast convergence speed. Compared with the
VLC, our unified loss is more discriminative and can provide better
generalization in downstream fine-tuning tasks. Experiments on image-text and
video-text retrieval benchmarks show that our unified loss can significantly
improve the performance of the state-of-the-art retrieval models.

In this paper, we examine the problem of real-world image deblurring and take
into account two key factors for improving the performance of the deep image
deblurring model, namely, training data synthesis and network architecture
design. Deblurring models trained on existing synthetic datasets perform poorly
on real blurry images due to domain shift. To reduce the domain gap between
synthetic and real domains, we propose a novel realistic blur synthesis
pipeline to simulate the camera imaging process. As a result of our proposed
synthesis method, existing deblurring models could be made more robust to
handle real-world blur. Furthermore, we develop an effective deblurring model
that captures non-local dependencies and local context in the feature domain
simultaneously. Specifically, we introduce the multi-path transformer module to
UNet architecture for enriched multi-scale features learning. A comprehensive
experiment on three real-world datasets shows that the proposed deblurring
model performs better than state-of-the-art methods.

Drug design is a crucial step in the drug discovery cycle. Recently, various
deep learning-based methods design drugs by generating novel molecules from
scratch, avoiding traversing large-scale drug libraries. However, they depend
on scarce experimental data or time-consuming docking simulation, leading to
overfitting issues with limited training data and slow generation speed. In
this study, we propose the zero-shot drug design method DESERT (Drug dEsign by
SkEtching and geneRaTing). Specifically, DESERT splits the design process into
two stages: sketching and generating, and bridges them with the molecular
shape. The two-stage fashion enables our method to utilize the large-scale
molecular database to reduce the need for experimental data and docking
simulation. Experiments show that DESERT achieves a new state-of-the-art at a
fast speed.

We present a new approach to Extended Reality (XR), denoted as iCOPYWAVES,
which seeks to offer naturally low-latency operation and cost-effectiveness,
overcoming the critical scalability issues faced by existing solutions.
iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in
wireless communications. Empowered by intelligent (meta)surfaces, PWEs
transform the wave propagation phenomenon into a software-defined process. We
leverage PWEs to i) create, and then ii) selectively copy the scattered RF
wavefront of an object from one location in space to another, where a machine
learning module, accelerated by FPGAs, translates it to visual input for an XR
headset using PWEdriven, RF imaging principles (XR-RF). This makes for an XR
system whose operation is bounded in the physical layer and, hence, has the
prospects for minimal end-to-end latency. Over large distances,
RF-to-fiber/fiber-to-RF is employed to provide intermediate connectivity. The
paper provides a tutorial on the iCOPYWAVES system architecture and workflow. A
proof-of-concept implementation via simulations is provided, demonstrating the
reconstruction of challenging objects in iCOPYWAVES produced computer graphics.

Clinical notes are an essential component of a health record. This paper
evaluates how natural language processing (NLP) can be used to identify the
risk of acute care use (ACU) in oncology patients, once chemotherapy starts.
Risk prediction using structured health data (SHD) is now standard, but
predictions using free-text formats are complex. This paper explores the use of
free-text notes for the prediction of ACU instead of SHD. Deep Learning models
were compared to manually engineered language features. Results show that SHD
models minimally outperform NLP models; an l1-penalised logistic regression
with SHD achieved a C-statistic of 0.748 (95%-CI: 0.735, 0.762), while the same
model with language features achieved 0.730 (95%-CI: 0.717, 0.745) and a
transformer-based model achieved 0.702 (95%-CI: 0.688, 0.717). This paper shows
how language models can be used in clinical applications and underlines how
risk bias is different for diverse patient groups, even using only free-text
data.

Black box models only provide results for deep learning tasks and lack
informative details about how these results were obtained. In this paper, we
propose a general theory that defines a variance tolerance factor (VTF) to
interpret the neural networks by ranking the importance of features and
constructing a novel architecture consisting of a base model and feature model
to demonstrate its utility. Two feature importance ranking methods and a
feature selection method based on the VTF are created. A thorough evaluation on
synthetic, benchmark, and real datasets is provided.

Nonresponse frequently arises in practice, and simply ignoring it may lead to
erroneous inference. Besides, the number of collected covariates may increase
as the sample size in modern statistics, so parametric imputation or propensity
score weighting usually leads to inefficiency without consideration of
sparsity. In this paper, we propose a nonparametric imputation method with
sparse learning by employing an efficient kernel-based learning gradient
algorithm to identify truly informative covariates. Moreover, an augmented
probability weighting framework is adopted to improve the estimation efficiency
of the nonparametric imputation method and establish the limiting distribution
of the corresponding estimator under regularity assumptions. The performance of
the proposed method is also supported by several simulated examples and one
real-life analysis.

With the advent of rich visual representations and pre-trained language
models, video captioning has seen continuous improvement over time. Despite the
performance improvement, video captioning models are prone to hallucination.
Hallucination refers to the generation of highly pathological descriptions that
are detached from the source material. In video captioning, there are two kinds
of hallucination: object and action hallucination. Instead of endeavoring to
learn better representations of a video, in this work, we investigate the
fundamental sources of the hallucination problem. We identify three main
factors: (i) inadequate visual features extracted from pre-trained models, (ii)
improper influences of source and target contexts during multi-modal fusion,
and (iii) exposure bias in the training strategy. To alleviate these problems,
we propose two robust solutions: (a) the introduction of auxiliary heads
trained in multi-label settings on top of the extracted visual features and (b)
the addition of context gates, which dynamically select the features during
fusion. The standard evaluation metrics for video captioning measures
similarity with ground truth captions and do not adequately capture object and
action relevance. To this end, we propose a new metric, COAHA (caption object
and action hallucination assessment), which assesses the degree of
hallucination. Our method achieves state-of-the-art performance on the
MSR-Video to Text (MSR-VTT) and the Microsoft Research Video Description Corpus
(MSVD) datasets, especially by a massive margin in CIDEr score.

Describing dynamic medical systems using machine learning is a challenging
topic with a wide range of applications. In this work, the possibility of
modeling the blood glucose level of diabetic patients purely on the basis of
measured data is described. A combination of the influencing variables insulin
and calories are used to find an interpretable model. The absorption speed of
external substances in the human body depends strongly on external influences,
which is why time-shifts are added for the influencing variables. The focus is
put on identifying the best timeshifts that provide robust models with good
prediction accuracy that are independent of other unknown external influences.
The modeling is based purely on the measured data using Sparse Identification
of Nonlinear Dynamics. A differential equation is determined which, starting
from an initial value, simulates blood glucose dynamics. By applying the best
model to test data, we can show that it is possible to simulate the long-term
blood glucose dynamics using differential equations and few, influencing
variables.

Shape-constrained symbolic regression (SCSR) allows to include prior
knowledge into data-based modeling. This inclusion allows to ensure that
certain expected behavior is better reflected by the resulting models. The
expected behavior is defined via constraints, which refer to the function form
e.g. monotonicity, concavity, convexity or the models image boundaries. In
addition to the advantage of obtaining more robust and reliable models due to
defining constraints over the functions shape, the use of SCSR allows to find
models which are more robust to noise and have a better extrapolation behavior.
This paper presents a mutlicriterial approach to minimize the approximation
error as well as the constraint violations. Explicitly the two algorithms
NSGA-II and NSGA-III are implemented and compared against each other in terms
of model quality and runtime. Both algorithms are capable of dealing with
multiple objectives, whereas NSGA-II is a well established multi-objective
approach performing well on instances with up-to 3 objectives. NSGA-III is an
extension of the NSGA-II algorithm and was developed to handle problems with
"many" objectives (more than 3 objectives). Both algorithms are executed on a
selected set of benchmark instances from physics textbooks. The results
indicate that both algorithms are able to find largely feasible solutions and
NSGA-III provides slight improvements in terms of model quality. Moreover, an
improvement in runtime can be observed using the many-objective approach.

Learning from demonstration (LfD) provides a convenient means to equip robots
with dexterous skills when demonstration can be obtained in robot intrinsic
coordinates. However, the problem of compounding errors in long and complex
skills reduces its wide deployment. Since most such complex skills are composed
of smaller movements that are combined, considering the target skill as a
sequence of compact motor primitives seems reasonable. Here the problem that
needs to be tackled is to ensure that a motor primitive ends in a state that
allows the successful execution of the subsequent primitive. In this study, we
focus on this problem by proposing to learn an explicit correction policy when
the expected transition state between primitives is not achieved. The
correction policy is itself learned via behavior cloning by the use of a
state-of-the-art movement primitive learning architecture, Conditional Neural
Motor Primitives (CNMPs). The learned correction policy is then able to produce
diverse movement trajectories in a context dependent way. The advantage of the
proposed system over learning the complete task as a single action is shown
with a table-top setup in simulation, where an object has to be pushed through
a corridor in two steps. Then, the applicability of the proposed method to
bi-manual knotting in the real world is shown by equipping an upper-body
humanoid robot with the skill of making knots over a bar in 3D space. The
experiments show that the robot can perform successful knotting even when the
faced correction cases are not part of the human demonstration set.

Objectives: To explore the capacity of deep learning algorithm to further
streamline and optimize urethral plate (UP) quality appraisal on 2D images
using the plate objective scoring tool (POST), aiming to increase the
objectivity and reproducibility of UP appraisal in hypospadias repair. Methods:
The five key POST landmarks were marked by specialists in a 691-image dataset
of prepubertal boys undergoing primary hypospadias repair. This dataset was
then used to develop and validate a deep learning-based landmark detection
model. The proposed framework begins with glans localization and detection,
where the input image is cropped using the predicted bounding box. Next, a deep
convolutional neural network (CNN) architecture is used to predict the
coordinates of the five POST landmarks. These predicted landmarks are then used
to assess UP quality in distal hypospadias. Results: The proposed model
accurately localized the glans area, with a mean average precision (mAP) of
99.5% and an overall sensitivity of 99.1%. A normalized mean error (NME) of
0.07152 was achieved in predicting the coordinates of the landmarks, with a
mean squared error (MSE) of 0.001 and a 20.2% failure rate at a threshold of
0.1 NME. Conclusions: This deep learning application shows robustness and high
precision in using POST to appraise UP quality. Further assessment using
international multi-centre image-based databases is ongoing. External
validation could benefit deep learning algorithms and lead to better
assessments, decision-making and predictions for surgical outcomes.

This research is intended to accomplish two goals: The first goal is to
curate a large and information rich dataset that contains crucial and succinct
summaries on the players' actions and positions and the back-and-forth travel
patterns of the volleyball in professional and NCAA Div-I indoor volleyball
games. While several prior studies have aimed to create similar datasets for
other sports (e.g. badminton and soccer), creating such a dataset for indoor
volleyball is not yet realized. The second goal is to introduce a volleyball
descriptive language to fully describe the rally processes in the games and
apply the language to our dataset. Based on the curated dataset and our
descriptive sports language, we introduce three tasks for automated volleyball
action and tactic analysis using our dataset: (1) Volleyball Rally Prediction,
aimed at predicting the outcome of a rally and helping players and coaches
improve decision-making in practice, (2) Setting Type and Hitting Type
Prediction, to help coaches and players prepare more effectively for the game,
and (3) Volleyball Tactics and Attacking Zone Statistics, to provide advanced
volleyball statistics and help coaches understand the game and opponent's
tactics better. We conducted case studies to show how experimental results can
provide insights to the volleyball analysis community. Furthermore,
experimental evaluation based on real-world data establishes a baseline for
future studies and applications of our dataset and language. This study bridges
the gap between the indoor volleyball field and computer science.

Labeling medical images depends on professional knowledge, making it
difficult to acquire large amount of annotated medical images with high quality
in a short time. Thus, making good use of limited labeled samples in a small
dataset to build a high-performance model is the key to medical image
classification problem. In this paper, we propose a deeply supervised Layer
Selective Attention Network (LSANet), which comprehensively uses label
information in feature-level and prediction-level supervision. For
feature-level supervision, in order to better fuse the low-level features and
high-level features, we propose a novel visual attention module, Layer
Selective Attention (LSA), to focus on the feature selection of different
layers. LSA introduces a weight allocation scheme which can dynamically adjust
the weighting factor of each auxiliary branch during the whole training process
to further enhance deeply supervised learning and ensure its generalization.
For prediction-level supervision, we adopt the knowledge synergy strategy to
promote hierarchical information interactions among all supervision branches
via pairwise knowledge matching. Using the public dataset, MedMNIST, which is a
large-scale benchmark for biomedical image classification covering diverse
medical specialties, we evaluate LSANet on multiple mainstream CNN
architectures and various visual attention modules. The experimental results
show the substantial improvements of our proposed method over its corresponding
counterparts, demonstrating that LSANet can provide a promising solution for
label-efficient learning in the field of medical image classification.

Reinforcement learning (RL) has exceeded human performance in many synthetic
settings such as video games and Go. However, real-world deployment of
end-to-end RL models is less common, as RL models can be very sensitive to
slight perturbation of the environment. The robust Markov decision process
(MDP) framework -- in which the transition probabilities belong to an
uncertainty set around a nominal model -- provides one way to develop robust
models. While previous analysis shows RL algorithms are effective assuming
access to a generative model, it remains unclear whether RL can be efficient
under a more realistic online setting, which requires a careful balance between
exploration and exploitation. In this work, we consider online robust MDP by
interacting with an unknown nominal system. We propose a robust optimistic
policy optimization algorithm that is provably efficient. To address the
additional uncertainty caused by an adversarial environment, our model features
a new optimistic update rule derived via Fenchel conjugates. Our analysis
establishes the first regret bound for online robust MDPs.

Kidney is an essential organ in human body. It maintains homeostasis and
removes harmful substances through urine. Renal cell carcinoma (RCC) is the
most common form of kidney cancer. Around 90\% of all kidney cancers are
attributed to RCC. Most harmful type of RCC is clear cell renal cell carcinoma
(ccRCC) that makes up about 80\% of all RCC cases. Early and accurate detection
of ccRCC is necessary to prevent further spreading of the disease in other
organs. In this article, a detailed experimentation is done to identify
important features which can aid in diagnosing ccRCC at different stages. The
ccRCC dataset is obtained from The Cancer Genome Atlas (TCGA). A novel mutual
information and ensemble based feature ranking approach considering the order
of features obtained from 8 popular feature selection methods is proposed.
Performance of the proposed method is evaluated by overall classification
accuracy obtained using 2 different classifiers (ANN and SVM). Experimental
results show that the proposed feature ranking method is able to attain a
higher accuracy (96.6\% and 98.6\% using SVM and NN, respectively) for
classifying different stages of ccRCC with a reduced feature set as compared to
existing work. It is also to be noted that, out of 3 distinguishing features as
mentioned by the existing TNM system (proposed by AJCC and UICC), our proposed
method was able to select two of them (size of tumour, metastasis status) as
the top-most ones. This establishes the efficacy of our proposed approach.

In this paper, we propose Suppression-Enhancing Mask based attention and
Interactive Channel transformatiON (SEMICON) to learn binary hash codes for
dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we
first develop a suppression-enhancing mask (SEM) based attention to dynamically
localize discriminative image regions. More importantly, different from
existing attention mechanism simply erasing previous discriminative regions,
our SEM is developed to restrain such regions and then discover other
complementary regions by considering the relation between activated regions in
a stage-by-stage fashion. In each stage, the interactive channel transformation
(ICON) module is afterwards designed to exploit correlations across channels of
attended activation tensors. Since channels could generally correspond to the
parts of fine-grained objects, the part correlation can be also modeled
accordingly, which further improves fine-grained retrieval accuracy. Moreover,
to be computational economy, ICON is realized by an efficient two-step process.
Finally, the hash learning of our SEMICON consists of both global- and
local-level branches for better representing fine-grained objects and then
generating binary hash codes explicitly corresponding to multiple levels.
Experiments on five benchmark fine-grained datasets show our superiority over

Instance-level Image Retrieval (IIR), or simply Instance Retrieval, deals
with the problem of finding all the images within an dataset that contain a
query instance (e.g. an object). This paper makes the first attempt that
tackles this problem using instance-discrimination based contrastive learning
(CL). While CL has shown impressive performance for many computer vision tasks,
the similar success has never been found in the field of IIR. In this work, we
approach this problem by exploring the capability of deriving discriminative
representations from pre-trained and fine-tuned CL models. To begin with, we
investigate the efficacy of transfer learning in IIR, by comparing
off-the-shelf features learned by a pre-trained deep neural network (DNN)
classifier with features learned by a CL model. The findings inspired us to
propose a new training strategy that optimizes CL towards learning IIR-oriented
features, by using an Average Precision (AP) loss together with a fine-tuning
method to learn contrastive feature representations that are tailored to IIR.
Our empirical evaluation demonstrates significant performance enhancement over
the off-the-shelf features learned from a pre-trained DNN classifier on the
challenging Oxford and Paris datasets.

Various Non-negative Matrix factorization (NMF) based methods add new terms
to the cost function to adapt the model to specific tasks, such as clustering,
or to preserve some structural properties in the reduced space (e.g., local
invariance). The added term is mainly weighted by a hyper-parameter to control
the balance of the overall formula to guide the optimization process towards
the objective. The result is a parameterized NMF method. However, NMF method
adopts unsupervised approaches to estimate the factorizing matrices. Thus, the
ability to perform prediction (e.g. classification) using the new obtained
features is not guaranteed. The objective of this work is to design an
evolutionary framework to learn the hyper-parameter of the parameterized NMF
and estimate the factorizing matrices in a supervised way to be more suitable
for classification problems. Moreover, we claim that applying NMF-based
algorithms separately to different class-pairs instead of applying it once to
the whole dataset improves the effectiveness of the matrix factorization
process. This results in training multiple parameterized NMF algorithms with
different balancing parameter values. A cross-validation combination learning
framework is adopted and a Genetic Algorithm is used to identify the optimal
set of hyper-parameter values. The experiments we conducted on both real and
synthetic datasets demonstrated the effectiveness of the proposed approach.

In contrast to multi-label learning, label distribution learning
characterizes the polysemy of examples by a label distribution to represent
richer semantics. In the learning process of label distribution, the training
data is collected mainly by manual annotation or label enhancement algorithms
to generate label distribution. Unfortunately, the complexity of the manual
annotation task or the inaccuracy of the label enhancement algorithm leads to
noise and uncertainty in the label distribution training set. To alleviate this
problem, we introduce the implicit distribution in the label distribution
learning framework to characterize the uncertainty of each label value.
Specifically, we use deep implicit representation learning to construct a label
distribution matrix with Gaussian prior constraints, where each row component
corresponds to the distribution estimate of each label value, and this row
component is constrained by a prior Gaussian distribution to moderate the noise
and uncertainty interference of the label distribution dataset. Finally, each
row component of the label distribution matrix is transformed into a standard
label distribution form by using the self-attention algorithm. In addition,
some approaches with regularization characteristics are conducted in the
training phase to improve the performance of the model.

Machine learning potentials (MLPs) developed from extensive datasets
constructed from density functional theory (DFT) calculations have become
increasingly appealing for many researchers. This paper presents a framework of
polynomial-based MLPs, called polynomial MLPs. The systematic development of
accurate and computationally efficient polynomial MLPs for many elemental and
binary alloy systems and their predictive powers for various properties are
also demonstrated. Consequently, many polynomial MLPs are available in a
repository website. The repository will help many scientists perform accurate
and efficient large-scale atomistic simulations and crystal structure searches.

Small lesions in magnetic resonance imaging (MRI) images are crucial for
clinical diagnosis of many kinds of diseases. However, the MRI quality can be
easily degraded by various noise, which can greatly affect the accuracy of
diagnosis of small lesion. Although some methods for denoising MR images have
been proposed, task-specific denoising methods for improving the diagnosis
confidence of small lesions are lacking. In this work, we propose a voxel-wise
hybrid residual MLP-CNN model to denoise three-dimensional (3D) MR images with
small lesions. We combine basic deep learning architecture, MLP and CNN, to
obtain an appropriate inherent bias for the image denoising and integrate each
output layers in MLP and CNN by adding residual connections to leverage
long-range information. We evaluate the proposed method on 720 T2-FLAIR brain
images with small lesions at different noise levels. The results show the
superiority of our method in both quantitative and visual evaluations on
testing dataset compared to state-of-the-art methods. Moreover, two experienced
radiologists agreed that at moderate and high noise levels, our method
outperforms other methods in terms of recovery of small lesions and overall
image denoising quality. The implementation of our method is available at
https://github.com/laowangbobo/Residual_MLP_CNN_Mixer.

Few-shot learning with N-way K-shot scheme is an open challenge in machine
learning. Many approaches have been proposed to tackle this problem, e.g., the
Matching Networks and CLIP-Adapter. Despite that these approaches have shown
significant progress, the mechanism of why these methods succeed has not been
well explored. In this paper, we interpret these few-shot learning methods via
causal mechanism. We show that the existing approaches can be viewed as
specific forms of front-door adjustment, which is to remove the effects of
confounders. Based on this, we introduce a general causal method for few-shot
learning, which considers not only the relationship between examples but also
the diversity of representations. Experimental results demonstrate the
superiority of our proposed method in few-shot classification on various
benchmark datasets. Code is available in the supplementary material.

The proliferation of unmanned aerial vehicles (UAVs) opens up new
opportunities for on-demand service provisioning anywhere and anytime, but it
also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is
regarded as a promising lightweight defense to actively protect mobile Internet
of things, especially UAV networks. Existing works primarily focused on
honeypot design and attack pattern recognition, the incentive issue for
motivating UAVs' participation (e.g., sharing trapped attack data in honeypots)
to collaboratively resist distributed and sophisticated attacks is still
under-explored. This paper proposes a novel game-based collaborative defense
approach to address optimal, fair, and feasible incentive mechanism design, in
the presence of network dynamics and UAVs' multi-dimensional private
information (e.g., valid defense data (VDD) volume, communication delay, and
UAV cost). Specifically, we first develop a honeypot game between UAVs under
both partial and complete information asymmetry scenarios. We then devise a
contract-theoretic method to solve the optimal VDD-reward contract design
problem with partial information asymmetry, while ensuring truthfulness,
fairness, and computational efficiency. Furthermore, under complete information
asymmetry, we devise a reinforcement learning based distributed method to
dynamically design optimal contracts for distinct types of UAVs in the
fast-changing network. Experimental simulations show that the proposed scheme
can motivate UAV's collaboration in VDD sharing and enhance defensive
effectiveness, compared with existing solutions.

Due to the increasing trend of performing spamming activities (e.g., Web
spam, deceptive reviews, fake followers, etc.) on various online platforms to
gain undeserved benefits, spam detection has emerged as a hot research issue.
Previous attempts to combat spam mainly employ features related to metadata,
user behaviors, or relational ties. These works have made considerable progress
in understanding and filtering spamming campaigns. However, this problem
remains far from fully solved. Almost all the proposed features focus on a
limited number of observed attributes or explainable phenomena, making it
difficult for existing methods to achieve further improvement. To broaden the
vision about solving the spam problem and address long-standing challenges
(class imbalance and graph incompleteness) in the spam detection area, we
propose a new attempt of utilizing signed latent factors to filter fraudulent
activities. The spam-contaminated relational datasets of multiple online
applications in this scenario are interpreted by the unified signed network.
Two competitive and highly dissimilar algorithms of latent factors mining (LFM)
models are designed based on multi-relational likelihoods estimation (LFM-MRLE)
and signed pairwise ranking (LFM-SPR), respectively. We then explore how to
apply the mined latent factors to spam detection tasks. Experiments on
real-world datasets of different kinds of Web applications (social media and
Web forum) indicate that LFM models outperform state-of-the-art baselines in
detecting spamming activities. By specifically manipulating experimental data,
the effectiveness of our methods in dealing with incomplete and imbalanced
challenges is valida

In many longitudinal settings, time-varying covariates may not be measured at
the same time as responses and are often prone to measurement error. Naive
last-observation-carried-forward methods incur estimation biases, and existing
kernel-based methods suffer from slow convergence rates and large variations.
To address these challenges, we propose a new functional calibration approach
to efficiently learn longitudinal covariate processes based on sparse
functional data with measurement error. Our approach, stemming from functional
principal component analysis, calibrates the unobserved synchronized covariate
values from the observed asynchronous and error-prone covariate values, and is
broadly applicable to asynchronous longitudinal regression with time-invariant
or time-varying coefficients. For regression with time-invariant coefficients,
our estimator is asymptotically unbiased, root-n consistent, and asymptotically
normal; for time-varying coefficient models, our estimator has the optimal
varying coefficient model convergence rate with inflated asymptotic variance
from the calibration. In both cases, our estimators present asymptotic
properties superior to the existing methods. The feasibility and usability of
the proposed methods are verified by simulations and an application to the
Study of Women's Health Across the Nation, a large-scale multi-site
longitudinal study on women's health during mid-life.

Federated Learning (FL) is a distributed machine learning framework to
alleviate the data silos, where decentralized clients collaboratively learn a
global model without sharing their private data. However, the clients'
Non-Independent and Identically Distributed (Non-IID) data negatively affect
the trained model, and clients with different numbers of local updates may
cause significant gaps to the local gradients in each communication round. In
this paper, we propose a Federated Vectorized Averaging (FedVeca) method to
address the above problem on Non-IID data. Specifically, we set a novel
objective for the global model which is related to the local gradients. The
local gradient is defined as a bi-directional vector with step size and
direction, where the step size is the number of local updates and the direction
is divided into positive and negative according to our definition. In FedVeca,
the direction is influenced by the step size, thus we average the
bi-directional vectors to reduce the effect of different step sizes. Then, we
theoretically analyze the relationship between the step sizes and the global
objective, and obtain upper bounds on the step sizes per communication round.
Based on the upper bounds, we design an algorithm for the server and the client
to adaptively adjusts the step sizes that make the objective close to the
optimum. Finally, we conduct experiments on different datasets, models and
scenarios by building a prototype system, and the experimental results
demonstrate the effectiveness and efficiency of the FedVeca method.

The combination of big data and deep learning is a world-shattering
technology that can greatly impact any objective if used properly. With the
availability of a large volume of health care datasets and progressions in deep
learning techniques, systems are now well equipped to predict the future trend
of any health problems. From the literature survey, we found the SVM was used
to predict the heart failure rate without relating objective factors. Utilizing
the intensity of important historical information in electronic health records
(EHR), we have built a smart and predictive model utilizing long short-term
memory (LSTM) and predict the future trend of heart failure based on that
health record. Hence the fundamental commitment of this work is to predict the
failure of the heart using an LSTM based on the patient's electronic medicinal
information. We have analyzed a dataset containing the medical records of 299
heart failure patients collected at the Faisalabad Institute of Cardiology and
the Allied Hospital in Faisalabad (Punjab, Pakistan). The patients consisted of
105 women and 194 men and their ages ranged from 40 and 95 years old. The
dataset contains 13 features, which report clinical, body, and lifestyle
information responsible for heart failure. We have found an increasing trend in
our analysis which will contribute to advancing the knowledge in the field of
heart stroke prediction.

Fast and efficient semantic segmentation of large-scale LiDAR point clouds is
a fundamental problem in autonomous driving. To achieve this goal, the existing
point-based methods mainly choose to adopt Random Sampling strategy to process
large-scale point clouds. However, our quantative and qualitative studies have
found that Random Sampling may be less suitable for the autonomous driving
scenario, since the LiDAR points follow an uneven or even long-tailed
distribution across the space, which prevents the model from capturing
sufficient information from points in different distance ranges and reduces the
model's learning capability. To alleviate this problem, we propose a new Polar
Cylinder Balanced Random Sampling method that enables the downsampled point
clouds to maintain a more balanced distribution and improve the segmentation
performance under different spatial distributions. In addition, a sampling
consistency loss is introduced to further improve the segmentation performance
and reduce the model's variance under different sampling methods. Extensive
experiments confirm that our approach produces excellent performance on both
SemanticKITTI and SemanticPOSS benchmarks, achieving a 2.8% and 4.0%
improvement, respectively.

With the spread of DeepFake techniques, this technology has become quite
accessible and good enough that there is concern about its malicious use. Faced
with this problem, detecting forged faces is of utmost importance to ensure
security and avoid socio-political problems, both on a global and private
scale. This paper presents a solution for the detection of DeepFakes using
convolution neural networks and a dataset developed for this purpose -
Celeb-DF. The results show that, with an overall accuracy of 95% in the
classification of these images, the proposed model is close to what exists in
the state of the art with the possibility of adjustment for better results in
the manipulation techniques that arise in the future.

Gradient Boosting Machines (GBMs) are derived from Taylor expansion in
functional space and have achieved state-of-the-art results on a variety of
problems. However, there is a dilemma for GBMs to maintain a balance between
performance and generality. Specifically, gradient descent-based GBMs employ
the first-order Taylor expansion to make them appropriate for all loss
functions. And Newton's method-based GBMs use the positive hessian information
to achieve better performance at the expense of generality. In this paper, a
generic Gradient Boosting Machine called Trust-region Boosting (TRBoost) is
presented to maintain this balance. In each iteration, we apply a constrained
quadratic model to approximate the objective and solve it by the Trust-region
algorithm to obtain a new learner. TRBoost offers the benefit that we do not
need the hessian to be positive definite, which generalizes GBMs to suit
arbitrary loss functions while keeping up the good performance as the
second-order algorithm. Several numerical experiments are conducted to confirm
that TRBoost is not only as general as the first-order GBMs but also able to
get competitive results with the second-order GBMs.

Traffic data chronically suffer from missing and corruption, leading to
accuracy and utility reduction in subsequent Intelligent Transportation System
(ITS) applications. Noticing the inherent low-rank property of traffic data,
numerous studies formulated missing traffic data recovery as a low-rank tensor
completion (LRTC) problem. Due to the non-convexity and discreteness of the
rank minimization in LRTC, existing methods either replaced rank with convex
surrogates that are quite far away from the rank function or approximated rank
with nonconvex surrogates involving many parameters. In this study, we proposed
a Parameter-Free Non-Convex Tensor Completion model (TC-PFNC) for traffic data
recovery, in which a log-based relaxation term was designed to approximate
tensor algebraic rank. Moreover, previous studies usually assumed the
observations are reliable without any outliers. Therefore, we extended the
TC-PFNC to a robust version (RTC-PFNC) by modeling potential traffic data
outliers, which can recover the missing value from partial and corrupted
observations and remove the anomalies in observations. The numerical solutions
of TC-PFNC and RTC-PFNC were elaborated based on the alternating direction
multiplier method (ADMM). The extensive experimental results conducted on four
real-world traffic data sets demonstrated that the proposed methods outperform
other state-of-the-art methods in both missing and corrupted data recovery. The
code used in this paper is available at:
https://github.com/YoungHe49/T-ITSPFNC.

Vision Transformers are increasingly embedded in industrial systems due to
their superior performance, but their memory and power requirements make
deploying them to edge devices a challenging task. Hence, model compression
techniques are now widely used to deploy models on edge devices as they
decrease the resource requirements and make model inference very fast and
efficient. But their reliability and robustness from a security perspective is
another major issue in safety-critical applications. Adversarial attacks are
like optical illusions for ML algorithms and they can severely impact the
accuracy and reliability of models. In this work we investigate the
transferability of adversarial samples across the SOTA Vision Transformer
models across 3 SOTA compressed versions and infer the effects different
compression techniques have on adversarial attacks.

Oil spill detection has attracted increasing attention in recent years since
marine oil spill accidents severely affect environments, natural resources, and
the lives of coastal inhabitants. Hyperspectral remote sensing images provide
rich spectral information which is beneficial for the monitoring of oil spills
in complex ocean scenarios. However, most of the existing approaches are based
on supervised and semi-supervised frameworks to detect oil spills from
hyperspectral images (HSIs), which require a huge amount of effort to annotate
a certain number of high-quality training sets. In this study, we make the
first attempt to develop an unsupervised oil spill detection method based on
isolation forest for HSIs. First, considering that the noise level varies among
different bands, a noise variance estimation method is exploited to evaluate
the noise level of different bands, and the bands corrupted by severe noise are
removed. Second, kernel principal component analysis (KPCA) is employed to
reduce the high dimensionality of the HSIs. Then, the probability of each pixel
belonging to one of the classes of seawater and oil spills is estimated with
the isolation forest, and a set of pseudo-labeled training samples is
automatically produced using the clustering algorithm on the detected
probability. Finally, an initial detection map can be obtained by performing
the support vector machine (SVM) on the dimension-reduced data, and then, the
initial detection result is further optimized with the extended random walker
(ERW) model so as to improve the detection accuracy of oil spills. Experiments
on airborne hyperspectral oil spill data (HOSD) created by ourselves
demonstrate that the proposed method obtains superior detection performance
with respect to other state-of-the-art detection approaches.

Solar flares, especially the M- and X-class flares, are often associated with
coronal mass ejections (CMEs). They are the most important sources of space
weather effects, that can severely impact the near-Earth environment. Thus it
is essential to forecast flares (especially the M-and X-class ones) to mitigate
their destructive and hazardous consequences. Here, we introduce several
statistical and Machine Learning approaches to the prediction of the AR's Flare
Index (FI) that quantifies the flare productivity of an AR by taking into
account the numbers of different class flares within a certain time interval.
Specifically, our sample includes 563 ARs appeared on solar disk from May 2010
to Dec 2017. The 25 magnetic parameters, provided by the Space-weather HMI
Active Region Patches (SHARP) from Helioseismic and Magnetic Imager (HMI) on
board the Solar Dynamics Observatory (SDO), characterize coronal magnetic
energy stored in ARs by proxy and are used as the predictors. We investigate
the relationship between these SHARP parameters and the FI of ARs with a
machine-learning algorithm (spline regression) and the resampling method
(Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise,
short by SMOGN). Based on the established relationship, we are able to predict
the value of FIs for a given AR within the next 1-day period. Compared with
other 4 popular machine learning algorithms, our methods improve the accuracy
of FI prediction, especially for large FI. In addition, we sort the importance
of SHARP parameters by Borda Count method calculated from the ranks that are
rendered by 9 different machine learning methods.

Semi-supervised few-shot learning consists in training a classifier to adapt
to new tasks with limited labeled data and a fixed quantity of unlabeled data.
Many sophisticated methods have been developed to address the challenges this
problem comprises. In this paper, we propose a simple but quite effective
approach to predict accurate negative pseudo-labels of unlabeled data from an
indirect learning perspective, and then augment the extremely label-constrained
support set in few-shot classification tasks. Our approach can be implemented
in just few lines of code by only using off-the-shelf operations, yet it is
able to outperform state-of-the-art methods on four benchmark datasets.

Normalizing flows model complex probability distributions using maps obtained
by composing invertible layers. Special linear layers such as masked and 1x1
convolutions play a key role in existing architectures because they increase
expressive power while having tractable Jacobians and inverses. We propose a
new family of invertible linear layers based on butterfly layers, which are
known to theoretically capture complex linear structures including permutations
and periodicity, yet can be inverted efficiently. This representational power
is a key advantage of our approach, as such structures are common in many
real-world datasets. Based on our invertible butterfly layers, we construct a
new class of normalizing flow models called ButterflyFlow. Empirically, we
demonstrate that ButterflyFlows not only achieve strong density estimation
results on natural images such as MNIST, CIFAR-10, and ImageNet 32x32, but also
obtain significantly better log-likelihoods on structured datasets such as
galaxy images and MIMIC-III patient cohorts -- all while being more efficient
in terms of memory and computation than relevant baselines.

The COVID-19 pandemic continues to bring up various topics discussed or
debated on social media. In order to explore the impact of pandemics on
people's lives, it is crucial to understand the public's concerns and attitudes
towards pandemic-related entities (e.g., drugs, vaccines) on social media.
However, models trained on existing named entity recognition (NER) or targeted
sentiment analysis (TSA) datasets have limited ability to understand
COVID-19-related social media texts because these datasets are not designed or
annotated from a medical perspective. This paper releases METS-CoV, a dataset
containing medical entities and targeted sentiments from COVID-19-related
tweets. METS-CoV contains 10,000 tweets with 7 types of entities, including 4
medical entity types (Disease, Drug, Symptom, and Vaccine) and 3 general entity
types (Person, Location, and Organization). To further investigate tweet users'
attitudes toward specific entities, 4 types of entities (Person, Organization,
Drug, and Vaccine) are selected and annotated with user sentiments, resulting
in a targeted sentiment dataset with 9,101 entities (in 5,278 tweets). To the
best of our knowledge, METS-CoV is the first dataset to collect medical
entities and corresponding sentiments of COVID-19-related tweets. We benchmark
the performance of classical machine learning models and state-of-the-art deep
learning models on NER and TSA tasks with extensive experiments. Results show
that the dataset has vast room for improvement for both NER and TSA tasks.
METS-CoV is an important resource for developing better medical social media
tools and facilitating computational social science research, especially in
epidemiology. Our data, annotation guidelines, benchmark models, and source
code are publicly available (https://github.com/YLab-Open/METS-CoV) to ensure
reproducibility.

Multimodal learning is a recent challenge that extends unimodal learning by
generalizing its domain to diverse modalities, such as texts, images, or
speech. This extension requires models to process and relate information from
multiple modalities. In Information Retrieval, traditional retrieval tasks
focus on the similarity between unimodal documents and queries, while
image-text retrieval hypothesizes that most texts contain the scene context
from images. This separation has ignored that real-world queries may involve
text content, image captions, or both. To address this, we introduce Multimodal
Retrieval on Representation of ImaGe witH Text (Mr. Right), a novel and
comprehensive dataset for multimodal retrieval. We utilize the Wikipedia
dataset with rich text-image examples and generate three types of text-based
queries with different modality information: text-related, image-related, and
mixed. To validate the effectiveness of our dataset, we provide a multimodal
training paradigm and evaluate previous text retrieval and image retrieval
frameworks. The results show that proposed multimodal retrieval can improve
retrieval performance, but creating a well-unified document representation with
texts and images is still a challenge. We hope Mr. Right allows us to broaden
current retrieval systems better and contributes to accelerating the
advancement of multimodal learning in the Information Retrieval.

The target of image-text clustering (ITC) is to find correct clusters by
integrating complementary and consistent information of multi-modalities for
these heterogeneous samples. However, the majority of current studies analyse
ITC on the ideal premise that the samples in every modality are complete. This
presumption, however, is not always valid in real-world situations. The missing
data issue degenerates the image-text feature learning performance and will
finally affect the generalization abilities in ITC tasks. Although a series of
methods have been proposed to address this incomplete image text clustering
issue (IITC), the following problems still exist: 1) most existing methods
hardly consider the distinct gap between heterogeneous feature domains. 2) For
missing data, the representations generated by existing methods are rarely
guaranteed to suit clustering tasks. 3) Existing methods do not tap into the
latent connections both inter and intra modalities. In this paper, we propose a
Clustering-Induced Generative Incomplete Image-Text Clustering(CIGIT-C) network
to address the challenges above. More specifically, we first use
modality-specific encoders to map original features to more distinctive
subspaces. The latent connections between intra and inter-modalities are
thoroughly explored by using the adversarial generating network to produce one
modality conditional on the other modality. Finally, we update the
corresponding modalityspecific encoders using two KL divergence losses.
Experiment results on public image-text datasets demonstrated that the
suggested method outperforms and is more effective in the IITC job.

Network analysis has been a powerful tool to unveil relationships and
interactions among a large number of objects. Yet its effectiveness in
accurately identifying important node-node interactions is challenged by the
rapidly growing network size, with data being collected at an unprecedented
granularity and scale. Common wisdom to overcome such high dimensionality is
collapsing nodes into smaller groups and conducting connectivity analysis on
the group level. Dividing efforts into two phases inevitably opens a gap in
consistency and drives down efficiency. Consensus learning emerges as a new
normal for common knowledge discovery with multiple data sources available. To
this end, this paper features developing a unified framework of simultaneous
grouping and connectivity analysis by combining multiple data sources. The
algorithm also guarantees a statistically optimal estimator.

The study proposes a quote-driven predictive automated market maker (AMM)
platform with on-chain custody and settlement functions, alongside off-chain
predictive reinforcement learning capabilities to improve liquidity provision
of real-world AMMs. The proposed AMM architecture is an augmentation to the
Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market
equilibrium pricing for reduced divergence and slippage loss. Further, the
proposed architecture involves a predictive AMM capability, utilizing a deep
hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning
framework that looks to improve market efficiency through better forecasts of
liquidity concentration ranges, so liquidity starts moving to expected
concentration ranges, prior to asset price movement, so that liquidity
utilization is improved. The augmented protocol framework is expected have
practical real-world implications, by (i) reducing divergence loss for
liquidity providers, (ii) reducing slippage for crypto-asset traders, while
(iii) improving capital efficiency for liquidity provision for the AMM
protocol. To our best knowledge, there are no known protocol or literature that
are proposing similar deep learning-augmented AMM that achieves similar capital
efficiency and loss minimization objectives for practical real-world

Deep Learning (DL) based Compressed Sensing (CS) has been applied for better
performance of image reconstruction than traditional CS methods. However, most
existing DL methods utilize the block-by-block measurement and each measurement
block is restored separately, which introduces harmful blocking effects for
reconstruction. Furthermore, the neuronal receptive fields of those methods are
designed to be the same size in each layer, which can only collect single-scale
spatial information and has a negative impact on the reconstruction process.
This paper proposes a novel framework named Multi-scale Dilated Convolution
Neural Network (MsDCNN) for CS measurement and reconstruction. During the
measurement period, we directly obtain all measurements from a trained
measurement network, which employs fully convolutional structures and is
jointly trained with the reconstruction network from the input image. It
needn't be cut into blocks, which effectively avoids the block effect. During
the reconstruction period, we propose the Multi-scale Feature Extraction (MFE)
architecture to imitate the human visual system to capture multi-scale features
from the same feature map, which enhances the image feature extraction ability
of the framework and improves the performance of image reconstruction. In the
MFE, there are multiple parallel convolution channels to obtain multi-scale
feature information. Then the multi-scale features information is fused and the
original image is reconstructed with high quality. Our experimental results
show that the proposed method performs favorably against the state-of-the-art
methods in terms of PSNR and SSIM.

It is well known that it is difficult to have a reliable and robust framework
to link multi-agent deep reinforcement learning algorithms with practical
multi-robot applications. To fill this gap, we propose and build an open-source
framework for multi-robot systems called MultiRoboLearn1. This framework builds
a unified setup of simulation and real-world applications. It aims to provide
standard, easy-to-use simulated scenarios that can also be easily deployed to
real-world multi-robot environments. Also, the framework provides researchers
with a benchmark system for comparing the performance of different
reinforcement learning algorithms. We demonstrate the generality, scalability,
and capability of the framework with two real-world scenarios2 using different
types of multi-agent deep reinforcement learning algorithms in discrete and
continuous action spaces.

Text segmentation aims to divide text into contiguous, semantically coherent
segments, while segment labeling deals with producing labels for each segment.
Past work has shown success in tackling segmentation and labeling for documents
and conversations. This has been possible with a combination of task-specific
pipelines, supervised and unsupervised learning objectives. In this work, we
propose a single encoder-decoder neural network that can handle long documents
and conversations, trained simultaneously for both segmentation and segment
labeling using only standard supervision. We successfully show a way to solve
the combined task as a pure generation task, which we refer to as structured
summarization. We apply the same technique to both document and conversational
data, and we show state of the art performance across datasets for both
segmentation and labeling, under both high- and low-resource settings. Our
results establish a strong case for considering text segmentation and segment
labeling as a whole, and moving towards general-purpose techniques that don't
depend on domain expertise or task-specific components.

Space-based infrared tiny ship detection aims at separating tiny ships from
the images captured by earth orbiting satellites. Due to the extremely large
image coverage area (e.g., thousands square kilometers), candidate targets in
these images are much smaller, dimer, more changeable than those targets
observed by aerial-based and land-based imaging devices. Existing short imaging
distance-based infrared datasets and target detection methods cannot be well
adopted to the space-based surveillance task. To address these problems, we
develop a space-based infrared tiny ship detection dataset (namely,
NUDT-SIRST-Sea) with 48 space-based infrared images and 17598 pixel-level tiny
ship annotations. Each image covers about 10000 square kilometers of area with
10000X10000 pixels. Considering the extreme characteristics (e.g., small, dim,
changeable) of those tiny ships in such challenging scenes, we propose a
multi-level TransUNet (MTU-Net) in this paper. Specifically, we design a Vision
Transformer (ViT) Convolutional Neural Network (CNN) hybrid encoder to extract
multi-level features. Local feature maps are first extracted by several
convolution layers and then fed into the multi-level feature extraction module
(MVTM) to capture long-distance dependency. We further propose a
copy-rotate-resize-paste (CRRP) data augmentation approach to accelerate the
training phase, which effectively alleviates the issue of sample imbalance
between targets and background. Besides, we design a FocalIoU loss to achieve
both target localization and shape description. Experimental results on the
NUDT-SIRST-Sea dataset show that our MTU-Net outperforms traditional and
existing deep learning based SIRST methods in terms of probability of
detection, false alarm rate and intersection over union.

Python was used to download and format NewsAPI article data relating to 400
publicly traded, low cap. Biotech companies. Crowd-sourcing was used to label a
subset of this data to then train and evaluate a variety of models to classify
the public sentiment of each company. The best performing models were then used
to show that trading entirely off public sentiment could provide market beating
returns.

With the shift towards on-device deep learning, ensuring a consistent
behavior of an AI service across diverse compute platforms becomes tremendously
important. Our work tackles the emergent problem of reducing predictive
inconsistencies arising as negative flips: test samples that are correctly
predicted by a less accurate model, but incorrectly by a more accurate one. We
introduce REGression constrained Neural Architecture Search (REG-NAS) to design
a family of highly accurate models that engender fewer negative flips. REG-NAS
consists of two components: (1) A novel architecture constraint that enables a
larger model to contain all the weights of the smaller one thus maximizing
weight sharing. This idea stems from our observation that larger weight sharing
among networks leads to similar sample-wise predictions and results in fewer
negative flips; (2) A novel search reward that incorporates both Top-1 accuracy
and negative flips in the architecture search metric. We demonstrate that
\regnas can successfully find desirable architectures with few negative flips
in three popular architecture search spaces. Compared to the existing
state-of-the-art approach, REG-NAS enables 33-48% relative reduction of
negative flips.

Seamlessly integrating rules in Learning-from-Demonstrations (LfD) policies
is a critical requirement to enable the real-world deployment of AI agents.
Recently Signal Temporal Logic (STL) has been shown to be an effective language
for encoding rules as spatio-temporal constraints. This work uses Monte Carlo
Tree Search (MCTS) as a means of integrating STL specification into a vanilla
LfD policy to improve constraint satisfaction. We propose augmenting the MCTS
heuristic with STL robustness values to bias the tree search towards branches
with higher constraint satisfaction. While the domain-independent method can be
applied to integrate STL rules online into any pre-trained LfD algorithm, we
choose goal-conditioned Generative Adversarial Imitation Learning as the
offline LfD policy. We apply the proposed method to the domain of planning
trajectories for General Aviation aircraft around a non-towered airfield.
Results using the simulator trained on real-world data showcase 60% improved
performance over baseline LfD methods that do not use STL heuristics.

BACKGROUND AND PURPOSE: Deep learning has been demonstrated effective in many
neuroimaging applications. However, in many scenarios, the number of imaging
sequences capturing information related to small vessel disease lesions is
insufficient to support data-driven techniques. Additionally, cohort-based
studies may not always have the optimal or essential imaging sequences for
accurate lesion detection. Therefore, it is necessary to determine which
imaging sequences are crucial for precise detection. This study introduces a
novel deep learning framework to detect enlarged perivascular spaces (ePVS) and
aims to find the optimal combination of MRI sequences for deep learning-based
quantification. MATERIALS AND METHODS: We implemented an effective lightweight
U-Net adapted for ePVS detection and comprehensively investigated different
combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted
(T2w) MRI sequences. The training data included 21 participants, which were
randomly selected from the MESA cohort. Participants had ePVS 683 lesions on
average. For T1w, T2w, and FLAIR images, the MESA study collected 3D isotropic
MRI scans at six different sites with Siemens scanners. Our training data
included participants from all these sites and all the scanner models, and the
proposed model was applied to the whole brain instead of selective regions.
RESULTS: The experimental results showed that T2w MRI is the most important for
accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the
deep neural network had minor improvements in accuracy and resulted in the
highest sensitivity and precision (sensitivity =0.82, precision =0.83). The
proposed method achieved comparable accuracy at a minimal time cost compared to
manual reading.

In this paper we address the problem of testing whether two observed trees
$(t,t')$ are sampled either independently or from a joint distribution under
which they are correlated.
  This problem, which we refer to as correlation detection in trees, plays a
key role in the study of graph alignment for two correlated random graphs.
Motivated by graph alignment, we investigate the conditions of existence of
one-sided tests, i.e. tests which have vanishing type I error and non-vanishing
power in the limit of large tree depth.
  For the correlated Galton-Watson model with Poisson offspring of mean
$\lambda>0$ and correlation parameter $s \in (0,1)$, we identify a phase
transition in the limit of large degrees at $s = \sqrt{\alpha}$, where $\alpha
\sim 0.3383$ is Otter's constant. Namely, we prove that no such test exists for
$s \leq \sqrt{\alpha}$, and that such a test exists whenever $s >
\sqrt{\alpha}$, for $\lambda$ large enough.
  This result sheds new light on the graph alignment problem in the sparse
regime (with $O(1)$ average node degrees) and on the performance of the MPAlign
method studied in Ganassali et al. (2021), Piccioli et al. (2021), proving in
particular the conjecture of Piccioli et al. (2021) that MPAlign succeeds in
the partial recovery task for correlation parameter $s>\sqrt{\alpha}$ provided
the average node degree $\lambda$ is large enough.

Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for
approximating integrals, for instance in the context of Bayesian inference. In
IS, the samples are simulated from the so-called proposal distribution, and the
choice of this proposal is key for achieving a high performance. In adaptive IS
(AIS) methods, a set of proposals is iteratively improved. AIS is a relevant
and timely methodology although many limitations remain yet to be overcome,
e.g., the curse of dimensionality in high-dimensional and multi-modal problems.
Moreover, the Hamiltonian Monte Carlo (HMC) algorithm has become increasingly
popular in machine learning and statistics. HMC has several appealing features
such as its exploratory behavior, especially in high-dimensional targets, when
other methods suffer. In this paper, we introduce the novel Hamiltonian
adaptive importance sampling (HAIS) method. HAIS implements a two-step adaptive
process with parallel HMC chains that cooperate at each iteration. The proposed
HAIS efficiently adapts a population of proposals, extracting the advantages of
HMC. HAIS can be understood as a particular instance of the generic layered AIS
family with an additional resampling step. HAIS achieves a significant
performance improvement in high-dimensional problems w.r.t. state-of-the-art
algorithms. We discuss the statistical properties of HAIS and show its high
performance in two challenging examples.

A single machine total weighted tardiness minimization (TWTM) problem in
operational planning is considered. The problem is formulated as an NP-hard
constrained combinatorial problem, which has no known deterministic polynomial
complexity solution using classical computing. Based on efficient Grover's
quantum search and Trugenberger's quantum optimization algorithms, a novel
efficient quantum optimization algorithm is proposed to solve the NP-hard
single machine TWTM problem, which makes the desired solution satisfying the
searching constraints and showing the minimal TWT value be measured with the
highest probability.

Randomized Controlled Trials (RCTs) represent a gold standard when developing
policy guidelines. However, RCTs are often narrow, and lack data on broader
populations of interest. Causal effects in these populations are often
estimated using observational datasets, which may suffer from unobserved
confounding and selection bias. Given a set of observational estimates (e.g.
from multiple studies), we propose a meta-algorithm that attempts to reject
observational estimates that are biased. We do so using validation effects,
causal effects that can be inferred from both RCT and observational data. After
rejecting estimators that do not pass this test, we generate conservative
confidence intervals on the extrapolated causal effects for subgroups not
observed in the RCT. Under the assumption that at least one observational
estimator is asymptotically normal and consistent for both the validation and
extrapolated effects, we provide guarantees on the coverage probability of the
intervals output by our algorithm. To facilitate hypothesis testing in settings
where causal effect transportation across datasets is necessary, we give
conditions under which a doubly-robust estimator of group average treatment
effects is asymptotically normal, even when flexible machine learning methods
are used for estimation of nuisance parameters. We illustrate the properties of
our approach on semi-synthetic and real world datasets, and show that it
compares favorably to standard meta-analysis techniques.

Cables are commonplace in homes, hospitals, and industrial warehouses and are
prone to tangling. This paper extends prior work on autonomously untangling
long cables by introducing novel uncertainty quantification metrics and actions
that interact with the cable to reduce perception uncertainty. We present
Sliding and Grasping for Tangle Manipulation 2.0 (SGTM 2.0), a system that
autonomously untangles cables approximately 3 meters in length with a bilateral
robot using estimates of uncertainty at each step to inform actions. By
interactively reducing uncertainty, Sliding and Grasping for Tangle
Manipulation 2.0 (SGTM 2.0) reduces the number of state-resetting moves it must
take, significantly speeding up run-time. Experiments suggest that SGTM 2.0 can
achieve 83% untangling success on cables with 1 or 2 overhand and figure-8
knots, and 70% termination detection success across these configurations,
outperforming SGTM 1.0 by 43% in untangling accuracy and 200% in full rollout
speed. Supplementary material, visualizations, and videos can be found at
sites.google.com/view/sgtm2.

Dataloaders, in charge of moving data from storage into GPUs while training
machine learning models, might hold the key to drastically improving the
performance of training jobs. Recent advances have shown promise not only by
considerably decreasing training time but also by offering new features such as
loading data from remote storage like S3. In this paper, we are the first to
distinguish the dataloader as a separate component in the Deep Learning (DL)
workflow and to outline its structure and features. Finally, we offer a
comprehensive comparison of the different dataloading libraries available,
their trade-offs in terms of functionality, usability, and performance and the
insights derived from them.

Recently, knowledge representation learning (KRL) is emerging as the
state-of-the-art approach to process queries over knowledge graphs (KGs),
wherein KG entities and the query are embedded into a latent space such that
entities that answer the query are embedded close to the query. Yet, despite
the intensive research on KRL, most existing studies either focus on homogenous
KGs or assume KG completion tasks (i.e., inference of missing facts), while
answering complex logical queries over KGs with multiple aspects (multi-view
KGs) remains an open challenge.
  To bridge this gap, in this paper, we present ROMA, a novel KRL framework for
answering logical queries over multi-view KGs. Compared with the prior work,
ROMA departs in major aspects. (i) It models a multi-view KG as a set of
overlaying sub-KGs, each corresponding to one view, which subsumes many types
of KGs studied in the literature (e.g., temporal KGs). (ii) It supports complex
logical queries with varying relation and view constraints (e.g., with complex
topology and/or from multiple views); (iii) It scales up to KGs of large sizes
(e.g., millions of facts) and fine-granular views (e.g., dozens of views); (iv)
It generalizes to query structures and KG views that are unobserved during
training. Extensive empirical evaluation on real-world KGs shows that \system
significantly outperforms alternative methods.

Generative models such as DALL-E 2 could represent a promising future tool
for image generation, augmentation, and manipulation for artificial
intelligence research in radiology provided that these models have sufficient
medical domain knowledge. Here we show that DALL-E 2 has learned relevant
representations of X-ray images with promising capabilities in terms of
zero-shot text-to-image generation of new images, continuation of an image
beyond its original boundaries, or removal of elements, while pathology
generation or CT, MRI, and ultrasound images are still limited. The use of
generative models for augmenting and generating radiological data thus seems
feasible, even if further fine-tuning and adaptation of these models to the
respective domain is required beforehand.

We propose a \underline{d}oubly \underline{o}ptimistic strategy for the
\underline{s}afe-\underline{l}inear-\underline{b}andit problem, DOSLB. The safe
linear bandit problem is to optimise an unknown linear reward whilst satisfying
unknown round-wise safety constraints on actions, using stochastic bandit
feedback of reward and safety-risks of actions. In contrast to prior work on
aggregated resource constraints, our formulation explicitly demands control on
roundwise safety risks.
  Unlike existing optimistic-pessimistic paradigms for safe bandits, DOSLB
exercises supreme optimism, using optimistic estimates of reward and safety
scores to select actions. Yet, and surprisingly, we show that DOSLB rarely
takes risky actions, and obtains $\tilde{O}(d \sqrt{T})$ regret, where our
notion of regret accounts for both inefficiency and lack of safety of actions.
Specialising to polytopal domains, we first notably show that the
$\sqrt{T}$-regret bound cannot be improved even with large gaps, and then
identify a slackened notion of regret for which we show tight
instance-dependent $O(\log^2 T)$ bounds. We further argue that in such domains,
the number of times an overly risky action is played is also bounded as
$O(\log^2T)$.

Achieving brain-like density and performance in neuromorphic computers
necessitates scaling down the size of nanodevices emulating neuro-synaptic
functionalities. However, scaling nanodevices results in reduction of
programming resolution and emergence of stochastic non-idealities. While prior
work has mainly focused on binary transitions, in this work we leverage the
stochastic switching of a three-state ferroelectric field effect transistor
(FeFET) to implement a long-term and short-term 2-tier stochastic synaptic
memory with a single device. Experimental measurements are performed on a
scaled 28nm high-$k$ metal gate technology-based device to develop a
probabilistic model of the hybrid stochastic synapse. In addition to the
advantage of ultra-low programming energies afforded by scaling, our
hardware-algorithm co-design analysis reveals the efficacy of the 2-tier memory
in comparison to binary stochastic synapses in on-chip learning tasks -- paving
the way for algorithms exploiting multi-state devices with probabilistic
transitions beyond deterministic ones.

Recent advancements in Vehicle-to-Everything communication technology have
enabled autonomous vehicles to share sensory information to obtain better
perception performance. With the rapid growth of autonomous vehicles and
intelligent infrastructure, the V2X perception systems will soon be deployed at
scale, which raises a safety-critical question: how can we evaluate and improve
its performance under challenging traffic scenarios before the real-world
deployment? Collecting diverse large-scale real-world test scenes seems to be
the most straightforward solution, but it is expensive and time-consuming, and
the collections can only cover limited scenarios. To this end, we propose the
first open adversarial scene generator V2XP-ASG that can produce realistic,
challenging scenes for modern LiDAR-based multi-agent perception system.
V2XP-ASG learns to construct an adversarial collaboration graph and
simultaneously perturb multiple agents' poses in an adversarial and plausible
manner. The experiments demonstrate that V2XP-ASG can effectively identify
challenging scenes for a large range of V2X perception systems. Meanwhile, by
training on the limited number of generated challenging scenes, the accuracy of
V2X perception systems can be further improved by 12.3% on challenging and 4%
on normal scenes.

While fairness-aware machine learning algorithms have been receiving
increasing attention, the focus has been on centralized machine learning,
leaving decentralized methods underexplored. Federated Learning is a
decentralized form of machine learning where clients train local models with a
server aggregating them to obtain a shared global model. Data heterogeneity
amongst clients is a common characteristic of Federated Learning, which may
induce or exacerbate discrimination of unprivileged groups defined by sensitive
attributes such as race or gender. In this work we propose FAIR-FATE: a novel
FAIR FederATEd Learning algorithm that aims to achieve group fairness while
maintaining high utility via a fairness-aware aggregation method that computes
the global model by taking into account the fairness of the clients. To achieve
that, the global model update is computed by estimating a fair model update
using a Momentum term that helps to overcome the oscillations of noisy non-fair
gradients. To the best of our knowledge, this is the first approach in machine
learning that aims to achieve fairness using a fair Momentum estimate.
Experimental results on four real-world datasets demonstrate that FAIR-FATE
significantly outperforms state-of-the-art fair Federated Learning algorithms
under different levels of data heterogeneity.

Planetary rover missions must utilize machine learning-based perception to
continue extra-terrestrial exploration with little to no human presence.
Martian terrain segmentation has been critical for rover navigation and hazard
avoidance to perform further exploratory tasks, e.g. soil sample collection and
searching for organic compounds. Current Martian terrain segmentation models
require a large amount of labeled data to achieve acceptable performance, and
also require retraining for deployment across different domains, i.e. different
rover missions, or different tasks, i.e. geological identification and
navigation. This research proposes a semi-supervised learning approach that
leverages unsupervised contrastive pretraining of a backbone for a
multi-mission semantic segmentation for Martian surfaces. This model will
expand upon the current Martian segmentation capabilities by being able to
deploy across different Martian rover missions for terrain navigation, by
utilizing a mixed-domain training set that ensures feature diversity.
Evaluation results of using average pixel accuracy show that a semi-supervised
mixed-domain approach improves accuracy compared to single domain training and
supervised training by reaching an accuracy of 97% for the Mars Science
Laboratory's Curiosity Rover and 79.6% for the Mars 2020 Perseverance Rover.
Further, providing different weighting methods to loss functions improved the
models correct predictions for minority or rare classes by over 30% using the
recall metric compared to standard cross-entropy loss. These results can inform
future multi-mission and multi-task semantic segmentation for rover missions in
a data-efficient manner.

Deep Convolutional Neural Networks (CNNs) for image classification
successively alternate convolutions and downsampling operations, such as
pooling layers or strided convolutions, resulting in lower resolution features
the deeper the network gets. These downsampling operations save computational
resources and provide some translational invariance as well as a bigger
receptive field at the next layers. However, an inherent side-effect of this is
that high-level features, produced at the deep end of the network, are always
captured in low resolution feature maps. The inverse is also true, as shallow
layers always contain small scale features. In biomedical image analysis
engineers are often tasked with classifying very small image patches which
carry only a limited amount of information. By their nature, these patches may
not even contain objects, with the classification depending instead on the
detection of subtle underlying patterns with an unknown scale in the image's
texture. In these cases every bit of information is valuable; thus, it is
important to extract the maximum number of informative features possible.
Driven by these considerations, we introduce a new CNN architecture which
preserves multi-scale features from deep, intermediate, and shallow layers by
utilizing skip connections along with consecutive contractions and expansions
of the feature maps. Using a dataset of very low resolution patches from
Pancreatic Ductal Adenocarcinoma (PDAC) CT scans we demonstrate that our
network can outperform current state of the art models.

Thousands of vulnerabilities are reported on a monthly basis to security
repositories, such as the National Vulnerability Database. Among these
vulnerabilities, software misconfiguration is one of the top 10 security risks
for web applications. With this large influx of vulnerability reports, software
fingerprinting has become a highly desired capability to discover distinctive
and efficient signatures and recognize reportedly vulnerable software
implementations. Due to the exponential worst-case complexity of fingerprint
matching, designing more efficient methods for fingerprinting becomes highly
desirable, especially for variability-intensive systems where optional features
add another exponential factor to its analysis. This position paper presents
our vision of a framework that lifts model learning and family-based analysis
principles to software fingerprinting. In this framework, we propose unifying
databases of signatures into a featured finite state machine and using presence
conditions to specify whether and in which circumstances a given input-output
trace is observed. We believe feature-based signatures can aid performance
improvements by reducing the size of fingerprints under analysis.

Multi-party computing (MPC) has been gaining popularity over the past years
as a secure computing model, particularly for machine learning (ML) inference.
Compared with its competitors, MPC has fewer overheads than homomorphic
encryption (HE) and has a more robust threat model than hardware-based trusted
execution environments (TEE) such as Intel SGX. Despite its apparent
advantages, MPC protocols still pay substantial performance penalties compared
to plaintext when applied to ML algorithms. The overhead is due to added
computation and communication costs. For multiplications that are ubiquitous in
ML algorithms, MPC protocols add 32x more computational costs and 1 round of
broadcasting among MPC servers. Moreover, ML computations that have trivial
costs in plaintext, such as Softmax, ReLU, and other non-linear operations
become very expensive due to added communication. Those added overheads make
MPC less palatable to deploy in real-time ML inference frameworks, such as
speech translation.
  In this work, we present MPC-Pipe, an MPC pipeline inference technique that
uses two ML-specific approaches. 1) inter-linear-layer pipeline and 2) inner
layer pipeline. Those two techniques shorten the total inference runtime for
machine learning models. Our experiments have shown to reduce ML inference
latency by up to 12.6% when model weights are private and 14.48\% when model
weights are public, compared to current MPC protocol implementations.

While current systems for autonomous robot navigation can produce safe and
efficient motion plans in static environments, they usually generate suboptimal
behaviors when multiple robots must navigate together in confined spaces. For
example, when two robots meet each other in a narrow hallway, they may either
turn around to find an alternative route or collide with each other. This paper
presents a new approach to navigation that allows two robots to pass each other
in a narrow hallway without colliding, stopping, or waiting. Our approach,
Perceptual Hallucination for Hallway Passing (PHHP), learns to synthetically
generate virtual obstacles (i.e., perceptual hallucination) to facilitate
passing in narrow hallways by multiple robots that utilize otherwise standard
autonomous navigation systems. Our experiments on physical robots in a variety
of hallways show improved performance compared to multiple baselines.

The pioneering method for unsupervised meta-learning, CACTUs, is a
clustering-based approach with pseudo-labeling. This approach is model-agnostic
and can be combined with supervised algorithms to learn from unlabeled data.
However, it often suffers from label inconsistency or limited diversity, which
leads to poor performance. In this work, we prove that the core reason for this
is lack of a clustering-friendly property in the embedding space. We address
this by minimizing the inter- to intra-class similarity ratio to provide
clustering-friendly embedding features, and validate our approach through
comprehensive experiments. Note that, despite only utilizing a simple
clustering algorithm (k-means) in our embedding space to obtain the
pseudo-labels, we achieve significant improvement. Moreover, we adopt a
progressive evaluation mechanism to obtain more diverse samples in order to
further alleviate the limited diversity problem. Finally, our approach is also
model-agnostic and can easily be integrated into existing supervised methods.
To demonstrate its generalization ability, we integrate it into two
representative algorithms: MAML and EP. The results on three main few-shot
benchmarks clearly show that the proposed method achieves significant
improvement compared to state-of-the-art models. Notably, our approach also
outperforms the corresponding supervised method in two tasks.

Real-time interception of a fast-moving object by a robotic arm in cluttered
environments filled with static or dynamic obstacles permits only tens of
milliseconds for reaction times, hence quite challenging and arduous for
state-of-the-art robotic planning algorithms to perform multiple robotic
skills, for instance, catching the dynamic object and avoiding obstacles, in
parallel. This paper proposes an unified framework of robotic path planning
through embedding the high-dimensional temporal information contained in the
event stream to distinguish between safe and colliding trajectories into a
low-dimension space manifested with a pre-constructed 2D densely connected
graph. We then leverage a fast graph-traversing strategy to generate the motor
commands necessary to effectively avoid the approaching obstacles while
simultaneously intercepting a fast-moving objects. The most distinctive feature
of our methodology is to conduct both object interception and obstacle
avoidance within the same algorithm framework based on deep manifold learning.
By leveraging a highly efficient diffusion-map based variational autoencoding
and Extended Kalman Filter(EKF), we demonstrate the effectiveness of our
approach on an autonomous 7-DoF robotic arm using only onboard sensing and
computation. Our robotic manipulator was capable of avoiding multiple obstacles
of different sizes and shapes while successfully capturing a fast-moving soft
ball thrown by hand at normal speed in different angles. Complete video
demonstrations of our experiments can be found in
https://sites.google.com/view/multirobotskill/home.

Autoregressive language models, which use deep learning to produce human-like
texts, have become increasingly widespread. Such models are powering popular
virtual assistants in areas like smart health, finance, and autonomous driving.
While the parameters of these large language models are improving, concerns
persist that these models might not work equally for all subgroups in society.
Despite growing discussions of AI fairness across disciplines, there lacks
systemic metrics to assess what equity means in dialogue systems and how to
engage different populations in the assessment loop. Grounded in theories of
deliberative democracy and science and technology studies, this paper proposes
an analytical framework for unpacking the meaning of equity in human-AI
dialogues. Using this framework, we conducted an auditing study to examine how
GPT-3 responded to different sub-populations on crucial science and social
topics: climate change and the Black Lives Matter (BLM) movement. Our corpus
consists of over 20,000 rounds of dialogues between GPT-3 and 3290 individuals
who vary in gender, race and ethnicity, education level, English as a first
language, and opinions toward the issues. We found a substantively worse user
experience with GPT-3 among the opinion and the education minority
subpopulations; however, these two groups achieved the largest knowledge gain,
changing attitudes toward supporting BLM and climate change efforts after the
chat. We traced these user experience divides to conversational differences and
found that GPT-3 used more negative expressions when it responded to the
education and opinion minority groups, compared to its responses to the
majority groups. We discuss the implications of our findings for a deliberative
conversational AI system that centralizes diversity, equity, and inclusion.

Many visual phenomena suggest that humans use top-down generative or
reconstructive processes to create visual percepts (e.g., imagery, object
completion, pareidolia), but little is known about the role reconstruction
plays in robust object recognition. We built an iterative encoder-decoder
network that generates an object reconstruction and used it as top-down
attentional feedback to route the most relevant spatial and feature information
to feed-forward object recognition processes. We tested this model using the
challenging out-of-distribution digit recognition dataset, MNIST-C, where 15
different types of transformation and corruption are applied to handwritten
digit images. Our model showed strong generalization performance against
various image perturbations, on average outperforming all other models
including feedforward CNNs and adversarially trained networks. Our model is
particularly robust to blur, noise, and occlusion corruptions, where shape
perception plays an important role. Ablation studies further reveal two
complementary roles of spatial and feature-based attention in robust object
recognition, with the former largely consistent with spatial masking benefits
in the attention literature (the reconstruction serves as a mask) and the
latter mainly contributing to the model's inference speed (i.e., number of time
steps to reach a certain confidence threshold) by reducing the space of
possible object hypotheses. We also observed that the model sometimes
hallucinates a non-existing pattern out of noise, leading to highly
interpretable human-like errors. Our study shows that modeling
reconstruction-based feedback endows AI systems with a powerful attention
mechanism, which can help us understand the role of generating perception in
human visual processing.

Frequently, trends in record AM1.5 power-conversion efficiencies versus time
are used to analyze relative merits of different photovoltaic material
technologies. However, this approach belies the effort expended in achieving
these levels of performance. We introduce cumulative publications as a proxy
for total R&D efforts and find surprisingly that silicon, Cu(In,Ga)Se2 (CIGSe),
CdTe, and halide perovskite technologies have each followed essentially the
same learning curve of 20-24% efficiency within 10,000 publications and a
consistent marginal rate of 5% efficiency per factor of 10 in publications.
While learning spillover from non-PV technologies, cross-pollination from other
PV technologies, and hidden commercial effort are not accounted for by this
metric, this analysis still yields useful and novel insights into PV technology
trajectories. Trajectories below this learning curve have required more total
effort per performance and plateaus of efficiency stagnation at large numbers
of publications may indicate (but do not guarantee) the existence of
fundamental barriers to commercially-relevant performance. Lastly, examples to
watch are identified for technologies currently exhibiting higher marginal
slopes, including some that appeared dormant by this metric in past years.

Operating under real world conditions is challenging due to the possibility
of a wide range of failures induced by partial observability. In relatively
benign settings, such failures can be overcome by retrying or executing one of
a small number of hand-engineered recovery strategies. By contrast,
contact-rich sequential manipulation tasks, like opening doors and assembling
furniture, are not amenable to exhaustive hand-engineering. To address this
issue, we present a general approach for robustifying manipulation strategies
in a sample-efficient manner. Our approach incrementally improves robustness by
first discovering the failure modes of the current strategy via exploration in
simulation and then learning additional recovery skills to handle these
failures. To ensure efficient learning, we propose an online algorithm Value
Upper Confidence Limit (Value-UCL) that selects what failure modes to
prioritize and which state to recover to such that the expected performance
improves maximally in every training episode. We use our approach to learn
recovery skills for door-opening and evaluate them both in simulation and on a
real robot with little fine-tuning. Compared to open-loop execution, our
experiments show that even a limited amount of recovery learning improves task
success substantially from 71\% to 92.4\% in simulation and from 75\% to 90\%
on a real robot.

No existing spherical convolutional neural network (CNN) framework is both
computationally scalable and rotationally equivariant. Continuous approaches
capture rotational equivariance but are often prohibitively computationally
demanding. Discrete approaches offer more favorable computational performance
but at the cost of equivariance. We develop a hybrid discrete-continuous
(DISCO) group convolution that is simultaneously equivariant and
computationally scalable to high-resolution. While our framework can be applied
to any compact group, we specialize to the sphere. Our DISCO spherical
convolutions not only exhibit $\text{SO}(3)$ rotational equivariance but also a
form of asymptotic $\text{SO}(3)/\text{SO}(2)$ rotational equivariance, which
is more desirable for many applications (where $\text{SO}(n)$ is the special
orthogonal group representing rotations in $n$-dimensions). Through a sparse
tensor implementation we achieve linear scaling in number of pixels on the
sphere for both computational cost and memory usage. For 4k spherical images we
realize a saving of $10^9$ in computational cost and $10^4$ in memory usage
when compared to the most efficient alternative equivariant spherical
convolution. We apply the DISCO spherical CNN framework to a number of
benchmark dense-prediction problems on the sphere, such as semantic
segmentation and depth estimation, on all of which we achieve the

A distinctive representation of image patches in form of features is a key
component of many computer vision and robotics tasks, such as image matching,
image retrieval, and visual localization. State-of-the-art descriptors, from
hand-crafted descriptors such as SIFT to learned ones such as HardNet, are
usually high dimensional; 128 dimensions or even more. The higher the
dimensionality, the larger the memory consumption and computational time for
approaches using such descriptors. In this paper, we investigate multi-layer
perceptrons (MLPs) to extract low-dimensional but high-quality descriptors. We
thoroughly analyze our method in unsupervised, self-supervised, and supervised
settings, and evaluate the dimensionality reduction results on four
representative descriptors. We consider different applications, including
visual localization, patch verification, image matching and retrieval. The
experiments show that our lightweight MLPs achieve better dimensionality
reduction than PCA. The lower-dimensional descriptors generated by our approach
outperform the original higher-dimensional descriptors in downstream tasks,
especially for the hand-crafted ones. The code will be available at
https://github.com/PRBonn/descriptor-dr.

Blind source separation (BSS) algorithms are unsupervised methods, which are
the cornerstone of hyperspectral data analysis by allowing for physically
meaningful data decompositions. BSS problems being ill-posed, the resolution
requires efficient regularization schemes to better distinguish between the
sources and yield interpretable solutions. For that purpose, we investigate a
semi-supervised source separation approach in which we combine a projected
alternating least-square algorithm with a learning-based regularization scheme.
In this article, we focus on constraining the mixing matrix to belong to a
learned manifold by making use of generative models. Altogether, we show that
this allows for an innovative BSS algorithm, with improved accuracy, which
provides physically interpretable solutions. The proposed method, coined sGMCA,
is tested on realistic hyperspectral astrophysical data in challenging
scenarios involving strong noise, highly correlated spectra and unbalanced
sources. The results highlight the significant benefit of the learned prior to
reduce the leakages between the sources, which allows an overall better
disentanglement.

We propose a self-supervised algorithm to learn representations from
egocentric video data. Recently, significant efforts have been made to capture
humans interacting with their own environments as they go about their daily
activities. In result, several large egocentric datasets of interaction-rich
multi-modal data have emerged. However, learning representations from videos
can be challenging. First, given the uncurated nature of long-form continuous
videos, learning effective representations require focusing on moments in time
when interactions take place. Second, visual representations of daily
activities should be sensitive to changes in the state of the environment.
However, current successful multi-modal learning frameworks encourage
representation invariance over time. To address these challenges, we leverage
audio signals to identify moments of likely interactions which are conducive to
better learning. We also propose a novel self-supervised objective that learns
from audible state changes caused by interactions. We validate these
contributions extensively on two large-scale egocentric datasets,
EPIC-Kitchens-100 and the recently released Ego4D, and show improvements on
several downstream tasks, including action recognition, long-term action
anticipation, and object state change classification.

Artificial intelligence (AI) systems are increasingly used for providing
advice to facilitate human decision making. While a large body of work has
explored how AI systems can be optimized to produce accurate and fair advice
and how algorithmic advice should be presented to human decision makers, in
this work we ask a different basic question: When should algorithms provide
advice? Motivated by limitations of the current practice of constantly
providing algorithmic advice, we propose the design of AI systems that interact
with the human user in a two-sided manner and provide advice only when it is
likely to be beneficial to the human in making their decision. Our AI systems
learn advising policies using past human decisions. Then, for new cases, the
learned policies utilize input from the human to identify cases where
algorithmic advice would be useful, as well as those where the human is better
off deciding alone. We conduct a large-scale experiment to evaluate our
approach by using data from the US criminal justice system on pretrial-release
decisions. In our experiment, participants were asked to assess the risk of
defendants to violate their release terms if released and were advised by
different advising approaches. The results show that our interactive-advising
approach manages to provide advice at times of need and to significantly
improve human decision making compared to fixed, non-interactive advising
approaches. Our approach has additional advantages in facilitating human
learning, preserving complementary strengths of human decision makers, and
leading to more positive responsiveness to the advice.

Efficient and automated design of optimizers plays a crucial role in
full-stack AutoML systems. However, prior methods in optimizer search are often
limited by their scalability, generability, or sample efficiency. With the goal
of democratizing research and application of optimizer search, we present the
first efficient, scalable and generalizable framework that can directly search
on the tasks of interest. We first observe that optimizer updates are
fundamentally mathematical expressions applied to the gradient. Inspired by the
innate tree structure of the underlying math expressions, we re-arrange the
space of optimizers into a super-tree, where each path encodes an optimizer.
This way, optimizer search can be naturally formulated as a path-finding
problem, allowing a variety of well-established tree traversal methods to be
used as the search algorithm. We adopt an adaptation of the Monte Carlo method
to tree search, equipped with rejection sampling and equivalent-form detection
that leverage the characteristics of optimizer update rules to further boost
the sample efficiency. We provide a diverse set of tasks to benchmark our
algorithm and demonstrate that, with only 128 evaluations, the proposed
framework can discover optimizers that surpass both human-designed counterparts
and prior optimizer search methods.

Sliced Wasserstein (SW) distance has been widely used in different
application scenarios since it can be scaled to a large number of supports
without suffering from the curse of dimensionality. The value of sliced
Wasserstein distance is the average of transportation cost between
one-dimensional representations (projections) of original measures that are
obtained by Radon Transform (RT). Despite its efficiency in the number of
supports, estimating the sliced Wasserstein requires a relatively large number
of projections in high-dimensional settings. Therefore, for applications where
the number of supports is relatively small compared with the dimension, e.g.,
several deep learning applications where the mini-batch approaches are
utilized, the complexities from matrix multiplication of Radon Transform become
the main computational bottleneck. To address this issue, we propose to derive
projections by linearly and randomly combining a smaller number of projections
which are named bottleneck projections. We explain the usage of these
projections by introducing Hierarchical Radon Transform (HRT) which is
constructed by applying Radon Transform variants recursively. We then formulate
the approach into a new metric between measures, named Hierarchical Sliced
Wasserstein (HSW) distance. By proving the injectivity of HRT, we derive the
metricity of HSW. Moreover, we investigate the theoretical properties of HSW
including its connection to SW variants and its computational and sample
complexities. Finally, we compare the computational cost and generative quality
of HSW with the conventional SW on the task of deep generative modeling using
various benchmark datasets including CIFAR10, CelebA, and Tiny ImageNet.

Training deep neural networks in low rank, i.e. with factorised layers, is of
particular interest to the community: it offers efficiency over unfactorised
training in terms of both memory consumption and training time. Prior work has
focused on low rank approximations of pre-trained networks and training in low
rank space with additional objectives, offering various ad hoc explanations for
chosen practice. We analyse techniques that work well in practice, and through
extensive ablations on models such as GPT2 we provide evidence falsifying
common beliefs in the field, hinting in the process at exciting research
opportunities that still need answering.

Computational models have become a powerful tool in the quantitative sciences
to understand the behaviour of complex systems that evolve in time. However,
they often contain a potentially large number of free parameters whose values
cannot be obtained from theory but need to be inferred from data. This is
especially the case for models in the social sciences, economics, or
computational epidemiology. Yet many current parameter estimation methods are
mathematically involved and computationally slow to run. In this paper we
present a computationally simple and fast method to retrieve accurate
probability densities for model parameters using neural differential equations.
We present a pipeline comprising multi-agent models acting as forward solvers
for systems of ordinary or stochastic differential equations, and a neural
network to then extract parameters from the data generated by the model. The
two combined create a powerful tool that can quickly estimate densities on
model parameters, even for very large systems. We demonstrate the method on
synthetic time series data of the SIR model of the spread of infection, and
perform an in-depth analysis of the Harris-Wilson model of economic activity on
a network, representing a non-convex problem. For the latter, we apply our
method both to synthetic data and to data of economic activity across Greater
London. We find that our method calibrates the model orders of magnitude more
accurately than a previous study of the same dataset using classical
techniques, while running between 195 and 390 times faster.

Cortical prostheses are devices implanted in the visual cortex that attempt
to restore lost vision by electrically stimulating neurons. Currently, the
vision provided by these devices is limited, and accurately predicting the
visual percepts resulting from stimulation is an open challenge. We propose to
address this challenge by utilizing 'brain-like' convolutional neural networks
(CNNs), which have emerged as promising models of the visual system. To
investigate the feasibility of adapting brain-like CNNs for modeling visual
prostheses, we developed a proof-of-concept model to predict the perceptions
resulting from electrical stimulation. We show that a neurologically-inspired
decoding of CNN activations produces qualitatively accurate phosphenes,
comparable to phosphenes reported by real patients. Overall, this is an
essential first step towards building brain-like models of electrical
stimulation, which may not just improve the quality of vision provided by
cortical prostheses but could also further our understanding of the neural code
of vision.

Fully supervised semantic segmentation learns from dense masks, which
requires heavy annotation cost for closed set. In this paper, we use natural
language as supervision without any pixel-level annotation for open world
segmentation. We call the proposed framework as FreeSeg, where the mask is
freely available from raw feature map of pretraining model. Compared with
zero-shot or openset segmentation, FreeSeg doesn't require any annotated masks,
and it widely predicts categories beyond class-agnostic unsupervised
segmentation. Specifically, FreeSeg obtains free mask from Image-Text
Similarity Map (ITSM) of Interpretable Contrastive Language-Image Pretraining
(ICLIP). And our core improvements are the smoothed min pooling for dense
ICLIP, with the partial label and pixel strategies for segmentation.
Furthermore, FreeSeg is very straight forward without complex design like
grouping, clustering or retrieval. Besides the simplicity, the performances of
FreeSeg surpass previous state-of-the-art at large margins, e.g. 13.4% higher
at mIoU on VOC dataset in the same settings.

Artificial intelligence-based methods are becoming increasingly effective at
screening libraries of polymers down to a selection that is manageable for
experimental inquiry. The vast majority of presently adopted approaches for
polymer screening rely on handcrafted chemostructural features extracted from
polymer repeat units- a burdensome task as polymer libraries, which approximate
the polymer chemical search space, progressively grow over time. Here, we
demonstrate that directly "machine-learning" important features from a polymer
repeat unit is a cheap and viable alternative to extracting expensive features
by hand. Our approach- based on graph neural networks, multitask learning, and
other advanced deep learning techniques- speeds up feature extraction by one to
two orders of magnitude relative to presently adopted handcrafted methods
without compromising model accuracy for a variety of polymer property
prediction tasks. We anticipate that our approach, which unlocks the screening
of truly massive polymer libraries at scale, will enable more sophisticated and
large scale screening technologies in the field of polymer informatics.

Zeroth-order methods are useful in solving black-box optimization and
reinforcement learning problems in unknown environments. It uses function
values to estimate the gradient. As optimization problems are often nonconvex,
it is a natural question to understand how zeroth-order methods escape saddle
points. In this paper, we consider zeroth-order methods, that at each
iteration, may freely choose 2$m$ function evaluations where $m$ ranges from 1
to $d$, with $d$ denoting the problem dimension. We show that by adding an
appropriate isotropic perturbation at each iteration, a zeroth-order algorithm
based on $2m$ function evaluations per iteration can not only find
$\epsilon$-second order stationary points polynomially fast, but do so using
only $\tilde{O}\left(\frac{d}{\epsilon^{2.5}}\right)$ function evaluations.

The problem of radio source detection is reformulated as a multi-class
classification problem and solved using deep learning frameworks. Incoming
waveforms are sampled using a centro-symmetric linear array with
omni-directional elements and the normalized upper triangle of the
autocorrelation matrix is extracted as the input feature to an uni-dimensional
(1D) CNN, trained to detect the sources in the presence of both uncorrelated
and correlated signals. The detection algorithms are introduced and
subsequently benchmarked against the conventional source detection algorithms.
We stress test the algorithms for challenging operational conditions and
present extensive evaluations to show the efficacy and contributions of the
introduced predictive models.

Time series data appears in a variety of applications such as smart
transportation and environmental monitoring. One of the fundamental problems
for time series analysis is time series forecasting. Despite the success of
recent deep time series forecasting methods, they require sufficient
observation of historical values to make accurate forecasting. In other words,
the ratio of the output length (or forecasting horizon) to the sum of the input
and output lengths should be low enough (e.g., 0.3). As the ratio increases
(e.g., to 0.8), the uncertainty for the forecasting accuracy increases
significantly. In this paper, we show both theoretically and empirically that
the uncertainty could be effectively reduced by retrieving relevant time series
as references. In the theoretical analysis, we first quantify the uncertainty
and show its connections to the Mean Squared Error (MSE). Then we prove that
models with references are easier to learn than models without references since
the retrieved references could reduce the uncertainty. To empirically
demonstrate the effectiveness of the retrieval based time series forecasting
models, we introduce a simple yet effective two-stage method, called ReTime
consisting of a relational retrieval and a content synthesis. We also show that
ReTime can be easily adapted to the spatial-temporal time series and time
series imputation settings. Finally, we evaluate ReTime on real-world datasets
to demonstrate its effectiveness.

Explaining neural network models is a challenging task that remains unsolved
in its entirety to this day. This is especially true for high dimensional and
complex data. With the present work, we introduce two notions for conceptual
views of a neural network, specifically a many-valued and a symbolic view. Both
provide novel analysis methods to enable a human AI analyst to grasp deeper
insights into the knowledge that is captured by the neurons of a network. We
test the conceptual expressivity of our novel views through different
experiments on the ImageNet and Fruit-360 data sets. Furthermore, we show to
which extent the views allow to quantify the conceptual similarity of different
learning architectures. Finally, we demonstrate how conceptual views can be
applied for abductive learning of human comprehensible rules from neurons. In
summary, with our work, we contribute to the most relevant task of globally
explaining neural networks models.

Functional connectivity (FC) between regions of the brain is commonly
estimated through statistical dependency measures applied to functional
magnetic resonance imaging (fMRI) data. The resulting functional connectivity
matrix (FCM) is often taken to represent the adjacency matrix of a brain graph.
Recently, graph neural networks (GNNs) have been successfully applied to FCMs
to learn brain graph representations. A common limitation of existing GNN
approaches, however, is that they require the graph adjacency matrix to be
known prior to model training. As such, it is implicitly assumed the
ground-truth dependency structure of the data is known. Unfortunately, for fMRI
this is not the case as the choice of which statistical measure best represents
the dependency structure of the data is non-trivial. Also, most GNN
applications to fMRI assume FC is static over time, which is at odds with
neuroscientific evidence that functional brain networks are time-varying and
dynamic. These compounded issues can have a detrimental effect on the capacity
of GNNs to learn representations of brain graphs. As a solution, we propose
Dynamic Brain Graph Structure Learning (DBGSL), a supervised method for
learning the optimal time-varying dependency structure of fMRI data.
Specifically, DBGSL learns a dynamic graph from fMRI timeseries via
spatial-temporal attention applied to brain region embeddings. The resulting
graph is then fed to a spatial-temporal GNN to learn a graph representation for
classification. Experiments on large resting-state as well as task fMRI
datasets for the task of gender classification demonstrate that DBGSL achieves
state-of-the-art performance. Moreover, analysis of the learnt dynamic graphs
highlights prediction-related brain regions which align with findings from
existing neuroscience literature.

Purely data-driven deep neural networks (DNNs) applied to physical
engineering systems can infer relations that violate physics laws, thus leading
to unexpected consequences. To address this challenge, we propose a
physics-model-based DNN framework, called Phy-Taylor, that accelerates learning
compliant representations with physical knowledge. The Phy-Taylor framework
makes two key contributions; it introduces a new architectural
Physics-compatible neural network (PhN), and features a novel compliance
mechanism, we call {\em Physics-guided Neural Network Editing\}. The PhN aims
to directly capture nonlinearities inspired by physical quantities, such as
kinetic energy, potential energy, electrical power, and aerodynamic drag force.
To do so, the PhN augments neural network layers with two key components: (i)
monomials of Taylor series expansion of nonlinear functions capturing physical
knowledge, and (ii) a suppressor for mitigating the influence of noise. The
neural-network editing mechanism further modifies network links and activation
functions consistently with physical knowledge. As an extension, we also
propose a self-correcting Phy-Taylor framework that introduces two additional
capabilities: (i) physics-model-based safety relationship learning, and (ii)
automatic output correction when violations of safety occur. Through
experiments, we show that (by expressing hard-to-learn nonlinearities directly
and by constraining dependencies) Phy-Taylor features considerably fewer
parameters, and a remarkably accelerated training process, while offering
enhanced model robustness and accuracy.

Accurate vehicle type classification serves a significant role in the
intelligent transportation system. It is critical for ruler to understand the
road conditions and usually contributive for the traffic light control system
to response correspondingly to alleviate traffic congestion. New technologies
and comprehensive data sources, such as aerial photos and remote sensing data,
provide richer and high-dimensional information. Also, due to the rapid
development of deep neural network technology, image based vehicle
classification methods can better extract underlying objective features when
processing data. Recently, several deep learning models have been proposed to
solve the problem. However, traditional pure convolutional based approaches
have constraints on global information extraction, and the complex environment,
such as bad weather, seriously limits the recognition capability. To improve
the vehicle type classification capability under complex environment, this
study proposes a novel Densely Connected Convolutional Transformer in
Transformer Neural Network (Dense-TNT) framework for the vehicle type
classification by stacking Densely Connected Convolutional Network (DenseNet)
and Transformer in Transformer (TNT) layers. Three-region vehicle data and four
different weather conditions are deployed for recognition capability
evaluation. Experimental findings validate the recognition ability of our
proposed vehicle classification model with little decay, even under the heavy
foggy weather condition.

Purpose: Object detection is rapidly evolving through machine learning
technology in automation systems. Well prepared data is necessary to train the
algorithms. Accordingly, the objective of this paper is to describe a
re-evaluation of the so-called Logistics Objects in Context (LOCO) dataset,
which is the first dataset for object detection in the field of intralogistics.
  Methodology: We use an experimental research approach with three steps to
evaluate the LOCO dataset. Firstly, the images on GitHub were analyzed to
understand the dataset better. Secondly, Google Drive Cloud was used for
training purposes to revisit the algorithmic implementation and training.
Lastly, the LOCO dataset was examined, if it is possible to achieve the same
training results in comparison to the original publications.
  Findings: The mean average precision, a common benchmark in object detection,
achieved in our study was 64.54%, and shows a significant increase from the
initial study of the LOCO authors, achieving 41%. However, improvement
potential is seen specifically within object types of forklifts and pallet
truck.
  Originality: This paper presents the first critical replication study of the
LOCO dataset for object detection in intralogistics. It shows that the training
with better hyperparameters based on LOCO can even achieve a higher accuracy
than presented in the original publication. However, there is also further room
for improving the LOCO dataset.

Motivation: The Venn diagram is one of the most popular methods to visualize
the overlap and differences between data sets. It is especially useful when it
is are 'area-proportional'; i.e. the sizes of the circles and the overlaps are
proportional to the sizes of the data sets. There are some tools available that
can generate area-proportional Venn Diagrams, but most of them are limited to
two or three circles, and others are not available as a web application or
accept only numbers and not lists of IDs as input. Some existing solutions also
have limited accuracy because of outdated algorithms to calculate the optimal
placement of the circles. The latest machine learning and deep learning
frameworks can offer a solution to this problem. Results: The DeepVenn web
application can create area-proportional Venn diagrams of up to ten sets.
Because of an algorithm implemented with the deep learning framework
Tensorflow.js, DeepVenn automatically finds the optimal solution in which the
overlap between the circles corresponds to the sizes of the overlap as much as
possible. The only required input is two to ten lists of IDs. Optional
parameters include the main title, the subtitle, the set titles and colours of
the circles and the background. The user can choose to display absolute numbers
or percentages in the final diagram. The image can be saved as a PNG file by
right-clicking on it and choosing "Save image as". The right side of the
interface also shows the numbers and contents of all intersections.
Availability: DeepVenn is available at https://www.deepvenn.com. Contact:
tim.hulsen@philips.com

Equatorial Plasma Bubbles (EPBs) are plumes of low density plasma that rise
up from the bottomside of the F layer towards the exosphere. EPBs are known
causes of radio wave scintillations which can degrade communications with
spacecraft. We build a random forest regressor to predict and forecast the
probability of an EPB [0-1] detected by the IBI processor on-board the SWARM
spacecraft. We use 8-years of Swarm data from 2014 to 2021 and transform the
data from a time series into a 5 dimensional space consisting of latitude,
longitude, mlt, year, and day-of-the-year. We also add Kp, F10.7cm and solar
wind speed. The observations of EPBs with respect to geolocation, local time,
season and solar activity mostly agrees with existing work, whilst the link
geomagnetic activity is less clear. The prediction has an accuracy of 88% and
performs well across the EPB specific spatiotemporal scales. This proves that
the XGBoost method is able to successfully capture the climatological and daily
variability of SWARM EPBs. Capturing the daily variance has long evaded
researchers because of local and stochastic features within the ionosphere. We
take advantage of Shapley Values to explain the model and to gain insight into
the physics of EPBs. We find that as the solar wind speed increases the
probability of an EPB decreases. We also identify a spike in EPB probability
around the Earth-Sun perihelion. Both of these insights were derived directly
from the XGBoost and Shapley technique.

Deep learning has achieved great success in the challenging circuit
annotation task by employing Convolutional Neural Networks (CNN) for the
segmentation of circuit structures. The deep learning approaches require a
large amount of manually annotated training data to achieve a good performance,
which could cause a degradation in performance if a deep learning model trained
on a given dataset is applied to a different dataset. This is commonly known as
the domain shift problem for circuit annotation, which stems from the possibly
large variations in distribution across different image datasets. The different
image datasets could be obtained from different devices or different layers
within a single device. To address the domain shift problem, we propose
Histogram-gated Image Translation (HGIT), an unsupervised domain adaptation
framework which transforms images from a given source dataset to the domain of
a target dataset, and utilize the transformed images for training a
segmentation network. Specifically, our HGIT performs generative adversarial
network (GAN)-based image translation and utilizes histogram statistics for
data curation. Experiments were conducted on a single labeled source dataset
adapted to three different target datasets (without labels for training) and
the segmentation performance was evaluated for each target dataset. We have
demonstrated that our method achieves the best performance compared to the
reported domain adaptation techniques, and is also reasonably close to the
fully supervised benchmark.

Recent studies on contrastive learning have achieved remarkable performance
solely by leveraging few labels in the context of medical image segmentation.
Existing methods mainly focus on instance discrimination and invariant mapping.
However, they face three common pitfalls: (1) tailness: medical image data
usually follows an implicit long-tail class distribution. Blindly leveraging
all pixels in training hence can lead to the data imbalance issues, and cause
deteriorated performance; (2) consistency: it remains unclear whether a
segmentation model has learned meaningful and yet consistent anatomical
features due to the intra-class variations between different anatomical
features; and (3) diversity: the intra-slice correlations within the entire
dataset have received significantly less attention. This motivates us to seek a
principled approach for strategically making use of the dataset itself to
discover similar yet distinct samples from different anatomical views. In this
paper, we introduce a novel semi-supervised medical image segmentation
framework termed Mine yOur owN Anatomy (MONA), and make three contributions.
First, prior work argues that every pixel equally matters to the model
training; we observe empirically that this alone is unlikely to define
meaningful anatomical features, mainly due to lacking the supervision signal.
We show two simple solutions towards learning invariances - through the use of
stronger data augmentations and nearest neighbors. Second, we construct a set
of objectives that encourage the model to be capable of decomposing medical
images into a collection of anatomical features in an unsupervised manner.
Lastly, our extensive results on three benchmark datasets with different
labeled settings validate the effectiveness of our proposed MONA which achieves
new state-of-the-art under different labeled settings.

Recent research has revealed that reducing the temporal and spatial
redundancy are both effective approaches towards efficient video recognition,
e.g., allocating the majority of computation to a task-relevant subset of
frames or the most valuable image regions of each frame. However, in most
existing works, either type of redundancy is typically modeled with another
absent. This paper explores the unified formulation of spatial-temporal dynamic
computation on top of the recently proposed AdaFocusV2 algorithm, contributing
to an improved AdaFocusV3 framework. Our method reduces the computational cost
by activating the expensive high-capacity network only on some small but
informative 3D video cubes. These cubes are cropped from the space formed by
frame height, width, and video duration, while their locations are adaptively
determined with a light-weighted policy network on a per-sample basis. At test
time, the number of the cubes corresponding to each video is dynamically
configured, i.e., video cubes are processed sequentially until a sufficiently
reliable prediction is produced. Notably, AdaFocusV3 can be effectively trained
by approximating the non-differentiable cropping operation with the
interpolation of deep features. Extensive empirical results on six benchmark
datasets (i.e., ActivityNet, FCVID, Mini-Kinetics, Something-Something V1&V2
and Diving48) demonstrate that our model is considerably more efficient than

Speed-control forecasting, a challenging problem in driver behavior analysis,
aims to predict the future actions of a driver in controlling vehicle speed
such as braking or acceleration. In this paper, we try to address this
challenge solely using egocentric video data, in contrast to the majority of
works in the literature using either third-person view data or extra vehicle
sensor data such as GPS, or both. To this end, we propose a novel graph
convolutional network (GCN) based network, namely, EgoSpeed-Net. We are
motivated by the fact that the position changes of objects over time can
provide us very useful clues for forecasting the speed change in future. We
first model the spatial relations among the objects from each class, frame by
frame, using fully-connected graphs, on top of which GCNs are applied for
feature extraction. Then we utilize a long short-term memory network to fuse
such features per class over time into a vector, concatenate such vectors and
forecast a speed-control action using a multilayer perceptron classifier. We
conduct extensive experiments on the Honda Research Institute Driving Dataset
and demonstrate the superior performance of EgoSpeed-Net.

Interpretable machine learning seeks to understand the reasoning process of
complex black-box systems that are long notorious for lack of explainability.
One growing interpreting approach is through counterfactual explanations, which
go beyond why a system arrives at a certain decision to further provide
suggestions on what a user can do to alter the outcome. A counterfactual
example must be able to counter the original prediction from the black-box
classifier, while also satisfying various constraints for practical
applications. These constraints exist at trade-offs between one and another
presenting radical challenges to existing works. To this end, we propose a
stochastic learning-based framework that effectively balances the
counterfactual trade-offs. The framework consists of a generation and a feature
selection module with complementary roles: the former aims to model the
distribution of valid counterfactuals whereas the latter serves to enforce
additional constraints in a way that allows for differentiable training and
amortized optimization. We demonstrate the effectiveness of our method in
generating actionable and plausible counterfactuals that are more diverse than
the existing methods and particularly in a more efficient manner than
counterparts of the same capacity.

In recent years, multi-task learning has turned out to be of great success in
various applications. Though single model training has promised great results
throughout these years, it ignores valuable information that might help us
estimate a metric better. Under learning-related tasks, multi-task learning has
been able to generalize the models even better. We try to enhance the feature
mapping of the multi-tasking models by sharing features among related tasks and
inductive transfer learning. Also, our interest is in learning the task
relationships among various tasks for acquiring better benefits from multi-task
learning. In this chapter, our objective is to visualize the existing
multi-tasking models, compare their performances, the methods used to evaluate
the performance of the multi-tasking models, discuss the problems faced during
the design and implementation of these models in various domains, and the
advantages and milestones achieved by them

With deep neural networks (DNNs) emerging as the backbone in a multitude of
computer vision tasks, their adoption in real-world consumer applications
broadens continuously. Given the abundance and omnipresence of smart devices,
"smart ecosystems" are being formed where sensing happens simultaneously rather
than standalone. This is shifting the on-device inference paradigm towards
deploying centralised neural processing units (NPUs) at the edge, where
multiple devices (e.g. in smart homes or autonomous vehicles) can stream their
data for processing with dynamic rates. While this provides enhanced potential
for input batching, naive solutions can lead to subpar performance and quality
of experience, especially under spiking loads. At the same time, the deployment
of dynamic DNNs, comprising stochastic computation graphs (e.g. early-exit (EE)
models), introduces a new dimension of dynamic behaviour in such systems. In
this work, we propose a novel early-exit-aware scheduling algorithm that allows
sample preemption at run time, to account for the dynamicity introduced both by
the arrival and early-exiting processes. At the same time, we introduce two
novel dimensions to the design space of the NPU hardware architecture, namely
Fluid Batching and Stackable Processing Elements, that enable run-time
adaptability to different batch sizes and significantly improve the NPU
utilisation even at small batch sizes. Our evaluation shows that our system
achieves an average 1.97x and 6.7x improvement over state-of-the-art DNN
streaming systems in terms of average latency and tail latency SLO
satisfaction, respectively.

Deep neural networks trained end-to-end to map a measurement of a (noisy)
image to a clean image perform excellent for a variety of linear inverse
problems. Current methods are only trained on a few hundreds or thousands of
images as opposed to the millions of examples deep networks are trained on in
other domains. In this work, we study whether major performance gains are
expected from scaling up the training set size. We consider image denoising,
accelerated magnetic resonance imaging, and super-resolution and empirically
determine the reconstruction quality as a function of training set size, while
optimally scaling the network size. For all three tasks we find that an
initially steep power-law scaling slows significantly already at moderate
training set sizes. Interpolating those scaling laws suggests that even
training on millions of images would not significantly improve performance. To
understand the expected behavior, we analytically characterize the performance
of a linear estimator learned with early stopped gradient descent. The result
formalizes the intuition that once the error induced by learning the signal
model is small relative to the error floor, more training examples do not
improve performance.

In this paper, we are interested in the acceleration of numerical
simulations. We focus on a hypersonic planetary reentry problem whose
simulation involves coupling fluid dynamics and chemical reactions. Simulating
chemical reactions takes most of the computational time but, on the other hand,
cannot be avoided to obtain accurate predictions. We face a trade-off between
cost-efficiency and accuracy: the simulation code has to be sufficiently
efficient to be used in an operational context but accurate enough to predict
the phenomenon faithfully. To tackle this trade-off, we design a hybrid
simulation code coupling a traditional fluid dynamic solver with a neural
network approximating the chemical reactions. We rely on their power in terms
of accuracy and dimension reduction when applied in a big data context and on
their efficiency stemming from their matrix-vector structure to achieve
important acceleration factors ($\times 10$ to $\times 18.6$). This paper aims
to explain how we design such cost-effective hybrid simulation codes in
practice. Above all, we describe methodologies to ensure accuracy guarantees,
allowing us to go beyond traditional surrogate modeling and to use these codes
as references.

Collocated tactile sensing is a fundamental enabling technology for dexterous
manipulation. However, deformable sensors introduce complex dynamics between
the robot, grasped object, and environment that must be considered for fine
manipulation. Here, we propose a method to learn soft tactile sensor membrane
dynamics that accounts for sensor deformations caused by the physical
interaction between the grasped object and environment. Our method combines the
perceived 3D geometry of the membrane with proprioceptive reaction wrenches to
predict future deformations conditioned on robot action. Grasped object poses
are recovered from membrane geometry and reaction wrenches, decoupling
interaction dynamics from the tactile observation model. We benchmark our
approach on two real-world contact-rich tasks: drawing with a grasped marker
and in-hand pivoting. Our results suggest that explicitly modeling membrane
dynamics achieves better task performance and generalization to unseen objects
than baselines.

Pre-training vision-language models with contrastive objectives has shown
promising results that are both scalable to large uncurated datasets and
transferable to many downstream applications. Some following works have
targeted to improve data efficiency by adding self-supervision terms, but
inter-domain (image-text) contrastive loss and intra-domain (image-image)
contrastive loss are defined on individual spaces in those works, so many
feasible combinations of supervision are overlooked. To overcome this issue, we
propose UniCLIP, a Unified framework for Contrastive Language-Image
Pre-training. UniCLIP integrates the contrastive loss of both inter-domain
pairs and intra-domain pairs into a single universal space. The discrepancies
that occur when integrating contrastive loss between different domains are
resolved by the three key components of UniCLIP: (1) augmentation-aware feature
embedding, (2) MP-NCE loss, and (3) domain dependent similarity measure.
UniCLIP outperforms previous vision-language pre-training methods on various
single- and multi-modality downstream tasks. In our experiments, we show that
each component that comprises UniCLIP contributes well to the final

Shapley value is a popular approach for measuring the influence of individual
features. While Shapley feature attribution is built upon desiderata from game
theory, some of its constraints may be less natural in certain machine learning
settings, leading to unintuitive model interpretation. In particular, the
Shapley value uses the same weight for all marginal contributions -- i.e. it
gives the same importance when a large number of other features are given
versus when a small number of other features are given. This property can be
problematic if larger feature sets are more or less informative than smaller
feature sets. Our work performs a rigorous analysis of the potential
limitations of Shapley feature attribution. We identify simple settings where
the Shapley value is mathematically suboptimal by assigning larger attributions
for less influential features. Motivated by this observation, we propose
WeightedSHAP, which generalizes the Shapley value and learns which marginal
contributions to focus directly from data. On several real-world datasets, we
demonstrate that the influential features identified by WeightedSHAP are better
able to recapitulate the model's predictions compared to the features
identified by the Shapley value.

LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), first launched
in February 2020, is a first-of-its-kind literature hub for tracking up-to-date
published research on COVID-19. The number of articles in LitCovid has
increased from 55,000 to ~300,000 over the past two and half years, with a
consistent growth rate of ~10,000 articles per month. In addition to the rapid
literature growth, the COVID-19 pandemic has evolved dramatically. For
instance, the Omicron variant has now accounted for over 98% of new infections
in the U.S. In response to the continuing evolution of the COVID-19 pandemic,
this article describes significant updates to LitCovid over the last two years.
First, we introduced the Long Covid collection consisting of the articles on
COVID-19 survivors experiencing ongoing multisystemic symptoms, including
respiratory issues, cardiovascular disease, cognitive impairment, and profound
fatigue. Second, we provided new annotations on the latest COVID-19 strains and
vaccines mentioned in the literature. Third, we improved several existing
features with more accurate machine learning algorithms for annotating topics
and classifying articles relevant to COVID-19. LitCovid has been widely used
with millions of accesses by users worldwide on various information needs and
continues to play a critical role in collecting, curating, and standardizing
the latest knowledge on the COVID-19 literature.

Improving the interactivity and interconnectivity between people is one of
the highlights of the Metaverse. The Metaverse relies on a core approach,
digital twinning, which is a means to replicate physical world objects, people,
actions and scenes onto the virtual world. Being able to access scenes and
information associated with the physical world, in the Metaverse in real-time
and under mobility, is essential in developing a highly accessible, interactive
and interconnective experience for all users. This development allows users
from other locations to access high-quality real-world and up-to-date
information about events happening in another location, and socialize with
others hyper-interactively. Nevertheless, receiving continual, smooth updates
generated by others from the Metaverse is a challenging task due to the large
data size of the virtual world graphics and the need for low latency
transmission. With the development of Mobile Augmented Reality (MAR), users can
interact via the Metaverse in a highly interactive manner, even under mobility.
Hence in our work, we considered an environment with users in moving Internet
of Vehicles (IoV), downloading real-time virtual world updates from Metaverse
Service Provider Cell Stations (MSPCSs) via wireless communications. We design
an environment with multiple cell stations, where there will be a handover of
users' virtual world graphic download tasks between cell stations. As
transmission latency is the primary concern in receiving virtual world updates
under mobility, our work aims to allocate system resources to minimize the
total time taken for users in vehicles to download their virtual world scenes
from the cell stations. We utilize deep reinforcement learning and evaluate the
performance of the algorithms under different environmental configurations. Our
work provides a use case of the Metaverse over AI-enabled 6G communications.

On-device session-based recommendation systems have been achieving increasing
attention on account of the low energy/resource consumption and privacy
protection while providing promising recommendation performance. To fit the
powerful neural session-based recommendation models in resource-constrained
mobile devices, tensor-train decomposition and its variants have been widely
applied to reduce memory footprint by decomposing the embedding table into
smaller tensors, showing great potential in compressing recommendation models.
However, these model compression techniques significantly increase the local
inference time due to the complex process of generating index lists and a
series of tensor multiplications to form item embeddings, and the resultant
on-device recommender fails to provide real-time response and recommendation.
To improve the online recommendation efficiency, we propose to learn
compositional encoding-based compact item representations. Specifically, each
item is represented by a compositional code that consists of several codewords,
and we learn embedding vectors to represent each codeword instead of each item.
Then the composition of the codeword embedding vectors from different embedding
matrices (i.e., codebooks) forms the item embedding. Since the size of
codebooks can be extremely small, the recommender model is thus able to fit in
resource-constrained devices and meanwhile can save the codebooks for fast
local inference.Besides, to prevent the loss of model capacity caused by
compression, we propose a bidirectional self-supervised knowledge distillation
framework. Extensive experimental results on two benchmark datasets demonstrate
that compared with existing methods, the proposed on-device recommender not
only achieves an 8x inference speedup with a large compression ratio but also
shows superior recommendation performance.

Domain adaptation is an attractive approach given the availability of a large
amount of labeled data with similar properties but different domains. It is
effective in image classification tasks where obtaining sufficient label data
is challenging. We propose a novel method, named SELDA, for stacking ensemble
learning via extending three domain adaptation methods for effectively solving
real-world problems. The major assumption is that when base domain adaptation
models are combined, we can obtain a more accurate and robust model by
exploiting the ability of each of the base models. We extend Maximum Mean
Discrepancy (MMD), Low-rank coding, and Correlation Alignment (CORAL) to
compute the adaptation loss in three base models. Also, we utilize a two-fully
connected layer network as a meta-model to stack the output predictions of
these three well-performing domain adaptation models to obtain high accuracy in
ophthalmic image classification tasks. The experimental results using
Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate
the effectiveness of the proposed model.

Commonly in reinforcement learning (RL), rewards are discounted over time
using an exponential function to model time preference, thereby bounding the
expected long-term reward. In contrast, in economics and psychology, it has
been shown that humans often adopt a hyperbolic discounting scheme, which is
optimal when a specific task termination time distribution is assumed. In this
work, we propose a theory for continuous-time model-based reinforcement
learning generalized to arbitrary discount functions. This formulation covers
the case in which there is a non-exponential random termination time. We derive
a Hamilton-Jacobi-Bellman (HJB) equation characterizing the optimal policy and
describe how it can be solved using a collocation method, which uses deep
learning for function approximation. Further, we show how the inverse RL
problem can be approached, in which one tries to recover properties of the
discount function given decision data. We validate the applicability of our
proposed approach on two simulated problems. Our approach opens the way for the
analysis of human discounting in sequential decision-making tasks.

Pathologists diagnose and grade prostate cancer by examining tissue from
needle biopsies on glass slides. The cancer's severity and risk of metastasis
are determined by the Gleason grade, a score based on the organization and
morphology of prostate cancer glands. For diagnostic work-up, pathologists
first locate glands in the whole biopsy core, and -- if they detect cancer --
they assign a Gleason grade. This time-consuming process is subject to errors
and significant inter-observer variability, despite strict diagnostic criteria.
This paper proposes an automated workflow that follows pathologists'
\textit{modus operandi}, isolating and classifying multi-scale patches of
individual glands in whole slide images (WSI) of biopsy tissues using distinct
steps: (1) two fully convolutional networks segment epithelium versus stroma
and gland boundaries, respectively; (2) a classifier network separates benign
from cancer glands at high magnification; and (3) an additional classifier
predicts the grade of each cancer gland at low magnification. Altogether, this
process provides a gland-specific approach for prostate cancer grading that we
compare against other machine-learning-based grading methods.

COVID-19 is a novel virus that attacks the upper respiratory tract and the
lungs. Its person-to-person transmissibility is considerably rapid and this has
caused serious problems in approximately every facet of individuals lives.
While some infected individuals may remain completely asymptomatic, others have
been frequently witnessed to have mild to severe symptoms. In addition to this,
thousands of death cases around the globe indicated that detecting COVID-19 is
an urgent demand in the communities. Practically, this is prominently done with
the help of screening medical images such as Computed Tomography (CT) and X-ray
images. However, the cumbersome clinical procedures and a large number of daily
cases have imposed great challenges on medical practitioners. Deep
Learning-based approaches have demonstrated a profound potential in a wide
range of medical tasks. As a result, we introduce a transformer-based method
for automatically detecting COVID-19 from X-ray images using Compact
Convolutional Transformers (CCT). Our extensive experiments prove the efficacy
of the proposed method with an accuracy of 98% which outperforms the previous
works.

To understand learning the dynamics of deep ReLU networks, we investigate the
dynamic system of gradient flow $w(t)$ by decomposing it to magnitude $w(t)$
and angle $\phi(t):= \pi - \theta(t) $ components. In particular, for
multi-layer single ReLU neurons with spherically symmetric data distribution
and the square loss function, we provide upper and lower bounds for magnitude
and angle components to describe the dynamics of gradient flow. Using the
obtained bounds, we conclude that small scale initialization induces slow
convergence speed for deep single ReLU neurons. Finally, by exploiting the
relation of gradient flow and gradient descent, we extend our results to the
gradient descent approach. All theoretical results are verified by experiments.

Although numerous methods to reduce the overfitting of convolutional neural
networks (CNNs) exist, it is still not clear how to confidently measure the
degree of overfitting. A metric reflecting the overfitting level might be,
however, extremely helpful for the comparison of different architectures and
for the evaluation of various techniques to tackle overfitting. Motivated by
the fact that overfitted neural networks tend to rather memorize noise in the
training data than generalize to unseen data, we examine how the training
accuracy changes in the presence of increasing data perturbations and study the
connection to overfitting. While previous work focused on label noise only, we
examine a spectrum of techniques to inject noise into the training data,
including adversarial perturbations and input corruptions. Based on this, we
define two new metrics that can confidently distinguish between correct and
overfitted models. For the evaluation, we derive a pool of models for which the
overfitting behavior is known beforehand. To test the effect of various
factors, we introduce several anti-overfitting measures in architectures based
on VGG and ResNet and study their impact, including regularization techniques,
training set size, and the number of parameters. Finally, we assess the
applicability of the proposed metrics by measuring the overfitting degree of
several CNN architectures outside of our model pool.

Pruning on neural networks before training not only compresses the original
models, but also accelerates the network training phase, which has substantial
application value. The current work focuses on fine-grained pruning, which uses
metrics to calculate weight scores for weight screening, and extends from the
initial single-order pruning to iterative pruning. Through these works, we
argue that network pruning can be summarized as an expressive force transfer
process of weights, where the reserved weights will take on the expressive
force from the removed ones for the purpose of maintaining the performance of
original networks. In order to achieve optimal expressive force scheduling, we
propose a pruning scheme before training called Neural Network Panning which
guides expressive force transfer through multi-index and multi-process steps,
and designs a kind of panning agent based on reinforcement learning to automate
processes. Experimental results show that Panning performs better than various
available pruning before training methods.

In this paper we address the problem of neural face reenactment, where, given
a pair of a source and a target facial image, we need to transfer the target's
pose (defined as the head pose and its facial expressions) to the source image,
by preserving at the same time the source's identity characteristics (e.g.,
facial shape, hair style, etc), even in the challenging case where the source
and the target faces belong to different identities. In doing so, we address
some of the limitations of the state-of-the-art works, namely, a) that they
depend on paired training data (i.e., source and target faces have the same
identity), b) that they rely on labeled data during inference, and c) that they
do not preserve identity in large head pose changes. More specifically, we
propose a framework that, using unpaired randomly generated facial images,
learns to disentangle the identity characteristics of the face from its pose by
incorporating the recently introduced style space $\mathcal{S}$ of StyleGAN2, a
latent representation space that exhibits remarkable disentanglement
properties. By capitalizing on this, we learn to successfully mix a pair of
source and target style codes using supervision from a 3D model. The resulting
latent code, that is subsequently used for reenactment, consists of latent
units corresponding to the facial pose of the target only and of units
corresponding to the identity of the source only, leading to notable
improvement in the reenactment performance compared to recent state-of-the-art
methods. In comparison to state of the art, we quantitatively and qualitatively
show that the proposed method produces higher quality results even on extreme
pose variations. Finally, we report results on real images by first embedding
them on the latent space of the pretrained generator. We make the code and
pretrained models publicly available at: https://github.com/StelaBou/StyleMask

Ensemble methods are a reliable way to combine several models to achieve
superior performance. However, research on the application of ensemble methods
in the remote sensing object detection scenario is mostly overlooked. Two
problems arise. First, one unique characteristic of remote sensing object
detection is the Oriented Bounding Boxes (OBB) of the objects and the fusion of
multiple OBBs requires further research attention. Second, the widely used deep
learning object detectors provide a score for each detected object as an
indicator of confidence, but how to use these indicators effectively in an
ensemble method remains a problem. Trying to address these problems, this paper
proposes OBBStacking, an ensemble method that is compatible with OBBs and
combines the detection results in a learned fashion. This ensemble method helps
take 1st place in the Challenge Track \textit{Fine-grained Object Recognition
in High-Resolution Optical Images}, which was featured in \textit{2021 Gaofen
Challenge on Automated High-Resolution Earth Observation Image Interpretation}.
The experiments on DOTA dataset and FAIR1M dataset demonstrate the improved
performance of OBBStacking and the features of OBBStacking are analyzed.

In this paper, we propose an accurate yet fast small object detection method
for RSI, named SuperYOLO, which fuses multimodal data and performs high
resolution (HR) object detection on multiscale objects by utilizing the
assisted super resolution (SR) learning and considering both the detection
accuracy and computation cost. First, we construct a compact baseline by
removing the Focus module to keep the HR features and significantly overcomes
the missing error of small objects. Second, we utilize pixel-level multimodal
fusion (MF) to extract information from various data to facilitate more
suitable and effective features for small objects in RSI. Furthermore, we
design a simple and flexible SR branch to learn HR feature representations that
can discriminate small objects from vast backgrounds with low-resolution (LR)
input, thus further improving the detection accuracy. Moreover, to avoid
introducing additional computation, the SR branch is discarded in the inference
stage and the computation of the network model is reduced due to the LR input.
Experimental results show that, on the widely used VEDAI RS dataset, SuperYOLO
achieves an accuracy of 73.61% (in terms of mAP50), which is more than 10%
higher than the SOTA large models such as YOLOv5l, YOLOv5x and RS designed
YOLOrs. Meanwhile, the GFOLPs and parameter size of SuperYOLO are about 18.1x
and 4.2x less than YOLOv5x. Our proposed model shows a favorable accuracy-speed
trade-off compared to the state-of-art models. The code will be open sourced at
https://github.com/icey-zhang/SuperYOLO.

Deep neural networks (DNNs) have demonstrated their superiority in practice.
Arguably, the rapid development of DNNs is largely benefited from high-quality
(open-sourced) datasets, based on which researchers and developers can easily
evaluate and improve their learning methods. Since the data collection is
usually time-consuming or even expensive, how to protect their copyrights is of
great significance and worth further exploration. In this paper, we revisit
dataset ownership verification. We find that existing verification methods
introduced new security risks in DNNs trained on the protected dataset, due to
the targeted nature of poison-only backdoor watermarks. To alleviate this
problem, in this work, we explore the untargeted backdoor watermarking scheme,
where the abnormal model behaviors are not deterministic. Specifically, we
introduce two dispersibilities and prove their correlation, based on which we
design the untargeted backdoor watermark under both poisoned-label and
clean-label settings. We also discuss how to use the proposed untargeted
backdoor watermark for dataset ownership verification. Experiments on benchmark
datasets verify the effectiveness of our methods and their resistance to
existing backdoor defenses. Our codes are available at
\url{https://github.com/THUYimingLi/Untargeted_Backdoor_Watermark}.

Aims. With the large amount of molecular emission data from (sub)millimeter
observatories and incoming James Webb Space Telescope infrared spectroscopy,
access to fast forward models of the chemical composition of protoplanetary
disks is of paramount importance.
  Methods. We used a thermo-chemical modeling code to generate a diverse
population of protoplanetary disk models. We trained a K-nearest neighbors
(KNN) regressor to instantly predict the chemistry of other disk models.
  Results. We show that it is possible to accurately reproduce chemistry using
just a small subset of physical conditions, thanks to correlations between the
local physical conditions in adopted protoplanetary disk models. We discuss the
uncertainties and limitations of this method.
  Conclusions. The proposed method can be used for Bayesian fitting of the line
emission data to retrieve disk properties from observations. We present a
pipeline for reproducing the same approach on other disk chemical model sets.

In this paper we present a class of convolutional neural networks (CNNs)
called non-overlapping CNNs in the study of approximation capabilities of CNNs.
We prove that such networks with sigmoidal activation function are capable of
approximating arbitrary continuous function defined on compact input sets with
any desired degree of accuracy. This result extends existing results where only
multilayer feedforward networks are a class of approximators. Evaluations
elucidate the accuracy and efficiency of our result and indicate that the
proposed non-overlapping CNNs are less sensitive to noise.

Evaluation of text generation to date has primarily focused on content
created sequentially, rather than improvements on a piece of text. Writing,
however, is naturally an iterative and incremental process that requires
expertise in different modular skills such as fixing outdated information or
making the style more consistent. Even so, comprehensive evaluation of a
model's capacity to perform these skills and the ability to edit remains
sparse. This work presents EditEval: An instruction-based, benchmark and
evaluation suite that leverages high-quality existing and new datasets for
automatic evaluation of editing capabilities such as making text more cohesive
and paraphrasing. We evaluate several pre-trained models, which shows that
InstructGPT and PEER perform the best, but that most baselines fall below the
supervised SOTA, particularly when neutralizing and updating information. Our
analysis also shows that commonly used metrics for editing tasks do not always
correlate well, and that optimization for prompts with the highest performance
does not necessarily entail the strongest robustness to different models.
Through the release of this benchmark and a publicly available leaderboard
challenge, we hope to unlock future research in developing models capable of
iterative and more controllable editing.

The macroscopic response of short fiber reinforced composites is dependent on
an extensive range of microstructural parameters. Thus, micromechanical
modeling of these materials is challenging and in some cases, computationally
expensive. This is particularly important when path-dependent plastic behavior
is needed to be predicted. A solution to this challenge is to enhance
micromechanical solutions with machine learning techniques such as artificial
neural networks. In this work, a recurrent deep neural network model is trained
to predict the path-dependent elasto-plastic stress response of short fiber
reinforced composites, given the microstructural parameters and the strain
path. Micromechanical meanfield simulations are conducted to create a data base
for training the validating the model. The model gives very accurate
predictions in a computationally efficient manner when compared with
independent micromechanical simulations.

Transformer architecture has become the fundamental element of the widespread
natural language processing~(NLP) models. With the trends of large NLP models,
the increasing memory and computation costs hinder their efficient deployment
on resource-limited devices. Therefore, transformer quantization attracts wide
research interest. Recent work recognizes that structured outliers are the
critical bottleneck for quantization performance. However, their proposed
methods increase the computation overhead and still leave the outliers there.
To fundamentally address this problem, this paper delves into the inherent
inducement and importance of the outliers. We discover that $\boldsymbol
\gamma$ in LayerNorm (LN) acts as a sinful amplifier for the outliers, and the
importance of outliers varies greatly where some outliers provided by a few
tokens cover a large area but can be clipped sharply without negative impacts.
Motivated by these findings, we propose an outlier suppression framework
including two components: Gamma Migration and Token-Wise Clipping. The Gamma
Migration migrates the outlier amplifier to subsequent modules in an equivalent
transformation, contributing to a more quantization-friendly model without any
extra burden. The Token-Wise Clipping takes advantage of the large variance of
token range and designs a token-wise coarse-to-fine pipeline, obtaining a
clipping range with minimal final quantization loss in an efficient way. This
framework effectively suppresses the outliers and can be used in a
plug-and-play mode. Extensive experiments prove that our framework surpasses
the existing works and, for the first time, pushes the 6-bit post-training BERT
quantization to the full-precision (FP) level. Our code is available at
https://github.com/wimh966/outlier_suppression.

In this paper, we present an approach to neural network mean-field-type
control and its stochastic stability analysis by means of adversarial inputs
(aka adversarial attacks). This is a class of data-driven mean-field-type
control where the distribution of the variables such as the system states and
control inputs are incorporated into the problem. Besides, we present a
methodology to validate the feasibility of the approximations of the solutions
via neural networks and evaluate their stability. Moreover, we enhance the
stability by enlarging the training set with adversarial inputs to obtain a
more robust neural network. Finally, a worked-out example based on the
linear-quadratic mean-field type control problem (LQ-MTC) is presented to
illustrate our methodology.

Safety is a crucial property of every robotic platform: any control policy
should always comply with actuator limits and avoid collisions with the
environment and humans. In reinforcement learning, safety is even more
fundamental for exploring an environment without causing any damage. While
there are many proposed solutions to the safe exploration problem, only a few
of them can deal with the complexity of the real world. This paper introduces a
new formulation of safe exploration for reinforcement learning of various
robotic tasks. Our approach applies to a wide class of robotic platforms and
enforces safety even under complex collision constraints learned from data by
exploring the tangent space of the constraint manifold. Our proposed approach
achieves state-of-the-art performance in simulated high-dimensional and dynamic
tasks while avoiding collisions with the environment. We show safe real-world
deployment of our learned controller on a TIAGo++ robot, achieving remarkable
performance in manipulation and human-robot interaction tasks.

Cross-modal retrieval between videos and texts has gained increasing research
interest due to the rapid emergence of videos on the web. Generally, a video
contains rich instance and event information and the query text only describes
a part of the information. Thus, a video can correspond to multiple different
text descriptions and queries. We call this phenomenon the ``Video-Text
Correspondence Ambiguity'' problem. Current techniques mostly concentrate on
mining local or multi-level alignment between contents of a video and text
(\textit{e.g.}, object to entity and action to verb). It is difficult for these
methods to alleviate the video-text correspondence ambiguity by describing a
video using only one single feature, which is required to be matched with
multiple different text features at the same time. To address this problem, we
propose a Text-Adaptive Multiple Visual Prototype Matching model, which
automatically captures multiple prototypes to describe a video by adaptive
aggregation of video token features. Given a query text, the similarity is
determined by the most similar prototype to find correspondence in the video,
which is termed text-adaptive matching. To learn diverse prototypes for
representing the rich information in videos, we propose a variance loss to
encourage different prototypes to attend to different contents of the video.
Our method outperforms state-of-the-art methods on four public video retrieval
datasets.

We address the problem of \emph{convex optimization with dueling feedback},
where the goal is to minimize a convex function given a weaker form of
\emph{dueling} feedback. Each query consists of two points and the dueling
feedback returns a (noisy) single-bit binary comparison of the function values
of the two queried points. The translation of the function values to the single
comparison bit is through a \emph{transfer function}. This problem has been
addressed previously for some restricted classes of transfer functions, but
here we consider a very general transfer function class which includes all
functions that can be approximated by a finite polynomial with a minimal degree
$p$. Our main contribution is an efficient algorithm with convergence rate of
$\smash{\widetilde O}(\epsilon^{-4p})$ for a smooth convex objective function,
and an optimal rate of $\smash{\widetilde O}(\epsilon^{-2p})$ when the
objective is smooth and strongly convex.

(Abridged) In this work, we study the 2.8x2.6 deg2 region in the emblematic
Rosette Nebula, centred at the young cluster NGC 2244, with the aim of
constructing the most reliable candidate member list to date, determining
various structural and kinematic parameters, and learning about the past and
the future of the region. Starting from a catalogue containing optical to
mid-infrared photometry, as well as positions and proper motions from Gaia
EDR3, we apply the Probabilistic Random Forest algorithm and derive membership
probability for each source. Based on the list of almost 3000 probable members,
of which about a third are concentrated within the radius of 20' from the
centre of NGC 2244, we identify various clustered sources and stellar
concentrations, and estimate the average distance of 1489+-37 pc (entire
region), 1440+-32 pc (NGC 2244) and 1525+-36 pc (NGC 2237). The masses,
extinction, and ages are derived by SED fitting, and the internal dynamic is
assessed via proper motions relative to the mean proper motion of NGC 2244. NGC
2244 is showing a clear expansion pattern, with an expansion velocity that
increases with radius. Its IMF is well represented by two power laws
(dN/dM\propto M^{-\alpha}), with slopes \alpha = 1.05+-0.02 for the mass range
0.2 - 1.5 MSun, and \alpha = 2.3+-0.3 for the mass range 1.5 - 20 MSun, in
agreement with other star forming regions. The mean age of the region is ~2
Myr. We find evidence for the difference in ages between NGC 2244 and the
region associated with the molecular cloud, which appears slightly younger. The
velocity dispersion of NGC 2244 is well above the virial velocity dispersion
derived from the total mass (1000+-70 MSun) and half-mass radius (3.4+-0.2 pc).
From the comparison to other clusters and to numerical simulations, we conclude
that NGC 2244 may be unbound, and possibly even formed in a super-virial state.

Dog owners are typically capable of recognizing behavioral cues that reveal
subjective states of their dogs, such as pain. But automatic recognition of the
pain state is very challenging. This paper proposes a novel video-based,
two-stream deep neural network approach for this problem. We extract and
preprocess body keypoints, and compute features from both keypoints and the RGB
representation over the video. We propose an approach to deal with
self-occlusions and missing keypoints. We also present a unique video-based dog
behavior dataset, collected by veterinary professionals, and annotated for
presence of pain, and report good classification results with the proposed
approach. This study is one of the first works on machine learning based
estimation of dog pain state.

To have the greatest impact, public health initiatives must be made using
evidence-based decision-making. Machine learning Algorithms are created to
gather, store, process, and analyse data to provide knowledge and guide
decisions. A crucial part of any surveillance system is image analysis. The
communities of computer vision and machine learning has ended up curious about
it as of late. This study uses a variety of machine learning and image
processing approaches to detect and forecast the malarial illness. In our
research, we discovered the potential of deep learning techniques as smart
tools with broader applicability for malaria detection, which benefits
physicians by assisting in the diagnosis of the condition. We examine the
common confinements of deep learning for computer frameworks and organising,
counting need of preparing data, preparing overhead, realtime execution, and
explain ability, and uncover future inquire about bearings focusing on these
restrictions.

IFISS is an established MATLAB finite element software package for studying
strategies for solving partial differential equations (PDEs). IFISS3D is a new
add-on toolbox that extends IFISS capabilities for elliptic PDEs from two to
three space dimensions. The open-source MATLAB framework provides a
computational laboratory for experimentation and exploration of finite element
approximation and error estimation, as well as iterative solvers. The package
is designed to be useful as a teaching tool for instructors and students who
want to learn about state-of-the-art finite element methodology. It will also
be useful for researchers as a source of reproducible test matrices of
arbitrarily large dimension.

Machine Learning may push research in precision medicine to unprecedented
heights. To succeed, machine learning needs a large amount of data, often
including personal data. Therefore, machine learning applied to precision
medicine is on a cliff edge: if it does not learn to fly, it will deeply fall
down. In this paper, we present Active Informed Consent (AIC) as a novel hybrid
legal-technological tool to foster the gathering of a large amount of data for
machine learning. We carefully analyzed the compliance of this technological
tool to the legal intricacies protecting the privacy of European Citizens.

The accelerated growth in synthetic visual media generation and manipulation
has now reached the point of raising significant concerns and posing enormous
intimidations towards society. There is an imperative need for automatic
detection networks towards false digital content and avoid the spread of
dangerous artificial information to contend with this threat. In this paper, we
utilize and compare two kinds of handcrafted features(SIFT and HoG) and two
kinds of deep features(Xception and CNN+RNN) for the deepfake detection task.
We also check the performance of these features when there are mismatches
between training sets and test sets. Evaluation is performed on the famous
FaceForensics++ dataset, which contains four sub-datasets, Deepfakes,
Face2Face, FaceSwap and NeuralTextures. The best results are from Xception,
where the accuracy could surpass over 99\% when the training and test set are
both from the same sub-dataset. In comparison, the results drop dramatically
when the training set mismatches the test set. This phenomenon reveals the
challenge of creating a universal deepfake detection system.

Federated learning (FL) has been proposed as a privacy-preserving approach in
distributed machine learning. A federated learning architecture consists of a
central server and a number of clients that have access to private, potentially
sensitive data. Clients are able to keep their data in their local machines and
only share their locally trained model's parameters with a central server that
manages the collaborative learning process. FL has delivered promising results
in real-life scenarios, such as healthcare, energy, and finance. However, when
the number of participating clients is large, the overhead of managing the
clients slows down the learning. Thus, client selection has been introduced as
a strategy to limit the number of communicating parties at every step of the
process. Since the early na\"{i}ve random selection of clients, several client
selection methods have been proposed in the literature. Unfortunately, given
that this is an emergent field, there is a lack of a taxonomy of client
selection methods, making it hard to compare approaches. In this paper, we
propose a taxonomy of client selection in Federated Learning that enables us to
shed light on current progress in the field and identify potential areas of
future research in this promising area of machine learning.

Modelling, forecasting and overall understanding of the dynamics of the power
grid and its frequency is essential for the safe operation of existing and
future power grids. Much previous research was focused on large continental
areas, while small systems, such as islands are less well-studied. These
natural island systems are ideal testing environments for microgrid proposals
and artificially islanded grid operation. In the present paper, we utilize
measurements of the power grid frequency obtained in European islands: the
Faroe Islands, Ireland, the Balearic Islands and Iceland and investigate how
their frequency can be predicted, compared to the Nordic power system, acting
as a reference. The Balearic islands are found to be particularly deterministic
and easy to predict in contrast to hard-to-predict Iceland. Furthermore, we
show that typically 2-4 weeks of data are needed to improve prediction
performance beyond simple benchmarks.

In this paper, we propose an algorithm to interpolate between a pair of
images of a dynamic scene. While in the past years significant progress in
frame interpolation has been made, current approaches are not able to handle
images with brightness and illumination changes, which are common even when the
images are captured shortly apart. We propose to address this problem by taking
advantage of the existing optical flow methods that are highly robust to the
variations in the illumination. Specifically, using the bidirectional flows
estimated using an existing pre-trained flow network, we predict the flows from
an intermediate frame to the two input images. To do this, we propose to encode
the bidirectional flows into a coordinate-based network, powered by a
hypernetwork, to obtain a continuous representation of the flow across time.
Once we obtain the estimated flows, we use them within an existing blending
network to obtain the final intermediate frame. Through extensive experiments,
we demonstrate that our approach is able to produce significantly better
results than state-of-the-art frame interpolation algorithms.

Semantic segmentation profits from deep learning and has shown its
possibilities in handling the graphical data from the on-site inspection. As a
result, visual damage in the facade images should be detected. Attention
mechanism and generative adversarial networks are two of the most popular
strategies to improve the quality of semantic segmentation. With specific
focuses on these two strategies, this paper adopts U-net, a representative
convolutional neural network, as the primary network and presents a comparative
study in two steps. First, cell images are utilized to respectively determine
the most effective networks among the U-nets with attention mechanism or
generative adversarial networks. Subsequently, selected networks from the first
test and their combination are applied for facade damage segmentation to
investigate the performances of these networks. Besides, the combined effect of
the attention mechanism and the generative adversarial network is discovered
and discussed.

Machine Translation System (MTS) serves as an effective tool for
communication by translating text or speech from one language to another
language. The need of an efficient translation system becomes obvious in a
large multilingual environment like India, where English and a set of Indian
Languages (ILs) are officially used. In contrast with English, ILs are still
entreated as low-resource languages due to unavailability of corpora. In order
to address such asymmetric nature, multilingual neural machine translation
(MNMT) system evolves as an ideal approach in this direction. In this paper, we
propose a MNMT system to address the issues related to low-resource language
translation. Our model comprises of two MNMT systems i.e. for English-Indic
(one-to-many) and the other for Indic-English (many-to-one) with a shared
encoder-decoder containing 15 language pairs (30 translation directions). Since
most of IL pairs have scanty amount of parallel corpora, not sufficient for
training any machine translation model. We explore various augmentation
strategies to improve overall translation quality through the proposed model. A
state-of-the-art transformer architecture is used to realize the proposed
model. Trials over a good amount of data reveal its superiority over the
conventional models. In addition, the paper addresses the use of language
relationships (in terms of dialect, script, etc.), particularly about the role
of high-resource languages of the same family in boosting the performance of
low-resource languages. Moreover, the experimental results also show the
advantage of backtranslation and domain adaptation for ILs to enhance the
translation quality of both source and target languages. Using all these key
approaches, our proposed model emerges to be more efficient than the baseline
model in terms of evaluation metrics i.e BLEU (BiLingual Evaluation Understudy)
score for a set of ILs.

A spatial AI that can perform complex tasks through visual signals and
cooperate with humans is highly anticipated. To achieve this, we need a visual
SLAM that easily adapts to new scenes without pre-training and generates dense
maps for downstream tasks in real-time. None of the previous learning-based and
non-learning-based visual SLAMs satisfy all needs due to the intrinsic
limitations of their components. In this work, we develop a visual SLAM named
Orbeez-SLAM, which successfully collaborates with implicit neural
representation (NeRF) and visual odometry to achieve our goals. Moreover,
Orbeez-SLAM can work with the monocular camera since it only needs RGB inputs,
making it widely applicable to the real world. We validate its effectiveness on
various challenging benchmarks. Results show that our SLAM is up to 800x faster
than the strong baseline with superior rendering outcomes.

Computing the Jacobian of the solution of an optimization problem is a
central problem in machine learning, with applications in hyperparameter
optimization, meta-learning, optimization as a layer, and dataset distillation,
to name a few. Unrolled differentiation is a popular heuristic that
approximates the solution using an iterative solver and differentiates it
through the computational path. This work provides a non-asymptotic
convergence-rate analysis of this approach on quadratic objectives for gradient
descent and the Chebyshev method. We show that to ensure convergence of the
Jacobian, we can either 1) choose a large learning rate leading to a fast
asymptotic convergence but accept that the algorithm may have an arbitrarily
long burn-in phase or 2) choose a smaller learning rate leading to an immediate
but slower convergence. We refer to this phenomenon as the curse of unrolling.
Finally, we discuss open problems relative to this approach, such as deriving a
practical update rule for the optimal unrolling strategy and making novel
connections with the field of Sobolev orthogonal polynomials.

Data-driven and deep learning approaches have demonstrated to have the
potential of replacing classical constitutive models for complex materials,
displaying path-dependency and possessing multiple inherent scales. Yet, the
necessity of structuring constitutive models with an incremental formulation
has given rise to data-driven approaches where physical quantities, e.g.
deformation, blend with artificial, non-physical ones, such as the increments
in deformation and time. Neural networks and the consequent constitutive models
depend, thus, on the particular incremental formulation, fail in identifying
material representations locally in time, and suffer from poor generalization.
  Here, we propose a new approach which allows, for the first time, to decouple
the material representation from the incremental formulation. Inspired by the
Thermodynamics-based Artificial Neural Networks (TANN) and the theory of the
internal variables, the evolution TANN (eTANN) are continuous-time, thus
independent of the aforementioned artificial quantities. Key feature of the
proposed approach is the discovery of the evolution equations of the internal
variables in the form of ordinary differential equations, rather than in an
incremental discrete-time form. In this work, we focus attention to juxtapose
and show how the various general notions of solid mechanics are implemented in
eTANN. The laws of thermodynamics are hardwired in the structure of the network
and allow predictions which are always consistent. We propose a methodology
that allows to discover, from data and first principles, admissible sets of
internal variables from the microscopic fields in complex materials. The
capabilities as well as the scalability of the proposed approach are
demonstrated through several applications involving a broad spectrum of complex
material behaviors, from plasticity to damage and viscosity.

The cubic regularization method (CR) is a popular algorithm for unconstrained
non-convex optimization. At each iteration, CR solves a cubically regularized
quadratic problem, called the cubic regularization subproblem (CRS). One way to
solve the CRS relies on solving the secular equation, whose computational
bottleneck lies in the computation of all eigenvalues of the Hessian matrix. In
this paper, we propose and analyze a novel CRS solver based on an approximate
secular equation, which requires only some of the Hessian eigenvalues and is
therefore much more efficient. Two approximate secular equations (ASEs) are
developed. For both ASEs, we first study the existence and uniqueness of their
roots and then establish an upper bound on the gap between the root and that of
the standard secular equation. Such an upper bound can in turn be used to bound
the distance from the approximate CRS solution based ASEs to the true CRS
solution, thus offering a theoretical guarantee for our CRS solver. A desirable
feature of our CRS solver is that it requires only matrix-vector multiplication
but not matrix inversion, which makes it particularly suitable for
high-dimensional applications of unconstrained non-convex optimization, such as
low-rank recovery and deep learning. Numerical experiments with synthetic and
real data-sets are conducted to investigate the practical performance of the
proposed CRS solver. Experimental results show that the proposed solver
outperforms two state-of-the-art methods.

Several recent studies have shown that the use of extra in-distribution data
can lead to a high level of adversarial robustness. However, there is no
guarantee that it will always be possible to obtain sufficient extra data for a
selected dataset. In this paper, we propose a biased multi-domain adversarial
training (BiaMAT) method that induces training data amplification on a primary
dataset using publicly available auxiliary datasets, without requiring the
class distribution match between the primary and auxiliary datasets. The
proposed method can achieve increased adversarial robustness on a primary
dataset by leveraging auxiliary datasets via multi-domain learning.
Specifically, data amplification on both robust and non-robust features can be
accomplished through the application of BiaMAT as demonstrated through a
theoretical and empirical analysis. Moreover, we demonstrate that while
existing methods are vulnerable to negative transfer due to the distributional
discrepancy between auxiliary and primary data, the proposed method enables
neural networks to flexibly leverage diverse image datasets for adversarial
training by successfully handling the domain discrepancy through the
application of a confidence-based selection strategy. The pre-trained models
and code are available at: \url{https://github.com/Saehyung-Lee/BiaMAT}.

Extraction of latent sources of complex stimuli is critical for making sense
of the world. While the brain solves this blind source separation (BSS) problem
continuously, its algorithms remain unknown. Previous work on
biologically-plausible BSS algorithms assumed that observed signals are linear
mixtures of statistically independent or uncorrelated sources, limiting the
domain of applicability of these algorithms. To overcome this limitation, we
propose novel biologically-plausible neural networks for the blind separation
of potentially dependent/correlated sources. Differing from previous work, we
assume some general geometric, not statistical, conditions on the source
vectors allowing separation of potentially dependent/correlated sources.
Concretely, we assume that the source vectors are sufficiently scattered in
their domains which can be described by certain polytopes. Then, we consider
recovery of these sources by the Det-Max criterion, which maximizes the
determinant of the output correlation matrix to enforce a similar spread for
the source estimates. Starting from this normative principle, and using a
weighted similarity matching approach that enables arbitrary linear
transformations adaptable by local learning rules, we derive two-layer
biologically-plausible neural network algorithms that can separate mixtures
into sources coming from a variety of source domains. We demonstrate that our
algorithms outperform other biologically-plausible BSS algorithms on correlated
source separation problems.

This paper addresses the problem of image reconstruction for
region-of-interest (ROI) computed tomography (CT). While model-based iterative
methods can be used for such a problem, their practicability is often limited
due to tedious parameterization and slow convergence. In addition, inadequate
solutions can be obtained when the retained priors do not perfectly fit the
solution space. Deep learning methods offer an alternative approach that is
fast, leverages information from large data sets, and thus can reach high
reconstruction quality. However, these methods usually rely on black boxes not
accounting for the physics of the imaging system, and their lack of
interpretability is often deplored. At the crossroads of both methods, unfolded
deep learning techniques have been recently proposed. They incorporate the
physics of the model and iterative optimization algorithms into a neural
network design, leading to superior performance in various applications. This
paper introduces a novel, unfolded deep learning approach called U-RDBFB
designed for ROI CT reconstruction from limited data. Few-view truncated data
are efficiently handled thanks to a robust non-convex data fidelity function
combined with sparsity-inducing regularization functions. Iterations of a block
dual forward-backward (DBFB) algorithm, embedded in an iterative reweighted
scheme, are then unrolled over a neural network architecture, allowing the
learning of various parameters in a supervised manner. Our experiments show an
improvement over various state-of-the-art methods, including model-based
iterative schemes, deep learning architectures, and deep unfolding methods.

Stochastic optimization of the Area Under the Precision-Recall Curve (AUPRC)
is a crucial problem for machine learning. Although various algorithms have
been extensively studied for AUPRC optimization, the generalization is only
guaranteed in the multi-query case. In this work, we present the first trial in
the single-query generalization of stochastic AUPRC optimization. For sharper
generalization bounds, we focus on algorithm-dependent generalization. There
are both algorithmic and theoretical obstacles to our destination. From an
algorithmic perspective, we notice that the majority of existing stochastic
estimators are biased only when the sampling strategy is biased, and is
leave-one-out unstable due to the non-decomposability. To address these issues,
we propose a sampling-rate-invariant unbiased stochastic estimator with
superior stability. On top of this, the AUPRC optimization is formulated as a
composition optimization problem, and a stochastic algorithm is proposed to
solve this problem. From a theoretical perspective, standard techniques of the
algorithm-dependent generalization analysis cannot be directly applied to such
a listwise compositional optimization problem. To fill this gap, we extend the
model stability from instancewise losses to listwise losses and bridge the
corresponding generalization and stability. Additionally, we construct state
transition matrices to describe the recurrence of the stability, and simplify
calculations by matrix spectrum. Practically, experimental results on three
image retrieval datasets on speak to the effectiveness and soundness of our

The current fusion positioning systems are mainly based on filtering
algorithms, such as Kalman filtering or particle filtering. However, the system
complexity of practical application scenarios is often very high, such as noise
modeling in pedestrian inertial navigation systems, or environmental noise
modeling in fingerprint matching and localization algorithms. To solve this
problem, this paper proposes a fusion positioning system based on deep learning
and proposes a transfer learning strategy for improving the performance of
neural network models for samples with different distributions. The results
show that in the whole floor scenario, the average positioning accuracy of the
fusion network is 0.506m. The experiment results of transfer learning show that
the estimation accuracy of the inertial navigation positioning step size and
rotation angle of different pedestrians can be improved by 53.3% on average,
the Bluetooth positioning accuracy of different devices can be improved by
33.4%, and the fusion can be improved by 31.6%.

Large-scale deep learning models contribute to significant performance
improvements on varieties of downstream tasks. Current data and model
parallelism approaches utilize model replication and partition techniques to
support the distributed training of ultra-large models. However, directly
deploying these systems often leads to sub-optimal training efficiency due to
the complex model architectures and the strict device memory constraints. In
this paper, we propose Optimal Sharded Data Parallel (OSDP), an automated
parallel training system that combines the advantages from both data and model
parallelism. Given the model description and the device information, OSDP makes
trade-offs between the memory consumption and the hardware utilization, thus
automatically generates the distributed computation graph and maximizes the
overall system throughput. In addition, OSDP introduces operator splitting to
further alleviate peak memory footprints during training with negligible
overheads, which enables the trainability of larger models as well as the
higher throughput. Extensive experimental results of OSDP on multiple different
kinds of large-scale models demonstrate that the proposed strategy outperforms
the state-of-the-art in multiple regards. Our code is available at
https://github.com/Youhe-Jiang/OptimalShardedDataParallel.

Real-world images used for training machine learning algorithms are often
unstructured and inconsistent. The process of analysing and tagging these
images can be costly and error prone (also availability, gaps and legal
conundrums). However, as we demonstrate in this article, the potential to
generate accurate graphical images that are indistinguishable from real-world
sources has a multitude of benefits in machine learning paradigms. One such
example of this is football data from broadcast services (television and other
streaming media sources). The football games are usually recorded from multiple
sources (cameras and phones) and resolutions, not to mention, occlusion of
visual details and other artefacts (like blurring, weathering and lighting
conditions) which make it difficult to accurately identify features. We
demonstrate an approach which is able to overcome these limitations using
generated tagged and structured images. The generated images are able to
simulate a variety views and conditions (including noise and blurring) which
may only occur sporadically in real-world data and make it difficult for
machine learning algorithm to 'cope' with these unforeseen problems in
real-data. This approach enables us to rapidly train and prepare a robust
solution that accurately extracts features (e.g., spacial locations, markers on
the pitch, player positions, ball location and camera FOV) from real-world
football match sources for analytical purposes.

Successful point cloud registration relies on accurate correspondences
established upon powerful descriptors. However, existing neural descriptors
either leverage a rotation-variant backbone whose performance declines under
large rotations, or encode local geometry that is less distinctive. To address
this issue, we introduce RIGA to learn descriptors that are Rotation-Invariant
by design and Globally-Aware. From the Point Pair Features (PPFs) of sparse
local regions, rotation-invariant local geometry is encoded into geometric
descriptors. Global awareness of 3D structures and geometric context is
subsequently incorporated, both in a rotation-invariant fashion. More
specifically, 3D structures of the whole frame are first represented by our
global PPF signatures, from which structural descriptors are learned to help
geometric descriptors sense the 3D world beyond local regions. Geometric
context from the whole scene is then globally aggregated into descriptors.
Finally, the description of sparse regions is interpolated to dense point
descriptors, from which correspondences are extracted for registration. To
validate our approach, we conduct extensive experiments on both object- and
scene-level data. With large rotations, RIGA surpasses the state-of-the-art
methods by a margin of 8\degree in terms of the Relative Rotation Error on
ModelNet40 and improves the Feature Matching Recall by at least 5 percentage
points on 3DLoMatch.

Understanding the origin and influence of the publication's idea is critical
to conducting scientific research. However, the proliferation of scientific
publications makes it difficult for researchers to sort out the evolution of
all relevant literature. To this end, we present IdeaReader, a machine reading
system that finds out which papers are most likely to inspire or be influenced
by the target publication and summarizes the ideas of these papers in natural
language. Specifically, IdeaReader first clusters the references and citations
(first-order or higher-order) of the target publication, and the obtained
clusters are regarded as the topics that inspire or are influenced by the
target publication. It then picks out the important papers from each cluster to
extract the skeleton of the idea flow. Finally, IdeaReader automatically
generates a literature review of the important papers in each topic. Our system
can help researchers gain insight into how scientific ideas flow from the
target publication's references to citations by the automatically generated
survey and the visualization of idea flow.

Semantic search is an important task which objective is to find the relevant
index from a database for query. It requires a retrieval model that can
properly learn the semantics of sentences. Transformer-based models are widely
used as retrieval models due to their excellent ability to learn semantic
representations. in the meantime, many regularization methods suitable for them
have also been proposed. In this paper, we propose a new regularization method:
Regularized Contrastive Learning, which can help transformer-based models to
learn a better representation of sentences. It firstly augments several
different semantic representations for every sentence, then take them into the
contrastive objective as regulators. These contrastive regulators can overcome
overfitting issues and alleviate the anisotropic problem. We firstly evaluate
our approach on 7 semantic search benchmarks with the outperforming pre-trained
model SRoBERTA. The results show that our method is more effective for learning
a superior sentence representation. Then we evaluate our approach on 2
challenging FAQ datasets, Cough and Faqir, which have long query and index. The
results of our experiments demonstrate that our method outperforms baseline
methods.

In recent years, with the large-scale deployment of space spacecraft entities
and the increase of satellite onboard capabilities, delay/disruption tolerant
network (DTN) emerged as a more robust communication protocol than TCP/IP in
the case of excessive network dynamics. DTN node buffer management is still an
active area of research, as the current implementation of the DTN core protocol
still relies on the assumption that there is always enough memory available in
different network nodes to store and forward bundles. In addition, the
classical queuing theory does not apply to the dynamic management of DTN node
buffers. Therefore, this paper proposes a centralized approach to automatically
manage cognitive DTN nodes in low earth orbit (LEO) satellite constellation
scenarios based on the advanced reinforcement learning (RL) strategy advantage
actor-critic (A2C). The method aims to explore training a geosynchronous earth
orbit intelligent agent to manage all DTN nodes in an LEO satellite
constellation scenario. The goal of the A2C agent is to maximize delivery
success rate and minimize network resource consumption cost while considering
node memory utilization. The intelligent agent can dynamically adjust the radio
data rate and perform drop operations based on bundle priority. In order to
measure the effectiveness of applying A2C technology to DTN node management
issues in LEO satellite constellation scenarios, this paper compares the
trained intelligent agent strategy with the other two non-RL policies,
including random and standard policies. Experiments show that the A2C strategy
balances delivery success rate and cost, and provides the highest reward and
the lowest node memory utilization.

These notes are an overview of some classical linear methods in Multivariate
Data Analysis. This is an good old domain, well established since the 60's, and
refreshed timely as a key step in statistical learning. It can be presented as
part of statistical learning, or as dimensionality reduction with a geometric
flavor. Both approaches are tightly linked: it is easier to learn patterns from
data in low dimensional spaces than in high-dimensional spaces. It is shown how
a diversity of methods and tools boil down to a single core methods, PCA with
SVD, such that the efforts to optimize codes for analyzing massive data sets
can focus on this shared core method, and benefit to all methods. An extension
to the study of several arrays is presented (Canonical Analysis).

We present a derivation of the gradients of feedforward neural networks using
Fr\'echet calculus which is arguably more compact than the ones usually
presented in the literature. We first derive the gradients for ordinary neural
networks working on vectorial data and show how these derived formulas can be
used to derive a simple and efficient algorithm for calculating a neural
networks gradients. Subsequently we show how our analysis generalizes to more
general neural network architectures including, but not limited to,
convolutional networks.

Data-efficient image classification is a challenging task that aims to solve
image classification using small training data. Neural network-based deep
learning methods are effective for image classification, but they typically
require large-scale training data and have major limitations such as requiring
expertise to design network architectures and having poor interpretability.
Evolutionary deep learning is a recent hot topic that combines evolutionary
computation with deep learning. However, most evolutionary deep learning
methods focus on evolving architectures of neural networks, which still suffer
from limitations such as poor interpretability. To address this, this paper
proposes a new genetic programming-based evolutionary deep learning approach to
data-efficient image classification. The new approach can automatically evolve
variable-length models using many important operators from both image and
classification domains. It can learn different types of image features from
colour or gray-scale images, and construct effective and diverse ensembles for
image classification. A flexible multi-layer representation enables the new
approach to automatically construct shallow or deep models/trees for different
tasks and perform effective transformations on the input data via multiple
internal nodes. The new approach is applied to solve five image classification
tasks with different training set sizes. The results show that it achieves
better performance in most cases than deep learning methods for data-efficient
image classification. A deep analysis shows that the new approach has good
convergence and evolves models with high interpretability, different
lengths/sizes/shapes, and good transferability.

Graph Neural Networks (GNNs) have gained momentum in graph representation
learning and boosted the state of the art in a variety of areas, such as data
mining (\emph{e.g.,} social network analysis and recommender systems), computer
vision (\emph{e.g.,} object detection and point cloud learning), and natural
language processing (\emph{e.g.,} relation extraction and sequence learning),
to name a few. With the emergence of Transformers in natural language
processing and computer vision, graph Transformers embed a graph structure into
the Transformer architecture to overcome the limitations of local neighborhood
aggregation while avoiding strict structural inductive biases. In this paper,
we present a comprehensive review of GNNs and graph Transformers in computer
vision from a task-oriented perspective. Specifically, we divide their
applications in computer vision into five categories according to the modality
of input data, \emph{i.e.,} 2D natural images, videos, 3D data, vision +
language, and medical images. In each category, we further divide the
applications according to a set of vision tasks. Such a task-oriented taxonomy
allows us to examine how each task is tackled by different GNN-based approaches
and how well these approaches perform. Based on the necessary preliminaries, we
provide the definitions and challenges of the tasks, in-depth coverage of the
representative approaches, as well as discussions regarding insights,
limitations, and future directions.

Annealed Importance Sampling (AIS) is a popular algorithm used to estimates
the intractable marginal likelihood of deep generative models. Although AIS is
guaranteed to provide unbiased estimate for any set of hyperparameters, the
common implementations rely on simple heuristics such as the geometric average
bridging distributions between initial and the target distribution which affect
the estimation performance when the computation budget is limited. In order to
reduce the number of sampling iterations, we present a parameteric AIS process
with flexible intermediary distributions defined by a residual density with
respect to the geometric mean path. Our method allows parameter sharing between
annealing distributions, the use of fix linear schedule for discretization and
amortization of hyperparameter selection in latent variable models. We assess
the performance of Optimized-Path AIS for marginal likelihood estimation of
deep generative models and compare it to compare it to more computationally
intensive AIS.

Existing imitation learning methods mainly focus on making an agent
effectively mimic a demonstrated behavior, but do not address the potential
contradiction between the behavior style and the objective of a task. There is
a general lack of efficient methods that allow an agent to partially imitate a
demonstrated behavior to varying degrees, while completing the main objective
of a task. In this paper we propose a method called Regularized Soft
Actor-Critic which formulates the main task and the imitation task under the
Constrained Markov Decision Process framework (CMDP). The main task is defined
as the maximum entropy objective used in Soft Actor-Critic (SAC) and the
imitation task is defined as a constraint. We evaluate our method on continuous
control tasks relevant to video games applications.

Image-based salient object detection (ISOD) in 360{\deg} scenarios is
significant for understanding and applying panoramic information. However,
research on 360{\deg} ISOD has not been widely explored due to the lack of
large, complex, high-resolution, and well-labeled datasets. Towards this end,
we construct a large scale 360{\deg} ISOD dataset with object-level pixel-wise
annotation on equirectangular projection (ERP), which contains rich panoramic
scenes with not less than 2K resolution and is the largest dataset for
360{\deg} ISOD by far to our best knowledge. By observing the data, we find
current methods face three significant challenges in panoramic scenarios:
diverse distortion degrees, discontinuous edge effects and changeable object
scales. Inspired by humans' observing process, we propose a view-aware salient
object detection method based on a Sample Adaptive View Transformer (SAVT)
module with two sub-modules to mitigate these issues. Specifically, the
sub-module View Transformer (VT) contains three transform branches based on
different kinds of transformations to learn various features under different
views and heighten the model's feature toleration of distortion, edge effects
and object scales. Moreover, the sub-module Sample Adaptive Fusion (SAF) is to
adjust the weights of different transform branches based on various sample
features and make transformed enhanced features fuse more appropriately. The
benchmark results of 20 state-of-the-art ISOD methods reveal the constructed
dataset is very challenging. Moreover, exhaustive experiments verify the
proposed approach is practical and outperforms the state-of-the-art methods.

Automaton based approaches have enabled robots to perform various complex
tasks. However, most existing automaton based algorithms highly rely on the
manually customized representation of states for the considered task, limiting
its applicability in deep reinforcement learning algorithms. To address this
issue, by incorporating Transformer into reinforcement learning, we develop a
Double-Transformer-guided Temporal Logic framework (T2TL) that exploits the
structural feature of Transformer twice, i.e., first encoding the LTL
instruction via the Transformer module for efficient understanding of task
instructions during the training and then encoding the context variable via the
Transformer again for improved task performance. Particularly, the LTL
instruction is specified by co-safe LTL. As a semantics-preserving rewriting
operation, LTL progression is exploited to decompose the complex task into
learnable sub-goals, which not only converts non-Markovian reward decision
process to Markovian ones, but also improves the sampling efficiency by
simultaneous learning of multiple sub-tasks. An environment-agnostic LTL
pre-training scheme is further incorporated to facilitate the learning of the
Transformer module resulting in improved representation of LTL. The simulation
and experiment results demonstrate the effectiveness of the T2TL framework.

This paper proposes a novel stroke-based rendering (SBR) method that
translates images into vivid oil paintings. Previous SBR techniques usually
formulate the oil painting problem as pixel-wise approximation. Different from
this technique route, we treat oil painting creation as an adaptive sampling
problem. Firstly, we compute a probability density map based on the texture
complexity of the input image. Then we use the Voronoi algorithm to sample a
set of pixels as the stroke anchors. Next, we search and generate an individual
oil stroke at each anchor. Finally, we place all the strokes on the canvas to
obtain the oil painting. By adjusting the hyper-parameter maximum sampling
probability, we can control the oil painting fineness in a linear manner.
Comparison with existing state-of-the-art oil painting techniques shows that
our results have higher fidelity and more realistic textures. A user opinion
test demonstrates that people behave more preference toward our oil paintings
than the results of other methods. More interesting results and the code are in
https://github.com/TZYSJTU/Im2Oil.

In this paper, we propose a Bi-layer Predictionbased Reduction Branch (BP-RB)
framework to speed up the process of finding a high-quality feasible solution
for Mixed Integer Programming (MIP) problems. A graph convolutional network
(GCN) is employed to predict binary variables' values. After that, a subset of
binary variables is fixed to the predicted value by a greedy method conditioned
on the predicted probabilities. By exploring the logical consequences, a
learning-based problem reduction method is proposed, significantly reducing the
variable and constraint sizes. With the reductive sub-MIP problem, the second
layer GCN framework is employed to update the prediction for the remaining
binary variables' values and to determine the selection of variables which are
then used for branching to generate the Branch and Bound (B&B) tree. Numerical
examples show that our BP-RB framework speeds up the primal heuristic and finds
the feasible solution with high quality.

This paper presents a reinforcement learning (RL) framework that utilizes
Frank-Wolfe policy optimization to solve Coding-Tree-Unit (CTU) bit allocation
for Region-of-Interest (ROI) intra-frame coding. Most previous RL-based methods
employ the single-critic design, where the rewards for distortion minimization
and rate regularization are weighted by an empirically chosen hyper-parameter.
Recently, the dual-critic design is proposed to update the actor by alternating
the rate and distortion critics. However, its convergence is not guaranteed. To
address these issues, we introduce Neural Frank-Wolfe Policy Optimization
(NFWPO) in formulating the CTU-level bit allocation as an action-constrained RL
problem. In this new framework, we exploit a rate critic to predict a feasible
set of actions. With this feasible set, a distortion critic is invoked to
update the actor to maximize the ROI-weighted image quality subject to a rate
constraint. Experimental results produced with x265 confirm the superiority of
the proposed method to the other baselines.

Speech Entity Linking aims to recognize and disambiguate named entities in
spoken languages. Conventional methods suffer gravely from the unfettered
speech styles and the noisy transcripts generated by ASR systems. In this
paper, we propose a novel approach called Knowledge Enhanced Named Entity
Recognition (KENER), which focuses on improving robustness through painlessly
incorporating proper knowledge in the entity recognition stage and thus
improving the overall performance of entity linking. KENER first retrieves
candidate entities for a sentence without mentions, and then utilizes the
entity descriptions as extra information to help recognize mentions. The
candidate entities retrieved by a dense retrieval module are especially useful
when the input is short or noisy. Moreover, we investigate various data
sampling strategies and design effective loss functions, in order to improve
the quality of retrieved entities in both recognition and disambiguation
stages. Lastly, a linking with filtering module is applied as the final
safeguard, making it possible to filter out wrongly-recognized mentions. Our
system achieves 1st place in Track 1 and 2nd place in Track 2 of NLPCC-2022
Shared Task 2.

We present a new approach to the global fairness verification of tree-based
classifiers. Given a tree-based classifier and a set of sensitive features
potentially leading to discrimination, our analysis synthesizes sufficient
conditions for fairness, expressed as a set of traditional propositional logic
formulas, which are readily understandable by human experts. The verified
fairness guarantees are global, in that the formulas predicate over all the
possible inputs of the classifier, rather than just a few specific test
instances. Our analysis is formally proved both sound and complete.
Experimental results on public datasets show that the analysis is precise,
explainable to human experts and efficient enough for practical adoption.

Retrosynthesis is a major task for drug discovery. It is formulated as a
graph-generating problem by many existing approaches. Specifically, these
methods firstly identify the reaction center, and break target molecule
accordingly to generate synthons. Reactants are generated by either adding
atoms sequentially to synthon graphs or directly adding proper leaving groups.
However, both two strategies suffer since adding atoms results in a long
prediction sequence which increases generation difficulty, while adding leaving
groups can only consider the ones in the training set which results in poor
generalization. In this paper, we propose a novel end-to-end graph generation
model for retrosynthesis prediction, which sequentially identifies the reaction
center, generates the synthons, and adds motifs to the synthons to generate
reactants. Since chemically meaningful motifs are bigger than atoms and smaller
than leaving groups, our method enjoys lower prediction complexity than adding
atoms and better generalization than adding leaving groups. Experiments on a
benchmark dataset show that the proposed model significantly outperforms
previous state-of-the-art algorithms.

Writing reports by analyzing medical images is error-prone for inexperienced
practitioners and time consuming for experienced ones. In this work, we present
RepsNet that adapts pre-trained vision and language models to interpret medical
images and generate automated reports in natural language. RepsNet consists of
an encoder-decoder model: the encoder aligns the images with natural language
descriptions via contrastive learning, while the decoder predicts answers by
conditioning on encoded images and prior context of descriptions retrieved by
nearest neighbor search. We formulate the problem in a visual question
answering setting to handle both categorical and descriptive natural language
answers. We perform experiments on two challenging tasks of medical visual
question answering (VQA-Rad) and report generation (IU-Xray) on radiology image
datasets. Results show that RepsNet outperforms state-of-the-art methods with
81.08 % classification accuracy on VQA-Rad 2018 and 0.58 BLEU-1 score on
IU-Xray. Supplementary details are available at
https://sites.google.com/view/repsnet

Visual microscopic study of diseased tissue by pathologists has been the
cornerstone for cancer diagnosis and prognostication for more than a century.
Recently, deep learning methods have made significant advances in the analysis
and classification of tissue images. However, there has been limited work on
the utility of such models in generating histopathology images. These synthetic
images have several applications in pathology including utilities in education,
proficiency testing, privacy, and data sharing. Recently, diffusion
probabilistic models were introduced to generate high quality images. Here, for
the first time, we investigate the potential use of such models along with
prioritized morphology weighting and color normalization to synthesize high
quality histopathology images of brain cancer. Our detailed results show that
diffusion probabilistic models are capable of synthesizing a wide range of
histopathology images and have superior performance compared to generative
adversarial networks.

Implicit neural representations have shown promising potential for the 3D
scene reconstruction. Recent work applies it to autonomous 3D reconstruction by
learning information gain for view path planning. Effective as it is, the
computation of the information gain is expensive, and compared with that using
volumetric representations, collision checking using the implicit
representation for a 3D point is much slower. In the paper, we propose to 1)
leverage a neural network as an implicit function approximator for the
information gain field and 2) combine the implicit fine-grained representation
with coarse volumetric representations to improve efficiency. Further with the
improved efficiency, we propose a novel informative path planning based on a
graph-based planner. Our method demonstrates significant improvements in the
reconstruction quality and planning efficiency compared with autonomous
reconstructions with implicit and explicit representations. We deploy the
method on a real UAV and the results show that our method can plan informative
views and reconstruct a scene with high quality.

Extremely large-scale massive multiple-input-multiple-output (XL-MIMO) is
regarded as a promising technology for next-generation communication systems.
In order to enhance the beamforming gains, codebook-based beam training is
widely adopted in XL-MIMO systems. However, in XL-MIMO systems, the near-field
domain expands, and near-field codebook should be adopted for beam training,
which significantly increases the pilot overhead. To tackle this problem, we
propose a deep learning-based beam training scheme where the near-field channel
model and the near-field codebook are considered. To be specific, we first
utilize the received signals corresponding to the far-field wide beams to
estimate the optimal near-field beam. Two training schemes are proposed, namely
the proposed original and the improved neural networks. The original scheme
estimates the optimal near-field codeword directly based on the output of the
neural networks. By contrast, the improved scheme performs additional beam
testing, which can significantly improve the performance of beam training.
Finally, the simulation results show that our proposed schemes can
significantly reduce the training overhead in the near-field domain and achieve
beamforming gains.

This study develops a framework for unmanned aerial systems (UASs) to monitor
fall hazard prevention systems near unprotected edges and openings in high-rise
building projects. A three-step machine-learning-based framework was developed
and tested to detect guardrail posts from the images captured by UAS. First, a
guardrail detector was trained to localize the candidate locations of posts
supporting the guardrail. Since images were used in this process collected from
an actual job site, several false detections were identified. Therefore,
additional constraints were introduced in the following steps to filter out
false detections. Second, the research team applied a horizontal line detector
to the image to properly detect floors and remove the detections that were not
close to the floors. Finally, since the guardrail posts are installed with
approximately normal distribution between each post, the space between them was
estimated and used to find the most likely distance between the two posts. The
research team used various combinations of the developed approaches to monitor
guardrail systems in the captured images from a high-rise building project.
Comparing the precision and recall metrics indicated that the cascade
classifier achieves better performance with floor detection and guardrail
spacing estimation. The research outcomes illustrate that the proposed
guardrail recognition system can improve the assessment of guardrails and
facilitate the safety engineer's task of identifying fall hazards in high-rise
building projects.

Offline Reinforcement Learning has attracted much interest in solving the
application challenge for traditional reinforcement learning. Offline
reinforcement learning uses previously-collected datasets to train agents
without any interaction. For addressing the overestimation of OOD
(out-of-distribution) actions, conservative estimates give a low value for all
inputs. Previous conservative estimation methods are usually difficult to avoid
the impact of OOD actions on Q-value estimates. In addition, these algorithms
usually need to lose some computational efficiency to achieve the purpose of
conservative estimation. In this paper, we propose a simple conservative
estimation method, double conservative estimates (DCE), which use two
conservative estimation method to constraint policy. Our algorithm introduces
V-function to avoid the error of in-distribution action while implicit
achieving conservative estimation. In addition, our algorithm uses a
controllable penalty term changing the degree of conservatism in training. We
theoretically show how this method influences the estimation of OOD actions and
in-distribution actions. Our experiment separately shows that two conservative
estimation methods impact the estimation of all state-action. DCE demonstrates
the state-of-the-art performance on D4RL.

With the advent of Deep Learning (DL), Super-Resolution (SR) has also become
a thriving research area. However, despite promising results, the field still
faces challenges that require further research e.g., allowing flexible
upsampling, more effective loss functions, and better evaluation metrics. We
review the domain of SR in light of recent advances, and examine
state-of-the-art models such as diffusion (DDPM) and transformer-based SR
models. We present a critical discussion on contemporary strategies used in SR,
and identify promising yet unexplored research directions. We complement
previous surveys by incorporating the latest developments in the field such as
uncertainty-driven losses, wavelet networks, neural architecture search, novel
normalization methods, and the latests evaluation techniques. We also include
several visualizations for the models and methods throughout each chapter in
order to facilitate a global understanding of the trends in the field. This
review is ultimately aimed at helping researchers to push the boundaries of DL
applied to SR.

3D scene flow characterizes how the points at the current time flow to the
next time in the 3D Euclidean space, which possesses the capacity to infer
autonomously the non-rigid motion of all objects in the scene. The previous
methods for estimating scene flow from images have limitations, which split the
holistic nature of 3D scene flow by estimating optical flow and disparity
separately. Learning 3D scene flow from point clouds also faces the
difficulties of the gap between synthesized and real data and the sparsity of
LiDAR point clouds. In this paper, the generated dense depth map is utilized to
obtain explicit 3D coordinates, which achieves direct learning of 3D scene flow
from 2D images. The stability of the predicted scene flow is improved by
introducing the dense nature of 2D pixels into the 3D space. Outliers in the
generated 3D point cloud are removed by statistical methods to weaken the
impact of noisy points on the 3D scene flow estimation task. Disparity
consistency loss is proposed to achieve more effective unsupervised learning of
3D scene flow. The proposed method of self-supervised learning of 3D scene flow
on real-world images is compared with a variety of methods for learning on the
synthesized dataset and learning on LiDAR point clouds. The comparisons of
multiple scene flow metrics are shown to demonstrate the effectiveness and
superiority of introducing pseudo-LiDAR point cloud to scene flow estimation.

The popularity in Deep Learning (DL) based creative endeavours continues to
grow without any signs of slowing down. Unpredictable to many a decade ago, the
achievements of DL models in a variety of creative domains are spectacular in
their own right. In this work, I combine multiple publicly available DL models
to create a fully automated system in the generation of multimedia
entertainment. The framework I propose is general enough for any genre of
entertainment, but I focus on the task of children's video literature
production.

Experimental data is costly to obtain, which makes it difficult to calibrate
complex models. For many models an experimental design that produces the best
calibration given a limited experimental budget is not obvious. This paper
introduces a deep reinforcement learning (RL) algorithm for design of
experiments that maximizes the information gain measured by Kullback-Leibler
(KL) divergence obtained via the Kalman filter (KF). This combination enables
experimental design for rapid online experiments where traditional methods are
too costly. We formulate possible configurations of experiments as a decision
tree and a Markov decision process (MDP), where a finite choice of actions is
available at each incremental step. Once an action is taken, a variety of
measurements are used to update the state of the experiment. This new data
leads to a Bayesian update of the parameters by the KF, which is used to
enhance the state representation. In contrast to the Nash-Sutcliffe efficiency
(NSE) index, which requires additional sampling to test hypotheses for forward
predictions, the KF can lower the cost of experiments by directly estimating
the values of new data acquired through additional actions. In this work our
applications focus on mechanical testing of materials. Numerical experiments
with complex, history-dependent models are used to verify the implementation
and benchmark the performance of the RL-designed experiments.

The non-uniform photoelectric response of infrared imaging systems results in
fixed-pattern stripe noise being superimposed on infrared images, which
severely reduces image quality. As the applications of degraded infrared images
are limited, it is crucial to effectively preserve original details. Existing
image destriping methods struggle to concurrently remove all stripe noise
artifacts, preserve image details and structures, and balance real-time
performance. In this paper we propose a novel algorithm for destriping degraded
images, which takes advantage of neighbouring column signal correlation to
remove independent column stripe noise. This is achieved through an iterative
deep unfolding algorithm where the estimated noise of one network iteration is
used as input to the next iteration. This progression substantially reduces the
search space of possible function approximations, allowing for efficient
training on larger datasets. The proposed method allows for a more precise
estimation of stripe noise to preserve scene details more accurately. Extensive
experimental results demonstrate that the proposed model outperforms existing
destriping methods on artificially corrupted images on both quantitative and
qualitative assessments.

Accurate traffic forecasting is vital to an intelligent transportation
system. Although many deep learning models have achieved state-of-art
performance for short-term traffic forecasting of up to 1 hour, long-term
traffic forecasting that spans multiple hours remains a major challenge.
Moreover, most of the existing deep learning traffic forecasting models are
black box, presenting additional challenges related to explainability and
interpretability. We develop Graph Pyramid Autoformer (X-GPA), an explainable
attention-based spatial-temporal graph neural network that uses a novel pyramid
autocorrelation attention mechanism. It enables learning from long temporal
sequences on graphs and improves long-term traffic forecasting accuracy. Our
model can achieve up to 35 % better long-term traffic forecast accuracy than
that of several state-of-the-art methods. The attention-based scores from the
X-GPA model provide spatial and temporal explanations based on the traffic
dynamics, which change for normal vs. peak-hour traffic and weekday vs. weekend
traffic.

Polarized Resonant Soft X-ray scattering (P-RSoXS) has emerged as a powerful
synchrotron-based tool that combines principles of X-ray scattering and X-ray
spectroscopy. P-RSoXS provides unique sensitivity to molecular orientation and
chemical heterogeneity in soft materials such as polymers and biomaterials.
Quantitative extraction of orientation information from P-RSoXS pattern data is
challenging because the scattering processes originate from sample properties
that must be represented as energy-dependent three-dimensional tensors with
heterogeneities at nanometer to sub-nanometer length scales. We overcome this
challenge by developing an open-source virtual instrument that uses GPUs to
simulate P-RSoXS patterns from real-space material representations with
nanoscale resolution. Our computational framework CyRSoXS
(https://github.com/usnistgov/cyrsoxs) is designed to maximize GPU performance.
We demonstrate the accuracy and robustness of our approach by validating
against an extensive set of test cases, which include both analytical solutions
and numerical comparisons, demonstrating a speedup of over three orders
relative to the current state-of-the-art simulation software. Such fast
simulations open up a variety of applications that were previously
computationally infeasible, including (a) pattern fitting, (b) co-simulation
with the physical instrument for operando analytics, data exploration, and
decision support, (c) data creation and integration into machine learning
workflows, and (d) utilization in multi-modal data assimilation approaches.
Finally, we abstract away the complexity of the computational framework from
the end-user by exposing CyRSoXS to Python using Pybind. This eliminates I/O
requirements for large-scale parameter exploration and inverse design, and
democratizes usage by enabling seamless integration with a Python ecosystem
(https://github.com/usnistgov/nrss).

Anomaly identification is highly dependent on the relationship between the
object and the scene, as different/same object actions in same/different scenes
may lead to various degrees of normality and anomaly. Therefore, object-scene
relation actually plays a crucial role in anomaly detection but is inadequately
explored in previous works. In this paper, we propose a Spatial-Temporal
Relation Learning (STRL) framework to tackle the video anomaly detection task.
First, considering dynamic characteristics of the objects as well as scene
areas, we construct a Spatio-Temporal Auto-Encoder (STAE) to jointly exploit
spatial and temporal evolution patterns for representation learning. For better
pattern extraction, two decoding branches are designed in the STAE module, i.e.
an appearance branch capturing spatial cues by directly predicting the next
frame, and a motion branch focusing on modeling the dynamics via optical flow
prediction. Then, to well concretize the object-scene relation, a Relation
Learning (RL) module is devised to analyze and summarize the normal relations
by introducing the Knowledge Graph Embedding methodology. Specifically in this
process, the plausibility of object-scene relation is measured by jointly
modeling object/scene features and optimizable object-scene relation maps.
Extensive experiments are conducted on three public datasets, and the superior
performance over the state-of-the-art methods demonstrates the effectiveness of

Personalized Federated Learning (PFL) is a new Federated Learning (FL)
approach to address the heterogeneity issue of the datasets generated by
distributed user equipments (UEs). However, most existing PFL implementations
rely on synchronous training to ensure good convergence performances, which may
lead to a serious straggler problem, where the training time is heavily
prolonged by the slowest UE. To address this issue, we propose a
semi-synchronous PFL algorithm, termed as Semi-Synchronous Personalized
FederatedAveraging (PerFedS$^2$), over mobile edge networks. By jointly
optimizing the wireless bandwidth allocation and UE scheduling policy, it not
only mitigates the straggler problem but also provides convergent training loss
guarantees. We derive an upper bound of the convergence rate of PerFedS2 in
terms of the number of participants per global round and the number of rounds.
On this basis, the bandwidth allocation problem can be solved using analytical
solutions and the UE scheduling policy can be obtained by a greedy algorithm.
Experimental results verify the effectiveness of PerFedS2 in saving training
time as well as guaranteeing the convergence of training loss, in contrast to
synchronous and asynchronous PFL algorithms.

Deep Neural Networks (DNNs) are susceptible to elaborately designed
perturbations, whether such perturbations are dependent or independent of
images. The latter one, called Universal Adversarial Perturbation (UAP), is
very attractive for model robustness analysis, since its independence of input
reveals the intrinsic characteristics of the model. Relatively, another
interesting observation is Neural Collapse (NC), which means the feature
variability may collapse during the terminal phase of training. Motivated by
this, we propose to generate UAP by attacking the layer where NC phenomenon
happens. Because of NC, the proposed attack could gather all the natural
images' features to its surrounding, which is hence called Feature-Gathering
UAP (FG-UAP).
  We evaluate the effectiveness our proposed algorithm on abundant experiments,
including untargeted and targeted universal attacks, attacks under limited
dataset, and transfer-based black-box attacks among different architectures
including Vision Transformers, which are believed to be more robust.
Furthermore, we investigate FG-UAP in the view of NC by analyzing the labels
and extracted features of adversarial examples, finding that collapse
phenomenon becomes stronger after the model is corrupted. The code will be
released when the paper is accepted.

Sex classification of children's voices allows for an investigation of the
development of secondary sex characteristics which has been a key interest in
the field of speech analysis. This research investigated a broad range of
acoustic features from scripted and spontaneous speech and applied a
hierarchical clustering-based machine learning model to distinguish the sex of
children aged between 5 and 15 years. We proposed an optimal feature set and
our modelling achieved an average F1 score (the harmonic mean of the precision
and recall) of 0.84 across all ages. Our results suggest that the sex
classification is generally more accurate when a model is developed for each
year group rather than for children in 4-year age bands, with classification
accuracy being better for older age groups. We found that spontaneous speech
could provide more helpful cues in sex classification than scripted speech,
especially for children younger than 7 years. For younger age groups, a broad
range of acoustic factors contributed evenly to sex classification, while for
older age groups, F0-related acoustic factors were found to be the most
critical predictors generally. Other important acoustic factors for older age
groups include vocal tract length estimators, spectral flux, loudness and
unvoiced features.

As free online encyclopedias with massive volumes of content, Wikipedia and
Wikidata are key to many Natural Language Processing (NLP) tasks, such as
information retrieval, knowledge base building, machine translation, text
classification, and text summarization. In this paper, we introduce WikiDes, a
novel dataset to generate short descriptions of Wikipedia articles for the
problem of text summarization. The dataset consists of over 80k English samples
on 6987 topics. We set up a two-phase summarization method - description
generation (Phase I) and candidate ranking (Phase II) - as a strong approach
that relies on transfer and contrastive learning. For description generation,
T5 and BART show their superiority compared to other small-scale pre-trained
models. By applying contrastive learning with the diverse input from beam
search, the metric fusion-based ranking models outperform the direct
description generation models significantly up to 22 ROUGE in topic-exclusive
split and topic-independent split. Furthermore, the outcome descriptions in
Phase II are supported by human evaluation in over 45.33% chosen compared to
23.66% in Phase I against the gold descriptions. In the aspect of sentiment
analysis, the generated descriptions cannot effectively capture all sentiment
polarities from paragraphs while doing this task better from the gold
descriptions. The automatic generation of new descriptions reduces the human
efforts in creating them and enriches Wikidata-based knowledge graphs. Our
paper shows a practical impact on Wikipedia and Wikidata since there are
thousands of missing descriptions. Finally, we expect WikiDes to be a useful
dataset for related works in capturing salient information from short
paragraphs. The curated dataset is publicly available at:
https://github.com/declare-lab/WikiDes.

The mean exit time escaping basin of attraction in the presence of white
noise is of practical importance in various scientific fields. In this work, we
propose a strategy to control mean exit time of general stochastic dynamical
systems to achieve a desired value based on the quasipotential concept and
machine learning. Specifically, we develop a neural network architecture to
compute the global quasipotential function. Then we design a systematic
iterated numerical algorithm to calculate the controller for a given mean exit
time. Moreover, we identify the most probable path between metastable
attractors with help of the effective Hamilton-Jacobi scheme and the trained
neural network. Numerical experiments demonstrate that our control strategy is
effective and sufficiently accurate.

The willingness to trust predictions formulated by automatic algorithms is
key in a vast number of domains. However, a vast number of deep architectures
are only able to formulate predictions without an associated uncertainty. In
this paper, we propose a method to convert a standard neural network into a
Bayesian neural network and estimate the variability of predictions by sampling
different networks similar to the original one at each forward pass. We couple
our methods with a tunable rejection-based approach that employs only the
fraction of the dataset that the model is able to classify with an uncertainty
below a user-set threshold. We test our model in a large cohort of brain images
from Alzheimer's Disease patients, where we tackle discrimination of patients
from healthy controls based on morphometric images only. We demonstrate how
combining the estimated uncertainty with a rejection-based approach increases
classification accuracy from 0.86 to 0.95 while retaining 75% of the test set.
In addition, the model can select cases to be recommended for manual evaluation
based on excessive uncertainty. We believe that being able to estimate the
uncertainty of a prediction, along with tools that can modulate the behavior of
the network to a degree of confidence that the user is informed about (and
comfortable with) can represent a crucial step in the direction of user
compliance and easier integration of deep learning tools into everyday tasks
currently performed by human operators.

While capable of segregating visual data, humans take time to examine a
single piece, let alone thousands or millions of samples. The deep learning
models efficiently process sizeable information with the help of modern-day
computing. However, their questionable decision-making process has raised
considerable concerns. Recent studies have identified a new approach to extract
image features from EEG signals and combine them with standard image features.
These approaches make deep learning models more interpretable and also enables
faster converging of models with fewer samples. Inspired by recent studies, we
developed an efficient way of encoding EEG signals as images to facilitate a
more subtle understanding of brain signals with deep learning models. Using two
variations in such encoding methods, we classified the encoded EEG signals
corresponding to 39 image classes with a benchmark accuracy of 70% on the
layered dataset of six subjects, which is significantly higher than the
existing work. Our image classification approach with combined EEG features
achieved an accuracy of 82% compared to the slightly better accuracy of a pure
deep learning approach; nevertheless, it demonstrates the viability of the
theory.

Current deep learning models trained to generate radiology reports from chest
radiographs are capable of producing clinically accurate, clear, and actionable
text that can advance patient care. However, such systems all succumb to the
same problem: making hallucinated references to non-existent prior reports.
Such hallucinations occur because these models are trained on datasets of
real-world patient reports that inherently refer to priors. To this end, we
propose two methods to remove references to priors in radiology reports: (1) a
GPT-3-based few-shot approach to rewrite medical reports without references to
priors; and (2) a BioBERT-based token classification approach to directly
remove words referring to priors. We use the aforementioned approaches to
modify MIMIC-CXR, a publicly available dataset of chest X-rays and their
associated free-text radiology reports; we then retrain CXR-RePaiR, a radiology
report generation system, on the adapted MIMIC-CXR dataset. We find that our
re-trained model--which we call CXR-ReDonE--outperforms previous report
generation methods on clinical metrics, achieving an average BERTScore of
0.2351 (2.57% absolute improvement). We expect our approach to be broadly
valuable in enabling current radiology report generation systems to be more
directly integrated into clinical pipelines.

We provide the first formal definition of reward hacking, a phenomenon where
optimizing an imperfect proxy reward function, $\mathcal{\tilde{R}}$, leads to
poor performance according to the true reward function, $\mathcal{R}$. We say
that a proxy is unhackable if increasing the expected proxy return can never
decrease the expected true return. Intuitively, it might be possible to create
an unhackable proxy by leaving some terms out of the reward function (making it
"narrower") or overlooking fine-grained distinctions between roughly equivalent
outcomes, but we show this is usually not the case. A key insight is that the
linearity of reward (in state-action visit counts) makes unhackability a very
strong condition. In particular, for the set of all stochastic policies, two
reward functions can only be unhackable if one of them is constant. We thus
turn our attention to deterministic policies and finite sets of stochastic
policies, where non-trivial unhackable pairs always exist, and establish
necessary and sufficient conditions for the existence of simplifications, an
important special case of unhackability. Our results reveal a tension between
using reward functions to specify narrow tasks and aligning AI systems with
human values.

In many contexts, simpler models are preferable to more complex models and
the control of this model complexity is the goal for many methods in machine
learning such as regularization, hyperparameter tuning and architecture design.
In deep learning, it has been difficult to understand the underlying mechanisms
of complexity control, since many traditional measures are not naturally
suitable for deep neural networks. Here we develop the notion of geometric
complexity, which is a measure of the variability of the model function,
computed using a discrete Dirichlet energy. Using a combination of theoretical
arguments and empirical results, we show that many common training heuristics
such as parameter norm regularization, spectral norm regularization, flatness
regularization, implicit gradient regularization, noise regularization and the
choice of parameter initialization all act to control geometric complexity,
providing a unifying framework in which to characterize the behavior of deep

Secure software leasing is a quantum cryptographic primitive that enables us
to lease software to a user by encoding it into a quantum state. Secure
software leasing has a mechanism that verifies whether a returned software is
valid or not. The security notion guarantees that once a user returns a
software in a valid form, the user no longer uses the software.
  In this work, we introduce the notion of secret-key functional encryption
(SKFE) with secure key leasing, where a decryption key can be securely leased
in the sense of secure software leasing. We also instantiate it with standard
cryptographic assumptions. More specifically, our contribution is as follows.
  - We define the syntax and security definitions for SKFE with secure key
leasing.
  - We achieve a transformation from standard SKFE into SKFE with secure key
leasing without using additional assumptions. Especially, we obtain bounded
collusion-resistant SKFE for P/poly with secure key leasing based on
post-quantum one-way functions since we can instantiate bounded
collusion-resistant SKFE for P/poly with the assumption.
  Some previous secure software leasing schemes capture only pirate software
that runs on an honest evaluation algorithm (on a legitimate platform).
However, our secure key leasing notion captures arbitrary attack strategies and
does not have such a limitation.
  We also introduce the notion of single-decryptor FE (SDFE), where each
functional decryption key is copy-protected. Since copy-protection is a
stronger primitive than secure software leasing, this notion can be seen as a
stronger cryptographic primitive than FE with secure key leasing. More
specifically:
  - We define the syntax and security definitions for SDFE.
  - We achieve collusion-resistant single-decryptor PKFE for P/poly from
post-quantum indistinguishability obfuscation and quantum hardness of the
learning with errors problem.

Recent advances in remote patient monitoring (RPM) systems can recognize
various human activities to measure vital signs, including subtle motions from
superficial vessels. There is a growing interest in applying artificial
intelligence (AI) to this area of healthcare by addressing known limitations
and challenges such as predicting and classifying vital signs and physical
movements, which are considered crucial tasks. Federated learning is a
relatively new AI technique designed to enhance data privacy by decentralizing
traditional machine learning modeling. However, traditional federated learning
requires identical architectural models to be trained across the local clients
and global servers. This limits global model architecture due to the lack of
local models heterogeneity. To overcome this, a novel federated learning
architecture, FedStack, which supports ensembling heterogeneous architectural
client models was proposed in this study. This work offers a protected privacy
system for hospitalized in-patients in a decentralized approach and identifies
optimum sensor placement. The proposed architecture was applied to a mobile
health sensor benchmark dataset from 10 different subjects to classify 12
routine activities. Three AI models, ANN, CNN, and Bi-LSTM were trained on
individual subject data. The federated learning architecture was applied to
these models to build local and global models capable of state of the art
performances. The local CNN model outperformed ANN and Bi-LSTM models on each
subject data. Our proposed work has demonstrated better performance for
heterogeneous stacking of the local models compared to homogeneous stacking.
This work sets the stage to build an enhanced RPM system that incorporates
client privacy to assist with clinical observations for patients in an acute
mental health facility and ultimately help to prevent unexpected death.

In this paper, we propose a two-stage optimization strategy for solving the
Large-scale Traveling Salesman Problems (LSTSPs) named CCPNRL-GA. First, we
hypothesize that the participation of a well-performed individual as an elite
can accelerate the convergence of optimization. Based on this hypothesis, in
the first stage, we cluster the cities and decompose the LSTSPs into multiple
subcomponents, and each subcomponent is optimized with a reusable Pointer
Network (PtrNet). After subcomponents optimization, we combine all sub-tours to
form a valid solution, this solution joins the second stage of optimization
with GA. We validate the performance of our proposal on 10 LSTSPs and compare
it with traditional EAs. Experimental results show that the participation of an
elite individual can greatly accelerate the optimization of LSTSPs, and our
proposal has broad prospects for dealing with LSTSPs.

The problem of estimating a linear functional based on observational data is
canonical in both the causal inference and bandit literatures. We analyze a
broad class of two-stage procedures that first estimate the treatment effect
function, and then use this quantity to estimate the linear functional. We
prove non-asymptotic upper bounds on the mean-squared error of such procedures:
these bounds reveal that in order to obtain non-asymptotically optimal
procedures, the error in estimating the treatment effect should be minimized in
a certain weighted $L^2$-norm. We analyze a two-stage procedure based on
constrained regression in this weighted norm, and establish its
instance-dependent optimality in finite samples via matching non-asymptotic
local minimax lower bounds. These results show that the optimal non-asymptotic
risk, in addition to depending on the asymptotically efficient variance,
depends on the weighted norm distance between the true outcome function and its
approximation by the richest function class supported by the sample size.

The Euclid Space Telescope will provide deep imaging at optical and
near-infrared wavelengths, along with slitless near-infrared spectroscopy,
across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion
astronomical sources, facilitating new insights into cosmology, galaxy
evolution, and various other topics. To optimally exploit the expected very
large data set, there is the need to develop appropriate methods and software.
Here we present a novel machine-learning based methodology for selection of
quiescent galaxies using broad-band Euclid I_E, Y_E, J_E, H_E photometry, in
combination with multiwavelength photometry from other surveys. The ARIADNE
pipeline uses meta-learning to fuse decision-tree ensembles,
nearest-neighbours, and deep-learning methods into a single classifier that
yields significantly higher accuracy than any of the individual learning
methods separately. The pipeline has `sparsity-awareness', so that missing
photometry values are still informative for the classification. Our pipeline
derives photometric redshifts for galaxies selected as quiescent, aided by the
`pseudo-labelling' semi-supervised method. After application of the outlier
filter, our pipeline achieves a normalized mean absolute deviation of ~< 0.03
and a fraction of catastrophic outliers of ~< 0.02 when measured against the
COSMOS2015 photometric redshifts. We apply our classification pipeline to mock
galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid
Deep Survey with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey
with ancillary ugriz, WISE, and radio data; (iii) Euclid Wide Survey only. Our
classification pipeline outperforms UVJ selection, in addition to the Euclid
I_E-Y_E, J_E-H_E and u-I_E,I_E-J_E colour-colour methods, with improvements in
completeness and the F1-score of up to a factor of 2. (Abridged)

Data sharing can be granted using different factors one of which is something
in a users or an IoT devices environment which is in this paper broadcast
signals. Using broadcast signals to measure Received Signal Strength Indicator
values and Machine Learning models this paper implements an IoT data sharing
scheme based on something that is in an IoT devices environment. The proposed
scheme is experimentally tested using different ML models and shows 97.78
percent as its highest accuracy.

Deep learning models for vision tasks are trained on large datasets under the
assumption that there exists a universal representation that can be used to
make predictions for all samples. Whereas high complexity models are proven to
be capable of learning such representations, a mixture of experts trained on
specific subsets of the data can infer the labels more efficiently. However
using mixture of experts poses two new problems, namely (i) assigning the
correct expert at inference time when a new unseen sample is presented. (ii)
Finding the optimal partitioning of the training data, such that the experts
rely the least on common features. In Dynamic Routing (DR) a novel architecture
is proposed where each layer is composed of a set of experts, however without
addressing the two challenges we demonstrate that the model reverts to using
the same subset of experts.
  In our method, Diversified Dynamic Routing (DivDR) the model is explicitly
trained to solve the challenge of finding relevant partitioning of the data and
assigning the correct experts in an unsupervised approach. We conduct several
experiments on semantic segmentation on Cityscapes and object detection and
instance segmentation on MS-COCO showing improved performance over several

We introduce VISOR, a new dataset of pixel annotations and a benchmark suite
for segmenting hands and active objects in egocentric video. VISOR annotates
videos from EPIC-KITCHENS, which comes with a new set of challenges not
encountered in current video segmentation datasets. Specifically, we need to
ensure both short- and long-term consistency of pixel-level annotations as
objects undergo transformative interactions, e.g. an onion is peeled, diced and
cooked - where we aim to obtain accurate pixel-level annotations of the peel,
onion pieces, chopping board, knife, pan, as well as the acting hands. VISOR
introduces an annotation pipeline, AI-powered in parts, for scalability and
quality. In total, we publicly release 272K manual semantic masks of 257 object
classes, 9.9M interpolated dense masks, 67K hand-object relations, covering 36
hours of 179 untrimmed videos. Along with the annotations, we introduce three
challenges in video object segmentation, interaction understanding and
long-term reasoning.
  For data, code and leaderboards: http://epic-kitchens.github.io/VISOR

Great successes have been achieved using deep learning techniques for image
super-resolution (SR) with fixed scales. To increase its real world
applicability, numerous models have also been proposed to restore SR images
with arbitrary scale factors, including asymmetric ones where images are
resized to different scales along horizontal and vertical directions. Though
most models are only optimized for the unidirectional upscaling task while
assuming a predefined downscaling kernel for low-resolution (LR) inputs, recent
models based on Invertible Neural Networks (INN) are able to increase upscaling
accuracy significantly by optimizing the downscaling and upscaling cycle
jointly. However, limited by the INN architecture, it is constrained to fixed
integer scale factors and requires one model for each scale. Without increasing
model complexity, a simple and effective invertible arbitrary rescaling network
(IARN) is proposed to achieve arbitrary image rescaling by training only one
model in this work. Using innovative components like position-aware scale
encoding and preemptive channel splitting, the network is optimized to convert
the non-invertible rescaling cycle to an effectively invertible process. It is
shown to achieve a state-of-the-art (SOTA) performance in bidirectional
arbitrary rescaling without compromising perceptual quality in LR outputs. It
is also demonstrated to perform well on tests with asymmetric scales using the
same network architecture.

Control design for robotic systems is complex and often requires solving an
optimization to follow a trajectory accurately. Online optimization approaches
like Model Predictive Control (MPC) have been shown to achieve great tracking
performance, but require high computing power. Conversely, learning-based
offline optimization approaches, such as Reinforcement Learning (RL), allow
fast and efficient execution on the robot but hardly match the accuracy of MPC
in trajectory tracking tasks. In systems with limited compute, such as aerial
vehicles, an accurate controller that is efficient at execution time is
imperative. We propose an Analytic Policy Gradient (APG) method to tackle this
problem. APG exploits the availability of differentiable simulators by training
a controller offline with gradient descent on the tracking error. We address
training instabilities that frequently occur with APG through curriculum
learning and experiment on a widely used controls benchmark, the CartPole, and
two common aerial robots, a quadrotor and a fixed-wing drone. Our proposed
method outperforms both model-based and model-free RL methods in terms of
tracking error. Concurrently, it achieves similar performance to MPC while
requiring more than an order of magnitude less computation time. Our work
provides insights into the potential of APG as a promising control method for
robotics. To facilitate the exploration of APG, we open-source our code and
make it available at https://github.com/lis-epfl/apg_trajectory_tracking.

Meta reinforcement learning (Meta-RL) is an approach wherein the experience
gained from solving a variety of tasks is distilled into a meta-policy. The
meta-policy, when adapted over only a small (or just a single) number of steps,
is able to perform near-optimally on a new, related task. However, a major
challenge to adopting this approach to solve real-world problems is that they
are often associated with sparse reward functions that only indicate whether a
task is completed partially or fully. We consider the situation where some
data, possibly generated by a sub-optimal agent, is available for each task. We
then develop a class of algorithms entitled Enhanced Meta-RL using
Demonstrations (EMRLD) that exploit this information even if sub-optimal to
obtain guidance during training. We show how EMRLD jointly utilizes RL and
supervised learning over the offline data to generate a meta-policy that
demonstrates monotone performance improvements. We also develop a warm started
variant called EMRLD-WS that is particularly efficient for sub-optimal
demonstration data. Finally, we show that our EMRLD algorithms significantly
outperform existing approaches in a variety of sparse reward environments,
including that of a mobile robot.

Hindsight goal relabeling has become a foundational technique for multi-goal
reinforcement learning (RL). The idea is quite simple: any arbitrary trajectory
can be seen as an expert demonstration for reaching the trajectory's end state.
Intuitively, this procedure trains a goal-conditioned policy to imitate a
sub-optimal expert. However, this connection between imitation and hindsight
relabeling is not well understood. Modern imitation learning algorithms are
described in the language of divergence minimization, and yet it remains an
open problem how to recast hindsight goal relabeling into that framework. In
this work, we develop a unified objective for goal-reaching that explains such
a connection, from which we can derive goal-conditioned supervised learning
(GCSL) and the reward function in hindsight experience replay (HER) from first
principles. Experimentally, we find that despite recent advances in
goal-conditioned behaviour cloning (BC), multi-goal Q-learning can still
outperform BC-like methods; moreover, a vanilla combination of both actually
hurts model performance. Under our framework, we study when BC is expected to
help, and empirically validate our findings. Our work further bridges
goal-reaching and generative modeling, illustrating the nuances and new
pathways of extending the success of generative models to RL.

In wearable sensing applications, data is inevitable to be irregularly
sampled or partially missing, which pose challenges for any downstream
application. An unique aspect of wearable data is that it is time-series data
and each channel can be correlated to another one, such as x, y, z axis of
accelerometer. We argue that traditional methods have rarely made use of both
times-series dynamics of the data as well as the relatedness of the features
from different sensors. We propose a model, termed as DynImp, to handle
different time point's missingness with nearest neighbors along feature axis
and then feeding the data into a LSTM-based denoising autoencoder which can
reconstruct missingness along the time axis. We experiment the model on the
extreme missingness scenario ($>50\%$ missing rate) which has not been widely
tested in wearable data. Our experiments on activity recognition show that the
method can exploit the multi-modality features from related sensors and also
learn from history time-series dynamics to reconstruct the data under extreme
missingness.

We present and discuss a runtime architecture that integrates sensorial data
and classifiers with a logic-based decision-making system in the context of an
e-Health system for the rehabilitation of children with neuromotor disorders.
In this application, children perform a rehabilitation task in the form of
games. The main aim of the system is to derive a set of parameters the child's
current level of cognitive and behavioral performance (e.g., engagement,
attention, task accuracy) from the available sensors and classifiers (e.g., eye
trackers, motion sensors, emotion recognition techniques) and take decisions
accordingly. These decisions are typically aimed at improving the child's
performance by triggering appropriate re-engagement stimuli when their
attention is low, by changing the game or making it more difficult when the
child is losing interest in the task as it is too easy. Alongside
state-of-the-art techniques for emotion recognition and head pose estimation,
we use a runtime variant of a probabilistic and epistemic logic programming
dialect of the Event Calculus, known as the Epistemic Probabilistic Event
Calculus. In particular, the probabilistic component of this symbolic framework
allows for a natural interface with the machine learning techniques. We
overview the architecture and its components, and show some of its
characteristics through a discussion of a running example and experiments.
Under consideration for publication in Theory and Practice of Logic Programming
(TPLP).

Humans make extensive use of vision and touch as complementary senses, with
vision providing global information about the scene and touch measuring local
information during manipulation without suffering from occlusions. In this
work, we propose a novel framework for learning multi-task visuo-tactile
representations in a self-supervised manner. We design a mechanism which
enables a robot to autonomously collect spatially aligned visual and tactile
data, a key property for downstream tasks. We then train visual and tactile
encoders to embed these paired sensory inputs into a shared latent space using
cross-modal contrastive loss. The learned representations are evaluated without
fine-tuning on 5 perception and control tasks involving deformable surfaces:
tactile classification, contact localization, anomaly detection (e.g., surgical
phantom tumor palpation), tactile search from a visual query (e.g., garment
feature localization under occlusion), and tactile servoing along cloth edges
and cables. The learned representations achieve an 80% success rate on towel
feature classification, a 73% average success rate on anomaly detection in
surgical materials, a 100% average success rate on vision-guided tactile
search, and 87.8% average servo distance along cables and garment seams. These
results suggest the flexibility of the learned representations and pose a step
toward task-agnostic visuo-tactile representation learning for robot control.

We introduce a new approach to image forensics: placing physical refractive
objects, which we call totems, into a scene so as to protect any photograph
taken of that scene. Totems bend and redirect light rays, thus providing
multiple, albeit distorted, views of the scene within a single image. A
defender can use these distorted totem pixels to detect if an image has been
manipulated. Our approach unscrambles the light rays passing through the totems
by estimating their positions in the scene and using their known geometric and
material properties. To verify a totem-protected image, we detect
inconsistencies between the scene reconstructed from totem viewpoints and the
scene's appearance from the camera viewpoint. Such an approach makes the
adversarial manipulation task more difficult, as the adversary must modify both
the totem and image pixels in a geometrically consistent manner without knowing
the physical properties of the totem. Unlike prior learning-based approaches,
our method does not require training on datasets of specific manipulations, and
instead uses physical properties of the scene and camera to solve the forensics
problem.

The ionization edges encoded in the electron energy loss spectroscopy (EELS)
spectra enable advanced material analysis including composition analyses and
elemental quantifications. The development of the parallel EELS instrument and
fast, sensitive detectors have greatly improved the acquisition speed of EELS
spectra. However, the traditional way of core-loss edge recognition is
experience based and human labor dependent, which limits the processing speed.
So far, the low signal-noise ratio and the low jump ratio of the core-loss
edges on the raw EELS spectra have been challenging for the automation of edge
recognition. In this work, a convolutional-bidirectional long short-term memory
neural network (CNN-BiLSTM) is proposed to automate the detection and elemental
identification of core-loss edges from raw spectra. An EELS spectral database
is synthesized by using our forward model to assist in the training and
validation of the neural network. To make the synthesized spectra resemble the
real spectra, we collected a large library of experimentally acquired EELS core
edges. In synthesize the training library, the edges are modeled by fitting the
multi-gaussian model to the real edges from experiments, and the noise and
instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM
network is tested against both the simulated spectra and real spectra collected
from experiments. The high accuracy of the network, 94.9 %, proves that,
without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM
network achieves the automation of core-loss edge recognition for EELS spectra
with high accuracy.

Performance metrics for medical image segmentation models are used to measure
the agreement between the reference annotation and the predicted segmentation.
Usually, overlap metrics, such as the Dice, are used as a metric to evaluate
the performance of these models in order for results to be comparable. However,
there is a mismatch between the distributions of cases and difficulty level of
segmentation tasks in public data sets compared to clinical practice. Common
metrics fail to measure the impact of this mismatch, especially for clinical
data sets that include low signal pathologies, a difficult segmentation task,
and uncertain, small, or empty reference annotations. This limitation may
result in ineffective research of machine learning practitioners in designing
and optimizing models. Dimensions of evaluating clinical value include
consideration of the uncertainty of reference annotations, independence from
reference annotation volume size, and evaluation of classification of empty
reference annotations. We study how uncertain, small, and empty reference
annotations influence the value of metrics for medical image segmentation on an
in-house data set regardless of the model. We examine metrics behavior on the
predictions of a standard deep learning framework in order to identify metrics
with clinical value. We compare to a public benchmark data set (BraTS 2019)
with a high-signal pathology and certain, larger, and no empty reference
annotations. We may show machine learning practitioners, how uncertain, small,
or empty reference annotations require a rethinking of the evaluation and
optimizing procedures. The evaluation code was released to encourage further
analysis of this topic.
https://github.com/SophieOstmeier/UncertainSmallEmpty.git

Traditional urban planning demands urban experts to spend considerable time
and effort producing an optimal urban plan under many architectural
constraints. The remarkable imaginative ability of deep generative learning
provides hope for renovating urban planning. While automated urban planners
have been examined, they are constrained because of the following: 1)
neglecting human requirements in urban planning; 2) omitting spatial
hierarchies in urban planning, and 3) lacking numerous urban plan data samples.
To overcome these limitations, we propose a novel, deep, human-instructed urban
planner. In the preliminary work, we formulate it into an encoder-decoder
paradigm. The encoder is to learn the information distribution of surrounding
contexts, human instructions, and land-use configuration. The decoder is to
reconstruct the land-use configuration and the associated urban functional
zones. The reconstruction procedure will capture the spatial hierarchies
between functional zones and spatial grids. Meanwhile, we introduce a
variational Gaussian mechanism to mitigate the data sparsity issue. Even though
early work has led to good results, the performance of generation is still
unstable because the way spatial hierarchies are captured may lead to unclear
optimization directions. In this journal version, we propose a cascading deep
generative framework based on generative adversarial networks (GANs) to solve
this problem, inspired by the workflow of urban experts. In particular, the
purpose of the first GAN is to build urban functional zones based on
information from human instructions and surrounding contexts. The second GAN
will produce the land-use configuration based on the functional zones that have
been constructed. Additionally, we provide a conditioning augmentation module
to augment data samples. Finally, we conduct extensive experiments to validate
the efficacy of our work.

Remote sensing benefits habitat conservation by making monitoring of large
areas easier compared to field surveying especially if the remote sensed data
can be automatically analyzed. An important aspect of monitoring is classifying
and mapping habitat types present in the monitored area. Automatic
classification is a difficult task, as classes have fine-grained differences
and their distributions are long-tailed and unbalanced. Usually training data
used for automatic land cover classification relies on fully annotated
segmentation maps, annotated from remote sensed imagery to a fairly high-level
taxonomy, i.e., classes such as forest, farmland, or urban area. A challenge
with automatic habitat classification is that reliable data annotation requires
field-surveys. Therefore, full segmentation maps are expensive to produce, and
training data is often sparse, point-like, and limited to areas accessible by
foot. Methods for utilizing these limited data more efficiently are needed.
  We address these problems by proposing a method for habitat classification
and mapping, and apply this method to classify the entire northern Finnish
Lapland area into Natura2000 classes. The method is characterized by using
finely-grained, sparse, single-pixel annotations collected from the field,
combined with large amounts of unannotated data to produce segmentation maps.
Supervised, unsupervised and semi-supervised methods are compared, and the
benefits of transfer learning from a larger out-of-domain dataset are
demonstrated. We propose a \ac{CNN} biased towards center pixel classification
ensembled with a random forest classifier, that produces higher quality
classifications than the models themselves alone. We show that cropping
augmentations, test-time augmentation and semi-supervised learning can help
classification even further.

Most of today's computer vision pipelines are built around deep neural
networks, where convolution operations require most of the generally high
compute effort. The Winograd convolution algorithm computes convolutions with
fewer MACs compared to the standard algorithm, reducing the operation count by
a factor of 2.25x for 3x3 convolutions when using the version with 2x2-sized
tiles $F_2$. Even though the gain is significant, the Winograd algorithm with
larger tile sizes, i.e., $F_4$, offers even more potential in improving
throughput and energy efficiency, as it reduces the required MACs by 4x.
Unfortunately, the Winograd algorithm with larger tile sizes introduces
numerical issues that prevent its use on integer domain-specific accelerators
and higher computational overhead to transform input and output data between
spatial and Winograd domains.
  To unlock the full potential of Winograd $F_4$, we propose a novel tap-wise
quantization method that overcomes the numerical issues of using larger tiles,
enabling integer-only inference. Moreover, we present custom hardware units
that process the Winograd transformations in a power- and area-efficient way,
and we show how to integrate such custom modules in an industrial-grade,
programmable DSA. An extensive experimental evaluation on a large set of
state-of-the-art computer vision benchmarks reveals that the tap-wise
quantization algorithm makes the quantized Winograd $F_4$ network almost as
accurate as the FP32 baseline. The Winograd-enhanced DSA achieves up to 1.85x
gain in energy efficiency and up to 1.83x end-to-end speed-up for
state-of-the-art segmentation and detection networks.

Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture
that enables openness, intelligence, and automated control. The RAN Intelligent
Controller (RIC) provides the platform to design and deploy RAN controllers.
xApps are the applications which will take this responsibility by leveraging
machine learning (ML) algorithms and acting in near-real time. Despite the
opportunities provided by this new architecture, the progress of practical
artificial intelligence (AI)-based solutions for network control and automation
has been slow. This is mostly because of the lack of an endto-end solution for
designing, deploying, and testing AI-based xApps fully executable in real O-RAN
network. In this paper we introduce an end-to-end O-RAN design and evaluation
procedure and provide a detailed discussion of developing a Reinforcement
Learning (RL) based xApp by using two different RL approaches and considering
the latest released O-RAN architecture and interfaces.

Effect of deformation on half-life of two-proton (2p) radioactivity is
investigated across the periodic chart for nuclei with Z$<$82. 2p-decay
half-lives are estimated by employing our newly proposed semi-empirical formula
wherein the nuclear deformation has been incorporated in a phenomenological
way. Robustness of the formula is demonstrated as it estimates the measured
values quite accurately and, hence, reliably applied to predict the other
possible 2p-emitters. For many proton rich nuclei for which experimental data
on the decay energies are not available, we have used the theoretical values
obtained from our calculations using the relativistic mean-field (RMF)
approach. The uncertainties in the theoretical decay energy values are
minimised by machine learning (ML) technique. Correlation of 2p-radioactivity
with 2p-halo and deformation is probed. Our calculations show the phenomenon of
shape coexistence in several 2p-emitters, wherein the prolate shape is found to
be more predominant for the ground state.

Recent global growth in the interest of smart cities has led to trillions of
dollars of investment toward research and development. These connected cities
have the potential to create a symbiosis of technology and society and
revolutionize the cost of living, safety, ecological sustainability, and
quality of life of societies on a world-wide scale. Some key components of the
smart city construct are connected smart grids, self-driving cars, federated
learning systems, smart utilities, large-scale public transit, and proactive
surveillance systems. While exciting in prospect, these technologies and their
subsequent integration cannot be attempted without addressing the potential
societal impacts of such a high degree of automation and data sharing.
Additionally, the feasibility of coordinating so many disparate tasks will
require a fast, extensible, unifying framework. To that end, we propose FaRO2,
a completely reimagined successor to FaRO1, built from the ground up. FaRO2
affords all of the same functionality as its predecessor, serving as a unified
biometric API harness that allows for seamless evaluation, deployment, and
simple pipeline creation for heterogeneous biometric software. FaRO2
additionally provides a fully declarative capability for defining and
coordinating custom machine learning and sensor pipelines, allowing the
distribution of processes across otherwise incompatible hardware and networks.
FaRO2 ultimately provides a way to quickly configure, hot-swap, and expand
large coordinated or federated systems online without interruptions for
maintenance. Because much of the data collected in a smart city contains
Personally Identifying Information (PII), FaRO2 also provides built-in tools
and layers to ensure secure and encrypted streaming, storage, and access of PII
data across distributed systems.

This paper develops a data-driven framework to synthesize local Volt/Var
control strategies for distributed energy resources (DERs) in power
distribution networks (DNs). Aiming to improve DN operational efficiency, as
quantified by a generic optimal reactive power flow (ORPF) problem, we propose
a two-stage approach. The first stage involves learning the manifold of optimal
operating points determined by an ORPF instance. To synthesize local Volt/Var
controllers, the learning task is partitioned into learning local surrogates
(one per DER) of the optimal manifold with voltage input and reactive power
output. Since these surrogates characterize efficient DN operating points, in
the second stage, we develop local control schemes that steer the DN to these
operating points. We identify the conditions on the surrogates and control
parameters to ensure that the locally acting controllers collectively converge,
in a global asymptotic sense, to a DN operating point agreeing with the local
surrogates. We use neural networks to model the surrogates and enforce the
identified conditions in the training phase. AC power flow simulations on the
IEEE 37-bus network empirically bolster the theoretical stability guarantees
obtained under linearized power flow assumptions. The tests further highlight
the optimality improvement compared to prevalent benchmark methods.

A proper parametrization of state transition matrices of linear state-space
models (SSMs) followed by standard nonlinearities enables them to efficiently
learn representations from sequential data, establishing the state-of-the-art
on a large series of long-range sequence modeling benchmarks. In this paper, we
show that we can improve further when the structural SSM such as S4 is given by
a linear liquid time-constant (LTC) state-space model. LTC neural networks are
causal continuous-time neural networks with an input-dependent state transition
module, which makes them learn to adapt to incoming inputs at inference. We
show that by using a diagonal plus low-rank decomposition of the state
transition matrix introduced in S4, and a few simplifications, the LTC-based
structural state-space model, dubbed Liquid-S4, achieves the new
state-of-the-art generalization across sequence modeling tasks with long-term
dependencies such as image, text, audio, and medical time-series, with an
average performance of 87.32% on the Long-Range Arena benchmark. On the full
raw Speech Command recognition, dataset Liquid-S4 achieves 96.78% accuracy with
a 30% reduction in parameter counts compared to S4. The additional gain in
performance is the direct result of the Liquid-S4's kernel structure that takes
into account the similarities of the input sequence samples during training and

Coarse graining (CG) enables the investigation of molecular properties for
larger systems and at longer timescales than the ones attainable at the
atomistic resolution. Machine learning techniques have been recently proposed
to learn CG particle interactions, i.e. develop CG force fields. Graph
representations of molecules and supervised training of a graph convolutional
neural network architecture are used to learn the potential of mean force
through a force matching scheme. In this work, the force acting on each CG
particle is correlated to a learned representation of its local environment
that goes under the name of SchNet, constructed via continuous filter
convolutions. We explore the application of SchNet models to obtain a CG
potential for liquid benzene, investigating the effect of model architecture
and hyperparameters on the thermodynamic, dynamical, and structural properties
of the simulated CG systems, reporting and discussing challenges encountered
and future directions envisioned.

Due to the wide range of timescales that are present in macromolecular
systems, hierarchical multiscale strategies are necessary for their
computational study. Coarse-graining (CG) allows to establish a link between
different system resolutions and provides the backbone for the development of
robust multiscale simulations and analyses. The CG mapping process is typically
system- and application-specific, and it relies on chemical intuition. In this
work, we explored the application of a Machine Learning strategy, based on
Variational Autoencoders, for the development of suitable mapping schemes from
the atomistic to the coarse-grained space of molecules with increasing chemical
complexity. An extensive evaluation of the effect of the model hyperparameters
on the training process and on the final output was performed, and an existing
method was extended with the definition of different loss functions and the
implementation of a selection criterion that ensures physical consistency of
the output. The relationship between the input feature choice and the
reconstruction accuracy was analyzed, supporting the need to introduce
rotational invariance into the system. Strengths and limitations of the
approach, both in the mapping and in the backmapping steps, are highlighted and
critically discussed.

Language models are pre-trained using large corpora of generic data like book
corpus, common crawl and Wikipedia, which is essential for the model to
understand the linguistic characteristics of the language. New studies suggest
using Domain Adaptive Pre-training (DAPT) and Task-Adaptive Pre-training (TAPT)
as an intermediate step before the final finetuning task. This step helps cover
the target domain vocabulary and improves the model performance on the
downstream task. In this work, we study the impact of training only the
embedding layer on the model's performance during TAPT and task-specific
finetuning. Based on our study, we propose a simple approach to make the
intermediate step of TAPT for BERT-based models more efficient by performing
selective pre-training of BERT layers. We show that training only the BERT
embedding layer during TAPT is sufficient to adapt to the vocabulary of the
target domain and achieve comparable performance. Our approach is
computationally efficient, with 78\% fewer parameters trained during TAPT. The
proposed embedding layer finetuning approach can also be an efficient domain
adaptation technique.

Learning to manipulate 3D objects in an interactive environment has been a
challenging problem in Reinforcement Learning (RL). In particular, it is hard
to train a policy that can generalize over objects with different semantic
categories, diverse shape geometry and versatile functionality. Recently, the
technique of visual affordance has shown great prospects in providing
object-centric information priors with effective actionable semantics. As such,
an effective policy can be trained to open a door by knowing how to exert force
on the handle. However, to learn the affordance, it often requires
human-defined action primitives, which limits the range of applicable tasks. In
this study, we take advantage of visual affordance by using the contact
information generated during the RL training process to predict contact maps of
interest. Such contact prediction process then leads to an end-to-end
affordance learning framework that can generalize over different types of
manipulation tasks. Surprisingly, the effectiveness of such framework holds
even under the multi-stage and the multi-agent scenarios. We tested our method
on eight types of manipulation tasks. Results showed that our methods
outperform baseline algorithms, including visual-based affordance methods and
RL methods, by a large margin on the success rate. The demonstration can be
found at https://sites.google.com/view/rlafford/.

Among various sensors for assisted and autonomous driving systems, automotive
radar has been considered as a robust and low-cost solution even in adverse
weather or lighting conditions. With the recent development of radar
technologies and open-sourced annotated data sets, semantic segmentation with
radar signals has become very promising. However, existing methods are either
computationally expensive or discard significant amounts of valuable
information from raw 3D radar signals by reducing them to 2D planes via
averaging. In this work, we introduce ERASE-Net, an Efficient RAdar
SEgmentation Network to segment the raw radar signals semantically. The core of
our approach is the novel detect-then-segment method for raw radar signals. It
first detects the center point of each object, then extracts a compact radar
signal representation, and finally performs semantic segmentation. We show that
our method can achieve superior performance on radar semantic segmentation task
compared to the state-of-the-art (SOTA) technique. Furthermore, our approach
requires up to 20x less computational resources. Finally, we show that the
proposed ERASE-Net can be compressed by 40% without significant loss in
performance, significantly more than the SOTA network, which makes it a more
promising candidate for practical automotive applications.

With recent advancements in artificial intelligence, its applications can be
seen in every aspect of humans' daily life. From voice assistants to mobile
healthcare and autonomous driving, we rely on the performance of AI methods for
many critical tasks; therefore, it is essential to assert the performance of
models in proper means to prevent damage. One of the shortfalls of AI models in
general, and deep machine learning in particular, is a drop in performance when
faced with shifts in the distribution of data. Nonetheless, these shifts are
always expected in real-world applications; thus, a field of study has emerged,
focusing on detecting out-of-distribution data subsets and enabling a more
comprehensive generalization. Furthermore, as many deep learning based models
have achieved near-perfect results on benchmark datasets, the need to evaluate
these models' reliability and trustworthiness for pushing towards real-world
applications is felt more strongly than ever. This has given rise to a growing
number of studies in the field of out-of-distribution detection and domain
generalization, which begs the need for surveys that compare these studies from
various perspectives and highlight their straightens and weaknesses. This paper
presents a survey that, in addition to reviewing more than 70 papers in this
field, presents challenges and directions for future works and offers a
unifying look into various types of data shifts and solutions for better

We provide practical and powerful schemes for learning many properties of an
unknown n-qubit quantum state using a sparing number of copies of the state.
Specifically, we present a depth-modulated randomized measurement scheme that
interpolates between two known classical shadows schemes based on random Pauli
measurements and random Clifford measurements. These can be seen within our
scheme as the special cases of zero and infinite depth, respectively. We focus
on the regime where depth scales logarithmically in n and provide evidence that
this retains the desirable properties of both extremal schemes whilst, in
contrast to the random Clifford scheme, also being experimentally feasible. We
present methods for two key tasks; estimating expectation values of certain
observables from generated classical shadows and, computing upper bounds on the
depth-modulated shadow norm, thus providing rigorous guarantees on the accuracy
of the output estimates. We consider observables that can be written as a
linear combination of poly(n) Paulis and observables that can be written as a
low bond dimension matrix product operator. For the former class of observables
both tasks are solved efficiently in n. For the latter class, we do not
guarantee efficiency but present a method that works in practice; by
variationally computing a heralded approximate inverses of a tensor network
that can then be used for efficiently executing both these tasks.

In the sixth-generation (6G) cellular networks, hybrid beamforming would be a
real-time optimization problem that is becoming progressively more challenging.
Although numerical computation-based iterative methods such as the minimal mean
square error (MMSE) and the alternative manifold-optimization (Alt-Min) can
already attain near-optimal performance, their computational cost renders them
unsuitable for real-time applications. However, recent studies have
demonstrated that machine learning techniques like deep neural networks (DNN)
can learn the mapping done by those algorithms between channel state
information (CSI) and near-optimal resource allocation, and then approximate
this mapping in near real-time. In light of this, we investigate various DNN
architectures for beamforming challenges in the terahertz (THz) band for
ultra-massive multiple-input multiple-output (UM-MIMO) and explore their
contextual mathematical modeling. Specifically, we design a sophisticated 1D
convolutional neural network and long short-term memory (1D CNN-LSTM) based
fusion-separation scheme, which can approach the performance of the Alt-Min
algorithm in terms of spectral efficiency (SE) and, at the same time, use
significantly less computational effort. Simulation results indicate that the
proposed system can attain almost the same level of SE as that of the numerical
iterative algorithms, while incurring a substantial reduction in computational
cost. Our DNN-based approach also exhibits exceptional adaptability to diverse
network setups and high scalability. Although the current model only addresses
the fully connected hybrid architecture, our approach can also be expanded to
address a variety of other network topologies.
  INDEX TERMS 6G, CNN, Hybrid Beamforming, LSTM, UM-MIMO

We explore a data-driven approach for learning to optimize neural networks.
We construct a dataset of neural network checkpoints and train a generative
model on the parameters. In particular, our model is a conditional diffusion
transformer that, given an initial input parameter vector and a prompted loss,
error, or return, predicts the distribution over parameter updates that achieve
the desired metric. At test time, it can optimize neural networks with unseen
parameters for downstream tasks in just one update. We find that our approach
successfully generates parameters for a wide range of loss prompts. Moreover,
it can sample multimodal parameter solutions and has favorable scaling
properties. We apply our method to different neural network architectures and
tasks in supervised and reinforcement learning.

Collaborative table-carrying is a complex task due to the continuous nature
of the action and state-spaces, multimodality of strategies, existence of
obstacles in the environment, and the need for instantaneous adaptation to
other agents. In this work, we present a method for predicting realistic motion
plans for cooperative human-robot teams on a table-carrying task. Using a
Variational Recurrent Neural Network, VRNN, to model the variation in the
trajectory of a human-robot team over time, we are able to capture the
distribution over the team's future states while leveraging information from
interaction history. The key to our approach is in our model's ability to
leverage human demonstration data and generate trajectories that synergize well
with humans during test time. We show that the model generates more human-like
motion compared to a baseline, centralized sampling-based planner,
Rapidly-exploring Random Trees (RRT). Furthermore, we evaluate the VRNN planner
with a human partner and show its ability to both generate more human-like
paths and achieve higher task success rate than RRT can while planning with a
human. Finally, we demonstrate that a LoCoBot using the VRNN planner can
complete the task successfully with a human controlling another LoCoBot.

Topological invariants of a dataset, such as the number of holes that survive
from one length scale to another (persistent Betti numbers) can be used to
analyse and classify data in machine learning applications. We present an
improved quantum algorithm for computing persistent Betti numbers, and provide
an end-to-end complexity analysis. Our approach provides large polynomial time
improvements, and an exponential space saving, over existing quantum
algorithms. Subject to gap dependencies, our algorithm obtains an almost
quintic speedup in the number of datapoints over rigorous state-of-the-art
classical algorithms for calculating the persistent Betti numbers to constant
additive error - the salient task for applications. However, this may be
reduced to closer to quadratic when compared against heuristic classical
methods and observed scalings. We discuss whether quantum algorithms can
achieve an exponential speedup for tasks of practical interest, as claimed
previously. We conclude that there is currently no evidence that this is the
case.

Variance reduction techniques are of crucial importance for the efficiency of
Monte Carlo simulations in finance applications. We propose the use of neural
SDEs, with control variates parameterized by neural networks, in order to learn
approximately optimal control variates and hence reduce variance as
trajectories of the SDEs are being simulated. We consider SDEs driven by
Brownian motion and, more generally, by L\'{e}vy processes including those with
infinite activity. For the latter case, we prove optimality conditions for the
variance reduction. Several numerical examples from option pricing are

Recently, Daniely and Granot [arXiv:1910.05697] introduced a new notion of
complexity called Approximate Description Length (ADL). They used it to derive
novel generalization bounds for neural networks, that despite substantial work,
were out of reach for more classical techniques such as discretization,
Covering Numbers and Rademacher Complexity. In this paper we explore how ADL
relates to classical notions of function complexity such as Covering Numbers
and VC Dimension. We find that for functions whose range is the reals, ADL is
essentially equivalent to these classical complexity measures. However, this
equivalence breaks for functions with high dimensional range.

Training deep reinforcement learning (DRL) locomotion policies often requires
massive amounts of data to converge to the desired behavior. In this regard,
simulators provide a cheap and abundant source. For successful sim-to-real
transfer, exhaustively engineered approaches such as system identification,
dynamics randomization, and domain adaptation are generally employed. As an
alternative, we investigate a simple strategy of random force injection (RFI)
to perturb system dynamics during training. We show that the application of
random forces enables us to emulate dynamics randomization.This allows us to
obtain locomotion policies that are robust to variations in system dynamics. We
further extend RFI, referred to as extended random force injection (ERFI), by
introducing an episodic actuation offset. We demonstrate that ERFI provides
additional robustness for variations in system mass offering on average a 61%
improved performance over RFI. We also show that ERFI is sufficient to perform
a successful sim-to-real transfer on two different quadrupedal platforms,
ANYmal C and Unitree A1, even for perceptive locomotion over uneven terrain in
outdoor environments.

Operator networks have emerged as promising deep learning tools for
approximating the solution to partial differential equations (PDEs). These
networks map input functions that describe material properties, forcing
functions and boundary data to the solution of a PDE. This work describes a new
architecture for operator networks that mimics the form of the numerical
solution obtained from an approximation of the variational or weak formulation
of the problem. The application of these ideas to a generic elliptic PDE leads
to a variationally mimetic operator network (VarMiON). Like the conventional
Deep Operator Network (DeepONet) the VarMiON is also composed of a sub-network
that constructs the basis functions for the output and another that constructs
the coefficients for these basis functions. However, in contrast to the
DeepONet, in the VarMiON the architecture of these networks is precisely
determined. An analysis of the error in the VarMiON solution reveals that it
contains contributions from the error in the training data, the training error,
quadrature error in sampling input and output functions, and a "covering error"
that measures the distance between the test input functions and the nearest
functions in the training dataset. It also depends on the stability constants
for the exact network and its VarMiON approximation. The application of the
VarMiON to a canonical elliptic PDE reveals that for approximately the same
number of network parameters, on average the VarMiON incurs smaller errors than
a standard DeepONet. Further, its performance is more robust to variations in
input functions, the techniques used to sample the input and output functions,
the techniques used to construct the basis functions, and the number of input
functions.

Imitation learning (IL) is a general learning paradigm for tackling
sequential decision-making problems. Interactive imitation learning, where
learners can interactively query for expert demonstrations, has been shown to
achieve provably superior sample efficiency guarantees compared with its
offline counterpart or reinforcement learning. In this work, we study
classification-based online imitation learning (abbrev. $\textbf{COIL}$) and
the fundamental feasibility to design oracle-efficient regret-minimization
algorithms in this setting, with a focus on the general nonrealizable case. We
make the following contributions: (1) we show that in the $\textbf{COIL}$
problem, any proper online learning algorithm cannot guarantee a sublinear
regret in general; (2) we propose $\textbf{Logger}$, an improper online
learning algorithmic framework, that reduces $\textbf{COIL}$ to online linear
optimization, by utilizing a new definition of mixed policy class; (3) we
design two oracle-efficient algorithms within the $\textbf{Logger}$ framework
that enjoy different sample and interaction round complexity tradeoffs, and
conduct finite-sample analyses to show their improvements over naive behavior
cloning; (4) we show that under the standard complexity-theoretic assumptions,
efficient dynamic regret minimization is infeasible in the $\textbf{Logger}$
framework. Our work puts classification-based online imitation learning, an
important IL setup, into a firmer foundation.

This thesis investigates share buybacks, specifically share buyback
announcements. It addresses how to recognize such announcements, the excess
return of share buybacks, and the prediction of returns after a share buyback
announcement. We illustrate two NLP approaches for the automated detection of
share buyback announcements. Even with very small amounts of training data, we
can achieve an accuracy of up to 90%. This thesis utilizes these NLP methods to
generate a large dataset consisting of 57,155 share buyback announcements. By
analyzing this dataset, this thesis aims to show that most companies, which
have a share buyback announced are underperforming the MSCI World. A minority
of companies, however, significantly outperform the MSCI World. This
significant overperformance leads to a net gain when looking at the averages of
all companies. If the benchmark index is adjusted for the respective size of
the companies, the average overperformance disappears, and the majority
underperforms even greater. However, it was found that companies that announce
a share buyback with a volume of at least 1% of their market cap, deliver, on
average, a significant overperformance, even when using an adjusted benchmark.
It was also found that companies that announce share buybacks in times of
crisis emerge better than the overall market. Additionally, the generated
dataset was used to train 72 machine learning models. Through this, it was able
to find many strategies that could achieve an accuracy of up to 77% and
generate great excess returns. A variety of performance indicators could be
improved across six different time frames and a significant overperformance was
identified. This was achieved by training several models for different tasks
and time frames as well as combining these different models, generating
significant improvement by fusing weak learners, in order to create one strong
learner.

In addition to being familiar with policies, high investment returns also
require extensive knowledge of relevant industry knowledge and news. In
addition, it is necessary to leverage relevant theories for investment to make
decisions, thereby amplifying investment returns. A effective investment return
estimate can feedback the future rate of return of investment behavior. In
recent years, deep learning are developing rapidly, and investment return
prediction based on deep learning has become an emerging research topic. This
paper proposes an embedding-based dual branch approach to predict an
investment's return. This approach leverages embedding to encode the investment
id into a low-dimensional dense vector, thereby mapping high-dimensional data
to a low-dimensional manifold, so that highdimensional features can be
represented competitively. In addition, the dual branch model realizes the
decoupling of features by separately encoding different information in the two
branches. In addition, the swish activation function further improves the model
performance. Our approach are validated on the Ubiquant Market Prediction
dataset. The results demonstrate the superiority of our approach compared to
Xgboost, Lightgbm and Catboost.

Detect-and-Avoid (DAA) capabilities are critical for safe operations of
unmanned aircraft systems (UAS). This paper introduces, AirTrack, a real-time
vision-only detect and tracking framework that respects the size, weight, and
power (SWaP) constraints of sUAS systems. Given the low Signal-to-Noise ratios
(SNR) of far away aircraft, we propose using full resolution images in a deep
learning framework that aligns successive images to remove ego-motion. The
aligned images are then used downstream in cascaded primary and secondary
classifiers to improve detection and tracking performance on multiple metrics.
We show that AirTrack outperforms state-of-the art baselines on the Amazon
Airborne Object Tracking (AOT) Dataset. Multiple real world flight tests with a
Cessna 172 interacting with general aviation traffic and additional
near-collision flight tests with a Bell helicopter flying towards a UAS in a
controlled setting showcase that the proposed approach satisfies the newly
introduced ASTM F3442/F3442M standard for DAA. Empirical evaluations show that
our system has a probability of track of more than 95% up to a range of 700m.
Video available at https://youtu.be/H3lL_Wjxjpw .

Community detection algorithms are in general evaluated by comparing
evaluation metric values for the communities obtained with different
algorithms. The evaluation metrics that are used for measuring quality of the
communities incorporate the topological information of entities like
connectivity of the nodes within or outside the communities. However, while
comparing the metric values it loses direct involvement of topological
information of the communities in the comparison process. In this paper, a
direct comparison approach is proposed where topological information of the
communities obtained with two algorithms are compared directly. A quality
measure namely \emph{Topological Variance (TV)} is designed based on direct
comparison of topological information of the communities. Considering the newly
designed quality measure, two ranking schemes are developed. The efficacy of
proposed quality metric as well as the ranking scheme is studied with eight
widely used real-world datasets and six community detection algorithms.

Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning
compact yet highly accurate binary neural networks simply by pruning and
quantizing randomly weighted full precision neural networks. However, the
accuracy of these multi-prize tickets (MPTs) is highly sensitive to the optimal
prune ratio, which limits their applicability. Furthermore, the original
implementation did not attain any training or inference speed benefits. In this
report, we discuss several improvements to overcome these limitations. We show
the benefit of the proposed techniques by performing experiments on CIFAR-10.

Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD)
have grown central to a wide range of applications, including hypothesis
testing, sampler selection, distribution approximation, and variational
inference. In each setting, these kernel-based discrepancy measures are
required to (i) separate a target P from other probability measures or even
(ii) control weak convergence to P. In this article we derive new sufficient
and necessary conditions to ensure (i) and (ii). For MMDs on separable metric
spaces, we characterize those kernels that separate Bochner embeddable measures
and introduce simple conditions for separating all measures with unbounded
kernels and for controlling convergence with bounded kernels. We use these
results on $\mathbb{R}^d$ to substantially broaden the known conditions for KSD
separation and convergence control and to develop the first KSDs known to
exactly metrize weak convergence to P. Along the way, we highlight the
implications of our results for hypothesis testing, measuring and improving
sample quality, and sampling with Stein variational gradient descent.

When dealing with clinical text classification on a small dataset recent
studies have confirmed that a well-tuned multilayer perceptron outperforms
other generative classifiers, including deep learning ones. To increase the
performance of the neural network classifier, feature selection for the
learning representation can effectively be used. However, most feature
selection methods only estimate the degree of linear dependency between
variables and select the best features based on univariate statistical tests.
Furthermore, the sparsity of the feature space involved in the learning
representation is ignored. Goal: Our aim is therefore to access an alternative
approach to tackle the sparsity by compressing the clinical representation
feature space, where limited French clinical notes can also be dealt with
effectively. Methods: This study proposed an autoencoder learning algorithm to
take advantage of sparsity reduction in clinical note representation. The
motivation was to determine how to compress sparse, high-dimensional data by
reducing the dimension of the clinical note representation feature space. The
classification performance of the classifiers was then evaluated in the trained
and compressed feature space. Results: The proposed approach provided overall
performance gains of up to 3% for each evaluation. Finally, the classifier
achieved a 92% accuracy, 91% recall, 91% precision, and 91% f1-score in
detecting the patient's condition. Furthermore, the compression working
mechanism and the autoencoder prediction process were demonstrated by applying
the theoretic information bottleneck framework.

The common approach for local navigation on challenging environments with
legged robots requires path planning, path following and locomotion, which
usually requires a locomotion control policy that accurately tracks a commanded
velocity. However, by breaking down the navigation problem into these
sub-tasks, we limit the robot's capabilities since the individual tasks do not
consider the full solution space. In this work, we propose to solve the
complete problem by training an end-to-end policy with deep reinforcement
learning. Instead of continuously tracking a precomputed path, the robot needs
to reach a target position within a provided time. The task's success is only
evaluated at the end of an episode, meaning that the policy does not need to
reach the target as fast as possible. It is free to select its path and the
locomotion gait. Training a policy in this way opens up a larger set of
possible solutions, which allows the robot to learn more complex behaviors. We
compare our approach to velocity tracking and additionally show that the time
dependence of the task reward is critical to successfully learn these new
behaviors. Finally, we demonstrate the successful deployment of policies on a
real quadrupedal robot. The robot is able to cross challenging terrains, which
were not possible previously, while using a more energy-efficient gait and
achieving a higher success rate.

Large-scale sound recognition data sets typically consist of acoustic
recordings obtained from multimedia libraries. As a consequence, modalities
other than audio can often be exploited to improve the outputs of models
designed for associated tasks. Frequently, however, not all contents are
available for all samples of such a collection: For example, the original
material may have been removed from the source platform at some point, and
therefore, non-auditory features can no longer be acquired.
  We demonstrate that a multi-encoder framework can be employed to deal with
this issue by applying this method to attention-based deep learning systems,
which are currently part of the state of the art in the domain of sound
recognition. More specifically, we show that the proposed model extension can
successfully be utilized to incorporate partially available visual information
into the operational procedures of such networks, which normally only use
auditory features during training and inference. Experimentally, we verify that
the considered approach leads to improved predictions in a number of evaluation
scenarios pertaining to audio tagging and sound event detection. Additionally,
we scrutinize some properties and limitations of the presented technique.

Complex wounds usually face partial or total loss of skin thickness, healing
by secondary intention. They can be acute or chronic, figuring infections,
ischemia and tissue necrosis, and association with systemic diseases. Research
institutes around the globe report countless cases, ending up in a severe
public health problem, for they involve human resources (e.g., physicians and
health care professionals) and negatively impact life quality. This paper
presents a new database for automatically categorizing complex wounds with five
categories, i.e., non-wound area, granulation, fibrinoid tissue, and dry
necrosis, hematoma. The images comprise different scenarios with complex wounds
caused by pressure, vascular ulcers, diabetes, burn, and complications after
surgical interventions. The dataset, called ComplexWoundDB, is unique because
it figures pixel-level classifications from $27$ images obtained in the wild,
i.e., images are collected at the patients' homes, labeled by four health
professionals. Further experiments with distinct machine learning techniques
evidence the challenges in addressing the problem of computer-aided complex
wound tissue categorization. The manuscript sheds light on future directions in
the area, with a detailed comparison among other databased widely used in the

This letter considers the problem of end-to-end learning for joint
optimization of transmitter precoding and receiver processing for mmWave
downlink positioning. Considering a multiple-input single-output (MISO)
scenario, we propose a novel autoencoder (AE) architecture to estimate
user-equipment(UE) position with multiple base-stations (BSs) and demonstrate
that end-to-end learning can match model-based design, both for angle of
departure (AoD) and position estimation, under ideal conditions without model
deficits and outperform it in the presence of hardware impairments.

This paper focuses on enhancing the captions generated by image-caption
generation systems. We propose an approach for improving caption generation
systems by choosing the most closely related output to the image rather than
the most likely output produced by the model. Our model revises the language
generation output beam search from a visual context perspective. We employ a
visual semantic measure in a word and sentence level manner to match the proper
caption to the related information in the image. The proposed approach can be
applied to any caption system as a post-processing based method.

Outlier detection tasks have been playing a critical role in AI safety. There
has been a great challenge to deal with this task. Observations show that deep
neural network classifiers usually tend to incorrectly classify
out-of-distribution (OOD) inputs into in-distribution classes with high
confidence. Existing works attempt to solve the problem by explicitly imposing
uncertainty on classifiers when OOD inputs are exposed to the classifier during
training. In this paper, we propose an alternative probabilistic paradigm that
is both practically useful and theoretically viable for the OOD detection
tasks. Particularly, we impose statistical independence between inlier and
outlier data during training, in order to ensure that inlier data reveals
little information about OOD data to the deep estimator during training.
Specifically, we estimate the statistical dependence between inlier and outlier
data through the Hilbert-Schmidt Independence Criterion (HSIC), and we penalize
such metric during training. We also associate our approach with a novel
statistical test during the inference time coupled with our principled
motivation. Empirical results show that our method is effective and robust for
OOD detection on various benchmarks. In comparison to SOTA models, our approach
achieves significant improvement regarding FPR95, AUROC, and AUPR metrics. Code
is available: \url{https://github.com/jylins/hood}.

When training a variational autoencoder (VAE) on a given dataset, determining
the optimal number of latent variables is mostly done by grid search: a costly
process in terms of computational time and carbon footprint. In this paper, we
explore the intrinsic dimension estimation (IDE) of the data and latent
representations learned by VAEs. We show that the discrepancies between the IDE
of the mean and sampled representations of a VAE after only a few steps of
training reveal the presence of passive variables in the latent space, which,
in well-behaved VAEs, indicates a superfluous number of dimensions. Using this
property, we propose FONDUE: an algorithm which quickly finds the number of
latent dimensions after which the mean and sampled representations start to
diverge (i.e., when passive variables are introduced), providing a principled
method for selecting the number of latent dimensions for VAEs and autoencoders.

We use a particular machine learning approach, called the genetic algorithms
(GA), in order to place constraints on deviations from general relativity (GR)
via a possible evolution of Newton's constant $\mu\equiv
G_\mathrm{eff}/G_\mathrm{N}$ and of the dark energy anisotropic stress $\eta$,
both defined to be equal to one in GR. Specifically, we use a plethora of
background and linear-order perturbations data, such as type Ia supernovae,
baryon acoustic oscillations, cosmic chronometers, redshift space distortions
and $E_g$ data. We find that although the GA is affected by the lower quality
of the currently available data, especially from the $E_g$ data, the
reconstruction of Newton's constant is consistent with a constant value within
the errors. On the other hand, the anisotropic stress deviates strongly from
unity due to the sparsity and the systematics of the $E_g$ data. Finally, we
also create synthetic data based on a next-generation survey and forecast the
limits of any possible detection of deviations from GR. In particular, we use
two fiducial models: one based on the cosmological constant $\Lambda$CDM model
and another on a model with an evolving Newton's constant, dubbed $\mu$CDM. We
find that the GA reconstructions of $\mu(z)$ and $\eta(z)$ can be constrained
to within a few percent of the fiducial models and in the case of the $\mu$CDM
mocks, they can also provide a strong detection of several $\sigma$s, thus
demonstrating the utility of the GA reconstruction approach.

Successful material selection is critical in designing and manufacturing
products for design automation. Designers leverage their knowledge and
experience to create high-quality designs by selecting the most appropriate
materials through performance, manufacturability, and sustainability
evaluation. Intelligent tools can help designers with varying expertise by
providing recommendations learned from prior designs. To enable this, we
introduce a graph representation learning framework that supports the material
prediction of bodies in assemblies. We formulate the material selection task as
a node-level prediction task over the assembly graph representation of CAD
models and tackle it using Graph Neural Networks (GNNs). Evaluations over three
experimental protocols performed on the Fusion 360 Gallery dataset indicate the
feasibility of our approach, achieving a 0.75 top-3 micro-f1 score. The
proposed framework can scale to large datasets and incorporate designers'
knowledge into the learning process. These capabilities allow the framework to
serve as a recommendation system for design automation and a baseline for
future work, narrowing the gap between human designers and intelligent design
agents.

Machine learning has blossomed in recent decades and has become essential in
many fields. It significantly solved some problems for particle physics --
particle reconstruction, event classification, etc. However, it is now time to
break the limitation of conventional machine learning with quantum computing. A
support-vector machine algorithm with a quantum kernel estimator (QSVM-Kernel)
leverages high-dimensional quantum state space to identify a signal from
backgrounds. In this study, we have employed this quantum machine learning
algorithm to study the $e^{+}e^{-} \rightarrow ZH$ process at the Circular
Electron-Positron Collider (CEPC), a Higgs factory proposed by Chinese
scientists. Using 6-qubits on quantum computer simulators, we have optimized
the QSVM-Kernel algorithm and obtained a similar classification performance to
the classical support-vector machine algorithm. We have also validated the
QSVM-Kernel algorithm using 6-qubits on quantum computer hardware from both IBM
and Origin Quantum (a quantum technology company in China), where the
classification performance is approaching noiseless quantum computer
simulators. Our study is one of the few examples that apply state-of-the-art
quantum computing technologies to particle physics, a branch of fundamental
science that relies on big experimental data.

Language models (LMs) trained on raw texts have no direct access to the
physical world. Gordon and Van Durme (2013) point out that LMs can thus suffer
from reporting bias: texts rarely report on common facts, instead focusing on
the unusual aspects of a situation. If LMs are only trained on text corpora and
naively memorise local co-occurrence statistics, they thus naturally would
learn a biased view of the physical world. While prior studies have repeatedly
verified that LMs of smaller scales (e.g., RoBERTa, GPT-2) amplify reporting
bias, it remains unknown whether such trends continue when models are scaled
up. We investigate reporting bias from the perspective of colour in larger
language models (LLMs) such as PaLM and GPT-3. Specifically, we query LLMs for
the typical colour of objects, which is one simple type of perceptually
grounded physical common sense. Surprisingly, we find that LLMs significantly
outperform smaller LMs in determining an object's typical colour and more
closely track human judgments, instead of overfitting to surface patterns
stored in texts. This suggests that very large models of language alone are
able to overcome certain types of reporting bias that are characterized by
local co-occurrences.

Generative flow networks (GFlowNets) are a family of algorithms for training
a sequential sampler of discrete objects under an unnormalized target density
and have been successfully used for various probabilistic modeling tasks.
Existing training objectives for GFlowNets are either local to states or
transitions, or propagate a reward signal over an entire sampling trajectory.
We argue that these alternatives represent opposite ends of a gradient
bias-variance tradeoff and propose a way to exploit this tradeoff to mitigate
its harmful effects. Inspired by the TD($\lambda$) algorithm in reinforcement
learning, we introduce subtrajectory balance or SubTB($\lambda$), a GFlowNet
training objective that can learn from partial action subsequences of varying
lengths. We show that SubTB($\lambda$) accelerates sampler convergence in
previously studied and new environments and enables training GFlowNets in
environments with longer action sequences and sparser reward landscapes than
what was possible before. We also perform a comparative analysis of stochastic
gradient dynamics, shedding light on the bias-variance tradeoff in GFlowNet
training and the advantages of subtrajectory balance.

We introduce a new visual-interactive tool: Explainable Labeling Assistant
(XLabel) that takes an explainable machine learning approach to data labeling.
The main component of XLabel is the Explainable Boosting Machine (EBM), a
predictive model that can calculate the contribution of each input feature
towards the final prediction. As a case study, we use XLabel to predict the
labels of four non-communicable diseases (NCDs): diabetes, hypertension,
chronic kidney disease, and dyslipidemia. We demonstrate that EBM is an
excellent choice of predictive model by comparing it against a rule-based and
four other machine learning models. By performing 5-fold cross-validation on
427 medical records, EBM's prediction accuracy, precision, and F1-score are
greater than 0.95 in all four NCDs. It performed as well as two black-box
models and outperformed the other models in these metrics. In an additional
experiment, when 40% of the records were intentionally mislabeled, EBM could
recall the correct labels of more than 90% of these records.

Food is essential to human survival. So much so that we have developed
different recipes to suit our taste needs. In this work, we propose a novel way
of creating new, fine-dining recipes from scratch using Transformers,
specifically auto-regressive language models. Given a small dataset of food
recipes, we try to train models to identify cooking techniques, propose novel
recipes, and test the power of fine-tuning with minimal data.

We investigate the resolution of second-order, potential, and monotone mean
field games with the generalized conditional gradient algorithm, an extension
of the Frank-Wolfe algorithm. We show that the method is equivalent to the
fictitious play method. We establish rates of convergence for the optimality
gap, the exploitability, and the distances of the variables to the unique
solution of the mean field game, for various choices of stepsizes. In
particular, we show that linear convergence can be achieved when the stepsizes
are computed by linesearch.

Hamiltonian Monte Carlo (HMC) is a Markov chain algorithm for sampling from a
high-dimensional distribution with density $e^{-f(x)}$, given access to the
gradient of $f$. A particular case of interest is that of a $d$-dimensional
Gaussian distribution with covariance matrix $\Sigma$, in which case $f(x) =
x^\top \Sigma^{-1} x$. We show that HMC can sample from a distribution that is
$\varepsilon$-close in total variation distance using
$\widetilde{O}(\sqrt{\kappa} d^{1/4} \log(1/\varepsilon))$ gradient queries,
where $\kappa$ is the condition number of $\Sigma$. Our algorithm uses long and
random integration times for the Hamiltonian dynamics. This contrasts with (and
was motivated by) recent results that give an $\widetilde\Omega(\kappa
d^{1/2})$ query lower bound for HMC with fixed integration times, even for the
Gaussian case.

3D point clouds have attracted increasing attention in architecture,
engineering, and construction due to their high-quality object representation
and efficient acquisition methods. Consequently, many point cloud feature
detection methods have been proposed in the literature to automate some
workflows, such as their classification or part segmentation. Nevertheless, the
performance of point cloud automated systems significantly lags behind their
image counterparts. While part of this failure stems from the irregularity,
unstructuredness, and disorder of point clouds, which makes the task of point
cloud feature detection significantly more challenging than the image one, we
argue that a lack of inspiration from the image domain might be the primary
cause of such a gap. Indeed, given the overwhelming success of Convolutional
Neural Networks (CNNs) in image feature detection, it seems reasonable to
design their point cloud counterparts, but none of the proposed approaches
closely resembles them. Specifically, even though many approaches generalise
the convolution operation in point clouds, they fail to emulate the CNNs
multiple-feature detection and pooling operations. For this reason, we propose
a graph convolution-based unit, dubbed Shrinking unit, that can be stacked
vertically and horizontally for the design of CNN-like 3D point cloud feature
extractors. Given that self, local and global correlations between points in a
point cloud convey crucial spatial geometric information, we also leverage them
during the feature extraction process. We evaluate our proposal by designing a
feature extractor model for the ModelNet-10 benchmark dataset and achieve
90.64% classification accuracy, demonstrating that our innovative idea is
effective. Our code is available at github.com/albertotamajo/Shrinking-unit.

This paper proposes DisCo, an automatic deep learning compilation module for
data-parallel distributed training. Unlike most deep learning compilers that
focus on training or inference on a single device, DisCo optimizes a DNN model
for distributed training over multiple GPU machines. Existing single-device
compilation strategies do not work well in distributed training, due mainly to
communication inefficiency that they incur. DisCo generates optimized, joint
computation operator and communication tensor fusion strategies to enable
highly efficient distributed training. A GNN-based simulator is built to
effectively estimate per-iteration training time achieved by operator/tensor
fusion candidates. A backtracking search algorithm is driven by the simulator,
navigating efficiently in the large strategy space to identify good
operator/tensor fusion strategies that minimize distributed training time. We
compare DisCo with existing DL fusion schemes and show that it achieves good
training speed-up close to the ideal, full computation-communication overlap
case.

Self-supervised learning (SSL) speech models have achieved unprecedented
success in speech representation learning, but some questions regarding their
representation ability remain unanswered. This paper addresses two of them: (1)
Can SSL speech models deal with non-speech audio?; (2) Would different SSL
speech models have insights into diverse aspects of audio features? To answer
the two questions, we conduct extensive experiments on abundant speech and
non-speech audio datasets to evaluate the representation ability of currently
state-of-the-art SSL speech models, which are wav2vec 2.0 and HuBERT in this
paper. These experiments are carried out during NeurIPS 2021 HEAR Challenge as
a standard evaluation pipeline provided by competition officials. Results show
that (1) SSL speech models could extract meaningful features of a wide range of
non-speech audio, while they may also fail on certain types of datasets; (2)
different SSL speech models have insights into different aspects of audio
features. The two conclusions provide a foundation for the ensemble of
representation models. We further propose an ensemble framework to fuse speech
representation models' embeddings. Our framework outperforms state-of-the-art
SSL speech/audio models and has generally superior performance on abundant
datasets compared with other teams in HEAR Challenge. Our code is available at
https://github.com/tony10101105/HEAR-2021-NeurIPS-Challenge -- NTU-GURA.

We present EasyRec, an easy-to-use, extendable and efficient recommendation
framework for building industrial recommendation systems. Our EasyRec framework
is superior in the following aspects: first, EasyRec adopts a modular and
pluggable design pattern to reduce the efforts to build custom models; second,
EasyRec implements hyper-parameter optimization and feature selection
algorithms to improve model performance automatically; third, EasyRec applies
online learning to fast adapt to the ever-changing data distribution. The code
is released: https://github.com/alibaba/EasyRec.

In a grid with a significant share of renewable generation, operators will
need additional tools to evaluate the operational risk due to the increased
volatility in load and generation. The computational requirements of the
forward uncertainty propagation problem, which must solve numerous
security-constrained economic dispatch (SCED) optimizations, is a major barrier
for such real-time risk assessment. This paper proposes a Just-In-Time Risk
Assessment Learning Framework (JITRALF) as an alternative. JITRALF trains risk
surrogates, one for each hour in the day, using Machine Learning (ML) to
predict the quantities needed to estimate risk, without explicitly solving the
SCED problem. This significantly reduces the computational burden of the
forward uncertainty propagation and allows for fast, real-time risk estimation.
The paper also proposes a novel, asymmetric loss function and shows that models
trained using the asymmetric loss perform better than those using symmetric
loss functions. JITRALF is evaluated on the French transmission system for
assessing the risk of insufficient operating reserves, the risk of load
shedding, and the expected operating cost.

Teaching an agent to perform new tasks using natural language can easily be
hindered by ambiguities in interpretation. When a teacher provides an
instruction to a learner about an object by referring to its features, the
learner can misunderstand the teacher's intentions, for instance if the
instruction ambiguously refer to features of the object, a phenomenon called
referential ambiguity. We study how two concepts derived from cognitive
sciences can help resolve those referential ambiguities: pedagogy (selecting
the right instructions) and pragmatism (learning the preferences of the other
agents using inductive reasoning). We apply those ideas to a teacher/learner
setup with two artificial agents on a simulated robotic task (block-stacking).
We show that these concepts improve sample efficiency for training the learner.

Existing Score-based Generative Models (SGMs) can be categorized into
constrained SGMs (CSGMs) or unconstrained SGMs (USGMs) according to their
parameterization approaches. CSGMs model the probability density functions as
Boltzmann distributions, and assign their predictions as the negative gradients
of some scalar-valued energy functions. On the other hand, USGMs employ
flexible architectures capable of directly estimating scores without the need
to explicitly model energy functions. In this paper, we demonstrate that the
architectural constraints of CSGMs may limit their score-matching ability. In
addition, we show that USGMs' inability to preserve the property of
conservativeness may lead to serious sampling inefficiency and degraded
sampling performance in practice. To address the above issues, we propose
Quasi-Conservative Score-based Generative Models (QCSGMs) for keeping the
advantages of both CSGMs and USGMs. Our theoretical derivations demonstrate
that the training objective of QCSGMs can be efficiently integrated into the
training processes by leveraging the Hutchinson trace estimator. In addition,
our experimental results on the Cifar-10, Cifar-100, ImageNet, and SVHN
datasets validate the effectiveness of QCSGMs. Finally, we justify the
advantage of QCSGMs using an example of a one-layered autoencoder.

As the methods evolve, inversion is mainly divided into two steps. The first
step is Image Embedding, in which an encoder or optimization process embeds
images to get the corresponding latent codes. Afterward, the second step aims
to refine the inversion and editing results, which we named Result Refinement.
Although the second step significantly improves fidelity, perception and
editability are almost unchanged, deeply dependent on inverse latent codes
attained in the first step. Therefore, a crucial problem is gaining the latent
codes with better perception and editability while retaining the reconstruction
fidelity. In this work, we first point out that these two characteristics are
related to the degree of alignment (or disalignment) of the inverse codes with
the synthetic distribution. Then, we propose Latent Space Alignment Inversion
Paradigm (LSAP), which consists of evaluation metric and solution for this
problem. Specifically, we introduce Normalized Style Space ($\mathcal{S^N}$
space) and $\mathcal{S^N}$ Cosine Distance (SNCD) to measure disalignment of
inversion methods. Since our proposed SNCD is differentiable, it can be
optimized in both encoder-based and optimization-based embedding methods to
conduct a uniform solution. Extensive experiments in various domains
demonstrate that SNCD effectively reflects perception and editability, and our
alignment paradigm archives the state-of-the-art in both two steps. Code is
available on https://github.com/caopulan/GANInverter.

Usage-based insurance is becoming the new standard in vehicle insurance; it
is therefore relevant to find efficient ways of using insureds' driving data.
Applying anomaly detection to vehicles' trip summaries, we develop a method
allowing to derive a "routine" and a "peculiarity" anomaly profile for each
vehicle. To this end, anomaly detection algorithms are used to compute a
routine and a peculiarity anomaly score for each trip a vehicle makes. The
former measures the anomaly degree of the trip compared to the other trips made
by the concerned vehicle, while the latter measures its anomaly degree compared
to trips made by any vehicle. The resulting anomaly scores vectors are used as
routine and peculiarity profiles. Features are then extracted from these
profiles, for which we investigate the predictive power in the claim
classification framework. Using real data, we find that features extracted from
the vehicles' peculiarity profile improve classification.

Even human intelligence system fails to offer 100% accuracy in identifying
speeches from a specific individual. Machine intelligence is trying to mimic
humans in speaker identification problems through various approaches to speech
feature extraction and speech modeling techniques. This paper presents a
text-independent speaker identification system that employs Mel Frequency
Cepstral Coefficients (MFCC) for feature extraction and k-Nearest Neighbor
(kNN) for classification. The maximum cross-validation accuracy obtained was
60%. This will be improved upon in subsequent research.

We illustrate an approach that can be exploited for constructing neural
networks which a priori obey physical laws. We start with a simple single-layer
neural network (NN) but refrain from choosing the activation functions yet.
Under certain conditions and in the infinite-width limit, we may apply the
central limit theorem, upon which the NN output becomes Gaussian. We may then
investigate and manipulate the limit network by falling back on Gaussian
process (GP) theory. It is observed that linear operators acting upon a GP
again yield a GP. This also holds true for differential operators defining
differential equations and describing physical laws. If we demand the GP, or
equivalently the limit network, to obey the physical law, then this yields an
equation for the covariance function or kernel of the GP, whose solution
equivalently constrains the model to obey the physical law. The central limit
theorem then suggests that NNs can be constructed to obey a physical law by
choosing the activation functions such that they match a particular kernel in
the infinite-width limit. The activation functions constructed in this way
guarantee the NN to a priori obey the physics, up to the approximation error of
non-infinite network width. Simple examples of the homogeneous 1D-Helmholtz
equation are discussed and compared to naive kernels and activations.

As the development of the encoder-decoder architecture, researchers are able
to study the text generation tasks with broader types of data. Among them,
KB-to-text aims at converting a set of knowledge triples into human readable
sentences. In the original setting, the task assumes that the input triples and
the text are exactly aligned in the perspective of the embodied
knowledge/information. In this paper, we extend this setting and explore how to
facilitate the trained model to generate more informative text, namely,
containing more information about the triple entities but not conveyed by the
input triples. To solve this problem, we propose a novel memory augmented
generator that employs a memory network to memorize the useful knowledge
learned during the training and utilizes such information together with the
input triples to generate text in the operational or testing phase. We derive a
dataset from WebNLG for our new setting and conduct extensive experiments to
investigate the effectiveness of our model as well as uncover the intrinsic
characteristics of the setting.

The current energy transition promotes the convergence of operation between
the power and natural gas systems. In that direction, it becomes paramount to
improve the modeling of non-convex natural gas flow dynamics within the
coordinated power and gas dispatch. In this work, we propose a
neural-network-constrained optimization method which includes a regression
model of the Weymouth equation, based on supervised machine learning. The
Weymouth equation links gas flow to inlet and outlet pressures for each
pipeline via a quadratic equality, which is captured by a neural network. The
latter is encoded via a tractable mixed-integer linear program into the set of
constraints. In addition, our proposed framework is capable of considering
bidirectionality without having recourse to complex and potentially inaccurate
convexification approaches. We further enhance our model by introducing a
reformulation of the activation function, which improves the computational
efficiency. An extensive numerical study based on the real-life Belgian power
and gas systems shows that the proposed methodology yields promising results in
terms of accuracy and tractability.

We propose DeepFusion, a modular multi-modal architecture to fuse lidars,
cameras and radars in different combinations for 3D object detection.
Specialized feature extractors take advantage of each modality and can be
exchanged easily, making the approach simple and flexible. Extracted features
are transformed into bird's-eye-view as a common representation for fusion.
Spatial and semantic alignment is performed prior to fusing modalities in the
feature space. Finally, a detection head exploits rich multi-modal features for
improved 3D detection performance. Experimental results for lidar-camera,
lidar-camera-radar and camera-radar fusion show the flexibility and
effectiveness of our fusion approach. In the process, we study the largely
unexplored task of faraway car detection up to 225 meters, showing the benefits
of our lidar-camera fusion. Furthermore, we investigate the required density of
lidar points for 3D object detection and illustrate implications at the example
of robustness against adverse weather conditions. Moreover, ablation studies on
our camera-radar fusion highlight the importance of accurate depth estimation.

Performance comparison of supervised machine learning (ML) models are widely
done in terms of different confusion matrix based scores obtained on test
datasets. However, a dataset comprises several instances having different
difficulty levels. Therefore, it is more logical to compare effectiveness of ML
models on individual instances instead of comparing scores obtained for the
entire dataset. In this paper, an alternative approach is proposed for direct
comparison of supervised ML models in terms of individual instances within the
dataset. A direct comparison matrix called \emph{Prayatul Matrix} is
introduced, which accounts for comparative outcome of two ML algorithms on
different instances of a dataset. Five different performance measures are
designed based on prayatul matrix. Efficacy of the proposed approach as well as
designed measures is analyzed with four classification techniques on three
datasets. Also analyzed on four large-scale complex image datasets with four
deep learning models namely ResNet50V2, MobileNetV2, EfficientNet, and
XceptionNet. Results are evident that the newly designed measure are capable of
giving more insight about the comparing ML algorithms, which were impossible
with existing confusion matrix based scores like accuracy, precision and
recall.

The choice of good distances and similarity measures between objects is
important for many machine learning methods. Therefore, many metric learning
algorithms have been developed in recent years, mainly for Euclidean data in
order to improve performance of classification or clustering methods. However,
due to difficulties in establishing computable, efficient and differentiable
distances between attributed graphs, few metric learning algorithms adapted to
graphs have been developed despite the strong interest of the community. In
this paper, we address this issue by proposing a new Simple Graph Metric
Learning - SGML - model with few trainable parameters based on Simple Graph
Convolutional Neural Networks - SGCN - and elements of Optimal Transport
theory. This model allows us to build an appropriate distance from a database
of labeled (attributed) graphs to improve the performance of simple
classification algorithms such as $k$-NN. This distance can be quickly trained
while maintaining good performances as illustrated by the experimental study
presented in this paper.

Understanding spatial and visual information is essential for a navigation
agent who follows natural language instructions. The current Transformer-based
VLN agents entangle the orientation and vision information, which limits the
gain from the learning of each information source. In this paper, we design a
neural agent with explicit Orientation and Vision modules. Those modules learn
to ground spatial information and landmark mentions in the instructions to the
visual environment more effectively. To strengthen the spatial reasoning and
visual perception of the agent, we design specific pre-training tasks to feed
and better utilize the corresponding modules in our final navigation model. We
evaluate our approach on both Room2room (R2R) and Room4room (R4R) datasets and
achieve the state of the art results on both benchmarks.

Noise is ubiquitous during image acquisition. Sufficient denoising is often
an important first step for image processing. In recent decades, deep neural
networks (DNNs) have been widely used for image denoising. Most DNN-based image
denoising methods require a large-scale dataset or focus on supervised
settings, in which single/pairs of clean images or a set of noisy images are
required. This poses a significant burden on the image acquisition process.
Moreover, denoisers trained on datasets of limited scale may incur
over-fitting. To mitigate these issues, we introduce a new self-supervised
framework for image denoising based on the Tucker low-rank tensor
approximation. With the proposed design, we are able to characterize our
denoiser with fewer parameters and train it based on a single image, which
considerably improves the model generalizability and reduces the cost of data
acquisition. Extensive experiments on both synthetic and real-world noisy
images have been conducted. Empirical results show that our proposed method
outperforms existing non-learning-based methods (e.g., low-pass filter,
non-local mean), single-image unsupervised denoisers (e.g., DIP, NN+BM3D)
evaluated on both in-sample and out-sample datasets. The proposed method even
achieves comparable performances with some supervised methods (e.g., DnCNN).

Extractive summarization produces summaries by identifying and concatenating
the most important sentences in a document. Since most summarization datasets
do not come with gold labels indicating whether document sentences are
summary-worthy, different labeling algorithms have been proposed to extrapolate
oracle extracts for model training. In this work, we identify two flaws with
the widely used greedy labeling approach: it delivers suboptimal and
deterministic oracles. To alleviate both issues, we propose a simple yet
effective labeling algorithm that creates soft, expectation-based sentence
labels. We define a new learning objective for extractive summarization which
incorporates learning signals from multiple oracle summaries and prove it is
equivalent to estimating the oracle expectation for each document sentence.
Without any architectural modifications, the proposed labeling scheme achieves
superior performance on a variety of summarization benchmarks across domains
and languages, in both supervised and zero-shot settings.

Communication helps agents to obtain information about others so that better
coordinated behavior can be learned. Some existing work communicates predicted
future trajectory with others, hoping to get clues about what others would do
for better coordination. However, circular dependencies sometimes can occur
when agents are treated synchronously so it is hard to coordinate
decision-making. In this paper, we propose a novel communication scheme,
Sequential Communication (SeqComm). SeqComm treats agents asynchronously (the
upper-level agents make decisions before the lower-level ones) and has two
communication phases. In negotiation phase, agents determine the priority of
decision-making by communicating hidden states of observations and comparing
the value of intention, which is obtained by modeling the environment dynamics.
In launching phase, the upper-level agents take the lead in making decisions
and communicate their actions with the lower-level agents. Theoretically, we
prove the policies learned by SeqComm are guaranteed to improve monotonically
and converge. Empirically, we show that SeqComm outperforms existing methods in
various multi-agent cooperative tasks.

Artificial intelligence (AI) has been widely applied in drug discovery with a
major task as molecular property prediction. Despite the boom of AI techniques
in molecular representation learning, some key aspects underlying molecular
property prediction haven't been carefully examined yet. In this study, we
conducted a systematic comparison on three representative models, random
forest, MolBERT and GROVER, which utilize three major molecular
representations, extended-connectivity fingerprints, SMILES strings and
molecular graphs, respectively. Notably, MolBERT and GROVER, are pretrained on
large-scale unlabelled molecule corpuses in a self-supervised manner. In
addition to the commonly used MoleculeNet benchmark datasets, we also assembled
a suite of opioids-related datasets for downstream prediction evaluation. We
first conducted dataset profiling on label distribution and structural
analyses; we also examined the activity cliffs issue in the opioids-related
datasets. Then, we trained 4,320 predictive models and evaluated the usefulness
of the learned representations. Furthermore, we explored into the model
evaluation by studying the effect of statistical tests, evaluation metrics and
task settings. Finally, we dissected the chemical space generalization into
inter-scaffold and intra-scaffold generalization and measured prediction
performance to evaluate model generalizbility under both settings. By taking
this respite, we reflected on the key aspects underlying molecular property
prediction, the awareness of which can, hopefully, bring better AI techniques
in this field.

Lyrics recognition is an important task in music processing. Despite
traditional algorithms such as the hybrid HMM- TDNN model achieving good
performance, studies on applying end-to-end models and self-supervised learning
(SSL) are limited. In this paper, we first establish an end-to-end baseline for
lyrics recognition and then explore the performance of SSL models on lyrics
recognition task. We evaluate a variety of upstream SSL models with different
training methods (masked reconstruction, masked prediction, autoregressive
reconstruction, and contrastive learning). Our end-to-end self-supervised
models, evaluated on the DAMP music dataset, outperform the previous
state-of-the-art (SOTA) system by 5.23% for the dev set and 2.4% for the test
set even without a language model trained by a large corpus. Moreover, we
investigate the effect of background music on the performance of
self-supervised learning models and conclude that the SSL models cannot extract
features efficiently in the presence of background music. Finally, we study the
out-of-domain generalization ability of the SSL features considering that those
models were not trained on music datasets.

With the continuous development of web technology, Web3.0 has attracted a
considerable amount of attention due to its unique decentralized
characteristics. The digital economy is an important driver of high-quality
economic development and is currently in a rapid development stage. In the
digital economy scenario, the centralized nature of the Internet and other
characteristics usually bring about security issues such as infringement and
privacy leakage. Therefore, it is necessary to investigate how to use Web3.0
technologies to solve the pain points encountered in the development of the
digital economy by fully exploring the critical technologies of digital economy
and Web3.0. In this paper, we discuss the aspects of Web3.0 that should be
integrated with the digital economy to better find the entry point to solve the
problems by examining the latest advances of Web3.0 in machine learning,
finance, and data management. We hope this research will inspire those who are
involved in both academia and industry, and finally help to build a favourable
ecology for the digital economy.

In cooperative multi-agent reinforcement learning (MARL), combining value
decomposition with actor-critic enables agents to learn stochastic policies,
which are more suitable for the partially observable environment. Given the
goal of learning local policies that enable decentralized execution, agents are
commonly assumed to be independent of each other, even in centralized training.
However, such an assumption may prohibit agents from learning the optimal joint
policy. To address this problem, we explicitly take the dependency among agents
into centralized training. Although this leads to the optimal joint policy, it
may not be factorized for decentralized execution. Nevertheless, we
theoretically show that from such a joint policy, we can always derive another
joint policy that achieves the same optimality but can be factorized for
decentralized execution. To this end, we propose multi-agent conditional policy
factorization (MACPF), which takes more centralized training but still enables
decentralized execution. We empirically verify MACPF in various cooperative
MARL tasks and demonstrate that MACPF achieves better performance or faster
convergence than baselines.

The goal of Adaptive UIs is to automatically change an interface so that the
UI better supports users in their tasks. A core challenge is to infer user
intent from user input and chose adaptations accordingly. Designing effective
online UI adaptations is challenging because it relies on tediously
hand-crafted rules or carefully collected, high-quality user data. To overcome
these challenges, we formulate UI adaptation as a multi-agent reinforcement
learning problem. In our formulation, a user agent learns to interact with a UI
to complete a task. Simultaneously, an interface agent learns UI adaptations to
maximize the user agent's performance. The interface agent is agnostic to the
goal. It learns the task structure from the behavior of the user agent and
based on that can support the user agent in completing its task. We show that
our approach leads to a significant reduction in necessary number of actions on
a photo editing task in silico. Furthermore, our user studies demonstrate the
generalization capabilities of our interface agent from a simulated user agent
to real users.

In this paper we consider the problem learning of variational models in the
context of supervised learning via risk minimization. Our goal is to provide a
deeper understanding of the two approaches of learning of variational models
via bilevel optimization and via algorithm unrolling. The former considers the
variational model as a lower level optimization problem below the risk
minimization problem, while the latter replaces the lower level optimization
problem by an algorithm that solves said problem approximately. Both approaches
are used in practice, but, unrolling is much simpler from a computational point
of view. To analyze and compare the two approaches, we consider a simple toy
model, and compute all risks and the respective estimators explicitly. We show
that unrolling can be better than the bilevel optimization approach, but also
that the performance of unrolling can depend significantly on further
parameters, sometimes in unexpected ways: While the stepsize of the unrolled
algorithm matters a lot, the number of unrolled iterations only matters if the
number is even or odd, and these two cases are notably different.

Demands for minimum parameter setup in machine learning models are desirable
to avoid time-consuming optimization processes. The $k$-Nearest Neighbors is
one of the most effective and straightforward models employed in numerous
problems. Despite its well-known performance, it requires the value of $k$ for
specific data distribution, thus demanding expensive computational efforts.
This paper proposes a $k$-Nearest Neighbors classifier that bypasses the need
to define the value of $k$. The model computes the $k$ value adaptively
considering the data distribution of the training set. We compared the proposed
model against the standard $k$-Nearest Neighbors classifier and two
parameterless versions from the literature. Experiments over 11 public datasets
confirm the robustness of the proposed approach, for the obtained results were
similar or even better than its counterpart versions.

In this paper, we introduce a novel online model-based reinforcement learning
algorithm that uses Unscented Transform to propagate uncertainty for the
prediction of the future reward. Previous approaches either approximate the
state distribution at each step of the prediction horizon with a Gaussian, or
perform Monte Carlo simulations to estimate the rewards. Our method, depending
on the number of sigma points employed, can propagate either mean and
covariance with minimal points, or higher-order moments with more points
similarly to Monte Carlo. The whole framework is implemented as a computational
graph for online training. Furthermore, in order to prevent explosion in the
number of sigma points when propagating through a generic state-dependent
uncertainty model, we add sigma-point expansion and contraction layers to our
graph, which are designed using the principle of moment matching. Finally, we
propose gradient descent inspired by Sequential Quadratic Programming to update
policy parameters in the presence of state constraints. We demonstrate the
proposed method with two applications in simulation. The first one designs a
stabilizing controller for the cart-pole problem when the dynamics is known
with state-dependent uncertainty. The second example, following up on our
previous work, tunes the parameters of a control barrier function-based
Quadratic Programming controller for a leader-follower problem in the presence
of input constraints.

While the distribution patterns of cold-water corals, such as Paragorgia
arborea, have received increasing attention in recent studies, little is known
about their in situ activity patterns. In this paper, we examine polyp activity
in P. arborea using machine learning techniques to analyze high-resolution time
series data and photographs obtained from an autonomous lander cluster deployed
in the Stjernsund, Norway. An interactive illustration of the models derived in
this paper is provided online as supplementary material. We find that the best
predictor of the degree of extension of the coral polyps is current direction
with a lag of three hours. Other variables that are not directly associated
with water currents, such as temperature and salinity, offer much less
information concerning polyp activity. Interestingly, the degree of polyp
extension can be predicted more reliably by sampling the laminar flows in the
water column above the measurement site than by sampling the more turbulent
flows in the direct vicinity of the corals. Our results show that the activity
patterns of the P. arborea polyps are governed by the strong tidal current
regime of the Stjernsund. It appears that P. arborea does not react to shorter
changes in the ambient current regime but instead adjusts its behavior in
accordance with the large-scale pattern of the tidal cycle itself in order to
optimize nutrient uptake.

In this paper, we propose a new low-rank matrix factorization model dubbed
bounded simplex-structured matrix factorization (BSSMF). Given an input matrix
$X$ and a factorization rank $r$, BSSMF looks for a matrix $W$ with $r$ columns
and a matrix $H$ with $r$ rows such that $X \approx WH$ where the entries in
each column of $W$ are bounded, that is, they belong to given intervals, and
the columns of $H$ belong to the probability simplex, that is, $H$ is column
stochastic. BSSMF generalizes nonnegative matrix factorization (NMF), and
simplex-structured matrix factorization (SSMF). BSSMF is particularly well
suited when the entries of the input matrix $X$ belong to a given interval; for
example when the rows of $X$ represent images, or $X$ is a rating matrix such
as in the Netflix and MovieLens data sets where the entries of $X$ belong to
the interval $[1,5]$. The simplex-structured matrix $H$ not only leads to an
easily understandable decomposition providing a soft clustering of the columns
of $X$, but implies that the entries of each column of $WH$ belong to the same
intervals as the columns of $W$. In this paper, we first propose a fast
algorithm for BSSMF, even in the presence of missing data in $X$. Then we
provide identifiability conditions for BSSMF, that is, we provide conditions
under which BSSMF admits a unique decomposition, up to trivial ambiguities.
Finally, we illustrate the effectiveness of BSSMF on two applications:
extraction of features in a set of images, and the matrix completion problem
for recommender systems.

Apparent parallels between natural language and biological sequence have led
to a recent surge in the application of deep language models (LMs) to the
analysis of antibody and other biological sequences. However, a lack of a
rigorous linguistic formalization of biological sequence languages, which would
define basic components, such as lexicon (i.e., the discrete units of the
language) and grammar (i.e., the rules that link sequence well-formedness,
structure, and meaning) has led to largely domain-unspecific applications of
LMs, which do not take into account the underlying structure of the biological
sequences studied. A linguistic formalization, on the other hand, establishes
linguistically-informed and thus domain-adapted components for LM applications.
It would facilitate a better understanding of how differences and similarities
between natural language and biological sequences influence the quality of LMs,
which is crucial for the design of interpretable models with extractable
sequence-functions relationship rules, such as the ones underlying the antibody
specificity prediction problem. Deciphering the rules of antibody specificity
is crucial to accelerating rational and in silico biotherapeutic drug design.
Here, we formalize the properties of the antibody language and thereby
establish not only a foundation for the application of linguistic tools in
adaptive immune receptor analysis but also for the systematic immunolinguistic
studies of immune receptor specificity in general.

Power system state estimation is being faced with different types of
anomalies. These might include bad data caused by gross measurement errors or
communication system failures. Sudden changes in load or generation can be
considered as anomaly depending on the implemented state estimation method.
Additionally, considering power grid as a cyber physical system, state
estimation becomes vulnerable to false data injection attacks. The existing
methods for anomaly classification cannot accurately classify (discriminate
between) the above mentioned three types of anomalies, especially when it comes
to discrimination between sudden load changes and false data injection attacks.
This paper presents a new algorithm for detecting anomaly presence, classifying
the anomaly type and identifying the origin of the anomaly, i.e., measurements
that contain gross errors in case of bad data, or buses associated with loads
experiencing a sudden change, or state variables targeted by false data
injection attack. The algorithm combines analytical and machine learning (ML)
approaches. The first stage exploits an analytical approach to detect anomaly
presence by combining $\chi^2$-test and anomaly detection index. The second
stage utilizes ML for classification of anomaly type and identification of its
origin, with particular reference to discrimination between sudden load changes
and false data injection attacks. The proposed ML based method is trained to be
independent of the network configuration which eliminates retraining of the
algorithm after network topology changes. The results obtained by implementing
the proposed algorithm on IEEE 14 bus test system demonstrate the accuracy and
effectiveness of the proposed algorithm.

Simulating time-domain observations of gravitational wave (GW) detector
environments will allow for a better understanding of GW sources, augment
datasets for GW signal detection and help in characterizing the noise of the
detectors, leading to better physics. This paper presents a novel approach to
simulating fixed-length time-domain signals using a three-player Wasserstein
Generative Adversarial Network (WGAN), called DVGAN, that includes an auxiliary
discriminator that discriminates on the derivatives of input signals. An
ablation study is used to compare the effects of including adversarial feedback
from an auxiliary derivative discriminator with a vanilla two-player WGAN. We
show that discriminating on derivatives can stabilize the learning of GAN
components on 1D continuous signals during their training phase. This results
in smoother generated signals that are less distinguishable from real samples
and better capture the distributions of the training data. DVGAN is also used
to simulate real transient noise events captured in the advanced LIGO GW
detector.

Image clustering is a very useful technique that is widely applied to various
areas, including remote sensing. Recently, visual representations by
self-supervised learning have greatly improved the performance of image
clustering. To further improve the well-trained clustering models, this paper
proposes a novel method by first ranking samples within each cluster based on
the confidence in their belonging to the current cluster and then using the
ranking to formulate a weighted cross-entropy loss to train the model. For
ranking the samples, we developed a method for computing the likelihood of
samples belonging to the current clusters based on whether they are situated in
densely populated neighborhoods, while for training the model, we give a
strategy for weighting the ranked samples. We present extensive experimental
results that demonstrate that the new technique can be used to improve the
State-of-the-Art image clustering models, achieving accuracy performance gains
ranging from $2.1\%$ to $15.9\%$. Performing our method on a variety of
datasets from remote sensing, we show that our method can be effectively
applied to remote--sensing images.

In recent years, great progress has been made to incorporate unlabeled data
to overcome the inefficiently supervised problem via semi-supervised learning
(SSL). Most state-of-the-art models are based on the idea of pursuing
consistent model predictions over unlabeled data toward the input noise, which
is called consistency regularization. Nonetheless, there is a lack of
theoretical insights into the reason behind its success. To bridge the gap
between theoretical and practical results, we propose a worst-case consistency
regularization technique for SSL in this paper. Specifically, we first present
a generalization bound for SSL consisting of the empirical loss terms observed
on labeled and unlabeled training data separately. Motivated by this bound, we
derive an SSL objective that minimizes the largest inconsistency between an
original unlabeled sample and its multiple augmented variants. We then provide
a simple but effective algorithm to solve the proposed minimax problem, and
theoretically prove that it converges to a stationary point. Experiments on
five popular benchmark datasets validate the effectiveness of our proposed
method.

In forensic voice comparison the speaker embedding has become widely popular
in the last 10 years. Most of the pretrained speaker embeddings are trained on
English corpora, because it is easily accessible. Thus, language dependency can
be an important factor in automatic forensic voice comparison, especially when
the target language is linguistically very different. There are numerous
commercial systems available, but their models are mainly trained on a
different language (mostly English) than the target language. In the case of a
low-resource language, developing a corpus for forensic purposes containing
enough speakers to train deep learning models is costly. This study aims to
investigate whether a model pre-trained on English corpus can be used on a
target low-resource language (here, Hungarian), different from the model is
trained on. Also, often multiple samples are not available from the offender
(unknown speaker). Therefore, samples are compared pairwise with and without
speaker enrollment for suspect (known) speakers. Two corpora are applied that
were developed especially for forensic purposes, and a third that is meant for
traditional speaker verification. Two deep learning based speaker embedding
vector extraction methods are used: the x-vector and ECAPA-TDNN. Speaker
verification was evaluated in the likelihood-ratio framework. A comparison is
made between the language combinations (modeling, LR calibration, evaluation).
The results were evaluated by minCllr and EER metrics. It was found that the
model pre-trained on a different language but on a corpus with a huge amount of
speakers performs well on samples with language mismatch. The effect of sample
durations and speaking styles were also examined. It was found that the longer
the duration of the sample in question the better the performance is. Also,
there is no real difference if various speaking styles are applied.

Hashing that projects data into binary codes has shown extraordinary talents
in cross-modal retrieval due to its low storage usage and high query speed.
Despite their empirical success on some scenarios, existing cross-modal hashing
methods usually fail to cross modality gap when fully-paired data with plenty
of labeled information is nonexistent. To circumvent this drawback, motivated
by the Divide-and-Conquer strategy, we propose Deep Manifold Hashing (DMH), a
novel method of dividing the problem of semi-paired unsupervised cross-modal
retrieval into three sub-problems and building one simple yet efficiency model
for each sub-problem. Specifically, the first model is constructed for
obtaining modality-invariant features by complementing semi-paired data based
on manifold learning, whereas the second model and the third model aim to learn
hash codes and hash functions respectively. Extensive experiments on three
benchmarks demonstrate the superiority of our DMH compared with the
state-of-the-art fully-paired and semi-paired unsupervised cross-modal hashing
methods.

Reinforcement learning algorithms have become more complex since the
invention of target networks. Unfortunately, target networks have not kept up
with this increased complexity, instead requiring approximate solutions to be
computationally feasible. These approximations increase noise in the Q-value
targets and in the replay sampling distribution. Paused Agent Replay Refresh
(PARR) is a drop-in replacement for target networks that supports more complex
learning algorithms without this need for approximation. Using a basic
Q-network architecture, and refreshing the novelty values, target values, and
replay sampling distribution, PARR gets 2500 points in Montezuma's Revenge
after only 30.9 million Atari frames. Finally, interpreting PARR in the context
of carbon-based learning offers a new reason for sleep.

In principle, applying variational autoencoders (VAEs) to sequential data
offers a method for controlled sequence generation, manipulation, and
structured representation learning. However, training sequence VAEs is
challenging: autoregressive decoders can often explain the data without
utilizing the latent space, known as posterior collapse. To mitigate this,
state-of-the-art models weaken the powerful decoder by applying uniformly
random dropout to the decoder input. We show theoretically that this removes
pointwise mutual information provided by the decoder input, which is
compensated for by utilizing the latent space. We then propose an adversarial
training strategy to achieve information-based stochastic dropout. Compared to
uniform dropout on standard text benchmark datasets, our targeted approach
increases both sequence modeling performance and the information captured in
the latent space.

Testing is essential for verifying and validating control designs, especially
in safety-critical applications. In particular, the control system governing an
automated driving vehicle must be proven reliable enough for its acceptance on
the market. Recently, much research has focused on scenario-based methods.
However, the number of possible driving scenarios to test is in principle
infinite. In this paper, we propose to generate corner test-cases by globally
optimizing a function expressing the criticality of the scenario. Thus, we aim
to reduce the effort of validating safety-critical control systems and support
controller design choices. In particular, we propose a learning-based
optimization framework for critical scenario detection based on a surrogate of
the objective function that we learn incrementally as new scenarios are tested.
We formalize the procedure and suggest the design of the objective function for
the case of automated driving. Numerical tests on two case studies using linear
time-varying model predictive controllers demonstrate that the approach can
identify critical scenarios within a limited number of closed-loop experiments.

Tail averaging improves on Polyak averaging's non-asymptotic behaviour by
excluding a number of leading iterates of stochastic optimization from its
calculations. In practice, with a finite number of optimization steps and a
learning rate that cannot be annealed to zero, tail averaging can get much
closer to a local minimum point of the training loss than either the individual
iterates or the Polyak average. However, the number of leading iterates to
ignore is an important hyperparameter, and starting averaging too early or too
late leads to inefficient use of resources or suboptimal solutions. Setting
this hyperparameter to improve generalization is even more difficult,
especially in the presence of other hyperparameters and overfitting.
Furthermore, before averaging starts, the loss is only weakly informative of
the final performance, which makes early stopping unreliable. To alleviate
these problems, we propose an anytime variant of tail averaging, that has no
hyperparameters and approximates the optimal tail at all optimization steps.
Our algorithm is based on two running averages with adaptive lengths bounded in
terms of the optimal tail length, one of which achieves approximate optimality
with some regularity. Requiring only the additional storage for two sets of
weights and periodic evaluation of the loss, the proposed two-tailed averaging
algorithm is a practical and widely applicable method for improving stochastic

Causal and attribution studies are essential for earth scientific discoveries
and critical for informing climate, ecology, and water policies. However, the
current generation of methods needs to keep pace with the complexity of
scientific and stakeholder challenges and data availability combined with the
adequacy of data-driven methods. Unless carefully informed by physics, they run
the risk of conflating correlation with causation or getting overwhelmed by
estimation inaccuracies. Given that natural experiments, controlled trials,
interventions, and counterfactual examinations are often impractical,
information-theoretic methods have been developed and are being continually
refined in the earth sciences. Here we show that transfer entropy-based causal
graphs, which have recently become popular in the earth sciences with
high-profile discoveries, can be spurious even when augmented with statistical
significance. We develop a subsample-based ensemble approach for robust
causality analysis. Simulated data, and observations in climate and
ecohydrology, suggest the robustness and consistency of this approach.

Nonnegative Matrix Factorization (NMF) models are widely used to recover
linearly mixed nonnegative data. When the data is made of samplings of
continuous signals, the factors in NMF can be constrained to be samples of
nonnegative rational functions, which allow fairly general models; this is
referred to as NMF using rational functions (R-NMF). We first show that, under
mild assumptions, R-NMF has an essentially unique factorization unlike NMF,
which is crucial in applications where ground-truth factors need to be
recovered such as blind source separation problems. Then we present different
approaches to solve R-NMF: the R-HANLS, R-ANLS and R-NLS methods. From our
tests, no method significantly outperforms the others, and a trade-off should
be done between time and accuracy. Indeed, R-HANLS is fast and accurate for
large problems, while R-ANLS is more accurate, but also more resources
demanding, both in time and memory. R-NLS is very accurate but only for small
problems. Moreover, we show that R-NMF outperforms NMF in various tasks
including the recovery of semi-synthetic continuous signals, and a
classification problem of real hyperspectral signals.

The backpropagation that drives the success of deep learning is most likely
different from the learning mechanism of the brain. In this paper, we develop a
biology-inspired learning rule that discovers features by local competitions
among neurons, following the idea of Hebb's famous proposal. It is demonstrated
that the unsupervised features learned by this local learning rule can serve as
a pre-training model to improve the performance of some supervised learning
tasks. More importantly, this local learning rule enables us to build a new
learning paradigm very different from the backpropagation, named activation
learning, where the output activation of the neural network roughly measures
how probable the input patterns are. The activation learning is capable of
learning plentiful local features from few shots of input patterns, and
demonstrates significantly better performances than the backpropagation
algorithm when the number of training samples is relatively small. This
learning paradigm unifies unsupervised learning, supervised learning and
generative models, and is also more secure against adversarial attack, paving a
road to some possibilities of creating general-task neural networks.

Localizing a semantic parser to support new languages requires effective
cross-lingual generalization. Recent work has found success with
machine-translation or zero-shot methods although these approaches can struggle
to model how native speakers ask questions. We consider how to effectively
leverage minimal annotated examples in new languages for few-shot cross-lingual
semantic parsing. We introduce a first-order meta-learning algorithm to train a
semantic parser with maximal sample efficiency during cross-lingual transfer.
Our algorithm uses high-resource languages to train the parser and
simultaneously optimizes for cross-lingual generalization for lower-resource
languages. Results across six languages on ATIS demonstrate that our
combination of generalization steps yields accurate semantic parsers sampling
$\le$10% of source training data in each new language. Our approach also trains
a competitive model on Spider using English with generalization to Chinese
similarly sampling $\le$10% of training data.

Audio is one of the most used way of human communication, but at the same
time it can be easily misused by to trick people. With the revolution of AI,
the related technologies are now accessible to almost everyone thus making it
simple for the criminals to commit crimes and forgeries. In this work, we
introduce a deep learning method to develop a classifier that will blindly
classify an input audio as real or mimicked. The proposed model was trained on
a set of important features extracted from a large dataset of audios to get a
classifier that was tested on the same set of features from different audios.
Two datasets were created for this work; an all English data set and a mixed
data set (Arabic and English). These datasets have been made available through
GitHub for the use of the research community at
https://github.com/SaSs7/Dataset. For the purpose of comparison, the audios
were also classified through human inspection with the subjects being the
native speakers. The ensued results were interesting and exhibited formidable
accuracy.

In this paper, we introduce a prototype of machine learning method to solve
the many-body Schr\"{o}dinger equation by the tensor neural network. Based on
the tensor product structure, we can do the direct numerical integration by
using fixed quadrature points for the functions constructed by the tensor
neural network within tolerable computational complexity. The corresponding
machine learning method is built for solving many-body Schr\"{o}dinger
equation. Some numerical examples are provided to validate the accuracy and
efficiency of the proposed algorithms.

Sleep stage recognition is crucial for assessing sleep and diagnosing chronic
diseases. Deep learning models, such as Convolutional Neural Networks and
Recurrent Neural Networks, are trained using grid data as input, making them
not capable of learning relationships in non-Euclidean spaces. Graph-based deep
models have been developed to address this issue when investigating the
external relationship of electrode signals across different brain regions.
However, the models cannot solve problems related to the internal relationships
between segments of electrode signals within a specific brain region. In this
study, we propose a Pearson correlation-based graph attention network, called
PearNet, as a solution to this problem. Graph nodes are generated based on the
spatial-temporal features extracted by a hierarchical feature extraction
method, and then the graph structure is learned adaptively to build node
connections. Based on our experiments on the Sleep-EDF-20 and Sleep-EDF-78
datasets, PearNet performs better than the state-of-the-art baselines.

This paper proposes a hybrid optimization and learning method for
impact-friendly catching objects at non-zero velocity. Through a constrained
Quadratic Programming problem, the method generates optimal trajectories up to
the contact point between the robot and the object to minimize their relative
velocity and reduce the initial impact forces. Next, the generated trajectories
are updated by Kernelized Movement Primitives which are based on human catching
demonstrations to ensure a smooth transition around the catching point. In
addition, the learned human variable stiffness (HVS) is sent to the robot's
Cartesian impedance controller to absorb the post-impact forces and stabilize
the catching position. Three experiments are conducted to compare our method
with and without HVS against a fixed-position impedance controller (FP-IC). The
results showed that the proposed methods outperform the FP-IC, while adding HVS
yields better results for absorbing the post-impact forces.

Successful Artificial Intelligence systems often require numerous labeled
data to extract information from document images. In this paper, we investigate
the problem of improving the performance of Artificial Intelligence systems in
understanding document images, especially in cases where training data is
limited. We address the problem by proposing a novel finetuning method using
reinforcement learning. Our approach treats the Information Extraction model as
a policy network and uses policy gradient training to update the model to
maximize combined reward functions that complement the traditional
cross-entropy losses. Our experiments on four datasets using labels and expert
feedback demonstrate that our finetuning mechanism consistently improves the
performance of a state-of-the-art information extractor, especially in the
small training data regime.

This work presents a deep learning-based plant disease diagnostic system
using images of fruits and leaves. Five state-of-the-art convolutional neural
networks (CNN) have been employed for implementing the system. Hitherto model
accuracy has been the focus for such applications and model optimization has
not been accounted for the model to be applicable to end-user devices. Two
model quantization techniques such as float16 and dynamic range quantization
have been applied to the five state-of-the-art CNN architectures. The study
shows that the quantized GoogleNet model achieved the size of 0.143 MB with an
accuracy of 97%, which is the best candidate model considering the size
criterion. The EfficientNet model achieved the size of 4.2MB with an accuracy
of 99%, which is the best model considering the performance criterion. The
source codes are available at
https://github.com/CompostieAI/Guava-disease-detection.

The evaluation of clustering results is difficult, highly dependent on the
evaluated data set and the perspective of the beholder. There are many
different clustering quality measures, which try to provide a general measure
to validate clustering results. A very popular measure is the Silhouette. We
discuss the efficient medoid-based variant of the Silhouette, perform a
theoretical analysis of its properties, and provide two fast versions for the
direct optimization. We combine ideas from the original Silhouette with the
well-known PAM algorithm and its latest improvements FasterPAM. One of the
versions guarantees equal results to the original variant and provides a run
speedup of $O(k^2)$. In experiments on real data with 30000 samples and
$k$=100, we observed a 10464$\times$ speedup compared to the original PAMMEDSIL

We propose a novel training algorithm for a multi-speaker neural
text-to-speech (TTS) model based on multi-task adversarial training. A
conventional generative adversarial network (GAN)-based training algorithm
significantly improves the quality of synthetic speech by reducing the
statistical difference between natural and synthetic speech. However, the
algorithm does not guarantee the generalization performance of the trained TTS
model in synthesizing voices of unseen speakers who are not included in the
training data. Our algorithm alternatively trains two deep neural networks:
multi-task discriminator and multi-speaker neural TTS model (i.e., generator of
GANs). The discriminator is trained not only to distinguish between natural and
synthetic speech but also to verify the speaker of input speech is existent or
non-existent (i.e., newly generated by interpolating seen speakers' embedding
vectors). Meanwhile, the generator is trained to minimize the weighted sum of
the speech reconstruction loss and adversarial loss for fooling the
discriminator, which achieves high-quality multi-speaker TTS even if the target
speaker is unseen. Experimental evaluation shows that our algorithm improves
the quality of synthetic speech better than a conventional GANSpeech algorithm.

Background: Quantitative prediction of the adolescents' spherical equivalent
based on their variable-length historical vision records.
  Methods: From October 2019 to March 2022, we examined binocular uncorrected
visual acuity, axial length, corneal curvature, and axial of 75,172 eyes from
37,586 adolescents aged 6-20 years in Chengdu, China. 80\% samples consist of
the training set and the remaining 20\% form the testing set. Time-Aware Long
Short-Term Memory was used to quantitatively predict the adolescents' spherical
equivalent within two and a half years.
  Result: The mean absolute prediction error on the testing set was 0.273-0.257
for spherical equivalent, ranging from 0.189-0.160 to 0.596-0.473 if we
consider different lengths of historical records and different prediction
durations.
  Conclusions: Time-Aware Long Short-Term Memory was applied to captured the
temporal features in irregularly sampled time series, which is more in line
with the characteristics of real data and thus has higher applicability, and
helps to identify the progression of myopia earlier. The overall error 0.273 is
much smaller than the criterion for clinically acceptable prediction, say 0.75.

Nonparametric regression subject to convexity or concavity constraints is
increasingly popular in economics, finance, operations research, machine
learning, and statistics. However, the conventional convex regression based on
the least squares loss function often suffers from overfitting and outliers.
This paper proposes to address these two issues by introducing the convex
support vector regression (CSVR) method, which effectively combines the key
elements of convex regression and support vector regression. Numerical
experiments demonstrate the performance of CSVR in prediction accuracy and
robustness that compares favorably with other state-of-the-art methods.

Binding kinetic parameters can be correlated with drug efficacy, which led to
the development of various computational methods for predicting binding kinetic
rates and gaining insight into protein-drug binding paths and mechanisms in
recent years. In this review, we introduce and compare computational methods
recently developed and applied to two systems, trypsin-benzamidine and
kinase-inhibitor complexes. Methods involving enhanced sampling in molecular
dynamics simulations or machine learning can be used not only to predict
kinetic rates, but also to reveal factors modulating the duration of residence
times, selectivity and drug resistance to mutations. Methods which require less
computational time to make predictions are highlighted, and suggestions to
reduce the error of computed kinetic rates are presented.

Specializing Directed Acyclic Graph Federated Learning(SDAGFL) is a new
federated learning framework which updates model from the devices with similar
data distribution through Directed Acyclic Graph Distributed Ledger Technology
(DAG-DLT). SDAGFL has the advantage of personalization, resisting single point
of failure and poisoning attack in fully decentralized federated learning.
Because of these advantages, the SDAGFL is suitable for the federated learning
in IoT scenario where the device is usually battery-powered. To promote the
application of SDAGFL in IoT, we propose an energy optimized SDAGFL based
event-triggered communication mechanism, called ESDAGFL. In ESDAGFL, the new
model is broadcasted only when it is significantly changed. We evaluate the
ESDAGFL on a clustered synthetically FEMNIST dataset and a dataset from texts
by Shakespeare and Goethe's works. The experiment results show that our
approach can reduce energy consumption by 33\% compared with SDAGFL, and
realize the same balance between training accuracy and specialization as
SDAGFL.

Multi-agent market simulators usually require careful calibration to emulate
real markets, which includes the number and the type of agents. Poorly
calibrated simulators can lead to misleading conclusions, potentially causing
severe loss when employed by investment banks, hedge funds, and traders to
study and evaluate trading strategies. In this paper, we propose a world model
simulator that accurately emulates a limit order book market -- it requires no
agent calibration but rather learns the simulated market behavior directly from
historical data. Traditional approaches fail short to learn and calibrate
trader population, as historical labeled data with details on each individual
trader strategy is not publicly available. Our approach proposes to learn a
unique "world" agent from historical data. It is intended to emulate the
overall trader population, without the need of making assumptions about
individual market agent strategies. We implement our world agent simulator
models as a Conditional Generative Adversarial Network (CGAN), as well as a
mixture of parametric distributions, and we compare our models against previous
work. Qualitatively and quantitatively, we show that the proposed approaches
consistently outperform previous work, providing more realism and
responsiveness.

Federated learning (FL) is increasingly deployed among multiple clients
(e.g., mobile devices) to train a shared model over decentralized data. To
address the privacy concerns, FL systems need to protect the clients' data from
being revealed during training and also control the data leakage through
trained models when exposed to untrusted domains. Distributed differential
privacy (DP) offers an appealing solution in this regard as it achieves an
informed tradeoff between privacy and utility without a trusted server.
However, existing distributed DP mechanisms work impractically in real world.
For instance, to handle realistic scenarios with \emph{client dropout}, these
existing mechanisms often make strong assumptions about client participation
yet still result in either poor privacy guarantees or unsatisfactory training
accuracy.
  We present Hyades, a distributed differentially private FL framework that is
highly efficient and resilient to client dropout. First, we develop a new
privacy accounting technique under the notion of Renyi DP that tightly bounds
the privacy loss in the presence of dropout before client sampling in FL. This
enables Hyades to set a minimum target noise level in each training round.
Second, we propose a novel 'add-then-remove' masking scheme to enforce this
target noise level, even though some sampled clients may still drop out in the
end. Third, we design an efficient secure aggregation mechanism that optimally
pipelines communication and computation for faster execution. Evaluation
through large-scale cloud deployment shows that Hyades can efficiently handle
client dropout in various realistic scenarios, attaining the optimal
privacy-utility tradeoff and accelerating the training by up to 2.1$\times$
compared to existing solutions.

Ensemble weather forecasts based on multiple runs of numerical weather
prediction models typically show systematic errors and require post-processing
to obtain reliable forecasts. Accurately modeling multivariate dependencies is
crucial in many practical applications, and various approaches to multivariate
post-processing have been proposed where ensemble predictions are first
post-processed separately in each margin and multivariate dependencies are then
restored via copulas. These two-step methods share common key limitations, in
particular the difficulty to include additional predictors in modeling the
dependencies. We propose a novel multivariate post-processing method based on
generative machine learning to address these challenges. In this new class of
nonparametric data-driven distributional regression models, samples from the
multivariate forecast distribution are directly obtained as output of a
generative neural network. The generative model is trained by optimizing a
proper scoring rule which measures the discrepancy between the generated and
observed data, conditional on exogenous input variables. Our method does not
require parametric assumptions on univariate distributions or multivariate
dependencies and allows for incorporating arbitrary predictors. In two case
studies on multivariate temperature and wind speed forecasting at weather
stations over Germany, our generative model shows significant improvements over
state-of-the-art methods and particularly improves the representation of
spatial dependencies.

Although Deep Neural Networks (DNNs) have great generalization and prediction
capabilities, their functioning does not allow a detailed explanation of their
behavior. Opaque deep learning models are increasingly used to make important
predictions in critical environments, and the danger is that they make and use
predictions that cannot be justified or legitimized. Several eXplainable
Artificial Intelligence (XAI) methods that separate explanations from machine
learning models have emerged, but have shortcomings in faithfulness to the
model actual functioning and robustness. As a result, there is a widespread
agreement on the importance of endowing Deep Learning models with explanatory
capabilities so that they can themselves provide an answer to why a particular
prediction was made. First, we address the problem of the lack of universal
criteria for XAI by formalizing what an explanation is. We also introduced a
set of axioms and definitions to clarify XAI from a mathematical perspective.
Finally, we present the Greybox XAI, a framework that composes a DNN and a
transparent model thanks to the use of a symbolic Knowledge Base (KB). We
extract a KB from the dataset and use it to train a transparent model (i.e., a
logistic regression). An encoder-decoder architecture is trained on RGB images
to produce an output similar to the KB used by the transparent model. Once the
two models are trained independently, they are used compositionally to form an
explainable predictive model. We show how this new architecture is accurate and
explainable in several datasets.

Despite the evolution of Convolutional Neural Networks (CNNs), their
performance is surprisingly dependent on the choice of hyperparameters.
However, it remains challenging to efficiently explore large hyperparameter
search space due to the long training times of modern CNNs. Multi-fidelity
optimization enables the exploration of more hyperparameter configurations
given budget by early termination of unpromising configurations. However, it
often results in selecting a sub-optimal configuration as training with the
high-performing configuration typically converges slowly in an early phase. In
this paper, we propose Multi-fidelity Optimization with a Recurring Learning
rate (MORL) which incorporates CNNs' optimization process into multi-fidelity
optimization. MORL alleviates the problem of slow-starter and achieves a more
precise low-fidelity approximation. Our comprehensive experiments on general
image classification, transfer learning, and semi-supervised learning
demonstrate the effectiveness of MORL over other multi-fidelity optimization
methods such as Successive Halving Algorithm (SHA) and Hyperband. Furthermore,
it achieves significant performance improvements over hand-tuned hyperparameter
configuration within a practical budget.

We experimentally validate a real-time machine learning framework, capable of
controlling the pump power values of Raman amplifiers to shape the signal power
evolution in two-dimensions (2D): frequency and fiber distance. In our setup,
power values of four first-order counter-propagating pumps are optimized to
achieve the desired 2D power profile. The pump power optimization framework
includes a convolutional neural network (CNN) followed by differential
evolution (DE) technique, applied online to the amplifier setup to
automatically achieve the target 2D power profiles. The results on achievable
2D profiles show that the framework is able to guarantee very low maximum
absolute error (MAE) (<0.5 dB) between the obtained and the target 2D profiles.
Moreover, the framework is tested in a multi-objective design scenario where
the goal is to achieve the 2D profiles with flat gain levels at the end of the
span, jointly with minimum spectral excursion over the entire fiber length. In
this case, the experimental results assert that for 2D profiles with the target
flat gain levels, the DE obtains less than 1 dB maximum gain deviation, when
the setup is not physically limited in the pump power values. The simulation
results also prove that with enough pump power available, better gain deviation
(less than 0.6 dB) for higher target gain levels is achievable.

Zero-shot cross-modal retrieval (ZS-CMR) deals with the retrieval problem
among heterogenous data from unseen classes. Typically, to guarantee
generalization, the pre-defined class embeddings from natural language
processing (NLP) models are used to build a common space. In this paper,
instead of using an extra NLP model to define a common space beforehand, we
consider a totally different way to construct (or learn) a common hamming space
from an information-theoretic perspective. We term our model the
Information-Theoretic Hashing (ITH), which is composed of two cascading
modules: an Adaptive Information Aggregation (AIA) module; and a Semantic
Preserving Encoding (SPE) module. Specifically, our AIA module takes the
inspiration from the Principle of Relevant Information (PRI) to construct a
common space that adaptively aggregates the intrinsic semantics of different
modalities of data and filters out redundant or irrelevant information. On the
other hand, our SPE module further generates the hashing codes of different
modalities by preserving the similarity of intrinsic semantics with the
element-wise Kullback-Leibler (KL) divergence. A total correlation
regularization term is also imposed to reduce the redundancy amongst different
dimensions of hash codes. Sufficient experiments on three benchmark datasets
demonstrate the superiority of the proposed ITH in ZS-CMR. Source code is
available in the supplementary material.

Unknown nonlinear dynamics often limit the tracking performance of
feedforward control. The aim of this paper is to develop a feedforward control
framework that can compensate these unknown nonlinear dynamics using universal
function approximators. The feedforward controller is parametrized as a
parallel combination of a physics-based model and a neural network, where both
share the same linear autoregressive (AR) dynamics. This parametrization allows
for efficient output-error optimization through Sanathanan-Koerner (SK)
iterations. Within each SK-iteration, the output of the neural network is
penalized in the subspace of the physics-based model through orthogonal
projection-based regularization, such that the neural network captures only the
unmodelled dynamics, resulting in interpretable models.

The merit of projecting data onto linear subspaces is well known from, e.g.,
dimension reduction. One key aspect of subspace projections, the maximum
preservation of variance (principal component analysis), has been thoroughly
researched and the effect of random linear projections on measures such as
intrinsic dimensionality still is an ongoing effort. In this paper, we
investigate the less explored depths of linear projections onto explicit
subspaces of varying dimensionality and the expectations of variance that
ensue. The result is a new family of bounds for Euclidean distances and inner
products. We showcase the quality of these bounds as well as investigate the
intimate relation to intrinsic dimensionality estimation.

The endeavor of artificial intelligence (AI) is to design autonomous agents
capable of achieving complex tasks. Namely, reinforcement learning (RL)
proposes a theoretical background to learn optimal behaviors. In practice, RL
algorithms rely on geometric discounts to evaluate this optimality.
Unfortunately, this does not cover decision processes where future returns are
not exponentially less valuable. Depending on the problem, this limitation
induces sample-inefficiency (as feed-backs are exponentially decayed) and
requires additional curricula/exploration mechanisms (to deal with sparse,
deceptive or adversarial rewards). In this paper, we tackle these issues by
generalizing the discounted problem formulation with a family of delayed
objective functions. We investigate the underlying RL problem to derive: 1) the
optimal stationary solution and 2) an approximation of the optimal
non-stationary control. The devised algorithms solved hard exploration problems
on tabular environment and improved sample-efficiency on classic simulated
robotics benchmarks.

TOR (The Onion Router) network is a widely used open source anonymous
communication tool, the abuse of TOR makes it difficult to monitor the
proliferation of online crimes such as to access criminal websites. Most
existing approches for TOR network de-anonymization heavily rely on manually
extracted features resulting in time consuming and poor performance. To tackle
the shortcomings, this paper proposes a neural representation learning approach
to recognize website fingerprint based on classification algorithm. We
constructed a new website fingerprinting attack model based on convolutional
neural network (CNN) with dilation and causal convolution, which can improve
the perception field of CNN as well as capture the sequential characteristic of
input data. Experiments on three mainstream public datasets show that the
proposed model is robust and effective for the website fingerprint
classification and improves the accuracy by 12.21% compared with the

In the era of deep learning, annotated datasets have become a crucial asset
to the remote sensing community. In the last decade, a plethora of different
datasets was published, each designed for a specific data type and with a
specific task or application in mind. In the jungle of remote sensing datasets,
it can be hard to keep track of what is available already. With this paper, we
introduce EOD - the IEEE GRSS Earth Observation Database (EOD) - an interactive
online platform for cataloguing different types of datasets leveraging remote
sensing imagery.

Explanatory Interactive Learning (XIL) collects user feedback on visual model
explanations to implement a Human-in-the-Loop (HITL) based interactive learning
scenario. Different user feedback types will have different impacts on user
experience and the cost associated with collecting feedback since different
feedback types involve different levels of image annotation. Although XIL has
been used to improve classification performance in multiple domains, the impact
of different user feedback types on model performance and explanation accuracy
is not well studied. To guide future XIL work we compare the effectiveness of
two different user feedback types in image classification tasks: (1)
instructing an algorithm to ignore certain spurious image features, and (2)
instructing an algorithm to focus on certain valid image features. We use
explanations from a Gradient-weighted Class Activation Mapping (GradCAM) based
XIL model to support both feedback types. We show that identifying and
annotating spurious image features that a model finds salient results in
superior classification and explanation accuracy than user feedback that tells
a model to focus on valid image features.

Optical coherence tomography (OCT) helps ophthalmologists assess macular
edema, accumulation of fluids, and lesions at microscopic resolution.
Quantification of retinal fluids is necessary for OCT-guided treatment
management, which relies on a precise image segmentation step. As manual
analysis of retinal fluids is a time-consuming, subjective, and error-prone
task, there is increasing demand for fast and robust automatic solutions. In
this study, a new convolutional neural architecture named RetiFluidNet is
proposed for multi-class retinal fluid segmentation. The model benefits from
hierarchical representation learning of textural, contextual, and edge features
using a new self-adaptive dual-attention (SDA) module, multiple self-adaptive
attention-based skip connections (SASC), and a novel multi-scale deep self
supervision learning (DSL) scheme. The attention mechanism in the proposed SDA
module enables the model to automatically extract deformation-aware
representations at different levels, and the introduced SASC paths further
consider spatial-channel interdependencies for concatenation of counterpart
encoder and decoder units, which improve representational capability.
RetiFluidNet is also optimized using a joint loss function comprising a
weighted version of dice overlap and edge-preserved connectivity-based losses,
where several hierarchical stages of multi-scale local losses are integrated
into the optimization process. The model is validated based on three publicly
available datasets: RETOUCH, OPTIMA, and DUKE, with comparisons against several
baselines. Experimental results on the datasets prove the effectiveness of the
proposed model in retinal OCT fluid segmentation and reveal that the suggested
method is more effective than existing state-of-the-art fluid segmentation
algorithms in adapting to retinal OCT scans recorded by various image scanning
instruments.

We present evidence that learned density functional theory (``DFT'') force
fields are ready for ground state catalyst discovery. Our key finding is that
relaxation using forces from a learned potential yields structures with similar
or lower energy to those relaxed using the RPBE functional in over 50\% of
evaluated systems, despite the fact that the predicted forces differ
significantly from the ground truth. This has the surprising implication that
learned potentials may be ready for replacing DFT in challenging catalytic
systems such as those found in the Open Catalyst 2020 dataset. Furthermore, we
show that a force field trained on a locally harmonic energy surface with the
same minima as a target DFT energy is also able to find lower or similar energy
structures in over 50\% of cases. This ``Easy Potential'' converges in fewer
steps than a standard model trained on true energies and forces, which further
accelerates calculations. Its success illustrates a key point: learned
potentials can locate energy minima even when the model has high force errors.
The main requirement for structure optimisation is simply that the learned
potential has the correct minima. Since learned potentials are fast and scale
linearly with system size, our results open the possibility of quickly finding
ground states for large systems.

High entropy alloys (HEA) represent a class of materials with promising
properties, such as high strength and ductility, radiation damage tolerance,
etc. At the same time, a combinatorially large variety of compositions and a
complex structure render them quite hard to study using conventional methods.
In this work, we present a computationally efficient methodology based on ab
initio calculations within the coherent potential approximation. To make the
methodology predictive, we apply an exchange-correlation correction to the
equation of state and take into account thermal effects on the magnetic state
and the equilibrium volume. The approach shows good agreement with available
experimental data on bulk properties of solid solutions. As a particular case,
the workflow is applied to a series of iron-group HEA to investigate their
solid solution strengthening within a parameter-free model based on the
effective medium representation of an alloy. The results reveal intricate
interactions between alloy components, which we analyze by means of a simple
model of local bonding. Thanks to its computational efficiency, the methodology
can be used as a basis for an adaptive learning workflow for optimal design of
HEA.

The variational quantum eigensolver (VQE) is a leading strategy that exploits
noisy intermediate-scale quantum (NISQ) machines to tackle chemical problems
outperforming classical approaches. To gain such computational advantages on
large-scale problems, a feasible solution is the QUantum DIstributed
Optimization (QUDIO) scheme, which partitions the original problem into $K$
subproblems and allocates them to $K$ quantum machines followed by the parallel
optimization. Despite the provable acceleration ratio, the efficiency of QUDIO
may heavily degrade by the synchronization operation. To conquer this issue,
here we propose Shuffle-QUDIO to involve shuffle operations into local
Hamiltonians during the quantum distributed optimization. Compared with QUDIO,
Shuffle-QUDIO significantly reduces the communication frequency among quantum
processors and simultaneously achieves better trainability. Particularly, we
prove that Shuffle-QUDIO enables a faster convergence rate over QUDIO.
Extensive numerical experiments are conducted to verify that Shuffle-QUDIO
allows both a wall-clock time speedup and low approximation error in the tasks
of estimating the ground state energy of molecule. We empirically demonstrate
that our proposal can be seamlessly integrated with other acceleration
techniques, such as operator grouping, to further improve the efficacy of VQE.

Quantum-inspired singular value decomposition (SVD) is a technique to perform
SVD in logarithmic time with respect to the dimension of a matrix, given access
to the matrix embedded in a segment-tree data structure. The speedup is
possible through the efficient sampling of matrix elements according to their
norms. Here, we apply it to extreme learning which is a machine learning
framework that performs linear regression using random feature vectors
generated through a random neural network. The extreme learning is suited for
the application of quantum-inspired SVD in that it first requires transforming
each data to a random feature during which we can construct the data structure
with a logarithmic overhead with respect to the number of data. We implement
the algorithm and observe that it works order-of-magnitude faster than the
exact SVD when we use high-dimensional feature vectors. However, we also
observe that, for random features generated by random neural networks, we can
replace the norm-based sampling in the quantum-inspired algorithm with uniform
sampling to obtain the same level of test accuracy due to the uniformity of the
matrix in this case. The norm-based sampling becomes effective for more
non-uniform matrices obtained by optimizing the feature mapping. It implies the
non-uniformity of matrix elements is a key property of the quantum-inspired
SVD. This work is a first step toward the practical application of the
quantum-inspired algorithm.

Can we see it all? Do we know it All? These are questions thrown to human
beings in our contemporary society to evaluate our tendency to solve problems.
Recent studies have explored several models in object detection; however, most
have failed to meet the demand for objectiveness and predictive accuracy,
especially in developing and under-developed countries. Consequently, several
global security threats have necessitated the development of efficient
approaches to tackle these issues. This paper proposes an object detection
model for cyber-physical systems known as Smart Surveillance Systems (3s). This
research proposes a 2-phase approach, highlighting the advantages of YOLO v3
deep learning architecture in real-time and visual object detection. A transfer
learning approach was implemented for this research to reduce training time and
computing resources. The dataset utilized for training the model is the MS COCO
dataset which contains 328,000 annotated image instances. Deep learning
techniques such as Pre-processing, Data pipelining, and detection was
implemented to improve efficiency. Compared to other novel research models, the
proposed model's results performed exceedingly well in detecting WILD objects
in surveillance footages. An accuracy of 99.71% was recorded, with an improved
mAP of 61.5.

Adopting data-based approaches leads to model improvement in numerous Oil&Gas
logging data processing problems. These improvements become even more sound due
to new capabilities provided by deep learning. However, usage of deep learning
is limited to areas where researchers possess large amounts of high-quality
data. We present an approach that provides universal data representations
suitable for solutions to different problems for different oil fields with
little additional data. Our approach relies on the self-supervised methodology
for sequential logging data for intervals from well, so it also doesn't require
labelled data from the start. For validation purposes of the received
representations, we consider classification and clusterization problems. We as
well consider the transfer learning scenario. We found out that using the
variational autoencoder leads to the most reliable and accurate models.
approach We also found that a researcher only needs a tiny separate data set
for the target oil field to solve a specific problem on top of universal

Reconstruction method based on the memory module for visual anomaly detection
attempts to narrow the reconstruction error for normal samples while enlarging
it for anomalous samples. Unfortunately, the existing memory module is not
fully applicable to the anomaly detection task, and the reconstruction error of
the anomaly samples remains small. Towards this end, this work proposes a new
unsupervised visual anomaly detection method to jointly learn effective normal
features and eliminate unfavorable reconstruction errors. Specifically, a novel
Partition Memory Bank (PMB) module is proposed to effectively learn and store
detailed features with semantic integrity of normal samples. It develops a new
partition mechanism and a unique query generation method to preserve the
context information and then improves the learning ability of the memory
module. The proposed PMB and the skip connection are alternatively explored to
make the reconstruction of abnormal samples worse. To obtain more precise
anomaly localization results and solve the problem of cumulative reconstruction
error, a novel Histogram Error Estimation module is proposed to adaptively
eliminate the unfavorable errors by the histogram of the difference image. It
improves the anomaly localization performance without increasing the cost. To
evaluate the effectiveness of the proposed method for anomaly detection and
localization, extensive experiments are conducted on three widely-used anomaly
detection datasets. The encouraging performance of the proposed method compared
to the recent approaches based on the memory module demonstrates its
superiority.

Super-resolving the coarse outputs of global climate simulations, termed
downscaling, is crucial in making political and social decisions on systems
requiring long-term climate change projections. Existing fast super-resolution
techniques, however, have yet to preserve the spatially correlated nature of
climatological data, which is particularly important when we address systems
with spatial expanse, such as the development of transportation infrastructure.
Herein, we show an adversarial network-based machine learning enables us to
correctly reconstruct the inter-regional spatial correlations in downscaling
with high magnification up to fifty, while maintaining the pixel-wise
statistical consistency. Direct comparison with the measured meteorological
data of temperature and precipitation distributions reveals that integrating
climatologically important physical information is essential for the accurate
downscaling, which prompts us to call our approach $\pi$SRGAN (Physics Informed
Super-Resolution Generative Adversarial Network). The present method has a
potential application to the inter-regionally consistent assessment of the
climate change impact.

We prove that optimistic-follow-the-regularized-leader (OFTRL), together with
smooth value updates, finds an $O(T^{-1})$-approximate Nash equilibrium in $T$
iterations for two-player zero-sum Markov games with full information. This
improves the $\tilde{O}(T^{-5/6})$ convergence rate recently shown in the paper
Zhang et al (2022). The refined analysis hinges on two essential ingredients.
First, the sum of the regrets of the two players, though not necessarily
non-negative as in normal-form games, is approximately non-negative in Markov
games. This property allows us to bound the second-order path lengths of the
learning dynamics. Second, we prove a tighter algebraic inequality regarding
the weights deployed by OFTRL that shaves an extra $\log T$ factor. This
crucial improvement enables the inductive analysis that leads to the final
$O(T^{-1})$ rate.

We enable efficient and effective coordination in unpredictable environments,
ie., in environments whose future evolution is unknown a priori and even
adversarial. We are motivated by the future of autonomy that involves multiple
robots coordinating in dynamic, unstructured, and adversarial environments to
complete complex tasks such as target tracking, image covering, and area
monitoring. Such tasks are often modeled as submodular maximization
coordination problems. We thus introduce the first submodular coordination
algorithm with bounded tracking regret, ie., with bounded suboptimality with
respect to optimal time-varying actions that know the future a priori. The
bound gracefully degrades with the environments' capacity to change
adversarially. It also quantifies how often the robots must re-select actions
to "learn" to coordinate as if they knew the future a priori. Our algorithm
generalizes the seminal Sequential Greedy algorithm by Fisher et al. to
unpredictable environments, leveraging submodularity and algorithms for the
problem of tracking the best expert. We validate our algorithm in simulated
scenarios of target tracking.

This paper proposes a method for learning continuous control policies for
active landmark localization and exploration using an information-theoretic
cost. We consider a mobile robot detecting landmarks within a limited sensing
range, and tackle the problem of learning a control policy that maximizes the
mutual information between the landmark states and the sensor observations. We
employ a Kalman filter to convert the partially observable problem in the
landmark state to Markov decision process (MDP), a differentiable field of view
to shape the reward, and an attention-based neural network to represent the
control policy. The approach is further unified with active volumetric mapping
to promote exploration in addition to landmark localization. The performance is
demonstrated in several simulated landmark localization tasks in comparison
with benchmark methods.

State-of-the-art (SOTA) deep learning mammogram classifiers, trained with
weakly-labelled images, often rely on global models that produce predictions
with limited interpretability, which is a key barrier to their successful
translation into clinical practice. On the other hand, prototype-based models
improve interpretability by associating predictions with training image
prototypes, but they are less accurate than global models and their prototypes
tend to have poor diversity. We address these two issues with the proposal of
BRAIxProtoPNet++, which adds interpretability to a global model by ensembling
it with a prototype-based model. BRAIxProtoPNet++ distills the knowledge of the
global model when training the prototype-based model with the goal of
increasing the classification accuracy of the ensemble. Moreover, we propose an
approach to increase prototype diversity by guaranteeing that all prototypes
are associated with different training images. Experiments on weakly-labelled
private and public datasets show that BRAIxProtoPNet++ has higher
classification accuracy than SOTA global and prototype-based models. Using
lesion localisation to assess model interpretability, we show BRAIxProtoPNet++
is more effective than other prototype-based models and post-hoc explanation of
global models. Finally, we show that the diversity of the prototypes learned by
BRAIxProtoPNet++ is superior to SOTA prototype-based approaches.

Smart monitoring using three-dimensional (3D) image sensors has been
attracting attention in the context of smart cities. In smart monitoring,
object detection from point cloud data acquired by 3D image sensors is
implemented for detecting moving objects such as vehicles and pedestrians to
ensure safety on the road. However, the features of point cloud data are
diversified due to the characteristics of light detection and ranging (LIDAR)
units used as 3D image sensors or the install position of the 3D image sensors.
Although a variety of deep learning (DL) models for object detection from point
cloud data have been studied to date, no research has considered how to use
multiple DL models in accordance with the features of the point cloud data. In
this work, we propose a feature-based model selection framework that creates
various DL models by using multiple DL methods and by utilizing training data
with pseudo incompleteness generated by two artificial techniques: sampling and
noise adding. It selects the most suitable DL model for the object detection
task in accordance with the features of the point cloud data acquired in the
real environment. To demonstrate the effectiveness of the proposed framework,
we compare the performance of multiple DL models using benchmark datasets
created from the KITTI dataset and present example results of object detection
obtained through a real outdoor experiment. Depending on the situation, the
detection accuracy varies up to 32% between DL models, which confirms the
importance of selecting an appropriate DL model according to the situation.

Cost-maps are used by robotic vehicles to plan collision-free paths. The cost
associated with each cell in the map represents the sensed environment
information which is often determined manually after several trial-and-error
efforts. In off-road environments, due to the presence of several types of
features, it is challenging to handcraft the cost values associated with each
feature. Moreover, different handcrafted cost values can lead to different
paths for the same environment which is not desirable. In this paper, we
address the problem of learning the cost-map values from the sensed environment
for robust vehicle path planning. We propose a novel framework called as CAMEL
using deep learning approach that learns the parameters through demonstrations
yielding an adaptive and robust cost-map for path planning. CAMEL has been
trained on multi-modal datasets such as RELLIS-3D. The evaluation of CAMEL is
carried out on an off-road scene simulator (MAVS) and on field data from
IISER-B campus. We also perform realworld implementation of CAMEL on a ground
rover. The results shows flexible and robust motion of the vehicle without
collisions in unstructured terrains.

Deep ensembles have been shown to extend the positive effect seen in typical
ensemble learning to neural networks and to reinforcement learning (RL).
However, there is still much to be done to improve the efficiency of such
ensemble models. In this work, we present Diverse Ensembles for Fast Transfer
in RL (DEFT), a new ensemble-based method for reinforcement learning in highly
multimodal environments and improved transfer to unseen environments. The
algorithm is broken down into two main phases: training of ensemble members,
and synthesis (or fine-tuning) of the ensemble members into a policy that works
in a new environment.
  The first phase of the algorithm involves training regular policy gradient or
actor-critic agents in parallel but adding a term to the loss that encourages
these policies to differ from each other. This causes the individual unimodal
agents to explore the space of optimal policies and capture more of the
multimodality of the environment than a single actor could. The second phase of
DEFT involves synthesizing the component policies into a new policy that works
well in a modified environment in one of two ways. To evaluate the performance
of DEFT, we start with a base version of the Proximal Policy Optimization (PPO)
algorithm and extend it with the modifications for DEFT. Our results show that
the pretraining phase is effective in producing diverse policies in multimodal
environments. DEFT often converges to a high reward significantly faster than
alternatives, such as random initialization without DEFT and fine-tuning of
ensemble members.
  While there is certainly more work to be done to analyze DEFT theoretically
and extend it to be even more robust, we believe it provides a strong framework
for capturing multimodality in environments while still using RL methods with
simple policy representations.

Language models are often trained on text alone, without additional
grounding. There is debate as to how much of natural language semantics can be
inferred from such a procedure. We prove that entailment judgments between
sentences can be extracted from an ideal language model that has perfectly
learned its target distribution, assuming the training sentences are generated
by Gricean agents, i.e., agents who follow fundamental principles of
communication from the linguistic theory of pragmatics. We also show entailment
judgments can be decoded from the predictions of a language model trained on
such Gricean data. Our results reveal a pathway for understanding the semantic
information encoded in unlabeled linguistic data and a potential framework for
extracting semantics from language models.

Increasing the layer number of on-chip photonic neural networks (PNNs) is
essential to improve its model performance. However, the successively cascading
of network hidden layers results in larger integrated photonic chip areas. To
address this issue, we propose the optical neural ordinary differential
equations (ON-ODE) architecture that parameterizes the continuous dynamics of
hidden layers with optical ODE solvers. The ON-ODE comprises the PNNs followed
by the photonic integrator and optical feedback loop, which can be configured
to represent residual neural networks (ResNet) and recurrent neural networks
with effectively reduced chip area occupancy. For the interference-based
optoelectronic nonlinear hidden layer, the numerical experiments demonstrate
that the single hidden layer ON-ODE can achieve approximately the same accuracy
as the two-layer optical ResNet in image classification tasks. Besides, the
ONODE improves the model classification accuracy for the diffraction-based
all-optical linear hidden layer. The time-dependent dynamics property of ON-ODE
is further applied for trajectory prediction with high accuracy.

The problem of sampling constrained continuous distributions has frequently
appeared in many machine/statistical learning models. Many Monte Carlo Markov
Chain (MCMC) sampling methods have been adapted to handle different types of
constraints on the random variables. Among these methods, Hamilton Monte Carlo
(HMC) and the related approaches have shown significant advantages in terms of
computational efficiency compared to other counterparts. In this article, we
first review HMC and some extended sampling methods, and then we concretely
explain three constrained HMC-based sampling methods, reflection,
reformulation, and spherical HMC. For illustration, we apply these methods to
solve three well-known constrained sampling problems, truncated multivariate
normal distributions, Bayesian regularized regression, and nonparametric
density estimation. In this review, we also connect constrained sampling with
another similar problem in the statistical design of experiments of constrained
design space.

In this paper, we propose the Generalized Parametric Contrastive Learning
(GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on
theoretical analysis, we observe that supervised contrastive loss tends to bias
high-frequency classes and thus increases the difficulty of imbalanced
learning. We introduce a set of parametric class-wise learnable centers to
rebalance from an optimization perspective. Further, we analyze our GPaCo/PaCo
loss under a balanced setting. Our analysis demonstrates that GPaCo/PaCo can
adaptively enhance the intensity of pushing samples of the same class close as
more samples are pulled together with their corresponding centers and benefit
hard example learning. Experiments on long-tailed benchmarks manifest the new
state-of-the-art for long-tailed recognition. On full ImageNet, models from
CNNs to vision transformers trained with GPaCo loss show better generalization
performance and stronger robustness compared with MAE models. Moreover, GPaCo
can be applied to the semantic segmentation task and obvious improvements are
observed on the 4 most popular benchmarks. Our code is available at
https://github.com/dvlab-research/Parametric-Contrastive-Learning.

Fair clustering aims to divide data into distinct clusters, while preventing
sensitive attributes (e.g., gender, race, RNA sequencing technique) from
dominating the clustering. Although a number of works have been conducted and
achieved huge success in recent, most of them are heuristical, and there lacks
a unified theory for algorithm design. In this work, we fill this blank by
developing a mutual information theory for deep fair clustering and accordingly
designing a novel algorithm, dubbed FCMI. In brief, through maximizing and
minimizing mutual information, FCMI is designed to achieve four characteristics
highly expected by deep fair clustering, i.e., compact, balanced, and fair
clusters, as well as informative features. Besides the contributions to theory
and algorithm, another contribution of this work is proposing a novel fair
clustering metric built upon information theory as well. Unlike existing
evaluation metrics, our metric measures the clustering quality and fairness in
a whole instead of separate manner. To verify the effectiveness of the proposed
FCMI, we carry out experiments on six benchmarks including a single-cell
RNA-seq atlas compared with 11 state-of-the-art methods in terms of five
metrics. Code will be released after the acceptance.

Deep convolutional neural networks (CNNs) are used for image denoising via
automatically mining accurate structure information. However, most of existing
CNNs depend on enlarging depth of designed networks to obtain better denoising
performance, which may cause training difficulty. In this paper, we propose a
multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three
stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet
transform and enhancement blocks (WEBs) and a residual block (RB). DCB uses a
dynamic convolution to dynamically adjust parameters of several convolutions
for making a tradeoff between denoising performance and computational costs.
WEB uses a combination of signal processing technique (i.e., wavelet
transformation) and discriminative learning to suppress noise for recovering
more detailed information in image denoising. To further remove redundant
features, RB is used to refine obtained features for improving denoising
effects and reconstruct clean images via improved residual dense architectures.
Experimental results show that the proposed MWDCNN outperforms some popular
denoising methods in terms of quantitative and qualitative analysis. Codes are
available at https://github.com/hellloxiaotian/MWDCNN.

We initiate the study of quantum algorithms for optimizing approximately
convex functions. Given a convex set ${\cal K}\subseteq\mathbb{R}^{n}$ and a
function $F\colon\mathbb{R}^{n}\to\mathbb{R}$ such that there exists a convex
function $f\colon\mathcal{K}\to\mathbb{R}$ satisfying $\sup_{x\in{\cal
K}}|F(x)-f(x)|\leq \epsilon/n$, our quantum algorithm finds an $x^{*}\in{\cal
K}$ such that $F(x^{*})-\min_{x\in{\cal K}} F(x)\leq\epsilon$ using
$\tilde{O}(n^{3})$ quantum evaluation queries to $F$. This achieves a
polynomial quantum speedup compared to the best-known classical algorithms. As
an application, we give a quantum algorithm for zeroth-order stochastic convex
bandits with $\tilde{O}(n^{5}\log^{2} T)$ regret, an exponential speedup in $T$
compared to the classical $\Omega(\sqrt{T})$ lower bound. Technically, we
achieve quantum speedup in $n$ by exploiting a quantum framework of simulated
annealing and adopting a quantum version of the hit-and-run walk. Our speedup
in $T$ for zeroth-order stochastic convex bandits is due to a quadratic quantum
speedup in multiplicative error of mean estimation.

Despite substantial progress in deep learning approaches to time-series
reconstruction, no existing methods are designed to uncover local activities
with minute signal strength due to their negligible contribution to the
optimization loss. Such local activities however can signify important abnormal
events in physiological systems, such as an extra foci triggering an abnormal
propagation of electrical waves in the heart. We discuss a novel technique for
reconstructing such local activity that, while small in signal strength, is the
cause of subsequent global activities that have larger signal strength. Our
central innovation is to approach this by explicitly modeling and disentangling
how the latent state of a system is influenced by potential hidden internal
interventions. In a novel neural formulation of state-space models (SSMs), we
first introduce causal-effect modeling of the latent dynamics via a system of
interacting neural ODEs that separately describes 1) the continuous-time
dynamics of the internal intervention, and 2) its effect on the trajectory of
the system's native state. Because the intervention can not be directly
observed but have to be disentangled from the observed subsequent effect, we
integrate knowledge of the native intervention-free dynamics of a system, and
infer the hidden intervention by assuming it to be responsible for differences
observed between the actual and hypothetical intervention-free dynamics. We
demonstrated a proof-of-concept of the presented framework on reconstructing
ectopic foci disrupting the course of normal cardiac electrical propagation
from remote observations.

Real-time biosignal processing on wearable devices has attracted worldwide
attention for its potential in healthcare applications. However, the
requirement of low-area, low-power and high adaptability to different patients
challenge conventional algorithms and hardware platforms. In this design, a
compact online learning neuromorphic hardware architecture with ultra-low power
consumption designed explicitly for biosignal processing is proposed. A
trace-based Spiking-Timing-Dependent-Plasticity (STDP) lgorithm is applied to
realize hardware-friendly online learning of a single-layer
excitatory-inhibitory spiking neural network. Several techniques, including
event-driven architecture and a fully optimized iterative computation approach,
are adopted to minimize the hardware utilization and power consumption for the
hardware implementation of online learning. Experiment results show that the
proposed design reaches the accuracy of 87.36% and 83% for the Mixed National
Institute of Standards and Technology database (MNIST) and ECG classification.
The hardware architecture is implemented on a Zynq-7020 FPGA. Implementation
results show that the Look-Up Table (LUT) and Flip Flops (FF) utilization
reduced by 14.87 and 7.34 times, respectively, and the power consumption
reduced by 21.69% compared to state of the art.

Score-based generative modeling (SGM) has grown to be a hugely successful
method for learning to generate samples from complex data distributions such as
that of images and audio. It is based on evolving an SDE that transforms white
noise into a sample from the learned distribution, using estimates of the score
function, or gradient log-pdf. Previous convergence analyses for these methods
have suffered either from strong assumptions on the data distribution or
exponential dependencies, and hence fail to give efficient guarantees for the
multimodal and non-smooth distributions that arise in practice and for which
good empirical performance is observed. We consider a popular kind of SGM --
denoising diffusion models -- and give polynomial convergence guarantees for
general data distributions, with no assumptions related to functional
inequalities or smoothness. Assuming $L^2$-accurate score estimates, we obtain
Wasserstein distance guarantees for any distribution of bounded support or
sufficiently decaying tails, as well as TV guarantees for distributions with
further smoothness assumptions.

Deep learning has been widely deployed for online ads systems to predict
Click-Through Rate (CTR). Machine learning researchers and practitioners
frequently retrain CTR models to test their new extracted features. However,
the CTR model training often relies on a large number of raw input data logs.
Hence, the feature extraction can take a significant proportion of the training
time for an industrial-level CTR model. In this paper, we propose FeatureBox, a
novel end-to-end training framework that pipelines the feature extraction and
the training on GPU servers to save the intermediate I/O of the feature
extraction. We rewrite computation-intensive feature extraction operators as
GPU operators and leave the memory-intensive operator on CPUs. We introduce a
layer-wise operator scheduling algorithm to schedule these heterogeneous
operators. We present a light-weight GPU memory management algorithm that
supports dynamic GPU memory allocation with minimal overhead. We experimentally
evaluate FeatureBox and compare it with the previous in-production feature
extraction framework on two real-world ads applications. The results confirm
the effectiveness of our proposed method.

In recent years, neuroscientists have been interested to the development of
brain-computer interface (BCI) devices. Patients with motor disorders may
benefit from BCIs as a means of communication and for the restoration of motor
functions. Electroencephalography (EEG) is one of most used for evaluating the
neuronal activity. In many computer vision applications, deep neural networks
(DNN) show significant advantages. Towards to ultimate usage of DNN, we present
here a shallow neural network that uses mainly two convolutional neural network
(CNN) layers, with relatively few parameters and fast to learn
spectral-temporal features from EEG. We compared this models to three other
neural network models with different depths applied to a mental arithmetic task
using eye-closed state adapted for patients suffering from motor disorders and
a decline in visual functions. Experimental results showed that the shallow CNN
model outperformed all the other models and achieved the highest classification
accuracy of 90.68%. It's also more robust to deal with cross-subject
classification issues: only 3% standard deviation of accuracy instead of 15.6%
from conventional method.

Thermal ablation procedures, such as high intensity focused ultrasound (HIFU)
and Radiofrequency Ablation (RFA), are often used to eliminate tumors by
minimally invasively heating a focal region. For this task, real-time 3D
temperature visualization is key to target the diseased tissues while
minimizing damage to the surroundings. Current CT thermometry is based on
energy-integrated CT, tissue-specific experimental data, and linear
relationships between attenuation and temperature. In this letter, we develop a
novel approach using photon-counting CT for material decomposition and a neural
network to predict temperature based on thermal characteristics of base
materials and spectral tomographic measurements of a volume of interest. In our
feasibility study, distilled water, 50 mM CaCl2, and 600 mM CaCl2 are chosen as
the base materials. Their attenuations are measured in four discrete energy
bins at various temperatures. The neural network trained on the experimental
data achieves a mean absolute error of 1.80 {\deg}C and 3.97 {\deg}C on 300 mM
CaCl2 and a milk-based protein shake respectively. These experimental results
indicate that our approach is promising for handling nonlinear thermal
properties for materials that are similar or dissimilar to our base materials.

Powered prosthetic legs must anticipate the user's intent when switching
between different locomotion modes (e.g., level walking, stair ascent/descent,
ramp ascent/descent). Numerous data-driven classification techniques have
demonstrated promising results for predicting user intent, but the performance
of these intent prediction models on novel subjects remains undesirable. In
other domains (e.g., image classification), transfer learning has improved
classification accuracy by using previously learned features from a large
dataset (i.e., pre-trained models) and then transferring this learned model to
a new task where a smaller dataset is available. In this paper, we develop a
deep convolutional neural network with intra-subject (subject-dependent) and
inter-subject (subject-independent) validations based on a human locomotion
dataset. We then apply transfer learning for the subject-independent model
using a small portion (10%) of the data from the left-out subject. We compare
the performance of these three models. Our results indicate that the transfer
learning (TL) model outperforms the subject-independent (IND) model and is
comparable to the subject-dependent (DEP) model (DEP Error: 0.74 $\pm$ 0.002%,
IND Error: 11.59 $\pm$ 0.076%, TL Error: 3.57 $\pm$ 0.02% with 10% data).
Moreover, as expected, transfer learning accuracy increases with the
availability of more data from the left-out subject. We also evaluate the
performance of the intent prediction system in various sensor configurations
that may be available in a prosthetic leg application. Our results suggest that
a thigh IMU on the the prosthesis is sufficient to predict locomotion intent in
practice.

We study the task of robust feature representations, aiming to generalize
well on multiple datasets for action recognition. We build our method on
Transformers for its efficacy. Although we have witnessed great progress for
video action recognition in the past decade, it remains challenging yet
valuable how to train a single model that can perform well across multiple
datasets. Here, we propose a novel multi-dataset training paradigm, MultiTrain,
with the design of two new loss terms, namely informative loss and projection
loss, aiming to learn robust representations for action recognition. In
particular, the informative loss maximizes the expressiveness of the feature
embedding while the projection loss for each dataset mines the intrinsic
relations between classes across datasets. We verify the effectiveness of our
method on five challenging datasets, Kinetics-400, Kinetics-700,
Moments-in-Time, Activitynet and Something-something-v2 datasets. Extensive
experimental results show that our method can consistently improve
state-of-the-art performance. Code and models are released.

Load frequency control (LFC) is a key factor to maintain the stable frequency
in multi-area power systems. As the modern power systems evolve from
centralized to distributed paradigm, LFC needs to consider the peer-to-peer
(P2P) based scheme that considers limited information from the
information-exchange graph for the generator control of each interconnected
area. This paper aims to solve a data-driven constrained LQR problem with
mean-variance risk constraints and output structured feedback, and applies this
framework to solve the LFC problem in multi-area power systems. By
reformulating the constrained optimization problem into a minimax problem, the
stochastic gradient descent max-oracle (SGDmax) algorithm with zero-order
policy gradient (ZOPG) is adopted to find the optimal feedback gain from the
learning, while guaranteeing convergence. In addition, to improve the
adaptation of the proposed learning method to new or varying models, we
construct an emulator grid that approximates the dynamics of a physical grid
and performs training based on this model. Once the feedback gain is obtained
from the emulator grid, it is applied to the physical grid with a robustness
test to check whether the controller from the approximated emulator applies to
the actual system. Numerical tests show that the obtained feedback controller
can successfully control the frequency of each area, while mitigating the
uncertainty from the loads, with reliable robustness that ensures the
adaptability of the obtained feedback gain to the actual physical grid.

In this paper, we present a fast monocular depth estimation method for
enabling 3D perception capabilities of low-cost underwater robots. We formulate
a novel end-to-end deep visual learning pipeline named UDepth, which
incorporates domain knowledge of image formation characteristics of natural
underwater scenes. First, we adapt a new input space from raw RGB image space
by exploiting underwater light attenuation prior, and then devise a
least-squared formulation for coarse pixel-wise depth prediction. Subsequently,
we extend this into a domain projection loss that guides the end-to-end
learning of UDepth on over 9K RGB-D training samples. UDepth is designed with a
computationally light MobileNetV2 backbone and a Transformer-based optimizer
for ensuring fast inference rates on embedded systems. By domain-aware design
choices and through comprehensive experimental analyses, we demonstrate that it
is possible to achieve state-of-the-art depth estimation performance while
ensuring a small computational footprint. Specifically, with 70%-80% less
network parameters than existing benchmarks, UDepth achieves comparable and
often better depth estimation performance. While the full model offers over 66
FPS (13 FPS) inference rates on a single GPU (CPU core), our domain projection
for coarse depth prediction runs at 51.5 FPS rates on single-board NVIDIA
Jetson TX2s. The inference pipelines are available at
https://github.com/uf-robopi/UDepth.

Meta-Learning has emerged as a research direction to better transfer
knowledge from related tasks to unseen but related tasks. However,
Meta-Learning requires many training tasks to learn representations that
transfer well to unseen tasks; otherwise, it leads to overfitting, and the
performance degenerates to worse than Multi-task Learning. We show that a
state-of-the-art data augmentation method worsens this problem of overfitting
when the task diversity is low. We propose a simple method, TaskMix, which
synthesizes new tasks by linearly interpolating existing tasks. We compare
TaskMix against many baselines on an in-house multilingual intent
classification dataset of N-Best ASR hypotheses derived from real-life
human-machine telephony utterances and two datasets derived from MTOP. We show
that TaskMix outperforms baselines, alleviates overfitting when task diversity
is low, and does not degrade performance even when it is high.

Advancements in machine learning have fuelled the popularity of using AI
decision algorithms in procedures such as bail hearings (Feller et al. 2016),
medical diagnoses (Rajkomar et al. 2018; Esteva et al. 2019) and recruitment
(Heilweil 2019, Van Esch et al. 2019). Academic articles (Floridi et al. 2018),
policy texts (HLEG 2019), and popularizing books (O'Neill 2016, Eubanks 2018)
alike warn that such algorithms tend to be _opaque_: they do not provide
explanations for their outcomes. Building on a causal account of transparency
and opacity as well as recent work on the value of causal explanation (Lombrozo
2011, Hitchcock 2012), I formulate a moral concern for opaque algorithms that
is yet to receive a systematic treatment in the literature: when such
algorithms are used in life-changing decisions, they can obstruct us from
effectively shaping our lives according to our goals and preferences, thus
undermining our autonomy. I argue that this concern deserves closer attention
as it furnishes the call for transparency in algorithmic decision-making with
both new tools and new challenges.

Finite element discretizations of problems in computational physics often
rely on adaptive mesh refinement (AMR) to preferentially resolve regions
containing important features during simulation. However, these spatial
refinement strategies are often heuristic and rely on domain-specific knowledge
or trial-and-error. We treat the process of adaptive mesh refinement as a
local, sequential decision-making problem under incomplete information,
formulating AMR as a partially observable Markov decision process. Using a deep
reinforcement learning approach, we train policy networks for AMR strategy
directly from numerical simulation. The training process does not require an
exact solution or a high-fidelity ground truth to the partial differential
equation at hand, nor does it require a pre-computed training dataset. The
local nature of our reinforcement learning formulation allows the policy
network to be trained inexpensively on much smaller problems than those on
which they are deployed. The methodology is not specific to any particular
partial differential equation, problem dimension, or numerical discretization,
and can flexibly incorporate diverse problem physics. To that end, we apply the
approach to a diverse set of partial differential equations, using a variety of
high-order discontinuous Galerkin and hybridizable discontinuous Galerkin
finite element discretizations. We show that the resultant deep reinforcement
learning policies are competitive with common AMR heuristics, generalize well
across problem classes, and strike a favorable balance between accuracy and
cost such that they often lead to a higher accuracy per problem degree of
freedom.

We focus on an unloading problem, typical of the logistics sector, modeled as
a sequential pick-and-place task. In this type of task, modern machine learning
techniques have shown to work better than classic systems since they are more
adaptable to stochasticity and better able to cope with large uncertainties.
More specifically, supervised and imitation learning have achieved outstanding
results in this regard, with the shortcoming of requiring some form of
supervision which is not always obtainable for all settings. On the other hand,
reinforcement learning (RL) requires much milder form of supervision but still
remains impracticable due to its inefficiency. In this paper, we propose and
theoretically motivate a novel Unsupervised Reward Shaping algorithm from
expert's observations which relaxes the level of supervision required by the
agent and works on improving RL performance in our task.

We investigate the use of animal videos (observations) to improve
Reinforcement Learning (RL) efficiency and performance in navigation tasks with
sparse rewards. Motivated by theoretical considerations, we make use of
weighted policy optimization for off-policy RL and describe the main challenges
when learning from animal videos. We propose solutions and test our ideas on a
series of 2D navigation tasks. We show how our methods can leverage animal
videos to improve performance over RL algorithms that do not leverage such
observations.

As children grow older, they develop an intuitive understanding of the
physical processes around them. They move along developmental trajectories,
which have been mapped out extensively in previous empirical research. We
investigate how children's developmental trajectories compare to the learning
trajectories of artificial systems. Specifically, we examine the idea that
cognitive development results from some form of stochastic optimization
procedure. For this purpose, we train a modern generative neural network model
using stochastic gradient descent. We then use methods from the developmental
psychology literature to probe the physical understanding of this model at
different degrees of optimization. We find that the model's learning trajectory
captures the developmental trajectories of children, thereby providing support
to the idea of development as stochastic optimization.

Novel object captioning (NOC) aims to describe images containing objects
without observing their ground truth captions during training. Due to the
absence of caption annotation, captioning models cannot be directly optimized
via sequence-to-sequence training or CIDEr optimization. As a result, we
present Paraphrasing-to-Captioning (P2C), a two-stage learning framework for
NOC, which would heuristically optimize the output captions via paraphrasing.
With P2C, the captioning model first learns paraphrasing from a language model
pre-trained on text-only corpus, allowing expansion of the word bank for
improving linguistic fluency. To further enforce the output caption
sufficiently describing the visual content of the input image, we perform
self-paraphrasing for the captioning model with fidelity and adequacy
objectives introduced. Since no ground truth captions are available for novel
object images during training, our P2C leverages cross-modality (image-text)
association modules to ensure the above caption characteristics can be properly
preserved. In the experiments, we not only show that our P2C achieves
state-of-the-art performances on nocaps and COCO Caption datasets, we also
verify the effectiveness and flexibility of our learning framework by replacing
language and cross-modality association models for NOC. Implementation details
and code are available in the supplementary materials.

We present a deep learning approximation, stochastic optimization based,
method for wave kinetic equations. To build confidence in our approach, we
apply the method to a Smoluchowski coagulation equation with multiplicative
kernel for which an analytic solution exists. Our deep learning approach is
then used to approximate the non-stationary solution to a 3-wave kinetic
equation corresponding to acoustic wave systems. To validate the neural network
approximation, we compare the decay rate of the total energy with previously
obtained theoretical results. A finite volume solution is presented and
compared with the present method.

In the study of subsurface seismic imaging, solving the acoustic wave
equation is a pivotal component in existing models. The advancement of deep
learning enables solving partial differential equations, including wave
equations, by applying neural networks to identify the mapping between the
inputs and the solution. This approach can be faster than traditional numerical
methods when numerous instances are to be solved. Previous works that
concentrate on solving the wave equation by neural networks consider either a
single velocity model or multiple simple velocity models, which is restricted
in practice. Instead, inspired by the idea of operator learning, this work
leverages the Fourier neural operator (FNO) to effectively learn the frequency
domain seismic wavefields under the context of variable velocity models. We
also propose a new framework paralleled Fourier neural operator (PFNO) for
efficiently training the FNO-based solver given multiple source locations and
frequencies. Numerical experiments demonstrate the high accuracy of both FNO
and PFNO with complicated velocity models in the OpenFWI datasets. Furthermore,
the cross-dataset generalization test verifies that PFNO adapts to
out-of-distribution velocity models. Moreover, PFNO has robust performance in
the presence of random noise in the labels. Finally, PFNO admits higher
computational efficiency on large-scale testing datasets than the traditional
finite-difference method. The aforementioned advantages endow the FNO-based
solver with the potential to build powerful models for research on seismic
waves.

A neural-networks predictor library has been developed to deploy machine
learning (ML) models into computational fluid dynamics (CFD) codes. The
pointer-to-implementation strategy is adopted to isolate the implementation
details in order to simplify the implementation to CFD solvers. The library
provides simplified model-managing functions by encapsulating the TensorFlow C
library, and it maintains self-belonging data containers to deal with data type
casting and memory layouts in the input/output (I/O) functions interfacing with
CFD solvers. On the language level, the library provides application
programming interfaces (APIs) for C++ and Fortran, the two commonly used
programming languages in the CFD community. High-level customized modules are
developed for two open-source CFD codes, OpenFOAM and CFL3D, written with C++
and Fortran, respectively. The basic usage of the predictor is demonstrated in
a simple data-driven heat transfer problem as the first tutorial case. Another
tutorial case of modeling the effect of turbulence in channel flow using the
library is implemented in both OpenFOAM and CFL3D codes. The developed ML
predictor library provides a powerful tool for the deployment of ML models in
CFD solvers.

Providing formal safety and performance guarantees for autonomous systems is
becoming increasingly important as they are integrated in our society.
Hamilton-Jacobi (HJ) reachability analysis is a popular formal verification
tool for providing these guarantees, since it can handle general nonlinear
system dynamics, bounded adversarial system disturbances, and state and input
constraints. However, it involves solving a PDE, whose computational and memory
complexity scales exponentially with respect to the state dimensionality,
making its direct use on large-scale systems intractable. A recently proposed
method called DeepReach overcomes this challenge by leveraging a sinusoidal
neural network PDE solver for high-dimensional reachability problems, whose
computational requirements scale with the complexity of the underlying
reachable tube rather than the state space dimension. Unfortunately, neural
networks can make errors and thus the computed solution may not be safe, which
falls short of achieving our overarching goal to provide formal safety
assurances. In this work, we propose a method to compute an error bound for the
DeepReach solution. This error bound can then be used for reachable tube
correction, resulting in a provably safe approximation of the true reachable
tube. We also propose a scenario optimization-based approach to compute this
error bound for general nonlinear dynamical systems. We demonstrate the
efficacy of the proposed approach in obtaining reachable tubes for
high-dimensional rocket-landing and multi-vehicle collision-avoidance problems.

We present a temporally extended variation of the successor representation,
which we term t-SR. t-SR captures the expected state transition dynamics of
temporally extended actions by constructing successor representations over
primitive action repeats. This form of temporal abstraction does not learn a
top-down hierarchy of pertinent task structures, but rather a bottom-up
composition of coupled actions and action repetitions. This lessens the amount
of decisions required in control without learning a hierarchical policy. As
such, t-SR directly considers the time horizon of temporally extended action
sequences without the need for predefined or domain-specific options. We show
that in environments with dynamic reward structure, t-SR is able to leverage
both the flexibility of the successor representation and the abstraction
afforded by temporally extended actions. Thus, in a series of sparsely rewarded
gridworld environments, t-SR optimally adapts learnt policies far faster than
comparable value-based, model-free reinforcement learning methods. We also show
that the manner in which t-SR learns to solve these tasks requires the learnt
policy to be sampled consistently less often than non-temporally extended
policies.

This work proposes aesthetic gradients, a method to personalize a
CLIP-conditioned diffusion model by guiding the generative process towards
custom aesthetics defined by the user from a set of images. The approach is
validated with qualitative and quantitative experiments, using the recent
stable diffusion model and several aesthetically-filtered datasets. Code is
released at https://github.com/vicgalle/stable-diffusion-aesthetic-gradients

Cross-lingual transfer learning has proven useful in a variety of Natural
Language Processing (NLP) tasks, but it is understudied in the context of legal
NLP, and not at all in Legal Judgment Prediction (LJP). We explore transfer
learning techniques on LJP using the trilingual Swiss-Judgment-Prediction
dataset, including cases written in three languages. We find that cross-lingual
transfer improves the overall results across languages, especially when we use
adapter-based fine-tuning. Finally, we further improve the model's performance
by augmenting the training dataset with machine-translated versions of the
original documents, using a 3x larger training corpus. Further on, we perform
an analysis exploring the effect of cross-domain and cross-regional transfer,
i.e., train a model across domains (legal areas), or regions. We find that in
both settings (legal areas, origin regions), models trained across all groups
perform overall better, while they also have improved results in the worst-case
scenarios. Finally, we report improved results when we ambitiously apply
cross-jurisdiction transfer, where we further augment our dataset with Indian
legal cases.

Unmanned aerial vehicles (UAVs) are widely applied for purposes of
inspection, search, and rescue operations by the virtue of low-cost,
large-coverage, real-time, and high-resolution data acquisition capacities.
Massive volumes of aerial videos are produced in these processes, in which
normal events often account for an overwhelming proportion. It is extremely
difficult to localize and extract abnormal events containing potentially
valuable information from long video streams manually. Therefore, we are
dedicated to developing anomaly detection methods to solve this issue. In this
paper, we create a new dataset, named DroneAnomaly, for anomaly detection in
aerial videos. This dataset provides 37 training video sequences and 22 testing
video sequences from 7 different realistic scenes with various anomalous
events. There are 87,488 color video frames (51,635 for training and 35,853 for
testing) with the size of $640 \times 640$ at 30 frames per second. Based on
this dataset, we evaluate existing methods and offer a benchmark for this task.
Furthermore, we present a new baseline model, ANomaly Detection with
Transformers (ANDT), which treats consecutive video frames as a sequence of
tubelets, utilizes a Transformer encoder to learn feature representations from
the sequence, and leverages a decoder to predict the next frame. Our network
models normality in the training phase and identifies an event with
unpredictable temporal dynamics as an anomaly in the test phase. Moreover, To
comprehensively evaluate the performance of our proposed method, we use not
only our Drone-Anomaly dataset but also another dataset. We will make our
dataset and code publicly available. A demo video is available at
https://youtu.be/ancczYryOBY. We make our dataset and code publicly available .

Estimating the causal effects of a spatially-varying intervention on a
spatially-varying outcome may be subject to non-local confounding (NLC), a
phenomenon that can bias estimates when the treatments and outcomes of a given
unit are dictated in part by the covariates of other nearby units. In
particular, NLC is a challenge for evaluating the effects of environmental
policies and climate events on health-related outcomes such as air pollution
exposure. This paper first formalizes NLC using the potential outcomes
framework, providing a comparison with the related phenomenon of causal
interference. Then, it proposes a broadly applicable framework, termed
"weather2vec", that uses the theory of balancing scores to learn
representations of non-local information into a scalar or vector defined for
each observational unit, which is subsequently used to adjust for confounding
in conjunction with causal inference methods. The framework is evaluated in a
simulation study and two case studies on air pollution where the weather is an
(inherently regional) known confounder.

Based on global tweets from 2017 to 2022, we implement traditional machine
learning and deep learning methods to build high-frequency measures of the
public's sentiment index towards inflation and analyze the correlation with
other online data sources such as google trend and market-oriented inflation
index. First, we test out several machine learning approaches using manually
labelled tri-grams and finally choose Bert model for our research. Second, we
calculate inflation sentiment index through sentiment score of the tweets
applying Bert model and analyse the regional and pre/post covid pattern.
Lastly, we take other online data sources of inflation into consideration and
prove that twitter-based inflation sentiment analysis method has an outstanding
capability to predict inflation. The results suggest that Twitter combined with
deep learning methods can be a novel and timely method to utilise existing
abundant data sources on inflation expectations and provide daily and weekly
indicators of consumers' perception on inflation.

Surges that have been observed at different periods in the number of COVID-19
cases are associated with the emergence of multiple SARS-CoV-2 (Severe Acute
Respiratory Virus) variants. The design of methods to support laboratory
detection are crucial in the monitoring of these variants. Hence, in this
paper, we develop a semi-automated method to design both forward and reverse
primer sets to detect SARS-CoV-2 variants. To proceed, we train deep
Convolution Neural Networks (CNNs) to classify labelled SARS-CoV-2 variants and
identify partial genomic features needed for the forward and reverse Polymerase
Chain Reaction (PCR) primer design. Our proposed approach supplements existing
ones while promoting the emerging concept of neural network assisted primer
design for PCR. Our CNN model was trained using a database of SARS-CoV-2
full-length genomes from GISAID and tested on a separate dataset from NCBI,
with 98\% accuracy for the classification of variants. This result is based on
the development of three different methods of feature extraction, and the
selected primer sequences for each SARS-CoV-2 variant detection (except
Omicron) were present in more than 95 \% of sequences in an independent set of
5000 same variant sequences, and below 5 \% in other independent datasets with
5000 sequences of each variant. In total, we obtain 22 forward and reverse
primer pairs with flexible length sizes (18-25 base pairs) with an expected
amplicon length ranging between 42 and 3322 nucleotides. Besides the feature
appearance, in-silico primer checks confirmed that the identified primer pairs
are suitable for accurate SARS-CoV-2 variant detection by means of PCR tests.

We propose an efficient algorithm for graph matching based on similarity
scores constructed from counting a certain family of weighted trees rooted at
each vertex. For two Erd\H{o}s-R\'enyi graphs $\mathcal{G}(n,q)$ whose edges
are correlated through a latent vertex correspondence, we show that this
algorithm correctly matches all but a vanishing fraction of the vertices with
high probability, provided that $nq\to\infty$ and the edge correlation
coefficient $\rho$ satisfies $\rho^2>\alpha \approx 0.338$, where $\alpha$ is
Otter's tree-counting constant. Moreover, this almost exact matching can be
made exact under an extra condition that is information-theoretically
necessary. This is the first polynomial-time graph matching algorithm that
succeeds at an explicit constant correlation and applies to both sparse and
dense graphs. In comparison, previous methods either require $\rho=1-o(1)$ or
are restricted to sparse graphs.
  The crux of the algorithm is a carefully curated family of rooted trees
called chandeliers, which allows effective extraction of the graph correlation
from the counts of the same tree while suppressing the undesirable correlation
between those of different trees.

Automunge is a tabular preprocessing library that encodes dataframes for
supervised learning. When selecting a default feature encoding strategy for
gradient boosted learning, one may consider metrics of training duration and
achieved predictive performance associated with the feature representations.
Automunge offers a default of binarization for categoric features and z-score
normalization for numeric. The presented study sought to validate those
defaults by way of benchmarking on a series of diverse data sets by encoding
variations with tuned gradient boosted learning. We found that on average our
chosen defaults were top performers both from a tuning duration and a model
performance standpoint. Another key finding was that one hot encoding did not
perform in a manner consistent with suitability to serve as a categoric default
in comparison to categoric binarization. We present here these and further

We consider the open federated learning (FL) systems, where clients may join
and/or leave the system during the FL process. Given the variability of the
number of present clients, convergence to a fixed model cannot be guaranteed in
open systems. Instead, we resort to a new performance metric that we term the
stability of open FL systems, which quantifies the magnitude of the learned
model in open systems. Under the assumption that local clients' functions are
strongly convex and smooth, we theoretically quantify the radius of stability
for two FL algorithms, namely local SGD and local Adam. We observe that this
radius relies on several key parameters, including the function condition
number as well as the variance of the stochastic gradient. Our theoretical
results are further verified by numerical simulations on both synthetic and
real-world benchmark data-sets.

Ovarian cancer is the most lethal gynaecological malignancy. The disease is
most commonly asymptomatic at its early stages and its diagnosis relies on
expert evaluation of transvaginal ultrasound images. Ultrasound is the
first-line imaging modality for characterising adnexal masses, it requires
significant expertise and its analysis is subjective and labour-intensive,
therefore open to error. Hence, automating processes to facilitate and
standardise the evaluation of scans is desired in clinical practice. Using
supervised learning, we have demonstrated that segmentation of adnexal masses
is possible, however, prevalence and label imbalance restricts the performance
on under-represented classes. To mitigate this we apply a novel
pathology-specific data synthesiser. We create synthetic medical images with
their corresponding ground truth segmentations by using Poisson image editing
to integrate less common masses into other samples. Our approach achieves the
best performance across all classes, including an improvement of up to 8% when
compared with nnU-Net baseline approaches.

Web archives have grown to petabytes. In addition to providing invaluable
background knowledge on many social and cultural developments over the last 30
years, they also provide vast amounts of training data for machine learning. To
benefit from recent developments in Deep Learning, the use of web archives
requires a scalable solution for their processing that supports inference with
and training of neural networks. To date, there is no publicly available
library for processing web archives in this way, and some existing applications
use workarounds. This paper presents WARC-DL, a deep learning-enabled pipeline
for web archive processing that scales to petabytes.

As modern problems such as autonomous driving, control of robotic components,
and medical diagnostics have become increasingly difficult to solve
analytically, data-driven decision-making has seen a large gain in interest.
Where there are problems with more dimensions of complexity than can be
understood by people, data-driven solutions are a strong option. Many of these
methods belong to a subdivision of machine learning known as reinforcement
learning. Unfortunately, data-driven models often come with uncertainty in how
they will perform in the worst of scenarios. Since the solutions are not
derived analytically many times, these models will fail unpredictably. In
fields such as autonomous driving and medicine, the consequences of these
failures could be catastrophic.
  Various methods are being explored to resolve this issue and one of them is
known as adversarial learning. It pits two models against each other by having
one model optimize its goals as the opposite of the other model's goals. This
type of training has the potential to find models which perform reliably in
complex and high stakes settings, although it is not certain when this type of
training will work. The goal is to gain insight about when these types of
models will reach stable solutions.

The Inverse Reinforcement Learning (\textit{IRL}) problem has seen rapid
evolution in the past few years, with important applications in domains like
robotics, cognition, and health. In this work, we explore the inefficacy of
current IRL methods in learning an agent's reward function from expert
trajectories depicting long-horizon, complex sequential tasks. We hypothesize
that imbuing IRL models with structural motifs capturing underlying tasks can
enable and enhance their performance. Subsequently, we propose a novel IRL
method, SMIRL, that first learns the (approximate) structure of a task as a
finite-state-automaton (FSA), then uses the structural motif to solve the IRL
problem. We test our model on both discrete grid world and high-dimensional
continuous domain environments. We empirically show that our proposed approach
successfully learns all four complex tasks, where two foundational IRL
baselines fail. Our model also outperforms the baselines in sample efficiency
on a simpler toy task. We further show promising test results in a modified
continuous domain on tasks with compositional reward functions.

Learning to optimize is a rapidly growing area that aims to solve
optimization problems or improve existing optimization algorithms using machine
learning (ML). In particular, the graph neural network (GNN) is considered a
suitable ML model for optimization problems whose variables and constraints are
permutation--invariant, for example, the linear program (LP). While the
literature has reported encouraging numerical results, this paper establishes
the theoretical foundation of applying GNNs to solving LPs. Given any size
limit of LPs, we construct a GNN that maps different LPs to different outputs.
We show that properly built GNNs can reliably predict feasibility, boundedness,
and an optimal solution for each LP in a broad class. Our proofs are based upon
the recently--discovered connections between the Weisfeiler--Lehman isomorphism
test and the GNN. To validate our results, we train a simple GNN and present
its accuracy in mapping LPs to their feasibilities and solutions.

Feature selection has drawn much attention over the last decades in machine
learning because it can reduce data dimensionality while maintaining the
original physical meaning of features, which enables better interpretability
than feature extraction. However, most existing feature selection approaches,
especially deep-learning-based, often focus on the features with great
importance scores only but neglect those with less importance scores during
training as well as the order of important candidate features. This can be
risky since some important and relevant features might be unfortunately ignored
during training, leading to suboptimal solutions or misleading selections. In
our work, we deal with feature selection by exploiting the features with less
importance scores and propose a feature selection framework based on a novel
complementary feature mask. Our method is generic and can be easily integrated
into existing deep-learning-based feature selection approaches to improve their
performance as well. Experiments have been conducted on benchmarking datasets
and shown that the proposed method can select more representative and
informative features than the state of the art.

In medicine, curated image datasets often employ discrete labels to describe
what is known to be a continuous spectrum of healthy to pathological
conditions, such as e.g. the Alzheimer's Disease Continuum or other areas where
the image plays a pivotal point in diagnosis. We propose an architecture for
image stratification based on a conditional variational autoencoder. Our
framework, VAESim, leverages a continuous latent space to represent the
continuum of disorders and finds clusters during training, which can then be
used for image/patient stratification. The core of the method learns a set of
prototypical vectors, each associated with a cluster. First, we perform a soft
assignment of each data sample to the clusters. Then, we reconstruct the sample
based on a similarity measure between the sample embedding and the prototypical
vectors of the clusters. To update the prototypical embeddings, we use an
exponential moving average of the most similar representations between actual
prototypes and samples in the batch size. We test our approach on the
MNIST-handwritten digit dataset and on a medical benchmark dataset called
PneumoniaMNIST. We demonstrate that our method outperforms baselines in terms
of kNN accuracy measured on a classification task against a standard VAE (up to
15% improvement in performance) in both datasets, and also performs at par with
classification models trained in a fully supervised way. We also demonstrate
how our model outperforms current, end-to-end models for unsupervised
stratification.

The conventional model aggregation-based federated learning (FL) approaches
require all local models to have the same architecture and fail to support
practical scenarios with heterogeneous local models. Moreover, the frequent
model exchange is costly for resource-limited wireless networks since modern
deep neural networks usually have over-million parameters. To tackle these
challenges, we first propose a novel knowledge-aided FL (KFL) framework, which
aggregates light high-level data features, namely knowledge, in the per-round
learning process. This framework allows devices to design their machine
learning models independently, and the KFL also reduces the communication
overhead in the training process. We then theoretically analyze the convergence
bound of the proposed framework under a non-convex loss function setting,
revealing that large data volumes should be scheduled in the early rounds if
the total data volumes during the entire learning course are fixed. Inspired by
this, we define a new objective function, i.e., the weighted scheduled data
sample volume, to transform the inexplicit global loss minimization problem
into a tractable one for device scheduling, bandwidth allocation and power
control. To deal with the unknown time-varying wireless channels, we transform
the problem into a deterministic problem with the assistance of the Lyapunov
optimization framework. Then, we also develop an efficient online device
scheduling algorithm to achieve an energy-learning trade-off in the learning
process. Experimental results on two typical datasets (i.e., MNIST and
CIFAR-10) under highly heterogeneous local data distribution show that the
proposed KFL is capable of reducing over 99% communication overhead while
achieving better learning performance than the conventional model
aggregation-based algorithms.

We study the problem of deleting user data from machine learning models
trained using empirical risk minimization. Our focus is on learning algorithms
which return the empirical risk minimizer and approximate unlearning algorithms
that comply with deletion requests that come streaming minibatches. Leveraging
the infintesimal jacknife, we develop an online unlearning algorithm that is
both computationally and memory efficient. Unlike prior memory efficient
unlearning algorithms, we target models that minimize objectives with
non-smooth regularizers, such as the commonly used $\ell_1$, elastic net, or
nuclear norm penalties. We also provide generalization, deletion capacity, and
unlearning guarantees that are consistent with state of the art methods. Across
a variety of benchmark datasets, our algorithm empirically improves upon the
runtime of prior methods while maintaining the same memory requirements and
test accuracy. Finally, we open a new direction of inquiry by proving that all
approximate unlearning algorithms introduced so far fail to unlearn in problem
settings where common hyperparameter tuning methods, such as cross-validation,
have been used to select models.

To navigate complex environments, robots must increasingly use
high-dimensional visual feedback (e.g. images) for control. However, relying on
high-dimensional image data to make control decisions raises important
questions; particularly, how might we prove the safety of a visual-feedback
controller? Control barrier functions (CBFs) are powerful tools for certifying
the safety of feedback controllers in the state-feedback setting, but CBFs have
traditionally been poorly-suited to visual feedback control due to the need to
predict future observations in order to evaluate the barrier function. In this
work, we solve this issue by leveraging recent advances in neural radiance
fields (NeRFs), which learn implicit representations of 3D scenes and can
render images from previously-unseen camera perspectives, to provide
single-step visual foresight for a CBF-based controller. This novel combination
is able to filter out unsafe actions and intervene to preserve safety. We
demonstrate the effect of our controller in real-time simulation experiments
where it successfully prevents the robot from taking dangerous actions.

Cooperative sensing and heterogeneous information fusion are critical to
realize vehicular cyber-physical systems (VCPSs). This paper makes the first
attempt to quantitatively measure the quality of VCPS by designing a new metric
called Age of View (AoV). Specifically, a vehicular edge computing (VEC)
architecture is presented, in which heterogeneous information can be
cooperatively sensed and uploaded via Vehicle-to-Infrastructure (V2I)
communications. Logical views can be constructed by fusing the heterogeneous
information at edge nodes. Further, we derive a cooperative sensing model based
on the multi-class M/G/1 priority queue. On this basis, we define the AoV by
modeling the timeliness, completeness, and consistency of the logical views.
Then, we formulate the problem, which aims at maximizing the quality of VCPS.
Further, a multi-agent deep reinforcement learning solution is proposed. The
system state includes vehicle sensed information, edge cached information, and
view requirements. The vehicle action space consists of the sensing frequencies
and uploading priorities of information, and the edge action space is the V2I
bandwidth allocation. The system reward is defined as the achieved VCPS
quality. In particular, a difference-reward-based credit assignment is designed
to divide the system reward into the difference reward for vehicles, reflecting
their individual contributions. Finally, we build the simulation model and give
a comprehensive performance evaluation, which conclusively demonstrates the
superiority of the proposed solution.

Noise in image sensors led to the development of a whole range of denoising
filters. A noisy image can become hard to recognize and often require several
types of post-processing compensation circuits. This paper proposes an adaptive
denoising system implemented using an analog in-memory neural computing
network. The proposed method can learn new noises and can be integrated into or
alone with CMOS image sensors. Three denoising network configurations are
implemented namely, (1) single layer network, (2) convolution network, and (3)
fusion network. The single layer network shows the processing time, energy
consumption, and on-chip area of 3.2us, 21nJ per image, and 0.3mm^2
respectively, meanwhile, the convolution denoising network correspondingly
shows 72ms, 236uJ, and 0.48mm^2. Among all the implemented networks, it is
observed that performance metrics SSIM, MSE, and PSNR show a maximum
improvement of 3.61, 21.7, and 7.7 times respectively.

Few-shot classification requires deep neural networks to learn generalized
representations only from limited training images, which is challenging but
significant in low-data regimes. Recently, CLIP-based methods have shown
promising few-shot performance benefited from the contrastive language-image
pre-training. Based on this point, we question if the large-scale pre-training
can alleviate the few-shot data deficiency and also assist the representation
learning by the pre-learned knowledge. In this paper, we propose CoMo, a
Collaboration of pre-trained Models that incorporates diverse prior knowledge
from various pre-training paradigms for better few-shot learning. Our CoMo
includes: CLIP's language-contrastive knowledge, DINO's vision-contrastive
knowledge, and DALL-E's language-generative knowledge. Specifically, CoMo works
in two aspects: few-shot data expansion and diverse knowledge ensemble. For
one, we generate synthetic images via zero-shot DALL-E to enrich the few-shot
training data without any manpower. For the other, we introduce a learnable
Multi-Knowledge Adapter (MK-Adapter) to adaptively blend the predictions from
CLIP and DINO. By such collaboration, CoMo can fully unleash the potential of
different pre-training methods and unify them to perform state-of-the-art for
few-shot classification. We conduct extensive experiments on 11 datasets to
demonstrate the superiority and generalization ability of our approach.

LiDAR and cameras are two complementary sensors for 3D perception in
autonomous driving. LiDAR point clouds have accurate spatial and geometry
information, while RGB images provide textural and color data for context
reasoning. To exploit LiDAR and cameras jointly, existing fusion methods tend
to align each 3D point to only one projected image pixel based on calibration,
namely one-to-one mapping. However, the performance of these approaches highly
relies on the calibration quality, which is sensitive to the temporal and
spatial synchronization of sensors. Therefore, we propose a Dynamic Cross
Attention (DCA) module with a novel one-to-many cross-modality mapping that
learns multiple offsets from the initial projection towards the neighborhood
and thus develops tolerance to calibration error. Moreover, a \textit{dynamic
query enhancement} is proposed to perceive the model-independent calibration,
which further strengthens DCA's tolerance to the initial misalignment. The
whole fusion architecture named Dynamic Cross Attention Network (DCAN) exploits
multi-level image features and adapts to multiple representations of point
clouds, which allows DCA to serve as a plug-in fusion module. Extensive
experiments on nuScenes and KITTI prove DCA's effectiveness. The proposed DCAN
outperforms state-of-the-art methods on the nuScenes detection challenge.

The combination of non-orthogonal multiple access (NOMA) and wireless power
transfer (WPT) is a promising solution to enhance the energy efficiency of
Device-to-Device (D2D) enabled wireless communication networks. In this paper,
we focus on maximizing the energy efficiency of a WPT-D2D pair in a
multiple-input single-output (MISO)-NOMA downlink network, by alternatively
optimizing the beamforming vectors of the base station (BS) and the time
switching coefficient of the WPT assisted D2D transmitter. The formulated
energy efficiency maximization problem is non-convex due to the highly coupled
variables. To efficiently address the non-convex problem, we first divide it
into two subproblems. Afterwards, an alternating algorithm based on the
Dinkelbach method and quadratic transform is proposed to solve the two
subproblems iteratively. To verify the proposed alternating algorithm's
accuracy, partial exhaustive search algorithm is proposed as a benchmark. We
also utilize a deep reinforcement learning (DRL) method to solve the non-convex
problem and compare it with the proposed algorithm. To demonstrate the
respective superiority of the proposed algorithm and DRL-based method,
simulations are performed for two scenarios of perfect and imperfect channel
state information (CSI). Simulation results are provided to compare NOMA and
orthogonal multiple access (OMA), which demonstrate the superior performance of
energy efficiency of the NOMA scheme.

Human trajectory forecasting in crowds presents the challenges of modelling
social interactions and outputting collision-free multimodal distribution.
Following the success of Social Generative Adversarial Networks (SGAN), recent
works propose various GAN-based designs to better model human motion in crowds.
Despite superior performance in reducing distance-based metrics, current
networks fail to output socially acceptable trajectories, as evidenced by high
collisions in model predictions. To counter this, we introduce SGANv2: an
improved safety-compliant SGAN architecture equipped with spatio-temporal
interaction modelling and a transformer-based discriminator. The
spatio-temporal modelling ability helps to learn the human social interactions
better while the transformer-based discriminator design improves temporal
sequence modelling. Additionally, SGANv2 utilizes the learned discriminator
even at test-time via a collaborative sampling strategy that not only refines
the colliding trajectories but also prevents mode collapse, a common phenomenon
in GAN training. Through extensive experimentation on multiple real-world and
synthetic datasets, we demonstrate the efficacy of SGANv2 to provide
socially-compliant multimodal trajectories.

Recent studies utilizing multi-modal data aimed at building a robust model
for facial Action Unit (AU) detection. However, due to the heterogeneity of
multi-modal data, multi-modal representation learning becomes one of the main
challenges. On one hand, it is difficult to extract the relevant features from
multi-modalities by only one feature extractor, on the other hand, previous
studies have not fully explored the potential of multi-modal fusion strategies.
For example, early fusion usually required all modalities to be present during
inference, while late fusion and middle fusion increased the network size for
feature learning. In contrast to a large amount of work on late fusion, there
are few works on early fusion to explore the channel information. This paper
presents a novel multi-modal network called Multi-modal Channel-Mixing (MCM),
as a pre-trained model to learn a robust representation in order to facilitate
the multi-modal fusion. We evaluate the learned representation on a downstream
task of automatic facial action units detection. Specifically, it is a single
stream encoder network that uses a channel-mixing module in early fusion,
requiring only one modality in the downstream detection task. We also utilize
the masked ViT encoder to learn features from the fusion image and reconstruct
back two modalities with two ViT decoders. We have conducted extensive
experiments on two public datasets, known as BP4D and DISFA, to evaluate the
effectiveness and robustness of the proposed multimodal framework. The results
show our approach is comparable or superior to the state-of-the-art baseline
methods.

We propose a stochastic projection-based gradient free physics-informed
neural network. The proposed approach, referred to as the stochastic projection
based physics informed neural network (SP-PINN), blends upscaled stochastic
projection theory with the recently proposed physics-informed neural network.
This results in a framework that is robust and can solve problems involving
complex solution domain and discontinuities. SP-PINN is a gradient-free
approach which addresses the computational bottleneck associated with automatic
differentiation in conventional PINN. Efficacy of the proposed approach is
illustrated by a number of examples involving regular domain, complex domain,
complex response and phase field based fracture mechanics problems. Case
studies by varying network architecture (activation function) and number of
collocation points have also been presented.

Continual Learning (CL) sequentially learns new tasks like human beings, with
the goal to achieve better Stability (S, remembering past tasks) and Plasticity
(P, adapting to new tasks). Due to the fact that past training data is not
available, it is valuable to explore the influence difference on S and P among
training examples, which may improve the learning pattern towards better SP.
Inspired by Influence Function (IF), we first study example influence via
adding perturbation to example weight and computing the influence derivation.
To avoid the storage and calculation burden of Hessian inverse in neural
networks, we propose a simple yet effective MetaSP algorithm to simulate the
two key steps in the computation of IF and obtain the S- and P-aware example
influence. Moreover, we propose to fuse two kinds of example influence by
solving a dual-objective optimization problem, and obtain a fused influence
towards SP Pareto optimality. The fused influence can be used to control the
update of model and optimize the storage of rehearsal. Empirical results show
that our algorithm significantly outperforms state-of-the-art methods on both
task- and class-incremental benchmark CL datasets.

Localizing anatomical landmarks are important tasks in medical image
analysis. However, the landmarks to be localized often lack prominent visual
features. Their locations are elusive and easily confused with the background,
and thus precise localization highly depends on the context formed by their
surrounding areas. In addition, the required precision is usually higher than
segmentation and object detection tasks. Therefore, localization has its unique
challenges different from segmentation or detection. In this paper, we propose
a zoom-in attentive network (ZIAN) for anatomical landmark localization in
ocular images. First, a coarse-to-fine, or "zoom-in" strategy is utilized to
learn the contextualized features in different scales. Then, an attentive
fusion module is adopted to aggregate multi-scale features, which consists of
1) a co-attention network with a multiple regions-of-interest (ROIs) scheme
that learns complementary features from the multiple ROIs, 2) an
attention-based fusion module which integrates the multi-ROIs features and
non-ROI features. We evaluated ZIAN on two open challenge tasks, i.e., the
fovea localization in fundus images and scleral spur localization in AS-OCT
images. Experiments show that ZIAN achieves promising performances and
outperforms state-of-the-art localization methods. The source code and trained
models of ZIAN are available at
https://github.com/leixiaofeng-astar/OMIA9-ZIAN.

The availability of well-curated datasets has driven the success of Machine
Learning (ML) models. Despite the increased access to earth observation data
for agriculture, there is a scarcity of curated, labelled datasets, which
limits the potential of its use in training ML models for remote sensing (RS)
in agriculture. To this end, we introduce a first-of-its-kind dataset, SICKLE,
having time-series images at different spatial resolutions from 3 different
satellites, annotated with multiple key cropping parameters for paddy
cultivation for the Cauvery Delta region in Tamil Nadu, India. The dataset
comprises of 2,398 season-wise samples from 388 unique plots distributed across
4 districts of the Delta. The dataset covers multi-spectral, thermal and
microwave data between the time period January 2018-March 2021. The paddy
samples are annotated with 4 key cropping parameters, i.e. sowing date,
transplanting date, harvesting date and crop yield. This is one of the first
studies to consider the growing season (using sowing and harvesting dates) as
part of a dataset. We also propose a yield prediction strategy that uses
time-series data generated based on the observed growing season and the
standard seasonal information obtained from Tamil Nadu Agricultural University
for the region. The consequent performance improvement highlights the impact of
ML techniques that leverage domain knowledge that are consistent with standard
practices followed by farmers in a specific region. We benchmark the dataset on
3 separate tasks, namely crop type, phenology date (sowing, transplanting,
harvesting) and yield prediction, and develop an end-to-end framework for
predicting key crop parameters in a real-world setting.

In this paper, a novel adaptive optimal control strategy is proposed to
achieve the cooperative optimal output regulation of continuous-time linear
multi-agent systems based on adaptive dynamic programming (ADP). The proposed
method is different from those in the existing literature of ADP and
cooperative output regulation in the sense that the knowledge of the exosystem
dynamics is not required in the design of the exostate observers for those
agents with no direct access to the exosystem. Moreover, an optimal control
policy is obtained without the prior knowledge of the modeling information of
any agent while achieving the cooperative output regulation. Instead, we use
the state/input information along the trajectories of the underlying dynamical
systems and the estimated exostates to learn the optimal control policy.
Simulation results show the efficacy of the proposed algorithm, where both
estimation errors of exosystem matrix and exostates, and the tracking errors
converge to zero in an optimal sense, which solves the cooperative optimal
output regulation problem.

Hand hygiene is a standard six-step hand-washing action proposed by the World
Health Organization (WHO). However, there is no good way to supervise medical
staff to do hand hygiene, which brings the potential risk of disease spread.
Existing action assessment works usually make an overall quality prediction on
an entire video. However, the internal structures of hand hygiene action are
important in hand hygiene assessment. Therefore, we propose a novel
fine-grained learning framework to perform step segmentation and key action
scorer in a joint manner for accurate hand hygiene assessment. Existing
temporal segmentation methods usually employ multi-stage convolutional network
to improve the segmentation robustness, but easily lead to over-segmentation
due to the lack of the long-range dependence. To address this issue, we design
a multi-stage convolution-transformer network for step segmentation. Based on
the observation that each hand-washing step involves several key actions which
determine the hand-washing quality, we design a set of key action scorers to
evaluate the quality of key actions in each step. In addition, there lacks a
unified dataset in hand hygiene assessment. Therefore, under the supervision of
medical staff, we contribute a video dataset that contains 300 video sequences
with fine-grained annotations. Extensive experiments on the dataset suggest
that our method well assesses hand hygiene videos and achieves outstanding

Deep learning methods have been shown to be effective for the automatic
segmentation of structures and pathologies in medical imaging. However, they
require large annotated datasets, whose manual segmentation is a tedious and
time-consuming task, especially for large structures. We present a new method
of partial annotations that uses a small set of consecutive annotated slices
from each scan with an annotation effort that is equal to that of only few
annotated cases. The training with partial annotations is performed by using
only annotated blocks, incorporating information about slices outside the
structure of interest and modifying a batch loss function to consider only the
annotated slices. To facilitate training in a low data regime, we use a
two-step optimization process. We tested the method with the popular soft Dice
loss for the fetal body segmentation task in two MRI sequences, TRUFI and
FIESTA, and compared full annotation regime to partial annotations with a
similar annotation effort. For TRUFI data, the use of partial annotations
yielded slightly better performance on average compared to full annotations
with an increase in Dice score from 0.936 to 0.942, and a substantial decrease
in Standard Deviations (STD) of Dice score by 22% and Average Symmetric Surface
Distance (ASSD) by 15%. For the FIESTA sequence, partial annotations also
yielded a decrease in STD of the Dice score and ASSD metrics by 27.5% and 33%
respectively for in-distribution data, and a substantial improvement also in
average performance on out-of-distribution data, increasing Dice score from
0.84 to 0.9 and decreasing ASSD from 7.46 to 4.01 mm. The two-step optimization
process was helpful for partial annotations for both in-distribution and
out-of-distribution data. The partial annotations method with the two-step
optimizer is therefore recommended to improve segmentation performance under
low data regime.

Cold start is an essential and persistent problem in recommender systems.
State-of-the-art solutions rely on training hybrid models for both cold-start
and existing users/items, based on the auxiliary information. Such a hybrid
model would compromise the performance of existing users/items, which might
make these solutions not applicable in real-worlds recommender systems where
the experience of existing users/items must be guaranteed. Meanwhile, graph
neural networks (GNNs) have been demonstrated to perform effectively warm
(non-cold-start) recommendations. However, they have never been applied to
handle the cold-start problem in a user-item bipartite graph. This is a
challenging but rewarding task since cold-start users/items do not have links.
Besides, it is nontrivial to design an appropriate GNN to conduct cold-start
recommendations while maintaining the performance for existing users/items. To
bridge the gap, we propose a tailored GNN-based framework (GPatch) that
contains two separate but correlated components. First, an efficient GNN
architecture -- GWarmer, is designed to model the warm users/items. Second, we
construct correlated Patching Networks to simulate and patch GWarmer by
conducting cold-start recommendations. Experiments on benchmark and large-scale
commercial datasets demonstrate that GPatch is significantly superior in
providing recommendations for both existing and cold-start users/items.

Recent studies on Click-Through Rate (CTR) prediction has reached new levels
by modeling longer user behavior sequences. Among others, the two-stage methods
stand out as the state-of-the-art (SOTA) solution for industrial applications.
The two-stage methods first train a retrieval model to truncate the long
behavior sequence beforehand and then use the truncated sequences to train a
CTR model. However, the retrieval model and the CTR model are trained
separately. So the retrieved subsequences in the CTR model is inaccurate, which
degrades the final performance. In this paper, we propose an end-to-end
paradigm to model long behavior sequences, which is able to achieve superior
performance along with remarkable cost-efficiency compared to existing models.
Our contribution is three-fold: First, we propose a hashing-based efficient
target attention (TA) network named ETA-Net to enable end-to-end user behavior
retrieval based on low-cost bit-wise operations. The proposed ETA-Net can
reduce the complexity of standard TA by orders of magnitude for sequential data
modeling. Second, we propose a general system architecture as one viable
solution to deploy ETA-Net on industrial systems. Particularly, ETA-Net has
been deployed on the recommender system of Taobao, and brought 1.8% lift on CTR
and 3.1% lift on Gross Merchandise Value (GMV) compared to the SOTA two-stage
methods. Third, we conduct extensive experiments on both offline datasets and
online A/B test. The results verify that the proposed model outperforms
existing CTR models considerably, in terms of both CTR prediction performance
and online cost-efficiency. ETA-Net now serves the main traffic of Taobao,
delivering services to hundreds of millions of users towards billions of items
every day.

Noise in seismic data arises from numerous sources and is continually
evolving. The use of supervised deep learning procedures for denoising of
seismic datasets often results in poor performance: this is due to the lack of
noise-free field data to act as training targets and the large difference in
characteristics between synthetic and field datasets. Self-supervised,
blind-spot networks typically overcome these limitation by training directly on
the raw, noisy data. However, such networks often rely on a random noise
assumption, and their denoising capabilities quickly decrease in the presence
of even minimally-correlated noise. Extending from blind-spots to blind-masks
can efficiently suppress coherent noise along a specific direction, but it
cannot adapt to the ever-changing properties of noise. To preempt the network's
ability to predict the signal and reduce its opportunity to learn the noise
properties, we propose an initial, supervised training of the network on a
frugally-generated synthetic dataset prior to fine-tuning in a self-supervised
manner on the field dataset of interest. Considering the change in peak
signal-to-noise ratio, as well as the volume of noise reduced and signal
leakage observed, we illustrate the clear benefit in initialising the
self-supervised network with the weights from a supervised base-training. This
is further supported by a test on a field dataset where the fine-tuned network
strikes the best balance between signal preservation and noise reduction.
Finally, the use of the unrealistic, frugally-generated synthetic dataset for
the supervised base-training includes a number of benefits: minimal prior
geological knowledge is required, substantially reduced computational cost for
the dataset generation, and a reduced requirement of re-training the network
should recording conditions change, to name a few.

Graph Convolutional Networks (GCNs) and their variants have achieved
significant performances on various recommendation tasks. However, many
existing GCN models tend to perform recursive aggregations among all related
nodes, which can arise severe computational burden to hinder their application
to large-scale recommendation tasks. To this end, this paper proposes the
flattened GCN~(FlatGCN) model, which is able to achieve superior performance
with remarkably less complexity compared with existing models. Our main
contribution is three-fold. First, we propose a simplified but powerful GCN
architecture which aggregates the neighborhood information using one flattened
GCN layer, instead of recursively. The aggregation step in FlatGCN is
parameter-free such that it can be pre-computed with parallel computation to
save memory and computational cost. Second, we propose an informative
neighbor-infomax sampling method to select the most valuable neighbors by
measuring the correlation among neighboring nodes based on a principled metric.
Third, we propose a layer ensemble technique which improves the expressiveness
of the learned representations by assembling the layer-wise neighborhood
representations at the final layer. Extensive experiments on three datasets
verify that our proposed model outperforms existing GCN models considerably and
yields up to a few orders of magnitude speedup in training efficiency.

The technology of optical coherence tomography (OCT) to fingerprint imaging
opens up a new research potential for fingerprint recognition owing to its
ability to capture depth information of the skin layers. Developing robust and
high security Automated Fingerprint Recognition Systems (AFRSs) are possible if
the depth information can be fully utilized. However, in existing studies,
Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction
based on depth information are treated as two independent branches, resulting
in high computation and complexity of AFRS building.Thus, this paper proposes a
uniform representation model for OCT-based fingerprint PAD and subsurface
fingerprint reconstruction. Firstly, we design a novel semantic segmentation
network which only trained by real finger slices of OCT-based fingerprints to
extract multiple subsurface structures from those slices (also known as
B-scans). The latent codes derived from the network are directly used to
effectively detect the PA since they contain abundant subsurface biological
information, which is independent with PA materials and has strong robustness
for unknown PAs. Meanwhile, the segmented subsurface structures are adopted to
reconstruct multiple subsurface 2D fingerprints. Recognition can be easily
achieved by using existing mature technologies based on traditional 2D
fingerprints. Extensive experiments are carried on our own established
database, which is the largest public OCT-based fingerprint database with 2449
volumes. In PAD task, our method can improve 0.33% Acc from the
state-of-the-art method. For reconstruction performance, our method achieves
the best performance with 0.834 mIOU and 0.937 PA. By comparing with the
recognition performance on surface 2D fingerprints, the effectiveness of our
proposed method on high quality subsurface fingerprint reconstruction is
further proved.

The impurity density in high-purity germanium detectors is crucial to
understand and simulate such detectors. However, the information about the
impurities provided by the manufacturer, based on Hall effect measurements, is
typically limited to a few locations and comes with a large uncertainty. As the
voltage dependence of the capacitance matrix of a detector strongly depends on
the impurity density distribution, capacitance measurements can provide a path
to improve the knowledge on the impurities. The novel method presented here
uses a machine-learned surrogate model, trained on precise GPU-accelerated
capacitance calculations, to perform full Bayesian inference of impurity
distribution parameters from capacitance measurements. All steps use
open-source Julia software packages. Capacitances are calculated with
SolidStateDetectors.jl, machine learning is done with Flux.jl and Bayesian
inference performed using BAT.jl. The capacitance matrix of a detector and its
dependence on the impurity density is explained and a capacitance bias-voltage
scan of an n-type true-coaxial test detector is presented. The study indicates
that the impurity density of the test detector also has a radial dependence.

This article provides convergence analysis of online stochastic gradient
descent algorithms for functional linear models. Adopting the characterizations
of the slope function regularity, the kernel space capacity, and the capacity
of the sampling process covariance operator, significant improvement on the
convergence rates is achieved. Both prediction problems and estimation problems
are studied, where we show that capacity assumption can alleviate the
saturation of the convergence rate as the regularity of the target function
increases. We show that with properly selected kernel, capacity assumptions can
fully compensate for the regularity assumptions for prediction problems (but
not for estimation problems). This demonstrates the significant difference
between the prediction problems and the estimation problems in functional data
analysis.

In the past few years, Convolutional Neural Networks (CNN) have demonstrated
promising performance in various real-world cybersecurity applications, such as
network and multimedia security. However, the underlying fragility of CNN
structures poses major security problems, making them inappropriate for use in
security-oriented applications including such computer networks. Protecting
these architectures from adversarial attacks necessitates using security-wise
architectures that are challenging to attack.
  In this study, we present a novel architecture based on an ensemble
classifier that combines the enhanced security of 1-Class classification (known
as 1C) with the high performance of conventional 2-Class classification (known
as 2C) in the absence of attacks.Our architecture is referred to as the
1.5-Class (SPRITZ-1.5C) classifier and constructed using a final dense
classifier, one 2C classifier (i.e., CNNs), and two parallel 1C classifiers
(i.e., auto-encoders). In our experiments, we evaluated the robustness of our
proposed architecture by considering eight possible adversarial attacks in
various scenarios. We performed these attacks on the 2C and SPRITZ-1.5C
architectures separately. The experimental results of our study showed that the
Attack Success Rate (ASR) of the I-FGSM attack against a 2C classifier trained
with the N-BaIoT dataset is 0.9900. In contrast, the ASR is 0.0000 for the
SPRITZ-1.5C classifier.

This paper follows series of our works on the applicability of various
machine learning methods to the morphological galaxy classification (Vavilova
et al., 2021, 2022). We exploited the sample of 315776 SDSS DR9 galaxies with
absolute stellar magnitudes of -24m<Mr<-19.4m at 0.003<z<0.1 as a target data
set for the CNN classifier based on the DenseNet-201. Because it is tightly
overlapped with the Galaxy Zoo 2 (GZ2) sample, we use these annotated data as
the training data set to classify galaxies into 34 detailed features. In the
presence of a pronounced difference of visual parameters between galaxies from
the GZ2 training data set and galaxies without known morphological parameters,
we applied novel procedures, which allowed us for the first time to get rid of
this difference for smaller and fainter SDSS galaxies.
  We describe in detail the adversarial validation technique as well as how we
managed the optimal train-test split of galaxies from the training data set. We
have also found optimal galaxy image transformations to increase the classifier
generalization ability. It can be considered as another way to improve the
human bias for those galaxy images that had a poor vote classification in the
GZ project. Such an approach, likely auto-immunization, when the CNN classifier
trained on very good images is able to retrain bad images from the same
homogeneous sample, can be considered co-planar to other methods of combating
the human bias.
  The accuracy of CNN classifier is in the range of 83.3-99.4 percent depending
on 32 features. As a result, for the first time, we assigned the detailed
morphological classification for more than 140K low-redshift galaxies,
especially at the fainter end. We accentuate on the typical problem points of
galaxy CNN image classification from the astronomical point of view. The
catalogs will be available through the VizieR.

In order to ensure the quality of software and prevent attacks from hackers
on critical systems, static analysis tools are frequently utilized to detect
vulnerabilities in the early development phase. However, these tools often
report a large number of warnings with a high false-positive rate, which causes
many difficulties for developers. In this paper, we introduce VulRG, a novel
approach to address this problem. Specifically, VulRG predicts and ranks the
warnings based on their likelihoods to be true positive. To predict that
likelihood, VulRG combines two deep learning models CNN and BiGRU to capture
the context of each warning in terms of program syntax, control flow, and
program dependence. Our experimental results on a real-world dataset of 6,620
warnings show that VulRG's Recall at Top-50% is 90.9%. This means that using
VulRG, 90% of the vulnerabilities can be found by examining only 50% of the
warnings. Moreover, at Top-5%, VulRG can improve the state-of-the-art approach
by +30% in both Precision and Recall.

Since radiology reports needed for clinical practice and research are written
and stored in free-text narrations, extraction of relative information for
further analysis is difficult. In these circumstances, natural language
processing (NLP) techniques can facilitate automatic information extraction and
transformation of free-text formats to structured data. In recent years, deep
learning (DL)-based models have been adapted for NLP experiments with promising
results. Despite the significant potential of DL models based on artificial
neural networks (ANN) and convolutional neural networks (CNN), the models face
some limitations to implement in clinical practice. Transformers, another new
DL architecture, have been increasingly applied to improve the process.
Therefore, in this study, we propose a transformer-based fine-grained named
entity recognition (NER) architecture for clinical information extraction. We
collected 88 abdominopelvic sonography reports in free-text formats and
annotated them based on our developed information schema. The text-to-text
transfer transformer model (T5) and Scifive, a pre-trained domain-specific
adaptation of the T5 model, were applied for fine-tuning to extract entities
and relations and transform the input into a structured format. Our
transformer-based model in this study outperformed previously applied
approaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and
BLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an
interpretable structured report.

Sparsity of the User-POI matrix is a well established problem for next POI
recommendation, which hinders effective learning of user preferences. Focusing
on a more granular extension of the problem, we propose a Joint Triplet Loss
Learning (JTLL) module for the Next New ($N^2$) POI recommendation task, which
is more challenging. Our JTLL module first computes additional training samples
from the users' historical POI visit sequence, then, a designed triplet loss
function is proposed to decrease and increase distances of POI and user
embeddings based on their respective relations. Next, the JTLL module is
jointly trained with recent approaches to additionally learn unvisited
relations for the recommendation task. Experiments conducted on two known
real-world LBSN datasets show that our joint training module was able to
improve the performances of recent existing works.

Self-supervised learning (SSL) has achieved remarkable performance on various
medical imaging tasks by dint of priors from massive unlabeled data. However,
for a specific downstream task, there is still a lack of an instruction book on
how to select suitable pretext tasks and implementation details. In this work,
we first review the latest applications of self-supervised methods in the field
of medical imaging analysis. Then, we conduct extensive experiments to explore
four significant issues in SSL for medical imaging, including (1) the effect of
self-supervised pretraining on imbalanced datasets, (2) network architectures,
(3) the applicability of upstream tasks to downstream tasks and (4) the
stacking effect of SSL and commonly used policies for deep learning, including
data resampling and augmentation. Based on the experimental results, potential
guidelines are presented for self-supervised pretraining in medical imaging.
Finally, we discuss future research directions and raise issues to be aware of
when designing new SSL methods and paradigms.

Intrinsic image decomposition is an important and long-standing computer
vision problem. Given an input image, recovering the physical scene properties
is ill-posed. Several physically motivated priors have been used to restrict
the solution space of the optimization problem for intrinsic image
decomposition. This work takes advantage of deep learning, and shows that it
can solve this challenging computer vision problem with high efficiency. The
focus lies in the feature encoding phase to extract discriminative features for
different intrinsic layers from an input image. To achieve this goal, we
explore the distinctive characteristics of different intrinsic components in
the high dimensional feature embedding space. We define feature distribution
divergence to efficiently separate the feature vectors of different intrinsic
components. The feature distributions are also constrained to fit the real ones
through a feature distribution consistency. In addition, a data refinement
approach is provided to remove data inconsistency from the Sintel dataset,
making it more suitable for intrinsic image decomposition. Our method is also
extended to intrinsic video decomposition based on pixel-wise correspondences
between adjacent frames. Experimental results indicate that our proposed
network structure can outperform the existing state-of-the-art.

We introduce a new in-context learning paradigm to measure Large Language
Models' (LLMs) ability to learn novel words during inference. In particular, we
rewrite Winograd-style co-reference resolution problems by replacing the key
concept word with a synthetic but plausible word that the model must understand
to complete the task. Solving this task requires the model to make use of the
dictionary definition of the new word given in the prompt. This benchmark
addresses word acquisition, one important aspect of the diachronic degradation
known to afflict LLMs. As LLMs are frozen in time at the moment they are
trained, they are normally unable to reflect the way language changes over
time. We show that the accuracy of LLMs compared to the original Winograd tasks
decreases radically in our benchmark, thus identifying a limitation of current
models and providing a benchmark to measure future improvements in LLMs ability
to do in-context learning.

Vision transformers (ViT) have shown promise in various vision tasks while
the U-Net based on a convolutional neural network (CNN) remains dominant in
diffusion models. We design a simple and general ViT-based architecture (named
U-ViT) for image generation with diffusion models. U-ViT is characterized by
treating all inputs including the time, condition and noisy image patches as
tokens and employing long skip connections between shallow and deep layers. We
evaluate U-ViT in unconditional and class-conditional image generation, as well
as text-to-image generation tasks, where U-ViT is comparable if not superior to
a CNN-based U-Net of a similar size. In particular, a latent diffusion model
with a small U-ViT achieves a record-breaking FID of 5.48 in text-to-image
generation on MS-COCO, among methods without accessing large external datasets
during the training of generative models. Besides, our results suggest that,
for diffusion-based image modeling, the long skip connection is crucial while
the down-sampling and up-sampling operators in CNN-based U-Net are not always
necessary. We believe that U-ViT can provide insights for future research on
backbones in diffusion models and benefit generative modeling on large scale
cross-modality datasets.

Anomaly detection has recently gained increasing attention in the field of
computer vision, likely due to its broad set of applications ranging from
product fault detection on industrial production lines and impending event
detection in video surveillance to finding lesions in medical scans. Regardless
of the domain, anomaly detection is typically framed as a one-class
classification task, where the learning is conducted on normal examples only.
An entire family of successful anomaly detection methods is based on learning
to reconstruct masked normal inputs (e.g. patches, future frames, etc.) and
exerting the magnitude of the reconstruction error as an indicator for the
abnormality level. Unlike other reconstruction-based methods, we present a
novel self-supervised masked convolutional transformer block (SSMCTB) that
comprises the reconstruction-based functionality at a core architectural level.
The proposed self-supervised block is extremely flexible, enabling information
masking at any layer of a neural network and being compatible with a wide range
of neural architectures. In this work, we extend our previous self-supervised
predictive convolutional attentive block (SSPCAB) with a 3D masked
convolutional layer, as well as a transformer for channel-wise attention.
Furthermore, we show that our block is applicable to a wider variety of tasks,
adding anomaly detection in medical images and thermal videos to the previously
considered tasks based on RGB images and surveillance videos. We exhibit the
generality and flexibility of SSMCTB by integrating it into multiple
state-of-the-art neural models for anomaly detection, bringing forth empirical
results that confirm considerable performance improvements on five benchmarks:
MVTec AD, BRATS, Avenue, ShanghaiTech, and Thermal Rare Event. We release our
code and data as open source at https://github.com/ristea/ssmctb.

The concept of small modular reactor has changed the outlook for tackling
future energy crises. This new reactor technology is very promising considering
its lower investment requirements, modularity, design simplicity, and enhanced
safety features. The application of artificial intelligence-driven multi-scale
modeling (neutronics, thermal hydraulics, fuel performance, etc.) incorporating
Digital Twin and associated uncertainties in the research of small modular
reactors is a recent concept. In this work, a comprehensive study is conducted
on the multiscale modeling of accident-tolerant fuels. The application of these
fuels in the light water-based small modular reactors is explored. This chapter
also focuses on the application of machine learning and artificial intelligence
in the design optimization, control, and monitoring of small modular reactors.
Finally, a brief assessment of the research gap on the application of
artificial intelligence to the development of high burnup composite
accident-tolerant fuels is provided. Necessary actions to fulfill these gaps
are also discussed.

Previous studies in the perimeter defense game have largely focused on the
fully observable setting where the true player states are known to all players.
However, this is unrealistic for practical implementation since defenders may
have to perceive the intruders and estimate their states. In this work, we
study the perimeter defense game in a photo-realistic simulator and the real
world, requiring defenders to estimate intruder states from vision. We train a
deep machine learning-based system for intruder pose detection with domain
randomization that aggregates multiple views to reduce state estimation errors
and adapt the defensive strategy to account for this. We newly introduce
performance metrics to evaluate the vision-based perimeter defense. Through
extensive experiments, we show that our approach improves state estimation, and
eventually, perimeter defense performance in both 1-defender-vs-1-intruder
games, and 2-defenders-vs-1-intruder games.

For higher degrees of freedom robot, mass matrix, Coriolis and centrifugal
force and gravity matrix are computationally heavy and require a long time to
execute. Due to the sequential structure of the programs, multicore processors
cannot boost performance. High processing power is required to maintain a
higher sampling rate. Neural network-based control is a great approach for
developing a parallel equivalent model of a sequential model. In this paper,
Deep learning algorithm-based controller is designed for 7 degrees of freedom
exoskeleton robot. A total of 49 densely connected neurons are arranged in four
layers to estimate joint torque requirements for tracking trajectories. For
training, the deep neural network analytical model-based data generation
technique is presented. A PD controller is added to handle prediction errors.
Since a deep learning network has a parallel structure, using a multicore
CPU/GPU can significantly improve controller performance. Simulation results
show very high trajectory tracking accuracies.

Scientists often use observational time series data to study complex natural
processes, from climate change to civil conflict to brain activity. But
regression analyses of these data often assume simplistic dynamics. Recent
advances in deep learning have yielded startling improvements to the
performance of models of complex processes, from speech comprehension to
nuclear physics to competitive gaming. But deep learning is generally not used
for scientific analysis. Here, we bridge this gap by showing that deep learning
can be used, not just to imitate, but to analyze complex processes, providing
flexible function approximation while preserving interpretability. Our approach
-- the continuous-time deconvolutional regressive neural network (CDRNN) --
relaxes standard simplifying assumptions (e.g., linearity, stationarity, and
homoscedasticity) that are implausible for many natural systems and may
critically affect the interpretation of data. We evaluate CDRNNs on incremental
human language processing, a domain with complex continuous dynamics. We
demonstrate dramatic improvements to predictive likelihood in behavioral and
neuroimaging data, and we show that CDRNNs enable flexible discovery of novel
patterns in exploratory analyses, provide robust control of possible confounds
in confirmatory analyses, and open up research questions that are otherwise
hard to study using observational data.

Neural architecture search (NAS) for transformers has been used to create
state-of-the-art models that target certain latency constraints. In this work
we present Bigger&Faster, a novel quantization-aware parameter sharing NAS that
finds architectures for 8-bit integer (int8) quantized transformers. Our
results show that our method is able to produce BERT models that outperform the
current state-of-the-art technique, AutoTinyBERT, at all latency targets we
tested, achieving up to a 2.68% accuracy gain. Additionally, although the
models found by our technique have a larger number of parameters than their
float32 counterparts, due to their parameters being int8, they have
significantly smaller memory footprints.

Along with the practical success of the discovery of dynamics using deep
learning, the theoretical analysis of this approach has attracted increasing
attention. Prior works have established the grid error estimation with
auxiliary conditions for the discovery of dynamics via linear multistep methods
and deep learning. And we extend the existing error analysis in this work. We
first introduce the inverse modified differential equations (IMDE) of linear
multistep methods and show that the learning model returns a close
approximation of the IMDE. Based on the IMDE, we prove that the error between
the discovered system and the target dynamical system is bounded by the sum of
the LMM discretization error and the learning loss. Several numerical
experiments are performed to verify the theoretical analysis.

Recovering key aspects of an incoming photon stream during projected FACET-II
experiments, such as their energy distributions and the original electron
beam's parameters, remains an unsolved computational problem. This paper
utilized data from simulated plasma wakefield acceleration betatron radiation
experiments and electron-positron pair production to determine which methods
could most reliably reconstruct these key properties. The data from these two
cases provided a large range of photon energies to help increase confidence in
each of the tested methods. In both cases, we compared the performance of
maximum likelihood estimation (MLE), a statistical technique used to determine
unknown parameters from the distribution of observed data, neural networks,
which detect patterns between datasets through repeated training, and a hybrid
approach combining the two. Furthermore, in the electron-positron production
case, the paper also compared QR decomposition, a matrix decomposition method.
The betatron radiation case demonstrated the effectiveness of a hybrid ML-MLE
approach, while the electron-positron pair production case illustrated the
effectiveness of the ML model in the face of noise. As such, the ML-MLE hybrid
approach proved to be the most generalizable of the methods.

We study the problem of allocating $T$ sequentially arriving items among $n$
homogeneous agents under the constraint that each agent must receive a
pre-specified fraction of all items, with the objective of maximizing the
agents' total valuation of items allocated to them. The agents' valuations for
the item in each round are assumed to be i.i.d. but their distribution is a
priori unknown to the central planner. Therefore, the central planner needs to
implicitly learn these distributions from the observed values in order to pick
a good allocation policy. However, an added challenge here is that the agents
are strategic with incentives to misreport their valuations in order to receive
better allocations. This sets our work apart both from the online auction
design settings which typically assume known valuation distributions and/or
involve payments, and from the online learning settings that do not consider
strategic agents. To that end, our main contribution is an online learning
based allocation mechanism that is approximately Bayesian incentive compatible,
and when all agents are truthful, guarantees a sublinear regret for individual
agents' utility compared to that under the optimal offline allocation policy.

We consider the problem of allocating a distribution of items to $n$
recipients where each recipient has to be allocated a fixed, prespecified
fraction of all items, while ensuring that each recipient does not experience
too much envy. We show that this problem can be formulated as a variant of the
semi-discrete optimal transport (OT) problem, whose solution structure in this
case has a concise representation and a simple geometric interpretation. Unlike
existing literature that treats envy-freeness as a hard constraint, our
formulation allows us to \emph{optimally} trade off efficiency and envy
continuously. Additionally, we study the statistical properties of the space of
our OT based allocation policies by showing a polynomial bound on the number of
samples needed to approximate the optimal solution from samples. Our approach
is suitable for large-scale fair allocation problems such as the blood donation
matching problem, and we show numerically that it performs well on a prior
realistic data simulator.

We briefly describe -- mainly through very simple examples -- different kinds
of answer-set programs with annotations that have been proposed for specifying:
database repairs and consistent query answering; secrecy view and query
evaluation with them; counterfactual interventions for causality in databases;
and counterfactual-based explanations in machine learning.

We study the $K$-armed dueling bandit problem, a variation of the traditional
multi-armed bandit problem in which feedback is obtained in the form of
pairwise comparisons. Previous learning algorithms have focused on the
$\textit{fully adaptive}$ setting, where the algorithm can make updates after
every comparison. The "batched" dueling bandit problem is motivated by
large-scale applications like web search ranking and recommendation systems,
where performing sequential updates may be infeasible. In this work, we ask:
$\textit{is there a solution using only a few adaptive rounds that matches the
asymptotic regret bounds of the best sequential algorithms for $K$-armed
dueling bandits?}$ We answer this in the affirmative $\textit{under the
Condorcet condition}$, a standard setting of the $K$-armed dueling bandit
problem. We obtain asymptotic regret of $O(K^2\log^2(K)) + O(K\log(T))$ in
$O(\log(T))$ rounds, where $T$ is the time horizon. Our regret bounds nearly
match the best regret bounds known in the fully sequential setting under the
Condorcet condition. Finally, in computational experiments over a variety of
real-world datasets, we observe that our algorithm using $O(\log(T))$ rounds
achieves almost the same performance as fully sequential algorithms (that use
$T$ rounds).

This research provides a novel framework to estimate the economic,
environmental, and social values of electrifying public transit buses, for
cities across the world, based on open-source data. Electric buses are a
compelling candidate to replace diesel buses for the environmental and social
benefits. However, the state-of-art models to evaluate the value of bus
electrification are limited in applicability because they require granular and
bespoke data on bus operation that can be difficult to procure. Our valuation
tool uses General Transit Feed Specification, a standard data format used by
transit agencies worldwide, to provide high-level guidance on developing a
prioritization strategy for electrifying a bus fleet. We develop
physics-informed machine learning models to evaluate the energy consumption,
the carbon emissions, the health impacts, and the total cost of ownership for
each transit route. We demonstrate the scalability of our tool with a case
study of the bus lines in the Greater Boston and Milan metropolitan areas.

MRI and CT are most widely used medical imaging modalities. It is often
necessary to acquire multi-modality images for diagnosis and treatment such as
radiotherapy planning. However, multi-modality imaging is not only costly but
also introduces misalignment between MRI and CT images. To address this
challenge, computational conversion is a viable approach between MRI and CT
images, especially from MRI to CT images. In this paper, we propose to use an
emerging deep learning framework called diffusion and score-matching models in
this context. Specifically, we adapt denoising diffusion probabilistic and
score-matching models, use four different sampling strategies, and compare
their performance metrics with that using a convolutional neural network and a
generative adversarial network model. Our results show that the diffusion and
score-matching models generate better synthetic CT images than the CNN and GAN
models. Furthermore, we investigate the uncertainties associated with the
diffusion and score-matching networks using the Monte-Carlo method, and improve
the results by averaging their Monte-Carlo outputs. Our study suggests that
diffusion and score-matching models are powerful to generate high quality
images conditioned on an image obtained using a complementary imaging modality,
analytically rigorous with clear explainability, and highly competitive with
CNNs and GANs for image synthesis.

We consider the problem of finding decentralized strategies for multi-agent
perimeter defense games. In this work, we design a graph neural network-based
learning framework to learn a mapping from defenders' local perceptions and the
communication graph to defenders' actions such that the learned actions are
close to that generated by a centralized expert algorithm. We demonstrate that
our proposed networks stay closer to the expert policy and are superior to
other baseline algorithms by capturing more intruders. Our GNN-based networks
are trained at a small scale and can generalize to large scales. To validate
our results, we run perimeter defense games in scenarios with different team
sizes and initial configurations to evaluate the performance of the learned
networks.

Recent advances in large pre-trained language models have demonstrated strong
results in generating natural languages and significantly improved performances
for many natural language generation (NLG) applications such as machine
translation and text summarization. However, when the generation tasks are more
open-ended and the content is under-specified, existing techniques struggle to
generate long-term coherent and creative content. Moreover, the models exhibit
and even amplify social biases that are learned from the training corpora. This
happens because the generation models are trained to capture the surface
patterns (i.e. sequences of words), instead of capturing underlying semantics
and discourse structures, as well as background knowledge including social
norms. In this paper, I introduce our recent works on controllable text
generation to enhance the creativity and fairness of language generation
models. We explore hierarchical generation and constrained decoding, with
applications to creative language generation including story, poetry, and
figurative languages, and bias mitigation for generation models.

Urbanization and its problems require an in-depth and comprehensive
understanding of urban dynamics, especially the complex and diversified
lifestyles in modern cities. Digitally acquired data can accurately capture
complex human activity, but it lacks the interpretability of demographic data.
In this paper, we study a privacy-enhanced dataset of the mobility visitation
patterns of 1.2 million people to 1.1 million places in 11 metro areas in the
U.S. to detect the latent mobility behaviors and lifestyles in the largest
American cities. Despite the considerable complexity of mobility visitations,
we found that lifestyles can be automatically decomposed into only 12 latent
interpretable activity behaviors on how people combine shopping, eating,
working, or using their free time. Rather than describing individuals with a
single lifestyle, we find that city dwellers' behavior is a mixture of those
behaviors. Those detected latent activity behaviors are equally present across
cities and cannot be fully explained by main demographic features. Finally, we
find those latent behaviors are associated with dynamics like experienced
income segregation, transportation, or healthy behaviors in cities, even after
controlling for demographic features. Our results signal the importance of
complementing traditional census data with activity behaviors to understand
urban dynamics.

We study how an autonomous agent learns to perform a task from demonstrations
in a different domain, such as a different environment or different agent. Such
cross-domain imitation learning is required to, for example, train an
artificial agent from demonstrations of a human expert. We propose a scalable
framework that enables cross-domain imitation learning without access to
additional demonstrations or further domain knowledge. We jointly train the
learner agent's policy and learn a mapping between the learner and expert
domains with adversarial training. We effect this by using a mutual information
criterion to find an embedding of the expert's state space that contains
task-relevant information and is invariant to domain specifics. This step
significantly simplifies estimating the mapping between the learner and expert
domains and hence facilitates end-to-end learning. We demonstrate successful
transfer of policies between considerably different domains, without extra
supervision such as additional demonstrations, and in situations where other
methods fail.

This paper addresses the Multi-Robot Active Information Acquisition (AIA)
problem, where a team of mobile robots, communicating through an underlying
graph, estimates a hidden state expressing a phenomenon of interest.
Applications like target tracking, coverage and SLAM can be expressed in this
framework. Existing approaches, though, are either not scalable, unable to
handle dynamic phenomena or not robust to changes in the communication graph.
To counter these shortcomings, we propose an Information-aware Graph Block
Network (I-GBNet), an AIA adaptation of Graph Neural Networks, that aggregates
information over the graph representation and provides sequential-decision
making in a distributed manner. The I-GBNet, trained via imitation learning
with a centralized sampling-based expert solver, exhibits permutation
equivariance and time invariance, while harnessing the superior scalability,
robustness and generalizability to previously unseen environments and robot
configurations. Experiments on significantly larger graphs and dimensionality
of the hidden state and more complex environments than those seen in training
validate the properties of the proposed architecture and its efficacy in the
application of localization and tracking of dynamic targets.

We consider the problem of learning Stochastic Differential Equations of the
form $dX_t = f(X_t)dt+\sigma(X_t)dW_t $ from one sample trajectory. This
problem is more challenging than learning deterministic dynamical systems
because one sample trajectory only provides indirect information on the unknown
functions $f$, $\sigma$, and stochastic process $dW_t$ representing the drift,
the diffusion, and the stochastic forcing terms, respectively. We propose a
method that combines Computational Graph Completion and data adapted kernels
learned via a new variant of cross validation. Our approach can be decomposed
as follows: (1) Represent the time-increment map $X_t \rightarrow X_{t+dt}$ as
a Computational Graph in which $f$, $\sigma$ and $dW_t$ appear as unknown
functions and random variables. (2) Complete the graph (approximate unknown
functions and random variables) via Maximum a Posteriori Estimation (given the
data) with Gaussian Process (GP) priors on the unknown functions. (3) Learn the
covariance functions (kernels) of the GP priors from data with randomized
cross-validation. Numerical experiments illustrate the efficacy, robustness,
and scope of our method.

Molecular machines composed of coupled subsystems transduce free energy
between different external reservoirs, in the process internally transducing
energy and information. While subsystem efficiencies of these molecular
machines have been measured in isolation, less is known about how they behave
in their natural setting when coupled together and acting in concert. Here we
derive upper and lower bounds on the subsystem efficiencies of a bipartite
molecular machine. We demonstrate their utility by estimating the efficiencies
of the $\mathrm{F}_\mathrm{o}$ and $\mathrm{F}_1$ subunits of ATP synthase and
that of kinesin pulling a diffusive cargo.

The application of models to assess the risk of the physical impacts of
weather and climate and their subsequent consequences for society and business
is of the utmost importance in our changing climate. The operation of such
models is historically bespoke and constrained to specific compute
infrastructure, driving datasets and predefined configurations. These
constraints introduce challenges with scaling model runs and putting the models
in the hands of interested users. Here we present a cloud-based modular
framework for the deployment and operation of geospatial models, initially
applied to climate impacts. The Climate Impact Modelling Frameworks (CIMF)
enables the deployment of modular workflows in a dynamic and flexible manner.
Users can specify workflow components in a streamlined manner, these components
can then be easily organised into different configurations to assess risk in
different ways and at different scales. This also enables different models
(physical simulation or machine learning models) and workflows to be connected
to produce combined risk assessment. Flood modelling is used as an end-to-end
example to demonstrate the operation of CIMF.

The technology of hyperspectral imaging (HSI) records the visual information
upon long-range-distributed spectral wavelengths. A representative
hyperspectral image acquisition procedure conducts a 3D-to-2D encoding by the
coded aperture snapshot spectral imager (CASSI), and requires a software
decoder for the 3D signal reconstruction. Based on this encoding procedure, two
major challenges stand in the way of a high-fidelity reconstruction: (i) To
obtain 2D measurements, CASSI dislocates multiple channels by disperser-titling
and squeezes them onto the same spatial region, yielding an entangled data
loss. (ii) The physical coded aperture (mask) will lead to a masked data loss
by selectively blocking the pixel-wise light exposure. To tackle these
challenges, we propose a spatial-spectral (S2-) transformer architecture with a
mask-aware learning strategy. Firstly, we simultaneously leverage spatial and
spectral attention modelings to disentangle the blended information in the 2D
measurement along both two dimensions. A series of Transformer structures
across spatial & spectral clues are systematically designed, which considers
the information inter-dependency between the two-fold cues. Secondly, the
masked pixels will induce higher prediction difficulty and should be treated
differently from unmasked ones. Thereby, we adaptively prioritize the loss
penalty attributing to the mask structure by inferring the difficulty-level
upon the mask-aware prediction. Our proposed method not only sets a new
state-of-the-art quantitatively, but also yields a better perceptual quality
upon structured areas.

Distinguishing if an action is performed as intended or if an intended action
fails is an important skill that not only humans have, but that is also
important for intelligent systems that operate in human environments.
Recognizing if an action is unintentional or anticipating if an action will
fail, however, is not straightforward due to lack of annotated data. While
videos of unintentional or failed actions can be found in the Internet in
abundance, high annotation costs are a major bottleneck for learning networks
for these tasks. In this work, we thus study the problem of self-supervised
representation learning for unintentional action prediction. While previous
works learn the representation based on a local temporal neighborhood, we show
that the global context of a video is needed to learn a good representation for
the three downstream tasks: unintentional action classification, localization
and anticipation. In the supplementary material, we show that the learned
representation can be used for detecting anomalies in videos as well.

Efficient and effective exploration in continuous space is a central problem
in applying reinforcement learning (RL) to autonomous driving. Skills learned
from expert demonstrations or designed for specific tasks can benefit the
exploration, but they are usually costly-collected, unbalanced/sub-optimal, or
failing to transfer to diverse tasks. However, human drivers can adapt to
varied driving tasks without demonstrations by taking efficient and structural
explorations in the entire skill space rather than a limited space with
task-specific skills. Inspired by the above fact, we propose an RL algorithm
exploring all feasible motion skills instead of a limited set of task-specific
and object-centric skills. Without demonstrations, our method can still perform
well in diverse tasks. First, we build a task-agnostic and ego-centric (TaEc)
motion skill library in a pure motion perspective, which is diverse enough to
be reusable in different complex tasks. The motion skills are then encoded into
a low-dimension latent skill space, in which RL can do exploration efficiently.
Validations in various challenging driving scenarios demonstrate that our
proposed method, TaEc-RL, outperforms its counterparts significantly in
learning efficiency and task performance.

The present paper is concerned with the question of how falsifiable a single
proposition is in the short and long run. Formal Learning theorists such as
Schulte and Juhl have argued that long-run falsifiability is characterized by
the topological notion of nowhere density in a suitable topological space. I
argue that the short-run falsifiability of a hypothesis is in turn
characterized by the VC finiteness of the hypothesis. Crucially, VC finite
hypotheses correspond precisely to definable sets in NIP structures. I end the
chapter by giving rigorous foundations for Mayo's conception of severe testing
by way of a combinatorial, non-probabilistic notion of surprise. VC finite
hypotheses again appear as the hypotheses with guaranteed short-run surprise
bounds. Therefore, NIP theories and VC finite hypotheses capture the notion of
short-run falsifiability.

Graph Neural Networks (GNNs) are powerful deep learning methods for
Non-Euclidean data. Popular GNNs are message-passing algorithms (MPNNs) that
aggregate and combine signals in a local graph neighborhood. However, shallow
MPNNs tend to miss long-range signals and perform poorly on some heterophilous
graphs, while deep MPNNs can suffer from issues like over-smoothing or
over-squashing. To mitigate such issues, existing works typically borrow
normalization techniques from training neural networks on Euclidean data or
modify the graph structures. Yet these approaches are not well-understood
theoretically and could increase the overall computational complexity. In this
work, we draw inspirations from spectral graph embedding and propose
$\texttt{PowerEmbed}$ -- a simple layer-wise normalization technique to boost
MPNNs. We show $\texttt{PowerEmbed}$ can provably express the top-$k$ leading
eigenvectors of the graph operator, which prevents over-smoothing and is
agnostic to the graph topology; meanwhile, it produces a list of
representations ranging from local features to global signals, which avoids
over-squashing. We apply $\texttt{PowerEmbed}$ in a wide range of simulated and
real graphs and demonstrate its competitive performance, particularly for
heterophilous graphs.

Although manipulating facial attributes by Generative Adversarial Networks
(GANs) has been remarkably successful recently, there are still some challenges
in explicit control of features such as pose, expression, lighting, etc. Recent
methods achieve explicit control over 2D images by combining 2D generative
model and 3DMM. However, due to the lack of realism and clarity in texture
reconstruction by 3DMM, there is a domain gap between the synthetic image and
the rendered image of 3DMM. Since rendered 3DMM images contain facial region
only without the background, directly computing the loss between these two
domains is not ideal and the resultant trained model will be biased. In this
study, we propose to explicitly edit the latent space of the pretrained
StyleGAN by controlling the parameters of the 3DMM. To address the domain gap
problem, we propose a noval network called 'Map and edit' and a simple but
effective attribute editing method to avoid direct loss computation between
rendered and synthesized images. Furthermore, since our model can accurately
generate multi-view face images while the identity remains unchanged. As a
by-product, combined with visibility masks, our proposed model can also
generate texture-rich and high-resolution UV facial textures. Our model relies
on pretrained StyleGAN, and the proposed model is trained in a self-supervised
manner without any manual annotations or datasets.

The overarching focus on predictive accuracy in mortality forecasting has
motivated an increasing shift towards more flexible representations of
age-specific period mortality trajectories at the cost of reduced
interpretability. While this perspective has led to successful predictive
strategies, the inclusion of interpretable structures in modeling of human
mortality can be, in fact, beneficial in improving forecasts. We pursue this
direction via a novel B-spline process with locally-adaptive dynamic
coefficients that outperforms state-of-the-art forecasting strategies by
explicitly incorporating core structures of period mortality trajectories
within an interpretable formulation that facilitates efficient computation via
closed-form Kalman filters and allows direct inference not only on age-specific
mortality trends but also on the associated rates of change across times. This
is obtained by modelling the age-specific death counts via an over-dispersed
Poisson log-normal model parameterized with a combination of B-spline bases
having dynamic coefficients that characterize time changes in mortality rates
through a set of carefully-defined stochastic differential equations. Such a
representation yields high flexibility, but also preserves tractability in
inference on dynamic changes of mortality patterns for different age profiles,
while accounting for shocks. As illustrated in applications to mortality data
from different countries, the proposed model yields more accurate forecasts
than state-of-the-art competitors and unveils substantial differences across
countries and age groups in mortality patterns, both in the past decades and
during the recent covid-19 pandemic.

This paper proposes a sensor data anonymization model that is trained on
decentralized data and strikes a desirable trade-off between data utility and
privacy, even in heterogeneous settings where the collected sensor data have
different underlying distributions. Our anonymization model, dubbed Blinder, is
based on a variational autoencoder and discriminator networks trained in an
adversarial fashion. We use the model-agnostic meta-learning framework to adapt
the anonymization model trained via federated learning to each user's data
distribution. We evaluate Blinder under different settings and show that it
provides end-to-end privacy protection at the cost of increasing privacy loss
by up to 4.00% and decreasing data utility by up to 4.24%, compared to the
state-of-the-art anonymization model trained on centralized data. Our
experiments confirm that Blinder can obscure multiple private attributes at
once, and has sufficiently low power consumption and computational overhead for
it to be deployed on edge devices and smartphones to perform real-time
anonymization of sensor data.

When songs are composed or performed, there is often an intent by the
singer/songwriter of expressing feelings or emotions through it. For humans,
matching the emotiveness in a musical composition or performance with the
subjective perception of an audience can be quite challenging. Fortunately, the
machine learning approach for this problem is simpler. Usually, it takes a
data-set, from which audio features are extracted to present this information
to a data-driven model, that will, in turn, train to predict what is the
probability that a given song matches a target emotion. In this paper, we
studied the most common features and models used in recent publications to
tackle this problem, revealing which ones are best suited for recognizing
emotion in a cappella songs.

The transcription quality of automatic speech recognition (ASR) systems
degrades significantly when transcribing audios coming from unseen domains. We
propose an unsupervised error correction method for unsupervised ASR domain
adaption, aiming to recover transcription errors caused by domain mismatch.
Unlike existing correction methods that rely on transcribed audios for
training, our approach requires only unlabeled data of the target domains in
which a pseudo-labeling technique is applied to generate correction training
samples. To reduce over-fitting to the pseudo data, we also propose an
encoder-decoder correction model that can take into account additional
information such as dialogue context and acoustic features. Experiment results
show that our method obtains a significant word error rate (WER) reduction over
non-adapted ASR systems. The correction model can also be applied on top of
other adaptation approaches to bring an additional improvement of 10%
relatively.

Dynamic Movement Primitives (DMPs) offer great versatility for encoding,
generating and adapting complex end-effector trajectories. DMPs are also very
well suited to learning manipulation skills from human demonstration. However,
the reactive nature of DMPs restricts their applicability for tool use and
object manipulation tasks involving non-holonomic constraints, such as scalpel
cutting or catheter steering. In this work, we extend the Cartesian space DMP
formulation by adding a coupling term that enforces a pre-defined set of
non-holonomic constraints. We obtain the closed-form expression for the
constraint forcing term using the Udwadia-Kalaba method. This approach offers a
clean and practical solution for guaranteed constraint satisfaction at
run-time. Further, the proposed analytical form of the constraint forcing term
enables efficient trajectory optimization subject to constraints. We
demonstrate the usefulness of this approach by showing how we can learn robotic
cutting skills from human demonstration.

Understanding collective phenomena in quantum materials from first principles
is a promising route toward engineering materials properties on demand and
designing new functionalities. This work examines the quantum paraelectric
state, an elusive state of matter characterized by the smooth saturation of the
ferroelectric instability at low temperature due to quantum fluctuations
associated with anharmonic phonon effects. The temperature-dependent evolution
of the soft ferroelectric phonon mode in the quantum paraelectric KTaO$_3$ in
the range 0-300 K is modelled by combining density functional theory (DFT)
calculations with the stochastic self-consistent harmonic approximation
assisted by an on-the-fly machine-learned force field. The calculated data show
that including anharmonic terms is essential to stabilize the spurious
imaginary ferroelectric phonon predicted by DFT, in agreement with experiments.
Augmenting the DFT workflow with machine-learned force fields allows for
efficient stochastic sampling of the configurational space using large
supercells in a broad and dense temperature range, inaccessible by conventional
ab initio protocols. This work proposes a robust computational workflow capable
of accounting for collective behaviors involving different degrees of freedom
and occurring at large time/length scales, paving the way for precise modeling
and control of quantum effects in materials.

A rapid transformation of current electric power and natural gas (NG)
infrastructure is imperative to meet the mid-century goal of CO2 emissions
reduction requires. This necessitates a long-term planning of the joint
power-NG system under representative demand and supply patterns, operational
constraints, and policy considerations. Our work is motivated by the
computational and practical challenges associated with solving the generation
and transmission expansion problem (GTEP) for joint planning of power-NG
systems. Specifically, we focus on efficiently extracting a set of
representative days from power and NG data in respective networks and using
this set to reduce the computational burden required to solve the GTEP. We
propose a Graph Autoencoder for Multiple time resolution Energy Systems (GAMES)
to capture the spatio-temporal demand patterns in interdependent networks and
account for differences in the temporal resolution of available data. The
resulting embeddings are used in a clustering algorithm to select
representative days. We evaluate the effectiveness of our approach in solving a
GTEP formulation calibrated for the joint power-NG system in New England. This
formulation accounts for the physical interdependencies between power and NG
systems, including the joint emissions constraint. Our results show that the
set of representative days obtained from GAMES not only allows us to tractably
solve the GTEP formulation, but also achieves a lower cost of implementing the
joint planning decisions.

While Reinforcement Learning can achieve impressive results for complex
tasks, the learned policies are generally prone to fail in downstream tasks
with even minor model mismatch or unexpected perturbations. Recent works have
demonstrated that a policy population with diverse behavior characteristics can
generalize to downstream environments with various discrepancies. However, such
policies might result in catastrophic damage during the deployment in practical
scenarios like real-world systems due to the unrestricted behaviors of trained
policies. Furthermore, training diverse policies without regulation of the
behavior can result in inadequate feasible policies for extrapolating to a wide
range of test conditions with dynamics shifts. In this work, we aim to train
diverse policies under the regularization of the behavior patterns. We motivate
our paradigm by observing the inverse dynamics in the environment with partial
state information and propose Diversity in Regulation(DiR) training diverse
policies with regulated behaviors to discover desired patterns that benefit the
generalization. Considerable empirical results on various variations of
different environments indicate that our method attains improvements over other
diversity-driven counterparts.

Lesion segmentation is a crucial step of the radiomic workflow. Manual
segmentation requires long execution time and is prone to variability,
impairing the realisation of radiomic studies and their robustness. In this
study, a deep-learning automatic segmentation method was applied on computed
tomography images of non-small-cell lung cancer patients. The use of manual vs
automatic segmentation in the performance of survival radiomic models was
assessed, as well. METHODS A total of 899 NSCLC patients were included (2
proprietary: A and B, 1 public datasets: C). Automatic segmentation of lung
lesions was performed by training a previously developed architecture, the
nnU-Net, including 2D, 3D and cascade approaches. The quality of automatic
segmentation was evaluated with DICE coefficient, considering manual contours
as reference. The impact of automatic segmentation on the performance of a
radiomic model for patient survival was explored by extracting radiomic
hand-crafted and deep-learning features from manual and automatic contours of
dataset A, and feeding different machine learning algorithms to classify
survival above/below median. Models' accuracies were assessed and compared.
RESULTS The best agreement between automatic and manual contours with DICE=0.78
+(0.12) was achieved by averaging predictions from 2D and 3D models, and
applying a post-processing technique to extract the maximum connected
component. No statistical differences were observed in the performances of
survival models when using manual or automatic contours, hand-crafted, or deep
features. The best classifier showed an accuracy between 0.65 and 0.78.
CONCLUSION The promising role of nnU-Net for automatic segmentation of lung
lesions was confirmed, dramatically reducing the time-consuming physicians'
workload without impairing the accuracy of survival predictive models based on
radiomics.

In this integrated study, a turboshaft engine was evaluated by adding inlet
air cooling and regenerative cooling based on energy-environment analysis.
First, impacts of flight-Mach number, flight altitude, the compression ratio of
compressor-1 in the main cycle, the turbine inlet temperature of turbine-1 in
the main cycle, temperature fraction of turbine-2, the compression ratio of the
accessory cycle, and inlet air temperature variation in inlet air cooling
system on some functional performance parameters of Regenerative turboshaft
engine cycle equipped with inlet air cooling system such as power-specific fuel
consumption, Power output, thermal efficiency, and mass flow rate of Nitride
oxides (NOx) including NO and NO2 has been investigated via using hydrogen as
fuel working. Consequently, based on the analysis, a model was developed to
predict the energy-environment performance of the Regenerative turboshaft
engine cycle equipped with a cooling air cooling system based on a deep neural
network (DNN) with 2 hidden layers with 625 neurons for each hidden layer. The
model proposed to predict the amount of thermal efficiency and the mass flow
rate of nitride oxide (NOx) containing NO and NO2. The results demonstrated the
accuracy of the integrated DNN model with the proper amount of the MSE, MAE,
and RMSD cost function for both predicted outputs to validate both testing and
training data. Also, R and R^2 are noticeably calculated very close to 1 for
both thermal Efficiency and NOx emission mass flow rate for both validations of
thermal efficiency and NOx emission mass flow rate prediction values with its
training and its testing data.

Controlling artificial agents from visual sensory data is an arduous task.
Reinforcement learning (RL) algorithms can succeed in this but require large
amounts of interactions between the agent and the environment. To alleviate the
issue, unsupervised RL proposes to employ self-supervised interaction and
learning, for adapting faster to future tasks. Yet, whether current
unsupervised strategies improve generalization capabilities is still unclear,
especially in visual control settings. In this work, we design an effective
unsupervised RL strategy for data-efficient visual control. First, we show that
world models pre-trained with data collected using unsupervised RL can
facilitate adaptation for future tasks. Then, we analyze several design choices
to adapt efficiently, effectively reusing the agents' pre-trained components,
and learning and planning in imagination, with our hybrid planner, which we dub
Dyna-MPC. By combining the findings of a large-scale empirical study, we
establish an approach that strongly improves performance on the Unsupervised RL
Benchmark, requiring 20$\times$ less data to match the performance of
supervised methods. The approach also demonstrates robust performance on the
Real-Word RL benchmark, hinting that the approach generalizes to noisy

Traditional machine learning methods have been widely studied in financial
innovation. My study focuses on the application of deep learning methods on
asset pricing. I investigate various deep learning methods for asset pricing,
especially for risk premia measurement. All models take the same set of
predictive signals (firm characteristics, systematic risks and macroeconomics).
I demonstrate high performance of all kinds of state-of-the-art (SOTA) deep
learning methods, and figure out that RNNs with memory mechanism and attention
have the best performance in terms of predictivity. Furthermore, I demonstrate
large economic gains to investors using deep learning forecasts. The results of
my comparative experiments highlight the importance of domain knowledge and
financial theory when designing deep learning models. I also show return
prediction tasks bring new challenges to deep learning. The time varying
distribution causes distribution shift problem, which is essential for
financial time series prediction. I demonstrate that deep learning methods can
improve asset risk premium measurement. Due to the booming deep learning
studies, they can constantly promote the study of underlying financial
mechanisms behind asset pricing. I also propose a promising research method
that learning from data and figuring out the underlying economic mechanisms
through explainable artificial intelligence (AI) methods. My findings not only
justify the value of deep learning in blooming fintech development, but also
highlight their prospects and advantages over traditional machine learning
methods.

Bandits with knapsacks (BwK) is an influential model of sequential
decision-making under uncertainty that incorporates resource consumption
constraints. In each round, the decision-maker observes an outcome consisting
of a reward and a vector of nonnegative resource consumptions, and the budget
of each resource is decremented by its consumption. In this paper we introduce
a natural generalization of the stochastic BwK problem that allows
non-monotonic resource utilization. In each round, the decision-maker observes
an outcome consisting of a reward and a vector of resource drifts that can be
positive, negative or zero, and the budget of each resource is incremented by
its drift. Our main result is a Markov decision process (MDP) policy that has
constant regret against a linear programming (LP) relaxation when the
decision-maker knows the true outcome distributions. We build upon this to
develop a learning algorithm that has logarithmic regret against the same LP
relaxation when the decision-maker does not know the true outcome
distributions. We also present a reduction from BwK to our model that shows our
regret bound matches existing results.

Understanding emerging behaviors of reinforcement learning (RL) agents may be
difficult since such agents are often trained in complex environments using
highly complex decision making procedures. This has given rise to a variety of
approaches to explainability in RL that aim to reconcile discrepancies that may
arise between the behavior of an agent and the behavior that is anticipated by
an observer. Most recent approaches have relied either on domain knowledge,
that may not always be available, on an analysis of the agent's policy, or on
an analysis of specific elements of the underlying environment, typically
modeled as a Markov Decision Process (MDP). Our key claim is that even if the
underlying MDP is not fully known (e.g., the transition probabilities have not
been accurately learned) or is not maintained by the agent (i.e., when using
model-free methods), it can nevertheless be exploited to automatically generate
explanations. For this purpose, we suggest using formal MDP abstractions and
transforms, previously used in the literature for expediting the search for
optimal policies, to automatically produce explanations. Since such transforms
are typically based on a symbolic representation of the environment, they may
represent meaningful explanations for gaps between the anticipated and actual
agent behavior. We formally define this problem, suggest a class of transforms
that can be used for explaining emergent behaviors, and suggest methods that
enable efficient search for an explanation. We demonstrate the approach on a
set of standard benchmarks.

The lack of large labeled medical imaging datasets, along with significant
inter-individual variability compared to clinically established disease
classes, poses significant challenges in exploiting medical imaging information
in a precision medicine paradigm, where in principle dense patient-specific
data can be employed to formulate individual predictions and/or stratify
patients into finer-grained groups which may follow more homogeneous
trajectories and therefore empower clinical trials. In order to efficiently
explore the effective degrees of freedom underlying variability in medical
images in an unsupervised manner, in this work we propose an unsupervised
autoencoder framework which is augmented with a contrastive loss to encourage
high separability in the latent space. The model is validated on (medical)
benchmark datasets. As cluster labels are assigned to each example according to
cluster assignments, we compare performance with a supervised transfer learning
baseline. Our method achieves similar performance to the supervised
architecture, indicating that separation in the latent space reproduces expert
medical observer-assigned labels. The proposed method could be beneficial for
patient stratification, exploring new subdivisions of larger classes or
pathological continua or, due to its sampling abilities in a variation setting,
data augmentation in medical image processing.

Bitcoin has been subject to illicit activities more often than probably any
other financial assets, due to the pseudo-anonymous nature of its transacting
entities. An ideal detection model is expected to achieve all the three
properties of (I) early detection, (II) good interpretability, and (III)
versatility for various illicit activities. However, existing solutions cannot
meet all these requirements, as most of them heavily rely on deep learning
without satisfying interpretability and are only available for retrospective
analysis of a specific illicit type.
  First, we present asset transfer paths, which aim to describe addresses'
early characteristics. Next, with a decision tree based strategy for feature
selection and segmentation, we split the entire observation period into
different segments and encode each as a segment vector. After clustering all
these segment vectors, we get the global status vectors, essentially the basic
unit to describe the whole intention. Finally, a hierarchical self-attention
predictor predicts the label for the given address in real time. A survival
module tells the predictor when to stop and proposes the status sequence,
namely intention. %
  With the type-dependent selection strategy and global status vectors, our
model can be applied to detect various illicit activities with strong
interpretability. The well-designed predictor and particular loss functions
strengthen the model's prediction speed and interpretability one step further.
Extensive experiments on three real-world datasets show that our proposed
algorithm outperforms state-of-the-art methods. Besides, additional case
studies justify our model can not only explain existing illicit patterns but
can also find new suspicious characters.

Belief Propagation (BP) is an important message-passing algorithm for various
reasoning tasks over graphical models, including solving the Constraint
Optimization Problems (COPs). It has been shown that BP can achieve
state-of-the-art performance on various benchmarks by mixing old and new
messages before sending the new one, i.e., damping. However, existing methods
of tuning a static damping factor for BP not only are laborious but also harm
their performance. Moreover, existing BP algorithms treat each variable node's
neighbors equally when composing a new message, which also limits their
exploration ability. To address these issues, we seamlessly integrate BP, Gated
Recurrent Units (GRUs), and Graph Attention Networks (GATs) within the
message-passing framework to reason about dynamic weights and damping factors
for composing new BP messages. Our model, Deep Attentive Belief Propagation
(DABP), takes the factor graph and the BP messages in each iteration as the
input and infers the optimal weights and damping factors through GRUs and GATs,
followed by a multi-head attention layer. Furthermore, unlike existing
neural-based BP variants, we propose a novel self-supervised learning algorithm
for DABP with a smoothed solution cost, which does not require expensive
training labels and also avoids the common out-of-distribution issue through
efficient online learning. Extensive experiments show that our model
significantly outperforms state-of-the-art baselines.

Visual perception and language understanding are - fundamental components of
human intelligence, enabling them to understand and reason about objects and
their interactions. It is crucial for machines to have this capacity to reason
using these two modalities to invent new robot-human collaborative systems.
Recent advances in deep learning have built separate sophisticated
representations of both visual scenes and languages. However, understanding the
associations between the two modalities in a shared context for multimodal
reasoning remains a challenge. Focusing on language and vision modalities, this
thesis advances the understanding of how to exploit and use pivotal aspects of
vision-and-language tasks with neural networks to support reasoning. We derive
these understandings from a series of works, making a two-fold contribution:
(i) effective mechanisms for content selection and construction of temporal
relations from dynamic visual scenes in response to a linguistic query and
preparing adequate knowledge for the reasoning process (ii) new frameworks to
perform reasoning with neural networks by exploiting visual-linguistic
associations, deduced either directly from data or guided by external priors.

In this study, the four joint time-frequency (TF) moments; mean, variance,
skewness, and kurtosis of TF matrix obtained from Multivariate Synchrosqueezing
Transform (MSST) are proposed as features for hand gesture recognition. A
publicly available dataset containing surface EMG (sEMG) signals of 40 subjects
performing 10 hand gestures, was used. The distinguishing power of the feature
variables for the tested gestures was evaluated according to their p values
obtained from the Kruskal-Wallis (KW) test. It is concluded that the mean,
variance, skewness, and kurtosis of TF matrices can be candidate feature sets
for the recognition of hand gestures.

Data science research into fake news has gathered much momentum in recent
years, arguably facilitated by the emergence of large public benchmark
datasets. While it has been well-established within media studies that gender
bias is an issue that pervades news media, there has been very little
exploration into the relationship between gender bias and fake news. In this
work, we provide the first empirical analysis of gender bias vis-a-vis fake
news, leveraging simple and transparent lexicon-based methods over public
benchmark datasets. Our analysis establishes the increased prevalance of gender
bias in fake news across three facets viz., abundance, affect and proximal
words. The insights from our analysis provide a strong argument that gender
bias needs to be an important consideration in research into fake news.

EEG signals are complex and low-frequency signals. Therefore, they are easily
influenced by external factors. EEG artifact removal is crucial in neuroscience
because artifacts have a significant impact on the results of EEG analysis. The
removal of ocular artifacts is the most challenging among these artifacts. In
this study, a novel ocular artifact removal method is presented by developing
bidirectional long-short term memory (BiLSTM)-based deep learning (DL) models.
We created a benchmarking dataset to train and test proposed DL models by
combining the EEGdenoiseNet and DEAP datasets. We also augmented the data by
contaminating ground-truth clean EEG signals with EOG at various SNR levels.
The BiLSTM network is then fed to features extracted from augmented signals
using highly-localized time-frequency (TF) coefficients obtained by wavelet
synchrosqueezed transform (WSST). We also compare the WSST-based DL model
results with traditional TF analysis (TFA) methods namely short-time Fourier
transformation (STFT) and continuous wavelet transform (CWT) as well as
augmented raw signals. The best average MSE value of 0.3066 was obtained by the
first time-proposed BiLSTM-based WSST-Net model. Our results demonstrated the
WSST-Net model significantly improves artifact removal performance compared to
traditional TF and raw signal methods. Also, the proposed EOG removal approach
reveals that it outperforms many conventional and DL-based ocular artifact
removal methods in the literature.

We demonstrate the applicability of model-agnostic algorithms for
meta-learning, specifically Reptile, to GNN models in molecular regression
tasks. Using meta-learning we are able to learn new chemical prediction tasks
with only a few model updates, as compared to using randomly initialized GNNs
which require learning each regression task from scratch. We experimentally
show that GNN layer expressivity is correlated to improved meta-learning.
Additionally, we also experiment with GNN emsembles which yield best
performance and rapid convergence for k-shot learning.

In light of widespread evidence of parameter instability in macroeconomic
models, many time-varying parameter (TVP) models have been proposed. This paper
proposes a nonparametric TVP-VAR model using Bayesian Additive Regression Trees
(BART). The novelty of this model arises from the law of motion driving the
parameters being treated nonparametrically. This leads to great flexibility in
the nature and extent of parameter change, both in the conditional mean and in
the conditional variance. In contrast to other nonparametric and machine
learning methods that are black box, inference using our model is
straightforward because, in treating the parameters rather than the variables
nonparametrically, the model remains conditionally linear in the mean.
Parsimony is achieved through adopting nonparametric factor structures and use
of shrinkage priors. In an application to US macroeconomic data, we illustrate
the use of our model in tracking both the evolving nature of the Phillips curve
and how the effects of business cycle shocks on inflationary measures vary
nonlinearly with movements in uncertainty.

In this paper, we introduce a novel artificial neural network (ANN) based
scheme to estimate the thickness of thin films deposited on a given substrate.
Here we consider the visible interference pattern between a plane wave and a
diverging wave reflected from the thin film surface that records the thickness
information of the thin film. We assume a uniform thickness profile of the
film. However, the thickness increases as the deposition takes place. We
extract the intensity data along a line through the center of the interference
pattern. We train our network by using a number of such line information of
known thickness profiles. The performance of the trained network is then tested
by estimating the thickness of unknown surfaces. The numerical simulation
results show that the proposed technique can be very much useful for automated
measurement of thickness, quickly and in real time, during deposition

Strong adversarial examples are the keys to evaluating and enhancing the
robustness of deep neural networks. The popular adversarial attack algorithms
maximize the non-concave loss function using the gradient ascent. However, the
performance of each attack is usually sensitive to, for instance, minor image
transformations due to insufficient information (only one input example, few
white-box source models and unknown defense strategies). Hence, the crafted
adversarial examples are prone to overfit the source model, which limits their
transferability to unidentified architectures. In this paper, we propose
Multiple Asymptotically Normal Distribution Attacks (MultiANDA), a novel method
that explicitly characterizes adversarial perturbations from a learned
distribution. Specifically, we approximate the posterior distribution over the
perturbations by taking advantage of the asymptotic normality property of
stochastic gradient ascent (SGA), then apply the ensemble strategy on this
procedure to estimate a Gaussian mixture model for a better exploration of the
potential optimization space. Drawing perturbations from the learned
distribution allow us to generate any number of adversarial examples for each
input. The approximated posterior essentially describes the stationary
distribution of SGA iterations, which captures the geometric information around
the local optimum. Thus, the samples drawn from the distribution reliably
maintain the transferability. Our proposed method outperforms nine
state-of-the-art black-box attacks on deep learning models with or without
defenses through extensive experiments on seven normally trained and seven
defence models.

In image classification, a lot of development has happened in detecting
out-of-distribution (OoD) data. However, most OoD detection methods are
evaluated on a standard set of datasets, arbitrarily different from training
data. There is no clear definition of what forms a ``good" OoD dataset.
Furthermore, the state-of-the-art OoD detection methods already achieve near
perfect results on these standard benchmarks. In this paper, we define 2
categories of OoD data using the subtly different concepts of perceptual/visual
and semantic similarity to in-distribution (iD) data. We define Near OoD
samples as perceptually similar but semantically different from iD samples, and
Shifted samples as points which are visually different but semantically akin to
iD data. We then propose a GAN based framework for generating OoD samples from
each of these 2 categories, given an iD dataset. Through extensive experiments
on MNIST, CIFAR-10/100 and ImageNet, we show that a) state-of-the-art OoD
detection methods which perform exceedingly well on conventional benchmarks are
significantly less robust to our proposed benchmark. Moreover, b) models
performing well on our setup also perform well on conventional real-world OoD
detection benchmarks and vice versa, thereby indicating that one might not even
need a separate OoD set, to reliably evaluate performance in OoD detection.

In hope of expanding training data, researchers often want to merge two or
more datasets that are created using different labeling schemes. This paper
considers two datasets that label part-of-speech (POS) tags under different
tagging schemes and leverage the supervised labels of one dataset to help
generate labels for the other dataset. This paper further discusses the
theoretical difficulties of this approach and proposes a novel supervised
architecture employing Transformers to tackle the problem of consolidating two
completely disjoint datasets. The results diverge from initial expectations and
discourage exploration into the use of disjoint labels to consolidate datasets
with different labels.

Seismic waveform modeling is a powerful tool for determining earth structure
models and unraveling earthquake rupture processes, but it is computationally
expensive. We introduce a scheme to vastly accelerate these calculations with a
recently developed machine learning paradigm called the neural operator. Once
trained, these models can simulate a full wavefield for arbitrary velocity
models at negligible cost. We use a U-shaped neural operator to learn a general
solution operator to the 2D elastic wave equation from an ensemble of numerical
simulations performed with random velocity models and source locations. We show
that full waveform modeling with neural operators is nearly two orders of
magnitude faster than conventional numerical methods, and more importantly, the
trained model enables accurate simulation for arbitrary velocity models, source
locations, and mesh discretization, even when distinctly different from the
training dataset. The method also enables efficient full-waveform inversion
with automatic differentiation.

A learning machine, like all machines, is an open system driven far from
thermal equilibrium by access to a low entropy source of free energy. We
discuss the connection between machines that learn, with low probability of
error, and the optimal use of thermodynamic resources for both classical and
quantum machines. Both fixed point and spiking perceptrons are discussed in the
context of possible physical implementations. An example of a single photon
quantum kernel evaluation illustrates the important role for quantum coherence
in data representation. Machine learning algorithms, implemented on
conventional complementary metal oxide semiconductor (CMOS) devices, currently
consume large amounts of energy. By focusing on the physical constraints of
learning machines rather than algorithms, we suggest that a more efficient
means of implementing learning may be possible based on quantum switches
operating at very low power. Single photon kernel evaluation is an example of
the energy efficiency that might be possible.

In this paper, we present our solution to the MuSe-Humor sub-challenge of the
Multimodal Emotional Challenge (MuSe) 2022. The goal of the MuSe-Humor
sub-challenge is to detect humor and calculate AUC from audiovisual recordings
of German football Bundesliga press conferences. It is annotated for humor
displayed by the coaches. For this sub-challenge, we first build a discriminant
model using the transformer module and BiLSTM module, and then propose a hybrid
fusion strategy to use the prediction results of each modality to improve the
performance of the model. Our experiments demonstrate the effectiveness of our
proposed model and hybrid fusion strategy on multimodal fusion, and the AUC of
our proposed model on the test set is 0.8972.

Machine programming (MP) is an emerging field at the intersection of
deterministic and probabilistic computing, and it aims to assist software and
hardware engineers, among other applications. Along with powerful compute
resources, MP systems often rely on vast amount of open-source code to learn
interesting properties about code and programming and solve problems in the
areas of debugging, code recommendation, auto-completion, etc. Unfortunately,
several of the existing MP systems either do not consider quality of code
repositories or use atypical quality measures than those typically used in
software engineering community to select them. As such, impact of quality of
code repositories on the performance of these systems needs to be studied.
  In this preliminary paper, we evaluate impact of different quality
repositories on the performance of a candidate MP system. Towards that
objective, we develop a framework, named GitRank, to rank open-source
repositories on quality, maintainability, and popularity by leveraging existing
research on this topic. We then apply GitRank to evaluate correlation between
the quality measures used by the candidate MP system and the quality measures
used by our framework. Our preliminary results reveal some correlation between
the quality measures used in GitRank and ControlFlag's performance, suggesting
that some of the measures used in GitRank are applicable to ControlFlag. But it
also raises questions around right quality measures for code repositories used
in MP systems. We believe that our findings also generate interesting insights
towards code quality measures that affect performance of MP systems.

Distributed machine learning enables scalability and computational
offloading, but requires significant levels of communication. Consequently,
communication efficiency in distributed learning settings is an important
consideration, especially when the communications are wireless and
battery-driven devices are employed. In this paper we develop a censoring-based
heavy ball (CHB) method for distributed learning in a server-worker
architecture. Each worker self-censors unless its local gradient is
sufficiently different from the previously transmitted one. The significant
practical advantages of the HB method for learning problems are well known, but
the question of reducing communications has not been addressed. CHB takes
advantage of the HB smoothing to eliminate reporting small changes, and
provably achieves a linear convergence rate equivalent to that of the classical
HB method for smooth and strongly convex objective functions. The convergence
guarantee of CHB is theoretically justified for both convex and nonconvex
cases. In addition we prove that, under some conditions, at least half of all
communications can be eliminated without any impact on convergence rate.
Extensive numerical results validate the communication efficiency of CHB on
both synthetic and real datasets, for convex, nonconvex, and nondifferentiable
cases. Given a target accuracy, CHB can significantly reduce the number of
communications compared to existing algorithms, achieving the same accuracy
without slowing down the optimization process.

Objects rarely sit in isolation in human environments. As such, we'd like our
robots to reason about how multiple objects relate to one another and how those
relations may change as the robot interacts with the world. To this end, we
propose a novel graph neural network framework for multi-object manipulation to
predict how inter-object relations change given robot actions. Our model
operates on partial-view point clouds and can reason about multiple objects
dynamically interacting during the manipulation. By learning a dynamics model
in a learned latent graph embedding space, our model enables multi-step
planning to reach target goal relations. We show our model trained purely in
simulation transfers well to the real world. Our planner enables the robot to
rearrange a variable number of objects with a range of shapes and sizes using
both push and pick and place skills.

The online knapsack problem is a classic online resource allocation problem
in networking and operations research. Its basic version studies how to pack
online arriving items of different sizes and values into a capacity-limited
knapsack. In this paper, we study a general version that includes item
departures, while also considering multiple knapsacks and multi-dimensional
item sizes. We design a threshold-based online algorithm and prove that the
algorithm can achieve order-optimal competitive ratios. Beyond worst-case
performance guarantees, we also aim to achieve near-optimal average performance
under typical instances. Towards this goal, we propose a data-driven online
algorithm that learns within a policy-class that guarantees a worst-case
performance bound. In trace-driven experiments, we show that our data-driven
algorithm outperforms other benchmark algorithms in an application of online
knapsack to job scheduling for cloud computing.

Large scale dynamics of the oceans and the atmosphere are governed by the
primitive equations (PEs). Due to the nonlinearity and nonlocality, the
numerical study of the PEs is in general a hard task. Neural network has been
shown a promising machine learning tool to tackle this challenge. In this work,
we employ the physics-informed neural networks (PINNs) to approximate the
solutions to the PEs and study the error estimates. We first establish the
higher-order regularity for the global solutions to the PEs with either full
viscosity and diffusivity, or with only the horizontal ones. Such higher-order
regularity results are new and required in the analysis under the PINNs
framework. Then we prove the existence of two layer tanh PINNs of which the
corresponding training error can be arbitrarily small by taking the width of
PINNs to be sufficiently wide, and the error between the true solution and the
approximation can be arbitrarily small provided that the training error is
small enough and the sample set is large enough. In particular, our analysis
includes higher-order (in spatial Sobolev norm) error estimates, and improves
existing results in PINNs' literature which concerns only the $L^2$ error.
Numerical results on prototype systems are presented for further illustrating
the advantage of using $H^s$ norm during the training.

Incomplete Multi-View Clustering aims to enhance clustering performance by
using data from multiple modalities. Despite the fact that several approaches
for studying this issue have been proposed, the following drawbacks still
persist: 1) It's difficult to learn latent representations that account for
complementarity yet consistency without using label information; 2) and thus
fails to take full advantage of the hidden information in incomplete data
results in suboptimal clustering performance when complete data is scarce. In
this paper, we propose Contrastive Incomplete Multi-View Image Clustering with
Generative Adversarial Networks (CIMIC-GAN), which uses GAN to fill in
incomplete data and uses double contrastive learning to learn consistency on
complete and incomplete data. More specifically, considering diversity and
complementary information among multiple modalities, we incorporate
autoencoding representation of complete and incomplete data into double
contrastive learning to achieve learning consistency. Integrating GANs into the
autoencoding process can not only take full advantage of new features of
incomplete data, but also better generalize the model in the presence of high
data missing rates. Experiments conducted on \textcolor{black}{four}
extensively-used datasets show that CIMIC-GAN outperforms state-of-the-art
incomplete multi-View clustering methods.

The theory of identifiable representation learning aims to build
general-purpose methods that extract high-level latent (causal) factors from
low-level sensory data. Most existing works focus on identifiable
representation learning with observational data, relying on distributional
assumptions on latent (causal) factors. However, in practice, we often also
have access to interventional data for representation learning. How can we
leverage interventional data to help identify high-level latents? To this end,
we explore the role of interventional data for identifiable representation
learning in this work. We study the identifiability of latent causal factors
with and without interventional data, under minimal distributional assumptions
on the latents. We prove that, if the true latent variables map to the observed
high-dimensional data via a polynomial function, then representation learning
via minimizing the standard reconstruction loss of autoencoders identifies the
true latents up to affine transformation. If we further have access to
interventional data generated by hard $do$ interventions on some of the
latents, then we can identify these intervened latents up to permutation, shift
and scaling.

Histone modifications play a critical role in gene regulation. Consequently,
predicting gene expression from histone modification signals is a highly
motivated problem in epigenetics. We build upon the work of DeepChrome by Singh
et al. (2016), who trained classifiers that map histone modification signals to
gene expression. We present a novel visualization technique for providing
insight into combinatorial relationships among histone modifications for gene
regulation that uses a generative adversarial network to generate histone
modification signals. We also explore and compare various architectural
changes, with results suggesting that the 645k-parameter convolutional neural
network from DeepChrome has the same predictive power as a 12-parameter linear
network. Results from cross-cell prediction experiments, where the model is
trained and tested on datasets of varying sizes, cell-types, and correlations,
suggest the relationship between histone modification signals and gene
expression is independent of cell type. We release our PyTorch
re-implementation of DeepChrome on GitHub
\footnote{\url{github.com/ssss1029/gene_expression_294}}.\parfillskip=0pt

We study momentum-based first-order optimization algorithms in which the
iterations utilize information from the two previous steps and are subject to
an additive white noise. This class of algorithms includes Polyak's heavy-ball
and Nesterov's accelerated methods as special cases and noise accounts for
uncertainty in either gradient evaluation or iteration updates. For strongly
convex quadratic problems, we use the steady-state variance of the error in the
optimization variable to quantify noise amplification and identify fundamental
stochastic performance tradeoffs. Our approach utilizes the Jury stability
criterion to provide a novel geometric characterization of conditions for
linear convergence, and it clarifies the relation between the noise
amplification and convergence rate as well as their dependence on the condition
number and the constant algorithmic parameters. This geometric insight leads to
simple alternative proofs of standard convergence results and allows us to
establish analytical lower bounds on the product between the settling time and
noise amplification that scale quadratically with the condition number. Our
analysis also identifies a key difference between the gradient and iterate
noise models: while the amplification of gradient noise can be made arbitrarily
small by sufficiently decelerating the algorithm, the best achievable variance
amplification for the iterate noise model increases linearly with the settling
time in decelerating regime. Furthermore, we introduce two parameterized
families of algorithms that strike a balance between noise amplification and
settling time while preserving order-wise Pareto optimality for both noise
models. Finally, by analyzing a class of accelerated gradient flow dynamics,
whose suitable discretization yields the two-step momentum algorithm, we
establish that stochastic performance tradeoffs also extend to continuous time.

In this paper, we predict conditional survival functions through a combined
regression strategy. We take weak learners as different random survival trees.
We propose to maximize concordance in the right-censored set up to find the
optimal parameters. We explore two approaches, a usual survival cobra and a
novel weighted predictor based on the concordance index. Our proposed
formulations use two different norms, say, Max-norm and Frobenius norm, to find
a proximity set of predictions from query points in the test dataset. We
illustrate our algorithms through three different real-life dataset

Learning from Demonstration (LfD) approaches empower end-users to teach
robots novel tasks via demonstrations of the desired behaviors, democratizing
access to robotics. However, current LfD frameworks are not capable of fast
adaptation to heterogeneous human demonstrations nor the large-scale deployment
in ubiquitous robotics applications. In this paper, we propose a novel LfD
framework, Fast Lifelong Adaptive Inverse Reinforcement learning (FLAIR). Our
approach (1) leverages learned strategies to construct policy mixtures for fast
adaptation to new demonstrations, allowing for quick end-user personalization,
(2) distills common knowledge across demonstrations, achieving accurate task
inference; and (3) expands its model only when needed in lifelong deployments,
maintaining a concise set of prototypical strategies that can approximate all
behaviors via policy mixtures. We empirically validate that FLAIR achieves
adaptability (i.e., the robot adapts to heterogeneous, user-specific task
preferences), efficiency (i.e., the robot achieves sample-efficient
adaptation), and scalability (i.e., the model grows sublinearly with the number
of demonstrations while maintaining high performance). FLAIR surpasses
benchmarks across three control tasks with an average 57% improvement in policy
returns and an average 78% fewer episodes required for demonstration modeling
using policy mixtures. Finally, we demonstrate the success of FLAIR in a table
tennis task and find users rate FLAIR as having higher task (p<.05) and
personalization (p<.05) performance.

Overlapped speech detection (OSD) is critical for speech applications in
scenario of multi-party conversion. Despite numerous research efforts and
progresses, comparing with speech activity detection (VAD), OSD remains an open
challenge and its overall performance is far from satisfactory. The majority of
prior research typically formulates the OSD problem as a standard
classification problem, to identify speech with binary (OSD) or three-class
label (joint VAD and OSD) at frame level. In contrast to the mainstream, this
study investigates the joint VAD and OSD task from a new perspective. In
particular, we propose to extend traditional classification network with
multi-exit architecture. Such an architecture empowers our system with unique
capability to identify class using either low-level features from early exits
or high-level features from last exit. In addition, two training schemes,
knowledge distillation and dense connection, are adopted to further boost our
system performance. Experimental results on benchmark datasets (AMI and
DIHARD-III) validated the effectiveness and generality of our proposed system.
Our ablations further reveal the complementary contribution of proposed
schemes. With $F_1$ score of 0.792 on AMI and 0.625 on DIHARD-III, our proposed
system outperforms several top performing models on these datasets, but also
surpasses the current state-of-the-art by large margins across both datasets.
Besides the performance benefit, our proposed system offers another appealing
potential for quality-complexity trade-offs, which is highly preferred for
efficient OSD deployment.

To address the monaural speech enhancement problem, numerous research studies
have been conducted to enhance speech via operations either in time-domain on
the inner-domain learned from the speech mixture or in time--frequency domain
on the fixed full-band short time Fourier transform (STFT) spectrograms. Very
recently, a few studies on sub-band based speech enhancement have been
proposed. By enhancing speech via operations on sub-band spectrograms, those
studies demonstrated competitive performances on the benchmark dataset of
DNS2020. Despite attractive, this new research direction has not been fully
explored and there is still room for improvement. As such, in this study, we
delve into the latest research direction and propose a sub-band based speech
enhancement system with perceptually-motivated optimization and dual
transformations, called PT-FSE. Specially, our proposed PT-FSE model improves
its backbone, a full-band and sub-band fusion model, by three efforts. First,
we design a frequency transformation module that aims to strengthen the global
frequency correlation. Then a temporal transformation is introduced to capture
long range temporal contexts. Lastly, a novel loss, with leverage of properties
of human auditory perception, is proposed to facilitate the model to focus on
low frequency enhancement. To validate the effectiveness of our proposed model,
extensive experiments are conducted on the DNS2020 dataset. Experimental
results show that our PT-FSE system achieves substantial improvements over its
backbone, but also outperforms the current state-of-the-art while being 27\%
smaller than the SOTA. With average NB-PESQ of 3.57 on the benchmark dataset,
our system offers the best speech enhancement results reported till date.

Recently cloud-based graph convolutional network (GCN) has demonstrated great
success and potential in many privacy-sensitive applications such as personal
healthcare and financial systems. Despite its high inference accuracy and
performance on cloud, maintaining data privacy in GCN inference, which is of
paramount importance to these practical applications, remains largely
unexplored. In this paper, we take an initial attempt towards this and develop
$\textit{CryptoGCN}$--a homomorphic encryption (HE) based GCN inference
framework. A key to the success of our approach is to reduce the tremendous
computational overhead for HE operations, which can be orders of magnitude
higher than its counterparts in the plaintext space. To this end, we develop an
approach that can effectively take advantage of the sparsity of matrix
operations in GCN inference to significantly reduce the computational overhead.
Specifically, we propose a novel AMA data formatting method and associated
spatial convolution methods, which can exploit the complex graph structure and
perform efficient matrix-matrix multiplication in HE computation and thus
greatly reduce the HE operations. We also develop a co-optimization framework
that can explore the trade offs among the accuracy, security level, and
computational overhead by judicious pruning and polynomial approximation of
activation module in GCNs. Based on the NTU-XVIEW skeleton joint dataset, i.e.,
the largest dataset evaluated homomorphically by far as we are aware of, our
experimental results demonstrate that $\textit{CryptoGCN}$ outperforms
state-of-the-art solutions in terms of the latency and number of homomorphic
operations, i.e., achieving as much as a 3.10$\times$ speedup on latency and
reduces the total Homomorphic Operation Count by 77.4\% with a small accuracy
loss of 1-1.5$\%$.

Representing a board game and its positions by text-based notation enables
the possibility of NLP applications. Language models, can help gain insight
into a variety of interesting problems such as unsupervised learning rules of a
game, detecting player behavior patterns, player attribution, and ultimately
learning the game to beat state of the art. In this study, we applied BERT
models, first to the simple Nim game to analyze its performance in the presence
of noise in a setup of a few-shot learning architecture. We analyzed the model
performance via three virtual players, namely Nim Guru, Random player, and
Q-learner. In the second part, we applied the game learning language model to
the chess game, and a large set of grandmaster games with exhaustive
encyclopedia openings. Finally, we have shown that model practically learns the
rules of the chess game and can survive games against Stockfish at a category-A
rating level.

We study and introduce new gradient operators in the complex and bicomplex
settings, inspired from the well-known Least Mean Square (LMS) algorithm
invented in 1960 by Widrow and Hoff for Adaptive Linear Neuron (ADALINE). These
gradient operators will be used to formulate new learning rules for the
Bicomplex Least Mean Square (BLMS) algorithms. This approach extends both the
classical real and complex LMS algorithms.

We present a cross-modal unsupervised framework for active speaker detection
in media content such as TV shows and movies. Machine learning advances have
enabled impressive performance in identifying individuals from speech and
facial images. We leverage speaker identity information from speech and faces,
and formulate active speaker detection as a speech-face assignment task such
that the active speaker's face and the underlying speech identify the same
person (character). We express the speech segments in terms of their associated
speaker identity distances, from all other speech segments, to capture a
relative identity structure for the video. Then we assign an active speaker's
face to each speech segment from the concurrently appearing faces such that the
obtained set of active speaker faces displays a similar relative identity
structure. Furthermore, we propose a simple and effective approach to address
speech segments where speakers are present off-screen. We evaluate the proposed
system on three benchmark datasets -- Visual Person Clustering dataset,
AVA-active speaker dataset, and Columbia dataset -- consisting of videos from
entertainment and broadcast media, and show competitive performance to
state-of-the-art fully supervised methods.

"Induction heads" are attention heads that implement a simple algorithm to
complete token sequences like [A][B] ... [A] -> [B]. In this work, we present
preliminary and indirect evidence for a hypothesis that induction heads might
constitute the mechanism for the majority of all "in-context learning" in large
transformer models (i.e. decreasing loss at increasing token indices). We find
that induction heads develop at precisely the same point as a sudden sharp
increase in in-context learning ability, visible as a bump in the training
loss. We present six complementary lines of evidence, arguing that induction
heads may be the mechanistic source of general in-context learning in
transformer models of any size. For small attention-only models, we present
strong, causal evidence; for larger models with MLPs, we present correlational
evidence.

In Simultaneous Localization and Mapping (SLAM), Loop Closure Detection (LCD)
is essential to minimize drift when recognizing previously visited places.
Visual Bag-of-Words (vBoW) has been an LCD algorithm of choice for many
state-of-the-art SLAM systems. It uses a set of visual features to provide
robust place recognition but fails to perceive the semantics or spatial
relationship between feature points. Previous work has mainly focused on
addressing these issues by combining vBoW with semantic and spatial information
from objects in the scene. However, they are unable to exploit spatial
information of local visual features and lack a structure that unifies semantic
objects and visual features, therefore limiting the symbiosis between the two
components. This paper proposes SymbioLCD2, which creates a unified graph
structure to integrate semantic objects and visual features symbiotically. Our
novel graph-based LCD system utilizes the unified graph structure by applying a
Weisfeiler-Lehman graph kernel with temporal constraints to robustly predict
loop closure candidates. Evaluation of the proposed system shows that having a
unified graph structure incorporating semantic objects and visual features
improves LCD prediction accuracy, illustrating that the proposed graph
structure provides a strong symbiosis between these two complementary
components. It also outperforms other Machine Learning algorithms - such as
SVM, Decision Tree, Random Forest, Neural Network and GNN based Graph Matching
Networks. Furthermore, it has shown good performance in detecting loop closure
candidates earlier than state-of-the-art SLAM systems, demonstrating that
extended semantic and spatial awareness from the unified graph structure
significantly impacts LCD performance.

Deep neural networks trained for predicting cellular events from DNA sequence
have become emerging tools to help elucidate the biological mechanism
underlying the associations identified in genome-wide association studies. To
enhance the training, multi-task learning (MTL) has been commonly exploited in
previous works where trained networks were needed for multiple profiles
differing in either event modality or cell type. All existing works adopted a
simple MTL framework where all tasks share a single feature extraction network.
Such a strategy even though effective to certain extent leads to substantial
negative transfer, meaning the existence of large portion of tasks for which
models obtained through MTL perform worse than those by single task learning.
There have been methods developed to address such negative transfer in other
domains, such as computer vision. However, these methods are generally
difficult to scale up to handle large amount of tasks. In this paper, we
propose a highly scalable task grouping framework to address negative transfer
by only jointly training tasks that are potentially beneficial to each other.
The proposed method exploits the network weights associated with task specific
classification heads that can be cheaply obtained by one-time joint training of
all tasks. Our results using a dataset consisting of 367 epigenetic profiles
demonstrate the effectiveness of the proposed approach and its superiority over
baseline methods.

Domain adaptation (DA) is an important technique for modern machine
learning-based medical data analysis, which aims at reducing distribution
differences between different medical datasets. A proper domain adaptation
method can significantly enhance the statistical power by pooling data acquired
from multiple sites/centers. To this end, we have developed the Domain
Adaptation Toolbox for Medical data analysis (DomainATM) - an open-source
software package designed for fast facilitation and easy customization of
domain adaptation methods for medical data analysis. The DomainATM is
implemented in MATLAB with a user-friendly graphical interface, and it consists
of a collection of popular data adaptation algorithms that have been
extensively applied to medical image analysis and computer vision. With
DomainATM, researchers are able to facilitate fast feature-level and
image-level adaptation, visualization and performance evaluation of different
adaptation methods for medical data analysis. More importantly, the DomainATM
enables the users to develop and test their own adaptation methods through
scripting, greatly enhancing its utility and extensibility. An overview
characteristic and usage of DomainATM is presented and illustrated with three
example experiments, demonstrating its effectiveness, simplicity, and
flexibility. The software, source code, and manual are available online.

Diffusion models can be used as learned priors for solving various inverse
problems. However, most existing approaches are restricted to linear inverse
problems, limiting their applicability to more general cases. In this paper, we
build upon Denoising Diffusion Restoration Models (DDRM) and propose a method
for solving some non-linear inverse problems. We leverage the pseudo-inverse
operator used in DDRM and generalize this concept for other measurement
operators, which allows us to use pre-trained unconditional diffusion models
for applications such as JPEG artifact correction. We empirically demonstrate
the effectiveness of our approach across various quality factors, attaining
performance levels that are on par with state-of-the-art methods trained
specifically for the JPEG restoration task.

Authorship attribution is the task of identifying the author of a given text.
The key is finding representations that can differentiate between authors.
Existing approaches typically use manually designed features that capture a
dataset's content and style, but these approaches are dataset-dependent and
yield inconsistent performance across corpora. In this work, we propose
\textit{learning} author-specific representations by fine-tuning pre-trained
generic language representations with a contrastive objective (Contra-X). We
show that Contra-X learns representations that form highly separable clusters
for different authors. It advances the state-of-the-art on multiple human and
machine authorship attribution benchmarks, enabling improvements of up to 6.8%
over cross-entropy fine-tuning. However, we find that Contra-X improves overall
accuracy at the cost of sacrificing performance for some authors. Resolving
this tension will be an important direction for future work. To the best of our
knowledge, we are the first to integrate contrastive learning with pre-trained
language model fine-tuning for authorship attribution.

Production forecast based on historical data provides essential value for
developing hydrocarbon resources. Classic history matching workflow is often
computationally intense and geometry-dependent. Analytical data-driven models
like decline curve analysis (DCA) and capacitance resistance models (CRM)
provide a grid-free solution with a relatively simple model capable of
integrating some degree of physics constraints. However, the analytical
solution may ignore subsurface geometries and is appropriate only for specific
flow regimes and otherwise may violate physics conditions resulting in degraded
model prediction accuracy. Machine learning-based predictive model for time
series provides non-parametric, assumption-free solutions for production
forecasting, but are prone to model overfit due to training data sparsity;
therefore may be accurate over short prediction time intervals.
  We propose a grid-free, physics-informed graph neural network (PI-GNN) for
production forecasting. A customized graph convolution layer aggregates
neighborhood information from historical data and has the flexibility to
integrate domain expertise into the data-driven model. The proposed method
relaxes the dependence on close-form solutions like CRM and honors the given
physics-based constraints. Our proposed method is robust, with improved
performance and model interpretability relative to the conventional CRM and GNN
baseline without physics constraints.

Recent approximations to backpropagation (BP) have mitigated many of BP's
computational inefficiencies and incompatibilities with biology, but important
limitations still remain. Moreover, the approximations significantly decrease
accuracy in benchmarks, suggesting that an entirely different approach may be
more fruitful. Here, grounded on recent theory for Hebbian learning in soft
winner-take-all networks, we present multilayer SoftHebb, i.e. an algorithm
that trains deep neural networks, without any feedback, target, or error
signals. As a result, it achieves efficiency by avoiding weight transport,
non-local plasticity, time-locking of layer updates, iterative equilibria, and
(self-) supervisory or other feedback signals -- which were necessary in other
approaches. Its increased efficiency and biological compatibility do not trade
off accuracy compared to state-of-the-art bio-plausible learning, but rather
improve it. With up to five hidden layers and an added linear classifier,
accuracies on MNIST, CIFAR-10, STL-10, and ImageNet, respectively reach 99.4%,
80.3%, 76.2%, and 27.3%. In conclusion, SoftHebb shows with a radically
different approach from BP that Deep Learning over few layers may be plausible
in the brain and increases the accuracy of bio-plausible machine learning.

This report explains, implements and extends the works presented in "Tighter
Variational Bounds are Not Necessarily Better" (T Rainforth et al., 2018). We
provide theoretical and empirical evidence that increasing the number of
importance samples $K$ in the importance weighted autoencoder (IWAE) (Burda et
al., 2016) degrades the signal-to-noise ratio (SNR) of the gradient estimator
in the inference network and thereby affecting the full learning process. In
other words, even though increasing $K$ decreases the standard deviation of the
gradients, it also reduces the magnitude of the true gradient faster, thereby
increasing the relative variance of the gradient updates. Extensive experiments
are performed to understand the importance of $K$. These experiments suggest
that tighter variational bounds are beneficial for the generative network,
whereas looser bounds are preferable for the inference network. With these
insights, three methods are implemented and studied: the partially importance
weighted autoencoder (PIWAE), the multiply importance weighted autoencoder
(MIWAE) and the combination importance weighted autoencoder (CIWAE). Each of
these three methods entails IWAE as a special case but employs the importance
weights in different ways to ensure a higher SNR of the gradient estimators. In
our research study and analysis, the efficacy of these algorithms is tested on
multiple datasets such as MNIST and Omniglot. Finally, we demonstrate that the
three presented IWAE variations are able to generate approximate posterior
distributions that are much closer to the true posterior distribution than for
the IWAE, while matching the performance of the IWAE generative network or
potentially outperforming it in the case of PIWAE.

Ultrasound Localization Microscopy (ULM) is an emerging technique that
employs the localization of echogenic microbubbles (MBs) to finely sample and
image the microcirculation beyond the diffraction limit of ultrasound imaging.
Conventional MB localization methods are mainly based on considering a specific
Point Spread Function (PSF) for MBs, which leads to loss of information caused
by overlapping MBs, non-stationary PSFs, and harmonic MB echoes. Therefore, it
is imperative to devise methods that can accurately localize MBs while being
resilient to MB nonlinearities and variations of MB concentrations that distort
MB PSFs. This paper proposes a transformer-based MB localization approach to
address this issue. We adopted DEtection TRansformer (DETR) arXiv:2005.12872 ,
which is an end-to-end object recognition method that detects a unique bounding
box for each of the detected objects using set-based Hungarian loss and
bipartite matching. To the authors' knowledge, this is the first time
transformers have been used for MB localization. To appraise the proposed
strategy, the pre-trained DETR network's performance has been tested for
detecting MBs using transfer learning principles. We have fine-tuned the
network on a subset of randomly selected frames of the dataset provided by the
IEEE IUS Ultra-SR challenge organizers and then tested on the rest using
cross-validation. For the simulation dataset, the paper supports the deployment
of transformer-based solutions for MB localization at high accuracy.

Whether it is in the form of transcribed conversations, blog posts, or
tweets, qualitative data provides a reader with rich insight into both the
overarching trends as well as the diversity of human ideas expressed through
text. Handling and analyzing large amounts of qualitative data, however, is
difficult, often requiring multiple time-intensive perusals in order to
identify patterns. This difficulty is multiplied with each additional question
or time point present in a data set. A primary challenge then is creating
visualizations that support the interpretation of qualitative data by making it
easier to identify and explore trends of interest. By combining the affordances
of both text and visualizations, WordStream has previously enabled ease of
information retrieval and processing of time-series text data, but the
data-wrangling necessary to produce a WordStream remains a significant barrier
for non-technical users. In response, this paper presents WordStream Maker: an
end-to-end platform with a pipeline that utilizes natural language processing
(NLP) to help non-technical users process raw text data and generate a
customizable visualization without programming practice. Lessons learned from
integrating NLP into visualization and scaling to large data sets are
discussed, along with use cases to demonstrate the usefulness of the platform.

The availability of big data has opened up big opportunities for individuals,
businesses and academics to view big into what is happening in their world.
Previous works of text representation mostly focused on informativeness from
massive words' frequency or cooccurrence. However, big data is a double-edged
sword which is big in volume but unstructured in format. The unstructured edge
requires specific techniques to transform 'big' into meaningful instead of
informative alone.
  This study presents KeypartX, a graph-based approach to represent perception
(text in general) by key parts of speech. Different from
bag-of-words/vector-based machine learning, this technique is human-like
learning that could extracts meanings from linguistic (semantic, syntactic and
pragmatic) information. Moreover, KeypartX is big-data capable but not hungry,
which is even applicable to the minimum unit of text:sentence.

Users are daily exposed to a large volume of harmful content on various
social network platforms. One solution is developing online moderation tools
using Machine Learning techniques. However, the processing of user data by
online platforms requires compliance with privacy policies. Federated Learning
(FL) is an ML paradigm where the training is performed locally on the users'
devices. Although the FL framework complies, in theory, with the GDPR policies,
privacy leaks can still occur. For instance, an attacker accessing the final
trained model can successfully perform unwanted inference of the data belonging
to the users who participated in the training process. In this paper, we
propose a privacy-preserving FL framework for online content moderation that
incorporates Differential Privacy (DP). To demonstrate the feasibility of our
approach, we focus on detecting harmful content on Twitter - but the overall
concept can be generalized to other types of misbehavior. We simulate a text
classifier - in FL fashion - which can detect tweets with harmful content. We
show that the performance of the proposed FL framework can be close to the
centralized approach - for both the DP and non-DP FL versions. Moreover, it has
a high performance even if a small number of clients (each with a small number
of data points) are available for the FL training. When reducing the number of
clients (from 50 to 10) or the data points per client (from 1K to 0.1K), the
classifier can still achieve ~81% AUC. Furthermore, we extend the evaluation to
four other Twitter datasets that capture different types of user misbehavior
and still obtain a promising performance (61% - 80% AUC). Finally, we explore
the overhead on the users' devices during the FL training phase and show that
the local training does not introduce excessive CPU utilization and memory
consumption overhead.

Speakers build rapport in the process of aligning conversational behaviors
with each other. Rapport engendered with a teachable agent while instructing
domain material has been shown to promote learning. Past work on lexical
alignment in the field of education suffers from limitations in both the
measures used to quantify alignment and the types of interactions in which
alignment with agents has been studied. In this paper, we apply alignment
measures based on a data-driven notion of shared expressions (possibly composed
of multiple words) and compare alignment in one-on-one human-robot (H-R)
interactions with the H-R portions of collaborative human-human-robot (H-H-R)
interactions. We find that students in the H-R setting align with a teachable
robot more than in the H-H-R setting and that the relationship between lexical
alignment and rapport is more complex than what is predicted by previous
theoretical and empirical work.

The ability to discern between true and false information is essential to
making sound decisions. However, with the recent increase in AI-based
disinformation campaigns, it has become critical to understand the influence of
deceptive systems on human information processing. In experiment (N=128), we
investigated how susceptible people are to deceptive AI systems by examining
how their ability to discern true news from fake news varies when AI systems
are perceived as either human fact-checkers or AI fact-checking systems, and
when explanations provided by those fact-checkers are either deceptive or
honest. We find that deceitful explanations significantly reduce accuracy,
indicating that people are just as likely to believe deceptive AI explanations
as honest AI explanations. Although before getting assistance from an
AI-system, people have significantly higher weighted discernment accuracy on
false headlines than true headlines, we found that with assistance from an AI
system, discernment accuracy increased significantly when given honest
explanations on both true headlines and false headlines, and decreased
significantly when given deceitful explanations on true headlines and false
headlines. Further, we did not observe any significant differences in
discernment between explanations perceived as coming from a human fact checker
compared to an AI-fact checker. Similarly, we found no significant differences
in trust. These findings exemplify the dangers of deceptive AI systems and the
need for finding novel ways to limit their influence human information
processing.

We study the problem of online dynamic pricing with two types of fairness
constraints: a "procedural fairness" which requires the proposed prices to be
equal in expectation among different groups, and a "substantive fairness" which
requires the accepted prices to be equal in expectation among different groups.
A policy that is simultaneously procedural and substantive fair is referred to
as "doubly fair". We show that a doubly fair policy must be random to have
higher revenue than the best trivial policy that assigns the same price to
different groups. In a two-group setting, we propose an online learning
algorithm for the 2-group pricing problems that achieves $\tilde{O}(\sqrt{T})$
regret, zero procedural unfairness and $\tilde{O}(\sqrt{T})$ substantive
unfairness over $T$ rounds of learning. We also prove two lower bounds showing
that these results on regret and unfairness are both information-theoretically
optimal up to iterated logarithmic factors. To the best of our knowledge, this
is the first dynamic pricing algorithm that learns to price while satisfying
two fairness constraints at the same time.

Regionalization is the act of breaking a dataset into contiguous homogeneous
regions that are heterogeneous from each other. Many different algorithms exist
for performing regionalization; however, using these algorithms on large real
world data sets have only become feasible in terms of compute power in recent
years. Very few studies have been done comparing different regionalization
methods, and those that do lack analysis in memory, scalability, geographic
metrics, and large-scale real-world applications. This study compares
state-of-the-art regionalization methods, namely, Agglomerative Clustering,
SKATER, REDCAP, AZP, and Max-P-Regions using real world social determinant of
health (SDOH) data. The scale of real world SDOH data, up to 1 million data
points in this study, not only compares the algorithms over different data sets
but provides a stress test for each individual regionalization algorithm, most
of which have never been run on such scales previously. We use several new
geographic metrics to compare algorithms as well as perform a comparative
memory analysis. The prevailing regionalization method is then compared with
unconstrained K-Means clustering on their ability to separate real health data
in Virginia and Washington DC.

Session-based recommender systems (SBRSs) have shown superior performance
over conventional methods. However, they show limited scalability on
large-scale industrial datasets since most models learn one embedding per item.
This leads to a large memory requirement (of storing one vector per item) and
poor performance on sparse sessions with cold-start or unpopular items. Using
one public and one large industrial dataset, we experimentally show that
state-of-the-art SBRSs have low performance on sparse sessions with sparse
items. We propose M2TRec, a Metadata-aware Multi-task Transformer model for
session-based recommendations. Our proposed method learns a transformation
function from item metadata to embeddings, and is thus, item-ID free (i.e.,
does not need to learn one embedding per item). It integrates item metadata to
learn shared representations of diverse item attributes. During inference, new
or unpopular items will be assigned identical representations for the
attributes they share with items previously observed during training, and thus
will have similar representations with those items, enabling recommendations of
even cold-start and sparse items. Additionally, M2TRec is trained in a
multi-task setting to predict the next item in the session along with its
primary category and subcategories. Our multi-task strategy makes the model
converge faster and significantly improves the overall performance.
Experimental results show significant performance gains using our proposed
approach on sparse items on the two datasets.

Learning-based behavior prediction methods are increasingly being deployed in
real-world autonomous systems, e.g., in fleets of self-driving vehicles, which
are beginning to commercially operate in major cities across the world. Despite
their advancements, however, the vast majority of prediction systems are
specialized to a set of well-explored geographic regions or operational design
domains, complicating deployment to additional cities, countries, or
continents. Towards this end, we present a novel method for efficiently
adapting behavior prediction models to new environments. Our approach leverages
recent advances in meta-learning, specifically Bayesian regression, to augment
existing behavior prediction models with an adaptive layer that enables
efficient domain transfer via offline fine-tuning, online adaptation, or both.
Experiments across multiple real-world datasets demonstrate that our method can
efficiently adapt to a variety of unseen environments.

Recently a multi-agent variant of the classical multi-armed bandit was
proposed to tackle fairness issues in online learning. Inspired by a long line
of work in social choice and economics, the goal is to optimize the Nash social
welfare instead of the total utility. Unfortunately previous algorithms either
are not efficient or achieve sub-optimal regret in terms of the number of
rounds $T$. We propose a new efficient algorithm with lower regret than even
previous inefficient ones. For $N$ agents, $K$ arms, and $T$ rounds, our
approach has a regret bound of $\tilde{O}(\sqrt{NKT} + NK)$. This is an
improvement to the previous approach, which has regret bound of $\tilde{O}(
\min(NK, \sqrt{N} K^{3/2})\sqrt{T})$. We also complement our efficient
algorithm with an inefficient approach with $\tilde{O}(\sqrt{KT} + N^2K)$
regret. The experimental findings confirm the effectiveness of our efficient
algorithm compared to the previous approaches.

We consider representation learning on periodic graphs encoding crystal
materials. Different from regular graphs, periodic graphs consist of a minimum
unit cell repeating itself on a regular lattice in 3D space. How to effectively
encode these periodic structures poses unique challenges not present in regular
graph representation learning. In addition to being E(3) invariant, periodic
graph representations need to be periodic invariant. That is, the learned
representations should be invariant to shifts of cell boundaries as they are
artificially imposed. Furthermore, the periodic repeating patterns need to be
captured explicitly as lattices of different sizes and orientations may
correspond to different materials. In this work, we propose a transformer
architecture, known as Matformer, for periodic graph representation learning.
Our Matformer is designed to be invariant to periodicity and can capture
repeating patterns explicitly. In particular, Matformer encodes periodic
patterns by efficient use of geometric distances between the same atoms in
neighboring cells. Experimental results on multiple common benchmark datasets
show that our Matformer outperforms baseline methods consistently. In addition,
our results demonstrate the importance of periodic invariance and explicit
repeating pattern encoding for crystal representation learning.

Deep learning models have achieved impressive prediction performance but
often sacrifice interpretability, a critical consideration in high-stakes
domains such as healthcare or policymaking. In contrast, generalized additive
models (GAMs) can maintain interpretability but often suffer from poor
prediction performance due to their inability to effectively capture feature
interactions. In this work, we aim to bridge this gap by using pre-trained
neural language models to extract embeddings for each input before learning a
linear model in the embedding space. The final model (which we call Emb-GAM) is
a transparent, linear function of its input features and feature interactions.
Leveraging the language model allows Emb-GAM to learn far fewer linear
coefficients, model larger interactions, and generalize well to novel inputs
(e.g. unseen ngrams in text). Across a variety of natural-language-processing
datasets, Emb-GAM achieves strong prediction performance without sacrificing
interpretability. All code is made available on Github.

Deep neural networks require specific layers to process point clouds, as the
scattered and irregular location of points prevents us from using convolutional
filters. Here we introduce the composite layer, a new convolutional operator
for point clouds. The peculiarity of our composite layer is that it extracts
and compresses the spatial information from the position of points before
combining it with their feature vectors. Compared to well-known
point-convolutional layers such as those of ConvPoint and KPConv, our composite
layer provides additional regularization and guarantees greater flexibility in
terms of design and number of parameters. To demonstrate the design
flexibility, we also define an aggregate composite layer that combines spatial
information and features in a nonlinear manner, and we use these layers to
implement a convolutional and an aggregate CompositeNet. We train our
CompositeNets to perform classification and, most remarkably, unsupervised
anomaly detection. Our experiments on synthetic and real-world datasets show
that, in both tasks, our CompositeNets outperform ConvPoint and achieve similar
results as KPConv despite having a much simpler architecture. Moreover, our
CompositeNets substantially outperform existing solutions for anomaly detection
on point clouds.

Learning a fast and discriminative patch descriptor is a challenging topic in
computer vision. Recently, many existing works focus on training various
descriptor learning networks by minimizing a triplet loss (or its variants),
which is expected to decrease the distance between each positive pair and
increase the distance between each negative pair. However, such an expectation
has to be lowered due to the non-perfect convergence of network optimizer to a
local solution. Addressing this problem and the open computational speed
problem, we propose a Descriptor Distillation framework for local descriptor
learning, called DesDis, where a student model gains knowledge from a
pre-trained teacher model, and it is further enhanced via a designed
teacher-student regularizer. This teacher-student regularizer is to constrain
the difference between the positive (also negative) pair similarity from the
teacher model and that from the student model, and we theoretically prove that
a more effective student model could be trained by minimizing a weighted
combination of the triplet loss and this regularizer, than its teacher which is
trained by minimizing the triplet loss singly. Under the proposed DesDis, many
existing descriptor networks could be embedded as the teacher model, and
accordingly, both equal-weight and light-weight student models could be
derived, which outperform their teacher in either accuracy or speed.
Experimental results on 3 public datasets demonstrate that the equal-weight
student models, derived from the proposed DesDis framework by utilizing three
typical descriptor learning networks as teacher models, could achieve
significantly better performances than their teachers and several other
comparative methods. In addition, the derived light-weight models could achieve
8 times or even faster speeds than the comparative methods under similar patch
verification performances

In recent years Landmark Complexes have been successfully employed for
localization-free and metric-free autonomous exploration using a group of
sensing-limited and communication-limited robots in a GPS-denied environment.
To ensure rapid and complete exploration, existing works make assumptions on
the density and distribution of landmarks in the environment. These assumptions
may be overly restrictive, especially in hazardous environments where landmarks
may be destroyed or completely missing. In this paper, we first propose a deep
reinforcement learning framework for multi-agent cooperative exploration in
environments with sparse landmarks while reducing client-server communication.
By leveraging recent development on partial observability and credit
assignment, our framework can train the exploration policy efficiently for
multi-robot systems. The policy receives individual rewards from actions based
on a proximity sensor with limited range and resolution, which is combined with
group rewards to encourage collaborative exploration and construction of the
Landmark Complex through observation of 0-, 1- and 2-dimensional simplices. In
addition, we employ a three-stage curriculum learning strategy to mitigate the
reward sparsity by gradually adding random obstacles and destroying random
landmarks. Experiments in simulation demonstrate that our method outperforms
the state-of-the-art landmark complex exploration method in efficiency among
different environments with sparse landmarks.

Collision avoidance is key for mobile robots and agents to operate safely in
the real world. In this work, we present an efficient and effective collision
avoidance system that combines real-world reinforcement learning (RL),
search-based online trajectory planning, and automatic emergency intervention,
e.g. automatic emergency braking (AEB). The goal of the RL is to learn
effective search heuristics that speed up the search for collision-free
trajectory and reduce the frequency of triggering automatic emergency
interventions. This novel setup enables RL to learn safely and directly on
mobile robots in a real-world indoor environment, minimizing actual crashes
even during training. Our real-world experiments show that, when compared with
several baselines, our approach enjoys a higher average speed, lower crash
rate, higher goals reached rate, smaller computation overhead, and smoother
overall control.

Energy-function-based safety certificates can provide provable safety
guarantees for the safe control tasks of complex robotic systems. However, all
recent studies about learning-based energy function synthesis only consider the
feasibility, which might cause over-conservativeness and result in less
efficient controllers. In this work, we proposed the magnitude regularization
technique to improve the efficiency of safe controllers by reducing the
conservativeness inside the energy function while keeping the promising
provable safety guarantees. Specifically, we quantify the conservativeness by
the magnitude of the energy function, and we reduce the conservativeness by
adding a magnitude regularization term to the synthesis loss. We propose the
SafeMR algorithm that uses reinforcement learning (RL) for the synthesis to
unify the learning processes of safe controllers and energy functions.
Experimental results show that the proposed method does reduce the
conservativeness of the energy functions and outperforms the baselines in terms
of the controller efficiency while guaranteeing safety.

We propose three novel pruning techniques to improve the cost and results of
inference-aware Differentiable Neural Architecture Search (DNAS). First, we
introduce , a stochastic bi-path building block for DNAS, which can search over
inner hidden dimensions with memory and compute complexity. Second, we present
an algorithm for pruning blocks within a stochastic layer of the SuperNet
during the search. Third, we describe a novel technique for pruning unnecessary
stochastic layers during the search. The optimized models resulting from the
search are called PruNet and establishes a new state-of-the-art Pareto frontier
for NVIDIA V100 in terms of inference latency for ImageNet Top-1 image
classification accuracy. PruNet as a backbone also outperforms GPUNet and
EfficientNet on the COCO object detection task on inference latency relative to
mean Average Precision (mAP).

In aerospace applications, multiple safety regulations were introduced to
address associated with pyrolysis. Predictive modeling of pyrolysis is a
challenging task since multiple thermo-chemo-mechanical laws need to be
concurrently solved at each time step. So far, classical modeling approaches
were mostly focused on defining the basic chemical processes (pyrolysis and
ignite) at micro-scale by decoupling them from thermal solution at the
micro-scale and then validating them using meso-scale experimental results. The
advent of Machine Learning (ML) and AI in recent years has provided an
opportunity to construct quick surrogate ML models to replace high fidelity
multi-physics models, which have a high computational cost and may not be
applicable for high nonlinear equations. This serves as the motivation for the
introduction of innovative Physics informed neural networks (PINNs) to simulate
multiple stiff, and semi-stiff ODEs that govern Pyrolysis and Ablation. Our
Engine is particularly developed to calculate the char formation and degree of
burning in the course of pyrolysis of crosslinked polymeric systems. A
multi-task learning approach is hired to assure the best fitting to the
training data. The proposed Hybrid-PINN (HPINN) solver was bench-marked against
finite element high fidelity solutions on different examples. We developed PINN
architectures using collocation training to forecast temperature distributions
and the degree of burning in the course of pyrolysis in multiple one- and
two-dimensional examples. By decoupling thermal and mechanical equations, we
can predict the loss of performance in the system by predicting the char
formation pattern and localized degree of burning at each continuum.

Robots have been used in all sorts of automation, and yet the design of
robots remains mainly a manual task. We seek to provide design tools to
automate the design of robots themselves. An important challenge in robot
design automation is the large and complex design search space which grows
exponentially with the number of components, making optimization difficult and
sample inefficient. In this work, we present Grammar-guided Latent Space
Optimization (GLSO), a framework that transforms design automation into a
low-dimensional continuous optimization problem by training a graph variational
autoencoder (VAE) to learn a mapping between the graph-structured design space
and a continuous latent space. This transformation allows optimization to be
conducted in a continuous latent space, where sample efficiency can be
significantly boosted by applying algorithms such as Bayesian Optimization.
GLSO guides training of the VAE using graph grammar rules and robot world space
features, such that the learned latent space focus on valid robots and is
easier for the optimization algorithm to explore. Importantly, the trained VAE
can be reused to search for designs specialized to multiple different tasks
without retraining. We evaluate GLSO by designing robots for a set of
locomotion tasks in simulation, and demonstrate that our method outperforms
related state-of-the-art robot design automation methods.

Finding unified complexity measures and algorithms for sample-efficient
learning is a central topic of research in reinforcement learning (RL). The
Decision-Estimation Coefficient (DEC) is recently proposed by Foster et al.
(2021) as a necessary and sufficient complexity measure for sample-efficient
no-regret RL. This paper makes progress towards a unified theory for RL with
the DEC framework. First, we propose two new DEC-type complexity measures:
Explorative DEC (EDEC), and Reward-Free DEC (RFDEC). We show that they are
necessary and sufficient for sample-efficient PAC learning and reward-free
learning, thereby extending the original DEC which only captures no-regret
learning. Next, we design new unified sample-efficient algorithms for all three
learning goals. Our algorithms instantiate variants of the
Estimation-To-Decisions (E2D) meta-algorithm with a strong and general model
estimation subroutine. Even in the no-regret setting, our algorithm E2D-TA
improves upon the algorithms of Foster et al. (2021) which require either
bounding a variant of the DEC which may be prohibitively large, or designing
problem-specific estimation subroutines. As applications, we recover existing
and obtain new sample-efficient learning results for a wide range of tractable
RL problems using essentially a single algorithm. We also generalize the DEC to
give sample-efficient algorithms for all-policy model estimation, with
applications for learning equilibria in Markov Games. Finally, as a connection,
we re-analyze two existing optimistic model-based algorithms based on Posterior
Sampling or Maximum Likelihood Estimation, showing that they enjoy similar
regret bounds as E2D-TA under similar structural conditions as the DEC.

Interpreting the meaning of a visual scene requires not only identification
of its constituent objects, but also a rich semantic characterization of object
interrelations. Here, we study the neural mechanisms underlying visuo-semantic
transformations by applying modern computational techniques to a large-scale 7T
fMRI dataset of human brain responses elicited by complex natural scenes. Using
semantic embeddings obtained by applying linguistic deep learning models to
human-generated scene descriptions, we identify a widely distributed network of
brain regions that encode semantic scene descriptions. Importantly, these
semantic embeddings better explain activity in these regions than traditional
object category labels. In addition, they are effective predictors of activity
despite the fact that the participants did not actively engage in a semantic
task, suggesting that visuo-semantic transformations are a default mode of
vision. In support of this view, we then show that highly accurate
reconstructions of scene captions can be directly linearly decoded from
patterns of brain activity. Finally, a recurrent convolutional neural network
trained on semantic embeddings further outperforms semantic embeddings in
predicting brain activity, providing a mechanistic model of the brain's
visuo-semantic transformations. Together, these experimental and computational
results suggest that transforming visual input into rich semantic scene
descriptions may be a central objective of the visual system, and that focusing
efforts on this new objective may lead to improved models of visual information
processing in the human brain.

Summary: Accurate phenotype prediction from genomic sequences is a highly
coveted task in biological and medical research. While machine-learning holds
the key to accurate prediction in a variety of fields, the complexity of
biological data can render many methodologies inapplicable. We introduce
BioKlustering, a user-friendly open-source and publicly available web app for
unsupervised and semi-supervised learning specialized for cases when sequence
alignment and/or experimental phenotyping of all classes are not possible.
Among its main advantages, BioKlustering 1) allows for maximally imbalanced
settings of partially observed labels including cases when only one class is
observed, which is currently prohibited in most semi-supervised methods, 2)
takes unaligned sequences as input and thus, allows learning for widely diverse
sequences (impossible to align) such as virus and bacteria, 3) is easy to use
for anyone with little or no programming expertise, and 4) works well with
small sample sizes.
  Availability and Implementation: BioKlustering
(https://bioklustering.wid.wisc.edu) is a freely available web app implemented
with Django, a Python-based framework, with all major browsers supported. The
web app does not need any installation, and it is publicly available and
open-source (https://github.com/solislemuslab/bioklustering).

Existing advertisements click-through rate (CTR) prediction models are mainly
dependent on behavior ID features, which are learned based on the historical
user-ad interactions. Nevertheless, behavior ID features relying on historical
user behaviors are not feasible to describe new ads without previous
interactions with users. To overcome the limitations of behavior ID features in
modeling new ads, we exploit the visual content in ads to boost the performance
of CTR prediction models. Specifically, we map each ad into a set of visual IDs
based on its visual content. These visual IDs are further used for generating
the visual embedding for enhancing CTR prediction models. We formulate the
learning of visual IDs into a supervised quantization problem. Due to a lack of
class labels for commercial images in advertisements, we exploit image textual
descriptions as the supervision to optimize the image extractor for generating
effective visual IDs. Meanwhile, since the hard quantization is
non-differentiable, we soften the quantization operation to make it support the
end-to-end network training. After mapping each image into visual IDs, we learn
the embedding for each visual ID based on the historical user-ad interactions
accumulated in the past. Since the visual ID embedding depends only on the
visual content, it generalizes well to new ads. Meanwhile, the visual ID
embedding complements the ad behavior ID embedding. Thus, it can considerably
boost the performance of the CTR prediction models previously relying on
behavior ID features for both new ads and ads that have accumulated rich user
behaviors. After incorporating the visual ID embedding in the CTR prediction
model of Baidu online advertising, the average CTR of ads improves by 1.46%,
and the total charge increases by 1.10%.

Deep Neural Networks (DNNs) are susceptible to backdoor attacks during
training. The model corrupted in this way functions normally, but when
triggered by certain patterns in the input, produces a predefined target label.
Existing defenses usually rely on the assumption of the universal backdoor
setting in which poisoned samples share the same uniform trigger. However,
recent advanced backdoor attacks show that this assumption is no longer valid
in dynamic backdoors where the triggers vary from input to input, thereby
defeating the existing defenses.
  In this work, we propose a novel technique, Beatrix (backdoor detection via
Gram matrix). Beatrix utilizes Gram matrix to capture not only the feature
correlations but also the appropriately high-order information of the
representations. By learning class-conditional statistics from activation
patterns of normal samples, Beatrix can identify poisoned samples by capturing
the anomalies in activation patterns. To further improve the performance in
identifying target labels, Beatrix leverages kernel-based testing without
making any prior assumptions on representation distribution. We demonstrate the
effectiveness of our method through extensive evaluation and comparison with
state-of-the-art defensive techniques. The experimental results show that our
approach achieves an F1 score of 91.1% in detecting dynamic backdoors, while
the state of the art can only reach 36.9%.

Recent progress in generative models, especially in text-guided diffusion
models, has enabled the production of aesthetically-pleasing imagery resembling
the works of professional human artists. However, one has to carefully compose
the textual description, called the prompt, and augment it with a set of
clarifying keywords. Since aesthetics are challenging to evaluate
computationally, human feedback is needed to determine the optimal prompt
formulation and keyword combination. In this paper, we present a
human-in-the-loop approach to learning the most useful combination of prompt
keywords using a genetic algorithm. We also show how such an approach can
improve the aesthetic appeal of images depicting the same descriptions.

An expert tells an advisee whether to take an action that may be good or bad.
He may provide a condition under which to take the action. This condition
predicts whether the action is good if and only if the expert is competent.
Providing the condition exposes the expert to reputational risk by allowing the
advisee to learn about his competence. He trades off the accuracy benefit and
reputational risk induced by providing the condition. He prefers not to provide
it -- i.e., to give "simple advice" -- when his payoff is sufficiently concave
in the posterior belief about his competence.

Volatility forecasts play a central role among equity risk measures. Besides
traditional statistical models, modern forecasting techniques, based on machine
learning, can readily be employed when treating volatility as a univariate,
daily time-series. However, econometric studies have shown that increasing the
number of daily observations with high-frequency intraday data helps to improve
predictions. In this work, we propose DeepVol, a model based on Dilated Causal
Convolutions to forecast day-ahead volatility by using high-frequency data. We
show that the dilated convolutional filters are ideally suited to extract
relevant information from intraday financial data, thereby naturally mimicking
(via a data-driven approach) the econometric models which incorporate realised
measures of volatility into the forecast. This allows us to take advantage of
the abundance of intraday observations, helping us to avoid the limitations of
models that use daily data, such as model misspecification or manually designed
handcrafted features, whose devise involves optimising the trade-off between
accuracy and computational efficiency and makes models prone to lack of
adaptation into changing circumstances. In our analysis, we use two years of
intraday data from NASDAQ-100 to evaluate DeepVol's performance. The reported
empirical results suggest that the proposed deep learning-based approach learns
global features from high-frequency data, achieving more accurate predictions
than traditional methodologies, yielding to more appropriate risk measures.

This paper studies a linear and additively separable model for
multidimensional panel data of three or more dimensions with unobserved
interactive fixed effects. Two approaches are considered to account for these
unobserved interactive fixed-effects when estimating coefficients on the
observed covariates. First, the model is embedded within the standard
two-dimensional panel framework and restrictions are derived under which the
factor structure methods in Bai (2009) lead to consistent estimation of model
parameters. The second approach considers group fixed-effects and kernel
methods that are more robust to the multidimensional nature of the problem.
Theoretical results and simulations show the benefit of standard
two-dimensional panel methods when the structure of the interactive
fixed-effect term is known, but also highlight how the group fixed-effects and
kernel methods perform well without knowledge of this structure. The methods
are implemented to estimate the demand elasticity for beer under a handful of
models for demand.

Recently neural scene representations have provided very impressive results
for representing 3D scenes visually, however, their study and progress have
mainly been limited to visualization of virtual models in computer graphics or
scene reconstruction in computer vision without explicitly accounting for
sensor and pose uncertainty. Using this novel scene representation in robotics
applications, however, would require accounting for this uncertainty in the
neural map. The aim of this paper is therefore to propose a novel method for
training {\em probabilistic neural scene representations} with uncertain
training data that could enable the inclusion of these representations in
robotics applications. Acquiring images using cameras or depth sensors contains
inherent uncertainty, and furthermore, the camera poses used for learning a 3D
model are also imperfect. If these measurements are used for training without
accounting for their uncertainty, then the resulting models are non-optimal,
and the resulting scene representations are likely to contain artifacts such as
blur and un-even geometry. In this work, the problem of uncertainty integration
to the learning process is investigated by focusing on training with uncertain
information in a probabilistic manner. The proposed method involves explicitly
augmenting the training likelihood with an uncertainty term such that the
learnt probability distribution of the network is minimized with respect to the
training uncertainty. It will be shown that this leads to more accurate image
rendering quality, in addition to more precise and consistent geometry.
Validation has been carried out on both synthetic and real datasets showing
that the proposed approach outperforms state-of-the-art methods. The results
show notably that the proposed method is capable of rendering novel
high-quality views even when the training data is limited.

Summary: Advances in 3D live cell microscopy are enabling high-resolution
capture of previously unobserved processes. Unleashing the power of modern
machine learning methods to fully benefit from these technologies is, however,
frustrated by the difficulty of manually annotating 3D training data.
MiCellAnnGELo virtual reality software offers an immersive environment for
viewing and interacting with 4D microscopy data, including efficient tools for
annotation. We present tools for labelling cell surfaces with a wide range of
applications, including cell motility, endocytosis, and intracellular
signalling. Availability and implementation: MiCellAnnGELo employs the cross
platform (Mac/Unix/Windows) Unity game engine and is available under the MIT
licence at https://github.com/CellDynamics/MiCellAnnGELo.git, together with
sample data. MiCellAnnGELo can be run in desktop mode on a 2D screen or in 3D
using a standard VR headset with compatible GPU.

Automatic coded audio quality predictors are typically designed for
evaluating single channels without considering any spatial aspects. With
InSE-NET [1], we demonstrated mimicking a state-of-the-art coded audio quality
metric (ViSQOL-v3 [2]) with deep neural networks (DNN) and subsequently
improving it - completely with programmatically generated data. In this study,
we take steps towards building a DNN-based coded stereo audio quality predictor
and we propose an extension of the InSE-NET for handling stereo signals. The
design considers stereo/spatial aspects by conditioning the model with left,
right, mid, and side channels; and we name our model Stereo InSE-NET. By
transferring selected weights from the pre-trained mono InSE-NET and retraining
with both real and synthetically augmented listening tests, we demonstrate a
significant improvement of 12% and 6% of Pearson and Spearman Rank correlation
coefficient, respectively, over the latest ViSQOL-v3 [3].

The solution of time dependent differential equations with neural networks
has attracted a lot of attention recently. The central idea is to learn the
laws that govern the evolution of the solution from data, which might be
polluted with random noise. However, in contrast to other machine learning
applications, usually a lot is known about the system at hand. For example, for
many dynamical systems physical quantities such as energy or (angular) momentum
are exactly conserved. Hence, the neural network has to learn these
conservation laws from data and they will only be satisfied approximately due
to finite training time and random noise. In this paper we present an
alternative approach which uses Noether's Theorem to inherently incorporate
conservation laws into the architecture of the neural network. We demonstrate
that this leads to better predictions for three model systems: the motion of a
non-relativistic particle in a three-dimensional Newtonian gravitational
potential, the motion of a massive relativistic particle in the Schwarzschild
metric and a system of two interacting particles in four dimensions.

Quantum machine learning is a growing research field that aims to perform
machine learning tasks assisted by a quantum computer. Kernel-based quantum
machine learning models are paradigmatic examples where the kernel involves
quantum states, and the Gram matrix is calculated from the overlap between
these states. With the kernel at hand, a regular machine learning model is used
for the learning process. In this paper we investigate the quantum support
vector machine and quantum kernel ridge models to predict the degree of
non-Markovianity of a quantum system. We perform digital quantum simulation of
amplitude damping and phase damping channels to create our quantum dataset. We
elaborate on different kernel functions to map the data and kernel circuits to
compute the overlap between quantum states. We show that our models deliver
accurate predictions that are comparable with the fully classical models.

At the CERN Large Hadron Collider experiment, the non-resonant double Higgs
production via vector-boson fusion represents a unique mean to probe the VVHH
(V=Z, W$^{\pm}$) Higgs self-coupling at the current center of mass energies.
Such a rare signal cannot be separated efficiently from huge backgrounds by
applying a few-observables cut-based selection. Indeed, in this work, a Deep
Learning algorithm is used to decide whether an event is more signal- or
background-like. In particular, we report on two main aspects: results of a
hyper-parameters parallel scanning strategy to distribute the training process
across multiple nodes on the ReCaS-Bari data center computing resources and the
discriminating performance of a Deep Neural Network architecture.

Compressed sensing is a scheme that allows for sparse signals to be acquired,
transmitted and stored using far fewer measurements than done by conventional
means employing Nyquist sampling theorem. Since many naturally occurring
signals are sparse (in some domain), compressed sensing has rapidly seen
popularity in a number of applied physics and engineering applications,
particularly in designing signal and image acquisition strategies, e.g.,
magnetic resonance imaging, quantum state tomography, scanning tunneling
microscopy, analog to digital conversion technologies. Contemporaneously,
causal inference has become an important tool for the analysis and
understanding of processes and their interactions in many disciplines of
science, especially those dealing with complex systems. Direct causal analysis
for compressively sensed data is required to avoid the task of reconstructing
the compressed data. Also, for some sparse signals, such as for sparse temporal
data, it may be difficult to discover causal relations directly using available
data-driven/ model-free causality estimation techniques. In this work, we
provide a mathematical proof that structured compressed sensing matrices,
specifically Circulant and Toeplitz, preserve causal relationships in the
compressed signal domain, as measured by Granger Causality. We then verify this
theorem on a number of bivariate and multivariate coupled sparse signal
simulations which are compressed using these matrices. We also demonstrate a
real world application of network causal connectivity estimation from sparse
neural spike train recordings from rat prefrontal cortex.

Transformer verification draws increasing attention in machine learning
research and industry. It formally verifies the robustness of transformers
against adversarial attacks such as exchanging words in a sentence with
synonyms. However, the performance of transformer verification is still not
satisfactory due to bound-centric computation which is significantly different
from standard neural networks. In this paper, we propose Faith, an efficient
framework for transformer verification on GPUs. We first propose a
semantic-aware computation graph transformation to identify semantic
information such as bound computation in transformer verification. We exploit
such semantic information to enable efficient kernel fusion at the computation
graph level. Second, we propose a verification-specialized kernel crafter to
efficiently map transformer verification to modern GPUs. This crafter exploits
a set of GPU hardware supports to accelerate verification specialized
operations which are usually memory-intensive. Third, we propose an
expert-guided autotuning to incorporate expert knowledge on GPU backends to
facilitate large search space exploration. Extensive evaluations show that
Faith achieves $2.1\times$ to $3.4\times$ ($2.6\times$ on average) speedup over
state-of-the-art frameworks.

Skeleton-based human action recognition has been drawing more interest
recently due to its low sensitivity to appearance changes and the accessibility
of more skeleton data. However, even the 3D skeletons captured in practice are
still sensitive to the viewpoint and direction gave the occlusion of different
human-body joints and the errors in human joint localization. Such view
variance of skeleton data may significantly affect the performance of action
recognition. To address this issue, we propose in this paper a new
view-invariant representation learning approach, without any manual action
labeling, for skeleton-based human action recognition. Specifically, we
leverage the multi-view skeleton data simultaneously taken for the same person
in the network training, by maximizing the mutual information between the
representations extracted from different views, and then propose a global-local
contrastive loss to model the multi-scale co-occurrence relationships in both
spatial and temporal domains. Extensive experimental results show that the
proposed method is robust to the view difference of the input skeleton data and
significantly boosts the performance of unsupervised skeleton-based human
action methods, resulting in new state-of-the-art accuracies on two challenging
multi-view benchmarks of PKUMMD and NTU RGB+D.

We propose a method for deploying a safety-critical machine-learning
component into continuously evolving environments where an increased degree of
automation in the engineering process is desired. We associate semantic tags
with the safety case argumentation and turn each piece of evidence into a
quantitative metric or a logic formula. With proper tool support, the impact
can be characterized by a query over the safety argumentation tree to highlight
evidence turning invalid. The concept is exemplified using a vision-based
emergency braking system of an autonomous guided vehicle for factory
automation.

Applied mathematics and machine computations have raised a lot of hope since
the recent success of supervised learning. Many practitioners in industries
have been trying to switch from their old paradigms to machine learning.
Interestingly, those data scientists spend more time scrapping, annotating and
cleaning data than fine-tuning models. This thesis is motivated by the
following question: can we derive a more generic framework than the one of
supervised learning in order to learn from clutter data?
  This question is approached through the lens of weakly supervised learning,
assuming that the bottleneck of data collection lies in annotation. We model
weak supervision as giving, rather than a unique target, a set of target
candidates. We argue that one should look for an ``optimistic'' function that
matches most of the observations. This allows us to derive a principle to
disambiguate partial labels. We also discuss the advantage to incorporate
unsupervised learning techniques into our framework, in particular manifold
regularization approached through diffusion techniques, for which we derived a
new algorithm that scales better with input dimension then the baseline method.
  Finally, we switch from passive to active weakly supervised learning,
introducing the ``active labeling'' framework, in which a practitioner can
query weak information about chosen data. Among others, we leverage the fact
that one does not need full information to access stochastic gradients and
perform stochastic gradient descent.

Grammatical inference is a classical problem in computational learning theory
and a topic of wider influence in natural language processing. We treat
grammars as a model of computation and propose a novel neural approach to
induction of regular grammars from positive and negative examples. Our model is
fully explainable, its intermediate results are directly interpretable as
partial parses, and it can be used to learn arbitrary regular grammars when
provided with sufficient data. We find that our method consistently attains
high recall and precision scores across a range of tests of varying complexity.

This paper describes speaker verification (SV) systems submitted by the
SpeakIn team to the Task 1 and Task 2 of the Far-Field Speaker Verification
Challenge 2022 (FFSVC2022). SV tasks of the challenge focus on the problem of
fully supervised far-field speaker verification (Task 1) and semi-supervised
far-field speaker verification (Task 2). In Task 1, we used the VoxCeleb and
FFSVC2020 datasets as train datasets. And for Task 2, we only used the VoxCeleb
dataset as train set. The ResNet-based and RepVGG-based architectures were
developed for this challenge. Global statistic pooling structure and MQMHA
pooling structure were used to aggregate the frame-level features across time
to obtain utterance-level representation. We adopted AM-Softmax and AAM-Softmax
to classify the resulting embeddings. We innovatively propose a staged transfer
learning method. In the pre-training stage we reserve the speaker weights, and
there are no positive samples to train them in this stage. Then we fine-tune
these weights with both positive and negative samples in the second stage.
Compared with the traditional transfer learning strategy, this strategy can
better improve the model performance. The Sub-Mean and AS-Norm backend methods
were used to solve the problem of domain mismatch. In the fusion stage, three
models were fused in Task1 and two models were fused in Task2. On the FFSVC2022
leaderboard, the EER of our submission is 3.0049% and the corresponding minDCF
is 0.2938 in Task1. In Task2, EER and minDCF are 6.2060% and 0.5232
respectively. Our approach leads to excellent performance and ranks 1st in both
challenge tasks.

With huge amounts of data explosively increasing in the mobile edge,
over-the-air federated learning (OA-FL) emerges as a promising technique to
reduce communication costs and privacy leak risks. However, when devices in a
relatively large area cooperatively train a machine learning model, the
attendant straggler issues will significantly reduce the learning performance.
In this paper, we propose an unmanned aerial vehicle (UAV) assisted OA-FL
system, where the UAV acts as a parameter server (PS) to aggregate the local
gradients hierarchically for global model updating. Under this UAV-assisted
hierarchical aggregation scheme, we carry out a gradient-correlation-aware FL
performance analysis. We then formulate a mean squared error (MSE) minimization
problem to tune the UAV trajectory and the global aggregation coefficients
based on the analysis results. An algorithm based on alternating optimization
(AO) and successive convex approximation (SCA) is developed to solve the
formulated problem. Simulation results demonstrate the great potential of our
UAV-assisted hierarchical aggregation scheme.

Artificial intelligence is gaining strength and materials science can both
contribute to and profit from it. In a simultaneous progress race, new
materials, systems and processes can be devised and optimized thanks to machine
learning techniques and such progress can be turned into in-novative computing
platforms. Future materials scientists will profit from understanding how
machine learning can boost the conception of advanced materials. This review
covers aspects of computation from the fundamentals to directions taken and
repercussions produced by compu-tation to account for the origins, procedures
and applications of artificial intelligence. Machine learning and its methods
are reviewed to provide basic knowledge on its implementation and its
potential. The materials and systems used to implement artificial intelligence
with electric charges are finding serious competition from other information
carrying and processing agents. The impact these techniques are having on the
inception of new advanced materials is so deep that a new paradigm is
developing where implicit knowledge is being mined to conceive materi-als and
systems for functions instead of finding applications to found materials. How
far this trend can be carried is hard to fathom as exemplified by the power to
discover unheard of mate-rials or physical laws buried in data.

Most domain adaptation methods for machine reading comprehension (MRC) use a
pre-trained question-answer (QA) construction model to generate pseudo QA pairs
for MRC transfer. Such a process will inevitably introduce mismatched pairs
(i.e., noisy correspondence) due to i) the unavailable QA pairs in target
documents, and ii) the domain shift during applying the QA construction model
to the target domain. Undoubtedly, the noisy correspondence will degenerate the
performance of MRC, which however is neglected by existing works. To solve such
an untouched problem, we propose to construct QA pairs by additionally using
the dialogue related to the documents, as well as a new domain adaptation
method for MRC. Specifically, we propose Robust Domain Adaptation for Machine
Reading Comprehension (RMRC) method which consists of an answer extractor (AE),
a question selector (QS), and an MRC model. Specifically, RMRC filters out the
irrelevant answers by estimating the correlation to the document via the AE,
and extracts the questions by fusing the candidate questions in multiple rounds
of dialogue chats via the QS. With the extracted QA pairs, MRC is fine-tuned
and provides the feedback to optimize the QS through a novel reinforced
self-training method. Thanks to the optimization of the QS, our method will
greatly alleviate the noisy correspondence problem caused by the domain shift.
To the best of our knowledge, this could be the first study to reveal the
influence of noisy correspondence in domain adaptation MRC models and show a
feasible way to achieve robustness to mismatched pairs. Extensive experiments
on three datasets demonstrate the effectiveness of our method.

Data-driven turbulence modeling is experiencing a surge in interest following
algorithmic and hardware developments in the data sciences. We discuss an
approach using the differentiable physics paradigm that combines known physics
with machine learning to develop closure models for Burgers' turbulence. We
consider the 1D Burgers system as a prototypical test problem for modeling the
unresolved terms in advection-dominated turbulence problems. We train a series
of models that incorporate varying degrees of physical assumptions on an a
posteriori loss function to test the efficacy of models across a range of
system parameters, including viscosity, time, and grid resolution. We find that
constraining models with inductive biases in the form of partial differential
equations that contain known physics or existing closure approaches produces
highly data-efficient, accurate, and generalizable models, outperforming
state-of-the-art baselines. Addition of structure in the form of physics
information also brings a level of interpretability to the models, potentially
offering a stepping stone to the future of closure modeling.

This work makes a substantial step in the field of split computing, i.e., how
to split a deep neural network to host its early part on an embedded device and
the rest on a server. So far, potential split locations have been identified
exploiting uniquely architectural aspects, i.e., based on the layer sizes.
Under this paradigm, the efficacy of the split in terms of accuracy can be
evaluated only after having performed the split and retrained the entire
pipeline, making an exhaustive evaluation of all the plausible splitting points
prohibitive in terms of time. Here we show that not only the architecture of
the layers does matter, but the importance of the neurons contained therein
too. A neuron is important if its gradient with respect to the correct class
decision is high. It follows that a split should be applied right after a layer
with a high density of important neurons, in order to preserve the information
flowing until then. Upon this idea, we propose Interpretable Split (I-SPLIT): a
procedure that identifies the most suitable splitting points by providing a
reliable prediction on how well this split will perform in terms of
classification accuracy, beforehand of its effective implementation. As a
further major contribution of I-SPLIT, we show that the best choice for the
splitting point on a multiclass categorization problem depends also on which
specific classes the network has to deal with. Exhaustive experiments have been
carried out on two networks, VGG16 and ResNet-50, and three datasets,
Tiny-Imagenet-200, notMNIST, and Chest X-Ray Pneumonia. The source code is
available at https://github.com/vips4/I-Split.

We study the community detection problem on a Gaussian mixture model, in
which (1) vertices are divided into $k\geq 2$ distinct communities that are not
necessarily equally-sized; (2) the Gaussian perturbations for different entries
in the observation matrix are not necessarily independent or identically
distributed. We prove necessary and sufficient conditions for the exact
recovery of the maximum likelihood estimation (MLE), and discuss the cases when
these necessary and sufficient conditions give sharp threshold. Applications
include the community detection on a graph where the Gaussian perturbations of
observations on each edge is the sum of i.i.d.~Gaussian random variables on its
end vertices, in which we explicitly obtain the threshold for the exact
recovery of the MLE.

Neural network calibration is an essential task in deep learning to ensure
consistency between the confidence of model prediction and the true correctness
likelihood. In this paper, we propose a new post-processing calibration method
called Neural Clamping, which employs a simple joint input-output
transformation on a pre-trained classifier via a learnable universal input
perturbation and an output temperature scaling parameter. Moreover, we provide
theoretical explanations on why Neural Clamping is provably better than
temperature scaling. Evaluated on CIFAR-100 and ImageNet image recognition
datasets and a variety of deep neural network models, our empirical results
show that Neural Clamping significantly outperforms state-of-the-art
post-processing calibration methods.

The energy consumption of the fifth generation(5G) of mobile networks is one
of the major concerns of the telecom industry. However, there is not currently
an accurate and tractable approach to evaluate 5G base stations (BSs) power
consumption. In this article, we propose a novel model for a realistic
characterisation of the power consumption of 5G multi-carrier BSs, which builds
on a large data collection campaign. At first, we define a machine learning
architecture that allows modelling multiple 5G BS products. Then, we exploit
the knowledge gathered by this framework to derive a realistic and analytically
tractable power consumption model, which can help driving both theoretical
analyses as well as feature standardisation, development and optimisation
frameworks. Notably, we demonstrate that such model has high precision, and it
is able of capturing the benefits of energy saving mechanisms. We believe this
analytical model represents a fundamental tool for understanding 5G BSs power
consumption, and accurately optimising the network energy efficiency.

A tool that could suggest new personalized research directions and ideas by
taking insights from the scientific literature could significantly accelerate
the progress of science. A field that might benefit from such an approach is
artificial intelligence (AI) research, where the number of scientific
publications has been growing exponentially over the last years, making it
challenging for human researchers to keep track of the progress. Here, we use
AI techniques to predict the future research directions of AI itself. We
develop a new graph-based benchmark based on real-world data -- the
Science4Cast benchmark, which aims to predict the future state of an evolving
semantic network of AI. For that, we use more than 100,000 research papers and
build up a knowledge network with more than 64,000 concept nodes. We then
present ten diverse methods to tackle this task, ranging from pure statistical
to pure learning methods. Surprisingly, the most powerful methods use a
carefully curated set of network features, rather than an end-to-end AI
approach. It indicates a great potential that can be unleashed for purely ML
approaches without human knowledge. Ultimately, better predictions of new
future research directions will be a crucial component of more advanced
research suggestion tools.

Training a robust policy is critical for policy deployment in real-world
systems or dealing with unknown dynamics mismatch in different dynamic systems.
Domain Randomization~(DR) is a simple and elegant approach that trains a
conservative policy to counter different dynamic systems without expert
knowledge about the target system parameters. However, existing works reveal
that the policy trained through DR tends to be over-conservative and performs
poorly in target domains. Our key insight is that dynamic systems with
different parameters provide different levels of difficulty for the policy, and
the difficulty of behaving well in a system is constantly changing due to the
evolution of the policy. If we can actively sample the systems with proper
difficulty for the policy on the fly, it will stabilize the training process
and prevent the policy from becoming over-conservative or over-optimistic. To
operationalize this idea, we introduce Active Dynamics Preference~(ADP), which
quantifies the informativeness and density of sampled system parameters. ADP
actively selects system parameters with high informativeness and low density.
We validate our approach in four robotic locomotion tasks with various
discrepancies between the training and testing environments. Extensive results
demonstrate that our approach has superior robustness for system inconsistency
compared to several baselines.

Learning a privacy-preserving model from distributed sensitive data is an
increasingly important problem, often formulated in the federated learning
context. Variational inference has recently been extended to the non-private
federated learning setting via the partitioned variational inference algorithm.
For privacy protection, the current gold standard is called differential
privacy. Differential privacy guarantees privacy in a strong, mathematically
clearly defined sense.
  In this paper, we present differentially private partitioned variational
inference, the first general framework for learning a variational approximation
to a Bayesian posterior distribution in the federated learning setting while
minimising the number of communication rounds and providing differential
privacy guarantees for data subjects.
  We propose three alternative implementations in the general framework, one
based on perturbing local optimisation done by individual parties, and two
based on perturbing global updates (one using a version of federated averaging,
one adding virtual parties to the protocol), and compare their properties both
theoretically and empirically. We show that perturbing the local optimisation
works well with simple and complex models as long as each party has enough
local data. However, the privacy is always guaranteed independently by each
party. In contrast, perturbing the global updates works best with relatively
simple models. Given access to suitable secure primitives, such as secure
aggregation or secure shuffling, the performance can be improved by all parties
guaranteeing privacy jointly.

Lagrangian and Hamiltonian neural networks (LNNs and HNNs, respectively)
encode strong inductive biases that allow them to outperform other models of
physical systems significantly. However, these models have, thus far, mostly
been limited to simple systems such as pendulums and springs or a single rigid
body such as a gyroscope or a rigid rotor. Here, we present a Lagrangian graph
neural network (LGNN) that can learn the dynamics of articulated rigid bodies
by exploiting their topology. We demonstrate the performance of LGNN by
learning the dynamics of ropes, chains, and trusses with the bars modeled as
rigid bodies. LGNN also exhibits generalizability -- LGNN trained on chains
with a few segments exhibits generalizability to simulate a chain with large
number of links and arbitrary link length. We also show that the LGNN can
simulate unseen hybrid systems including bars and chains, on which they have
not been trained on. Specifically, we show that the LGNN can be used to model
the dynamics of complex real-world structures such as the stability of
tensegrity structures. Finally, we discuss the non-diagonal nature of the mass
matrix and its ability to generalize in complex systems.

The ClearMind project aims to develop the TOF-PET position sensitive
detection module optimized for the time resolution, spatial resolution, and
detection efficiency. For this, the ClearMind project uses a large (59 $\times$
59 mm$^2$) monolithic PbWO$_4$ (PWO) scintillating crystal with a bialkali
photo-electric layer deposited directly on the crystal. Scintillation and
Cherenkov photons result together from the 511 keV gamma-ray interation into
the PWO crystal. A micro-channel plate photomultiplier tube (MCP-PMT)
encapsulating the PWO crystal amplifies photoelectrons generated at the
photocathode, and the corresponding anode signals are collected through the
transmission lines read out at both ends and digitized by a SAMPIC module. In
this work, we present a realistic Geant4 simulation of the ClearMind prototype
detector, including the propagation of the visible photons in the crystal, the
modelling of a realistic response of the photocathode and of the PMT, and the
propagation of the electrical signals over the transmission lines. The
reconstruction of the gamma conversion in the detector volume is performed from
the signals registered at both ends of the transmission lines. We compare the
reconstruction precision of a statistical algorithm against machine learning
algorithms developed using the TVMA package. We expect to reach a spatial
resolution down to a few mm$^3$ (FWHM). Finally, we will discuss prospects for
the ClearMind detector.

The video-based person re-identification (ReID) aims to identify the given
pedestrian video sequence across multiple non-overlapping cameras.
  To aggregate the temporal and spatial features of the video samples, the
graph neural networks (GNNs) are introduced.
  However, existing graph-based models, like STGCN, perform the
\textit{mean}/\textit{max pooling} on node features to obtain the graph
representation, which neglect the graph topology and node importance.
  In this paper, we propose the graph pooling network (GPNet) to learn the
multi-granularity graph representation for the video retrieval, where the
\textit{graph pooling layer} is implemented to downsample the graph.
  We first construct a multi-granular graph, whose node features denote image
embedding learned by backbone, and edges are established between the temporal
and Euclidean neighborhood nodes.
  We then implement multiple graph convolutional layers to perform the
neighborhood aggregation on the graphs.
  To downsample the graph, we propose a multi-head full attention graph pooling
(MHFAPool) layer, which integrates the advantages of existing node clustering
and node selection pooling methods.
  Specifically, MHFAPool takes the main eigenvector of full attention matrix as
the aggregation coefficients to involve the global graph information in each
pooled nodes.
  Extensive experiments demonstrate that our GPNet achieves the competitive
results on four widely-used datasets, i.e., MARS, DukeMTMC-VideoReID, iLIDS-VID
and PRID-2011.

Existing methods for video-based person re-identification (ReID) mainly learn
the appearance feature of a given pedestrian via a feature extractor and a
feature aggregator.
  However, the appearance models would fail when different pedestrians have
similar appearances.
  Considering that different pedestrians have different walking postures and
body proportions, we propose to learn the discriminative pose feature beyond
the appearance feature for video retrieval.
  Specifically, we implement a two-branch architecture to separately learn the
appearance feature and pose feature, and then concatenate them together for
  To learn the pose feature, we first detect the pedestrian pose in each frame
through an off-the-shelf pose detector, and construct a temporal graph using
the pose sequence.
  We then exploit a recurrent graph convolutional network (RGCN) to learn the
node embeddings of the temporal pose graph, which devises a global information
propagation mechanism to simultaneously achieve the neighborhood aggregation of
intra-frame nodes and message passing among inter-frame graphs.
  Finally, we propose a dual-attention method consisting of node-attention and
time-attention to obtain the temporal graph representation from the node
embeddings, where the self-attention mechanism is employed to learn the
importance of each node and each frame.
  We verify the proposed method on three video-based ReID datasets, i.e., Mars,
DukeMTMC and iLIDS-VID, whose experimental results demonstrate that the learned
pose feature can effectively improve the performance of existing appearance
models.

Artificial Neural Networks (ANN) are already heavily involved in methods and
applications for frequent tasks in the field of computational chemistry such as
representation of potential energy surfaces (PES) and spectroscopic
predictions. This perspective provides an overview of the foundations of neural
network-based full-dimensional potential energy surfaces, their architectures,
underlying concepts, their representation and applications to chemical systems.
Methods for data generation and training procedures for PES construction are
discussed and means for error assessment and refinement through transfer
learning are presented. A selection of recent results illustrates the latest
improvements regarding accuracy of PES representations and system size
limitations in dynamics simulations, but also NN application enabling direct
prediction of physical results without dynamics simulations. The aim is to
provide an overview for the current state-of-the-art NN approaches in
computational chemistry and also to point out the current challenges in
enhancing reliability and applicability of NN methods on larger scale.

The model-based gait recognition methods usually adopt the pedestrian walking
postures to identify human beings.
  However, existing methods did not explicitly resolve the large intra-class
variance of human pose due to camera views changing.
  In this paper, we propose to generate multi-view pose sequences for each
single-view pose sample by learning full-rank transformation matrices via
lower-upper generative adversarial network (LUGAN).
  By the prior of camera imaging, we derive that the spatial coordinates
between cross-view poses satisfy a linear transformation of a full-rank matrix,
thereby, this paper employs the adversarial training to learn transformation
matrices from the source pose and target views to obtain the target pose
  To this end, we implement a generator composed of graph convolutional (GCN)
layers, fully connected (FC) layers and two-branch convolutional (CNN) layers:
GCN layers and FC layers encode the source pose sequence and target view, then
CNN branches learn a lower triangular matrix and an upper triangular matrix,
respectively, finally they are multiplied to formulate the full-rank
transformation matrix.
  For the purpose of adversarial training, we further devise a condition
discriminator that distinguishes whether the pose sequence is true or
generated.
  To enable the high-level correlation learning, we propose a plug-and-play
module, named multi-scale hypergraph convolution (HGC), to replace the spatial
graph convolutional layer in baseline, which could simultaneously model the
joint-level, part-level and body-level correlations.
  Extensive experiments on two large gait recognition datasets, i.e., CASIA-B
and OUMVLP-Pose, demonstrate that our method outperforms the baseline model and
existing pose-based methods by a large margin.

As an increasingly popular task in multimedia information retrieval, video
moment retrieval (VMR) aims to localize the target moment from an untrimmed
video according to a given language query. Most previous methods depend heavily
on numerous manual annotations (i.e., moment boundaries), which are extremely
expensive to acquire in practice. In addition, due to the domain gap between
different datasets, directly applying these pre-trained models to an unseen
domain leads to a significant performance drop. In this paper, we focus on a
novel task: cross-domain VMR, where fully-annotated datasets are available in
one domain (``source domain''), but the domain of interest (``target domain'')
only contains unannotated datasets. As far as we know, we present the first
study on cross-domain VMR. To address this new task, we propose a novel
Multi-Modal Cross-Domain Alignment (MMCDA) network to transfer the annotation
knowledge from the source domain to the target domain. However, due to the
domain discrepancy between the source and target domains and the semantic gap
between videos and queries, directly applying trained models to the target
domain generally leads to a performance drop. To solve this problem, we develop
three novel modules: (i) a domain alignment module is designed to align the
feature distributions between different domains of each modality; (ii) a
cross-modal alignment module aims to map both video and query features into a
joint embedding space and to align the feature distributions between different
modalities in the target domain; (iii) a specific alignment module tries to
obtain the fine-grained similarity between a specific frame and the given query
for optimal localization. By jointly training these three modules, our MMCDA
can learn domain-invariant and semantic-aligned cross-modal representations.

Prompt learning is an effective paradigm that bridges gaps between the
pre-training tasks and the corresponding downstream applications. Approaches
based on this paradigm have achieved great transcendent results in various
applications. However, it still needs to be answered how to design a unified
framework based on the prompt learning paradigm for various information
extraction tasks. In this paper, we propose a novel composable prompt-based
generative framework, which could be applied to a wide range of tasks in the
field of Information Extraction. Specifically, we reformulate information
extraction tasks into the form of filling slots in pre-designed type-specific
prompts, which consist of one or multiple sub-prompts. A strategy of
constructing composable prompts is proposed to enhance the generalization
ability to extract events in data-scarce scenarios. Furthermore, to fit this
framework, we transform Relation Extraction into the task of determining
semantic consistency in prompts. The experimental results demonstrate that our
approach surpasses compared baselines on real-world datasets in data-abundant
and data-scarce scenarios. Further analysis of the proposed framework is
presented, as well as numerical experiments conducted to investigate impact
factors of performance on various tasks.

Conventional automatic doors cannot distinguish between people wishing to
pass through the door and people passing by the door, so they often open
unnecessarily. This leads to the need to adopt new systems in both commercial
and non-commercial environments: smart doors. In particular, a smart door
system predicts the intention of people near the door based on the social
context of the surrounding environment and then makes rational decisions about
whether or not to open the door. This work proposes the first position paper
related to smart doors, without bells and whistles. We first point out that the
problem not only concerns reliability, climate control, safety, and mode of
operation. Indeed, a system to predict the intention of people near the door
also involves a deeper understanding of the social context of the scene through
a complex combined analysis of proxemics and scene reasoning. Furthermore, we
conduct an exhaustive literature review about automatic doors, providing a
novel system formulation. Also, we present an analysis of the possible future
application of smart doors, a description of the ethical shortcomings, and
legislative issues.

There is a longstanding interest in capturing the error behaviour of object
detectors by finding images where their performance is likely to be
unsatisfactory. In real-world applications such as autonomous driving, it is
also crucial to characterise potential failures beyond simple requirements of
detection performance. For example, a missed detection of a pedestrian close to
an ego vehicle will generally require closer inspection than a missed detection
of a car in the distance. The problem of predicting such potential failures at
test time has largely been overlooked in the literature and conventional
approaches based on detection uncertainty fall short in that they are agnostic
to such fine-grained characterisation of errors. In this work, we propose to
reformulate the problem of finding "hard" images as a query-based hard image
retrieval task, where queries are specific definitions of "hardness", and offer
a simple and intuitive method that can solve this task for a large family of
queries. Our method is entirely post-hoc, does not require ground-truth
annotations, is independent of the choice of a detector, and relies on an
efficient Monte Carlo estimation that uses a simple stochastic model in place
of the ground-truth. We show experimentally that it can be applied successfully
to a wide variety of queries for which it can reliably identify hard images for
a given detector without any labelled data. We provide results on ranking and
classification tasks using the widely used RetinaNet, Faster-RCNN, Mask-RCNN,
and Cascade Mask-RCNN object detectors.

Machine learning (ML) refers to computer algorithms that predict a meaningful
output or categorise complex systems based on a large amount of data. ML
applied in a variety of areas, including natural science, engineering, space
exploration, and even gaming development. This article focused on the use of
machine learning in the field of chemical and biological oceanography. In the
prediction of global fixed nitrogen levels, partial carbon dioxide pressure,
and other chemical properties, the application of ML is a promising tool.
Machine learning is also utilised in the field of biological oceanography to
detect planktonic forms from various images (i.e., microscopy, FlowCAM and
video recorder), spectrometers, and other signal processing techniques.
Moreover, ML successfully classified the mammals using their acoustics,
detecting endangered mammalian and fish species in a specific environment. Most
importantly, using environmental data, the ML proved to be an effective method
for predicting hypoxic conditions and the harmful algal bloom events, an
important measurement in terms of environmental monitoring. Furthermore,
machine learning was used to construct a number of databases for various
species that will be useful to other researchers, and the creation of new
algorithms will help the marine research community better comprehend the
chemistry and biology of the ocean.

StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL),
of which the main difficulties include huge state space, varying action space,
and a long time horizon. In this work, we investigate a set of RL techniques
for the full-length game of StarCraft II. We investigate a hierarchical RL
approach involving extracted macro-actions and a hierarchical architecture of
neural networks. We investigate a curriculum transfer training procedure and
train the agent on a single machine with 4 GPUs and 48 CPU threads. On a 64x64
map and using restrictive units, we achieve a win rate of 99% against the
level-1 built-in AI. Through the curriculum transfer learning algorithm and a
mixture of combat models, we achieve a 93% win rate against the most difficult
non-cheating level built-in AI (level-7). In this extended version of the
paper, we improve our architecture to train the agent against the cheating
level AIs and achieve the win rate against the level-8, level-9, and level-10
AIs as 96%, 97%, and 94%, respectively. Our codes are at
https://github.com/liuruoze/HierNet-SC2. To provide a baseline referring the
AlphaStar for our work as well as the research and open-source community, we
reproduce a scaled-down version of it, mini-AlphaStar (mAS). The latest version
of mAS is 1.07, which can be trained on the raw action space which has 564
actions. It is designed to run training on a single common machine, by making
the hyper-parameters adjustable. We then compare our work with mAS using the
same resources and show that our method is more effective. The codes of
mini-AlphaStar are at https://github.com/liuruoze/mini-AlphaStar. We hope our
study could shed some light on the future research of efficient reinforcement
learning on SC2 and other large-scale games.

We offer a method for one-shot image synthesis that allows controlling
manipulations of a single image by inverting a quasi-robust classifier equipped
with strong regularizers. Our proposed method, entitled Magic, samples
structured gradients from a pre-trained quasi-robust classifier to better
preserve the input semantics while preserving its classification accuracy,
thereby guaranteeing credibility in the synthesis. Unlike current methods that
use complex primitives to supervise the process or use attention maps as a weak
supervisory signal, Magic aggregates gradients over the input, driven by a
guide binary mask that enforces a strong, spatial prior. Magic implements a
series of manipulations with a single framework achieving shape and location
control, intense non-rigid shape deformations, and copy/move operations in the
presence of repeating objects and gives users firm control over the synthesis
by requiring simply specifying binary guide masks. Our study and findings are
supported by various qualitative comparisons with the state-of-the-art on the
same images sampled from ImageNet and quantitative analysis using machine
perception along with a user survey of 100+ participants that endorse our
synthesis quality.

Robustness studies of black-box models is recognized as a necessary task for
numerical models based on structural equations and predictive models learned
from data. These studies must assess the model's robustness to possible
misspecification of regarding its inputs (e.g., covariate shift). The study of
black-box models, through the prism of uncertainty quantification (UQ), is
often based on sensitivity analysis involving a probabilistic structure imposed
on the inputs, while ML models are solely constructed from observed data. Our
work aim at unifying the UQ and ML interpretability approaches, by providing
relevant and easy-to-use tools for both paradigms. To provide a generic and
understandable framework for robustness studies, we define perturbations of
input information relying on quantile constraints and projections with respect
to the Wasserstein distance between probability measures, while preserving
their dependence structure. We show that this perturbation problem can be
analytically solved. Ensuring regularity constraints by means of isotonic
polynomial approximations leads to smoother perturbations, which can be more
suitable in practice. Numerical experiments on real case studies, from the UQ
and ML fields, highlight the computational feasibility of such studies and
provide local and global insights on the robustness of black-box models to
input perturbations.

Variational inference with Gaussian mixture models (GMMs) enables learning of
highly-tractable yet multi-modal approximations of intractable target
distributions. GMMs are particular relevant for problem settings with up to a
few hundred dimensions, for example in robotics, for modelling distributions
over trajectories or joint distributions. This work focuses on two very
effective methods for GMM-based variational inference that both employ
independent natural gradient updates for the individual components and the
categorical distribution of the weights. We show for the first time, that their
derived updates are equivalent, although their practical implementations and
theoretical guarantees differ. We identify several design choices that
distinguish both approaches, namely with respect to sample selection, natural
gradient estimation, stepsize adaptation, and whether trust regions are
enforced or the number of components adapted. We perform extensive ablations on
these design choices and show that they strongly affect the efficiency of the
optimization and the variability of the learned distribution. Based on our
insights, we propose a novel instantiation of our generalized framework, that
combines first-order natural gradient estimates with trust-regions and
component adaption, and significantly outperforms both previous methods in all
our experiments.

Electricity grids have become an essential part of daily life, even if they
are often not noticed in everyday life. We usually only become particularly
aware of this dependence by the time the electricity grid is no longer
available. However, significant changes, such as the transition to renewable
energy (photovoltaic, wind turbines, etc.) and an increasing number of energy
consumers with complex load profiles (electric vehicles, home battery systems,
etc.), pose new challenges for the electricity grid. To address these
challenges, we propose two first-of-its-kind datasets based on measurements in
a broadband powerline communications (PLC) infrastructure. Both datasets FiN-1
and FiN-2, were collected during real practical use in a part of the German
low-voltage grid that supplies around 4.4 million people and show more than 13
billion datapoints collected by more than 5100 sensors. In addition, we present
different use cases in asset management, grid state visualization, forecasting,
predictive maintenance, and novelty detection to highlight the benefits of
these types of data. For these applications, we particularly highlight the use
of novel machine learning architectures to extract rich information from
real-world data that cannot be captured using traditional approaches. By
publishing the first large-scale real-world dataset, we aim to shed light on
the previously largely unrecognized potential of PLC data and emphasize
machine-learning-based research in low-voltage distribution networks by
presenting a variety of different use cases.

Robust and reliable anonymization of chest radiographs constitutes an
essential step before publishing large datasets of such for research purposes.
The conventional anonymization process is carried out by obscuring personal
information in the images with black boxes and removing or replacing
meta-information. However, such simple measures retain biometric information in
the chest radiographs, allowing patients to be re-identified by a linkage
attack. Therefore, we see an urgent need to obfuscate the biometric information
appearing in the images. To the best of our knowledge, we propose the first
deep learning-based approach to targetedly anonymize chest radiographs while
maintaining data utility for diagnostic and machine learning purposes. Our
model architecture is a composition of three independent neural networks that,
when collectively used, allow for learning a deformation field that is able to
impede patient re-identification. The individual influence of each component is
investigated with an ablation study. Quantitative results on the ChestX-ray14
dataset show a reduction of patient re-identification from 81.8% to 58.6% in
the area under the receiver operating characteristic curve (AUC) with little
impact on the abnormality classification performance. This indicates the
ability to preserve underlying abnormality patterns while increasing patient
privacy. Furthermore, we compare the proposed deep learning-based anonymization
approach with differentially private image pixelization, and demonstrate the
superiority of our method towards resolving the privacy-utility trade-off for
chest radiographs.

Many high precision (dis)assembly tasks are still being performed by humans,
whereas this is an ideal opportunity for automation. This paper provides a
framework which enables a non-expert human operator to teach a robotic arm to
do complex precision tasks. The framework uses a variable Cartesian impedance
controller to execute trajectories learned from kinesthetic human
demonstrations. Feedback can be given to interactively reshape or speed up the
original demonstration. Board localization is done through a visual estimation
of the task board position and refined through haptic feedback. Our framework
is tested on the Robothon benchmark disassembly challenge, where the robot has
to perform complex precision tasks, such as a key insertion. The results show
high success rates for each of the manipulation subtasks, including cases when
the box is in novel poses. An ablation study is also performed to evaluate the
components of the framework.

In an effort to advocate the research for a deep learning-based machine
failure detection system, we present a case study of our proposed system based
on a tiny sound dataset. Our case study investigates a variational autoencoder
(VAE) for augmenting a small drill sound dataset from Valmet AB. A Valmet
dataset contains 134 sounds that have been divided into two categories:
"Anomaly" and "Normal" recorded from a drilling machine in Valmet AB, a company
in Sundsvall, Sweden that supplies equipment and processes for the production
of biofuels. Using deep learning models to detect failure drills on such a
small sound dataset is typically unsuccessful. We employed a VAE to increase
the number of sounds in the tiny dataset by synthesizing new sounds from
original sounds. The augmented dataset was created by combining these
synthesized sounds with the original sounds. We used a high-pass filter with a
passband frequency of 1000 Hz and a low-pass filter with a passband frequency
of 22\kern 0.16667em000 Hz to pre-process sounds in the augmented dataset
before transforming them to Mel spectrograms. The pre-trained 2D-CNN Alexnet
was then trained using these Mel spectrograms. When compared to using the
original tiny sound dataset to train pre-trained Alexnet, using the augmented
sound dataset enhanced the CNN model's classification results by 6.62\%(94.12\%
when trained on the augmented dataset versus 87.5\% when trained on the
original dataset).

Plants are dynamic organisms. Understanding temporal variations in vegetation
is an essential problem for all robots in the wild. However, associating
repeated 3D scans of plants across time is challenging. A key step in this
process is re-identifying and tracking the same individual plant components
over time. Previously, this has been achieved by comparing their global spatial
or topological location. In this work, we demonstrate how using shape features
improves temporal organ matching. We present a landmark-free shape compression
algorithm, which allows for the extraction of 3D shape features of leaves,
characterises leaf shape and curvature efficiently in few parameters, and makes
the association of individual leaves in feature space possible. The approach
combines 3D contour extraction and further compression using Principal
Component Analysis (PCA) to produce a shape space encoding, which is entirely
learned from data and retains information about edge contours and 3D curvature.
Our evaluation on temporal scan sequences of tomato plants shows, that
incorporating shape features improves temporal leaf-matching. A combination of
shape, location, and rotation information proves most informative for
recognition of leaves over time and yields a true positive rate of 75%, a 15%
improvement on sate-of-the-art methods. This is essential for robotic crop
monitoring, which enables whole-of-lifecycle phenotyping.

With the growth of smart building applications, occupancy information in
residential buildings is becoming more and more significant. In the context of
the smart buildings' paradigm, this kind of information is required for a wide
range of purposes, including enhancing energy efficiency and occupant comfort.
In this study, occupancy detection in residential building is implemented using
deep learning based on technical information of electric appliances. To this
end, a novel approach of occupancy detection for smart residential building
system is proposed. The dataset of electric appliances, sensors, light, and
HVAC, which is measured by smart metering system and is collected from 50
households, is used for simulations. To classify the occupancy among datasets,
the support vector machine and autoencoder algorithm are used. Confusion matrix
is utilized for accuracy, precision, recall, and F1 to demonstrate the
comparative performance of the proposed method in occupancy detection. The
proposed algorithm achieves occupancy detection using technical information of
electric appliances by 95.7~98.4%. To validate occupancy detection data,
principal component analysis and the t-distributed stochastic neighbor
embedding (t-SNE) algorithm are employed. Power consumption with renewable
energy system is reduced to 11.1~13.1% in smart buildings by using occupancy

Although analog semantic communication systems have received considerable
attention in the literature, there is less work on digital semantic
communication systems. In this paper, we develop a deep learning (DL)-enabled
vector quantized (VQ) semantic communication system for image transmission,
named VQ-DeepSC. Specifically, we propose a convolutional neural network
(CNN)-based transceiver to extract multi-scale semantic features of images and
introduce multi-scale semantic embedding spaces to perform semantic feature
quantization, rendering the data compatible with digital communication systems.
Furthermore, we employ adversarial training to improve the quality of received
images by introducing a PatchGAN discriminator. Experimental results
demonstrate that the proposed VQ-DeepSC outperforms traditional image
transmission methods in terms of SSIM.

Variational quantum algorithms (VQAs) offer the most promising path to
obtaining quantum advantages via noisy intermediate-scale quantum (NISQ)
processors. Such systems leverage classical optimization to tune the parameters
of a parameterized quantum circuit (PQC). The goal is minimizing a cost
function that depends on measurement outputs obtained from the PQC.
Optimization is typically implemented via stochastic gradient descent (SGD). On
NISQ computers, gate noise due to imperfections and decoherence affects the
stochastic gradient estimates by introducing a bias. Quantum error mitigation
(QEM) techniques can reduce the estimation bias without requiring any increase
in the number of qubits, but they in turn cause an increase in the variance of
the gradient estimates. This work studies the impact of quantum gate noise on
the convergence of SGD for the variational eigensolver (VQE), a fundamental
instance of VQAs. The main goal is ascertaining conditions under which QEM can
enhance the performance of SGD for VQEs. It is shown that quantum gate noise
induces a non-zero error-floor on the convergence error of SGD (evaluated with
respect to a reference noiseless PQC), which depends on the number of noisy
gates, the strength of the noise, as well as the eigenspectrum of the
observable being measured and minimized. In contrast, with QEM, any arbitrarily
small error can be obtained. Furthermore, for error levels attainable with or
without QEM, QEM can reduce the number of required iterations, but only as long
as the quantum noise level is sufficiently small, and a sufficiently large
number of measurements is allowed at each SGD iteration. Numerical examples for
a max-cut problem corroborate the main theoretical findings.

We give an asymptotic expansion of the relative entropy between the heat
kernel $q_Z(t,z,w)$ of a compact Riemannian manifold $Z$ and the normalized
Riemannian volume for small values of $t$ and for a fixed element $z\in Z$. We
prove that coefficients in the expansion can be expressed as universal
polynomials in the components of the curvature tensor and its covariant
derivatives at $z$, when they are expressed in terms of normal coordinates. We
describe a method to compute the coefficients, and we use the method to compute
the first three coefficients. The asymptotic expansion is necessary for an
unsupervised machine-learning algorithm called the Diffusion Variational
Autoencoder.

We introduce the Variational Open-Domain (VOD) framework for end-to-end
training and evaluation of retrieval-augmented models (open-domain question
answering and language modelling). We show that the R\'enyi variational bound,
a lower bound to the task marginal likelihood, can be exploited to aid
optimization and use importance sampling to estimate the task log-likelihood
lower bound and its gradients using samples drawn from an auxiliary retriever
(approximate posterior). The framework can be used to train modern
retrieval-augmented systems end-to-end using tractable and consistent estimates
of the R\'enyi variational bound and its gradients. We demonstrate the
framework's versatility by training reader-retriever BERT-based models on
multiple-choice medical exam questions (MedMCQA and USMLE). We registered a new
state-of-the-art for both datasets (MedMCQA: $62.9$\%, USMLE: $55.0$\%). Last,
we show that the retriever part of the learned reader-retriever model trained
on the medical board exam questions can be used in search engines for a medical
knowledge base.

This paper considers the task of learning users' preferences on a
combinatorial set of alternatives, as generally used by online configurators,
for example. In many settings, only a set of selected alternatives during past
interactions is available to the learner. Fargier et al. [2018] propose an
approach to learn, in such a setting, a model of the users' preferences that
ranks previously chosen alternatives as high as possible; and an algorithm to
learn, in this setting, a particular model of preferences: lexicographic
preferences trees (LP-trees). In this paper, we study complexity-theoretical
problems related to this approach. We give an upper bound on the sample
complexity of learning an LP-tree, which is logarithmic in the number of
attributes. We also prove that computing the LP tree that minimises the
empirical risk can be done in polynomial time when restricted to the class of
linear LP-trees.

Advanced feedforward control methods enable mechatronic systems to perform
varying motion tasks with extreme accuracy and throughput. The aim of this
paper is to develop a data-driven feedforward controller that addresses input
nonlinearities, which are common in typical applications such as semiconductor
back-end equipment. The developed method consists of parametric inverse-model
feedforward that is optimized for tracking error reduction by exploiting ideas
from iterative learning control. Results on a simulated set-up indicate
improved performance over existing identification methods for systems with
nonlinearities at the input.

Estimating causal effects from observational data in the presence of latent
variables sometimes leads to spurious relationships which can be misconceived
as causal. This is an important issue in many fields such as finance and
climate science. We propose Sequential Causal Effect Variational Autoencoder
(SCEVAE), a novel method for time series causality analysis under hidden
confounding. It is based on the CEVAE framework and recurrent neural networks.
The causal link's intensity of the confounded variables is calculated by using
direct causal criteria based on Pearl's do-calculus. We show the efficacy of
SCEVAE by applying it to synthetic datasets with both linear and nonlinear
causal links. Furthermore, we apply our method to real aerosol-cloud-climate
observation data. We compare our approach to a time series deconfounding method
with and without substitute confounders on the synthetic data. We demonstrate
that our method performs better by comparing both methods to the ground truth.
In the case of real data, we use the expert knowledge of causal links and show
how the use of correct proxy variables aids data reconstruction.

This study compares sequential image classification methods based on
recurrent neural networks. We describe methods based on recurrent neural
networks such as Long-Short-Term memory(LSTM), bidirectional Long-Short-Term
memory(BiLSTM) architectures, etc. We also review the state-of-the-art
sequential image classification architectures. We mainly focus on LSTM, BiLSTM,
temporal convolution network, and independent recurrent neural network
architecture in the study. It is known that RNN lacks in learning long-term
dependencies in the input sequence. We use a simple feature construction method
using orthogonal Ramanujan periodic transform on the input sequence.
Experiments demonstrate that if these features are given to LSTM or BiLSTM
networks, the performance increases drastically.
  Our focus in this study is to increase the training accuracy simultaneously
reducing the training time for the LSTM and BiLSTM architecture, but not on
pushing the state-of-the-art results, so we use simple LSTM/BiLSTM
architecture. We compare sequential input with the constructed feature as input
to single layer LSTM and BiLSTM network for MNIST and CIFAR datasets. We
observe that sequential input to the LSTM network with 128 hidden unit training
for five epochs results in training accuracy of 33% whereas constructed
features as input to the same LSTM network results in training accuracy of 90%
with 1/3 lesser time.

Person search is an integrated task of multiple sub-tasks such as
foreground/background classification, bounding box regression and person
re-identification. Therefore, person search is a typical multi-task learning
problem, especially when solved in an end-to-end manner. Recently, some works
enhance person search features by exploiting various auxiliary information,
e.g. person joint keypoints, body part position, attributes, etc., which brings
in more tasks and further complexifies a person search model. The inconsistent
convergence rate of each task could potentially harm the model optimization. A
straightforward solution is to manually assign different weights to different
tasks, compensating for the diverse convergence rates. However, given the
special case of person search, i.e. with a large number of tasks, it is
impractical to weight the tasks manually. To this end, we propose a Grouped
Adaptive Loss Weighting (GALW) method which adjusts the weight of each task
automatically and dynamically. Specifically, we group tasks according to their
convergence rates. Tasks within the same group share the same learnable weight,
which is dynamically assigned by considering the loss uncertainty. Experimental
results on two typical benchmarks, CUHK-SYSU and PRW, demonstrate the

We propose an approach based on function evaluations and Bayesian inference
to extract higher-order differential information of objective functions {from a
given ensemble of particles}. Pointwise evaluation $\{V(x^i)\}_i$ of some
potential $V$ in an ensemble $\{x^i\}_i$ contains implicit information about
first or higher order derivatives, which can be made explicit with little
computational effort (ensemble-based gradient inference -- EGI). We suggest to
use this information for the improvement of established ensemble-based
numerical methods for optimization and sampling such as Consensus-based
optimization and Langevin-based samplers. Numerical studies indicate that the
augmented algorithms are often superior to their gradient-free variants, in
particular the augmented methods help the ensembles to escape their initial
domain, to explore multimodal, non-Gaussian settings and to speed up the
collapse at the end of optimization dynamics.}
  The code for the numerical examples in this manuscript can be found in the
paper's Github repository
(https://github.com/MercuryBench/ensemble-based-gradient.git).

Large-scale place recognition is a fundamental but challenging task, which
plays an increasingly important role in autonomous driving and robotics.
Existing methods have achieved acceptable good performance, however, most of
them are concentrating on designing elaborate global descriptor learning
network structures. The importance of feature generalization and descriptor
post-enhancing has long been neglected. In this work, we propose a novel method
named GIDP to learn a Good Initialization and Inducing Descriptor Poseenhancing
for Large-scale Place Recognition. In particular, an unsupervised momentum
contrast point cloud pretraining module and a reranking-based descriptor
post-enhancing module are proposed respectively in GIDP. The former aims at
learning a good initialization for the point cloud encoding network before
training the place recognition model, while the later aims at post-enhancing
the predicted global descriptor through reranking at inference time. Extensive
experiments on both indoor and outdoor datasets demonstrate that our method can
achieve state-of-the-art performance using simple and general point cloud
encoding backbones.

Prompting method is regarded as one of the crucial progress for few-shot
nature language processing. Recent research on prompting moves from discrete
tokens based ``hard prompts'' to continuous ``soft prompts'', which employ
learnable vectors as pseudo prompt tokens and achieve better performance.
Though showing promising prospects, these soft-prompting methods are observed
to rely heavily on good initialization to take effect. Unfortunately, obtaining
a perfect initialization for soft prompts requires understanding of inner
language models working and elaborate design, which is no easy task and has to
restart from scratch for each new task. To remedy this, we propose a
generalized soft prompting method called MetaPrompting, which adopts the
well-recognized model-agnostic meta-learning algorithm to automatically find
better prompt initialization that facilitates fast adaptation to new prompting
tasks.Extensive experiments show MetaPrompting tackles soft prompt
initialization problem and brings significant improvement on four different
datasets (over 6 points improvement in accuracy for 1-shot setting), achieving
new state-of-the-art performance.

Conversational machine reading comprehension (CMRC) aims to assist computers
to understand an natural language text and thereafter engage in a multi-turn
conversation to answer questions related to the text. Existing methods
typically require three steps: (1) decision making based on entailment
reasoning; (2) span extraction if required by the above decision; (3) question
rephrasing based on the extracted span. However, for nearly all these methods,
the span extraction and question rephrasing steps cannot fully exploit the
fine-grained entailment reasoning information in decision making step because
of their relative independence, which will further enlarge the information gap
between decision making and question phrasing. Thus, to tackle this problem, we
propose a novel end-to-end framework for conversational machine reading
comprehension based on shared parameter mechanism, called entailment reasoning
T5 (ET5). Despite the lightweight of our proposed framework, experimental
results show that the proposed ET5 achieves new state-of-the-art results on the
ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are
publicly available at https://github.com/Yottaxx/ET5.

We consider a novel formulation of the problem of Active Few-Shot
Classification (AFSC) where the objective is to classify a small, initially
unlabeled, dataset given a very restrained labeling budget. This problem can be
seen as a rival paradigm to classical Transductive Few-Shot Classification
(TFSC), as both these approaches are applicable in similar conditions. We first
propose a methodology that combines statistical inference, and an original
two-tier active learning strategy that fits well into this framework. We then
adapt several standard vision benchmarks from the field of TFSC. Our
experiments show the potential benefits of AFSC can be substantial, with gains
in average weighted accuracy of up to 10% compared to state-of-the-art TFSC
methods for the same labeling budget. We believe this new paradigm could lead
to new developments and standards in data-scarce learning settings.

Fight detection in videos is an emerging deep learning application with
today's prevalence of surveillance systems and streaming media. Previous work
has largely relied on action recognition techniques to tackle this problem. In
this paper, we propose a simple but effective method that solves the task from
a new perspective: we design the fight detection model as a composition of an
action-aware feature extractor and an anomaly score generator. Also,
considering that collecting frame-level labels for videos is too laborious, we
design a weakly supervised two-stage training scheme, where we utilize
multiple-instance-learning loss calculated on video-level labels to train the
score generator, and adopt the self-training technique to further improve its
performance. Extensive experiments on a publicly available large-scale dataset,
UBI-Fights, demonstrate the effectiveness of our method, and the performance on
the dataset exceeds several previous state-of-the-art approaches. Furthermore,
we collect a new dataset, VFD-2000, that specializes in video fight detection,
with a larger scale and more scenarios than existing datasets. The
implementation of our method and the proposed dataset will be publicly
available at https://github.com/Hepta-Col/VideoFightDetection.

Recently, to improve the unsupervised image retrieval performance, plenty of
unsupervised hashing methods have been proposed by designing a semantic
similarity matrix, which is based on the similarities between image features
extracted by a pre-trained CNN model. However, most of these methods tend to
ignore high-level abstract semantic concepts contained in images. Intuitively,
concepts play an important role in calculating the similarity among images. In
real-world scenarios, each image is associated with some concepts, and the
similarity between two images will be larger if they share more identical
concepts. Inspired by the above intuition, in this work, we propose a novel
Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which
leverages a VLP model to construct a high-quality similarity matrix.
Specifically, a set of randomly chosen concepts is first collected. Then, by
employing a vision-language pretraining (VLP) model with the prompt engineering
which has shown strong power in visual representation learning, the set of
concepts is denoised according to the training images. Next, the proposed
method UHSCM applies the VLP model with prompting again to mine the concept
distribution of each image and construct a high-quality semantic similarity
matrix based on the mined concept distributions. Finally, with the semantic
similarity matrix as guiding information, a novel hashing loss with a modified
contrastive loss based regularization item is proposed to optimize the hashing
network. Extensive experiments on three benchmark datasets show that the
proposed method outperforms the state-of-the-art baselines in the image
retrieval task.

Prerequisites can play a crucial role in users' decision-making yet
recommendation systems have not fully utilized such contextual background
knowledge. Traditional recommendation systems (RS) mostly enrich user-item
interactions where the context consists of static user profiles and item
descriptions, ignoring the contextual logic and constraints that underlie them.
For example, an RS may recommend an item on the condition that the user has
interacted with another item as its prerequisite. Modeling prerequisite context
from conceptual side information can overcome this weakness. We propose
Prerequisite Driven Recommendation (PDR), a generic context-aware framework
where prerequisite context is explicitly modeled to facilitate recommendation.
We first design a Prerequisite Knowledge Linking (PKL) algorithm, to curate
datasets facilitating PDR research. Employing it, we build a 75k+ high-quality
prerequisite concept dataset which spans three domains. We then contribute
PDRS, a neural instantiation of PDR. By jointly optimizing both the
prerequisite learning and recommendation tasks through multi-layer perceptrons,
we find PDRS consistently outperforms baseline models in all three domains, by
an average margin of 7.41%. Importantly, PDRS performs especially well in
cold-start scenarios with improvements of up to 17.65%.

Online continual learning, especially when task identities and task
boundaries are unavailable, is a challenging continual learning setting. One
representative kind of methods for online continual learning is replay-based
methods, in which a replay buffer called memory is maintained to keep a small
part of past samples for overcoming catastrophic forgetting. When tackling with
online continual learning, most existing replay-based methods focus on
single-label problems in which each sample in the data stream has only one
label. But multi-label problems may also happen in the online continual
learning setting in which each sample may have more than one label. In the
online setting with multi-label samples, the class distribution in data stream
is typically highly imbalanced, and it is challenging to control class
distribution in memory since changing the number of samples belonging to one
class may affect the number of samples belonging to other classes. But class
distribution in memory is critical for replay-based memory to get good
performance, especially when the class distribution in data stream is highly
imbalanced. In this paper, we propose a simple but effective method, called
optimizing class distribution in memory (OCDM), for multi-label online
continual learning. OCDM formulates the memory update mechanism as an
optimization problem and updates the memory by solving this problem.
Experiments on two widely used multi-label datasets show that OCDM can control
the class distribution in memory well and can outperform other state-of-the-art
methods.

We propose a new sampling strategy, called smart active sapling, for quality
inspections outside the production line. Based on the principles of active
learning a machine learning model decides which samples are sent to quality
inspection. On the one hand, this minimizes the production of scrap parts due
to earlier detection of quality violations. On the other hand, quality
inspection costs are reduced for smooth operation.

Session-based recommendation (SBR) systems aim to utilize the user's
short-term behavior sequence to predict the next item without the detailed user
profile. Most recent works try to model the user preference by treating the
sessions as between-item transition graphs and utilize various graph neural
networks (GNNs) to encode the representations of pair-wise relations among
items and their neighbors. Some of the existing GNN-based models mainly focus
on aggregating information from the view of spatial graph structure, which
ignores the temporal relations within neighbors of an item during message
passing and the information loss results in a sub-optimal problem. Other works
embrace this challenge by incorporating additional temporal information but
lack sufficient interaction between the spatial and temporal patterns. To
address this issue, inspired by the uniformity and alignment properties of
contrastive learning techniques, we propose a novel framework called
Session-based Recommendation with Spatio-Temporal Contrastive Learning Enhanced
GNNs (RESTC). The idea is to supplement the GNN-based main supervised
recommendation task with the temporal representation via an auxiliary
cross-view contrastive learning mechanism. Furthermore, a novel global
collaborative filtering graph (CFG) embedding is leveraged to enhance the
spatial view in the main task. Extensive experiments demonstrate the
significant performance of RESTC compared with the state-of-the-art baselines
e.g., with an improvement as much as 27.08% gain on HR@20 and 20.10% gain on
MRR@20.

Despite their recent success, deep neural networks continue to perform poorly
when they encounter distribution shifts at test time. Many recently proposed
approaches try to counter this by aligning the model to the new distribution
prior to inference. With no labels available this requires unsupervised
objectives to adapt the model on the observed test data. In this paper, we
propose Test-Time Self-Training (TeST): a technique that takes as input a model
trained on some source data and a novel data distribution at test time, and
learns invariant and robust representations using a student-teacher framework.
We find that models adapted using TeST significantly improve over baseline
test-time adaptation algorithms. TeST achieves competitive performance to
modern domain adaptation algorithms, while having access to 5-10x less data at
time of adaption. We thoroughly evaluate a variety of baselines on two tasks:
object detection and image segmentation and find that models adapted with TeST.
We find that TeST sets the new state-of-the art for test-time domain adaptation

Glaucoma is a severe blinding disease, for which automatic detection methods
are urgently needed to alleviate the scarcity of ophthalmologists. Many works
have proposed to employ deep learning methods that involve the segmentation of
optic disc and cup for glaucoma detection, in which the segmentation process is
often considered merely as an upstream sub-task. The relationship between
fundus images and segmentation masks in terms of joint decision-making in
glaucoma assessment is rarely explored. We propose a novel segmentation-based
information extraction and amalgamation method for the task of glaucoma
detection, which leverages the robustness of segmentation masks without
disregarding the rich information in the original fundus images. Experimental
results on both private and public datasets demonstrate that our proposed
method outperforms all models that utilize solely either fundus images or
masks.

Background. The rapid and growing popularity of machine learning (ML)
applications has led to an increasing interest in MLOps, that is, the practice
of continuous integration and deployment (CI/CD) of ML-enabled systems. Aims.
Since changes may affect not only the code but also the ML model parameters and
the data themselves, the automation of traditional CI/CD needs to be extended
to manage model retraining in production. Method. In this paper, we present an
initial investigation of the MLOps practices implemented in a set of ML-enabled
systems retrieved from GitHub, focusing on GitHub Actions and CML, two
solutions to automate the development workflow. Results. Our preliminary
results suggest that the adoption of MLOps workflows in open-source GitHub
projects is currently rather limited. Conclusions. Issues are also identified,
which can guide future research work.

Auditing the information leakage of latent sensitive features during the
transborder data flow has attracted sufficient attention from global digital
regulators. However, there is missing a technical approach for the audit
practice due to two technical challenges. Firstly, there is a lack of theory
and tools for measuring the information of sensitive latent features in a
dataset. Secondly, the transborder data flow involves multi-stakeholders with
diverse interests, which means the audit must be trustless. Despite the
tremendous efforts in protecting data privacy, an important issue that has long
been neglected is that the transmitted data in data flows can leak other
regulated information that is not explicitly contained in the data, leading to
unaware information leakage risks. To unveil such risks trustfully before the
actual data transfer, we propose FIAT, a Fine-grained Information Audit system
for Trustless transborder data flow. In FIAT, we use a learning approach to
quantify the amount of information leakage, while the technologies of
zero-knowledge proof and smart contracts are applied to provide trustworthy and
privacy-preserving auditing results. Experiments show that large information
leakage can boost the predictability of uninvolved information using simple
machine-learning models, revealing the importance of information auditing.
Further performance benchmarking also validates the efficiency and scalability
of the FIAT auditing system.

Realistic dynamic garments on animated characters have many AR/VR
applications. While authoring such dynamic garment geometry is still a
challenging task, data-driven simulation provides an attractive alternative,
especially if it can be controlled simply using the motion of the underlying
character. In this work, we focus on motion guided dynamic 3D garments,
especially for loose garments. In a data-driven setup, we first learn a
generative space of plausible garment geometries. Then, we learn a mapping to
this space to capture the motion dependent dynamic deformations, conditioned on
the previous state of the garment as well as its relative position with respect
to the underlying body. Technically, we model garment dynamics, driven using
the input character motion, by predicting per-frame local displacements in a
canonical state of the garment that is enriched with frame-dependent skinning
weights to bring the garment to the global space. We resolve any remaining
per-frame collisions by predicting residual local displacements. The resultant
garment geometry is used as history to enable iterative rollout prediction. We
demonstrate plausible generalization to unseen body shapes and motion inputs,
and show improvements over multiple state-of-the-art alternatives.

Image dehazing is an active topic in low-level vision, and many image
dehazing networks have been proposed with the rapid development of deep
learning. Although these networks' pipelines work fine, the key mechanism to
improving image dehazing performance remains unclear. For this reason, we do
not target to propose a dehazing network with fancy modules; rather, we make
minimal modifications to popular U-Net to obtain a compact dehazing network.
Specifically, we swap out the convolutional blocks in U-Net for residual blocks
with the gating mechanism, fuse the feature maps of main paths and skip
connections using the selective kernel, and call the resulting U-Net variant
gUNet. As a result, with a significantly reduced overhead, gUNet is superior to
state-of-the-art methods on multiple image dehazing datasets. Finally, we
verify these key designs to the performance gain of image dehazing networks
through extensive ablation studies.

In contrast to conventional closed-set recognition, open-set recognition
(OSR) assumes the presence of an unknown class, which is not seen to a model
during training. One predominant approach in OSR is metric learning, where a
model is trained to separate the inter-class representations of known class
data. Numerous works in OSR reported that, even though the models are trained
only with the known class data, the models become aware of the unknown, and
learn to separate the unknown class representations from the known class
representations. This paper analyzes this emergent phenomenon by observing the
Jacobian norm of representation. We theoretically show that minimizing the
intra-class distances within the known set reduces the Jacobian norm of known
class representations while maximizing the inter-class distances within the
known set increases the Jacobian norm of the unknown class. The closed-set
metric learning thus separates the unknown from the known by forcing their
Jacobian norm values to differ. We empirically validate our theoretical
framework with ample pieces of evidence using standard OSR datasets. Moreover,
under our theoretical framework, we explain how the standard deep learning
techniques can be helpful for OSR and use the framework as a guiding principle
to develop an effective OSR model.

GAMMA Challenge is organized to encourage the AI models to screen the
glaucoma from a combination of 2D fundus image and 3D optical coherence
tomography volume, like the ophthalmologists.

People rely on news to know what is happening around the world and inform
their daily lives. In today's world, when the proliferation of fake news is
rampant, having a large-scale and high-quality source of authentic news
articles with the published category information is valuable to learning
authentic news' Natural Language syntax and semantics. As part of this work, we
present a News Category Dataset that contains around 210k news headlines from
the year 2012 to 2022 obtained from HuffPost, along with useful metadata to
enable various NLP tasks. In this paper, we also produce some novel insights
from the dataset and describe various existing and potential applications of

Timely and accurate detection of anomalies in power electronics is becoming
increasingly critical for maintaining complex production systems. Robust and
explainable strategies help decrease system downtime and preempt or mitigate
infrastructure cyberattacks. This work begins by explaining the types of
uncertainty present in current datasets and machine learning algorithm outputs.
Three techniques for combating these uncertainties are then introduced and
analyzed. We further present two anomaly detection and classification
approaches, namely the Matrix Profile algorithm and anomaly transformer, which
are applied in the context of a power electronic converter dataset.
Specifically, the Matrix Profile algorithm is shown to be well suited as a
generalizable approach for detecting real-time anomalies in streaming
time-series data. The STUMPY python library implementation of the iterative
Matrix Profile is used for the creation of the detector. A series of custom
filters is created and added to the detector to tune its sensitivity, recall,
and detection accuracy. Our numerical results show that, with simple parameter
tuning, the detector provides high accuracy and performance in a variety of
fault scenarios.

Repetition, a basic form of artistic creation, appears in most musical works
and delivers enthralling aesthetic experiences.

Cervical cancer is one of the most common types of cancer found in females.
It contributes to 6-29% of all cancers in women. It is caused by the Human
Papilloma Virus (HPV). The 5-year survival chances of cervical cancer range
from 17%-92% depending upon the stage at which it is detected. Early detection
of this disease helps in better treatment and survival rate of the patient.
Many deep learning algorithms are being used for the detection of cervical
cancer these days. A special category of deep learning techniques known as
Generative Adversarial Networks (GANs) are catching up with speed in the
screening, detection, and classification of cervical cancer. In this work, we
present a detailed analysis of the recent trends relating to the use of various
GAN models, their applications, and the evaluation metrics used for their
performance evaluation in the field of cervical cancer imaging.

Uncertainty on human behaviors poses a significant challenge to autonomous
driving in crowded urban environments. The partially observable Markov decision
processes (POMDPs) offer a principled framework for planning under uncertainty,
often leveraging Monte Carlo sampling to achieve online performance for complex
tasks. However, sampling also raises safety concerns by potentially missing
critical events. To address this, we propose a new algorithm, LEarning
Attention over Driving bEhavioRs (LEADER), that learns to attend to critical
human behaviors during planning. LEADER learns a neural network generator to
provide attention over human behaviors in real-time situations. It integrates
the attention into a belief-space planner, using importance sampling to bias
reasoning towards critical events. To train the algorithm, we let the attention
generator and the planner form a min-max game. By solving the min-max game,
LEADER learns to perform risk-aware planning without human labeling.

Heterogeneous graph learning has drawn significant attentions in recent
years, due to the success of graph neural networks (GNNs) and the broad
applications of heterogeneous information networks. Various heterogeneous graph
neural networks have been proposed to generalize GNNs for processing the
heterogeneous graphs. Unfortunately, these approaches model the heterogeneity
via various complicated modules. This paper aims to propose a simple yet
efficient framework to make the homogeneous GNNs have adequate ability to
handle heterogeneous graphs. Specifically, we propose Relation Embedding based
Graph Neural Networks (RE-GNNs), which employ only one parameter per relation
to embed the importance of edge type relations and self-loop connections. To
optimize these relation embeddings and the other parameters simultaneously, a
gradient scaling factor is proposed to constrain the embeddings to converge to
suitable values. Besides, we theoretically demonstrate that our RE-GNNs have
more expressive power than the meta-path based heterogeneous GNNs. Extensive
experiments on the node classification tasks validate the effectiveness of our

Domain adaptation is an important challenge for neural machine translation.
However, the traditional fine-tuning solution requires multiple extra training
and yields a high cost. In this paper, we propose a non-tuning paradigm,
resolving domain adaptation with a prompt-based method. Specifically, we
construct a bilingual phrase-level database and retrieve relevant pairs from it
as a prompt for the input sentences. By utilizing Retrieved Phrase-level
Prompts (RePP), we effectively boost the translation quality. Experiments show
that our method improves domain-specific machine translation for 6.2 BLEU
scores and improves translation constraints for 11.5% accuracy without
additional training.

Multi-Object Tracking (MOT) is one of the most fundamental computer vision
tasks which contributes to a variety of video analysis applications. Despite
the recent promising progress, current MOT research is still limited to a fixed
sampling frame rate of the input stream. In fact, we empirically find that the
accuracy of all recent state-of-the-art trackers drops dramatically when the
input frame rate changes. For a more intelligent tracking solution, we shift
the attention of our research work to the problem of Frame Rate Agnostic MOT
(FraMOT). In this paper, we propose a Frame Rate Agnostic MOT framework with
Periodic training Scheme (FAPS) to tackle the FraMOT problem for the first
time. Specifically, we propose a Frame Rate Agnostic Association Module (FAAM)
that infers and encodes the frame rate information to aid identity matching
across multi-frame-rate inputs, improving the capability of the learned model
in handling complex motion-appearance relations in FraMOT. Besides, the
association gap between training and inference is enlarged in FraMOT because
those post-processing steps not included in training make a larger difference
in lower frame rate scenarios. To address it, we propose Periodic Training
Scheme (PTS) to reflect all post-processing steps in training via tracking
pattern matching and fusion. Along with the proposed approaches, we make the
first attempt to establish an evaluation method for this new task of FraMOT in
two different modes, i.e., known frame rate and unknown frame rate, aiming to
handle a more complex situation. The quantitative experiments on the
challenging MOT datasets (FraMOT version) have clearly demonstrated that the
proposed approaches can handle different frame rates better and thus improve
the robustness against complicated scenarios.

What is the ideal regression (if any) for estimating average causal effects?
We study this question in the setting of discrete covariates, deriving
expressions for the finite-sample variance of various stratification
estimators. This approach clarifies the fundamental statistical phenomena
underlying many widely-cited results. Our exposition combines insights from
three distinct methodological traditions for studying causal effect estimation:
potential outcomes, causal diagrams, and structural models with additive
errors.

The universal approximation property (UAP) of neural networks is fundamental
for deep learning, and it is well known that wide neural networks are universal
approximators of continuous functions within both the $L^p$ norm and the
continuous/uniform norm. However, the exact minimum width, $w_{\min}$, for the
UAP has not been studied thoroughly. Recently, using a
decoder-memorizer-encoder scheme, \citet{Park2021Minimum} found that $w_{\min}
= \max(d_x+1,d_y)$ for both the $L^p$-UAP of ReLU networks and the $C$-UAP of
ReLU+STEP networks, where $d_x,d_y$ are the input and output dimensions,
respectively. In this paper, we consider neural networks with an arbitrary set
of activation functions. We prove that both $C$-UAP and $L^p$-UAP for functions
on compact domains share a universal lower bound of the minimal width; that is,
$w^*_{\min} = \max(d_x,d_y)$. In particular, the critical width, $w^*_{\min}$,
for $L^p$-UAP can be achieved by leaky-ReLU networks, provided that the input
or output dimension is larger than one. Our construction is based on the
approximation power of neural ordinary differential equations and the ability
to approximate flow maps by neural networks. The nonmonotone or discontinuous
activation functions case and the one-dimensional case are also discussed.

Objective: Dynamic cone-beam CT (CBCT) imaging is highly desired in
image-guided radiation therapy to provide volumetric images with high spatial
and temporal resolutions to enable applications including tumor motion
tracking/prediction and intra-delivery dose calculation/accumulation. However,
the dynamic CBCT reconstruction is a substantially challenging spatiotemporal
inverse problem, due to the extremely limited projection sample available for
each CBCT reconstruction (one projection for one CBCT volume). Approach: We
developed a simultaneous spatial and temporal implicit neural representation
(STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image
and the evolution of its motion into spatial and temporal multi-layer
perceptrons (MLPs), and iteratively optimized the neuron weighting of the MLPs
via acquired projections to represent the dynamic CBCT series. In addition to
the MLPs, we also introduced prior knowledge, in form of principal component
analysis (PCA)-based patient-specific motion models, to reduce the complexity
of the temporal INRs to address the ill-conditioned dynamic CBCT reconstruction
problem. We used the extended cardiac torso (XCAT) phantom to simulate
different lung motion/anatomy scenarios to evaluate STINR. The scenarios
contain motion variations including motion baseline shifts, motion
amplitude/frequency variations, and motion non-periodicity. The scenarios also
contain inter-scan anatomical variations including tumor shrinkage and tumor
position change. Main results: STINR shows consistently higher image
reconstruction and motion tracking accuracy than a traditional PCA-based method
and a polynomial-fitting based neural representation method. STINR tracks the
lung tumor to an averaged center-of-mass error of <2 mm, with corresponding
relative errors of reconstructed dynamic CBCTs <10%.

The challenge of labeling large example datasets for computer vision
continues to limit the availability and scope of image repositories. This
research provides a new method for automated data collection, curation,
labeling, and iterative training with minimal human intervention for the case
of overhead satellite imagery and object detection. The new operational scale
effectively scanned an entire city (68 square miles) in grid search and yielded
a prediction of car color from space observations. A partially trained yolov5
model served as an initial inference seed to output further, more refined model
predictions in iterative cycles. Soft labeling here refers to accepting label
noise as a potentially valuable augmentation to reduce overfitting and enhance
generalized predictions to previously unseen test data. The approach takes
advantage of a real-world instance where a cropped image of a car can
automatically receive sub-type information as white or colorful from pixel
values alone, thus completing an end-to-end pipeline without overdependence on
human labor.

Recent research has proposed a series of specialized optimization algorithms
for deep multi-task models. It is often claimed that these multi-task
optimization (MTO) methods yield solutions that are superior to the ones found
by simply optimizing a weighted average of the task losses. In this paper, we
perform large-scale experiments on a variety of language and vision tasks to
examine the empirical validity of these claims. We show that, despite the added
design and computational complexity of these algorithms, MTO methods do not
yield any performance improvements beyond what is achievable via traditional
optimization approaches. We highlight alternative strategies that consistently
yield improvements to the performance profile and point out common training
pitfalls that might cause suboptimal results. Finally, we outline challenges in
reliably evaluating the performance of MTO algorithms and discuss potential
solutions.

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment
(MCI) associated with brain changes remains a challenging task. Recent studies
have demonstrated that combination of multi-modality imaging techniques can
better reflect pathological characteristics and contribute to more accurate
diagnosis of AD and MCI. In this paper, we propose a novel tensor-based
multi-modality feature selection and regression method for diagnosis and
biomarker identification of AD and MCI from normal controls. Specifically, we
leverage the tensor structure to exploit high-level correlation information
inherent in the multi-modality data, and investigate tensor-level sparsity in
the multilinear regression model. We present the practical advantages of our
method for the analysis of ADNI data using three imaging modalities (VBM- MRI,
FDG-PET and AV45-PET) with clinical parameters of disease severity and
cognitive scores. The experimental results demonstrate the superior performance
of our proposed method against the state-of-the-art for the disease diagnosis
and the identification of disease-specific regions and modality-related
differences. The code for this work is publicly available at
https://github.com/junfish/BIOS22.

Kullback-Leibler (KL) divergence is widely used for variational inference of
Bayesian Neural Networks (BNNs). However, the KL divergence has limitations
such as unboundedness and asymmetry. We examine the Jensen-Shannon (JS)
divergence that is more general, bounded, and symmetric. We formulate a novel
loss function for BNNs based on the geometric JS divergence and show that the
conventional KL divergence-based loss function is its special case. We evaluate
the divergence part of the proposed loss function in a closed form for a
Gaussian prior. For any other general prior, Monte Carlo approximations can be
used. We provide algorithms for implementing both of these cases. We
demonstrate that the proposed loss function offers an additional parameter that
can be tuned to control the degree of regularisation. We derive the conditions
under which the proposed loss function regularises better than the KL
divergence-based loss function for Gaussian priors and posteriors. We
demonstrate performance improvements over the state-of-the-art KL
divergence-based BNN on the classification of a noisy CIFAR data set and a
biased histopathology data set.

Efficient custom pooling techniques that can aggressively trim the dimensions
of a feature map and thereby reduce inference compute and memory footprint for
resource-constrained computer vision applications have recently gained
significant traction. However, prior pooling works extract only the local
context of the activation maps, limiting their effectiveness. In contrast, we
propose a novel non-local self-attentive pooling method that can be used as a
drop-in replacement to the standard pooling layers, such as max/average pooling
or strided convolution. The proposed self-attention module uses patch
embedding, multi-head self-attention, and spatial-channel restoration, followed
by sigmoid activation and exponential soft-max. This self-attention mechanism
efficiently aggregates dependencies between non-local activation patches during
down-sampling. Extensive experiments on standard object classification and
detection tasks with various convolutional neural network (CNN) architectures
demonstrate the superiority of our proposed mechanism over the state-of-the-art
(SOTA) pooling techniques. In particular, we surpass the test accuracy of
existing pooling techniques on different variants of MobileNet-V2 on ImageNet
by an average of 1.2%. With the aggressive down-sampling of the activation maps
in the initial layers (providing up to 22x reduction in memory consumption),
our approach achieves 1.43% higher test accuracy compared to SOTA techniques
with iso-memory footprints. This enables the deployment of our models in
memory-constrained devices, such as micro-controllers (without losing
significant accuracy), because the initial activation maps consume a
significant amount of on-chip memory for high-resolution images required for
complex vision tasks. Our proposed pooling method also leverages the idea of
channel pruning to further reduce memory footprints.

The ultimate purpose of the statistical analysis of ordinal patterns is to
characterize the distribution of the features they induce. In particular,
knowing the joint distribution of the pair Entropy-Statistical Complexity for a
large class of time series models would allow statistical tests that are
unavailable to date. Working in this direction, we characterize the asymptotic
distribution of the empirical Shannon's Entropy for any model under which the
true normalized Entropy is neither zero nor one. We obtain the asymptotic
distribution from the Central Limit Theorem (assuming large time series), the
Multivariate Delta Method, and a third-order correction of its mean value. We
discuss the applicability of other results (exact, first-, and second-order
corrections) regarding their accuracy and numerical stability. Within a general
framework for building test statistics about Shannon's Entropy, we present a
bilateral test that verifies if there is enough evidence to reject the
hypothesis that two signals produce ordinal patterns with the same Shannon's
Entropy. We applied this bilateral test to the daily maximum temperature time
series from three cities (Dublin, Edinburgh, and Miami) and obtained sensible
results.

This paper proposes a probabilistic Bayesian formulation for system
identification (ID) and estimation of nonseparable Hamiltonian systems using
stochastic dynamic models. Nonseparable Hamiltonian systems arise in models
from diverse science and engineering applications such as astrophysics,
robotics, vortex dynamics, charged particle dynamics, and quantum mechanics.
The numerical experiments demonstrate that the proposed method recovers
dynamical systems with higher accuracy and reduced predictive uncertainty
compared to state-of-the-art approaches. The results further show that accurate
predictions far outside the training time interval in the presence of sparse
and noisy measurements are possible, which lends robustness and
generalizability to the proposed approach. A quantitative benefit is prediction
accuracy with less than 10% relative error for more than 12 times longer than a
comparable least-squares-based method on a benchmark problem.

Due to recent climate changes, we have seen more frequent and severe
wildfires in the United States. Predicting wildfires is critical for natural
disaster prevention and mitigation. Advances in technologies in data processing
and communication enabled us to access remote sensing data. With the remote
sensing data, valuable spatiotemporal statistical models can be created and
used for resource management practices. This paper proposes a distributed
learning framework that shares local data collected in ten locations in the
western USA throughout the local agents. The local agents aim to predict
wildfire grid maps one, two, three, and four weeks in advance while online
processing the remote sensing data stream. The proposed model has distinct
features that address the characteristic need in prediction evaluations,
including dynamic online estimation and time-series modeling. Local fire event
triggers are not isolated between locations, and there are confounding factors
when local data is analyzed due to incomplete state observations. Compared to
existing approaches that do not account for incomplete state observation within
wildfire time-series data, on average, we can achieve higher prediction

When evaluating the performance of clinical machine learning models, one must
consider the deployment population. When the population of patients with
observed labels is only a subset of the deployment population (label
selection), standard model performance estimates on the observed population may
be misleading. In this study we describe three classes of label selection and
simulate five causally distinct scenarios to assess how particular selection
mechanisms bias a suite of commonly reported binary machine learning model
performance metrics. Simulations reveal that when selection is affected by
observed features, naive estimates of model discrimination may be misleading.
When selection is affected by labels, naive estimates of calibration fail to
reflect reality. We borrow traditional weighting estimators from causal
inference literature and find that when selection probabilities are properly
specified, they recover full population estimates. We then tackle the
real-world task of monitoring the performance of deployed machine learning
models whose interactions with clinicians feed-back and affect the selection
mechanism of the labels. We train three machine learning models to flag
low-yield laboratory diagnostics, and simulate their intended consequence of
reducing wasteful laboratory utilization. We find that naive estimates of AUROC
on the observed population undershoot actual performance by up to 20%. Such a
disparity could be large enough to lead to the wrongful termination of a
successful clinical decision support tool. We propose an altered deployment
procedure, one that combines injected randomization with traditional weighted
estimates, and find it recovers true model performance.

We present a learning algorithm for human activity recognition in videos. Our
approach is designed for UAV videos, which are mainly acquired from obliquely
placed dynamic cameras that contain a human actor along with background motion.
Typically, the human actors occupy less than one-tenth of the spatial
resolution. Our approach simultaneously harnesses the benefits of frequency
domain representations, a classical analysis tool in signal processing, and
data driven neural networks. We build a differentiable static-dynamic frequency
mask prior to model the salient static and dynamic pixels in the video, crucial
for the underlying task of action recognition. We use this differentiable mask
prior to enable the neural network to intrinsically learn disentangled feature
representations via an identity loss function. Our formulation empowers the
network to inherently compute disentangled salient features within its layers.
Further, we propose a cost-function encapsulating temporal relevance and
spatial content to sample the most important frame within uniformly spaced
video segments. We conduct extensive experiments on the UAV Human dataset and
the NEC Drone dataset and demonstrate relative improvements of 5.72% - 13.00%
over the state-of-the-art and 14.28% - 38.05% over the corresponding baseline
model.

The technical report presents our emotion recognition pipeline for
high-dimensional emotion task (A-VB High) in The ACII Affective Vocal Bursts
(A-VB) 2022 Workshop \& Competition. Our proposed method contains three stages.
Firstly, we extract the latent features from the raw audio signal and its
Mel-spectrogram by self-supervised learning methods. Then, the features from
the raw signal are fed to the self-relation attention and temporal awareness
(SA-TA) module for learning the valuable information between these latent
features. Finally, we concatenate all the features and utilize a
fully-connected layer to predict each emotion's score. By empirical
experiments, our proposed method achieves a mean concordance correlation
coefficient (CCC) of 0.7295 on the test set, compared to 0.5686 on the baseline
model. The code of our method is available at
https://github.com/linhtd812/A-VB2022.

Discipline-based education researchers produce knowledge that aims to help
instructors improve student learning and educational outcomes. Yet, the
information produced may not even reach the educators it is intended to
influence. Prior work has found that instructors often face barriers to
implementing practices in peer-reviewed literature. Some of these barriers are
related to accessing the knowledge in the first place such as difficulty
finding and understanding research and a lack of time to do so. To lower these
barriers, we created an online blog, PERbites, that summarizes recent
discipline-based education research in short posts that use plain language.
Having covered nearly 100 papers to date, we conducted a survey to see if we
were addressing the need we had originally set out to address. We posted a
23-item survey on our website and received 24 usable responses. The results
suggested that readers do generally agree that we are meeting our original
goals. Readers reported that our articles were easier to understand and used
more plain language than a typical discipline-based education research (DBER)
journal article. At the same time, readers thought that all the important
information was still included. Finally, readers said that this approach helped
them keep up with DBER studies and read about papers they otherwise would not
have. However, most readers did not indicate they changed their teaching and
research practice as a result of reading our blog. Our results suggest that
alternative methods of sharing research (e.g., non-peer reviewed publications
or conference talks) can be an effective method of connecting research with
practitioners, and future work should consider how we as a community might
build on these efforts to ensure education research can make meaningful changes
in the classroom.

Context: The identification of bugs within the reported issues in an issue
tracker is crucial for the triage of issues. Machine learning models have shown
promising results regarding the performance of automated issue type prediction.
However, we have only limited knowledge beyond our assumptions how such models
identify bugs. LIME and SHAP are popular technique to explain the predictions
of classifiers.
  Objective: We want to understand if machine learning models provide
explanations for the classification that are reasonable to us as humans and
align with our assumptions of what the models should learn. We also want to
know if the prediction quality is correlated with the quality of explanations.
  Method: We conduct a study where we rate LIME and SHAP explanations based on
their quality of explaining the outcome of an issue type prediction model. For
this, we rate the quality of the explanations themselves, i.e., if they align
with our expectations and if they help us to understand the underlying machine
learning model.

A Stackelberg congestion game (SCG) is a bilevel program in which a leader
aims to maximize their own gain by anticipating and manipulating the
equilibrium state at which followers settle by playing a congestion game.
Large-scale SCGs are well known for their intractability and complexity. This
study approaches SCGs through differentiable programming, which marries the
latest developments in machine learning with conventional methodologies. The
core idea centers on representing the lower-level equilibrium problem using an
evolution path formed by the imitative logit dynamics. It enables the use of
automatic differentiation over the evolution path towards equilibrium, leading
to a double-loop gradient descent algorithm. We further show the fixation on
the lower-level equilibrium may be a self-imposed computational obstacle.
Instead, the leader may only look ahead along the followers' evolution path for
a few steps, while updating their decisions in sync with the followers through
a co-evolution process. The revelation gives rise to a single-loop algorithm
that is more efficient in terms of both memory consumption and computation
time. Through numerical experiments that cover a wide range of benchmark
problems, we find the single-loop algorithm consistently strikes a good balance
between solution quality and efficiency, outperforming not only the standard
double-loop implementation but also other methods from the literature.
Importantly, our results highlight both the wastefulness of "full anticipation"
and the peril of "zero anticipation". If a quick-and-dirty heuristic is needed
for solving a really large SCG, the proposed single-loop algorithm with a
one-step look-ahead makes an ideal candidate.

Sparsity has become one of the promising methods to compress and accelerate
Deep Neural Networks (DNNs). Among different categories of sparsity, structured
sparsity has gained more attention due to its efficient execution on modern
accelerators. Particularly, N:M sparsity is attractive because there are
already hardware accelerator architectures that can leverage certain forms of
N:M structured sparsity to yield higher compute-efficiency. In this work, we
focus on N:M sparsity and extensively study and evaluate various training
recipes for N:M sparsity in terms of the trade-off between model accuracy and
compute cost (FLOPs). Building upon this study, we propose two new decay-based
pruning methods, namely "pruning mask decay" and "sparse structure decay". Our
evaluations indicate that these proposed methods consistently deliver
state-of-the-art (SOTA) model accuracy, comparable to unstructured sparsity, on
a Transformer-based model for a translation task. The increase in the accuracy
of the sparse model using the new training recipes comes at the cost of
marginal increase in the total training compute (FLOPs).

Knowledge distillation (KD) is an effective tool for compressing deep
classification models for edge devices. However, the performance of KD is
affected by the large capacity gap between the teacher and student networks.
Recent methods have resorted to a multiple teacher assistant (TA) setting for
KD, which sequentially decreases the size of the teacher model to relatively
bridge the size gap between these models. This paper proposes a new technique
called Curriculum Expert Selection for Knowledge Distillation (CES-KD) to
efficiently enhance the learning of a compact student under the capacity gap
problem. This technique is built upon the hypothesis that a student network
should be guided gradually using stratified teaching curriculum as it learns
easy (hard) data samples better and faster from a lower (higher) capacity
teacher network. Specifically, our method is a gradual TA-based KD technique
that selects a single teacher per input image based on a curriculum driven by
the difficulty in classifying the image. In this work, we empirically verify
our hypothesis and rigorously experiment with CIFAR-10, CIFAR-100, CINIC-10,
and ImageNet datasets and show improved accuracy on VGG-like models, ResNets,
and WideResNets architectures.

Most existing Time series classification (TSC) models lack interpretability
and are difficult to inspect. Interpretable machine learning models can aid in
discovering patterns in data as well as give easy-to-understand insights to
domain specialists. In this study, we present Neuro-Symbolic Time Series
Classification (NSTSC), a neuro-symbolic model that leverages signal temporal
logic (STL) and neural network (NN) to accomplish TSC tasks using multi-view
data representation and expresses the model as a human-readable, interpretable
formula. In NSTSC, each neuron is linked to a symbolic expression, i.e., an STL
(sub)formula. The output of NSTSC is thus interpretable as an STL formula akin
to natural language, describing temporal and logical relations hidden in the
data. We propose an NSTSC-based classifier that adopts a decision-tree approach
to learn formula structures and accomplish a multiclass TSC task. The proposed
smooth activation functions for wSTL allow the model to be learned in an
end-to-end fashion. We test NSTSC on a real-world wound healing dataset from
mice and benchmark datasets from the UCR time-series repository, demonstrating
that NSTSC achieves comparable performance with the state-of-the-art models.
Furthermore, NSTSC can generate interpretable formulas that match with domain
knowledge.

In the last two decades we are witnessing a huge increase of valuable big
data structured in the form of graphs or networks. To apply traditional machine
learning and data analytic techniques to such data it is necessary to transform
graphs into vector-based representations that preserve the most essential
structural properties of graphs. For this purpose, a large number of graph
embedding methods have been proposed in the literature. Most of them produce
general-purpose embeddings suitable for a variety of applications such as node
clustering, node classification, graph visualisation and link prediction. In
this paper, we propose two novel graph embedding algorithms based on random
walks that are specifically designed for the node classification problem.
Random walk sampling strategies of the proposed algorithms have been designed
to pay special attention to hubs -- high-degree nodes that have the most
critical role for the overall connectedness in large-scale graphs. The proposed
methods are experimentally evaluated by analyzing the classification
performance of three classification algorithms trained on embeddings of
real-world networks. The obtained results indicate that our methods
considerably improve the predictive power of examined classifiers compared to
currently the most popular random walk method for generating general-purpose
graph embeddings (node2vec).

In this paper, we develop a neural network model to predict future human
motion from an observed human motion history. We propose a non-autoregressive
transformer architecture to leverage its parallel nature for easier training
and fast, accurate predictions at test time. The proposed architecture divides
human motion prediction into two parts: 1) the human trajectory, which is the
hip joint 3D position over time and 2) the human pose which is the all other
joints 3D positions over time with respect to a fixed hip joint. We propose to
make the two predictions simultaneously, as the shared representation can
improve the model performance. Therefore, the model consists of two sets of
encoders and decoders. First, a multi-head attention module applied to encoder
outputs improves human trajectory. Second, another multi-head self-attention
module applied to encoder outputs concatenated with decoder outputs facilitates
learning of temporal dependencies. Our model is well-suited for robotic
applications in terms of test accuracy and speed, and compares favorably with
respect to state-of-the-art methods. We demonstrate the real-world
applicability of our work via the Robot Follow-Ahead task, a challenging yet
practical case study for our proposed model.

Several existing works study either adversarial or natural distributional
robustness of deep neural networks separately. In practice, however, models
need to enjoy both types of robustness to ensure reliability. In this work, we
bridge this gap and show that in fact, explicit tradeoffs exist between
adversarial and natural distributional robustness. We first consider a simple
linear regression setting on Gaussian data with disjoint sets of core and
spurious features. In this setting, through theoretical and empirical analysis,
we show that (i) adversarial training with $\ell_1$ and $\ell_2$ norms
increases the model reliance on spurious features; (ii) For $\ell_\infty$
adversarial training, spurious reliance only occurs when the scale of the
spurious features is larger than that of the core features; (iii) adversarial
training can have an unintended consequence in reducing distributional
robustness, specifically when spurious correlations are changed in the new test
domain. Next, we present extensive empirical evidence, using a test suite of
twenty adversarially trained models evaluated on five benchmark datasets
(ObjectNet, RIVAL10, Salient ImageNet-1M, ImageNet-9, Waterbirds), that
adversarially trained classifiers rely on backgrounds more than their
standardly trained counterparts, validating our theoretical results. We also
show that spurious correlations in training data (when preserved in the test
domain) can improve adversarial robustness, revealing that previous claims that
adversarial vulnerability is rooted in spurious correlations are incomplete.

Deep learning algorithms for predicting neuroimaging data have shown
considerable promise in various applications. Prior work has demonstrated that
deep learning models that take advantage of the data's 3D structure can
outperform standard machine learning on several learning tasks. However, most
prior research in this area has focused on neuroimaging data from adults.
Within the Adolescent Brain and Cognitive Development (ABCD) dataset, a large
longitudinal development study, we examine structural MRI data to predict
gender and identify gender-related changes in brain structure. Results
demonstrate that gender prediction accuracy is exceptionally high (>97%) with
training epochs >200 and that this accuracy increases with age. Brain regions
identified as the most discriminative in the task under study include
predominantly frontal areas and the temporal lobe. When evaluating gender
predictive changes specific to a two-year increase in age, a broader set of
visual, cingulate, and insular regions are revealed. Our findings show a robust
gender-related structural brain change pattern, even over a small age range.
This suggests that it might be possible to study how the brain changes during
adolescence by looking at how these changes are related to different behavioral
and environmental factors.

Batch normalization is widely used in deep learning to normalize intermediate
activations. Deep networks suffer from notoriously increased training
complexity, mandating careful initialization of weights, requiring lower
learning rates, etc. These issues have been addressed by Batch Normalization
(\textbf{BN}), by normalizing the inputs of activations to zero mean and unit
standard deviation. Making this batch normalization part of the training
process dramatically accelerates the training process of very deep networks. A
new field of research has been going on to examine the exact theoretical
explanation behind the success of \textbf{BN}. Most of these theoretical
insights attempt to explain the benefits of \textbf{BN} by placing them on its
influence on optimization, weight scale invariance, and regularization. Despite
\textbf{BN} undeniable success in accelerating generalization, the gap of
analytically relating the effect of \textbf{BN} to the regularization parameter
is still missing. This paper aims to bring out the data-dependent auto-tuning
of the regularization parameter by \textbf{BN} with analytical proofs. We have
posed \textbf{BN} as a constrained optimization imposed on non-\textbf{BN}
weights through which we demonstrate its data statistics dependant auto-tuning
of regularization parameter. We have also given analytical proof for its
behavior under a noisy input scenario, which reveals the signal vs. noise
tuning of the regularization parameter. We have also substantiated our claim
with empirical results from the MNIST dataset experiments.

E-commerce queries are often short and ambiguous. Consequently, query
understanding often uses query rewriting to disambiguate user-input queries.
While using e-commerce search tools, users tend to enter multiple searches,
which we call context, before purchasing. These history searches contain
contextual insights about users' true shopping intents. Therefore, modeling
such contextual information is critical to a better query rewriting model.
However, existing query rewriting models ignore users' history behaviors and
consider only the instant search query, which is often a short string offering
limited information about the true shopping intent.
  We propose an end-to-end context-aware query rewriting model to bridge this
gap, which takes the search context into account. Specifically, our model
builds a session graph using the history search queries and their contained
words. We then employ a graph attention mechanism that models cross-query
relations and computes contextual information of the session. The model
subsequently calculates session representations by combining the contextual
information with the instant search query using an aggregation network. The
session representations are then decoded to generate rewritten queries.
Empirically, we demonstrate the superiority of our method to state-of-the-art
approaches under various metrics. On in-house data from an online shopping
platform, by introducing contextual information, our model achieves 11.6%
improvement under the MRR (Mean Reciprocal Rank) metric and 20.1% improvement
under the HIT@16 metric (a hit rate metric), in comparison with the best
baseline method (Transformer-based model).

We train a deep learning artificial neural network model, Spatial Attention
U-Net to recover useful ionospheric signals from noisy ionogram data measured
by Hualien's Vertical Incidence Pulsed Ionospheric Radar. Our results show that
the model can well identify F2 layer ordinary and extraordinary modes (F2o,
F2x) and the combined signals of the E layer (ordinary and extraordinary modes
and sporadic Es). The model is also capable of identifying some signals that
were not labeled. The performance of the model can be significantly degraded by
insufficient number of samples in the data set. From the recovered signals, we
determine the critical frequencies of F2o and F2x and the intersection
frequency between the two signals. The difference between the two critical
frequencies is peaking at 0.63 MHz, with the uncertainty being 0.18 MHz.

The edges of the characteristic imset polytope, $\operatorname{CIM}_p$, were
recently shown to have strong connections to causal discovery as many
algorithms could be interpreted as greedy restricted edge-walks, even though
only a strict subset of the edges are known. To better understand the general
edge structure of the polytope we describe the edge structure of faces with a
clear combinatorial interpretation: for any undirected graph $G$ we have the
face $\operatorname{CIM}_G$, the convex hull of the characteristic imsets of
DAGs with skeleton $G$. We give a full edge-description of
$\operatorname{CIM}_G$ when $G$ is a tree, leading to interesting connections
to other polytopes. In particular the well-studied stable set polytope can be
recovered as a face of $\operatorname{CIM}_G$ when $G$ is a tree. Building on
this connection we are also able to give a description of all edges of
$\operatorname{CIM}_G$ when $G$ is a cycle, suggesting possible inroads for
generalization. We then introduce an algorithm for learning directed trees from
data, utilizing our newly discovered edges, that outperforms classical methods
on simulated Gaussian data.

Participatory approaches to artificial intelligence (AI) and machine learning
(ML) are gaining momentum: the increased attention comes partly with the view
that participation opens the gateway to an inclusive, equitable, robust,
responsible and trustworthy AI.Among other benefits, participatory approaches
are essential to understanding and adequately representing the needs, desires
and perspectives of historically marginalized communities. However, there
currently exists lack of clarity on what meaningful participation entails and
what it is expected to do. In this paper we first review participatory
approaches as situated in historical contexts as well as participatory methods
and practices within the AI and ML pipeline. We then introduce three case
studies in participatory AI.Participation holds the potential for beneficial,
emancipatory and empowering technology design, development and deployment while
also being at risk for concerns such as cooptation and conflation with other
activities. We lay out these limitations and concerns and argue that as
participatory AI/ML becomes in vogue, a contextual and nuanced understanding of
the term as well as consideration of who the primary beneficiaries of
participatory activities ought to be constitute crucial factors to realizing
the benefits and opportunities that participation brings.

The rapidly-developing intersection of machine learning (ML) with high-energy
physics (HEP) presents both opportunities and challenges to our community. Far
beyond applications of standard ML tools to HEP problems, genuinely new and
potentially revolutionary approaches are being developed by a generation of
talent literate in both fields. There is an urgent need to support the needs of
the interdisciplinary community driving these developments, including funding
dedicated research at the intersection of the two fields, investing in
high-performance computing at universities and tailoring allocation policies to
support this work, developing of community tools and standards, and providing
education and career paths for young researchers attracted by the intellectual
vitality of machine learning for high energy physics.

We present ZeroEGGS, a neural network framework for speech-driven gesture
generation with zero-shot style control by example. This means style can be
controlled via only a short example motion clip, even for motion styles unseen
during training. Our model uses a Variational framework to learn a style
embedding, making it easy to modify style through latent space manipulation or
blending and scaling of style embeddings. The probabilistic nature of our
framework further enables the generation of a variety of outputs given the same
input, addressing the stochastic nature of gesture motion. In a series of
experiments, we first demonstrate the flexibility and generalizability of our
model to new speakers and styles. In a user study, we then show that our model
outperforms previous state-of-the-art techniques in naturalness of motion,
appropriateness for speech, and style portrayal. Finally, we release a
high-quality dataset of full-body gesture motion including fingers, with
speech, spanning across 19 different styles.

For more than 100 years, chemical, physical, and material scientists have
proposed competing constitutive models to best characterize the behavior of
natural and man-made materials in response to mechanical loading. Now, computer
science offers a universal solution: Neural Networks. Neural Networks are
powerful function approximators that can learn constitutive relations from
large data without any knowledge of the underlying physics. However, classical
Neural Networks ignore a century of research in constitutive modeling, violate
thermodynamic considerations, and fail to predict the behavior outside the
training regime. Here we design a new family of Constitutive Artificial Neural
Networks that inherently satisfy common kinematic, thermodynamic, and physic
constraints and, at the same time, constrain the design space of admissible
functions to create robust approximators, even in the presence of sparse data.
We revisit the non-linear field theories of mechanics and reverse-engineer the
network input to account for material objectivity, symmetry, and
incompressibility; the network output to enforce thermodynamic consistency; the
activation functions to implement physically reasonable restrictions; and the
network architecture to ensure polyconvexity. We demonstrate that this new
class of models is a generalization of the classical neo Hooke, Blatz Ko,
Mooney Rivlin, Yeoh, and Demiray models and that the network weights have a
clear physical interpretation. When trained with classical benchmark data for
rubber, our network autonomously selects the best constitutive model and learns
its parameters. Our findings suggests that Constitutive Artificial Neural
Networks have the potential to induce a paradigm shift in constitutive
modeling, from user-defined model selection to automated model discovery. Our
source code, data, and examples are available at
https://github.com/LivingMatterLab/CANN.

We consider the problem of learning the latent community structure in a
Multi-Layer Contextual Block Model introduced by Ma and Nandy (2021), where the
average degree for each of the observed networks is of constant order and
establish a sharp detection threshold for the community structure, above which
detection is possible asymptotically, while below the threshold no procedure
can perform better than random guessing. We further establish that the
detection threshold coincides with the threshold for weak recovery of the
common community structure using multiple correlated networks and co-variate
matrices. Finally, we provide a quasi-polynomial time algorithm to estimate the
latent communities in the recovery regime. Our results improve upon the results
of Ma and Nandy (2021), which considered the diverging degree regime and
recovers the results of Lu and Sen (2020) in the special case of a single
network structure.

The task of building general agents that perform well over a wide range of
tasks has been an important goal in reinforcement learning since its inception.
The problem has been subject of research of a large body of work, with
performance frequently measured by observing scores over the wide range of
environments contained in the Atari 57 benchmark. Agent57 was the first agent
to surpass the human benchmark on all 57 games, but this came at the cost of
poor data-efficiency, requiring nearly 80 billion frames of experience to
achieve. Taking Agent57 as a starting point, we employ a diverse set of
strategies to achieve a 200-fold reduction of experience needed to out perform
the human baseline. We investigate a range of instabilities and bottlenecks we
encountered while reducing the data regime, and propose effective solutions to
build a more robust and efficient agent. We also demonstrate competitive
performance with high-performing methods such as Muesli and MuZero. The four
key components to our approach are (1) an approximate trust region method which
enables stable bootstrapping from the online network, (2) a normalisation
scheme for the loss and priorities which improves robustness when learning a
set of value functions with a wide range of scales, (3) an improved
architecture employing techniques from NFNets in order to leverage deeper
networks without the need for normalization layers, and (4) a policy
distillation method which serves to smooth out the instantaneous greedy policy
overtime.

We introduce LAVIS, an open-source deep learning library for LAnguage-VISion
research and applications. LAVIS aims to serve as a one-stop comprehensive
library that brings recent advancements in the language-vision field accessible
for researchers and practitioners, as well as fertilizing future research and
development. It features a unified interface to easily access state-of-the-art
image-language, video-language models and common datasets. LAVIS supports
training, evaluation and benchmarking on a rich variety of tasks, including
multimodal classification, retrieval, captioning, visual question answering,
dialogue and pre-training. In the meantime, the library is also highly
extensible and configurable, facilitating future development and customization.
In this technical report, we describe design principles, key components and
functionalities of the library, and also present benchmarking results across
common language-vision tasks. The library is available at:
https://github.com/salesforce/LAVIS.

Joint synthesis of images and segmentation masks with generative adversarial
networks (GANs) is promising to reduce the effort needed for collecting image
data with pixel-wise annotations. However, to learn high-fidelity image-mask
synthesis, existing GAN approaches first need a pre-training phase requiring
large amounts of image data, which limits their utilization in restricted image
domains. In this work, we take a step to reduce this limitation, introducing
the task of one-shot image-mask synthesis. We aim to generate diverse images
and their segmentation masks given only a single labelled example, and
assuming, contrary to previous models, no access to any pre-training data. To
this end, inspired by the recent architectural developments of single-image
GANs, we introduce our OSMIS model which enables the synthesis of segmentation
masks that are precisely aligned to the generated images in the one-shot
regime. Besides achieving the high fidelity of generated masks, OSMIS
outperforms state-of-the-art single-image GAN models in image synthesis quality
and diversity. In addition, despite not using any additional data, OSMIS
demonstrates an impressive ability to serve as a source of useful data
augmentation for one-shot segmentation applications, providing performance
gains that are complementary to standard data augmentation techniques. Code is
available at https://github.com/ boschresearch/one-shot-synthesis

The modern time-domain photometric surveys collect a lot of observations of
various astronomical objects, and the coming era of large-scale surveys will
provide even more information. Most of the objects have never received a
spectroscopic follow-up, which is especially crucial for transients e.g.
supernovae. In such cases, observed light curves could present an affordable
alternative. Time series are actively used for photometric classification and
characterization, such as peak and luminosity decline estimation. However, the
collected time series are multidimensional, irregularly sampled, contain
outliers, and do not have well-defined systematic uncertainties. Machine
learning methods help extract useful information from available data in the
most efficient way. We consider several light curve approximation methods based
on neural networks: Multilayer Perceptrons, Bayesian Neural Networks, and
Normalizing Flows, to approximate observations of a single light curve. Tests
using both the simulated PLAsTiCC and real Zwicky Transient Facility data
samples demonstrate that even few observations are enough to fit networks and
achieve better approximation quality than other state-of-the-art methods. We
show that the methods described in this work have better computational
complexity and work faster than Gaussian Processes. We analyze the performance
of the approximation techniques aiming to fill the gaps in the observations of
the light curves, and show that the use of appropriate technique increases the
accuracy of peak finding and supernova classification. In addition, the study
results are organized in a Fulu Python library available on GitHub, which can
be easily used by the community.

Test-time training adapts to a new test distribution on the fly by optimizing
a model for each test input using self-supervision. In this paper, we use
masked autoencoders for this one-sample learning problem. Empirically, our
simple method improves generalization on many visual benchmarks for
distribution shifts. Theoretically, we characterize this improvement in terms
of the bias-variance trade-off.

We present a systematic study of domain generalization (DG) for tiny neural
networks. This problem is critical to on-device machine learning applications
but has been overlooked in the literature where research has been merely
focused on large models. Tiny neural networks have much fewer parameters and
lower complexity and therefore should not be trained the same way as their
large counterparts for DG applications. By conducting extensive experiments, we
find that knowledge distillation (KD), a well-known technique for model
compression, is much better for tackling the on-device DG problem than
conventional DG methods. Another interesting observation is that the
teacher-student gap on out-of-distribution data is bigger than that on
in-distribution data, which highlights the capacity mismatch issue as well as
the shortcoming of KD. We further propose a method called out-of-distribution
knowledge distillation (OKD) where the idea is to teach the student how the
teacher handles out-of-distribution data synthesized via disruptive data
augmentation. Without adding any extra parameter to the model -- hence keeping
the deployment cost unchanged -- OKD significantly improves DG performance for
tiny neural networks in a variety of on-device DG scenarios for image and
speech applications. We also contribute a scalable approach for synthesizing
visual domain shifts, along with a new suite of DG datasets to complement
existing testbeds.

Beam management is a challenging task for millimeter wave (mmWave) and
sub-terahertz communication systems, especially in scenarios with highly-mobile
users. Leveraging external sensing modalities such as vision, LiDAR, radar,
position, or a combination of them, to address this beam management challenge
has recently attracted increasing interest from both academia and industry.
This is mainly motivated by the dependency of the beam direction decision on
the user location and the geometry of the surrounding environment --
information that can be acquired from the sensory data. To realize the promised
beam management gains, such as the significant reduction in beam alignment
overhead, in practice, however, these solutions need to account for important
aspects. For example, these multi-modal sensing aided beam selection approaches
should be able to generalize their learning to unseen scenarios and should be
able to operate in realistic dense deployments.
  The "Multi-Modal Beam Prediction Challenge 2022: Towards Generalization"
competition is offered to provide a platform for investigating these critical
questions. In order to facilitate the generalizability study, the competition
offers a large-scale multi-modal dataset with co-existing communication and
sensing data collected across multiple real-world locations and different times
of the day. In this paper, along with the detailed descriptions of the problem
statement and the development dataset, we provide a baseline solution that
utilizes the user position data to predict the optimal beam indices. The
objective of this challenge is to go beyond a simple feasibility study and
enable necessary research in this direction, paving the way towards
generalizable multi-modal sensing-aided beam management for real-world future
communication systems.

Traditional automated metrics for evaluating conditional natural language
generation use pairwise comparisons between a single generated text and the
best-matching gold-standard ground truth text. When multiple ground truths are
available, scores are aggregated using an average or max operation across
references. While this approach works well when diversity in the ground truth
data (i.e. dispersion of the distribution of conditional texts) can be ascribed
to noise, such as in automated speech recognition, it does not allow for robust
evaluation in the case where diversity in the ground truths represents signal
for the model. In this work we argue that existing metrics are not appropriate
for domains such as visual description or summarization where ground truths are
semantically diverse, and where the diversity in those captions captures useful
additional information about the context. We propose a novel paradigm for
multi-candidate evaluation of conditional language generation models, and a new
family of metrics that compare the distributions of reference and
model-generated caption sets using small sample sets of each. We demonstrate
the utility of our approach with a case study in visual description: where we
show that existing models optimize for single-description quality over
diversity, and gain some insights into how sampling methods and temperature
impact description quality and diversity.

Gastro-Intestinal Tract cancer is considered a fatal malignant condition of
the organs in the GI tract. Due to its fatality, there is an urgent need for
medical image segmentation techniques to segment organs to reduce the treatment
time and enhance the treatment. Traditional segmentation techniques rely upon
handcrafted features and are computationally expensive and inefficient. Vision
Transformers have gained immense popularity in many image classification and
segmentation tasks. To address this problem from a transformers' perspective,
we introduced a hybrid CNN-transformer architecture to segment the different
organs from an image. The proposed solution is robust, scalable, and
computationally efficient, with a Dice and Jaccard coefficient of 0.79 and
0.72, respectively. The proposed solution also depicts the essence of deep
learning-based automation to improve the effectiveness of the treatment

Pre-trained vision-language models (e.g., CLIP) have shown promising
zero-shot generalization in many downstream tasks with properly designed text
prompts. Instead of relying on hand-engineered prompts, recent works learn
prompts using the training data from downstream tasks. While effective,
training on domain-specific data reduces a model's generalization capability to
unseen new domains. In this work, we propose test-time prompt tuning (TPT), a
method that can learn adaptive prompts on the fly with a single test sample.
For image classification, TPT optimizes the prompt by minimizing the entropy
with confidence selection so that the model has consistent predictions across
different augmented views of each test sample. In evaluating generalization to
natural distribution shifts, TPT improves the zero-shot top-1 accuracy of CLIP
by 3.6% on average, surpassing previous prompt tuning approaches that require
additional task-specific training data. In evaluating cross-dataset
generalization with unseen categories, TPT performs on par with the
state-of-the-art approaches that use additional training data. Project page:
https://azshue.github.io/TPT.

Neural networks trained with stochastic gradient descent (SGD) starting from
different random initialisations typically find functionally very similar
solutions, raising the question of whether there are meaningful differences
between different SGD solutions. Entezari et al.\ recently conjectured that
despite different initialisations, the solutions found by SGD lie in the same
loss valley after taking into account the permutation invariance of neural
networks. Concretely, they hypothesise that any two solutions found by SGD can
be permuted such that the linear interpolation between their parameters forms a
path without significant increases in loss. Here, we use a simple but powerful
algorithm to find such permutations that allows us to obtain direct empirical
evidence that the hypothesis is true in fully connected networks. Strikingly,
we find that two networks already live in the same loss valley at the time of
initialisation and averaging their random, but suitably permuted initialisation
performs significantly above chance. In contrast, for convolutional
architectures, our evidence suggests that the hypothesis does not hold.
Especially in a large learning rate regime, SGD seems to discover diverse
modes.

Transient phenomena play a key role in coordinating brain activity at
multiple scales, however,their underlying mechanisms remain largely unknown. A
key challenge for neural data science is thus to characterize the network
interactions at play during these events. Using the formalism of Structural
Causal Models and their graphical representation, we investigate the
theoretical and empirical properties of Information Theory based causal
strength measures in the context of recurring spontaneous transient events.
After showing the limitations of Transfer Entropy and Dynamic Causal Strength
in such a setting, we introduce a novel measure, relative Dynamic Causal
Strength, and provide theoretical and empirical support for its benefits. These
methods are applied to simulated and experimentally recorded neural time
series, and provide results in agreement with our current understanding of the
underlying brain circuits.

In offline model-based optimization, we strive to maximize a black-box
objective function by only leveraging a static dataset of designs and their
scores. This problem setting arises in numerous fields including the design of
materials, robots, DNA sequences, and proteins. Recent approaches train a deep
neural network (DNN) on the static dataset to act as a proxy function, and then
perform gradient ascent on the existing designs to obtain potentially
high-scoring designs. This methodology frequently suffers from the
out-of-distribution problem where the proxy function often returns poor
designs. To mitigate this problem, we propose BiDirectional learning for
offline Infinite-width model-based optimization (BDI). BDI consists of two
mappings: the forward mapping leverages the static dataset to predict the
scores of the high-scoring designs, and the backward mapping leverages the
high-scoring designs to predict the scores of the static dataset. The backward
mapping, neglected in previous work, can distill more information from the
static dataset into the high-scoring designs, which effectively mitigates the
out-of-distribution problem. For a finite-width DNN model, the loss function of
the backward mapping is intractable and only has an approximate form, which
leads to a significant deterioration of the design quality. We thus adopt an
infinite-width DNN model, and propose to employ the corresponding neural
tangent kernel to yield a closed-form loss for more accurate design updates.
Experiments on various tasks verify the effectiveness of BDI. The code is
available at https://github.com/GGchen1997/BDI.

Graph neural network (GNN) pre-training methods have been proposed to enhance
the power of GNNs. Specifically, a GNN is first pre-trained on a large-scale
unlabeled graph and then fine-tuned on a separate small labeled graph for
downstream applications, such as node classification. One popular pre-training
method is to mask out a proportion of the edges, and a GNN is trained to
recover them. However, such a generative method suffers from graph mismatch.
That is, the masked graph inputted to the GNN deviates from the original graph.
To alleviate this issue, we propose DiP-GNN (Discriminative Pre-training of
Graph Neural Networks). Specifically, we train a generator to recover
identities of the masked edges, and simultaneously, we train a discriminator to
distinguish the generated edges from the original graph's edges. In our
framework, the graph seen by the discriminator better matches the original
graph because the generator can recover a proportion of the masked edges.
Extensive experiments on large-scale homogeneous and heterogeneous graphs
demonstrate the effectiveness of the proposed framework.

Opinion summarization is the task of creating summaries capturing popular
opinions from user reviews. In this paper, we introduce Geodesic Summarizer
(GeoSumm), a novel system to perform unsupervised extractive opinion
summarization. GeoSumm involves an encoder-decoder based representation
learning model, that generates representations of text as a distribution over
latent semantic units. GeoSumm generates these representations by performing
dictionary learning over pre-trained text representations at multiple decoder
layers. We then use these representations to quantify the relevance of review
sentences using a novel approximate geodesic distance based scoring mechanism.
We use the relevance scores to identify popular opinions in order to compose
general and aspect-specific summaries. Our proposed model, GeoSumm, achieves
state-of-the-art performance on three opinion summarization datasets. We
perform additional experiments to analyze the functioning of our model and
showcase the generalization ability of {\X} across different domains.

Automatic depression detection on Twitter can help individuals privately and
conveniently understand their mental health status in the early stages before
seeing mental health professionals. Most existing black-box-like deep learning
methods for depression detection largely focused on improving classification
performance. However, explaining model decisions is imperative in health
research because decision-making can often be high-stakes and life-and-death.
Reliable automatic diagnosis of mental health problems including depression
should be supported by credible explanations justifying models' predictions. In
this work, we propose a novel explainable model for depression detection on
Twitter. It comprises a novel encoder combining hierarchical attention
mechanisms and feed-forward neural networks. To support psycholinguistic
studies, our model leverages metaphorical concept mappings as input. Thus, it
not only detects depressed individuals, but also identifies features of such
users' tweets and associated metaphor concept mappings.

Dynamic magnetic resonance imaging (MRI) is a popular medical imaging
technique to generate image sequences of the flow of a contrast material inside
tissues and organs. However, its application to imaging bolus movement through
the esophagus has only been demonstrated in few feasibility studies and is
relatively unexplored. In this work, we present a computational framework
called mechanics-informed MRI (MRI-MECH) that enhances that capability thereby
increasing the applicability of dynamic MRI for diagnosing esophageal
disorders. Pineapple juice was used as the swallowed contrast material for the
dynamic MRI and the MRI image sequence was used as input to the MRI-MECH. The
MRI-MECH modeled the esophagus as a flexible one-dimensional tube and the
elastic tube walls followed a linear tube law. Flow through the esophagus was
then governed by one-dimensional mass and momentum conservation equations.
These equations were solved using a physics-informed neural network (PINN). The
PINN minimized the difference between the measurements from the MRI and model
predictions ensuring that the physics of the fluid flow problem was always
followed. MRI-MECH calculated the fluid velocity and pressure during esophageal
transit and estimated the mechanical health of the esophagus by calculating
wall stiffness and active relaxation. Additionally, MRI-MECH predicted missing
information about the lower esophageal sphincter during the emptying process,
demonstrating its applicability to scenarios with missing data or poor image
resolution. In addition to potentially improving clinical decisions based on
quantitative estimates of the mechanical health of the esophagus, MRI-MECH can
also be enhanced for application to other medical imaging modalities to enhance
their functionality as well.

Markov Chain Monte Carlo methods for sampling from complex distributions and
estimating normalization constants often simulate samples from a sequence of
intermediate distributions along an annealing path, which bridges between a
tractable initial distribution and a target density of interest. Prior work has
constructed annealing paths using quasi-arithmetic means, and interpreted the
resulting intermediate densities as minimizing an expected divergence to the
endpoints. We provide a comprehensive analysis of this 'centroid' property
using Bregman divergences under a monotonic embedding of the density function,
thereby associating common divergences such as Amari's and Renyi's
${\alpha}$-divergences, ${(\alpha,\beta)}$-divergences, and the Jensen-Shannon
divergence with intermediate densities along an annealing path. Our analysis
highlights the interplay between parametric families, quasi-arithmetic means,
and divergence functions using the rho-tau Bregman divergence framework of
Zhang 2004;2013.

The increasing size of recently proposed Neural Networks makes it hard to
implement them on embedded devices, where memory, battery and computational
power are a non-trivial bottleneck. For this reason during the last years
network compression literature has been thriving and a large number of
solutions has been been published to reduce both the number of operations and
the parameters involved with the models. Unfortunately, most of these reducing
techniques are actually heuristic methods and usually require at least one
re-training step to recover the accuracy. The need of procedures for model
reduction is well-known also in the fields of Verification and Performances
Evaluation, where large efforts have been devoted to the definition of
quotients that preserve the observable underlying behaviour. In this paper we
try to bridge the gap between the most popular and very effective network
reduction strategies and formal notions, such as lumpability, introduced for
verification and evaluation of Markov Chains. Elaborating on lumpability we
propose a pruning approach that reduces the number of neurons in a network
without using any data or fine-tuning, while completely preserving the exact
behaviour. Relaxing the constraints on the exact definition of the quotienting
method we can give a formal explanation of some of the most common reduction

Recently vision transformers have been shown to be competitive with
convolution-based methods (CNNs) broadly across multiple vision tasks. The less
restrictive inductive bias of transformers endows greater representational
capacity in comparison with CNNs. However, in the image classification setting
this flexibility comes with a trade-off with respect to sample efficiency,
where transformers require ImageNet-scale training. This notion has carried
over to video where transformers have not yet been explored for video
classification in the low-labeled or semi-supervised settings. Our work
empirically explores the low data regime for video classification and discovers
that, surprisingly, transformers perform extremely well in the low-labeled
video setting compared to CNNs. We specifically evaluate video vision
transformers across two contrasting video datasets (Kinetics-400 and
SomethingSomething-V2) and perform thorough analysis and ablation studies to
explain this observation using the predominant features of video transformer
architectures. We even show that using just the labeled data, transformers
significantly outperform complex semi-supervised CNN methods that leverage
large-scale unlabeled data as well. Our experiments inform our recommendation
that semi-supervised learning video work should consider the use of video
transformers in the future.

The notion of omnipredictors (Gopalan, Kalai, Reingold, Sharan and Wieder
ITCS 2021), suggested a new paradigm for loss minimization. Rather than
learning a predictor based on a known loss function, omnipredictors can easily
be post-processed to minimize any one of a rich family of loss functions
compared with the loss of a class $C$. It has been shown that such
omnipredictors exist and are implied (for all convex and Lipschitz loss
functions) by the notion of multicalibration from the algorithmic fairness
literature. Nevertheless, it is often the case that the action selected must
obey some additional constraints (such as capacity or parity constraints). In
itself, the original notion of omnipredictors does not apply in this
well-motivated and heavily studied the context of constrained loss
minimization.
  In this paper, we introduce omnipredictors for constrained optimization and
study their complexity and implications. The notion that we introduce allows
the learner to be unaware of the loss function that will be later assigned as
well as the constraints that will be later imposed, as long as the
subpopulations that are used to define these constraints are known.
  The paper shows how to obtain omnipredictors for constrained optimization
problems, relying on appropriate variants of multicalibration. For some
interesting constraints and general loss functions and for general constraints
and some interesting loss functions, we show how omnipredictors are implied by
a variant of multicalibration that is similar in complexity to standard
multicalibration. We demonstrate that in the general case, standard
multicalibration is insufficient and show that omnipredictors are implied by
multicalibration with respect to a class containing all the level sets of
hypotheses in $C$. We also investigate the implications when the constraints
are group fairness notions.

High-resolution representations are important for vision-based robotic
grasping problems. Existing works generally encode the input images into
low-resolution representations via sub-networks and then recover
high-resolution representations. This will lose spatial information, and errors
introduced by the decoder will be more serious when multiple types of objects
are considered or objects are far away from the camera. To address these
issues, we revisit the design paradigm of CNN for robotic perception tasks. We
demonstrate that using parallel branches as opposed to serial stacked
convolutional layers will be a more powerful design for robotic visual grasping
tasks. In particular, guidelines of neural network design are provided for
robotic perception tasks, e.g., high-resolution representation and lightweight
design, which respond to the challenges in different manipulation scenarios. We
then develop a novel grasping visual architecture referred to as HRG-Net, a
parallel-branch structure that always maintains a high-resolution
representation and repeatedly exchanges information across resolutions.
Extensive experiments validate that these two designs can effectively enhance
the accuracy of visual-based grasping and accelerate network training. We show
a series of comparative experiments in real physical environments at Youtube:
https://youtu.be/Jhlsp-xzHFY.

Standard deep learning methods, such as Ensemble Models, Bayesian Neural
Networks and Quantile Regression Models provide estimates to prediction
uncertainties for data-driven deep learning models. However, they can be
limited in their applications due to their heavy memory, inference cost, and
ability to properly capture out-of-distribution uncertainties. Additionally,
some of these models require post-training calibration which limits their
ability to be used for continuous learning applications. In this paper, we
present a new approach to provide prediction with calibrated uncertainties that
includes out-of-distribution contributions and compare it to standard methods
on the Fermi National Accelerator Laboratory (FNAL) Booster accelerator
complex.

We study online learning problems in which a decision maker has to take a
sequence of decisions subject to $m$ long-term constraints. The goal of the
decision maker is to maximize their total reward, while at the same time
achieving small cumulative constraints violation across the $T$ rounds. We
present the first best-of-both-world type algorithm for this general class of
problems, with no-regret guarantees both in the case in which rewards and
constraints are selected according to an unknown stochastic model, and in the
case in which they are selected at each round by an adversary. Our algorithm is
the first to provide guarantees in the adversarial setting with respect to the
optimal fixed strategy that satisfies the long-term constraints. In particular,
it guarantees a $\rho/(1+\rho)$ fraction of the optimal reward and sublinear
regret, where $\rho$ is a feasibility parameter related to the existence of
strictly feasible solutions. Our framework employs traditional regret
minimizers as black-box components. Therefore, by instantiating it with an
appropriate choice of regret minimizers it can handle the full-feedback as well
as the bandit-feedback setting. Moreover, it allows the decision maker to
seamlessly handle scenarios with non-convex rewards and constraints. We show
how our framework can be applied in the context of budget-management mechanisms
for repeated auctions in order to guarantee long-term constraints that are not
packing (e.g., ROI constraints).

It is still a mystery how the human brain radically generalizes from previous
experiences to solve new problems with very little data, an ability that far
surpasses deep learning. Here, we propose a speculative hypothesis: that
'replay' in the brain implements a form of compositional computation where
entities are assembled into meaningful structures. We review recent advances in
the neuroscience of replay, highlighting how the hippocampus flexibly binds
objects to generalizable roles and how replay strings these role-bound objects
into compound statements. We suggest new experiments to test this hypothesis.
We end by noting the bidirectional interactions between replay and other brain
systems and their relationship to AI methods that hybridize neural networks
with compositional computation.

We consider the stochastic gradient descent (SGD) algorithm driven by a
general stochastic sequence, including i.i.d noise and random walk on an
arbitrary graph, among others; and analyze it in the asymptotic sense.
Specifically, we employ the notion of `efficiency ordering', a well-analyzed
tool for comparing the performance of Markov Chain Monte Carlo (MCMC) samplers,
for SGD algorithms in the form of Loewner ordering of covariance matrices
associated with the scaled iterate errors in the long term. Using this
ordering, we show that input sequences that are more efficient for MCMC
sampling also lead to smaller covariance of the errors for SGD algorithms in
the limit. This also suggests that an arbitrarily weighted MSE of SGD iterates
in the limit becomes smaller when driven by more efficient chains. Our finding
is of particular interest in applications such as decentralized optimization
and swarm learning, where SGD is implemented in a random walk fashion on the
underlying communication graph for cost issues and/or data privacy. We
demonstrate how certain non-Markovian processes, for which typical mixing-time
based non-asymptotic bounds are intractable, can outperform their Markovian
counterparts in the sense of efficiency ordering for SGD. We show the utility
of our method by applying it to gradient descent with shuffling and mini-batch
gradient descent, reaffirming key results from existing literature under a
unified framework. Empirically, we also observe efficiency ordering for
variants of SGD such as accelerated SGD and Adam, open up the possibility of
extending our notion of efficiency ordering to a broader family of stochastic
optimization algorithms.

We report on what seems to be an intriguing connection between variable
integration time and partial velocity refreshment of Ideal Hamiltonian Monte
Carlo samplers, both of which can be used for reducing the dissipative behavior
of the dynamics. More concretely, we show that on quadratic potentials,
efficiency can be improved through these means by a $\sqrt{\kappa}$ factor in
Wasserstein-2 distance, compared to classical constant integration time, fully
refreshed HMC.

Mean-Field Control (MFC) has recently been proven to be a scalable tool to
approximately solve large-scale multi-agent reinforcement learning (MARL)
problems. However, these studies are typically limited to unconstrained
cumulative reward maximization framework. In this paper, we show that one can
use the MFC approach to approximate the MARL problem even in the presence of
constraints. Specifically, we prove that, an $N$-agent constrained MARL
problem, with state, and action spaces of each individual agents being of sizes
$|\mathcal{X}|$, and $|\mathcal{U}|$ respectively, can be approximated by an
associated constrained MFC problem with an error, $e\triangleq
\mathcal{O}\left([\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}]/\sqrt{N}\right)$.
In a special case where the reward, cost, and state transition functions are
independent of the action distribution of the population, we prove that the
error can be improved to $e=\mathcal{O}(\sqrt{|\mathcal{X}|}/\sqrt{N})$. Also,
we provide a Natural Policy Gradient based algorithm and prove that it can
solve the constrained MARL problem within an error of $\mathcal{O}(e)$ with a
sample complexity of $\mathcal{O}(e^{-6})$.

The rapid advancement of models based on artificial intelligence demands
innovative monitoring techniques which can operate in real time with low
computational costs. In machine learning, especially if we consider neural
network (NN) learning algorithms, and in particular deep-learning
architectures, the models are often trained in a supervised manner.
Consequently, the learned relationship between the input and the output must
remain valid during the model's deployment. If this stationarity assumption
holds, we can conclude that the NN generates accurate predictions. Otherwise,
the retraining or rebuilding of the model is required. We propose to consider
the latent feature representation of the data (called "embedding") generated by
the NN for determining the time point when the data stream starts being
nonstationary. To be precise, we monitor embeddings by applying multivariate
control charts based on the calculation of the data depth and normalized ranks.
The performance of the introduced method is evaluated using various NNs with
different underlying data formats.

Humans have a remarkable ability to rapidly generalize to new tasks that is
difficult to reproduce in artificial learning systems. Compositionality has
been proposed as a key mechanism supporting generalization in humans, but
evidence of its neural implementation and impact on behavior is still scarce.
Here we study the computational properties associated with compositional
generalization in both humans and artificial neural networks (ANNs) on a highly
compositional task. First, we identified behavioral signatures of compositional
generalization in humans, along with their neural correlates using whole-cortex
functional magnetic resonance imaging (fMRI) data. Next, we designed
pretraining paradigms aided by a procedure we term {\em primitives pretraining}
to endow compositional task elements into ANNs. We found that ANNs with this
prior knowledge had greater correspondence with human behavior and neural
compositional signatures. Importantly, primitives pretraining induced abstract
internal representations, excellent zero-shot generalization, and
sample-efficient learning. Moreover, it gave rise to a hierarchy of abstract
representations that matched human fMRI data, where sensory rule abstractions
emerged in early sensory areas, and motor rule abstractions emerged in later
motor areas. Our findings give empirical support to the role of compositional
generalization in human behavior, implicate abstract representations as its
neural implementation, and illustrate that these representations can be
embedded into ANNs by designing simple and efficient pretraining procedures.

A scene graph is a semantic representation that expresses the objects,
attributes, and relationships between objects in a scene. Scene graphs play an
important role in many cross modality tasks, as they are able to capture the
interactions between images and texts. In this paper, we focus on scene graph
modification (SGM), where the system is required to learn how to update an
existing scene graph based on a natural language query. Unlike previous
approaches that rebuilt the entire scene graph, we frame SGM as a graph
expansion task by introducing the incremental structure expanding (ISE). ISE
constructs the target graph by incrementally expanding the source graph without
changing the unmodified structure. Based on ISE, we further propose a model
that iterates between nodes prediction and edges prediction, inferring more
accurate and harmonious expansion decisions progressively. In addition, we
construct a challenging dataset that contains more complicated queries and
larger scene graphs than existing datasets. Experiments on four benchmarks
demonstrate the effectiveness of our approach, which surpasses the previous
state-of-the-art model by large margins.

Two of the most fundamental challenges in Natural Language Understanding
(NLU) at present are: (a) how to establish whether deep learning-based models
score highly on NLU benchmarks for the 'right' reasons; and (b) to understand
what those reasons would even be. We investigate the behavior of reading
comprehension models with respect to two linguistic 'skills': coreference
resolution and comparison. We propose a definition for the reasoning steps
expected from a system that would be 'reading slowly', and compare that with
the behavior of five models of the BERT family of various sizes, observed
through saliency scores and counterfactual explanations. We find that for
comparison (but not coreference) the systems based on larger encoders are more
likely to rely on the 'right' information, but even they struggle with
generalization, suggesting that they still learn specific lexical patterns
rather than the general principles of comparison.

While neuromorphic computing architectures based on Spiking Neural Networks
(SNNs) are increasingly gaining interest as a pathway toward bio-plausible
machine learning, attention is still focused on computational units like the
neuron and synapse. Shifting from this neuro-synaptic perspective, this paper
attempts to explore the self-repair role of glial cells, in particular,
astrocytes. The work investigates stronger correlations with astrocyte
computational neuroscience models to develop macro-models with a higher degree
of bio-fidelity that accurately captures the dynamic behavior of the
self-repair process. Hardware-software co-design analysis reveals that
bio-morphic astrocytic regulation has the potential to self-repair hardware
realistic faults in neuromorphic hardware systems with significantly better
accuracy and repair convergence for unsupervised learning tasks on the MNIST
and F-MNIST datasets.

In recent years, reinforcement learning and its multi-agent analogue have
achieved great success in solving various complex control problems. However,
multi-agent reinforcement learning remains challenging both in its theoretical
analysis and empirical design of algorithms, especially for large swarms of
embodied robotic agents where a definitive toolchain remains part of active
research. We use emerging state-of-the-art mean-field control techniques in
order to convert many-agent swarm control into more classical single-agent
control of distributions. This allows profiting from advances in single-agent
reinforcement learning at the cost of assuming weak interaction between agents.
As a result, the mean-field model is violated by the nature of real systems
with embodied, physically colliding agents. Here, we combine collision
avoidance and learning of mean-field control into a unified framework for
tractably designing intelligent robotic swarm behavior. On the theoretical
side, we provide novel approximation guarantees for both general mean-field
control in continuous spaces and with collision avoidance. On the practical
side, we show that our approach outperforms multi-agent reinforcement learning
and allows for decentralized open-loop application while avoiding collisions,
both in simulation and real UAV swarms. Overall, we propose a framework for the
design of swarm behavior that is both mathematically well-founded and
practically useful, enabling the solution of otherwise intractable swarm
problems.

Pre-trained language models (PLMs) often take advantage of the monolingual
and multilingual dataset that is freely available online to acquire general or
mixed domain knowledge before deployment into specific tasks. Extra-large PLMs
(xLPLMs) are proposed very recently to claim supreme performances over
smaller-sized PLMs such as in machine translation (MT) tasks. These xLPLMs
include Meta-AI's wmt21-dense-24-wide-en-X (2021) and NLLB (2022). In this
work, we examine if xLPLMs are absolutely superior to smaller-sized PLMs in
fine-tuning toward domain-specific MTs. We use two different in-domain data of
different sizes: commercial automotive in-house data and clinical shared task
data from the ClinSpEn2022 challenge at WMT2022. We choose popular Marian
Helsinki as smaller sized PLM and two massive-sized Mega-Transformers from
Meta-AI as xLPLMs.
  Our experimental investigation shows that 1) on smaller-sized in-domain
commercial automotive data, xLPLM wmt21-dense-24-wide-en-X indeed shows much
better evaluation scores using SacreBLEU and hLEPOR metrics than smaller-sized
Marian, even though its score increase rate is lower than Marian after
fine-tuning; 2) on relatively larger-size well prepared clinical data
fine-tuning, the xLPLM NLLB tends to lose its advantage over smaller-sized
Marian on two sub-tasks (clinical terms and ontology concepts) using ClinSpEn
offered metrics METEOR, COMET, and ROUGE-L, and totally lost to Marian on
Task-1 (clinical cases) on all official metrics including SacreBLEU and BLEU;
3) metrics do not always agree with each other on the same tasks using the same
model outputs; 4) clinic-Marian ranked No.2 on Task- 1 (via SACREBLEU/BLEU) and
Task-3 (via METEOR and ROUGE) among all submissions.

Neural Architecture Search (NAS) has significantly improved productivity in
the design and deployment of neural networks (NN). As NAS typically evaluates
multiple models by training them partially or completely, the improved
productivity comes at the cost of significant carbon footprint. To alleviate
this expensive training routine, zero-shot/cost proxies analyze an NN at
initialization to generate a score, which correlates highly with its true
accuracy. Zero-cost proxies are currently designed by experts conducting
multiple cycles of empirical testing on possible algorithms, datasets, and
neural architecture design spaces. This experimentation lowers productivity and
is an unsustainable approach towards zero-cost proxy design as deep learning
use-cases diversify in nature. Additionally, existing zero-cost proxies fail to
generalize across neural architecture design spaces. In this paper, we propose
a genetic programming framework to automate the discovery of zero-cost proxies
for neural architecture scoring. Our methodology efficiently discovers an
interpretable and generalizable zero-cost proxy that gives state of the art
score-accuracy correlation on all datasets and search spaces of NASBench-201
and Network Design Spaces (NDS). We believe that this research indicates a
promising direction towards automatically discovering zero-cost proxies that
can work across network architecture design spaces, datasets, and tasks.

Social media has become the fulcrum of all forms of communication.
Classifying social texts such as fake news, rumour, sarcasm, etc. has gained
significant attention. The surface-level signals expressed by a social-text
itself may not be adequate for such tasks; therefore, recent methods attempted
to incorporate other intrinsic signals such as user behavior and the underlying
graph structure. Oftentimes, the `public wisdom' expressed through the
comments/replies to a social-text acts as a surrogate of crowd-sourced view and
may provide us with complementary signals. State-of-the-art methods on
social-text classification tend to ignore such a rich hierarchical signal.
Here, we propose Hyphen, a discourse-aware hyperbolic spectral co-attention
network. Hyphen is a fusion of hyperbolic graph representation learning with a
novel Fourier co-attention mechanism in an attempt to generalise the
social-text classification tasks by incorporating public discourse. We parse
public discourse as an Abstract Meaning Representation (AMR) graph and use the
powerful hyperbolic geometric representation to model graphs with hierarchical
structure. Finally, we equip it with a novel Fourier co-attention mechanism to
capture the correlation between the source post and public discourse. Extensive
experiments on four different social-text classification tasks, namely
detecting fake news, hate speech, rumour, and sarcasm, show that Hyphen
generalises well, and achieves state-of-the-art results on ten benchmark
datasets. We also employ a sentence-level fact-checked and annotated dataset to
evaluate how Hyphen is capable of producing explanations as analogous evidence
to the final prediction.

We study differentially private (DP) stochastic optimization (SO) with data
containing outliers and loss functions that are (possibly) not Lipschitz
continuous. To date, the vast majority of work on DP SO assumes that the loss
is uniformly Lipschitz over data (i.e. stochastic gradients are uniformly
bounded over all data points). While this assumption is convenient, it is often
unrealistic: in many practical problems, the loss function may not be uniformly
Lipschitz. Even when the loss function is Lipschitz continuous, the worst-case
Lipschitz parameter of the loss over all data points may be extremely large due
to outliers. In such cases, the error bounds for DP SO, which scale with the
worst-case Lipschitz parameter of the loss, are vacuous. To address these
limitations, this work does not require the loss function to be uniformly
Lipschitz. Instead, building on a recent line of work [WXDX20, KLZ22], we make
the weaker assumption that stochastic gradients have bounded $k$-th order
moments for some $k \geq 2$. Compared with works on DP Lipschitz SO, our excess
risk scales with the $k$-th moment bound instead of the Lipschitz parameter of
the loss, allowing for significantly faster rates in the presence of outliers.
For convex and strongly convex loss functions, we provide the first
asymptotically optimal excess risk bounds (up to a logarithmic factor). In
contrast to the prior works, our bounds do not require the loss function to be
differentiable/smooth. We also devise an accelerated algorithm for smooth
losses that runs in linear time and has excess risk that is tight in certain
practical parameter regimes. Additionally, our work is the first to address
non-convex non-Lipschitz loss functions satisfying the Proximal-PL inequality;
this covers some practical machine learning models. Our Proximal-PL algorithm
has near-optimal excess risk.

We provide a differentially private algorithm for producing synthetic data
simultaneously useful for multiple tasks: marginal queries and multitask
machine learning (ML). A key innovation in our algorithm is the ability to
directly handle numerical features, in contrast to a number of related prior
approaches which require numerical features to be first converted into {high
cardinality} categorical features via {a binning strategy}. Higher binning
granularity is required for better accuracy, but this negatively impacts
scalability. Eliminating the need for binning allows us to produce synthetic
data preserving large numbers of statistical queries such as marginals on
numerical features, and class conditional linear threshold queries. Preserving
the latter means that the fraction of points of each class label above a
particular half-space is roughly the same in both the real and synthetic data.
This is the property that is needed to train a linear classifier in a multitask
setting. Our algorithm also allows us to produce high quality synthetic data
for mixed marginal queries, that combine both categorical and numerical
features. Our method consistently runs 2-5x faster than the best comparable
techniques, and provides significant accuracy improvements in both marginal
queries and linear prediction tasks for mixed-type datasets.

In this paper, we ask whether Vision Transformers (ViTs) can serve as an
underlying architecture for improving the adversarial robustness of machine
learning models against evasion attacks. While earlier works have focused on
improving Convolutional Neural Networks, we show that also ViTs are highly
suitable for adversarial training to achieve competitive performance. We
achieve this objective using a custom adversarial training recipe, discovered
using rigorous ablation studies on a subset of the ImageNet dataset. The
canonical training recipe for ViTs recommends strong data augmentation, in part
to compensate for the lack of vision inductive bias of attention modules, when
compared to convolutions. We show that this recipe achieves suboptimal
performance when used for adversarial training. In contrast, we find that
omitting all heavy data augmentation, and adding some additional bag-of-tricks
($\varepsilon$-warmup and larger weight decay), significantly boosts the
performance of robust ViTs. We show that our recipe generalizes to different
classes of ViT architectures and large-scale models on full ImageNet-1k.
Additionally, investigating the reasons for the robustness of our models, we
show that it is easier to generate strong attacks during training when using
our recipe and that this leads to better robustness at test time. Finally, we
further study one consequence of adversarial training by proposing a way to
quantify the semantic nature of adversarial perturbations and highlight its
correlation with the robustness of the model. Overall, we recommend that the
community should avoid translating the canonical training recipes in ViTs to
robust training and rethink common training choices in the context of
adversarial training.

Treatment decisions in cancer care are guided by treatment effect estimates
from randomized controlled trials (RCTs). RCTs estimate the average effect of
one treatment versus another in a certain population. However, treatments may
not be equally effective for every patient in a population. Knowing the
effectiveness of treatments tailored to specific patient and tumor
characteristics would enable individualized treatment decisions. Getting
tailored treatment effects by averaging outcomes in different patient subgroups
in RCTs requires an unfeasible number of patients to have sufficient
statistical power in all relevant subgroups for all possible treatments.
  The American Joint Committee on Cancer (AJCC) recommends that researchers
develop outcome prediction models (OPMs) in an effort to individualize
treatment decisions. OPMs sometimes called risk models or prognosis models, use
patient and tumor characteristics to predict a patient outcome such as overall
survival. The assumption is that the predictions are useful for treatment
decisions using rules such as "prescribe chemotherapy only if the OPM predicts
the patient has a high risk of recurrence". Recognizing the importance of
reliable predictions, the AJCC published a checklist for OPMs to ensure
dependable OPM prediction accuracy in the patient population for which the OPM
was designed. However, accurate outcome predictions do not imply that these
predictions yield good treatment decisions. In this perspective, we show that
OPM rely on a fixed treatment policy which implies that OPM that were found to
accurately predict outcomes in validation studies can still lead to patient
harm when used to inform treatment decisions. We then give guidance on how to
develop models that are useful for individualized treatment decisions and how
to evaluate whether a model has value for decision-making.

Score-based divergences have been widely used in machine learning and
statistics applications. Despite their empirical success, a blindness problem
has been observed when using these for multi-modal distributions. In this work,
we discuss the blindness problem and propose a new family of divergences that
can mitigate the blindness problem. We illustrate our proposed divergence in
the context of density estimation and report improved performance compared to
traditional approaches.

Sustainable software engineering has received a lot of attention in recent
times, as we witness an ever-growing slice of energy use, for example, at data
centers, as software systems utilize the underlying infrastructure.
Characterizing servers for their energy use accurately without being intrusive,
is therefore important to make sustainable software deployment choices. In this
paper, we introduce ESAVE which is a machine learning-based approach that
leverages a small set of hardware attributes to characterize a server or
virtual machine's energy usage across different levels of utilization. This is
based upon an extensive exploration of multiple ML approaches, with a focus on
a minimal set of required attributes, while showcasing good accuracy. Early
validations show that ESAVE has only around 12% average prediction error,
despite being non-intrusive.

In this paper we provide a practical demonstration of how the modularity in a
Behavior Tree (BT) decreases the effort in programming a robot task when
compared to a Finite State Machine (FSM). In recent years the way to represent
a task plan to control an autonomous agent has been shifting from the standard
FSM towards BTs. Many works in the literature have highlighted and proven the
benefits of such design compared to standard approaches, especially in terms of
modularity, reactivity and human readability. However, these works have often
failed in providing a tangible comparison in the implementation of those
policies and the programming effort required to modify them. This is a relevant
aspect in many robotic applications, where the design choice is dictated both
by the robustness of the policy and by the time required to program it. In this
work, we compare backward chained BTs with a fault-tolerant design of FSMs by
evaluating the cost to modify them. We validate the analysis with a set of
experiments in a simulation environment where a mobile manipulator solves an
item fetching task.

We devise deep nearest centroids (DNC), a conceptually elegant yet
surprisingly effective network for large-scale visual recognition, by
revisiting Nearest Centroids, one of the most classic and simple classifiers.
Current deep models learn the classifier in a fully parametric manner, ignoring
the latent data structure and lacking simplicity and explainability. DNC
instead conducts nonparametric, case-based reasoning; it utilizes sub-centroids
of training samples to describe class distributions and clearly explains the
classification as the proximity of test data and the class sub-centroids in the
feature space. Due to the distance-based nature, the network output
dimensionality is flexible, and all the learnable parameters are only for data
embedding. That means all the knowledge learnt for ImageNet classification can
be completely transferred for pixel recognition learning, under the
"pre-training and fine-tuning" paradigm. Apart from its nested simplicity and
intuitive decision-making mechanism, DNC can even possess ad-hoc explainability
when the sub-centroids are selected as actual training images that humans can
view and inspect. Compared with parametric counterparts, DNC performs better on
image classification (CIFAR-10, ImageNet) and greatly boots pixel recognition
(ADE20K, Cityscapes), with improved transparency and fewer learnable
parameters, using various network architectures (ResNet, Swin) and segmentation
models (FCN, DeepLabV3, Swin). We feel this work brings fundamental insights
into related fields.

Aerial base stations (ABSs) allow smart farms to offload processing
responsibility of complex tasks from internet of things (IoT) devices to ABSs.
IoT devices have limited energy and computing resources, thus it is required to
provide an advanced solution for a system that requires the support of ABSs.
This paper introduces a novel multi-actor-based risk-sensitive reinforcement
learning approach for ABS task scheduling for smart agriculture. The problem is
defined as task offloading with a strict condition on completing the IoT tasks
before their deadlines. Moreover, the algorithm must also consider the limited
energy capacity of the ABSs. The results show that our proposed approach
outperforms several heuristics and the classic Q-Learning approach.
Furthermore, we provide a mixed integer linear programming solution to
determine a lower bound on the performance, and clarify the gap between our
risk-sensitive solution and the optimal solution, as well. The comparison
proves our extensive simulation results demonstrate that our method is a
promising approach for providing a guaranteed task processing services for the
IoT tasks in a smart farm, while increasing the hovering time of the ABSs in
this farm.

This paper provides a theoretical study of deep neural function approximation
in reinforcement learning (RL) with the $\epsilon$-greedy exploration under the
online setting. This problem setting is motivated by the successful deep
Q-networks (DQN) framework that falls in this regime. In this work, we provide
an initial attempt on theoretical understanding deep RL from the perspective of
function class and neural networks architectures (e.g., width and depth) beyond
the ``linear'' regime. To be specific, we focus on the value based algorithm
with the $\epsilon$-greedy exploration via deep (and two-layer) neural networks
endowed by Besov (and Barron) function spaces, respectively, which aims at
approximating an $\alpha$-smooth Q-function in a $d$-dimensional feature space.
We prove that, with $T$ episodes, scaling the width $m =
\widetilde{\mathcal{O}}(T^{\frac{d}{2\alpha + d}})$ and the depth
$L=\mathcal{O}(\log T)$ of the neural network for deep RL is sufficient for
learning with sublinear regret in Besov spaces. Moreover, for a two layer
neural network endowed by the Barron space, scaling the width
$\Omega(\sqrt{T})$ is sufficient. To achieve this, the key issue in our
analysis is how to estimate the temporal difference error under deep neural
function approximation as the $\epsilon$-greedy exploration is not enough to
ensure ``optimism''. Our analysis reformulates the temporal difference error in
an $L^2(\mathrm{d}\mu)$-integrable space over a certain averaged measure $\mu$,
and transforms it to a generalization problem under the non-iid setting. This
might have its own interest in RL theory for better understanding
$\epsilon$-greedy exploration in deep RL.

We show that combining human prior knowledge with end-to-end learning can
improve the robustness of deep neural networks by introducing a part-based
model for object classification. We believe that the richer form of annotation
helps guide neural networks to learn more robust features without requiring
more samples or larger models. Our model combines a part segmentation model
with a tiny classifier and is trained end-to-end to simultaneously segment
objects into parts and then classify the segmented object. Empirically, our
part-based models achieve both higher accuracy and higher adversarial
robustness than a ResNet-50 baseline on all three datasets. For instance, the
clean accuracy of our part models is up to 15 percentage points higher than the
baseline's, given the same level of robustness. Our experiments indicate that
these models also reduce texture bias and yield better robustness against
common corruptions and spurious correlations. The code is publicly available at
https://github.com/chawins/adv-part-model.

This paper introduces a new interpretation of the Variational Autoencoder
framework by taking a fully geometric point of view. We argue that vanilla VAE
models unveil naturally a Riemannian structure in their latent space and that
taking into consideration those geometrical aspects can lead to better
interpolations and an improved generation procedure. This new proposed sampling
method consists in sampling from the uniform distribution deriving
intrinsically from the learned Riemannian latent space and we show that using
this scheme can make a vanilla VAE competitive and even better than more
advanced versions on several benchmark datasets. Since generative models are
known to be sensitive to the number of training samples we also stress the
method's robustness in the low data regime.

We present a minimax optimal learner for the problem of learning predictors
robust to adversarial examples at test-time. Interestingly, we find that this
requires new algorithmic ideas and approaches to adversarially robust learning.
In particular, we show, in a strong negative sense, the suboptimality of the
robust learner proposed by Montasser, Hanneke, and Srebro (2019) and a broader
family of learners we identify as local learners. Our results are enabled by
adopting a global perspective, specifically, through a key technical
contribution: the global one-inclusion graph, which may be of independent
interest, that generalizes the classical one-inclusion graph due to Haussler,
Littlestone, and Warmuth (1994). Finally, as a byproduct, we identify a
dimension characterizing qualitatively and quantitatively what classes of
predictors $\mathcal{H}$ are robustly learnable. This resolves an open problem
due to Montasser et al. (2019), and closes a (potentially) infinite gap between
the established upper and lower bounds on the sample complexity of
adversarially robust learning.

Complex systems are ubiquitous in the real world and tend to have complicated
and poorly understood dynamics. For their control issues, the challenge is to
guarantee accuracy, robustness, and generalization in such bloated and troubled
environments. Fortunately, a complex system can be divided into multiple
modular structures that human cognition appears to exploit. Inspired by this
cognition, a novel control method, Causal Coupled Mechanisms (CCMs), is
proposed that explores the cooperation in division and competition in
combination. Our method employs the theory of hierarchical reinforcement
learning (HRL), in which 1) the high-level policy with competitive awareness
divides the whole complex system into multiple functional mechanisms, and 2)
the low-level policy finishes the control task of each mechanism. Specifically
for cooperation, a cascade control module helps the series operation of CCMs,
and a forward coupled reasoning module is used to recover the coupling
information lost in the division process. On both synthetic systems and a
real-world biological regulatory system, the CCM method achieves robust and
state-of-the-art control results even with unpredictable random noise.
Moreover, generalization results show that reusing prepared specialized CCMs
helps to perform well in environments with different confounders and dynamics.

The fifth and sixth generations of wireless communication networks are
enabling tools such as internet of things devices, unmanned aerial vehicles
(UAVs), and artificial intelligence, to improve the agricultural landscape
using a network of devices to automatically monitor farmlands. Surveying a
large area requires performing a lot of image classification tasks within a
specific period of time in order to prevent damage to the farm in case of an
incident, such as fire or flood. UAVs have limited energy and computing power,
and may not be able to perform all of the intense image classification tasks
locally and within an appropriate amount of time. Hence, it is assumed that the
UAVs are able to partially offload their workload to nearby multi-access edge
computing devices. The UAVs need a decision-making algorithm that will decide
where the tasks will be performed, while also considering the time constraints
and energy level of the other UAVs in the network. In this paper, we introduce
a Deep Q-Learning (DQL) approach to solve this multi-objective problem. The
proposed method is compared with Q-Learning and three heuristic baselines, and
the simulation results show that our proposed DQL-based method achieves
comparable results when it comes to the UAVs' remaining battery levels and
percentage of deadline violations. In addition, our method is able to reach
convergence 13 times faster than Q-Learning.

Abstraction has been widely studied as a way to improve the efficiency and
generalization of reinforcement learning algorithms. In this paper, we study
abstraction in the continuous-control setting. We extend the definition of MDP
homomorphisms to encompass continuous actions in continuous state spaces. We
derive a policy gradient theorem on the abstract MDP, which allows us to
leverage approximate symmetries of the environment for policy optimization.
Based on this theorem, we propose an actor-critic algorithm that is able to
learn the policy and the MDP homomorphism map simultaneously, using the lax
bisimulation metric. We demonstrate the effectiveness of our method on
benchmark tasks in the DeepMind Control Suite. Our method's ability to utilize
MDP homomorphisms for representation learning leads to improved performance
when learning from pixel observations.

The substantial increase in data volume and complexity expected from future
experiments will require significant investment to prepare experimental
algorithms. These algorithms include physics object reconstruction,
calibrations, and processing of observational data. In addition, the changing
computing architecture landscape, which will be primarily composed of
heterogeneous resources, will continue to pose major challenges with regard to
algorithmic migration. Portable tools need to be developed that can be shared
among the frontiers (e.g., for code execution on different platforms) and
opportunities, such as forums or cross-experimental working groups, need to be
provided where experiences and lessons learned can be shared between
experiments and frontiers. At the same time, individual experiments also need
to invest considerable resources to develop algorithms unique to their needs
(e.g., for facilities dedicated to the experiment), and ensure that their
specific algorithms will be able to efficiently exploit external heterogeneous
computing facilities. Common software tools represent a cost-effective
solution, providing ready-to-use software solutions as well as a platform for
R\&D work. These are particularly important for small experiments which
typically do not have dedicated resources needed to face the challenges imposed
by the evolving computing technologies. Workforce development is a key concern
across frontiers and experiments, and additional support is needed to provide
career opportunities for researchers working in the field of experimental
algorithm development. Finally, cross-discipline collaborations going beyond
high-energy physics are a key ingredient to address the challenges ahead and
more support for such collaborations needs to be created. This report targets
future experiments, observations and experimental algorithm development for the
next 10-15 years.

A parallel corpus is generally required to automatically evaluate the
translation quality using the metrics, such as BLEU, METEOR and BERTScore.
While the reference-based evaluation paradigm is widely used in many machine
translation tasks, it is difficult to be applied to translation with
low-resource languages, as those languages suffer from a deficiency of corpora.
Round-trip translation provides an encouraging way to alleviate the urgent
requirement of the parallel corpus, although it was unfortunately not observed
to correlate with forwarding translation in the era of statistical machine
translation. In this paper, we firstly observe that forward translation quality
consistently correlates to corresponding round-trip translation quality in the
scope of neural machine translation. Then, we carefully analyse and unveil the
reason for the contradictory results on statistical machine translation
systems. Secondly, we propose a simple yet effective regression method to
predict the performance of forward translation scores based on round-trip
translation scores for various language pairs, including those between very
low-resource languages. We conduct extensive experiments to show the
effectiveness and robustness of the predictive models on 1,000+ language pairs.
Finally, we test our method on challenging settings, such as predicting scores:
i) for unseen language pairs in training and ii) on real-world WMT shared tasks
but in new domains. The extensive experiments demonstrate the robustness and
utility of our approach. We believe our work will inspire works on very
low-resource multilingual machine translation.

In this letter, we address blockage detection and precoder design for
multiple-input multiple-output (MIMO) links, without communication overhead
required. Blockage detection is achieved by classifying light detection and
ranging (LIDAR) data through a physics-based graph neural network (GNN). For
precoder design, a preliminary channel estimate is obtained by running ray
tracing on a 3D surface obtained from LIDAR data. This estimate is successively
refined and the precoder is designed accordingly. Numerical simulations show
that blockage detection is successful with 95% accuracy. Our digital precoding
achieves 90% of the capacity and analog precoding outperforms previous works
exploiting LIDAR for precoder design.

As deep learning is now used in many real-world applications, research has
focused increasingly on the privacy of deep learning models and how to prevent
attackers from obtaining sensitive information about the training data.
However, image-text models like CLIP have not yet been looked at in the context
of privacy attacks. While membership inference attacks aim to tell whether a
specific data point was used for training, we introduce a new type of privacy
attack, named identity inference attack (IDIA), designed for multi-modal
image-text models like CLIP. Using IDIAs, an attacker can reveal whether a
particular person, was part of the training data by querying the model in a
black-box fashion with different images of the same person. Letting the model
choose from a wide variety of possible text labels, the attacker can probe the
model whether it recognizes the person and, therefore, was used for training.
Through several experiments on CLIP, we show that the attacker can identify
individuals used for training with very high accuracy and that the model learns
to connect the names with the depicted people. Our experiments show that a
multi-modal image-text model indeed leaks sensitive information about its
training data and, therefore, should be handled with care.

Adversarial training (AT) methods are effective against adversarial attacks,
yet they introduce severe disparity of accuracy and robustness between
different classes, known as the robust fairness problem. Previously proposed
Fair Robust Learning (FRL) adaptively reweights different classes to improve
fairness. However, the performance of the better-performed classes decreases,
leading to a strong performance drop. In this paper, we observed two unfair
phenomena during adversarial training: different difficulties in generating
adversarial examples from each class (source-class fairness) and disparate
target class tendencies when generating adversarial examples (target-class
fairness). From the observations, we propose Balance Adversarial Training (BAT)
to address the robust fairness problem. Regarding source-class fairness, we
adjust the attack strength and difficulties of each class to generate samples
near the decision boundary for easier and fairer model learning; considering
target-class fairness, by introducing a uniform distribution constraint, we
encourage the adversarial example generation process for each class with a fair
tendency. Extensive experiments conducted on multiple datasets (CIFAR-10,
CIFAR-100, and ImageNette) demonstrate that our method can significantly
outperform other baselines in mitigating the robust fairness problem (+5-10\%
on the worst class accuracy)

We study best-arm identification with a fixed budget and contextual
(covariate) information in stochastic multi-armed bandit problems. In each
round, after observing contextual information, we choose a treatment arm using
past observations and current context. Our goal is to identify the best
treatment arm, a treatment arm with the maximal expected reward marginalized
over the contextual distribution, with a minimal probability of
misidentification. First, we derive semiparametric lower bounds of the
misidentification probability for this problem, where we regard the gaps
between the expected rewards of the best and suboptimal treatment arms as
parameters of interest, and all other parameters, such as the expected rewards
conditioned on contexts, as the nuisance parameters. We then develop the
``Contextual RS-AIPW strategy,'' which consists of the random sampling (RS)
rule tracking a target allocation ratio and the recommendation rule using the
augmented inverse probability weighting (AIPW) estimator. Our proposed
Contextual RS-AIPW strategy is optimal because the upper bound for the
probability of misidentification by the strategy matches the semiparametric
lower bound, when the budget goes to infinity and the gaps converge to zero.

The traditional Machine Learning (ML) methodology requires to fragment the
development and experimental process into disconnected iterations whose
feedback is used to guide design or tuning choices. This methodology has
multiple efficiency and scalability disadvantages, such as leading to spend
significant resources into the creation of multiple trial models that do not
contribute to the final solution.The presented work is based on the intuition
that defining ML models as modular and extensible artefacts allows to introduce
a novel ML development methodology enabling the integration of multiple design
and evaluation iterations into the continuous enrichment of a single unbounded
intelligent system. We define a novel method for the generation of dynamic
multitask ML models as a sequence of extensions and generalizations. We first
analyze the capabilities of the proposed method by using the standard ML
empirical evaluation methodology. Finally, we propose a novel continuous
development methodology that allows to dynamically extend a pre-existing
multitask large-scale ML system while analyzing the properties of the proposed
method extensions. This results in the generation of an ML model capable of
jointly solving 124 image classification tasks achieving state of the art
quality with improved size and compute cost.

Lack of stability guarantees strongly limits the use of reinforcement
learning (RL) in safety critical robotic applications. Here we propose a
control system architecture for continuous RL control and derive corresponding
stability theorems via contraction analysis, yielding constraints on the
network weights to ensure stability. The control architecture can be
implemented in general RL algorithms and improve their stability, robustness,
and sample efficiency. We demonstrate the importance and benefits of such
guarantees for RL on two standard examples, PPO learning of a 2D problem and
HIRO learning of maze tasks.

Driven by the need to accelerate numerical simulations, the use of machine
learning techniques is rapidly growing in the field of computational solid
mechanics. Their application is especially advantageous in concurrent
multiscale finite element analysis (FE$^2$) due to the exceedingly high
computational costs often associated with it and the high number of similar
micromechanical analyses involved. To tackle the issue, using surrogate models
to approximate the microscopic behavior and accelerate the simulations is a
promising and increasingly popular strategy. However, several challenges
related to their data-driven nature compromise the reliability of surrogate
models in material modeling. The alternative explored in this work is to
reintroduce some of the physics-based knowledge of classical constitutive
modeling into a neural network by employing the actual material models used in
the full-order micromodel to introduce non-linearity. Thus, path-dependency
arises naturally since every material model in the layer keeps track of its own
internal variables. For the numerical examples, a composite Representative
Volume Element with elastic fibers and elasto-plastic matrix material is used
as the microscopic model. The network is tested in a series of challenging
scenarios and its performance is compared to that of a state-of-the-art
Recurrent Neural Network (RNN). A remarkable outcome of the novel framework is
the ability to naturally predict unloading/reloading behavior without ever
seeing it during training, a stark contrast with popular but data-hungry models
such as RNNs. Finally, the proposed network is applied to FE$^2$ examples to
assess its robustness for application in nonlinear finite element analysis.

We show how to take a regression function $\hat{f}$ that is appropriately
``multicalibrated'' and efficiently post-process it into an approximately error
minimizing classifier satisfying a large variety of fairness constraints. The
post-processing requires no labeled data, and only a modest amount of unlabeled
data and computation. The computational and sample complexity requirements of
computing $\hat f$ are comparable to the requirements for solving a single fair
learning task optimally, but it can in fact be used to solve many different
downstream fairness-constrained learning problems efficiently. Our
post-processing method easily handles intersecting groups, generalizing prior
work on post-processing regression functions to satisfy fairness constraints
that only applied to disjoint groups. Our work extends recent work showing that
multicalibrated regression functions are ``omnipredictors'' (i.e. can be
post-processed to optimally solve unconstrained ERM problems) to constrained

Point process models are of great importance in real world applications. In
certain critical applications, estimation of point process models involves
large amounts of sensitive personal data from users. Privacy concerns naturally
arise which have not been addressed in the existing literature. To bridge this
glaring gap, we propose the first general differentially private estimation
procedure for point process models. Specifically, we take the Hawkes process as
an example, and introduce a rigorous definition of differential privacy for
event stream data based on a discretized representation of the Hawkes process.
We then propose two differentially private optimization algorithms, which can
efficiently estimate Hawkes process models with the desired privacy and utility
guarantees under two different settings. Experiments are provided to back up
our theoretical analysis.

Graph neural networks (GNNs) have drawn more and more attention from material
scientists and demonstrated a high capacity to establish connections between
the structure and properties. However, with only unrelaxed structures provided
as input, few GNN models can predict the thermodynamic properties of relaxed
configurations with an acceptable level of error. In this work, we develop a
multi-task (MT) architecture based on DimeNet++ and mixture density networks to
improve the performance of such task. Taking CO adsorption on Cu-based
single-atom alloy catalysts as an illustration, we show that our method can
reliably estimate CO adsorption energy with a mean absolute error of 0.087 eV
from the initial CO adsorption structures without costly first-principles
calculations. Further, compared to other state-of-the-art GNN methods, our
model exhibits improved generalization ability when predicting catalytic
performance of out-of-domain configurations, built with either unseen substrate
surfaces or doping species. We show that the proposed MT GNN strategy can
facilitate catalyst discovery.

In this work, we study the simple yet universally applicable case of reward
shaping in value-based Deep Reinforcement Learning (DRL). We show that reward
shifting in the form of the linear transformation is equivalent to changing the
initialization of the $Q$-function in function approximation. Based on such an
equivalence, we bring the key insight that a positive reward shifting leads to
conservative exploitation, while a negative reward shifting leads to
curiosity-driven exploration. Accordingly, conservative exploitation improves
offline RL value estimation, and optimistic value estimation improves
exploration for online RL. We validate our insight on a range of RL tasks and
show its improvement over baselines: (1) In offline RL, the conservative
exploitation leads to improved performance based on off-the-shelf algorithms;
(2) In online continuous control, multiple value functions with different
shifting constants can be used to tackle the exploration-exploitation dilemma
for better sample efficiency; (3) In discrete control tasks, a negative reward
shifting yields an improvement over the curiosity-based exploration method.

In this paper, we propose to adopt the MDE paradigm for the development of
Machine Learning (ML)-enabled software systems with a focus on the Internet of
Things (IoT) domain. We illustrate how two state-of-the-art open-source
modeling tools, namely MontiAnna and ML-Quadrat can be used for this purpose as
demonstrated through a case study. The case study illustrates using ML, in
particular deep Artificial Neural Networks (ANNs), for automated image
recognition of handwritten digits using the MNIST reference dataset, and
integrating the machine learning components into an IoT system. Subsequently,
we conduct a functional comparison of the two frameworks, setting out an
analysis base to include a broad range of design considerations, such as the
problem domain, methods for the ML integration into larger systems, and
supported ML methods, as well as topics of recent intense interest to the ML
community, such as AutoML and MLOps. Accordingly, this paper is focused on
elucidating the potential of the MDE approach in the ML domain. This supports
the ML engineer in developing the (ML/software) model rather than implementing
the code, and additionally enforces reusability and modularity of the design
through enabling the out-of-the-box integration of ML functionality as a
component of the IoT or cyber-physical systems.

Due to the rapid development of autonomous driving, the Internet of Things
and streaming services, modern communication systems have to cope with varying
channel conditions and a steadily rising number of users and devices. This, and
the still rising bandwidth demands, can only be met by intelligent network
automation, which requires highly flexible and blind transceiver algorithms. To
tackle those challenges, we propose a novel adaptive equalization scheme, which
exploits the prosperous advances in deep learning by training an equalizer with
an adversarial network. The learning is only based on the statistics of the
transmit signal, so it is blind regarding the actual transmit symbols and
agnostic to the channel model. The proposed approach is independent of the
equalizer topology and enables the application of powerful neural network based
equalizers. In this work, we prove this concept in simulations of different --
both linear and nonlinear -- transmission channels and demonstrate the
capability of the proposed blind learning scheme to approach the performance of
non-blind equalizers. Furthermore, we provide a theoretical perspective and
highlight the challenges of the approach.

The rise of simulation environments has enabled learning-based approaches for
assembly planning, which is otherwise a labor-intensive and daunting task.
Assembling furniture is especially interesting since furniture are intricate
and pose challenges for learning-based approaches. Surprisingly, humans can
solve furniture assembly mostly given a 2D snapshot of the assembled product.
Although recent years have witnessed promising learning-based approaches for
furniture assembly, they assume the availability of correct connection labels
for each assembly step, which are expensive to obtain in practice. In this
paper, we alleviate this assumption and aim to solve furniture assembly with as
little human expertise and supervision as possible. To be specific, we assume
the availability of the assembled point cloud, and comparing the point cloud of
the current assembly and the point cloud of the target product, obtain a novel
reward signal based on two measures: Incorrectness and incompleteness. We show
that our novel reward signal can train a deep network to successfully assemble
different types of furniture. Code and networks available here:
https://github.com/METU-KALFA/AssembleRL

We study the average robustness notion in deep neural networks in (selected)
wide and narrow, deep and shallow, as well as lazy and non-lazy training
settings. We prove that in the under-parameterized setting, width has a
negative effect while it improves robustness in the over-parameterized setting.
The effect of depth closely depends on the initialization and the training
mode. In particular, when initialized with LeCun initialization, depth helps
robustness with the lazy training regime. In contrast, when initialized with
Neural Tangent Kernel (NTK) and He-initialization, depth hurts the robustness.
Moreover, under the non-lazy training regime, we demonstrate how the width of a
two-layer ReLU network benefits robustness. Our theoretical developments
improve the results by [Huang et al. NeurIPS21; Wu et al. NeurIPS21] and are
consistent with [Bubeck and Sellke NeurIPS21; Bubeck et al. COLT21].

Several recent works have focused on the search for bright, high-z quasars
(QSOs) in the South. Among them, the QUasars as BRIght beacons for Cosmology in
the Southern hemisphere (QUBRICS) survey has now delivered hundreds of new
spectroscopically confirmed QSOs selected by means of machine learning
algorithms. Building upon the results obtained by introducing the probabilistic
random forest (PRF) for the QUBRICS selection, we explore in this work the
feasibility of training the algorithm on synthetic data to improve the
completeness in the higher redshift bins. We also compare the performances of
the algorithm if colours are used as primary features instead of magnitudes. We
generate synthetic data based on a composite QSO spectral energy distribution.
We first train the PRF to identify QSOs among stars and galaxies, then separate
high-z quasar from low-z contaminants. We apply the algorithm on an updated
dataset, based on SkyMapper DR3, combined with Gaia eDR3, 2MASS and WISE
magnitudes. We find that employing colours as features slightly improves the
results with respect to the algorithm trained on magnitude data. Adding
synthetic data to the training set provides significantly better results with
respect to the PRF trained only on spectroscopically confirmed QSOs. We
estimate, on a testing dataset, a completeness of ~86% and a contamination of
~36%. Finally, 207 PRF-selected candidates were observed: 149/207 turned out to
be genuine QSOs with z > 2.5, 41 with z < 2.5, 3 galaxies and 14 stars. The
result confirms the ability of the PRF to select high-z quasars in large
datasets.

Multiple-objective optimization (MOO) aims to simultaneously optimize
multiple conflicting objectives and has found important applications in machine
learning, such as minimizing classification loss and discrepancy in treating
different populations for fairness. At optimality, further optimizing one
objective will necessarily harm at least another objective, and decision-makers
need to comprehensively explore multiple optima (called Pareto front) to
pinpoint one final solution. We address the efficiency of finding the Pareto
front. First, finding the front from scratch using stochastic multi-gradient
descent (SMGD) is expensive with large neural networks and datasets. We propose
to explore the Pareto front as a manifold from a few initial optima, based on a
predictor-corrector method. Second, for each exploration step, the predictor
solves a large-scale linear system that scales quadratically in the number of
model parameters and requires one backpropagation to evaluate a second-order
Hessian-vector product per iteration of the solver. We propose a Gauss-Newton
approximation that only scales linearly, and that requires only first-order
inner-product per iteration. This also allows for a choice between the MINRES
and conjugate gradient methods when approximately solving the linear system.
The innovations make predictor-corrector possible for large networks.
Experiments on multi-objective (fairness and accuracy) misinformation detection
tasks show that 1) the predictor-corrector method can find Pareto fronts better
than or similar to SMGD with less time; and 2) the proposed first-order method
does not harm the quality of the Pareto front identified by the second-order
method, while further reduce running time.

Neural Architecture Search (NAS) has fostered the automatic discovery of
state-of-the-art neural architectures. Despite the progress achieved with NAS,
so far there is little attention to theoretical guarantees on NAS. In this
work, we study the generalization properties of NAS under a unifying framework
enabling (deep) layer skip connection search and activation function search. To
this end, we derive the lower (and upper) bounds of the minimum eigenvalue of
the Neural Tangent Kernel (NTK) under the (in)finite-width regime using a
certain search space including mixed activation functions, fully connected, and
residual neural networks. We use the minimum eigenvalue to establish
generalization error bounds of NAS in the stochastic gradient descent training.
Importantly, we theoretically and experimentally show how the derived results
can guide NAS to select the top-performing architectures, even in the case
without training, leading to a train-free algorithm based on our theory.
Accordingly, our numerical validation shed light on the design of
computationally efficient methods for NAS. Our analysis is non-trivial due to
the coupling of various architectures and activation functions under the
unifying framework and has its own interest in providing the lower bound of the
minimum eigenvalue of NTK in deep learning theory.

Polynomial Networks (PNs) have demonstrated promising performance on face and
image recognition recently. However, robustness of PNs is unclear and thus
obtaining certificates becomes imperative for enabling their adoption in
real-world applications. Existing verification algorithms on ReLU neural
networks (NNs) based on classical branch and bound (BaB) techniques cannot be
trivially applied to PN verification. In this work, we devise a new bounding
method, equipped with BaB for global convergence guarantees, called
Verification of Polynomial Networks or VPN for short. One key insight is that
we obtain much tighter bounds than the interval bound propagation (IBP) and
DeepT-Fast [Bonaert et al., 2021] baselines. This enables sound and complete PN
verification with empirical validation on MNIST, CIFAR10 and STL10 datasets. We
believe our method has its own interest to NN verification. The source code is
publicly available at https://github.com/megaelius/PNVerification.

We study the problem of high-dimensional sparse linear regression in a
distributed setting under both computational and communication constraints.
Specifically, we consider a star topology network whereby several machines are
connected to a fusion center, with whom they can exchange relatively short
messages. Each machine holds noisy samples from a linear regression model with
the same unknown sparse $d$-dimensional vector of regression coefficients
$\theta$. The goal of the fusion center is to estimate the vector $\theta$ and
its support using few computations and limited communication at each machine.
In this work, we consider distributed algorithms based on Orthogonal Matching
Pursuit (OMP) and theoretically study their ability to exactly recover the
support of $\theta$. We prove that under certain conditions, even at low
signal-to-noise-ratios where individual machines are unable to detect the
support of $\theta$, distributed-OMP methods correctly recover it with total
communication sublinear in $d$. In addition, we present simulations that
illustrate the performance of distributed OMP-based algorithms and show that
they perform similarly to more sophisticated and computationally intensive
methods, and in some cases even outperform them.

Multi-agent reinforcement learning (MARL) recently has achieved tremendous
success in a wide range of fields. However, with a black-box neural network
architecture, existing MARL methods make decisions in an opaque fashion that
hinders humans from understanding the learned knowledge and how input
observations influence decisions. Our solution is MIXing Recurrent soft
decision Trees (MIXRTs), a novel interpretable architecture that can represent
explicit decision processes via the root-to-leaf path of decision trees. We
introduce a novel recurrent structure in soft decision trees to address partial
observability, and estimate joint action values via linearly mixing outputs of
recurrent trees based on local observations only. Theoretical analysis shows
that MIXRTs guarantees the structural constraint with additivity and
monotonicity in factorization. We evaluate MIXRTs on a range of challenging
StarCraft II tasks. Experimental results show that our interpretable learning
framework obtains competitive performance compared to widely investigated
baselines, and delivers more straightforward explanations and domain knowledge
of the decision processes.

Determining an appropriate number of attention heads on one hand and the
number of transformer-encoders, on the other hand, is an important choice for
Computer Vision (CV) tasks using the Transformer architecture. Computing
experiments confirmed the expectation that the total number of parameters has
to satisfy the condition of overdetermination (i.e., number of constraints
significantly exceeding the number of parameters). Then, good generalization
performance can be expected. This sets the boundaries within which the number
of heads and the number of transformers can be chosen. If the role of context
in images to be classified can be assumed to be small, it is favorable to use
multiple transformers with a low number of heads (such as one or two). In
classifying objects whose class may heavily depend on the context within the
image (i.e., the meaning of a patch being dependent on other patches), the
number of heads is equally important as that of transformers.

For the past decades, face recognition (FR) has been actively studied in
computer vision and pattern recognition society. Recently, due to the advances
in deep learning, the FR technology shows high performance for most of the
benchmark datasets. However, when the FR algorithm is applied to a real-world
scenario, the performance has been known to be still unsatisfactory. This is
mainly attributed to the mismatch between training and testing sets. Among such
mismatches, face misalignment between training and testing faces is one of the
factors that hinder successful FR. To address this limitation, we propose a
face shape-guided deep feature alignment framework for FR robust to the face
misalignment. Based on a face shape prior (e.g., face keypoints), we train the
proposed deep network by introducing alignment processes, i.e., pixel and
feature alignments, between well-aligned and misaligned face images. Through
the pixel alignment process that decodes the aggregated feature extracted from
a face image and face shape prior, we add the auxiliary task to reconstruct the
well-aligned face image. Since the aggregated features are linked to the face
feature extraction network as a guide via the feature alignment process, we
train the robust face feature to the face misalignment. Even if the face shape
estimation is required in the training stage, the additional face alignment
process, which is usually incorporated in the conventional FR pipeline, is not
necessarily needed in the testing phase. Through the comparative experiments,
we validate the effectiveness of the proposed method for the face misalignment
with the FR datasets.

The commitment to single-precision floating-point arithmetic is widespread in
the deep learning community. To evaluate whether this commitment is justified,
the influence of computing precision (single and double precision) on the
optimization performance of the Conjugate Gradient (CG) method (a second-order
optimization algorithm) and RMSprop (a first-order algorithm) has been
investigated. Tests of neural networks with one to five fully connected hidden
layers and moderate or strong nonlinearity with up to 4 million network
parameters have been optimized for Mean Square Error (MSE). The training tasks
have been set up so that their MSE minimum was known to be zero. Computing
experiments have disclosed that single-precision can keep up (with superlinear
convergence) with double-precision as long as line search finds an improvement.
First-order methods such as RMSprop do not benefit from double precision.
However, for moderately nonlinear tasks, CG is clearly superior. For strongly
nonlinear tasks, both algorithm classes find only solutions fairly poor in
terms of mean square error as related to the output variance. CG with double
floating-point precision is superior whenever the solutions have the potential
to be useful for the application goal.

The highest level in the Endsley situation awareness model is called
projection when the status of elements in the environment in the near future is
predicted. In cybersecurity situation awareness, the projection for an Advanced
Persistent Threat (APT) requires predicting the next step of the APT. The
threats are constantly changing and becoming more complex. As supervised and
unsupervised learning methods require APT datasets for projecting the next step
of APTs, they are unable to identify unknown APT threats. In reinforcement
learning methods, the agent interacts with the environment, and so it might
project the next step of known and unknown APTs. So far, reinforcement learning
has not been used to project the next step for APTs. In reinforcement learning,
the agent uses the previous states and actions to approximate the best action
of the current state. When the number of states and actions is abundant, the
agent employs a neural network which is called deep learning to approximate the
best action of each state. In this paper, we present a deep reinforcement
learning system to project the next step of APTs. As there exists some relation
between attack steps, we employ the Long- Short-Term Memory (LSTM) method to
approximate the best action of each state. In our proposed system, based on the
current situation, we project the next steps of APT threats.

The effective localization of mitosis is a critical precursory task for
deciding tumor prognosis and grade. Automated mitosis detection through deep
learning-oriented image analysis often fails on unseen patient data due to
inherent domain biases. This paper proposes a domain homogenizer for mitosis
detection that attempts to alleviate domain differences in histology images via
adversarial reconstruction of input images. The proposed homogenizer is based
on a U-Net architecture and can effectively reduce domain differences commonly
seen with histology imaging data. We demonstrate our domain homogenizer's
effectiveness by observing the reduction in domain differences between the
preprocessed images. Using this homogenizer, along with a subsequent retina-net
object detector, we were able to outperform the baselines of the 2021 MIDOG
challenge in terms of average precision of the detected mitotic figures.

In recent years, deep learning methods have become increasingly capable of
generating near photorealistic pictures and humanlike text up to the point that
humans can no longer recognize what is real and what is AI-generated.
Concerningly, there is evidence that some of these methods have already been
adopted to produce fake social media profiles and content. We hypothesize that
these advances have made detecting generated fake social media content in the
feed extremely difficult, if not impossible, for the average user of social
media. This paper presents the results of an experiment where 375 participants
attempted to label real and generated profiles and posts in a simulated social
media feed. The results support our hypothesis and suggest that even
fully-generated fake profiles with posts written by an advanced text generator
are difficult for humans to identify.

Breast tumor segmentation is one of the key steps that helps us characterize
and localize tumor regions. However, variable tumor morphology, blurred
boundary, and similar intensity distributions bring challenges for accurate
segmentation of breast tumors. Recently, many U-net variants have been proposed
and widely used for breast tumors segmentation. However, these architectures
suffer from two limitations: (1) Ignoring the characterize ability of the
benchmark networks, and (2) Introducing extra complex operations increases the
difficulty of understanding and reproducing the network. To alleviate these
challenges, this paper proposes a simple yet powerful nested U-net (NU-net) for
accurate segmentation of breast tumors. The key idea is to utilize U-Nets with
different depths and shared weights to achieve robust characterization of
breast tumors. NU-net mainly has the following advantages: (1) Improving
network adaptability and robustness to breast tumors with different scales, (2)
This method is easy to reproduce and execute, and (3) The extra operations
increase network parameters without significantly increasing computational
cost. Extensive experimental results with twelve state-of-the-art segmentation
methods on three public breast ultrasound datasets demonstrate that NU-net has
more competitive segmentation performance on breast tumors. Furthermore, the
robustness of NU-net is further illustrated on the segmentation of renal
ultrasound images. The source code is publicly available on
https://github.com/CGPzy/NU-net.

Over the last ten years, the popularity of Machine Learning (ML) has grown
exponentially in all scientific fields, including particle physics. The
industry has also developed new powerful tools that, imported into academia,
could revolutionise research. One recent industry development that has not yet
come to the attention of the particle physics community is Collaborative
Learning (CL), a framework that allows training the same ML model with
different datasets. This work explores the potential of CL, testing the library
Colearn with neutrino physics simulation. Colearn, developed by the British
Cambridge-based firm Fetch.AI, enables decentralised machine learning tasks.
Being a blockchain-mediated CL system, it allows multiple stakeholders to build
a shared ML model without needing to rely on a central authority. A generic
Liquid Argon Time-Projection Chamber (LArTPC) has been simulated and images
produced by fictitious neutrino interactions have been used to produce several
datasets. These datasets, called learners, participated successfully in
training a Deep Learning (DL) Keras model using blockchain technologies in a
decentralised way. This test explores the feasibility of training a single ML
model using different simulation datasets coming from different research
groups. In this work, we also discuss a framework that instead makes different
ML models compete against each other on the same dataset. The final goal is
then to train the most performant ML model across the entire scientific
community for a given experiment, either using all of the datasets available or
selecting the model which performs best among every model developed in the

Given a discriminating neural network, the problem of fairness improvement is
to systematically reduce discrimination without significantly scarifies its
performance (i.e., accuracy). Multiple categories of fairness improving methods
have been proposed for neural networks, including pre-processing, in-processing
and post-processing. Our empirical study however shows that these methods are
not always effective (e.g., they may improve fairness by paying the price of
huge accuracy drop) or even not helpful (e.g., they may even worsen both
fairness and accuracy). In this work, we propose an approach which adaptively
chooses the fairness improving method based on causality analysis. That is, we
choose the method based on how the neurons and attributes responsible for
unfairness are distributed among the input attributes and the hidden neurons.
Our experimental evaluation shows that our approach is effective (i.e., always
identify the best fairness improving method) and efficient (i.e., with an
average time overhead of 5 minutes).

Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic
fuzzy variables as antecedents and consequent to represent the human
understandable knowledge. They have been applied to various applications and
areas throughout the literature. However, FRBSs suffers from many drawbacks
such as uncertainty representation, high number of rules, interpretability
loss, high computational time for learning etc. To overcome these issues with
FRBSs, there exists many extentions of FRBSs. In this paper, we present an
overview and literature review for various types and prominent areas of fuzzy
systems (FRBSs) namely genetic fuzzy system (GFS), Hierarchical fuzzy system
(HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for big
data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which uses
cluster centroids as fuzzy rule, during the years 2010-2021. GFS uses
genetic/evolutionary approaches to improve the learning ability of FRBSs, HFS
solve the curse of dimensionality for FRBSs, NFS improves approximation ability
of FRBSs using neural networks and dynamic systems for streaming data is
considered in eFS. FRBSs are seen as good solutions for big data and imbalanced
data, in the recent years the interpretability in FRBSs has gained popularity
due to high dimensional and big data and rules are initialized with cluster
centroids to limit the number of rules in FRBSs. This paper also highlights
important contributions, publication statistics and current trends in the
field. The paper also addresses several open research areas which need further
attention from the FRBSs research community.

Graph convolutional neural network (GCN) has drawn increasing attention and
attained good performance in various computer vision tasks, however, there
lacks a clear interpretation of GCN's inner mechanism. For standard
convolutional neural networks (CNNs), class activation mapping (CAM) methods
are commonly used to visualize the connection between CNN's decision and image
region by generating a heatmap. Nonetheless, such heatmap usually exhibits
semantic-chaos when these CAMs are applied to GCN directly. In this paper, we
proposed a novel visualization method particularly applicable to GCN, Vertex
Semantic Class Activation Mapping (VS-CAM). VS-CAM includes two independent
pipelines to produce a set of semantic-probe maps and a semantic-base map,
respectively. Semantic-probe maps are used to detect the semantic information
from semantic-base map to aggregate a semantic-aware heatmap. Qualitative
results show that VS-CAM can obtain heatmaps where the highlighted regions
match the objects much more precisely than CNN-based CAM. The quantitative
evaluation further demonstrates the superiority of VS-CAM.

Spring-based actuators in legged locomotion provide energy-efficiency and
improved performance, but increase the difficulty of controller design. Whereas
previous works have focused on extensive modeling and simulation to find
optimal controllers for such systems, we propose to learn model-free
controllers directly on the real robot. In our approach, gaits are first
synthesized by central pattern generators (CPGs), whose parameters are
optimized to quickly obtain an open-loop controller that achieves efficient
locomotion. Then, to make that controller more robust and further improve the
performance, we use reinforcement learning to close the loop, to learn
corrective actions on top of the CPGs. We evaluate the proposed approach in
DLR's elastic quadruped bert. Our results in learning trotting and pronking
gaits show that exploitation of the spring actuator dynamics emerges naturally
from optimizing for dynamic motions, yielding high-performing locomotion
despite being model-free. The whole process takes no more than 1.5 hours on the
real robot and results in natural-looking gaits.

Data-driven optimization of sampling patterns in MRI has recently received a
significant attention. Following recent observations on the combinatorial
number of minimizers in off-the-grid optimization, we propose a framework to
globally optimize the sampling densities using Bayesian optimization. Using a
dimension reduction technique, we optimize the sampling trajectories more than
20 times faster than conventional off-the-grid methods, with a restricted
number of training samples. This method -- among other benefits -- discards the
need of automatic differentiation. Its performance is slightly worse than
state-of-the-art learned trajectories since it reduces the space of admissible
trajectories, but comes with significant computational advantages. Other
contributions include: i) a careful evaluation of the distance in probability
space to generate trajectories ii) a specific training procedure on families of
operators for unrolled reconstruction networks and iii) a gradient projection
based scheme for trajectory optimization.

Constituency Parse Extraction from Pre-trained Language Models (CPE-PLM) is a
recent paradigm that attempts to induce constituency parse trees relying only
on the internal knowledge of pre-trained language models. While attractive in
the perspective that similar to in-context learning, it does not require
task-specific fine-tuning, the practical effectiveness of such an approach
still remains unclear, except that it can function as a probe for investigating
language models' inner workings. In this work, we mathematically reformulate
CPE-PLM and propose two advanced ensemble methods tailored for it,
demonstrating that the new parsing paradigm can be competitive with common
unsupervised parsers by introducing a set of heterogeneous PLMs combined using
our techniques. Furthermore, we explore some scenarios where the trees
generated by CPE-PLM are practically useful. Specifically, we show that CPE-PLM
is more effective than typical supervised parsers in few-shot settings.

Adaptive Multi-Agent Systems (AMAS) transform dynamic problems into problems
of local cooperation between agents. We present smapy, an ensemble based AMAS
implementation for mobility prediction, whose agents are provided with machine
learning models in addition to their cooperation rules. With a detailed
methodology, we propose a framework to transform a classification problem into
a cooperative tiling of the input variable space. We show that it is possible
to use linear classifiers for online non-linear classification on three
benchmark toy problems chosen for their different levels of linear
separability, if they are integrated in a cooperative Multi-Agent structure.
The results obtained show a significant improvement of the performance of
linear classifiers in non-linear contexts in terms of classification accuracy
and decision boundaries, thanks to the cooperative approach.

This work examines the challenges and opportunities of Machine Learning (ML)
for Monitoring and Operational Data Analytics (MODA) in the context of
Quantitative Codesign of Supercomputers (QCS). MODA is employed to gain
insights into the behavior of current High Performance Computing (HPC) systems
to improve system efficiency, performance, and reliability (e.g. through
optimizing cooling infrastructure, job scheduling, and application parameter
tuning). In this work, we take the position that QCS in general, and MODA in
particular, require close exchange with the ML community to realize the full
potential of data-driven analysis for the benefit of existing and future HPC
systems. This exchange will facilitate identifying the appropriate ML methods
to gain insights into current HPC systems and to go beyond expert-based
knowledge and rules of thumb.

Diagnosis based on medical images, such as X-ray images, often involves
manual annotation of anatomical keypoints. However, this process involves
significant human efforts and can thus be a bottleneck in the diagnostic
process. To fully automate this procedure, deep-learning-based methods have
been widely proposed and have achieved high performance in detecting keypoints
in medical images. However, these methods still have clinical limitations:
accuracy cannot be guaranteed for all cases, and it is necessary for doctors to
double-check all predictions of models. In response, we propose a novel deep
neural network that, given an X-ray image, automatically detects and refines
the anatomical keypoints through a user-interactive system in which doctors can
fix mispredicted keypoints with fewer clicks than needed during manual
revision. Using our own collected data and the publicly available AASCE
dataset, we demonstrate the effectiveness of the proposed method in reducing
the annotation costs via extensive quantitative and qualitative results. A demo
video of our approach is available on our project webpage.

Deep neural networks have brought remarkable breakthroughs in medical image
analysis. However, due to their data-hungry nature, the modest dataset sizes in
medical imaging projects might be hindering their full potential. Generating
synthetic data provides a promising alternative, allowing to complement
training datasets and conducting medical image research at a larger scale.
Diffusion models recently have caught the attention of the computer vision
community by producing photorealistic synthetic images. In this study, we
explore using Latent Diffusion Models to generate synthetic images from
high-resolution 3D brain images. We used T1w MRI images from the UK Biobank
dataset (N=31,740) to train our models to learn about the probabilistic
distribution of brain images, conditioned on covariables, such as age, sex, and
brain structure volumes. We found that our models created realistic data, and
we could use the conditioning variables to control the data generation
effectively. Besides that, we created a synthetic dataset with 100,000 brain
images and made it openly available to the scientific community.

Variational Bayesian posterior inference often requires simplifying
approximations such as mean-field parametrisation to ensure tractability.
However, prior work has associated the variational mean-field approximation for
Bayesian neural networks with underfitting in the case of small datasets or
large model sizes. In this work, we show that invariances in the likelihood
function of over-parametrised models contribute to this phenomenon because
these invariances complicate the structure of the posterior by introducing
discrete and/or continuous modes which cannot be well approximated by Gaussian
mean-field distributions. In particular, we show that the mean-field
approximation has an additional gap in the evidence lower bound compared to a
purpose-built posterior that takes into account the known invariances.
Importantly, this invariance gap is not constant; it vanishes as the
approximation reverts to the prior. We proceed by first considering translation
invariances in a linear model with a single data point in detail. We show that,
while the true posterior can be constructed from a mean-field parametrisation,
this is achieved only if the objective function takes into account the
invariance gap. Then, we transfer our analysis of the linear model to neural
networks. Our analysis provides a framework for future work to explore
solutions to the invariance problem.

In decision-making problems such as the multi-armed bandit, an agent learns
sequentially by optimizing a certain feedback. While the mean reward criterion
has been extensively studied, other measures that reflect an aversion to
adverse outcomes, such as mean-variance or conditional value-at-risk (CVaR),
can be of interest for critical applications (healthcare, agriculture).
Algorithms have been proposed for such risk-aware measures under bandit
feedback without contextual information. In this work, we study contextual
bandits where such risk measures can be elicited as linear functions of the
contexts through the minimization of a convex loss. A typical example that fits
within this framework is the expectile measure, which is obtained as the
solution of an asymmetric least-square problem. Using the method of mixtures
for supermartingales, we derive confidence sequences for the estimation of such
risk measures. We then propose an optimistic UCB algorithm to learn optimal
risk-aware actions, with regret guarantees similar to those of generalized
linear bandits. This approach requires solving a convex problem at each round
of the algorithm, which we can relax by allowing only approximated solution
obtained by online gradient descent, at the cost of slightly higher regret. We
conclude by evaluating the resulting algorithms on numerical experiments.

Tabular data is the foundation of the information age and has been
extensively studied. Recent studies show that neural-based models are effective
in learning contextual representation for tabular data. The learning of an
effective contextual representation requires meaningful features and a large
amount of data. However, current methods often fail to properly learn a
contextual representation from the features without semantic information. In
addition, it's intractable to enlarge the training set through mixed tabular
datasets due to the difference between datasets. To address these problems, we
propose a novel framework PTab, using the Pre-trained language model to model
Tabular data. PTab learns a contextual representation of tabular data through a
three-stage processing: Modality Transformation(MT), Masked-Language
Fine-tuning(MF), and Classification Fine-tuning(CF). We initialize our model
with a pre-trained Model (PTM) which contains semantic information learned from
the large-scale language data. Consequently, contextual representation can be
learned effectively during the fine-tuning stages. In addition, we can
naturally mix the textualized tabular data to enlarge the training set to
further improve representation learning. We evaluate PTab on eight popular
tabular classification datasets. Experimental results show that our method has
achieved a better average AUC score in supervised settings compared to the
state-of-the-art baselines(e.g. XGBoost), and outperforms counterpart methods
under semi-supervised settings. We present visualization results that show PTab
has well instance-based interpretability.

Counterfactual risk minimization is a framework for offline policy
optimization with logged data which consists of context, action, propensity
score, and reward for each sample point. In this work, we build on this
framework and propose a learning method for settings where the rewards for some
samples are not observed, and so the logged data consists of a subset of
samples with unknown rewards and a subset of samples with known rewards. This
setting arises in many application domains, including advertising and
healthcare. While reward feedback is missing for some samples, it is possible
to leverage the unknown-reward samples in order to minimize the risk, and we
refer to this setting as semi-counterfactual risk minimization. To approach
this kind of learning problem, we derive new upper bounds on the true risk
under the inverse propensity score estimator. We then build upon these bounds
to propose a regularized counterfactual risk minimization method, where the
regularization term is based on the logged unknown-rewards dataset only; hence
it is reward-independent. We also propose another algorithm based on generating
pseudo-rewards for the logged unknown-rewards dataset. Experimental results
with neural networks and benchmark datasets indicate that these algorithms can
leverage the logged unknown-rewards dataset besides the logged known-reward
dataset.

In this work, we tackle one-shot visual search of object parts. Given a
single reference image of an object with annotated affordance regions, we
segment semantically corresponding parts within a target scene. We propose
AffCorrs, an unsupervised model that combines the properties of pre-trained
DINO-ViT's image descriptors and cyclic correspondences. We use AffCorrs to
find corresponding affordances both for intra- and inter-class one-shot part
segmentation. This task is more difficult than supervised alternatives, but
enables future work such as learning affordances via imitation and assisted
teleoperation.

Disordered elemental semiconductors, most notably a-C and a-Si, are
ubiquitous in a myriad of different applications. These exploit their unique
mechanical and electronic properties. In the past couple of decades, density
functional theory (DFT) and other quantum mechanics-based computational
simulation techniques have been successful at delivering a detailed
understanding of the atomic and electronic structure of crystalline
semiconductors. Unfortunately, the complex structure of disordered
semiconductors sets the time and length scales required for DFT simulation of
these materials out of reach. In recent years, machine learning (ML) approaches
to atomistic modeling have been developed that provide an accurate
approximation of the DFT potential energy surface for a small fraction of the
computational time. These ML approaches have now reached maturity and are
starting to deliver the first conclusive insights into some of the missing
details surrounding the intricate atomic structure of disordered
semiconductors. In this Topical Review we give a brief introduction to ML
atomistic modeling and its application to amorphous semiconductors. We then
take a look at how ML simulations have been used to improve our current
understanding of the atomic structure of a-C and a-Si.

The variational auto-encoder has become a leading framework for symbolic
music generation, and a popular research direction is to study how to
effectively control the generation process. A straightforward way is to control
a model using different conditions during inference. However, in music
practice, conditions are usually sequential (rather than simple categorical
labels), involving rich information that overlaps with the learned
representation. Consequently, the decoder gets confused about whether to
"listen to" the latent representation or the condition, and sometimes just
ignores the condition. To solve this problem, we leverage domain adversarial
training to disentangle the representation from condition cues for better
control. Specifically, we propose a condition corruption objective that uses
the representation to denoise a corrupted condition. Minimized by a
discriminator and maximized by the VAE encoder, this objective adversarially
induces a condition-invariant representation. In this paper, we focus on the
task of melody harmonization to illustrate our idea, while our methodology can
be generalized to other controllable generative tasks. Demos and experiments
show that our methodology facilitates not only condition-invariant
representation learning but also higher-quality controllability compared to

Video prediction is an important yet challenging problem; burdened with the
tasks of generating future frames and learning environment dynamics. Recently,
autoregressive latent video models have proved to be a powerful video
prediction tool, by separating the video prediction into two sub-problems:
pre-training an image generator model, followed by learning an autoregressive
prediction model in the latent space of the image generator. However,
successfully generating high-fidelity and high-resolution videos has yet to be
seen. In this work, we investigate how to train an autoregressive latent video
prediction model capable of predicting high-fidelity future frames with minimal
modification to existing models, and produce high-resolution (256x256) videos.
Specifically, we scale up prior models by employing a high-fidelity image
generator (VQ-GAN) with a causal transformer model, and introduce additional
techniques of top-k sampling and data augmentation to further improve video
prediction quality. Despite the simplicity, the proposed method achieves
competitive performance to state-of-the-art approaches on standard video
prediction benchmarks with fewer parameters, and enables high-resolution video
prediction on complex and large-scale datasets. Videos are available at
https://sites.google.com/view/harp-videos/home.

This paper presents COOL-MC, a tool that integrates state-of-the-art
reinforcement learning (RL) and model checking. Specifically, the tool builds
upon the OpenAI gym and the probabilistic model checker Storm. COOL-MC provides
the following features: (1) a simulator to train RL policies in the OpenAI gym
for Markov decision processes (MDPs) that are defined as input for Storm, (2) a
new model builder for Storm, which uses callback functions to verify (neural
network) RL policies, (3) formal abstractions that relate models and policies
specified in OpenAI gym or Storm, and (4) algorithms to obtain bounds on the
performance of so-called permissive policies. We describe the components and
architecture of COOL-MC and demonstrate its features on multiple benchmark

Recent years have witnessed the great success of blind image quality
assessment (BIQA) in various task-specific scenarios, which present invariable
distortion types and evaluation criteria. However, due to the rigid structure
and learning framework, they cannot apply to the cross-task BIQA scenario,
where the distortion types and evaluation criteria keep changing in practical
applications. This paper proposes a scalable incremental learning framework
(SILF) that could sequentially conduct BIQA across multiple evaluation tasks
with limited memory capacity. More specifically, we develop a dynamic parameter
isolation strategy to sequentially update the task-specific parameter subsets,
which are non-overlapped with each other. Each parameter subset is temporarily
settled to Remember one evaluation preference toward its corresponding task,
and the previously settled parameter subsets can be adaptively reused in the
following BIQA to achieve better performance based on the task relevance. To
suppress the unrestrained expansion of memory capacity in sequential tasks
learning, we develop a scalable memory unit by gradually and selectively
pruning unimportant neurons from previously settled parameter subsets, which
enable us to Forget part of previous experiences and free the limited memory
capacity for adapting to the emerging new tasks. Extensive experiments on
eleven IQA datasets demonstrate that our proposed method significantly
outperforms the other state-of-the-art methods in cross-task BIQA.

Federated learning (FL) is one of the most appealing alternatives to the
standard centralized learning paradigm, allowing heterogeneous set of devices
to train a machine learning model without sharing their raw data. However, FL
requires a central server to coordinate the learning process, thus introducing
potential scalability and security issues. In the literature, server-less FL
approaches like gossip federated learning (GFL) and blockchain-enabled
federated learning (BFL) have been proposed to mitigate these issues. In this
work, we propose a complete overview of these three techniques proposing a
comparison according to an integral set of performance indicators, including
model accuracy, time complexity, communication overhead, convergence time and
energy consumption. An extensive simulation campaign permits to draw a
quantitative analysis. In particular, GFL is able to save the 18% of training
time, the 68% of energy and the 51% of data to be shared with respect to the
CFL solution, but it is not able to reach the level of accuracy of CFL. On the
other hand, BFL represents a viable solution for implementing decentralized
learning with a higher level of security, at the cost of an extra energy usage
and data sharing. Finally, we identify open issues on the two decentralized
federated learning implementations and provide insights on potential extensions
and possible research directions on this new research field.

Reliable point cloud data is essential for perception tasks \textit{e.g.} in
robotics and autonomous driving applications. Adverse weather causes a specific
type of noise to light detection and ranging (LiDAR) sensor data, which
degrades the quality of the point clouds significantly. To address this issue,
this letter presents a novel point cloud adverse weather denoising deep
learning algorithm (4DenoiseNet). Our algorithm takes advantage of the time
dimension unlike deep learning adverse weather denoising methods in the
literature. It performs about 10\% better in terms of intersection over union
metric compared to the previous work and is more computationally efficient.
These results are achieved on our novel SnowyKITTI dataset, which has over
40000 adverse weather annotated point clouds. Moreover, strong qualitative
results on the Canadian Adverse Driving Conditions dataset indicate good
generalizability to domain shifts and to different sensor intrinsics.

Medical vision-and-language pre-training (Med-VLP) has received considerable
attention owing to its applicability to extracting generic vision-and-language
representations from medical images and texts. Most existing methods mainly
contain three elements: uni-modal encoders (i.e., a vision encoder and a
language encoder), a multi-modal fusion module, and pretext tasks, with few
studies considering the importance of medical domain expert knowledge and
explicitly exploiting such knowledge to facilitate Med-VLP. Although there
exist knowledge-enhanced vision-and-language pre-training (VLP) methods in the
general domain, most require off-the-shelf toolkits (e.g., object detectors and
scene graph parsers), which are unavailable in the medical domain. In this
paper, we propose a systematic and effective approach to enhance Med-VLP by
structured medical knowledge from three perspectives. First, considering
knowledge can be regarded as the intermediate medium between vision and
language, we align the representations of the vision encoder and the language
encoder through knowledge. Second, we inject knowledge into the multi-modal
fusion model to enable the model to perform reasoning using knowledge as the
supplementation of the input image and text. Third, we guide the model to put
emphasis on the most critical information in images and texts by designing
knowledge-induced pretext tasks. To perform a comprehensive evaluation and
facilitate further research, we construct a medical vision-and-language
benchmark including three tasks. Experimental results illustrate the
effectiveness of our approach, where state-of-the-art performance is achieved
on all downstream tasks. Further analyses explore the effects of different
components of our approach and various settings of pre-training.

Decentralized learning offers privacy and communication efficiency when data
are naturally distributed among agents communicating over an underlying graph.
Motivated by overparameterized learning settings, in which models are trained
to zero training loss, we study algorithmic and generalization properties of
decentralized learning with gradient descent on separable data. Specifically,
for decentralized gradient descent (DGD) and a variety of loss functions that
asymptote to zero at infinity (including exponential and logistic losses), we
derive novel finite-time generalization bounds. This complements a long line of
recent work that studies the generalization performance and the implicit bias
of gradient descent over separable data, but has thus far been limited to
centralized learning scenarios. Notably, our generalization bounds match in
order their centralized counterparts. Critical behind this, and of independent
interest, is establishing novel bounds on the training loss and the
rate-of-consensus of DGD for a class of self-bounded losses. Finally, on the
algorithmic front, we design improved gradient-based routines for decentralized
learning with separable data and empirically demonstrate orders-of-magnitude of
speed-up in terms of both training and generalization performance.

We propose a new sensitivity analysis model that combines copulas and
normalizing flows for causal inference under unobserved confounding. We refer
to the new model as $\rho$-GNF ($\rho$-Graphical Normalizing Flow), where
$\rho{\in}[-1,+1]$ is a bounded sensitivity parameter representing the backdoor
non-causal association due to unobserved confounding modeled using the most
well studied and widely popular Gaussian copula. Specifically, $\rho$-GNF
enables us to estimate and analyse the frontdoor causal effect or average
causal effect (ACE) as a function of $\rho$. We call this the $\rho_{curve}$.
The $\rho_{curve}$ enables us to specify the confounding strength required to
nullify the ACE. We call this the $\rho_{value}$. Further, the $\rho_{curve}$
also enables us to provide bounds for the ACE given an interval of $\rho$
values. We illustrate the benefits of $\rho$-GNF with experiments on simulated
and real-world data in terms of our empirical ACE bounds being narrower than
other popular ACE bounds.

Recently, Graph Neural Networks (GNNs) have significantly advanced the
performance of machine learning tasks on graphs. However, this technological
breakthrough makes people wonder: how does a GNN make such decisions, and can
we trust its prediction with high confidence? When it comes to some critical
fields, such as biomedicine, where making wrong decisions can have severe
consequences, it is crucial to interpret the inner working mechanisms of GNNs
before applying them. In this paper, we propose a model-agnostic model-level
explanation method for different GNNs that follow the message passing scheme,
GNNInterpreter, to explain the high-level decision-making process of the GNN
model. More specifically, GNNInterpreter learns a probabilistic generative
graph distribution that produces the most discriminative graph pattern the GNN
tries to detect when making a certain prediction by optimizing a novel
objective function specifically designed for the model-level explanation for
GNNs. Compared with the existing work, GNNInterpreter is more computationally
efficient and more flexible in generating explanation graphs with different
types of node features and edge features, without introducing another blackbox
to explain the GNN and without requiring manually specified domain-specific
knowledge. Additionally, the experimental studies conducted on four different
datasets demonstrate that the explanation graph generated by GNNInterpreter can
match the desired graph pattern when the model is ideal and reveal potential
model pitfalls if there exist any.

Medical vision-and-language pre-training provides a feasible solution to
extract effective vision-and-language representations from medical images and
texts. However, few studies have been dedicated to this field to facilitate
medical vision-and-language understanding. In this paper, we propose a
self-supervised learning paradigm with multi-modal masked autoencoders
(M$^3$AE), which learn cross-modal domain knowledge by reconstructing missing
pixels and tokens from randomly masked images and texts. There are three key
designs to make this simple approach work. First, considering the different
information densities of vision and language, we adopt different masking ratios
for the input image and text, where a considerably larger masking ratio is used
for images. Second, we use visual and textual features from different layers to
perform the reconstruction to deal with different levels of abstraction in
visual and language. Third, we develop different designs for vision and
language decoders (i.e., a Transformer for vision and a multi-layer perceptron
for language). To perform a comprehensive evaluation and facilitate further
research, we construct a medical vision-and-language benchmark including three
tasks. Experimental results demonstrate the effectiveness of our approach,
where state-of-the-art results are achieved on all downstream tasks. Besides,
we conduct further analysis to better verify the effectiveness of different
components of our approach and various settings of pre-training. The source
code is available at~\url{https://github.com/zhjohnchan/M3AE}.

Multi-objective optimization models that encode ordered sequential
constraints provide a solution to model various challenging problems including
encoding preferences, modeling a curriculum, and enforcing measures of safety.
A recently developed theory of topological Markov decision processes (TMDPs)
captures this range of problems for the case of discrete states and actions. In
this work, we extend TMDPs towards continuous spaces and unknown transition
dynamics by formulating, proving, and implementing the policy gradient theorem
for TMDPs. This theoretical result enables the creation of TMDP learning
algorithms that use function approximators, and can generalize existing deep
reinforcement learning (DRL) approaches. Specifically, we present a new
algorithm for a policy gradient in TMDPs by a simple extension of the proximal
policy optimization (PPO) algorithm. We demonstrate this on a real-world
multiple-objective navigation problem with an arbitrary ordering of objectives
both in simulation and on a real robot.

Safe reinforcement learning (RL) studies problems where an intelligent agent
has to not only maximize reward but also avoid exploring unsafe areas. In this
study, we propose CUP, a novel policy optimization method based on Constrained
Update Projection framework that enjoys rigorous safety guarantee. Central to
our CUP development is the newly proposed surrogate functions along with the
performance bound. Compared to previous safe RL methods, CUP enjoys the
benefits of 1) CUP generalizes the surrogate functions to generalized advantage
estimator (GAE), leading to strong empirical performance. 2) CUP unifies
performance bounds, providing a better understanding and interpretability for
some existing algorithms; 3) CUP provides a non-convex implementation via only
first-order optimizers, which does not require any strong approximation on the
convexity of the objectives. To validate our CUP method, we compared CUP
against a comprehensive list of safe RL baselines on a wide range of tasks.
Experiments show the effectiveness of CUP both in terms of reward and safety
constraint satisfaction. We have opened the source code of CUP at this link
https://github.com/zmsn-2077/ CUP-safe-rl.

Self-supervised monocular depth estimation has received much attention
recently in computer vision. Most of the existing works in literature aggregate
multi-scale features for depth prediction via either straightforward
concatenation or element-wise addition, however, such feature aggregation
operations generally neglect the contextual consistency between multi-scale
features. Addressing this problem, we propose the Self-Distilled Feature
Aggregation (SDFA) module for simultaneously aggregating a pair of low-scale
and high-scale features and maintaining their contextual consistency. The SDFA
employs three branches to learn three feature offset maps respectively: one
offset map for refining the input low-scale feature and the other two for
refining the input high-scale feature under a designed self-distillation
manner. Then, we propose an SDFA-based network for self-supervised monocular
depth estimation, and design a self-distilled training strategy to train the
proposed network with the SDFA module. Experimental results on the KITTI
dataset demonstrate that the proposed method outperforms the comparative
state-of-the-art methods in most cases. The code is available at
https://github.com/ZM-Zhou/SDFA-Net_pytorch.

Human cognition has compositionality. We understand a scene by decomposing
the scene into different concepts (e.g. shape and position of an object) and
learning the respective laws of these concepts which may be either natural
(e.g. laws of motion) or man-made (e.g. laws of a game). The automatic parsing
of these laws indicates the model's ability to understand the scene, which
makes law parsing play a central role in many visual tasks. In this paper, we
propose a deep latent variable model for Compositional LAw Parsing (CLAP). CLAP
achieves the human-like compositionality ability through an encoding-decoding
architecture to represent concepts in the scene as latent variables, and
further employ concept-specific random functions, instantiated with Neural
Processes, in the latent space to capture the law on each concept. Our
experimental results demonstrate that CLAP outperforms the compared baseline
methods in multiple visual tasks including intuitive physics, abstract visual
reasoning, and scene representation. In addition, CLAP can learn
concept-specific laws in a scene without supervision and one can edit laws
through modifying the corresponding latent random functions, validating its
interpretability and manipulability.

Seismic phase association connects earthquake arrival time measurements to
their causative sources. Effective association must determine the number of
discrete events, their location and origin times, and it must differentiate
real arrivals from measurement artifacts. The advent of deep learning pickers,
which provide high rates of picks from closely overlapping small magnitude
earthquakes, motivates revisiting the phase association problem and approaching
it using the methods of deep learning. We have developed a Graph Neural Network
associator that simultaneously predicts both source space-time localization,
and discrete source-arrival association likelihoods. The method is applicable
to arbitrary geometry, time-varying seismic networks of hundreds of stations,
and is robust to high rates of sources and input picks with variable noise and
quality. Our Graph Earthquake Neural Interpretation Engine (GENIE) uses one
graph to represent the station set and another to represent the spatial source
region. GENIE learns relationships from data in this combined representation
that enable it to determine robust source and source-arrival associations. We
train on synthetic data, and test our method on real data from the Northern
California (NC) seismic network using input generated by the PhaseNet deep
learning phase picker. We successfully re-detect ~96% of all events M>1
reported by the USGS during 500 random days between 2000$\unicode{x2013}$2022.
Over a 100-day continuous interval of processing in 2017$\unicode{x2013}$2018,
we detect ~4.2x the number of events reported by the USGS. Our new events have
small magnitude estimates below the magnitude of completeness of the USGS
catalog, and are located close to the active faults and quarries in the region.
Our results demonstrate that GENIE can effectively solve the association
problem under complex seismic monitoring conditions.

Solutions to differential equations are of significant scientific and
engineering relevance. Physics-Informed Neural Networks (PINNs) have emerged as
a promising method for solving differential equations, but they lack a
theoretical justification for the use of any particular loss function. This
work presents Differential Equation GAN (DEQGAN), a novel method for solving
differential equations using generative adversarial networks to "learn the loss
function" for optimizing the neural network. Presenting results on a suite of
twelve ordinary and partial differential equations, including the nonlinear
Burgers', Allen-Cahn, Hamilton, and modified Einstein's gravity equations, we
show that DEQGAN can obtain multiple orders of magnitude lower mean squared
errors than PINNs that use $L_2$, $L_1$, and Huber loss functions. We also show
that DEQGAN achieves solution accuracies that are competitive with popular
numerical methods. Finally, we present two methods to improve the robustness of
DEQGAN to different hyperparameter settings.

In this work, we propose a novel approach for layerwise representation
learning of a trained neural network. In particular, we form a Bregman
divergence based on the layer's transfer function and construct an extension of
the original Bregman PCA formulation by incorporating a mean vector and
normalizing the principal directions with respect to the geometry of the local
convex function around the mean. This generalization allows exporting the
learned representation as a fixed layer with a non-linearity. As an application
to knowledge distillation, we cast the learning problem for the student network
as predicting the compression coefficients of the teacher's representations,
which are passed as the input to the imported layer. Our empirical findings
indicate that our approach is substantially more effective for transferring
information between networks than typical teacher-student training using the
teacher's penultimate layer representations and soft labels.

There is still a significant gap between expectations and the successful
adoption of AI to innovate and improve businesses. Due to the emergence of deep
learning, AI adoption is more complex as it often incorporates big data and the
internet of things, affecting data privacy. Existing frameworks have identified
the need to focus on human-centered design, combining technical and
business/organizational perspectives. However, trust remains a critical issue
that needs to be designed from the beginning. The proposed framework expands
from the human-centered design approach, emphasizing and maintaining the trust
that underpins the process. This paper proposes a theoretical framework for
responsible artificial intelligence (AI) implementation. The proposed framework
emphasizes a synergistic business technology approach for the agile co-creation
process. The aim is to streamline the adoption process of AI to innovate and
improve business by involving all stakeholders throughout the project so that
the AI technology is designed, developed, and deployed in conjunction with
people and not in isolation. The framework presents a fresh viewpoint on
responsible AI implementation based on analytical literature review, conceptual
framework design, and practitioners' mediating expertise. The framework
emphasizes establishing and maintaining trust throughout the human-centered
design and agile development of AI. This human-centered approach is aligned
with and enabled by the privacy by design principle. The creators of the
technology and the end-users are working together to tailor the AI solution
specifically for the business requirements and human characteristics. An
illustrative case study on adopting AI for assisting planning in a hospital
will demonstrate that the proposed framework applies to real-life applications.

Deep learning based approaches like Physics-informed neural networks (PINNs)
and DeepONets have shown promise on solving PDE constrained optimization
(PDECO) problems. However, existing methods are insufficient to handle those
PDE constraints that have a complicated or nonlinear dependency on optimization
targets. In this paper, we present a novel bi-level optimization framework to
resolve the challenge by decoupling the optimization of the targets and
constraints. For the inner loop optimization, we adopt PINNs to solve the PDE
constraints only. For the outer loop, we design a novel method by using
Broyden's method based on the Implicit Function Theorem (IFT), which is
efficient and accurate for approximating hypergradients. We further present
theoretical explanations and error analysis of the hypergradients computation.
Extensive experiments on multiple large-scale and nonlinear PDE constrained
optimization problems demonstrate that our method achieves state-of-the-art
results compared with strong baselines.

Importance sampling (IS) is a popular technique in off-policy evaluation,
which re-weights the return of trajectories in the replay buffer to boost
sample efficiency. However, training with IS can be unstable and previous
attempts to address this issue mainly focus on analyzing the variance of IS. In
this paper, we reveal that the instability is also related to a new notion of
Reuse Bias of IS -- the bias in off-policy evaluation caused by the reuse of
the replay buffer for evaluation and optimization. We theoretically show that
the off-policy evaluation and optimization of the current policy with the data
from the replay buffer result in an overestimation of the objective, which may
cause an erroneous gradient update and degenerate the performance. We further
provide a high-probability upper bound of the Reuse Bias, and show that
controlling one term of the upper bound can control the Reuse Bias by
introducing the concept of stability for off-policy algorithms. Based on these
analyses, we finally present a novel Bias-Regularized Importance Sampling
(BIRIS) framework along with practical algorithms, which can alleviate the
negative impact of the Reuse Bias. Experimental results show that our
BIRIS-based methods can significantly improve the sample efficiency on a series
of continuous control tasks in MuJoCo.

In observational astronomy, noise obscures signals of interest. Large-scale
astronomical surveys are growing in size and complexity, which will produce
more data and increase the workload of data processing. Developing automated
tools, such as convolutional neural networks (CNN), for denoising has become a
promising area of research. We investigate the feasibility of CNN-based
self-supervised learning algorithms (e.g., Noise2Noise) for denoising
astronomical images. We experimented with Noise2Noise on simulated noisy
astronomical data. We evaluate the results based on the accuracy of recovering
flux and morphology. This algorithm can well recover the flux for Poisson noise
( $98.13${\raisebox{0.5ex}{\tiny$^{+0.77}_{-0.90} $}$\large\%$}) and for
Gaussian noise when image data has a smooth signal profile
($96.45${\raisebox{0.5ex}{\tiny$^{+0.80}_{-0.96} $}$\large\%$}).

This paper proposes a family of network centralities called fixed-point
centralities. This centrality family is defined via the fixed point of
permutation equivariant mappings related to the underlying network. Such a
centrality notion is immediately extended to define fixed-point centralities
for infinite graphs characterized by graphons. Variation bounds of such
centralities with respect to the variations of the underlying graphs and
graphons under mild assumptions are established. Fixed-point centralities
connect with a variety of different models on networks including graph neural
networks, static and dynamic games on networks, and Markov decision processes.

Since the preparation of labeled data for training semantic segmentation
networks of point clouds is a time-consuming process, weakly supervised
approaches have been introduced to learn from only a small fraction of data.
These methods are typically based on learning with contrastive losses while
automatically deriving per-point pseudo-labels from a sparse set of
user-annotated labels. In this paper, our key observation is that the selection
of what samples to annotate is as important as how these samples are used for
training. Thus, we introduce a method for weakly supervised segmentation of 3D
scenes that combines self-training with active learning. The active learning
selects points for annotation that likely result in performance improvements to
the trained model, while the self-training makes efficient use of the
user-provided labels for learning the model. We demonstrate that our approach
leads to an effective method that provides improvements in scene segmentation
over previous works and baselines, while requiring only a small number of user
annotations.

The task of Chinese Spelling Check (CSC) is aiming to detect and correct
spelling errors that can be found in the text. While manually annotating a
high-quality dataset is expensive and time-consuming, thus the scale of the
training dataset is usually very small (e.g., SIGHAN15 only contains 2339
samples for training), therefore supervised-learning based models usually
suffer the data sparsity limitation and over-fitting issue, especially in the
era of big language models. In this paper, we are dedicated to investigating
the \textbf{unsupervised} paradigm to address the CSC problem and we propose a
framework named \textbf{uChecker} to conduct unsupervised spelling error
detection and correction. Masked pretrained language models such as BERT are
introduced as the backbone model considering their powerful language diagnosis
capability. Benefiting from the various and flexible MASKing operations, we
propose a Confusionset-guided masking strategy to fine-train the masked
language model to further improve the performance of unsupervised detection and
correction. Experimental results on standard datasets demonstrate the
effectiveness of our proposed model uChecker in terms of character-level and
sentence-level Accuracy, Precision, Recall, and F1-Measure on tasks of spelling
error detection and correction respectively.

In domains where sample sizes are limited, efficient learning algorithms are
critical. Learning using privileged information (LuPI) offers increased sample
efficiency by allowing prediction models access to auxiliary information at
training time which is unavailable when the models are used. In recent work, it
was shown that for prediction in linear-Gaussian dynamical systems, a LuPI
learner with access to intermediate time series data is never worse and often
better in expectation than any unbiased classical learner. We provide new
insights into this analysis and generalize it to nonlinear prediction tasks in
latent dynamical systems, extending theoretical guarantees to the case where
the map connecting latent variables and observations is known up to a linear
transform. In addition, we propose algorithms based on random features and
representation learning for the case when this map is unknown. A suite of
empirical results confirm theoretical findings and show the potential of using
privileged time-series information in nonlinear prediction.

Nowadays self-paced learning (SPL) is an important machine learning paradigm
that mimics the cognitive process of humans and animals. The SPL regime
involves a self-paced regularizer and a gradually increasing age parameter,
which plays a key role in SPL but where to optimally terminate this process is
still non-trivial to determine. A natural idea is to compute the solution path
w.r.t. age parameter (i.e., age-path). However, current age-path algorithms are
either limited to the simplest regularizer, or lack solid theoretical
understanding as well as computational efficiency. To address this challenge,
we propose a novel \underline{G}eneralized \underline{Ag}e-path
\underline{A}lgorithm (GAGA) for SPL with various self-paced regularizers based
on ordinary differential equations (ODEs) and sets control, which can learn the
entire solution spectrum w.r.t. a range of age parameters. To the best of our
knowledge, GAGA is the first exact path-following algorithm tackling the
age-path for general self-paced regularizer. Finally the algorithmic steps of
classic SVM and Lasso are described in detail. We demonstrate the performance
of GAGA on real-world datasets, and find considerable speedup between our
algorithm and competing baselines.

As machine learning becomes prevalent, mitigating any unfairness present in
the training data becomes critical. Among the various notions of fairness, this
paper focuses on the well-known individual fairness, which states that similar
individuals should be treated similarly. While individual fairness can be
improved when training a model (in-processing), we contend that fixing the data
before model training (pre-processing) is a more fundamental solution. In
particular, we show that label flipping is an effective pre-processing
technique for improving individual fairness. Our system iFlipper solves the
optimization problem of minimally flipping labels given a limit to the
individual fairness violations, where a violation occurs when two similar
examples in the training data have different labels. We first prove that the
problem is NP-hard. We then propose an approximate linear programming algorithm
and provide theoretical guarantees on how close its result is to the optimal
solution in terms of the number of label flips. We also propose techniques for
making the linear programming solution more optimal without exceeding the
violations limit. Experiments on real datasets show that iFlipper significantly
outperforms other pre-processing baselines in terms of individual fairness and
accuracy on unseen test sets. In addition, iFlipper can be combined with
in-processing techniques for even better results.

Contrastive Language-Image pre-training (CLIP) learns rich representations
via readily available supervisions of natural language. It could improve
general performance on downstream vision tasks, including but not limited to
zero-shot, long tail, segmentation, retrieval, caption and video. However, to
the best of our knowledge, the visual interpretability of CLIP has not been
studied yet. To provide visual explanations of its predictions, we propose the
Image-Text Similarity Map (ITSM). Based on it, we surprisingly find that CLIP
prefers the background regions than the foregrounds, and presenting erroneous
visualization against human understanding. Experimentally, we find the devil is
in the pooling part, where inappropriate pooling methods lead to a phenomenon
called semantic shift. To correct and boost the visualization results, we
propose the Masked Max Pooling, with attention map from the self-supervised
image encoder. Meanwhile, interpretability task and recognition task require
different representations. To address the problem, we propose the dual
projections to cater this requirement. We integrate above methods as
Interpretable Contrastive Language-Image pre-training (ICLIP). And experiments
suggest ICLIP greatly improves the interpretability. For example, the
nontrivial improvements are $32.85\%$ and $49.10\%$, respectively, on VOC 2012
dataset.

It is now well understood that machine learning models, trained on data
without due care, often exhibit unfair and discriminatory behavior against
certain populations. Traditional algorithmic fairness research has mainly
focused on supervised learning tasks, particularly classification. While
fairness in unsupervised learning has received some attention, the literature
has primarily addressed fair representation learning of continuous embeddings.
In this paper, we conversely focus on unsupervised learning using probabilistic
graphical models with discrete latent variables. We develop a fair stochastic
variational inference technique for the discrete latent variables, which is
accomplished by including a fairness penalty on the variational distribution
that aims to respect the principles of intersectionality, a critical lens on
fairness from the legal, social science, and humanities literature, and then
optimizing the variational parameters under this penalty. We first show the
utility of our method in improving equity and fairness for clustering using
na\"ive Bayes and Gaussian mixture models on benchmark datasets. To demonstrate
the generality of our approach and its potential for real-world impact, we then
develop a special-purpose graphical model for criminal justice risk
assessments, and use our fairness approach to prevent the inferences from
encoding unfair societal biases.

Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes
that there exist smooth (non-binary) subnetworks within a dense network that
achieve the competitive performance of the dense network, we propose a few-shot
class incremental learning (FSCIL) method referred to as \emph{Soft-SubNetworks
(SoftNet)}. Our objective is to learn a sequence of sessions incrementally,
where each session only includes a few training instances per class while
preserving the knowledge of the previously learned ones. SoftNet jointly learns
the model weights and adaptive non-binary soft masks at a base training session
in which each mask consists of the major and minor subnetwork; the former aims
to minimize catastrophic forgetting during training, and the latter aims to
avoid overfitting to a few samples in each new training session. We provide
comprehensive empirical validations demonstrating that our SoftNet effectively
tackles the few-shot incremental learning problem by surpassing the performance
of state-of-the-art baselines over benchmark datasets.

Vision guided navigation requires processing complex visual information to
inform task-orientated decisions. Applications include autonomous robots,
self-driving cars, and assistive vision for humans. A key element is the
extraction and selection of relevant features in pixel space upon which to base
action choices, for which Machine Learning techniques are well suited. However,
Deep Reinforcement Learning agents trained in simulation often exhibit
unsatisfactory results when deployed in the real-world due to perceptual
differences known as the $\textit{reality gap}$. An approach that is yet to be
explored to bridge this gap is self-attention. In this paper we (1) perform a
systematic exploration of the hyperparameter space for self-attention based
navigation of 3D environments and qualitatively appraise behaviour observed
from different hyperparameter sets, including their ability to generalise; (2)
present strategies to improve the agents' generalisation abilities and
navigation behaviour; and (3) show how models trained in simulation are capable
of processing real world images meaningfully in real time. To our knowledge,
this is the first demonstration of a self-attention based agent successfully
trained in navigating a 3D action space, using less than 4000 parameters.

We propose a certainty-equivalence scheme for adaptive control of scalar
linear systems subject to additive, i.i.d. Gaussian disturbances and bounded
control input constraints, without requiring prior knowledge of the bounds of
the system parameters, nor the control direction. Assuming that the system is
at-worst marginally stable, mean square boundedness of the closed-loop system
states is proven. Lastly, numerical examples are presented to illustrate our
results.

Policy Decomposition (PoDec) is a framework that lessens the curse of
dimensionality when deriving policies to optimal control problems. For a given
system representation, i.e. the state variables and control inputs describing a
system, PoDec generates strategies to decompose the joint optimization of
policies for all control inputs. Thereby, policies for different inputs are
derived in a decoupled or cascaded fashion and as functions of some subsets of
the state variables, leading to reduction in computation. However, the choice
of system representation is crucial as it dictates the suboptimality of the
resulting policies. We present a heuristic method to find a representation more
amenable to decomposition. Our approach is based on the observation that every
decomposition enforces a sparsity pattern in the resulting policies at the cost
of optimality and a representation that already leads to a sparse optimal
policy is likely to produce decompositions with lower suboptimalities. As the
optimal policy is not known we construct a system representation that
sparsifies its LQR approximation. For a simplified biped, a 4 degree-of-freedom
manipulator, and a quadcopter, we discover decompositions that offer 10%
reduction in trajectory costs over those identified by vanilla PoDec. Moreover,
the decomposition policies produce trajectories with substantially lower costs
compared to policies obtained from state-of-the-art reinforcement learning

Markov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for
approximating intractable distributions. However, its usage is limited in the
context of deep latent variable models owing to costly datapoint-wise sampling
iterations and slow convergence. This paper proposes the amortized Langevin
dynamics (ALD), wherein datapoint-wise MCMC iterations are entirely replaced
with updates of an encoder that maps observations into latent variables. This
amortization enables efficient posterior sampling without datapoint-wise
iterations. Despite its efficiency, we prove that ALD is valid as an MCMC
algorithm, whose Markov chain has the target posterior as a stationary
distribution under mild assumptions. Based on the ALD, we also present a new
deep latent variable model named the Langevin autoencoder (LAE). Interestingly,
the LAE can be implemented by slightly modifying the traditional autoencoder.
Using multiple synthetic datasets, we first validate that ALD can properly
obtain samples from target posteriors. We also evaluate the LAE on the image
generation task, and show that our LAE can outperform existing methods based on
variational inference, such as the variational autoencoder, and other
MCMC-based methods in terms of the test likelihood.

Recently, there has been extensive use of artificial Intelligence (AI) in the
field of material engineering. This can be attributed to the development of
high performance computing and thereby feasibility to test deep learning models
with large parameters. In this article we tried to review some of the latest
developments in the applications of AI in material engineering.

We consider the problem of estimating the skeleton of a large causal polytree
from a relatively small i.i.d. sample. This is motivated by the problem of
determining causal structure when the number of variables is very large
compared to the sample size, such as in gene regulatory networks. We give an
algorithm that recovers the tree with high accuracy in such settings. The
algorithm works under essentially no distributional or modeling assumptions
other than some mild non-degeneracy conditions.

Time series classification is an important problem in real world. Due to its
non-stationary property that the distribution changes over time, it remains
challenging to build models for generalization to unseen distributions. In this
paper, we propose to view the time series classification problem from the
distribution perspective. We argue that the temporal complexity attributes to
the unknown latent distributions within. To this end, we propose DIVERSIFY to
learn generalized representations for time series classification. DIVERSIFY
takes an iterative process: it first obtains the worst-case distribution
scenario via adversarial training, then matches the distributions of the
obtained sub-domains. We also present some theoretical insights. We conduct
experiments on gesture recognition, speech commands recognition, wearable
stress and affect detection, and sensor-based human activity recognition with a
total of seven datasets in different settings. Results demonstrate that
DIVERSIFY significantly outperforms other baselines and effectively
characterizes the latent distributions by qualitative and quantitative
analysis.

There is a long history of an effort made to explore musical elements with
the entities and spaces around us, such as musique concr\`ete and ambient
music. In the context of computer music and digital art, interactive
experiences that concentrate on the surrounding objects and physical spaces
have also been designed. In recent years, with the development and
popularization of devices, an increasing number of works have been designed in
Extended Reality to create such musical experiences. In this paper, we describe
MR4MR, a sound installation work that allows users to experience melodies
produced from interactions with their surrounding space in the context of Mixed
Reality (MR). Using HoloLens, an MR head-mounted display, users can bump
virtual objects that emit sound against real objects in their surroundings.
Then, by continuously creating a melody following the sound made by the object
and re-generating randomly and gradually changing melody using music generation
machine learning models, users can feel their ambient melody "reincarnating".

Feature extraction methods help in dimensionality reduction and capture
relevant information. In time series forecasting (TSF), features can be used as
auxiliary information to achieve better accuracy. Traditionally, features used
in TSF are handcrafted, which requires domain knowledge and significant
data-engineering work. In this research, we first introduce a notion of static
and dynamic features, which then enables us to develop our autonomous Feature
Retrieving Autoregressive Network for Static features (FRANS) that does not
require domain knowledge. The method is based on a CNN classifier that is
trained to create for each series a collective and unique class representation
either from parts of the series or, if class labels are available, from a set
of series of the same class. It allows to discriminate series with similar
behaviour but from different classes and makes the features extracted from the
classifier to be maximally discriminatory. We explore the interpretability of
our features, and evaluate the prediction capabilities of the method within the
forecasting meta-learning environment FFORMA. Our results show that our
features lead to improvement in accuracy in most situations. Once trained our
approach creates features orders of magnitude faster than statistical methods.

The Natarajan dimension is a fundamental tool for characterizing multi-class
PAC learnability, generalizing the Vapnik-Chervonenkis (VC) dimension from
binary to multi-class classification problems. This note establishes upper
bounds on Natarajan dimensions for certain function classes, including (i)
multi-class decision tree and random forests, and (ii) multi-class neural
networks with binary, linear and ReLU activations. These results may be
relevant for describing the performance of certain multi-class learning

In recent years, deep learning has been at the center of analytics due to its
impressive empirical success in analyzing complex data objects. Despite this
success, most of the existing tools behave like black-box machines, thus the
increasing interest in interpretable, reliable, and robust deep learning models
applicable to a broad class of applications. Feature-selected deep learning has
emerged as a promising tool in this realm. However, the recent developments do
not accommodate ultra-high dimensional and highly correlated features, in
addition to the high noise level. In this article, we propose a novel screening
and cleaning method with the aid of deep learning for a data-adaptive
multi-resolutional discovery of highly correlated predictors with a controlled
error rate. Extensive empirical evaluations over a wide range of simulated
scenarios and several real datasets demonstrate the effectiveness of the
proposed method in achieving high power while keeping the false discovery rate
at a minimum.

Subgraph isomorphism or subgraph matching is generally considered as an
NP-complete problem, made more complex in practical applications where the edge
weights take real values and are subject to measurement noise and possible
anomalies. To the best of our knowledge, almost all subgraph matching methods
utilize node labels to perform node-node matching. In the absence of such
labels (in applications such as image matching and map matching among others),
these subgraph matching methods do not work. We propose a method for
identifying the node correspondence between a subgraph and a full graph in the
inexact case without node labels in two steps - (a) extract the minimal unique
topology preserving subset from the subgraph and find its feasible matching in
the full graph, and (b) implement a consensus-based algorithm to expand the
matched node set by pairing unique paths based on boundary commutativity. Going
beyond the existing subgraph matching approaches, the proposed method is shown
to have realistically sub-linear computational efficiency, robustness to random
measurement noise, and good statistical properties. Our method is also readily
applicable to the exact matching case without loss of generality. To
demonstrate the effectiveness of the proposed method, a simulation and a case
study is performed on the Erdos-Renyi random graphs and the image-based affine
covariant features dataset respectively.

Learning concise data representations without supervisory signals is a
fundamental challenge in machine learning. A prominent approach to this goal is
likelihood-based models such as variational autoencoders (VAE) to learn latent
representations based on a meta-prior, which is a general premise assumed
beneficial for downstream tasks (e.g., disentanglement). However, such
approaches often deviate from the original likelihood architecture to apply the
introduced meta-prior, causing undesirable changes in their training. In this
paper, we propose a novel representation learning method, Gromov-Wasserstein
Autoencoders (GWAE), which directly matches the latent and data distributions.
Instead of a likelihood-based objective, GWAE models have a trainable prior
optimized by minimizing the Gromov-Wasserstein (GW) metric. The GW metric
measures the distance structure-oriented discrepancy between distributions
supported on incomparable spaces, e.g., with different dimensionalities. By
restricting the family of the trainable prior, we can introduce meta-priors to
control latent representations for downstream tasks. The empirical comparison
with the existing VAE-based methods shows that GWAE models can learn
representations based on meta-priors by changing the prior family without
further modifying the GW objective.

A common study area in anomaly identification is industrial images anomaly
detection based on texture background. The interference of texture images and
the minuteness of texture anomalies are the main reasons why many existing
models fail to detect anomalies. We propose a strategy for anomaly detection
that combines dictionary learning and normalizing flow based on the
aforementioned questions. The two-stage anomaly detection approach already in
use is enhanced by our method. In order to improve baseline method, this
research add normalizing flow in representation learning and combines deep
learning and dictionary learning. Improved algorithms have exceeded 95$\%$
detection accuracy on all MVTec AD texture type data after experimental
validation. It shows strong robustness. The baseline method's detection
accuracy for the Carpet data was 67.9%. The article was upgraded, raising the
detection accuracy to 99.7%.

In recent years, face recognition systems have achieved exceptional success
due to promising advances in deep learning architectures. However, they still
fail to achieve expected accuracy when matching profile images against a
gallery of frontal images. Current approaches either perform pose normalization
(i.e., frontalization) or disentangle pose information for face recognition. We
instead propose a new approach to utilize pose as an auxiliary information via
an attention mechanism. In this paper, we hypothesize that pose attended
information using an attention mechanism can guide contextual and distinctive
feature extraction from profile faces, which further benefits a better
representation learning in an embedded domain. To achieve this, first, we
design a unified coupled profile-to-frontal face recognition network. It learns
the mapping from faces to a compact embedding subspace via a class-specific
contrastive loss. Second, we develop a novel pose attention block (PAB) to
specially guide the pose-agnostic feature extraction from profile faces. To be
more specific, PAB is designed to explicitly help the network to focus on
important features along both channel and spatial dimension while learning
discriminative yet pose invariant features in an embedding subspace. To
validate the effectiveness of our proposed method, we conduct experiments on
both controlled and in the wild benchmarks including Multi-PIE, CFP, IJBC, and
show superiority over the state of the arts.

The application of Data Science and Analytics to optimize or predict outcomes
is Ubiquitous in the Modern World. Data Science and Analytics have optimized
almost every domain that exists in the market. In our survey, we focus on how
the field of Analytics has been adopted in the field of sports, and how it has
contributed to the transformation of the game right from the assessment of
on-field players and their selection to the prediction of winning team and to
the marketing of tickets and business aspects of big sports tournaments. We
will present the analytical tools, algorithms, and methodologies adopted in the
field of Sports Analytics for different sports and also present our views on
the same and we will also compare and contrast these existing approaches. By
doing so, we will also present the best tools, algorithms, and analytical
methodologies to be considered by anyone who is looking to experiment with
sports data and analyze various aspects of the game.

The evolution of digital technology and the increasing popularity of sports
inspired the innovators to take the experience of users with a proclivity
towards sports to a whole new different level, by introducing Fantasy Sports
Platforms FSPs. The application of Data Science and Analytics is Ubiquitous in
the Modern World. Data Science and Analytics open doors to gain a deeper
understanding and help in the decision making process. We firmly believed that
we could adopt Data Science to predict the winning fantasy cricket team on the
FSP, Dream 11. We built a predictive model that predicts the performance of
players in a prospective game. We used a combination of Greedy and Knapsack
Algorithms to prescribe the combination of 11 players to create a fantasy
cricket team that has the most significant statistical odds of finishing as the
strongest team thereby giving us a higher chance of winning the pot of bets on
the Dream 11 FSP. We used PyCaret Python Library to help us understand and
adopt the best Regressor Algorithm for our problem statement to make precise
predictions. Further, we used Plotly Python Library to give us visual insights
into the team, and players performances by accounting for the statistical, and
subjective factors of a prospective game. The interactive plots help us to
bolster the recommendations of our predictive model. You either win big, win
small, or lose your bet based on the performance of the players selected for
your fantasy team in the prospective game, and our model increases the
probability of you winning big.

Determining subgroups that respond especially well (or poorly) to specific
interventions (medical or policy) requires new supervised learning methods
tailored specifically for causal inference. Bayesian Causal Forest (BCF) is a
recent method that has been documented to perform well on data generating
processes with strong confounding of the sort that is plausible in many
applications. This paper develops a novel algorithm for fitting the BCF model,
which is more efficient than the previously available Gibbs sampler. The new
algorithm can be used to initialize independent chains of the existing Gibbs
sampler leading to better posterior exploration and coverage of the associated
interval estimates in simulation studies. The new algorithm is compared to
related approaches via simulation studies as well as an empirical analysis.

With the development of machine learning techniques, the attention of
research has been moved from single-modal learning to multi-modal learning, as
real-world data exist in the form of different modalities. However, multi-modal
models often carry more information than single-modal models and they are
usually applied in sensitive scenarios, such as medical report generation or
disease identification. Compared with the existing membership inference against
machine learning classifiers, we focus on the problem that the input and output
of the multi-modal models are in different modalities, such as image
captioning. This work studies the privacy leakage of multi-modal models through
the lens of membership inference attack, a process of determining whether a
data record involves in the model training process or not. To achieve this, we
propose Multi-modal Models Membership Inference (M^4I) with two attack methods
to infer the membership status, named metric-based (MB) M^4I and feature-based
(FB) M^4I, respectively. More specifically, MB M^4I adopts similarity metrics
while attacking to infer target data membership. FB M^4I uses a pre-trained
shadow multi-modal feature extractor to achieve the purpose of data inference
attack by comparing the similarities from extracted input and output features.
Extensive experimental results show that both attack methods can achieve strong
performances. Respectively, 72.5% and 94.83% of attack success rates on average
can be obtained under unrestricted scenarios. Moreover, we evaluate multiple
defense mechanisms against our attacks. The source code of M^4I attacks is
publicly available at
https://github.com/MultimodalMI/Multimodal-membership-inference.git.

We propose PATRON, a new method that uses prompt-based uncertainty estimation
for data selection for pre-trained language model fine-tuning under cold-start
scenarios, i.e., no initial labeled data are available. In PATRON, we design
(1) a prompt-based uncertainty propagation approach to estimate the importance
of data points and (2) a partition-then-rewrite (PTR) strategy to promote
sample diversity when querying for annotations. Experiments on six text
classification datasets show that PATRON outperforms the strongest cold-start
data selection baselines by up to 6.9%. Besides, with 128 labels only, PATRON
achieves 91.0% and 92.1% of the fully supervised performance based on vanilla
fine-tuning and prompt-based learning respectively. Our implementation of
PATRON is available at \url{https://github.com/yueyu1030/Patron}.

Self-training has shown great potential in semi-supervised learning. Its core
idea is to use the model learned on labeled data to generate pseudo-labels for
unlabeled samples, and in turn teach itself. To obtain valid supervision,
active attempts typically employ a momentum teacher for pseudo-label prediction
yet observe the confirmation bias issue, where the incorrect predictions may
provide wrong supervision signals and get accumulated in the training process.
The primary cause of such a drawback is that the prevailing self-training
framework acts as guiding the current state with previous knowledge, because
the teacher is updated with the past student only. To alleviate this problem,
we propose a novel self-training strategy, which allows the model to learn from
the future. Concretely, at each training step, we first virtually optimize the
student (i.e., caching the gradients without applying them to the model
weights), then update the teacher with the virtual future student, and finally
ask the teacher to produce pseudo-labels for the current student as the
guidance. In this way, we manage to improve the quality of pseudo-labels and
thus boost the performance. We also develop two variants of our
future-self-training (FST) framework through peeping at the future both deeply
(FST-D) and widely (FST-W). Taking the tasks of unsupervised domain adaptive
semantic segmentation and semi-supervised semantic segmentation as the
instances, we experimentally demonstrate the effectiveness and superiority of
our approach under a wide range of settings. Code will be made publicly

Disentangled and invariant representations are two critical goals of
representation learning and many approaches have been proposed to achieve
either one of them. However, those two goals are actually complementary to each
other so that we propose a framework to accomplish both of them simultaneously.
We introduce a weakly supervised signal to learn disentangled representation
which consists of three splits containing predictive, known nuisance and
unknown nuisance information respectively. Furthermore, we incorporate
contrastive method to enforce representation invariance. Experiments shows that
the proposed method outperforms state-of-the-art (SOTA) methods on four
standard benchmarks and shows that the proposed method can have better
adversarial defense ability comparing to other methods without adversarial
training.

Automatic speech recognition (ASR) needs to be robust to speaker differences.
Voice Conversion (VC) modifies speaker characteristics of input speech. This is
an attractive feature for ASR data augmentation. In this paper, we demonstrate
that voice conversion can be used as a data augmentation technique to improve
ASR performance, even on LibriSpeech, which contains 2,456 speakers. For ASR
augmentation, it is necessary that the VC model be robust to a wide range of
input speech. This motivates the use of a non-autoregressive, non-parallel VC
model, and the use of a pretrained ASR encoder within the VC model. This work
suggests that despite including many speakers, speaker diversity may remain a
limitation to ASR quality. Finally, interrogation of our VC performance has
provided useful metrics for objective evaluation of VC quality.

Objective: There are several efforts to re-learn the 2013 ACC/AHA pooled
cohort equations (PCE) for patients with specific comorbidities and geographic
locations. With over 363 customized risk models in the literature, we aim to
evaluate such revised models to determine if the performance improvements
translate to gains in clinical utility.
  Methods: We re-train a baseline PCE using the ACC/AHA PCE variables and
revise it to incorporate subject-level geographic location and comorbidity
information. We apply fixed effects, random effects, and extreme gradient
boosting models to handle the correlation and heterogeneity induced by
locations. Models are trained using 2,464,522 claims records from Optum
Clinformatics Data Mart and validated in the hold-out set (N=1,056,224). We
evaluate models' performance overall and across subgroups defined by the
presence or absence of chronic kidney disease (CKD) or rheumatoid arthritis
(RA) and geographic locations. We evaluate models' expected net benefit using
decision curve analysis and models' statistical properties using several
discrimination and calibration metrics.
  Results: The baseline PCE is miscalibrated overall, in patients with CKD or
RA, and locations with small populations. Our revised models improved both the
overall (GND P-value=0.41) and subgroup calibration but only enhanced net
benefit in the underrepresented subgroups. The gains are larger in the
subgroups with comorbidities and heterogeneous across geographic locations.
  Conclusions: Revising the PCE with comorbidity and location information
significantly enhanced models' calibration; however, such improvements do not
necessarily translate to clinical gains. Thus, we recommend future works to
quantify the consequences from using risk calculators to guide clinical

To estimate causal effects, analysts performing observational studies in
health settings utilize several strategies to mitigate bias due to confounding
by indication. There are two broad classes of approaches for these purposes:
use of confounders and instrumental variables (IVs). Because such approaches
are largely characterized by untestable assumptions, analysts must operate
under an indefinite paradigm that these methods will work imperfectly. In this
tutorial, we formalize a set of general principles and heuristics for
estimating causal effects in the two approaches when the assumptions are
potentially violated. This crucially requires reframing the process of
observational studies as hypothesizing potential scenarios where the estimates
from one approach are less inconsistent than the other. While most of our
discussion of methodology centers around the linear setting, we touch upon
complexities in non-linear settings and flexible procedures such as target
minimum loss-based estimation (TMLE) and double machine learning (DML). To
demonstrate the application of our principles, we investigate the use of
donepezil off-label for mild cognitive impairment (MCI). We compare and
contrast results from confounder and IV methods, traditional and flexible,
within our analysis and to a similar observational study and clinical trial.

We propose a novel contextual bandit algorithm for generalized linear rewards
with an $\tilde{O}(\sqrt{\kappa^{-1} \phi T})$ regret over $T$ rounds where
$\phi$ is the minimum eigenvalue of the covariance of contexts and $\kappa$ is
a lower bound of the variance of rewards. In several practical cases where
$\phi=O(d)$, our result is the first regret bound for generalized linear model
(GLM) bandits with the order $\sqrt{d}$ without relying on the approach of Auer
[2002]. We achieve this bound using a novel estimator called double
doubly-robust (DDR) estimator, a subclass of doubly-robust (DR) estimator but
with a tighter error bound. The approach of Auer [2002] achieves independence
by discarding the observed rewards, whereas our algorithm achieves independence
considering all contexts using our DDR estimator. We also provide an
$O(\kappa^{-1} \phi \log (NT) \log T)$ regret bound for $N$ arms under a
probabilistic margin condition. Regret bounds under the margin condition are
given by Bastani and Bayati [2020] and Bastani et al. [2021] under the setting
that contexts are common to all arms but coefficients are arm-specific. When
contexts are different for all arms but coefficients are common, ours is the
first regret bound under the margin condition for linear models or GLMs. We
conduct empirical studies using synthetic data and real examples, demonstrating
the effectiveness of our algorithm.

The exponentially growing model size drives the continued success of deep
learning, but it brings prohibitive computation and memory cost. From the
algorithm perspective, model sparsification and quantization have been studied
to alleviate the problem. From the architecture perspective, hardware vendors
provide Tensor cores for acceleration. However, it is very challenging to gain
practical speedups from sparse, low-precision matrix operations on Tensor
cores, because of the strict requirements for data layout and lack of support
for efficiently manipulating the low-precision integers. We propose Magicube, a
high-performance sparse-matrix library for low-precision integers on Tensor
cores. Magicube supports SpMM and SDDMM, two major sparse operations in deep
learning with mixed precision. Experimental results on an NVIDIA A100 GPU show
that Magicube achieves on average 1.44x (up to 2.37x) speedup over the
vendor-optimized library for sparse kernels, and 1.43x speedup over the
state-of-the-art with a comparable accuracy for end-to-end sparse Transformer

While much has been learned about black holes by analyzing the latest LVK
catalog, GWTC-3, a measurement of the astrophysical distribution of the black
hole spin orientations remains elusive. This is usually probed by measuring the
cosine of the tilt angle ($\cos\tau$) between each black hole spin and the
orbital angular momentum, $\cos\tau=+1$ being perfect alignment. Abbott et al.
has modeled the $\cos\tau$ distribution as a mixture of an isotropic component
and a Gaussian component with mean fixed at +1 and width measured from the
data. We want to verify if the data require the existence of such a peak at
$\cos\tau=+1$. We use various alternative models for the astrophysical tilt
distribution and measure their parameters using the LVK GWTC-3 catalog. We find
that a) Augmenting the LVK model such that the mean $\mu$ of the Gaussian is
not fixed at +1 returns results that strongly depend on priors. If we allow
$\mu>+1$ then the resulting astrophysical $\cos\tau$ distribution peaks at +1
and looks linear, rather than Gaussian. If we constrain $-1\leq \mu\leq+1$ the
Gaussian component peaks at $\mu=0.48^{+0.46}_{-0.99}$ (median and 90%
symmetric credible interval). Two other 2-component mixture models yield
$\cos\tau$ distributions that either have a broad peak centered at
$0.19^{+0.22}_{-0.18}$ or a plateau that spans the range [-0.5, +1], without a
clear peak at +1. b) All of the models we considered agree on the fact that
there is no excess of black hole tilts at around -1. c) While yielding quite
different posteriors, the models considered in this work have Bayesian
evidences that are the same within error bars. We conclude that the current
dataset is not sufficiently informative to draw any model-independent
conclusions on the astrophysical distribution of spin tilts, except that there
is no excess of spins with negatively aligned tilts.

Clustering is an important exploratory data analysis technique to group
objects based on their similarity. The widely used $K$-means clustering method
relies on some notion of distance to partition data into a fewer number of
groups. In the Euclidean space, centroid-based and distance-based formulations
of the $K$-means are equivalent. In modern machine learning applications, data
often arise as probability distributions and a natural generalization to handle
measure-valued data is to use the optimal transport metric. Due to non-negative
Alexandrov curvature of the Wasserstein space, barycenters suffer from
regularity and non-robustness issues. The peculiar behaviors of Wasserstein
barycenters may make the centroid-based formulation fail to represent the
within-cluster data points, while the more direct distance-based $K$-means
approach and its semidefinite program (SDP) relaxation are capable of
recovering the true cluster labels. In the special case of clustering Gaussian
distributions, we show that the SDP relaxed Wasserstein $K$-means can achieve
exact recovery given the clusters are well-separated under the $2$-Wasserstein
metric. Our simulation and real data examples also demonstrate that
distance-based $K$-means can achieve better classification performance over the
standard centroid-based $K$-means for clustering probability distributions and
images.

Viscoelastic fluids are a class of fluids that exhibit both viscous and
elastic nature. Modeling such fluids requires constitutive equations for the
stress, and choosing the most appropriate constitutive relationship can be
difficult. We present viscoelasticNet, a physics-informed deep learning
framework that uses the velocity flow field to select the constitutive model
and learn the stress field. Our framework requires data only for the velocity
field, initial & boundary conditions for the stress tensor, and the boundary
condition for the pressure field. Using this information, we learn the model
parameters, the pressure field, and the stress tensor. In this work, we
consider three commonly used non-linear viscoelastic models: Oldroyd-B,
Giesekus, and linear PTT. We demonstrate that our framework works well with
noisy and sparse data. Our framework can be combined with velocity fields
acquired from experimental techniques like particle image velocimetry to get
the pressure & stress fields and model parameters for the constitutive
equation. Once the model has been discovered using viscoelasticNet, the fluid
can be simulated and modeled for further applications.

Despite recent success of self-supervised based contrastive learning model
for 3D point clouds representation, the adversarial robustness of such
pre-trained models raised concerns. Adversarial contrastive learning (ACL) is
considered an effective way to improve the robustness of pre-trained models. In
contrastive learning, the projector is considered an effective component for
removing unnecessary feature information during contrastive pretraining and
most ACL works also use contrastive loss with projected feature representations
to generate adversarial examples in pretraining, while "unprojected " feature
representations are used in generating adversarial inputs during
inference.Because of the distribution gap between projected and "unprojected"
features, their models are constrained of obtaining robust feature
representations for downstream tasks. We introduce a new method to generate
high-quality 3D adversarial examples for adversarial training by utilizing
virtual adversarial loss with "unprojected" feature representations in
contrastive learning framework. We present our robust aware loss function to
train self-supervised contrastive learning framework adversarially.
Furthermore, we find selecting high difference points with the Difference of
Normal (DoN) operator as additional input for adversarial self-supervised
contrastive learning can significantly improve the adversarial robustness of
the pre-trained model. We validate our method, PointACL on downstream tasks,
including 3D classification and 3D segmentation with multiple datasets. It
obtains comparable robust accuracy over state-of-the-art contrastive
adversarial learning methods.

Generative models (e.g., GANs, diffusion models) learn the underlying data
distribution in an unsupervised manner. However, many applications of interest
require sampling from a particular region of the output space or sampling
evenly over a range of characteristics. For efficient sampling in these
scenarios, we propose Generative Visual Prompt (PromptGen), a framework for
distributional control over pre-trained generative models by incorporating
knowledge of other off-the-shelf models. PromptGen defines control as
energy-based models (EBMs) and samples images in a feed-forward manner by
approximating the EBM with invertible neural networks, avoiding optimization at
inference. Our experiments demonstrate how PromptGen can efficiently sample
from several unconditional generative models (e.g., StyleGAN2, StyleNeRF,
diffusion autoencoder, NVAE) in a controlled or/and de-biased manner using
various off-the-shelf models: (1) with the CLIP model as control, PromptGen can
sample images guided by text, (2) with image classifiers as control, PromptGen
can de-bias generative models across a set of attributes or attribute
combinations, and (3) with inverse graphics models as control, PromptGen can
sample images of the same identity in different poses. (4) Finally, PromptGen
reveals that the CLIP model shows a "reporting bias" when used as control, and
PromptGen can further de-bias this controlled distribution in an iterative
manner. The code is available at
https://github.com/ChenWu98/Generative-Visual-Prompt.

Reservoir computing is a neuromorphic architecture that potentially offers
viable solutions to the growing energy costs of machine learning. The majority
of neuromorphic computing is currently software-based, where neural network
properties and performance can be reconfigured to suit different computational
tasks. This critical functionality is missing in ``physical" reservoir
computing schemes that exploit nonlinear and history-dependent memory responses
of physical systems for data processing. Here, we experimentally present a
`task-adaptive' approach to physical reservoir computing, capable of
reconfiguring key reservoir properties (nonlinearity, memory-capacity and
complexity) to optimise computational performance across a broad range of
tasks. As a model case of this, we use the temperature and magnetic-field
controlled spin-wave response of Cu$_2$OSeO$_3$ that hosts skyrmion, conical
and helical magnetic phases, providing on-demand access to a host of different
physical reservoir responses. We quantify phase-tunable reservoir performance,
characterise their properties and discuss the correlation between these in
physical reservoirs. This task-adaptive approach overcomes key prior
limitations of physical reservoirs, opening opportunities to apply
thermodynamically stable and metastable phase control across a wide variety of
physical reservoir systems.

We study the security of Probabilistic Data Structures (PDS) for handling
Approximate Membership Queries (AMQ); prominent examples of AMQ-PDS are Bloom
and Cuckoo filters. AMQ-PDS are increasingly being deployed in environments
where adversaries can gain benefit from carefully selecting inputs, for example
to increase the false positive rate of an AMQ-PDS. They are also being used in
settings where the inputs are sensitive and should remain private in the face
of adversaries who can access an AMQ-PDS through an API or who can learn its
internal state by compromising the system running the AMQ-PDS.
  We develop simulation-based security definitions that speak to correctness
and privacy of AMQ-PDS. Our definitions are general and apply to a broad range
of adversarial settings. We use our definitions to analyse the behaviour of
both Bloom filters and insertion-only Cuckoo filters. We show that these
AMQ-PDS can be provably protected through replacement or composition of hash
functions with keyed pseudorandom functions in their construction. We also
examine the practical impact on storage size and computation of providing
secure instances of Bloom and insertion-only Cuckoo filters.

Benefiting from large-scale Pretrained Vision-Language Models (VL-PMs), the
performance of Visual Question Answering (VQA) has started to approach human
oracle performance. However, finetuning large-scale VL-PMs with limited data
for VQA usually faces overfitting and poor generalization issues, leading to a
lack of robustness. In this paper, we aim to improve the robustness of VQA
systems (ie, the ability of the systems to defend against input variations and
human-adversarial attacks) from the perspective of Information Bottleneck when
finetuning VL-PMs for VQA. Generally, internal representations obtained by
VL-PMs inevitably contain irrelevant and redundant information for the
downstream VQA task, resulting in statistically spurious correlations and
insensitivity to input variations. To encourage representations to converge to
a minimal sufficient statistic in vision-language learning, we propose the
Correlation Information Bottleneck (CIB) principle, which seeks a tradeoff
between representation compression and redundancy by minimizing the mutual
information (MI) between the inputs and internal representations while
maximizing the MI between the outputs and the representations. Meanwhile, CIB
measures the internal correlations among visual and linguistic inputs and
representations by a symmetrized joint MI estimation. Extensive experiments on
five VQA benchmarks of input robustness and two VQA benchmarks of
human-adversarial robustness demonstrate the effectiveness and superiority of
the proposed CIB in improving the robustness of VQA systems.

In recent years novel architecture components for image classification have
been developed, starting with attention and patches used in transformers. While
prior works have analyzed the influence of some aspects of architecture
components on the robustness to adversarial attacks, in particular for vision
transformers, the understanding of the main factors is still limited. We
compare several (non)-robust classifiers with different architectures and study
their properties, including the effect of adversarial training on the
interpretability of the learnt features and robustness to unseen threat models.
An ablation from ResNet to ConvNeXt reveals key architectural changes leading
to almost $10\%$ higher $\ell_\infty$-robustness.

This study proposed a deep learning-based tracking method for ultrasound (US)
image-guided radiation therapy. The proposed cascade deep learning model is
composed of an attention network, a mask region-based convolutional neural
network (mask R-CNN), and a long short-term memory (LSTM) network. The
attention network learns a mapping from a US image to a suspected area of
landmark motion in order to reduce the search region. The mask R-CNN then
produces multiple region-of-interest (ROI) proposals in the reduced region and
identifies the proposed landmark via three network heads: bounding box
regression, proposal classification, and landmark segmentation. The LSTM
network models the temporal relationship among the successive image frames for
bounding box regression and proposal classification. To consolidate the final
proposal, a selection method is designed according to the similarities between
sequential frames. The proposed method was tested on the liver US tracking
datasets used in the Medical Image Computing and Computer Assisted
Interventions (MICCAI) 2015 challenges, where the landmarks were annotated by
three experienced observers to obtain their mean positions. Five-fold
cross-validation on the 24 given US sequences with ground truths shows that the
mean tracking error for all landmarks is 0.65+/-0.56 mm, and the errors of all
landmarks are within 2 mm. We further tested the proposed model on 69 landmarks
from the testing dataset that has a similar image pattern to the training
pattern, resulting in a mean tracking error of 0.94+/-0.83 mm. Our experimental
results have demonstrated the feasibility and accuracy of our proposed method
in tracking liver anatomic landmarks using US images, providing a potential
solution for real-time liver tracking for active motion management during
radiation therapy.

Freshly-synthesized r-process elements in kilonovae ejecta imprint absorption
features on optical spectra, as observed in the GW170817 binary neutron star
merger. These spectral features encode insights into the physical conditions of
the r-process and the origins of the ejected material, but associating features
with particular elements and inferring the resultant abundance pattern is
computationally challenging. We introduce Spectroscopic r-Process Abundance
Retrieval for Kilonovae (SPARK), a modular framework to perform Bayesian
inference on kilonova spectra with the goals of inferring elemental abundance
patterns and identifying absorption features at early times. SPARK inputs an
atomic line list and abundance patterns from reaction network calculations into
the TARDIS radiative transfer code. It then performs fast Bayesian inference on
observed kilonova spectra by training a Gaussian process surrogate for the
approximate posteriors of kilonova ejecta parameters, via active learning. We
use the spectrum of GW170817 at 1.4 days to perform the first inference on a
kilonova spectrum, and recover a complete abundance pattern. Our inference
shows that this ejecta was generated by an r-process with either (1) high
electron fraction Y_e ~ 0.35 and high entropy s/k_B ~ 25, or, (2) a more
moderate Y_e ~ 0.30 and s/k_B ~ 14. These parameters are consistent with a
shocked, polar dynamical component, and a viscously-driven outflow from a
remnant accretion disk, respectively. We also recover previous identifications
of strontium absorption at ~8000 AA, and tentatively identify yttrium and/or
zirconium at < 4500 AA. Our approach will enable computationally-tractable
inference on the spectra of future kilonovae discovered through multi-messenger

Denoising diffusion models have recently marked a milestone in high-quality
image generation. One may thus wonder if they are suitable for neural image
compression. This paper outlines an end-to-end optimized image compression
framework based on a conditional diffusion model, drawing on the
transform-coding paradigm. Besides the latent variables inherent to the
diffusion process, this paper introduces an additional discrete "content"
latent variable to condition the denoising process on. This variable is
equipped with a hierarchical prior for entropy coding. The remaining "texture"
latent variables characterizing the diffusion process are synthesized (either
stochastically or deterministically) at decoding time. We furthermore show that
the performance can be tuned toward perceptual metrics of interest. Our
extensive experiments involving five datasets and 16 image perceptual quality
assessment metrics show that our approach not only compares favorably in terms
of rate and perceptual distortion tradeoffs but also shows robust performance
under all metrics while other baselines show less consistent behavior.

Communal hill-climbing, via comparison of algorithm performances, can greatly
accelerate ML research. However, it requires task-relevant metrics. For
diseases involving parasite loads, e.g., malaria and neglected tropical
diseases (NTDs) such as schistosomiasis, the metrics currently reported in ML
papers (e.g., AUC, F1 score) are ill-suited to the clinical task. As a result,
the hill-climbing system is not enabling progress towards solutions that
address these dire illnesses. Drawing on examples from malaria and NTDs, this
paper highlights two gaps in current ML practice and proposes methods for
improvement: (i) We describe aspects of ML development, and performance metrics
in particular, that need to be firmly grounded in the clinical use case, and we
offer methods for acquiring this domain knowledge. (ii) We describe in detail
performance metrics to guide development of ML models for diseases involving
parasite loads. We highlight the importance of a patient-level perspective,
interpatient variability, false positive rates, limit of detection, and
different types of error. We also discuss problems with ROC curves and AUC as
commonly used in this context.

Handwriting recognition technology allows recognizing a written text from a
given data. The recognition task can target letters, symbols, or words, and the
input data can be a digital image or recorded by various sensors. A wide range
of applications from signature verification to electronic document processing
can be realized by implementing efficient and accurate handwriting recognition
algorithms. Over the years, there has been an increasing interest in
experimenting with different types of technology to collect handwriting data,
create datasets, and develop algorithms to recognize characters and symbols.
More recently, the OnHW-chars dataset has been published that contains
multivariate time series data of the English alphabet collected using a
ballpoint pen fitted with sensors. The authors of OnHW-chars also provided some
baseline results through their machine learning (ML) and deep learning (DL)
classifiers.
  In this paper, we develop handwriting recognition models on the OnHW-chars
dataset and improve the accuracy of previous models. More specifically, our ML
models provide $11.3\%$-$23.56\%$ improvements over the previous ML models, and
our optimized DL models with ensemble learning provide $3.08\%$-$7.01\%$
improvements over the previous DL models. In addition to our accuracy
improvements over the spectrum, we aim to provide some level of explainability
for our models to provide more logic behind chosen methods and why the models
make sense for the data type in the dataset. Our results are verifiable and
reproducible via the provided public repository.

Contrastive learning is among the most successful methods for visual
representation learning, and its performance can be further improved by jointly
performing clustering on the learned representations. However, existing methods
for joint clustering and contrastive learning do not perform well on
long-tailed data distributions, as majority classes overwhelm and distort the
loss of minority classes, thus preventing meaningful representations to be
learned. Motivated by this, we develop a novel joint clustering and contrastive
learning framework by adapting the debiased contrastive loss to avoid
under-clustering minority classes of imbalanced datasets. We show that our
proposed modified debiased contrastive loss and divergence clustering loss
improves the performance across multiple datasets and learning tasks. The
https://anonymous.4open.science/r/SSL-debiased-clustering

Collaborative robots (cobots) built to work alongside humans must be able to
quickly learn new skills and adapt to new task configurations. Learning from
demonstration (LfD) enables cobots to learn and adapt motions to different use
conditions. However, state-of-the-art LfD methods require manually tuning
intrinsic parameters and have rarely been used in industrial contexts without
experts.
  In this paper, the development and implementation of a LfD framework for
industrial applications with naive users is presented. We propose a
parameter-free method based on probabilistic movement primitives, where all the
parameters are pre-determined using Jensen-Shannon divergence and bayesian
optimization; thus, users do not have to perform manual parameter tuning. This
method learns motions from a small dataset of user demonstrations, and
generalizes the motion to various scenarios and conditions.
  We evaluate the method extensively in two field tests: one where the cobot
works on elevator door maintenance, and one where three Schindler workers teach
the cobot tasks useful for their workflow. Errors between the cobot
end-effector and target positions range from $0$ to $1.48\pm0.35$mm. For all
tests, no task failures were reported. Questionnaires completed by the
Schindler workers highlighted the method's ease of use, feeling of safety, and
the accuracy of the reproduced motion.
  Our code and recorded trajectories are made available online for
reproduction.

Leveraging sparse networks to connect successive layers in deep neural
networks has recently been shown to provide benefits to large scale
state-of-the-art models. However, network connectivity also plays a significant
role on the learning performance of shallow networks, such as the classic
Restricted Boltzmann Machines (RBM). Efficiently finding sparse connectivity
patterns that improve the learning performance of shallow networks is a
fundamental problem. While recent principled approaches explicitly include
network connections as model parameters that must be optimized, they often rely
on explicit penalization or have network sparsity as a hyperparameter. This
work presents a method to find optimal connectivity patterns for RBMs based on
the idea of network gradients (NCG): computing the gradient of every possible
connection, given a specific connection pattern, and using the gradient to
drive a continuous connection strength parameter that in turn is used to
determine the connection pattern. Thus, learning RBM parameters and learning
network connections is truly jointly performed, albeit with different learning
rates, and without changes to the objective function. The method is applied to
the MNIST and other datasets showing that better RBM models are found for the
benchmark tasks of sample generation and input classification. Results also
show that NCG is robust to network initialization, both adding and removing
network connections while learning.

The widespread adoption of deep neural networks in computer vision
applications has brought forth a significant interest in adversarial
robustness. Existing research has shown that maliciously perturbed inputs
specifically tailored for a given model (i.e., adversarial examples) can be
successfully transferred to another independently trained model to induce
prediction errors. Moreover, this property of adversarial examples has been
attributed to features derived from predictive patterns in the data
distribution. Thus, we are motivated to investigate the following question: Can
adversarial defenses, like adversarial examples, be successfully transferred to
other independently trained models? To this end, we propose a deep
learning-based pre-processing mechanism, which we refer to as a robust
transferable feature extractor (RTFE). After examining theoretical motivation
and implications, we experimentally show that our method can provide
adversarial robustness to multiple independently pre-trained classifiers that
are otherwise ineffective against an adaptive white box adversary. Furthermore,
we show that RTFEs can even provide one-shot adversarial robustness to models
independently trained on different datasets.

I survey, for a general scientific audience, three decades of research into
which sorts of problems admit exponential speedups via quantum computers --
from the classics (like the algorithms of Simon and Shor), to the breakthrough
of Yamakawa and Zhandry from April 2022. I discuss both the quantum circuit
model, which is what we ultimately care about in practice but where our
knowledge is radically incomplete, and the so-called oracle or black-box or
query complexity model, where we've managed to achieve a much more thorough
understanding that then informs our conjectures about the circuit model. I
discuss the strengths and weaknesses of switching attention to sampling tasks,
as was done in the recent quantum supremacy experiments. I make some skeptical
remarks about widely-repeated claims of exponential quantum speedups for
practical machine learning and optimization problems. Through many examples, I
try to convey the "law of conservation of weirdness," according to which every
problem admitting an exponential quantum speedup must have some unusual
property to allow the amplitude to be concentrated on the unknown right
answer(s).

The iterative weight update for the AdaBoost machine learning algorithm may
be realized as a dynamical map on a probability simplex. When learning a
low-dimensional data set this algorithm has a tendency towards cycling
behavior, which is the topic of this paper. AdaBoost's cycling behavior lends
itself to direct computational methods that are ineffective in the general,
non-cycling case of the algorithm. From these computational properties we give
a concrete correspondence between AdaBoost's cycling behavior and continued
fractions dynamics. Then we explore the results of this correspondence to
expound on how the algorithm comes to be in this periodic state at all. What we
intend for this work is to be a novel and self-contained explanation for the
cycling dynamics of this machine learning algorithm.

Image-based 3D reconstruction is one of the most important tasks in Computer
Vision with many solutions proposed over the last few decades. The objective is
to extract metric information i.e. the geometry of scene objects directly from
images. These can then be used in a wide range of applications such as film,
games, virtual reality, etc. Recently, deep learning techniques have been
proposed to tackle this problem. They rely on training on vast amounts of data
to learn to associate features between images through deep convolutional neural
networks and have been shown to outperform traditional procedural techniques.
In this paper, we improve on the state-of-the-art two-view
structure-from-motion(SfM) approach of [11] by incorporating 4D correlation
volume for more accurate feature matching and reconstruction. Furthermore, we
extend it to the general multi-view case and evaluate it on the complex
benchmark dataset DTU [4]. Quantitative evaluations and comparisons with
state-of-the-art multi-view 3D reconstruction methods demonstrate its
superiority in terms of the accuracy of reconstructions.

Multi-hop question answering (QA) requires reasoning over multiple documents
to answer a complex question and provide interpretable supporting evidence.
However, providing supporting evidence is not enough to demonstrate that a
model has performed the desired reasoning to reach the correct answer. Most
existing multi-hop QA methods fail to answer a large fraction of sub-questions,
even if their parent questions are answered correctly. In this paper, we
propose the Prompt-based Conservation Learning (PCL) framework for multi-hop
QA, which acquires new knowledge from multi-hop QA tasks while conserving old
knowledge learned on single-hop QA tasks, mitigating forgetting. Specifically,
we first train a model on existing single-hop QA tasks, and then freeze this
model and expand it by allocating additional sub-networks for the multi-hop QA
task. Moreover, to condition pre-trained language models to stimulate the kind
of reasoning required for specific multi-hop questions, we learn soft prompts
for the novel sub-networks to perform type-specific reasoning. Experimental
results on the HotpotQA benchmark show that PCL is competitive for multi-hop QA
and retains good performance on the corresponding single-hop sub-questions,
demonstrating the efficacy of PCL in mitigating knowledge loss by forgetting.

Nano quadcopters are small, agile, and cheap platforms that are well suited
for deployment in narrow, cluttered environments. Due to their limited payload,
these vehicles are highly constrained in processing power, rendering
conventional vision-based methods for safe and autonomous navigation
incompatible. Recent machine learning developments promise high-performance
perception at low latency, while dedicated edge computing hardware has the
potential to augment the processing capabilities of these limited devices. In
this work, we present NanoFlowNet, a lightweight convolutional neural network
for real-time dense optical flow estimation on edge computing hardware. We draw
inspiration from recent advances in semantic segmentation for the design of
this network. Additionally, we guide the learning of optical flow using motion
boundary ground truth data, which improves performance with no impact on
latency. Validation results on the MPI-Sintel dataset show the high performance
of the proposed network given its constrained architecture. Additionally, we
successfully demonstrate the capabilities of NanoFlowNet by deploying it on the
ultra-low power GAP8 microprocessor and by applying it to vision-based obstacle
avoidance on board a Bitcraze Crazyflie, a 34 g nano quadcopter.

Real-time remote control over wireless is an important-yet-challenging
application in 5G and beyond due to its mission-critical nature under limited
communication resources. Current solutions hinge on not only utilizing
ultra-reliable and low-latency communication (URLLC) links but also predicting
future states, which may consume enormous communication resources and struggle
with a short prediction time horizon. To fill this void, in this article we
propose a novel two-way Koopman autoencoder (AE) approach wherein: 1) a sensing
Koopman AE learns to understand the temporal state dynamics and predicts
missing packets from a sensor to its remote controller; and 2) a controlling
Koopman AE learns to understand the temporal action dynamics and predicts
missing packets from the controller to an actuator co-located with the sensor.
Specifically, each Koopman AE aims to learn the Koopman operator in the hidden
layers while the encoder of the AE aims to project the non-linear dynamics onto
a lifted subspace, which is reverted into the original non-linear dynamics by
the decoder of the AE. The Koopman operator describes the linearized temporal
dynamics, enabling long-term future prediction and coping with missing packets
and closed-form optimal control in the lifted subspace. Simulation results
corroborate that the proposed approach achieves a 38x lower mean squared
control error at 0 dBm signal-to-noise ratio (SNR) than the non-predictive
baseline.

Equilibrium Propagation (EP) is a powerful and more bio-plausible alternative
to conventional learning frameworks such as backpropagation. The effectiveness
of EP stems from the fact that it relies only on local computations and
requires solely one kind of computational unit during both of its training
phases, thereby enabling greater applicability in domains such as bio-inspired
neuromorphic computing. The dynamics of the model in EP is governed by an
energy function and the internal states of the model consequently converge to a
steady state following the state transition rules defined by the same. However,
by definition, EP requires the input to the model (a convergent RNN) to be
static in both the phases of training. Thus it is not possible to design a
model for sequence classification using EP with an LSTM or GRU like
architecture. In this paper, we leverage recent developments in modern hopfield
networks to further understand energy based models and develop solutions for
complex sequence classification tasks using EP while satisfying its convergence
criteria and maintaining its theoretical similarities with recurrent
backpropagation. We explore the possibility of integrating modern hopfield
networks as an attention mechanism with convergent RNN models used in EP,
thereby extending its applicability for the first time on two different
sequence classification tasks in natural language processing viz. sentiment
analysis (IMDB dataset) and natural language inference (SNLI dataset).

We propose and explore the possibility that language models can be studied as
effective proxies for specific human sub-populations in social science
research. Practical and research applications of artificial intelligence tools
have sometimes been limited by problematic biases (such as racism or sexism),
which are often treated as uniform properties of the models. We show that the
"algorithmic bias" within one such tool -- the GPT-3 language model -- is
instead both fine-grained and demographically correlated, meaning that proper
conditioning will cause it to accurately emulate response distributions from a
wide variety of human subgroups. We term this property "algorithmic fidelity"
and explore its extent in GPT-3. We create "silicon samples" by conditioning
the model on thousands of socio-demographic backstories from real human
participants in multiple large surveys conducted in the United States. We then
compare the silicon and human samples to demonstrate that the information
contained in GPT-3 goes far beyond surface similarity. It is nuanced,
multifaceted, and reflects the complex interplay between ideas, attitudes, and
socio-cultural context that characterize human attitudes. We suggest that
language models with sufficient algorithmic fidelity thus constitute a novel
and powerful tool to advance understanding of humans and society across a
variety of disciplines.

We compare the two largest galaxy morphology catalogues, which separate early
and late type galaxies at intermediate redshift. The two catalogues were built
by applying supervised deep learning (convolutional neural networks, CNNs) to
the Dark Energy Survey data down to a magnitude limit of $\sim$21 mag. The
methodologies used for the construction of the catalogues include differences
such as the cutout sizes, the labels used for training, and the input to the
CNN - monochromatic images versus $gri$-band normalized images. In addition,
one catalogue is trained using bright galaxies observed with DES ($i<18$),
while the other is trained with bright galaxies ($r<17.5$) and `emulated'
galaxies up to $r$-band magnitude $22.5$. Despite the different approaches, the
agreement between the two catalogues is excellent up to $i<19$, demonstrating
that CNN predictions are reliable for samples at least one magnitude fainter
than the training sample limit. It also shows that morphological
classifications based on monochromatic images are comparable to those based on
$gri$-band images, at least in the bright regime. At fainter magnitudes,
$i>19$, the overall agreement is good ($\sim$95\%), but is mostly driven by the
large spiral fraction in the two catalogues. In contrast, the agreement within
the elliptical population is not as good, especially at faint magnitudes. By
studying the mismatched cases we are able to identify lenticular galaxies (at
least up to $i<19$), which are difficult to distinguish using standard
classification approaches. The synergy of both catalogues provides an unique
opportunity to select a population of unusual galaxies.

Model mismatches prevail in real-world applications. Hence it is important to
design robust safe control algorithms for systems with uncertain dynamic
models. The major challenge is that uncertainty results in difficulty in
finding a feasible safe control in real-time. Existing methods usually simplify
the problem such as restricting uncertainty type, ignoring control limits, or
forgoing feasibility guarantees. In this work, we overcome these issues by
proposing a robust safe control framework for bounded state-dependent
uncertainties. We first guarantee the feasibility of safe control for uncertain
dynamics by learning a control-limits-aware, uncertainty-robust safety index.
Then we show that robust safe control can be formulated as convex problems
(Convex Semi-Infinite Programming or Second-Order Cone Programming) and propose
corresponding optimal solvers that can run in real-time. In addition, we
analyze when and how safety can be preserved under unmodeled uncertainties.
Experiment results show that our method successfully finds robust safe control
in real-time for different uncertainties and is much less conservative than a
strong baseline algorithm.

Humans leverage multiple sensor modalities when interacting with objects and
discovering their intrinsic properties. Using the visual modality alone is
insufficient for deriving intuition behind object properties (e.g., which of
two boxes is heavier), making it essential to consider non-visual modalities as
well, such as the tactile and auditory. Whereas robots may leverage various
modalities to obtain object property understanding via learned exploratory
interactions with objects (e.g., grasping, lifting, and shaking behaviors),
challenges remain: the implicit knowledge acquired by one robot via object
exploration cannot be directly leveraged by another robot with different
morphology, because the sensor models, observed data distributions, and
interaction capabilities are different across these different robot
configurations. To avoid the costly process of learning interactive object
perception tasks from scratch, we propose a multi-stage projection framework
for each new robot for transferring implicit knowledge of object properties
across heterogeneous robot morphologies. We evaluate our approach on the
object-property recognition and object-identity recognition tasks, using a
dataset containing two heterogeneous robots that perform 7,600 object
interactions. Results indicate that knowledge can be transferred across robots,
such that a newly-deployed robot can bootstrap its recognition models without
exhaustively exploring all objects. We also propose a data augmentation
technique and show that this technique improves the generalization of models.
We release our code and datasets, here:
https://github.com/gtatiya/Implicit-Knowledge-Transfer.

With exponential growth in the human population, it is vital to conserve
natural resources without compromising on producing enough food to feed
everyone. Doing so can improve people's livelihoods, health, and ecosystems for
the present and future generations. Sustainable development, a paradigm of the
United Nations, is rooted in food, crop, livestock, forest, population, and
even the emission of gases. By understanding the overall usage of natural
resources in different countries in the past, it is possible to forecast the
demand in each country. The proposed solution consists of implementing a
machine learning system using a statistical regression model that can predict
the top k products that would endure a shortage in each country in a specific
period in the future. The prediction performance in terms of absolute error and
root mean square error show promising results due to its low errors. This
solution could help organizations and manufacturers understand the productivity
and sustainability needed to satisfy the global demand.

This document, as the title stated, is meant to provide a vectorized
implementation of adjoint dynamics calculation for Graph Convolutional Neural
Ordinary Differential Equations (GCDE). The adjoint sensitivity method is the
gradient approximation method for neural ODEs that replaces the back
propagation. When implemented on libraries such as PyTorch or Tensorflow, the
adjoint can be calculated by autograd functions without the need for a
hand-derived formula. In applications such as edge computing and in memristor
crossbars, however, autograds are not available, and therefore we need a
vectorized derivation of adjoint dynamics to efficiently map the system on
hardware. This document will go over the basics, then move on to derive the
vectorized adjoint dynamics for GCDE.

In these proceedings I discuss the recent progress in the theory of spin
polarization in relativistic fluids. To date, a number of studies have begun to
examine the impact of the shear tensor on the local spin polarization and
whether this contribution can restore agreement between the measurements and
the predictions obtained from a polarization induced by the gradients of the
plasma. I present the derivation of the spin polarization vector of a fermion
at local thermal equilibrium and I discuss the role of pseudo-gauge
transformations and of dissipative effects. I list what we can learn from the
polarization measured at lower energies. Finally, I discuss possible
applications of spin polarization measurements in relativistic heavy ion

Despite decades of research on authorship attribution (AA) and authorship
verification (AV), inconsistent dataset splits/filtering and mismatched
evaluation methods make it difficult to assess the state of the art. In this
paper, we present a survey of the fields, resolve points of confusion,
introduce Valla that standardizes and benchmarks AA/AV datasets and metrics,
provide a large-scale empirical evaluation, and provide apples-to-apples
comparisons between existing methods. We evaluate eight promising methods on
fifteen datasets (including distribution-shifted challenge sets) and introduce
a new large-scale dataset based on texts archived by Project Gutenberg.
Surprisingly, we find that a traditional Ngram-based model performs best on 5
(of 7) AA tasks, achieving an average macro-accuracy of $76.50\%$ (compared to
$66.71\%$ for a BERT-based model). However, on the two AA datasets with the
greatest number of words per author, as well as on the AV datasets, BERT-based
models perform best. While AV methods are easily applied to AA, they are seldom
included as baselines in AA papers. We show that through the application of
hard-negative mining, AV methods are competitive alternatives to AA methods.
Valla and all experiment code can be found here:
https://github.com/JacobTyo/Valla

Constrained reinforcement learning is to maximize the expected reward subject
to constraints on utilities/costs. However, the training environment may not be
the same as the test one, due to, e.g., modeling error, adversarial attack,
non-stationarity, resulting in severe performance degradation and more
importantly constraint violation. We propose a framework of robust constrained
reinforcement learning under model uncertainty, where the MDP is not fixed but
lies in some uncertainty set, the goal is to guarantee that constraints on
utilities/costs are satisfied for all MDPs in the uncertainty set, and to
maximize the worst-case reward performance over the uncertainty set. We design
a robust primal-dual approach, and further theoretically develop guarantee on
its convergence, complexity and robust feasibility. We then investigate a
concrete example of $\delta$-contamination uncertainty set, design an online
and model-free algorithm and theoretically characterize its sample complexity.

Statistical shape modeling aims at capturing shape variations of an
anatomical structure that occur within a given population. Shape models are
employed in many tasks, such as shape reconstruction and image segmentation,
but also shape generation and classification. Existing shape priors either
require dense correspondence between training examples or lack robustness and
topological guarantees. We present FlowSSM, a novel shape modeling approach
that learns shape variability without requiring dense correspondence between
training instances. It relies on a hierarchy of continuous deformation flows,
which are parametrized by a neural network. Our model outperforms
state-of-the-art methods in providing an expressive and robust shape prior for
distal femur and liver. We show that the emerging latent representation is
discriminative by separating healthy from pathological shapes. Ultimately, we
demonstrate its effectiveness on two shape reconstruction tasks from partial
data. Our source code is publicly available
(https://github.com/davecasp/flowssm).

As input distributions evolve over a mission lifetime, maintaining
performance of learning-based models becomes challenging. This paper presents a
framework to incrementally retrain a model by selecting a subset of test inputs
to label, which allows the model to adapt to changing input distributions.
Algorithms within this framework are evaluated based on (1) model performance
throughout mission lifetime and (2) cumulative costs associated with labeling
and model retraining. We provide an open-source benchmark of a satellite pose
estimation model trained on images of a satellite in space and deployed in
novel scenarios (e.g., different backgrounds or misbehaving pixels), where
algorithms are evaluated on their ability to maintain high performance by
retraining on a subset of inputs. We also propose a novel algorithm to select a
diverse subset of inputs for labeling, by characterizing the information gain
from an input using Bayesian uncertainty quantification and choosing a subset
that maximizes collective information gain using concepts from batch active
learning. We show that our algorithm outperforms others on the benchmark, e.g.,
achieves comparable performance to an algorithm that labels 100% of inputs,
while only labeling 50% of inputs, resulting in low costs and high performance
over the mission lifetime.

Generative Adversarial Networks (GANs) have achieved great success in data
generation. However, its statistical properties are not fully understood. In
this paper, we consider the statistical behavior of the general $f$-divergence
formulation of GAN, which includes the Kullback--Leibler divergence that is
closely related to the maximum likelihood principle. We show that for
parametric generative models that are correctly specified, all $f$-divergence
GANs with the same discriminator classes are asymptotically equivalent under
suitable regularity conditions. Moreover, with an appropriately chosen local
discriminator, they become equivalent to the maximum likelihood estimate
asymptotically. For generative models that are misspecified, GANs with
different $f$-divergences {converge to different estimators}, and thus cannot
be directly compared. However, it is shown that for some commonly used
$f$-divergences, the original $f$-GAN is not optimal in that one can achieve a
smaller asymptotic variance when the discriminator training in the original
$f$-GAN formulation is replaced by logistic regression. The resulting
estimation method is referred to as Adversarial Gradient Estimation (AGE).
Empirical studies are provided to support the theory and to demonstrate the
advantage of AGE over the original $f$-GANs under model misspecification.

We train graph neural networks on halo catalogues from Gadget N-body
simulations to perform field-level likelihood-free inference of cosmological
parameters. The catalogues contain $\lesssim$5,000 halos with masses $\gtrsim
10^{10}~h^{-1}M_\odot$ in a periodic volume of $(25~h^{-1}{\rm Mpc})^3$; every
halo in the catalogue is characterized by several properties such as position,
mass, velocity, concentration, and maximum circular velocity. Our models, built
to be permutationally, translationally, and rotationally invariant, do not
impose a minimum scale on which to extract information and are able to infer
the values of $\Omega_{\rm m}$ and $\sigma_8$ with a mean relative error of
$\sim6\%$, when using positions plus velocities and positions plus masses,
respectively. More importantly, we find that our models are very robust: they
can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo
catalogues from thousands of N-body simulations run with five different N-body
codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model
trained to infer $\Omega_{\rm m}$ also works when tested on thousands of
state-of-the-art CAMELS hydrodynamic simulations run with four different codes
and subgrid physics implementations. Using halo properties such as
concentration and maximum circular velocity allow our models to extract more
information, at the expense of breaking the robustness of the models. This may
happen because the different N-body codes are not converged on the relevant
scales corresponding to these parameters.

Parametric 3D body models like SMPL only represent minimally-clothed people
and are hard to extend to clothing because they have a fixed mesh topology and
resolution. To address these limitations, recent work uses implicit surfaces or
point clouds to model clothed bodies. While not limited by topology, such
methods still struggle to model clothing that deviates significantly from the
body, such as skirts and dresses. This is because they rely on the body to
canonicalize the clothed surface by reposing it to a reference shape.
Unfortunately, this process is poorly defined when clothing is far from the
body. Additionally, they use linear blend skinning to pose the body and the
skinning weights are tied to the underlying body parts. In contrast, we model
the clothing deformation in a local coordinate space without canonicalization.
We also relax the skinning weights to let multiple body parts influence the
surface. Specifically, we extend point-based methods with a coarse stage, that
replaces canonicalization with a learned pose-independent "coarse shape" that
can capture the rough surface geometry of clothing like skirts. We then refine
this using a network that infers the linear blend skinning weights and pose
dependent displacements from the coarse representation. The approach works well
for garments that both conform to, and deviate from, the body. We demonstrate
the usefulness of our approach by learning person-specific avatars from
examples and then show how they can be animated in new poses and motions. We
also show that the method can learn directly from raw scans with missing data,
greatly simplifying the process of creating realistic avatars. Code is
available for research purposes at
{\small\url{https://qianlim.github.io/SkiRT}}.

Road construction projects maintain transportation infrastructures. These
projects range from the short-term (e.g., resurfacing or fixing potholes) to
the long-term (e.g., adding a shoulder or building a bridge). Deciding what the
next construction project is and when it is to be scheduled is traditionally
done through inspection by humans using special equipment. This approach is
costly and difficult to scale. An alternative is the use of computational
approaches that integrate and analyze multiple types of past and present
spatiotemporal data to predict location and time of future road constructions.
This paper reports on such an approach, one that uses a
deep-neural-network-based model to predict future constructions. Our model
applies both convolutional and recurrent components on a heterogeneous dataset
consisting of construction, weather, map and road-network data. We also report
on how we addressed the lack of adequate publicly available data - by building
a large scale dataset named "US-Constructions", that includes 6.2 million cases
of road constructions augmented by a variety of spatiotemporal attributes and
road-network features, collected in the contiguous United States (US) between
2016 and 2021. Using extensive experiments on several major cities in the US,
we show the applicability of our work in accurately predicting future
constructions - an average f1-score of 0.85 and accuracy 82.2% - that
outperform baselines. Additionally, we show how our training pipeline addresses
spatial sparsity of data.

Criminal recidivism models are tools that have gained widespread adoption by
parole boards across the United States to assist with parole decisions. These
models take in large amounts of data about an individual and then predict
whether an individual would commit a crime if released on parole. Although such
models are not the only or primary factor in making the final parole decision,
questions have been raised about their accuracy, fairness, and
interpretability. In this paper, various machine learning-based criminal
recidivism models are created based on a real-world parole decision dataset
from the state of Georgia in the United States. The recidivism models are
comparatively evaluated for their accuracy, fairness, and interpretability. It
is found that there are noted differences and trade-offs between accuracy,
fairness, and being inherently interpretable. Therefore, choosing the best
model depends on the desired balance between accuracy, fairness, and
interpretability, as no model is perfect or consistently the best across
different criteria.

The increasing size of input graphs for graph neural networks (GNNs)
highlights the demand for using multi-GPU platforms. However, existing
multi-GPU GNN solutions suffer from inferior performance due to imbalanced
computation and inefficient communication. To this end, we propose MGG, a novel
system design to accelerate GNNs on multi-GPU platforms via a GPU-centric
software pipeline. MGG explores the potential of hiding remote memory access
latency in GNN workloads through fine-grained computation-communication
pipelining. Specifically, MGG introduces a pipeline-aware workload management
strategy and a hybrid data layout design to facilitate
communication-computation overlapping. MGG implements an optimized
pipeline-centric kernel. It includes workload interleaving and warp-based
mapping for efficient GPU kernel operation pipelining and specialized memory
designs and optimizations for better data access performance. Besides, MGG
incorporates lightweight analytical modeling and optimization heuristics to
dynamically improve the GNN execution performance for different settings at
runtime. Comprehensive experiments demonstrate that MGG outperforms
state-of-the-art multi-GPU systems across various GNN settings: on average
3.65X faster than multi-GPU systems with a unified virtual memory design and on
average 7.38X faster than the DGCL framework.

We investigate models that can generate arbitrary natural language text (e.g.
all English sentences) from a bounded, convex and well-behaved control space.
We call them universal vec2text models. Such models would allow making semantic
decisions in the vector space (e.g. via reinforcement learning) while the
natural language generation is handled by the vec2text model. We propose four
desired properties: universality, diversity, fluency, and semantic structure,
that such vec2text models should possess and we provide quantitative and
qualitative methods to assess them. We implement a vec2text model by adding a
bottleneck to a 250M parameters Transformer model and training it with an
auto-encoding objective on 400M sentences (10B tokens) extracted from a massive
web corpus. We propose a simple data augmentation technique based on round-trip
translations and show in extensive experiments that the resulting vec2text
model surprisingly leads to vector spaces that fulfill our four desired
properties and that this model strongly outperforms both standard and denoising
auto-encoders.

This paper presents a method of decoupled pronunciation and prosody modeling
to improve the performance of meta-learning-based multilingual speech
synthesis. The baseline meta-learning synthesis method adopts a single text
encoder with a parameter generator conditioned on language embeddings and a
single decoder to predict mel-spectrograms for all languages. In contrast, our
proposed method designs a two-stream model structure that contains two encoders
and two decoders for pronunciation and prosody modeling, respectively,
considering that the pronunciation knowledge and the prosody knowledge should
be shared in different ways among languages. In our experiments, our proposed
method effectively improved the intelligibility and naturalness of multilingual
speech synthesis comparing with the baseline meta-learning synthesis method.

We study representations of data from an arbitrary metric space $\mathcal{X}$
in the space of univariate Gaussian mixtures with a transport metric (Delon and
Desolneux 2020). We derive embedding guarantees for feature maps implemented by
small neural networks called \emph{probabilistic transformers}. Our guarantees
are of memorization type: we prove that a probabilistic transformer of depth
about $n\log(n)$ and width about $n^2$ can bi-H\"{o}lder embed any $n$-point
dataset from $\mathcal{X}$ with low metric distortion, thus avoiding the curse
of dimensionality. We further derive probabilistic bi-Lipschitz guarantees,
which trade off the amount of distortion and the probability that a randomly
chosen pair of points embeds with that distortion. If $\mathcal{X}$'s geometry
is sufficiently regular, we obtain stronger, bi-Lipschitz guarantees for all
points in the dataset. As applications, we derive neural embedding guarantees
for datasets from Riemannian manifolds, metric trees, and certain types of
combinatorial graphs. When instead embedding into multivariate Gaussian
mixtures, we show that probabilistic transformers can compute bi-H\"{o}lder
embeddings with arbitrarily small distortion.

NLU systems deployed in the real world are expected to be regularly updated
by retraining or finetuning the underlying neural network on new training
examples accumulated over time. In our work, we focus on the multilingual
setting where we would want to further finetune a multilingual model on new
training data for the same NLU task on which the aforementioned model has
already been trained for. We show that under certain conditions, naively
updating the multilingual model can lead to losses in performance over a subset
of languages although the aggregated performance metric shows an improvement.
We establish this phenomenon over four tasks belonging to three task families
(token-level, sentence-level and seq2seq) and find that the baseline is far
from ideal for the setting at hand. We then build upon recent advances in
parameter-efficient finetuning to develop novel finetuning pipelines that allow
us to jointly minimize catastrophic forgetting while encouraging positive
cross-lingual transfer, hence improving the spread of gains over different
languages while reducing the losses incurred in this setup.

Development of controllers, novel robot kinematics, and learning-based
applications of robotics today happens almost exclusively in simulation first
before being implemented in the real world. In particular, Modular
Reconfigurable Robots (MRRs) are an exciting innovation in industrial robotics,
promising greater flexibility, improved maintainability, and cost-efficiency
compared to traditional manipulators. However, there is no tool or standardized
way to simulate and model assemblies of modules in the same way it has been
done for robotic manipulators for decades. We introduce the Toolbox for
Industrial Modular Robotics (Timor), a python toolbox to bridge this gap and
integrate modular robotics in existing simulation and optimization pipelines.
Our open-source library comes with various examples as well as tutorials and
can easily be integrated with existing simulation tools - not least by offering
URDF export of arbitrary modular robot assemblies, enabling rapid model

Cyber-physical systems (CPSs) are usually complex and safety-critical; hence,
it is difficult and important to guarantee that the system's requirements,
i.e., specifications, are fulfilled. Simulation-based falsification of CPSs is
a practical testing method that can be used to raise confidence in the
correctness of the system by only requiring that the system under test can be
simulated. As each simulation is typically computationally intensive, an
important step is to reduce the number of simulations needed to falsify a
specification. We study Bayesian optimization (BO), a sample-efficient method
that learns a surrogate model that describes the relationship between the
parametrization of possible input signals and the evaluation of the
specification.
  In this paper, we improve the falsification using BO by; first adopting two
prominent BO methods, one fits local surrogate models, and the other exploits
the user's prior knowledge. Secondly, the formulation of acquisition functions
for falsification is addressed in this paper. Benchmark evaluation shows
significant improvements in using local surrogate models of BO for falsifying
benchmark examples that were previously hard to falsify. Using prior knowledge
in the falsification process is shown to be particularly important when the
simulation budget is limited. For some of the benchmark problems, the choice of
acquisition function clearly affects the number of simulations needed for
successful falsification.

Monitoring plankton populations in situ is fundamental to preserve the
aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor
environmental perturbations, that can reflect into consequent morphological and
dynamical modifications. Nowadays, the availability of advanced automatic or
semi-automatic acquisition systems has been allowing the production of an
increasingly large amount of plankton image data. The adoption of machine
learning algorithms to classify such data may be affected by the significant
cost of manual annotation, due to both the huge quantity of acquired data and
the numerosity of plankton species. To address these challenges, we propose an
efficient unsupervised learning pipeline to provide accurate classification of
plankton microorganisms. We build a set of image descriptors exploiting a
two-step procedure. First, a Variational Autoencoder (VAE) is trained on
features extracted by a pre-trained neural network. We then use the learnt
latent space as image descriptor for clustering. We compare our method with
state-of-the-art unsupervised approaches, where a set of pre-defined
hand-crafted features is used for clustering of plankton images. The proposed
pipeline outperforms the benchmark algorithms for all the plankton datasets
included in our analysis, providing better image embedding properties.

Health literacy is the central focus of Healthy People 2030, the fifth
iteration of the U.S. national goals and objectives. People with low health
literacy usually have trouble understanding health information, following
post-visit instructions, and using prescriptions, which results in worse health
outcomes and serious health disparities. In this study, we propose to leverage
natural language processing techniques to improve health literacy in patient
education materials by automatically translating illiterate languages in a
given sentence. We scraped patient education materials from four online health
information websites: MedlinePlus.gov, Drugs.com, Mayoclinic.org and
Reddit.com. We trained and tested the state-of-the-art neural machine
translation (NMT) models on a silver standard training dataset and a gold
standard testing dataset, respectively. The experimental results showed that
the Bidirectional Long Short-Term Memory (BiLSTM) NMT model outperformed
Bidirectional Encoder Representations from Transformers (BERT)-based NMT
models. We also verified the effectiveness of NMT models in translating health
illiterate languages by comparing the ratio of health illiterate language in
the sentence. The proposed NMT models were able to identify the correct
complicated words and simplify into layman language while at the same time the
models suffer from sentence completeness, fluency, readability, and have
difficulty in translating certain medical terms.

Single-cell RNA-seq datasets are growing in size and complexity, enabling the
study of cellular composition changes in various biological/clinical contexts.
Scalable dimensionality reduction techniques are in need to disentangle
biological variation in them, while accounting for technical and biological
confounders. In this work, we extend a popular approach for probabilistic
non-linear dimensionality reduction, the Gaussian process latent variable
model, to scale to massive single-cell datasets while explicitly accounting for
technical and biological confounders. The key idea is to use an augmented
kernel which preserves the factorisability of the lower bound allowing for fast
stochastic variational inference. We demonstrate its ability to reconstruct
latent signatures of innate immunity recovered in Kumasaka et al. (2021) with
9x lower training time. We further analyze a COVID dataset and demonstrate
across a cohort of 130 individuals, that this framework enables data
integration while capturing interpretable signatures of infection.
Specifically, we explore COVID severity as a latent dimension to refine patient
stratification and capture disease-specific gene expression.

Robotic manipulation of cloth is a highly complex task because of its
infinite-dimensional shape-state space that makes cloth state estimation very
difficult. In this paper we introduce the dGLI Cloth Coordinates, a
low-dimensional representation of the state of a rectangular piece of cloth
that allows to efficiently distinguish key topological changes in a folding
sequence, opening the door to efficient learning methods for cloth manipulation
planning and control. Our representation is based on a directional derivative
of the Gauss Linking Integral and allows us to represent both planar and
spatial configurations in a consistent unified way. The proposed dGLI Cloth
Coordinates are shown to be more accurate in the classification of cloth states
and significantly more sensitive to changes in grasping affordances than other
classic shape distance methods. Finally, we apply our representation to real
images of a cloth, showing we can identify the different states using a simple
distance-based classifier.

Within textual emotion classification, the set of relevant labels depends on
the domain and application scenario and might not be known at the time of model
development. This conflicts with the classical paradigm of supervised learning
in which the labels need to be predefined. A solution to obtain a model with a
flexible set of labels is to use the paradigm of zero-shot learning as a
natural language inference task, which in addition adds the advantage of not
needing any labeled training data. This raises the question how to prompt a
natural language inference model for zero-shot learning emotion classification.
Options for prompt formulations include the emotion name anger alone or the
statement "This text expresses anger". With this paper, we analyze how
sensitive a natural language inference-based zero-shot-learning classifier is
to such changes to the prompt under consideration of the corpus: How carefully
does the prompt need to be selected? We perform experiments on an established
set of emotion datasets presenting different language registers according to
different sources (tweets, events, blogs) with three natural language inference
models and show that indeed the choice of a particular prompt formulation needs
to fit to the corpus. We show that this challenge can be tackled with
combinations of multiple prompts. Such ensemble is more robust across corpora
than individual prompts and shows nearly the same performance as the individual
best prompt for a particular corpus.

Iterative machine learning algorithms used to power recommender systems often
change people's preferences by trying to learn them. Further a recommender can
better predict what a user will do by making its users more predictable. Some
preference changes on the part of the user are self-induced and desired whether
the recommender caused them or not. This paper proposes that solutions to
preference manipulation in recommender systems must take into account certain
meta-preferences (preferences over another preference) in order to respect the
autonomy of the user and not be manipulative.

Binary analysis is an important capability required for many security and
software engineering applications. Consequently, there are many binary analysis
techniques and tools with varied capabilities. However, testing these tools
requires a large, varied binary dataset with corresponding source-level
information. In this paper, we present Cornucopia, an architecture agnostic
automated framework that can generate a plethora of binaries from corresponding
program source by exploiting compiler optimizations and feedback-guided
learning. Our evaluation shows that Cornucopia was able to generate 309K
binaries across four architectures (x86, x64, ARM, MIPS) with an average of 403
binaries for each program and outperforms Bintuner, a similar technique. Our
experiments revealed issues with the LLVM optimization scheduler resulting in
compiler crashes ($\sim$300). Our evaluation of four popular binary analysis
tools Angr, Ghidra, Idapro, and Radare, using Cornucopia generated binaries,
revealed various issues with these tools. Specifically, we found 263 crashes in
Angr and one memory corruption issue in Idapro. Our differential testing on the
analysis results revealed various semantic bugs in these tools. We also tested
machine learning tools, Asmvec, Safe, and Debin, that claim to capture binary
semantics and show that they perform poorly (For instance, Debin F1 score
dropped to 12.9% from reported 63.1%) on Cornucopia generated binaries. In
summary, our exhaustive evaluation shows that Cornucopia is an effective
mechanism to generate binaries for testing binary analysis techniques
effectively.

Gaze estimation is of great importance to many scientific fields and daily
applications, ranging from fundamental research in cognitive psychology to
attention-aware mobile systems. While recent advancements in deep learning have
yielded remarkable successes in building highly accurate gaze estimation
systems, the associated high computational cost and the reliance on large-scale
labeled gaze data for supervised learning place challenges on the practical use
of existing solutions. To move beyond these limitations, we present FreeGaze, a
resource-efficient framework for unsupervised gaze representation learning.
FreeGaze incorporates the frequency domain gaze estimation and the contrastive
gaze representation learning in its design. The former significantly alleviates
the computational burden in both system calibration and gaze estimation, and
dramatically reduces the system latency; while the latter overcomes the data
labeling hurdle of existing supervised learning-based counterparts, and ensures
efficient gaze representation learning in the absence of gaze label. Our
evaluation on two gaze estimation datasets shows that FreeGaze can achieve
comparable gaze estimation accuracy with existing supervised learning-based
approach, while enabling up to 6.81 and 1.67 times speedup in system
calibration and gaze estimation, respectively.

For a long time predicting, studying and analyzing financial indices has been
of major interest for the financial community. Recently, there has been a
growing interest in the Deep-Learning community to make use of reinforcement
learning which has surpassed many of the previous benchmarks in a lot of
fields. Our method provides a feature rich environment for the reinforcement
learning agent to work on. The aim is to provide long term profits to the user
so, we took into consideration the most reliable technical indicators. We have
also developed a custom indicator which would provide better insights of the
Bitcoin market to the user. The Bitcoin market follows the emotions and
sentiments of the traders, so another element of our trading environment is the
overall daily Sentiment Score of the market on Twitter. The agent is tested for
a period of 685 days which also included the volatile period of Covid-19. It
has been capable of providing reliable recommendations which give an average
profit of about 69%. Finally, the agent is also capable of suggesting the
optimal actions to the user through a website. Users on the website can also
access the visualizations of the indicators to help fortify their decisions.

This paper proposes a new concept called virtual inertia scheduling (VIS) to
efficiently handle the high penetration of inverter-based resources (IBRs). VIS
is an inertia management framework that targets security-constrained and
economy-oriented inertia scheduling and generation dispatch of power systems
with a large scale of renewable generations. Specifically, it schedules the
proper power setting points and reserved capacities of both synchronous
generators and IBRs, as well as the control modes and control parameters of
IBRs to provide secure and cost-effective inertia support. First, a uniform
system model is employed to quantify the frequency dynamics of the
IBRs-penetrated power system after disturbances. Based on the model, the
s-domain and time-domain analytical responses of IBRs with inertia support
capability are derived. Then, VIS-based real-time economic dispatch (VIS-RTED)
is formulated to minimize generation and reserve costs, with a full
consideration of dynamic frequency constraints and derived inertia support
reserve constraints. The virtual inertia and damping of IBRs are formulated as
decision variables. To address the non-linearity of dynamic constraints, deep
learning-assisted linearization is employed to solve the optimization problem.
Finally, the proposed VIS-RTED is demonstrated on a modified IEEE 39-bus
system. A full-order time-domain simulation is performed to verify the
scheduling results.

A modern vehicle fitted with sensors, actuators, and Electronic Control Units
(ECUs) can be divided into several operational subsystems called Functional
Working Groups (FWGs). Examples of these FWGs include the engine system,
transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels
that gauge vehicular operating conditions. This data rich environment is
conducive to the development of Predictive Maintenance (PdM) technologies.
Undercutting various PdM technologies is the need for robust anomaly detection
models that can identify events or observations which deviate significantly
from the majority of the data and do not conform to a well defined notion of
normal vehicular operational behavior. In this paper, we introduce the Vehicle
Performance, Reliability, and Operations (VePRO) dataset and use it to create a
multi-phased approach to anomaly detection. Utilizing Temporal Convolution
Networks (TCN), our anomaly detection system can achieve 96% detection accuracy
and accurately predicts 91% of true anomalies. The performance of our anomaly
detection system improves when sensor channels from multiple FWGs are utilized.

In this paper, we propose a novel grasp pipeline based on contact point
detection on the truncated signed distance function (TSDF) volume to achieve
closed-loop 7-degree-of-freedom (7-DoF) grasping on cluttered environments. The
key aspects of our method are that 1) the proposed pipeline exploits the TSDF
volume in terms of multi-view fusion, contact-point sampling and evaluation,
and collision checking, which provides reliable and collision-free 7-DoF
gripper poses with real-time performance; 2) the contact-based pose
representation effectively eliminates the ambiguity introduced by the
normal-based methods, which provides a more precise and flexible solution.
Extensive simulated and real-robot experiments demonstrate that the proposed
pipeline can select more antipodal and stable grasp poses and outperforms
normal-based baselines in terms of the grasp success rate in both simulated and
physical scenarios.

Latent representations of drugs and their targets produced by contemporary
graph autoencoder-based models have proved useful in predicting many types of
node-pair interactions on large networks, including drug-drug, drug-target, and
target-target interactions. However, most existing approaches model the node's
latent spaces in which node distributions are rigid and disjoint; these
limitations hinder the methods from generating new links among pairs of nodes.
In this paper, we present the effectiveness of variational graph autoencoders
(VGAE) in modeling latent node representations on multimodal networks. Our
approach can produce flexible latent spaces for each node type of the
multimodal graph; the embeddings are used later for predicting links among node
pairs under different edge types. To further enhance the models' performance,
we suggest a new method that concatenates Morgan fingerprints, which capture
the molecular structures of each drug, with their latent embeddings before
preceding them to the decoding stage for link prediction. Our proposed model
shows competitive results on two multimodal networks: (1) a multi-graph
consisting of drug and protein nodes, and (2) a multi-graph consisting of drug
and cell line nodes. Our source code is publicly available at
https://github.com/HySonLab/drug-interactions.

Beamforming-capable antenna arrays overcome the high free-space path loss at
higher carrier frequencies. However, the beams must be properly aligned to
ensure that the highest power is radiated towards (and received by) the user
equipment (UE). While there are methods that improve upon an exhaustive search
for optimal beams by some form of hierarchical search, they can be prone to
return only locally optimal solutions with small beam gains. Other approaches
address this problem by exploiting contextual information, e.g., the position
of the UE or information from neighboring base stations (BS), but the burden of
computing and communicating this additional information can be high. Methods
based on machine learning so far suffer from the accompanying training,
performance monitoring and deployment complexity that hinders their application
at scale.
  This paper proposes a novel method for solving the initial beam-discovery
problem. It is scalable, and easy to tune and to implement. Our algorithm is
based on a recommender system that associates groups (i.e., UEs) and
preferences (i.e., beams from a codebook) based on a training data set.
Whenever a new UE needs to be served our algorithm returns the best beams in
this user cluster. Our simulation results demonstrate the efficiency and
robustness of our approach, not only in single BS setups but also in setups
that require a coordination among several BSs. Our method consistently
outperforms standard baseline algorithms in the given task.

For the last two years, from 2020 to 2021, COVID-19 has broken disease
prevention measures in many countries, including Vietnam, and negatively
impacted various aspects of human life and the social community. Besides, the
misleading information in the community and fake news about the pandemic are
also serious situations. Therefore, we present the first Vietnamese
community-based question answering dataset for developing question answering
systems for COVID-19 called UIT-ViCoV19QA. The dataset comprises 4,500
question-answer pairs collected from trusted medical sources, with at least one
answer and at most four unique paraphrased answers per question. Along with the
dataset, we set up various deep learning models as baseline to assess the
quality of our dataset and initiate the benchmark results for further research
through commonly used metrics such as BLEU, METEOR, and ROUGE-L. We also
illustrate the positive effects of having multiple paraphrased answers
experimented on these models, especially on Transformer - a dominant
architecture in the field of study.

Recently, pre-training methods have shown remarkable success in task-oriented
dialog (TOD) systems. However, most existing pre-trained models for TOD focus
on either dialog understanding or dialog generation, but not both. In this
paper, we propose SPACE-3, a novel unified semi-supervised pre-trained
conversation model learning from large-scale dialog corpora with limited
annotations, which can be effectively fine-tuned on a wide range of downstream
dialog tasks. Specifically, SPACE-3 consists of four successive components in a
single transformer to maintain a task-flow in TOD systems: (i) a dialog
encoding module to encode dialog history, (ii) a dialog understanding module to
extract semantic vectors from either user queries or system responses, (iii) a
dialog policy module to generate a policy vector that contains high-level
semantics of the response, and (iv) a dialog generation module to produce
appropriate responses. We design a dedicated pre-training objective for each
component. Concretely, we pre-train the dialog encoding module with span mask
language modeling to learn contextualized dialog information. To capture the
structured dialog semantics, we pre-train the dialog understanding module via a
novel tree-induced semi-supervised contrastive learning objective with the help
of extra dialog annotations. In addition, we pre-train the dialog policy module
by minimizing the L2 distance between its output policy vector and the semantic
vector of the response for policy optimization. Finally, the dialog generation
model is pre-trained by language modeling. Results show that SPACE-3 achieves
state-of-the-art performance on eight downstream dialog benchmarks, including
intent prediction, dialog state tracking, and end-to-end dialog modeling. We
also show that SPACE-3 has a stronger few-shot ability than existing models
under the low-resource setting.

The Double Asteroid Redirection Test (DART) spacecraft will impact into the
asteroid Dimorphos on September 26, 2022 as a test of the kinetic impactor
technique for planetary defense. The efficiency of the deflection following a
kinetic impactor can be represented using the momentum enhancement factor,
Beta, which is dependent on factors such as impact geometry and the specific
target material properties. Currently, very little is known about Dimorphos and
its material properties that introduces uncertainty in the results of the
deflection efficiency observables, including crater formation, ejecta
distribution, and Beta. The DART Impact Modeling Working Group (IWG) is
responsible for using impact simulations to better understand the results of
the DART impact. Pre-impact simulation studies also provide considerable
insight into how different properties and impact scenarios affect momentum
enhancement following a kinetic impact. This insight provides a basis for
predicting the effects of the DART impact and the first understanding of how to
interpret results following the encounter. Following the DART impact, the
knowledge gained from these studies will inform the initial simulations that
will recreate the impact conditions, including providing estimates for
potential material properties of Dimorphos and Beta resulting from DARTs
impact. This paper summarizes, at a high level, what has been learned from the
IWG simulations and experiments in preparation for the DART impact. While
unknown, estimates for reasonable potential material properties of Dimorphos
provide predictions for Beta of 1-5, depending on end-member cases in the
strength regime.

Sequential recommender systems have shown effective suggestions by capturing
users' interest drift. There have been two groups of existing sequential
models: user- and item-centric models. The user-centric models capture
personalized interest drift based on each user's sequential consumption
history, but do not explicitly consider whether users' interest in items
sustains beyond the training time, i.e., interest sustainability. On the other
hand, the item-centric models consider whether users' general interest sustains
after the training time, but it is not personalized. In this work, we propose a
recommender system taking advantages of the models in both categories. Our
proposed model captures personalized interest sustainability, indicating
whether each user's interest in items will sustain beyond the training time or
not. We first formulate a task that requires to predict which items each user
will consume in the recent period of the training time based on users'
consumption history. We then propose simple yet effective schemes to augment
users' sparse consumption history. Extensive experiments show that the proposed
model outperforms 10 baseline models on 11 real-world datasets. The codes are
available at https://github.com/dmhyun/PERIS.

Pre-training methods with contrastive learning objectives have shown
remarkable success in dialog understanding tasks. However, current contrastive
learning solely considers the self-augmented dialog samples as positive samples
and treats all other dialog samples as negative ones, which enforces dissimilar
representations even for dialogs that are semantically related. In this paper,
we propose SPACE-2, a tree-structured pre-trained conversation model, which
learns dialog representations from limited labeled dialogs and large-scale
unlabeled dialog corpora via semi-supervised contrastive pre-training.
Concretely, we first define a general semantic tree structure (STS) to unify
the inconsistent annotation schema across different dialog datasets, so that
the rich structural information stored in all labeled data can be exploited.
Then we propose a novel multi-view score function to increase the relevance of
all possible dialogs that share similar STSs and only push away other
completely different dialogs during supervised contrastive pre-training. To
fully exploit unlabeled dialogs, a basic self-supervised contrastive loss is
also added to refine the learned representations. Experiments show that our
method can achieve new state-of-the-art results on the DialoGLUE benchmark
consisting of seven datasets and four popular dialog understanding tasks. For
reproducibility, we release the code and data at
https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/space-2.

Models of acoustic word embeddings (AWEs) learn to map variable-length spoken
word segments onto fixed-dimensionality vector representations such that
different acoustic exemplars of the same word are projected nearby in the
embedding space. In addition to their speech technology applications, AWE
models have been shown to predict human performance on a variety of auditory
lexical processing tasks. Current AWE models are based on neural networks and
trained in a bottom-up approach that integrates acoustic cues to build up a
word representation given an acoustic or symbolic supervision signal.
Therefore, these models do not leverage or capture high-level lexical knowledge
during the learning process. In this paper, we propose a multi-task learning
model that incorporates top-down lexical knowledge into the training procedure
of AWEs. Our model learns a mapping between the acoustic input and a lexical
representation that encodes high-level information such as word semantics in
addition to bottom-up form-based supervision. We experiment with three
languages and demonstrate that incorporating lexical knowledge improves the
embedding space discriminability and encourages the model to better separate
lexical categories.

Neural architecture search (NAS) has become a common approach to developing
and discovering new neural architectures for different target platforms and
purposes. However, scanning the search space is comprised of long training
processes of many candidate architectures, which is costly in terms of
computational resources and time. Regression algorithms are a common tool to
predicting a candidate architecture's accuracy, which can dramatically
accelerate the search procedure. We aim at proposing a new baseline that will
support the development of regression algorithms that can predict an
architecture's accuracy just from its scheme, or by only training it for a
minimal number of epochs. Therefore, we introduce the NAAP-440 dataset of 440
neural architectures, which were trained on CIFAR10 using a fixed recipe. Our
experiments indicate that by using off-the-shelf regression algorithms and
running up to 10% of the training process, not only is it possible to predict
an architecture's accuracy rather precisely, but that the values predicted for
the architectures also maintain their accuracy order with a minimal number of
monotonicity violations. This approach may serve as a powerful tool for
accelerating NAS-based studies and thus dramatically increase their efficiency.
The dataset and code used in the study have been made public.

In this paper, federated learning (FL) over wireless networks is
investigated. In each communication round, a subset of devices is selected to
participate in the aggregation with limited time and energy. In order to
minimize the convergence time, global loss and latency are jointly considered
in a Stackelberg game based framework. Specifically, age of information (AoI)
based device selection is considered at leader-level as a global loss
minimization problem, while sub-channel assignment, computational resource
allocation, and power allocation are considered at follower-level as a latency
minimization problem. By dividing the follower-level problem into two
sub-problems, the best response of the follower is obtained by a monotonic
optimization based resource allocation algorithm and a matching based
sub-channel assignment algorithm. By deriving the upper bound of convergence
rate, the leader-level problem is reformulated, and then a list based device
selection algorithm is proposed to achieve Stackelberg equilibrium. Simulation
results indicate that the proposed device selection scheme outperforms other
schemes in terms of the global loss, and the developed algorithms can
significantly decrease the time consumption of computation and communication.

Developing a safe, stable, and efficient obstacle avoidance policy in crowded
and narrow scenarios for multiple robots is challenging. Most existing studies
either use centralized control or need communication with other robots. In this
paper, we propose a novel logarithmic map-based deep reinforcement learning
method for obstacle avoidance in complex and communication-free multi-robot
scenarios. In particular, our method converts laser information into a
logarithmic map. As a step toward improving training speed and generalization
performance, our policies will be trained in two specially designed multi-robot
scenarios. Compared to other methods, the logarithmic map can represent
obstacles more accurately and improve the success rate of obstacle avoidance.
We finally evaluate our approach under a variety of simulation and real-world
scenarios. The results show that our method provides a more stable and
effective navigation solution for robots in complex multi-robot scenarios and
pedestrian scenarios. Videos are available at https://youtu.be/r0EsUXe6MZE.

Among the reasons hindering reinforcement learning (RL) applications to
real-world problems, two factors are critical: limited data and the mismatch
between the testing environment (real environment in which the policy is
deployed) and the training environment (e.g., a simulator). This paper attempts
to address these issues simultaneously with distributionally robust offline RL,
where we learn a distributionally robust policy using historical data obtained
from the source environment by optimizing against a worst-case perturbation
thereof. In particular, we move beyond tabular settings and consider linear
function approximation. More specifically, we consider two settings, one where
the dataset is well-explored and the other where the dataset has sufficient
coverage. We propose two algorithms -- one for each of the two settings -- that
achieve error bounds $\tilde{O}(d^{1/2}/N^{1/2})$ and
$\tilde{O}(d^{3/2}/N^{1/2})$ respectively, where $d$ is the dimension in the
linear function approximation and $N$ is the number of trajectories in the
dataset. To the best of our knowledge, they provide the first non-asymptotic
results of the sample complexity in this setting. Diverse experiments are
conducted to demonstrate our theoretical findings, showing the superiority of
our algorithm against the non-robust one.

Out-of-Domain (OOD) detection is a key component in a task-oriented dialog
system, which aims to identify whether a query falls outside the predefined
supported intent set. Previous softmax-based detection algorithms are proved to
be overconfident for OOD samples. In this paper, we analyze overconfident OOD
comes from distribution uncertainty due to the mismatch between the training
and test distributions, which makes the model can't confidently make
predictions thus probably causing abnormal softmax scores. We propose a
Bayesian OOD detection framework to calibrate distribution uncertainty using
Monte-Carlo Dropout. Our method is flexible and easily pluggable into existing
softmax-based baselines and gains 33.33\% OOD F1 improvements with increasing
only 0.41\% inference time compared to MSP. Further analyses show the
effectiveness of Bayesian learning for OOD detection.

Executing workflows on volunteer computing resources where individual tasks
may be forced to relinquish their resource for the resource's primary use leads
to unpredictability and often significantly increases execution time. Task
replication is one approach that can ameliorate this challenge. This comes at
the expense of a potentially significant increase in system load and energy
consumption. We propose the use of Reinforcement Learning (RL) such that a
system may `learn' the `best' number of replicas to run to increase the number
of workflows which complete promptly whilst minimising the additional workload
on the system when replicas are not beneficial. We show, through simulation,
that we can save 34% of the energy consumption using RL compared to a fixed
number of replicas with only a 4% decrease in workflows achieving a pre-defined
overhead bound.

Plasticity and stability are needed in class-incremental learning in order to
learn from new data while preserving past knowledge. Due to catastrophic
forgetting, finding a compromise between these two properties is particularly
challenging when no memory buffer is available. Mainstream methods need to
store two deep models since they integrate new classes using fine tuning with
knowledge distillation from the previous incremental state. We propose a method
which has similar number of parameters but distributes them differently in
order to find a better balance between plasticity and stability. Following an
approach already deployed by transfer-based incremental methods, we freeze the
feature extractor after the initial state. Classes in the oldest incremental
states are trained with this frozen extractor to ensure stability. Recent
classes are predicted using partially fine-tuned models in order to introduce
plasticity. Our proposed plasticity layer can be incorporated to any
transfer-based method designed for memory-free incremental learning, and we
apply it to two such methods. Evaluation is done with three large-scale
datasets. Results show that performance gains are obtained in all tested
configurations compared to existing methods.

Deep reinforcement learning has shown an ability to achieve super-human
performance in solving complex reinforcement learning (RL) tasks only from
raw-pixels. However, it fails to reuse knowledge from previously learnt tasks
to solve new, unseen ones. Generalizing and reusing knowledge are the
fundamental requirements for creating a truly intelligent agent. This work
proposes a general method for one-to-one transfer learning based on generative
adversarial network model tailored to RL task.

Model-based recursive partitioning (MOB) is a semi-parametric statistical
approach allowing the identification of subgroups that can be combined with a
broad range of outcome measures including continuous time-to-event outcomes.
When time is measured on a discrete scale, methods and models need to account
for this discreetness as otherwise subgroups might be spurious and effects
biased. The test underlying the splitting criterion of MOB, the M-fluctuation
test, assumes independent observations. However, for fitting discrete
time-to-event models the data matrix has to be modified resulting in an
augmented data matrix violating the independence assumption. We propose MOB for
discrete Survival data (MOB-dS) which controls the type I error rate of the
test used for data splitting and therefore the rate of identifying subgroups
although none is present. MOB-ds uses a permutation approach accounting for
dependencies in the augmented time-to-event data to obtain the distribution
under the null hypothesis of no subgroups being present. Through simulations we
investigate the type I error rate of the new MOB-dS and the standard MOB for
different patterns of survival curves and event rates. We find that the type I
error rates of the test is well controlled for MOB-dS, but observe some
considerable inflations of the error rate for MOB. To illustrate the proposed
methods, MOB-dS is applied to data on unemployment duration.

Graph neural networks (GNNs) have become compelling models designed to
perform learning and inference on graph-structured data, but little work has
been done on understanding the fundamental limitations of GNNs to be scalable
to larger graphs and generalized to out-of-distribution inputs. In this paper,
we use a random graph generator that allows us to systematically investigate
how the graph size and structural properties affect the predictive performance
of GNNs. We present specific evidence that, among the many graph properties,
the mean and modality of the node degree distribution are the key features that
determine whether GNNs can generalize to unseen graphs. Accordingly, we propose
flexible GNNs (Flex-GNNs), using multiple node update functions and the inner
loop optimization as a generalization to the single type of canonical nonlinear
transformation over aggregated inputs, allowing the network to adapt flexibly
to new graphs. The Flex-GNN framework improves the generalization out of the
training set on several inference tasks.

Multi-label image classification allows predicting a set of labels from a
given image. Unlike multiclass classification, where only one label per image
is assigned, such setup is applicable for a broader range of applications. In
this work we revisit two popular approaches to multilabel classification:
transformer-based heads and labels relations information graph processing
branches. Although transformer-based heads are considered to achieve better
results than graph-based branches, we argue that with the proper training
strategy graph-based methods can demonstrate just a small accuracy drop, while
spending less computational resources on inference. In our training strategy,
instead of Asymmetric Loss (ASL), which is the de-facto standard for multilabel
classification, we introduce its modification acting in the angle space. It
implicitly learns a proxy feature vector on the unit hypersphere for each
class, providing a better discrimination ability, than binary cross entropy
loss does on unnormalized features. With the proposed loss and training
strategy, we obtain SOTA results among single modality methods on widespread
multilabel classification benchmarks such as MS-COCO, PASCAL-VOC, NUS-Wide and
Visual Genome 500. Source code of our method is available as a part of the
OpenVINO Training Extensions
https://github.com/openvinotoolkit/deep-object-reid/tree/multilabel

With the rise of computing power, using data-driven approaches for
co-designing robots' morphology and controller has become a feasible way.
Nevertheless, evaluating the fitness of the controller under each morphology is
time-consuming. As a pioneering data-driven method, Co-adaptation utilizes a
double-network mechanism with the aim of learning a Q function conditioned on
morphology parameters to replace the traditional evaluation of a diverse set of
candidates, thereby speeding up optimization. In this paper, we find that
Co-adaptation ignores the existence of exploration error during training and
state-action distribution shift during parameter transmitting, which hurt the
performance. We propose the framework of the concurrent network that couples
online and offline RL methods. By leveraging the behavior cloning term
flexibly, we mitigate the impact of the above issues on the results. Simulation
and physical experiments are performed to demonstrate that our proposed method
outperforms baseline algorithms, which illustrates that the proposed method is
an effective way of discovering the optimal combination of morphology and
controller.

Multi-relational networks play an important role in today's world and are
utilized to capture complex relationships between the data. Their applications
span many domains such as biomedical, financial, social, etc., and because of
their increasing usability, it becomes crucial to find efficient ways to deal
with the added complexity of multiple layers. In this work, we propose a novel
approach to represent these complex networks using a single aggregated
adjacency matrix, by utilizing primes as surrogates for the relations. Due to
the fundamental theorem of arithmetic, this allows for a lossless, compact
representation of the whole multi-relational graph, using a single adjacency
matrix. Moreover, this representation enables the fast computation of multi-hop
adjacency matrices, that can be useful for a variety of downstream tasks. We
present simple and complex tasks in which this representation can be useful and
showcase its efficiency and performance. Finally, we also provide insights on
the advantages and the open challenges that still need to be addressed and
motivate future work.

From early days, a key and controversial question inside the artificial
intelligence community was whether Artificial General Intelligence (AGI) is
achievable. AGI is the ability of machines and computer programs to achieve
human-level intelligence and do all tasks that a human being can. While there
exist a number of systems in the literature claiming they realize AGI, several
other researchers argue that it is impossible to achieve it. In this paper, we
take a different view to the problem. First, we discuss that in order to
realize AGI, along with building intelligent machines and programs, an
intelligent world should also be constructed which is on the one hand, an
accurate approximation of our world and on the other hand, a significant part
of reasoning of intelligent machines is already embedded in this world. Then we
discuss that AGI is not a product or algorithm, rather it is a continuous
process which will become more and more mature over time (like human
civilization and wisdom). Then, we argue that pre-trained embeddings play a key
role in building this intelligent world and as a result, realizing AGI. We
discuss how pre-trained embeddings facilitate achieving several characteristics
of human-level intelligence, such as embodiment, common sense knowledge,
unconscious knowledge and continuality of learning, by machines.

We provide a new method for online learning, specifically prediction with
expert advice, in a changing environment. In a non-changing environment the
Squint algorithm has been designed to always function at least as well as other
known algorithms and in specific cases it functions much better. However, when
using a conventional black-box algorithm to make Squint suitable for a changing
environment, it loses its beneficial properties. Hence, we provide a new
algorithm, Squint-CE, which is suitable for a changing environment and
preserves the properties of Squint.

This paper presents a novel architecture for simultaneous estimation of
highly accurate optical flows and rigid scene transformations for difficult
scenarios where the brightness assumption is violated by strong shading
changes. In the case of rotating objects or moving light sources, such as those
encountered for driving cars in the dark, the scene appearance often changes
significantly from one view to the next. Unfortunately, standard methods for
calculating optical flows or poses are based on the expectation that the
appearance of features in the scene remain constant between views. These
methods may fail frequently in the investigated cases. The presented method
fuses texture and geometry information by combining image, vertex and normal
data to compute an illumination-invariant optical flow. By using a
coarse-to-fine strategy, globally anchored optical flows are learned, reducing
the impact of erroneous shading-based pseudo-correspondences. Based on the
learned optical flows, a second architecture is proposed that predicts robust
rigid transformations from the warped vertex and normal maps. Particular
attention is payed to situations with strong rotations, which often cause such
shading changes. Therefore a 3-step procedure is proposed that profitably
exploits correlations between the normals and vertices. The method has been
evaluated on a newly created dataset containing both synthetic and real data
with strong rotations and shading effects. This data represents the typical use
case in 3D reconstruction, where the object often rotates in large steps
between the partial reconstructions. Additionally, we apply the method to the
well-known Kitti Odometry dataset. Even if, due to fulfillment of the brighness
assumption, this is not the typical use case of the method, the applicability
to standard situations and the relation to other methods is therefore
established.

Graph contrastive learning (GCL) has emerged as an effective tool for
learning unsupervised representations of graphs. The key idea is to maximize
the agreement between two augmented views of each graph via data augmentation.
Existing GCL models mainly focus on applying \textit{identical augmentation
strategies} for all graphs within a given scenario. However, real-world graphs
are often not monomorphic but abstractions of diverse natures. Even within the
same scenario (e.g., macromolecules and online communities), different graphs
might need diverse augmentations to perform effective GCL. Thus, blindly
augmenting all graphs without considering their individual characteristics may
undermine the performance of GCL arts.To deal with this, we propose the first
principled framework, termed as \textit{G}raph contrastive learning with
\textit{P}ersonalized \textit{A}ugmentation (GPA), to advance conventional GCL
by allowing each graph to choose its own suitable augmentation operations.In
essence, GPA infers tailored augmentation strategies for each graph based on
its topology and node attributes via a learnable augmentation selector, which
is a plug-and-play module and can be effectively trained with downstream GCL
models end-to-end. Extensive experiments across 11 benchmark graphs from
different types and domains demonstrate the superiority of GPA against
state-of-the-art competitors.Moreover, by visualizing the learned augmentation
distributions across different types of datasets, we show that GPA can
effectively identify the most suitable augmentations for each graph based on
its characteristics.

Accurate in silico modeling of the antigen processing pathway is crucial to
enable personalized epitope vaccine design for cancer. An important step of
such pathway is the degradation of the vaccine into smaller peptides by the
proteasome, some of which are going to be presented to T cells by the MHC
complex. While predicting MHC-peptide presentation has received a lot of
attention recently, proteasomal cleavage prediction remains a relatively
unexplored area in light of recent advancesin high-throughput mass
spectrometry-based MHC ligandomics. Moreover, as such experimental techniques
do not allow to identify regions that cannot be cleaved, the latest predictors
generate decoy negative samples and treat them as true negatives when training,
even though some of them could actually be positives. In this work, we thus
present a new predictor trained with an expanded dataset and the solid
theoretical underpinning of positive-unlabeled learning, achieving a new
state-of-the-art in proteasomal cleavage prediction. The improved predictive
capabilities will in turn enable more precise vaccine development improving the
efficacy of epitope-based vaccines. Pretrained models are available on GitHub

Wearable sensor-based human activity recognition (HAR) has emerged as a
principal research area and is utilized in a variety of applications. Recently,
deep learning-based methods have achieved significant improvement in the HAR
field with the development of human-computer interaction applications. However,
they are limited to operating in a local neighborhood in the process of a
standard convolution neural network, and correlations between different sensors
on body positions are ignored. In addition, they still face significant
challenging problems with performance degradation due to large gaps in the
distribution of training and test data, and behavioral differences between
subjects. In this work, we propose a novel Transformer-based Adversarial
learning framework for human activity recognition using wearable sensors via
Self-KnowledgE Distillation (TASKED), that accounts for individual sensor
orientations and spatial and temporal features. The proposed method is capable
of learning cross-domain embedding feature representations from multiple
subjects datasets using adversarial learning and the maximum mean discrepancy
(MMD) regularization to align the data distribution over multiple domains. In
the proposed method, we adopt the teacher-free self-knowledge distillation to
improve the stability of the training procedure and the performance of human
activity recognition. Experimental results show that TASKED not only
outperforms state-of-the-art methods on the four real-world public HAR datasets
(alone or combined) but also improves the subject generalization effectively.

Data mining and information extraction from data is a field that has gained
relevance in recent years thanks to techniques based on artificial intelligence
and use of machine and deep learning. The main aim of the present work is the
development of a tool based on a previous behaviour study of security audit
tools (oriented to SQL pentesting) with the purpose of creating testing sets
capable of performing an accurate detection of a SQL attack. The study is based
on the information collected through the generated web server logs in a
pentesting laboratory environment. Then, making use of the common extracted
patterns from the logs, each attack vector has been classified in risk levels
(dangerous attack, normal attack, non-attack, etc.). Finally, a training with
the generated data was performed in order to obtain a classifier system that
has a variable performance between 97 and 99 percent in positive attack
detection. The training data is shared to other servers in order to create a
distributed network capable of deciding if a query is an attack or is a real
petition and inform to connected clients in order to block the petitions from
the attacker's IP.

Inspired by the ability of humans to perceive the surface texture of
unfamiliar objects without relying on vision, the sense of tactile can play a
crucial role in the process of robots exploring the environment, especially in
some scenes where vision is difficult to apply or occlusion is inevitable to
exist. Existing tactile surface reconstruction methods rely on external sensors
or have strong prior assumptions, which will limit their application scenarios
and make the operation more complex. This paper presents a surface
reconstruction algorithm that uses only a new vision-based tactile sensor where
the surface structure of an unfamiliar object is reconstructed by multiple
tactile measurements. Compared with existing algorithms, the proposed algorithm
doesn't rely on external devices and focuses on improving the reconstruction
accuracy of the large-scale object surface. Aiming at the difficulty that the
reconstruction accuracy is easily affected by the pressure of sampling, we
propose a correction algorithm to adapt it. Multi-frame tactile imprints
generated from many times contact can accurately reconstruct global object
surface by jointly using the point cloud registration algorithm, loop-closure
detection algorithm based on deep learning, and pose graph optimization
algorithm. Experiments verify the proposed algorithm can achieve
millimeter-level accuracy in reconstructing the surface of interactive objects
and provide accurate tactile information for the robot to perceive the
surrounding environment.

In this paper, we propose a patch-based architecture for multi-label
classification problems where only a single positive label is observed in
images of the dataset. Our contributions are twofold. First, we introduce a
light patch architecture based on the attention mechanism. Next, leveraging on
patch embedding self-similarities, we provide a novel strategy for estimating
negative examples and deal with positive and unlabeled learning problems.
Experiments demonstrate that our architecture can be trained from scratch,
whereas pre-training on similar databases is required for related methods from
the literature.

The privacy risks of machine learning models is a major concern when training
them on sensitive and personal data. We discuss the tradeoffs between data
privacy and the remaining goals of trustworthy machine learning (notably,
fairness, robustness, and explainability).

Nowadays Artificial Intelligence (AI) has become a fundamental component of
healthcare applications, both clinical and remote, but the best performing AI
systems are often too complex to be self-explaining. Explainable AI (XAI)
techniques are defined to unveil the reasoning behind the system's predictions
and decisions, and they become even more critical when dealing with sensitive
and personal health data. It is worth noting that XAI has not gathered the same
attention across different research areas and data types, especially in
healthcare. In particular, many clinical and remote health applications are
based on tabular and time series data, respectively, and XAI is not commonly
analysed on these data types, while computer vision and Natural Language
Processing (NLP) are the reference applications. To provide an overview of XAI
methods that are most suitable for tabular and time series data in the
healthcare domain, this paper provides a review of the literature in the last 5
years, illustrating the type of generated explanations and the efforts provided
to evaluate their relevance and quality. Specifically, we identify clinical
validation, consistency assessment, objective and standardised quality
evaluation, and human-centered quality assessment as key features to ensure
effective explanations for the end users. Finally, we highlight the main
research challenges in the field as well as the limitations of existing XAI
methods.

We propose an accurate and lightweight convolutional neural network for
stereo estimation with depth completion. We name this method
fully-convolutional deformable similarity network with depth completion
(FCDSN-DC). This method extends FC-DCNN by improving the feature extractor,
adding a network structure for training highly accurate similarity functions
and a network structure for filling inconsistent disparity estimates. The whole
method consists of three parts. The first part consists of fully-convolutional
densely connected layers that computes expressive features of rectified image
pairs. The second part of our network learns highly accurate similarity
functions between this learned features. It consists of densely-connected
convolution layers with a deformable convolution block at the end to further
improve the accuracy of the results. After this step an initial disparity map
is created and the left-right consistency check is performed in order to remove
inconsistent points. The last part of the network then uses this input together
with the corresponding left RGB image in order to train a network that fills in
the missing measurements. Consistent depth estimations are gathered around
invalid points and are parsed together with the RGB points into a shallow CNN
network structure in order to recover the missing values. We evaluate our
method on challenging real world indoor and outdoor scenes, in particular
Middlebury, KITTI and ETH3D were it produces competitive results. We
furthermore show that this method generalizes well and is well suited for many
applications without the need of further training. The code of our full
framework is available at: https://github.com/thedodo/FCDSN-DC

For the safe and successful navigation of autonomous vehicles in unstructured
environments, the traversability of terrain should vary based on the driving
capabilities of the vehicles. Actual driving experience can be utilized in a
self-supervised fashion to learn vehicle-specific traversability. However,
existing methods for learning self-supervised traversability are not highly
scalable for learning the traversability of various vehicles. In this work, we
introduce a scalable framework for learning self-supervised traversability,
which can learn the traversability directly from vehicle-terrain interaction
without any human supervision. We train a neural network that predicts the
proprioceptive experience that a vehicle would undergo from 3D point clouds.
Using a novel PU learning method, the network simultaneously identifies
non-traversable regions where estimations can be overconfident. With driving
data of various vehicles gathered from simulation and the real world, we show
that our framework is capable of learning the self-supervised traversability of
various vehicles. By integrating our framework with a model predictive
controller, we demonstrate that estimated traversability results in effective
navigation that enables distinct maneuvers based on the driving characteristics
of the vehicles. In addition, experimental results validate the ability of our
method to identify and avoid non-traversable regions.

Neural forecasting of spatiotemporal time series drives both research and
industrial innovation in several relevant application domains. Graph neural
networks (GNNs) are often the core component of the forecasting architecture.
However, in most spatiotemporal GNNs, the computational complexity scales up to
a quadratic factor with the length of the sequence times the number of links in
the graph, hence hindering the application of these models to large graphs and
long temporal sequences. While methods to improve scalability have been
proposed in the context of static graphs, few research efforts have been
devoted to the spatiotemporal case. To fill this gap, we propose a scalable
architecture that exploits an efficient encoding of both temporal and spatial
dynamics. In particular, we use a randomized recurrent neural network to embed
the history of the input time series into high-dimensional state
representations encompassing multi-scale temporal dynamics. Such
representations are then propagated along the spatial dimension using different
powers of the graph adjacency matrix to generate node embeddings characterized
by a rich pool of spatiotemporal features. The resulting node embeddings can be
efficiently pre-computed in an unsupervised manner, before being fed to a
feed-forward decoder that learns to map the multi-scale spatiotemporal
representations to predictions. The training procedure can then be parallelized
node-wise by sampling the node embeddings without breaking any dependency, thus
enabling scalability to large networks. Empirical results on relevant datasets
show that our approach achieves results competitive with the state of the art,
while dramatically reducing the computational burden.

Semantic localization (SeLo) refers to the task of obtaining the most
relevant locations in large-scale remote sensing (RS) images using semantic
information such as text. As an emerging task based on cross-modal retrieval,
SeLo achieves semantic-level retrieval with only caption-level annotation,
which demonstrates its great potential in unifying downstream tasks. Although
SeLo has been carried out successively, but there is currently no work has
systematically explores and analyzes this urgent direction. In this paper, we
thoroughly study this field and provide a complete benchmark in terms of
metrics and testdata to advance the SeLo task. Firstly, based on the
characteristics of this task, we propose multiple discriminative evaluation
metrics to quantify the performance of the SeLo task. The devised significant
area proportion, attention shift distance, and discrete attention distance are
utilized to evaluate the generated SeLo map from pixel-level and region-level.
Next, to provide standard evaluation data for the SeLo task, we contribute a
diverse, multi-semantic, multi-objective Semantic Localization Testset
(AIR-SLT). AIR-SLT consists of 22 large-scale RS images and 59 test cases with
different semantics, which aims to provide a comprehensive evaluations for
retrieval models. Finally, we analyze the SeLo performance of RS cross-modal
retrieval models in detail, explore the impact of different variables on this
task, and provide a complete benchmark for the SeLo task. We have also
established a new paradigm for RS referring expression comprehension, and
demonstrated the great advantage of SeLo in semantics through combining it with
tasks such as detection and road extraction. The proposed evaluation metrics,
semantic localization testsets, and corresponding scripts have been open to
access at github.com/xiaoyuan1996/SemanticLocalizationMetrics .

With the freedom of communication provided in online social media, hate
speech has increasingly generated. This leads to cyber conflicts affecting
social life at the individual and national levels. As a result, hateful content
classification is becoming increasingly demanded for filtering hate content
before being sent to the social networks. This paper focuses on classifying
hate speech in social media using multiple deep models that are implemented by
integrating recent transformer-based language models such as BERT, and neural
networks. To improve the classification performances, we evaluated with several
ensemble techniques, including soft voting, maximum value, hard voting and
stacking. We used three publicly available Twitter datasets (Davidson,
HatEval2019, OLID) that are generated to identify offensive languages. We fused
all these datasets to generate a single dataset (DHO dataset), which is more
balanced across different labels, to perform multi-label classification. Our
experiments have been held on Davidson dataset and the DHO corpora. The later
gave the best overall results, especially F1 macro score, even it required more
resources (time execution and memory). The experiments have shown good results
especially the ensemble models, where stacking gave F1 score of 97% on Davidson
dataset and aggregating ensembles 77% on the DHO dataset.

Crowd counting is an effective tool for situational awareness in public
places. Automated crowd counting using images and videos is an interesting yet
challenging problem that has gained significant attention in computer vision.
Over the past few years, various deep learning methods have been developed to
achieve state-of-the-art performance. The methods evolved over time vary in
many aspects such as model architecture, input pipeline, learning paradigm,
computational complexity, and accuracy gains etc. In this paper, we present a
systematic and comprehensive review of the most significant contributions in
the area of crowd counting. Although few surveys exist on the topic, our survey
is most up-to date and different in several aspects. First, it provides a more
meaningful categorization of the most significant contributions by model
architectures, learning methods (i.e., loss functions), and evaluation methods
(i.e., evaluation metrics). We chose prominent and distinct works and excluded
similar works. We also sort the well-known crowd counting models by their
performance over benchmark datasets. We believe that this survey can be a good
resource for novice researchers to understand the progressive developments and
contributions over time and the current state-of-the-art.

Total value adjustment (XVA) is the change in value to be added to the price
of a derivative to account for the bilateral default risk and the funding
costs. In this paper, we compute such a premium for American basket derivatives
whose payoff depends on multiple underlyings. In particular, in our model,
those underlying are supposed to follow the multidimensional Black-Scholes
stochastic model. In order to determine the XVA, we follow the approach
introduced by Burgard and Kjaer \cite{burgard2010pde} and afterward applied by
Arregui et al. \cite{arregui2017pde,arregui2019monte} for the one-dimensional
American derivatives. The evaluation of the XVA for basket derivatives is
particularly challenging as the presence of several underlings leads to a
high-dimensional control problem. We tackle such an obstacle by resorting to
Gaussian Process Regression, a machine learning technique that allows one to
address the curse of dimensionality effectively. Moreover, the use of numerical
techniques, such as control variates, turns out to be a powerful tool to
improve the accuracy of the proposed methods. The paper includes the results of
several numerical experiments that confirm the goodness of the proposed
methodologies.

Recent advancements in neural end-to-end TTS models have shown high-quality,
natural synthesized speech in a conventional sentence-based TTS. However, it is
still challenging to reproduce similar high quality when a whole paragraph is
considered in TTS, where a large amount of contextual information needs to be
considered in building a paragraph-based TTS model. To alleviate the difficulty
in training, we propose to model linguistic and prosodic information by
considering cross-sentence, embedded structure in training. Three sub-modules,
including linguistics-aware, prosody-aware and sentence-position networks, are
trained together with a modified Tacotron2. Specifically, to learn the
information embedded in a paragraph and the relations among the corresponding
component sentences, we utilize linguistics-aware and prosody-aware networks.
The information in a paragraph is captured by encoders and the inter-sentence
information in a paragraph is learned with multi-head attention mechanisms. The
relative sentence position in a paragraph is explicitly exploited by a
sentence-position network. Trained on a storytelling audio-book corpus (4.08
hours), recorded by a female Mandarin Chinese speaker, the proposed TTS model
demonstrates that it can produce rather natural and good-quality speech
paragraph-wise. The cross-sentence contextual information, such as break and
prosodic variations between consecutive sentences, can be better predicted and
rendered than the sentence-based model. Tested on paragraph texts, of which the
lengths are similar to, longer than, or much longer than the typical paragraph
length of the training data, the TTS speech produced by the new model is
consistently preferred over the sentence-based model in subjective tests and
confirmed in objective measures.

Self-Adaptive Multi-Agent Systems (AMAS) transform machine learning problems
into problems of local cooperation between agents. We present smapy, an
ensemble based AMAS implementation for mobility prediction, whose agents are
provided with machine learning models in addition to their cooperation rules.
With a detailed methodology, we show that it is possible to use linear models
for nonlinear classification on a benchmark transport mode detection dataset,
if they are integrated in a cooperative multi-agent structure. The results
obtained show a significant improvement of the performance of linear models in
non-linear contexts thanks to the multi-agent approach.

We propose a non-asymptotic convergence analysis of a two-step approach to
learn a conditional value-at-risk (VaR) and expected shortfall (ES) in a
nonparametric setting using Rademacher and Vapnik-Chervonenkis bounds. Our
approach for the VaR is extended to the problem of learning at once multiple
VaRs corresponding to different quantile levels. This results in efficient
learning schemes based on neural network quantile and least-squares
regressions. An a posteriori Monte Carlo (non-nested) procedure is introduced
to estimate distances to the ground-truth VaR and ES without access to the
latter. This is illustrated using numerical experiments in a Gaussian toy-model
and a financial case-study where the objective is to learn a dynamic initial
margin.

Deep Metric Learning algorithms aim to learn an efficient embedding space to
preserve the similarity relationships among the input data. Whilst these
algorithms have achieved significant performance gains across a wide plethora
of tasks, they have also failed to consider and increase comprehensive
similarity constraints; thus learning a sub-optimal metric in the embedding
space. Moreover, up until now; there have been few studies with respect to
their performance in the presence of noisy labels. Here, we address the concern
of learning a discriminative deep embedding space by designing a novel, yet
effective Deep Class-wise Discrepancy Loss (DCDL) function that segregates the
underlying similarity distributions (thus introducing class-wise discrepancy)
of the embedding points between each and every class. Our empirical results
across three standard image classification datasets and two fine-grained image
recognition datasets in the presence and absence of noise clearly demonstrate
the need for incorporating such class-wise similarity relationships along with
traditional algorithms while learning a discriminative embedding space.

Device-independent quantum key distribution (DIQKD) reduces the vulnerability
to side-channel attacks of standard QKD protocols by removing the need for
characterized quantum devices. The higher security guarantees come however, at
the price of a challenging implementation. Here, we tackle the question of the
conception of an experiment for implementing DIQKD with photonic devices. We
introduce a technique combining reinforcement learning, optimisation algorithm
and a custom efficient simulation of quantum optics experiments to automate the
design of photonic setups maximizing a given function of the measurement
statistics. Applying the algorithm to DIQKD, we get unexpected experimental
configurations leading to high key rates and to a high resistance to loss and
noise. These configurations might be helpful to facilitate a first
implementation of DIQKD with photonic devices and for future developments
targeting improved performances.

In this work, we extend the deep energy method (DEM), which has been used to
solve elastic deformation of structures, to problems involving classical
elastoplasticity. A loss function for elastoplastic DEM is proposed, inspired
by the discrete variational formulation of plasticity. The radial return
algorithm is coupled with DEM to update the plastic internal state variables
without violating the Kuhn-Tucker consistency conditions. Finite element shape
functions and their gradients are used to approximate the spatial gradients of
the DEM-predicted displacements, and Gauss quadrature is used to integrate the
loss function. Five numerical examples are presented to demonstrate the use of
the framework with different material models such as isotropic hardening,
perfect plasticity, and kinematic hardening. Monotonic and cyclic loading cases
are also considered. In all cases, the DEM solution shows high accuracy
compared to the reference solution obtained from the finite element method. We
also show that the DEM model trained on a coarse mesh retains high accuracy
when inferring state variables on a refined mesh. The current DEM model marks
the first time that DEM is extended to plasticity, and offers promising
potential to effectively solve elastoplasticity problems from scratch using
neural networks.

The Medical Internet of Things, a recent technological advancement in
medicine, is incredibly helpful in providing real-time monitoring of health
metrics. This paper presents a non-invasive IoT system that tracks patients'
emotions, especially those with autism spectrum disorder. With a few affordable
sensors and cloud computing services, the individual's heart rates are
monitored and analyzed to study the effects of changes in sweat and heartbeats
per minute for different emotions. Under normal resting conditions of the
individual, the proposed system could detect the right emotion using machine
learning algorithms with a performance of up to 92% accuracy. The result of the
proposed approach is comparable with the state-of-the-art solutions in medical
IoT.

Conventional frequentist FL schemes are known to yield overconfident
decisions. Bayesian FL addresses this issue by allowing agents to process and
exchange uncertainty information encoded in distributions over the model
parameters. However, this comes at the cost of a larger per-iteration
communication overhead. This letter investigates whether Bayesian FL can still
provide advantages in terms of calibration when constraining communication
bandwidth. We present compressed particle-based Bayesian FL protocols for FL
and federated "unlearning" that apply quantization and sparsification across
multiple particles. The experimental results confirm that the benefits of
Bayesian FL are robust to bandwidth constraints.

Decrypting intelligence from the human brain construct is vital in the
detection of particular neurological disorders. Recently, functional brain
connectomes have been used successfully to predict behavioral scores. However,
state-of-the-art methods, on one hand, neglect the topological properties of
the connectomes and, on the other hand, fail to solve the high inter-subject
brain heterogeneity. To address these limitations, we propose a novel
regression graph neural network through meta-learning namely Meta-RegGNN for
predicting behavioral scores from brain connectomes. The parameters of our
proposed regression GNN are explicitly trained so that a small number of
gradient steps combined with a small training data amount produces a good
generalization to unseen brain connectomes. Our results on verbal and
full-scale intelligence quotient (IQ) prediction outperform existing methods in
both neurotypical and autism spectrum disorder cohorts. Furthermore, we show
that our proposed approach ensures generalizability, particularly for autistic
subjects. Our Meta-RegGNN source code is available at
https://github.com/basiralab/Meta-RegGNN.

Symbolic regression is a nonlinear regression method which is commonly
performed by an evolutionary computation method such as genetic programming.
Quantification of uncertainty of regression models is important for the
interpretation of models and for decision making. The linear approximation and
so-called likelihood profiles are well-known possibilities for the calculation
of confidence and prediction intervals for nonlinear regression models. These
simple and effective techniques have been completely ignored so far in the
genetic programming literature. In this work we describe the calculation of
likelihood profiles in details and also provide some illustrative examples with
models created with three different symbolic regression algorithms on two
different datasets. The examples highlight the importance of the likelihood
profiles to understand the limitations of symbolic regression models and to
help the user taking an informed post-prediction decision.

Prompt-based fine-tuning for pre-trained models has proven effective for many
natural language processing tasks under few-shot settings in general domain.
However, tuning with prompt in biomedical domain has not been investigated
thoroughly. Biomedical words are often rare in general domain, but quite
ubiquitous in biomedical contexts, which dramatically deteriorates the
performance of pre-trained models on downstream biomedical applications even
after fine-tuning, especially in low-resource scenarios. We propose a simple
yet effective approach to helping models learn rare biomedical words during
tuning with prompt. Experimental results show that our method can achieve up to
6% improvement in biomedical natural language inference task without any extra
parameters or training steps using few-shot vanilla prompt settings.

Person Re-Identification (Re-ID) aims to search for a person of interest
(query) in a network of cameras. In the classic Re-ID setting the query is
sought in a gallery containing properly cropped images of entire bodies.
Recently, the live Re-ID setting was introduced to represent the practical
application context of Re-ID better. It consists in searching for the query in
short videos, containing whole scene frames. The initial live Re-ID baseline
used a pedestrian detector to build a large search gallery and a classic Re-ID
model to find the query in the gallery. However, the galleries generated were
too large and contained low-quality images, which decreased the live Re-ID
performance. Here, we present a new live Re-ID approach called TrADe, to
generate lower high-quality galleries. TrADe first uses a Tracking algorithm to
identify sequences of images of the same individual in the gallery. Following,
an Anomaly Detection model is used to select a single good representative of
each tracklet. TrADe is validated on the live Re-ID version of the PRID-2011
dataset and shows significant improvements over the baseline.

In this letter, a lightweight one-dimensional convolutional neural network
(1-D CNN)-based timing synchronization (TS) method is proposed to reduce the
computational complexity and processing delay and hold the timing accuracy in
orthogonal frequency division multiplexing (OFDM) systems. Specifically, the TS
task is first transformed into a deep learning (DL)-based classification task,
and then three iterations of the compressed sensing (CS)-based TS strategy are
simplified to form a lightweight network, whose CNN layers are specially
designed to highlight the classification features. Besides, to enhance the
generalization performance of the proposed method against the channel impulse
responses (CIR) uncertainty, the relaxed restriction for propagation delay is
exploited to augment the completeness of training data. Numerical results
reflect that the proposed 1-D CNN-based TS method effectively improves the TS
accuracy, reduces the computational complexity and processing delay, and
possesses a good generalization performance against the CIR uncertainty. The
source codes of the proposed method are available at
https://github.com/qingchj851/CNNTS.

This paper aims to improve the performance of text-to-SQL parsing by
exploring the intrinsic uncertainties in the neural network based approaches
(called SUN). From the data uncertainty perspective, it is indisputable that a
single SQL can be learned from multiple semantically-equivalent
questions.Different from previous methods that are limited to one-to-one
mapping, we propose a data uncertainty constraint to explore the underlying
complementary semantic information among multiple semantically-equivalent
questions (many-to-one) and learn the robust feature representations with
reduced spurious associations. In this way, we can reduce the sensitivity of
the learned representations and improve the robustness of the parser. From the
model uncertainty perspective, there is often structural information
(dependence) among the weights of neural networks. To improve the
generalizability and stability of neural text-to-SQL parsers, we propose a
model uncertainty constraint to refine the query representations by enforcing
the output representations of different perturbed encoding networks to be
consistent with each other. Extensive experiments on five benchmark datasets
demonstrate that our method significantly outperforms strong competitors and
achieves new state-of-the-art results. For reproducibility, we release our code
and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/sunsql.

Chronic pain is a pervasive disorder which is often very disabling and is
associated with comorbidities such as depression and anxiety. Neuropathic Pain
(NP) is a common sub-type which is often caused due to nerve damage and has a
known pathophysiology. Another common sub-type is Fibromyalgia (FM) which is
described as musculoskeletal, diffuse pain that is widespread through the body.
The pathophysiology of FM is poorly understood, making it very hard to
diagnose. Standard medications and treatments for FM and NP differ from one
another and if misdiagnosed it can cause an increase in symptom severity. To
overcome this difficulty, we propose a novel framework, PainPoints, which
accurately detects the sub-type of pain and generates clinical notes via
summarizing the patient interviews. Specifically, PainPoints makes use of large
language models to perform sentence-level classification of the text obtained
from interviews of FM and NP patients with a reliable AUC of 0.83. Using a
sufficiency-based interpretability approach, we explain how the fine-tuned
model accurately picks up on the nuances that patients use to describe their
pain. Finally, we generate summaries of these interviews via expert
interventions by introducing a novel facet-based approach. PainPoints thus
enables practitioners to add/drop facets and generate a custom summary based on
the notion of "facet-coverage" which is also introduced in this work.

We propose a lightweight and accurate method for detecting anomalies in
videos. Existing methods used multiple-instance learning (MIL) to determine the
normal/abnormal status of each segment of the video. Recent successful
researches argue that it is important to learn the temporal relationships among
segments to achieve high accuracy, instead of focusing on only a single
segment. Therefore we analyzed the existing methods that have been successful
in recent years, and found that while it is indeed important to learn all
segments together, the temporal orders among them are irrelevant to achieving
high accuracy. Based on this finding, we do not use the MIL framework, but
instead propose a lightweight model with a self-attention mechanism to
automatically extract features that are important for determining
normal/abnormal from all input segments. As a result, our neural network model
has 1.3\% of the number of parameters of the existing method. We evaluated the
frame-level detection accuracy of our method on three benchmark datasets
(UCF-Crime, ShanghaiTech, and XD-Violence) and demonstrate that our method can
achieve the comparable or better accuracy than state-of-the-art methods.

The pre-trained image-text models, like CLIP, have demonstrated the strong
power of vision-language representation learned from a large scale of
web-collected image-text data. In light of the well-learned visual features,
some existing works transfer image representation to video domain and achieve
good results. However, how to utilize image-language pre-trained model (e.g.,
CLIP) for video-language pre-training (post-pretraining) is still under
explored. In this paper, we investigate two questions: 1) what are the factors
hindering post-pretraining CLIP to further improve the performance on
video-language tasks? and 2) how to mitigate the impact of these factors?
Through a series of comparative experiments and analyses, we find that the data
scale and domain gap between language sources have great impacts. Motivated by
these, we propose a Omnisource Cross-modal Learning method equipped with a
Video Proxy mechanism on the basis of CLIP, namely CLIP-ViP. Extensive results
show that our approach improves the performance of CLIP on video-text retrieval
by a large margin. Our model also achieves SOTA results on a variety of
datasets, including MSR-VTT, DiDeMo, LSMDC, and ActivityNet. We will release
our code and pre-trained CLIP-ViP models at
https://github.com/microsoft/XPretrain/tree/main/CLIP-ViP.

Reed relay serves as the fundamental component of functional testing, which
closely relates to the successful quality inspection of electronics. To provide
accurate remaining useful life (RUL) estimation for reed relay, a hybrid deep
learning network with degradation pattern clustering is proposed based on the
following three considerations. First, multiple degradation behaviors are
observed for reed relay, and hence a dynamic time wrapping-based $K$-means
clustering is offered to distinguish degradation patterns from each other.
Second, although proper selections of features are of great significance, few
studies are available to guide the selection. The proposed method recommends
operational rules for easy implementation purposes. Third, a neural network for
remaining useful life estimation (RULNet) is proposed to address the weakness
of the convolutional neural network (CNN) in capturing temporal information of
sequential data, which incorporates temporal correlation ability after
high-level feature representation of convolutional operation. In this way,
three variants of RULNet are constructed with health indicators, features with
self-organizing map, or features with curve fitting. Ultimately, the proposed
hybrid model is compared with the typical baseline models, including CNN and
long short-term memory network (LSTM), through a practical reed relay dataset
with two distinct degradation manners. The results from both degradation cases
demonstrate that the proposed method outperforms CNN and LSTM regarding the
index root mean squared error.

Deep learning-based techniques have been introduced into the field of
trajectory optimization in recent years. Deep Neural Networks (DNNs) are
trained and used as the surrogates of conventional optimization process. They
can provide low thrust (LT) transfer cost estimation and enable more complex
preliminary mission designs. However, it is a challenge to efficiently obtain
the required amount of trajectory data for training. A Generative Adversarial
Network (GAN) is adapted to generate the feasible LT trajectory data
efficiently. The GAN consists of a generator and a discriminator, both of which
are deep networks. The generator generates fake LT transfer features using
random noise as input, while the discriminator distinguishes the generator's
fake LT transfer features from real LT transfer features. The GAN is trained
until the generator generates fake LT transfers that the discriminator cannot
identify. This indicates the generator generates low thrust transfer features
that have the same distribution as the real transfer features. The generated
low thrust transfer data have a high convergence rate, and they can be used to
efficiently produce training data for deep learning models. The proposed
approach is validated by generating feasible LT transfers in a Near-Earth
Asteroid (NEA) mission scenario. The convergence rate of GAN-generated samples
is 84.3%.

Inspired by online learning, data-dependent regret has recently been proposed
as a criterion for controller design. In the regret-optimal control paradigm,
causal controllers are designed to minimize regret against a hypothetical
optimal noncausal controller, which selects the globally cost-minimizing
sequence of control actions given noncausal access to the disturbance sequence.
  We extend regret-optimal control to the more challenging measurement-feedback
setting, where the online controller must compete against the optimal noncausal
controller without directly observing the state or the driving disturbance. We
show that no measurement-feedback controller can have bounded competitive ratio
or regret which is bounded by the pathlength of the measurement disturbance. We
do derive, however, a controller whose regret has optimal dependence on the
joint energy of the driving and measurement disturbances, and another
controller whose regret has optimal dependence on the pathlength of the driving
disturbance and the energy of the measurement disturbance. The key technique we
introduce is a reduction from regret-optimal measurement-feedback control to
$H_{\infty}$-optimal measurement-feedback control in a synthetic system. We
present numerical simulations which illustrate the efficacy of our proposed
control algorithms.

Recent years have seen growing interest in 3D human faces modelling due to
its wide applications in digital human, character generation and animation.
Existing approaches overwhelmingly emphasized on modeling the exterior shapes,
textures and skin properties of faces, ignoring the inherent correlation
between inner skeletal structures and appearance. In this paper, we present
SCULPTOR, 3D face creations with Skeleton Consistency Using a Learned
Parametric facial generaTOR, aiming to facilitate easy creation of both
anatomically correct and visually convincing face models via a hybrid
parametric-physical representation. At the core of SCULPTOR is LUCY, the first
large-scale shape-skeleton face dataset in collaboration with plastic surgeons.
Named after the fossils of one of the oldest known human ancestors, our LUCY
dataset contains high-quality Computed Tomography (CT) scans of the complete
human head before and after orthognathic surgeries, critical for evaluating
surgery results. LUCY consists of 144 scans of 72 subjects (31 male and 41
female) where each subject has two CT scans taken pre- and post-orthognathic
operations. Based on our LUCY dataset, we learn a novel skeleton consistent
parametric facial generator, SCULPTOR, which can create the unique and nuanced
facial features that help define a character and at the same time maintain
physiological soundness. Our SCULPTOR jointly models the skull, face geometry
and face appearance under a unified data-driven framework, by separating the
depiction of a 3D face into shape blend shape, pose blend shape and facial
expression blend shape. SCULPTOR preserves both anatomic correctness and visual
realism in facial generation tasks compared with existing methods. Finally, we
showcase the robustness and effectiveness of SCULPTOR in various fancy
applications unseen before.

As pre-trained language models become more resource-demanding, the inequality
between resource-rich languages such as English and resource-scarce languages
is worsening. This can be attributed to the fact that the amount of available
training data in each language follows the power-law distribution, and most of
the languages belong to the long tail of the distribution. Some research areas
attempt to mitigate this problem. For example, in cross-lingual transfer
learning and multilingual training, the goal is to benefit long-tail languages
via the knowledge acquired from resource-rich languages. Although being
successful, existing work has mainly focused on experimenting on as many
languages as possible. As a result, targeted in-depth analysis is mostly
absent. In this study, we focus on a single low-resource language and perform
extensive evaluation and probing experiments using cross-lingual post-training
(XPT). To make the transfer scenario challenging, we choose Korean as the
target language, as it is a language isolate and thus shares almost no typology
with English. Results show that XPT not only outperforms or performs on par
with monolingual models trained with orders of magnitudes more data but also is
highly efficient in the transfer process.

Multimodal machine learning has been widely studied for the development of
general intelligence. Recently, the remarkable multimodal algorithms, the
Perceiver and Perceiver IO, show competitive results for diverse dataset
domains and tasks. However, recent works, Perceiver and Perceiver IO, have
focused on heterogeneous modalities, including image, text, and speech, and
there are few research works for graph structured datasets. A graph is one of
the most generalized dataset structures, and we can represent the other
dataset, including images, text, and speech, as graph structured data. A graph
has an adjacency matrix different from other dataset domains such as text and
image, and it is not trivial to handle the topological information, relational
information, and canonical positional information. In this study, we provide a
Graph Perceiver IO, the Perceiver IO for the graph structured dataset. We keep
the main structure of the Graph Perceiver IO as the Perceiver IO because the
Perceiver IO already handles the diverse dataset well, except for the graph
structured dataset. The Graph Perceiver IO is a general method, and it can
handle diverse datasets such as graph structured data as well as text and
images. Comparing the graph neural networks, the Graph Perceiver IO requires a
lower complexity, and it can incorporate the local and global information
efficiently. We show that Graph Perceiver IO shows competitive results for
diverse graph-related tasks, including node classification, graph
classification, and link prediction.

Recently, convolutional neural networks (CNNs) have been widely used in image
denoising. Existing methods benefited from residual learning and achieved high
performance. Much research has been paid attention to optimizing the network
architecture of CNN but ignored the limitations of residual learning. This
paper suggests two limitations of it. One is that residual learning focuses on
estimating noise, thus overlooking the image information. The other is that the
image self-similarity is not effectively considered. This paper proposes a
compositional denoising network (CDN), whose image information path (IIP) and
noise estimation path (NEP) will solve the two problems, respectively. IIP is
trained by an image-to-image way to extract image information. For NEP, it
utilizes the image self-similarity from the perspective of training. This
similarity-based training method constrains NEP to output a similar estimated
noise distribution for different image patches with a specific kind of noise.
Finally, image information and noise distribution information will be
comprehensively considered for image denoising. Experiments show that CDN
achieves state-of-the-art results in synthetic and real-world image denoising.
Our code will be released on https://github.com/JiaHongZ/CDN.

The growing interest in developing corpora of persuasive texts has promoted
applications in automated systems, e.g., debating and essay scoring systems;
however, there is little prior work mining image persuasiveness from an
argumentative perspective. To expand persuasiveness mining into a multi-modal
realm, we present a multi-modal dataset, ImageArg, consisting of annotations of
image persuasiveness in tweets. The annotations are based on a persuasion
taxonomy we developed to explore image functionalities and the means of
persuasion. We benchmark image persuasiveness tasks on ImageArg using
widely-used multi-modal learning methods. The experimental results show that
our dataset offers a useful resource for this rich and challenging topic, and
there is ample room for modeling improvement.

In decentralized multi-robot navigation, the agents lack the world knowledge
to make safe and (near-)optimal plans reliably and make their decisions on
their neighbors' observable states. We present a reinforcement learning based
multi-agent navigation algorithm that performs inter-agent communications. In
order to deal with the variable number of neighbors for each agent, we use a
multi-head self-attention mechanism to encode neighbor information and create a
fixed-length observation vector. We pose communication selection as a link
prediction problem, where the network predicts whether communication is
necessary given the observable information. The communicated information
augments the observed neighbor information and is used to select a suitable
navigation plan. We highlight the benefits of our approach by performing safe
and efficient navigation among multiple robots in dense and challenging
benchmarks. We also compare the performance with other learning-based methods
and highlight improvements in terms of fewer collisions and time-to-goal in
dense scenarios.

Longitudinal fetal brain atlas is a powerful tool for understanding and
characterizing the complex process of fetus brain development. Existing fetus
brain atlases are typically constructed by averaged brain images on discrete
time points independently over time. Due to the differences in onto-genetic
trends among samples at different time points, the resulting atlases suffer
from temporal inconsistency, which may lead to estimating error of the brain
developmental characteristic parameters along the timeline. To this end, we
proposed a multi-stage deep-learning framework to tackle the time inconsistency
issue as a 4D (3D brain volume + 1D age) image data denoising task. Using
implicit neural representation, we construct a continuous and noise-free
longitudinal fetus brain atlas as a function of the 4D spatial-temporal
coordinate. Experimental results on two public fetal brain atlases (CRL and
FBA-Chinese atlases) show that the proposed method can significantly improve
the atlas temporal consistency while maintaining good fetus brain structure
representation. In addition, the continuous longitudinal fetus brain atlases
can also be extensively applied to generate finer 4D atlases in both spatial
and temporal resolution.

Fluorescence microscopy is a key driver to promote discoveries of biomedical
research. However, with the limitation of microscope hardware and
characteristics of the observed samples, the fluorescence microscopy images are
susceptible to noise. Recently, a few self-supervised deep learning (DL)
denoising methods have been proposed. However, the training efficiency and
denoising performance of existing methods are relatively low in real scene
noise removal. To address this issue, this paper proposed self-supervised image
denoising method Noise2SR (N2SR) to train a simple and effective image
denoising model based on single noisy observation. Our Noise2SR denoising model
is designed for training with paired noisy images of different dimensions.
Benefiting from this training strategy, Noise2SR is more efficiently
self-supervised and able to restore more image details from a single noisy
observation. Experimental results of simulated noise and real microscopy noise
removal show that Noise2SR outperforms two blind-spot based self-supervised
deep learning image denoising methods. We envision that Noise2SR has the
potential to improve more other kind of scientific imaging quality.

Classifiers have been widely implemented in practice, while how to evaluate
them properly remains a problem. Commonly used two types of metrics
respectively based on confusion matrix and loss function have different
advantages in flexibility and mathematical completeness, while they struggle in
different dilemmas like the insensitivity to slight improvements or the lack of
customizability in different tasks. In this paper, we propose a novel metric
named Meta Pattern Concern Score based on the abstract representation of the
probabilistic prediction, as well as the targeted design for processing
negative classes in multi-classification and reducing the discreteness of
metric value, to achieve advantages of both the two kinds of metrics and avoid
their weaknesses. Our metric provides customizability to pick out the model for
specific requirements in different practices, and make sure it is also fine
under traditional metrics at the same time. Evaluation in four kinds of models
and six datasets demonstrates the effectiveness and efficiency of our metric,
and a case study shows it can select a model to reduce 0.53% of dangerous
misclassifications by sacrificing only 0.04% of training accuracy.

Every autonomous driving dataset has a different configuration of sensors,
originating from distinct geographic regions and covering various scenarios. As
a result, 3D detectors tend to overfit the datasets they are trained on. This
causes a drastic decrease in accuracy when the detectors are trained on one
dataset and tested on another. We observe that lidar scan pattern differences
form a large component of this reduction in performance. We address this in our
approach, SEE-VCN, by designing a novel viewer-centred surface completion
network (VCN) to complete the surfaces of objects of interest within an
unsupervised domain adaptation framework, SEE. With SEE-VCN, we obtain a
unified representation of objects across datasets, allowing the network to
focus on learning geometry, rather than overfitting on scan patterns. By
adopting a domain-invariant representation, SEE-VCN can be classed as a
multi-target domain adaptation approach where no annotations or re-training is
required to obtain 3D detections for new scan patterns. Through extensive
experiments, we show that our approach outperforms previous domain adaptation
methods in multiple domain adaptation settings. Our code and data are available
at https://github.com/darrenjkt/SEE-VCN.

Computer vision (CV) is a big and important field in artificial intelligence
covering a wide range of applications. Image analysis is a major task in CV
aiming to extract, analyse and understand the visual content of images.
However, image-related tasks are very challenging due to many factors, e.g.,
high variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation (EC) approaches
have been widely used for image analysis with significant achievement. However,
there is no comprehensive survey of existing EC approaches to image analysis.
To fill this gap, this paper provides a comprehensive survey covering all
essential EC approaches to important image analysis tasks including edge
detection, image segmentation, image feature analysis, image classification,
object detection, and others. This survey aims to provide a better
understanding of evolutionary computer vision (ECV) by discussing the
contributions of different approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and trends
associated to this research field are also discussed and summarised to provide
further guidelines and opportunities for future research.

The rapidly expanding number of Internet of Things (IoT) devices is
generating huge quantities of data, but the data privacy and security exposure
in IoT devices, especially in the automatic driving system. Federated learning
(FL) is a paradigm that addresses data privacy, security, access rights, and
access to heterogeneous message issues by integrating a global model based on
distributed nodes. However, data poisoning attacks on FL can undermine the
benefits, destroying the global model's availability and disrupting model
training. To avoid the above issues, we build up a hierarchical defense data
poisoning (HDDP) system framework to defend against data poisoning attacks in
FL, which monitors each local model of individual nodes via abnormal detection
to remove the malicious clients. Whether the poisoning defense server has a
trusted test dataset, we design the \underline{l}ocal \underline{m}odel
\underline{t}est \underline{v}oting (LMTV) and
\underline{k}ullback-\underline{l}eibler divergence \underline{a}nomaly
parameters \underline{d}etection (KLAD) algorithms to defend against
label-flipping poisoning attacks. Specifically, the trusted test dataset is
utilized to obtain the evaluation results for each classification to recognize
the malicious clients in LMTV. More importantly, we adopt the kullback leibler
divergence to measure the similarity between local models without the trusted
test dataset in KLAD. Finally, through extensive evaluations and against the
various label-flipping poisoning attacks, LMTV and KLAD algorithms could
achieve the $100\%$ and $40\%$ to $85\%$ successful defense ratios under
different detection situations.

Learning robust feature matching between the template and search area is
crucial for 3D Siamese tracking. The core of Siamese feature matching is how to
assign high feature similarity on the corresponding points between the template
and search area for precise object localization. In this paper, we propose a
novel point cloud registration-driven Siamese tracking framework, with the
intuition that spatially aligned corresponding points (via 3D registration)
tend to achieve consistent feature representations. Specifically, our method
consists of two modules, including a tracking-specific nonlocal registration
module and a registration-aided Sinkhorn template-feature aggregation module.
The registration module targets at the precise spatial alignment between the
template and search area. The tracking-specific spatial distance constraint is
proposed to refine the cross-attention weights in the nonlocal module for
discriminative feature learning. Then, we use the weighted SVD to compute the
rigid transformation between the template and search area, and align them to
achieve the desired spatially aligned corresponding points. For the feature
aggregation model, we formulate the feature matching between the transformed
template and search area as an optimal transport problem and utilize the
Sinkhorn optimization to search for the outlier-robust matching solution. Also,
a registration-aided spatial distance map is built to improve the matching
robustness in indistinguishable regions (e.g., smooth surface). Finally, guided
by the obtained feature matching map, we aggregate the target information from
the template into the search area to construct the target-specific feature,
which is then fed into a CenterPoint-like detection head for object
localization. Extensive experiments on KITTI, NuScenes and Waymo datasets
verify the effectiveness of our proposed method.

Nowadays, transformer-based models gradually become the default choice for
artificial intelligence pioneers. The models also show superiority even in the
few-shot scenarios. In this paper, we revisit the classical methods and propose
a new few-shot alternative. Specifically, we investigate the few-shot one-class
problem, which actually takes a known sample as a reference to detect whether
an unknown instance belongs to the same class. This problem can be studied from
the perspective of sequence match. It is shown that with meta-learning, the
classical sequence match method, i.e. Compare-Aggregate, significantly
outperforms transformer ones. The classical approach requires much less
training cost. Furthermore, we perform an empirical comparison between two
kinds of sequence match approaches under simple fine-tuning and meta-learning.
Meta-learning causes the transformer models' features to have high-correlation
dimensions. The reason is closely related to the number of layers and heads of
transformer models. Experimental codes and data are available at
https://github.com/hmt2014/FewOne

We consider the multi-user detection (MUD) problem in uplink grant-free
non-orthogonal multiple access (NOMA), where the access point has to identify
the total number and correct identity of the active Internet of Things (IoT)
devices and decode their transmitted data. We assume that IoT devices use
complex spreading sequences and transmit information in a random-access manner
following the burst-sparsity model, where some IoT devices transmit their data
in multiple adjacent time slots with a high probability, while others transmit
only once during a frame. Exploiting the temporal correlation, we propose an
attention-based bidirectional long short-term memory (BiLSTM) network to solve
the MUD problem. The BiLSTM network creates a pattern of the device activation
history using forward and reverse pass LSTMs, whereas the attention mechanism
provides essential context to the device activation points. By doing so, a
hierarchical pathway is followed for detecting active devices in a grant-free
scenario. Then, by utilising the complex spreading sequences, blind data
detection for the estimated active devices is performed. The proposed framework
does not require prior knowledge of device sparsity levels and channels for
performing MUD. The results show that the proposed network achieves better
performance compared to existing benchmark schemes.

Road network and trajectory representation learning are essential for traffic
systems since the learned representation can be directly used in various
downstream tasks (e.g., traffic speed inference, and travel time estimation).
However, most existing methods only contrast within the same scale, i.e.,
treating road network and trajectory separately, which ignores valuable
inter-relations. In this paper, we aim to propose a unified framework that
jointly learns the road network and trajectory representations end-to-end. We
design domain-specific augmentations for road-road contrast and
trajectory-trajectory contrast separately, i.e., road segment with its
contextual neighbors and trajectory with its detour replaced and dropped
alternatives, respectively. On top of that, we further introduce the
road-trajectory cross-scale contrast to bridge the two scales by maximizing the
total mutual information. Unlike the existing cross-scale contrastive learning
methods on graphs that only contrast a graph and its belonging nodes, the
contrast between road segment and trajectory is elaborately tailored via novel
positive sampling and adaptive weighting strategies. We conduct prudent
experiments based on two real-world datasets with four downstream tasks,
demonstrating improved performance and effectiveness. The code is available at
https://github.com/mzy94/JCLRNT.

Deep neural network (DNN) classifiers are vulnerable to adversarial attacks.
Although the existing gradient-based attacks have achieved good performance in
feed-forward model and image recognition tasks, the extension for time series
classification in the recurrent neural network (RNN) remains a dilemma, because
the cyclical structure of RNN prevents direct model differentiation and the
visual sensitivity to perturbations of time series data challenges the
traditional local optimization objective to minimize perturbation. In this
paper, an efficient and widely applicable approach called TSFool for crafting
high-quality adversarial time series for the RNN classifier is proposed. We
propose a novel global optimization objective named Camouflage Coefficient to
consider how well the adversarial samples hide in class clusters, and
accordingly redefine the high-quality adversarial attack as a multi-objective
optimization problem. We also propose a new idea to use intervalized weighted
finite automata (IWFA) to capture deeply embedded vulnerable samples having
otherness between features and latent manifold to guide the approximation to
the optimization solution. Experiments on 22 UCR datasets are conducted to
confirm that TSFool is a widely effective, efficient and high-quality approach
with 93.22% less local perturbation, 32.33% better global camouflage, and 1.12
times speedup to existing methods.

Developing an effective automatic classifier to separate genuine sources from
artifacts is essential for transient follow-ups in wide-field optical surveys.
The identification of transient detections from the subtraction artifacts after
the image differencing process is a key step in such classifiers, known as
real-bogus classification problem. We apply a self-supervised machine learning
model, the deep-embedded self-organizing map (DESOM) to this "real-bogus"
classification problem. DESOM combines an autoencoder and a self-organizing map
to perform clustering in order to distinguish between real and bogus
detections, based on their dimensionality-reduced representations. We use 32x32
normalized detection thumbnails as the input of DESOM. We demonstrate different
model training approaches, and find that our best DESOM classifier shows a
missed detection rate of 6.6% with a false positive rate of 1.5%. DESOM offers
a more nuanced way to fine-tune the decision boundary identifying likely real
detections when used in combination with other types of classifiers, for
example built on neural networks or decision trees. We also discuss other
potential usages of DESOM and its limitations.

Security concerns about a machine learning model used in a
prediction-as-a-service include the privacy of the model, the query and the
result. Secure inference solutions based on homomorphic encryption (HE) and/or
multiparty computation (MPC) have been developed to protect all the sensitive
information. One of the most efficient type of solution utilizes HE for linear
layers, and MPC for non-linear layers. However, for such hybrid protocols with
semi-honest security, an adversary can malleate the intermediate features in
the inference process, and extract model information more effectively than
methods against inference service in plaintext. In this paper, we propose SEEK,
a general extraction method for hybrid secure inference services outputing only
class labels. This method can extract each layer of the target model
independently, and is not affected by the depth of the model. For ResNet-18,
SEEK can extract a parameter with less than 50 queries on average, with average
error less than $0.03\%$.

Flapping-fin unmanned underwater vehicle (UUV) propulsion systems provide
high maneuverability for naval tasks such as surveillance and terrain
exploration. Recent work has explored the use of time-series neural network
surrogate models to predict thrust from vehicle design and fin kinematics. We
develop a search-based inverse model that leverages a kinematics-to-thrust
neural network model for control system design. Our inverse model finds a set
of fin kinematics with the multi-objective goal of reaching a target thrust and
creating a smooth kinematic transition between flapping cycles. We demonstrate
how a control system integrating this inverse model can make online,
cycle-to-cycle adjustments to prioritize different system objectives.

Understanding causality helps to structure interventions to achieve specific
goals and enables predictions under interventions. With the growing importance
of learning causal relationships, causal discovery tasks have transitioned from
using traditional methods to infer potential causal structures from
observational data to the field of pattern recognition involved in deep
learning. The rapid accumulation of massive data promotes the emergence of
causal search methods with brilliant scalability. Existing summaries of causal
discovery methods mainly focus on traditional methods based on constraints,
scores and FCMs, there is a lack of perfect sorting and elaboration for deep
learning-based methods, also lacking some considers and exploration of causal
discovery methods from the perspective of variable paradigms. Therefore, we
divide the possible causal discovery tasks into three types according to the
variable paradigm and give the definitions of the three tasks respectively,
define and instantiate the relevant datasets for each task and the final causal
model constructed at the same time, then reviews the main existing causal
discovery methods for different tasks. Finally, we propose some roadmaps from
different perspectives for the current research gaps in the field of causal
discovery and point out future research directions.

Noise robustness in keyword spotting remains a challenge as many models fail
to overcome the heavy influence of noises, causing the deterioration of the
quality of feature embeddings. We proposed a contrastive regularization method
called Inter-Intra Contrastive Regularization (I2CR) to improve the feature
representations by guiding the model to learn the fundamental speech
information specific to the cluster. This involves maximizing the similarity
across Intra and Inter samples of the same class. As a result, it pulls the
instances closer to more generalized representations that form more prominent
clusters and reduces the adverse impact of noises. We show that our method
provides consistent improvements in accuracy over different backbone model
architectures under different noise environments. We also demonstrate that our
proposed framework has improved the accuracy of unseen out-of-domain noises and
unseen variant noise SNRs. This indicates the significance of our work with the
overall refinement in noise robustness.

We are currently unable to specify human goals and societal values in a way
that reliably directs AI behavior. Law-making and legal interpretation form a
computational engine that converts opaque human values into legible directives.
"Law Informs Code" is the research agenda capturing complex computational legal
processes, and embedding them in AI. Similar to how parties to a legal contract
cannot foresee every potential contingency of their future relationship, and
legislators cannot predict all the circumstances under which their proposed
bills will be applied, we cannot ex ante specify rules that provably direct
good AI behavior. Legal theory and practice have developed arrays of tools to
address these specification problems. For instance, legal standards allow
humans to develop shared understandings and adapt them to novel situations. In
contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior
through the threat of sanction), leveraged as an expression of how humans
communicate their goals, and what society values, Law Informs Code.
  We describe how data generated by legal processes (methods of law-making,
statutory interpretation, contract drafting, applications of legal standards,
legal reasoning, etc.) can facilitate the robust specification of inherently
vague human goals. This increases human-AI alignment and the local usefulness
of AI. Toward society-AI alignment, we present a framework for understanding
law as the applied philosophy of multi-agent alignment. Although law is partly
a reflection of historically contingent political power - and thus not a
perfect aggregation of citizen preferences - if properly parsed, its
distillation offers the most legitimate computational comprehension of societal
values available. If law eventually informs powerful AI, engaging in the
deliberative political process to improve law takes on even more meaning.

Automatic Speech Recognition models require large amount of speech data for
training, and the collection of such data often leads to privacy concerns.
Federated learning has been widely used and is considered to be an effective
decentralized technique by collaboratively learning a shared prediction model
while keeping the data local on different clients devices. However, the limited
computation and communication resources on clients devices present practical
difficulties for large models. To overcome such challenges, we propose
Federated Pruning to train a reduced model under the federated setting, while
maintaining similar performance compared to the full model. Moreover, the vast
amount of clients data can also be leveraged to improve the pruning results
compared to centralized training. We explore different pruning schemes and
provide empirical evidence of the effectiveness of our methods.

Non-reference speech quality models are important for a growing number of
applications. The VoiceMOS 2022 challenge provided a dataset of synthetic voice
conversion and text-to-speech samples with subjective labels. This study looks
at the amount of variance that can be explained in subjective ratings of speech
quality from metadata and the distribution imbalances of the dataset. Speech
quality models were constructed using wav2vec 2.0 with additional metadata
features that included rater groups and system identifiers and obtained
competitive metrics including a Spearman rank correlation coefficient (SRCC) of
0.934 and MSE of 0.088 at the system-level, and 0.877 and 0.198 at the
utterance-level. Using data and metadata that the test restricted or blinded
further improved the metrics. A metadata analysis showed that the system-level
metrics do not represent the model's system-level prediction as a result of the
wide variation in the number of utterances used for each system on the
validation and test datasets. We conclude that, in general, conditions should
have enough utterances in the test set to bound the sample mean error, and be
relatively balanced in utterance count between systems, otherwise the
utterance-level metrics may be more reliable and interpretable.

Image classification models often learn to predict a class based on
irrelevant co-occurrences between input features and an output class in
training data. We call the unwanted correlations "data biases," and the visual
features causing data biases "bias factors." It is challenging to identify and
mitigate biases automatically without human intervention. Therefore, we
conducted a design study to find a human-in-the-loop solution. First, we
identified user tasks that capture the bias mitigation process for image
classification models with three experts. Then, to support the tasks, we
developed a visual analytics system called DASH that allows users to visually
identify bias factors, to iteratively generate synthetic images using a
state-of-the-art image-to-image translation model, and to supervise the model
training process for improving the classification accuracy. Our quantitative
evaluation and qualitative study with ten participants demonstrate the
usefulness of DASH and provide lessons for future work.

Animals are able to rapidly infer from limited experience when sets of state
action pairs have equivalent reward and transition dynamics. On the other hand,
modern reinforcement learning systems must painstakingly learn through trial
and error that sets of state action pairs are value equivalent -- requiring an
often prohibitively large amount of samples from their environment. MDP
homomorphisms have been proposed that reduce the observed MDP of an environment
to an abstract MDP, which can enable more sample efficient policy learning.
Consequently, impressive improvements in sample efficiency have been achieved
when a suitable MDP homomorphism can be constructed a priori -- usually by
exploiting a practioner's knowledge of environment symmetries. We propose a
novel approach to constructing a homomorphism in discrete action spaces, which
uses a partial model of environment dynamics to infer which state action pairs
lead to the same state -- reducing the size of the state-action space by a
factor equal to the cardinality of the action space. We call this method
equivalent effect abstraction. In a gridworld setting, we demonstrate
empirically that equivalent effect abstraction can improve sample efficiency in
a model-free setting and planning efficiency for modelbased approaches.
Furthermore, we show on cartpole that our approach outperforms an existing
method for learning homomorphisms, while using 33x less training data.

When processing a batch of graphs in machine learning models such as Graph
Neural Networks (GNN), it is common to combine several small graphs into one
overall graph to accelerate processing and remove or reduce the overhead of
padding. This is for example supported in the PyG library. However, the sizes
of small graphs can vary substantially with respect to the number of nodes and
edges, and hence the size of the combined graph can still vary considerably,
especially for small batch sizes. Therefore, the costs of excessive padding and
wasted compute are still incurred when working with static shapes, which are
preferred for maximum acceleration. This paper proposes a new hardware agnostic
approach -- tuple packing -- for generating batches that cause minimal
overhead. The algorithm extends recently introduced sequence packing approaches
to work on the 2D tuples of (|nodes|, |edges|). A monotone heuristic is applied
to the 2D histogram of tuple values to define a priority for packing histogram
bins together with the objective to reach a limit on the number of nodes as
well as the number of edges. Experiments verify the effectiveness of the
algorithm on multiple datasets.

Deep convolutional neural networks for image segmentation do not learn the
label structure explicitly and may produce segmentations with an incorrect
structure, e.g., with disconnected cylindrical structures in the segmentation
of tree-like structures such as airways or blood vessels. In this paper, we
propose a novel label refinement method to correct such errors from an initial
segmentation, implicitly incorporating information about label structure. This
method features two novel parts: 1) a model that generates synthetic structural
errors, and 2) a label appearance simulation network that produces synthetic
segmentations (with errors) that are similar in appearance to the real initial
segmentations. Using these synthetic segmentations and the original images, the
label refinement network is trained to correct errors and improve the initial
segmentations. The proposed method is validated on two segmentation tasks:
airway segmentation from chest computed tomography (CT) scans and brain vessel
segmentation from 3D CT angiography (CTA) images of the brain. In both
applications, our method significantly outperformed a standard 3D U-Net and
other previous refinement approaches. Improvements are even larger when
additional unlabeled data is used for model training. In an ablation study, we
demonstrate the value of the different components of the proposed method.

Evolution is the theory that plants and animals today have come from kinds
that have existed in the past. Scientists such as Charles Darwin and Alfred
Wallace dedicate their life to observe how species interact with their
environment, grow, and change. We are able to predict future changes as well as
simulate the process using genetic algorithms. Genetic Algorithms give us the
opportunity to present multiple variables and parameters to an environment and
change values to simulate different situations. By optimizing genetic
algorithms to hold entities in an environment, we are able to assign varying
characteristics such as speed, size, and cloning probability, to the entities
to simulate real natural selection and evolution in a shorter period of time.
Learning about how species grow and evolve allows us to find ways to improve
technology, help animals going extinct to survive, and figure* out how diseases
spread and possible ways of making an environment uninhabitable for them. Using
data from an environment including genetic algorithms and parameters of speed,
size, and cloning percentage, the ability to test several changes in the
environment and observe how the species interacts within it appears. After
testing different environments with a varied amount of food while keeping the
number of starting population at 10 entities, it was found that an environment
with a scarce amount of food was not sustainable for small and slow entities.
All environments displayed an increase in speed, but the environments that were
richer in food allowed for the entities to live for the entire duration of 50
generations, as well as allowed the population to grow significantly.

It is important that the wireless network is well optimized and planned,
using the limited wireless spectrum resources, to serve the explosively growing
traffic and diverse applications needs of end users. Considering the challenges
of dynamics and complexity of the wireless systems, and the scale of the
networks, it is desirable to have solutions to automatically monitor, analyze,
optimize, and plan the network. This article discusses approaches and solutions
of data analytics and machine learning powered optimization and planning. The
approaches include analyzing some important metrics of performances and
experiences, at the lower layers and upper layers of open systems
interconnection (OSI) model, as well as deriving a metric of the end user
perceived network congestion indicator. The approaches include monitoring and
diagnosis such as anomaly detection of the metrics, root cause analysis for
poor performances and experiences. The approaches include enabling network
optimization with tuning recommendations, directly targeting to optimize the
end users experiences, via sensitivity modeling and analysis of the upper layer
metrics of the end users experiences v.s. the improvement of the lower layers
metrics due to tuning the hardware configurations. The approaches also include
deriving predictive metrics for network planning, traffic demand distributions
and trends, detection and prediction of the suppressed traffic demand, and the
incentives of traffic gains if the network is upgraded. These approaches of
optimization and planning are for accurate detection of optimization and
upgrading opportunities at a large scale, enabling more effective optimization
and planning such as tuning cells configurations, upgrading cells capacity with
more advanced technologies or new hardware, adding more cells, etc., improving
the network performances and providing better experiences to end users.

Self-supervised depth learning from monocular images normally relies on the
2D pixel-wise photometric relation between temporally adjacent image frames.
However, they neither fully exploit the 3D point-wise geometric
correspondences, nor effectively tackle the ambiguities in the photometric
warping caused by occlusions or illumination inconsistency. To address these
problems, this work proposes Density Volume Construction Network (DevNet), a
novel self-supervised monocular depth learning framework, that can consider 3D
spatial information, and exploit stronger geometric constraints among adjacent
camera frustums. Instead of directly regressing the pixel value from a single
image, our DevNet divides the camera frustum into multiple parallel planes and
predicts the pointwise occlusion probability density on each plane. The final
depth map is generated by integrating the density along corresponding rays.
During the training process, novel regularization strategies and loss functions
are introduced to mitigate photometric ambiguities and overfitting. Without
obviously enlarging model parameters size or running time, DevNet outperforms
several representative baselines on both the KITTI-2015 outdoor dataset and
NYU-V2 indoor dataset. In particular, the root-mean-square-deviation is reduced
by around 4% with DevNet on both KITTI-2015 and NYU-V2 in the task of depth
estimation. Code is available at https://github.com/gitkaichenzhou/DevNet.

Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20,
is a short form of cricket. In a Twenty20 game the two teams of 11 players have
a single innings each, which is restricted to a maximum of 20 overs. This
version of cricket is especially unpredictable and is one of the reasons it has
gained popularity over recent times. However, in this paper we try four
different approaches for predicting the results of T20 Cricket Matches.
Specifically we take in to account: previous performance statistics of the
players involved in the competing teams, ratings of players obtained from
reputed cricket statistics websites, clustering the players' with similar
performance statistics and using an ELO based approach to rate players. We
compare the performances of each of these approaches by using logistic
regression, support vector machines, bayes network, decision tree, random
forest.

BERT achieves remarkable results in text classification tasks, it is yet not
fully exploited, since only the last layer is used as a representation output
for downstream classifiers. The most recent studies on the nature of linguistic
features learned by BERT, suggest that different layers focus on different
kinds of linguistic features. We propose a CNN-Enhanced Transformer-Encoder
model which is trained on top of fixed BERT $[CLS]$ representations from all
layers, employing Convolutional Neural Networks to generate QKV feature maps
inside the Transformer-Encoder, instead of linear projections of the input into
the embedding space. CNN-Trans-Enc is relatively small as a downstream
classifier and doesn't require any fine-tuning of BERT, as it ensures an
optimal use of the $[CLS]$ representations from all layers, leveraging
different linguistic features with more meaningful, and generalizable QKV
representations of the input. Using BERT with CNN-Trans-Enc keeps $98.9\%$ and
$94.8\%$ of current state-of-the-art performance on the IMDB and SST-5 datasets
respectably, while obtaining new state-of-the-art on YELP-5 with $82.23$
($8.9\%$ improvement), and on Amazon-Polarity with $0.98\%$ ($0.2\%$
improvement) (K-fold Cross Validation on a 1M sample subset from both
datasets). On the AG news dataset CNN-Trans-Enc achieves $99.94\%$ of the
current state-of-the-art, and achieves a new top performance with an average
accuracy of $99.51\%$ on DBPedia-14.
  Index terms: Text Classification, Natural Language Processing, Convolutional
Neural Networks, Transformers, BERT

Human motion prediction is a fundamental part of many human-robot
applications. Despite the recent progress in human motion prediction, most
studies simplify the problem by predicting the human motion relative to a fixed
joint and/or only limit their model to predict one possible future motion.
While due to the complex nature of human motion, a single output cannot reflect
all the possible actions one can do. Also, for any robotics application, we
need the full human motion including the user trajectory not a 3d pose relative
to the hip joint.
  In this paper, we try to address these two issues by proposing a
transformer-based generative model for forecasting multiple diverse human
motions. Our model generates \textit{N} future possible motion by querying a
history of human motion. Our model first predicts the pose of the body relative
to the hip joint. Then the \textit{Hip Prediction Module} predicts the
trajectory of the hip movement for each predicted pose frame. To emphasize on
the diverse future motions we introduce a similarity loss that penalizes the
pairwise sample distance. We show that our system outperforms the
state-of-the-art in human motion prediction while it can predict diverse
multi-motion future trajectories with hip movements

Popular hypotheses about the origins of collective adaptation are related to
two basic behaviours: protection from predators and a combined search for food
resources. Among the anti-predator explanations, the predator confusion
hypothesis suggests that groups of individuals moving in a swarm aim to
overwhelm the predator while the dilution of risk hypothesis suggests that the
probability of a single prey being targeted by a predator is lower in larger
groups. In this paper, we explore how emergent behaviors arise from a
predator-driven process as an adaptive response to external stimuli perceived
as threatening. Moreover, we suggest a predator confusion process to provide a
selective pressure for the prey to evolve group formations. We analyze the
foraging and prey-predator dynamics evolved in terms of group density and
formation, behavior consistency, predator evasion and success rate, and
foraging rate. Two agents' perceptual models are compared. A local observation
model, where agents can only see what's in their immediate vicinity, and a
global observation model, where agents are able to see the predator at all
times. Both models were evolved for predator avoidance, foraging and collision
avoidance, using reinforcement learning in a simulated game environment. Our
results suggest that the dilution of risk factor is sufficient to evolve group
formations, and the predator confusion effect could play an important role in
the evolution of collaborative behaviors. Finally, we show how variations in
the information exchange of this social order can impact the global collective
behaviors.

In the articulatory synthesis task, speech is synthesized from input features
containing information about the physical behavior of the human vocal tract.
This task provides a promising direction for speech synthesis research, as the
articulatory space is compact, smooth, and interpretable. Current works have
highlighted the potential for deep learning models to perform articulatory
synthesis. However, it remains unclear whether these models can achieve the
efficiency and fidelity of the human speech production system. To help bridge
this gap, we propose a time-domain articulatory synthesis methodology and
demonstrate its efficacy with both electromagnetic articulography (EMA) and
synthetic articulatory feature inputs. Our model is computationally efficient
and achieves a transcription word error rate (WER) of 18.5% for the
EMA-to-speech task, yielding an improvement of 11.6% compared to prior work.
Through interpolation experiments, we also highlight the generalizability and
interpretability of our approach.

Unmanned Aerial Vehicles (UAV) have been standing out due to the wide range
of applications in which they can be used autonomously. However, they need
intelligent systems capable of providing a greater understanding of what they
perceive to perform several tasks. They become more challenging in complex
environments since there is a need to perceive the environment and act under
environmental uncertainties to make a decision. In this context, a system that
uses active perception can improve performance by seeking the best next view
through the recognition of targets while displacement occurs. This work aims to
contribute to the active perception of UAVs by tackling the problem of tracking
and recognizing water surface structures to perform a dynamic landing. We show
that our system with classical image processing techniques and a simple Deep
Reinforcement Learning (Deep-RL) agent is capable of perceiving the environment
and dealing with uncertainties without making the use of complex Convolutional
Neural Networks (CNN) or Contrastive Learning (CL).

Previous works showed that Deep-RL can be applied to perform mapless
navigation, including the medium transition of Hybrid Unmanned Aerial
Underwater Vehicles (HUAUVs). This paper presents new approaches based on the
state-of-the-art actor-critic algorithms to address the navigation and medium
transition problems for a HUAUV. We show that a double critic Deep-RL with
Recurrent Neural Networks improves the navigation performance of HUAUVs using
solely range data and relative localization. Our Deep-RL approaches achieved
better navigation and transitioning capabilities with a solid generalization of
learning through distinct simulated scenarios, outperforming previous

This paper presents a new technique for learning category-level manipulation
from raw RGB-D videos of task demonstrations, with no manual labels or
annotations. Category-level learning aims to acquire skills that can be
generalized to new objects, with geometries and textures that are different
from the ones of the objects used in the demonstrations. We address this
problem by first viewing both grasping and manipulation as special cases of
tool use, where a tool object is moved to a sequence of key-poses defined in a
frame of reference of a target object. Tool and target objects, along with
their key-poses, are predicted using a dynamic graph convolutional neural
network that takes as input an automatically segmented depth and color image of
the entire scene. Empirical results on object manipulation tasks with a real
robotic arm show that the proposed network can efficiently learn from real
visual demonstrations to perform the tasks on novel objects within the same
category, and outperforms alternative approaches.

Deterministic and Stochastic techniques in Deep Reinforcement Learning
(Deep-RL) have become a promising solution to improve motion control and the
decision-making tasks for a wide variety of robots. Previous works showed that
these Deep-RL algorithms can be applied to perform mapless navigation of mobile
robots in general. However, they tend to use simple sensing strategies since it
has been shown that they perform poorly with a high dimensional state spaces,
such as the ones yielded from image-based sensing. This paper presents a
comparative analysis of two Deep-RL techniques - Deep Deterministic Policy
Gradients (DDPG) and Soft Actor-Critic (SAC) - when performing tasks of mapless
navigation for mobile robots. We aim to contribute by showing how the neural
network architecture influences the learning itself, presenting quantitative
results based on the time and distance of navigation of aerial mobile robots
for each approach. Overall, our analysis of six distinct architectures
highlights that the stochastic approach (SAC) better suits with deeper
architectures, while the opposite happens with the deterministic approach
(DDPG).

With the rapid development of technology in genome-related fields,
researchers have proposed various approaches and algorithms in recent years.
However, they rarely publish the genomic datasets they used in their works for
others to reproduce and validate their methods, as sharing those data directly
can lead to significant privacy risks (e.g., against inference attacks). To
solve the problem and expedite cooperative scientific research, we propose a
novel differentially private sharing mechanism for genomic datasets that
protects the entire genomic dataset under differential privacy. To improve data
utility of the GWAS statistics, we further develop a post-processing scheme
that performs optimal transport (OT) on the empirical distributions of SNP
values. The distributions are also achieved in a privacy-preserving manner. We
evaluate our approach on several real genomic datasets and show in the
experiments that it provides better protection against both genomic and machine
learning-based membership inference attacks and offers higher GWAS utility than
the baseline approaches.

Prior work has shown that the order in which different components of the face
are learned using a sequential learner can play an important role in the
performance of facial expression recognition systems. We propose FaceTopoNet,
an end-to-end deep model for facial expression recognition, which is capable of
learning an effective tree topology of the face. Our model then traverses the
learned tree to generate a sequence, which is then used to form an embedding to
feed a sequential learner. The devised model adopts one stream for learning
structure and one stream for learning texture. The structure stream focuses on
the positions of the facial landmarks, while the main focus of the texture
stream is on the patches around the landmarks to learn textural information. We
then fuse the outputs of the two streams by utilizing an effective
attention-based fusion strategy. We perform extensive experiments on four
large-scale in-the-wild facial expression datasets - namely AffectNet, FER2013,
ExpW, and RAF-DB - and one lab-controlled dataset (CK+) to evaluate our
approach. FaceTopoNet achieves state-of-the-art performance on three of the
five datasets and obtains competitive results on the other two datasets. We
also perform rigorous ablation and sensitivity experiments to evaluate the
impact of different components and parameters in our model. Lastly, we perform
robustness experiments and demonstrate that FaceTopoNet is more robust against
occlusions in comparison to other leading methods in the area.

We present a novel method for placing a 3D human animation into a 3D scene
while maintaining any human-scene interactions in the animation. We use the
notion of computing the most important meshes in the animation for the
interaction with the scene, which we call "keyframes." These keyframes allow us
to better optimize the placement of the animation into the scene such that
interactions in the animations (standing, laying, sitting, etc.) match the
affordances of the scene (e.g., standing on the floor or laying in a bed). We
compare our method, which we call PAAK, with prior approaches, including POSA,
PROX ground truth, and a motion synthesis method, and highlight the benefits of
our method with a perceptual study. Human raters preferred our PAAK method over
the PROX ground truth data 64.6\% of the time. Additionally, in direct
comparisons, the raters preferred PAAK over competing methods including 61.5\%
compared to POSA.

Mass loss is a key property to understand stellar evolution and in particular
for low-metallicity environments. Our knowledge has improved dramatically over
the last decades both for single and binary evolutionary models. However,
episodic mass loss although definitely present observationally, is not included
in the models, while its role is currently undetermined. A major hindrance is
the lack of large enough samples of classified stars. We attempted to address
this by applying an ensemble machine-learning approach using color indices
(from IR/Spitzer and optical/Pan-STARRS photometry) as features and combining
the probabilities from three different algorithms. We trained on M31 and M33
sources with known spectral classification, which we grouped into
Blue/Yellow/Red/B[e] Supergiants, Luminous Blue Variables, classical Wolf-Rayet
and background galaxies/AGNs. We then applied the classifier to about one
million Spitzer point sources from 25 nearby galaxies, spanning a range of
metallicites ($1/15$ to $\sim3~Z_{\odot}$). Equipped with spectral
classifications we investigated the occurrence of these populations with
metallicity.

Forward gradients have been recently introduced to bypass backpropagation in
autodifferentiation, while retaining unbiased estimators of true gradients. We
derive an optimality condition to obtain best approximating forward gradients,
which leads us to mathematical insights that suggest optimization in high
dimension is challenging with forward gradients. Our extensive experiments on
test functions support this claim.

Deep Learning (DL) models increasingly power a diversity of applications.
Unfortunately, this pervasiveness also makes them attractive targets for
extraction attacks which can steal the architecture, parameters, and
hyper-parameters of a targeted DL model. Existing extraction attack studies
have observed varying levels of attack success for different DL models and
datasets, yet the underlying cause(s) behind their susceptibility often remain
unclear. Ascertaining such root-cause weaknesses would help facilitate secure
DL systems, though this requires studying extraction attacks in a wide variety
of scenarios to identify commonalities across attack success and DL
characteristics. The overwhelmingly high technical effort and time required to
understand, implement, and evaluate even a single attack makes it infeasible to
explore the large number of unique extraction attack scenarios in existence,
with current frameworks typically designed to only operate for specific attack
types, datasets and hardware platforms. In this paper we present PINCH: an
efficient and automated extraction attack framework capable of deploying and
evaluating multiple DL models and attacks across heterogeneous hardware
platforms. We demonstrate the effectiveness of PINCH by empirically evaluating
a large number of previously unexplored extraction attack scenarios, as well as
secondary attack staging. Our key findings show that 1) multiple
characteristics affect extraction attack success spanning DL model
architecture, dataset complexity, hardware, attack type, and 2) partially
successful extraction attacks significantly enhance the success of further
adversarial attack staging.

Multilingual transfer techniques often improve low-resource machine
translation (MT). Many of these techniques are applied without considering data
characteristics. We show in the context of Haitian-to-English translation that
transfer effectiveness is correlated with amount of training data and
relationships between knowledge-sharing languages. Our experiments suggest that
for some languages beyond a threshold of authentic data, back-translation
augmentation methods are counterproductive, while cross-lingual transfer from a
sufficiently related language is preferred. We complement this finding by
contributing a rule-based French-Haitian orthographic and syntactic engine and
a novel method for phonological embedding. When used with multilingual
techniques, orthographic transformation makes statistically significant
improvements over conventional methods. And in very low-resource Jamaican MT,
code-switching with a transfer language for orthographic resemblance yields a
6.63 BLEU point advantage.

We challenge AI models to "demonstrate understanding" of the sophisticated
multimodal humor of The New Yorker Caption Contest. Concretely, we develop
three carefully circumscribed tasks for which it suffices (but is not
necessary) to grasp potentially complex and unexpected relationships between
image and caption, and similarly complex and unexpected allusions to the wide
varieties of human experience; these are the hallmarks of a New Yorker-caliber
cartoon.
  We investigate vision-and-language models that take as input the cartoon
pixels and caption directly, as well as language-only models for which we
circumvent image-processing by providing textual descriptions of the image.
Even with the rich multifaceted annotations we provide for the cartoon images,
we identify performance gaps between high-quality machine learning models
(e.g., a fine-tuned, 175B parameter language model) and humans. We publicly
release our corpora including annotations describing the image's
locations/entities, what's unusual about the scene, and an explanation of the
joke.

Which volume to annotate next is a challenging problem in building medical
imaging datasets for deep learning. One of the promising methods to approach
this question is active learning (AL). However, AL has been a hard nut to crack
in terms of which AL algorithm and acquisition functions are most useful for
which datasets. Also, the problem is exacerbated with which volumes to label
first when there is zero labeled data to start with. This is known as the cold
start problem in AL. We propose two novel strategies for AL specifically for 3D
image segmentation. First, we tackle the cold start problem by proposing a
proxy task and then utilizing uncertainty generated from the proxy task to rank
the unlabeled data to be annotated. Second, we craft a two-stage learning
framework for each active iteration where the unlabeled data is also used in
the second stage as a semi-supervised fine-tuning strategy. We show the promise
of our approach on two well-known large public datasets from medical
segmentation decathlon. The results indicate that the initial selection of data
and semi-supervised framework both showed significant improvement for several
AL strategies.

Tongue twisters are meaningful sentences that are difficult to pronounce. The
process of automatically generating tongue twisters is challenging since the
generated utterance must satisfy two conditions at once: phonetic difficulty
and semantic meaning. Furthermore, phonetic difficulty is itself hard to
characterize and is expressed in natural tongue twisters through a
heterogeneous mix of phenomena such as alliteration and homophony. In this
paper, we propose PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue
Twisters Automatically. We leverage phoneme representations to capture the
notion of phonetic difficulty, and we train language models to generate
original tongue twisters on two proposed task settings. To do this, we curate a
dataset called PANCETTA, consisting of existing English tongue twisters.
Through automatic and human evaluation, as well as qualitative analysis, we
show that PANCETTA generates novel, phonetically difficult, fluent, and
semantically meaningful tongue twisters.

Assessing drug-target affinity is a critical step in the drug discovery and
development process, but to obtain such data experimentally is both time
consuming and expensive. For this reason, computational methods for predicting
binding strength are being widely developed. However, these methods typically
use a single-task approach for prediction, thus ignoring the additional
information that can be extracted from the data and used to drive the learning
process. Thereafter in this work, we present a multi-task approach for binding
strength prediction. Our results suggest that these prediction can indeed
benefit from a multi-task learning approach, by utilizing added information
from related tasks and multi-task induced regularization.

The safety of an automated vehicle hinges crucially upon the accuracy of
perception and decision-making latency. Under these stringent requirements,
future automated cars are usually equipped with multi-modal sensors such as
cameras and LiDARs. The sensor fusion is adopted to provide a confident context
of driving scenarios for better decision-making. A promising sensor fusion
technique is middle fusion that combines the feature representations from
intermediate layers that belong to different sensing modalities. However,
achieving both the accuracy and latency efficiency is challenging for middle
fusion, which is critical for driving automation applications. We present
A3Fusion, a software-hardware system specialized for an adaptive, agile, and
aligned fusion in driving automation. A3Fusion achieves a high efficiency for
the middle fusion of multiple CNN-based modalities by proposing an adaptive
multi-modal learning network architecture and a latency-aware, agile network
architecture optimization algorithm that enhances semantic segmentation
accuracy while taking the inference latency as a key trade-off. In addition,
A3Fusion proposes a FPGA-based accelerator that captures unique data flow
patterns of our middle fusion algorithm while reducing the overall compute
overheads. We enable these contributions by co-designing the neural network,
algorithm, and the accelerator architecture.

Optical Ising machines promise to solve complex optimization problems with an
optical hardware acceleration advantage. Here we study the ground state
properties of a nonlinear optical Ising machine realized by spatial light
modulator, Fourier optics, and second harmonic generation in a nonlinear
crystal. By tuning the ratio of the light intensities at the fundamental and
second harmonic frequencies, we experimentally observe two distinct
ferromagnetic-to-paramagnetic phase transitions: a second-order phase
transition where the magnetization changes to zero continuously and a
first-order phase transition where the magnetization drops to zero abruptly as
the effective temperature increases. Our experimental results are corroborated
by a numerical simulation based on the Monte Carlo Metropolis-Hastings
algorithm, and the physical mechanism for the distinct phase transitions can be
understood with a mean-field theory. Our results showcase the great flexibility
of the nonlinear optical Ising machine, which may find important potential
applications in solving combinatorial optimization problems.

Despite significant advances in recent years, the existing Computer-Assisted
Pronunciation Training (CAPT) methods detect pronunciation errors with a
relatively low accuracy (precision of 60% at 40%-80% recall). This Ph.D. work
proposes novel deep learning methods for detecting pronunciation errors in
non-native (L2) English speech, outperforming the state-of-the-art method in
AUC metric (Area under the Curve) by 41%, i.e., from 0.528 to 0.749. One of the
problems with existing CAPT methods is the low availability of annotated
mispronounced speech needed for reliable training of pronunciation error
detection models. Therefore, the detection of pronunciation errors is
reformulated to the task of generating synthetic mispronounced speech.
Intuitively, if we could mimic mispronounced speech and produce any amount of
training data, detecting pronunciation errors would be more effective.
Furthermore, to eliminate the need to align canonical and recognized phonemes,
a novel end-to-end multi-task technique to directly detect pronunciation errors
was proposed. The pronunciation error detection models have been used at Amazon
to automatically detect pronunciation errors in synthetic speech to accelerate
the research into new speech synthesis methods. It was demonstrated that the
proposed deep learning methods are applicable in the tasks of detecting and
reconstructing dysarthric speech.

Deep learning has become one of remote sensing scientists' most efficient
computer vision tools in recent years. However, the lack of training labels for
the remote sensing datasets means that scientists need to solve the domain
adaptation problem to narrow the discrepancy between satellite image datasets.
As a result, image segmentation models that are then trained, could better
generalize and use an existing set of labels instead of requiring new ones.
This work proposes an unsupervised domain adaptation model that preserves
semantic consistency and per-pixel quality for the images during the
style-transferring phase. This paper's major contribution is proposing the
improved architecture of the SemI2I model, which significantly boosts the
proposed model's performance and makes it competitive with the state-of-the-art
CyCADA model. A second contribution is testing the CyCADA model on the remote
sensing multi-band datasets such as WorldView-2 and SPOT-6. The proposed model
preserves semantic consistency and per-pixel quality for the images during the
style-transferring phase. Thus, the semantic segmentation model, trained on the
adapted images, shows substantial performance gain compared to the SemI2I model
and reaches similar results as the state-of-the-art CyCADA model. The future
development of the proposed method could include ecological domain transfer,
{\em a priori} evaluation of dataset quality in terms of data distribution, or
exploration of the inner architecture of the domain adaptation model.

We present a unified formalism for structure discovery of causal models and
predictive state representation (PSR) models in reinforcement learning (RL)
using higher-order category theory. Specifically, we model structure discovery
in both settings using simplicial objects, contravariant functors from the
category of ordinal numbers into any category. Fragments of causal models that
are equivalent under conditional independence -- defined as causal horns -- as
well as subsequences of potential tests in a predictive state representation --
defined as predictive horns -- are both special cases of horns of a simplicial
object, subsets resulting from the removal of the interior and the face
opposite a particular vertex. Latent structure discovery in both settings
involve the same fundamental mathematical problem of finding extensions of
horns of simplicial objects through solving lifting problems in commutative
diagrams, and exploiting weak homotopies that define higher-order symmetries.
Solutions to the problem of filling "inner" vs "outer" horns leads to various
notions of higher-order categories, including weak Kan complexes and
quasicategories. We define the abstract problem of structure discovery in both
settings in terms of adjoint functors between the category of universal causal
models or universal decision models and its simplicial object representation.

Tensegrity robots, composed of rigid rods and flexible cables, exhibit high
strength-to-weight ratios and extreme deformations, enabling them to navigate
unstructured terrain and even survive harsh impacts. However, they are hard to
control due to their high dimensionality, complex dynamics, and coupled
architecture. Physics-based simulation is one avenue for developing locomotion
policies that can then be transferred to real robots, but modeling tensegrity
robots is a complex task, so simulations experience a substantial sim2real gap.
To address this issue, this paper describes a Real2Sim2Real strategy for
tensegrity robots. This strategy is based on a differential physics engine that
can be trained given limited data from a real robot (i.e. offline measurements
and one random trajectory) and achieve a high enough accuracy to discover
transferable locomotion policies. Beyond the overall pipeline, key
contributions of this work include computing non-zero gradients at contact
points, a loss function, and a trajectory segmentation technique that avoid
conflicts in gradient evaluation during training. The proposed pipeline is
demonstrated and evaluated on a real 3-bar tensegrity robot.

The ability to accelerate the design of biological sequences can have a
substantial impact on the progress of the medical field. The problem can be
framed as a global optimization problem where the objective is an expensive
black-box function such that we can query large batches restricted with a
limitation of a low number of rounds. Bayesian Optimization is a principled
method for tackling this problem. However, the astronomically large state space
of biological sequences renders brute-force iterating over all possible
sequences infeasible. In this paper, we propose MetaRLBO where we train an
autoregressive generative model via Meta-Reinforcement Learning to propose
promising sequences for selection via Bayesian Optimization. We pose this
problem as that of finding an optimal policy over a distribution of MDPs
induced by sampling subsets of the data acquired in the previous rounds. Our
in-silico experiments show that meta-learning over such ensembles provides
robustness against reward misspecification and achieves competitive results
compared to existing strong baselines.

Discovering a meaningful, dimensionally homogeneous, symbolic expression that
explains experimental data is a fundamental challenge in many scientific
fields. We present a novel, open-source computational framework called
Scientist-Machine Equation Detector (SciMED), which integrates scientific
discipline wisdom in a scientist-in-the-loop approach with state-of-the-art
symbolic regression (SR) methods. SciMED combines a genetic algorithm-based
wrapper selection method with automatic machine learning and two levels of SR
methods. We test SciMED on four configurations of the settling of a sphere with
and without a non-linear aerodynamic drag force. We show that SciMED is
sufficiently robust to discover the correct physically meaningful symbolic
expressions from noisy data. Our results indicate better performance on these
tasks than the state-of-the-art SR software package.

We introduce a unified framework based on bi-level optimization schemes to
deal with parameter learning in the context of image processing. The goal is to
identify the optimal regularizer within a family depending on a parameter in a
general topological space. Our focus lies on the situation with non-compact
parameter domains, which is, for example, relevant when the commonly used box
constraints are disposed of. To overcome this lack of compactness, we propose a
natural extension of the upper-level functional to the closure of the parameter
domain via Gamma-convergence, which captures possible structural changes in the
reconstruction model at the edge of the domain. Under two main assumptions,
namely, Mosco-convergence of the regularizers and uniqueness of minimizers of
the lower-level problem, we prove that the extension coincides with the
relaxation, thus admitting minimizers that relate to the parameter optimization
problem of interest. We apply our abstract framework to investigate a quartet
of practically relevant models in image denoising, all featuring nonlocality.
The associated families of regularizers exhibit qualitatively different
parameter dependence, describing a weight factor, an amount of nonlocality, an
integrability exponent, and a fractional order, respectively. After the
asymptotic analysis that determines the relaxation in each of the four
settings, we finally establish theoretical conditions on the data that
guarantee structural stability of the models and give examples of when
stability is lost.

We present the joint contribution of IST and Unbabel to the WMT 2022 Shared
Task on Quality Estimation (QE). Our team participated on all three subtasks:
(i) Sentence and Word-level Quality Prediction; (ii) Explainable QE; and (iii)
Critical Error Detection. For all tasks we build on top of the COMET framework,
connecting it with the predictor-estimator architecture of OpenKiwi, and
equipping it with a word-level sequence tagger and an explanation extractor.
Our results suggest that incorporating references during pretraining improves
performance across several language pairs on downstream tasks, and that jointly
training with sentence and word-level objectives yields a further boost.
Furthermore, combining attention and gradient information proved to be the top
strategy for extracting good explanations of sentence-level QE models. Overall,
our submissions achieved the best results for all three tasks for almost all
language pairs by a considerable margin.

Nonlinear eigenvalue problems for pairs of homogeneous convex functions are
particular nonlinear constrained optimization problems that arise in a variety
of settings, including graph mining, machine learning, and network science. By
considering different notions of duality transforms from both classical and
recent convex geometry theory, in this work we show that one can move from the
primal to the dual nonlinear eigenvalue formulation maintaining the spectrum,
the variational spectrum as well as the corresponding multiplicities unchanged.
These nonlinear spectral duality properties can be used to transform the
original optimization problem into various alternative and possibly more
treatable dual problems. We illustrate the use of nonlinear spectral duality in
a variety of example settings involving optimization problems on graphs,
nonlinear Laplacians, and distances between convex bodies.

Despite the empirical successes of self-supervised learning (SSL) methods, it
is unclear what characteristics of their representations lead to high
downstream accuracies. In this work, we characterize properties that SSL
representations should ideally satisfy. Specifically, we prove necessary and
sufficient conditions such that for any task invariant to given data
augmentations, desired probes (e.g., linear or MLP) trained on that
representation attain perfect accuracy. These requirements lead to a unifying
conceptual framework for improving existing SSL methods and deriving new ones.
For contrastive learning, our framework prescribes simple but significant
improvements to previous methods such as using asymmetric projection heads. For
non-contrastive learning, we use our framework to derive a simple and novel
objective. Our resulting SSL algorithms outperform baselines on standard
benchmarks, including SwAV+multicrops on linear probing of ImageNet.

There is a growing need for machine learning-based anomaly detection
strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at
the Large Hadron Collider (LHC) and elsewhere. The first step of any anomaly
detection approach is to specify observables and then use them to decide on a
set of anomalous events. One common choice is to select events that have low
probability density. It is a well-known fact that probability densities are not
invariant under coordinate transformations, so the sensitivity can depend on
the initial choice of coordinates. The broader machine learning community has
recently connected coordinate sensitivity with anomaly detection and our goal
is to bring awareness of this issue to the growing high energy physics
literature on anomaly detection. In addition to analytical explanations, we
provide numerical examples from simple random variables and from the LHC
Olympics Dataset that show how using probability density as an anomaly score
can lead to events being classified as anomalous or not depending on the
coordinate frame.

Existing machine learning methods for causal inference usually estimate
quantities expressed via the mean of potential outcomes (e.g., average
treatment effect). However, such quantities do not capture the full information
about the distribution of potential outcomes. In this work, we estimate the
density of potential outcomes after Interventional Normalizing Flows.
Specifically, we combine two normalizing flows, namely (i) a teacher flow for
estimating nuisance parameters and (ii) a student flow for a parametric
estimation of the density of potential outcomes. We further develop a tractable
optimization objective via a one-step bias correction for an efficient and
doubly robust estimation of the student flow parameters. As a result our
Interventional Normalizing Flows offer a properly normalized density estimator.
Across various experiments, we demonstrate that our Interventional Normalizing
Flows are expressive and highly effective, and scale well with both sample size
and high-dimensional confounding. To the best of our knowledge, our
Interventional Normalizing Flows are the first fully-parametric, deep learning
method for density estimation of potential outcomes.

Recent deep learning approaches for multi-view depth estimation are employed
either in a depth-from-video or a multi-view stereo setting. Despite different
settings, these approaches are technically similar: they correlate multiple
source views with a keyview to estimate a depth map for the keyview. In this
work, we introduce the Robust Multi-View Depth Benchmark that is built upon a
set of public datasets and allows evaluation in both settings on data from
different domains. We evaluate recent approaches and find imbalanced
performances across domains. Further, we consider a third setting, where camera
poses are available and the objective is to estimate the corresponding depth
maps with their correct scale. We show that recent approaches do not generalize
across datasets in this setting. This is because their cost volume output runs
out of distribution. To resolve this, we present the Robust MVD Baseline model
for multi-view depth estimation, which is built upon existing components but
employs a novel scale augmentation procedure. It can be applied for robust
multi-view depth estimation, independent of the target data. We provide code
for the proposed benchmark and baseline model at
https://github.com/lmb-freiburg/robustmvd.

Recently, deep learning methods have been successfully applied to solve
numerous challenges in the field of digital pathology. However, many of these
approaches are fully supervised and require annotated images. Annotating a
histology image is a time-consuming and tedious process for even a highly
skilled pathologist, and, as such, most histology datasets lack
region-of-interest annotations and are weakly labeled. In this paper, we
introduce HistoPerm, a view generation approach designed for improving the
performance of representation learning techniques on histology images in weakly
supervised settings. In HistoPerm, we permute augmented views of patches
generated from whole-slide histology images to improve classification accuracy.
These permuted views belong to the same original slide-level class but are
produced from distinct patch instances. We tested adding HistoPerm to BYOL and
SimCLR, two prominent representation learning methods, on two public histology
datasets for Celiac disease and Renal Cell Carcinoma. For both datasets, we
found improved performance in terms of accuracy, F1-score, and AUC compared to
the standard BYOL and SimCLR approaches. Particularly, in a linear evaluation
configuration, HistoPerm increases classification accuracy on the Celiac
disease dataset by 8% for BYOL and 3% for SimCLR. Similarly, with HistoPerm,
classification accuracy increases by 2% for BYOL and 0.25% for SimCLR on the
Renal Cell Carcinoma dataset. The proposed permutation-based view generation
approach can be adopted in common representation learning frameworks to capture
histopathology features in weakly supervised settings and can lead to
whole-slide classification outcomes that are close to, or even better than,
fully supervised methods.

We present a simulation experiment of a pipeline based on machine learning
algorithms for neutral hydrogen (HI) intensity mapping (IM) surveys with
different telescopes. The simulation is conducted on HI signals, foreground
emission, thermal noise from instruments, strong radio frequency interference
(sRFI), and mild RFI (mRFI). We apply the Mini-Batch K-Means algorithm to
identify sRFI, and Adam algorithm to remove foregrounds and mRFI. Results show
that there exists a threshold of the sRFI amplitudes above which the
performance of our pipeline enhances greatly. In removing foregrounds and mRFI,
the performance of our pipeline is shown to have little dependence on the
apertures of telescopes. In addition, the results show that there are
thresholds of the signal amplitudes from which the performance of our pipeline
begins to change rapidly. We consider all these thresholds as the edges of the
signal amplitude ranges in which our pipeline can function well. Our work, for
the first time, explores the feasibility of applying machine learning
algorithms in the pipeline of IM surveys, especially for large surveys with the
next-generation telescopes.

Manual coding of text data from open-ended questions into different
categories is time consuming and expensive. Automated coding uses
statistical/machine learning to train on a small subset of manually coded text
answers. Recently, pre-training a general language model on vast amounts of
unrelated data and then adapting the model to the specific application has
proven effective in natural language processing. Using two data sets, we
empirically investigate whether BERT, the currently dominant pre-trained
language model, is more effective at automated coding of answers to open-ended
questions than other non-pre-trained statistical learning approaches. We found
fine-tuning the pre-trained BERT parameters is essential as otherwise BERT's is
not competitive. Second, we found fine-tuned BERT barely beats the
non-pre-trained statistical learning approaches in terms of classification
accuracy when trained on 100 manually coded observations. However, BERT's
relative advantage increases rapidly when more manually coded observations
(e.g. 200-400) are available for training. We conclude that for automatically
coding answers to open-ended questions BERT is preferable to non-pretrained
models such as support vector machines and boosting.

Homophily is a graph property describing the tendency of edges to connect
similar nodes; the opposite is called heterophily. While homophily is natural
for many real-world networks, there are also networks without this property. It
is often believed that standard message-passing graph neural networks (GNNs) do
not perform well on non-homophilous graphs, and thus such datasets need special
attention. While a lot of effort has been put into developing graph
representation learning methods for heterophilous graphs, there is no
universally agreed upon measure of homophily. Several metrics for measuring
homophily have been used in the literature, however, we show that all of them
have critical drawbacks preventing comparison of homophily levels between
different datasets. We formalize desirable properties for a proper homophily
measure and show how existing literature on the properties of classification
performance metrics can be linked to our problem. In doing so we find a measure
that we call adjusted homophily that satisfies more desirable properties than
existing homophily measures. Interestingly, this measure is related to two
classification performance metrics - Cohen's Kappa and Matthews correlation
coefficient. Then, we go beyond the homophily-heterophily dichotomy and propose
a new property that we call label informativeness (LI) that characterizes how
much information a neighbor's label provides about a node's label. We
theoretically show that LI is comparable across datasets with different numbers
of classes and class size balance. Through a series of experiments we show that
LI is a better predictor of the performance of GNNs on a dataset than
homophily. We show that LI explains why GNNs can sometimes perform well on
heterophilous datasets - a phenomenon recently observed in the literature.

We consider polynomial optimization problems (POP) on a semialgebraic set
contained in the nonnegative orthant (every POP on a compact set can be put in
this format by a simple translation of the origin). Such a POP can be converted
to an equivalent POP by squaring each variable. Using even symmetry and the
concept of factor width, we propose a hierarchy of semidefinite relaxations
based on the extension of P\'olya's Positivstellensatz by Dickinson-Povh. As
its distinguishing and crucial feature, the maximal matrix size of each
resulting semidefinite relaxation can be chosen arbitrarily and in addition, we
prove that the sequence of values returned by the new hierarchy converges to
the optimal value of the original POP at the rate $O(\varepsilon^{-c})$ if the
semialgebraic set has nonempty interior. When applied to (i) robustness
certification of multi-layer neural networks and (ii) computation of positive
maximal singular values, our method based on P\'olya's Positivstellensatz
provides better bounds and runs several hundred times faster than the standard
Moment-SOS hierarchy.

Biometric Authentication like Fingerprints has become an integral part of the
modern technology for authentication and verification of users. It is pervasive
in more ways than most of us are aware of. However, these fingerprint images
deteriorate in quality if the fingers are dirty, wet, injured or when sensors
malfunction. Therefore, extricating the original fingerprint by removing the
noise and inpainting it to restructure the image is crucial for its
authentication. Hence, this paper proposes a deep learning approach to address
these issues using Generative (GAN) and Segmentation models. Qualitative and
Quantitative comparison has been done between pix2pixGAN and cycleGAN
(generative models) as well as U-net (segmentation model). To train the model,
we created our own dataset NFD - Noisy Fingerprint Dataset meticulously with
different backgrounds along with scratches in some images to make it more
realistic and robust. In our research, the u-net model performed better than
the GAN networks

Ever since the Multilayered Perceptron was first introduced the connectionist
community has struggled with the concept of uncertainty and how this could be
represented in these types of models. This past decade has seen a lot of effort
in trying to join the principled approach of probabilistic modeling with the
scalable nature of deep neural networks. While the theoretical benefits of this
consolidation are clear, there are also several important practical aspects of
these endeavors; namely to force the models we create to represent, learn, and
report uncertainty in every prediction that is made. Many of these efforts have
been based on extending existing frameworks with additional structures. We
present Borch, a scalable deep universal probabilistic programming language,
built on top of PyTorch. The code is available for download and use in our
repository https://gitlab.com/desupervised/borch.

Due to various physical degradation factors and limited counts received, PET
image quality needs further improvements. The denoising diffusion probabilistic
models (DDPM) are distribution learning-based models, which try to transform a
normal distribution into a specific data distribution based on iterative
refinements. In this work, we proposed and evaluated different DDPM-based
methods for PET image denoising. Under the DDPM framework, one way to perform
PET image denoising is to provide the PET image and/or the prior image as the
network input. Another way is to supply the prior image as the input with the
PET image included in the refinement steps, which can fit for scenarios of
different noise levels. 120 18F-FDG datasets and 140 18F-MK-6240 datasets were
utilized to evaluate the proposed DDPM-based methods. Quantification show that
the DDPM-based frameworks with PET information included can generate better
results than the nonlocal mean and Unet-based denoising methods. Adding
additional MR prior in the model can help achieve better performance and
further reduce the uncertainty during image denoising. Solely relying on MR
prior while ignoring the PET information can result in large bias. Regional and
surface quantification shows that employing MR prior as the network input while
embedding PET image as a data-consistency constraint during inference can
achieve the best performance. In summary, DDPM-based PET image denoising is a
flexible framework, which can efficiently utilize prior information and achieve
better performance than the nonlocal mean and Unet-based denoising methods.

Meta-gradient methods (Xu et al., 2018; Zahavy et al., 2020) offer a
promising solution to the problem of hyperparameter selection and adaptation in
non-stationary reinforcement learning problems. However, the properties of
meta-gradients in such environments have not been systematically studied. In
this work, we bring new clarity to meta-gradients in non-stationary
environments. Concretely, we ask: (i) how much information should be given to
the learned optimizers, so as to enable faster adaptation and generalization
over a lifetime, (ii) what meta-optimizer functions are learned in this
process, and (iii) whether meta-gradient methods provide a bigger advantage in
highly non-stationary environments. To study the effect of information provided
to the meta-optimizer, as in recent works (Flennerhag et al., 2021; Almeida et
al., 2021), we replace the tuned meta-parameters of fixed update rules with
learned meta-parameter functions of selected context features. The context
features carry information about agent performance and changes in the
environment and hence can inform learned meta-parameter schedules. We find that
adding more contextual information is generally beneficial, leading to faster
adaptation of meta-parameter values and increased performance over a lifetime.
We support these results with a qualitative analysis of resulting
meta-parameter schedules and learned functions of context features. Lastly, we
find that without context, meta-gradients do not provide a consistent advantage
over the baseline in highly non-stationary environments. Our findings suggest
that contextualizing meta-gradients can play a pivotal role in extracting high
performance from meta-gradients in non-stationary settings.

Research articles are being shared in increasing numbers on multiple online
platforms. Although the scholarly impact of these articles has been widely
studied, the online interest determined by how long the research articles are
shared online remains unclear. Being cognizant of how long a research article
is mentioned online could be valuable information to the researchers. In this
paper, we analyzed multiple social media platforms on which users share and/or
discuss scholarly articles. We built three clusters for papers, based on the
number of yearly online mentions having publication dates ranging from the year
1920 to 2016. Using the online social media metrics for each of these three
clusters, we built machine learning models to predict the long-term online
interest in research articles. We addressed the prediction task with two
different approaches: regression and classification. For the regression
approach, the Multi-Layer Perceptron model performed best, and for the
classification approach, the tree-based models performed better than other
models. We found that old articles are most evident in the contexts of
economics and industry (i.e., patents). In contrast, recently published
articles are most evident in research platforms (i.e., Mendeley) followed by
social media platforms (i.e., Twitter).

The main objective of this paper is to propose an approach for developing an
Artificial Intelligence (AI)-powered Language Assessment (LA) tool. Such tools
can be used to assess language impairments associated with dementia in older
adults. The Machine Learning (ML) classifiers are the main parts of our
proposed approach, therefore to develop an accurate tool with high sensitivity
and specificity, we consider different binary classifiers and evaluate their
performances. We also assess the reliability and validity of our approach by
comparing the impact of different types of language tasks, features, and
recording media on the performance of ML classifiers.

Background: Computational fluid dynamics (CFD) is increasingly used to assess
blood flow conditions in patients with congenital heart disease (CHD). This
requires patient-specific anatomy, usually obtained from segmented 3D
cardiovascular magnetic resonance (CMR) images. However, segmentation is
time-consuming and needs expert input. This study aims to develop and validate
a machine learning (ML) method for segmentation of the aorta and pulmonary
arteries (PAs) for CFD studies.
  Methods: 90 CHD patients were retrospectively selected for this study. 3D CMR
images were manually segmented to obtain ground-truth (GT) background, aorta
and PA labels. These were used to train and optimize a U-Net model.
Segmentation performance was primarily evaluated using Dice score. CFD
simulations were set up from GT and ML segmentations using a semi-automatic
meshing and simulation pipeline. Pressure and velocity fields were computed,
and a mean average percentage error (MAPE) was calculated for each vessel pair.
A secondary observer (SO) segmented the test dataset to assess inter-observer
variability. Friedman tests were used to compare segmentation metrics and flow
field errors.
  Results: The model's Dice score (ML vs GT) was 0.945 for the aorta and 0.885
for the PAs. Differences with the inter-observer Dice score (SO vs GT) and ML
vs SO Dice scores were not statistically significant for either aorta or PAs.
The ML vs GT MAPEs for pressure and velocity in the aorta were 10.1% and 4.1%
respectively, and for the PAs 14.6% and 6.3%, respectively. Inter-observer (SO
vs GT) and ML vs SO pressure and velocity MAPEs were of a similar magnitude to
ML vs GT.
  Conclusions: The proposed method can successfully segment the great vessels
for CFD, with errors similar to inter-observer variability. This reduces the
time and effort needed for CFD analysis, making it more attractive for routine
clinical use.

Personalized medicine constitutes a growing area of research that benefits
from the many new developments in statistical learning. A key domain concerns
the prediction of individualized treatment effects, and models for this purpose
are increasingly common in the published literature. Aiming to facilitate the
validation of prediction models for individualized treatment effects, we extend
the classical concepts of discrimination and calibration performance to assess
causal (rather than associative) prediction models. Working within the
potential outcomes framework, we first evaluate properties of existing
statistics (including the c-for-benefit) and subsequently propose novel
model-based statistics. The main focus is on randomized trials with binary
endpoints. We use simulated data to provide insight into the characteristics of
discrimination and calibration statistics, and further illustrate all methods
in a trial in acute ischemic stroke treatment. Results demonstrate that the
proposed model-based statistics had the best characteristics in terms of bias
and variance. While resampling methods to adjust for optimism of performance
estimates in the development data were effective on average, they had a high
variance across replications that limits their accuracy in any particular
applied analysis. Thereto, individualized treatment effect models are best
validated in external data rather than in the original development sample.

Attention layers are an integral part of modern end-to-end automatic speech
recognition systems, for instance as part of the Transformer or Conformer
architecture. Attention is typically multi-headed, where each head has an
independent set of learned parameters and operates on the same input feature
sequence. The output of multi-headed attention is a fusion of the outputs from
the individual heads. We empirically analyze the diversity between
representations produced by the different attention heads and demonstrate that
the heads become highly correlated during the course of training. We
investigate a few approaches to increasing attention head diversity, including
using different attention mechanisms for each head and auxiliary training loss
functions to promote head diversity. We show that introducing
diversity-promoting auxiliary loss functions during training is a more
effective approach, and obtain WER improvements of up to 6% relative on the
Librispeech corpus. Finally, we draw a connection between the diversity of
attention heads and the similarity of the gradients of head parameters.

We present a deep generative model-based variational free energy approach to
the equations of state of dense hydrogen. We employ a normalizing flow network
to model the proton Boltzmann distribution and a fermionic neural network to
model the electron wavefunction at given proton positions. By jointly
optimizing the two neural networks we reached a comparable variational free
energy to the previous coupled electron-ion Monte Carlo calculation. Our result
suggests that hydrogen in the planetary condition is even denser compared to
previous Monte Carlo and ab initio molecular dynamics data, which is further
away from the empirical chemical model predictions. Obtaining reliable
equations of state of dense hydrogen, and in particular, direct access to
entropy and free energy opens new opportunities in planetary modeling and
high-pressure physics research.

We propose a manager-worker framework based on deep reinforcement learning to
tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP),
\ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where
customers who cannot be served before the deadline are subject to rejections.
Particularly, in the proposed framework, a manager agent learns to divide
mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a
Graph Isomorphism Network (GIN) based policy network. A worker agent learns to
solve sub-routing tasks by minimizing the cost in terms of both tour length and
rejection rate for each vehicle, the maximum of which is then fed back to the
manager agent to learn better assignments. Experimental results demonstrate
that the proposed framework outperforms strong baselines in terms of higher
solution quality and shorter computation time. More importantly, the trained
agents also achieve competitive performance for solving unseen larger
instances.

The University of Bologna has a long tradition in Digital Humanities, both at
the level of research and teaching. In this article, we want to introduce some
experiences in developing new educational models based on the idea of
transversal learning, collaborative approaches and projects-oriented outputs,
together with the definition of research fields within this vast domain,
accompanied by practical examples. The creation of an international master's
degree (DHDK), a PhD (CHeDE) and a research centre (/DH.arc) are the results of
refining our notion of Digital Humanities in a new bidirectional way: to
reflect on computational methodologies and models in the cultural sphere and to
suggest a cultural approach to Informatics.

Language models (LLMs) offer potential as a source of knowledge for agents
that need to acquire new task competencies within a performance environment. We
describe efforts toward a novel agent capability that can construct cues (or
"prompts") that result in useful LLM responses for an agent learning a new
task. Importantly, responses must not only be "reasonable" (a measure used
commonly in research on knowledge extraction from LLMs) but also specific to
the agent's task context and in a form that the agent can interpret given its
native language capacities. We summarize a series of empirical investigations
of prompting strategies and evaluate responses against the goals of targeted
and actionable responses for task learning. Our results demonstrate that
actionable task knowledge can be obtained from LLMs in support of online agent
task learning.

The average life expectancy is increasing globally due to advancements in
medical technology, preventive health care, and a growing emphasis on
gerontological health. Therefore, developing technologies that detect and track
aging-associated disease in cognitive function among older adult populations is
imperative. In particular, research related to automatic detection and
evaluation of Alzheimer's disease (AD) is critical given the disease's
prevalence and the cost of current methods. As AD impacts the acoustics of
speech and vocabulary, natural language processing and machine learning provide
promising techniques for reliably detecting AD. We compare and contrast the
performance of ten linear regression models for predicting Mini-Mental Status
Exam scores on the ADReSS challenge dataset. We extracted 13000+ handcrafted
and learned features that capture linguistic and acoustic phenomena. Using a
subset of 54 top features selected by two methods: (1) recursive elimination
and (2) correlation scores, we outperform a state-of-the-art baseline for the
same task. Upon scoring and evaluating the statistical significance of each of
the selected subset of features for each model, we find that, for the given
task, handcrafted linguistic features are more significant than acoustic and
learned features.

Model calibration measures the agreement between the predicted probability
estimates and the true correctness likelihood. Proper model calibration is
vital for high-risk applications. Unfortunately, modern deep neural networks
are poorly calibrated, compromising trustworthiness and reliability. Medical
image segmentation particularly suffers from this due to the natural
uncertainty of tissue boundaries. This is exasperated by their loss functions,
which favor overconfidence in the majority classes. We address these challenges
with DOMINO, a domain-aware model calibration method that leverages the
semantic confusability and hierarchical similarity between class labels. Our
experiments demonstrate that our DOMINO-calibrated deep neural networks
outperform non-calibrated models and state-of-the-art morphometric methods in
head image segmentation. Our results show that our method can consistently
achieve better calibration, higher accuracy, and faster inference times than
these methods, especially on rarer classes. This performance is attributed to
our domain-aware regularization to inform semantic model calibration. These
findings show the importance of semantic ties between class labels in building
confidence in deep learning models. The framework has the potential to improve
the trustworthiness and reliability of generic medical image segmentation
models. The code for this article is available at:
https://github.com/lab-smile/DOMINO.

The advent of nanoscale memristors raised hopes of being able to build CMOL
(CMOS/nanowire/moLecular) type ultra-dense in-memory-computing circuit
architectures. In CMOL, nanoscale memristors would be fabricated at the
intersection of nanowires. The CMOL concept can be exploited in neuromorphic
hardware by fabricating lower-density neurons on CMOS and placing massive
analog synaptic connectivity with nanowire and nanoscale-memristor fabric
post-fabricated on top. However, technical problems have hindered such
developments for presently available reliable commercial monolithic
CMOS-memristor technologies. On one hand, each memristor needs a MOS selector
transistor in series to guarantee forming and programming operations in large
arrays. This results in compound MOS-memristor synapses (called 1T1R) which are
no longer synapses at the crossing of nanowires. On the other hand, memristors
do not yet constitute highly reliable, stable analog memories for massive
analog-weight synapses with gradual learning. Here we demonstrate a pseudo-CMOL
monolithic chip core that circumvents the two technical problems mentioned
above by (a) exploiting a CMOL-like geometrical chip layout technique to
improve density despite the 1T1R limitation, and (b) exploiting a binary weight
stochastic Spike-Timing-Dependent-Plasticity (STDP) learning rule that takes
advantage of the more reliable binary memory capability of the memristors used.
Experimental results are provided for a spiking neural network (SNN) CMOL-core
with 64 input neurons, 64 output neurons, and 4096 1T1R synapses, fabricated in
130nm CMOS with 200nm-sized Ti/HfOx/TiN memristors on top. The CMOL-core uses
query-driven event read-out, which allows for memristor variability insensitive
computations.

We present SeRP, a framework for Self-Supervised Learning of 3D point clouds.
SeRP consists of encoder-decoder architecture that takes perturbed or corrupted
point clouds as inputs and aims to reconstruct the original point cloud without
corruption. The encoder learns the high-level latent representations of the
points clouds in a low-dimensional subspace and recovers the original
structure. In this work, we have used Transformers and PointNet-based
Autoencoders. The proposed framework also addresses some of the limitations of
Transformers-based Masked Autoencoders which are prone to leakage of location
information and uneven information density. We trained our models on the
complete ShapeNet dataset and evaluated them on ModelNet40 as a downstream
classification task. We have shown that the pretrained models achieved 0.5-1%
higher classification accuracies than the networks trained from scratch.
Furthermore, we also proposed VASP: Vector-Quantized Autoencoder for
Self-supervised Representation Learning for Point Clouds that employs
Vector-Quantization for discrete representation learning for Transformer-based
autoencoders.

Graphs in many applications, such as social networks and IoT, are inherently
streaming, involving continuous additions and deletions of vertices and edges
at high rates. Constructing random walks in a graph, i.e., sequences of
vertices selected with a specific probability distribution, is a prominent task
in many of these graph applications as well as machine learning (ML) on
graph-structured data. In a streaming scenario, random walks need to constantly
keep up with the graph updates to avoid stale walks and thus, performance
degradation in the downstream tasks. We present Wharf, a system that
efficiently stores and updates random walks on streaming graphs. It avoids a
potential size explosion by maintaining a compressed, high-throughput, and
low-latency data structure. It achieves (i) the succinct representation by
coupling compressed purely functional binary trees and pairing functions for
storing the walks, and (ii) efficient walk updates by effectively pruning the
walk search space. We evaluate Wharf, with real and synthetic graphs, in terms
of throughput and latency when updating random walks. The results show the high
superiority of Wharf over inverted index- and tree-based baselines.

Real-time music accompaniment generation has a wide range of applications in
the music industry, such as music education and live performances. However,
automatic real-time music accompaniment generation is still understudied and
often faces a trade-off between logical latency and exposure bias. In this
paper, we propose SongDriver, a real-time music accompaniment generation system
without logical latency nor exposure bias. Specifically, SongDriver divides one
accompaniment generation task into two phases: 1) The arrangement phase, where
a Transformer model first arranges chords for input melodies in real-time, and
caches the chords for the next phase instead of playing them out. 2) The
prediction phase, where a CRF model generates playable multi-track
accompaniments for the coming melodies based on previously cached chords. With
this two-phase strategy, SongDriver directly generates the accompaniment for
the upcoming melody, achieving zero logical latency. Furthermore, when
predicting chords for a timestep, SongDriver refers to the cached chords from
the first phase rather than its previous predictions, which avoids the exposure
bias problem. Since the input length is often constrained under real-time
conditions, another potential problem is the loss of long-term sequential
information. To make up for this disadvantage, we extract four musical features
from a long-term music piece before the current time step as global
information. In the experiment, we train SongDriver on some open-source
datasets and an original \`aiSong Dataset built from Chinese-style modern pop
music scores. The results show that SongDriver outperforms existing SOTA
(state-of-the-art) models on both objective and subjective metrics, meanwhile
significantly reducing the physical latency.

Natural Language Processing in the legal domain been benefited hugely by the
emergence of Transformer-based Pre-trained Language Models (PLMs) pre-trained
on legal text. There exist PLMs trained over European and US legal text, most
notably LegalBERT. However, with the rapidly increasing volume of NLP
applications on Indian legal documents, and the distinguishing characteristics
of Indian legal text, it has become necessary to pre-train LMs over Indian
legal text as well. In this work, we introduce transformer-based PLMs
pre-trained over a large corpus of Indian legal documents. We also apply these
PLMs over several benchmark legal NLP tasks over both Indian legal text, as
well as over legal text belonging to other domains (countries). The NLP tasks
with which we experiment include Legal Statute Identification from facts,
Semantic segmentation of court judgements, and Court Judgement Prediction. Our
experiments demonstrate the utility of the India-specific PLMs developed in
this work.

Graph neural networks (GNNs) have achieved extraordinary enhancements in
various areas including the fields medical imaging and network neuroscience
where they displayed a high accuracy in diagnosing challenging neurological
disorders such as autism. In the face of medical data scarcity and
high-privacy, training such data-hungry models remains challenging. Federated
learning brings an efficient solution to this issue by allowing to train models
on multiple datasets, collected independently by different hospitals, in fully
data-preserving manner. Although both state-of-the-art GNNs and federated
learning techniques focus on boosting classification accuracy, they overlook a
critical unsolved problem: investigating the reproducibility of the most
discriminative biomarkers (i.e., features) selected by the GNN models within a
federated learning paradigm. Quantifying the reproducibility of a predictive
medical model against perturbations of training and testing data distributions
presents one of the biggest hurdles to overcome in developing translational
clinical applications. To the best of our knowledge, this presents the first
work investigating the reproducibility of federated GNN models with application
to classifying medical imaging and brain connectivity datasets. We evaluated
our framework using various GNN models trained on medical imaging and
connectomic datasets. More importantly, we showed that federated learning
boosts both the accuracy and reproducibility of GNN models in such medical
learning tasks. Our source code is available at
https://github.com/basiralab/reproducibleFedGNN.

Traditional intent classification models are based on a pre-defined intent
set and only recognize limited in-domain (IND) intent classes. But users may
input out-of-domain (OOD) queries in a practical dialogue system. Such OOD
queries can provide directions for future improvement. In this paper, we define
a new task, Generalized Intent Discovery (GID), which aims to extend an IND
intent classifier to an open-world intent set including IND and OOD intents. We
hope to simultaneously classify a set of labeled IND intent classes while
discovering and recognizing new unlabeled OOD types incrementally. We construct
three public datasets for different application scenarios and propose two kinds
of frameworks, pipeline-based and end-to-end for future work. Further, we
conduct exhaustive experiments and qualitative analysis to comprehend key
challenges and provide new guidance for future GID research.

BlueMUSE is an integral field spectrograph in an early development stage for
the ESO VLT. For our design of the data reduction software for this instrument,
we are first reviewing capabilities and issues of the pipeline of the existing
MUSE instrument. MUSE has been in operation at the VLT since 2014 and led to
discoveries published in more than 600 refereed scientific papers. While
BlueMUSE and MUSE have many common properties we briefly point out a few key
differences between both instruments. We outline a first version of the
flowchart for the science reduction, and discuss the necessary changes due to
the blue wavelength range covered by BlueMUSE. We also detail specific new
features, for example, how the pipeline and subsequent analysis will benefit
from improved handling of the data covariance, and a more integrated approach
to the line-spread function, as well as improvements regarding the wavelength
calibration which is of extra importance in the blue optical range. We finally
discuss how simulations of BlueMUSE datacubes are being implemented and how
they will be used to prepare the science of the instrument.

This paper presents a novel control approach to dealing with object slip
during robotic manipulative movements. Slip is a major cause of failure in many
robotic grasping and manipulation tasks. Existing works increase grip force to
avoid/control slip. However, this may not be feasible when (i) the robot cannot
increase the gripping force -- the max gripping force is already applied or
(ii) increased force damages the grasped object, such as soft fruit. Moreover,
the robot fixes the gripping force when it forms a stable grasp on the surface
of an object, and changing the gripping force during real-time manipulation may
not be an effective control policy. We propose a novel control approach to slip
avoidance including a learned action-conditioned slip predictor and a
constrained optimiser avoiding a predicted slip given a desired robot action.
We show the effectiveness of the proposed trajectory adaptation method with
receding horizon controller with a series of real-robot test cases. Our
experimental results show our proposed data-driven predictive controller can
control slip for objects unseen in training.

Large variability between cell lines brings a difficult optimization problem
of drug selection for cancer therapy. Standard approaches use prediction of
value for this purpose, corresponding e.g. to expected value of their
distribution. This article shows superiority of working on, predicting the
entire probability distributions - proposing basic tools for this purpose. We
are mostly interested in the best drug in their batch to be tested - proper
optimization of their selection for extreme statistics requires knowledge of
the entire probability distributions, which for distributions of drug
properties among cell lines often turn out binomial, e.g. depending on
corresponding gene. Hence for basic prediction mechanism there is proposed
mixture of two Gaussians, trying to predict its weight based on additional

Deep learning training is an expensive process that extensively uses GPUs,
but not all model training saturates the modern powerful GPUs. Multi-Instance
GPU (MIG) is a new technology introduced by NVIDIA that can partition a GPU to
better fit workloads that don't require all the memory and compute resources of
a full GPU. In this paper, we examine the performance of a MIG-enabled A100 GPU
under deep learning workloads of three sizes focusing on image recognition
training with ResNet models. We investigate the behavior of these workloads
when running in isolation on a variety of MIG instances allowed by the GPU in
addition to running them in parallel on homogeneous instances co-located on the
same GPU.
  Our results demonstrate that employing MIG can significantly improve the
utilization of the GPU when the workload is too small to utilize the whole GPU
in isolation. By training multiple small models in parallel, more work can be
performed by the GPU per unit of time, despite the increase in time-per-epoch,
leading to $\sim$3 times the throughput. In contrast, for medium and
large-sized workloads, which already utilize the whole GPU well on their own,
MIG only provides marginal performance improvements. Nevertheless, we observe
that training models in parallel using separate MIG partitions does not exhibit
interference underlining the value of having a functionality like MIG on modern
GPUs.

Underwater Vehicles have become more sophisticated, driven by the off-shore
sector and the scientific community's rapid advancements in underwater
operations. Notably, many underwater tasks, including the assessment of subsea
infrastructure, are performed with the assistance of Autonomous Underwater
Vehicles (AUVs). There have been recent breakthroughs in Artificial
Intelligence (AI) and, notably, Deep Learning (DL) models and applications,
which have widespread usage in a variety of fields, including aerial unmanned
vehicles, autonomous car navigation, and other applications. However, they are
not as prevalent in underwater applications due to the difficulty of obtaining
underwater datasets for a specific application. In this sense, the current
study utilises recent advancements in the area of DL to construct a bespoke
dataset generated from photographs of items captured in a laboratory
environment. Generative Adversarial Networks (GANs) were utilised to translate
the laboratory object dataset into the underwater domain by combining the
collected images with photographs containing the underwater environment. The
findings demonstrated the feasibility of creating such a dataset, since the
resulting images closely resembled the real underwater environment when
compared with real-world underwater ship hull images. Therefore, the artificial
datasets of the underwater environment can overcome the difficulties arising
from the limited access to real-world underwater images and are used to enhance
underwater operations through underwater object image classification and

The connectional brain template (CBT) captures the shared traits across all
individuals of a given population of brain connectomes, thereby acting as a
fingerprint. Estimating a CBT from a population where brain graphs are derived
from diverse neuroimaging modalities (e.g., functional and structural) and at
different resolutions (i.e., number of nodes) remains a formidable challenge to
solve. Such network integration task allows for learning a rich and universal
representation of the brain connectivity across varying modalities and
resolutions. The resulting CBT can be substantially used to generate entirely
new multimodal brain connectomes, which can boost the learning of the
downs-stream tasks such as brain state classification. Here, we propose the
Multimodal Multiresolution Brain Graph Integrator Network (i.e.,
M2GraphIntegrator), the first multimodal multiresolution graph integration
framework that maps a given connectomic population into a well centered CBT.
M2GraphIntegrator first unifies brain graph resolutions by utilizing
resolution-specific graph autoencoders. Next, it integrates the resulting
fixed-size brain graphs into a universal CBT lying at the center of its
population. To preserve the population diversity, we further design a novel
clustering-based training sample selection strategy which leverages the most
heterogeneous training samples. To ensure the biological soundness of the
learned CBT, we propose a topological loss that minimizes the topological gap
between the ground-truth brain graphs and the learned CBT. Our experiments show
that from a single CBT, one can generate realistic connectomic datasets
including brain graphs of varying resolutions and modalities. We further
demonstrate that our framework significantly outperforms benchmarks in
reconstruction quality, augmentation task, centeredness and topological
soundness.

A central challenge in training one-shot learning models is the limited
representativeness of the available shots of the data space. Particularly in
the field of network neuroscience where the brain is represented as a graph,
such models may lead to low performance when classifying brain states (e.g.,
typical vs. autistic). To cope with this, most of the existing works involve a
data augmentation step to increase the size of the training set, its diversity
and representativeness. Though effective, such augmentation methods are limited
to generating samples with the same size as the input shots (e.g., generating
brain connectivity matrices from a single shot matrix). To the best of our
knowledge, the problem of generating brain multigraphs capturing multiple types
of connectivity between pairs of nodes (i.e., anatomical regions) from a single
brain graph remains unsolved. In this paper, we unprecedentedly propose a
hybrid graph neural network (GNN) architecture, namely Multigraph Generator
Network or briefly MultigraphGNet, comprising two subnetworks: (1) a
many-to-one GNN which integrates an input population of brain multigraphs into
a single template graph, namely a connectional brain temple (CBT), and (2) a
reverse one-to-many U-Net network which takes the learned CBT in each training
step and outputs the reconstructed input multigraph population. Both networks
are trained in an end-to-end way using a cyclic loss. Experimental results
demonstrate that our MultigraphGNet boosts the performance of an independent
classifier when trained on the augmented brain multigraphs in comparison with
training on a single CBT from each class. We hope that our framework can shed
some light on the future research of multigraph augmentation from a single
graph. Our MultigraphGNet source code is available at
https://github.com/basiralab/MultigraphGNet.

In detecting malicious websites, a common approach is the use of blacklists
which are not exhaustive in themselves and are unable to generalize to new
malicious sites. Detecting newly encountered malicious websites automatically
will help reduce the vulnerability to this form of attack. In this study, we
explored the use of ten machine learning models to classify malicious websites
based on lexical features and understand how they generalize across datasets.
Specifically, we trained, validated, and tested these models on different sets
of datasets and then carried out a cross-datasets analysis. From our analysis,
we found that K-Nearest Neighbor is the only model that performs consistently
high across datasets. Other models such as Random Forest, Decision Trees,
Logistic Regression, and Support Vector Machines also consistently outperform a
baseline model of predicting every link as malicious across all metrics and
datasets. Also, we found no evidence that any subset of lexical features
generalizes across models or datasets. This research should be relevant to
cybersecurity professionals and academic researchers as it could form the basis
for real-life detection systems or further research work.

Adversarial patch attacks are an emerging security threat for real world deep
learning applications. We present Demasked Smoothing, the first approach (up to
our knowledge) to certify the robustness of semantic segmentation models
against this threat model. Previous work on certifiably defending against patch
attacks has mostly focused on image classification task and often required
changes in the model architecture and additional training which is undesirable
and computationally expensive. In Demasked Smoothing, any segmentation model
can be applied without particular training, fine-tuning, or restriction of the
architecture. Using different masking strategies, Demasked Smoothing can be
applied both for certified detection and certified recovery. In extensive
experiments we show that Demasked Smoothing can on average certify 64% of the
pixel predictions for a 1% patch in the detection task and 48% against a 0.5%
patch for the recovery task on the ADE20K dataset.

Intelligent transport systems (ITS) are pivotal in the development of
sustainable and green urban living. ITS is data-driven and enabled by the
profusion of sensors ranging from pneumatic tubes to smart cameras. This work
explores a novel data source based on optical fibre-based distributed acoustic
sensors (DAS) for traffic analysis. Detecting the type of vehicle and
estimating the occupancy of vehicles are prime concerns in ITS. The first is
motivated by the need for tracking, controlling, and forecasting traffic flow.
The second targets the regulation of high occupancy vehicle lanes in an attempt
to reduce emissions and congestion. These tasks are often conducted by
individuals inspecting vehicles or through the use of emerging computer vision
technologies. The former is not scale-able nor efficient whereas the latter is
intrusive to passengers' privacy. To this end, we propose a deep learning
technique to analyse DAS signals to address this challenge through continuous
sensing and without exposing personal information. We propose a deep learning
method for processing DAS signals and achieve 92% vehicle classification
accuracy and 92-97% in occupancy detection based on DAS data collected under
controlled conditions.

Recent pre-trained language models (PLMs) achieved great success on many
natural language processing tasks through learning linguistic features and
contextualized sentence representation. Since attributes captured in stacked
layers of PLMs are not clearly identified, straightforward approaches such as
embedding the last layer are commonly preferred to derive sentence
representations from PLMs. This paper introduces the attention-based pooling
strategy, which enables the model to preserve layer-wise signals captured in
each layer and learn digested linguistic features for downstream tasks. The
contrastive learning objective can adapt the layer-wise attention pooling to
both unsupervised and supervised manners. It results in regularizing the
anisotropic space of pre-trained embeddings and being more uniform. We evaluate
our model on standard semantic textual similarity (STS) and semantic search
tasks. As a result, our method improved the performance of the base contrastive
learned BERT_base and variants.

Recently, there has been growing attention on an end-to-end deep
learning-based stitching model. However, the most challenging point in deep
learning-based stitching is to obtain pairs of input images with a narrow field
of view and ground truth images with a wide field of view captured from
real-world scenes. To overcome this difficulty, we develop a weakly-supervised
learning mechanism to train the stitching model without requiring genuine
ground truth images. In addition, we propose a stitching model that takes
multiple real-world fisheye images as inputs and creates a 360 output image in
an equirectangular projection format. In particular, our model consists of
color consistency corrections, warping, and blending, and is trained by
perceptual and SSIM losses. The effectiveness of the proposed algorithm is
verified on two real-world stitching datasets.

Numerical simulations within a cold dark matter (DM) cosmology form halos
whose density profiles have a steep inner slope (`cusp'), yet observations of
galaxies often point towards a flat central `core'. We develop a convolutional
mixture density neural network model to derive a probability density function
(PDF) of the inner density slopes of DM halos. We train the network on
simulated dwarf galaxies from the NIHAO and AURIGA projects, which include both
DM cusps and cores: line-of-sight velocities and 2D spatial distributions of
their stars are used as inputs to obtain a PDF representing the probability of
predicting a specific inner slope. The model recovers accurately the expected
DM profiles: $\sim$82$\%$ of the galaxies have a derived inner slope within
$\pm$0.1 of their true value, while $\sim$98$\%$ within $\pm$0.3. We apply our
model to four Local Group dwarf spheroidal galaxies and find results consistent
with those obtained with the Jeans modelling based code GravSphere: the Fornax
dSph has a strong indication of possessing a central DM core, Carina and
Sextans have cusps (although the latter with large uncertainties), while
Sculptor shows a double peaked PDF indicating that a cusp is preferred, but a
core can not be ruled out. Our results show that simulation-based inference
with neural networks provide a innovative and complementary method for the
determination of the inner matter density profiles in galaxies, which in turn
can help constrain the properties of the elusive DM.

We present evidence for the existence and effectiveness of adversarial
attacks on graph neural networks (GNNs) that aim to degrade fairness. These
attacks can disadvantage a particular subgroup of nodes in GNN-based node
classification, where nodes of the underlying network have sensitive
attributes, such as race or gender. We conduct qualitative and experimental
analyses explaining how adversarial link injection impairs the fairness of GNN
predictions. For example, an attacker can compromise the fairness of GNN-based
node classification by injecting adversarial links between nodes belonging to
opposite subgroups and opposite class labels. Our experiments on empirical
datasets demonstrate that adversarial fairness attacks can significantly
degrade the fairness of GNN predictions (attacks are effective) with a low
perturbation rate (attacks are efficient) and without a significant drop in
accuracy (attacks are deceptive). This work demonstrates the vulnerability of
GNN models to adversarial fairness attacks. We hope our findings raise
awareness about this issue in our community and lay a foundation for the future
development of GNN models that are more robust to such attacks.

Currently, large pre-trained models are widely applied in neural code
completion systems, such as Github Copilot, aiXcoder, and TabNine. Though large
models significantly outperform their smaller counterparts, a survey with 2,631
participants reveals that around 70\% displayed code completions from Copilot
are not accepted by developers. Being reviewed but not accepted, these
completions bring a threat to productivity. Besides, considering the high cost
of the large models, it is a huge waste of computing resources and energy,
which severely goes against the sustainable development principle of AI
technologies. Additionally, in code completion systems, the completion requests
are automatically and actively issued to the models as developers type out,
which significantly aggravates the workload. However, to the best of our
knowledge, such waste has never been realized, not to mention effectively
addressed, in the context of neural code completion. Hence, preventing such
profitless code completions from happening in a cost-friendly way is of urgent
need. To fill this gap, we first investigate the prompts of these completions
and find four observable prompt patterns, which demonstrate the feasibility of
identifying such prompts based on prompts themselves. Motivated by this
finding, we propose an early-rejection mechanism to turn down low-return
prompts by foretelling the completion qualities without sending them to the
LCM. Further, we propose a lightweight Transformer-based estimator to
demonstrate the feasibility of the mechanism. The experimental results show
that the estimator rejects low-return prompts with a promising accuracy of
83.2%.

Many emerging Internet of Things (IoT) applications rely on information
collected by sensor nodes where the freshness of information is an important
criterion. \textit{Age of Information} (AoI) is a metric that quantifies
information timeliness, i.e., the freshness of the received information or
status update. This work considers a setup of deployed sensors in an IoT
network, where multiple unmanned aerial vehicles (UAVs) serve as mobile relay
nodes between the sensors and the base station. We formulate an optimization
problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the
received messages. This ensures that the received information at the base
station is as fresh as possible. The complex optimization problem is
efficiently solved using a deep reinforcement learning (DRL) algorithm. In
particular, we propose a deep Q-network, which works as a function
approximation to estimate the state-action value function. The proposed scheme
is quick to converge and results in a lower AoI than the random walk scheme.
Our proposed algorithm reduces the average age by approximately $25\%$ and
requires down to $50\%$ less energy when compared to the baseline scheme.

In this work, we present a novel traffic prediction and fast uplink framework
for IoT networks controlled by binary Markovian events. First, we apply the
forward algorithm with hidden Markov models (HMM) in order to schedule the
available resources to the devices with maximum likelihood activation
probabilities via fast uplink grant. In addition, we evaluate the regret metric
as the number of wasted transmission slots to evaluate the performance of the
prediction. Next, we formulate a fairness optimization problem to minimize the
age of information while keeping the regret as minimum as possible. Finally, we
propose an iterative algorithm to estimate the model hyperparameters
(activation probabilities) in a real-time application and apply an
online-learning version of the proposed traffic prediction scheme. Simulation
results show that the proposed algorithms outperform baseline models such as
time division multiple access (TDMA) and grant-free (GF) random-access in terms
of regret, the efficiency of system usage, and age of information.

As recommender systems become increasingly central for sorting and
prioritizing the content available online, they have a growing impact on the
opportunities or revenue of their items producers. For instance, they influence
which recruiter a resume is recommended to, or to whom and how much a music
track, video or news article is being exposed. This calls for recommendation
approaches that not only maximize (a proxy of) user satisfaction, but also
consider some notion of fairness in the exposure of items or groups of items.
Formally, such recommendations are usually obtained by maximizing a concave
objective function in the space of randomized rankings. When the total exposure
of an item is defined as the sum of its exposure over users, the optimal
rankings of every users become coupled, which makes the optimization process
challenging. Existing approaches to find these rankings either solve the global
optimization problem in a batch setting, i.e., for all users at once, which
makes them inapplicable at scale, or are based on heuristics that have weak
theoretical guarantees. In this paper, we propose the first efficient online
algorithm to optimize concave objective functions in the space of rankings
which applies to every concave and smooth objective function, such as the ones
found for fairness of exposure. Based on online variants of the Frank-Wolfe
algorithm, we show that our algorithm is computationally fast, generating
rankings on-the-fly with computation cost dominated by the sort operation,
memory efficient, and has strong theoretical guarantees. Compared to baseline
policies that only maximize user-side performance, our algorithm allows to
incorporate complex fairness of exposure criteria in the recommendations with
negligible computational overhead.

Estimating an individual's potential outcomes under counterfactual treatments
is a challenging task for traditional causal inference and supervised learning
approaches when the outcome is high-dimensional (e.g. gene expressions, impulse
responses, human faces) and covariates are relatively limited. In this case, to
construct one's outcome under a counterfactual treatment, it is crucial to
leverage individual information contained in its observed factual outcome on
top of the covariates. We propose a deep variational Bayesian framework that
rigorously integrates two main sources of information for outcome construction
under a counterfactual treatment: one source is the individual features
embedded in the high-dimensional factual outcome; the other source is the
response distribution of similar subjects (subjects with the same covariates)
that factually received this treatment of interest.

One key challenge in multi-document summarization is to capture the relations
among input documents that distinguish between single document summarization
(SDS) and multi-document summarization (MDS). Few existing MDS works address
this issue. One effective way is to encode document positional information to
assist models in capturing cross-document relations. However, existing MDS
models, such as Transformer-based models, only consider token-level positional
information. Moreover, these models fail to capture sentences' linguistic
structure, which inevitably causes confusions in the generated summaries.
Therefore, in this paper, we propose document-aware positional encoding and
linguistic-guided encoding that can be fused with Transformer architecture for
MDS. For document-aware positional encoding, we introduce a general protocol to
guide the selection of document encoding functions. For linguistic-guided
encoding, we propose to embed syntactic dependency relations into the
dependency relation mask with a simple but effective non-linear encoding
learner for feature learning. Extensive experiments show the proposed model can
generate summaries with high quality.

Efficiency and robustness are increasingly needed for applications on 3D
point clouds, with the ubiquitous use of edge devices in scenarios like
autonomous driving and robotics, which often demand real-time and reliable
responses. The paper tackles the challenge by designing a general framework to
construct 3D learning architectures with SO(3) equivariance and network
binarization. However, a naive combination of equivariant networks and
binarization either causes sub-optimal computational efficiency or geometric
ambiguity. We propose to locate both scalar and vector features in our networks
to avoid both cases. Precisely, the presence of scalar features makes the major
part of the network binarizable, while vector features serve to retain rich
structural information and ensure SO(3) equivariance. The proposed approach can
be applied to general backbones like PointNet and DGCNN. Meanwhile, experiments
on ModelNet40, ShapeNet, and the real-world dataset ScanObjectNN, demonstrated
that the method achieves a great trade-off between efficiency, rotation
robustness, and accuracy. The codes are available at
https://github.com/zhuoinoulu/svnet.

Image binarization techniques are being popularly used in enhancement of
noisy and/or degraded images catering different Document Image Anlaysis (DIA)
applications like word spotting, document retrieval, and OCR. Most of the
existing techniques focus on feeding pixel images into the Convolution Neural
Networks to accomplish document binarization, which may not produce effective
results when working with compressed images that need to be processed without
full decompression. Therefore in this research paper, the idea of document
image binarization directly using JPEG compressed stream of document images is
proposed by employing Dual Discriminator Generative Adversarial Networks
(DD-GANs). Here the two discriminator networks - Global and Local work on
different image ratios and use focal loss as generator loss. The proposed model
has been thoroughly tested with different versions of DIBCO dataset having
challenges like holes, erased or smudged ink, dust, and misplaced fibres. The
model proved to be highly robust, efficient both in terms of time and space
complexities, and also resulted in state-of-the-art performance in JPEG
compressed domain.

Sparse document representations have been widely used to retrieve relevant
documents via exact lexical matching. Owing to the pre-computed inverted index,
it supports fast ad-hoc search but incurs the vocabulary mismatch problem.
Although recent neural ranking models using pre-trained language models can
address this problem, they usually require expensive query inference costs,
implying the trade-off between effectiveness and efficiency. Tackling the
trade-off, we propose a novel uni-encoder ranking model, Sparse retriever using
a Dual document Encoder (SpaDE), learning document representation via the dual
encoder. Each encoder plays a central role in (i) adjusting the importance of
terms to improve lexical matching and (ii) expanding additional terms to
support semantic matching. Furthermore, our co-training strategy trains the
dual encoder effectively and avoids unnecessary intervention in training each
other. Experimental results on several benchmarks show that SpaDE outperforms
existing uni-encoder ranking models.

Recent successes in generative modeling have accelerated studies on this
subject and attracted the attention of researchers. One of the most important
methods used to achieve this success is Generative Adversarial Networks (GANs).
It has many application areas such as; virtual reality (VR), augmented reality
(AR), super resolution, image enhancement. Despite the recent advances in hair
synthesis and style transfer using deep learning and generative modelling, due
to the complex nature of hair still contains unsolved challenges. The methods
proposed in the literature to solve this problem generally focus on making
high-quality hair edits on images. In this thesis, a generative adversarial
network method is proposed to solve the hair synthesis problem. While
developing this method, it is aimed to achieve real-time hair synthesis while
achieving visual outputs that compete with the best methods in the literature.
The proposed method was trained with the FFHQ dataset and then its results in
hair style transfer and hair reconstruction tasks were evaluated. The results
obtained in these tasks and the operating time of the method were compared with
MichiGAN, one of the best methods in the literature. The comparison was made at
a resolution of 128x128. As a result of the comparison, it has been shown that
the proposed method achieves competitive results with MichiGAN in terms of
realistic hair synthesis, and performs better in terms of operating time.

Scientific machine learning (SciML) is a field of increasing interest in
several different application fields. In an optimization context, SciML-based
tools have enabled the development of more efficient optimization methods.
However, implementing SciML tools for optimization must be rigorously evaluated
and performed with caution. This work proposes the deductions of a robustness
test that guarantees the robustness of multiobjective SciML-based optimization
by showing that its results respect the universal approximator theorem. The
test is applied in the framework of a novel methodology which is evaluated in a
series of benchmarks illustrating its consistency. Moreover, the proposed
methodology results are compared with feasible regions of rigorous
optimization, which requires a significantly higher computational effort.
Hence, this work provides a robustness test for guaranteed robustness in
applying SciML tools in multiobjective optimization with lower computational
effort than the existent alternative.

Sound event detection (SED) and Acoustic scene classification (ASC) are two
widely researched audio tasks that constitute an important part of research on
acoustic scene analysis. Considering shared information between sound events
and acoustic scenes, performing both tasks jointly is a natural part of a
complex machine listening system. In this paper, we investigate the usefulness
of several spatial audio features in training a joint deep neural network (DNN)
model performing SED and ASC. Experiments are performed for two different
datasets containing binaural recordings and synchronous sound event and
acoustic scene labels to analyse the differences between performing SED and ASC
separately or jointly. The presented results show that the use of specific
binaural features, mainly the Generalized Cross Correlation with Phase
Transform (GCC-phat) and sines and cosines of phase differences, result in a
better performing model in both separate and joint tasks as compared with
baseline methods based on logmel energies only.

Real-time detection of anomalies in streaming data is receiving increasing
attention as it allows us to raise alerts, predict faults, and detect
intrusions or threats across industries. Yet, little attention has been given
to compare the effectiveness and efficiency of anomaly detectors for streaming
data (i.e., of online algorithms). In this paper, we present a qualitative,
synthetic overview of major online detectors from different algorithmic
families (i.e., distance, density, tree or projection-based) and highlight
their main ideas for constructing, updating and testing detection models. Then,
we provide a thorough analysis of the results of a quantitative experimental
evaluation of online detection algorithms along with their offline
counterparts. The behavior of the detectors is correlated with the
characteristics of different datasets (i.e., meta-features), thereby providing
a meta-level analysis of their performance. Our study addresses several missing
insights from the literature such as (a) how reliable are detectors against a
random classifier and what dataset characteristics make them perform randomly;
(b) to what extent online detectors approximate the performance of offline
counterparts; (c) which sketch strategy and update primitives of detectors are
best to detect anomalies visible only within a feature subspace of a dataset;
(d) what are the tradeoffs between the effectiveness and the efficiency of
detectors belonging to different algorithmic families; (e) which specific
characteristics of datasets yield an online algorithm to outperform all others.

Machine learning applications are becoming increasingly pervasive in our
society. Since these decision-making systems rely on data-driven learning, risk
is that they will systematically spread the bias embedded in data. In this
paper, we propose to analyze biases by introducing a framework for generating
synthetic data with specific types of bias and their combinations. We delve
into the nature of these biases discussing their relationship to moral and
justice frameworks. Finally, we exploit our proposed synthetic data generator
to perform experiments on different scenarios, with various bias combinations.
We thus analyze the impact of biases on performance and fairness metrics both
in non-mitigated and mitigated machine learning models.

This paper proposes a transfer learning approach to recalibrate our
previously developed Wheel Odometry Neural Network (WhONet) for vehicle
positioning in environments where Global Navigation Satellite Systems (GNSS)
are unavailable. The WhONet has been shown to possess the capability to learn
the uncertainties in the wheel speed measurements needed for correction and
accurate positioning of vehicles. These uncertainties may be manifested as tyre
pressure changes from driving on muddy and uneven terrains or wheel slips.
However, a common cause for concern for data-driven approaches, such as the
WhONet model, is usually the inability to generalise the models to a new
vehicle. In scenarios where machine learning models are trained in a specific
domain but deployed in another domain, the model's performance degrades. In
real-life scenarios, several factors are influential to this degradation, from
changes to the dynamics of the vehicle to new pattern distributions of the
sensor's noise, and bias will make the test sensor data vary from training
data. Therefore, the challenge is to explore techniques that allow the trained
machine learning models to spontaneously adjust to new vehicle domains. As
such, we propose the Recalibrated-Wheel Odometry neural Network (R-WhONet),
that adapts the WhONet model from its source domain (a vehicle and environment
on which the model is initially trained) to the target domain (a new vehicle on
which the trained model is to be deployed). Through a performance evaluation on
several GNSS outage scenarios - short-term complex driving scenarios, and on
longer-term GNSS outage scenarios. We demonstrate that a model trained in the
source domain does not generalise well to a new vehicle in the target domain.
However, we show that our new proposed framework improves the generalisation of
the WhONet model to new vehicles in the target domains by up to 32%.

The vision of 5G lies in providing high data rates, low latency (for the aim
of near-real-time applications), significantly increased base station capacity,
and near-perfect quality of service (QoS) for users, compared to LTE networks.
In order to provide such services, 5G systems will support various combinations
of access technologies such as LTE, NR, NR-U and Wi-Fi. Each radio access
technology (RAT) provides different types of access, and these should be
allocated and managed optimally among the users. Besides resource management,
5G systems will also support a dual connectivity service. The orchestration of
the network therefore becomes a more difficult problem for system managers with
respect to legacy access technologies. In this paper, we propose an algorithm
for RAT allocation based on federated meta-learning (FML), which enables RAN
intelligent controllers (RICs) to adapt more quickly to dynamically changing
environments. We have designed a simulation environment which contains LTE and
5G NR service technologies. In the simulation, our objective is to fulfil UE
demands within the deadline of transmission to provide higher QoS values. We
compared our proposed algorithm with a single RL agent, the Reptile algorithm
and a rule-based heuristic method. Simulation results show that the proposed
FML method achieves higher caching rates at first deployment round 21% and 12%
respectively. Moreover, proposed approach adapts to new tasks and environments
most quickly amongst the compared methods.

Smart contract, one of the most successful applications of blockchain, is
taking the world by storm, playing an essential role in the blockchain
ecosystem. However, frequent smart contract security incidents not only result
in tremendous economic losses but also destroy the blockchain-based credit
system. The security and reliability of smart contracts thus gain extensive
attention from researchers worldwide. In this survey, we first summarize the
common types and typical cases of smart contract vulnerabilities from three
levels, i.e., Solidity code layer, EVM execution layer, and Block dependency
layer. Further, we review the research progress of smart contract vulnerability
detection and classify existing counterparts into five categories, i.e., formal
verification, symbolic execution, fuzzing detection, intermediate
representation, and deep learning. Empirically, we take 300 real-world smart
contracts deployed on Ethereum as the test samples and compare the
representative methods in terms of accuracy, F1-Score, and average detection
time. Finally, we discuss the challenges in the field of smart contract
vulnerability detection and combine with the deep learning technology to look
forward to future research directions.

Previous studies have proved that cross-lingual knowledge distillation can
significantly improve the performance of pre-trained models for cross-lingual
similarity matching tasks. However, the student model needs to be large in this
operation. Otherwise, its performance will drop sharply, thus making it
impractical to be deployed to memory-limited devices. To address this issue, we
delve into cross-lingual knowledge distillation and propose a multi-stage
distillation framework for constructing a small-size but high-performance
cross-lingual model. In our framework, contrastive learning, bottleneck, and
parameter recurrent strategies are combined to prevent performance from being
compromised during the compression process. The experimental results
demonstrate that our method can compress the size of XLM-R and MiniLM by more
than 50\%, while the performance is only reduced by about 1%.

High-precision atomic structure calculations require accurate modelling of
electronic correlations involving large multiconfiguration wave function
expansions. Here we develop a deep-learning approach which allows to preselect
the most relevant configurations out of large basis sets until the targeted
precision is achieved. Our method replaces a large multiconfiguration
Dirac-Hartree-Fock computation by a series of smaller ones performed on an
iteratively expanding basis subset managed by a convolutional neural network.
The results for several examples with many-electron atoms show that deep
learning can significantly reduce the required computational memory and running
time and renders possible large-scale computations on otherwise unaccessible
basis sets.

A formal write-up of the simple proof (1995) of the existence of calibrated
forecasts by the minimax theorem, which moreover shows that N^3 periods suffice
to guarantee a 1/N calibration error.

Matching user search queries with relevant keywords bid by advertisers in
real-time is a crucial problem in sponsored search. In the literature, two
broad set of approaches have been explored to solve this problem: (i) Dense
Retrieval (DR) - learning dense vector representations for queries and bid
keywords in a shared space, and (ii) Natural Language Generation (NLG) -
learning to directly generate bid keywords given queries. In this work, we
first conduct an empirical study of these two approaches and show that they
offer complementary benefits that are additive. In particular, a large fraction
of the keywords retrieved from NLG haven't been retrieved by DR and vice-versa.
We then show that it is possible to effectively combine the advantages of these
two approaches in one model. Specifically, we propose HEARTS: a novel
multi-task fusion framework where we jointly optimize a shared encoder to
perform both DR and non-autoregressive NLG. Through extensive experiments on
search queries from over 30+ countries spanning 20+ languages, we show that
HEARTS retrieves 40.3% more high-quality bid keywords than the baseline
approaches with the same GPU compute. We also demonstrate that inferring on a
single HEARTS model is as good as inferring on two different DR and NLG
baseline models, with 2x the compute. Further, we show that DR models trained
with the HEARTS objective are significantly better than those trained with the
standard contrastive loss functions. Finally, we show that our HEARTS objective
can be adopted to short-text retrieval tasks other than sponsored search and
achieve significant performance gains.

The flavor is the focal point in the flavor industry, which follows social
tendencies and behaviors. The research and development of new flavoring agents
and molecules are essential in this field. On the other hand, the development
of natural flavors plays a critical role in modern society. In light of this,
the present work proposes a novel framework based on Scientific Machine
Learning to undertake an emerging problem in flavor engineering and industry.
Therefore, this work brings an innovative methodology to design new natural
flavor molecules. The molecules are evaluated regarding the synthetic
accessibility, the number of atoms, and the likeness to a natural or
pseudo-natural product.

High-quality face images are required to guarantee the stability and
reliability of automatic face recognition (FR) systems in surveillance and
security scenarios. However, a massive amount of face data is usually
compressed before being analyzed due to limitations on transmission or storage.
The compressed images may lose the powerful identity information, resulting in
the performance degradation of the FR system. Herein, we make the first attempt
to study just noticeable difference (JND) for the FR system, which can be
defined as the maximum distortion that the FR system cannot notice. More
specifically, we establish a JND dataset including 3530 original images and
137,670 compressed images generated by advanced reference encoding/decoding
software based on the Versatile Video Coding (VVC) standard (VTM-15.0).
Subsequently, we develop a novel JND prediction model to directly infer JND
images for the FR system. In particular, in order to maximum redundancy removal
without impairment of robust identity information, we apply the encoder with
multiple feature extraction and attention-based feature decomposition modules
to progressively decompose face features into two uncorrelated components,
i.e., identity and residual features, via self-supervised learning. Then, the
residual feature is fed into the decoder to generate the residual map. Finally,
the predicted JND map is obtained by subtracting the residual map from the
original image. Experimental results have demonstrated that the proposed model
achieves higher accuracy of JND map prediction compared with the
state-of-the-art JND models, and is capable of saving more bits while
maintaining the performance of the FR system compared with VTM-15.0.

Existing clustering algorithms such as K-means often need to preset
parameters such as the number of categories K, and such parameters may lead to
the failure to output objective and consistent clustering results. This paper
introduces a clustering method based on the information theory, by which
clusters in the clustering result have maximum average information entropy
(called entropy payload in this paper). This method can bring the following
benefits: firstly, this method does not need to preset any super parameter such
as category number or other similar thresholds, secondly, the clustering
results have the maximum information expression efficiency. it can be used in
image segmentation, object classification, etc., and could be the basis of
unsupervised learning.

Federated Learning (FL) has opened the opportunity for collaboratively
training machine learning models on heterogeneous mobile or Edge devices while
keeping local data private.With an increase in its adoption, a growing concern
is related to its economic and environmental cost (as is also the case for
other machine learning techniques).Unfortunately, little work has been done to
optimize its energy consumption or emissions of carbon dioxide or equivalents,
as energy minimization is usually left as a secondary objective.In this paper,
we investigate the problem of minimizing the energy consumption of FL training
on heterogeneous devices by controlling the workload distribution.We model this
as the Minimal Cost FL Schedule problem, a total cost minimization problem with
identical, independent, and atomic tasks that have to be assigned to
heterogeneous resources with arbitrary cost functions.We propose a
pseudo-polynomial optimal solution to the problem based on the previously
unexplored Multiple-Choice Minimum-Cost Maximal Knapsack Packing Problem.We
also provide four algorithms for scenarios where cost functions are
monotonically increasing and follow the same behavior.These solutions are
likewise applicable on the minimization of other kinds of costs, and in other
one-dimensional data partition problems.

Cross-dataset emotion recognition as an extremely challenging task in the
field of EEG-based affective computing is influenced by many factors, which
makes the universal models yield unsatisfactory results. Facing the situation
that lacks EEG information decoding research, we first analyzed the impact of
different EEG information(individual, session, emotion and trial) for emotion
recognition by sample space visualization, sample aggregation phenomena
quantification, and energy pattern analysis on five public datasets. Based on
these phenomena and patterns, we provided the processing methods and
interpretable work of various EEG differences. Through the analysis of
emotional feature distribution patterns, the Individual Emotional Feature
Distribution Difference(IEFDD) was found, which was also considered as the main
factor of the stability for emotion recognition. After analyzing the
limitations of traditional modeling approach suffering from IEFDD, the
Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably
characterize emotional feature distribution patterns, four weight extraction
methods were designed, and the optimal was the correction T-test(CT) weight
extraction method. Finally, the performance of WCMF was validated on
cross-dataset tasks in two kinds of experiments that simulated different
practical scenarios, and the results showed that WCMF had more stable and
better emotion recognition ability.

Continuous software engineering has become commonplace in numerous fields.
However, in regulating intensive sectors, where additional concerns needs to be
taken into account, it is often considered difficult to apply continuous
development approaches, such as devops. In this paper, we present an approach
for using pull requests as design controls, and apply this approach to machine
learning in certified medical systems leveraging model cards, a novel technique
developed to add explainability to machine learning systems, as a regulatory
audit trail. The approach is demonstrated with an industrial system that we
have used previously to show how medical systems can be developed in a
continuous fashion.

The remarkable progress in deep learning in recent years is largely driven by
improvements in scale, where bigger models are trained on larger datasets for
longer schedules. To predict the benefit of scale empirically, we argue for a
more rigorous methodology based on the extrapolation loss, instead of reporting
the best-fitting (interpolating) parameters. We then present a recipe for
estimating scaling law parameters reliably from learning curves. We demonstrate
that it extrapolates more accurately than previous methods in a wide range of
architecture families across several domains, including image classification,
neural machine translation (NMT) and language modeling, in addition to tasks
from the BIG-Bench evaluation benchmark. Finally, we release a benchmark
dataset comprising of 90 evaluation tasks to facilitate research in this
domain.

In practice, many medical datasets have an underlying taxonomy defined over
the disease label space. However, existing classification algorithms for
medical diagnoses often assume semantically independent labels. In this study,
we aim to leverage class hierarchy with deep learning algorithms for more
accurate and reliable skin lesion recognition. We propose a hyperbolic network
to learn image embeddings and class prototypes jointly. The hyperbola provably
provides a space for modeling hierarchical relations better than Euclidean
geometry. Meanwhile, we restrict the distribution of hyperbolic prototypes with
a distance matrix that is encoded from the class hierarchy. Accordingly, the
learned prototypes preserve the semantic class relations in the embedding space
and we can predict the label of an image by assigning its feature to the
nearest hyperbolic class prototype. We use an in-house skin lesion dataset
which consists of around 230k dermoscopic images on 65 skin diseases to verify
our method. Extensive experiments provide evidence that our model can achieve
higher accuracy with less severe classification errors than models without
considering class relations.

We present a new multimodal dataset called Visual Recipe Flow, which enables
us to learn each cooking action result in a recipe text. The dataset consists
of object state changes and the workflow of the recipe text. The state change
is represented as an image pair, while the workflow is represented as a recipe
flow graph (r-FG). The image pairs are grounded in the r-FG, which provides the
cross-modal relation. With our dataset, one can try a range of applications,
from multimodal commonsense reasoning and procedural text generation.

Visual layouts of graphs representing SAT instances can highlight the
community structure of SAT instances. The community structure of SAT instances
has been associated with both instance hardness and known clause quality
heuristics. Our tool SATViz visualizes CNF formulas using the variable
interaction graph and a force-directed layout algorithm. With SATViz, clause
proofs can be animated to continuously highlight variables that occur in a
moving window of recently learned clauses. If needed, SATViz can also create
new layouts of the variable interaction graph with the adjusted edge weights.
In this paper, we describe the structure and feature set of SATViz. We also
present some interesting visualizations created with SATViz.

In this article, we explain the recent advance of subsampling methods in
knowledge graph embedding (KGE) starting from the original one used in
word2vec.

Artificial neural networks have a broad array of applications today due to
their high degree of flexibility and ability to model nonlinear functions from
data. However, the trustworthiness of neural networks is limited due to their
black-box nature, their poor ability to generalize from small datasets, and
their inconsistent convergence during training. Aluminum electrolysis is a
complex nonlinear process with many interrelated sub-processes. Artificial
neural networks can potentially be well suited for modeling the aluminum
electrolysis process, but the safety-critical nature of this process requires
trustworthy models. In this work, sparse neural networks are trained to model
the system dynamics of an aluminum electrolysis simulator. The sparse model
structure has a significantly reduction in model complexity compared to a
corresponding dense neural network. We argue that this makes the model more
interpretable. Furthermore, the empirical study shows that the sparse models
generalize better from small training sets than dense neural networks.
Moreover, training an ensemble of sparse neural networks with different
parameter initializations show that the models converge to similar model
structures with similar learned input features.

Finite mixture modelling is a popular method in the field of clustering and
is beneficial largely due to its soft cluster membership probabilities.
However, the most common algorithm for fitting finite mixture models, the EM
algorithm, falls victim to a number of issues. We address these issues that
plague clustering using finite mixture models, including convergence to
solutions corresponding to local maxima and algorithm speed concerns in high
dimensional cases. This is done by developing two novel algorithms that
incorporate a spectral decomposition of the data matrix and a non-parametric
bootstrap sampling scheme. Simulations show the validity of our algorithms and
demonstrate not only their flexibility but also their ability to avoid
solutions corresponding to local-maxima, when compared to other (bootstrapped)
clustering algorithms for estimating finite mixture models. Our novel
algorithms have a typically more consistent convergence criteria as well as a
significant increase in speed over other bootstrapped algorithms that fit
finite mixture models.

A novel framework has recently been proposed for designing the molecular
structure of chemical compounds with a desired chemical property, where design
of novel drugs is an important topic in bioinformatics and chemo-informatics.
The framework infers a desired chemical graph by solving a mixed integer linear
program (MILP) that simulates the computation process of a feature function
defined by a two-layered model on chemical graphs and a prediction function
constructed by a machine learning method. A set of graph theoretical
descriptors in the feature function plays a key role to derive a compact
formulation of such an MILP. To improve the learning performance of prediction
functions in the framework maintaining the compactness of the MILP, this paper
utilizes the product of two of those descriptors as a new descriptor and then
designs a method of reducing the number of descriptors. The results of our
computational experiments suggest that the proposed method improved the
learning performance for many chemical properties and can infer a chemical
structure with up to 50 non-hydrogen atoms.

Detecting mass in mammogram is significant due to the high occurrence and
mortality of breast cancer. In mammogram mass detection, modeling pairwise
lesion correspondence explicitly is particularly important. However, most of
the existing methods build relatively coarse correspondence and have not
utilized correspondence supervision. In this paper, we propose a new
transformer-based framework CL-Net to learn lesion detection and pairwise
correspondence in an end-to-end manner. In CL-Net, View-Interactive Lesion
Detector is proposed to achieve dynamic interaction across candidates of cross
views, while Lesion Linker employs the correspondence supervision to guide the
interaction process more accurately. The combination of these two designs
accomplishes precise understanding of pairwise lesion correspondence for
mammograms. Experiments show that CL-Net yields state-of-the-art performance on
the public DDSM dataset and our in-house dataset. Moreover, it outperforms
previous methods by a large margin in low FPI regime.

The use of deep neural networks in electromyogram (EMG) based prostheses
control provides a promising alternative to the hand-crafted features by
automatically learning muscle activation patterns from the EMG signals.
Meanwhile, the use of raw EMG signals as input to convolution neural networks
(CNN) offers a simple, fast, and ideal scheme for effective control of
prostheses. Therefore, this study investigates the relationship between window
length and overlap, which may influence the generation of robust raw EMG
2-dimensional (2D) signals for application in CNN. And a rule of thumb for a
proper combination of these parameters that could guarantee optimal network
performance was derived. Moreover, we investigate the relationship between the
CNN receptive window size and the raw EMG signal size. Experimental results
show that the performance of the CNN increases with the increase in overlap
within the generated signals, with the highest improvement of 9.49% accuracy
and 23.33% F1-score realized when the overlap is 75% of the window length.
Similarly, the network performance increases with the increase in receptive
window (kernel) size. Findings from this study suggest that a combination of
75% overlap in 2D EMG signals and wider network kernels may provide ideal motor
intents classification for adequate EMG-CNN based prostheses control scheme.

Interactions with articulated objects are a challenging but important task
for mobile robots. To tackle this challenge, we propose a novel closed-loop
control pipeline, which integrates manipulation priors from affordance
estimation with sampling-based whole-body control. We introduce the concept of
agent-aware affordances which fully reflect the agent's capabilities and
embodiment and we show that they outperform their state-of-the-art counterparts
which are only conditioned on the end-effector geometry. Additionally,
closed-loop affordance inference is found to allow the agent to divide a task
into multiple non-continuous motions and recover from failure and unexpected
states. Finally, the pipeline is able to perform long-horizon mobile
manipulation tasks, i.e. opening and closing an oven, in the real world with
high success rates (opening: 71%, closing: 72%).

Architectural photography is a genre of photography that focuses on capturing
a building or structure in the foreground with dramatic lighting in the
background. Inspired by recent successes in image-to-image translation methods,
we aim to perform style transfer for architectural photographs. However, the
special composition in architectural photography poses great challenges for
style transfer in this type of photographs. Existing neural style transfer
methods treat the architectural images as a single entity, which would generate
mismatched chrominance and destroy geometric features of the original
architecture, yielding unrealistic lighting, wrong color rendition, and visual
artifacts such as ghosting, appearance distortion, or color mismatching. In
this paper, we specialize a neural style transfer method for architectural
photography. Our method addresses the composition of the foreground and
background in an architectural photograph in a two-branch neural network that
separately considers the style transfer of the foreground and the background,
respectively. Our method comprises a segmentation module, a learning-based
image-to-image translation module, and an image blending optimization module.
We trained our image-to-image translation neural network with a new dataset of
unconstrained outdoor architectural photographs captured at different magic
times of a day, utilizing additional semantic information for better
chrominance matching and geometry preservation. Our experiments show that our
method can produce photorealistic lighting and color rendition on both the
foreground and background, and outperforms general image-to-image translation
and arbitrary style transfer baselines quantitatively and qualitatively. Our
code and data are available at
https://github.com/hkust-vgd/architectural_style_transfer.

Robotic cloth manipulation is a relevant challenging problem for autonomous
robotic systems. Highly deformable objects as textile items can adopt multiple
configurations and shapes during their manipulation. Hence, robots should not
only understand the current cloth configuration but also be able to predict the
future possible behaviors of the cloth. This paper addresses the problem of
indirectly controlling the configuration of certain points of a textile object,
by applying actions on other parts of the object through the use of a Model
Predictive Control (MPC) strategy, which also allows to foresee the behavior of
indirectly controlled points. The designed controller finds the optimal control
signals to attain the desired future target configuration. The explored
scenario in this paper considers tracking a reference trajectory with the lower
corners of a square piece of cloth by grasping its upper corners. To do so, we
propose and validate a linear cloth model that allows solving the MPC-related
optimization problem in real time. Reinforcement Learning (RL) techniques are
used to learn the optimal parameters of the proposed cloth model and also to
tune the resulting MPC. After obtaining accurate tracking results in
simulation, the full control scheme was implemented and executed in a real
robot, obtaining accurate tracking even in adverse conditions. While total
observed errors reach the 5 cm mark, for a 30x30 cm cloth, an analysis shows
the MPC contributes less than 30% to that value.

Logic obfuscation is introduced as a pivotal defense against multiple
hardware threats on Integrated Circuits (ICs), including reverse engineering
(RE) and intellectual property (IP) theft. The effectiveness of logic
obfuscation is challenged by the recently introduced Boolean satisfiability
(SAT) attack and its variants. A plethora of countermeasures has also been
proposed to thwart the SAT attack. Irrespective of the implemented defense
against SAT attacks, large power, performance, and area overheads are
indispensable. In contrast, we propose a cognitive solution: a neural
network-based unSAT clause translator, SATConda, that incurs a minimal area and
power overhead while preserving the original functionality with impenetrable
security. SATConda is incubated with an unSAT clause generator that translates
the existing conjunctive normal form (CNF) through minimal perturbations such
as the inclusion of pair of inverters or buffers or adding a new lightweight
unSAT block depending on the provided CNF. For efficient unSAT clause
generation, SATConda is equipped with a multi-layer neural network that first
learns the dependencies of features (literals and clauses), followed by a
long-short-term-memory (LSTM) network to validate and backpropagate the
SAT-hardness for better learning and translation. Our proposed SATConda is
evaluated on ISCAS85 and ISCAS89 benchmarks and is seen to defend against
multiple state-of-the-art successfully SAT attacks devised for hardware RE. In
addition, we also evaluate our proposed SATCondas empirical performance against
MiniSAT, Lingeling and Glucose SAT solvers that form the base for numerous
existing deobfuscation SAT attacks.

Analyzing building models for usable area, building safety, or energy
analysis requires function classification data of spaces and related objects.
Automated space function classification is desirable to reduce input model
preparation effort and errors. Existing space function classifiers use space
feature vectors or space connectivity graphs as input. The application of deep
learning (DL) image segmentation methods to space function classification has
not been studied. As an initial step towards addressing this gap, we present a
dataset, SFS-A68, that consists of input and ground truth images generated from
68 digital 3D models of space layouts of apartment buildings. The dataset is
suitable for developing DL models for space function segmentation. We use the
dataset to train and evaluate an experimental space function segmentation
network based on transfer learning and training from scratch. Test results
confirm the applicability of DL image segmentation for space function
classification. The code and the dataset of the experiments are publicly
available online (https://github.com/A2Amir/SFS-A68).

Interpretable Machine Learning (IML) is expected to remove significant
barriers for the application of Machine Learning (ML) algorithms in power
systems. This letter first seeks to showcase the benefits of SHapley Additive
exPlanations (SHAP) for understanding the outcomes of ML models, which are
increasingly being used. Second, we seek to demonstrate that SHAP explanations
are able to capture the underlying physics of the power system. To do so, we
demonstrate that the Power Transfer Distribution Factors (PTDF) -- a
physics-based linear sensitivity index -- can be derived from the SHAP values.
To do so, we take the derivatives of SHAP values from a ML model trained to
learn line flows from generator power injections, using a simple DC power flow
case in the 9-bus 3-generator test network. In demonstrating that SHAP values
can be related back to the physics that underpin the power system, we build
confidence in the explanations SHAP can offer.

Neural networks are vulnerable to adversarial attacks: adding well-crafted,
imperceptible perturbations to their input can modify their output. Adversarial
training is one of the most effective approaches to training robust models
against such attacks. Unfortunately, this method is much slower than vanilla
training of neural networks since it needs to construct adversarial examples
for the entire training data at every iteration. By leveraging the theory of
coreset selection, we show how selecting a small subset of training data
provides a principled approach to reducing the time complexity of robust
training. To this end, we first provide convergence guarantees for adversarial
coreset selection. In particular, we show that the convergence bound is
directly related to how well our coresets can approximate the gradient computed
over the entire training data. Motivated by our theoretical analysis, we
propose using this gradient approximation error as our adversarial coreset
selection objective to reduce the training set size effectively. Once built, we
run adversarial training over this subset of the training data. Unlike existing
methods, our approach can be adapted to a wide variety of training objectives,
including TRADES, $\ell_p$-PGD, and Perceptual Adversarial Training. We conduct
extensive experiments to demonstrate that our approach speeds up adversarial
training by 2-3 times while experiencing a slight degradation in the clean and
robust accuracy.

Machine Learning models are prone to fail when test data are different from
training data, a situation often encountered in real applications known as
distribution shift. While still valid, the training-time knowledge becomes less
effective, requiring a test-time adaptation to maintain high performance.
Following approaches that assume batch-norm layer and use their statistics for
adaptation, we propose a Test-Time Adaptation with Principal Component Analysis
(TTAwPCA), which presumes a fitted PCA and adapts at test time a spectral
filter based on the singular values of the PCA for robustness to corruptions.
TTAwPCA combines three components: the output of a given layer is decomposed
using a Principal Component Analysis (PCA), filtered by a penalization of its
singular values, and reconstructed with the PCA inverse transform. This generic
enhancement adds fewer parameters than current methods. Experiments on
CIFAR-10-C and CIFAR- 100-C demonstrate the effectiveness and limits of our
method using a unique filter of 2000 parameters.

Cardiac magnetic resonance (CMR) sequences visualise the cardiac function
voxel-wise over time. Simultaneously, deep learning-based deformable image
registration is able to estimate discrete vector fields which warp one time
step of a CMR sequence to the following in a self-supervised manner. However,
despite the rich source of information included in these 3D+t vector fields, a
standardised interpretation is challenging and the clinical applications remain
limited so far. In this work, we show how to efficiently use a deformable
vector field to describe the underlying dynamic process of a cardiac cycle in
form of a derived 1D motion descriptor. Additionally, based on the expected
cardiovascular physiological properties of a contracting or relaxing ventricle,
we define a set of rules that enables the identification of five cardiovascular
phases including the end-systole (ES) and end-diastole (ED) without the usage
of labels. We evaluate the plausibility of the motion descriptor on two
challenging multi-disease, -center, -scanner short-axis CMR datasets. First, by
reporting quantitative measures such as the periodic frame difference for the
extracted phases. Second, by comparing qualitatively the general pattern when
we temporally resample and align the motion descriptors of all instances across
both datasets. The average periodic frame difference for the ED, ES key phases
of our approach is $0.80\pm{0.85}$, $0.69\pm{0.79}$ which is slightly better
than the inter-observer variability ($1.07\pm{0.86}$, $0.91\pm{1.6}$) and the
supervised baseline method ($1.18\pm{1.91}$, $1.21\pm{1.78}$). Code and labels
will be made available on our GitHub repository.
https://github.com/Cardio-AI/cmr-phase-detection

The core problem of text-based person retrieval is how to bridge the
heterogeneous gap between multi-modal data. Many previous approaches contrive
to learning a latent common manifold mapping paradigm following a
\textbf{cross-modal distribution consensus prediction (CDCP)} manner. When
mapping features from distribution of one certain modality into the common
manifold, feature distribution of the opposite modality is completely
invisible. That is to say, how to achieve a cross-modal distribution consensus
so as to embed and align the multi-modal features in a constructed cross-modal
common manifold all depends on the experience of the model itself, instead of
the actual situation. With such methods, it is inevitable that the multi-modal
data can not be well aligned in the common manifold, which finally leads to a
sub-optimal retrieval performance. To overcome this \textbf{CDCP dilemma}, we
propose a novel algorithm termed LBUL to learn a Consistent Cross-modal Common
Manifold (C$^{3}$M) for text-based person retrieval. The core idea of our
method, just as a Chinese saying goes, is to `\textit{san si er hou xing}',
namely, to \textbf{Look Before yoU Leap (LBUL)}. The common manifold mapping
mechanism of LBUL contains a looking step and a leaping step. Compared to
CDCP-based methods, LBUL considers distribution characteristics of both the
visual and textual modalities before embedding data from one certain modality
into C$^{3}$M to achieve a more solid cross-modal distribution consensus, and
hence achieve a superior retrieval accuracy. We evaluate our proposed method on
two text-based person retrieval datasets CUHK-PEDES and RSTPReid. Experimental
results demonstrate that the proposed LBUL outperforms previous methods and
achieves the state-of-the-art performance.

The work-from-home policy affected people of all demographics and
professions, including students and faculty at universities. After the onset of
the COVID-19 pandemic in 2020, institutions moved their operations online,
affecting the motivation levels, communication abilities, and mental health of
students and faculty around the world. This paper is based mainly on primary
data collected from students from around the world, and professors at
universities in Bengaluru, India. It explores the effects of work-from-home as
a policy in terms of how it changed learning during the pandemic and how it has
permanently altered it in a post-pandemic future. Further, it suggests and
evaluates policies on how certain negative effects of the work-from-home policy
can be mitigated.

Image learning and colorization are hot spots in multimedia domain. Inspired
by the learning capability of humans, in this paper, we propose an automatic
colorization method with a learning framework. This method can be viewed as a
hybrid of exemplar-based and learning-based method, and it decouples the
colorization process and learning process so as to generate various color
styles for the same gray image. The matching process in the exemplar-based
colorization method can be regarded as a parameterized function, and we employ
a large amount of color images as the training samples to fit the parameters.
During the training process, the color images are the ground truths, and we
learn the optimal parameters for the matching process by minimizing the errors
in terms of the parameters for the matching function. To deal with images with
various compositions, a global feature is introduced, which can be used to
classify the images with respect to their compositions, and then learn the
optimal matching parameters for each image category individually. What's more,
a spatial consistency based post-processing is design to smooth the extracted
color information from the reference image to remove matching errors. Extensive
experiments are conducted to verify the effectiveness of the method, and it
achieves comparable performance against the state-of-the-art colorization

Given a natural language description, text-based person retrieval aims to
identify images of a target person from a large-scale person image database.
Existing methods generally face a \textbf{color over-reliance problem}, which
means that the models rely heavily on color information when matching
cross-modal data. Indeed, color information is an important decision-making
accordance for retrieval, but the over-reliance on color would distract the
model from other key clues (e.g. texture information, structural information,
etc.), and thereby lead to a sub-optimal retrieval performance. To solve this
problem, in this paper, we propose to \textbf{C}apture \textbf{A}ll-round
\textbf{I}nformation \textbf{B}eyond \textbf{C}olor (\textbf{CAIBC}) via a
jointly optimized multi-branch architecture for text-based person retrieval.
CAIBC contains three branches including an RGB branch, a grayscale (GRS) branch
and a color (CLR) branch. Besides, with the aim of making full use of all-round
information in a balanced and effective way, a mutual learning mechanism is
employed to enable the three branches which attend to varied aspects of
information to communicate with and learn from each other. Extensive
experimental analysis is carried out to evaluate our proposed CAIBC method on
the CUHK-PEDES and RSTPReid datasets in both \textbf{supervised} and
\textbf{weakly supervised} text-based person retrieval settings, which
demonstrates that CAIBC significantly outperforms existing methods and achieves
the state-of-the-art performance on all the three tasks.

The high-content image-based assay is commonly leveraged for identifying the
phenotypic impact of genetic perturbations in biology field. However, a
persistent issue remains unsolved during experiments: the interferential
technical noise caused by systematic errors (e.g., temperature, reagent
concentration, and well location) is always mixed up with the real biological
signals, leading to misinterpretation of any conclusion drawn. Here, we show a
mean teacher based deep learning model (DeepNoise) that can disentangle
biological signals from the experimental noise. Specifically, we aim to
classify the phenotypic impact of 1,108 different genetic perturbations
screened from 125,510 fluorescent microscopy images, which are totally
unrecognizable by human eye. We validate our model by participating in the
Recursion Cellular Image Classification Challenge, and our proposed method
achieves an extremely high classification score (Acc: 99.596%), ranking the 2nd
place among 866 participating groups. This promising result indicates the
successful separation of biological and technical factors, which might help
decrease the cost of treatment development and expedite the drug discovery
process.

Volumetric images from Magnetic Resonance Imaging (MRI) provide invaluable
information in preoperative staging of rectal cancer. Above all, accurate
preoperative discrimination between T2 and T3 stages is arguably both the most
challenging and clinically significant task for rectal cancer treatment, as
chemo-radiotherapy is usually recommended to patients with T3 (or greater)
stage cancer. In this study, we present a volumetric convolutional neural
network to accurately discriminate T2 from T3 stage rectal cancer with rectal
MR volumes. Specifically, we propose 1) a custom ResNet-based volume encoder
that models the inter-slice relationship with late fusion (i.e., 3D convolution
at the last layer), 2) a bilinear computation that aggregates the resulting
features from the encoder to create a volume-wise feature, and 3) a joint
minimization of triplet loss and focal loss. With MR volumes of pathologically
confirmed T2/T3 rectal cancer, we perform extensive experiments to compare
various designs within the framework of residual learning. As a result, our
network achieves an AUC of 0.831, which is higher than the reported accuracy of
the professional radiologist groups. We believe this method can be extended to
other volume analysis tasks

5G communication brings substantial improvements in the quality of service
provided to various applications by achieving higher throughput and lower
latency. However, interactive multimedia applications (e.g., ultra high
definition video conferencing, 3D and multiview video streaming, crowd-sourced
video streaming, cloud gaming, virtual and augmented reality) are becoming more
ambitious with high volume and low latency video streams putting strict demands
on the already congested networks. Mobile Edge Computing (MEC) is an emerging
paradigm that extends cloud computing capabilities to the edge of the network
i.e., at the base station level. To meet the latency requirements and avoid the
end-to-end communication with remote cloud data centers, MEC allows to store
and process video content (e.g., caching, transcoding, pre-processing) at the
base stations. Both video on demand and live video streaming can utilize MEC to
improve existing services and develop novel use cases, such as video analytics,
and targeted advertisements. MEC is expected to reshape the future of video
streaming by providing ultra-reliable and low latency streaming (e.g., in
augmented reality, virtual reality, and autonomous vehicles), pervasive
computing (e.g., in real-time video analytics), and blockchain-enabled
architecture for secure live streaming. This paper presents a comprehensive
survey of recent developments in MEC-enabled video streaming bringing
unprecedented improvement to enable novel use cases. A detailed review of the
state-of-the-art is presented covering novel caching schemes, optimal
computation offloading, cooperative caching and offloading and the use of
artificial intelligence (i.e., machine learning, deep learning, and
reinforcement learning) in MEC-assisted video streaming services.

The time needed to apply a hierarchical clustering algorithm is most often
dominated by the number of computations of a pairwise dissimilarity measure.
Such a constraint, for larger data sets, puts at a disadvantage the use of all
the classical linkage criteria but the single linkage one. However, it is known
that the single linkage clustering algorithm is very sensitive to outliers,
produces highly skewed dendrograms, and therefore usually does not reflect the
true underlying data structure -- unless the clusters are well-separated. To
overcome its limitations, we propose a new hierarchical clustering linkage
criterion called Genie. Namely, our algorithm links two clusters in such a way
that a chosen economic inequity measure (e.g., the Gini- or Bonferroni-index)
of the cluster sizes does not drastically increase above a given threshold. The
presented benchmarks indicate a high practical usefulness of the introduced
method: it most often outperforms the Ward or average linkage in terms of the
clustering quality while retaining the single linkage's speed. The Genie
algorithm is easily parallelizable and thus may be run on multiple threads to
speed up its execution even further. Its memory overhead is small: there is no
need to precompute the complete distance matrix to perform the computations in
order to obtain a desired clustering. It can be applied on arbitrary spaces
equipped with a dissimilarity measure, e.g., on real vectors, DNA or protein
sequences, images, rankings, informetric data, etc. A reference implementation
of the algorithm has been included in the open source 'genie' package for R.
See also https://genieclust.gagolewski.com for a new implementation
(genieclust) -- available for both R and Python.

Most object manipulation strategies for robots are based on the assumption
that the object is rigid (i.e., with fixed geometry) and the goal's details
have been fully specified (e.g., the exact target pose). However, there are
many tasks that involve spatial relations in human environments where these
conditions may be hard to satisfy, e.g., bending and placing a cable inside an
unknown container. To develop advanced robotic manipulation capabilities in
unstructured environments that avoid these assumptions, we propose a novel
long-horizon framework that exploits contrastive planning in finding promising
collaborative actions. Using simulation data collected by random actions, we
learn an embedding model in a contrastive manner that encodes the
spatio-temporal information from successful experiences, which facilitates the
subgoal planning through clustering in the latent space. Based on the keypoint
correspondence-based action parameterization, we design a leader-follower
control scheme for the collaboration between dual arms. All models of our
policy are automatically trained in simulation and can be directly transferred
to real-world environments. To validate the proposed framework, we conduct a
detailed experimental study on a complex scenario subject to environmental and
reachability constraints in both simulation and real environments.

In today's technological era, document images play an important and integral
part in our day to day life, and specifically with the surge of Covid-19,
digitally scanned documents have become key source of communication, thus
avoiding any sort of infection through physical contact. Storage and
transmission of scanned document images is a very memory intensive task, hence
compression techniques are being used to reduce the image size before archival
and transmission. To extract information or to operate on the compressed
images, we have two ways of doing it. The first way is to decompress the image
and operate on it and subsequently compress it again for the efficiency of
storage and transmission. The other way is to use the characteristics of the
underlying compression algorithm to directly process the images in their
compressed form without involving decompression and re-compression. In this
paper, we propose a novel idea of developing an OCR for CCITT (The
International Telegraph and Telephone Consultative Committee) compressed
machine printed TIFF document images directly in the compressed domain. After
segmenting text regions into lines and words, HMM is applied for recognition
using three coding modes of CCITT- horizontal, vertical and the pass mode.
Experimental results show that OCR on pass modes give a promising results.

Animating an avatar that reflects a user's action in the VR world enables
natural interactions with the virtual environment. It has the potential to
allow remote users to communicate and collaborate in a way as if they met in
person. However, a typical VR system provides only a very sparse set of up to
three positional sensors, including a head-mounted display (HMD) and optionally
two hand-held controllers, making the estimation of the user's full-body
movement a difficult problem. In this work, we present a data-driven
physics-based method for predicting the realistic full-body movement of the
user according to the transformations of these VR trackers and simulating an
avatar character to mimic such user actions in the virtual world in real-time.
We train our system using reinforcement learning with carefully designed
pretraining processes to ensure the success of the training and the quality of
the simulation. We demonstrate the effectiveness of the method with an
extensive set of examples.

Rank aggregation with pairwise comparisons has shown promising results in
elections, sports competitions, recommendations, and information retrieval.
However, little attention has been paid to the security issue of such
algorithms, in contrast to numerous research work on the computational and
statistical characteristics. Driven by huge profits, the potential adversary
has strong motivation and incentives to manipulate the ranking list. Meanwhile,
the intrinsic vulnerability of the rank aggregation methods is not well studied
in the literature. To fully understand the possible risks, we focus on the
purposeful adversary who desires to designate the aggregated results by
modifying the pairwise data in this paper. From the perspective of the
dynamical system, the attack behavior with a target ranking list is a fixed
point belonging to the composition of the adversary and the victim. To perform
the targeted attack, we formulate the interaction between the adversary and the
victim as a game-theoretic framework consisting of two continuous operators
while Nash equilibrium is established. Then two procedures against HodgeRank
and RankCentrality are constructed to produce the modification of the original
data. Furthermore, we prove that the victims will produce the target ranking
list once the adversary masters the complete information. It is noteworthy that
the proposed methods allow the adversary only to hold incomplete information or
imperfect feedback and perform the purposeful attack. The effectiveness of the
suggested target attack strategies is demonstrated by a series of toy
simulations and several real-world data experiments. These experimental results
show that the proposed methods could achieve the attacker's goal in the sense
that the leading candidate of the perturbed ranking list is the designated one
by the adversary.

Existing deep learning-based 3D object detectors typically rely on the
appearance of individual objects and do not explicitly pay attention to the
rich contextual information of the scene. In this work, we propose
Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D)
framework, which takes a 3D scene as input and strives to explicitly integrate
useful contextual information of the scene at multiple levels to predict a set
of object bounding-boxes along with their corresponding semantic labels. To
this end, we propose to utilize a context enhancement network that captures the
contextual information at different levels of granularity followed by a
multi-stage refinement module to progressively refine the box positions and
class predictions. Extensive experiments on the large-scale ScanNetV2 benchmark
reveal the benefits of our proposed method, leading to an absolute improvement
of 2.0% over the baseline. In addition to 3D object detection, we investigate
the effectiveness of our CMR3D framework for the problem of 3D object counting.
Our source code will be publicly released.

We present a novel reinforcement learning based algorithm for multi-robot
task allocation problem in warehouse environments. We formulate it as a Markov
Decision Process and solve via a novel deep multi-agent reinforcement learning
method (called RTAW) with attention inspired policy architecture. Hence, our
proposed policy network uses global embeddings that are independent of the
number of robots/tasks. We utilize proximal policy optimization algorithm for
training and use a carefully designed reward to obtain a converged policy. The
converged policy ensures cooperation among different robots to minimize total
travel delay (TTD) which ultimately improves the makespan for a sufficiently
large task-list. In our extensive experiments, we compare the performance of
our RTAW algorithm to state of the art methods such as myopic pickup distance
minimization (greedy) and regret based baselines on different navigation
schemes. We show an improvement of upto 14% (25-1000 seconds) in TTD on
scenarios with hundreds or thousands of tasks for different challenging
warehouse layouts and task generation schemes. We also demonstrate the
scalability of our approach by showing performance with up to $1000$ robots in
simulations.

Neural network pruning compresses automatic speech recognition (ASR) models
effectively. However, in multilingual ASR, language-agnostic pruning may lead
to severe performance drops on some languages because language-agnostic pruning
masks may not fit all languages and discard important language-specific
parameters. In this work, we present ASR pathways, a sparse multilingual ASR
model that activates language-specific sub-networks ("pathways"), such that the
parameters for each language are learned explicitly. With the overlapping
sub-networks, the shared parameters can also enable knowledge transfer for
lower-resource languages via joint multilingual training. We propose a novel
algorithm to learn ASR pathways, and evaluate the proposed method on 4
languages with a streaming RNN-T model. Our proposed ASR pathways outperform
both dense models and a language-agnostically pruned model, and provide better
performance on low-resource languages compared to the monolingual sparse
models.

This paper revisits an incredibly simple yet exceedingly effective computing
paradigm, Deep Mutual Learning (DML). We observe that the effectiveness
correlates highly to its excellent generalization quality. In the paper, we
interpret the performance improvement with DML from a novel perspective that it
is roughly an approximate Bayesian posterior sampling procedure. This also
establishes the foundation for applying the R\'{e}nyi divergence to improve the
original DML, as it brings in the variance control of the prior (in the context
of DML). Therefore, we propose R\'{e}nyi Divergence Deep Mutual Learning
(RDML). Our empirical results represent the advantage of the marriage of DML
and the R\'{e}nyi divergence. The flexible control imposed by the R\'{e}nyi
divergence is able to further improve DML to learn better generalized models.

Existing data-driven and feedback traffic control strategies do not consider
the heterogeneity of real-time data measurements. Besides, traditional
reinforcement learning (RL) methods for traffic control usually converge slowly
for lacking data efficiency. Moreover, conventional optimal perimeter control
schemes require exact knowledge of the system dynamics and thus would be
fragile to endogenous uncertainties. To handle these challenges, this work
proposes an integral reinforcement learning (IRL) based approach to learning
the macroscopic traffic dynamics for adaptive optimal perimeter control. This
work makes the following primary contributions to the transportation
literature: (a) A continuous-time control is developed with discrete gain
updates to adapt to the discrete-time sensor data. (b) To reduce the sampling
complexity and use the available data more efficiently, the experience replay
(ER) technique is introduced to the IRL algorithm. (c) The proposed method
relaxes the requirement on model calibration in a "model-free" manner that
enables robustness against modeling uncertainty and enhances the real-time
performance via a data-driven RL algorithm. (d) The convergence of the
IRL-based algorithms and the stability of the controlled traffic dynamics are
proven via the Lyapunov theory. The optimal control law is parameterized and
then approximated by neural networks (NN), which moderates the computational
complexity. Both state and input constraints are considered while no model
linearization is required. Numerical examples and simulation experiments are
presented to verify the effectiveness and efficiency of the proposed method.

Federated Learning (FL) provides a promising distributed learning paradigm,
since it seeks to protect users privacy by not sharing their private training
data. Recent research has demonstrated, however, that FL is susceptible to
model inversion attacks, which can reconstruct users' private data by
eavesdropping on shared gradients. Existing defense solutions cannot survive
stronger attacks and exhibit a poor trade-off between privacy and performance.
In this paper, we present a straightforward yet effective defense strategy
based on obfuscating the gradients of sensitive data with concealing data.
Specifically, we alter a few samples within a mini batch to mimic the sensitive
data at the gradient levels. Using a gradient projection technique, our method
seeks to obscure sensitive data without sacrificing FL performance. Our
extensive evaluations demonstrate that, compared to other defenses, our
technique offers the highest level of protection while preserving FL
performance. Our source code is located in the repository.

Image classification is one of the most important areas in computer vision.
Hierarchical multi-label classification applies when a multi-class image
classification problem is arranged into smaller ones based upon a hierarchy or
taxonomy. Thus, hierarchical classification modes generally provide multiple
class predictions on each instance, whereby these are expected to reflect the
structure of image classes as related to one another. In this paper, we propose
a multi-label capsule network (ML-CapsNet) for hierarchical classification. Our
ML-CapsNet predicts multiple image classes based on a hierarchical class-label
tree structure. To this end, we present a loss function that takes into account
the multi-label predictions of the network. As a result, the training approach
for our ML-CapsNet uses a coarse to fine paradigm while maintaining consistency
with the structure in the classification levels in the label-hierarchy. We also
perform experiments using widely available datasets and compare the model with
alternatives elsewhere in the literature. In our experiments, our ML-CapsNet
yields a margin of improvement with respect to these alternative methods.

The rise of social media platforms provides an unbounded, infinitely rich
source of aggregate knowledge of the world around us, both historic and
real-time, from a human perspective. The greatest challenge we face is how to
process and understand this raw and unstructured data, go beyond individual
observations and see the "big picture"--the domain of Situation Awareness. We
provide an extensive survey of Artificial Intelligence research, focusing on
microblog social media data with applications to Situation Awareness, that
gives the seminal work and state-of-the-art approaches across six thematic
areas: Crime, Disasters, Finance, Physical Environment, Politics, and Health
and Population. We provide a novel, unified methodological perspective,
identify key results and challenges, and present ongoing research directions.

Modeling and control of high-dimensional, nonlinear robotic systems remains a
challenging task. While various model- and learning-based approaches have been
proposed to address these challenges, they broadly lack generalizability to
different control tasks and rarely preserve the structure of the dynamics. In
this work, we propose a new, data-driven approach for extracting
low-dimensional models from data using Spectral Submanifold Reduction (SSMR).
In contrast to other data-driven methods which fit dynamical models to training
trajectories, we identify the dynamics on generic, low-dimensional attractors
embedded in the full phase space of the robotic system. This allows us to
obtain computationally-tractable models for control which preserve the system's
dominant dynamics and better track trajectories radically different from the
training data. We demonstrate the superior performance and generalizability of
SSMR in dynamic trajectory tracking tasks vis-a-vis the state of the art,
including Koopman operator-based approaches.

Reconstruction of large-sized data from small-sized ones is an important
problem in information science, and a typical example is the image
super-resolution reconstruction in computer vision. Combining machine learning
and quantum computing, quantum machine learning has shown the ability to
accelerate data processing and provides new methods for information processing.
In this paper, we propose two frameworks for data reconstruction based on
quantum neural networks (QNNs) and quantum autoencoder (QAE). The effects of
the two frameworks are evaluated by using the MNIST handwritten digits as
datasets. Simulation results show that QNNs and QAE can work well for data
reconstruction. We also compare our results with classical super-resolution
neural networks, and the results of one QNN are very close to classical ones.

Molecule generation, especially generating 3D molecular geometries from
scratch (i.e., 3D \textit{de novo} generation), has become a fundamental task
in drug designs. Existing diffusion-based 3D molecule generation methods could
suffer from unsatisfactory performances, especially when generating large
molecules. At the same time, the generated molecules lack enough diversity.
This paper proposes a novel diffusion model to address those two challenges.
  First, interatomic relations are not in molecules' 3D point cloud
representations. Thus, it is difficult for existing generative models to
capture the potential interatomic forces and abundant local constraints. To
tackle this challenge, we propose to augment the potential interatomic forces
and further involve dual equivariant encoders to encode interatomic forces of
different strengths. Second, existing diffusion-based models essentially shift
elements in geometry along the gradient of data density. Such a process lacks
enough exploration in the intermediate steps of the Langevin dynamics. To
address this issue, we introduce a distributional controlling variable in each
diffusion/reverse step to enforce thorough explorations and further improve
generation diversity.
  Extensive experiments on multiple benchmarks demonstrate that the proposed
model significantly outperforms existing methods for both unconditional and
conditional generation tasks. We also conduct case studies to help understand
the physicochemical properties of the generated molecules.

We analyze new generalization bounds for deep learning models trained by
transfer learning from a source to a target task. Our bounds utilize a quantity
called the majority predictor accuracy, which can be computed efficiently from
data. We show that our theory is useful in practice since it implies that the
majority predictor accuracy can be used as a transferability measure, a fact
that is also validated by our experiments.

Suicide is a major public health crisis. With more than 20,000,000 suicide
attempts each year, the early detection of suicidal intent has the potential to
save hundreds of thousands of lives. Traditional mental health screening
methods are time-consuming, costly, and often inaccessible to disadvantaged
populations; online detection of suicidal intent using machine learning offers
a viable alternative. Here we present Robin, the largest non-keyword generated
suicidal corpus to date, consisting of over 1.1 million online forum postings.
In addition to its unprecedented size, Robin is specially constructed to
include various categories of suicidal text, such as suicide bereavement and
flippant references, better enabling models trained on Robin to learn the
subtle nuances of text expressing suicidal ideation. Experimental results
achieve state-of-the-art performance for the classification of suicidal text,
both with traditional methods like logistic regression (F1=0.85), as well as
with large-scale pre-trained language models like BERT (F1=0.92). Finally, we
release the Robin dataset publicly as a machine learning resource with the
potential to drive the next generation of suicidal sentiment research.

Independent learners are learning agents that naively employ single-agent
learning algorithms in multi-agent systems, intentionally ignoring the effect
of other strategic agents present in their environment. This paper studies
$N$-player mean-field games from a decentralized learning perspective with two
primary objectives: (i) to study the convergence properties of independent
learners, and (ii) to identify structural properties of $N$-player mean-field
games that can guide algorithm design. Toward the first objective, we study the
learning iterates obtained by independent learners, and we use recent results
from POMDP theory to show that these iterates converge under mild conditions.
In particular, we consider four information structures corresponding to
information at each agent: (1) global state + local action; (2) local state,
mean-field state + local action; (3) local state, compressed mean-field state +
local action; (4) local state with local action. We present a notion of
subjective equilibrium suitable for the analysis of independent learners.
Toward the second objective, we study a family of dynamical systems on the set
of joint policies. The dynamical systems under consideration are subject to a
so-called $\epsilon$-satisficing condition: agents who are subjectively
$\epsilon$-best-responding at a given joint policy do not change their policy.
We establish a useful structural property relating to such dynamical systems.
Finally, we develop an independent learning algorithm for $N$-player mean-field
games that drives play to subjective $\epsilon$-equilibrium under self-play,
exploiting the aforementioned structural properties to guarantee convergence of
policies. Notably, we avoid requiring agents to follow the same policy (via a
representative agent) during the learning process, which has been the typical
approach in the existing literature on learning for mean-field games.

Vision transformers are emerging as a powerful tool to solve computer vision
problems. Recent techniques have also proven the efficacy of transformers
beyond the image domain to solve numerous video-related tasks. Among those,
human action recognition is receiving special attention from the research
community due to its widespread applications. This article provides the first
comprehensive survey of vision transformer techniques for action recognition.
We analyze and summarize the existing and emerging literature in this direction
while highlighting the popular trends in adapting transformers for action
recognition. Due to their specialized application, we collectively refer to
these methods as ``action transformers''. Our literature review provides
suitable taxonomies for action transformers based on their architecture,
modality, and intended objective. Within the context of action transformers, we
explore the techniques to encode spatio-temporal data, dimensionality
reduction, frame patch and spatio-temporal cube construction, and various
representation methods. We also investigate the optimization of spatio-temporal
attention in transformer layers to handle longer sequences, typically by
reducing the number of tokens in a single attention operation. Moreover, we
also investigate different network learning strategies, such as self-supervised
and zero-shot learning, along with their associated losses for
transformer-based action recognition. This survey also summarizes the progress
towards gaining grounds on evaluation metric scores on important benchmarks
with action transformers. Finally, it provides a discussion on the challenges,
outlook, and future avenues for this research direction.

The knowledge graph (KG) is an essential form of knowledge representation
that has grown in prominence in recent years. Because it concentrates on
nominal entities and their relationships, traditional knowledge graphs are
static and encyclopedic in nature. On this basis, event knowledge graph (Event
KG) models the temporal and spatial dynamics by text processing to facilitate
downstream applications, such as question-answering, recommendation and
intelligent search. Existing KG research, on the other hand, mostly focuses on
text processing and static facts, ignoring the vast quantity of dynamic
behavioral information included in photos, movies, and pre-trained neural
networks. In addition, no effort has been done to include behavioral
intelligence information into the knowledge graph for deep reinforcement
learning (DRL) and robot learning. In this paper, we propose a novel dynamic
knowledge and skill graph (KSG), and then we develop a basic and specific KSG
based on CN-DBpedia. The nodes are divided into entity and attribute nodes,
with entity nodes containing the agent, environment, and skill (DRL policy or
policy representation), and attribute nodes containing the entity description,
pre-train network, and offline dataset. KSG can search for different agents'
skills in various environments and provide transferable information for
acquiring new skills. This is the first study that we are aware of that looks
into dynamic KSG for skill retrieval and learning. Extensive experimental
results on new skill learning show that KSG boosts new skill learning

Word-level Quality Estimation (QE) of Machine Translation (MT) aims to find
out potential translation errors in the translated sentence without reference.
Typically, conventional works on word-level QE are designed to predict the
translation quality in terms of the post-editing effort, where the word labels
("OK" and "BAD") are automatically generated by comparing words between MT
sentences and the post-edited sentences through a Translation Error Rate (TER)
toolkit. While the post-editing effort can be used to measure the translation
quality to some extent, we find it usually conflicts with the human judgement
on whether the word is well or poorly translated. To overcome the limitation,
we first create a golden benchmark dataset, namely \emph{HJQE} (Human Judgement
on Quality Estimation), where the expert translators directly annotate the
poorly translated words on their judgements. Additionally, to further make use
of the parallel corpus, we propose the self-supervised pre-training with two
tag correcting strategies, namely tag refinement strategy and tree-based
annotation strategy, to make the TER-based artificial QE corpus closer to
\emph{HJQE}. We conduct substantial experiments based on the publicly available
WMT En-De and En-Zh corpora. The results not only show our proposed dataset is
more consistent with human judgment but also confirm the effectiveness of the
proposed tag correcting strategies.\footnote{The data can be found at
\url{https://github.com/ZhenYangIACAS/HJQE}.}

In this work I study the problem of adversarial perturbations to rewards, in
a Multi-armed bandit (MAB) setting. Specifically, I focus on an adversarial
attack to a UCB type best-arm identification policy applied to a stochastic
MAB. The UCB attack presented in [1] results in pulling a target arm K very
often. I used the attack model of [1] to derive the sample complexity required
for selecting target arm K as the best arm. I have proved that the stopping
condition of UCB based best-arm identification algorithm given in [2], can be
achieved by the target arm K in T rounds, where T depends only on the total
number of arms and $\sigma$ parameter of $\sigma^2-$ sub-Gaussian random
rewards of the arms.

The interpretability of machine learning models has been an essential area of
research for the safe deployment of machine learning systems. One particular
approach is to attribute model decisions to high-level concepts that humans can
understand. However, such concept-based explainability for Deep Neural Networks
(DNNs) has been studied mostly on image domain. In this paper, we extend TCAV,
the concept attribution approach, to tabular learning, by providing an idea on
how to define concepts over tabular data. On a synthetic dataset with
ground-truth concept explanations and a real-world dataset, we show the
validity of our method in generating interpretability results that match the
human-level intuitions. On top of this, we propose a notion of fairness based
on TCAV that quantifies what layer of DNN has learned representations that lead
to biased predictions of the model. Also, we empirically demonstrate the
relation of TCAV-based fairness to a group fairness notion, Demographic Parity.

One-shot Network Pruning at Initialization (OPaI) is an effective method to
decrease network pruning costs. Recently, there is a growing belief that data
is unnecessary in OPaI. However, we obtain an opposite conclusion by ablation
experiments in two representative OPaI methods, SNIP and GraSP. Specifically,
we find that informative data is crucial to enhancing pruning performance. In
this paper, we propose two novel methods, Discriminative One-shot Network
Pruning (DOP) and Super Stitching, to prune the network by high-level visual
discriminative image patches. Our contributions are as follows. (1) Extensive
experiments reveal that OPaI is data-dependent. (2) Super Stitching performs
significantly better than the original OPaI method on benchmark ImageNet,
especially in a highly compressed model.

Features, logits, and labels are the three primary data when a sample passes
through a deep neural network. Feature perturbation and label perturbation
receive increasing attention in recent years. They have been proven to be
useful in various deep learning approaches. For example, (adversarial) feature
perturbation can improve the robustness or even generalization capability of
learned models. However, limited studies have explicitly explored for the
perturbation of logit vectors. This work discusses several existing methods
related to class-level logit perturbation. A unified viewpoint between
positive/negative data augmentation and loss variations incurred by logit
perturbation is established. A theoretical analysis is provided to illuminate
why class-level logit perturbation is useful. Accordingly, new methodologies
are proposed to explicitly learn to perturb logits for both single-label and
multi-label classification tasks. Extensive experiments on benchmark image
classification data sets and their long-tail versions indicated the competitive
performance of our learning method. As it only perturbs on logit, it can be
used as a plug-in to fuse with any existing classification algorithms. All the
codes are available at https://github.com/limengyang1992/lpl.

Online news and information sources are convenient and accessible ways to
learn about current issues. For instance, more than 300 million people engage
with posts on Twitter globally, which provides the possibility to disseminate
misleading information. There are numerous cases where violent crimes have been
committed due to fake news. This research presents the CovidMis20 dataset
(COVID-19 Misinformation 2020 dataset), which consists of 1,375,592 tweets
collected from February to July 2020. CovidMis20 can be automatically updated
to fetch the latest news and is publicly available at:
https://github.com/everythingguy/CovidMis20. This research was conducted using
Bi-LSTM deep learning and an ensemble CNN+Bi-GRU for fake news detection. The
results showed that, with testing accuracy of 92.23% and 90.56%, respectively,
the ensemble CNN+Bi-GRU model consistently provided higher accuracy than the
Bi-LSTM model.

We introduce a new approach to probabilistic unsupervised learning based on
the recognition-parametrised model (RPM): a normalised semi-parametric
hypothesis class for joint distributions over observed and latent variables.
Under the key assumption that observations are conditionally independent given
the latents, RPMs directly encode the "recognition" process, parametrising both
the prior distribution on the latents and their conditional distributions given
observations. This recognition model is paired with non-parametric descriptions
of the marginal distribution of each observed variable. Thus, the focus is on
learning a good latent representation that captures dependence between the
measurements. The RPM permits exact maximum likelihood learning in settings
with discrete latents and a tractable prior, even when the mapping between
continuous observations and the latents is expressed through a flexible model
such as a neural network. We develop effective approximations for the case of
continuous latent variables with tractable priors. Unlike the approximations
necessary in dual-parametrised models such as Helmholtz machines and
variational autoencoders, these RPM approximations introduce only minor bias,
which may often vanish asymptotically. Furthermore, where the prior on latents
is intractable the RPM may be combined effectively with standard probabilistic
techniques such as variational Bayes. We demonstrate the model in high
dimensional data settings, including a form of weakly supervised learning on
MNIST digits and the discovery of latent maps from sensory observations. The
RPM provides an effective way to discover, represent and reason
probabilistically about the latent structure underlying observational data,
functions which are critical to both animal and artificial intelligence.

Recent Wave Energy Converters (WEC) are equipped with multiple legs and
generators to maximize energy generation. Traditional controllers have shown
limitations to capture complex wave patterns and the controllers must
efficiently maximize the energy capture. This paper introduces a Multi-Agent
Reinforcement Learning controller (MARL), which outperforms the traditionally
used spring damper controller. Our initial studies show that the complex nature
of problems makes it hard for training to converge. Hence, we propose a novel
skip training approach which enables the MARL training to overcome performance
saturation and converge to more optimum controllers compared to default MARL
training, boosting power generation. We also present another novel hybrid
training initialization (STHTI) approach, where the individual agents of the
MARL controllers can be initially trained against the baseline Spring Damper
(SD) controller individually and then be trained one agent at a time or all
together in future iterations to accelerate convergence. We achieved
double-digit gains in energy efficiency over the baseline Spring Damper
controller with the proposed MARL controllers using the Asynchronous Advantage
Actor-Critic (A3C) algorithm.

Video action segmentation and recognition tasks have been widely applied in
many fields. Most previous studies employ large-scale, high computational
visual models to understand videos comprehensively. However, few studies
directly employ the graph model to reason about the video. The graph model
provides the benefits of fewer parameters, low computational cost, a large
receptive field, and flexible neighborhood message aggregation. In this paper,
we present a graph-based method named Semantic2Graph, to turn the video action
segmentation and recognition problem into node classification of graphs. To
preserve fine-grained relations in videos, we construct the graph structure of
videos at the frame-level and design three types of edges: temporal, semantic,
and self-loop. We combine visual, structural, and semantic features as node
attributes. Semantic edges are used to model long-term spatio-temporal
relations, while the semantic features are the embedding of the label-text
based on the textual prompt. A Graph Neural Networks (GNNs) model is used to
learn multi-modal feature fusion. Experimental results show that Semantic2Graph
achieves improvement on GTEA and 50Salads, compared to the state-of-the-art
results. Multiple ablation experiments further confirm the effectiveness of
semantic features in improving model performance, and semantic edges enable
Semantic2Graph to capture long-term dependencies at a low cost.

We study the problem of estimating an unknown parameter in a distributed and
online manner. Existing work on distributed online learning typically either
focuses on asymptotic analysis, or provides bounds on regret. However, these
results may not directly translate into bounds on the error of the learned
model after a finite number of time-steps. In this paper, we propose a
distributed online estimation algorithm which enables each agent in a network
to improve its estimation accuracy by communicating with neighbors. We provide
non-asymptotic bounds on the estimation error, leveraging the statistical
properties of the underlying model. Our analysis demonstrates a trade-off
between estimation error and communication costs. Further, our analysis allows
us to determine a time at which the communication can be stopped (due to the
costs associated with communications), while meeting a desired estimation
accuracy. We also provide a numerical example to validate our results.

As robots across domains start collaborating with humans in shared
environments, algorithms that enable them to reason over human intent are
important to achieve safe interplay. In our work, we study human intent through
the problem of predicting trajectories in dynamic environments. We explore
domains where navigation guidelines are relatively strictly defined but not
clearly marked in their physical environments. We hypothesize that within these
domains, agents tend to exhibit short-term motion patterns that reveal context
information related to the agent's general direction, intermediate goals and
rules of motion, e.g., social behavior. From this intuition, we propose
Social-PatteRNN, an algorithm for recurrent, multi-modal trajectory prediction
that exploits motion patterns to encode the aforesaid contexts. Our approach
guides long-term trajectory prediction by learning to predict short-term motion
patterns. It then extracts sub-goal information from the patterns and
aggregates it as social context. We assess our approach across three domains:
humans crowds, humans in sports and manned aircraft in terminal airspace,
achieving state-of-the-art performance.

High-quality system-level message flow specifications are necessary for
comprehensive validation of system-on-chip (SoC) designs. However, manual
development and maintenance of such specifications are daunting tasks. We
propose a disruptive method that utilizes deep sequence modeling with the
attention mechanism to infer accurate flow specifications from SoC
communication traces. The proposed method can overcome the inherent complexity
of SoC traces induced by the concurrent executions of SoC designs that existing
mining tools often find extremely challenging. We conduct experiments on five
highly concurrent traces and find that the proposed approach outperforms
several existing state-of-the-art trace mining tools.

Recently there is an increasing scholarly interest in time-varying knowledge
graphs, or temporal knowledge graphs (TKG). Previous research suggests diverse
approaches to TKG reasoning that uses historical information. However, less
attention has been given to the hierarchies within such information at
different timestamps. Given that TKG is a sequence of knowledge graphs based on
time, the chronology in the sequence derives hierarchies between the graphs.
Furthermore, each knowledge graph has its hierarchical level which may differ
from one another. To address these hierarchical characteristics in TKG, we
propose HyperVC, which utilizes hyperbolic space that better encodes the
hierarchies than Euclidean space. The chronological hierarchies between
knowledge graphs at different timestamps are represented by embedding the
knowledge graphs as vectors in a common hyperbolic space. Additionally, diverse
hierarchical levels of knowledge graphs are represented by adjusting the
curvatures of hyperbolic embeddings of their entities and relations.
Experiments on four benchmark datasets show substantial improvements,
especially on the datasets with higher hierarchical levels.

To mitigate the noise in quantum channels, calibration is used to tune the
communication system to minimize error. Generally, calibration is performed by
transmitting pre-agreed-upon calibration states and determining an error cost
so the two parties can tune their devices accordingly. The calibration states
can be the same ones used for the desired protocol and so an untrusted party
could potentially learn which protocol is being performed by gathering
knowledge of the calibration states and cost function. Here, we assume an
insecure quantum channel and therefore the receiver should not be allowed to
determine the calibration states, but is honest in announcing the measurement
results to the sender. We limit the classical information that is openly
transmitted and in this regard, we propose a simple protocol that hides the
calibration states and cost function from the receiver, but still allows for
calibration to be performed efficiently. We show various numerical results
demonstrating the ability of the protocol under various channel noise
parameters and communication scenarios.

Semantic 3D scene understanding is a problem of critical importance in
robotics. While significant advances have been made in spatial perception,
robots are still far from having the common-sense knowledge about household
objects and locations of an average human. We thus investigate the use of large
language models to impart common sense for scene understanding. Specifically,
we introduce three paradigms for leveraging language for classifying rooms in
indoor environments based on their contained objects: (i) a zero-shot approach,
(ii) a feed-forward classifier approach, and (iii) a contrastive classifier
approach. These methods operate on 3D scene graphs produced by modern spatial
perception systems. We then analyze each approach, demonstrating notable
zero-shot generalization and transfer capabilities stemming from their use of
language. Finally, we show these approaches also apply to inferring building
labels from contained rooms and demonstrate our zero-shot approach on a real
environment. All code can be found at
https://github.com/MIT-SPARK/llm_scene_understanding.

Deep Linear and Nonlinear learning methods have already been vital machine
learning methods for investigating the hierarchical features such as functional
connectivity in the human brain via functional Magnetic Resonance signals;
however, there are three major shortcomings: 1). For deep linear learning
methods, although the identified hierarchy of functional connectivity is easily
explainable, it is challenging to reveal more hierarchical functional
connectivity; 2). For deep nonlinear learning methods, although non-fully
connected architecture reduces the complexity of neural network structures that
are easy to optimize and not vulnerable to overfitting, the functional
connectivity hierarchy is difficult to explain; 3). Importantly, it is
challenging for Deep Linear/Nonlinear methods to detect meta and sub-functional
connectivity even in the shallow layers; 4). Like most conventional Deep
Nonlinear Methods, such as Deep Neural Networks, the hyperparameters must be
tuned manually, which is time-consuming. Thus, in this work, we propose a novel
deep hybrid learning method named SEmi-Nonlinear Deep Efficient Reconstruction
(SENDER), to overcome the aforementioned shortcomings: 1). SENDER utilizes a
multiple-layer stacked structure for the linear learning methods to detect the
canonical functional connectivity; 2). SENDER implements a non-fully connected
architecture conducted for the nonlinear learning methods to reveal the
meta-functional connectivity through shallow and deeper layers; 3). SENDER
incorporates the proposed background components to extract the sub-functional
connectivity; 4). SENDER adopts a novel rank reduction operator to implement
the hyperparameters tuning automatically. To further validate the
effectiveness, we compared SENDER with four peer methodologies using real
functional Magnetic Resonance Imaging data for the human brain.

We consider the problem of category-level 6D pose estimation from a single
RGB image. Our approach represents an object category as a cuboid mesh and
learns a generative model of the neural feature activations at each mesh vertex
to perform pose estimation through differentiable rendering. A common problem
of rendering-based approaches is that they rely on bounding box proposals,
which do not convey information about the 3D rotation of the object and are not
reliable when objects are partially occluded. Instead, we introduce a
coarse-to-fine optimization strategy that utilizes the rendering process to
estimate a sparse set of 6D object proposals, which are subsequently refined
with gradient-based optimization. The key to enabling the convergence of our
approach is a neural feature representation that is trained to be scale- and
rotation-invariant using contrastive learning. Our experiments demonstrate an
enhanced category-level 6D pose estimation performance compared to prior work,
particularly under strong partial occlusion.

In this work we present a new approach for the task of predicting fingerings
for piano music. While prior neural approaches have often treated this as a
sequence tagging problem with independent predictions, we put forward a
checklist system, trained via reinforcement learning, that maintains a
representation of recent predictions in addition to a hidden state, allowing it
to learn soft constraints on output structure. We also demonstrate that by
modifying input representations -- which in prior work using neural models have
often taken the form of one-hot encodings over individual keys on the piano --
to encode relative position on the keyboard to the prior note instead, we can
achieve much better performance. Additionally, we reassess the use of raw
per-note labeling precision as an evaluation metric, noting that it does not
adequately measure the fluency, i.e. human playability, of a model's output. To
this end, we compare methods across several statistics which track the
frequency of adjacent finger predictions that while independently reasonable
would be physically challenging to perform in sequence, and implement a
reinforcement learning strategy to minimize these as part of our training loss.
Finally through human expert evaluation, we demonstrate significant gains in
performability directly attributable to improvements with respect to these
metrics.

We present a data-centric deep learning (DL) approach using neural networks
(NNs) to predict the thermodynamics of ternary solid solutions. We explore how
NNs can be trained with a dataset of Gibbs free energies computed from a
CALPHAD database to predict ternary systems as a function of composition and
temperature. We have chosen the energetics of the FCC solid solution phase in
226 binaries consisting of 23 elements at 11 different temperatures to
demonstrate the feasibility. The number of binary data points included in the
present study is 102,000. We select six ternaries to augment the binary dataset
to investigate their influence on the NN prediction accuracy. We examine the
sensitivity of data sampling on the prediction accuracy of NNs over selected
ternary systems. It is anticipated that the current DL workflow can be further
elevated by integrating advanced descriptors beyond the elemental composition
and more curated training datasets to improve prediction accuracy and
applicability.

Risk assessment of roadways is commonly practiced based on historical crash
data. Information on driver behaviors and real-time traffic situations is
sometimes missing. In this paper, the Safe Route Mapping (SRM) model, a
methodology for developing dynamic risk heat maps of roadways, is extended to
consider driver behaviors when making predictions. An Android App is designed
to gather drivers' information and upload it to a server. On the server, facial
recognition extracts drivers' data, such as facial landmarks, gaze directions,
and emotions. The driver's drowsiness and distraction are detected, and driving
performance is evaluated. Meanwhile, dynamic traffic information is captured by
a roadside camera and uploaded to the same server. A
longitudinal-scanline-based arterial traffic video analytics is applied to
recognize vehicles from the video to build speed and trajectory profiles. Based
on these data, a LightGBM model is introduced to predict conflict indices for
drivers in the next one or two seconds. Then, multiple data sources, including
historical crash counts and predicted traffic conflict indicators, are combined
using a Fuzzy logic model to calculate risk scores for road segments. The
proposed SRM model is illustrated using data collected from an actual traffic
intersection and a driving simulation platform. The prediction results show
that the model is accurate, and the added driver behavior features will improve
the model's performance. Finally, risk heat maps are generated for
visualization purposes. The authorities can use the dynamic heat map to
designate safe corridors and dispatch law enforcement and drivers for early
warning and trip planning.

Existing efforts to formulate computational definitions of fairness have
largely focused on distributional notions of equality, where equality is
defined by the resources or decisions given to individuals in the system. Yet
existing discrimination and injustice is often the result of unequal social
relations, rather than an unequal distribution of resources. Here, we show how
optimizing for existing computational and economic definitions of fairness and
equality fail to prevent unequal social relations. To do this, we provide an
example of a self-confirming equilibrium in a simple hiring market that is
relationally unequal but satisfies existing distributional notions of fairness.
In doing so, we introduce a notion of blatant relational unfairness for
complete-information games, and discuss how this definition helps initiate a
new approach to incorporating relational equality into computational systems.

Causal discovery (CD) from time-varying data is important in neuroscience,
medicine, and machine learning. Techniques for CD include randomized
experiments which are generally unbiased but expensive. It also includes
algorithms like regression, matching, and Granger causality, which are only
correct under strong assumptions made by human designers. However, as we found
in other areas of machine learning, humans are usually not quite right and
human expertise is usually outperformed by data-driven approaches. Here we test
if we can improve causal discovery in a data-driven way. We take a perturbable
system with a large number of causal components (transistors), the MOS 6502
processor, acquire the causal ground truth, and learn the causal discovery
procedure represented as a neural network. We find that this procedure far
outperforms human-designed causal discovery procedures, such as Mutual
Information, LiNGAM, and Granger Causality both on MOS 6502 processor and the
NetSim dataset which simulates functional magnetic resonance imaging (fMRI)
results. We argue that the causality field should consider, where possible, a
supervised approach, where CD procedures are learned from large datasets with
known causal relations instead of being designed by a human specialist. Our
findings promise a new approach toward improving CD in neural and medical data
and for the broader machine learning community.

We develop a polycrystal graph neural network (PGNN) model for predicting the
properties of three-dimensional (3D) polycrystalline microstructures. Compared
to existing GNN models in this domain, the PGNN model considers the physical
features of both the grains and grain boundaries, and therefore better aligns
with the physical principles. To evaluate the property prediction and transfer
learning performance of the PGNN model, a dataset of 5000 3D polycrystalline
microstructures and corresponding effective properties is generated by
high-throughput physics-based simulations. The effective Li-ion conductivities
and elastic stiffness coefficients of the polycrystalline Li7La3Zr2O12 ceramic
are utilized as the examples. Trained with 4000 data points, the PGNN model
achieves a property prediction error of <1.4%, which is superior to all the
baseline machine learning models, including a linear artificial neural network,
a convolutional neural network (CNN), and the ResNet. Furthermore, compared to
the PGNN model trained from scratch, a pretrained PGNN model demonstrates a
significant shorter training time and lower prediction error. Our fast,
accurate, and transferable PGNN model is therefore well suited for harnessing
large-scale 3D polycrystalline microstructure images that usually contain
billions of voxels per image, which can be too slow to address with voxel-based
machine learning models, such as the CNN.

Graph neural networks (GNNs), which are capable of learning representations
from graphical data, are naturally suitable for modeling molecular systems.
This review introduces GNNs and their various applications for small organic
molecules. GNNs rely on message-passing operations, a generic yet powerful
framework, to update node features iteratively. Many researches design GNN
architectures to effectively learn topological information of 2D molecule
graphs as well as geometric information of 3D molecular systems. GNNs have been
implemented in a wide variety of molecular applications, including molecular
property prediction, molecular scoring and docking, molecular optimization and
de novo generation, molecular dynamics simulation, etc. Besides, the review
also summarizes the recent development of self-supervised learning for
molecules with GNNs.

In this paper we present BayesLDM, a system for Bayesian longitudinal data
modeling consisting of a high-level modeling language with specific features
for modeling complex multivariate time series data coupled with a compiler that
can produce optimized probabilistic program code for performing inference in
the specified model. BayesLDM supports modeling of Bayesian network models with
a specific focus on the efficient, declarative specification of dynamic
Bayesian Networks (DBNs). The BayesLDM compiler combines a model specification
with inspection of available data and outputs code for performing Bayesian
inference for unknown model parameters while simultaneously handling missing
data. These capabilities have the potential to significantly accelerate
iterative modeling workflows in domains that involve the analysis of complex
longitudinal data by abstracting away the process of producing computationally
efficient probabilistic inference code. We describe the BayesLDM system
components, evaluate the efficiency of representation and inference
optimizations and provide an illustrative example of the application of the
system to analyzing heterogeneous and partially observed mobile health data.

With the growing rates of cyber-attacks and cyber espionage, the need for
better and more powerful intrusion detection systems (IDS) is even more
warranted nowadays. The basic task of an IDS is to act as the first line of
defense, in detecting attacks on the internet. As intrusion tactics from
intruders become more sophisticated and difficult to detect, researchers have
started to apply novel Machine Learning (ML) techniques to effectively detect
intruders and hence preserve internet users' information and overall trust in
the entire internet network security. Over the last decade, there has been an
explosion of research on intrusion detection techniques based on ML and Deep
Learning (DL) architectures on various cyber security-based datasets such as
the DARPA, KDDCUP'99, NSL-KDD, CAIDA, CTU-13, UNSW-NB15. In this research, we
review contemporary literature and provide a comprehensive survey of different
types of intrusion detection technique that applies Support Vector Machines
(SVMs) algorithms as a classifier. We focus only on studies that have been
evaluated on the two most widely used datasets in cybersecurity namely: the
KDDCUP'99 and the NSL-KDD datasets. We provide a summary of each method,
identifying the role of the SVMs classifier, and all other algorithms involved
in the studies. Furthermore, we present a critical review of each method, in
tabular form, highlighting the performance measures, strengths, and limitations
of each of the methods surveyed.

Federated learning (FL) aims to perform privacy-preserving machine learning
on distributed data held by multiple data owners. To this end, FL requires the
data owners to perform training locally and share the gradient updates (instead
of the private inputs) with the central server, which are then securely
aggregated over multiple data owners. Although aggregation by itself does not
provably offer privacy protection, prior work showed that it may suffice if the
batch size is sufficiently large. In this paper, we propose the Cocktail Party
Attack (CPA) that, contrary to prior belief, is able to recover the private
inputs from gradients aggregated over a very large batch size. CPA leverages
the crucial insight that aggregate gradients from a fully connected layer is a
linear combination of its inputs, which leads us to frame gradient inversion as
a blind source separation (BSS) problem (informally called the cocktail party
problem). We adapt independent component analysis (ICA)--a classic solution to
the BSS problem--to recover private inputs for fully-connected and
convolutional networks, and show that CPA significantly outperforms prior
gradient inversion attacks, scales to ImageNet-sized inputs, and works on large
batch sizes of up to 1024.

AI-generated synthetic data allows to distill the general patterns of
existing data, that can then be shared safely as granular-level representative,
yet novel data samples within the original semantics. In this work we explore
approaches of incorporating domain expertise into the data synthesis, to have
the statistical properties as well as pre-existing domain knowledge of rules be
represented. The resulting synthetic data generator, that can be probed for any
number of new samples, can then serve as a common source of intelligence, as a
lingua franca of learning, consumable by humans and machines alike. We
demonstrate the concept for a publicly available data set, and evaluate its
benefits via descriptive analysis as well as a downstream ML model.

Consider a Bayesian decision maker (DM) who, at each time period, chooses to
either continue paying attention to information about a state upon which her
utility depends, or stop and take action based on her best available
information. We show that all stopping times$\unicode{x2013}$the random times
at which DM stops paying attention$\unicode{x2013}$which can be implemented
through some dynamic information structure can be replicated by simple
structures which give the DM full information at random times, and the null
message otherwise. We use this to characterize the stopping times which can be
achieved through information. All stopping times can be implemented by simple
structures which steers the DM's beliefs deterministically towards a basin at
which her valuation for full information is maximized. We then introduce a
designer whose value function is any increasing function of the DM's stopping
time. We show that every optimal design leaves the DM with zero utility, and
that optimal designs take a block structure in which the DM (i) has some chance
to learn the state at the boundaries of each block; and (ii) receives no
information with probability one in the interior. Our results speak directly to
the attention economy.

The comparison of local characteristics of two random processes can shed
light on periods of time or space at which the processes differ the most. This
paper proposes a method that learns about regions with a certain volume, where
the marginal attributes of two processes are less similar. The proposed methods
are devised in full generality for the setting where the data of interest are
themselves stochastic processes, and thus the proposed method can be used for
pointing out the regions of maximum dissimilarity with a certain volume, in the
contexts of functional data, time series, and point processes. The parameter
functions underlying both stochastic processes of interest are modeled via a
basis representation, and Bayesian inference is conducted via an integrated
nested Laplace approximation. The numerical studies validate the proposed
methods, and we showcase their application with case studies on criminology,
finance, and medicine.

Designing profitable and reliable trading strategies is challenging in the
highly volatile cryptocurrency market. Existing works applied deep
reinforcement learning methods and optimistically reported increased profits in
backtesting, which may suffer from the false positive issue due to overfitting.
In this paper, we propose a practical approach to address backtest overfitting
for cryptocurrency trading using deep reinforcement learning. First, we
formulate the detection of backtest overfitting as a hypothesis test. Then, we
train the DRL agents, estimate the probability of overfitting, and reject the
overfitted agents, increasing the chance of good trading performance. Finally,
on 10 cryptocurrencies over a testing period from 05/01/2022 to 06/27/2022
(during which the crypto market crashed two times), we show that the less
overfitted deep reinforcement learning agents have a higher return than that of
more overfitted agents, an equal weight strategy, and the S&P DBM Index (market
benchmark), offering confidence in possible deployment to a real market.

Recently, Rissanen et al., (2022) have presented a new type of diffusion
process for generative modeling based on heat dissipation, or blurring, as an
alternative to isotropic Gaussian diffusion. Here, we show that blurring can
equivalently be defined through a Gaussian diffusion process with non-isotropic
noise. In making this connection, we bridge the gap between inverse heat
dissipation and denoising diffusion, and we shed light on the inductive bias
that results from this modeling choice. Finally, we propose a generalized class
of diffusion models that offers the best of both standard Gaussian denoising
diffusion and inverse heat dissipation, which we call Blurring Diffusion
Models.

The key to e-commerce search is how to best utilize the large yet noisy log
data. In this paper, we present our embedding-based model for grocery search at
Instacart. The system learns query and product representations with a two-tower
transformer-based encoder architecture. To tackle the cold-start problem, we
focus on content-based features. To train the model efficiently on noisy data,
we propose a self-adversarial learning method and a cascade training method.
AccOn an offline human evaluation dataset, we achieve 10% relative improvement
in RECALL@20, and for online A/B testing, we achieve 4.1% cart-adds per search
(CAPS) and 1.5% gross merchandise value (GMV) improvement. We describe how we
train and deploy the embedding based search model and give a detailed analysis
of the effectiveness of our method.

Classical force fields (FF) based on machine learning (ML) methods show great
potential for large scale simulations of materials. MLFFs have hitherto largely
been designed and fitted for specific systems and are not usually transferable
to chemistries beyond the specific training set. We develop a unified
atomisitic line graph neural network-based FF (ALIGNN-FF) that can model both
structurally and chemically diverse materials with any combination of 89
elements from the periodic table. To train the ALIGNN-FF model, we use the
JARVIS-DFT dataset which contains around 75000 materials and 4 million
energy-force entries, out of which 307113 are used in the training. We
demonstrate the applicability of this method for fast optimization of atomic
structures in the crystallography open database and by predicting accurate
crystal structures using genetic algorithm for alloys.

Social media platforms (SMPs) leverage algorithmic filtering (AF) as a means
of selecting the content that constitutes a user's feed with the aim of
maximizing their rewards. Selectively choosing the contents to be shown on the
user's feed may yield a certain extent of influence, either minor or major, on
the user's decision-making, compared to what it would have been under a
natural/fair content selection. As we have witnessed over the past decade,
algorithmic filtering can cause detrimental side effects, ranging from biasing
individual decisions to shaping those of society as a whole, for example,
diverting users' attention from whether to get the COVID-19 vaccine or inducing
the public to choose a presidential candidate. The government's constant
attempts to regulate the adverse effects of AF are often complicated, due to
bureaucracy, legal affairs, and financial considerations. On the other hand
SMPs seek to monitor their own algorithmic activities to avoid being fined for
exceeding the allowable threshold. In this paper, we mathematically formalize
this framework and utilize it to construct a data-driven statistical algorithm
to regulate the AF from deflecting users' beliefs over time, along with sample
and complexity guarantees. We show that our algorithm is robust against
potential adversarial users. This state-of-the-art algorithm can be used either
by authorities acting as external regulators or by SMPs for self-regulation.

We consider the problem of recovering the three-dimensional atomic structure
of a flexible macromolecule from a heterogeneous cryo-EM dataset. The dataset
contains noisy tomographic projections of the electrostatic potential of the
macromolecule, taken from different viewing directions, and in the
heterogeneous case, each image corresponds to a different conformation of the
macromolecule. Under the assumption that the macromolecule can be modelled as a
chain, or discrete curve (as it is for instance the case for a protein backbone
with a single chain of amino-acids), we introduce a method to estimate the
deformation of the atomic model with respect to a given conformation, which is
assumed to be known a priori. Our method consists on estimating the torsion and
bond angles of the atomic model in each conformation as a linear combination of
the eigenfunctions of the Laplace operator in the manifold of conformations.
These eigenfunctions can be approximated by means of a well-known technique in
manifold learning, based on the construction of a graph Laplacian using the
cryo-EM dataset. Finally, we test our approach with synthetic datasets, for
which we recover the atomic model of two-dimensional and three-dimensional
flexible structures from noisy tomographic projections.

We develop a new formulation of deep learning based on the Mori-Zwanzig (MZ)
formalism of irreversible statistical mechanics. The new formulation is built
upon the well-known duality between deep neural networks and discrete
stochastic dynamical systems, and it allows us to directly propagate quantities
of interest (conditional expectations and probability density functions)
forward and backward through the network by means of exact linear operator
equations. Such new equations can be used as a starting point to develop new
effective parameterizations of deep neural networks, and provide a new
framework to study deep-learning via operator theoretic methods. The proposed
MZ formulation of deep learning naturally introduces a new concept, i.e., the
memory of the neural network, which plays a fundamental role in low-dimensional
modeling and parameterization. By using the theory of contraction mappings, we
develop sufficient conditions for the memory of the neural network to decay
with the number of layers. This allows us to rigorously transform deep networks
into shallow ones, e.g., by reducing the number of neurons per layer (using
projection operators), or by reducing the total number of layers (using the
decay property of the memory operator).

The ability to read and reason about texts in an image is often lacking in
vision-and-language (V&L) models. How can we learn V&L models that exhibit
strong scene-text understanding (STU)? In this paper, we propose PreSTU, a
simple pre-training recipe specifically designed for scene-text understanding.
PreSTU combines a simple OCR-aware pre-training objective with a large-scale
image-text dataset with off-the-shelf OCR signals. We empirically demonstrate
the superiority of this pre-training objective on TextVQA, TextCaps, ST-VQA,
and VizWiz-VQA. We also study which factors affect STU performance, where we
highlight the importance of image resolution and dataset scale during

The operating room (OR) is a dynamic and complex environment consisting of a
multidisciplinary team working together in a high take environment to provide
safe and efficient patient care. Additionally, surgeons are frequently exposed
to multiple psycho-organisational stressors that may cause negative
repercussions on their immediate technical performance and long-term health.
Many factors can therefore contribute to increasing the Cognitive Workload
(CWL) such as temporal pressures, unfamiliar anatomy or distractions in the OR.
In this paper, a cascade of two machine learning approaches is suggested for
the multimodal recognition of CWL in four different surgical task conditions.
Firstly, a model based on the concept of transfer learning is used to identify
if a surgeon is experiencing any CWL. Secondly, a Convolutional Neural Network
(CNN) uses this information to identify different degrees of CWL associated to
each surgical task. The suggested multimodal approach considers adjacent
signals from electroencephalogram (EEG), functional near-infrared spectroscopy
(fNIRS) and eye pupil diameter. The concatenation of signals allows complex
correlations in terms of time (temporal) and channel location (spatial). Data
collection was performed by a Multi-sensing AI Environment for Surgical Task &
Role Optimisation platform (MAESTRO) developed at the Hamlyn Centre, Imperial
College London. To compare the performance of the proposed methodology, a
number of state-of-art machine learning techniques have been implemented. The
tests show that the proposed model has a precision of 93%.

Software architecture education remains challenging for instructors,
students, and software industry professionals. Several initiatives have been
proposed to mitigate the inherent challenges, including games, supporting
tools, collaborative courses, and hands-on projects. Case-based learning has
been introduced in software architecture, and its benefits are recognized.
However, choosing the right cases that cover the stated learning objectives and
developing learning activities to achieve high-order learning are also
challenging. The main goal of this paper is to present a case-based learning
approach that guides the development of learning objectives, the finding and
selection of real-world software architecture cases, and the design of
instructional activities. We applied our approach in software architecture
related courses during the past few years. The results show that it can
leverage the ways to adequately explore cases for educational purposes while
also motivating instructors and students to the software architecture

A promising way to improve the sample efficiency of reinforcement learning is
model-based methods, in which many explorations and evaluations can happen in
the learned models to save real-world samples. However, when the learned model
has a non-negligible model error, sequential steps in the model are hard to be
accurately evaluated, limiting the model's utilization. This paper proposes to
alleviate this issue by introducing multi-step plans to replace multi-step
actions for model-based RL. We employ the multi-step plan value estimation,
which evaluates the expected discounted return after executing a sequence of
action plans at a given state, and updates the policy by directly computing the
multi-step policy gradient via plan value estimation. The new model-based
reinforcement learning algorithm MPPVE (Model-based Planning Policy Learning
with Multi-step Plan Value Estimation) shows a better utilization of the
learned model and achieves a better sample efficiency than state-of-the-art
model-based RL approaches.

The widespread success of deep neural networks has revealed a surprise in
classical machine learning: very complex models often generalize well while
simultaneously overfitting training data. This phenomenon of benign overfitting
has been studied for a variety of classical models with the goal of better
understanding the mechanisms behind deep learning. Characterizing the
phenomenon in the context of quantum machine learning might similarly improve
our understanding of the relationship between overfitting,
overparameterization, and generalization. In this work, we provide a
characterization of benign overfitting in quantum models. To do this, we derive
the behavior of a classical interpolating Fourier features models for
regression on noisy signals, and show how a class of quantum models exhibits
analogous features, thereby linking the structure of quantum circuits (such as
data-encoding and state preparation operations) to overparameterization and
overfitting in quantum models. We intuitively explain these features according
to the ability of the quantum model to interpolate noisy data with locally
"spiky" behavior and provide a concrete demonstration example of benign
overfitting.

In this paper, we propose TEDL, a two-stage learning approach to quantify
uncertainty for deep learning models in classification tasks, inspired by our
findings in experimenting with Evidential Deep Learning (EDL) method, a
recently proposed uncertainty quantification approach based on the
Dempster-Shafer theory. More specifically, we observe that EDL tends to yield
inferior AUC compared with models learnt by cross-entropy loss and is highly
sensitive in training. Such sensitivity is likely to cause unreliable
uncertainty estimation, making it risky for practical applications. To mitigate
both limitations, we propose a simple yet effective two-stage learning approach
based on our analysis on the likely reasons causing such sensitivity, with the
first stage learning from cross-entropy loss, followed by a second stage
learning from EDL loss. We also re-formulate the EDL loss by replacing ReLU
with ELU to avoid the Dying ReLU issue. Extensive experiments are carried out
on varied sized training corpus collected from a large-scale commercial search
engine, demonstrating that the proposed two-stage learning framework can
increase AUC significantly and greatly improve training robustness.

We study the benefits of jet- and event-level deep learning methods in
distinguishing vector boson fusion (VBF) from gluon-gluon fusion (GGF) Higgs
production at the LHC. We show that a variety of classifiers (CNNs,
attention-based networks) trained on the complete low-level inputs of the full
event achieve significant performance gains over shallow machine learning
methods (BDTs) trained on jet kinematics and jet shapes, and we elucidate the
reasons for these performance gains. Finally, we take initial steps towards the
possibility of a VBF vs. GGF tagger that is agnostic to the Higgs decay mode,
by demonstrating that the performance of our event-level CNN does not change
when the Higgs decay products are removed. These results highlight the
potentially powerful benefits of event-level deep learning at the LHC.

Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and
fetal growth monitoring. The task of matching corresponding cross-sectional
planes in the 3D anatomy for a given 2D ultrasound brain scan is essential in
freehand scanning, but challenging. We propose AdLocUI, a framework that
Adaptively Localizes 2D Ultrasound Images in the 3D anatomical atlas without
using any external tracking sensor.. We first train a convolutional neural
network with 2D slices sampled from co-aligned 3D ultrasound volumes to predict
their locations in the 3D anatomical atlas. Next, we fine-tune it with 2D
freehand ultrasound images using a novel unsupervised cycle consistency, which
utilizes the fact that the overall displacement of a sequence of images in the
3D anatomical atlas is equal to the displacement from the first image to the
last in that sequence. We demonstrate that AdLocUI can adapt to three different
ultrasound datasets, acquired with different machines and protocols, and
achieves significantly better localization accuracy than the baselines. AdLocUI
can be used for sensorless 2D freehand ultrasound guidance by the bedside. The
source code is available at https://github.com/pakheiyeung/AdLocUI.

Transformers have revolutionized vision and natural language processing with
their ability to scale with large datasets. But in robotic manipulation, data
is both limited and expensive. Can manipulation still benefit from Transformers
with the right problem formulation? We investigate this question with PerAct, a
language-conditioned behavior-cloning agent for multi-task 6-DoF manipulation.
PerAct encodes language goals and RGB-D voxel observations with a Perceiver
Transformer, and outputs discretized actions by ``detecting the next best voxel
action''. Unlike frameworks that operate on 2D images, the voxelized 3D
observation and action space provides a strong structural prior for efficiently
learning 6-DoF actions. With this formulation, we train a single multi-task
Transformer for 18 RLBench tasks (with 249 variations) and 7 real-world tasks
(with 18 variations) from just a few demonstrations per task. Our results show
that PerAct significantly outperforms unstructured image-to-action agents and
3D ConvNet baselines for a wide range of tabletop tasks.

Deep neural networks have been shown to be vulnerable to adversarial attacks
that perturb inputs based on semantic features. Existing robustness analyzers
can reason about semantic feature neighborhoods to increase the networks'
reliability. However, despite the significant progress in these techniques,
they still struggle to scale to deep networks and large neighborhoods. In this
work, we introduce VeeP, an active learning approach that splits the
verification process into a series of smaller verification steps, each is
submitted to an existing robustness analyzer. The key idea is to build on prior
steps to predict the next optimal step. The optimal step is predicted by
estimating the certification velocity and sensitivity via parametric
regression. We evaluate VeeP on MNIST, Fashion-MNIST, CIFAR-10 and ImageNet and
show that it can analyze neighborhoods of various features: brightness,
contrast, hue, saturation, and lightness. We show that, on average, given a 90
minute timeout, VeeP verifies 96% of the maximally certifiable neighborhoods
within 29 minutes, while existing splitting approaches verify, on average, 73%
of the maximally certifiable neighborhoods within 58 minutes.

We define a broader family of corruption processes that generalizes
previously known diffusion models. To reverse these general diffusions, we
propose a new objective called Soft Score Matching that provably learns the
score function for any linear corruption process and yields state of the art
results for CelebA. Soft Score Matching incorporates the degradation process in
the network. Our new loss trains the model to predict a clean image,
\textit{that after corruption}, matches the diffused observation. We show that
our objective learns the gradient of the likelihood under suitable regularity
conditions for a family of corruption processes. We further develop a
principled way to select the corruption levels for general diffusion processes
and a novel sampling method that we call Momentum Sampler. We show
experimentally that our framework works for general linear corruption
processes, such as Gaussian blur and masking. We achieve state-of-the-art FID
score $1.85$ on CelebA-64, outperforming all previous linear diffusion models.
We also show significant computational benefits compared to vanilla denoising
diffusion.

In contrast to the traditional avatar creation pipeline which is a costly
process, contemporary generative approaches directly learn the data
distribution from photographs. While plenty of works extend unconditional
generative models and achieve some levels of controllability, it is still
challenging to ensure multi-view consistency, especially in large poses. In
this work, we propose a network that generates 3D-aware portraits while being
controllable according to semantic parameters regarding pose, identity,
expression and illumination. Our network uses neural scene representation to
model 3D-aware portraits, whose generation is guided by a parametric face model
that supports explicit control. While the latent disentanglement can be further
enhanced by contrasting images with partially different attributes, there still
exists noticeable inconsistency in non-face areas, e.g., hair and background,
when animating expressions. Wesolve this by proposing a volume blending
strategy in which we form a composite output by blending dynamic and static
areas, with two parts segmented from the jointly learned semantic field. Our
method outperforms prior arts in extensive experiments, producing realistic
portraits with vivid expression in natural lighting when viewed from free
viewpoints. It also demonstrates generalization ability to real images as well
as out-of-domain data, showing great promise in real applications.

FP8 is a natural progression for accelerating deep learning training
inference beyond the 16-bit formats common in modern processors. In this paper
we propose an 8-bit floating point (FP8) binary interchange format consisting
of two encodings - E4M3 (4-bit exponent and 3-bit mantissa) and E5M2 (5-bit
exponent and 2-bit mantissa). While E5M2 follows IEEE 754 conventions for
representatio of special values, E4M3's dynamic range is extended by not
representing infinities and having only one mantissa bit-pattern for NaNs. We
demonstrate the efficacy of the FP8 format on a variety of image and language
tasks, effectively matching the result quality achieved by 16-bit training
sessions. Our study covers the main modern neural network architectures - CNNs,
RNNs, and Transformer-based models, leaving all the hyperparameters unchanged
from the 16-bit baseline training sessions. Our training experiments include
large, up to 175B parameter, language models. We also examine FP8
post-training-quantization of language models trained using 16-bit formats that
resisted fixed point int8 quantization.

One of the challenging input settings for visual servoing is when the initial
and goal camera views are far apart. Such settings are difficult because the
wide baseline can cause drastic changes in object appearance and cause
occlusions. This paper presents a novel self-supervised visual servoing method
for wide baseline images which does not require 3D ground truth supervision.
Existing approaches that regress absolute camera pose with respect to an object
require 3D ground truth data of the object in the forms of 3D bounding boxes or
meshes. We learn a coherent visual representation by leveraging a geometric
property called 3D equivariance-the representation is transformed in a
predictable way as a function of 3D transformation. To ensure that the
feature-space is faithful to the underlying geodesic space, a geodesic
preserving constraint is applied in conjunction with the equivariance. We
design a Siamese network that can effectively enforce these two geometric
properties without requiring 3D supervision. With the learned model, the
relative transformation can be inferred simply by following the gradient in the
learned space and used as feedback for closed-loop visual servoing. Our method
is evaluated on objects from the YCB dataset, showing meaningful outperformance
on a visual servoing task, or object alignment task with respect to
state-of-the-art approaches that use 3D supervision. Ours yields more than 35%
average distance error reduction and more than 90% success rate with 3cm error
tolerance.

Physical interaction with textiles, such as assistive dressing, relies on
advanced dextreous capabilities. The underlying complexity in textile behavior
when being pulled and stretched, is due to both the yarn material properties
and the textile construction technique. Today, there are no commonly adopted
and annotated datasets on which the various interaction or property
identification methods are assessed. One important property that affects the
interaction is material elasticity that results from both the yarn material and
construction technique: these two are intertwined and, if not known a-priori,
almost impossible to identify through sensing commonly available on robotic
platforms. We introduce Elastic Context (EC), a concept that integrates various
properties that affect elastic behavior, to enable a more effective physical
interaction with textiles. The definition of EC relies on stress/strain curves
commonly used in textile engineering, which we reformulated for robotic
applications. We employ EC using Graph Neural Network (GNN) to learn
generalized elastic behaviors of textiles. Furthermore, we explore the effect
the dimension of the EC has on accurate force modeling of non-linear real-world
elastic behaviors, highlighting the challenges of current robotic setups to
sense textile properties.

Federated Learning (FL) wherein multiple institutions collaboratively train a
machine learning model without sharing data is becoming popular. Participating
institutions might not contribute equally, some contribute more data, some
better quality data or some more diverse data. To fairly rank the contribution
of different institutions, Shapley value (SV) has emerged as the method of
choice. Exact SV computation is impossibly expensive, especially when there are
hundreds of contributors. Existing SV computation techniques use
approximations. However, in healthcare where the number of contributing
institutions are likely not of a colossal scale, computing exact SVs is still
exorbitantly expensive, but not impossible. For such settings, we propose an
efficient SV computation technique called SaFE (Shapley Value for Federated
Learning using Ensembling). We empirically show that SaFE computes values that
are close to exact SVs, and that it performs better than current SV
approximations. This is particularly relevant in medical imaging setting where
widespread heterogeneity across institutions is rampant and fast accurate data
valuation is required to determine the contribution of each participant in
multi-institutional collaborative learning.

This tutorial survey provides an overview of recent non-asymptotic advances
in statistical learning theory as relevant to control and system
identification. While there has been substantial progress across all areas of
control, the theory is most well-developed when it comes to linear system
identification and learning for the linear quadratic regulator, which are the
focus of this manuscript. From a theoretical perspective, much of the labor
underlying these advances has been in adapting tools from modern
high-dimensional statistics and learning theory. While highly relevant to
control theorists interested in integrating tools from machine learning, the
foundational material has not always been easily accessible. To remedy this, we
provide a self-contained presentation of the relevant material, outlining all
the key ideas and the technical machinery that underpin recent results. We also
present a number of open problems and future directions.

With the rapid development of face forgery technology, deepfake videos have
attracted widespread attention in digital media. Perpetrators heavily utilize
these videos to spread disinformation and make misleading statements. Most
existing methods for deepfake detection mainly focus on texture features, which
are likely to be impacted by external fluctuations, such as illumination and
noise. Besides, detection methods based on facial landmarks are more robust
against external variables but lack sufficient detail. Thus, how to effectively
mine distinctive features in the spatial, temporal, and frequency domains and
fuse them with facial landmarks for forgery video detection is still an open
question. To this end, we propose a Landmark Enhanced Multimodal Graph Neural
Network (LEM-GNN) based on multiple modalities' information and geometric
features of facial landmarks. Specifically, at the frame level, we have
designed a fusion mechanism to mine a joint representation of the spatial and
frequency domain elements while introducing geometric facial features to
enhance the robustness of the model. At the video level, we first regard each
frame in a video as a node in a graph and encode temporal information into the
edges of the graph. Then, by applying the message passing mechanism of the
graph neural network (GNN), the multimodal feature will be effectively combined
to obtain a comprehensive representation of the video forgery. Extensive
experiments show that our method consistently outperforms the state-of-the-art
(SOTA) on widely-used benchmarks.

Reviews contain rich information about product characteristics and user
interests and thus are commonly used to boost recommender system performance.
Specifically, previous work show that jointly learning to perform review
generation improves rating prediction performance. Meanwhile, these
model-produced reviews serve as recommendation explanations, providing the user
with insights on predicted ratings. However, while existing models could
generate fluent, human-like reviews, it is unclear to what degree the reviews
fully uncover the rationale behind the jointly predicted rating. In this work,
we perform a series of evaluations that probes state-of-the-art models and
their review generation component. We show that the generated explanations are
brittle and need further evaluation before being taken as literal rationales
for the estimated ratings.

We propose Deterministic Sequencing of Exploration and Exploitation (DSEE)
algorithm with interleaving exploration and exploitation epochs for model-based
RL problems that aim to simultaneously learn the system model, i.e., a Markov
decision process (MDP), and the associated optimal policy. During exploration,
DSEE explores the environment and updates the estimates for expected reward and
transition probabilities. During exploitation, the latest estimates of the
expected reward and transition probabilities are used to obtain a robust policy
with high probability. We design the lengths of the exploration and
exploitation epochs such that the cumulative regret grows as a sub-linear
function of time.

Panoptic segmentation methods assign a known class to each pixel given in
input. Even for state-of-the-art approaches, this inherently enforces decisions
that systematically lead to wrong predictions for unknown objects that are not
part of the training categories. However, in safety-critical settings,
robustness against out-of-distribution samples and corner cases is crucial to
avoid dangerous consequences. Since real-world datasets cannot contain enough
data points to properly sample the long tail of the underlying distribution,
models must be able to deal with unknown and unseen scenarios as well. Previous
methods targeted this issue by re-identifying already seen unlabeled objects.
In this work, we propose the necessary step to extend segmentation with a new
task which we term holistic segmentation. The aim of holistic segmentation is
to identify and separate objects of unseen unknown categories into instances,
without any prior knowledge about them, while performing panoptic segmentation
of known classes. We tackle this new problem with U3HS, which finds unknowns as
highly uncertain regions, and clusters their corresponding instance-aware
embeddings into individual objects. By doing so, for the first time in panoptic
segmentation with unknown objects, our U3HS is not trained with unknown
categories, reducing assumptions and leaving the settings as unconstrained as
in real-life scenarios. Extensive experiments on publicly available data from
Cityscapes and Lost&Found demonstrate the effectiveness of U3HS for the new
challenging task of holistic segmentation.

Predicting traffic conditions is tremendously challenging since every road is
highly dependent on each other, both spatially and temporally. Recently, to
capture this spatial and temporal dependency, specially designed architectures
such as graph convolutional networks and temporal convolutional networks have
been introduced. While there has been remarkable progress in traffic
forecasting, we found that deep-learning-based traffic forecasting models still
fail in certain patterns, mainly in event situations (e.g., rapid speed drops).
Although it is commonly accepted that these failures are due to unpredictable
noise, we found that these failures can be corrected by considering previous
failures. Specifically, we observe autocorrelated errors in these failures,
which indicates that some predictable information remains. In this study, to
capture the correlation of errors, we introduce ResCAL, a residual estimation
module for traffic forecasting, as a widely applicable add-on module to
existing traffic forecasting models. Our ResCAL calibrates the prediction of
the existing models in real time by estimating future errors using previous
errors and graph signals. Extensive experiments on METR-LA and PEMS-BAY
demonstrate that our ResCAL can correctly capture the correlation of errors and
correct the failures of various traffic forecasting models in event situations.

Federated learning has attracted growing interest as it preserves the
clients' privacy. As a variant of federated learning, federated transfer
learning utilizes the knowledge from similar tasks and thus has also been
intensively studied. However, due to the limited radio spectrum, the
communication efficiency of federated learning via wireless links is critical
since some tasks may require thousands of Terabytes of uplink payload. In order
to improve the communication efficiency, we in this paper propose the
feature-based federated transfer learning as an innovative approach to reduce
the uplink payload by more than five orders of magnitude compared to that of
existing approaches. We first introduce the system design in which the
extracted features and outputs are uploaded instead of parameter updates, and
then determine the required payload with this approach and provide comparisons
with the existing approaches. Subsequently, we analyze the random shuffling
scheme that preserves the clients' privacy. Finally, we evaluate the
performance of the proposed learning scheme via experiments on an image
classification task to show its effectiveness.

Recent models can generate fluent and grammatical synthetic reviews while
accurately predicting user ratings. The generated reviews, expressing users'
estimated opinions towards related products, are often viewed as natural
language 'rationales' for the jointly predicted rating. However, previous
studies found that existing models often generate repetitive, universally
applicable, and generic explanations, resulting in uninformative rationales.
Further, our analysis shows that previous models' generated content often
contain factual hallucinations. These issues call for novel solutions that
could generate both informative and factually grounded explanations. Inspired
by recent success in using retrieved content in addition to parametric
knowledge for generation, we propose to augment the generator with a
personalized retriever, where the retriever's output serves as external
knowledge for enhancing the generator. Experiments on Yelp, TripAdvisor, and
Amazon Movie Reviews dataset show our model could generate explanations that
more reliably entail existing reviews, are more diverse, and are rated more
informative by human evaluators.

States of light encoding multiple polarizations - vector beams - offer unique
capabilities in metrology and communication. However, their practical
application is limited by the lack of methods for measuring many polarizations
in a scalable and compact way. Here we demonstrate polarimetry of vector beams
in a single shot without any polarization optics. We map the beam polarization
content into a spatial intensity distribution through multiple light scattering
and exploit supervised learning for single-shot measurements of multiple
polarizations. The method also allows us to classify beams with an unknown
number of polarization modes, a functionality missing in conventional
techniques. Our findings enable a fast and compact polarimeter for
polarization-structured light, a universal tool that may radically impact
optical devices for sensing, imaging, and computing.

Autism, also known as Autism Spectrum Disorder (or ASD), is a neurological
disorder. Its main symptoms include difficulty in (verbal and/or non-verbal)
communication, and rigid/repetitive behavior. These symptoms are often
indistinguishable from a normal (control) individual, due to which this
disorder remains undiagnosed in early childhood leading to delayed treatment.
Since the learning curve is steep during the initial age, an early diagnosis of
autism could allow to take adequate interventions at the right time, which
might positively affect the growth of an autistic child. Further, the
traditional methods of autism diagnosis require multiple visits to a
specialized psychiatrist, however this process can be time-consuming. In this
paper, we present a learning based approach to automate autism diagnosis using
simple and small action video clips of subjects. This task is particularly
challenging because the amount of annotated data available is small, and the
variations among samples from the two categories (ASD and control) are
generally indistinguishable. This is also evident from poor performance of a
binary classifier learned using the cross-entropy loss on top of a baseline
encoder. To address this, we adopt contrastive feature learning in both self
supervised and supervised learning frameworks, and show that these can lead to
a significant increase in the prediction accuracy of a binary classifier on
this task. We further validate this by conducting thorough experimental
analyses under different set-ups on two publicly available datasets.

Deep Neural Networks are, from a physical perspective, graphs whose `links`
and `vertices` iteratively process data and solve tasks sub-optimally. We use
Complex Network Theory (CNT) to represents Deep Neural Networks (DNNs) as
directed weighted graphs: within this framework, we introduce metrics to study
DNNs as dynamical systems, with a granularity that spans from weights to
layers, including neurons. CNT discriminates networks that differ in the number
of parameters and neurons, the type of hidden layers and activations, and the
objective task. We further show that our metrics discriminate low vs. high
performing networks. CNT is a comprehensive method to reason about DNNs and a
complementary approach to explain a model's behavior that is physically
grounded to networks theory and goes beyond the well-studied input-output
relation.

In this paper, we study convex optimization using a very general formulation
called BSGD (Block Stochastic Gradient Descent). At each iteration, some but
not necessary all components of the argument are updated. The direction of the
update can be one of two possibilities: (i) A noise-corrupted measurement of
the true gradient, or (ii) an approximate gradient computed using a first-order
approximation, using function values that might themselves be corrupted by
noise. This formulation embraces most of the currently used stochastic gradient
methods. We establish conditions for BSGD to converge to the global minimum,
based on stochastic approximation theory. Then we verify the predicted
convergence through numerical experiments. Out results show that when
approximate gradients are used, BSGD converges while momentum-based methods can
diverge. However, not just our BSGD, but also standard (full-update) gradient
descent, and various momentum-based methods, all converge, even with noisy
gradients.

A fundamental question on the use of ML models concerns the explanation of
their predictions for increasing transparency in decision-making. Although
several interpretability methods have emerged, some gaps regarding the
reliability of their explanations have been identified. For instance, most
methods are unstable (meaning that they give very different explanations with
small changes in the data), and do not cope well with irrelevant features (that
is, features not related to the label). This article introduces two new
interpretability methods, namely VarImp and SupClus, that overcome these issues
by using local regressions fits with a weighted distance that takes into
account variable importance. Whereas VarImp generates explanations for each
instance and can be applied to datasets with more complex relationships,
SupClus interprets clusters of instances with similar explanations and can be
applied to simpler datasets where clusters can be found. We compare our methods
with state-of-the art approaches and show that it yields better explanations
according to several metrics, particularly in high-dimensional problems with
irrelevant features, as well as when the relationship between features and
target is non-linear.

Machine-learned interatomic potentials (MLIPs) and force fields (i.e.
interaction laws for atoms and molecules) are typically trained on limited
data-sets that cover only a very small section of the full space of possible
input structures. MLIPs are nevertheless capable of making accurate predictions
of forces and energies in simulations involving (seemingly) much more complex
structures. In this article we propose a framework within which this kind of
generalisation can be rigorously understood. As a prototypical example, we
apply the framework to the case of simulating point defects in a crystalline
solid. Here, we demonstrate how the accuracy of the simulation depends
explicitly on the size of the training structures, on the kind of observations
(e.g., energies, forces, force constants, virials) to which the model has been
fitted, and on the fit accuracy. The new theoretical insights we gain partially
justify current best practices in the MLIP literature and in addition suggest a
new approach to the collection of training data and the design of loss

Influence functions efficiently estimate the effect of removing a single
training data point on a model's learned parameters. While influence estimates
align well with leave-one-out retraining for linear models, recent works have
shown this alignment is often poor in neural networks. In this work, we
investigate the specific factors that cause this discrepancy by decomposing it
into five separate terms. We study the contributions of each term on a variety
of architectures and datasets and how they vary with factors such as network
width and training time. While practical influence function estimates may be a
poor match to leave-one-out retraining for nonlinear networks, we show they are
often a good approximation to a different object we term the proximal Bregman
response function (PBRF). Since the PBRF can still be used to answer many of
the questions motivating influence functions, such as identifying influential
or mislabeled examples, our results suggest that current algorithms for
influence function estimation give more informative results than previous error
analyses would suggest.

A variety of different performance metrics are commonly used in the machine
learning literature for the evaluation of classification systems. Some of the
most common ones for measuring quality of hard decisions are standard and
balanced accuracy, standard and balanced error rate, F-beta score, and Matthews
correlation coefficient (MCC). In this document, we review the definition of
these and other metrics and compare them with the expected cost (EC), a metric
introduced in every statistical learning course but rarely used in the machine
learning literature. We show that both the standard and balanced error rates
are special cases of the EC. Further, we show its relation with F-score and MCC
and argue that EC is superior to these traditional metrics, being more elegant,
general, and intuitive, as well as being based on basic principles from
statistics.
  The metrics above measure the quality of hard decisions. Yet, most modern
classification systems output continuous scores for the classes which we may
want to evaluate directly. Metrics for measuring the quality of system scores
include the area under the ROC curve, equal error rate, cross-entropy, Brier
score, and Bayes EC or Bayes risk, among others. The last three metrics are
special cases of a family of metrics given by the expected value of proper
scoring rules (PSRs). We review the theory behind these metrics and argue that
they are the most principled way to measure the quality of the posterior
probabilities produced by a system. Finally, we show how to use these metrics
to compute the system's calibration loss and compare this metric with the
standard expected calibration error (ECE), arguing that calibration loss based
on PSRs is superior to the ECE for a variety of reasons.

Traditional learning systems have responded quickly to the COVID pandemic and
moved to online or distance learning. Online learning requires a
personalization method because the interaction between learners and instructors
is minimal, and learners have a specific learning method that works best for
them. One of the personalization methods is detecting the learners' learning
style. To detect learning styles, several works have been proposed using
classification techniques. However, the current detection models become
ineffective when learners have no dominant style or a mix of learning styles.
Thus, the objective of this study is twofold. Firstly, constructing a
prediction model based on regression analysis provides a probabilistic approach
for inferring the preferred learning style. Secondly, comparing regression
models and classification models for detecting learning style. To ground our
conceptual model, a set of machine learning algorithms have been implemented
based on a dataset collected from a sample of 72 students using visual,
auditory, reading/writing, and kinesthetic (VARK's) inventory questionnaire.
Results show that regression techniques are more accurate and representative
for real-world scenarios than classification algorithms, where students might
have multiple learning styles but with different probabilities. We believe that
this research will help educational institutes to engage learning styles in the
teaching process.

Estimating software effort has been a largely unsolved problem for decades.
One of the main reasons that hinders building accurate estimation models is the
often heterogeneous nature of software data with a complex structure.
Typically, building effort estimation models from local data tends to be more
accurate than using the entire data. Previous studies have focused on the use
of clustering techniques and decision trees to generate local and coherent data
that can help in building local prediction models. However, these approaches
may fall short in some aspect due to limitations in finding optimal clusters
and processing noisy data. In this paper we used a more sophisticated locality
approach that can mitigate these shortcomings that is Locally Weighted
Regression (LWR). This method provides an efficient solution to learn from
local data by building an estimation model that combines multiple local
regression models in k-nearest-neighbor based model. The main factor affecting
the accuracy of this method is the choice of the kernel function used to derive
the weights for local regression models. This paper investigates the effects of
choosing different kernels on the performance of Locally Weighted Regression of
a software effort estimation problem. After comprehensive experiments with 7
datasets, 10 kernels, 3 polynomial degrees and 4 bandwidth values with a total
of 840 Locally Weighted Regression variants, we found that: 1) Uniform kernel
functions cannot outperform non-uniform kernel functions, and 2) kernel type,
polynomial degrees and bandwidth parameters have no specific effect on the
estimation accuracy.

The recently introduced weakly disentangled representations proposed to relax
some constraints of the previous definitions of disentanglement, in exchange
for more flexibility. However, at the moment, weak disentanglement can only be
achieved by increasing the amount of supervision as the number of factors of
variations of the data increase. In this paper, we introduce modular
representations for weak disentanglement, a novel method that allows to keep
the amount of supervised information constant with respect the number of
generative factors. The experiments shows that models using modular
representations can increase their performance with respect to previous work
without the need of additional supervision.

Vision-language models trained on large, randomly collected data had
significant impact in many areas since they appeared. But as they show great
performance in various fields, such as image-text-retrieval, their inner
workings are still not fully understood. The current work analyses the true
zero-shot capabilities of those models. We start from the analysis of the
training corpus assessing to what extent (and which of) the test classes are
really zero-shot and how this correlates with individual classes performance.
We follow up with the analysis of the attribute-based zero-shot learning
capabilities of these models, evaluating how well this classical zero-shot
notion emerges from large-scale webly supervision. We leverage the recently
released LAION400M data corpus as well as the publicly available pretrained
models of CLIP, OpenCLIP, and FLAVA, evaluating the attribute-based zero-shot
capabilities on CUB and AWA2 benchmarks. Our analysis shows that: (i) most of
the classes in popular zero-shot benchmarks are observed (a lot) during
pre-training; (ii) zero-shot performance mainly comes out of models' capability
of recognizing class labels, whenever they are present in the text, and a
significantly lower performing capability of attribute-based zeroshot learning
is only observed when class labels are not used; (iii) the number of the
attributes used can have a significant effect on performance, and can easily
cause a significant performance decrease.

Effective exploration is critical for reinforcement learning agents in
environments with sparse rewards or high-dimensional state-action spaces.
Recent works based on state-visitation counts, curiosity and
entropy-maximization generate intrinsic reward signals to motivate the agent to
visit novel states for exploration. However, the agent can get distracted by
perturbations to sensor inputs that contain novel but task-irrelevant
information, e.g. due to sensor noise or changing background. In this work, we
introduce the sequential information bottleneck objective for learning
compressed and temporally coherent representations by modelling and compressing
sequential predictive information in time-series observations. For efficient
exploration in noisy environments, we further construct intrinsic rewards that
capture task-relevant state novelty based on the learned representations. We
derive a variational upper bound of our sequential information bottleneck
objective for practical optimization and provide an information-theoretic
interpretation of the derived upper bound. Our experiments on a set of
challenging image-based simulated control tasks show that our method achieves
better sample efficiency, and robustness to both white noise and natural video
backgrounds compared to state-of-art methods based on curiosity, entropy
maximization and information-gain.

A simple and versatile method to simulate the photon statistics of multimode
Gaussian states based on automatic differentiation of generating functions is
presented. The generating functions for the photon number distribution,
cumulative probabilities, moments, and factorial moments of the photon
statistics are derived. Related expressions for multimode photon-added and
photon-subtracted Gaussian states are presented. Numerical results are obtained
by using the machine learning framework PyTorch for automatic differentiation.
It is demonstrated that this approach is well suited for practical simulations
of the photon statistics of quantum optical experiments in realistic scenarios
with low photon numbers, in which various sources of imperfections have to be
taken into account. As an example, the detection probabilities of a recent
multipartite time-bin coding quantum key distribution setup are determined and
compared with the corresponding experimental values.

We introduce the \emph{temporal graphlet kernel} for classifying
dissemination processes in labeled temporal graphs. Such dissemination
processes can be spreading (fake) news, infectious diseases, or computer
viruses in dynamic networks. The networks are modeled as labeled temporal
graphs, in which the edges exist at specific points in time, and node labels
change over time. The classification problem asks to discriminate dissemination
processes of different origins or parameters, e.g., infectious diseases with
different infection probabilities. Our new kernel represents labeled temporal
graphs in the feature space of temporal graphlets, i.e., small subgraphs
distinguished by their structure, time-dependent node labels, and chronological
order of edges. We introduce variants of our kernel based on classes of
graphlets that are efficiently countable. For the case of temporal wedges, we
propose a highly efficient approximative kernel with low error in expectation.
We show that our kernels are faster to compute and provide better accuracy than

Learning powerful representations in bird's-eye-view (BEV) for perception
tasks is trending and drawing extensive attention both from industry and
academia. Conventional approaches for most autonomous driving algorithms
perform detection, segmentation, tracking, etc., in a front or perspective
view. As sensor configurations get more complex, integrating multi-source
information from different sensors and representing features in a unified view
come of vital importance. BEV perception inherits several advantages, as
representing surrounding scenes in BEV is intuitive and fusion-friendly; and
representing objects in BEV is most desirable for subsequent modules as in
planning and/or control. The core problems for BEV perception lie in (a) how to
reconstruct the lost 3D information via view transformation from perspective
view to BEV; (b) how to acquire ground truth annotations in BEV grid; (c) how
to formulate the pipeline to incorporate features from different sources and
views; and (d) how to adapt and generalize algorithms as sensor configurations
vary across different scenarios. In this survey, we review the most recent work
on BEV perception and provide an in-depth analysis of different solutions.
Moreover, several systematic designs of BEV approach from the industry are
depicted as well. Furthermore, we introduce a full suite of practical guidebook
to improve the performance of BEV perception tasks, including camera, LiDAR and
fusion inputs. At last, we point out the future research directions in this
area. We hope this report would shed some light on the community and encourage
more research effort on BEV perception. We keep an active repository to collect
the most recent work and provide a toolbox for bag of tricks at
https://github.com/OpenPerceptionX/BEVPerception-Survey-Recipe.

The statistical regularities of natural images, referred to as natural scene
statistics, play an important role in no-reference image quality assessment.
However, it has been widely acknowledged that screen content images (SCIs),
which are typically computer generated, do not hold such statistics. Here we
make the first attempt to learn the statistics of SCIs, based upon which the
quality of SCIs can be effectively determined. The underlying mechanism of the
proposed approach is based upon the wild assumption that the SCIs, which are
not physically acquired, still obey certain statistics that could be understood
in a learning fashion. We empirically show that the statistics deviation could
be effectively leveraged in quality assessment, and the proposed method is
superior when evaluated in different settings. Extensive experimental results
demonstrate the Deep Feature Statistics based SCI Quality Assessment (DFSS-IQA)
model delivers promising performance compared with existing NR-IQA models and
shows a high generalization capability in the cross-dataset settings. The
implementation of our method is publicly available at
https://github.com/Baoliang93/DFSS-IQA.

Deep neural networks (DNNs) are widely used in autonomous driving due to
their high accuracy for perception, decision, and control. In safety-critical
systems like autonomous driving, executing tasks like sensing and perception in
real-time is vital to the vehicle's safety, which requires the application's
execution time to be predictable. However, non-negligible time variations are
observed in DNN inference. Current DNN inference studies either ignore the time
variation issue or rely on the scheduler to handle it. None of the current work
explains the root causes of DNN inference time variations. Understanding the
time variations of the DNN inference becomes a fundamental challenge in
real-time scheduling for autonomous driving. In this work, we analyze the time
variation in DNN inference in fine granularity from six perspectives: data,
I/O, model, runtime, hardware, and end-to-end perception system. Six insights
are derived in understanding the time variations for DNN inference.

For industrial-scale advertising systems, prediction of ad click-through rate
(CTR) is a central problem. Ad clicks constitute a significant class of user
engagements and are often used as the primary signal for the usefulness of ads
to users. Additionally, in cost-per-click advertising systems where advertisers
are charged per click, click rate expectations feed directly into value
estimation. Accordingly, CTR model development is a significant investment for
most Internet advertising companies. Engineering for such problems requires
many machine learning (ML) techniques suited to online learning that go well
beyond traditional accuracy improvements, especially concerning efficiency,
reproducibility, calibration, credit attribution. We present a case study of
practical techniques deployed in Google's search ads CTR model. This paper
provides an industry case study highlighting important areas of current ML
research and illustrating how impactful new ML methods are evaluated and made
useful in a large-scale industrial setting.

Recent years have seen a surge in commercially-available and affordable
quadrupedal robots, with many of these platforms being actively used in
research and industry. As the availability of legged robots grows, so does the
need for controllers that enable these robots to perform useful skills.
However, most learning-based frameworks for controller development focus on
training robot-specific controllers, a process that needs to be repeated for
every new robot. In this work, we introduce a framework for training
generalized locomotion (GenLoco) controllers for quadrupedal robots. Our
framework synthesizes general-purpose locomotion controllers that can be
deployed on a large variety of quadrupedal robots with similar morphologies. We
present a simple but effective morphology randomization method that
procedurally generates a diverse set of simulated robots for training. We show
that by training a controller on this large set of simulated robots, our models
acquire more general control strategies that can be directly transferred to
novel simulated and real-world robots with diverse morphologies, which were not
observed during training.

The capacity for rapid domain adaptation is important to increasing the
applicability of reinforcement learning (RL) to real world problems.
Generalization of RL agents is critical to success in the real world, yet
zero-shot policy transfer is a challenging problem since even minor visual
changes could make the trained agent completely fail in the new task. We
propose USRA: Unified State Representation Learning under Data Augmentation, a
representation learning framework that learns a latent unified state
representation by performing data augmentations on its observations to improve
its ability to generalize to unseen target domains. We showcase the success of
our approach on the DeepMind Control Generalization Benchmark for the Walker
environment and find that USRA achieves higher sample efficiency and 14.3%
better domain adaptation performance compared to the best baseline results.

In this paper we address the application of pre-processing techniques to
multi-channel time series data with varying lengths, which we refer to as the
alignment problem, for downstream machine learning. The misalignment of
multi-channel time series data may occur for a variety of reasons, such as
missing data, varying sampling rates, or inconsistent collection times. We
consider multi-channel time series data collected from the MIT SuperCloud High
Performance Computing (HPC) center, where different job start times and varying
run times of HPC jobs result in misaligned data. This misalignment makes it
challenging to build AI/ML approaches for tasks such as compute workload
classification. Building on previous supervised classification work with the
MIT SuperCloud Dataset, we address the alignment problem via three broad, low
overhead approaches: sampling a fixed subset from a full time series,
performing summary statistics on a full time series, and sampling a subset of
coefficients from time series mapped to the frequency domain. Our best
performing models achieve a classification accuracy greater than 95%,
outperforming previous approaches to multi-channel time series classification
with the MIT SuperCloud Dataset by 5%. These results indicate our low overhead
approaches to solving the alignment problem, in conjunction with standard
machine learning techniques, are able to achieve high levels of classification
accuracy, and serve as a baseline for future approaches to addressing the
alignment problem, such as kernel methods.

Recently, Deepfake has drawn considerable public attention due to security
and privacy concerns in social media digital forensics. As the wildly spreading
Deepfake videos on the Internet become more realistic, traditional detection
techniques have failed in distinguishing between the real and fake. Most
existing deep learning methods mainly focus on local features and relations
within the face image using convolutional neural networks as a backbone.
However, local features and relations are insufficient for model training to
learn enough general information for Deepfake detection. Therefore, the
existing Deepfake detection methods have reached a bottleneck to further
improving the detection performance. To address this issue, we propose a deep
convolutional Transformer to incorporate the decisive image features both
locally and globally. Specifically, we apply convolutional pooling and
re-attention to enrich the extracted features and enhance the efficacy.
Moreover, we employ the barely discussed image keyframes in model training for
performance improvement and visualize the feature quantity gap between the key
and normal image frames caused by video compression. We finally illustrate the
transferability with extensive experiments on several Deepfake benchmark
datasets. The proposed solution consistently outperforms several
state-of-the-art baselines on both within- and cross-dataset experiments.

This paper proposes a simple yet effective interpolation-based data
augmentation approach termed DoubleMix, to improve the robustness of models in
text classification. DoubleMix first leverages a couple of simple augmentation
operations to generate several perturbed samples for each training data, and
then uses the perturbed data and original data to carry out a two-step
interpolation in the hidden space of neural models. Concretely, it first mixes
up the perturbed data to a synthetic sample and then mixes up the original data
and the synthetic perturbed data. DoubleMix enhances models' robustness by
learning the "shifted" features in hidden space. On six text classification
benchmark datasets, our approach outperforms several popular text augmentation
methods including token-level, sentence-level, and hidden-level data
augmentation techniques. Also, experiments in low-resource settings show our
approach consistently improves models' performance when the training data is
scarce. Extensive ablation studies and case studies confirm that each component
of our approach contributes to the final performance and show that our approach
exhibits superior performance on challenging counterexamples. Additionally,
visual analysis shows that text features generated by our approach are highly
interpretable. Our code for this paper can be found at
https://github.com/declare-lab/DoubleMix.git.

Quality control is a crucial activity performed by manufacturing enterprises
to ensure that their products meet quality standards and avoid potential damage
to the brand's reputation. The decreased cost of sensors and connectivity
enabled increasing digitalization of manufacturing. In addition, artificial
intelligence enables higher degrees of automation, reducing overall costs and
time required for defect inspection. This research compares three active
learning approaches (with single and multiple oracles) to visual inspection. We
propose a novel approach to probabilities calibration of classification models
and two new metrics to assess the performance of the calibration without the
need for ground truth. We performed experiments on real-world data provided by
Philips Consumer Lifestyle BV. Our results show that explored active learning
settings can reduce the data labeling effort by between three and four percent
without detriment to the overall quality goals, considering a threshold of
p=0.95. Furthermore, we show that the proposed metrics successfully capture
relevant information otherwise available to metrics used up to date only
through ground truth data. Therefore, the proposed metrics can be used to
estimate the quality of models' probability calibration without committing to a
labeling effort to obtain ground truth data.

The spread of hate speech on social media space is currently a serious issue.
The undemanding access to the enormous amount of information being generated on
these platforms has led people to post and react with toxic content that
originates violence. Though efforts have been made toward detecting and
restraining such content online, it is still challenging to identify it
accurately. Deep learning based solutions have been at the forefront of
identifying hateful content. However, the factors such as the context-dependent
nature of hate speech, the intention of the user, undesired biases, etc. make
this process overcritical. In this work, we deeply explore a wide range of
challenges in automatic hate speech detection by presenting a hierarchical
organization of these problems. We focus on challenges faced by machine
learning or deep learning based solutions to hate speech identification. At the
top level, we distinguish between data level, model level, and human level
challenges. We further provide an exhaustive analysis of each level of the
hierarchy with examples. This survey will help researchers to design their
solutions more efficiently in the domain of hate speech detection.

In machine learning, training data often capture the behaviour of multiple
subgroups of some underlying human population. When the nature of training data
for subgroups are not controlled carefully, under-representation bias arises.
To counter this effect we introduce two natural notions of subgroup fairness
and instantaneous fairness to address such under-representation bias in
time-series forecasting problems. Here we show globally convergent methods for
the fairness-constrained learning problems using hierarchies of
convexifications of non-commutative polynomial optimisation problems. Our
empirical results on a biased data set motivated by insurance applications and
the well-known COMPAS data set demonstrate the efficacy of our methods. We also
show that by exploiting sparsity in the convexifications, we can reduce the run
time of our methods considerably.

FFSVC2022 is the second challenge of far-field speaker verification.
FFSVC2022 provides the fully-supervised far-field speaker verification to
further explore the far-field scenario and proposes semi-supervised far-field
speaker verification. In contrast to FFSVC2020, FFSVC2022 focus on the
single-channel scenario. In addition, a supplementary set for the FFSVC2020
dataset is released this year. The supplementary set consists of more recording
devices and has the same data distribution as the FFSVC2022 evaluation set.
This paper summarizes the FFSVC 2022, including tasks description, trial
designing details, a baseline system and a summary of challenge results. The
challenge results indicate substantial progress made in the field but also
present that there are still difficulties with the far-field scenario.

In this paper, an LSTM autoencoder-based architecture is utilized for
drowsiness detection with ResNet-34 as feature extractor. The problem is
considered as anomaly detection for a single subject; therefore, only the
normal driving representations are learned and it is expected that drowsiness
representations, yielding higher reconstruction losses, are to be distinguished
according to the knowledge of the network. In our study, the confidence levels
of normal and anomaly clips are investigated through the methodology of label
assignment such that training performance of LSTM autoencoder and
interpretation of anomalies encountered during testing are analyzed under
varying confidence rates. Our method is experimented on NTHU-DDD and
benchmarked with a state-of-the-art anomaly detection method for driver
drowsiness. Results show that the proposed model achieves detection rate of
0.8740 area under curve (AUC) and is able to provide significant improvements
on certain scenarios.

Recent advances in high-throughput sequencing technologies have enabled the
extraction of multiple features that depict patient samples at diverse and
complementary molecular levels. The generation of such data has led to new
challenges in computational biology regarding the integration of
high-dimensional and heterogeneous datasets that capture the interrelationships
between multiple genes and their functions. Thanks to their versatility and
ability to learn synthetic latent representations of complex data, deep
learning methods offer promising perspectives for integrating multi-omics data.
These methods have led to the conception of many original architectures that
are primarily based on autoencoder models. However, due to the difficulty of
the task, the integration strategy is fundamental to take full advantage of the
sources' particularities without losing the global trends. This paper presents
a novel strategy to build a customizable autoencoder model that adapts to the
dataset used in the case of high-dimensional multi-source integration. We will
assess the impact of integration strategies on the latent representation and
combine the best strategies to propose a new method, CustOmics
(https://github.com/HakimBenkirane/CustOmics). We focus here on the integration
of data from multiple omics sources and demonstrate the performance of the
proposed method on test cases for several tasks such as classification and
survival analysis.

HAZOP is a safety paradigm undertaken to reveal hazards in industry, its
report covers valuable hazard events (HaE). The research on HaE classification
has much irreplaceable pragmatic values. However, no study has paid such
attention to this topic. In this paper, we present a novel deep learning model
termed DLF to explore the HaE classification through fractal method from the
perspective of language. The motivation is that (1): HaE can be naturally
regarded as a kind of time series; (2): the meaning of HaE is driven by word
arrangement. Specifically, first we employ BERT to vectorize HaE. Then, we
propose a new multifractal method termed HmF-DFA to calculate HaE fractal
series by analyzing the HaE vector who is regarded as a time series. Finally,
we design a new hierarchical gating neural network (HGNN) to process the HaE
fractal series to accomplish the classification of HaE. We take 18 processes
for case study. We launch the experiment on the basis of their HAZOP reports.
Experimental results demonstrate that our DLF classifier is satisfactory and
promising, the proposed HmF-DFA and HGNN are effective, and the introduction of
language fractal into HaE is feasible. Our HaE classification system can serve
HAZOP and bring application incentives to experts, engineers, employees, and
other enterprises, which is conducive to the intelligent development of
industrial safety. We hope our research can contribute added support to the
daily practice in industrial safety and fractal theory.

Background: The energy consumption of machine learning and its impact on the
environment has made energy efficient ML an emerging area of research. However,
most of the attention stays focused on the model creation and the training and
inferencing phase. Data oriented stages like preprocessing, cleaning and
exploratory analysis form a critical part of the machine learning workflow.
However, the energy efficiency of these stages have gained little attention
from the researchers. Aim: Our study aims to explore the energy consumption of
different dataframe processing libraries as a first step towards studying the
energy efficiency of the data oriented stages of the machine learning pipeline.
Method: We measure the energy consumption of 3 popular libraries used to work
with dataframes, namely Pandas, Vaex and Dask for 21 different operations
grouped under 4 categories on 2 datasets. Results: The results of our analysis
show that for a given dataframe processing operation, the choice of library can
indeed influence the energy consumption with some libraries consuming 202 times
lesser energy over others. Conclusion: The results of our study indicates that
there is a potential for optimizing the energy consumption of the data oriented
stages of the machine learning pipeline and further research is needed in the
direction.

In the absence of nonverbal cues during messaging communication, users
express part of their emotions using emojis. Thus, having emojis in the
vocabulary of text messaging language models can significantly improve many
natural language processing (NLP) applications such as online communication
analysis. On the other hand, word embedding models are usually trained on a
very large corpus of text such as Wikipedia or Google News datasets that
include very few samples with emojis. In this study, we create emojiSpace,
which is a combined word-emoji embedding using the word2vec model from the
Genism library in Python. We trained emojiSpace on a corpus of more than 4
billion tweets and evaluated it by implementing sentiment analysis on a Twitter
dataset containing more than 67 million tweets as an extrinsic task. For this
task, we compared the performance of two different classifiers of random forest
(RF) and linear support vector machine (SVM). For evaluation, we compared
emojiSpace performance with two other pre-trained embeddings and demonstrated
that emojiSpace outperforms both.

Cybersickness can be characterized by nausea, vertigo, headache, eye strain,
and other discomforts when using virtual reality (VR) systems. The previously
reported machine learning (ML) and deep learning (DL) algorithms for detecting
(classification) and predicting (regression) VR cybersickness use black-box
models; thus, they lack explainability. Moreover, VR sensors generate a massive
amount of data, resulting in complex and large models. Therefore, having
inherent explainability in cybersickness detection models can significantly
improve the model's trustworthiness and provide insight into why and how the
ML/DL model arrived at a specific decision. To address this issue, we present
three explainable machine learning (xML) models to detect and predict
cybersickness: 1) explainable boosting machine (EBM), 2) decision tree (DT),
and 3) logistic regression (LR). We evaluate xML-based models with publicly
available physiological and gameplay datasets for cybersickness. The results
show that the EBM can detect cybersickness with an accuracy of 99.75% and
94.10% for the physiological and gameplay datasets, respectively. On the other
hand, while predicting the cybersickness, EBM resulted in a Root Mean Square
Error (RMSE) of 0.071 for the physiological dataset and 0.27 for the gameplay
dataset. Furthermore, the EBM-based global explanation reveals exposure length,
rotation, and acceleration as key features causing cybersickness in the
gameplay dataset. In contrast, galvanic skin responses and heart rate are most
significant in the physiological dataset. Our results also suggest that
EBM-based local explanation can identify cybersickness-causing factors for
individual samples. We believe the proposed xML-based cybersickness detection
method can help future researchers understand, analyze, and design simpler
cybersickness detection and reduction models.

Robots working in real environments need to adapt to unexpected changes to
avoid failures. This is an open and complex challenge that requires robots to
timely predict and identify the causes of failures to prevent them. In this
paper, we present a causal method that will enable robots to predict when
errors are likely to occur and prevent them from happening by executing a
corrective action. First, we propose a causal-based method to detect the
cause-effect relationships between task executions and their consequences by
learning a causal Bayesian network (BN). The obtained model is transferred from
simulated data to real scenarios to demonstrate the robustness and
generalization of the obtained models. Based on the causal BN, the robot can
predict if and why the executed action will succeed or not in its current
state. Then, we introduce a novel method that finds the closest state
alternatives through a contrastive Breadth-First-Search if the current action
was predicted to fail. We evaluate our approach for the problem of stacking
cubes in two cases; a) single stacks (stacking one cube) and; b) multiple
stacks (stacking three cubes). In the single-stack case, our method was able to
reduce the error rate by 97%. We also show that our approach can scale to
capture multiple actions in one model, allowing to measure timely shifted
action effects, such as the impact of an imprecise stack of the first cube on
the stacking success of the third cube. For these complex situations, our model
was able to prevent around 75% of the stacking errors, even for the challenging
multiple-stack scenario. Thus, demonstrating that our method is able to
explain, predict, and prevent execution failures, which even scales to complex
scenarios that require an understanding of how the action history impacts
future actions.

Digital forensics is the process of extracting, preserving, and documenting
evidence in digital devices. A commonly used method in digital forensics is to
extract data from the main memory of a digital device. However, the main
challenge is identifying the important data to be extracted. Several pieces of
crucial information reside in the main memory, like usernames, passwords, and
cryptographic keys such as SSH session keys. In this paper, we propose
SmartKex, a machine-learning assisted method to extract session keys from heap
memory snapshots of an OpenSSH process. In addition, we release an openly
available dataset and the corresponding toolchain for creating additional data.
Finally, we compare SmartKex with naive brute-force methods and empirically
show that SmartKex can extract the session keys with high accuracy and high
throughput. With the provided resources, we intend to strengthen the research
on the intersection between digital forensics, cybersecurity, and machine
learning.

We present a framework for learning disentangled representation of CapsNet by
information bottleneck constraint that distills information into a compact form
and motivates to learn an interpretable factorized capsule. In our
$\beta$-CapsNet framework, hyperparameter $\beta$ is utilized to trade-off
disentanglement and other tasks, variational inference is utilized to convert
the information bottleneck term into a KL divergence that is approximated as a
constraint on the mean of the capsule. For supervised learning, class
independent mask vector is used for understanding the types of variations
synthetically irrespective of the image class, we carry out extensive
quantitative and qualitative experiments by tuning the parameter $\beta$ to
figure out the relationship between disentanglement, reconstruction and
classfication performance. Furthermore, the unsupervised $\beta$-CapsNet and
the corresponding dynamic routing algorithm is proposed for learning
disentangled capsule in an unsupervised manner, extensive empirical evaluations
suggest that our $\beta$-CapsNet achieves state-of-the-art disentanglement
performance compared to CapsNet and various baselines on several complex
datasets both in supervision and unsupervised scenes.

Recently, due to the poor performance of supervised person re-identification
(ReID) to an unseen domain, Domain Generalization (DG) person ReID has
attracted a lot of attention which aims to learn a domain-insensitive model and
can resist the influence of domain bias. In this paper, we first verify through
an experiment that style factors are a vital part of domain bias. Base on this
conclusion, we propose a Style Variable and Irrelevant Learning (SVIL) method
to eliminate the effect of style factors on the model. Specifically, we design
a Style Jitter Module (SJM) in SVIL. The SJM module can enrich the style
diversity of the specific source domain and reduce the style differences of
various source domains. This leads to the model focusing on identity-relevant
information and being insensitive to the style changes. Besides, we organically
combine the SJM module with a meta-learning algorithm, maximizing the benefits
and further improving the generalization ability of the model. Note that our
SJM module is plug-and-play and inference cost-free. Extensive experiments
confirm the effectiveness of our SVIL and our method outperforms the
state-of-the-art methods on DG-ReID benchmarks by a large margin.

Device Model Generalization (DMG) is a practical yet under-investigated
research topic for on-device machine learning applications. It aims to improve
the generalization ability of pre-trained models when deployed on
resource-constrained devices, such as improving the performance of pre-trained
cloud models on smart mobiles. While quite a lot of works have investigated the
data distribution shift across clouds and devices, most of them focus on model
fine-tuning on personalized data for individual devices to facilitate DMG.
Despite their promising, these approaches require on-device re-training, which
is practically infeasible due to the overfitting problem and high time delay
when performing gradient calculation on real-time data. In this paper, we argue
that the computational cost brought by fine-tuning can be rather unnecessary.
We consequently present a novel perspective to improving DMG without increasing
computational cost, i.e., device-specific parameter generation which directly
maps data distribution to parameters. Specifically, we propose an efficient
Device-cloUd collaborative parametErs generaTion framework DUET. DUET is
deployed on a powerful cloud server that only requires the low cost of
forwarding propagation and low time delay of data transmission between the
device and the cloud. By doing so, DUET can rehearse the device-specific model
weight realizations conditioned on the personalized real-time data for an
individual device. Importantly, our DUET elegantly connects the cloud and
device as a 'duet' collaboration, frees the DMG from fine-tuning, and enables a
faster and more accurate DMG paradigm. We conduct an extensive experimental
study of DUET on three public datasets, and the experimental results confirm
our framework's effectiveness and generalisability for different DMG tasks.

The prospect of combining human operators and virtual agents (bots) into an
effective hybrid system that provides proper customer service to clients is
promising yet challenging. The hybrid system decreases the customers'
frustration when bots are unable to provide appropriate service and increases
their satisfaction when they prefer to interact with human operators.
Furthermore, we show that it is possible to decrease the cost and efforts of
building and maintaining such virtual agents by enabling the virtual agent to
incrementally learn from the human operators. We employ queuing theory to
identify the key parameters that govern the behavior and efficiency of such
hybrid systems and determine the main parameters that should be optimized in
order to improve the service. We formally prove, and demonstrate in extensive
simulations and in a user study, that with the proper choice of parameters,
such hybrid systems are able to increase the number of served clients while
simultaneously decreasing their expected waiting time and increasing
satisfaction.

Simultaneous Localisation and Mapping (SLAM) is one of the fundamental
problems in autonomous mobile robots where a robot needs to reconstruct a
previously unseen environment while simultaneously localising itself with
respect to the map. In particular, Visual-SLAM uses various sensors from the
mobile robot for collecting and sensing a representation of the map.
Traditionally, geometric model-based techniques were used to tackle the SLAM
problem, which tends to be error-prone under challenging environments. Recent
advancements in computer vision, such as deep learning techniques, have
provided a data-driven approach to tackle the Visual-SLAM problem. This review
summarises recent advancements in the Visual-SLAM domain using various
learning-based methods. We begin by providing a concise overview of the
geometric model-based approaches, followed by technical reviews on the current
paradigms in SLAM. Then, we present the various learning-based approaches to
collecting sensory inputs from mobile robots and performing scene
understanding. The current paradigms in deep-learning-based semantic
understanding are discussed and placed under the context of Visual-SLAM.
Finally, we discuss challenges and further opportunities in the direction of
learning-based approaches in Visual-SLAM.

We propose a self-supervised training approach for learning view-invariant
dense visual descriptors using image augmentations. Unlike existing works,
which often require complex datasets, such as registered RGBD sequences, we
train on an unordered set of RGB images. This allows for learning from a single
camera view, e.g., in an existing robotic cell with a fix-mounted camera. We
create synthetic views and dense pixel correspondences using data
augmentations. We find our descriptors are competitive to the existing methods,
despite the simpler data recording and setup requirements. We show that
training on synthetic correspondences provides descriptor consistency across a
broad range of camera views. We compare against training with geometric
correspondence from multiple views and provide ablation studies. We also show a
robotic bin-picking experiment using descriptors learned from a fix-mounted
camera for defining grasp preferences.

A key goal of unsupervised learning is to go beyond density estimation and
sample generation to reveal the structure inherent within observed data. Such
structure can be expressed in the pattern of interactions between explanatory
latent variables captured through a probabilistic graphical model. Although the
learning of structured graphical models has a long history, much recent work in
unsupervised modelling has instead emphasised flexible deep-network-based
generation, either transforming independent latent generators to model complex
data or assuming that distinct observed variables are derived from different
latent nodes. Here, we extend amortised variational inference to incorporate
structured factors over multiple variables, able to capture the
observation-induced posterior dependence between latents that results from
``explaining away'' and thus allow complex observations to depend on multiple
nodes of a structured graph. We show that appropriately parametrised factors
can be combined efficiently with variational message passing in rich graphical
structures. We instantiate the framework in nonlinear Gaussian Process Factor
Analysis, evaluating the structured recognition framework using synthetic data
from known generative processes. We fit the GPFA model to high-dimensional
neural spike data from the hippocampus of freely moving rodents, where the
model successfully identifies latent signals that correlate with behavioural
covariates.

Network flow problems, which involve distributing traffic over a network such
that the underlying infrastructure is used effectively, are ubiquitous in
transportation and logistics. Due to the appeal of data-driven optimization,
these problems have increasingly been approached using graph learning methods.
Among them, the Multi-Commodity Network Flow (MCNF) problem is of particular
interest given its generality, since it concerns the distribution of multiple
flows (also called demands) of different sizes between several sources and
sinks. The widely-used objective that we focus on is the maximum utilization of
any link in the network, given traffic demands and a routing strategy.
  In this paper, we propose a novel approach based on Graph Neural Networks
(GNNs) for the MCNF problem which uses distinctly parametrized message
functions along each link, akin to a relational model where all edge types are
unique. We show that our proposed method yields substantial gains over existing
graph learning methods that constrain the routing unnecessarily. We extensively
evaluate the proposed approach by means of an Internet routing case study using
17 Service Provider topologies and two flow routing schemes. We find that, in
many networks, an MLP is competitive with a generic GNN that does not use our
mechanism. Furthermore, we shed some light on the relationship between graph
structure and the difficulty of data-driven routing of flows, an aspect that
has not been considered in the existing work in the area.

Optimization of heuristic functions for the A* algorithm, realized by deep
neural networks, is usually done by minimizing square root loss of estimate of
the cost to goal values. This paper argues that this does not necessarily lead
to a faster search of A* algorithm since its execution relies on relative
values instead of absolute ones. As a mitigation, we propose a L* loss, which
upper-bounds the number of excessively expanded states inside the A* search.
The L* loss, when used in the optimization of state-of-the-art deep neural
networks for automated planning in maze domains like Sokoban and maze with
teleports, significantly improves the fraction of solved problems, the quality
of founded plans, and reduces the number of expanded states to approximately
50%

We construct a neural network to learn the RN-AdS black hole metric based on
the data of optical conductivity by holography. The linear perturbative
equation for the Maxwell field is rewritten in terms of the optical
conductivity such that the neural network is constructed based on the
discretization of this differential equation. In contrast to all previous
models in AdS/DL (deep learning) duality, the derivative of the metric function
appears in the equation of motion and we propose distinct finite difference
methods to discretize this function. The notion of the reduced conductivity is
also proposed to avoid the divergence of the optical conductivity near the
horizon.The dependence of the training outcomes on the location of the cutoff,
the temperature as well as the frequency range is investigated in detail. This
work provides a concrete example for the reconstruction of the bulk geometry
with the given data on the boundary by deep learning.

When utilising PAC-Bayes theory for risk certification, it is usually
necessary to estimate and bound the Gibbs risk of the PAC-Bayes posterior. Many
works in the literature employ a method for this which requires a large number
of passes of the dataset, incurring high computational cost. This manuscript
presents a very general alternative which makes computational savings on the
order of the dataset size.

Lattice paths are functional entities that model efficient navigation in
discrete/grid maps. This paper presents a new scheme to generate collision-free
lattice paths with utmost efficiency using the bijective property to rooted
ordered trees, rendering a one-dimensional search problem. Our computational
studies using ten state-of-the-art and relevant nature-inspired swarm
heuristics in navigation scenarios with obstacles with convex and non-convex
geometry show the practical feasibility and efficiency in rendering
collision-free lattice paths. We believe our scheme may find use in devising
fast algorithms for planning and combinatorial optimization in discrete maps.

In an Markov decision process (MDP), unobservable confounders may exist and
have impacts on the data generating process, so that the classic off-policy
evaluation (OPE) estimators may fail to identify the true value function of the
target policy. In this paper, we study the statistical properties of OPE in
confounded MDPs with observable instrumental variables. Specifically, we
propose a two-stage estimator based on the instrumental variables and establish
its statistical properties in the confounded MDPs with a linear structure. For
non-asymptotic analysis, we prove a $\mathcal{O}(n^{-1/2})$-error bound where
$n$ is the number of samples. For asymptotic analysis, we prove that the
two-stage estimator is asymptotically normal with a typical rate of $n^{1/2}$.
To the best of our knowledge, we are the first to show such statistical results
of the two-stage estimator for confounded linear MDPs via instrumental

Trust plays an essential role in the development of human society. According
to the standard trust game, an investor decides whether to keep or transfer a
certain portion of initial stake to a trustee. In the latter case, the stake is
enhanced to signal the value of trust. The trustee then chooses how much to
return to the investor. We here distinguish two types of investors and two
types of trustees who can learn from each other. While a trustee can be
trustworthy or untrustworthy, an investor could be normal or punishing one. The
latter strategy punishes both untrustworthy trustees and normal investors who
are reluctant to control misbehaving trustees. Importantly, we assume a
hierarchical population where the portion of investors and trustees is fixed.
By means of replicator equation approach, we study the $N$-player trust game
and calculate the level of trust and trustworthiness. We find that the
introduction of punishment can induce a stable coexistence state between
punishing investors and trustworthy trustees. Furthermore, an intermediate
fraction of investors can better promote the evolution of trust when the
punishment intensity is low. For more intensive punishment, however, a higher
fraction of investors can be more efficient to elevate the trust level. In
addition, we reveal that appropriate increase of the punishment intensity can
enlarge the attraction domain of the coexistence state.

This paper aims to discuss a method of quantifying the 'shape' of data, via a
methodology called topological data analysis. The main tool within topological
data analysis is persistent homology; this is a means of measuring the shape of
data, from the homology of a simplicial complex, calculated over a range of
values. The required background theory and a method of computing persistent
homology is presented here, with applications specific to structural health
monitoring. These results allow for topological inference and the ability to
deduce features in higher-dimensional data, that might otherwise be overlooked.
  A simplicial complex is constructed for data for a given distance parameter.
This complex encodes information about the local proximity of data points. A
singular homology value can be calculated from this simplicial complex.
Extending this idea, the distance parameter is given for a range of values, and
the homology is calculated over this range. The persistent homology is a
representation of how the homological features of the data persist over this
interval. The result is characteristic to the data. A method that allows for
the comparison of the persistent homology for different data sets is also

Video highlights detection has been long researched as a topic in computer
vision tasks, digging the user-appealing clips out given unexposed raw video
inputs. However, in most case, the mainstream methods in this line of research
are built on the closed world assumption, where a fixed number of highlight
categories is defined properly in advance and need all training data to be
available at the same time, and as a result, leads to poor scalability with
respect to both the highlight categories and the size of the dataset. To tackle
the problem mentioned above, we propose a video highlights detector that is
able to learn incrementally, namely \textbf{G}lobal \textbf{P}rototype
\textbf{E}ncoding (GPE), capturing newly defined video highlights in the
extended dataset via their corresponding prototypes. Alongside, we present a
well annotated and costly dataset termed \emph{ByteFood}, including more than
5.1k gourmet videos belongs to four different domains which are \emph{cooking},
\emph{eating}, \emph{food material}, and \emph{presentation} respectively. To
the best of our knowledge, this is the first time the incremental learning
settings are introduced to video highlights detection, which in turn relieves
the burden of training video inputs and promotes the scalability of
conventional neural networks in proportion to both the size of the dataset and
the quantity of domains. Moreover, the proposed GPE surpasses current
incremental learning methods on \emph{ByteFood}, reporting an improvement of
1.57\% mAP at least. The code and dataset will be made available sooner.

Turn-taking is a fundamental aspect of human communication and can be
described as the ability to take turns, project upcoming turn shifts, and
supply backchannels at appropriate locations throughout a conversation. In this
work, we investigate the role of prosody in turn-taking using the recently
proposed Voice Activity Projection model, which incrementally models the
upcoming speech activity of the interlocutors in a self-supervised manner,
without relying on explicit annotation of turn-taking events, or the explicit
modeling of prosodic features. Through manipulation of the speech signal, we
investigate how these models implicitly utilize prosodic information. We show
that these systems learn to utilize various prosodic aspects of speech both on
aggregate quantitative metrics of long-form conversations and on single
utterances specifically designed to depend on prosody.

The prowess that makes few-shot learning desirable in medical image analysis
is the efficient use of the support image data, which are labelled to classify
or segment new classes, a task that otherwise requires substantially more
training images and expert annotations. This work describes a fully 3D
prototypical few-shot segmentation algorithm, such that the trained networks
can be effectively adapted to clinically interesting structures that are absent
in training, using only a few labelled images from a different institute.
First, to compensate for the widely recognised spatial variability between
institutions in episodic adaptation of novel classes, a novel spatial
registration mechanism is integrated into prototypical learning, consisting of
a segmentation head and an spatial alignment module. Second, to assist the
training with observed imperfect alignment, support mask conditioning module is
proposed to further utilise the annotation available from the support images.
Extensive experiments are presented in an application of segmenting eight
anatomical structures important for interventional planning, using a data set
of 589 pelvic T2-weighted MR images, acquired at seven institutes. The results
demonstrate the efficacy in each of the 3D formulation, the spatial
registration, and the support mask conditioning, all of which made positive
contributions independently or collectively. Compared with the previously
proposed 2D alternatives, the few-shot segmentation performance was improved
with statistical significance, regardless whether the support data come from
the same or different institutes.

Cross-coupled iterative learning control (ILC) can achieve high performance
for manufacturing applications in which tracking a contour is essential for the
quality of a product. The aim of this paper is to develop a framework for
norm-optimal cross-coupled ILC that enables the use of exact contour errors
that are calculated offline, and iteration- and time-varying weights.
Conditions for the monotonic convergence of this iteration-varying ILC
algorithm are developed. In addition, a resource-efficient implementation is
proposed in which the ILC update law is reframed as a linear quadratic tracking
problem, reducing the computational load significantly. The approach is
illustrated on a simulation example.

Internet censorship is a phenomenon of societal importance and attracts
investigation from multiple disciplines. Several research groups, such as
Censored Planet, have deployed large scale Internet measurement platforms to
collect network reachability data. However, existing studies generally rely on
manually designed rules (i.e., using censorship fingerprints) to detect
network-based Internet censorship from the data. While this rule-based approach
yields a high true positive detection rate, it suffers from several challenges:
it requires human expertise, is laborious, and cannot detect any censorship not
captured by the rules. Seeking to overcome these challenges, we design and
evaluate a classification model based on latent feature representation learning
and an image-based classification model to detect network-based Internet
censorship.
  To infer latent feature representations from network reachability data, we
propose a sequence-to-sequence autoencoder to capture the structure and the
order of data elements in the data. To estimate the probability of censorship
events from the inferred latent features, we rely on a densely connected
multi-layer neural network model.
  Our image-based classification model encodes a network reachability data
record as a gray-scale image and classifies the image as censored or not using
a dense convolutional neural network. We compare and evaluate both approaches
using data sets from Censored Planet via a hold-out evaluation. Both
classification models are capable of detecting network-based Internet
censorship as we were able to identify instances of censorship not detected by
the known fingerprints. Latent feature representations likely encode more
nuances in the data since the latent feature learning approach discovers a
greater quantity, and a more diverse set, of new censorship instances.

A Fourier neural operator (FNO) is one of the physics-inspired machine
learning methods. In particular, it is a neural operator. In recent times,
several types of neural operators have been developed, e.g., deep operator
networks, Graph neural operator (GNO), and Multiwavelet-based operator (MWTO).
Compared with other models, the FNO is computationally efficient and can learn
nonlinear operators between function spaces independent of a certain finite
basis. In this study, we investigated the bounding of the Rademacher complexity
of the FNO based on specific group norms. Using capacity based on these norms,
we bound the generalization error of the model. In addition, we investigated
the correlation between the empirical generalization error and the proposed
capacity of FNO. From the perspective of our result, we inferred that the type
of group norms determines the information about the weights and architecture of
the FNO model stored in the capacity. And then, we confirmed these inferences
through experiments. Based on this fact, we gained insight into the impact of
the number of modes used in the FNO model on the generalization error. And we
got experimental results that followed our insights.

In contrast to training traditional machine learning (ML) models in data
centers, federated learning (FL) trains ML models over local datasets contained
on resource-constrained heterogeneous edge devices. Existing FL algorithms aim
to learn a single global model for all participating devices, which may not be
helpful to all devices participating in the training due to the heterogeneity
of the data across the devices. Recently, Hanzely and Richt\'{a}rik (2020)
proposed a new formulation for training personalized FL models aimed at
balancing the trade-off between the traditional global model and the local
models that could be trained by individual devices using their private data
only. They derived a new algorithm, called Loopless Gradient Descent (L2GD), to
solve it and showed that this algorithms leads to improved communication
complexity guarantees in regimes when more personalization is required. In this
paper, we equip their L2GD algorithm with a bidirectional compression mechanism
to further reduce the communication bottleneck between the local devices and
the server. Unlike other compression-based algorithms used in the FL-setting,
our compressed L2GD algorithm operates on a probabilistic communication
protocol, where communication does not happen on a fixed schedule. Moreover,
our compressed L2GD algorithm maintains a similar convergence rate as vanilla
SGD without compression. To empirically validate the efficiency of our
algorithm, we perform diverse numerical experiments on both convex and
non-convex problems and using various compression techniques.

The violation of charge conjugation (C) and parity (P) symmetries are a
requirement for the observed dominance of matter over antimatter in the
Universe. As an established effect of beyond the Standard Model physics, this
could point towards additional CP violation in the Higgs-gauge sector. The
phenomenological footprint of the associated anomalous couplings can be small,
and designing measurement strategies with the highest sensitivity is therefore
of the utmost importance in order to maximise the discovery potential of the
Large Hadron Collider (LHC). There are, however, very few measurements of
CP-sensitive observables in processes that probe the weak-boson
self-interactions. In this article, we study the sensitivity to new sources of
CP violation for a range of experimentally-accessible electroweak processes,
including $W\gamma$ production, $WW$ production via photon fusion, electroweak
$Zjj$ production, electroweak $ZZjj$ production, and electroweak $W^\pm W^\pm
jj$ production. We study simple angular observables as well CP-sensitive
observables constructed using the outputs of machine-learning (ML) algorithms.
We find that the ML-constructed CP-sensitive observables improve the
sensitivity to CP-violating effects by up to a factor of five, depending on the
process. We also find that inclusive $W\gamma$ and electroweak $Zjj$ production
have the potential to set the best possible constraints on certain CP-odd
operators in the Higgs-gauge sector of dimension-six effective field theories.

We analyse and classify the sentiments of a text data constructed from movie
reviews. For this, we use the kernel-based approach from quantum machine
learning algorithms. In order to compose a quantum kernel, we use a circuit
constructed using a combination of different Pauli rotational gates where the
rotational parameter is a classical non-linear function of data points obtained
from the text data. For analysing the performance of the proposed model, we
analyse the quantum model using decision tree, gradient boosting classifier,
and classical and quantum support vector machines. Our results show that
quantum kernel model or quantum support vector machine outperforms all other
algorithms used for analysis in terms of all evaluation metrics. In comparison
to a classical support vector machine, the quantum support vector machine leads
to significantly better results even with increased number of features or
dimensions. The results clearly demonstrate increase in precision score by $9.4
\%$ using a quantum support vector machine as against a classical support
vector machine if the number of features are $15$.

Parameterized feedforward control is at the basis of many successful control
applications with varying references. The aim of this paper is to develop an
efficient data-driven approach to learn the feedforward parameters for MIMO
systems. To this end, a cost criterion is minimized using a stochastic gradient
descent algorithm, in which both the search direction and step size are
determined through system experiments. In particular, the search direction is
chosen as an unbiased estimate of the gradient which is obtained from a single
experiment, regardless of the size of the MIMO system. The approach is
illustrated using a simulation example, in which it is shown to be superior to
a deterministic method in terms of convergence speed and thus experimental
cost.

Learning fine-grained movements is among the most challenging topics in
robotics. This holds true especially for robotic hands. Robotic sign language
acquisition or, more specifically, fingerspelling sign language acquisition in
robots can be considered a specific instance of such challenge. In this paper,
we propose an approach for learning dexterous motor imitation from videos
examples, without the use of any additional information. We build an URDF model
of a robotic hand with a single actuator for each joint. By leveraging
pre-trained deep vision models, we extract the 3D pose of the hand from RGB
videos. Then, using state-of-the-art reinforcement learning algorithms for
motion imitation (namely, proximal policy optimisation), we train a policy to
reproduce the movement extracted from the demonstrations. We identify the best
set of hyperparameters to perform imitation based on a reference motion.
Additionally, we demonstrate the ability of our approach to generalise over 6
different fingerspelled letters.

Topological methods can provide a way of proposing new metrics and methods of
scrutinising data, that otherwise may be overlooked. In this work, a method of
quantifying the shape of data, via a topic called topological data analysis
will be introduced. The main tool within topological data analysis (TDA) is
persistent homology. Persistent homology is a method of quantifying the shape
of data over a range of length scales. The required background and a method of
computing persistent homology is briefly discussed in this work. Ideas from
topological data analysis are then used for nonlinear dynamics to analyse some
common attractors, by calculating their embedding dimension, and then to assess
their general topologies. A method will also be proposed, that uses topological
data analysis to determine the optimal delay for a time-delay embedding. TDA
will also be applied to a Z24 Bridge case study in structural health
monitoring, where it will be used to scrutinise different data partitions,
classified by the conditions at which the data were collected. A metric, from
topological data analysis, is used to compare data between the partitions. The
results presented demonstrate that the presence of damage alters the manifold
shape more significantly than the effects present from temperature.

Operational flare forecasting aims at providing predictions that can be used
to make decisions, typically at a daily scale, about the space weather impacts
of flare occurrence. This study shows that video-based deep learning can be
used for operational purposes when the training and validation sets used for
the network optimization are generated while accounting for the periodicity of
the solar cycle. Specifically, the paper describes an algorithm that can be
applied to build up sets of active regions that are balanced according to the
flare class rates associated to a specific cycle phase. These sets are used to
train and validate a Long-term Recurrent Convolutional Network made of a
combination of a convolutional neural network and a Long-Short Memory network.
The reliability of this approach is assessed in the case of two prediction
windows containing the solar storm of March 2015 and September 2017,

Data augmentation is an essential technique for improving recognition
accuracy in object recognition using deep learning. Methods that generate mixed
data from multiple data sets, such as mixup, can acquire new diversity that is
not included in the training data, and thus contribute significantly to
accuracy improvement. However, since the data selected for mixing are randomly
sampled throughout the training process, there are cases where appropriate
classes or data are not selected. In this study, we propose a data augmentation
method that calculates the distance between classes based on class
probabilities and can select data from suitable classes to be mixed in the
training process. Mixture data is dynamically adjusted according to the
training trend of each class to facilitate training. The proposed method is
applied in combination with conventional methods for generating mixed data.
Evaluation experiments show that the proposed method improves recognition
performance on general and long-tailed image recognition datasets.

Continual learning aims to rapidly and continually learn the current task
from a sequence of tasks.
  Compared to other kinds of methods, the methods based on experience replay
have shown great advantages to overcome catastrophic forgetting. One common
limitation of this method is the data imbalance between the previous and
current tasks, which would further aggravate forgetting. Moreover, how to
effectively address the stability-plasticity dilemma in this setting is also an
urgent problem to be solved. In this paper, we overcome these challenges by
proposing a novel framework called Meta-learning update via Multi-scale
Knowledge Distillation and Data Augmentation (MMKDDA). Specifically, we apply
multiscale knowledge distillation to grasp the evolution of long-range and
short-range spatial relationships at different feature levels to alleviate the
problem of data imbalance. Besides, our method mixes the samples from the
episodic memory and current task in the online continual training procedure,
thus alleviating the side influence due to the change of probability
distribution. Moreover, we optimize our model via the meta-learning update
resorting to the number of tasks seen previously, which is helpful to keep a
better balance between stability and plasticity. Finally, our experimental
evaluation on four benchmark datasets shows the effectiveness of the proposed
MMKDDA framework against other popular baselines, and ablation studies are also
conducted to further analyze the role of each component in our framework.

Expressing head-related transfer functions (HRTFs) in spherical harmonic (SH)
domain has been thoroughly studied as a method of obtaining continuity over
space. However, HRTFs are functions not only of direction but also of
frequency. This paper presents an extension of the SH-based method, utilizing
hyperspherical harmonics (HSHs) to obtain an HRTF representation that is
continuous over both space and frequency. The application of the HSH
approximation results in a relatively small set of coefficients which can be
decoded into HRTF values at any direction and frequency. The paper discusses
results obtained by applying the method to magnitude spectra extracted from
exemplary HRTF measurements. The HRTF representations based on SHs and HSHs
exhibit similar reproduction accuracy, with the latter one featuring continuity
over both space and frequency and requiring much lower number of coefficients.
The developed HSH-based continuous functional model can serve multiple
purposes, such as interpolation, compression or parametrization for
machine-learning applications.

To build recommender systems that not only consider user-item interactions
represented as ordinal variables, but also exploit the social network
describing the relationships between the users, we develop a hierarchical
Bayesian model termed ordinal graph factor analysis (OGFA), which jointly
models user-item and user-user interactions. OGFA not only achieves good
recommendation performance, but also extracts interpretable latent factors
corresponding to representative user preferences. We further extend OGFA to
ordinal graph gamma belief network, which is a multi-stochastic-layer deep
probabilistic model that captures the user preferences and social communities
at multiple semantic levels. For efficient inference, we develop a parallel
hybrid Gibbs-EM algorithm, which exploits the sparsity of the graphs and is
scalable to large datasets. Our experimental results show that the proposed
models not only outperform recent baselines on recommendation datasets with
explicit or implicit feedback, but also provide interpretable latent

Deep learning models tend not to be out-of-distribution robust primarily due
to their reliance on spurious features to solve the task. Counterfactual data
augmentations provide a general way of (approximately) achieving
representations that are counterfactual-invariant to spurious features, a
requirement for out-of-distribution (OOD) robustness. In this work, we show
that counterfactual data augmentations may not achieve the desired
counterfactual-invariance if the augmentation is performed by a
context-guessing machine, an abstract machine that guesses the most-likely
context of a given input. We theoretically analyze the invariance imposed by
such counterfactual data augmentations and describe an exemplar NLP task where
counterfactual data augmentation by a context-guessing machine does not lead to
robust OOD classifiers.

We present a sample-efficient deep learning strategy for topology
optimization. Our end-to-end approach is supervised and includes physics-based
preprocessing and equivariant networks. We analyze how different components of
our deep learning pipeline influence the number of required training samples
via a large-scale comparison. The results demonstrate that including physical
concepts not only drastically improves the sample efficiency but also the
predictions' physical correctness. Finally, we publish two topology
optimization datasets containing problems and corresponding ground truth
solutions. We are confident that these datasets will improve comparability and
future progress in the field.

Reproducibility is a cornerstone of science, as the replication of findings
is the process through which they become knowledge. It is widely considered
that many fields of science are undergoing a reproducibility crisis. This has
led to the publications of various guidelines in order to improve research
  This didactic chapter intends at being an introduction to reproducibility for
researchers in the field of machine learning for medical imaging. We first
distinguish between different types of reproducibility. For each of them, we
aim at defining it, at describing the requirements to achieve it and at
discussing its utility. The chapter ends with a discussion on the benefits of
reproducibility and with a plea for a non-dogmatic approach to this concept and
its implementation in research practice.

In this paper, we present a novel and generic data-driven method to
servo-control the 3-D shape of continuum robots embedded with fiber Bragg
grating (FBG) sensors. Developments of 3-D shape perception and control
technologies are crucial for continuum robots to perform the tasks autonomously
in surgical interventions. However, owing to the nonlinear properties of
continuum robots, one main difficulty lies in the modeling of them, especially
for soft robots with variable stiffness. To address this problem, we propose a
new robust adaptive controller by leveraging FBG shape feedback and neural
networks (NNs) that can online estimate the unknown model of continuum robot
and accounts for unexpected disturbances together with NN approximation errors,
which exhibits an adaptive behavior to the unmodeled system without priori data
exploration. Based on a new composite adaptation algorithm, the asymptotic
convergences of the closed-loop system with NNs learning parameters have been
proven by Lyapunov theory. To validate the proposed method, we present a
comprehensive experimental study by using two continuum robots both integrated
with multi-core FBGs, including a robotic-assisted colonoscope and
multi-section extensible soft manipulators. The results demonstrate the
feasibility, adaptability, and superiority of our controller in various
unstructured environments as well as phantom experiments.

We study the feature-based newsvendor problem, in which a decision-maker has
access to historical data consisting of demand observations and exogenous
features. In this setting, we investigate feature selection, aiming to derive
sparse, explainable models with improved out-of-sample performance. Up to now,
state-of-the-art methods utilize regularization, which penalizes the number of
selected features or the norm of the solution vector. As an alternative, we
introduce a novel bilevel programming formulation. The upper-level problem
selects a subset of features that minimizes an estimate of the out-of-sample
cost of ordering decisions based on a held-out validation set. The lower-level
problem learns the optimal coefficients of the decision function on a training
set, using only the features selected by the upper-level. We present a mixed
integer linear program reformulation for the bilevel program, which can be
solved to optimality with standard optimization solvers. Our computational
experiments show that the method accurately recovers ground-truth features
already for instances with a sample size of a few hundred observations. In
contrast, regularization-based techniques often fail at feature recovery or
require thousands of observations to obtain similar accuracy. Regarding
out-of-sample generalization, we achieve improved or comparable cost

Model explainability has become an important problem in machine learning (ML)
due to the increased effect that algorithmic predictions have on humans.
Explanations can help users understand not only why ML models make certain
predictions, but also how these predictions can be changed. In this thesis, we
examine the explainability of ML models from three vantage points: algorithms,
users, and pedagogy, and contribute several novel solutions to the
explainability problem.

A major focus of recent developments in stereo vision has been on how to
obtain accurate dense disparity maps in passive stereo vision. Active vision
systems enable more accurate estimations of dense disparity compared to passive
stereo. However, subpixel-accurate disparity estimation remains an open problem
that has received little attention. In this paper, we propose a new learning
strategy to train neural networks to estimate high-quality subpixel disparity
maps for semi-dense active stereo vision. The key insight is that neural
networks can double their accuracy if they are able to jointly learn how to
refine the disparity map while invalidating the pixels where there is
insufficient information to correct the disparity estimate. Our approach is
based on Bayesian modeling where validated and invalidated pixels are defined
by their stochastic properties, allowing the model to learn how to choose by
itself which pixels are worth its attention. Using active stereo datasets such
as Active-Passive SimStereo, we demonstrate that the proposed method
outperforms the current state-of-the-art active stereo models. We also
demonstrate that the proposed approach compares favorably with state-of-the-art
passive stereo models on the Middlebury dataset.

The unprecedented growth of the global Internet traffic, coupled with the
large spatio-temporal fluctuations that create, to some extent, predictable
tidal traffic conditions, are motivating the evolution from reactive to
proactive and eventually towards adaptive optical networks. In these networks,
traffic-driven service provisioning can address the problem of network
over-provisioning and better adapt to traffic variations, while keeping the
quality-of-service at the required levels. Such an approach will reduce network
resource over-provisioning and thus reduce the total network cost. This survey
provides a comprehensive review of the state of the art on machine learning
(ML)-based techniques at the optical layer for traffic-driven service
provisioning. The evolution of service provisioning in optical networks is
initially presented, followed by an overview of the ML techniques utilized for
traffic-driven service provisioning. ML-aided service provisioning approaches
are presented in detail, including predictive and prescriptive service
provisioning frameworks in proactive and adaptive networks. For all techniques
outlined, a discussion on their limitations, research challenges, and potential
opportunities is also presented.

Teaching graph theory with the most adequate tools requires time and ideas.
We present how an open community of teachers shares contents and ideas on an
innovative platform. The objective is to get the students autonomous in their
training with activities that give them immediate feedback on their
understanding. Beyond learning, the very large collection of exercises of
various levels can also be used to evaluate the student's level. The proposed
activities can be algorithm's code in classical programming languages (e.g.
Java, Python) that the student can test with predefined tests proposed by the
teacher or collections of generated questions.

Body-worn first-person vision (FPV) camera enables to extract a rich source
of information on the environment from the subject's viewpoint. However, the
research progress in wearable camera-based egocentric office activity
understanding is slow compared to other activity environments (e.g., kitchen
and outdoor ambulatory), mainly due to the lack of adequate datasets to train
more sophisticated (e.g., deep learning) models for human activity recognition
in office environments. This paper provides details of a large and publicly
available office activity dataset (BON) collected in different office settings
across three geographical locations: Barcelona (Spain), Oxford (UK) and Nairobi
(Kenya), using a chest-mounted GoPro Hero camera. The BON dataset contains
eighteen common office activities that can be categorised into person-to-person
interactions (e.g., Chat with colleagues), person-to-object (e.g., Writing on a
whiteboard), and proprioceptive (e.g., Walking). Annotation is provided for
each segment of video with 5-seconds duration. Generally, BON contains 25
subjects and 2639 total segments. In order to facilitate further research in
the sub-domain, we have also provided results that could be used as baselines
for future studies.

Neural ranking models (NRMs) have become one of the most important techniques
in information retrieval (IR). Due to the limitation of relevance labels, the
training of NRMs heavily relies on negative sampling over unlabeled data. In
general machine learning scenarios, it has shown that training with hard
negatives (i.e., samples that are close to positives) could lead to better
performance. Surprisingly, we find opposite results from our empirical studies
in IR. When sampling top-ranked results (excluding the labeled positives) as
negatives from a stronger retriever, the performance of the learned NRM becomes
even worse. Based on our investigation, the superficial reason is that there
are more false negatives (i.e., unlabeled positives) in the top-ranked results
with a stronger retriever, which may hurt the training process; The root is the
existence of pooling bias in the dataset constructing process, where annotators
only judge and label very few samples selected by some basic retrievers.
Therefore, in principle, we can formulate the false negative issue in training
NRMs as learning from labeled datasets with pooling bias. To solve this
problem, we propose a novel Coupled Estimation Technique (CET) that learns both
a relevance model and a selection model simultaneously to correct the pooling
bias for training NRMs. Empirical results on three retrieval benchmarks show
that NRMs trained with our technique can achieve significant gains on ranking
effectiveness against other baseline strategies.

Research has shown that human-agent relationships form in similar ways to
human-human relationships. Since children do not have the same critical
analysis skills as adults (and may over-trust technology, for example), this
relationship-formation is concerning. Nonetheless, little research investigates
children's perceptions of conversational agents in-depth, and even less
investigates how education might change these perceptions. We present K-12
workshops with associated conversational AI concepts to encourage healthier
understanding and relationships with agents. Through studies with the
curriculum, and children and parents from various countries, we found
participants' perceptions of agents -- specifically their partner models and
trust -- changed. When participants discussed changes in trust of agents, we
found they most often mentioned learning something. For example, they
frequently mentioned learning where agents obtained information, what agents do
with this information and how agents are programmed. Based on the results, we
developed recommendations for teaching conversational agent concepts, including
emphasizing the concepts students found most challenging, like training,
turn-taking and terminology; supplementing agent development activities with
related learning activities; fostering appropriate levels of trust towards
agents; and fostering accurate partner models of agents. Through such pedagogy,
students can learn to better understand conversational AI and what it means to
have it in the world.

Constructing an accurate atomistic model for the high-pressure phases of tin
(Sn) is challenging because properties of Sn are sensitive to pressures. We
develop machine-learning-based deep potentials for Sn with pressures ranging
from 0 to 50 GPa and temperatures ranging from 0 to 2000 K. In particular, we
find the deep potential, which is obtained by training the ab initio data from
density functional theory calculations with the state-of-the-art SCAN
exchange-correlation functional, is suitable to characterize high-pressure
phases of Sn. We systematically validate several structural and elastic
properties of the {\alpha} (diamond structure), {\beta}, bct, and bcc
structures of Sn, as well as the structural and dynamic properties of liquid
Sn. The thermodynamics integration method is further utilized to compute the
free energies of the {\alpha}, {\beta}, bct, and liquid phases, from which the
deep potential successfully predicts the phase diagram of Sn including the
existence of the triple-point that qualitatively agrees with the experiment.

Despite great recent advances achieved by deep neural networks (DNNs), they
are often vulnerable to adversarial attacks. Intensive research efforts have
been made to improve the robustness of DNNs; however, most empirical defenses
can be adaptively attacked again, and the theoretically certified robustness is
limited, especially on large-scale datasets. One potential root cause of such
vulnerabilities for DNNs is that although they have demonstrated powerful
expressiveness, they lack the reasoning ability to make robust and reliable
predictions. In this paper, we aim to integrate domain knowledge to enable
robust learning with the reasoning paradigm. In particular, we propose a
certifiably robust learning with reasoning pipeline (CARE), which consists of a
learning component and a reasoning component. Concretely, we use a set of
standard DNNs to serve as the learning component to make semantic predictions,
and we leverage the probabilistic graphical models, such as Markov logic
networks (MLN), to serve as the reasoning component to enable knowledge/logic
reasoning. However, it is known that the exact inference of MLN (reasoning) is
#P-complete, which limits the scalability of the pipeline. To this end, we
propose to approximate the MLN inference via variational inference based on an
efficient expectation maximization algorithm. In particular, we leverage graph
convolutional networks (GCNs) to encode the posterior distribution during
variational inference and update the parameters of GCNs (E-step) and the
weights of knowledge rules in MLN (M-step) iteratively. We conduct extensive
experiments on different datasets and show that CARE achieves significantly
higher certified robustness compared with the state-of-the-art baselines. We
additionally conducted different ablation studies to demonstrate the empirical
robustness of CARE and the effectiveness of different knowledge integration.

Learning-based methods have effectively promoted the community of image
compression. Meanwhile, variational autoencoder (VAE) based variable-rate
approaches have recently gained much attention to avoid the usage of a set of
different networks for various compression rates. Despite the remarkable
performance that has been achieved, these approaches would be readily corrupted
once multiple compression/decompression operations are executed, resulting in
the fact that image quality would be tremendously dropped and strong artifacts
would appear. Thus, we try to tackle the issue of high-fidelity fine
variable-rate image compression and propose the Invertible Activation
Transformation (IAT) module. We implement the IAT in a mathematical invertible
manner on a single rate Invertible Neural Network (INN) based model and the
quality level (QLevel) would be fed into the IAT to generate scaling and bias
tensors. IAT and QLevel together give the image compression model the ability
of fine variable-rate control while better maintaining the image fidelity.
Extensive experiments demonstrate that the single rate image compression model
equipped with our IAT module has the ability to achieve variable-rate control
without any compromise. And our IAT-embedded model obtains comparable
rate-distortion performance with recent learning-based image compression
methods. Furthermore, our method outperforms the state-of-the-art variable-rate
image compression method by a large margin, especially after multiple
re-encodings.

Reading computer program code and documentation written by others is, we are
told, one of the best ways to learn the art of writing intelligible and
maintainable code and documentation. The software resurrection exercise,
introduced in this paper, requires a motivated learner to compile and test a
historical release (e.g. 20 years old) version of a well maintained and widely
adopted open source software on a modern hardware and software platform. This
exercise concludes by writing a critique based on issues encountered while
compiling and testing a historical software release on a hardware and software
platform that could not have been foreseen at the time of release. The learner
is also required to fix the issues as a part of the software resurrection
exercise. The seemingly pointless exercise of resurrecting a historical
software allows motivated learners to experience the pain and joy of software
maintenance which is essential for understanding the factors that contribute to
intelligibility and maintainability of program code and documentation. The
concept of software resurrection exercise is illustrated using a version of the
SQLite database engine that was released 20 years ago. This illustration shows
that software engineering principles (or programming pearls) emerge when a
historical software release is adapted to run successfully on a modern
platform. The software resurrection exercise also has the potential to lay
foundations for a lifelong willingness to explore and learn from existing
program code.

Anomaly detection on the attributed network has recently received increasing
attention in many research fields, such as cybernetic anomaly detection and
financial fraud detection. With the wide application of deep learning on graph
representations, existing approaches choose to apply euclidean graph encoders
as their backbone, which may lose important hierarchical information,
especially in complex networks. To tackle this problem, we propose an efficient
anomaly detection framework using hyperbolic self-supervised contrastive
learning. Specifically, we first conduct the data augmentation by performing
subgraph sampling. Then we utilize the hierarchical information in hyperbolic
space through exponential mapping and logarithmic mapping and obtain the
anomaly score by subtracting scores of the positive pairs from the negative
pairs via a discriminating process. Finally, extensive experiments on four
real-world datasets demonstrate that our approach performs superior over
representative baseline approaches.

Nonsmooth nonconvex optimization problems broadly emerge in machine learning
and business decision making, whereas two core challenges impede the
development of efficient solution methods with finite-time convergence
guarantee: the lack of computationally tractable optimality criterion and the
lack of computationally powerful oracles. The contributions of this paper are
two-fold. First, we establish the relationship between the celebrated Goldstein
subdifferential~\citep{Goldstein-1977-Optimization} and uniform smoothing,
thereby providing the basis and intuition for the design of gradient-free
methods that guarantee the finite-time convergence to a set of Goldstein
stationary points. Second, we propose the gradient-free method (GFM) and
stochastic GFM for solving a class of nonsmooth nonconvex optimization problems
and prove that both of them can return a $(\delta,\epsilon)$-Goldstein
stationary point of a Lipschitz function $f$ at an expected convergence rate at
$O(d^{3/2}\delta^{-1}\epsilon^{-4})$ where $d$ is the problem dimension.
Two-phase versions of GFM and SGFM are also proposed and proven to achieve
improved large-deviation results. Finally, we demonstrate the effectiveness of
2-SGFM on training ReLU neural networks with the \textsc{Minst} dataset.

The convergence of policy gradient algorithms in reinforcement learning
hinges on the optimization landscape of the underlying optimal control problem.
Theoretical insights into these algorithms can often be acquired from analyzing
those of linear quadratic control. However, most of the existing literature
only considers the optimization landscape for static full-state or output
feedback policies (controllers). We investigate the more challenging case of
dynamic output-feedback policies for linear quadratic regulation (abbreviated
as dLQR), which is prevalent in practice but has a rather complicated
optimization landscape. We first show how the dLQR cost varies with the
coordinate transformation of the dynamic controller and then derive the optimal
transformation for a given observable stabilizing controller. At the core of
our results is the uniqueness of the stationary point of dLQR when it is
observable, which is in a concise form of an observer-based controller with the
optimal similarity transformation. These results shed light on designing
efficient algorithms for general decision-making problems with partially
observed information.

Open-shell benzenoid polycyclic hydrocarbons (BPHs) with unpaired electrons
can serve as functional materials for future quantum applications. However,
there is a grand challenge in the search and realization of molecules with
desired open-shell characters from a gigantic chemical space of BPHs, which
calls for strategies to direct both the fundamental understandings and
experimental progress. Machine learning (ML) has recently become one of the
most exciting tools entering materials science for accelerating data analysis
and data mining. In this work, we have built up a structure database of BPHs
through graphical enumeration, and performed data-driven analysis through the
ML model. Combined with a simple model Hamiltonian calculation, we discover
that the number of the internal vertices of the BPH graphs is strongly
correlated with their open-shell characters. We further demonstrate the role of
the phenalenyl subgraph in predicting the magnetic ground state. In addition,
we conceive a class of interesting open-shell BPHs whose spin densities are
globally distributed over the molecules, which may benefit their stability.
These data-driven results not only provide a database of open-shell BPHs, but
also extend the well-known Lieb's theorem and Ovchinnikov's rule and provide a
simple way for designing open-shell carbon nanostructures, which may facilitate
the exploration of emerging quantum phases and the realization of
technologically relevant magnetic materials.

With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP),
which aims to sort product reviews according to the predicted helpfulness
scores has become a research hotspot. Previous work on this task focuses on
attention-based modality fusion, information integration, and relation
modeling, which primarily exposes the following drawbacks: 1) the model may
fail to capture the really essential information due to its indiscriminate
attention formulation; 2) lack appropriate modeling methods that take full
advantage of correlation among provided data. In this paper, we propose SANCL:
Selective Attention and Natural Contrastive Learning for MRHP. SANCL adopts a
probe-based strategy to enforce high attention weights on the regions of
greater significance. It also constructs a contrastive learning framework based
on natural matching properties in the dataset. Experimental results on two
benchmark datasets with three categories show that SANCL achieves
state-of-the-art baseline performance with lower memory consumption.

When oncologists estimate cancer patient survival, they rely on multimodal
data. Even though some multimodal deep learning methods have been proposed in
the literature, the majority rely on having two or more independent networks
that share knowledge at a later stage in the overall model. On the other hand,
oncologists do not do this in their analysis but rather fuse the information in
their brain from multiple sources such as medical images and patient history.
This work proposes a deep learning method that mimics oncologists' analytical
behavior when quantifying cancer and estimating patient survival. We propose
TMSS, an end-to-end Transformer based Multimodal network for Segmentation and
Survival prediction that leverages the superiority of transformers that lies in
their abilities to handle different modalities. The model was trained and
validated for segmentation and prognosis tasks on the training dataset from the
HEad & NeCK TumOR segmentation and the outcome prediction in PET/CT images
challenge (HECKTOR). We show that the proposed prognostic model significantly
outperforms state-of-the-art methods with a concordance index of 0.763+/-0.14
while achieving a comparable dice score of 0.772+/-0.030 to a standalone
segmentation model. The code is publicly available.

In the present work, we propose a Self-supervised COordinate Projection
nEtwork (SCOPE) to reconstruct the artifacts-free CT image from a single SV
sinogram by solving the inverse tomography imaging problem. Compared with
recent related works that solve similar problems using implicit neural
representation network (INR), our essential contribution is an effective and
simple re-projection strategy that pushes the tomography image reconstruction
quality over supervised deep learning CT reconstruction works. The proposed
strategy is inspired by the simple relationship between linear algebra and
inverse problems. To solve the under-determined linear equation system, we
first introduce INR to constrain the solution space via image continuity prior
and achieve a rough solution. And secondly, we propose to generate a dense view
sinogram that improves the rank of the linear equation system and produces a
more stable CT image solution space. Our experiment results demonstrate that
the re-projection strategy significantly improves the image reconstruction
quality (+3 dB for PSNR at least). Besides, we integrate the recent hash
encoding into our SCOPE model, which greatly accelerates the model training.
Finally, we evaluate SCOPE in parallel and fan X-ray beam SVCT reconstruction
tasks. Experimental results indicate that the proposed SCOPE model outperforms
two latest INR-based methods and two well-popular supervised DL methods
quantitatively and qualitatively.

Transformers combined with convolutional encoders have been recently used for
hand gesture recognition (HGR) using micro-Doppler signatures. We propose a
vision-transformer-based architecture for HGR with multi-antenna
continuous-wave Doppler radar receivers. The proposed architecture consists of
three modules: a convolutional encoderdecoder, an attention module with three
transformer layers, and a multi-layer perceptron. The novel convolutional
decoder helps to feed patches with larger sizes to the attention module for
improved feature extraction. Experimental results obtained with a dataset
corresponding to a two-antenna continuous-wave Doppler radar receiver operating
at 24 GHz (published by Skaria et al.) confirm that the proposed architecture
achieves an accuracy of 98.3% which substantially surpasses the
state-of-the-art on the used dataset.

We investigate efficient learning from higher-order graph convolution and
learning directly from adjacency matrices for node classification. We revisit
the scaled graph residual network and remove ReLU activation from residual
layers and apply a single weight matrix at each residual layer. We show that
the resulting model lead to new graph convolution models as a polynomial of the
normalized adjacency matrix, the residual weight matrix, and the residual
scaling parameter. Additionally, we propose adaptive learning between directly
graph polynomial convolution models and learning directly from the adjacency
matrix. Furthermore, we propose fully adaptive models to learn scaling
parameters at each residual layer. We show that generalization bounds of
proposed methods are bounded as a polynomial of eigenvalue spectrum, scaling
parameters, and upper bounds of residual weights. By theoretical analysis, we
argue that the proposed models can obtain improved generalization bounds by
limiting the higher-orders of convolutions and direct learning from the
adjacency matrix. Using a wide set of real-data, we demonstrate that the
proposed methods obtain improved accuracy for node-classification of
non-homophilous graphs.

Machine learning (ML) has penetrated various fields in the era of big data.
The advantage of collaborative machine learning (CML) over most conventional ML
lies in the joint effort of decentralized nodes or agents that results in
better model performance and generalization. As the training of ML models
requires a massive amount of good quality data, it is necessary to eliminate
concerns about data privacy and ensure high-quality data. To solve this
problem, we cast our eyes on the integration of CML and smart contracts. Based
on blockchain, smart contracts enable automatic execution of data preserving
and validation, as well as the continuity of CML model training. In our
simulation experiments, we define incentive mechanisms on the smart contract,
investigate the important factors such as the number of features in the dataset
(num_words), the size of the training data, the cost for the data holders to
submit data, etc., and conclude how these factors impact the performance
metrics of the model: the accuracy of the trained model, the gap between the
accuracies of the model before and after simulation, and the time to use up the
balance of bad agent. For instance, the increase of the value of num_words
leads to higher model accuracy and eliminates the negative influence of
malicious agents in a shorter time from our observation of the experiment
results. Statistical analyses show that with the help of smart contracts, the
influence of invalid data is efficiently diminished and model robustness is
maintained. We also discuss the gap in existing research and put forward
possible future directions for further works.

Point cloud upsampling is necessary for Augmented Reality, Virtual Reality,
and telepresence scenarios. Although the geometry upsampling is well studied to
densify point cloud coordinates, the upsampling of colors has been largely
overlooked. In this paper, we propose CU-Net, the first deep-learning point
cloud color upsampling model. Leveraging a feature extractor based on sparse
convolution and a color prediction module based on neural implicit function,
CU-Net achieves linear time and space complexity. Therefore, CU-Net is
theoretically guaranteed to be more efficient than most existing methods with
quadratic complexity. Experimental results demonstrate that CU-Net can colorize
a photo-realistic point cloud with nearly a million points in real time, while
having better visual quality than baselines. Besides, CU-Net can adapt to an
arbitrary upsampling ratio and unseen objects. Our source code will be released
to the public soon.

3D point cloud representation-based view synthesis methods have demonstrated
effectiveness. However, existing methods usually synthesize novel views only
from a single source view, and it is non-trivial to generalize them to handle
multiple source views for pursuing higher reconstruction quality. In this
paper, we propose a new deep learning-based view synthesis paradigm, which
learns a unified 3D point cloud from different source views. Specifically, we
first construct sub-point clouds by projecting source views to 3D space based
on their depth maps. Then, we learn the unified 3D point cloud by adaptively
fusing points at a local neighborhood defined on the union of the sub-point
clouds. Besides, we also propose a 3D geometry-guided image restoration module
to fill the holes and recover high-frequency details of the rendered novel
views. Experimental results on three benchmark datasets demonstrate that our
method outperforms state-of-the-art view synthesis methods to a large extent
both quantitatively and visually.

In this paper, we present a novel perspective towards IR evaluation by
proposing a new family of evaluation metrics where the existing popular metrics
(e.g., nDCG, MAP) are customized by introducing a query-specific lower-bound
(LB) normalization term. While original nDCG, MAP etc. metrics are normalized
in terms of their upper bounds based on an ideal ranked list, a corresponding
LB normalization for them has not yet been studied. Specifically, we introduce
two different variants of the proposed LB normalization, where the lower bound
is estimated from a randomized ranking of the corresponding documents present
in the evaluation set. We next conducted two case-studies by instantiating the
new framework for two popular IR evaluation metric (with two variants, e.g.,
DCG_UL_V1,2 and MSP_UL_V1,2 ) and then comparing against the traditional metric
without the proposed LB normalization. Experiments on two different data-sets
with eight Learning-to-Rank (LETOR) methods demonstrate the following
properties of the new LB normalized metric: 1) Statistically significant
differences (between two methods) in terms of original metric no longer remain
statistically significant in terms of Upper Lower (UL) Bound normalized version
and vice-versa, especially for uninformative query-sets. 2) When compared
against the original metric, our proposed UL normalized metrics demonstrate
higher Discriminatory Power and better Consistency across different data-sets.
These findings suggest that the IR community should consider UL normalization
seriously when computing nDCG and MAP and more in-depth study of UL
normalization for general IR evaluation is warranted.

Generating new samples from data sets can mitigate extra expensive
operations, increased invasive procedures, and mitigate privacy issues. These
novel samples that are statistically robust can be used as a temporary and
intermediate replacement when privacy is a concern. This method can enable
better data sharing practices without problems relating to identification
issues or biases that are flaws for an adversarial attack.

The core structure of dislocations is critical to their mobility, cross slip,
and other plastic behaviors. Atomistic simulation of the core structure is
limited by the size of first-principles density functional theory (DFT)
calculation and the accuracy of classical molecular dynamics with empirical
interatomic potentials. Here, we utilize a Deep Potential (DP) method learned
from DFT calculations to investigate the dislocations of face-centered cubic
copper on a large scale and obtain their core structures and energies. The
validity of the DP description of the core structure and elastic strain from
dislocation is confirmed by a fully discrete Peierls model. Moreover, the DP
method can be further extended easily to dislocations with defects such as
surface or vacancy, and our study will pave a way in the large-scale atomistic
simulation of dislocation on the DFT level.

Deep Reinforcement Learning (DRL) has made tremendous advances in both
simulated and real-world robot control tasks in recent years. Nevertheless,
applying DRL to novel robot control tasks is still challenging, especially when
researchers have to design the action and observation space and the reward
function. In this paper, we investigate partial observability as a potential
failure source of applying DRL to robot control tasks, which can occur when
researchers are not confident whether the observation space fully represents
the underlying state. We compare the performance of three common DRL
algorithms, TD3, SAC and PPO under various partial observability conditions. We
find that TD3 and SAC become easily stuck in local optima and underperform PPO.
We propose multi-step versions of the vanilla TD3 and SAC to improve robustness
to partial observability based on one-step bootstrapping.

In this paper, we propose two efficient approximation methods to solve
high-dimensional fully nonlinear partial differential equations (PDEs) and
second-order backward stochastic differential equations (2BSDEs), where such
high-dimensional fully nonlinear PDEs are extremely difficult to solve because
the computational cost of standard approximation methods grows exponentially
with the number of dimensions. Therefore, we consider the following methods to
overcome this difficulty. For the merged fully nonlinear PDEs and 2BSDEs
system, combined with the time forward discretization and ReLU function, we use
multi-scale deep learning fusion and convolutional neural network (CNN)
techniques to obtain two numerical approximation schemes, respectively. In
numerical experiments, involving Allen-Cahn equations,
Black-Scholes-Barentblatt equations, and Hamiltonian-Jacobi-Bellman equations,
the first proposed method exhibits higher efficiency and accuracy than the
existing method; the second proposed method can extend the dimensionality of
the completely non-linear PDEs-2BSDEs system over $400$ dimensions, from which
the numerical results illustrate the effectiveness of proposed methods.

Online Knowledge Distillation (OKD) improves the involved models by
reciprocally exploiting the difference between teacher and student. Several
crucial bottlenecks over the gap between them -- e.g., Why and when does a
large gap harm the performance, especially for student? How to quantify the gap
between teacher and student? -- have received limited formal study. In this
paper, we propose Switchable Online Knowledge Distillation (SwitOKD), to answer
these questions. Instead of focusing on the accuracy gap at test phase by the
existing arts, the core idea of SwitOKD is to adaptively calibrate the gap at
training phase, namely distillation gap, via a switching strategy between two
modes -- expert mode (pause the teacher while keep the student learning) and
learning mode (restart the teacher). To possess an appropriate distillation
gap, we further devise an adaptive switching threshold, which provides a formal
criterion as to when to switch to learning mode or expert mode, and thus
improves the student's performance. Meanwhile, the teacher benefits from our
adaptive switching threshold and keeps basically on a par with other online
arts. We further extend SwitOKD to multiple networks with two basis topologies.
Finally, extensive experiments and analysis validate the merits of SwitOKD for
classification over the state-of-the-arts. Our code is available at
https://github.com/hfutqian/SwitOKD.

The multi-source electromechanical coupling makes the energy management of
fuel cell electric vehicles (FCEVs) relatively nonlinear and complex especially
in the types of 4-wheel-drive (4WD) FCEVs. Accurate state observing for
complicated nonlinear system is the basis for fantastic energy managing in
FCEVs. Aiming at releasing the energy-saving potential of FCEVs, a novel
learning-based robust model predictive control (LRMPC) strategy is proposed for
a 4WD FCEV, contributing to suitable power distribution among multiple energy
sources. The well-designed strategy based on machine learning (ML) translates
the knowledge of the nonlinear system to the explicit controlling scheme with
superior robust performance. To start with, ML methods with high regression
accuracy and superior generalization ability are trained offline to establish
the precise state observer for SOC. Then, explicit data tables for SOC
generated by state observer are used for grabbing accurate state changing,
whose input features include the vehicle status and the states of vehicle
components. To be specific, the vehicle velocity estimation for providing
future speed reference is constructed by deep forest. Next, the components
including explicit data tables and vehicle velocity estimation are combined
with model predictive control (MPC) to release the state-of-the-art
energy-saving ability for the multi-freedom system in FCEVs, whose name is
LRMPC. At last, the detailed assessment is performed in simulation test to
validate the advancing performance of LRMPC. The corresponding results
highlight the optimal control effect in energy-saving potential and strong
real-time application ability of LRMPC.

Recent advances in deep learning have greatly propelled the research on
semantic parsing. Improvement has since been made in many downstream tasks,
including natural language interface to web APIs, text-to-SQL generation, among
others. However, despite the close connection shared with these tasks, research
on question answering over knowledge bases (KBQA) has comparatively been
progressing slowly. We identify and attribute this to two unique challenges of
KBQA, schema-level complexity and fact-level complexity. In this survey, we
situate KBQA in the broader literature of semantic parsing and give a
comprehensive account of how existing KBQA approaches attempt to address the
unique challenges. Regardless of the unique challenges, we argue that we can
still take much inspiration from the literature of semantic parsing, which has
been overlooked by existing research on KBQA. Based on our discussion, we can
better understand the bottleneck of current KBQA research and shed light on
promising directions for KBQA to keep up with the literature of semantic
parsing, particularly in the era of pre-trained language models.

Learning conditional densities and identifying factors that influence the
entire distribution are vital tasks in data-driven applications. Conventional
approaches work mostly with summary statistics, and are hence inadequate for a
comprehensive investigation. Recently, there have been developments on
functional regression methods to model density curves as functional outcomes. A
major challenge for developing such models lies in the inherent constraint of
non-negativity and unit integral for the functional space of density outcomes.
To overcome this fundamental issue, we propose Wasserstein Distributional
Learning (WDL), a flexible density-on-scalar regression modeling framework that
starts with the Wasserstein distance $W_2$ as a proper metric for the space of
density outcomes. We then introduce a heterogeneous and flexible class of
Semi-parametric Conditional Gaussian Mixture Models (SCGMM) as the model class
$\mathfrak{F} \otimes \mathcal{T}$. The resulting metric space $(\mathfrak{F}
\otimes \mathcal{T}, W_2)$ satisfies the required constraints and offers a
dense and closed functional subspace. For fitting the proposed model, we
further develop an efficient algorithm based on Majorization-Minimization
optimization with boosted trees. Compared with methods in the previous
literature, WDL better characterizes and uncovers the nonlinear dependence of
the conditional densities, and their derived summary statistics. We demonstrate
the effectiveness of the WDL framework through simulations and real-world

In this work, we propose a semi-supervised triply robust inductive transfer
learning (STRIFLE) approach, which integrates heterogeneous data from label
rich source population and label scarce target population to improve the
learning accuracy in the target population. Specifically, we consider a high
dimensional covariate shift setting and employ two nuisance models, a density
ratio model and an imputation model, to combine transfer learning and
surrogate-assisted semi-supervised learning strategies organically and achieve
triple robustness. While the STRIFLE approach requires the target and source
populations to share the same conditional distribution of outcome Y given both
the surrogate features S and predictors X, it allows the true underlying model
of Y|X to differ between the two populations due to the potential covariate
shift in S and X. Different from double robustness, even if both nuisance
models are misspecified or the distribution of Y|S,X is not the same between
the two populations, when the transferred source population and the target
population share enough similarities, the triply robust STRIFLE estimator can
still partially utilize the source population, and it is guaranteed to be no
worse than the target-only surrogate-assisted semi-supervised estimator with
negligible errors. These desirable properties of our estimator are established
theoretically and verified in finite-sample via extensive simulation studies.
We utilize the STRIFLE estimator to train a Type II diabetes polygenic risk
prediction model for the African American target population by transferring
knowledge from electronic health records linked genomic data observed in a
larger European source population.

Although artificial intelligence (AI) has made significant progress in
understanding molecules in a wide range of fields, existing models generally
acquire the single cognitive ability from the single molecular modality. Since
the hierarchy of molecular knowledge is profound, even humans learn from
different modalities including both intuitive diagrams and professional texts
to assist their understanding. Inspired by this, we propose a molecular
multimodal foundation model which is pretrained from molecular graphs and their
semantically related textual data (crawled from published Scientific Citation
Index papers) via contrastive learning. This AI model represents a critical
attempt that directly bridges molecular graphs and natural language.
Importantly, through capturing the specific and complementary information of
the two modalities, our proposed model can better grasp molecular expertise.
Experimental results show that our model not only exhibits promising
performance in cross-modal tasks such as cross-modal retrieval and molecule
caption, but also enhances molecular property prediction and possesses
capability to generate meaningful molecular graphs from natural language
descriptions. We believe that our model would have a broad impact on
AI-empowered fields across disciplines such as biology, chemistry, materials,
environment, and medicine, among others.

Streaming videos is one of the methods for creators to share their creative
works with their audience. In these videos, the streamer share how they achieve
their final objective by using various tools in one or several programs for
creative projects. To this end, the steps required to achieve the final goal
can be discussed. As such, these videos could provide substantial educational
content that can be used to learn how to employ the tools used by the streamer.
However, one of the drawbacks is that the streamer might not provide enough
details for every step. Therefore, for the learners, it might be difficult to
catch up with all the steps. In order to alleviate this issue, one solution is
to link the streaming videos with the relevant tutorial available for the tools
used in the streaming video. More specifically, a system can analyze the
content of the live streaming video and recommend the most relevant tutorials.
Since the existing document recommendation models cannot handle this situation,
in this work, we present a novel dataset and model for the task of tutorial
recommendation for live-streamed videos. We conduct extensive analyses on the
proposed dataset and models, revealing the challenging nature of this task.

Machine learning (ML) models, such as SVM, for tasks like classification and
clustering of sequences, require a definition of distance/similarity between
pairs of sequences. Several methods have been proposed to compute the
similarity between sequences, such as the exact approach that counts the number
of matches between $k$-mers (sub-sequences of length $k$) and an approximate
approach that estimates pairwise similarity scores. Although exact methods
yield better classification performance, they pose high computational costs,
limiting their applicability to a small number of sequences. The approximate
algorithms are proven to be more scalable and perform comparably to (sometimes
better than) the exact methods -- they are designed in a "general" way to deal
with different types of sequences (e.g., music, protein, etc.). Although
general applicability is a desired property of an algorithm, it is not the case
in all scenarios. For example, in the current COVID-19 (coronavirus) pandemic,
there is a need for an approach that can deal specifically with the
coronavirus. To this end, we propose a series of ways to improve the
performance of the approximate kernel (using minimizers and information gain)
in order to enhance its predictive performance pm coronavirus sequences. More
specifically, we improve the quality of the approximate kernel using domain
knowledge (computed using information gain) and efficient preprocessing (using
minimizers computation) to classify coronavirus spike protein sequences
corresponding to different variants (e.g., Alpha, Beta, Gamma). We report
results using different classification and clustering algorithms and evaluate
their performance using multiple evaluation metrics. Using two datasets, we
show that our proposed method helps improve the kernel's performance compared
to the baseline and state-of-the-art approaches in the healthcare domain.

The Upper Indus Basin, Himalayas provides water for 270 million people and
countless ecosystems. However, precipitation, a key component to hydrological
modelling, is poorly understood in this area. A key challenge surrounding this
uncertainty comes from the complex spatial-temporal distribution of
precipitation across the basin. In this work we propose Gaussian processes with
structured non-stationary kernels to model precipitation patterns in the UIB.
Previous attempts to quantify or model precipitation in the Hindu Kush
Karakoram Himalayan region have often been qualitative or include crude
assumptions and simplifications which cannot be resolved at lower resolutions.
This body of research also provides little to no error propagation. We account
for the spatial variation in precipitation with a non-stationary Gibbs kernel
parameterised with an input dependent lengthscale. This allows the posterior
function samples to adapt to the varying precipitation patterns inherent in the
distinct underlying topography of the Indus region. The input dependent
lengthscale is governed by a latent Gaussian process with a stationary
squared-exponential kernel to allow the function level hyperparameters to vary
smoothly. In ablation experiments we motivate each component of the proposed
kernel by demonstrating its ability to model the spatial covariance, temporal
structure and joint spatio-temporal reconstruction. We benchmark our model with
a stationary Gaussian process and a Deep Gaussian processes.

Scene flow represents the 3D motion of each point in the scene, which
explicitly describes the distance and the direction of each point's movement.
Scene flow estimation is used in various applications such as autonomous
driving fields, activity recognition, and virtual reality fields. As it is
challenging to annotate scene flow with ground truth for real-world data, this
leaves no real-world dataset available to provide a large amount of data with
ground truth for scene flow estimation. Therefore, many works use synthesized
data to pre-train their network and real-world LiDAR data to finetune. Unlike
the previous unsupervised learning of scene flow in point clouds, we propose to
use odometry information to assist the unsupervised learning of scene flow and
use real-world LiDAR data to train our network. Supervised odometry provides
more accurate shared cost volume for scene flow. In addition, the proposed
network has mask-weighted warp layers to get a more accurate predicted point
cloud. The warp operation means applying an estimated pose transformation or
scene flow to a source point cloud to obtain a predicted point cloud and is the
key to refining scene flow from coarse to fine. When performing warp
operations, the points in different states use different weights for the pose
transformation and scene flow transformation. We classify the states of points
as static, dynamic, and occluded, where the static masks are used to divide
static and dynamic points, and the occlusion masks are used to divide occluded
points. The mask-weighted warp layer indicates that static masks and occlusion
masks are used as weights when performing warp operations. Our designs are
proved to be effective in ablation experiments. The experiment results show the
promising prospect of an odometry-assisted unsupervised learning method for 3D
scene flow in real-world data.

We propose a new Reject Option Classification technique to identify and
remove regions of uncertainty in the decision space for a given neural
classifier and dataset. Such existing formulations employ a learned rejection
(remove)/selection (keep) function and require either a known cost for
rejecting examples or strong constraints on the accuracy or coverage of the
selected examples. We consider an alternative formulation by instead analyzing
the complementary reject region and employing a validation set to learn
per-class softmax thresholds. The goal is to maximize the accuracy of the
selected examples subject to a natural randomness allowance on the rejected
examples (rejecting more incorrect than correct predictions). We provide
results showing the benefits of the proposed method over na\"ively thresholding
calibrated/uncalibrated softmax scores with 2-D points, imagery, and text
classification datasets using state-of-the-art pretrained models. Source code
is available at https://github.com/osu-cvl/learning-idk.

Product bundling is a common selling mechanism used in online retailing. To
set profitable bundle prices, the seller needs to learn consumer preferences
from the transaction data. When customers purchase bundles or multiple
products, classical methods such as discrete choice models cannot be used to
estimate customers' valuations. In this paper, we propose an approach to learn
the distribution of consumers' valuations toward the products using bundle
sales data. The approach reduces it to an estimation problem where the samples
are censored by polyhedral regions. Using the EM algorithm and Monte Carlo
simulation, our approach can recover the distribution of consumers' valuations.
The framework allows for unobserved no-purchases and clustered market segments.
We provide theoretical results on the identifiability of the probability model
and the convergence of the EM algorithm. The performance of the approach is
also demonstrated numerically.

This paper describes a novel framework for a human-machine interface that can
be used to control an upper-limb prosthesis. The objective is to estimate the
human's motor intent from noisy surface electromyography signals and to execute
the motor intent on the prosthesis (i.e., the robot) even in the presence of
previously unseen perturbations. The framework includes muscle-tendon models
for each degree of freedom, a method for learning the parameter values of
models used to estimate the user's motor intent, and a variable impedance
controller that uses the stiffness and damping values obtained from the muscle
models to adapt the prosthesis' motion trajectory and dynamics. We
experimentally evaluate our framework in the context of able-bodied humans
using a simulated version of the human-machine interface to perform reaching
tasks that primarily actuate one degree of freedom in the wrist, and consider
external perturbations in the form of a uniform force field that pushes the
wrist away from the target. We demonstrate that our framework provides the
desired adaptive performance, and substantially improves performance in
comparison with a data-driven baseline.

Industry assignment, which assigns firms to industries according to a
predefined Industry Classification System (ICS), is fundamental to a large
number of critical business practices, ranging from operations and strategic
decision making by firms to economic analyses by government agencies. Three
types of expert knowledge are essential to effective industry assignment:
definition-based knowledge (i.e., expert definitions of each industry),
structure-based knowledge (i.e., structural relationships among industries as
specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry
assignments performed by domain experts). Existing industry assignment methods
utilize only assignment-based knowledge to learn a model that classifies
unassigned firms to industries, and overlook definition-based and
structure-based knowledge. Moreover, these methods only consider which industry
a firm has been assigned to, but ignore the time-specificity of
assignment-based knowledge, i.e., when the assignment occurs. To address the
limitations of existing methods, we propose a novel deep learning-based method
that not only seamlessly integrates the three types of knowledge for industry
assignment but also takes the time-specificity of assignment-based knowledge
into account. Methodologically, our method features two innovations: dynamic
industry representation and hierarchical assignment. The former represents an
industry as a sequence of time-specific vectors by integrating the three types
of knowledge through our proposed temporal and spatial aggregation mechanisms.
The latter takes industry and firm representations as inputs, computes the
probability of assigning a firm to different industries, and assigns the firm
to the industry with the highest probability.

In the past decades, the rise of artificial intelligence has given us the
capabilities to solve the most challenging problems in our day-to-day lives,
such as cancer prediction and autonomous navigation. However, these
applications might not be reliable if not secured against adversarial attacks.
In addition, recent works demonstrated that some adversarial examples are
transferable across different models. Therefore, it is crucial to avoid such
transferability via robust models that resist adversarial manipulations. In
this paper, we propose a feature randomization-based approach that resists
eight adversarial attacks targeting deep learning models in the testing phase.
Our novel approach consists of changing the training strategy in the target
network classifier and selecting random feature samples. We consider the
attacker with a Limited-Knowledge and Semi-Knowledge conditions to undertake
the most prevalent types of adversarial attacks. We evaluate the robustness of
our approach using the well-known UNSW-NB15 datasets that include realistic and
synthetic attacks. Afterward, we demonstrate that our strategy outperforms the
existing state-of-the-art approach, such as the Most Powerful Attack, which
consists of fine-tuning the network model against specific adversarial attacks.
Finally, our experimental results show that our methodology can secure the
target network and resists adversarial attack transferability by over 60%.

Exploring search spaces is one of the most unpredictable challenges that has
attracted the interest of researchers for decades. One way to handle
unpredictability is to characterise the search spaces and take actions
accordingly. A well-characterised search space can assist in mapping the
problem states to a set of operators for generating new problem states. In this
paper, a landscape analysis-based set of features has been analysed using the
most renown machine learning approaches to determine the optimal feature set.
However, in order to deal with problem complexity and induce commonality for
transferring experience across domains, the selection of the most
representative features remains crucial. The proposed approach analyses the
predictivity of a set of features in order to determine the best
categorization.

IT helpdesks are charged with the task of responding quickly to user queries.
To give the user confidence that their query matters, the helpdesk will
auto-reply to the user with confirmation that their query has been received and
logged. This auto-reply may include generic `boiler-plate' text that addresses
common problems of the day, with relevant information and links. The approach
explored here is to tailor the content of the auto-reply to the user's problem,
so as to increase the relevance of the information included. Problem
classification is achieved by training a neural network on a suitable corpus of
IT helpdesk email data. While this is no substitute for follow-up by helpdesk
agents, the aim is that this system will provide a practical stop-gap.

This paper investigates the impact of feedback quantization on multi-agent
learning. In particular, we analyze the equilibrium convergence properties of
the well-known "follow the regularized leader" (FTRL) class of algorithms when
players can only observe a quantized (and possibly noisy) version of their
payoffs. In this information-constrained setting, we show that coarser
quantization triggers a qualitative shift in the convergence behavior of FTRL
schemes. Specifically, if the quantization error lies below a threshold value
(which depends only on the underlying game and not on the level of uncertainty
entering the process or the specific FTRL variant under study), then (i) FTRL
is attracted to the game's strict Nash equilibria with arbitrarily high
probability; and (ii) the algorithm's asymptotic rate of convergence remains
the same as in the non-quantized case. Otherwise, for larger quantization
levels, these convergence properties are lost altogether: players may fail to
learn anything beyond their initial state, even with full information on their
payoff vectors. This is in contrast to the impact of quantization in continuous
optimization problems, where the quality of the obtained solution degrades
smoothly with the quantization level.

Although deep reinforcement learning has recently been very successful at
learning complex behaviors, it requires a tremendous amount of data to learn a
task. One of the fundamental reasons causing this limitation lies in the nature
of the trial-and-error learning paradigm of reinforcement learning, where the
agent communicates with the environment and progresses in the learning only
relying on the reward signal. This is implicit and rather insufficient to learn
a task well. On the contrary, humans are usually taught new skills via natural
language instructions. Utilizing language instructions for robotic motion
control to improve the adaptability is a recently emerged topic and
challenging. In this paper, we present a meta-RL algorithm that addresses the
challenge of learning skills with language instructions in multiple
manipulation tasks. On the one hand, our algorithm utilizes the language
instructions to shape its interpretation of the task, on the other hand, it
still learns to solve task in a trial-and-error process. We evaluate our
algorithm on the robotic manipulation benchmark (Meta-World) and it
significantly outperforms state-of-the-art methods in terms of training and
testing task success rates. Codes are available at
\url{https://tumi6robot.wixsite.com/million}.

The most widely accepted view of memory in the brain holds that synapses are
the storage sites of memory, and that memories are formed through associative
modification of synapses. This view has been challenged on conceptual and
empirical grounds. As an alternative, it has been proposed that molecules
within the cell body are the storage sites of memory, and that memories are
formed through biochemical operations on these molecules. This paper proposes a
synthesis of these two views, grounded in a computational theory of memory.
Synapses are conceived as storage sites for the parameters of an approximate
posterior probability distribution over latent causes. Intracellular molecules
are conceived as storage sites for the parameters of a generative model. The
theory stipulates how these two components work together as part of an
integrated algorithm for learning and inference.

In this work, we investigate the problem of face reconstruction given a
facial feature representation extracted from a blackbox face recognition
engine. Indeed, it is very challenging problem in practice due to the
limitations of abstracted information from the engine. We therefore introduce a
new method named Attention-based Bijective Generative Adversarial Networks in a
Distillation framework (DAB-GAN) to synthesize faces of a subject given his/her
extracted face recognition features. Given any unconstrained unseen facial
features of a subject, the DAB-GAN can reconstruct his/her faces in high
definition. The DAB-GAN method includes a novel attention-based generative
structure with the new defined Bijective Metrics Learning approach. The
framework starts by introducing a bijective metric so that the distance
measurement and metric learning process can be directly adopted in image domain
for an image reconstruction task. The information from the blackbox face
recognition engine will be optimally exploited using the global distillation
process. Then an attention-based generator is presented for a highly robust
generator to synthesize realistic faces with ID preservation. We have evaluated
our method on the challenging face recognition databases, i.e. CelebA, LFW,
AgeDB, CFP-FP, and consistently achieved the state-of-the-art results. The
advancement of DAB-GAN is also proven on both image realism and ID preservation

Social media users share their ideas, thoughts, and emotions with other
users. However, it is not clear how online users would respond to new research
outcomes. This study aims to predict the nature of the emotions expressed by
Twitter users toward scientific publications. Additionally, we investigate what
features of the research articles help in such prediction. Identifying the
sentiments of research articles on social media will help scientists gauge a
new societal impact of their research articles.

In human environments, robots are expected to accomplish a variety of
manipulation tasks given simple natural language instructions. Yet, robotic
manipulation is extremely challenging as it requires fine-grained motor
control, long-term memory as well as generalization to previously unseen tasks
and environments. To address these challenges, we propose a unified
transformer-based approach that takes into account multiple inputs. In
particular, our transformer architecture integrates (i) natural language
instructions and (ii) multi-view scene observations while (iii) keeping track
of the full history of observations and actions. Such an approach enables
learning dependencies between history and instructions and improves
manipulation precision using multiple views. We evaluate our method on the
challenging RLBench benchmark and on a real-world robot. Notably, our approach
scales to 74 diverse RLBench tasks and outperforms the state of the art. We
also address instruction-conditioned tasks and demonstrate excellent
generalization to previously unseen variations.

Many works have shown the overfitting hazard of selecting a trading strategy
based only on good IS (in sample) performance. But most of them have merely
shown such phenomena exist without offering ways to avoid them. We propose an
approach to avoid overfitting: A good (meaning non-overfitting) trading
strategy should still work well on paths generated in accordance with the
distribution of the historical data. We use GAN with LSTM to learn or fit the
distribution of the historical time series . Then trading strategies are
backtested by the paths generated by GAN to avoid overfitting.(This paper is an
tanslated English version of a thesis (10.6342/NTU201801645) which was
originally written in Chinese in 2018, where some statements and claims are
outdated in 2022)

Despite great success in many applications, deep neural networks are not
always robust in practice. For instance, a convolutional neuron network (CNN)
model for classification tasks often performs unsatisfactorily in classifying
some particular classes of objects. In this work, we are concerned with
patching the weak part of a CNN model instead of improving it through the
costly retraining of the entire model. Inspired by the fundamental concepts of
modularization and composition in software engineering, we propose a compressed
modularization approach, CNNSplitter, which decomposes a strong CNN model for
$N$-class classification into $N$ smaller CNN modules. Each module is a
sub-model containing a part of the convolution kernels of the strong model. To
patch a weak CNN model that performs unsatisfactorily on a target class (TC),
we compose the weak CNN model with the corresponding module obtained from a
strong CNN model. The ability of the weak CNN model to recognize the TC can
thus be improved through patching. Moreover, the ability to recognize non-TCs
is also improved, as the samples misclassified as TC could be classified as
non-TCs correctly. Experimental results with two representative CNNs on three
widely-used datasets show that the averaged improvement on the TC in terms of
precision and recall are 12.54% and 2.14%, respectively. Moreover, patching
improves the accuracy of non-TCs by 1.18%. The results demonstrate that
CNNSplitter can patch a weak CNN model through modularization and composition,
thus providing a new solution for developing robust CNN models.

In order to identify expertise, forecasters should not be tested by their
calibration score, which can always be made arbitrarily small, but rather by
their Brier score. The Brier score is the sum of the calibration score and the
refinement score; the latter measures how good the sorting into bins with the
same forecast is, and thus attests to "expertise." This raises the question of
whether one can gain calibration without losing expertise, which we refer to as
"calibeating." We provide an easy way to calibeat any forecast, by a
deterministic online procedure. We moreover show that calibeating can be
achieved by a stochastic procedure that is itself calibrated, and then extend
the results to simultaneously calibeating multiple procedures, and to
deterministic procedures that are continuously calibrated.

The mechanical properties of a material are intimately related to its
microstructure. This is particularly important for predicting mechanical
behavior of polycrystalline metals, where microstructural variations dictate
the expected material strength. Until now, the lack of microstructural
variability in available datasets precluded the development of robust
physics-based theoretical models that account for randomness of
microstructures. To address this, we have developed a probabilistic machine
learning framework to predict the flow stress as a function of variations in
the microstructural features. In this framework, we first generated an
extensive database of flow stress for a set of over a million randomly sampled
microstructural features, and then applied a combination of mixture models and
neural networks on the generated database to quantify the flow stress
distribution and the relative importance of microstructural features. The
results show excellent agreement with experiments and demonstrate that across a
wide range of grain size, the conventional Hall-Petch relationship is
statistically valid for correlating the strength to the average grain size and
its comparative importance versus other microstructural features. This work
demonstrates the power of the machine-learning based probabilistic approach for
predicting polycrystalline strength, directly accounting for microstructural
variations, resulting in a tool to guide the design of polycrystalline metallic
materials with superior strength, and a method for overcoming sparse data
limitations.

Speech is inherently continuous, where discrete words, phonemes and other
units are not clearly segmented, and so speech recognition has been an active
research problem for decades. In this work we have fine-tuned wav2vec 2.0 to
recognize and transcribe Bengali speech -- training it on the Bengali Common
Voice Speech Dataset. After training for 71 epochs, on a training set
consisting of 36919 mp3 files, we achieved a training loss of 0.3172 and WER of
0.2524 on a validation set of size 7,747. Using a 5-gram language model, the
Levenshtein Distance was 2.6446 on a test set of size 7,747. Then the training
set and validation set were combined, shuffled and split into 85-15 ratio.
Training for 7 more epochs on this combined dataset yielded an improved
Levenshtein Distance of 2.60753 on the test set. Our model was the best
performing one, achieving a Levenshtein Distance of 6.234 on a hidden dataset,
which was 1.1049 units lower than other competing submissions.

The growing demands of remote detection and increasing amount of training
data make distributed machine learning under communication constraints a
critical issue. This work provides a communication-efficient quantum algorithm
that tackles two traditional machine learning problems, the least-square
fitting and softmax regression problem, in the scenario where the data set is
distributed across two parties. Our quantum algorithm finds the model
parameters with a communication complexity of $O(\frac{\log_2(N)}{\epsilon})$,
where $N$ is the number of data points and $\epsilon$ is the bound on parameter
errors. Compared to classical algorithms and other quantum algorithms that
achieve the same output task, our algorithm provides a communication advantage
in the scaling with the data volume. The building block of our algorithm, the
quantum-accelerated estimation of distributed inner product and Hamming
distance, could be further applied to various tasks in distributed machine
learning to accelerate communication.

Graph embedding methods aim at finding useful graph representations by
mapping nodes to a low-dimensional vector space. It is a task with important
downstream applications, such as link prediction, graph reconstruction, data
visualization, node classification, and language modeling. In recent years, the
field of graph embedding has witnessed a shift from linear algebraic approaches
towards local, gradient-based optimization methods combined with random walks
and deep neural networks to tackle the problem of embedding large graphs.
However, despite this improvement in the optimization tools, graph embedding
methods are still generically designed in a way that is oblivious to the
particularities of real-life networks. Indeed, there has been significant
progress in understanding and modeling complex real-life networks in recent
years. However, the obtained results have had a minor influence on the
development of graph embedding algorithms. This paper aims to remedy this by
designing a graph embedding method that takes advantage of recent valuable
insights from the field of network science. More precisely, we present a novel
graph embedding approach based on the popularity-similarity and local
attraction paradigms. We evaluate the performance of the proposed approach on
the link prediction task on a large number of real-life networks. We show,
using extensive experimental analysis, that the proposed method outperforms
state-of-the-art graph embedding algorithms. We also demonstrate its robustness
to data scarcity and the choice of embedding dimensionality.

Modern deep neural networks represent a formidable challenge for theorists:
even simple one hidden layer fully-connected architectures are in general not
analytically tractable with statistical physics techniques. This fact
constitutes a barrier against a comprehensive theoretical understanding of deep
learning. Huge simplifications arise in the so-called infinite-width limit,
where the size of the each hidden layer $N_\ell$ ($\ell=1, \dots, L$, $L$ being
the total number of hidden layers of the architecture) is much larger than the
size of the dataset $P$. Infinite-width neural networks are well understood
since they are equivalent to a particular kernel learning problem. Here we show
that the thermodynamics of deep learning with a quadratic loss function, i.e.
the calculation of the partition function, is an analytically tractable problem
also far away from this limit and, more importantly, at fixed realization of
the training set, at least in the asymptotic limit where the size of the hidden
layers tends to infinity at fixed ratios $\alpha_\ell = P/N_\ell$. This means
that the computation of an extensive number of integrals over the weights of
the deep neural networks can be reduced to a saddle-point integration over a
finite number of order parameters. In the case of one hidden layer
architectures we are able to prove that our result is correct as long as the
covariance matrix of the training data satisfies the assumptions of a
generalized central limit theorem due to Breuer and Major. In the general case
of $L$ hidden layers we derive a $2 L$-dimensional effective action for the
learning problem, which is obtained by iterative integration of the weights
starting from the input layer.

Transformer architectures have led to remarkable progress in many
state-of-art applications. However, despite their successes, modern
transformers rely on the self-attention mechanism, whose time- and
space-complexity is quadratic in the length of the input. Several approaches
have been proposed to speed up self-attention mechanisms to achieve
sub-quadratic running time; however, the large majority of these works are not
accompanied by rigorous error guarantees. In this work, we establish lower
bounds on the computational complexity of self-attention in a number of
scenarios. We prove that the time complexity of self-attention is necessarily
quadratic in the input length, unless the Strong Exponential Time Hypothesis
(SETH) is false. This argument holds even if the attention computation is
performed only approximately, and for a variety of attention mechanisms. As a
complement to our lower bounds, we show that it is indeed possible to
approximate dot-product self-attention using finite Taylor series in
linear-time, at the cost of having an exponential dependence on the polynomial
order.

Kronecker regression is a highly-structured least squares problem
$\min_{\mathbf{x}} \lVert \mathbf{K}\mathbf{x} - \mathbf{b} \rVert_{2}^2$,
where the design matrix $\mathbf{K} = \mathbf{A}^{(1)} \otimes \cdots \otimes
\mathbf{A}^{(N)}$ is a Kronecker product of factor matrices. This regression
problem arises in each step of the widely-used alternating least squares (ALS)
algorithm for computing the Tucker decomposition of a tensor. We present the
first subquadratic-time algorithm for solving Kronecker regression to a
$(1+\varepsilon)$-approximation that avoids the exponential term
$O(\varepsilon^{-N})$ in the running time. Our techniques combine leverage
score sampling and iterative methods. By extending our approach to block-design
matrices where one block is a Kronecker product, we also achieve
subquadratic-time algorithms for (1) Kronecker ridge regression and (2)
updating the factor matrix of a Tucker decomposition in ALS, which is not a
pure Kronecker regression problem, thereby improving the running time of all
steps of Tucker ALS. We demonstrate the speed and accuracy of this Kronecker
regression algorithm on synthetic data and real-world image tensors.

We study the potential of data-driven deep learning methods for separation of
two communication signals from an observation of their mixture. In particular,
we assume knowledge on the generation process of one of the signals, dubbed
signal of interest (SOI), and no knowledge on the generation process of the
second signal, referred to as interference. This form of the single-channel
source separation problem is also referred to as interference rejection. We
show that capturing high-resolution temporal structures (nonstationarities),
which enables accurate synchronization to both the SOI and the interference,
leads to substantial performance gains. With this key insight, we propose a
domain-informed neural network (NN) design that is able to improve upon both
"off-the-shelf" NNs and classical detection and interference rejection methods,
as demonstrated in our simulations. Our findings highlight the key role
communication-specific domain knowledge plays in the development of data-driven
approaches that hold the promise of unprecedented gains.

We study a fundamental model of online preference aggregation, where an
algorithm maintains an ordered list of $n$ elements. An input is a stream of
preferred sets $R_1, R_2, \dots, R_t, \dots$. Upon seeing $R_t$ and without
knowledge of any future sets, an algorithm has to rerank elements (change the
list ordering), so that at least one element of $R_t$ is found near the list
front. The incurred cost is a sum of the list update costs (the number of swaps
of neighboring list elements) and access costs (position of the first element
of $R_t$ on the list). This scenario occurs naturally in applications such as
ordering items in an online shop using aggregated preferences of shop
customers. The theoretical underpinning of this problem is known as Min-Sum Set
Cover.
  Unlike previous work (Fotakis et al., ICALP 2020, NIPS 2020) that mostly
studied the performance of an online algorithm ALG against the static optimal
solution (a single optimal list ordering), in this paper, we study an arguably
harder variant where the benchmark is the provably stronger optimal dynamic
solution OPT (that may also modify the list ordering). In terms of an online
shop, this means that the aggregated preferences of its user base evolve with
time. We construct a computationally efficient randomized algorithm whose
competitive ratio (ALG-to-OPT cost ratio) is $O(r^2)$ and prove the existence
of a deterministic $O(r^4)$-competitive algorithm. Here, $r$ is the maximum
cardinality of sets $R_t$. This is the first algorithm whose ratio does not
depend on $n$: the previously best algorithm for this problem was $O(r^{3/2}
\cdot \sqrt{n})$-competitive and $\Omega(r)$ is a lower bound on the
performance of any deterministic online algorithm.

The integration of discrete algorithmic components in deep learning
architectures has numerous applications. Recently, Implicit Maximum Likelihood
Estimation (IMLE, Niepert, Minervini, and Franceschi 2021), a class of gradient
estimators for discrete exponential family distributions, was proposed by
combining implicit differentiation through perturbation with the path-wise
gradient estimator. However, due to the finite difference approximation of the
gradients, it is especially sensitive to the choice of the finite difference
step size which needs to be specified by the user. In this work, we present
Adaptive IMLE (AIMLE) the first adaptive gradient estimator for complex
discrete distributions: it adaptively identifies the target distribution for
IMLE by trading off the density of gradient information with the degree of bias
in the gradient estimates. We empirically evaluate our estimator on synthetic
examples, as well as on Learning to Explain, Discrete Variational
Auto-Encoders, and Neural Relational Inference tasks. In our experiments, we
show that our adaptive gradient estimator can produce faithful estimates while
requiring orders of magnitude fewer samples than other gradient estimators.

The Shapley value (SV) is a fair and principled metric for contribution
evaluation in cross-silo federated learning (cross-silo FL), in which
organizations, i.e., clients, collaboratively train prediction models with the
coordination of a parameter server. However, existing SV calculation methods
for FL assume that the server can access the raw FL models and public test
data, which might not be a valid assumption in practice given the emerging
privacy attacks on FL models and that test data might be clients' private
assets. Hence, in this paper, we investigate the problem of secure SV
calculation for cross-silo FL. We first propose a one-server solution, HESV,
which is based solely on homomorphic encryption (HE) for privacy protection and
has some considerable limitations in efficiency. To overcome these limitations,
we further propose an efficient two-server protocol, SecSV, which has the
following novel features. First, SecSV utilizes a hybrid privacy protection
scheme to avoid ciphertext-ciphertext multiplications between test data and
models, which are extremely expensive under HE. Second, a more efficient secure
matrix multiplication method is proposed for SecSV. Third, SecSV strategically
identifies and skips some test samples without significantly affecting the
evaluation accuracy. Our experiments demonstrate that SecSV is 5.9-18.0 times
as fast as HESV while sacrificing a limited loss in the accuracy of calculated
SVs.

Choosing an appropriate parameter set for the designed controller is critical
for the final performance but usually requires a tedious and careful tuning
process, which implies a strong need for automatic tuning methods. However,
among existing methods, derivative-free ones suffer from poor scalability or
low efficiency, while gradient-based ones are often unavailable due to possibly
non-differentiable controller structure. To resolve the issues, we tackle the
controller tuning problem using a novel derivative-free reinforcement learning
(RL) framework, which performs timestep-wise perturbation in parameter space
during experience collection and integrates derivative-free policy updates into
the advanced actor-critic RL architecture to achieve high versatility and
efficiency. To demonstrate the framework's efficacy, we conduct numerical
experiments on two concrete examples from autonomous driving, namely, adaptive
cruise control with PID controller and trajectory tracking with MPC controller.
Experimental results show that the proposed method outperforms popular
baselines and highlight its strong potential for controller tuning.

With the remarkable progress of deep neural networks in computer vision, data
mixing augmentation techniques are widely studied to alleviate problems of
degraded generalization when the amount of training data is limited. However,
mixup strategies have not been well assembled in current vision toolboxes. In
this paper, we propose \texttt{OpenMixup}, an open-source all-in-one toolbox
for supervised, semi-, and self-supervised visual representation learning with
mixup. It offers an integrated model design and training platform, comprising a
rich set of prevailing network architectures and modules, a collection of data
mixing augmentation methods as well as practical model analysis tools. In
addition, we also provide standard mixup image classification benchmarks on
various datasets, which expedites practitioners to make fair comparisons among
state-of-the-art methods under the same settings. The source code and user
documents are available at \url{https://github.com/Westlake-AI/openmixup}.

Lossless and near-lossless image compression is of paramount importance to
professional users in many technical fields, such as medicine, remote sensing,
precision engineering and scientific research. But despite rapidly growing
research interests in learning-based image compression, no published method
offers both lossless and near-lossless modes. In this paper, we propose a
unified and powerful deep lossy plus residual (DLPR) coding framework for both
lossless and near-lossless image compression. In the lossless mode, the DLPR
coding system first performs lossy compression and then lossless coding of
residuals. We solve the joint lossy and residual compression problem in the
approach of VAEs, and add autoregressive context modeling of the residuals to
enhance lossless compression performance. In the near-lossless mode, we
quantize the original residuals to satisfy a given $\ell_\infty$ error bound,
and propose a scalable near-lossless compression scheme that works for variable
$\ell_\infty$ bounds instead of training multiple networks. To expedite the
DLPR coding, we increase the degree of algorithm parallelization by a novel
design of coding context, and accelerate the entropy coding with adaptive
residual interval. Experimental results demonstrate that the DLPR coding system
achieves both the state-of-the-art lossless and near-lossless image compression
performance with competitive coding speed.

The multi-panel array, as a state-of-the-art antenna-in-package technology,
is very suitable for millimeter-wave (mmWave)/terahertz (THz) systems, due to
its low-cost deployment and scalable configuration. But in the context of
nonuniform array structures it leads to intractable signal processing. Based on
such an array structure at the base station, this paper investigates a joint
active user detection (AUD) and channel estimation (CE) scheme based on
compressive sensing (CS) for application to the massive Internet of Things
(IoT). Specifically, by exploiting the structured sparsity of mmWave/THz
massive IoT access channels, we firstly formulate the multi-panel massive
multiple-input multiple-output (mMIMO)-based joint AUD and CE problem as a
multiple measurement vector (MMV)-CS problem. Then, we harness the expectation
maximization (EM) algorithm to learn the prior parameters (i.e., the noise
variance and the sparsity ratio) and an orthogonal approximate message passing
(OAMP)-EM-MMV algorithm is developed to solve this problem. Our simulation
results verify the improved AUD and CE performance of the proposed scheme
compared to conventional CS-based algorithms.

Continual Learning aims to learn multiple incoming new tasks continually, and
to keep the performance of learned tasks at a consistent level. However,
existing research on continual learning assumes the pose of the object is
pre-defined and well-aligned. For practical application, this work focuses on
pose-agnostic continual learning tasks, where the object's pose changes
dynamically and unpredictably. The point cloud augmentation adopted from past
approaches would sharply rise with the task increment in the continual learning
process. To address this problem, we inject the equivariance as the additional
prior knowledge into the networks. We proposed a novel continual learning model
that effectively distillates previous tasks' geometric equivariance
information. The experiments show that our method overcomes the challenge of
pose-agnostic scenarios in several mainstream point cloud datasets. We further
conduct ablation studies to evaluate the validation of each component of our
approach.

Given a valued graph, where both the nodes and the edges of the graph are
associated with one or several values, any network function for a given node
must be defined in terms of that node and its connected nodes in the graph.
Generally, applying the same definition to the whole graph or any given
subgraph of it would result in systematically different network functions. In
this paper we consider the feasibility of graph sampling approach to network
function learning, as well as the corresponding learning methods based on the
sample graphs. This can be useful either when the edges are unknown to start
with or the graph is too large (or dynamic) to be processed entirely.

The success of deep learning is due in large part to our ability to solve
certain massive non-convex optimization problems with relative ease. Though
non-convex optimization is NP-hard, simple algorithms -- often variants of
stochastic gradient descent -- exhibit surprising effectiveness in fitting
large neural networks in practice. We argue that neural network loss landscapes
contain (nearly) a single basin after accounting for all possible permutation
symmetries of hidden units a la Entezari et al. (2021). We introduce three
algorithms to permute the units of one model to bring them into alignment with
a reference model in order to merge the two models in weight space. This
transformation produces a functionally equivalent set of weights that lie in an
approximately convex basin near the reference model. Experimentally, we
demonstrate the single basin phenomenon across a variety of model architectures
and datasets, including the first (to our knowledge) demonstration of
zero-barrier linear mode connectivity between independently trained ResNet
models on CIFAR-10 and CIFAR-100. Additionally, we investigate intriguing
phenomena relating model width and training time to mode connectivity. Finally,
we discuss shortcomings of the linear mode connectivity hypothesis, including a
counterexample to the single basin theory.

We will show how to implement binary decision tree traversal in the language
of matrix computation. Our main contribution is to propose some equivalent
algorithms of binary tree traversal based on a novel matrix representation of
the hierarchical structure of the decision tree. Our key idea is to travel the
binary decision tree by maximum inner product search. We not only implement
decision tree methods without the recursive traverse but also delve into the
partitioning nature of tree-based methods.

Evaluating the efficiency of organizations and branches within an
organization is a challenging issue for managers. Evaluation criteria allow
organizations to rank their internal units, identify their position concerning
their competitors, and implement strategies for improvement and development
purposes. Among the methods that have been applied in the evaluation of bank
branches, non-parametric methods have captured the attention of researchers in
recent years. One of the most widely used non-parametric methods is the data
envelopment analysis (DEA) which leads to promising results. However, the
static DEA approaches do not consider the time in the model. Therefore, this
paper uses a dynamic DEA (DDEA) method to evaluate the branches of a private
Iranian bank over three years (2017-2019). The results are then compared with
static DEA. After ranking the branches, they are clustered using the K-means
method. Finally, a comprehensive sensitivity analysis approach is introduced to
help the managers to decide about changing variables to shift a branch from one
cluster to a more efficient one.

Learning representative, robust and discriminative information from images is
essential for effective person re-identification (Re-Id). In this paper, we
propose a compound approach for end-to-end discriminative deep feature learning
for person Re-Id based on both body and hand images. We carefully design the
Local-Aware Global Attention Network (LAGA-Net), a multi-branch deep network
architecture consisting of one branch for spatial attention, one branch for
channel attention, one branch for global feature representations and another
branch for local feature representations. The attention branches focus on the
relevant features of the image while suppressing the irrelevant backgrounds. In
order to overcome the weakness of the attention mechanisms, equivariant to
pixel shuffling, we integrate relative positional encodings into the spatial
attention module to capture the spatial positions of pixels. The global branch
intends to preserve the global context or structural information. For the the
local branch, which intends to capture the fine-grained information, we perform
uniform partitioning to generate stripes on the conv-layer horizontally. We
retrieve the parts by conducting a soft partition without explicitly
partitioning the images or requiring external cues such as pose estimation. A
set of ablation study shows that each component contributes to the increased
performance of the LAGA-Net. Extensive evaluations on four popular body-based
person Re-Id benchmarks and two publicly available hand datasets demonstrate
that our proposed method consistently outperforms existing state-of-the-art
methods.

The marriage between mean-field theory and reinforcement learning has shown a
great capacity to solve large-scale control problems with homogeneous agents.
To break the homogeneity restriction of mean-field theory, a recent interest is
to introduce graphon theory to the mean-field paradigm. In this paper, we
propose a graphon mean-field control (GMFC) framework to approximate
cooperative multi-agent reinforcement learning (MARL) with nonuniform
interactions and show that the approximate order is of
$\mathcal{O}(\frac{1}{\sqrt{N}})$, with $N$ the number of agents. By
discretizing the graphon index of GMFC, we further introduce a smaller class of
GMFC called block GMFC, which is shown to well approximate cooperative MARL.
Our empirical studies on several examples demonstrate that our GMFC approach is
comparable with the state-of-art MARL algorithms while enjoying better
scalability.

To improve the accuracy of direction-of-arrival (DOA) estimation, a deep
learning (DL)-based method called CDAE-DNN is proposed for hybrid analog and
digital (HAD) massive MIMO receive array with overlapped subarray (OSA)
architecture in this paper. In the proposed method, the sample covariance
matrix (SCM) is first input to a convolution denoise autoencoder (CDAE) to
remove the approximation error, then the output of CDAE is imported to a
fully-connected (FC) network to get the estimation result. Based on the
simulation results, the proposed CDAE-DNN has great performance advantages over
traditional MUSIC algorithm and CNN-based method, especially in the situations
with low signal to noise ratio (SNR) and low snapshot numbers. And the OSA
architecture has also been shown to significantly improve the estimation
accuracy compared to non-overlapped subarray (NOSA) architecture. In addition,
the Cramer-Rao lower bound (CRLB) for the HAD-OSA architecture is presented.

This study evaluates the discriminating capacity (uniqueness) of the EEG data
from the WAY EEG GAL public dataset to authenticate individuals against one
another as well as its permanence. In addition to the EEG data, Luciw et al.
provide EMG (Electromyography), and kinematics data for engineers and
researchers to utilize WAY EEG GAL for further studies. However, evaluating the
EMG and kinematics data is outside the scope of this study. The goal of the
state-of-the-art is to determine whether EEG data can be utilized to control
prosthetic devices. On the other hand, this study aims to evaluate the
separability of individuals through EEG data to perform user authentication. A
feature importance algorithm is utilized to select the best features for each
user to authenticate them against all others. The authentication platform
implemented for this study is based on Machine Learning models/classifiers. As
an initial test, two pilot studies are performed using Linear Discriminant
Analysis (LDA) and Support Vector Machine (SVM) to observe the learning trends
of the models by multi-labeling the EEG dataset. Utilizing kNN first as the
classifier for user authentication, accuracy around 75% is observed. Thereafter
to improve the performance both linear and non-linear SVMs are used to perform
classification. The overall average accuracies of 85.18% and 86.92% are
achieved using linear and non-linear SVMs respectively. In addition to
accuracy, F1 scores are also calculated. The overall average F1 score of 87.51%
and 88.94% are achieved for linear and non-linear SVMs respectively. Beyond the
overall performance, high performing individuals with 95.3% accuracy (95.3% F1
score) using linear SVM and 97.4% accuracy (97.3% F1 score) using non-linear
SVM are also observed.

Deep reinforcement learning is a technique for solving problems in a variety
of environments, ranging from Atari video games to stock trading. This method
leverages deep neural network models to make decisions based on observations of
a given environment with the goal of maximizing a reward function that can
incorporate cost and rewards for reaching goals. With the aim of pathfinding,
reward conditions can include reaching a specified target area along with costs
for movement. In this work, multiple Deep Q-Network (DQN) agents are trained to
operate in a partially observable environment with the goal of reaching a
target zone in minimal travel time. The agent operates based on a visual
representation of its surroundings, and thus has a restricted capability to
observe the environment. A comparison between DQN, DQN-GRU, and DQN-LSTM is
performed to examine each models capabilities with two different types of
input. Through this evaluation, it is been shown that with equivalent training
and analogous model architectures, a DQN model is able to outperform its
recurrent counterparts.

We propose a data collecting and annotation pipeline that extracts
information from Vietnamese radiology reports to provide accurate labels for
chest X-ray (CXR) images. This can benefit Vietnamese radiologists and
clinicians by annotating data that closely match their endemic diagnosis
categories which may vary from country to country. To assess the efficacy of
the proposed labeling technique, we built a CXR dataset containing 9,752
studies and evaluated our pipeline using a subset of this dataset. With an
F1-score of at least 0.9923, the evaluation demonstrates that our labeling tool
performs precisely and consistently across all classes. After building the
dataset, we train deep learning models that leverage knowledge transferred from
large public CXR datasets. We employ a variety of loss functions to overcome
the curse of imbalanced multi-label datasets and conduct experiments with
various model architectures to select the one that delivers the best
performance. Our best model (CheXpert-pretrained EfficientNet-B2) yields an
F1-score of 0.6989 (95% CI 0.6740, 0.7240), AUC of 0.7912, sensitivity of
0.7064 and specificity of 0.8760 for the abnormal diagnosis in general.
Finally, we demonstrate that our coarse classification (based on five specific
locations of abnormalities) yields comparable results to fine classification
(twelve pathologies) on the benchmark CheXpert dataset for general anomaly
detection while delivering better performance in terms of the average
performance of all classes.

A novel framework is proposed for handling the complex task of modelling and
analysis of longitudinal, multivariate, heterogeneous clinical data. This
method uses temporal abstraction to convert the data into a more appropriate
form for modelling, temporal pattern mining, to discover patterns in the
complex, longitudinal data and machine learning models of survival analysis to
select the discovered patterns. The method is applied to a real-world study of
Alzheimer's disease (AD), a progressive neurodegenerative disease that has no
cure. The patterns discovered were predictive of AD in survival analysis models
with a Concordance index of up to 0.8. This is the first work that performs
survival analysis of AD data using temporal data collections for AD. A
visualisation module also provides a clear picture of the discovered patterns
for ease of interpretability.

Detecting rare events, those defined to give rise to high impact but have a
low probability of occurring, is a challenge in a number of domains including
meteorological, environmental, financial and economic. The use of machine
learning to detect such events is becoming increasingly popular, since they
offer an effective and scalable solution when compared to traditional
signature-based detection methods. In this work, we begin by undertaking
exploratory data analysis, and present techniques that can be used in a
framework for employing machine learning methods for rare event detection.
Strategies to deal with the imbalance of classes including the selection of
performance metrics are also discussed. Despite their popularity, we believe
the performance of conventional machine learning classifiers could be further
improved, since they are agnostic to the natural order over time in which the
events occur. Stochastic processes on the other hand, model sequences of events
by exploiting their temporal structure such as clustering and dependence
between the different types of events. We develop a model for classification
based on Hawkes processes and apply it to a dataset of e-commerce transactions,
resulting in not only better predictive performance but also deriving
inferences regarding the temporal dynamics of the data.

Quality assurance is of great importance for deep learning (DL) systems,
especially when they are applied in safety-critical applications. While quality
issues of native DL applications have been extensively analyzed, the issues of
JavaScript-based DL applications have never been systematically studied.
Compared with native DL applications, JavaScript-based DL applications can run
on major browsers, making the platform- and device-independent. Specifically,
the quality of JavaScript-based DL applications depends on the 3 parts: the
application, the third-party DL library used and the underlying DL framework
(e.g., TensorFlow.js), called JavaScript-based DL system. In this paper, we
conduct the first empirical study on the quality issues of JavaScript-based DL
systems. Specifically, we collect and analyze 700 real-world faults from
relevant GitHub repositories, including the official TensorFlow.js repository,
13 third-party DL libraries, and 58 JavaScript-based DL applications. To better
understand the characteristics of these faults, we manually analyze and
construct taxonomies for the fault symptoms, root causes, and fix patterns,
respectively. Moreover, we also study the fault distributions of symptoms and
root causes, in terms of the different stages of the development lifecycle, the
3-level architecture in the DL system, and the 4 major components of
TensorFlow.js framework. Based on the results, we suggest actionable
implications and research avenues that can potentially facilitate the
development, testing, and debugging of JavaScript-based DL systems.

Healthcare is an important aspect of human life. Use of technologies in
healthcare has increased manifolds after the pandemic. Internet of Things based
systems and devices proposed in literature can help elders, children and adults
facing/experiencing health problems. This paper exhaustively reviews
thirty-nine wearable based datasets which can be used for evaluating the system
to recognize Activities of Daily Living and Falls. A comparative analysis on
the SisFall dataset using five machine learning methods i.e., Logistic
Regression, Linear Discriminant Analysis, K-Nearest Neighbor, Decision Tree and
Naive Bayes is performed in python. The dataset is modified in two ways, in
first all the attributes present in dataset are used as it is and labelled in
binary form. In second, magnitude of three axes(x,y,z) for three sensors value
are computed and then used in experiment with label attribute. The experiments
are performed on one subject, ten subjects and all the subjects and compared in
terms of accuracy, precision and recall. The results obtained from this study
proves that KNN outperforms other machine learning methods in terms of
accuracy, precision and recall. It is also concluded that personalization of
data improves accuracy.

As real-world graphs expand in size, larger GNN models with billions of
parameters are deployed. High parameter count in such models makes training and
inference on graphs expensive and challenging. To reduce the computational and
memory costs of GNNs, optimization methods such as pruning the redundant nodes
and edges in input graphs have been commonly adopted. However, model
compression, which directly targets the sparsification of model layers, has
been mostly limited to traditional Deep Neural Networks (DNNs) used for tasks
such as image classification and object detection. In this paper, we utilize
two state-of-the-art model compression methods (1) train and prune and (2)
sparse training for the sparsification of weight layers in GNNs. We evaluate
and compare the efficiency of both methods in terms of accuracy, training
sparsity, and training FLOPs on real-world graphs. Our experimental results
show that on the ia-email, wiki-talk, and stackoverflow datasets for link
prediction, sparse training with much lower training FLOPs achieves a
comparable accuracy with the train and prune method. On the brain dataset for
node classification, sparse training uses a lower number FLOPs (less than 1/7
FLOPs of train and prune method) and preserves a much better accuracy
performance under extreme model sparsity.

In this paper, the authors provide a weak decoding version of the traditional
source coding theorem of Claude Shannon. The central bound that is obtained is
\[ \chi>\log_{\epsilon}(2^{-n(H(X)+\epsilon)}) \] where \[
\chi=\frac{\log(k)}{n(H(X)+\epsilon)} \] and $k$ is the number of unsupervised
learning classes formed out of the non-typical source sequences. The bound
leads to the conclusion that if the number of classes is high enough, the
reliability function might possibly be improved. The specific regime in which
this improvement might be allowable is the one in which the atypical-sequence
clusters are small in size and sparsely placed; similar regimes might also show
an improvement.

In this paper, we propose a novel pipeline that leverages language foundation
models for temporal sequential pattern mining, such as for human mobility
forecasting tasks. For example, in the task of predicting Place-of-Interest
(POI) customer flows, typically the number of visits is extracted from
historical logs, and only the numerical data are used to predict visitor flows.
In this research, we perform the forecasting task directly on the natural
language input that includes all kinds of information such as numerical values
and contextual semantic information. Specific prompts are introduced to
transform numerical temporal sequences into sentences so that existing language
models can be directly applied. We design an AuxMobLCast pipeline for
predicting the number of visitors in each POI, integrating an auxiliary POI
category classification task with the encoder-decoder architecture. This
research provides empirical evidence of the effectiveness of the proposed
AuxMobLCast pipeline to discover sequential patterns in mobility forecasting
tasks. The results, evaluated on three real-world datasets, demonstrate that
pre-trained language foundation models also have good performance in
forecasting temporal sequences. This study could provide visionary insights and
lead to new research directions for predicting human mobility.

A semantic tableau method, called an argumentation tableau, that enables the
derivation of arguments, is proposed. First, the derivation of arguments for
standard propositional and predicate logic is addressed. Next, an extension
that enables reasoning with defeasible rules is presented. Finally, reasoning
by cases using an argumentation tableau is discussed.

The increasing availability of real-time data has fueled the prevalence of
algorithmic bidding (or autobidding) in online advertising markets, and has
enabled online ad platforms to produce signals through machine learning
techniques (i.e., ML advice) on advertisers' true perceived values for ad
conversions. Previous works have studied the auction design problem while
incorporating ML advice through various forms to improve total welfare of
advertisers. Yet, such improvements could come at the cost of individual
bidders' welfare, consequently eroding fairness of the ad platform. Motivated
by this, we study how ad platforms can utilize ML advice to improve welfare
guarantees and fairness on the individual bidder level in the autobidding
world. We focus on a practical setting where ML advice takes the form of lower
confidence bounds (or confidence intervals). We motivate a simple approach that
directly sets such advice as personalized reserve prices when the platform
consists of value-maximizing autobidders who are subject to return-on-ad spent
(ROAS) constraints competing in multiple parallel auctions. Under parallel VCG
auctions with ML advice-based reserves, we present a worst-case welfare
lower-bound guarantee for individual agents, and show that platform fairness is
positively correlated with ML advice quality. We also present an instance that
demonstrates our welfare guarantee is tight. Further, we prove an impossibility
result showing that no truthful, possibly randomized mechanism with anonymous
allocations and ML advice as personalized reserves can achieve universally
better fairness guarantees than VCG when coupled with ML advice of the same
quality. Finally, we extend our fairness guarantees with ML advice to
generalized first price (GFP) and generalized second price (GSP) auctions.

Denoising diffusion models represent a recent emerging topic in computer
vision, demonstrating remarkable results in the area of generative modeling. A
diffusion model is a deep generative model that is based on two stages, a
forward diffusion stage and a reverse diffusion stage. In the forward diffusion
stage, the input data is gradually perturbed over several steps by adding
Gaussian noise. In the reverse stage, a model is tasked at recovering the
original input data by learning to gradually reverse the diffusion process,
step by step. Diffusion models are widely appreciated for the quality and
diversity of the generated samples, despite their known computational burdens,
i.e. low speeds due to the high number of steps involved during sampling. In
this survey, we provide a comprehensive review of articles on denoising
diffusion models applied in vision, comprising both theoretical and practical
contributions in the field. First, we identify and present three generic
diffusion modeling frameworks, which are based on denoising diffusion
probabilistic models, noise conditioned score networks, and stochastic
differential equations. We further discuss the relations between diffusion
models and other deep generative models, including variational auto-encoders,
generative adversarial networks, energy-based models, autoregressive models and
normalizing flows. Then, we introduce a multi-perspective categorization of
diffusion models applied in computer vision. Finally, we illustrate the current
limitations of diffusion models and envision some interesting directions for
future research.

In this research a continuous model for resource allocations in a queuing
system is considered and a local prediction on the system behavior is
developed. As a result we obtain a set of possible cases, some of which lead to
quite clear optimization problems. Currently, the main result of this research
direction is an algorithm delivering an explicit solution to the problem of
minimization of the sum of all queues mean delays (which is not the overall
mean delay) in the case of the so-called uniform steadiness. Basically, in this
case we deal with convex optimization on a polytope.

An important problem across disciplines is the discovery of interventions
that produce a desired outcome. When the space of possible interventions is
large, making an exhaustive search infeasible, experimental design strategies
are needed. In this context, encoding the causal relationships between the
variables, and thus the effect of interventions on the system, is critical in
order to identify desirable interventions efficiently. We develop an iterative
causal method to identify optimal interventions, as measured by the discrepancy
between the post-interventional mean of the distribution and a desired target
mean. We formulate an active learning strategy that uses the samples obtained
so far from different interventions to update the belief about the underlying
causal model, as well as to identify samples that are most informative about
optimal interventions and thus should be acquired in the next batch. The
approach employs a Bayesian update for the causal model and prioritizes
interventions using a carefully designed, causally informed acquisition
function. This acquisition function is evaluated in closed form, allowing for
efficient optimization. The resulting algorithms are theoretically grounded
with information-theoretic bounds and provable consistency results. We
illustrate the method on both synthetic data and real-world biological data,
namely gene expression data from Perturb-CITE-seq experiments, to identify
optimal perturbations that induce a specific cell state transition; the
proposed causal approach is observed to achieve better sample efficiency
compared to several baselines. In both cases we observe that the causally
informed acquisition function notably outperforms existing criteria allowing
for optimal intervention design with significantly less experiments.

Thermal analysis provides deeper insights into electronic chips behavior
under different temperature scenarios and enables faster design exploration.
However, obtaining detailed and accurate thermal profile on chip is very
time-consuming using FEM or CFD. Therefore, there is an urgent need for
speeding up the on-chip thermal solution to address various system scenarios.
In this paper, we propose a thermal machine-learning (ML) solver to speed-up
thermal simulations of chips. The thermal ML-Solver is an extension of the
recent novel approach, CoAEMLSim (Composable Autoencoder Machine Learning
Simulator) with modifications to the solution algorithm to handle constant and
distributed HTC. The proposed method is validated against commercial solvers,
such as Ansys MAPDL, as well as a latest ML baseline, UNet, under different
scenarios to demonstrate its enhanced accuracy, scalability, and
generalizability.

In many applications (e.g., medical studies), the population of interest
(e.g., disease status) comprises heterogeneous subpopulations. The mixture of
probabilistic regression models is one of the most common techniques to
incorporate the information of covariates into learning of the population
heterogeneity. Despite its flexibility, the model may lead to unreliable
estimates in the presence of multicollinearity problem. In this paper, we
develop Liu-type shrinkage methods through an unsupervised learning approach to
estimate the model coefficients in multicollinearity. The performance of the
developed methods is evaluated via classification and stochastic versions of EM
algorithms. The numerical studies show that the proposed methods outperform
their Ridge and maximum likelihood counterparts. Finally, the developed methods
are applied to analyze the bone mineral data of women aged 50 and older.

Deriving governing equations of complex physical systems based on first
principles can be quite challenging when there are certain unknown terms and
hidden physical mechanisms in the systems. In this work, we apply a deep
learning architecture to learn fluid partial differential equations (PDEs) of a
plasma system based on the data acquired from a fully kinetic model. The
learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effects
such as Landau damping. Based on the learned fluid closure, the data-driven,
multi-moment fluid modeling can well reproduce all the physical quantities
derived from the fully kinetic model. The calculated damping rate of Landau
damping is consistent with both the fully kinetic simulation and the linear
theory. The data-driven fluid modeling of PDEs for complex physical systems may
be applied to improve fluid closure and reduce the computational cost of
multi-scale modeling of global systems.

Vision-Language Navigation requires the agent to follow natural language
instructions to reach a specific target. The large discrepancy between seen and
unseen environments makes it challenging for the agent to generalize well.
Previous studies propose data augmentation methods to mitigate the data bias
explicitly or implicitly and provide improvements in generalization. However,
they try to memorize augmented trajectories and ignore the distribution shifts
under unseen environments at test time. In this paper, we propose an Unseen
Discrepancy Anticipating Vision and Language Navigation (DAVIS) that learns to
generalize to unseen environments via encouraging test-time visual consistency.
Specifically, we devise: 1) a semi-supervised framework DAVIS that leverages
visual consistency signals across similar semantic observations. 2) a two-stage
learning procedure that encourages adaptation to test-time distribution. The
framework enhances the basic mixture of imitation and reinforcement learning
with Momentum Contrast to encourage stable decision-making on similar
observations under a joint training stage and a test-time adaptation stage.
Extensive experiments show that DAVIS achieves model-agnostic improvement over
previous state-of-the-art VLN baselines on R2R and RxR benchmarks. Our source
code and data are in supplemental materials.

As safety violations can lead to severe consequences in real-world robotic
applications, the increasing deployment of Reinforcement Learning (RL) in
robotic domains has propelled the study of safe exploration for reinforcement
learning (safe RL). In this work, we propose a risk preventive training method
for safe RL, which learns a statistical contrastive classifier to predict the
probability of a state-action pair leading to unsafe states. Based on the
predicted risk probabilities, we can collect risk preventive trajectories and
reshape the reward function with risk penalties to induce safe RL policies. We
conduct experiments in robotic simulation environments. The results show the
proposed approach has comparable performance with the state-of-the-art
model-based methods and outperforms conventional model-free safe RL approaches.

Bayesian Optimisation (BO) methods seek to find global optima of objective
functions which are only available as a black-box or are expensive to evaluate.
Such methods construct a surrogate model for the objective function,
quantifying the uncertainty in that surrogate through Bayesian inference.
Objective evaluations are sequentially determined by maximising an acquisition
function at each step. However, this ancilliary optimisation problem can be
highly non-trivial to solve, due to the non-convexity of the acquisition
function, particularly in the case of batch Bayesian optimisation, where
multiple points are selected in every step. In this work we reformulate batch
BO as an optimisation problem over the space of probability measures. We
construct a new acquisition function based on multipoint expected improvement
which is convex over the space of probability measures. Practical schemes for
solving this `inner' optimisation problem arise naturally as gradient flows of
this objective function. We demonstrate the efficacy of this new method on
different benchmark functions and compare with state-of-the-art batch BO
methods.

Supervised learning algorithms are heavily reliant on annotated datasets to
train machine learning models. However, the curation of the annotated datasets
is laborious and time consuming due to the manual effort involved and has
become a huge bottleneck in supervised learning. In this work, we apply the
theory of noisy learning to generate weak supervision signals instead of manual
annotation. We curate a noisy labeled dataset using a labeling heuristic to
identify epidemic related tweets. We evaluated the performance using a large
epidemic corpus and our results demonstrate that models trained with noisy data
in a class imbalanced and multi-classification weak supervision setting
achieved performance greater than 90%.

This work proposed kernel selection approaches for probabilistic classifiers
based on features produced by the convolutional encoder of a variational
autoencoder. Particularly, the developed methodologies allow the selection of
the most relevant subset of latent variables. In the proposed implementation,
each latent variable was sampled from the distribution associated with a single
kernel of the last encoder's convolution layer, as an individual distribution
was created for each kernel. Therefore, choosing relevant features on the
sampled latent variables makes it possible to perform kernel selection,
filtering the uninformative features and kernels. Such leads to a reduction in
the number of the model's parameters. Both wrapper and filter methods were
evaluated for feature selection. The second was of particular relevance as it
is based only on the distributions of the kernels. It was assessed by measuring
the Kullback-Leibler divergence between all distributions, hypothesizing that
the kernels whose distributions are more similar can be discarded. This
hypothesis was confirmed since it was observed that the most similar kernels do
not convey relevant information and can be removed. As a result, the proposed
methodology is suitable for developing applications for resource-constrained
devices.

Neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne
Muscular Dystrophy (DMD), cause progressive muscular degeneration and loss of
motor function for 1 in 6,000 children. Traditional upper limb motor function
assessments do not quantitatively measure patient-performed motions, which
makes it difficult to track progress for incremental changes. Assessing motor
function in children with neuromuscular disorders is particularly challenging
because they can be nervous or excited during experiments, or simply be too
young to follow precise instructions. These challenges translate to confounding
factors such as performing different parts of the arm curl slower or faster
(phase variability) which affects the assessed motion quality. This paper uses
curve registration and shape analysis to temporally align trajectories while
simultaneously extracting a mean reference shape. Distances from this mean
shape are used to assess the quality of motion. The proposed metric is
invariant to confounding factors, such as phase variability, while suggesting
several clinically relevant insights. First, there are statistically
significant differences between functional scores for the control and patient
populations (p$=$0.0213$\le$0.05). Next, several patients in the patient cohort
are able to perform motion on par with the healthy cohort and vice versa. Our
metric, which is computed based on wearables, is related to the Brooke's score
((p$=$0.00063$\le$0.05)), as well as motor function assessments based on
dynamometry ((p$=$0.0006$\le$0.05)). These results show promise towards
ubiquitous motion quality assessment in daily life.

COVID-19 is a disease caused by severe respiratory syndrome coronavirus. It
was identified in December 2019 in Wuhan, China. It has resulted in an ongoing
pandemic that caused infected cases including some deaths. Coronavirus is
primarily spread between people during close contact. Motivating to this
notion, this research proposes an artificial intelligence system for social
distancing classification of persons by using thermal images. By exploiting
YOLOv2 (you look at once), a deep learning detection technique is developed for
detecting and tracking people in indoor and outdoor scenarios. An algorithm is
also implemented for measuring and classifying the distance between persons and
automatically check if social distancing rules are respected or not. Hence,
this work aims at minimizing the spread of the COVID-19 virus by evaluating if
and how persons comply with social distancing rules. The proposed approach is
applied to images acquired through thermal cameras, to establish a complete AI
system for people tracking, social distancing classification, and body
temperature monitoring. The training phase is done with two datasets captured
from different thermal cameras. Ground Truth Labeler app is used for labeling
the persons in the images. The achieved results show that the proposed method
is suitable for the creation of a smart surveillance system in smart cities for
people detection, social distancing classification, and body temperature
analysis.

Few-shot text classification aims to classify the text under the few-shot
scenario. Most of the previous methods adopt optimization-based meta learning
to obtain task distribution. However, due to the neglect of matching between
the few amount of samples and complicated models, as well as the distinction
between useful and useless task features, these methods suffer from the
overfitting issue. To address this issue, we propose a novel Adaptive
Meta-learner via Gradient Similarity (AMGS) method to improve the model
generalization ability to a new task. Specifically, the proposed AMGS
alleviates the overfitting based on two aspects: (i) acquiring the potential
semantic representation of samples and improving model generalization through
the self-supervised auxiliary task in the inner loop, (ii) leveraging the
adaptive meta-learner via gradient similarity to add constraints on the
gradient obtained by base-learner in the outer loop. Moreover, we make a
systematic analysis of the influence of regularization on the entire framework.
Experimental results on several benchmarks demonstrate that the proposed AMGS
consistently improves few-shot text classification performance compared with
the state-of-the-art optimization-based meta-learning approaches.

Image quality is a crucial factor in the success of teledermatological
consultations. However, up to 50% of images sent by patients have quality
issues, thus increasing the time to diagnosis and treatment. An automated,
easily deployable, explainable method for assessing image quality is necessary
to improve the current teledermatological consultation flow. We introduce
ImageQX, a convolutional neural network trained for image quality assessment
with a learning mechanism for identifying the most common poor image quality
explanations: bad framing, bad lighting, blur, low resolution, and distance
issues. ImageQX was trained on 26635 photographs and validated on 9874
photographs, each annotated with image quality labels and poor image quality
explanations by up to 12 board-certified dermatologists. The photographic
images were taken between 2017-2019 using a mobile skin disease tracking
application accessible worldwide. Our method achieves expert-level performance
for both image quality assessment and poor image quality explanation. For image
quality assessment, ImageQX obtains a macro F1-score of 0.73 which places it
within standard deviation of the pairwise inter-rater F1-score of 0.77. For
poor image quality explanations, our method obtains F1-scores of between 0.37
and 0.70, similar to the inter-rater pairwise F1-score of between 0.24 and
0.83. Moreover, with a size of only 15 MB, ImageQX is easily deployable on
mobile devices. With an image quality detection performance similar to that of
dermatologists, incorporating ImageQX into the teledermatology flow can reduce
the image evaluation burden on dermatologists, while at the same time reducing
the time to diagnosis and treatment for patients. We introduce ImageQX, a first
of its kind explainable image quality assessor which leverages domain expertise
to improve the quality and efficiency of dermatological care in a virtual
setting.

Optimizing combinatorial structures is core to many real-world problems, such
as those encountered in life sciences. For example, one of the crucial steps
involved in antibody design is to find an arrangement of amino acids in a
protein sequence that improves its binding with a pathogen. Combinatorial
optimization of antibodies is difficult due to extremely large search spaces
and non-linear objectives. Even for modest antibody design problems, where
proteins have a sequence length of eleven, we are faced with searching over
2.05 x 10^14 structures. Applying traditional Reinforcement Learning algorithms
such as Q-learning to combinatorial optimization results in poor performance.
We propose Structured Q-learning (SQL), an extension of Q-learning that
incorporates structural priors for combinatorial optimization. Using a
molecular docking simulator, we demonstrate that SQL finds high binding energy
sequences and performs favourably against baselines on eight challenging
antibody design tasks, including designing antibodies for SARS-COV.

Machine learning models are nowadays ubiquitous in space missions, performing
a wide variety of tasks ranging from the prediction of multivariate time series
through the detection of specific patterns in the input data. Adopted models
are usually deep neural networks or other complex machine learning algorithms
providing predictions that are opaque, i.e., human users are not allowed to
understand the rationale behind the provided predictions. Several techniques
exist in the literature to combine the impressive predictive performance of
opaque machine learning models with human-intelligible prediction explanations,
as for instance the application of symbolic knowledge extraction procedures. In
this paper are reported the results of different knowledge extractors applied
to an ensemble predictor capable of reproducing cosmic-ray data gathered on
board the LISA Pathfinder space mission. A discussion about the
readability/fidelity trade-off of the extracted knowledge is also presented.

Flocking is a very challenging problem in a multi-agent system; traditional
flocking methods also require complete knowledge of the environment and a
precise model for control. In this paper, we propose Evolutionary Multi-Agent
Reinforcement Learning (EMARL) in flocking tasks, a hybrid algorithm that
combines cooperation and competition with little prior knowledge. As for
cooperation, we design the agents' reward for flocking tasks according to the
boids model. While for competition, agents with high fitness are designed as
senior agents, and those with low fitness are designed as junior, letting
junior agents inherit the parameters of senior agents stochastically. To
intensify competition, we also design an evolutionary selection mechanism that
shows effectiveness on credit assignment in flocking tasks. Experimental
results in a range of challenging and self-contrast benchmarks demonstrate that
EMARL significantly outperforms the full competition or cooperation methods.

Long-term planning of a robust power system requires the understanding of
changing demand patterns. Electricity demand is highly weather sensitive. Thus,
the supply side variation from introducing intermittent renewable sources,
juxtaposed with variable demand, will introduce additional challenges in the
grid planning process. By understanding the spatial and temporal variability of
temperature over the US, the response of demand to natural variability and
climate change-related effects on temperature can be separated, especially
because the effects due to the former factor are not known. Through this
project, we aim to better support the technology & policy development process
for power systems by developing machine and deep learning 'back-forecasting'
models to reconstruct multidecadal demand records and study the natural
variability of temperature and its influence on demand.

In this study, we propose an over-the-air computation (OAC) scheme to
calculate the majority vote (MV) for federated edge learning (FEEL). With the
proposed approach, edge devices (EDs) transmit the signs of local stochastic
gradients, i.e., votes, by activating one of two orthogonal resources. The MVs
at the edge server (ES) are obtained with non-coherent detectors by exploiting
the accumulations on the resources. Hence, the proposed scheme eliminates the
need for channel state information (CSI) at the EDs and ES. In this study, we
analyze various gradient-encoding strategies through the weight functions and
waveform configurations over orthogonal frequency division multiplexing (OFDM).
We show that specific weight functions that enable absentee EDs (i.e.,
hard-coded participation with absentees (HPA)) or weighted votes (i.e.,
soft-coded participation (SP)) can substantially reduce the probability of
detecting the incorrect MV. By taking path loss, power control, cell size, and
fading channel into account, we prove the convergence of the distributed
learning for a non-convex function for HPA. Through simulations, we show that
the proposed scheme with HPA and SP can provide high test accuracy even when
the time-synchronization and the power control are not ideal under
heterogeneous data distribution scenarios.

Recent trends towards training ever-larger language models have substantially
improved machine learning performance across linguistic tasks. However, the
huge cost of training larger models can make tuning them prohibitively
expensive, motivating the study of more efficient methods. Gradient-based
hyper-parameter optimization offers the capacity to tune hyper-parameters
during training, yet has not previously been studied in a sequence-to-sequence
setting. We apply a simple and general gradient-based hyperparameter
optimization method to sequence-to-sequence tasks for the first time,
demonstrating both efficiency and performance gains over strong baselines for
both Neural Machine Translation and Natural Language Understanding (NLU) tasks
(via T5 pretraining). For translation, we show the method generalizes across
language pairs, is more efficient than Bayesian hyper-parameter optimization,
and that learned schedules for some hyper-parameters can out-perform even
optimal constant-valued tuning. For T5, we show that learning hyper-parameters
during pretraining can improve performance across downstream NLU tasks. When
learning multiple hyper-parameters concurrently, we show that the global
learning rate can follow a schedule over training that improves performance and
is not explainable by the `short-horizon bias' of greedy methods
\citep{wu2018}. We release the code used to facilitate further research.

Object detection has always been practical. There are so many things in our
world that recognizing them can not only increase our automatic knowledge of
the surroundings, but can also be lucrative for those interested in starting a
new business. One of these attractive objects is the license plate (LP). In
addition to the security uses that license plate detection can have, it can
also be used to create creative businesses. With the development of object
detection methods based on deep learning models, an appropriate and
comprehensive dataset becomes doubly important. But due to the frequent
commercial use of license plate datasets, there are limited datasets not only
in Iran but also in the world. The largest Iranian dataset for detection
license plates has 1,466 images. Also, the largest Iranian dataset for
recognizing the characters of a license plate has 5,000 images. We have
prepared a complete dataset including 20,967 car images along with all the
detection annotation of the whole license plate and its characters, which can
be useful for various purposes. Also, the total number of license plate images
for character recognition application is 27,745 images.

In this paper we investigate a generalisation of a Boltzmann mean field game
(BMFG) for knowledge growth, originally introduced by the economists Lucas and
Moll. In BMFG the evolution of the agent density with respect to their
knowledge level is described by a Boltzmann equation. Agents increase their
knowledge through binary interactions with others; their increase is modulated
by the interaction and learning rate: Agents with similar knowledge learn more
in encounters, while agents with very different levels benefit less from
learning interactions. The optimal fraction of time spent on learning is
calculated by a Bellman equation, resulting in a highly nonlinear
forward-backward in time PDE system.
  The structure of solutions to the Boltzmann and Bellman equation depends
strongly on the learning rate in the Boltzmann collision kernel as well as the
utility function in the Bellman equation. In this paper we investigate the
monotonicity behavior of solutions for different learning and utility
functions, show existence of solutions and investigate how they impact the
existence of so-called balanced growth path solutions, that relate to
exponential growth of the overall economy. Furthermore we corroborate and
illustrate our analytical results with computational experiments.

Activation Functions introduce non-linearity in the deep neural networks.
This nonlinearity helps the neural networks learn faster and efficiently from
the dataset. In deep learning, many activation functions are developed and used
based on the type of problem statement. ReLU's variants, SWISH, and MISH are
goto activation functions. MISH function is considered having similar or even
better performance than SWISH, and much better than ReLU. In this paper, we
propose an activation function named APTx which behaves similar to MISH, but
requires lesser mathematical operations to compute. The lesser computational
requirements of APTx does speed up the model training, and thus also reduces
the hardware requirement for the deep learning model.

Advancing object detection to open-vocabulary and few-shot transfer has long
been a challenge for computer vision research. This work explores a continual
learning approach that enables a detector to expand its zero/few-shot
capabilities via multi-dataset vision-language pre-training. Using natural
language as knowledge representation, we explore methods to accumulate "visual
vocabulary" from different training datasets and unify the task as a
language-conditioned detection framework. Specifically, we propose a novel
language-aware detector OmDet and a novel training mechanism. The proposed
multimodal detection network can resolve the technical challenges in
multi-dataset joint training and it can generalize to arbitrary number of
training datasets without the requirements for manual label taxonomy merging.
Experiment results on COCO, Pascal VOC, and Wider Face/Pedestrian confirmed the
efficacy by achieving on par or higher scores in joint training compared to
training separately. Moreover, we pre-train on more than 20 million images with
4 million unique object vocabulary, and the resulting model is evaluated on 35
downstream tasks of ODinW. Results show that OmDet is able to achieve the
state-of-the-art fine-tuned performance on ODinW. And analysis shows that by
scaling up the proposed pre-training method, OmDet continues to improve its
zero/few-shot tuning performance, suggesting a promising way for further
scaling.

Solving temporally-extended tasks is a challenge for most reinforcement
learning (RL) algorithms [arXiv:1906.07343]. We investigate the ability of an
RL agent to learn to ask natural language questions as a tool to understand its
environment and achieve greater generalisation performance in novel,
temporally-extended environments. We do this by endowing this agent with the
ability of asking "yes-no" questions to an all-knowing Oracle. This allows the
agent to obtain guidance regarding the task at hand, while limiting the access
to new information. To study the emergence of such natural language questions
in the context of temporally-extended tasks we first train our agent in a
Mini-Grid environment. We then transfer the trained agent to a different,
harder environment. We observe a significant increase in generalisation
performance compared to a baseline agent unable to ask questions. Through
grounding its understanding of natural language in its environment, the agent
can reason about the dynamics of its environment to the point that it can ask
new, relevant questions when deployed in a novel environment.

We present CosmoGridV1: a large set of lightcone simulations for map-level
cosmological inference with probes of large scale structure. It is designed for
cosmological parameter measurement based on Stage-III photometric surveys with
non-Gaussian statistics and machine learning. CosmoGridV1 spans the $w$CDM
model by varying $\Omega_m$, $\sigma_8$, $w_0$, $H_0$, $n_s$, $\Omega_b$, and
assumes three degenerate neutrinos with $\sum m_\nu$ = 0.06 eV. This space is
covered by 2500 grid points on a Sobol sequence. At each grid point, we run 7
simulations with PkdGrav3 and store 69 particle maps at nside=2048 up to
$z$=3.5, as well as halo catalog snapshots. The fiducial cosmology has 200
independent simulations, along with their stencil derivatives. An important
part of CosmoGridV1 is the benchmark set of 28 simulations, which include
larger boxes, higher particle counts, and higher redshift resolution of shells.
They allow for testing if new types of analyses are sensitive to choices made
in CosmoGridV1. We add baryon feedback effects on the map level, using
shell-based baryon correction model. The shells are used to create maps of weak
gravitational lensing, intrinsic alignment, and galaxy clustering, using the
UFalcon code. The main part of CosmoGridV1 are the raw particle count shells
that can be used to create full-sky maps for a given $n(z)$. We also release
projected maps for a Stage-III forecast, as well as maps used previously in
KiDS-1000 deep learning constraints with CosmoGridV1. The data is available at
www.cosmogrid.ai.

Users on the internet usually require venues to provide better purchasing
recommendations. This can be provided by a reputation system that processes
ratings to provide recommendations. The rating aggregation process is a main
part of reputation system to produce global opinion about the product quality.
Naive methods that are frequently used do not consider consumer profiles in its
calculation and cannot discover unfair ratings and trends emerging in new
ratings. Other sophisticated rating aggregation methods that use weighted
average technique focus on one or a few aspects of consumers profile data. This
paper proposes a new reputation system using machine learning to predict
reliability of consumers from consumer profile. In particular, we construct a
new consumer profile dataset by extracting a set of factors that have great
impact on consumer reliability, which serve as an input to machine learning
algorithms. The predicted weight is then integrated with a weighted average
method to compute product reputation score. The proposed model has been
evaluated over three MovieLens benchmarking datasets, using 10-Folds cross
validation. Furthermore, the performance of the proposed model has been
compared to previous published rating aggregation models. The obtained results
were promising which suggest that the proposed approach could be a potential
solution for reputation systems. The results of comparison demonstrated the
accuracy of our models. Finally, the proposed approach can be integrated with
online recommendation systems to provide better purchasing recommendations and
facilitate user experience on online shopping markets.

The biliary tree is a network of tubes that connects the liver to the
gallbladder, an organ right beneath it. The bile duct is the major tube in the
biliary tree. The dilatation of a bile duct is a key indicator for more major
problems in the human body, such as stones and tumors, which are frequently
caused by the pancreas or the papilla of vater. The detection of bile duct
dilatation can be challenging for beginner or untrained medical personnel in
many circumstances. Even professionals are unable to detect bile duct
dilatation with the naked eye. This research presents a unique vision-based
model for biliary tree initial diagnosis. To segment the biliary tree from the
Magnetic Resonance Image, the framework used different image processing
approaches (MRI). After the image's region of interest was segmented, numerous
calculations were performed on it to extract 10 features, including major and
minor axes, bile duct area, biliary tree area, compactness, and some textural
features (contrast, mean, variance and correlation). This study used a database
of images from King Hussein Medical Center in Amman, Jordan, which included 200
MRI images, 100 normal cases, and 100 patients with dilated bile ducts. After
the characteristics are extracted, various classifiers are used to determine
the patients' condition in terms of their health (normal or dilated). The
findings demonstrate that the extracted features perform well with all
classifiers in terms of accuracy and area under the curve. This study is unique
in that it uses an automated approach to segment the biliary tree from MRI
images, as well as scientifically correlating retrieved features with biliary
tree status that has never been done before in the literature.

Dementia is a neuropsychiatric brain disorder that usually occurs when one or
more brain cells stop working partially or at all. Diagnosis of this disorder
in the early phases of the disease is a vital task to rescue patients lives
from bad consequences and provide them with better healthcare. Machine learning
methods have been proven to be accurate in predicting dementia in the early
phases of the disease. The prediction of dementia depends heavily on the type
of collected data which usually are gathered from Normalized Whole Brain Volume
(nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected
from Magnetic Resonance Imaging (MRIs). Other biological features such as age
and gender can also help in the diagnosis of dementia. Although many studies
use machine learning for predicting dementia, we could not reach a conclusion
on the stability of these methods for which one is more accurate under
different experimental conditions. Therefore, this paper investigates the
conclusion stability regarding the performance of machine learning algorithms
for dementia prediction. To accomplish this, a large number of experiments were
run using 7 machine learning algorithms and two feature reduction algorithms
namely, Information Gain (IG) and Principal Component Analysis (PCA). To
examine the stability of these algorithms, thresholds of feature selection were
changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has
resulted in 7x9 + 7x7= 112 experiments. In each experiment, various
classification evaluation data were recorded. The obtained results show that
among seven algorithms the support vector machine and Naive Bayes are the most
stable algorithms while changing the selection threshold. Also, it was found
that using IG would seem more efficient than using PCA for predicting Dementia.

Glass is very common in our daily life. Existing computer vision systems
neglect it and thus may have severe consequences, e.g., a robot may crash into
a glass wall. However, sensing the presence of glass is not straightforward.
The key challenge is that arbitrary objects/scenes can appear behind the glass.
In this paper, we propose an important problem of detecting glass surfaces from
a single RGB image. To address this problem, we construct the first large-scale
glass detection dataset (GDD) and propose a novel glass detection network,
called GDNet-B, which explores abundant contextual cues in a large
field-of-view via a novel large-field contextual feature integration (LCFI)
module and integrates both high-level and low-level boundary features with a
boundary feature enhancement (BFE) module. Extensive experiments demonstrate
that our GDNet-B achieves satisfying glass detection results on the images
within and beyond the GDD testing set. We further validate the effectiveness
and generalization capability of our proposed GDNet-B by applying it to other
vision tasks, including mirror segmentation and salient object detection.
Finally, we show the potential applications of glass detection and discuss
possible future research directions.

We derive an explicit form of the dilaton potential in improved holographic
QCD (IHQCD) from the QCD lattice data of the chiral condensate as a function of
the quark mass. This establishes a data-driven holographic modeling of QCD --
machine learning holographic QCD. The modeling consists of two steps for
solving inverse problems. The first inverse problem is to find the emergent
bulk geometry consistent with the lattice QCD simulation data at the boundary.
We solve this problem with the refinement of neural ordinary differential
equation, a machine learning technique. The second inverse problem is to derive
a bulk gravity action with a dilaton potential such that its solution is the
emergent bulk geometry. We solve this problem at non-zero temperature, and
derive the explicit form of the dilaton potential. The dilaton potential
determines the bulk action, the Einstein-dilaton system, thus we derive
holographically the bulk system from the QCD chiral condensate data. The
usefulness of the model is shown in the example of the prediction of the string
breaking distance, whose value is found to be consistent with another lattice
QCD data.

Decoders built on Gaussian processes (GPs) are enticing due to the
marginalisation over the non-linear function space. Such models (also known as
GP-LVMs) are often expensive and notoriously difficult to train in practice,
but can be scaled using variational inference and inducing points. In this
paper, we revisit active set approximations. We develop a new stochastic
estimate of the log-marginal likelihood based on recently discovered links to
cross-validation, and propose a computationally efficient approximation
thereof. We demonstrate that the resulting stochastic active sets (SAS)
approximation significantly improves the robustness of GP decoder training
while reducing computational cost. The SAS-GP obtains more structure in the
latent space, scales to many datapoints and learns better representations than
variational autoencoders, which is rarely the case for GP decoders.

The continued digitization of societal processes translates into a
proliferation of time series data that cover applications such as fraud
detection, intrusion detection, and energy management, where anomaly detection
is often essential to enable reliability and safety. Many recent studies target
anomaly detection for time series data. Indeed, area of time series anomaly
detection is characterized by diverse data, methods, and evaluation strategies,
and comparisons in existing studies consider only part of this diversity, which
makes it difficult to select the best method for a particular problem setting.
To address this shortcoming, we introduce taxonomies for data, methods, and
evaluation strategies, provide a comprehensive overview of unsupervised time
series anomaly detection using the taxonomies, and systematically evaluate and
compare state-of-the-art traditional as well as deep learning techniques. In
the empirical study using nine publicly available datasets, we apply the most
commonly-used performance evaluation metrics to typical methods under a fair
implementation standard. Based on the structuring offered by the taxonomies, we
report on empirical studies and provide guidelines, in the form of comparative
tables, for choosing the methods most suitable for particular application
settings. Finally, we propose research directions for this dynamic field.

Event cameras are becoming increasingly popular in robotics and computer
vision due to their beneficial properties, e.g., high temporal resolution, high
bandwidth, almost no motion blur, and low power consumption. However, these
cameras remain expensive and scarce in the market, making them inaccessible to
the majority. Using event simulators minimizes the need for real event cameras
to develop novel algorithms. However, due to the computational complexity of
the simulation, the event streams of existing simulators cannot be generated in
real-time but rather have to be pre-calculated from existing video sequences or
pre-rendered and then simulated from a virtual 3D scene. Although these offline
generated event streams can be used as training data for learning tasks, all
response time dependent applications cannot benefit from these simulators yet,
as they still require an actual event camera. This work proposes simulation
methods that improve the performance of event simulation by two orders of
magnitude (making them real-time capable) while remaining competitive in the
quality assessment.

The ongoing COVID-19 pandemic has caused immeasurable losses for people
worldwide. To contain the spread of virus and further alleviate the crisis,
various health policies (e.g., stay-at-home orders) have been issued which
spark heat discussion as users turn to share their attitudes on social media.
In this paper, we consider a more realistic scenario on stance detection (i.e.,
cross-target and zero-shot settings) for the pandemic and propose an
adversarial learning-based stance classifier to automatically identify the
public attitudes toward COVID-19-related health policies. Specifically, we
adopt adversarial learning which allows the model to train on a large amount of
labeled data and capture transferable knowledge from source topics, so as to
enable generalize to the emerging health policy with sparse labeled data.
Meanwhile, a GeoEncoder is designed which encourages model to learn unobserved
contextual factors specified by each region and represents them as non-text
information to enhance model's deeper understanding. We evaluate the
performance of a broad range of baselines in stance detection task for
COVID-19-related policies, and experimental results show that our proposed
method achieves state-of-the-art performance in both cross-target and zero-shot
settings.

Software defect prediction is an essential task during the software
development Lifecycle as it can help managers to identify the most
defect-proneness modules. Thus, it can reduce the test cost and assign testing
resources efficiently. Many classification methods can be used to determine if
the software is defective or not. Support Vector Machine (SVM) has not been
used extensively for such problems because of its instability when applied on
different datasets and parameter settings. The main parameter that influences
the accuracy is the choice of the kernel function. The use of kernel functions
has not been studied thoroughly in previous papers. Therefore, this research
examines the performance and accuracy of SVM with six different kernel
functions. Various public datasets from the PROMISE project empirically
validate our hypothesis. The results demonstrate that no kernel function can
give stable performance across different experimental settings. In addition,
the use of PCA as a feature reduction algorithm shows slight accuracy
improvement over some datasets.

Off-policy algorithms, in which a behavior policy differs from the target
policy and is used to gain experience for learning, have proven to be of great
practical value in reinforcement learning. However, even for simple convex
problems such as linear value function approximation, these algorithms are not
guaranteed to be stable. To address this, alternative algorithms that are
provably convergent in such cases have been introduced, the most well known
being gradient descent temporal difference (GTD) learning. This algorithm and
others like it, however, tend to converge much more slowly than conventional
temporal difference learning. In this paper we propose gradient descent
temporal difference-difference (Gradient-DD) learning in order to improve GTD2,
a GTD algorithm, by introducing second-order differences in successive
parameter updates. We investigate this algorithm in the framework of linear
value function approximation, theoretically proving its convergence by applying
the theory of stochastic approximation. %analytically showing its improvement
over GTD2. Studying the model empirically on the random walk task, the
Boyan-chain task, and the Baird's off-policy counterexample, we find
substantial improvement over GTD2 and, in several cases, better performance
even than conventional TD learning.

Convolutional neural networks trained using manually generated labels are
commonly used for semantic or instance segmentation. In precision agriculture,
automated flower detection methods use supervised models and post-processing
techniques that may not perform consistently as the appearance of the flowers
and the data acquisition conditions vary. We propose a self-supervised learning
strategy to enhance the sensitivity of segmentation models to different flower
species using automatically generated pseudo-labels. We employ a data
augmentation and refinement approach to improve the accuracy of the model
predictions. The augmented semantic predictions are then converted to panoptic
pseudo-labels to iteratively train a multi-task model. The self-supervised
model predictions can be refined with existing post-processing approaches to
further improve their accuracy. An evaluation on a multi-species fruit tree
flower dataset demonstrates that our method outperforms state-of-the-art models
without computationally expensive post-processing steps, providing a new
baseline for flower detection applications.

In this work, we aim to investigate Primal-Dual (PD) methods for
convex-strongly-concave saddle point problems (SPP). In many cases, the
computation of the proximal oracle over the primal-only function is
inefficient. Hence, we use its first-order linear approximation in the proximal
step resulting in a Linearized PD (LPD) method. Even when the coupling term is
bilinear, we observe that LPD has a suboptimal dependence on the Lipschitz
constant of the primal-only function. In contrast, LPD has optimal convergence
for the strongly-convex concave case. This observation induces us to present
our accelerated linearized primal-dual (ALPD) algorithm to solve convex
strongly-concave SPP. ALPD is a single-loop algorithm that combines features of
Nesterov's accelerated gradient descent (AGD) and LPD. We show that when the
coupling term is semi-linear (which contains bilinear as a specific case), ALPD
obtains the optimal dependence on the Lipschitz constant of primal-only
function. Hence, it is an optimal algorithm. When the coupling term has a
general nonlinear form, the ALPD algorithm has suboptimal dependence on the
Lipschitz constant of the primal part of the coupling term. To improve this
dependence, we present an inexact APD algorithm. This algorithm performs AGD
iterations in the inner loop to find an approximate solution to a proximal
subproblem of APD. We show that inexact APD maintains optimal number of
gradients evaluations (gradient complexity) of primal-only and dual parts of
the problem. It also significantly improves the gradient-complexity of the
primal coupling term.

Manual code reviews and static code analyzers are the traditional mechanisms
to verify if source code complies with coding policies. However, these
mechanisms are hard to scale. We formulate code compliance assessment as a
machine learning (ML) problem, to take as input a natural language policy and
code, and generate a prediction on the code's compliance, non-compliance, or
irrelevance. This can help scale compliance classification and search for
policies not covered by traditional mechanisms. We explore key research
questions on ML model formulation, training data, and evaluation setup. The
core idea is to obtain a joint code-text embedding space which preserves
compliance relationships via the vector distance of code and policy embeddings.
As there is no task-specific data, we re-interpret and filter commonly
available software datasets with additional pre-training and pre-finetuning
tasks that reduce the semantic gap. We benchmarked our approach on two listings
of coding policies (CWE and CBP). This is a zero-shot evaluation as none of the
policies occur in the training set. On CWE and CBP respectively, our tool
Policy2Code achieves classification accuracies of (59%, 71%) and search MRR of
(0.05, 0.21) compared to CodeBERT with classification accuracies of (37%, 54%)
and MRR of (0.02, 0.02). In a user study, 24% Policy2Code detections were
accepted compared to 7% for CodeBERT.

Federated Learning (FL) is a machine learning paradigm where local nodes
collaboratively train a central model while the training data remains
decentralized. Existing FL methods typically share model parameters or employ
co-distillation to address the issue of unbalanced data distribution. However,
they suffer from communication bottlenecks. More importantly, they risk privacy
leakage. In this work, we develop a privacy preserving and communication
efficient method in a FL framework with one-shot offline knowledge distillation
using unlabeled, cross-domain public data. We propose a quantized and noisy
ensemble of local predictions from completely trained local models for stronger
privacy guarantees without sacrificing accuracy. Based on extensive experiments
on image classification and text classification tasks, we show that our
privacy-preserving method outperforms baseline FL algorithms with superior
performance in both accuracy and communication efficiency.

Fully supervised human mesh recovery methods are data-hungry and have poor
generalizability due to the limited availability and diversity of 3D-annotated
benchmark datasets. Recent progress in self-supervised human mesh recovery has
been made using synthetic-data-driven training paradigms where the model is
trained from synthetic paired 2D representation (e.g., 2D keypoints and
segmentation masks) and 3D mesh. However, on synthetic dense correspondence
maps (i.e., IUV) few have been explored since the domain gap between synthetic
training data and real testing data is hard to address for 2D dense
representation. To alleviate this domain gap on IUV, we propose
cross-representation alignment utilizing the complementary information from the
robust but sparse representation (2D keypoints). Specifically, the alignment
errors between initial mesh estimation and both 2D representations are
forwarded into regressor and dynamically corrected in the following mesh
regression. This adaptive cross-representation alignment explicitly learns from
the deviations and captures complementary information: robustness from sparse
representation and richness from dense representation. We conduct extensive
experiments on multiple standard benchmark datasets and demonstrate competitive
results, helping take a step towards reducing the annotation effort needed to
produce state-of-the-art models in human mesh estimation.

Domain adaptation aims to transfer knowledge of labeled instances obtained
from a source domain to a target domain to fill the gap between the domains.
Most domain adaptation methods assume that the source and target domains have
the same dimensionality. Methods that are applicable when the number of
features is different in each domain have rarely been studied, especially when
no label information is given for the test data obtained from the target
domain. In this paper, it is assumed that common features exist in both domains
and that extra (new additional) features are observed in the target domain;
hence, the dimensionality of the target domain is higher than that of the
source domain. To leverage the homogeneity of the common features, the
adaptation between these source and target domains is formulated as an optimal
transport (OT) problem. In addition, a learning bound in the target domain for
the proposed OT-based method is derived. The proposed algorithm is validated
using both simulated and real-world data.

Recommender systems usually learn user interests from various user behaviors,
including clicks and post-click behaviors (e.g., like and favorite). However,
these behaviors inevitably exhibit popularity bias, leading to some unfairness
issues: 1) for items with similar quality, more popular ones get more exposure;
and 2) even worse the popular items with lower popularity might receive more
exposure. Existing work on mitigating popularity bias blindly eliminates the
bias and usually ignores the effect of item quality. We argue that the
relationships between different user behaviors (e.g., conversion rate) actually
reflect the item quality. Therefore, to handle the unfairness issues, we
propose to mitigate the popularity bias by considering multiple user behaviors.
  In this work, we examine causal relationships behind the interaction
generation procedure in multi-behavior recommendation. Specifically, we find
that: 1) item popularity is a confounder between the exposed items and users'
post-click interactions, leading to the first unfairness; and 2) some hidden
confounders (e.g., the reputation of item producers) affect both item
popularity and quality, resulting in the second unfairness. To alleviate these
confounding issues, we propose a causal framework to estimate the causal
effect, which leverages backdoor adjustment to block the backdoor paths caused
by the confounders. In the inference stage, we remove the negative effect of
popularity and utilize the good effect of quality for recommendation.
Experiments on two real-world datasets validate the effectiveness of our
proposed framework, which enhances fairness without sacrificing recommendation
accuracy.

Melanoma is the deadliest form of skin cancer. Uncontrollable growth of
melanocytes leads to melanoma. Melanoma has been growing wildly in the last few
decades. In recent years, the detection of melanoma using image processing
techniques has become a dominant research field. The Automatic Melanoma
Detection System (AMDS) helps to detect melanoma based on image processing
techniques by accepting infected skin area images as input. A single lesion
image is a source of multiple features. Therefore, It is crucial to select the
appropriate features from the image of the lesion in order to increase the
accuracy of AMDS. For melanoma detection, all extracted features are not
important. Some of the extracted features are complex and require more
computation tasks, which impacts the classification accuracy of AMDS. The
feature extraction phase of AMDS exhibits more variability, therefore it is
important to study the behaviour of AMDS using individual and extended feature
extraction approaches. A novel algorithm ExtFvAMDS is proposed for the
calculation of Extended Feature Vector Space. The six models proposed in the
comparative study revealed that the HSV feature vector space for automatic
detection of melanoma using Ensemble Bagged Tree classifier on Med-Node Dataset
provided 99% AUC, 95.30% accuracy, 94.23% sensitivity, and 96.96% specificity.

Traditional black-box optimization methods are inefficient when dealing with
multi-point measurement, i.e. when each query in the control domain requires a
set of measurements in a secondary domain to calculate the objective. In
particle accelerators, emittance tuning from quadrupole scans is an example of
optimization with multi-point measurements. Although the emittance is a
critical parameter for the performance of high-brightness machines, including
X-ray lasers and linear colliders, comprehensive optimization is often limited
by the time required for tuning. Here, we extend the recently-proposed Bayesian
Algorithm Execution (BAX) to the task of optimization with multi-point
measurements. BAX achieves sample-efficiency by selecting and modeling
individual points in the joint control-measurement domain. We apply BAX to
emittance minimization at the Linac Coherent Light Source (LCLS) and the
Facility for Advanced Accelerator Experimental Tests II (FACET-II) particle
accelerators. In an LCLS simulation environment, we show that BAX delivers a
20x increase in efficiency while also being more robust to noise compared to
traditional optimization methods. Additionally, we ran BAX live at both LCLS
and FACET-II, matching the hand-tuned emittance at FACET-II and achieving an
optimal emittance that was 24% lower than that obtained by hand-tuning at LCLS.
We anticipate that our approach can readily be adapted to other types of
optimization problems involving multi-point measurements commonly found in
scientific instruments.

We introduce a method for explaining the results of various linear and
hierarchical multi-criteria decision-making (MCDM) techniques such as WSM and
AHP. The two key ideas are (A) to maintain a fine-grained representation of the
values manipulated by these techniques and (B) to derive explanations from
these representations through merging, filtering, and aggregating operations.
An explanation in our model presents a high-level comparison of two
alternatives in an MCDM problem, presumably an optimal and a non-optimal one,
illuminating why one alternative was preferred over the other one. We show the
usefulness of our techniques by generating explanations for two well-known
examples from the MCDM literature. Finally, we show their efficacy by
performing computational experiments.

We demonstrate Tensor Query Processor (TQP): a query processor that
automatically compiles relational operators into tensor programs. By leveraging
tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools
(e.g., Pandas for data ingestion, Tensorboard for visualization); (2) target
different hardware (e.g., CPU, GPU) and software (e.g., browser) backends; and
(3) end-to-end accelerate queries containing both relational and ML operators.
TQP is generic enough to support the TPC-H benchmark, and it provides
performance that is comparable to, and often better than, that of specialized
CPU and GPU query processors.

Hazards can be exposed by HAZOP as text information, and studying their
classification is of great significance to the development of industrial
informatics, which is conducive to safety early warning, decision support,
policy evaluation, etc. However, there is no research on this important field
at present. In this paper, we propose a novel model termed DLGM via deep
learning for hazard classification. Specifically, first, we leverage BERT to
vectorize the hazard and treat it as a type of time series (HTS). Secondly, we
build a grey model FSGM(1, 1) to model it, and get the grey guidance in the
sense of the structural parameters. Finally, we design a hierarchical-feature
fusion neural network (HFFNN) to investigate the HTS with grey guidance (HTSGG)
from three themes, where, HFFNN is a hierarchical structure with four types of
modules: two feature encoders, a gating mechanism, and a deepening mechanism.
We take 18 industrial processes as application cases and launch a series of
experiments. The experimental results prove that DLGM has promising aptitudes
for hazard classification and that FSGM(1, 1) and HFFNN are effective. We hope
our research can contribute added value and support to the daily practice in
industrial safety.

Community detection is a classic problem in network science with extensive
applications in various fields. The most commonly used methods are the
algorithms designed to maximize a utility function, modularity, across
different ways that a network can be partitioned into communities. Despite
their name and design philosophy, current modularity maximization algorithms
generally fail to maximize modularity or guarantee any proximity to an optimal
solution. We propose the Bayan algorithm which, unlike the existing methods,
returns network partitions with a guarantee of either optimality or proximity
to an optimal solution. At the core of the Bayan algorithm is a branch-and-cut
scheme that solves a sparse integer programming formulation of the modularity
maximization problem to optimality or approximate it within a factor. We
analyze the performance of Bayan against 22 existing algorithms using synthetic
and real networks. Through extensive experiments, we demonstrate Bayan's
distinctive capabilities not only in maximizing modularity, but more
importantly in accurate retrieval of ground-truth communities. Bayan's
comparative level of performance remains stable over variations in the amount
of noise in the data (graph) generation process. The performance of Bayan as an
exact modularity maximization algorithm also reveals the theoretical capability
limits of maximum-modularity partitions in accurate retrieval of communities.
Overall our analysis points to Bayan as a suitable choice for a
methodologically grounded detection of communities through exact (approximate)
maximization of modularity in networks with up to $\sim10^3$ edges (and larger
networks). Prospective advances in graph optimization and integer programming
can push these limits further.

Deep learning has seen increasing applications in time series in recent
years. For time series anomaly detection scenarios, such as in finance,
Internet of Things, data center operations, etc., time series usually show very
flexible baselines depending on various external factors. Anomalies unveil
themselves by lying far away from the baseline. However, the detection is not
always easy due to some challenges including baseline shifting, lacking of
labels, noise interference, real time detection in streaming data, result
interpretability, etc. In this paper, we develop a novel deep architecture to
properly extract the baseline from time series, namely Deep Baseline Network
(DBLN). By using this deep network, we can easily locate the baseline position
and then provide reliable and interpretable anomaly detection result. Empirical
evaluation on both synthetic and public real-world datasets shows that our
purely unsupervised algorithm achieves superior performance compared with
state-of-art methods and has good practical applications.

In this paper, we propose a sample complexity bound for learning a simplex
from noisy samples. A dataset of size $n$ is given which includes i.i.d.
samples drawn from a uniform distribution over an unknown arbitrary simplex in
$\mathbb{R}^K$, where samples are assumed to be corrupted by an additive
Gaussian noise of an arbitrary magnitude. We propose a strategy which outputs a
simplex having, with high probability, a total variation distance of $\epsilon
+ O\left(\mathrm{SNR}^{-1}\right)$ from the true simplex, for any $\epsilon>0$.
We prove that to arrive this close to the true simplex, it is sufficient to
have $n\ge\tilde{O}\left(K^2/\epsilon^2\right)$ samples. Here, SNR stands for
the signal-to-noise ratio which can be viewed as the ratio of the diameter of
the simplex to the standard deviation of the noise. Our proofs are based on
recent advancements in sample compression techniques, which have already shown
promises in deriving tight bounds for density estimation in high-dimensional
Gaussian mixture models.

Cancer is the second leading cause of death in the world. Diagnosing cancer
early on can save many lives. Pathologists have to look at tissue microarray
(TMA) images manually to identify tumors, which can be time-consuming,
inconsistent and subjective. Existing algorithms that automatically detect
tumors have either not achieved the accuracy level of a pathologist or require
substantial human involvements. A major challenge is that TMA images with
different shapes, sizes, and locations can have the same score. Learning
staining patterns in TMA images requires a huge number of images, which are
severely limited due to privacy concerns and regulations in medical
organizations. TMA images from different cancer types may have common
characteristics that could provide valuable information, but using them
directly harms the accuracy. Transfer learning is adopted to increase the
training sample size by extracting knowledge from tissue images from different
cancer types. Transfer learning has made it possible for the algorithm to break
the critical accuracy barrier. The proposed algorithm reports an accuracy of
75.9% on breast cancer TMA images from the Stanford Tissue Microarray Database,
achieving the 75% accuracy level of pathologists. This will allow pathologists
to confidently use automatic algorithms to assist them in recognizing tumors
consistently with a higher accuracy in real time.

With the advances in deep learning, speaker verification has achieved very
high accuracy and is gaining popularity as a type of biometric authentication
option in many scenes of our daily life, especially the growing market of web
services. Compared to traditional passwords, "vocal passwords" are much more
convenient as they relieve people from memorizing different passwords. However,
new machine learning attacks are putting these voice authentication systems at
risk. Without a strong security guarantee, attackers could access legitimate
users' web accounts by fooling the deep neural network (DNN) based voice
recognition models. In this paper, we demonstrate an easy-to-implement data
poisoning attack to the voice authentication system, which can hardly be
captured by existing defense mechanisms. Thus, we propose a more robust defense
method, called Guardian, which is a convolutional neural network-based
discriminator. The Guardian discriminator integrates a series of novel
techniques including bias reduction, input augmentation, and ensemble learning.
Our approach is able to distinguish about 95% of attacked accounts from normal
accounts, which is much more effective than existing approaches with only 60%
accuracy.

Extensive research has been conducted, over recent years, on various ways of
enhancing heuristic search for combinatorial optimization problems with machine
learning algorithms. In this study, we investigate the use of predictions from
graph neural networks (GNNs) in the form of heatmaps to improve the Hybrid
Genetic Search (HGS), a state-of-the-art algorithm for the Capacitated Vehicle
Routing Problem (CVRP). The crossover and local-search components of HGS are
instrumental in finding improved solutions, yet these components essentially
rely on simple greedy or random choices. It seems intuitive to attempt to
incorporate additional knowledge at these levels. Throughout a vast
experimental campaign on more than 10,000 problem instances, we show that
exploiting more sophisticated strategies using measures of node relatedness
(heatmaps, or simply distance) within these algorithmic components can
significantly enhance performance. However, contrary to initial expectations,
we also observed that heatmaps did not present significant advantages over
simpler distance measures for these purposes. Therefore, we faced a common --
though rarely documented -- situation of overkill: GNNs can indeed improve
performance on an important optimization task, but an ablation analysis
demonstrated that simpler alternatives perform equally well.

Saddle-point problems appear in various settings including machine learning,
zero-sum stochastic games, and regression problems. We consider decomposable
saddle-point problems and study an extension of the alternating direction
method of multipliers to such saddle-point problems. Instead of solving the
original saddle-point problem directly, this algorithm solves smaller
saddle-point problems by exploiting the decomposable structure. We show the
convergence of this algorithm for convex-concave saddle-point problems under a
mild assumption. We also provide a sufficient condition for which the
assumption holds. We demonstrate the convergence properties of the saddle-point
alternating direction method of multipliers with numerical examples on a power
allocation problem in communication channels and a network routing problem with
adversarial costs.

This paper addresses a safe planning and control problem for mobile robots
operating in communication- and sensor-limited dynamic environments. In this
case the robots cannot sense the objects around them and must instead rely on
intermittent, external information about the environment, as e.g., in
underwater applications. The challenge in this case is that the robots must
plan using only this stale data, while accounting for any noise in the data or
uncertainty in the environment. To address this challenge we propose a
compositional technique which leverages neural networks to quickly plan and
control a robot through crowded and dynamic environments using only
intermittent information. Specifically, our tool uses reachability analysis and
potential fields to train a neural network that is capable of generating safe
control actions. We demonstrate our technique both in simulation with an
underwater vehicle crossing a crowded shipping channel and with real
experiments with ground vehicles in communication- and sensor-limited

The widespread adoption of speech-based online services raises security and
privacy concerns regarding the data that they use and share. If the data were
compromised, attackers could exploit user speech to bypass speaker verification
systems or even impersonate users. To mitigate this, we propose DeID-VC, a
speaker de-identification system that converts a real speaker to pseudo
speakers, thus removing or obfuscating the speaker-dependent attributes from a
spoken voice. The key components of DeID-VC include a Variational Autoencoder
(VAE) based Pseudo Speaker Generator (PSG) and a voice conversion Autoencoder
(AE) under zero-shot settings. With the help of PSG, DeID-VC can assign unique
pseudo speakers at speaker level or even at utterance level. Also, two novel
learning objectives are added to bridge the gap between training and inference
of zero-shot voice conversion. We present our experimental results with word
error rate (WER) and equal error rate (EER), along with three subjective
metrics to evaluate the generated output of DeID-VC. The result shows that our
method substantially improved intelligibility (WER 10% lower) and
de-identification effectiveness (EER 5% higher) compared to our baseline. Code
and listening demo: https://github.com/a43992899/DeID-VC

Modern neural network architectures have shown remarkable success in several
large-scale classification and prediction tasks. Part of the success of these
architectures is their flexibility to transform the data from the raw input
representations (e.g. pixels for vision tasks, or text for natural language
processing tasks) to one-hot output encoding. While much of the work has
focused on studying how the input gets transformed to the one-hot encoding,
very little work has examined the effectiveness of these one-hot labels.
  In this work, we demonstrate that more sophisticated label representations
are better for classification than the usual one-hot encoding. We propose
Learning with Adaptive Labels (LwAL) algorithm, which simultaneously learns the
label representation while training for the classification task. These learned
labels can significantly cut down on the training time (usually by more than
50%) while often achieving better test accuracies. Our algorithm introduces
negligible additional parameters and has a minimal computational overhead.
Along with improved training times, our learned labels are semantically
meaningful and can reveal hierarchical relationships that may be present in the
data.

Deep learning models achieve state-of-the art results in predicting blood
glucose trajectories, with a wide range of architectures being proposed.
However, the adaptation of such models in clinical practice is slow, largely
due to the lack of uncertainty quantification of provided predictions. In this
work, we propose to model the future glucose trajectory conditioned on the past
as an infinite mixture of basis distributions (i.e., Gaussian, Laplace, etc.).
This change allows us to learn the uncertainty and predict more accurately in
the cases when the trajectory has a heterogeneous or multi-modal distribution.
To estimate the parameters of the predictive distribution, we utilize the
Transformer architecture. We empirically demonstrate the superiority of our
method over existing state-of-the-art techniques both in terms of accuracy and
uncertainty on the synthetic and benchmark glucose data sets.

Adversarial examples, inputs designed to induce worst-case behavior in
machine learning models, have been extensively studied over the past decade.
Yet, our understanding of this phenomenon stems from a rather fragmented pool
of knowledge; at present, there are a handful of attacks, each with disparate
assumptions in threat models and incomparable definitions of optimality. In
this paper, we propose a systematic approach to characterize worst-case (i.e.,
optimal) adversaries. We first introduce an extensible decomposition of attacks
in adversarial machine learning by atomizing attack components into surfaces
and travelers. With our decomposition, we enumerate over components to create
576 attacks (568 of which were previously unexplored). Next, we propose the
Pareto Ensemble Attack (PEA): a theoretical attack that upper-bounds attack
performance. With our new attacks, we measure performance relative to the PEA
on: both robust and non-robust models, seven datasets, and three extended
lp-based threat models incorporating compute costs, formalizing the Space of
Adversarial Strategies. From our evaluation we find that attack performance to
be highly contextual: the domain, model robustness, and threat model can have a
profound influence on attack efficacy. Our investigation suggests that future
studies measuring the security of machine learning should: (1) be
contextualized to the domain & threat models, and (2) go beyond the handful of
known attacks used today.

In this work we present affinity-VAE: a framework for automatic clustering
and classification of objects in multidimensional image data based on their
similarity. The method expands on the concept of $\beta$-VAEs with an informed
similarity-based loss component driven by an affinity matrix. The affinity-VAE
is able to create rotationally-invariant, morphologically homogeneous clusters
in the latent representation, with improved cluster separation compared with a
standard $\beta$-VAE. We explore the extent of latent disentanglement and
continuity of the latent spaces on both 2D and 3D image data, including
simulated biological electron cryo-tomography (cryo-ET) volumes as an example
of a scientific application.

In this paper, we connect deep learning literature with non-linear factor
models and show that deep learning estimation makes a substantial improvement
in the non-linear additive factor model literature. We provide bounds on the
expected risk and show that these upper bounds are uniform over a set of
multiple response variables by extending Schmidt-Hieber (2020) theorems.
  We show that our risk bound does not depend on the number of factors.
  In order to construct a covariance matrix estimator for asset returns, we
develop a novel data-dependent estimator of the error covariance matrix in deep
neural networks. The estimator refers to a flexible adaptive thresholding
technique which is robust to outliers in the innovations. We prove that the
estimator is consistent in spectral norm. Then using that result, we show
consistency and rate of convergence of covariance matrix and precision matrix
estimator for asset returns. The rate of convergence in both results do not
depend on the number of factors, hence ours is a new result in the factor model
literature due to the fact that number of factors are impediment to better
estimation and prediction. Except from the precision matrix result, all our
results are obtained even with number of assets are larger than the time span,
and both quantities are growing.
  Various Monte Carlo simulations confirm our large sample findings and reveal
superior accuracies of the DNN-FM in estimating the true underlying functional
form which connects the factors and observable variables, as well as the
covariance and precision matrix compared to competing approaches. Moreover, in
an out-of-sample portfolio forecasting application it outperforms in most of
the cases alternative portfolio strategies in terms of out-of-sample portfolio
standard deviation and Sharpe ratio.

Employing large antenna arrays is a key characteristic of millimeter wave
(mmWave) and terahertz communication systems. Due to the hardware constraints
and the lack of channel knowledge, codebook based beamforming/combining is
normally adopted to achieve the desired array gain. However, most of the
existing codebooks focus only on improving the gain of their target user,
without taking interference into account. This can incur critical performance
degradation in dense networks. In this paper, we propose a sample-efficient
online reinforcement learning based beam pattern design algorithm that learns
how to shape the beam pattern to null the interfering directions. The proposed
approach does not require any explicit channel knowledge or any coordination
with the interferers. Simulation results show that the developed solution is
capable of learning well-shaped beam patterns that significantly suppress the
interference while sacrificing tolerable beamforming/combing gain from the
desired user. Furthermore, a hardware proof-of-concept prototype based on
mmWave phased arrays is built and used to implement and evaluate the developed
online beam learning solutions in realistic scenarios. The learned beam
patterns, measured in an anechoic chamber, show the performance gains of the
developed framework and highlight a promising machine learning based
beam/codebook optimization direction for mmWave and terahertz systems.

This paper introduces a new model for highly accurate distribution voltage
solutions, coined as a parameterized linear power flow model. The proffered
model is grounded on a physical model of linear power flow equations, and uses
learning-aided parameterization to increase the fidelity of voltage solutions
over a wide range of operating points. To this end, the closed-form analytic
solution of the parameterization approach is obtained via a Gaussian Process
using a deliberately small input sample and without the need for recomputation.
The resulting "self-adjusting" parameter is system-specific and controls how
accurate the proposed power flow equations are according to loading conditions.
Under a certain value of the resulting parameter, the proposed model can fully
recover the linearized formulation of a specialized branch flow model for
radial distribution systems, the so-called simplified DistFlow model. Numerical
examples are provided to illustrate the effectiveness of the proposed model as
well as the improvement in solution accuracy for voltage magnitudes over the
simplified DistFlow model and several other linear power flow models, at
multiple loading levels. Simulations were carried out on six small- and
medium-sized test systems.

Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging
technique that characterizes the anisotropic tissue magnetic susceptibility
with a second-order tensor model. STI has the potential to provide information
for both the reconstruction of white matter fiber pathways and detection of
myelin changes in the brain at mm resolution or less, which would be of great
value for understanding brain structure and function in healthy and diseased
brain. However, the application of STI in vivo has been hindered by its
cumbersome and time-consuming acquisition requirement of measuring
susceptibility induced MR phase changes at multiple (usually more than six)
head orientations. This complexity is enhanced by the limitation in head
rotation angles due to physical constraints of the head coil. As a result, STI
has not yet been widely applied in human studies in vivo. In this work, we
tackle these issues by proposing an image reconstruction algorithm for STI that
leverages data-driven priors. Our method, called DeepSTI, learns the data prior
implicitly via a deep neural network that approximates the proximal operator of
a regularizer function for STI. The dipole inversion problem is then solved
iteratively using the learned proximal network. Experimental results using both
simulation and in vivo human data demonstrate great improvement over
state-of-the-art algorithms in terms of the reconstructed tensor image,
principal eigenvector maps and tractography results, while allowing for tensor
reconstruction with MR phase measured at much less than six different
orientations. Notably, promising reconstruction results are achieved by our
method from only one orientation in human in vivo, and we demonstrate a
potential application of this technique for estimating lesion susceptibility
anisotropy in patients with multiple sclerosis.

Machine learning methods must be trusted to make appropriate decisions in
real-world environments, even when faced with out-of-distribution (OOD)
samples. Many current approaches simply aim to detect OOD examples and alert
the user when an unrecognized input is given. However, when the OOD sample
significantly overlaps with the training data, a binary anomaly detection is
not interpretable or explainable, and provides little information to the user.
We propose a new model for OOD detection that makes predictions at varying
levels of granularity as the inputs become more ambiguous, the model
predictions become coarser and more conservative. Consider an animal classifier
that encounters an unknown bird species and a car. Both cases are OOD, but the
user gains more information if the classifier recognizes that its uncertainty
over the particular species is too large and predicts bird instead of detecting
it as OOD. Furthermore, we diagnose the classifiers performance at each level
of the hierarchy improving the explainability and interpretability of the
models predictions. We demonstrate the effectiveness of hierarchical
classifiers for both fine- and coarse-grained OOD tasks.

Modern health care systems are conducting continuous, automated surveillance
of the electronic medical record (EMR) to identify adverse events with
increasing frequency; however, many events such as sepsis do not have clearly
elucidated prodromes (i.e., event chains) that can be used to identify and
intercept the adverse event early in its course. Currently there does not exist
a reliable framework for discovering or describing causal chains that precede
adverse hospital events. Clinically relevant and interpretable results require
a framework that can (1) infer temporal interactions across multiple patient
features found in EMR data (e.g., labs, vital signs, etc.) and (2) can identify
pattern(s) which precede and are specific to an impending adverse event (e.g.,
sepsis). In this work, we propose a linear multivariate Hawkes process model,
coupled with $g(x) = x^+$ link function to allow potential inhibition effects,
in order to recover a Granger Causal (GC) graph. We develop a two-phase
gradient-based scheme to maximize a surrogate of likelihood to estimate the
problem parameters. This two-phase algorithm is scalable and shown to be
effective via our numerical simulation. It is subsequently extended to a data
set of patients admitted to Grady hospital system in Atalanta, GA, where the
fitted Granger Causal graph identifies several highly interpretable chains that
precede sepsis.

We consider some aspects of mixed $\mathcal{H}_2/\mathcal{H}_\infty$ control
in a policy optimization setting. We study the convergence and robustness
properties of our proposed policy scheme for autonomous systems described by
stochastic differential equations with non-trivial additive Brownian motion as
a disturbance. We then propose efficiently \textit{learning} \textit{robustly
stabilizing optimal control policies} for such systems when the dynamics is
unknown. We evaluate our proposed schemes on two- and three-link kinematic
chains. Our evaluations demonstrate robust steady-state convergence to
equilibrium under worst-case disturbance and Brownian motion alike. This policy
optimization scheme is well-suited to reinforcement learning, and
learning-enabled control systems where modeling errors, unknown dynamics,
parametric and non-parametric uncertainties typically hamper system operations.

An accurate model of natural speech directivity is an important step toward
achieving realistic vocal presence within a virtual communication setting. In
this article, we propose a method to estimate and reconstruct the spatial
energy distribution pattern of natural, unconstrained speech. We detail our
method in two stages. Using recordings of speech captured by a real, static
microphone array, we create a virtual array that tracks with the movement of
the speaker over time. We use this egocentric virtual array to measure and
encode the high-resolution directivity pattern of the speech signal as it
dynamically evolves with natural speech and movement. Utilizing this encoded
directivity representation, we train a machine learning model that leverages to
estimate the full, dynamic directivity pattern when given a limited set of
speech signals, as would be the case when speech is recorded using the
microphones on a head-mounted display (HMD). We examine a variety of model
architectures and training paradigms, and discuss the utility and practicality
of each implementation. Our results demonstrate that neural networks can be
used to regress from limited speech information to an accurate, dynamic
estimation of the full directivity pattern.

The two-dimensional materials are an exciting platform for various types of
studies because of their tunability and versatility. However, creating devices
from such materials is cumbersome. One of the burden is searching for images of
candidate crystals from exfoliated bunch that vary in shapes and sizes. We
propose the methods to accurately search for desired crystals by combining
reinforcement learning and object detection. Our method utilizes the unique
image settings of exfoliated crystals to reduce the computation power. The
proposed method showed enhancement in performance compared to the baseline.
Finally, we quantify the effectiveness of the reinforcement learning agent in
searching. Codes are available at
\url{https://github.com/stephandkim/detect_crystals

The SKA pulsar search pipeline will be used for real time detection of
pulsars. Modern radio telescopes such as SKA will be generating petabytes of
data in their full scale of operation. Hence experience-based and data-driven
algorithms become indispensable for applications such as candidate detection.
Here we describe our findings from testing a state of the art object detection
algorithm called Mask R-CNN to detect candidate signatures in the SKA pulsar
search pipeline. We have trained the Mask R-CNN model to detect candidate
images. A custom annotation tool was developed to mark the regions of interest
in large datasets efficiently. We have successfully demonstrated this algorithm
by detecting candidate signatures on a simulation dataset. The paper presents
details of this work with a highlight on the future prospects.

We investigate the problem of private read update write (PRUW) in relation to
private federated submodel learning (FSL), where a machine learning model is
divided into multiple submodels based on the different types of data used to
train the model. In PRUW, each user downloads the required submodel without
revealing its index in the reading phase, and uploads the updates of the
submodel without revealing the submodel index or the values of the updates in
the writing phase. In this work, we first provide a basic communication
efficient PRUW scheme, and study further means of reducing the communication
cost via sparsification. Gradient sparsification is a widely used concept in
learning applications, where only a selected set of parameters is downloaded
and updated, which significantly reduces the communication cost. In this paper,
we study how the concept of sparsification can be incorporated in private FSL
with the goal of reducing the communication cost, while guaranteeing
information theoretic privacy of the updated submodel index as well as the
values of the updates. To this end, we introduce two schemes: PRUW with top $r$
sparsification and PRUW with random sparsification. The former communicates
only the most significant parameters/updates among the servers and the users,
while the latter communicates a randomly selected set of parameters/updates.
The two proposed schemes introduce novel techniques such as parameter/update
(noisy) permutations to handle the additional sources of information leakage in
PRUW caused by sparsification. Both schemes result in significantly reduced
communication costs compared to that of the basic (non-sparse) PRUW scheme.

We study the problem of sequential prediction and online minimax regret with
stochastically generated features under a general loss function. We introduce a
notion of expected worst case minimax regret that generalizes and encompasses
prior known minimax regrets. For such minimax regrets we establish tight upper
bounds via a novel concept of stochastic global sequential covering. We show
that for a hypothesis class of VC-dimension $\mathsf{VC}$ and $i.i.d.$
generated features of length $T$, the cardinality of the stochastic global
sequential covering can be upper bounded with high probability (whp) by
$e^{O(\mathsf{VC} \cdot \log^2 T)}$. We then improve this bound by introducing
a new complexity measure called the Star-Littlestone dimension, and show that
classes with Star-Littlestone dimension $\mathsf{SL}$ admit a stochastic global
sequential covering of order $e^{O(\mathsf{SL} \cdot \log T)}$. We further
establish upper bounds for real valued classes with finite fat-shattering
numbers. Finally, by applying information-theoretic tools of the fixed design
minimax regrets, we provide lower bounds for the expected worst case minimax
regret. We demonstrate the effectiveness of our approach by establishing tight
bounds on the expected worst case minimax regrets for logarithmic loss and
general mixable losses.

Point cloud compression (PCC) is a key enabler for various 3-D applications,
owing to the universality of the point cloud format. Ideally, 3D point clouds
endeavor to depict object/scene surfaces that are continuous. Practically, as a
set of discrete samples, point clouds are locally disconnected and sparsely
distributed. This sparse nature is hindering the discovery of local correlation
among points for compression. Motivated by an analysis with fractal dimension,
we propose a heterogeneous approach with deep learning for lossy point cloud
geometry compression. On top of a base layer compressing a coarse
representation of the input, an enhancement layer is designed to cope with the
challenging geometric residual/details. Specifically, a point-based network is
applied to convert the erratic local details to latent features residing on the
coarse point cloud. Then a sparse convolutional neural network operating on the
coarse point cloud is launched. It utilizes the continuity/smoothness of the
coarse geometry to compress the latent features as an enhancement bit-stream
that greatly benefits the reconstruction quality. When this bit-stream is
unavailable, e.g., due to packet loss, we support a skip mode with the same
architecture which generates geometric details from the coarse point cloud
directly. Experimentation on both dense and sparse point clouds demonstrate the
state-of-the-art compression performance achieved by our proposal. Our code is
available at https://github.com/InterDigitalInc/GRASP-Net.

To address computational challenges associated with power flow
nonconvexities, significant research efforts over the last decade have
developed convex relaxations and approximations of optimal power flow (OPF)
problems. However, benefits associated with the convexity of these relaxations
and approximations can have tradeoffs in terms of solution accuracy since they
may yield voltage phasors that are inconsistent with the power injections and
line flows, limiting their usefulness for some applications. Inspired by state
estimation (SE) techniques, this paper proposes a new method for obtaining an
AC power flow feasible point from the solution to a relaxed or approximated
optimal power flow (OPF) problem. By treating the inconsistent voltage phasors,
power injections, and line flows analogously to noisy measurements in a state
estimation algorithm, the proposed method yields power injections and voltage
phasors that are feasible with respect to the AC power flow equations while
incorporating information from many quantities in the solution to a relaxed or
approximated OPF problem. We improve this method by adjusting weighting terms
with a machine learning based approach. We demonstrate the proposed method
using several relaxations and approximations. The results show up to several
orders of magnitude improvement in accuracy over traditional methods.

In contrast to traditional exhaustive search, selective search first clusters
documents into several groups before all the documents are searched
exhaustively by a query, to limit the search executed within one group or only
a few groups. Selective search is designed to reduce the latency and
computation in modern large-scale search systems. In this study, we propose
MICO, a Mutual Information CO-training framework for selective search with
minimal supervision using the search logs. After training, MICO does not only
cluster the documents, but also routes unseen queries to the relevant clusters
for efficient retrieval. In our empirical experiments, MICO significantly
improves the performance on multiple metrics of selective search and
outperforms a number of existing competitive baselines.

Despite the success of neural networks in computer vision tasks, digital
'neurons' are a very loose approximation of biological neurons. Today's
learning approaches are designed to function on digital devices with digital
data representations such as image frames. In contrast, biological vision
systems are generally much more capable and efficient than state-of-the-art
digital computer vision algorithms. Event cameras are an emerging sensor
technology which imitates biological vision with asynchronously firing pixels,
eschewing the concept of the image frame. To leverage modern learning
techniques, many event-based algorithms are forced to accumulate events back to
image frames, somewhat squandering the advantages of event cameras.
  We follow the opposite paradigm and develop a new type of neural network
which operates closer to the original event data stream. We demonstrate
state-of-the-art performance in angular velocity regression and competitive
optical flow estimation, while avoiding difficulties related to training SNN.
Furthermore, the processing latency of our proposed approached is less than
1/10 any other implementation, while continuous inference increases this
improvement by another order of magnitude.

The algorithms employed by our communities are often underspecified, and thus
have multiple implementation choices, which do not effect the correctness of
the output, but do impact the efficiency or even tractability of its
production. In this extended abstract, to accompany a keynote talk at the 2021
SC-Square Workshop, we survey recent work (both the author's and from the
literature) on the use of Machine Learning technology to improve algorithms of
interest to SC-Square.

Cough audio signal classification is a potentially useful tool in screening
for respiratory disorders, such as COVID-19. Since it is dangerous to collect
data from patients with such contagious diseases, many research teams have
turned to crowdsourcing to quickly gather cough sound data, as it was done to
generate the COUGHVID dataset. The COUGHVID dataset enlisted expert physicians
to diagnose the underlying diseases present in a limited number of uploaded
recordings. However, this approach suffers from potential mislabeling of the
coughs, as well as notable disagreement between experts. In this work, we use a
semi-supervised learning (SSL) approach to improve the labeling consistency of
the COUGHVID dataset and the robustness of COVID-19 versus healthy cough sound
classification. First, we leverage existing SSL expert knowledge aggregation
techniques to overcome the labeling inconsistencies and sparsity in the
dataset. Next, our SSL approach is used to identify a subsample of re-labeled
COUGHVID audio samples that can be used to train or augment future cough
classification models. The consistency of the re-labeled data is demonstrated
in that it exhibits a high degree of class separability, 3x higher than that of
the user-labeled data, despite the expert label inconsistency present in the
original dataset. Furthermore, the spectral differences in the user-labeled
audio segments are amplified in the re-labeled data, resulting in significantly
different power spectral densities between healthy and COVID-19 coughs, which
demonstrates both the increased consistency of the new dataset and its
explainability from an acoustic perspective. Finally, we demonstrate how the
re-labeled dataset can be used to train a cough classifier. This SSL approach
can be used to combine the medical knowledge of several experts to improve the
database consistency for any diagnostic classification task.

In this paper, we consider a risk-averse multi-armed bandit (MAB) problem
where the goal is to learn a policy that minimizes the risk of low expected
return, as opposed to maximizing the expected return itself, which is the
objective in the usual approach to risk-neutral MAB. Specifically, we formulate
this problem as a transfer learning problem between an expert and a learner
agent in the presence of contexts that are only observable by the expert but
not by the learner. Thus, such contexts are unobserved confounders (UCs) from
the learner's perspective. Given a dataset generated by the expert that
excludes the UCs, the goal for the learner is to identify the true minimum-risk
arm with fewer online learning steps, while avoiding possible biased decisions
due to the presence of UCs in the expert's data.

The transformation of power grids into intelligent cyber-physical systems
brings numerous benefits, but also significantly increases the surface for
cyber-attacks, demanding appropriate countermeasures. However, the development,
validation, and testing of data-driven countermeasures against cyber-attacks,
such as machine learning-based detection approaches, lack important data from
real-world cyber incidents. Unlike attack data from real-world cyber incidents,
infrastructure knowledge and standards are accessible through expert and domain
knowledge. Our proposed approach uses domain knowledge to define the behavior
of a smart grid under non-attack conditions and detect attack patterns and
anomalies. Using a graph-based specification formalism, we combine cross-domain
knowledge that enables the generation of whitelisting rules not only for
statically defined protocol fields but also for communication ows and technical
operation boundaries. Finally, we evaluate our specification-based intrusion
detection system against various attack scenarios and assess detection quality
and performance. In particular, we investigate a data manipulation attack in a
future-orientated use case of an IEC 60870-based SCADA system that controls
distributed energy resources in the distribution grid. Our approach can detect
severe data manipulation attacks with high accuracy in a timely and reliable
manner.

Language model (LM) pretraining has led to consistent improvements in many
NLP downstream tasks, including named entity recognition (NER). In this paper,
we present T-NER (Transformer-based Named Entity Recognition), a Python library
for NER LM finetuning. In addition to its practical utility, T-NER facilitates
the study and investigation of the cross-domain and cross-lingual
generalization ability of LMs finetuned on NER. Our library also provides a web
app where users can get model predictions interactively for arbitrary text,
which facilitates qualitative model evaluation for non-expert programmers. We
show the potential of the library by compiling nine public NER datasets into a
unified format and evaluating the cross-domain and cross-lingual performance
across the datasets. The results from our initial experiments show that
in-domain performance is generally competitive across datasets. However,
cross-domain generalization is challenging even with a large pretrained LM,
which has nevertheless capacity to learn domain-specific features if fine-tuned
on a combined dataset. To facilitate future research, we also release all our
LM checkpoints via the Hugging Face model hub.

The performance of any Machine Learning (ML) algorithm is impacted by the
choice of its hyperparameters. As training and evaluating a ML algorithm is
usually expensive, the hyperparameter optimization (HPO) method needs to be
computationally efficient to be useful in practice. Most of the existing
approaches on multi-objective HPO use evolutionary strategies and
metamodel-based optimization. However, few methods have been developed to
account for uncertainty in the performance measurements. This paper presents
results on multi-objective hyperparameter optimization with uncertainty on the
evaluation of ML algorithms. We combine the sampling strategy of
Tree-structured Parzen Estimators (TPE) with the metamodel obtained after
training a Gaussian Process Regression (GPR) with heterogeneous noise.
Experimental results on three analytical test functions and three ML problems
show the improvement over multi-objective TPE and GPR, achieved with respect to
the hypervolume indicator.

Machine learning with formal privacy-preserving techniques like Differential
Privacy (DP) allows one to derive valuable insights from sensitive medical
imaging data while promising to protect patient privacy, but it usually comes
at a sharp privacy-utility trade-off. In this work, we propose to use steerable
equivariant convolutional networks for medical image analysis with DP. Their
improved feature quality and parameter efficiency yield remarkable accuracy
gains, narrowing the privacy-utility gap.

Advanced reactors to be deployed in the coming decades will face deregulated
energy markets, and may adopt flexible operation to boost profitability. To aid
in the transition from baseload to flexible operation paradigm, autonomous
operation is sought. This work focuses on the control aspect of autonomous
operation. Specifically, a hierarchical control system is designed to support
constraint enforcement during routine operational transients. Within the
system, data-driven modeling, physics-based state observation, and classical
control algorithms are integrated to provide an adaptable and robust solution.
A 320 MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design
basis for demonstrating the control system.
  The hierarchical control system consists of a supervisory layer and low-level
layer. The supervisory layer receives requests to change the system's operating
conditions, and accepts or rejects them based on constraints that have been
assigned. Constraints are issued to keep the plant within an optimal operating
region. The low-level layer interfaces with the actuators of the system to
fulfill requested changes, while maintaining tracking and regulation duties. To
accept requests at the supervisory layer, the Reference Governor algorithm was
adopted. To model the dynamics of the reactor, a system identification
algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution
of process variables that cannot be directly measured, the Unscented Kalman
Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The
composition of these algorithms led to a numerical demonstration of constraint
enforcement during a 40 % power drop transient. Adaptability was demonstrated
by modifying the constraint values, and enforcing them during the transient.
Robustness was demonstrated by enforcing constraints under noisy environments.

Previous unsupervised sentence embedding studies have focused on data
augmentation methods such as dropout masking and rule-based sentence
transformation methods. However, these approaches have a limitation of
controlling the fine-grained semantics of augmented views of a sentence. This
results in inadequate supervision signals for capturing a semantic similarity
of similar sentences. In this work, we found that using neighbor sentences
enables capturing a more accurate semantic similarity between similar
sentences. Based on this finding, we propose RankEncoder, which uses relations
between an input sentence and sentences in a corpus for training unsupervised
sentence encoders. We evaluate RankEncoder from three perspectives: 1) the
semantic textual similarity performance, 2) the efficacy on similar sentence
pairs, and 3) the universality of RankEncoder. Experimental results show that
RankEncoder achieves 80.07% Spearman's correlation, a 1.1% absolute improvement
compared to the previous state-of-the-art performance. The improvement is even
more significant, a 1.73% improvement, on similar sentence pairs. Also, we
demonstrate that RankEncoder is universally applicable to existing unsupervised
sentence encoders.

In this paper we propose a method for nonparametric estimation and inference
for heterogeneous bounds for causal effect parameters in general sample
selection models where the initial treatment can affect whether a
post-intervention outcome is observed or not. Treatment selection can be
confounded by observable covariates while the outcome selection can be
confounded by both observables and unobservables. The method provides
conditional effect bounds as functions of policy relevant pre-treatment
variables. It allows for conducting valid statistical inference on the
unidentified conditional effect curves. We use a flexible semiparametric
de-biased machine learning approach that can accommodate flexible functional
forms and high-dimensional confounding variables between treatment, selection,
and outcome processes. Easily verifiable high-level conditions for estimation
and misspecification robust inference guarantees are provided as well.

This work tackles a central machine learning problem of performance
degradation on out-of-distribution (OOD) test sets. The problem is particularly
salient in medical imaging based diagnosis system that appears to be accurate
but fails when tested in new hospitals/datasets. Recent studies indicate the
system might learn shortcut and non-relevant features instead of generalizable
features, so-called good features. We hypothesize that adversarial training can
eliminate shortcut features whereas saliency guided training can filter out
non-relevant features; both are nuisance features accounting for the
performance degradation on OOD test sets. With that, we formulate a novel model
training scheme for the deep neural network to learn good features for
classification and/or detection tasks ensuring a consistent generalization
performance on OOD test sets. The experimental results qualitatively and
quantitatively demonstrate the superior performance of our method using the
benchmark CXR image data sets on classification tasks.

Many robotic tasks involving some form of 3D visual perception greatly
benefit from a complete knowledge of the working environment. However, robots
often have to tackle unstructured environments and their onboard visual sensors
can only provide incomplete information due to limited workspaces, clutter or
object self-occlusion. In recent years, deep learning architectures for shape
completion have begun taking traction as effective means of inferring a
complete 3D object representation from partial visual data. Nevertheless, most
of the existing state-of-the-art approaches provide a fixed output resolution
in the form of voxel grids, strictly related to the size of the neural network
output stage. While this is enough for some tasks, e.g. obstacle avoidance in
navigation, grasping and manipulation require finer resolutions and simply
scaling up the neural network outputs is computationally expensive. In this
paper, we address this limitation by proposing an object shape completion
method based on an implicit 3D representation providing a confidence value for
each reconstructed point. As a second contribution, we propose a gradient-based
method for efficiently sampling such implicit function at an arbitrary
resolution, tunable at inference time. We experimentally validate our approach
by comparing reconstructed shapes with ground truths, and by deploying our
shape completion algorithm in a robotic grasping pipeline. In both cases, we
compare results with a state-of-the-art shape completion approach.

Reliable methods for automatic readability assessment have the potential to
impact a variety of fields, ranging from machine translation to self-informed
learning. Recently, large language models for the German language (such as
GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep
Learning based approaches that promise to further improve automatic readability
assessment. In this contribution, we studied the ability of ensembles of
fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability
of German sentences. We combined these models with linguistic features and
investigated the dependence of prediction performance on ensemble size and
composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than
ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models.
Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity
Assessment on data of German sentences. On out-of-sample data, our best
ensemble achieved a root mean squared error of 0.435.

Learning new tasks and skills in succession without losing prior learning
(i.e., catastrophic forgetting) is a computational challenge for both
artificial and biological neural networks, yet artificial systems struggle to
achieve parity with their biological analogues. Mammalian brains employ
numerous neural operations in support of continual learning during sleep. These
are ripe for artificial adaptation. Here, we investigate how modeling three
distinct components of mammalian sleep together affects continual learning in
artificial neural networks: (1) a veridical memory replay process observed
during non-rapid eye movement (NREM) sleep; (2) a generative memory replay
process linked to REM sleep; and (3) a synaptic downscaling process which has
been proposed to tune signal-to-noise ratios and support neural upkeep. We find
benefits from the inclusion of all three sleep components when evaluating
performance on a continual learning CIFAR-100 image classification benchmark.
Maximum accuracy improved during training and catastrophic forgetting was
reduced during later tasks. While some catastrophic forgetting persisted over
the course of network training, higher levels of synaptic downscaling lead to
better retention of early tasks and further facilitated the recovery of early
task accuracy during subsequent training. One key takeaway is that there is a
trade-off at hand when considering the level of synaptic downscaling to use -
more aggressive downscaling better protects early tasks, but less downscaling
enhances the ability to learn new tasks. Intermediate levels can strike a
balance with the highest overall accuracies during training. Overall, our
results both provide insight into how to adapt sleep components to enhance
artificial continual learning systems and highlight areas for future
neuroscientific sleep research to further such systems.

The use of neural networks in safety-critical systems requires safe and
robust models, due to the existence of adversarial attacks. Knowing the minimal
adversarial perturbation of any input x, or, equivalently, knowing the distance
of x from the classification boundary, allows evaluating the classification
robustness, providing certifiable predictions. Unfortunately, state-of-the-art
techniques for computing such a distance are computationally expensive and
hence not suited for online applications. This work proposes a novel family of
classifiers, namely Signed Distance Classifiers (SDCs), that, from a
theoretical perspective, directly output the exact distance of x from the
classification boundary, rather than a probability score (e.g., SoftMax). SDCs
represent a family of robust-by-design classifiers. To practically address the
theoretical requirements of a SDC, a novel network architecture named
Unitary-Gradient Neural Network is presented. Experimental results show that
the proposed architecture approximates a signed distance classifier, hence
allowing an online certifiable classification of x at the cost of a single

We demonstrate a spin-wave-based time-multiplexed Ising Machine (SWIM),
implemented using a 5 $\mu$m thick Yttrium Iron Garnet (YIG) film and
off-the-shelf microwave components. The artificial Ising spins consist of
34--68 ns long 3.125 GHz spinwave RF pulses with their phase binarized using a
phase-sensitive microwave amplifier. Thanks to the very low spinwave group
velocity, the 7 mm long YIG waveguide can host an 8-spin MAX-CUT problem and
solve it in less than 4 $\mu$s while consuming only 7 $\mu$J. Using a real-time
oscilloscope, we follow the temporal evolution of each spin as the SWIM
minimizes its energy and find both uniform and domain-propagation-like
switching of the spin state. The SWIM has the potential for substantial further
miniaturization, scalability, speed, and reduced power consumption, and may
become a versatile platform for commercially feasible optimization problem
solvers with high performance.

Action recognition is a fundamental capability for humanoid robots to
interact and cooperate with humans. This application requires the action
recognition system to be designed so that new actions can be easily added,
while unknown actions are identified and ignored. In recent years,
deep-learning approaches represented the principal solution to the Action
Recognition problem. However, most models often require a large dataset of
manually-labeled samples. In this work we target One-Shot deep-learning models,
because they can deal with just a single instance for class. Unfortunately,
One-Shot models assume that, at inference time, the action to recognize falls
into the support set and they fail when the action lies outside the support
set. Few-Shot Open-Set Recognition (FSOSR) solutions attempt to address that
flaw, but current solutions consider only static images and not sequences of
images. Static images remain insufficient to discriminate actions such as
sitting-down and standing-up. In this paper we propose a novel model that
addresses the FSOSR problem with a One-Shot model that is augmented with a
discriminator that rejects unknown actions. This model is useful for
applications in humanoid robotics, because it allows to easily add new classes
and determine whether an input sequence is among the ones that are known to the
system. We show how to train the whole model in an end-to-end fashion and we
perform quantitative and qualitative analyses. Finally, we provide real-world
examples.

Autonomous navigation in agricultural environments is often challenged by
varying field conditions that may arise in arable fields. The state-of-the-art
solutions for autonomous navigation in these agricultural environments will
require expensive hardware such as RTK-GPS. This paper presents a robust crop
row detection algorithm that can withstand those variations while detecting
crop rows for visual servoing. A dataset of sugar beet images was created with
43 combinations of 11 field variations found in arable fields. The novel crop
row detection algorithm is tested both for the crop row detection performance
and also the capability of visual servoing along a crop row. The algorithm only
uses RGB images as input and a convolutional neural network was used to predict
crop row masks. Our algorithm outperformed the baseline method which uses
colour-based segmentation for all the combinations of field variations. We use
a combined performance indicator that accounts for the angular and displacement
errors of the crop row detection. Our algorithm exhibited the worst performance
during the early growth stages of the crop.

Multiple Sclerosis (MS) is a chronic progressive neurological disease
characterized by the development of lesions in the white matter of the brain.
T2-fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance imaging
(MRI) provides superior visualization and characterization of MS lesions,
relative to other MRI modalities. Longitudinal brain FLAIR MRI in MS, involving
repetitively imaging a patient over time, provides helpful information for
clinicians towards monitoring disease progression. Predicting future whole
brain MRI examinations with variable time lag has only been attempted in
limited applications, such as healthy aging and structural degeneration in
Alzheimer's Disease. In this article, we present novel modifications to deep
learning architectures for MS FLAIR image synthesis, in order to support
prediction of longitudinal images in a flexible continuous way. This is
achieved with learned transposed convolutions, which support modelling time as
a spatially distributed array with variable temporal properties at different
spatial locations. Thus, this approach can theoretically model
spatially-specific time-dependent brain development, supporting the modelling
of more rapid growth at appropriate physical locations, such as the site of an
MS brain lesion. This approach also supports the clinician user to define how
far into the future a predicted examination should target. Accurate prediction
of future rounds of imaging can inform clinicians of potentially poor patient
outcomes, which may be able to contribute to earlier treatment and better
prognoses. Four distinct deep learning architectures have been developed. The
ISBI2015 longitudinal MS dataset was used to validate and compare our proposed
approaches. Results demonstrate that a modified ACGAN achieves the best
performance and reduces variability in model accuracy.

Adaptive training methods for physical-informed neural network (PINN) require
dedicated constructions of the distribution of weights assigned at each
training sample. To efficiently seek such an optimal weight distribution is not
a simple task and most existing methods choose the adaptive weights based on
approximating the full distribution or the maximum of residuals. In this paper,
we show that the bottleneck in the adaptive choice of samples for training
efficiency is the behavior of the tail distribution of the numerical residual.
Thus, we propose the Residual-Quantile Adjustment (RQA) method for a better
weight choice for each training sample. After initially setting the weights
proportional to the $p$-th power of the residual, our RQA method reassign all
weights above $q$-quantile ($90\%$ for example) to the median value, so that
the weight follows a quantile-adjusted distribution derived from the residuals.
With the iterative reweighting technique, RQA is also very easy to implement.
Experiment results show that the proposed method can outperform several
adaptive methods on various partial differential equation (PDE) problems.

Mobile parcel lockers (MPLs) have been recently proposed by logistics
operators as a technology that could help reduce traffic congestion and
operational costs in urban freight distribution. Given their ability to
relocate throughout their area of deployment, they hold the potential to
improve customer accessibility and convenience. In this study, we formulate the
Mobile Parcel Locker Problem (MPLP), a special case of the Location-Routing
Problem (LRP) which determines the optimal stopover location for MPLs
throughout the day and plans corresponding delivery routes. A Hybrid
Q-Learning-Network-based Method (HQM) is developed to resolve the computational
complexity of the resulting large problem instances while escaping local
optima. In addition, the HQM is integrated with global and local search
mechanisms to resolve the dilemma of exploration and exploitation faced by
classic reinforcement learning (RL) methods. We examine the performance of HQM
under different problem sizes (up to 200 nodes) and benchmarked it against the
Genetic Algorithm (GA). Our results indicate that the average reward obtained
by HQM is 1.96 times greater than GA, which demonstrates that HQM has a better
optimisation ability. Finally, we identify critical factors that contribute to
fleet size requirements, travel distances, and service delays. Our findings
outline that the efficiency of MPLs is mainly contingent on the length of time
windows and the deployment of MPL stopovers.

Deep Learning has received increased attention due to its unbeatable success
in many fields, such as computer vision, natural language processing,
recommendation systems, and most recently in simulating multiphysics problems
and predicting nonlinear dynamical systems. However, modeling and forecasting
the dynamics of chaotic systems remains an open research problem since training
deep learning models requires big data, which is not always available in many
cases. Such deep learners can be trained from additional information obtained
from simulated results and by enforcing the physical laws of the chaotic
systems. This paper considers extreme events and their dynamics and proposes
elegant models based on deep neural networks, called knowledge-based deep
learning (KDL). Our proposed KDL can learn the complex patterns governing
chaotic systems by jointly training on real and simulated data directly from
the dynamics and their differential equations. This knowledge is transferred to
model and forecast real-world chaotic events exhibiting extreme behavior. We
validate the efficiency of our model by assessing it on three real-world
benchmark datasets: El Nino sea surface temperature, San Juan Dengue viral
infection, and Bj{\o}rn{\o}ya daily precipitation, all governed by extreme
events' dynamics. Using prior knowledge of extreme events and physics-based
loss functions to lead the neural network learning, we ensure physically
consistent, generalizable, and accurate forecasting, even in a small data
regime.

The use of algorithm-agnostic approaches is an emerging area of research for
explaining the contribution of individual features towards the predicted
outcome. Whilst there is a focus on explaining the prediction itself, a little
has been done on explaining the robustness of these models, that is, how each
feature contributes towards achieving that robustness. In this paper, we
propose the use of Shapley values to explain the contribution of each feature
towards the model's robustness, measured in terms of Receiver-operating
Characteristics (ROC) curve and the Area under the ROC curve (AUC). With the
help of an illustrative example, we demonstrate the proposed idea of explaining
the ROC curve, and visualising the uncertainties in these curves. For
imbalanced datasets, the use of Precision-Recall Curve (PRC) is considered more
appropriate, therefore we also demonstrate how to explain the PRCs with the
help of Shapley values. The explanation of robustness can help analysts in a
number of ways, for example, it can help in feature selection by identifying
the irrelevant features that can be removed to reduce the computational
complexity. It can also help in identifying the features having critical
contributions or negative contributions towards robustness.

Learning spatiotemporal features is an important task for efficient video
understanding especially in medical images such as echocardiograms.
Convolutional neural networks (CNNs) and more recent vision transformers (ViTs)
are the most commonly used methods with limitations per each. CNNs are good at
capturing local context but fail to learn global information across video
frames. On the other hand, vision transformers can incorporate global details
and long sequences but are computationally expensive and typically require more
data to train. In this paper, we propose a method that addresses the
limitations we typically face when training on medical video data such as
echocardiographic scans. The algorithm we propose (EchoCoTr) utilizes the
strength of vision transformers and CNNs to tackle the problem of estimating
the left ventricular ejection fraction (LVEF) on ultrasound videos. We
demonstrate how the proposed method outperforms state-of-the-art work to-date
on the EchoNet-Dynamic dataset with MAE of 3.95 and $R^2$ of 0.82. These
results show noticeable improvement compared to all published research. In
addition, we show extensive ablations and comparisons with several algorithms,
including ViT and BERT. The code is available at
https://github.com/BioMedIA-MBZUAI/EchoCoTr.

Regression problems have been more and more embraced by deep learning (DL)
techniques. The increasing number of papers recently published in this domain,
including surveys and reviews, shows that deep regression has captured the
attention of the community due to efficiency and good accuracy in systems with
high-dimensional data. However, many DL methodologies have complex structures
that are not readily transparent to human users. Accessing the interpretability
of these models is an essential factor for addressing problems in sensitive
areas such as cyber-security systems, medical, financial surveillance, and
industrial processes. Fuzzy logic systems (FLS) are inherently interpretable
models, well known in the literature, capable of using nonlinear
representations for complex systems through linguistic terms with membership
degrees mimicking human thought. Within an atmosphere of explainable artificial
intelligence, it is necessary to consider a trade-off between accuracy and
interpretability for developing intelligent models. This paper aims to
investigate the state-of-the-art on existing methodologies that combine DL and
FLS, namely deep fuzzy systems, to address regression problems, configuring a
topic that is currently not sufficiently explored in the literature and thus
deserves a comprehensive survey.

Electronic Health Records (EHRs) aggregate diverse information at the patient
level, holding a trajectory representative of the evolution of the patient
health status throughout time. Although this information provides context and
can be leveraged by physicians to monitor patient health and make more accurate
prognoses/diagnoses, patient records can contain information from very long
time spans, which combined with the rapid generation rate of medical data makes
clinical decision making more complex. Patient trajectory modelling can assist
by exploring existing information in a scalable manner, and can contribute in
augmenting health care quality by fostering preventive medicine practices. We
propose a solution to model patient trajectories that combines different types
of information and considers the temporal aspect of clinical data. This
solution leverages two different architectures: one supporting flexible sets of
input features, to convert patient admissions into dense representations; and a
second exploring extracted admission representations in a recurrent-based
architecture, where patient trajectories are processed in sub-sequences using a
sliding window mechanism. The developed solution was evaluated on two different
clinical outcomes, unexpected patient readmission and disease progression,
using the publicly available MIMIC-III clinical database. The results obtained
demonstrate the potential of the first architecture to model readmission and
diagnoses prediction using single patient admissions. While information from
clinical text did not show the discriminative power observed in other existing
works, this may be explained by the need to fine-tune the clinicalBERT model.
Finally, we demonstrate the potential of the sequence-based architecture using
a sliding window mechanism to represent the input data, attaining comparable
performances to other existing solutions.

We propose a method for synthesizing cardiac MR images with plausible heart
shapes and realistic appearances for the purpose of generating labeled data for
deep-learning (DL) training. It breaks down the image synthesis into label
deformation and label-to-image translation tasks. The former is achieved via
latent space interpolation in a VAE model, while the latter is accomplished via
a conditional GAN model. We devise an approach for label manipulation in the
latent space of the trained VAE model, namely pathology synthesis, aiming to
synthesize a series of pseudo-pathological synthetic subjects with
characteristics of a desired heart disease. Furthermore, we propose to model
the relationship between 2D slices in the latent space of the VAE via
estimating the correlation coefficient matrix between the latent vectors and
utilizing it to correlate elements of randomly drawn samples before decoding to
image space. This simple yet effective approach results in generating 3D
consistent subjects from 2D slice-by-slice generations. Such an approach could
provide a solution to diversify and enrich the available database of cardiac MR
images and to pave the way for the development of generalizable DL-based image
analysis algorithms. The code will be available at
https://github.com/sinaamirrajab/CardiacPathologySynthesis.

graph neural networks (GNNs) are the dominant paradigm for modeling and
handling graph structure data by learning universal node representation. The
traditional way of training GNNs depends on a great many labeled data, which
results in high requirements on cost and time. In some special scene, it is
even unavailable and impracticable. Self-supervised representation learning,
which can generate labels by graph structure data itself, is a potential
approach to tackle this problem. And turning to research on self-supervised
learning problem for heterogeneous graphs is more challenging than dealing with
homogeneous graphs, also there are fewer studies about it. In this paper, we
propose a SElfsupervised learning method for heterogeneous graph via Structure
Information based on Metapath (SESIM). The proposed model can construct pretext
tasks by predicting jump number between nodes in each metapath to improve the
representation ability of primary task. In order to predict jump number, SESIM
uses data itself to generate labels, avoiding time-consuming manual labeling.
Moreover, predicting jump number in each metapath can effectively utilize graph
structure information, which is the essential property between nodes.
Therefore, SESIM deepens the understanding of models for graph structure. At
last, we train primary task and pretext tasks jointly, and use meta-learning to
balance the contribution of pretext tasks for primary task. Empirical results
validate the performance of SESIM method and demonstrate that this method can
improve the representation ability of traditional neural networks on link
prediction task and node classification task.

Bias in datasets can be very detrimental for appropriate statistical
estimation. In response to this problem, importance weighting methods have been
developed to match any biased distribution to its corresponding target unbiased
distribution. The seminal Kernel Mean Matching (KMM) method is, nowadays, still
considered as state of the art in this research field. However, one of the main
drawbacks of this method is the computational burden for large datasets.
Building on previous works by Huang et al. (2007) and de Mathelin et al.
(2021), we derive a novel importance weighting algorithm which scales to large
datasets by using a neural network to predict the instance weights. We show, on
multiple public datasets, under various sample biases, that our proposed
approach drastically reduces the computational time on large dataset while
maintaining similar sample bias correction performance compared to other
importance weighting methods. The proposed approach appears to be the only one
able to give relevant reweighting in a reasonable time for large dataset with
up to two million data.

This paper introduces an algorithm for the detection of change-points and the
identification of the corresponding subsequences in transient multivariate
time-series data (MTSD). The analysis of such data has become more and more
important due to the increase of availability in many industrial fields.
Labeling, sorting or filtering highly transient measurement data for training
condition based maintenance (CbM) models is cumbersome and error-prone. For
some applications it can be sufficient to filter measurements by simple
thresholds or finding change-points based on changes in mean value and
variation. But a robust diagnosis of a component within a component group for
example, which has a complex non-linear correlation between multiple sensor
values, a simple approach would not be feasible. No meaningful and coherent
measurement data which could be used for training a CbM model would emerge.
Therefore, we introduce an algorithm which uses a recurrent neural network
(RNN) based Autoencoder (AE) which is iteratively trained on incoming data. The
scoring function uses the reconstruction error and latent space information. A
model of the identified subsequence is saved and used for recognition of
repeating subsequences as well as fast offline clustering. For evaluation, we
propose a new similarity measure based on the curvature for a more intuitive
time-series subsequence clustering metric. A comparison with seven other
state-of-the-art algorithms and eight datasets shows the capability and the
increased performance of our algorithm to cluster MTSD online and offline in
conjunction with mechatronic systems.

Continual learning aims to learn a sequence of tasks by leveraging the
knowledge acquired in the past in an online-learning manner while being able to
perform well on all previous tasks, this ability is crucial to the artificial
intelligence (AI) system, hence continual learning is more suitable for most
real-word and complex applicative scenarios compared to the traditional
learning pattern. However, the current models usually learn a generic
representation base on the class label on each task and an effective strategy
is selected to avoid catastrophic forgetting. We postulate that selecting the
related and useful parts only from the knowledge obtained to perform each task
is more effective than utilizing the whole knowledge. Based on this fact, in
this paper we propose a new framework, named Selecting Related Knowledge for
Online Continual Learning (SRKOCL), which incorporates an additional efficient
channel attention mechanism to pick the particular related knowledge for every
task. Our model also combines experience replay and knowledge distillation to
circumvent the catastrophic forgetting. Finally, extensive experiments are
conducted on different benchmarks and the competitive experimental results
demonstrate that our proposed SRKOCL is a promised approach against the

Channel modeling has always been the core part in communication system design
and development, especially in 5G and 6G era. Traditional approaches like
stochastic channel modeling and ray-tracing (RT) based channel modeling depend
heavily on measurement data or simulation, which are usually expensive and time
consuming. In this paper, we propose a novel super resolution (SR) model for
generating channel characteristics data. The model is based on multi-task
learning (MTL) convolutional neural networks (CNN) with residual connection.
Experiments demonstrate that the proposed SR model could achieve excellent
performances in mean absolute error and standard deviation of error. Advantages
of the proposed model are demonstrated in comparisons with other
state-of-the-art deep learning models. Ablation study also proved the necessity
of multi-task learning and techniques in model design. The contribution in this
paper could be helpful in channel modeling, network optimization, positioning
and other wireless channel characteristics related work by largely reducing
workload of simulation or measurement.

In this paper, by introducing a low-noise condition, we study privacy and
utility (generalization) performances of differentially private stochastic
gradient descent (SGD) algorithms in a setting of stochastic convex
optimization (SCO) for both pointwise and pairwise learning problems. For
pointwise learning, we establish sharper excess risk bounds of order
$\mathcal{O}\Big( \frac{\sqrt{d\log(1/\delta)}}{n\epsilon} \Big)$ and
$\mathcal{O}\Big( {n^{-
\frac{1+\alpha}{2}}}+\frac{\sqrt{d\log(1/\delta)}}{n\epsilon}\Big)$ for the
$(\epsilon,\delta)$-differentially private SGD algorithm for strongly smooth
and $\alpha$-H\"older smooth losses, respectively, where $n$ is the sample size
and $d$ is the dimensionality. For pairwise learning, inspired by
\cite{lei2020sharper,lei2021generalization}, we propose a simple private SGD
algorithm based on gradient perturbation which satisfies
$(\epsilon,\delta)$-differential privacy, and develop novel utility bounds for
the proposed algorithm. In particular, we prove that our algorithm can achieve
excess risk rates
$\mathcal{O}\Big(\frac{1}{\sqrt{n}}+\frac{\sqrt{d\log(1/\delta)}}{n\epsilon}\Big)$
with gradient complexity $\mathcal{O}(n)$ and
$\mathcal{O}\big(n^{\frac{2-\alpha}{1+\alpha}}+n\big)$ for strongly smooth and
$\alpha$-H\"older smooth losses, respectively. Further, faster learning rates
are established in a low-noise setting for both smooth and non-smooth losses.
To the best of our knowledge, this is the first utility analysis which provides
excess population bounds better than
for privacy-preserving pairwise learning.

Although previous graph-based multi-view clustering algorithms have gained
significant progress, most of them are still faced with three limitations.
First, they often suffer from high computational complexity, which restricts
their applications in large-scale scenarios. Second, they usually perform graph
learning either at the single-view level or at the view-consensus level, but
often neglect the possibility of the joint learning of single-view and
consensus graphs. Third, many of them rely on the $k$-means for discretization
of the spectral embeddings, which lack the ability to directly learn the graph
with discrete cluster structure. In light of this, this paper presents an
efficient multi-view clustering approach via unified and discrete bipartite
graph learning (UDBGL). Specifically, the anchor-based subspace learning is
incorporated to learn the view-specific bipartite graphs from multiple views,
upon which the bipartite graph fusion is leveraged to learn a view-consensus
bipartite graph with adaptive weight learning. Further, the Laplacian rank
constraint is imposed to ensure that the fused bipartite graph has discrete
cluster structures (with a specific number of connected components). By
simultaneously formulating the view-specific bipartite graph learning, the
view-consensus bipartite graph learning, and the discrete cluster structure
learning into a unified objective function, an efficient minimization algorithm
is then designed to tackle this optimization problem and directly achieve a
discrete clustering solution without requiring additional partitioning, which
notably has linear time complexity in data size. Experiments on a variety of
multi-view datasets demonstrate the robustness and efficiency of our UDBGL
approach.

Federated learning (FL) is proving to be one of the most promising paradigms
for leveraging distributed resources, enabling a set of clients to
collaboratively train a machine learning model while keeping the data
decentralized. The explosive growth of interest in the topic has led to rapid
advancements in several core aspects like communication efficiency, handling
non-IID data, privacy, and security capabilities. However, the majority of FL
works only deal with supervised tasks, assuming that clients' training sets are
labeled. To leverage the enormous unlabeled data on distributed edge devices,
in this paper, we aim to extend the FL paradigm to unsupervised tasks by
addressing the problem of anomaly detection in decentralized settings. In
particular, we propose a novel method in which, through a preprocessing phase,
clients are grouped into communities, each having similar majority (i.e.,
inlier) patterns. Subsequently, each community of clients trains the same
anomaly detection model (i.e., autoencoders) in a federated fashion. The
resulting model is then shared and used to detect anomalies within the clients
of the same community that joined the corresponding federated process.
Experiments show that our method is robust, and it can detect communities
consistent with the ideal partitioning in which groups of clients having the
same inlier patterns are known. Furthermore, the performance is significantly
better than those in which clients train models exclusively on local data and
comparable with federated models of ideal communities' partition.

3D-aware generative models have demonstrated their superb performance to
generate 3D neural radiance fields (NeRF) from a collection of monocular 2D
images even for topology-varying object categories. However, these methods
still lack the capability to separately control the shape and appearance of the
objects in the generated radiance fields. In this paper, we propose a
generative model for synthesizing radiance fields of topology-varying objects
with disentangled shape and appearance variations. Our method generates
deformable radiance fields, which builds the dense correspondence between the
density fields of the objects and encodes their appearances in a shared
template field. Our disentanglement is achieved in an unsupervised manner
without introducing extra labels to previous 3D-aware GAN training. We also
develop an effective image inversion scheme for reconstructing the radiance
field of an object in a real monocular image and manipulating its shape and
appearance. Experiments show that our method can successfully learn the
generative model from unstructured monocular images and well disentangle the
shape and appearance for objects (e.g., chairs) with large topological
variance. The model trained on synthetic data can faithfully reconstruct the
real object in a given single image and achieve high-quality texture and shape
editing results.

In many machine learning applications, e.g., tree-based ensembles, floating
point numbers are extensively utilized due to their expressiveness. Nowadays
performing data analysis on embedded devices from dynamic data masses becomes
available, but such systems often lack hardware capabilities to process
floating point numbers, introducing large overheads for their processing. Even
if such hardware is present in general computing systems, using integer
operations instead of floating point operations promises to reduce operation
overheads and improve the performance.
  In this paper, we provide \mdname, a full precision floating point comparison
for random forests, by only using integer and logic operations. To ensure the
same functionality preserves, we formally prove the correctness of this
comparison. Since random forests only require comparison of floating point
numbers during inference, we implement \mdname~in low level realizations and
therefore eliminate the need for floating point hardware entirely, by keeping
the model accuracy unchanged. The usage of \mdname~basically boils down to a
one-by-one replacement of conditions: For instance, a comparison statement in
C: if(pX[3]<=(float)10.074347) becomes
if((*(((int*)(pX))+3))<=((int)(0x41213087))). Experimental evaluation on X86
and ARMv8 desktop and server class systems shows that the execution time can be
reduced by up to $\approx 30\%$ with our novel approach.

We give reconstruction algorithms for subclasses of depth-3 arithmetic
circuits. In particular, we obtain the first efficient algorithm for finding
tensor rank, and an optimal tensor decomposition as a sum of rank-one tensors,
when given black-box access to a tensor of super-constant rank. We obtain the
following results:
  1. A deterministic algorithm that reconstructs polynomials computed by
$\Sigma^{[k]}\bigwedge^{[d]}\Sigma$ circuits in time $\mathsf{poly}(n,d,c)
\cdot \mathsf{poly}(k)^{k^{k^{10}}}$
  2. A randomized algorithm that reconstructs polynomials computed by
multilinear $\Sigma^{k]}\prod^{[d]}\Sigma$ circuits in time
$\mathsf{poly}(n,d,c) \cdot k^{k^{k^{k^{O(k)}}}}$
  3. A randomized algorithm that reconstructs polynomials computed by
set-multilinear $\Sigma^{k]}\prod^{[d]}\Sigma$ circuits in time
$\mathsf{poly}(n,d,c) \cdot k^{k^{k^{k^{O(k)}}}}$, where $c=\log q$ if
$\mathbb{F}=\mathbb{F}_q$ is a finite field, and $c$ equals the maximum bit
complexity of any coefficient of $f$ if $\mathbb{F}$ is infinite.
  Prior to our work, polynomial time algorithms for the case when the rank,
$k$, is constant, were given by Bhargava, Saraf and Volkovich [BSV21].
  Another contribution of this work is correcting an error from a paper of
Karnin and Shpilka [KS09] that affected Theorem 1.6 of [BSV21]. Consequently,
the results of [KS09, BSV21] continue to hold, with a slightly worse setting of
parameters. For fixing the error we study the relation between syntactic and
semantic ranks of $\Sigma\Pi\Sigma$ circuits.
  We obtain our improvement by introducing a technique for learning rank
preserving coordinate-subspaces. [KS09] and [BSV21] tried all choices of
finding the "correct" coordinates, which led to having a fast growing function
of $k$ at the exponent of $n$. We find these spaces in time that is growing
fast with $k$, yet it is only a fixed polynomial in $n$.

Although recent advances in deep learning technology have boosted automatic
speech recognition (ASR) performance in the single-talker case, it remains
difficult to recognize multi-talker speech in which many voices overlap. One
conventional approach to tackle this problem is to use a cascade of a speech
separation or target speech extraction front-end with an ASR back-end. However,
the extra computation costs of the front-end module are a critical barrier to
quick response, especially for streaming ASR. In this paper, we propose a
target-speaker ASR (TS-ASR) system that implicitly integrates the target speech
extraction functionality within a streaming end-to-end (E2E) ASR system, i.e.
recurrent neural network-transducer (RNNT). Our system uses a similar idea as
adopted for target speech extraction, but implements it directly at the level
of the encoder of RNNT. This allows TS-ASR to be realized without placing extra
computation costs on the front-end. Note that this study presents two major
differences between prior studies on E2E TS-ASR; we investigate streaming
models and base our study on Conformer models, whereas prior studies used
RNN-based systems and considered only offline processing. We confirm in
experiments that our TS-ASR achieves comparable recognition performance with
conventional cascade systems in the offline setting, while reducing computation
costs and realizing streaming TS-ASR.

Most proposals in the anomaly detection field focus exclusively on the
detection stage, specially in the recent deep learning approaches. While
providing highly accurate predictions, these models often lack transparency,
acting as "black boxes". This criticism has grown to the point that explanation
is now considered very relevant in terms of acceptability and reliability. In
this paper, we addressed this issue by inspecting the ADMNC (Anomaly Detection
on Mixed Numerical and Categorical Spaces) model, an existing very accurate
although opaque anomaly detector capable to operate with both numerical and
categorical inputs. This work presents the extension EADMNC (Explainable
Anomaly Detection on Mixed Numerical and Categorical spaces), which adds
explainability to the predictions obtained with the original model. We
preserved the scalability of the original method thanks to the Apache Spark
framework. EADMNC leverages the formulation of the previous ADMNC model to
offer pre hoc and post hoc explainability, while maintaining the accuracy of
the original architecture. We present a pre hoc model that globally explains
the outputs by segmenting input data into homogeneous groups, described with
only a few variables. We designed a graphical representation based on
regression trees, which supervisors can inspect to understand the differences
between normal and anomalous data. Our post hoc explanations consist of a
text-based template method that locally provides textual arguments supporting
each detection. We report experimental results on extensive real-world data,
particularly in the domain of network intrusion detection. The usefulness of
the explanations is assessed by theory analysis using expert knowledge in the
network intrusion domain.

This study presents a methodology for anticounterfeiting of Non-Volatile
Memory (NVM) chips. In particular, we experimentally demonstrate a generalized
methodology for detecting (i) Integrated Circuit (IC) origin, (ii) recycled or
used NVM chips, and (iii) identification of used locations (addresses) in the
chip. Our proposed methodology inspects latency and variability signatures of
Commercial-Off-The-Shelf (COTS) NVM chips. The proposed technique requires
low-cycle (~100) pre-conditioning and utilizes Machine Learning (ML)
algorithms. We observe different trends in evolution of latency (sector erase
or page write) with cycling on different NVM technologies from different
vendors. ML assisted approach is utilized for detecting IC manufacturers with
95.1 % accuracy obtained on prepared test dataset consisting of 3 different NVM
technologies including 6 different manufacturers (9 types of chips).

Joint caching and transmission optimization problem is challenging due to the
deep coupling between decisions. This paper proposes an iterative distributed
multi-agent learning approach to jointly optimize caching and transmission. The
goal of this approach is to minimize the total transmission delay of all users.
In this iterative approach, each iteration includes caching optimization and
transmission optimization. A multi-agent reinforcement learning (MARL)-based
caching network is developed to cache popular tasks, such as answering which
files to evict from the cache and which files to storage. Based on the cached
files of the caching network, the transmission network transmits cached files
for users by single transmission (ST) or joint transmission (JT) with
multi-agent Bayesian learning automaton (MABLA) method. And then users access
the edge servers with the minimum transmission delay. The experimental results
demonstrate the performance of the proposed multi-agent learning approach.

A multi-label classifier estimates the binary label state (relevant vs
irrelevant) for each of a set of concept labels, for any given instance.
Probabilistic multi-label classifiers provide a predictive posterior
distribution over all possible labelset combinations of such label states (the
powerset of labels) from which we can provide the best estimate, simply by
selecting the labelset corresponding to the largest expected accuracy, over
that distribution. For example, in maximizing exact match accuracy, we provide
the mode of the distribution. But how does this relate to the confidence we may
have in such an estimate? Confidence is an important element of real-world
applications of multi-label classifiers (as in machine learning in general) and
is an important ingredient in explainability and interpretability. However, it
is not obvious how to provide confidence in the multi-label context and
relating to a particular accuracy metric, and nor is it clear how to provide a
confidence which correlates well with the expected accuracy, which would be
most valuable in real-world decision making. In this article we estimate the
expected accuracy as a surrogate for confidence, for a given accuracy metric.
We hypothesise that the expected accuracy can be estimated from the multi-label
predictive distribution. We examine seven candidate functions for their ability
to estimate expected accuracy from the predictive distribution. We found three
of these to correlate to expected accuracy and are robust. Further, we
determined that each candidate function can be used separately to estimate
Hamming similarity, but a combination of the candidates was best for expected
Jaccard index and exact match.

Edge training of Deep Neural Networks (DNNs) is a desirable goal for
continuous learning; however, it is hindered by the enormous computational
power required by training. Hardware approximate multipliers have shown their
effectiveness for gaining resource-efficiency in DNN inference accelerators;
however, training with approximate multipliers is largely unexplored. To build
resource efficient accelerators with approximate multipliers supporting DNN
training, a thorough evaluation of training convergence and accuracy for
different DNN architectures and different approximate multipliers is needed.
This paper presents ApproxTrain, an open-source framework that allows fast
evaluation of DNN training and inference using simulated approximate
multipliers. ApproxTrain is as user-friendly as TensorFlow (TF) and requires
only a high-level description of a DNN architecture along with C/C++ functional
models of the approximate multiplier. We improve the speed of the simulation at
the multiplier level by using a novel LUT-based approximate floating-point (FP)
multiplier simulator on GPU (AMSim). ApproxTrain leverages CUDA and efficiently
integrates AMSim into the TensorFlow library, in order to overcome the absence
of native hardware approximate multiplier in commercial GPUs. We use
ApproxTrain to evaluate the convergence and accuracy of DNN training with
approximate multipliers for small and large datasets (including ImageNet) using
LeNets and ResNets architectures. The evaluations demonstrate similar
convergence behavior and negligible change in test accuracy compared to FP32
and bfloat16 multipliers. Compared to CPU-based approximate multiplier
simulations in training and inference, the GPU-accelerated ApproxTrain is more
than 2500x faster. Based on highly optimized closed-source cuDNN/cuBLAS
libraries with native hardware multipliers, the original TensorFlow is only 8x
faster than ApproxTrain.

Slot filling and intent detection are two fundamental tasks in the field of
natural language understanding. Due to the strong correlation between these two
tasks, previous studies make efforts on modeling them with multi-task learning
or designing feature interaction modules to improve the performance of each
task. However, none of the existing approaches consider the relevance between
the structural information of sentences and the label semantics of two tasks.
The intent and semantic components of a utterance are dependent on the
syntactic elements of a sentence. In this paper, we investigate a multi-grained
label refinement network, which utilizes dependency structures and label
semantic embeddings. Considering to enhance syntactic representations, we
introduce the dependency structures of sentences into our model by graph
attention layer. To capture the semantic dependency between the syntactic
information and task labels, we combine the task specific features with
corresponding label embeddings by attention mechanism. The experimental results
demonstrate that our model achieves the competitive performance on two public
datasets.

Collaborative filtering problems are commonly solved based on matrix
completion techniques which recover the missing values of user-item interaction
matrices. In a matrix, the rating position specifically represents the user
given and the item rated. Previous matrix completion techniques tend to neglect
the position of each element (user, item and ratings) in the matrix but mainly
focus on semantic similarity between users and items to predict the missing
value in a matrix. This paper proposes a novel position-enhanced user/item
representation training model for recommendation, SUPER-Rec. We first capture
the rating position in the matrix using the relative positional rating encoding
and store the position-enhanced rating information and its user-item
relationship to the fixed dimension of embedding that is not affected by the
matrix size. Then, we apply the trained position-enhanced user and item
representations to the simplest traditional machine learning models to
highlight the pure novelty of our representation learning model. We contribute
the first formal introduction and quantitative analysis of position-enhanced
item representation in the recommendation domain and produce a principled
discussion about our SUPER-Rec to the outperformed performance of typical
collaborative filtering recommendation tasks with both explicit and implicit
feedback.

Oblivious Transfer (OT) is a major primitive for secure multiparty
computation. Indeed, combined with symmetric primitives along with garbled
circuits, it allows any secure function evaluation between two parties. In this
paper, we propose a new approach to build OT protocols. Interestingly, our new
paradigm features a security analysis in the Universal Composability (UC)
framework and may be instantiated from post-quantum primitives. In order to do
so, we define a new primitive named Smooth Projective Hash Function with Grey
Zone (SPHFwGZ) which can be seen as a relaxation of the classical Smooth
Projective Hash Functions, with a subset of the words for which one cannot
claim correctness nor smoothness: the grey zone. As a concrete application, we
provide two instantiations of SPHFwGZ respectively based on the Diffie-Hellman
and the Learning With Errors (LWE) problems. Hence, we propose a
quantum-resistant OT protocol with UC-security in the random oracle model.

Human personality decides various aspects of their daily life and working
behaviors. Since personality traits are relatively stable over time and unique
for each subject, previous approaches frequently infer personality from a
single frame or short-term behaviors. Moreover, most of them failed to
specifically extract person-specific and unique cues for personality
recognition. In this paper, we propose a novel video-based automatic
personality traits recognition approach which consists of: (1) a
\textbf{domain-specific facial behavior modelling} module that extracts
personality-related multi-scale short-term human facial behavior features; (2)
a \textbf{long-term behavior modelling} module that summarizes all short-term
features of a video as a long-term/video-level personality representation and
(3) a \textbf{multi-task personality traits prediction module} that models
underlying relationship among all traits and jointly predict them based on the
video-level personality representation. We conducted the experiments on
ChaLearn First Impression dataset, and our approach achieved comparable results
to the state-of-the-art. Importantly, we show that all three proposed modules
brought important benefits for personality recognition.

Bandit algorithms are often used in the e-commerce industry to train Machine
Learning (ML) systems when pre-labeled data is unavailable. However, the
industry setting poses various challenges that make implementing bandit
algorithms in practice non-trivial. In this paper, we elaborate on the
challenges of off-policy optimisation, delayed reward, concept drift, reward
design, and business rules constraints that practitioners at Booking.com
encounter when applying bandit algorithms. Our main contributions is an
extension to the Open Bandit Pipeline (OBP) framework. We provide simulation
components for some of the above-mentioned challenges to provide future
practitioners, researchers, and educators with a resource to address challenges
encountered in the e-commerce industry.

Policy makers need to predict the progression of an outcome before adopting a
new treatment policy, which defines when and how a sequence of treatments
affecting the outcome occurs in continuous time. Commonly, algorithms that
predict interventional future outcome trajectories take a fixed sequence of
future treatments as input. This either neglects the dependence of future
treatments on outcomes preceding them or implicitly assumes the treatment
policy is known, and hence excludes scenarios where the policy is unknown or a
counterfactual analysis is needed. To handle these limitations, we develop a
joint model for treatments and outcomes, which allows for the estimation of
treatment policies and effects from sequential treatment--outcome data. It can
answer interventional and counterfactual queries about interventions on
treatment policies, as we show with real-world data on blood glucose
progression and a simulation study building on top of this.

In the era of loT (Internet of Things) we are surrounded by a plethora of Al
enabled devices that can transcribe images, video, audio, and sensors signals
into text descriptions. When such transcriptions are captured in activity
reports for monitoring, life logging and anomaly detection applications, a user
would typically request a summary or ask targeted questions about certain
sections of the report they are interested in. Depending on the context and the
type of question asked, a question answering (QA) system would need to
automatically determine whether the answer covers single-span or multi-span
text components. Currently available QA datasets primarily focus on single span
responses only (such as SQuAD[4]) or contain a low proportion of examples with
multiple span answers (such as DROP[3]). To investigate automatic selection of
single/multi-span answers in the use case described, we created a new smart
home environment dataset comprised of questions paired with single-span or
multi-span answers depending on the question and context queried. In addition,
we propose a RoBERTa[6]-based multiple span extraction question answering
(MSEQA) model returning the appropriate answer span for a given question. Our
experiments show that the proposed model outperforms state-of-the-art QA models
on our dataset while providing comparable performance on published individual
single/multi-span task datasets.

Recently, convolutional auto-encoders (CAE) were introduced for image coding.
They achieved performance improvements over the state-of-the-art JPEG2000
method. However, these performances were obtained using massive CAEs featuring
a large number of parameters and whose training required heavy computational
power.\\ In this paper, we address the problem of lossy image compression using
a CAE with a small memory footprint and low computational power usage. In order
to overcome the computational cost issue, the majority of the literature uses
Lagrangian proximal regularization methods, which are time consuming
themselves.\\ In this work, we propose a constrained approach and a new
structured sparse learning method. We design an algorithm and test it on three
constraints: the classical $\ell_1$ constraint, the $\ell_{1,\infty}$ and the
new $\ell_{1,1}$ constraint. Experimental results show that the $\ell_{1,1}$
constraint provides the best structured sparsity, resulting in a high reduction
of memory and computational cost, with similar rate-distortion performance as
with dense networks.

Analyzing large graph data is an essential part of many modern applications,
such as social networks. Due to its large computational complexity, distributed
processing is frequently employed. This requires graph data to be divided
across nodes, and the choice of partitioning strategy has a great impact on the
execution time of the task. Yet, there is no one-size-fits-all partitioning
strategy that performs well on arbitrary graph data and algorithms. The
performance of a strategy depends on the characteristics of the graph data and
algorithms. Moreover, due to the complexity of graph data and algorithms,
manually identifying the best partitioning strategy is also infeasible. In this
work, we propose a machine learning-based approach to select the most
appropriate partitioning strategy for a given graph and processing algorithm.
Our approach enumerates viable partitioning strategies, predicts the execution
time of the target algorithm for each, and selects the partitioning strategy
with the fastest estimated execution time. Our machine learning model is
trained on features extracted from graph data and algorithm pseudo-code. We
also propose a method that augments real execution logs of graph tasks to
create a large synthetic dataset. Evaluation results show that the strategies
selected by our approach lead to 1.46X faster execution time on average
compared with the mean execution time of the partitioning strategies and about
0.95X the performance compared to the best partitioning strategy.

The availability of property data is one of the major bottlenecks in the
development of chemical processes, often requiring time-consuming and expensive
experiments or limiting the design space to a small number of known molecules.
This bottleneck has been the motivation behind the continuing development of
predictive property models. For the property prediction of novel molecules,
group contribution methods have been groundbreaking. In recent times, machine
learning has joined the more established property prediction models. However,
even with recent successes, the integration of physical constraints into
machine learning models remains challenging. Physical constraints are vital to
many thermodynamic properties, such as the Gibbs-Duhem relation, introducing an
additional layer of complexity into the prediction. Here, we introduce
SPT-NRTL, a machine learning model to predict thermodynamically consistent
activity coefficients and provide NRTL parameters for easy use in process
simulations. The results show that SPT-NRTL achieves higher accuracy than
UNIFAC in the prediction of activity coefficients across all functional groups
and is able to predict many vapor-liquid-equilibria with near experimental
accuracy, as illustrated for the exemplary mixtures water/ethanol and
chloroform/n-hexane. To ease the application of SPT-NRTL, NRTL-parameters of
100 000 000 mixtures are calculated with SPT-NRTL and provided online.

We explore the use of knowledge distillation (KD) for learning compact and
accurate models that enable classification of animal behavior from
accelerometry data on wearable devices. To this end, we take a deep and complex
convolutional neural network, known as residual neural network (ResNet), as the
teacher model. ResNet is specifically designed for multivariate time-series
classification. We use ResNet to distil the knowledge of animal behavior
classification datasets into soft labels, which consist of the predicted
pseudo-probabilities of every class for each datapoint. We then use the soft
labels to train our significantly less complex student models, which are based
on the gated recurrent unit (GRU) and multilayer perceptron (MLP). The
evaluation results using two real-world animal behavior classification datasets
show that the classification accuracy of the student GRU-MLP models improves
appreciably through KD, approaching that of the teacher ResNet model. To
further reduce the computational and memory requirements of performing
inference using the student models trained via KD, we utilize dynamic
fixed-point quantization through an appropriate modification of the
computational graphs of the models. We implement both unquantized and quantized
versions of the developed KD-based models on the embedded systems of our
purpose-built collar and ear tag devices to classify animal behavior in situ
and in real time. The results corroborate the effectiveness of KD and
quantization in improving the inference performance in terms of both
classification accuracy and computational and memory efficiency.

Infinite width limit has shed light on generalization and optimization
aspects of deep learning by establishing connections between neural networks
and kernel methods. Despite their importance, the utility of these kernel
methods was limited in large-scale learning settings due to their
(super-)quadratic runtime and memory complexities. Moreover, most prior works
on neural kernels have focused on the ReLU activation, mainly due to its
popularity but also due to the difficulty of computing such kernels for general
activations. In this work, we overcome such difficulties by providing methods
to work with general activations. First, we compile and expand the list of
activation functions admitting exact dual activation expressions to compute
neural kernels. When the exact computation is unknown, we present methods to
effectively approximate them. We propose a fast sketching method that
approximates any multi-layered Neural Network Gaussian Process (NNGP) kernel
and Neural Tangent Kernel (NTK) matrices for a wide range of activation
functions, going beyond the commonly analyzed ReLU activation. This is done by
showing how to approximate the neural kernels using the truncated Hermite
expansion of any desired activation functions. While most prior works require
data points on the unit sphere, our methods do not suffer from such limitations
and are applicable to any dataset of points in $\mathbb{R}^d$. Furthermore, we
provide a subspace embedding for NNGP and NTK matrices with near input-sparsity
runtime and near-optimal target dimension which applies to any
\emph{homogeneous} dual activation functions with rapidly convergent Taylor
expansion. Empirically, with respect to exact convolutional NTK (CNTK)
computation, our method achieves $106\times$ speedup for approximate CNTK of a
5-layer Myrtle network on CIFAR-10 dataset.

Various methods have been developed to combine inference across multiple sets
of results for unsupervised clustering, within the ensemble and consensus
clustering literature. The approach of reporting results from one `best' model
out of several candidate clustering models generally ignores the uncertainty
that arises from model selection, and results in inferences that are sensitive
to the particular model and parameters chosen, and assumptions made, especially
with small sample size or small cluster sizes. Bayesian model averaging (BMA)
is a popular approach for combining results across multiple models that offers
some attractive benefits in this setting, including probabilistic
interpretation of the combine cluster structure and quantification of
model-based uncertainty.
  In this work we introduce clusterBMA, a method that enables weighted model
averaging across results from multiple unsupervised clustering algorithms. We
use a combination of clustering internal validation criteria as a novel
approximation of the posterior model probability for weighting the results from
each model. From a combined posterior similarity matrix representing a weighted
average of the clustering solutions across models, we apply symmetric simplex
matrix factorisation to calculate final probabilistic cluster allocations. This
method is implemented in an accompanying R package.
  We explore the performance of this approach through a case study that aims to
to identify probabilistic clusters of individuals based on
electroencephalography (EEG) data. We also use simulated datasets to explore
the ability of the proposed technique to identify robust integrated clusters
with varying levels of separations between subgroups, and with varying numbers
of clusters between models.

Scroll wave chaos is thought to underlie life-threatening ventricular
fibrillation. However, currently there is no direct way to measure action
potential wave patterns transmurally throughout the thick ventricular heart
muscle. Consequently, direct observation of three-dimensional electrical scroll
wave chaos remains elusive. Here, we study whether it is possible to
reconstruct simulated three-dimensional scroll wave chaos inside a bulk-shaped
excitable medium from two-dimensional observations of the wave dynamics on the
bulk's surface using deep learning. We trained encoding-decoding convolutional
neural networks to predict three-dimensional scroll wave chaos inside opaque
and transparent as well as isotropic and anisotropic excitable media from
two-dimensional projections or observations of the wave dynamics on the
surface. We tested whether observations from one or two opposing surfaces would
be sufficient, whether incorporating measurements of the surface deformation
improves the reconstruction, and tested the feasibility of predicting the
bulk's thickness. We demonstrate that it is possible to fully reconstruct
three-dimensional scroll wave chaos in transparent excitable media with
anisotropy and to obtain partial reconstructions in opaque excitable media when
analyzing two opposing layers of the bulk. We found that anisotropy provides
crucial information for neural networks to decode depth, which facilitates the
reconstructions. In the future, deep neural networks could be used to visualize
transmural action potential wave patterns during ventricular fibrillation from
epi- or endocardial recordings.

Emotion cause pair extraction (ECPE), as one of the derived subtasks of
emotion cause analysis (ECA), shares rich inter-related features with emotion
extraction (EE) and cause extraction (CE). Therefore EE and CE are frequently
utilized as auxiliary tasks for better feature learning, modeled via multi-task
learning (MTL) framework by prior works to achieve state-of-the-art (SoTA) ECPE
results. However, existing MTL-based methods either fail to simultaneously
model the specific features and the interactive feature in between, or suffer
from the inconsistency of label prediction. In this work, we consider
addressing the above challenges for improving ECPE by performing two alignment
mechanisms with a novel A^2Net model. We first propose a feature-task alignment
to explicitly model the specific emotion-&cause-specific features and the
shared interactive feature. Besides, an inter-task alignment is implemented, in
which the label distance between the ECPE and the combinations of EE&CE are
learned to be narrowed for better label consistency. Evaluations of benchmarks
show that our methods outperform current best-performing systems on all ECA
subtasks. Further analysis proves the importance of our proposed alignment
mechanisms for the task.

In this paper, we propose a nonlinear probabilistic generative model of
Koopman mode decomposition based on an unsupervised Gaussian process. Existing
data-driven methods for Koopman mode decomposition have focused on estimating
the quantities specified by Koopman mode decomposition, namely, eigenvalues,
eigenfunctions, and modes. Our model enables the simultaneous estimation of
these quantities and latent variables governed by an unknown dynamical system.
Furthermore, we introduce an efficient strategy to estimate the parameters of
our model by low-rank approximations of covariance matrices. Applying the
proposed model to both synthetic data and a real-world epidemiological dataset,
we show that various analyses are available using the estimated parameters.

Imbalanced music genre classification is a crucial task in the Music
Information Retrieval (MIR) field for identifying the long-tail, data-poor
genre based on the related music audio segments, which is very prevalent in
real-world scenarios. Most of the existing models are designed for
class-balanced music datasets, resulting in poor performance in accuracy and
generalization when identifying the music genres at the tail of the
distribution. Inspired by the success of introducing Multi-instance Learning
(MIL) in various classification tasks, we propose a novel mechanism named
Multi-instance Attention (MATT) to boost the performance for identifying tail
classes. Specifically, we first construct the bag-level datasets by generating
the album-artist pair bags. Second, we leverage neural networks to encode the
music audio segments. Finally, under the guidance of a multi-instance attention
mechanism, the neural network-based models could select the most informative
genre to match the given music segment. Comprehensive experimental results on a
large-scale music genre benchmark dataset with long-tail distribution
demonstrate MATT significantly outperforms other state-of-the-art baselines.

Text-based games(TBG) are complex environments which allow users or computer
agents to make textual interactions and achieve game goals.In TBG agent design
and training process, balancing the efficiency and performance of the agent
models is a major challenge. Finding TBG agent deep learning modules'
performance in standardized environments, and testing their performance among
different evaluation types is also important for TBG agent research. We
constructed a standardized TBG agent with no hand-crafted rules, formally
categorized TBG evaluation types, and analyzed selected methods in our

We pose a new question: Can agents learn how to combine actions from previous
tasks to complete new tasks, just as humans? In contrast to imitation learning,
there is no expert data, only the data collected through environmental
exploration. Compared with offline reinforcement learning, the problem of data
distribution shift is more serious. Since the action sequence to solve the new
task may be the combination of trajectory segments of multiple training tasks,
in other words, the test task and the solving strategy do not exist directly in
the training data. This makes the problem more difficult. We propose a
Memory-related Multi-task Method (M3) to address this problem. The method
consists of three stages. First, task-agnostic exploration is carried out to
collect data. Different from previous methods, we organize the exploration data
into a knowledge graph. We design a model based on the exploration data to
extract action effect features and save them in memory, while an action
predictive model is trained. Secondly, for a new task, the action effect
features stored in memory are used to generate candidate actions by a feature
decomposition-based approach. Finally, a multi-scale candidate action pool and
the action predictive model are fused to generate a strategy to complete the
task. Experimental results show that the performance of our proposed method is
significantly improved compared with the baseline.

Fast multi-phase processes in methane hydrate-bearing samples are challenging
for micro-CT quantitative study because of complex tomographic data analysis
involving time-consuming segmentation procedures. This is due to the sample
multi-scale structure changing in time, low X-ray attenuation and phase
contrast between solid and fluid materials, as well as large amount of data
acquired during dynamic processes. We propose a hybrid approach for automatic
segmentation of tomographic data from time-resolved imaging of methane
gas-hydrate formation in sandy granular media. First, we use an optimized 3D
U-net neural network to perform segmentation of mineral grains that are
characterized by low contrast to the surrounding pore brine-saturated phases.
Then, we perform statistical clustering based on the Gaussian mixture model for
separating the pore-space phases that are characterized by gray-level
instabilities caused by dynamic processes during hydrate formation. The
proposed approach was used for segmenting several hundred gigabytes of data
acquired during an in-situ tomographic experiment at a synchrotron. Automatic
segmentation allowed for studying properties of the hydrate growth in pores, as
well as dynamic processes such as the incremental pore-brine flow and
redistribution.

Recent advances in deep learning technology have triggered radical progress
in the autonomy of ground vehicles. Marine coastal Autonomous Surface Vehicles
(ASVs) that are regularly used for surveillance, monitoring and other routine
tasks can benefit from this autonomy. Long haul deep sea transportation
activities are additional opportunities. These two use cases present very
different terrains -- the first being coastal waters -- with many obstacles,
structures and human presence while the latter is mostly devoid of such
obstacles. Variations in environmental conditions are common to both terrains.
Robust labeled datasets mapping such terrains are crucial in improving the
situational awareness that can drive autonomy. However, there are only limited
such maritime datasets available and these primarily consist of optical images.
Although, Long Wave Infrared (LWIR) is a strong complement to the optical
spectrum that helps in extreme light conditions, a labeled public dataset with
LWIR images does not currently exist. In this paper, we fill this gap by
presenting a labeled dataset of over 2,900 LWIR segmented images captured in
coastal maritime environment under diverse conditions. The images are labeled
using instance segmentation and classified in seven categories -- sky, water,
obstacle, living obstacle, bridge, self and background. We also evaluate this
dataset across three deep learning architectures (UNet, PSPNet, DeepLabv3) and
provide detailed analysis of its efficacy. While the dataset focuses on the
coastal terrain it can equally help deep sea use cases. Such terrain would have
less traffic, and the classifier trained on cluttered environment would be able
to handle sparse scenes effectively. We share this dataset with the research
community with the hope that it spurs new scene understanding capabilities in
the maritime environment.

Following recent outbreaks, monkeypox-related misinformation continues to
rapidly spread online. This negatively impacts response strategies and
disproportionately harms LGBTQ+ communities in the short-term, and ultimately
undermines the overall effectiveness of public health responses. In an attempt
to combat monkeypox-related misinformation, we present PoxVerifi, an
open-source, extensible tool that provides a comprehensive approach to
assessing the accuracy of monkeypox related claims. Leveraging information from
existing fact checking sources and published World Health Organization (WHO)
information, we created an open-sourced corpus of 225 rated monkeypox claims.
Additionally, we trained an open-sourced BERT-based machine learning model for
specifically classifying monkeypox information, which achieved 96%
cross-validation accuracy. PoxVerifi is a Google Chrome browser extension
designed to empower users to navigate through monkeypox-related misinformation.
Specifically, PoxVerifi provides users with a comprehensive toolkit to assess
the veracity of headlines on any webpage across the Internet without having to
visit an external site. Users can view an automated accuracy review from our
trained machine learning model, a user-generated accuracy review based on
community-member votes, and have the ability to see similar, vetted, claims.
Besides PoxVerifi's comprehensive approach to claim-testing, our platform
provides an efficient and accessible method to crowdsource accuracy ratings on
monkeypox related-claims, which can be aggregated to create new labeled
misinformation datasets.

It has already been observed that audio-visual embedding is more robust than
uni-modality embedding for person verification. Here, we proposed a novel
audio-visual strategy that considers aggregators from a fusion perspective.
First, we introduced weight-enhanced attentive statistics pooling for the first
time in face verification. We find that a strong correlation exists between
modalities during pooling, so joint attentive pooling is proposed which
contains cycle consistency to learn the implicit inter-frame weight. Finally,
each modality is fused with a gated attention mechanism to gain robust
audio-visual embedding. All the proposed models are trained on the VoxCeleb2
dev dataset and the best system obtains 0.18%, 0.27%, and 0.49% EER on three
official trial lists of VoxCeleb1 respectively, which is to our knowledge the
best-published results for person verification.

Stochastic compositional optimization (SCO) has attracted considerable
attention because of its broad applicability to important real-world problems.
However, existing works on SCO assume that the projection within a solution
update is simple, which fails to hold for problem instances where the
constraints are in the form of expectations, such as empirical conditional
value-at-risk constraints. We study a novel model that incorporates
single-level expected value and two-level compositional constraints into the
current SCO framework. Our model can be applied widely to data-driven
optimization and risk management, including risk-averse optimization and
high-moment portfolio selection, and can handle multiple constraints. We
further propose a class of primal-dual algorithms that generates sequences
converging to the optimal solution at the rate of
$\cO(\frac{1}{\sqrt{N}})$under both single-level expected value and two-level
compositional constraints, where $N$ is the iteration counter, establishing the
benchmarks in expected value constrained SCO.

Closed-loop optimal control design for high-dimensional nonlinear systems has
been a long-standing problem. Traditional methods, such as solving the
associated Hamilton-Jacobi-Bellman equation, suffer from the curse of
dimensionality. Recent literature proposed a new promising approach based on
supervised learning, by leveraging powerful open-loop optimal control solvers
to generate training data and neural networks as efficient high-dimensional
function approximators to fit the closed-loop optimal control. This approach
successfully handles certain high-dimensional optimal control problems but
still performs poorly on more challenging problems. One of the crucial reasons
for the failure of the approach is the so-called distribution mismatch
phenomenon brought by the controlled dynamics. In this paper, we investigate
this phenomenon and propose the initial value problem enhanced sampling method
to mitigate this problem. We theoretically prove that this sampling strategy
improves over the vanilla strategy on the classical linear-quadratic regulator
by a factor proportional to the total time duration. We further numerically
demonstrate that the proposed sampling strategy significantly improves the
performance on tested control problems, including the optimal landing problem
of a quadrotor and the optimal reaching problem of a 7 DoF manipulator.

Sense of hearing is crucial for autonomous vehicles (AVs) to better perceive
its surrounding environment. Although visual sensors of an AV, such as camera,
lidar, and radar, help to see its surrounding environment, an AV cannot see
beyond those sensors line of sight. On the other hand, an AV s sense of hearing
cannot be obstructed by line of sight. For example, an AV can identify an
emergency vehicle s siren through audio classification even though the
emergency vehicle is not within the line of sight of the AV. Thus, auditory
perception is complementary to the camera, lidar, and radar-based perception
systems. This paper presents a deep learning-based robust audio classification
framework aiming to achieve improved environmental perception for AVs. The
presented framework leverages a deep Convolution Neural Network (CNN) to
classify different audio classes. UrbanSound8k, an urban environment dataset,
is used to train and test the developed framework. Seven audio classes i.e.,
air conditioner, car horn, children playing, dog bark, engine idling, gunshot,
and siren, are identified from the UrbanSound8k dataset because of their
relevancy related to AVs. Our framework can classify different audio classes
with 97.82% accuracy. Moreover, the audio classification accuracies with all
ten classes are presented, which proves that our framework performed better in
the case of AV-related sounds compared to the existing audio classification
frameworks.

The lack of diversity in physics and astronomy PhD programmes is well known
but has not improved despite decades of efforts. PhD bridge programmes provide
an asset-based model to help overcome the societal and disciplinary obstacles
to improving access and inclusion for students from underrepresented groups and
are beginning to show some success. We describe several well-known PhD bridge
programmes in the United States and discuss lessons learned from their
experiences. Many of these lessons can be extended more broadly to physics and
astronomy PhD programmes to increase access, diversity and inclusion.

Metrics provide strong evidence to support hypotheses in online
experimentation and hence reduce debates in the decision-making process. In
this work, we introduce the concept of dropout buyers and categorize users with
incomplete metric values into two groups: visitors and dropout buyers. For the
analysis of incomplete metrics, we propose a cluster-based k-nearest
neighbors-based imputation method. Our proposed imputation method considers
both the experiment-specific features and users' activities along their
shopping paths, allowing different imputation values for different users. To
facilitate efficient imputation in large-scale data sets in online
experimentation, the proposed method uses a combination of stratification and
clustering. The performance of the proposed method was compared to several
conventional methods in a past experiment at eBay.

The brains of all bilaterally symmetric animals on Earth are are divided into
left and right hemispheres. The anatomy and functionality of the hemispheres
have a large degree of overlap, but they specialize to possess different
attributes. The left hemisphere is believed to specialize in specificity and
routine, the right in generalities and novelty. In this study, we propose an
artificial neural network that imitates that bilateral architecture using two
convolutional neural networks with different training objectives and test it on
an image classification task. The bilateral architecture outperforms
architectures of similar representational capacity that don't exploit
differential specialization. It demonstrates the efficacy of bilateralism and
constitutes a new principle that could be incorporated into other computational
neuroscientific models and used as an inductive bias when designing new ML
systems. An analysis of the model can help us to understand the human brain.

Prior work on safe Reinforcement Learning (RL) has studied risk-aversion to
randomness in dynamics (aleatory) and to model uncertainty (epistemic) in
isolation. We propose and analyze a new framework to jointly model the risk
associated with epistemic and aleatory uncertainties in finite-horizon and
discounted infinite-horizon MDPs. We call this framework that combines
Risk-Averse and Soft-Robust methods RASR. We show that when the risk-aversion
is defined using either EVaR or the entropic risk, the optimal policy in RASR
can be computed efficiently using a new dynamic program formulation with a
time-dependent risk level. As a result, the optimal risk-averse policies are
deterministic but time-dependent, even in the infinite-horizon discounted
setting. We also show that particular RASR objectives reduce to risk-averse RL
with mean posterior transition probabilities. Our empirical results show that
our new algorithms consistently mitigate uncertainty as measured by EVaR and
other standard risk measures.

We propose im2nerf, a learning framework that predicts a continuous neural
object representation given a single input image in the wild, supervised by
only segmentation output from off-the-shelf recognition methods. The standard
approach to constructing neural radiance fields takes advantage of multi-view
consistency and requires many calibrated views of a scene, a requirement that
cannot be satisfied when learning on large-scale image data in the wild. We
take a step towards addressing this shortcoming by introducing a model that
encodes the input image into a disentangled object representation that contains
a code for object shape, a code for object appearance, and an estimated camera
pose from which the object image is captured. Our model conditions a NeRF on
the predicted object representation and uses volume rendering to generate
images from novel views. We train the model end-to-end on a large collection of
input images. As the model is only provided with single-view images, the
problem is highly under-constrained. Therefore, in addition to using a
reconstruction loss on the synthesized input view, we use an auxiliary
adversarial loss on the novel rendered views. Furthermore, we leverage object
symmetry and cycle camera pose consistency. We conduct extensive quantitative
and qualitative experiments on the ShapeNet dataset as well as qualitative
experiments on Open Images dataset. We show that in all cases, im2nerf achieves
the state-of-the-art performance for novel view synthesis from a single-view
unposed image in the wild.

In their seminal paper that initiated the field of algorithmic mechanism
design, \citet{NR99} studied the problem of designing strategyproof mechanisms
for scheduling jobs on unrelated machines aiming to minimize the makespan. They
provided a strategyproof mechanism that achieves an $n$-approximation and they
made the bold conjecture that this is the best approximation achievable by any
deterministic strategyproof scheduling mechanism. After more than two decades
and several efforts, $n$ remains the best known approximation and very recent
work by \citet{CKK21} has been able to prove an $\Omega(\sqrt{n})$
approximation lower bound for all deterministic strategyproof mechanisms. This
strong negative result, however, heavily depends on the fact that the
performance of these mechanisms is evaluated using worst-case analysis. To
overcome such overly pessimistic, and often uninformative, worst-case bounds, a
surge of recent work has focused on the ``learning-augmented framework'', whose
goal is to leverage machine-learned predictions to obtain improved
approximations when these predictions are accurate (consistency), while also
achieving near-optimal worst-case approximations even when the predictions are
arbitrarily wrong (robustness).
  In this work, we study the classic strategic scheduling problem
of~\citet{NR99} using the learning-augmented framework and give a deterministic
polynomial-time strategyproof mechanism that is $6$-consistent and $2n$-robust.
We thus achieve the ``best of both worlds'': an $O(1)$ consistency and an
$O(n)$ robustness that asymptotically matches the best-known approximation. We
then extend this result to provide more general worst-case approximation
guarantees as a function of the prediction error. Finally, we complement our
positive results by showing that any $1$-consistent deterministic strategyproof
mechanism has unbounded robustness.

Generating power system states that have similar distribution and dependency
to the historical ones is essential for the tasks of system planning and
security assessment, especially when the historical data is insufficient. In
this paper, we described a generative model for load profiles of industrial and
commercial customers, based on the conditional variational autoencoder (CVAE)
neural network architecture, which is challenging due to the highly variable
nature of such profiles. Generated contextual load profiles were conditioned on
the month of the year and typical power exchange with the grid. Moreover, the
quality of generations was both visually and statistically evaluated. The
experimental results demonstrate our proposed CVAE model can capture temporal
features of historical load profiles and generate `realistic' data with
satisfying univariate distributions and multivariate dependencies.

Differential privacy is often applied with a privacy parameter that is larger
than the theory suggests is ideal; various informal justifications for
tolerating large privacy parameters have been proposed. In this work, we
consider partial differential privacy (DP), which allows quantifying the
privacy guarantee on a per-attribute basis. In this framework, we study several
basic data analysis and learning tasks, and design algorithms whose
per-attribute privacy parameter is smaller that the best possible privacy
parameter for the entire record of a person (i.e., all the attributes).

From higher computational efficiency to enabling the discovery of novel and
complex structures, deep learning has emerged as a powerful framework for the
design and optimization of nanophotonic circuits and components. However, both
data-driven and exploration-based machine learning strategies have limitations
in their effectiveness for nanophotonic inverse design. Supervised machine
learning approaches require large quantities of training data to produce
high-performance models and have difficulty generalizing beyond training data
given the complexity of the design space. Unsupervised and reinforcement
learning-based approaches on the other hand can have very lengthy training or
optimization times associated with them. Here we demonstrate a hybrid
supervised learning and reinforcement learning approach to the inverse design
of nanophotonic structures and show this approach can reduce training data
dependence, improve the generalizability of model predictions, and shorten
exploratory training times by orders of magnitude. The presented strategy thus
addresses a number of contemporary deep learning-based challenges, while
opening the door for new design methodologies that leverage multiple classes of
machine learning algorithms to produce more effective and practical solutions
for photonic design.

The symbolic AI community is increasingly trying to embrace machine learning
in neuro-symbolic architectures, yet is still struggling due to cultural
barriers. To break the barrier, this rather opinionated personal memo attempts
to explain and rectify the conventions in Statistics, Machine Learning, and
Deep Learning from the viewpoint of outsiders. It provides a step-by-step
protocol for designing a machine learning system that satisfies a minimum
theoretical guarantee necessary for being taken seriously by the symbolic AI
community, i.e., it discusses "in what condition we can stop worrying and
accept statistical machine learning." Unlike most textbooks which are written
for students trying to specialize in Stat/ML/DL and willing to accept jargons,
this memo is written for experienced symbolic researchers that hear a lot of
buzz but are still uncertain and skeptical. Information on Stat/ML/DL is
currently too scattered or too noisy to invest in. This memo prioritizes
compactness, citations to old papers (many in early 20th century), and concepts
that resonate well with symbolic paradigms in order to offer time savings. It
prioritizes general mathematical modeling and does not discuss any specific
function approximator, such as neural networks (NNs), SVMs, decision trees,
etc. Finally, it is open to corrections. Consider this memo as something
similar to a blog post taking the form of a paper on Arxiv.

Drowsiness is a major concern for drivers and one of the leading causes of
traffic accidents. Advances in Cognitive Neuroscience and Computer Science have
enabled the detection of drivers' drowsiness by using Brain-Computer Interfaces
(BCIs) and Machine Learning (ML). Nevertheless, several challenges remain open
and should be faced. First, a comprehensive enough evaluation of drowsiness
detection performance using a heterogeneous set of ML algorithms is missing in
the literature. Last, it is needed to study the detection performance of
scalable ML models suitable for groups of subjects and compare it with the
individual models proposed in the literature. To improve these limitations,
this work presents an intelligent framework that employs BCIs and features
based on electroencephalography (EEG) for detecting drowsiness in driving
scenarios. The SEED-VIG dataset is used to feed different ML regressors and
three-class classifiers and then evaluate, analyze, and compare the
best-performing models for individual subjects and groups of them. More in
detail, regarding individual models, Random Forest (RF) obtained a 78%
f1-score, improving the 58% obtained by models used in the literature such as
Support Vector Machine (SVM). Concerning scalable models, RF reached a 79%
f1-score, demonstrating the effectiveness of these approaches. The lessons
learned can be summarized as follows: i) not only SVM but also other models not
sufficiently explored in the literature are relevant for drowsiness detection,
and ii) scalable approaches suitable for groups of subjects are effective to
detect drowsiness, even when new subjects that are not included in the models
training are evaluated.

This project describes the application of the technologies of Machine
Learning and Internet-of-Things to assess the lower limb strength of
individuals undergoing rehabilitation or therapy. Specifically, it seeks to
measure and assess the progress of individuals by sensors attached to chairs
and processing the data through Google GPU Tensorflow CoLab. Pressure sensors
are attached to various locations on a chair, including but not limited to the
seating area, backrest, hand rests, and legs. Sensor data from the individual
performing both sit-to-stand transition and stand-to-sit transition provides a
time series dataset regarding the pressure distribution and vibratory motion on
the chair. The dataset and timing information can then be fed into a machine
learning model to estimate the relative strength and weakness during various
phases of the movement.

It is well-known that the parameterized family of functions representable by
fully-connected feedforward neural networks with ReLU activation function is
precisely the class of piecewise linear functions with finitely many pieces. It
is less well-known that for every fixed architecture of ReLU neural network,
the parameter space admits positive-dimensional spaces of symmetries, and hence
the local functional dimension near any given parameter is lower than the
parametric dimension. In this work we carefully define the notion of functional
dimension, show that it is inhomogeneous across the parameter space of ReLU
neural network functions, and continue an investigation - initiated in [14] and
[5] - into when the functional dimension achieves its theoretical maximum. We
also study the quotient space and fibers of the realization map from parameter
space to function space, supplying examples of fibers that are disconnected,
fibers upon which functional dimension is non-constant, and fibers upon which
the symmetry group acts non-transitively.

Federated learning (FL) provides an efficient paradigm to jointly train a
global model leveraging data from distributed users. As the local training data
come from different users who may not be trustworthy, several studies have
shown that FL is vulnerable to poisoning attacks. Meanwhile, to protect the
privacy of local users, FL is always trained in a differentially private way
(DPFL). Thus, in this paper, we ask: Can we leverage the innate privacy
property of DPFL to provide certified robustness against poisoning attacks? Can
we further improve the privacy of FL to improve such certification? We first
investigate both user-level and instance-level privacy of FL and propose novel
mechanisms to achieve improved instance-level privacy. We then provide two
robustness certification criteria: certified prediction and certified attack
cost for DPFL on both levels. Theoretically, we prove the certified robustness
of DPFL under a bounded number of adversarial users or instances. Empirically,
we conduct extensive experiments to verify our theories under a range of
attacks on different datasets. We show that DPFL with a tighter privacy
guarantee always provides stronger robustness certification in terms of
certified attack cost, but the optimal certified prediction is achieved under a
proper balance between privacy protection and utility loss.

Cost-effective depth and infrared sensors as alternatives to usual RGB
sensors are now a reality, and have some advantages over RGB in domains like
autonomous navigation and remote sensing. As such, building computer vision and
deep learning systems for depth and infrared data are crucial. However, large
labeled datasets for these modalities are still lacking. In such cases,
transferring knowledge from a neural network trained on a well-labeled large
dataset in the source modality (RGB) to a neural network that works on a target
modality (depth, infrared, etc.) is of great value. For reasons like memory and
privacy, it may not be possible to access the source data, and knowledge
transfer needs to work with only the source models. We describe an effective
solution, SOCKET: SOurce-free Cross-modal KnowledgE Transfer for this
challenging task of transferring knowledge from one source modality to a
different target modality without access to task-relevant source data. The
framework reduces the modality gap using paired task-irrelevant data, as well
as by matching the mean and variance of the target features with the batch-norm
statistics that are present in the source models. We show through extensive
experiments that our method significantly outperforms existing source-free
methods for classification tasks which do not account for the modality gap.

Neutrino physics is one of the fundamental areas of research into the origins
and properties of the Universe. Many experimental neutrino projects use
sophisticated detectors to observe properties of these particles, and have
turned to deep learning and artificial intelligence techniques to analyze their
data. From this, we have developed \texttt{larcv}, a \texttt{C++} and
\texttt{Python} based framework for efficient IO of sparse data with particle
physics applications in mind. We describe in this paper the \texttt{larcv}
framework and some benchmark IO performance tests. \texttt{larcv} is designed
to enable fast and efficient IO of ragged and irregular data, at scale on
modern HPC systems, and is compatible with the most popular open source data
analysis tools in the Python ecosystem.

Recent advances in machine learning have enabled its wide application in
different domains, and one of the most exciting applications is autonomous
vehicles (AVs), which have encouraged the development of a number of ML
algorithms from perception to prediction to planning. However, training AVs
usually requires a large amount of training data collected from different
driving environments (e.g., cities) as well as different types of personal
information (e.g., working hours and routes). Such collected large data,
treated as the new oil for ML in the data-centric AI era, usually contains a
large amount of privacy-sensitive information which is hard to remove or even
audit. Although existing privacy protection approaches have achieved certain
theoretical and empirical success, there is still a gap when applying them to
real-world applications such as autonomous vehicles. For instance, when
training AVs, not only can individually identifiable information reveal
privacy-sensitive information, but also population-level information such as
road construction within a city, and proprietary-level commercial secrets of
AVs. Thus, it is critical to revisit the frontier of privacy risks and
corresponding protection approaches in AVs to bridge this gap. Following this
goal, in this work, we provide a new taxonomy for privacy risks and protection
methods in AVs, and we categorize privacy in AVs into three levels: individual,
population, and proprietary. We explicitly list out recent challenges to
protect each of these levels of privacy, summarize existing solutions to these
challenges, discuss the lessons and conclusions, and provide potential future
directions and opportunities for both researchers and practitioners. We believe
this work will help to shape the privacy research in AV and guide the privacy
protection technology design.

In explainable machine learning, local post-hoc explanation algorithms and
inherently interpretable models are often seen as competing approaches. In this
work, we offer a partial reconciliation between these two approaches by showing
that Shapley Values correspond to Generalized Additive Models (GAMs). We
introduce $n$-Shapley Values, a parametric family of local post-hoc explanation
algorithms that explain individual predictions with interaction terms up to
order $n$. By varying the parameter $n$, these explanations cover the entire
range from Shapley Values up to a uniquely determined decomposition of the
function that we attempt to explain. The relationship between $n$-Shapley
Values and this decomposition offers a functionally-grounded characterization
of Shapley Values, and highlights the limitations of these explanations. We
then show that $n$-Shapley Values recover GAMs with interaction terms up to
order $n$, which implies that the original Shapely Values recover GAMs without
interaction terms. Taken together, our results offer a precise characterization
of Shapley Values as they are being used in explainable machine learning.
Python code to estimate $n$-Shapley Values and replicate the results in this
paper is available at \url{https://github.com/tml-tuebingen/nshap}.

Amazon Customer Service provides real-time support for millions of customer
contacts every year. While bot-resolver helps automate some traffic, we still
see high demand for human agents, also called subject matter experts (SMEs).
Customers outreach with questions in different domains (return policy, device
troubleshooting, etc.). Depending on their training, not all SMEs are eligible
to handle all contacts. Routing contacts to eligible SMEs turns out to be a
non-trivial problem because SMEs' domain eligibility is subject to training
quality and can change over time. To optimally recommend SMEs while
simultaneously learning the true eligibility status, we propose to formulate
the routing problem with a nonparametric contextual bandit algorithm (K-Boot)
plus an eligibility control (EC) algorithm. K-Boot models reward with a kernel
smoother on similar past samples selected by $k$-NN, and Bootstrap Thompson
Sampling for exploration. EC filters arms (SMEs) by the initially
system-claimed eligibility and dynamically validates the reliability of this
information. The proposed K-Boot is a general bandit algorithm, and EC is
applicable to other bandits. Our simulation studies show that K-Boot performs
on par with state-of-the-art Bandit models, and EC boosts K-Boot performance
when stochastic eligibility signal exists.

Cross-silo Federated learning (FL) has become a promising tool in machine
learning applications for healthcare. It allows hospitals/institutions to train
models with sufficient data while the data is kept private. To make sure the FL
model is robust when facing heterogeneous data among FL clients, most efforts
focus on personalizing models for clients. However, the latent relationships
between clients' data are ignored. In this work, we focus on a special non-iid
FL problem, called Domain-mixed FL, where each client's data distribution is
assumed to be a mixture of several predefined domains. Recognizing the
diversity of domains and the similarity within domains, we propose a novel
method, FedDAR, which learns a domain shared representation and domain-wise
personalized prediction heads in a decoupled manner. For simplified linear
regression settings, we have theoretically proved that FedDAR enjoys a linear
convergence rate. For general settings, we have performed intensive empirical
studies on both synthetic and real-world medical datasets which demonstrate its
superiority over prior FL methods.

Tensor decomposition has been widely used in machine learning and high-volume
data analysis. However, large-scale tensor factorization often consumes huge
memory and computing cost. Meanwhile, modernized computing hardware such as
tensor processing units (TPU) and Tensor Core GPU has opened a new window of
hardware-efficient computing via mixed- or low-precision arithmetic
representations. In this paper, we exploit the low-precision representation of
tensor factorization, and propose a mixed-precision block stochastic gradient
descent (SGD) method to reduce the costs of CP tensor decomposition. Our method
achieves robust and fast convergence via a two-stage optimization, i.e.,
SignSGD followed by mixed-precision SGD. Detailed theoretical analysis is
provided to prove the convergence of the proposed mixed-precision algorithm.
Numerical experiments on both synthetic and realistic tensor data sets show the
superior efficiency of our mixed-precision algorithm compared to full-precision
CP decomposition. This work can remarkably reduce the memory, computing and
energy cost on resource-constraint edge computing devices. We demonstrate this
benefit via an FPGA prototype.

We study the problem of \textit{online} low-rank matrix completion with
$\mathsf{M}$ users, $\mathsf{N}$ items and $\mathsf{T}$ rounds. In each round,
we recommend one item per user. For each recommendation, we obtain a (noisy)
reward sampled from a low-rank user-item reward matrix. The goal is to design
an online method with sub-linear regret (in $\mathsf{T}$). While the problem
can be mapped to the standard multi-armed bandit problem where each item is an
\textit{independent} arm, it leads to poor regret as the correlation between
arms and users is not exploited. In contrast, exploiting the low-rank structure
of reward matrix is challenging due to non-convexity of low-rank manifold. We
overcome this challenge using an explore-then-commit (ETC) approach that
ensures a regret of $O(\mathsf{polylog} (\mathsf{M}+\mathsf{N})
\mathsf{T}^{2/3})$. That is, roughly only $\mathsf{polylog}
(\mathsf{M}+\mathsf{N})$ item recommendations are required per user to get
non-trivial solution. We further improve our result for the rank-$1$ setting.
Here, we propose a novel algorithm OCTAL (Online Collaborative filTering using
iterAtive user cLustering) that ensures nearly optimal regret bound of
$O(\mathsf{polylog} (\mathsf{M}+\mathsf{N}) \mathsf{T}^{1/2})$. Our algorithm
uses a novel technique of clustering users and eliminating items jointly and
iteratively, which allows us to obtain nearly minimax optimal rate in
$\mathsf{T}$.

We study exact active learning of binary and multiclass classifiers with
margin. Given an $n$-point set $X \subset \mathbb{R}^m$, we want to learn any
unknown classifier on $X$ whose classes have finite strong convex hull margin,
a new notion extending the SVM margin. In the standard active learning setting,
where only label queries are allowed, learning a classifier with strong convex
hull margin $\gamma$ requires in the worst case
$\Omega\big(1+\frac{1}{\gamma}\big)^{(m-1)/2}$ queries. On the other hand,
using the more powerful seed queries (a variant of equivalence queries), the
target classifier could be learned in $O(m \log n)$ queries via Littlestone's
Halving algorithm; however, Halving is computationally inefficient. In this
work we show that, by carefully combining the two types of queries, a binary
classifier can be learned in time $\operatorname{poly}(n+m)$ using only $O(m^2
\log n)$ label queries and $O\big(m \log \frac{m}{\gamma}\big)$ seed queries;
the result extends to $k$-class classifiers at the price of a $k!k^2$
multiplicative overhead. Similar results hold when the input points have
bounded bit complexity, or when only one class has strong convex hull margin
against the rest. We complement the upper bounds by showing that in the worst
case any algorithm needs $\Omega\big(k m \log \frac{1}{\gamma}\big)$ seed and
label queries to learn a $k$-class classifier with strong convex hull margin
$\gamma$.

Recent works have shown that tackling offline reinforcement learning (RL)
with a conditional policy produces promising results. The Decision Transformer
(DT) combines the conditional policy approach and a transformer architecture,
showing competitive performance against several benchmarks. However, DT lacks
stitching ability -- one of the critical abilities for offline RL to learn the
optimal policy from sub-optimal trajectories. This issue becomes particularly
significant when the offline dataset only contains sub-optimal trajectories. On
the other hand, the conventional RL approaches based on Dynamic Programming
(such as Q-learning) do not have the same limitation; however, they suffer from
unstable learning behaviours, especially when they rely on function
approximation in an off-policy learning setting. In this paper, we propose the
Q-learning Decision Transformer (QDT) to address the shortcomings of DT by
leveraging the benefits of Dynamic Programming (Q-learning). It utilises the
Dynamic Programming results to relabel the return-to-go in the training data to
then train the DT with the relabelled data. Our approach efficiently exploits
the benefits of these two approaches and compensates for each other's
shortcomings to achieve better performance. We empirically show these in both
simple toy environments and the more complex D4RL benchmark, showing
competitive performance gains.

In recent years, the Neurosymbolic framework has attracted a lot of attention
in various applications, from recommender systems and information retrieval to
healthcare and finance. This success is due to its stellar performance combined
with attractive properties, such as learning and reasoning. The new emerging
Neurosymbolic field is currently experiencing a renaissance, as novel
frameworks and algorithms motivated by various practical applications are being
introduced, building on top of the classical neural and reasoning problem
setting. This article aims to provide a comprehensive review of significant
recent developments in real-world applications of Neurosymbolic Artificial
Intelligence. Specifically, we introduce a taxonomy of common Neurosymbolic
applications and summarize the state-of-the-art for each of those domains.
Furthermore, we identify important current trends and provide new perspectives
pertaining to the future of this burgeoning field.

Machine learning force fields (MLFFs) are gradually evolving towards enabling
molecular dynamics simulations of molecules and materials with ab initio
accuracy but at a small fraction of the computational cost. However, several
challenges remain to be addressed to enable predictive MLFF simulations of
realistic molecules, including: (1) developing efficient descriptors for
non-local interatomic interactions, which are essential to capture long-range
molecular fluctuations, and (2) reducing the dimensionality of the descriptors
in kernel methods (or a number of parameters in neural networks) to enhance the
applicability and interpretability of MLFFs. Here we propose an automatized
approach to substantially reduce the number of interatomic descriptor features
while preserving the accuracy and increasing the efficiency of MLFFs. To
simultaneously address the two stated challenges, we illustrate our approach on
the example of the global GDML MLFF; however, our methodology can be equally
applied to other models. We found that non-local features (atoms separated by
as far as 15$~\r{A}$ in studied systems) are crucial to retain the overall
accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and
supramolecular complexes. Interestingly, the number of required non-local
features in the reduced descriptors becomes comparable to the number of local
interatomic features (those below 5$~\r{A}$). These results pave the way to
constructing global molecular MLFFs whose cost increases linearly, instead of
quadratically, with system size.

Physics-informed neural networks (PINNs) have demonstrated promise in solving
forward and inverse problems involving partial differential equations. Despite
recent progress on expanding the class of problems that can be tackled by
PINNs, most of existing use-cases involve simple geometric domains. To date,
there is no clear way to inform PINNs about the topology of the domain where
the problem is being solved. In this work, we propose a novel positional
encoding mechanism for PINNs based on the eigenfunctions of the
Laplace-Beltrami operator. This technique allows to create an input space for
the neural network that represents the geometry of a given object. We
approximate the eigenfunctions as well as the operators involved in the partial
differential equations with finite elements. We extensively test and compare
the proposed methodology against traditional PINNs in complex shapes, such as a
coil, a heat sink and a bunny, with different physics, such as the Eikonal
equation and heat transfer. We also study the sensitivity of our method to the
number of eigenfunctions used, as well as the discretization used for the
eigenfunctions and the underlying operators. Our results show excellent
agreement with the ground truth data in cases where traditional PINNs fail to
produce a meaningful solution. We envision this new technique will expand the
effectiveness of PINNs to more realistic applications.

Quantum information, in the form of entanglement with an ancilla, can be
transmitted to a third system through interaction. Here, we investigate this
process of entanglement transmission perturbatively in time. Using the
entanglement monotone negativity, we determine how the proclivity of an
interaction to either generate, transfer or lose entanglement depends on the
choice of Hamiltonians and initial states. These three proclivities are
captured by Hamiltonian- and state-dependent quantities that we call negativity
susceptibility, negativity transmissibility and negativity vulnerability
respectively. These notions could serve, for example, as cost functions in
quantum technologies such as machine-learned quantum error correction.

Calibrating the redshift distributions of photometric galaxy samples is
essential in weak lensing studies. The self-calibration method combines angular
auto- and cross-correlations between galaxies in multiple photometric redshift
(photo-$z$) bins to reconstruct the scattering rates matrix between redshift
bins. In this paper, we test a recently proposed self-calibration algorithm
using the DECaLS Data Release 9 and investigate to what extent the scattering
rates are determined. We first mitigate the spurious angular correlations due
to imaging systematics by a machine learning based method. We then improve the
algorithm for $\chi^2$ minimization and error estimation. Finally, we solve for
the scattering matrices, carry out a series of consistency tests and find
reasonable agreements: (1) finer photo-$z$ bins return a high-resolution
scattering matrix, and it is broadly consistent with the low-resolution matrix
from wider bins; (2) the scattering matrix from the Northern Galactic Cap is
almost identical to that from Southern Galactic Cap; (3) the scattering
matrices are in reasonable agreement with those constructed from the power
spectrum and the weighted spectroscopic subsample. We also evaluate the impact
of cosmic magnification. Although it changes little the diagonal elements of
the scattering matrix, it affects the off-diagonals significantly. The
scattering matrix also shows some dependence on scale cut of input
correlations, which may be related to a known numerical degeneracy between
certain scattering pairs. This work demonstrates the feasibility of the
self-calibration method in real data and provides a practical alternative to
calibrate the redshift distributions of photometric samples.

We present LiDARGen, a novel, effective, and controllable generative model
that produces realistic LiDAR point cloud sensory readings. Our method
leverages the powerful score-matching energy-based model and formulates the
point cloud generation process as a stochastic denoising process in the
equirectangular view. This model allows us to sample diverse and high-quality
point cloud samples with guaranteed physical feasibility and controllability.
We validate the effectiveness of our method on the challenging KITTI-360 and
NuScenes datasets. The quantitative and qualitative results show that our
approach produces more realistic samples than other generative models.
Furthermore, LiDARGen can sample point clouds conditioned on inputs without
retraining. We demonstrate that our proposed generative model could be directly
used to densify LiDAR point clouds. Our code is available at:
https://www.zyrianov.org/lidargen/

We propose Fast text2StyleGAN, a natural language interface that adapts
pre-trained GANs for text-guided human face synthesis. Leveraging the recent
advances in Contrastive Language-Image Pre-training (CLIP), no text data is
required during training. Fast text2StyleGAN is formulated as a conditional
variational autoencoder (CVAE) that provides extra control and diversity to the
generated images at test time. Our model does not require re-training or
fine-tuning of the GANs or CLIP when encountering new text prompts. In contrast
to prior work, we do not rely on optimization at test time, making our method
orders of magnitude faster than prior work. Empirically, on FFHQ dataset, our
method offers faster and more accurate generation of images from natural
language descriptions with varying levels of detail compared to prior work.

Deep learning utilizing transformers has recently achieved a lot of success
in many vital areas such as natural language processing, computer vision,
anomaly detection, and recommendation systems, among many others. Among several
merits of transformers, the ability to capture long-range temporal dependencies
and interactions is desirable for time series forecasting, leading to its
progress in various time series applications. In this paper, we build a
transformer model for non-stationary time series. The problem is challenging
yet crucially important. We present a novel framework for univariate time
series representation learning based on the wavelet-based transformer encoder
architecture and call it W-Transformer. The proposed W-Transformers utilize a
maximal overlap discrete wavelet transformation (MODWT) to the time series data
and build local transformers on the decomposed datasets to vividly capture the
nonstationarity and long-range nonlinear dependencies in the time series.
Evaluating our framework on several publicly available benchmark time series
datasets from various domains and with diverse characteristics, we demonstrate
that it performs, on average, significantly better than the baseline
forecasters for short-term and long-term forecasting, even for datasets that
consist of only a few hundred training samples.

Recent advances in machine learning have led to growing interest in
Explainable AI (xAI) to enable humans to gain insight into the decision-making
of machine learning models. Despite this recent interest, the utility of xAI
techniques has not yet been characterized in human-machine teaming.
Importantly, xAI offers the promise of enhancing team situational awareness
(SA) and shared mental model development, which are the key characteristics of
effective human-machine teams. Rapidly developing such mental models is
especially critical in ad hoc human-machine teaming, where agents do not have a
priori knowledge of others' decision-making strategies. In this paper, we
present two novel human-subject experiments quantifying the benefits of
deploying xAI techniques within a human-machine teaming scenario. First, we
show that xAI techniques can support SA ($p<0.05)$. Second, we examine how
different SA levels induced via a collaborative AI policy abstraction affect ad
hoc human-machine teaming performance. Importantly, we find that the benefits
of xAI are not universal, as there is a strong dependence on the composition of
the human-machine team. Novices benefit from xAI providing increased SA
($p<0.05$) but are susceptible to cognitive overhead ($p<0.05$). On the other
hand, expert performance degrades with the addition of xAI-based support
($p<0.05$), indicating that the cost of paying attention to the xAI outweighs
the benefits obtained from being provided additional information to enhance SA.
Our results demonstrate that researchers must deliberately design and deploy
the right xAI techniques in the right scenario by carefully considering
human-machine team composition and how the xAI method augments SA.

Datasets scraped from the internet have been critical to the successes of
large-scale machine learning. Yet, this very success puts the utility of future
internet-derived datasets at potential risk, as model outputs begin to replace
human annotations as a source of supervision.
  In this work, we first formalize a system where interactions with one model
are recorded as history and scraped as training data in the future. We then
analyze its stability over time by tracking changes to a test-time bias
statistic (e.g. gender bias of model predictions). We find that the degree of
bias amplification is closely linked to whether the model's outputs behave like
samples from the training distribution, a behavior which we characterize and
define as consistent calibration. Experiments in three conditional prediction
scenarios - image classification, visual role-labeling, and language generation
- demonstrate that models that exhibit a sampling-like behavior are more
calibrated and thus more stable. Based on this insight, we propose an
intervention to help calibrate and stabilize unstable feedback systems.
  Code is available at https://github.com/rtaori/data_feedback.

Partial differential equations (PDEs) see widespread use in sciences and
engineering to describe simulation of physical processes as scalar and vector
fields interacting and coevolving over time. Due to the computationally
expensive nature of their standard solution methods, neural PDE surrogates have
become an active research topic to accelerate these simulations. However,
current methods do not explicitly take into account the relationship between
different fields and their internal components, which are often correlated.
Viewing the time evolution of such correlated fields through the lens of
multivector fields allows us to overcome these limitations. Multivector fields
consist of scalar, vector, as well as higher-order components, such as
bivectors and trivectors. Their algebraic properties, such as multiplication,
addition and other arithmetic operations can be described by Clifford algebras.
To our knowledge, this paper presents the first usage of such multivector
representations together with Clifford convolutions and Clifford Fourier
transforms in the context of deep learning. The resulting Clifford neural
layers are universally applicable and will find direct use in the areas of
fluid dynamics, weather forecasting, and the modeling of physical systems in
general. We empirically evaluate the benefit of Clifford neural layers by
replacing convolution and Fourier operations in common neural PDE surrogates by
their Clifford counterparts on two-dimensional Navier-Stokes and weather
modeling tasks, as well as three-dimensional Maxwell equations. Clifford neural
layers consistently improve generalization capabilities of the tested neural
PDE surrogates.

The term NeuralODE describes the structural combination of an Artifical
Neural Network (ANN) and a numerical solver for Ordinary Differential Equations
(ODEs), the former acts as the right-hand side of the ODE to be solved. This
concept was further extended by a black-box model in the form of a Functional
Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The
resulting structure features the advantages of first-principle and data-driven
modeling approaches in one single simulation model: A higher prediction
accuracy compared to conventional First Principle Models (FPMs), while also a
lower training effort compared to purely data-driven models. We present an
intuitive workflow to setup and use NeuralFMUs, enabling the encapsulation and
reuse of existing conventional models exported from common modeling tools.
Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption
simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a
typical use case in automotive industry. Related challenges that are often
neglected in scientific use cases, like real measurements (e.g. noise), an
unknown system state or high-frequent discontinuities, are handled in this
contribution. For the aim to build a hybrid model with a higher prediction
quality than the original FPM, we briefly highlight two open-source libraries:
FMI.jl for integrating FMUs into the Julia programming environment, as well as
an extension to this library called FMIFlux.jl, that allows for the integration
of FMUs into a neural network topology to finally obtain a NeuralFMU.

Machine learning (ML) techniques are increasingly prevalent in education,
from their use in predicting student dropout, to assisting in university
admissions, and facilitating the rise of MOOCs. Given the rapid growth of these
novel uses, there is a pressing need to investigate how ML techniques support
long-standing education principles and goals. In this work, we shed light on
this complex landscape drawing on qualitative insights from interviews with
education experts. These interviews comprise in-depth evaluations of ML for
education (ML4Ed) papers published in preeminent applied ML conferences over
the past decade. Our central research goal is to critically examine how the
stated or implied education and societal objectives of these papers are aligned
with the ML problems they tackle. That is, to what extent does the technical
problem formulation, objectives, approach, and interpretation of results align
with the education problem at hand. We find that a cross-disciplinary gap
exists and is particularly salient in two parts of the ML life cycle: the
formulation of an ML problem from education goals and the translation of
predictions to interventions. We use these insights to propose an extended ML
life cycle, which may also apply to the use of ML in other domains. Our work
joins a growing number of meta-analytical studies across education and ML
research, as well as critical analyses of the societal impact of ML.
Specifically, it fills a gap between the prevailing technical understanding of
machine learning and the perspective of education researchers working with
students and in policy.

We study a repeated information design problem faced by an informed sender
who tries to influence the behavior of a self-interested receiver. We consider
settings where the receiver faces a sequential decision making (SDM) problem.
At each round, the sender observes the realizations of random events in the SDM
problem. This begets the challenge of how to incrementally disclose such
information to the receiver to persuade them to follow (desirable) action
recommendations. We study the case in which the sender does not know random
events probabilities, and, thus, they have to gradually learn them while
persuading the receiver. We start by providing a non-trivial polytopal
approximation of the set of sender's persuasive information structures. This is
crucial to design efficient learning algorithms. Next, we prove a negative
result: no learning algorithm can be persuasive. Thus, we relax persuasiveness
requirements by focusing on algorithms that guarantee that the receiver's
regret in following recommendations grows sub-linearly. In the full-feedback
setting -- where the sender observes all random events realizations -- , we
provide an algorithm with $\tilde{O}(\sqrt{T})$ regret for both the sender and
the receiver. Instead, in the bandit-feedback setting -- where the sender only
observes the realizations of random events actually occurring in the SDM
problem -- , we design an algorithm that, given an $\alpha \in [1/2, 1]$ as
input, ensures $\tilde{O}({T^\alpha})$ and $\tilde{O}( T^{\max \{ \alpha,
1-\frac{\alpha}{2} \} })$ regrets, for the sender and the receiver
respectively. This result is complemented by a lower bound showing that such a
regrets trade-off is essentially tight.

This is the technical report of the 9th place in the final result of
PARSE2022 Challenge. We solve the segmentation problem of the pulmonary artery
by using a two-stage method based on a 3D CNN network. The coarse model is used
to locate the ROI, and the fine model is used to refine the segmentation
result. In addition, in order to improve the segmentation performance, we adopt
multi-view and multi-window level method, at the same time we employ a
fine-tune strategy to mitigate the impact of inconsistent labeling.

Masked autoencoders have become popular training paradigms for
self-supervised visual representation learning. These models randomly mask a
portion of the input and reconstruct the masked portion according to the target
representations. In this paper, we first show that a careful choice of the
target representation is unnecessary for learning good representations, since
different targets tend to derive similarly behaved models. Driven by this
observation, we propose a multi-stage masked distillation pipeline and use a
randomly initialized model as the teacher, enabling us to effectively train
high-capacity models without any efforts to carefully design target
representations. Interestingly, we further explore using teachers of larger
capacity, obtaining distilled students with remarkable transferring ability. On
different tasks of classification, transfer learning, object detection, and
semantic segmentation, the proposed method to perform masked knowledge
distillation with bootstrapped teachers (dBOT) outperforms previous
self-supervised methods by nontrivial margins. We hope our findings, as well as
the proposed method, could motivate people to rethink the roles of target
representations in pre-training masked autoencoders.

We present PixTrack, a vision based object pose tracking framework using
novel view synthesis and deep feature-metric alignment. Our evaluations
demonstrate that our method produces highly accurate, robust, and jitter-free
6DoF pose estimates of objects in RGB images without the need of any data
annotation or trajectory smoothing. Our method is also computationally
efficient making it easy to have multi-object tracking with no alteration to
our method and just using CPU multiprocessing.

Graph neural network (GNN) based methods have saturated the field of
recommender systems. The gains of these systems have been significant, showing
the advantages of interpreting data through a network structure. However,
despite the noticeable benefits of using graph structures in recommendation
tasks, this representational form has also bred new challenges which exacerbate
the complexity of mitigating algorithmic bias. When GNNs are integrated into
downstream tasks, such as recommendation, bias mitigation can become even more
difficult. Furthermore, the intractability of applying existing methods of
fairness promotion to large, real world datasets places even more serious
constraints on mitigation attempts. Our work sets out to fill in this gap by
taking an existing method for promoting individual fairness on graphs and
extending it to support mini-batch, or sub-sample based, training of a GNN,
thus laying the groundwork for applying this method to a downstream
recommendation task. We evaluate two popular GNN methods: Graph Convolutional
Network (GCN), which trains on the entire graph, and GraphSAGE, which uses
probabilistic random walks to create subgraphs for mini-batch training, and
assess the effects of sub-sampling on individual fairness. We implement an
individual fairness notion called \textit{REDRESS}, proposed by Dong et al.,
which uses rank optimization to learn individual fair node, or item,
embeddings. We empirically show on two real world datasets that GraphSAGE is
able to achieve, not just, comparable accuracy, but also, improved fairness as
compared with the GCN model. These finding have consequential ramifications to
individual fairness promotion, GNNs, and in downstream form, recommender
systems, showing that mini-batch training facilitate individual fairness
promotion by allowing for local nuance to guide the process of fairness
promotion in representation learning.

Identifying the quantum chromodynamics (QCD) color structure of processes
provides additional information to enhance the reach for new physics searches
at the Large Hadron Collider (LHC). Analyses of QCD color structure in the
decay process of a boosted particle have been spotted as information becomes
well localized in the limited phase space. While these kind of a boosted jet
analyses provide an efficient way to identify a color structure, the
constrained phase space reduces the number of available data, resulting in a
low significance. In this letter, we provide a simple but a novel data
preprocessing method using a Riemann sphere to utilize a full phase space by
decorrelating QCD structure from a kinematics. We can achieve a statistical
stability by enlarging the size of testable data set with focusing on QCD
structure effectively. We demonstrate the power of our method at the finite
statistics of the LHC Run 2. Our method is complementary to conventional
boosted jet analyses in utilizing QCD information over the wide range of a
phase space.

In this paper, we describe our participation in the subtask 1 of CASE-2022,
Event Causality Identification with Casual News Corpus. We address the Causal
Relation Identification (CRI) task by exploiting a set of simple yet
complementary techniques for fine-tuning language models (LMs) on a small
number of annotated examples (i.e., a few-shot configuration). We follow a
prompt-based prediction approach for fine-tuning LMs in which the CRI task is
treated as a masked language modeling problem (MLM). This approach allows LMs
natively pre-trained on MLM problems to directly generate textual responses to
CRI-specific prompts. We compare the performance of this method against
ensemble techniques trained on the entire dataset. Our best-performing
submission was fine-tuned with only 256 instances per class, 15.7% of the all
available data, and yet obtained the second-best precision (0.82), third-best
accuracy (0.82), and an F1-score (0.85) very close to what was reported by the
winner team (0.86).

Privacy-preserving data analysis has become prevailing in recent years. In
this paper, we propose a distributed group differentially private majority vote
mechanism for the sign selection problem in a distributed setup. To achieve
this, we apply the iterative peeling to the stability function and use the
exponential mechanism to recover the signs. As applications, we study the
private sign selection for mean estimation and linear regression problems in
distributed systems. Our method recovers the support and signs with the optimal
signal-to-noise ratio as in the non-private scenario, which is better than
contemporary works of private variable selections. Moreover, the sign selection
consistency is justified with theoretical guarantees. Simulation studies are
conducted to demonstrate the effectiveness of our proposed method.

The field of multi-agent reinforcement learning (MARL) has made considerable
progress towards controlling challenging multi-agent systems by employing
various learning methods. Numerous of these approaches focus on empirical and
algorithmic aspects of the MARL problems and lack a rigorous theoretical
foundation. Graphon mean field games (GMFGs) on the other hand provide a
scalable and mathematically well-founded approach to learning problems that
involve a large number of connected agents. In standard GMFGs, the connections
between agents are undirected, unweighted and invariant over time. Our paper
introduces colored digraphon mean field games (CDMFGs) which allow for weighted
and directed links between agents that are also adaptive over time. Thus,
CDMFGs are able to model more complex connections than standard GMFGs. Besides
a rigorous theoretical analysis including both existence and convergence
guarantees, we provide a learning scheme and illustrate our findings with an
epidemics model and a model of the systemic risk in financial markets.

Federated learning (FL) has emerged as a practical solution to tackle data
silo issues without compromising user privacy. One of its variants, vertical
federated learning (VFL), has recently gained increasing attention as the VFL
matches the enterprises' demands of leveraging more valuable features to build
better machine learning models while preserving user privacy. Current works in
VFL concentrate on developing a specific protection or attack mechanism for a
particular VFL algorithm. In this work, we propose an evaluation framework that
formulates the privacy-utility evaluation problem. We then use this framework
as a guide to comprehensively evaluate a broad range of protection mechanisms
against most of the state-of-the-art privacy attacks for three widely-deployed
VFL algorithms. These evaluations may help FL practitioners select appropriate
protection mechanisms given specific requirements. Our evaluation results
demonstrate that: the model inversion and most of the label inference attacks
can be thwarted by existing protection mechanisms; the model completion (MC)
attack is difficult to be prevented, which calls for more advanced MC-targeted
protection mechanisms. Based on our evaluation results, we offer concrete
advice on improving the privacy-preserving capability of VFL systems.

In soccer (or association football), players quickly go from heroes to
zeroes, or vice-versa. Performance is not a static measure but a somewhat
volatile one. Analyzing performance as a time series rather than a stationary
point in time is crucial to making better decisions. This paper introduces and
explores I-VAEP and O-VAEP models to evaluate actions and rate players'
intention and execution. Then, we analyze these ratings over time and propose
use cases to fundament our option of treating player ratings as a continuous
problem. As a result, we present who were the best players and how their
performance evolved, define volatility metrics to measure a player's
consistency, and build a player development curve to assist decision-making.

Although the field of multi-agent reinforcement learning (MARL) has made
considerable progress in the last years, solving systems with a large number of
agents remains a hard challenge. Graphon mean field games (GMFGs) enable the
scalable analysis of MARL problems that are otherwise intractable. By the
mathematical structure of graphons, this approach is limited to dense graphs
which are insufficient to describe many real-world networks such as power law
graphs. Our paper introduces a novel formulation of GMFGs, called LPGMFGs,
which leverages the graph theoretical concept of $L^p$ graphons and provides a
machine learning tool to efficiently and accurately approximate solutions for
sparse network problems. This especially includes power law networks which are
empirically observed in various application areas and cannot be captured by
standard graphons. We derive theoretical existence and convergence guarantees
and give empirical examples that demonstrate the accuracy of our learning
approach for systems with many agents. Furthermore, we rigorously extend the
Online Mirror Descent (OMD) learning algorithm to our setup to accelerate
learning speed, allow for agent interaction through the mean field in the
transition kernel, and empirically show its capabilities. In general, we
provide a scalable, mathematically well-founded machine learning approach to a
large class of otherwise intractable problems of great relevance in numerous
research fields.

Synthetic aperture sonar (SAS) imagery is crucial for several applications,
including target recognition and environmental segmentation. Deep learning
models have led to much success in SAS analysis; however, the features
extracted by these approaches may not be suitable for capturing certain
textural information. To address this problem, we present a novel application
of histogram layers on SAS imagery. The addition of histogram layer(s) within
the deep learning models improved performance by incorporating statistical
texture information on both synthetic and real-world datasets.

The analysis and control of large-population systems is of great interest to
diverse areas of research and engineering, ranging from epidemiology over
robotic swarms to economics and finance. An increasingly popular and effective
approach to realizing sequential decision-making in multi-agent systems is
through multi-agent reinforcement learning, as it allows for an automatic and
model-free analysis of highly complex systems. However, the key issue of
scalability complicates the design of control and reinforcement learning
algorithms particularly in systems with large populations of agents. While
reinforcement learning has found resounding empirical success in many scenarios
with few agents, problems with many agents quickly become intractable and
necessitate special consideration. In this survey, we will shed light on
current approaches to tractably understanding and analyzing large-population
systems, both through multi-agent reinforcement learning and through adjacent
areas of research such as mean-field games, collective intelligence, or complex
network theory. These classically independent subject areas offer a variety of
approaches to understanding or modeling large-population systems, which may be
of great use for the formulation of tractable MARL algorithms in the future.
Finally, we survey potential areas of application for large-scale control and
identify fruitful future applications of learning algorithms in practical
systems. We hope that our survey could provide insight and future directions to
junior and senior researchers in theoretical and applied sciences alike.

In recent years, graph neural networks (GNNs) combined with variants of
recurrent neural networks (RNNs) have reached state-of-the-art performance in
spatiotemporal forecasting tasks. This is particularly the case for traffic
forecasting, where GNN models use the graph structure of road networks to
account for spatial correlation between links and nodes. Recent solutions are
either based on complex graph operations or avoiding predefined graphs. This
paper proposes a new sequence-to-sequence architecture to extract the
spatiotemporal correlation at multiple levels of abstraction using GNN-RNN
cells with sparse architecture to decrease training time compared to more
complex designs. Encoding the same input sequence through multiple encoders,
with an incremental increase in encoder layers, enables the network to learn
general and detailed information through multilevel abstraction. We further
present a new benchmark dataset of street-level segment traffic data from
Montreal, Canada. Unlike highways, urban road segments are cyclic and
characterized by complicated spatial dependencies. Experimental results on the
METR-LA benchmark highway and our MSLTD street-level segment datasets
demonstrate that our model improves performance by more than 7% for one-hour
prediction compared to the baseline methods while reducing computing resource
requirements by more than half compared to other competing methods.

Multi-objective optimization problems are ubiquitous in robotics, e.g., the
optimization of a robot manipulation task requires a joint consideration of
grasp pose configurations, collisions and joint limits. While some demands can
be easily hand-designed, e.g., the smoothness of a trajectory, several
task-specific objectives need to be learned from data. This work introduces a
method for learning data-driven SE(3) cost functions as diffusion models.
Diffusion models can represent highly-expressive multimodal distributions and
exhibit proper gradients over the entire space due to their score-matching
training objective. Learning costs as diffusion models allows their seamless
integration with other costs into a single differentiable objective function,
enabling joint gradient-based motion optimization. In this work, we focus on
learning SE(3) diffusion models for 6DoF grasping, giving rise to a novel
framework for joint grasp and motion optimization without needing to decouple
grasp selection from trajectory generation. We evaluate the representation
power of our SE(3) diffusion models w.r.t. classical generative models, and we
showcase the superior performance of our proposed optimization framework in a
series of simulated and real-world robotic manipulation tasks against
representative baselines.

Automation of social network data assessment is one of the classic challenges
of natural language processing. During the COVID-19 pandemic, mining people's
stances from public messages have become crucial regarding understanding
attitudes towards health orders. In this paper, the authors propose the
predictive model based on transformer architecture to classify the presence of
premise in Twitter texts. This work is completed as part of the Social Media
Mining for Health (SMM4H) Workshop 2022. We explored modern transformer-based
classifiers in order to construct the pipeline efficiently capturing tweets
semantics. Our experiments on a Twitter dataset showed that RoBERTa is superior
to the other transformer models in the case of the premise prediction task. The
model achieved competitive performance with respect to ROC AUC value 0.807, and
0.7648 for the F1 score.

There exist opportunities for Multivariate Linear Regression (MLR) in the
prediction of Biochemical Oxygen Demand (BOD) in waste water, using the diverse
water quality parameters as the input variables. The goal of this work is to
examine the capability of MLR in prediction of BOD in waste water through four
input variables: Dissolved Oxygen (DO), Nitrogen, Fecal Coliform and Total
Coliform. The four input variables have higher correlation strength to BOD out
of the seven parameters examined for the strength of correlation. Machine
Learning (ML) was done with both 80% and 90% of the data as the training set
and 20% and 10% as the test set respectively. MLR performance was evaluated
through the coefficient of correlation (r), Root Mean Square Error (RMSE) and
the percentage accuracy in prediction of BOD. The performance indices for the
input variables of Dissolved Oxygen, Nitrogen, Fecal Coliform and Total
Coliform in prediction of BOD are: RMSE=6.77mg/L, r=0.60 and accuracy 70.3% for
training dataset of 80% and RMSE=6.74mg/L, r=0.60 and accuracy of 87.5% for
training set of 90% of the dataset. It was found that increasing the percentage
of the training set above 80% of the dataset improved the accuracy of the model
only but did not have a significant impact on the prediction capacity of the
model. The results showed that MLR model could be successfully employed in the
estimation of BOD in waste water using appropriately selected input parameters.

Fingerprint feature extraction is a task that is solved using either a global
or a local representation. State-of-the-art global approaches use heavy deep
learning models to process the full fingerprint image at once, which makes the
corresponding approach memory intensive. On the other hand, local approaches
involve minutiae based patch extraction, multiple feature extraction steps and
an expensive matching stage, which make the corresponding approach time
intensive. However, both these approaches provide useful and sometimes
exclusive insights for solving the problem. Using both approaches together for
extracting fingerprint representations is semantically useful but quite
inefficient. Our convolutional transformer based approach with an in-built
minutiae extractor provides a time and memory efficient solution to extract a
global as well as a local representation of the fingerprint. The use of these
representations along with a smart matching process gives us state-of-the-art
performance across multiple databases. The project page can be found at
https://saraansh1999.github.io/global-plus-local-fp-transformer.

Adversarial Training (AT) has been proven to be an effective method of
introducing strong adversarial robustness into deep neural networks. However,
the high computational cost of AT prohibits the deployment of large-scale AT on
resource-constrained edge devices, e.g., with limited computing power and small
memory footprint, in Federated Learning (FL) applications. Very few previous
studies have tried to tackle these constraints in FL at the same time. In this
paper, we propose a new framework named Federated Adversarial Decoupled
Learning (FADE) to enable AT on resource-constrained edge devices in FL. FADE
reduces the computation and memory usage by applying Decoupled Greedy Learning
(DGL) to federated adversarial training such that each client only needs to
perform AT on a small module of the entire model in each communication round.
In addition, we improve vanilla DGL by adding an auxiliary weight decay to
alleviate objective inconsistency and achieve better performance. FADE offers a
theoretical guarantee for the adversarial robustness and convergence. The
experimental results also show that FADE can significantly reduce the computing
resources consumed by AT while maintaining almost the same accuracy and
robustness as fully joint training.

Quantum computers based on gate-defined quantum dots (QDs) are expected to
scale. However, as the number of qubits increases, the burden of manually
calibrating these systems becomes unreasonable and autonomous tuning must be
used. There have been a range of recent demonstrations of automated tuning of
various QD parameters such as coarse gate ranges, global state topology (e.g.
single QD, double QD), charge, and tunnel coupling with a variety of methods.
Here, we demonstrate an intuitive, reliable, and data-efficient set of tools
for automated global state and charge tuning in a framework deemed
physics-informed tuning (PIT). The first module of PIT is an action-based
algorithm that combines a machine learning (ML) classifier with physics
knowledge to navigate to a target global state. The second module uses a series
of one-dimensional measurements to tune to a target charge state by first
emptying the QDs of charge, followed by calibrating capacitive couplings, and
navigating to the target charge state. The success rate for the action-based
tuning consistently surpasses $95~\%$ on both simulated and experimental data
suitable for off-line testing. The success rate for charge setting is
comparable when testing with simulated data, at $95.5(5.4)~\%$, and only
slightly worse for off-line experimental tests, with an average of
$89.7(17.4)~\%$ (median $97.5~\%$). It's noteworthy that the high performance
is demonstrated both on data from samples fabricated in an academic cleanroom
as well as on an industrial 300 mm process line, further underlining the
device-agnosticity of PIT. Together, these tests on a range of simulated and
experimental devices demonstrate the effectiveness and robustness of PIT.

Transformer-based pre-trained models have gained much advance in recent
years, becoming one of the most important backbones in natural language
processing. Recent work shows that the attention mechanism inside Transformer
may not be necessary, both convolutional neural networks and multi-layer
perceptron based models have also been investigated as Transformer
alternatives. In this paper, we consider a graph recurrent network for language
model pre-training, which builds a graph structure for each sequence with local
token-level communications, together with a sentence-level representation
decoupled from other tokens. The original model performs well in
domain-specific text classification under supervised training, however, its
potential in learning transfer knowledge by self-supervised way has not been
fully exploited. We fill this gap by optimizing the architecture and verifying
its effectiveness in more general language understanding tasks, for both
English and Chinese languages. As for model efficiency, instead of the
quadratic complexity in Transformer-based models, our model has linear
complexity and performs more efficiently during inference. Moreover, we find
that our model can generate more diverse outputs with less contextualized
feature redundancy than existing attention-based models.

While the methods exploiting the tensor low-rank prior are booming in
high-dimensional data processing and have obtained satisfying performance,
their applications in dynamic magnetic resonance (MR) image reconstruction are
limited. In this paper, we concentrate on the tensor singular value
decomposition (t-SVD), which is based on the Fast Fourier Transform (FFT) and
only provides the definite and limited tensor low-rank prior in the FFT domain,
heavily reliant upon how closely the data and the FFT domain match up. By
generalizing the FFT into an arbitrary unitary transformation of the
transformed t-SVD and proposing the transformed tensor nuclear norm (TTNN), we
introduce a flexible model based on TTNN with the ability to exploit the tensor
low-rank prior of a transformed domain in a larger transformation space and
elaborately design an iterative optimization algorithm based on the alternating
direction method of multipliers (ADMM), which is further unrolled into a
model-based deep unrolling reconstruction network to learn the transformed
tensor low-rank prior (T$^2$LR-Net). The convolutional neural network (CNN) is
incorporated within the T$^2$LR-Net to learn the best-matched transform from
the dynamic MR image dataset. The unrolling reconstruction network also
provides a new perspective on the low-rank prior utilization by exploiting the
low-rank prior in the CNN-extracted feature domain. Experimental results on two
cardiac cine MR datasets demonstrate that the proposed framework can provide
improved recovery results compared with the state-of-the-art optimization-based
and unrolling network-based methods.

This paper provides a detailed overview of a case study of applying Continual
Learning (CL) to a single-session Human-Robot Interaction (HRI) session (avg.
31 +- 10 minutes), where a robotic mental well-being coach conducted Positive
Psychology (PP) exercises with (n = 20) participants. We present the results of
a Thematic Analysis (TA) of data recorded from brief semi-structured interviews
that were conducted with participants after the interaction sessions, as well
as an analysis of statistical results demonstrating how participants'
personalities may affect how they perceive the robot and its interactions.

Machine learning algorithms are increasingly used to assist human
decision-making. When the goal of machine assistance is to improve the accuracy
of human decisions, it might seem appealing to design ML algorithms that
complement human knowledge. While neither the algorithm nor the human are
perfectly accurate, one could expect that their complementary expertise might
lead to improved outcomes. In this study, we demonstrate that in practice
decision aids that are not complementary, but make errors similar to human ones
may have their own benefits.
  In a series of human-subject experiments with a total of 901 participants, we
study how the similarity of human and machine errors influences human
perceptions of and interactions with algorithmic decision aids. We find that
(i) people perceive more similar decision aids as more useful, accurate, and
predictable, and that (ii) people are more likely to take opposing advice from
more similar decision aids, while (iii) decision aids that are less similar to
humans have more opportunities to provide opposing advice, resulting in a
higher influence on people's decisions overall.

Organizational knowledge bases are moving from passive archives to active
entities in the flow of people's work. We are seeing machine learning used to
enable systems that both collect and surface information as people are working,
making it possible to bring out connections between people and content that
were previously much less visible in order to automatically identify and
highlight experts on a given topic. When these knowledge bases begin to
actively bring attention to people and the content they work on, especially as
that work is still ongoing, we run into important challenges at the
intersection of work and the social. While such systems have the potential to
make certain parts of people's work more productive or enjoyable, they may also
introduce new workloads, for instance by putting people in the role of experts
for others to reach out to. And these knowledge bases can also have profound
social consequences by changing what parts of work are visible and, therefore,
acknowledged. We pose a number of open questions that warrant attention and
engagement across industry and academia. Addressing these questions is an
essential step in ensuring that the future of work becomes a good future for
those doing the work. With this position paper, we wish to enter into the
cross-disciplinary discussion we believe is required to tackle the challenge of
developing recommender systems that respect social values.

Explainability techniques for data-driven predictive models based on
artificial intelligence and machine learning algorithms allow us to better
understand the operation of such systems and help to hold them accountable. New
transparency approaches are developed at breakneck speed, enabling us to peek
inside these black boxes and interpret their decisions. Many of these
techniques are introduced as monolithic tools, giving the impression of
one-size-fits-all and end-to-end algorithms with limited customisability.
Nevertheless, such approaches are often composed of multiple interchangeable
modules that need to be tuned to the problem at hand to produce meaningful
explanations. This paper introduces a collection of hands-on training materials
-- slides, video recordings and Jupyter Notebooks -- that provide guidance
through the process of building and evaluating bespoke modular surrogate
explainers for tabular data. These resources cover the three core building
blocks of this technique: interpretable representation composition, data
sampling and explanation generation.

We consider a scenario where multiple agents are learning a common decision
vector from data which can be influenced by the agents' decisions. This leads
to the problem of multi-agent performative prediction (Multi-PfD). In this
paper, we formulate Multi-PfD as a decentralized optimization problem that
minimizes a sum of loss functions, where each loss function is based on a
distribution influenced by the local decision vector. We first prove the
necessary and sufficient condition for the Multi-PfD problem to admit a unique
multi-agent performative stable (Multi-PS) solution. We show that enforcing
consensus leads to a laxer condition for the existence of Multi-PS solution
with respect to the distributions' sensitivities, compared to the single agent
case. Then, we study a decentralized extension to the greedy deployment scheme
[Mendler-D\"unner et al., 2020], called the DSGD-GD scheme. We show that
DSGD-GD converges to the Multi-PS solution and analyze its non-asymptotic
convergence rate. Numerical results validate our analysis.

Predictive systems, in particular machine learning algorithms, can take
important, and sometimes legally binding, decisions about our everyday life. In
most cases, however, these systems and decisions are neither regulated nor
certified. Given the potential harm that these algorithms can cause, their
qualities such as fairness, accountability and transparency (FAT) are of
paramount importance. To ensure high-quality, fair, transparent and reliable
predictive systems, we developed an open source Python package called FAT
Forensics. It can inspect important fairness, accountability and transparency
aspects of predictive algorithms to automatically and objectively report them
back to engineers and users of such systems. Our toolbox can evaluate all
elements of a predictive pipeline: data (and their features), models and
predictions. Published under the BSD 3-Clause open source licence, FAT
Forensics is opened up for personal and commercial usage.

Natural disasters can cause substantial negative socio-economic impacts
around the world, due to mortality, relocation, rates, and reconstruction
decisions. Robotics has been successfully applied to identify and rescue
victims during the occurrence of a natural hazard. However, little effort has
been taken to deploy solutions where an autonomous robot can save the life of a
citizen by itself relocating it, without the need to wait for a rescue team
composed of humans. Reinforcement learning approaches can be used to deploy
such a solution, however, one of the most famous algorithms to deploy it, the
Q-learning, suffers from biased results generated when performing its learning
routines. In this research a solution for citizen relocation based on Partially
Observable Markov Decision Processes is adopted, where the capability of the
Double Q-learning in relocating citizens during a natural hazard is evaluated
under a proposed hazard simulation engine based on a grid world. The
performance of the solution was measured as a success rate of a citizen
relocation procedure, where the results show that the technique portrays a
performance above 100% for easy scenarios and near 50% for hard ones.

We propose a general framework to adapt various local explanation techniques
to recurrent neural networks. In particular, our explanations add temporal
information, which expand explanations generated from existing techniques to
cover data points that have different lengths compared to the original input
data point. Our approach is general as it only modifies the perturbation model
and feature representation of existing techniques without touching their core
algorithms. We have instantiated our approach on LIME and Anchors. Our
empirical evaluation shows that it effectively improves the usefulness of
explanations generated by these two techniques on a sentiment analysis network
and an anomaly detection network.

In this paper, we introduce novel lightweight generative adversarial
networks, which can effectively capture long-range dependencies in the image
generation process, and produce high-quality results with a much simpler
architecture. To achieve this, we first introduce a long-range module, allowing
the network to dynamically adjust the number of focused sampling pixels and to
also augment sampling locations. Thus, it can break the limitation of the fixed
geometric structure of the convolution operator, and capture long-range
dependencies in both spatial and channel-wise directions. Also, the proposed
long-range module can highlight negative relations between pixels, working as a
regularization to stabilize training. Furthermore, we propose a new generation
strategy through which we introduce metadata into the image generation process
to provide basic information about target images, which can stabilize and speed
up the training process. Our novel long-range module only introduces few
additional parameters and is easily inserted into existing models to capture
long-range dependencies. Extensive experiments demonstrate the competitive
performance of our method with a lightweight architecture.

Keyphrases are crucial for searching and systematizing scholarly documents.
Most current methods for keyphrase extraction are aimed at the extraction of
the most significant words in the text. But in practice, the list of keyphrases
often includes words that do not appear in the text explicitly. In this case,
the list of keyphrases represents an abstractive summary of the source text. In
this paper, we experiment with popular transformer-based models for abstractive
text summarization using four benchmark datasets for keyphrase extraction. We
compare the results obtained with the results of common unsupervised and
supervised methods for keyphrase extraction. Our evaluation shows that
summarization models are quite effective in generating keyphrases in the terms
of the full-match F1-score and BERTScore. However, they produce a lot of words
that are absent in the author's list of keyphrases, which makes summarization
models ineffective in terms of ROUGE-1. We also investigate several ordering
strategies to concatenate target keyphrases. The results showed that the choice
of strategy affects the performance of keyphrase generation.

In brain-computer interfaces (BCI) research, recording data is time-consuming
and expensive, which limits access to big datasets. This may influence the BCI
system performance as machine learning methods depend strongly on the training
dataset size. Important questions arise: taking into account neuronal signal
characteristics (e.g., non-stationarity), can we achieve higher decoding
performance with more data to train decoders? What is the perspective for
further improvement with time in the case of long-term BCI studies? In this
study, we investigated the impact of long-term recordings on motor imagery
decoding from two main perspectives: model requirements regarding dataset size
and potential for patient adaptation. We evaluated the multilinear model and
two deep learning (DL) models on a long-term BCI and Tetraplegia NCT02550522
clinical trial dataset containing 43 sessions of ECoG recordings performed with
a tetraplegic patient. In the experiment, a participant executed 3D virtual
hand translation using motor imagery patterns. We designed multiple
computational experiments in which training datasets were increased or
translated to investigate the relationship between models' performance and
different factors influencing recordings. Our analysis showed that adding more
data to the training dataset may not instantly increase performance for
datasets already containing 40 minutes of the signal. DL decoders showed
similar requirements regarding the dataset size compared to the multilinear
model while demonstrating higher decoding performance. Moreover, high decoding
performance was obtained with relatively small datasets recorded later in the
experiment, suggesting motor imagery patterns improvement and patient
adaptation. Finally, we proposed UMAP embeddings and local intrinsic
dimensionality as a way to visualize the data and potentially evaluate data
quality.

The ultimate goal of any sparse coding method is to accurately recover from a
few noisy linear measurements, an unknown sparse vector. Unfortunately, this
estimation problem is NP-hard in general, and it is therefore always approached
with an approximation method, such as lasso or orthogonal matching pursuit,
thus trading off accuracy for less computational complexity. In this paper, we
develop a quantum-inspired algorithm for sparse coding, with the premise that
the emergence of quantum computers and Ising machines can potentially lead to
more accurate estimations compared to classical approximation methods. To this
end, we formulate the most general sparse coding problem as a quadratic
unconstrained binary optimization (QUBO) task, which can be efficiently
minimized using quantum technology. To derive at a QUBO model that is also
efficient in terms of the number of spins (space complexity), we separate our
analysis into three different scenarios. These are defined by the number of
bits required to express the underlying sparse vector: binary, 2-bit, and a
general fixed-point representation. We conduct numerical experiments with
simulated data on LightSolver's quantum-inspired digital platform to verify the
correctness of our QUBO formulation and to demonstrate its advantage over

The growing complexity and capacity demands for mobile networks necessitate
innovative techniques for optimizing resource usage. Meanwhile, recent
breakthroughs have brought Reinforcement Learning (RL) into the domain of
continuous control of real-world systems. As a step towards RL-based network
control, this paper introduces a new framework for benchmarking the performance
of an RL agent in network environments simulated with ns-3. Within this
framework, we demonstrate that an RL agent without domain-specific knowledge
can learn how to efficiently adjust Radio Access Network (RAN) parameters to
match offline optimization in static scenarios, while also adapting on the fly
in dynamic scenarios, in order to improve the overall user experience. Our
proposed framework may serve as a foundation for further work in developing
workflows for designing RL-based RAN control algorithms.

Categorical data are present in key areas such as health or supply chain, and
this data require specific treatment. In order to apply recent machine learning
models on such data, encoding is needed. In order to build interpretable
models, one-hot encoding is still a very good solution, but such encoding
creates sparse data. Gradient estimators are not suited for sparse data: the
gradient is mainly considered as zero while it simply does not always exists,
thus a novel gradient estimator is introduced. We show what this estimator
minimizes in theory and show its efficiency on different datasets with multiple
model architectures. This new estimator performs better than common estimators
under similar settings. A real world retail dataset is also released after
anonymization. Overall, the aim of this paper is to thoroughly consider
categorical data and adapt models and optimizers to these key features.

Discriminative Feature Feedback is a setting proposed by Dastupta et al.
(2018), which provides a protocol for interactive learning based on feature
explanations that are provided by a human teacher. The features distinguish
between the labels of pairs of possibly similar instances. That work has shown
that learning in this model can have considerable statistical and computational
advantages over learning in standard label-based interactive learning models.
  In this work, we provide new robust interactive learning algorithms for the
Discriminative Feature Feedback model, with mistake bounds that are
significantly lower than those of previous robust algorithms for this setting.
In the adversarial setting, we reduce the dependence on the number of protocol
exceptions from quadratic to linear. In addition, we provide an algorithm for a
slightly more restricted model, which obtains an even smaller mistake bound for
large models with many exceptions.
  In the stochastic setting, we provide the first algorithm that converges to
the exception rate with a polynomial sample complexity. Our algorithm and
analysis for the stochastic setting involve a new construction that we call
Feature Influence, which may be of wider applicability.

Normal fetal adipose tissue (AT) development is essential for perinatal
well-being. AT, or simply fat, stores energy in the form of lipids.
Malnourishment may result in excessive or depleted adiposity. Although previous
studies showed a correlation between the amount of AT and perinatal outcome,
prenatal assessment of AT is limited by lacking quantitative methods. Using
magnetic resonance imaging (MRI), 3D fat- and water-only images of the entire
fetus can be obtained from two point Dixon images to enable AT lipid
quantification. This paper is the first to present a methodology for developing
a deep learning based method for fetal fat segmentation based on Dixon MRI. It
optimizes radiologists' manual fetal fat delineation time to produce annotated
training dataset. It consists of two steps: 1) model-based semi-automatic fetal
fat segmentations, reviewed and corrected by a radiologist; 2) automatic fetal
fat segmentation using DL networks trained on the resulting annotated dataset.
Three DL networks were trained. We show a significant improvement in
segmentation times (3:38 hours to < 1 hour) and observer variability (Dice of
0.738 to 0.906) compared to manual segmentation. Automatic segmentation of 24
test cases with the 3D Residual U-Net, nn-UNet and SWIN-UNetR transformer
networks yields a mean Dice score of 0.863, 0.787 and 0.856, respectively.
These results are better than the manual observer variability, and comparable
to automatic adult and pediatric fat segmentation. A radiologist reviewed and
corrected six new independent cases segmented using the best performing
network, resulting in a Dice score of 0.961 and a significantly reduced
correction time of 15:20 minutes. Using these novel segmentation methods and
short MRI acquisition time, whole body subcutaneous lipids can be quantified
for individual fetuses in the clinic and large-cohort research.

COVID-19 has affected more than 223 countries worldwide. There is a pressing
need for non invasive, low costs and highly scalable solutions to detect
COVID-19, especially in low-resource countries where PCR testing is not
ubiquitously available. Our aim is to develop a deep learning model identifying
COVID-19 using voice data recordings spontaneously provided by the general
population (voice recordings and a short questionnaire) via their personal
devices. The novelty of this work is in the development of a deep learning
model for the identification of COVID-19 patients from voice recordings.
Methods: We used the Cambridge University dataset consisting of 893 audio
samples, crowd-sourced from 4352 participants that used a COVID-19 Sounds app.
Voice features were extracted using a Mel-spectrogram analysis. Based on the
voice data, we developed deep learning classification models to detect positive
COVID-19 cases. These models included Long-Short Term Memory (LSTM) and
Convolutional Neural Network (CNN). We compared their predictive power to
baseline classification models, namely Logistic Regression and Support Vector
Machine. Results: LSTM based on a Mel-frequency cepstral coefficients (MFCC)
features achieved the highest accuracy (89%,) with a sensitivity and
specificity of respectively 89% and 89%, The results achieved with the proposed
model suggest a significant improvement in the prediction accuracy of COVID-19
diagnosis compared to the results obtained in the state of the art. Conclusion:
Deep learning can detect subtle changes in the voice of COVID-19 patients with
promising results. As an addition to the current testing techniques this model
may aid health professionals in fast diagnosis and tracing of COVID-19 cases
using simple voice analysis

Photovoltaic (PV) energy generation plays a crucial role in the energy
transition. Small-scale PV installations are deployed at an unprecedented pace,
and their integration into the grid can be challenging since public authorities
often lack quality data about them. Overhead imagery is increasingly used to
improve the knowledge of residential PV installations with machine learning
models capable of automatically mapping these installations. However, these
models cannot be easily transferred from one region or data source to another
due to differences in image acquisition. To address this issue known as domain
shift and foster the development of PV array mapping pipelines, we propose a
dataset containing aerial images, annotations, and segmentation masks. We
provide installation metadata for more than 28,000 installations. We provide
ground truth segmentation masks for 13,000 installations, including 7,000 with
annotations for two different image providers. Finally, we provide installation
metadata that matches the annotation for more than 8,000 installations. Dataset
applications include end-to-end PV registry construction, robust PV
installations mapping, and analysis of crowdsourced datasets.

Recent breakthroughs in NLP research, such as the advent of Transformer
models have indisputably contributed to major advancements in several tasks.
However, few works research robustness and explainability issues of their
evaluation strategies. In this work, we examine the behavior of high-performing
pre-trained language models, focusing on the task of semantic similarity for
visual vocabularies. First, we address the need for explainable evaluation
metrics, necessary for understanding the conceptual quality of retrieved
instances. Our proposed metrics provide valuable insights in local and global
level, showcasing the inabilities of widely used approaches. Secondly,
adversarial interventions on salient query semantics expose vulnerabilities of
opaque metrics and highlight patterns in learned linguistic representations.

Despite that leveraging the transferability of adversarial examples can
attain a fairly high attack success rate for non-targeted attacks, it does not
work well in targeted attacks since the gradient directions from a source image
to a targeted class are usually different in different DNNs. To increase the
transferability of target attacks, recent studies make efforts in aligning the
feature of the generated adversarial example with the feature distributions of
the targeted class learned from an auxiliary network or a generative
adversarial network. However, these works assume that the training dataset is
available and require a lot of time to train networks, which makes it hard to
apply to real-world scenarios. In this paper, we revisit adversarial examples
with targeted transferability from the perspective of universality and find
that highly universal adversarial perturbations tend to be more transferable.
Based on this observation, we propose the Locality of Images (LI) attack to
improve targeted transferability. Specifically, instead of using the
classification loss only, LI introduces a feature similarity loss between
intermediate features from adversarial perturbed original images and randomly
cropped images, which makes the features from adversarial perturbations to be
more dominant than that of benign images, hence improving targeted
transferability. Through incorporating locality of images into optimizing
perturbations, the LI attack emphasizes that targeted perturbations should be
universal to diverse input patterns, even local image patches. Extensive
experiments demonstrate that LI can achieve high success rates for
transfer-based targeted attacks. On attacking the ImageNet-compatible dataset,
LI yields an improvement of 12\% compared with existing state-of-the-art
methods.

Unsupervised video object segmentation aims to segment a target object in the
video without a ground truth mask in the initial frame. This challenging task
requires extracting features for the most salient common objects within a video
sequence. This difficulty can be solved by using motion information such as
optical flow, but using only the information between adjacent frames results in
poor connectivity between distant frames and poor performance. To solve this
problem, we propose a novel prototype memory network architecture. The proposed
model effectively extracts the RGB and motion information by extracting
superpixel-based component prototypes from the input RGB images and optical
flow maps. In addition, the model scores the usefulness of the component
prototypes in each frame based on a self-learning algorithm and adaptively
stores the most useful prototypes in memory and discards obsolete prototypes.
We use the prototypes in the memory bank to predict the next query frames mask,
which enhances the association between distant frames to help with accurate
mask prediction. Our method is evaluated on three datasets, achieving
state-of-the-art performance. We prove the effectiveness of the proposed model
with various ablation studies.

The training of deep neural networks and other modern machine learning models
usually consists in solving non-convex optimisation problems that are
high-dimensional and subject to large-scale data. Here, momentum-based
stochastic optimisation algorithms have become especially popular in recent
years. The stochasticity arises from data subsampling which reduces
computational cost. Moreover, both, momentum and stochasticity are supposed to
help the algorithm to overcome local minimisers and, hopefully, converge
globally. Theoretically, this combination of stochasticity and momentum is
badly understood.
  In this work, we propose and analyse a continuous-time model for stochastic
gradient descent with momentum. This model is a piecewise-deterministic Markov
process that represents the particle movement by an underdamped dynamical
system and the data subsampling through a stochastic switching of the dynamical
system. In our analysis, we investigate longtime limits, the
subsampling-to-no-subsampling limit, and the momentum-to-no-momentum limit. We
are particularly interested in the case of reducing the momentum over time:
intuitively, the momentum helps to overcome local minimisers in the initial
phase of the algorithm, but prohibits fast convergence to a global minimiser
later. Under convexity assumptions, we show convergence of our dynamical system
to the global minimiser when reducing momentum over time and let the
subsampling rate go to infinity.
  We then propose a stable, symplectic discretisation scheme to construct an
algorithm from our continuous-time dynamical system. In numerical experiments,
we study our discretisation scheme in convex and non-convex test problems.
Additionally, we train a convolutional neural network to solve the CIFAR-10
image classification problem. Here, our algorithm reaches competitive results
compared to stochastic gradient descent with momentum.

Transpose convolution has shown prominence in many deep learning
applications. However, transpose convolution layers are computationally
intensive due to the increased feature map size due to adding zeros after each
element in each row and column. Thus, convolution operation on the expanded
input feature map leads to poor utilization of hardware resources. The main
reason for unnecessary multiplication operations is zeros at predefined
positions in the input feature map. We propose an algorithmic-level
optimization technique for the effective transpose convolution implementation
to solve these problems. Based on kernel activations, we segregated the
original kernel into four sub-kernels. This scheme could reduce memory
requirements and unnecessary multiplications. Our proposed method was $3.09
(3.02) \times$ faster computation using the Titan X GPU (Intel Dual Core CPU)
with a flower dataset from the Kaggle website. Furthermore, the proposed
optimization method can be generalized to existing devices without additional
hardware requirements. A simple deep learning model containing one transpose
convolution layer was used to evaluate the optimization method. It showed $2.2
\times$ faster training using the MNIST dataset with an Intel Dual-core CPU
than the conventional implementation.

This study presents incremental correction methods for refining neural
network parameters or control functions entering into a continuous-time dynamic
system to achieve improved solution accuracy in satisfying the interim point
constraints placed on the performance output variables. The proposed approach
is to linearise the dynamics around the baseline values of its arguments, and
then to solve for the corrective input required to transfer the perturbed
trajectory to precisely known or desired values at specific time points, i.e.,
the interim points. Depending on the type of decision variables to adjust,
parameter correction and control function correction methods are developed.
These incremental correction methods can be utilised as a means to compensate
for the prediction errors of pre-trained neural networks in real-time
applications where high accuracy of the prediction of dynamical systems at
prescribed time points is imperative. In this regard, the online update
approach can be useful for enhancing overall targeting accuracy of
finite-horizon control subject to point constraints using a neural policy.
Numerical example demonstrates the effectiveness of the proposed approach in an
application to a powered descent problem at Mars.

A fundamental property of deep learning normalization techniques, such as
batch normalization, is making the pre-normalization parameters scale
invariant. The intrinsic domain of such parameters is the unit sphere, and
therefore their gradient optimization dynamics can be represented via spherical
optimization with varying effective learning rate (ELR), which was studied
previously. However, the varying ELR may obscure certain characteristics of the
intrinsic loss landscape structure. In this work, we investigate the properties
of training scale-invariant neural networks directly on the sphere using a
fixed ELR. We discover three regimes of such training depending on the ELR
value: convergence, chaotic equilibrium, and divergence. We study these regimes
in detail both on a theoretical examination of a toy example and on a thorough
empirical analysis of real scale-invariant deep learning models. Each regime
has unique features and reflects specific properties of the intrinsic loss
landscape, some of which have strong parallels with previous research on both
regular and scale-invariant neural networks training. Finally, we demonstrate
how the discovered regimes are reflected in conventional training of normalized
networks and how they can be leveraged to achieve better optima.

With an increased amount and availability of unmanned aerial vehicles (UAVs)
and other remote sensing devices (e.g. satellites), we have recently seen a
vast increase in computer vision methods for aerial view data. One application
of such technologies is within search-and-rescue (SAR), where the task is to
localize and assist one or several people who are missing, for example after a
natural disaster. In many cases the rough location may be known and a UAV can
be deployed to explore a given, confined area to precisely localize the missing
people. Due to time and battery constraints it is often critical that
localization is performed as efficiently as possible. In this work, we approach
this type of problem by abstracting it as an aerial view goal localization task
in a framework that emulates a SAR-like setup without requiring access to
actual UAVs. In this framework, an agent operates on top of an aerial image
(proxy for a search area) and is tasked with localizing a goal that is
described in terms of visual cues. To further mimic the situation on an actual
UAV, the agent is not able to observe the search area in its entirety, not even
at low resolution, and thus it has to operate solely based on partial glimpses
when navigating towards the goal. To tackle this task, we propose AiRLoc, a
reinforcement learning (RL)-based model that decouples exploration (searching
for distant goals) and exploitation (localizing nearby goals). Extensive
evaluations show that AiRLoc outperforms heuristic search methods as well as
alternative learnable approaches. We also conduct a proof-of-concept study
which indicates that the learnable methods outperform humans on average. Code
has been made publicly available: https://github.com/aleksispi/airloc.

Networks of social interactions are the substrate upon which civilizations
are built. Often, we create new bonds with people that we like or feel that our
relationships are damaged through the intervention of third parties. Despite
their importance and the huge impact that these processes have in our lives,
quantitative scientific understanding of them is still in its infancy, mainly
due to the difficulty of collecting large datasets of social networks including
individual attributes. In this work, we present a thorough study of real social
networks of 13 schools, with more than 3,000 students and 60,000 declared
positive and negative relations, including tests for personal traits of all the
students. We introduce a metric -- the `triadic influence' -- that measures the
influence of nearest-neighbors in the relationships of their contacts. We use
neural networks to predict the relationships and to extract the probability
that two students are friends or enemies depending on their personal attributes
or the triadic influence. We alternatively use a high-dimensional embedding of
the network structure to also predict the relationships. Remarkably, the
triadic influence (a simple one-dimensional metric) achieves the highest
accuracy at predicting the relationship between two students. We postulate that
the probabilities extracted from the neural networks -- functions of the
triadic influence and the personalities of the students -- control the
evolution of real social networks, opening a new avenue for the quantitative
study of these systems.

Motion-compensated MR reconstruction (MCMR) is a powerful concept with
considerable potential, consisting of two coupled sub-problems: Motion
estimation, assuming a known image, and image reconstruction, assuming known
motion. In this work, we propose a learning-based self-supervised framework for
MCMR, to efficiently deal with non-rigid motion corruption in cardiac MR
imaging. Contrary to conventional MCMR methods in which the motion is estimated
prior to reconstruction and remains unchanged during the iterative optimization
process, we introduce a dynamic motion estimation process and embed it into the
unrolled optimization. We establish a cardiac motion estimation network that
leverages temporal information via a group-wise registration approach, and
carry out a joint optimization between the motion estimation and
reconstruction. Experiments on 40 acquired 2D cardiac MR CINE datasets
demonstrate that the proposed unrolled MCMR framework can reconstruct high
quality MR images at high acceleration rates where other state-of-the-art
methods fail. We also show that the joint optimization mechanism is mutually
beneficial for both sub-tasks, i.e., motion estimation and image
reconstruction, especially when the MR image is highly undersampled.

Predict+Optimize is a recently proposed framework which combines machine
learning and constrained optimization, tackling optimization problems that
contain parameters that are unknown at solving time. The goal is to predict the
unknown parameters and use the estimates to solve for an estimated optimal
solution to the optimization problem. However, all prior works have focused on
the case where unknown parameters appear only in the optimization objective and
not the constraints, for the simple reason that if the constraints were not
known exactly, the estimated optimal solution might not even be feasible under
the true parameters. The contributions of this paper are two-fold. First, we
propose a novel and practically relevant framework for the Predict+Optimize
setting, but with unknown parameters in both the objective and the constraints.
We introduce the notion of a correction function, and an additional penalty
term in the loss function, modelling practical scenarios where an estimated
optimal solution can be modified into a feasible solution after the true
parameters are revealed, but at an additional cost. Second, we propose a
corresponding algorithmic approach for our framework, which handles all packing
and covering linear programs. Our approach is inspired by the prior work of
Mandi and Guns, though with crucial modifications and re-derivations for our
very different setting. Experimentation demonstrates the superior empirical
performance of our method over classical approaches.

Finding online research papers relevant to one's interests is very
challenging due to the increasing number of publications. Therefore,
personalized research paper recommendation has become a significant and timely
research topic. Collaborative filtering is a successful recommendation
approach, which exploits the ratings given to items by users as a source of
information for learning to make accurate recommendations. However, the ratings
are often very sparse as in the research paper domain, due to the huge number
of publications growing every year. Therefore, more attention has been drawn to
hybrid methods that consider both ratings and content information.
Nevertheless, most of the hybrid recommendation approaches that are based on
text embedding have utilized bag-of-words techniques, which ignore word order
and semantic meaning. In this paper, we propose a hybrid approach that
leverages deep semantic representation of research papers based on social tags
assigned by users. The experimental evaluation is performed on CiteULike, a
real and publicly available dataset. The obtained findings show that the
proposed model is effective in recommending research papers even when the
rating data is very sparse.

Foundation Models (FMs) have demonstrated unprecedented capabilities
including zero-shot learning, high fidelity data synthesis, and out of domain
generalization. However, as we show in this paper, FMs still have poor
out-of-the-box performance on expert tasks (e.g. retrieval of car manuals
technical illustrations from language queries), data for which is either unseen
or belonging to a long-tail part of the data distribution of the huge datasets
used for FM pre-training. This underlines the necessity to explicitly evaluate
and finetune FMs on such expert tasks, arguably ones that appear the most in
practical real-world applications. In this paper, we propose a first of its
kind FETA benchmark built around the task of teaching FMs to understand
technical documentation, via learning to match their graphical illustrations to
corresponding language descriptions. Our FETA benchmark focuses on
text-to-image and image-to-text retrieval in public car manuals and sales
catalogue brochures. FETA is equipped with a procedure for completely automatic
annotation extraction (code would be released upon acceptance), allowing easy
extension of FETA to more documentation types and application domains in the
future. Our automatic annotation leads to an automated performance metric shown
to be consistent with metrics computed on human-curated annotations (also
released). We provide multiple baselines and analysis of popular FMs on FETA
leading to several interesting findings that we believe would be very valuable
to the FM community, paving the way towards real-world application of FMs for
practical expert tasks currently 'overlooked' by standard benchmarks focusing
on common objects.

Fast and precise beam alignment is crucial to support high-quality data
transmission in millimeter wave (mmWave) communication systems. In this work,
we propose a novel deep learning based hierarchical beam alignment method that
learns two tiers of probing codebooks (PCs) and uses their measurements to
predict the optimal beam in a coarse-to-fine searching manner. Specifically,
the proposed method first performs coarse channel measurement using the tier-1
PC, then selects a tier-2 PC for fine channel measurement, and finally predicts
the optimal beam based on both coarse and fine measurements. The proposed deep
neural network (DNN) architecture is trained in two steps. First, the tier-1 PC
and the tier-2 PC selector are trained jointly. After that, all the tier-2 PCs
together with the optimal beam predictors are trained jointly. The learned
hierarchical PCs can capture the features of propagation environment. Numerical
results based on realistic ray-tracing datasets demonstrate that the proposed
method is superior to the state-of-art beam alignment methods in both alignment
accuracy and sweeping overhead.

Knowledge Graphs (KGs) have proven to be a reliable way of structuring data.
They can provide a rich source of contextual information about cultural
heritage collections. However, cultural heritage KGs are far from being
complete. They are often missing important attributes such as geographical
location, especially for sculptures and mobile or indoor entities such as
paintings. In this paper, we first present a framework for ingesting knowledge
about tangible cultural heritage entities from various data sources and their
connected multi-hop knowledge into a geolocalized KG. Secondly, we propose a
multi-view learning model for estimating the relative distance between a given
pair of cultural heritage entities, based on the geographical as well as the
knowledge connections of the entities.

Enabling secure and reliable high-bandwidth lowlatency connectivity between
automated vehicles and external servers, intelligent infrastructure, and other
road users is a central step in making fully automated driving possible. The
availability of data interfaces, which allow this kind of connectivity, has the
potential to distinguish artificial agents' capabilities in connected,
cooperative, and automated mobility systems from the capabilities of human
operators, who do not possess such interfaces. Connected agents can for example
share data to build collective environment models, plan collective behavior,
and learn collectively from the shared data that is centrally combined. This
paper presents multiple solutions that allow connected entities to exchange
data. In particular, we propose a new universal communication interface which
uses the Message Queuing Telemetry Transport (MQTT) protocol to connect agents
running the Robot Operating System (ROS). Our work integrates methods to assess
the connection quality in the form of various key performance indicators in
real-time. We compare a variety of approaches that provide the connectivity
necessary for the exemplary use case of edge-cloud lidar object detection in a
5G network. We show that the mean latency between the availability of
vehicle-based sensor measurements and the reception of a corresponding object
list from the edge-cloud is below 87 ms. All implemented solutions are made
open-source and free to use. Source code is available at
https://github.com/ika-rwth-aachen/ros-v2x-benchmarking-suite.

Accurate traffic conditions prediction provides a solid foundation for
vehicle-environment coordination and traffic control tasks. Because of the
complexity of road network data in spatial distribution and the diversity of
deep learning methods, it becomes challenging to effectively define traffic
data and adequately capture the complex spatial nonlinear features in the data.
This paper applies two hierarchical graph pooling approaches to the traffic
prediction task to reduce graph information redundancy. First, this paper
verifies the effectiveness of hierarchical graph pooling methods in traffic
prediction tasks. The hierarchical graph pooling methods are contrasted with
the other baselines on predictive performance. Second, two mainstream
hierarchical graph pooling methods, node clustering pooling and node drop
pooling, are applied to analyze advantages and weaknesses in traffic
prediction. Finally, for the mentioned graph neural networks, this paper
compares the predictive effects of different graph network inputs on traffic
prediction accuracy. The efficient ways of defining graph networks are analyzed
and summarized.

The lack of effective target regions makes it difficult to perform several
visual functions in low intensity light, including pedestrian recognition, and
image-to-image translation. In this situation, with the accumulation of
high-quality information by the combined use of infrared and visible images it
is possible to detect pedestrians even in low light. In this study we are going
to use advanced deep learning models like pix2pixGAN and YOLOv7 on LLVIP
dataset, containing visible-infrared image pairs for low light vision. This
dataset contains 33672 images and most of the images were captured in dark
scenes, tightly synchronized with time and location.

Modelling the mapping from scene irradiance to image intensity is essential
for many computer vision tasks. Such mapping is known as the camera response.
Most digital cameras use a nonlinear function to map irradiance, as measured by
the sensor to an image intensity used to record the photograph. Modelling of
the response is necessary for the nonlinear calibration. In this paper, a new
high-performance camera response model that uses a single latent variable and
fully connected neural network is proposed. The model is produced using
unsupervised learning with an autoencoder on real-world (example) camera
responses. Neural architecture searching is then used to find the optimal
neural network architecture. A latent distribution learning approach was
introduced to constrain the latent distribution. The proposed model achieved
state-of-the-art CRF representation accuracy in a number of benchmark tests,
but is almost twice as fast as the best current models when performing the
maximum likelihood estimation during camera response calibration due to the
simple yet efficient model representation.

Malware is one of the most common and severe cyber-attack today. Malware
infects millions of devices and can perform several malicious activities
including mining sensitive data, encrypting data, crippling system performance,
and many more. Hence, malware detection is crucial to protect our computers and
mobile devices from malware attacks. Deep learning (DL) is one of the emerging
and promising technologies for detecting malware. The recent high production of
malware variants against desktop and mobile platforms makes DL algorithms
powerful approaches for building scalable and advanced malware detection models
as they can handle big datasets. This work explores current deep learning
technologies for detecting malware attacks on the Windows, Linux, and Android
platforms. Specifically, we present different categories of DL algorithms,
network optimizers, and regularization methods. Different loss functions,
activation functions, and frameworks for implementing DL models are presented.
We also present feature extraction approaches and a review of recent DL-based
models for detecting malware attacks on the above platforms. Furthermore, this
work presents major research issues on malware detection including future
directions to further advance knowledge and research in this field.

When a model informs decisions about people, distribution shifts can create
undue disparities. However, it is hard for external entities to check for
distribution shift, as the model and its training set are often proprietary. In
this paper, we introduce and study a black-box auditing method to detect cases
of distribution shift that lead to a performance disparity of the model across
demographic groups. By extending techniques used in membership and property
inference attacks -- which are designed to expose private information from
learned models -- we demonstrate that an external auditor can gain the
information needed to identify these distribution shifts solely by querying the
model. Our experimental results on real-world datasets show that this approach
is effective, achieving 80--100% AUC-ROC in detecting shifts involving the
underrepresentation of a demographic group in the training set. Researchers and
investigative journalists can use our tools to perform non-collaborative audits
of proprietary models and expose cases of underrepresentation in the training
datasets.

Evolutionary Computation (EC) has been shown to be able to quickly train Deep
Artificial Neural Networks (DNNs) to solve Reinforcement Learning (RL)
problems. While a Genetic Algorithm (GA) is well-suited for exploiting reward
functions that are neither deceptive nor sparse, it struggles when the reward
function is either of those. To that end, Novelty Search (NS) has been shown to
be able to outperform gradient-following optimizers in some cases, while
under-performing in others. We propose a new algorithm: Explore-Exploit
$\gamma$-Adaptive Learner ($E^2\gamma AL$, or EyAL). By preserving a
dynamically-sized niche of novelty-seeking agents, the algorithm manages to
maintain population diversity, exploiting the reward signal when possible and
exploring otherwise. The algorithm combines both the exploitation power of a GA
and the exploration power of NS, while maintaining their simplicity and
elegance. Our experiments show that EyAL outperforms NS in most scenarios,
while being on par with a GA -- and in some scenarios it can outperform both.
EyAL also allows the substitution of the exploiting component (GA) and the
exploring component (NS) with other algorithms, e.g., Evolution Strategy and
Surprise Search, thus opening the door for future research.

Subsampling or subdata selection is a useful approach in large-scale
statistical learning. Most existing studies focus on model-based subsampling
methods which significantly depend on the model assumption. In this paper, we
consider the model-free subsampling strategy for generating subdata from the
original full data. In order to measure the goodness of representation of a
subdata with respect to the original data, we propose a criterion, generalized
empirical F-discrepancy (GEFD), and study its theoretical properties in
connection with the classical generalized L2-discrepancy in the theory of
uniform designs. These properties allow us to develop a kind of low-GEFD
data-driven subsampling method based on the existing uniform designs. By
simulation examples and a real case study, we show that the proposed
subsampling method is superior to the random sampling method. Moreover, our
method keeps robust under diverse model specifications while other popular
subsampling methods are under-performing. In practice, such a model-free
property is more appealing than the model-based subsampling methods, where the
latter may have poor performance when the model is misspecified, as
demonstrated in our simulation studies.

Understanding human mobility is essential for the development of smart cities
and social behavior research. Human mobility models may be used in numerous
applications, including pandemic control, urban planning, and traffic
management. The existing models' accuracy in predicting users' mobility
patterns is less than 25%. The low accuracy may be justified by the flexible
nature of the human movement. Indeed, humans are not rigid in their daily
movement. In addition, the rigid mobility models may result in missing the
hidden regularities in users' records. Thus, we propose a novel perspective to
study and analyze human mobility patterns and capture their flexibility.
Typically, the mobility patterns are represented by a sequence of locations. We
propose to define the mobility patterns by abstracting these locations into a
set of places. Labeling these locations will allow us to detect
close-to-reality hidden patterns. We present IMAP, an Individual huMAn mobility
Patterns visualizing platform. Our platform enables users to visualize a graph
of the places they visited based on their history records. In addition, our
platform displays the most frequent mobility patterns computed using a modified
PrefixSpan approach.

We present DIY-IPS - Do It Yourself - Indoor Positioning System, an
open-source real-time indoor positioning mobile application. DIY-IPS detects
users' indoor position by employing dual-band RSSI fingerprinting of available
WiFi access points. The app can be used, without additional infrastructural
costs, to detect users' indoor positions in real time. We published our app as
an open source to save other researchers time recreating it. The app enables
researchers/users to (1) collect indoor positioning datasets with a ground
truth label, (2) customize the app for higher accuracy or other research
purposes (3) test the accuracy of modified methods by live testing with ground
truth. We ran preliminary experiments to demonstrate the effectiveness of the
app.

Continual Learning (CL) focuses on developing algorithms with the ability to
adapt to new environments and learn new skills. This very challenging task has
generated a lot of interest in recent years, with new solutions appearing
rapidly. In this paper, we propose a nVFNet-RDC approach for continual object
detection. Our nVFNet-RDC consists of teacher-student models, and adopts replay
and feature distillation strategies. As the 1st place solutions, we achieve
55.94% and 54.65% average mAP on the 3rd CLVision Challenge Track 2 and Track
3, respectively.

Clustering is a usual unsupervised machine learning technique for grouping
the data points into groups based upon similar features. We focus here on
unsupervised clustering for contaminated data, i.e in the case where K-medians
should be preferred to K-means because of its robustness. More precisely, we
concentrate on a common question in clustering: how to chose the number of
clusters? The answer proposed here is to consider the choice of the optimal
number of clusters as the minimization of a risk function via penalization. In
this paper, we obtain a suitable penalty shape for our criterion and derive an
associated oracle-type inequality. Finally, the performance of this approach
with different types of K-medians algorithms is compared on a simulation study
with other popular techniques. All studied algorithms are available in the R
package Kmedians on CRAN.

We study a posterior sampling approach to efficient exploration in
constrained reinforcement learning. Alternatively to existing algorithms, we
propose two simple algorithms that are more efficient statistically, simpler to
implement and computationally cheaper. The first algorithm is based on a linear
formulation of CMDP, and the second algorithm leverages the saddle-point
formulation of CMDP. Our empirical results demonstrate that, despite its
simplicity, posterior sampling achieves state-of-the-art performance and, in
some cases, significantly outperforms optimistic algorithms.

A novel scene text recognizer based on Vision-Language Transformer (VLT) is
presented. Inspired by Levenshtein Transformer in the area of NLP, the proposed
method (named Levenshtein OCR, and LevOCR for short) explores an alternative
way for automatically transcribing textual content from cropped natural images.
Specifically, we cast the problem of scene text recognition as an iterative
sequence refinement process. The initial prediction sequence produced by a pure
vision model is encoded and fed into a cross-modal transformer to interact and
fuse with the visual features, to progressively approximate the ground truth.
The refinement process is accomplished via two basic character-level
operations: deletion and insertion, which are learned with imitation learning
and allow for parallel decoding, dynamic length change and good
interpretability. The quantitative experiments clearly demonstrate that LevOCR
achieves state-of-the-art performances on standard benchmarks and the
qualitative analyses verify the effectiveness and advantage of the proposed
LevOCR algorithm. Code is available at
https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/LevOCR.

Machine learning methods are increasingly widely used in high-risk settings
such as healthcare, transportation, and finance. In these settings, it is
important that a model produces calibrated uncertainty to reflect its own
confidence and avoid failures. In this paper we survey recent works on
uncertainty quantification (UQ) for deep learning, in particular
distribution-free Conformal Prediction method for its mathematical properties
and wide applicability. We will cover the theoretical guarantees of conformal
methods, introduce techniques that improve calibration and efficiency for UQ in
the context of spatiotemporal data, and discuss the role of UQ in the context
of safe decision making.

In this paper, we determine deuteron's static properties, low energy
scattering parameters, total cross-section and form factors from inverse S-wave
potentials constructed using Morse function. The scattering phase shifts (SPS)
at different lab energies are determined using phase function method. The model
parameters are optimised using both machine learning algorithm and traditional
data analysis by choosing mean squared error as cost function. The mean
absolute error between experimental and obtained SPS for states 3S1 and 1S0 are
found to be 0.35 and 0.70 respectively. The low energy scattering parameters
are matching well with expected values. The contribution due to S-waves SPS
towards total cross-section at various energies have been obtained and are
matching well with experimental values. The analytical ground state deuteron
wave-function (DWF) is obtained by utilizing the experimental value for
Quadrupole moment. Other static properties and form factors determined from
obtained DWF are found to be in close agreement with experimental ones.

A transfer learning method, domain adversarial neural network (DANN), is
introduced to study the phase transition of two-dimensional q-state Potts
model. With the DANN, we only need to choose a few labeled configurations
automatically as input data, then the critical points can be obtained after
training the algorithm. By an additional iterative process, the critical points
can be captured to comparable accuracy to Monte Carlo simulations as we
demonstrate it for q = 3, 5, 7 and 10. The type of phase transition (first or
second-order) is also determined at the same time. Meanwhile, for the
second-order phase transition at q = 3, we can calculate the critical exponent
$\nu$ by data collapse. Furthermore, compared with the traditional supervised
learning, the DANN is of higher accuracy with lower cost.

In this paper, an unrolling algorithm of the iterative subspace-based
optimization method (SOM) is proposed for solving full-wave inverse scattering
problems (ISPs). The unrolling network, named SOM-Net, inherently embeds the
Lippmann- Schwinger physical model into the design of network structures. The
SOM-Net takes the deterministic induced current and the raw permittivity image
obtained from back-propagation (BP) as the input. It then updates the induced
current and the permittivity successively in sub-network blocks of the SOM- Net
by imitating iterations of the SOM. The final output of the SOM-Net is the full
predicted induced current, from which the scattered field and the permittivity
image can also be deduced analytically. The parameters of the SOM-Net are
optimized in a supervised manner with the total loss to simultaneously ensure
the consistency of the induced current, the scattered field, and the
permittivity in the governing equations. Numerical tests on both synthetic and
experimental data verify the superior performance of the proposed SOM-Net over
typical ones. The results on challenging examples like scatterers with tough
profiles or high permittivity demonstrate the good generalization ability of
the SOM-Net. With the use of deep unrolling technology, this work builds a
bridge between traditional iterative methods and deep learning methods for
solving ISPs.

Recent years have witnessed significant success in Self-Supervised Learning
(SSL), which facilitates various downstream tasks. However, attackers may steal
such SSL models and commercialize them for profit, making it crucial to protect
their Intellectual Property (IP). Most existing IP protection solutions are
designed for supervised learning models and cannot be used directly since they
require that the models' downstream tasks and target labels be known and
available during watermark embedding, which is not always possible in the
domain of SSL. To address such a problem especially when downstream tasks are
diverse and unknown during watermark embedding, we propose a novel black-box
watermarking solution, named SSL-WM, for protecting the ownership of SSL
models. SSL-WM maps watermarked inputs by the watermarked encoders into an
invariant representation space, which causes any downstream classifiers to
produce expected behavior, thus allowing the detection of embedded watermarks.
We evaluate SSL-WM on numerous tasks, such as Computer Vision (CV) and Natural
Language Processing (NLP), using different SSL models, including
contrastive-based and generative-based. Experimental results demonstrate that
SSL-WM can effectively verify the ownership of stolen SSL models in various
downstream tasks. Furthermore, SSL-WM is robust against model fine-tuning and
pruning attacks. Lastly, SSL-WM can also evade detection from evaluated
watermark detection approaches, demonstrating its promising application in
protecting the IP of SSL models.

Law enforcement and city safety are significantly impacted by detecting
violent incidents in surveillance systems. Although modern (smart) cameras are
widely available and affordable, such technological solutions are impotent in
most instances. Furthermore, personnel monitoring CCTV recordings frequently
show a belated reaction, resulting in the potential cause of catastrophe to
people and property. Thus automated detection of violence for swift actions is
very crucial. The proposed solution uses a novel end-to-end deep learning-based
video vision transformer (ViViT) that can proficiently discern fights, hostile
movements, and violent events in video sequences. The study presents utilizing
a data augmentation strategy to overcome the downside of weaker inductive
biasness while training vision transformers on a smaller training datasets. The
evaluated results can be subsequently sent to local concerned authority, and
the captured video can be analyzed. In comparison to state-of-theart (SOTA)
approaches the proposed method achieved auspicious performance on some of the
challenging benchmark datasets.

[Purpose] To understand the meaning of a sentence, humans can focus on
important words in the sentence, which reflects our eyes staying on each word
in different gaze time or times. Thus, some studies utilize eye-tracking values
to optimize the attention mechanism in deep learning models. But these studies
lack to explain the rationality of this approach. Whether the attention
mechanism possesses this feature of human reading needs to be explored.
[Design/methodology/approach] We conducted experiments on a sentiment
classification task. Firstly, we obtained eye-tracking values from two
open-source eye-tracking corpora to describe the feature of human reading.
Then, the machine attention values of each sentence were learned from a
sentiment classification model. Finally, a comparison was conducted to analyze
machine attention values and eye-tracking values. [Findings] Through
experiments, we found the attention mechanism can focus on important words,
such as adjectives, adverbs, and sentiment words, which are valuable for
judging the sentiment of sentences on the sentiment classification task. It
possesses the feature of human reading, focusing on important words in
sentences when reading. Due to the insufficient learning of the attention
mechanism, some words are wrongly focused. The eye-tracking values can help the
attention mechanism correct this error and improve the model performance.
[Originality/value] Our research not only provides a reasonable explanation for
the study of using eye-tracking values to optimize the attention mechanism, but
also provides new inspiration for the interpretability of attention mechanism.

Incorporating tagging into neural machine translation (NMT) systems has shown
promising results in helping translate rare words such as named entities (NE).
However, translating NE in low-resource setting remains a challenge. In this
work, we investigate the effect of using tags and NE hypernyms from knowledge
graphs (KGs) in parallel corpus in different levels of resource conditions. We
find the tag-and-copy mechanism (tag the NEs in the source sentence and copy
them to the target sentence) improves translation in high-resource settings
only. Introducing copying also results in polarizing effects in translating
different parts-of-speech (POS). Interestingly, we find that copy accuracy for
hypernyms is consistently higher than that of entities. As a way of avoiding
"hard" copying and utilizing hypernym in bootstrapping rare entities, we
introduced a "soft" tagging mechanism and found consistent improvement in high
and low-resource settings.

The popularity of Windows attracts the attention of hackers/cyber-attackers,
making Windows devices the primary target of malware attacks in recent years.
Several sophisticated malware variants and anti-detection methods have been
significantly enhanced and as a result, traditional malware detection
techniques have become less effective. This work presents MalBehavD-V1, a new
behavioural dataset of Windows Application Programming Interface (API) calls
extracted from benign and malware executable files using the dynamic analysis
approach. In addition, we present MalDetConV, a new automated behaviour-based
framework for detecting both existing and zero-day malware attacks. MalDetConv
uses a text processing-based encoder to transform features of API calls into a
suitable format supported by deep learning models. It then uses a hybrid of
convolutional neural network (CNN) and bidirectional gated recurrent unit
(CNN-BiGRU) automatic feature extractor to select high-level features of the
API Calls which are then fed to a fully connected neural network module for
malware classification. MalDetConv also uses an explainable component that
reveals features that contributed to the final classification outcome, helping
the decision-making process for security analysts. The performance of the
proposed framework is evaluated using our MalBehavD-V1 dataset and other
benchmark datasets. The detection results demonstrate the effectiveness of
MalDetConv over the state-of-the-art techniques with detection accuracy of
96.10%, 95.73%, 98.18%, and 99.93% achieved while detecting unseen malware from
MalBehavD-V1, Allan and John, Brazilian, and Ki-D datasets, respectively. The
experimental results show that MalDetConv is highly accurate in detecting both
known and zero-day malware attacks on Windows devices.

A mesh generation method that can generate an optimal mesh for a blade
passage at a single attempt is developed using deep reinforcement learning
(DRL). Unlike the conventional methods, where meshing parameters must be
specified by the user or iteratively optimized from scratch for a newly given
geometry, the developed method employs DRL-based multi-condition (MC)
optimization to define meshing parameters for various geometries optimally. The
method involves the following steps: (1) development of a base algorithm for
structured mesh generation of a blade passage; (2) formulation of an MC
optimization problem to optimize meshing parameters introduced while developing
the base algorithm; and (3) development of a DRL-based mesh generation
algorithm by solving the MC optimization problem using DRL. As a result, the
developed algorithm is able to successfully generate optimal meshes at a single
trial for various blades.

Most reinforcement learning algorithms implicitly assume strong synchrony. We
present novel attacks targeting Q-learning that exploit a vulnerability
entailed by this assumption by delaying the reward signal for a limited time
period. We consider two types of attack goals: targeted attacks, which aim to
cause a target policy to be learned, and untargeted attacks, which simply aim
to induce a policy with a low reward. We evaluate the efficacy of the proposed
attacks through a series of experiments. Our first observation is that
reward-delay attacks are extremely effective when the goal is simply to
minimize reward. Indeed, we find that even naive baseline reward-delay attacks
are also highly successful in minimizing the reward. Targeted attacks, on the
other hand, are more challenging, although we nevertheless demonstrate that the
proposed approaches remain highly effective at achieving the attacker's
targets. In addition, we introduce a second threat model that captures a
minimal mitigation that ensures that rewards cannot be used out of sequence. We
find that this mitigation remains insufficient to ensure robustness to attacks
that delay, but preserve the order, of rewards.

Existing differentiable channel pruning methods often attach scaling factors
or masks behind channels to prune filters with less importance, and implicitly
assume uniform contribution of input samples to filter importance.
Specifically, the effects of instance complexity on pruning performance are not
yet fully investigated in static network pruning. In this paper, we propose a
simple yet effective differentiable network pruning method CWP based on
instance complexity weighted filter importance scores. We define instance
complexity related weight for each instance by giving higher weights to hard
instances, and measure the weighted sum of instance-specific soft masks to
model non-uniform contribution of different inputs, which encourages hard
instances to dominate the pruning process and the model performance to be well
preserved. In addition, we introduce a regularizer to maximize polarization of
the masks, such that a sweet spot can be easily found to identify the filters
to be pruned. Performance evaluations on various network architectures and
datasets demonstrate CWP has advantages over the state-of-the-arts in pruning
large networks. For instance, CWP improves the accuracy of ResNet56 on CIFAR-10
dataset by 0.32% aftering removing 64.11% FLOPs, and prunes 87.75% FLOPs of
ResNet50 on ImageNet dataset with only 0.93% Top-1 accuracy loss.

This paper summarizes the CLaC submission for SMM4H 2022 Task 10 which
concerns the recognition of diseases mentioned in Spanish tweets. Before
classifying each token, we encode each token with a transformer encoder using
features from Multilingual RoBERTa Large, UMLS gazetteer, and DISTEMIST
gazetteer, among others. We obtain a strict F1 score of 0.869, with competition
mean of 0.675, standard deviation of 0.245, and median of 0.761.

Full waveform inversion (FWI) commonly stands for the state-of-the-art
approach for imaging subsurface structures and physical parameters, however,
its implementation usually faces great challenges, such as building a good
initial model to escape from local minima, and evaluating the uncertainty of
inversion results. In this paper, we propose the implicit full waveform
inversion (IFWI) algorithm using continuously and implicitly defined deep
neural representations. Compared to FWI, which is sensitive to the initial
model, IFWI benefits from the increased degrees of freedom with deep learning
optimization, thus allowing to start from a random initialization, which
greatly reduces the risk of non-uniqueness and being trapped in local minima.
Both theoretical and experimental analyses indicates that, given a random
initial model, IFWI is able to converge to the global minimum and produce a
high-resolution image of subsurface with fine structures. In addition,
uncertainty analysis of IFWI can be easily performed by approximating Bayesian
inference with various deep learning approaches, which is analyzed in this
paper by adding dropout neurons. Furthermore, IFWI has a certain degree of
robustness and strong generalization ability that are exemplified in the
experiments of various 2D geological models. With proper setup, IFWI can also
be well suited for multi-scale joint geophysical inversion.

Healthcare digitalization requires effective applications of human sensors,
when various parameters of the human body are instantly monitored in everyday
life due to the Internet of Things (IoT). In particular, machine learning (ML)
sensors for the prompt diagnosis of COVID-19 are an important option for IoT
application in healthcare and ambient assisted living (AAL). Determining a
COVID-19 infected status with various diagnostic tests and imaging results is
costly and time-consuming. This study provides a fast, reliable and
cost-effective alternative tool for the diagnosis of COVID-19 based on the
routine blood values (RBVs) measured at admission. The dataset of the study
consists of a total of 5296 patients with the same number of negative and
positive COVID-19 test results and 51 routine blood values. In this study, 13
popular classifier machine learning models and the LogNNet neural network model
were exanimated. The most successful classifier model in terms of time and
accuracy in the detection of the disease was the histogram-based gradient
boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified
the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride,
amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy.
In addition, the importance of single, double and triple combinations of these
features in the diagnosis of the disease was discussed. We propose to use these
11 features and their binary combinations as important biomarkers for ML
sensors in the diagnosis of the disease, supporting edge computing on Arduino
and cloud IoT service.

We proposed classification models that utilize the result from the
Quasi-Reversibility Method, which solves the Black-Scholes equation to forecast
the option prices one day in advance. Combining the minimizer from QRM with our
machine learning classifications, we can classify the option as an increase or
decrease in value. Based on the different classifications of the options, we
can apply various trading strategies which we aim to figure out ways to improve
the results from QRM's extrapolations. To further test the viability of our
model, we collected 23548 options data from the real-world market for our
model, and we will then feed in the data along with the minimizer from QRM to
form decision trees and random forests, which we will later test for accuracy,
precision, and recall.

Robotic arms are widely used in automatic industries. However, with wide
applications of deep learning in robotic arms, there are new challenges such as
the allocation of grasping computing power and the growing demand for security.
In this work, we propose a robotic arm grasping approach based on deep learning
and edge-cloud collaboration. This approach realizes the arbitrary grasp
planning of the robot arm and considers the grasp efficiency and information
security. In addition, the encoder and decoder trained by GAN enable the images
to be encrypted while compressing, which ensures the security of privacy. The
model achieves 92% accuracy on the OCID dataset, the image compression ratio
reaches 0.03%, and the structural difference value is higher than 0.91.

In software engineering, different approaches and machine learning models
leverage different types of data: source code, textual information, historical
data. An important part of any project is its dependencies. The list of
dependencies is relatively small but carries a lot of semantics with it, which
can be used to compare projects or make judgements about them.
  In this paper, we focus on Python projects and their PyPi dependencies in the
form of requirements.txt files. We compile a dataset of 7,132 Python projects
and their dependencies, as well as use Git to pull their versions from previous
years. Using this data, we build 32-dimensional embeddings of libraries by
applying Singular Value Decomposition to the co-occurrence matrix of projects
and libraries. We then cluster the embeddings and study their semantic
relations.
  To showcase the usefulness of such lightweight library embeddings, we
introduce a prototype tool for suggesting relevant libraries to a given
project. The tool computes project embeddings and uses dependencies of projects
with similar embeddings to form suggestions. To compare different library
recommenders, we have created a benchmark based on the evolution of dependency
sets in open-source projects. Approaches based on the created embeddings
significantly outperform the baseline of showing the most popular libraries in
a given year. We have also conducted a user study that showed that the
suggestions differ in quality for different project domains and that even
relevant suggestions might be not particularly useful. Finally, to facilitate
potentially more useful recommendations, we extended the recommender system
with an option to suggest rarer libraries.

We present Neural Feature Fusion Fields (N3F), a method that improves dense
2D image feature extractors when the latter are applied to the analysis of
multiple images reconstructible as a 3D scene. Given an image feature
extractor, for example pre-trained using self-supervision, N3F uses it as a
teacher to learn a student network defined in 3D space. The 3D student network
is similar to a neural radiance field that distills said features and can be
trained with the usual differentiable rendering machinery. As a consequence,
N3F is readily applicable to most neural rendering formulations, including
vanilla NeRF and its extensions to complex dynamic scenes. We show that our
method not only enables semantic understanding in the context of scene-specific
neural fields without the use of manual labels, but also consistently improves
over the self-supervised 2D baselines. This is demonstrated by considering
various tasks, such as 2D object retrieval, 3D segmentation, and scene editing,
in diverse sequences, including long egocentric videos in the EPIC-KITCHENS

Artificial neural networks took a lot of inspiration from their biological
counterparts in becoming our best machine perceptual systems. This work
summarizes some of that history and incorporates modern theoretical
neuroscience into experiments with artificial neural networks from the field of
deep learning. Specifically, iterative magnitude pruning is used to train
sparsely connected networks with 33x fewer weights without loss in performance.
These are used to test and ultimately reject the hypothesis that weight
sparsity alone improves image noise robustness. Recent work mitigated
catastrophic forgetting using weight sparsity, activation sparsity, and active
dendrite modeling. This paper replicates those findings, and extends the method
to train convolutional neural networks on a more challenging continual learning
task. The code has been made publicly available.

We show that in a cooperative $N$-agent network, one can design locally
executable policies for the agents such that the resulting discounted sum of
average rewards (value) well approximates the optimal value computed over all
(including non-local) policies. Specifically, we prove that, if $|\mathcal{X}|,
|\mathcal{U}|$ denote the size of state, and action spaces of individual
agents, then for sufficiently small discount factor, the approximation error is
given by $\mathcal{O}(e)$ where $e\triangleq
\frac{1}{\sqrt{N}}\left[\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}\right]$.
Moreover, in a special case where the reward and state transition functions are
independent of the action distribution of the population, the error improves to
$\mathcal{O}(e)$ where $e\triangleq \frac{1}{\sqrt{N}}\sqrt{|\mathcal{X}|}$.
Finally, we also devise an algorithm to explicitly construct a local policy.
With the help of our approximation results, we further establish that the
constructed local policy is within $\mathcal{O}(\max\{e,\epsilon\})$ distance
of the optimal policy, and the sample complexity to achieve such a local policy
is $\mathcal{O}(\epsilon^{-3})$, for any $\epsilon>0$.

HELM Learning (Helping Everyone Learn More) is the first online peer-to-peer
learning platform which allows students (typically middle-to-high school
students) to teach classes and students (typically elementary-to-middle school
students) to learn from classes for free. This method of class structure
(peer-to-peer learning) has been proven effective for learning, as it promotes
teamwork and collaboration, and enables active learning. HELM is a unique
platform as it provides an easy process for students to create, teach and learn
topics in a structured, peer-to-peer environment. Since HELM was created in
April 2020, it has gotten over 4000 student sign ups and 80 teachers, in 4
continents around the world. HELM has grown from a simple
website-and-Google-Form platform to having a backend system coded with Python,
SQL, JavaScript and HTML, hosted on an AWS service. This not only makes it
easier for students to sign up (as the students' information is saved in an SQL
database, meaning they can sign up for classes without having to put in their
information again, as well as getting automated emails about their classes),
but also makes it easier for teachers to teach (as supplemental processes such
as creating Zoom links, class recording folders, sending emails to students,
etc. are done automatically). In addition, HELM has a recommendation machine
learning algorithm which suggests classes and subjects students would enjoy
taking, based on the previous classes a student has taken. This has created an
easier experience for students to sign up for classes they are interested in.

In this short note, we propose a new method for quantizing the weights of a
fully trained neural network. A simple deterministic pre-processing step allows
us to quantize network layers via memoryless scalar quantization while
preserving the network performance on given training data. On one hand, the
computational complexity of this pre-processing slightly exceeds that of
state-of-the-art algorithms in the literature. On the other hand, our approach
does not require any hyper-parameter tuning and, in contrast to previous
methods, allows a plain analysis. We provide rigorous theoretical guarantees in
the case of quantizing single network layers and show that the relative error
decays with the number of parameters in the network if the training data
behaves well, e.g., if it is sampled from suitable random distributions. The
developed method also readily allows the quantization of deep networks by
consecutive application to single layers.

The primary focus of this paper is centered on the numerical analysis and
optimal control of vertical axis wind turbines (VAWT) using Bayesian
reinforcement learning (RL). We specifically tackle small-scale wind turbines
with permanent magnet synchronous generator, which are well-suited to local and
compact production of electrical energy in small scale such as urban and rural
infrastructure installations. Through this work, we formulate and implement an
RL strategy using Markov chain Monte Carlo (MCMC) algorithm to optimize the
long-term energy output of the wind turbine. Our MCMC-based RL algorithm is a
model-free and gradient-free algorithm, where the designer does not have to
know the precise dynamics of the plant and their uncertainties. The method
specifically overcomes the shortcomings typically associated with conventional
solutions including but not limited to component aging, modeling errors and
inaccuracies in the estimation of wind speed patterns. It has been observed to
be especially successful in capturing power from wind transients; it modulates
the generator load and hence rotor torque load so that the rotor tip speed
reaches the optimum value for the anticipated wind speed. This ratio of rotor
tip speed to wind speed is known to be critical in wind power applications. The
wind to load energy efficiency of the proposed method is shown to be superior
to the classical maximum power point tracking method.

The bootstrap resampling method has been popular for performing significance
analysis on word error rate (WER) in automatic speech recognition (ASR)
evaluation. To deal with dependent speech data, the blockwise bootstrap
approach is also introduced. By dividing utterances into uncorrelated blocks,
this approach resamples these blocks instead of original data. However, it is
typically nontrivial to uncover the dependent structure among utterances and
identify the blocks, which might lead to subjective conclusions in statistical
testing. In this paper, we present graphical lasso based methods to explicitly
model such dependency and estimate uncorrelated blocks of utterances in a
rigorous way, after which blockwise bootstrap is applied on top of the inferred
blocks. We show the resulting variance estimator of WER in ASR evaluation is
statistically consistent under mild conditions. We also demonstrate the
validity of proposed approach on LibriSpeech dataset.

With the recent surge in big data analytics for hyper-dimensional data there
is a renewed interest in dimensionality reduction techniques for machine
learning applications. In order for these methods to improve performance gains
and understanding of the underlying data, a proper metric needs to be
identified. This step is often overlooked and metrics are typically chosen
without consideration of the underlying geometry of the data. In this paper, we
present a method for incorporating elastic metrics into the t-distributed
Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and
Projection (UMAP). We apply our method to functional data, which is uniquely
characterized by rotations, parameterization, and scale. If these properties
are ignored, they can lead to incorrect analysis and poor classification
performance. Through our method we demonstrate improved performance on shape
identification tasks for three benchmark data sets (MPEG-7, Car data set, and
Plane data set of Thankoor), where we achieve 0.77, 0.95, and 1.00 F1 score,

We present an approach for the reconstruction of textured 3D meshes of human
heads from one or few views. Since such few-shot reconstruction is
underconstrained, it requires prior knowledge which is hard to impose on
traditional 3D reconstruction algorithms. In this work, we rely on the recently
introduced 3D representation $\unicode{x2013}$ neural implicit functions
$\unicode{x2013}$ which, being based on neural networks, allows to naturally
learn priors about human heads from data, and is directly convertible to
textured mesh. Namely, we extend NeuS, a state-of-the-art neural implicit
function formulation, to represent multiple objects of a class (human heads in
our case) simultaneously. The underlying neural net architecture is designed to
learn the commonalities among these objects and to generalize to unseen ones.
Our model is trained on just a hundred smartphone videos and does not require
any scanned 3D data. Afterwards, the model can fit novel heads in the few-shot
or one-shot modes with good results.

Analog and mixed-signal (AMS) circuit designs still rely on human design
expertise. Machine learning has been assisting circuit design automation by
replacing human experience with artificial intelligence. This paper presents
TAG, a new paradigm of learning the circuit representation from layouts
leveraging text, self-attention and graph. The embedding network model learns
spatial information without manual labeling. We introduce text embedding and a
self-attention mechanism to AMS circuit learning. Experimental results
demonstrate the ability to predict layout distances between instances with
industrial FinFET technology benchmarks. The effectiveness of the circuit
representation is verified by showing the transferability to three other
learning tasks with limited data in the case studies: layout matching
prediction, wirelength estimation, and net parasitic capacitance prediction.

The Science Gateways Community Institute (SGCI) is an NSF Software
Infrastructure for Sustained Innovation (S2I2) funded project that leads and
supports the science gateway community. Major activities for SGCI include a)
sustainability training, including the Focus Week week-long course designed to
help science gateway operators develop sustainability plans, and the Jumpstart
virtual short-course; b) usability and user experience consulting; c) a
community catalog of science gateways and science gateway software; d)
workforce development activities, including a coding institute for students,
internship opportunities, and hackathons; e) an annual conference; and f)
in-depth technical support for client gateway projects. The goals of SGCI's
Embedded Technical Support component are to help the institute's clients to
create new science gateways or to significantly enhance existing science
gateways. Examples of the latter include helping to implement major new
capabilities and to implement significant usability improvements suggested by
SGCI's usability consultants. The Embedded Technical Support component was
managed by Indiana University and involved research software engineers at San
Diego Supercomputer Center, Texas Advanced Computing Center, Indiana
University, and Purdue University (through 2019). Since 2016, the component has
involved 20 research software engineers as consultants and has conducted 59
client consultations. This short paper provides a summary of lessons learned
from the Embedded Technical Support program that may be useful for the research
software engineering community.

The paper describes a study of wet foams in microgravity whose bubble size
distribution evolves due to diffusive gas exchange. We focus on the comparison
between the size of bubbles determined from images of the foam surface and the
size of bubbles in the bulk foam, determined from Diffuse Transmission
Spectroscopy (DTS). Extracting the bubble size distribution from images of a
foam surface is difficult so we have used three different procedures : manual
analysis, automatic analysis with a customized Python script and machine
learning analysis. Once various pitfalls were identified and taken into
account, all the three procedures yield identical results within error bars.
DTS only allows the determination of an average bubble radius which is
proportional to the photon transport mean free path $\ell^*$. The relation
between the measured diffuse transmitted light intensity and {$\ell^*$}
previously derived for slab-shaped samples of infinite lateral extent does not
apply to the cuboid geometry of the cells used in the microgravity experiment.
A new more general expression of the diffuse intensity transmitted with
specific optical boundary conditions has been derived and applied to determine
the average bubble radius. The temporal evolution of the average bubble radii
deduced from DTS and of the same average radii of the bubbles measured at the
sample surface are in very good agreement throughout the coarsening. Finally,
ground experiments were performed to compare bubble size distributions in a
bulk wet foam and at its surface at times so short that diffusive gas exchange
is insignificant. They were found to be similar, confirming that bubbles seen
at the surface are representative of the bulk foam bubbles.

In this paper, we seek to draw connections between the frontal and profile
face images in an abstract embedding space. We exploit this connection using a
coupled-encoder network to project frontal/profile face images into a common
latent embedding space. The proposed model forces the similarity of
representations in the embedding space by maximizing the mutual information
between two views of the face. The proposed coupled-encoder benefits from three
contributions for matching faces with extreme pose disparities. First, we
leverage our pose-aware contrastive learning to maximize the mutual information
between frontal and profile representations of identities. Second, a memory
buffer, which consists of latent representations accumulated over past
iterations, is integrated into the model so it can refer to relatively much
more instances than the mini-batch size. Third, a novel pose-aware adversarial
domain adaptation method forces the model to learn an asymmetric mapping from
profile to frontal representation. In our framework, the coupled-encoder learns
to enlarge the margin between the distribution of genuine and imposter faces,
which results in high mutual information between different views of the same
identity. The effectiveness of the proposed model is investigated through
extensive experiments, evaluations, and ablation studies on four benchmark
datasets, and comparison with the compelling state-of-the-art algorithms.

This paper describes the models developed by the AILAB-Udine team for the
SMM4H 22 Shared Task. We explored the limits of Transformer based models on
text classification, entity extraction and entity normalization, tackling Tasks
1, 2, 5, 6 and 10. The main take-aways we got from participating in different
tasks are: the overwhelming positive effects of combining different
architectures when using ensemble learning, and the great potential of
generative models for term normalization.

We study binary activated neural networks in the context of regression tasks,
provide guarantees on the expressiveness of these particular networks and
propose a greedy algorithm for building such networks. Aiming for predictors
having small resources needs, the greedy approach does not need to fix in
advance an architecture for the network: this one is built one layer at a time,
one neuron at a time, leading to predictors that aren't needlessly wide and
deep for a given task. Similarly to boosting algorithms, our approach
guarantees a training loss reduction every time a neuron is added to a layer.
This greatly differs from most binary activated neural networks training
schemes that rely on stochastic gradient descent (circumventing the
0-almost-everywhere derivative problem of the binary activation function by
surrogates such as the straight through estimator or continuous binarization).
We show that our method provides compact and sparse predictors while obtaining
similar performances to state-of-the-art methods for training binary activated
networks.

This paper presents a new statistical analysis aiming to explain the recent
superior achievements of the pre-training techniques in natural language
processing (NLP). We prove that when the classes of the pre-training task
(e.g., different words in the masked language model task) are sufficiently
diverse, in the sense that the least singular value of the last linear layer in
pre-training (denoted as $\tilde{\nu}$) is large, then pre-training can
significantly improve the sample efficiency of downstream tasks. Specially, we
show the transfer learning excess risk enjoys an $O\left(\frac{1}{\tilde{\nu}
\sqrt{n}}\right)$ rate, in contrast to the $O\left(\frac{1}{\sqrt{m}}\right)$
rate in the standard supervised learning. Here, $n$ is the number of
pre-training data and $m$ is the number of data in the downstream task, and
typically $n \gg m$. Our proof relies on a vector-form Rademacher complexity
chain rule for disassembling composite function classes and a modified
self-concordance condition. These techniques can be of independent interest.

Despite having been discovered more than three decades ago, High Temperature
Superconductors (HTSs) lack both an explanation for their mechanisms and a
systematic way to search for them. To aid this search, this project proposes
ScGAN, a Generative Adversarial Network (GAN) to efficiently predict new
superconductors. ScGAN was trained on compounds in OQMD and then transfer
learned onto the SuperCon database or a subset of it. Once trained, the GAN was
used to predict superconducting candidates, and approximately 70\% of them were
determined to be superconducting by a classification model--a 23-fold increase
in discovery rate compared to manual search methods. Furthermore, more than
99\% of predictions were novel materials, demonstrating that ScGAN was able to
potentially predict completely new superconductors, including several promising
HTS candidates. This project presents a novel, efficient way to search for new
superconductors, which may be used in technological applications or provide
insight into the unsolved problem of high temperature superconductivity.

As the first-line diagnostic imaging modality, radiography plays an essential
role in the early detection of developmental dysplasia of the hip (DDH).
Clinically, the diagnosis of DDH relies on manual measurements and subjective
evaluation of different anatomical features from pelvic radiographs. This
process is inefficient and error-prone and requires years of clinical
experience. In this study, we propose a deep learning-based system that
automatically detects 14 keypoints from a radiograph, measures three anatomical
angles (center-edge, T\"onnis, and Sharp angles), and classifies DDH hips as
grades I-IV based on the Crowe criteria. Moreover, a novel data-driven scoring
system is proposed to quantitatively integrate the information from the three
angles for DDH diagnosis. The proposed keypoint detection model achieved a mean
(95% confidence interval [CI]) average precision of 0.807 (0.804-0.810). The
mean (95% CI) intraclass correlation coefficients between the center-edge,
Tonnis, and Sharp angles measured by the proposed model and the ground-truth
were 0.957 (0.952-0.962), 0.947 (0.941-0.953), and 0.953 (0.947-0.960),
respectively, which were significantly higher than those of experienced
orthopedic surgeons (p<0.0001). In addition, the mean (95% CI) test diagnostic
agreement (Cohen's kappa) obtained using the proposed scoring system was 0.84
(0.83-0.85), which was significantly higher than those obtained from diagnostic
criteria for individual angle (0.76 [0.75-0.77]) and orthopedists (0.71
[0.63-0.79]). To the best of our knowledge, this is the first study for
objective DDH diagnosis by leveraging deep learning keypoint detection and
integrating different anatomical measurements, which can provide reliable and
explainable support for clinical decision-making.

Aircraft industry is constantly striving for more efficient design
optimization methods in terms of human efforts, computation time, and resource
consumption. Hybrid surrogate optimization maintains high results quality while
providing rapid design assessments when both the surrogate model and the switch
mechanism for eventually transitioning to the HF model are calibrated properly.
Feedforward neural networks (FNNs) can capture highly nonlinear input-output
mappings, yielding efficient surrogates for aircraft performance factors.
However, FNNs often fail to generalize over the out-of-distribution (OOD)
samples, which hinders their adoption in critical aircraft design optimization.
Through SmOOD, our smoothness-based out-of-distribution detection approach, we
propose to codesign a model-dependent OOD indicator with the optimized FNN
surrogate, to produce a trustworthy surrogate model with selective but credible
predictions. Unlike conventional uncertainty-grounded methods, SmOOD exploits
inherent smoothness properties of the HF simulations to effectively expose OODs
through revealing their suspicious sensitivities, thereby avoiding
over-confident uncertainty estimates on OOD samples. By using SmOOD, only
high-risk OOD inputs are forwarded to the HF model for re-evaluation, leading
to more accurate results at a low overhead cost. Three aircraft performance
models are investigated. Results show that FNN-based surrogates outperform
their Gaussian Process counterparts in terms of predictive performance.
Moreover, SmOOD does cover averagely 85% of actual OODs on all the study cases.
When SmOOD plus FNN surrogates are deployed in hybrid surrogate optimization
settings, they result in a decrease error rate of 34.65% and a computational
speed up rate of 58.36 times, respectively.

Explainable Artificial Intelligence (XAI) methods are intended to help human
users better understand the decision making of an AI agent. However, many
modern XAI approaches are unintuitive to end users, particularly those without
prior AI or ML knowledge. In this paper, we present a novel XAI approach we
call Responsibility that identifies the most responsible training example for a
particular decision. This example can then be shown as an explanation: "this is
what I (the AI) learned that led me to do that". We present experimental
results across a number of domains along with the results of an Amazon
Mechanical Turk user study, comparing responsibility and existing XAI methods
on an image classification task. Our results demonstrate that responsibility
can help improve accuracy for both human end users and secondary ML models.

In the context of aircraft system performance assessment, deep learning
technologies allow to quickly infer models from experimental measurements, with
less detailed system knowledge than usually required by physics-based modeling.
However, this inexpensive model development also comes with new challenges
regarding model trustworthiness. This work presents a novel approach,
physics-guided adversarial machine learning (ML), that improves the confidence
over the physics consistency of the model. The approach performs, first, a
physics-guided adversarial testing phase to search for test inputs revealing
behavioral system inconsistencies, while still falling within the range of
foreseeable operational conditions. Then, it proceeds with physics-informed
adversarial training to teach the model the system-related physics domain
foreknowledge through iteratively reducing the unwanted output deviations on
the previously-uncovered counterexamples. Empirical evaluation on two aircraft
system performance models shows the effectiveness of our adversarial ML
approach in exposing physical inconsistencies of both models and in improving
their propensity to be consistent with physics domain knowledge.

Multimodal machine learning is a vibrant multi-disciplinary research field
that aims to design computer agents with intelligent capabilities such as
understanding, reasoning, and learning through integrating multiple
communicative modalities, including linguistic, acoustic, visual, tactile, and
physiological messages. With the recent interest in video understanding,
embodied autonomous agents, text-to-image generation, and multisensor fusion in
application domains such as healthcare and robotics, multimodal machine
learning has brought unique computational and theoretical challenges to the
machine learning community given the heterogeneity of data sources and the
interconnections often found between modalities. However, the breadth of
progress in multimodal research has made it difficult to identify the common
themes and open questions in the field. By synthesizing a broad range of
application domains and theoretical frameworks from both historical and recent
perspectives, this paper is designed to provide an overview of the
computational and theoretical foundations of multimodal machine learning. We
start by defining two key principles of modality heterogeneity and
interconnections that have driven subsequent innovations, and propose a
taxonomy of 6 core technical challenges: representation, alignment, reasoning,
generation, transference, and quantification covering historical and recent
trends. Recent technical achievements will be presented through the lens of
this taxonomy, allowing researchers to understand the similarities and
differences across new approaches. We end by motivating several open problems
for future research as identified by our taxonomy.

Reconstructing the causal relationships behind the phenomena we observe is a
fundamental challenge in all areas of science. Discovering causal relationships
through experiments is often infeasible, unethical, or expensive in complex
systems. However, increases in computational power allow us to process the
ever-growing amount of data that modern science generates, leading to an
emerging interest in the causal discovery problem from observational data. This
work evaluates the LPCMCI algorithm, which aims to find generators compatible
with a multi-dimensional, highly autocorrelated time series while some
variables are unobserved. We find that LPCMCI performs much better than a
random algorithm mimicking not knowing anything but is still far from optimal
detection. Furthermore, LPCMCI performs best on auto-dependencies, then
contemporaneous dependencies, and struggles most with lagged dependencies. The
source code of this project is available online.

We present a neural network architecture, Bispectral Neural Networks (BNNs)
for learning representations that are invariant to the actions of compact
commutative groups on the space over which a signal is defined. The model
incorporates the ansatz of the bispectrum, an analytically defined group
invariant that is complete -- that is, it preserves all signal structure while
removing only the variation due to group actions. Here, we demonstrate that
BNNs are able to simultaneously learn groups, their irreducible
representations, and corresponding complete invariant maps purely from the
symmetries implicit in data. Further, we demonstrate that the completeness
property endows these networks with strong adversarial robustness. This work
establishes Bispectral Neural Networks as a powerful computational primitive
for robust invariant representation learning.

Neural networks (NNs) and decision trees (DTs) are both popular models of
machine learning, yet coming with mutually exclusive advantages and
limitations. To bring the best of the two worlds, a variety of approaches are
proposed to integrate NNs and DTs explicitly or implicitly. In this survey,
these approaches are organized in a school which we term as neural trees (NTs).
This survey aims to present a comprehensive review of NTs and attempts to
identify how they enhance the model interpretability. We first propose a
thorough taxonomy of NTs that expresses the gradual integration and
co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their
interpretability and performance, and suggest possible solutions to the
remaining challenges. Finally, this survey concludes with a discussion about
other considerations like conditional computation and promising directions
towards this field. A list of papers reviewed in this survey, along with their
corresponding codes, is available at:
https://github.com/zju-vipa/awesome-neural-trees

The A* algorithm is commonly used to solve NP-hard combinatorial optimization
problems. When provided with an accurate heuristic function, A* can solve such
problems in time complexity that is polynomial in the solution depth. This fact
implies that accurate heuristic approximation for many such problems is also
NP-hard. In this context, we examine a line of recent publications that propose
the use of deep neural networks for heuristic approximation. We assert that
these works suffer from inherent scalability limitations since -- under the
assumption that P$\ne$NP -- such approaches result in either (a) network sizes
that scale exponentially in the instance sizes or (b) heuristic approximation
accuracy that scales inversely with the instance sizes. Our claim is supported
by experimental results for three representative NP-hard search problems that
show that fitting deep neural networks accurately to heuristic functions
necessitates network sizes that scale exponentially with the instance size.

Data science pipelines to train and evaluate models with machine learning may
contain bugs just like any other code. Leakage between training and test data
can lead to overestimating the model's accuracy during offline evaluations,
possibly leading to deployment of low-quality models in production. Such
leakage can happen easily by mistake or by following poor practices, but may be
tedious and challenging to detect manually. We develop a static analysis
approach to detect common forms of data leakage in data science code. Our
evaluation shows that our analysis accurately detects data leakage and that
such leakage is pervasive among over 100,000 analyzed public notebooks. We
discuss how our static analysis approach can help both practitioners and
educators, and how leakage prevention can be designed into the development
process.

Multiport interferometers based on integrated beamsplitter meshes have
recently captured interest as a platform for many emerging technologies. In
this paper, we present a novel architecture for multiport interferometers based
on the Sine-Cosine fractal decomposition of a unitary matrix. Our architecture
is unique in that it is self-similar, enabling the construction of modular
multi-chiplet devices. Due to this modularity, our design enjoys improved
resilience to hardware imperfections as compared to conventional multiport
interferometers. Additionally, the structure of our circuit enables systematic
truncation, which is key in reducing the hardware footprint of the chip as well
as compute time in training optical neural networks, while maintaining full
connectivity. Numerical simulations show that truncation of these meshes gives
robust performance even under large fabrication errors. This design is a step
forward in the construction of large-scale programmable photonics, removing a
major hurdle in scaling up to practical machine learning and quantum computing

Distinguishing between computer-generated (CG) and natural photographic (PG)
images is of great importance to verify the authenticity and originality of
digital images. However, the recent cutting-edge generation methods enable high
qualities of synthesis in CG images, which makes this challenging task even
trickier. To address this issue, a joint learning strategy with deep texture
and high-frequency features for CG image detection is proposed. We first
formulate and deeply analyze the different acquisition processes of CG and PG
images. Based on the finding that multiple different modules in image
acquisition will lead to different sensitivity inconsistencies to the
convolutional neural network (CNN)-based rendering in images, we propose a deep
texture rendering module for texture difference enhancement and discriminative
texture representation. Specifically, the semantic segmentation map is
generated to guide the affine transformation operation, which is used to
recover the texture in different regions of the input image. Then, the
combination of the original image and the high-frequency components of the
original and rendered images are fed into a multi-branch neural network
equipped with attention mechanisms, which refines intermediate features and
facilitates trace exploration in spatial and channel dimensions respectively.
Extensive experiments on two public datasets and a newly constructed dataset
with more realistic and diverse images show that the proposed approach
outperforms existing methods in the field by a clear margin. Besides, results
also demonstrate the detection robustness and generalization ability of the
proposed approach to postprocessing operations and generative adversarial
network (GAN) generated images.

Quantum amplitude estimation is a key subroutine in a number of powerful
quantum algorithms, including quantum-enhanced Monte Carlo simulation and
quantum machine learning. Maximum-likelihood quantum amplitude estimation
(MLQAE) is one of a number of recent approaches that employ much simpler
quantum circuits than the original algorithm based on quantum phase estimation.
In this article, we deepen the analysis of MLQAE to put the algorithm in a more
prescriptive form, including scenarios where quantum circuit depth is limited.
In the process, we observe and explain particular ranges of `exceptional'
values of the target amplitude for which the algorithm fails to achieve the
desired precision. We then propose and numerically validate a heuristic
modification to the algorithm to overcome this problem, bringing the algorithm
even closer to being useful as a practical subroutine on near- and mid-term
quantum hardware.

Open vocabulary models are a promising new paradigm for image classification.
Unlike traditional classification models, open vocabulary models classify among
any arbitrary set of categories specified with natural language during
inference. This natural language, called "prompts", typically consists of a set
of hand-written templates (e.g., "a photo of a {}") which are completed with
each of the category names. This work introduces a simple method to generate
higher accuracy prompts, without using explicit knowledge of the image domain
and with far fewer hand-constructed sentences. To achieve this, we combine open
vocabulary models with large language models (LLMs) to create Customized
Prompts via Language models (CuPL, pronounced "couple"). In particular, we
leverage the knowledge contained in LLMs in order to generate many descriptive
sentences that are customized for each object category. We find that this
straightforward and general approach improves accuracy on a range of zero-shot
image classification benchmarks, including over one percentage point gain on
ImageNet. Finally, this method requires no additional training and remains
completely zero-shot. Code is available at https://github.com/sarahpratt/CuPL.

Pre-Training (PT) of text representations has been successfully applied to
low-resource Neural Machine Translation (NMT). However, it usually fails to
achieve notable gains (sometimes, even worse) on resource-rich NMT on par with
its Random-Initialization (RI) counterpart. We take the first step to
investigate the complementarity between PT and RI in resource-rich scenarios
via two probing analyses, and find that: 1) PT improves NOT the accuracy, but
the generalization by achieving flatter loss landscapes than that of RI; 2) PT
improves NOT the confidence of lexical choice, but the negative diversity by
assigning smoother lexical probability distributions than that of RI. Based on
these insights, we propose to combine their complementarities with a model
fusion algorithm that utilizes optimal transport to align neurons between PT
and RI. Experiments on two resource-rich translation benchmarks, WMT'17
English-Chinese (20M) and WMT'19 English-German (36M), show that PT and RI
could be nicely complementary to each other, achieving substantial improvements
considering both translation accuracy, generalization, and negative diversity.
Probing tools and code are released at: https://github.com/zanchangtong/PTvsRI.

This paper studies the statistical model of the non-centered mixture of
scaled Gaussian distributions (NC-MSG). Using the Fisher-Rao information
geometry associated to this distribution, we derive a Riemannian gradient
descent algorithm. This algorithm is leveraged for two minimization problems.
The first one is the minimization of a regularized negative log- likelihood
(NLL). The latter makes the trade-off between a white Gaussian distribution and
the NC-MSG. Conditions on the regularization are given so that the existence of
a minimum to this problem is guaranteed without assumptions on the samples.
Then, the Kullback-Leibler (KL) divergence between two NC-MSG is derived. This
divergence enables us to define a minimization problem to compute centers of
mass of several NC-MSGs. The proposed Riemannian gradient descent algorithm is
leveraged to solve this second minimization problem. Numerical experiments show
the good performance and the speed of the Riemannian gradient descent on the
two problems. Finally, a Nearest centroid classifier is implemented leveraging
the KL divergence and its associated center of mass. Applied on the large scale
dataset Breizhcrops, this classifier shows good accuracies as well as
robustness to rigid transformations of the test set.

Spiking neural networks (SNNs) have attracted much attention for their high
energy efficiency and for recent advances in their classification performance.
However, unlike traditional deep learning approaches, the analysis and study of
the robustness of SNNs to adversarial examples remains relatively
underdeveloped. In this work we advance the field of adversarial machine
learning through experimentation and analyses of three important SNN security
attributes. First, we show that successful white-box adversarial attacks on
SNNs are highly dependent on the underlying surrogate gradient technique.
Second, we analyze the transferability of adversarial examples generated by
SNNs and other state-of-the-art architectures like Vision Transformers and Big
Transfer CNNs. We demonstrate that SNNs are not often deceived by adversarial
examples generated by Vision Transformers and certain types of CNNs. Lastly, we
develop a novel white-box attack that generates adversarial examples capable of
fooling both SNN models and non-SNN models simultaneously. Our experiments and
analyses are broad and rigorous covering two datasets (CIFAR-10 and CIFAR-100),
five different white-box attacks and twelve different classifier models.

This short note is a critical discussion of the quantification of aleatoric
and epistemic uncertainty in terms of conditional entropy and mutual
information, respectively, which has recently been proposed in machine learning
and has become quite common since then. More generally, we question the idea of
an additive decomposition of total uncertainty into its aleatoric and epistemic
constituents.

Artificial intelligence on graphs (graph AI) has achieved remarkable success
in modeling complex systems, ranging from dynamical systems in biology to
interacting particle systems in physics. The increasingly heterogeneous graph
datasets call for multimodal graph AI algorithms to combine multiple inductive
biases -- the set of assumptions that algorithms use to predict outputs of
given inputs that they have not yet encountered. Learning on multimodal graph
datasets presents fundamental challenges because inductive biases can vary by
data modality and graphs might not be explicitly given in the input. To address
these challenges, multimodal graph AI methods combine multiple modalities while
leveraging cross-modal dependencies. Here, we survey 142 studies in graph AI
and realize that diverse datasets are increasingly combined using graphs and
fed into sophisticated multimodal models. These models stratify into image-,
language-, and knowledge-grounded multimodal graph AI methods. Using this
categorization of state-of-the-art methods, we put forward an algorithmic
blueprint for multimodal graph AI, which we use to study existing methods and
standardize the design of future methods for highly complex systems.

In recent years, significant progress has been made in transportation
electrification. And lithium-ion batteries (LIB), as the main energy storage
devices, have received widespread attention. Accurately predicting the state of
health (SOH) can not only ease the anxiety of users about the battery life but
also provide important information for the management of the battery. This
paper presents a prediction method for SOH based on Vision Transformer (ViT)
model. First, discrete charging data of a predefined voltage range is used as
an input data matrix. Then, the cycle features of the battery are captured by
the ViT which can obtain the global features, and the SOH is obtained by
combining the cycle features with the full connection (FC) layer. At the same
time, transfer learning (TL) is introduced, and the prediction model based on
source task battery training is further fine-tuned according to the early cycle
data of the target task battery to provide an accurate prediction. Experiments
show that our method can obtain better feature expression compared with
existing deep learning methods so that better prediction effect and transfer
effect can be achieved.

In recent years, machine learning potentials (MLP) for atomistic simulations
have attracted a lot of attention in chemistry and materials science. Many new
approaches have been developed with the primary aim to transfer the accuracy of
electronic structure calculations to large condensed systems containing
thousands of atoms. In spite of these advances, the reliability of modern MLPs
in reproducing the subtle details of the multi-dimensional potential-energy
surface is still difficult to assess for such systems. On the other hand,
moderately sized systems enabling the application of tools for thorough and
systematic quality-control are nowadays rarely investigated. In this work we
use benchmark-quality harmonic and anharmonic vibrational frequencies as a
sensitive probe for the validation of high-dimensional neural network
potentials. For the case of the formic acid dimer, a frequently studied model
system for which stringent spectroscopic data became recently available, we
show that high-quality frequencies can be obtained from state-of-the-art
calculations in excellent agreement with coupled cluster theory and
experimental data.

The occurrence of West Nile Virus (WNV) represents one of the most common
mosquito-borne zoonosis viral infections. Its circulation is usually associated
with climatic and environmental conditions suitable for vector proliferation
and virus replication. On top of that, several statistical models have been
developed to shape and forecast WNV circulation: in particular, the recent
massive availability of Earth Observation (EO) data, coupled with the
continuous advances in the field of Artificial Intelligence, offer valuable
opportunities.
  In this paper, we seek to predict WNV circulation by feeding Deep Neural
Networks (DNNs) with satellite images, which have been extensively shown to
hold environmental and climatic features. Notably, while previous approaches
analyze each geographical site independently, we propose a spatial-aware
approach that considers also the characteristics of close sites. Specifically,
we build upon Graph Neural Networks (GNN) to aggregate features from
neighbouring places, and further extend these modules to consider multiple
relations, such as the difference in temperature and soil moisture between two
sites, as well as the geographical distance. Moreover, we inject time-related
information directly into the model to take into account the seasonality of
virus spread.
  We design an experimental setting that combines satellite images - from
Landsat and Sentinel missions - with ground truth observations of WNV
circulation in Italy. We show that our proposed Multi-Adjacency Graph Attention
Network (MAGAT) consistently leads to higher performance when paired with an
appropriate pre-training stage. Finally, we assess the importance of each
component of MAGAT in our ablation studies.

Visual imitation learning provides efficient and intuitive solutions for
robotic systems to acquire novel manipulation skills. However, simultaneously
learning geometric task constraints and control policies from visual inputs
alone remains a challenging problem. In this paper, we propose an approach for
keypoint-based visual imitation (K-VIL) that automatically extracts sparse,
object-centric, and embodiment-independent task representations from a small
number of human demonstration videos. The task representation is composed of
keypoint-based geometric constraints on principal manifolds, their associated
local frames, and the movement primitives that are then needed for the task
execution. Our approach is capable of extracting such task representations from
a single demonstration video, and of incrementally updating them when new
demonstrations become available. To reproduce manipulation skills using the
learned set of prioritized geometric constraints in novel scenes, we introduce
a novel keypoint-based admittance controller. We evaluate our approach in
several real-world applications, showcasing its ability to deal with cluttered
scenes, new instances of categorical objects, and large object pose and shape
variations, as well as its efficiency and robustness in both one-shot and
few-shot imitation learning settings. Videos and source code are available at
https://sites.google.com/view/k-vil.

We propose a solution strategy for parameter identification in multiphase
thermo-hydro-mechanical (THM) processes in porous media using physics-informed
neural networks (PINNs). We employ a dimensionless form of the THM governing
equations that is particularly well suited for the inverse problem, and we
leverage the sequential multiphysics PINN solver we developed in previous work.
We validate the proposed inverse-modeling approach on multiple benchmark
problems, including Terzaghi's isothermal consolidation problem, Barry-Mercer's
isothermal injection-production problem, and nonisothermal consolidation of an
unsaturated soil layer. We report the excellent performance of the proposed
sequential PINN-THM inverse solver, thus paving the way for the application of
PINNs to inverse modeling of complex nonlinear multiphysics problems.

This paper proposes the MBURST, a novel multimodal solution for audio-visual
speech enhancements that consider the most recent neurological discoveries
regarding pyramidal cells of the prefrontal cortex and other brain regions. The
so-called burst propagation implements several criteria to address the credit
assignment problem in a more biologically plausible manner: steering the sign
and magnitude of plasticity through feedback, multiplexing the feedback and
feedforward information across layers through different weight connections,
approximating feedback and feedforward connections, and linearizing the
feedback signals. MBURST benefits from such capabilities to learn correlations
between the noisy signal and the visual stimuli, thus attributing meaning to
the speech by amplifying relevant information and suppressing noise.
Experiments conducted over a Grid Corpus and CHiME3-based dataset show that
MBURST can reproduce similar mask reconstructions to the multimodal
backpropagation-based baseline while demonstrating outstanding energy
efficiency management, reducing the neuron firing rates to values up to
\textbf{$70\%$} lower. Such a feature implies more sustainable implementations,
suitable and desirable for hearing aids or any other similar embedded systems.

This paper reported a bespoke adder-based deep learning network for
time-domain fluorescence lifetime imaging (FLIM). By leveraging the l1-norm
extraction method, we propose a 1-D Fluorescence Lifetime AdderNet (FLAN)
without multiplication-based convolutions to reduce the computational
complexity. Further, we compressed fluorescence decays in temporal dimension
using a log-scale merging technique to discard redundant temporal information
derived as log-scaling FLAN (FLAN+LS). FLAN+LS achieves 0.11 and 0.23
compression ratios compared with FLAN and a conventional 1-D convolutional
neural network (1-D CNN) while maintaining high accuracy in retrieving
lifetimes. We extensively evaluated FLAN and FLAN+LS using synthetic and real
data. A traditional fitting method and other non-fitting, high-accuracy
algorithms were compared with our networks for synthetic data. Our networks
attained a minor reconstruction error in different photon-count scenarios. For
real data, we used fluorescent beads' data acquired by a confocal microscope to
validate the effectiveness of real fluorophores, and our networks can
differentiate beads with different lifetimes. Additionally, we implemented the
network architecture on a field-programmable gate array (FPGA) with a
post-quantization technique to shorten the bit-width, thereby improving
computing efficiency. FLAN+LS on hardware achieves the highest computing
efficiency compared to 1-D CNN and FLAN. We also discussed the applicability of
our network and hardware architecture for other time-resolved biomedical
applications using photon-efficient, time-resolved sensors.

Riemannian optimization is a principled framework for solving optimization
problems where the desired optimum is constrained to a smooth manifold
$\mathcal{M}$. Algorithms designed in this framework usually require some
geometrical description of the manifold, which typically includes tangent
spaces, retractions, and gradients of the cost function. However, in many
cases, only a subset (or none at all) of these elements can be accessed due to
lack of information or intractability. In this paper, we propose a novel
approach that can perform approximate Riemannian optimization in such cases,
where the constraining manifold is a submanifold of $\R^{D}$. At the bare
minimum, our method requires only a noiseless sample set of the cost function
$(\x_{i}, y_{i})\in {\mathcal{M}} \times \mathbb{R}$ and the intrinsic
dimension of the manifold $\mathcal{M}$. Using the samples, and utilizing the
Manifold-MLS framework (Sober and Levin 2020), we construct approximations of
the missing components entertaining provable guarantees and analyze their
computational costs. In case some of the components are given analytically
(e.g., if the cost function and its gradient are given explicitly, or if the
tangent spaces can be computed), the algorithm can be easily adapted to use the
accurate expressions instead of the approximations. We analyze the global
convergence of Riemannian gradient-based methods using our approach, and we
demonstrate empirically the strength of this method, together with a
conjugate-gradients type method based upon similar principles.

Self-supervised visual representation learning has recently attracted
significant research interest. While a common way to evaluate self-supervised
representations is through transfer to various downstream tasks, we instead
investigate the problem of measuring their interpretability, i.e. understanding
the semantics encoded in raw representations. We formulate the latter as
estimating the mutual information between the representation and a space of
manually labelled concepts. To quantify this we introduce a decoding
bottleneck: information must be captured by simple predictors, mapping concepts
to clusters in representation space. This approach, which we call reverse
linear probing, provides a single number sensitive to the semanticity of the
representation. This measure is also able to detect when the representation
contains combinations of concepts (e.g., "red apple") instead of just
individual attributes ("red" and "apple" independently). Finally, we propose to
use supervised classifiers to automatically label large datasets in order to
enrich the space of concepts used for probing. We use our method to evaluate a
large number of self-supervised representations, ranking them by
interpretability, highlight the differences that emerge compared to the
standard evaluation with linear probes and discuss several qualitative
insights. Code at: {\scriptsize{\url{https://github.com/iro-cp/ssl-qrp}}}.

Open-source software (OSS) vulnerability management process is important
nowadays, as the number of discovered OSS vulnerabilities is increasing over
time. Monitoring vulnerability-fixing commits is a part of the standard process
to prevent vulnerability exploitation. Manually detecting vulnerability-fixing
commits is, however, time consuming due to the possibly large number of commits
to review. Recently, many techniques have been proposed to automatically detect
vulnerability-fixing commits using machine learning. These solutions either:
(1) did not use deep learning, or (2) use deep learning on only limited sources
of information. This paper proposes VulCurator, a tool that leverages deep
learning on richer sources of information, including commit messages, code
changes and issue reports for vulnerability-fixing commit classifica- tion. Our
experimental results show that VulCurator outperforms the state-of-the-art
baselines up to 16.1% in terms of F1-score. VulCurator tool is publicly
available at https://github.com/ntgiang71096/VFDetector and
https://zenodo.org/record/7034132#.Yw3MN-xBzDI, with a demo video at
https://youtu.be/uMlFmWSJYOE.

3D textured shape recovery from partial scans is crucial for many real-world
applications. Existing approaches have demonstrated the efficacy of implicit
function representation, but they suffer from partial inputs with severe
occlusions and varying object types, which greatly hinders their application
value in the real world. This technical report presents our approach to address
these limitations by incorporating learned geometric priors. To this end, we
generate a SMPL model from learned pose prediction and fuse it into the partial
input to add prior knowledge of human bodies. We also propose a novel
completeness-aware bounding box adaptation for handling different levels of
scales and partialness of partial scans.

Brain-computer interface (BCI) provides a direct communication pathway
between human brain and external devices. Before a new subject could use BCI, a
calibration procedure is usually required. Because the inter- and intra-subject
variances are so large that the models trained by the existing subjects perform
poorly on new subjects. Therefore, effective subject-transfer and calibration
method is essential. In this paper, we propose a semi-supervised meta learning
(SSML) method for subject-transfer learning in BCIs. The proposed SSML learns a
meta model with the existing subjects first, then fine-tunes the model in a
semi-supervised learning manner, i.e. using few labeled and many unlabeled
samples of target subject for calibration. It is significant for BCI
applications where the labeled data are scarce or expensive while unlabeled
data are readily available. To verify the SSML method, three different BCI
paradigms are tested: 1) event-related potential detection; 2) emotion
recognition; and 3) sleep staging. The SSML achieved significant improvements
of over 15% on the first two paradigms and 4.9% on the third. The experimental
results demonstrated the effectiveness and potential of the SSML method in BCI

Constructing a static call graph requires trade-offs between soundness and
precision. Program analysis techniques for constructing call graphs are
unfortunately usually imprecise. To address this problem, researchers have
recently proposed call graph pruning empowered by machine learning to
post-process call graphs constructed by static analysis. A machine learning
model is built to capture information from the call graph by extracting
structural features for use in a random forest classifier. It then removes
edges that are predicted to be false positives. Despite the improvements shown
by machine learning models, they are still limited as they do not consider the
source code semantics and thus often are not able to effectively distinguish
true and false positives. In this paper, we present a novel call graph pruning
technique, AutoPruner, for eliminating false positives in call graphs via both
statistical semantic and structural analysis. Given a call graph constructed by
traditional static analysis tools, AutoPruner takes a Transformer-based
approach to capture the semantic relationships between the caller and callee
functions associated with each edge in the call graph. To do so, AutoPruner
fine-tunes a model of code that was pre-trained on a large corpus to represent
source code based on descriptions of its semantics. Next, the model is used to
extract semantic features from the functions related to each edge in the call
graph. AutoPruner uses these semantic features together with the structural
features extracted from the call graph to classify each edge via a feed-forward
neural network. Our empirical evaluation on a benchmark dataset of real-world
programs shows that AutoPruner outperforms the state-of-the-art baselines,
improving on F-measure by up to 13% in identifying false-positive edges in a
static call graph.

The contextual bandit problem is a theoretically justified framework with
wide applications in various fields. While the previous study on this problem
usually requires independence between noise and contexts, our work considers a
more sensible setting where the noise becomes a latent confounder that affects
both contexts and rewards. Such a confounded setting is more realistic and
could expand to a broader range of applications. However, the unresolved
confounder will cause a bias in reward function estimation and thus lead to a
large regret. To deal with the challenges brought by the confounder, we apply
the dual instrumental variable regression, which can correctly identify the
true reward function. We prove the convergence rate of this method is
near-optimal in two types of widely used reproducing kernel Hilbert spaces.
Therefore, we can design computationally efficient and regret-optimal
algorithms based on the theoretical guarantees for confounded bandit problems.
The numerical results illustrate the efficacy of our proposed algorithms in the
confounded bandit setting.

Quantum reservoir computing is a promising approach to quantum neural
networks capable of solving hard learning tasks on both classical and quantum
input data. However, current approaches with qubits are limited by low
connectivity. We propose an implementation for quantum reservoir that obtains a
large number of densely connected neurons by using parametrically coupled
quantum oscillators instead of physically coupled qubits. We analyse a specific
hardware implementation based on superconducting circuits. Our results give the
coupling and dissipation requirements in the system and show how they affect
the performance of the quantum reservoir. Beyond quantum reservoir computation,
the use of parametrically coupled bosonic modes holds promise for realizing
large quantum neural network architectures.

Positive and negative relations play an essential role in human behavior and
shape the communities we live in. Despite their importance, data about signed
relations is rare and commonly gathered through surveys. Interaction data is
more abundant, for instance, in the form of proximity or communication data. So
far, though, it could not be utilized to detect signed relations. In this
paper, we show how the underlying signed relations can be extracted with such
data. Employing a statistical network approach, we construct networks of signed
relations in four communities. We then show that these relations correspond to
the ones reported in surveys. Additionally, the inferred relations allow us to
study the homophily of individuals with respect to gender, religious beliefs,
and financial backgrounds. We evaluate the importance of triads in the signed
network to study group cohesion.

Conversational recommender systems (CRS) are interactive agents that support
their users in recommendation-related goals through multi-turn conversations.
Generally, a CRS can be evaluated in various dimensions. Today's CRS mainly
rely on offline(computational) measures to assess the performance of their
algorithms in comparison to different baselines. However, offline measures can
have limitations, for example, when the metrics for comparing a newly generated
response with a ground truth do not correlate with human perceptions, because
various alternative generated responses might be suitable too in a given dialog
situation. Current research on machine learning-based CRS models therefore
acknowledges the importance of humans in the evaluation process, knowing that
pure offline measures may not be sufficient in evaluating a highly interactive
system like a CRS.

Driverless road maintenance could potentially be highly beneficial to all its
stakeholders, with the key goals being increased safety for all road
participants, more efficient traffic management, and reduced road maintenance
costs such that the standard of the road infrastructure is sufficient for it to
be used in Automated Driving (AD). This paper addresses how the current state
of technology could be expanded to reach those goals. Within the project
'System for Teleoperated Road-marking' (SToRM), using the road-marking machine
as the system, different operation modes based on teleoperation were discussed
and developed. Furthermore, a functional system overview considering both
hardware and software elements was experimentally validated with an actual
road-marking machine and should serve as a baseline for future efforts in this
and similar areas.

Achieving highly accurate kinematic or simulator models that are close to the
real robot can facilitate model-based controls (e.g., model predictive control
or linear-quadradic regulators), model-based trajectory planning (e.g.,
trajectory optimization), and decrease the amount of learning time necessary
for reinforcement learning methods. Thus, the objective of this work is to
learn the residual errors between a kinematic and/or simulator model and the
real robot. This is achieved using auto-tuning and neural networks, where the
parameters of a neural network are updated using an auto-tuning method that
applies equations from an Unscented Kalman Filter (UKF) formulation. Using this
method, we model these residual errors with only small amounts of data - a
necessity as we improve the simulator/kinematic model by learning directly from
hardware operation. We demonstrate our method on robotic hardware (e.g.,
manipulator arm), and show that with the learned residual errors, we can
further close the reality gap between kinematic models, simulations, and the
real robot.

The robustness of any machine learning solution is fundamentally bound by the
data it was trained on. One way to generalize beyond the original training is
through human-informed augmentation of the original dataset; however, it is
impossible to specify all possible failure cases that can occur during
deployment. To address this limitation we combine model-based reinforcement
learning and model-interpretability methods to propose a solution that
self-generates simulated scenarios constrained by environmental concepts and
dynamics learned in an unsupervised manner. In particular, an internal model of
the agent's environment is conditioned on low-dimensional concept
representations of the input space that are sensitive to the agent's actions.
We demonstrate this method within a standard realistic driving simulator in a
simple point-to-point navigation task, where we show dramatic improvements in
one-shot generalization to different instances of specified failure cases as
well as zero-shot generalization to similar variations compared to model-based
and model-free approaches.

The academic evaluation of the publication record of researchers is relevant
for identifying talented candidates for promotion and funding. A key tool for
this is the use of the indexes provided by Web of Science and SCOPUS, costly
databases that sometimes exceed the possibilities of academic institutions in
many parts of the world. We show here how the data in one of the bases can be
used to infer the main index of the other one. Methods of data analysis used in
Machine Learning allow us to select just a few of the hundreds of variables in
a database, which later are used in a panel regression, yielding a good
approximation to the main index in the other database. Since the information of
SCOPUS can be freely scraped from the Web, this approach allows to infer for
free the Impact Factor of publications, the main index used in research
assessments around the globe.

Deep Reinforcement Learning uses a deep neural network to encode a policy,
which achieves very good performance in a wide range of applications but is
widely regarded as a black box model. A more interpretable alternative to deep
networks is given by neuro-fuzzy controllers. Unfortunately, neuro-fuzzy
controllers often need a large number of rules to solve relatively simple
tasks, making them difficult to interpret. In this work, we present an
algorithm to distill the policy from a deep Q-network into a compact
neuro-fuzzy controller. This allows us to train compact neuro-fuzzy controllers
through distillation to solve tasks that they are unable to solve directly,
combining the flexibility of deep reinforcement learning and the
interpretability of compact rule bases. We demonstrate the algorithm on three
well-known environments from OpenAI Gym, where we nearly match the performance
of a DQN agent using only 2 to 6 fuzzy rules.

Machine learning techniques are increasingly used to predict material
behavior in scientific applications and offer a significant advantage over
conventional numerical methods. In this work, an Artificial Neural Network
(ANN) model is used in a finite element formulation to define the flow law of a
metallic material as a function of plastic strain, plastic strain rate and
temperature. First, we present the general structure of the neural network, its
operation and focus on the ability of the network to deduce, without prior
learning, the derivatives of the flow law with respect to the model inputs. In
order to validate the robustness and accuracy of the proposed model, we compare
and analyze the performance of several network architectures with respect to
the analytical formulation of a Johnson-Cook behavior law for a 42CrMo4 steel.
In a second part, after having selected an Artificial Neural Network
architecture with $2$ hidden layers, we present the implementation of this
model in the Abaqus Explicit computational code in the form of a VUHARD
subroutine. The predictive capability of the proposed model is then
demonstrated during the numerical simulation of two test cases: the necking of
a circular bar and a Taylor impact test. The results obtained show a very high
capability of the ANN to replace the analytical formulation of a Johnson-Cook
behavior law in a finite element code, while remaining competitive in terms of
numerical simulation time compared to a classical approach.

Inferring chemical reaction networks (CRN) from time series data is a
challenge encouraged by the growing availability of quantitative temporal data
at the cellular level. This motivates the design of algorithms to infer the
preponderant reactions between the molecular species observed in a given
biochemical process, and help to build CRN model structure and kinetics.
Existing ODE-based inference methods such as SINDy resort to least square
regression combined with sparsity-enforcing penalization, such as Lasso.
However, when the input time series are only available in wild type conditions
in which all reactions are present, we observe that current methods fail to
learn sparse models. Results: We present Reactmine, a CRN learning algorithm
which enforces sparsity by inferring reactions in a sequential fashion within a
search tree of bounded depth, ranking the inferred reaction candidates
according to the variance of their kinetics, and re-optimizing the CRN kinetic
parameters on the whole trace in a final pass to rank the inferred CRN
candidates. We first evaluate its performance on simulation data from a
benchmark of hidden CRNs, together with algorithmic hyperparameter sensitivity
analyses, and then on two sets of real experimental data: one from protein
fluorescence videomicroscopy of cell cycle and circadian clock markers, and one
from biomedical measurements of systemic circadian biomarkers possibly acting
on clock gene expression in peripheral organs. We show that Reactmine succeeds
both on simulation data by retrieving hidden CRNs where SINDy fails, and on the
two real datasets by inferring reactions in agreement with previous studies.

To improve the performance-critical stability and brightness of the electron
bunch at injection into the proton-driven plasma wakefield at the AWAKE CERN
experiment, automation approaches based on unsupervised Machine Learning (ML)
were developed and deployed. Numerical optimisers were tested together with
different model-free reinforcement learning agents. In order to avoid any bias,
reinforcement learning agents have been trained also using a completely
unsupervised state encoding using auto-encoders. To aid hyper-parameter
selection, a full synthetic model of the beamline was constructed using a
variational auto-encoder trained to generate surrogate data from equipment
settings. This paper describes the novel approaches based on deep learning and
reinforcement learning to aid the automatic setup of a low energy line, as the
one used to deliver beam to the AWAKE facility. The results obtained with the
different ML approaches, including automatic unsupervised feature extraction
from images using computer vision are presented. The prospects for operational
deployment and wider applicability are discussed.

Language models pre-trained on biomedical corpora, such as BioBERT, have
recently shown promising results on downstream biomedical tasks. Many existing
pre-trained models, on the other hand, are resource-intensive and
computationally heavy owing to factors such as embedding size, hidden
dimension, and number of layers. The natural language processing (NLP)
community has developed numerous strategies to compress these models utilising
techniques such as pruning, quantisation, and knowledge distillation, resulting
in models that are considerably faster, smaller, and subsequently easier to use
in practice. By the same token, in this paper we introduce six lightweight
models, namely, BioDistilBERT, BioTinyBERT, BioMobileBERT, DistilBioBERT,
TinyBioBERT, and CompactBioBERT which are obtained either by knowledge
distillation from a biomedical teacher or continual learning on the Pubmed
dataset via the Masked Language Modelling (MLM) objective. We evaluate all of
our models on three biomedical tasks and compare them with BioBERT-v1.1 to
create efficient lightweight models that perform on par with their larger
counterparts. All the models will be publicly available on our Huggingface
profile at https://huggingface.co/nlpie and the codes used to run the
experiments will be available at
https://github.com/nlpie-research/Compact-Biomedical-Transformers.

Human anatomy, morphology, and associated diseases can be studied using
medical imaging data. However, access to medical imaging data is restricted by
governance and privacy concerns, data ownership, and the cost of acquisition,
thus limiting our ability to understand the human body. A possible solution to
this issue is the creation of a model able to learn and then generate synthetic
images of the human body conditioned on specific characteristics of relevance
(e.g., age, sex, and disease status). Deep generative models, in the form of
neural networks, have been recently used to create synthetic 2D images of
natural scenes. Still, the ability to produce high-resolution 3D volumetric
imaging data with correct anatomical morphology has been hampered by data
scarcity and algorithmic and computational limitations. This work proposes a
generative model that can be scaled to produce anatomically correct,
high-resolution, and realistic images of the human brain, with the necessary
quality to allow further downstream analyses. The ability to generate a
potentially unlimited amount of data not only enables large-scale studies of
human anatomy and pathology without jeopardizing patient privacy, but also
significantly advances research in the field of anomaly detection, modality
synthesis, learning under limited data, and fair and ethical AI. Code and
trained models are available at: https://github.com/AmigoLab/SynthAnatomy.

Hate speech detection is a common downstream application of natural language
processing (NLP) in the real world. In spite of the increasing accuracy,
current data-driven approaches could easily learn biases from the imbalanced
data distributions originating from humans. The deployment of biased models
could further enhance the existing social biases. But unlike handling tabular
data, defining and mitigating biases in text classifiers, which deal with
unstructured data, are more challenging. A popular solution for improving
machine learning fairness in NLP is to conduct the debiasing process with a
list of potentially discriminated words given by human annotators. In addition
to suffering from the risks of overlooking the biased terms, exhaustively
identifying bias with human annotators are unsustainable since discrimination
is variable among different datasets and may evolve over time. To this end, we
propose an automatic misuse detector (MiD) relying on an explanation method for
detecting potential bias. And built upon that, an end-to-end debiasing
framework with the proposed staged correction is designed for text classifiers
without any external resources required.

Nowadays, owners and developers of deep learning models must consider
stringent privacy-preservation rules of their training data, usually
crowd-sourced and retaining sensitive information. The most widely adopted
method to enforce privacy guarantees of a deep learning model nowadays relies
on optimization techniques enforcing differential privacy. According to the
literature, this approach has proven to be a successful defence against several
models' privacy attacks, but its downside is a substantial degradation of the
models' performance. In this work, we compare the effectiveness of the
differentially-private stochastic gradient descent (DP-SGD) algorithm against
standard optimization practices with regularization techniques. We analyze the
resulting models' utility, training performance, and the effectiveness of
membership inference and model inversion attacks against the learned models.
Finally, we discuss differential privacy's flaws and limits and empirically
demonstrate the often superior privacy-preserving properties of dropout and
l2-regularization.

Similarity caching allows requests for an item \(i\) to be served by a
similar item \(i'\). Applications include recommendation systems, multimedia
retrieval, and machine learning. Recently, many similarity caching policies
have been proposed, but still we do not know how to compute the hit rate even
for the simplest policies, like SIM-LRU and RND-LRU that are straightforward
modifications of classical caching algorithms. This paper proposes the first
algorithm to compute the hit rate of similarity caching policies under the
independent reference model for the request process. In particular, our work
shows how to extend the popular TTL approximation from classic caching to
similarity caching. The algorithm is evaluated on both synthetic and real world
traces.

High-entropy alloys (HEAs) have attracted extensive interest due to their
exceptional mechanical properties and the vast compositional space for new
HEAs. However, understanding their novel physical mechanisms and then using
these mechanisms to design new HEAs are confronted with their high-dimensional
chemical complexity, which presents unique challenges to (i) the theoretical
modeling that needs accurate atomic interactions for atomistic simulations and
(ii) constructing reliable macro-scale models for high-throughput screening of
vast amounts of candidate alloys. Machine learning (ML) sheds light on these
problems with its capability to represent extremely complex relations. This
review highlights the success and promising future of utilizing ML to overcome
these challenges. We first introduce the basics of ML algorithms and
application scenarios. We then summarize the state-of-the-art ML models
describing atomic interactions and atomistic simulations of thermodynamic and
mechanical properties. Special attention is paid to phase predictions,
planar-defect calculations, and plastic deformation simulations. Next, we
review ML models for macro-scale properties, such as lattice structures, phase
formations, and mechanical properties. Examples of machine-learned
phase-formation rules and order parameters are used to illustrate the workflow.
Finally, we discuss the remaining challenges and present an outlook of research
directions, including uncertainty quantification and ML-guided inverse
materials design.

Recent research has shown that criminal networks have complex organizational
structures, but whether this can be used to predict static and dynamic
properties of criminal networks remains little explored. Here, by combining
graph representation learning and machine learning methods, we show that
structural properties of political corruption, police intelligence, and money
laundering networks can be used to recover missing criminal partnerships,
distinguish among different types of criminal and legal associations, as well
as predict the total amount of money exchanged among criminal agents, all with
outstanding accuracy. We also show that our approach can anticipate future
criminal associations during the dynamic growth of corruption networks with
significant accuracy. Thus, similar to evidence found at crime scenes, we
conclude that structural patterns of criminal networks carry crucial
information about illegal activities, which allows machine learning methods to
predict missing information and even anticipate future criminal behavior.

COVID-19 impacted every part of the world, although the misinformation about
the outbreak traveled faster than the virus. Misinformation spread through
online social networks (OSN) often misled people from following correct medical
practices. In particular, OSN bots have been a primary source of disseminating
false information and initiating cyber propaganda. Existing work neglects the
presence of bots that act as a catalyst in the spread and focuses on fake news
detection in 'articles shared in posts' rather than the post (textual) content.
Most work on misinformation detection uses manually labeled datasets that are
hard to scale for building their predictive models. In this research, we
overcome this challenge of data scarcity by proposing an automated approach for
labeling data using verified fact-checked statements on a Twitter dataset. In
addition, we combine textual features with user-level features (such as
followers count and friends count) and tweet-level features (such as number of
mentions, hashtags and urls in a tweet) to act as additional indicators to
detect misinformation. Moreover, we analyzed the presence of bots in tweets and
show that bots change their behavior over time and are most active during the
misinformation campaign. We collected 10.22 Million COVID-19 related tweets and
used our annotation model to build an extensive and original ground truth
dataset for classification purposes. We utilize various machine learning models
to accurately detect misinformation and our best classification model achieves
precision (82%), recall (96%), and false positive rate (3.58%). Also, our bot
analysis indicates that bots generated approximately 10% of misinformation
tweets. Our methodology results in substantial exposure of false information,
thus improving the trustworthiness of information disseminated through social
media platforms.

Real-time object detection on Unmanned Aerial Vehicles (UAVs) is a
challenging issue due to the limited computing resources of edge GPU devices as
Internet of Things (IoT) nodes. To solve this problem, in this paper, we
propose a novel lightweight deep learning architectures named FasterX based on
YOLOX model for real-time object detection on edge GPU. First, we design an
effective and lightweight PixSF head to replace the original head of YOLOX to
better detect small objects, which can be further embedded in the depthwise
separable convolution (DS Conv) to achieve a lighter head. Then, a slimmer
structure in the Neck layer termed as SlimFPN is developed to reduce parameters
of the network, which is a trade-off between accuracy and speed. Furthermore,
we embed attention module in the Head layer to improve the feature extraction
effect of the prediction head. Meanwhile, we also improve the label assignment
strategy and loss function to alleviate category imbalance and box optimization
problems of the UAV dataset. Finally, auxiliary heads are presented for online
distillation to improve the ability of position embedding and feature
extraction in PixSF head. The performance of our lightweight models are
validated experimentally on the NVIDIA Jetson NX and Jetson Nano GPU embedded
platforms.Extensive experiments show that FasterX models achieve better
trade-off between accuracy and latency on VisDrone2021 dataset compared to

Electric Vehicle (EV) charging demand and charging station availability
forecasting is one of the challenges in the intelligent transportation system.
With the accurate EV station situation prediction, suitable charging behaviors
could be scheduled in advance to relieve range anxiety. Many existing deep
learning methods are proposed to address this issue, however, due to the
complex road network structure and comprehensive external factors, such as
point of interests (POIs) and weather effects, many commonly used algorithms
could just extract the historical usage information without considering
comprehensive influence of external factors. To enhance the prediction accuracy
and interpretability, the Attribute-Augmented Spatial-Temporal Graph Informer
(AST-GIN) structure is proposed in this study by combining the Graph
Convolutional Network (GCN) layer and the Informer layer to extract both
external and internal spatial-temporal dependence of relevant transportation
data. And the external factors are modeled as dynamic attributes by the
attribute-augmented encoder for training. AST-GIN model is tested on the data
collected in Dundee City and experimental results show the effectiveness of our
model considering external factors influence over various horizon settings

We introduce AudioLM, a framework for high-quality audio generation with
long-term consistency. AudioLM maps the input audio to a sequence of discrete
tokens and casts audio generation as a language modeling task in this
representation space. We show how existing audio tokenizers provide different
trade-offs between reconstruction quality and long-term structure, and we
propose a hybrid tokenization scheme to achieve both objectives. Namely, we
leverage the discretized activations of a masked language model pre-trained on
audio to capture long-term structure and the discrete codes produced by a
neural audio codec to achieve high-quality synthesis. By training on large
corpora of raw audio waveforms, AudioLM learns to generate natural and coherent
continuations given short prompts. When trained on speech, and without any
transcript or annotation, AudioLM generates syntactically and semantically
plausible speech continuations while also maintaining speaker identity and
prosody for unseen speakers. Furthermore, we demonstrate how our approach
extends beyond speech by generating coherent piano music continuations, despite
being trained without any symbolic representation of music.

Mis- and disinformation are now a substantial global threat to our security
and safety. To cope with the scale of online misinformation, one viable
solution is to automate the fact-checking of claims by retrieving and verifying
against relevant evidence. While major recent advances have been achieved in
pushing forward the automatic fact-verification, a comprehensive evaluation of
the possible attack vectors against such systems is still lacking.
Particularly, the automated fact-verification process might be vulnerable to
the exact disinformation campaigns it is trying to combat. In this work, we
assume an adversary that automatically tampers with the online evidence in
order to disrupt the fact-checking model via camouflaging the relevant
evidence, or planting a misleading one. We first propose an exploratory
taxonomy that spans these two targets and the different threat model
dimensions. Guided by this, we design and propose several potential attack
methods. We show that it is possible to subtly modify claim-salient snippets in
the evidence, in addition to generating diverse and claim-aligned evidence. As
a result, we highly degrade the fact-checking performance under many different
permutations of the taxonomy's dimensions. The attacks are also robust against
post-hoc modifications of the claim. Our analysis further hints at potential
limitations in models' inference when faced with contradicting evidence. We
emphasize that these attacks can have harmful implications on the inspectable
and human-in-the-loop usage scenarios of such models, and we conclude by
discussing challenges and directions for future defenses.

A novel cross-domain attentional multi-task architecture - xDom - for robust
real-world wireless radio frequency (RF) fingerprinting is presented in this
work. To the best of our knowledge, this is the first time such comprehensive
attention mechanism is applied to solve RF fingerprinting problem. In this
paper, we resort to real-world IoT WiFi and Bluetooth (BT) emissions (instead
of synthetic waveform generation) in a rich multipath and unavoidable
interference environment in an indoor experimental testbed. We show the impact
of the time-frame of capture by including waveforms collected over a span of
months and demonstrate the same time-frame and multiple time-frame
fingerprinting evaluations. The effectiveness of resorting to a multi-task
architecture is also experimentally proven by conducting single-task and
multi-task model analyses. Finally, we demonstrate the significant gain in
performance achieved with the proposed xDom architecture by benchmarking
against a well-known state-of-the-art model for fingerprinting. Specifically,
we report performance improvements by up to 59.3% and 4.91x under single-task
WiFi and BT fingerprinting respectively, and up to 50.5% increase in
fingerprinting accuracy under the multi-task setting.

Picking the first arrival times of prestack gathers is called First Arrival
Time (FAT) picking, which is an indispensable step in seismic data processing,
and is mainly solved manually in the past. With the current increasing density
of seismic data collection, the efficiency of manual picking has been unable to
meet the actual needs. Therefore, automatic picking methods have been greatly
developed in recent decades, especially those based on deep learning. However,
few of the current supervised deep learning-based method can avoid the
dependence on labeled samples. Besides, since the gather data is a set of
signals which are greatly different from the natural images, it is difficult
for the current method to solve the FAT picking problem in case of a low Signal
to Noise Ratio (SNR). In this paper, for hard rock seismic gather data, we
propose a Multi-Stage Segmentation Pickup Network (MSSPN), which solves the
generalization problem across worksites and the picking problem in the case of
low SNR. In MSSPN, there are four sub-models to simulate the manually picking
processing, which is assumed to four stages from coarse to fine. Experiments on
seven field datasets with different qualities show that our MSSPN outperforms
benchmarks by a large margin.Particularly, our method can achieve more than
90\% accurate picking across worksites in the case of medium and high SNRs, and
even fine-tuned model can achieve 88\% accurate picking of the dataset with low
SNR.

Autonomous surface vehicles (ASV) allow the investigation of coastal areas,
ports and harbors as well as harsh and dangerous environments such as the
arctic regions. Despite receiving increasing attention, the hydrodynamic
analysis of ASV performance subject to variable operational parameters is
little investigated. In this context, this paper presents a multi-fidelity (MF)
hydrodynamic analysis of an ASV, namely the Shallow Water Autonomous
Multipurpose Platform (SWAMP), at surveying speed in calm water and subject to
variable payload and location of the center of mass, accounting for the variety
of equipment that the vehicle can carry. The analysis is conducted in deep
water, which is the condition mostly encountered by the ASV during surveys of
coastal and harbors areas. Quantities of interest are the resistance, the
vehicle attitude, and the wave generated in the region between the catamaran
hulls. These are assessed using a Reynolds Averaged Navier Stokes Equation
(RANSE) code and a linear potential flow (PF) solver. The objective is to
accurately assess the quantities of interest, along with identifying the
limitation of PF analysis in the current context. Finally, a multi-fidelity
Gaussian Process (MF-GP) model is obtained combining RANSE and PF solutions.
The latter also include variable grid refinement and coupling between
hydrodynamic loads and rigid body equations of motion. The surrogate model is
iteratively refined using an active learning approach. Numerical results show
that the MF-GP is effective in producing response surfaces of the SWAMP
performance with a limited computational cost. It is highlighted how the SWAMP
performance is significantly affected not only by the payload, but also by the
location of the center of mass. The latter can be therefore properly calibrated
to minimize the resistance and allow for longer-range operations.

There is increasing interest in the use of multimodal data in various web
applications, such as digital advertising and e-commerce. Typical methods for
extracting important information from multimodal data rely on a mid-fusion
architecture that combines the feature representations from multiple encoders.
However, as the number of modalities increases, several potential problems with
the mid-fusion model structure arise, such as an increase in the dimensionality
of the concatenated multimodal features and missing modalities. To address
these problems, we propose a new concept that considers multimodal inputs as a
set of sequences, namely, deep multimodal sequence sets (DM$^2$S$^2$). Our
set-aware concept consists of three components that capture the relationships
among multiple modalities: (a) a BERT-based encoder to handle the inter- and
intra-order of elements in the sequences, (b) intra-modality residual attention
(IntraMRA) to capture the importance of the elements in a modality, and (c)
inter-modality residual attention (InterMRA) to enhance the importance of
elements with modality-level granularity further. Our concept exhibits
performance that is comparable to or better than the previous set-aware models.
Furthermore, we demonstrate that the visualization of the learned InterMRA and
IntraMRA weights can provide an interpretation of the prediction results.

With the application of machine learning to security-critical and sensitive
domains, there is a growing need for integrity and privacy in computation using
accelerators, such as GPUs. Unfortunately, the support for trusted execution on
GPUs is currently very limited - trusted execution on accelerators is
particularly challenging since the attestation mechanism should not reduce
performance. Although hardware support for trusted execution on GPUs is
emerging, we study purely software-based approaches for trusted GPU execution.
A software-only approach offers distinct advantages: (1) complement
hardware-based approaches, enhancing security especially when vulnerabilities
in the hardware implementation degrade security, (2) operate on GPUs without
hardware support for trusted execution, and (3) achieve security without
reliance on secrets embedded in the hardware, which can be extracted as history
has shown. In this work, we present SAGE, a software-based attestation
mechanism for GPU execution. SAGE enables secure code execution on NVIDIA GPUs
of the Ampere architecture (A100), providing properties of code integrity and
secrecy, computation integrity, as well as data integrity and secrecy - all in
the presence of malicious code running on the GPU and CPU. Our evaluation
demonstrates that SAGE is already practical today for executing code in a
trustworthy way on GPUs without specific hardware support.

In course of this work, we examine the process of plastic profile extrusion,
where a polymer melt is shaped inside the so-called extrusion die and fixed in
its shape by solidification in the downstream calibration unit. More precise,
we focus on the development of a data-driven reduced order model (ROM) for the
purpose of predicting temperature distributions within the extruded profiles
inside the calibration unit. Therein, the ROM functions as a first step to our
overall goal of prediction based process control in order to avoid undesired
warpage and damages of the final product.

Watermarks should be introduced in the natural language outputs of AI systems
in order to maintain the distinction between human and machine-generated text.
The ethical imperative to not blur this distinction arises from the asemantic
nature of large language models and from human projections of emotional and
cognitive states on machines, possibly leading to manipulation, spreading
falsehoods or emotional distress. Enforcing this distinction requires
unintrusive, yet easily accessible marks of the machine origin. We propose to
implement a code based on equidistant letter sequences. While no such code
exists in human-written texts, its appearance in machine-generated ones would
prove helpful for ethical reasons.

Due to the rapid growth of Electrical Capacitance Tomography (ECT)
applications in several industrial fields, there is a crucial need for
developing high quality, yet fast, methodologies of image reconstruction from
raw capacitance measurements. Deep learning, as an effective non-linear mapping
tool for complicated functions, has been going viral in many fields including
electrical tomography. In this paper, we propose a Conditional Generative
Adversarial Network (CGAN) model for reconstructing ECT images from capacitance
measurements. The initial image of the CGAN model is constructed from the
capacitance measurement. To our knowledge, this is the first time to represent
the capacitance measurements in an image form. We have created a new massive
ECT dataset of 320K synthetic image measurements pairs for training, and
testing the proposed model. The feasibility and generalization ability of the
proposed CGAN-ECT model are evaluated using testing dataset, contaminated data
and flow patterns that are not exposed to the model during the training phase.
The evaluation results prove that the proposed CGAN-ECT model can efficiently
create more accurate ECT images than traditional and other deep learning-based
image reconstruction algorithms. CGAN-ECT achieved an average image correlation
coefficient of more than 99.3% and an average relative image error about 0.07.

Standard GPs offer a flexible modelling tool for well-behaved processes.
However, deviations from Gaussianity are expected to appear in real world
datasets, with structural outliers and shocks routinely observed. In these
cases GPs can fail to model uncertainty adequately and may over-smooth
inferences. Here we extend the GP framework into a new class of time-changed
GPs that allow for straightforward modelling of heavy-tailed non-Gaussian
behaviours, while retaining a tractable conditional GP structure through an
infinite mixture of non-homogeneous GPs representation. The conditional GP
structure is obtained by conditioning the observations on a latent transformed
input space and the random evolution of the latent transformation is modelled
using a L\'{e}vy process which allows Bayesian inference in both the posterior
predictive density and the latent transformation function. We present Markov
chain Monte Carlo inference procedures for this model and demonstrate the
potential benefits compared to a standard GP.

Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge of
and reason about the relationship between an object and its parts. In this
paper we specify a generative model for such data, and derive a variational
algorithm for inferring the transformation of each model object in a scene, and
the assignments of observed parts to the objects. We derive a learning
algorithm for the object models, based on variational expectation maximization
(Jordan et al., 1999). We also study an alternative inference algorithm based
on the RANSAC method of Fischler and Bolles (1981). We apply these inference
methods to (i) data generated from multiple geometric objects like squares and
triangles ("constellations"), and (ii) data from a parts-based model of faces.
Recent work by Kosiorek et al. (2019) has used amortized inference via stacked
capsule autoencoders (SCAEs) to tackle this problem -- our results show that we
significantly outperform them where we can make comparisons (on the
constellations data).

We investigate the computational efficiency of multitask learning of Boolean
functions over the $d$-dimensional hypercube, that are related by means of a
feature representation of size $k \ll d$ shared across all tasks. We present a
polynomial time multitask learning algorithm for the concept class of
halfspaces with margin $\gamma$, which is based on a simultaneous boosting
technique and requires only $\textrm{poly}(k/\gamma)$ samples-per-task and
$\textrm{poly}(k\log(d)/\gamma)$ samples in total.
  In addition, we prove a computational separation, showing that assuming there
exists a concept class that cannot be learned in the attribute-efficient model,
we can construct another concept class such that can be learned in the
attribute-efficient model, but cannot be multitask learned efficiently --
multitask learning this concept class either requires super-polynomial time
complexity or a much larger total number of samples.

This paper proposes a procedural content generator which evolves Minecraft
buildings according to an open-ended and intrinsic definition of novelty. To
realize this goal we evaluate individuals' novelty in the latent space using a
3D autoencoder, and alternate between phases of exploration and transformation.
During exploration the system evolves multiple populations of CPPNs through
CPPN-NEAT and constrained novelty search in the latent space (defined by the
current autoencoder). We apply a set of repair and constraint functions to
ensure candidates adhere to basic structural rules and constraints during
evolution. During transformation, we reshape the boundaries of the latent space
to identify new interesting areas of the solution space by retraining the
autoencoder with novel content. In this study we evaluate five different
approaches for training the autoencoder during transformation and its impact on
populations' quality and diversity during evolution. Our results show that by
retraining the autoencoder we can achieve better open-ended complexity compared
to a static model, which is further improved when retraining using larger
datasets of individuals with diverse complexities.

We propose a new hybrid modelling approach that combines a mechanistic model
with a machine-learnt model to predict the limit cycle oscillations of physical
systems with a Hopf bifurcation. The mechanistic model is an ordinary
differential equation normal-form model capturing the bifurcation structure of
the system. A data-driven mapping from this model to the experimental
observations is then identified based on experimental data using machine
learning techniques. The proposed method is first demonstrated numerically on a
Van der Pol oscillator and a three-degree-of-freedom aeroelastic model. It is
then applied to model the behaviour of a physical aeroelastic structure
exhibiting limit cycle oscillations during wind tunnel tests. The method is
shown to be general, data-efficient and to offer good accuracy without any
prior knowledge about the system other than its bifurcation structure.

The pandemic outbreak has profoundly changed our life, especially our social
habits and communication behaviors. While this dramatic shock has heavily
impacted human interaction rules, novel localization techniques are emerging to
help society in complying with new policies, such as social distancing.
Wireless sensing and machine learning are well suited to alleviate viruses
propagation in a privacy-preserving manner. However, its wide deployment
requires cost-effective installation and operational solutions. In public
environments, individual localization information-such as social
distancing-needs to be monitored to avoid safety threats when not properly
observed. To this end, the high penetration of wireless devices can be
exploited to continuously analyze-and-learn the propagation environment,
thereby passively detecting breaches and triggering alerts if required. In this
paper, we describe a novel passive and privacy-preserving human localization
solution that relies on the directive transmission properties of mmWave
communications to monitor social distancing and notify people in the area in
case of violations. Thus, addressing the social distancing challenge in a
privacy-preserving and cost-efficient manner. Our solution provides an overall
accuracy of about 99% in the tested scenarios.

Multi-robot navigation is a challenging task in which multiple robots must be
coordinated simultaneously within dynamic environments. We apply deep
reinforcement learning (DRL) to learn a decentralized end-to-end policy which
maps raw sensor data to the command velocities of the agent. In order to enable
the policy to generalize, the training is performed in different environments
and scenarios. The learned policy is tested and evaluated in common multi-robot
scenarios like switching a place, an intersection and a bottleneck situation.
This policy allows the agent to recover from dead ends and to navigate through
complex environments.

The number of clinical citations received from clinical guidelines or
clinical trials has been considered as one of the most appropriate indicators
for quantifying the clinical impact of biomedical papers. Therefore, the early
prediction of the clinical citation count of biomedical papers is critical to
scientific activities in biomedicine, such as research evaluation, resource
allocation, and clinical translation. In this study, we designed a four-layer
multilayer perceptron neural network (MPNN) model to predict the clinical
citation count of biomedical papers in the future by using 9,822,620 biomedical
papers published from 1985 to 2005. We extracted ninety-one paper features from
three dimensions as the input of the model, including twenty-one features in
the paper dimension, thirty-five in the reference dimension, and thirty-five in
the citing paper dimension. In each dimension, the features can be classified
into three categories, i.e., the citation-related features, the clinical
translation-related features, and the topic-related features. Besides, in the
paper dimension, we also considered the features that have previously been
demonstrated to be related to the citation counts of research papers. The
results showed that the proposed MPNN model outperformed the other five
baseline models, and the features in the reference dimension were the most
important.

Federated learning is an approach to train machine learning models on the
edge of the networks, as close as possible where the data is produced,
motivated by the emerging problem of the inability to stream and centrally
store the large amount of data produced by edge devices as well as by data
privacy concerns. This learning paradigm is in need of robust algorithms to
device heterogeneity and data heterogeneity. This paper proposes ModFL as a
federated learning framework that splits the models into a configuration module
and an operation module enabling federated learning of the individual modules.
This modular approach makes it possible to extract knowlege from a group of
heterogeneous devices as well as from non-IID data produced from its users.
This approach can be viewed as an extension of the federated learning with
personalisation layers FedPer framework that addresses data heterogeneity. We
show that ModFL outperforms FedPer for non-IID data partitions of CIFAR-10 and
STL-10 using CNNs. Our results on time-series data with HAPT, RWHAR, and WISDM
datasets using RNNs remain inconclusive, we argue that the chosen datasets do
not highlight the advantages of ModFL, but in the worst case scenario it
performs as well as FedPer.

Demographic classification is essential in fairness assessment in recommender
systems or in measuring unintended bias in online networks and voting systems.
Important fields like education and politics, which often lay a foundation for
the future of equality in society, need scrutiny to design policies that can
better foster equality in resource distribution constrained by the unbalanced
demographic distribution of people in the country.
  We collect three publicly available datasets to train state-of-the-art
classifiers in the domain of gender and caste classification. We train the
models in the Indian context, where the same name can have different styling
conventions (Jolly Abraham/Kumar Abhishikta in one state may be written as
Abraham Jolly/Abishikta Kumar in the other). Finally, we also perform
cross-testing (training and testing on different datasets) to understand the
efficacy of the above models.
  We also perform an error analysis of the prediction models. Finally, we
attempt to assess the bias in the existing Indian system as case studies and
find some intriguing patterns manifesting in the complex demographic layout of
the sub-continent across the dimensions of gender and caste.

This work introduces the concept of an autonomous cooking process based on
Digital Twin method- ology. It proposes a hybrid approach of physics-based full
order simulations followed by a data-driven system identification process with
low errors. It makes faster-than-real-time simulations of Digital Twins
feasible on a device level, without the need for cloud or high-performance
computing. The concept is universally applicable to various physical processes.

The variational lower bound (a.k.a. ELBO or free energy) is the central
objective for many established as well as many novel algorithms for
unsupervised learning. Learning algorithms change model parameters such that
the variational lower bound increases. Learning usually proceeds until
parameters have converged to values close to a stationary point of the learning
dynamics. In this purely theoretical contribution, we show that (for a very
large class of generative models) the variational lower bound is at all
stationary points of learning equal to a sum of entropies. For standard machine
learning models with one set of latents and one set observed variables, the sum
consists of three entropies: (A) the (average) entropy of the variational
distributions, (B) the negative entropy of the model's prior distribution, and
(C) the (expected) negative entropy of the observable distributions. The
obtained result applies under realistic conditions including: finite numbers of
data points, at any stationary points (including saddle points) and for any
family of (well behaved) variational distributions. The class of generative
models for which we show the equality to entropy sums contains many well-known
generative models. As concrete examples we discuss Sigmoid Belief Networks,
probabilistic PCA and (Gaussian and non-Gaussian) mixture models. The
prerequisites we use to show equality to entropy sums are relatively mild.
Concretely, the distributions of a given generative model have to be of the
exponential family (with constant base measure), and the model has to satisfy a
parameterization criterion (which is usually fulfilled). Proving the equality
of the ELBO to entropy sums at stationary points (under the stated conditions)
is the main contribution of this work.

Deep learning is currently the most important branch of machine learning,
with applications in speech recognition, computer vision, image classification,
and medical imaging analysis. Plant recognition is one of the areas where image
classification can be used to identify plant species through their leaves.
Botanists devote a significant amount of time to recognizing plant species by
personally inspecting. This paper describes a method for dissecting color
images of Swedish leaves and identifying plant species. To achieve higher
accuracy, the task is completed using transfer learning with the help of
pre-trained classifier VGG-19. The four primary processes of classification are
image preprocessing, image augmentation, feature extraction, and recognition,
which are performed as part of the overall model evaluation. The VGG-19
classifier grasps the characteristics of leaves by employing pre-defined hidden
layers such as convolutional layers, max pooling layers, and fully connected
layers, and finally uses the soft-max layer to generate a feature
representation for all plant classes. The model obtains knowledge connected to
aspects of the Swedish leaf dataset, which contains fifteen tree classes, and
aids in predicting the proper class of an unknown plant with an accuracy of
99.70% which is higher than previous research works reported.

We consider the tasks of learning quantum states, measurements and channels
generated by continuous-variable (CV) quantum circuits. This family of circuits
is suited to describe optical quantum technologies and in particular it
includes state-of-the-art photonic processors capable of showing quantum
advantage. We define classes of functions that map classical variables, encoded
into the CV circuit parameters, to outcome probabilities evaluated on those
circuits. We then establish efficient learnability guarantees for such classes,
by computing bounds on their pseudo-dimension or covering numbers, showing that
CV quantum circuits can be learned with a sample complexity that scales
polynomially with the circuit's size, i.e., the number of modes. Our results
establish that CV circuits can be trained efficiently using a number of
training samples that, unlike their finite-dimensional counterpart, does not
scale with the circuit depth.

Fog computing becomes a promising technology to process user's requests near
the proximity of users to reduce response time for latency-sensitive requests.
Despite its advantages, the properties such as resource heterogeneity and
limitations, and its dynamic and unpredictable nature greatly reduce the
efficiency of fog computing. Therefore, predicting the dynamic behavior of the
fog and managing resources accordingly is of utmost importance. In this work,
we provide a review of machine learning-based predictive resource management
approaches in a fog environment. Resource management is classified into six
sub-areas: resource provisioning, application placement, scheduling, resource
allocation, task offloading, and load balancing. Reviewed resource management
approaches are analyzed based on the objective metrics, tools, datasets, and
utilized techniques.

Recent masked image modeling (MIM) has received much attention in
self-supervised learning (SSL), which requires the target model to recover the
masked part of the input image. Although MIM-based pre-training methods achieve
new state-of-the-art performance when transferred to many downstream tasks, the
visualizations show that the learned representations are less separable,
especially compared to those based on contrastive learning pre-training. This
inspires us to think whether the linear separability of MIM pre-trained
representation can be further improved, thereby improving the pre-training
performance. Since MIM and contrastive learning tend to utilize different data
augmentations and training strategies, combining these two pretext tasks is not
trivial. In this work, we propose a novel and flexible pre-training framework,
named MimCo, which combines MIM and contrastive learning through two-stage
pre-training. Specifically, MimCo takes a pre-trained contrastive learning
model as the teacher model and is pre-trained with two types of learning
targets: patch-level and image-level reconstruction losses.
  Extensive transfer experiments on downstream tasks demonstrate the superior
performance of our MimCo pre-training framework. Taking ViT-S as an example,
when using the pre-trained MoCov3-ViT-S as the teacher model, MimCo only needs
100 epochs of pre-training to achieve 82.53% top-1 finetuning accuracy on
Imagenet-1K, which outperforms the state-of-the-art self-supervised learning

Single-Image Super-Resolution can support robotic tasks in environments where
a reliable visual stream is required to monitor the mission, handle
teleoperation or study relevant visual details. In this work, we propose an
efficient Generative Adversarial Network model for real-time Super-Resolution.
We adopt a tailored architecture of the original SRGAN and model quantization
to boost the execution on CPU and Edge TPU devices, achieving up to 200 fps
inference. We further optimize our model by distilling its knowledge to a
smaller version of the network and obtain remarkable improvements compared to
the standard training approach. Our experiments show that our fast and
lightweight model preserves considerably satisfying image quality compared to
heavier state-of-the-art models. Finally, we conduct experiments on image
transmission with bandwidth degradation to highlight the advantages of the
proposed system for mobile robotic applications.

Modern defenses against cyberattacks increasingly rely on proactive
approaches, e.g., to predict the adversary's next actions based on past events.
Building accurate prediction models requires knowledge from many organizations;
alas, this entails disclosing sensitive information, such as network
structures, security postures, and policies, which might often be undesirable
or outright impossible. In this paper, we explore the feasibility of using
Federated Learning (FL) to predict future security events. To this end, we
introduce Cerberus, a system enabling collaborative training of Recurrent
Neural Network (RNN) models for participating organizations. The intuition is
that FL could potentially offer a middle-ground between the non-private
approach where the training data is pooled at a central server and the
low-utility alternative of only training local models. We instantiate Cerberus
on a dataset obtained from a major security company's intrusion prevention
product and evaluate it vis-a-vis utility, robustness, and privacy, as well as
how participants contribute to and benefit from the system. Overall, our work
sheds light on both the positive aspects and the challenges of using FL for
this task and paves the way for deploying federated approaches to predictive
security.

Multimodal Variational Autoencoders (VAEs) have been a subject of intense
research in the past years as they can integrate multiple modalities into a
joint representation and can thus serve as a promising tool for both data
classification and generation. Several approaches toward multimodal VAE
learning have been proposed so far, their comparison and evaluation have
however been rather inconsistent. One reason is that the models differ at the
implementation level, another problem is that the datasets commonly used in
these cases were not initially designed for the evaluation of multimodal
generative models. This paper addresses both mentioned issues. First, we
propose a toolkit for systematic multimodal VAE training and comparison.
Second, we present a synthetic bimodal dataset designed for a comprehensive
evaluation of the joint generation and cross-generation capabilities. We
demonstrate the utility of the dataset by comparing state-of-the-art models.

As the length scales of the smallest technology continue to advance beyond
the micron scale it becomes increasingly important to equip robotic components
with the means for intelligent and autonomous decision making with limited
information. With the help of a tabular Q-learning algorithm, we design a model
for training a microswimmer, to navigate quickly through an environment given
by various different scalar motility fields, while receiving a limited amount
of local information. We compare the performances of the microswimmer, defined
via time of first passage to a target, with performances of suitable reference
cases. We show that the strategy obtained with our reinforcement learning model
indeed represents an efficient navigation strategy, that outperforms the
reference cases. By confronting the swimmer with a variety of unfamiliar
environments after the finalised training, we show that the obtained strategy
generalises to different classes of random fields.

IceCube, a cubic-kilometer array of optical sensors built to detect
atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed
1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The
classification and reconstruction of events from the in-ice detectors play a
central role in the analysis of data from IceCube. Reconstructing and
classifying events is a challenge due to the irregular detector geometry,
inhomogeneous scattering and absorption of light in the ice and, below 100 GeV,
the relatively low number of signal photons produced per event. To address this
challenge, it is possible to represent IceCube events as point cloud graphs and
use a Graph Neural Network (GNN) as the classification and reconstruction
method. The GNN is capable of distinguishing neutrino events from cosmic-ray
backgrounds, classifying different neutrino event types, and reconstructing the
deposited energy, direction and interaction vertex. Based on simulation, we
provide a comparison in the 1-100 GeV energy range to the current
state-of-the-art maximum likelihood techniques used in current IceCube
analyses, including the effects of known systematic uncertainties. For neutrino
event classification, the GNN increases the signal efficiency by 18% at a fixed
false positive rate (FPR), compared to current IceCube methods. Alternatively,
the GNN offers a reduction of the FPR by over a factor 8 (to below half a
percent) at a fixed signal efficiency. For the reconstruction of energy,
direction, and interaction vertex, the resolution improves by an average of
13%-20% compared to current maximum likelihood techniques in the energy range
of 1-30 GeV. The GNN, when run on a GPU, is capable of processing IceCube
events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz,
which opens the possibility of using low energy neutrinos in online searches
for transient events.

Histology images with multi-gigapixel of resolution yield rich information
for cancer diagnosis and prognosis. Most of the time, only slide-level label is
available because pixel-wise annotation is labour intensive task. In this
paper, we propose a deep learning pipeline for classification in histology
images. Using multiple instance learning, we attempt to predict the latent
membrane protein 1 (LMP1) status of nasopharyngeal carcinoma (NPC) based on
haematoxylin and eosin-stain (H&E) histology images. We utilised attention
mechanism with residual connection for our aggregation layers. In our 3-fold
cross-validation experiment, we achieved average accuracy, AUC and F1-score
0.936, 0.995 and 0.862, respectively. This method also allows us to examine the
model interpretability by visualising attention scores. To the best of our
knowledge, this is the first attempt to predict LMP1 status on NPC using deep
learning.

Few-shot visual recognition refers to recognize novel visual concepts from a
few labeled instances. Many few-shot visual recognition methods adopt the
metric-based meta-learning paradigm by comparing the query representation with
class representations to predict the category of query instance. However,
current metric-based methods generally treat all instances equally and
consequently often obtain biased class representation, considering not all
instances are equally significant when summarizing the instance-level
representations for the class-level representation. For example, some instances
may contain unrepresentative information, such as too much background and
information of unrelated concepts, which skew the results. To address the above
issues, we propose a novel metric-based meta-learning framework termed
instance-adaptive class representation learning network (ICRL-Net) for few-shot
visual recognition. Specifically, we develop an adaptive instance revaluing
network with the capability to address the biased representation issue when
generating the class representation, by learning and assigning adaptive weights
for different instances according to their relative significance in the support
set of corresponding class. Additionally, we design an improved bilinear
instance representation and incorporate two novel structural losses, i.e.,
intra-class instance clustering loss and inter-class representation
distinguishing loss, to further regulate the instance revaluation process and
refine the class representation. We conduct extensive experiments on four
commonly adopted few-shot benchmarks: miniImageNet, tieredImageNet, CIFAR-FS,
and FC100 datasets. The experimental results compared with the state-of-the-art
approaches demonstrate the superiority of our ICRL-Net.

Overfitting and generalization is an important concept in Machine Learning as
only models that generalize are interesting for general applications. Yet some
students have trouble learning this important concept through lectures and
exercises. In this paper we describe common examples of students
misunderstanding overfitting, and provide recommendations for possible
solutions. We cover student misconceptions about overfitting, about solutions
to overfitting, and implementation mistakes that are commonly confused with
overfitting issues. We expect that our paper can contribute to improving
student understanding and lectures about this important topic.

This paper presents a Bayesian framework to construct non-linear,
parsimonious, shallow models for multitask regression. The proposed framework
relies on the fact that Random Fourier Features (RFFs) enables the
approximation of an RBF kernel by an extreme learning machine whose hidden
layer is formed by RFFs. The main idea is to combine both dual views of a same
model under a single Bayesian formulation that extends the Sparse Bayesian
Extreme Learning Machines to multitask problems. From the kernel methods point
of view, the proposed formulation facilitates the introduction of prior domain
knowledge through the RBF kernel parameter. From the extreme learning machines
perspective, the new formulation helps control overfitting and enables a
parsimonious overall model (the models that serve each task share a same set of
RFFs selected within the joint Bayesian optimisation). The experimental results
show that combining advantages from kernel methods and extreme learning
machines within the same framework can lead to significant improvements in the
performance achieved by each of these two paradigms independently.

Precise and accurate localization in outdoor and indoor environments is a
challenging problem that currently constitutes a significant limitation for
several practical applications. Ultra-wideband (UWB) localization technology
represents a valuable low-cost solution to the problem. However,
non-line-of-sight (NLOS) conditions and complexity of the specific radio
environment can easily introduce a positive bias in the ranging measurement,
resulting in highly inaccurate and unsatisfactory position estimation. In the
light of this, we leverage the latest advancement in deep neural network
optimization techniques and their implementation on ultra-low-power
microcontrollers to introduce an effective range error mitigation solution that
provides corrections in either NLOS or LOS conditions with a few mW of power.
Our extensive experimentation endorses the advantages and improvements of our
low-cost and power-efficient methodology.

We present quantitative probing as a model-agnostic framework for validating
causal models in the presence of quantitative domain knowledge. The method is
constructed as an analogue of the train/test split in correlation-based machine
learning and as an enhancement of current causal validation strategies that are
consistent with the logic of scientific discovery. The effectiveness of the
method is illustrated using Pearl's sprinkler example, before a thorough
simulation-based investigation is conducted. Limits of the technique are
identified by studying exemplary failing scenarios, which are furthermore used
to propose a list of topics for future research and improvements of the
presented version of quantitative probing. The code for integrating
quantitative probing into causal analysis, as well as the code for the
presented simulation-based studies of the effectiveness of quantitative probing
is provided in two separate open-source Python packages.

Shadow tomography for quantum states provides a sample efficient approach for
predicting the properties of quantum systems when the properties are restricted
to expectation values of $2$-outcome POVMs. However, these shadow tomography
procedures yield poor bounds if there are more than 2 outcomes per measurement.
In this paper, we consider a general problem of learning properties from
unknown quantum states: given an unknown $d$-dimensional quantum state $\rho$
and $M$ unknown quantum measurements $\mathcal{M}_1,...,\mathcal{M}_M$ with
$K\geq 2$ outcomes, estimating the probability distribution for applying
$\mathcal{M}_i$ on $\rho$ to within total variation distance $\epsilon$.
Compared to the special case when $K=2$, we need to learn unknown distributions
instead of values. We develop an online shadow tomography procedure that solves
this problem with high success probability requiring $\tilde{O}(K\log^2M\log
d/\epsilon^4)$ copies of $\rho$. We further prove an information-theoretic
lower bound that at least $\Omega(\min\{d^2,K+\log M\}/\epsilon^2)$ copies of
$\rho$ are required to solve this problem with high success probability. Our
shadow tomography procedure requires sample complexity with only logarithmic
dependence on $M$ and $d$ and is sample-optimal for the dependence on $K$.

We present rectified flow, a surprisingly simple approach to learning
(neural) ordinary differential equation (ODE) models to transport between two
empirically observed distributions \pi_0 and \pi_1, hence providing a unified
solution to generative modeling and domain transfer, among various other tasks
involving distribution transport. The idea of rectified flow is to learn the
ODE to follow the straight paths connecting the points drawn from \pi_0 and
\pi_1 as much as possible. This is achieved by solving a straightforward
nonlinear least squares optimization problem, which can be easily scaled to
large models without introducing extra parameters beyond standard supervised
learning. The straight paths are special and preferred because they are the
shortest paths between two points, and can be simulated exactly without time
discretization and hence yield computationally efficient models. We show that
the procedure of learning a rectified flow from data, called rectification,
turns an arbitrary coupling of \pi_0 and \pi_1 to a new deterministic coupling
with provably non-increasing convex transport costs. In addition, recursively
applying rectification allows us to obtain a sequence of flows with
increasingly straight paths, which can be simulated accurately with coarse time
discretization in the inference phase. In empirical studies, we show that
rectified flow performs superbly on image generation, image-to-image
translation, and domain adaptation. In particular, on image generation and
translation, our method yields nearly straight flows that give high quality
results even with a single Euler discretization step.

For performing robotic manipulation tasks, the core problem is determining
suitable trajectories that fulfill the task requirements. Various approaches to
compute such trajectories exist, being learning and optimization the main
driving techniques. Our work builds on the learning-from-demonstration (LfD)
paradigm, where an expert demonstrates motions, and the robot learns to imitate
them. However, expert demonstrations are not sufficient to capture all sorts of
task specifications, such as the timing to grasp an object. In this paper, we
propose a new method that considers formal task specifications within LfD
skills. Precisely, we leverage Signal Temporal Logic (STL), an expressive form
of temporal properties of systems, to formulate task specifications and use
black-box optimization (BBO) to adapt an LfD skill accordingly. We demonstrate
our approach in simulation and on a real industrial setting using several tasks
that showcase how our approach addresses the LfD limitations using STL and BBO.

Creating a vision pipeline for different datasets to solve a computer vision
task is a complex and time consuming process. Currently, these pipelines are
developed with the help of domain experts. Moreover, there is no systematic
structure to construct a vision pipeline apart from relying on experience,
trial and error or using template-based approaches. As the search space for
choosing suitable algorithms for achieving a particular vision task is large,
human exploration for finding a good solution requires time and effort. To
address the following issues, we propose a dynamic and data-driven way to
identify an appropriate set of algorithms that would be fit for building the
vision pipeline in order to achieve the goal task. We introduce a Transformer
Architecture complemented with Deep Reinforcement Learning to recommend
algorithms that can be incorporated at different stages of the vision workflow.
This system is both robust and adaptive to dynamic changes in the environment.
Experimental results further show that our method also generalizes well to
recommend algorithms that have not been used while training and hence
alleviates the need of retraining the system on a new set of algorithms
introduced during test time.

Retinal optical coherence tomography (OCT) and optical coherence tomography
angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's
disease (AD). These non-invasive imaging techniques are cost-effective and more
accessible than alternative neuroimaging tools. However, interpreting and
classifying multi-slice scans produced by OCT devices is time-consuming and
challenging even for trained practitioners.
  There are surveys on machine learning and deep learning approaches concerning
the automated analysis of OCT scans for various diseases such as glaucoma.
However, the current literature lacks an extensive survey on the diagnosis of
Alzheimer's disease or cognitive impairment using OCT or OCTA. This has
motivated us to do a comprehensive survey aimed at machine/deep learning
scientists or practitioners who require an introduction to the problem. The
paper contains 1) an introduction to the medical background of Alzheimer's
Disease and Cognitive Impairment and their diagnosis using OCT and OCTA imaging
modalities, 2) a review of various technical proposals for the problem and the
sub-problems from an automated analysis perspective, 3) a systematic review of
the recent deep learning studies and available OCT/OCTA datasets directly aimed
at the diagnosis of Alzheimer's Disease and Cognitive Impairment. For the
latter, we used Publish or Perish Software to search for the relevant studies
from various sources such as Scopus, PubMed, and Web of Science. We followed
the PRISMA approach to screen an initial pool of 3073 references and determined
ten relevant studies (N=10, out of 3073) that directly targeted AD diagnosis.
We identified the lack of open OCT/OCTA datasets (about Alzheimer's disease) as
the main issue that is impeding the progress in the field.

The application of the context-adaptive entropy model significantly improves
the rate-distortion (R-D) performance, in which hyperpriors and autoregressive
models are jointly utilized to effectively capture the spatial redundancy of
the latent representations. However, the latent representations still contain
some spatial correlations. In addition, these methods based on the
context-adaptive entropy model cannot be accelerated in the decoding process by
parallel computing devices, e.g. FPGA or GPU. To alleviate these limitations,
we propose a learned multi-resolution image compression framework, which
exploits the recently developed octave convolutions to factorize the latent
representations into the high-resolution (HR) and low-resolution (LR) parts,
similar to wavelet transform, which further improves the R-D performance. To
speed up the decoding, our scheme does not use context-adaptive entropy model.
Instead, we exploit an additional hyper layer including hyper encoder and hyper
decoder to further remove the spatial redundancy of the latent representation.
Moreover, the cross-resolution parameter estimation (CRPE) is introduced into
the proposed framework to enhance the flow of information and further improve
the rate-distortion performance. An additional information-fidelity loss is
proposed to the total loss function to adjust the contribution of the LR part
to the final bit stream. Experimental results show that our method separately
reduces the decoding time by approximately 73.35 % and 93.44 % compared with
that of state-of-the-art learned image compression methods, and the R-D
performance is still better than H.266/VVC(4:2:0) and some learning-based
methods on both PSNR and MS-SSIM metrics across a wide bit rates.

Amidst the COVID-19 pandemic, the authors of this paper organized a
Reinforcement Learning (RL) course for a graduate school in the field of data
science. We describe the strategy and materials for creating an exciting
learning experience despite the ubiquitous Zoom fatigue and evaluate the course
qualitatively. The key organizational features are a focus on a competitive
hands-on setting in teams, supported by a minimum of lectures providing the
essential background on RL. The practical part of the course revolved around
Hearts Gym, an RL environment for the card game Hearts that we developed as an
entry-level tutorial to RL. Participants were tasked with training agents to
explore reward shaping and other RL hyperparameters. For a final evaluation,
the agents of the participants competed against each other.

Skeleton-based human action recognition is a longstanding challenge due to
its complex dynamics. Some fine-grain details of the dynamics play a vital role
in classification. The existing work largely focuses on designing incremental
neural networks with more complicated adjacent matrices to capture the details
of joints relationships. However, they still have difficulties distinguishing
actions that have broadly similar motion patterns but belong to different
categories. Interestingly, we found that the subtle differences in motion
patterns can be significantly amplified and become easy for audience to
distinct through specified view directions, where this property haven't been
fully explored before. Drastically different from previous work, we boost the
performance by proposing a conceptually simple yet effective Multi-view
strategy that recognizes actions from a collection of dynamic view features.
Specifically, we design a novel Skeleton-Anchor Proposal (SAP) module which
contains a Multi-head structure to learn a set of views. For feature learning
of different views, we introduce a novel Angle Representation to transform the
actions under different views and feed the transformations into the baseline
model. Our module can work seamlessly with the existing action classification
model. Incorporated with baseline models, our SAP module exhibits clear
performance gains on many challenging benchmarks. Moreover, comprehensive
experiments show that our model consistently beats down the state-of-the-art
and remains effective and robust especially when dealing with corrupted data.
Related code will be available on https://github.com/ideal-idea/SAP .

Interactive Machine Learning (IML) shall enable intelligent systems to
interactively learn from their end-users, and is quickly becoming more and more
important. Although it puts the human in the loop, interactions are mostly
performed via mutual explanations that miss contextual information.
Furthermore, current model-agnostic IML strategies like CAIPI are limited to
'destructive' feedback, meaning they solely allow an expert to prevent a
learner from using irrelevant features. In this work, we propose a novel
interaction framework called Semantic Interactive Learning for the text domain.
We frame the problem of incorporating constructive and contextual feedback into
the learner as a task to find an architecture that (a) enables more semantic
alignment between humans and machines and (b) at the same time helps to
maintain statistical characteristics of the input domain when generating
user-defined counterexamples based on meaningful corrections. Therefore, we
introduce a technique called SemanticPush that is effective for translating
conceptual corrections of humans to non-extrapolating training examples such
that the learner's reasoning is pushed towards the desired behavior. In several
experiments, we show that our method clearly outperforms CAIPI, a state of the
art IML strategy, in terms of Predictive Performance as well as Local
Explanation Quality in downstream multi-class classification tasks.

While several benefits were realized for multilingual vision-language
pretrained models, recent benchmarks across various tasks and languages showed
poor cross-lingual generalisation when multilingually pre-trained
vision-language models are applied to non-English data, with a large gap
between (supervised) English performance and (zero-shot) cross-lingual
transfer. In this work, we explore the poor performance of these models on a
zero-shot cross-lingual visual question answering (VQA) task, where models are
fine-tuned on English visual-question data and evaluated on 7 typologically
diverse languages. We improve cross-lingual transfer with three strategies: (1)
we introduce a linguistic prior objective to augment the cross-entropy loss
with a similarity-based loss to guide the model during training, (2) we learn a
task-specific subnetwork that improves cross-lingual generalisation and reduces
variance without model modification, (3) we augment training examples using
synthetic code-mixing to promote alignment of embeddings between source and
target languages. Our experiments on xGQA using the pretrained multilingual
multimodal transformers UC2 and M3P demonstrate the consistent effectiveness of
the proposed fine-tuning strategy for 7 languages, outperforming existing
transfer methods with sparse models. Code and data to reproduce our findings
are publicly available.

ISO 14971 is the primary standard used for medical device risk management.
While it specifies the requirements for medical device risk management, it does
not specify a particular method for performing risk management. Hence, medical
device manufacturers are free to develop or use any appropriate methods for
managing the risk of medical devices. The most commonly used methods, such as
Fault Tree Analysis (FTA), are unable to provide a reasonable basis for
computing risk estimates when there are limited or no historical data available
or where there is second-order uncertainty about the data. In this paper, we
present a novel method for medical device risk management using hybrid Bayesian
networks (BNs) that resolves the limitations of classical methods such as FTA
and incorporates relevant factors affecting the risk of medical devices. The
proposed BN method is generic but can be instantiated on a system-by-system
basis, and we apply it to a Defibrillator device to demonstrate the process
involved for medical device risk management during production and
post-production. The example is validated against real-world data.

The evolution of language follows the rule of gradual change. Grammar,
vocabulary, and lexical semantic shifts take place over time, resulting in a
diachronic linguistic gap. As such, a considerable amount of texts are written
in languages of different eras, which creates obstacles for natural language
processing tasks, such as word segmentation and machine translation. Although
the Chinese language has a long history, previous Chinese natural language
processing research has primarily focused on tasks within a specific era.
Therefore, we propose a cross-era learning framework for Chinese word
segmentation (CWS), CROSSWISE, which uses the Switch-memory (SM) module to
incorporate era-specific linguistic knowledge. Experiments on four corpora from
different eras show that the performance of each corpus significantly improves.
Further analyses also demonstrate that the SM can effectively integrate the
knowledge of the eras into the neural network.

Foundation models are considered a breakthrough in all applications of AI,
promising robust and reusable mechanisms for feature extraction, alleviating
the need for large amounts of high quality training data for task-specific
prediction models. However, foundation models may potentially encode and even
reinforce existing biases present in historic datasets. Given the limited
ability to scrutinize foundation models, it remains unclear whether the
opportunities outweigh the risks in safety critical applications such as
clinical decision making. In our statistical bias analysis of a recently
published, and publicly available chest X-ray foundation model, we found
reasons for concern as the model seems to encode protected characteristics
including biological sex and racial identity, which may lead to disparate
performance across subgroups in downstream applications. While research into
foundation models for healthcare applications is in an early stage, we believe
it is important to make the community aware of these risks to avoid harm.

This paper presents Adam Mickiewicz University's (AMU) submissions to the
constrained track of the WMT 2022 General MT Task. We participated in the
Ukrainian $\leftrightarrow$ Czech translation directions. The systems are a
weighted ensemble of four models based on the Transformer (big) architecture.
The models use source factors to utilize the information about named entities
present in the input. Each of the models in the ensemble was trained using only
the data provided by the shared task organizers. A noisy back-translation
technique was used to augment the training corpora. One of the models in the
ensemble is a document-level model, trained on parallel and synthetic longer
sequences. During the sentence-level decoding process, the ensemble generated
the n-best list. The n-best list was merged with the n-best list generated by a
single document-level model which translated multiple sentences at a time.
Finally, existing quality estimation models and minimum Bayes risk decoding
were used to rerank the n-best list so that the best hypothesis was chosen
according to the COMET evaluation metric. According to the automatic evaluation
results, our systems rank first in both translation directions.

Long-tailed datasets, where head classes comprise much more training samples
than tail classes, cause recognition models to get biased towards the head
classes. Weighted loss is one of the most popular ways of mitigating this
issue, and a recent work has suggested that class-difficulty might be a better
clue than conventionally used class-frequency to decide the distribution of
weights. A heuristic formulation was used in the previous work for quantifying
the difficulty, but we empirically find that the optimal formulation varies
depending on the characteristics of datasets. Therefore, we propose
Difficulty-Net, which learns to predict the difficulty of classes using the
model's performance in a meta-learning framework. To make it learn reasonable
difficulty of a class within the context of other classes, we newly introduce
two key concepts, namely the relative difficulty and the driver loss. The
former helps Difficulty-Net take other classes into account when calculating
difficulty of a class, while the latter is indispensable for guiding the
learning to a meaningful direction. Extensive experiments on popular
long-tailed datasets demonstrated the effectiveness of the proposed method, and
it achieved state-of-the-art performance on multiple long-tailed datasets.

Fully-supervised salient object detection (SOD) methods have made great
progress, but such methods often rely on a large number of pixel-level
annotations, which are time-consuming and labour-intensive. In this paper, we
focus on a new weakly-supervised SOD task under hybrid labels, where the
supervision labels include a large number of coarse labels generated by the
traditional unsupervised method and a small number of real labels. To address
the issues of label noise and quantity imbalance in this task, we design a new
pipeline framework with three sophisticated training strategies. In terms of
model framework, we decouple the task into label refinement sub-task and
salient object detection sub-task, which cooperate with each other and train
alternately. Specifically, the R-Net is designed as a two-stream
encoder-decoder model equipped with Blender with Guidance and Aggregation
Mechanisms (BGA), aiming to rectify the coarse labels for more reliable
pseudo-labels, while the S-Net is a replaceable SOD network supervised by the
pseudo labels generated by the current R-Net. Note that, we only need to use
the trained S-Net for testing. Moreover, in order to guarantee the
effectiveness and efficiency of network training, we design three training
strategies, including alternate iteration mechanism, group-wise incremental
mechanism, and credibility verification mechanism. Experiments on five SOD
benchmarks show that our method achieves competitive performance against
weakly-supervised/unsupervised methods both qualitatively and quantitatively.

In this paper, we propose a new agency-guided semi-supervised counting
approach. First, we build a learnable auxiliary structure, namely the density
agency to bring the recognized foreground regional features close to
corresponding density sub-classes (agents) and push away background ones.
Second, we propose a density-guided contrastive learning loss to consolidate
the backbone feature extractor. Third, we build a regression head by using a
transformer structure to refine the foreground features further. Finally, an
efficient noise depression loss is provided to minimize the negative influence
of annotation noises. Extensive experiments on four challenging crowd counting
datasets demonstrate that our method achieves superior performance to the
state-of-the-art semi-supervised counting methods by a large margin. Code is

With the development of industry, drones are appearing in various field. In
recent years, deep reinforcement learning has made impressive gains in games,
and we are committed to applying deep reinforcement learning algorithms to the
field of robotics, moving reinforcement learning algorithms from game scenarios
to real-world application scenarios. We are inspired by the LunarLander of
OpenAI Gym, we decided to make a bold attempt in the field of reinforcement
learning to control drones. At present, there is still a lack of work applying
reinforcement learning algorithms to robot control, the physical simulation
platform related to robot control is only suitable for the verification of
classical algorithms, and is not suitable for accessing reinforcement learning
algorithms for the training. In this paper, we will face this problem, bridging
the gap between physical simulation platforms and intelligent agent, connecting
intelligent agents to a physical simulation platform, allowing agents to learn
and complete drone flight tasks in a simulator that approximates the real
world. We proposed a reinforcement learning framework based on Gazebo that is a
kind of physical simulation platform (ROS-RL), and used three continuous action
space reinforcement learning algorithms in the framework to dealing with the
problem of autonomous landing of drones. Experiments show the effectiveness of
the algorithm, the task of autonomous landing of drones based on reinforcement
learning achieved full success.

Recent studies demonstrated that X-ray radiography showed higher accuracy
than Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore,
applying deep learning models to X-rays and radiography images increases the
speed and accuracy of determining COVID-19 cases. However, due to Health
Insurance Portability and Accountability (HIPAA) compliance, the hospitals were
unwilling to share patient data due to privacy concerns. To maintain privacy,
we propose differential private deep learning models to secure the patients'
private information. The dataset from the Kaggle website is used to evaluate
the designed model for COVID-19 detection. The EfficientNet model version was
selected according to its highest test accuracy. The injection of differential
privacy constraints into the best-obtained model was made to evaluate
performance. The accuracy is noted by varying the trainable layers, privacy
loss, and limiting information from each sample. We obtained 84\% accuracy with
a privacy loss of 10 during the fine-tuning process.

Learning the underlying casual structure, represented by Directed Acyclic
Graphs (DAGs), of concerned events from fully-observational data is a crucial
part of causal reasoning, but it is challenging due to the combinatorial and
large search space. A recent flurry of developments recast this combinatorial
problem into a continuous optimization problem by leveraging an algebraic
equality characterization of acyclicity. However, these methods suffer from the
fixed-threshold step after optimization, which is not a flexible and systematic
way to rule out the cycle-inducing edges or false discoveries edges with small
values caused by numerical precision. In this paper, we develop a data-driven
DAG structure learning method without the predefined threshold, called adaptive
NOTEARS [30], achieved by applying adaptive penalty levels to each parameters
in the regularization term. We show that adaptive NOTEARS enjoys the oracle
properties under some specific conditions. Furthermore, simulation experimental
results validate the effectiveness of our method, without setting any gap of
edges weights around zero.

Impact due to demographic factors such as age, sex, race, etc., has been
studied extensively in automated face recognition systems. However, the impact
of \textit{digitally modified} demographic and facial attributes on face
recognition is relatively under-explored. In this work, we study the effect of
attribute manipulations induced via generative adversarial networks (GANs) on
face recognition performance. We conduct experiments on the CelebA dataset by
intentionally modifying thirteen attributes using AttGAN and STGAN and
evaluating their impact on two deep learning-based face verification methods,
ArcFace and VGGFace. Our findings indicate that some attribute manipulations
involving eyeglasses and digital alteration of sex cues can significantly
impair face recognition by up to 73% and need further analysis.

Graph pooling is a crucial operation for encoding hierarchical structures
within graphs. Most existing graph pooling approaches formulate the problem as
a node clustering task which effectively captures the graph topology.
Conventional methods ask users to specify an appropriate number of clusters as
a hyperparameter, then assume that all input graphs share the same number of
clusters. In inductive settings where the number of clusters can vary, however,
the model should be able to represent this variation in its pooling layers in
order to learn suitable clusters. Thus we propose GMPool, a novel
differentiable graph pooling architecture that automatically determines the
appropriate number of clusters based on the input data. The main intuition
involves a grouping matrix defined as a quadratic form of the pooling operator,
which induces use of binary classification probabilities of pairwise
combinations of nodes. GMPool obtains the pooling operator by first computing
the grouping matrix, then decomposing it. Extensive evaluations on molecular
property prediction tasks demonstrate that our method outperforms conventional
methods.

There is no, nor will there ever be, single best clustering algorithm, but we
would still like to be able to pinpoint those which are well-performing on
certain task types and filter out the systematically disappointing ones.
Clustering algorithms are traditionally evaluated using either internal or
external validity measures. Internal measures quantify different aspects of the
obtained partitions, e.g., the average degree of cluster compactness or point
separability. Yet, their validity is questionable because the clusterings they
promote can sometimes be meaningless. External measures, on the other hand,
compare the algorithms' outputs to the reference, ground truth groupings that
are provided by experts. The commonly-used classical partition similarity
scores, such as the normalised mutual information, Fowlkes-Mallows, or adjusted
Rand index, might not possess all the desirable properties, e.g., they do not
identify pathological edge cases correctly. Furthermore, they are not nicely
interpretable: it is hard to say what a score of 0.8 really means. Its
behaviour might also vary as the number of true clusters changes. This makes
comparing clustering algorithms across many benchmark datasets difficult. To
remedy this, we propose and analyse a new measure: an asymmetric version of the
optimal set-matching accuracy. It is corrected for chance and the
imbalancedness of cluster sizes.

The coronavirus disease 2019 (COVID-19) continues to have a negative impact
on healthcare systems around the world, though the vaccines have been developed
and national vaccination coverage rate is steadily increasing. At the current
stage, automatically segmenting the lung infection area from CT images is
essential for the diagnosis and treatment of COVID-19. Thanks to the
development of deep learning technology, some deep learning solutions for lung
infection segmentation have been proposed. However, due to the scattered
distribution, complex background interference and blurred boundaries, the
accuracy and completeness of the existing models are still unsatisfactory. To
this end, we propose a boundary guided semantic learning network (BSNet) in
this paper. On the one hand, the dual-branch semantic enhancement module that
combines the top-level semantic preservation and progressive semantic
integration is designed to model the complementary relationship between
different high-level features, thereby promoting the generation of more
complete segmentation results. On the other hand, the mirror-symmetric boundary
guidance module is proposed to accurately detect the boundaries of the lesion
regions in a mirror-symmetric way. Experiments on the publicly available
dataset demonstrate that our BSNet outperforms the existing state-of-the-art
competitors and achieves a real-time inference speed of 44 FPS.

In this work, we develop an efficient solver based on deep neural networks
for the Poisson equation with variable coefficients and singular sources
expressed by the Dirac delta function $\delta(\mathbf{x})$. This class of
problems covers general point sources, line sources and point-line
combinations, and has a broad range of practical applications. The proposed
approach is based on decomposing the true solution into a singular part that is
known analytically using the fundamental solution of the Laplace equation and a
regular part that satisfies a suitable elliptic PDE with smoother sources, and
then solving for the regular part using the deep Ritz method. A path-following
strategy is suggested to select the penalty parameter for penalizing the
Dirichlet boundary condition. Extensive numerical experiments in two- and
multi-dimensional spaces with point sources, line sources or their combinations
are presented to illustrate the efficiency of the proposed approach, and a
comparative study with several existing approaches is also given, which shows
clearly its competitiveness for the specific class of problems. In addition, we
briefly discuss the error analysis of the approach.

Deep neural networks (DNN) have achieved unprecedented performance in
computer-vision tasks almost ubiquitously in business, technology, and science.
While substantial efforts are made to engineer highly accurate architectures
and provide usable model explanations, most state-of-the-art approaches are
first designed for natural vision and then translated to the medical domain.
This dissertation seeks to address this gap by proposing novel architectures
that integrate the domain-specific constraints of medical imaging into the DNN
model and explanation design.

In modern cities, the number of Electric vehicles (EV) is increasing rapidly
for their low emission and better dynamic performance, leading to increasing
demand for EV charging. However, due to the limited number of EV charging
facilities, catering to the huge demand for time-consuming EV charging becomes
a critical problem. It is quite a challenge to dispatch EVs in the dynamic
traffic environment and coordinate interaction among agents. To better serve
further research on various related Deep Reinforcment Learning (DRL) EV
dispatching algorithms, an efficient simulation environment is necessary to
ensure success. As simulator Simulation Urban Mobility (SUMO) is one of the
most widely used open-source simulators, it has great significance in creating
an environment that satisfies research requirements on SUMO. We aim to improve
the efficiency of EV charging station usage and save time for EV users in
further work. As a result, we design an EV navigation system on the basis of
the traffic simulator SUMO using Jurong Area, Singapore in this paper. Various
state-of-the-art DRL algorithms are deployed on the designed testbed to
validate the feasibility of the framework in terms of EV charging dispatching
problems. Besides EV dispatching problems, the environment can also serve for
other reinforcement learning (RL) traffic control problems

Sampling is an important process in many GNN structures in order to train
larger datasets with a smaller computational complexity. However, compared to
other processes in GNN (such as aggregate, backward propagation), the sampling
process still costs tremendous time, which limits the speed of training. To
reduce the time of sampling, hardware acceleration is an ideal choice. However,
state of the art GNN acceleration proposal did not specify how to accelerate
the sampling process. What's more, directly accelerating traditional sampling
algorithms will make the structure of the accelerator very complicated.
  In this work, we made two contributions: (1) Proposed a new neighbor sampler:
CONCAT Sampler, which can be easily accelerated on hardware level while
guaranteeing the test accuracy. (2) Designed a CONCAT-sampler-accelerator based
on FPGA, with which the neighbor sampling process boosted to about 300-1000
times faster compared to the sampling process without it.

This paper explores a new sequential selection framework for the optimal
sensor placement (OSP) in Electrocardiography imaging networks (ECGI). The
proposed methodology incorporates the use a recent experimental design method
for the sequential selection of landmarkings on biological objects, namely,
Gaussian process landmarking (GPLMK) for better exploration of the candidate
sensors. The two experimental design methods work as a source of the training
and the validation locations which is fitted using a spatiotemporal Gaussian
process (STGP). The STGP is fitted using the training set to predict for the
current validation set generated using GPLMK, and the sensor with the largest
prediction absolute error is selected from the current validation set and added
to the selected sensors. Next, a new validation set is generated and predicted
using the current training set. The process continues until selecting a
specific number of sensor locations. The study is conducted on a dataset of
body surface potential mapping (BSPM) of 352 electrodes of four human subjects.
A number of 30 sensor locations is selected using the proposed algorithm. The
selected sensor locations achieved average $R^2 = 94.40 \%$ for estimating the
whole-body QRS segment. The proposed method adds to design efforts for a more
clinically practical ECGI system by improving its wearability and reduce the
design cost as well.

Social network alignment shows fundamental importance in a wide spectrum of
applications. To the best of our knowledge, existing studies mainly focus on
network alignment at the individual user level, requiring abundant common
information between shared individual users. For the networks that cannot meet
such requirements, social community structures actually provide complementary
and critical information at a slightly coarse-grained level, alignment of which
will provide additional information for user alignment. In turn, user alignment
also reveals more clues for community alignment. Hence, in this paper, we
introduce the problem of joint social network alignment, which aims to align
users and communities across social networks simultaneously. Key challenges lie
in that 1) how to learn the representations of both users and communities, and
2) how to make user alignment and community alignment benefit from each other.
To address these challenges, we first elaborate on the characteristics of
real-world networks with the notion of delta-hyperbolicity, and show the
superiority of hyperbolic space for representing social networks. Then, we
present a novel hyperbolic embedding approach for the joint social network
alignment, referred to as PERFECT, in a unified optimization. Extensive
experiments on real-world datasets show the superiority of PERFECT in both user
alignment and community alignment.

Global teams frequently consist of language-based subgroups who put together
complementary information to achieve common goals. Previous research outlines a
two-step work communication flow in these teams. There are team meetings using
a required common language (i.e., English); in preparation for those meetings,
people have subgroup conversations in their native languages. Work
communication at team meetings is often less effective than in subgroup
conversations. In the current study, we investigate the idea of leveraging
machine translation (MT) to facilitate global team meetings. We hypothesize
that exchanging subgroup conversation logs before a team meeting offers
contextual information that benefits teamwork at the meeting. MT can translate
these logs, which enables comprehension at a low cost. To test our hypothesis,
we conducted a between-subjects experiment where twenty quartets of
participants performed a personnel selection task. Each quartet included two
English native speakers (NS) and two non-native speakers (NNS) whose native
language was Mandarin. All participants began the task with subgroup
conversations in their native languages, then proceeded to team meetings in
English. We manipulated the exchange of subgroup conversation logs prior to
team meetings: with MT-mediated exchanges versus without. Analysis of
participants' subjective experience, task performance, and depth of discussions
as reflected through their conversational moves jointly indicates that team
meeting quality improved when there were MT-mediated exchanges of subgroup
conversation logs as opposed to no exchanges. We conclude with reflections on
when and how MT could be applied to enhance global teamwork across a language
barrier.

Many real-world problems can be formulated as the alignment between two
geometric patterns. Previously, a great amount of research focus on the
alignment of 2D or 3D patterns in the field of computer vision. Recently, the
alignment problem in high dimensions finds several novel applications in
practice. However, the research is still rather limited in the algorithmic
aspect. To the best of our knowledge, most existing approaches are just simple
extensions of their counterparts for 2D and 3D cases, and often suffer from the
issues such as high computational complexities. In this paper, we propose an
effective framework to compress the high dimensional geometric patterns. Any
existing alignment method can be applied to the compressed geometric patterns
and the time complexity can be significantly reduced. Our idea is inspired by
the observation that high dimensional data often has a low intrinsic dimension.
Our framework is a "data-dependent" approach that has the complexity depending
on the intrinsic dimension of the input data. Our experimental results reveal
that running the alignment algorithm on compressed patterns can achieve similar
qualities, comparing with the results on the original patterns, but the
runtimes (including the times cost for compression) are substantially lower.

Backdoor attack is a powerful attack algorithm to deep learning model.
Recently, GNN's vulnerability to backdoor attack has been proved especially on
graph classification task. In this paper, we propose the first backdoor
detection and defense method on GNN. Most backdoor attack depends on injecting
small but influential trigger to the clean sample. For graph data, current
backdoor attack focus on manipulating the graph structure to inject the
trigger. We find that there are apparent differences between benign samples and
malicious samples in some explanatory evaluation metrics, such as fidelity and
infidelity. After identifying the malicious sample, the explainability of the
GNN model can help us capture the most significant subgraph which is probably
the trigger in a trojan graph. We use various dataset and different attack
settings to prove the effectiveness of our defense method. The attack success
rate all turns out to decrease considerably.

Magnetic Resonance Imaging (MRI) is important in clinic to produce high
resolution images for diagnosis, but its acquisition time is long for high
resolution images. Deep learning based MRI super resolution methods can reduce
scan time without complicated sequence programming, but may create additional
artifacts due to the discrepancy between training data and testing data. Data
consistency layer can improve the deep learning results but needs raw k-space
data. In this work, we propose a magnitude-image based data consistency deep
learning MRI super resolution method to improve super resolution images'
quality without raw k-space data. Our experiments show that the proposed method
can improve NRMSE and SSIM of super resolution images compared to the same
Convolutional Neural Network (CNN) block without data consistency module.

Recent trends in self-supervised representation learning have focused on
removing inductive biases from training pipelines. However, inductive biases
can be useful in settings when limited data are available or provide additional
insight into the underlying data distribution. We present spatial prior
attention (SPAN), a framework that takes advantage of consistent spatial and
semantic structure in unlabeled image datasets to guide Vision Transformer
attention. SPAN operates by regularizing attention masks from separate
transformer heads to follow various priors over semantic regions. These priors
can be derived from data statistics or a single labeled sample provided by a
domain expert. We study SPAN through several detailed real-world scenarios,
including medical image analysis and visual quality assurance. We find that the
resulting attention masks are more interpretable than those derived from
domain-agnostic pretraining. SPAN produces a 58.7 mAP improvement for lung and
heart segmentation. We also find that our method yields a 2.2 mAUC improvement
compared to domain-agnostic pretraining when transferring the pretrained model
to a downstream chest disease classification task. Lastly, we show that SPAN
pretraining leads to higher downstream classification performance in low-data
regimes compared to domain-agnostic pretraining.

Catalyzed by the recent emergence of site-specific, high-fidelity radio
frequency (RF) modeling and simulation tools purposed for radar, data-driven
formulations of classical methods in radar have rapidly grown in popularity
over the past decade. Despite this surge, limited focus has been directed
toward the theoretical foundations of these classical methods. In this regard,
as part of our ongoing data-driven approach to radar space-time adaptive
processing (STAP), we analyze the asymptotic performance guarantees of select
subspace separation methods in the context of radar target localization, and
augment this analysis through a proposed deep learning framework for target
location estimation. In our approach, we generate comprehensive datasets by
randomly placing targets of variable strengths in predetermined constrained
areas using RFView, a site-specific RF modeling and simulation tool developed
by ISL Inc. For each radar return signal from these constrained areas, we
generate heatmap tensors in range, azimuth, and elevation of the normalized
adaptive matched filter (NAMF) test statistic, and of the output power of a
generalized sidelobe canceller (GSC). Using our deep learning framework, we
estimate target locations from these heatmap tensors to demonstrate the
feasibility of and significant improvements provided by our data-driven
approach in matched and mismatched settings.

In real world scenarios, out-of-distribution (OOD) datasets may have a large
distributional shift from training datasets. This phenomena generally occurs
when a trained classifier is deployed on varying dynamic environments, which
causes a significant drop in performance. To tackle this issue, we are
proposing an end-to-end deep multi-task network in this work. Observing a
strong relationship between rotation prediction (self-supervised) accuracy and
semantic classification accuracy on OOD tasks, we introduce an additional
auxiliary classification head in our multi-task network along with semantic
classification and rotation prediction head. To observe the influence of this
addition classifier in improving the rotation prediction head, our proposed
learning method is framed into bi-level optimisation problem where the
upper-level is trained to update the parameters for semantic classification and
rotation prediction head. In the lower-level optimisation, only the auxiliary
classification head is updated through semantic classification head by fixing
the parameters of the semantic classification head. The proposed method has
been validated through three unseen OOD datasets where it exhibits a clear
improvement in semantic classification accuracy than other two baseline
methods. Our code is available on GitHub
\url{https://github.com/harshita-555/OSSL}

Recent neuroimaging studies that focus on predicting brain disorders via
modern machine learning approaches commonly include a single modality and rely
on supervised over-parameterized models.However, a single modality provides
only a limited view of the highly complex brain. Critically, supervised models
in clinical settings lack accurate diagnostic labels for training. Coarse
labels do not capture the long-tailed spectrum of brain disorder phenotypes,
which leads to a loss of generalizability of the model that makes them less
useful in diagnostic settings. This work presents a novel multi-scale
coordinated framework for learning multiple representations from multimodal
neuroimaging data. We propose a general taxonomy of informative inductive
biases to capture unique and joint information in multimodal self-supervised
fusion. The taxonomy forms a family of decoder-free models with reduced
computational complexity and a propensity to capture multi-scale relationships
between local and global representations of the multimodal inputs. We conduct a
comprehensive evaluation of the taxonomy using functional and structural
magnetic resonance imaging (MRI) data across a spectrum of Alzheimer's disease
phenotypes and show that self-supervised models reveal disorder-relevant brain
regions and multimodal links without access to the labels during pre-training.
The proposed multimodal self-supervised learning yields representations with
improved classification performance for both modalities. The concomitant rich
and flexible unsupervised deep learning framework captures complex multimodal
relationships and provides predictive performance that meets or exceeds that of
a more narrow supervised classification analysis. We present elaborate
quantitative evidence of how this framework can significantly advance our
search for missing links in complex brain disorders.

Convolutional Neural Networks (CNNs) compression is crucial to deploying
these models in edge devices with limited resources. Existing channel pruning
algorithms for CNNs have achieved plenty of success on complex models. They
approach the pruning problem from various perspectives and use different
metrics to guide the pruning process. However, these metrics mainly focus on
the model's `outputs' or `weights' and neglect its `interpretations'
information. To fill in this gap, we propose to address the channel pruning
problem from a novel perspective by leveraging the interpretations of a model
to steer the pruning process, thereby utilizing information from both inputs
and outputs of the model. However, existing interpretation methods cannot get
deployed to achieve our goal as either they are inefficient for pruning or may
predict non-coherent explanations. We tackle this challenge by introducing a
selector model that predicts real-time smooth saliency masks for pruned models.
We parameterize the distribution of explanatory masks by Radial Basis Function
(RBF)-like functions to incorporate geometric prior of natural images in our
selector model's inductive bias. Thus, we can obtain compact representations of
explanations to reduce the computational costs of our pruning method. We
leverage our selector model to steer the network pruning by maximizing the
similarity of explanatory representations for the pruned and original models.
Extensive experiments on CIFAR-10 and ImageNet benchmark datasets demonstrate
the efficacy of our proposed method. Our implementations are available at
\url{https://github.com/Alii-Ganjj/InterpretationsSteeredPruning}

This paper looks at a common law legal system as a learning algorithm, models
specific features of legal proceedings, and asks whether this system learns
efficiently. A particular feature of our model is explicitly viewing various
aspects of court proceedings as learning algorithms. This viewpoint enables
directly pointing out that when the costs of going to court are not
commensurate with the benefits of going to court, there is a failure of
learning and inaccurate outcomes will persist in cases that settle.
Specifically, cases are brought to court at an insufficient rate. On the other
hand, when individuals can be compelled or incentivized to bring their cases to
court, the system can learn and inaccuracy vanishes over time.

We present a novel reinforcement learning (RL) based task allocation and
decentralized navigation algorithm for mobile robots in warehouse environments.
Our approach is designed for scenarios in which multiple robots are used to
perform various pick up and delivery tasks. We consider the problem of joint
decentralized task allocation and navigation and present a two level approach
to solve it. At the higher level, we solve the task allocation by formulating
it in terms of Markov Decision Processes and choosing the appropriate rewards
to minimize the Total Travel Delay (TTD). At the lower level, we use a
decentralized navigation scheme based on ORCA that enables each robot to
perform these tasks in an independent manner, and avoid collisions with other
robots and dynamic obstacles. We combine these lower and upper levels by
defining rewards for the higher level as the feedback from the lower level
navigation algorithm. We perform extensive evaluation in complex warehouse
layouts with large number of agents and highlight the benefits over
state-of-the-art algorithms based on myopic pickup distance minimization and
regret-based task selection. We observe improvement up to 14% in terms of task
completion time and up-to 40% improvement in terms of computing collision-free
trajectories for the robots.

In reinforcement learning, Monte Carlo algorithms update the Q function by
averaging the episodic returns. In the Monte Carlo UCB (MC-UCB) algorithm, the
action taken in each state is the action that maximizes the Q function plus a
UCB exploration term, which biases the choice of actions to those that have
been chosen less frequently. Although there has been significant work on
establishing regret bounds for MC-UCB, most of that work has been focused on
finite-horizon versions of the problem, for which each episode terminates after
a constant number of steps. For such finite-horizon problems, the optimal
policy depends both on the current state and the time within the episode.
However, for many natural episodic problems, such as games like Go and Chess
and robotic tasks, the episode is of random length and the optimal policy is
stationary. For such environments, it is an open question whether the
Q-function in MC-UCB will converge to the optimal Q function; we conjecture
that, unlike Q-learning, it does not converge for all MDPs. We nevertheless
show that for a large class of MDPs, which includes stochastic MDPs such as
blackjack and deterministic MDPs such as Go, the Q-function in MC-UCB converges
almost surely to the optimal Q function. An immediate corollary of this result
is that it also converges almost surely for all finite-horizon MDPs. We also
provide numerical experiments, providing further insights into MC-UCB.

This paper describes the analysis and simulation performed on a three phase
squirrel cage induction motor, fed by a Voltage Source Inverter (VSI),
modulated using the space vector modulation scheme. The study mainly focused on
analyzing the motor performance when fed by a space vector modulated voltage
source inverter. The effect of switching frequency on the motor current ripple
as well as the frequency spectra of motor current, voltage and torque were
studied. The transient response of the inverter-fed motor for a sudden load
change was studied and compared to that of a motor fed with an ideal AC source.
Results from these studies performed as computer simulations are presented and
analyzed.

We revisit heavy-tailed corrupted least-squares linear regression assuming to
have a corrupted $n$-sized label-feature sample of at most $\epsilon n$
arbitrary outliers. We wish to estimate a $p$-dimensional parameter $b^*$ given
such sample of a label-feature pair $(y,x)$ satisfying $y=\langle
x,b^*\rangle+\xi$ with heavy-tailed $(x,\xi)$. We only assume $x$ is $L^4-L^2$
hypercontractive with constant $L>0$ and has covariance matrix $\Sigma$ with
minimum eigenvalue $1/\mu^2>0$ and bounded condition number $\kappa>0$. The
noise $\xi$ can be arbitrarily dependent on $x$ and nonsymmetric as long as
$\xi x$ has finite covariance matrix $\Xi$. We propose a near-optimal
computationally tractable estimator, based on the power method, assuming no
knowledge on $(\Sigma,\Xi)$ nor the operator norm of $\Xi$. With probability at
least $1-\delta$, our proposed estimator attains the statistical rate
$\mu^2\Vert\Xi\Vert^{1/2}(\frac{p}{n}+\frac{\log(1/\delta)}{n}+\epsilon)^{1/2}$
and breakdown-point $\epsilon\lesssim\frac{1}{L^4\kappa^2}$, both optimal in
the $\ell_2$-norm, assuming the near-optimal minimum sample size
$L^4\kappa^2(p\log p + \log(1/\delta))\lesssim n$, up to a log factor. To the
best of our knowledge, this is the first computationally tractable algorithm
satisfying simultaneously all the mentioned properties. Our estimator is based
on a two-stage Multiplicative Weight Update algorithm. The first stage
estimates a descent direction $\hat v$ with respect to the (unknown)
pre-conditioned inner product $\langle\Sigma(\cdot),\cdot\rangle$. The second
stage estimate the descent direction $\Sigma\hat v$ with respect to the (known)
inner product $\langle\cdot,\cdot\rangle$, without knowing nor estimating
$\Sigma$.

This paper presents a novel method for restoring digital videos via a Deep
Plug-and-Play (PnP) approach. Under a Bayesian formalism, the method consists
in using a deep convolutional denoising network in place of the proximal
operator of the prior in an alternating optimization scheme. We distinguish
ourselves from prior PnP work by directly applying that method to restore a
digital video from a degraded video observation. This way, a network trained
once for denoising can be repurposed for other video restoration tasks. Our
experiments in video deblurring, super-resolution, and interpolation of random
missing pixels all show a clear benefit to using a network specifically
designed for video denoising, as it yields better restoration performance and
better temporal stability than a single image network with similar denoising
performance using the same PnP formulation. Moreover, our method compares
favorably to applying a different state-of-the-art PnP scheme separately on
each frame of the sequence. This opens new perspectives in the field of video
restoration.

For many students, introductory college science courses are often the only
opportunity in their formal higher education to be exposed to science, shaping
their view of the subject, their scientific literacy, and their attitudes
towards their own ability in STEM. While science writing instruction has been
demonstrated to impact attitudes and outlooks of STEM majors in their
coursework, this instructional strategy has yet to be explored for non-majors.
In this work, we investigate student attitudes towards STEM before and after
taking a writing-intensive introductory astronomy course. We find that students
cite writing about science as beneficial to their learning, deepening their
understanding of science topics and their perspective on science as a field and
finding writing to be a "bridge" between STEM content and their focus on
humanities in their majors. Students also report increased perceptions of their
own ability and confidence in engaging with STEM across multiple metrics,
leaving the course more prepared to be informed, engaged, and science literate
citizens.

We introduce three algorithms that invert simulated gravity data to 3D
subsurface rock/flow properties. The first algorithm is a data-driven, deep
learning-based approach, the second mixes a deep learning approach with
physical modeling into a single workflow, and the third considers the time
dependence of surface gravity monitoring. The target application of these
proposed algorithms is the prediction of subsurface CO$_2$ plumes as a
complementary tool for monitoring CO$_2$ sequestration deployments. Each
proposed algorithm outperforms traditional inversion methods and produces
high-resolution, 3D subsurface reconstructions in near real-time. Our proposed
methods achieve Dice scores of up to 0.8 for predicted plume geometry and near
perfect data misfit in terms of $\mu$Gals. These results indicate that
combining 4D surface gravity monitoring with deep learning techniques
represents a low-cost, rapid, and non-intrusive method for monitoring CO$_2$
storage sites.

Monkeypox virus is emerging slowly with the decline of COVID-19 virus
infections around the world. People are afraid of it, thinking that it would
appear as a pandemic like COVID-19. As such, it is crucial to detect them
earlier before widespread community transmission. AI-based detection could help
identify them at the early stage. In this paper, we aim to compare 13 different
pre-trained deep learning (DL) models for the Monkeypox virus detection. For
this, we initially fine-tune them with the addition of universal custom layers
for all of them and analyse the results using four well-established measures:
Precision, Recall, F1-score, and Accuracy. After the identification of the
best-performing DL models, we ensemble them to improve the overall performance
using a majority voting over the probabilistic outputs obtained from them. We
perform our experiments on a publicly available dataset, which results in
average Precision, Recall, F1-score, and Accuracy of 85.44\%, 85.47\%, 85.40\%,
and 87.13\%, respectively with the help of our proposed ensemble approach.
These encouraging results, which outperform the state-of-the-art methods,
suggest that the proposed approach is applicable to health practitioners for
mass screening.

Art is an artistic method of using digital technologies as a part of the
generative or creative process. With the advent of digital currency and NFTs
(Non-Fungible Token), the demand for digital art is growing aggressively. In
this manuscript, we advocate the concept of using deep generative networks with
adversarial training for a stable and variant art generation. The work mainly
focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN)
and explores the techniques to address the common pitfalls in GAN training. We
compare various architectures and designs of DC-GANs to arrive at a
recommendable design choice for a stable and realistic generation. The main
focus of the work is to generate realistic images that do not exist in reality
but are synthesised from random noise by the proposed model. We provide visual
results of generated animal face images (some pieces of evidence showing a
blend of species) along with recommendations for training, architecture and
design choices. We also show how training image preprocessing plays a massive
role in GAN training.

Production theory, defined as the study of the economic process of
transforming inputs into outputs, consists of two simultaneous economic forces:
cost minimization and profit maximization. The cost minimization problem
involves deriving conditional factor demand functions and the cost function.
The profit maximization problem involves deriving the output supply function,
the profit function, and unconditional factor demand functions. Nested within
the process are Shephard's lemma, Hotelling's lemmas, direct and indirect
mathematical relations, and other elements contributing to the dynamics of the
process. The intricacies and hidden underlying influences pose difficulties in
presenting the material for an instructor, and inhibit learning by students.
Simply put, the primary aim of this paper is to facilitate the teaching and
learning of the production theory realm of Economics through the use of a
conceptual visual model. This paper proposes a pedagogical tool in the form of
a detailed graphic illustrating t he relationship between profit maximization
and cost minimization under technical constraints, with an emphasis on the
similarities and differences between the perfect competition and monopoly
cases. The potential that such a visual has to enhance learning when
supplementing traditional context is discussed under the context of
contemporary learning literature. Embedded in the discussion is an example of
how we believe our model could be conceptualized and utilized in a real-world
setting to evaluate an industrial project with an economic point of view.

We consider risk-averse learning in repeated unknown games where the goal of
the agents is to minimize their individual risk of incurring significantly high
cost. Specifically, the agents use the conditional value at risk (CVaR) as a
risk measure and rely on bandit feedback in the form of the cost values of the
selected actions at every episode to estimate their CVaR values and update
their actions. A major challenge in using bandit feedback to estimate CVaR is
that the agents can only access their own cost values, which, however, depend
on the actions of all agents. To address this challenge, we propose a new
risk-averse learning algorithm with momentum that utilizes the full historical
information on the cost values. We show that this algorithm achieves sub-linear
regret and matches the best known algorithms in the literature. We provide
numerical experiments for a Cournot game that show that our method outperforms

In this work, we study how the performance and evaluation of generative image
models are impacted by the racial composition of their training datasets. By
examining and controlling the racial distributions in various training
datasets, we are able to observe the impacts of different training
distributions on generated image quality and the racial distributions of the
generated images. Our results show that the racial compositions of generated
images successfully preserve that of the training data. However, we observe
that truncation, a technique used to generate higher quality images during
inference, exacerbates racial imbalances in the data. Lastly, when examining
the relationship between image quality and race, we find that the highest
perceived visual quality images of a given race come from a distribution where
that race is well-represented, and that annotators consistently prefer
generated images of white people over those of Black people.

Nowadays, it's a very significant way for researchers and other individuals
to achieve their interests because it provides short solutions to satisfy their
demands. Because there are so many pieces of information on the internet, news
recommendation systems allow us to filter content and deliver it to the user in
proportion to his desires and interests. RSs have three techniques:
content-based filtering, collaborative filtering, and hybrid filtering. We will
use the MIND dataset with our system, which was collected in 2019, the big
challenge in this dataset because there is a lot of ambiguity and complex text
processing. In this paper, will present our proposed recommendation system. The
core of our system we have used the GloVe algorithm for word embeddings and
representation. Besides, the Multi-head Attention Layer calculates the
attention of words, to generate a list of recommended news. Finally, we achieve
good results more than some other related works in AUC 71.211, MRR 35.72,
nDCG@5 38.05, and nDCG@10 44.45.

Sketches make an intuitive and powerful visual expression as they are fast
executed freehand drawings. We present a method for synthesizing realistic
photos from scene sketches. Without the need for sketch and photo pairs, our
framework directly learns from readily available large-scale photo datasets in
an unsupervised manner. To this end, we introduce a standardization module that
provides pseudo sketch-photo pairs during training by converting photos and
sketches to a standardized domain, i.e. the edge map. The reduced domain gap
between sketch and photo also allows us to disentangle them into two
components: holistic scene structures and low-level visual styles such as color
and texture. Taking this advantage, we synthesize a photo-realistic image by
combining the structure of a sketch and the visual style of a reference photo.
Extensive experimental results on perceptual similarity metrics and human
perceptual studies show the proposed method could generate realistic photos
with high fidelity from scene sketches and outperform state-of-the-art photo
synthesis baselines. We also demonstrate that our framework facilitates a
controllable manipulation of photo synthesis by editing strokes of
corresponding sketches, delivering more fine-grained details than previous
approaches that rely on region-level editing.

The growing demand for the internet of things (IoT) makes it necessary to
implement computer vision tasks such as object recognition in low-power
devices. Convolutional neural networks (CNNs) are a potential approach for
object recognition and detection. However, the convolutional layer in CNN
consumes significant energy compared to the fully connected layers. To mitigate
this problem, a new approach based on the Hadamard transformation as an
alternative to the convolution operation is demonstrated using two fundamental
datasets, MNIST and CIFAR10. The mathematical expression of the Hadamard method
shows the clear potential to save energy consumption compared to convolutional
layers, which are helpful with BigData applications. In addition, to the test
accuracy of the MNIST dataset, the Hadamard method performs similarly to the
convolution method. In contrast, with the CIFAR10 dataset, test data accuracy
is dropped (due to complex data and multiple channels) compared to the
convolution method. Finally, the demonstrated method is helpful for other
computer vision tasks when the kernel size is smaller than the input image
size.

In this work, we introduce a learning model designed to meet the needs of
applications in which computational resources are limited, and robustness and
interpretability are prioritized. Learning problems can be formulated as
constrained stochastic optimization problems, with the constraints originating
mainly from model assumptions that define a trade-off between complexity and
performance. This trade-off is closely related to over-fitting, generalization
capacity, and robustness to noise and adversarial attacks, and depends on both
the structure and complexity of the model, as well as the properties of the
optimization methods used. We develop an online prototype-based learning
algorithm based on annealing optimization that is formulated as an online
gradient-free stochastic approximation algorithm. The learning model can be
viewed as an interpretable and progressively growing competitive-learning
neural network model to be used for supervised, unsupervised, and reinforcement
learning. The annealing nature of the algorithm contributes to minimal
hyper-parameter tuning requirements, poor local minima prevention, and
robustness with respect to the initial conditions. At the same time, it
provides online control over the performance-complexity trade-off by
progressively increasing the complexity of the learning model as needed,
through an intuitive bifurcation phenomenon. Finally, the use of stochastic
approximation enables the study of the convergence of the learning algorithm
through mathematical tools from dynamical systems and control, and allows for
its integration with reinforcement learning algorithms, constructing an
adaptive state-action aggregation scheme.

Early prediction is clinically considered one of the essential parts of
cerebral palsy (CP) treatment. We propose to implement a low-cost and
interpretable classification system for supporting CP prediction based on
General Movement Assessment (GMA). We design a Pytorch-based attention-informed
graph convolutional network to early identify infants at risk of CP from
skeletal data extracted from RGB videos. We also design a frequency-binning
module for learning the CP movements in the frequency domain while filtering
noise. Our system only requires consumer-grade RGB videos for training to
support interactive-time CP prediction by providing an interpretable CP
classification result.

Neutron stars provide a unique laboratory for studying matter at extreme
pressures and densities. While there is no direct way to explore their interior
structure, X-rays emitted from these stars can indirectly provide clues to the
equation of state (EOS) of superdense nuclear matter through the inference of
the star's mass and radius. However, inference of EOS directly from a star's
X-ray spectra is extremely challenging and is complicated by systematic
uncertainties. The current state of the art is to use simulation-based
likelihoods in a piece-wise method, which first infer the star's mass and
radius to reduce the dimensionality of the problem, and from those quantities
infer the EOS. We demonstrate a series of enhancements to the state of the art,
in terms of realistic uncertainty quantification and improved regression of
physical properties with machine learning. We also demonstrate novel inference
of the EOS directly from the high-dimensional spectra of observed stars,
avoiding the intermediate mass-radius step. Our network is conditioned on the
sources of uncertainty of each star, allowing for natural and complete
propagation of uncertainties to the EOS.

Machine learning (ML), being now widely accessible to the research community
at large, has fostered a proliferation of new and striking applications of
these emergent mathematical techniques across a wide range of disciplines. In
this paper, we will focus on a particular case study: the field of
paleoanthropology, which seeks to understand the evolution of the human species
based on biological and cultural evidence. As we will show, the easy
availability of ML algorithms and lack of expertise on their proper use among
the anthropological research community has led to foundational misapplications
that have appeared throughout the literature. The resulting unreliable results
not only undermine efforts to legitimately incorporate ML into anthropological
research, but produce potentially faulty understandings about our human
evolutionary and behavioral past.
  The aim of this paper is to provide a brief introduction to some of the ways
in which ML has been applied within paleoanthropology; we also include a survey
of some basic ML algorithms for those who are not fully conversant with the
field, which remains under active development. We discuss a series of missteps,
errors, and violations of correct protocols of ML methods that appear
disconcertingly often within the accumulating body of anthropological
literature. These mistakes include use of outdated algorithms and practices;
inappropriate train/test splits, sample composition, and textual explanations;
as well as an absence of transparency due to the lack of data/code sharing, and
the subsequent limitations imposed on independent replication. We assert that
expanding samples, sharing data and code, re-evaluating approaches to peer
review, and, most importantly, developing interdisciplinary teams that include
experts in ML are all necessary for progress in future research incorporating
ML within anthropology.

Crop diseases significantly affect the quantity and quality of agricultural
production. In a context where the goal of precision agriculture is to minimize
or even avoid the use of pesticides, weather and remote sensing data with deep
learning can play a pivotal role in detecting crop diseases, allowing localized
treatment of crops. However, combining heterogeneous data such as weather and
images remains a hot topic and challenging task. Recent developments in
transformer architectures have shown the possibility of fusion of data from
different domains, for instance text-image. The current trend is to custom only
one transformer to create a multimodal fusion model. Conversely, we propose a
new approach to realize data fusion using three transformers. In this paper, we
first solved the missing satellite images problem, by interpolating them with a
ConvLSTM model. Then, proposed a multimodal fusion architecture that jointly
learns to process visual and weather information. The architecture is built
from three main components, a Vision Transformer and two transformer-encoders,
allowing to fuse both image and weather modalities. The results of the proposed
method are promising achieving 97\% overall accuracy.

In this work we try to perform emotional style transfer on audios. In
particular, MelGAN-VC architecture is explored for various emotion-pair
transfers. The generated audio is then classified using an LSTM-based emotion
classifier for audio. We find that "sad" audio is generated well as compared to
"happy" or "anger" as people have similar expressions of sadness.

High throughput and low latency data processing is essential for systems
requiring live decision making, control, and machine learning-optimized data
reduction. We focus on two distinct use cases for in-flight streaming data
processing for a) X-ray pulse reconstruction at SLAC's LCLS-II Free-Electron
Laser and b) control diagnostics at the DIII-D tokamak fusion reactor. Both
cases exemplify high throughput and low latency control feedback and motivate
our focus on machine learning at the edge where data processing and machine
learning algorithms can be implemented in field programmable gate array based
hardware immediately after the diagnostic sensors. We present our recent work
on a data preprocessing chain which requires fast featurization for information
encoding. We discuss several options for such algorithms with the primary focus
on our discrete cosine and sine transform-based approach adapted for streaming
data. These algorithms are primarily aimed at implementation in field
programmable gate arrays, favoring linear algebra operations that are also
aligned with the recent advances in inference accelerators for the
computational edge.

The existing Optical Character Recognition (OCR) systems are capable of
recognizing images with horizontal texts. However, when the rotation of the
texts increases, it becomes harder to recognizing these texts. The performance
of the OCR systems decreases. Thus predicting the rotations of the texts and
correcting the images are important. Previous work mainly uses traditional
Computer Vision methods like Hough Transform and Deep Learning methods like
Convolutional Neural Network. However, all of these methods are prone to
background noises commonly existing in general images with texts. To tackle
this problem, in this work, we introduce a new masked bounding-box selection
method, that incorporating the bounding box information into the system. By
training a ResNet predictor to focus on the bounding box as the region of
interest (ROI), the predictor learns to overlook the background noises.
Evaluations on the text rotation prediction tasks show that our method improves
the performance by a large margin.

We study a modular approach to tackle long-horizon mobile manipulation tasks
for object rearrangement, which decomposes a full task into a sequence of
subtasks. To tackle the entire task, prior work chains multiple stationary
manipulation skills with a point-goal navigation skill, which are learned
individually on subtasks. Although more effective than monolithic end-to-end RL
policies, this framework suffers from compounding errors in skill chaining,
e.g., navigating to a bad location where a stationary manipulation skill can
not reach its target to manipulate. To this end, we propose that the
manipulation skills should include mobility to have flexibility in interacting
with the target object from multiple locations and at the same time the
navigation skill could have multiple end points which lead to successful
manipulation. We operationalize these ideas by implementing mobile manipulation
skills rather than stationary ones and training a navigation skill trained with
region goal instead of point goal. We evaluate our multi-skill mobile
manipulation method M3 on 3 challenging long-horizon mobile manipulation tasks
in the Home Assistant Benchmark (HAB), and show superior performance as
compared to the baselines.

Deep Operator Networks (DeepONets) offer a powerful, data-driven tool for
solving parametric PDEs by learning operators, i.e. maps between
infinite-dimensional function spaces. In this work, we employ physics-informed
DeepONets in the context of high-dimensional, Bayesian inverse problems.
Traditional solution strategies necessitate an enormous, and frequently
infeasible, number of forward model solves, as well as the computation of
parametric derivatives. In order to enable efficient solutions, we extend
DeepONets by employing a realNVP architecture which yields an invertible and
differentiable map between the parametric input and the branch net output. This
allows us to construct accurate approximations of the full posterior which can
be readily adapted irrespective of the number of observations and the magnitude
of the observation noise. As a result, no additional forward solves are
required, nor is there any need for costly sampling procedures. We demonstrate
the efficacy and accuracy of the proposed methodology in the context of inverse
problems based on a anti-derivative, a reaction-diffusion and a Darcy-flow
equation.

Purpose: To improve dynamic speech imaging at 3 Tesla.
  Methods: A novel scheme combining a 16-channel vocal tract coil, variable
density spirals (VDS), and manifold regularization was developed. Short readout
duration spirals (1.3 ms long) were used to minimize sensitivity to
off-resonance. The manifold model leveraged similarities between frames sharing
similar vocal tract postures without explicit motion binning. Reconstruction
was posed as a SENSE-based non-local soft weighted temporal regularization
scheme. The self-navigating capability of VDS was leveraged to learn the
structure of the manifold. Our approach was compared against low-rank and
finite difference reconstruction constraints on two volunteers performing
repetitive and arbitrary speaking tasks. Blinded image quality evaluation in
the categories of alias artifacts, spatial blurring, and temporal blurring were
performed by three experts in voice research.
  Results: We achieved a spatial resolution of 2.4mm2/pixel and a temporal
resolution of 17.4 ms/frame for single slice imaging, and 52.2 ms/frame for
concurrent 3-slice imaging. Implicit motion binning of the manifold scheme for
both repetitive and fluent speaking tasks was demonstrated. The manifold scheme
provided superior fidelity in modeling articulatory motion compared to low rank
and temporal finite difference schemes. This was reflected by higher image
quality scores in spatial and temporal blurring categories. Our technique
exhibited faint alias artifacts, but offered a reduced interquartile range of
scores compared to other methods in alias artifact category.
  Conclusion: Synergistic combination of a custom vocal-tract coil, variable
density spirals and manifold regularization enables robust dynamic speech
imaging at 3 Tesla.

A fundamental component of user-level social media language based clinical
depression modelling is depression symptoms detection (DSD). Unfortunately,
there does not exist any DSD dataset that reflects both the clinical insights
and the distribution of depression symptoms from the samples of self-disclosed
depressed population. In our work, we describe a Semi-supervised Learning (SSL)
framework which uses an initial supervised learning model that leverages 1) a
state-of-the-art large mental health forum text pre-trained language model
further fine-tuned on a clinician annotated DSD dataset, 2) a Zero-Shot
learning model for DSD, and couples them together to harvest depression
symptoms related samples from our large self-curated Depression Tweets
Repository (DTR). Our clinician annotated dataset is the largest of its kind.
Furthermore, DTR is created from the samples of tweets in self-disclosed
depressed users Twitter timeline from two datasets, including one of the
largest benchmark datasets for user-level depression detection from Twitter.
This further helps preserve the depression symptoms distribution of
self-disclosed Twitter users tweets. Subsequently, we iteratively retrain our
initial DSD model with the harvested data. We discuss the stopping criteria and
limitations of this SSL process, and elaborate the underlying constructs which
play a vital role in the overall SSL process. We show that we can produce a
final dataset which is the largest of its kind. Furthermore, a DSD and a
Depression Post Detection (DPD) model trained on it achieves significantly
better accuracy than their initial version.

As complex machine learning models are increasingly used in sensitive
applications like banking, trading or credit scoring, there is a growing demand
for reliable explanation mechanisms. Local feature attribution methods have
become a popular technique for post-hoc and model-agnostic explanations.
However, attribution methods typically assume a stationary environment in which
the predictive model has been trained and remains stable. As a result, it is
often unclear how local attributions behave in realistic, constantly evolving
settings such as streaming and online applications. In this paper, we discuss
the impact of temporal change on local feature attributions. In particular, we
show that local attributions can become obsolete each time the predictive model
is updated or concept drift alters the data generating distribution.
Consequently, local feature attributions in data streams provide high
explanatory power only when combined with a mechanism that allows us to detect
and respond to local changes over time. To this end, we present CDLEEDS, a
flexible and model-agnostic framework for detecting local change and concept
drift. CDLEEDS serves as an intuitive extension of attribution-based
explanation techniques to identify outdated local attributions and enable more
targeted recalculations. In experiments, we also show that the proposed
framework can reliably detect both local and global concept drift. Accordingly,
our work contributes to a more meaningful and robust explainability in online

In order to determine a suitable automobile insurance policy premium one
needs to take into account three factors, the risk associated with the drivers
and cars on the policy, the operational costs associated with management of the
policy and the desired profit margin. The premium should then be some function
of these three values. We focus on risk assessment using a Data Science
approach. Instead of using the traditional frequency and severity metrics we
instead predict the total claims that will be made by a new customer using
historical data of current and past policies. Given multiple features of the
policy (age and gender of drivers, value of car, previous accidents, etc.) one
can potentially try to provide personalized insurance policies based
specifically on these features as follows. We can compute the average claims
made per year of all past and current policies with identical features and then
take an average over these claim rates. Unfortunately there may not be
sufficient samples to obtain a robust average. We can instead try to include
policies that are "similar" to obtain sufficient samples for a robust average.
We therefore face a trade-off between personalization (only using closely
similar policies) and robustness (extending the domain far enough to capture
sufficient samples). This is known as the Bias-Variance Trade-off. We model
this problem and determine the optimal trade-off between the two (i.e. the
balance that provides the highest prediction accuracy) and apply it to the
claim rate prediction problem. We demonstrate our approach using real data.

Component-based synthesis seeks to build programs using the APIs provided by
a set of libraries. Oftentimes, these APIs have effects, which make it
challenging to reason about the correctness of potential synthesis candidates.
This is because changes to global state made by effectful library procedures
affect how they may be composed together, yielding an intractably large search
space that can confound typical enumerative synthesis techniques. If the nature
of these effects are exposed as part of their specification, however, deductive
synthesis approaches can be used to help guide the search for components. In
this paper, we present a new specification-guided synthesis procedure that uses
Hoare-style pre- and post-conditions to express fine-grained effects of
potential library component candidates to drive a bi-directional synthesis
search strategy. The procedure alternates between a forward search process that
seeks to build larger terms given an existing context but which is otherwise
unaware of the actual goal, alongside a backward search mechanism that seeks
terms consistent with the desired goal but which is otherwise unaware of the
context from which these terms must be synthesized. To further improve
efficiency and scalability, we integrate a conflict-driven learning procedure
into the synthesis algorithm that provides a semantic characterization of
previously encountered unsuccessful search paths that is used to prune the
space of possible candidates as synthesis proceeds. We have implemented our
ideas in a tool called Cobalt and demonstrate its effectiveness on a number of
challenging synthesis problems defined over OCaml libraries equipped with
effectful specifications.

Differential equations based on physical principals are used to represent
complex dynamic systems in all fields of science and engineering. Through
repeated use in both academics and industry, these equations have been shown to
represent real-world dynamics well. Since the true dynamics of these complex
systems are generally unknown, learning the governing equations can improve our
understanding of the mechanisms driving the systems. Here, we develop a
Bayesian approach to data-driven discovery of non-linear spatio-temporal
dynamic equations. Our approach can accommodate measurement noise and missing
data, both of which are common in real-world data, and accounts for parameter
uncertainty. The proposed framework is illustrated using three simulated
systems with varying amounts of observational uncertainty and missing data and
applied to a real-world system to infer the temporal evolution of the vorticity
of the streamfunction.

Several techniques have recently aimed to improve the performance of deep
learning models for Scene Graph Generation (SGG) by incorporating background
knowledge. State-of-the-art techniques can be divided into two families: one
where the background knowledge is incorporated into the model in a subsymbolic
fashion, and another in which the background knowledge is maintained in
symbolic form. Despite promising results, both families of techniques face
several shortcomings: the first one requires ad-hoc, more complex neural
architectures increasing the training or inference cost; the second one suffers
from limited scalability w.r.t. the size of the background knowledge. Our work
introduces a regularization technique for injecting symbolic background
knowledge into neural SGG models that overcomes the limitations of prior art.
Our technique is model-agnostic, does not incur any cost at inference time, and
scales to previously unmanageable background knowledge sizes. We demonstrate
that our technique can improve the accuracy of state-of-the-art SGG models, by
up to 33%.

How can an entire CS faculty, who together have been teaching the ACM
standard CS curricula, shift to teaching elements of inclusive design across a
4-year undergraduate CS program? And will they even want to try? To investigate
these questions, we developed an educate-the-educators curriculum to support
this shift. The overall goal of the educate-the-educators curriculum was to
enable CS faculty to creatively engage with embedding inclusive design into
their courses in "minimally invasive" ways. GenderMag, an inclusive design
evaluation method, was selected for use. The curriculum targeted the following
learning outcomes: to enable CS faculty: (1) to analyze the costs and benefits
of integrating inclusive design into their own course(s); (2) to evaluate
software using the GenderMag method, and recognize its use to identify
meaningful issues in software; (3) to integrate inclusive design into existing
course materials with provided resources and collaboration; and (4) to prepare
to engage and guide students on learning GenderMag concepts. We conducted a
field study over a spring/summer followed by end-of-fall interviews, during
which we worked with 18 faculty members to integrate inclusive design into 13
courses. Ten of these faculty then taught 7 of these courses that were on the
Fall 2021 schedule, across 16 sections. We present the new
educate-the-educators curriculum and report on the faculty's experiences acting
upon it over the three-month field study and subsequent interviews. Our results
showed that, of the 18 faculty we worked with, 83% chose to modify their
courses; by Fall 2021, faculty across all four years of a CS degree program had
begun teaching inclusive design concepts. When we followed up with the 10 Fall
2021 faculty, 91% of their reported outcomes indicated that the incorporations
of inclusive design concepts in their courses went as well as or better than
expected.

Clinical investigations of anatomy's structural changes over time could
greatly benefit from population-level quantification of shape, or
spatiotemporal statistic shape modeling (SSM). Such a tool enables
characterizing patient organ cycles or disease progression in relation to a
cohort of interest. Constructing shape models requires establishing a
quantitative shape representation (e.g., corresponding landmarks).
Particle-based shape modeling (PSM) is a data-driven SSM approach that captures
population-level shape variations by optimizing landmark placement. However, it
assumes cross-sectional study designs and hence has limited statistical power
in representing shape changes over time. Existing methods for modeling
spatiotemporal or longitudinal shape changes require predefined shape atlases
and pre-built shape models that are typically constructed cross-sectionally.
This paper proposes a data-driven approach inspired by the PSM method to learn
population-level spatiotemporal shape changes directly from shape data. We
introduce a novel SSM optimization scheme that produces landmarks that are in
correspondence both across the population (inter-subject) and across
time-series (intra-subject). We apply the proposed method to 4D cardiac data
from atrial-fibrillation patients and demonstrate its efficacy in representing
the dynamic change of the left atrium. Furthermore, we show that our method
outperforms an image-based approach for spatiotemporal SSM with respect to a
generative time-series model, the Linear Dynamical System (LDS). LDS fit using
a spatiotemporal shape model optimized via our approach provides better
generalization and specificity, indicating it accurately captures the
underlying time-dependency.

In this paper, we revisit the long-standing problem of automatic
reconstruction of 3D objects from single line drawings. Previous
optimization-based methods can generate compact and accurate 3D models, but
their success rates depend heavily on the ability to (i) identifying a
sufficient set of true geometric constraints, and (ii) choosing a good initial
value for the numerical optimization. In view of these challenges, we propose
to train deep neural networks to detect pairwise relationships among geometric
entities (i.e., edges) in the 3D object, and to predict initial depth value of
the vertices. Our experiments on a large dataset of CAD models show that, by
leveraging deep learning in a geometric constraint solving pipeline, the
success rate of optimization-based 3D reconstruction can be significantly
improved.

This paper revisits the principle of uniform convergence in statistical
learning, discusses how it acts as the foundation behind machine learning, and
attempts to gain a better understanding of the essential problem that current
deep learning algorithms are solving. Using computer vision as an example
domain in machine learning, the discussion shows that recent research trends in
leveraging increasingly large-scale data to perform pre-training for
representation learning are largely to reduce the discrepancy between a
practically tractable empirical loss and its ultimately desired but intractable
expected loss. Furthermore, this paper suggests a few future research
directions, predicts the continued increase of data, and argues that more
fundamental research is needed on robustness, interpretability, and reasoning
capabilities of machine learning by incorporating structure and knowledge.

We consider multi-party protocols for classification that are motivated by
applications such as e-discovery in court proceedings. We identify a protocol
that guarantees that the requesting party receives all responsive documents and
the sending party discloses the minimal amount of non-responsive documents
necessary to prove that all responsive documents have been received. This
protocol can be embedded in a machine learning framework that enables automated
labeling of points and the resulting multi-party protocol is equivalent to the
standard one-party classification problem (if the one-party classification
problem satisfies a natural independence-of-irrelevant-alternatives property).
Our formal guarantees focus on the case where there is a linear classifier that
correctly partitions the documents.

Image-to-image translation has played an important role in enabling synthetic
data for computer vision. However, if the source and target domains have a
large semantic mismatch, existing techniques often suffer from source content
corruption aka semantic flipping. To address this problem, we propose a new
paradigm for image-to-image translation using Vector Symbolic Architectures
(VSA), a theoretical framework which defines algebraic operations in a
high-dimensional vector (hypervector) space. We introduce VSA-based constraints
on adversarial learning for source-to-target translations by learning a
hypervector mapping that inverts the translation to ensure consistency with
source content. We show both qualitatively and quantitatively that our method
improves over other state-of-the-art techniques.

The applications of artificial neural networks in the cosmological field have
shone successfully during the past decade, this is due to their great ability
of modeling large amounts of datasets and complex nonlinear functions. However,
in some cases, their use still remains controversial becasue their ease of
producing inaccurate results when the hyperparameters are not carefully
selected. In this paper, to find the optimal combination of hyperparameters
that describe the artificial neural networks, we propose to take advantage of
the genetic algorithms. As a proof of the concept, we analyze three different
cosmological cases to test the performance of the new architecture achieved
with the genetic algorithms and compare the output with the standard process,
consisting of a grid with all possible configurations. First, we carry out a
model-independent reconstruction of the distance modulus using a Type Ia
Supernovae compilation. Second, the neural networks learn to solve dynamical
system of the Universe's content, and finally with the latest Sloan Digital Sky
Survey data release we train the networks for the classification of
astronomical objects. We found that the genetic algorithms improve considerably
the generation of the architecture, which can ensure more confidence in their
physical results because of the better performance in the metrics with respect
to the grid method.

Inspired by biological neurons, the activation functions play an essential
part in the learning process of any artificial neural network commonly used in
many real-world problems. Various activation functions have been proposed in
the literature for classification as well as regression tasks. In this work, we
survey the activation functions that have been employed in the past as well as
the current state-of-the-art. In particular, we present various developments in
activation functions over the years and the advantages as well as disadvantages
or limitations of these activation functions. We also discuss classical (fixed)
activation functions, including rectifier units, and adaptive activation
functions. In addition to presenting the taxonomy of activation functions based
on characterization, a taxonomy of activation functions based on applications
is also presented. To this end, the systematic comparison of various fixed and
adaptive activation functions is performed for classification data sets such as
the MNIST, CIFAR-10, and CIFAR-100. In recent years, a physics-informed machine
learning framework has emerged for solving problems related to scientific
computations. To this purpose, we also discuss various requirements for
activation functions that have been used in the physics-informed machine
learning framework. Furthermore, various comparisons are made among different
fixed and adaptive activation functions using various machine learning
libraries such as TensorFlow, Pytorch, and JAX.

With the development of machine-learning algorithms, many attempts have been
made to use Artificial Neural Networks (ANN) for complicated tasks related to
data classification, pattern recognition, and predictive modeling. Among such
applications include Binary Black Hole (BBH) and Binary Neutron Star (BNS)
merger Gravitational Wave (GW) signal detection and forecasting. Image neural
networks that use time-frequency spectrograms as inputs remain one of the most
prominent methods due to their relevance to highly efficient and robust ANN
architectures. Earlier studies used traditional Fourier transform-based
time-frequency decomposition methods for spectrogram generation, which have had
difficulties identifying rapid frequency changes in merger signals with heavy
background noise. The primary objective of this study is to develop a signal
decomposition technique for improved GW signal classification and detection
performance using ANN. We introduce the Joint-Chirp-rate-Time-Frequency
transform (JCTFT), in which complex-valued window functions are used to
modulate the amplitude, frequency, and phase of the input signal. In addition,
we outline general techniques for generating chirp rate enhanced time-frequency
spectrograms from the results of a JCTFT. We found improved signal localization
performance of the JCTFT in comparison to the short-time-Fourier-transform
method with a moderate-to-high amount of background noise. The JCTFT can be
applied to existing and next-generation GW detector signals. The inclusion of
the chirp rate makes the JCTFT computation more time-consuming. Further studies
will aim to improve the efficiency and performance of JCTFT numerical

While quantum computing holds great potential in several fields including
combinatorial optimization, electronic structure calculation, and number
theory, the current era of quantum computing is limited by noisy hardware. Many
quantum compilation approaches, including noise-adaptive compilation and
efficient qubit routing, can mitigate the effects of imperfect hardware by
optimizing quantum circuits for objectives such as critical path length. Few of
these approaches, however, consider quantum circuits in terms of the set of
vendor-calibrated operations (i.e., native gates) available on target hardware.
In this paper, we review and expand both analytical and numerical methodology
for optimizing quantum circuits at this abstraction level. Additionally, we
present a procedure for combining the strengths of analytical native gate-level
optimization with numerical optimization. We use these methods to produce
optimized implementations of the Toffoli gate, a fundamental building block of
several quantum algorithms with near-term applications in quantum compilation
and machine learning. This paper focuses on the IBMQ native gate set, but the
methods presented are generalizable to any superconducting qubit architecture.
Our analytically optimized implementation demonstrated a $18\%$ reduction in
infidelity compared with the canonical implementation as benchmarked on IBM
Jakarta with quantum process tomography. Our numerical methods produced
implementations with six multi-qubit gates assuming the inclusion of
multi-qubit cross-resonance gates in the IBMQ native gate set, a $25\%$
reduction from the canonical eight multi-qubit implementation for
linearly-connected qubits. These results demonstrate the efficacy of native
gate-level optimization of quantum circuits and motivate further research into
this topic.

Syllabus is essentially a concise outline of a course of study, and
conventionally a text document. In the past few decades, however, two novel
variations of syllabus have emerged, namely "the Graphic Syllabus" and "the
Interactive Syllabus". Each of these two variations of syllabus has its own
special advantages. The present paper argues that there could be devised a new
combined version of the two mentioned variations, called "the Interactive
Graphic Syllabus", which can potentially bring us the advantages of both at the
same time. Specifically, using a well-designed Interactive Graphic syllabus can
bring about many advantages such as clarifying complex relationships; causing a
better retention; needing less cognitive energy for interpretation; helping
instructors identify any snags in their course organization; capability of
being integrated easily into a course management system; appealing to many of
learning styles and engaging students with different learning styles.

With the introduction of spectrum sharing and heterogeneous services in
next-generation networks, the base stations need to sense the wideband spectrum
and identify the spectrum resources to meet the quality-of-service, bandwidth,
and latency constraints. Sub-Nyquist sampling (SNS) enables digitization for
sparse wideband spectrum without needing Nyquist speed analog-to-digital
converters. However, SNS demands additional signal processing algorithms for
spectrum reconstruction, such as the well-known orthogonal matching pursuit
(OMP) algorithm. OMP is also widely used in other compressed sensing
applications. The first contribution of this work is efficiently mapping the
OMP algorithm on the Zynq system-on-chip (ZSoC) consisting of an ARM processor
and FPGA. Experimental analysis shows a significant degradation in OMP
performance for sparse spectrum. Also, OMP needs prior knowledge of spectrum
sparsity. We address these challenges via deep-learning-based architectures and
efficiently map them on the ZSoC platform as second contribution. Via
hardware-software co-design, different versions of the proposed architecture
obtained by partitioning between software (ARM processor) and hardware (FPGA)
are considered. The resource, power, and execution time comparisons for given
memory constraints and a wide range of word lengths are presented for these

Analyzing concentration of large random matrices is a common task in a wide
variety of fields. Given independent random variables, many tools are available
to analyze random matrices whose entries are linear in the variables, e.g. the
matrix-Bernstein inequality. However, in many applications, we need to analyze
random matrices whose entries are polynomials in the variables. These arise
naturally in the analysis of spectral algorithms, e.g., Hopkins et al. [STOC
2016], Moitra-Wein [STOC 2019]; and in lower bounds for semidefinite programs
based on the Sum of Squares hierarchy, e.g. Barak et al. [FOCS 2016], Jones et
al. [FOCS 2021]. In this work, we present a general framework to obtain such
bounds, based on the matrix Efron-Stein inequalities developed by
Paulin-Mackey-Tropp [Annals of Probability 2016]. The Efron-Stein inequality
bounds the norm of a random matrix by the norm of another simpler (but still
random) matrix, which we view as arising by "differentiating" the starting
matrix. By recursively differentiating, our framework reduces the main task to
analyzing far simpler matrices. For Rademacher variables, these simpler
matrices are in fact deterministic and hence, analyzing them is far easier. For
general non-Rademacher variables, the task reduces to scalar concentration,
which is much easier. Moreover, in the setting of polynomial matrices, our
results generalize the work of Paulin-Mackey-Tropp. Using our basic framework,
we recover known bounds in the literature for simple "tensor networks" and
"dense graph matrices". Using our general framework, we derive bounds for
"sparse graph matrices", which were obtained only recently by Jones et al.
[FOCS 2021] using a nontrivial application of the trace power method, and was a
core component in their work. We expect our framework to be helpful for other
applications involving concentration phenomena for nonlinear random matrices.

We consider the problem of explaining the temporal behavior of black-box
systems using human-interpretable models. To this end, based on recent research
trends, we rely on the fundamental yet interpretable models of deterministic
finite automata (DFAs) and linear temporal logic (LTL) formulas. In contrast to
most existing works for learning DFAs and LTL formulas, we rely on only
positive examples. Our motivation is that negative examples are generally
difficult to observe, in particular, from black-box systems. To learn
meaningful models from positive examples only, we design algorithms that rely
on conciseness and language minimality of models as regularizers. To this end,
our algorithms adopt two approaches: a symbolic and a counterexample-guided
one. While the symbolic approach exploits an efficient encoding of language
minimality as a constraint satisfaction problem, the counterexample-guided one
relies on generating suitable negative examples to prune the search. Both the
approaches provide us with effective algorithms with theoretical guarantees on
the learned models. To assess the effectiveness of our algorithms, we evaluate
all of them on synthetic data.

Deep learning shows excellent potential in generation tasks thanks to deep
latent representation. Generative models are classes of models that can
generate observations randomly concerning certain implied parameters. Recently,
the diffusion Model has become a rising class of generative models by its
power-generating ability. Nowadays, great achievements have been reached. More
applications except for computer vision, speech generation, bioinformatics, and
natural language processing are to be explored in this field. However, the
diffusion model has its genuine drawback of a slow generation process, single
data types, low likelihood, and the inability for dimension reduction. They are
leading to many enhanced works. This survey makes a summary of the field of the
diffusion model. We first state the main problem with two landmark works --
DDPM and DSM, and a unified landmark work -- Score SDE. Then, we present
improved techniques for existing problems in the diffusion-based model field,
including speed-up improvement For model speed-up improvement, data structure
diversification, likelihood optimization, and dimension reduction. Regarding
existing models, we also provide a benchmark of FID score, IS, and NLL
according to specific NFE. Moreover, applications with diffusion models are
introduced including computer vision, sequence modeling, audio, and AI for
science. Finally, there is a summarization of this field together with
limitations \& further directions. The summation of existing well-classified
methods is in our
Github:https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model.

A new type of experiment that aims to determine the optimal quantities of a
sequence of factors is eliciting considerable attention in medical science,
bioengineering, and many other disciplines. Such studies require the
simultaneous optimization of both quantities and the sequence orders of several
components which are called quantitative-sequence (QS) factors. Given the large
and semi-discrete solution spaces in such experiments, efficiently identifying
optimal or near-optimal solutions by using a small number of experimental
trials is a nontrivial task. To address this challenge, we propose a novel
active learning approach, called QS-learning, to enable effective modeling and
efficient optimization for experiments with QS factors. QS-learning consists of
three parts: a novel mapping-based additive Gaussian process (MaGP) model, an
efficient global optimization scheme (QS-EGO), and a new class of optimal
designs (QS-design). The theoretical properties of the proposed method are
investigated, and optimization techniques using analytical gradients are
developed. The performance of the proposed method is demonstrated via a real
drug experiment on lymphoma treatment and several simulation studies.

Transportation electrification requires an increasing number of electric
components (e.g., electric motors and electric energy storage systems) on
vehicles, and control of the electric powertrains usually involves multiple
inputs and multiple outputs (MIMO). This paper focused on the online
optimization of energy management strategy for a multi-mode hybrid electric
vehicle based on multi-agent reinforcement learning (MARL) algorithms that aim
to address MIMO control optimization while most existing methods only deal with
single output control. A new collaborative cyber-physical learning with
multi-agents is proposed based on the analysis of the evolution of energy
efficiency of the multi-mode hybrid electric vehicle (HEV) optimized by a deep
deterministic policy gradient (DDPG)-based MARL algorithm. Then a learning
driving cycle is set by a novel random method to speed up the training process.
Eventually, network design, learning rate, and policy noise are incorporated in
the sensibility analysis and the DDPG-based algorithm parameters are
determined, and the learning performance with the different relationships of
multi-agents is studied and demonstrates that the not completely independent
relationship with Ratio 0.2 is the best. The compassion study with the
single-agent and multi-agent suggests that the multi-agent can achieve
approximately 4% improvement of total energy over the single-agent scheme.
Therefore, the multi-objective control by MARL can achieve good optimization
effects and application efficiency.

We reformulate and reframe a series of increasingly complex parametric
statistical topics into a framework of response-vs-covariate (Re-Co) dynamics
that is described without any explicit functional structures. Then we resolve
these topics' data analysis tasks by discovering major factors underlying such
Re-Co dynamics by only making use of data's categorical nature. The major
factor selection protocol at the heart of Categorical Exploratory Data Analysis
(CEDA) paradigm is illustrated and carried out by employing Shannon's
conditional entropy (CE) and mutual information ($I[Re; Co] $) as two key
Information Theoretical measurements. Through the process of evaluating these
two entropy-based measurements and resolving statistical tasks, we acquire
several computational guidelines for carrying out the major factor selection
protocol in a do-and-learn fashion. Specifically, practical guidelines are
established for evaluating CE and $I[Re; Co] $ in accord with the criterion
called [C1:confirmable]. Via [C1:confirmable] criterion, we make no attempts on
acquiring consistent estimations of these theoretical information measurements.
All evaluations are carried out on a contingency table platform, upon which the
practical guidelines also provide ways of lessening effects of curse of
dimensionality. We explicitly carry out six examples of Re-Co dynamics, within
each of which, several widely extended scenarios are also explored and

Customer churn prediction is a valuable task in many industries. In
telecommunications it presents great challenges, given the high dimensionality
of the data, and how difficult it is to identify underlying frustration
signatures, which may represent an important driver regarding future churn
behaviour. Here, we propose a novel Bayesian hierarchical joint model that is
able to characterise customer profiles based on how many events take place
within different television watching journeys, and how long it takes between
events. The model drastically reduces the dimensionality of the data from
thousands of observations per customer to 11 customer-level parameter estimates
and random effects. We test our methodology using data from 40 BT customers (20
active and 20 who eventually cancelled their subscription) whose TV watching
behaviours were recorded from October to December 2019, totalling approximately
half a million observations. Employing different machine learning techniques
using the parameter estimates and random effects from the Bayesian hierarchical
model as features yielded up to 92\% accuracy predicting churn, associated with
100\% true positive rates and false positive rates as low as 14\% on a
validation set. Our proposed methodology represents an efficient way of
reducing the dimensionality of the data, while at the same time maintaining
high descriptive and predictive capabilities. We provide code to implement the
Bayesian model at https://github.com/rafamoral/profiling_tv_watching_behaviour.

In many real-world applications, one object (e.g., image) can be represented
or described by multiple instances (e.g., image patches) and simultaneously
associated with multiple labels. Such applications can be formulated as
multi-instance multi-label learning (MIML) problems and have been extensively
studied during the past few years. Existing MIML methods are useful in many
applications but most of which suffer from relatively low accuracy and training
efficiency due to several issues: i) the inter-label correlations (i.e., the
probabilistic correlations between the multiple labels corresponding to an
object) are neglected; ii) the inter-instance correlations cannot be learned
directly (or jointly) with other types of correlations due to the missing
instance labels; iii) diverse inter-correlations (e.g., inter-label
correlations, inter-instance correlations) can only be learned in multiple
stages. To resolve these issues, a new single-stage framework called broad
multi-instance multi-label learning (BMIML) is proposed. In BMIML, there are
three innovative modules: i) an auto-weighted label enhancement learning
(AWLEL) based on broad learning system (BLS); ii) A specific MIML neural
network called scalable multi-instance probabilistic regression (SMIPR); iii)
Finally, an interactive decision optimization (IDO). As a result, BMIML can
achieve simultaneous learning of diverse inter-correlations between whole
images, instances, and labels in single stage for higher classification
accuracy and much faster training time. Experiments show that BMIML is highly
competitive to (or even better than) existing methods in accuracy and much
faster than most MIML methods even for large medical image data sets (> 90K
images).

Learning in games has been widely used to solve many cooperative multi-agent
problems such as coverage control, consensus, self-reconfiguration or
vehicle-target assignment. One standard approach in this domain is to formulate
the problem as a potential game and to use an algorithm such as log-linear
learning to achieve the stochastic stability of globally optimal
configurations. Standard versions of such learning algorithms are asynchronous,
i.e., only one agent updates its action at each round of the learning process.
To enable faster learning, we propose a synchronization strategy based on
decentralized random prioritization of agents, which allows multiple agents to
change their actions simultaneously when they do not affect each other's
utility or feasible actions. We show that the proposed approach can be
integrated into any standard asynchronous learning algorithm to improve the
convergence speed while maintaining the limiting behavior (e.g., stochastically
stable configurations). We support our theoretical results with simulations in
a coverage control scenario.

In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane
signals are typically of lower resolution than the in-plane signals. While
contemporary super-resolution (SR) methods aim to recover the underlying
high-resolution volume, the estimated high-frequency information is implicit
via end-to-end data-driven training rather than being explicitly stated and
sought. To address this, we reframe the SR problem statement in terms of
perfect reconstruction filter banks, enabling us to identify and directly
estimate the missing information. In this work, we propose a two-stage approach
to approximate the completion of a perfect reconstruction filter bank
corresponding to the anisotropic acquisition of a particular scan. In stage 1,
we estimate the missing filters using gradient descent and in stage 2, we use
deep networks to learn the mapping from coarse coefficients to detail
coefficients. In addition, the proposed formulation does not rely on external
training data, circumventing the need for domain shift correction. Under our
approach, SR performance is improved particularly in "slice gap" scenarios,
likely due to the constrained solution space imposed by the framework.

The computational resources required to train a model have been increasing
since the inception of deep networks. Training neural networks on massive
datasets have become a challenging and time-consuming task. So, there arises a
need to reduce the dataset without compromising the accuracy. In this paper, we
present novel variations of an earlier approach called reduction through
homogeneous clustering for reducing dataset size. The proposed methods are
based on the idea of partitioning the dataset into homogeneous clusters and
selecting images that contribute significantly to the accuracy. We propose two
variations: Geometrical Homogeneous Clustering for Image Data Reduction
(GHCIDR) and Merged-GHCIDR upon the baseline algorithm - Reduction through
Homogeneous Clustering (RHC) to achieve better accuracy and training time. The
intuition behind GHCIDR involves selecting data points by cluster weights and
geometrical distribution of the training set. Merged-GHCIDR involves merging
clusters having the same labels using complete linkage clustering. We used
three deep learning models- Fully Connected Networks (FCN), VGG1, and VGG16. We
experimented with the two variants on four datasets- MNIST, CIFAR10,
Fashion-MNIST, and Tiny-Imagenet. Merged-GHCIDR with the same percentage
reduction as RHC showed an increase of 2.8%, 8.9%, 7.6% and 3.5% accuracy on
MNIST, Fashion-MNIST, CIFAR10, and Tiny-Imagenet, respectively.

Site preparation by mounding is a commonly used silvicultural treatment that
improves tree growth conditions by mechanically creating planting microsites
called mounds. Following site preparation, the next critical step is to count
the number of mounds, which provides forest managers with a precise estimate of
the number of seedlings required for a given plantation block. Counting the
number of mounds is generally conducted through manual field surveys by
forestry workers, which is costly and prone to errors, especially for large
areas. To address this issue, we present a novel framework exploiting advances
in Unmanned Aerial Vehicle (UAV) imaging and computer vision to accurately
estimate the number of mounds on a planting block. The proposed framework
comprises two main components. First, we exploit a visual recognition method
based on a deep learning algorithm for multiple object detection by pixel-based
segmentation. This enables a preliminary count of visible mounds, as well as
other frequently seen objects (e.g. trees, debris, accumulation of water), to
be used to characterize the planting block. Second, since visual recognition
could limited by several perturbation factors (e.g. mound erosion, occlusion),
we employ a machine learning estimation function that predicts the final number
of mounds based on the local block properties extracted in the first stage. We
evaluate the proposed framework on a new UAV dataset representing numerous
planting blocks with varying features. The proposed method outperformed manual
counting methods in terms of relative counting precision, indicating that it
has the potential to be advantageous and efficient in difficult situations.

There are many frameworks for deep generative modeling, each often presented
with their own specific training algorithms and inference methods. We present a
short note on the connections between existing deep generative models and the
GFlowNet framework, shedding light on their overlapping traits and providing a
unifying viewpoint through the lens of learning with Markovian trajectories.
This provides a means for unifying training and inference algorithms, and
provides a route to construct an agglomeration of generative models.

Many modern online 3D applications and video games rely on parametric models
of human faces for creating believable avatars. However, manually reproducing
someone's facial likeness with a parametric model is difficult and
time-consuming. Machine Learning solution for that task is highly desirable but
is also challenging. The paper proposes a novel approach to the so-called
Face-to-Parameters problem (F2P for short), aiming to reconstruct a parametric
face from a single image. The proposed method utilizes synthetic data, domain
decomposition, and domain adaptation to address multifaceted challenges in
solving the F2P. The open-sourced codebase illustrates our key observations and
provides means for quantitative evaluation. The presented approach proves
practical in an industrial application; it improves accuracy and allows for
more efficient models training. The techniques have the potential to extend to
other types of parametric models.

Single-pixel imaging (SPI) is significant for applications constrained by
transmission bandwidth or lighting band, where 3D SPI can be further realized
through capturing signals carrying depth. Sampling strategy and reconstruction
algorithm are the key issues of SPI. Traditionally, random patterns are often
adopted for sampling, but this blindly passive strategy requires a high
sampling rate, and even so, it is difficult to develop a reconstruction
algorithm that can maintain higher accuracy and robustness. In this paper, an
active strategy is proposed to perform sampling with targeted scanning by
designed patterns, from which the spatial information can be easily reordered
well. Then, deep learning methods are introduced further to achieve 3D
reconstruction, and the ability of deep learning to reconstruct desired
information under low sampling rates are analyzed. Abundant experiments verify
that our method improves the precision of SPI even if the sampling rate is very
low, which has the potential to be extended flexibly in similar systems
according to practical needs.

In this work, we propose a meta algorithm that can solve a multivariate
global optimization problem using univariate global optimizers. Although the
univariate global optimization does not receive much attention compared to the
multivariate case, which is more emphasized in academia and industry; we show
that it is still relevant and can be directly used to solve problems of
multivariate optimization. We also provide the corresponding regret bounds in
terms of the time horizon $T$ and the average regret of the univariate
optimizer, when it is robust against nonnegative noises with robust regret
guarantees.

As convolutional neural networks (CNNs) become more accurate at object
recognition, their representations become more similar to the primate visual
system. This finding has inspired us and other researchers to ask if the
implication also runs the other way: If CNN representations become more
brain-like, does the network become more accurate? Previous attempts to address
this question showed very modest gains in accuracy, owing in part to
limitations of the regularization method. To overcome these limitations, we
developed a new neural data regularizer for CNNs that uses Deep Canonical
Correlation Analysis (DCCA) to optimize the resemblance of the CNN's image
representations to that of the monkey visual cortex. Using this new neural data
regularizer, we see much larger performance gains in both classification
accuracy and within-super-class accuracy, as compared to the previous
state-of-the-art neural data regularizers. These networks are also more robust
to adversarial attacks than their unregularized counterparts. Together, these
results confirm that neural data regularization can push CNN performance
higher, and introduces a new method that obtains a larger performance boost.

We study the problem of multi-armed bandits with $\epsilon$-global
Differential Privacy (DP). First, we prove the minimax and problem-dependent
regret lower bounds for stochastic and linear bandits that quantify the
hardness of bandits with $\epsilon$-global DP. These bounds suggest the
existence of two hardness regimes depending on the privacy budget $\epsilon$.
In the high-privacy regime (small $\epsilon$), the hardness depends on a
coupled effect of privacy and partial information about the reward
distributions. In the low-privacy regime (large $\epsilon$), bandits with
$\epsilon$-global DP are not harder than the bandits without privacy. For
stochastic bandits, we further propose a generic framework to design a
near-optimal $\epsilon$ global DP extension of an index-based optimistic bandit
algorithm. The framework consists of three ingredients: the Laplace mechanism,
arm-dependent adaptive episodes, and usage of only the rewards collected in the
last episode for computing private statistics. Specifically, we instantiate
$\epsilon$-global DP extensions of UCB and KL-UCB algorithms, namely AdaP-UCB
and AdaP-KLUCB. AdaP-KLUCB is the first algorithm that both satisfies
$\epsilon$-global DP and yields a regret upper bound that matches the
problem-dependent lower bound up to multiplicative constants.

Capsule networks are a neural network architecture specialized for visual
scene recognition. Features and pose information are extracted from a scene and
then dynamically routed through a hierarchy of vector-valued nodes called
'capsules' to create an implicit scene graph, with the ultimate aim of learning
vision directly as inverse graphics. Despite these intuitions, however, capsule
networks are not formulated as explicit probabilistic generative models;
moreover, the routing algorithms typically used are ad-hoc and primarily
motivated by algorithmic intuition. In this paper, we derive an alternative
capsule routing algorithm utilizing iterative inference under sparsity
constraints. We then introduce an explicit probabilistic generative model for
capsule networks based on the self-attention operation in transformer networks
and show how it is related to a variant of predictive coding networks using
Von-Mises-Fisher (VMF) circular Gaussian distributions.

Recent increases in aerial image access and volume, increases in
computational power, and interest in applications have opened the door to
scaling up object detection and domain adaptation research to production.
Aerial data sets are very large in size, and each frame of the data set
contains a huge number of dense and small objects. Deep learning applications
for aerial imagery are behind due to a lack of training data, and researchers
have recently turned to domain adaptation (DA) from a labeled data set to an
unlabeled data set to alleviate the issue. These factors create two major
challenges: the high variety between datasets (e.g. object sizes, class
distributions, object feature uniformity, image acquisition, distance, weather
conditions), and the size of objects in satellite imagery and subsequent
failure of state-of-the-art to capture small objects, local features, and
region proposals for densely overlapped objects in satellite image. In this
paper, we propose two solutions to these problems: a domain discriminator to
better align the local feature space between domains; and a novel pipeline that
improves the back-end by spatial pyramid pooling, cross-stage partial network,
region proposal network via heatmap-based region proposals, and object
localization and identification through a novel image difficulty score that
adapts the overall focal loss measure based on the image difficulty. Our
proposed model outperformed the state-of-the-art method by 7.4%.

We propose to build a reinforcement learning prover of independent
components: a deductive system (an environment), the proof state representation
(how an agent sees the environment), and an agent training algorithm. To that
purpose, we contribute an additional Vampire-based environment to
$\texttt{gym-saturation}$ package of OpenAI Gym environments for saturation
provers. We demonstrate a prototype of using $\texttt{gym-saturation}$ together
with a popular reinforcement learning framework (Ray $\texttt{RLlib}$).
Finally, we discuss our plans for completing this work in progress to a
competitive automated theorem prover.

The scientific outcomes of the 2022 Landslide4Sense (L4S) competition
organized by the Institute of Advanced Research in Artificial Intelligence
(IARAI) are presented here. The objective of the competition is to
automatically detect landslides based on large-scale multiple sources of
satellite imagery collected globally. The 2022 L4S aims to foster
interdisciplinary research on recent developments in deep learning (DL) models
for the semantic segmentation task using satellite imagery. In the past few
years, DL-based models have achieved performance that meets expectations on
image interpretation, due to the development of convolutional neural networks
(CNNs). The main objective of this article is to present the details and the
best-performing algorithms featured in this competition. The winning solutions
are elaborated with state-of-the-art models like the Swin Transformer,
SegFormer, and U-Net. Advanced machine learning techniques and strategies such
as hard example mining, self-training, and mix-up data augmentation are also
considered. Moreover, we describe the L4S benchmark data set in order to
facilitate further comparisons, and report the results of the accuracy
assessment online. The data is accessible on \textit{Future Development
Leaderboard} for future evaluation at
\url{https://www.iarai.ac.at/landslide4sense/challenge/}, and researchers are
invited to submit more prediction results, evaluate the accuracy of their
methods, compare them with those of other users, and, ideally, improve the
landslide detection results reported in this article.

Greedy-GQ with linear function approximation, originally proposed in
\cite{maei2010toward}, is a value-based off-policy algorithm for optimal
control in reinforcement learning, and it has a non-linear two timescale
structure with a non-convex objective function. This paper develops its
finite-time error bounds. We show that the Greedy-GQ algorithm converges as
fast as $\mathcal{O}({1}/{\sqrt{T}})$ under the i.i.d.\ setting and
$\mathcal{O}({\log T}/{\sqrt{T}})$ under the Markovian setting. We further
design a variant of the vanilla Greedy-GQ algorithm using the nested-loop
approach, and show that its sample complexity is
$\mathcal{O}({\log(1/\epsilon)\epsilon^{-2}})$, which matches with the one of
the vanilla Greedy-GQ. Our finite-time error bounds match with one of the
stochastic gradient descent algorithms for general smooth non-convex
optimization problems. Our finite-sample analysis provides theoretical guidance
on choosing step-sizes for faster convergence in practice and suggests the
trade-off between the convergence rate and the quality of the obtained policy.
Our techniques in this paper provide a general approach for finite-sample
analysis of non-convex two timescale value-based reinforcement learning

Despite the advances provided by large-scale photometric surveys, stellar
features - such as metallicity - generally remain limited to spectroscopic
observations often of bright, nearby low-extinction stars. To rectify this, we
present a neural network approach for estimating the metallicities and
distances of red giant stars with 8-band photometry and parallaxes from Gaia
EDR3 and the 2MASS and WISE surveys. The algorithm accounts for uncertainties
in the predictions arising from the range of possible outputs at each input and
from the range of models compatible with the training set (through drop-out). A
two-stage procedure is adopted where an initial network to estimate
photo-astrometric parallaxes is trained using a large sample of noisy parallax
data from Gaia EDR3 and then a secondary network is trained using spectroscopic
metallicities from the APOGEE and LAMOST surveys and an augmented feature space
utilising the first-stage parallax estimates. The algorithm produces
metallicity predictions with an average uncertainty of $\pm0.19$ dex. The
methodology is applied to stars within the Galactic bar/bulge with particular
focus on a sample of 1.69 million objects with Gaia radial velocities. We
demonstrate the use and validity of our approach by inspecting both spatial and
kinematic gradients with metallicity in the Galactic bar/bulge recovering
previous results on the vertical metallicity gradient ($-0.528 \pm 0.002$
dex/kpc) and the vertex deviation of the bar ($-21.29 \pm 2.74$ deg).

The goal of Explainable AI (XAI) is to design methods to provide insights
into the reasoning process of black-box models, such as deep neural networks,
in order to explain them to humans. Social science research states that such
explanations should be conversational, similar to human-to-human explanations.
In this work, we show how to incorporate XAI in a conversational agent, using a
standard design for the agent comprising natural language understanding and
generation components. We build upon an XAI question bank which we extend by
quality-controlled paraphrases to understand the user's information needs. We
further systematically survey the literature for suitable explanation methods
that provide the information to answer those questions, and present a
comprehensive list of suggestions. Our work is the first step towards truly
natural conversations about machine learning models with an explanation agent.
The comprehensive list of XAI questions and the corresponding explanation
methods may support other researchers in providing the necessary information to
address users' demands.

In many digital contexts such as online news and e-tailing with many new
users and items, recommendation systems face several challenges: i) how to make
initial recommendations to users with little or no response history (i.e.,
cold-start problem), ii) how to learn user preferences on items (test and
learn), and iii) how to scale across many users and items with myriad
demographics and attributes. While many recommendation systems accommodate
aspects of these challenges, few if any address all. This paper introduces a
Collaborative Filtering (CF) Multi-armed Bandit (B) with Attributes (A)
recommendation system (CFB-A) to jointly accommodate all of these
considerations. Empirical applications including an offline test on MovieLens
data, synthetic data simulations, and an online grocery experiment indicate the
CFB-A leads to substantial improvement on cumulative average rewards (e.g.,
total money or time spent, clicks, purchased quantities, average ratings, etc.)
relative to the most powerful extant baseline methods.

Microservice-based architecture has become prevalent for cloud-native
applications. With an increasing number of applications being deployed on cloud
platforms every day leveraging this architecture, more research efforts are
required to understand how different strategies can be applied to effectively
manage various cloud resources at scale. A large body of research has deployed
automatic resource allocation algorithms using reactive and proactive
autoscaling policies. However, there is still a gap in the efficiency of
current algorithms in capturing the important features of microservices from
their architecture and deployment environment, for example, lack of
consideration of graphical dependency. To address this challenge, we propose
Graph-PHPA, a graph-based proactive horizontal pod autoscaling strategy for
allocating cloud resources to microservices leveraging long short-term memory
(LSTM) and graph neural network (GNN) based prediction methods. We evaluate the
performance of Graph-PHPA using the Bookinfo microservices deployed in a
dedicated testing environment with real-time workloads generated based on
realistic datasets. We demonstrate the efficacy of Graph-PHPA by comparing it
with the rule-based resource allocation scheme in Kubernetes as our baseline.
Extensive experiments have been implemented and our results illustrate the
superiority of our proposed approach in resource savings over the reactive
rule-based baseline algorithm in different testing scenarios.

We develop an approach to policy selection in active inference that allows us
to efficiently search large policy spaces by mapping each policy to its
embedding in a vector space. We sample the expected free energy of
representative points in the space, then perform a more thorough policy search
around the most promising point in this initial sample. We consider various
approaches to creating the policy embedding space, and propose using k-means
clustering to select representative points. We apply our technique to a
goal-oriented graph-traversal problem, for which naive policy selection is
intractable for even moderately large graphs.

Contrastive learning (CL) has recently been demonstrated critical in
improving recommendation performance. The fundamental idea of CL-based
recommendation models is to maximize the consistency between representations
learned from different graph augmentations of the user-item bipartite graph. In
such a self-supervised manner, CL-based recommendation models are expected to
extract general features from the raw data to tackle the data sparsity issue.
Despite the effectiveness of this paradigm, we still have no clue what
underlies the performance gains. In this paper, we first reveal that CL
enhances recommendation through endowing the model with the ability to learn
more evenly distributed user/item representations, which can implicitly
alleviate the pervasive popularity bias and promote long-tail items. Meanwhile,
we find that the graph augmentations, which were considered a necessity in
prior studies, are relatively unreliable and less significant in CL-based
recommendation. On top of these findings, we put forward an eXtremely Simple
Graph Contrastive Learning method (XSimGCL) for recommendation, which discards
the ineffective graph augmentations and instead employs a simple yet effective
noise-based embedding augmentation to create views for CL. A comprehensive
experimental study on three large and highly sparse benchmark datasets
demonstrates that, though the proposed method is extremely simple, it can
smoothly adjust the uniformity of learned representations and outperforms its
graph augmentation-based counterparts by a large margin in both recommendation
accuracy and training efficiency. The code is released at
https://github.com/Coder-Yu/SELFRec.

In freemium games, the revenue from a player comes from the in-app purchases
made and the advertisement to which that player is exposed. The longer a player
is playing the game, the higher will be the chances that he or she will
generate a revenue within the game. Within this scenario, it is extremely
important to be able to detect promptly when a player is about to quit playing
(churn) in order to react and attempt to retain the player within the game,
thus prolonging his or her game lifetime. In this article we investigate how to
improve the current state-of-the-art in churn prediction by combining
sequential and aggregate data using different neural network architectures. The
results of the comparative analysis show that the combination of the two data
types grants an improvement in the prediction accuracy over predictors based on
either purely sequential or purely aggregated data.

Semantic image synthesis enables control over unconditional image generation
by allowing guidance on what is being generated. We conditionally synthesize
the latent space from a vector quantized model (VQ-model) pre-trained to
autoencode images. Instead of training an autoregressive Transformer on
separately learned conditioning latents and image latents, we find that jointly
learning the conditioning and image latents significantly improves the modeling
capabilities of the Transformer model. While our jointly trained VQ-model
achieves a similar reconstruction performance to a vanilla VQ-model for both
semantic and image latents, tying the two modalities at the autoencoding stage
proves to be an important ingredient to improve autoregressive modeling
performance. We show that our model improves semantic image synthesis using
autoregressive models on popular semantic image datasets ADE20k, Cityscapes and
COCO-Stuff.

Understanding Transformer-based models has attracted significant attention,
as they lie at the heart of recent technological advances across machine
learning. While most interpretability methods rely on running models over
inputs, recent work has shown that a zero-pass approach, where parameters are
interpreted directly without a forward/backward pass is feasible for some
Transformer parameters, and for two-layer attention networks. In this work, we
present a theoretical analysis where all parameters of a trained Transformer
are interpreted by projecting them into the embedding space, that is, the space
of vocabulary items they operate on. We derive a simple theoretical framework
to support our arguments and provide ample evidence for its validity. First, an
empirical analysis showing that parameters of both pretrained and fine-tuned
models can be interpreted in embedding space. Second, we present two
applications of our framework: (a) aligning the parameters of different models
that share a vocabulary, and (b) constructing a classifier without training by
``translating'' the parameters of a fine-tuned classifier to parameters of a
different model that was only pretrained. Overall, our findings open the door
to interpretation methods that, at least in part, abstract away from model
specifics and operate in the embedding space only.

Nonnegative matrix factorization (NMF) is widely used for clustering with
strong interpretability. Among general NMF problems, symmetric NMF is a special
one that plays an important role in graph clustering where each element
measures the similarity between data points. Most existing symmetric NMF
algorithms require factor matrices to be nonnegative, and only focus on
minimizing the gap between similarity matrix and its approximation for
clustering, without giving a consideration to other potential regularization
terms which can yield better clustering. In this paper, we explore factorizing
a symmetric matrix that does not have to be nonnegative, presenting an
efficient factorization algorithm with a regularization term to boost the
clustering performance. Moreover, a more general framework is proposed to solve
symmetric matrix factorization problems with different constraints on the
factor matrices.

The Fermilab Linac delivers 400 MeV H- beam to the rest of the accelerator
chain. Providing stable intensity, energy, and emittance is key since it
directly affects downstream machines. To counter fluctuations of Linac output
due to various effects to be described below we are working on implementing
dynamic longitudinal parameter optimization based on Machine Learning (ML). As
inputs for the ML model, signals from beam diagnostics have to be well
understood and reliable. In this paper we discuss the status and plans for
ML-based optimization as well as preliminary results of diagnostics studies.

We establish a disintegrated PAC-Bayesian bound, for classifiers that are
trained via continuous-time (non-stochastic) gradient descent. Contrarily to
what is standard in the PAC-Bayesian setting, our result applies to a training
algorithm that is deterministic, conditioned on a random initialisation,
without requiring any $\textit{de-randomisation}$ step. We provide a broad
discussion of the main features of the bound that we propose, and we study
analytically and empirically its behaviour on linear models, finding promising
results.

In multi-task learning (MTL) for visual scene understanding, it is crucial to
transfer useful information between multiple tasks with minimal interferences.
In this paper, we propose a novel architecture that effectively transfers
informative features by applying the attention mechanism to the multi-scale
features of the tasks. Since applying the attention module directly to all
possible features in terms of scale and task requires a high complexity, we
propose to apply the attention module sequentially for the task and scale. The
cross-task attention module (CTAM) is first applied to facilitate the exchange
of relevant information between the multiple task features of the same scale.
The cross-scale attention module (CSAM) then aggregates useful information from
feature maps at different resolutions in the same task. Also, we attempt to
capture long range dependencies through the self-attention module in the
feature extraction network. Extensive experiments demonstrate that our method
achieves state-of-the-art performance on the NYUD-v2 and PASCAL-Context
dataset.

We consider a simple classification problem to show that the dynamics of
finite-width Deep Neural Networks in the underparametrized regime gives rise to
effects similar to those associated with glassy systems, namely a slow
evolution of the loss function and aging. Remarkably, the aging is sublinear in
the waiting time (subaging) and the power-law exponent characterizing it is
robust to different architectures under the constraint of a constant total
number of parameters. Our results are maintained in the more complex scenario
of the MNIST database. We find that for this database there is a unique
exponent ruling the subaging behavior in the whole phase.

Beyond achieving higher compression efficiency over classical image
compression codecs, deep image compression is expected to be improved with
additional side information, e.g., another image from a different perspective
of the same scene. To better utilize the side information under the distributed
compression scenario, the existing method (Ayzik and Avidan 2020) only
implements patch matching at the image domain to solve the parallax problem
caused by the difference in viewing points. However, the patch matching at the
image domain is not robust to the variance of scale, shape, and illumination
caused by the different viewing angles, and can not make full use of the rich
texture information of the side information image. To resolve this issue, we
propose Multi-Scale Feature Domain Patch Matching (MSFDPM) to fully utilizes
side information at the decoder of the distributed image compression model.
Specifically, MSFDPM consists of a side information feature extractor, a
multi-scale feature domain patch matching module, and a multi-scale feature
fusion network. Furthermore, we reuse inter-patch correlation from the shallow
layer to accelerate the patch matching of the deep layer. Finally, we nd that
our patch matching in a multi-scale feature domain further improves compression
rate by about 20% compared with the patch matching method at image domain
(Ayzik and Avidan 2020).

Most existing semi-supervised graph-based clustering methods exploit the
supervisory information by either refining the affinity matrix or directly
constraining the low-dimensional representations of data points. The affinity
matrix represents the graph structure and is vital to the performance of
semi-supervised graph-based clustering. However, existing methods adopt a
static affinity matrix to learn the low-dimensional representations of data
points and do not optimize the affinity matrix during the learning process. In
this paper, we propose a novel dynamic graph structure learning method for
semi-supervised clustering. In this method, we simultaneously optimize the
affinity matrix and the low-dimensional representations of data points by
leveraging the given pairwise constraints. Moreover, we propose an alternating
minimization approach with proven convergence to solve the proposed nonconvex
model. During the iteration process, our method cyclically updates the
low-dimensional representations of data points and refines the affinity matrix,
leading to a dynamic affinity matrix (graph structure). Specifically, for the
update of the affinity matrix, we enforce the data points with remarkably
different low-dimensional representations to have an affinity value of 0.
Furthermore, we construct the initial affinity matrix by integrating the local
distance and global self-representation among data points. Experimental results
on eight benchmark datasets under different settings show the advantages of the

As AI is more and more pervasive in everyday life, humans have an increasing
demand to understand its behavior and decisions. Most research on explainable
AI builds on the premise that there is one ideal explanation to be found. In
fact, however, everyday explanations are co-constructed in a dialogue between
the person explaining (the explainer) and the specific person being explained
to (the explainee). In this paper, we introduce a first corpus of dialogical
explanations to enable NLP research on how humans explain as well as on how AI
can learn to imitate this process. The corpus consists of 65 transcribed
English dialogues from the Wired video series \emph{5 Levels}, explaining 13
topics to five explainees of different proficiency. All 1550 dialogue turns
have been manually labeled by five independent professionals for the topic
discussed as well as for the dialogue act and the explanation move performed.
We analyze linguistic patterns of explainers and explainees, and we explore
differences across proficiency levels. BERT-based baseline results indicate
that sequence information helps predicting topics, acts, and moves effectively

For microservice applications with detected performance anomalies, localizing
root causes based on monitoring data is important to enabling rapid recovery
and loss mitigation. Existing research mainly focuses on coarse-grained faulty
service localization. However, the fine-grained root cause localization to
identify not only faulty service but also the root cause metric in the service
is more helpful for operators to fix application anomalies, which is also more
challenging. Recently, causal inference (CI) based methods is becoming popular
but currently used CI methods have limitations, such as linear causal relations
assumption. Therefore, this paper provides a framework named CausalRCA to
implement fine-grained, automated, and real-time root cause localization. The
CausalRCA works with a gradient-based causal structure learning method to
generate weighted causal graphs and a root cause inference method to localize
root cause metrics. We conduct coarse-grained and fine-grained root cause
localization to validate the localization performance of CausalRCA.
Experimental results show that CausalRCA performs best localization accuracy
compared with baseline methods, e.g., the average $AC@3$ of the fine-grained
root cause metric localization in the faulty service is 0.719, and the average
improvement is 17\% compared with baseline methods.

The deep learning research in computer vision has been growing extremely fast
in the past decade, many of which have been translated into novel image
analysis methods for biomedical problems. Broadly speaking, many deep learning
based biomedical image analysis methods can be considered as a general
image-to-image transformation framework. In this work, we introduce a new open
source python package MMV_Im2Im for image-to-image transformation in bioimaging
applications. The overall package is designed with a generic image-to-image
transformation framework, which could be directly used for semantic
segmentation, instance segmentation, image restoration, image generation, etc..
The implementation takes advantage of the state-of-the-art machine learning
engineering techniques for users to focus on the research without worrying
about the engineering details. We demonstrate the effectiveness of MMV_Im2Im in
more than ten different biomedical problems. For biomedical machine learning
researchers, we hope this new package could serve as the starting point for
their specific problems to stimulate new biomedical image analysis or machine
learning methods. For experimental biomedical researchers, we hope this work
can provide a holistic view of the image-to-image transformation concept with
diverse examples, so that deep learning based image-to-image transformation
could be further integrated into the assay development process and permit new
biomedical studies that can hardly be done only with traditional experimental
methods. Source code can be found at https://github.com/MMV-Lab/mmv_im2im.

The Proton Improvement Plan-II (PIP-II, [1]) linac will include 35 Single
Spoke Resonators type 2 (SSR2). A total of eight pre-production SSR2 jacketed
cavities will be procured and five installed in the first pre-production
cryomodule. The mechanical design of the jacketed cavity has been finalized and
it will be presented in this paper along with fabrication and processing
experience. The importance of interfaces, quality controls and procurement
aspects in the design phase will be remarked as well as lessons learned during
the fabrication process. Furthermore, development studies will be presented
together with other design validation tests.

Argument mining tasks require an informed range of low to high complexity
linguistic phenomena and commonsense knowledge. Previous work has shown that
pre-trained language models are highly effective at encoding syntactic and
semantic linguistic phenomena when applied with transfer learning techniques
and built on different pre-training objectives. It remains an issue of how much
the existing pre-trained language models encompass the complexity of argument
mining tasks. We rely on experimentation to shed light on how language models
obtained from different lexical semantic families leverage the performance of
the identification of argumentative discourse units task. Experimental results
show that transfer learning techniques are beneficial to the task and that
current methods may be insufficient to leverage commonsense knowledge from
different lexical semantic families.

Nowadays, yoga has gained worldwide attention because of increasing levels of
stress in the modern way of life, and there are many ways or resources to learn
yoga. The word yoga means a deep connection between the mind and body. Today
there is substantial Medical and scientific evidence to show that the very
fundamentals of the activity of our brain, our chemistry even our genetic
content can be changed by practicing different systems of yoga. Suryanamaskar,
also known as salute to the sun, is a yoga practice that combines eight
different forms and 12 asanas(4 asana get repeated) devoted to the Hindu Sun
God, Surya. Suryanamaskar offers a number of health benefits such as
strengthening muscles and helping to control blood sugar levels. Here the
Mediapipe Library is used to analyze Surya namaskar situations. Standing is
detected in real time with advanced software, as one performs Surya namaskar in
front of the camera. The class divider identifies the form as one of the
following: Pranamasana, Hasta Padasana, Hasta Uttanasana, Ashwa - Sanchalan
asana, Ashtanga Namaskar, Dandasana, or Bhujangasana and Svanasana. Deep
learning-based techniques(CNN) are used to develop this model with model
accuracy of 98.68 percent and an accuracy score of 0.75 to detect correct yoga
(Surya Namaskar ) posture. With this method, the users can practice the desired
pose and can check if the pose that the person is doing is correct or not. It
will help in doing all the different poses of surya namaskar correctly and
increase the efficiency of the yoga practitioner. This paper describes the
whole framework which is to be implemented in the model.

Logo retrieval is a challenging problem since the definition of similarity is
more subjective compared to image retrieval tasks and the set of known
similarities is very scarce. To tackle this challenge, in this paper, we
propose a simple but effective segment-based augmentation strategy to introduce
artificially similar logos for training deep networks for logo retrieval. In
this novel augmentation strategy, we first find segments in a logo and apply
transformations such as rotation, scaling, and color change, on the segments,
unlike the conventional image-level augmentation strategies. Moreover, we
evaluate whether the recently introduced ranking-based loss function,
Smooth-AP, is a better approach for learning similarity for logo retrieval. On
the large scale METU Trademark Dataset, we show that (i) our segment-based
augmentation strategy improves retrieval performance compared to the baseline
model or image-level augmentation strategies, and (ii) Smooth-AP indeed
performs better than conventional losses for logo retrieval.

Larger deep learning models usually lead to higher model quality with an
ever-increasing GPU memory footprint. Although tensor checkpointing techniques
have been proposed to enable training under a restricted GPU memory budget, the
input tensor dynamics have been unexploited for optimizing performance while
reducing GPU memory footprint. Specifically, due to the diverse datasets and
subsequent data argumentation, the input tensor size per mini-batch is dynamic
during the training process, leading to a changing GPU memory footprint.
However, to leverage such input tensor dynamics in checkpointing, there are two
challenges to be solved. First, the checkpointing plan needs to be determined
during runtime due to the dynamics of input tensors. Second, the checkpointing
plan needs to be applied on the fly without significantly deteriorating the
  In this paper, we propose Mimose, an input-aware tensor checkpointing planner
respecting the memory budget while enabling efficient model training on GPU.
Mimose builds a lightweight but accurate prediction model of GPU memory usage
online, without pre-analyzing the model. It generates a tensor checkpointing
plan based on per-layer memory prediction and applies it to training progress
on the fly. It also adopts a caching strategy to avoid having to regenerate the
plan for repeated input size. Our experiments show that Mimose achieves
superior training throughput compared to state-of-the-art memory planners under
the same GPU memory budgets.

There is a limited amount of publicly available data to support research in
malware analysis technology. Particularly, there are virtually no publicly
available datasets generated from rich sandboxes such as Cuckoo/CAPE. The
benefit of using dynamic sandboxes is the realistic simulation of file
execution in the target machine and obtaining a log of such execution. The
machine can be infected by malware hence there is a good chance of capturing
the malicious behavior in the execution logs, thus allowing researchers to
study such behavior in detail. Although the subsequent analysis of log
information is extensively covered in industrial cybersecurity backends, to our
knowledge there has been only limited effort invested in academia to advance
such log analysis capabilities using cutting edge techniques. We make this
sample dataset available to support designing new machine learning methods for
malware detection, especially for automatic detection of generic malicious
behavior. The dataset has been collected in cooperation between Avast Software
and Czech Technical University - AI Center (AIC).

Cardiac Magnetic Resonance Imaging is commonly used for the assessment of the
cardiac anatomy and function. The delineations of left and right ventricle
blood pools and left ventricular myocardium are important for the diagnosis of
cardiac diseases. Unfortunately, the movement of a patient during the CMR
acquisition procedure may result in motion artifacts appearing in the final
image. Such artifacts decrease the diagnostic quality of CMR images and force
redoing of the procedure. In this paper, we present a Multi-task Swin UNEt
TRansformer network for simultaneous solving of two tasks in the CMRxMotion
challenge: CMR segmentation and motion artifacts classification. We utilize
both segmentation and classification as a multi-task learning approach which
allows us to determine the diagnostic quality of CMR and generate masks at the
same time. CMR images are classified into three diagnostic quality classes,
whereas, all samples with non-severe motion artifacts are being segmented.
Ensemble of five networks trained using 5-Fold Cross-validation achieves
segmentation performance of DICE coefficient of 0.871 and classification
accuracy of 0.595.

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson
(Z=118), it is estimated that as many as about 3000 atomic nuclei could exist
in nature. Most of these nuclei are too short-lived to be occurring on Earth,
but they play an essential role in astrophysical events such as supernova
explosions or neutron star mergers that are presumed to be at the origin of
most heavy elements in the Universe. Understanding the structure, reactions,
and decays of nuclei across the entire chart of nuclides is an enormous
challenge because of the experimental difficulties in measuring properties of
interest in such fleeting objects and the theoretical and computational issues
of simulating strongly-interacting quantum many-body systems. Nuclear density
functional theory (DFT) is a fully microscopic theoretical framework which has
the potential of providing such a quantitatively accurate description of
nuclear properties for every nucleus in the chart of nuclides. Thanks to
high-performance computing facilities, it has already been successfully applied
to predict nuclear masses, global patterns of radioactive decay like $\beta$ or
$\gamma$ decay, and several aspects of the nuclear fission process such as,
e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear
spectroscopy or of nuclear fission, or the quantification of theoretical
uncertainties and their propagation to applications, would require several
orders of magnitude more calculations than currently possible. However, most of
this computational effort would be spent into generating a suitable basis of
DFT wavefunctions. Such a task could potentially be considerably accelerated by
borrowing tools from the field of machine learning and artificial intelligence.
In this paper, we review different approaches to applying supervised and
unsupervised learning techniques to nuclear DFT.

Learning from positive and unlabeled (PU) data is a setting where the learner
only has access to positive and unlabeled samples while having no information
on negative examples. Such PU setting is of great importance in various tasks
such as medical diagnosis, social network analysis, financial markets analysis,
and knowledge base completion, which also tend to be intrinsically imbalanced,
i.e., where most examples are actually negatives. Most existing approaches for
PU learning, however, only consider artificially balanced datasets and it is
unclear how well they perform in the realistic scenario of imbalanced and
long-tail data distribution. This paper proposes to tackle this challenge via
robust and efficient self-supervised pretraining. However, training
conventional self-supervised learning methods when applied with highly
imbalanced PU distribution needs better reformulation. In this paper, we
present \textit{ImPULSeS}, a unified representation learning framework for
\underline{Im}balanced \underline{P}ositive \underline{U}nlabeled
\underline{L}earning leveraging \underline{Se}lf-\underline{S}upervised debiase
pre-training. ImPULSeS uses a generic combination of large-scale unsupervised
learning with debiased contrastive loss and additional reweighted PU loss. We
performed different experiments across multiple datasets to show that ImPULSeS
is able to halve the error rate of the previous state-of-the-art, even compared
with previous methods that are given the true prior. Moreover, our method
showed increased robustness to prior misspecification and superior performance
even when pretraining was performed on an unrelated dataset. We anticipate such
robustness and efficiency will make it much easier for practitioners to obtain
excellent results on other PU datasets of interest. The source code is
available at \url{https://github.com/JSchweisthal/ImPULSeS}

The universal approximation theorem asserts that a single hidden layer neural
network approximates continuous functions with any desired precision on compact
sets. As an existential result, the universal approximation theorem supports
the use of neural networks for various applications, including regression and
classification tasks. The universal approximation theorem is not limited to
real-valued neural networks but also holds for complex, quaternion, tessarines,
and Clifford-valued neural networks. This paper extends the universal
approximation theorem for a broad class of hypercomplex-valued neural networks.
Precisely, we first introduce the concept of non-degenerate hypercomplex
algebra. Complex numbers, quaternions, and tessarines are examples of
non-degenerate hypercomplex algebras. Then, we state the universal
approximation theorem for hypercomplex-valued neural networks defined on a
non-degenerate algebra.

Compared with conventional numerical approaches to solving partial
differential equations (PDEs), physics-informed neural networks (PINN) have
manifested the capability to save development effort and computational cost,
especially in scenarios of reconstructing the physics field and solving the
inverse problem. Considering the advantages of parameter sharing, spatial
feature extraction and low inference cost, convolutional neural networks (CNN)
are increasingly used in PINN. However, some challenges still remain as
follows. To adapt convolutional PINN to solve different PDEs, considerable
effort is usually needed for tuning critical hyperparameters. Furthermore, the
effects of the finite difference accuracy, and the mesh resolution on the
predictivity of convolutional PINN are not settled. To fill the gaps above, we
propose three initiatives in this paper: (1) A Multi-Receptive-Field PINN
(MRF-PINN) model is established to solve different types of PDEs on various
mesh resolutions without manual tuning; (2) The dimensional balance method is
used to estimate the loss weights when solving Navier-Stokes equations; (3) The
Taylor polynomial is used to pad the virtual nodes near the boundaries for
implementing high-order finite difference. The proposed MRF-PINN is tested for
solving three typical linear PDEs (elliptic, parabolic, hyperbolic) and a
series of nonlinear PDEs (Navier-Stokes PDEs) to demonstrate its generality and
superiority. This paper shows that MRF-PINN can adapt to completely different
equation types and mesh resolutions without any hyperparameter tuning. The
dimensional balance method saves computational time and improves the
convergence for solving Navier-Stokes PDEs. Further, the solving error is
significantly decreased under high-order finite difference, large channel
number, and high mesh resolution, which is expected to be a general
convolutional PINN scheme.

The inability of a computer to think has been a limiter in its usefulness and
a point of reassurance for humanity since the first computers were created. The
semantic web is the first step toward removing that barrier, enabling computers
to operate based on conceptual understanding, and AI and ML are the second.
Both semantic knowledge and the ability to learn are fundamental to web3, as
are blockchain, decentralization, transactional transparency, and ownership.
Web3 is the next generational step in the information age, where the web
evolves into a more digestible medium for users and machines to browse
knowledge. The slow introduction of Web3 across the global software ecosystem
will impact the people who enable the current iteration. This evolution of the
internet space will expand the way knowledge is shared, consumed, and owned,
which will lessen the requirement for a global standard and allow data to
interact efficiently, no matter the construction of the knowledge. The heart of
this paper understands the: 1) Enablement of Web3 across the digital ecosystem.
2) What a Web3 developer will look like. 3) How this alteration will evolve the
market around software and knowledge in general.

Function-level binary code similarity detection is essential in the field of
cyberspace security. It helps us find bugs and detect patent infringements in
released software and plays a key role in the prevention of supply chain
attacks. A practical embedding learning framework relies on the robustness of
vector representation system of assembly code and the accuracy of the
annotation of function pairs. Supervised learning based methods are
traditionally emploied. But annotating different function pairs with accurate
labels is very difficult. These supervised learning methods are easily
overtrained and suffer from vector robustness issues. To mitigate these
problems, we propose Fun2Vec: a contrastive learning framework of
function-level representation for binary. We take an unsupervised learning
approach and formulate the binary code similarity detection as instance
discrimination. Fun2Vec works directly on disassembled binary functions, and
could be implemented with any encoder. It does not require manual labeled
similar or dissimilar information. We use the compiler optimization options and
code obfuscation techniques to generate augmented data. Our experimental
results demonstrate that our method surpasses the state-of-the-art in accuracy
and have great advantage in few-shot settings.

Probabilistic (Bayesian) modeling has experienced a surge of applications in
almost all quantitative sciences and industrial areas. This development is
driven by a combination of several factors, including better probabilistic
estimation algorithms, flexible software, increased computing power, and a
growing awareness of the benefits of probabilistic learning. However, a
principled Bayesian model building workflow is far from complete and many
challenges remain. To aid future research and applications of a principled
Bayesian workflow, we ask and provide answers for what we perceive as two
fundamental questions of Bayesian modeling, namely (a) "What actually is a
Bayesian model?" and (b) "What makes a good Bayesian model?". As an answer to
the first question, we propose the PAD model taxonomy that defines four basic
kinds of Bayesian models, each representing some combination of the assumed
joint distribution of all (known or unknown) variables (P), a posterior
approximator (A), and training data (D). As an answer to the second question,
we propose ten utility dimensions according to which we can evaluate Bayesian
models holistically, namely, (1) causal consistency, (2) parameter
recoverability, (3) predictive performance, (4) fairness, (5) structural
faithfulness, (6) parsimony, (7) interpretability, (8) convergence, (9)
estimation speed, and (10) robustness. Further, we propose two example utility
decision trees that describe hierarchies and trade-offs between utilities
depending on the inferential goals that drive model building and testing.

On-road obstacle detection is an important field of research that falls in
the scope of intelligent transportation infrastructure systems. The use of
vision-based approaches results in an accurate and cost-effective solution to
such systems. In this research paper, we propose a threat detection mechanism
for autonomous self-driving cars using dashcam videos to ensure the presence of
any unwanted obstacle on the road that falls within its visual range. This
information can assist the vehicle's program to en route safely. There are four
major components, namely, YOLO to identify the objects, advanced lane detection
algorithm, multi regression model to measure the distance of the object from
the camera, the two-second rule for measuring the safety, and limiting speed.
In addition, we have used the Car Crash Dataset(CCD) for calculating the
accuracy of the model. The YOLO algorithm gives an accuracy of around 93%. The
final accuracy of our proposed Threat Detection Model (TDM) is 82.65%.

Apprenticeship learning is a framework in which an agent learns a policy to
perform a given task in an environment using example trajectories provided by
an expert. In the real world, one might have access to expert trajectories in
different environments where the system dynamics is different while the
learning task is the same. For such scenarios, two types of learning objectives
can be defined. One where the learned policy performs very well in one specific
environment and another when it performs well across all environments. To
balance these two objectives in a principled way, our work presents the cross
apprenticeship learning (CAL) framework. This consists of an optimization
problem where an optimal policy for each environment is sought while ensuring
that all policies remain close to each other. This nearness is facilitated by
one tuning parameter in the optimization problem. We derive properties of the
optimizers of the problem as the tuning parameter varies. Since the problem is
nonconvex, we provide a convex outer approximation. Finally, we demonstrate the
attributes of our framework in the context of a navigation task in a windy
gridworld environment.

We present a machine learning-based mesh refinement technique for steady and
unsteady flows. The clustering technique proposed by Otmani et al.
arXiv:2207.02929 [physics.flu-dyn] is used to mark the viscous and turbulent
regions for the flow past a cylinder at Re=40 (steady laminar flow) and Re=3900
(unsteady turbulent flow). Within this clustered region, we increase the
polynomial order to show that it is possible to obtain similar levels of
accuracy to a uniformly refined mesh. The method is effective as the clustering
successfully identifies the two flow regions, a viscous/turbulent dominated
region (including the boundary layer and wake) and an inviscid/irrotational
region (a potential flow region). The data used within this framework are
generated using a high-order discontinuous Galerkin solver, allowing to locally
refine the polynomial order (p-refinement) in each element of the clustered
region. For the steady laminar test case we are able to reduce the
computational cost up to 32% and for the unsteady turbulent case up to 33%.

Scene-text image synthesis techniques aimed at naturally composing text
instances on background scene images are very appealing for training deep
neural networks because they can provide accurate and comprehensive annotation
information. Prior studies have explored generating synthetic text images on
two-dimensional and three-dimensional surfaces based on rules derived from
real-world observations. Some of these studies have proposed generating
scene-text images from learning; however, owing to the absence of a suitable
training dataset, unsupervised frameworks have been explored to learn from
existing real-world data, which may not result in a robust performance. To ease
this dilemma and facilitate research on learning-based scene text synthesis, we
propose DecompST, a real-world dataset prepared using public benchmarks, with
three types of annotations: quadrilateral-level BBoxes, stroke-level text
masks, and text-erased images. Using the DecompST dataset, we propose an image
synthesis engine that includes a text location proposal network (TLPNet) and a
text appearance adaptation network (TAANet). TLPNet first predicts the suitable
regions for text embedding. TAANet then adaptively changes the geometry and
color of the text instance according to the context of the background. Our
comprehensive experiments verified the effectiveness of the proposed method for
generating pretraining data for scene text detectors.

According to the Complementary Learning Systems (CLS) theory
\cite{mcclelland1995there} in neuroscience, humans do effective \emph{continual
learning} through two complementary systems: a fast learning system centered on
the hippocampus for rapid learning of the specifics, individual experiences;
and a slow learning system located in the neocortex for the gradual acquisition
of structured knowledge about the environment. Motivated by this theory, we
propose \emph{DualNets} (for Dual Networks), a general continual learning
framework comprising a fast learning system for supervised learning of
pattern-separated representation from specific tasks and a slow learning system
for representation learning of task-agnostic general representation via
Self-Supervised Learning (SSL). DualNets can seamlessly incorporate both
representation types into a holistic framework to facilitate better continual
learning in deep neural networks. Via extensive experiments, we demonstrate the
promising results of DualNets on a wide range of continual learning protocols,
ranging from the standard offline, task-aware setting to the challenging
online, task-free scenario. Notably, on the CTrL \cite{veniat2020efficient}
benchmark that has unrelated tasks with vastly different visual images,
DualNets can achieve competitive performance with existing state-of-the-art
dynamic architecture strategies \cite{ostapenko2021continual}. Furthermore, we
conduct comprehensive ablation studies to validate DualNets efficacy,
robustness, and scalability. Code is publicly available at
\url{https://github.com/phquang/DualNet}.

Insect outbreaks are biotic disturbances in forests and agroecosystems that
cause economic and ecological damage. This phenomenon depends on a variety of
biological and physical factors. The complexity and practical importance of the
issue have made the problem of predicting outbreaks a focus of recent research.
Here, we propose the Pattern-Based Prediction (PBP) method for predicting
population outbreaks. It is based on the Alert Zone Procedure, combined with
elements from machine learning. It uses information on previous time series
values that precede an outbreak event as predictors of future outbreaks, which
can be useful when monitoring pest species. We illustrate the methodology using
simulated datasets and real time series data obtained by monitoring aphids in
wheat crops in Southern Brazil. We obtained an average test accuracy of
$84.6\%$ in the simulation studies implemented with stochastic models, and
$95.0\%$ for predicting outbreaks using the real dataset. This shows the
feasibility of the PBP method in predicting outbreaks in population dynamics.
We benchmarked our results against established state-of-the-art machine
learning methods, namely Support Vector Machines, Deep Neural Networks, Long
Short Term Memory and Random Forests. The PBP method yielded a competitive
performance, associated with higher true-positive rates in most comparisons,
while being able to provide interpretability rather than being a black-box
method. This is an improvement over current state-of-the-art machine learning
tools, especially when being used by non-specialists, such as ecologists aiming
to use a quantitative approach for pest monitoring. We provide open-source code
to implement the PBP method in Python, through the \texttt{pypbp} package,
which may be directly downloaded from the Python Package Index server or
accessed through \url{https://pypbp-documentation.readthedocs.io}

Text information including extensive prior knowledge about land cover classes
has been ignored in hyperspectral image classification (HSI) tasks. It is
necessary to explore the effectiveness of linguistic mode in assisting HSI
classification. In addition, the large-scale pre-training image-text foundation
models have demonstrated great performance in a variety of downstream
applications, including zero-shot transfer. However, most domain generalization
methods have never addressed mining linguistic modal knowledge to improve the
generalization performance of model. To compensate for the inadequacies listed
above, a Language-aware Domain Generalization Network (LDGnet) is proposed to
learn cross-domain invariant representation from cross-domain shared prior
knowledge. The proposed method only trains on the source domain (SD) and then
transfers the model to the target domain (TD). The dual-stream architecture
including image encoder and text encoder is used to extract visual and
linguistic features, in which coarse-grained and fine-grained text
representations are designed to extract two levels of linguistic features.
Furthermore, linguistic features are used as cross-domain shared semantic
space, and visual-linguistic alignment is completed by supervised contrastive
learning in semantic space. Extensive experiments on three datasets demonstrate
the superiority of the proposed method when compared with state-of-the-art

Large transformer models display promising performance on a wide range of
natural language processing (NLP) tasks. Although the AI community has expanded
the model scale to the trillion parameter level, the practical deployment of
10-100 billion parameter models is still uncertain due to the latency,
throughput, and memory constraints.
  In this paper, we proposed EnergonAI to solve the challenges of the efficient
deployment of 10-100 billion parameter transformer models on single- or
multi-GPU systems. EnergonAI adopts a hierarchy-controller system architecture
to coordinate multiple devices and efficiently support different parallel
patterns. It delegates the execution of sub-models to multiple workers in the
single-controller style and applies tensor parallelism and pipeline parallelism
among the workers in a multi-controller style. Upon the novel architecture, we
propose three techniques, i.e. non-blocking pipeline parallelism, distributed
redundant computation elimination, and peer memory pooling. EnergonAI enables
the users to program complex parallel code the same as a serial one. Compared
with the FasterTransformer, we have proven that EnergonAI has superior
performance on latency and throughput. In our experiments, EnergonAI can
achieve 37% latency reduction in tensor parallelism, 10% scalability
improvement in pipeline parallelism, and it improves the model scale inferred
on a single GPU by using a larger heterogeneous memory space at cost of limited
performance reduction.

Object detection is the foundation of various critical computer-vision tasks
such as segmentation, object tracking, and event detection. To train an object
detector with satisfactory accuracy, a large amount of data is required.
However, due to the intensive workforce involved with annotating large
datasets, such a data curation task is often outsourced to a third party or
relied on volunteers. This work reveals severe vulnerabilities of such data
curation pipeline. We propose MACAB that crafts clean-annotated images to
stealthily implant the backdoor into the object detectors trained on them even
when the data curator can manually audit the images. We observe that the
backdoor effect of both misclassification and the cloaking are robustly
achieved in the wild when the backdoor is activated with inconspicuously
natural physical triggers. Backdooring non-classification object detection with
clean-annotation is challenging compared to backdooring existing image
classification tasks with clean-label, owing to the complexity of having
multiple objects within each frame, including victim and non-victim objects.
The efficacy of the MACAB is ensured by constructively i abusing the
image-scaling function used by the deep learning framework, ii incorporating
the proposed adversarial clean image replica technique, and iii combining
poison data selection criteria given constrained attacking budget. Extensive
experiments demonstrate that MACAB exhibits more than 90% attack success rate
under various real-world scenes. This includes both cloaking and
misclassification backdoor effect even restricted with a small attack budget.
The poisoned samples cannot be effectively identified by state-of-the-art
detection techniques.The comprehensive video demo is at
https://youtu.be/MA7L_LpXkp4, which is based on a poison rate of 0.14% for
YOLOv4 cloaking backdoor and Faster R-CNN misclassification backdoor.

In this manuscript, we argue for the need to further look at search as
learning (SAL) with children as the primary stakeholders. Inspired by how
children learn and considering the classroom (regardless of the teaching
modality) as a natural educational ecosystem, we posit that scaffolding is the
tie that can simultaneously allow for learning to search while searching for
learning. The main contribution of this work is a list of open challenges
focused on the primary school classroom for the IR community to consider when
setting up to explore and make progress on SAL research with and for children
and beyond.

Self-supervised methods have shown tremendous success in the field of
computer vision, including applications in remote sensing and medical imaging.
Most popular contrastive-loss based methods like SimCLR, MoCo, MoCo-v2 use
multiple views of the same image by applying contrived augmentations on the
image to create positive pairs and contrast them with negative examples.
Although these techniques work well, most of these techniques have been tuned
on ImageNet (and similar computer vision datasets). While there have been some
attempts to capture a richer set of deformations in the positive samples, in
this work, we explore a promising alternative to generating positive examples
for remote sensing data within the contrastive learning framework. Images
captured from different sensors at the same location and nearby timestamps can
be thought of as strongly augmented instances of the same scene, thus removing
the need to explore and tune a set of hand crafted strong augmentations. In
this paper, we propose a simple dual-encoder framework, which is pre-trained on
a large unlabeled dataset (~1M) of Sentinel-1 and Sentinel-2 image pairs. We
test the embeddings on two remote sensing downstream tasks: flood segmentation
and land cover mapping, and empirically show that embeddings learnt from this
technique outperform the conventional technique of collecting positive examples
via aggressive data augmentations.

Generating highly detailed, complex data is a long-standing and frequently
considered problem in the machine learning field. However, developing
detail-aware generators remains an challenging and open problem. Generative
adversarial networks are the basis of many state-of-the-art methods. However,
they introduce a second network to be trained as a loss function, making the
interpretation of the learned functions much more difficult. As an alternative,
we present a new method based on a wavelet loss formulation, which remains
transparent in terms of what is optimized. The wavelet-based loss function is
used to overcome the limitations of conventional distance metrics, such as L1
or L2 distances, when it comes to generate data with high-frequency details. We
show that our method can successfully reconstruct high-frequency details in an
illustrative synthetic test case. Additionally, we evaluate the performance
when applied to more complex surfaces based on physical simulations. Taking a
roughly approximated simulation as input, our method infers corresponding
spatial details while taking into account how they evolve. We consider this
problem in terms of spatial and temporal frequencies, and leverage generative
networks trained with our wavelet loss to learn the desired spatio-temporal
signal for the surface dynamics. We test the capabilities of our method with a
set of synthetic wave function tests and complex 2D and 3D dynamics of
elasto-plastic materials.

In the (special) smoothing spline problem one considers a variational problem
with a quadratic data fidelity penalty and Laplacian regularisation. Higher
order regularity can be obtained via replacing the Laplacian regulariser with a
poly-Laplacian regulariser. The methodology is readily adapted to graphs and
here we consider graph poly-Laplacian regularisation in a fully supervised,
non-parametric, noise corrupted, regression problem. In particular, given a
dataset $\{x_i\}_{i=1}^n$ and a set of noisy labels
$\{y_i\}_{i=1}^n\subset\mathbb{R}$ we let $u_n:\{x_i\}_{i=1}^n\to\mathbb{R}$ be
the minimiser of an energy which consists of a data fidelity term and an
appropriately scaled graph poly-Laplacian term. When $y_i = g(x_i)+\xi_i$, for
iid noise $\xi_i$, and using the geometric random graph, we identify (with high
probability) the rate of convergence of $u_n$ to $g$ in the large data limit
$n\to\infty$. Furthermore, our rate, up to logarithms, coincides with the known
rate of convergence in the usual smoothing spline model.

Today, computer systems hold large amounts of personal data. Yet while such
an abundance of data allows breakthroughs in artificial intelligence, and
especially machine learning (ML), its existence can be a threat to user
privacy, and it can weaken the bonds of trust between humans and AI. Recent
regulations now require that, on request, private information about a user must
be removed from both computer systems and from ML models, i.e. ``the right to
be forgotten''). While removing data from back-end databases should be
straightforward, it is not sufficient in the AI context as ML models often
`remember' the old data. Contemporary adversarial attacks on trained models
have proven that we can learn whether an instance or an attribute belonged to
the training data. This phenomenon calls for a new paradigm, namely machine
unlearning, to make ML models forget about particular data. It turns out that
recent works on machine unlearning have not been able to completely solve the
problem due to the lack of common frameworks and resources. Therefore, this
paper aspires to present a comprehensive examination of machine unlearning's
concepts, scenarios, methods, and applications. Specifically, as a category
collection of cutting-edge studies, the intention behind this article is to
serve as a comprehensive resource for researchers and practitioners seeking an
introduction to machine unlearning and its formulations, design criteria,
removal requests, algorithms, and applications. In addition, we aim to
highlight the key findings, current trends, and new research areas that have
not yet featured the use of machine unlearning but could benefit greatly from
it. We hope this survey serves as a valuable resource for ML researchers and
those seeking to innovate privacy technologies. Our resources are publicly
available at https://github.com/tamlhp/awesome-machine-unlearning.

The accurate and personalized estimation of task difficulty provides many
opportunities for optimizing user experience. However, user diversity makes
such difficulty estimation hard, in that empirical measurements from some user
sample do not necessarily generalize to others. In this paper, we contribute a
new approach for personalized difficulty estimation of game levels, borrowing
methods from content recommendation. Using factorization machines (FM) on a
large dataset from a commercial puzzle game, we are able to predict difficulty
as the number of attempts a player requires to pass future game levels, based
on observed attempt counts from earlier levels and levels played by others. In
addition to performance and scalability, FMs offer the benefit that the learned
latent variable model can be used to study the characteristics of both players
and game levels that contribute to difficulty. We compare the approach to a
simple non-personalized baseline and a personalized prediction using Random
Forests. Our results suggest that FMs are a promising tool enabling game
designers to both optimize player experience and learn more about their players
and the game.

As aspect-level sentiment labels are expensive and labor-intensive to
acquire, zero-shot aspect-level sentiment classification is proposed to learn
classifiers applicable to new domains without using any annotated aspect-level
data. In contrast, document-level sentiment data with ratings are more easily
accessible. In this work, we achieve zero-shot aspect-level sentiment
classification by only using document-level reviews. Our key intuition is that
the sentiment representation of a document is composed of the sentiment
representations of all the aspects of that document. Based on this, we propose
the AF-DSC method to explicitly model such sentiment composition in reviews.
AF-DSC first learns sentiment representations for all potential aspects and
then aggregates aspect-level sentiments into a document-level one to perform
document-level sentiment classification. In this way, we obtain the
aspect-level sentiment classifier as the by-product of the document-level
sentiment classifier. Experimental results on aspect-level sentiment
classification benchmarks demonstrate the effectiveness of explicit utilization
of sentiment composition in document-level sentiment classification. Our model
with only 30k training data outperforms previous work utilizing millions of
data.

This paper proposes a novel non-intrusive system failure prediction technique
using available information from developers and minimal information from raw
logs (rather than mining entire logs) but keeping the data entirely private
with the data owners. A neural network based multi-class classifier is
developed for failure prediction, using artificially generated anonymous data
set, applying a combination of techniques, viz., genetic algorithm (steps),
pattern repetition, etc., to train and test the network. The proposed mechanism
completely decouples the data set used for training process from the actual
data which is kept private. Moreover, multi-criteria decision making (MCDM)
schemes are used to prioritize failures meeting business requirements. Results
show high accuracy in failure prediction under different parameter
configurations. On a broader context, any classification problem, beyond
failure prediction, can be performed using the proposed mechanism with
artificially generated data set without looking into the actual data as long as
the input features can be translated to binary values (e.g. output from private
binary classifiers) and can provide classification-as-a-service.

We introduce a novel technique and an associated high resolution dataset that
aims to precisely evaluate wireless signal based indoor positioning algorithms.
The technique implements an augmented reality (AR) based positioning system
that is used to annotate the wireless signal parameter data samples with high
precision position data. We track the position of a practical and low cost
navigable setup of cameras and a Bluetooth Low Energy (BLE) beacon in an area
decorated with AR markers. We maximize the performance of the AR-based
localization by using a redundant number of markers. Video streams captured by
the cameras are subjected to a series of marker recognition, subset selection
and filtering operations to yield highly precise pose estimations. Our results
show that we can reduce the positional error of the AR localization system to a
rate under 0.05 meters. The position data are then used to annotate the BLE
data that are captured simultaneously by the sensors stationed in the
environment, hence, constructing a wireless signal data set with the ground
truth, which allows a wireless signal based localization system to be evaluated
accurately.

Understanding the intention of the users and recognizing the semantic
entities from their sentences, aka natural language understanding (NLU), is the
upstream task of many natural language processing tasks. One of the main
challenges is to collect a sufficient amount of annotated data to train a
model. Existing research about text augmentation does not abundantly consider
entity and thus performs badly for NLU tasks. To solve this problem, we propose
a novel NLP data augmentation technique, Entity Aware Data Augmentation (EADA),
which applies a tree structure, Entity Aware Syntax Tree (EAST), to represent
sentences combined with attention on the entity. Our EADA technique
automatically constructs an EAST from a small amount of annotated data, and
then generates a large number of training instances for intent detection and
slot filling. Experimental results on four datasets showed that the proposed
technique significantly outperforms the existing data augmentation methods in
terms of both accuracy and generalization ability.

Beamforming technique realized by the multiple-input-multiple-output (MIMO)
antenna arrays has been widely used to compensate for the severe path loss in
the millimeter wave (mmWave) bands. In 5G NR system, the beam sweeping and beam
refinement are employed to find out the best beam codeword aligned to the
mobile. Due to the complicated handshaking and finite resolution of the
codebook, today's 5G-based beam management strategy is ineffective in various
scenarios in terms of the data rate, energy consumption, and also processing
latency. An aim of this article is to introduce a new type of beam management
framework based on the computer vision (CV) technique. In this framework
referred to as computer vision-aided beam management (CVBM), a camera attached
to the BS captures the image and then the deep learning-based object detector
identifies the 3D location of the mobile. Since the base station can directly
set the beam direction without codebook quantization and feedback delay, CVBM
achieves the significant beamforming gain and latency reduction. Using the
specially designed dataset called Vision Objects for Beam Management (VOBEM),
we demonstrate that CVBM achieves more than 40% improvement in the beamforming
gain and 40% reduction in the beam training overhead over the 5G NR beam

Federated learning (FL) is a subfield of machine learning where multiple
clients try to collaboratively learn a model over a network under communication
constraints. We consider finite-sum federated optimization under a second-order
function similarity condition and strong convexity, and propose two new
algorithms: SVRP and Catalyzed SVRP. This second-order similarity condition has
grown popular recently, and is satisfied in many applications including
distributed statistical learning and differentially private empirical risk
minimization. The first algorithm, SVRP, combines approximate stochastic
proximal point evaluations, client sampling, and variance reduction. We show
that SVRP is communication efficient and achieves superior performance to many
existing algorithms when function similarity is high enough. Our second
algorithm, Catalyzed SVRP, is a Catalyst-accelerated variant of SVRP that
achieves even better performance and uniformly improves upon existing
algorithms for federated optimization under second-order similarity and strong
convexity. In the course of analyzing these algorithms, we provide a new
analysis of the Stochastic Proximal Point Method (SPPM) that might be of
independent interest. Our analysis of SPPM is simple, allows for approximate
proximal point evaluations, does not require any smoothness assumptions, and
shows a clear benefit in communication complexity over ordinary distributed
stochastic gradient descent.

We present an approach for interpreting a black-box alarming system for
forecasting accidents and anomalies during the drilling of oil and gas wells.
The interpretation methodology aims to explain the local behavior of the
accident predictive model to drilling engineers. The explanatory model uses
Shapley additive explanations analysis of features, obtained through
Bag-of-features representation of telemetry logs used during the drilling
accident forecasting phase. Validation shows that the explanatory model has 15%
precision at 70% recall, and overcomes the metric values of a random baseline
and multi-head attention neural network. These results justify that the
developed explanatory model is better aligned with explanations of drilling
engineers, than the state-of-the-art method. The joint performance of
explanatory and Bag-of-features models allows drilling engineers to understand
the logic behind the system decisions at the particular moment, pay attention
to highlighted telemetry regions, and correspondingly, increase the trust level
in the accident forecasting alarms.

Emotion Classification through EEG signals has achieved many advancements.
However, the problems like lack of data and learning the important features and
patterns have always been areas with scope for improvement both computationally
and in prediction accuracy. This works analyses the baseline machine learning
classifiers' performance on DEAP Dataset along with a tabular learning approach
that provided state-of-the-art comparable results leveraging the performance
boost due to its deep learning architecture without deploying heavy neural
networks.

Renal structure segmentation from computed tomography angiography~(CTA) is
essential for many computer-assisted renal cancer treatment applications.
Kidney PArsing~(KiPA 2022) Challenge aims to build a fine-grained
multi-structure dataset and improve the segmentation of multiple renal
structures. Recently, U-Net has dominated the medical image segmentation. In
the KiPA challenge, we evaluated several U-Net variants and selected the best
models for the final submission.

Koopman operators globally linearize nonlinear dynamical systems and their
spectral information is a powerful tool for the analysis and decomposition of
nonlinear dynamical systems. However, Koopman operators are
infinite-dimensional, and computing their spectral information is a
considerable challenge. We introduce measure-preserving extended dynamic mode
decomposition ($\texttt{mpEDMD}$), the first truncation method whose
eigendecomposition converges to the spectral quantities of Koopman operators
for general measure-preserving dynamical systems. $\texttt{mpEDMD}$ is a
data-driven algorithm based on an orthogonal Procrustes problem that enforces
measure-preserving truncations of Koopman operators using a general dictionary
of observables. It is flexible and easy to use with any pre-existing DMD-type
method, and with different types of data. We prove convergence of
$\texttt{mpEDMD}$ for projection-valued and scalar-valued spectral measures,
spectra, and Koopman mode decompositions. For the case of delay embedding
(Krylov subspaces), our results include the first convergence rates of the
approximation of spectral measures as the size of the dictionary increases. We
demonstrate $\texttt{mpEDMD}$ on a range of challenging examples, its increased
robustness to noise compared with other DMD-type methods, and its ability to
capture the energy conservation and cascade of experimental measurements of a
turbulent boundary layer flow with Reynolds number $> 6\times 10^4$ and
state-space dimension $>10^5$.

In stockbreeding of beef cattle, computer vision-based approaches have been
widely employed to monitor cattle conditions (e.g. the physical, physiology,
and health). To this end, the accurate and effective recognition of cattle
action is a prerequisite. Generally, most existing models are confined to
individual behavior that uses video-based methods to extract spatial-temporal
features for recognizing the individual actions of each cattle. However, there
is sociality among cattle and their interaction usually reflects important
conditions, e.g. estrus, and also video-based method neglects the real-time
capability of the model. Based on this, we tackle the challenging task of
real-time recognizing interactions between cattle in a single frame in this
paper. The pipeline of our method includes two main modules: Cattle
Localization Network and Interaction Recognition Network. At every moment,
cattle localization network outputs high-quality interaction proposals from
every detected cattle and feeds them into the interaction recognition network
with a triple-stream architecture. Such a triple-stream network allows us to
fuse different features relevant to recognizing interactions. Specifically, the
three kinds of features are a visual feature that extracts the appearance
representation of interaction proposals, a geometric feature that reflects the
spatial relationship between cattle, and a semantic feature that captures our
prior knowledge of the relationship between the individual action and
interaction of cattle. In addition, to solve the problem of insufficient
quantity of labeled data, we pre-train the model based on self-supervised
learning. Qualitative and quantitative evaluation evidences the performance of
our framework as an effective method to recognize cattle interaction in real
time.

Source code documentation is an important artifact for efficient software
development. Code documentation could greatly benefit from automation since
manual documentation is often labouring, resource and time-intensive. In this
paper, we employed Codex for automatic code documentation creation. Codex is a
GPT-3 based model pre-trained on both natural and programming languages. We
find that Codex outperforms existing techniques even with basic settings like
one-shot learning (i.e., providing only one example for training). Codex
achieves an overall BLEU score of 20.6 for six different programming languages
(11.2% improvement over earlier state-of-the-art techniques). Thus, Codex shows
promise and warrants in-depth future studies for automatic code documentation
generation to support diverse development tasks.

The recent advances of conversational recommendations provide a promising way
to efficiently elicit users' preferences via conversational interactions. To
achieve this, the recommender system conducts conversations with users, asking
their preferences for different items or item categories. Most existing
conversational recommender systems for cold-start users utilize a multi-armed
bandit framework to learn users' preference in an online manner. However, they
rely on a pre-defined conversation frequency for asking about item categories
instead of individual items, which may incur excessive conversational
interactions that hurt user experience. To enable more flexible questioning
about key-terms, we formulate a new conversational bandit problem that allows
the recommender system to choose either a key-term or an item to recommend at
each round and explicitly models the rewards of these actions. This motivates
us to handle a new exploration-exploitation (EE) trade-off between key-term
asking and item recommendation, which requires us to accurately model the
relationship between key-term and item rewards. We conduct a survey and analyze
a real-world dataset to find that, unlike assumptions made in prior works,
key-term rewards are mainly affected by rewards of representative items. We
propose two bandit algorithms, Hier-UCB and Hier-LinUCB, that leverage this
observed relationship and the hierarchical structure between key-terms and
items to efficiently learn which items to recommend. We theoretically prove
that our algorithm can reduce the regret bound's dependency on the total number
of items from previous work. We validate our proposed algorithms and regret
bound on both synthetic and real-world data.

Octree-based point cloud representation and compression have been adopted by
the MPEG G-PCC standard. However, it only uses handcrafted methods to predict
the probability that a leaf node is non-empty, which is then used for entropy
coding. We propose a novel approach for predicting such probabilities for
geometry coding, which applies a denoising neural network to a "noisy" context
cube that includes both neighboring decoded voxels as well as uncoded voxels.
We further propose a convolution-based model to upsample the decoded point
cloud at a coarse resolution on the decoder side. Integration of the two
approaches significantly improves the rate-distortion performance for geometry
coding compared to the original G-PCC standard and other baseline methods for
dense point clouds. The proposed octree-based entropy coding approach is
naturally scalable, which is desirable for dynamic rate adaptation in point
cloud streaming systems.

The problem of self-tuning control of cooperative manipulators forming a
closed kinematic chain in the presence of an inaccurate kinematics model is
addressed using adaptive machine learning. The kinematic parameters pertaining
to the relative position/orientation uncertainties of the interconnected
manipulators are updated online by two cascaded estimators in order to tune a
cooperative controller for achieving accurate motion tracking with minimum-norm
actuation force. This technique permits accurate calibration of the relative
kinematics of the involved manipulators without needing high precision
end-point sensing or force measurements, and hence it is economically
justified. Investigating the stability of the entire real-time
estimator/controller system reveals that the convergence and stability of the
adaptive control process can be ensured if i) the direction of the angular
velocity vector does not remain constant over time, and ii) the initial
kinematic parameter error is upper bounded by a scaler function of some known
parameters. The adaptive controller is proved to be singularity-free even
though the control law involves inverting the approximation of a matrix
computed at the estimated parameters. Experimental results demonstrate the
sensitivity of the tracking performance of the conventional inverse dynamic
control scheme to kinematic inaccuracies, while the tracking error is
significantly reduced by the self-tuning cooperative controller.

Underwater images are inevitably affected by color distortion and reduced
contrast. Traditional statistic-based methods such as white balance and
histogram stretching attempted to adjust the imbalance of color channels and
narrow distribution of intensities a priori thus with limited performance.
Recently, deep-learning-based methods have achieved encouraging results.
However, the involved complicate architecture and high computational costs may
hinder their deployment in practical constrained platforms. Inspired by above
works, we propose a statistically guided lightweight underwater image
enhancement network (USLN). Concretely, we first develop a dual-statistic white
balance module which can learn to use both average and maximum of images to
compensate the color distortion for each specific pixel. Then this is followed
by a multi-color space stretch module to adjust the histogram distribution in
RGB, HSI, and Lab color spaces adaptively. Extensive experiments show that,
with the guidance of statistics, USLN significantly reduces the required
network capacity (over98%) and achieves state-of-the-art performance. The code
and relevant resources are available at https://github.com/deepxzy/USLN.

Reference resolution, which aims to identify entities being referred to by a
speaker, is more complex in real world settings: new referents may be created
by processes the agents engage in and/or be salient only because they belong to
the shared physical setting. Our focus is on resolving references to
visualizations on a large screen display in multimodal dialogue; crucially,
reference resolution is directly involved in the process of creating new
visualizations. We describe our annotations for user references to
visualizations appearing on a large screen via language and hand gesture and
also new entity establishment, which results from executing the user request to
create a new visualization. We also describe our reference resolution pipeline
which relies on an information-state architecture to maintain dialogue context.
We report results on detecting and resolving references, effectiveness of
contextual information on the model, and under-specified requests for creating
visualizations. We also experiment with conventional CRF and deep learning /
transformer models (BiLSTM-CRF and BERT-CRF) for tagging references in user
utterance text. Our results show that transfer learning significantly boost
performance of the deep learning methods, although CRF still out-performs them,
suggesting that conventional methods may generalize better for low resource
data.

Reliable and fast channel estimation is crucial for next-generation wireless
networks supporting a wide range of vehicular and low-latency services.
Recently, deep learning (DL) based channel estimation is being explored as an
efficient alternative to conventional least-square (LS) and linear minimum mean
square error (LMMSE) based channel estimation. Unlike LMMSE, DL methods do not
need prior knowledge of channel statistics. Most of these approaches have not
been realized on system-on-chip (SoC), and preliminary study shows that their
complexity exceeds the complexity of the entire physical layer (PHY). The high
latency of DL is another concern. This paper considers the design and
implementation of deep neural network (DNN) augmented LS-based channel
estimation (LSDNN) on Zynq multi-processor SoC (ZMPSoC). We demonstrate the
gain in performance compared to the conventional LS and LMMSE channel
estimation schemes. Via software-hardware co-design, word-length optimization,
and reconfigurable architectures, we demonstrate the superiority of the LSDNN
architecture over the LS and LMMSE for a wide range of SNR, number of pilots,
preamble types, and wireless channels. Further, we evaluate the performance,
power, and area (PPA) of the LS and LSDNN application-specific integrated
circuit (ASIC) implementations in 45 nm technology. We demonstrate that the
word-length optimization can substantially improve PPA for the proposed
architecture in ASIC implementations.

This paper introduces the informational multi-armed bandit (IMAB) model in
which at each round, a player chooses an arm, observes a symbol, and receives
an unobserved reward in the form of the symbol's self-information. Thus, the
expected reward of an arm is the Shannon entropy of the probability mass
function of the source that generates its symbols. The player aims to maximize
the expected total reward associated with the entropy values of the arms
played. Under the assumption that the alphabet size is known, two UCB-based
algorithms are proposed for the IMAB model which consider the biases of the
plug-in entropy estimator. The first algorithm optimistically corrects the bias
term in the entropy estimation. The second algorithm relies on data-dependent
confidence intervals that adapt to sources with small entropy values.
Performance guarantees are provided by upper bounding the expected regret of
each of the algorithms. Furthermore, in the Bernoulli case, the asymptotic
behavior of these algorithms is compared to the Lai-Robbins lower bound for the
pseudo regret. Additionally, under the assumption that the \textit{exact}
alphabet size is unknown, and instead the player only knows a loose upper bound
on it, a UCB-based algorithm is proposed, in which the player aims to reduce
the regret caused by the unknown alphabet size in a finite time regime.
Numerical results illustrating the expected regret of the algorithms presented
in the paper are provided.

Humans are talented with the ability to perform diverse interactions in the
teaching process. However, when humans want to teach AI, existing interactive
systems only allow humans to perform repetitive labeling, causing an
unsatisfactory teaching experience. My Ph.D. research studies Interactive
Machine Teaching (IMT), an emerging field of HCI research that aims to enhance
humans' teaching experience in the AI creation process. My research builds IMT
systems that exploit and guide user interaction and shows that such in-depth
integration of human interaction can benefit both AI models and user
experience.

Event argument extraction (EAE) has been well studied at the sentence level
but under-explored at the document level. In this paper, we study to capture
event arguments that actually spread across sentences in documents. Prior works
mainly assume full access to rich document supervision, ignoring the fact that
the argument supervision is limited in documents. To fill this gap, we present
FewDocAE, a Few-Shot Document-Level Event Argument Extraction benchmark, based
on the largest document-level event extraction dataset DocEE. We first define
the new problem and reconstruct the corpus by a novel N-Way-D-Doc sampling
instead of the traditional N-Way-K-Shot strategy. Then we adjust the advanced
document-level neural models into the few-shot setting to provide baseline
results under in- and cross-domain settings. Since the argument extraction
depends on the context from multiple sentences and the learning process is
limited to very few examples, we find the task to be very challenging with
substantively low performance. Considering FewDocAE is closely related to
practical use under low-resource regimes, we hope this benchmark encourages
more research in this direction. Our data and codes will be available online.

Post-training dropout based approaches achieve high sparsity and are well
established means of deciphering problems relating to computational cost and
overfitting in Neural Network architectures. Contrastingly, pruning at
initialization is still far behind. Initialization pruning is more efficacious
when it comes to scaling computation cost of the network. Furthermore, it
handles overfitting just as well as post training dropout.
  In approbation of the above reasons, the paper presents two approaches to
prune at initialization. The goal is to achieve higher sparsity while
preserving performance. 1) K-starts, begins with k random p-sparse matrices at
initialization. In the first couple of epochs the network then determines the
"fittest" of these p-sparse matrices in an attempt to find the "lottery ticket"
p-sparse network. The approach is adopted from how evolutionary algorithms find
the best individual. Depending on the Neural Network architecture, fitness
criteria can be based on magnitude of network weights, magnitude of gradient
accumulation over an epoch or a combination of both. 2) Dissipating gradients
approach, aims at eliminating weights that remain within a fraction of their
initial value during the first couple of epochs. Removing weights in this
manner despite their magnitude best preserves performance of the network.
Contrarily, the approach also takes the most epochs to achieve higher sparsity.
3) Combination of dissipating gradients and kstarts outperforms either methods
and random dropout consistently.
  The benefits of using the provided pertaining approaches are: 1) They do not
require specific knowledge of the classification task, fixing of dropout
threshold or regularization parameters 2) Retraining of the model is neither
necessary nor affects the performance of the p-sparse network.

Light field (LF) images with the multi-view property have many applications,
which can be severely affected by the low-light imaging. Recent learning-based
methods for low-light enhancement have their own disadvantages, such as no
noise suppression, complex training process and poor performance in extremely
low-light conditions. Targeted on solving these deficiencies while fully
utilizing the multi-view information, we propose an efficient Low-light
Restoration Transformer (LRT) for LF images, with multiple heads to perform
specific intermediate tasks, including denoising, luminance adjustment,
refinement and detail enhancement, within a single network, achieving
progressive restoration from small scale to full scale. We design an angular
transformer block with a view-token scheme to model the global angular
relationship efficiently, and a multi-scale window-based transformer block to
encode the multi-scale local and global spatial information. To solve the
problem of insufficient training data, we formulate a synthesis pipeline by
simulating the major noise with the estimated noise parameters of LF camera.
Experimental results demonstrate that our method can achieve superior
performance on the restoration of extremely low-light and noisy LF images with
high efficiency.

One key technical challenge in the age of autonomous machines is the
programming of autonomous machines, which demands the synergy across multiple
domains, including fundamental computer science, computer architecture, and
robotics, and requires expertise from both academia and industry. This paper
discusses the programming theory and practices tied to producing real-life
autonomous machines, and covers aspects from high-level concepts down to
low-level code generation in the context of specific functional requirements,
performance expectation, and implementation constraints of autonomous machines.

The vast network of bridges in the United States raises a high requirement
for maintenance and rehabilitation. The massive cost of manual visual
inspection to assess bridge conditions is a burden to some extent. Advanced
robots have been leveraged to automate inspection data collection. Automating
the segmentations of multiclass elements and surface defects on the elements in
the large volume of inspection image data would facilitate an efficient and
effective assessment of the bridge condition. Training separate single-task
networks for element parsing (i.e., semantic segmentation of multiclass
elements) and defect segmentation fails to incorporate the close connection
between these two tasks. Both recognizable structural elements and apparent
surface defects are present in the inspection images. This paper is motivated
to develop a multitask deep learning model that fully utilizes such
interdependence between bridge elements and defects to boost the model's task
performance and generalization. Furthermore, the study investigated the
effectiveness of the proposed model designs for improving task performance,
including feature decomposition, cross-talk sharing, and multi-objective loss
function. A dataset with pixel-level labels of bridge elements and corrosion
was developed for model training and testing. Quantitative and qualitative
results from evaluating the developed multitask deep model demonstrate its
advantages over the single-task-based model not only in performance (2.59%
higher mIoU on bridge parsing and 1.65% on corrosion segmentation) but also in
computational time and implementation capability.

We propose a novel approach to perform approximate Bayesian inference in
complex models such as Bayesian neural networks. The approach is more scalable
to large data than Markov Chain Monte Carlo, it embraces more expressive models
than Variational Inference, and it does not rely on adversarial training (or
density ratio estimation). We adopt the recent approach of constructing two
models: (1) a primary model, tasked with performing regression or
classification; and (2) a secondary, expressive (e.g. implicit) model that
defines an approximate posterior distribution over the parameters of the
primary model. However, we optimise the parameters of the posterior model via
gradient descent according to a Monte Carlo estimate of the posterior
predictive distribution -- which is our only approximation (other than the
posterior model). Only a likelihood needs to be specified, which can take
various forms such as loss functions and synthetic likelihoods, thus providing
a form of a likelihood-free approach. Furthermore, we formulate the approach
such that the posterior samples can either be independent of, or conditionally
dependent upon the inputs to the primary model. The latter approach is shown to
be capable of increasing the apparent complexity of the primary model. We see
this being useful in applications such as surrogate and physics-based models.
To promote how the Bayesian paradigm offers more than just uncertainty
quantification, we demonstrate: uncertainty quantification, multi-modality, as
well as an application with a recent deep forecasting neural network

This paper studies a policy optimization problem arising from collaborative
multi-agent reinforcement learning in a decentralized setting where agents
communicate with their neighbors over an undirected graph to maximize the sum
of their cumulative rewards. A novel decentralized natural policy gradient
method, dubbed Momentum-based Decentralized Natural Policy Gradient (MDNPG), is
proposed, which incorporates natural gradient, momentum-based variance
reduction, and gradient tracking into the decentralized stochastic gradient
ascent framework. The $\mathcal{O}(n^{-1}\epsilon^{-3})$ sample complexity for
MDNPG to converge to an $\epsilon$-stationary point has been established under
standard assumptions, where $n$ is the number of agents. It indicates that
MDNPG can achieve the optimal convergence rate for decentralized policy
gradient methods and possesses a linear speedup in contrast to centralized
optimization methods. Moreover, superior empirical performance of MDNPG over
other state-of-the-art algorithms has been demonstrated by extensive numerical

The popular methods for semi-supervised semantic segmentation mostly adopt a
unitary network model using convolutional neural networks (CNNs) and enforce
consistency of the model predictions over small perturbations applied to the
inputs or model. However, such a learning paradigm suffers from a) limited
learning capability of the CNN-based model; b) limited capacity of learning the
discriminative features for the unlabeled data; c) limited learning for both
global and local information from the whole image. In this paper, we propose a
novel Semi-supervised Learning approach, called Transformer-CNN Cohort (TCC),
that consists of two students with one based on the vision transformer (ViT)
and the other based on the CNN. Our method subtly incorporates the multi-level
consistency regularization on the predictions and the heterogeneous feature
spaces via pseudo labeling for the unlabeled data. First, as the inputs of the
ViT student are image patches, the feature maps extracted encode crucial
class-wise statistics. To this end, we propose class-aware feature consistency
distillation (CFCD) that first leverages the outputs of each student as the
pseudo labels and generates class-aware feature (CF) maps. It then transfers
knowledge via the CF maps between the students. Second, as the ViT student has
more uniform representations for all layers, we propose consistency-aware cross
distillation to transfer knowledge between the pixel-wise predictions from the
cohort. We validate the TCC framework on Cityscapes and Pascal VOC 2012
datasets, which significantly outperforms existing semi-supervised methods by a

The present world is badly affected by novel coronavirus (COVID-19). Using
medical kits to identify the coronavirus affected persons are very slow. What
happens in the next, nobody knows. The world is facing erratic problem and do
not know what will happen in near future. This paper is trying to make
prognosis of the coronavirus recovery cases using LSTM (Long Short Term
Memory). This work exploited data of 258 regions, their latitude and longitude
and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021.
Specifically, advanced deep learning-based algorithms known as the LSTM, play a
great effect on extracting highly essential features for time series data (TSD)
analysis.There are lots of methods which already use to analyze propagation
prediction. The main task of this paper culminates in analyzing the spreading
of Coronavirus across worldwide recovery cases using LSTM deep learning-based

Adversarial examples against AI systems pose both risks via malicious attacks
and opportunities for improving robustness via adversarial training. In
multiagent settings, adversarial policies can be developed by training an
adversarial agent to minimize a victim agent's rewards. Prior work has studied
black-box attacks where the adversary only sees the state observations and
effectively treats the victim as any other part of the environment. In this
work, we experiment with white-box adversarial policies to study whether an
agent's internal state can offer useful information for other agents. We make
three contributions. First, we introduce white-box adversarial policies in
which an attacker can observe a victim's internal state at each timestep.
Second, we demonstrate that white-box access to a victim makes for better
attacks in two-agent environments, resulting in both faster initial learning
and higher asymptotic performance against the victim. Third, we show that
training against white-box adversarial policies can be used to make learners in
single-agent environments more robust to domain shifts.

We consider multiobjective ranking and selection problems, where the goal is
to correctly identify the Pareto optimal solutions among a finite set of
candidates for which the multiple objective outcomes have been observed with
uncertainty (e.g., after running a multiobjective stochastic simulation
optimization procedure). When identifying these solutions, the noise perturbing
the observed performance may lead to two types of errors: solutions that are
truly Pareto-optimal can be wrongly considered dominated, and solutions that
are truly dominated can be wrongly considered Pareto-optimal. We propose a
novel Bayesian multiobjective ranking and selection method (MORS-SK) that
sequentially allocates extra samples to competitive solutions, in view of
reducing the misclassification errors when identifying the solutions with the
best expected performance. The approach uses stochastic kriging to build
reliable predictive distributions of the objective outcomes, and exploits this
information to decide how to resample. Experimental results show that the
proposed method outperforms a standard allocation method, as well as the
state-of-the-art MOCBA approach. Moreover, we show that the use of stochastic
kriging information would also benefit both the standard and the MOCBA
allocation approach; yet, MORS-SK remains superior.

We consider a network of smart sensors for edge computing application that
sample a signal of interest and send updates to a base station for remote
global monitoring. Sensors are equipped with sensing and compute, and can
either send raw data or process them on-board before transmission. Limited
hardware resources at the edge generate a fundamental latency-accuracy
trade-off: raw measurements are inaccurate but timely, whereas accurate
processed updates are available after computational delay. Also, if sensor
on-board processing entails data compression, latency caused by wireless
communication might be higher for raw measurements. Hence, one needs to decide
when sensors should transmit raw measurements or rely on local processing to
maximize overall network performance. To tackle this sensing design problem, we
model an estimation-theoretic optimization framework that embeds computation
and communication delays, and propose a Reinforcement Learning-based approach
to dynamically allocate computational resources at each sensor. Effectiveness
of our proposed approach is validated through numerical simulations with case
studies motivated by the Internet of Drones and self-driving vehicles.

This paper introduces a reinforcement learning-based tracking control
approach for a class of nonlinear systems using neural networks. In this
approach, adversarial attacks were considered both in the actuator and on the
outputs. This approach incorporates a simultaneous tracking and optimization
process. It is necessary to be able to solve the Hamilton-Jacobi-Bellman
equation (HJB) in order to obtain optimal control input, but this is difficult
due to the strong nonlinearity terms in the equation. In order to find the
solution to the HJB equation, we used a reinforcement learning approach. In
this online adaptive learning approach, three neural networks are
simultaneously adapted: the critic neural network, the actor neural network,
and the adversary neural network. Ultimately, simulation results are presented
to demonstrate the effectiveness of the introduced method on a manipulator.

Deep Learning Gaussian Processes (DL-GP) are proposed as a methodology for
analyzing (approximating) computer models that produce heteroskedastic and
high-dimensional output. Computer simulation models have many areas of
applications, including social-economic processes, agriculture, environmental,
biology, engineering and physics problems. A deterministic transformation of
inputs is performed by deep learning and predictions are calculated by
traditional Gaussian Processes. We illustrate our methodology using a
simulation of motorcycle accidents and simulations of an Ebola outbreak.
Finally, we conclude with directions for future research.

There is inadequate attention to using mobile Augmented Reality (AR) in
fitness, despite mobile AR being easy to use, requiring no extra cost, and can
be a powerful learning tool. In this work, we present a mobile AR application
that can help users learn exercises with a virtual personal trainer. We conduct
a user study with 10 participants to investigate the learning quality of the
ARFit (i.e., the proposed mobile AR application) in comparison to traditional
methods such as Image-based learning and Video-based learning. Our results
indicate that participants have a higher learning quality of exercise with
mobile AR than (1) Image-based learning among all exercises selected and (2)
video-based learning with exercise that requires greater spatial knowledge,
with the performance evaluated by a qualified personal trainer. In addition,
ARFit has an excellent rating in usability, is deemed to be highly acceptable,
and is the preferred exercise learning method by most participants (N=9)

Assistive Robotics is a class of robotics concerned with aiding humans in
daily care tasks that they may be inhibited from doing due to disabilities or
age. While research has demonstrated that classical control methods can be used
to design policies to complete these tasks, these methods can be difficult to
generalize to a variety of instantiations of a task. Reinforcement learning can
provide a solution to this issue, wherein robots are trained in simulation and
their policies are transferred to real-world machines. In this work, we
replicate a published baseline for training robots on three tasks in the
Assistive Gym environment, and we explore the usage of a Recurrent Neural
Network and Phasic Policy Gradient learning to augment the original work. Our
baseline implementation meets or exceeds the baseline of the original work,
however, we found that our explorations into the new methods was not as
effective as we anticipated. We discuss the results of our baseline and some
thoughts on why our new methods were not as successful.

Training multiple agents to perform safe and cooperative control in the
complex scenarios of autonomous driving has been a challenge. For a small fleet
of cars moving together, this paper proposes Lepus, a new approach to training
multiple agents. Lepus adopts a pure cooperative manner for training multiple
agents, featured with the shared parameters of policy networks and the shared
reward function of multiple agents. In particular, Lepus pre-trains the policy
networks via an adversarial process, improving its collaborative
decision-making capability and further the stability of car driving. Moreover,
for alleviating the problem of sparse rewards, Lepus learns an approximate
reward function from expert trajectories by combining a random network and a
distillation network. We conduct extensive experiments on the MADRaS simulation
platform. The experimental results show that multiple agents trained by Lepus
can avoid collisions as many as possible while driving simultaneously and
outperform the other four methods, that is, DDPG-FDE, PSDDPG, MADDPG, and
MAGAIL(DDPG) in terms of stability.

Smart cars, smartphones and other devices in the Internet of Things (IoT),
which usually have more than one sensors, produce multimodal data. Federated
Learning supports collecting a wealth of multimodal data from different devices
without sharing raw data. Transfer Learning methods help transfer knowledge
from some devices to others. Federated Transfer Learning methods benefit both
Federated Learning and Transfer Learning. This newly proposed Federated
Transfer Learning framework aims at connecting data islands with privacy
protection. Our construction is based on Federated Learning and Transfer
Learning. Compared with previous Federated Transfer Learnings, where each user
should have data with identical modalities (either all unimodal or all
multimodal), our new framework is more generic, it allows a hybrid distribution
of user data. The core strategy is to use two different but inherently
connected training methods for our two types of users. Supervised Learning is
adopted for users with only unimodal data (Type 1), while Self-Supervised
Learning is applied to user with multimodal data (Type 2) for both the feature
of each modality and the connection between them. This connection knowledge of
Type 2 will help Type 1 in later stages of training. Training in the new
framework can be divided in three steps. In the first step, users who have data
with the identical modalities are grouped together. For example, user with only
sound signals are in group one, and those with only images are in group two,
and users with multimodal data are in group three, and so on. In the second
step, Federated Learning is executed within the groups, where Supervised
Learning and Self-Supervised Learning are used depending on the group's nature.
Most of the Transfer Learning happens in the third step, where the related
parts in the network obtained from the previous steps are aggregated
(federated).

We present a new approach to unsupervised shape correspondence learning
between pairs of point clouds. We make the first attempt to adapt the classical
locally linear embedding algorithm (LLE) -- originally designed for nonlinear
dimensionality reduction -- for shape correspondence. The key idea is to find
dense correspondences between shapes by first obtaining high-dimensional
neighborhood-preserving embeddings of low-dimensional point clouds and
subsequently aligning the source and target embeddings using locally linear
transformations. We demonstrate that learning the embedding using a new
LLE-inspired point cloud reconstruction objective results in accurate shape
correspondences. More specifically, the approach comprises an end-to-end
learnable framework of extracting high-dimensional neighborhood-preserving
embeddings, estimating locally linear transformations in the embedding space,
and reconstructing shapes via divergence measure-based alignment of
probabilistic density functions built over reconstructed and target shapes. Our
approach enforces embeddings of shapes in correspondence to lie in the same
universal/canonical embedding space, which eventually helps regularize the
learning process and leads to a simple nearest neighbors approach between shape
embeddings for finding reliable correspondences. Comprehensive experiments show
that the new method makes noticeable improvements over state-of-the-art
approaches on standard shape correspondence benchmark datasets covering both
human and nonhuman shapes.

We examine the inducement of rare but severe errors in English-Chinese and
Chinese-English in-domain neural machine translation by minimal deletion of the
source text with character-based models. By deleting a single character, we can
induce severe translation errors. We categorize these errors and compare the
results of deleting single characters and single words. We also examine the
effect of training data size on the number and types of pathological cases
induced by these minimal perturbations, finding significant variation. We find
that deleting a word hurts overall translation score more than deleting a
character, but certain errors are more likely to occur when deleting
characters, with language direction also influencing the effect.

We develop a machine learning based algorithm using a convolutional neural
network (CNN) to identify low HI column density Ly$\alpha$ absorption systems
($\log{N_{\mathrm{HI}}}/{\rm cm}^{-2}<17$) in the Ly$\alpha$ forest, and
predict their physical properties, such as their HI column density
($\log{N}_{\mathrm{HI}}/{\rm cm}^{-2}$), redshift ($z_{\mathrm{HI}}$), and
Doppler width ($b_{\mathrm{HI}}$). Our CNN models are trained using simulated
spectra (S/N $\simeq10$), and we test their performance on high quality spectra
of quasars at redshift $z\sim2.5-2.9$ observed with the High Resolution Echelle
Spectrometer on the Keck I telescope. We find that $\sim78\%$ of the systems
identified by our algorithm are listed in the manual Voigt profile fitting
catalogue. We demonstrate that the performance of our CNN is stable and
consistent for all simulated and observed spectra with S/N $\gtrsim10$. Our
model can therefore be consistently used to analyse the enormous number of both
low and high S/N data available with current and future facilities. Our CNN
provides state-of-the-art predictions within the range
$12.5\leq\log{N_{\mathrm{HI}}}/\mathrm{cm^{-2}}<15.5$ with a mean absolute
error of $\Delta(\log{N}_{\mathrm{HI}}/{\rm cm}^{-2})=0.13$,
$\Delta(z_{\mathrm{HI}})=2.7\times{10}^{-5}$, and $\Delta(b_{\mathrm{HI}})=4.1\
\mathrm{km\ s^{-1}}$. The CNN prediction costs $<3$ minutes per model per
spectrum with a size of 120\,000 pixels using a laptop computer. We demonstrate
that CNNs can significantly increase the efficiency of analysing Ly$\alpha$
forest spectra, and thereby greatly increase the statistics of Ly$\alpha$
absorbers.

The estimation of coverage probabilities, and in particular of the missing
mass, is a classical statistical problem with applications in numerous
scientific fields. In this paper, we study this problem in relation to
randomized data compression, or sketching. This is a novel but practically
relevant perspective, and it refers to situations in which coverage
probabilities must be estimated based on a compressed and imperfect summary, or
sketch, of the true data, because neither the full data nor the empirical
frequencies of distinct symbols can be observed directly. Our contribution is a
Bayesian nonparametric methodology to estimate coverage probabilities from data
sketched through random hashing, which also solves the challenging problems of
recovering the numbers of distinct counts in the true data and of distinct
counts with a specified empirical frequency of interest. The proposed Bayesian
estimators are shown to be easily applicable to large-scale analyses in
combination with a Dirichlet process prior, although they involve some open
computational challenges under the more general Pitman-Yor process prior. The
empirical effectiveness of our methodology is demonstrated through numerical
experiments and applications to real data sets of Covid DNA sequences, classic
English literature, and IP addresses.

Pre-training weakly related image-text pairs in the contrastive style shows
great power in learning semantic aligning cross-modal models. The common choice
to measure the distance between the feature representations of the image-text
pairs is the cosine similarity, which can be considered as the negative inner
product of features embedded on a sphere mathematically. While such topology
benefits from the low computational resources consumption and a properly
defined uniformity, typically, there are two major drawbacks when applied.
First, it is vulnerable to the semantic ambiguity phenomenon resulting from the
noise in the weakly-related image-text pairs. Second, the learning progress is
unstable and fragile at the beginning. Although, in the practice of former
studies, a learnable softmax temperature parameter and a long warmup scheme are
employed to meliorate the training progress, still there lacks an in-depth
analysis of these problems. In this work, we discuss the desired properties of
the topology and its endowed distance function for the embedding vectors of
feature representations from the view of optimization. We then propose a rather
simple solution to improve the aforementioned problem. That is, we map the
feature representations onto the oblique manifold endowed with the negative
inner product as the distance function. In the experimental analysis, we show
that we can improve the baseline performance by a large margin (e.g. 4% in the
zero-shot image to text retrieval task) by changing only two lines of the
training codes.

Prostate biopsy and image-guided treatment procedures are often performed
under the guidance of ultrasound fused with magnetic resonance images (MRI).
Accurate image fusion relies on accurate segmentation of the prostate on
ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g.,
speckle and shadowing) in ultrasound images limit the performance of automated
prostate segmentation techniques and generalizing these methods to new image
domains is inherently difficult. In this study, we address these challenges by
introducing a novel 2.5D deep neural network for prostate segmentation on
ultrasound images. Our approach addresses the limitations of transfer learning
and finetuning methods (i.e., drop in performance on the original training data
when the model weights are updated) by combining a supervised domain adaptation
technique and a knowledge distillation loss. The knowledge distillation loss
allows the preservation of previously learned knowledge and reduces the
performance drop after model finetuning on new datasets. Furthermore, our
approach relies on an attention module that considers model feature positioning
information to improve the segmentation accuracy. We trained our model on 764
subjects from one institution and finetuned our model using only ten subjects
from subsequent institutions. We analyzed the performance of our method on
three large datasets encompassing 2067 subjects from three different
institutions. Our method achieved an average Dice Similarity Coefficient (Dice)
of $94.0\pm0.03$ and Hausdorff Distance (HD95) of 2.28 $mm$ in an independent
set of subjects from the first institution. Moreover, our model generalized
well in the studies from the other two institutions (Dice: $91.0\pm0.03$; HD95:
3.7$mm$ and Dice: $82.0\pm0.03$; HD95: 7.1 $mm$).

Post-hurricane damage assessment is crucial towards managing resource
allocations and executing an effective response. Traditionally, this evaluation
is performed through field reconnaissance, which is slow, hazardous, and
arduous. Instead, in this paper we furthered the idea of implementing deep
learning through convolutional neural networks in order to classify
post-hurricane satellite imagery of buildings as Flooded/Damaged or Undamaged.
The experimentation was conducted employing a dataset containing post-hurricane
satellite imagery from the Greater Houston area after Hurricane Harvey in 2017.
This paper implemented three convolutional neural network model architectures
paired with additional model considerations in order to achieve high accuracies
(over 99%), reinforcing the effective use of machine learning in post-hurricane
disaster assessment.

Catastrophic forgetting has been a significant problem hindering the
deployment of deep learning algorithms in the continual learning setting.
Numerous methods have been proposed to address the catastrophic forgetting
problem where an agent loses its generalization power of old tasks while
learning new tasks. We put forward an alternative strategy to handle the
catastrophic forgetting with knowledge amalgamation (CFA), which learns a
student network from multiple heterogeneous teacher models specializing in
previous tasks and can be applied to current offline methods. The knowledge
amalgamation process is carried out in a single-head manner with only a
selected number of memorized samples and no annotations. The teachers and
students do not need to share the same network structure, allowing
heterogeneous tasks to be adapted to a compact or sparse data representation.
We compare our method with competitive baselines from different strategies,
demonstrating our approach's advantages.

Over the past few years, a significant progress has been made in deep
convolutional neural networks (CNNs)-based image recognition. This is mainly
due to the strong ability of such networks in mining discriminative object pose
and parts information from texture and shape. This is often inappropriate for
fine-grained visual classification (FGVC) since it exhibits high intra-class
and low inter-class variances due to occlusions, deformation, illuminations,
etc. Thus, an expressive feature representation describing global structural
information is a key to characterize an object/ scene. To this end, we propose
a method that effectively captures subtle changes by aggregating context-aware
features from most relevant image-regions and their importance in
discriminating fine-grained categories avoiding the bounding-box and/or
distinguishable part annotations. Our approach is inspired by the recent
advancement in self-attention and graph neural networks (GNNs) approaches to
include a simple yet effective relation-aware feature transformation and its
refinement using a context-aware attention mechanism to boost the
discriminability of the transformed feature in an end-to-end learning process.
Our model is evaluated on eight benchmark datasets consisting of fine-grained
objects and human-object interactions. It outperforms the state-of-the-art
approaches by a significant margin in recognition accuracy.

Autonomous driving decision-making is a challenging task due to the inherent
complexity and uncertainty in traffic. For example, adjacent vehicles may
change their lane or overtake at any time to pass a slow vehicle or to help
traffic flow. Anticipating the intention of surrounding vehicles, estimating
their future states and integrating them into the decision-making process of an
automated vehicle can enhance the reliability of autonomous driving in complex
driving scenarios. This paper proposes a Prediction-based Deep Reinforcement
Learning (PDRL) decision-making model that considers the manoeuvre intentions
of surrounding vehicles in the decision-making process for highway driving. The
model is trained using real traffic data and tested in various traffic
conditions through a simulation platform. The results show that the proposed
PDRL model improves the decision-making performance compared to a Deep
Reinforcement Learning (DRL) model by decreasing collision numbers, resulting
in safer driving.

In this paper, we propose a mesh-free method to solve full stokes equation
which models the glacier movement with nonlinear rheology. Our approach is
inspired by the Deep-Ritz method proposed in [12]. We first formulate the
solution of non-Newtonian ice flow model into the minimizer of a variational
integral with boundary constraints. The solution is then approximated by a deep
neural network whose loss function is the variational integral plus soft
constraint from the mixed boundary conditions. Instead of introducing mesh
grids or basis functions to evaluate the loss function, our method only
requires uniform samplers of the domain and boundaries. To address instability
in real-world scaling, we re-normalize the input of the network at the first
layer and balance the regularizing factors for each individual boundary.
Finally, we illustrate the performance of our method by several numerical
experiments, including a 2D model with analytical solution, Arolla glacier
model with real scaling and a 3D model with periodic boundary conditions.
Numerical results show that our proposed method is efficient in solving the
non-Newtonian mechanics arising from glacier modeling with nonlinear rheology.

Ionized gas in the halo circumgalactic medium leaves an imprint on the cosmic
microwave background via the thermal Sunyaev-Zeldovich (tSZ) effect. Feedback
from active galactic nuclei (AGN) and supernovae can affect the measurements of
the integrated tSZ flux of halos ($Y_\mathrm{SZ}$) and cause its relation with
the halo mass ($Y_\mathrm{SZ}-M$) to deviate from the self-similar power-law
prediction of the virial theorem. We perform a comprehensive study of such
deviations using CAMELS, a suite of hydrodynamic simulations with extensive
variations in feedback prescriptions.
  We use a combination of two machine learning tools (random forest and
symbolic regression) to search for analogues of the $Y-M$ relation which are
more robust to feedback processes for low masses ($M\lesssim 10^{14}\, h^{-1}
\, M_\odot$); we find that simply replacing $Y\rightarrow
Y(1+M_*/M_\mathrm{gas})$ in the relation makes it remarkably self-similar. This
could serve as a robust multiwavelength mass proxy for low-mass clusters and
galaxy groups. Our methodology can also be generally useful to improve the
domain of validity of other astrophysical scaling relations.
  We also forecast that measurements of the $Y-M$ relation could provide
percent-level constraints on certain combinations of feedback parameters and/or
rule out a major part of the parameter space of supernova and AGN feedback
models used in current state-of-the-art hydrodynamic simulations. Our results
can be useful for using upcoming SZ surveys (e.g. SO, CMB-S4) and galaxy
surveys (e.g. DESI and Rubin) to constrain the nature of baryonic feedback.
Finally, we find that the an alternative relation, $Y-M_*$, provides
complementary information on feedback than $Y-M$.

Few-shot classification aims to learn to classify new object categories well
using only a few labeled examples. Transferring feature representations from
other models is a popular approach for solving few-shot classification
problems. In this work we perform a systematic study of various feature
representations for few-shot classification, including representations learned
from MAML, supervised classification, and several common self-supervised tasks.
We find that learning from more complex tasks tend to give better
representations for few-shot classification, and thus we propose the use of
representations learned from multiple tasks for few-shot classification.
Coupled with new tricks on feature selection and voting to handle the issue of
small sample size, our direct transfer learning method offers performance
comparable to state-of-art on several benchmark datasets.

The properties of soft electronic materials depend on the coupling of
electronic and conformational degrees of freedom over a wide range of
spatiotemporal scales. State-of-the-art modeling of such properties requires
multiscale approaches that connect quantum-chemical calculations to
coarse-grained (CG) methodologies via backmapping to atomistic resolution. The
computational cost of such approaches, however, seriously hampers their
application in high-throughput computer-aided soft materials discovery. Here,
we present a machine learning-enabled method that can replace the
backmapping-based approach. We illustrate the method for an emerging class of
soft electronic materials: nonconjugated, radical-containing polymers. We train
supervised machine learning models to learn the dependence of electronic
properties on conformations at CG resolutions. We then develop CG models that
retain electronic structure information, simulate CG condensed phases, and
predict electronic properties of such phases solely from the CG degrees of
freedom. We validate our method by comparing it against the backmapping-based
approach. This work demonstrates the capabilities of the proposed method to
accelerate multiscale workflows and informs the development of CG models that
retain electronic structure information.

Fact-checking has gained increasing attention due to the widespread of
falsified information. Most fact-checking approaches focus on claims made in
English only due to the data scarcity issue in other languages. The lack of
fact-checking datasets in low-resource languages calls for an effective
cross-lingual transfer technique for fact-checking. Additionally, trustworthy
information in different languages can be complementary and helpful in
verifying facts. To this end, we present the first fact-checking framework
augmented with cross-lingual retrieval that aggregates evidence retrieved from
multiple languages through a cross-lingual retriever. Given the absence of
cross-lingual information retrieval datasets with claim-like queries, we train
the retriever with our proposed Cross-lingual Inverse Cloze Task (X-ICT), a
self-supervised algorithm that creates training instances by translating the
title of a passage. The goal for X-ICT is to learn cross-lingual retrieval in
which the model learns to identify the passage corresponding to a given
translated title. On the X-Fact dataset, our approach achieves 2.23% absolute
F1 improvement in the zero-shot cross-lingual setup over prior systems. The
source code and data are publicly available at
https://github.com/khuangaf/CONCRETE.

The challenge of language grounding is to fully understand natural language
by grounding language in real-world referents. While AI techniques are
available, the widespread adoption and effectiveness of such technologies for
human-robot teams relies critically on user trust. This survey provides three
contributions relating to the newly emerging field of trust in language
grounding, including a) an overview of language grounding research in terms of
AI technologies, data sets, and user interfaces; b) six hypothesised trust
factors relevant to language grounding, which are tested empirically on a
human-robot cleaning team; and c) future research directions for trust in
language grounding.

Performance of classifiers is often measured in terms of average accuracy on
test data. Despite being a standard measure, average accuracy fails in
characterizing the fit of the model to the underlying conditional law of labels
given the features vector ($Y|X$), e.g. due to model misspecification, over
fitting, and high-dimensionality. In this paper, we consider the fundamental
problem of assessing the goodness-of-fit for a general binary classifier. Our
framework does not make any parametric assumption on the conditional law $Y|X$,
and treats that as a black box oracle model which can be accessed only through
queries. We formulate the goodness-of-fit assessment problem as a tolerance
hypothesis testing of the form \[ H_0: \mathbb{E}\Big[D_f\Big({\sf
Bern}(\eta(X))\|{\sf Bern}(\hat{\eta}(X))\Big)\Big]\leq \tau\,, \] where $D_f$
represents an $f$-divergence function, and $\eta(x)$, $\hat{\eta}(x)$
respectively denote the true and an estimate likelihood for a feature vector
$x$ admitting a positive label. We propose a novel test, called \grasp for
testing $H_0$, which works in finite sample settings, no matter the features
(distribution-free). We also propose model-X \grasp designed for model-X
settings where the joint distribution of the features vector is known. Model-X
\grasp uses this distributional information to achieve better power. We
evaluate the performance of our tests through extensive numerical experiments.

Machine Learning permeates many industries, which brings new source of
benefits for companies. However within the life insurance industry, Machine
Learning is not widely used in practice as over the past years statistical
models have shown their efficiency for risk assessment. Thus insurers may face
difficulties to assess the value of the artificial intelligence. Focusing on
the modification of the life insurance industry over time highlights the stake
of using Machine Learning for insurers and benefits that it can bring by
unleashing data value. This paper reviews traditional actuarial methodologies
for survival modeling and extends them with Machine Learning techniques. It
points out differences with regular machine learning models and emphasizes
importance of specific implementations to face censored data with machine
learning models family. In complement to this article, a Python library has
been developed. Different open-source Machine Learning algorithms have been
adjusted to adapt the specificities of life insurance data, namely censoring
and truncation. Such models can be easily applied from this SCOR library to
accurately model life insurance risks.

The concept of Label Distribution Learning (LDL) is a technique to stabilize
classification and regression problems with ambiguous and/or imbalanced labels.
A prototypical use-case of LDL is human age estimation based on profile images.
Regarding this regression problem, a so called Deep Label Distribution Learning
(DLDL) method has been developed. The main idea is the joint regression of the
label distribution and its expectation value. However, the original DLDL method
uses loss components with different mathematical motivation and, thus,
different scales, which is why the use of a hyperparameter becomes necessary.
In this work, we introduce a loss function for DLDL whose components are
completely defined by Kullback-Leibler (KL) divergences and, thus, are directly
comparable to each other without the need of additional hyperparameters. It
generalizes the concept of DLDL with regard to further use-cases, in particular
for multi-dimensional or multi-scale distribution learning tasks.

Recent year has brought considerable advancements in Electric Vehicles (EVs)
and associated infrastructures/communications. Intrusion Detection Systems
(IDS) are widely deployed for anomaly detection in such critical
infrastructures. This paper presents an Interpretable Anomaly Detection System
(RX-ADS) for intrusion detection in CAN protocol communication in EVs.
Contributions include: 1) window based feature extraction method; 2) deep
Autoencoder based anomaly detection method; and 3) adversarial machine learning
based explanation generation methodology. The presented approach was tested on
two benchmark CAN datasets: OTIDS and Car Hacking. The anomaly detection
performance of RX-ADS was compared against the state-of-the-art approaches on
these datasets: HIDS and GIDS. The RX-ADS approach presented performance
comparable to the HIDS approach (OTIDS dataset) and has outperformed HIDS and
GIDS approaches (Car Hacking dataset). Further, the proposed approach was able
to generate explanations for detected abnormal behaviors arising from various
intrusions. These explanations were later validated by information used by
domain experts to detect anomalies. Other advantages of RX-ADS include: 1) the
method can be trained on unlabeled data; 2) explanations help experts in
understanding anomalies and root course analysis, and also help with AI model
debugging and diagnostics, ultimately improving user trust in AI systems.

The use of machine learning algorithms to predict behaviors of complex
systems is booming. However, the key to an effective use of machine learning
tools in multi-physics problems, including combustion, is to couple them to
physical and computer models. The performance of these tools is enhanced if all
the prior knowledge and the physical constraints are embodied. In other words,
the scientific method must be adapted to bring machine learning into the
picture, and make the best use of the massive amount of data we have produced,
thanks to the advances in numerical computing. The present chapter reviews some
of the open opportunities for the application of data-driven reduced-order
modeling of combustion systems. Examples of feature extraction in turbulent
combustion data, empirical low-dimensional manifold (ELDM) identification,
classification, regression, and reduced-order modeling are provided.

Airway segmentation is crucial for the examination, diagnosis, and prognosis
of lung diseases, while its manual delineation is unduly burdensome. To
alleviate this time-consuming and potentially subjective manual procedure,
researchers have proposed methods to automatically segment airways from
computerized tomography (CT) images. However, some small-sized airway branches
(e.g., bronchus and terminal bronchioles) significantly aggravate the
difficulty of automatic segmentation by machine learning models. In particular,
the variance of voxel values and the severe data imbalance in airway branches
make the computational module prone to discontinuous and false-negative
predictions. especially for cohorts with different lung diseases. Attention
mechanism has shown the capacity to segment complex structures, while fuzzy
logic can reduce the uncertainty in feature representations. Therefore, the
integration of deep attention networks and fuzzy theory, given by the fuzzy
attention layer, should be an escalated solution for better generalization and
robustness. This paper presents an efficient method for airway segmentation,
comprising a novel fuzzy attention neural network and a comprehensive loss
function to enhance the spatial continuity of airway segmentation. The deep
fuzzy set is formulated by a set of voxels in the feature map and a learnable
Gaussian membership function. Different from the existing attention mechanism,
the proposed channel-specific fuzzy attention addresses the issue of
heterogeneous features in different channels. Furthermore, a novel evaluation
metric is proposed to assess both the continuity and completeness of airway
structures. The efficiency, generalization and robustness of the proposed
method have been proved by training on normal lung disease while testing on
datasets of lung cancer, COVID-19 and pulmonary fibrosis.

Density is one of the most commonly measured or estimated materials
properties, especially for glasses and melts that are of significant interest
to many fields, including metallurgy, geology, materials science and
sustainable cements. Here, two types of machine learning (ML) models (i.e.,
random forest (RF) and artificial neural network (ANN)) have been developed to
predict the room-temperature density of glasses in the compositional space of
CaO-MgO-Al2O3-SiO2-TiO2-FeO-Fe2O3-Na2O-K2O-MnO (CMASTFNKM), based on ~2100 data
points mined from ~140 literature studies. The results show that the RF and ANN
models give accurate predictions of glass density with R2 values, RMSE, and
MAPE of ~0.96-0.98, ~0.02-0.03 g/cm3 and ~0.59-0.79%, respectively, for the 15%
testing set, which are more accurate compared with empirical density models
based on ionic packing ratio (with R2 values, RMSE, and MAPE of ~0.28-0.91,
~0.05-0.15 g/cm3, and ~1.40-4.61%, respectively). Furthermore, glass density is
shown to be a reliable reactivity indicator for a range of CaO-Al2O3-SiO2 (CAS)
and volcanic glasses due to its strong correlation (R2 values above ~0.90) with
the average metal-oxygen dissociation energy (a structural descriptor) of these
glasses. Analysis of the predicted density-composition relationships from these
models (for selected compositional subspaces) suggests that the ANN model
exhibits a certain level of transferability (i.e., ability to extrapolate to
compositional space not (or less) covered in the database) and captures known
features including the mixed alkaline earth effects for
(CaO-MgO)0.5-(Al2O3-SiO2)0.5 glasses.

The classification of internet traffic has become increasingly important due
to the rapid growth of today's networks and applications. The number of
connections and the addition of new applications in our networks causes a vast
amount of log data and complicates the search for common patterns by experts.
Finding such patterns among specific classes of applications is necessary to
fulfill various requirements in network analytics. Deep learning methods
provide both feature extraction and classification from data in a single
system. However, these networks are very complex and are used as black-box
models, which weakens the experts' trust in the classifications. Moreover, by
using them as a black-box, new knowledge cannot be obtained from the model
predictions despite their excellent performance. Therefore, the explainability
of the classifications is crucial. Besides increasing trust, the explanation
can be used for model evaluation gaining new insights from the data and
improving the model. In this paper, we present a visual interactive tool that
combines the classification of network data with an explanation technique to
form an interface between experts, algorithms, and data.

Over the last decade, signal processing on graphs has become a very active
area of research. Specifically, the number of applications, for instance in
statistical or deep learning, using frames built from graphs, such as wavelets
on graphs, has increased significantly. We consider in particular the case of
signal denoising on graphs via a data-driven wavelet tight frame methodology.
This adaptive approach is based on a threshold calibrated using Stein's
unbiased risk estimate adapted to a tight-frame representation. We make it
scalable to large graphs using Chebyshev-Jackson polynomial approximations,
which allow fast computation of the wavelet coefficients, without the need to
compute the Laplacian eigendecomposition. However, the overcomplete nature of
the tight-frame, transforms a white noise into a correlated one. As a result,
the covariance of the transformed noise appears in the divergence term of the
SURE, thus requiring the computation and storage of the frame, which leads to
an impractical calculation for large graphs. To estimate such covariance, we
develop and analyze a Monte-Carlo strategy, based on the fast transformation of
zero mean and unit variance random variables. This new data-driven denoising
methodology finds a natural application in differential privacy. A
comprehensive performance analysis is carried out on graphs of varying size,
from real and simulated data.

Thanks to their ease of implementation, multilayer perceptrons (MLPs) have
become ubiquitous in deep learning applications. The graph underlying an MLP is
indeed multipartite, i.e. each layer of neurons only connects to neurons
belonging to the adjacent layer. In constrast, in vivo brain connectomes at the
level of individual synapses suggest that biological neuronal networks are
characterized by scale-free degree distributions or exponentially truncated
power law strength distributions, hinting at potentially novel avenues for the
exploitation of evolution-derived neuronal networks. In this paper, we present
"4Ward", a method and Python library capable of generating flexible and
efficient neural networks (NNs) from arbitrarily complex directed acyclic
graphs. 4Ward is inspired by layering algorithms drawn from the graph drawing
discipline to implement efficient forward passes, and provides significant time
gains in computational experiments with various Erd\H{o}s-R\'enyi graphs. 4Ward
overcomes the sequential nature of the learning matrix method by parallelizing
the computation of activations and provides the designer with freedom to
customize weight initialization and activation functions. Our algorithm can be
of aid for any investigator seeking to exploit complex topologies in a NN
design framework at the microscale.

Every year, millions of brain MRI scans are acquired in hospitals, which is a
figure considerably larger than the size of any research dataset. Therefore,
the ability to analyse such scans could transform neuroimaging research. Yet,
their potential remains untapped, since no automated algorithm can cope with
the high variability in clinical acquisitions (MR contrast, resolution,
orientation, etc.). Here we present SynthSeg+, an AI segmentation suite that
enables, for the first time, robust analysis of heterogeneous clinical
datasets. Specifically, in addition to whole-brain segmentation, SynthSeg+ also
performs cortical parcellation, intracranial volume estimation, and automated
detection of faulty segmentations (mainly caused by scans of very low quality).
We demonstrate SynthSeg+ in seven experiments, including an ageing study on
14,000 scans, where it accurately replicates atrophy patterns observed on data
of much higher quality. SynthSeg+ is publicly released as a ready-to-use tool
to unlock the potential of quantitative morphometry in wide-ranging settings.

All-sky searches for continuous gravitational waves are generally model
dependent and computationally costly to run. By contrast, SOAP is a
model-agnostic search that rapidly returns candidate signal tracks in the
time-frequency plane. In this work we extend the SOAP search to return broad
Bayesian posteriors on the astrophysical parameters of a specific signal model.
These constraints drastically reduce the volume of parameter space that any
follow-up search needs to explore, so increasing the speed at which candidates
can be identified and confirmed. Our method uses a machine learning technique,
specifically a conditional variational auto-encoder, and delivers a rapid
estimation of the posterior distribution of the four Doppler parameters of a
continuous wave signal. It does so without requiring a clear definition of a
likelihood function, or being shown any true Bayesian posteriors in training.
We demonstrate how the Doppler parameter space volume can be reduced by a
factor of $\mathcal{O}(10^{-7})$ for signals of SNR 100.

Connectionist temporal classification (CTC) -based models are attractive
because of their fast inference in automatic speech recognition (ASR). Language
model (LM) integration approaches such as shallow fusion and rescoring can
improve the recognition accuracy of CTC-based ASR by taking advantage of the
knowledge in text corpora. However, they significantly slow down the inference
of CTC. In this study, we propose to distill the knowledge of BERT for
CTC-based ASR, extending our previous study for attention-based ASR. CTC-based
ASR learns the knowledge of BERT during training and does not use BERT during
testing, which maintains the fast inference of CTC. Different from
attention-based models, CTC-based models make frame-level predictions, so they
need to be aligned with token-level predictions of BERT for distillation. We
propose to obtain alignments by calculating the most plausible CTC paths.
Experimental evaluations on the Corpus of Spontaneous Japanese (CSJ) and
TED-LIUM2 show that our method improves the performance of CTC-based ASR
without the cost of inference speed.

Self-Modeling is the process by which an agent, such as an animal or machine,
learns to create a predictive model of its own dynamics. Once captured, this
self-model can then allow the agent to plan and evaluate various potential
behaviors internally using the self-model, rather than using costly physical
experimentation. Here, we quantify the benefits of such self-modeling against
the complexity of the robot. We find a R2 =0.90 correlation between the number
of degrees of freedom a robot has, and the added value of self-modeling as
compared to a direct learning baseline. This result may help motivate self
modeling in increasingly complex robotic systems, as well as shed light on the
origins of self-modeling, and ultimately self-awareness, in animals and humans.

This paper proposes and develops a new algorithm for trading wind energy in
electricity markets, within an online learning and optimization framework. In
particular, we combine a component-wise adaptive variant of the gradient
descent algorithm with recent advances in the feature-driven newsvendor model.
This results in an online offering approach capable of leveraging data-rich
environments, while adapting to non-stationary characteristics of energy
generation and electricity markets, and with a minimal computational burden.
The performance of our approach is analyzed based on several numerical
experiments, showing both better adaptability to non-stationary uncertain
parameters and significant economic gains.

3D motion estimation from cine cardiac magnetic resonance (CMR) images is
important for the assessment of cardiac function and diagnosis of
cardiovascular diseases. Most of the previous methods focus on estimating
pixel-/voxel-wise motion fields in the full image space, which ignore the fact
that motion estimation is mainly relevant and useful within the object of
interest, e.g., the heart. In this work, we model the heart as a 3D geometric
mesh and propose a novel deep learning-based method that can estimate 3D motion
of the heart mesh from 2D short- and long-axis CMR images. By developing a
differentiable mesh-to-image rasterizer, the method is able to leverage the
anatomical shape information from 2D multi-view CMR images for 3D motion
estimation. The differentiability of the rasterizer enables us to train the
method end-to-end. One advantage of the proposed method is that by tracking the
motion of each vertex, it is able to keep the vertex correspondence of 3D
meshes between time frames, which is important for quantitative assessment of
the cardiac function on the mesh. We evaluate the proposed method on CMR images
acquired from the UK Biobank study. Experimental results show that the proposed
method quantitatively and qualitatively outperforms both conventional and
learning-based cardiac motion tracking methods.

We exhibit examples of high-dimensional unimodal posterior distributions
arising in non-linear regression models with Gaussian process priors for which
worst-case (`cold start') initialised MCMC methods typically take an
exponential run-time to enter the regions where the bulk of the posterior
measure concentrates. The counter-examples hold for general MCMC schemes based
on gradient or random walk steps, and the theory is illustrated for
Metropolis-Hastings adjusted methods such as pCN and MALA.

Zero-shot learning is a learning regime that recognizes unseen classes by
generalizing the visual-semantic relationship learned from the seen classes. To
obtain an effective ZSL model, one may resort to curating training samples from
multiple sources, which may inevitably raise the privacy concerns about data
sharing across different organizations. In this paper, we propose a novel
Federated Zero-Shot Learning FedZSL framework, which learns a central model
from the decentralized data residing on edge devices. To better generalize to
previously unseen classes, FedZSL allows the training data on each device
sampled from the non-overlapping classes, which are far from the i.i.d. that
traditional federated learning commonly assumes. We identify two key challenges
in our FedZSL protocol: 1) the trained models are prone to be biased to the
locally observed classes, thus failing to generalize to the unseen classes
and/or seen classes appeared on other devices; 2) as each category in the
training data comes from a single source, the central model is highly
vulnerable to model replacement (backdoor) attacks. To address these issues, we
propose three local objectives for visual-semantic alignment and cross-device
alignment through relation distillation, which leverages the normalized
class-wise covariance to regularize the consistency of the prediction logits
across devices. To defend against the backdoor attacks, a feature magnitude
defending technique is proposed. As malicious samples are less correlated to
the given semantic attributes, the visual features of low magnitude will be
discarded to stabilize model updates. The effectiveness and robustness of
FedZSL are demonstrated by extensive experiments conducted on three zero-shot

Convolutional Neural Networks (CNNs) are widely used in fault diagnosis of
mechanical systems due to their powerful feature extraction and classification
capabilities. However, the CNN is a typical black-box model, and the mechanism
of CNN's decision-making are not clear, which limits its application in
high-reliability-required fault diagnosis scenarios. To tackle this issue, we
propose a novel interpretable neural network termed as Time-Frequency Network
(TFN), where the physically meaningful time-frequency transform (TFT) method is
embedded into the traditional convolutional layer as an adaptive preprocessing
layer. This preprocessing layer named as time-frequency convolutional (TFconv)
layer, is constrained by a well-designed kernel function to extract
fault-related time-frequency information. It not only improves the diagnostic
performance but also reveals the logical foundation of the CNN prediction in
the frequency domain. Different TFT methods correspond to different kernel
functions of the TFconv layer. In this study, four typical TFT methods are
considered to formulate the TFNs and their effectiveness and interpretability
are proved through three mechanical fault diagnosis experiments. Experimental
results also show that the proposed TFconv layer can be easily generalized to
other CNNs with different depths. The code of TFN is available on
https://github.com/ChenQian0618/TFN.

Accurate abnormality localization in chest X-rays (CXR) can benefit the
clinical diagnosis of various thoracic diseases. However, the lesion-level
annotation can only be performed by experienced radiologists, and it is tedious
and time-consuming, thus difficult to acquire. Such a situation results in a
difficulty to develop a fully-supervised abnormality localization system for
CXR. In this regard, we propose to train the CXR abnormality localization
framework via a weakly semi-supervised strategy, termed Point Beyond Class
(PBC), which utilizes a small number of fully annotated CXRs with lesion-level
bounding boxes and extensive weakly annotated samples by points. Such a point
annotation setting can provide weakly instance-level information for
abnormality localization with a marginal annotation cost. Particularly, the
core idea behind our PBC is to learn a robust and accurate mapping from the
point annotations to the bounding boxes against the variance of annotated
points. To achieve that, a regularization term, namely multi-point consistency,
is proposed, which drives the model to generate the consistent bounding box
from different point annotations inside the same abnormality. Furthermore, a
self-supervision, termed symmetric consistency, is also proposed to deeply
exploit the useful information from the weakly annotated data for abnormality
localization. Experimental results on RSNA and VinDr-CXR datasets justify the
effectiveness of the proposed method. When less than 20% box-level labels are
used for training, an improvement of ~5 in mAP can be achieved by our PBC,
compared to the current state-of-the-art method (i.e., Point DETR). Code is
available at https://github.com/HaozheLiu-ST/Point-Beyond-Class.

In contrast to classical techniques for exploratory analysis of
high-dimensional data sets, such as principal component analysis (PCA),
neighbor embedding (NE) techniques tend to better preserve the local
structure/topology of high-dimensional data. However, the ability to preserve
local structure comes at the expense of interpretability: Techniques such as
t-Distributed Stochastic Neighbor Embedding (t-SNE) or Uniform Manifold
Approximation and Projection (UMAP) do not give insights into which input
variables underlie the topological (cluster) structure seen in the
corresponding embedding. We here propose different "tricks" from the
chemometrics field based on PCA, Q-residuals and Hotelling's T2 contributions
in combination with novel visualization approaches to derive local and global
explanations of neighbor embedding. We show how our approach is capable of
identifying discriminatory features between groups of data points that remain
unnoticed when exploring NEs using standard univariate or multivariate

The quantification of uncertainty is important for the adoption of machine
learning, especially to reject out-of-distribution (OOD) data back to human
experts for review. Yet progress has been slow, as a balance must be struck
between computational efficiency and the quality of uncertainty estimates. For
this reason many use deep ensembles of neural networks or Monte Carlo dropout
for reasonable uncertainty estimates at relatively minimal compute and memory.
Surprisingly, when we focus on the real-world applicable constraint of $\leq
1\%$ false positive rate (FPR), prior methods fail to reliably detect OOD
samples as such. Notably, even Gaussian random noise fails to trigger these
popular OOD techniques. We help to alleviate this problem by devising a simple
adversarial training scheme that incorporates an attack of the epistemic
uncertainty predicted by the dropout ensemble. We demonstrate this method
improves OOD detection performance on standard data (i.e., not adversarially
crafted), and improves the standardized partial AUC from near-random guessing
performance to $\geq 0.75$.

Probabilistic graphical models are a powerful concept for modeling
high-dimensional distributions. Besides modeling distributions, probabilistic
graphical models also provide an elegant framework for performing statistical
inference; because of the high-dimensional nature, however, one must often use
approximate methods for this purpose. Belief propagation performs approximate
inference, is efficient, and looks back on a long success-story. Yet, in most
cases, belief propagation lacks any performance and convergence guarantees.
Many realistic problems are presented by graphical models with loops, however,
in which case belief propagation is neither guaranteed to provide accurate
estimates nor that it converges at all. This thesis investigates how the model
parameters influence the performance of belief propagation. We are particularly
interested in their influence on (i) the number of fixed points, (ii) the
convergence properties, and (iii) the approximation quality.

Event detection tasks can help people quickly determine the domain from
complex texts. It can also provides powerful support for downstream tasks of
natural language processing.Existing methods implement fixed-type learning only
based on large amounts of data. When extending to new classes, it is often
required to retain the original data and retrain the model.Incremental event
detection tasks enables lifelong learning of new classes, but most existing
methods need to retain a large number of original data or face the problem of
catastrophic forgetting. Apart from that, it is difficult to obtain enough data
for model training due to the lack of high-quality data in practical.To address
the above problems, we define a new task in the domain of event detection,
which is few-shot incremental event detection.This task require that the model
should retain previous type when learning new event type in each round with
limited input. We recreate and release a benchmark dataset in the few-shot
incremental event detection task based on FewEvent.The dataset we published is
more appropriate than other in this new task. In addition, we propose two
benchmark approaches, IFSED-K and IFSED-KP, which can address the task in
different ways. Experiments results have shown that our approach has a higher
F1 score and is more stable than baseline.

Disentangling speaker and content attributes of a speech signal into separate
latent representations followed by decoding the content with an exchanged
speaker representation is a popular approach for voice conversion, which can be
trained with non-parallel and unlabeled speech data. However, previous
approaches perform disentanglement only implicitly via some sort of information
bottleneck or normalization, where it is usually hard to find a good trade-off
between voice conversion and content reconstruction. Further, previous works
usually do not consider an adaptation of the speaking rate to the target
speaker or they put some major restrictions to the data or use case. Therefore,
the contribution of this work is two-fold. First, we employ an explicit and
fully unsupervised disentanglement approach, which has previously only been
used for representation learning, and show that it allows to obtain both
superior voice conversion and content reconstruction. Second, we investigate
simple and generic approaches to linearly adapt the length of a speech signal,
and hence the speaking rate, to a target speaker and show that the proposed
adaptation allows to increase the speaking rate similarity with respect to the
target speaker.

Many recent approaches to natural language tasks are built on the remarkable
abilities of large language models. Large language models can perform
in-context learning, where they learn a new task from a few task
demonstrations, without any parameter updates. This work examines the
implications of in-context learning for the creation of datasets for new
natural language tasks. Departing from recent in-context learning methods, we
formulate an annotation-efficient, two-step framework: selective annotation
that chooses a pool of examples to annotate from unlabeled data in advance,
followed by prompt retrieval that retrieves task examples from the annotated
pool at test time. Based on this framework, we propose an unsupervised,
graph-based selective annotation method, voke-k, to select diverse,
representative examples to annotate. Extensive experiments on 10 datasets
(covering classification, commonsense reasoning, dialogue, and text/code
generation) demonstrate that our selective annotation method improves the task
performance by a large margin. On average, vote-k achieves a 12.9%/11.4%
relative gain under an annotation budget of 18/100, as compared to randomly
selecting examples to annotate. Compared to state-of-the-art supervised
finetuning approaches, it yields similar performance with 10-100x less
annotation cost across 10 tasks. We further analyze the effectiveness of our
framework in various scenarios: language models with varying sizes, alternative
selective annotation methods, and cases where there is a test data domain
shift. We hope that our studies will serve as a basis for data annotations as
large language models are increasingly applied to new tasks. Our code is
available at https://github.com/HKUNLP/icl-selective-annotation.

To run a cloud application with the required service quality, operators have
to continuously monitor the cloud application's run-time status, detect
potential performance anomalies, and diagnose the root causes of anomalies.
However, existing models of performance anomaly detection often suffer from low
re-usability and robustness due to the diversity of system-level metrics being
monitored and the lack of high-quality labeled monitoring data for anomalies.
Moreover, the current coarse-grained analysis models make it difficult to
locate system-level root causes of the application performance anomalies for
effective adaptation decisions. We provide a FIne-grained Robust pErformance
Diagnosis (FIRED) framework to tackle those challenges. The framework offers an
ensemble of several well-selected base models for anomaly detection using a
deep neural network, which adopts weakly-supervised learning considering fewer
labels exist in reality. The framework also employs a real-time fine-grained
analysis model to locate dependent system metrics of the anomaly. Our
experiments show that the framework can achieve the best detection accuracy and
algorithm robustness, and it can predict anomalies in four minutes with F1
score higher than 0.8. In addition, the framework can accurately localize the
first root causes, and with an average accuracy higher than 0.7 of locating
first four root causes.

The construction of spatiotemporal networks using graph convolution networks
(GCNs) has become one of the most popular methods for predicting traffic
signals. However, when using a GCN for traffic speed prediction, the
conventional approach generally assumes the relationship between the sensors as
a homogeneous graph and learns an adjacency matrix using the data accumulated
by the sensors. However, the spatial correlation between sensors is not
specified as one but defined differently from various viewpoints. To this end,
we aim to study the heterogeneous characteristics inherent in traffic signal
data to learn the hidden relationships between sensors in various ways.
Specifically, we designed a method to construct a heterogeneous graph for each
module by dividing the spatial relationship between sensors into static and
dynamic modules. We propose a network decentralization attention based
heterogeneity-aware graph convolution network (HAGCN) method that aggregates
the hidden states of adjacent nodes by considering the importance of each
channel in a heterogeneous graph. Experimental results on real traffic datasets
verified the effectiveness of the proposed method, achieving a 6.35%
improvement over the existing model and realizing state-of-the-art prediction

Intelligent robots rely on object detection models to perceive the
environment. Following advances in deep learning security it has been revealed
that object detection models are vulnerable to adversarial attacks. However,
prior research primarily focuses on attacking static images or offline videos.
Therefore, it is still unclear if such attacks could jeopardize real-world
robotic applications in dynamic environments. This paper bridges this gap by
presenting the first real-time online attack against object detection models.
We devise three attacks that fabricate bounding boxes for nonexistent objects
at desired locations. The attacks achieve a success rate of about 90% within
about 20 iterations. The demo video is available at:
https://youtu.be/zJZ1aNlXsMU.

Quantum machine learning (QML) is the spearhead of quantum computer
applications. In particular, quantum neural networks (QNN) are actively studied
as the method that works both in near-term quantum computers and fault-tolerant
quantum computers. Recent studies have shown that supervised machine learning
with QNN can be interpreted as the quantum kernel method (QKM), suggesting that
enhancing the practicality of the QKM is the key to building near-term
applications of QML. However, the QKM is also known to have two severe issues.
One is that the QKM with the (inner-product based) quantum kernel defined in
the original large Hilbert space does not generalize; namely, the model fails
to find patterns of unseen data. The other one is that the classical
computational cost of the QKM increases at least quadratically with the number
of data, and therefore, QKM is not scalable with data size. This paper aims to
provide algorithms free from both of these issues. That is, for a class of
quantum kernels with generalization capability, we show that the QKM with those
quantum kernels can be made scalable by using our proposed deterministic and
random features. Our numerical experiment, using datasets including $O(1,000)
\sim O(10,000)$ training data, supports the validity of our method.

Estimating the average treatment effect (ATE) from observational data is
challenging due to selection bias. Existing works mainly tackle this challenge
in two ways. Some researchers propose constructing a score function that
satisfies the orthogonal condition, which guarantees that the established ATE
estimator is "orthogonal" to be more robust. The others explore representation
learning models to achieve a balanced representation between the treated and
the controlled groups. However, existing studies fail to 1) discriminate
treated units from controlled ones in the representation space to avoid the
over-balanced issue; 2) fully utilize the "orthogonality information". In this
paper, we propose a moderately-balanced representation learning (MBRL)
framework based on recent covariates balanced representation learning methods
and orthogonal machine learning theory. This framework protects the
representation from being over-balanced via multi-task learning.
Simultaneously, MBRL incorporates the noise orthogonality information in the
training and validation stages to achieve a better ATE estimation. The
comprehensive experiments on benchmark and simulated datasets show the
superiority and robustness of our method on treatment effect estimations
compared with existing state-of-the-art methods.

Machine learning frameworks based on correlations of interatomic positions
begin with a discretized description of the density of other atoms in the
neighbourhood of each atom in the system. Symmetry considerations support the
use of spherical harmonics to expand the angular dependence of this density,
but there is as yet no clear rationale to choose one radial basis over another.
Here we investigate the basis that results from the solution of the Laplacian
eigenvalue problem within a sphere around the atom of interest. We show that
this generates the smoothest possible basis of a given size within the sphere,
and that a tensor product of Laplacian eigenstates also provides the smoothest
possible basis for expanding any higher-order correlation of the atomic density
within the appropriate hypersphere. We consider several unsupervised metrics of
the quality of a basis for a given dataset, and show that the Laplacian
eigenstate basis has a performance that is much better than some widely used
basis sets and is competitive with data-driven bases that numerically optimize
each metric. In supervised machine learning tests, we find that the optimal
function smoothness of the Laplacian eigenstates leads to comparable or better
performance than can be obtained from a data-driven basis of a similar size
that has been optimized to describe the atom-density correlation for the
specific dataset. We conclude that the smoothness of the basis functions is a
key and hitherto largely overlooked aspect of successful atomic density

The ability to discover useful behaviours from past experience and transfer
them to new tasks is considered a core component of natural embodied
intelligence. Inspired by neuroscience, discovering behaviours that switch at
bottleneck states have been long sought after for inducing plans of minimum
description length across tasks. Prior approaches have either only supported
online, on-policy, bottleneck state discovery, limiting sample-efficiency, or
discrete state-action domains, restricting applicability. To address this, we
introduce Model-Based Offline Options (MO2), an offline hindsight framework
supporting sample-efficient bottleneck option discovery over continuous
state-action spaces. Once bottleneck options are learnt offline over source
domains, they are transferred online to improve exploration and value
estimation on the transfer domain. Our experiments show that on complex
long-horizon continuous control tasks with sparse, delayed rewards, MO2's
properties are essential and lead to performance exceeding recent option
learning methods. Additional ablations further demonstrate the impact on option
predictability and credit assignment.

We propose a deep importance sampling method that is suitable for estimating
rare event probabilities in high-dimensional problems. We approximate the
optimal importance distribution in a general importance sampling problem as the
pushforward of a reference distribution under a composition of order-preserving
transformations, in which each transformation is formed by a squared
tensor-train decomposition. The squared tensor-train decomposition provides a
scalable ansatz for building order-preserving high-dimensional transformations
via density approximations. The use of composition of maps moving along a
sequence of bridging densities alleviates the difficulty of directly
approximating concentrated density functions. To compute expectations over
unnormalized probability distributions, we design a ratio estimator that
estimates the normalizing constant using a separate importance distribution,
again constructed via a composition of transformations in tensor-train format.
This offers better theoretical variance reduction compared with self-normalized
importance sampling, and thus opens the door to efficient computation of rare
event probabilities in Bayesian inference problems. Numerical experiments on
problems constrained by differential equations show little to no increase in
the computational complexity with the event probability going to zero, and
allow to compute hitherto unattainable estimates of rare event probabilities
for complex, high-dimensional posterior densities.

Explainable Artificial Intelligence (XAI) has mainly focused on static
learning scenarios so far. We are interested in dynamic scenarios where data is
sampled progressively, and learning is done in an incremental rather than a
batch mode. We seek efficient incremental algorithms for computing feature
importance (FI) measures, specifically, an incremental FI measure based on
feature marginalization of absent features similar to permutation feature
importance (PFI). We propose an efficient, model-agnostic algorithm called iPFI
to estimate this measure incrementally and under dynamic modeling conditions
including concept drift. We prove theoretical guarantees on the approximation
quality in terms of expectation and variance. To validate our theoretical
findings and the efficacy of our approaches compared to traditional batch PFI,
we conduct multiple experimental studies on benchmark data with and without
concept drift.

Using deep learning techniques, anomalies in the paranasal sinus system can
be detected automatically in MRI images and can be further analyzed and
classified based on their volume, shape and other parameters like local
contrast. However due to limited training data, traditional supervised learning
methods often fail to generalize. Existing deep learning methods in paranasal
anomaly classification have been used to diagnose at most one anomaly. In our
work, we consider three anomalies. Specifically, we employ a 3D CNN to separate
maxillary sinus volumes without anomalies from maxillary sinus volumes with
anomalies. To learn robust representations from a small labelled dataset, we
propose a novel learning paradigm that combines contrastive loss and
cross-entropy loss. Particularly, we use a supervised contrastive loss that
encourages embeddings of maxillary sinus volumes with and without anomaly to
form two distinct clusters while the cross-entropy loss encourages the 3D CNN
to maintain its discriminative ability. We report that optimising with both
losses is advantageous over optimising with only one loss. We also find that
our training strategy leads to label efficiency. With our method, a 3D CNN
classifier achieves an AUROC of 0.85 while a 3D CNN classifier optimised with
cross-entropy loss achieves an AUROC of 0.66.

Recapture detection of face and document images is an important forensic
task. With deep learning, the performances of face anti-spoofing (FAS) and
recaptured document detection have been improved significantly. However, the
performances are not yet satisfactory on samples with weak forensic cues. The
amount of forensic cues can be quantified to allow a reliable forensic result.
In this work, we propose a forensicability assessment network to quantify the
forensicability of the questioned samples. The low-forensicability samples are
rejected before the actual recapturing detection process to improve the
efficiency of recapturing detection systems. We first extract forensicability
features related to both image quality assessment and forensic tasks. By
exploiting domain knowledge of the forensic application in image quality and
forensic features, we define three task-specific forensicability classes and
the initialized locations in the feature space. Based on the extracted features
and the defined centers, we train the proposed forensic assessment network
(FANet) with cross-entropy loss and update the centers with a momentum-based
update method. We integrate the trained FANet with practical recapturing
detection schemes in face anti-spoofing and recaptured document detection
tasks. Experimental results show that, for a generic CNN-based FAS scheme,
FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by
rejecting samples with the lowest 30% forensicability scores. The performance
of FAS schemes is poor in the rejected samples, with EER as high as 56.48%.
Similar performances in rejecting low-forensicability samples have been
observed for the state-of-the-art approaches in FAS and recaptured document
detection tasks. To the best of our knowledge, this is the first work that
assesses the forensicability of recaptured document images and improves the
system efficiency.

Brain-computer interface (BCI) systems can be utilized for kinematics
decoding from scalp brain activation to control rehabilitation or
power-augmenting devices. In this study, the hand kinematics decoding for grasp
and lift task is performed in three-dimensional (3D) space using scalp
electroencephalogram (EEG) signals. Twelve subjects from the publicly available
database WAY-EEG-GAL, has been utilized in this study. In particular,
multi-layer perceptron (MLP) and convolutional neural network-long short-term
memory (CNN-LSTM) based deep learning frameworks are proposed that utilize the
motor-neural information encoded in the pre-movement EEG data. Spectral
features are analyzed for hand kinematics decoding using EEG data filtered in
seven frequency ranges. The best performing frequency band spectral features
has been considered for further analysis with different EEG window sizes and
lag windows. Appropriate lag windows from movement onset, make the approach
pre-movement in true sense. Additionally, inter-subject hand trajectory
decoding analysis is performed using leave-one-subject-out (LOSO) approach. The
Pearson correlation coefficient and hand trajectory are considered as
performance metric to evaluate decoding performance for the neural decoders.
This study explores the feasibility of inter-subject 3-D hand trajectory
decoding using EEG signals only during reach and grasp task, probably for the
first time. The results may provide the viable information to decode 3D hand
kinematics using pre-movement EEG signals for practical BCI applications such
as exoskeleton/exosuit and prosthesis.

Deep Learning could drastically boost the classification accuracy for
Hyperspectral Imaging (HSI). Still, the training on the mostly small
hyperspectral data sets is not trivial. Two key challenges are the large
channel dimension of the recordings and the incompatibility between cameras of
different manufacturers. By introducing a suitable model bias and continuously
defining the channel dimension, we propose a 2D convolution optimized for these
challenges of Hyperspectral Imaging. We evaluate the method based on two
different hyperspectral applications (inline inspection and remote sensing).
Besides the shown superiority of the model, the modification adds additional
explanatory power. In addition, the model learns the necessary camera filters
in a data-driven manner. Based on these camera filters, an optimal camera can
be designed.

Compared with the rapid development of single-frequency multi-polarization
SAR image classification technology, there is less research on the land cover
classification of multifrequency polarimetric SAR (MF-PolSAR) images. In
addition, the current deep learning methods for MF-PolSAR classification are
mainly based on convolutional neural networks (CNNs), only local spatiality is
considered but the nonlocal relationship is ignored. Therefore, based on
semantic interaction and nonlocal topological structure, this paper proposes
the MF semantics and topology fusion network (MF-STFnet) to improve MF-PolSAR
classification performance. In MF-STFnet, two kinds of classification are
implemented for each band, semantic information-based (SIC) and topological
property-based (TPC). They work collaboratively during MF-STFnet training,
which can not only fully leverage the complementarity of bands, but also
combine local and nonlocal spatial information to improve the discrimination
between different categories. For SIC, the designed crossband interactive
feature extraction module (CIFEM) is embedded to explicitly model the deep
semantic correlation among bands, thereby leveraging the complementarity of
bands to make ground objects more separable. For TPC, the graph sample and
aggregate network (GraphSAGE) is employed to dynamically capture the
representation of nonlocal topological relations between land cover categories.
In this way, the robustness of classification can be further improved by
combining nonlocal spatial information. Finally, an adaptive weighting fusion
(AWF) strategy is proposed to merge inference from different bands, so as to
make the MF joint classification decisions of SIC and TPC. The comparative
experiments show that MF-STFnet can achieve more competitive classification
performance than some state-of-the-art methods.

Automated analysis of electron microscopy datasets poses multiple challenges,
such as limitation in the size of the training dataset, variation in data
distribution induced by variation in sample quality and experiment conditions,
etc. It is crucial for the trained model to continue to provide acceptable
segmentation/classification performance on new data, and quantify the
uncertainty associated with its predictions. Among the broad applications of
machine learning, various approaches have been adopted to quantify uncertainty,
such as Bayesian modeling, Monte Carlo dropout, ensembles, etc. With the aim of
addressing the challenges specific to the data domain of electron microscopy,
two different types of ensembles of pre-trained neural networks were
implemented in this work. The ensembles performed semantic segmentation of ice
crystal within a two-phase mixture, thereby tracking its phase transformation
to water. The first ensemble (EA) is composed of U-net style networks having
different underlying architectures, whereas the second series of ensembles
(ER-i) are composed of randomly initialized U-net style networks, wherein each
base learner has the same underlying architecture 'i'. The encoders of the base
learners were pre-trained on the Imagenet dataset. The performance of EA and ER
were evaluated on three different metrics: accuracy, calibration, and
uncertainty. It is seen that EA exhibits a greater classification accuracy and
is better calibrated, as compared to ER. While the uncertainty quantification
of these two types of ensembles are comparable, the uncertainty scores
exhibited by ER were found to be dependent on the specific architecture of its
base member ('i') and not consistently better than EA. Thus, the challenges
posed for the analysis of electron microscopy datasets appear to be better
addressed by an ensemble design like EA, as compared to an ensemble design like
ER.

Robust learning is an important issue in Scientific Machine Learning (SciML).
There are several works in the literature addressing this topic. However, there
is an increasing demand for methods that can simultaneously consider all the
different uncertainty components involved in SciML model identification. Hence,
this work proposes a comprehensive methodology for uncertainty evaluation of
the SciML that also considers several possible sources of uncertainties
involved in the identification process. The uncertainties considered in the
proposed method are the absence of theory and causal models, the sensitiveness
to data corruption or imperfection, and the computational effort. Therefore, it
was possible to provide an overall strategy for the uncertainty-aware models in
the SciML field. The methodology is validated through a case study, developing
a Soft Sensor for a polymerization reactor. The results demonstrated that the
identified Soft Sensor are robust for uncertainties, corroborating with the
consistency of the proposed approach.

The realization of open-source-defined wireless networks in the
telecommunication domain is accomplished through the fifth-generation network
(5G). In contrast to its predecessors (3G and 4G), the 5G network can support a
wide variety of heterogeneous use cases with challenging requirements from both
the Internet and the Internet of Things (IoT). The future sixth-generation (6G)
network will not only extend 5G capabilities but also innovate new
functionalities to address emerging academic and engineering challenges. The
research community has identified these challenges could be overcome by
open-source-defined wireless networks, which is based on open-source software
and hardware. In this survey, we present an overview of different aspects of
open-source-defined wireless networks, comprising motivation, frameworks, key
technologies, and implementation. We start by introducing the motivation and
explore several frameworks with classification into three different categories:
black-box, grey-box, and white-box. We review research efforts related to
open-source-defined Core Network (CN), Radio Access Network (RAN), Multi-access
Edge Computing (MEC), the capabilities of security threats, open-source
hardware, and various implementations, including testbeds. The last but most
important in this survey, lessons learned, future research direction, open
research issues, pitfalls, and limitations of existing surveys on open-source
wireless networks are included to motivate and encourage future research.

As the pre-trained language models (PLMs) continue to grow, so do the
hardware and data requirements for fine-tuning PLMs. Therefore, the researchers
have come up with a lighter method called \textit{Prompt Learning}. However,
during the investigations, we observe that the prompt learning methods are
vulnerable and can easily be attacked by some illegally constructed prompts,
resulting in classification errors, and serious security problems for PLMs.
Most of the current research ignores the security issue of prompt-based
methods. Therefore, in this paper, we propose a malicious prompt template
construction method (\textbf{PromptAttack}) to probe the security performance
of PLMs. Several unfriendly template construction approaches are investigated
to guide the model to misclassify the task. Extensive experiments on three
datasets and three PLMs prove the effectiveness of our proposed approach
PromptAttack. We also conduct experiments to verify that our method is
applicable in few-shot scenarios.

One of the primary challenges in Semi-supervised Domain Adaptation (SSDA) is
the skewed ratio between the number of labeled source and target samples,
causing the model to be biased towards the source domain. Recent works in SSDA
show that aligning only the labeled target samples with the source samples
potentially leads to incomplete domain alignment of the target domain to the
source domain. In our approach, to align the two domains, we leverage
contrastive losses to learn a semantically meaningful and a domain agnostic
feature space using the supervised samples from both domains. To mitigate
challenges caused by the skewed label ratio, we pseudo-label the unlabeled
target samples by comparing their feature representation to those of the
labeled samples from both the source and target domains. Furthermore, to
increase the support of the target domain, these potentially noisy
pseudo-labels are gradually injected into the labeled target dataset over the
course of training. Specifically, we use a temperature scaled cosine similarity
measure to assign a soft pseudo-label to the unlabeled target samples.
Additionally, we compute an exponential moving average of the soft
pseudo-labels for each unlabeled sample. These pseudo-labels are progressively
injected or removed) into the (from) the labeled target dataset based on a
confidence threshold to supplement the alignment of the source and target
distributions. Finally, we use a supervised contrastive loss on the labeled and
pseudo-labeled datasets to align the source and target distributions. Using our
proposed approach, we showcase state-of-the-art performance on SSDA benchmarks
- Office-Home, DomainNet and Office-31.

The performance of modern deep learning-based systems dramatically depends on
the quality of input objects. For example, face recognition quality would be
lower for blurry or corrupted inputs. However, it is hard to predict the
influence of input quality on the resulting accuracy in more complex scenarios.
We propose an approach for deep metric learning that allows direct estimation
of the uncertainty with almost no additional computational cost. The developed
\textit{ScaleFace} algorithm uses trainable scale values that modify
similarities in the space of embeddings. These input-dependent scale values
represent a measure of confidence in the recognition result, thus allowing
uncertainty estimation. We provide comprehensive experiments on face
recognition tasks that show the superior performance of ScaleFace compared to
other uncertainty-aware face recognition approaches. We also extend the results
to the task of text-to-image retrieval showing that the proposed approach beats
the competitors with significant margin.

We consider the problem of sequential recommendations, where at each step an
agent proposes some slate of $N$ distinct items to a user from a much larger
catalog of size $K>>N$. The user has unknown preferences towards the
recommendations and the agent takes sequential actions that optimise (in our
case minimise) some user-related cost, with the help of Reinforcement Learning.
The possible item combinations for a slate is $\binom{K}{N}$, an enormous
number rendering value iteration methods intractable. We prove that the
slate-MDP can actually be decomposed using just $K$ item-related $Q$ functions
per state, which describe the problem in a more compact and efficient way.
Based on this, we propose a novel model-free SARSA and Q-learning algorithm
that performs $N$ parallel iterations per step, without any prior user
knowledge. We call this method \texttt{SlateFree}, i.e. free-of-slates, and we
show numerically that it converges very fast to the exact optimum for arbitrary
user profiles, and that it outperforms alternatives from the literature.

Unsupervised domain adaptation (UDA) has attracted considerable attention,
which transfers knowledge from a label-rich source domain to a related but
unlabeled target domain. Reducing inter-domain differences has always been a
crucial factor to improve performance in UDA, especially for tasks where there
is a large gap between source and target domains. To this end, we propose a
novel style-aware feature fusion method (SAFF) to bridge the large domain gap
and transfer knowledge while alleviating the loss of class-discriminative
information. Inspired by the human transitive inference and learning ability, a
novel style-aware self-intermediate domain (SSID) is investigated to link two
seemingly unrelated concepts through a series of intermediate auxiliary
synthesized concepts. Specifically, we propose a novel learning strategy of
SSID, which selects samples from both source and target domains as anchors, and
then randomly fuses the object and style features of these anchors to generate
labeled and style-rich intermediate auxiliary features for knowledge transfer.
Moreover, we design an external memory bank to store and update specified
labeled features to obtain stable class features and class-wise style features.
Based on the proposed memory bank, the intra- and inter-domain loss functions
are designed to improve the class recognition ability and feature
compatibility, respectively. Meanwhile, we simulate the rich latent feature
space of SSID by infinite sampling and the convergence of the loss function by
mathematical theory. Finally, we conduct comprehensive experiments on commonly
used domain adaptive benchmarks to evaluate the proposed SAFF, and the
experimental results show that the proposed SAFF can be easily combined with
different backbone networks and obtain better performance as a plug-in-plug-out
module.

Human visual brain use three main component such as color, texture and shape
to detect or identify environment and objects. Hence, texture analysis has been
paid much attention by scientific researchers in last two decades. Texture
features can be used in many different applications in commuter vision or
machine learning problems. Since now, many different approaches have been
proposed to classify textures. Most of them consider the classification
accuracy as the main challenge that should be improved. In this article, a new
approach is proposed based on combination of two efficient texture descriptor,
co-occurrence matrix and local ternary patterns (LTP). First of all, basic
local binary pattern and LTP are performed to extract local textural
information. Next, a subset of statistical features is extracted from
gray-level co-occurrence matrixes. Finally, concatenated features are used to
train classifiers. The performance is evaluated on Brodatz benchmark dataset in
terms of accuracy. Experimental results show that proposed approach provide
higher classification rate in comparison with some state-of-the-art approaches.

Chemical kinetics mechanisms are essential for understanding, analyzing, and
simulating complex combustion phenomena. In this study, a Neural Ordinary
Differential Equation (Neural ODE) framework is employed to optimize kinetics
parameters of reaction mechanisms. Given experimental or high-cost simulated
observations as training data, the proposed algorithm can optimally recover the
hidden characteristics in the data. Different datasets of various sizes, types,
and noise levels are tested. A classic toy problem of stiff Robertson ODE is
first used to demonstrate the learning capability, efficiency, and robustness
of the Neural ODE approach. A 41-species, 232-reactions JP-10 skeletal
mechanism and a 34-species, 121-reactions n-heptane skeletal mechanism are then
optimized with species' temporal profiles and ignition delay times,
respectively. Results show that the proposed algorithm can optimize stiff
chemical models with sufficient accuracy and efficiency. It is noted that the
trained mechanism not only fits the data perfectly but also retains its
physical interpretability, which can be further integrated and validated in
practical turbulent combustion simulations.

Deep learning approaches achieve state-of-the-art performance for classifying
radiology images, but rely on large labelled datasets that require
resource-intensive annotation by specialists. Both semi-supervised learning and
active learning can be utilised to mitigate this annotation burden. However,
there is limited work on combining the advantages of semi-supervised and active
learning approaches for multi-label medical image classification. Here, we
introduce a novel Consistency-based Semi-supervised Evidential Active Learning
framework (CSEAL). Specifically, we leverage predictive uncertainty based on
theories of evidence and subjective logic to develop an end-to-end integrated
approach that combines consistency-based semi-supervised learning with
uncertainty-based active learning. We apply our approach to enhance four
leading consistency-based semi-supervised learning methods: Pseudo-labelling,
Virtual Adversarial Training, Mean Teacher and NoTeacher. Extensive evaluations
on multi-label Chest X-Ray classification tasks demonstrate that CSEAL achieves
substantive performance improvements over two leading semi-supervised active
learning baselines. Further, a class-wise breakdown of results shows that our
approach can substantially improve accuracy on rarer abnormalities with fewer
labelled samples.

Although being a question in the very methodological core of machine
learning, there is still no unanimous consensus on how to compare classifiers.
Every comparison framework is confronted with (at least) three fundamental
challenges: the multiplicity of quality criteria, the multiplicity of data sets
and the randomness/arbitrariness of the selection of data sets. In this paper,
we add a fresh view to the vivid debate by adopting recent developments in
decision theory. Our resulting framework, based on so-called preference
systems, ranks classifiers by a generalized concept of stochastic dominance,
which powerfully circumvents the cumbersome, and often even self-contradictory,
reliance on aggregates. Moreover, we show that generalized stochastic dominance
can be operationalized by solving easy-to-handle linear programs and
statistically tested by means of an adapted two-sample
observation-randomization test. This indeed yields a powerful framework for the
statistical comparison of classifiers with respect to multiple quality criteria
simultaneously. We illustrate and investigate our framework in a simulation
study and with standard benchmark data sets.

This paper proposes a novel dual inexact splitting algorithm (DISA) for the
distributed convex composite optimization problem, where the local loss
function consists of an $L$-smooth term and a possibly nonsmooth term which is
composed with a linear operator. We prove that DISA is convergent when the
primal and dual stepsizes $\tau$, $\beta$ satisfy $0<\tau<{2}/{L}$ and
$0<\tau\beta <1$. Compared with existing primal-dual proximal splitting
algorithms (PD-PSAs), DISA overcomes the dependence of the convergence stepsize
range on the Euclidean norm of the linear operator. It implies that DISA allows
for larger stepsizes when the Euclidean norm is large, thus ensuring fast
convergence of it. Moreover, we establish the sublinear and linear convergence
rate of DISA under general convexity and metric subregularity, respectively.
Furthermore, an approximate iterative version of DISA is provided, and the
global convergence and sublinear convergence rate of this approximate version
are proved. Finally, numerical experiments not only corroborate the theoretical
analyses but also indicate that DISA achieves a significant acceleration
compared with the existing PD-PSAs.

Deep Neural Network (DNN) models are increasingly evaluated using new
replication test datasets, which have been carefully created to be similar to
older and popular benchmark datasets. However, running counter to expectations,
DNN classification models show significant, consistent, and largely unexplained
degradation in accuracy on these replication test datasets. While the popular
evaluation approach is to assess the accuracy of a model by making use of all
the datapoints available in the respective test datasets, we argue that doing
so hinders us from adequately capturing the behavior of DNN models and from
having realistic expectations about their accuracy. Therefore, we propose a
principled evaluation protocol that is suitable for performing comparative
investigations of the accuracy of a DNN model on multiple test datasets,
leveraging subsets of datapoints that can be selected using different criteria,
including uncertainty-related information. By making use of this new evaluation
protocol, we determined the accuracy of $564$ DNN models on both (1) the
CIFAR-10 and ImageNet datasets and (2) their replication datasets. Our
experimental results indicate that the observed accuracy degradation between
established benchmark datasets and their replications is consistently lower
(that is, models do perform better on the replication test datasets) than the
accuracy degradation reported in published works, with these published works
relying on conventional evaluation approaches that do not utilize
uncertainty-related information.

Entity alignment aims to discover unique equivalent entity pairs with the
same meaning across different knowledge graphs (KGs). Existing models have
focused on projecting KGs into a latent embedding space so that inherent
semantics between entities can be captured for entity alignment. However, the
adverse impacts of alignment conflicts have been largely overlooked during
training, thereby limiting the entity alignment performance. To address this
issue, we propose a novel Conflict-aware Pseudo Labeling via Optimal Transport
model (CPL-OT) for entity alignment. The key idea is to iteratively
pseudo-label alignment pairs empowered with conflict-aware optimal transport
(OT) modeling to boost the precision of entity alignment. CPL-OT is composed of
two key components -- entity embedding learning with global-local aggregation
and iterative conflict-aware pseudo labeling -- that mutually reinforce each
other. To mitigate alignment conflicts during pseudo labeling, we propose to
use optimal transport as an effective means to warrant one-to-one entity
alignment between two KGs with the minimal overall transport cost. Extensive
experiments on benchmark datasets validate the superiority of CPL-OT over
state-of-the-art baselines under both settings with and without prior alignment
seeds.

Aided by advances in neural density estimation, considerable progress has
been made in recent years towards a suite of simulation-based inference (SBI)
methods capable of performing flexible, black-box, approximate Bayesian
inference for stochastic simulation models. While it has been demonstrated that
neural SBI methods can provide accurate posterior approximations, the
simulation studies establishing these results have considered only
well-specified problems -- that is, where the model and the data generating
process coincide exactly. However, the behaviour of such algorithms in the case
of model misspecification has received little attention. In this work, we
provide the first comprehensive study of the behaviour of neural SBI algorithms
in the presence of various forms of model misspecification. We find that
misspecification can have a profoundly deleterious effect on performance. Some
mitigation strategies are explored, but no approach tested prevents failure in
all cases. We conclude that new approaches are required to address model
misspecification if neural SBI algorithms are to be relied upon to derive
accurate scientific conclusions.

Generative Adversarial Networks (GANs) are powerful Machine Learning models
capable of generating fully synthetic samples of a desired phenomenon with a
high resolution. Despite their success, the training process of a GAN is highly
unstable and typically it is necessary to implement several accessory
heuristics to the networks to reach an acceptable convergence of the model. In
this paper, we introduce a novel method to analyze the convergence and
stability in the training of Generative Adversarial Networks. For this purpose,
we propose to decompose the objective function of the adversary min-max game
defining a periodic GAN into its Fourier series. By studying the dynamics of
the truncated Fourier series for the continuous Alternating Gradient Descend
algorithm, we are able to approximate the real flow and to identify the main
features of the convergence of the GAN. This approach is confirmed empirically
by studying the training flow in a $2$-parametric GAN aiming to generate an
unknown exponential distribution. As byproduct, we show that convergent orbits
in GANs are small perturbations of periodic orbits so the Nash equillibria are
spiral attractors. This theoretically justifies the slow and unstable training
observed in GANs.

Human intuition allows to detect abnormal driving scenarios in situations
they never experienced before. Like humans detect those abnormal situations and
take countermeasures to prevent collisions, self-driving cars need anomaly
detection mechanisms. However, the literature lacks a standard benchmark for
the comparison of anomaly detection algorithms. We fill the gap and propose the
R-U-MAAD benchmark for unsupervised anomaly detection in multi-agent
trajectories. The goal is to learn a representation of the normal driving from
the training sequences without labels, and afterwards detect anomalies. We use
the Argoverse Motion Forecasting dataset for the training and propose a test
dataset of 160 sequences with human-annotated anomalies in urban environments.
To this end we combine a replay of real-world trajectories and scene-dependent
abnormal driving in the simulation. In our experiments we compare 11 baselines
including linear models, deep auto-encoders and one-class classification models
using standard anomaly detection metrics. The deep reconstruction and
end-to-end one-class methods show promising results. The benchmark and the
baseline models will be publicly available.

The issue of shortcut learning is widely known in NLP and has been an
important research focus in recent years. Unintended correlations in the data
enable models to easily solve tasks that were meant to exhibit advanced
language understanding and reasoning capabilities. In this survey paper, we
focus on the field of machine reading comprehension (MRC), an important task
for showcasing high-level language understanding that also suffers from a range
of shortcuts. We summarize the available techniques for measuring and
mitigating shortcuts and conclude with suggestions for further progress in
shortcut research. Most importantly, we highlight two main concerns for
shortcut mitigation in MRC: the lack of public challenge sets, a necessary
component for effective and reusable evaluation, and the lack of certain
mitigation techniques that are prominent in other areas.

Automated anomaly detection from medical images, such as MRIs and X-rays, can
significantly reduce human effort in disease diagnosis. Owing to the complexity
of modeling anomalies and the high cost of manual annotation by domain experts
(e.g., radiologists), a typical technique in the current medical imaging
literature has focused on deriving diagnostic models from healthy subjects
only, assuming the model will detect the images from patients as outliers.
However, in many real-world scenarios, unannotated datasets with a mix of both
healthy and diseased individuals are abundant. Therefore, this paper poses the
research question of how to improve unsupervised anomaly detection by utilizing
(1) an unannotated set of mixed images, in addition to (2) the set of healthy
images as being used in the literature. To answer the question, we propose
HealthyGAN, a novel one-directional image-to-image translation method, which
learns to translate the images from the mixed dataset to only healthy images.
Being one-directional, HealthyGAN relaxes the requirement of cycle consistency
of existing unpaired image-to-image translation methods, which is unattainable
with mixed unannotated data. Once the translation is learned, we generate a
difference map for any given image by subtracting its translated output.
Regions of significant responses in the difference map correspond to potential
anomalies (if any). Our HealthyGAN outperforms the conventional
state-of-the-art methods by significant margins on two publicly available
datasets: COVID-19 and NIH ChestX-ray14, and one institutional dataset
collected from Mayo Clinic. The implementation is publicly available at
https://github.com/mahfuzmohammad/HealthyGAN.

Traditional policy gradient methods are fundamentally flawed. Natural
gradients converge quicker and better, forming the foundation of contemporary
Reinforcement Learning such as Trust Region Policy Optimization (TRPO) and
Proximal Policy Optimization (PPO). This lecture note aims to clarify the
intuition behind natural policy gradients, focusing on the thought process and
the key mathematical constructs.

The task of Human-Object Interaction (HOI) detection targets fine-grained
visual parsing of humans interacting with their environment, enabling a broad
range of applications. Prior work has demonstrated the benefits of effective
architecture design and integration of relevant cues for more accurate HOI
detection. However, the design of an appropriate pre-training strategy for this
task remains underexplored by existing approaches. To address this gap, we
propose Relational Language-Image Pre-training (RLIP), a strategy for
contrastive pre-training that leverages both entity and relation descriptions.
To make effective use of such pre-training, we make three technical
contributions: (1) a new Parallel entity detection and Sequential relation
inference (ParSe) architecture that enables the use of both entity and relation
descriptions during holistically optimized pre-training; (2) a synthetic data
generation framework, Label Sequence Extension, that expands the scale of
language data available within each minibatch; (3) mechanisms to account for
ambiguity, Relation Quality Labels and Relation Pseudo-Labels, to mitigate the
influence of ambiguous/noisy samples in the pre-training data. Through
extensive experiments, we demonstrate the benefits of these contributions,
collectively termed RLIP-ParSe, for improved zero-shot, few-shot and
fine-tuning HOI detection performance as well as increased robustness to
learning from noisy annotations. Code will be available at
https://github.com/JacobYuan7/RLIP.

Under-Display Camera (UDC) has been widely exploited to help smartphones
realize full screen display. However, as the screen could inevitably affect the
light propagation process, the images captured by the UDC system usually
contain flare, haze, blur, and noise. Particularly, flare and blur in UDC
images could severely deteriorate the user experience in high dynamic range
(HDR) scenes. In this paper, we propose a new deep model, namely UDC-UNet, to
address the UDC image restoration problem with the known Point Spread Function
(PSF) in HDR scenes. On the premise that Point Spread Function (PSF) of the UDC
system is known, we treat UDC image restoration as a non-blind image
restoration problem and propose a novel learning-based approach. Our network
consists of three parts, including a U-shape base network to utilize
multi-scale information, a condition branch to perform spatially variant
modulation, and a kernel branch to provide the prior knowledge of the given
PSF. According to the characteristics of HDR data, we additionally design a
tone mapping loss to stabilize network optimization and achieve better visual
quality. Experimental results show that the proposed UDC-UNet outperforms the
state-of-the-art methods in quantitative and qualitative comparisons. Our
approach won the second place in the UDC image restoration track of MIPI
challenge. Codes will be publicly available.

Many practical decision-making problems in economics and healthcare seek to
estimate the average treatment effect (ATE) from observational data. The
Double/Debiased Machine Learning (DML) is one of the prevalent methods to
estimate ATE in the observational study. However, the DML estimators can suffer
an error-compounding issue and even give an extreme estimate when the
propensity scores are misspecified or very close to 0 or 1. Previous studies
have overcome this issue through some empirical tricks such as propensity score
trimming, yet none of the existing literature solves this problem from a
theoretical standpoint. In this paper, we propose a Robust Causal Learning
(RCL) method to offset the deficiencies of the DML estimators. Theoretically,
the RCL estimators i) are as consistent and doubly robust as the DML
estimators, and ii) can get rid of the error-compounding issue. Empirically,
the comprehensive experiments show that i) the RCL estimators give more stable
estimations of the causal parameters than the DML estimators, and ii) the RCL
estimators outperform the traditional estimators and their variants when
applying different machine learning models on both simulation and benchmark
datasets.

Sound event localization and detection (SELD) is a joint task of sound event
detection and direction-of-arrival estimation. In DCASE 2022 Task 3, types of
data transform from computationally generated spatial recordings to recordings
of real-sound scenes. Our system submitted to the DCASE 2022 Task 3 is based on
our previous proposed Event-Independent Network V2 (EINV2) with a novel data
augmentation method. Our method employs EINV2 with a track-wise output format,
permutation-invariant training, and a soft parameter-sharing strategy, to
detect different sound events of the same class but in different locations. The
Conformer structure is used for extending EINV2 to learn local and global
features. A data augmentation method, which contains several data augmentation
chains composed of stochastic combinations of several different data
augmentation operations, is utilized to generalize the model. To mitigate the
lack of real-scene recordings in the development dataset and the presence of
sound events being unbalanced, we exploit FSD50K, AudioSet, and TAU Spatial
Room Impulse Response Database (TAU-SRIR DB) to generate simulated datasets for
training. We present results on the validation set of Sony-TAu Realistic
Spatial Soundscapes 2022 (STARSS22) in detail. Experimental results indicate
that the ability to generalize to different environments and unbalanced
performance among different classes are two main challenges. We evaluate our
proposed method in Task 3 of the DCASE 2022 challenge and obtain the second
rank in the teams ranking. Source code is released.

The deep convolutional neural networks (CNNs)-based single image dehazing
methods have achieved significant success. The previous methods are devoted to
improving the network's performance by increasing the network's depth and
width. The current methods focus on increasing the convolutional kernel size to
enhance its performance by benefiting from the larger receptive field. However,
directly increasing the size of the convolutional kernel introduces a massive
amount of computational overhead and parameters. Thus, a novel Large Kernel
Convolution Dehaze Block (LKD Block) consisting of the Decomposition deep-wise
Large Kernel Convolution Block (DLKCB) and the Channel Enhanced Feed-forward
Network (CEFN) is devised in this paper. The designed DLKCB can split the
deep-wise large kernel convolution into a smaller depth-wise convolution and a
depth-wise dilated convolution without introducing massive parameters and
computational overhead. Meanwhile, the designed CEFN incorporates a channel
attention mechanism into Feed-forward Network to exploit significant channels
and enhance robustness. By combining multiple LKD Blocks and Up-Down sampling
modules, the Large Kernel Convolution Dehaze Network (LKD-Net) is conducted.
The evaluation results demonstrate the effectiveness of the designed DLKCB and
CEFN, and our LKD-Net outperforms the state-of-the-art. On the SOTS indoor
dataset, our LKD-Net dramatically outperforms the Transformer-based method
Dehamer with only 1.79% #Param and 48.9% FLOPs. The source code of our LKD-Net
is available at https://github.com/SWU-CS-MediaLab/LKD-Net.

Generative learning is a powerful tool for representation learning, and shows
particular promise for problems in biomedical imaging. However, in this
context, sampling from the distribution is secondary to finding representations
of real images, which often come with labels and explicitly represent the
content and quality of the target distribution. It remains difficult to
faithfully reconstruct images from generative models, particularly those as
complex as histological images. In this work, two existing methods
(autoencoders and adversarial latent autoencoders) are combined in attempt to
improve our ability to encode and decode real images of non-melanoma skin
cancer, specifically intra-epidermal carcinoma (IEC). Utilising a dataset of
high-quality images of IEC (256 x 256), this work assesses the result of both
image reconstruction quality and representation learning. It is shown that
adversarial training can improve baseline FID scores from 76 to 50, and that
benchmarks on representation learning can be improved by up to 3%. Smooth and
realistic interpolations of the variation in the morphological structure are
also presented for the first time, positioning representation learning as a
promising direction in the context of computational pathology.

Multi-agent reinforcement learning was performed in this study for indoor
path planning of two unmanned aerial vehicles (UAVs). Each UAV performed the
task of moving as fast as possible from a randomly paired initial position to a
goal position in an environment with obstacles. To minimize training time and
prevent the damage of UAVs, learning was performed by simulation. Considering
the non-stationary characteristics of the multi-agent environment wherein the
optimal behavior varies based on the actions of other agents, the action of the
other UAV was also included in the state space of each UAV. Curriculum learning
was performed in two stages to increase learning efficiency. A goal rate of
89.0% was obtained compared with other learning strategies that obtained goal
rates of 73.6% and 79.9%.

Robots are integrating more huge-size models to enrich functions and improve
accuracy, which leads to out-of-control computing pressure. And thus robots are
encountering bottlenecks in computing power and battery capacity. Fog or cloud
robotics is one of the most anticipated theories to address these issues.
Approaches of cloud robotics have developed from system-level to node-level.
However, the present node-level systems are not flexible enough to dynamically
adapt to changing conditions. To address this, we present ElasticROS, which
evolves the present node-level systems into an algorithm-level one. ElasticROS
is based on ROS and ROS2. For fog and cloud robotics, it is the first robot
operating system with algorithm-level collaborative computing. ElasticROS
develops elastic collaborative computing to achieve adaptability to dynamic
conditions. The collaborative computing algorithm is the core and challenge of
ElasticROS. We abstract the problem and then propose an algorithm named
ElasAction to address. It is a dynamic action decision algorithm based on
online learning, which determines how robots and servers cooperate. The
algorithm dynamically updates parameters to adapt to changes of conditions
where the robot is currently in. It achieves elastically distributing of
computing tasks to robots and servers according to configurations. In addition,
we prove that the regret upper bound of the ElasAction is sublinear, which
guarantees its convergence and thus enables ElasticROS to be stable in its
elasticity. Finally, we conducted experiments with ElasticROS on common tasks
of robotics, including SLAM, grasping and human-robot dialogue, and then
measured its performances in latency, CPU usage and power consumption. The
algorithm-level ElasticROS performs significantly better than the present
node-level system.

This work introduces a B-frame coding framework, termed B-CANF, that exploits
conditional augmented normalizing flows for B-frame coding. Learned B-frame
coding is less explored and more challenging. Motivated by recent advances in
conditional P-frame coding, B-CANF is the first attempt at applying flow-based
models to both conditional motion and inter-frame coding. B-CANF features
frame-type adaptive coding that learns better bit allocation for hierarchical
B-frame coding. B-CANF also introduces a special type of B-frame, called
B*-frame, to mimic P-frame coding. On commonly used datasets, B-CANF achieves
the state-of-the-art compression performance, showing comparable BD-rate
results (in terms of PSNR-RGB) to HM-16.23 under the random access
configuration.

Recently, several studies have applied deep convolutional neural networks
(CNNs) in image compressive sensing (CS) tasks to improve reconstruction
quality. However, convolutional layers generally have a small receptive field;
therefore, capturing long-range pixel correlations using CNNs is challenging,
which limits their reconstruction performance in image CS tasks. Considering
this limitation, we propose a U-shaped transformer for image CS tasks, called
the Uformer-ICS. We develop a projection-based transformer block by integrating
the prior projection knowledge of CS into the original transformer blocks, and
then build a symmetrical reconstruction model using the projection-based
transformer blocks and residual convolutional blocks. Compared with previous
CNN-based CS methods that can only exploit local image features, the proposed
reconstruction model can simultaneously utilize the local features and
long-range dependencies of an image, and the prior projection knowledge of the
CS theory. Additionally, we design an adaptive sampling model that can
adaptively sample image blocks based on block sparsity, which can ensure that
the compressed results retain the maximum possible information of the original
image under a fixed sampling ratio. The proposed Uformer-ICS is an end-to-end
framework that simultaneously learns the sampling and reconstruction processes.
Experimental results demonstrate that it achieves significantly better
reconstruction performance than existing state-of-the-art deep learning-based
CS methods.

Similar to colorization in computer vision, instrument separation is to
assign instrument labels (e.g. piano, guitar...) to notes from unlabeled
mixtures which contain only performance information. To address the problem, we
adopt diffusion models and explicitly guide them to preserve consistency
between mixtures and music. The quantitative results show that our proposed
model can generate high-fidelity samples for multitrack symbolic music with
creativity.

The empirical risk minimization approach to data-driven decision making
assumes that we can learn a decision rule from training data drawn under the
same conditions as the ones we want to deploy it under. However, in a number of
settings, we may be concerned that our training sample is biased, and that some
groups (characterized by either observable or unobservable attributes) may be
under- or over-represented relative to the general population; and in this
setting empirical risk minimization over the training set may fail to yield
rules that perform well at deployment. Building on concepts from
distributionally robust optimization and sensitivity analysis, we propose a
method for learning a decision rule that minimizes the worst-case risk incurred
under a family of test distributions whose conditional distributions of
outcomes $Y$ given covariates $X$ differ from the conditional training
distribution by at most a constant factor, and whose covariate distributions
are absolutely continuous with respect to the covariate distribution of the
training data. We apply a result of Rockafellar and Uryasev to show that this
problem is equivalent to an augmented convex risk minimization problem. We give
statistical guarantees for learning a robust model using the method of sieves
and propose a deep learning algorithm whose loss function captures our
robustness target. We empirically validate our proposed method in simulations
and a case study with the MIMIC-III dataset.

Recently, federated learning (FL) has received intensive research because of
its ability in preserving data privacy for scattered clients to collaboratively
train machine learning models. Commonly, a parameter server (PS) is deployed
for aggregating model parameters contributed by different clients.
Decentralized federated learning (DFL) is upgraded from FL which allows clients
to aggregate model parameters with their neighbours directly. DFL is
particularly feasible for vehicular networks as vehicles communicate with each
other in a vehicle-to-vehicle (V2V) manner. However, due to the restrictions of
vehicle routes and communication distances, it is hard for individual vehicles
to sufficiently exchange models with others. Data sources contributing to
models on individual vehicles may not diversified enough resulting in poor
model accuracy. To address this problem, we propose the DFL-DDS (DFL with
diversified Data Sources) algorithm to diversify data sources in DFL.
Specifically, each vehicle maintains a state vector to record the contribution
weight of each data source to its model. The Kullback-Leibler (KL) divergence
is adopted to measure the diversity of a state vector. To boost the convergence
of DFL, a vehicle tunes the aggregation weight of each data source by
minimizing the KL divergence of its state vector, and its effectiveness in
diversifying data sources can be theoretically proved. Finally, the superiority
of DFL-DDS is evaluated by extensive experiments (with MNIST and CIFAR-10
datasets) which demonstrate that DFL-DDS can accelerate the convergence of DFL
and improve the model accuracy significantly compared with state-of-the-art

Image enhancement aims at improving the aesthetic visual quality of photos by
retouching the color and tone, and is an essential technology for professional
digital photography. Recent years deep learning-based image enhancement
algorithms have achieved promising performance and attracted increasing
popularity. However, typical efforts attempt to construct a uniform enhancer
for all pixels' color transformation. It ignores the pixel differences between
different content (e.g., sky, ocean, etc.) that are significant for
photographs, causing unsatisfactory results. In this paper, we propose a novel
learnable context-aware 4-dimensional lookup table (4D LUT), which achieves
content-dependent enhancement of different contents in each image via
adaptively learning of photo context. In particular, we first introduce a
lightweight context encoder and a parameter encoder to learn a context map for
the pixel-level category and a group of image-adaptive coefficients,
respectively. Then, the context-aware 4D LUT is generated by integrating
multiple basis 4D LUTs via the coefficients. Finally, the enhanced image can be
obtained by feeding the source image and context map into fused context-aware
4D~LUT via quadrilinear interpolation. Compared with traditional 3D LUT, i.e.,
RGB mapping to RGB, which is usually used in camera imaging pipeline systems or
tools, 4D LUT, i.e., RGBC(RGB+Context) mapping to RGB, enables finer control of
color transformations for pixels with different content in each image, even
though they have the same RGB values. Experimental results demonstrate that our
method outperforms other state-of-the-art methods in widely-used benchmarks.

Effectively preserving and encoding structure features from objects in
irregular and sparse LiDAR points is a key challenge to 3D object detection on
point cloud. Recently, Transformer has demonstrated promising performance on
many 2D and even 3D vision tasks. Compared with the fixed and rigid convolution
kernels, the self-attention mechanism in Transformer can adaptively exclude the
unrelated or noisy points and thus suitable for preserving the local spatial
structure in irregular LiDAR point cloud. However, Transformer only performs a
simple sum on the point features, based on the self-attention mechanism, and
all the points share the same transformation for value. Such isotropic
operation lacks the ability to capture the direction-distance-oriented local
structure which is important for 3D object detection. In this work, we propose
a Structure-Embedding transFormer (SEFormer), which can not only preserve local
structure as traditional Transformer but also have the ability to encode the
local structure. Compared to the self-attention mechanism in traditional
Transformer, SEFormer learns different feature transformations for value points
based on the relative directions and distances to the query point. Then we
propose a SEFormer based network for high-performance 3D object detection.
Extensive experiments show that the proposed architecture can achieve SOTA
results on Waymo Open Dataset, the largest 3D detection benchmark for
autonomous driving. Specifically, SEFormer achieves 79.02% mAP, which is 1.2%
higher than existing works. We will release the codes.

Video denoising for raw image has always been the difficulty of camera image
processing. On the one hand, image denoising performance largely determines the
image quality, moreover denoising effect in raw image will affect the accuracy
of the following operations of ISP processing flow. On the other hand, compared
with image, video have motion information in time sequence, thus motion
estimation which is complex and computationally expensive is needed in video
denoising. In view of the above problems, this paper proposes a video denoising
algorithm for raw image, performing multiple cascading processing stages on
raw-RGB image based on convolutional neural network, and carries out implicit
motion estimation in the network. The denoising performance is far superior to
that of traditional algorithms with minimal computation and bandwidth, and has
computational advantages compared with most deep learning algorithms.

The logistic regression model is one of the most powerful statistical methods
for the analysis of binary data. The logistic regression allows to use a set of
covariates to explain the binary responses. The mixture of logistic regression
models is used to fit heterogeneous populations through an unsupervised
learning approach. The multicollinearity problem is one of the most common
problems in logistics and a mixture of logistic regressions where the
covariates are highly correlated. This problem results in unreliable maximum
likelihood estimates for the regression coefficients. This research developed
shrinkage methods to deal with the multicollinearity in a mixture of logistic
regression models. These shrinkage methods include ridge and Liu-type
estimators. Through extensive numerical studies, we show that the developed
methods provide more reliable results in estimating the coefficients of the
mixture. Finally, we applied the shrinkage methods to analyze the bone disorder
status of women aged 50 and older.

From early image processing to modern computational imaging, successful
models and algorithms have relied on a fundamental property of natural signals:
symmetry. Here symmetry refers to the invariance property of signal sets to
transformations such as translation, rotation or scaling. Symmetry can also be
incorporated into deep neural networks in the form of equivariance, allowing
for more data-efficient learning. While there has been important advances in
the design of end-to-end equivariant networks for image classification in
recent years, computational imaging introduces unique challenges for
equivariant network solutions since we typically only observe the image through
some noisy ill-conditioned forward operator that itself may not be equivariant.
We review the emerging field of equivariant imaging and show how it can provide
improved generalization and new imaging opportunities. Along the way we show
the interplay between the acquisition physics and group actions and links to
iterative reconstruction, blind compressed sensing and self-supervised
learning.

Deep learning (DL), a branch of artificial intelligence (AI) techniques, has
shown great promise in various disciplines such as image classification and
segmentation, speech recognition, language translation, among others. This
remarkable success of DL has stimulated increasing interest in applying this
paradigm to wireless channel estimation in recent years. Since DL principles
are inductive in nature and distinct from the conventional rule-based
algorithms, when one tries to use DL technique to the channel estimation, one
might easily get stuck and confused by so many knobs to control and small
details to be aware of. The primary purpose of this paper is to discuss key
issues and possible solutions in DL-based wireless channel estimation and
channel state information (CSI) feedback including the DL model selection,
training data acquisition, and neural network design for 6G. Specifically, we
present several case studies together with the numerical experiments to
demonstrate the effectiveness of the DL-based wireless channel estimation

Trojan backdoor is a poisoning attack against Neural Network (NN) classifiers
in which adversaries try to exploit the (highly desirable) model reuse property
to implant Trojans into model parameters for backdoor breaches through a
poisoned training process. Most of the proposed defenses against Trojan attacks
assume a white-box setup, in which the defender either has access to the inner
state of NN or is able to run back-propagation through it. In this work, we
propose a more practical black-box defense, dubbed TrojDef, which can only run
forward-pass of the NN. TrojDef tries to identify and filter out Trojan inputs
(i.e., inputs augmented with the Trojan trigger) by monitoring the changes in
the prediction confidence when the input is repeatedly perturbed by random
noise. We derive a function based on the prediction outputs which is called the
prediction confidence bound to decide whether the input example is Trojan or
not. The intuition is that Trojan inputs are more stable as the
misclassification only depends on the trigger, while benign inputs will suffer
when augmented with noise due to the perturbation of the classification
features.
  Through mathematical analysis, we show that if the attacker is perfect in
injecting the backdoor, the Trojan infected model will be trained to learn the
appropriate prediction confidence bound, which is used to distinguish Trojan
and benign inputs under arbitrary perturbations. However, because the attacker
might not be perfect in injecting the backdoor, we introduce a nonlinear
transform to the prediction confidence bound to improve the detection accuracy
in practical settings. Extensive empirical evaluations show that TrojDef
significantly outperforms the-state-of-the-art defenses and is highly stable
under different settings, even when the classifier architecture, the training
process, or the hyper-parameters change.

Graph Attention Networks (GATs) have been intensively studied and widely used
in graph data learning tasks. Existing GATs generally adopt the self-attention
mechanism to conduct graph edge attention learning, requiring expensive
computation. It is known that Spiking Neural Networks (SNNs) can perform
inexpensive computation by transmitting the input signal data into discrete
spike trains and can also return sparse outputs. Inspired by the merits of
SNNs, in this work, we propose a novel Graph Spiking Attention Network (GSAT)
for graph data representation and learning. In contrast to self-attention
mechanism in existing GATs, the proposed GSAT adopts a SNN module architecture
which is obvious energy-efficient. Moreover, GSAT can return sparse attention
coefficients in natural and thus can perform feature aggregation on the
selective neighbors which makes GSAT perform robustly w.r.t graph edge noises.
Experimental results on several datasets demonstrate the effectiveness, energy
efficiency and robustness of the proposed GSAT model.

Over a five-year period, computing methods for generating high-fidelity,
fictional depictions of people and events moved from exotic demonstrations by
computer science research teams into ongoing use as a tool of disinformation.
The methods, referred to with the portmanteau of "deepfakes," have been used to
create compelling audiovisual content. Here, I share challenges ahead with
malevolent uses of two classes of deepfakes that we can expect to come into
practice with costly implications for society: interactive and compositional
deepfakes. Interactive deepfakes have the capability to impersonate people with
realistic interactive behaviors, taking advantage of advances in multimodal
interaction. Compositional deepfakes leverage synthetic content in larger
disinformation plans that integrate sets of deepfakes over time with observed,
expected, and engineered world events to create persuasive synthetic histories.
Synthetic histories can be constructed manually but may one day be guided by
adversarial generative explanation (AGE) techniques. In the absence of
mitigations, interactive and compositional deepfakes threaten to move us closer
to a post-epistemic world, where fact cannot be distinguished from fiction. I
shall describe interactive and compositional deepfakes and reflect about
cautions and potential mitigations to defend against them.

Large pretrained models such as GPT-3 have had tremendous impact on modern
natural language processing by leveraging self-supervised learning to learn
salient representations that can be used to readily finetune on a wide variety
of downstream tasks. We investigate the possibility of transferring such
advances to molecular machine learning by building a chemical foundation model,
ChemBERTa-2, using the language of SMILES. While labeled data for molecular
prediction tasks is typically scarce, libraries of SMILES strings are readily
available. In this work, we build upon ChemBERTa by optimizing the pretraining
process. We compare multi-task and self-supervised pretraining by varying
hyperparameters and pretraining dataset size, up to 77M compounds from PubChem.
To our knowledge, the 77M set constitutes one of the largest datasets used for
molecular pretraining to date. We find that with these pretraining
improvements, we are competitive with existing state-of-the-art architectures
on the MoleculeNet benchmark suite. We analyze the degree to which improvements
in pretraining translate to improvement on downstream tasks.

As Autonomous Vehicle (AV) development has progressed, concerns regarding the
safety of passengers and agents in their environment have risen. Each real
world traffic collision involving autonomously controlled vehicles has
compounded this concern. Open source autonomous driving implementations show a
software architecture with complex interdependent tasks, heavily reliant on
machine learning and Deep Neural Networks (DNN), which are vulnerable to non
deterministic faults and corner cases. These complex subsystems work together
to fulfill the mission of the AV while also maintaining safety. Although
significant improvements are being made towards increasing the empirical
reliability and confidence in these systems, the inherent limitations of DNN
verification create an, as yet, insurmountable challenge in providing
deterministic safety guarantees in AV.
  We propose Synergistic Redundancy (SR), a safety architecture for complex
cyber physical systems, like AV. SR provides verifiable safety guarantees
against specific faults by decoupling the mission and safety tasks of the
system. Simultaneous to independently fulfilling their primary roles, the
partially functionally redundant mission and safety tasks are able to aid each
other, synergistically improving the combined system. The synergistic safety
layer uses only verifiable and logically analyzable software to fulfill its
tasks. Close coordination with the mission layer allows easier and early
detection of safety critical faults in the system. SR simplifies the mission
layer's optimization goals and improves its design. SR provides safe deployment
of high performance, although inherently unverifiable, machine learning
software. In this work, we first present the design and features of the SR
architecture and then evaluate the efficacy of the solution, focusing on the
crucial problem of obstacle existence detection faults in AV.

Automatic Speaker Verification (ASV) technology has become commonplace in
virtual assistants. However, its performance suffers when there is a mismatch
between the train and test domains. Mixed bandwidth training, i.e., pooling
training data from both domains, is a preferred choice for developing a
universal model that works for both narrowband and wideband domains. We propose
complementing this technique by performing neural upsampling of narrowband
signals, also known as bandwidth extension. Our main goal is to discover and
analyze high-performing time-domain Generative Adversarial Network (GAN) based
models to improve our downstream state-of-the-art ASV system. We choose GANs
since they (1) are powerful for learning conditional distribution and (2) allow
flexible plug-in usage as a pre-processor during the training of downstream
task (ASV) with data augmentation. Prior works mainly focus on feature-domain
bandwidth extension and limited experimental setups. We address these
limitations by 1) using time-domain extension models, 2) reporting results on
three real test sets, 2) extending training data, and 3) devising new test-time
schemes. We compare supervised (conditional GAN) and unsupervised GANs
(CycleGAN) and demonstrate average relative improvement in Equal Error Rate of
8.6% and 7.7%, respectively. For further analysis, we study changes in
spectrogram visual quality, audio perceptual quality, t-SNE embeddings, and ASV
score distributions. We show that our bandwidth extension leads to phenomena
such as a shift of telephone (test) embeddings towards wideband (train)
signals, a negative correlation of perceptual quality with downstream
performance, and condition-independent score calibration.

Even though a train/test split of the dataset randomly performed is a common
practice, could not always be the best approach for estimating performance
generalization under some scenarios. The fact is that the usual machine
learning methodology can sometimes overestimate the generalization error when a
dataset is not representative or when rare and elusive examples are a
fundamental aspect of the detection problem. In the present work, we analyze
strategies based on the predictors' variability to split in training and
testing sets. Such strategies aim at guaranteeing the inclusion of rare or
unusual examples with a minimal loss of the population's representativeness and
provide a more accurate estimation about the generalization error when the
dataset is not representative. Two baseline classifiers based on decision trees
were used for testing the four splitting strategies considered. Both
classifiers were applied on CTU19 a low-representative dataset for a network
security detection problem. Preliminary results showed the importance of
applying the three alternative strategies to the Monte Carlo splitting strategy
in order to get a more accurate error estimation on different but feasible

Small neural networks (NNs) used for error correction were shown to improve
on classic channel codes and to address channel model changes. We extend the
code dimension of any such structure by using the same NN under one-hot
encoding multiple times, which are serially-concatenated with an outer classic
code. We design NNs with the same network parameters, where each Reed-Solomon
codeword symbol is an input to a different NN. Significant improvements in
block error probabilities for an additive Gaussian noise channel as compared to
the small neural code are illustrated, as well as robustness to channel model
changes.

The emerging field of Explainable Artificial Intelligence focuses on
researching methods of explaining the decision making processes of complex
machine learning models. In the field of explainability for Computer Vision,
explanations are provided as saliency maps, which visualize the importance of
individual pixels of the input w.r.t. the model's prediction. In this work we
focus on a perturbation-based, model-agnostic explainability method called
RISE, elaborate on observed shortcomings of its grid-based approach and propose
two modifications: replacement of square occlusions with convex polygonal
occlusions based on cells of a Voronoi mesh and addition of an informativeness
guarantee to the occlusion mask generator. These modifications, collectively
called VRISE (Voronoi-RISE), are meant to, respectively, improve the accuracy
of maps generated using large occlusions and accelerate convergence of saliency
maps in cases where sampling density is either very low or very high. We
perform a quantitative comparison of accuracy of saliency maps produced by
VRISE and RISE on the validation split of ILSVRC2012, using a saliency-guided
content insertion/deletion metric and a localization metric based on bounding
boxes. Additionally, we explore the space of configurable occlusion pattern
parameters to better understand their influence on saliency maps produced by
RISE and VRISE. We also describe and demonstrate two effects observed over the
course of experimentation, arising from the random sampling approach of RISE:
"feature slicing" and "saliency misattribution". Our results show that convex
polygonal occlusions yield more accurate maps for coarse occlusion meshes and
multi-object images, but improvement is not guaranteed in other cases. The
informativeness guarantee is shown to increase the convergence rate without
incurring a significant computational overhead.

In this paper we develop a novel method of combining many forecasts based on
a machine learning algorithm called Graphical LASSO. We visualize forecast
errors from different forecasters as a network of interacting entities and
generalize network inference in the presence of common factor structure and
structural breaks. First, we note that forecasters often use common information
and hence make common mistakes, which makes the forecast errors exhibit common
factor structures. We propose the Factor Graphical LASSO (Factor GLASSO), which
separates common forecast errors from the idiosyncratic errors and exploits
sparsity of the precision matrix of the latter. Second, since the network of
experts changes over time as a response to unstable environments such as
recessions, it is unreasonable to assume constant forecast combination weights.
Hence, we propose Regime-Dependent Factor Graphical LASSO (RD-Factor GLASSO)
and develop its scalable implementation using the Alternating Direction Method
of Multipliers (ADMM) to estimate regime-dependent forecast combination
weights. The empirical application to forecasting macroeconomic series using
the data of the European Central Bank's Survey of Professional Forecasters (ECB
SPF) demonstrates superior performance of a combined forecast using Factor
GLASSO and RD-Factor GLASSO.

Variational inference (VI) is a specific type of approximate Bayesian
inference that approximates an intractable posterior distribution with a
tractable one. VI casts the inference problem as an optimization problem, more
specifically, the goal is to maximize a lower bound of the logarithm of the
marginal likelihood with respect to the parameters of the approximate
posterior. Reinforcement learning (RL) on the other hand deals with autonomous
agents and how to make them act optimally such as to maximize some notion of
expected future cumulative reward. In the non-sequential setting where agents'
actions do not have an impact on future states of the environment, RL is
covered by contextual bandits and Bayesian optimization. In a proper sequential
scenario, however, where agents' actions affect future states, instantaneous
rewards need to be carefully traded off against potential long-term rewards.
This manuscript shows how the apparently different subjects of VI and RL are
linked in two fundamental ways. First, the optimization objective of RL to
maximize future cumulative rewards can be recovered via a VI objective under a
soft policy constraint in both the non-sequential and the sequential setting.
This policy constraint is not just merely artificial but has proven as a useful
regularizer in many RL tasks yielding significant improvements in agent
performance. And second, in model-based RL where agents aim to learn about the
environment they are operating in, the model-learning part can be naturally
phrased as an inference problem over the process that governs environment
dynamics. We are going to distinguish between two scenarios for the latter: VI
when environment states are fully observable by the agent and VI when they are
only partially observable through an observation distribution.

The primary hyperparameter in kernel regression (KR) is the choice of kernel.
In most theoretical studies of KR, one assumes the kernel is fixed before
seeing the training data. Under this assumption, it is known that the optimal
kernel is equal to the prior covariance of the target function. In this note,
we consider KR in which the kernel may be updated after seeing the training
data. We point out that an analogous choice of kernel using the posterior of
the target function is optimal in this setting. Connections to the view of deep
neural networks as data-dependent kernel learners are discussed.

Existing studies on neural architecture search (NAS) mainly focus on
efficiently and effectively searching for network architectures with better
performance. Little progress has been made to systematically understand if the
NAS-searched architectures are robust to privacy attacks while abundant work
has already shown that human-designed architectures are prone to privacy
attacks. In this paper, we fill this gap and systematically measure the privacy
risks of NAS architectures. Leveraging the insights from our measurement study,
we further explore the cell patterns of cell-based NAS architectures and
evaluate how the cell patterns affect the privacy risks of NAS-searched
architectures. Through extensive experiments, we shed light on how to design
robust NAS architectures against privacy attacks, and also offer a general
methodology to understand the hidden correlation between the NAS-searched
architectures and other privacy risks.

Individual probabilities refer to the probabilities of outcomes that are
realized only once: the probability that it will rain tomorrow, the probability
that Alice will die within the next 12 months, the probability that Bob will be
arrested for a violent crime in the next 18 months, etc. Individual
probabilities are fundamentally unknowable. Nevertheless, we show that two
parties who agree on the data -- or on how to sample from a data distribution
-- cannot agree to disagree on how to model individual probabilities. This is
because any two models of individual probabilities that substantially disagree
can together be used to empirically falsify and improve at least one of the two
models. This can be efficiently iterated in a process of "reconciliation" that
results in models that both parties agree are superior to the models they
started with, and which themselves (almost) agree on the forecasts of
individual probabilities (almost) everywhere. We conclude that although
individual probabilities are unknowable, they are contestable via a
computationally and data efficient process that must lead to agreement. Thus we
cannot find ourselves in a situation in which we have two equally accurate and
unimprovable models that disagree substantially in their predictions --
providing an answer to what is sometimes called the predictive or model
multiplicity problem.

We propose a novel output layer activation function, which we name ASTra
(Asymmetric Sigmoid Transfer function), which makes the classification of
minority examples, in scenarios of high imbalance, more tractable. We combine
this with a loss function that helps to effectively target minority
misclassification. These two methods can be used together or separately, with
their combination recommended for the most severely imbalanced cases. The
proposed approach is tested on datasets with IRs from 588.24 to 4000 and very
few minority examples (in some datasets, as few as five). Results using neural
networks with from two to 12 hidden units are demonstrated to be comparable to,
or better than, equivalent results obtained in a recent study that deployed a
wide range of complex, hybrid data-level ensemble classifiers.

This research proposes a new database and method to detect the reduction of
alertness conditions due to alcohol, drug consumption and sleepiness
deprivation from Near-Infra-Red (NIR) periocular eye images. The study focuses
on determining the effect of external factors on the Central Nervous System
(CNS). The goal is to analyse how this impacts iris and pupil movement
behaviours and if it is possible to classify these changes with a standard iris
NIR capture device. This paper proposes a modified MobileNetV2 to classify iris
NIR images taken from subjects under alcohol/drugs/sleepiness influences. The
results show that the MobileNetV2-based classifier can detect the Unfit
alertness condition from iris samples captured after alcohol and drug
consumption robustly with a detection accuracy of 91.3% and 99.1%,
respectively. The sleepiness condition is the most challenging with 72.4%. For
two-class grouped images belonging to the Fit/Unfit classes, the model obtained
an accuracy of 94.0% and 84.0%, respectively, using a smaller number of
parameters than the standard Deep learning Network algorithm. This work is a
step forward in biometric applications for developing an automatic system to
classify "Fitness for Duty" and prevent accidents due to alcohol/drug
consumption and sleepiness.

Vertical distributed learning exploits the local features collected by
multiple learning workers to form a better global model. However, the exchange
of data between the workers and the model aggregator for parameter training
incurs a heavy communication burden, especially when the learning system is
built upon capacity-constrained wireless networks. In this paper, we propose a
novel hierarchical distributed learning framework, where each worker separately
learns a low-dimensional embedding of their local observed data. Then, they
perform communication efficient distributed max-pooling for efficiently
transmitting the synthesized input to the aggregator. For data exchange over a
shared wireless channel, we propose an opportunistic carrier sensing-based
protocol to implement the max-pooling operation for the output data from all
the learning workers. Our simulation experiments show that the proposed
learning framework is able to achieve almost the same model accuracy as the
learning model using the concatenation of all the raw outputs from the learning
workers, while requiring a communication load that is independent of the number
of workers.

In recent years, designing fairness-aware methods has received much attention
in various domains, including machine learning, natural language processing,
and information retrieval. However, understanding structural bias and
inequalities in social networks and designing fairness-aware methods for
various research problems in social network analysis (SNA) have not received
much attention. In this work, we highlight how the structural bias of social
networks impacts the fairness of different SNA methods. We further discuss
fairness aspects that should be considered while proposing network
structure-based solutions for different SNA problems, such as link prediction,
influence maximization, centrality ranking, and community detection. This paper
clearly highlights that very few works have considered fairness and bias while
proposing solutions; even these works are mainly focused on some research
topics, such as link prediction, influence maximization, and PageRank. However,
fairness has not yet been addressed for other research topics, such as
influence blocking and community detection. We review state-of-the-art for
different research topics in SNA, including the considered fairness
constraints, their limitations, and our vision. This paper also covers
evaluation metrics, available datasets, and synthetic network generating models
used in such studies. Finally, we highlight various open research directions
that require researchers' attention to bridge the gap between fairness and SNA.

Features learned from single radiologic images are unable to provide
information about whether and how much a lesion may be changing over time.
Time-dependent features computed from repeated images can capture those changes
and help identify malignant lesions by their temporal behavior. However,
longitudinal medical imaging presents the unique challenge of sparse, irregular
time intervals in data acquisition. While self-attention has been shown to be a
versatile and efficient learning mechanism for time series and natural images,
its potential for interpreting temporal distance between sparse, irregularly
sampled spatial features has not been explored. In this work, we propose two
interpretations of a time-distance vision transformer (ViT) by using (1) vector
embeddings of continuous time and (2) a temporal emphasis model to scale
self-attention weights. The two algorithms are evaluated based on benign versus
malignant lung cancer discrimination of synthetic pulmonary nodules and lung
screening computed tomography studies from the National Lung Screening Trial
(NLST). Experiments evaluating the time-distance ViTs on synthetic nodules show
a fundamental improvement in classifying irregularly sampled longitudinal
images when compared to standard ViTs. In cross-validation on screening chest
CTs from the NLST, our methods (0.785 and 0.786 AUC respectively) significantly
outperform a cross-sectional approach (0.734 AUC) and match the discriminative
performance of the leading longitudinal medical imaging algorithm (0.779 AUC)
on benign versus malignant classification. This work represents the first
self-attention-based framework for classifying longitudinal medical images. Our
code is available at https://github.com/tom1193/time-distance-transformer.

Unsupervised video object segmentation (VOS) aims to detect the most salient
object in a video sequence at the pixel level. In unsupervised VOS, most
state-of-the-art methods leverage motion cues obtained from optical flow maps
in addition to appearance cues to exploit the property that salient objects
usually have distinctive movements compared to the background. However, as they
are overly dependent on motion cues, which may be unreliable in some cases,
they cannot achieve stable prediction. To reduce this motion dependency of
existing two-stream VOS methods, we propose a novel motion-as-option network
that optionally utilizes motion cues. Additionally, to fully exploit the
property of the proposed network that motion is not always required, we
introduce a collaborative network learning strategy. On all the public
benchmark datasets, our proposed network affords state-of-the-art performance
with real-time inference speed.

Sparse expert models are a thirty-year old concept re-emerging as a popular
architecture in deep learning. This class of architecture encompasses
Mixture-of-Experts, Switch Transformers, Routing Networks, BASE layers, and
others, all with the unifying idea that each example is acted on by a subset of
the parameters. By doing so, the degree of sparsity decouples the parameter
count from the compute per example allowing for extremely large, but efficient
models. The resulting models have demonstrated significant improvements across
diverse domains such as natural language processing, computer vision, and
speech recognition. We review the concept of sparse expert models, provide a
basic description of the common algorithms, contextualize the advances in the
deep learning era, and conclude by highlighting areas for future work.

Named Entity Recognition is the task to locate and classify the entities in
the text. However, Unlabeled Entity Problem in NER datasets seriously hinders
the improvement of NER performance. This paper proposes SCL-RAI to cope with
this problem. Firstly, we decrease the distance of span representations with
the same label while increasing it for different ones via span-based
contrastive learning, which relieves the ambiguity among entities and improves
the robustness of the model over unlabeled entities. Then we propose retrieval
augmented inference to mitigate the decision boundary shifting problem. Our
method significantly outperforms the previous SOTA method by 4.21% and 8.64%
F1-score on two real-world datasets.

Fraud detection is a challenging task due to the changing nature of fraud
patterns over time and the limited availability of fraud examples to learn such
sophisticated patterns. Thus, fraud detection with the aid of smart versions of
machine learning (ML) tools is essential to assure safety. Fraud detection is a
primary ML classification task; however, the optimum performance of the
corresponding ML tool relies on the usage of the best hyperparameter values.
Moreover, classification under imbalanced classes is quite challenging as it
causes poor performance in minority classes, which most ML classification
techniques ignore. Thus, we investigate four state-of-the-art ML techniques,
namely, logistic regression, decision trees, random forest, and extreme
gradient boost, that are suitable for handling imbalance classes to maximize
precision and simultaneously reduce false positives. First, these classifiers
are trained on two original benchmark unbalanced fraud detection datasets,
namely, phishing website URLs and fraudulent credit card transactions. Then,
three synthetically balanced datasets are produced for each original data set
by implementing the sampling frameworks, namely, RandomUnderSampler, SMOTE, and
SMOTEENN. The optimum hyperparameters for all the 16 experiments are revealed
using the method RandomzedSearchCV. The validity of the 16 approaches in the
context of fraud detection is compared using two benchmark performance metrics,
namely, area under the curve of receiver operating characteristics (AUC ROC)
and area under the curve of precision and recall (AUC PR). For both phishing
website URLs and credit card fraud transaction datasets, the results indicate
that extreme gradient boost trained on the original data shows trustworthy
performance in the imbalanced dataset and manages to outperform the other three
methods in terms of both AUC ROC and AUC PR.

While it is encouraging to witness the recent development in
privacy-preserving Machine Learning as a Service (MLaaS), there still exists a
significant performance gap for its deployment in real-world applications. We
observe the state-of-the-art frameworks follow a compute-and-share principle
for every function output where the summing in linear functions, which is the
last of two steps for function output, involves all rotations (which is the
most expensive HE operation), and the multiplexing in nonlinear functions,
which is also the last of two steps for function output, introduces noticeable
communication rounds. Therefore, we challenge the conventional
compute-and-share logic and introduce the first joint linear and nonlinear
computation across functions that features by 1) the PHE triplet for computing
the nonlinear function, with which the multiplexing is eliminated; 2) the
matrix encoding to calculate the linear function, with which all rotations for
summing is removed; and 3) the network adaptation to reassemble the model
structure, with which the joint computation module is utilized as much as
possible. The boosted efficiency is verified by the numerical complexity, and
the experiments demonstrate up to 13x speedup for various functions used in the
state-of-the-art models and up to 5x speedup over mainstream neural networks.

Currently, cross-scene hyperspectral image (HSI) classification has drawn
increasing attention. It is necessary to train a model only on source domain
(SD) and directly transferring the model to target domain (TD), when TD needs
to be processed in real time and cannot be reused for training. Based on the
idea of domain generalization, a Single-source Domain Expansion Network
(SDEnet) is developed to ensure the reliability and effectiveness of domain
extension. The method uses generative adversarial learning to train in SD and
test in TD. A generator including semantic encoder and morph encoder is
designed to generate the extended domain (ED) based on
encoder-randomization-decoder architecture, where spatial and spectral
randomization are specifically used to generate variable spatial and spectral
information, and the morphological knowledge is implicitly applied as domain
invariant information during domain expansion. Furthermore, the supervised
contrastive learning is employed in the discriminator to learn class-wise
domain invariant representation, which drives intra-class samples of SD and ED.
Meanwhile, adversarial training is designed to optimize the generator to drive
intra-class samples of SD and ED to be separated. Extensive experiments on two
public HSI datasets and one additional multispectral image (MSI) dataset
demonstrate the superiority of the proposed method when compared with

Massive Open Online Courses (MOOCs) once offered the promise of accessibility
and affordability. However, MOOCs typically lack expert feedback and social
interaction, and have low student engagement and retention. Thus, alternative
programs for online education have emerged including an online graduate program
in computer science at a major public university in USA. This program is
considered a success with over 9000 students now enrolled in the program. We
adopt the perspective of cognitive science to answer the question why do only
some online educational courses succeed? We measure learner motivation and
self-regulation in one course in the program, specifically a course on
artificial intelligence (AI). Surveys of students indicate that students
self-reported assessments of self-efficacy, cognitive strategy use, and
intrinsic value of the course are not only fairly high, but also generally
increase over the course of learning. This data suggests that the online AI
course might be a success because the students have high self-efficacy and the
class fosters self-regulated learning.

In computer vision there has been significant research interest in assessing
potential demographic bias in deep learning models. One of the main causes of
such bias is imbalance in the training data. In medical imaging, where the
potential impact of bias is arguably much greater, there has been less
interest. In medical imaging pipelines, segmentation of structures of interest
plays an important role in estimating clinical biomarkers that are subsequently
used to inform patient management. Convolutional neural networks (CNNs) are
starting to be used to automate this process. We present the first systematic
study of the impact of training set imbalance on race and sex bias in CNN-based
segmentation. We focus on segmentation of the structures of the heart from
short axis cine cardiac magnetic resonance images, and train multiple CNN
segmentation models with different levels of race/sex imbalance. We find no
significant bias in the sex experiment but significant bias in two separate
race experiments, highlighting the need to consider adequate representation of
different demographic groups in health datasets.

ProBoost, a new boosting algorithm for probabilistic classifiers, is proposed
in this work. This algorithm uses the epistemic uncertainty of each training
sample to determine the most challenging/uncertain ones; the relevance of these
samples is then increased for the next weak learner, producing a sequence that
progressively focuses on the samples found to have the highest uncertainty. In
the end, the weak learners' outputs are combined into a weighted ensemble of
classifiers. Three methods are proposed to manipulate the training set:
undersampling, oversampling, and weighting the training samples according to
the uncertainty estimated by the weak learners. Furthermore, two approaches are
studied regarding the ensemble combination. The weak learner herein considered
is a standard convolutional neural network, and the probabilistic models
underlying the uncertainty estimation use either variational inference or Monte
Carlo dropout. The experimental evaluation carried out on MNIST benchmark
datasets shows that ProBoost yields a significant performance improvement. The
results are further highlighted by assessing the relative achievable
improvement, a metric proposed in this work, which shows that a model with only
four weak learners leads to an improvement exceeding 12% in this metric (for
either accuracy, sensitivity, or specificity), in comparison to the model
learned without ProBoost.

This paper reviews concepts, modeling approaches, and recent findings along a
spectrum of different levels of abstraction of neural network models including
generalization across (1) Samples, (2) Distributions, (3) Domains, (4) Tasks,
(5) Modalities, and (6) Scopes. Results on (1) sample generalization show that,
in the case of ImageNet, nearly all the recent improvements reduced training
error while overfitting stayed flat; with nearly all the training error
eliminated, future progress will require a focus on reducing overfitting.
Perspectives from statistics highlight how (2) distribution generalization can
be viewed alternately as a change in sample weights or a change in the
input-output relationship. Transfer learning approaches to (3) domain
generalization are summarized, as are recent advances and the wealth of domain
adaptation benchmark datasets available. Recent breakthroughs surveyed in (4)
task generalization include few-shot meta-learning approaches and the BERT NLP
engine, and recent (5) modality generalization studies are discussed that
integrate image and text data and that apply a biologically-inspired network
across olfactory, visual, and auditory modalities. Recent (6) scope
generalization results are reviewed that embed knowledge graphs into deep NLP
approaches. Additionally, concepts from neuroscience are discussed on the
modular architecture of brains and the steps by which dopamine-driven
conditioning leads to abstract thinking.

Adaptive gradient algorithms such as ADAGRAD and its variants have gained
popularity in the training of deep neural networks. While many works as for
adaptive methods have focused on the static regret as a performance metric to
achieve a good regret guarantee, the dynamic regret analyses of these methods
remain unclear. As opposed to the static regret, dynamic regret is considered
to be a stronger concept of performance measurement in the sense that it
explicitly elucidates the non-stationarity of the environment. In this paper,
we go through a variant of ADAGRAD (referred to as M-ADAGRAD ) in a strong
convex setting via the notion of dynamic regret, which measures the performance
of an online learner against a reference (optimal) solution that may change
over time. We demonstrate a regret bound in terms of the path-length of the
minimizer sequence that essentially reflects the non-stationarity of
environments. In addition, we enhance the dynamic regret bound by exploiting
the multiple accesses of the gradient to the learner in each round. Empirical
results indicate that M-ADAGRAD works also well in practice.

This study presents machine learning models that forecast and categorize lost
circulation severity preemptively using a large class imbalanced drilling
dataset. We demonstrate reproducible core techniques involved in tackling a
large drilling engineering challenge utilizing easily interpretable machine
learning approaches.
  We utilized a 65,000+ records data with class imbalance problem from Azadegan
oilfield formations in Iran. Eleven of the dataset's seventeen parameters are
chosen to be used in the classification of five lost circulation events. To
generate classification models, we used six basic machine learning algorithms
and four ensemble learning methods. Linear Discriminant Analysis (LDA),
Logistic Regression (LR), Support Vector Machines (SVM), Classification and
Regression Trees (CART), k-Nearest Neighbors (KNN), and Gaussian Naive Bayes
(GNB) are the six fundamental techniques. We also used bagging and boosting
ensemble learning techniques in the investigation of solutions for improved
predicting performance. The performance of these algorithms is measured using
four metrics: accuracy, precision, recall, and F1-score. The F1-score weighted
to represent the data imbalance is chosen as the preferred evaluation
criterion.
  The CART model was found to be the best in class for identifying drilling
fluid circulation loss events with an average weighted F1-score of 0.9904 and
standard deviation of 0.0015. Upon application of ensemble learning techniques,
a Random Forest ensemble of decision trees showed the best predictive
performance. It identified and classified lost circulation events with a
perfect weighted F1-score of 1.0. Using Permutation Feature Importance (PFI),
the measured depth was found to be the most influential factor in accurately
recognizing lost circulation events while drilling.

Medical report generation is a challenging task since it is time-consuming
and requires expertise from experienced radiologists. The goal of medical
report generation is to accurately capture and describe the image findings.
Previous works pretrain their visual encoding neural networks with large
datasets in different domains, which cannot learn general visual representation
in the specific medical domain. In this work, we propose a medical report
generation framework that uses a contrastive learning approach to pretrain the
visual encoder and requires no additional meta information. In addition, we
adopt lung segmentation as an augmentation method in the contrastive learning
framework. This segmentation guides the network to focus on encoding the visual
feature within the lung region. Experimental results show that the proposed
framework improves the performance and the quality of the generated medical
reports both quantitatively and qualitatively.

Deep learning techniques have been applied widely in industrial
recommendation systems. However, far less attention has been paid to the
overfitting problem of models in recommendation systems, which, on the
contrary, is recognized as a critical issue for deep neural networks. In the
context of Click-Through Rate (CTR) prediction, we observe an interesting
one-epoch overfitting problem: the model performance exhibits a dramatic
degradation at the beginning of the second epoch. Such a phenomenon has been
witnessed widely in real-world applications of CTR models. Thereby, the best
performance is usually achieved by training with only one epoch. To understand
the underlying factors behind the one-epoch phenomenon, we conduct extensive
experiments on the production data set collected from the display advertising
system of Alibaba. The results show that the model structure, the optimization
algorithm with a fast convergence rate, and the feature sparsity are closely
related to the one-epoch phenomenon. We also provide a likely hypothesis for
explaining such a phenomenon and conduct a set of proof-of-concept experiments.
We hope this work can shed light on future research on training more epochs for
better performance.

We study the problem of target stabilization with robust obstacle avoidance
in robots and vehicles that have access only to vision-based sensors for the
purpose of realtime localization. This problem is particularly challenging due
to the topological obstructions induced by the obstacle, which preclude the
existence of smooth feedback controllers able to achieve simultaneous
stabilization and robust obstacle avoidance. To overcome this issue, we develop
a vision-based hybrid controller that switches between two different feedback
laws depending on the current position of the vehicle using a hysteresis
mechanism and a data-assisted supervisor. The main innovation of the paper is
the incorporation of suitable perception maps into the hybrid controller. These
maps can be learned from data obtained from cameras in the vehicles and trained
via convolutional neural networks (CNN). Under suitable assumptions on this
perception map, we establish theoretical guarantees for the trajectories of the
vehicle in terms of convergence and obstacle avoidance. Moreover, the proposed
vision-based hybrid controller is numerically tested under different scenarios,
including noisy data, sensors with failures, and cameras with occlusions.

The pervasiveness of graphs in today's real life systems is quite evident,
where the system either explicitly exists as graph or can be readily modelled
as one. Such graphical structure is thus a store house rich information. This
has various implication depending on whether we are interested in a node or the
graph as a whole. In this paper, we are primarily concerned with the later,
that is, the inference that the structure of the graph influences the property
of the real life system it represents. A model of such structural influence
would be useful in inferencing useful properties of complex and large systems,
like VLSI circuits, through its structural property. However, before we can
apply some machine learning (ML) based technique to model such relationship, an
effective representation of the graph is imperative. In this paper, we propose
a graph representation which is lossless, linear-sized in terms of number of
vertices and gives a 1-D representation of the graph. Our representation is
based on Prufer encoding for trees. Moreover, our method is based on a novel
technique, called $\mathcal{GT}$-enhancement whereby we first transform the
graph such that it can be represented by a singular tree. The encoding also
provides scope to include additional graph property and improve the
interpretability of the code.

Adaptive filtering is a well-known problem with a wide range of applications,
including echo cancellation. Extensive research during the past few decades has
led to the invention of various algorithms. However, the known
computationally-efficient solutions show a tradeoff between convergence speed
and accuracy. Moreover, running these algorithms involves heuristically setting
various parameters that considerably affect their performances. In this paper,
we propose a new algorithm which we refer to as online block maximum likelihood
(OBML). OBML is a computationally-efficient online learning algorithm that
employs maximum likelihood (ML) estimation every $P$ samples. We fully
characterize the expected performance of OBML and show that i) OBML is able to
asymptotically recover the unknown coefficients and ii) its expected estimation
error asymptotically converges to zero as $O({1\over t})$. We also derive an
alternative version of OBML, which we refer to as incremental maximum
likelihood (IML), which incrementally updates its ML estimate of the
coefficients at every sample. Our simulation results verify the analytical
conclusions for memoryless inputs, and also show excellent performance of both
OBML and IML in an audio echo cancellation application with strongly correlated
input signals.

Web-based phishing accounts for over 90% of data breaches, and most
web-browsers and security vendors rely on machine-learning (ML) models as
mitigation. Despite this, links posted regularly on anti-phishing aggregators
such as PhishTank and VirusTotal are shown to easily bypass existing detectors.
Prior art suggests that automated website cloning, with light mutations, is
gaining traction with attackers. This has limited exposure in current
literature and leads to sub-optimal ML-based countermeasures. The work herein
conducts the first empirical study that compiles and evaluates a variety of
state-of-the-art cloning techniques in wide circulation. We collected 13,394
samples and found 8,566 confirmed phishing pages targeting 4 popular websites
using 7 distinct cloning mechanisms. These samples were replicated with
malicious code removed within a controlled platform fortified with precautions
that prevent accidental access. We then reported our sites to VirusTotal and
other platforms, with regular polling of results for 7 days, to ascertain the
efficacy of each cloning technique. Results show that no security vendor
detected our clones, proving the urgent need for more effective detectors.
Finally, we posit 4 recommendations to aid web developers and ML-based defences
to alleviate the risks of cloning attacks.

A staple food in more than a hundred nations worldwide is rice (Oryza
sativa). The cultivation of rice is vital to global economic growth. However,
the main issue facing the agricultural industry is rice leaf disease. The
quality and quantity of the crops have declined, and this is the main cause. As
farmers in any country do not have much knowledge about rice leaf disease, they
cannot diagnose rice leaf disease properly. That's why they cannot take proper
care of rice leaves. As a result, the production is decreasing. From literature
survey, it has seen that YOLOv5 exhibit the better result compare to others
deep learning method. As a result of the continual advancement of object
detection technology, YOLO family algorithms, which have extraordinarily high
precision and better speed have been used in various scene recognition tasks to
build rice leaf disease monitoring systems. We have annotate 1500 collected
data sets and propose a rice leaf disease classification and detection method
based on YOLOv5 deep learning. We then trained and evaluated the YOLOv5 model.
The simulation outcomes show improved object detection result for the augmented
YOLOv5 network proposed in this article. The required levels of recognition
precision, recall, mAP value, and F1 score are 90\%, 67\%, 76\%, and 81\%
respectively are considered as performance metrics.

In the existing methods, LiDAR odometry shows superior performance, but
visual odometry is still widely used for its price advantage. Conventionally,
the task of visual odometry mainly rely on the input of continuous images.
However, it is very complicated for the odometry network to learn the epipolar
geometry information provided by the images. In this paper, the concept of
pseudo-LiDAR is introduced into the odometry to solve this problem. The
pseudo-LiDAR point cloud back-projects the depth map generated by the image
into the 3D point cloud, which changes the way of image representation.
Compared with the stereo images, the pseudo-LiDAR point cloud generated by the
stereo matching network can get the explicit 3D coordinates. Since the 6
Degrees of Freedom (DoF) pose transformation occurs in 3D space, the 3D
structure information provided by the pseudo-LiDAR point cloud is more direct
than the image. Compared with sparse LiDAR, the pseudo-LiDAR has a denser point
cloud. In order to make full use of the rich point cloud information provided
by the pseudo-LiDAR, a projection-aware dense odometry pipeline is adopted.
Most previous LiDAR-based algorithms sampled 8192 points from the point cloud
as input to the odometry network. The projection-aware dense odometry pipeline
takes all the pseudo-LiDAR point clouds generated from the images except for
the error points as the input to the network. While making full use of the 3D
geometric information in the images, the semantic information in the images is
also used in the odometry task. The fusion of 2D-3D is achieved in an
image-only based odometry. Experiments on the KITTI dataset prove the
effectiveness of our method. To the best of our knowledge, this is the first
visual odometry method using pseudo-LiDAR.

Deep code generation is a topic of deep learning for software engineering
(DL4SE), which adopts neural models to generate code for the intended
functions. Since end-to-end neural methods lack the awareness of domain
knowledge and software hierarchy, the results often require manual correction.
To systematically explore the potential improvements of code generation, we let
it participate in the whole top-down development from intentions to
realizations, which is possible in limited scopes. In the process, it benefits
from massive samples, features, and knowledge. As the foundation, we suggest
building a taxonomy on code data, namely code taxonomy, leveraging the
categorization of code information. Moreover, we introduce a three-layer
semantic pyramid (SP) to associate text data and code data. It identifies the
information of different abstraction levels, and thus introduces the domain
knowledge on development and reveals the hierarchy of software. Furthermore, we
propose a semantic pyramid framework (SPF) as the approach, focusing on
softwares of high modularity and low complexity. SPF divides the code
generation process into stages and reserves spots for potential interactions.
Eventually, we conceived application scopes for SPF.

Variable selection is an important problem in statistics and machine
learning. Copula Entropy (CE) is a mathematical concept for measuring
statistical independence and has been applied to variable selection recently.
In this paper we propose to apply the CE-based method for variable selection to
survival analysis. The idea is to measure the correlation between variables and
time-to-event with CE and then select variables according to their CE value.
Experiments on simulated data and two real cancer data were conducted to
compare the proposed method with two related methods: random survival forest
and Lasso-Cox. Experimental results showed that the proposed method can select
the 'right' variables out that are more interpretable and lead to better
prediction performance.

Adversarial continual learning is effective for continual learning problems
because of the presence of feature alignment process generating task-invariant
features having low susceptibility to the catastrophic forgetting problem.
Nevertheless, the ACL method imposes considerable complexities because it
relies on task-specific networks and discriminators. It also goes through an
iterative training process which does not fit for online (one-epoch) continual
learning problems. This paper proposes a scalable adversarial continual
learning (SCALE) method putting forward a parameter generator transforming
common features into task-specific features and a single discriminator in the
adversarial game to induce common features. The training process is carried out
in meta-learning fashions using a new combination of three loss functions.
SCALE outperforms prominent baselines with noticeable margins in both accuracy
and execution time.

Graph Neural Networks (GNNs) have become the backbone for a myriad of tasks
pertaining to graphs and similar topological data structures. While many works
have been established in domains related to node and graph
classification/regression tasks, they mostly deal with a single task. Continual
learning on graphs is largely unexplored and existing graph continual learning
approaches are limited to the task-incremental learning scenarios. This paper
proposes a graph continual learning strategy that combines the
architecture-based and memory-based approaches. The structural learning
strategy is driven by reinforcement learning, where a controller network is
trained in such a way to determine an optimal number of nodes to be
added/pruned from the base network when new tasks are observed, thus assuring
sufficient network capacities. The parameter learning strategy is underpinned
by the concept of Dark Experience replay method to cope with the catastrophic
forgetting problem. Our approach is numerically validated with several graph
continual learning benchmark problems in both task-incremental learning and
class-incremental learning settings. Compared to recently published works, our
approach demonstrates improved performance in both the settings. The
implementation code can be found at \url{https://github.com/codexhammer/gcl}.

Many real-world classification problems have imbalanced frequency of class
labels; a well-known issue known as the "class imbalance" problem. Classic
classification algorithms tend to be biased towards the majority class, leaving
the classifier vulnerable to misclassification of the minority class. While the
literature is rich with methods to fix this problem, as the dimensionality of
the problem increases, many of these methods do not scale-up and the cost of
running them become prohibitive. In this paper, we present an end-to-end deep
generative classifier. We propose a domain-constraint autoencoder to preserve
the latent-space as prior for a generator, which is then used to play an
adversarial game with two other deep networks, a discriminator and a
classifier. Extensive experiments are carried out on three different
multi-class imbalanced problems and a comparison with state-of-the-art methods.
Experimental results confirmed the superiority of our method over popular
algorithms in handling high-dimensional imbalanced classification problems. Our
code is available on https://github.com/TanmDL/SLPPL-GAN.

Deception is prevalent in human social settings. However, studies into the
effect of deception on reinforcement learning algorithms have been limited to
simplistic settings, restricting their applicability to complex real-world
problems. This paper addresses this by introducing a new mixed
competitive-cooperative multi-agent reinforcement learning (MARL) environment
inspired by popular role-based deception games such as Werewolf, Avalon, and
Among Us. The environment's unique challenge lies in the necessity to cooperate
with other agents despite not knowing if they are friend or foe. Furthermore,
we introduce a model of deception, which we call Bayesian belief manipulation
(BBM) and demonstrate its effectiveness at deceiving other agents in this
environment while also increasing the deceiving agent's performance.

A cross domain multistream classification is a challenging problem calling
for fast domain adaptations to handle different but related streams in
never-ending and rapidly changing environments. Notwithstanding that existing
multistream classifiers assume no labelled samples in the target stream, they
still incur expensive labelling cost since they require fully labelled samples
of the source stream. This paper aims to attack the problem of extreme label
shortage in the cross domain multistream classification problems where only
very few labelled samples of the source stream are provided before process
runs. Our solution, namely Learning Streaming Process from Partial Ground Truth
(LEOPARD), is built upon a flexible deep clustering network where its hidden
nodes, layers and clusters are added and removed dynamically in respect to
varying data distributions. A deep clustering strategy is underpinned by a
simultaneous feature learning and clustering technique leading to
clustering-friendly latent spaces. A domain adaptation strategy relies on the
adversarial domain adaptation technique where a feature extractor is trained to
fool a domain classifier classifying source and target streams. Our numerical
study demonstrates the efficacy of LEOPARD where it delivers improved
performances compared to prominent algorithms in 15 of 24 cases. Source codes
of LEOPARD are shared in \url{https://github.com/wengweng001/LEOPARD.git} to
enable further study.

Detecting conditional independencies plays a key role in several statistical
and machine learning tasks, especially in causal discovery algorithms. In this
study, we introduce LCIT (Latent representation based Conditional Independence
Test)-a novel non-parametric method for conditional independence testing based
on representation learning. Our main contribution involves proposing a
generative framework in which to test for the independence between X and Y
given Z, we first learn to infer the latent representations of target variables
X and Y that contain no information about the conditioning variable Z. The
latent variables are then investigated for any significant remaining
dependencies, which can be performed using the conventional partial correlation
test. The empirical evaluations show that LCIT outperforms several
state-of-the-art baselines consistently under different evaluation metrics, and
is able to adapt really well to both non-linear and high-dimensional settings
on a diverse collection of synthetic and real data sets.

Binary Neural Networks (BNNs) show great promise for real-world embedded
devices. As one of the critical steps to achieve a powerful BNN, the scale
factor calculation plays an essential role in reducing the performance gap to
their real-valued counterparts. However, existing BNNs neglect the intrinsic
bilinear relationship of real-valued weights and scale factors, resulting in a
sub-optimal model caused by an insufficient training process. To address this
issue, Recurrent Bilinear Optimization is proposed to improve the learning
process of BNNs (RBONNs) by associating the intrinsic bilinear variables in the
back propagation process. Our work is the first attempt to optimize BNNs from
the bilinear perspective. Specifically, we employ a recurrent optimization and
Density-ReLU to sequentially backtrack the sparse real-valued weight filters,
which will be sufficiently trained and reach their performance limits based on
a controllable learning process. We obtain robust RBONNs, which show impressive
performance over state-of-the-art BNNs on various models and datasets.
Particularly, on the task of object detection, RBONNs have great generalization
performance. Our code is open-sourced on https://github.com/SteveTsui/RBONN .

Masked visual modeling (MVM) has been recently proven effective for visual
pre-training. While similar reconstructive objectives on video inputs (e.g.,
masked frame modeling) have been explored in video-language (VidL)
pre-training, the pre-extracted video features in previous studies cannot be
refined through MVM during pre-training, and thus leading to unsatisfactory
downstream performance. In this work, we systematically examine the potential
of MVM in the context of VidL learning. Specifically, we base our study on a
fully end-to-end VIdeO-LanguagE Transformer (VIOLET), which mitigates the
disconnection between fixed video representations and MVM training. In total,
eight different reconstructive targets of MVM are explored, from low-level
pixel values and oriented gradients to high-level depth maps, optical flow,
discrete visual tokens and latent visual features. We conduct comprehensive
experiments and provide insights on the factors leading to effective MVM
training. Empirically, we show VIOLET pre-trained with MVM objective achieves
notable improvements on 13 VidL benchmarks, ranging from video question
answering, video captioning, to text-to-video retrieval.

Integrating multiple online social networks (OSNs) has important implications
for many downstream social mining tasks, such as user preference modelling,
recommendation, and link prediction. However, it is unfortunately accompanied
by growing privacy concerns about leaking sensitive user information. How to
fully utilize the data from different online social networks while preserving
user privacy remains largely unsolved. To this end, we propose a Cross-network
Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive
representations of users in a privacy-preserving way. We jointly consider
information from partially aligned social networks with differential privacy
guarantees. In particular, for each heterogeneous social network, we first
introduce a hybrid differential privacy notion to capture the variation of
privacy expectations for heterogeneous data types. Next, to find user linkages
across social networks, we make unsupervised user embedding-based alignment in
which the user embeddings are achieved by the heterogeneous network embedding
technology. To further enhance user embeddings, a novel cross-network GCN
embedding model is designed to transfer knowledge across networks through those
aligned users. Extensive experiments on three real-world datasets demonstrate
that our approach makes a significant improvement on user interest prediction
tasks as well as defending user attribute inference attacks from embedding.

Auditing Data Provenance (ADP), i.e., auditing if a certain piece of data has
been used to train a machine learning model, is an important problem in data
provenance. The feasibility of the task has been demonstrated by existing
auditing techniques, e.g., shadow auditing methods, under certain conditions
such as the availability of label information and the knowledge of training
protocols for the target model. Unfortunately, both of these conditions are
often unavailable in real applications. In this paper, we introduce Data
Provenance via Differential Auditing (DPDA), a practical framework for auditing
data provenance with a different approach based on statistically significant
differentials, i.e., after carefully designed transformation, perturbed input
data from the target model's training set would result in much more drastic
changes in the output than those from the model's non-training set. This
framework allows auditors to distinguish training data from non-training ones
without the need of training any shadow models with the help of labeled output
data. Furthermore, we propose two effective auditing function implementations,
an additive one and a multiplicative one. We report evaluations on real-world
data sets demonstrating the effectiveness of our proposed auditing technique.

The growing societal dependence on social media and user generated content
for news and information has increased the influence of unreliable sources and
fake content, which muddles public discourse and lessens trust in the media.
Validating the credibility of such information is a difficult task that is
susceptible to confirmation bias, leading to the development of algorithmic
techniques to distinguish between fake and real news. However, most existing
methods are challenging to interpret, making it difficult to establish trust in
predictions, and make assumptions that are unrealistic in many real-world
scenarios, e.g., the availability of audiovisual features or provenance. In
this work, we focus on fake news detection of textual content using
interpretable features and methods. In particular, we have developed a deep
probabilistic model that integrates a dense representation of textual news
using a variational autoencoder and bi-directional Long Short-Term Memory
(LSTM) networks with semantic topic-related features inferred from a Bayesian
admixture model. Extensive experimental studies with 3 real-world datasets
demonstrate that our model achieves comparable performance to state-of-the-art
competing models while facilitating model interpretability from the learned
topics. Finally, we have conducted model ablation studies to justify the
effectiveness and accuracy of integrating neural embeddings and topic features
both quantitatively by evaluating performance and qualitatively through
separability in lower dimensional embeddings.

Self-supervised learning (SSL) enables learning useful inductive biases
through utilizing pretext tasks that require no labels. The unlabeled nature of
SSL makes it especially important for whole slide histopathological images
(WSIs), where patch-level human annotation is difficult. Masked Autoencoders
(MAE) is a recent SSL method suitable for digital pathology as it does not
require negative sampling and requires little to no data augmentations.
However, the domain shift between natural images and digital pathology images
requires further research in designing MAE for patch-level WSIs. In this paper,
we investigate several design choices for MAE in histopathology. Furthermore,
we introduce a multi-modal MAE (MMAE) that leverages the specific
compositionality of Hematoxylin & Eosin (H&E) stained WSIs. We performed our
experiments on the public patch-level dataset NCT-CRC-HE-100K. The results show
that the MMAE architecture outperforms supervised baselines and other
state-of-the-art SSL techniques for an eight-class tissue phenotyping task,
utilizing only 100 labeled samples for fine-tuning. Our code is available at
https://github.com/wisdomikezogwo/MMAE_Pathology

In a multilingual neural machine translation model that fully shares
parameters across all languages, an artificial language token is usually used
to guide translation into the desired target language. However, recent studies
show that prepending language tokens sometimes fails to navigate the
multilingual neural machine translation models into right translation
directions, especially on zero-shot translation. To mitigate this issue, we
propose two methods, language embedding embodiment and language-aware
multi-head attention, to learn informative language representations to channel
translation into right directions. The former embodies language embeddings into
different critical switching points along the information flow from the source
to the target, aiming at amplifying translation direction guiding signals. The
latter exploits a matrix, instead of a vector, to represent a language in the
continuous space. The matrix is chunked into multiple heads so as to learn
language representations in multiple subspaces. Experiment results on two
datasets for massively multilingual neural machine translation demonstrate that
language-aware multi-head attention benefits both supervised and zero-shot
translation and significantly alleviates the off-target translation issue.
Further linguistic typology prediction experiments show that matrix-based
language representations learned by our methods are capable of capturing rich
linguistic typology features.

User review data is helpful in alleviating the data sparsity problem in many
recommender systems. In review-based recommendation methods, review data is
considered as auxiliary information that can improve the quality of learned
user/item or interaction representations for the user rating prediction task.
However, these methods usually model user-item interactions in a holistic
manner and neglect the entanglement of the latent factors behind them, e.g.,
price, quality, or appearance, resulting in suboptimal representations and
reducing interpretability. In this paper, we propose a Disentangled Graph
Contrastive Learning framework for Review-based recommendation (DGCLR), to
separately model the user-item interactions based on different latent factors
through the textual review data. To this end, we first model the distributions
of interactions over latent factors from both semantic information in review
data and structural information in user-item graph data, forming several factor
graphs. Then a factorized message passing mechanism is designed to learn
disentangled user/item representations on the factor graphs, which enable us to
further characterize the interactions and adaptively combine the predicted
ratings from multiple factors via a devised attention mechanism. Finally, we
set two factor-wise contrastive learning objectives to alleviate the sparsity
issue and model the user/item and interaction features pertinent to each factor
more accurately. Empirical results over five benchmark datasets validate the
superiority of DGCLR over the state-of-the-art methods. Further analysis is
offered to interpret the learned intent factors and rating prediction in DGCLR.

Here we present Symplectically Integrated Symbolic Regression (SISR), a novel
technique for learning physical governing equations from data. SISR employs a
deep symbolic regression approach, using a multi-layer LSTM-RNN with mutation
to probabilistically sample Hamiltonian symbolic expressions. Using symplectic
neural networks, we develop a model-agnostic approach for extracting meaningful
physical priors from the data that can be imposed on-the-fly into the RNN
output, limiting its search space. Hamiltonians generated by the RNN are
optimized and assessed using a fourth-order symplectic integration scheme;
prediction performance is used to train the LSTM-RNN to generate increasingly
better functions via a risk-seeking policy gradients approach. Employing these
techniques, we extract correct governing equations from oscillator, pendulum,
two-body, and three-body gravitational systems with noisy and extremely small
datasets.

Generative patent language models can assist humans to write patent text more
effectively. The question is how to measure effectiveness from a human-centric
perspective and how to improve effectiveness. In this manuscript, a simplified
design of the autocomplete function is proposed to increase effectiveness by
more than 10%. With the new design, the effectiveness of autocomplete can reach
more than 60%, which means that more than 60% of keystrokes can be saved by
autocomplete. Since writing patent text does not necessarily start from the
beginning to the end, a question is whether the generative model can assist a
user no matter where to start writing. To answer the question, the generative
models in this manuscript are pre-trained with training data in both
directions. The generative models become bidirectional. Since text generation
is bidirectional, the calculation of autocomplete effectiveness can be
bidirectional and starts from anywhere in the text. After thorough experiments,
a key finding is that the autocomplete effectiveness of a model for the same
text remains similar no matter where the calculation starts. The finding
indicates that such bidirectional models can assist a user at a similar level,
no matter where the user starts to write.

Stopwords carry little semantic information and are often removed from text
data to reduce dataset size and improve machine learning model performance.
Consequently, researchers have sought to develop techniques for generating
effective stopword sets. Previous approaches have ranged from qualitative
techniques relying upon linguistic experts, to statistical approaches that
extract word importance using correlations or frequency-dependent metrics
computed on a corpus. We present a novel quantitative approach that employs
iterative and recursive feature deletion algorithms to see which words can be
deleted from a pre-trained transformer's vocabulary with the least degradation
to its performance, specifically for the task of sentiment analysis.
Empirically, stopword lists generated via this approach drastically reduce
dataset size while negligibly impacting model performance, in one such example
shrinking the corpus by 28.4% while improving the accuracy of a trained
logistic regression model by 0.25%. In another instance, the corpus was shrunk
by 63.7% with a 2.8% decrease in accuracy. These promising results indicate
that our approach can generate highly effective stopword sets for specific NLP
tasks.

Preoperative and noninvasive prediction of the meningioma grade is important
in clinical practice, as it directly influences the clinical decision making.
What's more, brain invasion in meningioma (i.e., the presence of tumor tissue
within the adjacent brain tissue) is an independent criterion for the grading
of meningioma and influences the treatment strategy. Although efforts have been
reported to address these two tasks, most of them rely on hand-crafted features
and there is no attempt to exploit the two prediction tasks simultaneously. In
this paper, we propose a novel task-aware contrastive learning algorithm to
jointly predict meningioma grade and brain invasion from multi-modal MRIs.
Based on the basic multi-task learning framework, our key idea is to adopt
contrastive learning strategy to disentangle the image features into
task-specific features and task-common features, and explicitly leverage their
inherent connections to improve feature representation for the two prediction
tasks. In this retrospective study, an MRI dataset was collected, for which 800
patients (containing 148 high-grade, 62 invasion) were diagnosed with
meningioma by pathological analysis. Experimental results show that the
proposed algorithm outperforms alternative multi-task learning methods,
achieving AUCs of 0:8870 and 0:9787 for the prediction of meningioma grade and
brain invasion, respectively. The code is available at
https://github.com/IsDling/predictTCL.

Humans can attribute mental states to others, a capacity known as Theory of
Mind. However, it is unknown to what extent this ability results from an innate
biological endowment or from experience accrued through child development,
particularly exposure to language describing others' mental states. We test the
viability of the language exposure hypothesis by assessing whether models
exposed to large quantities of human language develop evidence of Theory of
Mind. In pre-registered analyses, we present a linguistic version of the False
Belief Task, widely used to assess Theory of Mind, to both human participants
and a state-of-the-art Large Language Model, GPT-3. Both are sensitive to
others' beliefs, but while the language model significantly exceeds chance
behavior, it does not perform as well as the humans, nor does it explain the
full extent of their behavior -- despite being exposed to more language than a
human would in a lifetime. This suggests that while statistical learning from
language exposure may in part explain how humans develop Theory of Mind, other
mechanisms are also responsible.

K-Nearest Neighbors algorithm is one of the most used classifiers in terms of
simplicity and performance. Although, when a dataset has many outliers or when
it is small or unbalanced, KNN doesn't work well. This paper aims to propose a
novel classifier, based on K-Nearest Neighbors which calculates the local means
of every class using the Power Muirhead Mean operator to overcome alluded
issues. We called our new algorithm Power Muirhead Mean K-Nearest Neighbors
(PMM-KNN). Eventually, we used five well-known datasets to assess PMM-KNN
performance. The research results demonstrate that the PMM-KNN has outperformed
three state-of-the-art classification methods in all experiments.

Objective: Steady-state visually evoked potentials (SSVEPs), measured with
EEG (electroencephalogram), yield decent information transfer rates (ITR) in
brain-computer interface (BCI) spellers. However, the current high performing
SSVEP BCI spellers in the literature require an initial lengthy and tiring
user-specific training for each new user for system adaptation, including data
collection with EEG experiments, algorithm training and calibration (all are
before the actual use of the system). This impedes the widespread use of BCIs.
To ensure practicality, we propose a highly novel target identification method
based on an ensemble of deep neural networks (DNNs), which does not require any
sort of user-specific training. Method: We exploit already-existing literature
datasets from participants of previously conducted EEG experiments to train a
global target identifier DNN first, which is then fine-tuned to each
participant. We transfer this ensemble of fine-tuned DNNs to the new user
instance, determine the k most representative DNNs according to the
participants' statistical similarities to the new user, and predict the target
character through a weighted combination of the ensemble predictions. Results:
On two large-scale benchmark and BETA datasets, our method achieves impressive
155.51 bits/min and 114.64 bits/min ITRs. Code is available for
reproducibility: https://github.com/osmanberke/Ensemble-of-DNNs Conclusion: The
proposed method significantly outperforms all the state-of-the-art alternatives
for all stimulation durations in [0.2-1.0] seconds on both datasets.
Significance: Our Ensemble-DNN method has the potential to promote the
practical widespread deployment of BCI spellers in daily lives as we provide
the highest performance while enabling the immediate system use without any
user-specific training.

Deep learning research has generated widespread interest leading to emergence
of a large variety of technological innovations and applications. As
significant proportion of deep learning research focuses on vision based
applications, there exists a potential for using some of these techniques to
enable low-power portable health-care diagnostic support solutions. In this
paper, we propose an embedded-hardware-based implementation of microscopy
diagnostic support system for PoC case study on: (a) Malaria in thick blood
smears, (b) Tuberculosis in sputum samples, and (c) Intestinal parasite
infection in stool samples. We use a Squeeze-Net based model to reduce the
network size and computation time. We also utilize the Trained Quantization
technique to further reduce memory footprint of the learned models. This
enables microscopy-based detection of pathogens that classifies with laboratory
expert level accuracy as a standalone embedded hardware platform. The proposed
implementation is 6x more power-efficient compared to conventional CPU-based
implementation and has an inference time of $\sim$ 3 ms/sample.

AlphaZero, Leela Chess Zero and Stockfish NNUE revolutionized Computer Chess.
This book gives a complete introduction into the technical inner workings of
such engines. The book is split into four main chapters -- excluding chapter 1
(introduction) and chapter 6 (conclusion): Chapter 2 introduces neural networks
and covers all the basic building blocks that are used to build deep networks
such as those used by AlphaZero. Contents include the perceptron,
back-propagation and gradient descent, classification, regression, multilayer
perceptron, vectorization techniques, convolutional networks, squeeze and
excitation networks, fully connected networks, batch normalization and
rectified linear units, residual layers, overfitting and underfitting. Chapter
3 introduces classical search techniques used for chess engines as well as
those used by AlphaZero. Contents include minimax, alpha-beta search, and Monte
Carlo tree search. Chapter 4 shows how modern chess engines are designed. Aside
from the ground-breaking AlphaGo, AlphaGo Zero and AlphaZero we cover Leela
Chess Zero, Fat Fritz, Fat Fritz 2 and Efficiently Updatable Neural Networks
(NNUE) as well as Maia. Chapter 5 is about implementing a miniaturized
AlphaZero. Hexapawn, a minimalistic version of chess, is used as an example for
that. Hexapawn is solved by minimax search and training positions for
supervised learning are generated. Then as a comparison, an AlphaZero-like
training loop is implemented where training is done via self-play combined with
reinforcement learning. Finally, AlphaZero-like training and supervised
training are compared.

The paradigm of machine intelligence moves from purely supervised learning to
a more practical scenario when many loosely related unlabeled data are
available and labeled data is scarce. Most existing algorithms assume that the
underlying task distribution is stationary. Here we consider a more realistic
and challenging setting in that task distributions evolve over time. We name
this problem as Semi-supervised meta-learning with Evolving Task diStributions,
abbreviated as SETS. Two key challenges arise in this more realistic setting:
(i) how to use unlabeled data in the presence of a large amount of unlabeled
out-of-distribution (OOD) data; and (ii) how to prevent catastrophic forgetting
on previously learned task distributions due to the task distribution shift. We
propose an OOD Robust and knowleDge presErved semi-supeRvised meta-learning
approach (ORDER), to tackle these two major challenges. Specifically, our ORDER
introduces a novel mutual information regularization to robustify the model
with unlabeled OOD data and adopts an optimal transport regularization to
remember previously learned knowledge in feature space. In addition, we test
our method on a very challenging dataset: SETS on large-scale non-stationary
semi-supervised task distributions consisting of (at least) 72K tasks. With
extensive experiments, we demonstrate the proposed ORDER alleviates forgetting
on evolving task distributions and is more robust to OOD data than related
strong baselines.

We present StreamNet, an autoencoder architecture for the analysis of the
highly heterogeneous geometry of large collections of white matter streamlines.
This proposed framework takes advantage of geometry-preserving properties of
the Wasserstein-1 metric in order to achieve direct encoding and reconstruction
of entire bundles of streamlines. We show that the model not only accurately
captures the distributive structures of streamlines in the population, but is
also able to achieve superior reconstruction performance between real and
synthetic streamlines. Experimental model performance is evaluated on white
matter streamlines resulting from T1-weighted diffusion imaging of 40 healthy
controls using recent state of the art bundle comparison metric that measures
fiber-shape similarities.

This article provides a background and descriptive analysis of insect memory
and the coding of olfactory sensation in Drosophila, presenting graphs and
summary statistics from a large dataset of neurons and synapses that was
recently made publicly available and also discussing findings from the existing
empirical literature. Some general principles from Drosophila olfaction are
discussed as they apply to the design of analogous systems in artificial neural
networks: (1) the networks used for coding are shallow; (2) the level of
connectedness varies widely across neurons in the same layer; (3) much
communication is between neurons in the same layer; (4) in most olfactory
learning, the manner in which sensory inputs are represented in stored memory
is largely fixed, and the learning process involves developing positive or
negative associations with existing categories of inputs.

Modeling sequential patterns from data is at the core of various time series
forecasting tasks. Deep learning models have greatly outperformed many
traditional models, but these black-box models generally lack explainability in
prediction and decision making. To reveal the underlying trend with
understandable mathematical expressions, scientists and economists tend to use
partial differential equations (PDEs) to explain the highly nonlinear dynamics
of sequential patterns. However, it usually requires domain expert knowledge
and a series of simplified assumptions, which is not always practical and can
deviate from the ever-changing world. Is it possible to learn the differential
relations from data dynamically to explain the time-evolving dynamics? In this
work, we propose an learning framework that can automatically obtain
interpretable PDE models from sequential data. Particularly, this framework is
comprised of learnable differential blocks, named $P$-blocks, which is proved
to be able to approximate any time-evolving complex continuous functions in
theory. Moreover, to capture the dynamics shift, this framework introduces a
meta-learning controller to dynamically optimize the hyper-parameters of a
hybrid PDE model. Extensive experiments on times series forecasting of
financial, engineering, and health data show that our model can provide
valuable interpretability and achieve comparable performance to
state-of-the-art models. From empirical studies, we find that learning a few
differential operators may capture the major trend of sequential dynamics
without massive computational complexity.

This paper compares two deep reinforcement learning approaches for cyber
security in software defined networking. Neural Episodic Control to Deep
Q-Network has been implemented and compared with that of Double Deep
Q-Networks. The two algorithms are implemented in a format similar to that of a
zero-sum game. A two-tailed T-test analysis is done on the two game results
containing the amount of turns taken for the defender to win. Another
comparison is done on the game scores of the agents in the respective games.
The analysis is done to determine which algorithm is the best in game performer
and whether there is a significant difference between them, demonstrating if
one would have greater preference over the other. It was found that there is no
significant statistical difference between the two approaches.

Physics-constrained data-driven computing is an emerging computational
paradigm that allows simulation of complex materials directly based on material
database and bypass the classical constitutive model construction. However, it
remains difficult to deal with high-dimensional applications and extrapolative
generalization. This paper introduces deep learning techniques under the
data-driven framework to address these fundamental issues in nonlinear
materials modeling. To this end, an autoencoder neural network architecture is
introduced to learn the underlying low-dimensional representation (embedding)
of the given material database. The offline trained autoencoder and the
discovered embedding space are then incorporated in the online data-driven
computation such that the search of optimal material state from database can be
performed on a low-dimensional space, aiming to enhance the robustness and
predictability with projected material data. To ensure numerical stability and
representative constitutive manifold, a convexity-preserving interpolation
scheme tailored to the proposed autoencoder-based data-driven solver is
proposed for constructing the material state. In this study, the applicability
of the proposed approach is demonstrated by modeling nonlinear biological
tissues. A parametric study on data noise, data size and sparsity, training
initialization, and model architectures, is also conducted to examine the
robustness and convergence property of the proposed approach.

Self-supervised methods have emerged as a promising avenue for representation
learning in the recent years since they alleviate the need for labeled
datasets, which are scarce and expensive to acquire. Contrastive methods are a
popular choice for self-supervision in the audio domain, and typically provide
a learning signal by forcing the model to be invariant to some transformations
of the input. These methods, however, require measures such as negative
sampling or some form of regularisation to be taken to prevent the model from
collapsing on trivial solutions. In this work, instead of invariance, we
propose to use equivariance as a self-supervision signal to learn audio tempo
representations from unlabelled data. We derive a simple loss function that
prevents the network from collapsing on a trivial solution during training,
without requiring any form of regularisation or negative sampling. Our
experiments show that it is possible to learn meaningful representations for
tempo estimation by solely relying on equivariant self-supervision, achieving
performance comparable with supervised methods on several benchmarks. As an
added benefit, our method only requires moderate compute resources and
therefore remains accessible to a wide research community.

Physical systems are commonly represented as a combination of particles, the
individual dynamics of which govern the system dynamics. However, traditional
approaches require the knowledge of several abstract quantities such as the
energy or force to infer the dynamics of these particles. Here, we present a
framework, namely, Lagrangian graph neural network (LGnn), that provides a
strong inductive bias to learn the Lagrangian of a particle-based system
directly from the trajectory. We test our approach on challenging systems with
constraints and drag -- LGnn outperforms baselines such as feed-forward
Lagrangian neural network (Lnn) with improved performance. We also show the
zero-shot generalizability of the system by simulating systems two orders of
magnitude larger than the trained one and also hybrid systems that are unseen
by the model, a unique feature. The graph architecture of LGnn significantly
simplifies the learning in comparison to Lnn with ~25 times better performance
on ~20 times smaller amounts of data. Finally, we show the interpretability of
LGnn, which directly provides physical insights on drag and constraint forces
learned by the model. LGnn can thus provide a fillip toward understanding the
dynamics of physical systems purely from observable quantities.

In this paper, we introduce a neural rendering pipeline for transferring the
facial expressions, head pose and body movements of one person in a source
video to another in a target video. We apply our method to the challenging case
of Sign Language videos: given a source video of a sign language user, we can
faithfully transfer the performed manual (e.g. handshape, palm orientation,
movement, location) and non-manual (e.g. eye gaze, facial expressions, head
movements) signs to a target video in a photo-realistic manner. To effectively
capture the aforementioned cues, which are crucial for sign language
communication, we build upon an effective combination of the most robust and
reliable deep learning methods for body, hand and face tracking that have been
introduced lately. Using a 3D-aware representation, the estimated motions of
the body parts are combined and retargeted to the target signer. They are then
given as conditional input to our Video Rendering Network, which generates
temporally consistent and photo-realistic videos. We conduct detailed
qualitative and quantitative evaluations and comparisons, which demonstrate the
effectiveness of our approach and its advantages over existing approaches. Our
method yields promising results of unprecedented realism and can be used for
Sign Language Anonymization. In addition, it can be readily applicable to
reenactment of other types of full body activities (dancing, acting
performance, exercising, etc.), as well as to the synthesis module of Sign
Language Production systems.

Chronic kidney disease (CKD) represents a slowly progressive disorder that
can eventually require renal replacement therapy (RRT) including dialysis or
renal transplantation. Early identification of patients who will require RRT
(as much as 1 year in advance) improves patient outcomes, for example by
allowing higher-quality vascular access for dialysis. Therefore, early
recognition of the need for RRT by care teams is key to successfully managing
the disease. Unfortunately, there is currently no commonly used predictive tool
for RRT initiation. In this work, we present a machine learning model that
dynamically identifies CKD patients at risk of requiring RRT up to one year in
advance using only claims data. To evaluate the model, we studied approximately
3 million Medicare beneficiaries for which we made over 8 million predictions.
We showed that the model can identify at risk patients with over 90%
sensitivity and specificity. Although additional work is required before this
approach is ready for clinical use, this study provides a basis for a screening
tool to identify patients at risk within a time window that enables early
proactive interventions intended to improve RRT outcomes.

Many cyberattacks start with disseminating phishing URLs. When clicking these
phishing URLs, the victim's private information is leaked to the attacker.
There have been proposed several machine learning methods to detect phishing
URLs. However, it still remains under-explored to detect phishing URLs with
evasion, i.e., phishing URLs that pretend to be benign by manipulating
patterns. In many cases, the attacker i) reuses prepared phishing web pages
because making a completely brand-new set costs non-trivial expenses, ii)
prefers hosting companies that do not require private information and are
cheaper than others, iii) prefers shared hosting for cost efficiency, and iv)
sometimes uses benign domains, IP addresses, and URL string patterns to evade
existing detection methods. Inspired by those behavioral characteristics, we
present a network-based inference method to accurately detect phishing URLs
camouflaged with legitimate patterns, i.e., robust to evasion. In the network
approach, a phishing URL will be still identified as phishy even after evasion
unless a majority of its neighbors in the network are evaded at the same time.
Our method consistently shows better detection performance throughout various
experimental tests than state-of-the-art methods, e.g., F-1 of 0.89 for our
method vs. 0.84 for the best feature-based method.

We study the revenue-maximizing mechanism when a buyer's value evolves
endogenously because of learning-by-consuming. A seller sells one unit of a
divisible good, while the buyer relies on his private, rough valuation to
choose his first-stage consumption level. Consuming more leads to a more
precise valuation estimate, after which the buyer determines the second-stage
consumption level. The optimum is a menu of try-and-decide contracts,
consisting of a first-stage price-quantity pair and a second-stage per-unit
price for the remaining quantity. In equilibrium, a higher first-stage
valuation buyer pays more for higher first-stage consumption and enjoys a lower
second-stage per-unit price. Methodologically, we deal with the difficulty that
due to the failure of single-crossing condition, monotonicity in allocation
plus the envelope condition is insufficient for incentive compatibility. Our
results help to understand contracts about sequential consumption with the
learning feature; e.g., leasing contracts for experience goods and trial
sessions for certain courses.

Minimally invasive surgery is highly operator dependant with lengthy
procedural times causing fatigue and risk to patients. In order to mitigate
these risks, real-time systems can help assist surgeons to navigate and track
tools, by providing clear understanding of scene and avoid miscalculations
during operation. While several efforts have been made in this direction, a
lack of diverse datasets, as well as very dynamic scenes and its variability in
each patient entails major hurdle in accomplishing robust systems. In this
work, we present a systematic review of recent machine learning-based
approaches including surgical tool localisation, segmentation, tracking and 3D
scene perception. Furthermore, we present current gaps and directions of these
invented methods and provide rational behind clinical integration of these

Reinforcement learning methods as a promising technique have achieved
superior results in the motion planning of free-floating space robots. However,
due to the increase in planning dimension and the intensification of system
dynamics coupling, the motion planning of dual-arm free-floating space robots
remains an open challenge. In particular, the current study cannot handle the
task of capturing a non-cooperative object due to the lack of the pose
constraint of the end-effectors. To address the problem, we propose a novel
algorithm, EfficientLPT, to facilitate RL-based methods to improve planning
accuracy efficiently. Our core contributions are constructing a mixed policy
with prior knowledge guidance and introducing infinite norm to build a more
reasonable reward function. Furthermore, our method successfully captures a
rotating object with different spinning speeds.

In this paper, we study the mixed-integer nonlinear set given by a separable
quadratic constraint on continuous variables, where each continuous variable is
controlled by an additional indicator. This set occurs pervasively in
optimization problems with uncertainty and in machine learning. We show that
optimization over this set is NP-hard. Despite this negative result, we
characterize the structure of the convex hull, and show that it can be formally
studied using polyhedral theory. Moreover, we show that although perspective
relaxation in the literature for this set fails to match the structure of its
convex hull, it is guaranteed to be a close approximation.

In this paper we study probabilistic and neural network approximations for
solutions to Poisson equation subject to H\" older or $C^2$ data in general
bounded domains of $\mathbb{R}^d$. We aim at two fundamental goals.
  The first, and the most important, we show that the solution to Poisson
equation can be numerically approximated in the sup-norm by Monte Carlo methods
based on a slight change of the walk on spheres algorithm. This provides
estimates which are efficient with respect to the prescribed approximation
error and without the curse of dimensionality. In addition, the overall number
of samples does not not depend on the point at which the approximation is
performed.
  As a second goal, we show that the obtained Monte Carlo solver renders ReLU
deep neural network (DNN) solutions to Poisson problem, whose sizes depend at
most polynomially in the dimension $d$ and in the desired error. In fact we
show that the random DNN provides with high probability a small approximation
error and low polynomial complexity in the dimension.

The goal of fine-grained action recognition is to successfully discriminate
between action categories with subtle differences. To tackle this, we derive
inspiration from the human visual system which contains specialized regions in
the brain that are dedicated towards handling specific tasks. We design a novel
Dynamic Spatio-Temporal Specialization (DSTS) module, which consists of
specialized neurons that are only activated for a subset of samples that are
highly similar. During training, the loss forces the specialized neurons to
learn discriminative fine-grained differences to distinguish between these
similar samples, improving fine-grained recognition. Moreover, a
spatio-temporal specialization method further optimizes the architectures of
the specialized neurons to capture either more spatial or temporal fine-grained
information, to better tackle the large range of spatio-temporal variations in
the videos. Lastly, we design an Upstream-Downstream Learning algorithm to
optimize our model's dynamic decisions during training, improving the
performance of our DSTS module. We obtain state-of-the-art performance on two
widely-used fine-grained action recognition datasets.

Embodied agents are regularly faced with the challenge to learn new tasks. In
order to do that they need to be able to predict their next sensory state by
forming an internal world model. We theorize that agents require a high value
of information integration to update that world model in light of new
information. This can be seen in the context of Integrated Information Theory,
which provides a quantitative approach to consciousness and can be applied to
neural networks. We use the sensorimotor loop to model the interactions among
the agent's brain, body and environment. Thereby we can calculate various
information theoretic measures that quantify different information flows in the
system, one of which corresponds to Integrated Information. Additionally we are
able to measure the interaction among the body and the environment, which leads
to the concept of Morphological Computation. Previous research reveals an
antagonistic relationship between Integrated Information and Morphological
Computation. A morphology adapted well to a task can reduce the necessity for
Integrated Information significantly. This creates the problem that embodied
intelligence is correlated with reduced conscious experience. Here we propose a
solution to this problem, namely that the agents need Integrated Information to
learn. We support our hypothesis with results from a simple experimental setup
in which the agents learn by using the em-algorithm.

Smart sensing provides an easier and convenient data-driven mechanism for
monitoring and control in the built environment. Data generated in the built
environment are privacy sensitive and limited. Federated learning is an
emerging paradigm that provides privacy-preserving collaboration among multiple
participants for model training without sharing private and limited data. The
noisy labels in the datasets of the participants degrade the performance and
increase the number of communication rounds for convergence of federated
learning. Such large communication rounds require more time and energy to train
the model. In this paper, we propose a federated learning approach to suppress
the unequal distribution of the noisy labels in the dataset of each
participant. The approach first estimates the noise ratio of the dataset for
each participant and normalizes the noise ratio using the server dataset. The
proposed approach can handle bias in the server dataset and minimizes its
impact on the participants' dataset. Next, we calculate the optimal weighted
contributions of the participants using the normalized noise ratio and
influence of each participant. We further derive the expression to estimate the
number of communication rounds required for the convergence of the proposed
approach. Finally, experimental results demonstrate the effectiveness of the
proposed approach over existing techniques in terms of the communication rounds
and achieved performance in the built environment.

Multi-modal knowledge graphs (MKGs) include not only the relation triplets,
but also related multi-modal auxiliary data (i.e., texts and images), which
enhance the diversity of knowledge. However, the natural incompleteness has
significantly hindered the applications of MKGs. To tackle the problem,
existing studies employ the embedding-based reasoning models to infer the
missing knowledge after fusing the multi-modal features. However, the reasoning
performance of these methods is limited due to the following problems: (1)
ineffective fusion of multi-modal auxiliary features; (2) lack of complex
reasoning ability as well as inability to conduct the multi-hop reasoning which
is able to infer more missing knowledge. To overcome these problems, we propose
a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning).
Specifically, the model contains the following two components: (1) a unified
gate-attention network which is designed to generate effective multi-modal
complementary features through sufficient attention interaction and noise
reduction; (2) a complementary feature-aware reinforcement learning method
which is proposed to predict missing elements by performing the multi-hop
reasoning process, based on the features obtained in component (1). The
experimental results demonstrate that MMKGR outperforms the state-of-the-art
approaches in the MKG reasoning task.

Generating unsafe sub-requirements from a partitioned input space to support
verification-guided test cases for formal verification of black-box models is a
challenging problem for researchers. The size of the search space makes
exhaustive search computationally impractical. This paper investigates a
meta-heuristic approach to search for unsafe candidate sub-requirements in
partitioned input space. We present a Negative Selection Algorithm (NSA) for
identifying the candidates' unsafe regions within given safety properties. The
Meta-heuristic capability of the NSA algorithm made it possible to estimate
vast unsafe regions while validating a subset of these regions. We utilize a
parallel execution of partitioned input space to produce safe areas. The NSA
based on the prior knowledge of the safe regions is used to identify candidate
unsafe region areas and the Marabou framework is then used to validate the NSA
results. Our preliminary experimentation and evaluation show that the procedure
finds candidate unsafe sub-requirements when validated with the Marabou
framework with high precision.

Existing Binary Neural Networks (BNNs) mainly operate on local convolutions
with binarization function. However, such simple bit operations lack the
ability of modeling contextual dependencies, which is critical for learning
discriminative deep representations in vision models. In this work, we tackle
this issue by presenting new designs of binary neural modules, which enables
BNNs to learn effective contextual dependencies. First, we propose a binary
multi-layer perceptron (MLP) block as an alternative to binary convolution
blocks to directly model contextual dependencies. Both short-range and
long-range feature dependencies are modeled by binary MLPs, where the former
provides local inductive bias and the latter breaks limited receptive field in
binary convolutions. Second, to improve the robustness of binary models with
contextual dependencies, we compute the contextual dynamic embeddings to
determine the binarization thresholds in general binary convolutional blocks.
Armed with our binary MLP blocks and improved binary convolution, we build the
BNNs with explicit Contextual Dependency modeling, termed as BCDNet. On the
standard ImageNet-1K classification benchmark, the BCDNet achieves 72.3% Top-1
accuracy and outperforms leading binary methods by a large margin. In
particular, the proposed BCDNet exceeds the state-of-the-art ReActNet-A by 2.9%
Top-1 accuracy with similar operations. Our code is available at
https://github.com/Sense-GVT/BCDN

Tumor burden assessment by magnetic resonance imaging (MRI) is central to the
evaluation of treatment response for glioblastoma. This assessment is complex
to perform and associated with high variability due to the high heterogeneity
and complexity of the disease. In this work, we tackle this issue and propose a
deep learning pipeline for the fully automated end-to-end analysis of
glioblastoma patients. Our approach simultaneously identifies tumor
sub-regions, including the enhancing tumor, peritumoral edema and surgical
cavity in the first step, and then calculates the volumetric and bidimensional
measurements that follow the current Response Assessment in Neuro-Oncology
(RANO) criteria. Also, we introduce a rigorous manual annotation process which
was followed to delineate the tumor sub-regions by the human experts, and to
capture their segmentation confidences that are later used while training the
deep learning models. The results of our extensive experimental study performed
over 760 pre-operative and 504 post-operative adult patients with glioma
obtained from the public database (acquired at 19 sites in years 2021-2020) and
from a clinical treatment trial (47 and 69 sites for pre-/post-operative
patients, 2009-2011) and backed up with thorough quantitative, qualitative and
statistical analysis revealed that our pipeline performs accurate segmentation
of pre- and post-operative MRIs in a fraction of the manual delineation time
(up to 20 times faster than humans). The bidimensional and volumetric
measurements were in strong agreement with expert radiologists, and we showed
that RANO measurements are not always sufficient to quantify tumor burden.

Human drivers have distinct driving techniques, knowledge, and sentiments due
to unique driving traits. Driver drowsiness has been a serious issue
endangering road safety; therefore, it is essential to design an effective
drowsiness detection algorithm to bypass road accidents. Miscellaneous research
efforts have been approached the problem of detecting anomalous human driver
behaviour to examine the frontal face of the driver and automobile dynamics via
computer vision techniques. Still, the conventional methods cannot capture
complicated driver behaviour features. However, with the origin of deep
learning architectures, a substantial amount of research has also been executed
to analyze and recognize driver's drowsiness using neural network algorithms.
This paper introduces a novel framework based on vision transformers and YoloV5
architectures for driver drowsiness recognition. A custom YoloV5 pre-trained
architecture is proposed for face extraction with the aim of extracting Region
of Interest (ROI). Owing to the limitations of previous architectures, this
paper introduces vision transformers for binary image classification which is
trained and validated on a public dataset UTA-RLDD. The model had achieved
96.2\% and 97.4\% as it's training and validation accuracies respectively. For
the further evaluation, proposed framework is tested on a custom dataset of 39
participants in various light circumstances and achieved 95.5\% accuracy. The
conducted experimentations revealed the significant potential of our framework
for practical applications in smart transportation systems.

Top-k error has become a popular metric for large-scale classification
benchmarks due to the inevitable semantic ambiguity among classes. Existing
literature on top-k optimization generally focuses on the optimization method
of the top-k objective, while ignoring the limitations of the metric itself. In
this paper, we point out that the top-k objective lacks enough discrimination
such that the induced predictions may give a totally irrelevant label a top
rank. To fix this issue, we develop a novel metric named partial Area Under the
top-k Curve (AUTKC). Theoretical analysis shows that AUTKC has a better
discrimination ability, and its Bayes optimal score function could give a
correct top-K ranking with respect to the conditional probability. This shows
that AUTKC does not allow irrelevant labels to appear in the top list.
Furthermore, we present an empirical surrogate risk minimization framework to
optimize the proposed metric. Theoretically, we present (1) a sufficient
condition for Fisher consistency of the Bayes optimal score function; (2) a
generalization upper bound which is insensitive to the number of classes under
a simple hyperparameter setting. Finally, the experimental results on four
benchmark datasets validate the effectiveness of our proposed framework.

Inductive link prediction (ILP) is to predict links for unseen entities in
emerging knowledge graphs (KGs), considering the evolving nature of KGs. A more
challenging scenario is that emerging KGs consist of only unseen entities,
called as disconnected emerging KGs (DEKGs). Existing studies for DEKGs only
focus on predicting enclosing links, i.e., predicting links inside the emerging
KG. The bridging links, which carry the evolutionary information from the
original KG to DEKG, have not been investigated by previous work so far. To
fill in the gap, we propose a novel model entitled DEKG-ILP (Disconnected
Emerging Knowledge Graph Oriented Inductive Link Prediction) that consists of
the following two components. (1) The module CLRM (Contrastive Learning-based
Relation-specific Feature Modeling) is developed to extract global
relation-based semantic features that are shared between original KGs and DEKGs
with a novel sampling strategy. (2) The module GSM (GNN-based Subgraph
Modeling) is proposed to extract the local subgraph topological information
around each link in KGs. The extensive experiments conducted on several
benchmark datasets demonstrate that DEKG-ILP has obvious performance
improvements compared with state-of-the-art methods for both enclosing and
bridging link prediction. The source code is available online.

This paper constitutes the novel hypergraph convolutional neural networkbased
clustering technique. This technique is employed to solve the clustering
problem for the Citeseer dataset and the Cora dataset. Each dataset contains
the feature matrix and the incidence matrix of the hypergraph (i.e.,
constructed from the feature matrix). This novel clustering method utilizes
both matrices. Initially, the hypergraph auto-encoders are employed to
transform both the incidence matrix and the feature matrix from high
dimensional space to low dimensional space. In the end, we apply the k-means
clustering technique to the transformed matrix. The hypergraph convolutional
neural network (CNN)-based clustering technique presented a better result on
performance during experiments than those of the other classical clustering

Deep generative models have the potential to fundamentally change the way we
create high-fidelity digital content but are often hard to control. Prompting a
generative model is a promising recent development that in principle enables
end-users to creatively leverage zero-shot and few-shot learning to assign new
tasks to an AI ad-hoc, simply by writing them down. However, for the majority
of end-users writing effective prompts is currently largely a trial and error
process. To address this, we discuss the key opportunities and challenges for
interactive creative applications that use prompting as a new paradigm for
Human-AI interaction. Based on our analysis, we propose four design goals for
user interfaces that support prompting. We illustrate these with concrete UI
design sketches, focusing on the use case of creative writing. The research
community in HCI and AI can take these as starting points to develop adequate
user interfaces for models capable of zero- and few-shot learning.

This paper proposes SaleNet - an end-to-end convolutional neural network
(CNN) for sustained attention level evaluation using prefrontal
electroencephalogram (EEG). A bias-driven pruning method is proposed together
with group convolution, global average pooling (GAP), near-zero pruning, weight
clustering and quantization for the model compression, achieving a total
compression ratio of 183.11x. The compressed SaleNet obtains a state-of-the-art
subject-independent sustained attention level classification accuracy of 84.2%
on the recorded 6-subject EEG database in this work. The SaleNet is implemented
on a Artix-7 FPGA with a competitive power consumption of 0.11 W and an
energy-efficiency of 8.19 GOps/W.

Several training strategies and temporal models have been recently proposed
for isolated word lip-reading in a series of independent works. However, the
potential of combining the best strategies and investigating the impact of each
of them has not been explored. In this paper, we systematically investigate the
performance of state-of-the-art data augmentation approaches, temporal models
and other training strategies, like self-distillation and using word boundary
indicators. Our results show that Time Masking (TM) is the most important
augmentation followed by mixup and Densely-Connected Temporal Convolutional
Networks (DC-TCN) are the best temporal model for lip-reading of isolated
words. Using self-distillation and word boundary indicators is also beneficial
but to a lesser extent. A combination of all the above methods results in a
classification accuracy of 93.4%, which is an absolute improvement of 4.6% over
the current state-of-the-art performance on the LRW dataset. The performance
can be further improved to 94.1% by pre-training on additional datasets. An
error analysis of the various training strategies reveals that the performance
improves by increasing the classification accuracy of hard-to-recognise words.

Breast cancer is the most common cancer among women worldwide. Early-stage
diagnosis of breast cancer can significantly improve the efficiency of
treatment. Computer-aided diagnosis (CAD) systems are widely adopted in this
issue due to their reliability, accuracy and affordability. There are different
imaging techniques for a breast cancer diagnosis; one of the most accurate ones
is histopathology which is used in this paper. Deep feature transfer learning
is used as the main idea of the proposed CAD system's feature extractor.
Although 16 different pre-trained networks have been tested in this study, our
main focus is on the classification phase. The Inception-ResNet-v2 which has
both residual and inception networks profits together has shown the best
feature extraction capability in the case of breast cancer histopathology
images among all tested CNNs. In the classification phase, the ensemble of
CatBoost, XGBoost and LightGBM has provided the best average accuracy. The
BreakHis dataset was used to evaluate the proposed method. BreakHis contains
7909 histopathology images (2,480 benign and 5,429 malignant) in four
magnification factors. The proposed method's accuracy (IRv2-CXL) using 70% of
BreakHis dataset as training data in 40x, 100x, 200x and 400x magnification is
96.82%, 95.84%, 97.01% and 96.15%, respectively. Most studies on automated
breast cancer detection have focused on feature extraction, which made us
attend to the classification phase. IRv2-CXL has shown better or comparable
results in all magnifications due to using the soft voting ensemble method
which could combine the advantages of CatBoost, XGBoost and LightGBM together.

A Recurrent Neural Network that operates on several time lags, called an
RNN(p), is the natural generalization of an Autoregressive ARX(p) model. It is
a powerful forecasting tool when different time scales can influence a given
phenomenon, as it happens in the energy sector where hourly, daily, weekly and
yearly interactions coexist. The cost-effective BPTT is the industry standard
as learning algorithm for RNNs. We prove that, when training RNN(p) models,
other learning algorithms turn out to be much more efficient in terms of both
time and space complexity. We also introduce a new learning algorithm, the Tree
Recombined Recurrent Learning, that leverages on a tree representation of the
unrolled network and appears to be even more effective. We present an
application of RNN(p) models for power consumption forecasting on the hourly
scale: experimental results demonstrate the efficiency of the proposed
algorithm and the excellent predictive accuracy achieved by the selected model
both in point and in probabilistic forecasting of the energy consumption.

Flowers play an essential role in removing the duller from the environment.
The life cycle of the flowering plants involves pollination, fertilization,
flowering, seed-formation, dispersion, and germination. Honeybees pollinate
approximately 75% of all flowering plants. Environmental pollution, climate
change, natural landscape demolition, and so on, threaten the natural habitats,
thus continuously reducing the number of honeybees. As a result, several
researchers are attempting to resolve this issue. Applying acoustic
classification to recordings of beehive sounds may be a way of detecting
changes within them. In this research, we use deep learning techniques, namely
Sequential Neural Network, Convolutional Neural Network, and Recurrent Neural
Network, on the recorded sounds to classify bee sounds from the nonbeehive
noises. In addition, we perform a comparative study among some popular non-deep
learning techniques, namely Support Vector Machine, Decision Tree, Random
Forest, and Na\"ive Bayes, with the deep learning techniques. The techniques
are also verified on the combined recorded sounds (25-75% noises).

Adverse weather conditions such as haze, rain, and snow often impair the
quality of captured images, causing detection networks trained on normal images
to generalize poorly in these scenarios. In this paper, we raise an intriguing
question - if the combination of image restoration and object detection, can
boost the performance of cutting-edge detectors in adverse weather conditions.
To answer it, we propose an effective yet unified detection paradigm that
bridges these two subtasks together via dynamic enhancement learning to discern
objects in adverse weather conditions, called TogetherNet. Different from
existing efforts that intuitively apply image dehazing/deraining as a
pre-processing step, TogetherNet considers a multi-task joint learning problem.
Following the joint learning scheme, clean features produced by the restoration
network can be shared to learn better object detection in the detection
network, thus helping TogetherNet enhance the detection capacity in adverse
weather conditions. Besides the joint learning architecture, we design a new
Dynamic Transformer Feature Enhancement module to improve the feature
extraction and representation capabilities of TogetherNet. Extensive
experiments on both synthetic and real-world datasets demonstrate that our
TogetherNet outperforms the state-of-the-art detection approaches by a large
margin both quantitatively and qualitatively. Source code is available at
https://github.com/yz-wang/TogetherNet.

We determine sharp bounds on the price of bandit feedback for several
variants of the mistake-bound model. The first part of the paper presents
bounds on the $r$-input weak reinforcement model and the $r$-input delayed,
ambiguous reinforcement model. In both models, the adversary gives $r$ inputs
in each round and only indicates a correct answer if all $r$ guesses are
correct. The only difference between the two models is that in the delayed,
ambiguous model, the learner must answer each input before receiving the next
input of the round, while the learner receives all $r$ inputs at once in the
weak reinforcement model. In the second part of the paper, we introduce models
for online learning with permutation patterns, in which a learner attempts to
learn a permutation from a set of permutations by guessing statistics related
to sub-permutations. For these permutation models, we prove sharp bounds on the
price of bandit feedback.

Deep neural networks (DNNs) allow digital receivers to learn to operate in
complex environments. To do so, DNNs should preferably be trained using large
labeled data sets with a similar statistical relationship as the one under
which they are to infer. For DNN-aided receivers, obtaining labeled data
conventionally involves pilot signalling at the cost of reduced spectral
efficiency, typically resulting in access to limited data sets. In this paper,
we study how one can enrich a small set of labeled pilots data into a larger
data set for training deep receivers. Motivated by the widespread use of data
augmentation techniques for enriching visual and text data, we propose
dedicated augmentation schemes that exploits the characteristics of digital
communication data. We identify the key considerations in data augmentations
for deep receivers as the need for domain orientation, class (constellation)
diversity, and low complexity. Following these guidelines, we devise three
complementing augmentations that exploit the geometric properties of digital
constellations. Our combined augmentation approach builds on the merits of
these different augmentations to synthesize reliable data from a momentary
channel distribution, to be used for training deep receivers. Furthermore, we
exploit previous channel realizations to increase the reliability of the
augmented samples.

Determination of crystal structures of nanocrystalline or amorphous compounds
is a great challenge in solid states chemistry and physics. Pair distribution
function (PDF) analysis of X-Ray or neutron total scattering data has proven to
be a key element in tackling this challenge. However, in most cases a reliable
structural motif is needed as starting configuration for structure refinements.
Here, we present an algorithm that is able to determine the crystal structure
of an unknown compound by means of an on-the-fly trained machine learning model
that combines density functional theory (DFT) calculations with comparison of
calculated and measured PDFs for global optimization in an artificial
landscape. Due to the nature of this landscape, even metastable configurations
can be determined.

Computed Tomography (CT) is an imaging technique where information about an
object are collected at different angles (called projections or scans). Then
the cross-sectional image showing the internal structure of the slice is
produced by solving an inverse problem. Limited by certain factors such as
radiation dosage, projection angles, the produced images can be noisy or
contain artifacts. Inspired by the success of transformer for natural language
processing, the core idea of this preliminary study is to consider a projection
of tomography as a word token, and the whole scan of the cross-section (A.K.A.
sinogram) as a sentence in the context of natural language processing. Then we
explore the idea of foundation model by training a masked sinogram model (MSM)
and fine-tune MSM for various downstream applications including CT
reconstruction under data collections restriction (e.g., photon-budget) and a
data-driven solution to approximate solutions of the inverse problem for CT
reconstruction. Models and data used in this study are available at
https://github.com/lzhengchun/TomoTx.

Most machine vision tasks (e.g., semantic segmentation) are based on images
encoded and decoded by image compression algorithms (e.g., JPEG). However,
these decoded images in the pixel domain introduce distortion, and they are
optimized for human perception, making the performance of machine vision tasks
suboptimal. In this paper, we propose a method based on the compressed domain
to improve segmentation tasks. i) A dynamic and a static channel selection
method are proposed to reduce the redundancy of compressed representations that
are obtained by encoding. ii) Two different transform modules are explored and
analyzed to help the compressed representation be transformed as the features
in the segmentation network. The experimental results show that we can save up
to 15.8\% bitrates compared with a state-of-the-art compressed domain-based
work while saving up to about 83.6\% bitrates and 44.8\% inference time
compared with the pixel domain-based method.

Offloading computation to nearby edge/fog computing nodes, including the ones
carried by moving vehicles, e.g., vehicular fog nodes (VFN), has proved to be a
promising approach for enabling low-latency and compute-intensive mobility
applications, such as cooperative and autonomous driving. This work considers
vehicular fog computing scenarios where the clients of computation offloading
services try to minimize their own costs while deciding which VFNs to offload
their tasks. We focus on decentralized multi-agent decision-making in a
repeated unknown game where each agent, e.g., service client, can observe only
its own action and realized cost. In other words, each agent is unaware of the
game composition or even the existence of opponents. We apply a completely
uncoupled learning rule to generalize the decentralized decision-making
algorithm presented in \cite{Cho2021} for the multi-agent case. The multi-agent
solution proposed in this work can capture the unknown offloading cost
variations susceptive to resource congestion under an adversarial framework
where each agent may take implicit cost estimation and suitable resource choice
adapting to the dynamics associated with volatile supply and demand. According
to the evaluation via simulation, this work reveals that such individual
perturbations for robustness to uncertainty and adaptation to dynamicity ensure
a certain level of optimality in terms of social welfare, e.g., converging the
actual sequence of play with unknown and asymmetric attributes and lowering the
correspondent cost in social welfare due to the self-interested behaviors of
agents.

The task of link prediction aims to solve the problem of incomplete knowledge
caused by the difficulty of collecting facts from the real world. GCNs-based
models are widely applied to solve link prediction problems due to their
sophistication, but GCNs-based models are suffering from two problems in the
structure and training process. 1) The transformation methods of GCN layers
become increasingly complex in GCN-based knowledge representation models; 2)
Due to the incompleteness of the knowledge graph collection process, there are
many uncollected true facts in the labeled negative samples. Therefore, this
paper investigates the characteristic of the information aggregation
coefficient (self-attention) of adjacent nodes and redesigns the self-attention
mechanism of the GAT structure. Meanwhile, inspired by human thinking habits,
we designed a semi-supervised self-training method over pre-trained models.
Experimental results on the benchmark datasets FB15k-237 and WN18RR show that
our proposed self-attention mechanism and semi-supervised self-training method
can effectively improve the performance of the link prediction task. If you
look at FB15k-237, for example, the proposed method improves Hits@1 by about
30%.

The paper presents a joint beamforming algorithm using statistical channel
state information (S-CSI) for reconfigurable intelligent surfaces (RIS) for
multiuser MISO wireless communications. We used S-CSI, which is a long-term
average of the cascaded channel as opposed to instantaneous CSI utilized in
most existing works. Through this method, the overhead of channel estimation is
dramatically reduced. We propose a proximal policy optimization (PPO) algorithm
which is a well-known actor-critic based reinforcement learning (RL) algorithm
to solve the optimization problem. To test the efficacy of this algorithm,
simulation results are presented along with evaluations of key system
parameters, including the Rician factor and RIS location, on the achievable sum
rate of the users.

Recently, contrastive learning has been applied to the sequential
recommendation task to address data sparsity caused by users with few item
interactions and items with few user adoptions. Nevertheless, the existing
contrastive learning-based methods fail to ensure that the positive (or
negative) sequence obtained by some random augmentation (or sequence sampling)
on a given anchor user sequence remains to be semantically similar (or
different). When the positive and negative sequences turn out to be false
positive and false negative respectively, it may lead to degraded
recommendation performance. In this work, we address the above problem by
proposing Explanation Guided Augmentations (EGA) and Explanation Guided
Contrastive Learning for Sequential Recommendation (EC4SRec) model framework.
The key idea behind EGA is to utilize explanation method(s) to determine items'
importance in a user sequence and derive the positive and negative sequences
accordingly. EC4SRec then combines both self-supervised and supervised
contrastive learning over the positive and negative sequences generated by EGA
operations to improve sequence representation learning for more accurate
recommendation results. Extensive experiments on four real-world benchmark
datasets demonstrate that EC4SRec outperforms the state-of-the-art sequential
recommendation methods and two recent contrastive learning-based sequential
recommendation methods, CL4SRec and DuoRec. Our experiments also show that
EC4SRec can be easily adapted for different sequence encoder backbones (e.g.,
GRU4Rec and Caser), and improve their recommendation performance.

Numerous microarchitectural optimizations unlocked tremendous processing
power for deep neural networks that in turn fueled the AI revolution. With the
exhaustion of such optimizations, the growth of modern AI is now gated by the
performance of training systems, especially their data movement. Instead of
focusing on single accelerators, we investigate data-movement characteristics
of large-scale training at full system scale. Based on our workload analysis,
we design HammingMesh, a novel network topology that provides high bandwidth at
low cost with high job scheduling flexibility. Specifically, HammingMesh can
support full bandwidth and isolation to deep learning training jobs with two
dimensions of parallelism. Furthermore, it also supports high global bandwidth
for generic traffic. Thus, HammingMesh will power future large-scale deep
learning systems with extreme bandwidth requirements.

In this paper, we present a density estimation framework based on tree
tensor-network states. The proposed method consists of determining the tree
topology with Chow-Liu algorithm, and obtaining a linear system of equations
that defines the tensor-network components via sketching techniques. Novel
choices of sketch functions are developed in order to consider graphical models
that contain loops. Sample complexity guarantees are provided and further
corroborated by numerical experiments.

Federated Learning (FL) is a paradigm for jointly training machine learning
algorithms in a decentralized manner which allows for parties to communicate
with an aggregator to create and train a model, without exposing the underlying
raw data distribution of the local parties involved in the training process.
Most research in FL has been focused on Neural Network-based approaches,
however Tree-Based methods, such as XGBoost, have been underexplored in
Federated Learning due to the challenges in overcoming the iterative and
additive characteristics of the algorithm. Decision tree-based models, in
particular XGBoost, can handle non-IID data, which is significant for
algorithms used in Federated Learning frameworks since the underlying
characteristics of the data are decentralized and have risks of being non-IID
by nature. In this paper, we focus on investigating the effects of how
Federated XGBoost is impacted by non-IID distributions by performing
experiments on various sample size-based data skew scenarios and how these
models perform under various non-IID scenarios. We conduct a set of extensive
experiments across multiple different datasets and different data skew
partitions. Our experimental results demonstrate that despite the various
partition ratios, the performance of the models stayed consistent and performed
close to or equally well against models that were trained in a centralized
manner.

With the enhancement of people's living standards and rapid growth of
communication technologies, residential environments are becoming smart and
well-connected, increasing overall energy consumption substantially. As
household appliances are the primary energy consumers, their recognition
becomes crucial to avoid unattended usage, thereby conserving energy and making
smart environments more sustainable. An appliance recognition model is
traditionally trained at a central server (service provider) by collecting
electricity consumption data, recorded via smart plugs, from the clients
(consumers), causing a privacy breach. Besides that, the data are susceptible
to noisy labels that may appear when an appliance gets connected to a
non-designated smart plug. While addressing these issues jointly, we propose a
novel federated learning approach to appliance recognition, called FedAR+,
enabling decentralized model training across clients in a privacy preserving
way even with mislabeled training data. FedAR+ introduces an adaptive noise
handling method, essentially a joint loss function incorporating weights and
label distribution, to empower the appliance recognition model against noisy
labels. By deploying smart plugs in an apartment complex, we collect a labeled
dataset that, along with two existing datasets, are utilized to evaluate the
performance of FedAR+. Experimental results show that our approach can
effectively handle up to $30\%$ concentration of noisy labels while
outperforming the prior solutions by a large margin on accuracy.

Deep neural networks trained with standard cross-entropy loss are more prone
to memorize noisy labels, which degrades their performance. Negative learning
using complementary labels is more robust when noisy labels intervene but with
an extremely slow model convergence speed. In this paper, we first introduce a
bidirectional learning scheme, where positive learning ensures convergence
speed while negative learning robustly copes with label noise. Further, a
dynamic sample reweighting strategy is proposed to globally weaken the effect
of noise-labeled samples by exploiting the excellent discriminatory ability of
negative learning on the sample probability distribution. In addition, we
combine self-distillation to further improve the model performance. The code is
available at \url{https://github.com/chenchenzong/BLDR}.

Few-Shot Learning (FSL) has attracted growing attention in computer vision
due to its capability in model training without the need for excessive data.
FSL is challenging because the training and testing categories (the base vs.
novel sets) can be largely diversified. Conventional transfer-based solutions
that aim to transfer knowledge learned from large labeled training sets to
target testing sets are limited, as critical adverse impacts of the shift in
task distribution are not adequately addressed. In this paper, we extend the
solution of transfer-based methods by incorporating the concept of
metric-learning and channel attention. To better exploit the feature
representations extracted by the feature backbone, we propose Class-Specific
Channel Attention (CSCA) module, which learns to highlight the discriminative
channels in each class by assigning each class one CSCA weight vector. Unlike
general attention modules designed to learn global-class features, the CSCA
module aims to learn local and class-specific features with very effective
computation. We evaluated the performance of the CSCA module on standard
benchmarks including miniImagenet, Tiered-ImageNet, CIFAR-FS, and CUB-200-2011.
Experiments are performed in inductive and in/cross-domain settings. We achieve
new state-of-the-art results.

It might be inadequate for the line search technique for Newton's method to
use only one floating point number. A column vector of the same size as the
gradient might be better than a mere float number to accelerate each of the
gradient elements with different rates. Moreover, a square matrix of the same
order as the Hessian matrix might be helpful to correct the Hessian matrix.
Chiang applied something between a column vector and a square matrix, namely a
diagonal matrix, to accelerate the gradient and further proposed a faster
gradient variant called quadratic gradient. In this paper, we present a new way
to build a new version of the quadratic gradient. This new quadratic gradient
doesn't satisfy the convergence conditions of the fixed Hessian Newton's
method. However, experimental results show that it sometimes has a better
performance than the original one in convergence rate. Also, Chiang speculates
that there might be a relation between the Hessian matrix and the learning rate
for the first-order gradient descent method. We prove that the floating number
$\frac{1}{\epsilon + \max \{| \lambda_i | \}}$ can be a good learning rate of
the gradient methods, where $\epsilon$ is a number to avoid division by zero
and $\lambda_i$ the eigenvalues of the Hessian matrix.

Convolutional neural networks can achieve remarkable performance in semantic
segmentation tasks. However, such neural network approaches heavily rely on
costly pixel-level annotation. Semi-supervised learning is a promising
resolution to tackle this issue, but its performance still far falls behind the
fully supervised counterpart. This work proposes a cross-teacher training
framework with three modules that significantly improves traditional
semi-supervised learning approaches. The core is a cross-teacher module, which
could simultaneously reduce the coupling among peer networks and the error
accumulation between teacher and student networks. In addition, we propose two
complementary contrastive learning modules. The high-level module can transfer
high-quality knowledge from labeled data to unlabeled ones and promote
separation between classes in feature space. The low-level module can encourage
low-quality features learning from the high-quality features among peer
networks. In experiments, the cross-teacher module significantly improves the
performance of traditional student-teacher approaches, and our framework
outperforms stateof-the-art methods on benchmark datasets. Our source code of
CTT will be released.

To detect the existing steganographic algorithms, recent steganalysis methods
usually train a Convolutional Neural Network (CNN) model on the dataset
consisting of corresponding paired cover/stego-images. However, it is
inefficient and impractical for those steganalysis tools to completely retrain
the CNN model to make it effective against both the existing steganographic
algorithms and a new emerging steganographic algorithm. Thus, existing
steganalysis models usually lack dynamic extensibility for new steganographic
algorithms, which limits their application in real-world scenarios. To address
this issue, we propose an accurate parameter importance estimation (APIE)
based-continual learning scheme for steganalysis. In this scheme, when a
steganalysis model is trained on the new image dataset generated by the new
steganographic algorithm, its network parameters are effectively and
efficiently updated with sufficient consideration of their importance evaluated
in the previous training process. This approach can guide the steganalysis
model to learn the patterns of the new steganographic algorithm without
significantly degrading the detectability against the previous steganographic
algorithms. Experimental results demonstrate the proposed scheme has promising
extensibility for new emerging steganographic algorithms.

Low resolution of positron emission tomography (PET) limits its diagnostic
performance. Deep learning has been successfully applied to achieve
super-resolution PET. However, commonly used supervised learning methods in
this context require many pairs of low- and high-resolution (LR and HR) PET
images. Although unsupervised learning utilizes unpaired images, the results
are not as good as that obtained with supervised deep learning. In this paper,
we propose a quasi-supervised learning method, which is a new type of
weakly-supervised learning methods, to recover HR PET images from LR
counterparts by leveraging similarity between unpaired LR and HR image patches.
Specifically, LR image patches are taken from a patient as inputs, while the
most similar HR patches from other patients are found as labels. The similarity
between the matched HR and LR patches serves as a prior for network
construction. Our proposed method can be implemented by designing a new network
or modifying an existing network. As an example in this study, we have modified
the cycle-consistent generative adversarial network (CycleGAN) for
super-resolution PET. Our numerical and experimental results qualitatively and
quantitatively show the merits of our method relative to the state-ofthe-art
methods. The code is publicly available at
https://github.com/PigYang-ops/CycleGAN-QSDL.

We provide the first comprehensive study on how to classify trajectories
using only their spatial representations, measured on 5 real-world data sets.
Our comparison considers 20 distinct classifiers arising either as a KNN
classifier of a popular distance, or as a more general type of classifier using
a vectorized representation of each trajectory. We additionally develop new
methods for how to vectorize trajectories via a data-driven method to select
the associated landmarks, and these methods prove among the most effective in
our study. These vectorized approaches are simple and efficient to use, and
also provide state-of-the-art accuracy on an established transportation mode
classification task. In all, this study sets the standard for how to classify
trajectories, including introducing new simple techniques to achieve these
results, and sets a rigorous standard for the inevitable future study on this
topic.

Deep learning models have achieved promising disease prediction performance
of the Electronic Health Records (EHR) of patients. However, most models
developed under the I.I.D. hypothesis fail to consider the agnostic
distribution shifts, diminishing the generalization ability of deep learning
models to Out-Of-Distribution (OOD) data. In this setting, spurious statistical
correlations that may change in different environments will be exploited, which
can cause sub-optimal performances of deep learning models. The unstable
correlation between procedures and diagnoses existed in the training
distribution can cause spurious correlation between historical EHR and future
diagnosis. To address this problem, we propose to use a causal representation
learning method called Causal Healthcare Embedding (CHE). CHE aims at
eliminating the spurious statistical relationship by removing the dependencies
between diagnoses and procedures. We introduce the Hilbert-Schmidt Independence
Criterion (HSIC) to measure the degree of independence between the embedded
diagnosis and procedure features. Based on causal view analyses, we perform the
sample weighting technique to get rid of such spurious relationship for the
stable learning of EHR across different environments. Moreover, our proposed
CHE method can be used as a flexible plug-and-play module that can enhance
existing deep learning models on EHR. Extensive experiments on two public
datasets and five state-of-the-art baselines unequivocally show that CHE can
improve the prediction accuracy of deep learning models on out-of-distribution
data by a large margin. In addition, the interpretability study shows that CHE
could successfully leverage causal structures to reflect a more reasonable
contribution of historical records for predictions.

Contrastive learning (CL) has shown impressive advances in image
representation learning in whichever supervised multi-class classification or
unsupervised learning. However, these CL methods fail to be directly adapted to
multi-label image classification due to the difficulty in defining the positive
and negative instances to contrast a given anchor image in multi-label
scenario, let the label missing one alone, implying that borrowing a
commonly-used way from contrastive multi-class learning to define them will
incur a lot of false negative instances unfavorable for learning. In this
paper, with the introduction of a label correction mechanism to identify
missing labels, we first elegantly generate positives and negatives for
individual semantic labels of an anchor image, then define a unique contrastive
loss for multi-label image classification with missing labels (CLML), the loss
is able to accurately bring images close to their true positive images and
false negative images, far away from their true negative images. Different from
existing multi-label CL losses, CLML also preserves low-rank global and local
label dependencies in the latent representation space where such dependencies
have been shown to be helpful in dealing with missing labels. To the best of
our knowledge, this is the first general multi-label CL loss in the
missing-label scenario and thus can seamlessly be paired with those losses of
any existing multi-label learning methods just via a single hyperparameter. The
proposed strategy has been shown to improve the classification performance of
the Resnet101 model by margins of 1.2%, 1.6%, and 1.3% respectively on three
standard datasets, MSCOCO, VOC, and NUS-WIDE. Code is available at
https://github.com/chuangua/ContrastiveLossMLML.

Knowledge distillation is an effective transfer of knowledge from a heavy
network (teacher) to a small network (student) to boost students' performance.
Self-knowledge distillation, the special case of knowledge distillation, has
been proposed to remove the large teacher network training process while
preserving the student's performance. This paper introduces a novel
Self-knowledge distillation approach via Siamese representation learning, which
minimizes the difference between two representation vectors of the two
different views from a given sample. Our proposed method, SKD-SRL, utilizes
both soft label distillation and the similarity of representation vectors.
Therefore, SKD-SRL can generate more consistent predictions and representations
in various views of the same data point. Our benchmark has been evaluated on
various standard datasets. The experimental results have shown that SKD-SRL
significantly improves the accuracy compared to existing supervised learning
and knowledge distillation methods regardless of the networks.

Accurate and efficient prediction of polymer properties is of great
significance in polymer development and design. Conventionally, expensive and
time-consuming experiments or simulations are required to evaluate the function
of polymers. Recently, Transformer models, equipped with attention mechanisms,
have exhibited superior performance in various natural language processing
tasks. However, such methods have not been investigated in polymer sciences.
Herein, we report TransPolymer, a Transformer-based language model for polymer
property prediction. Owing to our proposed polymer tokenizer with chemical
awareness, TransPolymer can learn representations directly from polymer
sequences. The model learns expressive representations by pretraining on a
large unlabeled dataset, followed by finetuning the model on downstream
datasets concerning various polymer properties. TransPolymer achieves superior
performance in all eight datasets and surpasses other baselines significantly
on most downstream tasks. Moreover, the improvement by the pretrained
TransPolymer over supervised TransPolymer and other language models strengthens
the significant benefits of pretraining on large unlabeled data in
representation learning. Experiment results further demonstrate the important
role of the attention mechanism in understanding polymer sequences. We
highlight this model as a promising computational tool for promoting rational
polymer design and understanding structure-property relationships in a data
science view.

The Expectation--Maximization (EM) algorithm is a simple meta-algorithm that
has been used for many years as a methodology for statistical inference when
there are missing measurements in the observed data or when the data is
composed of observables and unobservables. Its general properties are well
studied, and also, there are countless ways to apply it to individual problems.
In this paper, we introduce the $em$ algorithm, an information geometric
formulation of the EM algorithm, and its extensions and applications to various
problems. Specifically, we will see that it is possible to formulate an
outlier-robust inference algorithm, an algorithm for calculating channel
capacity, parameter estimation methods on probability simplex, particular
multivariate analysis methods such as principal component analysis in a space
of probability models and modal regression, matrix factorization, and learning
generative models, which have recently attracted attention in deep learning,
from the geometric perspective.

Unsupervised domain adaptation (UDA) is one of the key technologies to solve
a problem where it is hard to obtain ground truth labels needed for supervised
learning. In general, UDA assumes that all samples from source and target
domains are available during the training process. However, this is not a
realistic assumption under applications where data privacy issues are
concerned. To overcome this limitation, UDA without source data, referred to
source-free unsupervised domain adaptation (SFUDA) has been recently proposed.
Here, we propose a SFUDA method for medical image segmentation. In addition to
the entropy minimization method, which is commonly used in UDA, we introduce a
loss function for avoiding feature norms in the target domain small and a prior
to preserve shape constraints of the target organ. We conduct experiments using
datasets including multiple types of source-target domain combinations in order
to show the versatility and robustness of our method. We confirm that our
method outperforms the state-of-the-art in all datasets.

Models can expose sensitive information about their training data. In an
attribute inference attack, an adversary has partial knowledge of some training
records and access to a model trained on those records, and infers the unknown
values of a sensitive feature of those records. We study a fine-grained variant
of attribute inference we call \emph{sensitive value inference}, where the
adversary's goal is to identify with high confidence some records from a
candidate set where the unknown attribute has a particular sensitive value. We
explicitly compare attribute inference with data imputation that captures the
training distribution statistics, under various assumptions about the training
data available to the adversary. Our main conclusions are: (1) previous
attribute inference methods do not reveal more about the training data from the
model than can be inferred by an adversary without access to the trained model,
but with the same knowledge of the underlying distribution as needed to train
the attribute inference attack; (2) black-box attribute inference attacks
rarely learn anything that cannot be learned without the model; but (3)
white-box attacks, which we introduce and evaluate in the paper, can reliably
identify some records with the sensitive value attribute that would not be
predicted without having access to the model. Furthermore, we show that
proposed defenses such as differentially private training and removing
vulnerable records from training do not mitigate this privacy risk. The code
for our experiments is available at
\url{https://github.com/bargavj/EvaluatingDPML}.

In this article, we describe a {\tt R} package for sampling from an empirical
likelihood-based posterior using a Hamiltonian Monte Carlo method. Empirical
likelihood-based methodologies have been used in Bayesian modeling of many
problems of interest in recent times. This semiparametric procedure can easily
combine the flexibility of a non-parametric distribution estimator together
with the interpretability of a parametric model. The model is specified by
estimating equations-based constraints. Drawing an inference from a Bayesian
empirical likelihood (BayesEL) posterior is challenging. The likelihood is
computed numerically, so no closed expression of the posterior exists.
Moreover, for any sample of finite size, the support of the likelihood is
non-convex, which hinders the fast mixing of many Markov Chain Monte Carlo
(MCMC) procedures. It has been recently shown that using the properties of the
gradient of log empirical likelihood, one can devise an efficient Hamiltonian
Monte Carlo (HMC) algorithm to sample from a BayesEL posterior.
  The package requires the user to specify only the estimating equations, the
prior, and their respective gradients. An MCMC sample drawn from the BayesEL
posterior of the parameters, with various details required by the user is
obtained.

In Multi-Agent Reinforcement Learning, communication is critical to encourage
cooperation among agents. Communication in realistic wireless networks can be
highly unreliable due to network conditions varying with agents' mobility, and
stochasticity in the transmission process. We propose a framework to learn
practical communication strategies by addressing three fundamental questions:
(1) When: Agents learn the timing of communication based on not only message
importance but also wireless channel conditions. (2) What: Agents augment
message contents with wireless network measurements to better select the game
and communication actions. (3) How: Agents use a novel neural message encoder
to preserve all information from received messages, regardless of the number
and order of messages. Simulating standard benchmarks under realistic wireless
network settings, we show significant improvements in game performance,
convergence speed and communication efficiency compared with state-of-the-art.

Six 650 MHz high-power couplers will be integrated into the prototype High
Beta 650 MHz (HB650) cryomodule for the PIP-II project at Fermilab. The design
of the coupler is described, including design optimizations from the previous
generation. This paper then describes the coupler life-cycle, including
manufacturing, assembly, testing, conditioning, and the lessons learned at each
stage.

Many studies on scaling laws consider basic factors such as model size, model
shape, dataset size, and compute power. These factors are easily tunable and
represent the fundamental elements of any machine learning setup. But
researchers have also employed more complex factors to estimate the test error
and generalization performance with high predictability. These factors are
generally specific to the domain or application. For example, feature diversity
was primarily used for promoting syn-to-real transfer by Chen et al. (2021).
With numerous scaling factors defined in previous works, it would be
interesting to investigate how these factors may affect overall generalization
performance in the context of self-supervised learning with CNN models. How do
individual factors promote generalization, which includes varying depth, width,
or the number of training epochs with early stopping? For example, does higher
feature diversity result in higher accuracy held in complex settings other than
a syn-to-real transfer? How do these factors depend on each other? We found
that the last layer is the most diversified throughout the training. However,
while the model's test error decreases with increasing epochs, its diversity
drops. We also discovered that diversity is directly related to model width.

In recent times empirical likelihood has been widely applied under Bayesian
framework. Markov chain Monte Carlo (MCMC) methods are frequently employed to
sample from the posterior distribution of the parameters of interest. However,
complex, especially non-convex nature of the likelihood support erects enormous
hindrances in choosing an appropriate MCMC algorithm. Such difficulties have
restricted the use of Bayesian empirical likelihood (BayesEL) based methods in
many applications. In this article, we propose a two-step Metropolis Hastings
algorithm to sample from the BayesEL posteriors. Our proposal is specified
hierarchically, where the estimating equations determining the empirical
likelihood are used to propose values of a set of parameters depending on the
proposed values of the remaining parameters. Furthermore, we discuss Bayesian
model selection using empirical likelihood and extend our two-step Metropolis
Hastings algorithm to a reversible jump Markov chain Monte Carlo procedure to
sample from the resulting posterior. Finally, several applications of our
proposed methods are presented.

This paper presents Deep-PANTHER, a learning-based perception-aware
trajectory planner for unmanned aerial vehicles (UAVs) in dynamic environments.
Given the current state of the UAV, and the predicted trajectory and size of
the obstacle, Deep-PANTHER generates multiple trajectories to avoid a dynamic
obstacle while simultaneously maximizing its presence in the field of view
(FOV) of the onboard camera. To obtain a computationally tractable real-time
solution, imitation learning is leveraged to train a Deep-PANTHER policy using
demonstrations provided by a multimodal optimization-based expert. Extensive
simulations show replanning times that are two orders of magnitude faster than
the optimization-based expert, while achieving a similar cost. By ensuring that
each expert trajectory is assigned to one distinct student trajectory in the
loss function, Deep-PANTHER can also capture the multimodality of the problem
and achieve a mean squared error (MSE) loss with respect to the expert that is
up to 18 times smaller than state-of-the-art (Relaxed) Winner-Takes-All
approaches. Deep-PANTHER is also shown to generalize well to obstacle
trajectories that differ from the ones used in training.

The world consists of objects: distinct entities possessing independent
properties and dynamics. For agents to interact with the world intelligently,
they must translate sensory inputs into the bound-together features that
describe each object. These object-based representations form a natural basis
for planning behavior. Active inference (AIF) is an influential unifying
account of perception and action, but existing AIF models have not leveraged
this important inductive bias. To remedy this, we introduce 'object-based
active inference' (OBAI), marrying AIF with recent deep object-based neural
networks. OBAI represents distinct objects with separate variational beliefs,
and uses selective attention to route inputs to their corresponding object
slots. Object representations are endowed with independent action-based
dynamics. The dynamics and generative model are learned from experience with a
simple environment (active multi-dSprites). We show that OBAI learns to
correctly segment the action-perturbed objects from video input, and to
manipulate these objects towards arbitrary goals.

In this paper, the problem of decentralized eigenvalue decomposition of a
general symmetric matrix that is important, e.g., in Principal Component
Analysis, is studied, and a decentralized online learning algorithm is
proposed. Instead of collecting all information in a fusion center, the
proposed algorithm involves only local interactions among adjacent agents. It
benefits from the representation of the matrix as a sum of rank-one components
which makes the algorithm attractive for online eigenvalue and eigenvector
tracking applications. We examine the performance of the proposed algorithm in
two types of important application examples: First, we consider the online
eigendecomposition of a sample covariance matrix over the network, with
application in decentralized Direction-of-Arrival (DoA) estimation and DoA
tracking applications. Then, we investigate the online computation of the
spectra of the graph Laplacian that is important in, e.g., Graph Fourier
Analysis and graph dependent filter design. We apply our proposed algorithm to
track the spectra of the graph Laplacian in static and dynamic networks.
Simulation results reveal that the proposed algorithm outperforms existing
decentralized algorithms both in terms of estimation accuracy as well as
communication cost.

In this paper, we study a learning problem in which a forecaster only
observes partial information. By properly rescaling the problem, we
heuristically derive a limiting PDE on Wasserstein space which characterizes
the asymptotic behavior of the regret of the forecaster. Using a verification
type argument, we show that the problem of obtaining regret bounds and
efficient algorithms can be tackled by finding appropriate smooth
sub/supersolutions of this parabolic PDE.

Matrix functions are utilized to rewrite smooth spectral constrained matrix
optimization problems as smooth unconstrained problems over the set of
symmetric matrices which are then solved via the cubic-regularized Newton
method. A second-order chain rule identity for matrix functions is proven to
compute the higher-order derivatives to implement cubic-regularized Newton, and
a new convergence analysis is provided for cubic-regularized Newton for matrix
vector spaces. We demonstrate the applicability of our approach by conducting
numerical experiments on both synthetic and real datasets. In our experiments,
we formulate a new model for estimating fair and robust covariance matrices in
the spirit of the Tyler's M-estimator (TME) model and demonstrate its
advantage.

The PIP-II Injector Test 1 PIP2IT facility accelerator was assembled in
multiple stages in 2014 $-$ 2021 to test concepts and components of the future
PIPII linac that is being constructed at Fermilab. In its final configuration,
PIP2IT accelerated a 0.55 ms x 20 Hz x 2 mA H$-$ beam to 16 MeV. To protect
elements of the beam line, a Machine Protection System MPS was implemented and
commissioned. The beam was interrupted faster than 10$\mu$s when excessive beam
loss was detected. The paper describes the MPS architecture, methods of the
loss detection, procedure of the beam interruption, and operational experience
at PIP2IT.

The co-adaptation of robots has been a long-standing research endeavour with
the goal of adapting both body and behaviour of a system for a given task,
inspired by the natural evolution of animals. Co-adaptation has the potential
to eliminate costly manual hardware engineering as well as improve the
performance of systems. The standard approach to co-adaptation is to use a
reward function for optimizing behaviour and morphology. However, defining and
constructing such reward functions is notoriously difficult and often a
significant engineering effort. This paper introduces a new viewpoint on the
co-adaptation problem, which we call co-imitation: finding a morphology and a
policy that allow an imitator to closely match the behaviour of a demonstrator.
To this end we propose a co-imitation methodology for adapting behaviour and
morphology by matching state distributions of the demonstrator. Specifically,
we focus on the challenging scenario with mismatched state- and action-spaces
between both agents. We find that co-imitation increases behaviour similarity
across a variety of tasks and settings, and demonstrate co-imitation by
transferring human walking, jogging and kicking skills onto a simulated
humanoid.

Deep learning-based algorithms have seen a massive popularity in different
areas of remote sensing image analysis over the past decade. Recently,
transformers-based architectures, originally introduced in natural language
processing, have pervaded computer vision field where the self-attention
mechanism has been utilized as a replacement to the popular convolution
operator for capturing long-range dependencies. Inspired by recent advances in
computer vision, remote sensing community has also witnessed an increased
exploration of vision transformers for a diverse set of tasks. Although a
number of surveys have focused on transformers in computer vision in general,
to the best of our knowledge we are the first to present a systematic review of
recent advances based on transformers in remote sensing. Our survey covers more
than 60 recent transformers-based methods for different remote sensing problems
in sub-areas of remote sensing: very high-resolution (VHR), hyperspectral (HSI)
and synthetic aperture radar (SAR) imagery. We conclude the survey by
discussing different challenges and open issues of transformers in remote
sensing. Additionally, we intend to frequently update and maintain the latest
transformers in remote sensing papers with their respective code at:
https://github.com/VIROBO-15/Transformer-in-Remote-Sensing

Knowledge graphs (KGs) are known for their large scale and knowledge
inference ability, but are also notorious for the incompleteness associated
with them. Due to the long-tail distribution of the relations in KGs, few-shot
KG completion has been proposed as a solution to alleviate incompleteness and
expand the coverage of KGs. It aims to make predictions for triplets involving
novel relations when only a few training triplets are provided as reference.
Previous methods have mostly focused on designing local neighbor aggregators to
learn entity-level information and/or imposing sequential dependency assumption
at the triplet level to learn meta relation information. However, valuable
pairwise triplet-level interactions and context-level relational information
have been largely overlooked for learning meta representations of few-shot
relations. In this paper, we propose a hierarchical relational learning method
(HiRe) for few-shot KG completion. By jointly capturing three levels of
relational information (entity-level, triplet-level and context-level), HiRe
can effectively learn and refine the meta representation of few-shot relations,
and consequently generalize very well to new unseen relations. Extensive
experiments on two benchmark datasets validate the superiority of HiRe against

Despite the fundamental distinction between adversarial and natural training
(AT and NT), AT methods generally adopt momentum SGD (MSGD) for the outer
optimization. This paper aims to analyze this choice by investigating the
overlooked role of outer optimization in AT. Our exploratory evaluations reveal
that AT induces higher gradient norm and variance compared to NT. This
phenomenon hinders the outer optimization in AT since the convergence rate of
MSGD is highly dependent on the variance of the gradients. To this end, we
propose an optimization method called ENGM which regularizes the contribution
of each input example to the average mini-batch gradients. We prove that the
convergence rate of ENGM is independent of the variance of the gradients, and
thus, it is suitable for AT. We introduce a trick to reduce the computational
cost of ENGM using empirical observations on the correlation between the norm
of gradients w.r.t. the network parameters and input examples. Our extensive
evaluations and ablation studies on CIFAR-10, CIFAR-100, and TinyImageNet
demonstrate that ENGM and its variants consistently improve the performance of
a wide range of AT methods. Furthermore, ENGM alleviates major shortcomings of
AT including robust overfitting and high sensitivity to hyperparameter
settings.

Prediction of correlation matrices from given time series data has several
applications for a range of problems, such as inferring neuronal connections
from spiking data, deducing causal dependencies between genes from expression
data, and discovering long spatial range influences in climate variations.
Traditional methods of predicting correlation matrices utilize time series data
of all the nodes of the underlying networks. Here, we use a supervised machine
learning technique to predict the correlation matrix of entire systems from
finite time series information of a few randomly selected nodes. The accuracy
of the prediction from the model confirms that only a limited time series of a
subset of the entire system is enough to make good correlation matrix
predictions. Furthermore, using an unsupervised learning algorithm, we provide
insights into the success of the predictions from our model. Finally, we apply
the machine learning model developed here to real-world data sets.

Tensor network quantum machine learning (QML) models are promising
applications on near-term quantum hardware. While decoherence of qubits is
expected to decrease the performance of QML models, it is unclear to what
extent the diminished performance can be compensated for by adding ancillas to
the models and accordingly increasing the virtual bond dimension of the models.
We investigate here the competition between decoherence and adding ancillas on
the classification performance of two models, with an analysis of the
decoherence effect from the perspective of regression. We present numerical
evidence that the fully-decohered unitary tree tensor network (TTN) with two
ancillas performs at least as well as the non-decohered unitary TTN, suggesting
that it is beneficial to add at least two ancillas to the unitary TTN
regardless of the amount of decoherence may be consequently introduced.

Recent advances in neural radiance fields (NeRFs) achieve state-of-the-art
novel view synthesis and facilitate dense estimation of scene properties.
However, NeRFs often fail for large, unbounded scenes that are captured under
very sparse views with the scene content concentrated far away from the camera,
as is typical for field robotics applications. In particular, NeRF-style
algorithms perform poorly: (1) when there are insufficient views with little
pose diversity, (2) when scenes contain saturation and shadows, and (3) when
finely sampling large unbounded scenes with fine structures becomes
computationally intensive.
  This paper proposes CLONeR, which significantly improves upon NeRF by
allowing it to model large outdoor driving scenes that are observed from sparse
input sensor views. This is achieved by decoupling occupancy and color learning
within the NeRF framework into separate Multi-Layer Perceptrons (MLPs) trained
using LiDAR and camera data, respectively. In addition, this paper proposes a
novel method to build differentiable 3D Occupancy Grid Maps (OGM) alongside the
NeRF model, and leverage this occupancy grid for improved sampling of points
along a ray for volumetric rendering in metric space.
  Through extensive quantitative and qualitative experiments on scenes from the
KITTI dataset, this paper demonstrates that the proposed method outperforms
state-of-the-art NeRF models on both novel view synthesis and dense depth
prediction tasks when trained on sparse input data.

The aim of this study was to investigate the influence of MRI and patient
data on the prediction of knee osteoarthritis (OA) incidence using different
deep learning architectures. Knee OA incidence within 24 months was predicted
using the intermediate-weighted turbo spin-echo (IW-TSE) sequence of 593
patients from the Osteoarthritis Initiative. To extract a region of interest
containing the knee joint from the IW-TSE sequence, a U-Net model was trained
and used to segment bone on a dual echo steady state (DESS) sequence.
Subsequently, IW-TSE and DESS sequences were registered and the DESS
segmentations were transformed to the corresponding IW-TSE scans. The
performance of MRI-based features in the prediction of knee OA incidence was
tested using three different deep learning architectures: a residual network
(ResNet), a densely connected convolutional network (DenseNet), and a
convolutional variational autoencoder (CVAE). To evaluate the predictive
performance of MRI-based features alone, the outputs of ResNet, DenseNet, and
CVAE were coupled with patient data (i.e., age, gender, BMI) and used as input
to a Logistic Regression (LR) Classifier. Knee OA was defined based on visual
MRI and X-ray-based OA features. The ResNet and DenseNet showed similar
results, with both methods having the area under the receiver operating
characteristic curve (AUC) values up to 0.6269. The best performing OA
detection model was CVAE with an AUC of 0.6699 when combined with patient data
and an AUC of 0.6689 when used alone as input to the LR classifier. The results
showed that three deep learning algorithms have similar metrics when using
IW-TSE MRIs and their performance increased with the inclusion of patient data,
which shows the strong influence of variables such as age, gender, and BMI on
the detection of knee OA.

Many NLP tasks benefit from using large language models (LLMs) that often
have more than 100 billion parameters. With the release of BLOOM-176B and
OPT-175B, everyone can download pretrained models of this scale. Still, using
these models requires high-end hardware unavailable to many researchers. In
some cases, LLMs can be used more affordably via RAM offloading or hosted APIs.
However, these techniques have innate limitations: offloading is too slow for
interactive inference, while APIs are not flexible enough for research. In this
work, we propose Petals $-$ a system for inference and fine-tuning of large
models collaboratively by joining the resources of multiple parties trusted to
process client's data. We demonstrate that this strategy significantly
outperforms offloading for very large models, running inference of BLOOM-176B
on consumer GPUs with $\approx$ 1 step per second. Unlike most inference APIs,
Petals also natively exposes the hidden states of served models, allowing its
users to train and share custom model extensions based on efficient fine-tuning
methods.

As the academic community debates the future of in-person conferences, it is
important to understand how effective they are at diffusing ideas. Most
previous attempts to analyze this question have struggled to separate causation
from correlation and used potentially biased measures like self-reported
learning. Here, we propose a novel approach using scheduling conflicts. When
multiple presentations of interest to an attendee are scheduled at the same
time, the attendee is less able to see them, on average. If seeing
presentations influences future research, then conflicting presentations should
influence research less than unconflicting ones. Analyzing conflicts in the
personalized schedules of 1960 attendees of 20 computer science conferences
reveals that when an attendee is able to see a paper presentation, she is
almost twice as likely to cite the paper in her future work. The effect is
robust to underlying differences between attendees, papers, and paper authors,
and is even larger for a stronger measure of influence -- citing the presented
paper multiple times. Given the substantial learning effects of in-person
presentations, it will be important to ensure that attempts to turn conferences
hybrid or virtual do not imperil knowledge diffusion.

While Transformer language models (LMs) are state-of-the-art for information
extraction, long text introduces computational challenges requiring suboptimal
preprocessing steps or alternative model architectures. Sparse-attention LMs
can represent longer sequences, overcoming performance hurdles. However, it
remains unclear how to explain predictions from these models, as not all tokens
attend to each other in the self-attention layers, and long sequences pose
computational challenges for explainability algorithms when runtime depends on
document length. These challenges are severe in the medical context where
documents can be very long, and machine learning (ML) models must be auditable
and trustworthy. We introduce a novel Masked Sampling Procedure (MSP) to
identify the text blocks that contribute to a prediction, apply MSP in the
context of predicting diagnoses from medical text, and validate our approach
with a blind review by two clinicians. Our method identifies about 1.7x more
clinically informative text blocks than the previous state-of-the-art, runs up
to 100x faster, and is tractable for generating important phrase pairs. MSP is
particularly well-suited to long LMs but can be applied to any text classifier.
We provide a general implementation of MSP.

In this note, we study how neural networks with a single hidden layer and
ReLU activation interpolate data drawn from a radially symmetric distribution
with target labels 1 at the origin and 0 outside the unit ball, if no labels
are known inside the unit ball. With weight decay regularization and in the
infinite neuron, infinite data limit, we prove that a unique radially symmetric
minimizer exists, whose weight decay regularizer and Lipschitz constant grow as
$d$ and $\sqrt{d}$ respectively.
  We furthermore show that the weight decay regularizer grows exponentially in
$d$ if the label $1$ is imposed on a ball of radius $\varepsilon$ rather than
just at the origin. By comparison, a neural networks with two hidden layers can
approximate the target function without encountering the curse of
dimensionality.

As a special infinite-order vector autoregressive (VAR) model, the vector
autoregressive moving average (VARMA) model can capture much richer temporal
patterns than the widely used finite-order VAR model. However, its practicality
has long been hindered by its non-identifiability, computational
intractability, and relative difficulty of interpretation. This paper
introduces a novel infinite-order VAR model which, with only a little sacrifice
of generality, inherits the essential temporal patterns of the VARMA model but
avoids all of the above drawbacks. As another attractive feature, the temporal
and cross-sectional dependence structures of this model can be interpreted
separately, since they are characterized by different sets of parameters. For
high-dimensional time series, this separation motivates us to impose sparsity
on the parameters determining the cross-sectional dependence. As a result,
greater statistical efficiency and interpretability can be achieved, while no
loss of temporal information is incurred by the imposed sparsity. We introduce
an $\ell_1$-regularized estimator for the proposed model and derive the
corresponding nonasymptotic error bounds. An efficient block coordinate descent
algorithm and a consistent model order selection method are developed. The
merit of the proposed approach is supported by simulation studies and a
real-world macroeconomic data analysis.

We study the effect of normalization on the layers of deep neural networks of
feed-forward type. A given layer $i$ with $N_{i}$ hidden units is allowed to be
normalized by $1/N_{i}^{\gamma_{i}}$ with $\gamma_{i}\in[1/2,1]$ and we study
the effect of the choice of the $\gamma_{i}$ on the statistical behavior of the
neural network's output (such as variance) as well as on the test accuracy on
the MNIST data set. We find that in terms of variance of the neural network's
output and test accuracy the best choice is to choose the $\gamma_{i}$'s to be
equal to one, which is the mean-field scaling. We also find that this is
particularly true for the outer layer, in that the neural network's behavior is
more sensitive in the scaling of the outer layer as opposed to the scaling of
the inner layers. The mechanism for the mathematical analysis is an asymptotic
expansion for the neural network's output. An important practical consequence
of the analysis is that it provides a systematic and mathematically informed
way to choose the learning rate hyperparameters. Such a choice guarantees that
the neural network behaves in a statistically robust way as the $N_i$ grow to
infinity.

We propose a family of First Hitting Diffusion Models (FHDM), deep generative
models that generate data with a diffusion process that terminates at a random
first hitting time. This yields an extension of the standard fixed-time
diffusion models that terminate at a pre-specified deterministic time. Although
standard diffusion models are designed for continuous unconstrained data, FHDM
is naturally designed to learn distributions on continuous as well as a range
of discrete and structure domains. Moreover, FHDM enables instance-dependent
terminate time and accelerates the diffusion process to sample higher quality
data with fewer diffusion steps. Technically, we train FHDM by maximum
likelihood estimation on diffusion trajectories augmented from observed data
with conditional first hitting processes (i.e., bridge) derived based on Doob's
$h$-transform, deviating from the commonly used time-reversal mechanism. We
apply FHDM to generate data in various domains such as point cloud (general
continuous distribution), climate and geographical events on earth (continuous
distribution on the sphere), unweighted graphs (distribution of binary
matrices), and segmentation maps of 2D images (high-dimensional categorical
distribution). We observe considerable improvement compared with the
state-of-the-art approaches in both quality and speed.

This study explores the association between music preferences and moral
values by applying text analysis techniques to lyrics. Harvesting data from a
Facebook-hosted application, we align psychometric scores of 1,386 users to
lyrics from the top 5 songs of their preferred music artists as emerged from
Facebook Page Likes. We extract a set of lyrical features related to each
song's overarching narrative, moral valence, sentiment, and emotion. A machine
learning framework was designed to exploit regression approaches and evaluate
the predictive power of lyrical features for inferring moral values. Results
suggest that lyrics from top songs of artists people like inform their
morality. Virtues of hierarchy and tradition achieve higher prediction scores
($.20 \leq r \leq .30$) than values of empathy and equality ($.08 \leq r \leq
.11$), while basic demographic variables only account for a small part in the
models' explainability. This shows the importance of music listening
behaviours, as assessed via lyrical preferences, alone in capturing moral
values. We discuss the technological and musicological implications and
possible future improvements.

Traditional process mining techniques take event data as input where each
event is associated with exactly one object. An object represents the
instantiation of a process. Object-centric event data contain events associated
with multiple objects expressing the interaction of multiple processes. As
traditional process mining techniques assume events associated with exactly one
object, these techniques cannot be applied to object-centric event data. To use
traditional process mining techniques, the object-centric event data are
flattened by removing all object references but one. The flattening process is
lossy, leading to inaccurate features extracted from flattened data.
Furthermore, the graph-like structure of object-centric event data is lost when
flattening. In this paper, we introduce a general framework for extracting and
encoding features from object-centric event data. We calculate features
natively on the object-centric event data, leading to accurate measures.
Furthermore, we provide three encodings for these features: tabular,
sequential, and graph-based. While tabular and sequential encodings have been
heavily used in process mining, the graph-based encoding is a new technique
preserving the structure of the object-centric event data. We provide six use
cases: a visualization and a prediction use case for each of the three
encodings. We use explainable AI in the prediction use cases to show the
utility of both the object-centric features and the structure of the sequential
and graph-based encoding for a predictive model.

Existing deep networks for histopathology image synthesis cannot generate
accurate boundaries for clustered nuclei and cannot output image styles that
align with different organs. To address these issues, we propose a style-guided
instance-adaptive normalization (SIAN) to synthesize realistic color
distributions and textures for different organs. SIAN contains four phases,
semantization, stylization, instantiation, and modulation. The four phases work
together and are integrated into a generative network to embed image semantics,
style, and instance-level boundaries. Experimental results demonstrate the
effectiveness of all components in SIAN, and show that the proposed method
outperforms the state-of-the-art conditional GANs for histopathology image
synthesis using the Frechet Inception Distance (FID), structural similarity
Index (SSIM), detection quality(DQ), segmentation quality(SQ), and panoptic
quality(PQ). Furthermore, the performance of a segmentation network could be
significantly improved by incorporating synthetic images generated using SIAN.

Reverse Engineering a CAD shape from other representations is an important
geometric processing step for many downstream applications. In this work, we
introduce a novel neural network architecture to solve this challenging task
and approximate a smoothed signed distance function with an editable,
constrained, prismatic CAD model. During training, our method reconstructs the
input geometry in the voxel space by decomposing the shape into a series of 2D
profile images and 1D envelope functions. These can then be recombined in a
differentiable way allowing a geometric loss function to be defined. During
inference, we obtain the CAD data by first searching a database of 2D
constrained sketches to find curves which approximate the profile images, then
extrude them and use Boolean operations to build the final CAD model. Our
method approximates the target shape more closely than other methods and
outputs highly editable constrained parametric sketches which are compatible
with existing CAD software.

Medication mistaking is one of the risks that can result in unpredictable
consequences for patients. To mitigate this risk, we develop an automatic
system that correctly identifies pill-prescription from mobile images.
Specifically, we define a so-called pill-prescription matching task, which
attempts to match the images of the pills taken with the pills' names in the
prescription. We then propose PIMA, a novel approach using Graph Neural Network
(GNN) and contrastive learning to address the targeted problem. In particular,
GNN is used to learn the spatial correlation between the text boxes in the
prescription and thereby highlight the text boxes carrying the pill names. In
addition, contrastive learning is employed to facilitate the modeling of
cross-modal similarity between textual representations of pill names and visual
representations of pill images. We conducted extensive experiments and
demonstrated that PIMA outperforms baseline models on a real-world dataset of
pill and prescription images that we constructed. Specifically, PIMA improves
the accuracy from 19.09% to 46.95% compared to other baselines. We believe our
work can open up new opportunities to build new clinical applications and
improve medication safety and patient care.

We study one of the key tools in data approximation and optimization:
low-discrepancy colorings. Formally, given a finite set system $(X,\mathcal
S)$, the \emph{discrepancy} of a two-coloring $\chi:X\to\{-1,1\}$ is defined as
$\max_{S \in \mathcal S}|{\chi(S)}|$, where $\chi(S)=\sum\limits_{x \in
S}\chi(x)$.
  We propose a randomized algorithm which, for any $d>0$ and $(X,\mathcal S)$
with dual shatter function $\pi^*(k)=O(k^d)$, returns a coloring with expected
discrepancy $O\left({\sqrt{|X|^{1-1/d}\log|\mathcal S|}}\right)$ (this bound is
tight) in time $\tilde O\left({|\mathcal S|\cdot|X|^{1/d}+|X|^{2+1/d}}\right)$,
improving upon the previous-best time of $O\left(|\mathcal S|\cdot|X|^3\right)$
by at least a factor of $|X|^{2-1/d}$ when $|\mathcal S|\geq|X|$. This setup
includes many geometric classes, families of bounded dual VC-dimension, and
others. As an immediate consequence, we obtain an improved algorithm to
construct $\varepsilon$-approximations of sub-quadratic size.
  Our method uses primal-dual reweighing with an improved analysis of randomly
updated weights and exploits the structural properties of the set system via
matchings with low crossing number -- a fundamental structure in computational
geometry. In particular, we get the same $|X|^{2-1/d}$ factor speed-up on the
construction time of matchings with crossing number
$O\left({|X|^{1-1/d}}\right)$, which is the first improvement since the 1980s.
  The proposed algorithms are very simple, which makes it possible, for the
first time, to compute colorings with near-optimal discrepancies and
near-optimal sized approximations for abstract and geometric set systems in
dimensions higher than $2$.

One of the key challenges of learning an online recommendation model is the
temporal domain shift, which causes the mismatch between the training and
testing data distribution and hence domain generalization error. To overcome,
we propose to learn a meta future gradient generator that forecasts the
gradient information of the future data distribution for training so that the
recommendation model can be trained as if we were able to look ahead at the
future of its deployment. Compared with Batch Update, a widely used paradigm,
our theory suggests that the proposed algorithm achieves smaller temporal
domain generalization error measured by a gradient variation term in a local
regret. We demonstrate the empirical advantage by comparing with various

In this paper, we derive a novel method as a generalization over LCEs such as
E2C. The method develops the idea of learning a locally linear state space, by
adding a multi-step prediction, thus allowing for more explicit control over
the curvature. We show, that the method outperforms E2C without drastic model
changes which come with other works, such as PCC and P3C. We discuss the
relation between E2C and the presented method and derived update equations. We
provide empirical evidence, which suggests that by considering the multi-step
prediction our method - ms-E2C - allows to learn much better latent state
spaces in terms of curvature and next state predictability. Finally, we also
discuss certain stability challenges we encounter with multi-step predictions
and the ways to mitigate them.

Multi-server queueing systems are widely used models for job scheduling in
machine learning, wireless networks, and crowdsourcing. This paper considers a
multi-server system with multiple servers and multiple types of jobs. The
system maintains a separate queue for each type of jobs. For each time slot,
each available server picks a job from a queue and then serves the job until it
is complete. The arrival rates of the queues and the mean service times are
unknown and even nonstationary. We propose the MaxWeight with discounted upper
confidence bound (UCB) algorithm, which simultaneously learns the statistics
and schedules jobs to servers. We prove that the proposed algorithm can
stabilize the queues when the arrival rates are strictly within the service
capacity region. Specifically, we prove that the queue lengths are bounded in
the mean under the assumption that the mean service times change relatively
slowly over time and the arrival rates are bounded away from the capacity
region by a constant whose value depends on the discount factor used in the
discounted UCB. Simulation results confirm that the proposed algorithm can
stabilize the queues and that it outperforms MaxWeight with empirical mean and
MaxWeight with discounted empirical mean. The proposed algorithm is also better
than MaxWeight with UCB in the nonstationary setting.

Domain Generalization (DG) studies the capability of a deep learning model to
generalize to out-of-training distributions. In the last decade, literature has
been massively filled with a collection of training methodologies that claim to
obtain more abstract and robust data representations to tackle domain shifts.
Recent research has provided a reproducible benchmark for DG, pointing out the
effectiveness of naive empirical risk minimization (ERM) over existing
algorithms. Nevertheless, researchers persist in using the same outdated
feature extractors, and no attention has been given to the effects of different
backbones yet. In this paper, we start back to backbones proposing a
comprehensive analysis of their intrinsic generalization capabilities, so far
ignored by the research community. We evaluate a wide variety of feature
extractors, from standard residual solutions to transformer-based
architectures, finding an evident linear correlation between large-scale
single-domain classification accuracy and DG capability. Our extensive
experimentation shows that by adopting competitive backbones in conjunction
with effective data augmentation, plain ERM outperforms recent DG solutions and
achieves state-of-the-art accuracy. Moreover, our additional qualitative
studies reveal that novel backbones give more similar representations to
same-class samples, separating different domains in the feature space. This
boost in generalization capabilities leaves marginal room for DG algorithms and
suggests a new paradigm for investigating the problem, placing backbones in the
spotlight and encouraging the development of consistent algorithms on top of
them.

Robot design optimization, imitation learning and system identification share
a common problem which requires optimization over robot or task parameters at
the same time as optimizing the robot motion. To solve these problems, we can
use differentiable optimal control for which the gradients of the robot's
motion with respect to the parameters are required. We propose a method to
efficiently compute these gradients analytically via the differential dynamic
programming (DDP) algorithm using sensitivity analysis (SA). We show that we
must include second-order dynamics terms when computing the gradients. However,
we do not need to include them when computing the motion. We validate our
approach on the pendulum and double pendulum systems. Furthermore, we compare
against using the derivatives of the iterative linear quadratic regulator
(iLQR), which ignores these second-order terms everywhere, on a co-design task
for the Kinova arm, where we optimize the link lengths of the robot for a
target reaching task. We show that optimizing using iLQR gradients diverges as
ignoring the second-order dynamics affects the computation of the derivatives.
Instead, optimizing using DDP gradients converges to the same optimum for a
range of initial designs allowing our formulation to scale to complex systems.

This paper demonstrates a novel approach to improve face-recognition
pose-invariance using semantic-segmentation features. The proposed
Seg-Distilled-ID network jointly learns identification and
semantic-segmentation tasks, where the segmentation task is then "distilled"
(MobileNet encoder). Performance is benchmarked against three state-of-the-art
encoders on a publicly available data-set emphasizing head-pose variations.
Experimental evaluations show the Seg-Distilled-ID network shows notable
robustness benefits, achieving 99.9% test-accuracy in comparison to 81.6% on
ResNet-101, 96.1% on VGG-19 and 96.3% on InceptionV3. This is achieved using
approximately one-tenth of the top encoder's inference parameters. These
results demonstrate distilling semantic-segmentation features can efficiently
address face-recognition pose-invariance.

Deep neural networks (DNNs) have shown incredible promise in learning
fixed-length representations from fingerprints. Since the representation
learning is often focused on capturing specific prior knowledge (e.g.,
minutiae), there is no universal representation that comprehensively
encapsulates all the discriminatory information available in a fingerprint.
While learning an ensemble of representations can mitigate this problem, two
critical challenges need to be addressed: (i) How to extract multiple diverse
representations from the same fingerprint image? and (ii) How to optimally
exploit these representations during the matching process? In this work, we
train multiple instances of DeepPrint (a state-of-the-art DNN-based fingerprint
encoder) on different transformations of the input image to generate an
ensemble of fingerprint embeddings. We also propose a feature fusion technique
that distills these multiple representations into a single embedding, which
faithfully captures the diversity present in the ensemble without increasing
the computational complexity. The proposed approach has been comprehensively
evaluated on five databases containing rolled, plain, and latent fingerprints
(NIST SD4, NIST SD14, NIST SD27, NIST SD302, and FVC2004 DB2A) and
statistically significant improvements in accuracy have been consistently
demonstrated across a range of verification as well as closed- and open-set
identification settings. The proposed approach serves as a wrapper capable of
improving the accuracy of any DNN-based recognition system.

With the fast adoption of machine learning (ML) techniques, sharing of ML
models is becoming popular. However, ML models are vulnerable to privacy
attacks that leak information about the training data. In this work, we focus
on a particular type of privacy attacks named property inference attack (PIA)
which infers the sensitive properties of the training data through the access
to the target ML model. In particular, we consider Graph Neural Networks (GNNs)
as the target model, and distribution of particular groups of nodes and links
in the training graph as the target property. While the existing work has
investigated PIAs that target at graph-level properties, no prior works have
studied the inference of node and link properties at group level yet.
  In this work, we perform the first systematic study of group property
inference attacks (GPIA) against GNNs. First, we consider a taxonomy of threat
models under both black-box and white-box settings with various types of
adversary knowledge, and design six different attacks for these settings. We
evaluate the effectiveness of these attacks through extensive experiments on
three representative GNN models and three real-world graphs. Our results
demonstrate the effectiveness of these attacks whose accuracy outperforms the
baseline approaches. Second, we analyze the underlying factors that contribute
to GPIA's success, and show that the target model trained on the graphs with or
without the target property represents some dissimilarity in model parameters
and/or model outputs, which enables the adversary to infer the existence of the
property. Further, we design a set of defense mechanisms against the GPIA
attacks, and demonstrate that these mechanisms can reduce attack accuracy
effectively with small loss on GNN model accuracy.

In the context of modern environmental and societal concerns, there is an
increasing demand for methods able to identify management strategies for civil
engineering systems, minimizing structural failure risks while optimally
planning inspection and maintenance (I&M) processes. Most available methods
simplify the I&M decision problem to the component level due to the
computational complexity associated with global optimization methodologies
under joint system-level state descriptions. In this paper, we propose an
efficient algorithmic framework for inference and decision-making under
uncertainty for engineering systems exposed to deteriorating environments,
providing optimal management strategies directly at the system level. In our
approach, the decision problem is formulated as a factored partially observable
Markov decision process, whose dynamics are encoded in Bayesian network
conditional structures. The methodology can handle environments under equal or
general, unequal deterioration correlations among components, through Gaussian
hierarchical structures and dynamic Bayesian networks. In terms of policy
optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC)
reinforcement learning approach, in which the policies are approximated by
actor neural networks guided by a critic network. By including deterioration
dependence in the simulated environment, and by formulating the cost model at
the system level, DDMAC policies intrinsically consider the underlying
system-effects. This is demonstrated through numerical experiments conducted
for both a 9-out-of-10 system and a steel frame under fatigue deterioration.
Results demonstrate that DDMAC policies offer substantial benefits when
compared to state-of-the-art heuristic approaches. The inherent consideration
of system-effects by DDMAC strategies is also interpreted based on the learned
policies.

We study a neural network framework for the numerical evaluation of Feynman
loop integrals that are fundamental building blocks for perturbative
computations of physical observables in gauge and gravity theories. We show
that such a machine learning approach improves the convergence of the Monte
Carlo algorithm for high-precision evaluation of multi-dimensional integrals
compared to traditional algorithms. In particular, we use a neural network to
improve the importance sampling. For a set of representative integrals
appearing in the computation of the conservative dynamics for a compact binary
system in General Relativity, we perform a quantitative comparison between the
Monte Carlo integrators VEGAS and i-flow, an integrator based on neural network
sampling.

Temporal networks have been widely used to model real-world complex systems
such as financial systems and e-commerce systems. In a temporal network, the
joint neighborhood of a set of nodes often provides crucial structural
information on predicting whether they may interact at a certain time. However,
recent representation learning methods for temporal networks often fail to
extract such information or depend on extremely time-consuming feature
construction approaches. To address the issue, this work proposes
Neighborhood-Aware Temporal network model (NAT). For each node in the network,
NAT abandons the commonly-used one-single-vector-based representation while
adopting a novel dictionary-type neighborhood representation. Such a dictionary
representation records a down-sampled set of the neighboring nodes as keys, and
allows fast construction of structural features for a joint neighborhood of
multiple nodes. We also design dedicated data structure termed N-cache to
support parallel access and update of those dictionary representations on GPUs.
NAT gets evaluated over seven real-world large-scale temporal networks. NAT not
only outperforms all cutting-edge baselines by averaged 5.9% and 6.0% in
transductive and inductive link prediction accuracy, respectively, but also
keeps scalable by achieving a speed-up of 4.1-76.7x against the baselines that
adopts joint structural features and achieves a speed-up of 1.6-4.0x against
the baselines that cannot adopt those features. The link to the code:
https://github.com/Graph-COM/Neighborhood-Aware-Temporal-Network.

Machine learning (ML) is becoming increasingly crucial in many fields of
engineering but has not yet played out its full potential in bioprocess
engineering. While experimentation has been accelerated by increasing levels of
lab automation, experimental planning and data modeling are still largerly
depend on human intervention. ML can be seen as a set of tools that contribute
to the automation of the whole experimental cycle, including model building and
practical planning, thus allowing human experts to focus on the more demanding
and overarching cognitive tasks. First, probabilistic programming is used for
the autonomous building of predictive models. Second, machine learning
automatically assesses alternative decisions by planning experiments to test
hypotheses and conducting investigations to gather informative data that focus
on model selection based on the uncertainty of model predictions. This review
provides a comprehensive overview of ML-based automation in bioprocess
development. On the one hand, the biotech and bioengineering community should
be aware of the potential and, most importantly, the limitation of existing ML
solutions for their application in biotechnology and biopharma. On the other
hand, it is essential to identify the missing links to enable the easy
implementation of ML and Artificial Intelligence (AI) tools in valuable
solutions for the bio-community.

Planar graph drawings tend to be aesthetically pleasing. In this poster we
explore a Neural Network's capability of learning various planar graph classes.
Additionally, we also investigate the effectiveness of the model in
generalizing beyond planarity. We find that the model can outperform
conventional techniques for certain graph classes. The model, however, appears
to be more susceptible to randomness in the data, and seems to be less robust
than expected.

Centralised training (CT) is the basis for many popular multi-agent
reinforcement learning (MARL) methods because it allows agents to quickly learn
high-performing policies. However, CT relies on agents learning from one-off
observations of other agents' actions at a given state. Because MARL agents
explore and update their policies during training, these observations often
provide poor predictions about other agents' behaviour and the expected return
for a given action. CT methods therefore suffer from high variance and
error-prone estimates, harming learning. CT methods also suffer from explosive
growth in complexity due to the reliance on global observations, unless strong
factorisation restrictions are imposed (e.g., monotonic reward functions for
QMIX). We address these challenges with a new semi-centralised MARL framework
that performs policy-embedded training and decentralised execution. Our method,
policy embedded reinforcement learning algorithm (PERLA), is an enhancement
tool for Actor-Critic MARL algorithms that leverages a novel parameter sharing
protocol and policy embedding method to maintain estimates that account for
other agents' behaviour. Our theory proves PERLA dramatically reduces the
variance in value estimates. Unlike various CT methods, PERLA, which seamlessly
adopts MARL algorithms, scales easily with the number of agents without the
need for restrictive factorisation assumptions. We demonstrate PERLA's superior
empirical performance and efficient scaling in benchmark environments including
StarCraft Micromanagement II and Multi-agent Mujoco

Classifications organize entities into categories that identify similarities
within a category and discern dissimilarities among categories, and they
powerfully classify information in support of analysis. We propose a new
classification scheme premised on the reality of imperfect data. Our
computational model uses uncertain data envelopment analysis to define a
classification's proximity to equitable efficiency, which is an aggregate
measure of intra-similarity within a classification's categories. Our
classification process has two overriding computational challenges, those being
a loss of convexity and a combinatorially explosive search space. We overcome
the first by establishing lower and upper bounds on the proximity value, and
then by searching this range with a first-order algorithm. We overcome the
second by adapting the p-median problem to initiate our exploration, and by
then employing an iterative neighborhood search to finalize a classification.
We conclude by classifying the thirty stocks in the Dow Jones Industrial
average into performant tiers and by classifying prostate treatments into
clinically effectual categories.

This paper discusses the relevance of information overload for explaining
environmental degradation. Our argument goes that information overload and
detachment from nature, caused by energy abundance, have made individuals
unaware of the unsustainable effects of their choices and lifestyles.

Surprising events trigger measurable brain activity and influence human
behavior by affecting learning, memory, and decision-making. Currently there
is, however, no consensus on the definition of surprise. Here we identify 18
mathematical definitions of surprise in a unifying framework. We first propose
a technical classification of these definitions into three groups based on
their dependence on an agent's belief, show how they relate to each other, and
prove under what conditions they are indistinguishable. Going beyond this
technical analysis, we propose a taxonomy of surprise definitions and classify
them into four conceptual categories based on the quantity they measure: (i)
'prediction surprise' measures a mismatch between a prediction and an
observation; (ii) 'change-point detection surprise' measures the probability of
a change in the environment; (iii) 'confidence-corrected surprise' explicitly
accounts for the effect of confidence; and (iv) 'information gain surprise'
measures the belief-update upon a new observation. The taxonomy poses the
foundation for principled studies of the functional roles and physiological
signatures of surprise in the brain.

In recent years, deep learning has been widely applied in communications and
achieved remarkable performance improvement. Most of the existing works are
based on data-driven deep learning, which requires a significant amount of
training data for the communication model to adapt to new environments and
results in huge computing resources for collecting data and retraining the
model. In this paper, we will significantly reduce the required amount of
training data for new environments by leveraging the learning experience from
the known environments. Therefore, we introduce few-shot learning to enable the
communication model to generalize to new environments, which is realized by an
attention-based method. With the attention network embedded into the deep
learning-based communication model, environments with different power delay
profiles can be learnt together in the training process, which is called the
learning experience. By exploiting the learning experience, the communication
model only requires few pilot blocks to perform well in the new environment.
Through an example of deep-learning-based channel estimation, we demonstrate
that this novel design method achieves better performance than the existing
data-driven approach designed for few-shot learning.

Motivation: The development of novel compounds targeting proteins of interest
is one of the most important tasks in the pharmaceutical industry. Deep
generative models have been applied to targeted molecular design and have shown
promising results. Recently, target-specific molecule generation has been
viewed as a translation between the protein language and the chemical language.
However, such a model is limited by the availability of interacting
protein-ligand pairs. On the other hand, large amounts of unlabeled protein
sequences and chemical compounds are available and have been used to train
language models that learn useful representations. In this study, we propose
exploiting pretrained biochemical language models to initialize (i.e. warm
start) targeted molecule generation models. We investigate two warm start
strategies: (i) a one-stage strategy where the initialized model is trained on
targeted molecule generation (ii) a two-stage strategy containing a
pre-finetuning on molecular generation followed by target specific training. We
also compare two decoding strategies to generate compounds: beam search and
sampling.
  Results: The results show that the warm-started models perform better than a
baseline model trained from scratch. The two proposed warm-start strategies
achieve similar results to each other with respect to widely used metrics from
benchmarks. However, docking evaluation of the generated compounds for a number
of novel proteins suggests that the one-stage strategy generalizes better than
the two-stage strategy. Additionally, we observe that beam search outperforms
sampling in both docking evaluation and benchmark metrics for assessing
compound quality.
  Availability and implementation: The source code is available at
https://github.com/boun-tabi/biochemical-lms-for-drug-design and the materials
are archived in Zenodo at https://doi.org/10.5281/zenodo.6832145

To understand the origins of materials properties, neutron scattering
experiments at three-axes spectrometers (TAS) investigate magnetic and lattice
excitations in a sample by measuring intensity distributions in its momentum
(Q) and energy (E) space. The high demand and limited availability of beam time
for TAS experiments however raise the natural question whether we can improve
their efficiency or make better use of the experimenter's time. In fact, using
TAS, there are a number of scientific questions that require searching for
signals of interest in a particular region of Q-E space, but when done
manually, it is time consuming and inefficient since the measurement points may
be placed in uninformative regions such as the background. Active learning is a
promising general machine learning approach that allows to iteratively detect
informative regions of signal autonomously, i.e., without human interference,
thus avoiding unnecessary measurements and speeding up the experiment. In
addition, the autonomous mode allows experimenters to focus on other relevant
tasks in the meantime. The approach that we describe in this article exploits
log-Gaussian processes which, due to the log transformation, have the largest
approximation uncertainties in regions of signal. Maximizing uncertainty as an
acquisition function hence directly yields locations for informative
measurements. We demonstrate the benefits of our approach on outcomes of a real
neutron experiment at the thermal TAS EIGER (PSI) as well as on results of a
benchmark in a synthetic setting including numerous different excitations.

Multimodal information is frequently available in medical tasks. By combining
information from multiple sources, clinicians are able to make more accurate
judgments. In recent years, multiple imaging techniques have been used in
clinical practice for retinal analysis: 2D fundus photographs, 3D optical
coherence tomography (OCT) and 3D OCT angiography, etc. Our paper investigates
three multimodal information fusion strategies based on deep learning to solve
retinal analysis tasks: early fusion, intermediate fusion, and hierarchical
fusion. The commonly used early and intermediate fusions are simple but do not
fully exploit the complementary information between modalities. We developed a
hierarchical fusion approach that focuses on combining features across multiple
dimensions of the network, as well as exploring the correlation between
modalities. These approaches were applied to glaucoma and diabetic retinopathy
classification, using the public GAMMA dataset (fundus photographs and OCT) and
a private dataset of PlexElite 9000 (Carl Zeis Meditec Inc.) OCT angiography
acquisitions, respectively. Our hierarchical fusion method performed the best
in both cases and paved the way for better clinical diagnosis.

Image smoothing is a fundamental low-level vision task that aims to preserve
salient structures of an image while removing insignificant details. Deep
learning has been explored in image smoothing to deal with the complex
entanglement of semantic structures and trivial details. However, current
methods neglect two important facts in smoothing: 1) naive pixel-level
regression supervised by the limited number of high-quality smoothing
ground-truth could lead to domain shift and cause generalization problems
towards real-world images; 2) texture appearance is closely related to object
semantics, so that image smoothing requires awareness of semantic difference to
apply adaptive smoothing strengths. To address these issues, we propose a novel
Contrastive Semantic-Guided Image Smoothing Network (CSGIS-Net) that combines
both contrastive prior and semantic prior to facilitate robust image smoothing.
The supervision signal is augmented by leveraging undesired smoothing effects
as negative teachers, and by incorporating segmentation tasks to encourage
semantic distinctiveness. To realize the proposed network, we also enrich the
original VOC dataset with texture enhancement and smoothing labels, namely
VOC-smooth, which first bridges image smoothing and semantic segmentation.
Extensive experiments demonstrate that the proposed CSGIS-Net outperforms
state-of-the-art algorithms by a large margin. Code and dataset are available
at https://github.com/wangjie6866/CSGIS-Net.

Standard views in two-dimensional echocardiography are well established but
the quality of acquired images are highly dependent on operator skills and are
assessed subjectively. This study is aimed at providing an objective assessment
pipeline for echocardiogram image quality by defining a new set of
domain-specific quality indicators. Consequently, image quality assessment can
thus be automated to enhance clinical measurements, interpretation, and
real-time optimization. We have developed deep neural networks for the
automated assessment of echocardiographic frame which were randomly sampled
from 11,262 adult patients. The private echocardiography dataset consists of
33,784 frames, previously acquired between 2010 and 2020. Deep learning
approaches were used to extract the spatiotemporal features and the image
quality indicators were evaluated against the mean absolute error. Our quality
indicators encapsulate both anatomical and pathological elements to provide
multivariate assessment scores for anatomical visibility, clarity, depth-gain
and foreshortedness, respectively.

In Novel Class Discovery (NCD), the goal is to find new classes in an
unlabeled set given a labeled set of known but different classes. While NCD has
recently gained attention from the community, no framework has yet been
proposed for heterogeneous tabular data, despite being a very common
representation of data. In this paper, we propose TabularNCD, a new method for
discovering novel classes in tabular data. We show a way to extract knowledge
from already known classes to guide the discovery process of novel classes in
the context of tabular data which contains heterogeneous variables. A part of
this process is done by a new method for defining pseudo labels, and we follow
recent findings in Multi-Task Learning to optimize a joint objective function.
Our method demonstrates that NCD is not only applicable to images but also to
heterogeneous tabular data. Extensive experiments are conducted to evaluate our
method and demonstrate its effectiveness against 3 competitors on 7 diverse
public classification datasets.

End-to-end deep trainable models are about to exceed the performance of the
traditional handcrafted compression techniques on videos and images. The core
idea is to learn a non-linear transformation, modeled as a deep neural network,
mapping input image into latent space, jointly with an entropy model of the
latent distribution. The decoder is also learned as a deep trainable network,
and the reconstructed image measures the distortion. These methods enforce the
latent to follow some prior distributions. Since these priors are learned by
optimization over the entire training set, the performance is optimal in
average. However, it cannot fit exactly on every single new instance, hence
damaging the compression performance by enlarging the bit-stream. In this
paper, we propose a simple yet efficient instance-based parameterization method
to reduce this amortization gap at a minor cost. The proposed method is
applicable to any end-to-end compressing methods, improving the compression
bitrate by 1% without any impact on the reconstruction quality.

Monocular depth estimation is an essential task in the computer vision
community. While tremendous successful methods have obtained excellent results,
most of them are computationally expensive and not applicable for real-time
on-device inference. In this paper, we aim to address more practical
applications of monocular depth estimation, where the solution should consider
not only the precision but also the inference time on mobile devices. To this
end, we first develop an end-to-end learning-based model with a tiny weight
size (1.4MB) and a short inference time (27FPS on Raspberry Pi 4). Then, we
propose a simple yet effective data augmentation strategy, called R2 crop, to
boost the model performance. Moreover, we observe that the simple lightweight
model trained with only one single loss term will suffer from performance
bottleneck. To alleviate this issue, we adopt multiple loss terms to provide
sufficient constraints during the training stage. Furthermore, with a simple
dynamic re-weight strategy, we can avoid the time-consuming hyper-parameter
choice of loss terms. Finally, we adopt the structure-aware distillation to
further improve the model performance. Notably, our solution named LiteDepth
ranks 2nd in the MAI&AIM2022 Monocular Depth Estimation Challenge}, with a
si-RMSE of 0.311, an RMSE of 3.79, and the inference time is 37$ms$ tested on
the Raspberry Pi 4. Notably, we provide the fastest solution to the challenge.
Codes and models will be released at
\url{https://github.com/zhyever/LiteDepth}.

Echocardiography image quality assessment is not a trivial issue in
transthoracic examination. As the in vivo examination of heart structures
gained prominence in cardiac diagnosis, it has been affirmed that accurate
diagnosis of the left ventricle functions is hugely dependent on the quality of
echo images. Up till now, visual assessment of echo images is highly subjective
and requires specific definition under clinical pathologies. While poor-quality
images impair quantifications and diagnosis, the inherent variations in
echocardiographic image quality standards indicates the complexity faced among
different observers and provides apparent evidence for incoherent assessment
under clinical trials, especially with less experienced cardiologists. In this
research, our aim was to analyse and define specific quality attributes mostly
discussed by experts and present a fully trained convolutional neural network
model for assessing such quality features objectively.

In this paper, we propose SATformer, a novel Transformer-based solution for
Boolean satisfiability (SAT) solving. Different from existing learning-based
SAT solvers that learn at the problem instance level, SATformer learns the
minimum unsatisfiable cores (MUC) of unsatisfiable problem instances, which
provide rich information for the causality of such problems. Specifically, we
apply a graph neural network (GNN) to obtain the embeddings of the clauses in
the conjunctive normal format (CNF). A hierarchical Transformer architecture is
applied on the clause embeddings to capture the relationships among clauses,
and the self-attention weight is learned to be high when those clauses forming
UNSAT cores are attended together, and set to be low otherwise. By doing so,
SATformer effectively learns the correlations among clauses for SAT prediction.
Experimental results show that SATformer is more powerful than existing
end-to-end learning-based SAT solvers.

Processing 3D pointclouds with Deep Learning methods is not an easy task. A
common choice is to do so with Graph Neural Networks, but this framework
involves the creation of edges between points, which are explicitly not related
between them. Historically, naive and handcrafted methods like k Nearest
Neighbors (k-NN) or query ball point over xyz features have been proposed,
focusing more attention on improving the network than improving the graph. In
this work, we propose a more principled way of creating a graph from a 3D
pointcloud. Our method is based on performing k-NN over a transformation of the
input 3D pointcloud. This transformation is done by an Multi-Later Perceptron
(MLP) with learnable parameters that is optimized through backpropagation
jointly with the rest of the network. We also introduce a regularization method
based on stress minimization, which allows to control how distant is the learnt
graph from our baseline: k-NN over xyz space. This framework is tested on
ModelNet40, where graphs generated by our network outperformed the baseline by
0.3 points in overall accuracy.

Predicting the economy's short-term dynamics -- a vital input to economic
agents' decision-making process -- often uses lagged indicators in linear
models. This is typically sufficient during normal times but could prove
inadequate during crisis periods. This paper aims to demonstrate that
non-traditional and timely data such as retail and wholesale payments, with the
aid of nonlinear machine learning approaches, can provide policymakers with
sophisticated models to accurately estimate key macroeconomic indicators in
near real-time. Moreover, we provide a set of econometric tools to mitigate
overfitting and interpretability challenges in machine learning models to
improve their effectiveness for policy use. Our models with payments data,
nonlinear methods, and tailored cross-validation approaches help improve
macroeconomic nowcasting accuracy up to 40\% -- with higher gains during the
COVID-19 period. We observe that the contribution of payments data for economic
predictions is small and linear during low and normal growth periods. However,
the payments data contribution is large, asymmetrical, and nonlinear during
strong negative or positive growth periods.

Many data analysis tasks heavily rely on a deep understanding of tables
(multi-dimensional data). Across the tasks, there exist comonly used metadata
attributes of table fields / columns. In this paper, we identify four such
analysis metadata: Measure/dimension dichotomy, common field roles, semantic
field type, and default aggregation function. While those metadata face
challenges of insufficient supervision signals, utilizing existing knowledge
and understanding distribution. To inference these metadata for a raw table, we
propose our multi-tasking Metadata model which fuses field distribution and
knowledge graph information into pre-trained tabular models. For model training
and evaluation, we collect a large corpus (~582k tables from private
spreadsheet and public tabular datasets) of analysis metadata by using diverse
smart supervisions from downstream tasks. Our best model has accuracy = 98%,
hit rate at top-1 > 67%, accuracy > 80%, and accuracy = 88% for the four
analysis metadata inference tasks, respectively. It outperforms a series of
baselines that are based on rules, traditional machine learning methods, and
pre-trained tabular models. Analysis metadata models are deployed in a popular
data analysis product, helping downstream intelligent features such as insights
mining, chart / pivot table recommendation, and natural language QA...

Recent advances in machine learning showed that pre-training representations
acquired via self-supervised learning could achieve high accuracy on tasks with
small training data. Unlike in vision and natural language processing domains,
such pre-training for IMU-based applications is challenging, as there are only
a few publicly available datasets with sufficient size and diversity to learn
generalizable representations. To overcome this problem, we propose IMG2IMU, a
novel approach that adapts pre-train representation from large-scale images to
diverse few-shot IMU sensing tasks. We convert the sensor data into visually
interpretable spectrograms for the model to utilize the knowledge gained from
vision. Further, we apply contrastive learning on an augmentation set we
designed to learn representations that are tailored to interpreting sensor
data. Our extensive evaluations on five different IMU sensing tasks show that
IMG2IMU consistently outperforms the baselines, illustrating that vision
knowledge can be incorporated into a few-shot learning environment for IMU
sensing tasks.

Predicting the country where a picture has been taken from has many potential
applications, like detection of false claims, impostors identification,
prevention of disinformation campaigns, identification of fake news and so on.
Previous works have focused mostly on the estimation of the geo-coordinates
where a picture has been taken. Yet, recognizing the country where an image has
been taken could potentially be more important, from a semantic and forensic
point of view, than identifying its spatial coordinates. So far only a few
works have addressed this task, mostly by relying on images containing
characteristic landmarks, like iconic monuments. In the above framework, this
paper provides two main contributions. First, we introduce a new dataset, the
VIPPGeo dataset, containing almost 4 million images, that can be used to train
DL models for country classification. The dataset contains only urban images
given the relevance of this kind of image for country recognition, and it has
been built by paying attention to removing non-significant images, like images
portraying faces or specific, non-relevant objects, like airplanes or ships.
Secondly, we used the dataset to train a deep learning architecture casting the
country recognition problem as a classification problem. The experiments, we
performed, show that our network provides significantly better results than
current state of the art. In particular, we found that asking the network to
directly identify the country provides better results than estimating the
geo-coordinates first and then using them to trace back to the country where
the picture was taken.

Command and Control (C2) communication is a key component of any structured
cyber-attack. As such, security operations actively try to detect this type of
communication in their networks. This poses a problem for legitimate pentesters
that try to remain undetected, since commonly used pentesting tools, such as
Metasploit, generate constant traffic patterns that are easily distinguishable
from regular web traffic. In this paper we start with these identifiable
patterns in Metasploit's C2 traffic and show that a machine learning-based
detector is able to detect the presence of such traffic with high accuracy,
even when encrypted. We then outline and implement a set of modifications to
the Metasploit framework in order to decrease the detection rates of such
classifier. To evaluate the performance of these modifications, we use two
threat models with increasing awareness of these modifications. We look at the
detection evasion performance and at the byte count and runtime overhead of the
modifications. Our results show that for the second, increased-awareness threat
model the framework-side traffic modifications yield a better detection
avoidance rate (90%) than payload-side only modifications (50%). We also show
that although the modifications use up to 3 times more TLS payload bytes than
the original, the runtime does not significantly change and the total number of
bytes (including TLS payload) reduces.

The use of a thermodynamic framework in the study of learning systems has a
long tradition. Recently, new tools relating perfect thermodynamic adaptation
to the adaptation process have been developed. These results, known as
fluctuation theorems, have been tested experimentally in several physical
scenarios and, moreover, they have been shown to be true under broad
mathematical conditions. Hence, although not experimentally challenged yet,
they are presumed to apply to learning systems as well. We address this issue
here by testing the applicability of fluctuation theorems in learning systems,
more specifically, in human sensorimotor learning.

As an edge intelligence algorithm for multi-device collaborative training,
federated learning (FL) can reduce the communication burden but increase the
computing load of wireless devices. In contrast, split learning (SL) can reduce
the computing load of devices by using model splitting and assignment, but
increase the communication burden to transmit intermediate results. In this
paper, to exploit the advantages of FL and SL, we propose a hybrid federated
split learning (HFSL) framework in wireless networks, which combines the
multi-worker parallel update of FL and flexible splitting of SL. To reduce the
computational idleness in model splitting, we design a parallel computing
scheme for model splitting without label sharing, and theoretically analyze the
influence of the delayed gradient caused by the scheme on the convergence
speed. Aiming to obtain the trade-off between the training time and energy
consumption, we optimize the splitting decision, the bandwidth and computing
resource allocation. The optimization problem is multi-objective, and we thus
propose a predictive generative adversarial network (GAN)-powered
multi-objective optimization algorithm to obtain the Pareto front of the
problem. Experimental results show that the proposed algorithm outperforms
others in finding Pareto optimal solutions, and the solutions of the proposed
HFSL dominate the solution of FL.

Removing the influence of a specified subset of training data from a machine
learning model may be required to address issues such as privacy, fairness, and
data quality. Retraining the model from scratch on the remaining data after
removal of the subset is an effective but often infeasible option, due to its
computational expense. The past few years have therefore seen several novel
approaches towards efficient removal, forming the field of "machine
unlearning", however, many aspects of the literature published thus far are
disparate and lack consensus. In this paper, we summarise and compare seven
state-of-the-art machine unlearning algorithms, consolidate definitions of core
concepts used in the field, reconcile different approaches for evaluating
algorithms, and discuss issues related to applying machine unlearning in
practice.

Graph Neural Networks (GNNs) are widely used for graph representation
learning. Despite its prevalence, GNN suffers from two drawbacks in the graph
classification task, the neglect of graph-level relationships, and the
generalization issue. Each graph is treated separately in GNN message
passing/graph pooling, and existing methods to address overfitting operate on
each individual graph. This makes the graph representations learnt less
effective in the downstream classification. In this paper, we propose a
Class-Aware Representation rEfinement (CARE) framework for the task of graph
classification. CARE computes simple yet powerful class representations and
injects them to steer the learning of graph representations towards better
class separability. CARE is a plug-and-play framework that is highly flexible
and able to incorporate arbitrary GNN backbones without significantly
increasing the computational cost. We also theoretically prove that CARE has a
better generalization upper bound than its GNN backbone through
Vapnik-Chervonenkis (VC) dimension analysis. Our extensive experiments with 10
well-known GNN backbones on 9 benchmark datasets validate the superiority and
effectiveness of CARE over its GNN counterparts.

In this work, we explore recurrent neural network architectures for
tuberculosis (TB) cough classification. In contrast to previous unsuccessful
attempts to implement deep architectures in this domain, we show that a basic
bidirectional long short-term memory network (BiLSTM) can achieve improved
performance. In addition, we show that by performing greedy feature selection
in conjunction with a newly-proposed attention-based architecture that learns
patient invariant features, substantially better generalisation can be achieved
compared to a baseline and other considered architectures. Furthermore, this
attention mechanism allows an inspection of the temporal regions of the audio
signal considered to be important for classification to be performed. Finally,
we develop a neural style transfer technique to infer idealised inputs which
can subsequently be analysed. We find distinct differences between the
idealised power spectra of TB and non-TB coughs, which provide clues about the
origin of the features in the audio signal.

In this paper, we propose to leverage the unique characteristics of dialogues
sharing commonsense knowledge across participants, to resolve the difficulties
in summarizing them. We present SICK, a framework that uses commonsense
inferences as additional context. Compared to previous work that solely relies
on the input dialogue, SICK uses an external knowledge model to generate a rich
set of commonsense inferences and selects the most probable one with a
similarity-based selection method. Built upon SICK, SICK++ utilizes commonsense
as supervision, where the task of generating commonsense inferences is added
upon summarizing the dialogue in a multi-task learning setting. Experimental
results show that with injected commonsense knowledge, our framework generates
more informative and consistent summaries than existing methods.

Longitudinal imaging is able to capture both static anatomical structures and
dynamic changes in disease progression towards earlier and better
patient-specific pathology management. However, conventional approaches for
detecting diabetic retinopathy (DR) rarely take advantage of longitudinal
information to improve DR analysis. In this work, we investigate the benefit of
exploiting self-supervised learning with a longitudinal nature for DR diagnosis
purposes. We compare different longitudinal self-supervised learning (LSSL)
methods to model the disease progression from longitudinal retinal color fundus
photographs (CFP) to detect early DR severity changes using a pair of
consecutive exams. The experiments were conducted on a longitudinal DR
screening dataset with or without those trained encoders (LSSL) acting as a
longitudinal pretext task. Results achieve an AUC of 0.875 for the baseline
(model trained from scratch) and an AUC of 0.96 (95% CI: 0.9593-0.9655 DeLong
test) with a p-value < 2.2e-16 on early fusion using a simple ResNet alike
architecture with frozen LSSL weights, suggesting that the LSSL latent space
enables to encode the dynamic of DR progression.

While representation learning has been central to the rise of machine
learning and artificial intelligence, a key problem remains in making the
learnt representations meaningful. For this the typical approach is to
regularize the learned representation through prior probability distributions.
However such priors are usually unavailable or ad hoc. To deal with this, we
propose a dynamics-constrained representation learning framework. Instead of
using predefined probabilities, we restrict the latent representation to follow
specific dynamics, which is a more natural constraint for representation
learning in dynamical systems. Our belief stems from a fundamental observation
in physics that though different systems can have different marginalized
probability distributions, they typically obey the same dynamics, such as
Newton's and Schrodinger's equations. We validate our framework for different
systems including a real-world fluorescent DNA movie dataset. We show that our
algorithm can uniquely identify an uncorrelated, isometric and meaningful
latent representation.

Noisy labels are unavoidable yet troublesome in the ecosystem of deep
learning because models can easily overfit them. There are many types of label
noise, such as symmetric, asymmetric and instance-dependent noise (IDN), with
IDN being the only type that depends on image information. Such dependence on
image information makes IDN a critical type of label noise to study, given that
labelling mistakes are caused in large part by insufficient or ambiguous
information about the visual classes present in images. Aiming to provide an
effective technique to address IDN, we present a new graphical modelling
approach called InstanceGM, that combines discriminative and generative models.
The main contributions of InstanceGM are: i) the use of the continuous
Bernoulli distribution to train the generative model, offering significant
training advantages, and ii) the exploration of a state-of-the-art noisy-label
discriminative classifier to generate clean labels from instance-dependent
noisy-label samples. InstanceGM is competitive with current noisy-label
learning approaches, particularly in IDN benchmarks using synthetic and
real-world datasets, where our method shows better accuracy than the
competitors in most experiments.

Graph Neural Networks achieve state-of-the-art performance on a plethora of
graph classification tasks, especially due to pooling operators, which
aggregate learned node embeddings hierarchically into a final graph
representation. However, they are not only questioned by recent work showing on
par performance with random pooling, but also ignore completely higher-order
connectivity patterns. To tackle this issue, we propose HoscPool, a
clustering-based graph pooling operator that captures higher-order information
hierarchically, leading to richer graph representations. In fact, we learn a
probabilistic cluster assignment matrix end-to-end by minimising relaxed
formulations of motif spectral clustering in our objective function, and we
then extend it to a pooling operator. We evaluate HoscPool on graph
classification tasks and its clustering component on graphs with ground-truth
community structure, achieving best performance. Lastly, we provide a deep
empirical analysis of pooling operators' inner functioning.

SPIRAL2 is a superconducting accelerator for protons, deuterons and heavy
ions delivering a maximum beam power of 200 kW at 40 MeV (for deuteron beams).
26 superconducting quarter wave cavities are operated at 4.4 K, plunged in a
liquid helium bath with a drastic phase separator pressure control. Previous
years have seen the development of advanced process control for cryogenics
allowing to cope with high heat load dynamics thanks to an automatic heat
dissipation compensation and a model based control. The latter is based on
models, using the Simcryogenics library, optimized and linearised in the
Programmable Logic Controllers. The SPIRAL2 operation has demonstrated that
such control allows to keep the specified conditions for RF and beam operation
even at levels of heat load dissipation approaching the physical limits of the
cryogenic system. These developments allowed to synthesise a virtual observer
of the dynamic heat load dissipated by the cavities. The present paper
summarises the development of such observer based on the physical thermodynamic
model and on machine learning techniques.

Bayesian optimization is a popular formalism for global optimization, but its
computational costs limit it to expensive-to-evaluate functions. A competing,
computationally more efficient, global optimization framework is optimistic
optimization, which exploits prior knowledge about the geometry of the search
space in form of a dissimilarity function. We investigate to which degree the
conceptual advantages of Bayesian Optimization can be combined with the
computational efficiency of optimistic optimization. By mapping the kernel to a
dissimilarity, we obtain an optimistic optimization algorithm for the Bayesian
Optimization setting with a run-time of up to $\mathcal{O}(N \log N)$. As a
high-level take-away we find that, when using stationary kernels on objectives
of relatively low evaluation cost, optimistic optimization can be strongly
preferable over Bayesian optimization, while for strongly coupled and
parametric models, good implementations of Bayesian optimization can perform
much better, even at low evaluation cost. We argue that there is a new research
domain between geometric and probabilistic search, i.e. methods that run
drastically faster than traditional Bayesian optimization, while retaining some
of the crucial functionality of Bayesian optimization.

Multi-modal entity alignment aims to identify equivalent entities between two
different multi-modal knowledge graphs, which consist of structural triples and
images associated with entities. Most previous works focus on how to utilize
and encode information from different modalities, while it is not trivial to
leverage multi-modal knowledge in entity alignment because of the modality
heterogeneity. In this paper, we propose MCLEA, a Multi-modal Contrastive
Learning based Entity Alignment model, to obtain effective joint
representations for multi-modal entity alignment. Different from previous
works, MCLEA considers task-oriented modality and models the inter-modal
relationships for each entity representation. In particular, MCLEA firstly
learns multiple individual representations from multiple modalities, and then
performs contrastive learning to jointly model intra-modal and inter-modal
interactions. Extensive experimental results show that MCLEA outperforms
state-of-the-art baselines on public datasets under both supervised and
unsupervised settings.

With a prevalence of 5 to 50%, Dry Eye Disease (DED) is one of the leading
reasons for ophthalmologist consultations. The diagnosis and quantification of
DED usually rely on ocular surface analysis through slit-lamp examinations.
However, evaluations are subjective and non-reproducible. To improve the
diagnosis, we propose to 1) track the ocular surface in 3-D using video
recordings acquired during examinations, and 2) grade the severity using
registered frames. Our registration method uses unsupervised image-to-depth
learning. These methods learn depth from lights and shadows and estimate pose
based on depth maps. However, DED examinations undergo unresolved challenges
including a moving light source, transparent ocular tissues, etc. To overcome
these and estimate the ego-motion, we implement joint CNN architectures with
multiple losses incorporating prior known information, namely the shape of the
eye, through semantic segmentation as well as sphere fitting. The achieved
tracking errors outperform the state-of-the-art, with a mean Euclidean distance
as low as 0.48% of the image width on our test set. This registration improves
the DED severity classification by a 0.20 AUC difference. The proposed approach
is the first to address DED diagnosis with supervision from monocular videos

We analyze the cumulative regret of the Dyadic Search algorithm of Bachoc et
al. [2022].

Front-end electronics equipped with high-speed digitizers are being used and
proposed for future nuclear detectors. Recent literature reveals that deep
learning models, especially one-dimensional convolutional neural networks, are
promising when dealing with digital signals from nuclear detectors. Simulations
and experiments demonstrate the satisfactory accuracy and additional benefits
of neural networks in this area. However, specific hardware accelerating such
models for online operations still needs to be studied. In this work, we
introduce PulseDL-II, a system-on-chip (SoC) specially designed for
applications of event feature (time, energy, etc.) extraction from pulses with
deep learning. Based on the previous version, PulseDL-II incorporates a RISC
CPU into the system structure for better functional flexibility and integrity.
The neural network accelerator in the SoC adopts a three-level (arithmetic
unit, processing element, neural network) hierarchical architecture and
facilitates parameter optimization of the digital design. Furthermore, we
devise a quantization scheme and associated implementation methods (rescale &
bit-shift) for full compatibility with deep learning frameworks (e.g.,
TensorFlow) within a selected subset of layer types. With the current scheme,
the quantization-aware training of neural networks is supported, and network
models are automatically transformed into software of RISC CPU by dedicated
scripts, with nearly no loss of accuracy. We validate PulseDL-II on field
programmable gate arrays (FPGA). Finally, system validation is done with an
experimental setup made up of a direct digital synthesis (DDS) signal generator
and an FPGA development board with analog-to-digital converters (ADC). The
proposed system achieved 60 ps time resolution and 0.40% energy resolution with
online neural network inference at signal to noise ratio (SNR) of 47.4 dB.

We study an intermediate-age open cluster NGC 2506 using the
\textit{ASTROSAT}/UVIT data and other archival data. We identified 2175 cluster
members using a machine learning-based algorithm, ML--MOC, on Gaia EDR3 data.
Among the cluster members detected in UVIT filters, F148W, F154W, and F169M, we
detect 9 blue straggler stars (BSS), 3 yellow straggler stars (YSS) and 3 red
clump (RC) stars. We construct multi-wavelength spectral energy distributions
(SEDs) of these objects to characterize them and to estimate their parameters.
We discovered hot companions to 3 BSS, 2 YSS and 3 RC candidates and estimated
their properties. The hot companions with estimated temperatures,
T$\mathrm{_{eff}}$ $\sim$ 13250--31000 K, are WDs of extremely low-mass ($\sim$
0.20 M$_\odot$), low-mass ($\sim$ 0.20--0.40 M$_\odot$), normal mass ($\sim$
0.40--0.60 M$_\odot$), and high-mass ($\sim$ 0.8 M$_\odot$). We suggest that
systems with extremely low mass and low mass WDs as companions are formed via
Case-A/Case-B mass transfer mechanism. A BSS is the likely progenitor of the
high mass WD, as a star with more than the turn-off mass of the cluster is
needed to form a high mass WD. Thus, systems with high mass WD are likely to be
formed through merger in triple systems. We conclude that mass transfer as well
as merger pathways of BSS formation are present in this cluster.

Learning analytics (LA) provides data-driven feedback that aims to improve
learning and inform action. For learners, LA-based feedback may scaffold
self-regulated learning skills, which are crucial to learning success. For
teachers, LA-based feedback may help the evaluation of teaching effects and the
need for interventions. However, the current development of LA has presented
problems related to the cognitive, social-affective, and structural dimensions
of feedback. In light of this, this position paper argues that attention needs
to shift from the design of LA as a feedback product to one that facilitates a
process in which both teachers and students play active roles in
meaning-making. To this end, implications for feedback literacy in the context
of LA are discussed.

Task-oriented dialogue systems aim to fulfill user goals through natural
language interactions. They are ideally evaluated with human users, which
however is unattainable to do at every iteration of the development phase.
Simulated users could be an alternative, however their development is
nontrivial. Therefore, researchers resort to offline metrics on existing
human-human corpora, which are more practical and easily reproducible. They are
unfortunately limited in reflecting real performance of dialogue systems. BLEU
for instance is poorly correlated with human judgment, and existing
corpus-based metrics such as success rate overlook dialogue context mismatches.
There is still a need for a reliable metric for task-oriented systems with good
generalization and strong correlation with human judgements. In this paper, we
propose the use of offline reinforcement learning for dialogue evaluation based
on a static corpus. Such an evaluator is typically called a critic and utilized
for policy optimization. We go one step further and show that offline RL
critics can be trained on a static corpus for any dialogue system as external
evaluators, allowing dialogue performance comparisons across various types of
systems. This approach has the benefit of being corpus- and model-independent,
while attaining strong correlation with human judgements, which we confirm via
an interactive user trial.

Restricted Boltzmann Machines (RBMs) offer a versatile architecture for
unsupervised machine learning that can in principle approximate any target
probability distribution with arbitrary accuracy. However, the RBM model is
usually not directly accessible due to its computational complexity, and
Markov-chain sampling is invoked to analyze the learned probability
distribution. For training and eventual applications, it is thus desirable to
have a sampler that is both accurate and efficient. We highlight that these two
goals generally compete with each other and cannot be achieved simultaneously.
More specifically, we identify and quantitatively characterize three regimes of
RBM learning: independent learning, where the accuracy improves without losing
efficiency; correlation learning, where higher accuracy entails lower
efficiency; and degradation, where both accuracy and efficiency no longer
improve or even deteriorate. These findings are based on numerical experiments
and heuristic arguments.

Deep neural networks (DNNs) have have shown state-of-the-art performance for
computer vision applications like image classification, segmentation and object
detection. Whereas recent advances have shown their vulnerability to manual
digital perturbations in the input data, namely adversarial attacks. The
accuracy of the networks is significantly affected by the data distribution of
their training dataset. Distortions or perturbations on color space of input
images generates out-of-distribution data, which make networks more likely to
misclassify them. In this work, we propose a color-variation dataset by
distorting their RGB color on a subset of the ImageNet with 27 different
combinations. The aim of our work is to study the impact of color variation on
the performance of DNNs. We perform experiments on several state-of-the-art DNN
architectures on the proposed dataset, and the result shows a significant
correlation between color variation and loss of accuracy. Furthermore, based on
the ResNet50 architecture, we demonstrate some experiments of the performance
of recently proposed robust training techniques and strategies, such as Augmix,
revisit, and free normalizer, on our proposed dataset. Experimental results
indicate that these robust training techniques can improve the robustness of
deep networks to color variation.

AI-based molecule generation provides a promising approach to a large area of
biomedical sciences and engineering, such as antibody design, hydrolase
engineering, or vaccine development. Because the molecules are governed by
physical laws, a key challenge is to incorporate prior information into the
training procedure to generate high-quality and realistic molecules. We propose
a simple and novel approach to steer the training of diffusion-based generative
models with physical and statistics prior information. This is achieved by
constructing physically informed diffusion bridges, stochastic processes that
guarantee to yield a given observation at the fixed terminal time. We develop a
Lyapunov function based method to construct and determine bridges, and propose
a number of proposals of informative prior bridges for both high-quality
molecule generation and uniformity-promoted 3D point cloud generation. With
comprehensive experiments, we show that our method provides a powerful approach
to the 3D generation task, yielding molecule structures with better quality and
stability scores and more uniformly distributed point clouds of high qualities.

An ensemble of classifiers combines several single classifiers to deliver a
final prediction or classification decision. An increasingly provoking question
is whether such systems can outperform the single best classifier. If so, what
form of an ensemble of classifiers (also known as multiple classifier learning
systems or multiple classifiers) yields the most significant benefits in the
size or diversity of the ensemble itself? Given that the tests used to detect
autism traits are time-consuming and costly, developing a system that will
provide the best outcome and measurement of autism spectrum disorder (ASD) has
never been critical. In this paper, several single and later multiple
classifiers learning systems are evaluated in terms of their ability to predict
and identify factors that influence or contribute to ASD for early screening
purposes. A dataset of behavioural data and robot-enhanced therapy of 3,000
sessions and 300 hours, recorded from 61 children are utilised for this task.
Simulation results show the superior predictive performance of multiple
classifier learning systems (especially those with three classifiers per
ensemble) compared to individual classifiers, with bagging and boosting
achieving excellent results. It also appears that social communication gestures
remain the critical contributing factor to the ASD problem among children.

One of the key topics in network security research is the autonomous COA
(Couse-of-Action) attack search method. Traditional COA attack search methods
that passively search for attacks can be difficult, especially as the network
gets bigger. To address these issues, new autonomous COA techniques are being
developed, and among them, an intelligent spatial algorithm is designed in this
paper for efficient operations in scalable networks. On top of the spatial
search, a Monte-Carlo (MC)- based temporal approach is additionally considered
for taking care of time-varying network behaviors. Therefore, we propose a
spatio-temporal attack COA search algorithm for scalable and time-varying
networks.

LiDAR-based 3D single object tracking is a challenging issue in robotics and
autonomous driving. Currently, existing approaches usually suffer from the
problem that objects at long distance often have very sparse or
partially-occluded point clouds, which makes the features extracted by the
model ambiguous. Ambiguous features will make it hard to locate the target
object and finally lead to bad tracking results. To solve this problem, we
utilize the powerful Transformer architecture and propose a
Point-Track-Transformer (PTT) module for point cloud-based 3D single object
tracking task. Specifically, PTT module generates fine-tuned attention features
by computing attention weights, which guides the tracker focusing on the
important features of the target and improves the tracking ability in complex
scenarios. To evaluate our PTT module, we embed PTT into the dominant method
and construct a novel 3D SOT tracker named PTT-Net. In PTT-Net, we embed PTT
into the voting stage and proposal generation stage, respectively. PTT module
in the voting stage could model the interactions among point patches, which
learns context-dependent features. Meanwhile, PTT module in the proposal
generation stage could capture the contextual information between object and
background. We evaluate our PTT-Net on KITTI and NuScenes datasets.
Experimental results demonstrate the effectiveness of PTT module and the
superiority of PTT-Net, which surpasses the baseline by a noticeable margin,
~10% in the Car category. Meanwhile, our method also has a significant
performance improvement in sparse scenarios. In general, the combination of
transformer and tracking pipeline enables our PTT-Net to achieve
state-of-the-art performance on both two datasets. Additionally, PTT-Net could
run in real-time at 40FPS on NVIDIA 1080Ti GPU. Our code is open-sourced for
the research community at https://github.com/shanjiayao/PTT.

Recent works have shown huge success of deep learning models for common in
vocabulary (IV) scene text recognition. However, in real-world scenarios,
out-of-vocabulary (OOV) words are of great importance and SOTA recognition
models usually perform poorly on OOV settings. Inspired by the intuition that
the learned language prior have limited OOV preformence, we design a framework
named Vision Language Adaptive Mutual Decoder (VLAMD) to tackle OOV problems
partly. VLAMD consists of three main conponents. Firstly, we build an attention
based LSTM decoder with two adaptively merged visual-only modules, yields a
vision-language balanced main branch. Secondly, we add an auxiliary query based
autoregressive transformer decoding head for common visual and language prior
representation learning. Finally, we couple these two designs with
bidirectional training for more diverse language modeling, and do mutual
sequential decoding to get robuster results. Our approach achieved 70.31\% and
59.61\% word accuracy on IV+OOV and OOV settings respectively on Cropped Word
Recognition Task of OOV-ST Challenge at ECCV 2022 TiE Workshop, where we got
1st place on both settings.

Indirect discrimination is an issue of major concern in algorithmic models.
This is particularly the case in insurance pricing where protected policyholder
characteristics are not allowed to be used for insurance pricing. Simply
disregarding protected policyholder information is not an appropriate solution
because this still allows for the possibility of inferring the protected
characteristics from the non-protected ones. This leads to so-called proxy or
indirect discrimination. Though proxy discrimination is qualitatively different
from the group fairness concepts in machine learning, these group fairness
concepts are proposed to 'smooth out' the impact of protected characteristics
in the calculation of insurance prices. The purpose of this note is to share
some thoughts about group fairness concepts in the light of insurance pricing
and to discuss their implications. We present a statistical model that is free
of proxy discrimination, thus, unproblematic from an insurance pricing point of
view. However, we find that the canonical price in this statistical model does
not satisfy any of the three most popular group fairness axioms. This seems
puzzling and we welcome feedback on our example and on the usefulness of these
group fairness axioms for non-discriminatory insurance pricing.

Object Rearrangement is to move objects from an initial state to a goal
state. Here, we focus on a more practical setting in object rearrangement,
i.e., rearranging objects from shuffled layouts to a normative target
distribution without explicit goal specification. However, it remains
challenging for AI agents, as it is hard to describe the target distribution
(goal specification) for reward engineering or collect expert trajectories as
demonstrations. Hence, it is infeasible to directly employ reinforcement
learning or imitation learning algorithms to address the task. This paper aims
to search for a policy only with a set of examples from a target distribution
instead of a handcrafted reward function. We employ the score-matching
objective to train a Target Gradient Field (TarGF), indicating a direction on
each object to increase the likelihood of the target distribution. For object
rearrangement, the TarGF can be used in two ways: 1) For model-based planning,
we can cast the target gradient into a reference control and output actions
with a distributed path planner; 2) For model-free reinforcement learning, the
TarGF is not only used for estimating the likelihood-change as a reward but
also provides suggested actions in residual policy learning. Experimental
results in ball and room rearrangement demonstrate that our method
significantly outperforms the state-of-the-art methods in the quality of the
terminal state, the efficiency of the control process, and scalability.

Mesh denoising is a fundamental problem in digital geometry processing. It
seeks to remove surface noise, while preserving surface intrinsic signals as
accurately as possible. While the traditional wisdom has been built upon
specialized priors to smooth surfaces, learning-based approaches are making
their debut with great success in generalization and automation. In this work,
we provide a comprehensive review of the advances in mesh denoising, containing
both traditional geometric approaches and recent learning-based methods. First,
to familiarize readers with the denoising tasks, we summarize four common
issues in mesh denoising. We then provide two categorizations of the existing
denoising methods. Furthermore, three important categories, including
optimization-, filter-, and data-driven-based techniques, are introduced and
analyzed in detail, respectively. Both qualitative and quantitative comparisons
are illustrated, to demonstrate the effectiveness of the state-of-the-art
denoising methods. Finally, potential directions of future work are pointed out
to solve the common problems of these approaches. A mesh denoising benchmark is
also built in this work, and future researchers will easily and conveniently
evaluate their methods with the state-of-the-art approaches.

Human Activity Recognition (HAR) based on inertial data is an increasingly
diffused task on embedded devices, from smartphones to ultra low-power sensors.
Due to the high computational complexity of deep learning models, most embedded
HAR systems are based on simple and not-so-accurate classic machine learning
algorithms. This work bridges the gap between on-device HAR and deep learning,
proposing a set of efficient one-dimensional Convolutional Neural Networks
(CNNs) deployable on general purpose microcontrollers (MCUs). Our CNNs are
obtained combining hyper-parameters optimization with sub-byte and
mixed-precision quantization, to find good trade-offs between classification
results and memory occupation. Moreover, we also leverage adaptive inference as
an orthogonal optimization to tune the inference complexity at runtime based on
the processed input, hence producing a more flexible HAR system. With
experiments on four datasets, and targeting an ultra-low-power RISC-V MCU, we
show that (i) We are able to obtain a rich set of Pareto-optimal CNNs for HAR,
spanning more than 1 order of magnitude in terms of memory, latency and energy
consumption; (ii) Thanks to adaptive inference, we can derive >20 runtime
operating modes starting from a single CNN, differing by up to 10% in
classification scores and by more than 3x in inference complexity, with a
limited memory overhead; (iii) on three of the four benchmarks, we outperform
all previous deep learning methods, reducing the memory occupation by more than
100x. The few methods that obtain better performance (both shallow and deep)
are not compatible with MCU deployment. (iv) All our CNNs are compatible with
real-time on-device HAR with an inference latency <16ms. Their memory
occupation varies in 0.05-23.17 kB, and their energy consumption in 0.005 and
61.59 uJ, allowing years of continuous operation on a small battery supply.

Recently, score-based diffusion models have shown satisfactory performance in
MRI reconstruction. Most of these methods require a large amount of fully
sampled MRI data as a training set, which, sometimes, is difficult to acquire
in practice. This paper proposes a fully-sampled-data-free score-based
diffusion model for MRI reconstruction, which learns the fully sampled MR image
prior in a self-supervised manner on undersampled data. Specifically, we first
infer the fully sampled MR image distribution from the undersampled data by
Bayesian deep learning, then perturb the data distribution and approximate
their probability density gradient by training a score function. Leveraging the
learned score function as a prior, we can reconstruct the MR image by
performing conditioned Langevin Markov chain Monte Carlo (MCMC) sampling.
Experiments on the public dataset show that the proposed method outperforms
existing self-supervised MRI reconstruction methods and achieves comparable
performances with the conventional (fully sampled data trained) score-based
diffusion methods.

In recent years, a growing body of work has emerged on how to learn machine
learning models under fairness constraints, often expressed with respect to
some sensitive attributes. In this work, we consider the setting in which an
adversary has black-box access to a target model and show that information
about this model's fairness can be exploited by the adversary to enhance his
reconstruction of the sensitive attributes of the training data. More
precisely, we propose a generic reconstruction correction method, which takes
as input an initial guess made by the adversary and corrects it to comply with
some user-defined constraints (such as the fairness information) while
minimizing the changes in the adversary's guess. The proposed method is
agnostic to the type of target model, the fairness-aware learning method as
well as the auxiliary knowledge of the adversary. To assess the applicability
of our approach, we have conducted a thorough experimental evaluation on two
state-of-the-art fair learning methods, using four different fairness metrics
with a wide range of tolerances and with three datasets of diverse sizes and
sensitive attributes. The experimental results demonstrate the effectiveness of
the proposed approach to improve the reconstruction of the sensitive attributes
of the training set.

Natural language processing (NLP) algorithms are rapidly improving but often
struggle when applied to out-of-distribution examples. A prominent approach to
mitigate the domain gap is domain adaptation, where a model trained on a source
domain is adapted to a new target domain. We present a new learning setup,
``domain adaptation from scratch'', which we believe to be crucial for
extending the reach of NLP to sensitive domains in a privacy-preserving manner.
In this setup, we aim to efficiently annotate data from a set of source domains
such that the trained model performs well on a sensitive target domain from
which data is unavailable for annotation. Our study compares several approaches
for this challenging setup, ranging from data selection and domain adaptation
algorithms to active learning paradigms, on two NLP tasks: sentiment analysis
and Named Entity Recognition. Our results suggest that using the abovementioned
approaches eases the domain gap, and combining them further improves the
results.

Graph representations for real-world social networks in the past have missed
two important elements: the multiplexity of connections as well as representing
time. To this end, in this paper, we present a new dynamic heterogeneous graph
representation for social networks which includes time in every single
component of the graph, i.e., nodes and edges, each of different types that
captures heterogeneity. We illustrate the power of this representation by
presenting four time-dependent queries and deep learning problems that cannot
easily be handled in conventional homogeneous graph representations commonly
used. As a proof of concept we present a detailed representation of a new
social media platform (Steemit), which we use to illustrate both the dynamic
querying capability as well as prediction tasks using graph neural networks
(GNNs). The results illustrate the power of the dynamic heterogeneous graph
representation to model social networks. Given that this is a relatively
understudied area we also illustrate opportunities for future work in query
optimization as well as new dynamic prediction tasks on heterogeneous graph
structures.

We describe a novel technique, based on image compression and machine
learning, for transverse phase space tomography in two degrees of freedom in an
accelerator beamline. The technique has been used in the CLARA accelerator test
facility at Daresbury Laboratory: results from the machine learning method are
compared with those from a conventional tomography algorithm (algebraic
reconstruction), applied to the same data. The use of machine learning allows
reconstruction of the 4D phase space distribution of the beam to be carried out
much more rapidly than using conventional tomography algorithms, and also
enables the use of image compression to reduce significantly the size of the
data sets involved in the analysis. Results from the machine learning technique
are at least as good as those from the algebraic reconstruction tomography in
characterising the beam behaviour, in terms of the variation of the beam size
in response to variation of the quadrupole strengths.

Guaranteeing runtime integrity of embedded system software is an open
problem. Trade-offs between security and other priorities (e.g., cost or
performance) are inherent, and resolving them is both challenging and
important. The proliferation of runtime attacks that introduce malicious code
(e.g., by injection) into embedded devices has prompted a range of mitigation
techniques. One popular approach is Remote Attestation (RA), whereby a trusted
entity (verifier) checks the current software state of an untrusted remote
device (prover). RA yields a timely authenticated snapshot of prover state that
verifier uses to decide whether an attack occurred.
  Current RA schemes require verifier to explicitly initiate RA, based on some
unclear criteria. Thus, in case of prover's compromise, verifier only learns
about it late, upon the next RA instance. While sufficient for compromise
detection, some applications would benefit from a more proactive,
prevention-based approach. To this end, we construct CASU: Compromise Avoidance
via Secure Updates. CASU is an inexpensive hardware/software co-design
enforcing: (i) runtime software immutability, thus precluding any illegal
software modification, and (ii) authenticated updates as the sole means of
modifying software. In CASU, a successful RA instance serves as a proof of
successful update, and continuous subsequent software integrity is implicit,
due to the runtime immutability guarantee. This obviates the need for RA in
between software updates and leads to unobtrusive integrity assurance with
guarantees akin to those of prior RA techniques, with better overall

Machine learning (ML)-based Android malware detection has been one of the
most popular research topics in the mobile security community. An increasing
number of research studies have demonstrated that machine learning is an
effective and promising approach for malware detection, and some works have
even claimed that their proposed models could achieve 99\% detection accuracy,
leaving little room for further improvement. However, numerous prior studies
have suggested that unrealistic experimental designs bring substantial biases,
resulting in over-optimistic performance in malware detection. Unlike previous
research that examined the detection performance of ML classifiers to locate
the causes, this study employs Explainable AI (XAI) approaches to explore what
ML-based models learned during the training process, inspecting and
interpreting why ML-based malware classifiers perform so well under unrealistic
experimental settings. We discover that temporal sample inconsistency in the
training dataset brings over-optimistic classification performance (up to 99\%
F1 score and accuracy). Importantly, our results indicate that ML models
classify malware based on temporal differences between malware and benign,
rather than the actual malicious behaviors. Our evaluation also confirms the
fact that unrealistic experimental designs lead to not only unrealistic
detection performance but also poor reliability, posing a significant obstacle
to real-world applications. These findings suggest that XAI approaches should
be used to help practitioners/researchers better understand how do AI/ML models
(i.e., malware detection) work -- not just focusing on accuracy improvement.

Preconditioning has long been a staple technique in optimization, often
applied to reduce the condition number of a matrix and speed up the convergence
of algorithms. Although there are many popular preconditioning techniques in
practice, most lack guarantees on reductions in condition number. Moreover, the
degree to which we can improve over existing heuristic preconditioners remains
an important practical question. In this paper, we study the problem of optimal
diagonal preconditioning that achieves maximal reduction in the condition
number of any full-rank matrix by scaling its rows and/or columns. We first
reformulate the problem as a quasi-convex problem and provide a simple
algorithm based on bisection. Then we develop an interior point algorithm with
$O(\log(1/\epsilon))$ iteration complexity, where each iteration consists of a
Newton update based on the Nesterov-Todd direction. Next, we specialize to
one-sided optimal diagonal preconditioning problems, and demonstrate that they
can be formulated as standard dual SDP problems. We then develop efficient
customized solvers and study the empirical performance of our optimal diagonal
preconditioning procedures through extensive experiments on large matrices. Our
findings suggest that optimal diagonal preconditioners can significantly
improve upon existing heuristics-based diagonal preconditioners at reducing
condition numbers and speeding up iterative methods. Moreover, our
implementation of customized solvers, combined with a random row/column
sampling step, can find near-optimal diagonal preconditioners for matrices up
to size 200,000 in reasonable time, demonstrating their practical appeal.

Temporal graph neural networks (TGNNs) have been widely used for modeling
time-evolving graph-related tasks due to their ability to capture both graph
topology dependency and non-linear temporal dynamic. The explanation of TGNNs
is of vital importance for a transparent and trustworthy model. However, the
complex topology structure and temporal dependency make explaining TGNN models
very challenging. In this paper, we propose a novel explainer framework for
TGNN models. Given a time series on a graph to be explained, the framework can
identify dominant explanations in the form of a probabilistic graphical model
in a time period. Case studies on the transportation domain demonstrate that
the proposed approach can discover dynamic dependency structures in a road
network for a time period.

Large-scale graphs are ubiquitous in real-world scenarios and can be trained
by Graph Neural Networks (GNNs) to generate representation for downstream
tasks. Given the abundant information and complex topology of a large-scale
graph, we argue that redundancy exists in such graphs and will degrade the
training efficiency. Unfortunately, the model scalability severely restricts
the efficiency of training large-scale graphs via vanilla GNNs. Despite recent
advances in sampling-based training methods, sampling-based GNNs generally
overlook the redundancy issue. It still takes intolerable time to train these
models on large-scale graphs. Thereby, we propose to drop redundancy and
improve efficiency of training large-scale graphs with GNNs, by rethinking the
inherent characteristics in a graph.
  In this paper, we pioneer to propose a once-for-all method, termed DropReef,
to drop the redundancy in large-scale graphs. Specifically, we first conduct
preliminary experiments to explore potential redundancy in large-scale graphs.
Next, we present a metric to quantify the neighbor heterophily of all nodes in
a graph. Based on both experimental and theoretical analysis, we reveal the
redundancy in a large-scale graph, i.e., nodes with high neighbor heterophily
and a great number of neighbors. Then, we propose DropReef to detect and drop
the redundancy in large-scale graphs once and for all, helping reduce the
training time while ensuring no sacrifice in the model accuracy. To demonstrate
the effectiveness of DropReef, we apply it to recent state-of-the-art
sampling-based GNNs for training large-scale graphs, owing to the high
precision of such models. With DropReef leveraged, the training efficiency of
models can be greatly promoted. DropReef is highly compatible and is offline
performed, benefiting the state-of-the-art sampling-based GNNs in the present
and future to a significant extent.

Recovering high quality surfaces from noisy point clouds, known as point
cloud denoising, is a fundamental yet challenging problem in geometry
processing. Most of the existing methods either directly denoise the noisy
input or filter raw normals followed by updating point positions. Motivated by
the essential interplay between point cloud denoising and normal filtering, we
revisit point cloud denoising from a multitask perspective, and propose an
end-to-end network, named PCDNF, to denoise point clouds via joint normal
filtering. In particular, we introduce an auxiliary normal filtering task to
help the overall network remove noise more effectively while preserving
geometric features more accurately. In addition to the overall architecture,
our network has two novel modules. On one hand, to improve noise removal
performance, we design a shape-aware selector to construct the latent tangent
space representation of the specific point by comprehensively considering the
learned point and normal features and geometry priors. On the other hand, point
features are more suitable for describing geometric details, and normal
features are more conducive for representing geometric structures (e.g., sharp
edges and corners). Combining point and normal features allows us to overcome
their weaknesses. Thus, we design a feature refinement module to fuse point and
normal features for better recovering geometric information. Extensive
evaluations, comparisons, and ablation studies demonstrate that the proposed
method outperforms state-of-the-arts for both point cloud denoising and normal
filtering.

Diffusion models have emerged as a powerful new family of deep generative
models with record-breaking performance in many applications, including image
synthesis, video generation, and molecule design. In this survey, we provide an
overview of the rapidly expanding body of work on diffusion models,
categorizing the research into three key areas: efficient sampling, improved
likelihood estimation, and handling data with special structures. We also
discuss the potential for combining diffusion models with other generative
models for enhanced results. We further review the wide-ranging applications of
diffusion models in fields spanning from computer vision, natural language
processing, temporal data modeling, to interdisciplinary applications in other
scientific disciplines. This survey aims to provide a contextualized, in-depth
look at the state of diffusion models, identifying the key areas of focus and
pointing to potential areas for further exploration. Github:
https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy.

Though graph representation learning (GRL) has made significant progress, it
is still a challenge to extract and embed the rich topological structure and
feature information in an adequate way. Most existing methods focus on local
structure and fail to fully incorporate the global topological structure. To
this end, we propose a novel Structure-Preserving Graph Representation Learning
(SPGRL) method, to fully capture the structure information of graphs.
Specifically, to reduce the uncertainty and misinformation of the original
graph, we construct a feature graph as a complementary view via k-Nearest
Neighbor method. The feature graph can be used to contrast at node-level to
capture the local relation. Besides, we retain the global topological structure
information by maximizing the mutual information (MI) of the whole graph and
feature embeddings, which is theoretically reduced to exchanging the feature
embeddings of the feature and the original graphs to reconstruct themselves.
Extensive experiments show that our method has quite superior performance on
semi-supervised node classification task and excellent robustness under noise
perturbation on graph structure or node features.

Deep learning based device fingerprinting has emerged as a key method of
identifying and authenticating devices solely via their captured RF
transmissions. Conventional approaches are not portable to different domains in
that if a model is trained on data from one domain, it will not perform well on
data from a different but related domain. Examples of such domains include the
receiver hardware used for collecting the data, the day/time on which data was
captured, and the protocol configuration of devices. This work proposes Tweak,
a technique that, using metric learning and a calibration process, enables a
model trained with data from one domain to perform well on data from another
domain. This process is accomplished with only a small amount of training data
from the target domain and without changing the weights of the model, which
makes the technique computationally lightweight and thus suitable for
resource-limited IoT networks. This work evaluates the effectiveness of Tweak
vis-a-vis its ability to identify IoT devices using a testbed of real
LoRa-enabled devices under various scenarios. The results of this evaluation
show that Tweak is viable and especially useful for networks with limited
computational resources and applications with time-sensitive missions.

Analyses of large observational datasets tend to be complicated and prone to
fault depending upon the variable selection, data cleaning and analytic methods
employed. Here, we discuss analyses of 2016 US environmental epidemiology data
and outline some potential implications of our Non-parametric and Unsupervised
Learning perspective. Readers are invited to download the CSV files we have
contributed to dryad and apply the analytic approaches they think appropriate.
We hope to encourage development of a broad-based "consensus view" of the
potential effects of Secondary Organic Aerosols (Volatile Organic Compounds
that have predominantly Biogenic or Anthropogenic origin) within PM2:5
particulate matter on Circulatory and/or Respiratory mortality. The analyses
described here ultimately focus on the question: "Is life in a region with
relatively high air-borne Biogenic particulate matter also relatively dangerous
in terms of Circulatory and/or Respiratory mortality?"

Typo-squatting domains are a common cyber-attack technique. It involves
utilising domain names, that exploit possible typographical errors of commonly
visited domains, to carry out malicious activities such as phishing, malware
installation, etc. Current approaches typically revolve around string
comparison algorithms like the Demaru-Levenschtein Distance (DLD) algorithm.
Such techniques do not take into account keyboard distance, which researchers
find to have a strong correlation with typical typographical errors and are
trying to take account of. In this paper, we present the TypoSwype framework
which converts strings to images that take into account keyboard location
innately. We also show how modern state of the art image recognition techniques
involving Convolutional Neural Networks, trained via either Triplet Loss or
NT-Xent Loss, can be applied to learn a mapping to a lower dimensional space
where distances correspond to image, and equivalently, textual similarity.
Finally, we also demonstrate our method's ability to improve typo-squatting
detection over the widely used DLD algorithm, while maintaining the
classification accuracy as to which domain the input domain was attempting to
typo-squat.

Rapid digitalisation spurred by the Covid-19 pandemic has resulted in more
cyber crime. Malware-as-a-service is now a booming business for cyber
criminals. With the surge in malware activities, it is vital for cyber
defenders to understand more about the malware samples they have at hand as
such information can greatly influence their next course of actions during a
breach. Recently, researchers have shown how malware family classification can
be done by first converting malware binaries into grayscale images and then
passing them through neural networks for classification. However, most work
focus on studying the impact of different neural network architectures on
classification performance. In the last year, researchers have shown that
augmenting supervised learning with self-supervised learning can improve
performance. Even more recently, Data2Vec was proposed as a modality agnostic
self-supervised framework to train neural networks. In this paper, we present
BinImg2Vec, a framework of training malware binary image classifiers that
incorporates both self-supervised learning and supervised learning to produce a
model that consistently outperforms one trained only via supervised learning.
We were able to achieve a 4% improvement in classification performance and a
0.5% reduction in performance variance over multiple runs. We also show how our
framework produces embeddings that can be well clustered, facilitating model
explanability.

A significant number of equity funds are preferred by index funds nowadays,
and market sensitivities are instrumental in managing them. Index funds might
replicate the index identically, which is, however, cost-ineffective and
impractical. Moreover, to utilize market sensitivities to replicate the index
partially, they must be predicted or estimated accurately. Accordingly, first,
we examine deep learning models to predict market sensitivities. Also, we
present pragmatic applications of data processing methods to aid training and
generate target data for the prediction. Then, we propose a
partial-index-tracking optimization model controlling the net predicted market
sensitivities of the portfolios and index to be the same. These processes'
efficacy is corroborated by the Korea Stock Price Index 200. Our experiments
show a significant reduction of the prediction errors compared with historical
estimations, and competitive tracking errors of replicating the index using
fewer than half of the entire constituents. Therefore, we show that applying
deep learning to predict market sensitivities is promising and that our
portfolio construction methods are practically effective. Additionally, to our
knowledge, this is the first study that addresses market sensitivities focused
on deep learning.

As an important cyber-physical system (CPS), smart grid is highly vulnerable
to cyber attacks. Amongst various types of attacks, false data injection attack
(FDIA) proves to be one of the top-priority cyber-related issues and has
received increasing attention in recent years. However, so far little attention
has been paid to privacy preservation issues in the detection of FDIAs in smart
grid. Inspired by federated learning, a FDIA detection method based on secure
federated deep learning is proposed in this paper by combining Transformer,
federated learning and Paillier cryptosystem. The Transformer, as a detector
deployed in edge nodes, delves deep into the connection between individual
electrical quantities by using its multi-head self-attention mechanism. By
using federated learning framework, our approach utilizes the data from all
nodes to collaboratively train a detection model while preserving data privacy
by keeping the data locally during training. To improve the security of
federated learning, a secure federated learning scheme is designed by combing
Paillier cryptosystem with federated learning. Through extensive experiments on
the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of
the proposed method are verifed.

Ophthalmic images and derivatives such as the retinal nerve fiber layer
(RNFL) thickness map are crucial for detecting and monitoring ophthalmic
diseases (e.g., glaucoma). For computer-aided diagnosis of eye diseases, the
key technique is to automatically extract meaningful features from ophthalmic
images that can reveal the biomarkers (e.g., RNFL thinning patterns) linked to
functional vision loss. However, representation learning from ophthalmic images
that links structural retinal damage with human vision loss is non-trivial
mostly due to large anatomical variations between patients. The task becomes
even more challenging in the presence of image artifacts, which are common due
to issues with image acquisition and automated segmentation. In this paper, we
propose an artifact-tolerant unsupervised learning framework termed EyeLearn
for learning representations of ophthalmic images. EyeLearn has an artifact
correction module to learn representations that can best predict artifact-free
ophthalmic images. In addition, EyeLearn adopts a clustering-guided contrastive
learning strategy to explicitly capture the intra- and inter-image affinities.
During training, images are dynamically organized in clusters to form
contrastive samples in which images in the same or different clusters are
encouraged to learn similar or dissimilar representations, respectively. To
evaluate EyeLearn, we use the learned representations for visual field
prediction and glaucoma detection using a real-world ophthalmic image dataset
of glaucoma patients. Extensive experiments and comparisons with
state-of-the-art methods verified the effectiveness of EyeLearn for learning
optimal feature representations from ophthalmic images.

In performative prediction, a predictive model impacts the distribution that
generates future data, a phenomenon that is being ignored in classical
supervised learning. In this closed-loop setting, the natural measure of
performance named performative risk ($\mathrm{PR}$), captures the expected loss
incurred by a predictive model \emph{after} deployment. The core difficulty of
using the performative risk as an optimization objective is that the data
distribution itself depends on the model parameters. This dependence is
governed by the environment and not under the control of the learner. As a
consequence, even the choice of a convex loss function can result in a highly
non-convex $\mathrm{PR}$ minimization problem. Prior work has identified a pair
of general conditions on the loss and the mapping from model parameters to
distributions that implies the convexity of the performative risk. In this
paper, we relax these assumptions and focus on obtaining weaker notions of
convexity, without sacrificing the amenability of the $\mathrm{PR}$
minimization problem for iterative optimization methods.

Federated learning refers to the task of performing machine learning with
decentralized data from multiple clients while protecting data security and
privacy. Works have been done to incorporate quantum advantage in such
scenarios. However, when the clients' data are not independent and identically
distributed (IID), the performance of conventional federated algorithms
deteriorates. In this work, we explore this phenomenon in the quantum regime
with both theoretical and numerical analysis. We further prove that a global
quantum channel can be exactly decomposed into channels trained by each client
with the help of local density estimators. It leads to a general framework for
quantum federated learning on non-IID data with one-shot communication
complexity. We demonstrate it on classification tasks with numerical

We present ConCur, a contrastive video representation learning method that
uses curriculum learning to impose a dynamic sampling strategy in contrastive
training. More specifically, ConCur starts the contrastive training with easy
positive samples (temporally close and semantically similar clips), and as the
training progresses, it increases the temporal span effectively sampling hard
positives (temporally away and semantically dissimilar). To learn better
context-aware representations, we also propose an auxiliary task of predicting
the temporal distance between a positive pair of clips. We conduct extensive
experiments on two popular action recognition datasets, UCF101 and HMDB51, on
which our proposed method achieves state-of-the-art performance on two
benchmark tasks of video action recognition and video retrieval. We explore the
impact of encoder backbones and pre-training strategies by using R(2+1)D and
C3D encoders and pre-training on Kinetics-400 and Kinetics-200 datasets.
Moreover, a detailed ablation study shows the effectiveness of each of the
components of our proposed method.

Grain boundaries (GBs) can be treated as two-dimensional (2-D) interfacial
phases (also called 'complexions') that can undergo interfacial phase-like
transitions. As bulk phase diagrams and calculation of phase diagram (CALPHAD)
methods are a foundation for modern materials science, we propose to extend
them to GBs to have equally significant impacts. This perspective article
reviews a series of studies to compute the GB counterparts to bulk phase
diagrams. First, a phenomenological interfacial thermodynamic model was
developed to construct GB lambda diagrams to forecast high-temperature GB
disordering and related trends in sintering and other properties for both
metallic and ceramic materials. In parallel, an Ising-type lattice statistical
thermodynamic model was utilized to construct GB adsorption diagrams, which
predicted first-order GB adsorption (a.k.a. segregation) transitions and
critical phenomena. These two simplified thermodynamic models emphasize on the
GB structural (disordering) and chemical (adsorption) aspects, respectively.
Subsequently, hybrid Monte Carlo and molecular dynamics atomistic simulations
were used to compute more rigorous and accurate GB 'phase' diagrams. Computed
GB diagrams of thermodynamic and structural properties were further extended to
include mechanical properties. Moreover, machine learning was combined with
atomistic simulations to predict GB properties as functions of four independent
compositional variables and temperature in a 5-D space for a given GB in
high-entropy alloys or as functions of five GB macroscopic (crystallographic)
degrees of freedom (DOFs) plus temperature and composition for a binary alloy
in a 7-D space. Relevant other studies are examined. Future perspective and
outlook, including two emerging fields of high-entropy grain boundaries (HEGBs)
and electrically (or electrochemically) induced GB transitions, are discussed.

Several papers have rightly included minority groups in artificial
intelligence (AI) training data to improve test inference for minority groups
and/or society-at-large. A society-at-large consists of both minority and
majority stakeholders. A common misconception is that minority inclusion does
not increase performance for majority groups alone. In this paper, we make the
surprising finding that including minority samples can improve test error for
the majority group. In other words, minority group inclusion leads to majority
group enhancements (MIME) in performance. A theoretical existence proof of the
MIME effect is presented and found to be consistent with experimental results
on six different datasets. Project webpage:
https://visual.ee.ucla.edu/mime.htm/

Neural Networks (NNs) struggle to efficiently learn certain problems, such as
parity problems, even when there are simple learning algorithms for those
problems. Can NNs discover learning algorithms on their own? We exhibit a NN
architecture that, in polynomial time, learns as well as any efficient learning
algorithm describable by a constant-sized learning algorithm. For example, on
parity problems, the NN learns as well as row reduction, an efficient algorithm
that can be succinctly described. Our architecture combines both recurrent
weight-sharing between layers and convolutional weight-sharing to reduce the
number of parameters down to a constant, even though the network itself may
have trillions of nodes. While in practice the constants in our analysis are
too large to be directly meaningful, our work suggests that the synergy of
Recurrent and Convolutional NNs (RCNNs) may be more powerful than either alone.

Model order estimation (MOE) is often a pre-requisite for Direction of
Arrival (DoA) estimation. Due to limits imposed by array geometry, it is
typically not possible to estimate spatial parameters for an arbitrary number
of sources; an estimate of the signal model is usually required. MOE is the
process of selecting the most likely signal model from several candidates.
While classic methods fail at MOE in the presence of coherent multipath
interference, data-driven supervised learning models can solve this problem.
Instead of the classic MLP (Multiple Layer Perceptions) or CNN (Convolutional
Neural Networks) architectures, we propose the application of Residual
Convolutional Neural Networks (RCNN), with grouped symmetric kernel filters to
deliver state-of-art estimation accuracy of up to 95.2\% in the presence of
coherent multipath, and a weighted loss function to eliminate underestimation
error of the model order. We show the benefit of the approach by demonstrating
its impact on an overall signal processing flow that determines the number of
total signals received by the array, the number of independent sources, and the
association of each of the paths with those sources . Moreover, we show that
the proposed estimator provides accurate performance over a variety of array
types, can identify the overloaded scenario, and ultimately provides strong DoA
estimation and signal association performance.

High-resolution satellite images can provide abundant, detailed spatial
information for land cover classification, which is particularly important for
studying the complicated built environment. However, due to the complex land
cover patterns, the costly training sample collections, and the severe
distribution shifts of satellite imageries, few studies have applied
high-resolution images to land cover mapping in detailed categories at large
scale. To fill this gap, we present a large-scale land cover dataset,
Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150
high-resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category
system covering artificial-constructed, agricultural, and natural classes. In
addition, we propose a deep-learning-based unsupervised domain adaptation
approach that can transfer classification models trained on labeled dataset
(referred to as the source domain) to unlabeled data (referred to as the target
domain) for large-scale land cover mapping. Specifically, we introduce an
end-to-end Siamese network employing dynamic pseudo-label assignment and class
balancing strategy to perform adaptive domain joint learning. To validate the
generalizability of our dataset and the proposed approach across different
sensors and different geographical regions, we carry out land cover mapping on
five megacities in China and six cities in other five Asian countries severally
using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite
images. Over a total study area of 60,000 square kilometers, the experiments
show promising results even though the input images are entirely unlabeled. The
proposed approach, trained with the Five-Billion-Pixels dataset, enables
high-quality and detailed land cover mapping across the whole country of China
and some other Asian countries at meter-resolution.

Learning spatial-temporal correspondences in cardiac motion from images is
important for understanding the underlying dynamics of cardiac anatomical
structures. Many methods explicitly impose smoothness constraints such as the
$\mathcal{L}_2$ norm on the displacement vector field (DVF), while usually
ignoring biomechanical feasibility in the transformation. Other geometric
constraints either regularize specific regions of interest such as imposing
incompressibility on the myocardium or introduce additional steps such as
training a separate network-based regularizer on physically simulated datasets.
In this work, we propose an explicit biomechanics-informed prior as
regularization on the predicted DVF in modeling a more generic biomechanically
plausible transformation within all cardiac structures without introducing
additional training complexity. We validate our methods on two publicly
available datasets in the context of 2D MRI data and perform extensive
experiments to illustrate the effectiveness and robustness of our proposed
methods compared to other competing regularization schemes. Our proposed
methods better preserve biomechanical properties by visual assessment and show
advantages in segmentation performance using quantitative evaluation metrics.
\url{https://github.com/Voldemort108X/bioinformed_reg}.

Fully supervised skeleton-based action recognition has achieved great
progress with the blooming of deep learning techniques. However, these methods
require sufficient labeled data which is not easy to obtain. In contrast,
self-supervised skeleton-based action recognition has attracted more attention.
With utilizing the unlabeled data, more generalizable features can be learned
to alleviate the overfitting problem and reduce the demand of massive labeled
training data. Inspired by the MAE, we propose a spatial-temporal masked
autoencoder framework for self-supervised 3D skeleton-based action recognition
(SkeletonMAE). Following MAE's masking and reconstruction pipeline, we utilize
a skeleton based encoder-decoder transformer architecture to reconstruct the
masked skeleton sequences. A novel masking strategy, named Spatial-Temporal
Masking, is introduced in terms of both joint-level and frame-level for the
skeleton sequence. This pre-training strategy makes the encoder output
generalizable skeleton features with spatial and temporal dependencies. Given
the unmasked skeleton sequence, the encoder is fine-tuned for the action
recognition task. Extensive experiments show that our SkeletonMAE achieves
remarkable performance and outperforms the state-of-the-art methods on both NTU
RGB+D and NTU RGB+D 120 datasets.

The constantly evolving digital transformation imposes new requirements on
our society. Aspects relating to reliance on the networking domain and the
difficulty of achieving security by design pose a challenge today. As a result,
data-centric and machine-learning approaches arose as feasible solutions for
securing large networks. Although, in the network security domain, ML-based
solutions face a challenge regarding the capability to generalize between
different contexts. In other words, solutions based on specific network data
usually do not perform satisfactorily on other networks. This paper describes
the stacked-unsupervised federated learning (FL) approach to generalize on a
cross-silo configuration for a flow-based network intrusion detection system
(NIDS). The proposed approach we have examined comprises a deep autoencoder in
conjunction with an energy flow classifier in an ensemble learning task. Our
approach performs better than traditional local learning and naive
cross-evaluation (training in one context and testing on another network data).
Remarkably, the proposed approach demonstrates a sound performance in the case
of non-iid data silos. In conjunction with an informative feature in an
ensemble architecture for unsupervised learning, we advise that the proposed
FL-based NIDS results in a feasible approach for generalization between
heterogeneous networks. To the best of our knowledge, our proposal is the first
successful approach to applying unsupervised FL on the problem of network
intrusion detection generalization using flow-based data.

With the increasing dependency of daily life over computer networks, the
importance of these networks security becomes prominent. Different intrusion
attacks to networks have been designed and the attackers are working on
improving them. Thus the ability to detect intrusion with limited number of
labeled data is desirable to provide networks with higher level of security. In
this paper we design an intrusion detection system based on a deep neural
network. The proposed system is based on self-supervised contrastive learning
where a huge amount of unlabeled data can be used to generate informative
representation suitable for various downstream tasks with limited number of
labeled data. Using different experiments, we have shown that the proposed
system presents an accuracy of 94.05% over the UNSW-NB15 dataset, an
improvement of 4.22% in comparison to previous method based on self-supervised
learning. Our simulations have also shown impressive results when the size of
labeled training data is limited. The performance of the resulting Encoder
Block trained on UNSW-NB15 dataset has also been tested on other datasets for
representation extraction which shows competitive results in downstream tasks.

Board games, with the exception of solo games, need at least one other player
to play. Because of this, we created Artificial Intelligent (AI) agents to play
against us when an opponent is missing. These AI agents are created in a number
of ways, but one challenge with these agents is that an agent can have superior
ability compared to us. In this work, we describe how to create weaker AI
agents that play board games. We use Tic-Tac-Toe, Nine-Men's Morris, and
Mancala, and our technique uses a Reinforcement Learning model where an agent
uses the Q-learning algorithm to learn these games. We show how these agents
can learn to play the board game perfectly, and we then describe our approach
to making weaker versions of these agents. Finally, we provide a methodology to
compare AI agents.

Video communication has been rapidly increasing over the past decade, with
YouTube providing a medium where users can post, discover, share, and react to
videos. There has also been an increase in the number of videos citing research
articles, especially since it has become relatively commonplace for academic
conferences to require video submissions. However, the relationship between
research articles and YouTube videos is not clear, and the purpose of the
present paper is to address this issue. We created new datasets using YouTube
videos and mentions of research articles on various online platforms. We found
that most of the articles cited in the videos are related to medicine and
biochemistry. We analyzed these datasets through statistical techniques and
visualization, and built machine learning models to predict (1) whether a
research article is cited in videos, (2) whether a research article cited in a
video achieves a level of popularity, and (3) whether a video citing a research
article becomes popular. The best models achieved F1 scores between 80% and
94%. According to our results, research articles mentioned in more tweets and
news coverage have a higher chance of receiving video citations. We also found
that video views are important for predicting citations and increasing research
articles' popularity and public engagement with science.

Generative Adversarial Networks (GANs) have been widely applied in modeling
diverse image distributions. However, despite its impressive applications, the
structure of the latent space in GANs largely remains as a black-box, leaving
its controllable generation an open problem, especially when spurious
correlations between different semantic attributes exist in the image
distributions. To address this problem, previous methods typically learn linear
directions or individual channels that control semantic attributes in the image
space. However, they often suffer from imperfect disentanglement, or are unable
to obtain multi-directional controls. In this work, in light of the above
challenges, we propose a novel approach that discovers nonlinear controls,
which enables multi-directional manipulation as well as effective
disentanglement, based on gradient information in the learned GAN latent space.
More specifically, we first learn interpolation directions by following the
gradients from classification networks trained separately on the attributes,
and then navigate the latent space by exclusively controlling channels
activated for the target attribute in the learned directions. Empirically, with
small training data, our approach is able to gain fine-grained controls over a
diverse set of bi-directional and multi-directional attributes, and we showcase
its ability to achieve disentanglement significantly better than
state-of-the-art methods both qualitatively and quantitatively.

Video conferencing has become the preferred way of interacting virtually.
Current video conferencing applications, like Zoom, Teams or WebEx, are
centralized, cloud-based platforms whose performance crucially depends on the
proximity of clients to their data centers. Clients from low-income countries
are particularly affected as most data centers from major cloud providers are
located in economically advanced nations. Centralized conferencing applications
also suffer from occasional outages and are embattled by serious privacy
violation allegations. In recent years, decentralized video conferencing
applications built over p2p networks and incentivized through blockchain are
becoming popular. A key characteristic of these networks is their openness:
anyone can host a media server on the network and gain reward for providing
service. Strong economic incentives combined with lower entry barrier to join
the network, makes increasing server coverage to even remote regions of the
world. These reasons, however, also lead to a security problem: a server may
obfuscate its true location in order to gain an unfair business advantage. In
this paper, we consider the problem of multicast tree construction for video
conferencing sessions in open p2p conferencing applications. We propose DecVi,
a decentralized multicast tree construction protocol that adaptively discovers
efficient tree structures based on an exploration-exploitation framework. DecVi
is motivated by the combinatorial multi-armed bandit problem and uses a
succinct learning model to compute effective actions. Despite operating in a
multi-agent setting with each server having only limited knowledge of the
global network and without cooperation among servers, experimentally we show
DecVi achieves similar quality-of-experience compared to a centralized globally
optimal algorithm while achieving higher reliability and flexibility.

The ML community recognizes the importance of anticipating and mitigating the
potential negative impacts of benchmark research. In this position paper, we
argue that more attention must be paid to areas of ethical risk at the
technical and scientific core of ML benchmarks. We identify overlooked
structural similarities between human IQ and ML benchmarks. These share
similarities in setting standards for describing, evaluating, and comparing
performance on tasks relevant to intelligence. Drawing on prior research on IQ
benchmarks from feminist philosophy of science, we argue that values need to be
considered when creating ML benchmarks and datasets, and that it is not
possible to avoid this choice by creating benchmarks that are value-neutral.
Finally, we outline practical recommendations for benchmark research ethics and
ethics review.

When creating 3D content, highly specialized skills are generally needed to
design and generate models of objects and other assets by hand. We address this
problem through high-quality 3D asset retrieval from multi-modal inputs,
including 2D sketches, images and text. We use CLIP as it provides a bridge to
higher-level latent features. We use these features to perform a multi-modality
fusion to address the lack of artistic control that affects common data-driven
approaches. Our approach allows for multi-modal conditional feature-driven
retrieval through a 3D asset database, by utilizing a combination of input
latent embeddings. We explore the effects of different combinations of feature
embeddings across different input types and weighting methods.

Today, data collection has improved in various areas, and the medical domain
is no exception. Auscultation, as an important diagnostic technique for
physicians, due to the progress and availability of digital stethoscopes, lends
itself well to applications of machine learning. Due to the large number of
auscultations performed, the availability of data opens up an opportunity for
more effective analysis of sounds where prognostic accuracy even among experts
remains low. In this study, digital 6-channel auscultations of 45 patients were
used in various machine learning scenarios, with the aim of distinguishing
between normal and anomalous pulmonary sounds. Audio features (such as
fundamental frequencies F0-4, loudness, HNR, DFA, as well as descriptive
statistics of log energy, RMS and MFCC) were extracted using the Python library
Surfboard. Windowing and feature aggregation and concatenation strategies were
used to prepare data for tree-based ensemble models in unsupervised (fair-cut
forest) and supervised (random forest) machine learning settings. The
evaluation was carried out using 9-fold stratified cross-validation repeated 30
times. Decision fusion by averaging outputs for a subject was tested and found
to be useful. Supervised models showed a consistent advantage over unsupervised
ones, achieving mean AUC ROC of 0.691 (accuracy 71.11%, Kappa 0.416, F1-score
0.771) in side-based detection and mean AUC ROC of 0.721 (accuracy 68.89%,
Kappa 0.371, F1-score 0.650) in patient-based detection.

Estimation of physical quantities is at the core of most scientific research
and the use of quantum devices promises to enhance its performances. In real
scenarios, it is fundamental to consider that the resources are limited and
Bayesian adaptive estimation represents a powerful approach to efficiently
allocate, during the estimation process, all the available resources. However,
this framework relies on the precise knowledge of the system model, retrieved
with a fine calibration that often results computationally and experimentally
demanding. Here, we introduce a model-free and deep learning-based approach to
efficiently implement realistic Bayesian quantum metrology tasks accomplishing
all the relevant challenges, without relying on any a-priori knowledge on the
system. To overcome this need, a neural network is trained directly on
experimental data to learn the multiparameter Bayesian update. Then, the system
is set at its optimal working point through feedbacks provided by a
reinforcement learning algorithm trained to reconstruct and enhance experiment
heuristics of the investigated quantum sensor. Notably, we prove experimentally
the achievement of higher estimation performances than standard methods,
demonstrating the strength of the combination of these two black-box algorithms
on an integrated photonic circuit. This work represents an important step
towards fully artificial intelligence-based quantum metrology.

We survey the phase diagram of high-pressure molecular hydrogen with path
integral molecular dynamics using a machine-learned interatomic potential
trained with Quantum Monte Carlo forces and energies. Besides the HCP and
C2/c-24 phases, we find two new stable phases both with molecular centers in
the Fmmm-4 structure, separated by a molecular orientation transition with
temperature. The high temperature isotropic Fmmm-4 phase has a reentrant
melting line with a maximum at higher temperature (1450K at 150GPa) than
previously estimated and crosses the liquid-liquid transition line around 1200K
and 200GPa.

We propose X-NeRF, a novel method to learn a Cross-Spectral scene
representation given images captured from cameras with different light spectrum
sensitivity, based on the Neural Radiance Fields formulation. X-NeRF optimizes
camera poses across spectra during training and exploits Normalized
Cross-Device Coordinates (NXDC) to render images of different modalities from
arbitrary viewpoints, which are aligned and at the same resolution. Experiments
on 16 forward-facing scenes, featuring color, multi-spectral and infrared
images, confirm the effectiveness of X-NeRF at modeling Cross-Spectral scene

In this work, we address the problem of generating speech from silent lip
videos for any speaker in the wild. In stark contrast to previous works, our
method (i) is not restricted to a fixed number of speakers, (ii) does not
explicitly impose constraints on the domain or the vocabulary and (iii) deals
with videos that are recorded in the wild as opposed to within laboratory
settings. The task presents a host of challenges, with the key one being that
many features of the desired target speech, like voice, pitch and linguistic
content, cannot be entirely inferred from the silent face video. In order to
handle these stochastic variations, we propose a new VAE-GAN architecture that
learns to associate the lip and speech sequences amidst the variations. With
the help of multiple powerful discriminators that guide the training process,
our generator learns to synthesize speech sequences in any voice for the lip
movements of any person. Extensive experiments on multiple datasets show that
we outperform all baselines by a large margin. Further, our network can be
fine-tuned on videos of specific identities to achieve a performance comparable
to single-speaker models that are trained on $4\times$ more data. We conduct
numerous ablation studies to analyze the effect of different modules of our
architecture. We also provide a demo video that demonstrates several
qualitative results along with the code and trained models on our website:
\url{http://cvit.iiit.ac.in/research/projects/cvit-projects/lip-to-speech-synthesis}}

This paper addresses the problem of 2D pose representation during
unsupervised 2D to 3D pose lifting to improve the accuracy, stability and
generalisability of 3D human pose estimation (HPE) models. All unsupervised
2D-3D HPE approaches provide the entire 2D kinematic skeleton to a model during
training. We argue that this is sub-optimal and disruptive as long-range
correlations are induced between independent 2D key points and predicted 3D
ordinates during training. To this end, we conduct the following study. With a
maximum architecture capacity of 6 residual blocks, we evaluate the performance
of 5 models which each represent a 2D pose differently during the adversarial
unsupervised 2D-3D HPE process. Additionally, we show the correlations between
2D key points which are learned during the training process, highlighting the
unintuitive correlations induced when an entire 2D pose is provided to a
lifting model. Our results show that the most optimal representation of a 2D
pose is that of two independent segments, the torso and legs, with no shared
features between each lifting network. This approach decreased the average
error by 20\% on the Human3.6M dataset when compared to a model with a near
identical parameter count trained on the entire 2D kinematic skeleton.
Furthermore, due to the complex nature of adversarial learning, we show how
this representation can also improve convergence during training allowing for
an optimum result to be obtained more often.

To mitigate the effects of undesired biases in models, several approaches
propose to pre-process the input dataset to reduce the risks of discrimination
by preventing the inference of sensitive attributes. Unfortunately, most of
these pre-processing methods lead to the generation a new distribution that is
very different from the original one, thus often leading to unrealistic data.
As a side effect, this new data distribution implies that existing models need
to be re-trained to be able to make accurate predictions. To address this
issue, we propose a novel pre-processing method, that we coin as fair mapping,
based on the transformation of the distribution of protected groups onto a
chosen target one, with additional privacy constraints whose objective is to
prevent the inference of sensitive attributes. More precisely, we leverage on
the recent works of the Wasserstein GAN and AttGAN frameworks to achieve the
optimal transport of data points coupled with a discriminator enforcing the
protection against attribute inference. Our proposed approach, preserves the
interpretability of data and can be used without defining exactly the sensitive
groups. In addition, our approach can be specialized to model existing
state-of-the-art approaches, thus proposing a unifying view on these methods.
Finally, several experiments on real and synthetic datasets demonstrate that
our approach is able to hide the sensitive attributes, while limiting the
distortion of the data and improving the fairness on subsequent data analysis
tasks.

Classic algorithms and machine learning systems like neural networks are both
abundant in everyday life. While classic computer science algorithms are
suitable for precise execution of exactly defined tasks such as finding the
shortest path in a large graph, neural networks allow learning from data to
predict the most likely answer in more complex tasks such as image
classification, which cannot be reduced to an exact algorithm. To get the best
of both worlds, this thesis explores combining both concepts leading to more
robust, better performing, more interpretable, more computationally efficient,
and more data efficient architectures. The thesis formalizes the idea of
algorithmic supervision, which allows a neural network to learn from or in
conjunction with an algorithm. When integrating an algorithm into a neural
architecture, it is important that the algorithm is differentiable such that
the architecture can be trained end-to-end and gradients can be propagated back
through the algorithm in a meaningful way. To make algorithms differentiable,
this thesis proposes a general method for continuously relaxing algorithms by
perturbing variables and approximating the expectation value in closed form,
i.e., without sampling. In addition, this thesis proposes differentiable
algorithms, such as differentiable sorting networks, differentiable renderers,
and differentiable logic gate networks. Finally, this thesis presents
alternative training strategies for learning with algorithms.

Several studies have empirically compared in-distribution (ID) and
out-of-distribution (OOD) performance of various models. They report frequent
positive correlations on benchmarks in computer vision and NLP. Surprisingly,
they never observe inverse correlations suggesting necessary trade-offs. This
matters to determine whether ID performance can serve as a proxy for OOD
generalization. This paper shows that inverse correlations between ID and OOD
performance do happen in real-world benchmarks. They could be missed in past
studies because of a biased selection of models. We show an example on the
WILDS-Camelyon17 dataset, using models from multiple training epochs and random
seeds. Our observations are particularly striking with models trained with a
regularizer that diversifies the solutions to the ERM objective. We nuance
recommendations and conclusions made in past studies. (1) High OOD performance
may sometimes require trading off ID performance.(2) Focusing on ID performance
alone may not lead to optimal OOD performance: it can lead to diminishing and
eventually negative returns in OOD performance. (3) Our example reminds that
empirical studies only chart regimes achievable with existing methods: care is
warranted in deriving prescriptive recommendations.

Heterogeneous graph neural networks (HGNNs) have attracted increasing
research interest in recent three years. Most existing HGNNs fall into two
classes. One class is meta-path-based HGNNs which either require domain
knowledge to handcraft meta-paths or consume huge amount of time and memory to
automatically construct meta-paths. The other class does not rely on meta-path
construction. It takes homogeneous convolutional graph neural networks
(Conv-GNNs) as backbones and extend them to heterogeneous graphs by introducing
node-type- and edge-type-dependent parameters. Regardless of the meta-path
dependency, most existing HGNNs employ shallow Conv-GNNs such as GCN and GAT to
aggregate neighborhood information, and may have limited capability to capture
information from high-order neighborhood. In this work, we propose two
heterogeneous graph tree network models: Heterogeneous Graph Tree Convolutional
Network (HetGTCN) and Heterogeneous Graph Tree Attention Network (HetGTAN),
which do not rely on meta-paths to encode heterogeneity in both node features
and graph structure. Extensive experiments on three real-world heterogeneous
graph data demonstrate that the proposed HetGTCN and HetGTAN are efficient and
consistently outperform all state-of-the-art HGNN baselines on semi-supervised
node classification tasks, and can go deep without compromising performance.

As its core computation, a self-attention mechanism gauges pairwise
correlations across the entire input sequence. Despite favorable performance,
calculating pairwise correlations is prohibitively costly. While recent work
has shown the benefits of runtime pruning of elements with low attention
scores, the quadratic complexity of self-attention mechanisms and their on-chip
memory capacity demands are overlooked. This work addresses these constraints
by architecting an accelerator, called SPRINT, which leverages the inherent
parallelism of ReRAM crossbar arrays to compute attention scores in an
approximate manner. Our design prunes the low attention scores using a
lightweight analog thresholding circuitry within ReRAM, enabling SPRINT to
fetch only a small subset of relevant data to on-chip memory. To mitigate
potential negative repercussions for model accuracy, SPRINT re-computes the
attention scores for the few fetched data in digital. The combined in-memory
pruning and on-chip recompute of the relevant attention scores enables SPRINT
to transform quadratic complexity to a merely linear one. In addition, we
identify and leverage a dynamic spatial locality between the adjacent attention
operations even after pruning, which eliminates costly yet redundant data
fetches. We evaluate our proposed technique on a wide range of state-of-the-art
transformer models. On average, SPRINT yields 7.5x speedup and 19.6x energy
reduction when total 16KB on-chip memory is used, while virtually on par with
iso-accuracy of the baseline models (on average 0.36% degradation).

Predictive models of thermodynamic properties of mixtures are paramount in
chemical engineering and chemistry. Classical thermodynamic models are
successful in generalizing over (continuous) conditions like temperature and
concentration. On the other hand, matrix completion methods (MCMs) from machine
learning successfully generalize over (discrete) binary systems; these MCMs can
make predictions without any data for a given binary system by implicitly
learning commonalities across systems. In the present work, we combine the
strengths of both worlds in a hybrid approach. The underlying idea is to
predict the pair-interaction energies, as they are used in basically all
physical models of liquid mixtures, by an MCM. As an example, we embed an MCM
into UNIQUAC, a widely-used physical model for the Gibbs excess energy. We
train the resulting hybrid model in a Bayesian machine-learning framework on
experimental data for activity coefficients in binary systems of 1146
components from the Dortmund Data Bank. We thereby obtain, for the first time,
a complete set of UNIQUAC parameters for all binary systems of these
components, which allows us to predict, in principle, activity coefficients at
arbitrary temperature and composition for any combination of these components,
not only for binary but also for multicomponent systems. The hybrid model even
outperforms the best available physical model for predicting activity
coefficients, the modified UNIFAC (Dortmund) model.

Python has become a standard scientific computing language with fast-growing
support of machine learning and data analysis modules, as well as an increasing
usage of big data. The Dynamic Distributed Dimensional Data Model (D4M) offers
a highly composable, unified data model with strong performance built to handle
big data fast and efficiently. In this work we present an implementation of D4M
in Python. $D4M\! .py$ implements all foundational functionality of D4M and
includes Accumulo and SQL database support via Graphulo. We describe the
mathematical background and motivation, an explanation of the approaches made
for its fundamental functions and building blocks, and performance results
which compare $D4M\! .py$'s performance to D4M-MATLAB and D4M.jl

Many contextualized word representations are now learned by intricate neural
network models, such as masked neural language models (MNLMs) which are made up
of huge neural network structures and trained to restore the masked text. Such
representations demonstrate superhuman performance in some reading
comprehension (RC) tasks which extract a proper answer in the context given a
question. However, identifying the detailed knowledge trained in MNLMs is
challenging owing to numerous and intermingled model parameters. This paper
provides new insights and empirical analyses on commonsense knowledge included
in pretrained MNLMs. First, we use a diagnostic test that evaluates whether
commonsense knowledge is properly trained in MNLMs. We observe that a large
proportion of commonsense knowledge is not appropriately trained in MNLMs and
MNLMs do not often understand the semantic meaning of relations accurately. In
addition, we find that the MNLM-based RC models are still vulnerable to
semantic variations that require commonsense knowledge. Finally, we discover
the fundamental reason why some knowledge is not trained. We further suggest
that utilizing an external commonsense knowledge repository can be an effective
solution. We exemplify the possibility to overcome the limitations of the
MNLM-based RC models by enriching text with the required knowledge from an
external commonsense knowledge repository in controlled experiments.

The rapid pace of recent advancements in numerical computation, notably the
rise of GPU and TPU hardware accelerators, have allowed tensor network (TN)
algorithms to scale to even larger quantum simulation problems, and to be
employed more broadly for solving machine learning tasks. The
"quantum-inspired" nature of TNs permits them to be mapped to parametrized
quantum circuits (PQCs), a fact which has inspired recent proposals for
enhancing the performance of TN algorithms using near-term quantum devices, as
well as enabling joint quantum-classical training frameworks which benefit from
the distinct strengths of TN and PQC models. However, the success of any such
methods depends on efficient and accurate methods for approximating TN states
using realistic quantum circuits, something which remains an unresolved
question. In this work, we compare a range of novel and previously-developed
algorithmic protocols for decomposing matrix product states (MPS) of arbitrary
bond dimensions into low-depth quantum circuits consisting of stacked linear
layers of two-qubit unitaries. These protocols are formed from different
combinations of a preexisting analytical decomposition scheme with constrained
optimization of circuit unitaries, and all possess efficient classical
runtimes. Our experimental results reveal one particular protocol, involving
sequential growth and optimization of the quantum circuit, to outperform all
other methods, with even greater benefits seen in the setting of limited
computational resources. Given these promising results, we expect our proposed
decomposition protocol to form a useful ingredient within any joint application
of TNs and PQCs, in turn further unlocking the rich and complementary benefits
of classical and quantum computation.

Tiny machine learning (TinyML) in IoT systems exploits MCUs as edge devices
for data processing. However, traditional TinyML methods can only perform
inference, limited to static environments or classes. Real case scenarios
usually work in dynamic environments, thus drifting the context where the
original neural model is no more suitable. For this reason, pre-trained models
reduce accuracy and reliability during their lifetime because the data recorded
slowly becomes obsolete or new patterns appear. Continual learning strategies
maintain the model up to date, with runtime fine-tuning of the parameters. This
paper compares four state-of-the-art algorithms in two real applications: i)
gesture recognition based on accelerometer data and ii) image classification.
Our results confirm these systems' reliability and the feasibility of deploying
them in tiny-memory MCUs, with a drop in the accuracy of a few percentage
points with respect to the original models for unconstrained computing

Deep reinforcement learning agents are notoriously sample inefficient, which
considerably limits their application to real-world problems. Recently, many
model-based methods have been designed to address this issue, with learning in
the imagination of a world model being one of the most prominent approaches.
However, while virtually unlimited interaction with a simulated environment
sounds appealing, the world model has to be accurate over extended periods of
time. Motivated by the success of Transformers in sequence modeling tasks, we
introduce IRIS, a data-efficient agent that learns in a world model composed of
a discrete autoencoder and an autoregressive Transformer. With the equivalent
of only two hours of gameplay in the Atari 100k benchmark, IRIS achieves a mean
human normalized score of 1.046, and outperforms humans on 10 out of 26 games.
Our approach sets a new state of the art for methods without lookahead search,
and even surpasses MuZero. To foster future research on Transformers and world
models for sample-efficient reinforcement learning, we release our codebase at
https://github.com/eloialonso/iris.

Stain color variation in histological images, caused by a variety of factors,
is a challenge not only for the visual diagnosis of pathologists but also for
cell segmentation algorithms. To eliminate the color variation, many stain
normalization approaches have been proposed. However, most were designed for
hematoxylin and eosin staining images and performed poorly on
immunohistochemical staining images. Current cell segmentation methods
systematically apply stain normalization as a preprocessing step, but the
impact brought by color variation has not been quantitatively investigated yet.
In this paper, we produced five groups of NeuN staining images with different
colors. We applied a deep learning image-recoloring method to perform color
transfer between histological image groups. Finally, we altered the color of a
segmentation set and quantified the impact of color variation on cell
segmentation. The results demonstrated the necessity of color normalization
prior to subsequent analysis.

Robots and intelligent systems that sense or interact with the world are
increasingly being used to automate a wide array of tasks. The ability of these
systems to complete these tasks depends on a large range of technologies such
as the mechanical and electrical parts that make up the physical body of the
robot and its sensors, perception algorithms to perceive the environment, and
planning and control algorithms to produce meaningful actions. Therefore, it is
often necessary to consider the interactions between these components when
designing an embodied system. This thesis explores work on the task-driven
co-optimization of robotics systems in an end-to-end manner, simultaneously
optimizing the physical components of the system with inference or control
algorithms directly for task performance. We start by considering the problem
of optimizing a beacon-based localization system directly for localization
accuracy. Designing such a system involves placing beacons throughout the
environment and inferring location from sensor readings. In our work, we
develop a deep learning approach to optimize both beacon placement and location
inference directly for localization accuracy. We then turn our attention to the
related problem of task-driven optimization of robots and their controllers. In
our work, we start by proposing a data-efficient algorithm based on multi-task
reinforcement learning. Our approach efficiently optimizes both physical design
and control parameters directly for task performance by leveraging a
design-conditioned controller capable of generalizing over the space of
physical designs. We then follow this up with an extension to allow for the
optimization over discrete morphological parameters such as the number and
configuration of limbs. Finally, we conclude by exploring the fabrication and
deployment of optimized soft robots.

This paper investigates theoretical properties of subsampling and hashing as
tools for approximate Euclidean norm-preserving embeddings for vectors with
(unknown) additive Gaussian noises. Such embeddings are sometimes called
Johnson-lindenstrauss embeddings due to their celebrated lemma. Previous work
shows that as sparse embeddings, the success of subsampling and hashing closely
depends on the $l_\infty$ to $l_2$ ratios of the vector to be mapped. This
paper shows that the presence of noise removes such constrain in
high-dimensions, in other words, sparse embeddings such as subsampling and
hashing with comparable embedding dimensions to dense embeddings have similar
approximate norm-preserving dimensionality-reduction properties. The key is
that the noise should be treated as an information to be exploited, not simply
something to be removed. Theoretical bounds for subsampling and hashing to
recover the approximate norm of a high dimension vector in the presence of
noise are derived, with numerical illustrations showing better performances are
achieved in the presence of noise.

Modern AAA video games feature huge game levels and maps which are
increasingly hard for level testers to cover exhaustively. As a result, games
often ship with catastrophic bugs such as the player falling through the floor
or being stuck in walls. We propose an approach specifically targeted at
reachability bugs in simulated 3D environments based on the powerful
exploration algorithm, Go-Explore, which saves unique checkpoints across the
map and then identifies promising ones to explore from. We show that when
coupled with simple heuristics derived from the game's navigation mesh,
Go-Explore finds challenging bugs and comprehensively explores complex
environments without the need for human demonstration or knowledge of the game
dynamics. Go-Explore vastly outperforms more complicated baselines including
reinforcement learning with intrinsic curiosity in both covering the navigation
mesh and number of unique positions across the map discovered. Finally, due to
our use of parallel agents, our algorithm can fully cover a vast 1.5km x 1.5km
game world within 10 hours on a single machine making it extremely promising
for continuous testing suites.

Extracting temporal relationships between pairs of events in texts is a
crucial yet challenging problem for natural language understanding. Depending
on the distance between the events, models must learn to differently balance
information from local and global contexts surrounding the event pair for
temporal relation prediction. Learning how to fuse this information has proved
challenging for transformer-based language models. Therefore, we present MulCo:
Multi-Scale Contrastive Co-Training, a technique for the better fusion of local
and global contextualized features. Our model uses a BERT-based language model
to encode local context and a Graph Neural Network (GNN) to represent global
document-level syntactic and temporal characteristics. Unlike previous
state-of-the-art methods, which use simple concatenation on multi-view features
or select optimal sentences using sophisticated reinforcement learning
approaches, our model co-trains GNN and BERT modules using a multi-scale
contrastive learning objective. The GNN and BERT modules learn a synergistic
parameterization by contrasting GNN multi-layer multi-hop subgraphs (i.e.,
global context embeddings) and BERT outputs (i.e., local context embeddings)
through end-to-end back-propagation. We empirically demonstrate that MulCo
provides improved ability to fuse local and global contexts encoded using BERT
and GNN compared to the current state-of-the-art. Our experimental results show
that MulCo achieves new state-of-the-art results on several temporal relation
extraction datasets.

In the problem of online load balancing on uniformly related machines with
bounded migration, jobs arrive online one after another and have to be
immediately placed on one of a given set of machines without knowledge about
jobs that may arrive later on. Each job has a size and each machine has a
speed, and the load due to a job assigned to a machine is obtained by dividing
the first value by the second. The goal is to minimize the maximum overall load
any machine receives. However, unlike in the pure online case, each time a new
job arrives it contributes a migration potential equal to the product of its
size and a certain migration factor. This potential can be spend to reassign
jobs either right away (non-amortized case) or at any later time (amortized
case). Semi-online models of this flavor have been studied intensively for
several fundamental problems, e.g., load balancing on identical machines and
bin packing, but uniformly related machines have not been considered up to now.
In the present paper, the classical doubling strategy on uniformly related
machines is combined with migration to achieve an
$(8/3+\varepsilon)$-competitive algorithm and a $(4+\varepsilon)$-competitive
algorithm with $O(1/\varepsilon)$ amortized and non-amortized migration,
respectively, while the best known competitive ratio in the pure online setting
is roughly $5.828$.

Learning Electronic Health Records (EHRs) representation is a preeminent yet
under-discovered research topic. It benefits various clinical decision support
applications, e.g., medication outcome prediction or patient similarity search.
Current approaches focus on task-specific label supervision on vectorized
sequential EHR, which is not applicable to large-scale unsupervised scenarios.
Recently, contrastive learning shows great success on self-supervised
representation learning problems. However, complex temporality often degrades
the performance. We propose Graph Kernel Infomax, a self-supervised graph
kernel learning approach on the graphical representation of EHR, to overcome
the previous problems. Unlike the state-of-the-art, we do not change the graph
structure to construct augmented views. Instead, we use Kernel Subspace
Augmentation to embed nodes into two geometrically different manifold views.
The entire framework is trained by contrasting nodes and graph representations
on those two manifold views through the commonly used contrastive objectives.
Empirically, using publicly available benchmark EHR datasets, our approach
yields performance on clinical downstream tasks that exceeds the
state-of-the-art. Theoretically, the variation on distance metrics naturally
creates different views as data augmentation without changing graph structures.

The use of models, even if efficient, must be accompanied by an understanding
at all levels of the process that transforms data (upstream and downstream).
Thus, needs increase to define the relationships between individual data and
the choice that an algorithm could make based on its analysis (e.g. the
recommendation of one product or one promotional offer, or an insurance rate
representative of the risk). Model users must ensure that models do not
discriminate and that it is also possible to explain their results. This paper
introduces the importance of model interpretation and tackles the notion of
model transparency. Within an insurance context, it specifically illustrates
how some tools can be used to enforce the control of actuarial models that can
nowadays leverage on machine learning. On a simple example of loss frequency
estimation in car insurance, we show the interest of some interpretability
methods to adapt explanation to the target audience.

In this work we introduce an incremental learning framework for
Click-Through-Rate (CTR) prediction and demonstrate its effectiveness for
Taboola's massive-scale recommendation service. Our approach enables rapid
capture of emerging trends through warm-starting from previously deployed
models and fine tuning on "fresh" data only. Past knowledge is maintained via a
teacher-student paradigm, where the teacher acts as a distillation technique,
mitigating the catastrophic forgetting phenomenon. Our incremental learning
framework enables significantly faster training and deployment cycles (x12
speedup). We demonstrate a consistent Revenue Per Mille (RPM) lift over
multiple traffic segments and a significant CTR increase on newly introduced
items.

The processing of legal texts has been developing as an emerging field in
natural language processing (NLP). Legal texts contain unique jargon and
complex linguistic attributes in vocabulary, semantics, syntax, and morphology.
Therefore, the development of text simplification (TS) methods specific to the
legal domain is of paramount importance for facilitating comprehension of legal
text by ordinary people and providing inputs to high-level models for
mainstream legal NLP applications. While a recent study proposed a rule-based
TS method for legal text, learning-based TS in the legal domain has not been
considered previously. Here we introduce an unsupervised simplification method
for legal texts (USLT). USLT performs domain-specific TS by replacing complex
words and splitting long sentences. To this end, USLT detects complex words in
a sentence, generates candidates via a masked-transformer model, and selects a
candidate for substitution based on a rank score. Afterward, USLT recursively
decomposes long sentences into a hierarchy of shorter core and context
sentences while preserving semantic meaning. We demonstrate that USLT
outperforms state-of-the-art domain-general TS methods in text simplicity while
keeping the semantics intact.

This research considers the problem of identifying safety constraints and
developing Run Time Assurance (RTA) for Deep Reinforcement Learning (RL)
Tactical Autopilots that use neural network control systems (NNCS). This
research studies a specific use case of an NNCS performing autonomous formation
flight while an RTA system provides collision avoidance and geofence
assurances. First, Systems Theoretic Accident Models and Processes (STAMP) is
applied to identify accidents, hazards, and safety constraints as well as
define a functional control system block diagram of the ground station, manned
flight lead, and surrogate unmanned wingman. Then, Systems Theoretic Process
Analysis (STPA) is applied to the interactions of the the ground station,
manned flight lead, surrogate unmanned wingman, and internal elements of the
wingman aircraft to identify unsafe control actions, scenarios leading to each,
and safety requirements to mitigate risks. This research is the first
application of STAMP and STPA to an NNCS bounded by RTA.

Signed and directed networks are ubiquitous in real-world applications.
However, there has been relatively little work proposing spectral graph neural
networks (GNNs) for such networks. Here we introduce a signed directed
Laplacian matrix, which we call the magnetic signed Laplacian, as a natural
generalization of both the signed Laplacian on signed graphs and the magnetic
Laplacian on directed graphs. We then use this matrix to construct a novel
efficient spectral GNN architecture and conduct extensive experiments on both
node clustering and link prediction tasks. In these experiments, we consider
tasks related to signed information, tasks related to directional information,
and tasks related to both signed and directional information. We demonstrate
that our proposed spectral GNN is effective for incorporating both signed and
directional information, and attains leading performance on a wide range of
data sets. Additionally, we provide a novel synthetic network model, which we
refer to as the signed directed stochastic block model, and a number of novel
real-world data sets based on lead-lag relationships in financial time series.

Tensor completion and tensor decomposition are important problems in many
domains. In this work, we leverage the connection between these problems to
learn a distance metric that improves both decomposition and completion. We
show that the optimal Mahalanobis distance metric for the completion task is
closely related to the Tucker decomposition of the completed tensor. Then, we
formulate a bilevel optimization problem to perform joint tensor completion and
decomposition, subject to metric learning constraints. The metric learning
constraints also allow us to flexibly incorporate similarity side information
and coupled matrices, when available, into the tensor recovery process. We
derive an algorithm to solve the bilevel optimization problem and prove its
global convergence. When evaluated on real data, our approach performs
significantly better compared to previous methods.

This paper proposes a novel methodology to measure political leaders'
attention that combines text data with machine learning algorithms. We use this
method on a hand-collected database of presidential ``state-of-the-union''-type
speeches spanning ten countries and two centuries to study the determinants of
political attention, its shifts over time, and its impacts on countries'
outcomes. We find that presidential attention can be characterized by a compact
set of topics whose relative importance remains stable over long periods.
Contrary to presidential rhetoric, using a differences-in-differences design,
we show that presidents' attention has precisely-estimated null effects on
growth and other policy outcomes.

A widely-studied deep reinforcement learning (RL) technique known as
Prioritized Experience Replay (PER) allows agents to learn from transitions
sampled with non-uniform probability proportional to their temporal-difference
(TD) error. Although it has been shown that PER is one of the most crucial
components for the overall performance of deep RL methods in discrete action
domains, many empirical studies indicate that it considerably underperforms
actor-critic algorithms in continuous control. We theoretically show that actor
networks cannot be effectively trained with transitions that have large TD
errors. As a result, the approximate policy gradient computed under the
Q-network diverges from the actual gradient computed under the optimal
Q-function. Motivated by this, we introduce a novel experience replay sampling
framework for actor-critic methods, which also regards issues with stability
and recent findings behind the poor empirical performance of PER. The
introduced algorithm suggests a new branch of improvements to PER and schedules
effective and efficient training for both actor and critic networks. An
extensive set of experiments verifies our theoretical claims and demonstrates
that the introduced method significantly outperforms the competing approaches
and obtains state-of-the-art results over the standard off-policy actor-critic

Equilibrium propagation (EP) is an alternative to backpropagation (BP) that
allows the training of deep neural networks with local learning rules. It thus
provides a compelling framework for training neuromorphic systems and
understanding learning in neurobiology. However, EP requires infinitesimal
teaching signals, thereby limiting its applicability in noisy physical systems.
Moreover, the algorithm requires separate temporal phases and has not been
applied to large-scale problems. Here we address these issues by extending EP
to holomorphic networks. We show analytically that this extension naturally
leads to exact gradients even for finite-amplitude teaching signals.
Importantly, the gradient can be computed as the first Fourier coefficient from
finite neuronal activity oscillations in continuous time without requiring
separate phases. Further, we demonstrate in numerical simulations that our
approach permits robust estimation of gradients in the presence of noise and
that deeper models benefit from the finite teaching signals. Finally, we
establish the first benchmark for EP on the ImageNet 32x32 dataset and show
that it matches the performance of an equivalent network trained with BP. Our
work provides analytical insights that enable scaling EP to large-scale
problems and establishes a formal framework for how oscillations could support
learning in biological and neuromorphic systems.

Tree-based demappers for multiple-input multiple-output (MIMO) detection such
as the sphere decoder can achieve near-optimal performance but incur high
computational cost due to their sequential nature. In this paper, we propose
the perturbed linear demapper (PLM), which is a novel data-driven model for
computing soft outputs in parallel. To achieve this, the PLM learns a
distribution centered on an initial linear estimate and a log-likelihood ratio
clipping parameter using end-to-end Bayesian optimization. Furthermore, we show
that lattice-reduction can be naturally incorporated into the PLM pipeline,
which allows to trade off computational cost against coded block error rate
reduction. We find that the optimized PLM can achieve near maximum-likelihood
(ML) performance in Rayleigh channels, making it an efficient alternative to
tree-based demappers.

The task of learning a quantum circuit to prepare a given mixed state is a
fundamental quantum subroutine. We present a variational quantum algorithm
(VQA) to learn mixed states which is suitable for near-term hardware. Our
algorithm represents a generalization of previous VQAs that aimed at learning
preparation circuits for pure states. We consider two different ans\"{a}tze for
compiling the target state; the first is based on learning a purification of
the state and the second on representing it as a convex combination of pure
states. In both cases, the resources required to store and manipulate the
compiled state grow with the rank of the approximation. Thus, by learning a
lower rank approximation of the target state, our algorithm provides a means of
compressing a state for more efficient processing. As a byproduct of our
algorithm, one effectively learns the principal components of the target state,
and hence our algorithm further provides a new method for principal component
analysis. We investigate the efficacy of our algorithm through extensive
numerical implementations, showing that typical random states and thermal
states of many body systems may be learnt this way. Additionally, we
demonstrate on quantum hardware how our algorithm can be used to study hardware
noise-induced states.

Deep neural networks use multiple layers of functions to map an object
represented by an input vector progressively to different representations, and
with sufficient training, eventually to a single score for each class that is
the output of the final decision function. Ideally, in this output space, the
objects of different classes achieve maximum separation. Motivated by the need
to better understand the inner working of a deep neural network, we analyze the
effectiveness of the learned representations in separating the classes from a
data complexity perspective. Using a simple complexity measure, a popular
benchmarking task, and a well-known architecture design, we show how the data
complexity evolves through the network, how it changes during training, and how
it is impacted by the network design and the availability of training samples.
We discuss the implications of the observations and the potentials for further
studies.

Surface defect detection is one of the most essential processes for
industrial quality inspection. Deep learning-based surface defect detection
methods have shown great potential. However, the well-performed models usually
require large training data and can only detect defects that appeared in the
training stage. When facing incremental few-shot data, defect detection models
inevitably suffer from catastrophic forgetting and misclassification problem.
To solve these problems, this paper proposes a new knowledge distillation
network, called Dual Knowledge Align Network (DKAN). The proposed DKAN method
follows a pretraining-finetuning transfer learning paradigm and a knowledge
distillation framework is designed for fine-tuning. Specifically, an
Incremental RCNN is proposed to achieve decoupled stable feature representation
of different categories. Under this framework, a Feature Knowledge Align (FKA)
loss is designed between class-agnostic feature maps to deal with catastrophic
forgetting problems, and a Logit Knowledge Align (LKA) loss is deployed between
logit distributions to tackle misclassification problems. Experiments have been
conducted on the incremental Few-shot NEU-DET dataset and results show that
DKAN outperforms other methods on various few-shot scenes, up to 6.65% on the
mean Average Precision metric, which proves the effectiveness of the proposed
method.

The standard approaches to neural network implementation yield powerful
function approximation capabilities but are limited in their abilities to learn
meta representations and reason probabilistic uncertainties in their
predictions. Gaussian processes, on the other hand, adopt the Bayesian learning
scheme to estimate such uncertainties but are constrained by their efficiency
and approximation capacity. The Neural Processes Family (NPF) intends to offer
the best of both worlds by leveraging neural networks for meta-learning
predictive uncertainties. Such potential has brought substantial research
activity to the family in recent years. Therefore, a comprehensive survey of
NPF models is needed to organize and relate their motivation, methodology, and
experiments. This paper intends to address this gap while digging deeper into
the formulation, research themes, and applications concerning the family
members. We shed light on their potential to bring several recent advances in
other deep learning domains under one umbrella. We then provide a rigorous
taxonomy of the family and empirically demonstrate their capabilities for
modeling data generating functions operating on 1-d, 2-d, and 3-d input
domains. We conclude by discussing our perspectives on the promising directions
that can fuel the research advances in the field. Code for our experiments will
be made available at https://github.com/srvCodes/neural-processes-survey.

We introduce an explorative active learning (AL) algorithm based on Gaussian
process regression and marginalized graph kernel (GPR-MGK) to explore chemical
space with minimum cost. Using high-throughput molecular dynamics simulation to
generate data and graph neural network (GNN) to predict, we constructed an
active learning molecular simulation framework for thermodynamic property
prediction. In specific, targeting 251,728 alkane molecules consisting of 4 to
19 carbon atoms and their liquid physical properties: densities, heat
capacities, and vaporization enthalpies, we use the AL algorithm to select the
most informative molecules to represent the chemical space. Validation of
computational and experimental test sets shows that only 313 (0.124\% of the
total) molecules were sufficient to train an accurate GNN model with $\rm R^2 >
0.99$ for computational test sets and $\rm R^2 > 0.94$ for experimental test
sets. We highlight two advantages of the presented AL algorithm: compatibility
with high-throughput data generation and reliable uncertainty quantification.

Subgraphs are rich substructures in graphs, and their nodes and edges can be
partially observed in real-world tasks. Under partial observation, existing
node- or subgraph-level message-passing produces suboptimal representations. In
this paper, we formulate a novel task of learning representations of partially
observed subgraphs. To solve this problem, we propose Partial Subgraph InfoMax
(PSI) framework and generalize existing InfoMax models, including DGI,
InfoGraph, MVGRL, and GraphCL, into our framework. These models maximize the
mutual information between the partial subgraph's summary and various
substructures from nodes to full subgraphs. In addition, we suggest a novel
two-stage model with $k$-hop PSI, which reconstructs the representation of the
full subgraph and improves its expressiveness from different local-global
structures. Under training and evaluation protocols designed for this problem,
we conduct experiments on three real-world datasets and demonstrate that PSI
models outperform baselines.

Current technologies in quantum-based communications bring a new integration
of quantum data with classical data for hybrid processing. However, the
frameworks of these technologies are restricted to a single classical or
quantum task, which limits their flexibility in near-term applications. We
propose a quantum reservoir processor to harness quantum dynamics in
computational tasks requiring both classical and quantum inputs. This analog
processor comprises a network of quantum dots in which quantum data is incident
to the network and classical data is encoded via a coherent field exciting the
network. We perform a multitasking application of quantum tomography and
nonlinear equalization of classical channels. Interestingly, the tomography can
be performed in a closed-loop manner via the feedback control of classical
data. Therefore, if the classical input comes from a dynamical system,
embedding this system in a closed loop enables hybrid processing even if access
to the external classical input is interrupted. Finally, we demonstrate
preparing quantum depolarizing channels as a novel quantum machine learning
technique for quantum data processing.

We introduce TempCLR, a new time-coherent contrastive learning approach for
the structured regression task of 3D hand reconstruction. Unlike previous
time-contrastive methods for hand pose estimation, our framework considers
temporal consistency in its augmentation scheme, and accounts for the
differences of hand poses along the temporal direction. Our data-driven method
leverages unlabelled videos and a standard CNN, without relying on synthetic
data, pseudo-labels, or specialized architectures. Our approach improves the
performance of fully-supervised hand reconstruction methods by 15.9% and 7.6%
in PA-V2V on the HO-3D and FreiHAND datasets respectively, thus establishing
new state-of-the-art performance. Finally, we demonstrate that our approach
produces smoother hand reconstructions through time, and is more robust to
heavy occlusions compared to the previous state-of-the-art which we show
quantitatively and qualitatively. Our code and models will be available at
https://eth-ait.github.io/tempclr.

Automatic medical question summarization can significantly help the system to
understand consumer health questions and retrieve correct answers. The Seq2Seq
model based on maximum likelihood estimation (MLE) has been applied in this
task, which faces two general problems: the model can not capture well question
focus and and the traditional MLE strategy lacks the ability to understand
sentence-level semantics. To alleviate these problems, we propose a novel
question focus-driven contrastive learning framework (QFCL). Specially, we
propose an easy and effective approach to generate hard negative samples based
on the question focus, and exploit contrastive learning at both encoder and
decoder to obtain better sentence level representations. On three medical
benchmark datasets, our proposed model achieves new state-of-the-art results,
and obtains a performance gain of 5.33, 12.85 and 3.81 points over the baseline
BART model on three datasets respectively. Further human judgement and detailed
analysis prove that our QFCL model learns better sentence representations with
the ability to distinguish different sentence meanings, and generates
high-quality summaries by capturing question focus.

Numerical weather forecasts can exhibit systematic errors due to simplifying
model assumptions and computational approximations. Statistical postprocessing
is a statistical approach to correcting such biases. A statistical
postprocessing model takes input data from a numerical forecast model, and
outputs a parametric predictive distribution of a real-world observation, with
model parameters learned from past forecast-observation pairs. In this paper we
develop and discuss methods for postprocessing of gridded data. We show that
estimates of postprocessing parameters on a spatial grid can be improved by
Bayesian hierarchical modelling with spatial priors. We use the
"Max-and-Smooth" approach [Hrafnkelsson et al., 2021] to approximate a fully
Bayesian inference in two steps. First we calculate maximum-likelihood
estimates (MLEs) of postprocessing parameters at individual grid points. Second
we smooth the MLEs using a measurement error model with a spatial prior. Our
approach provides the theoretical basis for the parameter smoothing approach by
Kharin et al. [2017], and simplifies and generalises the Bayesian hierarchical
modelling approach by Moeller et al. [2015]. A new derivation of Max-and-Smooth
is presented. The method is applicable to arbitrary postprocessing models, as
illustrated on Model Output Statistics, Logistic Regression, and Nonhomogeneous
Gaussian Regression. We report consistent improvements in forecast accuracy,
calibration, and probabilistic skill in postprocessing of temperature and
precipitation forecasts.

Current study on next POI recommendation mainly explores user sequential
transitions with the fine-grained individual-user POI check-in trajectories
only, which suffers from the severe check-in data sparsity issue. In fact,
coarse-grained signals (i.e., region- and global-level check-ins) in such
sparse check-ins would also benefit to augment user preference learning.
Specifically, our data analysis unveils that user movement exhibits noticeable
patterns w.r.t. the regions of visited POIs. Meanwhile, the global all-user
check-ins can help reflect sequential regularities shared by the crowd. We are,
therefore, inspired to propose the MCMG: a Multi-Channel next POI
recommendation framework with Multi-Granularity signals categorized from two
orthogonal perspectives, i.e., fine-coarse grained check-ins at either
POI/region level or local/global level. Being equipped with three modules
(i.e., global user behavior encoder, local multi-channel encoder, and
region-aware weighting strategy), MCMG is capable of capturing both fine- and
coarse-grained sequential regularities as well as exploring the dynamic impact
of multi-channel by differentiating the region check-in patterns. Extensive
experiments on four real-world datasets show that our MCMG significantly
outperforms state-of-the-art next POI recommendation approaches.

As structured data are often insufficient, labels need to be extracted from
free text in electronic health records when developing models for clinical
information retrieval and decision support systems. One of the most important
contextual properties in clinical text is negation, which indicates the absence
of findings. We aimed to improve large scale extraction of labels by comparing
three methods for negation detection in Dutch clinical notes. We used the
Erasmus Medical Center Dutch Clinical Corpus to compare a rule-based method
based on ContextD, a biLSTM model using MedCAT and (finetuned) RoBERTa-based
models. We found that both the biLSTM and RoBERTa models consistently
outperform the rule-based model in terms of F1 score, precision and recall. In
addition, we systematically categorized the classification errors for each
model, which can be used to further improve model performance in particular
applications. Combining the three models naively was not beneficial in terms of
performance. We conclude that the biLSTM and RoBERTa-based models in particular
are highly accurate accurate in detecting clinical negations, but that
ultimately all three approaches can be viable depending on the use case at
hand.

Ensembling neural networks is a long-standing technique for improving the
generalization error of neural networks by combining networks with orthogonal
properties via a committee decision. We show that this technique is an ideal
fit for machine learning on medical data: First, ensembles are amenable to
parallel and asynchronous learning, thus enabling efficient training of
patient-specific component neural networks. Second, building on the idea of
minimizing generalization error by selecting uncorrelated patient-specific
networks, we show that one can build an ensemble of a few selected
patient-specific models that outperforms a single model trained on much larger
pooled datasets. Third, the non-iterative ensemble combination step is an
optimal low-dimensional entry point to apply output perturbation to guarantee
the privacy of the patient-specific networks. We exemplify our framework of
differentially private ensembles on the task of early prediction of sepsis,
using real-life intensive care unit data labeled by clinical experts.

Fu and Tantithamthavorn have recently proposed GPT2SP, a Transformer-based
deep learning model for SP estimation of user stories. They empirically
evaluated the performance of GPT2SP on a dataset shared by Choetkiertikul et al
including 16 projects with a total of 23,313 issues. They benchmarked GPT2SP
against two baselines (namely the naive Mean and Median estimators) and the
method previously proposed by Choetkiertikul et al. (which we will refer to as
DL2SP from now on) for both within- and cross-project estimation scenarios, and
evaluated the extent to which each components of GPT2SP contribute towards the
accuracy of the SP estimates. Their results show that GPT2SP outperforms DL2SP
with a 6%-47% improvement over MAE for the within-project scenario and a 3%-46%
improvement for the cross-project scenarios. However, when we attempted to use
the GPT2SP source code made available by Fu and Tantithamthavorn to reproduce
their experiments, we found a bug in the computation of the Mean Absolute Error
(MAE), which may have inflated the GPT2SP's accuracy reported in their work.
Therefore, we had issued a pull request to fix such a bug, which has been
accepted and merged into their repository at
https://github.com/awsm-research/gpt2sp/pull/2.
  In this report, we describe the results we achieved by using the fixed
version of GPT2SP to replicate the experiments conducted in the original paper
for RQ1 and RQ2. Following the original study, we analyse the results
considering the Medan Absolute Error (MAE) of the estimation methods over all
issues in each project, but we also report the Median Absolute Error (MdAE) and
the Standard accuracy (SA) for completeness.

This work provides several fundamental characterizations of the optimal
classification function under the demographic parity constraint. In the
awareness framework, akin to the classical unconstrained classification case,
we show that maximizing accuracy under this fairness constraint is equivalent
to solving a corresponding regression problem followed by thresholding at level
$1/2$. We extend this result to linear-fractional classification measures
(e.g., ${\rm F}$-score, AM measure, balanced accuracy, etc.), highlighting the
fundamental role played by the regression problem in this framework. Our
results leverage recently developed connection between the demographic parity
constraint and the multi-marginal optimal transport formulation. Informally,
our result shows that the transition between the unconstrained problems and the
fair one is achieved by replacing the conditional expectation of the label by
the solution of the fair regression problem. Finally, leveraging our analysis,
we demonstrate an equivalence between the awareness and the unawareness setups
in the case of two sensitive groups.

Current state-of-the-art automatic speaker verification (ASV) systems are
vulnerable to presentation attacks, and several countermeasures (CMs), which
distinguish bona fide trials from spoofing ones, have been explored to protect
ASV. However, ASV systems and CMs are generally developed and optimized
independently without considering their inter-relationship. In this paper, we
propose a new spoofing-aware ASV back-end module that efficiently computes a
combined ASV score based on speaker similarity and CM score. In addition to the
learnable fusion function of the two scores, the proposed back-end module has
two types of attention components, scaled-dot and feed-forward self-attention,
so that intra-relationship information of multiple enrollment utterances can
also be learned at the same time. Moreover, a new effective trials-sampling
strategy is designed for simulating new spoofing-aware verification scenarios
introduced in the Spoof-Aware Speaker Verification (SASV) challenge 2022.

In recent years, the number of machine learning (ML) technologies gaining
regulatory approval for healthcare has increased significantly allowing them to
be placed on the market. However, the regulatory frameworks applied to them
were originally devised for traditional software, which has largely rule-based
behaviour, compared to the data-driven and learnt behaviour of ML. As the
frameworks are in the process of reformation, there is a need to proactively
assure the safety of ML to prevent patient safety being compromised. The
Assurance of Machine Learning for use in Autonomous Systems (AMLAS) methodology
was developed by the Assuring Autonomy International Programme based on
well-established concepts in system safety. This review has appraised the
methodology by consulting ML manufacturers to understand if it converges or
diverges from their current safety assurance practices, whether there are gaps
and limitations in its structure and if it is fit for purpose when applied to
the healthcare domain. Through this work we offer the view that there is clear
utility for AMLAS as a safety assurance methodology when applied to healthcare
machine learning technologies, although development of healthcare specific
supplementary guidance would benefit those implementing the methodology.

Recognizing human actions from point cloud videos has attracted tremendous
attention from both academia and industry due to its wide applications like
automatic driving, robotics, and so on. However, current methods for point
cloud action recognition usually require a huge amount of data with manual
annotations and a complex backbone network with high computation costs, which
makes it impractical for real-world applications. Therefore, this paper
considers the task of semi-supervised point cloud action recognition. We
propose a Masked Pseudo-Labeling autoEncoder (\textbf{MAPLE}) framework to
learn effective representations with much fewer annotations for point cloud
action recognition. In particular, we design a novel and efficient
\textbf{De}coupled \textbf{s}patial-\textbf{t}emporal Trans\textbf{Former}
(\textbf{DestFormer}) as the backbone of MAPLE. In DestFormer, the spatial and
temporal dimensions of the 4D point cloud videos are decoupled to achieve
efficient self-attention for learning both long-term and short-term features.
Moreover, to learn discriminative features from fewer annotations, we design a
masked pseudo-labeling autoencoder structure to guide the DestFormer to
reconstruct features of masked frames from the available frames. More
importantly, for unlabeled data, we exploit the pseudo-labels from the
classification head as the supervision signal for the reconstruction of
features from the masked frames. Finally, comprehensive experiments demonstrate
that MAPLE achieves superior results on three public benchmarks and outperforms
the state-of-the-art method by 8.08\% accuracy on the MSR-Action3D dataset.

This paper introduces a dual-based algorithm framework for solving the
regularized online resource allocation problems, which have cumulative convex
rewards, hard resource constraints, and a non-separable regularizer. Under a
strategy of adaptively updating the resource constraints, the proposed
framework only requests an approximate solution to the empirical dual problem
up to a certain accuracy, and yet delivers an optimal logarithmic regret under
a locally strongly convex assumption. Surprisingly, a delicate analysis of dual
objective function enables us to eliminate the notorious loglog factor in
regret bound. The flexible framework renders renowned and computationally fast
algorithms immediately applicable, e.g., dual gradient descent and stochastic
gradient descent. A worst-case square-root regret lower bound is established if
the resource constraints are not adaptively updated during dual optimization,
which underscores the critical role of adaptive dual variable update.
Comprehensive numerical experiments and real data application demonstrate the
merits of proposed algorithm framework.

This paper develops a general framework for estimation of high-dimensional
conditional factor models via nuclear norm regularization. We establish large
sample properties of the estimators, and provide an efficient computing
algorithm for finding the estimators as well as a cross validation procedure
for choosing the regularization parameter. The general framework allows us to
estimate a variety of conditional factor models in a unified way and quickly
deliver new asymptotic results. We apply the method to analyze the cross
section of individual US stock returns, and find that imposing homogeneity may
improve the model's out-of-sample predictability.

The rapid analysis of thermal stress and deformation plays a pivotal role in
the thermal control measures and optimization of the structural design of
satellites. For achieving real-time thermal stress and thermal deformation
analysis of satellite motherboards, this paper proposes a novel Multi-Task
Attention UNet (MTA-UNet) neural network which combines the advantages of both
Multi-Task Learning (MTL) and U-Net with attention mechanism. Besides, a
physics-informed strategy is used in the training process, where partial
differential equations (PDEs) are integrated into the loss functions as
residual terms. Finally, an uncertainty-based loss balancing approach is
applied to weight different loss functions of multiple training tasks.
Experimental results show that the proposed MTA-UNet effectively improves the
prediction accuracy of multiple physics tasks compared with Single-Task
Learning (STL) models. In addition, the physics-informed method brings less
error in the prediction of each task, especially on small data sets. The code
can be downloaded at: \url{https://github.com/KomorebiTso/MTA-UNet}.

In this paper, we describe a graph-based algorithm that uses the features
obtained by a self-supervised transformer to detect and segment salient objects
in images and videos. With this approach, the image patches that compose an
image or video are organised into a fully connected graph, where the edge
between each pair of patches is labeled with a similarity score between patches
using features learned by the transformer. Detection and segmentation of
salient objects is then formulated as a graph-cut problem and solved using the
classical Normalized Cut algorithm. Despite the simplicity of this approach, it
achieves state-of-the-art results on several common image and video detection
and segmentation tasks. For unsupervised object discovery, this approach
outperforms the competing approaches by a margin of 6.1%, 5.7%, and 2.6%,
respectively, when tested with the VOC07, VOC12, and COCO20K datasets. For the
unsupervised saliency detection task in images, this method improves the score
for Intersection over Union (IoU) by 4.4%, 5.6% and 5.2%. When tested with the
ECSSD, DUTS, and DUT-OMRON datasets, respectively, compared to current
state-of-the-art techniques. This method also achieves competitive results for
unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS
datasets.

Holistic scene understanding is pivotal for the performance of autonomous
machines. In this paper we propose a new end-to-end model for performing
semantic segmentation and depth completion jointly. The vast majority of recent
approaches have developed semantic segmentation and depth completion as
independent tasks. Our approach relies on RGB and sparse depth as inputs to our
model and produces a dense depth map and the corresponding semantic
segmentation image. It consists of a feature extractor, a depth completion
branch, a semantic segmentation branch and a joint branch which further
processes semantic and depth information altogether. The experiments done on
Virtual KITTI 2 dataset, demonstrate and provide further evidence, that
combining both tasks, semantic segmentation and depth completion, in a
multi-task network can effectively improve the performance of each task. Code
is available at https://github.com/juanb09111/semantic depth.

The prediction of product translational, vibrational, and rotational energy
distributions for arbitrary initial conditions for reactive atom+diatom
collisions is of considerable practical interest in atmospheric re-entry. Due
to the large number of accessible states, determination of the necessary
information from explicit (quasi-classical or quantum) dynamics studies is
impractical. Here, a machine-learned (ML) model based on translational energy
and product vibrational states assigned from a spectroscopic, ro-vibrational
coupled energy expression based on the Dunham expansion is developed and tested
quantitatively. All models considered in this work reproduce final state
distributions determined from quasi-classical trajectory (QCT) simulations with
$R^2 \sim 0.98$. As a further validation, thermal rates determined from the
machine-learned models agree with those from explicit QCT simulations and
demonstrate that the atomistic details are retained by the machine learning
which makes them suitable for applications in more coarse-grained simulations.
More generally, it is found that ML is suitable for designing robust and
accurate models from mixed computational/experimental data which may also be of
interest in other areas of the physical sciences.

Data tensors of orders 3 and greater are routinely being generated. These
data collections are increasingly huge and growing. They are either tensor
fields (e.g., images, videos, geographic data) in which each location of data
contains important information or permutation invariant general tensors (e.g.,
unsupervised latent space learning, graph network analysis, recommendation
systems, etc.). Directly accessing such large data tensor collections for
information has become increasingly prohibitive. We learn approximate full-rank
and compact tensor sketches with decompositive representations providing
compact space, time and spectral embeddings of both tensor fields (P-SCT) and
general tensors (P-SCT-Permute). All subsequent information querying with high
accuracy is performed on the generative sketches. We produce optimal rank-r
Tucker decompositions of arbitrary order data tensors by building tensor
sketches from a sample-efficient sub-sampling of tensor slices. Our sample
efficient policy is learned via an adaptable stochastic Thompson sampling using
Dirichlet distributions with conjugate priors.

Texture-based studies and designs have been in focus recently. Whisker-based
multidimensional surface texture data is missing in the literature. This data
is critical for robotics and machine perception algorithms in the
classification and regression of textural surfaces. In this study, we present a
novel sensor design to acquire multidimensional texture information. The
surface texture's roughness and hardness were measured experimentally using
sweeping and dabbing. Three machine learning models (SVM, RF, and MLP) showed
excellent classification accuracy for the roughness and hardness of surface
textures. We show that the combination of pressure and accelerometer data,
collected from a standard machined specimen using the whisker sensor, improves
classification accuracy. Further, we experimentally validate that the sensor
can classify texture with roughness depths as low as $2.5\mu m$ at an accuracy
of $90\%$ or more and segregate materials based on their roughness and
hardness. We present a novel metric to consider while designing a whisker
sensor to guarantee the quality of texture data acquisition beforehand. The
machine learning model performance was validated against the data collected
from the laser sensor from the same set of surface textures. As part of our
work, we are releasing two-dimensional texture data: roughness and hardness to
the research community.

Position detection of hydraulic cylinder pistons is crucial for numerous
industrial automation applications. A typical traditional method is to excite
electromagnetic waves in the cylinder structure and analytically solve the
piston position based on the scattering parameters measured by a sensor. The
core of this approach is a physical model that mathematically describes the
relationship between the measured scattering parameters and the targeted piston
position. However, this physical model has shortcomings in accuracy and
adaptability, especially in extreme conditions. To overcome this problem, we
propose Artificial Intelligence (AI)-based methods to learn the relationship
directly data-driven. As a result, all Artificial Neural Network (ANN) models
in this paper consistently outperform the physical one by a large margin. Given
the success of AI-based models for our task, we further deliberate the choice
of models based on domain knowledge and provide in-depth analyses combining
model performance with the physical characteristics. Specifically, we use
Convolutional Neural Network (CNN)s to discover local interactions of input
among adjacent frequencies, apply Complex-Valued Neural Network (CVNN) to
exploit the complex-valued nature of electromagnetic scattering parameters, and
introduce a novel technique named Frequency Encoding to add weighted frequency
information to the model input. By combining these three techniques, our best
performing model, a complex-valued CNN with Frequency Encoding, manages to
significantly reduce the test error to hardly 1/12 of the one given by the
traditional physical model.

While variance reduction methods have shown great success in solving large
scale optimization problems, many of them suffer from accumulated errors and,
therefore, should periodically require the full gradient computation. In this
paper, we present a single-loop algorithm named SLEDGE (Single-Loop mEthoD for
Gradient Estimator) for finite-sum nonconvex optimization, which does not
require periodic refresh of the gradient estimator but achieves nearly optimal
gradient complexity. Unlike existing methods, SLEDGE has the advantage of
versatility; (i) second-order optimality, (ii) exponential convergence in the
PL region, and (iii) smaller complexity under less heterogeneity of data.
  We build an efficient federated learning algorithm by exploiting these
favorable properties. We show the first and second-order optimality of the
output and also provide analysis under PL conditions. When the local budget is
sufficiently large and clients are less (Hessian-)~heterogeneous, the algorithm
requires fewer communication rounds then existing methods such as FedAvg,
SCAFFOLD, and Mime. The superiority of our method is verified in numerical

Existing studies for gait recognition are dominated by in-the-lab scenarios.
Since people live in real-world senses, gait recognition in the wild is a more
practical problem that has recently attracted the attention of the community of
multimedia and computer vision. Current methods that obtain state-of-the-art
performance on in-the-lab benchmarks achieve much worse accuracy on the
recently proposed in-the-wild datasets because these methods can hardly model
the varied temporal dynamics of gait sequences in unconstrained scenes.
Therefore, this paper presents a novel multi-hop temporal switch method to
achieve effective temporal modeling of gait patterns in real-world scenes.
Concretely, we design a novel gait recognition network, named Multi-hop
Temporal Switch Network (MTSGait), to learn spatial features and multi-scale
temporal features simultaneously. Different from existing methods that use 3D
convolutions for temporal modeling, our MTSGait models the temporal dynamics of
gait sequences by 2D convolutions. By this means, it achieves high efficiency
with fewer model parameters and reduces the difficulty in optimization compared
with 3D convolution-based models. Based on the specific design of the 2D
convolution kernels, our method can eliminate the misalignment of features
among adjacent frames. In addition, a new sampling strategy, i.e., non-cyclic
continuous sampling, is proposed to make the model learn more robust temporal
features. Finally, the proposed method achieves superior performance on two
public gait in-the-wild datasets, i.e., GREW and Gait3D, compared with

Automatic extraction of funding information from academic articles adds
significant value to industry and research communities, such as tracking
research outcomes by funding organizations, profiling researchers and
universities based on the received funding, and supporting open access
policies. Two major challenges of identifying and linking funding entities are:
(i) sparse graph structure of the Knowledge Base (KB), which makes the commonly
used graph-based entity linking approaches suboptimal for the funding domain,
(ii) missing entities in KB, which (unlike recent zero-shot approaches)
requires marking entity mentions without KB entries as NIL. We propose an
entity linking model that can perform NIL prediction and overcome data scarcity
issues in a time and data-efficient manner. Our model builds on a
transformer-based mention detection and bi-encoder model to perform entity
linking. We show that our model outperforms strong existing baselines.

A key challenge of continual reinforcement learning (CRL) in dynamic
environments is to promptly adapt the RL agent's behavior as the environment
changes over its lifetime, while minimizing the catastrophic forgetting of the
learned information. To address this challenge, in this article, we propose
DaCoRL, i.e., dynamics-adaptive continual RL. DaCoRL learns a
context-conditioned policy using progressive contextualization, which
incrementally clusters a stream of stationary tasks in the dynamic environment
into a series of contexts and opts for an expandable multihead neural network
to approximate the policy. Specifically, we define a set of tasks with similar
dynamics as an environmental context and formalize context inference as a
procedure of online Bayesian infinite Gaussian mixture clustering on
environment features, resorting to online Bayesian inference to infer the
posterior distribution over contexts. Under the assumption of a Chinese
restaurant process prior, this technique can accurately classify the current
task as a previously seen context or instantiate a new context as needed
without relying on any external indicator to signal environmental changes in
advance. Furthermore, we employ an expandable multihead neural network whose
output layer is synchronously expanded with the newly instantiated context, and
a knowledge distillation regularization term for retaining the performance on
learned tasks. As a general framework that can be coupled with various deep RL
algorithms, DaCoRL features consistent superiority over existing methods in
terms of the stability, overall performance and generalization ability, as
verified by extensive experiments on several robot navigation and MuJoCo
locomotion tasks.

Modern approximations to Gaussian processes are suitable for "tall data",
with a cost that scales well in the number of observations, but under-performs
on ``wide data'', scaling poorly in the number of input features. That is, as
the number of input features grows, good predictive performance requires the
number of summarising variables, and their associated cost, to grow rapidly. We
introduce a kernel that allows the number of summarising variables to grow
exponentially with the number of input features, but requires only linear cost
in both number of observations and input features. This scaling is achieved
through our introduction of the B\'ezier buttress, which allows approximate
inference without computing matrix inverses or determinants. We show that our
kernel has close similarities to some of the most used kernels in Gaussian
process regression, and empirically demonstrate the kernel's ability to scale
to both tall and wide datasets.

Due to the nonstationary nature, the distribution of real-world multivariate
time series (MTS) changes over time, which is known as distribution drift. Most
existing MTS forecasting models greatly suffer from distribution drift and
degrade the forecasting performance over time. Existing methods address
distribution drift via adapting to the latest arrived data or self-correcting
per the meta knowledge derived from future data. Despite their great success in
MTS forecasting, these methods hardly capture the intrinsic distribution
changes, especially from a distributional perspective. Accordingly, we propose
a novel framework temporal conditional variational autoencoder (TCVAE) to model
the dynamic distributional dependencies over time between historical
observations and future data in MTSs and infer the dependencies as a temporal
conditional distribution to leverage latent variables. Specifically, a novel
temporal Hawkes attention mechanism represents temporal factors subsequently
fed into feed-forward networks to estimate the prior Gaussian distribution of
latent variables. The representation of temporal factors further dynamically
adjusts the structures of Transformer-based encoder and decoder to distribution
changes by leveraging a gated attention mechanism. Moreover, we introduce
conditional continuous normalization flow to transform the prior Gaussian to a
complex and form-free distribution to facilitate flexible inference of the
temporal conditional distribution. Extensive experiments conducted on six
real-world MTS datasets demonstrate the TCVAE's superior robustness and
effectiveness over the state-of-the-art MTS forecasting baselines. We further
illustrate the TCVAE applicability through multifaceted case studies and
visualization in real-world scenarios.

Computational phenotyping allows for unsupervised discovery of subgroups of
patients as well as corresponding co-occurring medical conditions from
electronic health records (EHR). Typically, EHR data contains demographic
information, diagnoses and laboratory results. Discovering (novel) phenotypes
has the potential to be of prognostic and therapeutic value. Providing medical
practitioners with transparent and interpretable results is an important
requirement and an essential part for advancing precision medicine. Low-rank
data approximation methods such as matrix (e.g., non-negative matrix
factorization) and tensor decompositions (e.g., CANDECOMP/PARAFAC) have
demonstrated that they can provide such transparent and interpretable insights.
Recent developments have adapted low-rank data approximation methods by
incorporating different constraints and regularizations that facilitate
interpretability further. In addition, they offer solutions for common
challenges within EHR data such as high dimensionality, data sparsity and
incompleteness. Especially extracting temporal phenotypes from longitudinal EHR
has received much attention in recent years. In this paper, we provide a
comprehensive review of low-rank approximation-based approaches for
computational phenotyping. The existing literature is categorized into temporal
vs. static phenotyping approaches based on matrix vs. tensor decompositions.
Furthermore, we outline different approaches for the validation of phenotypes,
i.e., the assessment of clinical significance.

Lithium borosilicate (LBS) glass is a prototypical lithium-ion conducting
oxide glasses available for an all-solid state buttery. Nevertheless, the
atomistic modeling of LBS glass using $ab$ $initio$ (AIMD) and classical
molecular dynamics (CMD) simulations have critical limitations due to
computational cost and inaccuracy in reproducing the glass microstructures,
respectively. To overcome these difficulties, a machine-learning potential
(MLP) was examined in this work for modeling LBS glasses using DeepMD. The
glass structures obtained by this MLP possessed fourhold-coordinated boron
($^4$B) confirmed well with the experimental data and abundance of
three-membered rings. The models were energetically more stable compared with
those constructed with a functional force-field even though both the models
included reasonable $^4$B. The results confirmed MLP to be superior to model
the boron-containing glasses and address the inherent shortcomings of the AIMD
and CMD. This study also discusses some limitations of MLP for modeling
glasses.

Supervised machine learning provides state-of-the-art solutions to a wide
range of computer vision problems. However, the need for copious labelled
training data limits the capabilities of these algorithms in scenarios where
such input is scarce or expensive. Self-supervised learning offers a way to
lower the need for manually annotated data by pretraining models for a specific
domain on unlabelled data. In this approach, labelled data are solely required
to fine-tune models for downstream tasks. Medical image segmentation is a field
where labelling data requires expert knowledge and collecting large labelled
datasets is challenging; therefore, self-supervised learning algorithms promise
substantial improvements in this field. Despite this, self-supervised learning
algorithms are used rarely to pretrain medical image segmentation networks. In
this paper, we elaborate and analyse the effectiveness of supervised and
self-supervised pretraining approaches on downstream medical image
segmentation, focusing on convergence and data efficiency. We find that
self-supervised pretraining on natural images and target-domain-specific images
leads to the fastest and most stable downstream convergence. In our experiments
on the ACDC cardiac segmentation dataset, this pretraining approach achieves
4-5 times faster fine-tuning convergence compared to an ImageNet pretrained
model. We also show that this approach requires less than five epochs of
pretraining on domain-specific data to achieve such improvement in the
downstream convergence time. Finally, we find that, in low-data scenarios,
supervised ImageNet pretraining achieves the best accuracy, requiring less than
100 annotated samples to realise close to minimal error.

Black-box optimization has potential in numerous applications such as
hyperparameter optimization in machine learning and optimization in design of
experiments. Ising machines are useful for binary optimization problems because
variables can be represented by a single binary variable of Ising machines.
However, conventional approaches using an Ising machine cannot handle black-box
optimization problems with non-binary values. To overcome this limitation, we
propose an approach for integer-variable black-box optimization problems by
using Ising/annealing machines and factorization machines in cooperation with
three different integer-encoding methods. The performance of our approach is
numerically evaluated with different encoding methods using a simple problem of
calculating the energy of the hydrogen molecule in the most stable state. The
proposed approach can calculate the energy using any of the integer-encoding
methods. However, one-hot encoding is useful for problems with a small size.

In this work, we ask for and answer what makes classical reinforcement
learning cooperative. Cooperating in social dilemma situations is vital for
animals, humans, and machines. While evolutionary theory revealed a range of
mechanisms promoting cooperation, the conditions under which agents learn to
cooperate are contested. Here, we demonstrate which and how individual elements
of the multi-agent learning setting lead to cooperation. Specifically, we
consider the widely used temporal-difference reinforcement learning algorithm
with epsilon-greedy exploration in the classic environment of an iterated
Prisoner's dilemma with one-period memory. Each of the two learning agents
learns a strategy that conditions the following action choices on both agents'
action choices of the last round. We find that next to a high caring for future
rewards, a low exploration rate, and a small learning rate, it is primarily
intrinsic stochastic fluctuations of the reinforcement learning process which
double the final rate of cooperation to up to 80\%. Thus, inherent noise is not
a necessary evil of the iterative learning process. It is a critical asset for
the learning of cooperation. However, we also point out the trade-off between a
high likelihood of cooperative behavior and achieving this in a reasonable
amount of time. Our findings are relevant for purposefully designing
cooperative algorithms and regulating undesired collusive effects.

Integration of multimodal information from various sources has been shown to
boost the performance of machine learning models and thus has received
increased attention in recent years. Often such models use deep
modality-specific networks to obtain unimodal features which are combined to
obtain "late-fusion" representations. However, these designs run the risk of
information loss in the respective unimodal pipelines. On the other hand,
"early-fusion" methodologies, which combine features early, suffer from the
problems associated with feature heterogeneity and high sample complexity. In
this work, we present an iterative representation refinement approach, called
Progressive Fusion, which mitigates the issues with late fusion
representations. Our model-agnostic technique introduces backward connections
that make late stage fused representations available to early layers, improving
the expressiveness of the representations at those stages, while retaining the
advantages of late fusion designs. We test Progressive Fusion on tasks
including affective sentiment detection, multimedia analysis, and time series
fusion with different models, demonstrating its versatility. We show that our
approach consistently improves performance, for instance attaining a 5%
reduction in MSE and 40% improvement in robustness on multimodal time series
prediction.

Deep reinforcement learning has achieved great success in laser-based
collision avoidance works because the laser can sense accurate depth
information without too much redundant data, which can maintain the robustness
of the algorithm when it is migrated from the simulation environment to the
real world. However, high-cost laser devices are not only difficult to deploy
for a large scale of robots but also demonstrate unsatisfactory robustness
towards the complex obstacles, including irregular obstacles, e.g., tables,
chairs, and shelves, as well as complex ground and special materials. In this
paper, we propose a novel monocular camera-based complex obstacle avoidance
framework. Particularly, we innovatively transform the captured RGB images to
pseudo-laser measurements for efficient deep reinforcement learning. Compared
to the traditional laser measurement captured at a certain height that only
contains one-dimensional distance information away from the neighboring
obstacles, our proposed pseudo-laser measurement fuses the depth and semantic
information of the captured RGB image, which makes our method effective for
complex obstacles. We also design a feature extraction guidance module to
weight the input pseudo-laser measurement, and the agent has more reasonable
attention for the current state, which is conducive to improving the accuracy
and efficiency of the obstacle avoidance policy.

Creating a complex work of art like music necessitates profound creativity.
With recent advancements in deep learning and powerful models such as
transformers, there has been huge progress in automatic music generation. In an
accompaniment generation context, creating a coherent drum pattern with
apposite fills and improvisations at proper locations in a song is a
challenging task even for an experienced drummer. Drum beats tend to follow a
repetitive pattern through stanzas with fills or improvisation at section
boundaries. In this work, we tackle the task of drum pattern generation
conditioned on the accompanying music played by four melodic instruments:
Piano, Guitar, Bass, and Strings. We use the transformer sequence to sequence
model to generate a basic drum pattern conditioned on the melodic accompaniment
to find that improvisation is largely absent, attributed possibly to its
expectedly relatively low representation in the training data. We propose a
novelty function to capture the extent of improvisation in a bar relative to
its neighbors. We train a model to predict improvisation locations from the
melodic accompaniment tracks. Finally, we use a novel BERT-inspired in-filling
architecture, to learn the structure of both the drums and melody to in-fill
elements of improvised music.

The systematic study of alternating minimization problems was initiated by
Csisz\'ar and Tusn\'ady. The area gained renewed interest recently due to its
applications in machine learning. We will use this theory in the context of
conditional graph entropy. The unconditioned version of this entropy notion was
introduced by K\"orner. The conditional version is important due to a result of
Orlitsky and Roche showing that the minimal rate for a natural functional
compression problem (with side information at the receiver) is given by
conditional graph entropy.
  In this paper we show that conditional graph entropy can be formulated as an
alternating minimization problem. More specifically, we present a function of
two (vector-valued) variables such that, fixing any of the two variables, the
optimal choice for the other variable can be expressed explicitly. Then
minimizing in the fixed variable gives us the global minimum. Depending on
which variable we start with, we get two different minimization problems with
the same optimum. In one case we get back the original formula for conditional
graph entropy. In the other we obtain a new formula. This new formula shows
that conditional graph entropy is part of a more general framework: the
solution of an optimization problem over a so-called convex corner. In the
unconditioned case (i.e., graph entropy) this was known due to Csisz\'ar,
K\"orner, Lov\'asz, Marton, and Simonyi. In that case the role of the convex
corner was played by the so-called vertex packing polytope. In the conditional
version it is a more intricate convex body but the function to minimize is the
same.
  Furthermore, if we alternate in optimally choosing one variable given the
other, then we get a decreasing sequence of function values converging to the
minimum, which allows one to numerically compute (conditional) graph entropy.

We present a novel generative method for the creation of city-scale road
layouts. While the output of recent methods is limited in both size of the
covered area and diversity, our framework produces large traversable graphs of
high quality consisting of vertices and edges representing complete street
networks covering 400 square kilometers or more. While our framework can
process general 2D embedded graphs, we focus on street networks due to the wide
availability of training data.
  Our generative framework consists of a transformer decoder that is used in a
sliding window manner to predict a field of indices, with each index encoding a
representation of the local neighborhood. The semantics of each index is
determined by a dictionary of context vectors. The index field is then input to
a decoder to compute the street graph.
  Using data from OpenStreetMap, we train our system on whole cities and even
across large countries such as the US, and finally compare it to the state of
the art.

Portfolio management is a fundamental problem in finance. It involves
periodic reallocations of assets to maximize the expected returns within an
appropriate level of risk exposure. Deep reinforcement learning (RL) has been
considered a promising approach to solving this problem owing to its strong
capability in sequential decision making. However, due to the non-stationary
nature of financial markets, applying RL techniques to portfolio optimization
remains a challenging problem. Extracting trading knowledge from various expert
strategies could be helpful for agents to accommodate the changing markets. In
this paper, we propose MetaTrader, a novel two-stage RL-based approach for
portfolio management, which learns to integrate diverse trading policies to
adapt to various market conditions. In the first stage, MetaTrader incorporates
an imitation learning objective into the reinforcement learning framework.
Through imitating different expert demonstrations, MetaTrader acquires a set of
trading policies with great diversity. In the second stage, MetaTrader learns a
meta-policy to recognize the market conditions and decide on the most proper
learned policy to follow. We evaluate the proposed approach on three real-world
index datasets and compare it to state-of-the-art baselines. The empirical
results demonstrate that MetaTrader significantly outperforms those baselines
in balancing profits and risks. Furthermore, thorough ablation studies validate
the effectiveness of the components in the proposed approach.

In this paper, we introduce a new task, spoken video grounding (SVG), which
aims to localize the desired video fragments from spoken language descriptions.
Compared with using text, employing audio requires the model to directly
exploit the useful phonemes and syllables related to the video from raw speech.
Moreover, we randomly add environmental noises to this speech audio, further
increasing the difficulty of this task and better simulating real applications.
To rectify the discriminative phonemes and extract video-related information
from noisy audio, we develop a novel video-guided curriculum learning (VGCL)
during the audio pre-training process, which can make use of the vital visual
perceptions to help understand the spoken language and suppress the external
noise. Considering during inference the model can not obtain ground truth video
segments, we design a curriculum strategy that gradually shifts the input video
from the ground truth to the entire video content during pre-training. Finally,
the model can learn how to extract critical visual information from the entire
video clip to help understand the spoken language. In addition, we collect the
first large-scale spoken video grounding dataset based on ActivityNet, which is
named as ActivityNet Speech dataset. Extensive experiments demonstrate our
proposed video-guided curriculum learning can facilitate the pre-training
process to obtain a mutual audio encoder, significantly promoting the
performance of spoken video grounding tasks. Moreover, we prove that in the
case of noisy sound, our model outperforms the method that grounding video with
ASR transcripts, further demonstrating the effectiveness of our curriculum
strategy.

Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models
for achieving satisfactory results. ID is the occurrence of a situation where
the quantity of the samples belonging to one class outnumbers that of the other
by a wide margin, making such models learning process biased towards the
majority class. In recent years, to address this issue, several solutions have
been put forward, which opt for either synthetically generating new data for
the minority class or reducing the number of majority classes for balancing the
data. Hence, in this paper, we investigate the effectiveness of methods based
on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), mixed
with a variety of well-known imbalanced data solutions meaning oversampling and
undersampling. To evaluate our methods, we have used KEEL, breast cancer, and
Z-Alizadeh Sani datasets. In order to achieve reliable results, we conducted
our experiments 100 times with randomly shuffled data distributions. The
classification results demonstrate that the mixed Synthetic Minority
Oversampling Technique (SMOTE)-Normalization-CNN outperforms different
methodologies achieving 99.08% accuracy on the 24 imbalanced datasets.
Therefore, the proposed mixed model can be applied to imbalanced binary
classification problems on other real datasets.

Most existing methods that cope with noisy labels usually assume that the
class distributions are well balanced, which has insufficient capacity to deal
with the practical scenarios where training samples have imbalanced
distributions. To this end, this paper makes an early effort to tackle the
image classification task with both long-tailed distribution and label noise.
Existing noise-robust learning methods cannot work in this scenario as it is
challenging to differentiate noisy samples from clean samples of tail classes.
To deal with this problem, we propose a new learning paradigm based on matching
between inferences on weak and strong data augmentations to screen out noisy
samples and introduce a leave-noise-out regularization to eliminate the effect
of the recognized noisy samples. Furthermore, we incorporate a novel prediction
penalty based on online prior distribution to avoid bias towards head classes.
This mechanism has superiority in capturing the class fitting degree in
realtime compared to the existing long-tail classification methods. Exhaustive
experiments demonstrate that the proposed method outperforms state-of-the-art
algorithms that address the distribution imbalance problem in long-tailed
classification under noisy labels.

Privacy issues on social networks have been extensively discussed in recent
years. The user identity linkage (UIL) task, aiming at finding corresponding
users across different social networks, would be a threat to privacy if
unethically applied. The sensitive user information might be detected through
connected identities. A promising and novel solution to this issue is to design
an adversarial strategy to degrade the matching performance of UIL models.
However, most existing adversarial attacks on graphs are designed for models
working in a single network, while UIL is a cross-network learning task.
Meanwhile, privacy protection against UIL works unilaterally in real-world
scenarios, i.e., the service provider can only add perturbations to its own
network to protect its users from being linked. To tackle these challenges,
this paper proposes a novel adversarial attack strategy that poisons one target
network to prevent its nodes from being linked to other networks by UIL
algorithms. Specifically, we reformalize the UIL problem in the perspective of
kernelized topology consistency and convert the attack objective to maximizing
the structural changes within the target network before and after attacks. A
novel graph kernel is then defined with Earth mover's distance (EMD) on the
edge-embedding space. In terms of efficiency, a fast attack strategy is
proposed by greedy searching and replacing EMD with its lower bound. Results on
three real-world datasets indicate that the proposed attacks can best fool a
wide range of UIL models and reach a balance between attack effectiveness and
imperceptibility.

Cybersecurity has become a primary global concern with the rapid increase in
security attacks and data breaches. Artificial intelligence is promising to
help humans analyzing and identifying attacks. However, labeling millions of
packets for supervised learning is never easy. This study aims to leverage
transfer learning technique that stores the knowledge gained from well-defined
attack lifecycle documents and applies it to hundred thousands of unlabeled
attacks (packets) for identifying their attack tactics. We anticipate the
knowledge of an attack is well-described in the documents, and the cutting edge
transformer-based language model can embed the knowledge into a
high-dimensional latent space. Then, reusing the information from the language
model for the learning of attack tactic carried by packets to improve the
learning efficiency. We propose a system, PELAT, that fine-tunes BERT model
with 1,417 articles from MITRE ATT&CK lifecycle framework to enhance its attack
knowledge (including syntax used and semantic meanings embedded). PELAT then
transfers its knowledge to perform semi-supervised learning for unlabeled
packets to generate their tactic labels. Further, when a new attack packet
arrives, the packet payload will be processed by the PELAT language model with
a downstream classifier to predict its tactics. In this way, we can effectively
reduce the burden of manually labeling big datasets. In a one-week honeypot
attack dataset (227 thousand packets per day), PELAT performs 99% of precision,
recall, and F1 on testing dataset. PELAT can infer over 99% of tactics on two
other testing datasets (while nearly 90% of tactics are identified).

Clinical text processing has gained more and more attention in recent years.
The access to sensitive patient data, on the other hand, is still a big
challenge, as text cannot be shared without legal hurdles and without removing
personal information. There are many techniques to modify or remove patient
related information, each with different strengths. This paper investigates the
influence of different anonymization techniques on the performance of ML models
using multiple datasets corresponding to five different NLP tasks. Several
learnings and recommendations are presented. This work confirms that
particularly stronger anonymization techniques lead to a significant drop of
performance. In addition to that, most of the presented techniques are not
secure against a re-identification attack based on similarity search.

Citrinet is an end-to-end convolutional Connectionist Temporal Classification
(CTC) based automatic speech recognition (ASR) model. To capture local and
global contextual information, 1D time-channel separable convolutions combined
with sub-word encoding and squeeze-and-excitation (SE) are used in Citrinet,
making the whole architecture to be as deep as including 23 blocks with 235
convolution layers and 46 linear layers. This pure convolutional and deep
architecture makes Critrinet relatively slow at convergence. In this paper, we
propose to introduce multi-head attentions together with feed-forward networks
in the convolution module in Citrinet blocks while keeping the SE module and
residual module unchanged. For speeding up, we remove 8 convolution layers in
each attention-enhanced Citrinet block and reduce 23 blocks to 13. Experiments
on the Japanese CSJ-500h and Magic-1600h dataset show that the
attention-enhanced Citrinet with less layers and blocks and converges faster
with lower character error rates than (1) Citrinet with 80\% training time and
(2) Conformer with 40\% training time and 29.8\% model size.

Conformer has achieved impressive results in Automatic Speech Recognition
(ASR) by leveraging transformer's capturing of content-based global
interactions and convolutional neural network's exploiting of local features.
In Conformer, two macaron-like feed-forward layers with half-step residual
connections sandwich the multi-head self-attention and convolution modules
followed by a post layer normalization. We improve Conformer's long-sequence
representation ability in two directions, \emph{sparser} and \emph{deeper}. We
adapt a sparse self-attention mechanism with $\mathcal{O}(L\text{log}L)$ in
time complexity and memory usage. A deep normalization strategy is utilized
when performing residual connections to ensure our training of hundred-level
Conformer blocks. On the Japanese CSJ-500h dataset, this deep sparse Conformer
achieves respectively CERs of 5.52\%, 4.03\% and 4.50\% on the three evaluation
sets and 4.16\%, 2.84\% and 3.20\% when ensembling five deep sparse Conformer
variants from 12 to 16, 17, 50, and finally 100 encoder layers.

The visual quality of point clouds has been greatly emphasized since the
ever-increasing 3D vision applications are expected to provide cost-effective
and high-quality experiences for users. Looking back on the development of
point cloud quality assessment (PCQA) methods, the visual quality is usually
evaluated by utilizing single-modal information, i.e., either extracted from
the 2D projections or 3D point cloud. The 2D projections contain rich texture
and semantic information but are highly dependent on viewpoints, while the 3D
point clouds are more sensitive to geometry distortions and invariant to
viewpoints. Therefore, to leverage the advantages of both point cloud and
projected image modalities, we propose a novel no-reference point cloud quality
assessment (NR-PCQA) metric in a multi-modal fashion. In specific, we split the
point clouds into sub-models to represent local geometry distortions such as
point shift and down-sampling. Then we render the point clouds into 2D image
projections for texture feature extraction. To achieve the goals, the
sub-models and projected images are encoded with point-based and image-based
neural networks. Finally, symmetric cross-modal attention is employed to fuse
multi-modal quality-aware information. Experimental results show that our
approach outperforms all compared state-of-the-art methods and is far ahead of
previous NR-PCQA methods, which highlights the effectiveness of the proposed
method.

Continual relation extraction (CRE) requires the model to continually learn
new relations from class-incremental data streams. In this paper, we propose a
Frustratingly easy but Effective Approach (FEA) method with two learning stages
for CRE: 1) Fast Adaption (FA) warms up the model with only new data. 2)
Balanced Tuning (BT) finetunes the model on the balanced memory data. Despite
its simplicity, FEA achieves comparable (on TACRED or superior (on FewRel)
performance compared with the state-of-the-art baselines. With careful
examinations, we find that the data imbalance between new and old relations
leads to a skewed decision boundary in the head classifiers over the pretrained
encoders, thus hurting the overall performance. In FEA, the FA stage unleashes
the potential of memory data for the subsequent finetuning, while the BT stage
helps establish a more balanced decision boundary. With a unified view, we find
that two strong CRE baselines can be subsumed into the proposed training
pipeline. The success of FEA also provides actionable insights and suggestions
for future model designing in CRE.

Statistical decision problems are the foundation of statistical machine
learning. The simplest problems are binary and multiclass classification and
class probability estimation. Central to their definition is the choice of loss
function, which is the means by which the quality of a solution is evaluated.
In this paper we systematically develop the theory of loss functions for such
problems from a novel perspective whose basic ingredients are convex sets with
a particular structure. The loss function is defined as the subgradient of the
support function of the convex set. It is consequently automatically proper
(calibrated for probability estimation). This perspective provides three novel
opportunities. It enables the development of a fundamental relationship between
losses and (anti)-norms that appears to have not been noticed before. Second,
it enables the development of a calculus of losses induced by the calculus of
convex sets which allows the interpolation between different losses, and thus
is a potential useful design tool for tailoring losses to particular problems.
In doing this we build upon, and considerably extend, existing results on
M-sums of convex sets. Third, the perspective leads to a natural theory of
`polar' (or `inverse') loss functions, which are derived from the polar dual of
the convex set defining the loss, and which form a natural universal
substitution function for Vovk's aggregating algorithm.

Capsule networks (CapsNets) aim to parse images into a hierarchical component
structure that consists of objects, parts, and their relations. Despite their
potential, they are computationally expensive and pose a major drawback, which
limits utilizing these networks efficiently on more complex datasets. The
current CapsNet models only compare their performance with the capsule
baselines and do not perform at the same level as deep CNN-based models on
complicated tasks. This paper proposes an efficient way for learning capsules
that detect atomic parts of an input image, through a group of SubCapsules,
upon which an input vector is projected. Subsequently, we present the
Wasserstein Embedding Module that first measures the dissimilarity between the
input and components modeled by the SubCapsules, and then finds their degree of
alignment based on the learned optimal transport. This strategy leverages new
insights on defining alignment between the input and SubCapsules based on the
similarity between their respective component distributions. Our proposed
model, (i) is lightweight and allows to apply capsules for more complex vision
tasks; (ii) performs better than or at par with CNN-based models on these
challenging tasks. Our experimental results indicate that Wasserstein Embedding
Capsules (WECapsules) perform more robustly on affine transformations,
effectively scale up to larger datasets, and outperform the CNN and CapsNet
models in several vision tasks.

Federated learning (FL) enables participating parties to collaboratively
build a global model with boosted utility without disclosing private data
information. Appropriate protection mechanisms have to be adopted to fulfill
the opposing requirements in preserving \textit{privacy} and maintaining high
model \textit{utility}. In addition, it is a mandate for a federated learning
system to achieve high \textit{efficiency} in order to enable large-scale model
training and deployment. We propose a unified federated learning framework that
reconciles horizontal and vertical federated learning. Based on this framework,
we formulate and quantify the trade-offs between privacy leakage, utility loss,
and efficiency reduction, which leads us to the No-Free-Lunch (NFL) theorem for
the federated learning system. NFL indicates that it is unrealistic to expect
an FL algorithm to simultaneously provide excellent privacy, utility, and
efficiency in certain scenarios. We then analyze the lower bounds for the
privacy leakage, utility loss and efficiency reduction for several
widely-adopted protection mechanisms including \textit{Randomization},
\textit{Homomorphic Encryption}, \textit{Secret Sharing} and
\textit{Compression}. Our analysis could serve as a guide for selecting
protection parameters to meet particular requirements.

High-performance traffic flow prediction model designing, a core technology
of Intelligent Transportation System, is a long-standing but still challenging
task for industrial and academic communities. The lack of integration between
physical principles and data-driven models is an important reason for limiting
the development of this field. In the literature, physics-based methods can
usually provide a clear interpretation of the dynamic process of traffic flow
systems but are with limited accuracy, while data-driven methods, especially
deep learning with black-box structures, can achieve improved performance but
can not be fully trusted due to lack of a reasonable physical basis. To bridge
the gap between purely data-driven and physics-driven approaches, we propose a
physics-guided deep learning model named Spatio-Temporal Differential Equation
Network (STDEN), which casts the physical mechanism of traffic flow dynamics
into a deep neural network framework. Specifically, we assume the traffic flow
on road networks is driven by a latent potential energy field (like water flows
are driven by the gravity field), and model the spatio-temporal dynamic process
of the potential energy field as a differential equation network. STDEN absorbs
both the performance advantage of data-driven models and the interpretability
of physics-based models, so is named a physics-guided prediction model.
Experiments on three real-world traffic datasets in Beijing show that our model
outperforms state-of-the-art baselines by a significant margin. A case study
further verifies that STDEN can capture the mechanism of urban traffic and
generate accurate predictions with physical meaning. The proposed framework of
differential equation network modeling may also cast light on other similar

Multi-instance point cloud registration is the problem of estimating multiple
poses of source point cloud instances within a target point cloud. Solving this
problem is challenging since inlier correspondences of one instance constitute
outliers of all the other instances. Existing methods often rely on
time-consuming hypothesis sampling or features leveraging spatial consistency,
resulting in limited performance. In this paper, we propose PointCLM, a
contrastive learning-based framework for mutli-instance point cloud
registration. We first utilize contrastive learning to learn well-distributed
deep representations for the input putative correspondences. Then based on
these representations, we propose a outlier pruning strategy and a clustering
strategy to efficiently remove outliers and assign the remaining
correspondences to correct instances. Our method outperforms the
state-of-the-art methods on both synthetic and real datasets by a large margin.

Statistical power is a measure of the goodness/strength of a hypothesis test.
Formally, it is the probability of detecting an effect, if there is a true
effect present to detect. Hence, optimizing statistical power as a function of
some parameters of a hypothesis test is desirable. However, for most hypothesis
tests, the explicit functional form of statistical power as a function of those
parameters is unknown but calculating statistical power for a given set of
values of those parameters is possible using simulated experiments. These
simulated experiments are usually computationally expensive. Hence, developing
the entire statistical power manifold using simulations can be very
time-consuming. Motivated by this, we propose a novel genetic algorithm-based
framework for learning statistical power manifold. For a multiple linear
regression $F$-test, we show that the proposed algorithm/framework learns the
statistical power manifold much faster as compared to a brute-force approach as
the number of queries to the power oracle is significantly reduced. We also
show that the quality of learning the manifold improves as the number of
iterations increases for the genetic algorithm.

In cities around the world, locating public parking lots with vacant parking
spots is a major problem, costing commuters time and adding to traffic
congestion. This work illustrates how a dataset of Geo-tagged images from a
mobile phone camera, can be used in navigating to the most convenient public
parking lot in Johannesburg with an available parking space, detected by a
neural network powered-public camera. The images are used to fine-tune a
Detectron2 model pre-trained on the ImageNet dataset to demonstrate detection
and segmentation of vacant parking spots, we then add the parking lot's
corresponding longitude and latitude coordinates to recommend the most
convenient parking lot to the driver based on the Haversine distance and number
of available parking spots. Using the VGG Image Annotation (VIA) we use images
from an expanding dataset of images, and annotate these with polygon outlines
of the four different types of objects of interest: cars, open parking spots,
people, and car number plates. We use the segmentation model to ensure number
plates can be occluded in production for car registration anonymity purposes.
We get an 89% and 82% intersection over union cover score on cars and parking
spaces respectively. This work has the potential to help reduce the amount of
time commuters spend searching for free public parking, hence easing traffic
congestion in and around shopping complexes and other public places, and
maximize people's utility with respect to driving on public roads.

Story ending generation aims at generating reasonable endings for a given
story context. Most existing studies in this area focus on generating coherent
or diversified story endings, while they ignore that different characters may
lead to different endings for a given story. In this paper, we propose a
Character-oriented Story Ending Generator (CoSEG) to customize an ending for
each character in a story. Specifically, we first propose a character modeling
module to learn the personalities of characters from their descriptive
experiences extracted from the story context. Then, inspired by the ion
exchange mechanism in chemical reactions, we design a novel vector
breaking/forming module to learn the intrinsic interactions between each
character and the corresponding context through an analogical information
exchange procedure. Finally, we leverage the attention mechanism to learn
effective character-specific interactions and feed each interaction into a
decoder to generate character-orient endings. Extensive experimental results
and case studies demonstrate that CoSEG achieves significant improvements in
the quality of generated endings compared with state-of-the-art methods, and it
effectively customizes the endings for different characters.

Machine learning models have been deployed in mobile networks to deal with
the data from different layers to enable automated network management and
intelligence on devices. To overcome high communication cost and severe privacy
concerns of centralized machine learning, Federated Learning (FL) has been
proposed to achieve distributed machine learning among networked devices. While
the computation and communication limitation has been widely studied in FL, the
impact of on-device storage on the performance of FL is still not explored.
Without an efficient and effective data selection policy to filter the abundant
streaming data on devices, classical FL can suffer from much longer model
training time (more than $4\times$) and significant inference accuracy
reduction (more than $7\%$), observed in our experiments. In this work, we take
the first step to consider the online data selection for FL with limited
on-device storage. We first define a new data valuation metric for data
selection in FL: the projection of local gradient over an on-device data sample
onto the global gradient over the data from all devices. We further design
\textbf{ODE}, a framework of \textbf{O}nline \textbf{D}ata s\textbf{E}lection
for FL, to coordinate networked devices to store valuable data samples
collaboratively, with theoretical guarantees for speeding up model convergence
and enhancing final model accuracy, simultaneously. Experimental results on one
industrial task (mobile network traffic classification) and three public tasks
(synthetic task, image classification, human activity recognition) show the
remarkable advantages of ODE over the state-of-the-art approaches.
Particularly, on the industrial dataset, ODE achieves as high as $2.5\times$
speedup of training time and $6\%$ increase in final inference accuracy, and is
robust to various factors in the practical environment.

As a significant ingredient regarding health status, data-driven
state-of-health (SOH) estimation has become dominant for lithium-ion batteries
(LiBs). To handle data discrepancy across batteries, current SOH estimation
models engage in transfer learning (TL), which reserves apriori knowledge
gained through reusing partial structures of the offline trained model.
However, multiple degradation patterns of a complete life cycle of a battery
make it challenging to pursue TL. The concept of the stage is introduced to
describe the collection of continuous cycles that present a similar degradation
pattern. A transferable multi-stage SOH estimation model is proposed to perform
TL across batteries in the same stage, consisting of four steps. First, with
identified stage information, raw cycling data from the source battery are
reconstructed into the phase space with high dimensions, exploring hidden
dynamics with limited sensors. Next, domain invariant representation across
cycles in each stage is proposed through cycling discrepancy subspace with
reconstructed data. Third, considering the unbalanced discharge cycles among
different stages, a switching estimation strategy composed of a lightweight
model with the long short-term memory network and a powerful model with the
proposed temporal capsule network is proposed to boost estimation accuracy.
Lastly, an updating scheme compensates for estimation errors when the cycling
consistency of target batteries drifts. The proposed method outperforms its
competitive algorithms in various transfer tasks for a run-to-failure benchmark
with three batteries.

Traditional federated optimization methods perform poorly with heterogeneous
data (ie, accuracy reduction), especially for highly skewed data. In this
paper, we investigate the label distribution skew in FL, where the distribution
of labels varies across clients. First, we investigate the label distribution
skew from a statistical view. We demonstrate both theoretically and empirically
that previous methods based on softmax cross-entropy are not suitable, which
can result in local models heavily overfitting to minority classes and missing
classes. Additionally, we theoretically introduce a deviation bound to measure
the deviation of the gradient after local update. At last, we propose FedLC
(\textbf {Fed} erated learning via\textbf {L} ogits\textbf {C} alibration),
which calibrates the logits before softmax cross-entropy according to the
probability of occurrence of each class. FedLC applies a fine-grained
calibrated cross-entropy loss to local update by adding a pairwise label
margin. Extensive experiments on federated datasets and real-world datasets
demonstrate that FedLC leads to a more accurate global model and much improved
performance. Furthermore, integrating other FL methods into our approach can
further enhance the performance of the global model.

Long-latency load requests continue to limit the performance of
high-performance processors. To increase the latency tolerance of a processor,
architects have primarily relied on two key techniques: sophisticated data
prefetchers and large on-chip caches. In this work, we show that: 1) even a
sophisticated state-of-the-art prefetcher can only predict half of the off-chip
load requests on average across a wide range of workloads, and 2) due to the
increasing size and complexity of on-chip caches, a large fraction of the
latency of an off-chip load request is spent accessing the on-chip cache
hierarchy. The goal of this work is to accelerate off-chip load requests by
removing the on-chip cache access latency from their critical path. To this
end, we propose a new technique called Hermes, whose key idea is to: 1)
accurately predict which load requests might go off-chip, and 2) speculatively
fetch the data required by the predicted off-chip loads directly from the main
memory, while also concurrently accessing the cache hierarchy for such loads.
To enable Hermes, we develop a new lightweight, perceptron-based off-chip load
prediction technique that learns to identify off-chip load requests using
multiple program features (e.g., sequence of program counters). For every load
request, the predictor observes a set of program features to predict whether or
not the load would go off-chip. If the load is predicted to go off-chip, Hermes
issues a speculative request directly to the memory controller once the load's
physical address is generated. If the prediction is correct, the load
eventually misses the cache hierarchy and waits for the ongoing speculative
request to finish, thus hiding the on-chip cache hierarchy access latency from
the critical path of the off-chip load. Our evaluation shows that Hermes
significantly improves performance of a state-of-the-art baseline. We
open-source Hermes.

2D animation is a common factor in game development, used for characters,
effects and background art. It involves work that takes both skill and time,
but parts of which are repetitive and tedious. Automated animation approaches
exist, but are designed without animators in mind. The focus is heavily on
real-life video, which follows strict laws of how objects move, and does not
account for the stylistic movement often present in 2D animation. We propose a
problem formulation that more closely adheres to the standard workflow of
animation. We also demonstrate a model, SketchBetween, which learns to map
between keyframes and sketched in-betweens to rendered sprite animations. We
demonstrate that our problem formulation provides the required information for
the task and that our model outperforms an existing method.

Medical image classification has been widely adopted in medical image
analysis. However, due to the difficulty of collecting and labeling data in the
medical area, medical image datasets are usually highly-imbalanced. To address
this problem, previous works utilized class samples as prior for re-weighting
or re-sampling but the feature representation is usually still not
discriminative enough. In this paper, we adopt the contrastive learning to
tackle the long-tailed medical imbalance problem. Specifically, we first
propose the category prototype and adversarial proto-instance to generate
representative contrastive pairs. Then, the prototype recalibration strategy is
proposed to address the highly imbalanced data distribution. Finally, a unified
proto-loss is designed to train our framework. The overall framework, namely as
Prototype-aware Contrastive learning (ProCo), is unified as a single-stage
pipeline in an end-to-end manner to alleviate the imbalanced problem in medical
image classification, which is also a distinct progress than existing works as
they follow the traditional two-stage pipeline. Extensive experiments on two
highly-imbalanced medical image classification datasets demonstrate that our
method outperforms the existing state-of-the-art methods by a large margin.

Structure is one of the most essential aspects of music, and music structure
is commonly indicated through repetition. However, the nature of repetition and
structure in music is still not well understood, especially in the context of
music generation, and much remains to be explored with Music Information
Retrieval (MIR) techniques. Analyses of two popular music datasets (Chinese and
American) illustrate important music construction principles: (1) structure
exists at multiple hierarchical levels, (2) songs use repetition and limited
vocabulary so that individual songs do not follow general statistics of song
collections, (3) structure interacts with rhythm, melody, harmony, and
predictability, and (4) over the course of a song, repetition is not random,
but follows a general trend as revealed by cross-entropy. These and other
findings offer challenges as well as opportunities for deep-learning music
generation and suggest new formal music criteria and evaluation methods. Music
from recent music generation systems is analyzed and compared to human-composed
music in our datasets, often revealing striking differences from a structural
perspective.

The distribution shifts between training and test data typically undermine
the performance of deep learning models. In recent years, lots of work pays
attention to domain generalization (DG) where distribution shift exists and
target data are unseen. Despite the progress in algorithm design, two
foundational factors have long been ignored: 1) the optimization for
regularization-based objectives (e.g., distribution alignment), and 2) the
model selection for DG since no knowledge about the target domain can be
utilized. In this paper, we propose Mixup guided optimization and selection
techniques for domain generalization. For optimization, we utilize an adapted
Mixup to generate an out-of-distribution dataset that can guide the preference
direction and optimize with Pareto optimization. For model selection, we
generate a validation dataset with a closer distance to the target
distribution, and thereby it can better represent the target data. We also
present some theoretical insights behind our proposals. Comprehensive
experiments on one visual classification benchmark and three time-series
benchmarks demonstrate that our model optimization and selection techniques can
largely improve the performance of existing domain generalization algorithms
and even achieve new state-of-the-art results.

This paper presents Vision-Language Universal Search (VL-UnivSearch), which
builds a unified model for multi-modality retrieval. VL-UnivSearch encodes
query and multi-modality sources in a universal embedding space for searching
related candidates and routing modalities. To learn a tailored embedding space
for multi-modality retrieval, VL-UnivSearch proposes two techniques: 1)
Universal embedding optimization, which contrastively optimizes the embedding
space using the modality-balanced hard negatives; 2) Image verbalization
method, which bridges the modality gap between images and texts in the raw data
space. VL-UnivSearch achieves the state-of-the-art on the multi-modality
open-domain question answering benchmark, WebQA, and outperforms all retrieval
models in each single modality task. It demonstrates that universal
multi-modality search is feasible to replace the divide-and-conquer pipeline
with a united model and also benefit per modality tasks. All source codes of
this work will be released via Github.

The most pressing problems in science are neither empirical nor theoretical,
but infrastructural. Scientific practice is defined by coproductive, mutually
reinforcing infrastructural deficits and incentive systems that everywhere
constrain and contort our art of curiosity in service of profit and prestige.
Our infrastructural problems are not unique to science, but reflective of the
broader logic of digital enclosure where platformatized control of information
production and extraction fuels some of the largest corporations in the world.
I have taken lessons learned from decades of intertwined digital cultures
within and beyond academia like wikis, pirates, and librarians in order to
draft a path towards more liberatory infrastructures for both science and
society. Based on a system of peer-to-peer linked data, I sketch interoperable
systems for shared data, tools, and knowledge that map onto three domains of
platform capture: storage, computation and communication. The challenge of
infrastructure is not solely technical, but also social and cultural, and so I
attempt to ground a practical development blueprint in an ethics for organizing
and maintaining it. I intend this draft as a rallying call for organization, to
be revised with the input of collaborators and through the challenges posed by
its implementation. I argue that a more liberatory future for science is
neither utopian nor impractical -- the truly impractical choice is to continue
to organize science as prestige fiefdoms resting on a pyramid scheme of
underpaid labor, playing out the clock as every part of our work is swallowed
whole by circling information conglomerates. It was arguably scientists looking
for a better way to communicate that created something as radical as the
internet in the first place, and I believe we can do it again.

Particle filtering is a standard Monte-Carlo approach for a wide range of
sequential inference tasks. The key component of a particle filter is a set of
particles with importance weights that serve as a proxy of the true posterior
distribution of some stochastic process. In this work, we propose continuous
latent particle filters, an approach that extends particle filtering to the
continuous-time domain. We demonstrate how continuous latent particle filters
can be used as a generic plug-in replacement for inference techniques relying
on a learned variational posterior. Our experiments with different model
families based on latent neural stochastic differential equations demonstrate
superior performance of continuous-time particle filtering in inference tasks
like likelihood estimation and sequential prediction for a variety of
stochastic processes.

Cybersecurity breaches are the common anomalies for distributed
cyber-physical systems (CPS). However, the cyber security breach classification
is still a difficult problem, even using cutting-edge artificial intelligence
(AI) approaches. In this paper, we study the multi-class classification problem
in cyber security for attack detection. A challenging multi-node data-censoring
case is considered. In such a case, data within each data center/node cannot be
shared while the local data is incomplete. Particularly, local nodes contain
only a part of the multiple classes. In order to train a global multi-class
classifier without sharing the raw data across all nodes, the main result of
our study is designing a multi-node multi-class classification ensemble
approach. By gathering the estimated parameters of the binary classifiers and
data densities from each local node, the missing information for each local
node is completed to build the global multi-class classifier. Numerical
experiments are given to validate the effectiveness of the proposed approach
under the multi-node data-censoring case. Under such a case, we even show the
out-performance of the proposed approach over the full-data approach.

An oracle is a mechanism to decide whether the outputs of the program for the
executed test cases are correct. For machine learning programs, such oracle is
not available or too difficult to apply. Metamorphic testing is a testing
approach that uses metamorphic relations, which are necessary properties of the
software under test to help verify the correctness of a program. Prioritization
of metamorphic relations helps to reduce the cost of metamorphic testing [1].
However, prioritizing metamorphic relations based on code coverage is often not
effective for prioritizing MRs for machine learning programs, since the
decision logic of a machine learning model is learned from training data, and
100% code coverage can be easily achieved with a single test input. To this
end, in this work, we propose a cost-effective approach based on diversity in
the source and follow-up data set to prioritize metamorphic relations for
machine learning programs. We show that the proposed data diversity-based
prioritization approach increase the fault detection effectiveness by up to 40%
when compared to the code coverage-based approach and reduce the time taken to
detect a fault by 29% when compared to random execution of MRs. Overall, our
approach leads to saving time and cost during testing.

We present a qualitative analysis of the (potentially erroneous) outputs of
contextualized embedding-based methods for detecting diachronic semantic
change. First, we introduce an ensemble method outperforming previously
described contextualized approaches. This method is used as a basis for an
in-depth analysis of the degrees of semantic change predicted for English words
across 5 decades. Our findings show that contextualized methods can often
predict high change scores for words which are not undergoing any real
diachronic semantic shift in the lexicographic sense of the term (or at least
the status of these shifts is questionable). Such challenging cases are
discussed in detail with examples, and their linguistic categorization is
proposed. Our conclusion is that pre-trained contextualized language models are
prone to confound changes in lexicographic senses and changes in contextual
variance, which naturally stem from their distributional nature, but is
different from the types of issues observed in methods based on static
embeddings. Additionally, they often merge together syntactic and semantic
aspects of lexical entities. We propose a range of possible future solutions to
these issues.

The Infinitesimal Jackknife is a general method for estimating variances of
parametric models, and more recently also for some ensemble methods. In this
paper we extend the Infinitesimal Jackknife to estimate the covariance between
any two models. This can be used to quantify uncertainty for combinations of
models, or to construct test statistics for comparing different models or
ensembles of models fitted using the same training dataset. Specific examples
in this paper use boosted combinations of models like random forests and
M-estimators. We also investigate its application on neural networks and
ensembles of XGBoost models. We illustrate the efficacy of variance estimates
through extensive simulations and its application to the Beijing Housing data,
and demonstrate the theoretical consistency of the Infinitesimal Jackknife
covariance estimate.

Variational quantum algorithms (VQAs) may have the capacity to provide a
quantum advantage in the Noisy Intermediate-scale Quantum (NISQ) era. Here, we
present a quantum machine learning (QML) framework, inspired by VQAs, to tackle
the problem of finding time-independent Hamiltonians that generate desired
unitary evolutions, i.e. multi-qubit quantum gates. The Hamiltonians are
designed by tuning local fields and two-body interaction terms only. We find
that our approach achieves high fidelity quantum gates, such as the Toffoli
gate, with a significantly lower computational complexity than is possible
classically. This method can also be extended to realize higher-order
multi-controlled quantum gates, which could be directly applied to quantum
error correction (QEC) schemes.

This paper investigates the problem of bounding possible output from a
counterfactual query given a set of observational data. While various works of
literature have described methodologies to generate efficient algorithms that
provide an optimal bound for the counterfactual query, all of them assume a
finite-horizon causal diagram. This paper aims to extend the previous work by
modifying Q-learning algorithm to provide informative bounds of a causal query
given an infinite-horizon causal diagram. Through simulations, our algorithms
are proven to perform better compared to existing algorithm.

Recent years have seen considerable advances in audio synthesis with deep
generative models. However, the state-of-the-art is very difficult to quantify;
different studies often use different evaluation methodologies and different
metrics when reporting results, making a direct comparison to other systems
difficult if not impossible. Furthermore, the perceptual relevance and meaning
of the reported metrics in most cases unknown, prohibiting any conclusive
insights with respect to practical usability and audio quality. This paper
presents a study that investigates state-of-the-art approaches side-by-side
with (i) a set of previously proposed objective metrics for audio
reconstruction, and with (ii) a listening study. The results indicate that
currently used objective metrics are insufficient to describe the perceptual
quality of current systems.

Learning to detect objects, such as humans, in imagery captured by an
unmanned aerial vehicle (UAV) usually suffers from tremendous variations caused
by the UAV's position towards the objects. In addition, existing UAV-based
benchmark datasets do not provide adequate dataset metadata, which is essential
for precise model diagnosis and learning features invariant to those
variations. In this paper, we introduce Archangel, the first UAV-based object
detection dataset composed of real and synthetic subsets captured with similar
imagining conditions and UAV position and object pose metadata. A series of
experiments are carefully designed with a state-of-the-art object detector to
demonstrate the benefits of leveraging the metadata during model evaluation.
Moreover, several crucial insights involving both real and synthetic data
during model fine-tuning are presented. In the end, we discuss the advantages,
limitations, and future directions regarding Archangel to highlight its
distinct value for the broader machine learning community.

Kernel-based tests provide a simple yet effective framework that use the
theory of reproducing kernel Hilbert spaces to design non-parametric testing
procedures. In this paper we propose new theoretical tools that can be used to
study the asymptotic behaviour of kernel-based tests in several data scenarios,
and in many different testing problems. Unlike current approaches, our methods
avoid using lengthy $U$ and $V$ statistics expansions and limit theorems, that
commonly appear in the literature, and works directly with random functionals
on Hilbert spaces. Therefore, our framework leads to a much simpler and clean
analysis of kernel tests, only requiring mild regularity conditions.
Furthermore, we show that, in general, our analysis cannot be improved by
proving that the regularity conditions required by our methods are both
sufficient and necessary. To illustrate the effectiveness of our approach we
present a new kernel-test for the conditional independence testing problem, as
well as new analyses for already known kernel-based tests.

Due to the imbalanced and limited data, semi-supervised medical image
segmentation methods often fail to produce superior performance for some
specific tailed classes. Inadequate training for those particular classes could
introduce more noise to the generated pseudo labels, affecting overall
learning. To alleviate this shortcoming and identify the under-performing
classes, we propose maintaining a confidence array that records class-wise
performance during training. A fuzzy fusion of these confidence scores is
proposed to adaptively prioritize individual confidence metrics in every sample
rather than traditional ensemble approaches, where a set of predefined fixed
weights are assigned for all the test cases. Further, we introduce a robust
class-wise sampling method and dynamic stabilization for a better training
strategy. Our proposed method considers all the under-performing classes with
dynamic weighting and tries to remove most of the noises during training. Upon
evaluation on two cardiac MRI datasets, ACDC and MMWHS, our proposed method
shows effectiveness and generalizability and outperforms several
state-of-the-art methods found in the literature.

We extend the Kearns-Vazirani learning algorithm to be able to handle systems
that change over time. We present a new learning algorithm that can reuse and
update previously learned behavior, implement it in the LearnLib library, and
evaluate it on large examples, to which we make small adjustments between two
runs of the algorithm. In these experiments our algorithm significantly
outperforms both the classic Kearns-Vazirani learning algorithm and the current
state-of-the-art adaptive algorithm.

Estimation of causal effects is the core objective of many scientific
disciplines. However, it remains a challenging task, especially when the
effects are estimated from observational data. Recently, several promising
machine learning models have been proposed for causal effect estimation. The
evaluation of these models has been based on the mean values of the error of
the Average Treatment Effect (ATE) as well as of the Precision in Estimation of
Heterogeneous Effect (PEHE). In this paper, we propose to complement the
evaluation of causal inference models using concrete statistical evidence,
including the performance profiles of Dolan and Mor{\'e}, as well as
non-parametric and post-hoc statistical tests. The main motivation behind this
approach is the elimination of the influence of a small number of instances or
simulation on the benchmarking process, which in some cases dominate the
results. We use the proposed evaluation methodology to compare several
state-of-the-art causal effect estimation models.

We study a regularized interacting particle method for computing aggregation
patterns and near singular solutions of a Keller-Segal (KS) chemotaxis system
in two and three space dimensions, then further develop DeepParticle (DP)
method to learn and generate solutions under variations of physical parameters.
The KS solutions are approximated as empirical measures of particles which
self-adapt to the high gradient part of solutions. We utilize the
expressiveness of deep neural networks (DNNs) to represent the transform of
samples from a given initial (source) distribution to a target distribution at
finite time T prior to blowup without assuming invertibility of the transforms.
In the training stage, we update the network weights by minimizing a discrete
2-Wasserstein distance between the input and target empirical measures. To
reduce computational cost, we develop an iterative divide-and-conquer algorithm
to find the optimal transition matrix in the Wasserstein distance. We present
numerical results of DP framework for successful learning and generation of KS
dynamics in the presence of laminar and chaotic flows. The physical parameter
in this work is either the small diffusivity of chemo-attractant or the
reciprocal of the flow amplitude in the advection-dominated regime.

In 2020 the Australia New Zealand Standard Research Classification Fields of
Research Codes (ANZSRC FoR codes) were updated by their owners. This has led
the sector to need to update their systems of reference and has caused
suppliers working in the research information sphere to need to update both
systems and data. This paper describes the approach developed by Digital
Science's Dimensions team to the creation of an improved machine learning
training set, and the mapping of that set from FoR 2008 codes to FoR 2020 codes
so that Dimensions classification approach for the ANZSRC codes could be
improved and updated.

Recent years have witnessed a growing number of attack vectors against
increasingly interconnected traffic networks. Informational attacks have
emerged as the prominent ones that aim to poison traffic data, misguide users,
and manipulate traffic patterns. To study the impact of this class of attacks,
we propose a game-theoretic framework where the attacker, as a Stackelberg
leader, falsifies the traffic conditions to change the traffic pattern
predicted by the Wardrop traffic equilibrium, achieved by the users, or the
followers. The intended shift of the Wardrop equilibrium is a consequence of
strategic informational poisoning. Leveraging game-theoretic and sensitivity
analysis, we quantify the system-level impact of the attack by characterizing
the concept of poisoned Price of Anarchy, which compares the poisoned Wardrop
equilibrium and its non-poisoned system optimal counterpart. We use an
evacuation case study to show that the Stackelberg equilibrium can be found
through a two-time scale zeroth-order learning process and demonstrate the
disruptive effects of informational poisoning, indicating a compelling need for
defense policies to mitigate such security threats.

Brain Computer Interface (BCI) has great potential for solving many brain
signal analysis limitations, mental disorder resolutions, and restoring missing
limb functionality via neural-controlled implants. However, there is no single
available, and safe implant for daily life usage exists yet. Most of the
proposed implants have several implementation issues, such as infection hazards
and heat dissipation, which limits their usability and makes it more
challenging to pass regulations and quality control production. The wireless
implant does not require a chronic wound in the skull. However, the current
complex clustering neuron identification algorithms inside the implant chip
consume a lot of power and bandwidth, causing higher heat dissipation issues
and draining the implant's battery. The spike sorting is the core unit of an
invasive BCI chip, which plays a significant role in power consumption,
accuracy, and area. Therefore, in this study, we propose a low-power adaptive
simplified VLSI architecture, "Zydeco-Style," for BCI spike sorting that is
computationally less complex with higher accuracy that performs up to 93.5% in
the worst-case scenario. The architecture uses a low-power Bluetooth Wireless
communication module with external IoT medical ICU devices. The proposed
architecture was implemented and simulated in Verilog. In addition, we are
proposing an implant conceptual design.

Recurrent Neural Networks (RNNs) are used in applications that learn
dependencies in data sequences, such as speech recognition, human activity
recognition, and anomaly detection. In recent years, newer RNN variants, such
as GRUs and LSTMs, have been used for implementing these applications. As many
of these applications are employed in real-time scenarios, accelerating
RNN/LSTM/GRU inference is crucial. In this paper, we propose a novel photonic
hardware accelerator called RecLight for accelerating simple RNNs, GRUs, and
LSTMs. Simulation results indicate that RecLight achieves 37x lower
energy-per-bit and 10% better throughput compared to the state-of-the-art.

I present my recollections of Richard Feynman's mid-1980s interest in
artificial intelligence and neural networks, set in the technical context of
the physics-related approaches to neural networks of that time. I attempt to
evaluate his ideas in the light of the substantial advances in the field since
then, and vice versa. There are aspects of Feynman's interests that I think
have been largely achieved and others that remain excitingly open, notably in
computational science, and potentially including the revival of symbolic
methods therein.

In this paper, we address the problem of multi-view 3D shape reconstruction.
While recent differentiable rendering approaches associated to implicit shape
representations have provided breakthrough performance, they are still
computationally heavy and often lack precision on the estimated geometries. To
overcome these limitations we investigate a new computational approach that
builds on a novel shape representation that is volumetric, as in recent
differentiable rendering approaches, but parameterized with depth maps to
better materialize the shape surface. The shape energy associated to this
representation evaluates 3D geometry given color images and does not need
appearance prediction but still benefits from volumetric integration when
optimized. In practice we propose an implicit shape representation, the SRDF,
based on signed distances which we parameterize by depths along camera rays.
The associated shape energy considers the agreement between depth prediction
consistency and photometric consistency, this at 3D locations within the
volumetric representation. Various photo-consistency priors can be accounted
for such as a median based baseline, or a more elaborated criterion as with a
learned function. The approach retains pixel-accuracy with depth maps and is
parallelizable. Our experiments over standard datasets shows that it provides
state-of-the-art results with respect to recent approaches with implicit shape
representations as well as with respect to traditional multi-view stereo
methods.

Unsupervised contrastive learning (UCL) is a self-supervised learning
technique that aims to learn a useful representation function by pulling
positive samples close to each other while pushing negative samples far apart
in the embedding space. To improve the performance of UCL, several works
introduced hard-negative unsupervised contrastive learning (H-UCL) that aims to
select the "hard" negative samples in contrast to a random sampling strategy
used in UCL. In another approach, under the assumption that the label
information is available, supervised contrastive learning (SCL) has developed
recently by extending the UCL to a fully-supervised setting. In this paper,
motivated by the effectiveness of hard-negative sampling strategies in H-UCL
and the usefulness of label information in SCL, we propose a contrastive
learning framework called hard-negative supervised contrastive learning
(H-SCL). Our numerical results demonstrate the effectiveness of H-SCL over both
SCL and H-UCL on several image datasets. In addition, we theoretically prove
that, under certain conditions, the objective function of H-SCL can be bounded
by the objective function of H-UCL but not by the objective function of UCL.
Thus, minimizing the H-UCL loss can act as a proxy to minimize the H-SCL loss
while minimizing UCL loss cannot. As we numerically showed that H-SCL
outperforms other contrastive learning methods, our theoretical result
(bounding H-SCL loss by H-UCL loss) helps to explain why H-UCL outperforms UCL
in practice.

In many scenarios such as genome-wide association studies where dependences
between variables commonly exist, it is often of interest to infer the
interaction effects in the model. However, testing pairwise interactions among
millions of variables in complex and high-dimensional data suffers from low
statistical power and huge computational cost. To address these challenges, we
propose a two-stage testing procedure with false discovery rate (FDR) control,
which is known as a less conservative multiple-testing correction.
Theoretically, the difficulty in the FDR control dues to the data dependence
among test statistics in two stages, and the fact that the number of hypothesis
tests conducted in the second stage depends on the screening result in the
first stage. By using the Cram\'er type moderate deviation technique, we show
that our procedure controls FDR at the desired level asymptotically in the
generalized linear model (GLM), where the model is allowed to be misspecified.
In addition, the asymptotic power of the FDR control procedure is rigorously
established. We demonstrate via comprehensive simulation studies that our
two-stage procedure is computationally more efficient than the classical BH
procedure, with a comparable or improved statistical power. Finally, we apply
the proposed method to a bladder cancer data from dbGaP where the scientific
goal is to identify genetic susceptibility loci for bladder cancer.

In this paper, we present Tetris, a new task of Goal-Oriented Script
Completion. Unlike previous work, it considers a more realistic and more
general setting, where the input includes not only the goal but also additional
user context, including preferences and history. To address the problem using a
knowledge-based approach, we introduce Task Concept Graph, an automatically
constructed knowledge base from instructional websites. Different from
Commonsense Knowledge Base like ConceptNet, the task concept graph schema
introduces various types of noun phrases-based nodes specifically for
accomplishing a task. To integrate such graphs into script learning, we devise
two methods that acquire concepts from the knowledge base as prompts to
downstream script completion. On our WikiHow-based dataset, we find that
incorporating concepts from the Task Concept Graph consistently improves
performance, demonstrating the benefit of Task Concept Graph for this task.
Furthermore, the model with gold-standard concepts as prompt outperforms the
baseline significantly, further confirming the need for task-specific knowledge
in goal-oriented script completion. The dataset, repository, models, and demo
will be publicly available to facilitate further research on this new task.

Current self-supervised approaches for skeleton action representation
learning often focus on constrained scenarios, where videos and skeleton data
are recorded in laboratory settings. When dealing with estimated skeleton data
in real-world videos, such methods perform poorly due to the large variations
across subjects and camera viewpoints. To address this issue, we introduce ViA,
a novel View-Invariant Autoencoder for self-supervised skeleton action
representation learning. ViA leverages motion retargeting between different
human performers as a pretext task, in order to disentangle the latent
action-specific `Motion' features on top of the visual representation of a 2D
or 3D skeleton sequence. Such `Motion' features are invariant to skeleton
geometry and camera view and allow ViA to facilitate both, cross-subject and
cross-view action classification tasks. We conduct a study focusing on
transfer-learning for skeleton-based action recognition with self-supervised
pre-training on real-world data (e.g., Posetics). Our results showcase that
skeleton representations learned from ViA are generic enough to improve upon
state-of-the-art action classification accuracy, not only on 3D laboratory
datasets such as NTU-RGB+D 60 and NTU-RGB+D 120, but also on real-world
datasets where only 2D data are accurately estimated, e.g., Toyota Smarthome,
UAV-Human and Penn Action.

Predicting the possible future trajectories of the surrounding dynamic agents
is an essential requirement in autonomous driving. These trajectories mainly
depend on the surrounding static environment, as well as the past movements of
those dynamic agents. Furthermore, the multimodal nature of agent intentions
makes the trajectory prediction problem more challenging. All of the existing
models consider the target agent as well as the surrounding agents similarly,
without considering the variation of physical properties. In this paper, we
present a novel deep-learning based framework for multimodal trajectory
prediction in autonomous driving, which considers the physical properties of
the target and surrounding vehicles such as the object class and their physical
dimensions through a weighted attention module, that improves the accuracy of
the predictions. Our model has achieved the highest results in the nuScenes
trajectory prediction benchmark, out of the models which use rasterized maps to
input environment information. Furthermore, our model is able to run in
real-time, achieving a high inference rate of over 300 FPS.

Hail risk assessment is necessary to estimate and reduce damage to crops,
orchards, and infrastructure. Also, it helps to estimate and reduce consequent
losses for businesses and, particularly, insurance companies. But hail
forecasting is challenging. Data used for designing models for this purpose are
tree-dimensional geospatial time series. Hail is a very local event with
respect to the resolution of available datasets. Also, hail events are rare -
only 1% of targets in observations are marked as "hail". Models for nowcasting
and short-term hail forecasts are improving. Introducing machine learning
models to the meteorology field is not new. There are also various climate
models reflecting possible scenarios of climate change in the future. But there
are no machine learning models for data-driven forecasting of changes in hail
frequency for a given area.
  The first possible approach for the latter task is to ignore spatial and
temporal structure and develop a model capable of classifying a given vertical
profile of meteorological variables as favorable to hail formation or not.
Although such an approach certainly neglects important information, it is very
light weighted and easily scalable because it treats observations as
independent from each other. The more advanced approach is to design a neural
network capable to process geospatial data. Our idea here is to combine
convolutional layers responsible for the processing of spatial data with
recurrent neural network blocks capable to work with temporal structure.
  This study compares two approaches and introduces a model suitable for the
task of forecasting changes in hail frequency for ongoing decades.

Biofilms pose significant problems for engineers in diverse fields, such as
marine science, bioenergy, and biomedicine, where effective biofilm control is
a long-term goal. The adhesion and surface mechanics of biofilms play crucial
roles in generating and removing biofilm. Designing customized nano-surfaces
with different surface topologies can alter the adhesive properties to remove
biofilms more easily and greatly improve long-term biofilm control. To rapidly
design such topologies, we employ individual-based modeling and Bayesian
optimization to automate the design process and generate different active
surfaces for effective biofilm removal. Our framework successfully generated
ideal nano-surfaces for biofilm removal through applied shear and vibration.
Densely distributed short pillar topography is the optimal geometry to prevent
biofilm formation. Under fluidic shearing, the optimal topography is to
sparsely distribute tall, slim, pillar-like structures. When subjected to
either vertical or lateral vibrations, thick trapezoidal cones are found to be
optimal. Optimizing the vibrational loading indicates a small vibration
magnitude with relatively low frequencies is more efficient in removing
biofilm. Our results provide insights into various engineering fields that
require surface-mediated biofilm control. Our framework can also be applied to
more general materials design and optimization.

Climate change increases the number of extreme weather events (wind and
snowstorms, heavy rains, wildfires) that compromise power system reliability
and lead to multiple equipment failures. Real-time and accurate detecting of
potential line failures is the first step to mitigating the extreme weather
impact and activating emergency controls. Power balance equations nonlinearity,
increased uncertainty in generation during extreme events, and lack of grid
observability compromise the efficiency of traditional data-driven failure
detection methods. At the same time, modern problem-oblivious machine learning
methods based on neural networks require a large amount of data to detect an
accident, especially in a time-changing environment. This paper proposes a
Physics-InformEd Line failure Detector (FIELD) that leverages grid topology
information to reduce sample and time complexities and improve localization
accuracy. Finally, we illustrate the superior empirical performance of our
approach compared to state-of-the-art methods over various test cases.

Conservation laws are key theoretical and practical tools for understanding,
characterizing, and modeling nonlinear dynamical systems. However, for many
complex dynamical systems, the corresponding conserved quantities are difficult
to identify, making it hard to analyze their dynamics and build efficient,
stable predictive models. Current approaches for discovering conservation laws
often depend on detailed dynamical information, such as the equation of motion
or fine-grained time measurements, with many recent proposals also relying on
black box parametric deep learning methods. We instead reformulate this task as
a manifold learning problem and propose a non-parametric approach, combining
the Wasserstein metric from optimal transport with diffusion maps, to discover
conserved quantities that vary across trajectories sampled from a dynamical
system. We test this new approach on a variety of physical
systems$\unicode{x2014}$including conservative Hamiltonian systems, dissipative
systems, and spatiotemporal systems$\unicode{x2014}$and demonstrate that our
manifold learning method is able to both identify the number of conserved
quantities and extract their values. Using tools from optimal transport theory
and manifold learning, our proposed method provides a direct geometric approach
to identifying conservation laws that is both robust and interpretable without
requiring an explicit model of the system nor accurate time information.

This paper introduces a new type of causal structure, namely multiscale
non-stationary directed acyclic graph (MN-DAG), that generalizes DAGs to the
time-frequency domain. Our contribution is twofold. First, by leveraging
results from spectral and causality theories, we expose a novel probabilistic
generative model, which allows to sample an MN-DAG according to user-specified
priors concerning the time-dependence and multiscale properties of the causal
graph. Second, we devise a Bayesian method for the estimation of MN-DAGs, by
means of stochastic variational inference (SVI), called Multiscale
Non-Stationary Causal Structure Learner (MN-CASTLE). In addition to direct
observations, MN-CASTLE exploits information from the decomposition of the
total power spectrum of time series over different time resolutions. In our
experiments, we first use the proposed model to generate synthetic data
according to a latent MN-DAG, showing that the data generated reproduces
well-known features of time series in different domains. Then we compare our
learning method MN-CASTLE against baseline models on synthetic data generated
with different multiscale and non-stationary settings, confirming the good
performance of MN-CASTLE. Finally, we show some insights derived from the
application of MN-CASTLE to study the causal structure of 7 global equity
markets during the Covid-19 pandemic.

In this work, we present a novel way of computing IPS using a position-bias
model for deterministic logging policies. This technique significantly widens
the policies on which OPE can be used. We validate this technique using two
different experiments on industry-scale data. The OPE results are clearly
strongly correlated with the online results, with some constant bias. The
estimator requires the examination model to be a reasonably accurate
approximation of real user behaviour.

The ability to detect zero-day (novel) attacks has become essential in the
network security industry. Due to ever evolving attack signatures, existing
network intrusion detection systems often fail to detect these threats. This
project aims to solve the task of detecting zero-day DDoS (distributed
denial-of-service) attacks by utilizing network traffic that is captured before
entering a private network. Modern feature extraction techniques are used in
conjunction with neural networks to determine if a network packet is either
benign or malicious.

Concept-based interpretations of black-box models are often more intuitive
for humans to understand. The most widely adopted approach for concept-based
interpretation is Concept Activation Vector (CAV). CAV relies on learning a
linear relation between some latent representation of a given model and
concepts. The linear separability is usually implicitly assumed but does not
hold true in general. In this work, we started from the original intent of
concept-based interpretation and proposed Concept Gradient (CG), extending
concept-based interpretation beyond linear concept functions. We showed that
for a general (potentially non-linear) concept, we can mathematically evaluate
how a small change of concept affecting the model's prediction, which leads to
an extension of gradient-based interpretation to the concept space. We
demonstrated empirically that CG outperforms CAV in both toy examples and real
world datasets.

Deep-learning-based device fingerprinting has recently been recognized as a
key enabler for automated network access authentication. Its robustness to
impersonation attacks due to the inherent difficulty of replicating physical
features is what distinguishes it from conventional cryptographic solutions.
Although device fingerprinting has shown promising performances, its
sensitivity to changes in the network operating environment still poses a major
limitation. This paper presents an experimental framework that aims to study
and overcome the sensitivity of LoRa-enabled device fingerprinting to such
changes. We first begin by describing RF datasets we collected using our
LoRa-enabled wireless device testbed. We then propose a new fingerprinting
technique that exploits out-of-band distortion information caused by hardware
impairments to increase the fingerprinting accuracy. Finally, we experimentally
study and analyze the sensitivity of LoRa RF fingerprinting to various network
setting changes. Our results show that fingerprinting does relatively well when
the learning models are trained and tested under the same settings. However,
when trained and tested under different settings, these models exhibit moderate
sensitivity to channel condition changes and severe sensitivity to protocol
configuration and receiver hardware changes when IQ data is used as input.
However, with FFT data is used as input, they perform poorly under any change.

Gaussian processes are arguably the most important model class in spatial
statistics. They encode prior information about the modeled function and can be
used for exact or approximate Bayesian inference. In many applications,
particularly in physical sciences and engineering, but also in areas such as
geostatistics and neuroscience, invariance to symmetries is one of the most
fundamental forms of prior information one can consider. The invariance of a
Gaussian process' covariance to such symmetries gives rise to the most natural
generalization of the concept of stationarity to such spaces. In this work, we
develop constructive and practical techniques for building stationary Gaussian
processes on a very large class of non-Euclidean spaces arising in the context
of symmetries. Our techniques make it possible to (i) calculate covariance
kernels and (ii) sample from prior and posterior Gaussian processes defined on
such spaces, both in a practical manner. This work is split into two parts,
each involving different technical considerations: part I studies compact
spaces, while part II studies non-compact spaces possessing certain structure.
Our contributions make the non-Euclidean Gaussian process models we study
compatible with well-understood computational techniques available in standard
Gaussian process software packages, thereby making them accessible to
practitioners.

A considerable amount of research is concerned with the generation of
realistic sensor data. LiDAR point clouds are generated by complex simulations
or learned generative models. The generated data is usually exploited to enable
or improve downstream perception algorithms. Two major questions arise from
these procedures: First, how to evaluate the realism of the generated data?
Second, does more realistic data also lead to better perception performance?
This paper addresses both questions and presents a novel metric to quantify the
realism of LiDAR point clouds. Relevant features are learned from real-world
and synthetic point clouds by training on a proxy classification task. In a
series of experiments, we demonstrate the application of our metric to
determine the realism of generated LiDAR data and compare the realism
estimation of our metric to the performance of a segmentation model. We confirm
that our metric provides an indication for the downstream segmentation

The use of Environmental Microorganisms (EMs) offers a highly efficient, low
cost and harmless remedy to environmental pollution, by monitoring and
decomposing of pollutants. This relies on how the EMs are correctly segmented
and identified. With the aim of enhancing the segmentation of weakly visible EM
images which are transparent, noisy and have low contrast, a Pairwise Deep
Learning Feature Network (PDLF-Net) is proposed in this study. The use of PDLFs
enables the network to focus more on the foreground (EMs) by concatenating the
pairwise deep learning features of each image to different blocks of the base
model SegNet. Leveraging the Shi and Tomas descriptors, we extract each image's
deep features on the patches, which are centered at each descriptor using the
VGG-16 model. Then, to learn the intermediate characteristics between the
descriptors, pairing of the features is performed based on the Delaunay
triangulation theorem to form pairwise deep learning features. In this
experiment, the PDLF-Net achieves outstanding segmentation results of 89.24%,
63.20%, 77.27%, 35.15%, 89.72%, 91.44% and 89.30% on the accuracy, IoU, Dice,
VOE, sensitivity, precision and specificity, respectively.

The mutual incompatibility of distinct spectroscopic systems is among the
most limiting factors in Laser-Induced Breakdown Spectroscopy (LIBS). The cost
related to setting up a new LIBS system is increased, as its extensive
calibration is required. Solving the problem would enable inter-laboratory
reference measurements and shared spectral libraries, which are fundamental for
other spectroscopic techniques. In this work, we study a simplified version of
this challenge where LIBS systems differ only in used spectrometers and
collection optics but share all other parts of the apparatus, and collect
spectra simultaneously from the same plasma plume. Extensive datasets measured
as hyperspectral images of heterogeneous specimens are used to train machine
learning models that can transfer spectra between systems. The transfer is
realized by a pipeline that consists of a variational autoencoder (VAE) and a
fully-connected artificial neural network (ANN). In the first step, we obtain a
latent representation of the spectra which were measured on the Primary system
(by using the VAE). In the second step, we map spectra from the Secondary
system to corresponding locations in the latent space (by the ANN). Finally,
Secondary system spectra are reconstructed from the latent space to the space
of the Primary system. The transfer is evaluated by several figures of merit
(Euclidean and cosine distances, both spatially resolved; k-means clustering of
transferred spectra). The methodology is compared to several baseline

This survey presents a comprehensive review of current literature on
Explainable Artificial Intelligence (XAI) methods for cyber security
applications. Due to the rapid development of Internet-connected systems and
Artificial Intelligence in recent years, Artificial Intelligence including
Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the
fields of cyber security including intrusion detection, malware detection, and
spam filtering. However, although Artificial Intelligence-based approaches for
the detection and defense of cyber attacks and threats are more advanced and
efficient compared to the conventional signature-based and rule-based cyber
security strategies, most ML-based techniques and DL-based techniques are
deployed in the black-box manner, meaning that security experts and customers
are unable to explain how such procedures reach particular conclusions. The
deficiencies of transparency and interpretability of existing Artificial
Intelligence techniques would decrease human users' confidence in the models
utilized for the defense against cyber attacks, especially in current
situations where cyber attacks become increasingly diverse and complicated.
Therefore, it is essential to apply XAI in the establishment of cyber security
models to create more explainable models while maintaining high accuracy and
allowing human users to comprehend, trust, and manage the next generation of
cyber defense mechanisms. Although there are papers reviewing Artificial
Intelligence applications in cyber security areas and the vast literature on
applying XAI in many fields including healthcare, financial services, and
criminal justice, the surprising fact is that there are currently no survey
research articles that concentrate on XAI applications in cyber security.

Machine learning models are vulnerable to membership inference attacks in
which an adversary aims to predict whether or not a particular sample was
contained in the target model's training dataset. Existing attack methods have
commonly exploited the output information (mostly, losses) solely from the
given target model. As a result, in practical scenarios where both the member
and non-member samples yield similarly small losses, these methods are
naturally unable to differentiate between them. To address this limitation, in
this paper, we propose a new attack method, called \system, which can exploit
the membership information from the whole training process of the target model
for improving the attack performance. To mount the attack in the common
black-box setting, we leverage knowledge distillation, and represent the
membership information by the losses evaluated on a sequence of intermediate
models at different distillation epochs, namely \emph{distilled loss
trajectory}, together with the loss from the given target model. Experimental
results over different datasets and model architectures demonstrate the great
advantage of our attack in terms of different metrics. For example, on
CINIC-10, our attack achieves at least 6$\times$ higher true-positive rate at a
low false-positive rate of 0.1\% than existing methods. Further analysis
demonstrates the general effectiveness of our attack in more strict scenarios.

A plethora of deep-neural-networks (DNN) based methods were proposed over the
past few years to address the challenging ill-posed inverse problem of MRI
reconstruction from undersampled "k-space" (Fourier domain) data. However,
instability against variations in the acquisition process and the anatomical
distribution, indicates a poor generalization of the relevant physical models
by the DNN architectures compared to their classical counterparts. The poor
generalization effectively precludes DNN applicability for undersampled MRI
reconstruction in the clinical setting. We improve the generalization capacity
of DNN methods for undersampled MRI reconstruction by introducing a
physically-primed DNN architecture and training approach. Our architecture
encodes the undersampling mask in addition to the observed data in the model
architecture and employs an appropriate training approach that uses data
generated with various undersampling masks to encourage the model to generalize
the undersampled MRI reconstruction problem. We demonstrated the added-value of
our approach through extensive experimentation with the publicly available
Fast-MRI dataset. Our physically-primed approach achieved an enhanced
generalization capacity which resulted in significantly improved robustness
against variations in the acquisition process and in the anatomical
distribution, especially in pathological regions, compared to both vanilla DNN
methods and DNN trained with undersampling mask augmentation. Trained models
and code to replicate our experiments will become available for research
purposes upon acceptance.

In this paper, we introduce Latent Go-Explore (LGE), a simple and general
approach based on the Go-Explore paradigm for exploration in reinforcement
learning (RL). Go-Explore was initially introduced with a strong domain
knowledge constraint for partitioning the state space into cells. However, in
most real-world scenarios, drawing domain knowledge from raw observations is
complex and tedious. If the cell partitioning is not informative enough,
Go-Explore can completely fail to explore the environment. We argue that the
Go-Explore approach can be generalized to any environment without domain
knowledge and without cells by exploiting a learned latent representation.
Thus, we show that LGE can be flexibly combined with any strategy for learning
a latent representation. We show that LGE, although simpler than Go-Explore, is
more robust and outperforms all state-of-the-art algorithms in terms of pure
exploration on multiple hard-exploration environments. The LGE implementation
is available as open-source at https://github.com/qgallouedec/lge.

In this work, we present a neural approach to reconstructing rooted tree
graphs describing hierarchical interactions, using a novel representation we
term the Lowest Common Ancestor Generations (LCAG) matrix. This compact
formulation is equivalent to the adjacency matrix, but enables learning a
tree's structure from its leaves alone without the prior assumptions required
if using the adjacency matrix directly. Employing the LCAG therefore enables
the first end-to-end trainable solution which learns the hierarchical structure
of varying tree sizes directly, using only the terminal tree leaves to do so.
In the case of high-energy particle physics, a particle decay forms a
hierarchical tree structure of which only the final products can be observed
experimentally, and the large combinatorial space of possible trees makes an
analytic solution intractable. We demonstrate the use of the LCAG as a target
in the task of predicting simulated particle physics decay structures using
both a Transformer encoder and a Neural Relational Inference encoder Graph
Neural Network. With this approach, we are able to correctly predict the LCAG
purely from leaf features for a maximum tree-depth of $8$ in $92.5\%$ of cases
for trees up to $6$ leaves (including) and $59.7\%$ for trees up to $10$ in our
simulated dataset.

Clinical Natural Language Processing (NLP) has become an emerging technology
in healthcare that leverages a large amount of free-text data in electronic
health records (EHRs) to improve patient care, support clinical decisions, and
facilitate clinical and translational science research. Recently, deep learning
has achieved state-of-the-art performance in many clinical NLP tasks. However,
training deep learning models usually requires large annotated datasets, which
are normally not publicly available and can be time-consuming to build in
clinical domains. Working with smaller annotated datasets is typical in
clinical NLP and therefore, ensuring that deep learning models perform well is
crucial for the models to be used in real-world applications. A widely adopted
approach is fine-tuning existing Pre-trained Language Models (PLMs), but these
attempts fall short when the training dataset contains only a few annotated
samples. Few-Shot Learning (FSL) has recently been investigated to tackle this
problem. Siamese Neural Network (SNN) has been widely utilized as an FSL
approach in computer vision, but has not been studied well in NLP. Furthermore,
the literature on its applications in clinical domains is scarce. In this
paper, we propose two SNN-based FSL approaches for clinical NLP, including
Pre-Trained SNN (PT-SNN) and SNN with Second-Order Embeddings (SOE-SNN). We
evaluated the proposed approaches on two clinical tasks, namely clinical text
classification and clinical named entity recognition. We tested three few-shot
settings including 4-shot, 8-shot, and 16-shot learning. Both clinical NLP
tasks were benchmarked using three PLMs, including BERT,BioBERT, and
BioClinicalBERT. The experimental results verified the effectiveness of the
proposed SNN-based FSL approaches in both NLP tasks.

The autoregressive moving average (ARMA) model is a classical, and arguably
one of the most studied approaches to model time series data. It has compelling
theoretical properties and is widely used among practitioners. More recent deep
learning approaches popularize recurrent neural networks (RNNs) and, in
particular, long short-term memory (LSTM) cells that have become one of the
best performing and most common building blocks in neural time series modeling.
While advantageous for time series data or sequences with long-term effects,
complex RNN cells are not always a must and can sometimes even be inferior to
simpler recurrent approaches. In this work, we introduce the ARMA cell, a
simpler, modular, and effective approach for time series modeling in neural
networks. This cell can be used in any neural network architecture where
recurrent structures are present and naturally handles multivariate time series
using vector autoregression. We also introduce the ConvARMA cell as a natural
successor for spatially-correlated time series. Our experiments show that the
proposed methodology is competitive with popular alternatives in terms of
performance while being more robust and compelling due to its simplicity.

Every moment, countless meteoroids enter our atmosphere unseen. The detection
and measurement of meteors offer the unique opportunity to gain insights into
the composition of our solar systems' celestial bodies. Researchers, therefore,
carry out a wide-area-sky-monitoring to secure 360-degree video material,
saving every single entry of a meteor. Existing machine intelligence cannot
accurately recognize events of meteors intersecting the earth's atmosphere due
to a lack of high-quality training data publicly available. This work presents
four reusable open source solutions for researchers trained on data we
collected due to the lack of available labeled high-quality training data. We
refer to the proposed dataset as the NightSkyUCP dataset, consisting of a
balanced set of 10,000 meteor- and 10,000 non-meteor-events. Our solutions
apply various machine learning techniques, namely classification, feature
learning, anomaly detection, and extrapolation. For the classification task, a
mean accuracy of 99.1\% is achieved. The code and data are made public at
figshare with DOI: 10.6084/m9.figshare.16451625

Setting the transmit power setting of 5G cells has been a long-term topic of
discussion, as optimized power settings can help reduce interference and
improve the quality of service to users. Recently, machine learning (ML)-based,
especially reinforcement learning (RL)-based control methods have received much
attention. However, there is little discussion about the generalisation ability
of the trained RL models. This paper points out that an RL agent trained in a
specific indoor environment is room-dependent, and cannot directly serve new
heterogeneous environments. Therefore, in the context of Open Radio Access
Network (O-RAN), this paper proposes a distributed cell power-control scheme
based on Federated Reinforcement Learning (FRL). Models in different indoor
environments are aggregated to the global model during the training process,
and then the central server broadcasts the updated model back to each client.
The model will also be used as the base model for adaptive training in the new
environment. The simulation results show that the FRL model has similar
performance to a single RL agent, and both are better than the random power
allocation method and exhaustive search method. The results of the
generalisation test show that using the FRL model as the base model improves
the convergence speed of the model in the new environment.

Physics-constrained machine learning is emerging as an important topic in the
field of machine learning for physics. One of the most significant advantages
of incorporating physics constraints into machine learning methods is that the
resulting model requires significantly less data to train. By incorporating
physical rules into the machine learning formulation itself, the predictions
are expected to be physically plausible. Gaussian process (GP) is perhaps one
of the most common methods in machine learning for small datasets. In this
paper, we investigate the possibility of constraining a GP formulation with
monotonicity on three different material datasets, where one experimental and
two computational datasets are used. The monotonic GP is compared against the
regular GP, where a significant reduction in the posterior variance is
observed. The monotonic GP is strictly monotonic in the interpolation regime,
but in the extrapolation regime, the monotonic effect starts fading away as one
goes beyond the training dataset. Imposing monotonicity on the GP comes at a
small accuracy cost, compared to the regular GP. The monotonic GP is perhaps
most useful in applications where data is scarce and noisy, and monotonicity is
supported by strong physical evidence.

Point cloud registration aims at estimating the geometric transformation
between two point cloud scans, in which point-wise correspondence estimation is
the key to its success. In addition to previous methods that seek
correspondences by hand-crafted or learnt geometric features, recent point
cloud registration methods have tried to apply RGB-D data to achieve more
accurate correspondence. However, it is not trivial to effectively fuse the
geometric and visual information from these two distinctive modalities,
especially for the registration problem. In this work, we propose a new
Geometry-Aware Visual Feature Extractor (GAVE) that employs multi-scale local
linear transformation to progressively fuse these two modalities, where the
geometric features from the depth data act as the geometry-dependent
convolution kernels to transform the visual features from the RGB data. The
resultant visual-geometric features are in canonical feature spaces with
alleviated visual dissimilarity caused by geometric changes, by which more
reliable correspondence can be achieved. The proposed GAVE module can be
readily plugged into recent RGB-D point cloud registration framework. Extensive
experiments on 3D Match and ScanNet demonstrate that our method outperforms the
state-of-the-art point cloud registration methods even without correspondence
or pose supervision. The code is available at: https://github.com/514DNA/LLT.

A recent paper by Piliouras et al. [2021, 2022] introduces an uncoupled
learning algorithm for normal-form games -- called Clairvoyant MWU (CMWU). In
this note we show that CMWU is equivalent to the conceptual prox method
described by Nemirovski [2004]. This connection immediately shows that it is
possible to extend the CMWU algorithm to any convex game, a question left open
by Piliouras et al. We call the resulting algorithm -- again equivalent to the
conceptual prox method -- Clairvoyant OMD. At the same time, we show that our
analysis yields an improved regret bound compared to the original bound by
Piliouras et al., in that the regret of CMWU scales only with the square root
of the number of players, rather than the number of players themselves.

Most unsupervised domain adaptation (UDA) methods assume that labeled source
images are available during model adaptation. However, this assumption is often
infeasible owing to confidentiality issues or memory constraints on mobile
devices. To address these problems, we propose a simple yet effective
source-free UDA method that uses only a pre-trained source model and unlabeled
target images. Our method captures the aleatoric uncertainty by incorporating
data augmentation and trains the feature generator with two consistency
objectives. The feature generator is encouraged to learn consistent visual
features away from the decision boundaries of the head classifier. Inspired by
self-supervised learning, our method promotes inter-space alignment between the
prediction space and the feature space while incorporating intra-space
consistency within the feature space to reduce the domain gap between the
source and target domains. We also consider epistemic uncertainty to boost the
model adaptation performance. Extensive experiments on popular UDA benchmarks
demonstrate that the performance of our approach is comparable or even superior
to vanilla UDA methods without using source images or network modifications.

Recent deep learning approaches in table detection achieved outstanding
performance and proved to be effective in identifying document layouts.
Currently, available table detection benchmarks have many limitations,
including the lack of samples diversity, simple table structure, the lack of
training cases, and samples quality. In this paper, we introduce a diverse
large-scale dataset for table detection with more than seven thousand samples
containing a wide variety of table structures collected from many diverse
sources. In addition to that, we also present baseline results using a
convolutional neural network-based method to detect table structure in
documents. Experimental results show the superiority of applying convolutional
deep learning methods over classical computer vision-based methods. The
introduction of this diverse table detection dataset will enable the community
to develop high throughput deep learning methods for understanding document
layout and tabular data processing.

Unsupervised hashing has attracted much attention for binary representation
learning due to the requirement of economical storage and efficiency of binary
codes. It aims to encode high-dimensional features in the Hamming space with
similarity preservation between instances. However, most existing methods learn
hash functions in manifold-based approaches. Those methods capture the local
geometric structures (i.e., pairwise relationships) of data, and lack
satisfactory performance in dealing with real-world scenarios that produce
similar features (e.g. color and shape) with different semantic information. To
address this challenge, in this work, we propose an effective unsupervised
method, namely Jointly Personalized Sparse Hashing (JPSH), for binary
representation learning. To be specific, firstly, we propose a novel
personalized hashing module, i.e., Personalized Sparse Hashing (PSH). Different
personalized subspaces are constructed to reflect category-specific attributes
for different clusters, adaptively mapping instances within the same cluster to
the same Hamming space. In addition, we deploy sparse constraints for different
personalized subspaces to select important features. We also collect the
strengths of the other clusters to build the PSH module with avoiding
over-fitting. Then, to simultaneously preserve semantic and pairwise
similarities in our JPSH, we incorporate the PSH and manifold-based hash
learning into the seamless formulation. As such, JPSH not only distinguishes
the instances from different clusters, but also preserves local neighborhood
structures within the cluster. Finally, an alternating optimization algorithm
is adopted to iteratively capture analytical solutions of the JPSH model.
Extensive experiments on four benchmark datasets verify that the JPSH
outperforms several hashing algorithms on the similarity search task.

This paper studies the multimedia problem of temporal sentence grounding
(TSG), which aims to accurately determine the specific video segment in an
untrimmed video according to a given sentence query. Traditional TSG methods
mainly follow the top-down or bottom-up framework and are not end-to-end. They
severely rely on time-consuming post-processing to refine the grounding
results. Recently, some transformer-based approaches are proposed to
efficiently and effectively model the fine-grained semantic alignment between
video and query. Although these methods achieve significant performance to some
extent, they equally take frames of the video and words of the query as
transformer input for correlating, failing to capture their different levels of
granularity with distinct semantics. To address this issue, in this paper, we
propose a novel Hierarchical Local-Global Transformer (HLGT) to leverage this
hierarchy information and model the interactions between different levels of
granularity and different modalities for learning more fine-grained multi-modal
representations. Specifically, we first split the video and query into
individual clips and phrases to learn their local context (adjacent dependency)
and global correlation (long-range dependency) via a temporal transformer.
Then, a global-local transformer is introduced to learn the interactions
between the local-level and global-level semantics for better multi-modal
reasoning. Besides, we develop a new cross-modal cycle-consistency loss to
enforce interaction between two modalities and encourage the semantic alignment
between them. Finally, we design a brand-new cross-modal parallel transformer
decoder to integrate the encoded visual and textual features for final
grounding. Extensive experiments on three challenging datasets show that our
proposed HLGT achieves a new state-of-the-art performance.

The use of counterfactual explanations (CFXs) is an increasingly popular
explanation strategy for machine learning models. However, recent studies have
shown that these explanations may not be robust to changes in the underlying
model (e.g., following retraining), which raises questions about their
reliability in real-world applications. Existing attempts towards solving this
problem are heuristic, and the robustness to model changes of the resulting
CFXs is evaluated with only a small number of retrained models, failing to
provide exhaustive guarantees. To remedy this, we propose the first notion to
formally and deterministically assess the robustness (to model changes) of CFXs
for neural networks, that we call {\Delta}-robustness. We introduce an
abstraction framework based on interval neural networks to verify the
{\Delta}-robustness of CFXs against a possibly infinite set of changes to the
model parameters, i.e., weights and biases. We then demonstrate the utility of
this approach in two distinct ways. First, we analyse the {\Delta}-robustness
of a number of CFX generation methods from the literature and show that they
unanimously host significant deficiencies in this regard. Second, we
demonstrate how embedding {\Delta}-robustness within existing methods can
provide CFXs which are provably robust.

Softwarization, programmable network control and the use of all-encompassing
controllers acting at different timescales are heralded as the key drivers for
the evolution to next-generation cellular networks. These technologies have
fostered newly designed intelligent data-driven solutions for managing large
sets of diverse cellular functionalities, basically impossible to implement in
traditionally closed cellular architectures. Despite the evident interest of
industry on Artificial Intelligence (AI) and Machine Learning (ML) solutions
for closed-loop control of the Radio Access Network (RAN), and several research
works in the field, their design is far from mainstream, and it is still a
sophisticated and often overlooked operation. In this paper, we discuss how to
design AI/ML solutions for the intelligent closed-loop control of the Open RAN,
providing guidelines and insights based on exemplary solutions with
high-performance record. We then show how to embed these solutions into xApps
instantiated on the O-RAN near-real-time RAN Intelligent Controller (RIC)
through OpenRAN Gym, the first publicly available toolbox for data-driven O-RAN
experimentation at scale. We showcase a use case of an xApp developed with
OpenRAN Gym and tested on a cellular network with 7 base stations and 42 users
deployed on the Colosseum wireless network emulator. Our demonstration shows
the high degree of flexibility of the OpenRAN Gym-based xApp development
environment, which is independent of deployment scenarios and traffic demand.

To improve the purity of coal and prevent damage to the coal mining machine,
it is necessary to identify coal and rock in underground coal mines. At the
same time, the mined coal needs to be purified to remove rock and gangue. These
two procedures are manually operated by workers in most coal mines. The
realization of automatic identification and purification is not only conducive
to the automation of coal mines, but also ensures the safety of workers. We
discuss the possibility of using image-based methods to distinguish them. In
order to find a solution that can be used in both scenarios, a model that
forwards image feature extracted by DenseNet to Gaussian process is proposed,
which is trained on images taken on surface and achieves high accuracy on
images taken underground. This indicates our method is powerful in few-shot
learning such as identification of coal and rock/gangue and might be beneficial
for realizing automation in coal mines.

We propose a system for rendering a symbolic piano performance with flexible
musical expression. It is necessary to actively control musical expression for
creating a new music performance that conveys various emotions or nuances.
However, previous approaches were limited to following the composer's
guidelines of musical expression or dealing with only a part of the musical
attributes. We aim to disentangle the entire musical expression and structural
attribute of piano performance using a conditional VAE framework. It
stochastically generates expressive parameters from latent representations and
given note structures. In addition, we employ self-supervised approaches that
force the latent variables to represent target attributes. Finally, we leverage
a two-step encoder and decoder that learn hierarchical dependency to enhance
the naturalness of the output. Experimental results show that our system can
stably generate performance parameters relevant to the given musical scores,
learn disentangled representations, and control musical attributes
independently of each other.

Contextual multi-armed bandit (MAB) is an important sequential
decision-making problem in recommendation systems. A line of works, called the
clustering of bandits (CLUB), utilize the collaborative effect over users and
dramatically improve the recommendation quality. Owing to the increasing
application scale and public concerns about privacy, there is a growing demand
to keep user data decentralized and push bandit learning to the local server
side. Existing CLUB algorithms, however, are designed under the centralized
setting where data are available at a central server. We focus on studying the
federated online clustering of bandit (FCLUB) problem, which aims to minimize
the total regret while satisfying privacy and communication considerations. We
design a new phase-based scheme for cluster detection and a novel asynchronous
communication protocol for cooperative bandit learning for this problem. To
protect users' privacy, previous differential privacy (DP) definitions are not
very suitable, and we propose a new DP notion that acts on the user cluster
level. We provide rigorous proofs to show that our algorithm simultaneously
achieves (clustered) DP, sublinear communication complexity and sublinear
regret. Finally, experimental evaluations show our superior performance
compared with benchmark algorithms.

We present a novel method of learning style-agnostic representation using
both style transfer and adversarial learning in the reinforcement learning
framework. The style, here, refers to task-irrelevant details such as the color
of the background in the images, where generalizing the learned policy across
environments with different styles is still a challenge. Focusing on learning
style-agnostic representations, our method trains the actor with diverse image
styles generated from an inherent adversarial style perturbation generator,
which plays a min-max game between the actor and the generator, without
demanding expert knowledge for data augmentation or additional class labels for
adversarial training. We verify that our method achieves competitive or better
performances than the state-of-the-art approaches on Procgen and Distracting
Control Suite benchmarks, and further investigate the features extracted from
our model, showing that the model better captures the invariants and is less
distracted by the shifted style. The code is available at
https://github.com/POSTECH-CVLab/style-agnostic-RL.

Temporal Action Localization (TAL) aims to predict both action category and
temporal boundary of action instances in untrimmed videos, i.e., start and end
time. Fully-supervised solutions are usually adopted in most existing works,
and proven to be effective. One of the practical bottlenecks in these solutions
is the large amount of labeled training data required. To reduce expensive
human label cost, this paper focuses on a rarely investigated yet practical
task named semi-supervised TAL and proposes an effective active learning
method, named AL-STAL. We leverage four steps for actively selecting video
samples with high informativeness and training the localization model, named
\emph{Train, Query, Annotate, Append}. Two scoring functions that consider the
uncertainty of localization model are equipped in AL-STAL, thus facilitating
the video sample rank and selection. One takes entropy of predicted label
distribution as measure of uncertainty, named Temporal Proposal Entropy (TPE).
And the other introduces a new metric based on mutual information between
adjacent action proposals and evaluates the informativeness of video samples,
named Temporal Context Inconsistency (TCI). To validate the effectiveness of
proposed method, we conduct extensive experiments on two benchmark datasets
THUMOS'14 and ActivityNet 1.3. Experiment results show that AL-STAL outperforms
the existing competitors and achieves satisfying performance compared with
fully-supervised learning.

Modeling the human body in a canonical space is a common practice for
capturing and animation. But when involving the neural radiance field (NeRF),
learning a static NeRF in the canonical space is not enough because the
lighting of the body changes when the person moves even though the scene
lighting is constant. Previous methods alleviate the inconsistency of lighting
by learning a per-frame embedding, but this operation does not generalize to
unseen poses. Given that the lighting condition is static in the world space
while the human body is consistent in the canonical space, we propose a
dual-space NeRF that models the scene lighting and the human body with two MLPs
in two separate spaces. To bridge these two spaces, previous methods mostly
rely on the linear blend skinning (LBS) algorithm. However, the blending
weights for LBS of a dynamic neural field are intractable and thus are usually
memorized with another MLP, which does not generalize to novel poses. Although
it is possible to borrow the blending weights of a parametric mesh such as
SMPL, the interpolation operation introduces more artifacts. In this paper, we
propose to use the barycentric mapping, which can directly generalize to unseen
poses and surprisingly achieves superior results than LBS with neural blending
weights. Quantitative and qualitative results on the Human3.6M and the
ZJU-MoCap datasets show the effectiveness of our method.

Heart murmurs are abnormal sounds present in heartbeats, caused by turbulent
blood flow through the heart. The PhysioNet 2022 challenge targets automatic
detection of murmur from audio recordings of the heart and automatic detection
of normal vs. abnormal clinical outcome. The recordings are captured from
multiple locations around the heart. Our participation investigates the
effectiveness of selfsupervised learning for murmur detection. We train the
layers of a backbone CNN in a self-supervised way with data from both this
year's and the 2016 challenge. We use two different augmentations on each
training sample, and normalized temperature-scaled cross-entropy loss. We
experiment with different augmentations to learn effective phonocardiogram
representations. To build the final detectors we train two classification
heads, one for each challenge task. We present evaluation results for all
combinations of the available augmentations, and for our multipleaugmentation
approach. Our team's, Listen2YourHeart, SSL murmur detection classifier
received a weighted accuracy score of 0.737 (ranked 13th out of 40 teams) and
an outcome identification challenge cost score of 11946 (ranked 7th out of 39
teams) on the hidden test set.

The logic behind design decisions, called design rationale, is very valuable.
In the past, researchers have tried to automatically extract and exploit this
information, but prior techniques are only applicable to specific contexts and
there is insufficient progress on an end-to-end rationale information
extraction pipeline. Here we outline a path towards such a pipeline that
leverages several Machine Learning (ML) and Natural Language Processing (NLP)
techniques. Our proposed context-independent approach, called Kantara, produces
a knowledge graph representation of decisions and of their rationales, which
considers their historical evolution and traceability. We also propose
validation mechanisms to ensure the correctness of the extracted information
and the coherence of the development process. We conducted a preliminary
evaluation of our proposed approach on a small example sourced from the Linux
Kernel, which shows promising results.

In this paper, we study the combinatorial semi-bandits (CMAB) and focus on
reducing the dependency of the batch-size $K$ in the regret bound, where $K$ is
the total number of arms that can be pulled or triggered in each round. First,
for the setting of CMAB with probabilistically triggered arms (CMAB-T), we
discover a novel (directional) triggering probability and variance modulated
(TPVM) condition that can replace the previously-used smoothness condition for
various applications, such as cascading bandits, online network exploration and
online influence maximization. Under this new condition, we propose a BCUCB-T
algorithm with variance-aware confidence intervals and conduct regret analysis
which reduces the $O(K)$ factor to $O(\log K)$ or $O(\log^2 K)$ in the regret
bound, significantly improving the regret bounds for the above applications.
Second, for the setting of non-triggering CMAB with independent arms, we
propose a SESCB algorithm which leverages on the non-triggering version of the
TPVM condition and completely removes the dependency on $K$ in the leading
regret. As a valuable by-product, the regret analysis used in this paper can
improve several existing results by a factor of $O(\log K)$. Finally,
experimental evaluations show our superior performance compared with benchmark
algorithms in different applications.

Semi-supervised Anomaly Detection (AD) is a kind of data mining task which
aims at learning features from partially-labeled datasets to help detect
outliers. In this paper, we classify existing semi-supervised AD methods into
two categories: unsupervised-based and supervised-based, and point out that
most of them suffer from insufficient exploitation of labeled data and
under-exploration of unlabeled data. To tackle these problems, we propose Deep
Anomaly Detection and Search (DADS), which applies Reinforcement Learning (RL)
to balance exploitation and exploration. During the training process, the agent
searches for possible anomalies with hierarchically-structured datasets and
uses the searched anomalies to enhance performance, which in essence draws
lessons from the idea of ensemble learning. Experimentally, we compare DADS
with several state-of-the-art methods in the settings of leveraging labeled
known anomalies to detect both other known anomalies and unknown anomalies.
Results show that DADS can efficiently and precisely search anomalies from
unlabeled data and learn from them, thus achieving good performance.

The probability of a drought for a particular region is crucial when making
decisions related to agriculture. Forecasting this probability is critical for
management and challenging at the same time. The prediction model should
consider multiple factors with complex relationships across the region of
interest and neighbouring regions.
  We approach this problem by presenting an end-to-end solution based on a
spatio-temporal neural network. The model predicts the Palmer Drought Severity
Index (PDSI) for subregions of interest. Predictions by climate models provide
an additional source of knowledge of the model leading to more accurate drought
  Our model has better accuracy than baseline Gradient boosting solutions, as
the $R^2$ score for it is $0.90$ compared to $0.85$ for Gradient boosting.
Specific attention is on the range of applicability of the model. We examine
various regions across the globe to validate them under different conditions.
  We complement the results with an analysis of how future climate changes for
different scenarios affect the PDSI and how our model can help to make better
decisions and more sustainable economics.

Recent years have seen a surge of novel neural network architectures for the
integration of multi-omics data for prediction. Most of the architectures
include either encoders alone or encoders and decoders, i.e., autoencoders of
various sorts, to transform multi-omics data into latent representations. One
important parameter is the depth of integration: the point at which the latent
representations are computed or merged, which can be either early,
intermediate, or late. The literature on integration methods is growing
steadily, however, close to nothing is known about the relative performance of
these methods under fair experimental conditions and under consideration of
different use cases. We developed a comparison framework that trains and
optimizes multi-omics integration methods under equal conditions. We
incorporated early integration and four recently published deep learning
methods: MOLI, Super.FELT, OmiEmbed, and MOMA. Further, we devised a novel
method, Omics Stacking, that combines the advantages of intermediate and late
integration. Experiments were conducted on a public drug response data set with
multiple omics data (somatic point mutations, somatic copy number profiles and
gene expression profiles) that was obtained from cell lines, patient-derived
xenografts, and patient samples. Our experiments confirmed that early
integration has the lowest predictive performance. Overall, architectures that
integrate triplet loss achieved the best results. Statistical differences can,
overall, rarely be observed, however, in terms of the average ranks of methods,
Super.FELT is consistently performing best in a cross-validation setting and
Omics Stacking best in an external test set setting. The source code of all
experiments is available under
\url{https://github.com/kramerlab/Multi-Omics_analysis}

Complex Event Recognition and Forecasting (CER/F) techniques attempt to
detect, or even forecast ahead of time, event occurrences in streaming input
using predefined event patterns. Such patterns are not always known in advance,
or they frequently change over time, making machine learning techniques,
capable of extracting such patterns from data, highly desirable in CER/F. Since
many CER/F systems use symbolic automata to represent such patterns, we propose
a family of such automata where the transition-enabling conditions are defined
by Answer Set Programming (ASP) rules, and which, thanks to the strong
connections of ASP to symbolic learning, are directly learnable from data. We
present such a learning approach in ASP and an incremental version thereof that
trades optimality for efficiency and is capable to scale to large datasets. We
evaluate our approach on two CER datasets and compare it to state-of-the-art
automata learning techniques, demonstrating empirically a superior performance,
both in terms of predictive accuracy and scalability.

Cadences are complex structures that have been driving music from the
beginning of contrapuntal polyphony until today. Detecting such structures is
vital for numerous MIR tasks such as musicological analysis, key detection, or
music segmentation. However, automatic cadence detection remains challenging
mainly because it involves a combination of high-level musical elements like
harmony, voice leading, and rhythm. In this work, we present a graph
representation of symbolic scores as an intermediate means to solve the cadence
detection task. We approach cadence detection as an imbalanced node
classification problem using a Graph Convolutional Network. We obtain results
that are roughly on par with the state of the art, and we present a model
capable of making predictions at multiple levels of granularity, from
individual notes to beats, thanks to the fine-grained, note-by-note
representation. Moreover, our experiments suggest that graph convolution can
learn non-local features that assist in cadence detection, freeing us from the
need of having to devise specialized features that encode non-local context. We
argue that this general approach to modeling musical scores and classification
tasks has a number of potential advantages, beyond the specific recognition
task presented here.

Thanks to the development of deep learning, research on machine anomalous
sound detection based on self-supervised learning has made remarkable
achievements. However, there are differences in the acoustic characteristics of
the test set and the training set under different operating conditions of the
same machine (domain shifts). It is challenging for the existing detection
methods to learn the domain shifts features stably with low computation
overhead. To address these problems, we propose a domain shift-oriented machine
anomalous sound detection model based on self-supervised learning
(TranSelf-DyGCN) in this paper. Firstly, we design a time-frequency domain
feature modeling network to capture global and local spatial and time-domain
features, thus improving the stability of machine anomalous sound detection
stability under domain shifts. Then, we adopt a Dynamic Graph Convolutional
Network (DyGCN) to model the inter-dependence relationship between domain
shifts features, enabling the model to perceive domain shifts features
efficiently. Finally, we use a Domain Adaptive Network (DAN) to compensate for
the performance decrease caused by domain shifts, making the model adapt to
anomalous sound better in the self-supervised environment. The performance of
the suggested model is validated on DCASE 2020 task 2 and DCASE 2022 task 2.

Flaky tests are tests that yield different outcomes when run on the same
version of a program. This non-deterministic behaviour plagues continuous
integration with false signals, wasting developers' time and reducing their
trust in test suites. Studies highlighted the importance of keeping tests
flakiness-free. Recently, the research community has been pushing forward the
detection of flaky tests by suggesting many static and dynamic approaches.
While promising, those approaches mainly focus on classifying tests as flaky or
not and, even when high performances are reported, it remains challenging to
understand the cause of flakiness. This part is crucial for researchers and
developers that aim to fix it. To help with the comprehension of a given flaky
test, we propose FlakyCat, the first approach for classifying flaky tests based
on their root cause category. FlakyCat relies on CodeBERT for code
representation and leverages a Siamese network-based Few-Shot learning method
to train a multi-class classifier with few data. We train and evaluate FlakyCat
on a set of 343 flaky tests collected from open-source Java projects. Our
evaluation shows that FlakyCat categorises flaky tests accurately, with a
weighted F1 score of 70%. Furthermore, we investigate the performance of our
approach for each category, revealing that Async waits, Unordered collections
and Time-related flaky tests are accurately classified, while
Concurrency-related flaky tests are more challenging to predict. Finally, to
facilitate the comprehension of FlakyCat's predictions, we present a new
technique for CodeBERT-based model interpretability that highlights code
statements influencing the categorization.

In this paper, we consider a ${\rm U}(1)$-connection graph, that is, a graph
where each oriented edge is endowed with a unit modulus complex number which is
simply conjugated under orientation flip. A natural replacement for the
combinatorial Laplacian is then the so-called magnetic Laplacian, an Hermitian
matrix that includes information about the graph's connection. Connection
graphs and magnetic Laplacians appear, e.g., in the problem of angular
synchronization. In the context of large and dense graphs, we study here
sparsifiers of the magnetic Laplacian, i.e., spectral approximations based on
subgraphs with few edges. Our approach relies on sampling multi-type spanning
forests (MTSFs) using a custom determinantal point process, a distribution over
edges that favours diversity. In a word, an MTSF is a spanning subgraph whose
connected components are either trees or cycle-rooted trees. The latter
partially capture the angular inconsistencies of the connection graph, and thus
provide a way to compress information contained in the connection.
Interestingly, when this connection graph has weakly inconsistent cycles,
samples of this distribution can be obtained by using a random walk with cycle
popping. We provide statistical guarantees for a choice of natural estimators
of the connection Laplacian, and investigate the practical application of our
sparsifiers in two applications.

Capturing both local and global features of irregular point clouds is
essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g.,
VoteNet and its variants, either abandon considerable local features during
pooling operations or ignore many global features in the whole scene context.
This paper explores new modules to simultaneously learn local-global features
of scene point clouds that serve 3OD positively. To this end, we propose an
effective 3OD network via simultaneous local-global feature learning (dubbed
3DLG-Detector). 3DLG-Detector has two key contributions. First, it develops a
Dynamic Points Interaction (DPI) module that preserves effective local features
during pooling. Besides, DPI is detachable and can be incorporated into
existing 3OD networks to boost their performance. Second, it develops a Global
Context Aggregation module to aggregate multi-scale features from different
layers of the encoder to achieve scene context-awareness. Our method shows
improvements over thirteen competitors in terms of detection accuracy and
robustness on both the SUN RGB-D and ScanNet datasets. Source code will be
available upon publication.

Gradual pattern extraction is a field in (KDD) Knowledge Discovery in
Databases that maps correlations between attributes of a data set as gradual
dependencies. A gradual dependency may take a form of "the more Attribute K ,
the less Attribute L". In this paper, we propose an ant colony optimization
technique that uses a probabilistic approach to learn and extract frequent
gradual patterns. Through computational experiments on real-world data sets, we
compared the performance of our ant-based algorithm to an existing gradual item
set extraction algorithm and we found out that our algorithm outperforms the
later especially when dealing with large data sets.

In this work we propose a new paradigm for designing efficient deep unrolling
networks using dimensionality reduction schemes, including minibatch gradient
approximation and operator sketching. The deep unrolling networks are currently
the state-of-the-art solutions for imaging inverse problems. However, for
high-dimensional imaging tasks, especially X-ray CT and MRI imaging, the deep
unrolling schemes typically become inefficient both in terms of memory and
computation, due to the need of computing multiple times the high-dimensional
forward and adjoint operators. Recently researchers have found that such
limitations can be partially addressed by unrolling the stochastic gradient
descent (SGD), inspired by the success of stochastic first-order optimization.
In this work, we explore further this direction and propose first a more
expressive and practical stochastic primal-dual unrolling, based on the
state-of-the-art Learned Primal-Dual (LPD) network, and also a further
acceleration upon stochastic primal-dual unrolling, using sketching techniques
to approximate products in the high-dimensional image space. The operator
sketching can be jointly applied with stochastic unrolling for the best
acceleration and compression performance. Our numerical experiments on X-ray CT
image reconstruction demonstrate the remarkable effectiveness of our
accelerated unrolling schemes.

We present a new method based on information theory to find the optimal
number of bands required to measure the physical properties of galaxies with a
desired accuracy. As a proof of concept, using the recently updated COSMOS
catalog (COSMOS2020), we identify the most relevant wavebands for measuring the
physical properties of galaxies in a Hawaii Two-0 (H20)- and UVISTA-like survey
for a sample of $i<25$ AB mag galaxies. We find that with available $i$-band
fluxes, $r$, $u$, IRAC/$ch2$ and $z$ bands provide most of the information
regarding the redshift with importance decreasing from $r$-band to $z$-band. We
also find that for the same sample, IRAC/$ch2$, $Y$, $r$ and $u$ bands are the
most relevant bands in stellar mass measurements with decreasing order of
importance. Investigating the inter-correlation between the bands, we train a
model to predict UVISTA observations in near-IR from H20-like observations. We
find that magnitudes in $YJH$ bands can be simulated/predicted with an accuracy
of $1\sigma$ mag scatter $\lesssim 0.2$ for galaxies brighter than 24 AB mag in
near-IR bands. One should note that these conclusions depend on the selection
criteria of the sample. For any new sample of galaxies with a different
selection, these results should be remeasured. Our results suggest that in the
presence of a limited number of bands, a machine learning model trained over
the population of observed galaxies with extensive spectral coverage
outperforms template-fitting. Such a machine learning model maximally comprises
the information acquired over available extensive surveys and breaks
degeneracies in the parameter space of template-fitting inevitable in the
presence of a few bands.

Electrokinetic energy conversion provides a scheme for energy harvesting and
storage for on-chip applications. However, the major drawback of electrokinetic
energy conversion is its low conversion efficiency. Researchers are in a quest
to find ways to improve this efficiency. With the same motive, we investigated
the generation of streaming potential by applying surface modification and
employing a non-Newtonian fluid to flow through the microchannel under constant
pressure difference across its ends. Shear-thickening liquids tend to lessen
electrokinetic effects, whereas shear-thinning liquids favour them. Also,
having superhydrophobic surfaces improve the magnitude of generated streaming
potential. We examine the combined effect of fluid rheology and surface
modification on electrokinetic energy generation. We have learned intriguing
insights about using non-Newtonian fluid in hydrophobic microchannels as an
outcome of our combined research. Hydrophobic surfaces do not enhance the
efficiency for a fluid with below a power law index of 0.7. The findings of
this research can be used towards the selection of fluid-substrate combination
that will optimize electrokinetic power generation efficiency.

The classification of electroencephalography (EEG) signals is useful in a
wide range of applications such as seizure detection/prediction, motor imagery
classification, emotion classification and drug effects diagnosis, amongst
others. With the large number of EEG channels acquired, it has become vital
that efficient data-reduction methods are developed, with varying importance
from one application to another. It is also important that online
classification is achieved during EEG recording for many applications, to
monitor changes as they happen. In this paper we introduce a method based on
Mutual Information (MI), for channel selection. Obtained results show that
whilst there is a penalty on classification accuracy scores, promising speed-up
gains can be achieved using MI techniques. Using MI with signal epochs (3secs)
containing signal transitions enhances these speed-up gains. This work is
exploratory and we suggest further research to be carried out for validation
and development. Benefits to improving classification speed include improving
application in clinical or educational settings.

Classifying Electroencephalogram(EEG) signals helps in understanding
Brain-Computer Interface (BCI). EEG signals are vital in studying how the human
mind functions. In this paper, we have used an Arithmetic Calculation dataset
consisting of Before Calculation Signals (BCS) and During Calculation Signals
(DCS). The dataset consisted of 36 participants. In order to understand the
functioning of neurons in the brain, we classified BCS vs DCS. For this
classification, we extracted various features such as Mutual Information (MI),
Phase Locking Value (PLV), and Entropy namely Permutation entropy, Spectral
entropy, Singular value decomposition entropy, Approximate entropy, Sample
entropy. The classification of these features was done using RNN-based
classifiers such as LSTM, BLSTM, ConvLSTM, and CNN-LSTM. The model achieved an
accuracy of 99.72% when entropy was used as a feature and ConvLSTM as a
classifier.

In large-scale retrieval, the lexicon-weighting paradigm, learning weighted
sparse representations in vocabulary space, has shown promising results with
high quality and low latency. Despite it deeply exploiting the
lexicon-representing capability of pre-trained language models, a crucial gap
remains between language modeling and lexicon-weighting retrieval -- the former
preferring certain or low-entropy words whereas the latter favoring pivot or
high-entropy words -- becoming the main barrier to lexicon-weighting
performance for large-scale retrieval. To bridge this gap, we propose a
brand-new pre-training framework, lexicon-bottlenecked masked autoencoder
(LexMAE), to learn importance-aware lexicon representations. Essentially, we
present a lexicon-bottlenecked module between a normal language modeling
encoder and a weakened decoder, where a continuous bag-of-words bottleneck is
constructed to learn a lexicon-importance distribution in an unsupervised
fashion. The pre-trained LexMAE is readily transferred to the lexicon-weighting
retrieval via fine-tuning, achieving 42.6\% MRR@10 with 45.83 QPS on a CPU
machine for the passage retrieval benchmark, MS-Marco. And LexMAE shows
state-of-the-art zero-shot transfer capability on BEIR benchmark with 12
datasets.

In multi-goal reinforcement learning in an environment, agents learn policies
to achieve multiple goals by using experiences gained from interactions with
the environment. With a sparse binary reward, training agents is particularly
challenging, due to a lack of successful experiences. To solve this problem,
hindsight experience replay (HER) generates successful experiences from
unsuccessful experiences. However, generating successful experiences without
consideration of the property of achieved goals is less efficient. In this
paper, a novel cluster-based sampling strategy exploiting the property of
achieved goals is proposed. The proposed sampling strategy groups episodes with
different achieved goals and samples experiences in the manner of HER. For the
grouping, K-means clustering algorithm is used. The centroids of the clusters
are obtained from the distribution of failed goals defined as the original
goals not achieved. The proposed method is validated by experiments with three
robotic control tasks of the OpenAI Gym. The results of experiments demonstrate
that the proposed method significantly reduces the number of epochs required
for convergence in two of the three tasks and marginally increases the success
rates in the remaining one. It is also shown that the proposed method can be
combined with other sampling strategies for HER.

The class of Basic Feasible Functionals BFF is the second-order counterpart
of the class of first-order functions computable in polynomial time. We present
several implicit characterizations of BFF based on a typed programming language
of terms. These terms may perform calls to imperative procedures, which are not
recursive. The type discipline has two layers: the terms follow a standard
simply-typed discipline and the procedures follow a standard tier-based type
discipline. BFF consists exactly of the second-order functionals that are
computed by typable and terminating programs. The completeness of this
characterization surprisingly still holds in the absence of lambda-abstraction.
Moreover, the termination requirement can be specified as a
completeness-preserving instance, which can be decided in time quadratic in the
size of the program. As typing is decidable in polynomial time, we obtain the
first tractable (i.e., decidable in polynomial time), sound, complete, and
implicit characterization of BFF, thus solving a problem opened for more than
20 years.

Reinforcement learning is commonly associated with training of
reward-maximizing (or cost-minimizing) agents, in other words, controllers. It
can be applied in model-free or model-based fashion, using a priori or online
collected system data to train involved parametric architectures. In general,
online reinforcement learning does not guarantee closed loop stability unless
special measures are taken, for instance, through learning constraints or
tailored training rules. Particularly promising are hybrids of reinforcement
learning with "classical" control approaches. In this work, we suggest a method
to guarantee practical stability of the system-controller closed loop in a
purely online learning setting, i.e., without offline training. Moreover, we
assume only partial knowledge of the system model. To achieve the claimed
results, we employ techniques of classical adaptive control. The implementation
of the overall control scheme is provided explicitly in a digital, sampled
setting. That is, the controller receives the state of the system and computes
the control action at discrete, specifically, equidistant moments in time. The
method is tested in adaptive traction control and cruise control where it
proved to significantly reduce the cost.

Machine learning using quantum convolutional neural networks (QCNNs) has
demonstrated success in both quantum and classical data classification. In
previous studies, QCNNs attained a higher classification accuracy than their
classical counterparts under the same training conditions in the few-parameter
regime. However, the general performance of large-scale quantum models is
difficult to examine because of the limited size of quantum circuits, which can
be reliably implemented in the near future. We propose transfer learning as an
effective strategy for utilizing small QCNNs in the noisy intermediate-scale
quantum era to the full extent. In the classical-to-quantum transfer learning
framework, a QCNN can solve complex classification problems without requiring a
large-scale quantum circuit by utilizing a pre-trained classical convolutional
neural network (CNN). We perform numerical simulations of QCNN models with
various sets of quantum convolution and pooling operations for MNIST data
classification under transfer learning, in which a classical CNN is trained
with Fashion-MNIST data. The results show that transfer learning from classical
to quantum CNN performs considerably better than purely classical transfer
learning models under similar training conditions.

Automatic segmentation of tumor lesions is a critical initial processing step
for quantitative PET/CT analysis. However, numerous tumor lesion with different
shapes, sizes, and uptake intensity may be distributed in different anatomical
contexts throughout the body, and there is also significant uptake in healthy
organs. Therefore, building a systemic PET/CT tumor lesion segmentation model
is a challenging task. In this paper, we propose a novel training strategy to
build deep learning models capable of systemic tumor segmentation. Our method
is validated on the training set of the AutoPET 2022 Challenge. We achieved
0.7574 Dice score, 0.0299 false positive volume and 0.2538 false negative
volume on preliminary test set.The code of our work is available on the
following link: https://github.com/ZZZsn/MICCAI2022-autopet.

Diffusion-based generative models have achieved promising results recently,
but raise an array of open questions in terms of conceptual understanding,
theoretical analysis, algorithm improvement and extensions to discrete,
structured, non-Euclidean domains. This work tries to re-exam the overall
framework, in order to gain better theoretical understandings and develop
algorithmic extensions for data from arbitrary domains. By viewing diffusion
models as latent variable models with unobserved diffusion trajectories and
applying maximum likelihood estimation (MLE) with latent trajectories imputed
from an auxiliary distribution, we show that both the model construction and
the imputation of latent trajectories amount to constructing diffusion bridge
processes that achieve deterministic values and constraints at end point, for
which we provide a systematic study and a suit of tools. Leveraging our
framework, we present 1) a first theoretical error analysis for learning
diffusion generation models, and 2) a simple and unified approach to learning
on data from different discrete and constrained domains. Experiments show that
our methods perform superbly on generating images, semantic segments and 3D
point clouds.

We study the combinatorial assignment domain, which includes combinatorial
auctions and course allocation. The main challenge in this domain is that the
bundle space grows exponentially in the number of items. To address this,
several papers have recently proposed machine learning-based preference
elicitation algorithms that aim to elicit only the most important information
from agents. However, the main shortcoming of this prior work is that it does
not model a mechanism's uncertainty over values for not yet elicited bundles.
In this paper, we address this shortcoming by presenting a Bayesian
Optimization-based Combinatorial Assignment (BOCA) mechanism. Our key technical
contribution is to integrate a method for capturing model uncertainty into an
iterative combinatorial auction mechanism. Concretely, we design a new method
for estimating an upper uncertainty bound that can be used as an acquisition
function to determine the next query to the agents. This enables the mechanism
to properly explore (and not just exploit) the bundle space during its
preference elicitation phase. We run computational experiments in several
spectrum auction domains to evaluate BOCA's performance. Our results show that
BOCA achieves higher allocative efficiency than state-of-the-art approaches.

For orthogonal multiple access (OMA) systems, the number of served user
equipments (UEs) is limited to the number of available orthogonal resources. On
the other hand, non-orthogonal multiple access (NOMA) schemes allow multiple
UEs to use the same orthogonal resource. This extra degree of freedom
introduces new challenges for resource allocation. Buffer state information
(BSI), like the size and age of packets waiting for transmission, can be used
to improve scheduling in OMA systems. In this paper, we investigate the impact
of BSI on the performance of a centralized scheduler in an uplink multi-carrier
NOMA scenario with UEs having various data rate and latency requirements. To
handle the large combinatorial space of allocating UEs to the resources, we
propose a novel scheduler based on actor-critic reinforcement learning
incorporating BSI. Training and evaluation are carried out using Nokia's
"wireless suite". We propose various novel techniques to both stabilize and
speed up training. The proposed scheduler outperforms benchmark schedulers.

Table structure recognition is a crucial part of document image analysis
domain. Its difficulty lies in the need to parse the physical coordinates and
logical indices of each cell at the same time. However, the existing methods
are difficult to achieve both these goals, especially when the table splitting
lines are blurred or tilted. In this paper, we propose an accurate and
end-to-end transformer-based table structure recognition method, referred to as
TRUST. Transformers are suitable for table structure recognition because of
their global computations, perfect memory, and parallel computation. By
introducing novel Transformer-based Query-based Splitting Module and
Vertex-based Merging Module, the table structure recognition problem is
decoupled into two joint optimization sub-tasks: multi-oriented table
row/column splitting and table grid merging. The Query-based Splitting Module
learns strong context information from long dependencies via Transformer
networks, accurately predicts the multi-oriented table row/column separators,
and obtains the basic grids of the table accordingly. The Vertex-based Merging
Module is capable of aggregating local contextual information between adjacent
basic grids, providing the ability to merge basic girds that belong to the same
spanning cell accurately. We conduct experiments on several popular benchmarks
including PubTabNet and SynthTable, our method achieves new state-of-the-art
results. In particular, TRUST runs at 10 FPS on PubTabNet, surpassing the
previous methods by a large margin.

We present the design and baseline results for a new challenge in the
ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on
"cross-domain" meta-learning. Meta-learning aims to leverage experience gained
from previous tasks to solve new tasks efficiently (i.e., with better
performance, little training data, and/or modest computational resources).
While previous challenges in the series focused on within-domain few-shot
learning problems, with the aim of learning efficiently N-way k-shot tasks
(i.e., N class classification problems with k training examples), this
competition challenges the participants to solve "any-way" and "any-shot"
problems drawn from various domains (healthcare, ecology, biology,
manufacturing, and others), chosen for their humanitarian and societal impact.
To that end, we created Meta-Album, a meta-dataset of 40 image classification
datasets from 10 domains, from which we carve out tasks with any number of
"ways" (within the range 2-20) and any number of "shots" (within the range
1-20). The competition is with code submission, fully blind-tested on the
CodaLab challenge platform. The code of the winners will be open-sourced,
enabling the deployment of automated machine learning solutions for few-shot
image classification across several domains.

Tactile maps are commonly used to give visually impaired users access to
geographical representations. Although those relief maps are efficient tools
for acquisition of spatial knowledge, they present several limitations and
issues such as the need to read braille. Several research projects have been
led during the past three decades in order to improve access to maps using
interactive technologies. In this chapter, we present an exhaustive review of
interactive map prototypes. We classified existing interactive maps into two
categories: Digital Interactive Maps (DIMs) that are displayed on a flat
surface such as a screen; and Hybrid Interactive Maps (HIMs) that include both
a digital and a physical representation. In each family, we identified several
subcategories depending on the technology being used. We compared the
categories and subcategories according to cost, availability and technological
limitations, but also in terms of content, comprehension and interactivity.
Then we reviewed a number of studies showing that those maps can support
spatial learning for visually impaired users. Finally, we identified new
technologies and methods that could improve the accessibility of graphics for
visually impaired users in the future.

Deep Neural Networks (DNNs) have achieved excellent performance in various
fields. However, DNNs' vulnerability to Adversarial Examples (AE) hinders their
deployments to safety-critical applications. This paper presents a novel AE
detection framework, named BEYOND, for trustworthy predictions. BEYOND performs
the detection by distinguishing the AE's abnormal relation with its augmented
versions, i.e. neighbors, from two prospects: representation similarity and
label consistency. An off-the-shelf Self-Supervised Learning (SSL) model is
used to extract the representation and predict the label for its highly
informative representation capacity compared to supervised learning models. For
clean samples, their representations and predictions are closely consistent
with their neighbors, whereas those of AEs differ greatly. Furthermore, we
explain this observation and show that by leveraging this discrepancy BEYOND
can effectively detect AEs. We develop a rigorous justification for the
effectiveness of BEYOND. Furthermore, as a plug-and-play model, BEYOND can
easily cooperate with the Adversarial Trained Classifier (ATC), achieving the
state-of-the-art (SOTA) robustness accuracy. Experimental results show that
BEYOND outperforms baselines by a large margin, especially under adaptive
attacks. Empowered by the robust relation net built on SSL, we found that
BEYOND outperforms baselines in terms of both detection ability and speed. Our
code will be publicly available.

Active learning is a paradigm of machine learning which aims at reducing the
amount of labeled data needed to train a classifier. Its overall principle is
to sequentially select the most informative data points, which amounts to
determining the uncertainty of regions of the input space. The main challenge
lies in building a procedure that is computationally efficient and that offers
appealing theoretical properties; most of the current methods satisfy only one
or the other. In this paper, we use the classification with rejection in a
novel way to estimate the uncertain regions. We provide an active learning
algorithm and prove its theoretical benefits under classical assumptions. In
addition to the theoretical results, numerical experiments have been carried
out on synthetic and non-synthetic datasets. These experiments provide
empirical evidence that the use of rejection arguments in our active learning
algorithm is beneficial and allows good performance in various statistical
situations.

Novices need to overcome initial barriers while programming cyber-physical
systems behavior, like coding quadcopter missions, and should thus be supported
by an adequately designed programming environment. Using multiple
representations by including graphical previews is a common approach to ease
coding and program understanding. However, novices struggle to map information
of the code and graphical previews. Previous studies imply that mapping aids in
a live programming environment might support novices while programming and
foster a deeper understanding of the content. To implement these mapping aids
in a domain independent way Source Location Tracking based on run-time
information can be used. In our study, we tested N=82 participants while
interacting and learning in an online programming environment. Using our 2x2
between-subject design study, we investigated the effects of two mapping aids:
highlighting and dynamic linking on coding correctness including typical
errors, and learning outcomes. Based on process data, successful strategies
were analyzed. Combining both mapping aids compared to one aid resulted in
higher performance. While highlights were more helpful for implementing the
quadcopter missions, dynamic linking improved learning outcomes on the
comprehension and application level . Traces of learning strategies were
related to higher coding correctness and higher learning outcomes. Based on
process data, users in the group with both aids had a higher chance of avoiding
certain typical implementation mistakes. Implementing dynamic linking and
highlighting through source location tracking is a promising approach to
support novices to develop a better semantic understanding of the domain
specific language. Depending on the coding tasks different mapping aids might
be effective.

Hardware security has been a key concern in modern information technologies.
Especially, as the number of Internet-of-Things (IoT) devices grows rapidly, to
protect the device security with low-cost security primitives becomes
essential, among which Physical Unclonable Function (PUF) is a widely-used
solution. In this paper, we propose the first FeFET-based strong PUF exploiting
the cycle-to-cycle (C2C) variation of FeFETs as the entropy source. Based on
the experimental measurements, the proposed PUF shows satisfying performance
including high uniformity, uniqueness, reconfigurability and reliability. To
resist machine-learning attack, XOR structure was introduced, and simulations
show that our proposed PUF has similar resistance to existing attack models
with traditional arbiter PUFs. Furthermore, our design is shown to be
power-efficient, and highly robust to write voltage, temperature and device
size, which makes it a competitive security solution for Internet-of-Things
edge devices.

Diagonal linear networks (DLNs) are a toy simplification of artificial neural
networks; they consist in a quadratic reparametrization of linear regression
inducing a sparse implicit regularization. In this paper, we describe the
trajectory of the gradient flow of DLNs in the limit of small initialization.
We show that incremental learning is effectively performed in the limit:
coordinates are successively activated, while the iterate is the minimizer of
the loss constrained to have support on the active coordinates only. This shows
that the sparse implicit regularization of DLNs decreases with time. This work
is restricted to the underparametrized regime with anti-correlated features for
technical reasons.

Most current methods generate adversarial examples with the $L_p$ norm
specification. As a result, many defense methods utilize this property to
eliminate the impact of such attacking algorithms. In this paper,we instead
introduce "unrestricted" perturbations that create adversarial samples by using
spurious relations which were learned by model training. Specifically, we find
feature clusters in non-semantic features that are strongly correlated with
model judgment results, and treat them as spurious relations learned by the
model. Then we create adversarial samples by using them to replace the
corresponding feature clusters in the target image. Experimental evaluations
show that in both black-box and white-box situations. Our adversarial examples
do not change the semantics of images, while still being effective at fooling
an adversarially trained DNN image classifier.

Currently, deep learning methods with stacking small size convolutional
filters are widely used for automatic modulation classification (AMC). In this
report, we find some experienced improvements by using large kernel size for
convolutional deep convolution neural network based AMC, which is more
efficient in extracting multi-scale features of the raw signal I/Q sequence
data. Also, Squeeze-and-Excitation (SE) mechanisms can significantly help AMC
networks to focus on the more important features of the signal. As a result, we
propose a multi-scale feature network with large kernel size and SE mechanism
(SE-MSFN) in this paper. SE-MSFN achieves state-of-the-art classification
performance on the public well-known RADIOML 2018.01A dataset, with average
classification accuracy of 64.50%, surpassing CLDNN by 1.42%, maximum
classification accuracy of 98.5%, and an average classification accuracy of
85.53% in the lower SNR range 0dB to 10dB, surpassing CLDNN by 2.85%. In
addition, we also verified that ensemble learning can help further improve
classification performance. We hope this report can provide some references for
developers and researchers in practical scenes.

Object detection networks have reached an impressive performance level, yet a
lack of suitable data in specific applications often limits it in practice.
Typically, additional data sources are utilized to support the training task.
In these, however, domain gaps between different data sources pose a challenge
in deep learning. GAN-based image-to-image style-transfer is commonly applied
to shrink the domain gap, but is unstable and decoupled from the object
detection task. We propose AWADA, an Attention-Weighted Adversarial Domain
Adaptation framework for creating a feedback loop between style-transformation
and detection task. By constructing foreground object attention maps from
object detector proposals, we focus the transformation on foreground object
regions and stabilize style-transfer training. In extensive experiments and
ablation studies, we show that AWADA reaches state-of-the-art unsupervised
domain adaptation object detection performance in the commonly used benchmarks
for tasks such as synthetic-to-real, adverse weather and cross-camera

With the increasing use of Machine Learning (ML) in critical autonomous
systems, runtime monitors have been developed to detect prediction errors and
keep the system in a safe state during operations. Monitors have been proposed
for different applications involving diverse perception tasks and ML models,
and specific evaluation procedures and metrics are used for different contexts.
This paper introduces three unified safety-oriented metrics, representing the
safety benefits of the monitor (Safety Gain), the remaining safety gaps after
using it (Residual Hazard), and its negative impact on the system's performance
(Availability Cost). To compute these metrics, one requires to define two
return functions, representing how a given ML prediction will impact expected
future rewards and hazards. Three use-cases (classification, drone landing, and
autonomous driving) are used to demonstrate how metrics from the literature can
be expressed in terms of the proposed metrics. Experimental results on these
examples show how different evaluation choices impact the perceived performance
of a monitor. As our formalism requires us to formulate explicit safety
assumptions, it allows us to ensure that the evaluation conducted matches the
high-level system requirements.

Image retrieval systems help users to browse and search among extensive
images in real-time. With the rise of cloud computing, retrieval tasks are
usually outsourced to cloud servers. However, the cloud scenario brings a
daunting challenge of privacy protection as cloud servers cannot be fully
trusted. To this end, image-encryption-based privacy-preserving image retrieval
schemes have been developed, which first extract features from cipher-images,
and then build retrieval models based on these features. Yet, most existing
approaches extract shallow features and design trivial retrieval models,
resulting in insufficient expressiveness for the cipher-images. In this paper,
we propose a novel paradigm named Encrypted Vision Transformer (EViT), which
advances the discriminative representations capability of cipher-images. First,
in order to capture comprehensive ruled information, we extract multi-level
local length sequence and global Huffman-code frequency features from the
cipher-images which are encrypted by stream cipher during JPEG compression
process. Second, we design the Vision Transformer-based retrieval model to
couple with the multi-level features, and propose two adaptive data
augmentation methods to improve representation power of the retrieval model.
Our proposal can be easily adapted to unsupervised and supervised settings via
self-supervised contrastive learning manner. Extensive experiments reveal that
EViT achieves both excellent encryption and retrieval performance,
outperforming current schemes in terms of retrieval accuracy by large margins
while protecting image privacy effectively. Code is publicly available at
\url{https://github.com/onlinehuazai/EViT}.

Demonstration learning aims to guide the prompt prediction via providing
answered demonstrations in the few shot settings. Despite achieving promising
results, existing work only concatenates the answered examples as
demonstrations to the prompt template (including the raw context) without any
additional operation, neglecting the prompt-demonstration dependencies.
Besides, prior research found that randomly replacing the labels of
demonstrations marginally hurts performance, illustrating that the model could
not properly learn the knowledge brought by the demonstrations. Inspired by the
human learning process, in this paper, we introduce Imitation DEMOnstration
Learning (Imitation-Demo) to strengthen demonstration learning via explicitly
imitating human review behaviour, which includes: (1) contrastive learning
mechanism to concentrate on the similar demonstrations. (2) demonstration-label
re-prediction method to consolidate known knowledge. Experiment results show
that our proposed method achieves state-of-the-art performance on 11 out of 14
classification corpora. Further studies also prove that Imitation-Demo
strengthen the association between prompt and demonstrations, which could
provide the basis for exploring how demonstration learning works.

To date, there is a lack of research on learning environments for pre-service
physics teachers that allow them to learn and practise diagnosing students'
conceptions that are (currently) not covered in physics education textbooks
(e.g. students' conceptions about viscosity). In this study, we developed and
piloted such a learning environment, which was implemented and piloted twice in
a seminar for pre-service physics teachers. As coping with a diagnostic process
is particularly demanding for pre-service physics teachers, our accompanying
research aims to identify learning barriers within our developed learning
environment. The results indicate that the participants experience the learning
environment with varying degrees of difficulty. One main difficulty for
pre-service physics teachers seems to be in interconnecting their content
knowledge with their pedagogical content knowledge in the diagnostic process.

This paper examines the drivers of CPI inflation through the lens of a
simple, but computationally intensive machine learning technique. More
specifically, it predicts inflation across 20 advanced countries between 2000
and 2021, relying on 1,000 regression trees that are constructed based on six
key macroeconomic variables. This agnostic, purely data driven method delivers
(relatively) good outcome prediction performance. Out of sample root mean
square errors (RMSE) systematically beat even the in-sample benchmark
econometric models. Partial effects of inflation expectations on CPI outcomes
are also elicited in the paper. Overall, the results highlight the role of
expectations for inflation outcomes in advanced economies, even though their
importance appears to have declined somewhat during the last 10 years.

Vision Transformer and its variants have demonstrated great potential in
various computer vision tasks. But conventional vision transformers often focus
on global dependency at a coarse level, which suffer from a learning challenge
on global relationships and fine-grained representation at a token level. In
this paper, we introduce Multi-scale Attention Fusion into transformer
(MAFormer), which explores local aggregation and global feature extraction in a
dual-stream framework for visual recognition. We develop a simple but effective
module to explore the full potential of transformers for visual representation
by learning fine-grained and coarse-grained features at a token level and
dynamically fusing them. Our Multi-scale Attention Fusion (MAF) block consists
of: i) a local window attention branch that learns short-range interactions
within windows, aggregating fine-grained local features; ii) global feature
extraction through a novel Global Learning with Down-sampling (GLD) operation
to efficiently capture long-range context information within the whole image;
iii) a fusion module that self-explores the integration of both features via
attention. Our MAFormer achieves state-of-the-art performance on common vision
tasks. In particular, MAFormer-L achieves 85.9$\%$ Top-1 accuracy on ImageNet,
surpassing CSWin-B and LV-ViT-L by 1.7$\%$ and 0.6$\%$ respectively. On MSCOCO,
MAFormer outperforms the prior art CSWin by 1.7$\%$ mAPs on object detection
and 1.4$\%$ on instance segmentation with similar-sized parameters,
demonstrating the potential to be a general backbone network.

Dialogue bots have been widely applied in customer service scenarios to
provide timely and user-friendly experience. These bots must classify the
appropriate domain of a dialogue, understand the intent of users, and generate
proper responses. Existing dialogue pre-training models are designed only for
several dialogue tasks and ignore weakly-supervised expert knowledge in
customer service dialogues. In this paper, we propose a novel unified knowledge
prompt pre-training framework, UFA (\textbf{U}nified Model \textbf{F}or
\textbf{A}ll Tasks), for customer service dialogues. We formulate all the tasks
of customer service dialogues as a unified text-to-text generation task and
introduce a knowledge-driven prompt strategy to jointly learn from a mixture of
distinct dialogue tasks. We pre-train UFA on a large-scale Chinese customer
service corpus collected from practical scenarios and get significant
improvements on both natural language understanding (NLU) and natural language
generation (NLG) benchmarks.

We analyse the drivers of European Power Exchange (EPEX) wholesale
electricity prices between 2012 and early 2022 using machine learning. The
agnostic random forest approach that we use is able to reduce in-sample root
mean square errors (RMSEs) by around 50% when compared to a standard linear
least square model. This indicates that non-linearities and interaction effects
are key in wholesale electricity markets. Out-of-sample prediction errors using
machine learning are (slightly) lower than even in-sample least square errors
using a least square model. The effects of efforts to limit power consumption
and green the energy matrix on wholesale electricity prices are first order.
CO2 permit prices strongly impact electricity prices, as do the prices of
source energy commodities. And carbon permit prices impact has clearly
increased post-2021 (particularly for baseload prices). Among energy sources,
natural gas has the largest effect on electricity prices. Importantly, the role
of wind energy feed-in has slowly risen over time, and its impact is now
roughly on par with that of coal.

This paper provides a brief overview of our submission to the no interaction
track of SAPIEN ManiSkill Challenge 2021. Our approach follows an end-to-end
pipeline which mainly consists of two steps: we first extract the point cloud
features of multiple objects; then we adopt these features to predict the
action score of the robot simulators through a deep and wide transformer-based
network. More specially, %to give guidance for future work, to open up avenues
for exploitation of learning manipulation skill, we present an empirical study
that includes a bag of tricks and abortive attempts. Finally, our method
achieves a promising ranking on the leaderboard. All code of our solution is
available at https://github.com/liu666666/bigfish\_codes.

Optical Coherence Tomography(OCT) is a non-invasive technique capturing
cross-sectional area of the retina in micro-meter resolutions. It has been
widely used as a auxiliary imaging reference to detect eye-related pathology
and predict longitudinal progression of the disease characteristics. Retina
layer segmentation is one of the crucial feature extraction techniques, where
the variations of retinal layer thicknesses and the retinal layer deformation
due to the presence of the fluid are highly correlated with multiple epidemic
eye diseases like Diabetic Retinopathy(DR) and Age-related Macular Degeneration
(AMD). However, these images are acquired from different devices, which have
different intensity distribution, or in other words, belong to different
imaging domains. This paper proposes a segmentation-guided domain-adaptation
method to adapt images from multiple devices into single image domain, where
the state-of-art pre-trained segmentation model is available. It avoids the
time consumption of manual labelling for the upcoming new dataset and the
re-training of the existing network. The semantic consistency and global
feature consistency of the network will minimize the hallucination effect that
many researchers reported regarding Cycle-Consistent Generative Adversarial
Networks(CycleGAN) architecture.

Recently, with the continuous development of deep learning, the performance
of named entity recognition tasks has been dramatically improved. However, the
privacy and the confidentiality of data in some specific fields, such as
biomedical and military, cause insufficient data to support the training of
deep neural networks. In this paper, we propose an encryption learning
framework to address the problems of data leakage and inconvenient disclosure
of sensitive data in certain domains. We introduce multiple encryption
algorithms to encrypt training data in the named entity recognition task for
the first time. In other words, we train the deep neural network using the
encrypted data. We conduct experiments on six Chinese datasets, three of which
are constructed by ourselves. The experimental results show that the encryption
method achieves satisfactory results. The performance of some models trained
with encrypted data even exceeds the performance of the unencrypted method,
which verifies the effectiveness of the introduced encryption method and solves
the problem of data leakage to a certain extent.

Camera traps, unmanned observation devices, and deep learning-based image
recognition systems have greatly reduced human effort in collecting and
analyzing wildlife images. However, data collected via above apparatus exhibits
1) long-tailed and 2) open-ended distribution problems. To tackle the open-set
long-tailed recognition problem, we propose the Temporal Flow Mask Attention
Network that comprises three key building blocks: 1) an optical flow module, 2)
an attention residual module, and 3) a meta-embedding classifier. We extract
temporal features of sequential frames using the optical flow module and learn
informative representation using attention residual blocks. Moreover, we show
that applying the meta-embedding technique boosts the performance of the method
in open-set long-tailed recognition. We apply this method on a Korean
Demilitarized Zone (DMZ) dataset. We conduct extensive experiments, and
quantitative and qualitative analyses to prove that our method effectively
tackles the open-set long-tailed recognition problem while being robust to
unknown classes.

Learning curves plot the expected error of a learning algorithm as a function
of the number of labeled samples it receives from a target distribution. They
are widely used as a measure of an algorithm's performance, but classic PAC
learning theory cannot explain their behavior.
  As observed by Antos and Lugosi (1996 , 1998), the classic `No Free Lunch'
lower bounds only trace the upper envelope above all learning curves of
specific target distributions. For a concept class with VC dimension $d$ the
classic bound decays like $d/n$, yet it is possible that the learning curve for
\emph{every} specific distribution decays exponentially. In this case, for each
$n$ there exists a different `hard' distribution requiring $d/n$ samples. Antos
and Lugosi asked which concept classes admit a `strong minimax lower bound' --
a lower bound of $d'/n$ that holds for a fixed distribution for infinitely many
$n$.
  We solve this problem in a principled manner, by introducing a combinatorial
dimension called VCL that characterizes the best $d'$ for which $d'/n$ is a
strong minimax lower bound. Our characterization strengthens the lower bounds
of Bousquet, Hanneke, Moran, van Handel, and Yehudayoff (2021), and it refines
their theory of learning curves, by showing that for classes with finite VCL
the learning rate can be decomposed into a linear component that depends only
on the hypothesis class and an exponential component that depends also on the
target distribution. As a corollary, we recover the lower bound of Antos and
Lugosi (1996 , 1998) for half-spaces in $\mathbb{R}^d$.
  Finally, to provide another viewpoint on our work and how it compares to
traditional PAC learning bounds, we also present an alternative formulation of
our results in a language that is closer to the PAC setting.

Conversational recommender systems (CRS) dynamically obtain the user
preferences via multi-turn questions and answers. The existing CRS solutions
are widely dominated by deep reinforcement learning algorithms. However, deep
reinforcement learning methods are often criticised for lacking
interpretability and requiring a large amount of training data to perform.
  In this paper, we explore a simpler alternative and propose a decision tree
based solution to CRS. The underlying challenge in CRS is that the same item
can be described differently by different users. We show that decision trees
are sufficient to characterize the interactions between users and items, and
solve the key challenges in multi-turn CRS: namely which questions to ask, how
to rank the candidate items, when to recommend, and how to handle negative
feedback on the recommendations. Firstly, the training of decision trees
enables us to find questions which effectively narrow down the search space.
Secondly, by learning embeddings for each item and tree nodes, the candidate
items can be ranked based on their similarity to the conversation context
encoded by the tree nodes. Thirdly, the diversity of items associated with each
tree node allows us to develop an early stopping strategy to decide when to
make recommendations. Fourthly, when the user rejects a recommendation, we
adaptively choose the next decision tree to improve subsequent questions and
recommendations. Extensive experiments on three publicly available benchmark
CRS datasets show that our approach provides significant improvement to the
state of the art CRS methods.

Multi-source data fusion, in which multiple data sources are jointly analyzed
to obtain improved information, has considerable research attention. For the
datasets of multiple medical institutions, data confidentiality and
cross-institutional communication are critical. In such cases, data
collaboration (DC) analysis by sharing dimensionality-reduced intermediate
representations without iterative cross-institutional communications may be
appropriate. Identifiability of the shared data is essential when analyzing
data including personal information. In this study, the identifiability of the
DC analysis is investigated. The results reveals that the shared intermediate
representations are readily identifiable to the original data for supervised
learning. This study then proposes a non-readily identifiable DC analysis only
sharing non-readily identifiable data for multiple medical datasets including
personal information. The proposed method solves identifiability concerns based
on a random sample permutation, the concept of interpretable DC analysis, and
usage of functions that cannot be reconstructed. In numerical experiments on
medical datasets, the proposed method exhibits a non-readily identifiability
while maintaining a high recognition performance of the conventional DC
analysis. For a hospital dataset, the proposed method exhibits a nine
percentage point improvement regarding the recognition performance over the
local analysis that uses only local dataset.

We propose the Sparse Abstract Machine (SAM), an intermediate representation
and abstract machine model for targeting sparse tensor algebra to
reconfigurable and fixed-function spatial dataflow accelerators. SAM defines a
streaming abstraction with sparse primitives that encompass a large space of
scheduled tensor algebra expressions. SAM dataflow graphs naturally separate
tensor formats from algorithms and is expressive enough to incorporate many
sparse-iteration and hardware-specific optimizations. We show an automatic
compilation technique from a high-level language to SAM and a set of hardware
primitives which implement it. We evaluate the generality and extensibility of
our sparse abstract machine, explore the performance space of sparse tensor
algebra optimizations using SAM, and provide an example implementation of our
SAM architecture.

Fine-grained visual categorization (FGVC) aims at recognizing objects from
similar subordinate categories, which is challenging and practical for human's
accurate automatic recognition needs. Most FGVC approaches focus on the
attention mechanism research for discriminative regions mining while neglecting
their interdependencies and composed holistic object structure, which are
essential for model's discriminative information localization and understanding
ability. To address the above limitations, we propose the Structure Information
Modeling Transformer (SIM-Trans) to incorporate object structure information
into transformer for enhancing discriminative representation learning to
contain both the appearance information and structure information.
Specifically, we encode the image into a sequence of patch tokens and build a
strong vision transformer framework with two well-designed modules: (i) the
structure information learning (SIL) module is proposed to mine the spatial
context relation of significant patches within the object extent with the help
of the transformer's self-attention weights, which is further injected into the
model for importing structure information; (ii) the multi-level feature
boosting (MFB) module is introduced to exploit the complementary of multi-level
features and contrastive learning among classes to enhance feature robustness
for accurate recognition. The proposed two modules are light-weighted and can
be plugged into any transformer network and trained end-to-end easily, which
only depends on the attention weights that come with the vision transformer
itself. Extensive experiments and analyses demonstrate that the proposed
SIM-Trans achieves state-of-the-art performance on fine-grained visual
categorization benchmarks. The code is available at
https://github.com/PKU-ICST-MIPL/SIM-Trans_ACMMM2022.

QA models with lifelong learning (LL) abilities are important for practical
QA applications, and architecture-based LL methods are reported to be an
effective implementation for these models. However, it is non-trivial to extend
previous approaches to QA tasks since they either require access to task
identities in the testing phase or do not explicitly model samples from unseen
tasks. In this paper, we propose Diana: a dynamic architecture-based lifelong
QA model that tries to learn a sequence of QA tasks with a prompt enhanced
language model. Four types of hierarchically organized prompts are used in
Diana to capture QA knowledge from different granularities. Specifically, we
dedicate task-level prompts to capture task-specific knowledge to retain high
LL performances and maintain instance-level prompts to learn knowledge shared
across different input samples to improve the model's generalization
performance. Moreover, we dedicate separate prompts to explicitly model unseen
tasks and introduce a set of prompt key vectors to facilitate knowledge sharing
between tasks. Extensive experiments demonstrate that Diana outperforms
state-of-the-art lifelong QA models, especially in handling unseen tasks.

With the development of intelligent operation and maintenance of substations,
the daily inspection of substations needs to process massive video and image
data. This puts forward higher requirements on the processing speed and
accuracy of defect detection. Based on the end-to-end learning paradigm, this
paper proposes an intelligent detection method for substation insulator defects
based on CenterMask. First, the backbone network VoVNet is improved according
to the residual connection and eSE module, which effectively solves the
problems of deep network saturation and gradient information loss. On this
basis, an insulator mask generation method based on a spatial attentiondirected
mechanism is proposed. Insulators with complex image backgrounds are accurately
segmented. Then, three strategies of pixel-wise regression prediction,
multi-scale features and centerness are introduced. The anchor-free
single-stage target detector accurately locates the defect points of
insulators. Finally, an example analysis is carried out with the substation
inspection image of a power supply company in a certain area to verify the
effectiveness and robustness of the proposed method.

This paper looks at semi-supervised learning (SSL) for image-based text
recognition. One of the most popular SSL approaches is pseudo-labeling (PL). PL
approaches assign labels to unlabeled data before re-training the model with a
combination of labeled and pseudo-labeled data. However, PL methods are
severely degraded by noise and are prone to over-fitting to noisy labels, due
to the inclusion of erroneous high confidence pseudo-labels generated from
poorly calibrated models, thus, rendering threshold-based selection
ineffective. Moreover, the combinatorial complexity of the hypothesis space and
the error accumulation due to multiple incorrect autoregressive steps posit
pseudo-labeling challenging for sequence models. To this end, we propose a
pseudo-label generation and an uncertainty-based data selection framework for
semi-supervised text recognition. We first use Beam-Search inference to yield
highly probable hypotheses to assign pseudo-labels to the unlabelled examples.
Then we adopt an ensemble of models, sampled by applying dropout, to obtain a
robust estimate of the uncertainty associated with the prediction, considering
both the character-level and word-level predictive distribution to select good
quality pseudo-labels. Extensive experiments on several benchmark handwriting
and scene-text datasets show that our method outperforms the baseline
approaches and the previous state-of-the-art semi-supervised text-recognition
methods.

In one-class recommendation systems, the goal is to learn a model from a
small set of interacted users and items and then identify the
positively-related user-item pairs among a large number of pairs with unknown
interactions. Most previous loss functions rely on dissimilar pairs of users
and items, which are selected from the ones with unknown interactions, to
obtain better prediction performance. This strategy introduces several
challenges such as increasing training time and hurting the performance by
picking "similar pairs with the unknown interactions" as dissimilar pairs. In
this paper, the goal is to only use the similar set to train the models. We
point out three trivial solutions that the models converge to when they are
trained only on similar pairs: collapsed, partially collapsed, and shrinking
solutions. We propose two terms that can be added to the objective functions in
the literature to avoid these solutions. The first one is a hinge pairwise
distance loss that avoids the shrinking and collapsed solutions by keeping the
average pairwise distance of all the representations greater than a margin. The
second one is an orthogonality term that minimizes the correlation between the
dimensions of the representations and avoids the partially collapsed solution.
We conduct experiments on a variety of tasks on public and real-world datasets.
The results show that our approach using only similar pairs outperforms
state-of-the-art methods using similar pairs and a large number of dissimilar
pairs.

Temporal variations of apparent magnitude, called light curves, are
observational statistics of interest captured by telescopes over long periods
of time. Light curves afford the exploration of Space Domain Awareness (SDA)
objectives such as object identification or pose estimation as latent variable
inference problems. Ground-based observations from commercial off the shelf
(COTS) cameras remain inexpensive compared to higher precision instruments,
however, limited sensor availability combined with noisier observations can
produce gappy time-series data that can be difficult to model. These external
factors confound the automated exploitation of light curves, which makes light
curve prediction and extrapolation a crucial problem for applications.
Traditionally, image or time-series completion problems have been approached
with diffusion-based or exemplar-based methods. More recently, Deep Neural
Networks (DNNs) have become the tool of choice due to their empirical success
at learning complex nonlinear embeddings. However, DNNs often require large
training data that are not necessarily available when looking at unique
features of a light curve of a single satellite.
  In this paper, we present a novel approach to predicting missing and future
data points of light curves using Gaussian Processes (GPs). GPs are non-linear
probabilistic models that infer posterior distributions over functions and
naturally quantify uncertainty. However, the cubic scaling of GP inference and
training is a major barrier to their adoption in applications. In particular, a
single light curve can feature hundreds of thousands of observations, which is
well beyond the practical realization limits of a conventional GP on a single
machine. Consequently, we employ MuyGPs, a scalable framework for
hyperparameter estimation of GP models that uses nearest neighbors
sparsification and local cross-validation. MuyGPs...

Matrix splitting methods play a vital role in solving linear systems, and
their efficiency heavily depends on the optimal splitting parameters. However,
the splitting parameter selection method has not been well developed. Existing
direct traversal method is computationally expensive, and theoretical
estimation method is available on a case-by-case basis and usually relies on
the upper bound of the spectral radius of iterative matrix. In this paper, we
present a multitask kernel-learning parameter prediction method to
automatically obtain accurate splitting parameters. For application to solving
time-dependent linear systems, we propose a class of matrix splitting Kronecker
product methods and give corresponding convergence analysis. Finally, we apply
our approaches to solve two-dimensional diffusion and convection-diffusion
equations, and the differential Sylvester matrix equation. Numerical results
illustrate the efficiency and superiority of our methods.

In object detection, data amount and cost are a trade-off, and collecting a
large amount of data in a specific domain is labor intensive. Therefore,
existing large-scale datasets are used for pre-training. However, conventional
transfer learning and domain adaptation cannot bridge the domain gap when the
target domain differs significantly from the source domain. We propose a data
synthesis method that can solve the large domain gap problem. In this method, a
part of the target image is pasted onto the source image, and the position of
the pasted region is aligned by utilizing the information of the object
bounding box. In addition, we introduce adversarial learning to discriminate
whether the original or the pasted regions. The proposed method trains on a
large number of source images and a few target domain images. The proposed
method achieves higher accuracy than conventional methods in a very different
domain problem setting, where RGB images are the source domain, and thermal
infrared images are the target domain. Similarly, the proposed method achieves
higher accuracy in the cases of simulation images to real images.

Like many team sports, basketball involves two groups of players who engage
in collaborative and adversarial activities to win a game. Players and teams
are executing various complex strategies to gain an advantage over their
opponents. Defining, identifying, and analyzing different types of activities
is an important task in sports analytics, as it can lead to better strategies
and decisions by the players and coaching staff. The objective of this paper is
to automatically recognize basketball group activities from tracking data
representing locations of players and the ball during a game. We propose a
novel deep learning approach for group activity recognition (GAR) in team
sports called NETS. To efficiently model the player relations in team sports,
we combined a Transformer-based architecture with LSTM embedding, and a
team-wise pooling layer to recognize the group activity. Training such a neural
network generally requires a large amount of annotated data, which incurs high
labeling cost. To address scarcity of manual labels, we generate weak-labels
and pretrain the neural network on a self-supervised trajectory prediction
task. We used a large tracking data set from 632 NBA games to evaluate our
approach. The results show that NETS is capable of learning group activities
with high accuracy, and that self- and weak-supervised training in NETS have a
positive impact on GAR accuracy.

A significant body of recent research in the field of Learning Analytics has
focused on leveraging machine learning approaches for predicting at-risk
students in order to initiate timely interventions and thereby elevate
retention and completion rates. The overarching feature of the majority of
these research studies has been on the science of prediction only. The
component of predictive analytics concerned with interpreting the internals of
the models and explaining their predictions for individual cases to
stakeholders has largely been neglected. Additionally, works that attempt to
employ data-driven prescriptive analytics to automatically generate
evidence-based remedial advice for at-risk learners are in their infancy.
eXplainable AI is a field that has recently emerged providing cutting-edge
tools which support transparent predictive analytics and techniques for
generating tailored advice for at-risk students. This study proposes a novel
framework that unifies both transparent machine learning as well as techniques
for enabling prescriptive analytics. This work practically demonstrates the
proposed framework using predictive models for identifying at-risk learners of
programme non-completion. The study then further demonstrates how predictive
modelling can be augmented with prescriptive analytics on two case studies in
order to generate human-readable prescriptive feedback for those who are at
risk.

Transformer-based neural networks have achieved state-of-the-art task
performance in a number of machine learning domains including natural language
processing and computer vision. To further improve their accuracy, recent work
has explored the integration of dynamic behavior into these networks in the
form of mixture-of-expert (MoE) layers. In this paper, we explore the
introduction of MoE layers to optimize a different metric: inference latency.
We introduce a novel system named PLANER that takes an existing
Transformer-based network and a user-defined latency target and produces an
optimized, sparsely-activated version of the original network that tries to
meet the latency target while maintaining baseline accuracy. We evaluate PLANER
on two real-world language modeling tasks using the Transformer-XL network and
achieve inference latency reductions of over 2x at iso-accuracy.

Recovering underlying Directed Acyclic Graph structures (DAG) from
observational data is highly challenging due to the combinatorial nature of the
DAG-constrained optimization problem. Recently, DAG learning has been cast as a
continuous optimization problem by characterizing the DAG constraint as a
smooth equality one, generally based on polynomials over adjacency matrices.
Existing methods place very small coefficients on high-order polynomial terms
for stabilization, since they argue that large coefficients on the higher-order
terms are harmful due to numeric exploding. On the contrary, we discover that
large coefficients on higher-order terms are beneficial for DAG learning, when
the spectral radiuses of the adjacency matrices are small, and that larger
coefficients for higher-order terms can approximate the DAG constraints much
better than the small counterparts. Based on this, we propose a novel DAG
learning method with efficient truncated matrix power iteration to approximate
geometric series-based DAG constraints. Empirically, our DAG learning method
outperforms the previous state-of-the-arts in various settings, often by a
factor of 3 or more in terms of structural Hamming distance.

We study how fads emerge from social learning in a changing environment. We
consider a sequential learning model in which rational agents arrive in order,
each acting only once, and the underlying unknown state is constantly evolving.
Each agent receives a private signal, observes all past actions of others, and
chooses an action to match the current state. Since the state changes over
time, cascades cannot last forever, and actions fluctuate too. We show that in
the long run, actions change more often than the state. This describes many
real-life faddish behaviors in which people often change their actions more
frequently than what is necessary.

In this paper, we introduce LINKS, a dataset of 100 million one degree of
freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more
than 1000 times larger than any existing database of planar mechanisms and is
not limited to specific kinds of mechanisms such as four-bars, six-bars, \etc
which are typically what most databases include. LINKS is made up of various
components including 100 million mechanisms, the simulation data for each
mechanism, normalized paths generated by each mechanism, a curated set of
paths, the code used to generate the data and simulate mechanisms, and a live
web demo for interactive design of linkage mechanisms. The curated paths are
provided as a measure for removing biases in the paths generated by mechanisms
that enable a more even design space representation. In this paper, we discuss
the details of how we can generate such a large dataset and how we can overcome
major issues with such scales. To be able to generate such a large dataset we
introduce a new operator to generate 1-DOF mechanism topologies, furthermore,
we take many steps to speed up slow simulations of mechanisms by vectorizing
our simulations and parallelizing our simulator on a large number of threads,
which leads to a simulation 800 times faster than the simple simulation
algorithm. This is necessary given on average, 1 out of 500 candidates that are
generated are valid~(and all must be simulated to determine their validity),
which means billions of simulations must be performed for the generation of
this dataset. Then we demonstrate the depth of our dataset through a
bi-directional chamfer distance-based shape retrieval study where we show how
our dataset can be used directly to find mechanisms that can trace paths very
close to desired target paths.

We present an efficient bi-encoder framework for named entity recognition
(NER), which applies contrastive learning to map candidate text spans and
entity types into the same vector representation space. Prior work
predominantly approaches NER as sequence labeling or span classification. We
instead frame NER as a metric learning problem that maximizes the similarity
between the vector representations of an entity mention and its type. This
makes it easy to handle nested and flat NER alike, and can better leverage
noisy self-supervision signals. A major challenge to this bi-encoder
formulation for NER lies in separating non-entity spans from entity mentions.
Instead of explicitly labeling all non-entity spans as the same class Outside
(O) as in most prior methods, we introduce a novel dynamic thresholding loss,
which is learned in conjunction with the standard contrastive loss. Experiments
show that our method performs well in both supervised and distantly supervised
settings, for nested and flat NER alike, establishing new state of the art
across standard datasets in the general domain (e.g., ACE2004, ACE2005) and
high-value verticals such as biomedicine (e.g., GENIA, NCBI, BC5CDR, JNLPBA).

Earthquake monitoring workflows are designed to detect earthquake signals and
to determine source characteristics from continuous waveform data. Recent
developments in deep learning seismology have been used to improve tasks within
earthquake monitoring workflows that allow the fast and accurate detection of
up to orders of magnitude more small events than are present in conventional
catalogs. To facilitate the application of machine-learning algorithms to
large-volume seismic records, we developed a cloud-based earthquake monitoring
workflow, QuakeFlow, that applies multiple processing steps to generate
earthquake catalogs from raw seismic data. QuakeFlow uses a deep learning
model, PhaseNet, for picking P/S phases and a machine learning model, GaMMA,
for phase association with approximate earthquake location and magnitude. Each
component in QuakeFlow is containerized, allowing straightforward updates to
the pipeline with new deep learning/machine learning models, as well as the
ability to add new components, such as earthquake relocation algorithms. We
built QuakeFlow in Kubernetes to make it auto-scale for large datasets and to
make it easy to deploy on cloud platforms, which enables large-scale parallel
processing. We used QuakeFlow to process three years of continuous archived
data from Puerto Rico, and found more than a factor of ten more events that
occurred on much the same structures as previously known seismicity. We applied
Quakeflow to monitoring frequent earthquakes in Hawaii and found over an order
of magnitude more events than are in the standard catalog, including many
events that illuminate the deep structure of the magmatic system. We also added
Kafka and Spark streaming to deliver real-time earthquake monitoring results.
QuakeFlow is an effective and efficient approach both for improving realtime
earthquake monitoring and for mining archived seismic data sets.

Bandit algorithms have become a reference solution for interactive
recommendation. However, as such algorithms directly interact with users for
improved recommendations, serious privacy concerns have been raised regarding
its practical use. In this work, we propose a differentially private linear
contextual bandit algorithm, via a tree-based mechanism to add Laplace or
Gaussian noise to model parameters. Our key insight is that as the model
converges during online update, the global sensitivity of its parameters
shrinks over time (thus named dynamic global sensitivity). Compared with
existing solutions, our dynamic global sensitivity analysis allows us to inject
less noise to obtain $(\epsilon, \delta)$-differential privacy with added
regret caused by noise injection in $\tilde O(\log{T}\sqrt{T}/\epsilon)$. We
provide a rigorous theoretical analysis over the amount of noise added via
dynamic global sensitivity and the corresponding upper regret bound of our
proposed algorithm. Experimental results on both synthetic and real-world
datasets confirmed the algorithm's advantage against existing solutions.

Some languages allow arguments to be omitted in certain contexts. Yet human
language comprehenders reliably infer the intended referents of these zero
pronouns, in part because they construct expectations about which referents are
more likely. We ask whether Neural Language Models also extract the same
expectations. We test whether 12 contemporary language models display
expectations that reflect human behavior when exposed to sentences with zero
pronouns from five behavioral experiments conducted in Italian by Carminati
(2005). We find that three models - XGLM 2.9B, 4.5B, and 7.5B - capture the
human behavior from all the experiments, with others successfully modeling some
of the results. This result suggests that human expectations about coreference
can be derived from exposure to language, and also indicates features of
language models that allow them to better reflect human behavior.

We present BioSLAM, a lifelong SLAM framework for learning various new
appearances incrementally and maintaining accurate place recognition for
previously visited areas. Unlike humans, artificial neural networks suffer from
catastrophic forgetting and may forget the previously visited areas when
trained with new arrivals. For humans, researchers discover that there exists a
memory replay mechanism in the brain to keep the neuron active for previous
events. Inspired by this discovery, BioSLAM designs a gated generative replay
to control the robot's learning behavior based on the feedback rewards.
Specifically, BioSLAM provides a novel dual-memory mechanism for maintenance:
1) a dynamic memory to efficiently learn new observations and 2) a static
memory to balance new-old knowledge. When combined with a visual-/LiDAR- based
SLAM system, the complete processing pipeline can help the agent incrementally
update the place recognition ability, robust to the increasing complexity of
long-term place recognition. We demonstrate BioSLAM in two incremental SLAM
scenarios. In the first scenario, a LiDAR-based agent continuously travels
through a city-scale environment with a 120km trajectory and encounters
different types of 3D geometries (open streets, residential areas, commercial
buildings). We show that BioSLAM can incrementally update the agent's place
recognition ability and outperform the state-of-the-art incremental approach,
Generative Replay, by 24%. In the second scenario, a LiDAR-vision-based agent
repeatedly travels through a campus-scale area on a 4.5km trajectory. BioSLAM
can guarantee the place recognition accuracy to outperform 15\% over the
state-of-the-art approaches under different appearances. To our knowledge,
BioSLAM is the first memory-enhanced lifelong SLAM system to help incremental
place recognition in long-term navigation tasks.

Weakly supervised video object localization (WSVOL) allows locating object in
videos using only global video tags such as object class. State-of-art methods
rely on multiple independent stages, where initial spatio-temporal proposals
are generated using visual and motion cues, then prominent objects are
identified and refined. Localization is done by solving an optimization problem
over one or more videos, and video tags are typically used for video
clustering. This requires a model per-video or per-class making for costly
inference. Moreover, localized regions are not necessary discriminant because
of unsupervised motion methods like optical flow, or because video tags are
discarded from optimization. In this paper, we leverage the successful class
activation mapping (CAM) methods, designed for WSOL based on still images. A
new Temporal CAM (TCAM) method is introduced to train a discriminant deep
learning (DL) model to exploit spatio-temporal information in videos, using an
aggregation mechanism, called CAM-Temporal Max Pooling (CAM-TMP), over
consecutive CAMs. In particular, activations of regions of interest (ROIs) are
collected from CAMs produced by a pretrained CNN classifier to build pixel-wise
pseudo-labels for training the DL model. In addition, a global unsupervised
size constraint, and local constraint such as CRF are used to yield more
accurate CAMs. Inference over single independent frames allows parallel
processing of a clip of frames, and real-time localization. Extensive
experiments on two challenging YouTube-Objects datasets for unconstrained
videos, indicate that CAM methods (trained on independent frames) can yield
decent localization accuracy. Our proposed TCAM method achieves a new
state-of-art in WSVOL accuracy, and visual results suggest that it can be
adapted for subsequent tasks like visual object tracking and detection. Code is
publicly available.

We adapt concepts, methodology, and theory originally developed in the areas
of multidimensional scaling and dimensionality reduction for multivariate data
to the functional setting. We focus on classical scaling and Isomap --
prototypical methods that have played important roles in these area -- and
showcase their use in the context of functional data analysis. In the process,
we highlight the crucial role that the ambient metric plays.

Fifth-generation (5G) and beyond systems are expected to accelerate the
ongoing transformation of power systems towards the smart grid. However, the
inherent heterogeneity in smart grid services and requirements pose significant
challenges towards the definition of a unified network architecture. In this
context, radio access network (RAN) slicing emerges as a key 5G enabler to
ensure interoperable connectivity and service management in the smart grid.
This article introduces a novel RAN slicing framework which leverages the
potential of artificial intelligence (AI) to support IEC 61850 smart grid
services. With the aid of deep reinforcement learning, efficient radio resource
management for RAN slices is attained, while conforming to the stringent
performance requirements of a smart grid self-healing use case. Our research
outcomes advocate the adoption of emerging AI-native approaches for RAN slicing
in beyond-5G systems, and lay the foundations for differentiated service
provisioning in the smart grid.

In this work, the capabilities of an encoder-decoder learning framework are
leveraged to predict soft-failure evolution over a long future horizon. This
enables the triggering of timely repair actions with low
quality-of-transmission (QoT) margins before a costly hard-failure occurs,
ultimately reducing the frequency of repair actions and associated operational
expenses. Specifically, it is shown that the proposed scheme is capable of
triggering a repair action several days prior to the expected day of a
hard-failure, contrary to soft-failure detection schemes utilizing rule-based
fixed QoT margins, that may lead either to premature repair actions (i.e.,
several months before the event of a hard-failure) or to repair actions that
are taken too late (i.e., after the hard failure has occurred). Both frameworks
are evaluated and compared for a lightpath established in an elastic optical
network, where soft-failure evolution can be modeled by analyzing
bit-error-rate information monitored at the coherent receivers.

Protein structures and functions are determined by a contiguous arrangement
of amino acid sequences. Designing novel protein sequences and structures with
desired geometry and functions is a complex task with large state spaces. Here
we develop a novel protein design pipeline consisting of two deep learning
algorithms, ProteinSolver and AlphaFold2. ProteinSolver is a deep graph neural
network that generates amino acid sequences such that the forces between
interacting amino acids are favorable and compatible with the fold while
AlphaFold2 is a deep learning algorithm that predicts the protein structures
from protein sequences. We present forty de novo designed binding sites of the
PTP1B and P53 proteins with high precision, out of which thirty proteins are
novel. Using ProteinSolver and AlphaFold2 in conjunction, we can trim the
exploration of the large protein conformation space, thus expanding the ability
to find novel and diverse de novo protein designs.

With the advantages that cloud computing offers in terms of platform as a
service, software as a service, and infrastructure as a service, data engineers
and data scientists are able to leverage cloud computing for their ETL/ELT
(extract, transform and load) and ML (machine learning) requirements and
deployments. The proposed framework for the comparative review of cloud
computing platforms for data science workflows uses an amalgamation of the
analytical hierarchy process, Saaty's fundamental scale of absolute numbers,
and a selection of relevant evaluation criteria (namely: automation, error
handling, fault tolerance, performance quality, unit testing, data encryption,
monitoring, role based access, security, availability, ease of use, integration
and interoperability). The framework enables users to evaluate criteria
pertaining to cloud platforms for data science workflows, and additionally is
able to recommend which cloud platform would be suitable for the user based on
the relative importance of the above criteria. Evaluations of the criteria are
shown to be consistent and thus the weighting of criteria against the goal of
cloud service provider or cloud platform selection are sensible. The proposed
framework is robust enough to accommodate for changes in criteria and
alternatives, depending on user cloud platform requirements and scope of cloud
platform selection.

We draw on the latest advancements in the physics community to propose a
novel method for discovering the governing non-linear dynamics of physical
systems in reinforcement learning (RL). We establish that this method is
capable of discovering the underlying dynamics using significantly fewer
trajectories (as little as one rollout with $\leq 30$ time steps) than state of
the art model learning algorithms. Further, the technique learns a model that
is accurate enough to induce near-optimal policies given significantly fewer
trajectories than those required by model-free algorithms. It brings the
benefits of model-based RL without requiring a model to be developed in
advance, for systems that have physics-based dynamics.
  To establish the validity and applicability of this algorithm, we conduct
experiments on four classic control tasks. We found that an optimal policy
trained on the discovered dynamics of the underlying system can generalize
well. Further, the learned policy performs well when deployed on the actual
physical system, thus bridging the model to real system gap. We further compare
our method to state-of-the-art model-based and model-free approaches, and show
that our method requires fewer trajectories sampled on the true physical system
compared other methods. Additionally, we explored approximate dynamics models
and found that they also can perform well.

Obtaining text datasets with semantic annotations is an effortful process,
yet crucial for supervised training in natural language processsing (NLP). In
general, developing and applying new NLP pipelines in domain-specific contexts
for tasks often requires custom designed datasets to address NLP tasks in
supervised machine learning fashion. When operating in non-English languages
for medical data processing, this exposes several minor and major,
interconnected problems such as lack of task-matching datasets as well as
task-specific pre-trained models. In our work we suggest to leverage pretrained
language models for training data acquisition in order to retrieve sufficiently
large datasets for training smaller and more efficient models for use-case
specific tasks. To demonstrate the effectiveness of your approach, we create a
custom dataset which we use to train a medical NER model for German texts,
GPTNERMED, yet our method remains language-independent in principle. Our
obtained dataset as well as our pre-trained models are publicly available at:
https://github.com/frankkramer-lab/GPTNERMED

Jets in relativistic heavy-ion collisions (HICs) interact with the
quark-gluon plasma (QGP), leading to effects such as a suppression of jet
yields and a modification of internal jet structure that are used to measure
the properties of the QGP. These proceedings show the inclusive jet nuclear
modification factors in Pb--Pb collisions in various centrality classes at
$\sqrt{s_{\rm NN}}$ = 5.02 TeV recorded with the ALICE detector for resolution
parameters up to $R$ = 0.6 for momenta down to 40 GeV/$c$. The analysis
utilizes machine learning techniques to correct the large background in HICs,
extending the measurement of inclusive jets to lower jet $p_{T}$ and larger $R$
than previously achieved in HICs at the LHC. A new suite of measurements
characterizing groomed jet splittings using both the Soft Drop and Dynamical
Grooming algorithms in central and semi-central Pb--Pb collisions will also be
presented. The groomed jet radius, $\theta_{\rm g}\equiv R_{\rm g}/R$, the
groomed jet momentum fraction, $z_{\rm g}$, and the transverse momentum of the
groomed splitting, $k_{\rm T, g}$ are also reported. All measurements are fully
corrected through unfolding and compared to various theoretical calculations.

A well-known failure mode of neural networks is that they may confidently
return erroneous predictions. Such unsafe behaviour is particularly frequent
when the use case slightly differs from the training context, and/or in the
presence of an adversary. This work presents a novel direction to address these
issues in a broad, general manner: imposing class-aware constraints on a
model's internal activation patterns. Specifically, we assign to each class a
unique, fixed, randomly-generated binary vector - hereafter called class code -
and train the model so that its cross-depths activation patterns predict the
appropriate class code according to the input sample's class. The resulting
predictors are dubbed total activation classifiers (TAC), and TACs may either
be trained from scratch, or used with negligible cost as a thin add-on on top
of a frozen, pre-trained neural network. The distance between a TAC's
activation pattern and the closest valid code acts as an additional confidence
score, besides the default unTAC'ed prediction head's. In the add-on case, the
original neural network's inference head is completely unaffected (so its
accuracy remains the same) but we now have the option to use TAC's own
confidence and prediction when determining which course of action to take in an
hypothetical production workflow. In particular, we show that TAC strictly
improves the value derived from models allowed to reject/defer. We provide
further empirical evidence that TAC works well on multiple types of
architectures and data modalities and that it is at least as good as
state-of-the-art alternative confidence scores derived from existing models.

Objective: Endophenotypes like brain age and fluid intelligence are important
biomarkers of disease status. However, brain imaging studies to identify these
biomarkers often encounter limited numbers of subjects and high dimensional
imaging features, hindering reproducibility. Therefore, we develop an
interpretable, multivariate classification/regression algorithm, called Latent
Similarity (LatSim), suitable for small sample size, high feature dimension
datasets. Methods: LatSim combines metric learning with a kernel similarity
function and softmax aggregation to identify task-related similarities between
subjects. Inter-subject similarity is utilized to improve performance on three
prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm,
made possible by LatSim's computational efficiency, is developed as an
interpretability method. Results: LatSim achieved significantly higher
predictive accuracy at small sample sizes on the Philadelphia
Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave
superior discriminative power compared to those identified by other methods. We
identified 4 functional brain networks enriched in connections for predicting
brain age, sex, and intelligence. Conclusion: We find that most information for
a predictive task comes from only a few (1-5) connections. Additionally, we
find that the default mode network is over-represented in the top connections
of all predictive tasks. Significance: We propose a novel algorithm for small
sample, high feature dimension datasets and use it to identify connections in
task fMRI data. Our work should lead to new insights in both algorithm design
and neuroscience research. Code and demo are available at
https://github.com/aorliche/LatentSimilarity/.

Using a taxonomy to organize information requires classifying objects
(documents, images, etc) with appropriate taxonomic classes. The flexible
nature of zero-shot learning is appealing for this task because it allows
classifiers to naturally adapt to taxonomy modifications. This work studies
zero-shot multi-label document classification with fine-tuned language models
under realistic taxonomy expansion scenarios in the human resource domain.
Experiments show that zero-shot learning can be highly effective in this
setting. When controlling for training data budget, zero-shot classifiers
achieve a 12% relative increase in macro-AP when compared to a traditional
multi-label classifier trained on all classes. Counterintuitively, these
results suggest in some settings it would be preferable to adopt zero-shot
techniques and spend resources annotating more documents with an incomplete set
of classes, rather than spreading the labeling budget uniformly over all
classes and using traditional classification techniques. Additional experiments
demonstrate that adopting the well-known filter/re-rank decomposition from the
recommender systems literature can significantly reduce the computational
burden of high-performance zero-shot classifiers, empirically resulting in a
98% reduction in computational overhead for only a 2% relative decrease in
performance. The evidence presented here demonstrates that zero-shot learning
has the potential to significantly increase the flexibility of taxonomies and
highlights directions for future research.

In Federated Learning (FL) of click-through rate (CTR) prediction, users'
data is not shared for privacy protection. The learning is performed by
training locally on client devices and communicating only model changes to the
server. There are two main challenges: (i) the client heterogeneity, making FL
algorithms that use the weighted averaging to aggregate model updates from the
clients have slow progress and unsatisfactory learning results; and (ii) the
difficulty of tuning the server learning rate with trial-and-error methodology
due to the big computation time and resources needed for each experiment. To
address these challenges, we propose a simple online meta-learning method to
learn a strategy of aggregating the model updates, which adaptively weighs the
importance of the clients based on their attributes and adjust the step sizes
of the update. We perform extensive evaluations on public datasets. Our method
significantly outperforms the state-of-the-art in both the speed of convergence
and the quality of the final learning results.

Relevant metrological scenarios involve the simultaneous estimation of
multiple parameters. The fundamental ingredient to achieve quantum-enhanced
performances is based on the use of appropriately tailored quantum probes.
However, reaching the ultimate resolution allowed by physical laws requires non
trivial estimation strategies both from a theoretical and a practical point of
view. A crucial tool for this purpose is the application of adaptive learning
techniques. Indeed, adaptive strategies provide a flexible approach to obtain
optimal parameter-independent performances, and optimize convergence to the
fundamental bounds with limited amount of resources. Here, we combine on the
same platform quantum-enhanced multiparameter estimation attaining the
corresponding quantum limit and adaptive techniques. We demonstrate the
simultaneous estimation of three optical phases in a programmable integrated
photonic circuit, in the limited resource regime. The obtained results show the
possibility of successfully combining different fundamental methodologies
towards transition to quantum sensors applications.

We use Gaia DR3 data to study the Collinder 132-Gulliver 21 region via the
machine learning algorithm StarGO, and find eight subgroups of stars (ASCC 32,
Collinder 132 gp 1--6, Gulliver 21) located in close proximity. Three co-moving
populations were identified among these eight subgroups: (i) a coeval 25
Myr-old moving group (Collinder 132); (ii) an intermediate-age (50--100 Myr)
group; and (iii) the 275 Myr-old dissolving cluster Gulliver 21. These three
populations form parallel diagonal stripe-shape over-densities in the U--V
distribution, which differ from open clusters and stellar groups in the solar
neighborhood. We name this kinematic structure the Collinder 132-Gulliver 21
stream, as it extends over 270 pc in the 3D space. The oldest population
Gulliver21 is spatially surrounded by the Collinder 132 moving group and the
intermediate-age group. Stars in the Collinder 132-Gulliver 21 stream have an
age difference up to 250 Myr. Metallicity information shows a variation of 0.3
dex between the youngest and oldest populations. The formation of the
Collinder132-Gulliver 21 stream involves both star formation and dynamical
heating. The youngest population (Collinder 132 moving group) with homogeneous
metallicity is probably formed through filamentary star formation. The
intermediate-age and the oldest population were then scatted by the Galactic
bar or spiral structure resonance to intercept Collinder 132's orbit. Without
mutual interaction between each population, the three populations are flying by
each other currently and will become distinct three groups again in
approximately ~50Myr.

While self-supervised learning has been shown to benefit a number of vision
tasks, existing techniques mainly focus on image-level manipulation, which may
not generalize well to downstream tasks at patch or pixel levels. Moreover,
existing SSL methods might not sufficiently describe and associate the above
representations within and across image scales. In this paper, we propose a
Self-Supervised Pyramid Representation Learning (SS-PRL) framework. The
proposed SS-PRL is designed to derive pyramid representations at patch levels
via learning proper prototypes, with additional learners to observe and relate
inherent semantic information within an image. In particular, we present a
cross-scale patch-level correlation learning in SS-PRL, which allows the model
to aggregate and associate information learned across patch scales. We show
that, with our proposed SS-PRL for model pre-training, one can easily adapt and
fine-tune the models for a variety of applications including multi-label

We present MapTR, a structured end-to-end framework for efficient online
vectorized HD map construction. We propose a unified permutation-based modeling
approach, i.e., modeling map element as a point set with a group of equivalent
permutations, which avoids the definition ambiguity of map element and eases
learning. We adopt a hierarchical query embedding scheme to flexibly encode
structured map information and perform hierarchical bipartite matching for map
element learning. MapTR achieves the best performance and efficiency among
existing vectorized map construction approaches on nuScenes dataset. In
particular, MapTR-nano runs at real-time inference speed ($25.1$ FPS) on RTX
3090, $8\times$ faster than the existing state-of-the-art camera-based method
while achieving $3.3$ higher mAP. MapTR-tiny significantly outperforms the
existing state-of-the-art multi-modality method by $13.5$ mAP while being
faster. Qualitative results show that MapTR maintains stable and robust map
construction quality in complex and various driving scenes. Abundant demos are
available at \url{https://github.com/hustvl/MapTR} to prove the effectiveness
in real-world scenarios. MapTR is of great application value in autonomous
driving. Code will be released for facilitating further research and

Runtime enforcement refers to the theories, techniques, and tools for
enforcing correct behavior with respect to a formal specification of systems at
runtime. In this paper, we are interested in techniques for constructing
runtime enforcers for the concrete application domain of enforcing safety in
AI. We discuss how safety is traditionally handled in the field of AI and how
more formal guarantees on the safety of a self-learning agent can be given by
integrating a runtime enforcer. We survey a selection of work on such
enforcers, where we distinguish between approaches for discrete and continuous
action spaces. The purpose of this paper is to foster a better understanding of
advantages and limitations of different enforcement techniques, focusing on the
specific challenges that arise due to their application in AI. Finally, we
present some open challenges and avenues for future work.

A colloquial interpretation of entropy is that it is the knowledge gained
upon learning the outcome of a random experiment. Conditional entropy is then
interpreted as the knowledge gained upon learning the outcome of one random
experiment after learning the outcome of another, possibly statistically
dependent, random experiment. In the classical world, entropy and conditional
entropy take only non-negative values, consistent with the intuition that one
has regarding the aforementioned interpretations. However, for certain
entangled states, one obtains negative values when evaluating commonly accepted
and information-theoretically justified formulas for the quantum conditional
entropy, leading to the confounding conclusion that one can know less than
nothing in the quantum world. Here, we introduce a physically motivated
framework for defining quantum conditional entropy, based on two simple
postulates inspired by the second law of thermodynamics (non-decrease of
entropy) and extensivity of entropy, and we argue that all plausible
definitions of quantum conditional entropy should respect these two postulates.
We then prove that all plausible quantum conditional entropies take on negative
values for certain entangled states, so that it is inevitable that one can know
less than nothing in the quantum world. All of our arguments are based on
constructions of physical processes that respect the first postulate, the one
inspired by the second law of thermodynamics.

Competition between traditional platforms is known to improve user utility by
aligning the platform's actions with user preferences. But to what extent is
alignment exhibited in data-driven marketplaces? To study this question from a
theoretical perspective, we introduce a duopoly market where platform actions
are bandit algorithms and the two platforms compete for user participation. A
salient feature of this market is that the quality of recommendations depends
on both the bandit algorithm and the amount of data provided by interactions
from users. This interdependency between the algorithm performance and the
actions of users complicates the structure of market equilibria and their
quality in terms of user utility. Our main finding is that competition in this
market does not perfectly align market outcomes with user utility.
Interestingly, market outcomes exhibit misalignment not only when the platforms
have separate data repositories, but also when the platforms have a shared data
repository. Nonetheless, the data sharing assumptions impact what mechanism
drives misalignment and also affect the specific form of misalignment (e.g. the
quality of the best-case and worst-case market outcomes). More broadly, our
work illustrates that competition in digital marketplaces has subtle
consequences for user utility that merit further investigation.

Disordered rocksalt (DRX) Li3V2O5 is a promising candidate for anode in
rechargeable lithium-ion batteries because of its ideal low voltage, high rate
capability, and superior cycling stability. Herein, we presents a comprehensive
study of intercalation chemistry of the DRX-Li3V2O5 anode using density
functional theory calculations combined with machine learning cluster
expansions and interatomic potentials. The predicted voltage profile of the
disordered Li3V2O5 anode at room temperature based on Monte Carlo simulations
with a fitted cluster expansion model is in excellent agreement with
experiments. In contrast to previous DFT results, we find that Li ions
predominately intercalate into tetrahedral sites during charging, while the
majority of Li and V ions at octahedral sites remain stable. In addition, MD
simulations with a fitted moment tensor potential attribute the fast-charging
capability of DRX-Li3V2O5 to the facile diffusivity of Li+ via tetrahedral -
octahedral - tetrahedral pathway. We further suggest tuning the Li:V ratio as a
means to trade off increased lithiation capacity and decreased anode voltage in
this system. This work provides in-depth insights into the high-performance
DRX-Li3V2O5 anode, and paves the way to the discovery of other disordered anode
materials.

Standardized datasets and benchmarks have spurred innovations in computer
vision, natural language processing, multi-modal and tabular settings. We note
that, as compared to other well researched fields fraud detection has numerous
differences. The differences include a high class imbalance, diverse feature
types, frequently changing fraud patterns, and adversarial nature of the
problem. Due to these differences, the modeling approaches that are designed
for other classification tasks may not work well for the fraud detection. We
introduce Fraud Dataset Benchmark (FDB), a compilation of publicly available
datasets catered to fraud detection. FDB comprises variety of fraud related
tasks, ranging from identifying fraudulent card-not-present transactions,
detecting bot attacks, classifying malicious URLs, predicting risk of loan to
content moderation. The Python based library from FDB provides consistent API
for data loading with standardized training and testing splits. For reference,
we also provide baseline evaluations of different modeling approaches on FDB.
Considering the increasing popularity of Automated Machine Learning (AutoML)
for various research and business problems, we used AutoML frameworks for our
baseline evaluations. For fraud prevention, the organizations that operate with
limited resources and lack ML expertise often hire a team of investigators, use
blocklists and manual rules, all of which are inefficient and do not scale
well. Such organizations can benefit from AutoML solutions that are easy to
deploy in production and pass the bar of fraud prevention requirements. We hope
that FDB helps in the development of customized fraud detection techniques
catered to different fraud modus operandi (MOs) as well as in the improvement
of AutoML systems that can work well for all datasets in the benchmark.

Many methods for Model-based Reinforcement learning (MBRL) provide guarantees
for both the accuracy of the Markov decision process (MDP) model they can
deliver and the learning efficiency. At the same time, state abstraction
techniques allow for a reduction of the size of an MDP while maintaining a
bounded loss with respect to the original problem. It may come as a surprise,
therefore, that no such guarantees are available when combining both
techniques, i.e., where MBRL merely observes abstract states. Our theoretical
analysis shows that abstraction can introduce a dependence between samples
collected online (e.g., in the real world), which means that most results for
MBRL can not be directly extended to this setting. The new results in this work
show that concentration inequalities for martingales can be used to overcome
this problem and allows for extending the results of algorithms such as R-MAX
to the setting with abstraction. Thus producing the first performance
guarantees for Abstracted RL: model-based reinforcement learning with an
abstracted model.

A reliable locoregional recurrence (LRR) prediction model is important for
the personalized management of head and neck cancers (HNC) patients. This work
aims to develop a delta-radiomics feature-based multi-classifier,
multi-objective, and multi-modality (Delta-mCOM) model for post-treatment HNC
LRR prediction and adopting a learning with rejection option (LRO) strategy to
boost the prediction reliability by rejecting samples with high prediction
uncertainties. In this retrospective study, we collected PET/CT image and
clinical data from 224 HNC patients. We calculated the differences between
radiomics features extracted from PET/CT images acquired before and after
radiotherapy as the input features. Using clinical parameters, PET and CT
radiomics features, we built and optimized three separate single-modality
models. We used multiple classifiers for model construction and employed
sensitivity and specificity simultaneously as the training objectives. For
testing samples, we fused the output probabilities from all these
single-modality models to obtain the final output probabilities of the
Delta-mCOM model. In the LRO strategy, we estimated the epistemic and aleatoric
uncertainties when predicting with Delta-mCOM model and identified patients
associated with prediction of higher reliability. Predictions with higher
epistemic uncertainty or higher aleatoric uncertainty than given thresholds
were deemed unreliable, and they were rejected before providing a final
prediction. Different thresholds corresponding to different low-reliability
prediction rejection ratios were applied. The inclusion of the delta-radiomics
feature improved the accuracy of HNC LRR prediction, and the proposed
Delta-mCOM model can give more reliable predictions by rejecting predictions
for samples of high uncertainty using the LRO strategy.

The next-generation wireless networks are required to satisfy a variety of
services and criteria concurrently. To address upcoming strict criteria, a new
open radio access network (O-RAN) with distinguishing features such as flexible
design, disaggregated virtual and programmable components, and intelligent
closed-loop control was developed. O-RAN slicing is being investigated as a
critical strategy for ensuring network quality of service (QoS) in the face of
changing circumstances. However, distinct network slices must be dynamically
controlled to avoid service level agreement (SLA) variation caused by rapid
changes in the environment. Therefore, this paper introduces a novel framework
able to manage the network slices through provisioned resources intelligently.
Due to diverse heterogeneous environments, intelligent machine learning
approaches require sufficient exploration to handle the harshest situations in
a wireless network and accelerate convergence. To solve this problem, a new
solution is proposed based on evolutionary-based deep reinforcement learning
(EDRL) to accelerate and optimize the slice management learning process in the
radio access network's (RAN) intelligent controller (RIC) modules. To this end,
the O-RAN slicing is represented as a Markov decision process (MDP) which is
then solved optimally for resource allocation to meet service demand using the
EDRL approach. In terms of reaching service demands, simulation results show
that the proposed approach outperforms the DRL baseline by 62.2%.

We study the convergence of divergence-regularized optimal transport as the
regularization parameter vanishes. Sharp rates for general divergences
including relative entropy or $L^{p}$ regularization, general transport costs
and multi-marginal problems are obtained. A novel methodology using
quantization and martingale couplings is suitable for non-compact marginals and
achieves, in particular, the sharp leading-order term of entropically
regularized 2-Wasserstein distance for all marginals with finite
$(2+\delta)$-moment.

This work proposes a clusterization algorithm called k-Morphological Sets
(k-MS), based on morphological reconstruction and heuristics. k-MS is faster
than the CPU-parallel k-Means in worst case scenarios and produces enhanced
visualizations of the dataset as well as very distinct clusterizations. It is
also faster than similar clusterization methods that are sensitive to density
and shapes such as Mitosis and TRICLUST. In addition, k-MS is deterministic and
has an intrinsic sense of maximal clusters that can be created for a given
input sample and input parameters, differing from k-Means and other
clusterization algorithms. In other words, given a constant k, a structuring
element and a dataset, k-MS produces k or less clusters without using random/
pseudo-random functions. Finally, the proposed algorithm also provides a
straightforward means for removing noise from images or datasets in general.

Diagnosing erythema migrans (EM) skin lesion, the most common early symptom
of Lyme disease using deep learning techniques can be effective to prevent
long-term complications. Existing works on deep learning based EM recognition
only utilizes lesion image due to the lack of a dataset of Lyme disease related
images with associated patient data. Physicians rely on patient information
about the background of the skin lesion to confirm their diagnosis. In order to
assist the deep learning model with a probability score calculated from patient
data, this study elicited opinion from fifteen doctors. For the elicitation
process, a questionnaire with questions and possible answers related to EM was
prepared. Doctors provided relative weights to different answers to the
questions. We converted doctors evaluations to probability scores using
Gaussian mixture based density estimation. For elicited probability model
validation, we exploited formal concept analysis and decision tree. The
elicited probability scores can be utilized to make image based deep learning
Lyme disease pre-scanners robust.

Base stations (BSs) are the most energy-consuming segment of mobile networks.
To reduce BS energy consumption, different components of BSs can sleep when BS
is not active. According to the activation/deactivation time of the BS
components, multiple sleep modes (SMs) are defined in the literature. In this
study, we model the problem of BS energy saving utilizing multiple sleep modes
as a sequential MDP and propose an online traffic-aware deep reinforcement
learning approach to maximize the long-term energy saving. However, there is a
risk that BS is not sleeping at the right time and incurs large delays to the
users. To tackle this issue, we propose to use a digital twin model to
encapsulate the dynamics underlying the investigated system and estimate the
risk of decision-making (RDM) in advance. We define a novel metric to quantify
RDM and predict the performance degradation. The RDM calculated by DT is
compared with a tolerable threshold set by the mobile operator. Based on this
comparison, BS can decide to deactivate the SMs, re-train when needed to avoid
taking high risks, or activate the SMs to benefit from energy savings. For deep
reinforcement learning, we use long-short term memory (LSTM), to take into
account the long and short-term dependencies in input traffic, and approximate
the Q-function. We train the LSTM network using the experience replay method
over a real traffic data set obtained from an operator BS in Stockholm. The
data set contains data rate information with very coarse-grained time
granularity. Thus, we propose a scheme to generate a new data set using the
real network data set which 1) has finer-grained time granularity and 2)
considers the bursty behavior of traffic data. Simulation results show that
using proposed methods, considerable energy saving is obtained, compared to the
baselines at cost of negligible number of delayed users.

The network embedding task is to represent the node in the network as a
low-dimensional vector while incorporating the topological and structural
information. Most existing approaches solve this problem by factorizing a
proximity matrix, either directly or implicitly. In this work, we introduce a
network embedding method from a new perspective, which leverages Modern
Hopfield Networks (MHN) for associative learning. Our network learns
associations between the content of each node and that node's neighbors. These
associations serve as memories in the MHN. The recurrent dynamics of the
network make it possible to recover the masked node, given that node's
neighbors. Our proposed method is evaluated on different downstream tasks such
as node classification and linkage prediction. The results show competitive
performance compared to the common matrix factorization techniques and deep
learning based methods.

This work proposes the use of Genetic Algorithms (GA) in tracing and
recognizing the pericardium contour of the human heart using Computed
Tomography (CT) images. We assume that each slice of the pericardium can be
modelled by an ellipse, the parameters of which need to be optimally
determined. An optimal ellipse would be one that closely follows the
pericardium contour and, consequently, separates appropriately the epicardial
and mediastinal fats of the human heart. Tracing and automatically identifying
the pericardium contour aids in medical diagnosis. Usually, this process is
done manually or not done at all due to the effort required. Besides, detecting
the pericardium may improve previously proposed automated methodologies that
separate the two types of fat associated to the human heart. Quantification of
these fats provides important health risk marker information, as they are
associated with the development of certain cardiovascular pathologies. Finally,
we conclude that GA offers satisfiable solutions in a feasible amount of
processing time.

We propose a methodology to predict the cardiac epicardial and mediastinal
fat volumes in computed tomography images using regression algorithms. The
obtained results indicate that it is feasible to predict these fats with a high
degree of correlation, thus alleviating the requirement for manual or automatic
segmentation of both fat volumes. Instead, segmenting just one of them
suffices, while the volume of the other may be predicted fairly precisely. The
correlation coefficient obtained by the Rotation Forest algorithm using MLP
Regressor for predicting the mediastinal fat based on the epicardial fat was
0.9876, with a relative absolute error of 14.4% and a root relative squared
error of 15.7%. The best correlation coefficient obtained in the prediction of
the epicardial fat based on the mediastinal was 0.9683 with a relative absolute
error of 19.6% and a relative squared error of 24.9%. Moreover, we analysed the
feasibility of using linear regressors, which provide an intuitive
interpretation of the underlying approximations. In this case, the obtained
correlation coefficient was 0.9534 for predicting the mediastinal fat based on
the epicardial, with a relative absolute error of 31.6% and a root relative
squared error of 30.1%. On the prediction of the epicardial fat based on the
mediastinal fat, the correlation coefficient was 0.8531, with a relative
absolute error of 50.43% and a root relative squared error of 52.06%. In
summary, it is possible to speed up general medical analyses and some
segmentation and quantification methods that are currently employed in the
state-of-the-art by using this prediction approach, which consequently reduces
costs and therefore enables preventive treatments that may lead to a reduction
of health problems.

Intrinsic image decomposition (IID) is an under-constrained problem.
Therefore, traditional approaches use hand crafted priors to constrain the
problem. However, these constraints are limited when coping with complex
scenes. Deep learning-based approaches learn these constraints implicitly
through the data, but they often suffer from dataset biases (due to not being
able to include all possible imaging conditions).
  In this paper, a combination of the two is proposed. Component specific
priors like semantics and invariant features are exploited to obtain
semantically and physically plausible reflectance transitions. These
transitions are used to steer a progressive CNN with implicit homogeneity
constraints to decompose reflectance and shading maps.
  An ablation study is conducted showing that the use of the proposed priors
and progressive CNN increase the IID performance. State of the art performance
on both our proposed dataset and the standard real-world IIW dataset shows the
effectiveness of the proposed method. Code is made available at
https://github.com/Morpheus3000/SIGNet

Text-based person search is a challenging task that aims to search pedestrian
images with the same identity from the image gallery given a query text
description. In recent years, text-based person search has made good progress,
and state-of-the-art methods achieve superior performance by learning local
fine-grained correspondence between images and texts. However, the existing
methods explicitly extract image parts and text phrases from images and texts
by hand-crafted split or external tools and then conduct complex cross-modal
local matching. Moreover, the existing methods seldom consider the problem of
information inequality between modalities caused by image-specific information.
In this paper, we propose an efficient joint Information and Semantic Alignment
Network (ISANet) for text-based person search. Specifically, we first design an
image-specific information suppression module, which suppresses image
background and environmental factors by relation-guide localization and channel
attention filtration respectively. This design can effectively alleviate the
problem of information inequality and realize the information alignment between
images and texts. Secondly, we propose an implicit local alignment module to
adaptively aggregate image and text features to a set of modality-shared
semantic topic centers, and implicitly learn the local fine-grained
correspondence between images and texts without additional supervision
information and complex cross-modal interactions. Moreover, a global alignment
is introduced as a supplement to the local perspective. Extensive experiments
on multiple databases demonstrate the effectiveness and superiority of the
proposed ISANet.

Fracture is one of the main causes of failure in engineering structures.
Phase field methods coupled with adaptive mesh refinement (AMR) techniques have
been widely used to model crack propagation due to their ease of implementation
and scalability. However, phase field methods can still be computationally
demanding making them unfeasible for high-throughput design applications.
Machine learning (ML) models such as Graph Neural Networks (GNNs) have shown
their ability to emulate complex dynamic problems with speed-ups orders of
magnitude faster compared to high-fidelity simulators. In this work, we present
a dynamic mesh-based GNN framework for emulating phase field simulations of
crack propagation with AMR for different crack configurations. The developed
framework - ADAPTive mesh-based graph neural network (ADAPT-GNN) - exploits the
benefits of both ML methods and AMR by describing the graph representation at
each time-step as the refined mesh itself. Using ADAPT-GNN, we predict the
evolution of displacement fields and scalar damage field (or phase field) with
high accuracy compared to conventional phase field fracture model. We also
compute crack stress fields with high accuracy using the predicted
displacements and phase field parameter. Finally, we observe speed up of 15-36x
compared to serial execution of the phase field model.

Weak supervision (WS) is a powerful method to build labeled datasets for
training supervised models in the face of little-to-no labeled data. It
replaces hand-labeling data with aggregating multiple noisy-but-cheap label
estimates expressed by labeling functions (LFs). While it has been used
successfully in many domains, weak supervision's application scope is limited
by the difficulty of constructing labeling functions for domains with complex
or high-dimensional features. To address this, a handful of methods have
proposed automating the LF design process using a small set of ground truth
labels. In this work, we introduce AutoWS-Bench-101: a framework for evaluating
automated WS (AutoWS) techniques in challenging WS settings -- a set of diverse
application domains on which it has been previously difficult or impossible to
apply traditional WS techniques. While AutoWS is a promising direction toward
expanding the application-scope of WS, the emergence of powerful methods such
as zero-shot foundation models reveals the need to understand how AutoWS
techniques compare or cooperate with modern zero-shot or few-shot learners.
This informs the central question of AutoWS-Bench-101: given an initial set of
100 labels for each task, we ask whether a practitioner should use an AutoWS
method to generate additional labels or use some simpler baseline, such as
zero-shot predictions from a foundation model or supervised learning. We
observe that in many settings, it is necessary for AutoWS methods to
incorporate signal from foundation models if they are to outperform simple
few-shot baselines, and AutoWS-Bench-101 promotes future research in this
direction. We conclude with a thorough ablation study of AutoWS methods.

Medical images used in clinical practice are heterogeneous and not the same
quality as scans studied in academic research. Preprocessing breaks down in
extreme cases when anatomy, artifacts, or imaging parameters are unusual or
protocols are different. Methods robust to these variations are most needed. A
novel deep learning method is proposed for fast and accurate segmentation of
the human brain into 132 regions. The proposed model uses an efficient
U-Net-like network and benefits from the intersection points of different views
and hierarchical relations for the fusion of the orthogonal 2D planes and brain
labels during the end-to-end training. Weakly supervised learning is deployed
to take the advantage of partially labeled data for the whole brain
segmentation and estimation of the intracranial volume (ICV). Moreover, data
augmentation is used to expand the magnetic resonance imaging (MRI) data by
generating realistic brain scans with high variability for robust training of
the model while preserving data privacy. The proposed method can be applied to
brain MRI data including skull or any other artifacts without preprocessing the
images or a drop in performance. Several experiments using different atlases
are conducted to evaluate the segmentation performance of the trained model
compared to the state-of-the-art, and the results show higher segmentation
accuracy and robustness of the proposed model compared to the existing methods
across different intra- and inter-domain datasets.

With the rapid development of self-supervised learning (e.g., contrastive
learning), the importance of having large-scale images (even without
annotations) for training a more generalizable AI model has been widely
recognized in medical image analysis. However, collecting large-scale
task-specific unannotated data at scale can be challenging for individual labs.
Existing online resources, such as digital books, publications, and search
engines, provide a new resource for obtaining large-scale images. However,
published images in healthcare (e.g., radiology and pathology) consist of a
considerable amount of compound figures with subplots. In order to extract and
separate compound figures into usable individual images for downstream
learning, we propose a simple compound figure separation (SimCFS) framework
without using the traditionally required detection bounding box annotations,
with a new loss function and a hard case simulation. Our technical contribution
is four-fold: (1) we introduce a simulation-based training framework that
minimizes the need for resource extensive bounding box annotations; (2) we
propose a new side loss that is optimized for compound figure separation; (3)
we propose an intra-class image augmentation method to simulate hard cases; and
(4) to the best of our knowledge, this is the first study that evaluates the
efficacy of leveraging self-supervised learning with compound image separation.
From the results, the proposed SimCFS achieved state-of-the-art performance on
the ImageCLEF 2016 Compound Figure Separation Database. The pretrained
self-supervised learning model using large-scale mined figures improved the
accuracy of downstream image classification tasks with a contrastive learning
algorithm. The source code of SimCFS is made publicly available at
https://github.com/hrlblab/ImageSeperation.

The quantification of fat depots on the surroundings of the heart is an
accurate procedure for evaluating health risk factors correlated with several
diseases. However, this type of evaluation is not widely employed in clinical
practice due to the required human workload. This work proposes a novel
technique for the automatic segmentation of cardiac fat pads. The technique is
based on applying classification algorithms to the segmentation of cardiac CT
images. Furthermore, we extensively evaluate the performance of several
algorithms on this task and discuss which provided better predictive models.
Experimental results have shown that the mean accuracy for the classification
of epicardial and mediastinal fats has been 98.4% with a mean true positive
rate of 96.2%. On average, the Dice similarity index, regarding the segmented
patients and the ground truth, was equal to 96.8%. Therfore, our technique has
achieved the most accurate results for the automatic segmentation of cardiac
fats, to date.

Human usually composes music by organizing elements according to the musical
form to express music ideas. However, for neural network-based music
generation, it is difficult to do so due to the lack of labelled data on
musical form. In this paper, we develop MeloForm, a system that generates
melody with musical form using expert systems and neural networks.
Specifically, 1) we design an expert system to generate a melody by developing
musical elements from motifs to phrases then to sections with repetitions and
variations according to pre-given musical form; 2) considering the generated
melody is lack of musical richness, we design a Transformer based refinement
model to improve the melody without changing its musical form. MeloForm enjoys
the advantages of precise musical form control by expert systems and musical
richness learning via neural models. Both subjective and objective experimental
evaluations demonstrate that MeloForm generates melodies with precise musical
form control with 97.79% accuracy, and outperforms baseline systems in terms of
subjective evaluation score by 0.75, 0.50, 0.86 and 0.89 in structure,
thematic, richness and overall quality, without any labelled musical form data.
Besides, MeloForm can support various kinds of forms, such as verse and chorus
form, rondo form, variational form, sonata form, etc.

We aim to resolve this problem by introducing a comprehensive distributed
deep learning (DDL) profiler, which can determine the various execution
"stalls" that DDL suffers from while running on a public cloud. We have
implemented the profiler by extending prior work to additionally estimate two
types of communication stalls - interconnect and network stalls. We train
popular DNN models using the profiler to characterize various AWS GPU instances
and list their advantages and shortcomings for users to make an informed
decision. We observe that the more expensive GPU instances may not be the most
performant for all DNN models and AWS may sub-optimally allocate hardware
interconnect resources. Specifically, the intra-machine interconnect can
introduce communication overheads up to 90% of DNN training time and
network-connected instances can suffer from up to 5x slowdown compared to
training on a single instance. Further, we model the impact of DNN macroscopic
features such as the number of layers and the number of gradients on
communication stalls. Finally, we propose a measurement-based recommendation
model for users to lower their public cloud monetary costs for DDL, given a
time budget.

Owing to the resource-constrained feature of Internet of Things (IoT)
devices, offloading tasks from IoT devices to the nearby mobile edge computing
(MEC) servers can not only save the energy of IoT devices but also reduce the
response time of executing the tasks. However, offloading a task to the nearest
MEC server may not be the optimal solution due to the limited computing
resources of the MEC server. Thus, jointly optimizing the offloading decision
and resource management is critical, but yet to be explored. Here, offloading
decision refers to where to offload a task and resource management implies how
much computing resource in an MEC server is allocated to a task. By considering
the waiting time of a task in the communication and computing queues (which are
ignored by most of the existing works) as well as tasks priorities, we propose
the \ul{D}eep reinforcement l\ul{E}arning based offloading de\ul{C}ision and
r\ul{E}source manageme\ul{NT} (DECENT) algorithm, which leverages the advantage
actor critic method to optimize the offloading decision and computing resource
allocation for each arriving task in real-time such that the cumulative
weighted response time can be minimized. The performance of DECENT is
demonstrated via different experiments.

While neural network models are making significant progress in piano
transcription, they are becoming more resource-consuming due to requiring
larger model size and more computing power. In this paper, we attempt to apply
more prior about piano to reduce model size and improve the transcription
performance. The sound of a piano note contains various overtones, and the
pitch of a key does not change over time. To make full use of such latent
information, we propose HPPNet that using the Harmonic Dilated Convolution to
capture the harmonic structures and the Frequency Grouped Recurrent Neural
Network to model the pitch-invariance over time. Experimental results on the
MAESTRO dataset show that our piano transcription system achieves
state-of-the-art performance both in frame and note scores (frame F1 93.15%,
note F1 97.18%). Moreover, the model size is much smaller than the previous
state-of-the-art deep learning models.

Deep Reinforcement Learning (or just "RL") is gaining popularity for
industrial and research applications. However, it still suffers from some key
limits slowing down its widespread adoption. Its performance is sensitive to
initial conditions and non-determinism. To unlock those challenges, we propose
a procedure for building ensembles of RL agents to efficiently build better
local decisions toward long-term cumulated rewards. For the first time,
hundreds of experiments have been done to compare different ensemble
constructions procedures in 2 electricity control environments. We discovered
an ensemble of 4 agents improves accumulated rewards by 46%, improves
reproducibility by a factor of 3.6, and can naturally and efficiently train and
predict in parallel on GPUs and CPUs.

Anomalies in time-series provide insights of critical scenarios across a
range of industries, from banking and aerospace to information technology,
security, and medicine. However, identifying anomalies in time-series data is
particularly challenging due to the imprecise definition of anomalies, the
frequent absence of labels, and the enormously complex temporal correlations
present in such data. The LSTM Autoencoder is an Encoder-Decoder scheme for
Anomaly Detection based on Long Short Term Memory Networks that learns to
reconstruct time-series behavior and then uses reconstruction error to identify
abnormalities. We introduce the Denoising Architecture as a complement to this
LSTM Encoder-Decoder model and investigate its effect on real-world as well as
artificially generated datasets. We demonstrate that the proposed architecture
increases both the accuracy and the training speed, thereby, making the LSTM
Autoencoder more efficient for unsupervised anomaly detection tasks.

The rich longitudinal individual level data available from electronic health
records (EHRs) can be used to examine treatment effect heterogeneity. However,
estimating treatment effects using EHR data poses several challenges, including
time-varying confounding, repeated and temporally non-aligned measurements of
covariates, treatment assignments and outcomes, and loss-to-follow-up due to
dropout. Here, we develop the Subgroup Discovery for Longitudinal Data (SDLD)
algorithm, a tree-based algorithm for discovering subgroups with heterogeneous
treatment effects using longitudinal data by combining the generalized
interaction tree algorithm, a general data-driven method for subgroup
discovery, with longitudinal targeted maximum likelihood estimation. We apply
the algorithm to EHR data to discover subgroups of people living with human
immunodeficiency virus (HIV) who are at higher risk of weight gain when
receiving dolutegravir-containing antiretroviral therapies (ARTs) versus when
receiving non dolutegravir-containing ARTs.

As an important biomarker for human identification, human gait can be
collected at a distance by passive sensors without subject cooperation, which
plays an essential role in crime prevention, security detection and other human
identification applications. At present, most research works are based on
cameras and computer vision techniques to perform gait recognition. However,
vision-based methods are not reliable when confronting poor illuminations,
leading to degrading performances. In this paper, we propose a novel multimodal
gait recognition method, namely GaitFi, which leverages WiFi signals and videos
for human identification. In GaitFi, Channel State Information (CSI) that
reflects the multi-path propagation of WiFi is collected to capture human
gaits, while videos are captured by cameras. To learn robust gait information,
we propose a Lightweight Residual Convolution Network (LRCN) as the backbone
network, and further propose the two-stream GaitFi by integrating WiFi and
vision features for the gait retrieval task. The GaitFi is trained by the
triplet loss and classification loss on different levels of features. Extensive
experiments are conducted in the real world, which demonstrates that the GaitFi
outperforms state-of-the-art gait recognition methods based on single WiFi or
camera, achieving 94.2% for human identification tasks of 12 subjects.

Knowledge graphs (KGs) have gained prominence for their ability to learn
representations for uni-relational facts. Recently, research has focused on
modeling hyper-relational facts, which move beyond the restriction of
uni-relational facts and allow us to represent more complex and real-world
information. However, existing approaches for learning representations on
hyper-relational KGs majorly focus on enhancing the communication from
qualifiers to base triples while overlooking the flow of information from base
triple to qualifiers. This can lead to suboptimal qualifier representations,
especially when a large amount of qualifiers are presented. It motivates us to
design a framework that utilizes multiple aggregators to learn representations
for hyper-relational facts: one from the perspective of the base triple and the
other one from the perspective of the qualifiers. Experiments demonstrate the
effectiveness of our framework for hyper-relational knowledge graph completion
across multiple datasets. Furthermore, we conduct an ablation study that
validates the importance of the various components in our framework. The code
to reproduce our results can be found at
\url{https://github.com/HarryShomer/QUAD}.

With the mass construction of Gen III nuclear reactors, it is a popular trend
to use deep learning (DL) techniques for fast and effective diagnosis of
possible accidents. To overcome the common problems of previous work in
diagnosing reactor accidents using deep learning theory, this paper proposes a
diagnostic process that ensures robustness to noisy and crippled data and is
interpretable. First, a novel Denoising Padded Autoencoder (DPAE) is proposed
for representation extraction of monitoring data, with representation extractor
still effective on disturbed data with signal-to-noise ratios up to 25.0 and
monitoring data missing up to 40.0%. Secondly, a diagnostic framework using
DPAE encoder for extraction of representations followed by shallow statistical
learning algorithms is proposed, and such stepwise diagnostic approach is
tested on disturbed datasets with 41.8% and 80.8% higher classification and
regression task evaluation metrics, in comparison with the end-to-end
diagnostic approaches. Finally, a hierarchical interpretation algorithm using
SHAP and feature ablation is presented to analyze the importance of the input
monitoring parameters and validate the effectiveness of the high importance
parameters. The outcomes of this study provide a referential method for
building robust and interpretable intelligent reactor anomaly diagnosis systems
in scenarios with high safety requirements.

Training deep neural networks (DNNs) is an important and challenging
optimization problem in machine learning due to its non-convexity and
non-separable structure. The alternating minimization (AM) approaches split the
composition structure of DNNs and have drawn great interest in the deep
learning and optimization communities. In this paper, we propose a unified
framework for analyzing the convergence rate of AM-type network training
methods. Our analysis are based on the $j$-step sufficient decrease conditions
and the Kurdyka-Lojasiewicz (KL) property, which relaxes the requirement of
designing descent algorithms. We show the detailed local convergence rate if
the KL exponent $\theta$ varies in $[0,1)$. Moreover, the local R-linear
convergence is discussed under a stronger $j$-step sufficient decrease
condition.

We study the prices of European Emission Allowances (EUA), whereby we analyze
their uncertainty and dependencies on related energy prices (natural gas, coal,
and oil). We propose a probabilistic multivariate conditional time series model
with a VECM-Copula-GARCH structure which exploits key characteristics of the
data. We normalize the EUA and fuel price data with respect to inflation and
carbon emissions to allow for proper cross-series evaluation. The forecasting
performance is evaluated in an extensive rolling-window forecasting study,
covering eight years out-of-sample. The accuracy of the multivariate
probabilistic forecasts is assessed using the energy score. We discuss our
findings for both levels- and log-transformed data, focusing on time-varying
correlations, and in view of the Russian invasion of Ukraine.

Continual Learning (CL, sometimes also termed incremental learning) is a
flavor of machine learning where the usual assumption of stationary data
distribution is relaxed or omitted. When naively applying, e.g., DNNs in CL
problems, changes in the data distribution can cause the so-called catastrophic
forgetting (CF) effect: an abrupt loss of previous knowledge. Although many
significant contributions to enabling CL have been made in recent years, most
works address supervised (classification) problems. This article reviews
literature that study CL in other settings, such as learning with reduced
supervision, fully unsupervised learning, and reinforcement learning. Besides
proposing a simple schema for classifying CL approaches w.r.t. their level of
autonomy and supervision, we discuss the specific challenges associated with
each setting and the potential contributions to the field of CL in general.

Size-based separation of bioparticles/cells is crucial to a variety of
biomedical processing steps for applications such as exosomes and DNA
isolation. Design and improvement of such microfluidic devices is a challenge
to best answer the demand for producing homogeneous end-result for study and
use. Deterministic lateral displacement (DLD) exploits a similar principle that
has drawn extensive attention over years. However, the lack of predictive
understanding of the particle trajectory and its induced mode makes designing a
DLD device an iterative procedure. Therefore, this paper investigates a fast
versatile design automation platform to address this issue. To do so,
convolutional and artificial neural networks were employed to learn velocity
fields and critical diameters of a wide range of DLD configurations. Later,
these networks were combined with a multi-objective evolutionary algorithm to
construct the automation tool. After ensuring the accuracy of the neural
networks, the developed tool was tested for 12 critical conditions. Reaching
the imposed conditions, the automation components performed reliably with
errors of less than 4%. Moreover, this tool is generalizable to other
field-based problems and since the neural network is an integral part of this
method, it enables transfer learning for similar physics. All the codes
generated and used in this study alongside the pre-trained neural network
models are available on https://github.com/HoseynAAmiri/DLDNN.

Adversarial machine learning is an emerging area showing the vulnerability of
deep learning models. Exploring attack methods to challenge state of the art
artificial intelligence (A.I.) models is an area of critical concern. The
reliability and robustness of such A.I. models are one of the major concerns
with an increasing number of effective adversarial attack methods.
Classification tasks are a major vulnerable area for adversarial attacks. The
majority of attack strategies are developed for colored or gray-scaled images.
Consequently, adversarial attacks on binary image recognition systems have not
been sufficiently studied. Binary images are simple two possible pixel-valued
signals with a single channel. The simplicity of binary images has a
significant advantage compared to colored and gray scaled images, namely
computation efficiency. Moreover, most optical character recognition systems
(O.C.R.s), such as handwritten character recognition, plate number
identification, and bank check recognition systems, use binary images or
binarization in their processing steps. In this paper, we propose a simple yet
efficient attack method, Efficient Combinatorial Black-box Adversarial Attack,
on binary image classifiers. We validate the efficiency of the attack technique
on two different data sets and three classification networks, demonstrating its
performance. Furthermore, we compare our proposed method with state-of-the-art
methods regarding advantages and disadvantages as well as applicability.

The Reynolds-Averaged Navier-Stokes (RANS) approach remains a backbone for
turbulence modeling due to its high cost-effectiveness. Its accuracy is largely
based on a reliable Reynolds stress anisotropy tensor closure model. There has
been an amount of work aiming at improving traditional closure models, while
they are still not satisfactory to some complex flow configurations. In recent
years, advances in computing power have opened up a new way to address this
problem: the machine-learning-assisted turbulence modeling. In this paper, we
employ neural networks to fully predict the Reynolds stress anisotropy tensor
of turbulent channel flows at different friction Reynolds numbers, for both
interpolation and extrapolation scenarios. Several generic neural networks of
Multi-Layer Perceptron (MLP) type are trained with different input feature
combinations to acquire a complete grasp of the role of each parameter. The
best performance is yielded by the model with the dimensionless mean streamwise
velocity gradient $\alpha$, the dimensionless wall distance $y^+$ and the
friction Reynolds number $\mathrm{Re}_\tau$ as inputs. A deeper theoretical
insight into the Tensor Basis Neural Network (TBNN) clarifies some remaining
ambiguities found in the literature concerning its application of Pope's
general eddy viscosity model. We emphasize the sensitivity of the TBNN on the
constant tensor $\textbf{T}^{*(0)}$ upon the turbulent channel flow data set,
and newly propose a generalized $\textbf{T}^{*(0)}$, which considerably
enhances its performance. Through comparison between the MLP and the augmented
TBNN model with both $\{\alpha, y^+, \mathrm{Re}_\tau\}$ as input set, it is
concluded that the former outperforms the latter and provides excellent
interpolation and extrapolation predictions of the Reynolds stress anisotropy
tensor in the specific case of turbulent channel flow.

Deep-learning-based approaches for retinal lesion segmentation often require
an abundant amount of precise pixel-wise annotated data. However, coarse
annotations such as circles or ellipses for outlining the lesion area can be
six times more efficient than pixel-level annotation. Therefore, this paper
proposes an annotation refinement network to convert a coarse annotation into a
pixel-level segmentation mask. Our main novelty is the application of the
prototype learning paradigm to enhance the generalization ability across
different datasets or types of lesions. We also introduce a prototype weighing
module to handle challenging cases where the lesion is overly small. The
proposed method was trained on the publicly available IDRiD dataset and then
generalized to the public DDR and our real-world private datasets. Experiments
show that our approach substantially improved the initial coarse mask and
outperformed the non-prototypical baseline by a large margin. Moreover, we
demonstrate the usefulness of the prototype weighing module in both
cross-dataset and cross-class settings.

The state identification problem seeks to identify power usage patterns of
any system, like buildings or factories, of interest. In this challenge paper,
we make power usage dataset available from 8 institutions in manufacturing,
education and medical institutions from the US and India, and an initial
un-supervised machine learning based solution as a baseline for the community
to accelerate research in this area.

Multi-view data are commonly encountered in data mining applications.
Effective extraction of information from multi-view data requires specific
design of clustering methods to cater for data with multiple views, which is
non-trivial and challenging. In this paper, we propose a novel one-step
multi-view clustering method by exploiting the dual representation of both the
common and specific information of different views. The motivation originates
from the rationale that multi-view data contain not only the consistent
knowledge between views but also the unique knowledge of each view. Meanwhile,
to make the representation learning more specific to the clustering task, a
one-step learning framework is proposed to integrate representation learning
and clustering partition as a whole. With this framework, the representation
learning and clustering partition mutually benefit each other, which
effectively improve the clustering performance. Results from extensive
experiments conducted on benchmark multi-view datasets clearly demonstrate the
superiority of the proposed method.

Audio-visual source localization is a challenging task that aims to predict
the location of visual sound sources in a video. Since collecting ground-truth
annotations of sounding objects can be costly, a plethora of weakly-supervised
localization methods that can learn from datasets with no bounding-box
annotations have been proposed in recent years, by leveraging the natural
co-occurrence of audio and visual signals. Despite significant interest,
popular evaluation protocols have two major flaws. First, they allow for the
use of a fully annotated dataset to perform early stopping, thus significantly
increasing the annotation effort required for training. Second, current
evaluation metrics assume the presence of sound sources at all times. This is
of course an unrealistic assumption, and thus better metrics are necessary to
capture the model's performance on (negative) samples with no visible sound
sources. To accomplish this, we extend the test set of popular benchmarks,
Flickr SoundNet and VGG-Sound Sources, in order to include negative samples,
and measure performance using metrics that balance localization accuracy and
recall. Using the new protocol, we conducted an extensive evaluation of prior
methods, and found that most prior works are not capable of identifying
negatives and suffer from significant overfitting problems (rely heavily on
early stopping for best results). We also propose a new approach for visual
sound source localization that addresses both these problems. In particular, we
found that, through extreme visual dropout and the use of momentum encoders,
the proposed approach combats overfitting effectively, and establishes a new
state-of-the-art performance on both Flickr SoundNet and VGG-Sound Source. Code
and pre-trained models are available at https://github.com/stoneMo/SLAVC.

In this work, we propose a data generation pipeline by leveraging the 3D
suite Blender to produce synthetic RGBD image datasets with 6D poses for
robotic picking. The proposed pipeline can efficiently generate large amounts
of photo-realistic RGBD images for the object of interest. In addition, a
collection of domain randomization techniques is introduced to bridge the gap
between real and synthetic data. Furthermore, we develop a real-time two-stage
6D pose estimation approach by integrating the object detector YOLO-V4-tiny and
the 6D pose estimation algorithm PVN3D for time sensitive robotics
applications. With the proposed data generation pipeline, our pose estimation
approach can be trained from scratch using only synthetic data without any
pre-trained models. The resulting network shows competitive performance
compared to state-of-the-art methods when evaluated on LineMod dataset. We also
demonstrate the proposed approach in a robotic experiment, grasping a household
object from cluttered background under different lighting conditions.

Quantization is a technique to reduce the computation and memory cost of DNN
models, which are getting increasingly large. Existing quantization solutions
use fixed-point integer or floating-point types, which have limited benefits,
as both require more bits to maintain the accuracy of original models. On the
other hand, variable-length quantization uses low-bit quantization for normal
values and high-precision for a fraction of outlier values. Even though this
line of work brings algorithmic benefits, it also introduces significant
hardware overheads due to variable-length encoding and decoding.
  In this work, we propose a fixed-length adaptive numerical data type called
ANT to achieve low-bit quantization with tiny hardware overheads. Our data type
ANT leverages two key innovations to exploit the intra-tensor and inter-tensor
adaptive opportunities in DNN models. First, we propose a particular data type,
flint, that combines the advantages of float and int for adapting to the
importance of different values within a tensor. Second, we propose an adaptive
framework that selects the best type for each tensor according to its
distribution characteristics. We design a unified processing element
architecture for ANT and show its ease of integration with existing DNN
accelerators. Our design results in 2.8$\times$ speedup and 2.5$\times$ energy
efficiency improvement over the state-of-the-art quantization accelerators.

Estimation of a multivariate regression function from independent and
identically distributed data is considered. An estimate is defined which fits a
deep neural network consisting of a large number of fully connected neural
networks, which are computed in parallel, via gradient descent to the data. The
estimate is over-parametrized in the sense that the number of its parameters is
much larger than the sample size. It is shown that in case of a suitable random
initialization of the network, a suitable small stepsize of the gradient
descent, and a number of gradient descent steps which is slightly larger than
the reciprocal of the stepsize of the gradient descent, the estimate is
universally consistent in the sense that its expected L2 error converges to
zero for all distributions of the data where the response variable is square
integrable.

3D face modeling has been an active area of research in computer vision and
computer graphics, fueling applications ranging from facial expression transfer
in virtual avatars to synthetic data generation. Existing 3D deep learning
generative models (e.g., VAE, GANs) allow generating compact face
representations (both shape and texture) that can model non-linearities in the
shape and appearance space (e.g., scatter effects, specularities, etc.).
However, they lack the capability to control the generation of subtle
expressions. This paper proposes a new 3D face generative model that can
decouple identity and expression and provides granular control over
expressions. In particular, we propose using a pair of supervised auto-encoder
and generative adversarial networks to produce high-quality 3D faces, both in
terms of appearance and shape. Experimental results in the generation of 3D
faces learned with holistic expression labels, or Action Unit labels, show how
we can decouple identity and expression; gaining fine-control over expressions
while preserving identity.

It is often difficult to correctly infer a writer's emotion from text
exchanged online, and differences in recognition between writers and readers
can be problematic. In this paper, we propose a new framework for detecting
sentences that create differences in emotion recognition between the writer and
the reader and for detecting the kinds of expressions that cause such
differences. The proposed framework consists of a bidirectional encoder
representations from transformers (BERT)-based detector that detects sentences
causing differences in emotion recognition and an analysis that acquires
expressions that characteristically appear in such sentences. The detector,
based on a Japanese SNS-document dataset with emotion labels annotated by both
the writer and three readers of the social networking service (SNS) documents,
detected "hidden-anger sentences" with AUC = 0.772; these sentences gave rise
to differences in the recognition of anger. Because SNS documents contain many
sentences whose meaning is extremely difficult to interpret, by analyzing the
sentences detected by this detector, we obtained several expressions that
appear characteristically in hidden-anger sentences. The detected sentences and
expressions do not convey anger explicitly, and it is difficult to infer the
writer's anger, but if the implicit anger is pointed out, it becomes possible
to guess why the writer is angry. Put into practical use, this framework would
likely have the ability to mitigate problems based on misunderstandings.

Time series forecasting is an important problem, with many real world
applications. Ensembles of deep neural networks have recently achieved
impressive forecasting accuracy, but such large ensembles are impractical in
many real world settings. Transformer models been successfully applied to a
diverse set of challenging problems. We propose a novel adaptation of the
original Transformer architecture focusing on the task of time series
forecasting, called Persistence Initialization. The model is initialized as a
naive persistence model by using a multiplicative gating mechanism combined
with a residual skip connection. We use a decoder Transformer with ReZero
normalization and Rotary positional encodings, but the adaptation is applicable
to any auto-regressive neural network model. We evaluate our proposed
architecture on the challenging M4 dataset, achieving competitive performance
compared to ensemble based methods. We also compare against existing recently
proposed Transformer models for time series forecasting, showing superior
performance on the M4 dataset. Extensive ablation studies show that Persistence
Initialization leads to better performance and faster convergence. As the size
of the model increases, only the models with our proposed adaptation gain in
performance. We also perform an additional ablation study to determine the
importance of the choice of normalization and positional encoding, and find
both the use of Rotary encodings and ReZero normalization to be essential for
good forecasting performance.

Many U.S. metropolitan cities are notorious for their severe shortage of
parking spots. To this end, we present a proactive prediction-driven
optimization framework to dynamically adjust parking prices. We use
state-of-the-art deep learning technologies such as neural ordinary
differential equations (NODEs) to design our future parking occupancy rate
prediction model given historical occupancy rates and price information. Owing
to the continuous and bijective characteristics of NODEs, in addition, we
design a one-shot price optimization method given a pre-trained prediction
model, which requires only one iteration to find the optimal solution. In other
words, we optimize the price input to the pre-trained prediction model to
achieve targeted occupancy rates in the parking blocks. We conduct experiments
with the data collected in San Francisco and Seattle for years. Our prediction
model shows the best accuracy in comparison with various temporal or
spatio-temporal forecasting models. Our one-shot optimization method greatly
outperforms other black-box and white-box search methods in terms of the search
time and always returns the optimal price solution.

Distributed synchronized GPU training is commonly used for deep learning. The
resource constraint of using fixed GPUs makes large-scale deep learning
training jobs suffer, and also lowers the cluster utilization. However,
incorporating resource elasticity often introduces non-determinism in model
accuracy, which is mainly due to the lack of capability to isolate the model
training procedure from hardware resources.
  We introduce EasyScale, an elastic framework that scales distributed training
on heterogeneous GPUs while producing deterministic deep learning models.
EasyScale follows the data-parallel training flow strictly, traces the
accuracy-relevant factors carefully, utilizes the deep learning characteristics
for context switching efficiently, thus achieving elastic accuracy-consistent
model training. To saturate the computation capability of heterogeneous GPUs,
EasyScale dynamically assigns workers based on our intra-job and inter-job
scheduling policies, minimizing GPU idle time and maximizing aggregated job
throughput accordingly. Deployed in an online serving cluster of CompanyA,
EasyScale powers elastic deep learning training jobs to utilize free GPUs
opportunistically, improving the overall cluster utilization by 62.1% without
violating SLA.

Node similarity scores constitute a foundation for machine learning in
graphs. Besides clustering, node classification, and anomaly detection, they
are a basis for link prediction with critical applications in biological
systems, information networks, and recommender systems. Recent works on link
prediction use vector space embeddings to calculate node similarities. While
these methods can provide good performance in undirected networks, they have
several disadvantages: limited interpretability, problem-specific
hyperparameter tuning, manual model fitting through dimensionality reduction,
and poor performance of symmetric similarities in directed link prediction. To
address these issues, we propose MapSim, a novel information-theoretic approach
to assess node similarities based on modular compression of network flows.
Different from vector space embeddings, MapSim represents nodes in a discrete,
non-metric space of communities and yields asymmetric similarities suitable to
predict directed and undirected links in an unsupervised fashion. The resulting
similarities can be explained based on a network's hierarchical modular
organisation, facilitating interpretability. MapSim naturally accounts for
Occam's razor, leading to parsimonious representations of clusters at multiple
scales. Addressing unsupervised link prediction, we compare MapSim to popular
embedding-based algorithms across 47 data sets of networks from a few hundred
to hundreds of thousands of nodes and millions of links. Our analysis shows
that MapSim's average performance across all networks is more than 7% higher
than its closest competitor, outperforming all embedding methods in 14 of the
47 networks, and a more than 33% better worst-case performance. Our method
demonstrates the potential of compression-based approaches in graph
representation learning, with promising applications in other graph learning
tasks.

Staining reveals the micro structure of the aspirate while creating
histopathology slides. Stain variation, defined as a chromatic difference
between the source and the target, is caused by varying characteristics during
staining, resulting in a distribution shift and poor performance on the target.
The goal of stain normalization is to match the target's chromatic distribution
to that of the source. However, stain normalisation causes the underlying
morphology to distort, resulting in an incorrect diagnosis. We propose FUSION,
a new method for promoting stain-adaption by adjusting the model to the target
in an unsupervised test-time scenario, eliminating the necessity for
significant labelling at the target end. FUSION works by altering the target's
batch normalization statistics and fusing them with source statistics using a
weighting factor. The algorithm reduces to one of two extremes based on the
weighting factor. Despite the lack of training or supervision, FUSION surpasses
existing equivalent algorithms for classification and dense predictions
(segmentation), as demonstrated by comprehensive experiments on two public
datasets.

Deep learning has been widely employed to solve the Electrical Impedance
Tomography (EIT) image reconstruction problem. Most existing physical
model-based and learning-based approaches focus on 2D EIT image reconstruction.
However, when they are directly extended to the 3D domain, the reconstruction
performance in terms of image quality and noise robustness is hardly guaranteed
mainly due to the significant increase in dimensionality. This paper presents a
learning-based approach for 3D EIT image reconstruction, which is named
Transposed convolution with Neurons Network (TN-Net). Simulation and
experimental results show the superior performance and generalization ability
of TN-Net compared with prevailing 3D EIT image reconstruction algorithms.

While machine learning and ranking-based systems are in widespread use for
sensitive decision-making processes (e.g., determining job candidates,
assigning credit scores), they are rife with concerns over unintended biases in
their outcomes, which makes algorithmic fairness (e.g., demographic parity,
equal opportunity) an objective of interest. 'Algorithmic recourse' offers
feasible recovery actions to change unwanted outcomes through the modification
of attributes. We introduce the notion of ranked group-level recourse fairness,
and develop a 'recourse-aware ranking' solution that satisfies ranked recourse
fairness constraints while minimizing the cost of suggested modifications. Our
solution suggests interventions that can reorder the ranked list of database
records and mitigate group-level unfairness; specifically, disproportionate
representation of sub-groups and recourse cost imbalance. This re-ranking
identifies the minimum modifications to data points, with these attribute
modifications weighted according to their ease of recourse. We then present an
efficient block-based extension that enables re-ranking at any granularity
(e.g., multiple brackets of bank loan interest rates, multiple pages of search
engine results). Evaluation on real datasets shows that, while existing methods
may even exacerbate recourse unfairness, our solution -- RAGUEL --
significantly improves recourse-aware fairness. RAGUEL outperforms alternatives
at improving recourse fairness, through a combined process of counterfactual
generation and re-ranking, whilst remaining efficient for large-scale datasets.

Multi-source domain adaptation (MSDA) learns to predict the labels in target
domain data, under the setting that data from multiple source domains are
labelled and data from the target domain are unlabelled. Most methods for this
task focus on learning invariant representations across domains. However, their
success relies heavily on the assumption that the label distribution remains
consistent across domains, which may not hold in general real-world problems.
In this paper, we propose a new and more flexible assumption, termed
\textit{latent covariate shift}, where a latent content variable $\mathbf{z}_c$
and a latent style variable $\mathbf{z}_s$ are introduced in the generative
process, with the marginal distribution of $\mathbf{z}_c$ changing across
domains and the conditional distribution of the label given $\mathbf{z}_c$
remaining invariant across domains. We show that although (completely)
identifying the proposed latent causal model is challenging, the latent content
variable can be identified up to scaling by using its dependence with labels
from source domains, together with the identifiability conditions of nonlinear
ICA. This motivates us to propose a novel method for MSDA, which learns the
invariant label distribution conditional on the latent content variable,
instead of learning invariant representations. Empirical evaluation on
simulation and real data demonstrates the effectiveness of the proposed method.

The task of causal representation learning aims to uncover latent
higher-level causal representations that affect lower-level observations.
Identifying true latent causal representations from observed data, while
allowing instantaneous causal relations among latent variables, remains a
challenge, however. To this end, we start from the analysis of three intrinsic
properties in identifying latent space from observations: transitivity,
permutation indeterminacy, and scaling indeterminacy. We find that transitivity
acts as a key role in impeding the identifiability of latent causal
representations. To address the unidentifiable issue due to transitivity, we
introduce a novel identifiability condition where the underlying latent causal
model satisfies a linear-Gaussian model, in which the causal coefficients and
the distribution of Gaussian noise are modulated by an additional observed
variable. Under some mild assumptions, we can show that the latent causal
representations can be identified up to trivial permutation and scaling.
Furthermore, based on this theoretical result, we propose a novel method,
termed Structural caUsAl Variational autoEncoder, which directly learns latent
causal representations and causal relationships among them, together with the
mapping from the latent causal variables to the observed ones. We show that the
proposed method learns the true parameters asymptotically. Experimental results
on synthetic and real data demonstrate the identifiability and consistency
results and the efficacy of the proposed method in learning latent causal

Education for sustainable development supports the improvement of knowledge,
skills, attitudes and behaviors related to global challenges such as climate
change, global warming and environmental degradation, among others. It is
increasingly taking place through projects based on information and
communication technologies. The effectiveness of the actions taken depends not
only on the quality of the project activities or the sophistication of the
innovative tools used. Social commitment also depends on the beliefs and moral
judgements manifested by potential recipients of educational activities on
environmental issues. This study aimed to identify the beliefs and moral
judgements that may facilitate or hinder the implementation of educational
activities based on information and communication technology, shaping
pro-environmental attitudes and behavior among city dwellers. Based on the
co-creation workshops conducted, five general categories emerged:
responsibility, sense of empowerment, local leadership, real eco-approach, and
eco-knowledge. The research findings may contribute to the design of
educational activities dedicated to shaping the pro-environmental behavior of
city dwellers.

For the Hamiltonian system, this work considers the learning and prediction
of the position (q) and momentum (p) variables generated by a symplectic
evolution map. Similar to Chen & Tao (2021), the symplectic map is represented
by the generating function. In addition, we develop a new learning scheme by
splitting the time series (q_i, p_i) into several partitions, and then train a
leap-frog neural network (LFNN) to approximate the generating function between
the first (i.e. initial condition) and one of the rest partitions. For
predicting the system evolution in a short timescale, the LFNN could
effectively avoid the issue of accumulative error. Then the LFNN is applied to
learn the behavior of the 2:3 resonant Kuiper belt objects, in a much longer
time period, and there are two significant improvements on the neural network
constructed in our previous work (Li et al. 2022): (1) conservation of the
Jacobi integral ; (2) highly accurate prediction of the orbital evolution. We
propose that the LFNN may be useful to make the prediction of the long time
evolution of the Hamiltonian system.

Deep neural networks have achieved impressive results in many image
classification tasks. However, since their performance is usually measured in
controlled settings, it is important to ensure that their decisions remain
correct when deployed in noisy environments. In fact, deep networks are not
robust to a large variety of semantic-preserving image modifications, even to
imperceptible image changes known as adversarial perturbations. The poor
robustness of image classifiers to small data distribution shifts raises
serious concerns regarding their trustworthiness. To build reliable machine
learning models, we must design principled methods to analyze and understand
the mechanisms that shape robustness and invariance. This is exactly the focus
of this thesis.
  First, we study the problem of computing sparse adversarial perturbations. We
exploit the geometry of the decision boundaries of image classifiers for
computing sparse perturbations very fast, and reveal a qualitative connection
between adversarial examples and the data features that image classifiers
learn. Then, to better understand this connection, we propose a geometric
framework that connects the distance of data samples to the decision boundary,
with the features existing in the data. We show that deep classifiers have a
strong inductive bias towards invariance to non-discriminative features, and
that adversarial training exploits this property to confer robustness. Finally,
we focus on the challenging problem of generalization to unforeseen corruptions
of the data, and we propose a novel data augmentation scheme for achieving
state-of-the-art robustness to common corruptions of the images.
  Overall, our results contribute to the understanding of the fundamental
mechanisms of deep image classifiers, and pave the way for building more
reliable machine learning systems that can be deployed in real-world

Several chronic lung diseases, like idiopathic pulmonary fibrosis (IPF) are
characterised by abnormal dilatation of the airways. Quantification of airway
features on computed tomography (CT) can help characterise disease progression.
Physics based airway measurement algorithms have been developed, but have met
with limited success in part due to the sheer diversity of airway morphology
seen in clinical practice. Supervised learning methods are also not feasible
due to the high cost of obtaining precise airway annotations. We propose
synthesising airways by style transfer using perceptual losses to train our
model, Airway Transfer Network (ATN). We compare our ATN model with a
state-of-the-art GAN-based network (simGAN) using a) qualitative assessment; b)
assessment of the ability of ATN and simGAN based CT airway metrics to predict
mortality in a population of 113 patients with IPF. ATN was shown to be quicker
and easier to train than simGAN. ATN-based airway measurements were also found
to be consistently stronger predictors of mortality than simGAN-derived airway
metrics on IPF CTs. Airway synthesis by a transformation network that refines
synthetic data using perceptual losses is a realistic alternative to GAN-based
methods for clinical CT analyses of idiopathic pulmonary fibrosis. Our source
code can be found at https://github.com/ashkanpakzad/ATN that is compatible
with the existing open-source airway analysis framework, AirQuant.

The concepts in knowledge graphs (KGs) enable machines to understand natural
language, and thus play an indispensable role in many applications. However,
existing KGs have the poor coverage of concepts, especially fine-grained
concepts. In order to supply existing KGs with more fine-grained and new
concepts, we propose a novel concept extraction framework, namely MRC-CE, to
extract large-scale multi-granular concepts from the descriptive texts of
entities. Specifically, MRC-CE is built with a machine reading comprehension
model based on BERT, which can extract more fine-grained concepts with a
pointer network. Furthermore, a random forest and rule-based pruning are also
adopted to enhance MRC-CE's precision and recall simultaneously. Our
experiments evaluated upon multilingual KGs, i.e., English Probase and Chinese
CN-DBpedia, justify MRC-CE's superiority over the state-of-the-art extraction
models in KG completion. Particularly, after running MRC-CE for each entity in
CN-DBpedia, more than 7,053,900 new concepts (instanceOf relations) are
supplied into the KG. The code and datasets have been released at
https://github.com/fcihraeipnusnacwh/MRC-CE

As machine learning (ML) models are increasingly being deployed in
high-stakes applications, policymakers have suggested tighter data protection
regulations (e.g., GDPR, CCPA). One key principle is the "right to be
forgotten" which gives users the right to have their data deleted. Another key
principle is the right to an actionable explanation, also known as algorithmic
recourse, allowing users to reverse unfavorable decisions. To date, it is
unknown whether these two principles can be operationalized simultaneously.
Therefore, we introduce and study the problem of recourse invalidation in the
context of data deletion requests. More specifically, we theoretically and
empirically analyze the behavior of popular state-of-the-art algorithms and
demonstrate that the recourses generated by these algorithms are likely to be
invalidated if a small number of data deletion requests (e.g., 1 or 2) warrant
updates of the predictive model. For the setting of linear models and
overparameterized neural networks -- studied through the lens of neural tangent
kernels (NTKs) -- we suggest a framework to identify a minimal subset of
critical training points which, when removed, maximize the fraction of
invalidated recourses. Using our framework, we empirically show that the
removal of as little as 2 data instances from the training set can invalidate
up to 95 percent of all recourses output by popular state-of-the-art
algorithms. Thus, our work raises fundamental questions about the compatibility
of "the right to an actionable explanation" in the context of the "right to be
forgotten" while also providing constructive insights on the determining
factors of recourse robustness.

Deep generative models (DGMs) are data-eager because learning a complex model
on limited data suffers from a large variance and easily overfits. Inspired by
the classical perspective of the bias-variance tradeoff, we propose regularized
deep generative model (Reg-DGM), which leverages a nontransferable pre-trained
model to reduce the variance of generative modeling with limited data.
Formally, Reg-DGM optimizes a weighted sum of a certain divergence and the
expectation of an energy function, where the divergence is between the data and
the model distributions, and the energy function is defined by the pre-trained
model w.r.t. the model distribution. We analyze a simple yet representative
Gaussian-fitting case to demonstrate how the weighting hyperparameter trades
off the bias and the variance. Theoretically, we characterize the existence and
the uniqueness of the global minimum of Reg-DGM in a non-parametric setting and
prove its convergence with neural networks trained by gradient-based methods.
Empirically, with various pre-trained feature extractors and a data-dependent
energy function, Reg-DGM consistently improves the generation performance of
strong DGMs with limited data and achieves competitive results to the

Diffusion models are a class of generative models, showing superior
performance as compared to other generative models in creating realistic images
when trained on natural image datasets. We introduce DISPR, a diffusion-based
model for solving the inverse problem of three-dimensional (3D) cell shape
prediction from two-dimensional (2D) single cell microscopy images. Using the
2D microscopy image as a prior, DISPR is conditioned to predict realistic 3D
shape reconstructions. To showcase the applicability of DISPR as a data
augmentation tool in a feature-based single cell classification task, we
extract morphological features from the cells grouped into six highly
imbalanced classes. Adding features from predictions of DISPR to the three
minority classes improved the macro F1 score from $F1_\text{macro} = 55.2 \pm
4.6\%$ to $F1_\text{macro} = 72.2 \pm 4.9\%$. With our method being the first
to employ a diffusion-based model in this context, we demonstrate that
diffusion models can be applied to inverse problems in 3D, and that they learn
to reconstruct 3D shapes with realistic morphological features from 2D
microscopy images.

Nowadays, we are living in an era of extreme device heterogeneity. Despite
the high variety of conventional CPU architectures, accelerator devices, such
as GPUs and FPGAs, also appear in the foreground exploding the pool of
available solutions to execute applications. However, choosing the appropriate
device per application needs is an extremely challenging task due to the
abstract relationship between hardware and software. Automatic optimization
algorithms that are accurate are required to cope with the complexity and
variety of current hardware and software. Optimal execution has always relied
on time-consuming trial and error approaches. Machine learning (ML) and Natural
Language Processing (NLP) has flourished over the last decade with research
focusing on deep architectures. In this context, the use of natural language
processing techniques to source code in order to conduct autotuning tasks is an
emerging field of study. In this paper, we extend the work of Cummins et al.,
namely Deeptune, that tackles the problem of optimal device selection (CPU or
GPU) for accelerated OpenCL kernels. We identify three major limitations of
Deeptune and, based on these, we propose four different DNN models that provide
enhanced contextual information of source codes. Experimental results show that
our proposed methodology surpasses that of Cummins et al. work, providing up to
4\% improvement in prediction accuracy.

Traffic accidents are the leading cause of death among young people, a
problem that today costs an enormous number of victims. Several technologies
have been proposed to prevent accidents, being Brain-Computer Interfaces (BCIs)
one of the most promising. In this context, BCIs have been used to detect
emotional states, concentration issues, or stressful situations, which could
play a fundamental role in the road since they are directly related to the
drivers' decisions. However, there is no extensive literature applying BCIs to
detect subjects' emotions in driving scenarios. In such a context, there are
some challenges to be solved, such as (i) the impact of performing a driving
task on the emotion detection and (ii) which emotions are more detectable in
driving scenarios. To improve these challenges, this work proposes a framework
focused on detecting emotions using electroencephalography with machine
learning and deep learning algorithms. In addition, a use case has been
designed where two scenarios are presented. The first scenario consists in
listening to sounds as the primary task to perform, while in the second
scenario listening to sound becomes a secondary task, being the primary task
using a driving simulator. In this way, it is intended to demonstrate whether
BCIs are useful in this driving scenario. The results improve those existing in
the literature , achieving 99% accuracy for the detection of two emotions
(non-stimuli and angry), 93% for three emotions (non-stimuli, angry and
neutral) and 75% for four emotions (non-stimuli, angry, neutral and joy).

This paper studies sequential information acquisition by an ambiguity-averse
decision maker (DM), who decides how long to collect information before taking
an irreversible action. The agent optimizes against the worst-case belief and
updates prior by prior. We show that the consideration of ambiguity gives rise
to rich dynamics: compared to the Bayesian DM, the DM here tends to experiment
excessively when facing modest uncertainty and, to counteract it, may stop
experimenting prematurely when facing high uncertainty. In the latter case, the
DM's stopping rule is non-monotonic in beliefs and features randomized
stopping.

A learning based method for obtaining feedback laws for nonlinear optimal
control problems is proposed. The learning problem is posed such that the open
loop value function is its optimal solution.
  This infinite dimensional, function space, problem, is approximated by a
polynomial ansatz and its convergence is analyzed. An $\ell_1$ penalty term is
employed, which combined with the proximal point method, allows to find sparse
solutions for the learning problem. The approach requires multiple evaluations
of the elements of the polynomial basis and of their derivatives. In order to
do this efficiently a graph-theoretic algorithm is devised. Several examples
underline that the proposed methodology provides a promising approach for
mitigating the curse of dimensionality which would be involved in case the
optimal feedback law was obtained by solving the Hamilton Jacobi Bellman
equation.

Recent successes suggest that an image can be manipulated by a text prompt,
e.g., a landscape scene on a sunny day is manipulated into the same scene on a
rainy day driven by a text input "raining". These approaches often utilize a
StyleCLIP-based image generator, which leverages multi-modal (text and image)
embedding space. However, we observe that such text inputs are often
bottlenecked in providing and synthesizing rich semantic cues, e.g.,
differentiating heavy rain from rain with thunderstorms. To address this issue,
we advocate leveraging an additional modality, sound, which has notable
advantages in image manipulation as it can convey more diverse semantic cues
(vivid emotions or dynamic expressions of the natural world) than texts. In
this paper, we propose a novel approach that first extends the image-text joint
embedding space with sound and applies a direct latent optimization method to
manipulate a given image based on audio input, e.g., the sound of rain. Our
extensive experiments show that our sound-guided image manipulation approach
produces semantically and visually more plausible manipulation results than the
state-of-the-art text and sound-guided image manipulation methods, which are
further confirmed by our human evaluations. Our downstream task evaluations
also show that our learned image-text-sound joint embedding space effectively
encodes sound inputs.

A Tone Mapping Operator (TMO) is required to render images with a High
Dynamic Range (HDR) on media with limited dynamic capabilities. TMOs compress
the dynamic range with the aim of preserving the visually perceptual cues of
the scene. Previous literature has established the benefits of TMOs being
semantic aware, understanding the content in the scene to preserve the cues
better. Expert photographers analyze the semantic and the contextual
information of a scene and decide tonal transformations or local luminance
adjustments. This process can be considered a manual analogy to tone mapping.
In this work, we draw inspiration from an expert photographer's approach and
present a Graph-based Semantic-aware Tone Mapping Operator, G-SemTMO. We
leverage semantic information as well as the contextual information of the
scene in the form of a graph capturing the spatial arrangements of its semantic
segments. Using Graph Convolutional Network (GCN), we predict intermediate
parameters called Semantic Hints and use these parameters to apply tonal
adjustments locally to different semantic segments in the image. In addition,
we also introduce LocHDR, a dataset of 781 HDR images tone mapped manually by
an expert photo-retoucher with local tonal enhancements. We conduct ablation
studies to show that our approach, G-SemTMO\footnote{Code and dataset to be
published with the final version of the manuscript}, can learn both global and
local tonal transformations from a pair of input linear and manually retouched
images by leveraging the semantic graphs and produce better results than both
classical and learning based TMOs. We also conduct ablation experiments to
validate the advantage of using GCN.

Activation functions can have a significant impact on reducing the
topological complexity of input data and therefore improve the performance of
the model. Selecting a suitable activation function is an essential step in
neural model design. However, the choice of activation function is seldom
discussed or explored in Transformer-based language models. Their activation
functions are chosen beforehand and then remain fixed from pre-training to
fine-tuning. As a result, the inductive biases they imposed on models cannot be
adjusted during this long life cycle. Moreover, subsequently developed models
(e.g., RoBERTa, BART, and GPT-3) often follow up prior work (e.g., BERT) to use
the same activation function without justification. In this paper, we
investigate the effectiveness of using Rational Activation Function (RAF), a
learnable activation function, in the Transformer architecture. In contrast to
conventional, predefined activation functions, RAFs can adaptively learn
optimal activation functions during training according to input data. Our
experiments show the RAF-based Transformer (RAFT) achieves a lower validation
perplexity than a vanilla BERT with the GELU function. We further evaluate RAFT
on downstream tasks in low- and full-data settings. Our results show that RAFT
outperforms the counterpart model across the majority of tasks and settings.
For instance, RAFT outperforms vanilla BERT on the GLUE benchmark by 5.71
points on average in low-data scenario (where 100 training examples are
available) and by 2.05 points on SQuAD in full-data setting. Analysis of the
shapes of learned RAFs further unveils that they substantially vary between
different layers of the pre-trained model and mostly look very different from
conventional activation functions. RAFT opens a new research direction for
analyzing and interpreting pre-trained models according to the learned
activation functions.

Recently, deep reinforcement learning (RL) has shown some impressive
successes in robotic manipulation applications. However, training robots in the
real world is nontrivial owing to sample efficiency and safety concerns.
Sim-to-real transfer is proposed to address the aforementioned concerns but
introduces a new issue called the reality gap. In this work, we introduce a
sim-to-real learning framework for vision-based assembly tasks and perform
training in a simulated environment by employing inputs from a single camera to
address the aforementioned issues. We present a domain adaptation method based
on cycle-consistent generative adversarial networks (CycleGAN) and a force
control transfer approach to bridge the reality gap. We demonstrate that the
proposed framework trained in a simulated environment can be successfully
transferred to a real peg-in-hole setup.

Structure-based drug design is drawing growing attentions in computer-aided
drug discovery. Compared with the virtual screening approach where a
pre-defined library of compounds are computationally screened, de novo drug
design based on the structure of a target protein can provide novel drug
candidates. In this paper, we present a generative solution named TamGent
(Target-aware molecule generator with Transformer) that can directly generate
candidate drugs from scratch for a given target, overcoming the limits imposed
by existing compound libraries. Following the Transformer framework (a
state-of-the-art framework in deep learning), we design a variant of
Transformer encoder to process 3D geometric information of targets and
pre-train the Transformer decoder on 10 million compounds from PubChem for
candidate drug generation. Systematical evaluation on candidate compounds
generated for targets from DrugBank shows that both binding affinity and
drugability are largely improved. TamGent outperforms previous baselines in
terms of both effectiveness and efficiency. The method is further verified by
generating candidate compounds for the SARS-CoV-2 main protease and the
oncogenic mutant KRAS G12C. The results show that our method not only
re-discovers previously verified drug molecules , but also generates novel
molecules with better docking scores, expanding the compound pool and
potentially leading to the discovery of novel drugs.

The alternating current (AC) chance-constrained optimal power flow (CC-OPF)
problem addresses the economic efficiency of electricity generation and
delivery under generation uncertainty. The latter is intrinsic to modern power
grids because of the high amount of renewables. Despite its academic success,
the AC CC-OPF problem is highly nonlinear and computationally demanding, which
limits its practical impact. For improving the AC-OPF problem
complexity/accuracy trade-off, the paper proposes a fast data-driven setup that
uses the sparse and hybrid Gaussian processes (GP) framework to model the power
flow equations with input uncertainty. We advocate the efficiency of the
proposed approach by a numerical study over multiple IEEE test cases showing up
to two times faster and more accurate solutions compared to the

The traditional homography estimation pipeline consists of four main steps:
feature detection, feature matching, outlier removal and transformation
estimation. Recent deep learning models intend to address the homography
estimation problem using a single convolutional network. While these models are
trained in an end-to-end fashion to simplify the homography estimation problem,
they lack the feature matching step and/or the outlier removal step, which are
important steps in the traditional homography estimation pipeline. In this
paper, we attempt to build a deep learning model that mimics all four steps in
the traditional homography estimation pipeline. In particular, the feature
matching step is implemented using the cost volume technique. To remove
outliers in the cost volume, we treat this outlier removal problem as a
denoising problem and propose a novel self-supervised loss to solve the
problem. Extensive experiments on synthetic and real datasets demonstrate that
the proposed model outperforms existing deep learning models.

This paper attempts to establish the theoretical foundation for the emerging
super-model paradigm via domain adaptation, where one first trains a very
large-scale model, {\it i.e.}, super model (or foundation model in some other
papers), on a large amount of data and then adapts it to various specific
domains. Super-model paradigms help reduce computational and data cost and
carbon emission, which is critical to AI industry, especially enormous small
and medium-sized enterprises. We model the super-model paradigm as a two-stage
diffusion process: (1) in the pre-training stage, the model parameter diffuses
from random initials and converges to a steady distribution; and (2) in the
fine-tuning stage, the model parameter is transported to another steady
distribution. Both training stages can be mathematically modeled by the
Uhlenbeck-Ornstein process which converges to two Maxwell-Boltzmann
distributions, respectively, each of which characterizes the corresponding
convergent model. An $\mathcal O(1/\sqrt{N})$ generalization bound is then
established via PAC-Bayesian framework. The theory finds that the
generalization error of the fine-tuning stage is dominant in domain adaptation.
In addition, our theory suggests that the generalization is determined by a new
measure that characterizes the domain discrepancy between the source domain and
target domain, based on the covariance matrices and the shift of the converged
local minimum.

Multi-user delay constrained scheduling is important in many real-world
applications including wireless communication, live streaming, and cloud
computing. Yet, it poses a critical challenge since the scheduler needs to make
real-time decisions to guarantee the delay and resource constraints
simultaneously without prior information of system dynamics, which can be
time-varying and hard to estimate. Moreover, many practical scenarios suffer
from partial observability issues, e.g., due to sensing noise or hidden
correlation. To tackle these challenges, we propose a deep reinforcement
learning (DRL) algorithm, named Recurrent Softmax Delayed Deep Double
Deterministic Policy Gradient ($\mathtt{RSD4}$), which is a data-driven method
based on a Partially Observed Markov Decision Process (POMDP) formulation.
$\mathtt{RSD4}$ guarantees resource and delay constraints by Lagrangian dual
and delay-sensitive queues, respectively. It also efficiently tackles partial
observability with a memory mechanism enabled by the recurrent neural network
(RNN) and introduces user-level decomposition and node-level merging to ensure
scalability. Extensive experiments on simulated/real-world datasets demonstrate
that $\mathtt{RSD4}$ is robust to system dynamics and partially observable
environments, and achieves superior performances over existing DRL and
non-DRL-based methods.

Representation learning on temporal graphs has drawn considerable research
attention owing to its fundamental importance in a wide spectrum of real-world
applications. Though a number of studies succeed in obtaining time-dependent
representations, it still faces significant challenges. On the one hand, most
of the existing methods restrict the embedding space with a certain curvature.
However, the underlying geometry in fact shifts among the positive curvature
hyperspherical, zero curvature Euclidean and negative curvature hyperbolic
spaces in the evolvement over time. On the other hand, these methods usually
require abundant labels to learn temporal representations, and thereby notably
limit their wide use in the unlabeled graphs of the real applications. To
bridge this gap, we make the first attempt to study the problem of
self-supervised temporal graph representation learning in the general
Riemannian space, supporting the time-varying curvature to shift among
hyperspherical, Euclidean and hyperbolic spaces. In this paper, we present a
novel self-supervised Riemannian graph neural network (SelfRGNN). Specifically,
we design a curvature-varying Riemannian GNN with a theoretically grounded time
encoding, and formulate a functional curvature over time to model the
evolvement shifting among the positive, zero and negative curvature spaces. To
enable the self-supervised learning, we propose a novel reweighting
self-contrastive approach, exploring the Riemannian space itself without
augmentation, and propose an edge-based self-supervised curvature learning with
the Ricci curvature. Extensive experiments show the superiority of SelfRGNN,
and moreover, the case study shows the time-varying curvature of temporal graph

In ridepooling systems with electric fleets, charging is a complex
decision-making process. Most electric vehicle (EV) taxi services require
drivers to make egoistic decisions, leading to decentralized ad-hoc charging
strategies. The current state of the mobility system is often lacking or not
shared between vehicles, making it impossible to make a system-optimal
decision. Most existing approaches do not combine time, location and duration
into a comprehensive control algorithm or are unsuitable for real-time
operation. We therefore present a real-time predictive charging method for
ridepooling services with a single operator, called Idle Time Exploitation
(ITX), which predicts the periods where vehicles are idle and exploits these
periods to harvest energy. It relies on Graph Convolutional Networks and a
linear assignment algorithm to devise an optimal pairing of vehicles and
charging stations, in pursuance of maximizing the exploited idle time. We
evaluated our approach through extensive simulation studies on real-world
datasets from New York City. The results demonstrate that ITX outperforms all
baseline methods by at least 5% (equivalent to $70,000 for a 6,000 vehicle
operation) per week in terms of a monetary reward function which was modeled to
replicate the profitability of a real-world ridepooling system. Moreover, ITX
can reduce delays by at least 4.68% in comparison with baseline methods and
generally increase passenger comfort by facilitating a better spread of
customers across the fleet. Our results also demonstrate that ITX enables
vehicles to harvest energy during the day, stabilizing battery levels and
increasing resilience to unexpected surges in demand. Lastly, compared to the
best-performing baseline strategy, peak loads are reduced by 17.39% which
benefits grid operators and paves the way for more sustainable use of the
electrical grid.

To improve the overall performance of processors, computer architects use
various performance optimization techniques in modern processors, such as
speculative execution, branch prediction, and chaotic execution. Both now and
in the future, these optimization techniques are critical for improving the
execution speed of processor instructions. However, researchers have discovered
that these techniques introduce hidden inherent security flaws, such as
meltdown and ghost attacks in recent years. They exploit techniques such as
chaotic execution or speculative execution combined with cache-based
side-channel attacks to leak protected data. The impact of these
vulnerabilities is enormous because they are prevalent in existing or future
processors. However, until today, meltdown and ghost have not been effectively
addressed, but instead, multiple attack variants and different attack
implementations have evolved from them. This paper proposes to optimize four
different hardware performance events through feature selection and use machine
learning algorithms to build a real-time detection mechanism for Spectre
v1,v2,v4, and different implementations of meltdown attacks, ultimately
achieving an accuracy rate of over 99\%. In order to verify the practicality of
the attack detection model, this paper is tested with a variety of benign
programs and different implementations of Spectre attacks different from the
modeling process, and the absolute accuracy also exceeds 99\%, showing that
this paper can cope with different attack variants and different
implementations of the same attack that may occur daily.

Camera traps have revolutionized the animal research of many species that
were previously nearly impossible to observe due to their habitat or behavior.
They are cameras generally fixed to a tree that take a short sequence of images
when triggered. Deep learning has the potential to overcome the workload to
automate image classification according to taxon or empty images. However, a
standard deep neural network classifier fails because animals often represent a
small portion of the high-definition images. That is why we propose a workflow
named Weakly Object Detection Faster-RCNN+FPN which suits this challenge. The
model is weakly supervised because it requires only the animal taxon label per
image but doesn't require any manual bounding box annotations. First, it
automatically performs the weakly-supervised bounding box annotation using the
motion from multiple frames. Then, it trains a Faster-RCNN+FPN model using this
weak supervision. Experimental results have been obtained with two datasets
from a Papua New Guinea and Missouri biodiversity monitoring campaign, then on
an easily reproducible testbed.

Many fundamental properties of a quantum system are captured by its
Hamiltonian and ground state. Despite the significance of ground states
preparation (GSP), this task is classically intractable for large-scale
Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern
quantum machines, have emerged as a leading protocol to conquer this issue. As
such, how to enhance the performance of QNNs becomes a crucial topic in GSP.
Empirical evidence showed that QNNs with handcraft symmetric ansatzes generally
experience better trainability than those with asymmetric ansatzes, while
theoretical explanations have not been explored. To fill this knowledge gap,
here we propose the effective quantum neural tangent kernel (EQNTK) and connect
this concept with over-parameterization theory to quantify the convergence of
QNNs towards the global optima. We uncover that the advance of symmetric
ansatzes attributes to their large EQNTK value with low effective dimension,
which requests few parameters and quantum circuit depth to reach the
over-parameterization regime permitting a benign loss landscape and fast
convergence. Guided by EQNTK, we further devise a symmetric pruning (SP) scheme
to automatically tailor a symmetric ansatz from an over-parameterized and
asymmetric one to greatly improve the performance of QNNs when the explicit
symmetry information of Hamiltonian is unavailable. Extensive numerical
simulations are conducted to validate the analytical results of EQNTK and the
effectiveness of SP.

Ensembles of Deep Neural Networks (DNNs) have achieved qualitative
predictions but they are computing and memory intensive. Therefore, the demand
is growing to make them answer a heavy workload of requests with available
computational resources. Unlike recent initiatives on inference servers and
inference frameworks, which focus on the prediction of single DNNs, we propose
a new software layer to serve with flexibility and efficiency ensembles of
DNNs.
  Our inference system is designed with several technical innovations. First,
we propose a novel procedure to find a good allocation matrix between devices
(CPUs or GPUs) and DNN instances. It runs successively a worst-fit to allocate
DNNs into the memory devices and a greedy algorithm to optimize allocation
settings and speed up the ensemble. Second, we design the inference system
based on multiple processes to run asynchronously: batching, prediction, and
the combination rule with an efficient internal communication scheme to avoid
overhead.
  Experiments show the flexibility and efficiency under extreme scenarios: It
successes to serve an ensemble of 12 heavy DNNs into 4 GPUs and at the
opposite, one single DNN multi-threaded into 16 GPUs. It also outperforms the
simple baseline consisting of optimizing the batch size of DNNs by a speedup up
to 2.7X on the image classification task.

Automated Machine Learning with ensembling (or AutoML with ensembling) seeks
to automatically build ensembles of Deep Neural Networks (DNNs) to achieve
qualitative predictions. Ensemble of DNNs are well known to avoid over-fitting
but they are memory and time consuming approaches. Therefore, an ideal AutoML
would produce in one single run time different ensembles regarding accuracy and
inference speed. While previous works on AutoML focus to search for the best
model to maximize its generalization ability, we rather propose a new AutoML to
build a larger library of accurate and diverse individual models to then
construct ensembles. First, our extensive benchmarks show asynchronous
Hyperband is an efficient and robust way to build a large number of diverse
models to combine them. Then, a new ensemble selection method based on a
multi-objective greedy algorithm is proposed to generate accurate ensembles by
controlling their computing cost. Finally, we propose a novel algorithm to
optimize the inference of the DNNs ensemble in a GPU cluster based on
allocation optimization. The produced AutoML with ensemble method shows robust
results on two datasets using efficiently GPU clusters during both the training
phase and the inference phase.

Detecting anomalous regions in images is a frequently encountered problem in
industrial monitoring. A relevant example is the analysis of tissues and other
products that in normal conditions conform to a specific texture, while defects
introduce changes in the normal pattern. We address the anomaly detection
problem by training a deep autoencoder, and we show that adopting a loss
function based on Complex Wavelet Structural Similarity (CW-SSIM) yields
superior detection performance on this type of images compared to traditional
autoencoder loss functions. Our experiments on well-known anomaly detection
benchmarks show that a simple model trained with this loss function can achieve
comparable or superior performance to state-of-the-art methods leveraging
deeper, larger and more computationally demanding neural networks.

The research of extending deep reinforcement learning (drl) to multi-agent
field has solved many complicated problems and made great achievements.
However, almost all these studies only focus on discrete or continuous action
space and there are few works having ever used multi-agent deep reinforcement
learning to real-world environment problems which mostly have a hybrid action
space. Therefore, in this paper, we propose two algorithms: deep multi-agent
hybrid soft actor-critic (MAHSAC) and multi-agent hybrid deep deterministic
policy gradients (MAHDDPG) to fill this gap. This two algorithms follow the
centralized training and decentralized execution (CTDE) paradigm and could
handle hybrid action space problems. Our experiences are running on multi-agent
particle environment which is an easy multi-agent particle world, along with
some basic simulated physics. The experimental results show that these
algorithms have good performances.

Machine ethics has received increasing attention over the past few years
because of the need to ensure safe and reliable artificial intelligence (AI).
The two dominantly used theories in machine ethics are deontological and
utilitarian ethics. Virtue ethics, on the other hand, has often been mentioned
as an alternative ethical theory. While this interesting approach has certain
advantages over popular ethical theories, little effort has been put into
engineering artificial virtuous agents due to challenges in their
formalization, codifiability, and the resolution of ethical dilemmas to train
virtuous agents. We propose to bridge this gap by using role-playing games
riddled with moral dilemmas. There are several such games in existence, such as
Papers, Please and Life is Strange, where the main character encounters
situations where they must choose the right course of action by giving up
something else dear to them. We draw inspiration from such games to show how a
systemic role-playing game can be designed to develop virtues within an
artificial agent. Using modern day AI techniques, such as affinity-based
reinforcement learning and explainable AI, we motivate the implementation of
virtuous agents that play such role-playing games, and the examination of their
decisions through a virtue ethical lens. The development of such agents and
environments is a first step towards practically formalizing and demonstrating
the value of virtue ethics in the development of ethical agents.

We address the problem of online change detection in multivariate
datastreams, and we introduce QuantTree Exponentially Weighted Moving Average
(QT-EWMA), a nonparametric change-detection algorithm that can control the
expected time before a false alarm, yielding a desired Average Run Length
(ARL$_0$). Controlling false alarms is crucial in many applications and is
rarely guaranteed by online change-detection algorithms that can monitor
multivariate datastreams without knowing the data distribution. Like many
change-detection algorithms, QT-EWMA builds a model of the data distribution,
in our case a QuantTree histogram, from a stationary training set. To monitor
datastreams even when the training set is extremely small, we propose
QT-EWMA-update, which incrementally updates the QuantTree histogram during
monitoring, always keeping the ARL$_0$ under control. Our experiments,
performed on synthetic and real-world datastreams, demonstrate that QT-EWMA and
QT-EWMA-update control the ARL$_0$ and the false alarm rate better than
state-of-the-art methods operating in similar conditions, achieving lower or
comparable detection delays.

Detecting test data deviating from training data is a central problem for
safe and robust machine learning. Likelihoods learned by a generative model,
e.g., a normalizing flow via standard log-likelihood training, perform poorly
as an anomaly score. We propose to use an unlabelled auxiliary dataset and a
probabilistic outlier score for anomaly detection. We use a self-supervised
feature extractor trained on the auxiliary dataset and train a normalizing flow
on the extracted features by maximizing the likelihood on in-distribution data
and minimizing the likelihood on the auxiliary dataset. We show that this is
equivalent to learning the normalized positive difference between the
in-distribution and the auxiliary feature density. We conduct experiments on
benchmark datasets and show a robust improvement compared to likelihood,
likelihood ratio methods and state-of-the-art anomaly detection methods.

Human pose forecasting is a challenging problem involving complex human body
motion and posture dynamics. In cases that there are multiple people in the
environment, one's motion may also be influenced by the motion and dynamic
movements of others. Although there are several previous works targeting the
problem of multi-person dynamic pose forecasting, they often model the entire
pose sequence as time series (ignoring the underlying relationship between
joints) or only output the future pose sequence of one person at a time. In
this paper, we present a new method, called Social Motion Transformer
(SoMoFormer), for multi-person 3D pose forecasting. Our transformer
architecture uniquely models human motion input as a joint sequence rather than
a time sequence, allowing us to perform attention over joints while predicting
an entire future motion sequence for each joint in parallel. We show that with
this problem reformulation, SoMoFormer naturally extends to multi-person scenes
by using the joints of all people in a scene as input queries. Using learned
embeddings to denote the type of joint, person identity, and global position,
our model learns the relationships between joints and between people, attending
more strongly to joints from the same or nearby people. SoMoFormer outperforms
state-of-the-art methods for long-term motion prediction on the SoMoF benchmark
as well as the CMU-Mocap and MuPoTS-3D datasets. Code will be made available
after publication.

Fluoroscopy is an imaging technique that uses X-ray to obtain a real-time 2D
video of the interior of a 3D object, helping surgeons to observe pathological
structures and tissue functions especially during intervention. However, it
suffers from heavy noise that mainly arises from the clinical use of a low dose
X-ray, thereby necessitating the technology of fluoroscopy denoising. Such
denoising is challenged by the relative motion between the object being imaged
and the X-ray imaging system. We tackle this challenge by proposing a
self-supervised, three-stage framework that exploits the domain knowledge of
fluoroscopy imaging. (i) Stabilize: we first construct a dynamic panorama based
on optical flow calculation to stabilize the non-stationary background induced
by the motion of the X-ray detector. (ii) Decompose: we then propose a novel
mask-based Robust Principle Component Analysis (RPCA) decomposition method to
separate a video with detector motion into a low-rank background and a sparse
foreground. Such a decomposition accommodates the reading habit of experts.
(iii) Denoise: we finally denoise the background and foreground separately by a
self-supervised learning strategy and fuse the denoised parts into the final
output via a bilateral, spatiotemporal filter. To assess the effectiveness of
our work, we curate a dedicated fluoroscopy dataset of 27 videos (1,568 frames)
and corresponding ground truth. Our experiments demonstrate that it achieves
significant improvements in terms of denoising and enhancement effects when
compared with standard approaches. Finally, expert rating confirms this
efficacy.

As an alcoholic beverage, wine has remained prevalent for thousands of years,
and the quality assessment of wines has been significant in wine production and
trade. Scholars have proposed various deep learning and machine learning
algorithms for wine quality prediction, such as Support vector machine (SVM),
Random Forest (RF), K-nearest neighbors (KNN), Deep neural network (DNN), and
Logistic regression (LR). However, these methods ignore the inner relationship
between the physical and chemical properties of the wine, for example, the
correlations between pH values, fixed acidity, citric acid, and so on. To fill
the gap, this paper conducts the Pearson correlation analysis, PCA analysis,
and Shapiro-Wilk test on those properties and incorporates 1D-CNN architecture
to capture the correlations among neighboring features. In addition, it
implemented dropout and batch normalization techniques to improve the
robustness of the proposed model. Massive experiments have shown that our
method can outperform baseline approaches in wine quality prediction. Moreover,
ablation experiments also demonstrate the effectiveness of incorporating the
1-D CNN module, Dropout, and normalization techniques.

Obesity is a serious issue in the modern society since it associates to a
significantly reduced quality of life. Current research conducted to explore
the obesity-related neurological evidences using electroencephalography (EEG)
data are limited to traditional approaches. In this study, we developed a novel
machine learning model to identify brain networks of obese females using alpha
band functional connectivity features derived from EEG data. An overall
classification accuracy of 0.912 is achieved. Our finding suggests that the
obese brain is characterized by a dysfunctional network in which the areas that
are responsible for processing self-referential information such as energy
requirement are impaired.

Ejection fraction (EF) is a key indicator of cardiac function, allowing
identification of patients prone to heart dysfunctions such as heart failure.
EF is estimated from cardiac ultrasound videos known as echocardiograms (echo)
by manually tracing the left ventricle and estimating its volume on certain
frames. These estimations exhibit high inter-observer variability due to the
manual process and varying video quality. Such sources of inaccuracy and the
need for rapid assessment necessitate reliable and explainable machine learning
techniques. In this work, we introduce EchoGNN, a model based on graph neural
networks (GNNs) to estimate EF from echo videos. Our model first infers a
latent echo-graph from the frames of one or multiple echo cine series. It then
estimates weights over nodes and edges of this graph, indicating the importance
of individual frames that aid EF estimation. A GNN regressor uses this weighted
graph to predict EF. We show, qualitatively and quantitatively, that the
learned graph weights provide explainability through identification of critical
frames for EF estimation, which can be used to determine when human
intervention is required. On EchoNet-Dynamic public EF dataset, EchoGNN
achieves EF prediction performance that is on par with state of the art and
provides explainability, which is crucial given the high inter-observer
variability inherent in this task.

We present a set of peculiar radio sources detected using an unsupervised
machine learning method. We use data from the Australian Square Kilometre Array
Pathfinder (ASKAP) telescope to train a self-organizing map (SOM). The radio
maps from three ASKAP surveys, Evolutionary Map of Universe pilot survey
(EMU-PS), Deep Investigation of Neutral Gas Origins pilot survey (DINGO) and
Survey With ASKAP of GAMA-09 + X-ray (SWAG-X), are used to search for the
rarest or unknown radio morphologies. We use an extension of the SOM algorithm
that implements rotation and flipping invariance on astronomical sources. The
SOM is trained using the images of all "complex" radio sources in the EMU-PS
which we define as all sources catalogued as "multi-component". The trained SOM
is then used to estimate a similarity score for complex sources in all surveys.
We select 0.5\% of the sources that are most complex according to the
similarity metric, and visually examine them to find the rarest radio
morphologies. Among these, we find two new odd radio circle (ORC) candidates
and five other peculiar morphologies. We discuss multiwavelength properties and
the optical/infrared counterparts of selected peculiar sources. In addition, we
present examples of conventional radio morphologies including: diffuse emission
from galaxy clusters, and resolved, bent-tailed, and FR-I and FR-II type radio
galaxies. We discuss the overdense environment that may be the reason behind
the circular shape of ORC candidates.

Elucidating and accurately predicting the druggability and bioactivities of
molecules plays a pivotal role in drug design and discovery and remains an open
challenge. Recently, graph neural networks (GNN) have made remarkable
advancements in graph-based molecular property prediction. However, current
graph-based deep learning methods neglect the hierarchical information of
molecules and the relationships between feature channels. In this study, we
propose a well-designed hierarchical informative graph neural networks
framework (termed HiGNN) for predicting molecular property by utilizing a
co-representation learning of molecular graphs and chemically synthesizable
BRICS fragments. Furthermore, a plug-and-play feature-wise attention block is
first designed in HiGNN architecture to adaptively recalibrate atomic features
after the message passing phase. Extensive experiments demonstrate that HiGNN
achieves state-of-the-art predictive performance on many challenging drug
discovery-associated benchmark datasets. In addition, we devise a
molecule-fragment similarity mechanism to comprehensively investigate the
interpretability of HiGNN model at the subgraph level, indicating that HiGNN as
a powerful deep learning tool can help chemists and pharmacists identify the
key components of molecules for designing better molecules with desired
properties or functions. The source code is publicly available at
https://github.com/idruglab/hignn.

Source-free unsupervised domain adaptation (SFUDA) aims to learn a target
domain model using unlabeled target data and the knowledge of a well-trained
source domain model. Most previous SFUDA works focus on inferring semantics of
target data based on the source knowledge. Without measuring the
transferability of the source knowledge, these methods insufficiently exploit
the source knowledge, and fail to identify the reliability of the inferred
target semantics. However, existing transferability measurements require either
source data or target labels, which are infeasible in SFUDA. To this end,
firstly, we propose a novel Uncertainty-induced Transferability Representation
(UTR), which leverages uncertainty as the tool to analyse the channel-wise
transferability of the source encoder in the absence of the source data and
target labels. The domain-level UTR unravels how transferable the encoder
channels are to the target domain and the instance-level UTR characterizes the
reliability of the inferred target semantics. Secondly, based on the UTR, we
propose a novel Calibrated Adaption Framework (CAF) for SFUDA, including i)the
source knowledge calibration module that guides the target model to learn the
transferable source knowledge and discard the non-transferable one, and ii)the
target semantics calibration module that calibrates the unreliable semantics.
With the help of the calibrated source knowledge and the target semantics, the
model adapts to the target domain safely and ultimately better. We verified the
effectiveness of our method using experimental results and demonstrated that
the proposed method achieves state-of-the-art performances on the three SFUDA
benchmarks. Code is available at https://github.com/SPIresearch/UTR.

Exposure to bio-aerosols such as mold spores and pollen can lead to adverse
health effects. There is a need for a portable and cost-effective device for
long-term monitoring and quantification of various bio-aerosols. To address
this need, we present a mobile and cost-effective label-free bio-aerosol sensor
that takes holographic images of flowing particulate matter concentrated by a
virtual impactor, which selectively slows down and guides particles larger than
~6 microns to fly through an imaging window. The flowing particles are
illuminated by a pulsed laser diode, casting their inline holograms on a CMOS
image sensor in a lens-free mobile imaging device. The illumination contains
three short pulses with a negligible shift of the flowing particle within one
pulse, and triplicate holograms of the same particle are recorded at a single
frame before it exits the imaging field-of-view, revealing different
perspectives of each particle. The particles within the virtual impactor are
localized through a differential detection scheme, and a deep neural network
classifies the aerosol type in a label-free manner, based on the acquired
holographic images. We demonstrated the success of this mobile bio-aerosol
detector with a virtual impactor using different types of pollen (i.e.,
bermuda, elm, oak, pine, sycamore, and wheat) and achieved a blind
classification accuracy of 92.91%. This mobile and cost-effective device weighs
~700 g and can be used for label-free sensing and quantification of various
bio-aerosols over extended periods since it is based on a cartridge-free
virtual impactor that does not capture or immobilize particulate matter.

Since multidrug combination is widely applied, the accurate prediction of
drug-drug interaction (DDI) is becoming more and more critical. In our method,
we use graph to represent drug-drug interaction: nodes represent drug; edges
represent drug-drug interactions. Based on our assumption, we convert the
prediction of DDI to link prediction problem, utilizing known drug node
characteristics and DDI types to predict unknown DDI types. This work proposes
a Graph Distance Neural Network (GDNN) to predict drug-drug interactions.
Firstly, GDNN generates initial features for nodes via target point method,
fully including the distance information in the graph. Secondly, GDNN adopts an
improved message passing framework to better generate each drug node embedded
expression, comprehensively considering the nodes and edges characteristics
synchronously. Thirdly, GDNN aggregates the embedded expressions, undergoing
MLP processing to generate the final predicted drug interaction type. GDNN
achieved Test Hits@20=0.9037 on the ogb-ddi dataset, proving GDNN can predict
DDI efficiently.

In this paper, we present a new neural architectural block for the vision
domain, named Mixing Regionally and Locally (MRL), developed with the aim of
effectively and efficiently mixing the provided input features. We bifurcate
the input feature mixing task as mixing at a regional and local scale. To
achieve an efficient mix, we exploit the domain-wide receptive field provided
by self-attention for regional-scale mixing and convolutional kernels
restricted to local scale for local-scale mixing. More specifically, our
proposed method mixes regional features associated with local features within a
defined region, followed by a local-scale features mix augmented by regional
features. Experiments show that this hybridization of self-attention and
convolution brings improved capacity, generalization (right inductive bias),
and efficiency. Under similar network settings, MRL outperforms or is at par
with its counterparts in classification, object detection, and segmentation
tasks. We also show that our MRL-based network architecture achieves
state-of-the-art performance for H&E histology datasets. We achieved DICE of
0.843, 0.855, and 0.892 for Kumar, CoNSep, and CPM-17 datasets, respectively,
while highlighting the versatility offered by the MRL framework by
incorporating layers like group convolutions to improve dataset-specific

Lossless image compression is an essential research field in image
compression. Recently, learning-based image compression methods achieved
impressive performance compared with traditional lossless methods, such as
WebP, JPEG2000, and FLIF. However, there are still many impressive lossy
compression methods that can be applied to lossless compression. Therefore, in
this paper, we explore the methods widely used in lossy compression and apply
them to lossless compression. Inspired by the impressive performance of the
Gaussian mixture model (GMM) shown in lossy compression, we generate a lossless
network architecture with GMM. Besides noticing the successful achievements of
attention modules and autoregressive models, we propose to utilize attention
modules and add an extra autoregressive model for raw images in our network
architecture to boost the performance. Experimental results show that our
approach outperforms most classical lossless compression methods and existing
learning-based methods.

The lung airway tree modeling is essential to work for the diagnosis of
pulmonary diseases, especially for X-Ray computed tomography (CT). The airway
tree modeling on CT images can provide the experts with 3-dimension
measurements like wall thickness, etc. This information can tremendously aid
the diagnosis of pulmonary diseases like chronic obstructive pulmonary disease
[1-4]. Many scholars have attempted various ways to model the lung airway tree,
which can be split into two major categories based on its nature. Namely, the
model-based approach and the deep learning approach. The performance of a
typical model-based approach usually depends on the manual tuning of the model
parameter, which can be its advantages and disadvantages. The advantage is its
don't require a large amount of training data which can be beneficial for a
small dataset like medical imaging. On the other hand, the performance of
model-based may be a misconcep-tion [5,6].
  In recent years, deep learning has achieved good results in the field of
medical image processing, and many scholars have used UNet-based methods in
medical image segmentation [7-11]. Among all the variation of UNet, the UNet 3+
[11] have relatively good result compare to the rest of the variation of UNet.
Therefor to further improve the accuracy of lung airway tree modeling, this
study combines the Frangi filter [5] with UNet 3+ [11] to develop a
dual-channel 3D UNet 3+. The Frangi filter is used to extracting vessel-like
feature. The vessel-like feature then used as input to guide the dual-channel
UNet 3+ training and testing procedures.

This paper proposes a neural architecture search (NAS) method for split
computing. Split computing is an emerging machine-learning inference technique
that addresses the privacy and latency challenges of deploying deep learning in
IoT systems. In split computing, neural network models are separated and
cooperatively processed using edge servers and IoT devices via networks. Thus,
the architecture of the neural network model significantly impacts the
communication payload size, model accuracy, and computational load. In this
paper, we address the challenge of optimizing neural network architecture for
split computing. To this end, we proposed NASC, which jointly explores optimal
model architecture and a split point to achieve higher accuracy while meeting
latency requirements (i.e., smaller total latency of computation and
communication than a certain threshold). NASC employs a one-shot NAS that does
not require repeating model training for a computationally efficient
architecture search. Our performance evaluation using hardware (HW)-NAS-Bench
of benchmark data demonstrates that the proposed NASC can improve the
``communication latency and model accuracy" trade-off, i.e., reduce the latency
by approximately 40-60% from the baseline, with slight accuracy degradation.

A multi-task learning (MTL) framework, called gradient kernel ridge
regression, for nuclear masses and separation energies is developed by
introducing gradient kernel functions to the kernel ridge regression (KRR)
approach. By taking the WS4 mass model as an example, the gradient KRR network
is trained with the mass model residuals, i.e., deviations between experimental
and theoretical values of masses and one-nucleon separation energies, to
improve the accuracy of theoretical predictions. Significant improvements are
achieved by the gradient KRR approach in both the interpolation and the
extrapolation predictions of nuclear masses and separation energies. This
demonstrates the advantage of the present MTL framework that integrates the
information of nuclear masses and separation energies and improves the
predictions for both of them.

Partial label learning is a type of weakly supervised learning, where each
training instance corresponds to a set of candidate labels, among which only
one is true. In this paper, we introduce ProPaLL, a novel probabilistic
approach to this problem, which has at least three advantages compared to the
existing approaches: it simplifies the training process, improves performance,
and can be applied to any deep architecture. Experiments conducted on
artificial and real-world datasets indicate that ProPaLL outperforms the

Humans have been developing and playing musical instruments for millennia.
With technological advancements, instruments were becoming ever more
sophisticated. In recent decades computer-supported innovations have also been
introduced in hardware design, usability, and aesthetics. One of the most
commonly digitally augmented instruments is the piano. Besides electronic
keyboards, several prototypes augmenting pianos with different projections
providing various levels of interactivity on and around the keyboard have been
implemented in order to support piano players. However, it is still not
understood if these solutions are indeed supporting the learning process. In
this paper we present a systematic review of augmented piano prototypes
focusing on instrument learning, which is based on the four themes derived from
interviews of piano experts to better understand the problems of teaching the
piano. These themes are: (i) synchronised movement and body posture, (ii)
sight-reading, (iii) ensuring motivation, and (iv) encouraging improvisation.
We found that prototypes are saturated on the synchronisation themes, and there
are opportunities for sight-reading, motivation, and improvisation themes. We
conclude by presenting recommendations on augmenting piano systems towards
enriching the piano learning experience as well as on possible directions to
expand knowledge in the area.

Recent advancements in AI, especially deep learning, have contributed to a
significant increase in the creation of new realistic-looking synthetic media
(video, image, and audio) and manipulation of existing media, which has led to
the creation of the new term ``deepfake''. Based on both the research
literature and resources in English and in Chinese, this paper gives a
comprehensive overview of deepfake, covering multiple important aspects of this
emerging concept, including 1) different definitions, 2) commonly used
performance metrics and standards, and 3) deepfake-related datasets,
challenges, competitions and benchmarks. In addition, the paper also reports a
meta-review of 12 selected deepfake-related survey papers published in 2020 and
2021, focusing not only on the mentioned aspects, but also on the analysis of
key challenges and recommendations. We believe that this paper is the most
comprehensive review of deepfake in terms of aspects covered, and the first one
covering both the English and Chinese literature and sources.

Surgical tool detection in minimally invasive surgery is an essential part of
computer-assisted interventions. Current approaches are mostly based on
supervised methods which require large fully labeled data to train supervised
models and suffer from pseudo label bias because of class imbalance issues.
However large image datasets with bounding box annotations are often scarcely
available. Semi-supervised learning (SSL) has recently emerged as a means for
training large models using only a modest amount of annotated data; apart from
reducing the annotation cost. SSL has also shown promise to produce models that
are more robust and generalizable. Therefore, in this paper we introduce a
semi-supervised learning (SSL) framework in surgical tool detection paradigm
which aims to mitigate the scarcity of training data and the data imbalance
through a knowledge distillation approach. In the proposed work, we train a
model with labeled data which initialises the Teacher-Student joint learning,
where the Student is trained on Teacher-generated pseudo labels from unlabeled
data. We propose a multi-class distance with a margin based classification loss
function in the region-of-interest head of the detector to effectively
segregate foreground classes from background region. Our results on
m2cai16-tool-locations dataset indicate the superiority of our approach on
different supervised data settings (1%, 2%, 5%, 10% of annotated data) where
our model achieves overall improvements of 8%, 12% and 27% in mAP (on 1%
labeled data) over the state-of-the-art SSL methods and a fully supervised
baseline, respectively. The code is available at
https://github.com/Mansoor-at/Semi-supervised-surgical-tool-det

Airlines are critical today for carrying people and commodities on time. Any
delay in the schedule of these planes can potentially disrupt the business and
trade of thousands of employees at any given time. Therefore, precise flight
delay prediction is beneficial for the aviation industry and passenger travel.
Recent research has focused on using artificial intelligence algorithms to
predict the possibility of flight delays. Earlier prediction algorithms were
designed for a specific air route or airfield. Many present flight delay
prediction algorithms rely on tiny samples and are challenging to understand,
allowing almost no room for machine learning implementation. This research
study develops a flight delay prediction system by analyzing data from domestic
flights inside the United States of America. The proposed models learn about
the factors that cause flight delays and cancellations and the link between
departure and arrival delays.

We propose the first unified theoretical analysis of mixed sample data
augmentation (MSDA), such as Mixup and CutMix. Our theoretical results show
that regardless of the choice of the mixing strategy, MSDA behaves as a
pixel-level regularization of the underlying training loss and a regularization
of the first layer parameters. Similarly, our theoretical results support that
the MSDA training strategy can improve adversarial robustness and
generalization compared to the vanilla training strategy. Using the theoretical
results, we provide a high-level understanding of how different design choices
of MSDA work differently. For example, we show that the most popular MSDA
methods, Mixup and CutMix, behave differently, e.g., CutMix regularizes the
input gradients by pixel distances, while Mixup regularizes the input gradients
regardless of pixel distances. Our theoretical results also show that the
optimal MSDA strategy depends on tasks, datasets, or model parameters. From
these observations, we propose generalized MSDAs, a Hybrid version of Mixup and
CutMix (HMix) and Gaussian Mixup (GMix), simple extensions of Mixup and CutMix.
Our implementation can leverage the advantages of Mixup and CutMix, while our
implementation is very efficient, and the computation cost is almost
neglectable as Mixup and CutMix. Our empirical study shows that our HMix and
GMix outperform the previous state-of-the-art MSDA methods in CIFAR-100 and
ImageNet classification tasks. Source code is available at
https://github.com/naver-ai/hmix-gmix

Atmospheric pollution remains one of the major public health threat worldwide
with an estimated 7 millions deaths annually. In Africa, rapid urbanization and
poor transport infrastructure are worsening the problem. In this paper, we have
analysed spatio-temporal variations of PM2.5 across different geographical
regions in Africa. The West African region remains the most affected by the
high levels of pollution with a daily average of 40.856 $\mu g/m^3$ in some
cities like Lagos, Abuja and Bamako. In East Africa, Uganda is reporting the
highest pollution level with a daily average concentration of 56.14 $\mu g/m^3$
and 38.65 $\mu g/m^3$ for Kigali. In countries located in the central region of
Africa, the highest daily average concentration of PM2.5 of 90.075 $\mu g/m^3$
was recorded in N'Djamena. We compare three data driven models in predicting
future trends of pollution levels. Neural network is outperforming Gaussian
processes and ARIMA models.

A well formed query is defined as a query which is formulated in the manner
of an inquiry, and with correct interrogatives, spelling and grammar. While
identifying well formed queries is an important task, few works have attempted
to address it. In this paper we propose transformer based language model -
Bidirectional Encoder Representations from Transformers (BERT) to this task. We
further imbibe BERT with parts-of-speech information inspired from earlier
works. Furthermore, we also train the model in multiple curriculum settings for
improvement in performance. Curriculum Learning over the task is experimented
with Baby Steps and One Pass techniques. Proposed architecture performs
exceedingly well on the task. The best approach achieves accuracy of 83.93%,
outperforming previous state-of-the-art at 75.0% and reaching close to the
approximate human upper bound of 88.4%.

Graph pre-training strategies have been attracting a surge of attention in
the graph mining community, due to their flexibility in parameterizing graph
neural networks (GNNs) without any label information. The key idea lies in
encoding valuable information into the backbone GNNs, by predicting the masked
graph signals extracted from the input graphs. In order to balance the
importance of diverse graph signals (e.g., nodes, edges, subgraphs), the
existing approaches are mostly hand-engineered by introducing hyperparameters
to re-weight the importance of graph signals. However, human interventions with
sub-optimal hyperparameters often inject additional bias and deteriorate the
generalization performance in the downstream applications. This paper addresses
these limitations from a new perspective, i.e., deriving curriculum for
pre-training GNNs. We propose an end-to-end model named MentorGNN that aims to
supervise the pre-training process of GNNs across graphs with diverse
structures and disparate feature spaces. To comprehend heterogeneous graph
signals at different granularities, we propose a curriculum learning paradigm
that automatically re-weighs graph signals in order to ensure a good
generalization in the target domain. Moreover, we shed new light on the problem
of domain adaption on relational data (i.e., graphs) by deriving a natural and
interpretable upper bound on the generalization error of the pre-trained GNNs.
Extensive experiments on a wealth of real graphs validate and verify the
performance of MentorGNN.

While building machine learning models, Feature selection (FS) stands out as
an essential preprocessing step used to handle the uncertainty and vagueness in
the data. Recently, the minimum Redundancy and Maximum Relevance (mRMR)
approach has proven to be effective in obtaining the irredundant feature
subset. Owing to the generation of voluminous datasets, it is essential to
design scalable solutions using distributed/parallel paradigms. MapReduce
solutions are proven to be one of the best approaches to designing
fault-tolerant and scalable solutions. This work analyses the existing
MapReduce approaches for mRMR feature selection and identifies the limitations
thereof. In the current study, we proposed VMR_mRMR, an efficient vertical
partitioning-based approach using a memorization approach, thereby overcoming
the extant approaches limitations. The experiment analysis says that VMR_mRMR
significantly outperformed extant approaches and achieved a better
computational gain (C.G). In addition, we also conducted a comparative analysis
with the horizontal partitioning approach HMR_mRMR [1] to assess the strengths
and limitations of the proposed approach.

Adaptive Moment Estimation (Adam) optimizer is widely used in deep learning
tasks because of its fast convergence properties. However, the convergence of
Adam is still not well understood. In particular, the existing analysis of Adam
cannot clearly demonstrate the advantage of Adam over SGD. We attribute this
theoretical embarrassment to $L$-smooth condition (i.e., assuming the gradient
is globally Lipschitz continuous with constant $L$) adopted by literature,
which has been pointed out to often fail in practical neural networks. To
tackle this embarrassment, we analyze the convergence of Adam under a relaxed
condition called $(L_0,L_1)$ smoothness condition, which allows the gradient
Lipschitz constant to change with the local gradient norm. $(L_0,L_1)$ is
strictly weaker than $L$-smooth condition and it has been empirically verified
to hold for practical deep neural networks. Under the $(L_0,L_1)$ smoothness
condition, we establish the convergence for Adam with practical
hyperparameters. Specifically, we argue that Adam can adapt to the local
smoothness condition, justifying the \emph{adaptivity} of Adam. In contrast,
SGD can be arbitrarily slow under this condition. Our result might shed light
on the benefit of adaptive gradient methods over non-adaptive ones.

Recent works have demonstrated a double descent phenomenon in
over-parameterized learning. Although this phenomenon has been investigated by
recent works, it has not been fully understood in theory. In this paper, we
consider a double random feature model (DRFM) which is the concatenation of two
types of random features, and study the excess risk achieved by the DRFM in
ridge regression. We calculate the precise limit of the excess risk under the
high dimensional framework where the training sample size, the dimension of
data, and the dimension of random features tend to infinity proportionally.
Based on the calculation, we further theoretically demonstrate that the risk
curves of DRFMs can exhibit triple descent. We then provide a thorough
experimental study to verify our theory. At last, we extend our study to the
multiple random feature model (MRFM), and show that MRFMs ensembling $K$ types
of random features may exhibit $(K+1)$-fold descent. Our analysis points out
that risk curves with a specific number of descent generally exist in random
feature learning and ensemble learning with feature concatenation. Another
interesting finding is that our result can help understand the risk peak
locations reported in the literature when learning neural networks in the
"neural tangent kernel" regime.

The increasing popularity of the federated learning framework due to its
success in a wide range of collaborative learning tasks also induces certain
security concerns regarding the learned model due to the possibility of
malicious clients participating in the learning process. Hence, the objective
is to neutralize the impact of the malicious participants and to ensure the
final model is trustable. One common observation regarding the Byzantine
attacks is that the higher the variance among the clients' models/updates, the
more space for attacks to be hidden. To this end, it has been recently shown
that by utilizing momentum, thus reducing the variance, it is possible to
weaken the strength of the known Byzantine attacks. The Centered Clipping
framework (ICML 2021) has further shown that, besides reducing the variance,
the momentum term from the previous iteration can be used as a reference point
to neutralize the Byzantine attacks and show impressive performance against
well-known attacks. However, in the scope of this work, we show that the
centered clipping framework has certain vulnerabilities, and existing attacks
can be revised based on these vulnerabilities to circumvent the centered
clipping defense. Hence, we introduce a strategy to design an attack to
circumvent the centered clipping framework and numerically illustrate its
effectiveness against centered clipping as well as other known defense
strategies by reducing test accuracy to 5-40 on best-case scenarios.

Many genetic mutations adversely affect the structure and function of
load-bearing soft tissues, with clinical sequelae often responsible for
disability or death. Parallel advances in genetics and histomechanical
characterization provide significant insight into these conditions, but there
remains a pressing need to integrate such information. We present a novel
genotype-to-biomechanical-phenotype neural network (G2{\Phi}net) for
characterizing and classifying biomechanical properties of soft tissues, which
serve as important functional readouts of tissue health or disease. We
illustrate the utility of our approach by inferring the nonlinear,
genotype-dependent constitutive behavior of the aorta for four mouse models
involving defects or deficiencies in extracellular constituents. We show that
G2{\Phi}net can infer the biomechanical response while simultaneously ascribing
the associated genotype correctly by utilizing limited, noisy, and unstructured
experimental data. More broadly, G2{\Phi}net provides a powerful method and a
paradigm shift for correlating genotype and biomechanical phenotype
quantitatively, promising a better understanding of their interplay in
biological tissues.

Given data with label noise (i.e., incorrect data), deep neural networks
would gradually memorize the label noise and impair model performance. To
relieve this issue, curriculum learning is proposed to improve model
performance and generalization by ordering training samples in a meaningful
(e.g., easy to hard) sequence. Previous work takes incorrect samples as generic
hard ones without discriminating between hard samples (i.e., hard samples in
correct data) and incorrect samples. Indeed, a model should learn from hard
samples to promote generalization rather than overfit to incorrect ones. In
this paper, we address this problem by appending a novel loss function
DiscrimLoss, on top of the existing task loss. Its main effect is to
automatically and stably estimate the importance of easy samples and difficult
samples (including hard and incorrect samples) at the early stages of training
to improve the model performance. Then, during the following stages,
DiscrimLoss is dedicated to discriminating between hard and incorrect samples
to improve the model generalization. Such a training strategy can be formulated
dynamically in a self-supervised manner, effectively mimicking the main
principle of curriculum learning. Experiments on image classification, image
regression, text sequence regression, and event relation reasoning demonstrate
the versatility and effectiveness of our method, particularly in the presence
of diversified noise levels.

In this paper, we present a deep learning-based numerical method for
approximating high dimensional stochastic partial differential equations
(SPDEs). At each time step, our method relies on a predictor-corrector
procedure. More precisely, we decompose the original SPDE into a degenerate
SPDE and a deterministic PDE. Then in the prediction step, we solve the
degenerate SPDE with the Euler scheme, while in the correction step we solve
the second-order deterministic PDE by deep neural networks via its equivalent
backward stochastic differential equation (BSDE). Under standard assumptions,
error estimates and the rate of convergence of the proposed algorithm are
presented. The efficiency and accuracy of the proposed algorithm are
illustrated by numerical examples.

Breast cancer is one of the leading causes of cancer deaths in women. As the
primary output of breast screening, breast ultrasound (US) video contains
exclusive dynamic information for cancer diagnosis. However, training models
for video analysis is non-trivial as it requires a voluminous dataset which is
also expensive to annotate. Furthermore, the diagnosis of breast lesion faces
unique challenges such as inter-class similarity and intra-class variation. In
this paper, we propose a pioneering approach that directly utilizes US videos
in computer-aided breast cancer diagnosis. It leverages masked video modeling
as pretraining to reduce reliance on dataset size and detailed annotations.
Moreover, a correlation-aware contrastive loss is developed to facilitate the
identifying of the internal and external relationship between benign and
malignant lesions. Experimental results show that our proposed approach
achieved promising classification performance and can outperform other

The prosperity of deep learning contributes to the rapid progress in scene
text detection. Among all the methods with convolutional networks,
segmentation-based ones have drawn extensive attention due to their superiority
in detecting text instances of arbitrary shapes and extreme aspect ratios.
However, the bottom-up methods are limited to the performance of their
segmentation models. In this paper, we propose DPTNet (Dual-Path Transformer
Network), a simple yet effective architecture to model the global and local
information for the scene text detection task. We further propose a parallel
design that integrates the convolutional network with a powerful self-attention
mechanism to provide complementary clues between the attention path and
convolutional path. Moreover, a bi-directional interaction module across the
two paths is developed to provide complementary clues in the channel and
spatial dimensions. We also upgrade the concentration operation by adding an
extra multi-head attention layer to it. Our DPTNet achieves state-of-the-art
results on the MSRA-TD500 dataset, and provides competitive results on other
standard benchmarks in terms of both detection accuracy and speed.

Over the past few years, graph representation learning (GRL) has been a
powerful strategy for analyzing graph-structured data. Recently, GRL methods
have shown promising results by adopting self-supervised learning methods
developed for learning representations of images. Despite their success,
existing GRL methods tend to overlook an inherent distinction between images
and graphs, i.e., images are assumed to be independently and identically
distributed, whereas graphs exhibit relational information among data
instances, i.e., nodes. To fully benefit from the relational information
inherent in the graph-structured data, we propose a novel GRL method, called
RGRL, that learns from the relational information generated from the graph
itself. RGRL learns node representations such that the relationship among nodes
is invariant to augmentations, i.e., augmentation-invariant relationship, which
allows the node representations to vary as long as the relationship among the
nodes is preserved. By considering the relationship among nodes in both global
and local perspectives, RGRL overcomes limitations of previous contrastive and
non-contrastive methods, and achieves the best of both worlds. Extensive
experiments on fourteen benchmark datasets over various downstream tasks
demonstrate the superiority of RGRL over state-of-the-art baselines. The source
code for RGRL is available at https://github.com/Namkyeong/RGRL.

A successful automated program proof is, in software verification, the
ultimate triumph. In practice, however, the road to such success is paved with
many failed proof attempts. Unlike a failed test, which provides concrete
evidence of an actual bug in the program, a failed proof leaves the programmer
in the dark. Can we instead learn something useful from it?
  The work reported here takes advantage of the rich internal information that
some automatic provers collect about the program when attempting a proof. If
the proof fails, the Proof2Test tool presented in this article uses the
counterexample generated by the prover (specifically, the SMT solver underlying
the proof environment Boogie, used in the AutoProof system to perform
correctness proofs of contract-equipped Eiffel programs) to produce a failed
test, which provides the programmer with immediately exploitable information to
correct the program. The discussion presents the Proof2Test tool and
demonstrates the application of the ideas and tool to a collection of
representative examples.

The robustness of deep neural networks is crucial to modern AI-enabled
systems and should be formally verified. Sigmoid-like neural networks have been
adopted in a wide range of applications. Due to their non-linearity,
Sigmoid-like activation functions are usually over-approximated for efficient
verification, which inevitably introduces imprecision. Considerable efforts
have been devoted to finding the so-called tighter approximations to obtain
more precise verification results. However, existing tightness definitions are
heuristic and lack theoretical foundations. We conduct a thorough empirical
analysis of existing neuron-wise characterizations of tightness and reveal that
they are superior only on specific neural networks. We then introduce the
notion of network-wise tightness as a unified tightness definition and show
that computing network-wise tightness is a complex non-convex optimization
problem. We bypass the complexity from different perspectives via two
efficient, provably tightest approximations. The results demonstrate the
promising performance achievement of our approaches over state of the art: (i)
achieving up to 251.28% improvement to certified lower robustness bounds; and
(ii) exhibiting notably more precise verification results on convolutional
networks.

The research process includes many decisions, e.g., how to entitle and where
to publish the paper. In this paper, we introduce a general framework for
investigating the effects of such decisions. The main difficulty in
investigating the effects is that we need to know counterfactual results, which
are not available in reality. The key insight of our framework is inspired by
the existing counterfactual analysis using twins, where the researchers regard
twins as counterfactual units. The proposed framework regards a pair of papers
that cite each other as twins. Such papers tend to be parallel works, on
similar topics, and in similar communities. We investigate twin papers that
adopted different decisions, observe the progress of the research impact
brought by these studies, and estimate the effect of decisions by the
difference in the impacts of these studies. We release our code and data, which
we believe are highly beneficial owing to the scarcity of the dataset on
counterfactual studies.

Self-supervised pre-training is gaining increasingly more popularity in
AI-aided drug discovery, leading to more and more pre-trained models with the
promise that they can extract better feature representations for molecules.
Yet, the quality of learned representations have not been fully explored. In
this work, inspired by the two phenomena of Activity Cliffs (ACs) and Scaffold
Hopping (SH) in traditional Quantitative Structure-Activity Relationship (QSAR)
analysis, we propose a method named Representation-Property Relationship
Analysis (RePRA) to evaluate the quality of the representations extracted by
the pre-trained model and visualize the relationship between the
representations and properties. The concepts of ACs and SH are generalized from
the structure-activity context to the representation-property context, and the
underlying principles of RePRA are analyzed theoretically. Two scores are
designed to measure the generalized ACs and SH detected by RePRA, and therefore
the quality of representations can be evaluated. In experiments,
representations of molecules from 10 target tasks generated by 7 pre-trained
models are analyzed. The results indicate that the state-of-the-art pre-trained
models can overcome some shortcomings of canonical Extended-Connectivity
FingerPrints (ECFP), while the correlation between the basis of the
representation space and specific molecular substructures are not explicit.
Thus, some representations could be even worse than the canonical fingerprints.
Our method enables researchers to evaluate the quality of molecular
representations generated by their proposed self-supervised pre-trained models.
And our findings can guide the community to develop better pre-training
techniques to regularize the occurrence of ACs and SH.

Large-scale deep neural networks consume expensive training costs, but the
training results in less-interpretable weight matrices constructing the
networks. Here, we propose a mode decomposition learning that can interpret the
weight matrices as a hierarchy of latent modes. These modes are akin to
patterns in physics studies of memory networks. The mode decomposition learning
not only saves a significant large amount of training costs, but also explains
the network performance with the leading modes. The mode learning scheme shows
a progressively compact latent space across the network hierarchy, and the
least number of modes increases only logarithmically with the network width.
Our mode decomposition learning is also studied in an analytic on-line learning
setting, which reveals multi-stage of learning dynamics. Therefore, the
proposed mode decomposition learning points to a cheap and interpretable route
towards the magical deep learning.

The theory of learning in games is prominent in the AI community, motivated
by several rising applications such as multi-agent reinforcement learning and
Generative Adversarial Networks. We propose Mutation-driven Multiplicative
Weights Update (M2WU) for learning an equilibrium in two-player zero-sum
normal-form games and prove that it exhibits the last-iterate convergence
property in both full- and noisy-information feedback settings. In the
full-information feedback setting, the players observe their exact gradient
vectors of the utility functions. On the other hand, in the noisy-information
feedback setting, they can only observe the noisy gradient vectors. Existing
algorithms, including the well-known Multiplicative Weights Update (MWU) and
Optimistic MWU (OMWU) algorithms, fail to converge to a Nash equilibrium with
noisy-information feedback. In contrast, M2WU exhibits the last-iterate
convergence to a stationary point near a Nash equilibrium in both of the
feedback settings. We then prove that it converges to an exact Nash equilibrium
by adapting the mutation term iteratively. We empirically confirm that M2WU
outperforms MWU and OMWU in exploitability and convergence rates.

Image clustering is an important, and open challenge task in computer vision.
Although many methods have been proposed to solve the image clustering task,
they only explore images and uncover clusters according to the image features,
thus are unable to distinguish visually similar but semantically different
images. In this paper, we propose to investigate the task of image clustering
with the help of visual-language pre-training model. Different from the
zero-shot setting in which the class names are known, we only know the number
of clusters in this setting. Therefore, how to map images to a proper semantic
space and how to cluster images from both image and semantic spaces are two key
problems. To solve the above problems, we propose a novel image clustering
method guided by the visual-language pre-training model CLIP, named as
\textbf{Semantic-enhanced Image Clustering (SIC)}. In this new method, we
propose a method to map the given images to a proper semantic space first and
efficient methods to generate pseudo-labels according to the relationships
between images and semantics. Finally, we propose to perform clustering with
the consistency learning in both image space and semantic space, in a
self-supervised learning fashion. Theoretical result on convergence analysis
shows that our proposed method can converge in sublinear speed. Theoretical
analysis on expectation risk also shows that we can reduce the expectation risk
by improving the neighborhood consistency or prediction confidence or reducing
neighborhood imbalance. Experimental results on five benchmark datasets clearly
show the superiority of our new method.

Monocular depth estimation and defocus estimation are two fundamental tasks
in computer vision. Most existing methods treat depth estimation and defocus
estimation as two separate tasks, ignoring the strong connection between them.
In this work, we propose a multi-task learning network consisting of an encoder
with two decoders to estimate the depth and defocus map from a single focused
image. Through the multi-task network, the depth estimation facilitates the
defocus estimation to get better results in the weak texture region and the
defocus estimation facilitates the depth estimation by the strong physical
connection between the two maps. We set up a dataset (named ALL-in-3D dataset)
which is the first all-real image dataset consisting of 100K sets of
all-in-focus images, focused images with focus depth, depth maps, and defocus
maps. It enables the network to learn features and solid physical connections
between the depth and real defocus images. Experiments demonstrate that the
network learns more solid features from the real focused images than the
synthetic focused images. Benefiting from this multi-task structure where
different tasks facilitate each other, our depth and defocus estimations
achieve significantly better performance than other state-of-art algorithms.
The code and dataset will be publicly available at
https://github.com/cubhe/MDDNet.

Pre-training and fine-tuning have achieved significant advances in the
information retrieval (IR). A typical approach is to fine-tune all the
parameters of large-scale pre-trained models (PTMs) on downstream tasks. As the
model size and the number of tasks increase greatly, such approach becomes less
feasible and prohibitively expensive. Recently, a variety of
parameter-efficient tuning methods have been proposed in natural language
processing (NLP) that only fine-tune a small number of parameters while still
attaining strong performance. Yet there has been little effort to explore
parameter-efficient tuning for IR.
  In this work, we first conduct a comprehensive study of existing
parameter-efficient tuning methods at both the retrieval and re-ranking stages.
Unlike the promising results in NLP, we find that these methods cannot achieve
comparable performance to full fine-tuning at both stages when updating less
than 1\% of the original model parameters. More importantly, we find that the
existing methods are just parameter-efficient, but not learning-efficient as
they suffer from unstable training and slow convergence. To analyze the
underlying reason, we conduct a theoretical analysis and show that the
separation of the inserted trainable modules makes the optimization difficult.
To alleviate this issue, we propose to inject additional modules alongside the
\acp{PTM} to make the original scattered modules connected. In this way, all
the trainable modules can form a pathway to smooth the loss surface and thus
help stabilize the training process. Experiments at both retrieval and
re-ranking stages show that our method outperforms existing parameter-efficient
methods significantly, and achieves comparable or even better performance over
full fine-tuning.

Dense retrieval (DR) has shown promising results in information retrieval. In
essence, DR requires high-quality text representations to support effective
search in the representation space. Recent studies have shown that pre-trained
autoencoder-based language models with a weak decoder can provide high-quality
text representations, boosting the effectiveness and few-shot ability of DR
models. However, even a weak autoregressive decoder has the bypass effect on
the encoder. More importantly, the discriminative ability of learned
representations may be limited since each token is treated equally important in
decoding the input texts. To address the above problems, in this paper, we
propose a contrastive pre-training approach to learn a discriminative
autoencoder with a lightweight multi-layer perception (MLP) decoder. The basic
idea is to generate word distributions of input text in a non-autoregressive
fashion and pull the word distributions of two masked versions of one text
close while pushing away from others. We theoretically show that our
contrastive strategy can suppress the common words and highlight the
representative words in decoding, leading to discriminative representations.
Empirical results show that our method can significantly outperform the
state-of-the-art autoencoder-based language models and other pre-trained models
for dense retrieval.

Visible-infrared person re-identification (VI-ReID) is a task of matching the
same individuals across the visible and infrared modalities. Its main challenge
lies in the modality gap caused by cameras operating on different spectra.
Existing VI-ReID methods mainly focus on learning general features across
modalities, often at the expense of feature discriminability. To address this
issue, we present a novel cycle-construction-based network for neutral yet
discriminative feature learning, termed CycleTrans. Specifically, CycleTrans
uses a lightweight Knowledge Capturing Module (KCM) to capture rich semantics
from the modality-relevant feature maps according to pseudo queries.
Afterwards, a Discrepancy Modeling Module (DMM) is deployed to transform these
features into neutral ones according to the modality-irrelevant prototypes. To
ensure feature discriminability, another two KCMs are further deployed for
feature cycle constructions. With cycle construction, our method can learn
effective neutral features for visible and infrared images while preserving
their salient semantics. Extensive experiments on SYSU-MM01 and RegDB datasets
validate the merits of CycleTrans against a flurry of state-of-the-art methods,
+4.57% on rank-1 in SYSU-MM01 and +2.2% on rank-1 in RegDB.

Image-Text Retrieval (ITR) is challenging in bridging visual and lingual
modalities. Contrastive learning has been adopted by most prior arts. Except
for limited amount of negative image-text pairs, the capability of constrastive
learning is restricted by manually weighting negative pairs as well as
unawareness of external knowledge. In this paper, we propose our novel Coupled
Diversity-Sensitive Momentum Constrastive Learning (CODER) for improving
cross-modal representation. Firstly, a novel diversity-sensitive contrastive
learning (DCL) architecture is invented. We introduce dynamic dictionaries for
both modalities to enlarge the scale of image-text pairs, and
diversity-sensitiveness is achieved by adaptive negative pair weighting.
Furthermore, two branches are designed in CODER. One learns instance-level
embeddings from image/text, and it also generates pseudo online clustering
labels for its input image/text based on their embeddings. Meanwhile, the other
branch learns to query from commonsense knowledge graph to form concept-level
descriptors for both modalities. Afterwards, both branches leverage DCL to
align the cross-modal embedding spaces while an extra pseudo clustering label
prediction loss is utilized to promote concept-level representation learning
for the second branch. Extensive experiments conducted on two popular
benchmarks, i.e. MSCOCO and Flicker30K, validate CODER remarkably outperforms
the state-of-the-art approaches.

Multimodal sentiment analysis has become an increasingly popular research
area as the demand for multimodal online content is growing. For multimodal
sentiment analysis, words can have different meanings depending on the
linguistic context and non-verbal information, so it is crucial to understand
the meaning of the words accordingly. In addition, the word meanings should be
interpreted within the whole utterance context that includes nonverbal
information. In this paper, we present a Context-driven Modality Shifting BERT
with Contrastive Learning for linguistic, visual, acoustic Representations
(CMSBERT-CLR), which incorporates the whole context's non-verbal and verbal
information and aligns modalities more effectively through contrastive
learning. First, we introduce a Context-driven Modality Shifting (CMS) to
incorporate the non-verbal and verbal information within the whole context of
the sentence utterance. Then, for improving the alignment of different
modalities within a common embedding space, we apply contrastive learning.
Furthermore, we use an exponential moving average parameter and label smoothing
as optimization strategies, which can make the convergence of the network more
stable and increase the flexibility of the alignment. In our experiments, we
demonstrate that our approach achieves state-of-the-art results.

This paper presents Tyche, a Python library to facilitate probabilistic
reasoning in uncertain worlds through the construction, querying, and learning
of belief models. Tyche uses aleatoric description logic (ADL), which provides
computational advantages in its evaluation over other description logics. Tyche
belief models can be succinctly created by defining classes of individuals, the
probabilistic beliefs about them (concepts), and the probabilistic
relationships between them (roles). We also introduce a method of observation
propagation to facilitate learning from complex ADL observations. A
demonstration of Tyche to predict the author of anonymised messages, and to
extract author writing tendencies from anonymised messages, is provided. Tyche
has the potential to assist in the development of expert systems, knowledge
extraction systems, and agents to play games with incomplete and probabilistic

With the recent advances of conversational recommendations, the recommender
system is able to actively and dynamically elicit user preference via
conversational interactions. To achieve this, the system periodically queries
users' preference on attributes and collects their feedback. However, most
existing conversational recommender systems only enable the user to provide
absolute feedback to the attributes. In practice, the absolute feedback is
usually limited, as the users tend to provide biased feedback when expressing
the preference. Instead, the user is often more inclined to express comparative
preferences, since user preferences are inherently relative. To enable users to
provide comparative preferences during conversational interactions, we propose
a novel comparison-based conversational recommender system. The relative
feedback, though more practical, is not easy to be incorporated since its
feedback scale is always mismatched with users' absolute preferences. With
effectively collecting and understanding the relative feedback from an
interactive manner, we further propose a new bandit algorithm, which we call
RelativeConUCB. The experiments on both synthetic and real-world datasets
validate the advantage of our proposed method, compared to the existing bandit
algorithms in the conversational recommender systems.

Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows
potential in providing quantitative imaging biomarkers that indirectly reflect
fetal lung maturation. However, fetal motion during the acquisition hampered
quantitative analysis of the acquired DWI data and, consequently, reliable
clinical utilization. We introduce qDWI-morph, an unsupervised
deep-neural-network architecture for motion compensated quantitative DWI (qDWI)
analysis. Our approach couples a registration sub-network with a quantitative
DWI model fitting sub-network. We simultaneously estimate the qDWI parameters
and the motion model by minimizing a bio-physically-informed loss function
integrating a registration loss and a model fitting quality loss. We
demonstrated the added-value of qDWI-morph over: 1) a baseline qDWI analysis
without motion compensation and 2) a baseline deep-learning model incorporating
registration loss solely. The qDWI-morph achieved a substantially improved
correlation with the gestational age through in-vivo qDWI analysis of fetal
lung DWI data (R-squared=0.32 vs. 0.13, 0.28). Our qDWI-morph has the potential
to enable motion-compensated quantitative analysis of DWI data and to provide
clinically feasible bio-markers for non-invasive fetal lung maturity
assessment. Our code is available at:
https://github.com/TechnionComputationalMRILab/qDWI-Morph.

Attention has become one of the most commonly used mechanisms in deep
learning approaches. The attention mechanism can help the system focus more on
the feature space's critical regions. For example, high amplitude regions can
play an important role for Speech Emotion Recognition (SER). In this paper, we
identify misalignments between the attention and the signal amplitude in the
existing multi-head self-attention. To improve the attention area, we propose
to use a Focus-Attention (FA) mechanism and a novel Calibration-Attention (CA)
mechanism in combination with the multi-head self-attention. Through the FA
mechanism, the network can detect the largest amplitude part in the segment. By
employing the CA mechanism, the network can modulate the information flow by
assigning different weights to each attention head and improve the utilization
of surrounding contexts. To evaluate the proposed method, experiments are
performed with the IEMOCAP and RAVDESS datasets. Experimental results show that
the proposed framework significantly outperforms the state-of-the-art
approaches on both datasets.

Robust learning on noisy-labeled data has been an important task in real
applications, because label noise directly leads to the poor generalization of
deep learning models. Existing label-noise learning methods usually assume that
the ground-truth classes of the training data are balanced. However, the
real-world data is often imbalanced, leading to the inconsistency between
observed and intrinsic class distribution due to label noises. Distribution
inconsistency makes the problem of label-noise learning more challenging
because it is hard to distinguish clean samples from noisy samples on the
intrinsic tail classes. In this paper, we propose a learning framework for
label-noise learning with intrinsically long-tailed data. Specifically, we
propose a robust sample selection method called two-stage bi-dimensional sample
selection (TBSS) to better separate clean samples from noisy samples,
especially for the tail classes. TBSS consists of two new separation metrics to
jointly separate samples in each class. Extensive experiments on multiple
noisy-labeled datasets with intrinsically long-tailed class distribution
demonstrate the effectiveness of our method.

Learning expressive representation is crucial in deep learning. In speech
emotion recognition (SER), vacuum regions or noises in the speech interfere
with expressive representation learning. However, traditional RNN-based models
are susceptible to such noise. Recently, Graph Neural Network (GNN) has
demonstrated its effectiveness for representation learning, and we adopt this
framework for SER. In particular, we propose a cosine similarity-based graph as
an ideal graph structure for representation learning in SER. We present a
Cosine similarity-based Graph Convolutional Network (CoGCN) that is robust to
perturbation and noise. Experimental results show that our method outperforms
state-of-the-art methods or provides competitive results with a significant
model size reduction with only 1/30 parameters.

Knowledge graph (KG) embedding seeks to learn vector representations for
entities and relations. Conventional models reason over graph structures, but
they suffer from the issues of graph incompleteness and long-tail entities.
Recent studies have used pre-trained language models to learn embeddings based
on the textual information of entities and relations, but they cannot take
advantage of graph structures. In the paper, we show empirically that these two
kinds of features are complementary for KG embedding. To this end, we propose
CoLE, a Co-distillation Learning method for KG Embedding that exploits the
complementarity of graph structures and text information. Its graph embedding
model employs Transformer to reconstruct the representation of an entity from
its neighborhood subgraph. Its text embedding model uses a pre-trained language
model to generate entity representations from the soft prompts of their names,
descriptions, and relational neighbors. To let the two model promote each
other, we propose co-distillation learning that allows them to distill
selective knowledge from each other's prediction logits. In our co-distillation
learning, each model serves as both a teacher and a student. Experiments on
benchmark datasets demonstrate that the two models outperform their related
baselines, and the ensemble method CoLE with co-distillation learning advances
the state-of-the-art of KG embedding.

The advent of deep learning has resulted in a number of applications which
have transformed the landscape of the research area in which it has been
applied. However, with an increase in popularity, the complexity of classical
deep neural networks has increased over the years. As a result, this has leads
to considerable problems during deployment on devices with space and time
constraints. In this work, we perform a review of the present advancements in
non-volatile memory and how the use of resistive RAM memory, particularly
memristors, can help to progress the state of research in deep learning. In
other words, we wish to present an ideology that advances in the field of
memristive technology can greatly influence and impact deep learning inference
on edge devices.

GEMM with the small size of input matrices is becoming widely used in many
fields like HPC and machine learning. Although many famous BLAS libraries
already supported small GEMM, they cannot achieve near-optimal performance.
This is because the costs of pack operations are high and frequent boundary
processing cannot be neglected. This paper proposes an input-aware adaptive
tuning framework(IAAT) for small GEMM to overcome the performance bottlenecks
in state-of-the-art implementations. IAAT consists of two stages, the
install-time stage and the run-time stage. In the run-time stage, IAAT tiles
matrices into blocks to alleviate boundary processing. This stage utilizes an
input-aware adaptive tile algorithm and plays the role of runtime tuning. In
the install-time stage, IAAT auto-generates hundreds of kernels of different
sizes to remove pack operations. Finally, IAAT finishes the computation of
small GEMM by invoking different kernels, which corresponds to the size of
blocks. The experimental results show that IAAT gains better performance than
other BLAS libraries on ARMv8 platform.

Bandit algorithms are widely used in sequential decision problems to maximize
the cumulative reward. One potential application is mobile health, where the
goal is to promote the user's health through personalized interventions based
on user specific information acquired through wearable devices. Important
considerations include the type of, and frequency with which data is collected
(e.g. GPS, or continuous monitoring), as such factors can severely impact app
performance and users' adherence. In order to balance the need to collect data
that is useful with the constraint of impacting app performance, one needs to
be able to assess the usefulness of variables. Bandit feedback data are
sequentially correlated, so traditional testing procedures developed for
independent data cannot apply. Recently, a statistical testing procedure was
developed for the actor-critic bandit algorithm. An actor-critic algorithm
maintains two separate models, one for the actor, the action selection policy,
and the other for the critic, the reward model. The performance of the
algorithm as well as the validity of the test are guaranteed only when the
critic model is correctly specified. However, misspecification is frequent in
practice due to incorrect functional form or missing covariates. In this work,
we propose a modified actor-critic algorithm which is robust to critic
misspecification and derive a novel testing procedure for the actor parameters
in this case.

Practical results have shown that deep learning optimizers using small
constant learning rates, hyperparameters close to one, and large batch sizes
can find the model parameters of deep neural networks that minimize the loss
functions. We first show theoretical evidence that the momentum method
(Momentum) and adaptive moment estimation (Adam) perform well in the sense that
the upper bound of the theoretical performance measure is small with a small
constant learning rate, hyperparameters close to one, and a large batch size.
Next, we show that there exists a batch size called the critical batch size
minimizing the stochastic first-order oracle (SFO) complexity, which is the
stochastic gradient computation cost, and that SFO complexity increases once
the batch size exceeds the critical batch size. Finally, we provide numerical
results that support our theoretical results. That is, the numerical results
indicate that Adam using a small constant learning rate, hyperparameters close
to one, and the critical batch size minimizing SFO complexity has faster
convergence than Momentum and stochastic gradient descent (SGD).

Single-strand breaks (SSBs) are the major DNA damage in the genome arising
spontaneously as the outcome of genotoxins and intermediates of DNA
transactions. SSBs play a crucial role in various biological processes and show
a non-random distribution in the genome. Several SSB detection approaches such
as S1 END-seq and SSiNGLe-ILM emerged to characterize the genomic landscape of
SSB with nucleotide resolution. However, these sequencing-based methods are
costly and unfeasible for large-scale analysis of diverse species. Thus, we
proposed the first computational approach, SSBlazer, which is an explainable
and scalable deep learning framework for genome-wide nucleotide-resolution SSB
site prediction. We demonstrated that SSBlazer can accurately predict SSB sites
and sufficiently alleviate false positives by constructing an imbalanced
dataset to simulate the realistic SSB distribution. The model interpretation
analysis reveals that SSBlazer captures the pattern of individual CpG in
genomic context and the motif of TGCC in the center region as critical
features. Besides, SSBlazer is a lightweight model with robust cross-species
generalization ability in the cross-species evaluation, which enables the
large-scale genome-wide application in diverse species. Strikingly, the
putative SSB genomic landscapes of 216 vertebrates reveal a negative
correlation between SSB frequency and evolutionary hierarchy, suggesting that
the genome tends to be integrity during evolution.

The input/output complexity, which is the complexity of data exchange between
the main memory and the external memory, has been elaborately studied by a lot
of former researchers. However, the existing works failed to consider the
input/output complexity in a computation model point of view. In this paper we
remedy this by proposing three variants of Turing machine that include external
memory and the mechanism of exchanging data between main memory and external
memory. Based on these new models, the input/output complexity is deeply
studied. We discussed the relationship between input/output complexity and the
other complexity measures such as time complexity and parameterized complexity,
which is not considered by former researchers. We also define the external
access trace complexity, which reflects the physical behavior of magnetic disks
and gives a theoretical evidence of IO-efficient algorithms.

Many effective attempts have been made for deepfake audio detection. However,
they can only distinguish between real and fake. For many practical application
scenarios, what tool or algorithm generated the deepfake audio also is needed.
This raises a question: Can we detect the system fingerprints of deepfake
audio? Therefore, this paper conducts a preliminary investigation to detect
system fingerprints of deepfake audio. Experiments are conducted on deepfake
audio datasets from five latest deep-learning speech synthesis systems. The
results show that LFCC features are relatively more suitable for system
fingerprints detection. Moreover, the ResNet achieves the best detection
results among LCNN and x-vector based models. The t-SNE visualization shows
that different speech synthesis systems generate distinct system fingerprints.

In today's modern digital world, we have a number of online Question and
Answer platforms like Stack Exchange, Quora, and GFG that serve as a medium for
people to communicate and help each other. In this paper, we analyzed the
effectiveness of Stack Overflow in helping newbies to programming. Every user
on this platform goes through a journey. For the first 12 months, we consider
them to be a newbie. Post 12 months they come under one of the following
categories: Experienced, Lurkers, or Inquisitive. Each question asked has tags
assigned to it and we observe that questions with some specific tags have a
faster response time indicating an active community in that field over others.
The platform had a steady growth up to 2013 after which it started declining,
but recently during the pandemic 2020, we can see rejuvenated activity on the
platform.

3D Point cloud is becoming a critical data representation in many real-world
applications like autonomous driving, robotics, and medical imaging. Although
the success of deep learning further accelerates the adoption of 3D point
clouds in the physical world, deep learning is notorious for its vulnerability
to adversarial attacks. In this work, we first identify that the
state-of-the-art empirical defense, adversarial training, has a major
limitation in applying to 3D point cloud models due to gradient obfuscation. We
further propose PointDP, a purification strategy that leverages diffusion
models to defend against 3D adversarial attacks. We extensively evaluate
PointDP on six representative 3D point cloud architectures, and leverage 10+
strong and adaptive attacks to demonstrate its lower-bound robustness. Our
evaluation shows that PointDP achieves significantly better robustness than
state-of-the-art purification methods under strong attacks. Results of
certified defenses on randomized smoothing combined with PointDP will be
included in the near future.

Dental age is one of the most reliable methods to identify an individual's
age. By using dental panoramic radiography (DPR) images, physicians and
pathologists in forensic sciences try to establish the chronological age of
individuals with no valid legal records or registered patients. The current
methods in practice demand intensive labor, time, and qualified experts. The
development of deep learning algorithms in the field of medical image
processing has improved the sensitivity of predicting truth values while
reducing the processing speed of imaging time. This study proposed an automated
approach to estimate the forensic ages of individuals ranging in age from 8 to
68 using 1,332 DPR images. Initially, experimental analyses were performed with
the transfer learning-based models, including InceptionV3, DenseNet201,
EfficientNetB4, MobileNetV2, VGG16, and ResNet50V2; and accordingly, the
best-performing model, InceptionV3, was modified, and a new neural network
model was developed. Reducing the number of the parameters already available in
the developed model architecture resulted in a faster and more accurate dental
age estimation. The performance metrics of the results attained were as
follows: mean absolute error (MAE) was 3.13, root mean square error (RMSE) was
4.77, and correlation coefficient R$^2$ was 87%. It is conceivable to propose
the new model as potentially dependable and practical ancillary equipment in
forensic sciences and dental medicine.

Many people with some form of hearing loss consider lipreading as their
primary mode of day-to-day communication. However, finding resources to learn
or improve one's lipreading skills can be challenging. This is further
exacerbated in the COVID19 pandemic due to restrictions on direct interactions
with peers and speech therapists. Today, online MOOCs platforms like Coursera
and Udemy have become the most effective form of training for many types of
skill development. However, online lipreading resources are scarce as creating
such resources is an extensive process needing months of manual effort to
record hired actors. Because of the manual pipeline, such platforms are also
limited in vocabulary, supported languages, accents, and speakers and have a
high usage cost. In this work, we investigate the possibility of replacing real
human talking videos with synthetically generated videos. Synthetic data can
easily incorporate larger vocabularies, variations in accent, and even local
languages and many speakers. We propose an end-to-end automated pipeline to
develop such a platform using state-of-the-art talking head video generator
networks, text-to-speech models, and computer vision techniques. We then
perform an extensive human evaluation using carefully thought out lipreading
exercises to validate the quality of our designed platform against the existing
lipreading platforms. Our studies concretely point toward the potential of our
approach in developing a large-scale lipreading MOOC platform that can impact
millions of people with hearing loss.

Early classification algorithms help users react faster to their machine
learning model's predictions. Early warning systems in hospitals, for example,
let clinicians improve their patients' outcomes by accurately predicting
infections. While early classification systems are advancing rapidly, a major
gap remains: existing systems do not consider irregular time series, which have
uneven and often-long gaps between their observations. Such series are
notoriously pervasive in impactful domains like healthcare. We bridge this gap
and study early classification of irregular time series, a new setting for
early classifiers that opens doors to more real-world problems. Our solution,
Stop&Hop, uses a continuous-time recurrent network to model ongoing irregular
time series in real time, while an irregularity-aware halting policy, trained
with reinforcement learning, predicts when to stop and classify the streaming
series. By taking real-valued step sizes, the halting policy flexibly decides
exactly when to stop ongoing series in real time. This way, Stop&Hop seamlessly
integrates information contained in the timing of observations, a new and vital
source for early classification in this setting, with the time series values to
provide early classifications for irregular time series. Using four synthetic
and three real-world datasets, we demonstrate that Stop&Hop consistently makes
earlier and more-accurate predictions than state-of-the-art alternatives
adapted to this new problem. Our code is publicly available at
https://github.com/thartvigsen/StopAndHop.

The Cox proportional hazards model is a canonical method in survival analysis
for prediction of the life expectancy of a patient given clinical or genetic
covariates -- it is a linear model in its original form. In recent years,
several methods have been proposed to generalize the Cox model to neural
networks, but none of these are both numerically correct and computationally
efficient. We propose FastCPH, a new method that runs in linear time and
supports both the standard Breslow and Efron methods for tied events. We also
demonstrate the performance of FastCPH combined with LassoNet, a neural network
that provides interpretability through feature sparsity, on survival datasets.
The final procedure is efficient, selects useful covariates and outperforms
existing CoxPH approaches.

Doubles play an indispensable role in the movie industry. They take the place
of the actors in dangerous stunt scenes or scenes where the same actor plays
multiple characters. The double's face is later replaced with the actor's face
and expressions manually using expensive CGI technology, costing millions of
dollars and taking months to complete. An automated, inexpensive, and fast way
can be to use face-swapping techniques that aim to swap an identity from a
source face video (or an image) to a target face video. However, such methods
cannot preserve the source expressions of the actor important for the scene's
context. To tackle this challenge, we introduce video-to-video (V2V)
face-swapping, a novel task of face-swapping that can preserve (1) the identity
and expressions of the source (actor) face video and (2) the background and
pose of the target (double) video. We propose FaceOff, a V2V face-swapping
system that operates by learning a robust blending operation to merge two face
videos following the constraints above. It reduces the videos to a quantized
latent space and then blends them in the reduced space. FaceOff is trained in a
self-supervised manner and robustly tackles the non-trivial challenges of V2V
face-swapping. As shown in the experimental section, FaceOff significantly
outperforms alternate approaches qualitatively and quantitatively.

Node classification using Graph Neural Networks (GNNs) has been widely
applied in various real-world scenarios. However, in recent years, compelling
evidence emerges that the performance of GNN-based node classification may
deteriorate substantially by topological perturbation, such as random
connections or adversarial attacks. Various solutions, such as topological
denoising methods and mechanism design methods, have been proposed to develop
robust GNN-based node classifiers but none of these works can fully address the
problems related to topological perturbations. Recently, the Bayesian label
transition model is proposed to tackle this issue but its slow convergence may
lead to inferior performance. In this work, we propose a new label inference
model, namely LInDT, which integrates both Bayesian label transition and
topology-based label propagation for improving the robustness of GNNs against
topological perturbations. LInDT is superior to existing label transition
methods as it improves the label prediction of uncertain nodes by utilizing
neighborhood-based label propagation leading to better convergence of label
inference. Besides, LIndT adopts asymmetric Dirichlet distribution as a prior,
which also helps it to improve label inference. Extensive experiments on five
graph datasets demonstrate the superiority of LInDT for GNN-based node
classification under three scenarios of topological perturbations.

With the recent developments in speech synthesis via machine learning, this
study explores incorporating linguistics knowledge to visualise and evaluate
synthetic speech model training. If changes to the first and second formant (in
turn, the vowel space) can be seen and heard in synthetic speech, this
knowledge can inform speech synthesis technology developers. A speech synthesis
model trained on a large General American English database was fine-tuned into
a New Zealand English voice to identify if the changes in the vowel space of
synthetic speech could be seen and heard. The vowel spaces at different
intervals during the fine-tuning were analysed to determine if the model
learned the New Zealand English vowel space. Our findings based on vowel space
analysis show that we can visualise how a speech synthesis model learns the
vowel space of the database it is trained on. Perception tests confirmed that
humans could perceive when a speech synthesis model has learned characteristics
of the speech database it is training on. Using the vowel space as an
intermediary evaluation helps understand what sounds are to be added to the
training database and build speech synthesis models based on linguistics

A robust real-world deployment of autonomous edge devices requires on-chip
adaptation to user-, environment- and task-induced variability. Due to on-chip
memory constraints, prior learning devices were limited to static stimuli with
no temporal contents. We propose a 0.45-mm$^2$ spiking RNN processor enabling
task-agnostic online learning over seconds, which we demonstrate for
navigation, gesture recognition, and keyword spotting within a 0.8-% memory
overhead and a <150-$\mu$W training power budget.

The mathematical equivalence between finite state stochastic machine and
non-dissipative and dissipative quantum tight-binding and Schroedinger model is
derived. Stochastic Finite state machine is also expressed by classical
epidemic model and can reproduce the quantum entanglement emerging in the case
of electrostatically coupled qubits described by von-Neumann entropy both in
non-dissipative and dissipative case. The obtained results shows that quantum
mechanical phenomena might be simulated by classical statistical model as
represented by finite state stochastic machine. It includes the quantum like
entanglement and superposition of states. Therefore coupled epidemic models
expressed by classical systems in terms of classical physics can be the base
for possible incorporation of quantum technologies and in particular for
quantum like computation and quantum like communication. The classical density
matrix is derived and described by the equation of motion in terms of
anticommutator. Existence of Rabi like oscillations is pointed in classical
epidemic model. Furthermore the existence of Aharonov-Bohm effect in quantum
systems can also be reproduced by the classical epidemic model or in broader
sense by finite state stochastic machine. Every quantum system made from
quantum dots and described by simplistic tight-binding model by use of
position-based qubits can be effectively described by classical statistical
model encoded in finite stochastic state machine with very specific structure
of S matrix that has twice bigger size as it is the case of quantum matrix
Hamiltonian. Furthermore the description of linear and non-linear stochastic
finite state machine is mapped to tight-binding and Schroedinger model. The
concept of N dimensional complex time is incorporated into tight-binding model,
so the description of dissipation in most general case is possible.

Deep Learning failure cases are abundant, particularly in the medical area.
Recent studies in out-of-distribution generalization have advanced considerably
on well-controlled synthetic datasets, but they do not represent medical
imaging contexts. We propose a pipeline that relies on artifacts annotation to
enable generalization evaluation and debiasing for the challenging skin lesion
analysis context. First, we partition the data into levels of increasingly
higher biased training and test sets for better generalization assessment.
Then, we create environments based on skin lesion artifacts to enable domain
generalization methods. Finally, after robust training, we perform a test-time
debiasing procedure, reducing spurious features in inference images. Our
experiments show our pipeline improves performance metrics in biased cases, and
avoids artifacts when using explanation methods. Still, when evaluating such
models in out-of-distribution data, they did not prefer clinically-meaningful
features. Instead, performance only improved in test sets that present similar
artifacts from training, suggesting models learned to ignore the known set of
artifacts. Our results raise a concern that debiasing models towards a single
aspect may not be enough for fair skin lesion analysis.

This research is the second phase in a series of investigations on developing
an Optical Character Recognition (OCR) of Arabic historical documents and
examining how different modeling procedures interact with the problem. The
first research studied the effect of Transformers on our custom-built Arabic
dataset. One of the downsides of the first research was the size of the
training data, a mere 15000 images from our 30 million images, due to lack of
resources. Also, we add an image enhancement layer, time and space
optimization, and Post-Correction layer to aid the model in predicting the
correct word for the correct context. Notably, we propose an end-to-end text
recognition approach using Vision Transformers as an encoder, namely BEIT, and
vanilla Transformer as a decoder, eliminating CNNs for feature extraction and
reducing the model's complexity. The experiments show that our end-to-end model
outperforms Convolutions Backbones. The model attained a CER of 4.46%.

Classical federated learning approaches yield significant performance
degradation in the presence of Non-IID data distributions of participants. When
the distribution of each local dataset is highly different from the global one,
the local objective of each client will be inconsistent with the global optima
which incur a drift in the local updates. This phenomenon highly impacts the
performance of clients. This is while the primary incentive for clients to
participate in federated learning is to obtain better personalized models. To
address the above-mentioned issue, we present a new algorithm, FLIS, which
groups the clients population in clusters with jointly trainable data
distributions by leveraging the inference similarity of clients' models. This
framework captures settings where different groups of users have their own
objectives (learning tasks) but by aggregating their data with others in the
same cluster (same learning task) to perform more efficient and personalized
federated learning. We present experimental results to demonstrate the benefits
of FLIS over the state-of-the-art benchmarks on CIFAR-100/10, SVHN, and FMNIST
datasets. Our code is available at https://github.com/MMorafah/FLIS.

A reliable evaluation method is essential for building a robust
out-of-distribution (OOD) detector. Current robustness evaluation protocols for
OOD detectors rely on injecting perturbations to outlier data. However, the
perturbations are unlikely to occur naturally or not relevant to the content of
data, providing a limited assessment of robustness. In this paper, we propose
Evaluation-via-Generation for OOD detectors (EvG), a new protocol for
investigating the robustness of OOD detectors under more realistic modes of
variation in outliers. EvG utilizes a generative model to synthesize plausible
outliers, and employs MCMC sampling to find outliers misclassified as
in-distribution with the highest confidence by a detector. We perform a
comprehensive benchmark comparison of the performance of state-of-the-art OOD
detectors using EvG, uncovering previously overlooked weaknesses.

Machine learning (ML) algorithms are showing a growing trend in helping the
scientific communities across different disciplines and institutions to address
large and diverse data problems. However, many available ML tools are
programmatically demanding and computationally costly. The MLExchange project
aims to build a collaborative platform equipped with enabling tools that allow
scientists and facility users who do not have a profound ML background to use
ML and computational resources in scientific discovery. At the high level, we
are targeting a full user experience where managing and exchanging ML
algorithms, workflows, and data are readily available through web applications.
Since each component is an independent container, the whole platform or its
individual service(s) can be easily deployed at servers of different scales,
ranging from a personal device (laptop, smart phone, etc.) to high performance
clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange
renders flexible using scenarios -- users could either access the services and
resources from a remote server or run the whole platform or its individual
service(s) within their local network.

In this paper, we establish efficient and uncoupled learning dynamics so
that, when employed by all players in multiplayer perfect-recall
imperfect-information extensive-form games, the \emph{trigger regret} of each
player grows as $O(\log T)$ after $T$ repetitions of play. This improves
exponentially over the prior best known trigger-regret bound of $O(T^{1/4})$,
and settles a recent open question by Bai et al. (2022). As an immediate
consequence, we guarantee convergence to the set of \emph{extensive-form
correlated equilibria} and \emph{coarse correlated equilibria} at a
near-optimal rate of $\frac{\log T}{T}$.
  Building on prior work, at the heart of our construction lies a more general
result regarding fixed points deriving from rational functions with
\emph{polynomial degree}, a property that we establish for the fixed points of
\emph{(coarse) trigger deviation functions}. Moreover, our construction
leverages a refined \textit{regret circuit} for the convex hull, which --
unlike prior guarantees -- preserves the \emph{RVU property} introduced by
Syrgkanis et al. (NIPS, 2015); this observation has an independent interest in
establishing near-optimal regret under learning dynamics based on a CFR-type
decomposition of the regret.

The increasing number and scale of federated learning (FL) jobs necessitates
resource efficient scheduling and management of aggregation to make the
economics of cloud-hosted aggregation work. Existing FL research has focused on
the design of FL algorithms and optimization, and less on the efficacy of
aggregation. Existing FL platforms often employ aggregators that actively wait
for model updates. This wastes computational resources on the cloud, especially
in large scale FL settings where parties are intermittently available for
training.
  In this paper, we propose a new FL aggregation paradigm -- "just-in-time"
(JIT) aggregation that leverages unique properties of FL jobs, especially the
periodicity of model updates, to defer aggregation as much as possible and free
compute resources for other FL jobs or other datacenter workloads. We describe
a novel way to prioritize FL jobs for aggregation, and demonstrate using
multiple datasets, models and FL aggregation algorithms that our techniques can
reduce resource usage by 60+\% when compared to eager aggregation used in
existing FL platforms. We also demonstrate that using JIT aggregation has
negligible overhead and impact on the latency of the FL job.

Multi-view unsupervised feature selection (MUFS) has been demonstrated as an
effective technique to reduce the dimensionality of multi-view unlabeled data.
The existing methods assume that all of views are complete. However, multi-view
data are usually incomplete, i.e., a part of instances are presented on some
views but not all views. Besides, learning the complete similarity graph, as an
important promising technology in existing MUFS methods, cannot achieve due to
the missing views. In this paper, we propose a complementary and consensus
learning-based incomplete multi-view unsupervised feature selection method
(C$^{2}$IMUFS) to address the aforementioned issues. Concretely, C$^{2}$IMUFS
integrates feature selection into an extended weighted non-negative matrix
factorization model equipped with adaptive learning of view-weights and a
sparse $\ell_{2,p}$-norm, which can offer better adaptability and flexibility.
By the sparse linear combinations of multiple similarity matrices derived from
different views, a complementary learning-guided similarity matrix
reconstruction model is presented to obtain the complete similarity graph in
each view. Furthermore, C$^{2}$IMUFS learns a consensus clustering indicator
matrix across different views and embeds it into a spectral graph term to
preserve the local geometric structure. Comprehensive experimental results on
real-world datasets demonstrate the effectiveness of C$^{2}$IMUFS compared with

While distributed optimization algorithms have the merits in parallel
processing and protecting local data security, they often suffer from slow
convergence compared with centralized optimization algorithms. This paper
focuses on how small amount of data sharing could benefit distributed
optimization and learning for more advanced optimization algorithms.
Specifically, we consider how data sharing could benefit distributed
multi-block alternating direction method of multipliers (ADMM) and
preconditioned conjugate gradient method (PCG) with application in machine
learning tasks of linear and logistic regression. These algorithms are commonly
known as algorithms between the first and the second order methods, and we show
that data sharing could hugely boost the convergence speed for this class of
the algorithms. Theoretically, we prove that a small amount of data share leads
to improvements from near-worst to near-optimal convergence rate when applying
ADMM and PCG methods to machine learning tasks. We further propose a meta
randomized data-sharing scheme and provide its tailored applications in
multi-block ADMM and PCG methods in order to enjoy both the benefit from
data-sharing and from the efficiency of distributed computing. From the
numerical evidences, we are convinced that our algorithms provide good quality
of estimators in both the least square and the logistic regressions within much
fewer iterations by only sharing 5% of pre-fixed data, while purely distributed
optimization algorithms may take hundreds more times of iterations to converge.
We hope that the discovery resulted from this paper would encourage even a
small amount of data sharing among different regions to combat difficult global
learning problems.

With increasing concerns for data privacy and ownership, recent years have
witnessed a paradigm shift in machine learning (ML). An emerging paradigm,
federated learning (FL), has gained great attention and has become a novel
design for machine learning implementations. FL enables the ML model training
at data silos under the coordination of a central server, eliminating
communication overhead and without sharing raw data. In this paper, we conduct
a review of the FL paradigm and, in particular, compare the types, the network
structures, and the global model aggregation methods. Then, we conducted a
comprehensive review of FL applications in the energy domain (refer to the
smart grid in this paper). We provide a thematic classification of FL to
address a variety of energy-related problems, including demand response,
identification, prediction, and federated optimizations. We describe the
taxonomy in detail and conclude with a discussion of various aspects, including
challenges, opportunities, and limitations in its energy informatics
applications, such as energy system modeling and design, privacy, and
evolution.

This paper studies class incremental learning (CIL) of continual learning
(CL). Many approaches have been proposed to deal with catastrophic forgetting
(CF) in CIL. Most methods incrementally construct a single classifier for all
classes of all tasks in a single head network. To prevent CF, a popular
approach is to memorize a small number of samples from previous tasks and
replay them during training of the new task. However, this approach still
suffers from serious CF as the parameters learned for previous tasks are
updated or adjusted with only the limited number of saved samples in the
memory. This paper proposes an entirely different approach that builds a
separate classifier (head) for each task (called a multi-head model) using a
transformer network, called MORE. Instead of using the saved samples in memory
to update the network for previous tasks/classes in the existing approach, MORE
leverages the saved samples to build a task specific classifier (adding a new
classification head) without updating the network learned for previous
tasks/classes. The model for the new task in MORE is trained to learn the
classes of the task and also to detect samples that are not from the same data
distribution (i.e., out-of-distribution (OOD)) of the task. This enables the
classifier for the task to which the test instance belongs to produce a high
score for the correct class and the classifiers of other tasks to produce low
scores because the test instance is not from the data distributions of these
classifiers. Experimental results show that MORE outperforms state-of-the-art
baselines and is also naturally capable of performing OOD detection in the
continual learning setting.

Advances in Deep Learning have led to the emergence of Large Language Models
(LLMs) such as OpenAI Codex which powers GitHub Copilot. LLMs have been fine
tuned and packaged so that programmers can use them in an Integrated
Development Environment (IDE) to write code. An emerging line of work is
assessing the code quality of code written with the help of these LLMs, with
security studies warning that LLMs do not fundamentally have any understanding
of the code they are writing, so they are more likely to make mistakes that may
be exploitable. We thus conducted a user study (N=58) to assess the security of
code written by student programmers when guided by LLMs. Half of the students
in our study had the help of the LLM and the other half did not. The students
were asked to write code in C that performed operations over a singly linked
list, including node operations such as inserting, updating, removing,
combining, and others. Our study shows that the students who had the help of an
LLM were more likely to write functional code, and although security impacts
were observed for certain individual functions, no statistically significant
impact on security was observed across all functions. We also investigate
systematic stylistic differences between unaided and LLM-assisted code, finding
that LLM code is more repetitive, which may have an amplifying effect if
vulnerable code is repeated in addition to the impact on source code
attribution.

In the context of structured nonconvex optimization, we estimate the increase
in minimum value for a decision that is robust to parameter perturbations as
compared to the value of a nominal problem. The estimates rely on detailed
expressions for subgradients and local Lipschitz moduli of min-value functions
in nonconvex robust optimization and require only the solution of the nominal
problem. The theoretical results are illustrated by examples from military
operations research involving mixed-integer optimization models. Across 54
cases examined, the median error in estimating the increase in minimum value is
12%. Therefore, the derived expressions for subgradients and local Lipschitz
moduli may accurately inform analysts about the possibility of obtaining
cost-effective, parameter-robust decisions in nonconvex optimization.

While uniform sampling has been widely studied in the matrix completion
literature, CUR sampling approximates a low-rank matrix via row and column
samples. Unfortunately, both sampling models lack flexibility for various
circumstances in real-world applications. In this work, we propose a novel and
easy-to-implement sampling strategy, coined Cross-Concentrated Sampling (CCS).
By bridging uniform sampling and CUR sampling, CCS provides extra flexibility
that can potentially save sampling costs in applications. In addition, we also
provide a sufficient condition for CCS-based matrix completion. Moreover, we
propose a highly efficient non-convex algorithm, termed Iterative CUR
Completion (ICURC), for the proposed CCS model. Numerical experiments verify
the empirical advantages of CCS and ICURC against uniform sampling and its
baseline algorithms, on both synthetic and real-world datasets.

Can deep language models be explanatory models of human cognition? If so,
what are their limits? In order to explore this question, we propose an
approach called hyperparameter hypothesization that uses predictive
hyperparameter tuning in order to find individuating descriptors of
cognitive-behavioral profiles. We take the first step in this approach by
predicting human performance in the semantic fluency task (SFT), a well-studied
task in cognitive science that has never before been modeled using
transformer-based language models (TLMs). In our task setup, we compare several
approaches to predicting which word an individual performing SFT will utter
next. We report preliminary evidence suggesting that, despite obvious
implementational differences in how people and TLMs learn and use language,
TLMs can be used to identify individual differences in human fluency task
behaviors better than existing computational models, and may offer insights
into human memory retrieval strategies -- cognitive process not typically
considered to be the kinds of things TLMs can model. Finally, we discuss the
implications of this work for cognitive modeling of knowledge representations.

Few-shot learning (FSL) aims to learn a classifier that can be easily adapted
to recognize novel classes,given only a few labeled examples per class.Limited
data keep this task challenging for deep learning.Recent metric-based methods
has achieved promising performance based on image-level features.However,these
global features ignore abundant local and structural information that is
transferable and consistent between seen and unseen classes.Some study in
cognitive science argue that humans can recognize novel classes with the
learned primitives.We expect to mine both transferable and discriminative
representation from base classes and adopt them to recognize novel
classes.Building on the episodic training mechanism,We propose a Primitive
Mining and Reasoning Network(PMRN) to learn primitive-aware representation in
an end-to-end manner for metric-based FSL model.We first add self-supervision
auxiliary task,forcing feature extractor to learn tvisual pattern corresponding
to primitives.To further mine and produce transferable primitive-aware
representations,we design an Adaptive Channel Grouping(ACG)module to synthesize
a set of visual primitives from object embedding by enhancing informative
channel maps while suppressing useless ones. Based on the learned primitive
feature,a Semantic Correlation Reasoning (SCR) module is proposed to capture
internal relations among them.Finally,we learn the task-specific importance of
primitives and conduct primitive-level metric based on the task-specific
attention feature.Extensive experiments show that our method achieves
state-of-the-art results on six standard benchmarks.

Orbital characteristics based on Gaia Early Data Release 3 astrometric
parameters are analyzed for ${\sim} 1700$ $r$-process-enhanced (RPE; [Eu/Fe] $>
+0.3$) metal-poor stars ([Fe/H] $\leq -0.8$) compiled from the $R$-Process
Alliance, the GALactic Archaeology with HERMES (GALAH) DR3 survey, and
additional literature sources. We find dynamical clusters of these stars based
on their orbital energies and cylindrical actions using the \HDBSCAN
~unsupervised learning algorithm. We identify $36$ Chemo-Dynamically Tagged
Groups (CDTGs) containing between $5$ and $22$ members; $17$ CDTGs have at
least $10$ member stars. Previously known Milky Way (MW) substructures such as
Gaia-Sausage-Enceladus, the Splashed Disk, the Metal-Weak Thick Disk, the Helmi
Stream, LMS-1 (Wukong), and Thamnos are re-identified. Associations with MW
globular clusters are determined for $7$ CDTGs; no recognized MW dwarf galaxy
satellites were associated with any of our CDTGs. Previously identified
dynamical groups are also associated with our CDTGs, adding structural
determination information and possible new identifications. Carbon-Enhanced
Metal-Poor RPE (CEMP-$r$) stars are identified among the targets; we assign
these to morphological groups in the Yoon-Beers $A$(C)$_{c}$ vs. [Fe/H]
Diagram. Our results confirm previous dynamical analyses that showed RPE stars
in CDTGs share common chemical histories, influenced by their birth

Machine learning algorithms have been widely used in intrusion detection
systems, including Multi-layer Perceptron (MLP). In this study, we proposed a
two-stage model that combines the Birch clustering algorithm and MLP classifier
to improve the performance of network anomaly multi-classification. In our
proposed method, we first apply Birch or Kmeans as an unsupervised clustering
algorithm to the CICIDS-2017 dataset to pre-group the data. The generated
pseudo-label is then added as an additional feature to the training of the
MLP-based classifier. The experimental results show that using Birch and
K-Means clustering for data pre-grouping can improve intrusion detection system
performance. Our method can achieve 99.73% accuracy in multi-classification
using Birch clustering, which is better than similar researches using a
stand-alone MLP model.

As graph data becomes more ubiquitous, the need for robust inferential graph
algorithms to operate in these complex data domains is crucial. In many cases
of interest, inference is further complicated by the presence of adversarial
data contamination. The effect of the adversary is frequently to change the
data distribution in ways that negatively affect statistical and algorithmic
performance. We study this phenomenon in the context of vertex nomination, a
semi-supervised information retrieval task for network data. Here, a common
suite of methods relies on spectral graph embeddings, which have been shown to
provide both good algorithmic performance and flexible settings in which
regularization techniques can be implemented to help mitigate the effect of an
adversary. Many current regularization methods rely on direct network trimming
to effectively excise the adversarial contamination, although this direct
trimming often gives rise to complicated dependency structures in the resulting
graph. We propose a new trimming method that operates in model space which can
address both block structure contamination and white noise contamination
(contamination whose distribution is unknown). This model trimming is more
amenable to theoretical analysis while also demonstrating superior performance
in a number of simulations, compared to direct trimming.

Deploying deep neural networks on low-resource edge devices is challenging
due to their ever-increasing resource requirements. Recent investigations
propose multiplication-free neural networks to reduce computation and memory
consumption. Shift neural network is one of the most effective tools towards
these reductions. However, existing low-bit shift networks are not as accurate
as their full precision counterparts and cannot efficiently transfer to a wide
range of tasks due to their inherent design flaws. We propose DenseShift
network that exploits the following novel designs. First, we demonstrate that
the zero-weight values in low-bit shift networks are neither useful to the
model capacity nor simplify the model inference. Therefore, we propose to use a
zero-free shifting mechanism to simplify inference while increasing the model
capacity. Second, we design a new metric to measure the weight freezing issue
in training low-bit shift networks, and propose a sign-scale decomposition to
improve the training efficiency. Third, we propose the low-variance random
initialization strategy to improve the model's performance in transfer learning
scenarios. We run extensive experiments on various computer vision and speech
tasks. The experimental results show that DenseShift network significantly
outperforms existing low-bit multiplication-free networks and can achieve
competitive performance to the full-precision counterpart. It also exhibits
strong transfer learning performance with no drop in accuracy.

While deep Embedding Learning approaches have witnessed widespread success in
multiple computer vision tasks, the state-of-the-art methods for representing
natural images need not necessarily perform well on images from other domains,
such as paintings, cartoons, and sketch. This is because of the huge shift in
the distribution of data from across these domains, as compared to natural
images. Domains like sketch often contain sparse informative pixels. However,
recognizing objects in such domains is crucial, given multiple relevant
applications leveraging such data, for instance, sketch to image retrieval.
Thus, achieving an Embedding Learning model that could perform well across
multiple domains is not only challenging, but plays a pivotal role in computer
vision. To this end, in this paper, we propose a novel Embedding Learning
approach with the goal of generalizing across different domains. During
training, given a query image from a domain, we employ gated fusion and
attention to generate a positive example, which carries a broad notion of the
semantics of the query object category (from across multiple domains). By
virtue of Contrastive Learning, we pull the embeddings of the query and
positive, in order to learn a representation which is robust across domains. At
the same time, to teach the model to be discriminative against examples from
different semantic categories (across domains), we also maintain a pool of
negative embeddings (from different categories). We show the prowess of our
method using the DomainBed framework, on the popular PACS (Photo, Art painting,
Cartoon, and Sketch) dataset.

Knowledge distillation is an effective approach for training compact
recognizers required in autonomous driving. Recent studies on image
classification have shown that matching student and teacher on a wide range of
data points is critical for improving performance in distillation. This concept
(called function matching) is suitable for driving scene recognition, where
generally an almost infinite amount of unlabeled data are available. In this
study, we experimentally investigate the impact of using such a large amount of
unlabeled data for distillation on the performance of student models in
structured prediction tasks for autonomous driving. Through extensive
experiments, we demonstrate that the performance of the compact student model
can be improved dramatically and even match the performance of the large-scale
teacher by knowledge distillation with massive unlabeled data.

Data sparsity is a well-known problem for grammatical error correction (GEC).
Generating synthetic training data is one widely proposed solution to this
problem, and has allowed models to achieve state-of-the-art (SOTA) performance
in recent years. However, these methods often generate unrealistic errors, or
aim to generate sentences with only one error. We propose a learning based two
stage method for synthetic data generation for GEC that relaxes this constraint
on sentences containing only one error. Errors are generated in accordance with
sentence merit. We show that a GEC model trained on our synthetically generated
corpus outperforms models trained on synthetic data from prior work.

In the present work we detail how the many-body potential energy landscape of
interatomic potentials for carbon can be explored by utilising the nested
sampling algorithm, allowing the calculation of their pressure-temperature
phase diagram up to high pressures. We present a comparison of three
interatomic potential models, Tersoff, EDIP and GAP-20, focusing on their
macroscopic properties, particularly on their melting transition and on
identifying thermodynamically stable solid structures up to at least 100 GPa.
The studied models all form graphite structures upon freezing at lower
pressure, then the diamond structure as the pressure increases. We were able to
locate the transition between these phases in case of the Tersoff and EDIP
models. We placed particular focus on the state-of-the-art machine learning
(ML) model, GAP-20, and calculated its phase diagram up to 1 TPa to evaluate
its predictive capabilities well outside of the model's fitting conditions. The
phase diagram showed a remarkably good agreement with the experimental phase
diagram up to 200 GPa, despite a variety of unexpected graphite layer spacing.
Above that nested sampling identified two novel stable solid structures, a
strained diamond structure and above 800 GPa a strained hexagonal-close-packed
structure. However, the stability of these two phases were not confirmed by DFT
calculations, highlighting potential routes to further improve the ML model.

Min-max optimization problems (i.e., min-max games) have attracted a great
deal of attention recently as their applicability to a wide range of machine
learning problems has become evident. In this paper, we study min-max games
with dependent strategy sets, where the strategy of the first player constrains
the behavior of the second. Such games are best understood as sequential, i.e.,
Stackelberg, games, for which the relevant solution concept is Stackelberg
equilibrium, a generalization of Nash. One of the most popular algorithms for
solving min-max games is gradient descent ascent (GDA). We present a
straightforward generalization of GDA to min-max Stackelberg games with
dependent strategy sets, but show that it may not converge to a Stackelberg
equilibrium. We then introduce two variants of GDA, which assume access to a
solution oracle for the optimal Karush Kuhn Tucker (KKT) multipliers of the
games' constraints. We show that such an oracle exists for a large class of
convex-concave min-max Stackelberg games, and provide proof that our GDA
variants with such an oracle converge in $O(\frac{1}{\varepsilon^2})$
iterations to an $\varepsilon$-Stackelberg equilibrium, improving on the most
efficient algorithms currently known which converge in
$O(\frac{1}{\varepsilon^3})$ iterations. We then show that solving Fisher
markets, a canonical example of a min-max Stackelberg game, using our novel
algorithm, corresponds to buyers and sellers using myopic best-response
dynamics in a repeated market, allowing us to prove the convergence of these
dynamics in $O(\frac{1}{\varepsilon^2})$ iterations in Fisher markets. We close
by describing experiments on Fisher markets which suggest potential ways to
extend our theoretical results, by demonstrating how different properties of
the objective function can affect the convergence and convergence rate of our

Light field (LF) depth estimation plays a key role in many LF-based
applications. Existing LF depth estimation methods generally consider depth
estimation as a regression problem, supervised by a pixel-wise L1 loss between
the regressed disparity map and the groundtruth one. However, the disparity map
is only a sub-space projection (i.e., an expectation) of the disparity
distribution, which is essential for models to learn. In this paper, we propose
a simple yet effective method to learn the sub-pixel disparity distribution by
fully utilizing the power of deep networks, especially for LF of narrow
baselines. We construct the cost volume at the sub-pixel level to produce a
finer disparity distribution and design an uncertainty-aware focal loss to
supervise the predicted disparity distribution to be close to the groundtruth
one. Extensive experimental results demonstrate the effectiveness of our
method. Our method ranks the first place over 105 submitted algorithms on the
HCI 4D LF Benchmark in terms of all the four accuracy metrics (i.e., BadPix
0.01, BadPix 0.03, BadPix 0.07, and MSE). The Code and model are available at
\url{https://github.com/chaowentao/SubFocal}.

In this paper, microchannel designs with secondary channels and with ribs are
investigated using computational fluid dynamics and are coupled with a
multi-objective optimization algorithm to determine and propose optimal
solutions based on observed thermal resistance and pumping power. A workflow
that combines Latin hypercube sampling, machine learning-based surrogate
modeling and multi-objective optimization is proposed. Random forests, gradient
boosting algorithms and neural networks were considered during the search for
the best surrogate. We demonstrated that tuned neural networks can make
accurate predictions and be used to create an acceptable surrogate model.
Optimized solutions show a negligible difference in overall performance when
compared to the conventional optimization approach. Additionally, solutions are
calculated in one-fifth of the original time. Generated designs attain
temperatures that are lower by more than 10% under the same pressure limits as
a convectional microchannel design. When limited by temperature, pressure drops
are reduced by more than 25%. Finally, the influence of each design variable on
the thermal resistance and pumping power was investigated by employing the
SHapley Additive exPlanations technique. Overall, we have demonstrated that the
proposed framework has merit and can be used as a viable methodology in
microchannel heat sink design optimization.

Machine learning has been used to recognize emotions in faces, typically by
looking for 8 different emotional states (neutral, happy, sad, surprise, fear,
disgust, anger and contempt). We consider two approaches: feature recognition
based on facial landmarks and deep learning on all pixels; each produced 58%
overall accuracy. However, they produced different results on different images
and thus we propose a new meta-classifier combining these approaches. It
produces far better results with 77% accuracy

This paper describes and discusses our vision to develop and reason about
best practices and novel ways of curating data-centric geosciences knowledge
(data, experiments, models, methods, conclusions, and interpretations). This
knowledge is produced from applying statistical modelling, Machine Learning,
and modern data analytics methods on geo-data collections. The problems address
open methodological questions in model building, models' assessment,
prediction, and forecasting workflows.

We propose a new setting that relaxes the assumption in the conventional
CoSOD setting by allowing the presence of \enquote{noisy images} which do not
share the common salient object. We call this new setting Generalised
Co-Salient Object Detection (GCoSOD). We propose a novel random sampling based
Generalised CoSOD Training (GCT) strategy to distill the awareness of
inter-image absence of co-salient object into CoSOD models. It employs a
Diverse Sampling Self-Supervised Learning (D$\text{S}^{3}$L) that, in addition
to the provided supervised co-salient label, introduces additional
self-supervised labels for images (being null that no co-salient object is
present). Further, the random sampling process inherent in GCT enables the
generation of a high-quality uncertainty map highlighting potential
false-positive predictions at instance level. To evaluate the performance of
CoSOD models under the GCoSOD setting, we propose two new testing datasets,
namely CoCA-Common and CoCA-Zero, where a common salient object is partially
present in the former and completely absent in the latter. Extensive
experiments demonstrate that our proposed method significantly improves the
performance of CoSOD models in terms of the performance under the GCoSOD
setting as well as the model calibration degrees.

Assessing image aesthetics is a challenging computer vision task. One reason
is that aesthetic preference is highly subjective and may vary significantly
among people for certain images. Thus, it is important to properly model and
quantify such \textit{subjectivity}, but there has not been much effort to
resolve this issue. In this paper, we propose a novel unified probabilistic
framework that can model and quantify subjective aesthetic preference based on
the subjective logic. In this framework, the rating distribution is modeled as
a beta distribution, from which the probabilities of being definitely pleasing,
being definitely unpleasing, and being uncertain can be obtained. We use the
probability of being uncertain to define an intuitive metric of subjectivity.
Furthermore, we present a method to learn deep neural networks for prediction
of image aesthetics, which is shown to be effective in improving the
performance of subjectivity prediction via experiments. We also present an
application scenario where the framework is beneficial for aesthetics-based
image recommendation.

Recent advances in artificial intelligence largely benefit from better neural
network architectures. These architectures are a product of a costly process of
trial-and-error. To ease this process, we develop ArchExplorer, a visual
analysis method for understanding a neural architecture space and summarizing
design principles. The key idea behind our method is to make the architecture
space explainable by exploiting structural distances between architectures. We
formulate the pairwise distance calculation as solving an all-pairs shortest
path problem. To improve efficiency, we decompose this problem into a set of
single-source shortest path problems. The time complexity is reduced from
O(kn^2N) to O(knN). Architectures are hierarchically clustered according to the
distances between them. A circle-packing-based architecture visualization has
been developed to convey both the global relationships between clusters and
local neighborhoods of the architectures in each cluster. Two case studies and
a post-analysis are presented to demonstrate the effectiveness of ArchExplorer
in summarizing design principles and selecting better-performing architectures.

Recent efforts have shown machine learning to be useful for the prediction of
nonlinear fluid dynamics. Predictive accuracy is often a central motivation for
employing neural networks, but the pattern recognition central to the network
function is equally valuable for purposes of enhancing our dynamical insight
into confounding dynamics. In this paper, convolutional neural networks (CNNs)
were trained to recognize several qualitatively different subsonic buffet flows
over a high-incidence airfoil, and a near-perfect accuracy was performed with
only a small training dataset. The convolutional kernels and corresponding
feature maps, developed by the model with no temporal information provided,
identified large-scale coherent structures in agreement with those known to be
associated with buffet flows. An approach named Gradient-weighted Class
Activation Mapping (Grad-CAM) was then applied to the trained model to indicate
the importance of these feature maps in classification. Sensitivity to
hyperparameters including network architecture and convolutional kernel size
was also explored, and results show that smaller kernels are better at coherent
structure identification than are larger kernels. A long-short term memory CNN
was subsequently used to demonstrate that with the inclusion of temporal
information, the coherent structures remained qualitatively comparable to those
of the conventional CNN. The coherent structures identified by these models
enhance our dynamical understanding of subsonic buffet over high-incidence
airfoils over a wide range of Reynolds numbers.

Offline Handwritten Mathematical Expression Recognition (HMER) is a major
area in the field of mathematical expression recognition. Offline HMER is often
viewed as a much harder problem as compared to online HMER due to a lack of
temporal information and variability in writing style. In this paper, we
purpose a encoder-decoder model that uses paired adversarial learning.
Semantic-invariant features are extracted from handwritten mathematical
expression images and their printed mathematical expression counterpart in the
encoder. Learning of semantic-invariant features combined with the DenseNet
encoder and transformer decoder, helped us to improve the expression rate from
previous studies. Evaluated on the CROHME dataset, we have been able to improve
latest CROHME 2019 test set results by 4% approx.

Network science established itself as a prominent tool for modeling time
series and complex systems. This modeling process consists of transforming a
set or a single time series into a network. Nodes may represent complete time
series, segments, or single values, while links define associations or
similarities between the represented parts. R is one of the main programming
languages used in data science, statistics, and machine learning, with many
packages available. However, no single package provides the necessary methods
to transform time series into networks. This paper presents ts2net, an R
package for modeling one or multiple time series into networks. The package
provides the time series distance functions that can be easily computed in
parallel and in supercomputers to process larger data sets and methods to
transform distance matrices into networks. Ts2net also provides methods to
transform a single time series into a network, such as recurrence networks,
visibility graphs, and transition networks. Together with other packages,
ts2net permits using network science and graph mining tools to extract
information from time series.

Event detection (ED), aiming to detect events from texts and categorize them,
is vital to understanding actual happenings in real life. However, mainstream
event detection models require high-quality expert human annotations of
triggers, which are often costly and thus deter the application of ED to new
domains. Therefore, in this paper, we focus on low-resource ED without triggers
and aim to tackle the following formidable challenges: multi-label
classification, insufficient clues, and imbalanced events distribution. We
propose a novel trigger-free ED method via Derangement mechanism on a machine
Reading Comprehension (DRC) framework. More specifically, we treat the input
text as Context and concatenate it with all event type tokens that are deemed
as Answers with an omitted default question. So we can leverage the
self-attention in pre-trained language models to absorb semantic relations
between input text and the event types. Moreover, we design a simple yet
effective event derangement module (EDM) to prevent major events from being
excessively learned so as to yield a more balanced training process. The
experiment results show that our proposed trigger-free ED model is remarkably
competitive to mainstream trigger-based models, showing its strong performance
on low-source event detection.

While generative models have recently become ubiquitous in many scientific
areas, less attention has been paid to their evaluation. For molecular
generative models, the state-of-the-art examines their output in isolation or
in relation to its input. However, their biological and functional properties,
such as ligand-target interaction is not being addressed. In this study, a
novel biologically-inspired benchmark for the evaluation of molecular
generative models is proposed. Specifically, three diverse reference datasets
are designed and a set of metrics are introduced which are directly relevant to
the drug discovery process. In particular we propose a recreation metric, apply
drug-target affinity prediction and molecular docking as complementary
techniques for the evaluation of generative outputs. While all three metrics
show consistent results across the tested generative models, a more detailed
comparison of drug-target affinity binding and molecular docking scores
revealed that unimodal predictiors can lead to erroneous conclusions about
target binding on a molecular level and a multi-modal approach is thus
preferrable. The key advantage of this framework is that it incorporates prior
physico-chemical domain knowledge into the benchmarking process by focusing
explicitly on ligand-target interactions and thus creating a highly efficient
tool not only for evaluating molecular generative outputs in particular, but
also for enriching the drug discovery process in general.

Annotated data is a requirement for applying supervised machine learning
methods, and the quality of annotations is crucial for the result. Especially
when working with cultural heritage collections that inhere a manifold of
uncertainties, annotating data remains a manual, arduous task to be carried out
by domain experts. Our project started with two already annotated sets of
medieval manuscript images which however were incomplete and comprised
conflicting metadata based on scholarly and linguistic differences. Our aims
were to create (1) a uniform set of descriptive labels for the combined data
set, and (2) a hierarchical classification of a high quality that can be used
as a valuable input for supervised machine learning. To reach these goals, we
developed a visual analytics system to enable medievalists to combine,
regularize and extend the vocabulary used to describe these data sets. Visual
interfaces for word and image embeddings as well as co-occurrences of the
annotations across the data sets enable annotating multiple images at the same
time, recommend annotation label candidates and support composing a
hierarchical classification of labels. Our system itself implements a
semi-supervised method as it updates visual representations based on the
medievalists' feedback, and a series of usage scenarios document its value for
the target community.

Deep Learning systems have achieved great success in the past few years, even
surpassing human intelligence in several cases. As of late, they have also
established themselves in the biomedical and healthcare domains, where they
have shown a lot of promise, but have not yet achieved widespread adoption.
This is in part due to the fact that most methods fail to maintain their
performance when they are called to make decisions on data that originate from
a different distribution than the one they were trained on, namely
Out-Of-Distribution (OOD) data. For example, in the case of biosignal
classification, models often fail to generalize well on datasets from different
hospitals, due to the distribution discrepancy amongst different sources of
data. Our goal is to demonstrate the Domain Generalization problem present
between distinct hospital databases and propose a method that classifies
abnormalities on 12-lead Electrocardiograms (ECGs), by leveraging information
extracted across the architecture of a Deep Neural Network, and capturing the
underlying structure of the signal. To this end, we adopt a ResNet-18 as the
backbone model and extract features from several intermediate convolutional
layers of the network. To evaluate our method, we adopt publicly available ECG
datasets from four sources and handle them as separate domains. To simulate the
distributional shift present in real-world settings, we train our model on a
subset of the domains and leave-out the remaining ones. We then evaluate our
model both on the data present at training time (intra-distribution) and the
held-out data (out-of-distribution), achieving promising results and surpassing
the baseline of a vanilla Residual Network in most of the cases.

Data-driven predictive methods which can efficiently and accurately transform
protein sequences into biologically active structures are highly valuable for
scientific research and therapeutical development. Determining accurate folding
landscape using co-evolutionary information is fundamental to the success of
modern protein structure prediction methods. As the state of the art,
AlphaFold2 has dramatically raised the accuracy without performing explicit
co-evolutionary analysis. Nevertheless, its performance still shows strong
dependence on available sequence homologs. We investigated the cause of such
dependence and presented EvoGen, a meta generative model, to remedy the
underperformance of AlphaFold2 for poor MSA targets. EvoGen allows us to
manipulate the folding landscape either by denoising the searched MSA or by
generating virtual MSA, and helps AlphaFold2 fold accurately in low-data regime
or even achieve encouraging performance with single-sequence predictions. Being
able to make accurate predictions with few-shot MSA not only generalizes
AlphaFold2 better for orphan sequences, but also democratizes its use for
high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a
probabilistic structure generation method which could explore alternative
conformations of protein sequences, and the task-aware differentiable algorithm
for sequence generation will benefit other related tasks including protein
design.

Adsorption of carbon monoxide (CO) on transition-metal surfaces is a
prototypical process in surface sciences and catalysis. Despite its simplicity
it has posed great challenges to theoretical modeling. Pretty much all existing
density functionals fail to accurately describe surface energies, CO adsorption
site preference, as well as adsorption energies simultaneously. Although the
random phase approximation (RPA) cures these DFT failures, its large
computational cost makes it prohibitive to study the CO adsorption for any but
the simplest ordered cases. Here, we address these challenges by developing a
machine-learned force field (MLFF) with near RPA accuracy for the prediction of
coverage-dependent adsorption of CO on the Rh(111) surface through an efficient
on-the-fly active learning procedure and a $\Delta$-machine learning approach.
We show that the RPA derived MLFF is capable to accurately predict the Rh(111)
surface energy, CO adsorption site preference as well as adsorption energies at
different coverages that are all in good agreement with experiments. Moreover,
the coverage-dependent ground-state adsorption patterns and adsorption
saturation coverage are identified.

We study the computational complexity of some explainable clustering problems
in the framework proposed by [Dasgupta et al., ICML 2020], where explainability
is achieved via axis-aligned decision trees. We consider the $k$-means,
$k$-medians, $k$-centers and the spacing cost functions. We prove that the
first three are hard to optimize while the latter can be optimized in
polynomial time.

Fourier transformation is an extensively studied problem in many research
fields. It has many applications in machine learning, signal processing,
compressed sensing, and so on. In many real-world applications, approximated
Fourier transformation is sufficient and we only need to do the Fourier
transform on a subset of coordinates. Given a vector $x \in \mathbb{C}^{n}$, an
approximation parameter $\epsilon$ and a query set $S \subset [n]$ of size $k$,
we propose an algorithm to compute an approximate Fourier transform result $x'$
which uses $O(\epsilon^{-1} k \log(n/\delta))$ Fourier measurements, runs in
$O(\epsilon^{-1} k \log(n/\delta))$ time and outputs a vector $x'$ such that
$\| ( x' - \widehat{x} )_S \|_2^2 \leq \epsilon \| \widehat{x}_{\bar{S}} \|_2^2
+ \delta \| \widehat{x} \|_1^2 $ holds with probability of at least $9/10$.

Ever since Reddi et al. 2018 pointed out the divergence issue of Adam, many
new variants have been designed to obtain convergence. However, vanilla Adam
remains exceptionally popular and it works well in practice. Why is there a gap
between theory and practice? We point out there is a mismatch between the
settings of theory and practice: Reddi et al. 2018 pick the problem after
picking the hyperparameters of Adam, i.e., $(\beta_1, \beta_2)$; while
practical applications often fix the problem first and then tune $(\beta_1,
\beta_2)$. Due to this observation, we conjecture that the empirical
convergence can be theoretically justified, only if we change the order of
picking the problem and hyperparameter. In this work, we confirm this
conjecture. We prove that, when $\beta_2$ is large and $\beta_1 <
\sqrt{\beta_2}<1$, Adam converges to the neighborhood of critical points. The
size of the neighborhood is propositional to the variance of stochastic
gradients. Under an extra condition (strong growth condition), Adam converges
to critical points. It is worth mentioning that our results cover a wide range
of hyperparameters: as $\beta_2$ increases, our convergence result can cover
any $\beta_1 \in [0,1)$ including $\beta_1=0.9$, which is the default setting
in deep learning libraries. To our knowledge, this is the first result showing
that Adam can converge without any modification on its update rules. Further,
our analysis does not require assumptions of bounded gradients or bounded
2nd-order momentum. When $\beta_2$ is small, we further point out a large
region of $(\beta_1,\beta_2)$ where Adam can diverge to infinity. Our
divergence result considers the same setting as our convergence result,
indicating a phase transition from divergence to convergence when increasing
$\beta_2$. These positive and negative results can provide suggestions on how
to tune Adam hyperparameters.

Credit cards play an exploding role in modern economies. Its popularity and
ubiquity have created a fertile ground for fraud, assisted by the cross boarder
reach and instantaneous confirmation. While transactions are growing, the fraud
percentages are also on the rise as well as the true cost of a dollar fraud.
Volume of transactions, uniqueness of frauds and ingenuity of the fraudster are
main challenges in detecting frauds. The advent of machine learning, artificial
intelligence and big data has opened up new tools in the fight against frauds.
Given past transactions, a machine learning algorithm has the ability to
'learn' infinitely complex characteristics in order to identify frauds in
real-time, surpassing the best human investigators. However, the developments
in fraud detection algorithms has been challenging and slow due the massively
unbalanced nature of fraud data, absence of benchmarks and standard evaluation
metrics to identify better performing classifiers, lack of sharing and
disclosure of research findings and the difficulties in getting access to
confidential transaction data for research. This work investigates the
properties of typical massively imbalanced fraud data sets, their availability,
suitability for research use while exploring the widely varying nature of fraud
distributions. Furthermore, we show how human annotation errors compound with
machine classification errors. We also carry out experiments to determine the
effect of PCA obfuscation (as a means of disseminating sensitive transaction
data for research and machine learning) on algorithmic performance of
classifiers and show that while PCA does not significantly degrade performance,
care should be taken to use the appropriate principle component size
(dimensions) to avoid overfitting.

Indoor thermal comfort in smart buildings has a significant impact on the
health and performance of occupants. Consequently, machine learning (ML) is
increasingly used to solve challenges related to indoor thermal comfort.
Temporal variability of thermal comfort perception is an important problem that
regulates occupant well-being and energy consumption. However, in most ML-based
thermal comfort studies, temporal aspects such as the time of day, circadian
rhythm, and outdoor temperature are not considered. This work addresses these
problems. It investigates the impact of circadian rhythm and outdoor
temperature on the prediction accuracy and classification performance of ML
models. The data is gathered through month-long field experiments carried out
in 14 classrooms of 5 schools, involving 512 primary school students. Four
thermal comfort metrics are considered as the outputs of Deep Neural Networks
and Support Vector Machine models for the dataset. The effect of temporal
variability on school children's comfort is shown through a "time of day"
analysis. Temporal variability in prediction accuracy is demonstrated (up to
80%). Furthermore, we show that outdoor temperature (varying over time)
positively impacts the prediction performance of thermal comfort models by up
to 30%. The importance of spatio-temporal context is demonstrated by
contrasting micro-level (location specific) and macro-level (6 locations across
a city) performance. The most important finding of this work is that a
definitive improvement in prediction accuracy is shown with an increase in the
time of day and sky illuminance, for multiple thermal comfort metrics.

Modeling what makes an advertisement persuasive, i.e., eliciting the desired
response from consumer, is critical to the study of propaganda, social
psychology, and marketing. Despite its importance, computational modeling of
persuasion in computer vision is still in its infancy, primarily due to the
lack of benchmark datasets that can provide persuasion-strategy labels
associated with ads. Motivated by persuasion literature in social psychology
and marketing, we introduce an extensive vocabulary of persuasion strategies
and build the first ad image corpus annotated with persuasion strategies. We
then formulate the task of persuasion strategy prediction with multi-modal
learning, where we design a multi-task attention fusion model that can leverage
other ad-understanding tasks to predict persuasion strategies. Further, we
conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500
companies where we use our model's predictions to analyze which strategies work
with different demographics (age and gender). The dataset also provides image
segmentation masks, which labels persuasion strategies in the corresponding ad
images on the test split. We publicly release our code and dataset
https://midas-research.github.io/persuasion-advertisements/.

Knowledge-enhanced pre-trained models for language representation have been
shown to be more effective in knowledge base construction tasks (i.e.,~relation
extraction) than language models such as BERT. These knowledge-enhanced
language models incorporate knowledge into pre-training to generate
representations of entities or relationships. However, existing methods
typically represent each entity with a separate embedding. As a result, these
methods struggle to represent out-of-vocabulary entities and a large amount of
parameters, on top of their underlying token models (i.e.,~the transformer),
must be used and the number of entities that can be handled is limited in
practice due to memory constraints. Moreover, existing models still struggle to
represent entities and relationships simultaneously. To address these problems,
we propose a new pre-trained model that learns representations of both entities
and relationships from token spans and span pairs in the text respectively. By
encoding spans efficiently with span modules, our model can represent both
entities and their relationships but requires fewer parameters than existing
models. We pre-trained our model with the knowledge graph extracted from
Wikipedia and test it on a broad range of supervised and unsupervised
information extraction tasks. Results show that our model learns better
representations for both entities and relationships than baselines, while in
supervised settings, fine-tuning our model outperforms RoBERTa consistently and
achieves competitive results on information extraction tasks.

The high cost of the test can be dramatically reduced, provided that the
coverability as an inherent feature of the code under test is predictable. This
article offers a machine learning model to predict the extent to which the test
could cover a class in terms of a new metric called Coverageability. The
prediction model consists of an ensemble of four regression models. The
learning samples consist of feature vectors, where features are source code
metrics computed for a class. The samples are labeled by the Coverageability
values computed for their corresponding classes. We offer a mathematical model
to evaluate test effectiveness in terms of size and coverage of the test suite
generated automatically for each class. We extend the size of the feature space
by introducing a new approach to defining sub-metrics in terms of existing
source code metrics. Using feature importance analysis on the learned
prediction models, we sort source code metrics in the order of their impact on
the test effectiveness. As a result of which, we found the class strict
cyclomatic complexity as the most influential source code metric. Our
experiments with the prediction models on a large corpus of Java projects
containing about 23,000 classes demonstrate the Mean Absolute Error (MAE) of
0.032, Mean Squared Error (MSE) of 0.004, and an R2-score of 0.855. Compared
with the state-of-the-art coverage prediction models, our models improve MAE,
MSE, and an R2-score by 5.78%, 2.84%, and 20.71%, respectively.

Class imbalance is a frequently occurring scenario in classification tasks.
Learning from imbalanced data poses a major challenge, which has instigated a
lot of research in this area. Data preprocessing using sampling techniques is a
standard approach to deal with the imbalance present in the data. Since
standard classification algorithms do not perform well on imbalanced data, the
dataset needs to be adequately balanced before training. This can be
accomplished by oversampling the minority class or undersampling the majority
class. In this study, a novel hybrid sampling algorithm has been proposed. To
overcome the limitations of the sampling techniques while ensuring the quality
of the retained sampled dataset, a sophisticated framework has been developed
to properly combine three different sampling techniques. Neighborhood Cleaning
rule is first applied to reduce the imbalance. Random undersampling is then
strategically coupled with the SMOTE algorithm to obtain an optimal balance in
the dataset. This proposed hybrid methodology, termed "SMOTE-RUS-NC", has been
compared with other state-of-the-art sampling techniques. The strategy is
further incorporated into the ensemble learning framework to obtain a more
robust classification algorithm, termed "SRN-BRF". Rigorous experimentation has
been conducted on 26 imbalanced datasets with varying degrees of imbalance. In
virtually all datasets, the proposed two algorithms outperformed existing
sampling strategies, in many cases by a substantial margin. Especially in
highly imbalanced datasets where popular sampling techniques failed utterly,
they achieved unparalleled performance. The superior results obtained
demonstrate the efficacy of the proposed models and their potential to be
powerful sampling algorithms in imbalanced domain.

The existing fake audio detection systems often rely on expert experience to
design the acoustic features or manually design the hyperparameters of the
network structure. However, artificial adjustment of the parameters can have a
relatively obvious influence on the results. It is almost impossible to
manually set the best set of parameters. Therefore this paper proposes a fully
automated end-toend fake audio detection method. We first use wav2vec
pre-trained model to obtain a high-level representation of the speech.
Furthermore, for the network structure, we use a modified version of the
differentiable architecture search (DARTS) named light-DARTS. It learns deep
speech representations while automatically learning and optimizing complex
neural structures consisting of convolutional operations and residual blocks.
The experimental results on the ASVspoof 2019 LA dataset show that our proposed
system achieves an equal error rate (EER) of 1.08%, which outperforms the
state-of-the-art single system.

Unlike most other software quality attributes, testability cannot be
evaluated solely based on the characteristics of the source code. The
effectiveness of the test suite and the budget assigned to the test highly
impact the testability of the code under test. The size of a test suite
determines the test effort and cost, while the coverage measure indicates the
test effectiveness. Therefore, testability can be measured based on the
coverage and number of test cases provided by a test suite, considering the
test budget. This paper offers a new equation to estimate testability regarding
the size and coverage of a given test suite. The equation has been used to
label 23,000 classes belonging to 110 Java projects with their testability
measure. The labeled classes were vectorized using 262 metrics. The labeled
vectors were fed into a family of supervised machine learning algorithms,
regression, to predict testability in terms of the source code metrics.
Regression models predicted testability with an R2 of 0.68 and a mean squared
error of 0.03, suitable in practice. Fifteen software metrics highly affecting
testability prediction were identified using a feature importance analysis
technique on the learned model. The proposed models have improved mean absolute
error by 38% due to utilizing new criteria, metrics, and data compared with the
relevant study on predicting branch coverage as a test criterion. As an
application of testability prediction, it is demonstrated that automated
refactoring of 42 smelly Java classes targeted at improving the 15 influential
software metrics could elevate their testability by an average of 86.87%.

The task of argument mining aims to detect all possible argumentative
components and identify their relationships automatically. As a thriving field
in natural language processing, there has been a large amount of corpus for
academic study and application development in argument mining. However, the
research in this area is still constrained by the inherent limitations of
existing datasets. Specifically, all the publicly available datasets are
relatively small in scale, and few of them provide information from other
modalities to facilitate the learning process. Moreover, the statements and
expressions in these corpora are usually in a compact form, which means
non-adjacent clauses or text segments will always be regarded as multiple
individual components, thus restricting the generalization ability of models.
To this end, we collect and contribute a novel dataset AntCritic to serve as a
helpful complement to this area, which consists of about 10k free-form and
visually-rich financial comments and supports both argument component detection
and argument relation prediction tasks. Besides, in order to cope with the
challenges and difficulties brought by scenario expansion and problem setting
modification, we thoroughly explore the fine-grained relation prediction and
structure reconstruction scheme for free-form documents and discuss the
encoding mechanism for visual styles and layouts. And based on these analyses,
we design two simple but effective model architectures and conduct various
experiments on this dataset to provide benchmark performances as a reference
and verify the practicability of our proposed architecture.

We study inverse reinforcement learning (IRL) and imitation learning (IM),
the problems of recovering a reward or policy function from expert's
demonstrated trajectories. We propose a new way to improve the learning process
by adding a weight function to the maximum entropy framework, with the
motivation of having the ability to learn and recover the stochasticity (or the
bounded rationality) of the expert policy. Our framework and algorithms allow
to learn both a reward (or policy) function and the structure of the entropy
terms added to the Markov Decision Processes, thus enhancing the learning
procedure. Our numerical experiments using human and simulated demonstrations
and with discrete and continuous IRL/IM tasks show that our approach
outperforms prior algorithms.

We propose a hybrid machine learning architecture that simultaneously employs
multiple deep learning models analyzing contextual and behavioral
characteristics of Windows portable executable, producing a final prediction
based on a decision from the meta-model. The detection heuristic in
contemporary machine learning Windows malware classifiers is typically based on
the static properties of the sample since dynamic analysis through
virtualization is challenging for vast quantities of samples. To surpass this
limitation, we employ a Windows kernel emulation that allows the acquisition of
behavioral patterns across large corpora with minimal temporal and
computational costs. We partner with a security vendor for a collection of more
than 100k int-the-wild samples that resemble the contemporary threat landscape,
containing raw PE files and filepaths of applications at the moment of
execution. The acquired dataset is at least ten folds larger than reported in
related works on behavioral malware analysis. Files in the training dataset are
labeled by a professional threat intelligence team, utilizing manual and
automated reverse engineering tools. We estimate the hybrid classifier's
operational utility by collecting an out-of-sample test set three months later
from the acquisition of the training set. We report an improved detection rate,
above the capabilities of the current state-of-the-art model, especially under
low false-positive requirements. Additionally, we uncover a meta-model's
ability to identify malicious activity in validation and test sets even if none
of the individual models express enough confidence to mark the sample as
malevolent. We conclude that the meta-model can learn patterns typical to
malicious samples from representation combinations produced by different
analysis techniques. We publicly release pre-trained models and anonymized
dataset of emulation reports.

Training keyphrase generation (KPG) models requires a large amount of
annotated data, which can be prohibitively expensive and often limited to
specific domains. In this study, we first demonstrate that large distribution
shifts among different domains severely hinder the transferability of KPG
models. We then propose a three-stage pipeline, which gradually guides KPG
models' learning focus from general syntactical features to domain-related
semantics, in a data-efficient manner. With Domain-general Phrase pre-training,
we pre-train Sequence-to-Sequence models with generic phrase annotations that
are widely available on the web, which enables the models to generate phrases
in a wide range of domains. The resulting model is then applied in the Transfer
Labeling stage to produce domain-specific pseudo keyphrases, which help adapt
models to a new domain. Finally, we fine-tune the model with limited data with
true labels to fully adapt it to the target domain. Our experiment results show
that the proposed process can produce good quality keyphrases in new domains
and achieve consistent improvements after adaptation with limited in-domain
annotated data.

Recently, e-learning platforms have grown as a place where students can post
doubts (as a snap taken with smart phones) and get them resolved in minutes.
However, the significant increase in the number of student-posted doubts with
high variance in quality on these platforms not only presents challenges for
teachers' navigation to address them but also increases the resolution time per
doubt. Both are not acceptable, as high doubt resolution time hinders the
students learning progress. This necessitates ways to automatically identify if
there exists a similar doubt in repository and then serve it to the teacher as
the plausible solution to validate and communicate with the student. Supervised
learning techniques (like Siamese architecture) require labels to identify the
matches, which is not feasible as labels are scarce and expensive. In this
work, we, thus, developed a label-agnostic doubt matching paradigm based on the
representations learnt via self-supervised technique. Building on prior
theoretical insights of BYOL (bootstrap your own latent space), we propose
custom BYOL which combines domain-specific augmentation with contrastive
objective over a varied set of appropriately constructed data views. Results
highlighted that, custom BYOL improves the top-1 matching accuracy by
approximately 6\% and 5\% as compared to both BYOL and supervised learning
instances, respectively. We further show that both BYOL-based learning
instances performs either on par or better than human labeling.

We introduce a new differential privacy (DP) accountant called the
saddle-point accountant (SPA). SPA approximates privacy guarantees for the
composition of DP mechanisms in an accurate and fast manner. Our approach is
inspired by the saddle-point method -- a ubiquitous numerical technique in
statistics. We prove rigorous performance guarantees by deriving upper and
lower bounds for the approximation error offered by SPA. The crux of SPA is a
combination of large-deviation methods with central limit theorems, which we
derive via exponentially tilting the privacy loss random variables
corresponding to the DP mechanisms. One key advantage of SPA is that it runs in
constant time for the $n$-fold composition of a privacy mechanism. Numerical
experiments demonstrate that SPA achieves comparable accuracy to
state-of-the-art accounting methods with a faster runtime.

Predicting disruptions across different tokamaks is a great obstacle to
overcome. Future tokamaks can hardly tolerate disruptions at high performance
discharge. Few disruption discharges at high performance can hardly compose an
abundant training set, which makes it difficult for current data-driven methods
to obtain an acceptable result. A machine learning method capable of
transferring a disruption prediction model trained on one tokamak to another is
required to solve the problem. The key is a disruption prediction model
containing a feature extractor that is able to extract common disruption
precursor traces in tokamak diagnostic data, and a transferable disruption
classifier. Based on the concerns above, the paper first presents a deep fusion
feature extractor designed specifically for extracting disruption precursor
features from common diagnostics on tokamaks according to currently known
precursors of disruption, providing a promising foundation for transferable
models. The fusion feature extractor is proved by comparing with manual feature
extraction on J-TEXT. Based on the feature extractor trained on J-TEXT, the
disruption prediction model was transferred to EAST data with mere 20
discharges from EAST experiment. The performance is comparable with a model
trained with 1896 discharges from EAST. From the comparison among other model
training scenarios, transfer learning showed its potential in predicting
disruptions across different tokamaks.

Structural topology optimization, which aims to find the optimal physical
structure that maximizes mechanical performance, is vital in engineering design
applications in aerospace, mechanical, and civil engineering. Generative
adversarial networks (GANs) have recently emerged as a popular alternative to
traditional iterative topology optimization methods. However, these models are
often difficult to train, have limited generalizability, and due to their goal
of mimicking optimal topologies, neglect manufacturability and performance
objectives like mechanical compliance. We propose TopoDiff, a conditional
diffusion-model-based architecture to perform performance-aware and
manufacturability-aware topology optimization that overcomes these issues. Our
model introduces a surrogate model-based guidance strategy that actively favors
structures with low compliance and good manufacturability. Our method
significantly outperforms a state-of-art conditional GAN by reducing the
average error on physical performance by a factor of eight and by producing 11
times fewer infeasible samples. By introducing diffusion models to topology
optimization, we show that conditional diffusion models have the ability to
outperform GANs in engineering design synthesis applications too. Our work also
suggests a general framework for engineering optimization problems using
diffusion models and external performance and constraint-aware guidance.

In many fields of scientific research and real-world applications, unbiased
estimation of causal effects from non-experimental data is crucial for
understanding the mechanism underlying the data and for decision-making on
effective responses or interventions. A great deal of research has been
conducted on this challenging problem from different angles. For causal effect
estimation in data, assumptions such as Markov property, faithfulness and
causal sufficiency are always made. Under the assumptions, full knowledge such
as, a set of covariates or an underlying causal graph, is still required. A
practical challenge is that in many applications, no such full knowledge or
only some partial knowledge is available. In recent years, research has emerged
to use a search strategy based on graphical causal modelling to discover useful
knowledge from data for causal effect estimation, with some mild assumptions,
and has shown promose in tackling the practical challenge. In this survey, we
review the methods and focus on the challenges the data-driven methods face. We
discuss the assumptions, strengths and limitations of the data-driven methods.
We hope this review will motivate more researchers to design better data-driven
methods based on graphical causal modelling for the challenging problem of
causal effect estimation.

The recent popularity of edge devices and Artificial Intelligent of Things
(AIoT) has driven a new wave of contextual recommendations, such as location
based Point of Interest (PoI) recommendations and computing resource-aware
mobile app recommendations. In many such recommendation scenarios, contexts are
drifting over time. For example, in a mobile game recommendation, contextual
features like locations, battery, and storage levels of mobile devices are
frequently drifting over time. However, most existing graph-based collaborative
filtering methods are designed under the assumption of static features.
Therefore, they would require frequent retraining and/or yield graphical models
burgeoning in sizes, impeding their suitability for context-drifting
  In this work, we propose a specifically tailor-made Hybrid Static and
Adaptive Graph Embedding (HySAGE) network for context-drifting recommendations.
Our key idea is to disentangle the relatively static user-item interaction and
rapidly drifting contextual features. Specifically, our proposed HySAGE network
learns a relatively static graph embedding from user-item interaction and an
adaptive embedding from drifting contextual features. These embeddings are
incorporated into an interest network to generate the user interest in some
certain context. We adopt an interactive attention module to learn the
interactions among static graph embeddings, adaptive contextual embeddings, and
user interest, helping to achieve a better final representation. Extensive
experiments on real-world datasets demonstrate that HySAGE significantly
improves the performance of the existing state-of-the-art recommendation

Sketch-and-project is a framework which unifies many known iterative methods
for solving linear systems and their variants, as well as further extensions to
non-linear optimization problems. It includes popular methods such as
randomized Kaczmarz, coordinate descent, variants of the Newton method in
convex optimization, and others. In this paper, we obtain sharp guarantees for
the convergence rate of sketch-and-project methods via new tight spectral
bounds for the expected sketched projection matrix. Our estimates reveal a
connection between the sketch-and-project convergence rate and the
approximation error of another well-known but seemingly unrelated family of
algorithms, which use sketching to accelerate popular matrix factorizations
such as QR and SVD. This connection brings us closer to precisely quantifying
how the performance of sketch-and-project solvers depends on their sketch size.
Our analysis covers not only Gaussian and sub-gaussian sketching matrices, but
also a family of efficient sparse sketching methods known as LESS embeddings.
Our experiments back up the theory and demonstrate that even extremely sparse
sketches show the same convergence properties in practice.

Sight and hearing are two senses that play a vital role in human
communication and scene understanding. To mimic human perception ability,
audio-visual learning, aimed at developing computational approaches to learn
from both audio and visual modalities, has been a flourishing field in recent
years. A comprehensive survey that can systematically organize and analyze
studies of the audio-visual field is expected. Starting from the analysis of
audio-visual cognition foundations, we introduce several key findings that have
inspired our computational studies. Then, we systematically review the recent
audio-visual learning studies and divide them into three categories:
audio-visual boosting, cross-modal perception and audio-visual collaboration.
Through our analysis, we discover that, the consistency of audio-visual data
across semantic, spatial and temporal support the above studies. To revisit the
current development of the audio-visual learning field from a more macro view,
we further propose a new perspective on audio-visual scene understanding, then
discuss and analyze the feasible future direction of the audio-visual learning
area. Overall, this survey reviews and outlooks the current audio-visual
learning field from different aspects. We hope it can provide researchers with
a better understanding of this area. A website including constantly-updated
survey is released: \url{https://gewu-lab.github.io/audio-visual-learning/}.

Despite recent progress in improving the performance of misinformation
detection systems, classifying misinformation in an unseen domain remains an
elusive challenge. To address this issue, a common approach is to introduce a
domain critic and encourage domain-invariant input features. However, early
misinformation often demonstrates both conditional and label shifts against
existing misinformation data (e.g., class imbalance in COVID-19 datasets),
rendering such methods less effective for detecting early misinformation. In
this paper, we propose contrastive adaptation network for early misinformation
detection (CANMD). Specifically, we leverage pseudo labeling to generate
high-confidence target examples for joint training with source data. We
additionally design a label correction component to estimate and correct the
label shifts (i.e., class priors) between the source and target domains.
Moreover, a contrastive adaptation loss is integrated in the objective function
to reduce the intra-class discrepancy and enlarge the inter-class discrepancy.
As such, the adapted model learns corrected class priors and an invariant
conditional distribution across both domains for improved estimation of the
target data distribution. To demonstrate the effectiveness of the proposed
CANMD, we study the case of COVID-19 early misinformation detection and perform
extensive experiments using multiple real-world datasets. The results suggest
that CANMD can effectively adapt misinformation detection systems to the unseen
COVID-19 target domain with significant improvements compared to the

Considering the prospects of public key embedding (PKE) mechanism in active
forensics on the integrity or identity of ciphertext for distributed deep
learning security, two reversible data hiding in encrypted domain (RDH-ED)
algorithms with PKE mechanism are proposed, in which all the elements of the
embedding function shall be open to the public, while the extraction function
could be performed only by legitimate users. The first algorithm is difference
expansion in single bit encrypted domain (DE-SBED), which is optimized from the
homomorphic embedding framework based on the bit operations of DE in spatial
domain. DE-SBED is suitable for the ciphertext of images encrypted from any
single bit encryption and learning with errors (LWE) encryption is selected in
this paper. Pixel value ordering is introduced to reduce the distortion of
decryption and improve the embedding rates (ER). To apply to more flexible
applications, public key recoding on encryption redundancy (PKR-ER) algorithm
is proposed. Public embedding key is constructed by recoding on the redundancy
from the probabilistic decryption of LWE. It is suitable for any plaintext
regardless of the type of medium or the content. By setting different
quantization rules for recoding, decryption and extraction functions are
separable. No distortion exists in the directly decrypted results of the marked
ciphertext and ER could reach over 1.0 bits per bit of plaintext. Correctness
and security of the algorithms are proved theoretically by deducing the
probability distributions of ciphertext and quantization variable. Experimental
results demonstrate the performances in correctness, one-way attribute of
security and efficiency of the algorithms.

A novel sparse array (SA) structure is proposed based on the maximum
inter-element spacing (IES) constraint (MISC) criterion. Compared with the
traditional MISC array, the proposed SA configurations, termed as improved MISC
(IMISC) has significantly increased uniform degrees of freedom (uDOF) and
reduced mutual coupling. In particular, the IMISC arrays are composed of six
uniform linear arrays (ULAs), which can be determined by an IES set. The IES
set is constrained by two parameters, namely the maximum IES and the number of
sensors. The uDOF of the IMISC arrays is derived and the weight function of the
IMISC arrays is analyzed as well. The proposed IMISC arrays have a great
advantage in terms of uDOF against the existing SAs, while their mutual
coupling remains at a low level. Simulations are carried out to demonstrate the
advantages of the IMISC arrays.

In reinforcement learning, different reward functions can be equivalent in
terms of the optimal policies they induce. A particularly well-known and
important example is potential shaping, a class of functions that can be added
to any reward function without changing the optimal policy set under arbitrary
transition dynamics. Potential shaping is conceptually similar to potentials,
conservative vector fields and gauge transformations in math and physics, but
this connection has not previously been formally explored. We develop a
formalism for discrete calculus on graphs that abstract a Markov Decision
Process, and show how potential shaping can be formally interpreted as a
gradient within this framework. This allows us to strengthen results from Ng et
al. (1999) describing conditions under which potential shaping is the only
additive reward transformation to always preserve optimal policies. As an
additional application of our formalism, we define a rule for picking a single
unique reward function from each potential shaping equivalence class.

For the first time, we propose using a multiple instance learning based
convolution-free transformer model, called Multiple Instance Neuroimage
Transformer (MINiT), for the classification of T1weighted (T1w) MRIs. We first
present several variants of transformer models adopted for neuroimages. These
models extract non-overlapping 3D blocks from the input volume and perform
multi-headed self-attention on a sequence of their linear projections. MINiT,
on the other hand, treats each of the non-overlapping 3D blocks of the input
MRI as its own instance, splitting it further into non-overlapping 3D patches,
on which multi-headed self-attention is computed. As a proof-of-concept, we
evaluate the efficacy of our model by training it to identify sex from T1w-MRIs
of two public datasets: Adolescent Brain Cognitive Development (ABCD) and the
National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA).
The learned attention maps highlight voxels contributing to identifying sex
differences in brain morphometry. The code is available at
https://github.com/singlaayush/MINIT.

In computer vision, multi-label classification, including zero-shot
multi-label classification are important tasks with many real-world
applications. In this paper, we propose a novel algorithm, Aligned Dual
moDality ClaSsifier (ADDS), which includes a Dual-Modal decoder (DM-decoder)
with alignment between visual and textual features, for multi-label
classification tasks. Moreover, we design a simple and yet effective method
called Pyramid-Forwarding to enhance the performance for inputs with high
resolutions. Extensive experiments conducted on standard multi-label benchmark
datasets, MS-COCO and NUS-WIDE, demonstrate that our approach significantly
outperforms previous methods and provides state-of-the-art performance for
conventional multi-label classification, zero-shot multi-label classification,
and an extreme case called single-to-multi label classification where models
trained on single-label datasets (ImageNet-1k, ImageNet-21k) are tested on
multi-label ones (MS-COCO and NUS-WIDE). We also analyze how visual-textual
alignment contributes to the proposed approach, validate the significance of
the DM-decoder, and demonstrate the effectiveness of Pyramid-Forwarding on
vision transformer.

Developing models with high interpretability and even deriving formulas to
quantify relationships between biological data is an emerging need. We propose
here a framework for ab initio derivation of sequence motifs and linear formula
using a new approach based on the interpretable neural network model called
contextual regression model. We showed that this linear model could predict
gene expression levels using promoter sequences with a performance comparable
to deep neural network models. We uncovered a list of 300 motifs with important
regulatory roles on gene expression and showed that they also had significant
contributions to cell-type specific gene expression in 154 diverse cell types.
This work illustrates the possibility of deriving formulas to represent biology
laws that may not be easily elucidated.
(https://github.com/Wang-lab-UCSD/Motif_Finding_Contextual_Regression)

Teaching programming effectively is difficult. This paper explores the
benefits of using Minecraft Education Edition to teach Python programming.
Educators can use the game to teach various programming concepts ranging from
fundamental programming concepts, object-oriented programming, event-driven
programming, and parallel programming. It has several benefits, including being
highly engaging, sharpen creativity and problem-solving skill, motivating the
study of mathematics, and making students realizes the importance of
programming.

Online autonomous agents are able to draw on a wide variety of potential
sources of task knowledge; however current approaches invariably focus on only
one or two. Here we investigate the challenges and impact of exploiting diverse
knowledge sources to learn, in one-shot, new tasks for a simulated household
mobile robot. The resulting agent, developed in the Soar cognitive
architecture, uses the following sources of domain and task knowledge:
interaction with the environment, task execution and planning knowledge, human
natural language instruction, and responses retrieved from a large language
model (GPT-3). We explore the distinct contributions of these knowledge sources
and evaluate the performance of different combinations in terms of learning
correct task knowledge, human workload, and computational costs. The results
from combining all sources demonstrate that integration improves one-shot task
learning overall in terms of computational costs and human workload.

A variety of problems in econometrics and machine learning, including
instrumental variable regression and Bellman residual minimization, can be
formulated as satisfying a set of conditional moment restrictions (CMR). We
derive a general, game-theoretic strategy for satisfying CMR that scales to
nonlinear problems, is amenable to gradient-based optimization, and is able to
account for finite sample uncertainty. We recover the approaches of Dikkala et
al. and Dai et al. as special cases of our general framework before detailing
various extensions and how to efficiently solve the game defined by CMR.

In many problems in modern statistics and machine learning, it is often of
interest to establish that a first order method on a non-convex risk function
eventually enters a region of parameter space in which the risk is locally
convex. We derive an asymptotic comparison inequality, which we call the
Sudakov-Fernique post-AMP inequality, which, in a certain class of problems
involving a GOE matrix, is able to probe properties of an optimization
landscape locally around the iterates of an approximate message passing (AMP)
algorithm. As an example of its use, we provide a new, and arguably simpler,
proof of some of the results of Celentano et al. (2021), which establishes that
the so-called TAP free energy in the $\mathbb{Z}_2$-synchronization problem is
locally convex in the region to which AMP converges. We further prove a
conjecture of El Alaoui et al. (2022) involving the local convexity of a
related but distinct TAP free energy, which, as a consequence, confirms that
their algorithm efficiently samples from the Sherrington-Kirkpatrick Gibbs
measure throughout the "easy" regime.

The knockoff filter of Barber and Candes (arXiv:1404.5609) is a flexible
framework for multiple testing in supervised learning models, based on
introducing synthetic predictor variables to control the false discovery rate
(FDR). Using the conditional calibration framework of Fithian and Lei
(arXiv:2007.10438), we introduce the calibrated knockoff procedure, a method
that uniformly improves the power of any knockoff procedure. We implement our
method for fixed-X knockoffs and show theoretically and empirically that the
improvement is especially notable in two contexts where knockoff methods can be
nearly powerless: when the rejection set is small, and when the structure of
the design matrix prevents us from constructing good knockoff variables. In
these contexts, calibrated knockoffs even outperform competing FDR-controlling
methods like the (dependence-adjusted) Benjamini-Hochberg procedure in many

In this paper, we consider the meta learning problem for estimating the
graphs associated with high-dimensional Ising models, using the method of
$\ell_1$-regularized logistic regression for neighborhood selection of each
node. Our goal is to use the information learned from the auxiliary tasks in
the learning of the novel task to reduce its sufficient sample complexity. To
this end, we propose a novel generative model as well as an improper estimation
method. In our setting, all the tasks are \emph{similar} in their \emph{random}
model parameters and supports. By pooling all the samples from the auxiliary
tasks to \emph{improperly} estimate a single parameter vector, we can recover
the true support union, assumed small in size, with a high probability with a
sufficient sample complexity of $\Omega(1) $ per task, for $K = \Omega(d^3 \log
p ) $ tasks of Ising models with $p$ nodes and a maximum neighborhood size $d$.
Then, with the support for the novel task restricted to the estimated support
union, we prove that consistent neighborhood selection for the novel task can
be obtained with a reduced sufficient sample complexity of $\Omega(d^3 \log
d)$.

Recently, there have been significant developments in neural networks; thus,
neural networks have been frequently used in the physics literature. This work
estimates the masses of exotic hadrons, doubly charmed and bottomed baryons
from the meson and baryon masses using neural networks. Subsequently, the
number of data has been increased using the artificial data augmentation
technique proposed recently. We have observed that the neural network's
predictive ability increases using augmented data. This study has shown that
data augmentation techniques play an essential role in improving neural network
predictions; moreover, neural networks can make reasonable predictions for
exotic hadrons, doubly charmed, and doubly bottomed baryons. The results are
also comparable to Gaussian Process and Constituent Quark Model.

This report covers our reproduction effort of the paper 'Differentiable
Spatial Planning using Transformers' by Chaplot et al. . In this paper, the
problem of spatial path planning in a differentiable way is considered. They
show that their proposed method of using Spatial Planning Transformers
outperforms prior data-driven models and leverages differentiable structures to
learn mapping without a ground truth map simultaneously. We verify these claims
by reproducing their experiments and testing their method on new data. We also
investigate the stability of planning accuracy with maps with increased
obstacle complexity. Efforts to investigate and verify the learnings of the
Mapper module were met with failure stemming from a paucity of computational
resources and unreachable authors.

A common way to avoid overfitting in supervised learning is early stopping,
where a held-out set is used for iterative evaluation during training to find a
sweet spot in the number of training steps that gives maximum generalization.
However, such a method requires a disjoint validation set, thus part of the
labeled data from the training set is usually left out for this purpose, which
is not ideal when training data is scarce. Furthermore, when the training
labels are noisy, the performance of the model over a validation set may not be
an accurate proxy for generalization. In this paper, we propose a method to
spot an early stopping point in the training iterations without the need for a
validation set. We first show that in the overparameterized regime the randomly
initialized weights of a linear model converge to the same direction during
training. Using this result, we propose to train two parallel instances of a
linear model, initialized with different random seeds, and use their
intersection as a signal to detect overfitting. In order to detect
intersection, we use the cosine distance between the weights of the parallel
models during training iterations. Noticing that the final layer of a NN is a
linear map of pre-last layer activations to output logits, we build on our
criterion for linear models and propose an extension to multi-layer networks,
using the new notion of counterfactual weights. We conduct experiments on two
areas that early stopping has noticeable impact on preventing overfitting of a
NN: (i) learning from noisy labels; and (ii) learning to rank in IR. Our
experiments on four widely used datasets confirm the effectiveness of our
method for generalization. For a wide range of learning rates, our method,
called Cosine-Distance Criterion (CDC), leads to better generalization on
average than all the methods that we compare against in almost all of the
tested cases.

The highly transmissible COVID-19 disease is a serious threat to people's
health and life. To automate tracing those who have been in close physical
contact with newly infected people and/or to analyse tracing-related data,
researchers have proposed various ad-hoc programs that require being executed
on users' smartphones. Nevertheless, the existing solutions have two primary
limitations: (1) lack of generality: for each type of analytic task, a certain
kind of data needs to be sent to an analyst; (2) lack of transparency: parties
who provide data to an analyst are not necessarily infected individuals;
therefore, infected individuals' data can be shared with others (e.g., the
analyst) without their fine-grained and direct consent. In this work, we
present Glass-Vault, a protocol that addresses both limitations simultaneously.
It allows an analyst to run authorised programs over the collected data of
infectious users, without learning the input data. Glass-Vault relies on a new
variant of generic Functional Encryption that we propose in this work. This new
variant, called DD-Steel, offers these two additional properties: dynamic and
decentralised. We illustrate the security of both Glass-Vault and DD-Steel in
the Universal Composability setting. Glass-Vault is the first UC-secure
protocol that allows analysing the data of Exposure Notification users in a
privacy-preserving manner. As a sample application, we indicate how it can be
used to generate "infection heatmaps".

Conventional anti-jamming methods mainly focus on preventing single jammer
attacks with an invariant jamming policy or jamming attacks from multiple
jammers with similar jamming policies. These anti-jamming methods are
ineffective against a single jammer following several different jamming
policies or multiple jammers with distinct policies. Therefore, this paper
proposes an anti-jamming method that can adapt its policy to the current
jamming attack. Moreover, for the multiple jammers scenario, an anti-jamming
method that estimates the future occupied channels using the jammers' occupied
channels in previous time slots is proposed. In both single and multiple
jammers scenarios, the interaction between the users and jammers is modeled
using recurrent neural networks (RNN)s. The performance of the proposed
anti-jamming methods is evaluated by calculating the users' successful
transmission rate (STR) and ergodic rate (ER), and compared to a baseline based
on Q-learning (DQL). Simulation results show that for the single jammer
scenario, all the considered jamming policies are perfectly detected and high
STR and ER are maintained. Moreover, when 70 % of the spectrum is under jamming
attacks from multiple jammers, the proposed method achieves an STR and ER
greater than 75 % and 80 %, respectively. These values rise to 90 % when 30 %
of the spectrum is under jamming attacks. In addition, the proposed
anti-jamming methods significantly outperform the DQL method for all the
considered cases and jamming scenarios.

Popularity bias is the idea that a recommender system will unduly favor
popular artists when recommending artists to users. As such, they may
contribute to a winner-take-all marketplace in which a small number of artists
receive nearly all of the attention, while similarly meritorious artists are
unlikely to be discovered. In this paper, we attempt to measure popularity bias
in three state-of-art recommender system models (e.g., SLIM, Multi-VAE, WRMF)
and on three commercial music streaming services (Spotify, Amazon Music,
YouTube). We find that the most accurate model (SLIM) also has the most
popularity bias while less accurate models have less popularity bias. We also
find no evidence of popularity bias in the commercial recommendations based on
a simulated user experiment.

Representation learning often plays a critical role in reinforcement learning
by managing the curse of dimensionality. A representative class of algorithms
exploits a spectral decomposition of the stochastic transition dynamics to
construct representations that enjoy strong theoretical properties in an
idealized setting. However, current spectral methods suffer from limited
applicability because they are constructed for state-only aggregation and
derived from a policy-dependent transition kernel, without considering the
issue of exploration. To address these issues, we propose an alternative
spectral method, Spectral Decomposition Representation (SPEDER), that extracts
a state-action abstraction from the dynamics without inducing spurious
dependence on the data collection policy, while also balancing the
exploration-versus-exploitation trade-off during learning. A theoretical
analysis establishes the sample efficiency of the proposed algorithm in both
the online and offline settings. In addition, an experimental investigation
demonstrates superior performance over current state-of-the-art algorithms
across several benchmarks.

The Solar Dynamics Observatory (SDO), a NASA multi-spectral decade-long
mission that has been daily producing terabytes of observational data from the
Sun, has been recently used as a use-case to demonstrate the potential of
machine learning methodologies and to pave the way for future deep-space
mission planning. In particular, the idea of using image-to-image translation
to virtually produce extreme ultra-violet channels has been proposed in several
recent studies, as a way to both enhance missions with less available channels
and to alleviate the challenges due to the low downlink rate in deep space.
This paper investigates the potential and the limitations of such a deep
learning approach by focusing on the permutation of four channels and an
encoder--decoder based architecture, with particular attention to how
morphological traits and brightness of the solar surface affect the neural
network predictions. In this work we want to answer the question: can synthetic
images of the solar corona produced via image-to-image translation be used for
scientific studies of the Sun? The analysis highlights that the neural network
produces high-quality images over three orders of magnitude in count rate
(pixel intensity) and can generally reproduce the covariance across channels
within a 1% error. However the model performance drastically diminishes in
correspondence of extremely high energetic events like flares, and we argue
that the reason is related to the rareness of such events posing a challenge to
model training.

Systems capable of analyzing and quantifying human physical or behavioral
traits, known as biometrics systems, are growing in use and application
variability. Since its evolution from handcrafted features and traditional
machine learning to deep learning and automatic feature extraction, the
performance of biometric systems increased to outstanding values. Nonetheless,
the cost of this fast progression is still not understood. Due to its opacity,
deep neural networks are difficult to understand and analyze, hence, hidden
capacities or decisions motivated by the wrong motives are a potential risk.
Researchers have started to pivot their focus towards the understanding of deep
neural networks and the explanation of their predictions. In this paper, we
provide a review of the current state of explainable biometrics based on the
study of 47 papers and discuss comprehensively the direction in which this
field should be developed.

Machine learning on source code (MLOnCode) promises to transform how software
is delivered. By mining the context and relationship between software
artefacts, MLOnCode augments the software developers capabilities with code
auto-generation, code recommendation, code auto-tagging and other data-driven
enhancements. For many of these tasks a script level representation of code is
sufficient, however, in many cases a repository level representation that takes
into account various dependencies and repository structure is imperative, for
example, auto-tagging repositories with topics or auto-documentation of
repository code etc. Existing methods for computing repository level
representations suffer from (a) reliance on natural language documentation of
code (for example, README files) (b) naive aggregation of method/script-level
representation, for example, by concatenation or averaging. This paper
introduces Topical a deep neural network to generate repository level
embeddings of publicly available GitHub code repositories directly from source
code. Topical incorporates an attention mechanism that projects the source
code, the full dependency graph and the script level textual information into a
dense repository-level representation. To compute the repository-level
representations, Topical is trained to predict the topics associated with a
repository, on a dataset of publicly available GitHub repositories that were
crawled along with their ground truth topic tags. Our experiments show that the
embeddings computed by Topical are able to outperform multiple baselines,
including baselines that naively combine the method-level representations
through averaging or concatenation at the task of repository auto-tagging.

Given independent standard Gaussian points $v_1, \ldots, v_n$ in dimension
$d$, for what values of $(n, d)$ does there exist with high probability an
origin-symmetric ellipsoid that simultaneously passes through all of the
points? This basic problem of fitting an ellipsoid to random points has
connections to low-rank matrix decompositions, independent component analysis,
and principal component analysis. Based on strong numerical evidence,
Saunderson, Parrilo, and Willsky [Proc. of Conference on Decision and Control,
pp. 6031-6036, 2013] conjecture that the ellipsoid fitting problem transitions
from feasible to infeasible as the number of points $n$ increases, with a sharp
threshold at $n \sim d^2/4$. We resolve this conjecture up to logarithmic
factors by constructing a fitting ellipsoid for some $n = \Omega( \,
d^2/\log^5(d) \,)$, improving prior work of Ghosh et al. [Proc. of Symposium on
Foundations of Computer Science, pp. 954-965, 2020] that requires $n =
o(d^{3/2})$. Our proof demonstrates feasibility of the least squares
construction of Saunderson et al. using a careful analysis of the eigenvectors
and eigenvalues of a certain non-standard random matrix.

Federated learning is a machine learning method in which data is not
aggregated on a server, but is distributed to the edges, in consideration of
security and privacy. ResNet is a classic but representative neural network
that succeeds in deepening the neural network by learning a residual function
that adds the inputs and outputs together. In federated learning, communication
is performed between the server and edge devices to exchange weight parameters,
but ResNet has deep layers and a large number of parameters, so communication
size becomes large. In this paper, we use Neural ODE as a lightweight model of
ResNet to reduce communication size in federated learning. In addition, we
newly introduce a flexible federated learning using Neural ODE models with
different number of iterations, which correspond to ResNet with different
depths. The CIFAR-10 dataset is used in the evaluation, and the use of Neural
ODE reduces communication size by approximately 90% compared to ResNet. We also
show that the proposed flexible federated learning can merge models with
different iteration counts.

Methods of eXplainable Artificial Intelligence (XAI) are used in
geoscientific applications to gain insights into the decision-making strategy
of Neural Networks (NNs) highlighting which features in the input contribute
the most to a NN prediction. Here, we discuss our lesson learned that the task
of attributing a prediction to the input does not have a single solution.
Instead, the attribution results and their interpretation depend greatly on the
considered baseline (sometimes referred to as reference point) that the XAI
method utilizes; a fact that has been overlooked so far in the literature. This
baseline can be chosen by the user or it is set by construction in the method s
algorithm, often without the user being aware of that choice. We highlight that
different baselines can lead to different insights for different science
questions and, thus, should be chosen accordingly. To illustrate the impact of
the baseline, we use a large ensemble of historical and future climate
simulations forced with the SSP3-7.0 scenario and train a fully connected NN to
predict the ensemble- and global-mean temperature (i.e., the forced global
warming signal) given an annual temperature map from an individual ensemble
member. We then use various XAI methods and different baselines to attribute
the network predictions to the input. We show that attributions differ
substantially when considering different baselines, as they correspond to
answering different science questions. We conclude by discussing some important
implications and considerations about the use of baselines in XAI research.

Grammatical Error Correction (GEC) systems perform a sequence-to-sequence
task, where an input word sequence containing grammatical errors, is corrected
for these errors by the GEC system to output a grammatically correct word
sequence. With the advent of deep learning methods, automated GEC systems have
become increasingly popular. For example, GEC systems are often used on speech
transcriptions of English learners as a form of assessment and feedback - these
powerful GEC systems can be used to automatically measure an aspect of a
candidate's fluency. The count of \textit{edits} from a candidate's input
sentence (or essay) to a GEC system's grammatically corrected output sentence
is indicative of a candidate's language ability, where fewer edits suggest
better fluency. The count of edits can thus be viewed as a \textit{fluency
score} with zero implying perfect fluency. However, although deep learning
based GEC systems are extremely powerful and accurate, they are susceptible to
adversarial attacks: an adversary can introduce a small, specific change at the
input of a system that causes a large, undesired change at the output. When
considering the application of GEC systems to automated language assessment,
the aim of an adversary could be to cheat by making a small change to a
grammatically incorrect input sentence that conceals the errors from a GEC
system, such that no edits are found and the candidate is unjustly awarded a
perfect fluency score. This work examines a simple universal substitution
adversarial attack that non-native speakers of English could realistically
employ to deceive GEC systems used for assessment.

State-of-the-art machine-learning based models are a popular choice for
modelling and forecasting energy behaviour in buildings because given enough
data, they are good at finding spatiotemporal patterns and structures even in
scenarios where the complexity prohibits analytical descriptions. However,
machine-learning based models for building energy forecasting have difficulty
generalizing to out-of-sample scenarios that are not represented in the data
because their architecture typically does not hold physical correspondence to
mechanistic structures linked with governing phenomena of energy transfer.
Thus, their ability to forecast for unseen initial conditions and boundary
conditions wholly depends on the representativeness in the data, which is not
guaranteed in building measurement data. Consequently, these limitations impede
their application to real-world engineering applications such as energy
management in Digital Twins. In response, we present a Domain Adaptation
framework that aims to leverage well-known understanding of phenomenon
governing energy behavior in buildings to forecast for out of sample scenarios
beyond building measurement data. More specifically, we represent mechanistic
knowledge of energy behavior using low-rank linear time-invariant state space
models and subsequently leverage their governing structure to forecast for a
target energy system for which only building measurement data is available. We
achieve this by aligning the Physics-derived subspace that governs global state
space behavior closer towards the target subspace derived from the measurement
data. In this initial exploration we focus on linear energy systems; we test
the subspace-based DA framework on a 1D heat conduction scenario by varying the
thermophysical properties of the source and target systems to demonstrate the
transferability of mechanistic models from Physics to measurement data.

This paper addresses policy learning in non-stationary environments and games
with continuous actions. Rather than the classical reward maximization
mechanism, inspired by the ideas of follow-the-regularized-leader (FTRL) and
mirror descent (MD) update, we propose a no-regret style reinforcement learning
algorithm PORL for continuous action tasks. We prove that PORL has a
last-iterate convergence guarantee, which is important for adversarial and
cooperative games. Empirical studies show that, in stationary environments such
as MuJoCo locomotion controlling tasks, PORL performs equally well as, if not
better than, the soft actor-critic (SAC) algorithm; in non-stationary
environments including dynamical environments, adversarial training, and
competitive games, PORL is superior to SAC in both a better final policy
performance and a more stable training process.

In this work, we propose a robust framework that employs adversarially robust
training to safeguard the machine learning models against perturbed testing
data. We achieve this by incorporating the worst-case additive adversarial
error within a fixed budget for each sample during model estimation. Our main
focus is to provide a plug-and-play solution that can be incorporated in the
existing machine learning algorithms with minimal changes. To that end, we
derive the closed-form ready-to-use solution for several widely used loss
functions with a variety of norm constraints on adversarial perturbation.
Finally, we validate our approach by showing significant performance
improvement on real-world datasets for supervised problems such as regression
and classification, as well as for unsupervised problems such as matrix
completion and learning graphical models, with very little computational
overhead.

Monocular 3D object detection is a fundamental but very important task to
many applications including autonomous driving, robotic grasping and augmented
reality. Existing leading methods tend to estimate the depth of the input image
first, and detect the 3D object based on point cloud. This routine suffers from
the inherent gap between depth estimation and object detection. Besides, the
prediction error accumulation would also affect the performance. In this paper,
a novel method named MonoPCNS is proposed. The insight behind introducing
MonoPCNS is that we propose to simulate the feature learning behavior of a
point cloud based detector for monocular detector during the training period.
Hence, during inference period, the learned features and prediction would be
similar to the point cloud based detector as possible. To achieve it, we
propose one scene-level simulation module, one RoI-level simulation module and
one response-level simulation module, which are progressively used for the
detector's full feature learning and prediction pipeline. We apply our method
to the famous M3D-RPN detector and CaDDN detector, conducting extensive
experiments on KITTI and Waymo Open dataset. Results show that our method
consistently improves the performance of different monocular detectors for a
large margin without changing their network architectures. Our method finally

Event logs are widely used for anomaly detection and prediction in complex
systems. Existing log-based anomaly detection methods usually consist of four
main steps: log collection, log parsing, feature extraction, and anomaly
detection, wherein the feature extraction step extracts useful features for
anomaly detection by counting log events. For a complex system, such as a
lithography machine consisting of a large number of subsystems, its log may
contain thousands of different events, resulting in abounding extracted
features. However, when anomaly detection is performed at the subsystem level,
analyzing all features becomes expensive and unnecessary. To mitigate this
problem, we develop a feature selection method for log-based anomaly detection
and prediction, largely improving the effectiveness and efficiency.

Intent detection is a key part of any Natural Language Understanding (NLU)
system of a conversational assistant. Detecting the correct intent is essential
yet difficult for email conversations where multiple directives and intents are
present. In such settings, conversation context can become a key disambiguating
factor for detecting the user's request from the assistant. One prominent way
of incorporating context is modeling past conversation history like
task-oriented dialogue models. However, the nature of email conversations (long
form) restricts direct usage of the latest advances in task-oriented dialogue
models. So in this paper, we provide an effective transfer learning framework
(EMToD) that allows the latest development in dialogue models to be adapted for
long-form conversations. We show that the proposed EMToD framework improves
intent detection performance over pre-trained language models by 45% and over
pre-trained dialogue models by 30% for task-oriented email conversations.
Additionally, the modular nature of the proposed framework allows plug-and-play
for any future developments in both pre-trained language and task-oriented
dialogue models.

Semantic textual similarity (STS) in the clinical domain helps improve
diagnostic efficiency and produce concise texts for downstream data mining
tasks. However, given the high degree of domain knowledge involved in clinic
text, it remains challenging for general language models to infer implicit
medical relationships behind clinical sentences and output similarities
correctly. In this paper, we present a graph-augmented cyclic learning
framework for similarity estimation in the clinical domain. The framework can
be conveniently implemented on a state-of-art backbone language model, and
improve its performance by leveraging domain knowledge through co-training with
an auxiliary graph convolution network (GCN) based network. We report the
success of introducing domain knowledge in GCN and the co-training framework by
improving the Bio-clinical BERT baseline by 16.3% and 27.9%, respectively.

Estimating a Gibbs density function given a sample is an important problem in
computational statistics and statistical learning. Although the well
established maximum likelihood method is commonly used, it requires the
computation of the partition function (i.e., the normalization of the density).
  This function can be easily calculated for simple low-dimensional problems
but its computation is difficult or even intractable for general densities and
high-dimensional problems. In this paper we propose an alternative approach
based on Maximum A-Posteriori (MAP) estimators, we name Maximum Recovery MAP
(MR-MAP), to derive estimators that do not require the computation of the
partition function, and reformulate the problem as an optimization problem. We
further propose a least-action type potential that allows us to quickly solve
the optimization problem as a feed-forward hyperbolic neural network. We
demonstrate the effectiveness of our methods on some standard data sets.

Federated learning (FL) is a framework for machine learning across
heterogeneous client devices in a privacy-preserving fashion. To date, most FL
algorithms learn a "global" server model across multiple rounds. At each round,
the same server model is broadcast to all participating clients, updated
locally, and then aggregated across clients. In this work, we propose a more
general procedure in which clients "select" what values are sent to them.
Notably, this allows clients to operate on smaller, data-dependent slices. In
order to make this practical, we outline a primitive, federated select, which
enables client-specific selection in realistic FL systems. We discuss how to
use federated select for model training and show that it can lead to drastic
reductions in communication and client memory usage, potentially enabling the
training of models too large to fit on-device. We also discuss the implications
of federated select on privacy and trust, which in turn affect possible system
constraints and design. Finally, we discuss open questions concerning model
architectures, privacy-preserving technologies, and practical FL systems.

Multi-task learning of dense prediction tasks, by sharing both the encoder
and decoder, as opposed to sharing only the encoder, provides an attractive
front to increase both accuracy and computational efficiency. When the tasks
are similar, sharing the decoder serves as an additional inductive bias
providing more room for tasks to share complementary information among
themselves. However, increased sharing exposes more parameters to task
interference which likely hinders both generalization and robustness. Effective
ways to curb this interference while exploiting the inductive bias of sharing
the decoder remains an open challenge. To address this challenge, we propose
Progressive Decoder Fusion (PDF) to progressively combine task decoders based
on inter-task representation similarity. We show that this procedure leads to a
multi-task network with better generalization to in-distribution and
out-of-distribution data and improved robustness to adversarial attacks.
Additionally, we observe that the predictions of different tasks of this
multi-task network are more consistent with each other.

Recently action recognition has received more and more attention for its
comprehensive and practical applications in intelligent surveillance and
human-computer interaction. However, few-shot action recognition has not been
well explored and remains challenging because of data scarcity. In this paper,
we propose a novel hierarchical compositional representations (HCR) learning
approach for few-shot action recognition. Specifically, we divide a complicated
action into several sub-actions by carefully designed hierarchical clustering
and further decompose the sub-actions into more fine-grained spatially
attentional sub-actions (SAS-actions). Although there exist large differences
between base classes and novel classes, they can share similar patterns in
sub-actions or SAS-actions. Furthermore, we adopt the Earth Mover's Distance in
the transportation problem to measure the similarity between video samples in
terms of sub-action representations. It computes the optimal matching flows
between sub-actions as distance metric, which is favorable for comparing
fine-grained patterns. Extensive experiments show our method achieves the
state-of-the-art results on HMDB51, UCF101 and Kinetics datasets.

Tactics in StarCraft II are closely related to group behavior of the game
agents. In other words, human players in the game often group spatially near
agents into a team and control the team to defeat opponents. In this light,
clustering the agents in StarCraft II has been studied for various purposes
such as the efficient control of the agents in multi-agent reinforcement
learning and game analytic tools for the game users. However, these works do
not aim to learn and predict dynamics of the clusters, limiting the
applications to currently observed game status. In this paper, we present a
hybrid AI model that couples unsupervised and self-supervised learning to
forecast evolution of the clusters in StarCraft II. We develop an unsupervised
Hebbian learning method in a set-to-cluster module to efficiently create a
variable number of the clusters, and it also features lower inference time
complexity than conventional k-means clustering. For the prediction task, a
long short-term memory based prediction module is designed to recursively
forecast state vectors generated by the set-to-cluster module. We observe the
proposed model successfully predicts complex evolution of the clusters with
regard to cluster centroids and their radii.

Interpretability of Deep Learning (DL) models is arguably the barrier in
front of trustworthy AI. Despite great efforts made by the Explainable AI (XAI)
community, explanations lack robustness--indistinguishable input perturbations
may lead to different XAI results. Thus, it is vital to assess how robust DL
interpretability is, given an XAI technique. To this end, we identify the
following challenges that state-of-the-art is unable to cope with collectively:
i) XAI techniques are highly heterogeneous; ii) misinterpretations are normally
rare events; iii) both worst-case and overall robustness are of practical
interest. In this paper, we propose two evaluation methods to tackle them--i)
they are of black-box nature, based on Genetic Algorithm (GA) and Subset
Simulation (SS); ii) bespoke fitness functions are used by GA to solve a
constrained optimisation efficiently, while SS is dedicated to estimating rare
event probabilities; iii) two diverse metrics are introduced, concerning the
worst-case interpretation discrepancy and a probabilistic notion of
\textit{how} robust in general, respectively. We conduct experiments to study
the accuracy, sensitivity and efficiency of our methods that outperform
state-of-the-arts. Finally, we show two applications of our methods for ranking
robust XAI methods and selecting training schemes to improve both
classification and interpretation robustness.

Multi-modal aspect-based sentiment classification (MABSC) is an emerging
classification task that aims to classify the sentiment of a given target such
as a mentioned entity in data with different modalities. In typical multi-modal
data with text and image, previous approaches do not make full use of the
fine-grained semantics of the image, especially in conjunction with the
semantics of the text and do not fully consider modeling the relationship
between fine-grained image information and target, which leads to insufficient
use of image and inadequate to identify fine-grained aspects and opinions. To
tackle these limitations, we propose a new framework SeqCSG including a method
to construct sequential cross-modal semantic graphs and an encoder-decoder
model. Specifically, we extract fine-grained information from the original
image, image caption, and scene graph, and regard them as elements of the
cross-modal semantic graph as well as tokens from texts. The cross-modal
semantic graph is represented as a sequence with a multi-modal visible matrix
indicating relationships between elements. In order to effectively utilize the
cross-modal semantic graph, we propose an encoder-decoder method with a target
prompt template. Experimental results show that our approach outperforms
existing methods and achieves the state-of-the-art on two standard datasets
MABSC. Further analysis demonstrates the effectiveness of each component and
our model can implicitly learn the correlation between the target and
fine-grained information of the image.

We study Stackelberg games where a principal repeatedly interacts with a
long-lived, non-myopic agent, without knowing the agent's payoff function.
Although learning in Stackelberg games is well-understood when the agent is
myopic, non-myopic agents pose additional complications. In particular,
non-myopic agents may strategically select actions that are inferior in the
present to mislead the principal's learning algorithm and obtain better
outcomes in the future.
  We provide a general framework that reduces learning in presence of
non-myopic agents to robust bandit optimization in the presence of myopic
agents. Through the design and analysis of minimally reactive bandit
algorithms, our reduction trades off the statistical efficiency of the
principal's learning algorithm against its effectiveness in inducing
near-best-responses. We apply this framework to Stackelberg security games
(SSGs), pricing with unknown demand curve, strategic classification, and
general finite Stackelberg games. In each setting, we characterize the type and
impact of misspecifications present in near-best-responses and develop a
learning algorithm robust to such misspecifications.
  Along the way, we improve the query complexity of learning in SSGs with $n$
targets from the state-of-the-art $O(n^3)$ to a near-optimal $\widetilde{O}(n)$
by uncovering a fundamental structural property of such games. This result is
of independent interest beyond learning with non-myopic agents.

We present CycleDance, a dance style transfer system to transform an existing
motion clip in one dance style to a motion clip in another dance style while
attempting to preserve motion context of the dance. Our method extends an
existing CycleGAN architecture for modeling audio sequences and integrates
multimodal transformer encoders to account for music context. We adopt sequence
length-based curriculum learning to stabilize training. Our approach captures
rich and long-term intra-relations between motion frames, which is a common
challenge in motion transfer and synthesis work. We further introduce new
metrics for gauging transfer strength and content preservation in the context
of dance movements. We perform an extensive ablation study as well as a human
study including 30 participants with 5 or more years of dance experience. The
results demonstrate that CycleDance generates realistic movements with the
target style, significantly outperforming the baseline CycleGAN on naturalness,
transfer strength, and content preservation.

The imputation of missing values represents a significant obstacle for many
real-world data analysis pipelines. Here, we focus on time series data and put
forward SSSD, an imputation model that relies on two emerging technologies,
(conditional) diffusion models as state-of-the-art generative models and
structured state space models as internal model architecture, which are
particularly suited to capture long-term dependencies in time series data. We
demonstrate that SSSD matches or even exceeds state-of-the-art probabilistic
imputation and forecasting performance on a broad range of data sets and
different missingness scenarios, including the challenging blackout-missing
scenarios, where prior approaches failed to provide meaningful results.

Modern machine learning models use an ever-increasing number of parameters to
train (175 billion parameters for GPT-3) with large datasets to obtain better
performance. Bigger is better has been the norm. Optical computing has been
reawakened as a potential solution to large-scale computing through optical
accelerators that carry out linear operations while reducing electrical power.
However, to achieve efficient computing with light, creating and controlling
nonlinearity optically rather than electronically remains a challenge. This
study explores a reservoir computing (RC) approach whereby a 14 mm long
few-mode waveguide in LiNbO3 on insulator is used as a complex nonlinear
optical processor. A dataset is encoded digitally on the spectrum of a
femtosecond pulse which is then launched in the waveguide. The output spectrum
depends nonlinearly on the input. We experimentally show that a simple digital
linear classifier with 784 parameters using the output spectrum from the
waveguide as input increased the classification accuracy of several databases
compared to non-transformed data, approximately 10$\%$. In comparison, a deep
digital neural network (NN) with 40000 parameters was necessary to achieve the
same accuracy. Reducing the number of parameters by a factor of $\sim$50
illustrates that a compact optical RC approach can perform on par with a deep
digital NN.

Standard diffusion models involve an image transform -- adding Gaussian noise
-- and an image restoration operator that inverts this degradation. We observe
that the generative behavior of diffusion models is not strongly dependent on
the choice of image degradation, and in fact an entire family of generative
models can be constructed by varying this choice. Even when using completely
deterministic degradations (e.g., blur, masking, and more), the training and
test-time update rules that underlie diffusion models can be easily generalized
to create generative models. The success of these fully deterministic models
calls into question the community's understanding of diffusion models, which
relies on noise in either gradient Langevin dynamics or variational inference,
and paves the way for generalized diffusion models that invert arbitrary
processes. Our code is available at
https://github.com/arpitbansal297/Cold-Diffusion-Models

Accurate protein-ligand binding affinity prediction is essential in drug
design and many other molecular recognition problems. Despite many advances in
affinity prediction based on machine learning techniques, they are still
limited since the protein-ligand binding is determined by the dynamics of atoms
and molecules. To this end, we curated an MD dataset containing 3,218 dynamic
protein-ligand complexes and further developed Dynaformer, a graph-based deep
learning framework. Dynaformer can fully capture the dynamic binding modes by
considering various geometric characteristics of the interaction. Our method
shows superior performance on the benchmark dataset over the methods hitherto
reported. Moreover, we performed virtual drug-screening on heat shock protein
90 (HSP90) by integrating our model with structure-based docking. We
benchmarked our performance against the conventional knowledge-based approach,
demonstrating that our method can identify two lead compounds with
submicromolar activity (0.207\textmu{}M and 0.517\textmu{}M). We anticipate
that large-scale MD dataset and machine learning models will form a new
synergy, providing a new route toward accelerated drug discovery.

Federated Learning (FL) is a distributed machine learning paradigm that
enables learning models from decentralized private datasets, where the labeling
effort is entrusted to the clients. While most existing FL approaches assume
high-quality labels are readily available on users' devices; in reality, label
noise can naturally occur in FL and follows a non-i.i.d. distribution among
clients. Due to the non-iid-ness challenges, existing state-of-the-art
centralized approaches exhibit unsatisfactory performance, while previous FL
studies rely on data exchange or repeated server-side aid to improve model's
performance. Here, we propose FedLN, a framework to deal with label noise
across different FL training stages; namely, FL initialization, on-device model
training, and server model aggregation. Specifically, FedLN computes per-client
noise-level estimation in a single federated round and improves the models'
performance by correcting (or limiting the effect of) noisy samples. Extensive
experiments on various publicly available vision and audio datasets demonstrate
a 24% improvement on average compared to other existing methods for a label
noise level of 70%. We further validate the efficiency of FedLN in
human-annotated real-world noisy datasets and report a 9% increase on average
in models' recognition rate, highlighting that FedLN can be useful for
improving FL services provided to everyday users.

Ultracool dwarfs (UCDs) comprise the lowest mass members of the stellar
population and brown dwarfs, from M7 V to cooler objects with L, T, and Y
spectral types. Most of them have been discovered using wide-field imaging
surveys, for which the Virtual Observatory (VO) has proven to be of great
utility. We aim to perform a search for UCDs in the entire Javalambre
Photometric Local Universe Survey (J-PLUS) second data release (2176 deg$^2$)
following a VO methodology. We also explore the ability to reproduce this
search with a purely machine learning (ML)-based methodology that relies solely
on J-PLUS photometry. We followed three different approaches based on
parallaxes, proper motions, and colours, respectively, using the VOSA tool to
estimate the effective temperatures. For the ML methodology, we built a
two-step method based on principal component analysis and support vector
machine algorithms. We identified a total of 7827 new candidate UCDs, which
represents an increase of about 135% in the number of UCDs reported in the sky
coverage of the J-PLUS second data release. Among the candidate UCDs, we found
122 possible unresolved binary systems, 78 wide multiple systems, and 48
objects with a high Bayesian probability of belonging to a young association.
We also identified four objects with strong excess in the filter corresponding
to the Ca II H and K emission lines and four other objects with excess emission
in the H$\alpha$ filter. With the ML approach, we obtained a recall score of
92% and 91% in the test and blind test, respectively. We consolidated the
proposed search methodology for UCDs, which will be used in deeper and larger
upcoming surveys such as J-PAS and Euclid. We concluded that the ML methodology
is more efficient in the sense that it allows for a larger number of true
negatives to be discarded prior to analysis with VOSA, although it is more
photometrically restrictive.

Federated recommendation applies federated learning techniques in
recommendation systems to help protect user privacy by exchanging models
instead of raw user data between user devices and the central server. Due to
the heterogeneity in user's attributes and local data, attaining personalized
models is critical to help improve the federated recommendation performance. In
this paper, we propose a Graph Neural Network based Personalized Federated
Recommendation (PerFedRec) framework via joint representation learning, user
clustering, and model adaptation. Specifically, we construct a collaborative
graph and incorporate attribute information to jointly learn the representation
through a federated GNN. Based on these learned representations, we cluster
users into different user groups and learn personalized models for each
cluster. Then each user learns a personalized model by combining the global
federated model, the cluster-level federated model, and the user's fine-tuned
local model. To alleviate the heavy communication burden, we intelligently
select a few representative users (instead of randomly picked users) from each
cluster to participate in training. Experiments on real-world datasets show
that our proposed method achieves superior performance over existing methods.

Image and language modeling is of crucial importance for vision-language
pre-training (VLP), which aims to learn multi-modal representations from
large-scale paired image-text data. However, we observe that most existing VLP
methods focus on modeling the interactions between image and text features
while neglecting the information disparity between image and text, thus
suffering from focal bias. To address this problem, we propose a
vision-language masked autoencoder framework (VLMAE). VLMAE employs visual
generative learning, facilitating the model to acquire fine-grained and
unbiased features. Unlike the previous works, VLMAE pays attention to almost
all critical patches in an image, providing more comprehensive understanding.
Extensive experiments demonstrate that VLMAE achieves better performance in
various vision-language downstream tasks, including visual question answering,
image-text retrieval and visual grounding, even with up to 20% pre-training
speedup.

This paper presents a novel non-stationary dynamic pricing algorithm design,
where pricing agents face incomplete demand information and market environment
shifts. The agents run price experiments to learn about each product's demand
curve and the profit-maximizing price, while being aware of market environment
shifts to avoid high opportunity costs from offering sub-optimal prices. The
proposed ACIDP extends information-directed sampling (IDS) algorithms from
statistical machine learning to include microeconomic choice theory, with a
novel pricing strategy auditing procedure to escape sub-optimal pricing after
market environment shift. The proposed ACIDP outperforms competing bandit
algorithms including Upper Confidence Bound (UCB) and Thompson sampling (TS) in
a series of market environment shifts.

Simulating multi-scale phenomena such as turbulent fluid flows is typically
computationally very expensive. Filtering the smaller scales allows for using
coarse discretizations, however, this requires closure models to account for
the effects of the unresolved on the resolved scales. The common approach is to
filter the continuous equations, but this gives rise to several commutator
errors due to nonlinear terms, non-uniform filters, or boundary conditions.
  We propose a new approach to filtering, where the equations are discretized
first and then filtered. For a non-uniform filter applied to the linear
convection equation, we show that the discretely filtered convection operator
can be inferred using three methods: intrusive (`explicit reconstruction') or
non-intrusive operator inference, either via `derivative fitting' or
`trajectory fitting' (embedded learning). We show that explicit reconstruction
and derivative fitting identify a similar operator and produce small errors,
but that trajectory fitting requires significant effort to train to achieve
similar performance. However, the explicit reconstruction approach is more
prone to instabilities.

Background and Objective: Existing deep learning platforms for medical image
segmentation mainly focus on fully supervised segmentation that assumes full
and accurate pixel-level annotations are available. We aim to develop a new
deep learning toolkit to support annotation-efficient learning for medical
image segmentation, which can accelerate and simply the development of deep
learning models with limited annotation budget, e.g., learning from partial,
sparse or noisy annotations.
  Methods: Our proposed toolkit named PyMIC is a modular deep learning platform
for medical image segmentation tasks. In addition to basic components that
support development of high-performance models for fully supervised
segmentation, it contains several advanced components that are tailored for
learning from imperfect annotations, such as loading annotated and unannounced
images, loss functions for unannotated, partially or inaccurately annotated
images, and training procedures for co-learning between multiple networks, etc.
PyMIC is built on the PyTorch framework and supports development of
semi-supervised, weakly supervised and noise-robust learning methods for
medical image segmentation.
  Results: We present four illustrative medical image segmentation tasks based
on PyMIC: (1) Achieving competitive performance on fully supervised learning;
(2) Semi-supervised cardiac structure segmentation with only 10% training
images annotated; (3) Weakly supervised segmentation using scribble
annotations; and (4) Learning from noisy labels for chest radiograph
  Conclusions: The PyMIC toolkit is easy to use and facilitates efficient
development of medical image segmentation models with imperfect annotations. It
is modular and flexible, which enables researchers to develop high-performance
models with low annotation cost. The source code is available at:
https://github.com/HiLab-git/PyMIC.

Prototypical network (PN) is a simple yet effective few shot learning
strategy. It is a metric-based meta-learning technique where classification is
performed by computing Euclidean distances to prototypical representations of
each class. Conventional PN attributes equal importance to all samples and
generates prototypes by simply averaging the support sample embeddings
belonging to each class. In this work, we propose a novel version of PN that
attributes weights to support samples corresponding to their influence on the
support sample distribution. Influence weights of samples are calculated based
on maximum mean discrepancy (MMD) between the mean embeddings of sample
distributions including and excluding the sample. Further, the influence factor
of a sample is measured using MMD based on the shift in the distribution in the
absence of that sample.

While many graph drawing algorithms consider nodes as points, graph
visualization tools often represent them as shapes. These shapes support the
display of information such as labels or encode various data with size or
color. However, they can create overlaps between nodes which hinder the
exploration process by hiding parts of the information. It is therefore of
utmost importance to remove these overlaps to improve graph visualization
readability. If not handled by the layout process, Overlap Removal (OR)
algorithms have been proposed as layout post-processing. As graph layouts
usually convey information about their topology, it is important that OR
algorithms preserve them as much as possible. We propose a novel algorithm that
models OR as a joint stress and scaling optimization problem, and leverages
efficient stochastic gradient descent. This approach is compared with
state-of-the-art algorithms, and several quality metrics demonstrate its
efficiency to quickly remove overlaps while retaining the initial layout

We propose a technique to authenticate received packets in underwater
acoustic networks based on the physical layer features of the underwater
acoustic channel (UWAC). Several sensors a) locally estimate features (e.g.,
the number of taps or the delay spread) of the UWAC over which the packet is
received, b) obtain a compressed feature representation through a neural
network (NN), and c) transmit their representations to a central sink node
that, using a NN, decides whether the packet has been transmitted by the
legitimate node or by an impersonating attacker. Although the purpose of the
system is to make a binary decision as to whether a packet is authentic or not,
we show the importance of having a rich set of compressed features, while still
taking into account transmission rate limits among the nodes. We consider both
global training, where all NNs are trained together, and local training, where
each NN is trained individually. For the latter scenario, several alternatives
for the NN structure and loss function were used for training.

As post hoc explanations are increasingly used to understand the behavior of
graph neural networks (GNNs), it becomes crucial to evaluate the quality and
reliability of GNN explanations. However, assessing the quality of GNN
explanations is challenging as existing graph datasets have no or unreliable
ground-truth explanations for a given task. Here, we introduce a synthetic
graph data generator, ShapeGGen, which can generate a variety of benchmark
datasets (e.g., varying graph sizes, degree distributions, homophilic vs.
heterophilic graphs) accompanied by ground-truth explanations. Further, the
flexibility to generate diverse synthetic datasets and corresponding
ground-truth explanations allows us to mimic the data generated by various
real-world applications. We include ShapeGGen and several real-world graph
datasets into an open-source graph explainability library, GraphXAI. In
addition to synthetic and real-world graph datasets with ground-truth
explanations, GraphXAI provides data loaders, data processing functions,
visualizers, GNN model implementations, and evaluation metrics to benchmark the
performance of GNN explainability methods.

Social media platforms give rise to an abundance of posts and comments on
every topic imaginable. Many of these posts express opinions on various aspects
of society, but their unfalsifiable nature makes them ill-suited to
fact-checking pipelines. In this work, we aim to distill such posts into a
small set of narratives that capture the essential claims related to a given
topic. Understanding and visualizing these narratives can facilitate more
informed debates on social media. As a first step towards systematically
identifying the underlying narratives on social media, we introduce PAPYER, a
fine-grained dataset of online comments related to hygiene in public restrooms,
which contains a multitude of unfalsifiable claims. We present a
human-in-the-loop pipeline that uses a combination of machine and human kernels
to discover the prevailing narratives and show that this pipeline outperforms
recent large transformer models and state-of-the-art unsupervised topic models.

Despite having achieved great success for sentiment analysis, existing neural
models struggle with implicit sentiment analysis. This may be due to the fact
that they may latch onto spurious correlations ("shortcuts", e.g., focusing
only on explicit sentiment words), resulting in undermining the effectiveness
and robustness of the learned model. In this work, we propose a causal
intervention model for Implicit Sentiment Analysis using Instrumental Variable
(ISAIV). We first review sentiment analysis from a causal perspective and
analyze the confounders existing in this task. Then, we introduce an
instrumental variable to eliminate the confounding causal effects, thus
extracting the pure causal effect between sentence and sentiment. We compare
the proposed ISAIV model with several strong baselines on both the general
implicit sentiment analysis and aspect-based implicit sentiment analysis tasks.
The results indicate the great advantages of our model and the efficacy of
implicit sentiment reasoning.

Choice modeling has been a central topic in the study of individual
preference or utility across many fields including economics, marketing,
operations research, and psychology. While the vast majority of the literature
on choice models has been devoted to the analytical properties that lead to
managerial and policy-making insights, the existing methods to learn a choice
model from empirical data are often either computationally intractable or
sample inefficient. In this paper, we develop deep learning-based choice models
under two settings of choice modeling: (i) feature-free and (ii) feature-based.
Our model captures both the intrinsic utility for each candidate choice and the
effect that the assortment has on the choice probability. Synthetic and real
data experiments demonstrate the performances of proposed models in terms of
the recovery of the existing choice models, sample complexity, assortment
effect, architecture design, and model interpretation.

Deep reinforcement learning has gained a lot of success with the presence of
trust region policy optimization (TRPO) and proximal policy optimization (PPO),
for their scalability and efficiency. However, the pessimism of both
algorithms, among which it either is constrained in a trust region or strictly
excludes all suspicious gradients, has been proven to suppress the exploration
and harm the performance of the agent. To address those issues, we propose a
shifted Markov decision process (MDP), or rather, with entropy augmentation, to
encourage the exploration and reinforce the ability of escaping from
suboptimums. Our method is extensible and adapts to either reward shaping or
bootstrapping. With convergence analysis given, we find it is crucial to
control the temperature coefficient. However, if appropriately tuning it, we
can achieve remarkable performance, even on other algorithms, since it is
simple yet effective. Our experiments test augmented TRPO and PPO on MuJoCo
benchmark tasks, of an indication that the agent is heartened towards higher
reward regions, and enjoys a balance between exploration and exploitation. We
verify the exploration bonus of our method on two grid world environments.

Visual place recognition (VPR) using deep networks has achieved
state-of-the-art performance. However, most of them require a training set with
ground truth sensor poses to obtain positive and negative samples of each
observation's spatial neighborhood for supervised learning. When such
information is unavailable, temporal neighborhoods from a sequentially
collected data stream could be exploited for self-supervised training, although
we find its performance suboptimal. Inspired by noisy label learning, we
propose a novel self-supervised framework named \textit{TF-VPR} that uses
temporal neighborhoods and learnable feature neighborhoods to discover unknown
spatial neighborhoods. Our method follows an iterative training paradigm which
alternates between: (1) representation learning with data augmentation, (2)
positive set expansion to include the current feature space neighbors, and (3)
positive set contraction via geometric verification. We conduct comprehensive
experiments on both simulated and real datasets, with either RGB images or
point clouds as inputs. The results show that our method outperforms our
baselines in recall rate, robustness, and heading diversity, a novel metric we
propose for VPR. Our code and datasets can be found at
https://ai4ce.github.io/TF-VPR/.

Compared with the image-based static facial expression recognition (SFER)
task, the dynamic facial expression recognition (DFER) task based on video
sequences is closer to the natural expression recognition scene. However, DFER
is often more challenging. One of the main reasons is that video sequences
often contain frames with different expression intensities, especially for the
facial expressions in the real-world scenarios, while the images in SFER
frequently present uniform and high expression intensities. However, if the
expressions with different intensities are treated equally, the features
learned by the networks will have large intra-class and small inter-class
differences, which is harmful to DFER. To tackle this problem, we propose the
global convolution-attention block (GCA) to rescale the channels of the feature
maps. In addition, we introduce the intensity-aware loss (IAL) in the training
process to help the network distinguish the samples with relatively low
expression intensities. Experiments on two in-the-wild dynamic facial
expression datasets (i.e., DFEW and FERV39k) indicate that our method
outperforms the state-of-the-art DFER approaches. The source code will be made

Graph convolutional networks are a popular class of deep neural network
algorithms which have shown success in a number of relational learning tasks.
Despite their success, graph convolutional networks exhibit a number of
peculiar features, including a bias towards learning oversmoothed and
homophilic functions, which are not easily diagnosed due to the complex nature
of these algorithms. We propose to bridge this gap in understanding by studying
the neural tangent kernel of sheaf convolutional networks--a topological
generalization of graph convolutional networks. To this end, we derive a
parameterization of the neural tangent kernel for sheaf convolutional networks
which separates the function into two parts: one driven by a forward diffusion
process determined by the graph, and the other determined by the composite
effect of nodes' activations on the output layer. This geometrically-focused
derivation produces a number of immediate insights which we discuss in detail.

A reliable and efficient representation of multivariate time series is
crucial in various downstream machine learning tasks. In multivariate time
series forecasting, each variable depends on its historical values and there
are inter-dependencies among variables as well. Models have to be designed to
capture both intra- and inter-relationships among the time series. To move
towards this goal, we propose the Time Series Attention Transformer (TSAT) for
multivariate time series representation learning. Using TSAT, we represent both
temporal information and inter-dependencies of multivariate time series in
terms of edge-enhanced dynamic graphs. The intra-series correlations are
represented by nodes in a dynamic graph; a self-attention mechanism is modified
to capture the inter-series correlations by using the super-empirical mode
decomposition (SMD) module. We applied the embedded dynamic graphs to times
series forecasting problems, including two real-world datasets and two
benchmark datasets. Extensive experiments show that TSAT clearly outerperforms
six state-of-the-art baseline methods in various forecasting horizons. We
further visualize the embedded dynamic graphs to illustrate the graph
representation power of TSAT. We share our code at
https://github.com/RadiantResearch/TSAT.

Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has
become the most popular algorithm for aspect modeling. While sufficiently
successful in text topic extraction from large corpora, VB is less successful
in identifying aspects in the presence of limited data. We present a novel
variational message passing algorithm as applied to Latent Dirichlet Allocation
(LDA) and compare it with the gold standard VB and collapsed Gibbs sampling. In
situations where marginalisation leads to non-conjugate messages, we use ideas
from sampling to derive approximate update equations. In cases where conjugacy
holds, Loopy Belief update (LBU) (also known as Lauritzen-Spiegelhalter) is
used. Our algorithm, ALBU (approximate LBU), has strong similarities with
Variational Message Passing (VMP) (which is the message passing variant of VB).
To compare the performance of the algorithms in the presence of limited data,
we use data sets consisting of tweets and news groups. Using coherence measures
we show that ALBU learns latent distributions more accurately than does VB,
especially for smaller data sets.

Technique of emotion recognition enables computers to classify human
affective states into discrete categories. However, the emotion may fluctuate
instead of maintaining a stable state even within a short time interval. There
is also a difficulty to take the full use of the EEG spatial distribution due
to its 3-D topology structure. To tackle the above issues, we proposed a
locally temporal-spatial pattern learning graph attention network (LTS-GAT) in
the present study. In the LTS-GAT, a divide-and-conquer scheme was used to
examine local information on temporal and spatial dimensions of EEG patterns
based on the graph attention mechanism. A dynamical domain discriminator was
added to improve the robustness against inter-individual variations of the EEG
statistics to learn robust EEG feature representations across different
participants. We evaluated the LTS-GAT on two public datasets for affective
computing studies under individual-dependent and independent paradigms. The
effectiveness of LTS-GAT model was demonstrated when compared to other existing
mainstream methods. Moreover, visualization methods were used to illustrate the
relations of different brain regions and emotion recognition. Meanwhile, the
weights of different time segments were also visualized to investigate emotion
sparsity problems.

In many applications, machine learned (ML) models are required to hold some
invariance qualities, such as rotation, size, intensity, and background
invariance. Unlike many types of variance, the variants of background scenes
cannot be ordered easily, which makes it difficult to analyze the robustness
and biases of the models concerned. In this work, we present a technical
solution for ordering background scenes according to their semantic proximity
to a target image that contains a foreground object being tested. We make use
of the results of object recognition as the semantic description of each image,
and construct an ontology for storing knowledge about relationships among
different objects using association analysis. This ontology enables (i)
efficient and meaningful search for background scenes of different semantic
distances to a target image, (ii) quantitative control of the distribution and
sparsity of the sampled background scenes, and (iii) quality assurance using
visual representations of invariance testing results (referred to as variance
matrices). In this paper, we also report the training of an ML4ML assessor to
evaluate the invariance quality of ML models automatically.

We describe a local surrogate model for use in conjunction with global
structure search methods. The model follows the Gaussian approximation
potential (GAP) formalism and is based on a the smooth overlap of atomic
positions descriptor with sparsification in terms of a reduced number of local
environments using mini-batch $k$-means. The model is implemented in the
Atomistic Global Optimization X framework and used as a partial replacement of
the local relaxations in basin hopping structure search. The approach is shown
to be robust for a wide range of atomistic system including molecules,
nano-particles, surface supported clusters and surface thin films. The benefits
in a structure search context of a local surrogate model are demonstrated. This
includes the ability to transfer learning from smaller systems as well as the
possibility to perform concurrent multi-stoichiometry searches.

Robust speech emotion recognition relies on the quality of the speech
features. We present speech features enhancement strategy that improves speech
emotion recognition. We used the INTERSPEECH 2010 challenge feature-set. We
identified subsets from the features set and applied Principle Component
Analysis to the subsets. Finally, the features are fused horizontally. The
resulting feature set is analyzed using t-distributed neighbour embeddings
(t-SNE) before the application of features for emotion recognition. The method
is compared with the state-of-the-art methods used in the literature. The
empirical evidence is drawn using two well-known datasets: Emotional Speech
Dataset (EMO-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song
(RAVDESS) for two languages, German and English, respectively. Our method
achieved an average recognition gain of 11.5\% for six out of seven emotions
for the EMO-DB dataset, and 13.8\% for seven out of eight emotions for the
RAVDESS dataset as compared to the baseline study.

We elaborate on a metamorphic approach for testing industrial control
systems. The proposed approach consists of two phases: an exploration phase in
which we learn about fault patterns of the system under test and an
exploitation phase where the observed fault patterns are used for targeted
testing. Our method extracts metamorphic relations and input space of the
system from its requirements. The seed input used for testing is extracted from
the execution logs of the system and used to generate source tests and
follow-up tests automatically. The morphed input is constructed based on the
seed input and refined using a set of constraints. The approach is exemplified
on a position control system and the results show that it is effective in
discovering faults with an increased level of automation.

We study the Improving Multi-Armed Bandit (IMAB) problem, where the reward
obtained from an arm increases with the number of pulls it receives. This model
provides an elegant abstraction for many real-world problems in domains such as
education and employment, where decisions about the distribution of
opportunities can affect the future capabilities of communities and the
disparity between them. A decision-maker in such settings must consider the
impact of her decisions on future rewards in addition to the standard objective
of maximizing her cumulative reward at any time. In many of these applications,
the time horizon is unknown to the decision-maker beforehand, which motivates
the study of the IMAB problem in the technically more challenging
horizon-unaware setting. We study the tension that arises between two seemingly
conflicting objectives in the horizon-unaware setting: a) maximizing the
cumulative reward at any time based on current rewards of the arms, and b)
ensuring that arms with better long-term rewards get sufficient opportunities
even if they initially have low rewards. We show that, surprisingly, the two
objectives are aligned with each other in this setting. Our main contribution
is an anytime algorithm for the IMAB problem that achieves the best possible
cumulative reward while ensuring that the arms reach their true potential given
sufficient time. Our algorithm mitigates the initial disparity due to lack of
opportunity and continues pulling an arm till it stops improving. We prove the
optimality of our algorithm by showing that a) any algorithm for the IMAB
problem, no matter how utilitarian, must suffer $\Omega(T)$ policy regret and
$\Omega(k)$ competitive ratio with respect to the optimal offline policy, and
b) the competitive ratio of our algorithm is $O(k)$.

Deep learning driven joint source-channel coding (JSCC) for wireless image or
video transmission, also called DeepJSCC, has been a topic of interest recently
with very promising results. The idea is to map similar source samples to
nearby points in the channel input space such that, despite the noise
introduced by the channel, the input can be recovered with minimal distortion.
In DeepJSCC, this is achieved by an autoencoder architecture with a
non-trainable channel layer between the encoder and decoder. DeepJSCC has many
favorable properties, such as better end-to-end distortion performance than its
separate source and channel coding counterpart as well as graceful degradation
with respect to channel quality. However, due to the inherent correlation
between the source sample and channel input, DeepJSCC is vulnerable to
eavesdropping attacks. In this paper, we propose the first DeepJSCC scheme for
wireless image transmission that is secure against eavesdroppers, called
DeepJSCEC. DeepJSCEC not only preserves the favorable properties of DeepJSCC,
it also provides security against chosen-plaintext attacks from the
eavesdropper, without the need to make assumptions about the eavesdropper's
channel condition, or its intended use of the intercepted signal. Numerical
results show that DeepJSCEC achieves similar or better image quality than
separate source coding using BPG compression, AES encryption, and LDPC codes
for channel coding, while preserving the graceful degradation of image quality
with respect to channel quality. We also show that the proposed encryption
method is problem agnostic, meaning it can be applied to other end-to-end JSCC
problems, such as remote classification, without modification. Given the
importance of security in modern wireless communication systems, we believe
this work brings DeepJSCC schemes much closer to adoption in practice.

Anomaly detection of multivariate time series is meaningful for system
behavior monitoring. This paper proposes an anomaly detection method based on
unsupervised Short- and Long-term Mask Representation learning (SLMR). The main
idea is to extract short-term local dependency patterns and long-term global
trend patterns of the multivariate time series by using multi-scale residual
dilated convolution and Gated Recurrent Unit(GRU) respectively. Furthermore,
our approach can comprehend temporal contexts and feature correlations by
combining spatial-temporal masked self-supervised representation learning and
sequence split. It considers the importance of features is different, and we
introduce the attention mechanism to adjust the contribution of each feature.
Finally, a forecasting-based model and a reconstruction-based model are
integrated to focus on single timestamp prediction and latent representation of
time series. Experiments show that the performance of our method outperforms
other state-of-the-art models on three real-world datasets. Further analysis
shows that our method is good at interpretability.

When quantizing neural networks for efficient inference, low-bit integers are
the go-to format for efficiency. However, low-bit floating point numbers have
an extra degree of freedom, assigning some bits to work on an exponential scale
instead. This paper in-depth investigates this benefit of the floating point
format for neural network inference. We detail the choices that can be made for
the FP8 format, including the important choice of the number of bits for the
mantissa and exponent, and show analytically in which settings these choices
give better performance. Then we show how these findings translate to real
networks, provide an efficient implementation for FP8 simulation, and a new
algorithm that enables the learning of both the scale parameters and the number
of exponent bits in the FP8 format. Our chief conclusion is that when doing
post-training quantization for a wide range of networks, the FP8 format is
better than INT8 in terms of accuracy, and the choice of the number of exponent
bits is driven by the severity of outliers in the network. We also conduct
experiments with quantization-aware training where the difference in formats
disappears as the network is trained to reduce the effect of outliers.

Multi-person motion prediction remains a challenging problem, especially in
the joint representation learning of individual motion and social interactions.
Most prior methods only involve learning local pose dynamics for individual
motion (without global body trajectory) and also struggle to capture complex
interaction dependencies for social interactions. In this paper, we propose a
novel Social-Aware Motion Transformer (SoMoFormer) to effectively model
individual motion and social interactions in a joint manner. Specifically,
SoMoFormer extracts motion features from sub-sequences in displacement
trajectory space to effectively learn both local and global pose dynamics for
each individual. In addition, we devise a novel social-aware motion attention
mechanism in SoMoFormer to further optimize dynamics representations and
capture interaction dependencies simultaneously via motion similarity
calculation across time and social dimensions. On both short- and long-term
horizons, we empirically evaluate our framework on multi-person motion datasets
and demonstrate that our method greatly outperforms state-of-the-art methods of
single- and multi-person motion prediction. Code will be made publicly

We study the problem of designing mechanisms for trading networks that
satisfy four desired properties: dominant-strategy incentive compatibility,
efficiency, weak budget balance (WBB), and individual rationality (IR).
Although there exist mechanisms that simultaneously satisfy these properties ex
post for combinatorial auctions, we prove the impossibility that such
mechanisms do not exist for a broad class of trading networks. We thus propose
approaches for computing and learning the mechanisms that satisfy the four
properties, in a Bayesian setting, where WBB and IR, respectively, are relaxed
to ex ante and interim. For computational and sample efficiency, we introduce
several techniques, including game theoretical analysis to reduce the input
feature space. We empirically demonstrate that the proposed approaches
successfully find the mechanisms with the four properties for those trading
networks where the impossibility holds ex post.

Evaluation of generative models is mostly based on the comparison between the
estimated distribution and the ground truth distribution in a certain feature
space. To embed samples into informative features, previous works often use
convolutional neural networks optimized for classification, which is criticized
by recent studies. Therefore, various feature spaces have been explored to
discover alternatives. Among them, a surprising approach is to use a randomly
initialized neural network for feature embedding. However, the fundamental
basis to employ the random features has not been sufficiently justified. In
this paper, we rigorously investigate the feature space of models with random
weights in comparison to that of trained models. Furthermore, we provide an
empirical evidence to choose networks for random features to obtain consistent
and reliable results. Our results indicate that the features from random
networks can evaluate generative models well similarly to those from trained
networks, and furthermore, the two types of features can be used together in a
complementary way.

When studying unconstrained behaviour and allowing mice to leave their cage
to navigate a complex labyrinth, the mice exhibit foraging behaviour in the
labyrinth searching for rewards, returning to their home cage now and then,
e.g. to drink. Surprisingly, when executing such a ``home run'', the mice do
not follow the exact reverse path, in fact, the entry path and home path have
very little overlap. Recent work proposed a hierarchical active inference model
for navigation, where the low level model makes inferences about hidden states
and poses that explain sensory inputs, whereas the high level model makes
inferences about moving between locations, effectively building a map of the
environment. However, using this ``map'' for planning, only allows the agent to
find trajectories that it previously explored, far from the observed mice's
behaviour. In this paper, we explore ways of incorporating before-unvisited
paths in the planning algorithm, by using the low level generative model to
imagine potential, yet undiscovered paths. We demonstrate a proof of concept in
a grid-world environment, showing how an agent can accurately predict a new,
shorter path in the map leading to its starting point, using a generative model
learnt from pixel-based observations.

We study the problem of best arm identification in a federated learning
multi-armed bandit setup with a central server and multiple clients. Each
client is associated with a multi-armed bandit in which each arm yields {\em
i.i.d.}\ rewards following a Gaussian distribution with an unknown mean and
known variance. The set of arms is assumed to be the same at all the clients.
We define two notions of best arm -- local and global. The local best arm at a
client is the arm with the largest mean among the arms local to the client,
whereas the global best arm is the arm with the largest average mean across all
the clients. We assume that each client can only observe the rewards from its
local arms and thereby estimate its local best arm. The clients communicate
with a central server on uplinks that entail a cost of $C\ge0$ units per usage
per uplink. The global best arm is estimated at the server. The goal is to
identify the local best arms and the global best arm with minimal total cost,
defined as the sum of the total number of arm selections at all the clients and
the total communication cost, subject to an upper bound on the error
probability. We propose a novel algorithm {\sc FedElim} that is based on
successive elimination and communicates only in exponential time steps and
obtain a high probability instance-dependent upper bound on its total cost. The
key takeaway from our paper is that for any $C\geq 0$ and error probabilities
sufficiently small, the total number of arm selections (resp.\ the total cost)
under {\sc FedElim} is at most~$2$ (resp.~$3$) times the maximum total number
of arm selections under its variant that communicates in every time step.
Additionally, we show that the latter is optimal in expectation up to a
constant factor, thereby demonstrating that communication is almost cost-free
in {\sc FedElim}. We numerically validate the efficacy of {\sc FedElim}.

The recent rapid growth in mobile data traffic entails a pressing demand for
improving the throughput of the underlying wireless communication networks.
Network node deployment has been considered as an effective approach for
throughput enhancement which, however, often leads to highly non-trivial
non-convex optimizations. Although convex approximation based solutions are
considered in the literature, their approximation to the actual throughput may
be loose and sometimes lead to unsatisfactory performance. With this
consideration, in this paper, we propose a novel graph neural network (GNN)
method for the network node deployment problem. Specifically, we fit a GNN to
the network throughput and use the gradients of this GNN to iteratively update
the locations of the network nodes. Besides, we show that an expressive GNN has
the capacity to approximate both the function value and the gradients of a
multivariate permutation-invariant function, as a theoretic support to the
proposed method. To further improve the throughput, we also study a hybrid node
deployment method based on this approach. To train the desired GNN, we adopt a
policy gradient algorithm to create datasets containing good training samples.
Numerical experiments show that the proposed methods produce competitive
results compared to the baselines.

We apply post-processing to the class probability distribution outputs of
audio event classification models and employ reinforcement learning to jointly
discover the optimal parameters for various stages of a post-processing stack,
such as the classification thresholds and the kernel sizes of median filtering
algorithms used to smooth out model predictions. To achieve this we define a
reinforcement learning environment where: 1) a state is the class probability
distribution provided by the model for a given audio sample, 2) an action is
the choice of a candidate optimal value for each parameter of the
post-processing stack, 3) the reward is based on the classification accuracy
metric we aim to optimize, which is the audio event-based macro F1-score in our
case. We apply our post-processing to the class probability distribution
outputs of two audio event classification models submitted to the DCASE Task4
2020 challenge. We find that by using reinforcement learning to discover the
optimal per-class parameters for the post-processing stack that is applied to
the outputs of audio event classification models, we can improve the audio
event-based macro F1-score (the main metric used in the DCASE challenge to
compare audio event classification accuracy) by 4-5% compared to using the same
post-processing stack with manually tuned parameters.

With the increasing prevalence of video recordings there is a growing need
for tools that can maintain the privacy of those recorded. In this paper, we
define an approach for redacting personally identifiable text from videos using
a combination of optical character recognition (OCR) and natural language
processing (NLP) techniques. We examine the relative performance of this
approach when used with different OCR models, specifically Tesseract and the
OCR system from Google Cloud Vision (GCV). For the proposed approach the
performance of GCV, in both accuracy and speed, is significantly higher than
Tesseract. Finally, we explore the advantages and disadvantages of both models
in real-world applications.

Automatic image segmentation technology is critical to the visual analysis.
The autoencoder architecture has satisfying performance in various image
segmentation tasks. However, autoencoders based on convolutional neural
networks (CNN) seem to encounter a bottleneck in improving the accuracy of
semantic segmentation. Increasing the inter-class distance between foreground
and background is an inherent characteristic of the segmentation network.
However, segmentation networks pay too much attention to the main visual
difference between foreground and background, and ignores the detailed edge
information, which leads to a reduction in the accuracy of edge segmentation.
In this paper, we propose a light-weight end-to-end segmentation framework
based on multi-task learning, termed Edge Attention autoencoder Network
(EAA-Net), to improve edge segmentation ability. Our approach not only utilizes
the segmentation network to obtain inter-class features, but also applies the
reconstruction network to extract intra-class features among the foregrounds.
We further design a intra-class and inter-class features fusion module -- I2
fusion module. The I2 fusion module is used to merge intra-class and
inter-class features, and use a soft attention mechanism to remove invalid
background information. Experimental results show that our method performs well
in medical image segmentation tasks. EAA-Net is easy to implement and has small
calculation cost.

DNN-based video object detection (VOD) powers autonomous driving and video
surveillance industries with rising importance and promising opportunities.
However, adversarial patch attack yields huge concern in live vision tasks
because of its practicality, feasibility, and powerful attack effectiveness.
This work proposes Themis, a software/hardware system to defend against
adversarial patches for real-time robust video object detection. We observe
that adversarial patches exhibit extremely localized superficial feature
importance in a small region with non-robust predictions, and thus propose the
adversarial region detection algorithm for adversarial effect elimination.
Themis also proposes a systematic design to efficiently support the algorithm
by eliminating redundant computations and memory traffics. Experimental results
show that the proposed methodology can effectively recover the system from the
adversarial attack with negligible hardware overhead.

Optional type annotations allow for enriching dynamic programming languages
with static typing features like better Integrated Development Environment
(IDE) support, more precise program analysis, and early detection and
prevention of type-related runtime errors. Machine learning-based type
inference promises interesting results for automating this task. However, the
practical usage of such systems depends on their ability to generalize across
different domains, as they are often applied outside their training domain. In
this work, we investigate the generalization ability of Type4Py as a
representative for state-of-the-art deep learning-based type inference systems,
by conducting extensive cross-domain experiments. Thereby, we address the
following problems: dataset shifts, out-of-vocabulary words, unknown classes,
and rare classes. To perform such experiments, we use the datasets ManyTypes4Py
and CrossDomainTypes4Py. The latter we introduce in this paper. Our dataset has
over 1,000,000 type annotations and enables cross-domain evaluation of type
inference systems in different domains of software projects using data from the
two domains web development and scientific calculation. Through our
experiments, we detect shifts in the dataset and that it has a long-tailed
distribution with many rare and unknown data types which decreases the
performance of the deep learning-based type inference system drastically. In
this context, we test unsupervised domain adaptation methods and fine-tuning to
overcome the issues. Moreover, we investigate the impact of out-of-vocabulary
words.

We propose formulating the finite-horizon stochastic optimal control problem
for colloidal self-assembly in the space of probability density functions
(PDFs) of the underlying state variables (namely, order parameters). The
control objective is formulated in terms of steering the state PDFs from a
prescribed initial probability measure towards a prescribed terminal
probability measure with minimum control effort. For specificity, we use a
univariate stochastic state model from the literature. Both the analysis and
the computational steps for control synthesis as developed in this paper
generalize for multivariate stochastic state dynamics given by generic
nonlinear in state and non-affine in control models. We derive the conditions
of optimality for the associated optimal control problem. This derivation
yields a system of three coupled partial differential equations together with
the boundary conditions at the initial and terminal times. The resulting system
is a generalized instance of the so-called Schr\"{o}dinger bridge problem. We
then determine the optimal control policy by training a physics-informed deep
neural network, where the "physics" are the derived conditions of optimality.
The performance of the proposed solution is demonstrated via numerical
simulations on a benchmark colloidal self-assembly problem.

Natural language understanding (NLU) is the task of semantic decoding of
human languages by machines. NLU models rely heavily on large training data to
ensure good performance. However, substantial languages and domains have very
few data resources and domain experts. It is necessary to overcome the data
scarcity challenge, when very few or even zero training samples are available.
In this thesis, we focus on developing cross-lingual and cross-domain methods
to tackle the low-resource issues. First, we propose to improve the model's
cross-lingual ability by focusing on the task-related keywords, enhancing the
model's robustness and regularizing the representations. We find that the
representations for low-resource languages can be easily and greatly improved
by focusing on just the keywords. Second, we present Order-Reduced Modeling
methods for the cross-lingual adaptation, and find that modeling partial word
orders instead of the whole sequence can improve the robustness of the model
against word order differences between languages and task knowledge transfer to
low-resource languages. Third, we propose to leverage different levels of
domain-related corpora and additional masking of data in the pre-training for
the cross-domain adaptation, and discover that more challenging pre-training
can better address the domain discrepancy issue in the task knowledge transfer.
Finally, we introduce a coarse-to-fine framework, Coach, and a cross-lingual
and cross-domain parsing framework, X2Parser. Coach decomposes the
representation learning process into a coarse-grained and a fine-grained
feature learning, and X2Parser simplifies the hierarchical task structures into
flattened ones. We observe that simplifying task structures makes the
representation learning more effective for low-resource languages and domains.

Self-supervised monocular methods can efficiently learn depth information of
weakly textured surfaces or reflective objects. However, the depth accuracy is
limited due to the inherent ambiguity in monocular geometric modeling. In
contrast, multi-frame depth estimation methods improve the depth accuracy
thanks to the success of Multi-View Stereo (MVS), which directly makes use of
geometric constraints. Unfortunately, MVS often suffers from texture-less
regions, non-Lambertian surfaces, and moving objects, especially in real-world
video sequences without known camera motion and depth supervision. Therefore,
we propose MOVEDepth, which exploits the MOnocular cues and VElocity guidance
to improve multi-frame Depth learning. Unlike existing methods that enforce
consistency between MVS depth and monocular depth, MOVEDepth boosts multi-frame
depth learning by directly addressing the inherent problems of MVS. The key of
our approach is to utilize monocular depth as a geometric priority to construct
MVS cost volume, and adjust depth candidates of cost volume under the guidance
of predicted camera velocity. We further fuse monocular depth and MVS depth by
learning uncertainty in the cost volume, which results in a robust depth
estimation against ambiguity in multi-view geometry. Extensive experiments show
MOVEDepth achieves state-of-the-art performance: Compared with Monodepth2 and
PackNet, our method relatively improves the depth accuracy by 20\% and 19.8\%
on the KITTI benchmark. MOVEDepth also generalizes to the more challenging DDAD
benchmark, relatively outperforming ManyDepth by 7.2\%. The code is available
at https://github.com/JeffWang987/MOVEDepth.

The European energy system is increasing its renewable share, primarily that
of wind and solar photovoltaic energy. Going forward, the system will need
better interconnections and storage integration to balance the mismatch between
electricity demand and renewable generation. Europe has already a large
capacity of pumped-hydro storage installed. Moreover, Li-Ion batteries are
emerging as a promising cost-effective storage option. Taking that into
consideration, we analyze to which extent a highly renewable system benefits
from an additional storage technology, named storage-X, in addition to
pumped-hydro storage and batteries. In our analysis, we identify which
characteristics of storage-X are required for it to play a prominent role in
the system. For the study, we use an overnight capacity and
dispatch-optimization model of a future decarbonized sector-coupled European
energy system. In total, the system is optimized for 2,016 configurations of
storage-X characteristics, including efficiency and cost properties, to define
the space of feasible configurations. By comparing this space with the
characteristics of storage technologies currently under development (e.g.,
pumped thermal energy storage, compressed air energy storage, etc.), we learn
that significant improvements are needed for them to become part of the optimal
solutions. Reducing energy capacity cost, increasing discharge efficiency, and
reducing the cost of charging power capacity must be prioritized. As other
sectors are electrified, the design space for storage-X shows similar
characteristics. We find a potential system cost reduction of 10% with the best
storage configuration. But for this to be realized, the storage is required to
provide a high load coverage. Even in that case, renewable curtailment of 1% is
still present in the optimal system.

Of late, in order to have better acceptability among various domain,
researchers have argued that machine intelligence algorithms must be able to
provide explanations that humans can understand causally. This aspect, also
known as causability, achieves a specific level of human-level explainability.
A specific class of algorithms known as counterfactuals may be able to provide
causability. In statistics, causality has been studied and applied for many
years, but not in great detail in artificial intelligence (AI). In a
first-of-its-kind study, we employed the principles of causal inference to
provide explainability for solving the analytical customer relationship
management (ACRM) problems. In the context of banking and insurance, current
research on interpretability tries to address causality-related questions like
why did this model make such decisions, and was the model's choice influenced
by a particular factor? We propose a solution in the form of an intervention,
wherein the effect of changing the distribution of features of ACRM datasets is
studied on the target feature. Subsequently, a set of counterfactuals is also
obtained that may be furnished to any customer who demands an explanation of
the decision taken by the bank/insurance company. Except for the credit card
churn prediction dataset, good quality counterfactuals were generated for the
loan default, insurance fraud detection, and credit card fraud detection
datasets, where changes in no more than three features are observed.

Recently, Graph Neural Networks (GNNs) have been receiving a spotlight as a
powerful tool that can effectively serve various inference tasks on graph
structured data. As the size of real-world graphs continues to scale, the GNN
training system faces a scalability challenge. Distributed training is a
popular approach to address this challenge by scaling out CPU nodes. However,
not much attention has been paid to disk-based GNN training, which can scale up
the single-node system in a more cost-effective manner by leveraging
high-performance storage devices like NVMe SSDs. We observe that the data
movement between the main memory and the disk is the primary bottleneck in the
SSD-based training system, and that the conventional GNN training pipeline is
sub-optimal without taking this overhead into account. Thus, we propose Ginex,
the first SSD-based GNN training system that can process billion-scale graph
datasets on a single machine. Inspired by the inspector-executor execution
model in compiler optimization, Ginex restructures the GNN training pipeline by
separating sample and gather stages. This separation enables Ginex to realize a
provably optimal replacement algorithm, known as Belady's algorithm, for
caching feature vectors in memory, which account for the dominant portion of
I/O accesses. According to our evaluation with four billion-scale graph
datasets, Ginex achieves 2.11x higher training throughput on average (up to
2.67x at maximum) than the SSD-extended PyTorch Geometric.

In this paper, we study the problem of one-shot skeleton-based action
recognition, which poses unique challenges in learning transferable
representation from base classes to novel classes, particularly for
fine-grained actions. Existing meta-learning frameworks typically rely on the
body-level representations in spatial dimension, which limits the
generalisation to capture subtle visual differences in the fine-grained label
space. To overcome the above limitation, we propose a part-aware prototypical
representation for one-shot skeleton-based action recognition. Our method
captures skeleton motion patterns at two distinctive spatial levels, one for
global contexts among all body joints, referred to as body level, and the other
attends to local spatial regions of body parts, referred to as the part level.
We also devise a class-agnostic attention mechanism to highlight important
parts for each action class. Specifically, we develop a part-aware prototypical
graph network consisting of three modules: a cascaded embedding module for our
dual-level modelling, an attention-based part fusion module to fuse parts and
generate part-aware prototypes, and a matching module to perform classification
with the part-aware representations. We demonstrate the effectiveness of our
method on two public skeleton-based action recognition datasets: NTU RGB+D 120
and NW-UCLA.

Much research has been devoted to the problem of learning fair
representations; however, they do not explicitly the relationship between
latent representations. In many real-world applications, there may be causal
relationships between latent representations. Furthermore, most fair
representation learning methods focus on group-level fairness and are based on
correlations, ignoring the causal relationships underlying the data. In this
work, we theoretically demonstrate that using the structured representations
enable downstream predictive models to achieve counterfactual fairness, and
then we propose the Counterfactual Fairness Variational AutoEncoder (CF-VAE) to
obtain structured representations with respect to domain knowledge. The
experimental results show that the proposed method achieves better fairness and
accuracy performance than the benchmark fairness methods.

We propose a new semi-analytic physics informed neural network (PINN) to
solve singularly perturbed boundary value problems. The PINN is a scientific
machine learning framework that offers a promising perspective for finding
numerical solutions to partial differential equations. The PINNs have shown
impressive performance in solving various differential equations including
time-dependent and multi-dimensional equations involved in a complex geometry
of the domain. However, when considering stiff differential equations, neural
networks in general fail to capture the sharp transition of solutions, due to
the spectral bias. To resolve this issue, here we develop the semi-analytic
PINN methods, enriched by using the so-called corrector functions obtained from
the boundary layer analysis. Our new enriched PINNs accurately predict
numerical solutions to the singular perturbation problems. Numerical
experiments include various types of singularly perturbed linear and nonlinear
differential equations.

Given a learning problem with real-world tradeoffs, which cost function
should the model be trained to optimize? This is the metric selection problem
in machine learning. Despite its practical interest, there is limited formal
guidance on how to select metrics for machine learning applications. This
thesis outlines metric elicitation as a principled framework for selecting the
performance metric that best reflects implicit user preferences. Once
specified, the evaluation metric can be used to compare and train models. In
this manuscript, we formalize the problem of Metric Elicitation and devise
novel strategies for eliciting classification performance metrics using
pairwise preference feedback over classifiers. Specifically, we provide novel
strategies for eliciting linear and linear-fractional metrics for binary and
multiclass classification problems, which are then extended to a framework that
elicits group-fair performance metrics in the presence of multiple sensitive
groups. All the elicitation strategies that we discuss are robust to both
finite sample and feedback noise, thus are useful in practice for real-world
applications. Using the tools and the geometric characterizations of the
feasible confusion statistics sets from the binary, multiclass, and
multiclass-multigroup classification setups, we further provide strategies to
elicit from a wider range of complex, modern multiclass metrics defined by
quadratic functions of confusion statistics by exploiting their local linear
structure. From application perspective, we also propose to use the metric
elicitation framework in optimizing complex black box metrics that is amenable
to deep network training. Lastly, to bring theory closer to practice, we
conduct a preliminary real-user study that shows the efficacy of the metric
elicitation framework in recovering the users' preferred performance metric in
a binary classification setup.

Sign Language Production (SLP) aims to translate spoken languages into sign
sequences automatically. The core process of SLP is to transform sign gloss
sequences into their corresponding sign pose sequences (G2P). Most existing G2P
models usually perform this conditional long-range generation in an
autoregressive manner, which inevitably leads to an accumulation of errors. To
address this issue, we propose a vector quantized diffusion method for
conditional pose sequences generation, called PoseVQ-Diffusion, which is an
iterative non-autoregressive method. Specifically, we first introduce a vector
quantized variational autoencoder (Pose-VQVAE) model to represent a pose
sequence as a sequence of latent codes. Then we model the latent discrete space
by an extension of the recently developed diffusion architecture. To better
leverage the spatial-temporal information, we introduce a novel architecture,
namely CodeUnet, to generate higher quality pose sequence in the discrete
space. Moreover, taking advantage of the learned codes, we develop a novel
sequential k-nearest-neighbours method to predict the variable lengths of pose
sequences for corresponding gloss sequences. Consequently, compared with the
autoregressive G2P models, our model has a faster sampling speed and produces
significantly better results. Compared with previous non-autoregressive G2P
methods, PoseVQ-Diffusion improves the predicted results with iterative
refinements, thus achieving state-of-the-art results on the SLP evaluation

We consider the problem of OOD generalization, where the goal is to train a
model that performs well on test distributions that are different from the
training distribution. Deep learning models are known to be fragile to such
shifts and can suffer large accuracy drops even for slightly different test
distributions. We propose a new method - DAFT - based on the intuition that
adversarially robust combination of a large number of rich features should
provide OOD robustness. Our method carefully distills the knowledge from a
powerful teacher that learns several discriminative features using standard
training while combining them using adversarial training. The standard
adversarial training procedure is modified to produce teachers which can guide
the student better. We evaluate DAFT on standard benchmarks in the DomainBed
framework, and demonstrate that DAFT achieves significant improvements over the
current state-of-the-art OOD generalization methods. DAFT consistently
out-performs well-tuned ERM and distillation baselines by up to 6%, with more
pronounced gains for smaller networks.

Knowledge graph completion (KGC) aims to discover missing relationships
between entities in knowledge graphs (KGs). Most prior KGC work focuses on
learning representations for entities and relations. Yet, a higher-dimensional
embedding space is usually required for a better reasoning capability, which
leads to a larger model size and hinders applicability to real-world problems
(e.g., large-scale KGs or mobile/edge computing). A lightweight modularized KGC
solution, called GreenKGC, is proposed in this work to address this issue.
GreenKGC consists of three modules: 1) representation learning, 2) feature
pruning, and 3) decision learning. In Module 1, we leverage existing KG
embedding models to learn high-dimensional representations for entities and
relations. In Module 2, the KG is partitioned into several relation groups
followed by a feature pruning process to find the most discriminant features
for each relation group. Finally, a classifier is assigned to each relation
group to cope with low-dimensional triple features for KGC tasks in Module 3.
We evaluate the performance of GreenKGC on four widely used link prediction
datasets and observe that GreenKGC can achieve comparable or even better
performance against original high-dimensional embeddings with a much smaller
model size. Furthermore, we experiment on two triple classification datasets to
demonstrate that the same methodology can generalize to more tasks.

In an era of information explosion, recommendation systems play an important
role in people's daily life by facilitating content exploration. It is known
that user activeness, i.e., number of behaviors, tends to follow a long-tail
distribution, where the majority of users are with low activeness. In practice,
we observe that tail users suffer from significantly lower-quality
recommendation than the head users after joint training. We further identify
that a model trained on tail users separately still achieve inferior results
due to limited data. Though long-tail distributions are ubiquitous in
recommendation systems, improving the recommendation performance on the tail
users still remains challenge in both research and industry. Directly applying
related methods on long-tail distribution might be at risk of hurting the
experience of head users, which is less affordable since a small portion of
head users with high activeness contribute a considerate portion of platform
revenue. In this paper, we propose a novel approach that significantly improves
the recommendation performance of the tail users while achieving at least
comparable performance for the head users over the base model. The essence of
this approach is a novel Gradient Aggregation technique that learns common
knowledge shared by all users into a backbone model, followed by separate
plugin prediction networks for the head users and the tail users
personalization. As for common knowledge learning, we leverage the backward
adjustment from the causality theory for deconfounding the gradient estimation
and thus shielding off the backbone training from the confounder, i.e., user
activeness. We conduct extensive experiments on two public recommendation
benchmark datasets and a large-scale industrial datasets collected from the
Alipay platform. Empirical studies validate the rationality and effectiveness

Generalized text representations are the foundation of many natural language
understanding tasks. To fully utilize the different corpus, it is inevitable
that models need to understand the relevance among them. However, many methods
ignore the relevance and adopt a single-channel model (a coarse paradigm)
directly for all tasks, which lacks enough rationality and interpretation. In
addition, some existing works learn downstream tasks by stitches skill block(a
fine paradigm), which might cause irrationalresults due to its redundancy and
noise. Inthis work, we first analyze the task correlation through three
different perspectives, i.e., data property, manual design, and model-based
relevance, based on which the similar tasks are grouped together. Then, we
propose a hierarchical framework with a coarse-to-fine paradigm, with the
bottom level shared to all the tasks, the mid-level divided to different
groups, and the top-level assigned to each of the tasks. This allows our model
to learn basic language properties from all tasks, boost performance on
relevant tasks, and reduce the negative impact from irrelevant tasks. Our
experiments on 13 benchmark datasets across five natural language understanding
tasks demonstrate the superiority of our method.

Recently, test time adaptation (TTA) has attracted increasing attention due
to its power of handling the distribution shift issue in the real world. Unlike
what has been developed for convolutional neural networks (CNNs) for image
data, TTA is less explored for Graph Neural Networks (GNNs). There is still a
lack of efficient algorithms tailored for graphs with irregular structures. In
this paper, we present a novel test time adaptation strategy named Graph
Adversarial Pseudo Group Contrast (GAPGC), for graph neural networks TTA, to
better adapt to the Out Of Distribution (OOD) test data. Specifically, GAPGC
employs a contrastive learning variant as a self-supervised task during TTA,
equipped with Adversarial Learnable Augmenter and Group Pseudo-Positive Samples
to enhance the relevance between the self-supervised task and the main task,
boosting the performance of the main task. Furthermore, we provide theoretical
evidence that GAPGC can extract minimal sufficient information for the main
task from information theory perspective. Extensive experiments on molecular
scaffold OOD dataset demonstrated that the proposed approach achieves
state-of-the-art performance on GNNs.

Invoking the manifold assumption in machine learning requires knowledge of
the manifold's geometry and dimension, and theory dictates how many samples are
required. However, in applications data are limited, sampling may not be
uniform, and manifold properties are unknown and (possibly) non-pure; this
implies that neighborhoods must adapt to the local structure. We introduce an
algorithm for inferring adaptive neighborhoods for data given by a similarity
kernel. Starting with a locally-conservative neighborhood (Gabriel) graph, we
sparsify it iteratively according to a weighted counterpart. In each step, a
linear program yields minimal neighborhoods globally and a volumetric statistic
reveals neighbor outliers likely to violate manifold geometry. We apply our
adaptive neighborhoods to non-linear dimensionality reduction, geodesic
computation and dimension estimation. A comparison against standard algorithms
using, e.g., k-nearest neighbors, demonstrates their usefulness.

Facial pose estimation refers to the task of predicting face orientation from
a single RGB image. It is an important research topic with a wide range of
applications in computer vision. Label distribution learning (LDL) based
methods have been recently proposed for facial pose estimation, which achieve
promising results. However, there are two major issues in existing LDL methods.
First, the expectations of label distributions are biased, leading to a biased
pose estimation. Second, fixed distribution parameters are applied for all
learning samples, severely limiting the model capability. In this paper, we
propose an Anisotropic Spherical Gaussian (ASG)-based LDL approach for facial
pose estimation. In particular, our approach adopts the spherical Gaussian
distribution on a unit sphere which constantly generates unbiased expectation.
Meanwhile, we introduce a new loss function that allows the network to learn
the distribution parameter for each learning sample flexibly. Extensive
experimental results show that our method sets new state-of-the-art records on
AFLW2000 and BIWI datasets.

It is well known that translations generated by an excellent document-level
neural machine translation (NMT) model are consistent and coherent. However,
existing sentence-level evaluation metrics like BLEU can hardly reflect the
model's performance at the document level. To tackle this issue, we propose a
Discourse Cohesion Evaluation Method (DCoEM) in this paper and contribute a new
test suite that considers four cohesive manners (reference, conjunction,
substitution, and lexical cohesion) to measure the cohesiveness of document
translations. The evaluation results on recent document-level NMT systems show
that our method is practical and essential in estimating translations at the
document level.

This paper considers the problem of voltage regulation in distribution
networks. The primary motivation is to keep voltages within preassigned
operating limits by commanding the reactive power output of distributed energy
resources (DERs) deployed in the grid. We develop a framework for developing
local Volt/Var control that comprises two main steps. In the first, by
exploiting historical data and for each DER, we learn a function representing
the desirable equilibrium points for the power network. These points
approximate solutions of an Optimal Power Flow (OPF) problem. In the second, we
propose a control scheme for steering the network towards these favorable
configurations. Theoretical conditions are derived to formally guarantee the
stability of the developed control scheme, and numerical simulations illustrate
the effectiveness of the proposed approach.

With the rapid development of the Internet, more and more applications (app)
are playing an important role in various aspects of the world. Among all apps,
mobile apps and web apps are dominant in people's daily life and all
industries. In order to tackle the challenges in ensuring the app quality, many
approaches have been adopted to improve app GUI testing, including random
technologies, model-based technologies, etc. However, existing approaches are
still insufficient in reaching high code coverage, constructing high quality
models, and achieving generalizability. Besides, current approaches is heavily
dependent on the execution platforms (i.e., Android, Web). Apps of distinct
platforms share commonalities in GUI design, which inspires us to propose a
platform-independent approach with the development of computer vision
  In this paper, we propose UniRLTest. It is a reinforcement learning based
approach utilizing a universal framework with computer vision algorithms to
conduct automated testing on apps from different platforms. UniRLTest extracts
the GUI widgets from GUI pages and characterizes the GUI corresponding layouts,
embedding the GUI pages as states. UniRLTest explores apps with the guidance of
a novelly designed curiosity-driven strategy, which uses a Q-network to
estimate the values of specific states and actions to encourage more
exploration in uncovered pages without platform dependency. The state embedding
similarity is used to calculate the rewards of each exploration step. We
conduct an empirical study on 20 mobile apps and 5 web apps, and the results
show that UniRLTest can perform better than the baselines, especially in the
exploration of new states.

As an indispensable ingredient of computer-assisted pronunciation training
(CAPT), automatic pronunciation assessment (APA) plays a pivotal role in aiding
self-directed language learners by providing multi-aspect and timely feedback.
However, there are at least two potential obstacles that might hinder its
performance for practical use. On one hand, most of the studies focus
exclusively on leveraging segmental (phonetic)-level features such as goodness
of pronunciation (GOP); this, however, may cause a discrepancy of feature
granularity when performing suprasegmental (prosodic)-level pronunciation
assessment. On the other hand, automatic pronunciation assessments still suffer
from the lack of large-scale labeled speech data of non-native speakers, which
inevitably limits the performance of pronunciation assessment. In this paper,
we tackle these problems by integrating multiple prosodic and phonological
features to provide a multi-view, multi-granularity, and multi-aspect (3M)
pronunciation modeling. Specifically, we augment GOP with prosodic and
self-supervised learning (SSL) features, and meanwhile develop a
vowel/consonant positional embedding for a more phonology-aware automatic
pronunciation assessment. A series of experiments conducted on the
publicly-available speechocean762 dataset show that our approach can obtain
significant improvements on several assessment granularities in comparison with
previous work, especially on the assessment of speaking fluency and speech
prosody.

Standard deep reinforcement learning (DRL) aims to maximize expected reward,
considering collected experiences equally in formulating a policy. This differs
from human decision-making, where gains and losses are valued differently and
outlying outcomes are given increased consideration. It also fails to
capitalize on opportunities to improve safety and/or performance through the
incorporation of distributional context. Several approaches to distributional
DRL have been investigated, with one popular strategy being to evaluate the
projected distribution of returns for possible actions. We propose a more
direct approach whereby risk-sensitive objectives, specified in terms of the
cumulative distribution function (CDF) of the distribution of full-episode
rewards, are optimized. This approach allows for outcomes to be weighed based
on relative quality, can be used for both continuous and discrete action
spaces, and may naturally be applied in both constrained and unconstrained
settings. We show how to compute an asymptotically consistent estimate of the
policy gradient for a broad class of risk-sensitive objectives via sampling,
subsequently incorporating variance reduction and regularization measures to
facilitate effective on-policy learning. We then demonstrate that the use of
moderately "pessimistic" risk profiles, which emphasize scenarios where the
agent performs poorly, leads to enhanced exploration and a continual focus on
addressing deficiencies. We test the approach using different risk profiles in
six OpenAI Safety Gym environments, comparing to state of the art on-policy
methods. Without cost constraints, we find that pessimistic risk profiles can
be used to reduce cost while improving total reward accumulation. With cost
constraints, they are seen to provide higher positive rewards than risk-neutral
approaches at the prescribed allowable cost.

Knowledge tracing (KT) is a field of study that predicts the future
performance of students based on prior performance datasets collected from
educational applications such as intelligent tutoring systems, learning
management systems, and online courses. Some previous studies on KT have
concentrated only on the interpretability of the model, whereas others have
focused on enhancing the performance. Models that consider both
interpretability and the performance improvement have been insufficient.
Moreover, models that focus on performance improvements have not shown an
overwhelming performance compared with existing models. In this study, we
propose MonaCoBERT, which achieves the best performance on most benchmark
datasets and has significant interpretability. MonaCoBERT uses a BERT-based
architecture with monotonic convolutional multihead attention, which reflects
forgetting behavior of the students and increases the representation power of
the model. We can also increase the performance and interpretability using a
classical test-theory-based (CTT-based) embedding strategy that considers the
difficulty of the question. To determine why MonaCoBERT achieved the best
performance and interpret the results quantitatively, we conducted ablation
studies and additional analyses using Grad-CAM, UMAP, and various visualization
techniques. The analysis results demonstrate that both attention components
complement one another and that CTT-based embedding represents information on
both global and local difficulties. We also demonstrate that our model
represents the relationship between concepts.

Discovering the underlying dynamics of complex systems from data is an
important practical topic. Constrained optimization algorithms are widely
utilized and lead to many successes. Yet, such purely data-driven methods may
bring about incorrect physics in the presence of random noise and cannot easily
handle the situation with incomplete data. In this paper, a new iterative
learning algorithm for complex turbulent systems with partial observations is
developed that alternates between identifying model structures, recovering
unobserved variables, and estimating parameters. First, a causality-based
learning approach is utilized for the sparse identification of model
structures, which takes into account certain physics knowledge that is
pre-learned from data. It has unique advantages in coping with indirect
coupling between features and is robust to the stochastic noise. A practical
algorithm is designed to facilitate the causal inference for high-dimensional
systems. Next, a systematic nonlinear stochastic parameterization is built to
characterize the time evolution of the unobserved variables. Closed analytic
formula via an efficient nonlinear data assimilation is exploited to sample the
trajectories of the unobserved variables, which are then treated as synthetic
observations to advance a rapid parameter estimation. Furthermore, the
localization of the state variable dependence and the physics constraints are
incorporated into the learning procedure, which mitigate the curse of
dimensionality and prevent the finite time blow-up issue. Numerical experiments
show that the new algorithm succeeds in identifying the model structure and
providing suitable stochastic parameterizations for many complex nonlinear
systems with chaotic dynamics, spatiotemporal multiscale structures,
intermittency, and extreme events.

Investment professionals rely on extrapolating company revenue into the
future (i.e. revenue forecast) to approximate the valuation of scaleups
(private companies in a high-growth stage) and inform their investment
decision. This task is manual and empirical, leaving the forecast quality
heavily dependent on the investment professionals' experiences and insights.
Furthermore, financial data on scaleups is typically proprietary, costly and
scarce, ruling out the wide adoption of data-driven approaches. To this end, we
propose a simulation-informed revenue extrapolation (SiRE) algorithm that
generates fine-grained long-term revenue predictions on small datasets and
short time-series. SiRE models the revenue dynamics as a linear dynamical
system (LDS), which is solved using the EM algorithm. The main innovation lies
in how the noisy revenue measurements are obtained during training and
inferencing. SiRE works for scaleups that operate in various sectors and
provides confidence estimates. The quantitative experiments on two practical
tasks show that SiRE significantly surpasses the baseline methods by a large
margin. We also observe high performance when SiRE extrapolates long-term
predictions from short time-series. The performance-efficiency balance and
result explainability of SiRE are also validated empirically. Evaluated from
the perspective of investment professionals, SiRE can precisely locate the
scaleups that have a great potential return in 2 to 5 years. Furthermore, our
qualitative inspection illustrates some advantageous attributes of the SiRE
revenue forecasts.

Autonomous materials research systems allow scientists to fail smarter, learn
faster, and spend less resources in their studies. As these systems grow in
number, capability, and complexity, a new challenge arises - how will they work
together across large facilities? We explore one solution to this question - a
multi-agent laboratory control frame-work. We demonstrate this framework with
an autonomous material science lab in mind - where information from diverse
research campaigns can be combined to ad-dress the scientific question at hand.
This framework can 1) account for realistic resource limits such as equipment
use, 2) allow for machine learning agents with diverse learning capabilities
and goals capable of running re-search campaigns, and 3) facilitate multi-agent
collaborations and teams. The framework is dubbed the MULTI-agent auTonomous
fAcilities - a Scalable frameworK aka MULTITASK. MULTITASK makes possible
facility-wide simulations, including agent-instrument and agent-agent
interactions. Through MULTITASK's modularity, real-world facilities can come
on-line in phases, with simulated instruments gradually replaced by real-world
instruments. We hope MULTITASK opens new areas of study in large-scale
autonomous and semi-autonomous research campaigns and facilities.

Although many near-Earth objects have been found by ground-based telescopes,
some fast-moving ones, especially those near detection limits, have been missed
by observatories. We developed a convolutional neural network for detecting
faint fast-moving near-Earth objects. It was trained with artificial streaks
generated from simulations and was able to find these asteroid streaks with an
accuracy of 98.7% and a false positive rate of 0.02% on simulated data. This
program was used to search image data from the Zwicky Transient Facility (ZTF)
in four nights in 2019, and it identified six previously undiscovered
asteroids. The visual magnitudes of our detections range from ~19.0 - 20.3 and
motion rates range from ~6.8 - 24 deg/day, which is very faint compared to
other ZTF detections moving at similar motion rates. Our asteroids are also ~1
- 51 m diameter in size and ~5 - 60 lunar distances away at close approach,
assuming their albedo values follow the albedo distribution function of known
asteroids. The use of a purely simulated dataset to train our model enables the
program to gain sensitivity in detecting faint and fast-moving objects while
still being able to recover nearly all discoveries made by previously designed
neural networks which used real detections to train neural networks. Our
approach can be adopted by any observatory for detecting fast-moving asteroid
streaks.

Labeling and maintaining a commercial sound effects library is a
time-consuming task exacerbated by databases that continually grow in size and
undergo taxonomy updates. Moreover, sound search and taxonomy creation are
complicated by non-uniform metadata, an unrelenting problem even with the
introduction of a new industry standard, the Universal Category System. To
address these problems and overcome dataset-dependent limitations that inhibit
the successful training of deep learning models, we pursue representation
learning to train generalized embeddings that can be used for a wide variety of
sound effects libraries and are a taxonomy-agnostic representation of sound. We
show that a task-specific but dataset-independent representation can
successfully address data issues such as class imbalance, inconsistent class
labels, and insufficient dataset size, outperforming established
representations such as OpenL3. Detailed experimental results show the impact
of metric learning approaches and different cross-dataset training methods on
representational effectiveness.

Modern deep generative models can assign high likelihood to inputs drawn from
outside the training distribution, posing threats to models in open-world
deployments. While much research attention has been placed on defining new
test-time measures of OOD uncertainty, these methods do not fundamentally
change how deep generative models are regularized and optimized in training. In
particular, generative models are shown to overly rely on the background
information to estimate the likelihood. To address the issue, we propose a
novel frequency-regularized learning FRL framework for OOD detection, which
incorporates high-frequency information into training and guides the model to
focus on semantically relevant features. FRL effectively improves performance
on a wide range of generative architectures, including variational
auto-encoder, GLOW, and PixelCNN++. On a new large-scale evaluation task, FRL
achieves the state-of-the-art performance, outperforming a strong baseline
Likelihood Regret by 10.7% (AUROC) while achieving 147$\times$ faster inference
speed. Extensive ablations show that FRL improves the OOD detection performance
while preserving the image generation quality. Code is available at
https://github.com/mu-cai/FRL.

Wildfires are increasingly impacting the environment, human health and
safety. Among the top 20 California wildfires, those in 2020-2021 burned more
acres than the last century combined. California's 2018 wildfire season caused
damages of $148.5 billion. Among millions of impacted people, those living with
disabilities (around 15% of the world population) are disproportionately
impacted due to inadequate means of alerts. In this project, a multi-modal
wildfire prediction and personalized early warning system has been developed
based on an advanced machine learning architecture. Sensor data from the
Environmental Protection Agency and historical wildfire data from 2012 to 2018
have been compiled to establish a comprehensive wildfire database, the largest
of its kind. Next, a novel U-Convolutional-LSTM (Long Short-Term Memory) neural
network was designed with a special architecture for extracting key spatial and
temporal features from contiguous environmental parameters indicative of
impending wildfires. Environmental and meteorological factors were incorporated
into the database and classified as leading indicators and trailing indicators,
correlated to risks of wildfire conception and propagation respectively.
Additionally, geological data was used to provide better wildfire risk
assessment. This novel spatio-temporal neural network achieved >97% accuracy
vs. around 76% using traditional convolutional neural networks, successfully
predicting 2018's five most devastating wildfires 5-14 days in advance.
Finally, a personalized early warning system, tailored to individuals with
sensory disabilities or respiratory exacerbation conditions, was proposed. This
technique would enable fire departments to anticipate and prevent wildfires
before they strike and provide early warnings for at-risk individuals for
better preparation, thereby saving lives and reducing economic damages.

Session-based recommender systems capture the short-term interest of a user
within a session. Session contexts (i.e., a user's high-level interests or
intents within a session) are not explicitly given in most datasets, and
implicitly inferring session context as an aggregation of item-level attributes
is crude. In this paper, we propose ISCON, which implicitly contextualizes
sessions. ISCON first generates implicit contexts for sessions by creating a
session-item graph, learning graph embeddings, and clustering to assign
sessions to contexts. ISCON then trains a session context predictor and uses
the predicted contexts' embeddings to enhance the next-item prediction
accuracy. Experiments on four datasets show that ISCON has superior next-item
prediction accuracy than state-of-the-art models. A case study of ISCON on the
Reddit dataset confirms that assigned session contexts are unique and
meaningful.

This paper presents a novel probabilistic approach to deep robot learning
from demonstrations (LfD). Deep movement primitives (DMPs) are deterministic
LfD model that maps visual information directly into a robot trajectory. This
paper extends DMPs and presents a deep probabilistic model that maps the visual
information into a distribution of effective robot trajectories. The
architecture that leads to the highest level of trajectory accuracy is
presented and compared with the existing methods. Moreover, this paper
introduces a novel training method for learning domain-specific latent
features. We show the superiority of the proposed probabilistic approach and
novel latent space learning in the lab's real-robot task of strawberry
harvesting. The experimental results demonstrate that latent space learning can
significantly improve model prediction performances. The proposed approach
allows to sample trajectories from distribution and optimises the robot
trajectory to meet a secondary objective, e.g. collision avoidance.

Networks-on-chips (NoCs) are an integral part of emerging manycore computing
chips. They play a key role in facilitating communication among processing
cores and between cores and memory. To meet the aggressive performance and
energy-efficiency targets of machine learning and big data applications, NoCs
have been evolving to leverage emerging paradigms such as silicon photonics and
wireless communication. Increasingly, these NoC fabrics are becoming
susceptible to security vulnerabilities, such as from hardware trojans that can
snoop, corrupt, or disrupt information transfers on NoCs. This article surveys
the landscape of security challenges and countermeasures across electronic,
wireless, and photonic NoCs.

This paper investigates whether socioeconomic factors are important for the
hurricane performance of the electric power system in Florida. The
investigation is performed using the Random Forest classifier with Mean
Decrease of Accuracy (MDA) for measuring the importance of a set of factors
that include hazard intensity, time to recovery from maximum impact, and
socioeconomic characteristics of the affected population. The data set (at
county scale) for this study includes socioeconomic variables from the 5-year
American Community Survey (ACS), as well as wind velocities, and outage data of
five hurricanes including Alberto and Michael in 2018, Dorian in 2019, and Eta
and Isaias in 2020. The study shows that socioeconomic variables are
considerably important for the system performance model. This indicates that
social disparities may exist in the occurrence of power outages, which directly
impact the resilience of communities and thus require immediate attention.

Utilization of internet in everyday life has made us vulnerable in terms of
privacy and security of our data and systems. Therefore, there are pressing
needs to protect our data and systems by improving authentication mechanisms,
which needs to be low cost, unobtrusive, and ideally ubiquitous in nature.
Behavioral biometrics modalities such as mouse dynamics (i.e., mouse behaviors
on a graphical user interface-GUI) and widget interactions (i.e., another
modality closely related to mouse dynamics that also considers the target
(widget) of a GUI interaction, such as links, button, and combo-box) can
bolster the security of existing authentication systems because of their
ability to distinguish an individual based on their unique features. As a
result, it can be difficult for an imposter to impersonate these behavioral
biometrics, making them suitable for authentication. In this paper, we survey
the literature on mouse dynamics and widget interactions dated from 1897 to
2021. We begin our survey with an account of the psychological perspectives on
behavioral biometrics. We then analyze the literature along the following
dimensions: tasks and experimental settings for data collection, taxonomy of
raw attributes, feature extractions and mathematical definitions, publicly
available datasets, algorithms (statistical, machine learning, and deep
learning), fusion and performance, and limitations. Lastly, we end the paper
with presenting more opportunities for future research

In this paper, we consider the problem of finding a meta-learning online
control algorithm that can learn across the tasks when faced with a sequence of
$N$ (similar) control tasks. Each task involves controlling a linear dynamical
system for a finite horizon of $T$ time steps. The cost function and system
noise at each time step are adversarial and unknown to the controller before
taking the control action. Meta-learning is a broad approach where the goal is
to prescribe an online policy for any new unseen task exploiting the
information from other tasks and the similarity between the tasks. We propose a
meta-learning online control algorithm for the control setting and characterize
its performance by \textit{meta-regret}, the average cumulative regret across
the tasks. We show that when the number of tasks are sufficiently large, our
proposed approach achieves a meta-regret that is smaller by a factor $D/D^{*}$
compared to an independent-learning online control algorithm which does not
perform learning across the tasks, where $D$ is a problem constant and $D^{*}$
is a scalar that decreases with increase in the similarity between tasks. Thus,
when the sequence of tasks are similar the regret of the proposed meta-learning
online control is significantly lower than that of the naive approaches without
meta-learning. We also present experiment results to demonstrate the superior
performance achieved by our meta-learning algorithm.

In order to explore the potential of adaptive learning techniques to big data
sets, the SNAD team used Active Anomaly Discovery (AAD) as a tool to search for
new supernova (SN) candidates in the photometric data from the first 9.4 months
of the Zwicky Transient Facility survey - between 2018 March 17 and December 31
(58194 < MJD < 58483). We analysed 70 ZTF fields with high galactic latitude
and visually inspected 2100 outliers. This resulted in 104 supernova-like
objects found, 57 of them were reported to the Transient Name Server for the
first time and 47 were previously mentioned in other catalogues either as
supernovae with known types or as supernova candidates. We visually inspected
the multi-colour light curves of the non-catalogued transients and performed
their fit with different supernova models to assign it to a proper class: Ia,
Ib/c, IIP, IIL, IIn. Moreover, we also identified unreported slow-evolving
transients which are good superluminous SN candidates, and a few others
non-catalogued objects, such as red dwarf flares and active galactic nuclei.
Beyond confirming the effectiveness of human-machine integration underlying the
AAD strategy, our results shed light on potential leaks in currently available
pipelines and can help avoid similar losses in future large scale astronomical
surveys. The algorithm enables directed search of any type of data and
definition of anomaly chosen by the expert.

Uncertainty is ubiquitous in games, both in the agents playing games and
often in the games themselves. Working with uncertainty is therefore an
important component of successful deep reinforcement learning agents. While
there has been substantial effort and progress in understanding and working
with uncertainty for supervised learning, the body of literature for
uncertainty aware deep reinforcement learning is less developed. While many of
the same problems regarding uncertainty in neural networks for supervised
learning remain for reinforcement learning, there are additional sources of
uncertainty due to the nature of an interactable environment. In this work, we
provide an overview motivating and presenting existing techniques in
uncertainty aware deep reinforcement learning. These works show empirical
benefits on a variety of reinforcement learning tasks. This work serves to help
to centralize the disparate results and promote future research in this area.

We present Deep Control - new ML architecture of cortico-striatal brain
circuits, which use whole cortical column as a structural element, instead of a
singe neuron. Based on this architecture, we present MARTI - new model of human
brain, considering neocortex and basal ganglia. This model is de-signed to
implement expedient behavior and is capable to learn and achieve goals in
unknown environments. We introduce a novel surprise feeling mechanism, that
significantly improves reinforcement learning process through inner rewards. We
use OpenAI Gym environment to demonstrate MARTI learning on a single CPU just
in several hours.

We propose a data-driven mean-curvature solver for the level-set method. This
work is the natural extension to $\mathbb{R}^3$ of our two-dimensional strategy
in [arXiv:2201.12342][1] and the hybrid inference system of [DOI:
10.1016/j.jcp.2022.111291][2]. However, in contrast to [1,2], which built
resolution-dependent neural-network dictionaries, here we develop a pair of
models in $\mathbb{R}^3$, regardless of the mesh size. Our feedforward networks
ingest transformed level-set, gradient, and curvature data to fix numerical
mean-curvature approximations selectively for interface nodes. To reduce the
problem's complexity, we have used the Gaussian curvature to classify stencils
and fit our models separately to non-saddle and saddle patterns. Non-saddle
stencils are easier to handle because they exhibit a curvature error
distribution characterized by monotonicity and symmetry. While the latter has
allowed us to train only on half the mean-curvature spectrum, the former has
helped us blend the data-driven and the baseline estimations seamlessly near
flat regions. On the other hand, the saddle-pattern error structure is less
clear; thus, we have exploited no latent information beyond what is known. In
this regard, we have trained our models on not only spherical but also
sinusoidal and hyperbolic paraboloidal patches. Our approach to building their
data sets is systematic but gleans samples randomly while ensuring
well-balancedness. We have also resorted to standardization and dimensionality
reduction as a preprocessing step and integrated regularization to minimize
outliers. In addition, we leverage curvature rotation/reflection invariance to
improve precision at inference time. Several experiments confirm that our
proposed system can yield more accurate mean-curvature estimations than modern
particle-based interface reconstruction and level-set schemes around
under-resolved regions.

This paper studies how to train machine-learning models that directly
approximate the optimal solutions of constrained optimization problems. This is
an empirical risk minimization under constraints, which is challenging as
training must balance optimality and feasibility conditions. Supervised
learning methods often approach this challenge by training the model on a large
collection of pre-solved instances. This paper takes a different route and
proposes the idea of Primal-Dual Learning (PDL), a self-supervised training
method that does not require a set of pre-solved instances or an optimization
solver for training and inference. Instead, PDL mimics the trajectory of an
Augmented Lagrangian Method (ALM) and jointly trains primal and dual neural
networks. Being a primal-dual method, PDL uses instance-specific penalties of
the constraint terms in the loss function used to train the primal network.
Experiments show that, on a set of nonlinear optimization benchmarks, PDL
typically exhibits negligible constraint violations and minor optimality gaps,
and is remarkably close to the ALM optimization. PDL also demonstrated improved
or similar performance in terms of the optimality gaps, constraint violations,
and training times compared to existing approaches.

This paper concerns the approximation of smooth, high-dimensional functions
from limited samples using polynomials. This task lies at the heart of many
applications in computational science and engineering -- notably, those arising
from parametric modelling and uncertainty quantification. It is common to use
Monte Carlo (MC) sampling in such applications, so as not to succumb to the
curse of dimensionality. However, it is well known this strategy is
theoretically suboptimal. There are many polynomial spaces of dimension $n$ for
which the sample complexity scales log-quadratically in $n$. This
well-documented phenomenon has led to a concerted effort to design improved, in
fact, near-optimal strategies, whose sample complexities scale log-linearly, or
even linearly in $n$.
  Paradoxically, in this work we show that MC is actually a perfectly good
strategy in high dimensions. We first document this phenomenon via several
numerical examples. Next, we present a theoretical analysis that resolves this
paradox for holomorphic functions of infinitely-many variables. We show that
there is a least-squares scheme based on $m$ MC samples whose error decays
algebraically fast in $m/\log(m)$, with a rate that is the same as that of the
best $n$-term polynomial approximation. This result is non-constructive, since
it assumes knowledge of a suitable polynomial space in which to perform the
approximation. We next present a compressed sensing-based scheme that achieves
the same rate, except for a larger polylogarithmic factor. This scheme is
practical, and numerically it performs as well as or better than well-known
adaptive least-squares schemes.
  Overall, our findings demonstrate that MC sampling is eminently suitable for
smooth function approximation when the dimension is sufficiently high. Hence
the benefits of improved sampling strategies are generically limited to
lower-dimensional settings.

The introduction of computational techniques to analyze chemical data has
given rise to the analytical study of biological systems, known as
"bioinformatics". One facet of bioinformatics is using machine learning (ML)
technology to detect multivariable trends in various cases. Amongst the most
pressing cases is predicting blood-brain barrier (BBB) permeability. The
development of new drugs to treat central nervous system disorders presents
unique challenges due to poor penetration efficacy across the blood-brain
barrier. In this research, we aim to mitigate this problem through an ML model
that analyzes chemical features. To do so: (i) An overview into the relevant
biological systems and processes as well as the use case is given. (ii) Second,
an in-depth literature review of existing computational techniques for
detecting BBB permeability is undertaken. From there, an aspect unexplored
across current techniques is identified and a solution is proposed. (iii)
Lastly, a two-part in silico model to quantify likelihood of permeability of
drugs with defined features across the BBB through passive diffusion is
developed, tested, and reflected on. Testing and validation with the dataset
determined the predictive logBB model's mean squared error to be around 0.112
units and the neuroinflammation model's mean squared error to be approximately
0.3 units, outperforming all relevant studies found.

In his G\'eom\'etrie (1637) Descartes introduces the algebra of segments.
This is a fundamental step in the mathematical treatment of variable quantities
before the creation of differential calculus. It is an algebra with symbols but
without numbers, in which the covariation between geometric variables,
constrained by ruler and compass constructions or with other geometric
constructions, can be expressed with symbolic equations. By using algebraic
manipulations, it is possible to easily deduce the properties of the
corresponding geometric constructions, including those that produce graphs of
rational functions. We believe that the study of functions through Descartes's
algebra can be didactically effective in teaching and learning the concept of
function in secondary school. Firstly, it avoids the reference to real numbers;
secondly, the interpretation of formulas as geometric constructions and vice
versa facilitates the "transition" from functions understood as processes to
functions understood as objects.

We show that deep belief networks with binary hidden units can approximate
any multivariate probability density under very mild integrability requirements
on the parental density of the visible nodes. The approximation is measured in
the $L^q$-norm for $q\in[1,\infty]$ ($q=\infty$ corresponding to the supremum
norm) and in Kullback-Leibler divergence. Furthermore, we establish sharp
quantitative bounds on the approximation error in terms of the number of hidden
units.

We investigate top-$m$ arm identification, a basic problem in bandit theory,
in a multi-agent learning model in which agents collaborate to learn an
objective function. We are interested in designing collaborative learning
algorithms that achieve maximum speedup (compared to single-agent learning
algorithms) using minimum communication cost, as communication is frequently
the bottleneck in multi-agent learning. We give both algorithmic and
impossibility results, and conduct a set of experiments to demonstrate the
effectiveness of our algorithms.

Current Graph Neural Networks (GNNs) suffer from the over-smoothing problem,
which results in indistinguishable node representations and low model
performance with more GNN layers. Many methods have been put forward to tackle
this problem in recent years. However, existing tackling over-smoothing methods
emphasize model performance and neglect the over-smoothness of node
representations. Additional, different approaches are applied one at a time,
while there lacks an overall framework to jointly leverage multiple solutions
to the over-smoothing challenge. To solve these problems, we propose GraTO, a
framework based on neural architecture search to automatically search for GNNs
architecture. GraTO adopts a novel loss function to facilitate striking a
balance between model performance and representation smoothness. In addition to
existing methods, our search space also includes DropAttribute, a novel scheme
for alleviating the over-smoothing challenge, to fully leverage diverse
solutions. We conduct extensive experiments on six real-world datasets to
evaluate GraTo, which demonstrates that GraTo outperforms baselines in the
over-smoothing metrics and achieves competitive performance in accuracy. GraTO
is especially effective and robust with increasing numbers of GNN layers.
Further experiments bear out the quality of node representations learned with
GraTO and the effectiveness of model architecture. We make cide of GraTo
available at Github (\url{https://github.com/fxsxjtu/GraTO}).

Open-World Instance Segmentation (OWIS) is an emerging research topic that
aims to segment class-agnostic object instances from images. The mainstream
approaches use a two-stage segmentation framework, which first locates the
candidate object bounding boxes and then performs instance segmentation. In
this work, we instead promote a single-stage framework for OWIS. We argue that
the end-to-end training process in the single-stage framework can be more
convenient for directly regularizing the localization of class-agnostic object
pixels. Based on the single-stage instance segmentation framework, we propose a
regularization model to predict foreground pixels and use its relation to
instance segmentation to construct a cross-task consistency loss. We show that
such a consistency loss could alleviate the problem of incomplete instance
annotation -- a common problem in the existing OWIS datasets. We also show that
the proposed loss lends itself to an effective solution to semi-supervised OWIS
that could be considered an extreme case that all object annotations are absent
for some images. Our extensive experiments demonstrate that the proposed method
achieves impressive results in both fully-supervised and semi-supervised
settings. Compared to SOTA methods, the proposed method significantly improves
the $AP_{100}$ score by 4.75\% in UVO$\rightarrow$UVO setting and 4.05\% in
COCO$\rightarrow$UVO setting. In the case of semi-supervised learning, our
model learned with only 30\% labeled data, even outperforms its
fully-supervised counterpart with 50\% labeled data. The code will be released
soon.

We propose the Vision-and-Augmented-Language Transformer (VAuLT). VAuLT is an
extension of the popular Vision-and-Language Transformer (ViLT), and improves
performance on vision-and-language (VL) tasks that involve more complex text
inputs than image captions while having minimal impact on training and
inference efficiency. ViLT, importantly, enables efficient training and
inference in VL tasks, achieved by encoding images using a linear projection of
patches instead of an object detector. However, it is pretrained on captioning
datasets, where the language input is simple, literal, and descriptive,
therefore lacking linguistic diversity. So, when working with multimedia data
in the wild, such as multimodal social media data, there is a notable shift
from captioning language data, as well as diversity of tasks. We indeed find
evidence that the language capacity of ViLT is lacking. The key insight of
VAuLT is to propagate the output representations of a large language model (LM)
like BERT to the language input of ViLT. We show that joint training of the LM
and ViLT in VAuLT can yield relative improvements up to 20% over ViLT on VL
tasks involving richer language inputs and affective constructs, such as for
Target-Oriented Sentiment Classification in TWITTER-2015 and TWITTER-2017, and
Sentiment Classification in MVSA-Single and MVSA-Multiple. Our code is
available at https://github.com/gchochla/VAuLT.

We address the task of controlled generation of small molecules, which
entails finding novel molecules with desired properties under certain
constraints (e.g., similarity to a reference molecule). Here we introduce
MolMIM, a probabilistic auto-encoder for small molecule drug discovery that
learns an informative and clustered latent space. MolMIM is trained with Mutual
Information Machine (MIM) learning, and provides a fixed length representation
of variable length SMILES strings. Since encoder-decoder models can learn
representations with ``holes'' of invalid samples, here we propose a novel
extension to the training procedure which promotes a dense latent space, and
allows the model to sample valid molecules from random perturbations of latent
codes. We provide a thorough comparison of MolMIM to several variable-size and
fixed-size encoder-decoder models, demonstrating MolMIM's superior generation
as measured in terms of validity, uniqueness, and novelty. We then utilize
CMA-ES, a naive black-box and gradient free search algorithm, over MolMIM's
latent space for the task of property guided molecule optimization. We achieve
state-of-the-art results in several constrained single property optimization
tasks as well as in the challenging task of multi-objective optimization,
improving over previous success rate SOTA by more than 5\% . We attribute the
strong results to MolMIM's latent representation which clusters similar
molecules in the latent space, whereas CMA-ES is often used as a baseline
optimization method. We also demonstrate MolMIM to be favourable in a compute
limited regime, making it an attractive model for such cases.

Standard inference and training with transformer based architectures scale
quadratically with input sequence length. This is prohibitively large for a
variety of applications especially in web-page translation, query-answering
etc. Consequently, several approaches have been developed recently to speedup
attention computation by enforcing different attention structures such as
sparsity, low-rank, approximating attention using kernels. In this work, we
view attention computation as that of nearest neighbor retrieval, and use
decision tree based hierarchical navigation to reduce the retrieval cost per
query token from linear in sequence length to nearly logarithmic. Based on such
hierarchical navigation, we design Treeformer which can use one of two
efficient attention layers -- TF-Attention and TC-Attention. TF-Attention
computes the attention in a fine-grained style, while TC-Attention is a coarse
attention layer which also ensures that the gradients are "dense". To optimize
such challenging discrete layers, we propose a two-level bootstrapped training
method. Using extensive experiments on standard NLP benchmarks, especially for
long-sequences, we demonstrate that our Treeformer architecture can be almost
as accurate as baseline Transformer while using 30x lesser FLOPs in the
attention layer. Compared to Linformer, the accuracy can be as much as 12%
higher while using similar FLOPs in the attention layer.

In this paper, we tackle a new computer vision task, open-vocabulary panoptic
segmentation, that aims to perform panoptic segmentation (background semantic
labeling + foreground instance segmentation) for arbitrary categories of
text-based descriptions. We first build a baseline method without finetuning
nor distillation to utilize the knowledge in the existing CLIP model. We then
develop a new method, MaskCLIP, that is a Transformer-based approach using mask
queries with the ViT-based CLIP backbone to perform semantic segmentation and
object instance segmentation. Here we design a Relative Mask Attention (RMA)
module to account for segmentations as additional tokens to the ViT CLIP model.
MaskCLIP learns to efficiently and effectively utilize pre-trained dense/local
CLIP features by avoiding the time-consuming operation to crop image patches
and compute feature from an external CLIP image model. We obtain encouraging
results for open-vocabulary panoptic segmentation and state-of-the-art results
for open-vocabulary semantic segmentation on ADE20K and PASCAL datasets. We
show qualitative illustration for MaskCLIP with custom categories.

Context: An increasing demand is observed in various domains to employ
Machine Learning (ML) for solving complex problems. ML models are implemented
as software components and deployed in Machine Learning Software Systems
(MLSSs). Problem: There is a strong need for ensuring the serving quality of
MLSSs. False or poor decisions of such systems can lead to malfunction of other
systems, significant financial losses, or even threat to human life. The
quality assurance of MLSSs is considered as a challenging task and currently is
a hot research topic. Moreover, it is important to cover all various aspects of
the quality in MLSSs. Objective: This paper aims to investigate the
characteristics of real quality issues in MLSSs from the viewpoint of
practitioners. This empirical study aims to identify a catalog of bad-practices
related to poor quality in MLSSs. Method: We plan to conduct a set of
interviews with practitioners/experts, believing that interviews are the best
method to retrieve their experience and practices when dealing with quality
issues. We expect that the catalog of issues developed at this step will also
help us later to identify the severity, root causes, and possible remedy for
quality issues of MLSSs, allowing us to develop efficient quality assurance
tools for ML models and MLSSs.

We propose a method for using a large language model, such as GPT-3, to
simulate responses of different humans in a given context. We test our method
by attempting to reproduce well-established economic, psycholinguistic, and
social experiments. The method requires prompt templates for each experiment.
Simulations are run by varying the (hypothetical) subject details, such as
name, and analyzing the text generated by the language model. To validate our
methodology, we use GPT-3 to simulate the Ultimatum Game, garden path
sentences, risk aversion, and the Milgram Shock experiments. In order to
address concerns of exposure to these studies in training data, we also
evaluate simulations on novel variants of these studies. We show that it is
possible to simulate responses of different people and that their responses are
largely consistent with prior human studies from the literature. Using large
language models as simulators offers advantages but also poses risks. Our use
of a language model for simulation is contrasted with anthropomorphic views of
a language model as having its own behavior.

At the interface of Earth's upper and lower mantle, the post-spinel
transition boundary controls the dynamics and morphologies of downwelling slabs
and upwelling plumes, and its Clapeyron slope is hence one of the most
important constraints on mantle convection. In this study, we reported a new in
situ experimental dataset on phase stability in Mg$_{2}$SiO$_{4}$ at mantle
transition zone pressures from laser-heated diamond anvil cell experiments,
along with a compilation of corrected in situ experimental datasets from the
literature. We presented a machine learning framework for high-pressure phase
diagram determination and focused on its application to constrain the location
and Clapeyron slope of the post-spinel transition: ringwoodite
$\leftrightarrow$ bridgmanite + periclase. We found that the post-spinel
boundary is nonlinear and its Clapeyron slope varies locally from
$-2.3_{-1.4}^{+0.6}$ MPa/K at 1900 K, to $-1.0_{-1.7}^{+1.3}$ MPa/K at 1700 K,
and to $0.0_{-2.0}^{+1.7}$ MPa/K at 1500 K. We applied the
temperature-dependent post-spinel Clapeyron slope to estimate its lateral
variation across the "660-km" seismic discontinuity in subducting slabs and
hotspot-associated plumes worldwide, as well as the ambient mantle. We found
that, in the present-day mantle, the average post spinel Clapeyron slope in the
plumes is three times more negative than that in slabs, and we then discussed
the effects of a nonlinear post-spinel transition on the dynamics of Earth's
mantle.

Blind image deblurring (BID) has been extensively studied in computer vision
and adjacent fields. Modern methods for BID can be grouped into two categories:
single-instance methods that deal with individual instances using statistical
inference and numerical optimization, and data-driven methods that train
deep-learning models to deblur future instances directly. Data-driven methods
can be free from the difficulty in deriving accurate blur models, but are
fundamentally limited by the diversity and quality of the training data --
collecting sufficiently expressive and realistic training data is a standing
challenge. In this paper, we focus on single-instance methods that remain
competitive and indispensable. However, most such methods do not prescribe how
to deal with unknown kernel size and substantial noise, precluding practical
deployment. Indeed, we show that several state-of-the-art (SOTA)
single-instance methods are unstable when the kernel size is overspecified,
and/or the noise level is high. On the positive side, we propose a practical
BID method that is stable against both, the first of its kind. Our method
builds on the recent ideas of solving inverse problems by integrating the
physical models and structured deep neural networks, without extra training
data. We introduce several crucial modifications to achieve the desired
stability. Extensive empirical tests on standard synthetic datasets, as well as
real-world NTIRE2020 and RealBlur datasets, show the superior effectiveness and
practicality of our BID method compared to SOTA single-instance as well as
data-driven methods. The code of our method is available at:
\url{https://github.com/sun-umn/Blind-Image-Deblurring}.

In a decentralized household energy system comprised of various devices such
as home appliances, electric vehicles, and solar panels, end-users are able to
dig deeper into the system's details and further achieve energy sustainability
if they are presented with data on the electric energy consumption and
production at the granularity of the device. However, many databases in this
field are siloed from other domains, including solely information pertaining to
energy. This may result in the loss of information (e.g. weather) on each
device's energy use. Meanwhile, a large number of these datasets have been
extensively used in computational modeling techniques such as machine learning
models. While such computational approaches achieve great accuracy and
performance by concentrating only on a local view of datasets, model
reliability cannot be guaranteed since such models are very vulnerable to data
input fluctuations when information omission is taken into account. This
article tackles the data isolation issue in the field of smart energy systems
by examining Semantic Web methods on top of a household energy system. We offer
an ontology-based approach for managing decentralized data at the device-level
resolution in a system. As a consequence, the scope of the data associated with
each device may easily be expanded in an interoperable manner throughout the
Web, and additional information, such as weather, can be obtained from the Web,
provided that the data is organized according to W3C standards.

Tree-based regression and classification has become a standard tool in modern
data science. Bayesian Additive Regression Trees (BART) has in particular
gained wide popularity due its flexibility in dealing with interactions and
non-linear effects. BART is a Bayesian tree-based machine learning method that
can be applied to both regression and classification problems and yields
competitive or superior results when compared to other predictive models. As a
Bayesian model, BART allows the practitioner to explore the uncertainty around
predictions through the posterior distribution. In this paper, we present new
visualization techniques for exploring BART models. We construct conventional
plots to analyze a model's performance and stability as well as create new
tree-based plots to analyze variable importance, interaction, and tree
structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct
heatmaps that display variable importance and interactions jointly using color
scale to represent posterior uncertainty. Our new visualizations are designed
to work with the most popular BART R packages available, namely BART, dbarts,
and bartMachine. Our approach is implemented in the R package bartMan (BART
Model ANalysis).

Grounded Situation Recognition (GSR) aims to generate structured semantic
summaries of images for "human-like" event understanding. Specifically, GSR
task not only detects the salient activity verb (e.g. buying), but also
predicts all corresponding semantic roles (e.g. agent and goods). Inspired by
object detection and image captioning tasks, existing methods typically employ
a two-stage framework: 1) detect the activity verb, and then 2) predict
semantic roles based on the detected verb. Obviously, this illogical framework
constitutes a huge obstacle to semantic understanding. First, pre-detecting
verbs solely without semantic roles inevitably fails to distinguish many
similar daily activities (e.g., offering and giving, buying and selling).
Second, predicting semantic roles in a closed auto-regressive manner can hardly
exploit the semantic relations among the verb and roles. To this end, in this
paper we propose a novel two-stage framework that focuses on utilizing such
bidirectional relations within verbs and roles. In the first stage, instead of
pre-detecting the verb, we postpone the detection step and assume a pseudo
label, where an intermediate representation for each corresponding semantic
role is learned from images. In the second stage, we exploit transformer layers
to unearth the potential semantic relations within both verbs and semantic
roles. With the help of a set of support images, an alternate learning scheme
is designed to simultaneously optimize the results: update the verb using nouns
corresponding to the image, and update nouns using verbs from support images.
Extensive experimental results on challenging SWiG benchmarks show that our
renovated framework outperforms other state-of-the-art methods under various
metrics.

Simulation of turbulent flows, especially at the edges of clouds in the
atmosphere, is an inherently challenging task. Hitherto, the best possible
computational method to perform such experiments is the Direct Numerical
Simulation (DNS). DNS involves solving non-linear partial differential
equations for fluid flows, also known as Navier-Stokes equations, on
discretized grid boxes in a three-dimensional space. It is a valuable paradigm
that has guided the numerical weather prediction models to compute rainfall
formation. However, DNS cannot be performed for large domains of practical
utility to the weather forecast community. Here, we introduce DeepClouds.ai, a
3D-UNET that simulates the outputs of a rising cloud DNS experiment. The
problem of increasing the domain size in DNS is addressed by mapping an inner
3D cube to the complete 3D cube from the output of the DNS discretized grid
simulation. Our approach effectively captures turbulent flow dynamics without
having to solve the complex dynamical core. The baseline shows that the deep
learning-based simulation is comparable to the partial-differential
equation-based model as measured by various score metrics. This framework can
be used to further the science of turbulence and cloud flows by enabling
simulations over large physical domains in the atmosphere. It would lead to
cascading societal benefits by improved weather predictions via advanced
parameterization schemes.

KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind
power dataset, in which the participants are required to predict the future
generation given the historical context factors. The evaluation metrics contain
RMSE and MAE. This paper describes the solution of Team 88VIP, which mainly
comprises two types of models: a gradient boosting decision tree to memorize
the basic data patterns and a recurrent neural network to capture the deep and
latent probabilistic transitions. Ensembling these models contributes to tackle
the fluctuation of wind power, and training submodels targets on the
distinguished properties in heterogeneous timescales of forecasting, from
minutes to days. In addition, feature engineering, imputation techniques and
the design of offline evaluation are also described in details. The proposed
solution achieves an overall online score of -45.213 in Phase 3.

AI Planning, Machine Learning and Process Mining have so far developed into
separate research fields. At the same time, many interesting concepts and
insights have been gained at the intersection of these areas in recent years.
For example, the behavior of future processes is now comprehensively predicted
with the aid of Machine Learning. For the practical application of these
findings, however, it is also necessary not only to know the expected course,
but also to give recommendations and hints for the achievement of goals, i.e.
to carry out comprehensive process planning. At the same time, an adequate
integration of the aforementioned research fields is still lacking. In this
article, we present a research project in which researchers from the AI and BPM
field work jointly together. Therefore, we discuss the overall research
problem, the relevant fields of research and our overall research framework to
automatically derive process models from executional process data, derive
subsequent planning problems and conduct automated planning in order to
adaptively plan and execute business processes using real-time forecasts.

We study the meta-learning for support (i.e. the set of non-zero entries)
recovery in high-dimensional Principal Component Analysis. We reduce the
sufficient sample complexity in a novel task with the information that is
learned from auxiliary tasks. We assume each task to be a different random
Principal Component (PC) matrix with a possibly different support and that the
support union of the PC matrices is small. We then pool the data from all the
tasks to execute an improper estimation of a single PC matrix by maximising the
$l_1$-regularised predictive covariance to establish that with high probability
the true support union can be recovered provided a sufficient number of tasks
$m$ and a sufficient number of samples $ O\left(\frac{\log(p)}{m}\right)$ for
each task, for $p$-dimensional vectors. Then, for a novel task, we prove that
the maximisation of the $l_1$-regularised predictive covariance with the
additional constraint that the support is a subset of the estimated support
union could reduce the sufficient sample complexity of successful support
recovery to $O(\log |J|)$, where $J$ is the support union recovered from the
auxiliary tasks. Typically, $|J|$ would be much less than $p$ for sparse
matrices. Finally, we demonstrate the validity of our experiments through

Federated learning (FL) enables independent parties to collaboratively build
machine learning (ML) models while protecting data privacy. Vertical federated
learning (VFL), a variant of FL, has recently drawn increasing attention as the
VFL matches the enterprises' demands of leveraging more valuable features to
achieve better model performance without jeopardizing data privacy. However,
conventional VFL may run into data deficiency as it is only able to exploit
aligned samples (belonging to different parties) with labels, leaving often the
majority of unaligned and unlabeled samples unused. The data deficiency hampers
the effort of the federation. In this work, we propose a Federated Hybrid
Self-Supervised Learning framework, coined FedHSSL, to utilize all available
data (including unaligned and unlabeled samples) of participants to train the
joint VFL model. The core idea of FedHSSL is to utilize cross-party views
(i.e., dispersed features) of samples aligned among parties and local views
(i.e., augmentations) of samples within each party to improve the
representation learning capability of the joint VFL model through SSL (e.g.,
SimSiam). FedHSSL further exploits generic features shared among parties to
boost the performance of the joint model through partial model aggregation. We
empirically demonstrate that our FedHSSL achieves significant performance gains
compared with baseline methods, especially when the number of labeled samples
is small. We provide an in-depth analysis of FedHSSL regarding privacy leakage,
which is rarely discussed in existing self-supervised VFL works. We investigate
the protection mechanism for FedHSSL. The results show our protection can
thwart the state-of-the-art label inference attack.

Missing data scenarios are very common in ML applications in general and
time-series/sequence applications are no exceptions. This paper pertains to a
novel Recurrent Neural Network (RNN) based solution for sequence prediction
under missing data. Our method is distinct from all existing approaches. It
tries to encode the missingness patterns in the data directly without trying to
impute data either before or during model building. Our encoding is lossless
and achieves compression. It can be employed for both sequence classification
and forecasting. We focus on forecasting here in a general context of
multi-step prediction in presence of possible exogenous inputs. In particular,
we propose novel variants of Encoder-Decoder (Seq2Seq) RNNs for this. The
encoder here adopts the above mentioned pattern encoding, while at the decoder
which has a different structure, multiple variants are feasible. We demonstrate
the utility of our proposed architecture via multiple experiments on both
single and multiple sequence (real) data-sets. We consider both scenarios where
(i)data is naturally missing and (ii)data is synthetically masked.

Normalizing Flows (NFs) are flexible explicit generative models that have
been shown to accurately model complex real-world data distributions. However,
their invertibility constraint imposes limitations on data distributions that
reside on lower dimensional manifolds embedded in higher dimensional space.
Practically, this shortcoming is often bypassed by adding noise to the data
which impacts the quality of the generated samples. In contrast to prior work,
we approach this problem by generating samples from the original data
distribution given full knowledge about the perturbed distribution and the
noise model. To this end, we establish that NFs trained on perturbed data
implicitly represent the manifold in regions of maximum likelihood. Then, we
propose an optimization objective that recovers the most likely point on the
manifold given a sample from the perturbed distribution. Finally, we focus on
3D point clouds for which we utilize the explicit nature of NFs, i.e. surface
normals extracted from the gradient of the log-likelihood and the
log-likelihood itself, to apply Poisson surface reconstruction to refine
generated point sets.

Though vision transformers (ViTs) have exhibited impressive ability for
representation learning, we empirically find that they cannot generalize well
to unseen domains with previous domain generalization algorithms. In this
paper, we propose a novel approach DoPrompt based on prompt learning to embed
the knowledge of source domains in domain prompts for target domain prediction.
Specifically, domain prompts are prepended before ViT input tokens from the
corresponding source domain. Each domain prompt learns domain-specific
knowledge efficiently since it is optimized only for one domain. Meanwhile, we
train a prompt adapter to produce a suitable prompt for each input image based
on the learned source domain prompts. At test time, the adapted prompt
generated by the prompt adapter can exploit the similarity between the feature
of the out-of-domain image and source domains to properly integrate the source
domain knowledge. Extensive experiments are conducted on four benchmark
datasets. Our approach achieves 1.4% improvements in the averaged accuracy,
which is 3.5 times the improvement of the state-of-the-art algorithm with a ViT
backbone.

Wind speed retrieval at sea surface is of primary importance for scientific
and operational applications. Besides weather models, in-situ measurements and
remote sensing technologies, especially satellite sensors, provide
complementary means to monitor wind speed. As sea surface winds produce sounds
that propagate underwater, underwater acoustics recordings can also deliver
fine-grained wind-related information. Whereas model-driven schemes, especially
data assimilation approaches, are the state-of-the-art schemes to address
inverse problems in geoscience, machine learning techniques become more and
more appealing to fully exploit the potential of observation datasets. Here, we
introduce a deep learning approach for the retrieval of wind speed time series
from underwater acoustics possibly complemented by other data sources such as
weather model reanalyses. Our approach bridges data assimilation and
learning-based frameworks to benefit both from prior physical knowledge and
computational efficiency. Numerical experiments on real data demonstrate that
we outperform the state-of-the-art data-driven methods with a relative gain up
to 16% in terms of RMSE. Interestingly, these results support the relevance of
the time dynamics of underwater acoustic data to better inform the time
evolution of wind speed. They also show that multimodal data, here underwater
acoustics data combined with ECMWF reanalysis data, may further improve the
reconstruction performance, including the robustness with respect to missing
underwater acoustics data.

Despite the constant evolution of similarity searching research, it continues
to face the same challenges stemming from the complexity of the data, such as
the curse of dimensionality and computationally expensive distance functions.
Various machine learning techniques have proven capable of replacing elaborate
mathematical models with combinations of simple linear functions, often gaining
speed and simplicity at the cost of formal guarantees of accuracy and
correctness of querying.
  The authors explore the potential of this research trend by presenting a
lightweight solution for the complex problem of 3D protein structure search.
The solution consists of three steps -- (i) transformation of 3D protein
structural information into very compact vectors, (ii) use of a probabilistic
model to group these vectors and respond to queries by returning a given number
of similar objects, and (iii) a final filtering step which applies basic vector
distance functions to refine the result.

GomalizingFlow.jl: is a package to generate configurations for quantum field
theory on the lattice using the flow based sampling algorithm in Julia
programming language. This software serves two main purposes: to accelerate
research of lattice QCD with machine learning with easy prototyping, and to
provide an independent implementation to an existing public Jupyter notebook in
Python/PyTorch. GomalizingFlow.jl implements, the flow based sampling
algorithm, namely, RealNVP and Metropolis-Hastings test for two dimension and
three dimensional scalar field, which can be switched by a parameter file. HMC
for that theory also implemented for comparison. This package has Docker image,
which reduces effort for environment construction. This code works both on CPU
and NVIDIA GPU.

This report presents a comprehensive view of our vision on the development
path of the human-machine symbiotic art creation. We propose a classification
of the creative system with a hierarchy of 5 classes, showing the pathway of
creativity evolving from a mimic-human artist (Turing Artists) to a Machine
artist in its own right. We begin with an overview of the limitations of the
Turing Artists then focus on the top two-level systems, Machine Artists,
emphasizing machine-human communication in art creation. In art creation, it is
necessary for machines to understand humans' mental states, including desires,
appreciation, and emotions, humans also need to understand machines' creative
capabilities and limitations. The rapid development of immersive environment
and further evolution into the new concept of metaverse enable symbiotic art
creation through unprecedented flexibility of bi-directional communication
between artists and art manifestation environments. By examining the latest
sensor and XR technologies, we illustrate the novel way for art data collection
to constitute the base of a new form of human-machine bidirectional
communication and understanding in art creation. Based on such communication
and understanding mechanisms, we propose a novel framework for building future
Machine artists, which comes with the philosophy that a human-compatible AI
system should be based on the "human-in-the-loop" principle rather than the
traditional "end-to-end" dogma. By proposing a new form of inverse
reinforcement learning model, we outline the platform design of machine
artists, demonstrate its functions and showcase some examples of technologies
we have developed. We also provide a systematic exposition of the ecosystem for
AI-based symbiotic art form and community with an economic model built on NFT
technology. Ethical issues for the development of machine artists are also

The cost of moving data between the memory units and the compute units is a
major contributor to the execution time and energy consumption of modern
workloads in computing systems. At the same time, we are witnessing an enormous
amount of data being generated across multiple application domains. These
trends suggest a need for a paradigm shift towards a data-centric approach
where computation is performed close to where the data resides. Further, a
data-centric approach can enable a data-driven view where we take advantage of
vast amounts of available data to improve architectural decisions.
  As a step towards modern architectures, this dissertation contributes to
various aspects of the data-centric approach and proposes several data-driven
mechanisms.
  First, we design NERO, a data-centric accelerator for a real-world weather
prediction application. Second, we explore the applicability of different
number formats, including fixed-point, floating-point, and posit, for different
stencil kernels. Third, we propose NAPEL, an ML-based application performance
and energy prediction framework for data-centric architectures. Fourth, we
present LEAPER, the first use of few-shot learning to transfer FPGA-based
computing models across different hardware platforms and applications. Fifth,
we propose Sibyl, the first reinforcement learning-based mechanism for data
placement in hybrid storage systems.
  Overall, this thesis provides two key conclusions: (1) hardware acceleration
on an FPGA+HBM fabric is a promising solution to overcome the data movement
bottleneck of our current computing systems; (2) data should drive system and
design decisions by leveraging inherent data characteristics to make our
computing systems more efficient.

Originating from several sources (Big Bang, stars, cosmic rays) and being
strongly depleted during stellar lifetime, the lithium element (Li) is of great
interest as its chemical evolution in the Milky Way is not yet well understood.
To help constrain stellar and galactic chemical evolution models, numerous and
precise Li abundances are necessary for a large range of evolutionary stages,
metallicities, and Galactic volume. In the age of industrial parametrization,
spectroscopic surveys such as APOGEE, GALAH, RAVE, and LAMOST have used
data-driven methods to rapidly and precisely infer stellar labels (atmospheric
parameters and abundances). To prepare grounds for future spectroscopic surveys
like 4MOST and WEAVE, we aim to apply machine-learning techniques for Li
study/measurement. We train a Convolution Neural-Network (CNN) coupling
Gaia-ESO Survey iDR6 stellar labels ($\mathrm{\textit{T}_{\text{eff}}}$,
log(g), [Fe/H] and A(Li)) and GIRAFFE HR15N spectra, to infer the atmospheric
parameters and Li abundances for $\sim$40000 stars. We show that the CNN
properly learns the physics of the stellar labels, from relevant spectral
features, over a large range of evolutionary stages and stellar parameters. The
Li feature at 6707.8 A is successfully singled out by our CNN, among the
thousands of lines in the GIRAFFE HR15N setup. Rare objects like Li-rich giants
are found in our sample. Such performances are achieved thanks to a
meticulously built high-quality and homogeneous training sample. The CNN
approach is very well adapted for the next generations of spectroscopic surveys
aiming at studying (among other elements) Li, such as the 4MIDABLE-LR/HR (4MOST
Milky Way disk and bulge low- and high-resolution) surveys. In this context,
the caveats of the machine-learning applications should be properly
investigated along with realistic label uncertainties and upper limits for
abundances.

We aim to prepare the machine-learning ground for the next generation of
spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that
convolutional neural networks can predict accurate stellar labels from relevant
spectral features in a physically meaningful way. We built a neural network and
trained it on GIRAFFE spectra with associated stellar labels from the sixth
internal Gaia-ESO data release. Our neural network predicts the atmospheric
parameters Teff and log(g) as well as the chemical abundances [Mg/Fe], [Al/Fe],
and [Fe/H] for 30115 stellar spectra. The scatter of predictions from eight
slightly different network models shows a high internal precision of the
network results: 24 K for Teff, 0.03 for log(g), 0.02 dex for [Mg/Fe], 0.03 dex
for [Al/Fe], and 0.02 dex for [Fe/H]. The network gradients reveal that the
network is inferring the labels in a physically meaningful way from spectral
features. Validation with benchmark stars and several scientific applications
confirm that our network predictions are accurate for individual stars and
recover the properties of different stellar populations in the Milky Way
galaxy. Such a study provides very good insights into the application of
machine-learning for the spectral analysis of large-scale spectroscopic
surveys, such as WEAVE and 4MIDABLE-LR and -HR (4MOST Milky Way disk and bulge
low- and high-resolution). The community will have to put a substantial effort
into building proactive training sets for machine-learning methods to minimize
the possible systematics.

A major challenge for causal inference from time-series data is the trade-off
between computational feasibility and accuracy. Motivated by process motifs for
lagged covariance in an autoregressive model with slow mean-reversion, we
propose to infer networks of causal relations via pairwise edge measure (PEMs)
that one can easily compute from lagged correlation matrices. Motivated by
contributions of process motifs to covariance and lagged variance, we formulate
two PEMs that correct for confounding factors and for reverse causation. To
demonstrate the performance of our PEMs, we consider network interference from
simulations of linear stochastic processes, and we show that our proposed PEMs
can infer networks accurately and efficiently. Specifically, for slightly
autocorrelated time-series data, our approach achieves accuracies higher than
or similar to Granger causality, transfer entropy, and convergent crossmapping
-- but with much shorter computation time than possible with any of these
methods. Our fast and accurate PEMs are easy-to-implement methods for network
inference with a clear theoretical underpinning. They provide promising
alternatives to current paradigms for the inference of linear models from
time-series data, including Granger causality, vector-autoregression, and
sparse inverse covariance estimation.

The use of Deep Learning (DL) algorithms has improved the performance of
vision-based space applications in recent years. However, generating large
amounts of annotated data for training these DL algorithms has proven
challenging. While synthetically generated images can be used, the DL models
trained on synthetic data are often susceptible to performance degradation,
when tested in real-world environments. In this context, the Interdisciplinary
Center of Security, Reliability and Trust (SnT) at the University of Luxembourg
has developed the 'SnT Zero-G Lab', for training and validating vision-based
space algorithms in conditions emulating real-world space environments. An
important aspect of the SnT Zero-G Lab development was the equipment selection.
From the lessons learned during the lab development, this article presents a
systematic approach combining market survey and experimental analyses for
equipment selection. In particular, the article focus on the image acquisition
equipment in a space lab: background materials, cameras and illumination lamps.
The results from the experiment analyses show that the market survey
complimented by experimental analyses is required for effective equipment
selection in a space lab development project.

Experimental progress in qubit manufacturing calls for the development of new
theoretical tools to analyze quantum data. We show how an unsupervised
machine-learning technique can be used to understand short-range entangled
many-qubit systems using data of local measurements. The method successfully
constructs the phase diagram of a cluster-state model and detects the
respective order parameters of its phases, including string order parameters.
For the toric code subject to external magnetic fields, the machine identifies
the explicit forms of its two stabilizers. Prior information of the underlying
Hamiltonian or the quantum states is not needed; instead, the machine outputs
their characteristic observables. Our work opens the door for a
first-principles application of hybrid algorithms that aim at strong
interpretability without supervision.

Musculoskeletal and neurological disorders are the most common causes of
walking problems among older people, and they often lead to diminished quality
of life. Analyzing walking motion data manually requires trained professionals
and the evaluations may not always be objective. To facilitate early diagnosis,
recent deep learning-based methods have shown promising results for automated
analysis, which can discover patterns that have not been found in traditional
machine learning methods. We observe that existing work mostly applies deep
learning on individual joint features such as the time series of joint
positions. Due to the challenge of discovering inter-joint features such as the
distance between feet (i.e. the stride width) from generally smaller-scale
medical datasets, these methods usually perform sub-optimally. As a result, we
propose a solution that explicitly takes both individual joint features and
inter-joint features as input, relieving the system from the need of
discovering more complicated features from small data. Due to the distinctive
nature of the two types of features, we introduce a two-stream framework, with
one stream learning from the time series of joint position and the other from
the time series of relative joint displacement. We further develop a mid-layer
fusion module to combine the discovered patterns in these two streams for
diagnosis, which results in a complementary representation of the data for
better prediction performance. We validate our system with a benchmark dataset
of 3D skeleton motion that involves 45 patients with musculoskeletal and
neurological disorders, and achieve a prediction accuracy of 95.56%,
outperforming state-of-the-art methods.

Graph neural networks (GNNs) have achieved remarkable success in recommender
systems by representing users and items based on their historical interactions.
However, little attention was paid to GNN's vulnerability to exposure bias:
users are exposed to a limited number of items so that a system only learns a
biased view of user preference to result in suboptimal recommendation quality.
Although inverse propensity weighting is known to recognize and alleviate
exposure bias, it usually works on the final objective with the model outputs,
whereas GNN can also be biased during neighbor aggregation. In this paper, we
propose a simple but effective approach, neighbor aggregation via inverse
propensity (Navip) for GNNs. Specifically, given a user-item bipartite graph,
we first derive propensity score of each user-item interaction in the graph.
Then, inverse of the propensity score with Laplacian normalization is applied
to debias neighbor aggregation from exposure bias. We validate the
effectiveness of our approach through our extensive experiments on two public
and Amazon Alexa datasets where the performance enhances up to 14.2%.

The quality of datasets plays a crucial role in the successful training and
deployment of deep learning models. Especially in the medical field, where
system performance may impact the health of patients, clean datasets are a
safety requirement for reliable predictions. Therefore, outlier detection is an
essential process when building autonomous clinical decision systems. In this
work, we assess the suitability of Self-Organizing Maps for outlier detection
specifically on a medical dataset containing quantitative phase images of white
blood cells. We detect and evaluate outliers based on quantization errors and
distance maps. Our findings confirm the suitability of Self-Organizing Maps for
unsupervised Out-Of-Distribution detection on the dataset at hand.
Self-Organizing Maps perform on par with a manually specified filter based on
expert domain knowledge. Additionally, they show promise as a tool in the
exploration and cleaning of medical datasets. As a direction for future
research, we suggest a combination of Self-Organizing Maps and feature
extraction based on deep learning.

Automatically discovering failures in vision models under real-world settings
remains an open challenge. This work demonstrates how off-the-shelf,
large-scale, image-to-text and text-to-image models, trained on vast amounts of
data, can be leveraged to automatically find such failures. In essence, a
conditional text-to-image generative model is used to generate large amounts of
synthetic, yet realistic, inputs given a ground-truth label. Misclassified
inputs are clustered and a captioning model is used to describe each cluster.
Each cluster's description is used in turn to generate more inputs and assess
whether specific clusters induce more failures than expected. We use this
pipeline to demonstrate that we can effectively interrogate classifiers trained
on ImageNet to find specific failure cases and discover spurious correlations.
We also show that we can scale the approach to generate adversarial datasets
targeting specific classifier architectures. This work serves as a
proof-of-concept demonstrating the utility of large-scale generative models to
automatically discover bugs in vision models in an open-ended manner. We also
describe a number of limitations and pitfalls related to this approach.

Recent trackers adopt the Transformer to combine or replace the widely used
ResNet as their new backbone network. Although their trackers work well in
regular scenarios, however, they simply flatten the 2D features into a sequence
to better match the Transformer. We believe these operations ignore the spatial
prior of the target object which may lead to sub-optimal results only. In
addition, many works demonstrate that self-attention is actually a low-pass
filter, which is independent of input features or key/queries. That is to say,
it may suppress the high-frequency component of the input features and preserve
or even amplify the low-frequency information. To handle these issues, in this
paper, we propose a unified Spatial-Frequency Transformer that models the
Gaussian spatial Prior and High-frequency emphasis Attention (GPHA)
simultaneously. To be specific, Gaussian spatial prior is generated using dual
Multi-Layer Perceptrons (MLPs) and injected into the similarity matrix produced
by multiplying Query and Key features in self-attention. The output will be fed
into a Softmax layer and then decomposed into two components, i.e., the direct
signal and high-frequency signal. The low- and high-pass branches are rescaled
and combined to achieve all-pass, therefore, the high-frequency features will
be protected well in stacked self-attention layers. We further integrate the
Spatial-Frequency Transformer into the Siamese tracking framework and propose a
novel tracking algorithm, termed SFTransT. The cross-scale fusion based
SwinTransformer is adopted as the backbone, and also a multi-head
cross-attention module is used to boost the interaction between search and
template features. The output will be fed into the tracking head for target
localization. Extensive experiments on both short-term and long-term tracking
benchmarks all demonstrate the effectiveness of our proposed framework.

It is asserted that consciousness functionally is a vision. Biologically it
is a sensation of work of a brain that converts external and internal signals
into visual output. However, as we well know, human consciousness includes
meaning (interpretation), and therefore functionally possesses an additional
level of complexity. It consists of concepts which are its minimal components
(elementary constituents). A concept is an abstract (i.e. non-existent in
nature) common property of different observables, as perceived by humans. It is
an irreducible entity since it cannot be divided into any other functional
components. At the biological and physical level each concept is a unique
sensation encoded in a nervous system as a sensory and visual image. Concepts
evolve throughout one's lifetime. We continuously create and acquire new
concepts, develop and expand the existing ones, and eliminate and abandon some
others. 'I' ('self') is one of the concepts. To acquire a concept one has to
learn (to teach one's nervous system) feeling or sensing it, and to connect it
to existing concepts, if any. We make a conjecture that both the creation and
the acquisition of a concept is a phase transition. The ability to generate and
acquire concepts is unique to humans and is the only principal difference
between human and animal. We offer scenarios for how this ability could have
been acquired by our predecessors, thus making them humans. It is suggested
that the animal's brain, being a processor of visual signals that converts them
into internal images, has developed this ability as a result of "guessing" or
imagining details that are missing in the actual visible picture.

Timely and accurate land use mapping is a long-standing problem, which is
critical for effective land and space planning and management. Due to complex
and mixed use, it is challenging for accurate land use mapping from widely-used
remote sensing images (RSI) directly, especially for high-density cities. To
address this issue, in this paper, we propose a coarse-to-fine machine
learning-based approach for parcel-level urban land use mapping, integrating
multisource geospatial data, including RSI, points-of-interest (POI), and
area-of-interest (AOI) data. Specifically, we first divide the city into
built-up and non-built-up regions based on parcels generated from road
networks. Then, we adopt different classification strategies for parcels in
different regions, and finally combine the classified results into an
integrated land use map. The results show that the proposed approach can
significantly outperform baseline method that mixes built-up and non-built-up
regions, with accuracy increase of 25% and 30% for level-1 and level-2
classification, respectively. In addition, we examine the rarely explored AOI
data, which can further boost the level-1 and level-2 classification accuracy
by 13% and 14%. These results demonstrate the effectiveness of the proposed
approach and also indicate the usefulness of AOIs for land use mapping, which
are valuable for further studies.

Contrastive Self-supervised Learning (CSL) is a practical solution that
learns meaningful visual representations from massive data in an unsupervised
approach. The ordinary CSL embeds the features extracted from neural networks
onto specific topological structures. During the training progress, the
contrastive loss draws the different views of the same input together while
pushing the embeddings from different inputs apart. One of the drawbacks of CSL
is that the loss term requires a large number of negative samples to provide
better mutual information bound ideally. However, increasing the number of
negative samples by larger running batch size also enhances the effects of
false negatives: semantically similar samples are pushed apart from the anchor,
hence downgrading downstream performance. In this paper, we tackle this problem
by introducing a simple but effective contrastive learning framework. The key
insight is to employ siamese-style metric loss to match intra-prototype
features, while increasing the distance between inter-prototype features. We
conduct extensive experiments on various benchmarks where the results
demonstrate the effectiveness of our method on improving the quality of visual
representations. Specifically, our unsupervised pre-trained ResNet-50 with a
linear probe, out-performs the fully-supervised trained version on the
ImageNet-1K dataset.

State-of-the-art object pose estimation handles multiple instances in a test
image by using multi-model formulations: detection as a first stage and then
separately trained networks per object for 2D-3D geometric correspondence
prediction as a second stage. Poses are subsequently estimated using the
Perspective-n-Points algorithm at runtime. Unfortunately, multi-model
formulations are slow and do not scale well with the number of object instances
involved. Recent approaches show that direct 6D object pose estimation is
feasible when derived from the aforementioned geometric correspondences. We
present an approach that learns an intermediate geometric representation of
multiple objects to directly regress 6D poses of all instances in a test image.
The inherent end-to-end trainability overcomes the requirement of separately
processing individual object instances. By calculating the mutual
Intersection-over-Unions, pose hypotheses are clustered into distinct
instances, which achieves negligible runtime overhead with respect to the
number of object instances. Results on multiple challenging standard datasets
show that the pose estimation performance is superior to single-model
state-of-the-art approaches despite being more than ~35 times faster. We
additionally provide an analysis showing real-time applicability (>24 fps) for
images where more than 90 object instances are present. Further results show
the advantage of supervising geometric-correspondence-based object pose
estimation with the 6D pose.

In many multi-agent settings, participants can form teams to achieve
collective outcomes that may far surpass their individual capabilities.
Measuring the relative contributions of agents and allocating them shares of
the reward that promote long-lasting cooperation are difficult tasks.
Cooperative game theory offers solution concepts identifying distribution
schemes, such as the Shapley value, that fairly reflect the contribution of
individuals to the performance of the team or the Core, which reduces the
incentive of agents to abandon their team. Applications of such methods include
identifying influential features and sharing the costs of joint ventures or
team formation. Unfortunately, using these solutions requires tackling a
computational barrier as they are hard to compute, even in restricted settings.
In this work, we show how cooperative game-theoretic solutions can be distilled
into a learned model by training neural networks to propose fair and stable
payoff allocations. We show that our approach creates models that can
generalize to games far from the training distribution and can predict
solutions for more players than observed during training. An important
application of our framework is Explainable AI: our approach can be used to
speed-up Shapley value computations on many instances.

Explainable Artificial Intelligence (XAI) research gained prominence in
recent years in response to the demand for greater transparency and trust in AI
from the user communities. This is especially critical because AI is adopted in
sensitive fields such as finance, medicine etc., where implications for
society, ethics, and safety are immense. Following thorough systematic
evaluations, work in XAI has primarily focused on Machine Learning (ML) for
categorization, decision, or action. To the best of our knowledge, no work is
reported that offers an Explainable Reinforcement Learning (XRL) method for
trading financial stocks. In this paper, we proposed to employ SHapley Additive
exPlanation (SHAP) on a popular deep reinforcement learning architecture viz.,
deep Q network (DQN) to explain an action of an agent at a given instance in
financial stock trading. To demonstrate the effectiveness of our method, we
tested it on two popular datasets namely, SENSEX and DJIA, and reported the
results.

Histopathological images contain abundant phenotypic information and
pathological patterns, which are the gold standards for disease diagnosis and
essential for the prediction of patient prognosis and treatment outcome. In
recent years, computer-automated analysis techniques for histopathological
images have been urgently required in clinical practice, and deep learning
methods represented by convolutional neural networks have gradually become the
mainstream in the field of digital pathology. However, obtaining large numbers
of fine-grained annotated data in this field is a very expensive and difficult
task, which hinders the further development of traditional supervised
algorithms based on large numbers of annotated data. More recent studies have
started to liberate from the traditional supervised paradigm, and the most
representative ones are the studies on weakly supervised learning paradigm
based on weak annotation, semi-supervised learning paradigm based on limited
annotation, and self-supervised learning paradigm based on pathological image
representation learning. These new methods have led a new wave of automatic
pathological image diagnosis and analysis targeted at annotation efficiency.
With a survey of over 130 papers, we present a comprehensive and systematic
review of the latest studies on weakly supervised learning, semi-supervised
learning, and self-supervised learning in the field of computational pathology
from both technical and methodological perspectives. Finally, we present the
key challenges and future trends for these techniques.

The abundance of gaps in satellite image time series often complicates the
application of deep learning models such as convolutional neural networks for
spatiotemporal modeling. Based on previous work in computer vision on image
inpainting, this paper shows how three-dimensional spatiotemporal partial
convolutions can be used as layers in neural networks to fill gaps in satellite
image time series. To evaluate the approach, we apply a U-Net-like model on
incomplete image time series of quasi-global carbon monoxide observations from
the Sentinel-5P satellite. Prediction errors were comparable to two considered
statistical approaches while computation times for predictions were up to three
orders of magnitude faster, making the approach applicable to process large
amounts of satellite data. Partial convolutions can be added as layers to other
types of neural networks, making it relatively easy to integrate with existing
deep learning models. However, the approach does not quantify prediction errors
and further research is needed to understand and improve model transferability.
The implementation of spatiotemporal partial convolutions and the U-Net-like
model is available as open-source software.

We introduce a novel mathematical formulation for the training of
feed-forward neural networks with (potentially non-smooth) proximal maps as
activation functions. This formulation is based on Bregman distances and a key
advantage is that its partial derivatives with respect to the network's
parameters do not require the computation of derivatives of the network's
activation functions. Instead of estimating the parameters with a combination
of first-order optimisation method and back-propagation (as is the
state-of-the-art), we propose the use of non-smooth first-order optimisation
methods that exploit the specific structure of the novel formulation. We
present several numerical results that demonstrate that these training
approaches can be equally well or even better suited for the training of neural
network-based classifiers and (denoising) autoencoders with sparse coding
compared to more conventional training frameworks.

This paper analyzes the graph embedding method introduced in
\cite{Qiu_Recipe}, which is a bind-and-sum approach using spherical codes and
the tensor product to represent the edge set of a graph. We compare our
spherical/tensor method to similar methods, and in particular we examine the
competing scheme of Rademacher codes paired with the Hadamard product. We show
that the while the Hadamard product doesn't increase the dimension like the
tensor product, it suffers a proportional penalty in the number of edges it can
accurately store in superposition. In fact, the memory capacity ratio of the
Rademacher/Hadmard scheme is the same as the spherical/tensor scheme, and so
there is no true memory savings when using the Hadamard and its related binding
operations. We present some experimental results confirming our theoretical
ones and show that while on the surface our method might require more
parameters than competing methods, in reality it has the same efficiency.

Reconstructing 3D human body shapes from 3D partial textured scans remains a
fundamental task for many computer vision and graphics applications -- e.g.,
body animation, and virtual dressing. We propose a new neural network
architecture for 3D body shape and high-resolution texture completion --
BCom-Net -- that can reconstruct the full geometry from mid-level to high-level
partial input scans. We decompose the overall reconstruction task into two
stages - first, a joint implicit learning network (SCom-Net and TCom-Net) that
takes a voxelized scan and its occupancy grid as input to reconstruct the full
body shape and predict vertex textures. Second, a high-resolution texture
completion network, that utilizes the predicted coarse vertex textures to
inpaint the missing parts of the partial 'texture atlas'. A thorough
experimental evaluation on 3DBodyTex.V2 dataset shows that our method achieves
competitive results with respect to the state-of-the-art while generalizing to
different types and levels of partial shapes. The proposed method has also
ranked second in the track1 of SHApe Recovery from Partial textured 3D scans
(SHARP [38,1]) 2022 challenge1.

In this paper, our goal is to adapt a pre-trained Convolutional Neural
Network to domain shifts at test time. We do so continually with the incoming
stream of test batches, without labels. Existing literature mostly operates on
artificial shifts obtained via adversarial perturbations of a test image.
Motivated by this, we evaluate the state of the art on two realistic and
challenging sources of domain shifts, namely contextual and semantic shifts.
Contextual shifts correspond to the environment types, for example a model
pre-trained on indoor context has to adapt to the outdoor context on CORe-50
[7]. Semantic shifts correspond to the capture types, for example a model
pre-trained on natural images has to adapt to cliparts, sketches and paintings
on DomainNet [10]. We include in our analysis recent techniques such as
Prediction-Time Batch Normalization (BN) [8], Test Entropy Minimization (TENT)
[16] and Continual Test-Time Adaptation (CoTTA) [17]. Our findings are
three-fold: i) Test-time adaptation methods perform better and forget less on
contextual shifts compared to semantic shifts, ii) TENT outperforms other
methods on short-term adaptation, whereas CoTTA outpeforms other methods on
long-term adaptation, iii) BN is most reliable and robust.

We introduce a method for embedding graphs as vectors in a
structure-preserving manner. In this paper, we showcase its rich
representational capacity and give some theoretical properties of our method.
In particular, our procedure falls under the bind-and-sum approach, and we show
that our binding operation -- the tensor product -- is the most general binding
operation that respects the principle of superposition. Similarly, we show that
the spherical code achieves optimal compression. We then establish some precise
results characterizing the performance our method as well as some experimental
results showcasing how it can accurately perform various graph operations even
when the number of edges is quite large. Finally, we conclude with establishing
a link to adjacency matrices, showing that our method is, in some sense, a
generalization of adjacency matrices with applications towards large sparse
graphs.

Federated learning (FL) trains machine learning (ML) models on devices using
locally generated data and exchanges models without transferring raw data to a
distant server. This exchange incurs a communication overhead and impacts the
performance of FL training. There is limited understanding of how communication
protocols specifically contribute to the performance of FL. Such an
understanding is essential for selecting the right communication protocol when
designing an FL system. This paper presents FedComm, a benchmarking methodology
to quantify the impact of optimized application layer protocols, namely Message
Queue Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and
ZeroMQ Message Transport Protocol (ZMTP), and non-optimized application layer
protocols, namely as TCP and UDP, on the performance of FL. FedComm measures
the overall performance of FL in terms of communication time and accuracy under
varying computational and network stress and packet loss rates. Experiments on
a lab-based testbed demonstrate that TCP outperforms UDP as a non-optimized
application layer protocol with higher accuracy and shorter communication times
for 4G and Wi-Fi networks. Optimized application layer protocols such as AMQP,
MQTT, and ZMTP outperformed non-optimized application layer protocols in most
network conditions, resulting in a 2.5x reduction in communication time
compared to TCP while maintaining accuracy. The experimental results enable us
to highlight a number of open research issues for further investigation.
FedComm is available for download from https://github.com/qub-blesson/FedComm.

One-shot voice conversion (VC) with only a single target speaker's speech for
reference has become a hot research topic. Existing works generally disentangle
timbre, while information about pitch, rhythm and content is still mixed
together. To perform one-shot VC effectively with further disentangling these
speech components, we employ random resampling for pitch and content encoder
and use the variational contrastive log-ratio upper bound of mutual information
and gradient reversal layer based adversarial mutual information learning to
ensure the different parts of the latent space containing only the desired
disentangled representation during training. Experiments on the VCTK dataset
show the model achieves state-of-the-art performance for one-shot VC in terms
of naturalness and intellgibility. In addition, we can transfer characteristics
of one-shot VC on timbre, pitch and rhythm separately by speech representation
disentanglement. Our code, pre-trained models and demo are available at
https://im1eon.github.io/IS2022-SRDVC/.

Formaldehyde (HCHO) is one one of the most important trace gas in the
atmosphere, as it is a pollutant causing respiratory and other diseases. It is
also a precursor of tropospheric ozone which damages crops and deteriorates
human health. Study of HCHO chemistry and long-term monitoring using satellite
data is important from the perspective of human health, food security and air
pollution. Dynamic atmospheric chemistry models struggle to simulate
atmospheric formaldehyde and often overestimate by up to two times relative to
satellite observations and reanalysis. Spatial distribution of modelled HCHO
also fail to match satellite observations. Here, we present deep learning
approach using a simple super-resolution based convolutional neural network
towards simulating fast and reliable atmospheric HCHO. Our approach is an
indirect method of HCHO estimation without the need to chemical equations. We
find that deep learning outperforms dynamical model simulations which involves
complicated atmospheric chemistry representation. Causality establishing the
nonlinear relationships of different variables to target formaldehyde is
established in our approach by using a variety of precursors from meteorology
and chemical reanalysis to target OMI AURA satellite based HCHO predictions. We
choose South Asia for testing our implementation as it doesnt have in situ
measurements of formaldehyde and there is a need for improved quality data over
the region. Moreover, there are spatial and temporal data gaps in the satellite
product which can be removed by trustworthy modelling of atmospheric
formaldehyde. This study is a novel attempt using computer vision for
trustworthy modelling of formaldehyde from remote sensing can lead to cascading
societal benefits.

Facilitated by messaging protocols (MP), many home devices are connected to
the Internet, bringing convenience and accessibility to customers. However,
most deployed MPs on IoT platforms are fragmented and are not implemented
carefully to support secure communication. To the best of our knowledge, there
is no systematic solution to perform automatic security checks on MP
implementations yet.
  To bridge the gap, we present MPInspector, the first automatic and systematic
solution for vetting the security of MP implementations. MPInspector combines
model learning with formal analysis and operates in three stages: (a) using
parameter semantics extraction and interaction logic extraction to
automatically infer the state machine of an MP implementation, (b) generating
security properties based on meta properties and the state machine, and (c)
applying automatic property based formal verification to identify property
violations. We evaluate MPInspector on three popular MPs, including MQTT, CoAP
and AMQP, implemented on nine leading IoT platforms. It identifies 252 property
violations, leveraging which we further identify eleven types of attacks under
two realistic attack scenarios. In addition, we demonstrate that MPInspector is
lightweight (the average overhead of end-to-end analysis is ~4.5 hours) and
effective with a precision of 100% in identifying property violations.

Recently, the attention-enhanced multi-layer encoder, such as Transformer,
has been extensively studied in Machine Reading Comprehension (MRC). To predict
the answer, it is common practice to employ a predictor to draw information
only from the final encoder layer which generates the coarse-grained
representations of the source sequences, i.e., passage and question. The
analysis shows that the representation of source sequence becomes more
coarse-grained from finegrained as the encoding layer increases. It is
generally believed that with the growing number of layers in deep neural
networks, the encoding process will gather relevant information for each
location increasingly, resulting in more coarse-grained representations, which
adds the likelihood of similarity to other locations (referring to
homogeneity). Such phenomenon will mislead the model to make wrong judgement
and degrade the performance. In this paper, we argue that it would be better if
the predictor could exploit representations of different granularity from the
encoder, providing different views of the source sequences, such that the
expressive power of the model could be fully utilized. To this end, we propose
a novel approach called Adaptive Bidirectional Attention-Capsule Network
(ABA-Net), which adaptively exploits the source representations of different
levels to the predictor. Furthermore, due to the better representations are at
the core for boosting MRC performance, the capsule network and self-attention
module are carefully designed as the building blocks of our encoders, which
provides the capability to explore the local and global representations,
respectively. Experimental results on three benchmark datasets, i.e., SQuAD
1.0, SQuAD 2.0 and COQA, demonstrate the effectiveness of our approach. In
particular, we set the new state-of-the-art performance on the SQuAD 1.0
dataset

Quantum machine learning offers a promising advantage in extracting
information about quantum states, e.g. phase diagram. However, access to
training labels is a major bottleneck for any supervised approach, preventing
extracting insights about new physics. In this work, using quantum
convolutional neural networks we overcome this limit with the determination of
the phase diagram of a model where no analytical solutions are known, by
training on marginal points of the phase diagram where integrable models are
represented. More specifically, we consider the Axial Next Nearest Neighbor
Ising (ANNNI) Hamiltonian, which possesses a ferro-, para-magnetic and
antiphase and we show that the whole phase diagram can be reproduced.

Email phishing has become more prevalent and grows more sophisticated over
time. To combat this rise, many machine learning (ML) algorithms for detecting
phishing emails have been developed. However, due to the limited email data
sets on which these algorithms train, they are not adept at recognising varied
attacks and, thus, suffer from concept drift; attackers can introduce small
changes in the statistical characteristics of their emails or websites to
successfully bypass detection. Over time, a gap develops between the reported
accuracy from literature and the algorithm's actual effectiveness in the real
world. This realises itself in frequent false positive and false negative
classifications.
  To this end, we propose a multidimensional risk assessment of emails to
reduce the feasibility of an attacker adapting their email and avoiding
detection. This horizontal approach to email phishing detection profiles an
incoming email on its main features. We develop a risk assessment framework
that includes three models which analyse an email's (1) threat level, (2)
cognitive manipulation, and (3) email type, which we combine to return the
final risk assessment score. The Profiler does not require large data sets to
train on to be effective and its analysis of varied email features reduces the
impact of concept drift. Our Profiler can be used in conjunction with ML
approaches, to reduce their misclassifications or as a labeller for large email
data sets in the training stage.
  We evaluate the efficacy of the Profiler against a machine learning ensemble
using state-of-the-art ML algorithms on a data set of 9000 legitimate and 900
phishing emails from a large Australian research organisation. Our results
indicate that the Profiler's mitigates the impact of concept drift, and
delivers 30% less false positive and 25% less false negative email
classifications over the ML ensemble's approach.

The actor-critic (AC) reinforcement learning algorithms have been the
powerhouse behind many challenging applications. Nevertheless, its convergence
is fragile in general. To study its instability, existing works mostly consider
the uncommon double-loop variant or basic models with finite state and action
space. We investigate the more practical single-sample two-timescale AC for
solving the canonical linear quadratic regulator (LQR) problem, where the actor
and the critic update only once with a single sample in each iteration on an
unbounded continuous state and action space. Existing analysis cannot conclude
the convergence for such a challenging case. We develop a new analysis
framework that allows establishing the global convergence to an
$\epsilon$-optimal solution with at most an
$\tilde{\mathcal{O}}(\epsilon^{-2.5})$ sample complexity. To our knowledge,
this is the first finite-time convergence analysis for the single sample
two-timescale AC for solving LQR with global optimality. The sample complexity
improves those of other variants by orders, which sheds light on the practical
wisdom of single sample algorithms. We also further validate our theoretical
findings via comprehensive simulation comparisons.

The number of internet-connected devices has been exponentially growing with
the massive volume of heterogeneous data generated from various devices,
resulting in a highly intertwined cyber-physical system. Currently, the Edge
Intelligence System (EIS) concept that leverages the merits of edge computing
and Artificial Intelligence (AI) is utilized to provide smart cloud services
with powerful computational processing and reduce decision-making delays. Thus,
EIS offers a possible solution to realizing future Intelligent Transportation
Systems (ITS), especially in a vehicular network framework. However, since the
central aggregator server supervises the entire system orchestration, the
existing EIS framework faces several challenges and is still potentially
susceptible to numerous malicious attacks. Hence, to solve the issues mentioned
earlier, this paper presents the notion of secure edge intelligence, merging
the benefits of Federated Learning (FL), blockchain, and Local Differential
Privacy (LDP). The blockchain-assisted FL approach efficiently improves traffic
prediction accuracy and enhances user privacy and security by recording
transactions in immutable distributed ledger networks and providing a
decentralized reward mechanism system. Furthermore, LDP is empowered to
strengthen the confidentiality of data sharing transactions, especially in
protecting users' private data from various attacks. The proposed framework has
been implemented in two scenarios, i.e., blockchain-based FL to efficiently
develop the decentralized traffic management for vehicular networks and
LDP-based FL to produce randomized privacy protection using the IBM Library for
differential privacy.

Bayesian optimization (BO) is a well-established method to optimize black-box
functions whose direct evaluations are costly. In this paper, we tackle the
problem of incorporating expert knowledge into BO, with the goal of further
accelerating the optimization, which has received very little attention so far.
We design a multi-task learning architecture for this task, with the goal of
jointly eliciting the expert knowledge and minimizing the objective function.
In particular, this allows for the expert knowledge to be transferred into the
BO task. We introduce a specific architecture based on Siamese neural networks
to handle the knowledge elicitation from pairwise queries. Experiments on
various benchmark functions with both simulated and actual human experts show
that the proposed method significantly speeds up BO even when the expert
knowledge is biased compared to the objective function.

Compared to traditional learning from scratch, knowledge distillation
sometimes makes the DNN achieve superior performance. This paper provides a new
perspective to explain the success of knowledge distillation, i.e., quantifying
knowledge points encoded in intermediate layers of a DNN for classification,
based on the information theory. To this end, we consider the signal processing
in a DNN as the layer-wise information discarding. A knowledge point is
referred to as an input unit, whose information is much less discarded than
other input units. Thus, we propose three hypotheses for knowledge distillation
based on the quantification of knowledge points. 1. The DNN learning from
knowledge distillation encodes more knowledge points than the DNN learning from
scratch. 2. Knowledge distillation makes the DNN more likely to learn different
knowledge points simultaneously. In comparison, the DNN learning from scratch
tends to encode various knowledge points sequentially. 3. The DNN learning from
knowledge distillation is often optimized more stably than the DNN learning
from scratch. In order to verify the above hypotheses, we design three types of
metrics with annotations of foreground objects to analyze feature
representations of the DNN, \textit{i.e.} the quantity and the quality of
knowledge points, the learning speed of different knowledge points, and the
stability of optimization directions. In experiments, we diagnosed various DNNs
for different classification tasks, i.e., image classification, 3D point cloud
classification, binary sentiment classification, and question answering, which
verified above hypotheses.

Detecting tiny objects is one of the main obstacles hindering the development
of object detection. The performance of generic object detectors tends to
drastically deteriorate on tiny object detection tasks. In this paper, we point
out that either box prior in the anchor-based detector or point prior in the
anchor-free detector is sub-optimal for tiny objects. Our key observation is
that the current anchor-based or anchor-free label assignment paradigms will
incur many outlier tiny-sized ground truth samples, leading to detectors
imposing less focus on the tiny objects. To this end, we propose a Gaussian
Receptive Field based Label Assignment (RFLA) strategy for tiny object
detection. Specifically, RFLA first utilizes the prior information that the
feature receptive field follows Gaussian distribution. Then, instead of
assigning samples with IoU or center sampling strategy, a new Receptive Field
Distance (RFD) is proposed to directly measure the similarity between the
Gaussian receptive field and ground truth. Considering that the IoU-threshold
based and center sampling strategy are skewed to large objects, we further
design a Hierarchical Label Assignment (HLA) module based on RFD to achieve
balanced learning for tiny objects. Extensive experiments on four datasets
demonstrate the effectiveness of the proposed methods. Especially, our approach
outperforms the state-of-the-art competitors with 4.0 AP points on the AI-TOD
dataset. Codes are available at https://github.com/Chasel-Tsui/mmdet-rfla

According to cognitive psychology and related disciplines, the development of
complex problem-solving behaviour in biological agents depends on hierarchical
cognitive mechanisms. Hierarchical reinforcement learning is a promising
computational approach that may eventually yield comparable problem-solving
behaviour in artificial agents and robots. However, to date the problem-solving
abilities of many human and non-human animals are clearly superior to those of
artificial systems. Here, we propose steps to integrate biologically inspired
hierarchical mechanisms to enable advanced problem-solving skills in artificial
agents. Therefore, we first review the literature in cognitive psychology to
highlight the importance of compositional abstraction and predictive
processing. Then we relate the gained insights with contemporary hierarchical
reinforcement learning methods. Interestingly, our results suggest that all
identified cognitive mechanisms have been implemented individually in isolated
computational architectures, raising the question of why there exists no single
unifying architecture that integrates them. As our final contribution, we
address this question by providing an integrative perspective on the
computational challenges to develop such a unifying architecture. We expect our
results to guide the development of more sophisticated cognitively inspired
hierarchical machine learning architectures.

In tokamak operations, accurate equilibrium reconstruction is essential for
reliable real-time control and realistic post-shot instability analysis. The
safety factor (q) profile defines the magnetic field line pitch angle, which is
the central element in equilibrium reconstruction. The motional Stark effect
(MSE) diagnostic has been a standard measurement for the magnetic field line
pitch angle in tokamaks that are equipped with neutral beams. However, the MSE
data are not always available due to experimental constraints, especially in
future devices without neutral beams. Here we develop a deep learning-based
surrogate model of the gyrokinetic toroidal code for q profile reconstruction
(SGTC-QR) that can reconstruct the q profile with the measurements without MSE
to mimic the traditional equilibrium reconstruction with the MSE constraint.
The model demonstrates promising performance, and the sub-millisecond inference
time is compatible with the real-time plasma control system.

A basic premise in graph signal processing (GSP) is that a graph encoding
pairwise (anti-)correlations of the targeted signal as edge weights is
exploited for graph filtering. However, existing fast graph sampling schemes
are designed and tested only for positive graphs describing positive
correlations. In this paper, we show that for datasets with strong inherent
anti-correlations, a suitable graph contains both positive and negative edge
weights. In response, we propose a linear-time signed graph sampling method
centered on the concept of balanced signed graphs. Specifically, given an
empirical covariance data matrix $\bar{\bf{C}}$, we first learn a sparse
inverse matrix (graph Laplacian) $\mathcal{L}$ corresponding to a signed graph
$\mathcal{G}$. We define the eigenvectors of Laplacian $\mathcal{L}_B$ for a
balanced signed graph $\mathcal{G}_B$ -- approximating $\mathcal{G}$ via edge
weight augmentation -- as graph frequency components. Next, we choose samples
to minimize the low-pass filter reconstruction error in two steps. We first
align all Gershgorin disc left-ends of Laplacian $\mathcal{L}_B$ at smallest
eigenvalue $\lambda_{\min}(\mathcal{L}_B)$ via similarity transform
$\mathcal{L}_p = \S \mathcal{L}_B \S^{-1}$, leveraging a recent linear algebra
theorem called Gershgorin disc perfect alignment (GDPA). We then perform
sampling on $\mathcal{L}_p$ using a previous fast Gershgorin disc alignment
sampling (GDAS) scheme. Experimental results show that our signed graph
sampling method outperformed existing fast sampling schemes noticeably on
various datasets.

Social recommendations utilize social relations to enhance the representation
learning for recommendations. Most social recommendation models unify user
representations for the user-item interactions (collaborative domain) and
social relations (social domain). However, such an approach may fail to model
the users heterogeneous behavior patterns in two domains, impairing the
expressiveness of user representations. In this work, to address such
limitation, we propose a novel Disentangled contrastive learning framework for
social Recommendations DcRec. More specifically, we propose to learn
disentangled users representations from the item and social domains. Moreover,
disentangled contrastive learning is designed to perform knowledge transfer
between disentangled users representations for social recommendations.
Comprehensive experiments on various real-world datasets demonstrate the
superiority of our proposed model.

Active inference provides a general framework for behavior and learning in
autonomous agents. It states that an agent will attempt to minimize its
variational free energy, defined in terms of beliefs over observations,
internal states and policies. Traditionally, every aspect of a discrete active
inference model must be specified by hand, i.e. by manually defining the hidden
state space structure, as well as the required distributions such as likelihood
and transition probabilities. Recently, efforts have been made to learn state
space representations automatically from observations using deep neural
networks. In this paper, we present a novel approach of learning state spaces
using quantum physics-inspired tensor networks. The ability of tensor networks
to represent the probabilistic nature of quantum states as well as to reduce
large state spaces makes tensor networks a natural candidate for active
inference. We show how tensor networks can be used as a generative model for
sequential data. Furthermore, we show how one can obtain beliefs from such a
generative model and how an active inference agent can use these to compute the
expected free energy. Finally, we demonstrate our method on the classic T-maze

We study the approximation of functions which are invariant with respect to
certain permutations of the input indices using flow maps of dynamical systems.
Such invariant functions includes the much studied translation-invariant ones
involving image tasks, but also encompasses many permutation-invariant
functions that finds emerging applications in science and engineering. We prove
sufficient conditions for universal approximation of these functions by a
controlled equivariant dynamical system, which can be viewed as a general
abstraction of deep residual networks with symmetry constraints. These results
not only imply the universal approximation for a variety of commonly employed
neural network architectures for symmetric function approximation, but also
guide the design of architectures with approximation guarantees for
applications involving new symmetry requirements.

Fast and user-controllable music generation could enable novel ways of
composing or performing music. However, state-of-the-art music generation
systems require large amounts of data and computational resources for training,
and are slow at inference. This makes them impractical for real-time
interactive use. In this work, we introduce Musika, a music generation system
that can be trained on hundreds of hours of music using a single consumer GPU,
and that allows for much faster than real-time generation of music of arbitrary
length on a consumer CPU. We achieve this by first learning a compact
invertible representation of spectrogram magnitudes and phases with adversarial
autoencoders, then training a Generative Adversarial Network (GAN) on this
representation for a particular music domain. A latent coordinate system
enables generating arbitrarily long sequences of excerpts in parallel, while a
global context vector allows the music to remain stylistically coherent through
time. We perform quantitative evaluations to assess the quality of the
generated samples and showcase options for user control in piano and techno
music generation. We release the source code and pretrained autoencoder weights
at github.com/marcoppasini/musika, such that a GAN can be trained on a new
music domain with a single GPU in a matter of hours.

The security of deep learning (DL) systems is an extremely important field of
study as they are being deployed in several applications due to their
ever-improving performance to solve challenging tasks. Despite overwhelming
promises, the deep learning systems are vulnerable to crafted adversarial
examples, which may be imperceptible to the human eye, but can lead the model
to misclassify. Protections against adversarial perturbations on ensemble-based
techniques have either been shown to be vulnerable to stronger adversaries or
shown to lack an end-to-end evaluation. In this paper, we attempt to develop a
new ensemble-based solution that constructs defender models with diverse
decision boundaries with respect to the original model. The ensemble of
classifiers constructed by (1) transformation of the input by a method called
Split-and-Shuffle, and (2) restricting the significant features by a method
called Contrast-Significant-Features are shown to result in diverse gradients
with respect to adversarial attacks, which reduces the chance of transferring
adversarial examples from the original to the defender model targeting the same
class. We present extensive experimentations using standard image
classification datasets, namely MNIST, CIFAR-10 and CIFAR-100 against
state-of-the-art adversarial attacks to demonstrate the robustness of the
proposed ensemble-based defense. We also evaluate the robustness in the
presence of a stronger adversary targeting all the models within the ensemble
simultaneously. Results for the overall false positives and false negatives
have been furnished to estimate the overall performance of the proposed
methodology.

Recommender systems deployed in real-world applications can have inherent
exposure bias, which leads to the biased logged data plaguing the researchers.
A fundamental way to address this thorny problem is to collect users'
interactions on randomly expose items, i.e., the missing-at-random data. A few
works have asked certain users to rate or select randomly recommended items,
e.g., Yahoo!, Coat, and OpenBandit. However, these datasets are either too
small in size or lack key information, such as unique user ID or the features
of users/items. In this work, we present KuaiRand, an unbiased sequential
recommendation dataset containing millions of intervened interactions on
randomly exposed videos, collected from the video-sharing mobile App, Kuaishou.
Different from existing datasets, KuaiRand records 12 kinds of user feedback
signals (e.g., click, like, and view time) on randomly exposed videos inserted
in the recommendation feeds in two weeks. To facilitate model learning, we
further collect rich features of users and items as well as users' behavior
history. By releasing this dataset, we enable the research of advanced
debiasing large-scale recommendation scenarios for the first time. Also, with
its distinctive features, KuaiRand can support various other research
directions such as interactive recommendation, long sequential behavior
modeling, and multi-task learning. The dataset and its news will be available
at https://kuairand.com.

Shared control can help in teleoperated object manipulation by assisting with
the execution of the user's intention. To this end, robust and prompt intention
estimation is needed, which relies on behavioral observations. Here, an
intention estimation framework is presented, which uses natural gaze and motion
features to predict the current action and the target object. The system is
trained and tested in a simulated environment with pick and place sequences
produced in a relatively cluttered scene and with both hands, with possible
hand-over to the other hand. Validation is conducted across different users and
hands, achieving good accuracy and earliness of prediction. An analysis of the
predictive power of single features shows the predominance of the grasping
trigger and the gaze features in the early identification of the current
action. In the current framework, the same probabilistic model can be used for
the two hands working in parallel and independently, while a rule-based model
is proposed to identify the resulting bimanual action. Finally, limitations and
perspectives of this approach to more complex, full-bimanual manipulations are

In this paper, we present an end-to-end approach to generate high-resolution
person images conditioned on texts only. State-of-the-art text-to-image
generation models are mainly designed for center-object generation, e.g.,
flowers and birds. Unlike center-placed objects with similar shapes and
orientation, person image generation is a more challenging task, for which we
observe the followings: 1) the generated images for the same person exhibit
visual details with identity-consistency, e.g., identity-related
textures/clothes/shoes across the images, and 2) those images should be
discriminant for being robust against the inter-person variations caused by
visual ambiguities. To address the above challenges, we develop an effective
generative model to produce person images with two novel mechanisms. In
particular, our first mechanism (called T-Person-GAN-ID) is to integrate the
one-stream generator with an identity-preserving network such that the
representations of generated data are regularized in their feature space to
ensure the identity-consistency. The second mechanism (called
T-Person-GAN-ID-MM) is based on the manifold mix-up to produce mixed images via
the linear interpolation across generated images from different manifold
identities, and we further enforce such interpolated images to be linearly
classified in the feature space. This amounts to learning a linear
classification boundary that can perfectly separate images from two identities.
Our proposed method is empirically validated to achieve a remarkable
improvement in text-to-person image generation. Our architecture is orthogonal
to StackGAN++ , and focuses on person image generation, with all of them
together to enrich the spectrum of GANs for the image generation task. Codes
are available on
\url{https://github.com/linwu-github/Person-Image-Generation.git}.

The following paper is a reproducibility report for "Social NCE: Contrastive
Learning of Socially-aware Motion Representations" {\cite{liu2020snce}}
published in ICCV 2021 as part of the ML Reproducibility Challenge 2021. The
original code was made available by the author
\footnote{\href{https://github.com/vita-epfl/social-nce}{https://github.com/vita-epfl/social-nce}}.
We attempted to verify the results claimed by the authors and reimplemented
their code in PyTorch Lightning.

Maximizing a monotone submodular function is a fundamental task in machine
learning, economics, and statistics. In this paper, we present two
communication-efficient decentralized online algorithms for the monotone
continuous DR-submodular maximization problem, both of which reduce the number
of per-function gradient evaluations and per-round communication complexity
from $T^{3/2}$ to $1$. The first one, One-shot Decentralized Meta-Frank-Wolfe
(Mono-DMFW), achieves a $(1-1/e)$-regret bound of $O(T^{4/5})$. As far as we
know, this is the first one-shot and projection-free decentralized online
algorithm for monotone continuous DR-submodular maximization. Next, inspired by
the non-oblivious boosting function \citep{zhang2022boosting}, we propose the
Decentralized Online Boosting Gradient Ascent (DOBGA) algorithm, which attains
a $(1-1/e)$-regret of $O(\sqrt{T})$. To the best of our knowledge, this is the
first result to obtain the optimal $O(\sqrt{T})$ against a
$(1-1/e)$-approximation with only one gradient inquiry for each local objective
function per step. Finally, various experimental results confirm the

Cross-domain sentiment analysis aims to predict the sentiment of texts in the
target domain using the model trained on the source domain to cope with the
scarcity of labeled data. Previous studies are mostly cross-entropy-based
methods for the task, which suffer from instability and poor generalization. In
this paper, we explore contrastive learning on the cross-domain sentiment
analysis task. We propose a modified contrastive objective with in-batch
negative samples so that the sentence representations from the same class will
be pushed close while those from the different classes become further apart in
the latent space. Experiments on two widely used datasets show that our model
can achieve state-of-the-art performance in both cross-domain and multi-domain
sentiment analysis tasks. Meanwhile, visualizations demonstrate the
effectiveness of transferring knowledge learned in the source domain to the
target domain and the adversarial test verifies the robustness of our model.

Malicious attackers can generate targeted adversarial examples by imposing
human-imperceptible noise on images, forcing neural network models to produce
specific incorrect outputs. With cross-model transferable adversarial examples,
the vulnerability of neural networks remains even if the model information is
kept secret from the attacker. Recent studies have shown the effectiveness of
ensemble-based methods in generating transferable adversarial examples.
However, existing methods fall short under the more challenging scenario of
creating targeted attacks transferable among distinct models. In this work, we
propose Diversified Weight Pruning (DWP) to further enhance the ensemble-based
methods by leveraging the weight pruning method commonly used in model
compression. Specifically, we obtain multiple diverse models by a random weight
pruning method. These models preserve similar accuracies and can serve as
additional models for ensemble-based methods, yielding stronger transferable
targeted attacks. Experiments on ImageNet-Compatible Dataset under the more
challenging scenarios are provided: transferring to distinct architectures and
to adversarially trained models. The results show that our proposed DWP
improves the targeted attack success rates with up to 4.1% and 8.0% on the
combination of state-of-the-art methods, respectively

Manual labeling of tree species remains a challenging task, especially in
tropical regions, owing to inaccessibility and labor-intensive ground-based
surveys. Hyperspectral images (HSIs), through their narrow and contiguous
bands, can assist in distinguishing tree species based on their spectral
properties. Therefore, automated classification algorithms on HSI images can
help augment the limited labeled information and generate a real-time
classification map for various tree species. Achieving high classification
accuracy with a limited amount of labeled information in an image is one of the
key challenges that researchers have started addressing in recent years. We
propose a novel graph-regularized neural network (GRNN) algorithm that
encompasses the superpixel-based segmentation for graph construction, a
pixel-wise neural network classifier, and the label propagation technique to
generate an accurate classification map. GRNN outperforms several
state-of-the-art techniques not only for the standard Indian Pines HSI but also
achieves a high classification accuracy (approx. 92%) on a new HSI data set
collected over the forests of French Guiana (FG) even when less than 1% of the
pixels are labeled. We show that GRNN is not only competitive with the
state-of-the-art semi-supervised methods, but also exhibits lower variance in
accuracy for different number of training samples and over different
independent random sampling of the labeled pixels for training.

Respiratory rate (RR) is an important biomarker as RR changes can reflect
severe medical events such as heart disease, lung disease, and sleep disorders.
Unfortunately, however, standard manual RR counting is prone to human error and
cannot be performed continuously. This study proposes a method for continuously
estimating RR, RRWaveNet. The method is a compact end-to-end deep learning
model which does not require feature engineering and can use low-cost raw
photoplethysmography (PPG) as input signal. RRWaveNet was tested
subject-independently and compared to baseline in three datasets (BIDMC,
CapnoBase, and WESAD) and using three window sizes (16, 32, and 64 seconds).
RRWaveNet outperformed current state-of-the-art methods with mean absolute
errors at optimal window size of 1.66 \pm 1.01, 1.59 \pm 1.08, and 1.92 \pm
0.96 breaths per minute for each dataset. In remote monitoring settings, such
as in the WESAD dataset, we apply transfer learning to two other ICU datasets,
reducing the MAE to 1.52 \pm 0.50 breaths per minute, showing this model allows
accurate and practical estimation of RR on affordable and wearable devices. Our
study shows feasibility of remote RR monitoring in the context of telemedicine
and at home.

A major problem of structural health monitoring (SHM) has been the prognosis
of damage and the definition of the remaining useful life of a structure. Both
tasks depend on many parameters, many of which are often uncertain. Many models
have been developed for the aforementioned tasks but they have been either
deterministic or stochastic with the ability to take into account only a
restricted amount of past states of the structure. In the current work, a
generative model is proposed in order to make predictions about the damage
evolution of structures. The model is able to perform in a population-based SHM
(PBSHM) framework, to take into account many past states of the damaged
structure, to incorporate uncertainties in the modelling process and to
generate potential damage evolution outcomes according to data acquired from a
structure. The algorithm is tested on a simulated damage evolution example and
the results reveal that it is able to provide quite confident predictions about
the remaining useful life of structures within a population.

Denoising diffusion probabilistic models (DDPMs) are a recent family of
generative models that achieve state-of-the-art results. In order to obtain
class-conditional generation, it was suggested to guide the diffusion process
by gradients from a time-dependent classifier. While the idea is theoretically
sound, deep learning-based classifiers are infamously susceptible to
gradient-based adversarial attacks. Therefore, while traditional classifiers
may achieve good accuracy scores, their gradients are possibly unreliable and
might hinder the improvement of the generation results. Recent work discovered
that adversarially robust classifiers exhibit gradients that are aligned with
human perception, and these could better guide a generative process towards
semantically meaningful images. We utilize this observation by defining and
training a time-dependent adversarially robust classifier and use it as
guidance for a generative diffusion model. In experiments on the highly
challenging and diverse ImageNet dataset, our scheme introduces significantly
more intelligible intermediate gradients, better alignment with theoretical
findings, as well as improved generation results under several evaluation
metrics. Furthermore, we conduct an opinion survey whose findings indicate that
human raters prefer our method's results.

Secure multi-party computation-based machine learning, referred to as MPL,
has become an important technology to utilize data from multiple parties with
privacy preservation. While MPL provides rigorous security guarantees for the
computation process, the models trained by MPL are still vulnerable to attacks
that solely depend on access to the models. Differential privacy could help to
defend against such attacks. However, the accuracy loss brought by differential
privacy and the huge communication overhead of secure multi-party computation
protocols make it highly challenging to balance the 3-way trade-off between
privacy, efficiency, and accuracy.
  In this paper, we are motivated to resolve the above issue by proposing a
solution, referred to as PEA (Private, Efficient, Accurate), which consists of
a secure DPSGD protocol and two optimization methods. First, we propose a
secure DPSGD protocol to enforce DPSGD in secret sharing-based MPL frameworks.
Second, to reduce the accuracy loss led by differential privacy noise and the
huge communication overhead of MPL, we propose two optimization methods for the
training process of MPL: (1) the data-independent feature extraction method,
which aims to simplify the trained model structure; (2) the local data-based
global model initialization method, which aims to speed up the convergence of
the model training. We implement PEA in two open-source MPL frameworks:
TF-Encrypted and Queqiao. The experimental results on various datasets
demonstrate the efficiency and effectiveness of PEA. E.g. when ${\epsilon}$ =
2, we can train a differentially private classification model with an accuracy
of 88% for CIFAR-10 within 7 minutes under the LAN setting. This result
significantly outperforms the one from CryptGPU, one SOTA MPL framework: it
costs more than 16 hours to train a non-private deep neural network model on
CIFAR-10 with the same accuracy.

Recent domain generalization (DG) approaches typically use the hypothesis
learned on source domains for inference on the unseen target domain. However,
such a hypothesis can be arbitrarily far from the optimal one for the target
domain, induced by a gap termed ``adaptivity gap''. Without exploiting the
domain information from the unseen test samples, adaptivity gap estimation and
minimization are intractable, which hinders us to robustify a model to any
unknown distribution. In this paper, we first establish a generalization bound
that explicitly considers the adaptivity gap. Our bound motivates two
strategies to reduce the gap: the first one is ensembling multiple classifiers
to enrich the hypothesis space, then we propose effective gap estimation
methods for guiding the selection of a better hypothesis for the target. The
other method is minimizing the gap directly by adapting model parameters using
online target samples. We thus propose \textbf{Domain-specific Risk
Minimization (DRM)}. During training, DRM models the distributions of different
source domains separately; for inference, DRM performs online model steering
using the source hypothesis for each arriving target sample. Extensive
experiments demonstrate the effectiveness of the proposed DRM for domain
generalization with the following advantages: 1) it significantly outperforms
competitive baselines on different distributional shift settings; 2) it
achieves either comparable or superior accuracies on all source domains
compared to vanilla empirical risk minimization; 3) it remains simple and
efficient during training, and 4) it is complementary to invariant learning

Clinical data usually cannot be freely distributed due to their highly
confidential nature and this hampers the development of machine learning in the
healthcare domain. One way to mitigate this problem is by generating realistic
synthetic datasets using generative adversarial networks (GANs). However, GANs
are known to suffer from mode collapse and thus creating outputs of low
diveristy. In this paper, we extend the classic GAN setup with an external
memory to replay features from real samples. Using antiretroviral therapy for
human immunodeficiency virus (ART for HIV) as a case study, we show that our
extended setup increases convergence and more importantly, it is effective in
capturing the severe class imbalanced distributions common to real world
clinical data.

There is currently no established method for evaluating human response timing
across a range of naturalistic traffic conflict types. Traditional notions
derived from controlled experiments, such as perception-response time, fail to
account for the situation-dependency of human responses and offer no clear way
to define the stimulus in many common traffic conflict scenarios. As a result,
they are not well suited for application in naturalistic settings. Our main
contribution is the development of a novel framework for measuring and modeling
response times in naturalistic traffic conflicts applicable to automated
driving systems as well as other traffic safety domains. The framework suggests
that response timing must be understood relative to the subject's current
(prior) belief and is always embedded in, and dependent on, the dynamically
evolving situation. The response process is modeled as a belief update process
driven by perceived violations to this prior belief, that is, by surprising
stimuli. The framework resolves two key limitations with traditional notions of
response time when applied in naturalistic scenarios: (1) The strong
situation-dependence of response timing and (2) how to unambiguously define the
stimulus. Resolving these issues is a challenge that must be addressed by any
response timing model intended to be applied in naturalistic traffic conflicts.
We show how the framework can be implemented by means of a relatively simple
heuristic model fit to naturalistic human response data from real crashes and
near crashes from the SHRP2 dataset and discuss how it is, in principle,
generalizable to any traffic conflict scenario. We also discuss how the
response timing framework can be implemented computationally based on evidence
accumulation enhanced by machine learning-based generative models and the
information-theoretic concept of surprise.

Game theory has been an effective tool in the control of disease spread and
in suggesting optimal policies at both individual and area levels. In this AMS
Notices article, we focus on the decision-making development for the
intervention of COVID-19, aiming to provide mathematical models and efficient
machine learning methods, and justifications for related policies that have
been implemented in the past and explain how the authorities' decisions affect
their neighboring regions from a game theory viewpoint.

When using deep learning techniques to model program languages, neural
networks with tree or graph structures are widely adopted to capture the rich
structural information within program abstract syntax trees (AST). However,
long-term/global dependencies widely exist in programs, and most of these
neural architectures fail to capture these dependencies. In this paper, we
propose Tree-Transformer, a novel recursive tree-structured neural network
which aims to overcome the above limitations. Tree-Transformer leverages two
multi-head attention units to model the dependency between siblings and
parent-children node pairs. Moreover, we propose a bi-directional propagation
strategy to allow node information passing in two directions: bottom-up and
top-down along trees. By combining bottom-up and top-down propagation,
Tree-Transformer can learn both global contexts and meaningful node features.
The extensive experimental results show that our Tree-Transformer outperforms
existing tree-based or graph-based neural networks in program-related tasks
with tree-level and node-level prediction tasks, indicating that
Tree-Transformer performs well on learning both tree-level and node-level

Many two-sample network hypothesis testing methodologies operate under the
implicit assumption that the vertex correspondence across networks is a priori
known. In this paper, we consider the degradation of power in two-sample graph
hypothesis testing when there are misaligned/label-shuffled vertices across
networks. In the context of stochastic block model networks, we theoretically
explore the power loss due to shuffling for a pair of hypothesis tests based on
Frobenius norm differences between estimated edge probability matrices or
between adjacency matrices. The loss in testing power is further reinforced by
numerous simulations and experiments, both in the stochastic block model and in
the random dot product graph model, where we compare the power loss across
multiple recently proposed tests in the literature. Lastly, we demonstrate the
impact that shuffling can have in real-data testing in a pair of examples from
neuroscience and from social network analysis.

In this paper, we develop a physics-informed neural network (PINN) model for
parabolic problems with a sharply perturbed initial condition. As an example of
a parabolic problem, we consider the advection-dispersion equation (ADE) with a
point (Gaussian) source initial condition. In the $d$-dimensional ADE,
perturbations in the initial condition decay with time $t$ as $t^{-d/2}$, which
can cause a large approximation error in the PINN solution. Localized large
gradients in the ADE solution make the (common in PINN) Latin hypercube
sampling of the equation's residual highly inefficient. Finally, the PINN
solution of parabolic equations is sensitive to the choice of weights in the
loss function. We propose a normalized form of ADE where the initial
perturbation of the solution does not decrease in amplitude and demonstrate
that this normalization significantly reduces the PINN approximation error. We
propose criteria for weights in the loss function that produce a more accurate
PINN solution than those obtained with the weights selected via other methods.
Finally, we proposed an adaptive sampling scheme that significantly reduces the
PINN solution error for the same number of the sampling (residual) points. We
demonstrate the accuracy of the proposed PINN model for forward, inverse, and
backward ADEs.

We present a novel semi-supervised learning framework that intelligently
leverages the consistency regularization between the model's predictions from
two strongly-augmented views of an image, weighted by a confidence of
pseudo-label, dubbed ConMatch. While the latest semi-supervised learning
methods use weakly- and strongly-augmented views of an image to define a
directional consistency loss, how to define such direction for the consistency
regularization between two strongly-augmented views remains unexplored. To
account for this, we present novel confidence measures for pseudo-labels from
strongly-augmented views by means of weakly-augmented view as an anchor in
non-parametric and parametric approaches. Especially, in parametric approach,
we present, for the first time, to learn the confidence of pseudo-label within
the networks, which is learned with backbone model in an end-to-end manner. In
addition, we also present a stage-wise training to boost the convergence of
training. When incorporated in existing semi-supervised learners, ConMatch
consistently boosts the performance. We conduct experiments to demonstrate the
effectiveness of our ConMatch over the latest methods and provide extensive
ablation studies. Code has been made publicly available at
https://github.com/JiwonCocoder/ConMatch.

We present a conceptually simple, flexible, and universal visual perception
head for variant visual tasks, e.g., classification, object detection, instance
segmentation and pose estimation, and different frameworks, such as one-stage
or two-stage pipelines. Our approach effectively identifies an object in an
image while simultaneously generating a high-quality bounding box or
contour-based segmentation mask or set of keypoints. The method, called
UniHead, views different visual perception tasks as the dispersible points
learning via the transformer encoder architecture. Given a fixed spatial
coordinate, UniHead adaptively scatters it to different spatial points and
reasons about their relations by transformer encoder. It directly outputs the
final set of predictions in the form of multiple points, allowing us to perform
different visual tasks in different frameworks with the same head design. We
show extensive evaluations on ImageNet classification and all three tracks of
the COCO suite of challenges, including object detection, instance segmentation
and pose estimation. Without bells and whistles, UniHead can unify these visual
tasks via a single visual head design and achieve comparable performance
compared to expert models developed for each task.We hope our simple and
universal UniHead will serve as a solid baseline and help promote universal
visual perception research. Code and models are available at
https://github.com/Sense-X/UniHead.

Building dialogue generation systems in a zero-shot scenario remains a huge
challenge, since the typical zero-shot approaches in dialogue generation rely
heavily on large-scale pre-trained language generation models such as GPT-3 and
T5. The research on zero-shot dialogue generation without cumbersome language
models is limited due to lacking corresponding parallel dialogue corpora. In
this paper, we propose a simple but effective Multilingual learning framework
for Zero-shot Dialogue Generation (dubbed as MulZDG) that can effectively
transfer knowledge from an English corpus with large-scale training samples to
a non-English corpus with zero samples. Besides, MulZDG can be viewed as a
multilingual data augmentation method to improve the performance of the
resource-rich language. First, we construct multilingual code-switching
dialogue datasets via translation utterances randomly selected from monolingual
English datasets. Then we employ MulZDG to train a unified multilingual
dialogue model based on the code-switching datasets. The MulZDG can conduct
implicit semantic alignment between different languages. Experiments on
DailyDialog and DSTC7 datasets demonstrate that MulZDG not only achieve
competitive performance under zero-shot case compared to training with
sufficient examples but also greatly improve the performance of the source
language.

We consider the inverse problem for the Partial Differential Equations (PDEs)
such that the parameters of the dependency structure can exhibit random
changepoints over time. This can arise, for example, when the physical system
is either under malicious attack (e.g., hacker attacks on power grids and
internet networks) or subject to extreme external conditions (e.g., weather
conditions impacting electricity grids or large market movements impacting
valuations of derivative contracts). For that purpose, we employ Physics
Informed Neural Networks (PINNs) -- universal approximators that can
incorporate prior information from any physical law described by a system of
PDEs. This prior knowledge acts in the training of the neural network as a
regularization that limits the space of admissible solutions and increases the
correctness of the function approximation. We show that when the true data
generating process exhibits changepoints in the PDE dynamics, this
regularization can lead to a complete miss-calibration and a failure of the
model. Therefore, we propose an extension of PINNs using a Total-Variation
penalty which accommodates (multiple) changepoints in the PDE dynamics. These
changepoints can occur at random locations over time, and they are estimated
together with the solutions. We propose an additional refinement algorithm that
combines changepoints detection with a reduced dynamic programming method that
is feasible for the computationally intensive PINNs methods, and we demonstrate
the benefits of the proposed model empirically using examples of different
equations with changes in the parameters. In case of no changepoints in the
data, the proposed model reduces to the original PINNs model. In the presence
of changepoints, it leads to improvements in parameter estimation, better model
fitting, and a lower training error compared to the original PINNs model.

Recently unsupervised person re-identification (re-ID) has drawn much
attention due to its open-world scenario settings where limited annotated data
is available. Existing supervised methods often fail to generalize well on
unseen domains, while the unsupervised methods, mostly lack multi-granularity
information and are prone to suffer from confirmation bias. In this paper, we
aim at finding better feature representations on the unseen target domain from
two aspects, 1) performing unsupervised domain adaptation on the labeled source
domain and 2) mining potential similarities on the unlabeled target domain.
Besides, a collaborative pseudo re-labeling strategy is proposed to alleviate
the influence of confirmation bias. Firstly, a generative adversarial network
is utilized to transfer images from the source domain to the target domain.
Moreover, person identity preserving and identity mapping losses are introduced
to improve the quality of generated images. Secondly, we propose a novel
collaborative multiple feature clustering framework (CMFC) to learn the
internal data structure of target domain, including global feature and partial
feature branches. The global feature branch (GB) employs unsupervised
clustering on the global feature of person images while the Partial feature
branch (PB) mines similarities within different body regions. Finally,
extensive experiments on two benchmark datasets show the competitive
performance of our method under unsupervised person re-ID settings.

In this work, we identify open research opportunities in applying Neural
Temporal Point Process (NTPP) models to industry scale customer behavior data
by carefully reproducing NTPP models published up to date on known literature
benchmarks as well as applying NTPP models to a novel, real world consumer
behavior dataset that is twice as large as the largest publicly available NTPP
benchmark. We identify the following challenges. First, NTPP models, albeit
their generative nature, remain vulnerable to dataset imbalances and cannot
forecast rare events. Second, NTPP models based on stochastic differential
equations, despite their theoretical appeal and leading performance on
literature benchmarks, do not scale easily to large industry-scale data. The
former is in light of previously made observations on deep generative models.
Additionally, to combat a cold-start problem, we explore a novel addition to
NTPP models - a parametrization based on static user features.

Recent progress in 4D implicit representation focuses on globally controlling
the shape and motion with low dimensional latent vectors, which is prone to
missing surface details and accumulating tracking error. While many deep local
representations have shown promising results for 3D shape modeling, their 4D
counterpart does not exist yet. In this paper, we fill this blank by proposing
a novel Local 4D implicit Representation for Dynamic clothed human, named LoRD,
which has the merits of both 4D human modeling and local representation, and
enables high-fidelity reconstruction with detailed surface deformations, such
as clothing wrinkles. Particularly, our key insight is to encourage the network
to learn the latent codes of local part-level representation, capable of
explaining the local geometry and temporal deformations. To make the inference
at test-time, we first estimate the inner body skeleton motion to track local
parts at each time step, and then optimize the latent codes for each part via
auto-decoding based on different types of observed data. Extensive experiments
demonstrate that the proposed method has strong capability for representing 4D
human, and outperforms state-of-the-art methods on practical applications,
including 4D reconstruction from sparse points, non-rigid depth fusion, both
qualitatively and quantitatively.

Maximum Common induced Subgraph (MCS) is an important NP-hard problem with
wide real-world applications. Branch-and-Bound (BnB) is the basis of a class of
efficient algorithms for MCS, consisting in successively selecting vertices to
match and pruning when it is discovered that a solution better than the best
solution found so far does not exist. The method of selecting the vertices to
match is essential for the performance of BnB. In this paper, we propose a new
value function and a hybrid selection strategy used in reinforcement learning
to define a new vertex selection method, and propose a new BnB algorithm,
called McSplitDAL, for MCS. Extensive experiments show that McSplitDAL
significantly improves the current best BnB algorithms, McSplit+LL and
McSplit+RL. An empirical analysis is also performed to illustrate why the new
value function and the hybrid selection strategy are effective.

An important problem in microrobotics is how to control a large group of
microrobots with a global control signal. This paper focuses on controlling a
large-scale swarm of MicroStressBots with on-board physical finite-state
machines. We introduce the concept of group-based control, which makes it
possible to scale up the swarm size while reducing the complexity both of robot
fabrication as well as swarm control. We prove that the group-based control
system is locally accessible in terms of the robot positions. We further
hypothesize based on extensive simulations that the system is globally
controllable. A nonlinear optimization strategy is proposed to control the
swarm by minimizing control effort. We also propose a probabilistically
complete collision avoidance method that is suitable for online use. The paper
concludes with an evaluation of the proposed methods in simulations.

Robot navigation with deep reinforcement learning (RL) achieves higher
performance and performs well under complex environment. Meanwhile, the
interpretation of the decision-making of deep RL models becomes a critical
problem for more safety and reliability of autonomous robots. In this paper, we
propose a visual explanation method based on an attention branch for deep RL
models. We connect attention branch with pre-trained deep RL model and the
attention branch is trained by using the selected action by the trained deep RL
model as a correct label in a supervised learning manner. Because the attention
branch is trained to output the same result as the deep RL model, the obtained
attention maps are corresponding to the agent action with higher
interpretability. Experimental results with robot navigation task show that the
proposed method can generate interpretable attention maps for a visual
explanation.

Person-job fit is the core technique of online recruitment platforms, which
can improve the efficiency of recruitment by accurately matching the job
positions with the job seekers. Existing works mainly focus on modeling the
unidirectional process or overall matching. However, recruitment is a two-way
selection process, which means that both candidate and employer involved in the
interaction should meet the expectation of each other, instead of unilateral
satisfaction. In this paper, we propose a dual-perspective graph representation
learning approach to model directed interactions between candidates and jobs.
To model the two-way selection preference from the dual-perspective of job
seekers and employers, we incorporate two different nodes for each candidate
(or job) and characterize both successful matching and failed matching via a
unified dual-perspective interaction graph. To learn dual-perspective node
representations effectively, we design an effective optimization algorithm,
which involves a quadruple-based loss and a dual-perspective contrastive
learning loss. Extensive experiments on three large real-world recruitment
datasets have shown the effectiveness of our approach.

Intellectual properties is increasingly important in the economic
development. To solve the pain points by traditional methods in IP evaluation,
we are developing a new technology with machine learning as the core. We have
built an online platform and will expand our business in the Greater Bay Area
with plans.

With the expanding role of neural networks, the need for complete and sound
verification of their property has become critical. In the recent years, it was
established that Binary Neural Networks (BNNs) have an equivalent
representation in Boolean logic and can be formally analyzed using logical
reasoning tools such as SAT solvers. However, to date, only BNNs can be
transformed into a SAT formula. In this work, we introduce Truth Table Deep
Convolutional Neural Networks (TTnets), a new family of SAT-encodable models
featuring for the first time real-valued weights. Furthermore, it admits, by
construction, some valuable conversion features including post-tuning and
tractability in the robustness verification setting. The latter property leads
to a more compact SAT symbolic encoding than BNNs. This enables the use of
general SAT solvers, making property verification easier. We demonstrate the
value of TTnets regarding the formal robustness property: TTnets outperform the
verified accuracy of all BNNs with a comparable computation time. More
generally, they represent a relevant trade-off between all known complete
verification methods: TTnets achieve high verified accuracy with fast
verification time, being complete with no timeouts. We are exploring here a
proof of concept of TTnets for a very important application (complete
verification of robustness) and we believe this novel real-valued network
constitutes a practical response to the rising need for functional formal
verification. We postulate that TTnets can apply to various CNN-based
architectures and be extended to other properties such as fairness, fault
attack and exact rule extraction.

Text-based person retrieval aims to find the query person based on a textual
description. The key is to learn a common latent space mapping between
visual-textual modalities. To achieve this goal, existing works employ
segmentation to obtain explicitly cross-modal alignments or utilize attention
to explore salient alignments. These methods have two shortcomings: 1) Labeling
cross-modal alignments are time-consuming. 2) Attention methods can explore
salient cross-modal alignments but may ignore some subtle and valuable pairs.
To relieve these issues, we introduce an Implicit Visual-Textual (IVT)
framework for text-based person retrieval. Different from previous models, IVT
utilizes a single network to learn representation for both modalities, which
contributes to the visual-textual interaction. To explore the fine-grained
alignment, we further propose two implicit semantic alignment paradigms:
multi-level alignment (MLA) and bidirectional mask modeling (BMM). The MLA
module explores finer matching at sentence, phrase, and word levels, while the
BMM module aims to mine \textbf{more} semantic alignments between visual and
textual modalities. Extensive experiments are carried out to evaluate the
proposed IVT on public datasets, i.e., CUHK-PEDES, RSTPReID, and ICFG-PEDES.
Even without explicit body part alignment, our approach still achieves
state-of-the-art performance. Code is available at:
https://github.com/TencentYoutuResearch/PersonRetrieval-IVT.

Along with the fast development of network technology and the rapid growth of
network equipment, the data throughput is sharply increasing. To handle the
problem of backhaul bottleneck in cellular network and satisfy people's
requirements about latency, the network architecture like information-centric
network (ICN) intends to proactively keep limited popular content at the edge
of network based on predicted results. Meanwhile, the interactions between the
content (e.g., deep neural network models, Wikipedia-alike knowledge base) and
users could be regarded as a dynamic bipartite graph. In this paper, to
maximize the cache hit rate, we leverage an effective dynamic graph neural
network (DGNN) to jointly learn the structural and temporal patterns embedded
in the bipartite graph. Furthermore, in order to have deeper insights into the
dynamics within the evolving graph, we propose an age of information (AoI)
based attention mechanism to extract valuable historical information while
avoiding the problem of message staleness. Combining this aforementioned
prediction model, we also develop a cache selection algorithm to make caching
decisions in accordance with the prediction results. Extensive results
demonstrate that our model can obtain a higher prediction accuracy than other
state-of-the-art schemes in two real-world datasets. The results of hit rate
further verify the superiority of the caching policy based on our proposed
model over other traditional ways.

Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance for medical image segmentation, yet need plenty of manual
annotations for training. Semi-Supervised Learning (SSL) methods are promising
to reduce the requirement of annotations, but their performance is still
limited when the dataset size and the number of annotated images are small.
Leveraging existing annotated datasets with similar anatomical structures to
assist training has a potential for improving the model's performance. However,
it is further challenged by the cross-anatomy domain shift due to the different
appearance and even imaging modalities from the target structure. To solve this
problem, we propose Contrastive Semi-supervised learning for Cross Anatomy
Domain Adaptation (CS-CADA) that adapts a model to segment similar structures
in a target domain, which requires only limited annotations in the target
domain by leveraging a set of existing annotated images of similar structures
in a source domain. We use Domain-Specific Batch Normalization (DSBN) to
individually normalize feature maps for the two anatomical domains, and propose
a cross-domain contrastive learning strategy to encourage extracting domain
invariant features. They are integrated into a Self-Ensembling Mean-Teacher
(SE-MT) framework to exploit unlabeled target domain images with a prediction
consistency constraint. Extensive experiments show that our CS-CADA is able to
solve the challenging cross-anatomy domain shift problem, achieving accurate
segmentation of coronary arteries in X-ray images with the help of retinal
vessel images and cardiac MR images with the help of fundus images,
respectively, given only a small number of annotations in the target domain.

Phase retrieval (PR), a long-established challenge for recovering a
complex-valued signal from its Fourier intensity-only measurements, has
attracted considerable attention due to its widespread applications in digital
imaging. Recently, deep learning-based approaches were developed that achieved
some success in single-shot PR. These approaches require a single Fourier
intensity measurement without the need to impose any additional constraints on
the measured data. Nevertheless, vanilla deep neural networks (DNN) do not give
good performance due to the substantial disparity between the input and output
domains of the PR problems. Physics-informed approaches try to incorporate the
Fourier intensity measurements into an iterative approach to increase the
reconstruction accuracy. It, however, requires a lengthy computation process,
and the accuracy still cannot be guaranteed. Besides, many of these approaches
work on simulation data that ignore some common problems such as saturation and
quantization errors in practical optical PR systems. In this paper, a novel
physics-driven multi-scale DNN structure dubbed PPRNet is proposed. Similar to
other deep learning-based PR methods, PPRNet requires only a single Fourier
intensity measurement. It is physics-driven that the network is guided to
follow the Fourier intensity measurement at different scales to enhance the
reconstruction accuracy. PPRNet has a feedforward structure and can be
end-to-end trained. Thus, it is much faster and more accurate than the
traditional physics-driven PR approaches. Extensive simulations and experiments
on a practical optical platform were conducted. The results demonstrate the
superiority and practicality of the proposed PPRNet over the traditional
learning-based PR methods.

User videos shared on social media platforms usually suffer from degradations
caused by unknown proprietary processing procedures, which means that their
visual quality is poorer than that of the originals. This paper presents a new
general video restoration framework for the restoration of user videos shared
on social media platforms. In contrast to most deep learning-based video
restoration methods that perform end-to-end mapping, where feature extraction
is mostly treated as a black box, in the sense that what role a feature plays
is often unknown, our new method, termed Video restOration through adapTive
dEgradation Sensing (VOTES), introduces the concept of a degradation feature
map (DFM) to explicitly guide the video restoration process. Specifically, for
each video frame, we first adaptively estimate its DFM to extract features
representing the difficulty of restoring its different regions. We then feed
the DFM to a convolutional neural network (CNN) to compute hierarchical
degradation features to modulate an end-to-end video restoration backbone
network, such that more attention is paid explicitly to potentially more
difficult to restore areas, which in turn leads to enhanced restoration
performance. We will explain the design rationale of the VOTES framework and
present extensive experimental results to show that the new VOTES method
outperforms various state-of-the-art techniques both quantitatively and
qualitatively. In addition, we contribute a large scale real-world database of
user videos shared on different social media platforms. Codes and datasets are
available at https://github.com/luohongming/VOTES.git

The prevailing graph neural network models have achieved significant progress
in graph representation learning. However, in this paper, we uncover an
ever-overlooked phenomenon: the pre-trained graph representation learning model
tested with full graphs underperforms the model tested with well-pruned graphs.
This observation reveals that there exist confounders in graphs, which may
interfere with the model learning semantic information, and current graph
representation learning methods have not eliminated their influence. To tackle
this issue, we propose Robust Causal Graph Representation Learning (RCGRL) to
learn robust graph representations against confounding effects. RCGRL
introduces an active approach to generate instrumental variables under
unconditional moment restrictions, which empowers the graph representation
learning model to eliminate confounders, thereby capturing discriminative
information that is causally related to downstream predictions. We offer
theorems and proofs to guarantee the theoretical effectiveness of the proposed
approach. Empirically, we conduct extensive experiments on a synthetic dataset
and multiple benchmark datasets. The results demonstrate that compared with
state-of-the-art methods, RCGRL achieves better prediction performance and
generalization ability.

To recommend relevant merchandises for seasonal retail events, we rely on
item retrieval from marketplace inventory. With feedback to expand query scope,
we discuss keyword expansion candidate selection using word embedding
similarity, and an enhanced tf-idf formula for expanded words in search
ranking.

We propose to utilize self-supervised techniques in the 2D domain for
fine-grained 3D shape segmentation tasks. This is inspired by the observation
that view-based surface representations are more effective at modeling
high-resolution surface details and texture than their 3D counterparts based on
point clouds or voxel occupancy. Specifically, given a 3D shape, we render it
from multiple views, and set up a dense correspondence learning task within the
contrastive learning framework. As a result, the learned 2D representations are
view-invariant and geometrically consistent, leading to better generalization
when trained on a limited number of labeled shapes compared to alternatives
that utilize self-supervision in 2D or 3D alone. Experiments on textured
(RenderPeople) and untextured (PartNet) 3D datasets show that our method
outperforms state-of-the-art alternatives in fine-grained part segmentation.
The improvements over baselines are greater when only a sparse set of views is
available for training or when shapes are textured, indicating that MvDeCor
benefits from both 2D processing and 3D geometric reasoning.

Conditional randomization tests (CRTs) assess whether a variable $x$ is
predictive of another variable $y$, having observed covariates $z$. CRTs
require fitting a large number of predictive models, which is often
computationally intractable. Existing solutions to reduce the cost of CRTs
typically split the dataset into a train and test portion, or rely on
heuristics for interactions, both of which lead to a loss in power. We propose
the decoupled independence test (DIET), an algorithm that avoids both of these
issues by leveraging marginal independence statistics to test conditional
independence relationships. DIET tests the marginal independence of two random
variables: $F(x \mid z)$ and $F(y \mid z)$ where $F(\cdot \mid z)$ is a
conditional cumulative distribution function (CDF). These variables are termed
"information residuals." We give sufficient conditions for DIET to achieve
finite sample type-1 error control and power greater than the type-1 error
rate. We then prove that when using the mutual information between the
information residuals as a test statistic, DIET yields the most powerful
conditionally valid test. Finally, we show DIET achieves higher power than
other tractable CRTs on several synthetic and real benchmarks.

Malicious architecture extraction has been emerging as a crucial concern for
deep neural network (DNN) security. As a defense, architecture obfuscation is
proposed to remap the victim DNN to a different architecture. Nonetheless, we
observe that, with only extracting an obfuscated DNN architecture, the
adversary can still retrain a substitute model with high performance (e.g.,
accuracy), rendering the obfuscation techniques ineffective. To mitigate this
under-explored vulnerability, we propose ObfuNAS, which converts the DNN
architecture obfuscation into a neural architecture search (NAS) problem. Using
a combination of function-preserving obfuscation strategies, ObfuNAS ensures
that the obfuscated DNN architecture can only achieve lower accuracy than the
victim. We validate the performance of ObfuNAS with open-source architecture
datasets like NAS-Bench-101 and NAS-Bench-301. The experimental results
demonstrate that ObfuNAS can successfully find the optimal mask for a victim
model within a given FLOPs constraint, leading up to 2.6% inference accuracy
degradation for attackers with only 0.14x FLOPs overhead. The code is available
at: https://github.com/Tongzhou0101/ObfuNAS.

In this work we study high probability bounds for stochastic subgradient
methods under heavy tailed noise. In this case the noise is only assumed to
have finite variance as opposed to a sub-Gaussian distribution for which it is
known that standard subgradient methods enjoys high probability bounds. We
analyzed a clipped version of the projected stochastic subgradient method,
where subgradient estimates are truncated whenever they have large norms. We
show that this clipping strategy leads both to near optimal any-time and finite
horizon bounds for many classical averaging schemes. Preliminary experiments
are shown to support the validity of the method.

A ''technology lottery'' describes a research idea or technology succeeding
over others because it is suited to the available software and hardware, not
necessarily because it is superior to alternative directions--examples abound,
from the synergies of deep learning and GPUs to the disconnect of urban design
and autonomous vehicles. The nascent field of Self-Driving Laboratories (SDL),
particularly those implemented as Materials Acceleration Platforms (MAPs), is
at risk of an analogous pitfall: the next logical step for building MAPs is to
take existing lab equipment and workflows and mix in some AI and automation. In
this whitepaper, we argue that the same simulation and AI tools that will
accelerate the search for new materials, as part of the MAPs research program,
also make possible the design of fundamentally new computing mediums. We need
not be constrained by existing biases in science, mechatronics, and
general-purpose computing, but rather we can pursue new vectors of engineering
physics with advances in cyber-physical learning and closed-loop,
self-optimizing systems. Here we outline a simulation-based MAP program to
design computers that use physics itself to solve optimization problems. Such
systems mitigate the hardware-software-substrate-user information losses
present in every other class of MAPs and they perfect alignment between
computing problems and computing mediums eliminating any technology lottery. We
offer concrete steps toward early ''Physical Computing (PC) -MAP'' advances and
the longer term cyber-physical R&D which we expect to introduce a new era of
innovative collaboration between materials researchers and computer scientists.

Is it possible to restructure the non-linear activation functions in a deep
network to create hardware-efficient models? To address this question, we
propose a new paradigm called Restructurable Activation Networks (RANs) that
manipulate the amount of non-linearity in models to improve their
hardware-awareness and efficiency. First, we propose RAN-explicit (RAN-e) -- a
new hardware-aware search space and a semi-automatic search algorithm -- to
replace inefficient blocks with hardware-aware blocks. Next, we propose a
training-free model scaling method called RAN-implicit (RAN-i) where we
theoretically prove the link between network topology and its expressivity in
terms of number of non-linear units. We demonstrate that our networks achieve
state-of-the-art results on ImageNet at different scales and for several types
of hardware. For example, compared to EfficientNet-Lite-B0, RAN-e achieves a
similar accuracy while improving Frames-Per-Second (FPS) by 1.5x on Arm
micro-NPUs. On the other hand, RAN-i demonstrates up to 2x reduction in #MACs
over ConvNexts with a similar or better accuracy. We also show that RAN-i
achieves nearly 40% higher FPS than ConvNext on Arm-based datacenter CPUs.
Finally, RAN-i based object detection networks achieve a similar or higher mAP
and up to 33% higher FPS on datacenter CPUs compared to ConvNext based models.
The code to train and evaluate RANs and the pretrained networks are available
at https://github.com/ARM-software/ML-restructurable-activation-networks.

The scattering transform is a multilayered, wavelet-based transform initially
introduced as a model of convolutional neural networks (CNNs) that has played a
foundational role in our understanding of these networks' stability and
invariance properties. Subsequently, there has been widespread interest in
extending the success of CNNs to data sets with non-Euclidean structure, such
as graphs and manifolds, leading to the emerging field of geometric deep
learning. In order to improve our understanding of the architectures used in
this new field, several papers have proposed generalizations of the scattering
transform for non-Euclidean data structures such as undirected graphs and
compact Riemannian manifolds without boundary.
  In this paper, we introduce a general, unified model for geometric scattering
on measure spaces. Our proposed framework includes previous work on geometric
scattering as special cases but also applies to more general settings such as
directed graphs, signed graphs, and manifolds with boundary. We propose a new
criterion that identifies to which groups a useful representation should be
invariant and show that this criterion is sufficient to guarantee that the
scattering transform has desirable stability and invariance properties.
Additionally, we consider finite measure spaces that are obtained from randomly
sampling an unknown manifold. We propose two methods for constructing a
data-driven graph on which the associated graph scattering transform
approximates the scattering transform on the underlying manifold. Moreover, we
use a diffusion-maps based approach to prove quantitative estimates on the rate
of convergence of one of these approximations as the number of sample points
tends to infinity. Lastly, we showcase the utility of our method on spherical
images, directed graphs, and on high-dimensional single-cell data.

Graph neural networks (GNNs) have attracted increasing attention due to their
superior performance in deep learning on graph-structured data. GNNs have
succeeded across various domains such as social networks, chemistry, and
electronic design automation (EDA). Electronic circuits have a long history of
being represented as graphs, and to no surprise, GNNs have demonstrated
state-of-the-art performance in solving various EDA tasks. More importantly,
GNNs are now employed to address several hardware security problems, such as
detecting intellectual property (IP) piracy and hardware Trojans (HTs), to name
a few.
  In this survey, we first provide a comprehensive overview of the usage of
GNNs in hardware security and propose the first taxonomy to divide the
state-of-the-art GNN-based hardware security systems into four categories: (i)
HT detection systems, (ii) IP piracy detection systems, (iii) reverse
engineering platforms, and (iv) attacks on logic locking. We summarize the
different architectures, graph types, node features, benchmark data sets, and
model evaluation of the employed GNNs. Finally, we elaborate on the lessons
learned and discuss future directions.

Recent years have seen significant advances in explainable AI as the need to
understand deep learning models has gained importance with the increased
emphasis on trust and ethics in AI. Comprehensible models for sequential
decision tasks are a particular challenge as they require understanding not
only individual predictions but a series of predictions that interact with
environmental dynamics. We present a framework for learning comprehensible
models of sequential decision tasks in which agent strategies are characterized
using temporal logic formulas. Given a set of agent traces, we first cluster
the traces using a novel embedding method that captures frequent action
patterns. We then search for logical formulas that explain the agent strategies
in the different clusters. We evaluate our framework on combat scenarios in
StarCraft II (SC2), using traces from a handcrafted expert policy and a trained
reinforcement learning agent. We implemented a feature extractor for SC2
environments that extracts traces as sequences of high-level features
describing both the state of the environment and the agent's local behavior
from agent replays. We further designed a visualization tool depicting the
movement of units in the environment that helps understand how different task
conditions lead to distinct agent behavior patterns in each trace cluster.
Experimental results show that our framework is capable of separating agent
traces into distinct groups of behaviors for which our approach to strategy
inference produces consistent, meaningful, and easily understood strategy
descriptions.

Estimating individual treatment effects (ITEs) from observational data is
relevant in many fields such as personalized medicine. However, in practice,
the treatment assignment is usually confounded by unobserved variables and thus
introduces bias. A remedy to remove the bias is the use of instrumental
variables (IVs). Such settings are widespread in medicine (e.g., trials where
compliance is used as binary IV). In this paper, we propose a novel, multiply
robust machine learning framework, called MRIV, for estimating ITEs using
binary IVs and thus yield an unbiased ITE estimator. Different from previous
work for binary IVs, our framework estimates the ITE directly via a pseudo
outcome regression. (1) We provide a theoretical analysis where we show that
our framework yields multiply robust convergence rates: our ITE estimator
achieves fast convergence even if several nuisance estimators converge slowly.
(2) We further show that our framework asymptotically outperforms
state-of-the-art plug-in IV methods for ITE estimation. (3) We build upon our
theoretical results and propose a tailored deep neural network architecture
called MRIV-Net for ITE estimation using binary IVs. Across various
computational experiments, we demonstrate empirically that our MRIV-Net
achieves state-of-the-art performance. To the best of our knowledge, our MRIV
is the first machine learning framework for estimating ITEs in the binary IV
setting shown to be multiply robust.

We consider protocols where users communicate with multiple servers to
perform a computation on the users' data. An adversary exerts semi-honest
control over many of the parties but its view is differentially private with
respect to honest users. Prior work described protocols that required multiple
rounds of interaction or offered privacy against a computationally bounded
adversary. Our work presents limitations of non-interactive protocols that
offer privacy against unbounded adversaries. We show these protocols demand
exponentially more samples for some learning and estimation tasks than
centrally private counterparts. This means performing as well as the central
model requires interactivity or computational differential privacy, or both.

Scientists are increasingly interested in discovering community structure
from modern relational data arising on large-scale social networks. While many
methods have been proposed for learning community structure, few account for
the fact that these modern networks arise from processes of interactions in the
population. We introduce block edge exchangeable models (BEEM) for the study of
interaction networks with latent node-level community structure. The block
vertex components model (B-VCM) is derived as a canonical example. Several
theoretical and practical advantages over traditional vertex-centric approaches
are highlighted. In particular, BEEMs allow for sparse degree structure and
power-law degree distributions within communities. Our theoretical analysis
bounds the misspecification rate of block assignments, while supporting
simulations show the properties of the network can be recovered. A
computationally tractable Gibbs algorithm is derived. We demonstrate the
proposed model using post-comment interaction data from Talklife, a large-scale
online peer-to-peer support network, and contrast the learned communities from
those using standard algorithms including spectral clustering and
degree-correct stochastic block models.

One of the major open challenges in MIMO-OFDM receive processing is how to
efficiently and effectively utilize the extremely limited over-the-air pilot
symbols to detect the transmitted data symbols. Recent advances have been
devoted to investigating effective ways to utilize the limited pilots. However,
we notice that besides exploiting the pilots, one can take advantage of the
data symbols to improve the detection performance. Thus, this paper introduces
an online subframe-based approach, namely RC-StructNet, that can efficiently
learn from the precious pilot symbols and be dynamically updated with the
detected payload data using the decision feedback (DF) approach. The network
consists of a reservoir computing (RC) module in the time domain and a neural
network StructNet in the frequency domain. The unique design of the network
allows it to be dynamically updated with the changes of the channel by learning
from the detected data symbols. Experiments demonstrate the effectiveness of
RC-StructNet in detection under dynamic transmission modes and in reducing the
training overhead requirement when taking the DF approach.

We investigate robustness properties of pre-trained neural models for
automatic speech recognition. Real life data in machine learning is usually
very noisy and almost never clean, which can be attributed to various factors
depending on the domain, e.g. outliers, random noise and adversarial noise.
Therefore, the models we develop for various tasks should be robust to such
kinds of noisy data, which led to the thriving field of robust machine
learning. We consider this important issue in the setting of automatic speech
recognition. With the increasing popularity of pre-trained models, it's an
important question to analyze and understand the robustness of such models to
noise. In this work, we perform a robustness analysis of the pre-trained neural
models wav2vec2, HuBERT and DistilHuBERT on the LibriSpeech and TIMIT datasets.
We use different kinds of noising mechanisms and measure the model performances
as quantified by the inference time and the standard Word Error Rate metric. We
also do an in-depth layer-wise analysis of the wav2vec2 model when injecting
noise in between layers, enabling us to predict at a high level what each layer
learns. Finally for this model, we visualize the propagation of errors across
the layers and compare how it behaves on clean versus noisy data. Our
experiments conform the predictions of Pasad et al. [2021] and also raise
interesting directions for future work.

Research and development in hypersonics have progressed significantly in
recent years, with various military and commercial applications being
demonstrated increasingly. Public and private organizations in several
countries have been investing in hypersonics, with the aim to overtake their
competitors and secure/improve strategic advantage and deterrence. For these
organizations, being able to identify emerging technologies in a timely and
reliable manner is paramount. Recent advances in information technology have
made it possible to analyze large amounts of data, extract hidden patterns, and
provide decision-makers with new insights. In this study, we focus on
scientific publications about hypersonics within the period of 2000-2020, and
employ natural language processing and machine learning to characterize the
research landscape by identifying 12 key latent research themes and analyzing
their temporal evolution. Our publication similarity analysis revealed patterns
that are indicative of cycles during two decades of research. The study offers
a comprehensive analysis of the research field and the fact that the research
themes are algorithmically extracted removes subjectivity from the exercise and
enables consistent comparisons between topics and between time intervals.

To enable learning on edge devices with fast convergence and low memory, we
present a novel backpropagation-free optimization algorithm dubbed Target
Projection Stochastic Gradient Descent (tpSGD). tpSGD generalizes direct random
target projection to work with arbitrary loss functions and extends target
projection for training recurrent neural networks (RNNs) in addition to
feedforward networks. tpSGD uses layer-wise stochastic gradient descent (SGD)
and local targets generated via random projections of the labels to train the
network layer-by-layer with only forward passes. tpSGD doesn't require
retaining gradients during optimization, greatly reducing memory allocation
compared to SGD backpropagation (BP) methods that require multiple instances of
the entire neural network weights, input/output, and intermediate results. Our
method performs comparably to BP gradient-descent within 5% accuracy on
relatively shallow networks of fully connected layers, convolutional layers,
and recurrent layers. tpSGD also outperforms other state-of-the-art
gradient-free algorithms in shallow models consisting of multi-layer
perceptrons, convolutional neural networks (CNNs), and RNNs with competitive
accuracy and less memory and time. We evaluate the performance of tpSGD in
training deep neural networks (e.g. VGG) and extend the approach to multi-layer
RNNs. These experiments highlight new research directions related to optimized
layer-based adaptor training for domain-shift using tpSGD at the edge.

We propose \emph{Choquet regularizers} to measure and manage the level of
exploration for reinforcement learning (RL), and reformulate the
continuous-time entropy-regularized RL problem of Wang et al. (2020, JMLR,
21(198)) in which we replace the differential entropy used for regularization
with a Choquet regularizer. We derive the Hamilton--Jacobi--Bellman equation of
the problem, and solve it explicitly in the linear--quadratic (LQ) case via
maximizing statically a mean--variance constrained Choquet regularizer. Under
the LQ setting, we derive explicit optimal distributions for several specific
Choquet regularizers, and conversely identify the Choquet regularizers that
generate a number of broadly used exploratory samplers such as
$\epsilon$-greedy, exponential, uniform and Gaussian.

Federated learning (FL) has received a surge of interest in recent years
thanks to its benefits in data privacy protection, efficient communication, and
parallel data processing. Also, with appropriate algorithmic designs, one could
achieve the desirable linear speedup for convergence effect in FL. However,
most existing works on FL are limited to systems with i.i.d. data and
centralized parameter servers and results on decentralized FL with
heterogeneous datasets remains limited. Moreover, whether or not the linear
speedup for convergence is achievable under fully decentralized FL with data
heterogeneity remains an open question. In this paper, we address these
challenges by proposing a new algorithm, called NET-FLEET, for fully
decentralized FL systems with data heterogeneity. The key idea of our algorithm
is to enhance the local update scheme in FL (originally intended for
communication efficiency) by incorporating a recursive gradient correction
technique to handle heterogeneous datasets. We show that, under appropriate
parameter settings, the proposed NET-FLEET algorithm achieves a linear speedup
for convergence. We further conduct extensive numerical experiments to evaluate
the performance of the proposed NET-FLEET algorithm and verify our theoretical
findings.

Scale has been a major driving force in improving machine learning
performance, and understanding scaling laws is essential for strategic planning
for a sustainable model quality performance growth, long-term resource planning
and developing efficient system infrastructures to support large-scale models.
In this paper, we study empirical scaling laws for DLRM style recommendation
models, in particular Click-Through Rate (CTR). We observe that model quality
scales with power law plus constant in model size, data size and amount of
compute used for training. We characterize scaling efficiency along three
different resource dimensions, namely data, parameters and compute by comparing
the different scaling schemes along these axes. We show that parameter scaling
is out of steam for the model architecture under study, and until a
higher-performing model architecture emerges, data scaling is the path forward.
The key research questions addressed by this study include: Does a
recommendation model scale sustainably as predicted by the scaling laws? Or are
we far off from the scaling law predictions? What are the limits of scaling?
What are the implications of the scaling laws on long-term hardware/system
development?

The effective representation, precessing, analysis, and visualization of
large-scale structured data over graphs are gaining a lot of attention. So far
most of the literature has focused on real-valued signals. However, signals are
often sparse in the Fourier domain, and more informative and compact
representations for them can be obtained using the complex envelope of their
spectral components, as opposed to the original real-valued signals. Motivated
by this fact, in this work we generalize graph convolutional neural networks
(GCN) to the complex domain, deriving the theory that allows to incorporate a
complex-valued graph shift operators (GSO) in the definition of graph filters
(GF) and process complex-valued graph signals (GS). The theory developed can
handle spatio-temporal complex network processes. We prove that complex-valued
GCNs are stable with respect to perturbations of the underlying graph support,
the bound of the transfer error and the bound of error propagation through
multiply layers. Then we apply complex GCN to power grid state forecasting,
power grid cyber-attack detection and localization.

We investigate the problems of model estimation and reward-free learning in
episodic Block MDPs. In these MDPs, the decision maker has access to rich
observations or contexts generated from a small number of latent states. We are
first interested in estimating the latent state decoding function (the mapping
from the observations to latent states) based on data generated under a fixed
behavior policy. We derive an information-theoretical lower bound on the error
rate for estimating this function and present an algorithm approaching this
fundamental limit. In turn, our algorithm also provides estimates of all the
components of the MDP. We then study the problem of learning near-optimal
policies in the reward-free framework. Based on our efficient model estimation
algorithm, we show that we can infer a policy converging (as the number of
collected samples grows large) to the optimal policy at the best possible rate.
Interestingly, our analysis provides necessary and sufficient conditions under
which exploiting the block structure yields improvements in the sample
complexity for identifying near-optimal policies. When these conditions are
met, the sample complexity in the minimax reward-free setting is improved by a
multiplicative factor $n$, where $n$ is the number of possible contexts.

In human neuroscience, machine learning can help reveal lower-dimensional
neural representations relevant to subjects' behavior. However,
state-of-the-art models typically require large datasets to train, so are prone
to overfitting on human neuroimaging data that often possess few samples but
many input dimensions. Here, we capitalized on the fact that the features we
seek in human neuroscience are precisely those relevant to subjects' behavior.
We thus developed a Task-Relevant Autoencoder via Classifier Enhancement
(TRACE), and tested its ability to extract behaviorally-relevant, separable
representations compared to a standard autoencoder for two severely truncated
machine learning datasets. We then evaluated both models on fMRI data where
subjects observed animals and objects. TRACE outperformed both the autoencoder
and raw inputs nearly unilaterally, showing up to 30% increased classification
accuracy and up to threefold improvement in discovering "cleaner",
task-relevant representations. These results showcase TRACE's potential for a
wide variety of data related to human behavior.

In supervised machine learning, use of correct labels is extremely important
to ensure high accuracy. Unfortunately, most datasets contain corrupted labels.
Machine learning models trained on such datasets do not generalize well. Thus,
detecting their label errors can significantly increase their efficacy. We
propose a novel framework, called CTRL (Clustering TRaining Losses for label
error detection), to detect label errors in multi-class datasets. It detects
label errors in two steps based on the observation that models learn clean and
noisy labels in different ways. First, we train a neural network using the
noisy training dataset and obtain the loss curve for each sample. Then, we
apply clustering algorithms to the training losses to group samples into two
categories: cleanly-labeled and noisily-labeled. After label error detection,
we remove samples with noisy labels and retrain the model. Our experimental
results demonstrate state-of-the-art error detection accuracy on both image
(CIFAR-10 and CIFAR-100) and tabular datasets under simulated noise. We also
use a theoretical analysis to provide insights into why CTRL performs so well.

Color and structure are the two pillars that combine to give an image its
meaning. Interested in critical structures for neural network recognition, we
isolate the influence of colors by limiting the color space to just a few bits,
and find structures that enable network recognition under such constraints. To
this end, we propose a color quantization network, ColorCNN, which learns to
structure an image in limited color spaces by minimizing the classification
loss. Building upon the architecture and insights of ColorCNN, we introduce
ColorCNN+, which supports multiple color space size configurations, and
addresses the previous issues of poor recognition accuracy and undesirable
visual fidelity under large color spaces. Via a novel imitation learning
approach, ColorCNN+ learns to cluster colors like traditional color
quantization methods. This reduces overfitting and helps both visual fidelity
and recognition accuracy under large color spaces. Experiments verify that
ColorCNN+ achieves very competitive results under most circumstances,
preserving both key structures for network recognition and visual fidelity with
accurate colors. We further discuss differences between key structures and
accurate colors, and their specific contributions to network recognition. For
potential applications, we show that ColorCNNs can be used as image compression
methods for network recognition.

Semi-supervised semantic segmentation needs rich and robust supervision on
unlabeled data. Consistency learning enforces the same pixel to have similar
features in different augmented views, which is a robust signal but neglects
relationships with other pixels. In comparison, contrastive learning considers
rich pairwise relationships, but it can be a conundrum to assign binary
positive-negative supervision signals for pixel pairs. In this paper, we take
the best of both worlds and propose multi-view correlation consistency (MVCC)
learning: it considers rich pairwise relationships in self-correlation matrices
and matches them across views to provide robust supervision. Together with this
correlation consistency loss, we propose a view-coherent data augmentation
strategy that guarantees pixel-pixel correspondence between different views. In
a series of semi-supervised settings on two datasets, we report competitive
accuracy compared with the state-of-the-art methods. Notably, on Cityscapes, we
achieve 76.8% mIoU with 1/8 labeled data, just 0.6% shy from the fully
supervised oracle.

Human Activity Recognition (HAR) is a problem of interpreting sensor data to
human movement using an efficient machine learning (ML) approach. The HAR
systems rely on data from untrusted users, making them susceptible to data
poisoning attacks. In a poisoning attack, attackers manipulate the sensor
readings to contaminate the training set, misleading the HAR to produce
erroneous outcomes. This paper presents the design of a label flipping data
poisoning attack for a HAR system, where the label of a sensor reading is
maliciously changed in the data collection phase. Due to high noise and
uncertainty in the sensing environment, such an attack poses a severe threat to
the recognition system. Besides, vulnerability to label flipping attacks is
dangerous when activity recognition models are deployed in safety-critical
applications. This paper shades light on how to carry out the attack in
practice through smartphone-based sensor data collection applications. This is
an earlier research work, to our knowledge, that explores attacking the HAR
models via label flipping poisoning. We implement the proposed attack and test
it on activity recognition models based on the following machine learning
algorithms: multi-layer perceptron, decision tree, random forest, and XGBoost.
Finally, we evaluate the effectiveness of K-nearest neighbors (KNN)-based
defense mechanism against the proposed attack.

To increase the training speed of distributed learning, recent years have
witnessed a significant amount of interest in developing both synchronous and
asynchronous distributed stochastic variance-reduced optimization methods.
However, all existing synchronous and asynchronous distributed training
algorithms suffer from various limitations in either convergence speed or
implementation complexity. This motivates us to propose an algorithm called
STNTHESIS (semi-asynchronous path-integrated stochastic gradient search), which
leverages the special structure of the variance-reduction framework to overcome
the limitations of both synchronous and asynchronous distributed learning
algorithms while retaining their salient features. We consider two
implementations of STNTHESIS under distributed and shared memory architectures.
We show that our STNTHESIS algorithms have
$O(\sqrt{N}\epsilon^{-2}(\Delta+1)+N)$ and $O(\sqrt{N}\epsilon^{-2}(\Delta+1)
d+N)$ computational complexities for achieving an $\epsilon$-stationary point
in non-convex learning under distributed and shared memory architectures,
respectively, where N denotes the total number of training samples and $\Delta$
represents the maximum delay of the workers. Moreover, we investigate the
generalization performance of \algname by establishing algorithmic stability
bounds for quadratic strongly convex and non-convex optimization. We further
conduct extensive numerical experiments to verify our theoretical findings

Conformal inference is a powerful tool for quantifying the uncertainty around
predictions made by black-box models (e.g. neural nets, random forests).
Formally, this methodology guarantees that if the training and test data are
exchangeable (e.g. i.i.d.) then we can construct a prediction set $C$ for the
target $Y$ such that $P(Y \in C) = 1-\alpha$ for any target level $\alpha$. In
this article, we extend this methodology to an online prediction setting where
the distribution generating the data is allowed to vary over time. To account
for the non-exchangeability, we develop a protective layer that lies on top of
conformal inference and gradually re-calibrates its predictions to adapt to the
observed changes in the environment. Our methods are highly flexible and can be
used in combination with any predictive algorithm that produces estimates of
the target or its conditional distribution and without any assumptions on the
size or type of the distribution shift. We test our techniques on two
real-world datasets aimed at predicting stock market volatility and COVID-19
case counts and find that they are robust and adaptive to real-world

Given an unexpected change in the output metric of a large-scale system, it
is important to answer why the change occurred: which inputs caused the change
in metric? A key component of such an attribution question is estimating the
counterfactual: the (hypothetical) change in the system metric due to a
specified change in a single input. However, due to inherent stochasticity and
complex interactions between parts of the system, it is difficult to model an
output metric directly. We utilize the computational structure of a system to
break up the modelling task into sub-parts, such that each sub-part corresponds
to a more stable mechanism that can be modelled accurately over time. Using the
system's structure also helps to view the metric as a computation over a
structural causal model (SCM), thus providing a principled way to estimate
counterfactuals. Specifically, we propose a method to estimate counterfactuals
using time-series predictive models and construct an attribution score,
CF-Shapley, that is consistent with desirable axioms for attributing an
observed change in the output metric. Unlike past work on causal shapley
values, our proposed method can attribute a single observed change in output
(rather than a population-level effect) and thus provides more accurate
attribution scores when evaluated on simulated datasets. As a real-world
application, we analyze a query-ad matching system with the goal of attributing
observed change in a metric for ad matching density. Attribution scores explain
how query volume and ad demand from different query categories affect the ad
matching density, leading to actionable insights and uncovering the role of
external events (e.g., "Cheetah Day") in driving the matching density.

Prognostics and Health Management (PHM) is an emerging field which has
received much attention from the manufacturing industry because of the benefits
and efficiencies it brings to the table. And Remaining Useful Life (RUL)
prediction is at the heart of any PHM system. Most recent data-driven research
demand substantial volumes of labelled training data before a performant model
can be trained under the supervised learning paradigm. This is where Transfer
Learning (TL) and Domain Adaptation (DA) methods step in and make it possible
for us to generalize a supervised model to other domains with different data
distributions with no labelled data. In this paper, we propose
\textit{LAMA-Net}, an encoder-decoder based model (Transformer) with an induced
bottleneck, Latent Alignment using Maximum Mean Discrepancy (MMD) and manifold
learning is proposed to tackle the problem of Unsupervised Homogeneous Domain
Adaptation for RUL prediction. \textit{LAMA-Net} is validated using the C-MAPSS
Turbofan Engine dataset by NASA and compared against other state-of-the-art
techniques for DA. The results suggest that the proposed method offers a
promising approach to perform domain adaptation in RUL prediction. Code will be
made available once the paper comes out of review.

We propose a new kind of embedding for natural language text that deeply
represents semantic meaning. Standard text embeddings use the vector output of
a pretrained language model. In our method, we let a language model learn from
the text and then literally pick its brain, taking the actual weights of the
model's neurons to generate a vector. We call this representation of the text a
neural embedding. The technique may generalize beyond text and language models,
but we first explore its properties for natural language processing. We compare
neural embeddings with GPT sentence (SGPT) embeddings on several datasets. We
observe that neural embeddings achieve comparable performance with a far
smaller model, and the errors are different.

Published studies have suggested the bias of automated face-based gender
classification algorithms across gender-race groups. Specifically, unequal
accuracy rates were obtained for women and dark-skinned people. To mitigate the
bias of gender classifiers, the vision community has developed several
strategies. However, the efficacy of these mitigation strategies is
demonstrated for a limited number of races mostly, Caucasian and
African-American. Further, these strategies often offer a trade-off between
bias and classification accuracy. To further advance the state-of-the-art, we
leverage the power of generative views, structured learning, and evidential
learning towards mitigating gender classification bias. We demonstrate the
superiority of our bias mitigation strategy in improving classification
accuracy and reducing bias across gender-racial groups through extensive
experimental validation, resulting in state-of-the-art performance in intra-
and cross dataset evaluations.

Effective Human-AI teaming requires the ability to communicate the goals of
the team and constraints under which you need the agent to operate. Providing
the ability to specify the shared intent or operation criteria of the team can
enable an AI agent to perform its primary function while still being able to
cater to the specific desires of the current team. While significant work has
been conducted to instruct an agent to perform a task, via language or
demonstrations, prior work lacks a focus on building agents which can operate
within the parameters specified by a team. Worse yet, there is a dearth of
research pertaining to enabling humans to provide their specifications through
unstructured, naturalist language. In this paper, we propose the use of goals
and constraints as a scaffold to modulate and evaluate autonomous agents. We
contribute to this field by presenting a novel dataset, and an associated data
collection protocol, which maps language descriptions to goals and constraints
corresponding to specific strategies developed by human participants for the
board game Risk. Leveraging state-of-the-art language models and augmentation
procedures, we develop a machine learning framework which can be used to
identify goals and constraints from unstructured strategy descriptions. To
empirically validate our approach we conduct a human-subjects study to
establish a human-baseline for our dataset. Our results show that our machine
learning architecture is better able to interpret unstructured language
descriptions into strategy specifications than human raters tasked with
performing the same machine translation task (F(1,272.53) = 17.025, p < 0.001).

The theory of the strong force, quantum chromodynamics, describes the proton
in terms of quarks and gluons. The proton is a state of two up quarks and one
down quark bound by gluons, but quantum theory predicts that in addition there
is an infinite number of quark-antiquark pairs. Both light and heavy quarks,
whose mass is respectively smaller or bigger than the mass of the proton, are
revealed inside the proton in high-energy collisions. However, it is unclear
whether heavy quarks also exist as a part of the proton wavefunction, which is
determined by non-perturbative dynamics and accordingly unknown: so-called
intrinsic heavy quarks. It has been argued for a long time that the proton
could have a sizable intrinsic component of the lightest heavy quark, the charm
quark. Innumerable efforts to establish intrinsic charm in the proton have
remained inconclusive. Here we provide evidence for intrinsic charm by
exploiting a high-precision determination of the quark-gluon content of the
nucleon based on machine learning and a large experimental dataset. We
disentangle the intrinsic charm component from charm-anticharm pairs arising
from high-energy radiation. We establish the existence of intrinsic charm at
the 3-standard-deviation level, with a momentum distribution in remarkable
agreement with model predictions. We confirm these findings by comparing to
very recent data on Z-boson production with charm jets from the LHCb

In this paper, we study the Radial Basis Function (RBF) approximation to
differential operators on smooth tensor fields defined on closed Riemannian
submanifolds of Euclidean space, identified by randomly sampled point cloud
data. Theoretically, we establish the spectral convergence for the classical
pointwise RBF discrete non-symmetric approximation of Laplacians. Numerically,
we found that this formulation produces a very accurate estimation of leading
spectra with large enough data. When the manifolds are unknown, the error bound
of the pointwise operator estimation depends on the accuracy of the approximate
local tangent spaces. To improve this approximation accuracy, we develop a
second-order local SVD technique for estimating local tangent spaces on the
manifold. For robust manifold learning, we introduce a symmetric RBF discrete
approximation of the Laplacians induced by a weak formulation on appropriate
Hilbert spaces. Unlike the non-symmetric approximation, this formulation
guarantees non-negative real-valued spectra and the orthogonality of the
eigenvectors. Theoretically, we establish the convergence of the eigenpairs of
both the Laplace-Beltrami operator and Bochner Laplacian in the limit of large
data with convergence rates. Numerically, we provide supporting examples for
approximations of the Laplace-Beltrami operator and various vector Laplacians,
including the Bochner, Hodge, and Lichnerowicz Laplacians.

Lifelong language learning aims to stream learning NLP tasks while retaining
knowledge of previous tasks. Previous works based on the language model and
following data-free constraint approaches have explored formatting all data as
"begin token (\textit{B}) + context (\textit{C}) + question (\textit{Q}) +
answer (\textit{A})" for different tasks. However, they still suffer from
catastrophic forgetting and are exacerbated when the previous task's pseudo
data is insufficient for the following reasons: (1) The model has difficulty
generating task-corresponding pseudo data, and (2) \textit{A} is prone to error
when \textit{A} and \textit{C} are separated by \textit{Q} because the
information of the \textit{C} is diminished before generating \textit{A}.
Therefore, we propose the Ask Question First and Replay Question (AQF-RQ),
including a novel data format "\textit{BQCA}" and a new training task to train
pseudo questions of previous tasks. Experimental results demonstrate that
AQF-RQ makes it easier for the model to generate more pseudo data that match
corresponding tasks, and is more robust to both sufficient and insufficient
pseudo-data when the task boundary is both clear and unclear. AQF-RQ can
achieve only 0.36\% lower performance than multi-task learning.

This paper refers to the extraction of multiple fundamental frequencies
(multiple F0) based on PYIN, an algorithm for extracting the fundamental
frequency (F0) of monophonic music, and a trained convolutional neural networks
(CNN) model, where a pitch salience function of the input signal is produced to
estimate the multiple F0. The implementation of these two algorithms and their
corresponding advantages and disadvantages are discussed in this article.
Analysing the different performance of these two methods, PYIN is applied to
supplement the F0 extracted from the trained CNN model to combine the
advantages of these two algorithms. For evaluation, four pieces played by two
violins are used, and the performance of the models are evaluated accoring to
the flatness of the F0 curve extracted. The result shows the combined model
outperforms the original algorithms when extracting F0 from monophonic music
and polyphonic music.

Colonoscopy is widely recognised as the gold standard procedure for the early
detection of colorectal cancer (CRC). Segmentation is valuable for two
significant clinical applications, namely lesion detection and classification,
providing means to improve accuracy and robustness. The manual segmentation of
polyps in colonoscopy images is time-consuming. As a result, the use of deep
learning (DL) for automation of polyp segmentation has become important.
However, DL-based solutions can be vulnerable to overfitting and the resulting
inability to generalise to images captured by different colonoscopes. Recent
transformer-based architectures for semantic segmentation both achieve higher
performance and generalise better than alternatives, however typically predict
a segmentation map of $\frac{h}{4}\times\frac{w}{4}$ spatial dimensions for a
$h\times w$ input image. To this end, we propose a new architecture for
full-size segmentation which leverages the strengths of a transformer in
extracting the most important features for segmentation in a primary branch,
while compensating for its limitations in full-size prediction with a secondary
fully convolutional branch. The resulting features from both branches are then
fused for final prediction of a $h\times w$ segmentation map. We demonstrate
our method's state-of-the-art performance with respect to the mDice, mIoU,
mPrecision, and mRecall metrics, on both the Kvasir-SEG and CVC-ClinicDB
dataset benchmarks. Additionally, we train the model on each of these datasets
and evaluate on the other to demonstrate its superior generalisation

We consider the problem of minimum cost cover of adaptive-submodular
functions, and provide a 4(ln Q+1)-approximation algorithm, where Q is the goal
value. This bound is nearly the best possible as the problem does not admit any
approximation ratio better than ln Q (unless P=NP). Our result is the first
O(ln Q)-approximation algorithm for this problem. Previously, O(ln Q)
approximation algorithms were only known assuming either independent items or
unit-cost items. Furthermore, our result easily extends to the setting where
one wants to simultaneously cover multiple adaptive-submodular functions: we
obtain the first approximation algorithm for this generalization.

Real world data often exhibits a long-tailed and open-ended (with unseen
classes) distribution. A practical recognition system must balance between
majority (head) and minority (tail) classes, generalize across the
distribution, and acknowledge novelty upon the instances of unseen classes
(open classes). We define Open Long-Tailed Recognition++ (OLTR++) as learning
from such naturally distributed data and optimizing for the classification
accuracy over a balanced test set which includes both known and open classes.
OLTR++ handles imbalanced classification, few-shot learning, open-set
recognition, and active learning in one integrated algorithm, whereas existing
classification approaches often focus only on one or two aspects and deliver
poorly over the entire spectrum. The key challenges are: 1) how to share visual
knowledge between head and tail classes, 2) how to reduce confusion between
tail and open classes, and 3) how to actively explore open classes with learned
knowledge. Our algorithm, OLTR++, maps images to a feature space such that
visual concepts can relate to each other through a memory association mechanism
and a learned metric (dynamic meta-embedding) that both respects the closed
world classification of seen classes and acknowledges the novelty of open
classes. Additionally, we propose an active learning scheme based on visual
memory, which learns to recognize open classes in a data-efficient manner for
future expansions. On three large-scale open long-tailed datasets we curated
from ImageNet (object-centric), Places (scene-centric), and MS1M (face-centric)
data, as well as three standard benchmarks (CIFAR-10-LT, CIFAR-100-LT, and
iNaturalist-18), our approach, as a unified framework, consistently
demonstrates competitive performance. Notably, our approach also shows strong
potential for the active exploration of open classes and the fairness analysis
of minority groups.

Causal models of agents have been used to analyse the safety aspects of
machine learning systems. But identifying agents is non-trivial -- often the
causal model is just assumed by the modeler without much justification -- and
modelling failures can lead to mistakes in the safety analysis. This paper
proposes the first formal causal definition of agents -- roughly that agents
are systems that would adapt their policy if their actions influenced the world
in a different way. From this we derive the first causal discovery algorithm
for discovering agents from empirical data, and give algorithms for translating
between causal models and game-theoretic influence diagrams. We demonstrate our
approach by resolving some previous confusions caused by incorrect causal
modelling of agents.

Recent progress in deep learning (DL)-based joint source-channel coding
(DeepJSCC) has led to a new paradigm of semantic communications. Two salient
features of DeepJSCC-based semantic communications are the exploitation of
semantic-aware features directly from the source signal, and the discrete-time
analog transmission (DTAT) of these features. Compared with traditional digital
communications, semantic communications with DeepJSCC provide superior
reconstruction performance at the receiver and graceful degradation with
diminishing channel quality, but also exhibit a large peak-to-average power
ratio (PAPR) in the transmitted signal. An open question has been whether the
gains of DeepJSCC come from the additional freedom brought by the high-PAPR
continuous-amplitude signal. In this paper, we address this question by
exploring three PAPR reduction techniques in the application of image
transmission. We confirm that the superior image reconstruction performance of
DeepJSCC-based semantic communications can be retained while the transmitted
PAPR is suppressed to an acceptable level. This observation is an important
step towards the implementation of DeepJSCC in practical semantic communication
systems.

With the emergence of large pre-trained vison-language model like CLIP,
transferrable representations can be adapted to a wide range of downstream
tasks via prompt tuning. Prompt tuning tries to probe the beneficial
information for downstream tasks from the general knowledge stored in both the
image and text encoders of the pre-trained vision-language model. A recently
proposed method named Context Optimization (CoOp) introduces a set of learnable
vectors as text prompt from the language side, while tuning the text prompt
alone can not affect the computed visual features of the image encoder, thus
leading to sub-optimal. In this paper, we propose a dual modality prompt tuning
paradigm through learning text prompts and visual prompts for both the text and
image encoder simultaneously. In addition, to make the visual prompt
concentrate more on the target visual concept, we propose Class-Aware Visual
Prompt Tuning (CAVPT), which is generated dynamically by performing the cross
attention between language descriptions of template prompts and visual class
token embeddings. Our method provides a new paradigm for tuning the large
pre-trained vision-language model and extensive experimental results on 8
datasets demonstrate the effectiveness of the proposed method. Our code is
available in the supplementary materials.

6D pose estimation of rigid objects from RGB-D images is crucial for object
grasping and manipulation in robotics. Although RGB channels and the depth (D)
channel are often complementary, providing respectively the appearance and
geometry information, it is still non-trivial how to fully benefit from the two
cross-modal data. From the simple yet new observation, when an object rotates,
its semantic label is invariant to the pose while its keypoint offset direction
is variant to the pose. To this end, we present SO(3)-Pose, a new
representation learning network to explore SO(3)-equivariant and
SO(3)-invariant features from the depth channel for pose estimation. The
SO(3)-invariant features facilitate to learn more distinctive representations
for segmenting objects with similar appearance from RGB channels. The
SO(3)-equivariant features communicate with RGB features to deduce the (missed)
geometry for detecting keypoints of an object with the reflective surface from
the depth channel. Unlike most of existing pose estimation methods, our
SO(3)-Pose not only implements the information communication between the RGB
and depth channels, but also naturally absorbs the SO(3)-equivariance geometry
knowledge from depth images, leading to better appearance and geometry
representation learning. Comprehensive experiments show that our method
achieves the state-of-the-art performance on three benchmarks.

Uncertainty quantification (UQ) is important to machine learning (ML) force
fields to assess the level of confidence during prediction, as ML models are
not inherently physical and can therefore yield catastrophically incorrect
predictions. Established a-posteriori UQ methods, including ensemble methods,
the dropout method, the delta method, and various heuristic distance metrics,
have limitations such as being computationally challenging for large models due
to model re-training. In addition, the uncertainty estimates are often not
rigorously calibrated. In this work, we propose combining the distribution-free
UQ method, known as conformal prediction (CP), with the distances in the neural
network's latent space to estimate the uncertainty of energies predicted by
neural network force fields. We evaluate this method (CP+latent) along with
other UQ methods on two essential aspects, calibration, and sharpness, and find
this method to be both calibrated and sharp under the assumption of independent
and identically-distributed (i.i.d.) data. We show that the method is
relatively insensitive to hyperparameters selected, and test the limitations of
the method when the i.i.d. assumption is violated. Finally, we demonstrate that
this method can be readily applied to trained neural network force fields with
traditional and graph neural network architectures to obtain estimates of
uncertainty with low computational costs on a training dataset of 1 million
images to showcase its scalability and portability. Incorporating the CP method
with latent distances offers a calibrated, sharp and efficient strategy to
estimate the uncertainty of neural network force fields. In addition, the CP
approach can also function as a promising strategy for calibrating uncertainty
estimated by other approaches.

Spatiotemporal forecasting is an imperative topic in data science due to its
diverse and critical applications in smart cities. Existing works mostly
perform consecutive predictions of following steps with observations completely
and continuously obtained, where nearest observations can be exploited as key
knowledge for instantaneous status estimation. However, the practical issues of
early activity planning and sensor failures elicit a brand-new task, i.e.,
non-consecutive forecasting. In this paper, we define spatiotemporal learning
systems with missing observation as Grey Spatiotemporal Systems (G2S) and
propose a Factor-Decoupled learning framework for G2S (FDG2S), where the core
idea is to hierarchically decouple multi-level factors and enable both flexible
aggregations and disentangled uncertainty estimations. Firstly, to compensate
for missing observations, a generic semantic-neighboring sequence sampling is
devised, which selects representative sequences to capture both periodical
regularity and instantaneous variations. Secondly, we turn the predictions of
non-consecutive statuses into inferring statuses under expected combined
exogenous factors. In particular, a factor-decoupled aggregation scheme is
proposed to decouple factor-induced predictive intensity and region-wise
proximity by two energy functions of conditional random field. To infer
region-wise proximity under flexible factor-wise combinations and enable
dynamic neighborhood aggregations, we further disentangle compounded influences
of exogenous factors on region-wise proximity and learn to aggregate them.
Given the inherent incompleteness and critical applications of G2S, a
DisEntangled Uncertainty Quantification is put forward, to identify two types
of uncertainty for reliability guarantees and model interpretations.

Recently, a lot of automated white blood cells (WBC) or leukocyte
classification techniques have been developed. However, all of these methods
only utilize a single modality microscopic image i.e. either blood smear or
fluorescence based, thus missing the potential of a better learning from
multimodal images. In this work, we develop an efficient multimodal
architecture based on a first of its kind multimodal WBC dataset for the task
of WBC classification. Specifically, our proposed idea is developed in two
steps - 1) First, we learn modality specific independent subnetworks inside a
single network only; 2) We further enhance the learning capability of the
independent subnetworks by distilling knowledge from high complexity
independent teacher networks. With this, our proposed framework can achieve a
high performance while maintaining low complexity for a multimodal dataset. Our
unique contribution is two-fold - 1) We present a first of its kind multimodal
WBC dataset for WBC classification; 2) We develop a high performing multimodal
architecture which is also efficient and low in complexity at the same time.

Twitter bot detection is an important and meaningful task. Existing
text-based methods can deeply analyze user tweet content, achieving high
performance. However, novel Twitter bots evade these detections by stealing
genuine users' tweets and diluting malicious content with benign tweets. These
novel bots are proposed to be characterized by semantic inconsistency. In
addition, methods leveraging Twitter graph structure are recently emerging,
showing great competitiveness. However, hardly a method has made text and graph
modality deeply fused and interacted to leverage both advantages and learn the
relative importance of the two modalities. In this paper, we propose a novel
model named BIC that makes the text and graph modalities deeply interactive and
detects tweet semantic inconsistency. Specifically, BIC contains a text
propagation module, a graph propagation module to conduct bot detection
respectively on text and graph structure, and a proven effective text-graph
interactive module to make the two interact. Besides, BIC contains a semantic
consistency detection module to learn semantic consistency information from
tweets. Extensive experiments demonstrate that our framework outperforms
competitive baselines on a comprehensive Twitter bot benchmark. We also prove
the effectiveness of the proposed interaction and semantic consistency

Nominal set plays a central role in a group-theoretic extension of finite
automata to those over an infinite set of data values. Moerman et al. proposed
an active learning algorithm for nominal word automata with the equality
symmetry. In this paper, we introduce deterministic bottom-up nominal tree
automata (DBNTA), which operate on trees whose nodes are labelled with elements
of an orbit finite nominal set. We then prove a Myhill-Nerode theorem for the
class of languages recognized by DBNTA and propose an active learning algorithm
for DBNTA. The algorithm can deal with any data symmetry that admits least
support, not restricted to the equality symmetry and/or the total order
symmetry. To prove the termination of the algorithm, we define a partial order
on nominal sets and show that there is no infinite chain of orbit finite
nominal sets with respect to this partial order between any two orbit finite
sets.

We propose Video-TransUNet, a deep architecture for instance segmentation in
medical CT videos constructed by integrating temporal feature blending into the
TransUNet deep learning framework. In particular, our approach amalgamates
strong frame representation via a ResNet CNN backbone, multi-frame feature
blending via a Temporal Context Module (TCM), non-local attention via a Vision
Transformer, and reconstructive capabilities for multiple targets via a
UNet-based convolutional-deconvolutional architecture with multiple heads. We
show that this new network design can significantly outperform other
state-of-the-art systems when tested on the segmentation of bolus and
pharynx/larynx in Videofluoroscopic Swallowing Study (VFSS) CT sequences. On
our VFSS2022 dataset it achieves a dice coefficient of 0.8796 and an average
surface distance of 1.0379 pixels. Note that tracking the pharyngeal bolus
accurately is a particularly important application in clinical practice since
it constitutes the primary method for diagnostics of swallowing impairment. Our
findings suggest that the proposed model can indeed enhance the TransUNet
architecture via exploiting temporal information and improving segmentation
performance by a significant margin. We publish key source code, network
weights, and ground truth annotations for simplified performance reproduction.

Exploration of unknown environments is a fundamental problem in robotics and
an essential component in numerous applications of autonomous systems. A major
challenge in exploring unknown environments is that the robot has to plan with
the limited information available at each time step. While most current
approaches rely on heuristics and assumption to plan paths based on these
partial observations, we instead propose a novel way to integrate deep learning
into exploration by leveraging 3D scene completion for informed, safe, and
interpretable exploration mapping and planning. Our approach, SC-Explorer,
combines scene completion using a novel incremental fusion mechanism and a
newly proposed hierarchical multi-layer mapping approach, to guarantee safety
and efficiency of the robot. We further present an informative path planning
method, leveraging the capabilities of our mapping approach and a novel
scene-completion-aware information gain. While our method is generally
applicable, we evaluate it in the use case of a Micro Aerial Vehicle (MAV). We
thoroughly study each component in high-fidelity simulation experiments using
only mobile hardware, and show that our method can speed up coverage of an
environment by 73% compared to the baselines with only minimal reduction in map
accuracy. Even if scene completions are not included in the final map, we show
that they can be used to guide the robot to choose more informative paths,
speeding up the measurement of the scene with the robot's sensors by 35%. We
validate our system on a fully autonomous MAV, showing rapid and reliable scene
coverage even in a complex cluttered environment. We make our methods available
as open-source.

Topology-imbalance is a graph-specific imbalance problem caused by the uneven
topology positions of labeled nodes, which significantly damages the
performance of GNNs. What topology-imbalance means and how to measure its
impact on graph learning remain under-explored. In this paper, we provide a new
understanding of topology-imbalance from a global view of the supervision
information distribution in terms of under-reaching and over-squashing, which
motivates two quantitative metrics as measurements. In light of our analysis,
we propose a novel position-aware graph structure learning framework named
PASTEL, which directly optimizes the information propagation path and solves
the topology-imbalance issue in essence. Our key insight is to enhance the
connectivity of nodes within the same class for more supervision information,
thereby relieving the under-reaching and over-squashing phenomena.
Specifically, we design an anchor-based position encoding mechanism, which
better incorporates relative topology position and enhances the intra-class
inductive bias by maximizing the label influence. We further propose a
class-wise conflict measure as the edge weights, which benefits the separation
of different node classes. Extensive experiments demonstrate the superior
potential and adaptability of PASTEL in enhancing GNNs' power in different data
annotation scenarios.

In modern capital market the price of a stock is often considered to be
highly volatile and unpredictable because of various social, financial,
political and other dynamic factors. With calculated and thoughtful investment,
stock market can ensure a handsome profit with minimal capital investment,
while incorrect prediction can easily bring catastrophic financial loss to the
investors. This paper introduces the application of a recently introduced
machine learning model - the Transformer model, to predict the future price of
stocks of Dhaka Stock Exchange (DSE), the leading stock exchange in Bangladesh.
The transformer model has been widely leveraged for natural language processing
and computer vision tasks, but, to the best of our knowledge, has never been
used for stock price prediction task at DSE. Recently the introduction of
time2vec encoding to represent the time series features has made it possible to
employ the transformer model for the stock price prediction. This paper
concentrates on the application of transformer-based model to predict the price
movement of eight specific stocks listed in DSE based on their historical daily
and weekly data. Our experiments demonstrate promising results and acceptable
root mean squared error on most of the stocks.

Addressing the need for explainable Machine Learning has emerged as one of
the most important research directions in modern Artificial Intelligence (AI).
While the current dominant paradigm in the field is based on black-box models,
typically in the form of (deep) neural networks, these models lack direct
interpretability for human users, i.e., their outcomes (and, even more so,
their inner working) are opaque and hard to understand. This is hindering the
adoption of AI in safety-critical applications, where high interests are at
stake. In these applications, explainable by design models, such as decision
trees, may be more suitable, as they provide interpretability. Recent works
have proposed the hybridization of decision trees and Reinforcement Learning,
to combine the advantages of the two approaches. So far, however, these works
have focused on the optimization of those hybrid models. Here, we apply
MAP-Elites for diversifying hybrid models over a feature space that captures
both the model complexity and its behavioral variability. We apply our method
on two well-known control problems from the OpenAI Gym library, on which we
discuss the "illumination" patterns projected by MAP-Elites, comparing its
results against existing similar approaches.

Grayscale image colorization is a fascinating application of AI for
information restoration. The inherently ill-posed nature of the problem makes
it even more challenging since the outputs could be multi-modal. The
learning-based methods currently in use produce acceptable results for
straightforward cases but usually fail to restore the contextual information in
the absence of clear figure-ground separation. Also, the images suffer from
color bleeding and desaturated backgrounds since a single model trained on full
image features is insufficient for learning the diverse data modes. To address
these issues, we present a parallel GAN-based colorization framework. In our
approach, each separately tailored GAN pipeline colorizes the foreground (using
object-level features) or the background (using full-image features). The
foreground pipeline employs a Residual-UNet with self-attention as its
generator trained using the full-image features and the corresponding
object-level features from the COCO dataset. The background pipeline relies on
full-image features and additional training examples from the Places dataset.
We design a DenseFuse-based fusion network to obtain the final colorized image
by feature-based fusion of the parallelly generated outputs. We show the
shortcomings of the non-perceptual evaluation metrics commonly used to assess
multi-modal problems like image colorization and perform extensive performance
evaluation of our framework using multiple perceptual metrics. Our approach
outperforms most of the existing learning-based methods and produces results
comparable to the state-of-the-art. Further, we performed a runtime analysis
and obtained an average inference time of 24ms per image.

Resistive AC-coupled Silicon Detectors (RSDs) are based on the Low Gain
Avalanche Diode (LGAD) technology, characterized by a continuous gain layer,
and by the innovative introduction of resistive read-out. Thanks to a novel
electrode design aimed at maximizing signal sharing, RSD2, the second RSD
production by Fondazione Bruno Kessler (FBK), achieves a position resolution on
the whole pixel surface of about 8 $\mu m$ for 200-$\mu m$ pitch. RSD2 arrays
have been tested using a Transient Current Technique setup equipped with a
16-channel digitizer, and results on spatial resolution have been obtained with
machine learning algorithms.

We study generic inference on identified linear functionals of nonunique
nuisances defined as solutions to underidentified conditional moment
restrictions. This problem appears in a variety of applications, including
nonparametric instrumental variable models, proximal causal inference under
unmeasured confounding, and missing-not-at-random data with shadow variables.
Although the linear functionals of interest, such as average treatment effect,
are identifiable under suitable conditions, nonuniqueness of nuisances pose
serious challenges to statistical inference, since in this setting common
nuisance estimators can be unstable and lack fixed limits. In this paper, we
propose penalized minimax estimators for the nuisance functions and show they
enable valid inference in this challenging setting. The proposed nuisance
estimators can accommodate flexible function classes, and importantly, they can
converge to fixed limits determined by the penalization, regardless of whether
the nuisances are unique or not. We use the penalized nuisance estimators to
form a debiased estimator for the linear functional of interest and prove its
asymptotic normality under generic high-level conditions, which provide for
asymptotically valid confidence intervals.

Code completion, a highly valuable topic in the software development domain,
has been increasingly promoted for use by recent advances in large language
models (LLMs). To date, visible LLM-based code completion frameworks like
GitHub Copilot and GPT are trained using deep learning over vast quantities of
unstructured text and open source codes. As the paramount component and the
cornerstone in daily programming tasks, code completion has largely boosted
professionals' efficiency in building real-world software systems. In contrast
to this flourishing market, we find that code completion models often output
suspicious results, and to date, an automated testing and enhancement framework
for code completion models is not available. This research proposes CCTEST, a
framework to test and repair code completion systems in blackbox settings.
CCTEST features a novel mutation strategy, namely program structure-consistency
(PSC) mutations, to generate mutated code completion inputs. Then, it detects
inconsistent outputs, representing likely erroneous cases, from all the
completed code cases. Moreover, CCTEST repairs the code completion outputs by
selecting the output that mostly reflects the "average" appearance of all
output cases, as the final output of the code completion systems. We detected a
total of 33,540 inputs that can trigger likely erroneous cases from eight
popular LLM-based code completion systems. With repairing, we show that the
performance of code completion models notably increased by 53.51% on average.

Metal artifact correction is a challenging problem in cone beam computed
tomography (CBCT) scanning. Metal implants inserted into the anatomy cause
severe artifacts in reconstructed images. Widely used inpainting-based metal
artifact reduction (MAR) methods require segmentation of metal traces in the
projections as a first step which is a challenging task. One approach is to use
a deep learning method to segment metals in the projections. However, the
success of deep learning methods is limited by the availability of realistic
training data. It is challenging and time consuming to get reliable ground
truth annotations due to unclear implant boundary and large number of
projections. We propose to use X-ray simulations to generate synthetic metal
segmentation training dataset from clinical CBCT scans. We compare the effect
of simulations with different number of photons and also compare several
training strategies to augment the available data. We compare our model's
performance on real clinical scans with conventional threshold-based MAR and a
recent deep learning method. We show that simulations with relatively small
number of photons are suitable for the metal segmentation task and that
training the deep learning model with full size and cropped projections
together improves the robustness of the model. We show substantial improvement
in the image quality affected by severe motion, voxel size under-sampling, and
out-of-FOV metals. Our method can be easily implemented into the existing
projection-based MAR pipeline to get improved image quality. This method can
provide a novel paradigm to accurately segment metals in CBCT projections.

Tensor decomposition is a powerful tool for extracting physically meaningful
latent factors from multi-dimensional nonnegative data, and has been an
increasing interest in a variety of fields such as image processing, machine
learning, and computer vision. In this paper, we propose a sparse nonnegative
Tucker decomposition and completion method for the recovery of underlying
nonnegative data under noisy observations. Here the underlying nonnegative data
tensor is decomposed into a core tensor and several factor matrices with all
entries being nonnegative and the factor matrices being sparse. The loss
function is derived by the maximum likelihood estimation of the noisy
observations, and the $\ell_0$ norm is employed to enhance the sparsity of the
factor matrices. We establish the error bound of the estimator of the proposed
model under generic noise scenarios, which is then specified to the
observations with additive Gaussian noise, additive Laplace noise, and Poisson
observations, respectively. Our theoretical results are better than those by
existing tensor-based or matrix-based methods. Moreover, the minimax lower
bounds are shown to be matched with the derived upper bounds up to logarithmic
factors. Numerical examples on both synthetic and real-world data sets
demonstrate the superiority of the proposed method for nonnegative tensor data
completion.

Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment
analysis task that aims to extract the corresponding opinion words of a given
opinion target from the sentence. Recently, deep learning approaches have made
remarkable progress on this task. Nevertheless, the TOWE task still suffers
from the scarcity of training data due to the expensive data annotation
process. Limited labeled data increase the risk of distribution shift between
test data and training data. In this paper, we propose exploiting massive
unlabeled data to reduce the risk by increasing the exposure of the model to
varying distribution shifts. Specifically, we propose a novel Multi-Grained
Consistency Regularization (MGCR) method to make use of unlabeled data and
design two filters specifically for TOWE to filter noisy data at different
granularity. Extensive experimental results on four TOWE benchmark datasets
indicate the superiority of MGCR compared with current state-of-the-art
methods. The in-depth analysis also demonstrates the effectiveness of the
different-granularity filters. Our codes are available at
https://github.com/TOWESSL/TOWESSL.

Vision transformers are nowadays the de-facto choice for image classification
tasks. There are two broad categories of classification tasks, fine-grained and
coarse-grained. In fine-grained classification, the necessity is to discover
subtle differences due to the high level of similarity between sub-classes.
Such distinctions are often lost as we downscale the image to save the memory
and computational cost associated with vision transformers (ViT). In this work,
we present an in-depth analysis and describe the critical components for
developing a system for the fine-grained categorization of plants from
herbarium sheets. Our extensive experimental analysis indicated the need for a
better augmentation technique and the ability of modern-day neural networks to
handle higher dimensional images. We also introduce a convolutional transformer
architecture called Conviformer which, unlike the popular Vision Transformer
(ConViT), can handle higher resolution images without exploding memory and
computational cost. We also introduce a novel, improved pre-processing
technique called PreSizer to resize images better while preserving their
original aspect ratios, which proved essential for classifying natural plants.
With our simple yet effective approach, we achieved SoTA on Herbarium 202x and
iNaturalist 2019 dataset.

Machine learning poses severe privacy concerns as it is shown that the
learned models can reveal sensitive information about their training data. Many
works have investigated the effect of widely-adopted data augmentation (DA) and
adversarial training (AT) techniques, termed data enhancement in the paper, on
the privacy leakage of machine learning models. Such privacy effects are often
measured by membership inference attacks (MIAs), which aim to identify whether
a particular example belongs to the training set or not. We propose to
investigate privacy from a new perspective called memorization. Through the
lens of memorization, we find that previously deployed MIAs produce misleading
results as they are less likely to identify samples with higher privacy risks
as members compared to samples with low privacy risks. To solve this problem,
we deploy a recent attack that can capture the memorization degrees of
individual samples for evaluation. Through extensive experiments, we unveil
non-trivial findings about the connections between three important properties
of machine learning models, including privacy, generalization gap, and
adversarial robustness. We demonstrate that, unlike existing results, the
generalization gap is shown not highly correlated with privacy leakage.
Moreover, stronger adversarial robustness does not necessarily imply that the
model is more susceptible to privacy attacks.

Oral Food Challenges (OFCs) are essential to accurately diagnosing food
allergy in patients. However, patients are hesitant to undergo OFCs, and for
those that do, there is limited access to allergists in rural/community
healthcare settings. The prediction of OFC outcomes through machine learning
methods can facilitate the de-labeling of food allergens at home, improve
patient and physician comfort during OFCs, and economize medical resources by
minimizing the number of OFCs performed. Clinical data was gathered from 1,112
patients who collectively underwent a total of 1,284 OFCs, and consisted of
clinical factors including serum specific IgE, total IgE, skin prick tests
(SPTs), symptoms, sex, and age. Using these clinical features, machine learning
models were constructed to predict outcomes for peanut, egg, and milk
challenge. The best performing model for each allergen was created using the
Learning Using Concave and Convex Kernels (LUCCK) method, which achieved an
Area under the Curve (AUC) for peanut, egg, and milk OFC prediction of 0.76,
0.68, and 0.70, respectively. Model interpretation via SHapley Additive
exPlanations (SHAP) indicate that specific IgE, along with wheal and flare
values from SPTs, are highly predictive of OFC outcomes. The results of this
analysis suggest that machine learning has the potential to predict OFC
outcomes and reveal relevant clinical factors for further study.

Nonequilibrium statistical mechanics inherits the challenges of the
equilibrium, including accurately describing the joint distribution of a large
number of variables. It also poses new challenges as the joint distribution
evolves over time. While a number of methods have been proposed, e.g., tensor
networks for one-dimensional lattices, we lack a method for arbitrary finite
time in higher dimensions. In this work, we propose a general approach to study
the time evolution of nonequilibrium systems in statistical mechanics by
autoregressive neural networks. Specifically, our method offers direct sampling
and efficient computation of the normalized probabilities and dynamical
partition functions, uncovering the dynamical phase transition over time. We
apply the method to a prototype model of nonequilibrium statistical mechanics,
the kinetically constrained models of structural glasses up to three
dimensions. The obtained results reveal the phase diagram of the time and
counting field, as well as the scaling relations. Our approach paves the way
toward solving nonequilibrium statistical mechanics using tools in modern

In this paper we propose a novel method for semi-supervised anomaly detection
(SSAD). Our classifier is named QMS22 as its inception was dated 2022 upon the
framework of quadratic multiform separation (QMS), a recently introduced
classification model. QMS22 tackles SSAD by solving a multi-class
classification problem involving both the training set and the test set of the
original problem. The classification problem intentionally includes classes
with overlapping samples. One of the classes contains mixture of normal samples
and outliers, and all other classes contain only normal samples. An outlier
score is then calculated for every sample in the test set using the outcome of
the classification problem. We also include performance evaluation of QMS22
against top performing classifiers using ninety-five benchmark imbalanced
datasets from the KEEL repository. These classifiers are BRM (Bagging-Random
Miner), OCKRA (One-Class K-means with Randomly-projected features Algorithm),
ISOF (Isolation Forest), and ocSVM (One-Class Support Vector Machine). It is
shown by using the area under the curve of the receiver operating
characteristic curve as the performance measure, QMS22 significantly
outperforms ISOF and ocSVM. Moreover, the Wilcoxon signed-rank tests reveal
that there is no statistically significant difference when testing QMS22
against BRM nor QMS22 against OCKRA.

Multimodal learning, especially large-scale multimodal pre-training, has
developed rapidly over the past few years and led to the greatest advances in
artificial intelligence (AI). Despite its effectiveness, understanding the
underlying mechanism of multimodal pre-training models still remains a grand
challenge. Revealing the explainability of such models is likely to enable
breakthroughs of novel learning paradigms in the AI field. To this end, given
the multimodal nature of the human brain, we propose to explore the
explainability of multimodal learning models with the aid of non-invasive brain
imaging technologies such as functional magnetic resonance imaging (fMRI).
Concretely, we first present a newly-designed multimodal foundation model
pre-trained on 15 million image-text pairs, which has shown strong multimodal
understanding and generalization abilities in a variety of cognitive downstream
tasks. Further, from the perspective of neural encoding (based on our
foundation model), we find that both visual and lingual encoders trained
multimodally are more brain-like compared with unimodal ones. Particularly, we
identify a number of brain regions where multimodally-trained encoders
demonstrate better neural encoding performance. This is consistent with the
findings in existing studies on exploring brain multi-sensory integration.
Therefore, we believe that multimodal foundation models are more suitable tools
for neuroscientists to study the multimodal signal processing mechanisms in the
human brain. Our findings also demonstrate the potential of multimodal
foundation models as ideal computational simulators to promote both
AI-for-brain and brain-for-AI research.

Precise and efficient control of quantum systems is essential to perform
quantum information processing tasks. In terms of adiabatic speedup via leakage
elimination operator approach, for a closed system, the ideal pulse control
conditions have been theoretically derived by P-Q partitioning technique.
However, it is a challenge to design the corresponding control pulses for an
open system, which requires to tackle noisy environments. In this paper, we
apply the stochastic search procedures to an open qutrit system and
successfully obtain the optimal control pulses for significant adiabatic
speedup. The calculation results show that these optimal pulses allow us to
acquire higher fidelities than the ideal pulses. The improvement of fidelity is
large for relatively strong system-bath coupling strength and high bath
temperature. For certain coupling strength and bath temperature, the maximal
improvement can be achieved for a critical characteristic frequency which
represents the memory time of the environment. Our investigation indicates that
the stochastic search procedures are powerful tools to design control pulses
for combating the detrimental effects of the environment.

The use of data-driven decision support by public agencies is becoming more
widespread and already influences the allocation of public resources. This
raises ethical concerns, as it has adversely affected minorities and
historically discriminated groups. In this paper, we use an approach that
combines statistics and machine learning with dynamical modeling to assess
long-term fairness effects of labor market interventions. Specifically, we
develop and use a model to investigate the impact of decisions caused by a
public employment authority that selectively supports job-seekers through
targeted help. The selection of who receives what help is based on a
data-driven intervention model that estimates an individual's chances of
finding a job in a timely manner and is based on data that describes a
population in which skills relevant to the labor market are unevenly
distributed between two groups (e.g., males and females). The intervention
model has incomplete access to the individual's actual skills and can augment
this with knowledge of the individual's group affiliation, thus using a
protected attribute to increase predictive accuracy. We assess this
intervention model's dynamics -- especially fairness-related issues and
trade-offs between different fairness goals -- over time and compare it to an
intervention model that does not use group affiliation as a predictive feature.
We conclude that in order to quantify the trade-off correctly and to assess the
long-term fairness effects of such a system in the real-world, careful modeling
of the surrounding labor market is indispensable.

Causal discovery has become a vital tool for scientists and practitioners
wanting to discover causal relationships from observational data. While most
previous approaches to causal discovery have implicitly assumed that no expert
domain knowledge is available, practitioners can often provide such domain
knowledge from prior experience. Recent work has incorporated domain knowledge
into constraint-based causal discovery. The majority of such constraint-based
methods, however, assume causal faithfulness, which has been shown to be
frequently violated in practice. Consequently, there has been renewed attention
towards exact-search score-based causal discovery methods, which do not assume
causal faithfulness, such as A*-based methods. However, there has been no
consideration of these methods in the context of domain knowledge. In this
work, we focus on efficiently integrating several types of domain knowledge
into A*-based causal discovery. In doing so, we discuss and explain how domain
knowledge can reduce the graph search space and then provide an analysis of the
potential computational gains. We support these findings with experiments on
synthetic and real data, showing that even small amounts of domain knowledge
can dramatically speed up A*-based causal discovery and improve its performance
and practicality.

Bootstrapping from pre-trained language models has been proven to be an
efficient approach for building foundation vision-language models (VLM) for
tasks such as image captioning or visual question answering. However, it is
difficult-if not impossible-to utilize it to make the model conform with user's
rationales for specific answers. To elicit and reinforce commonsense reasons,
we propose an iterative sampling and tuning paradigm, called ILLUME, that
executes the following loop: Given an image-question-answer prompt, the VLM
samples multiple candidate rationales, and a human critic provides minimal
feedback via preference selection, used for fine-tuning. This loop increases
the training data and gradually carves out the VLM's rationalization
capabilities. Our exhaustive experiments demonstrate that ILLUME is competitive
with standard supervised fine-tuning while using significantly fewer training
data and only requiring minimal feedback.

In this paper, a semantic communication framework is proposed for textual
data transmission. In the studied model, a base station (BS) extracts the
semantic information from textual data, and transmits it to each user. The
semantic information is modeled by a knowledge graph (KG) that consists of a
set of semantic triples. After receiving the semantic information, each user
recovers the original text using a graph-to-text generation model. To measure
the performance of the considered semantic communication framework, a metric of
semantic similarity (MSS) that jointly captures the semantic accuracy and
completeness of the recovered text is proposed. Due to wireless resource
limitations, the BS may not be able to transmit the entire semantic information
to each user and satisfy the transmission delay constraint. Hence, the BS must
select an appropriate resource block for each user as well as determine and
transmit part of the semantic information to the users. As such, we formulate
an optimization problem whose goal is to maximize the total MSS by jointly
optimizing the resource allocation policy and determining the partial semantic
information to be transmitted. To solve this problem, a
proximal-policy-optimization-based reinforcement learning (RL) algorithm
integrated with an attention network is proposed. The proposed algorithm can
evaluate the importance of each triple in the semantic information using an
attention network and then, build a relationship between the importance
distribution of the triples in the semantic information and the total MSS.
Compared to traditional RL algorithms, the proposed algorithm can dynamically
adjust its learning rate thus ensuring convergence to a locally optimal
solution.

Autonomous Systems (AS) are increasingly proposed, or used, in Safety
Critical (SC) applications. Many such systems make use of sophisticated sensor
suites and processing to provide scene understanding which informs the AS'
decision-making. The sensor processing typically makes use of Machine Learning
(ML) and has to work in challenging environments, further the ML-algorithms
have known limitations,e.g., the possibility of false-negatives or
false-positives in object classification. The well-established safety-analysis
methods developed for conventional SC systems are not well-matched to AS, ML,
or the sensing systems used by AS. This paper proposes an adaptation of
well-established safety-analysis methods to address the specifics of
perception-systems for AS, including addressing environmental effects and the
potential failure-modes of ML, and provides a rationale for choosing particular
sets of guidewords, or prompts, for safety-analysis. It goes on to show how the
results of the analysis can be used to inform the design and verification of
the AS and illustrates the new method by presenting a partial analysis of a
road vehicle. Illustrations in the paper are primarily based on optical
sensing, however the paper discusses the applicability of the method to other
sensing modalities and its role in a wider safety process addressing the
overall capabilities of AS.

Machine learning assisted modeling of the inter-atomic potential energy
surface (PES) is revolutionizing the field of molecular simulation. With the
accumulation of high-quality electronic structure data, a model that can be
pretrained on all available data and finetuned on downstream tasks with a small
additional effort would bring the field to a new stage. Here we propose DPA-1,
a Deep Potential model with a novel attention mechanism, which is highly
effective for representing the conformation and chemical spaces of atomic
systems and learning the PES. We tested DPA-1 on a number of systems and
observed superior performance compared with existing benchmarks. When
pretrained on large-scale datasets containing 56 elements, DPA-1 can be
successfully applied to various downstream tasks with a great improvement of
sample efficiency. Surprisingly, for different elements, the learned type
embedding parameters form a $spiral$ in the latent space and have a natural
correspondence with their positions on the periodic table, showing interesting
interpretability of the pretrained DPA-1 model.

This paper studies a framework, projected fixed-point method, for graph
matching. The framework contains a class of popular graph matching algorithms,
including graduated assignment (GA), integer projected fixed-point method
(IPFP) and doubly stochastic projected fixed-point method (DSPFP). We propose
an adaptive strategy to tune the step size parameter in this framework. Such a
strategy improves these algorithms in efficiency and accuracy. Further, it
guarantees the convergence of the underlying algorithms. Some preliminary
analysis based on distance geometry seems to support that the optimal step size
parameter has a high probability of 1 when graphs are fully connected.
Secondly, it is observed that a popular projection method, softassign, is
sensitive to graphs' cardinality(size). We proposed a dynamical softassign
algorithm that is robust to graphs' cardinality. Combining the adaptive step
size and the dynamical softassign, we propose a novel graph matching algorithm:
the adaptive projected fixed-point method with dynamical softassign. Various
experiments demonstrate that the proposed algorithm is significantly faster
than several other state-of-art algorithms with no loss of accuracy.

Controlling the text generated by language models and customizing the content
has been a long-standing challenge. Existing prompting techniques proposed in
pursuit of providing control are task-specific and lack generality; this
provides overwhelming choices for non-expert users to find a suitable method
for their task. The effort associated with those techniques, such as in writing
examples, explanations, instructions, etc. further limits their adoption among
non-expert users. In this paper, we propose a simple prompting strategy HELP ME
THINK where we encourage GPT3 to help non-expert users by asking a set of
relevant questions and leveraging user answers to execute the task. We
demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks.
Specifically, we focus on tasks that are hard for average humans and require
significant thinking to perform. We hope our work will encourage the
development of unconventional ways to harness the power of large language
models.

This paper proposes a deep autoencoder model based on Pytorch. This algorithm
introduces the idea of Pytorch into the auto-encoder, and randomly clears the
input weights connected to the hidden layer neurons with a certain probability,
so as to achieve the effect of sparse network, which is similar to the starting
point of the sparse auto-encoder. The new algorithm effectively solves the
problem of possible overfitting of the model and improves the accuracy of image
classification. Finally, the experiment is carried out, and the experimental
results are compared with ELM, RELM, AE, SAE, DAE.

In this paper, we address the problem of conducting statistical inference in
settings involving large-scale data that may be high-dimensional and
contaminated by outliers. The high volume and dimensionality of the data
require distributed processing and storage solutions. We propose a two-stage
distributed and robust statistical inference procedures coping with
high-dimensional models by promoting sparsity. In the first stage, known as
model selection, relevant predictors are locally selected by applying robust
Lasso estimators to the distinct subsets of data. The variable selections from
each computation node are then fused by a voting scheme to find the sparse
basis for the complete data set. It identifies the relevant variables in a
robust manner. In the second stage, the developed statistically robust and
computationally efficient bootstrap methods are employed. The actual inference
constructs confidence intervals, finds parameter estimates and quantifies
standard deviation. Similar to stage 1, the results of local inference are
communicated to the fusion center and combined there. By using analytical
methods, we establish the favorable statistical properties of the robust and
computationally efficient bootstrap methods including consistency for a fixed
number of predictors, and robustness. The proposed two-stage robust and
distributed inference procedures demonstrate reliable performance and
robustness in variable selection, finding confidence intervals and bootstrap
approximations of standard deviations even when data is high-dimensional and
contaminated by outliers.

We report the simulation results for angular resolution of an electromagnetic
(EM) sampling calorimeter with photons in the range of 100 MeV to 2 GeV. The
simulation model of the EM calorimeter consists of alternating layers of an
1-mm-thick Pb plate and a 5-mm-thick plastic scintillator plate. The
scintillator plates are alternately segmented into horizontal and vertical
strips. In this study, we obtained energy deposits in individual strips using
Geant4 simulations and reconstructed the incident photon angles using XGBoost
with gradient-boosted decision trees. The performance of the angle
reconstruction depends on both the detector configuration and accuracy of
machine learning. The angular resolution so obtained can be expressed as $0.24
\oplus 1.24/\sqrt{E_{\gamma}}$, where $E_{\gamma}$ is the incident photon
energy in GeV. This energy dependence is consistent for different incident
angles in the range of 15$^{\circ}$ to 40$^{\circ}$.

Large language models have demonstrated the ability to condition on and
generate both natural language and programming language text. Such models open
up the possibility of multi-language code generation: could code generation
models generalize knowledge from one language to another? Although contemporary
code generation models can generate semantically correct Python code, little is
known about their abilities with other languages. We facilitate the exploration
of this topic by proposing MultiPL-E, the first multi-language parallel
benchmark for natural-language-to-code-generation.
  MultiPL-E extends the HumanEval benchmark (Chen et al, 2021) to support 18
more programming languages, encompassing a range of programming paradigms and
popularity. We evaluate two state-of-the-art code generation models on
MultiPL-E: Codex and InCoder. We find that on several languages, Codex matches
and even exceeds its performance on Python. The range of programming languages
represented in MultiPL-E allow us to explore the impact of language frequency
and language features on model performance. Finally, the MultiPL-E approach of
compiling code generation benchmarks to new programming languages is both
scalable and extensible. We describe a general approach for easily adding
support for new benchmarks and languages to MultiPL-E.

Accurate geometry representation is essential in developing finite element
models. Although generally good, deep-learning segmentation approaches with
only few data have difficulties in accurately segmenting fine features, e.g.,
gaps and thin structures. Subsequently, segmented geometries need
labor-intensive manual modifications to reach a quality where they can be used
for simulation purposes. We propose a strategy that uses transfer learning to
reuse datasets with poor segmentation combined with an interactive learning
step where fine-tuning of the data results in anatomically accurate
segmentations suitable for simulations. We use a modified MultiPlanar UNet that
is pre-trained using inferior hip joint segmentation combined with a dedicated
loss function to learn the gap regions and post-processing to correct tiny
inaccuracies on symmetric classes due to rotational invariance. We demonstrate
this robust yet conceptually simple approach applied with clinically validated
results on publicly available computed tomography scans of hip joints. Code and
resulting 3D models are available at:
https://github.com/MICCAI2022-155/AuToSeg}

Open Access (OA) facilitates access to articles. But, authors or funders
often must pay the publishing costs preventing authors who do not receive
financial support from participating in OA publishing and citation advantage
for OA articles. OA may exacerbate existing inequalities in the publication
system rather than overcome them. To investigate this, we studied 522,664
articles published by Springer Nature. Employing statistical methods, we
describe the relationship between authors affiliated with countries from
different income levels, their choice of publishing (OA or closed access), and
the citation impact of their papers. A machine learning classification method
helped us to explore the association between OA-publishing and attributes of
the author, especially eligibility for APC-waivers or discounts, journal,
country, and paper. The results indicate that authors eligible for the
APC-waivers publish more in gold-OA-journals than other authors. In contrast,
authors eligible for an APC discount have the lowest ratio of OA publications,
leading to the assumption that this discount insufficiently motivates authors
to publish in a gold-OA-journal. The rank of journals is a significant driver
for publishing in a gold-OA-journal, whereas the OA option is mostly avoided in
hybrid journals. Seniority, experience with OA publications, and the scientific
field are the most decisive factors in OA-publishing.

Although the smart camera parking system concept has existed for decades, a
few approaches have fully addressed the system's scalability and reliability.
As the cornerstone of a smart parking system is the ability to detect
occupancy, traditional methods use the classification backbone to predict spots
from a manual labeled grid. This is time-consuming and loses the system's
scalability. Additionally, most of the approaches use deep learning models,
making them not error-free and not reliable at scale. Thus, we propose an
end-to-end smart camera parking system where we provide an autonomous detecting
occupancy by an object detector called OcpDet. Our detector also provides
meaningful information from contrastive modules: training and spatial
knowledge, which avert false detections during inference. We benchmark OcpDet
on the existing PKLot dataset and reach competitive results compared to
traditional classification solutions. We also introduce an additional SNU-SPS
dataset, in which we estimate the system performance from various views and
conduct system evaluation in parking assignment tasks. The result from our
dataset shows that our system is promising for real-world applications.

Supervised representation learning with deep networks tends to overfit the
training classes and the generalization to novel classes is a challenging
question. It is common to evaluate a learned embedding on held-out images of
the same training classes. In real applications however, data comes from new
sources and novel classes are likely to arise. We hypothesize that
incorporating unlabelled images of novel classes in the training set in a
semi-supervised fashion would be beneficial for the efficient retrieval of
novel-class images compared to a vanilla supervised representation. To verify
this hypothesis in a comprehensive way, we propose an original evaluation
methodology that varies the degree of novelty of novel classes by partitioning
the dataset category-wise either randomly, or semantically, i.e. by minimizing
the shared semantics between base and novel classes. This evaluation procedure
allows to train a representation blindly to any novel-class labels and evaluate
the frozen representation on the retrieval of base or novel classes. We find
that a vanilla supervised representation falls short on the retrieval of novel
classes even more so when the semantics gap is higher. Semi-supervised
algorithms allow to partially bridge this performance gap but there is still
much room for improvement.

Recently, as the demand for cleaning robots has steadily increased, therefore
household electricity consumption is also increasing. To solve this electricity
consumption issue, the problem of efficient path planning for cleaning robot
has become important and many studies have been conducted. However, most of
them are about moving along a simple path segment, not about the whole path to
clean all places. As the emerging deep learning technique, reinforcement
learning (RL) has been adopted for cleaning robot. However, the models for RL
operate only in a specific cleaning environment, not the various cleaning
environment. The problem is that the models have to retrain whenever the
cleaning environment changes. To solve this problem, the proximal policy
optimization (PPO) algorithm is combined with an efficient path planning that
operates in various cleaning environments, using transfer learning (TL),
detection nearest cleaned tile, reward shaping, and making elite set methods.
The proposed method is validated with an ablation study and comparison with
conventional methods such as random and zigzag. The experimental results
demonstrate that the proposed method achieves improved training performance and
increased convergence speed over the original PPO. And it also demonstrates
that this proposed method is better performance than conventional methods
(random, zigzag).

Modern industrial facilities generate large volumes of raw sensor data during
production process. This data is used to monitor and control the processes and
can be analyzed to detect and predict process abnormalities. Typically, the
data has to be annotated by experts to be further used in predictive modeling.
Most of today's research is focusing on either unsupervised anomaly detection
algorithms or supervised methods, that require manually annotated data. The
studies are often done using process simulator generated data for a narrow
class of events and proposed algorithms are rarely verified on publicly
available datasets. In this paper, we propose a novel method SensorSCAN for
unsupervised fault detection and diagnosis designed for industrial chemical
sensor data. We demonstrate our model performance on two publicly available
datasets based on the Tennessee Eastman Process with various fault types.
Results show that our method significantly outperforms existing approaches
(+0.2-0.3 TPR for a fixed FPR) and detects most of the process faults without
the use of expert annotation. In addition, we performed experiments to show
that our method is suitable for real-world applications where the number of
fault types is not known in advance.

Brain biometrics based on electroencephalography (EEG) have been used
increasingly for personal identification. Traditional machine learning
techniques as well as modern day deep learning methods have been applied with
promising results. In this paper we present EEG-BBNet, a hybrid network which
integrates convolutional neural networks (CNN) with graph convolutional neural
networks (GCNN). The benefit of the CNN in automatic feature extraction and the
capability of GCNN in learning connectivity between EEG electrodes through
graph representation are jointly exploited. We examine various connectivity
measures, namely the Euclidean distance, Pearson's correlation coefficient,
phase-locked value, phase-lag index, and Rho index. The performance of the
proposed method is assessed on a benchmark dataset consisting of various
brain-computer interface (BCI) tasks and compared to other state-of-the-art
approaches. We found that our models outperform all baselines in the
event-related potential (ERP) task with an average correct recognition rates up
to 99.26% using intra-session data. EEG-BBNet with Pearson's correlation and
RHO index provide the best classification results. In addition, our model
demonstrates greater adaptability using inter-session and inter-task data. We
also investigate the practicality of our proposed model with smaller number of
electrodes. Electrode placements over the frontal lobe region appears to be
most appropriate with minimal lost in performance.

In this competition, participants will address two fundamental causal
challenges in machine learning in the context of education using time-series
data. The first is to identify the causal relationships between different
constructs, where a construct is defined as the smallest element of learning.
The second challenge is to predict the impact of learning one construct on the
ability to answer questions on other constructs. Addressing these challenges
will enable optimisation of students' knowledge acquisition, which can be
deployed in a real edtech solution impacting millions of students. Participants
will run these tasks in an idealised environment with synthetic data and a
real-world scenario with evaluation data collected from a series of A/B tests.

Graph anomaly detection on attributed networks has become a prevalent
research topic due to its broad applications in many influential domains. In
real-world scenarios, nodes and edges in attributed networks usually display
distinct heterogeneity, i.e. attributes of different types of nodes show great
variety, different types of relations represent diverse meanings. Anomalies
usually perform differently from the majority in various perspectives of
heterogeneity in these networks. However, existing graph anomaly detection
approaches do not leverage heterogeneity in attributed networks, which is
highly related to anomaly detection. In light of this problem, we propose
AHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach
based on the encoder-decoder framework. Specifically, for the encoder, we
design three levels of attention, i.e. attribute level, node type level, and
edge level attentions to capture the heterogeneity of network structure, node
properties and information of a single node, respectively. In the decoder, we
exploit structure, attribute, and node type reconstruction terms to obtain an
anomaly score for each node. Extensive experiments show the superiority of
AHEAD on several real-world heterogeneous information networks compared with
the state-of-arts in the unsupervised setting. Further experiments verify the
effectiveness and robustness of our triple attention, model backbone, and
decoder in general.

The European Machinery Directive and related harmonized standards do consider
that software is used to generate safety-relevant behavior of the machinery but
do not consider all kinds of software. In particular, software based on machine
learning (ML) are not considered for the realization of safety-relevant
behavior. This limits the introduction of suitable safety concepts for
autonomous mobile robots and other autonomous machinery, which commonly depend
on ML-based functions. We investigated this issue and the way safety standards
define safety measures to be implemented against software faults. Functional
safety standards use Safety Integrity Levels (SILs) to define which safety
measures shall be implemented. They provide rules for determining the SIL and
rules for selecting safety measures depending on the SIL. In this paper, we
argue that this approach can hardly be adopted with respect to ML and other
kinds of Artificial Intelligence (AI). Instead of simple rules for determining
an SIL and applying related measures against faults, we propose the use of
assurance cases to argue that the individually selected and applied measures
are sufficient in the given case. To get a first rating regarding the
feasibility and usefulness of our proposal, we presented and discussed it in a
workshop with experts from industry, German statutory accident insurance
companies, work safety and standardization commissions, and representatives
from various national, European, and international working groups dealing with
safety and AI. In this paper, we summarize the proposal and the workshop
discussion. Moreover, we check to which extent our proposal is in line with the
European AI Act proposal and current safety standardization initiatives
addressing AI and Autonomous Systems

Recombining known primitive concepts into larger novel combinations is a
quintessentially human cognitive capability. Whether large neural models in NLP
can acquire this ability while learning from data is an open question. In this
paper, we investigate this problem from the perspective of formal languages. We
use deterministic finite-state transducers to make an unbounded number of
datasets with controllable properties governing compositionality. By randomly
sampling over many transducers, we explore which of their properties contribute
to learnability of a compositional relation by a neural network. We find that
the models either learn the relations completely or not at all. The key is
transition coverage, setting a soft learnability limit at 400 examples per
transition.

Vision-Transformers are widely used in various vision tasks. Meanwhile, there
is another line of works starting with the MLP-mixer trying to achieve similar
performance using mlp-based architectures. Interestingly, until now those
mlp-based architectures have not been adapted for NLP tasks. Additionally,
until now, mlp-based architectures have failed to achieve state-of-the-art
performance in vision tasks. In this paper, we analyze the expressive power of
mlp-based architectures in modeling dependencies between multiple different
inputs simultaneously, and show an exponential gap between the attention and
the mlp-based mechanisms. Our results suggest a theoretical explanation for the
mlp inability to compete with attention-based mechanisms in NLP problems, they
also suggest that the performance gap in vision tasks may be due to the mlp
relative weakness in modeling dependencies between multiple different
locations, and that combining smart input permutations with mlp architectures
may not be enough to close the performance gap alone.

With the recent development of Deep Learning applied to Computer Vision,
sport video understanding has gained a lot of attention, providing much richer
information for both sport consumers and leagues. This paper introduces
DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks
for automated sport understanding. The main purpose of this framework is to
close the gap between academic research and real world settings. To this end,
the datasets provide high-resolution raw images, camera parameters and high
quality annotations. DeepSportradar currently supports four challenging tasks
related to basketball: ball 3D localization, camera calibration, player
instance segmentation and player re-identification. For each of the four tasks,
a detailed description of the dataset, objective, performance metrics, and the
proposed baseline method are provided. To encourage further research on
advanced methods for sport understanding, a competition is organized as part of
the MMSports workshop from the ACM Multimedia 2022 conference, where
participants have to develop state-of-the-art methods to solve the above tasks.
The four datasets, development kits and baselines are publicly available.

GANs are able to model accurately the distribution of complex,
high-dimensional datasets, e.g. images. This makes high-quality GANs useful for
unsupervised anomaly detection in medical imaging. However, differences in
training datasets such as output image dimensionality and appearance of
semantically meaningful features mean that GAN models from the natural image
domain may not work `out-of-the-box' for medical imaging, necessitating
re-implementation and re-evaluation. In this work we adapt and evaluate three
GAN models to the task of modelling 3D healthy image patches for pulmonary CT.
To the best of our knowledge, this is the first time that such an evaluation
has been performed. The DCGAN, styleGAN and the bigGAN architectures were
investigated due to their ubiquity and high performance in natural image
processing. We train different variants of these methods and assess their
performance using the FID score. In addition, the quality of the generated
images was evaluated by a human observer study, the ability of the networks to
model 3D domain-specific features was investigated, and the structure of the
GAN latent spaces was analysed. Results show that the 3D styleGAN produces
realistic-looking images with meaningful 3D structure, but suffer from mode
collapse which must be addressed during training to obtain samples diversity.
Conversely, the 3D DCGAN models show a greater capacity for image variability,
but at the cost of poor-quality images. The 3D bigGAN models provide an
intermediate level of image quality, but most accurately model the distribution
of selected semantically meaningful features. The results suggest that future
development is required to realise a 3D GAN with sufficient capacity for
patch-based lung CT anomaly detection and we offer recommendations for future
areas of research, such as experimenting with other architectures and
incorporation of position-encoding.

Deep neural networks for survival prediction outper-form classical approaches
in discrimination, which is the ordering of patients according to their
time-of-event. Conversely, classical approaches like the Cox Proportional
Hazards model display much better calibration, the correct temporal prediction
of events of the underlying distribution. Especially in the medical domain,
where it is critical to predict the survival of a single patient, both
discrimination and calibration are important performance metrics. Here we
present Discrete Calibrated Survival (DCS), a novel deep neural network for
discriminated and calibrated survival prediction that outperforms competing
survival models in discrimination on three medical datasets, while achieving
best calibration among all discrete time models. The enhanced performance of
DCS can be attributed to two novel features, the variable temporal output node
spacing and the novel loss term that optimizes the use of uncensored and
censored patient data. We believe that DCS is an important step towards
clinical application of deep-learning-based survival prediction with
state-of-the-art discrimination and good calibration.

Hidden Markov Chains (HMC) and Recurrent Neural Networks (RNN) are two well
known tools for predicting time series. Even though these solutions were
developed independently in distinct communities, they share some similarities
when considered as probabilistic structures. So in this paper we first consider
HMC and RNN as generative models, and we embed both structures in a common
generative unified model (GUM). We next address a comparative study of the
expressivity of these models. To that end we assume that the models are
furthermore linear and Gaussian. The probability distributions produced by
these models are characterized by structured covariance series, and as a
consequence expressivity reduces to comparing sets of structured covariance
series, which enables us to call for stochastic realization theory (SRT). We
finally provide conditions under which a given covariance series can be
realized by a GUM, an HMC or an RNN.

Hyperparameter tuning is a common technique for improving the performance of
neural networks. Most techniques for hyperparameter search involve an iterated
process where the model is retrained at every iteration. However, the expected
accuracy improvement from every additional search iteration, is still unknown.
Calculating the expected improvement can help create stopping rules for
hyperparameter tuning and allow for a wiser allocation of a project's
computational budget. In this paper, we establish an empirical estimate for the
expected accuracy improvement from an additional iteration of hyperparameter
search. Our results hold for any hyperparameter tuning method which is based on
random search \cite{bergstra2012random} and samples hyperparameters from a
fixed distribution. We bound our estimate with an error of
$O\left(\sqrt{\frac{\log k}{k}}\right)$ w.h.p. where $k$ is the current number
of iterations. To the best of our knowledge this is the first bound on the
expected gain from an additional iteration of hyperparameter search. Finally,
we demonstrate that the optimal estimate for the expected accuracy will still
have an error of $\frac{1}{k}$.

Radiographs are a versatile diagnostic tool for the detection and assessment
of pathologies, for treatment planning or for navigation and localization
purposes in clinical interventions. However, their interpretation and
assessment by radiologists can be tedious and error-prone. Thus, a wide variety
of deep learning methods have been proposed to support radiologists
interpreting radiographs. Mostly, these approaches rely on convolutional neural
networks (CNN) to extract features from images. Especially for the multi-label
classification of pathologies on chest radiographs (Chest X-Rays, CXR), CNNs
have proven to be well suited. On the Contrary, Vision Transformers (ViTs) have
not been applied to this task despite their high classification performance on
generic images and interpretable local saliency maps which could add value to
clinical interventions. ViTs do not rely on convolutions but on patch-based
self-attention and in contrast to CNNs, no prior knowledge of local
connectivity is present. While this leads to increased capacity, ViTs typically
require an excessive amount of training data which represents a hurdle in the
medical domain as high costs are associated with collecting large medical data
sets. In this work, we systematically compare the classification performance of
ViTs and CNNs for different data set sizes and evaluate more data-efficient ViT
variants (DeiT). Our results show that while the performance between ViTs and
CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a
reasonably large data set is available for training.

Spatial models of preference, in the form of vector embeddings, are learned
by many deep learning systems including recommender systems. Often these models
are assumed to approximate a Euclidean structure, where an individual prefers
alternatives positioned closer to their "ideal point", as measured by the
Euclidean metric. However, Bogomolnaia and Laslier (2007) showed that there
exist ordinal preference profiles that cannot be represented with this
structure if the Euclidean space has two fewer dimensions than there are
individuals or alternatives. We extend this result, showing that there are
realistic situations in which almost all preference profiles cannot be
represented with the Euclidean model, and derive a theoretical lower bound on
the information lost when approximating non-representable preferences with the
Euclidean model. Our results have implications for the interpretation and use
of vector embeddings, because in some cases close approximation of arbitrary,
true preferences is possible only if the dimensionality of the embeddings is a
substantial fraction of the number of individuals or alternatives.

The rate of heart morbidity and heart mortality increases significantly which
affect the global public health and world economy. Early prediction of heart
disease is crucial for reducing heart morbidity and mortality. This paper
proposes two quantum machine learning methods i.e. hybrid quantum neural
network and hybrid random forest quantum neural network for early detection of
heart disease. The methods are applied on the Cleveland and Statlog datasets.
The results show that hybrid quantum neural network and hybrid random forest
quantum neural network are suitable for high dimensional and low dimensional
problems respectively. The hybrid quantum neural network is sensitive to
outlier data while hybrid random forest is robust on outlier data. A comparison
between different machine learning methods shows that the proposed quantum
methods are more appropriate for early heart disease prediction where 96.43%
and 97.78% area under curve are obtained for Cleveland and Statlog dataset

In this work, a parameter-efficient attention module is presented for emotion
classification using a limited, or relatively small, number of
electroencephalogram (EEG) signals. This module is called the Monotonicity
Constrained Attention Module (MCAM) due to its capability of incorporating
priors on the monotonicity when converting features' Gram matrices into
attention matrices for better feature refinement. Our experiments have shown
that MCAM's effectiveness is comparable to state-of-the-art attention modules
in boosting the backbone network's performance in prediction while requiring
less parameters. Several accompanying sensitivity analyses on trained models'
prediction concerning different attacks are also performed. These attacks
include various frequency domain filtering levels and gradually morphing
between samples associated with multiple labels. Our results can help better
understand different modules' behaviour in prediction and can provide guidance
in applications where data is limited and are with noises.

Interpretability has become an essential topic for artificial intelligence in
some high-risk domains such as healthcare, bank and security. For commonly-used
tabular data, traditional methods trained end-to-end machine learning models
with numerical and categorical data only, and did not leverage human
understandable knowledge such as data descriptions. Yet mining human-level
knowledge from tabular data and using it for prediction remain a challenge.
Therefore, we propose a concept and argumentation based model (CAM) that
includes the following two components: a novel concept mining method to obtain
human understandable concepts and their relations from both descriptions of
features and the underlying data, and a quantitative argumentation-based method
to do knowledge representation and reasoning. As a result of it, CAM provides
decisions that are based on human-level knowledge and the reasoning process is
intrinsically interpretable. Finally, to visualize the purposed interpretable
model, we provide a dialogical explanation that contain dominated reasoning
path within CAM. Experimental results on both open source benchmark dataset and
real-word business dataset show that (1) CAM is transparent and interpretable,
and the knowledge inside the CAM is coherent with human understanding; (2) Our
interpretable approach can reach competitive results comparing with other
state-of-art models.

We show that $d$-variate polynomials of degree $R$ can be represented on
$[0,1]^d$ as shallow neural networks of width $2(R+d)^d$. Also, by SNN
representation of localized Taylor polynomials of univariate $C^\beta$-smooth
functions, we derive for shallow networks the minimax optimal rate of
convergence, up to a logarithmic factor, to unknown univariate regression
function.

Model-Agnostic Meta-Learning (MAML) is one of the most successful
meta-learning techniques for few-shot learning. It uses gradient descent to
learn commonalities between various tasks, enabling the model to learn the
meta-initialization of its own parameters to quickly adapt to new tasks using a
small amount of labeled training data. A key challenge to few-shot learning is
task uncertainty. Although a strong prior can be obtained from meta-learning
with a large number of tasks, a precision model of the new task cannot be
guaranteed because the volume of the training dataset is normally too small. In
this study, first,in the process of choosing initialization parameters, the new
method is proposed for task-specific learner adaptively learn to select
initialization parameters that minimize the loss of new tasks. Then, we propose
two improved methods for the meta-loss part: Method 1 generates weights by
comparing meta-loss differences to improve the accuracy when there are few
classes, and Method 2 introduces the homoscedastic uncertainty of each task to
weigh multiple losses based on the original gradient descent,as a way to
enhance the generalization ability to novel classes while ensuring accuracy
improvement. Compared with previous gradient-based meta-learning methods, our
model achieves better performance in regression tasks and few-shot
classification and improves the robustness of the model to the learning rate
and query sets in the meta-test set.

Goal-conditioned reinforcement learning (GCRL) has a wide range of potential
real-world applications, including manipulation and navigation problems in
robotics. Especially in such robotics task, sample efficiency is of the utmost
importance for GCRL since, by default, the agent is only rewarded when it
reaches its goal. While several methods have been proposed to improve the
sample efficiency of GCRL, one relatively under-studied approach is the design
of neural architectures to support sample efficiency. In this work, we
introduce a novel neural architecture for GCRL that achieves significantly
better sample efficiency than the commonly-used monolithic network
architecture. They key insight is that the optimal action value function Q^*(s,
a, g) must satisfy the triangle inequality in a specific sense. Furthermore, we
introduce the metric residual network (MRN) that deliberately decomposes the
action-value function Q(s,a,g) into the negated summation of a metric plus a
residual asymmetric component. MRN provably approximates any optimal
action-value function Q^*(s,a,g), thus making it a fitting neural architecture
for GCRL. We conduct comprehensive experiments across 12 standard benchmark
environments in GCRL. The empirical results demonstrate that MRN uniformly
outperforms other state-of-the-art GCRL neural architectures in terms of sample

Meta-learning is an effective method to handle imbalanced and noisy-label
learning, but it depends on a validation set containing randomly selected,
manually labelled and balanced distributed samples. The random selection and
manual labelling and balancing of this validation set is not only sub-optimal
for meta-learning, but it also scales poorly with the number of classes. Hence,
recent meta-learning papers have proposed ad-hoc heuristics to automatically
build and label this validation set, but these heuristics are still sub-optimal
for meta-learning. In this paper, we analyse the meta-learning algorithm and
propose new criteria to characterise the utility of the validation set, based
on: 1) the informativeness of the validation set; 2) the class distribution
balance of the set; and 3) the correctness of the labels of the set.
Furthermore, we propose a new imbalanced noisy-label meta-learning (INOLML)
algorithm that automatically builds a validation set by maximising its utility
using the criteria above. Our method shows significant improvements over
previous meta-learning approaches and sets the new state-of-the-art on several

Semi-supervised learning and domain adaptation techniques have drawn
increasing attention in the field of domestic sound event detection thanks to
the availability of large amounts of unlabeled data and the relative ease to
generate synthetic strongly-labeled data. In a previous work, several
semi-supervised learning strategies were designed to boost the performance of a
mean-teacher model. Namely, these strategies include shift consistency training
(SCT), interpolation consistency training (ICT), and pseudo-labeling. However,
adversarial domain adaptation (ADA) did not seem to improve the event detection
accuracy further when we attempt to compensate for the domain gap between
synthetic and real data. In this research, we empirically found that ICT tends
to pull apart the distributions of synthetic and real data in t-SNE plots.
Therefore, ICT is abandoned while SCT, in contrast, is applied to train both
the student and the teacher models. With these modifications, the system
successfully integrates with an ADA network, and we achieve 47.2% in the F1
score on the DCASE 2020 task 4 dataset, which is 2.1% higher than what was
reported in the previous work.

The recent works on a deep learning (DL)-based joint design of preamble set
for the transmitters and data-aided active user detection (AUD) in the receiver
has demonstrated a significant performance improvement for grant-free sparse
code multiple access (GF-SCMA) system. The autoencoder for the joint design can
be trained only in a given environment, but in an actual situation where the
operating environment is constantly changing, it is difficult to optimize the
preamble set for every possible environment. Therefore, a conventional, yet
general approach may implement the data-aided AUD while relying on the preamble
set that is designed independently rather than the joint design. In this paper,
the activity detection error rate (ADER) performance of the data-aided AUD
subject to the two preamble designs, i.e., independently designed preamble and
jointly designed preamble, were directly compared. Fortunately, it was found
that the performance loss in the data-aided AUD induced by the independent
preamble design is limited to only 1dB. Furthermore, such performance
characteristics of jointly designed preamble set is interpreted through average
cross-correlation among the preambles associated with the same codebook (CB)
(average intra-CB cross-correlation) and average cross-correlation among
preambles associated with the different CBs (average inter-CB
cross-correlation).

Research in machine learning for autism spectrum disorder (ASD)
classification bears the promise to improve clinical diagnoses. However, recent
studies in clinical imaging have shown the limited generalization of biomarkers
across and beyond benchmark datasets. Despite increasing model complexity and
sample size in neuroimaging, the classification performance of ASD remains far
away from clinical application. This raises the question of how we can overcome
these barriers to develop early biomarkers for ASD. One approach might be to
rethink how we operationalize the theoretical basis of this disease in machine
learning models. Here we introduced unsupervised graph representations that
explicitly map the neural mechanisms of a core aspect of ASD, deficits in
dyadic social interaction, as assessed by dual brain recordings, termed
hyperscanning, and evaluated their predictive performance. The proposed method
differs from existing approaches in that it is more suitable to capture social
interaction deficits on a neural level and is applicable to young children and
infants. First results from functional near-infrared spectroscopy data indicate
potential predictive capacities of a task-agnostic, interpretable graph
representation. This first effort to leverage interaction-related deficits on
neural level to classify ASD may stimulate new approaches and methods to
enhance existing models to achieve developmental ASD biomarkers in the future.

Tabular data typically contains private and important information; thus,
precautions must be taken before they are shared with others. Although several
methods (e.g., differential privacy and k-anonymity) have been proposed to
prevent information leakage, in recent years, tabular data synthesis models
have become popular because they can well trade-off between data utility and
privacy. However, recent research has shown that generative models for image
data are susceptible to the membership inference attack, which can determine
whether a given record was used to train a victim synthesis model. In this
paper, we investigate the membership inference attack in the context of tabular
data synthesis. We conduct experiments on 4 state-of-the-art tabular data
synthesis models under two attack scenarios (i.e., one black-box and one
white-box attack), and find that the membership inference attack can seriously
jeopardize these models. We next conduct experiments to evaluate how well two
popular differentially-private deep learning training algorithms, DP-SGD and
DP-GAN, can protect the models against the attack. Our key finding is that both
algorithms can largely alleviate this threat by sacrificing the generation
quality.

Pre-trained representation is one of the key elements in the success of
modern deep learning. However, existing works on continual learning methods
have mostly focused on learning models incrementally from scratch. In this
paper, we explore an alternative framework to incremental learning where we
continually fine-tune the model from a pre-trained representation. Our method
takes advantage of linearization technique of a pre-trained neural network for
simple and effective continual learning. We show that this allows us to design
a linear model where quadratic parameter regularization method is placed as the
optimal continual learning policy, and at the same time enjoying the high
performance of neural networks. We also show that the proposed algorithm
enables parameter regularization methods to be applied to class-incremental
problems. Additionally, we provide a theoretical reason why the existing
parameter-space regularization algorithms such as EWC underperform on neural
networks trained with cross-entropy loss. We show that the proposed method can
prevent forgetting while achieving high continual fine-tuning performance on
image classification tasks. To show that our method can be applied to general
continual learning settings, we evaluate our method in data-incremental,
task-incremental, and class-incremental learning problems.

Curriculum Data Augmentation (CDA) improves neural models by presenting
synthetic data with increasing difficulties from easy to hard. However,
traditional CDA simply treats the ratio of word perturbation as the difficulty
measure and goes through the curriculums only once. This paper presents
\textbf{PCC}: \textbf{P}araphrasing with Bottom-k Sampling and \textbf{C}yclic
Learning for \textbf{C}urriculum Data Augmentation, a novel CDA framework via
paraphrasing, which exploits the textual paraphrase similarity as the
curriculum difficulty measure. We propose a curriculum-aware paraphrase
generation module composed of three units: a paraphrase candidate generator
with bottom-k sampling, a filtering mechanism and a difficulty measure. We also
propose a cyclic learning strategy that passes through the curriculums multiple
times. The bottom-k sampling is proposed to generate super-hard instances for
the later curriculums. Experimental results on few-shot text classification as
well as dialogue generation indicate that PCC surpasses competitive baselines.
Human evaluation and extensive case studies indicate that bottom-k sampling
effectively generates super-hard instances, and PCC significantly improves the
baseline dialogue agent.

Facial expression recognition (FER) is a challenging problem because the
expression component is always entangled with other irrelevant factors, such as
identity and head pose. In this work, we propose an identity and pose
disentangled facial expression recognition (IPD-FER) model to learn more
discriminative feature representation. We regard the holistic facial
representation as the combination of identity, pose and expression. These three
components are encoded with different encoders. For identity encoder, a well
pre-trained face recognition model is utilized and fixed during training, which
alleviates the restriction on specific expression training data in previous
works and makes the disentanglement practicable on in-the-wild datasets. At the
same time, the pose and expression encoder are optimized with corresponding
labels. Combining identity and pose feature, a neutral face of input individual
should be generated by the decoder. When expression feature is added, the input
image should be reconstructed. By comparing the difference between synthesized
neutral and expressional images of the same individual, the expression
component is further disentangled from identity and pose. Experimental results
verify the effectiveness of our method on both lab-controlled and in-the-wild
databases and we achieve state-of-the-art recognition performance.

GitHub commits, which record the code changes with natural language messages
for description, play a critical role for software developers to comprehend the
software evolution. To promote the development of the open-source software
community, we collect a commit benchmark including over 7.99 million commits
across 7 programming languages. Based on this benchmark, we present CommitBART,
a large pre-trained encoder-decoder Transformer model for GitHub commits. The
model is pre-trained by three categories (i.e., denoising objectives,
cross-modal generation and contrastive learning) for six pre-training tasks to
learn commit fragment representations. Furthermore, we unify a "commit
intelligence" framework with one understanding task and three generation tasks
for commits. The comprehensive experiments on these tasks demonstrate that
CommitBART significantly outperforms previous pre-trained works for code.
Further analysis also reveals each pre-training task enhances the model
performance. We encourage the follow-up researchers to contribute more
commit-related downstream tasks to our framework in the future.

This paper introduces a novel Self-supervised Fine-grained Dialogue
Evaluation framework (SelF-Eval). The core idea is to model the correlation
between turn quality and the entire dialogue quality. We first propose a novel
automatic data construction method that can automatically assign fine-grained
scores for arbitrarily dialogue data. Then we train \textbf{SelF-Eval} with a
multi-level contrastive learning schema which helps to distinguish different
score levels. Experimental results on multiple benchmarks show that SelF-Eval
is highly consistent with human evaluations and better than the
state-of-the-art models. We give a detailed analysis of the experiments in this
paper. Our code is available on GitHub.

Ring Learning With Error (RLWE) algorithm is used in Post Quantum
Cryptography (PQC) and Homomorphic Encryption (HE) algorithm. The existing
classical crypto algorithms may be broken in quantum computers. The adversaries
can store all encrypted data. While the quantum computer will be available,
these encrypted data can be exposed by the quantum computer. Therefore, the PQC
algorithms are an essential solution in recent applications. On the other hand,
the HE allows operations on encrypted data which is appropriate for getting
services from third parties without revealing confidential plain-texts. The
FPGA based PQC and HE hardware accelerators like RLWE is much cost-effective
than processor based platform and Application Specific Integrated Circuit
(ASIC). FPGA based hardware accelerators still consume more power compare to
ASIC based design. Near Threshold Computation (NTC) may be a convenient
solution for FPGA based RLWE implementation. In this paper, we have implemented
RLWE hardware accelerator which has 14 subcomponents. This paper creates
clusters based on the critical path of all 14 subcomponents. Each cluster is
implemented in an FPGA partition which has the same biasing voltage
$V_{ccint}$. The clusters that have higher critical paths use higher Vccint to
avoid timing failure. The clusters have lower critical paths use lower biasing
voltage Vccint. This voltage scaled, partitioned RLWE can save ~6% and ~11%
power in Vivado and VTR platform respectively. The resource usage and
throughput of the implemented RLWE hardware accelerator is comparatively better
than existing literature.

Controllable image synthesis with user scribbles is a topic of keen interest
in the computer vision community. In this paper, for the first time we study
the problem of photorealistic image synthesis from incomplete and primitive
human paintings. In particular, we propose a novel approach paint2pix, which
learns to predict (and adapt) "what a user wants to draw" from rudimentary
brushstroke inputs, by learning a mapping from the manifold of incomplete human
paintings to their realistic renderings. When used in conjunction with recent
works in autonomous painting agents, we show that paint2pix can be used for
progressive image synthesis from scratch. During this process, paint2pix allows
a novice user to progressively synthesize the desired image output, while
requiring just few coarse user scribbles to accurately steer the trajectory of
the synthesis process. Furthermore, we find that our approach also forms a
surprisingly convenient approach for real image editing, and allows the user to
perform a diverse range of custom fine-grained edits through the addition of
only a few well-placed brushstrokes. Supplemental video and demo are available
at https://1jsingh.github.io/paint2pix

Wearable sensor-based Human Action Recognition (HAR) has achieved remarkable
success recently. However, the accuracy performance of wearable sensor-based
HAR is still far behind the ones from the visual modalities-based system (i.e.,
RGB video, skeleton, and depth). Diverse input modalities can provide
complementary cues and thus improve the accuracy performance of HAR, but how to
take advantage of multi-modal data on wearable sensor-based HAR has rarely been
explored. Currently, wearable devices, i.e., smartwatches, can only capture
limited kinds of non-visual modality data. This hinders the multi-modal HAR
association as it is unable to simultaneously use both visual and non-visual
modality data. Another major challenge lies in how to efficiently utilize
multimodal data on wearable devices with their limited computation resources.
In this work, we propose a novel Progressive Skeleton-to-sensor Knowledge
Distillation (PSKD) model which utilizes only time-series data, i.e.,
accelerometer data, from a smartwatch for solving the wearable sensor-based HAR
problem. Specifically, we construct multiple teacher models using data from
both teacher (human skeleton sequence) and student (time-series accelerometer
data) modalities. In addition, we propose an effective progressive learning
scheme to eliminate the performance gap between teacher and student models. We
also designed a novel loss function called Adaptive-Confidence Semantic (ACS),
to allow the student model to adaptively select either one of the teacher
models or the ground-truth label it needs to mimic. To demonstrate the
effectiveness of our proposed PSKD method, we conduct extensive experiments on
Berkeley-MHAD, UTD-MHAD, and MMAct datasets. The results confirm that the
proposed PSKD method has competitive performance compared to the previous mono
sensor-based HAR methods.

Few-shot learning (FSL) is an emergent paradigm of learning that attempts to
learn with low sample complexity to mimic the way humans can learn, generalise
and extrapolate based on only a few examples. While FSL attempts to mimic these
human characteristics, fundamentally, the task of FSL as conventionally
described and modelled using meta-learning with episodic-based training does
not fully align with how humans acquire and reason with knowledge. FSL with
episodic training, while only using $K$ instances of each test class, still
requires a large number of labelled instances from disjoint training classes.
In this paper, we introduce the novel task of constrained few-shot learning
(CFSL), a special case of FSL where the number of training instances of each
class is constrained to be less than some value $M$ thus applying a similar
restriction during training and test. We propose a method for CFSL leveraging
Cat2Vec using a novel categorical contrastive loss inspired by cognitive
theories such as fuzzy trace theory and prototype theory.

In recent years, general matrix-matrix multiplication with non-regular-shaped
input matrices has been widely used in many applications like deep learning and
has drawn more and more attention. However, conventional implementations are
not suited for non-regular-shaped matrix-matrix multiplications, and few works
focus on optimizing tall-and-skinny matrix-matrix multiplication on CPUs. This
paper proposes an auto-tuning framework, AutoTSMM, to build high-performance
tall-and-skinny matrix-matrix multiplication. AutoTSMM selects the optimal
inner kernels in the install-time stage and generates an execution plan for the
pre-pack tall-and-skinny matrix-matrix multiplication in the runtime stage.
Experiments demonstrate that AutoTSMM achieves competitive performance
comparing to state-of-the-art tall-and-skinny matrix-matrix multiplication.
And, it outperforms all conventional matrix-matrix multiplication

Resource allocation is one of the most critical issues in planning
construction projects, due to its direct impact on cost, time, and quality.
There are usually specific allocation methods for autonomous resource
management according to the projects objectives. However, integrated planning
and optimization of utilizing resources in an entire construction organization
are scarce. The purpose of this study is to present an automatic resource
allocation structure for construction companies based on Deep Reinforcement
Learning (DRL), which can be used in various situations. In this structure,
Data Harvesting (DH) gathers resource information from the distributed Internet
of Things (IoT) sensor devices all over the companys projects to be employed in
the autonomous resource management approach. Then, Coverage Resources
Allocation (CRA) is compared to the information obtained from DH in which the
Autonomous Resource Management (ARM) determines the project of interest.
Likewise, Double Deep Q-Networks (DDQNs) with similar models are trained on two
distinct assignment situations based on structured resource information of the
company to balance objectives with resource constraints. The suggested
technique in this paper can efficiently adjust to large resource management
systems by combining portfolio information with adopted individual project
information. Also, the effects of important information processing parameters
on resource allocation performance are analyzed in detail. Moreover, the
results of the generalizability of management approaches are presented,
indicating no need for additional training when the variables of situations
change.

A plethora of modern machine learning tasks requires the utilization of
large-scale distributed clusters as a critical component of the training
pipeline. However, abnormal Byzantine behavior of the worker nodes can derail
the training and compromise the quality of the inference. Such behavior can be
attributed to unintentional system malfunctions or orchestrated attacks; as a
result, some nodes may return arbitrary results to the parameter server (PS)
that coordinates the training. Recent work considers a wide range of attack
models and has explored robust aggregation and/or computational redundancy to
correct the distorted gradients.
  In this work, we consider attack models ranging from strong ones: $q$
omniscient adversaries with full knowledge of the defense protocol that can
change from iteration to iteration to weak ones: $q$ randomly chosen
adversaries with limited collusion abilities that only change every few
iterations at a time. Our algorithms rely on redundant task assignments coupled
with detection of adversarial behavior. For strong attacks, we demonstrate a
reduction in the fraction of distorted gradients ranging from 16%-99% as
compared to the prior state-of-the-art. Our top-1 classification accuracy
results on the CIFAR-10 data set demonstrate a 25% advantage in accuracy
(averaged over strong and weak scenarios) under the most sophisticated attacks
compared to state-of-the-art methods.

Due to its rapid response time and a high degree of robustness, the selective
fixed-filter active noise control (SFANC) method appears to be a viable
candidate for widespread use in a variety of practical active noise control
(ANC) systems. In comparison to conventional fixed-filter ANC methods, SFANC
can select the pre-trained control filters for different types of noise. Deep
learning technologies, thus, can be used in SFANC methods to enable a more
flexible selection of the most appropriate control filters for attenuating
various noises. Furthermore, with the assistance of a deep neural network, the
selecting strategy can be learned automatically from noise data rather than
through trial and error, which significantly simplifies and improves the
practicability of ANC design. Therefore, this paper investigates the
performance of SFANC based on different one-dimensional and two-dimensional
convolutional neural networks. Additionally, we conducted comparative analyses
of several network training strategies and discovered that fine-tuning could
improve selection performance.

Deep neural networks (DNNs) are vulnerable to adversarial examples, in which
DNNs are misled to false outputs due to inputs containing imperceptible
perturbations. Adversarial training, a reliable and effective method of
defense, may significantly reduce the vulnerability of neural networks and
becomes the de facto standard for robust learning. While many recent works
practice the data-centric philosophy, such as how to generate better
adversarial examples or use generative models to produce additional training
data, we look back to the models themselves and revisit the adversarial
robustness from the perspective of deep feature distribution as an insightful
complementarity. In this paper, we propose Branch Orthogonality adveRsarial
Training (BORT) to obtain state-of-the-art performance with solely the original
dataset for adversarial training. To practice our design idea of integrating
multiple orthogonal solution spaces, we leverage a simple and straightforward
multi-branch neural network that eclipses adversarial attacks with no increase
in inference time. We heuristically propose a corresponding loss function,
branch-orthogonal loss, to make each solution space of the multi-branch model
orthogonal. We evaluate our approach on CIFAR-10, CIFAR-100, and SVHN against
\ell_{\infty} norm-bounded perturbations of size \epsilon = 8/255,
respectively. Exhaustive experiments are conducted to show that our method goes
beyond all state-of-the-art methods without any tricks. Compared to all methods
that do not use additional data for training, our models achieve 67.3% and
41.5% robust accuracy on CIFAR-10 and CIFAR-100 (improving upon the
state-of-the-art by +7.23% and +9.07%). We also outperform methods using a
training set with a far larger scale than ours. All our models and codes are
available online at https://github.com/huangd1999/BORT.

The selective fixed-filter active noise control (SFANC) method selecting the
best pre-trained control filters for various types of noise can achieve a fast
response time. However, it may lead to large steady-state errors due to
inaccurate filter selection and the lack of adaptability. In comparison, the
filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower
steady-state errors through adaptive optimization. Nonetheless, its slow
convergence has a detrimental effect on dynamic noise attenuation. Therefore,
this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive
algorithm's slow convergence and provide a better noise reduction level than
the SFANC method. A lightweight one-dimensional convolutional neural network
(1D CNN) is designed to automatically select the most suitable pre-trained
control filter for each frame of the primary noise. Meanwhile, the FxNLMS
algorithm continues to update the coefficients of the chosen pre-trained
control filter at the sampling rate. Owing to the effective combination of the
two algorithms, experimental results show that the hybrid SFANC-FxNLMS
algorithm can achieve a rapid response time, a low noise reduction error, and a
high degree of robustness.

Distance education had existed for a long time, then it has undergone a
renaissance with the advent of computers and the Internet. Distance education
relied on physically delivered material and assessments to students, who work
offline at home. More recently, online learning or e-learning introduced
virtual classrooms, assessments, online tests and transformed the classroom an
into interactive online classroom. Despite the large number of online degrees
offered, face-to-face remained the dominant mode and e-learning was just used
to complement the classroom. The Covid-19 pandemic continues to impact higher
education, and online learning is a forgone conclusion. However, the digital
divide hindered the disadvantaged schools and students efforts to transition to
online learning. As the pandemic continues to change the education landscape,
many challenges arise and prevent student from realising the full potential of
e-learning. One of those is the access to physical labs in science,
engineering, and computer science programs. This study evaluates practical
solutions for virtual labs to be used in teaching information security and
ethical hacking. The course ran over five semesters, and 164 students were
surveyed. The survey measured perceptions, enjoyment, experiences and attitudes
towards virtual labs, and the results were supporting adoption and acceptance
of virtual labs.

Lecture slide presentations, a sequence of pages that contain text and
figures accompanied by speech, are constructed and presented carefully in order
to optimally transfer knowledge to students. Previous studies in multimedia and
psychology attribute the effectiveness of lecture presentations to their
multimodal nature. As a step toward developing AI to aid in student learning as
intelligent teacher assistants, we introduce the Multimodal Lecture
Presentations dataset as a large-scale benchmark testing the capabilities of
machine learning models in multimodal understanding of educational content. Our
dataset contains aligned slides and spoken language, for 180+ hours of video
and 9000+ slides, with 10 lecturers from various subjects (e.g., computer
science, dentistry, biology). We introduce two research tasks which are
designed as stepping stones towards AI agents that can explain (automatically
captioning a lecture presentation) and illustrate (synthesizing visual figures
to accompany spoken explanations) educational content. We provide manual
annotations to help implement these two research tasks and evaluate
state-of-the-art models on them. Comparing baselines and human student
performances, we find that current models struggle in (1) weak crossmodal
alignment between slides and spoken text, (2) learning novel visual mediums,
(3) technical language, and (4) long-range sequences. Towards addressing this
issue, we also introduce PolyViLT, a multimodal transformer trained with a
multi-instance learning loss that is more effective than current approaches. We
conclude by shedding light on the challenges and opportunities in multimodal
understanding of educational presentations.

The idea of \textit{Virtual Try-ON} (VTON) benefits e-retailing by giving an
user the convenience of trying a clothing at the comfort of their home. In
general, most of the existing VTON methods produce inconsistent results when a
person posing with his arms folded i.e., bent or crossed, wants to try an
outfit. The problem becomes severe in the case of long-sleeved outfits. As
then, for crossed arm postures, overlap among different clothing parts might
happen. The existing approaches, especially the warping-based methods employing
\textit{Thin Plate Spline (TPS)} transform can not tackle such cases. To this
end, we attempt a solution approach where the clothing from the source person
is segmented into semantically meaningful parts and each part is warped
independently to the shape of the person. To address the bending issue, we
employ hand-crafted geometric features consistent with human body geometry for
warping the source outfit. In addition, we propose two learning-based modules:
a synthesizer network and a mask prediction network. All these together attempt
to produce a photo-realistic, pose-robust VTON solution without requiring any
paired training data. Comparison with some of the benchmark methods clearly
establishes the effectiveness of the approach.

Malware developers use combinations of techniques such as compression,
encryption, and obfuscation to bypass anti-virus software. Malware with
anti-analysis technologies can bypass AI-based anti-virus software and malware
analysis tools. Therefore, classifying pack files is one of the big challenges.
Problems arise if the malware classifiers learn packers' features, not those of
malware. Training the models with unintended erroneous data turn into poisoning
attacks, adversarial attacks, and evasion attacks. Therefore, researchers
should consider packing to build appropriate malware classifier models. In this
paper, we propose a multi-step framework for classifying and identifying packed
samples which consists of pseudo-optimal feature selection, machine
learning-based classifiers, and packer identification steps. In the first step,
we use the CART algorithm and the permutation importance to preselect important
20 features. In the second step, each model learns 20 preselected features for
classifying the packed files with the highest performance. As a result, the
XGBoost, which learned the features preselected by XGBoost with the permutation
importance, showed the highest performance of any other experiment scenarios
with an accuracy of 99.67%, an F1-Score of 99.46%, and an area under the curve
(AUC) of 99.98%. In the third step, we propose a new approach that can identify
packers only for samples classified as Well-Known Packed.

Quantum Bayesian AI (Q-B) is an emerging field that levers the computational
gains available in Quantum computing. The promise is an exponential speed-up in
many Bayesian algorithms. Our goal is to apply these methods directly to
statistical and machine learning problems. We provide a duality between
classical and quantum probability for calculating of posterior quantities of
interest. Our framework unifies MCMC, Deep Learning and Quantum Learning
calculations from the viewpoint from von Neumann's principle of quantum
measurement. Quantum embeddings and neural gates are also an important part of
data encoding and feature selection. There is a natural duality with well-known
kernel methods in statistical learning. We illustrate the behaviour of quantum
algorithms on two simple classification algorithms. Finally, we conclude with
directions for future research.

Neural clone detection has attracted the attention of software engineering
researchers and practitioners. However, most neural clone detection methods do
not generalize beyond the scope of clones that appear in the training dataset.
This results in poor model performance, especially in terms of model recall. In
this paper, we present an Abstract Syntax Tree (AST) assisted approach for
generalizable neural clone detection, or ASTRO, a framework for finding clones
in codebases reflecting industry practices. We present three main components:
(1) an AST-inspired representation for source code that leverages program
structure and semantics, (2) a global graph representation that captures the
context of an AST among a corpus of programs, and (3) a graph embedding for
programs that, in combination with extant large-scale language models, improves
state-of-the-art code clone detection. Our experimental results show that ASTRO
improves state-of-the-art neural clone detection approaches in both recall and
F-1 scores.

Many Machine Learning (ML) systems, especially neural networks, are
fundamentally regarded as black boxes since it is difficult to grasp how they
function once they have been trained. Here, we tackle the issue of the
interpretability of a high-accuracy ML model created to model the flux of
Earth's radiation belt electrons. The Outer RadIation belt Electron Neural net
model (ORIENT) uses only solar wind conditions and geomagnetic indices as
input. Using the Deep SHAPley additive explanations (DeepSHAP) method, we show
that the `black box' ORIENT model can be successfully explained. Two
significant electron flux enhancement events observed by Van Allen Probes
during the storm interval of 17 to 18 March 2013 and non storm interval of 19
to 20 September 2013 are investigated using the DeepSHAP method. The results
show that the feature importances calculated from the purely data driven ORIENT
model identify physically meaningful behavior consistent with current physical

About forty years ago, in a now--seminal contribution, Rosenbaum & Rubin
(1983) introduced a critical characterization of the propensity score as a
central quantity for drawing causal inferences in observational study settings.
In the decades since, much progress has been made across several research
fronts in causal inference, notably including the re-weighting and matching
paradigms. Focusing on the former and specifically on its intersection with
machine learning and semiparametric efficiency theory, we re-examine the role
of the propensity score in modern methodological developments. As Rosenbaum &
Rubin (1983)'s contribution spurred a focus on the balancing property of the
propensity score, we re-examine the degree to which and how this property plays
a role in the development of asymptotically efficient estimators of causal
effects; moreover, we discuss a connection between the balancing property and
efficient estimation in the form of score equations and propose a score test
for evaluating whether an estimator achieves balance.

The centrality and diversity of the labeled data are very influential to the
performance of semi-supervised learning (SSL), but most SSL models select the
labeled data randomly. This study first construct a leading forest that forms a
partially ordered topological space in an unsupervised way, and select a group
of most representative samples to label with one shot (differs from active
learning essentially) using property of homeomorphism. Then a kernelized large
margin metric is efficiently learned for the selected data to classify the
remaining unlabeled sample. Optimal leading forest (OLF) has been observed to
have the advantage of revealing the difference evolution along a path within a
subtree. Therefore, we formulate an optimization problem based on OLF to select
the samples. Also with OLF, the multiple local metrics learning is facilitated
to address multi-modal and mix-modal problem in SSL, especially when the number
of class is large. Attribute to this novel design, stableness and accuracy of
the performance is significantly improved when compared with the
state-of-the-art graph SSL methods. The extensive experimental studies have
shown that the proposed method achieved encouraging accuracy and efficiency.
Code has been made available at https://github.com/alanxuji/DeLaLA.

Sampling is ubiquitous in machine learning methodologies. Due to the growth
of large datasets and model complexity, we want to learn and adapt the sampling
process while training a representation. Towards achieving this grand goal, a
variety of sampling techniques have been proposed. However, most of them either
use a fixed sampling scheme or adjust the sampling scheme based on simple
heuristics. They cannot choose the best sample for model training in different
stages. Inspired by "Think, Fast and Slow" (System 1 and System 2) in cognitive
science, we propose a reward-guided sampling strategy called Adaptive Sample
with Reward (ASR) to tackle this challenge. To the best of our knowledge, this
is the first work utilizing reinforcement learning (RL) to address the sampling
problem in representation learning. Our approach optimally adjusts the sampling
process to achieve optimal performance. We explore geographical relationships
among samples by distance-based sampling to maximize overall cumulative reward.
We apply ASR to the long-standing sampling problems in similarity-based loss
functions. Empirical results in information retrieval and clustering
demonstrate ASR's superb performance across different datasets. We also discuss
an engrossing phenomenon which we name as "ASR gravity well" in experiments.

With the thriving of deep learning in processing point cloud data, recent
works show that backdoor attacks pose a severe security threat to 3D vision
applications. The attacker injects the backdoor into the 3D model by poisoning
a few training samples with trigger, such that the backdoored model performs
well on clean samples but behaves maliciously when the trigger pattern appears.
Existing attacks often insert some additional points into the point cloud as
the trigger, or utilize a linear transformation (e.g., rotation) to construct
the poisoned point cloud. However, the effects of these poisoned samples are
likely to be weakened or even eliminated by some commonly used pre-processing
techniques for 3D point cloud, e.g., outlier removal or rotation augmentation.
In this paper, we propose a novel imperceptible and robust backdoor attack
(IRBA) to tackle this challenge. We utilize a nonlinear and local
transformation, called weighted local transformation (WLT), to construct
poisoned samples with unique transformations. As there are several
hyper-parameters and randomness in WLT, it is difficult to produce two similar
transformations. Consequently, poisoned samples with unique transformations are
likely to be resistant to aforementioned pre-processing techniques. Besides, as
the controllability and smoothness of the distortion caused by a fixed WLT, the
generated poisoned samples are also imperceptible to human inspection.
Extensive experiments on three benchmark datasets and four models show that
IRBA achieves 80%+ ASR in most cases even with pre-processing techniques, which
is significantly higher than previous state-of-the-art attacks.

Power distribution networks are approaching their voltage stability
boundaries due to the severe voltage violations and the inadequate reactive
power reserves caused by the increasing renewable generations and dynamic
loads. In the broad endeavor to resolve this concern, we focus on enhancing
voltage stability through stochastic distribution network reconfiguration
(SDNR), which optimizes the (radial) topology of a distribution network under
uncertain generations and loads. We propose a deep learning method to solve
this computationally challenging problem. Specifically, we build a
convolutional neural network model to predict the relevant voltage stability
index from the SDNR decisions. Then we integrate this prediction model into
successive branch reduction algorithms to reconfigure a radial network with
optimized performance in terms of power loss reduction and voltage stability
enhancement. Numerical results on two IEEE network models verify the
significance of enhancing voltage stability through SDNR and the computational
efficiency of the proposed method.

Analysis of overhead imagery using computer vision is a problem that has
received considerable attention in academic literature. Most techniques that
operate in this space are both highly specialised and require expensive manual
annotation of large datasets. These problems are addressed here through the
development of a more generic framework, incorporating advances in
representation learning which allows for more flexibility in analysing new
categories of imagery with limited labeled data. First, a robust representation
of an unlabeled aerial imagery dataset was created based on the momentum
contrast mechanism. This was subsequently specialised for different tasks by
building accurate classifiers with as few as 200 labeled images. The successful
low-level detection of urban infrastructure evolution over a 10-year period
from 60 million unlabeled images, exemplifies the substantial potential of our
approach to advance quantitative urban research.

Soft demodulation of received symbols into bit log-likelihood ratios (LLRs)
is at the very heart of multiple-input-multiple-output (MIMO) detection.
However, the optimal maximum a posteriori (MAP) detector is complicated and
infeasible to be used in a practical system. In this paper, we propose a soft
MIMO detection algorithm based on marginal posterior probability statistics
(MPPS). With the help of optimal transport theory and order statistics theory,
we transform the posteriori probability distribution of each layer into a
Gaussian distribution. Then the full sampling paths can be implicitly restored
from the first- and second-order moment statistics of the transformed
distribution. A lightweight network is designed to learn to recovery the
log-MAP LLRs from the moment statistics with low complexity. Simulation results
show that the proposed algorithm can improve the performance significantly with
reduced samples under fading and correlated channels.

3D multi-object tracking (MOT) is a key problem for autonomous vehicles,
required to perform well-informed motion planning in dynamic environments.
Particularly for densely occupied scenes, associating existing tracks to new
detections remains challenging as existing systems tend to omit critical
contextual information. Our proposed solution, InterTrack, introduces the
Interaction Transformer for 3D MOT to generate discriminative object
representations for data association. We extract state and shape features for
each track and detection, and efficiently aggregate global information via
attention. We then perform a learned regression on each track/detection feature
pair to estimate affinities, and use a robust two-stage data association and
track management approach to produce the final tracks. We validate our approach
on the nuScenes 3D MOT benchmark, where we observe significant improvements,
particularly on classes with small physical sizes and clustered objects. As of
submission, InterTrack ranks 1st in overall AMOTA among methods using
CenterPoint detections.

Conventional multi-view clustering seeks to partition data into respective
groups based on the assumption that all views are fully observed. However, in
practical applications, such as disease diagnosis, multimedia analysis, and
recommendation system, it is common to observe that not all views of samples
are available in many cases, which leads to the failure of the conventional
multi-view clustering methods. Clustering on such incomplete multi-view data is
referred to as incomplete multi-view clustering. In view of the promising
application prospects, the research of incomplete multi-view clustering has
noticeable advances in recent years. However, there is no survey to summarize
the current progresses and point out the future research directions. To this
end, we review the recent studies of incomplete multi-view clustering.
Importantly, we provide some frameworks to unify the corresponding incomplete
multi-view clustering methods, and make an in-depth comparative analysis for
some representative methods from theoretical and experimental perspectives.
Finally, some open problems in the incomplete multi-view clustering field are
offered for researchers.

Terahertz (THz) wireless networks are expected to catalyze the beyond fifth
generation (B5G) era. However, due to the directional nature and the
line-of-sight demand of THz links, as well as the ultra-dense deployment of THz
networks, a number of challenges that the medium access control (MAC) layer
needs to face are created. In more detail, the need of rethinking user
association and resource allocation strategies by incorporating artificial
intelligence (AI) capable of providing "real-time" solutions in complex and
frequently changing environments becomes evident. Moreover, to satisfy the
ultra-reliability and low-latency demands of several B5G applications, novel
mobility management approaches are required. Motivated by this, this article
presents a holistic MAC layer approach that enables intelligent user
association and resource allocation, as well as flexible and adaptive mobility
management, while maximizing systems' reliability through blockage
minimization. In more detail, a fast and centralized joint user association,
radio resource allocation, and blockage avoidance by means of a novel
metaheuristic-machine learning framework is documented, that maximizes the THz
networks performance, while minimizing the association latency by approximately
three orders of magnitude. To support, within the access point (AP) coverage
area, mobility management and blockage avoidance, a deep reinforcement learning
(DRL) approach for beam-selection is discussed. Finally, to support user
mobility between coverage areas of neighbor APs, a proactive hand-over
mechanism based on AI-assisted fast channel prediction is~reported.

To satisfy various user needs, different subtasks of graphic layout
generation have been explored intensively in recent years. Existing studies
usually propose task-specific methods with diverse input-output formats,
dedicated model architectures, and different learning methods. However, those
specialized approaches make the adaption to unseen subtasks difficult, hinder
the knowledge sharing between different subtasks, and are contrary to the trend
of devising general-purpose models. In this work, we propose UniLayout, which
handles different subtasks for graphic layout generation in a unified manner.
First, we uniformly represent diverse inputs and outputs of subtasks as the
sequences of tokens. Then, based on the unified sequence format, we naturally
leverage an identical encoder-decoder architecture with Transformers for
different subtasks. Moreover, based on the above two kinds of unification, we
further develop a single model that supports all subtasks concurrently.
Experiments on two public datasets demonstrate that while simple, UniLayout
significantly outperforms the previous task-specific methods.

This paper presents a Deep Reinforcement Learning based navigation approach
in which we define the occupancy observations as heuristic evaluations of
motion primitives, rather than using raw sensor data. Our method enables fast
mapping of the occupancy data, generated by multi-sensor fusion, into
trajectory values in 3D workspace. The computationally efficient trajectory
evaluation allows dense sampling of the action space. We utilize our occupancy
observations in different data structures to analyze their effects on both
training process and navigation performance. We train and test our methodology
on two different robots within challenging physics-based simulation
environments including static and dynamic obstacles. We benchmark our occupancy
representations with other conventional data structures from state-of-the-art
methods. The trained navigation policies are also validated successfully with
physical robots in dynamic environments. The results show that our method not
only decreases the required training time but also improves the navigation
performance as compared to other occupancy representations. The open-source
implementation of our work and all related info are available at
\url{https://github.com/RIVeR-Lab/tentabot}.

With the goal of electricity system decarbonization, conventional synchronous
generators are gradually replaced by converter-interfaced renewable
generations. Such transition is causing concerns over system frequency and
rate-of-change-of-frequency (RoCoF) security due to significant reduction in
system inertia. Existing efforts are mostly derived from uniform system
frequency response model which may fail to capture all characteristics of the
systems. To ensure the locational frequency security, this paper presents a
deep neural network (DNN) based RoCoF-constrained unit commitment (DNN-RCUC)
model. RoCoF predictor is trained to predict the highest locational RoCoF based
on a high-fidelity simulation dataset. Training samples are generated from
models over various scenarios, which can avoid simulation divergence and system
instability. The trained network is then reformulated into a set of
mixed-integer linear constraints representing the locational RoCoF-limiting
constraints in unit commitment. The proposed DNN-RCUC model is studied on the
IEEE 24-bus system. Time domain simulation results on PSS/E demonstrate the

The aggressive performance optimizations in modern microprocessors can result
in security vulnerabilities. For example, the timing-based attacks in processor
caches are shown to be successful in stealing secret keys or causing privilege
escalation. So far, finding cache-timing vulnerabilities is mostly performed by
human experts, which is inefficient and laborious. There is a need for
automatic tools that can explore vulnerabilities because unreported
vulnerabilities leave the systems at risk. In this paper, we propose AutoCAT,
an automated exploration framework that finds cache timing-channel attacks
using reinforcement learning (RL). Specifically, AutoCAT formulates the cache
timing-channel attack as a guessing game between the attacker program and the
victim program holding a secret, which can thus be solved via modern deep RL
techniques. AutoCAT can explore attacks in various cache configurations without
knowing design details and under different attacker and victim configurations,
and also find attacks to bypass known detection and defense mechanisms. In
particular, AutoCAT discovered StealthyStreamline, a new attack that is able to
bypass detection based on performance counters and has up to a 71% higher
information leakage rate than the state-of-the-art LRU-based attacks on real
processors. AutoCAT is the first of its kind using RL for crafting
microarchitectural timing-channel attack sequences and can accelerate cache
timing-channel exploration for secure microprocessor designs.

Micro-video recommender systems suffer from the ubiquitous noises in users'
behaviors, which might render the learned user representation indiscriminating,
and lead to trivial recommendations (e.g., popular items) or even weird ones
that are far beyond users' interests. Contrastive learning is an emergent
technique for learning discriminating representations with random data
augmentations. However, due to neglecting the noises in user behaviors and
treating all augmented samples equally, the existing contrastive learning
framework is insufficient for learning discriminating user representations in
recommendation. To bridge this research gap, we propose the Contrast over
Contrastive Learning framework for training recommender models, named CCL4Rec,
which models the nuances of different augmented views by further contrasting
augmented positives/negatives with adaptive pulling/pushing strengths, i.e.,
the contrast over (vanilla) contrastive learning. To accommodate these
contrasts, we devise the hardness-aware augmentations that track the importance
of behaviors being replaced in the query user and the relatedness of
substitutes, and thus determining the quality of augmented positives/negatives.
The hardness-aware augmentation also permits controllable contrastive learning,
leading to performance gains and robust training. In this way, CCL4Rec captures
the nuances of historical behaviors for a given user, which explicitly shields
off the learned user representation from the effects of noisy behaviors. We
conduct extensive experiments on two micro-video recommendation benchmarks,
which demonstrate that CCL4Rec with far less model parameters could achieve
comparable performance to existing state-of-the-art method, and improve the
training/inference speed by several orders of magnitude.

Many sequential decision making problems can be formulated as an adaptive
submodular maximization problem. However, most of existing studies in this
field focus on pool-based setting, where one can pick items in any order, and
there have been few studies for the stream-based setting where items arrive in
an arbitrary order and one must immediately decide whether to select an item or
not upon its arrival. In this paper, we introduce a new class of utility
functions, semi-policywise submodular functions. We develop a series of
effective algorithms to maximize a semi-policywise submodular function under
the stream-based setting.

We revisit the detection of luminous dark matter in direct detection
experiments. In this scenario, dark matter scatters endothermically to produce
an excited state, which decays to produce a photon. We explore ways in which
the electron recoil signal from the decay photon can be differentiated from
other potential electron recoil signals with a narrow spectral shape. We find
that larger volume/exposure xenon detectors will be unable to differentiate the
signal origin without significant improvements in detector energy resolution of
around an order of magnitude. We also explore what can be learned about a
generic luminous dark matter signal with a higher resolution detector.
Motivated by the advancements in energy resolution by solid-state detectors, we
find that sub-eV resolution enables the discovery of LDM in the presence of
background levels that would otherwise make observation impossible. We also
find that sub-eV resolution can be used to determine the shape of the luminous
dark matter decay spectrum and thus constrain the dark matter mass and velocity

Symbol detection is a fundamental and challenging problem in modern
communication systems, e.g., multiuser multiple-input multiple-output (MIMO)
setting. Iterative Soft Interference Cancellation (SIC) is a state-of-the-art
method for this task and recently motivated data-driven neural network models,
e.g. DeepSIC, that can deal with unknown non-linear channels. However, these
neural network models require thorough timeconsuming training of the networks
before applying, and is thus not readily suitable for highly dynamic channels
in practice. We introduce an online training framework that can swiftly adapt
to any changes in the channel. Our proposed framework unifies the recent deep
unfolding approaches with the emerging generative adversarial networks (GANs)
to capture any changes in the channel and quickly adjust the networks to
maintain the top performance of the model. We demonstrate that our framework
significantly outperforms recent neural network models on highly dynamic
channels and even surpasses those on the static channel in our experiments.

Cross-Domain Few Shot Classification (CDFSC) leverages prior knowledge
learned from a supervised auxiliary dataset to solve a target task with limited
supervised information available, where the auxiliary and target datasets come
from the different domains. It is challenging due to the domain shift between
these datasets. Inspired by Multisource Domain Adaptation (MDA), the recent
works introduce the multiple domains to improve the performance. However, they,
on the one hand, evaluate only on the benchmark with natural images, and on the
other hand, they need many annotations even in the source domains can be
costly. To address the above mentioned issues, this paper explore a new
Multisource CDFSC setting (MCDFSC) where only one source domain is fully
labeled while the rest source domains remain unlabeled. These sources are from
different fileds, means they are not only natural images. Considering the
inductive bias of CNNs, this paper proposed Inter-Source stylization network
(ISSNet) for this new MCDFSC setting. It transfers the styles of unlabeled
sources to labeled source, which expands the distribution of labeled source and
further improves the model generalization ability. Experiments on 8 target
datasets demonstrate ISSNet effectively suppresses the performance degradation
caused by different domains.

Domain mismatch problem caused by speaker-unrelated feature has been a major
topic in speaker recognition. In this paper, we propose an explicit
disentanglement framework to unravel speaker-relevant features from
speaker-unrelated features via mutual information (MI) minimization. To achieve
our goal of minimizing MI between speaker-related and speaker-unrelated
features, we adopt a contrastive log-ratio upper bound (CLUB), which exploits
the upper bound of MI. Our framework is constructed in a 3-stage structure.
First, in the front-end encoder, input speech is encoded into shared initial
embedding. Next, in the decoupling block, shared initial embedding is split
into separate speaker-related and speaker-unrelated embeddings. Finally,
disentanglement is conducted by MI minimization in the last stage. Experiments
on Far-Field Speaker Verification Challenge 2022 (FFSVC2022) demonstrate that
our proposed framework is effective for disentanglement. Also, to utilize
domain-unknown datasets containing numerous speakers, we pre-trained the
front-end encoder with VoxCeleb datasets. We then fine-tuned the speaker
embedding model in the disentanglement framework with FFSVC 2022 dataset. The
experimental results show that fine-tuning with a disentanglement framework on
a existing pre-trained model is valid and can further improve performance.

Effectively representing users lie at the core of modern recommender systems.
Since users' interests naturally exhibit multiple aspects, it is of increasing
interest to develop multi-interest frameworks for recommendation, rather than
represent each user with an overall embedding. Despite their effectiveness,
existing methods solely exploit the encoder (the forward flow) to represent
multiple aspects of interests. However, without explicit regularization, the
interest embeddings may not be distinct from each other nor semantically
reflect representative historical items. Towards this end, we propose the Re4
framework, which leverages the backward flow to reexamine each interest
embedding. Specifically, Re4 encapsulates three backward flows, i.e., 1)
Re-contrast, which drives each interest embedding to be distinct from other
interests using contrastive learning; 2) Re-attend, which ensures the
interest-item correlation estimation in the forward flow to be consistent with
the criterion used in final recommendation; and 3) Re-construct, which ensures
that each interest embedding can semantically reflect the information of
representative items that relate to the corresponding interest. We demonstrate
the novel forward-backward multi-interest paradigm on ComiRec, and perform
extensive experiments on three real-world datasets. Empirical studies validate
that Re4 helps to learn learning distinct and effective multi-interest

Benchmark datasets play an important role in evaluating Natural Language
Understanding (NLU) models. However, shortcuts -- unwanted biases in the
benchmark datasets -- can damage the effectiveness of benchmark datasets in
revealing models' real capabilities. Since shortcuts vary in coverage,
productivity, and semantic meaning, it is challenging for NLU experts to
systematically understand and avoid them when creating benchmark datasets. In
this paper, we develop a visual analytics system, ShortcutLens, to help NLU
experts explore shortcuts in NLU benchmark datasets. The system allows users to
conduct multi-level exploration of shortcuts. Specifically, Statistics View
helps users grasp the statistics such as coverage and productivity of shortcuts
in the benchmark dataset. Template View employs hierarchical and interpretable
templates to summarize different types of shortcuts. Instance View allows users
to check the corresponding instances covered by the shortcuts. We conduct case
studies and expert interviews to evaluate the effectiveness and usability of
the system. The results demonstrate that ShortcutLens supports users in gaining
a better understanding of benchmark dataset issues through shortcuts, inspiring
them to create challenging and pertinent benchmark datasets.

Feature embeddings are one of the most essential steps when training deep
learning based Click-Through Rate prediction models, which map high-dimensional
sparse features to dense embedding vectors. Classic human-crafted embedding
size selection methods are shown to be "sub-optimal" in terms of the trade-off
between memory usage and model capacity. The trending methods in Neural
Architecture Search (NAS) have demonstrated their efficiency to search for
embedding sizes. However, most existing NAS-based works suffer from expensive
computational costs, the curse of dimensionality of the search space, and the
discrepancy between continuous search space and discrete candidate space. Other
works that prune embeddings in an unstructured manner fail to reduce the
computational costs explicitly. In this paper, to address those limitations, we
propose a novel strategy that searches for the optimal mixed-dimension
embedding scheme by structurally pruning a super-net via Hard Auxiliary Mask.
Our method aims to directly search candidate models in the discrete space using
a simple and efficient gradient-based method. Furthermore, we introduce
orthogonal regularity on embedding tables to reduce correlations within
embedding columns and enhance representation capacity. Extensive experiments
demonstrate it can effectively remove redundant embedding dimensions without
great performance loss.

Increasing the size of overparameterized neural networks has been shown to
improve their generalization performance. However, real-world datasets often
contain a significant fraction of noisy labels, which can drastically harm the
performance of the models trained on them. In this work, we study how neural
networks' test loss changes with model size when the training set contains
noisy labels. We show that under a sufficiently large noise-to-sample size
ratio, generalization error eventually increases with model size. First, we
provide a theoretical analysis on random feature regression and show that this
phenomenon occurs as the variance of the generalization loss experiences a
second ascent under large noise-to-sample size ratio. Then, we present
extensive empirical evidence confirming that our theoretical results hold for
neural networks. Furthermore, we empirically observe that the adverse effect of
network size is more pronounced when robust training methods are employed to
learn from noisy-labeled data. Our results have important practical
implications: First, larger models should be employed with extra care,
particularly when trained on smaller dataset or using robust learning methods.
Second, a large sample size can alleviate the effect of noisy labels and allow
larger models to achieve a superior performance even under noise.

Automated vehicles are gradually entering people's daily life to provide a
comfortable driving experience for the users. The generic and user-agnostic
automated vehicles have limited ability to accommodate the different driving
styles of different users. This limitation not only impacts users' satisfaction
but also causes safety concerns. Learning from user demonstrations can provide
direct insights regarding users' driving preferences. However, it is difficult
to understand a driver's preference with limited data. In this study, we use a
model-free inverse reinforcement learning method to study drivers'
characteristics in the car-following scenario from a naturalistic driving
dataset, and show this method is capable of representing users' preferences
with reward functions. In order to predict the driving styles for drivers with
limited data, we apply Gaussian Mixture Models and compute the similarity of a
specific driver to the clusters of drivers. We design a personalized adaptive
cruise control (P-ACC) system through a partially observable Markov decision
process (POMDP) model. The reward function with the model to mimic drivers'
driving style is integrated, with a constraint on the relative distance to
ensure driving safety. Prediction of the driving styles achieves 85.7% accuracy
with the data of less than 10 car-following events. The model-based
experimental driving trajectories demonstrate that the P-ACC system can provide
a personalized driving experience.

Weak supervision has been applied to various Natural Language Understanding
tasks in recent years. Due to technical challenges with scaling weak
supervision to work on long-form documents, spanning up to hundreds of pages,
applications in the document understanding space have been limited. At Lexion,
we built a weak supervision-based system tailored for long-form (10-200 pages
long) PDF documents. We use this platform for building dozens of language
understanding models and have applied it successfully to various domains, from
commercial agreements to corporate formation documents.
  In this paper, we demonstrate the effectiveness of supervised learning with
weak supervision in a situation with limited time, workforce, and training
data. We built 8 high quality machine learning models in the span of one week,
with the help of a small team of just 3 annotators working with a dataset of
under 300 documents. We share some details about our overall architecture, how
we utilize weak supervision, and what results we are able to achieve. We also
include the dataset for researchers who would like to experiment with alternate
approaches or refine ours.
  Furthermore, we shed some light on the additional complexities that arise
when working with poorly scanned long-form documents in PDF format, and some of
the techniques that help us achieve state-of-the-art performance on such data.

Rapid progress in machine learning for natural language processing has the
potential to transform debates about how humans learn language. However, the
learning environments and biases of current artificial learners and humans
diverge in ways that weaken the impact of the evidence obtained from learning
simulations. For example, today's most effective neural language models are
trained on roughly one thousand times the amount of linguistic data available
to a typical child. To increase the relevance of learnability results from
computational models, we need to train model learners without significant
advantages over humans. If an appropriate model successfully acquires some
target linguistic knowledge, it can provide a proof of concept that the target
is learnable in a hypothesized human learning scenario. Plausible model
learners will enable us to carry out experimental manipulations to make causal
inferences about variables in the learning environment, and to rigorously test
poverty-of-the-stimulus-style claims arguing for innate linguistic knowledge in
humans on the basis of speculations about learnability. Comparable experiments
will never be possible with human subjects due to practical and ethical
considerations, making model learners an indispensable resource. So far,
attempts to deprive current models of unfair advantages obtain sub-human
results for key grammatical behaviors such as acceptability judgments. But
before we can justifiably conclude that language learning requires more prior
domain-specific knowledge than current models possess, we must first explore
non-linguistic inputs in the form of multimodal stimuli and multi-agent
interaction as ways to make our learners more efficient at learning from
limited linguistic input.

This paper presents a method for time-lapse 3D cell analysis. Specifically,
we consider the problem of accurately localizing and quantitatively analyzing
sub-cellular features, and for tracking individual cells from time-lapse 3D
confocal cell image stacks. The heterogeneity of cells and the volume of
multi-dimensional images presents a major challenge for fully automated
analysis of morphogenesis and development of cells. This paper is motivated by
the pavement cell growth process, and building a quantitative morphogenesis
model. We propose a deep feature based segmentation method to accurately detect
and label each cell region. An adjacency graph based method is used to extract
sub-cellular features of the segmented cells. Finally, the robust graph based
tracking algorithm using multiple cell features is proposed for associating
cells at different time instances. Extensive experiment results are provided
and demonstrate the robustness of the proposed method. The code is available on
Github and the method is available as a service through the BisQue portal.

Every sound that we hear is the result of successive convolutional operations
(e.g. room acoustics, microphone characteristics, resonant properties of the
instrument itself, not to mention characteristics and limitations of the sound
reproduction system). In this work we seek to determine the best room in which
to perform a particular piece using AI. Additionally, we use room acoustics as
a way to enhance the perceptual qualities of a given sound. Historically, rooms
(particularly Churches and concert halls) were designed to host and serve
specific musical functions. In some cases the architectural acoustical
qualities enhanced the music performed there. We try to mimic this, as a first
step, by designating room impulse responses that would correlate to producing
enhanced sound quality for particular music. A convolutional architecture is
first trained to take in an audio sample and mimic the ratings of experts with
about 78 % accuracy for various instrument families and notes for perceptual
qualities. This gives us a scoring function for any audio sample which can rate
the perceptual pleasantness of a note automatically. Now, via a library of
about 60,000 synthetic impulse responses mimicking all kinds of room,
materials, etc, we use a simple convolution operation, to transform the sound
as if it was played in a particular room. The perceptual evaluator is used to
rank the musical sounds, and yield the "best room or the concert hall" to play
a sound. As a byproduct it can also use room acoustics to turn a poor quality
sound into a "good" sound.

Blockchain is an emerging decentralized data collection, sharing and storage
technology, which have provided abundant transparent, secure, tamper-proof,
secure and robust ledger services for various real-world use cases. Recent
years have witnessed notable developments of blockchain technology itself as
well as blockchain-adopting applications. Most existing surveys limit the
scopes on several particular issues of blockchain or applications, which are
hard to depict the general picture of current giant blockchain ecosystem. In
this paper, we investigate recent advances of both blockchain technology and
its most active research topics in real-world applications. We first review the
recent developments of consensus mechanisms and storage mechanisms in general
blockchain systems. Then extensive literature is conducted on blockchain
enabled IoT, edge computing, federated learning and several emerging
applications including healthcare, COVID-19 pandemic, social network and supply
chain, where detailed specific research topics are discussed in each. Finally,
we discuss the future directions, challenges and opportunities in both academia
and industry.

Event extraction in the clinical domain is an under-explored research area.
The lack of training data in addition to the high volume of domain-specific
jargon that includes long entities with vague boundaries make the task
especially challenging. In this paper, we introduce DICE, a robust and
data-efficient generative model for clinical event extraction. DICE frames
event extraction as a conditional generation problem and utilizes descriptions
provided by domain experts to boost the performance under low-resource
settings. Furthermore, DICE learns to locate and bound biomedical mentions with
an auxiliary mention identification task trained jointly with event extraction
tasks to leverage inter-task dependencies and further incorporates the
identified mentions as trigger and argument candidates for their respective
tasks. We also introduce MACCROBAT-EE, the first clinical event extraction
dataset with event argument annotation. Our experiments demonstrate the
robustness of DICE under low data settings for the clinical domain and the
benefits of incorporating flexible joint training and mention markers into
generative approaches.

Virtualized network slicing allows a multitude of logical networks to be
created on a common substrate infrastructure to support diverse services. A
virtualized network slice is a logical combination of multiple virtual network
functions, which run on virtual machines (VMs) as software applications by
virtualization techniques. As the performance of network slices hinges on the
normal running of VMs, detecting and analyzing anomalies in VMs are critical.
Based on the three-tier management framework of virtualized network slicing, we
first develop a federated learning (FL) based three-tier distributed VM anomaly
detection framework, which enables distributed network slice managers to
collaboratively train a global VM anomaly detection model while keeping metrics
data locally. The high-dimensional, imbalanced, and distributed data features
in virtualized network slicing scenarios invalidate the existing anomaly
detection models. Considering the powerful ability of generative adversarial
network (GAN) in capturing the distribution from complex data, we design a new
multi-discriminator Bidirectional Wasserstein GAN with Gradient Penalty
(BiWGAN-GP) model to learn the normal data distribution from high-dimensional
resource metrics datasets that are spread on multiple VM monitors. The
multi-discriminator BiWGAN-GP model can be trained over distributed data
sources, which avoids high communication and computation overhead caused by the
centralized collection and processing of local data. We define an anomaly score
as the discriminant criterion to quantify the deviation of new metrics data
from the learned normal distribution to detect abnormal behaviors arising in
VMs. The efficiency and effectiveness of the proposed collaborative anomaly
detection algorithm are validated through extensive experimental evaluation on
a real-world dataset.

We initiate the study of differentially private (DP) estimation with access
to a small amount of public data. For private estimation of d-dimensional
Gaussians, we assume that the public data comes from a Gaussian that may have
vanishing similarity in total variation distance with the underlying Gaussian
of the private data. We show that under the constraints of pure or concentrated
DP, d+1 public data samples are sufficient to remove any dependence on the
range parameters of the private data distribution from the private sample
complexity, which is known to be otherwise necessary without public data. For
separated Gaussian mixtures, we assume that the underlying public and private
distributions are the same, and we consider two settings: (1) when given a
dimension-independent amount of public data, the private sample complexity can
be improved polynomially in terms of the number of mixture components, and any
dependence on the range parameters of the distribution can be removed in the
approximate DP case; (2) when given an amount of public data linear in the
dimension, the private sample complexity can be made independent of range
parameters even under concentrated DP, and additional improvements can be made
to the overall sample complexity.

The focus of this paper is a proof of concept, machine learning (ML) pipeline
that extracts heart rate from pressure sensor data acquired on low-power edge
devices. The ML pipeline consists an upsampler neural network, a signal quality
classifier, and a 1D-convolutional neural network optimized for efficient and
accurate heart rate estimation. The models were designed so the pipeline was
less than 40 kB. Further, a hybrid pipeline consisting of the upsampler and
classifier, followed by a peak detection algorithm was developed. The pipelines
were deployed on ESP32 edge device and benchmarked against signal processing to
determine the energy usage, and inference times. The results indicate that the
proposed ML and hybrid pipeline reduces energy and time per inference by 82%
and 28% compared to traditional algorithms. The main trade-off for ML pipeline
was accuracy, with a mean absolute error (MAE) of 3.28, compared to 2.39 and
1.17 for the hybrid and signal processing pipelines. The ML models thus show
promise for deployment in energy and computationally constrained devices.
Further, the lower sampling rate and computational requirements for the ML
pipeline could enable custom hardware solutions to reduce the cost and energy
needs of wearable devices.

With increasing concern about user data privacy, federated learning (FL) has
been developed as a unique training paradigm for training machine learning
models on edge devices without access to sensitive data. Traditional FL and
existing methods directly employ aggregation methods on all edges of the same
models and training devices for a cloud server. Although these methods protect
data privacy, they are not capable of model heterogeneity, even ignore the
heterogeneous computing power, and incur steep communication costs. In this
paper, we purpose a resource-aware FL to aggregate an ensemble of local
knowledge extracted from edge models, instead of aggregating the weights of
each local model, which is then distilled into a robust global knowledge as the
server model through knowledge distillation. The local model and the global
knowledge are extracted into a tiny size knowledge network by deep mutual
learning. Such knowledge extraction allows the edge client to deploy a
resource-aware model and perform multi-model knowledge fusion while maintaining
communication efficiency and model heterogeneity. Empirical results show that
our approach has significantly improved over existing FL algorithms in terms of
communication cost and generalization performance in heterogeneous data and
models. Our approach reduces the communication cost of VGG-11 by up to
102$\times$ and ResNet-32 by up to 30$\times$ when training ResNet-20 as the
knowledge network.

In this paper, we present a decentralized control approach based on a
Nonlinear Model Predictive Control (NMPC) method that employs barrier
certificates for safe navigation of multiple nonholonomic wheeled mobile robots
in unknown environments with static and/or dynamic obstacles. This method
incorporates a Learned Barrier Function (LBF) into the NMPC design in order to
guarantee safe robot navigation, i.e., prevent robot collisions with other
robots and the obstacles. We refer to our proposed control approach as
NMPC-LBF. Since each robot does not have a priori knowledge about the obstacles
and other robots, we use a Deep Neural Network (DeepNN) running in real-time on
each robot to learn the Barrier Function (BF) only from the robot's LiDAR and
odometry measurements. The DeepNN is trained to learn the BF that separates
safe and unsafe regions. We implemented our proposed method on simulated and
actual Turtlebot3 Burger robot(s) in different scenarios. The implementation
results show the effectiveness of the NMPC-LBF method at ensuring safe
navigation of the robots.

For nearly the past 30 years, Centroid Molecular Dynamics (CMD) has proven to
be a viable classical-like phase space formulation for the calculation of
quantum dynamical properties. However, calculation of the centroid effective
force remains a significant computational cost and limits the ability of CMD to
be an efficient approach to study condensed phase quantum dynamics. In this
paper we introduce a neural network-based methodology for first learning the
centroid effective force from path integral molecular dynamics data, which is
subsequently used as an effective force field to evolve the centroids directly
with the CMD algorithm. This method, called Machine-Learned Centroid Molecular
Dynamics (ML-CMD) is faster and far less costly than both standard on the fly
CMD and ring polymer molecular dynamics (RPMD). The training aspect of ML-CMD
is also straightforwardly implemented utilizing the DeepMD software kit. ML-CMD
is then applied to two model systems to illustrate the approach: liquid
para-hydrogen and water. The results show comparable accuracy to both CMD and
RPMD in the estimation of quantum dynamical properties, including the
self-diffusion constant and velocity time correlation function, but for
significantly reduced overall computational cost.

Couples generally manage chronic diseases together and the management takes
an emotional toll on both patients and their romantic partners. Consequently,
recognizing the emotions of each partner in daily life could provide an insight
into their emotional well-being in chronic disease management. Currently, the
process of assessing each partner's emotions is manual, time-intensive, and
costly. Despite the existence of works on emotion recognition among couples,
none of these works have used data collected from couples' interactions in
daily life. In this work, we collected 85 hours (1,021 5-minute samples) of
real-world multimodal smartwatch sensor data (speech, heart rate,
accelerometer, and gyroscope) and self-reported emotion data (n=612) from 26
partners (13 couples) managing diabetes mellitus type 2 in daily life. We
extracted physiological, movement, acoustic, and linguistic features, and
trained machine learning models (support vector machine and random forest) to
recognize each partner's self-reported emotions (valence and arousal). Our
results from the best models (balanced accuracies of 63.8% and 78.1% for
arousal and valence respectively) are better than chance and our prior work
that also used data from German-speaking, Swiss-based couples, albeit, in the
lab. This work contributes toward building automated emotion recognition
systems that would eventually enable partners to monitor their emotions in
daily life and enable the delivery of interventions to improve their emotional
well-being.

Iteration of training and evaluating a machine learning model is an important
process to improve its performance. However, while teachable interfaces enable
blind users to train and test an object recognizer with photos taken in their
distinctive environment, accessibility of training iteration and evaluation
steps has received little attention. Iteration assumes visual inspection of the
training photos, which is inaccessible for blind users. We explore this
challenge through MyCam, a mobile app that incorporates automatically estimated
descriptors for non-visual access to the photos in the users' training sets. We
explore how blind participants (N=12) interact with MyCam and the descriptors
through an evaluation study in their homes. We demonstrate that the real-time
photo-level descriptors enabled blind users to reduce photos with cropped
objects, and that participants could add more variations by iterating through
and accessing the quality of their training sets. Also, Participants found the
app simple to use indicating that they could effectively train it and that the
descriptors were useful. However, subjective responses were not reflected in
the performance of their models, partially due to little variation in training
and cluttered backgrounds.

This paper presents a first end-to-end application of a Quantum Support
Vector Machine (QSVM) algorithm for a classification problem in the financial
payment industry using the IBM Safer Payments and IBM Quantum Computers via the
Qiskit software stack. Based on real card payment data, a thorough comparison
is performed to assess the complementary impact brought in by the current
state-of-the-art Quantum Machine Learning algorithms with respect to the
Classical Approach. A new method to search for best features is explored using
the Quantum Support Vector Machine's feature map characteristics. The results
are compared using fraud specific key performance indicators: Accuracy, Recall,
and False Positive Rate, extracted from analyses based on human expertise (rule
decisions), classical machine learning algorithms (Random Forest, XGBoost) and
quantum based machine learning algorithms using QSVM. In addition, a hybrid
classical-quantum approach is explored by using an ensemble model that combines
classical and quantum algorithms to better improve the fraud prevention
decision. We found, as expected, that the results highly depend on feature
selections and algorithms that are used to select them. The QSVM provides a
complementary exploration of the feature space which led to an improved
accuracy of the mixed quantum-classical method for fraud detection, on a
drastically reduced data set to fit current state of Quantum Hardware.

Online learning of Hawkes processes has received increasing attention in the
last couple of years especially for modeling a network of actors. However,
these works typically either model the rich interaction between the events or
the latent cluster of the actors or the network structure between the actors.
We propose to model the latent structure of the network of actors as well as
their rich interaction across events for real-world settings of medical and
financial applications. Experimental results on both synthetic and real-world
data showcase the efficacy of our approach.

Generative design has been growing across the design community as a viable
method for design space exploration. Thermal design is more complex than
mechanical or aerodynamic design because of the additional convection-diffusion
equation and its pertinent boundary interaction. We present a generative
thermal design using cooperative multi-agent deep reinforcement learning and
continuous geometric representation of the fluid and solid domain. The proposed
framework consists of a pre-trained neural network surrogate model as an
environment to predict heat transfer and pressure drop of the generated
geometries. The design space is parameterized by composite Bezier curve to
solve multiple fin shape optimization. We show that our multi-agent framework
can learn the policy for design strategy using multi-objective reward without
the need for shape derivation or differentiable objective function.

Generalization analyses of deep learning typically assume that the training
converges to a fixed point. But, recent results indicate that in practice, the
weights of deep neural networks optimized with stochastic gradient descent
often oscillate indefinitely. To reduce this discrepancy between theory and
practice, this paper focuses on the generalization of neural networks whose
training dynamics do not necessarily converge to fixed points. Our main
contribution is to propose a notion of statistical algorithmic stability (SAS)
that extends classical algorithmic stability to non-convergent algorithms and
to study its connection to generalization. This ergodic-theoretic approach
leads to new insights when compared to the traditional optimization and
learning theory perspectives. We prove that the stability of the
time-asymptotic behavior of a learning algorithm relates to its generalization
and empirically demonstrate how loss dynamics can provide clues to
generalization performance. Our findings provide evidence that networks that
"train stably generalize better" even when the training continues indefinitely
and the weights do not converge.

Diffusion models are recent state-of-the-art methods for image generation and
likelihood estimation. In this work, we generalize continuous-time diffusion
models to arbitrary Riemannian manifolds and derive a variational framework for
likelihood estimation. Computationally, we propose new methods for computing
the Riemannian divergence which is needed in the likelihood estimation.
Moreover, in generalizing the Euclidean case, we prove that maximizing this
variational lower-bound is equivalent to Riemannian score matching.
Empirically, we demonstrate the expressive power of Riemannian diffusion models
on a wide spectrum of smooth manifolds, such as spheres, tori, hyperboloids,
and orthogonal groups. Our proposed method achieves new state-of-the-art
likelihoods on all benchmarks.

In this paper, we focus on the problem of statistical dependence estimation
using characteristic functions. We propose a statistical dependence measure,
based on the maximum-norm of the difference between joint and product-marginal
characteristic functions. The proposed measure can detect arbitrary statistical
dependence between two random vectors of possibly different dimensions, is
differentiable, and easily integrable into modern machine learning and deep
learning pipelines. We also conduct experiments both with simulated and real
data. Our simulations show, that the proposed method can measure statistical
dependencies in high-dimensional, non-linear data, and is less affected by the
curse of dimensionality, compared to the previous work in this line of
research. The experiments with real data demonstrate the potential
applicability of our statistical measure for two different empirical inference
scenarios, showing statistically significant improvement in the performance
characteristics when applied for supervised feature extraction and deep neural
network regularization. In addition, we provide a link to the accompanying
open-source repository https://bit.ly/3d4ch5I.

In modern machine learning applications, frequent encounters of covariate
shift and label scarcity have posed challenges to robust model training and
evaluation. Numerous transfer learning methods have been developed to robustly
adapt the model itself to some unlabeled target populations using existing
labeled data in a source population. However, there is a paucity of literature
on transferring performance metrics of a trained model. In this paper, we aim
to evaluate the performance of a trained binary classifier on unlabeled target
population based on receiver operating characteristic (ROC) analysis. We
proposed $\bf S$emi-supervised $\bf T$ransfer l$\bf E$arning of $\bf A$ccuracy
$\bf M$easures (STEAM), an efficient three-step estimation procedure that
employs 1) double-index modeling to construct calibrated density ratio weights
and 2) robust imputation to leverage the large amount of unlabeled data to
improve estimation efficiency. We establish the consistency and asymptotic
normality of the proposed estimators under correct specification of either the
density ratio model or the outcome model. We also correct for potential
overfitting bias in the estimators in finite samples with cross-validation. We
compare our proposed estimators to existing methods and show reductions in bias
and gains in efficiency through simulations. We illustrate the practical
utility of the proposed method on evaluating prediction performance of a
phenotyping model for Rheumatoid Arthritis (RA) on temporally evolving EHR
cohorts.

Federated Learning (FL) is a distributed learning paradigm that enables
mutually untrusting clients to collaboratively train a common machine learning
model. Client data privacy is paramount in FL. At the same time, the model must
be protected from poisoning attacks from adversarial clients. Existing
solutions address these two problems in isolation. We present FedPerm, a new FL
algorithm that addresses both these problems by combining a novel intra-model
parameter shuffling technique that amplifies data privacy, with Private
Information Retrieval (PIR) based techniques that permit cryptographic
aggregation of clients' model updates. The combination of these techniques
further helps the federation server constrain parameter updates from clients so
as to curtail effects of model poisoning attacks by adversarial clients. We
further present FedPerm's unique hyperparameters that can be used effectively
to trade off computation overheads with model utility. Our empirical evaluation
on the MNIST dataset demonstrates FedPerm's effectiveness over existing
Differential Privacy (DP) enforcement solutions in FL.

Data owners face increasing liability for how the use of their data could
harm under-priviliged communities. Stakeholders would like to identify the
characteristics of data that lead to algorithms being biased against any
particular demographic groups, for example, defined by their race, gender, age,
and/or religion. Specifically, we are interested in identifying subsets of the
feature space where the ground truth response function from features to
observed outcomes differs across demographic groups. To this end, we propose
FORESEE, a FORESt of decision trEEs algorithm, which generates a score that
captures how likely an individual's response varies with sensitive attributes.
Empirically, we find that our approach allows us to identify the individuals
who are most likely to be misclassified by several classifiers, including
Random Forest, Logistic Regression, Support Vector Machine, and k-Nearest
Neighbors. The advantage of our approach is that it allows stakeholders to
characterize risky samples that may contribute to discrimination, as well as,
use the FORESEE to estimate the risk of upcoming samples.

Many real-world problems involve multiple, possibly conflicting, objectives.
Multi-objective reinforcement learning (MORL) approaches have emerged to tackle
these problems by maximizing a joint objective function weighted by a
preference vector. These approaches find fixed customized policies
corresponding to preference vectors specified during training. However, the
design constraints and objectives typically change dynamically in real-life
scenarios. Furthermore, storing a policy for each potential preference is not
scalable. Hence, obtaining a set of Pareto front solutions for the entire
preference space in a given domain with a single training is critical. To this
end, we propose a novel MORL algorithm that trains a single universal network
to cover the entire preference space. The proposed approach, Preference-Driven
MORL (PD-MORL), utilizes the preferences as guidance to update the network
parameters. After demonstrating PD-MORL using classical Deep Sea Treasure and
Fruit Tree Navigation benchmarks, we evaluate its performance on challenging
multi-objective continuous control tasks.

FOLD-R++ is a highly efficient and explainable rule-based machine learning
algorithm for binary classification tasks. It generates a stratified normal
logic program as an (explainable) trained model. We present an improvement over
the FOLD-R++ algorithm, termed FOLD-SE, that provides scalable explainability
(SE) while inheriting all the merits of FOLD-R++. Scalable explainability means
that regardless of the size of the dataset, the number of learned rules and
learned literals stay small and, hence, understandable by human beings, while
maintaining good performance in classification. FOLD-SE is competitive in
performance with state-of-the-art algorithms such as XGBoost and Multi-Layer
Perceptrons (MLP). However, unlike XGBoost and MLP, the FOLD-SE algorithm
generates a model with scalable explainability. The FOLD-SE algorithm
outperforms FOLD-R++ and RIPPER algorithms in efficiency, performance, and
explainability, especially for large datasets. The FOLD-RM algorithm is an
extension of FOLD-R++ for multi-class classification tasks. An improved FOLD-RM
algorithm built upon FOLD-SE is also presented.

The sensitivity of astronomical X-ray detectors is limited by the
instrumental background. The background is especially important when observing
low surface brightness sources that are critical for many of the science cases
targeted by future X-ray observatories, including Athena and future US-led
flagship or probe-class X-ray missions. Above 2keV, the background is dominated
by signals induced by cosmic rays interacting with the spacecraft and detector.
We develop novel machine learning algorithms to identify events in
next-generation X-ray imaging detectors and to predict the probability that an
event is induced by a cosmic ray vs. an astrophysical X-ray photon, enabling
enhanced filtering of the cosmic ray-induced background. We find that by
learning the typical correlations between the secondary events that arise from
a single primary, machine learning algorithms are able to successfully identify
cosmic ray-induced background events that are missed by traditional filtering
methods employed on current-generation X-ray missions, reducing the unrejected
background by as much as 30 per cent.

The development of technologies for causal inference with the privacy
preservation of distributed data has attracted considerable attention in recent
years. To address this issue, we propose a quasi-experiment based on data
collaboration (DC-QE) that enables causal inference from distributed data with
privacy preservation. Our method preserves the privacy of private data by
sharing only dimensionality-reduced intermediate representations, which are
individually constructed by each party. Moreover, our method can reduce both
random errors and biases, whereas existing methods can only reduce random
errors in the estimation of treatment effects. Through numerical experiments on
both artificial and real-world data, we confirmed that our method can lead to
better estimation results than individual analyses. With the spread of our
method, intermediate representations can be published as open data to help
researchers find causalities and accumulated as a knowledge base.

Multi-server Federated learning (FL) has been considered as a promising
solution to address the limited communication resource problem of single-server
FL. We consider a typical multi-server FL architecture, where the coverage
areas of regional servers may overlap. The key point of this architecture is
that the clients located in the overlapping areas update their local models
based on the average model of all accessible regional models, which enables
indirect model sharing among different regional servers. Due to the complicated
network topology, the convergence analysis is much more challenging than
single-server FL. In this paper, we firstly propose a novel MS-FedAvg algorithm
for this multi-server FL architecture and analyze its convergence on non-iid
datasets for general non-convex settings. Since the number of clients located
in each regional server is much less than in single-server FL, the bandwidth of
each client should be large enough to successfully communicate training models
with the server, which indicates that full client participation can work in
multi-server FL. Also, we provide the convergence analysis of the partial
client participation scheme and develop a new biased partial participation
strategy to further accelerate convergence. Our results indicate that the
convergence results highly depend on the ratio of the number of clients in each
area type to the total number of clients in all three strategies. The extensive
experiments show remarkable performance and support our theoretical results.

The applications of Artificial Intelligence (AI) surround decisions on
increasingly many aspects of human lives. Society responds by imposing legal
and social expectations for the accountability of such automated decision
systems (ADSs). Fairness, a fundamental constituent of AI accountability, is
concerned with just treatment of individuals and sensitive groups (e.g., based
on sex, race). While many studies focus on fair learning and fairness testing
for the classification tasks, the literature is rather limited on how to
examine fairness in regression tasks. This work presents error parity as a
regression fairness notion and introduces a testing methodology to assess group
fairness based on a statistical hypothesis testing procedure. The error parity
test checks whether prediction errors are distributed similarly across
sensitive groups to determine if an ADS is fair. It is followed by a suitable
permutation test to compare groups on several statistics to explore disparities
and identify impacted groups. The usefulness and applicability of the proposed
methodology are demonstrated via a case study on COVID-19 projections in the US
at the county level, which revealed race-based differences in forecast errors.
Overall, the proposed regression fairness testing methodology fills a gap in
the fair machine learning literature and may serve as a part of larger
accountability assessments and algorithm audits.

Deep reinforcement learning is a promising approach to learning policies in
uncontrolled environments that do not require domain knowledge. Unfortunately,
due to sample inefficiency, deep RL applications have primarily focused on
simulated environments. In this work, we demonstrate that the recent
advancements in machine learning algorithms and libraries combined with a
carefully tuned robot controller lead to learning quadruped locomotion in only
20 minutes in the real world. We evaluate our approach on several indoor and
outdoor terrains which are known to be challenging for classical model-based
controllers. We observe the robot to be able to learn walking gait consistently
on all of these terrains. Finally, we evaluate our design decisions in a
simulated environment.

Image Segmentation is one of the core tasks in Computer Vision and solving it
often depends on modeling the image appearance data via the color distributions
of each it its constituent regions. Whereas many segmentation algorithms handle
the appearance models dependence using alternation or implicit methods, we
propose here a new approach to directly estimate them from the image without
prior information on the underlying segmentation. Our method uses local high
order color statistics from the image as an input to tensor factorization-based
estimator for latent variable models. This approach is able to estimate models
in multiregion images and automatically output the regions proportions without
prior user interaction, overcoming the drawbacks from a prior attempt to this
problem. We also demonstrate the performance of our proposed method in many
challenging synthetic and real imaging scenarios and show that it leads to an
efficient segmentation algorithm.

State-of-the-art neural language models can now be used to solve ad-hoc
language tasks through zero-shot prompting without the need for supervised
training. This approach has gained popularity in recent years, and researchers
have demonstrated prompts that achieve strong accuracy on specific NLP tasks.
However, finding a prompt for new tasks requires experimentation. Different
prompt templates with different wording choices lead to significant accuracy
differences. PromptIDE allows users to experiment with prompt variations,
visualize prompt performance, and iteratively optimize prompts. We developed a
workflow that allows users to first focus on model feedback using small data
before moving on to a large data regime that allows empirical grounding of
promising prompts using quantitative measures of the task. The tool then allows
easy deployment of the newly created ad-hoc models. We demonstrate the utility
of PromptIDE (demo at http://prompt.vizhub.ai) and our workflow using several
real-world use cases.

We analyze the complexity of learning $n$-qubit quantum phase states. A
degree-$d$ phase state is defined as a superposition of all $2^n$ basis vectors
$x$ with amplitudes proportional to $(-1)^{f(x)}$, where $f$ is a degree-$d$
Boolean polynomial over $n$ variables. We show that the sample complexity of
learning an unknown degree-$d$ phase state is $\Theta(n^d)$ if we allow
separable measurements and $\Theta(n^{d-1})$ if we allow entangled
measurements. Our learning algorithm based on separable measurements has
runtime $\textsf{poly}(n)$ (for constant $d$) and is well-suited for near-term
demonstrations as it requires only single-qubit measurements in the Pauli $X$
and $Z$ bases. We show similar bounds on the sample complexity for learning
generalized phase states with complex-valued amplitudes. We further consider
learning phase states when $f$ has sparsity-$s$, degree-$d$ in its
$\mathbb{F}_2$ representation (with sample complexity $O(2^d sn)$), $f$ has
Fourier-degree-$t$ (with sample complexity $O(2^{2t})$), and learning quadratic
phase states with $\varepsilon$-global depolarizing noise (with sample
complexity $O(n^{1+\varepsilon})$). These learning algorithms give us a
procedure to learn the diagonal unitaries of the Clifford hierarchy and IQP
circuits.

Diagnosis of COVID-19 is necessary to prevent and control the disease. Deep
learning methods have been considered a fast and accurate method. In this
paper, by the parallel combination of three well-known pre-trained networks, we
attempted to distinguish coronavirus-infected samples from healthy samples. The
negative log-likelihood loss function has been used for model training. CT scan
images in the SARS-CoV-2 dataset were used for diagnosis. The SARS-CoV-2
dataset contains 2482 images of lung CT scans, of which 1252 images belong to
COVID-19-infected samples. The proposed model was close to 97% accurate.

Face anti-spoofing (FAS) aims at distinguishing face spoof attacks from the
authentic ones, which is typically approached by learning proper models for
performing the associated classification task. In practice, one would expect
such models to be generalized to FAS in different image domains. Moreover, it
is not practical to assume that the type of spoof attacks would be known in
advance. In this paper, we propose a deep learning model for addressing the
aforementioned domain-generalized face anti-spoofing task. In particular, our
proposed network is able to disentangle facial liveness representation from the
irrelevant ones (i.e., facial content and image domain features). The resulting
liveness representation exhibits sufficient domain invariant properties, and
thus it can be applied for performing domain-generalized FAS. In our
experiments, we conduct experiments on five benchmark datasets with various
settings, and we verify that our model performs favorably against
state-of-the-art approaches in identifying novel types of spoof attacks in
unseen image domains.

Wireless sensor networks consist of randomly distributed sensor nodes for
monitoring targets or areas of interest. Maintaining the network for continuous
surveillance is a challenge due to the limited battery capacity in each sensor.
Wireless power transfer technology is emerging as a reliable solution for
energizing the sensors by deploying a mobile charger (MC) to recharge the
sensor. However, designing an optimal charging path for the MC is challenging
because of uncertainties arising in the networks. The energy consumption rate
of the sensors may fluctuate significantly due to unpredictable changes in the
network topology, such as node failures. These changes also lead to shifts in
the importance of each sensor, which are often assumed to be the same in
existing works. We address these challenges in this paper by proposing a novel
adaptive charging scheme using a deep reinforcement learning (DRL) approach.
Specifically, we endow the MC with a charging policy that determines the next
sensor to charge conditioning on the current state of the network. We then use
a deep neural network to parametrize this charging policy, which will be
trained by reinforcement learning techniques. Our model can adapt to
spontaneous changes in the network topology. The empirical results show that
the proposed algorithm outperforms the existing on-demand algorithms by a
significant margin.

In this paper, we investigate a reconfigurable intelligent surface
(RIS)-aided multiuser full-duplex secure communication system with hardware
impairments at transceivers and RIS, where multiple eavesdroppers overhear the
two-way transmitted signals simultaneously, and an RIS is applied to enhance
the secrecy performance. Aiming at maximizing the sum secrecy rate (SSR), a
joint optimization problem of the transmit beamforming at the base station (BS)
and the reflecting beamforming at the RIS is formulated under the transmit
power constraint of the BS and the unit modulus constraint of the phase
shifters. As the environment is time-varying and the system is
high-dimensional, this non-convex optimization problem is mathematically
intractable. A deep reinforcement learning (DRL)-based algorithm is explored to
obtain the satisfactory solution by repeatedly interacting with and learning
from the dynamic environment. Extensive simulation results illustrate that the
DRL-based secure beamforming algorithm is proved to be significantly effective
in improving the SSR. It is also found that the performance of the DRL-based
method can be greatly improved and the convergence speed of neural network can
be accelerated with appropriate neural network parameters.

Auto-encoding Variational Bayes (AEVB) is a powerful and general algorithm
for fitting latent variable models (a promising direction for unsupervised
learning), and is well-known for training the Variational Auto-Encoder (VAE).
In this tutorial, we focus on motivating AEVB from the classic Expectation
Maximization (EM) algorithm, as opposed to from deterministic auto-encoders.
Though natural and somewhat self-evident, the connection between EM and AEVB is
not emphasized in the recent deep learning literature, and we believe that
emphasizing this connection can improve the community's understanding of AEVB.
In particular, we find it especially helpful to view (1) optimizing the
evidence lower bound (ELBO) with respect to inference parameters as approximate
E-step and (2) optimizing ELBO with respect to generative parameters as
approximate M-step; doing both simultaneously as in AEVB is then simply
tightening and pushing up ELBO at the same time. We discuss how approximate
E-step can be interpreted as performing variational inference. Important
concepts such as amortization and the reparametrization trick are discussed in
great detail. Finally, we derive from scratch the AEVB training procedures of a
non-deep and several deep latent variable models, including VAE, Conditional
VAE, Gaussian Mixture VAE and Variational RNN. It is our hope that readers
would recognize AEVB as a general algorithm that can be used to fit a wide
range of latent variable models (not just VAE), and apply AEVB to such models
that arise in their own fields of research. PyTorch code for all included
models are publicly available.

We present adaptive measurement techniques tailored for variational quantum
algorithms on near-term small and noisy devices. In particular, we generalise
earlier "learning to measure" strategies in two ways. First, by considering a
class of adaptive positive operator valued measures (POVMs) that can be
simulated with simple projective measurements without ancillary qubits, we
decrease the amount of required qubits and two-qubit gates. Second, by
introducing a method based on Quantum Detector Tomography to mitigate the
effect of noise, we are able to optimise the POVMs as well as to infer
expectation values reliably in the currently available noisy quantum devices.
Our numerical simulations clearly indicate that the presented strategies can
significantly reduce the number of needed shots to achieve chemical accuracy in
variational quantum eigensolvers, thus helping to solve one of the bottlenecks
of near-term quantum computing.

Using quantum computing, this paper addresses two scientifically pressing and
day-to-day relevant problems, namely, chemical retrosynthesis which is an
important step in drug/material discovery and security of the semiconductor
supply chain. We show that Quantum Long Short-Term Memory (QLSTM) is a viable
tool for retrosynthesis. We achieve 65% training accuracy with QLSTM, whereas
classical LSTM can achieve 100%. However, in testing, we achieve 80% accuracy
with the QLSTM while classical LSTM peaks at only 70% accuracy! We also
demonstrate an application of Quantum Neural Network (QNN) in the hardware
security domain, specifically in Hardware Trojan (HT) detection using a set of
power and area Trojan features. The QNN model achieves detection accuracy as
high as 97.27%.

In the deep learning era, improving the neural network performance in jet
physics is a rewarding task as it directly contributes to more accurate physics
measurements at the LHC. Recent research has proposed various network designs
in consideration of the full Lorentz symmetry, but its benefit is still not
systematically asserted, given that there remain many successful networks
without taking it into account. We conduct a detailed study on the
Lorentz-symmetric design. We propose two generalized approaches for modifying a
network - these methods are experimented on PFN, ParticleNet, and LorentzNet,
and exhibit a general performance gain. We also reveal for the first time that
the underlying reason behind the known improvement the "pairwise mass" feature
brings to the network is that it introduces a structure that adheres to full
Lorentz symmetry. We confirm that Lorentz-symmetry preservation serves as a
strong inductive bias of jet physics, hence calling for attention to such
general recipes in future network designs.

Learning invariant (causal) features for out-of-distribution (OOD)
generalization has attracted extensive attention recently, and among the
proposals invariant risk minimization (IRM) (Arjovsky et al., 2019) is a
notable solution. In spite of its theoretical promise for linear regression,
the challenges of using IRM in linear classification problems yet remain
(Rosenfeld et al.,2020, Nagarajan et al., 2021). Along this line, a recent
study (Arjovsky et al., 2019) has made a first step and proposes a learning
principle of information bottleneck based invariant risk minimization (IB-IRM).
In this paper, we first show that the key assumption of support overlap of
invariant features used in (Arjovsky et al., 2019) is rather strong for the
guarantee of OOD generalization and it is still possible to achieve the optimal
solution without such assumption. To further answer the question of whether
IB-IRM is sufficient for learning invariant features in linear classification
problems, we show that IB-IRM would still fail in two cases whether or not the
invariant features capture all information about the label. To address such
failures, we propose a \textit{Counterfactual Supervision-based Information
Bottleneck (CSIB)} learning algorithm that provably recovers the invariant
features. The proposed algorithm works even when accessing data from a single
environment, and has theoretically consistent results for both binary and
multi-class problems. We present empirical experiments on three synthetic
datasets that verify the efficacy of our proposed method.

Regression is one of the core problems tackled in supervised learning.
Rectified linear unit (ReLU) neural networks generate continuous and
piecewise-linear (CPWL) mappings and are the state-of-the-art approach for
solving regression problems. In this paper, we propose an alternative method
that leverages the expressivity of CPWL functions. In contrast to deep neural
networks, our CPWL parameterization guarantees stability and is interpretable.
Our approach relies on the partitioning of the domain of the CPWL function by a
Delaunay triangulation. The function values at the vertices of the
triangulation are our learnable parameters and identify the CPWL function
uniquely. Formulating the learning scheme as a variational problem, we use the
Hessian total variation (HTV) as regularizer to favor CPWL functions with few
affine pieces. In this way, we control the complexity of our model through a
single hyperparameter. By developing a computational framework to compute the
HTV of any CPWL function parameterized by a triangulation, we discretize the
learning problem as the generalized least absolute shrinkage and selection
operator (LASSO). Our experiments validate the usage of our method in
low-dimensional scenarios.

Inverse Kinematics (IK) systems are often rigid with respect to their input
character, thus requiring user intervention to be adapted to new skeletons. In
this paper we aim at creating a flexible, learned IK solver applicable to a
wide variety of human morphologies. We extend a state-of-the-art machine
learning IK solver to operate on the well known Skinned Multi-Person Linear
model (SMPL). We call our model SMPL-IK, and show that when integrated into
real-time 3D software, this extended system opens up opportunities for defining
novel AI-assisted animation workflows. For example, pose authoring can be made
more flexible with SMPL-IK by allowing users to modify gender and body shape
while posing a character. Additionally, when chained with existing pose
estimation algorithms, SMPL-IK accelerates posing by allowing users to
bootstrap 3D scenes from 2D images while allowing for further editing. Finally,
we propose a novel SMPL Shape Inversion mechanism (SMPL-SI) to map arbitrary
humanoid characters to the SMPL space, allowing artists to leverage SMPL-IK on
custom characters. In addition to qualitative demos showing proposed tools, we
present quantitative SMPL-IK baselines on the H36M and AMASS datasets.

Unsupervised domain adaptation (UDA) has been widely used to transfer
knowledge from a labeled source domain to an unlabeled target domain to counter
the difficulty of labeling in a new domain. The training of conventional
solutions usually relies on the existence of both source and target domain
data. However, privacy of the large-scale and well-labeled data in the source
domain and trained model parameters can become the major concern of cross
center/domain collaborations. In this work, to address this, we propose a
practical solution to UDA for segmentation with a black-box segmentation model
trained in the source domain only, rather than original source data or a
white-box source model. Specifically, we resort to a knowledge distillation
scheme with exponential mixup decay (EMD) to gradually learn target-specific
representations. In addition, unsupervised entropy minimization is further
applied to regularization of the target domain confidence. We evaluated our
framework on the BraTS 2018 database, achieving performance on par with
white-box source model adaptation approaches.

The NASA Dawn spacecraft took off from Cape Canaveral in September 2007 atop
a Delta II rocket starting an ambitious journey to Vesta and Ceres, the two
most massive worlds in the largest reservoir of asteroids in the Solar System,
the Main Belt. Prior to the Dawn launch, Earth-bound observations of Vesta and
Ceres revealed intriguing features--from Vesta's rugged shape to Ceres' tenuous
water exosphere--, but these objects remained fuzzy speckles of light even
through the lenses of the most powerful telescopes. With Dawn's exploration of
Vesta (2011-2012) and Ceres (2015-2018) these two worlds came into focus.
Breath-taking details emerged of how large collisions sculpted Vesta liberating
massive amounts of material in the inner Main Belt, providing the source of an
important family of meteorites recovered on Earth. Ceres' complex geology,
which may rival that of the Earth and Mars, unveiled recent cryovolcanic
activity. This book is dedicated to these highlights, and many more discoveries
of the Dawn mission. By the time Dawn completed its mission in 2018, our
understanding of the formation of the Solar System had greatly evolved thanks
to new theoretical models and to a new trove of meteorite geochemical data, and
Dawn observations of Vesta and Ceres provide new, vital constraints to
synergistically interpret models and data. The broader implications of the Dawn
legacy are presented in a series of dedicated chapters. The editors hope this
book will serve as a solid reference for the younger generations as well as for
more seasoned researchers to successfully pursue future exploration of the Main
Belt. We certainly have learned a lot thanks to Dawn, and yet we know that we
have barely scratched the surface of what Main Belt asteroids can tell us about
the dawn of our Solar System.

Modern computational science and engineering applications are being improved
by the advances in scientific machine learning. Data-driven methods such as
Dynamic Mode Decomposition (DMD) can extract coherent structures from
spatio-temporal data generated from dynamical systems and infer different
scenarios for said systems. The spatio-temporal data comes as snapshots
containing spatial information for each time instant. In modern engineering
applications, the generation of high-dimensional snapshots can be time and/or
resource-demanding. In the present study, we consider two strategies for
enhancing DMD workflow in large numerical simulations: (i) snapshots
compression to relieve disk pressure; (ii) the use of in situ visualization
images to reconstruct the dynamics (or part of) in runtime. We evaluate our
approaches with two 3D fluid dynamics simulations and consider DMD to
reconstruct the solutions. Results reveal that snapshot compression
considerably reduces the required disk space. We have observed that lossy
compression reduces storage by almost $50\%$ with low relative errors in the
signal reconstructions and other quantities of interest. We also extend our
analysis to data generated on-the-fly, using in-situ visualization tools to
generate image files of our state vectors during runtime. On large simulations,
the generation of snapshots may be slow enough to use batch algorithms for
inference. Streaming DMD takes advantage of the incremental SVD algorithm and
updates the modes with the arrival of each new snapshot. We use streaming DMD
to reconstruct the dynamics from in-situ generated images. We show that this
process is efficient, and the reconstructed dynamics are accurate.

Unsupervised domain adaptation (UDA) has been successfully applied to
transfer knowledge from a labeled source domain to target domains without their
labels. Recently introduced transferable prototypical networks (TPN) further
addresses class-wise conditional alignment. In TPN, while the closeness of
class centers between source and target domains is explicitly enforced in a
latent space, the underlying fine-grained subtype structure and the
cross-domain within-class compactness have not been fully investigated. To
counter this, we propose a new approach to adaptively perform a fine-grained
subtype-aware alignment to improve performance in the target domain without the
subtype label in both domains. The insight of our approach is that the
unlabeled subtypes in a class have the local proximity within a subtype, while
exhibiting disparate characteristics, because of different conditional and
label shifts. Specifically, we propose to simultaneously enforce subtype-wise
compactness and class-wise separation, by utilizing intermediate pseudo-labels.
In addition, we systematically investigate various scenarios with and without
prior knowledge of subtype numbers, and propose to exploit the underlying
subtype structure. Furthermore, a dynamic queue framework is developed to
evolve the subtype cluster centroids steadily using an alternative processing
scheme. Experimental results, carried out with multi-view congenital heart
disease data and VisDA and DomainNet, show the effectiveness and validity of
our subtype-aware UDA, compared with state-of-the-art UDA methods.

SOTA multiagent reinforcement algorithms distinguish themselves in many ways
from their single-agent equivalences, except that they still totally inherit
the single-agent exploration-exploitation strategy. We report that naively
inheriting this strategy from single-agent algorithms causes potential
collaboration failures, in which the agents blindly follow mainstream behaviors
and reject taking minority responsibility. We named this problem the diffusion
of responsibility (DR) as it shares similarities with a same-name social
psychology effect. In this work, we start by theoretically analyzing the cause
of the DR problem, emphasizing it is not relevant to the reward crafting or the
credit assignment problems. We propose a Policy Resonance approach to address
the DR problem by modifying the multiagent exploration-exploitation strategy.
Next, we show that most SOTA algorithms can equip this approach to promote
collaborative agent performance in complex cooperative tasks. Experiments are
performed in multiple test benchmark tasks to illustrate the effectiveness of
this approach.

A growing number of universities worldwide use various forms of online and
blended learning as part of their academic curricula. Furthermore, the recent
changes caused by the COVID-19 pandemic have led to a drastic increase in
importance and ubiquity of online education. Among the major advantages of
e-learning is not only improving students' learning experience and widening
their educational prospects, but also an opportunity to gain insights into
students' learning processes with learning analytics. This study contributes to
the topic of improving and understanding e-learning processes in the following
ways. First, we demonstrate that accurate predictive models can be built based
on sequential patterns derived from students' behavioral data, which are able
to identify underperforming students early in the course. Second, we
investigate the specificity-generalizability trade-off in building such
predictive models by investigating whether predictive models should be built
for every course individually based on course-specific sequential patterns, or
across several courses based on more general behavioral patterns. Finally, we
present a methodology for capturing temporal aspects in behavioral data and
analyze its influence on the predictive performance of the models. The results
of our improved sequence classification technique are capable to predict
student performance with high levels of accuracy, reaching 90 percent for
course-specific models.

Dimensionality reduction is the essence of many data processing problems,
including filtering, data compression, reduced-order modeling and pattern
analysis. While traditionally tackled using linear tools in the fluid dynamics
community, nonlinear tools from machine learning are becoming increasingly
popular. This article, halfway between a review and a tutorial, introduces a
general framework for linear and nonlinear dimensionality reduction techniques.
Differences and links between autoencoders and manifold learning methods are
highlighted, and popular nonlinear techniques such as kernel Principal
Component Analysis (kPCA), isometric feature learning (ISOMAPs) and Locally
Linear Embedding (LLE) are placed in this framework. These algorithms are
benchmarked in three classic problems: 1) filtering, 2) identification of
oscillatory patterns, and 3) data compression. Their performances are compared
against the traditional Proper Orthogonal Decomposition (POD) to provide a
perspective on their diffusion in fluid dynamics.

Many methods that build powerful variational distributions based on
unadjusted Langevin transitions exist. Most of these were developed using a
wide range of different approaches and techniques. Unfortunately, the lack of a
unified analysis and derivation makes developing new methods and reasoning
about existing ones a challenging task. We address this giving a single
analysis that unifies and generalizes these existing techniques. The main idea
is to augment the target and variational by numerically simulating the
underdamped Langevin diffusion process and its time reversal. The benefits of
this approach are twofold: it provides a unified formulation for many existing
methods, and it simplifies the development of new ones. In fact, using our
formulation we propose a new method that combines the strengths of previously
existing algorithms; it uses underdamped Langevin transitions and powerful
augmentations parameterized by a score network. Our empirical evaluation shows
that our proposed method consistently outperforms relevant baselines in a wide
range of tasks.

Frequency-domain simulation of seismic waves plays an important role in
seismic inversion, but it remains challenging in large models. The recently
proposed physics-informed neural network (PINN), as an effective deep learning
method, has achieved successful applications in solving a wide range of partial
differential equations (PDEs), and there is still room for improvement on this
front. For example, PINN can lead to inaccurate solutions when PDE coefficients
are non-smooth and describe structurally-complex media. In this paper, we solve
the acoustic and visco-acoustic scattered-field wave equation in the frequency
domain with PINN instead of the wave equation to remove source singularity. We
first illustrate that non-smooth velocity models lead to inaccurate wavefields
when no boundary conditions are implemented in the loss function. Then, we add
the perfectly matched layer (PML) conditions in the loss function of PINN and
design a quadratic neural network to overcome the detrimental effects of
non-smooth models in PINN. We show that PML and quadratic neurons improve the
results as well as attenuation and discuss the reason for this improvement. We
also illustrate that a network trained during a wavefield simulation can be
used to pre-train the neural network of another wavefield simulation after
PDE-coefficient alteration and improve the convergence speed accordingly. This
pre-training strategy should find application in iterative full waveform
inversion (FWI) and time-lag target-oriented imaging when the model
perturbation between two consecutive iterations or two consecutive experiments
can be small.

Missing data is an inevitable and common problem in data-driven intelligent
transportation systems (ITS). In the past decade, scholars have done many
research on the recovery of missing traffic data, however how to make full use
of spatio-temporal traffic patterns to improve the recovery performance is
still an open problem. Aiming at the spatio-temporal characteristics of traffic
speed data, this paper regards the recovery of missing data as a matrix
completion problem, and proposes a spatio-temporal traffic data completion
method based on hidden feature analysis, which discovers spatio-temporal
patterns and underlying structures from incomplete data to complete the
recovery task. Therefore, we introduce spatial and temporal correlation to
capture the main underlying features of each dimension. Finally, these latent
features are applied to recovery traffic data through latent feature analysis.
The experimental and evaluation results show that the evaluation criterion
value of the model is small, which indicates that the model has better
performance. The results show that the model can accurately estimate the
continuous missing data.

Bilevel planning, in which a high-level search over an abstraction of an
environment is used to guide low-level decision making, is an effective
approach to solving long-horizon tasks in continuous state and action spaces.
Recent work has shown that action abstractions that enable such bilevel
planning can be learned in the form of symbolic operators and neural samplers
given symbolic predicates and demonstrations that achieve known goals. In this
work, we show that existing approaches fall short in environments where actions
tend to cause a large number of predicates to change. To address this issue, we
propose to learn operators with ignore effects. The key idea motivating our
approach is that modeling every observed change in the predicates is
unnecessary; the only changes that need be modeled are those that are necessary
for high-level search to achieve the specified goal. Experimentally, we show
that our approach is able to learn operators with ignore effects across six
hybrid robotic domains that enable an agent to solve novel variations of a
task, with different initial states, goals, and numbers of objects,
significantly more efficiently than several baselines.

Existing deraining methods mainly focus on a single input image. With just a
single input image, it is extremely difficult to accurately detect rain
streaks, remove rain streaks, and restore rain-free images. Compared with a
single 2D image, a light field image (LFI) embeds abundant 3D structure and
texture information of the target scene by recording the direction and position
of each incident ray via a plenoptic camera, which has emerged as a popular
device in the computer vision and graphics research communities. In this paper,
we propose a novel network, 4D-MGP-SRRNet, for rain streak removal from an LFI.
Our method takes as input all sub-views of a rainy LFI. In order to make full
use of the LFI, we adopt 4D convolutional layers to build the proposed rain
steak removal network to simultaneously process all sub-views of the LFI. In
the proposed network, the rain detection model, MGPDNet, with a novel
Multi-scale Self-guided Gaussian Process (MSGP) module is proposed to detect
rain streaks from all sub-views of the input LFI. Semi-supervised learning is
introduced to accurately detect rain streaks by training on both virtual-world
rainy LFIs and real-world rainy LFIs at multiple scales via calculating pseudo
ground truth for real-world rain streaks. All sub-views subtracting the
predicted rain streaks are then fed into a 4D residual model to estimate depth
maps. Finally, all sub-views concatenated with the corresponding rain streaks
and fog maps converted from the estimated depth maps are fed into a rainy LFI
restoring model that is based on the adversarial recurrent neural network to
progressively eliminate rain streaks and recover the rain-free LFI. Extensive
quantitative and qualitative evaluations conducted on both synthetic LFIs and
real-world LFIs demonstrate the effectiveness of our proposed method.

Self-supervised learning (SSL) has emerged as a promising alternative to
create supervisory signals to real-world tasks, avoiding the extensive cost of
labeling. SSL is particularly attractive for unsupervised tasks such as anomaly
detection (AD), where labeled anomalies are costly to secure, difficult to
simulate, or even nonexistent. A large catalog of augmentation functions have
been used for SSL-based AD (SSAD) on image data, and recent works have observed
that the type of augmentation has a significant impact on performance.
Motivated by those, this work sets out to put image-based SSAD under a larger
lens and carefully investigate the role of data augmentation in AD through
extensive experiments on three different models across 420 different tasks. Our
main finding is that self-supervision acts as a yet-another model
hyperparameter and should be chosen carefully in regard to the nature of true
anomalies. That is, the alignment between data augmentation and the underlying
anomaly-generating mechanism in given data is the key to the success of SSAD,
and in the lack thereof, SSL even impairs (!) the accuracy. Moving beyond
proposing another SSAD method, our study contributes to a better understanding
of this growing area and lays out new directions for future research.

K-means clustering, as a classic unsupervised machine learning algorithm, is
the key step to select the interpolation sampling points in interpolative
separable density fitting (ISDF) decomposition. Real-valued K-means clustering
for accelerating the ISDF decomposition has been demonstrated for large-scale
hybrid functional ab initio molecular dynamics (AIMD) within plane-wave basis
sets where the Kohn-Sham orbitals are real-valued. However, it is unclear
whether such K-means clustering works for complex-valued Kohn-Sham orbitals.
Here, we apply the K-means clustering into AIMD for complex-valued Kohn-Sham
orbitals and use an improved weight function defined as the sum of the square
modulus of complex-valued Kohn-Sham orbitals in K-means clustering. Numerical
results demonstrate that this improved weight function in K-means clustering
algorithm yields smoother and more delocalized interpolation sampling points,
resulting in smoother energy potential, smaller energy drift and longer time
steps for AIMD compared to the previous weight function used in the real-valued
K-means algorithm. In particular, we find that this improved algorithm can
obtain more accurate oxygen-oxygen radial distribution functions in liquid
water molecules compared to the previous K-means algorithm. Finally, we
describe a massively parallel implementation of this ISDF decomposition to
accelerate large-scale complex-valued hybrid DFT calculations containing
thousands of atoms (2,744 atoms), which can scale up to 5,504 CPU cores on
modern heterogeneous supercomputers.

Semantic segmentation is a key technique involved in automatic interpretation
of high-resolution remote sensing (HRS) imagery and has drawn much attention in
the remote sensing community. Deep convolutional neural networks (DCNNs) have
been successfully applied to the HRS imagery semantic segmentation task due to
their hierarchical representation ability. However, the heavy dependency on a
large number of training data with dense annotation and the sensitiveness to
the variation of data distribution severely restrict the potential application
of DCNNs for the semantic segmentation of HRS imagery. This study proposes a
novel unsupervised domain adaptation semantic segmentation network
(MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet
constructs an output space adversarial learning scheme to bridge the domain
distribution discrepancy between source domain and target domain and to narrow
the influence of domain shift. Specifically, we embed an invariant feature
memory module to store invariant domain-level context information because the
features obtained from adversarial learning only tend to represent the variant
feature of current limited inputs. This module is integrated by a category
attention-driven invariant domain-level context aggregation module to current
pseudo invariant feature for further augmenting the pixel representations. An
entropy-based pseudo label filtering strategy is used to update the memory
module with high-confident pseudo invariant feature of current target images.
Extensive experiments under three cross-domain tasks indicate that our proposed
MemoryAdaptNet is remarkably superior to the state-of-the-art methods.

More than twenty years after its introduction, Annealed Importance Sampling
(AIS) remains one of the most effective methods for marginal likelihood
estimation. It relies on a sequence of distributions interpolating between a
tractable initial distribution and the target distribution of interest which we
simulate from approximately using a non-homogeneous Markov chain. To obtain an
importance sampling estimate of the marginal likelihood, AIS introduces an
extended target distribution to reweight the Markov chain proposal. While much
effort has been devoted to improving the proposal distribution used by AIS, an
underappreciated issue is that AIS uses a convenient but suboptimal extended
target distribution. We here leverage recent progress in score-based generative
modeling (SGM) to approximate the optimal extended target distribution
minimizing the variance of the marginal likelihood estimate for AIS proposals
corresponding to the discretization of Langevin and Hamiltonian dynamics. We
demonstrate these novel, differentiable, AIS procedures on a number of
synthetic benchmark distributions and variational auto-encoders.

Generative Adversarial Networks (GANs) are state of the art for image
synthesis. Here, we present dapi2ck, a novel GAN-based approach to synthesize
cytokeratin (CK) staining from immunofluorescent (IF) DAPI staining of nuclei
in non-small cell lung cancer (NSCLC) images. We use the synthetic CK to
segment epithelial regions, which, compared to expert annotations, yield
equally good results as segmentation on stained CK. Considering the limited
number of markers in a multiplexed IF (mIF) panel, our approach allows to
replace CK by another marker addressing the complexity of the tumor
micro-environment (TME) to facilitate patient selection for immunotherapies. In
contrast to stained CK, dapi2ck does not suffer from issues like unspecific CK
staining or loss of tumoral CK expression.

Fake news existed ever since there was news, from rumors to printed media
then radio and television. Recently, the information age, with its
communications and Internet breakthroughs, exacerbated the spread of fake news.
Additionally, aside from e-Commerce, the current Internet economy is dependent
on advertisements, views and clicks, which prompted many developers to bait the
end users to click links or ads. Consequently, the wild spread of fake news
through social media networks has impacted real world issues from elections to
5G adoption and the handling of the Covid- 19 pandemic. Efforts to detect and
thwart fake news has been there since the advent of fake news, from fact
checkers to artificial intelligence-based detectors. Solutions are still
evolving as more sophisticated techniques are employed by fake news
propagators. In this paper, R code have been used to study and visualize a
modern fake news dataset. We use clustering, classification, correlation and
various plots to analyze and present the data. The experiments show high
efficiency of classifiers in telling apart real from fake news.

Terrain-aware locomotion has become an emerging topic in legged robotics.
However, it is hard to generate diverse, challenging, and realistic
unstructured terrains in simulation, which limits the way researchers evaluate
their locomotion policies. In this paper, we prototype the generation of a
terrain dataset via terrain authoring and active learning, and the learned
samplers can stably generate diverse high-quality terrains. We expect the
generated dataset to make a terrain-robustness benchmark for legged locomotion.
The dataset, the code implementation, and some policy evaluations are released
at https://bit.ly/3bn4j7f.

As a promising privacy-preserving machine learning method, Federated Learning
(FL) enables global model training across clients without compromising their
confidential local data. However, existing FL methods suffer from the problem
of low inference performance for unevenly distributed data, since most of them
rely on Federated Averaging (FedAvg)-based aggregation. By averaging model
parameters in a coarse manner, FedAvg eclipses the individual characteristics
of local models, which strongly limits the inference capability of FL. Worse
still, in each round of FL training, FedAvg dispatches the same initial local
models to clients, which can easily result in stuck-at-local-search for optimal
global models. To address the above issues, this paper proposes a novel and
effective FL paradigm named FedMR (Federating Model Recombination). Unlike
conventional FedAvg-based methods, the cloud server of FedMR shuffles each
layer of collected local models and recombines them to achieve new models for
local training on clients. Due to the fine-grained model recombination and
local training in each FL round, FedMR can quickly figure out one globally
optimal model for all the clients. Comprehensive experimental results
demonstrate that, compared with state-of-the-art FL methods, FedMR can
significantly improve the inference accuracy without causing extra
communication overhead.

Outlier detection is a challenging activity. Several machine learning
techniques are proposed in the literature for outlier detection. In this
article, we propose a new training approach for bidirectional GAN (BiGAN) to
detect outliers. To validate the proposed approach, we train a BiGAN with the
proposed training approach to detect taxpayers, who are manipulating their tax
returns. For each taxpayer, we derive six correlation parameters and three
ratio parameters from tax returns submitted by him/her. We train a BiGAN with
the proposed training approach on this nine-dimensional derived ground-truth
data set. Next, we generate the latent representation of this data set using
the $encoder$ (encode this data set using the $encoder$) and regenerate this
data set using the $generator$ (decode back using the $generator$) by giving
this latent representation as the input. For each taxpayer, compute the cosine
similarity between his/her ground-truth data and regenerated data. Taxpayers
with lower cosine similarity measures are potential return manipulators. We
applied our method to analyze the iron and steel taxpayers data set provided by
the Commercial Taxes Department, Government of Telangana, India.

Extracting query-document relevance from the sparse, biased clickthrough log
is among the most fundamental tasks in the web search system. Prior art mainly
learns a relevance judgment model with semantic features of the query and
document and ignores directly counterfactual relevance evaluation from the
clicking log. Though the learned semantic matching models can provide relevance
signals for tail queries as long as the semantic feature is available. However,
such a paradigm lacks the capability to introspectively adjust the biased
relevance estimation whenever it conflicts with massive implicit user feedback.
The counterfactual evaluation methods, on the contrary, ensure unbiased
relevance estimation with sufficient click information. However, they suffer
from the sparse or even missing clicks caused by the long-tailed query
  In this paper, we propose to unify the counterfactual evaluating and learning
approaches for unbiased relevance estimation on search queries with various
popularities. Specifically, we theoretically develop a doubly robust estimator
with low bias and variance, which intentionally combines the benefits of
existing relevance evaluating and learning approaches. We further instantiate
the proposed unbiased relevance estimation framework in Baidu search, with
comprehensive practical solutions designed regarding the data pipeline for
click behavior tracking and online relevance estimation with an approximated
deep neural network. Finally, we present extensive empirical evaluations to
verify the effectiveness of our proposed framework, finding that it is robust
in practice and manages to improve online ranking performance substantially.

Dense passage retrieval aims to retrieve the relevant passages of a query
from a large corpus based on dense representations (i.e., vectors) of the query
and the passages. Recent studies have explored improving pre-trained language
models to boost dense retrieval performance. This paper proposes CoT-MAE
(ConTextual Masked Auto-Encoder), a simple yet effective generative
pre-training method for dense passage retrieval. CoT-MAE employs an asymmetric
encoder-decoder architecture that learns to compress the sentence semantics
into a dense vector through self-supervised and context-supervised masked
auto-encoding. Precisely, self-supervised masked auto-encoding learns to model
the semantics of the tokens inside a text span, and context-supervised masked
auto-encoding learns to model the semantical correlation between the text
spans. We conduct experiments on large-scale passage retrieval benchmarks and
show considerable improvements over strong baselines, demonstrating the high
efficiency of CoT-MAE.

With the increasing application of deep learning in mission-critical systems,
there is a growing need to obtain formal guarantees about the behaviors of
neural networks. Indeed, many approaches for verifying neural networks have
been recently proposed, but these generally struggle with limited scalability
or insufficient accuracy. A key component in many state-of-the-art verification
schemes is computing lower and upper bounds on the values that neurons in the
network can obtain for a specific input domain -- and the tighter these bounds,
the more likely the verification is to succeed. Many common algorithms for
computing these bounds are variations of the symbolic-bound propagation method;
and among these, approaches that utilize a process called back-substitution are
particularly successful. In this paper, we present an approach for making
back-substitution produce tighter bounds. To achieve this, we formulate and
then minimize the imprecision errors incurred during back-substitution. Our
technique is general, in the sense that it can be integrated into numerous
existing symbolic-bound propagation techniques, with only minor modifications.
We implement our approach as a proof-of-concept tool, and present favorable
results compared to state-of-the-art verifiers that perform back-substitution.

Circular trading is a form of tax evasion in Goods and Services Tax where a
group of fraudulent taxpayers (traders) aims to mask illegal transactions by
superimposing several fictitious transactions (where no value is added to the
goods or service) among themselves in a short period. Due to the vast database
of taxpayers, it is infeasible for authorities to manually identify groups of
circular traders and the illegitimate transactions they are involved in. This
work uses big data analytics and graph representation learning techniques to
propose a framework to identify communities of circular traders and isolate the
illegitimate transactions in the respective communities. Our approach is tested
on real-life data provided by the Department of Commercial Taxes, Government of
Telangana, India, where we uncovered several communities of circular traders.

Edge Federation is a new computing paradigm that seamlessly interconnects the
resources of multiple edge service providers. A key challenge in such systems
is the deployment of latency-critical and AI based resource-intensive
applications in constrained devices. To address this challenge, we propose a
novel memory-efficient deep learning based model, namely generative
optimization networks (GON). Unlike GANs, GONs use a single network to both
discriminate input and generate samples, significantly reducing their memory
footprint. Leveraging the low memory footprint of GONs, we propose a
decentralized fault-tolerance method called DRAGON that runs simulations (as
per a digital modeling twin) to quickly predict and optimize the performance of
the edge federation. Extensive experiments with real-world edge computing
benchmarks on multiple Raspberry-Pi based federated edge configurations show
that DRAGON can outperform the baseline methods in fault-detection and Quality
of Service (QoS) metrics. Specifically, the proposed method gives higher F1
scores for fault-detection than the best deep learning (DL) method, while
consuming lower memory than the heuristic methods. This allows for improvement
in energy consumption, response time and service level agreement violations by
up to 74, 63 and 82 percent, respectively.

Optimization of modern ASR architectures is among the highest priority tasks
since it saves many computational resources for model training and inference.
The work proposes a new Uconv-Conformer architecture based on the standard
Conformer model. It consistently reduces the input sequence length by 16 times,
which results in speeding up the work of the intermediate layers. To solve the
convergence issue connected with such a significant reduction of the time
dimension, we use upsampling blocks like in the U-Net architecture to ensure
the correct CTC loss calculation and stabilize network training. The
Uconv-Conformer architecture appears to be not only faster in terms of training
and inference speed but also shows better WER compared to the baseline
Conformer. Our best Uconv-Conformer model shows 47.8% and 23.5% inference
acceleration on the CPU and GPU, respectively. Relative WER reduction is 7.3%
and 9.2% on LibriSpeech test_clean and test_other respectively.

Pathologists need to combine information from differently stained
pathological slices to obtain accurate diagnostic results. Deformable image
registration is a necessary technique for fusing multi-modal pathological
slices. This paper proposes a hybrid deep feature-based deformable image
registration framework for stained pathological samples. We first extract dense
feature points and perform points matching by two deep learning feature
networks. Then, to further reduce false matches, an outlier detection method
combining the isolation forest statistical model and the local affine
correction model is proposed. Finally, the interpolation method generates the
DVF for pathology image registration based on the above matching points. We
evaluate our method on the dataset of the Non-rigid Histology Image
Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019
conference. Our technique outperforms the traditional approaches by 17% with
the Average-Average registration target error (rTRE) reaching 0.0034. The
proposed method achieved state-of-the-art performance and ranking it 1 in
evaluating the test dataset. The proposed hybrid deep feature-based
registration method can potentially become a reliable method for pathology
image registration.

Representation learning approaches typically rely on images of objects
captured from a single perspective that are transformed using affine
transformations. Additionally, self-supervised learning, a successful paradigm
of representation learning, relies on instance discrimination and
self-augmentations which cannot always bridge the gap between observations of
the same object viewed from a different perspective. Viewing an object from
multiple perspectives aids holistic understanding of an object which is
particularly important in situations where data annotations are limited. In
this paper, we present an approach that combines self-supervised learning with
a multi-perspective matching technique and demonstrate its effectiveness on
learning higher quality representations on data captured by a robotic vacuum
with an embedded camera. We show that the availability of multiple views of the
same object combined with a variety of self-supervised pretraining algorithms
can lead to improved object classification performance without extra labels.

Knowledge-intensive language tasks (KILT) usually require a large body of
information to provide correct answers. A popular paradigm to solve this
problem is to combine a search system with a machine reader, where the former
retrieves supporting evidences and the latter examines them to produce answers.
Recently, the reader component has witnessed significant advances with the help
of large-scale pre-trained generative models. Meanwhile most existing solutions
in the search component rely on the traditional ``index-retrieve-then-rank''
pipeline, which suffers from large memory footprint and difficulty in
end-to-end optimization. Inspired by recent efforts in constructing model-based
IR models, we propose to replace the traditional multi-step search pipeline
with a novel single-step generative model, which can dramatically simplify the
search process and be optimized in an end-to-end manner. We show that a strong
generative retrieval model can be learned with a set of adequately designed
pre-training tasks, and be adopted to improve a variety of downstream KILT
tasks with further fine-tuning. We name the pre-trained generative retrieval
model as CorpusBrain as all information about the corpus is encoded in its
parameters without the need of constructing additional index. Empirical results
show that CorpusBrain can significantly outperform strong baselines for the
retrieval task on the KILT benchmark and establish new state-of-the-art
downstream performances. We also show that CorpusBrain works well under zero-

The present study is aimed at analysing the benefits of an ontological
approach in Functional Structural Plant Modelling. The ontological approach has
been used at two levels, to refine the conceptual modelling approach, and to
define the nomenclature of the plant. To the scope available domain-specific
ontologies describing plant entities and their relations have been analysed to
verify how they support botanical and phenological descriptions at different
scales. The analysis put in evidence how ontologies have a large number of
shared terms and also host a large number of structural and dynamical relations
among entities, however, they still lack semantic annotation useful for a
complete and consistent Conceptual Modelling, as put in evidence by Foundation
Ontologies. Nonetheless, the analysis also put in evidence the potential of the
approach and the basis for designing a bridged ontology to be used to produce
tools for learning botany, growing techniques, and supporting precision
Agriculture.

Early and precise diagnosis of diseases in plants can help to develop an
early treatment technique. Plant diseases degrade both the quantity and quality
of crops, thus posing a threat to food security and resulting in huge economic
losses. Traditionally identification is performed manually, which is
inaccurate, time-consuming, and expensive. This paper presents a simple and
efficient model to detect grapes leaf diseases using transfer learning. A
pre-trained deep convolutional neural network is used as a feature extractor
and random forest as a classifier. The performance of the model is interpreted
in terms of accuracy, precision, recall, and f1 score. Total 1003 images of
four different classes are used and 91.66% accuracy is obtained.

The convergence of 5G architecture and deep learning has gained a lot of
research interests in both the fields of wireless communication and artificial
intelligence. This is because deep learning technologies have been identified
to be the potential driver of the 5G technologies, that make up the 5G
architecture. Hence, there have been extensive surveys on the convergence of 5G
architecture and deep learning. However, most of the existing survey papers
mainly focused on how deep learning can converge with a specific 5G technology,
thus, not covering the full spectrum of the 5G architecture. Although there is
a recent survey paper that appears to be robust, a review of that paper shows
that it is not well structured to specifically cover the convergence of deep
learning and the 5G technologies. Hence, this paper provides a robust overview
of the convergence of the key 5G technologies and deep learning. The challenges
faced by such convergence are discussed. In addition, a brief overview of the
future 6G architecture, and how it can converge with deep learning is also

Feedback is critical in education. This Innovative Practice Full Paper
reports lessons learned from improving the quality of feedback in a
semi-capstone software engineering course, with particular focus on how to
deliver productive feedback in large scale during project work. The
bachelor-level introduction to software engineering course is taken by about
500 students from eight study programs, organised into 72 project teams. The
course aims to educate reflective systems developers. The teaching staff
includes 29 teaching assistants as supervisor and product owners for teams.
Project teams get feedback on seven deliverables as part of formative portfolio
assessment. Students expressed frustration on feedback not being aligned, that
they got critique on topics not stated in assignments and that teaching
assistants were reluctant to discuss the feedback. This article provides a
description of the course design, an assessment of the quality of feedback and
lessons learned from three main changes: Revising assignments and rubrics,
reorganising the teaching staff and increasing training of teaching assistants.
In discussing the changes, we draw on a survey to students with 142
respondents, a survey to teaching assistants with 18 respondents, meeting
minutes from a student reference group and experience reports from teaching
assistants as well as literature and own experience. The article concludes with
three actionable lessons learned for large-scale semi-capstone courses.

Image compression is an essential and last processing unit in the camera
image signal processing (ISP) pipeline. While many studies have been made to
replace the conventional ISP pipeline with a single end-to-end optimized deep
learning model, image compression is barely considered as a part of the model.
In this paper, we investigate the designing of a fully end-to-end optimized
camera ISP incorporating image compression. To this end, we propose RAWtoBit
network (RBN) that can effectively perform both tasks simultaneously. RBN is
further improved with a novel knowledge distillation scheme by introducing two
teacher networks specialized in each task. Extensive experiments demonstrate
that our proposed method significantly outperforms alternative approaches in
terms of rate-distortion trade-off.

Knowledge graph (KG) embeddings have been a mainstream approach for reasoning
over incomplete KGs. However, limited by their inherently shallow and static
architectures, they can hardly deal with the rising focus on complex logical
queries, which comprise logical operators, imputed edges, multiple source
entities, and unknown intermediate entities. In this work, we present the
Knowledge Graph Transformer (kgTransformer) with masked pre-training and
fine-tuning strategies. We design a KG triple transformation method to enable
Transformer to handle KGs, which is further strengthened by the
Mixture-of-Experts (MoE) sparse activation. We then formulate the complex
logical queries as masked prediction and introduce a two-stage masked
pre-training strategy to improve transferability and generalizability.
Extensive experiments on two benchmarks demonstrate that kgTransformer can
consistently outperform both KG embedding-based baselines and advanced encoders
on nine in-domain and out-of-domain reasoning tasks. Additionally,
kgTransformer can reason with explainability via providing the full reasoning
paths to interpret given answers.

In this paper, we revisit the online non-monotone continuous DR-submodular
maximization problem over a down-closed convex set, which finds wide real-world
applications in the domain of machine learning, economics, and operations
research. At first, we present the Meta-MFW algorithm achieving a $1/e$-regret
of $O(\sqrt{T})$ at the cost of $T^{3/2}$ stochastic gradient evaluations per
round. As far as we know, Meta-MFW is the first algorithm to obtain
$1/e$-regret of $O(\sqrt{T})$ for the online non-monotone continuous
DR-submodular maximization problem over a down-closed convex set. Furthermore,
in sharp contrast with ODC algorithm \citep{thang2021online}, Meta-MFW relies
on the simple online linear oracle without discretization, lifting, or rounding
operations. Considering the practical restrictions, we then propose the
Mono-MFW algorithm, which reduces the per-function stochastic gradient
evaluations from $T^{3/2}$ to 1 and achieves a $1/e$-regret bound of
$O(T^{4/5})$. Next, we extend Mono-MFW to the bandit setting and propose the
Bandit-MFW algorithm which attains a $1/e$-regret bound of $O(T^{8/9})$. To the
best of our knowledge, Mono-MFW and Bandit-MFW are the first sublinear-regret
algorithms to explore the one-shot and bandit setting for online non-monotone
continuous DR-submodular maximization problem over a down-closed convex set,
respectively. Finally, we conduct numerical experiments on both synthetic and
real-world datasets to verify the effectiveness of our methods.

Many ontologies, i.e., Description Logic (DL) knowledge bases, have been
developed to provide rich knowledge about various domains, and a lot of them
are based on ALC, i.e., a prototypical and expressive DL, or its extensions.
The main task that explores ALC ontologies is to compute semantic entailment.
Symbolic approaches can guarantee sound and complete semantic entailment but
are sensitive to inconsistency and missing information. To this end, we propose
FALCON, a Fuzzy ALC Ontology Neural reasoner. FALCON uses fuzzy logic operators
to generate single model structures for arbitrary ALC ontologies, and uses
multiple model structures to compute semantic entailments. Theoretical results
demonstrate that FALCON is guaranteed to be a sound and complete algorithm for
computing semantic entailments over ALC ontologies. Experimental results show
that FALCON enables not only approximate reasoning (reasoning over incomplete
ontologies) and paraconsistent reasoning (reasoning over inconsistent
ontologies), but also improves machine learning in the biomedical domain by
incorporating background knowledge from ALC ontologies. FALCON is available at
https://github.com/bio-ontology-research-group/FALCON.

When we use algorithms to produce recommendations, we typically think of
these recommendations as providing helpful information, such as when risk
assessments are presented to judges or doctors. But when a decision-maker
obtains a recommendation, they may not only react to the information. The
decision-maker may view the recommendation as a default action, making it
costly for them to deviate, for example when a judge is reluctant to overrule a
high-risk assessment of a defendant or a doctor fears the consequences of
deviating from recommended procedures. In this article, we consider the effect
and design of recommendations when they affect choices not just by shifting
beliefs, but also by altering preferences. We motivate our model from
institutional factors, such as a desire to avoid audits, as well as from
well-established models in behavioral science that predict loss aversion
relative to a reference point, which here is set by the algorithm. We show that
recommendation-dependent preferences create inefficiencies where the
decision-maker is overly responsive to the recommendation, which changes the
optimal design of the algorithm towards providing less conservative
recommendations. As a potential remedy, we discuss an algorithm that
strategically withholds recommendations, and show how it can improve the
quality of final decisions.

Knowledge Graph Embeddings (KGE) aim to map entities and relations to low
dimensional spaces and have become the \textit{de-facto} standard for knowledge
graph completion. Most existing KGE methods suffer from the sparsity challenge,
where it is harder to predict entities that appear less frequently in knowledge
graphs. In this work, we propose a novel framework KRACL to alleviate the
widespread sparsity in KGs with graph context and contrastive learning.
Firstly, we propose the Knowledge Relational Attention Network (KRAT) to
leverage the graph context by simultaneously projecting neighboring triples to
different latent spaces and jointly aggregating messages with the attention
mechanism. KRAT is capable of capturing the subtle semantic information and
importance of different context triples as well as leveraging multi-hop
information in knowledge graphs. Secondly, we propose the knowledge contrastive
loss by combining the contrastive loss with cross entropy loss, which
introduces more negative samples and thus enriches the feedback to sparse
entities. Our experiments demonstrate that KRACL achieves superior results
across various standard knowledge graph benchmarks, especially on WN18RR and
NELL-995 which have large numbers of low in-degree entities. Extensive
experiments also bear out KRACL's effectiveness in handling sparse knowledge
graphs and robustness against noisy triples.

Yes - This study investigates the impact of commonplace lossy image
compression on face recognition algorithms with regard to the racial
characteristics of the subject. We adopt a recently proposed racial
phenotype-based bias analysis methodology to measure the effect of varying
levels of lossy compression across racial phenotype categories. Additionally,
we determine the relationship between chroma-subsampling and race-related
phenotypes for recognition performance. Prior work investigates the impact of
lossy JPEG compression algorithm on contemporary face recognition performance.
However, there is a gap in how this impact varies with different race-related
inter-sectional groups and the cause of this impact. Via an extensive
experimental setup, we demonstrate that common lossy image compression
approaches have a more pronounced negative impact on facial recognition
performance for specific racial phenotype categories such as darker skin tones
(by up to 34.55\%). Furthermore, removing chroma-subsampling during compression
improves the false matching rate (up to 15.95\%) across all phenotype
categories affected by the compression, including darker skin tones, wide
noses, big lips, and monolid eye categories. In addition, we outline the
characteristics that may be attributable as the underlying cause of such
phenomenon for lossy compression algorithms such as JPEG.

Study of properties of chemicals, drugs, biomaterials and alloys requires
decades of dedicated work. Often times the outcome, however, is not what is
expected for practical applications. This research procedure can be inverted by
the new artificial intelligence and optimization methods. Instead of studying
the properties of a material and its structurally close derivatives, the
chemical and structural parameter space that contains all possible derivatives
of that material can be scanned in a fast and smart way at the HPC centers. As
a result of this, the particular material that has the specific physical or
chemical properties can be found. Here we show how Bayesian optimization,
Gaussian regression and artificial neural networks can be used towards this
goal. We present an example smart search on the doped graphene quantum dot
parameter space.

In this paper, we consider Barron functions $f : [0,1]^d \to \mathbb{R}$ of
smoothness $\sigma > 0$, which are functions that can be written as \[
  f(x) = \int_{\mathbb{R}^d} F(\xi) \, e^{2 \pi i \langle x, \xi \rangle} \, d
\xi
  \quad \text{with} \quad
  \int_{\mathbb{R}^d} |F(\xi)| \cdot (1 + |\xi|)^{\sigma} \, d \xi < \infty. \]
For $\sigma = 1$, these functions play a prominent role in machine learning,
since they can be efficiently approximated by (shallow) neural networks without
suffering from the curse of dimensionality.
  For these functions, we study the following question: Given $m$ point samples
$f(x_1),\dots,f(x_m)$ of an unknown Barron function $f : [0,1]^d \to
\mathbb{R}$ of smoothness $\sigma$, how well can $f$ be recovered from these
samples, for an optimal choice of the sampling points and the reconstruction
procedure? Denoting the optimal reconstruction error measured in $L^p$ by $s_m
(\sigma; L^p)$, we show that \[
  m^{- \frac{1}{\max \{ p,2 \}} - \frac{\sigma}{d}}
  \lesssim s_m(\sigma;L^p)
  \lesssim (\ln (e + m))^{\alpha(\sigma,d) / p}
  \cdot m^{- \frac{1}{\max \{ p,2 \}} - \frac{\sigma}{d}}
  , \] where the implied constants only depend on $\sigma$ and $d$ and where
$\alpha(\sigma,d)$ stays bounded as $d \to \infty$.

The fifth-generation (5G) wireless communication is useful for positioning
due to its large bandwidth and low cost. However, the presence of obstacles
that block the line-of-sight (LOS) path between devices would affect
localization accuracy severely. In this paper, we propose an online learning
approach to mitigate ranging error directly in non-line-of-sight (NLOS)
channels. The distribution of NLOS ranging error is learned from received raw
signals, where a network with neural processes regressor (NPR) is utilized to
learn the environment and range-related information precisely. The network can
be implemented for online learning free from retraining the network, which is
computationally efficient. Simulation results show that the proposed approach
outperforms conventional techniques in terms of NLOS ranging error mitigation.

How to build and use dialogue data efficiently, and how to deploy models in
different domains at scale can be two critical issues in building a
task-oriented dialogue system. In this paper, we propose a novel manual-guided
dialogue scheme to alleviate these problems, where the agent learns the tasks
from both dialogue and manuals. The manual is an unstructured textual document
that guides the agent in interacting with users and the database during the
conversation. Our proposed scheme reduces the dependence of dialogue models on
fine-grained domain ontology, and makes them more flexible to adapt to various
domains. We then contribute a fully-annotated multi-domain dataset MagDial to
support our scheme. It introduces three dialogue modeling subtasks: instruction
matching, argument filling, and response generation. Modeling these subtasks is
consistent with the human agent's behavior patterns. Experiments demonstrate
that the manual-guided dialogue scheme improves data efficiency and domain
scalability in building dialogue systems. The dataset and benchmark will be
publicly available for promoting future research.

Integrated sensing and communication (ISAC) represents a paradigm shift,
where previously competing wireless transmissions are jointly designed to
operate in harmony via the shared use of the hardware platform for improving
the spectral and energy efficiencies. However, due to adversarial factors such
as fading and interference, ISAC may suffer from high sensing uncertainties.
This paper presents a multi-point ISAC (MPISAC) system that fuses the outputs
from multiple ISAC devices for achieving higher sensing performance by
exploiting multi-view data redundancy. Furthermore, we propose to effectively
explore the performance trade-off between sensing and communication via a
functionality selection module that adaptively determines the working state
(i.e., sensing or communication) of an ISAC device. The crux of our approach is
to derive a fusion model that predicts the fusion accuracy via hypothesis
testing and optimal voting analysis. Simulation results demonstrate the
superiority of MPISAC over various benchmark schemes and show that the proposed
approach can effectively span the trade-off region in ISAC systems.

Source-free domain adaptation (SFDA) aims to adapt a classifier to an
unlabelled target data set by only using a pre-trained source model. However,
the absence of the source data and the domain shift makes the predictions on
the target data unreliable. We propose quantifying the uncertainty in the
source model predictions and utilizing it to guide the target adaptation. For
this, we construct a probabilistic source model by incorporating priors on the
network parameters inducing a distribution over the model predictions.
Uncertainties are estimated by employing a Laplace approximation and
incorporated to identify target data points that do not lie in the source
manifold and to down-weight them when maximizing the mutual information on the
target data. Unlike recent works, our probabilistic treatment is
computationally lightweight, decouples source training and target adaptation,
and requires no specialized source training or changes of the model
architecture. We show the advantages of uncertainty-guided SFDA over
traditional SFDA in the closed-set and open-set settings and provide empirical
evidence that our approach is more robust to strong domain shifts even without
tuning.

Risk assessment for extreme events requires accurate estimation of high
quantiles that go beyond the range of historical observations. When the risk
depends on the values of observed predictors, regression techniques are used to
interpolate in the predictor space. We propose the EQRN model that combines
tools from neural networks and extreme value theory into a method capable of
extrapolation in the presence of complex predictor dependence. Neural networks
can naturally incorporate additional structure in the data. We develop a
recurrent version of EQRN that is able to capture complex sequential dependence
in time series. We apply this method to forecasting of flood risk in the Swiss
Aare catchment. It exploits information from multiple covariates in space and
time to provide one-day-ahead predictions of return levels and exceedances
probabilities. This output complements the static return level from a
traditional extreme value analysis and the predictions are able to adapt to
distributional shifts as experienced in a changing climate. Our model can help
authorities to manage flooding more effectively and to minimize their
disastrous impacts through early warning systems.

With the proliferation of user-generated online videos, Multimodal Sentiment
Analysis (MSA) has attracted increasing attention recently. Despite significant
progress, there are still two major challenges on the way towards robust MSA:
1) inefficiency when modeling cross-modal interactions in unaligned multimodal
data; and 2) vulnerability to random modality feature missing which typically
occurs in realistic settings. In this paper, we propose a generic and unified
framework to address them, named Efficient Multimodal Transformer with
Dual-Level Feature Restoration (EMT-DLFR). Concretely, EMT employs
utterance-level representations from each modality as the global multimodal
context to interact with local unimodal features and mutually promote each
other. It not only avoids the quadratic scaling cost of previous local-local
cross-modal interaction methods but also leads to better performance. To
improve model robustness in the incomplete modality setting, on the one hand,
DLFR performs low-level feature reconstruction to implicitly encourage the
model to learn semantic information from incomplete data. On the other hand, it
innovatively regards complete and incomplete data as two different views of one
sample and utilizes siamese representation learning to explicitly attract their
high-level representations. Comprehensive experiments on three popular datasets
demonstrate that our method achieves superior performance in both complete and
incomplete modality settings.

The SWIMMER environment is a standard benchmark in reinforcement learning
(RL). In particular, it is often used in papers comparing or combining RL
methods with direct policy search methods such as genetic algorithms or
evolution strategies. A lot of these papers report poor performance on SWIMMER
from RL methods and much better performance from direct policy search methods.
In this technical report we show that the low performance of RL methods on
SWIMMER simply comes from the inadequate tuning of an important
hyper-parameter, the discount factor. Furthermore we show that, by setting this
hyper-parameter to a correct value, the issue can be easily fixed. Finally, for
a set of often used RL algorithms, we provide a set of successful
hyper-parameters obtained with the Stable Baselines3 library and its RL Zoo.

Maximizing a submodular function is a fundamental task in machine learning
and in this paper we study the deletion robust version of the problem under the
classic matroids constraint. Here the goal is to extract a small size summary
of the dataset that contains a high value independent set even after an
adversary deleted some elements. We present constant-factor approximation
algorithms, whose space complexity depends on the rank $k$ of the matroid and
the number $d$ of deleted elements. In the centralized setting we present a
$(4.597+O(\varepsilon))$-approximation algorithm with summary size $O(
\frac{k+d}{\varepsilon^2}\log \frac{k}{\varepsilon})$ that is improved to a
$(3.582+O(\varepsilon))$-approximation with $O(k + \frac{d}{\varepsilon^2}\log
\frac{k}{\varepsilon})$ summary size when the objective is monotone. In the
streaming setting we provide a $(9.435 + O(\varepsilon))$-approximation
algorithm with summary size and memory $O(k + \frac{d}{\varepsilon^2}\log
\frac{k}{\varepsilon})$; the approximation factor is then improved to
$(5.582+O(\varepsilon))$ in the monotone case.

In second-order uncertain Bayesian networks, the conditional probabilities
are only known within distributions, i.e., probabilities over probabilities.
The delta-method has been applied to extend exact first-order inference methods
to propagate both means and variances through sum-product networks derived from
Bayesian networks, thereby characterizing epistemic uncertainty, or the
uncertainty in the model itself. Alternatively, second-order belief propagation
has been demonstrated for polytrees but not for general directed acyclic graph
structures. In this work, we extend Loopy Belief Propagation to the setting of
second-order Bayesian networks, giving rise to Second-Order Loopy Belief
Propagation (SOLBP). For second-order Bayesian networks, SOLBP generates
inferences consistent with those generated by sum-product networks, while being
more computationally efficient and scalable.

Risk management in many environmental settings requires an understanding of
the mechanisms that drive extreme events. Useful metrics for quantifying such
risk are extreme quantiles of response variables conditioned on predictor
variables that describe e.g., climate, biosphere and environmental states.
Typically these quantiles lie outside the range of observable data and so, for
estimation, require specification of parametric extreme value models within a
regression framework. Classical approaches in this context utilise linear or
additive relationships between predictor and response variables and suffer in
either their predictive capabilities or computational efficiency; moreover,
their simplicity is unlikely to capture the truly complex structures that lead
to the creation of extreme wildfires. In this paper, we propose a new
methodological framework for performing extreme quantile regression using
artificial neutral networks, which are able to capture complex non-linear
relationships and scale well to high-dimensional data. The "black box" nature
of neural networks means that they lack the desirable trait of interpretability
often favoured by practitioners; thus, we combine aspects of linear, and
additive, models with deep learning to create partially interpretable neural
networks that can be used for statistical inference but retain high prediction
accuracy. To complement this methodology, we further propose a novel point
process model for extreme values which overcomes the finite lower-endpoint
problem associated with the generalised extreme value class of distributions.
Efficacy of our unified framework is illustrated on U.S. wildfire data with a
high-dimensional predictor set and we illustrate vast improvements in
predictive performance over linear and spline-based regression techniques.

Weakly Supervised Object Detection (WSOD) is a task that detects objects in
an image using a model trained only on image-level annotations. Current
state-of-the-art models benefit from self-supervised instance-level
supervision, but since weak supervision does not include count or location
information, the most common ``argmax'' labeling method often ignores many
instances of objects. To alleviate this issue, we propose a novel multiple
instance labeling method called object discovery. We further introduce a new
contrastive loss under weak supervision where no instance-level information is
available for sampling, called weakly supervised contrastive loss (WSCL). WSCL
aims to construct a credible similarity threshold for object discovery by
leveraging consistent features for embedding vectors in the same class. As a
result, we achieve new state-of-the-art results on MS-COCO 2014 and 2017 as
well as PASCAL VOC 2012, and competitive results on PASCAL VOC 2007.

Context: Test smells are symptoms of sub-optimal design choices adopted when
developing test cases. Previous studies have proved their harmfulness for test
code maintainability and effectiveness. Therefore, researchers have been
proposing automated, heuristic-based techniques to detect them. However, the
performance of such detectors is still limited and dependent on thresholds to
be tuned.
  Objective: We propose the design and experimentation of a novel test smell
detection approach based on machine learning to detect four test smells.
  Method: We plan to develop the largest dataset of manually-validated test
smells. This dataset will be leveraged to train six machine learners and assess
their capabilities in within- and cross-project scenarios. Finally, we plan to
compare our approach with state-of-the-art heuristic-based techniques.

Two-sample hypothesis testing for comparing two networks is an important yet
difficult problem. Major challenges include: potentially different sizes and
sparsity levels; non-repeated observations of adjacency matrices; computational
scalability; and theoretical investigations, especially on finite-sample
accuracy and minimax optimality. In this article, we propose the first provably
higher-order accurate two-sample inference method by comparing network moments.
Our method extends the classical two-sample t-test to the network setting. We
make weak modeling assumptions and can effectively handle networks of different
sizes and sparsity levels. We establish strong finite-sample theoretical
guarantees, including rate-optimality properties. Our method is easy to
implement and computes fast. We also devise a novel nonparametric framework of
offline hashing and fast querying particularly effective for maintaining and
querying very large network databases. We demonstrate the effectiveness of our
method by comprehensive simulations. We apply our method to two real-world data
sets and discover interesting novel structures.

The fetal cortical plate (CP) undergoes drastic morphological changes during
the in utero development. Therefore, CP growth and folding patterns are key
indicator in the assessment of the brain development and maturation. Magnetic
resonance imaging (MRI) offers specific insights for the analysis of
quantitative imaging biomarkers. Nonetheless, accurate and, more importantly,
topologically correct MR image segmentation remains the key baseline to such
analysis. In this study, we propose a deep learning segmentation framework for
automatic and morphologically consistent segmentation of the CP in fetal brain
MRI. Our contribution is two fold. First, we generalized a multi-dimensional
topological loss function in order to enhance the topological accuracy. Second,
we introduced hole ratio, a new topology-based validation measure that
quantifies the size of the topological defects taking into account the size of
the structure of interest. Using two publicly available datasets, we
quantitatively evaluated our proposed method based on three complementary
metrics which are overlap-, distance- and topology-based on 27 fetal brains.
Our results evidence that our topology-integrative framework outperforms
state-of-the-art training loss functions on super-resolution reconstructed
clinical MRI, not only in shape correctness but also in the classical
evaluation metrics. Furthermore, results on additional 31 out-of-domain SR
reconstructions from clinical acquisitions were qualitatively assessed by three
experts. The experts' consensus ranked our TopoCP method as the best
segmentation in 100\% of the cases with a high inter-expert agreement. Overall,
both quantitative and qualitative results, on a wide range of gestational ages
and number of cases, support the generalizability and added value of our
topology-guided framework for fetal CP segmentation.

Ranking lies at the core of many Information Retrieval (IR) tasks. While
existing research on Learning to Rank (LTR) using Deep Neural Network (DNN) has
achieved great success, it is somewhat limited because of its dependence on
fine-grained labels. In practice, fine-grained labels are often expensive to
acquire, i.e. explicit relevance judgements, or suffer from biases, i.e. click
logs. Compared to fine-grained labels, coarse-grained labels are easier and
cheaper to collect. Some recent works propose utilizing only coarse-grained
labels for LTR tasks. A most representative line of work introduces
Reinforcement Learning (RL) algorithms. RL can help train the LTR model with
little reliance on fine-grained labels compared to Supervised Learning. To
study the effectiveness of the RL-based LTR algorithm on coarse-grained labels,
in this paper, we implement four different RL paradigms and conduct extensive
experiments on two well-established LTR datasets. The results on simulated
coarse-grained labeled dataset show that while using coarse-grained labels to
train an RL model for LTR tasks still can not outperform traditional approaches
using fine-grained labels, it still achieve somewhat promising results and is
potentially helpful for future research in LTR. Our code implementations will
be released after this work is accepted.

We propose a data-driven method for controlling the frequency and convergence
rate of black-box nonlinear dynamical systems based on the Koopman operator
theory. With the proposed method, a policy network is trained such that the
eigenvalues of a Koopman operator of controlled dynamics are close to the
target eigenvalues. The policy network consists of a neural network to find a
Koopman invariant subspace, and a pole placement module to adjust the
eigenvalues of the Koopman operator. Since the policy network is
differentiable, we can train it in an end-to-end fashion using reinforcement
learning. We demonstrate that the proposed method achieves better performance
than model-free reinforcement learning and model-based control with system
identification.

Denoising of magnetic resonance images is beneficial in improving the quality
of low signal-to-noise ratio images. Recently, denoising using deep neural
networks has demonstrated promising results. Most of these networks, however,
utilize supervised learning, which requires large training images of
noise-corrupted and clean image pairs. Obtaining training images, particularly
clean images, is expensive and time-consuming. Hence, methods such as
Noise2Noise (N2N) that require only pairs of noise-corrupted images have been
developed to reduce the burden of obtaining training datasets. In this study,
we propose a new self-supervised denoising method, Coil2Coil (C2C), that does
not require the acquisition of clean images or paired noise-corrupted images
for training. Instead, the method utilizes multichannel data from phased-array
coils to generate training images. First, it divides and combines multichannel
coil images into two images, one for input and the other for label. Then, they
are processed to impose noise independence and sensitivity normalization such
that they can be used for the training images of N2N. For inference, the method
inputs a coil-combined image (e.g., DICOM image), enabling a wide application
of the method. When evaluated using synthetic noise-added images, C2C shows the
best performance against several self-supervised methods, reporting comparable
outcomes to supervised methods. When testing the DICOM images, C2C successfully
denoised real noise without showing structure-dependent residuals in the error
maps. Because of the significant advantage of not requiring additional scans
for clean or paired images, the method can be easily utilized for various
clinical applications.

Unmanned aerial vehicles (UAVs) have been actively studied as moving
cloudlets to provide application offloading opportunities and to enhance the
security level of user equipments (UEs). In this correspondence, we propose a
hybrid UAV-aided secure offloading system in which a UAV serves as a helper by
switching the mode between jamming and relaying to maximize the secrecy
sum-rate of UEs. This work aims to optimize (i) the trajectory of the helper
UAV, (ii) the mode selection strategy and (iii) the UEs' offloading decisions
under the constraints of offloading accomplishment and the UAV's operational
limitations. The solution is provided via a deep deterministic policy gradient
(DDPG)-based method, whose superior performance is verified via a numerical
simulation and compared to those of traditional approaches.

Human activity recognition (HAR) with wearables is promising research that
can be widely adopted in many smart healthcare applications. In recent years,
the deep learning-based HAR models have achieved impressive recognition
performance. However, most HAR algorithms are susceptible to the multi-class
windows problem that is essential yet rarely exploited. In this paper, we
propose to relieve this challenging problem by introducing the segmentation
technology into HAR, yielding joint activity segmentation and recognition.
Especially, we introduce the Multi-Stage Temporal Convolutional Network
(MS-TCN) architecture for sample-level activity prediction to joint segment and
recognize the activity sequence. Furthermore, to enhance the robustness of HAR
against the inter-class similarity and intra-class heterogeneity, a multi-level
contrastive loss, containing the sample-level and segment-level contrast, has
been proposed to learn a well-structured embedding space for better activity
segmentation and recognition performance. Finally, with comprehensive
experiments, we verify the effectiveness of the proposed method on two public
HAR datasets, achieving significant improvements in the various evaluation
metrics.

No human drives a car in a vacuum; she/he must negotiate with other road
users to achieve their goals in social traffic scenes. A rational human driver
can interact with other road users in a socially-compatible way through
implicit communications to complete their driving tasks smoothly in
interaction-intensive, safety-critical environments. This paper aims to review
the existing approaches and theories to help understand and rethink the
interactions among human drivers toward social autonomous driving. We take this
survey to seek the answers to a series of fundamental questions: 1) What is
social interaction in road traffic scenes? 2) How to measure and evaluate
social interaction? 3) How to model and reveal the process of social
interaction? 4) How do human drivers reach an implicit agreement and negotiate
smoothly in social interaction? This paper reviews various approaches to
modeling and learning the social interactions between human drivers, ranging
from optimization theory and graphical models to social force theory and
behavioral & cognitive science. We also highlight some new directions, critical
challenges, and opening questions for future research.

Federated learning is an emerging technique for training models from
decentralized data sets. In many applications, data owners participating in the
federated learning system hold not only the data but also a set of domain
knowledge. Such knowledge includes human know-how and craftsmanship that can be
extremely helpful to the federated learning task. In this work, we propose a
federated learning framework that allows the injection of participants' domain
knowledge, where the key idea is to refine the global model with knowledge
locally. The scenario we consider is motivated by a real industry-level
application, and we demonstrate the effectiveness of our approach to this

Social media platforms struggle to protect users from harmful content through
content moderation. These platforms have recently leveraged machine learning
models to cope with the vast amount of user-generated content daily. Since
moderation policies vary depending on countries and types of products, it is
common to train and deploy the models per policy. However, this approach is
highly inefficient, especially when the policies change, requiring dataset
re-labeling and model re-training on the shifted data distribution. To
alleviate this cost inefficiency, social media platforms often employ
third-party content moderation services that provide prediction scores of
multiple subtasks, such as predicting the existence of underage personnel, rude
gestures, or weapons, instead of directly providing final moderation decisions.
However, making a reliable automated moderation decision from the prediction
scores of the multiple subtasks for a specific target policy has not been
widely explored yet. In this study, we formulate real-world scenarios of
content moderation and introduce a simple yet effective threshold optimization
method that searches the optimal thresholds of the multiple subtasks to make a
reliable moderation decision in a cost-effective way. Extensive experiments
demonstrate that our approach shows better performance in content moderation
compared to existing threshold optimization methods and heuristics.

Spiking neural network is a kind of neuromorphic computing which is believed
to improve on the level of intelligence and provide advabtages for quantum
computing. In this work, we address this issue by designing an optical spiking
neural network and prove that it can be used to accelerate the speed of
computation, especially on the combinatorial optimization problems. Here the
spiking neural network is constructed by the antisymmetrically coupled
degenerate optical parametric oscillator pulses and dissipative pulses. A
nonlinear transfer function is chosen to mitigate amplitude inhomogeneities and
destabilize the resulting local minima according to the dynamical behavior of
spiking neurons. It is numerically proved that the spiking neural
network-coherent Ising machines has excellent performance on combinatorial
optimization problems, for which is expected to offer a new applications for
neural computing and optical computing.

In software development, the identification of source code file experts is an
important task. Identifying these experts helps to improve software maintenance
and evolution activities, such as developing new features, code reviews, and
bug fixes. Although some studies have proposed repository mining techniques to
automatically identify source code experts, there are still gaps in this area
that can be explored. For example, investigating new variables related to
source code knowledge and applying machine learning aiming to improve the
performance of techniques to identify source code experts. The goal of this
study is to investigate opportunities to improve the performance of existing
techniques to recommend source code files experts. We built an oracle by
collecting data from the development history and surveying developers of 113
software projects. Then, we use this oracle to: (i) analyze the correlation
between measures extracted from the development history and the developers
source code knowledge and (ii) investigate the use of machine learning
classifiers by evaluating their performance in identifying source code files
experts. First Authorship and Recency of Modification are the variables with
the highest positive and negative correlations with source code knowledge,
respectively. Machine learning classifiers outperformed the linear techniques
(F-Measure = 71% to 73%) in the public dataset, but this advantage is not clear
in the private dataset, with F-Measure ranging from 55% to 68% for the linear
techniques and 58% to 67% for ML techniques. Overall, the linear techniques and
the machine learning classifiers achieved similar performance, particularly if
we analyze F-Measure. However, machine learning classifiers usually get higher
precision while linear techniques obtained the highest recall values.

This paper provides a theoretical framework on the solution of feedforward
ReLU networks for interpolations, in terms of what is called an interpolation
matrix, which is the summary, extension and generalization of our three
preceding works, with the expectation that the solution of engineering could be
included in this framework and finally understood. To three-layer networks, we
classify different kinds of solutions and model them in a normalized form; the
solution finding is investigated by three dimensions, including data, networks
and the training; the mechanism of a type of overparameterization solution is
interpreted. To deep-layer networks, we present a general result called
sparse-matrix principle, which could describe some basic behavior of deep
layers and explain the phenomenon of the sparse-activation mode that appears in
engineering applications associated with brain science; an advantage of deep
layers compared to shallower ones is manifested in this principle. As
applications, a general solution of deep neural networks for classifications is
constructed by that principle; and we also use the principle to study the
data-disentangling property of encoders. Analogous to the three-layer case, the
solution of deep layers is also explored through several dimensions. The
mechanism of multi-output neural networks is explained from the perspective of
interpolation matrices.

This paper considers optimization proxies for Optimal Power Flow (OPF), i.e.,
machine-learning models that approximate the input/output relationship of OPF.
Recent work has focused on showing that such proxies can be of high fidelity.
However, their training requires significant data, each instance necessitating
the (offline) solving of an OPF for a sample of the input distribution. To meet
the requirements of market-clearing applications, this paper proposes Active
Bucketized Sampling (ABS), a novel active learning framework that aims at
training the best possible OPF proxy within a time limit. ABS partitions the
input distribution into buckets and uses an acquisition function to determine
where to sample next. It relies on an adaptive learning rate that increases and
decreases over time. Experimental results demonstrate the benefits of ABS.

Horizontal federated learning (HFL) enables distributed clients to train a
shared model and keep their data privacy. In training high-quality HFL models,
the data heterogeneity among clients is one of the major concerns. However, due
to the security issue and the complexity of deep learning models, it is
challenging to investigate data heterogeneity across different clients. To
address this issue, based on a requirement analysis we developed a visual
analytics tool, HetVis, for participating clients to explore data
heterogeneity. We identify data heterogeneity through comparing prediction
behaviors of the global federated model and the stand-alone model trained with
local data. Then, a context-aware clustering of the inconsistent records is
done, to provide a summary of data heterogeneity. Combining with the proposed
comparison techniques, we develop a novel set of visualizations to identify
heterogeneity issues in HFL. We designed three case studies to introduce how
HetVis can assist client analysts in understanding different types of
heterogeneity issues. Expert reviews and a comparative study demonstrate the
effectiveness of HetVis.

We present surrogate models for heat transfer and pressure drop prediction of
complex fin geometries generated using composite Bezier curves. Thermal design
process includes iterative high fidelity simulation which is complex,
computationally expensive, and time-consuming. With the advancement in machine
learning algorithms as well as Graphics Processing Units (GPUs), we can utilize
the parallel processing architecture of GPUs rather than solely relying on CPUs
to accelerate the thermo-fluid simulation. In this study, Convolutional Neural
Networks (CNNs) are used to predict results of Computational Fluid Dynamics
(CFD) directly from topologies saved as images. The case with a single fin as
well as multiple morphable fins are studied. A comparison of Xception network
and regular CNN is presented for the case with a single fin design. Results
show that high accuracy in prediction is observed for single fin design
particularly using Xception network. Increasing design freedom to multiple fins
increases the error in prediction. This error, however, remains within three
percent for pressure drop and heat transfer estimation which is valuable for
design purpose.

As the practicality of Artificial Intelligence (AI) and Machine Learning (ML)
based techniques grow, there is an ever increasing threat of adversarial
attacks. There is a need to red team this ecosystem to identify system
vulnerabilities, potential threats, characterize properties that will enhance
system robustness, and encourage the creation of effective defenses. A
secondary need is to share this AI security threat intelligence between
different stakeholders like, model developers, users, and AI/ML security
professionals. In this paper, we create and describe a prototype system CTI4AI,
to overcome the need to methodically identify and share AI/ML specific
vulnerabilities and threat intelligence.

Many fundamental low-rank optimization problems, such as matrix completion,
phase synchronization/retrieval, power system state estimation, and robust PCA,
can be formulated as the matrix sensing problem. Two main approaches for
solving matrix sensing are based on semidefinite programming (SDP) and
Burer-Monteiro (B-M) factorization. The SDP method suffers from high
computational and space complexities, whereas the B-M method may return a
spurious solution due to the non-convexity of the problem. The existing
theoretical guarantees for the success of these methods have led to similar
conservative conditions, which may wrongly imply that these methods have
comparable performances. In this paper, we shed light on some major differences
between these two methods. First, we present a class of structured matrix
completion problems for which the B-M methods fail with an overwhelming
probability, while the SDP method works correctly. Second, we identify a class
of highly sparse matrix completion problems for which the B-M method works and
the SDP method fails. Third, we prove that although the B-M method exhibits the
same performance independent of the rank of the unknown solution, the success
of the SDP method is correlated to the rank of the solution and improves as the
rank increases. Unlike the existing literature that has mainly focused on those
instances of matrix sensing for which both SDP and B-M work, this paper offers
the first result on the unique merit of each method over the alternative
approach.

Neuromorphic computers perform computations by emulating the human brain, and
use extremely low power. They are expected to be indispensable for
energy-efficient computing in the future. While they are primarily used in
spiking neural network-based machine learning applications, neuromorphic
computers are known to be Turing-complete, and thus, capable of general-purpose
computation. However, to fully realize their potential for general-purpose,
energy-efficient computing, it is important to devise efficient mechanisms for
encoding numbers. Current encoding approaches have limited applicability and
may not be suitable for general-purpose computation. In this paper, we present
the virtual neuron as an encoding mechanism for integers and rational numbers.
We evaluate the performance of the virtual neuron on physical and simulated
neuromorphic hardware and show that it can perform an addition operation using
23 nJ of energy on average using a mixed-signal memristor-based neuromorphic
processor. We also demonstrate its utility by using it in some of the
mu-recursive functions, which are the building blocks of general-purpose
computation.

In this discussion paper, we survey recent research surrounding robustness of
machine learning models. As learning algorithms become increasingly more
popular in data-driven control systems, their robustness to data uncertainty
must be ensured in order to maintain reliable safety-critical operations. We
begin by reviewing common formalisms for such robustness, and then move on to
discuss popular and state-of-the-art techniques for training robust machine
learning models as well as methods for provably certifying such robustness.
From this unification of robust machine learning, we identify and discuss
pressing directions for future research in the area.

While parameter efficient tuning (PET) methods have shown great potential
with transformer architecture on Natural Language Processing (NLP) tasks, their
effectiveness is still under-studied with large-scale ConvNets on Computer
Vision (CV) tasks. This paper proposes Conv-Adapter, a PET module designed for
ConvNets. Conv-Adapter is light-weight, domain-transferable, and
architecture-agnostic with generalized performance on different tasks. When
transferring on downstream tasks, Conv-Adapter learns tasks-specific feature
modulation to the intermediate representations of backbone while keeping the
pre-trained parameters frozen. By introducing only a tiny amount of learnable
parameters, e.g., only 3.5% full fine-tuning parameters of ResNet50,
Conv-Adapter outperforms previous PET baseline methods and achieves comparable
or surpasses the performance of full fine-tuning on 23 classification tasks of
various domains. It also presents superior performance on few-shot
classifications, with an average margin of 3.39%. Beyond classification,
Conv-Adapter can generalize to detection and segmentation tasks with more than
50% reduction of parameters but comparable performance to the traditional full
fine-tuning.

Graph representations of programs are commonly a central element of machine
learning for code research. We introduce an open source Python library
python_graphs that applies static analysis to construct graph representations
of Python programs suitable for training machine learning models. Our library
admits the construction of control-flow graphs, data-flow graphs, and composite
``program graphs'' that combine control-flow, data-flow, syntactic, and lexical
information about a program. We present the capabilities and limitations of the
library, perform a case study applying the library to millions of competitive
programming submissions, and showcase the library's utility for machine
learning research.

We propose a new graph neural network (GNN) module, based on relaxations of
recently proposed geometric scattering transforms, which consist of a cascade
of graph wavelet filters. Our learnable geometric scattering (LEGS) module
enables adaptive tuning of the wavelets to encourage band-pass features to
emerge in learned representations. The incorporation of our LEGS-module in GNNs
enables the learning of longer-range graph relations compared to many popular
GNNs, which often rely on encoding graph structure via smoothness or similarity
between neighbors. Further, its wavelet priors result in simplified
architectures with significantly fewer learned parameters compared to competing
GNNs. We demonstrate the predictive performance of LEGS-based networks on graph
classification benchmarks, as well as the descriptive quality of their learned
features in biochemical graph data exploration tasks. Our results show that
LEGS-based networks match or outperforms popular GNNs, as well as the original
geometric scattering construction, on many datasets, in particular in
biochemical domains, while retaining certain mathematical properties of
handcrafted (non-learned) geometric scattering.

We study p-Laplacians and spectral clustering for a recently proposed
hypergraph model that incorporates edge-dependent vertex weights (EDVWs). These
weights can reflect different importance of vertices within a hyperedge, thus
conferring the hypergraph model higher expressivity and flexibility. By
constructing submodular EDVWs-based splitting functions, we convert hypergraphs
with EDVWs into submodular hypergraphs for which the spectral theory is better
developed. In this way, existing concepts and theorems such as p-Laplacians and
Cheeger inequalities proposed under the submodular hypergraph setting can be
directly extended to hypergraphs with EDVWs. For submodular hypergraphs with
EDVWs-based splitting functions, we propose an efficient algorithm to compute
the eigenvector associated with the second smallest eigenvalue of the
hypergraph 1-Laplacian. We then utilize this eigenvector to cluster the
vertices, achieving higher clustering accuracy than traditional spectral
clustering based on the 2-Laplacian. More broadly, the proposed algorithm works
for all submodular hypergraphs that are graph reducible. Numerical experiments
using real-world data demonstrate the effectiveness of combining spectral
clustering based on the 1-Laplacian and EDVWs.

We present a generalized theoretical framework for olfactory representation,
learning, and perception using the theory of smooth manifolds and sheaves. This
framework enables the simultaneous depiction of sampling-based physical
similarity and learning-dependent perceptual similarity among odors, including
related perceptual phenomena such as generalization gradients, hierarchical
categorical perception, configural learning, and the speed-precision tradeoff.
Beginning with the space of all possible instantaneous afferent inputs to the
olfactory system, we develop a dynamic model for perceptual learning that
culminates in a perceptual space in which categorical odor representations are
hierarchically constructed, exhibiting statistically appropriate consequential
regions and clear relationships between the broader and narrower identities to
which a given odor might be assigned. Individual training and experience
generates correspondingly more sophisticated odor identification capabilities.
Critically, because these idiosyncratic hierarchies are constructed from
experience and depend on local plasticity mechanisms, geometries that fix
curvature, whether Euclidean, hyperbolic, or otherwise, are insufficient to
describe the capabilities of the system. This generative framework also
encompasses hypotheses explaining representational drift in postbulbar circuits
and the context-dependent remapping of perceptual similarity relationships.

Invariant inference algorithms such as interpolation-based inference and
IC3/PDR show that it is feasible, in practice, to find inductive invariants for
many interesting systems, but non-trivial upper bounds on the computational
complexity of such algorithms are scarce, and limited to simple syntactic forms
of invariants. In this paper we achieve invariant inference algorithms, in the
domain of propositional transition systems, with provable upper bounds on the
number of SAT calls. We do this by building on the monotone theory, developed
by Bshouty for exact learning Boolean formulas. We prove results for two
invariant inference frameworks: (i) model-based interpolation, where we show an
algorithm that, under certain conditions about reachability, efficiently infers
invariants when they have both short CNF and DNF representations (transcending
previous results about monotone invariants); and (ii) abstract interpretation
in a domain based on the monotone theory that was previously studied in
relation to property-directed reachability, where we propose an efficient
implementation of the best abstract transformer, leading to overall complexity
bounds on the number of SAT calls. These results build on a novel procedure for
computing least monotone overapproximations.

Electroencephalogram (EEG) signals are effective tools towards seizure
analysis where one of the most important challenges is accurate detection of
seizure events and brain regions in which seizure happens or initiates.
However, all existing machine learning-based algorithms for seizure analysis
require access to the labeled seizure data while acquiring labeled data is very
labor intensive, expensive, as well as clinicians dependent given the
subjective nature of the visual qualitative interpretation of EEG signals. In
this paper, we propose to detect seizure channels and clips in a
self-supervised manner where no access to the seizure data is needed. The
proposed method considers local structural and contextual information embedded
in EEG graphs by employing positive and negative sub-graphs. We train our
method through minimizing contrastive and generative losses. The employ of
local EEG sub-graphs makes the algorithm an appropriate choice when accessing
to the all EEG channels is impossible due to complications such as skull
fractures. We conduct an extensive set of experiments on the largest seizure
dataset and demonstrate that our proposed framework outperforms the
state-of-the-art methods in the EEG-based seizure study. The proposed method is
the only study that requires no access to the seizure data in its training
phase, yet establishes a new state-of-the-art to the field, and outperforms all
related supervised methods.

Self-supervised learning (SSL) has drawn an increased attention in the field
of speech processing. Recent studies have demonstrated that contrastive
learning is able to learn discriminative speaker embeddings in a
self-supervised manner. However, base contrastive self-supervised learning
(CSSL) assumes that the pairs generated from a view of anchor instance and any
view of other instances are all negative, which introduces many false negative
pairs in constructing the loss function. The problem is referred as
$class$-$collision$, which remains as one major issue that impedes the CSSL
based speaker verification (SV) systems from achieving better performances. In
the meanwhile, studies reveal that negative sample free SSL frameworks perform
well in learning speaker or image representations. In this study, we
investigate SSL techniques that lead to an improved SV performance. We first
analyse the impact of false negative pairs in the CSSL systems. Then, a
multi-stage Class-Collision Correction (C3) method is proposed, which leads to
the state-of-the-art CSSL based speaker embedding system. On the basis of the
pretrained CSSL model, we further propose to employ a negative sample free SSL
objective (i.e., DINO) to fine-tune the speaker embedding network. The
resulting speaker embedding system (C3-DINO) achieves 2.5% EER with a simple
Cosine Distance Scoring method on Voxceleb1 test set, which outperforms the
previous SOTA SSL system (4.86%) by a significant +45% relative improvement.
With speaker clustering and pseudo labeling on Voxceleb2 training set, a
LDA/CDS back-end applying on the C3-DINO speaker embeddings is able to further
push the EER to 2.2%. Comprehensive experimental investigations of the Voxceleb
benchmarks and our internal dataset demonstrate the effectiveness of our
proposed methods, and the performance gap between the SSL SV and the supervised
counterpart narrows further.

Medical coding is a complex task, requiring assignment of a subset of over
72,000 ICD codes to a patient's notes. Modern natural language processing
approaches to these tasks have been challenged by the length of the input and
size of the output space. We limit our model inputs to a small window around
medical entities found in our documents. From those local contexts, we build
contextualized representations of both ICD codes and entities, and aggregate
over these representations to form document-level predictions. In contrast to
existing methods which use a representation fixed either in size or by codes
seen in training, we represent ICD codes by encoding the code description with
local context. We discuss metrics appropriate to deploying coding systems in
practice. We show that our approach is superior to existing methods in both
standard and deployable measures, including performance on rare and unseen
codes.

This study delved into the viability of Robot-supported flipped classes in
English for Medical Purposes reading comprehension. In a 16-session course, the
reading comprehension and then workspace performance of 444 students, with
Commercially-Off-The-Shelf and Self-Generated robot flipped classes were
compared. The results indicated that the flipped classes brought about a good
instructional-learning ambience in postsecondary education for English for
Medical Purposes (EMP) reading comprehension and adopting proactive approach
for workspace performance. In tandem, the Mixed Effect Model revealed that
student participation in the self-generated robot-supported flipped classes
yielded a larger effect size (+17.6\%) than Commercially-Off-The-Shelf
robot-supported flipped classes. Analyses produced five contributing moderators
of EMP reading comprehension and workspace performance: reading proficiency,
attitude, manner of practicing, as well as student and teacher role.

The pedestrian intention prediction problem is to estimate whether or not the
target pedestrian will cross the street. State-of-the-art approaches heavily
rely on visual information collected with the front camera of the ego-vehicle
to make a prediction of the pedestrian's intention. As such, the performance of
existing methods significantly degrades when the visual information is not
accurate, e.g., when the distance between the pedestrian and ego-vehicle is
far, or the lighting conditions are not good enough. In this paper, we design,
implement, and evaluate the first pedestrian intention prediction model based
on integration of motion sensor data gathered with the smartwatch (or
smartphone) of the pedestrian. A novel machine learning architecture is
proposed to effectively incorporate the motion sensor data to reinforce the
visual information to significantly improve the performance in adverse
situations where the visual information may be unreliable. We also conduct a
large-scale data collection and present the first pedestrian intention
prediction dataset integrated with time-synchronized motion sensor data. The
dataset consists of a total of 128 video clips with different distances and
varying levels of lighting conditions. We trained our model using the
widely-used JAAD and our own datasets and compare the performance with a
state-of-the-art model. The results demonstrate that our model outperforms the
state-of-the-art method particularly when the distance to the pedestrian is far
(over 70m), and the lighting conditions are not sufficient.

Quantum mechanics has about a dozen exactly solvable potentials. Normally,
the time-independent Schroedinger equation for them is solved by using a
generalized series solution for the bound states (using the Froebenius method)
and then an analytic continuation for the continuum states (if present). In
this work, we present an alternative way to solve these problems, based on the
Laplace method. This technique uses a similar procedure for the bound states
and for the continuum states. It was originally used by Schroedinger when he
solved for the wavefunctions of hydrogen. Dirac advocated using this method
too. We discuss why it is a powerful approach for graduate students to learn
and describe how it can be employed to solve all problems whose wavefunctions
are represented in terms of confluent hypergeometric functions.

This paper investigates reconfigurable intelligent surface (RIS)-assisted
full-duplex multiple-input single-output wireless system, where the beamforming
and RIS phase shifts are optimized to maximize the sum-rate for both single and
distributed RIS deployment schemes. The preference of using the single or
distributed RIS deployment scheme is investigated through three practical
scenarios based on the links' quality. The closed-form solution is derived to
optimize the beamforming vectors and a novel deep reinforcement learning (DRL)
algorithm is proposed to optimize the RIS phase shifts. Simulation results
illustrate that the choice of the deployment scheme depends on the scenario and
the links' quality. It is further shown that the proposed algorithm
significantly improves the sum-rate compared to the non-optimized scenario in
both single and distributed RIS deployment schemes. Besides, the proposed
beamforming derivation achieves a remarkable improvement compared to the
approximated derivation in previous works. Finally, the complexity analysis
confirms that the proposed DRL algorithm reduces the computation complexity
compared to the DRL algorithm in the literature.

Deep learning has become the method of choice to tackle real-world problems
in different domains, partly because of its ability to learn from data and
achieve impressive performance on a wide range of applications. However, its
success usually relies on two assumptions: (i) vast troves of labeled datasets
are required for accurate model fitting, and (ii) training and testing data are
independent and identically distributed. Its performance on unseen target
domains, thus, is not guaranteed, especially when encountering
out-of-distribution data at the adaptation stage. The performance drop on data
in a target domain is a critical problem in deploying deep neural networks that
are successfully trained on data in a source domain. Unsupervised domain
adaptation (UDA) is proposed to counter this, by leveraging both labeled source
domain data and unlabeled target domain data to carry out various tasks in the
target domain. UDA has yielded promising results on natural image processing,
video analysis, natural language processing, time-series data analysis, medical
image analysis, etc. In this review, as a rapidly evolving topic, we provide a
systematic comparison of its methods and applications. In addition, the
connection of UDA with its closely related tasks, e.g., domain generalization
and out-of-distribution detection, has also been discussed. Furthermore,
deficiencies in current methods and possible promising directions are
highlighted.

Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing
radiation therapy planning. Existing deep learning-based multi-scale fusion
architectures have demonstrated a tremendous capacity for 2D medical image
segmentation. The key to their success is aggregating global context and
maintaining high resolution representations. However, when translated into 3D
segmentation problems, existing multi-scale fusion architectures might
underperform due to their heavy computation overhead and substantial data diet.
To address this issue, we propose a new OAR segmentation framework, called
OARFocalFuseNet, which fuses multi-scale features and employs focal modulation
for capturing global-local context across multiple scales. Each resolution
stream is enriched with features from different resolution scales, and
multi-scale information is aggregated to model diverse contextual ranges. As a
result, feature representations are further boosted. The comprehensive
comparisons in our experimental setup with OAR segmentation as well as
multi-organ segmentation show that our proposed OARFocalFuseNet outperforms the
recent state-of-the-art methods on publicly available OpenKBP datasets and
Synapse multi-organ segmentation. Both of the proposed methods (3D-MSF and
OARFocalFuseNet) showed promising performance in terms of standard evaluation
metrics. Our best performing method (OARFocalFuseNet) obtained a dice
coefficient of 0.7995 and hausdorff distance of 5.1435 on OpenKBP datasets and
dice coefficient of 0.8137 on Synapse multi-organ segmentation dataset.

Optical emission spectroscopy of laser-produced plasmas, commonly known as
laser-induced breakdown spectroscopy (LIBS), is an emerging analytical tool for
rapid soil analysis. However, specific challenges with LIBS exist, such as
matrix effects and quantification issues, that require further study in the
application of LIBS, particularly for analysis of heterogeneous samples such as
soils. Advancements in the applications of Machine Learning (ML) methods can
address some of these issues, advancing the potential for LIBS in soil
analysis. This article aims to review the progress of LIBS application combined
with ML methods, focusing on methodological approaches used in reducing matrix
effect, feature selection, quantification analysis, soil classification, and
self-absorption. The performance of various adopted ML approaches is discussed,
including their shortcomings and advantages, to provide researchers with a
clear picture of the current status of ML applications in LIBS for improving
its analytical capability. The challenges and prospects of LIBS development in
soil analysis are proposed, offering a path toward future research. This review
article emphasize ML tools for LIBS soil analysis that are broadly relevant for
other LIBS applications.

We introduce a new method for releasing answers to statistical queries with
differential privacy, based on the Johnson-Lindenstrauss lemma. The key idea is
to randomly project the query answers to a lower dimensional space so that the
distance between any two vectors of feasible query answers is preserved up to
an additive error. Then we answer the projected queries using a simple
noise-adding mechanism, and lift the answers up to the original dimension.
Using this method, we give, for the first time, purely differentially private
mechanisms with optimal worst case sample complexity under average error for
answering a workload of $k$ queries over a universe of size $N$. As other
applications, we give the first purely private efficient mechanisms with
optimal sample complexity for computing the covariance of a bounded
high-dimensional distribution, and for answering 2-way marginal queries. We
also show that, up to the dependence on the error, a variant of our mechanism
is nearly optimal for every given query workload.

Dental disease is one of the most common chronic diseases despite being
largely preventable. However, professional advice on optimal oral hygiene
practices is often forgotten or abandoned by patients. Therefore patients may
benefit from timely and personalized encouragement to engage in oral self-care
behaviors. In this paper, we develop an online reinforcement learning (RL)
algorithm for use in optimizing the delivery of mobile-based prompts to
encourage oral hygiene behaviors. One of the main challenges in developing such
an algorithm is ensuring that the algorithm considers the impact of the current
action on the effectiveness of future actions (i.e., delayed effects),
especially when the algorithm has been made simple in order to run stably and
autonomously in a constrained, real-world setting (i.e., highly noisy, sparse
data). We address this challenge by designing a quality reward which maximizes
the desired health outcome (i.e., high-quality brushing) while minimizing user
burden. We also highlight a procedure for optimizing the hyperparameters of the
reward by building a simulation environment test bed and evaluating candidates
using the test bed. The RL algorithm discussed in this paper will be deployed
in Oralytics, an oral self-care app that provides behavioral strategies to
boost patient engagement in oral hygiene practices.

A common approach to aggregate classification estimates in an ensemble of
decision trees is to either use voting or to average the probabilities for each
class. The latter takes uncertainty into account, but not the reliability of
the uncertainty estimates (so to say, the "uncertainty about the uncertainty").
More generally, much remains unknown about how to best combine probabilistic
estimates from multiple sources. In this paper, we investigate a number of
alternative prediction methods. Our methods are inspired by the theories of
probability, belief functions and reliable classification, as well as a
principle that we call evidence accumulation. Our experiments on a variety of
data sets are based on random decision trees which guarantees a high diversity
in the predictions to be combined. Somewhat unexpectedly, we found that taking
the average over the probabilities is actually hard to beat. However, evidence
accumulation showed consistently better results on all but very small leafs.

Speech manuscript (German + English) of the impulse lecture for the panel
discussion "May machines (be able to) think?" at the 102nd Katholikentag on May
28, 2022 in Stuttgart. Panel: Winfried Kretschmann (MdL, Prime Minister
Baden-W\"urttemberg, Stuttgart), Ursula Nothelle-Wildfeuer (Freiburg), Michael
Resch (Stuttgart),Karsten Wendland (Aalen). Moderation: Stefanie Rentsch
(Fulda). Advocate of the audience: Verena Neuhausen (Stuttgart).

We propose a Predictive Group Elevator Scheduler by using predictive
information of passengers arrivals from a Transformer based destination
predictor and a linear regression model that predicts remaining time to
destinations. Through extensive empirical evaluation, we find that the savings
of Average Waiting Time (AWT) could be as high as above 50% for light arrival
streams and around 15% for medium arrival streams in afternoon down-peak
traffic regimes. Such results can be obtained after carefully setting the
Predicted Probability of Going to Elevator (PPGE) threshold, thus avoiding a
majority of false predictions for people heading to the elevator, while
achieving as high as 80% of true predictive elevator landings as early as after
having seen only 60% of the whole trajectory of a passenger.

Unsupervised video domain adaptation is a practical yet challenging task. In
this work, for the first time, we tackle it from a disentanglement view. Our
key idea is to disentangle the domain-related information from the data during
the adaptation process. Specifically, we consider the generation of
cross-domain videos from two sets of latent factors, one encoding the static
domain-related information and another encoding the temporal and
semantic-related information. A Transfer Sequential VAE (TranSVAE) framework is
then developed to model such generation. To better serve for adaptation, we
further propose several objectives to constrain the latent factors in TranSVAE.
Extensive experiments on the UCF-HMDB, Jester, and Epic-Kitchens datasets
verify the effectiveness and superiority of TranSVAE compared with several
state-of-the-art methods. Code is publicly available at
https://github.com/ldkong1205/TranSVAE.

Robust and accurate localization is a basic requirement for mobile autonomous
systems. Pole-like objects, such as traffic signs, poles, and lamps are
frequently used landmarks for localization in urban environments due to their
local distinctiveness and long-term stability. In this paper, we present a
novel, accurate, and fast pole extraction approach based on geometric features
that runs online and has little computational demands. Our method performs all
computations directly on range images generated from 3D LiDAR scans, which
avoids processing 3D point clouds explicitly and enables fast pole extraction
for each scan. We further use the extracted poles as pseudo labels to train a
deep neural network for online range image-based pole segmentation. We test
both our geometric and learning-based pole extraction methods for localization
on different datasets with different LiDAR scanners, routes, and seasonal
changes. The experimental results show that our methods outperform other
state-of-the-art approaches. Moreover, boosted with pseudo pole labels
extracted from multiple datasets, our learning-based method can run across
different datasets and achieve even better localization results compared to our
geometry-based method. We released our pole datasets to the public for
evaluating the performance of pole extractors, as well as the implementation of

Simulated humanoids are an appealing research domain due to their physical
capabilities. Nonetheless, they are also challenging to control, as a policy
must drive an unstable, discontinuous, and high-dimensional physical system.
One widely studied approach is to utilize motion capture (MoCap) data to teach
the humanoid agent low-level skills (e.g., standing, walking, and running) that
can then be re-used to synthesize high-level behaviors. However, even with
MoCap data, controlling simulated humanoids remains very hard, as MoCap data
offers only kinematic information. Finding physical control inputs to realize
the demonstrated motions requires computationally intensive methods like
reinforcement learning. Thus, despite the publicly available MoCap data, its
utility has been limited to institutions with large-scale compute. In this
work, we dramatically lower the barrier for productive research on this topic
by training and releasing high-quality agents that can track over three hours
of MoCap data for a simulated humanoid in the dm_control physics-based
environment. We release MoCapAct (Motion Capture with Actions), a dataset of
these expert agents and their rollouts, which contain proprioceptive
observations and actions. We demonstrate the utility of MoCapAct by using it to
train a single hierarchical policy capable of tracking the entire MoCap dataset
within dm_control and show the learned low-level component can be re-used to
efficiently learn downstream high-level tasks. Finally, we use MoCapAct to
train an autoregressive GPT model and show that it can control a simulated
humanoid to perform natural motion completion given a motion prompt.
  Videos of the results and links to the code and dataset are available at
https://microsoft.github.io/MoCapAct.

This paper compares and ranks 11 UDA validation methods. Validators estimate
model accuracy, which makes them an essential component of any UDA train-test
pipeline. We rank these validators to indicate which of them are most useful
for the purpose of selecting optimal models, checkpoints, and hyperparameters.
In addition, we propose and compare new effective validators and significantly
improved versions of existing validators. To the best of our knowledge, this
large-scale benchmark study is the first of its kind in the UDA field.

Linear regression is a fundamental tool for statistical analysis. This has
motivated the development of linear regression methods that also satisfy
differential privacy and thus guarantee that the learned model reveals little
about any one data point used to construct it. However, existing differentially
private solutions assume that the end user can easily specify good data bounds
and hyperparameters. Both present significant practical obstacles. In this
paper, we study an algorithm which uses the exponential mechanism to select a
model with high Tukey depth from a collection of non-private regression models.
Given $n$ samples of $d$-dimensional data used to train $m$ models, we
construct an efficient analogue using an approximate Tukey depth that runs in
time $O(d^2n + dm\log(m))$. We find that this algorithm obtains strong
empirical performance in the data-rich setting with no data bounds or
hyperparameter selection required.

Machine learning models of visual action recognition are typically trained
and tested on data from specific situations where actions are associated with
certain objects. It is an open question how action-object associations in the
training set influence a model's ability to generalize beyond trained
situations. We set out to identify properties of training data that lead to
action recognition models with greater generalization ability. To do this, we
take inspiration from a cognitive mechanism called cross-situational learning,
which states that human learners extract the meaning of concepts by observing
instances of the same concept across different situations. We perform
controlled experiments with various types of action-object associations, and
identify key properties of action-object co-occurrence in training data that
lead to better classifiers. Given that these properties are missing in the
datasets that are typically used to train action classifiers in the computer
vision literature, our work provides useful insights on how we should best
construct datasets for efficiently training for better generalization.

Understanding the spread of COVID-19 has been the subject of numerous
studies, highlighting the significance of reliable epidemic models. Here, we
introduce a novel epidemic model using a latent Hawkes process with temporal
covariates for modelling the infections. Unlike other models, we model the
reported cases via a probability distribution driven by the underlying Hawkes
process. Modelling the infections via a Hawkes process allows us to estimate by
whom an infected individual was infected. We propose a Kernel Density Particle
Filter (KDPF) for inference of both latent cases and reproduction number and
for predicting the new cases in the near future. The computational effort is
proportional to the number of infections making it possible to use particle
filter type algorithms, such as the KDPF. We demonstrate the performance of the
proposed algorithm on synthetic data sets and COVID-19 reported cases in
various local authorities in the UK, and benchmark our model to alternative

Large language models have been widely adopted but require significant GPU
memory for inference. We develop a procedure for Int8 matrix multiplication for
feed-forward and attention projection layers in transformers, which cut the
memory needed for inference by half while retaining full precision performance.
With our method, a 175B parameter 16/32-bit checkpoint can be loaded, converted
to Int8, and used immediately without performance degradation. This is made
possible by understanding and working around properties of highly systematic
emergent features in transformer language models that dominate attention and
transformer predictive performance. To cope with these features, we develop a
two-part quantization procedure, LLM.int8(). We first use vector-wise
quantization with separate normalization constants for each inner product in
the matrix multiplication, to quantize most of the features. However, for the
emergent outliers, we also include a new mixed-precision decomposition scheme,
which isolates the outlier feature dimensions into a 16-bit matrix
multiplication while still more than 99.9% of values are multiplied in 8-bit.
Using LLM.int8(), we show empirically it is possible to perform inference in
LLMs with up to 175B parameters without any performance degradation. This
result makes such models much more accessible, for example making it possible
to use OPT-175B/BLOOM on a single server with consumer GPUs. We open-source our
software.

Conventional physics-based modeling is a time-consuming bottleneck in control
design for complex nonlinear systems like autonomous underwater vehicles
(AUVs). In contrast, purely data-driven models, though convenient and quick to
obtain, require a large number of observations and lack operational guarantees
for safety-critical systems. Data-driven models leveraging available partially
characterized dynamics have potential to provide reliable systems models in a
typical data-limited scenario for high value complex systems, thereby avoiding
months of expensive expert modeling time. In this work we explore this
middle-ground between expert-modeled and pure data-driven modeling. We present
control-oriented parametric models with varying levels of domain-awareness that
exploit known system structure and prior physics knowledge to create
constrained deep neural dynamical system models. We employ universal
differential equations to construct data-driven blackbox and graybox
representations of the AUV dynamics. In addition, we explore a hybrid
formulation that explicitly models the residual error related to imperfect
graybox models. We compare the prediction performance of the learned models for
different distributions of initial conditions and control inputs to assess
their accuracy, generalization, and suitability for control.

Predictions about people, such as their expected educational achievement or
their credit risk, can be performative and shape the outcome that they aim to
predict. Understanding the causal effect of these predictions on the eventual
outcomes is crucial for foreseeing the implications of future predictive models
and selecting which models to deploy. However, this causal estimation task
poses unique challenges: model predictions are usually deterministic functions
of input features and highly correlated with outcomes. This can make the causal
effects of predictions on outcomes impossible to disentangle from the direct
effect of the covariates. We study this problem through the lens of causal
identifiability, and despite the hardness of this problem in full generality,
we highlight three natural scenarios where the causal relationship between
covariates, predictions and outcomes can be identified from observational data:
randomization in predictions, overparameterization of the predictive model
deployed during data collection, and discrete prediction outputs. Empirically
we show that given our identifiability conditions hold, standard variants of
supervised learning that predict from predictions by treating the prediction as
an input feature can indeed find transferable functional relationships that
allow for conclusions about newly deployed predictive models. These positive
results fundamentally rely on model predictions being recorded during data
collection, bringing forward the importance of rethinking standard data
collection practices to enable progress towards a better understanding of
social outcomes and performative feedback loops.

Graph convolutional networks (GCNs) and its variants are designed for
unsigned graphs containing only positive links. Many existing GCNs have been
derived from the spectral domain analysis of signals lying over (unsigned)
graphs and in each convolution layer they perform low-pass filtering of the
input features followed by a learnable linear transformation. Their extension
to signed graphs with positive as well as negative links imposes multiple
issues including computational irregularities and ambiguous frequency
interpretation, making the design of computationally efficient low pass filters
challenging. In this paper, we address these issues via spectral analysis of
signed graphs and propose two different signed graph neural networks, one keeps
only low-frequency information and one also retains high-frequency information.
We further introduce magnetic signed Laplacian and use its eigendecomposition
for spectral analysis of directed signed graphs. We test our methods for node
classification and link sign prediction tasks on signed graphs and achieve
state-of-the-art performances.

Multi-instance learning (MIL) is widely used in the computer-aided
interpretation of pathological Whole Slide Images (WSIs) to solve the lack of
pixel-wise or patch-wise annotations. Often, this approach directly applies
"natural image driven" MIL algorithms which overlook the multi-scale (i.e.
pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed
on a single-scale of WSIs (e.g., 20x magnification), while human pathologists
usually aggregate the global and local patterns in a multi-scale manner (e.g.,
by zooming in and out between different magnifications). In this study, we
propose a novel cross-scale attention mechanism to explicitly aggregate
inter-scale interactions into a single MIL network for Crohn's Disease (CD),
which is a form of inflammatory bowel disease. The contribution of this paper
is two-fold: (1) a cross-scale attention mechanism is proposed to aggregate
features from different resolutions with multi-scale interaction; and (2)
differential multi-scale attention visualizations are generated to localize
explainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon
(AC) patches from 20 CD patient and 30 healthy control samples at different
scales, our approach achieved a superior Area under the Curve (AUC) score of
0.8924 compared with baseline models. The official implementation is publicly
available at https://github.com/hrlblab/CS-MIL.

Recently proposed BERT-based evaluation metrics perform well on standard
evaluation benchmarks but are vulnerable to adversarial attacks, e.g., relating
to factuality errors. We argue that this stems (in part) from the fact that
they are models of semantic similarity. In contrast, we develop evaluation
metrics based on Natural Language Inference (NLI), which we deem a more
appropriate modeling. We design a preference-based adversarial attack framework
and show that our NLI based metrics are much more robust to the attacks than
the recent BERT-based metrics. On standard benchmarks, our NLI based metrics
outperform existing summarization metrics, but perform below SOTA MT metrics.
However, when we combine existing metrics with our NLI metrics, we obtain both
higher adversarial robustness (+20% to +30%) and higher quality metrics as
measured on standard benchmarks (+5% to +25%).

In order to be able to use artificial intelligence (AI) in medicine without
scepticism and to recognise and assess its growing potential, a basic
understanding of this topic is necessary among current and future medical
staff. Under the premise of "trust through understanding", we developed an
innovative online course as a learning opportunity within the framework of the
German KI Campus (AI campus) project, which is a self-guided course that
teaches the basics of AI for the analysis of medical image data. The main goal
is to provide a learning environment for a sufficient understanding of AI in
medical image analysis so that further interest in this topic is stimulated and
inhibitions towards its use can be overcome by means of positive application
experience. The focus was on medical applications and the fundamentals of
machine learning. The online course was divided into consecutive lessons, which
include theory in the form of explanatory videos, practical exercises in the
form of Streamlit and practical exercises and/or quizzes to check learning
progress. A survey among the participating medical students in the first run of
the course was used to analyse our research hypotheses quantitatively.

The StarCraft II Multi-Agent Challenge (SMAC) was created to be a challenging
benchmark problem for cooperative multi-agent reinforcement learning (MARL).
SMAC focuses exclusively on the problem of StarCraft micromanagement and
assumes that each unit is controlled individually by a learning agent that acts
independently and only possesses local information; centralized training is
assumed to occur with decentralized execution (CTDE). To perform well in SMAC,
MARL algorithms must handle the dual problems of multi-agent credit assignment
and joint action evaluation.
  This paper introduces a new architecture TransMix, a transformer-based joint
action-value mixing network which we show to be efficient and scalable as
compared to the other state-of-the-art cooperative MARL solutions. TransMix
leverages the ability of transformers to learn a richer mixing function for
combining the agents' individual value functions. It achieves comparable
performance to previous work on easy SMAC scenarios and outperforms other
techniques on hard scenarios, as well as scenarios that are corrupted with
Gaussian noise to simulate fog of war.

With the increment of interest in leveraging machine learning technology in
safety-critical systems, the robustness of neural networks under external
disturbance receives more and more concerns. Global robustness is a robustness
property defined on the entire input domain. And a certified globally robust
network can ensure its robustness on any possible network input. However, the
state-of-the-art global robustness certification algorithm can only certify
networks with at most several thousand neurons. In this paper, we propose the
GPU-supported global robustness certification framework GROCET, which is more
efficient than the previous optimization-based certification approach.
Moreover, GROCET provides differentiable global robustness, which is leveraged
in the training of globally robust neural networks.

Purpose: The Targeted Learning roadmap provides a systematic guide for
generating and evaluating real-world evidence (RWE). From a regulatory
perspective, RWE arises from diverse sources such as randomized controlled
trials that make use of real-world data, observational studies, and other study
designs. This paper illustrates a principled approach to assessing the validity
and interpretability of RWE.
  Methods: We applied the roadmap to a published observational study of the
dose-response association between ritodrine hydrochloride and pulmonary edema
among women pregnant with twins in Japan. The goal was to identify barriers to
causal effect estimation beyond unmeasured confounding reported by the study's
authors, and to explore potential options for overcoming the barriers that
robustify results.
  Results: Following the roadmap raised issues that led us to formulate
alternative causal questions that produced more reliable, interpretable RWE.
The process revealed a lack of information in the available data to identify a
causal dose-response curve. However, under explicit assumptions the effect of
treatment with any amount of ritodrine versus none, albeit a less ambitious
parameter, can be estimated from data.
  Conclusion: Before RWE can be used in support of clinical and regulatory
decision-making, its quality and reliability must be systematically evaluated.
The TL roadmap prescribes how to carry out a thorough, transparent, and
realistic assessment of RWE. We recommend this approach be a routine part of
any decision-making process.

In this paper, we propose a differentiable WORLD synthesizer and demonstrate
its use in end-to-end audio style transfer tasks such as (singing) voice
conversion and the DDSP timbre transfer task. Accordingly, our baseline
differentiable synthesizer has no model parameters, yet it yields adequate
synthesis quality. We can extend the baseline synthesizer by appending
lightweight black-box postnets which apply further processing to the baseline
output in order to improve fidelity. An alternative differentiable approach
considers extraction of the source excitation spectrum directly, which can
improve naturalness albeit for a narrower class of style transfer applications.
The acoustic feature parameterization used by our approaches has the added
benefit that it naturally disentangles pitch and timbral information so that
they can be modeled separately. Moreover, as there exists a robust means of
estimating these acoustic features from monophonic audio sources, it allows for
parameter loss terms to be added to an end-to-end objective function, which can
help convergence and/or further stabilize (adversarial) training.

Debiased recommender models have recently attracted increasing attention from
the academic and industry communities. Existing models are mostly based on the
technique of inverse propensity score (IPS). However, in the recommendation
domain, IPS can be hard to estimate given the sparse and noisy nature of the
observed user-item exposure data. To alleviate this problem, in this paper, we
assume that the user preference can be dominated by a small amount of latent
factors, and propose to cluster the users for computing more accurate IPS via
increasing the exposure densities. Basically, such method is similar with the
spirit of stratification models in applied statistics. However, unlike previous
heuristic stratification strategy, we learn the cluster criterion by presenting
the users with low ranking embeddings, which are future shared with the user
representations in the recommender model. At last, we find that our model has
strong connections with the previous two types of debiased recommender models.
We conduct extensive experiments based on real-world datasets to demonstrate
the effectiveness of the proposed method.

The traditional adaptive algorithms will face the non-uniqueness problem when
dealing with stereophonic acoustic echo cancellation (SAEC). In this paper, we
first propose an efficient multi-input and multi-output (MIMO) scheme based on
deep learning to filter out echoes from all microphone signals at once. Then,
we employ a lightweight complex spectral mapping framework (LCSM) for
end-to-end SAEC without decorrelation preprocessing to the loudspeaker signals.
Inplace convolution and channel-wise spatial modeling are utilized to ensure
the near-end signal information is preserved. Finally, a cross-domain loss
function is designed for better generalization capability. Experiments are
evaluated on a variety of untrained conditions and results demonstrate that the
LCSM significantly outperforms previous methods. Moreover, the proposed causal
framework only has 0.55 million parameters, much less than the similar deep
learning-based methods, which is important for the resource-limited devices.

The pervasiveness of Wi-Fi signals provides significant opportunities for
human sensing and activity recognition in fields such as healthcare. The
sensors most commonly used for passive Wi-Fi sensing are based on passive Wi-Fi
radar (PWR) and channel state information (CSI) data, however current systems
do not effectively exploit the information acquired through multiple sensors to
recognise the different activities. In this paper, we explore new properties of
the Transformer architecture for multimodal sensor fusion. We study different
signal processing techniques to extract multiple image-based features from PWR
and CSI data such as spectrograms, scalograms and Markov transition field
(MTF). We first propose the Fusion Transformer, an attention-based model for
multimodal and multi-sensor fusion. Experimental results show that our Fusion
Transformer approach can achieve competitive results compared to a ResNet
architecture but with much fewer resources. To further improve our model, we
propose a simple and effective framework for multimodal and multi-sensor
self-supervised learning (SSL). The self-supervised Fusion Transformer
outperforms the baselines, achieving a F1-score of 95.9%. Finally, we show how
this approach significantly outperforms the others when trained with as little
as 1% (2 minutes) of labelled training data to 20% (40 minutes) of labelled

Search systems are often used to support learning-oriented goals. This trend
has given rise to the "search-as-learning" movement, which proposes that search
systems should be designed to support learning. To this end, an important
research question is: How does a searcher's \emph{type} of learning objective
influence their trajectory (or \emph{pathway}) towards that objective? We
report on a lab study ($N=36$) in which participants gathered information to
meet a specific type of learning objective. To characterize learning objectives
\emph{and pathways}, we leveraged Anderson and Krathwohl's (A\&K's) taxonomy
\cite{anderson2001taxonomy}. Participants completed learning-oriented search
tasks that varied along three cognitive processes (apply, evaluate, create) and
three knowledge types (factual, conceptual, procedural knowledge). A
\emph{pathway} is defined as a sequence of \emph{learning instances} (e.g.,
subgoals) that were also each classified into cells from A\&K's taxonomy. Our
study used a think-aloud protocol, and pathways were generated through a
qualitative analysis of participants' think-aloud comments and recorded screen
activities. We investigate three research questions. First, in RQ1, we study
the impact of the learning objective on pathway characteristics (e.g., pathway
length). Second, in RQ2, we study the impact of the learning objective on the
types of A\&K cells traversed along the pathway. Third, in RQ3, we study common
and uncommon \emph{transitions} between A\&K cells along pathways conditioned
on the knowledge type of the objective. We discuss implications of our results
for designing search systems to support learning.

The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises
the need for accurate option pricing models. Yet, existing methodologies fail
to cope with the volatile nature of the emerging DAs. Many models have been
proposed to address the unorthodox market dynamics and frequent disruptions in
the microstructure caused by the non-stationarity, and peculiar statistics, in
DA markets. However, they are either prone to the curse of dimensionality, as
additional complexity is required to employ traditional theories, or they
overfit historical patterns that may never repeat. Instead, we leverage recent
advances in market regime (MR) clustering with the Implied Stochastic
Volatility Model (ISVM). Time-regime clustering is a temporal clustering
method, that clusters the historic evolution of a market into different
volatility periods accounting for non-stationarity. ISVM can incorporate
investor expectations in each of the sentiment-driven periods by using implied
volatility (IV) data. In this paper, we applied this integrated time-regime
clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC
options at the popular trading platform Deribit. We demonstrate that MR-ISVM
contributes to overcome the burden of complex adaption to jumps in higher order
characteristics of option pricing models. This allows us to price the market
based on the expectations of its participants in an adaptive fashion.

Nearest neighbor-based methods are commonly used for classification tasks and
as subroutines of other data-analysis methods. An attacker with the capability
of inserting their own data points into the training set can manipulate the
inferred nearest neighbor structure. We distill this goal to the task of
performing a training-set data insertion attack against $k$-Nearest Neighbor
classification ($k$NN). We prove that computing an optimal training-time
(a.k.a. poisoning) attack against $k$NN classification is NP-Hard, even when $k
= 1$ and the attacker can insert only a single data point. We provide an
anytime algorithm to perform such an attack, and a greedy algorithm for general
$k$ and attacker budget. We provide theoretical bounds and empirically
demonstrate the effectiveness and practicality of our methods on synthetic and
real-world datasets. Empirically, we find that $k$NN is vulnerable in practice
and that dimensionality reduction is an effective defense. We conclude with a
discussion of open problems illuminated by our analysis.

We present tile2tile, an approach for style transfer between levels of
tile-based platformer games. Our method involves training models that translate
levels from a lower-resolution sketch representation based on tile affordances
to the original tile representation for a given game. This enables these
models, which we refer to as filters, to translate level sketches into the
style of a specific game. Moreover, by converting a level of one game into
sketch form and then translating the resulting sketch into the tiles of another
game, we obtain a method of style transfer between two games. We use Markov
random fields and autoencoders for learning the game filters and apply them to
demonstrate style transfer between levels of Super Mario Bros, Kid Icarus, Mega
Man and Metroid.

This work explores the search for heterogeneous approximate multiplier
configurations for neural networks that produce high accuracy and low energy
consumption. We discuss the validity of additive Gaussian noise added to
accurate neural network computations as a surrogate model for behavioral
simulation of approximate multipliers. The continuous and differentiable
properties of the solution space spanned by the additive Gaussian noise model
are used as a heuristic that generates meaningful estimates of layer robustness
without the need for combinatorial optimization techniques. Instead, the amount
of noise injected into the accurate computations is learned during network
training using backpropagation. A probabilistic model of the multiplier error
is presented to bridge the gap between the domains; the model estimates the
standard deviation of the approximate multiplier error, connecting solutions in
the additive Gaussian noise space to actual hardware instances. Our experiments
show that the combination of heterogeneous approximation and neural network
retraining reduces the energy consumption for multiplications by 70% to 79% for
different ResNet variants on the CIFAR-10 dataset with a Top-1 accuracy loss
below one percentage point. For the more complex Tiny ImageNet task, our VGG16
model achieves a 53 % reduction in energy consumption with a drop in Top-5
accuracy of 0.5 percentage points. We further demonstrate that our error model
can predict the parameters of an approximate multiplier in the context of the
commonly used additive Gaussian noise (AGN) model with high accuracy. Our
software implementation is available under
https://github.com/etrommer/agn-approx.

Large-scale social networks are thought to contribute to polarization by
amplifying people's biases. However, the complexity of these technologies makes
it difficult to identify the mechanisms responsible and to evaluate mitigation
strategies. Here we show under controlled laboratory conditions that
information transmission through social networks amplifies motivational biases
on a simple perceptual decision-making task. Participants in a large behavioral
experiment showed increased rates of biased decision-making when part of a
social network relative to asocial participants, across 40 independently
evolving populations. Drawing on techniques from machine learning and Bayesian
statistics, we identify a simple adjustment to content-selection algorithms
that is predicted to mitigate bias amplification. This algorithm generates a
sample of perspectives from within an individual's network that is more
representative of the population as a whole. In a second large experiment, this
strategy reduced bias amplification while maintaining the benefits of
information sharing.

We analyze the behavior of projected stochastic gradient descent focusing on
the case where the optimum is on the boundary of the constraint set and the
gradient does not vanish at the optimum. Here iterates may in expectation make
progress against the objective at each step. When this and an appropriate
moment condition on noise holds, we prove that the convergence rate to the
optimum of the constrained stochastic gradient descent will be different and
typically be faster than the unconstrained stochastic gradient descent
algorithm. Our results argue that the concentration around the optimum is
exponentially distributed rather than normally distributed, which typically
determines the limiting convergence in the unconstrained case. The methods that
we develop rely on a geometric ergodicity proof. This extends a result on
Markov chains by Hajek (1982) to the area of stochastic approximation
algorithms. As examples, we show how the results apply to linear programming
and tabular reinforcement learning.

This paper proposes a non-interactive end-to-end solution for secure fusion
and matching of biometric templates using fully homomorphic encryption (FHE).
Given a pair of encrypted feature vectors, we perform the following ciphertext
operations, i) feature concatenation, ii) fusion and dimensionality reduction
through a learned linear projection, iii) scale normalization to unit
$\ell_2$-norm, and iv) match score computation. Our method, dubbed HEFT
(Homomorphically Encrypted Fusion of biometric Templates), is custom-designed
to overcome the unique constraint imposed by FHE, namely the lack of support
for non-arithmetic operations. From an inference perspective, we systematically
explore different data packing schemes for computationally efficient linear
projection and introduce a polynomial approximation for scale normalization.
From a training perspective, we introduce an FHE-aware algorithm for learning
the linear projection matrix to mitigate errors induced by approximate
normalization. Experimental evaluation for template fusion and matching of face
and voice biometrics shows that HEFT (i) improves biometric verification
performance by 11.07% and 9.58% AUROC compared to the respective unibiometric
representations while compressing the feature vectors by a factor of 16 (512D
to 32D), and (ii) fuses a pair of encrypted feature vectors and computes its
match score against a gallery of size 1024 in 884 ms. Code and data are
available at https://github.com/human-analysis/encrypted-biometric-fusion

Graph Neural Networks (GNNs) have been successfully applied to many
real-world static graphs. However, the success of static graphs has not fully
translated to dynamic graphs due to the limitations in model design, evaluation
settings, and training strategies. Concretely, existing dynamic GNNs do not
incorporate state-of-the-art designs from static GNNs, which limits their
performance. Current evaluation settings for dynamic GNNs do not fully reflect
the evolving nature of dynamic graphs. Finally, commonly used training methods
for dynamic GNNs are not scalable. Here we propose ROLAND, an effective graph
representation learning framework for real-world dynamic graphs. At its core,
the ROLAND framework can help researchers easily repurpose any static GNN to
dynamic graphs. Our insight is to view the node embeddings at different GNN
layers as hierarchical node states and then recurrently update them over time.
We then introduce a live-update evaluation setting for dynamic graphs that
mimics real-world use cases, where GNNs are making predictions and being
updated on a rolling basis. Finally, we propose a scalable and efficient
training approach for dynamic GNNs via incremental training and meta-learning.
We conduct experiments over eight different dynamic graph datasets on future
link prediction tasks. Models built using the ROLAND framework achieve on
average 62.7% relative mean reciprocal rank (MRR) improvement over
state-of-the-art baselines under the standard evaluation settings on three
datasets. We find state-of-the-art baselines experience out-of-memory errors
for larger datasets, while ROLAND can easily scale to dynamic graphs with 56
million edges. After re-implementing these baselines using the ROLAND training
strategy, ROLAND models still achieve on average 15.5% relative MRR improvement
over the baselines.

Federated learning (FL) enables mobile devices to collaboratively learn a
shared prediction model while keeping data locally. However, there are two
major research challenges to practically deploy FL over mobile devices: (i)
frequent wireless updates of huge size gradients v.s. limited spectrum
resources, and (ii) energy-hungry FL communication and local computing during
training v.s. battery-constrained mobile devices. To address those challenges,
in this paper, we propose a novel multi-bit over-the-air computation
(M-AirComp) approach for spectrum-efficient aggregation of local model updates
in FL and further present an energy-efficient FL design for mobile devices.
Specifically, a high-precision digital modulation scheme is designed and
incorporated in the M-AirComp, allowing mobile devices to upload model updates
at the selected positions simultaneously in the multi-access channel. Moreover,
we theoretically analyze the convergence property of our FL algorithm. Guided
by FL convergence analysis, we formulate a joint transmission probability and
local computing control optimization, aiming to minimize the overall energy
consumption (i.e., iterative local computing + multi-round communications) of
mobile devices in FL. Extensive simulation results show that our proposed
scheme outperforms existing ones in terms of spectrum utilization, energy
efficiency, and learning accuracy.

In this paper, we study the problem of 3D scene geometry decomposition and
manipulation from 2D views. By leveraging the recent implicit neural
representation techniques, particularly the appealing neural radiance fields,
we introduce an object field component to learn unique codes for all individual
objects in 3D space only from 2D supervision. The key to this component is a
series of carefully designed loss functions to enable every 3D point,
especially in non-occupied space, to be effectively optimized even without 3D
labels. In addition, we introduce an inverse query algorithm to freely
manipulate any specified 3D object shape in the learned scene representation.
Notably, our manipulation algorithm can explicitly tackle key issues such as
object collisions and visual occlusions. Our method, called DM-NeRF, is among
the first to simultaneously reconstruct, decompose, manipulate and render
complex 3D scenes in a single pipeline. Extensive experiments on three datasets
clearly show that our method can accurately decompose all 3D objects from 2D
views, allowing any interested object to be freely manipulated in 3D space such
as translation, rotation, size adjustment, and deformation.

Atmospheric effects, such as turbulence and background thermal noise, inhibit
the propagation of coherent light used in ON-OFF keying free-space optical
communication. Here we present and experimentally validate a convolutional
neural network to reduce the bit error rate of free-space optical communication
in post-processing that is significantly simpler and cheaper than existing
solutions based on advanced optics. Our approach consists of two neural
networks, the first determining the presence of coherent bit sequences in
thermal noise and turbulence and the second demodulating the coherent bit
sequences. All data used for training and testing our network is obtained
experimentally by generating ON-OFF keying bit streams of coherent light,
combining these with thermal light, and passing the resultant light through a
turbulent water tank which we have verified mimics turbulence in the air to a
high degree of accuracy. Our convolutional neural network improves detection
accuracy over threshold classification schemes and has the capability to be
integrated with current demodulation and error correction schemes.

Paper recommendation with user-generated keyword is to suggest papers that
simultaneously meet user's interests and are relevant to the input keyword.
This is a recommendation task with two queries, a.k.a. user ID and keyword.
However, existing methods focus on recommendation according to one query,
a.k.a. user ID, and are not applicable to solving this problem. In this paper,
we propose a novel click-through rate (CTR) prediction model with heterogeneous
graph neural network, called AMinerGNN, to recommend papers with two queries.
Specifically, AMinerGNN constructs a heterogeneous graph to project user,
paper, and keyword into the same embedding space by graph representation
learning. To process two queries, a novel query attentive fusion layer is
designed to recognize their importances dynamically and then fuse them as one
query to build a unified and end-to-end recommender system. Experimental
results on our proposed dataset and online A/B tests prove the superiority of
AMinerGNN.

Since the traffic conditions change over time, machine learning models that
predict traffic flows must be updated continuously and efficiently in smart
public transportation. Federated learning (FL) is a distributed machine
learning scheme that allows buses to receive model updates without waiting for
model training on the cloud. However, FL is vulnerable to poisoning or DDoS
attacks since buses travel in public. Some work introduces blockchain to
improve reliability, but the additional latency from the consensus process
reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a scheme
that reduces the latency of aggregation to improve efficiency, but the learning
performance is unstable due to unreasonably weighted local models. To address
the above challenges, this paper offers a blockchain-based asynchronous
federated learning scheme with a dynamic scaling factor (DBAFL). Specifically,
the novel committee-based consensus algorithm for blockchain improves
reliability at the lowest possible cost of time. Meanwhile, the devised dynamic
scaling factor allows AFL to assign reasonable weights to stale local models.
Extensive experiments conducted on heterogeneous devices validate outperformed
learning performance, efficiency, and reliability of DBAFL.

In recent years WiFi became the primary source of information to locate a
person or device indoor. Collecting RSSI values as reference measurements with
known positions, known as WiFi fingerprinting, is commonly used in various
positioning methods and algorithms that appear in literature. However,
measuring the spatial distance between given set of WiFi fingerprints is
heavily affected by the selection of the signal distance function used to model
signal space as geospatial distance. In this study, the authors proposed
utilization of machine learning to improve the estimation of geospatial
distance between fingerprints. This research examined data collected from 13
different open datasets to provide a broad representation aiming for general
model that can be used in any indoor environment. The proposed novel approach
extracted data features by examining a set of commonly used signal distance
metrics via feature selection process that includes feature analysis and
genetic algorithm. To demonstrate that the output of this research is venue
independent, all models were tested on datasets previously excluded during the
training and validation phase. Finally, various machine learning algorithms
were compared using wide variety of evaluation metrics including ability to
scale out the test bed to real world unsolicited datasets.

Secure aggregation is widely used in horizontal Federated Learning (FL), to
prevent leakage of training data when model updates from data owners are
aggregated. Secure aggregation protocols based on Homomorphic Encryption (HE)
have been utilized in industrial cross-silo FL systems, one of the settings
involved with privacy-sensitive organizations such as financial or medical,
presenting more stringent requirements on privacy security. However, existing
HE-based solutions have limitations in efficiency and security guarantees
against colluding adversaries without a Trust Third Party.
  This paper proposes an efficient Doubly Homomorphic Secure Aggregation (DHSA)
scheme for cross-silo FL, which utilizes multi-key Homomorphic Encryption
(MKHE) and seed homomorphic pseudorandom generator (SHPRG) as cryptographic
primitives. The application of MKHE provides strong security guarantees against
up to $N-2$ participates colluding with the aggregator, with no TTP required.
To mitigate the large computation and communication cost of MKHE, we leverage
the homomorphic property of SHPRG to replace the majority of MKHE computation
by computationally-friendly mask generation from SHPRG, while preserving the
security. Overall, the resulting scheme satisfies the stringent security
requirements of typical cross-silo FL scenarios, at the same time providing
high computation and communication efficiency for practical usage. We
experimentally demonstrate our scheme brings a speedup to 20$\times$ over the
state-of-the-art HE-based secure aggregation, and reduces the traffic volume to
approximately 1.5$\times$ inflation over the plain learning setting.

Magnetic resonance imaging serves as an essential tool for clinical
diagnosis. However, it suffers from a long acquisition time. The utilization of
deep learning, especially the deep generative models, offers aggressive
acceleration and better reconstruction in magnetic resonance imaging.
Nevertheless, learning the data distribution as prior knowledge and
reconstructing the image from limited data remains challenging. In this work,
we propose a novel Hankel-k-space generative model (HKGM), which can generate
samples from a training set of as little as one k-space data. At the prior
learning stage, we first construct a large Hankel matrix from k-space data,
then extract multiple structured k-space patches from the large Hankel matrix
to capture the internal distribution among different patches. Extracting
patches from a Hankel matrix enables the generative model to be learned from
redundant and low-rank data space. At the iterative reconstruction stage, it is
observed that the desired solution obeys the learned prior knowledge. The
intermediate reconstruction solution is updated by taking it as the input of
the generative model. The updated result is then alternatively operated by
imposing low-rank penalty on its Hankel matrix and data consistency con-strain
on the measurement data. Experimental results confirmed that the internal
statistics of patches within a single k-space data carry enough information for
learning a powerful generative model and provide state-of-the-art
reconstruction.

Thanks to the increasing power of CPUs and GPUs in embedded systems,
deep-learning-enabled object detection systems have become pervasive in a
multitude of robotic applications. While deep learning models are vulnerable to
several well-known adversarial attacks, the applicability of these attacks is
severely limited by strict assumptions on, for example, access to the detection
system. Inspired by Man-in-the-Middle attacks in cryptography, we propose a
novel hardware attack on object detection systems that overcomes these
limitations. Experiments prove that it is possible to generate an efficient
Universal Adversarial Perturbation (UAP) within one minute and then use the
perturbation to attack a detection system via the Man-in-the-Middle attack.
These findings raise serious concerns for applications of deep learning models
in safety-critical systems, such as autonomous driving. Demo Video:
https://youtu.be/OvIpe-R3ZS8.

Cooperative guidance of multiple missiles is a challenging task with rigorous
constraints of time and space consensus, especially when attacking dynamic
targets. In this paper, the cooperative guidance task is described as a
distributed multi-objective cooperative optimization problem. To address the
issues of non-stationarity and continuous control faced by cooperative
guidance, the natural evolutionary strategy (NES) is improved along with an
elitist adaptive learning technique to develop a novel natural co-evolutionary
strategy (NCES). The gradients of original evolutionary strategy are rescaled
to reduce the estimation bias caused by the interaction between the multiple
missiles. Then, a hybrid co-evolutionary cooperative guidance law (HCCGL) is
proposed by integrating the highly scalable co-evolutionary mechanism and the
traditional guidance strategy. Finally, three simulations under different
conditions demonstrate the effectiveness and superiority of this guidance law
in solving cooperative guidance tasks with high accuracy. The proposed
co-evolutionary approach has great prospects not only in cooperative guidance,
but also in other application scenarios of multi-objective optimization,
dynamic optimization and distributed control.

Urban downscaling is a link to transfer the knowledge from coarser climate
information to city scale assessments. These high-resolution assessments need
multiyear climatology of past data and future projections, which are complex
and computationally expensive to generate using traditional numerical weather
prediction models. The city of Austin, Texas, USA has seen tremendous growth in
the past decade. Systematic planning for the future requires the availability
of fine resolution city-scale datasets. In this study, we demonstrate a novel
approach generating a general purpose operator using deep learning to perform
urban downscaling. The algorithm employs an iterative super-resolution
convolutional neural network (Iterative SRCNN) over the city of Austin, Texas,
USA. We show the development of a high-resolution gridded precipitation product
(300 m) from a coarse (10 km) satellite-based product (JAXA GsMAP). High
resolution gridded datasets of precipitation offer insights into the spatial
distribution of heavy to low precipitation events in the past. The algorithm
shows improvement in the mean peak-signal-to-noise-ratio and mutual information
to generate high resolution gridded product of size 300 m X 300 m relative to
the cubic interpolation baseline. Our results have implications for developing
high-resolution gridded-precipitation urban datasets and the future planning of
smart cities for other cities and other climatic variables.

The rapid proliferation of learning systems in an arbitrarily changing
environment mandates the need for managing tensions between exploration and
exploitation. This work proposes a quantum-inspired bandit learning approach
for the learning-and-adapting-based offloading problem where a client observes
and learns the costs of each task offloaded to the candidate resource
providers, e.g., fog nodes. In this approach, a new action update strategy and
novel probabilistic action selection are adopted, provoked by the amplitude
amplification and collapse postulate in quantum computation theory,
respectively. We devise a locally linear mapping between a quantum-mechanical
phase in a quantum domain, e.g., Grover-type search algorithm, and a distilled
probability-magnitude in a value-based decision-making domain, e.g.,
adversarial multi-armed bandit algorithm. The proposed algorithm is
generalized, via the devised mapping, for better learning weight adjustments on
favourable/unfavourable actions and its effectiveness is verified via
simulation.

A novel clustering technique based on the projection onto convex set (POCS)
method, called POCS-based clustering algorithm, is proposed in this paper. The
proposed POCS-based clustering algorithm exploits a parallel projection method
of POCS to find appropriate cluster prototypes in the feature space. The
algorithm considers each data point as a convex set and projects the cluster
prototypes parallelly to the member data points. The projections are convexly
combined to minimize the objective function for data clustering purpose. The
performance of the proposed POCS-based clustering algorithm is verified through
experiments on various synthetic datasets. The experimental results show that
the proposed POCS-based clustering algorithm is competitive and efficient in
terms of clustering error and execution speed when compared with other
conventional clustering methods including Fuzzy C-Means (FCM) and K-means
clustering algorithms.

Aerodynamics plays an important role in aviation industry and aircraft
design. Detecting and minimizing the phenomenon of flow separation from
scattered pressure data on airfoil is critical for ensuring stable and
efficient aviation. However, since it is challenging to understand the
mechanics of flow field separation, the aerodynamic parameters are emphasized
for the identification and control of flow separation. It has been investigated
extensively using traditional algorithms and machine learning methods such as
the support vector machine (SVM) models. Recently, a growing interest in
quantum computing and its applications among wide research communities sheds
light upon the use of quantum techniques to solve aerodynamic problems. In this
paper, we apply qSVM, a quantum SVM algorithm based on the quantum annealing
model, to identify whether there is flow separation, with their performance in
comparison to the widely-used classical SVM. We show that our approach
outperforms the classical SVM with an 11.1% increase of the accuracy, from
0.818 to 0.909, for this binary classification task. We further develop
multi-class qSVMs based on one-against-all algorithm. We apply it to classify
multiple types of the attack angles to the wings, where the advantage over the
classical multi-class counterpart is maintained with an accuracy increased from
0.67 to 0.79, by 17.9%. Our work demonstrates a useful quantum technique for
classifying flow separation scenarios, and may promote rich investigations for
quantum computing applications in fluid dynamics.

Matchmaking systems are vital for creating fair matches in online multiplayer
games, which directly affects players' satisfactions and game experience. Most
of the matchmaking systems largely rely on precise estimation of players' game
skills to construct equitable games. However, the skill rating of a novice is
usually inaccurate, as current matchmaking rating algorithms require
considerable amount of games for learning the true skill of a new player. Using
these unreliable skill scores at early stages for matchmaking usually leads to
disparities in terms of team performance, which causes negative game
experience. This is known as the ''cold-start'' problem for matchmaking rating
  To overcome this conundrum, this paper proposes QuickSKill, a deep learning
based novice skill estimation framework to quickly probe abilities of new
players in online multiplayer games. QuickSKill extracts sequential performance
features from initial few games of a player to predict his/her future skill
rating with a dedicated neural network, thus delivering accurate skill
estimation at the player's early game stage. By employing QuickSKill for
matchmaking, game fairness can be dramatically improved in the initial
cold-start period. We conduct experiments in a popular mobile multiplayer game
in both offline and online scenarios. Results obtained with two real-world
anonymized gaming datasets demonstrate that proposed QuickSKill delivers
precise estimation of game skills for novices, leading to significantly lower
team skill disparities and better player game experience. To the best of our
knowledge, proposed QuickSKill is the first framework that tackles the
cold-start problem for traditional skill rating algorithms.

Compared to the existing function-based models in deep generative modeling,
the recently proposed diffusion models have achieved outstanding performance
with a stochastic-process-based approach. But a long sampling time is required
for this approach due to many timesteps for discretization. Schr\"odinger
bridge (SB)-based models attempt to tackle this problem by training
bidirectional stochastic processes between distributions. However, they still
have a slow sampling speed compared to generative models such as generative
adversarial networks. And due to the training of the bidirectional stochastic
processes, they require a relatively long training time. Therefore, this study
tried to reduce the number of timesteps and training time required and proposed
regularization terms to the existing SB models to make the bidirectional
stochastic processes consistent and stable with a reduced number of timesteps.
Each regularization term was integrated into a single term to enable more
efficient training in computation time and memory usage. Applying this
regularized stochastic process to various generation tasks, the desired
translations between different distributions were obtained, and accordingly,
the possibility of generative modeling based on a stochastic process with
faster sampling speed could be confirmed. The code is available at
https://github.com/KiUngSong/RSB.

Deception is rapidly growing as an important tool for cyber defence,
complementing existing perimeter security measures to rapidly detect breaches
and data theft. One of the factors limiting the use of deception has been the
cost of generating realistic artefacts by hand. Recent advances in Machine
Learning have, however, created opportunities for scalable, automated
generation of realistic deceptions. This vision paper describes the
opportunities and challenges involved in developing models to mimic many common
elements of the IT stack for deception effects.

The 3D Bin Packing Problem (3D-BPP) is one of the most demanded yet
challenging problems in industry, where an agent must pack variable size items
delivered in sequence into a finite bin with the aim to maximize the space
utilization. It represents a strongly NP-Hard optimization problem such that no
solution has been offered to date with high performance in space utilization.
In this paper, we present a new reinforcement learning (RL) framework for a
3D-BPP solution for improving performance. First, a buffer is introduced to
allow multi-item action selection. By increasing the degree of freedom in
action selection, a more complex policy that results in better packing
performance can be derived. Second, we propose an agnostic data augmentation
strategy that exploits both bin item symmetries for improving sample
efficiency. Third, we implement a model-based RL method adapted from the
popular algorithm AlphaGo, which has shown superhuman performance in zero-sum
games. Our adaptation is capable of working in single-player and score based
environments. In spite of the fact that AlphaGo versions are known to be
computationally heavy, we manage to train the proposed framework with a single
thread and GPU, while obtaining a solution that outperforms the
state-of-the-art results in space utilization.

Neural network-based Text-to-Speech has significantly improved the quality of
synthesized speech. Prominent methods (e.g., Tacotron2, FastSpeech, FastPitch)
usually generate Mel-spectrogram from text and then synthesize speech using
vocoder (e.g., WaveNet, WaveGlow, HiFiGAN). Compared with traditional
parametric approaches (e.g., STRAIGHT and WORLD), neural vocoder based
end-to-end models suffer from slow inference speed, and the synthesized speech
is usually not robust and lack of controllability. In this work, we propose a
novel updated vocoder, which is a simple signal model to train and easy to
generate waveforms. We use the Gaussian-Markov model toward robust learning of
spectral envelope and wavelet-based statistical signal processing to
characterize and decompose F0 features. It can retain the fine spectral
envelope and achieve high controllability of natural speech. The experimental
results demonstrate that our proposed vocoder achieves better naturalness of
reconstructed speech than the conventional STRAIGHT vocoder, slightly better
than WaveNet, and somewhat worse than the WaveRNN.

In games, as in and many other domains, design validation and testing is a
huge challenge as systems are growing in size and manual testing is becoming
infeasible. This paper proposes a new approach to automated game validation and
testing. Our method leverages a data-driven imitation learning technique, which
requires little effort and time and no knowledge of machine learning or
programming, that designers can use to efficiently train game testing agents.
We investigate the validity of our approach through a user study with industry
experts. The survey results show that our method is indeed a valid approach to
game validation and that data-driven programming would be a useful aid to
reducing effort and increasing quality of modern playtesting. The survey also
highlights several open challenges. With the help of the most recent
literature, we analyze the identified challenges and propose future research
directions suitable for supporting and maximizing the utility of our approach.

Predictive Coding Networks (PCNs) aim to learn a generative model of the
world. Given observations, this generative model can then be inverted to infer
the causes of those observations. However, when training PCNs, a noticeable
pathology is often observed where inference accuracy peaks and then declines
with further training. This cannot be explained by overfitting since both
training and test accuracy decrease simultaneously. Here we provide a thorough
investigation of this phenomenon and show that it is caused by an imbalance
between the speeds at which the various layers of the PCN converge. We
demonstrate that this can be prevented by regularising the weight matrices at
each layer: by restricting the relative size of matrix singular values, we
allow the weight matrix to change but restrict the overall impact which a layer
can have on its neighbours. We also demonstrate that a similar effect can be
achieved through a more biologically plausible and simple scheme of just
capping the weights.

With the development of streaming media technology, increasing communication
relies on sound and visual information, which puts a massive burden on online
media. Data compression becomes increasingly important to reduce the volume of
data transmission and storage. To further improve the efficiency of image
compression, researchers utilize various image processing methods to compensate
for the limitations of conventional codecs and advanced learning-based
compression methods. Instead of modifying the image compression oriented
approaches, we propose a unified image compression preprocessing framework,
called Kuchen, which aims to further improve the performance of existing
codecs. The framework consists of a hybrid data labeling system along with a
learning-based backbone to simulate personalized preprocessing. As far as we
know, this is the first exploration of setting a unified preprocessing
benchmark in image compression tasks. Results demonstrate that the modern
codecs optimized by our unified preprocessing framework constantly improve the
efficiency of the state-of-the-art compression.

The scene graph generation has gained tremendous progress in recent years.
However, its intrinsic long-tailed distribution of predicate classes is a
challenging problem. Almost all existing scene graph generation (SGG) methods
follow the same framework where they use a similar backbone network for object
detection and a customized network for scene graph generation. These methods
often design the sophisticated context-encoder to extract the inherent
relevance of scene context w.r.t the intrinsic predicates and complicated
networks to improve the learning capabilities of the network model for highly
imbalanced data distributions. To address the unbiased SGG problem, we present
a simple yet effective method called Context-Aware Mixture-of-Experts (CAME) to
improve the model diversity and alleviate the biased SGG without a
sophisticated design. Specifically, we propose to use the mixture of experts to
remedy the heavily long-tailed distributions of predicate classes, which is
suitable for most unbiased scene graph generators. With a mixture of relation
experts, the long-tailed distribution of predicates is addressed in a divide
and ensemble manner. As a result, the biased SGG is mitigated and the model
tends to make more balanced predicates predictions. However, experts with the
same weight are not sufficiently diverse to discriminate the different levels
of predicates distributions. Hence, we simply use the build-in context-aware
encoder, to help the network dynamically leverage the rich scene
characteristics to further increase the diversity of the model. By utilizing
the context information of the image, the importance of each expert w.r.t the
scene context is dynamically assigned. We have conducted extensive experiments
on three tasks on the Visual Genome dataset to show that came achieved superior
performance over previous methods.

Time series underwent the transition from statistics to deep learning, as did
many other machine learning fields. Although it appears that the accuracy has
been increasing as the model is updated in a number of publicly available
datasets, it typically only increases the scale by several times in exchange
for a slight difference in accuracy. Through this experiment, we point out a
different line of thinking, time series, especially long-term forecasting, may
differ from other fields. It is not necessary to use extensive and complex
models to grasp all aspects of time series, but to use pure models to grasp the
core rules of time series changes. With this simple but effective idea, we
created PureTS, a network with three pure linear layers that achieved
state-of-the-art in 80% of the long sequence prediction tasks while being
nearly the lightest model and having the fastest running speed. On this basis,
we discuss the potential of pure linear layers in both phenomena and essence.
The ability to understand the core law contributes to the high precision of
long-distance prediction, and reasonable fluctuation prevents it from
distorting the curve in multi-step prediction like mainstream deep learning
models, which is summarized as a pure linear neural network that avoids
over-fluctuating. Finally, we suggest the fundamental design standards for
lightweight long-step time series tasks: input and output should try to have
the same dimension, and the structure avoids fragmentation and complex
operations.

Recently, the performance of blind speech separation (BSS) and target speech
extraction (TSE) has greatly progressed. Most works, however, focus on
relatively well-controlled conditions using, e.g., read speech. The performance
may degrade in more realistic situations. One of the factors causing such
degradation may be intrinsic speaker variability, such as emotions, occurring
commonly in realistic speech. In this paper, we investigate the influence of
emotions on TSE and BSS. We create a new test dataset of emotional mixtures for
the evaluation of TSE and BSS. This dataset combines LibriSpeech and Ryerson
Audio-Visual Database of Emotional Speech and Song (RAVDESS). Through
controlled experiments, we can analyze the impact of different emotions on the
performance of BSS and TSE. We observe that BSS is relatively robust to
emotions, while TSE, which requires identifying and extracting the speech of a
target speaker, is much more sensitive to emotions. On comparative speaker
verification experiments we show that identifying the target speaker may be
particularly challenging when dealing with emotional speech. Using our
findings, we outline potential future directions that could improve the
robustness of BSS and TSE systems toward emotional speech.

Nowadays, an increasing number of people are being diagnosed with
cardiovascular diseases (CVDs), the leading cause of death globally. The gold
standard for identifying these heart problems is via electrocardiogram (ECG).
The standard 12-lead ECG is widely used in clinical practice and the majority
of current research. However, using a lower number of leads can make ECG more
pervasive as it can be integrated with portable or wearable devices. This
article introduces two novel techniques to improve the performance of the
current deep learning system for 3-lead ECG classification, making it
comparable with models that are trained using standard 12-lead ECG.
Specifically, we propose a multi-task learning scheme in the form of the number
of heartbeats regression and an effective mechanism to integrate patient
demographic data into the system. With these two advancements, we got
classification performance in terms of F1 scores of 0.9796 and 0.8140 on two
large-scale ECG datasets, i.e., Chapman and CPSC-2018, respectively, which
surpassed current state-of-the-art ECG classification methods, even those
trained on 12-lead data. To encourage further development, our source code is
publicly available at https://github.com/lhkhiem28/LightX3ECG.

Intent discovery is a fundamental task in NLP, and it is increasingly
relevant for a variety of industrial applications (Quarteroni 2018). The main
challenge resides in the need to identify from input utterances novel unseen
in-tents. Herein, we propose Z-BERT-A, a two-stage method for intent discovery
relying on a Transformer architecture (Vaswani et al. 2017; Devlin et al.
2018), fine-tuned with Adapters (Pfeiffer et al. 2020), initially trained for
Natural Language Inference (NLI), and later applied for unknown in-tent
classification in a zero-shot setting. In our evaluation, we firstly analyze
the quality of the model after adaptive fine-tuning on known classes. Secondly,
we evaluate its performance casting intent classification as an NLI task.
Lastly, we test the zero-shot performance of the model on unseen classes,
showing how Z-BERT-A can effectively perform in-tent discovery by generating
intents that are semantically similar, if not equal, to the ground truth ones.
Our experiments show how Z-BERT-A is outperforming a wide variety of baselines
in two zero-shot settings: known intents classification and unseen intent
discovery. The proposed pipeline holds the potential to be widely applied in a
variety of application for customer care. It enables automated dynamic triage
using a lightweight model that, unlike large language models, can be easily
deployed and scaled in a wide variety of business scenarios. Especially when
considering a setting with limited hardware availability and performance
whereon-premise or low resource cloud deployments are imperative. Z-BERT-A,
predicting novel intents from a single utterance, represents an innovative
approach for intent discovery, enabling online generation of novel intents. The
pipeline is available as an installable python package at the following link:
https://github.com/GT4SD/zberta.

Recent years have seen a surge in research on dynamic graph representation
learning, which aims to model temporal graphs that are dynamic and evolving
constantly over time. However, current work typically models graph dynamics
with recurrent neural networks (RNNs), making them suffer seriously from
computation and memory overheads on large temporal graphs. So far, scalability
of dynamic graph representation learning on large temporal graphs remains one
of the major challenges. In this paper, we present a scalable framework, namely
SpikeNet, to efficiently capture the temporal and structural patterns of
temporal graphs. We explore a new direction in that we can capture the evolving
dynamics of temporal graphs with spiking neural networks (SNNs) instead of
RNNs. As a low-power alternative to RNNs, SNNs explicitly model graph dynamics
as spike trains of neuron populations and enable spike-based propagation in an
efficient way. Experiments on three large real-world temporal graph datasets
demonstrate that SpikeNet outperforms strong baselines on the temporal node
classification task with lower computational costs. Particularly, SpikeNet
generalizes to a large temporal graph (2M nodes and 13M edges) with
significantly fewer parameters and computation overheads. Our code is publicly
available at https://github.com/EdisonLeeeee/SpikeNet

Inferring linear relationships lies at the heart of many empirical
investigations. A measure of linear dependence should correctly evaluate the
strength of the relationship as well as qualify whether it is meaningful for
the population. Pearson's correlation coefficient (PCC), the \textit{de-facto}
measure for bivariate relationships, is known to lack in both regards. The
estimated strength $r$ maybe wrong due to limited sample size, and nonnormality
of data. In the context of statistical significance testing, erroneous
interpretation of a $p$-value as posterior probability leads to Type I errors
-- a general issue with significance testing that extends to PCC. Such errors
are exacerbated when testing multiple hypotheses simultaneously. To tackle
these issues, we propose a machine-learning-based predictive data calibration
method which essentially conditions the data samples on the expected linear
relationship. Calculating PCC using calibrated data yields a calibrated
$p$-value that can be interpreted as posterior probability together with a
calibrated $r$ estimate, a desired outcome not provided by other methods.
Furthermore, the ensuing independent interpretation of each test might
eliminate the need for multiple testing correction. We provide empirical
evidence favouring the proposed method using several simulations and
application to real-world data.

We take a systematic look at the problem of storing whole files in a cache
with limited capacity in the context of optimistic learning, where the caching
policy has access to a prediction oracle (provided by, e.g., a Neural Network).
The successive file requests are assumed to be generated by an adversary, and
no assumption is made on the accuracy of the oracle. In this setting, we
provide a universal lower bound for prediction-assisted online caching and
proceed to design a suite of policies with a range of performance-complexity
trade-offs. All proposed policies offer sublinear regret bounds commensurate
with the accuracy of the oracle. Our results substantially improve upon all
recently-proposed online caching policies, which, being unable to exploit the
oracle predictions, offer only $O(\sqrt{T})$ regret. In this pursuit, we
design, to the best of our knowledge, the first comprehensive optimistic
Follow-the-Perturbed leader policy, which generalizes beyond the caching
problem. We also study the problem of caching files with different sizes and
the bipartite network caching problem. Finally, we evaluate the efficacy of the
proposed policies through extensive numerical experiments using real-world
traces.

JPEG image compression algorithm is a widely used technique for image size
reduction in edge and cloud computing settings. However, applying such lossy
compression on images processed by deep neural networks can lead to significant
accuracy degradation. Inspired by the curriculum learning paradigm, we present
a novel training approach called curriculum pre-training (CPT) for crowd
counting on compressed images, which alleviates the drop in accuracy resulting
from lossy compression. We verify the effectiveness of our approach by
extensive experiments on three crowd counting datasets, two crowd counting DNN
models and various levels of compression. Our proposed training method is not
overly sensitive to hyper-parameters, and reduces the error, particularly for
heavily compressed images, by up to 19.70%.

Rolling bearings are the most crucial components of rotating machinery.
Identifying defective bearings in a timely manner may prevent the malfunction
of an entire machinery system. The mechanical condition monitoring field has
entered the big data phase as a result of the fast advancement of machine
parts. When working with large amounts of data, the manual feature extraction
approach has the drawback of being inefficient and inaccurate. Data-driven
methods like the Deep Learning method have been successfully used in recent
years for mechanical intelligent fault detection. Convolutional neural networks
(CNNs) were mostly used in earlier research to detect and identify bearing
faults. The CNN model, however, suffers from the drawback of having trouble
managing fault-time information, which results in a lack of classification
results. In this study, bearing defects have been classified using a
state-of-the-art Vision Transformer (ViT). Bearing defects were classified
using Case Western Reserve University (CWRU) bearing failure laboratory
experimental data. The research took into account 13 distinct kinds of defects
under 0-load situations in addition to normal bearing conditions. Using the
short-time Fourier transform (STFT), the vibration signals were converted into
2D time-frequency images. The 2D time-frequency images are used as input
parameters for the ViT. The model achieved an overall accuracy of 98.8%.

Malware detection plays a crucial role in cyber-security with the increase in
malware growth and advancements in cyber-attacks. Previously unseen malware
which is not determined by security vendors are often used in these attacks and
it is becoming inevitable to find a solution that can self-learn from unlabeled
sample data. This paper presents SHERLOCK, a self-supervision based deep
learning model to detect malware based on the Vision Transformer (ViT)
architecture. SHERLOCK is a novel malware detection method which learns unique
features to differentiate malware from benign programs with the use of
image-based binary representation. Experimental results using 1.2 million
Android applications across a hierarchy of 47 types and 696 families, shows
that self-supervised learning can achieve an accuracy of 97% for the binary
classification of malware which is higher than existing state-of-the-art
techniques. Our proposed model is also able to outperform state-of-the-art
techniques for multi-class malware classification of types and family with
macro-F1 score of .497 and .491 respectively.

Hybrid modeling combining data-driven techniques and numerical methods is an
emerging and promising research direction for efficient climate simulation.
However, previous works lack practical platforms, making developing hybrid
modeling a challenging programming problem. Furthermore, the lack of standard
data sets and evaluation metrics may hamper researchers from comprehensively
comparing various algorithms under a uniform condition. To address these
problems, we propose a framework called NeuroClim for hybrid modeling under the
real-world scenario, a basic setting to simulate the real climate that we live
in. NeuroClim consists of three parts: (1) Platform. We develop a user-friendly
platform NeuroGCM for efficiently developing hybrid modeling in climate
simulation. (2) Dataset. We provide an open-source dataset for data-driven
methods in hybrid modeling. We investigate the characteristics of the data,
i.e., heterogeneity and stiffness, which reveals the difficulty of regressing
climate simulation data; (3) Metrics. We propose a methodology for
quantitatively evaluating hybrid modeling, including the approximation ability
of machine learning models and the stability during simulation. We believe that
NeuroClim allows researchers to work without high level of climate-related
expertise and focus only on machine learning algorithm design, which will
accelerate hybrid modeling research in the AI-Climate intersection. The codes
and data are released at https://github.com/x-w19/NeuroClim.

Few-shot object detection (FSOD) aims to classify and detect few images of
novel categories. Existing meta-learning methods insufficiently exploit
features between support and query images owing to structural limitations. We
propose a hierarchical attention network with sequentially large receptive
fields to fully exploit the query and support images. In addition,
meta-learning does not distinguish the categories well because it determines
whether the support and query images match. In other words, metric-based
learning for classification is ineffective because it does not work directly.
Thus, we propose a contrastive learning method called meta-contrastive
learning, which directly helps achieve the purpose of the meta-learning
strategy. Finally, we establish a new state-of-the-art network, by realizing
significant margins. Our method brings 2.3, 1.0, 1.3, 3.4 and 2.4% AP
improvements for 1-30 shots object detection on COCO dataset. Our code is
available at: https://github.com/infinity7428/hANMCL

Most industrial processes in real-world manufacturing applications are
characterized by the scalability property, which requires an automated strategy
to self-adapt machine learning (ML) software systems to the new conditions. In
this paper, we investigate an Electroslag Remelting (ESR) use case process from
the Uddeholms AB steel company. The use case involves predicting the minimum
pressure value for a vacuum pumping event. Taking into account the long time
required to collect new records and efficiently integrate the new machines with
the built ML software system. Additionally, to accommodate the changes and
satisfy the non-functional requirement of the software system, namely
adaptability, we propose an automated and adaptive approach based on a drift
handling technique called importance weighting. The aim is to address the
problem of adding a new furnace to production and enable the adaptability
attribute of the ML software. The overall results demonstrate the improvements
in ML software performance achieved by implementing the proposed approach over
the classical non-adaptive approach.

In tasks where the goal or configuration varies between iterations,
human-robot interaction (HRI) can allow the robot to handle repeatable aspects
and the human to provide information which adapts to the current state.
Advanced interactive robot behaviors are currently realized by inferring human
goal or, for physical interaction, adapting robot impedance. While many
application-specific heuristics have been proposed for interactive robot
behavior, they are often limited in scope, e.g. only considering human
ergonomics or task performance. To improve generality, this paper proposes a
framework which plans both trajectory and impedance online, handles a mix of
task and human objectives, and can be efficiently applied to a new task. This
framework can consider many types of uncertainty: contact constraint variation,
uncertainty in human goals, or task disturbances. An uncertainty-aware task
model is learned from a few demonstrations using Gaussian Processes. This task
model is used in a nonlinear model predictive control (MPC) problem to optimize
robot trajectory and impedance according to belief in discrete human goals,
human kinematics, safety constraints, contact stability, and frequency-domain
disturbance rejection. This MPC formulation is introduced, analyzed with
respect to convexity, and validated in co-manipulation with multiple goals, a
collaborative polishing task, and a collaborative assembly task.

The heart of Quantum Federated Learning (QFL) is associated with a
distributed learning architecture across several local quantum devices and a
more efficient training algorithm for the QFL is expected to minimize the
communication overhead among different quantum participants. In this work, we
put forth an efficient learning algorithm, namely federated quantum natural
gradient descent (FQNGD), applied in a QFL framework which consists of the
variational quantum circuit (VQC)-based quantum neural networks (QNN). The
FQNGD algorithm admits much fewer training iterations for the QFL model to get
converged and it can significantly reduce the total communication cost among
local quantum devices. Compared with other federated learning algorithms, our
experiments on a handwritten digit classification dataset corroborate the
effectiveness of the FQNGD algorithm for the QFL in terms of a faster
convergence rate on the training dataset and higher accuracy on the test one.

Heuristic search has traditionally relied on hand-crafted or programmatically
derived heuristics. Neural networks (NNs) are newer powerful tools which can be
used to learn complex mappings from states to cost-to-go heuristics. However,
their slow single inference time is a large overhead that can substantially
slow down planning time in optimized heuristic search implementations. Several
recent works have described ways to take advantage of NN's batch computations
to decrease overhead in planning, while retaining bounds on (sub)optimality.
However, all these methods have used the NN heuristic in a "blocking" manner
while building up their batches, and have ignored possible fast-to-compute
admissible heuristics (e.g. existing classically derived heuristics) that are
usually available to use. We introduce Non-Blocking Batch A* (NBBA*), a bounded
suboptimal method which lazily computes the NN heuristic in batches while
allowing expansions informed by a non-NN heuristic. We show how this subtle but
important change can lead to substantial reductions in expansions compared to
the current blocking alternative, and see that the performance is related to
the information difference between the batch computed NN and fast non-NN
heuristic.

Built upon the decision tree (DT) classification and regression idea, the
subspace learning machine (SLM) has been recently proposed to offer higher
performance in general classification and regression tasks. Its performance
improvement is reached at the expense of higher computational complexity. In
this work, we investigate two ways to accelerate SLM. First, we adopt the
particle swarm optimization (PSO) algorithm to speed up the search of a
discriminant dimension that is expressed as a linear combination of current
dimensions. The search of optimal weights in the linear combination is
computationally heavy. It is accomplished by probabilistic search in original
SLM. The acceleration of SLM by PSO requires 10-20 times fewer iterations.
Second, we leverage parallel processing in the SLM implementation. Experimental
results show that the accelerated SLM method achieves a speed up factor of 577
in training time while maintaining comparable classification/regression
performance of original SLM.

We introduce a memory-driven semi-parametric approach to text-to-image
generation, which is based on both parametric and non-parametric techniques.
The non-parametric component is a memory bank of image features constructed
from a training set of images. The parametric component is a generative
adversarial network. Given a new text description at inference time, the memory
bank is used to selectively retrieve image features that are provided as basic
information of target images, which enables the generator to produce realistic
synthetic results. We also incorporate the content information into the
discriminator, together with semantic features, allowing the discriminator to
make a more reliable prediction. Experimental results demonstrate that the
proposed memory-driven semi-parametric approach produces more realistic images
than purely parametric approaches, in terms of both visual fidelity and
text-image semantic consistency.

Physics-based models have been mainstream in fluid dynamics for developing
predictive models. In recent years, machine learning has offered a renaissance
to the fluid community due to the rapid developments in data science,
processing units, neural network based technologies, and sensor adaptations. So
far in many applications in fluid dynamics, machine learning approaches have
been mostly focused on a standard process that requires centralizing the
training data on a designated machine or in a data center. In this letter, we
present a federated machine learning approach that enables localized clients to
collaboratively learn an aggregated and shared predictive model while keeping
all the training data on each edge device. We demonstrate the feasibility and
prospects of such decentralized learning approach with an effort to forge a
deep learning surrogate model for reconstructing spatiotemporal fields. Our
results indicate that federated machine learning might be a viable tool for
designing highly accurate predictive decentralized digital twins relevant to
fluid dynamics.

Graph Neural Networks (GNNs) have shown expressive performance on graph
representation learning by aggregating information from neighbors. Recently,
some studies have discussed the importance of modeling neighborhood
distribution on the graph. However, most existing GNNs aggregate neighbors'
features through single statistic (e.g., mean, max, sum), which loses the
information related to neighbor's feature distribution and therefore degrades
the model performance. In this paper, inspired by the method of moment in
statistical theory, we propose to model neighbor's feature distribution with
multi-order moments. We design a novel GNN model, namely Mix-Moment Graph
Neural Network (MM-GNN), which includes a Multi-order Moment Embedding (MME)
module and an Element-wise Attention-based Moment Adaptor module. MM-GNN first
calculates the multi-order moments of the neighbors for each node as
signatures, and then use an Element-wise Attention-based Moment Adaptor to
assign larger weights to important moments for each node and update node
representations. We conduct extensive experiments on 15 real-world graphs
(including social networks, citation networks and web-page networks etc.) to
evaluate our model, and the results demonstrate the superiority of MM-GNN over
existing state-of-the-art models.

Controlling fish feeding machine is challenging problem because experienced
fishermen can adequately control based on assumption. To build robust method
for reasonable application, we propose automatic controlling fish feeding
machine based on computer vision using combination of counting nutriments and
estimating ripple behavior using regression and textural feature, respectively.
To count number of nutriments, we apply object detection and tracking methods
to acknowledge the nutriments moving to sea surface. Recently, object tracking
is active research and challenging problem in computer vision. Unfortunately,
the robust tracking method for multiple small objects with dense and complex
relationships is unsolved problem in aquaculture field with more appearance
creatures. Based on the number of nutriments and ripple behavior, we can
control fish feeding machine which consistently performs well in real
environment. Proposed method presents the agreement for automatic controlling
fish feeding by the activation graphs and textural feature of ripple behavior.
Our tracking method can precisely track the nutriments in next frame comparing
with other methods. Based on computational time, proposed method reaches 3.86
fps while other methods spend lower than 1.93 fps. Quantitative evaluation can
promise that proposed method is valuable for aquaculture fish farm with widely
applied to real environment.

Comprehensive global cooperation is essential to limit global temperature
increases while continuing economic development, e.g., reducing severe
inequality or achieving long-term economic growth. Achieving long-term
cooperation on climate change mitigation with n strategic agents poses a
complex game-theoretic problem. For example, agents may negotiate and reach
climate agreements, but there is no central authority to enforce adherence to
those agreements. Hence, it is critical to design negotiation and agreement
frameworks that foster cooperation, allow all agents to meet their individual
policy objectives, and incentivize long-term adherence. This is an
interdisciplinary challenge that calls for collaboration between researchers in
machine learning, economics, climate science, law, policy, ethics, and other
fields. In particular, we argue that machine learning is a critical tool to
address the complexity of this domain. To facilitate this research, here we
introduce RICE-N, a multi-region integrated assessment model that simulates the
global climate and economy, and which can be used to design and evaluate the
strategic outcomes for different negotiation and agreement frameworks. We also
describe how to use multi-agent reinforcement learning to train rational agents
using RICE-N. This framework underpinsAI for Global Climate Cooperation, a
working group collaboration and competition on climate negotiation and
agreement design. Here, we invite the scientific community to design and
evaluate their solutions using RICE-N, machine learning, economic intuition,
and other domain knowledge. More information can be found on
www.ai4climatecoop.org.

This paper studies the problem of holistic 3D wireframe perception (HoW-3D),
a new task of perceiving both the visible 3D wireframes and the invisible ones
from single-view 2D images. As the non-front surfaces of an object cannot be
directly observed in a single view, estimating the non-line-of-sight (NLOS)
geometries in HoW-3D is a fundamentally challenging problem and remains open in
computer vision. We study the problem of HoW-3D by proposing an ABC-HoW
benchmark, which is created on top of CAD models sourced from the ABC-dataset
with 12k single-view images and the corresponding holistic 3D wireframe models.
With our large-scale ABC-HoW benchmark available, we present a novel Deep
Spatial Gestalt (DSG) model to learn the visible junctions and line segments as
the basis and then infer the NLOS 3D structures from the visible cues by
following the Gestalt principles of human vision systems. In our experiments,
we demonstrate that our DSG model performs very well in inferring the holistic
3D wireframes from single-view images. Compared with the strong baseline
methods, our DSG model outperforms the previous wireframe detectors in
detecting the invisible line geometry in single-view images and is even very
competitive with prior arts that take high-fidelity PointCloud as inputs on
reconstructing 3D wireframes.

Housing quality is an essential proxy for regional wealth, security and
health. Understanding the distribution of housing quality is crucial for
unveiling rural development status and providing political proposals.
However,present rural house quality data highly depends on a top-down,
time-consuming survey at the national or provincial level but fails to unpack
the housing quality at the village level. To fill the gap between accurately
depicting rural housing quality conditions and deficient data,we collect
massive rural images and invite users to assess their housing quality at scale.
Furthermore, a deep learning framework is proposed to automatically and
efficiently predict housing quality based on crowd-sourcing rural images.

This paper proposes a theoretical framework on the mechanism of autoencoders.
To the encoder part, under the main use of dimensionality reduction, we
investigate its two fundamental properties: bijective maps and data
disentangling. The general construction methods of an encoder that satisfies
either or both of the above two properties are given. The generalization
mechanism of autoencoders is modeled. Based on the theoretical framework above,
we explain some experimental results of variational autoencoders, denoising
autoencoders, and linear-unit autoencoders, with emphasis on the interpretation
of the lower-dimensional representation of data via encoders; and the mechanism
of image restoration through autoencoders is natural to be understood by those
explanations. Compared to PCA and decision trees, the advantages of
(generalized) autoencoders on dimensionality reduction and classification are
demonstrated, respectively. Convolutional neural networks and randomly weighted
neural networks are also interpreted by this framework.

Accurate sleep stage classification is significant for sleep health
assessment. In recent years, several deep learning and machine learning based
sleep staging algorithms have been developed and they have achieved performance
on par with human annotation. Despite improved performance, a limitation of
most deep-learning based algorithms is their Black-box behavior, which which
have limited their use in clinical settings. Here, we propose Cross-Modal
Transformers, which is a transformer-based method for sleep stage
classification. Our models achieve both competitive performance with the
state-of-the-art approaches and eliminates the Black-box behavior of
deep-learning models by utilizing the interpretability aspect of the attention
modules. The proposed cross-modal transformers consist of a novel cross-modal
transformer encoder architecture along with a multi-scale 1-dimensional
convolutional neural network for automatic representation learning. Our sleep
stage classifier based on this design was able to achieve sleep stage
classification performance on par with or better than the state-of-the-art
approaches, along with interpretability, a fourfold reduction in the number of
parameters and a reduced training time compared to the current
state-of-the-art. Our code is available at
https://github.com/Jathurshan0330/Cross-Modal-Transformer.

The principle of maximum entropy is a broadly applicable technique for
computing a distribution with the least amount of information possible while
constrained to match empirically estimated feature expectations. However, in
many real-world applications that use noisy sensors computing the feature
expectations may be challenging due to partial observation of the relevant
model variables. For example, a robot performing apprenticeship learning may
lose sight of the agent it is learning from due to environmental occlusion. We
show that in generalizing the principle of maximum entropy to these types of
scenarios we unavoidably introduce a dependency on the learned model to the
empirical feature expectations. We introduce the principle of uncertain maximum
entropy and present an expectation-maximization based solution generalized from
the principle of latent maximum entropy. Finally, we experimentally demonstrate
the improved robustness to noisy data offered by our technique in a maximum
causal entropy inverse reinforcement learning domain.

Machine learning methods can be unreliable when deployed in domains that
differ from the domains on which they were trained. To address this, we may
wish to learn representations of data that are domain-invariant in the sense
that we preserve data structure that is stable across domains, but throw out
spuriously-varying parts. There are many representation-learning approaches of
this type, including methods based on data augmentation, distributional
invariances, and risk invariance. Unfortunately, when faced with any particular
real-world domain shift, it is unclear which, if any, of these methods might be
expected to work. The purpose of this paper is to show how the different
methods relate to each other, and clarify the real-world circumstances under
which each is expected to succeed. The key tool is a new notion of domain shift
relying on the idea that causal relationships are invariant, but non-causal
relationships (e.g., due to confounding) may vary.

The phenomenon of adversarial examples has been revealed in variant
scenarios. Recent studies show that well-designed adversarial defense
strategies can improve the robustness of deep learning models against
adversarial examples. However, with the rapid development of defense
technologies, it also tends to be more difficult to evaluate the robustness of
the defensed model due to the weak performance of existing manually designed
adversarial attacks. To address the challenge, given the defensed model, the
efficient adversarial attack with less computational burden and lower robust
accuracy is needed to be further exploited. Therefore, we propose a
multi-objective memetic algorithm for auto adversarial attack optimization
design, which realizes the automatical search for the near-optimal adversarial
attack towards defensed models. Firstly, the more general mathematical model of
auto adversarial attack optimization design is constructed, where the search
space includes not only the attacker operations, magnitude, iteration number,
and loss functions but also the connection ways of multiple adversarial
attacks. In addition, we develop a multi-objective memetic algorithm combining
NSGA-II and local search to solve the optimization problem. Finally, to
decrease the evaluation cost during the search, we propose a representative
data selection strategy based on the sorting of cross entropy loss values of
each images output by models. Experiments on CIFAR10, CIFAR100, and ImageNet
datasets show the effectiveness of our proposed method.

Two R\'{e}nyi-type generalizations of the Shannon cross-entropy, the
R\'{e}nyi cross-entropy and the Natural R\'{e}nyi cross-entropy, were recently
used as loss functions for the improved design of deep learning generative
adversarial networks. In this work, we build upon our results in [1] by
deriving the R\'{e}nyi and Natural R\'{e}nyi differential cross-entropy
measures in closed form for a wide class of common continuous distributions
belonging to the exponential family and tabulating the results for ease of
reference. We also summarise the R\'{e}nyi-type cross-entropy rates between
stationary Gaussian processes and between finite-alphabet time-invariant Markov
sources.

The judiciary has historically been conservative in its use of Artificial
Intelligence, but recent advances in machine learning have prompted scholars to
reconsider such use in tasks like sentence prediction. This paper investigates
by experimentation the potential use of explainable artificial intelligence for
predicting imprisonment sentences in assault cases in New Zealand's courts. We
propose a proof-of-concept explainable model and verify in practice that it is
fit for purpose, with predicted sentences accurate to within one year. We
further analyse the model to understand the most influential phrases in
sentence length prediction. We conclude the paper with an evaluative discussion
of the future benefits and risks of different ways of using such an AI model in
New Zealand's courts.

With the growing adoption of deep learning for on-device TinyML applications,
there has been an ever-increasing demand for more efficient neural network
backbones optimized for the edge. Recently, the introduction of attention
condenser networks have resulted in low-footprint, highly-efficient,
self-attention neural networks that strike a strong balance between accuracy
and speed. In this study, we introduce a new faster attention condenser design
called double-condensing attention condensers that enable more condensed
feature embedding. We further employ a machine-driven design exploration
strategy that imposes best practices design constraints for greater efficiency
and robustness to produce the macro-micro architecture constructs of the
backbone. The resulting backbone (which we name AttendNeXt) achieves
significantly higher inference throughput on an embedded ARM processor when
compared to several other state-of-the-art efficient backbones (>10X faster
than FB-Net C at higher accuracy and speed and >10X faster than MobileOne-S1 at
smaller size) while having a small model size (>1.37X smaller than
MobileNetv3-L at higher accuracy and speed) and strong accuracy (1.1% higher
top-1 accuracy than MobileViT XS on ImageNet at higher speed). These promising
results demonstrate that exploring different efficient architecture designs and
self-attention mechanisms can lead to interesting new building blocks for
TinyML applications.

Estimated time of arrival (ETA) prediction, also known as travel time
estimation, is a fundamental task for a wide range of intelligent
transportation applications, such as navigation, route planning, and
ride-hailing services. To accurately predict the travel time of a route, it is
essential to take into account both contextual and predictive factors, such as
spatial-temporal interaction, driving behavior, and traffic congestion
propagation inference. The ETA prediction models previously deployed at Baidu
Maps have addressed the factors of spatial-temporal interaction (ConSTGAT) and
driving behavior (SSML). In this work, we focus on modeling traffic congestion
propagation patterns to improve ETA performance. Traffic congestion propagation
pattern modeling is challenging, and it requires accounting for impact regions
over time and cumulative effect of delay variations over time caused by traffic
events on the road network. In this paper, we present a practical
industrial-grade ETA prediction framework named DuETA. Specifically, we
construct a congestion-sensitive graph based on the correlations of traffic
patterns, and we develop a route-aware graph transformer to directly learn the
long-distance correlations of the road segments. This design enables DuETA to
capture the interactions between the road segment pairs that are spatially
distant but highly correlated with traffic conditions. Extensive experiments
are conducted on large-scale, real-world datasets collected from Baidu Maps.
Experimental results show that ETA prediction can significantly benefit from
the learned traffic congestion propagation patterns. In addition, DuETA has
already been deployed in production at Baidu Maps, serving billions of requests
every day. This demonstrates that DuETA is an industrial-grade and robust
solution for large-scale ETA prediction services.

Graph coloring, a classical and critical NP-hard problem, is the problem of
assigning connected nodes as different colors as possible. However, we observe
that state-of-the-art GNNs are less successful in the graph coloring problem.
We analyze the reasons from two perspectives. First, most GNNs fail to
generalize the task under homophily to heterophily, i.e., graphs where
connected nodes are assigned different colors. Second, GNNs are bounded by the
network depth, making them possible to be a local method, which has been
demonstrated to be non-optimal in Maximum Independent Set (MIS) problem. In
this paper, we focus on the aggregation-combine GNNs (AC-GNNs), a popular class
of GNNs. We first define the power of AC-GNNs in the coloring problem as the
capability to assign nodes different colors. The definition is different with
previous one that is based on the assumption of homophily. We identify node
pairs that AC-GNNs fail to discriminate. Furthermore, we show that any AC-GNN
is a local coloring method, and any local coloring method is non-optimal by
exploring the limits of local methods over sparse random graphs, thereby
demonstrating the non-optimality of AC-GNNs due to its local property. We then
prove the positive correlation between model depth and its coloring power.
Moreover, we discuss the color equivariance of graphs to tackle some practical
constraints such as the pre-fixing constraints. Following the discussions
above, we summarize a series of rules a series of rules that make a GNN color
equivariant and powerful in the coloring problem. Then, we propose a simple
AC-GNN variation satisfying these rules. We empirically validate our
theoretical findings and demonstrate that our simple model substantially
outperforms state-of-the-art heuristic algorithms in both quality and runtime.

Finding dense semantic correspondence is a fundamental problem in computer
vision, which remains challenging in complex scenes due to background clutter,
extreme intra-class variation, and a severe lack of ground truth. In this
paper, we aim to address the challenge of label sparsity in semantic
correspondence by enriching supervision signals from sparse keypoint
annotations. To this end, we first propose a teacher-student learning paradigm
for generating dense pseudo-labels and then develop two novel strategies for
denoising pseudo-labels. In particular, we use spatial priors around the sparse
annotations to suppress the noisy pseudo-labels. In addition, we introduce a
loss-driven dynamic label selection strategy for label denoising. We
instantiate our paradigm with two variants of learning strategies: a single
offline teacher setting, and mutual online teachers setting. Our approach
achieves notable improvements on three challenging benchmarks for semantic
correspondence and establishes the new state-of-the-art. Project page:
https://shuaiyihuang.github.io/publications/SCorrSAN.

State-of-the-art Graph Neural Networks (GNNs) have achieved tremendous
success in social event detection tasks when restricted to a closed set of
events. However, considering the large amount of data needed for training a
neural network and the limited ability of a neural network in handling
previously unknown data, it remains a challenge for existing GNN-based methods
to operate in an open set setting. To address this problem, we design a
Quality-aware Self-improving Graph Neural Network (QSGNN) which extends the
knowledge from known to unknown by leveraging the best of known samples and
reliable knowledge transfer. Specifically, to fully exploit the labeled data,
we propose a novel supervised pairwise loss with an additional orthogonal
inter-class relation constraint to train the backbone GNN encoder. The learnt,
already-known events further serve as strong reference bases for the unknown
ones, which greatly prompts knowledge acquisition and transfer. When the model
is generalized to unknown data, to ensure the effectiveness and reliability, we
further leverage the reference similarity distribution vectors for pseudo
pairwise label generation, selection and quality assessment. Following the
diversity principle of active learning, our method selects diverse pair samples
with the generated pseudo labels to fine-tune the GNN encoder. Besides, we
propose a novel quality-guided optimization in which the contributions of
pseudo labels are weighted based on consistency. We thoroughly evaluate our
model on two large real-world social event datasets. Experiments demonstrate
that our model achieves state-of-the-art results and extends well to unknown
events.

We propose a video feature representation learning framework called STAR-GNN,
which applies a pluggable graph neural network component on a multi-scale
lattice feature graph. The essence of STAR-GNN is to exploit both the temporal
dynamics and spatial contents as well as visual connections between regions at
different scales in the frames. It models a video with a lattice feature graph
in which the nodes represent regions of different granularity, with weighted
edges that represent the spatial and temporal links. The contextual nodes are
aggregated simultaneously by graph neural networks with parameters trained with
retrieval triplet loss. In the experiments, we show that STAR-GNN effectively
implements a dynamic attention mechanism on video frame sequences, resulting in
the emphasis for dynamic and semantically rich content in the video, and is
robust to noise and redundancies. Empirical results show that STAR-GNN achieves
state-of-the-art performance for Content-Based Video Retrieval.

As an efficient neural network model for graph data, graph neural networks
(GNNs) recently find successful applications for various wireless optimization
problems. Given that the inference stage of GNNs can be naturally implemented
in a decentralized manner, GNN is a potential enabler for decentralized
control/management in the next-generation wireless communications. Privacy
leakage, however, may occur due to the information exchanges among neighbors
during decentralized inference with GNNs. To deal with this issue, in this
paper, we analyze and enhance the privacy of decentralized inference with GNNs
in wireless networks. Specifically, we adopt local differential privacy as the
metric, and design novel privacy-preserving signals as well as
privacy-guaranteed training algorithms to achieve privacy-preserving inference.
We also define the SNR-privacy trade-off function to analyze the performance
upper bound of decentralized inference with GNNs in wireless networks. To
further enhance the communication and computation efficiency, we adopt the
over-the-air computation technique and theoretically demonstrate its advantage
in privacy preservation. Through extensive simulations on the synthetic graph
data, we validate our theoretical analysis, verify the effectiveness of
proposed privacy-preserving wireless signaling and privacy-guaranteed training
algorithm, and offer some guidance on practical implementation.

After a survey for person-tracking system-induced privacy concerns, we
propose a black-box adversarial attack method on state-of-the-art human
detection models called InvisibiliTee. The method learns printable adversarial
patterns for T-shirts that cloak wearers in the physical world in front of
person-tracking systems. We design an angle-agnostic learning scheme which
utilizes segmentation of the fashion dataset and a geometric warping process so
the adversarial patterns generated are effective in fooling person detectors
from all camera angles and for unseen black-box detection models. Empirical
results in both digital and physical environments show that with the
InvisibiliTee on, person-tracking systems' ability to detect the wearer drops

Although representational retrieval models based on Transformers have been
able to make major advances in the past few years, and despite the widely
accepted conventions and best-practices for testing such models, a
$\textit{standardized}$ evaluation framework for testing them has not been
developed. In this work, we formalize the best practices and conventions
followed by researchers in the literature, paving the path for more
standardized evaluations - and therefore more fair comparisons between the
models. Our framework (1) embeds the documents and queries; (2) for each
query-document pair, computes the relevance score based on the dot product of
the document and query embedding; (3) uses the $\texttt{dev}$ set of the
MSMARCO dataset to evaluate the models; (4) uses the $\texttt{trec_eval}$
script to calculate MRR@100, which is the primary metric used to evaluate the
models. Most importantly, we showcase the use of this framework by
experimenting on some of the most well-known dense retrieval models.

In low resource settings, data augmentation strategies are commonly leveraged
to improve performance. Numerous approaches have attempted document-level
augmentation (e.g., text classification), but few studies have explored
token-level augmentation. Performed naively, data augmentation can produce
semantically incongruent and ungrammatical examples. In this work, we compare
simple masked language model replacement and an augmentation method using
constituency tree mutations to improve the performance of named entity
recognition in low-resource settings with the aim of preserving linguistic
cohesion of the augmented sentences.

Contrastive learning is an effective unsupervised method in graph
representation learning, and the key component of contrastive learning lies in
the construction of positive and negative samples. Previous methods usually
utilize the proximity of nodes in the graph as the principle. Recently, the
data augmentation based contrastive learning method has advanced to show great
power in the visual domain, and some works extended this method from images to
graphs. However, unlike the data augmentation on images, the data augmentation
on graphs is far less intuitive and much harder to provide high-quality
contrastive samples, which leaves much space for improvement. In this work, by
introducing an adversarial graph view for data augmentation, we propose a
simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to
extract informative contrastive samples within reasonable constraints. We
develop a new technique called information regularization for stable training
and use subgraph sampling for scalability. We generalize our method from
node-level contrastive learning to the graph-level by treating each graph
instance as a supernode. ARIEL consistently outperforms the current graph
contrastive learning methods for both node-level and graph-level classification
tasks on real-world datasets. We further demonstrate that ARIEL is more robust
in face of adversarial attacks.

Pre-trained and fine-tuned transformer models like BERT and T5 have improved
the state of the art in ad-hoc retrieval and question-answering, but not as yet
in high-recall information retrieval, where the objective is to retrieve
substantially all relevant documents. We investigate whether the use of
transformer-based models for reranking and/or featurization can improve the
Baseline Model Implementation of the TREC Total Recall Track, which represents
the current state of the art for high-recall information retrieval. We also
introduce CALBERT, a model that can be used to continuously fine-tune a
BERT-based model based on relevance feedback.

The Internet of things (IoT) is increasingly prevalent in domains such as
emergency response, smart cities and autonomous vehicles. Simulation plays a
key role in the testing of IoT systems, noting that field testing of a complete
IoT product may be infeasible or prohibitively expensive. In this paper, we
propose a domain-specific language (DSL) for generating edge-to-cloud
simulators. An edge-to-cloud simulator executes the functionality of a large
array of edge devices that communicate with cloud applications. Our DSL, named
IoTECS, is the result of a collaborative project with an IoT analytics company,
Cheetah Networks. The industrial use case that motivates IoTECS is ensuring the
scalability of cloud applications by putting them under extreme loads from IoT
devices connected to the edge. We implement IoTECS using Xtext and empirically
evaluate its usefulness. We further reflect on the lessons learned.

In this paper, we analyze the impact of data freshness on real-time
supervised learning, where a neural network is trained to infer a time-varying
target (e.g., the position of the vehicle in front) based on features (e.g.,
video frames) observed at a sensing node (e.g., camera or lidar). One might
expect that the performance of real-time supervised learning degrades
monotonically as the feature becomes stale. Using an information-theoretic
analysis, we show that this is true if the feature and target data sequence can
be closely approximated as a Markov chain; it is not true if the data sequence
is far from Markovian. Hence, the prediction error of real-time supervised
learning is a function of the Age of Information (AoI), where the function
could be non-monotonic. Several experiments are conducted to illustrate the
monotonic and non-monotonic behaviors of the prediction error. To minimize the
inference error in real-time, we propose a new "selection-from-buffer" model
for sending the features, which is more general than the "generate-at-will"
model used in earlier studies. By using Gittins and Whittle indices,
low-complexity scheduling strategies are developed to minimize the inference
error, where a new connection between the Gittins index theory and Age of
Information (AoI) minimization is discovered. These scheduling results hold (i)
for minimizing general AoI functions (monotonic or non-monotonic) and (ii) for
general feature transmission time distributions. Data-driven evaluations are
presented to illustrate the benefits of the proposed scheduling algorithms.

In this paper, we have proposed STC-GEF, a novel Spatio-Temporal
Cross-platform Graph Embedding Fusion approach for the urban traffic flow
prediction. We have designed a spatial embedding module based on graph
convolutional networks (GCN) to extract the complex spatial features within
traffic flow data. Furthermore, to capture the temporal dependencies between
the traffic flow data from various time intervals, we have designed a temporal
embedding module based on recurrent neural networks. Based on the observations
that different transportation platforms trip data (e.g., taxis, Uber, and Lyft)
can be correlated, we have designed an effective fusion mechanism that combines
the trip data from different transportation platforms and further uses them for
cross-platform traffic flow prediction (e.g., integrating taxis and
ride-sharing platforms for taxi traffic flow prediction). We have conducted
extensive real-world experimental studies based on real-world trip data of
yellow taxis and ride-sharing (Lyft) from the New York City (NYC), and
validated the accuracy and effectiveness of STC-GEF in fusing different
transportation platform data and predicting traffic flows.

The security of passwords depends on a thorough understanding of the
strategies used by attackers. Unfortunately, real-world adversaries use
pragmatic guessing tactics like dictionary attacks, which are difficult to
simulate in password security research. Dictionary attacks must be carefully
configured and modified to represent an actual threat. This approach, however,
needs domain-specific knowledge and expertise that are difficult to duplicate.
This paper reviews various deep learning-based password guessing approaches
that do not require domain knowledge or assumptions about users' password
structures and combinations. It also introduces GNPassGAN, a password guessing
tool built on generative adversarial networks for trawling offline attacks. In
comparison to the state-of-the-art PassGAN model, GNPassGAN is capable of
guessing 88.03\% more passwords and generating 31.69\% fewer duplicates.

The subject area known as computational neuroscience involves the
investigation of brain function using mathematical techniques and theories. In
order to comprehend how the brain processes information, it can also include
various methods from signal processing, computer science, and physics. In the
present work, for the first time a neurobiological based unsupervised machine
learning algorithm i.e., Self-Organizing Map Neural Network is implemented for
determining the fracture location in dissimilar friction stir welded
AA5754-C11000 alloys. Too Shoulder Diameter (mm), Tool Rotational Speed (RPM),
and Tool Traverse Speed (mm/min) are input parameters while the Fracture
location i.e. whether the specimen fracture at Thermo-Mechanically Affected
Zone (TMAZ) of copper or it fractures at TMAZ of Aluminium. The results showed
that the implemented algorithm is able to predict the fracture location with
96.92% accuracy.

We propose ordering-based approaches for learning the maximal ancestral graph
(MAG) of a structural equation model (SEM) up to its Markov equivalence class
(MEC) in the presence of unobserved variables. Existing ordering-based methods
in the literature recover a graph through learning a causal order (c-order). We
advocate for a novel order called removable order (r-order) as they are
advantageous over c-orders for structure learning. This is because r-orders are
the minimizers of an appropriately defined optimization problem that could be
either solved exactly (using a reinforcement learning approach) or
approximately (using a hill-climbing search). Moreover, the r-orders (unlike
c-orders) are invariant among all the graphs in a MEC and include c-orders as a
subset. Given that set of r-orders is often significantly larger than the set
of c-orders, it is easier for the optimization problem to find an r-order
instead of a c-order. We evaluate the performance and the scalability of our
proposed approaches on both real-world and randomly generated networks.

Visual (re)localization addresses the problem of estimating the 6-DoF (Degree
of Freedom) camera pose of a query image captured in a known scene, which is a
key building block of many computer vision and robotics applications. Recent
advances in structure-based localization solve this problem by memorizing the
mapping from image pixels to scene coordinates with neural networks to build
2D-3D correspondences for camera pose optimization. However, such memorization
requires training by amounts of posed images in each scene, which is heavy and
inefficient. On the contrary, few-shot images are usually sufficient to cover
the main regions of a scene for a human operator to perform visual
localization. In this paper, we propose a scene region classification approach
to achieve fast and effective scene memorization with few-shot images. Our
insight is leveraging a) pre-learned feature extractor, b) scene region
classifier, and c) meta-learning strategy to accelerate training while
mitigating overfitting. We evaluate our method on both indoor and outdoor
benchmarks. The experiments validate the effectiveness of our method in the
few-shot setting, and the training time is significantly reduced to only a few
minutes. Code available at: \url{https://github.com/siyandong/SRC}

Continual learning of a stream of tasks is an active area in deep neural
networks. The main challenge investigated has been the phenomenon of
catastrophic forgetting or interference of newly acquired knowledge with
knowledge from previous tasks. Recent work has investigated forward knowledge
transfer to new tasks. Backward transfer for improving knowledge gained during
previous tasks has received much less attention. There is in general limited
understanding of how knowledge transfer could aid tasks learned continually. We
present a theory for knowledge transfer in continual supervised learning, which
considers both forward and backward transfer. We aim at understanding their
impact for increasingly knowledgeable learners. We derive error bounds for each
of these transfer mechanisms. These bounds are agnostic to specific
implementations (e.g. deep neural networks). We demonstrate that, for a
continual learner that observes related tasks, both forward and backward
transfer can contribute to an increasing performance as more tasks are
observed.

Spatial and temporal features are studied with respect to their predictive
value for failure time prediction in subcritical failure with machine learning
(ML). Data are generated from simulations of a novel, brittle random fuse model
(RFM), as well as elasto-plastic finite element simulations (FEM) of a
stochastic plasticity model with damage, both models considering stochastic
thermally activated damage/failure processes in disordered materials. Fuse
networks are generated with hierarchical and nonhierarchical architectures.
Random forests - a specific ML algorithm - allow us to measure the feature
importance through a feature's average error reduction. RFM simulation data are
found to become more predictable with increasing system size and temperature.
Increasing the load or the scatter in local materials properties has the
opposite effect. Damage accumulation in these models proceeds in stochastic
avalanches, and statistical signatures such as avalanche rate or magnitude have
been discussed in the literature as predictors of incipient failure. However,
in the present study such features proved of no measurable use to the ML
models, which mostly rely on global or local strain for prediction. This
suggests the strain as viable quantity to monitor in future experimental
studies as it is accessible via digital image correlation.

There have been many studies on intelligent robotic systems for patients with
motor impairments, where different sensor types and different human-machine
interface (HMI) methods have been developed. However, these studies fail to
achieve complex activity detection at the minimum sensing level. In this paper,
exploratory approaches are adopted to investigate ocular activity dynamics and
complex activity estimation using a single-channel EOG device. First, the
stationarity of ocular activities during a static motion is investigated and
some activities are found to be non-stationary. Further, no statistical
difference is found between the envelope sequences in the temporal domain.
However, when utilized as an alternative to a low-pass filter, high-frequency
harmonic components in the frequency domain are found to improve contrasting
ocular activities and the performance of the EOG-HMI-based activity detection
system substantially. The activities are trained with different classifiers and
their prediction success is evaluated with leave-one-session-out
cross-validation. Accordingly, the two-dimensional CNN model achieved the
highest performance with the accuracy of 72.35\%. Furthermore, the clustering
performance is assessed using unsupervised learning and the results are
evaluated in terms of how well the feature sets are grouped. The system is
further tested in real-time with the graphical user interface and the scores
and survey data of the subjects are used to verify the effectiveness.

Lateral inhibitory connections have been observed in the cortex of the
biological brain, and has been extensively studied in terms of its role in
cognitive functions. However, in the vanilla version of backpropagation in deep
learning, all gradients (which can be understood to comprise of both signal and
noise gradients) flow through the network during weight updates. This may lead
to overfitting. In this work, inspired by biological lateral inhibition, we
propose Gradient Mask, which effectively filters out noise gradients in the
process of backpropagation. This allows the learned feature information to be
more intensively stored in the network while filtering out noisy or unimportant
features. Furthermore, we demonstrate analytically how lateral inhibition in
artificial neural networks improves the quality of propagated gradients. A new
criterion for gradient quality is proposed which can be used as a measure
during training of various convolutional neural networks (CNNs). Finally, we
conduct several different experiments to study how Gradient Mask improves the
performance of the network both quantitatively and qualitatively.
Quantitatively, accuracy in the original CNN architecture, accuracy after
pruning, and accuracy after adversarial attacks have shown improvements.
Qualitatively, the CNN trained using Gradient Mask has developed saliency maps
that focus primarily on the object of interest, which is useful for data
augmentation and network interpretability.

Diabetic Retinopathy (DR) has become one of the leading causes of vision
impairment in working-aged people and is a severe problem worldwide. However,
most of the works ignored the ordinal information of labels. In this project,
we propose a novel design MTCSNN, a Multi-task Clinical Siamese Neural Network
for Diabetic Retinopathy severity prediction task. The novelty of this project
is to utilize the ordinal information among labels and add a new regression
task, which can help the model learn more discriminative feature embedding for
fine-grained classification tasks. We perform comprehensive experiments over
the RetinaMNIST, comparing MTCSNN with other models like ResNet-18, 34, 50. Our
results indicate that MTCSNN outperforms the benchmark models in terms of AUC
and accuracy on the test dataset.

Sharpness-Aware Minimization (SAM) and adaptive sharpness-aware minimization
(ASAM) aim to improve the model generalization. And in this project, we
proposed three experiments to valid their generalization from the sharpness
aware perspective. And our experiments show that sharpness aware-based
optimization techniques could help to provide models with strong generalization
ability. Our experiments also show that ASAM could improve the generalization
performance on un-normalized data, but further research is needed to confirm
this.

Drori et al. (2022) report that "A neural network solves, explains, and
generates university math problems by program synthesis and few-shot learning
at human level ... [It] automatically answers 81\% of university-level
mathematics problems." The system they describe is indeed impressive; however,
the above description is very much overstated. The work of solving the problems
is done, not by a neural network, but by the symbolic algebra package Sympy.
Problems of various formats are excluded from consideration. The so-called
"explanations" are just rewordings of lines of code. Answers are marked as
correct that are not in the form specified in the problem. Most seriously, it
seems that in many cases the system uses the correct answer given in the test
corpus to guide its path to solving the problem.

We introduce a new sorting algorithm that is the combination of ML-enhanced
sorting with the In-place Super Scalar Sample Sort (IPS4o). The main
contribution of our work is to achieve parallel ML-enhanced sorting, as
previous algorithms were limited to sequential implementations. We introduce
the In-Place Parallel Learned Sort (IPLS) algorithm and compare it extensively
against other sorting approaches. IPLS combines the IPS4o framework with linear
models trained using the Fastest Minimum Conflict Degree algorithm to partition
data. The experimental results do not crown IPLS as the fastest algorithm.
However, they do show that IPLS is competitive among its peers and that using
the IPS4o framework is a promising approach towards parallel learned sorting.

Spiking neural networks (SNNs) are receiving increased attention as a means
to develop "biologically plausible" machine learning models. These networks
mimic synaptic connections in the human brain and produce spike trains, which
can be approximated by binary values, precluding high computational cost with
floating-point arithmetic circuits. Recently, the addition of convolutional
layers to combine the feature extraction power of convolutional networks with
the computational efficiency of SNNs has been introduced. In this paper, the
feasibility of using a convolutional spiking neural network (CSNN) as a
classifier to detect anticipatory slow cortical potentials related to braking
intention in human participants using an electroencephalogram (EEG) was
studied. The EEG data was collected during an experiment wherein participants
operated a remote controlled vehicle on a testbed designed to simulate an urban
environment. Participants were alerted to an incoming braking event via an
audio countdown to elicit anticipatory potentials that were then measured using
an EEG. The CSNN's performance was compared to a standard convolutional neural
network (CNN) and three graph neural networks (GNNs) via 10-fold
cross-validation. The results showed that the CSNN outperformed the other

Deep neural networks used for image classification often use convolutional
filters to extract distinguishing features before passing them to a linear
classifier. Most interpretability literature focuses on providing semantic
meaning to convolutional filters to explain a model's reasoning process and
confirm its use of relevant information from the input domain. Fully connected
layers can be studied by decomposing their weight matrices using a singular
value decomposition, in effect studying the correlations between the rows in
each matrix to discover the dynamics of the map. In this work we define a
singular value decomposition for the weight tensor of a convolutional layer,
which provides an analogous understanding of the correlations between filters,
exposing the dynamics of the convolutional map. We validate our definition
using recent results in random matrix theory. By applying the decomposition
across the linear layers of an image classification network we suggest a
framework against which interpretability methods might be applied using
hypergraphs to model class separation. Rather than looking to the activations
to explain the network, we use the singular vectors with the greatest
corresponding singular values for each linear layer to identify those features
most important to the network. We illustrate our approach with examples and
introduce the DeepDataProfiler library, the analysis tool used for this study.

Joint super-resolution and inverse tone-mapping (SR-ITM) aims to enhance the
visual quality of videos that have quality deficiencies in resolution and
dynamic range. This problem arises when using 4K high dynamic range (HDR) TVs
to watch a low-resolution standard dynamic range (LR SDR) video. Previous
methods that rely on learning local information typically cannot do well in
preserving color conformity and long-range structural similarity, resulting in
unnatural color transition and texture artifacts. In order to tackle these
challenges, we propose a global priors guided modulation network (GPGMNet) for
joint SR-ITM. In particular, we design a global priors extraction module (GPEM)
to extract color conformity prior and structural similarity prior that are
beneficial for ITM and SR tasks, respectively. To further exploit the global
priors and preserve spatial information, we devise multiple global priors
guided spatial-wise modulation blocks (GSMBs) with a few parameters for
intermediate feature modulation, in which the modulation parameters are
generated by the shared global priors and the spatial features map from the
spatial pyramid convolution block (SPCB). With these elaborate designs, the
GPGMNet can achieve higher visual quality with lower computational complexity.
Extensive experiments demonstrate that our proposed GPGMNet is superior to the
state-of-the-art methods. Specifically, our proposed model exceeds the
state-of-the-art by 0.64 dB in PSNR, with 69$\%$ fewer parameters and
3.1$\times$ speedup. The code will be released soon.

In the real world, the class of a time series is usually labeled at the final
time, but many applications require to classify time series at every time
point. e.g. the outcome of a critical patient is only determined at the end,
but he should be diagnosed at all times for timely treatment. Thus, we propose
a new concept: Continuous Classification of Time Series (CCTS). It requires the
model to learn data in different time stages. But the time series evolves
dynamically, leading to different data distributions. When a model learns
multi-distribution, it always forgets or overfits. We suggest that meaningful
learning scheduling is potential due to an interesting observation: Measured by
confidence, the process of model learning multiple distributions is similar to
the process of human learning multiple knowledge. Thus, we propose a novel
Confidence-guided method for CCTS (C3TS). It can imitate the alternating human
confidence described by the Dunning-Kruger Effect. We define the objective-
confidence to arrange data, and the self-confidence to control the learning
duration. Experiments on four real-world datasets show that C3TS is more
accurate than all baselines for CCTS.

Much like most of cognition research, music cognition is an interdisciplinary
field, which attempts to apply methods of cognitive science (neurological,
computational and experimental) to understand the perception and process of
composition of music. In this paper, we first motivate why music is relevant to
cognitive scientists and give an overview of the approaches to computational
modelling of music cognition. We then review literature on the various models
of music perception, including non-computational models, computational
non-cognitive models and computational cognitive models. Lastly, we review
literature on modelling the creative behaviour and on computer systems capable
of composing music. Since a lot of technical terms from music theory have been
used, we have appended a list of relevant terms and their definitions at the
end.

Users of a recommender system may want part of their data being deleted, not
only from the data repository but also from the underlying machine learning
model, for privacy or utility reasons. Such right-to-be-forgotten requests
could be fulfilled by simply retraining the recommendation model from scratch,
but that would be too slow and too expensive in practice. In this paper, we
investigate fast machine unlearning techniques for recommender systems that can
remove the effect of a small amount of training data from the recommendation
model without incurring the full cost of retraining. A natural idea to speed
this process up is to fine-tune the current recommendation model on the
remaining training data instead of starting from a random initialization. This
warm-start strategy indeed works for neural recommendation models using
standard 1st-order neural network optimizers (like AdamW). However, we have
found that even greater acceleration could be achieved by employing 2nd-order
(Newton or quasi-Newton) optimization methods instead. To overcome the
prohibitively high computational cost of 2nd-order optimizers, we propose a new
recommendation unlearning approach AltEraser which divides the optimization
problem of unlearning into many small tractable sub-problems. Extensive
experiments on three real-world recommendation datasets show promising results
of AltEraser in terms of consistency (forgetting thoroughness), accuracy
(recommendation effectiveness), and efficiency (unlearning speed). To our
knowledge, this work represents the first attempt at fast approximate machine
unlearning for state-of-the-art neural recommendation models.

Multilingual Neural Machine Translation has been showing great success using
transformer models. Deploying these models is challenging because they usually
require large vocabulary (vocab) sizes for various languages. This limits the
speed of predicting the output tokens in the last vocab projection layer. To
alleviate these challenges, this paper proposes a fast vocabulary projection
method via clustering which can be used for multilingual transformers on GPUs.
First, we offline split the vocab search space into disjoint clusters given the
hidden context vector of the decoder output, which results in much smaller
vocab columns for vocab projection. Second, at inference time, the proposed
method predicts the clusters and candidate active tokens for hidden context
vectors at the vocab projection. This paper also includes analysis of different
ways of building these clusters in multilingual settings. Our results show
end-to-end speed gains in float16 GPU inference up to 25% while maintaining the
BLEU score and slightly increasing memory cost. The proposed method speeds up
the vocab projection step itself by up to 2.6x. We also conduct an extensive
human evaluation to verify the proposed method preserves the quality of the
translations from the original model.

Frouros is a Python library capable of detecting drift in machine learning
problems. It provides a combination of classical and more recent algorithms for
drift detection: both supervised and unsupervised, as well as some capable of
acting in a semi-supervised manner. We have designed it with the objective of
being easily integrated with the scikit-learn library, implementing the same
application programming interface. The library is developed following a set of
best development and continuous integration practices to ensure ease of
maintenance and extensibility. The source code is available at
https://github.com/IFCA/frouros.

Image retrieval has become an increasingly appealing technique with broad
multimedia application prospects, where deep hashing serves as the dominant
branch towards low storage and efficient retrieval. In this paper, we carried
out in-depth investigations on metric learning in deep hashing for establishing
a powerful metric space in multi-label scenarios, where the pair loss suffers
high computational overhead and converge difficulty, while the proxy loss is
theoretically incapable of expressing the profound label dependencies and
exhibits conflicts in the constructed hypersphere space. To address the
problems, we propose a novel metric learning framework with Hybrid Proxy-Pair
Loss (HyP$^2$ Loss) that constructs an expressive metric space with efficient
training complexity w.r.t. the whole dataset. The proposed HyP$^2$ Loss focuses
on optimizing the hypersphere space by learnable proxies and excavating
data-to-data correlations of irrelevant pairs, which integrates sufficient data
correspondence of pair-based methods and high-efficiency of proxy-based
methods. Extensive experiments on four standard multi-label benchmarks justify
the proposed method outperforms the state-of-the-art, is robust among different
hash bits and achieves significant performance gains with a faster, more stable
convergence speed. Our code is available at
https://github.com/JerryXu0129/HyP2-Loss.

Cosmological shock waves are essential to understanding the formation of
cosmological structures. To study them, scientists run computationally
expensive high-resolution 3D hydrodynamic simulations. Interpreting the
simulation results is challenging because the resulting data sets are enormous,
and the shock wave surfaces are hard to separate and classify due to their
complex morphologies and multiple shock fronts intersecting. We introduce a
novel pipeline, Virgo, combining physical motivation, scalability, and
probabilistic robustness to tackle this unsolved unsupervised classification
problem. To this end, we employ kernel principal component analysis with
low-rank matrix approximations to denoise data sets of shocked particles and
create labeled subsets. We perform supervised classification to recover full
data resolution with stochastic variational deep kernel learning. We evaluate
on three state-of-the-art data sets with varying complexity and achieve good
results. The proposed pipeline runs automatically, has only a few
hyperparameters, and performs well on all tested data sets. Our results are
promising for large-scale applications, and we highlight now enabled future
scientific work.

In this paper, we present a ranking-based underwater image quality assessment
(UIQA) method, abbreviated as URanker. The URanker is built on the efficient
conv-attentional image Transformer. In terms of underwater images, we specially
devise (1) the histogram prior that embeds the color distribution of an
underwater image as histogram token to attend global degradation and (2) the
dynamic cross-scale correspondence to model local degradation. The final
prediction depends on the class tokens from different scales, which
comprehensively considers multi-scale dependencies. With the margin ranking
loss, our URanker can accurately rank the order of underwater images of the
same scene enhanced by different underwater image enhancement (UIE) algorithms
according to their visual quality. To achieve that, we also contribute a
dataset, URankerSet, containing sufficient results enhanced by different UIE
algorithms and the corresponding perceptual rankings, to train our URanker.
Apart from the good performance of URanker, we found that a simple U-shape UIE
network can obtain promising performance when it is coupled with our
pre-trained URanker as additional supervision. In addition, we also propose a
normalization tail that can significantly improve the performance of UIE
networks. Extensive experiments demonstrate the state-of-the-art performance of
our method. The key designs of our method are discussed. We will release our
dataset and code.

Integrating logical reasoning and machine learning by approximating logical
inference with differentiable operators is a widely used technique in
Neuro-Symbolic systems.
  However, some differentiable operators could bring a significant bias during
backpropagation and degrade the performance of Neuro-Symbolic learning.
  In this paper, we reveal that this bias, named \textit{Implication Bias} is
common in loss functions derived from fuzzy logic operators.
  Furthermore, we propose a simple yet effective method to transform the biased
loss functions into \textit{Reduced Implication-bias Logic Loss (RILL)} to
address the above problem.
  Empirical study shows that RILL can achieve significant improvements compared
with the biased logic loss functions, especially when the knowledge base is
incomplete, and keeps more robust than the compared methods when labelled data
is insufficient.

In this paper, a collision avoidance system is presented to detect red light
running and warn nearby vehicles and pedestrians in real time in order to
prevent possible accidents. No complex infrastructure-based solution such as
those based on radars or cameras is here required. Instead, a new solution
based on smartphones carried by drivers and pedestrians is proposed so that it
is the device inside the vehicle violating a traffic light, the one that
self-reports the offence in order to generate alerts and warn nearby vehicles
and pedestrians to prevent accidents. The proposal could also be used by road
authorities to collect data on traffic lights that are most frequently violated
in order to define an action plan to investigate causes and look for solutions.
It includes a classifier for learning and estimating driver behaviour based on
collected data, which is used to predict whether he/she is about to run a red
light or detect whether that has already happened. In the first case, the
system broadcasts warnings directly to close vehicles and pedestrians through
Wi-Fi, while in the second case, the proposal warns vehicles and pedestrians in
the neighbourhood through a server. The solution also includes a prioritization
system based on changing traffic lights at intersections according to the needs
and characteristics of the traffic at all times, giving the top priority to
emergency vehicles. Furthermore, the proposal involves the use of cryptographic
schemes to protect authenticity and integrity of messages sent from traffic
lights, smartphones and servers, and privacy and anonymity to promote the use
of the system. A beta version with some parts of the proposal has been
implemented and the obtained results are promising.

Multinomial logistic regression, also known by other names such as multiclass
logistic regression and softmax regression, is a fundamental classification
method that generalizes binary logistic regression to multiclass problems. A
recently work proposed a faster gradient called $\texttt{quadratic gradient}$
that can accelerate the binary logistic regression training, and presented an
enhanced Nesterov's accelerated gradient (NAG) method for binary logistic
regression.
  In this paper, we extend this work to multiclass logistic regression and
propose an enhanced Adaptive Gradient Algorithm (Adagrad) that can accelerate
the original Adagrad method. We test the enhanced NAG method and the enhanced
Adagrad method on some multiclass-problem datasets. Experimental results show
that both enhanced methods converge faster than their original ones

Language is a method by which individuals express their thoughts. Each
language has its own set of alphabetic and numeric characters. People can
communicate with one another through either oral or written communication.
However, each language has a sign language counterpart. Individuals who are
deaf and/or mute communicate through sign language. The Bangla language also
has a sign language, which is called BDSL. The dataset is about Bangla hand
sign images. The collection contains 49 individual Bangla alphabet images in
sign language. BDSL49 is a dataset that consists of 29,490 images with 49
labels. Images of 14 different adult individuals, each with a distinct
background and appearance, have been recorded during data collection. Several
strategies have been used to eliminate noise from datasets during preparation.
This dataset is available to researchers for free. They can develop automated
systems using machine learning, computer vision, and deep learning techniques.
In addition, two models were used in this dataset. The first is for detection,
while the second is for recognition.

De novo molecular design has facilitated the exploration of large chemical
space to accelerate drug discovery. Structure-based de novo method can overcome
the data scarcity of active ligands by incorporating drug-target interaction
into deep generative architectures. However, these strategies are bottlenecked
by the small fraction of experimentally determined protein or complex
structures. In addition, the cost of molecular generation is computationally
expensive due to 3D representations of both molecule and protein. Here, we
demonstrate a widely used and fast protein sequence-based reinforcement
learning (RL) model for drug discovery. In the generative model, one of the
reward components, a binding affinity predictor, is based on 1D protein
sequence and molecular SMILES. As a proof of concept, the RL model was utilized
to design molecules for four targets. The generated compounds showed
bioactivities by the validation of both QSAR and molecular docking with
experimental 3D binding pockets. We also found that the performance of
generated molecules depends on the selection of data source training for the
binding predictor. Furthermore, drug design for a kinase without any
experimental structure, CDK20, was studied by our model. With only 1D protein
sequence as input, the generated novel compounds showed favorable binding
affinity based on the AlphaFold predicted structure.

The remarkable performance gains realized by large pretrained models, e.g.,
GPT-3, hinge on the massive amounts of data they are exposed to during
training. Analogously, distilling such large models to compact models for
efficient deployment also necessitates a large amount of (labeled or unlabeled)
training data. In this paper, we propose the teacher-guided training (TGT)
framework for training a high-quality compact model that leverages the
knowledge acquired by pretrained generative models, while obviating the need to
go through a large volume of data. TGT exploits the fact that the teacher has
acquired a good representation of the underlying data domain, which typically
corresponds to a much lower dimensional manifold than the input space.
Furthermore, we can use the teacher to explore input space more efficiently
through sampling or gradient-based methods; thus, making TGT especially
attractive for limited data or long-tail settings. We formally capture this
benefit of proposed data-domain exploration in our generalization bounds. We
find that TGT can improve accuracy on several image classification benchmarks
as well as a range of text classification and retrieval tasks.

Learning radiance fields has shown remarkable results for novel view
synthesis. The learning procedure usually costs lots of time, which motivates
the latest methods to speed up the learning procedure by learning without
neural networks or using more efficient data structures. However, these
specially designed approaches do not work for most of radiance fields based
methods. To resolve this issue, we introduce a general strategy to speed up the
learning procedure for almost all radiance fields based methods. Our key idea
is to reduce the redundancy by shooting much fewer rays in the multi-view
volume rendering procedure which is the base for almost all radiance fields
based methods. We find that shooting rays at pixels with dramatic color change
not only significantly reduces the training burden but also barely affects the
accuracy of the learned radiance fields. In addition, we also adaptively
subdivide each view into a quadtree according to the average rendering error in
each node in the tree, which makes us dynamically shoot more rays in more
complex regions with larger rendering error. We evaluate our method with
different radiance fields based methods under the widely used benchmarks.
Experimental results show that our method achieves comparable accuracy to the
state-of-the-art with much faster training.

Measurement of the cardiac pulse from facial video has become an interesting
pursuit of research over the last few years. This is mainly due to the
increasing importance of obtaining the heart rate of an individual in a
non-invasive manner, which can be highly useful for applications in gaming and
the medical industry. Another instrumental area of research over the past few
years has been the advent of Deep Learning and using Deep Neural networks to
enhance task performance. In this work, we propose to use efficient
convolutional networks to accurately measure the heart rate of user from low
resolution facial videos. Furthermore, to ensure that we are able to obtain the
heart rate in real time, we compress the deep learning model by pruning it,
thereby reducing its memory footprint. We benchmark the performance of our
approach on the MAHNOB dataset and compare its performance across multiple

Most practical scheduling applications involve some uncertainty about the
arriving times and lengths of the jobs. Stochastic online scheduling is a
well-established model capturing this. Here the arrivals occur online, while
the processing times are random. For this model, Gupta, Moseley, Uetz, and Xie
recently devised an efficient policy for non-preemptive scheduling on unrelated
machines with the objective to minimize the expected total weighted completion
time. We improve upon this policy by adroitly combining greedy job assignment
with $\alpha_j$-point scheduling on each machine. In this way we obtain a
$(3+\sqrt 5)(2+\Delta)$-competitive deterministic and an
$(8+4\Delta)$-competitive randomized stochastic online scheduling policy, where
$\Delta$ is an upper bound on the squared coefficients of variation of the
processing times. We also give constant performance guarantees for these
policies within the class of all fixed-assignment policies. The
$\alpha_j$-point scheduling on a single machine can be enhanced when the upper
bound $\Delta$ is known a priori or the processing times are known to be
$\delta$-NBUE for some $\delta \ge 1$. This implies improved competitive ratios
for unrelated machines but may also be of independent interest.

Point cloud filtering and normal estimation are two fundamental research
problems in the 3D field. Existing methods usually perform normal estimation
and filtering separately and often show sensitivity to noise and/or inability
to preserve sharp geometric features such as corners and edges. In this paper,
we propose a novel deep learning method to jointly estimate normals and filter
point clouds. We first introduce a 3D patch based contrastive learning
framework, with noise corruption as an augmentation, to train a feature encoder
capable of generating faithful representations of point cloud patches while
remaining robust to noise. These representations are consumed by a simple
regression network and supervised by a novel joint loss, simultaneously
estimating point normals and displacements that are used to filter the patch
centers. Experimental results show that our method well supports the two tasks
simultaneously and preserves sharp features and fine details. It generally
outperforms state-of-the-art techniques on both tasks.

Deep learning-based video inpainting has yielded promising results and gained
increasing attention from researchers. Generally, these methods usually assume
that the corrupted region masks of each frame are known and easily obtained.
However, the annotation of these masks are labor-intensive and expensive, which
limits the practical application of current methods. Therefore, we expect to
relax this assumption by defining a new semi-supervised inpainting setting,
making the networks have the ability of completing the corrupted regions of the
whole video using the annotated mask of only one frame. Specifically, in this
work, we propose an end-to-end trainable framework consisting of completion
network and mask prediction network, which are designed to generate corrupted
contents of the current frame using the known mask and decide the regions to be
filled of the next frame, respectively. Besides, we introduce a cycle
consistency loss to regularize the training parameters of these two networks.
In this way, the completion network and the mask prediction network can
constrain each other, and hence the overall performance of the trained model
can be maximized. Furthermore, due to the natural existence of prior knowledge
(e.g., corrupted contents and clear borders), current video inpainting datasets
are not suitable in the context of semi-supervised video inpainting. Thus, we
create a new dataset by simulating the corrupted video of real-world scenarios.
Extensive experimental results are reported to demonstrate the superiority of
our model in the video inpainting task. Remarkably, although our model is
trained in a semi-supervised manner, it can achieve comparable performance as
fully-supervised methods.

There has been an extensive use of Unmanned Aerial Vehicles in search and
rescue missions to distribute first aid kits and food packets. It is important
that these UAVs are able to identify and distinguish the markers from one
another for effective distribution. One of the common ways to mark the
locations is via the use of characters superimposed on shapes of various colors
which gives rise to wide variety of markers based on combination of different
shapes, characters, and their respective colors.
  In this paper, we propose an object detection and classification pipeline
which prevents false positives and minimizes misclassification of alphanumeric
characters and shapes in aerial images. Our method makes use of traditional
computer vision techniques and unsupervised machine learning methods for
identifying region proposals, segmenting the image targets and removing false
positives. We make use of a computationally light model for classification,
making it easy to be deployed on any aerial vehicle.

Spatiotemporal activity prediction, aiming to predict user activities at a
specific location and time, is crucial for applications like urban planning and
mobile advertising. Existing solutions based on tensor decomposition or graph
embedding suffer from the following two major limitations: 1) ignoring the
fine-grained similarities of user preferences; 2) user's modeling is entangled.
In this work, we propose a hypergraph neural network model called DisenHCN to
bridge the above gaps. In particular, we first unify the fine-grained user
similarity and the complex matching between user preferences and spatiotemporal
activity into a heterogeneous hypergraph. We then disentangle the user
representations into different aspects (location-aware, time-aware, and
activity-aware) and aggregate corresponding aspect's features on the
constructed hypergraph, capturing high-order relations from different aspects
and disentangles the impact of each aspect for final prediction. Extensive
experiments show that our DisenHCN outperforms the state-of-the-art methods by
14.23% to 18.10% on four real-world datasets. Further studies also convincingly
verify the rationality of each component in our DisenHCN.

The splitting method is a powerful method for solving partial differential
equations. Various splitting methods have been designed to separate different
physics, nonlinearities, and so on. Recently, a new splitting approach has been
proposed where some degrees of freedom are handled implicitly while other
degrees of freedom are handled explicitly. As a result, the scheme contains two
equations, one implicit and the other explicit. The stability of this approach
has been studied. It was shown that the time step scales as the coarse spatial
mesh size, which can provide a significant computational advantage. However,
the implicit solution part can still be expensive, especially for nonlinear
problems. In this paper, we introduce modified partial machine learning
algorithms to replace the implicit solution part of the algorithm. These
algorithms are first introduced in arXiv:2109.02147, where a homogeneous source
term is considered along with the Transformer, which is a neural network that
can predict future dynamics. In this paper, we consider time-dependent source
terms which is a generalization of the previous work. Moreover, we use the
whole history of the solution to train the network. As the implicit part of the
equations is more complicated to solve, we design a neural network to predict
it based on training. Furthermore, we compute the explicit part of the solution
using our splitting strategy. In addition, we use Proper Orthogonal
Decomposition based model reduction in machine learning. The machine learning
algorithms provide computational saving without sacrificing accuracy. We
present three numerical examples which show that our machine learning scheme is
stable and accurate.

We propose high dynamic range radiance (HDR) fields, HDR-Plenoxels, that
learn a plenoptic function of 3D HDR radiance fields, geometry information, and
varying camera settings inherent in 2D low dynamic range (LDR) images. Our
voxel-based volume rendering pipeline reconstructs HDR radiance fields with
only multi-view LDR images taken from varying camera settings in an end-to-end
manner and has a fast convergence speed. To deal with various cameras in
real-world scenarios, we introduce a tone mapping module that models the
digital in-camera imaging pipeline (ISP) and disentangles radiometric settings.
Our tone mapping module allows us to render by controlling the radiometric
settings of each novel view. Finally, we build a multi-view dataset with
varying camera conditions, which fits our problem setting. Our experiments show
that HDR-Plenoxels can express detail and high-quality HDR novel views from
only LDR images with various cameras.

Given a task, human learns from easy to hard, whereas the model learns
randomly. Undeniably, difficulty insensitive learning leads to great success in
NLP, but little attention has been paid to the effect of text difficulty in
NLP. In this research, we propose the Human Learning Matching Index (HLM Index)
to investigate the effect of text difficulty. Experiment results show: (1) LSTM
has more human-like learning behavior than BERT. (2) UID-SuperLinear gives the
best evaluation of text difficulty among four text difficulty criteria. (3)
Among nine tasks, some tasks' performance is related to text difficulty,
whereas some are not. (4) Model trained on easy data performs best in easy and
medium data, whereas trains on a hard level only perform well on hard data. (5)
Training the model from easy to hard leads to fast convergence.

A nonnegative latent factorization of tensors (NLFT) model can well model the
temporal pattern hidden in nonnegative quality-of-service (QoS) data for
predicting the unobserved ones with high accuracy. However, existing NLFT
models' objective function is based on Euclidean distance, which is only a
special case of $\beta$-divergence. Hence, can we build a generalized NLFT
model via adopting $\beta$-divergence to achieve prediction accuracy gain? To
tackle this issue, this paper proposes a $\beta$-divergence-based NLFT model
($\beta$-NLFT). Its ideas are two-fold 1) building a learning objective with
$\beta$-divergence to achieve higher prediction accuracy, and 2) implementing
self-adaptation of hyper-parameters to improve practicability. Empirical
studies on two dynamic QoS datasets demonstrate that compared with
state-of-the-art models, the proposed $\beta$-NLFT model achieves the higher
prediction accuracy for unobserved QoS data.

Federated learning offers a framework of training a machine learning model in
a distributed fashion while preserving privacy of the participants. As the
server cannot govern the clients' actions, nefarious clients may attack the
global model by sending malicious local gradients. In the meantime, there could
also be unreliable clients who are benign but each has a portion of low-quality
training data (e.g., blur or low-resolution images), thus may appearing similar
as malicious clients. Therefore, a defense mechanism will need to perform a
three-fold differentiation which is much more challenging than the conventional
(two-fold) case. This paper introduces MUD-HoG, a novel defense algorithm that
addresses this challenge in federated learning using long-short history of
gradients, and treats the detected malicious and unreliable clients
differently. Not only this, but we can also distinguish between targeted and
untargeted attacks among malicious clients, unlike most prior works which only
consider one type of the attacks. Specifically, we take into account
sign-flipping, additive-noise, label-flipping, and multi-label-flipping
attacks, under a non-IID setting. We evaluate MUD-HoG with six state-of-the-art
methods on two datasets. The results show that MUD-HoG outperforms all of them
in terms of accuracy as well as precision and recall, in the presence of a
mixture of multiple (four) types of attackers as well as unreliable clients.
Moreover, unlike most prior works which can only tolerate a low population of
harmful users, MUD-HoG can work with and successfully detect a wide range of
malicious and unreliable clients - up to 47.5% and 10%, respectively, of the
total population. Our code is open-sourced at
https://github.com/LabSAINT/MUD-HoG_Federated_Learning.

Link prediction, inferring the undiscovered or potential links of the graph,
is widely applied in the real-world. By facilitating labeled links of the graph
as the training data, numerous deep learning based link prediction methods have
been studied, which have dominant prediction accuracy compared with non-deep
methods. However,the threats of maliciously crafted training graph will leave a
specific backdoor in the deep model, thus when some specific examples are fed
into the model, it will make wrong prediction, defined as backdoor attack. It
is an important aspect that has been overlooked in the current literature. In
this paper, we prompt the concept of backdoor attack on link prediction, and
propose Link-Backdoor to reveal the training vulnerability of the existing link
prediction methods. Specifically, the Link-Backdoor combines the fake nodes
with the nodes of the target link to form a trigger. Moreover, it optimizes the
trigger by the gradient information from the target model. Consequently, the
link prediction model trained on the backdoored dataset will predict the link
with trigger to the target state. Extensive experiments on five benchmark
datasets and five well-performing link prediction models demonstrate that the
Link-Backdoor achieves the state-of-the-art attack success rate under both
white-box (i.e., available of the target model parameter)and black-box (i.e.,
unavailable of the target model parameter) scenarios. Additionally, we testify
the attack under defensive circumstance, and the results indicate that the
Link-Backdoor still can construct successful attack on the well-performing link
prediction methods. The code and data are available at
https://github.com/Seaocn/Link-Backdoor.

YouTube users looking for instructions for a specific task may spend a long
time browsing content trying to find the right video that matches their needs.
Creating a visual summary (abridged version of a video) provides viewers with a
quick overview and massively reduces search time. In this work, we focus on
summarizing instructional videos, an under-explored area of video
summarization. In comparison to generic videos, instructional videos can be
parsed into semantically meaningful segments that correspond to important steps
of the demonstrated task. Existing video summarization datasets rely on manual
frame-level annotations, making them subjective and limited in size. To
overcome this, we first automatically generate pseudo summaries for a corpus of
instructional videos by exploiting two key assumptions: (i) relevant steps are
likely to appear in multiple videos of the same task (Task Relevance), and (ii)
they are more likely to be described by the demonstrator verbally (Cross-Modal
Saliency). We propose an instructional video summarization network that
combines a context-aware temporal video encoder and a segment scoring
transformer. Using pseudo summaries as weak supervision, our network constructs
a visual summary for an instructional video given only video and transcribed
speech. To evaluate our model, we collect a high-quality test set, WikiHow
Summaries, by scraping WikiHow articles that contain video demonstrations and
visual depictions of steps allowing us to obtain the ground-truth summaries. We
outperform several baselines and a state-of-the-art video summarization model
on this new benchmark.

A powerful category of (invisible) data poisoning attacks modify a subset of
training examples by small adversarial perturbations to change the prediction
of certain test-time data. Existing defense mechanisms are not desirable to
deploy in practice, as they often either drastically harm the generalization
performance, or are attack-specific, and prohibitively slow to apply. Here, we
propose a simple but highly effective approach that unlike existing methods
breaks various types of invisible poisoning attacks with the slightest drop in
the generalization performance. We make the key observation that attacks
introduce local sharp regions of high training loss, which when minimized,
results in learning the adversarial perturbations and makes the attack
successful. To break poisoning attacks, our key idea is to alleviate the sharp
loss regions introduced by poisons. To do so, our approach comprises two
components: an optimized friendly noise that is generated to maximally perturb
examples without degrading the performance, and a randomly varying noise
component. The combination of both components builds a very light-weight but
extremely effective defense against the most powerful triggerless targeted and
hidden-trigger backdoor poisoning attacks, including Gradient Matching,
Bulls-eye Polytope, and Sleeper Agent. We show that our friendly noise is
transferable to other architectures, and adaptive attacks cannot break our
defense due to its random noise component.

Computer Tomography (CT) images have become quite important to diagnose
diseases. CT scan slice contains a vast amount of data that may not be properly
examined with the requisite precision and speed using normal visual inspection.
A computer-assisted skull fracture classification expert system is needed to
assist physicians. Convolutional Neural Networks (CNNs) are the most
extensively used deep learning models for image categorization since most often
time they outperform other models in terms of accuracy and results. The CNN
models were then developed and tested, and several convolutional neural network
(CNN) architectures were compared. ResNet50, which was used for feature
extraction combined with a gradient boosted decision tree machine learning
algorithm to act as a classifier for the categorization of skull fractures from
brain CT scans into three fracture categories, had the best overall F1-score of
96%, Hamming Score of 95%, Balanced accuracy Score of 94% & ROC AUC curve of
96% for the classification of skull fractures.

A data sketch algorithm scans a big data set, collecting a small amount of
data -- the sketch, which can be used to statistically infer properties of the
big data set. Some data sketch algorithms take a fixed-size random sample of a
big data set, and use that sample to infer frequencies of items that meet
various criteria in the big data set. This paper shows how to statistically
infer probably approximately correct (PAC) bounds for those frequencies,
efficiently, and precisely enough that the frequency bounds are either sharp or
off by only one, which is the best possible result without exact computation.

Tensor algebra lies at the core of computational science and machine
learning. Due to its high usage, entire libraries exist dedicated to improving
its performance. Conventional tensor algebra performance boosts focus on
algorithmic optimizations, which in turn lead to incremental improvements. In
this paper, we describe a method to accelerate tensor algebra a different way:
by outsourcing operations to an optical microchip. We outline a numerical
programming language developed to perform tensor algebra computations that is
designed to leverage our optical hardware's full potential. We introduce the
language's current grammar and go over the compiler design. We then show a new
way to store sparse rank-n tensors in RAM that outperforms conventional array
storage (used by C++, Java, etc.). This method is more memory-efficient than
Compressed Sparse Fiber (CSF) format and is specifically tuned for our optical
hardware. Finally, we show how the scalar-tensor product, rank-$n$ Kronecker
product, tensor dot product, Khatri-Rao product, face-splitting product, and
vector cross product can be compiled into operations native to our optical
microchip through various tensor decompositions.

We regularly consider answering counterfactual questions in practice, such as
"Would people with diabetes take a turn for the better had they choose another
medication?". Observational studies are growing in significance in answering
such questions due to their widespread accumulation and comparatively easier
acquisition than Randomized Control Trials (RCTs). Recently, some works have
introduced representation learning and domain adaptation into counterfactual
inference. However, most current works focus on the setting of binary
treatments. None of them considers that different treatments' sample sizes are
imbalanced, especially data examples in some treatment groups are relatively
limited due to inherent user preference. In this paper, we design a new
algorithmic framework for counterfactual inference, which brings an idea from
Meta-learning for Estimating Individual Treatment Effects (MetaITE) to fill the
above research gaps, especially considering multiple imbalanced treatments.
Specifically, we regard data episodes among treatment groups in counterfactual
inference as meta-learning tasks. We train a meta-learner from a set of source
treatment groups with sufficient samples and update the model by gradient
descent with limited samples in target treatment. Moreover, we introduce two
complementary losses. One is the supervised loss on multiple source treatments.
The other loss which aligns latent distributions among various treatment groups
is proposed to reduce the discrepancy. We perform experiments on two real-world
datasets to evaluate inference accuracy and generalization ability.
Experimental results demonstrate that the model MetaITE matches/outperforms

Recently, there has been a surging interest in formulating recommendations in
the context of causal inference. The studies regard the recommendation as an
intervention in causal inference and frame the users' preferences as
interventional effects to improve recommender systems' generalization. Many
studies in the field of causal inference for recommender systems have been
focusing on utilizing propensity scores from the causal community that reduce
the bias while inducing additional variance. Alternatively, some studies
suggest the existence of a set of unbiased data from randomized controlled
trials while it requires to satisfy certain assumptions that may be challenging
in practice. In this paper, we first design a causal graph representing
recommender systems' data generation and propagation process. Then, we reveal
that the underlying exposure mechanism biases the maximum likelihood estimation
(MLE) on observational feedback. In order to figure out users' preferences in
terms of causality behind data, we leverage the back-door adjustment and
do-calculus, which induces an interventional recommendation model (IREC).
Furthermore, considering the confounder may be inaccessible for measurement, we
propose a contrastive counterfactual learning method (CCL) for simulating the
intervention. In addition, we present two extra novel sampling strategies and
show an intriguing finding that sampling from counterfactual sets contributes
to superior performance. We perform extensive experiments on two real-world
datasets to evaluate and analyze the performance of our model IREC-CCL on
unbiased test sets. Experimental results demonstrate our model outperforms the

Graph Neural Networks (GNNs) have achieved great success in learning graph
representations and thus facilitating various graph-related tasks. However,
most GNN methods adopt a supervised learning setting, which is not always
feasible in real-world applications due to the difficulty to obtain labeled
data. Hence, graph self-supervised learning has been attracting increasing
attention. Graph contrastive learning (GCL) is a representative framework for
self-supervised learning. In general, GCL learns node representations by
contrasting semantically similar nodes (positive samples) and dissimilar nodes
(negative samples) with anchor nodes. Without access to labels, positive
samples are typically generated by data augmentation, and negative samples are
uniformly sampled from the entire graph, which leads to a sub-optimal
objective. Specifically, data augmentation naturally limits the number of
positive samples that involve in the process (typically only one positive
sample is adopted). On the other hand, the random sampling process would
inevitably select false-negative samples (samples sharing the same semantics
with the anchor). These issues limit the learning capability of GCL. In this
work, we propose an enhanced objective that addresses the aforementioned
issues. We first introduce an unachievable ideal objective that contains all
positive samples and no false-negative samples. This ideal objective is then
transformed into a probabilistic form based on the distributions for sampling
positive and negative samples. We then model these distributions with node
similarity and derive the enhanced objective. Comprehensive experiments on
various datasets demonstrate the effectiveness of the proposed enhanced
objective under different settings.

Day-ahead operations involves a complex and computationally intensive
optimization process to determine the generator commitment schedule and
dispatch. The optimization process is a mixed-integer linear program (MILP)
also known as security-constrained unit commitment (SCUC). Independent system
operators (ISOs) run SCUC daily and require state-of-the-art algorithms to
speed up the process. Existing patterns in historical information can be
leveraged for model reduction of SCUC, which can provide significant time
savings. In this paper, machine learning (ML) based classification approaches,
namely logistic regression, neural networks, random forest and K-nearest
neighbor, were studied for model reduction of SCUC. The ML was then aided with
a feasibility layer (FL) and post-process technique to ensure high-quality
solutions. The proposed approach is validated on several test systems namely,
IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, 500-Bus system,
and Polish 2383-Bus system. Moreover, model reduction of a stochastic SCUC
(SSCUC) was demonstrated utilizing a modified IEEE 24-Bus system with renewable
generation. Simulation results demonstrate a high training accuracy to identify
commitment schedule while FL and post-process ensure ML predictions do not lead
to infeasible solutions with minimal loss in solution quality.

Purpose; The purpose of this study is to classify glial tumors into grade II,
III and IV categories noninvasively by application of machine learning to
multi-modal MRI features in comparison with volumetric analysis. Methods; We
retrospectively studied 57 glioma patients with pre and postcontrast T1
weighted, T2 weighted, FLAIR images, and ADC maps acquired on a 3T MRI. The
tumors were segmented into enhancing and nonenhancing portions, tumor necrosis,
cyst and edema using semiautomated segmentation of ITK-SNAP open source tool.
We measured total tumor volume, enhancing-nonenhancing tumor, edema, necrosis
volume and the ratios to the total tumor volume. Training of a support vector
machine (SVM) classifier and artificial neural network (ANN) was performed with
labeled data designed to answer the question of interest. Specificity,
sensitivity, and AUC of the predictions were computed by means of ROC analysis.
Differences in continuous measures between groups were assessed by using
Kruskall Wallis, with post hoc Dunn correction for multiple comparisons.
Results; When we compared the volume ratios between groups, there was
statistically significant difference between grade IV and grade II-III glial
tumors. Edema and tumor necrosis volume ratios for grade IV glial tumors were
higher than that of grade II and III. Volumetric ratio analysis could not
distinguish grade II and III tumors successfully. However, SVM and ANN
correctly classified each group with accuracies up to 98% and 96%. Conclusion;
Application of machine learning methods to MRI features can be used to classify
brain tumors noninvasively and more readily in clinical settings.

It is crucial to successfully quantify causal effects of a policy
intervention to determine whether the policy achieved the desired outcomes. We
present a deterministic approach to a classical method of policy evaluation,
synthetic control (Abadie and Gardeazabal, 2003), to estimate the unobservable
outcome of a treatment unit using ellipsoidal optimal recovery (EOpR). EOpR
provides policy evaluators with "worst-case" outcomes and "typical" outcomes to
help in decision making. It is an approximation-theoretic technique that also
relates to the theory of principal components, which recovers unknown
observations given a learned signal class and a set of known observations. We
show that EOpR can improve pre-treatment fit and bias of the post-treatment
estimation relative to other econometrics methods. Beyond recovery of the unit
of interest, an advantage of EOpR is that it produces worst-case estimates over
the estimations produced by the recovery. We assess our approach on
artificially-generated data, on datasets commonly used in the econometrics
literature, and also derive results in the context of the COVID-19 pandemic.
Such an approach is novel in the econometrics literature for causality and
policy evaluation.

A statistical emulator can be used as a surrogate of complex physics-based
calculations to drastically reduce the computational cost. Its successful
implementation hinges on an accurate representation of the nonlinear response
surface with a high-dimensional input space. Conventional ``space-filling''
designs, including random sampling and Latin hypercube sampling, become
inefficient as the dimensionality of the input variables increases, and the
predictive accuracy of the emulator can degrade substantially for a test input
distant from the training input set. To address this fundamental challenge, we
develop a reliable emulator for predicting complex functionals by active
learning with error control (ALEC). The algorithm is applicable to
infinite-dimensional mapping with high-fidelity predictions and a controlled
predictive error. The computational efficiency has been demonstrated by
emulating the classical density functional theory (cDFT) calculations, a
statistical-mechanical method widely used in modeling the equilibrium
properties of complex molecular systems. We show that ALEC is much more
accurate than conventional emulators based on the Gaussian processes with
``space-filling'' designs and alternative active learning methods. Besides, it
is computationally more efficient than direct cDFT calculations. ALEC can be a
reliable building block for emulating expensive functionals owing to its
minimal computational cost, controllable predictive error, and fully automatic
features.

Following QUIC protocol ratification on May 2021, the third major version of
the Hypertext Transfer Protocol, namely HTTP/3, was published around one year
later in RFC 9114. In light of these consequential advancements, the current
work aspires to provide a full-blown coverage of the following issues, which to
our knowledge have received feeble or no attention in the literature so far.
First, we provide a complete review of attacks against HTTP/2, and elaborate on
if and in which way they can be migrated to HTTP/3. Second, through the
creation of a testbed comprising the at present six most popular HTTP/3-enabled
servers, we examine the effectiveness of a quartet of attacks, either stemming
directly from the HTTP/2 relevant literature or being entirely new. This
scrutiny led to the assignment of at least one CVE ID with a critical base
score by MITRE. No less important, by capitalizing on a realistic, abundant in
devices testbed, we compiled a voluminous, labeled corpus containing traces of
ten diverse attacks against HTTP and QUIC services. An initial evaluation of
the dataset mainly by means of machine learning techniques is included as well.
Given that the 30 GB dataset is made available in both pcap and CSV formats,
forthcoming research can easily take advantage of any subset of features,
contingent upon the specific network topology and configuration.

Recent advances in the reinforcement learning (RL) literature have enabled
roboticists to automatically train complex policies in simulated environments.
However, due to the poor sample complexity of these methods, solving
reinforcement learning problems using real-world data remains a challenging
problem. This paper introduces a novel cost-shaping method which aims to reduce
the number of samples needed to learn a stabilizing controller. The method adds
a term involving a control Lyapunov function (CLF) -- an `energy-like' function
from the model-based control literature -- to typical cost formulations.
Theoretical results demonstrate the new costs lead to stabilizing controllers
when smaller discount factors are used, which is well-known to reduce sample
complexity. Moreover, the addition of the CLF term `robustifies' the search for
a stabilizing controller by ensuring that even highly sub-optimal polices will
stabilize the system. We demonstrate our approach with two hardware examples
where we learn stabilizing controllers for a cartpole and an A1 quadruped with
only seconds and a few minutes of fine-tuning data, respectively.

This paper studies a natural generalization of the problem of minimizing a
univariate convex function $f$ by querying its values sequentially. At each
time-step $t$, the optimizer can invest a budget $b_t$ in a query point $X_t$
of their choice to obtain a fuzzy evaluation of $f$ at $X_t$ whose accuracy
depends on the amount of budget invested in $X_t$ across times. This setting is
motivated by the minimization of objectives whose values can only be determined
approximately through lengthy or expensive computations. We design an any-time
parameter-free algorithm called Dyadic Search, for which we prove near-optimal
optimization error guarantees. As a byproduct of our analysis, we show that the
classical dependence on the global Lipschitz constant in the error bounds is an
artifact of the granularity of the budget. Finally, we illustrate our
theoretical findings with numerical simulations.

A longstanding challenge surrounding deep learning algorithms is unpacking
and understanding how they make their decisions. Explainable Artificial
Intelligence (XAI) offers methods to provide explanations of internal functions
of algorithms and reasons behind their decisions in ways that are interpretable
and understandable to human users. . Numerous XAI approaches have been
developed thus far, and a comparative analysis of these strategies seems
necessary to discern their relevance to clinical prediction models. To this
end, we first implemented two prediction models for short- and long-term
outcomes of traumatic brain injury (TBI) utilizing structured tabular as well
as time-series physiologic data, respectively. Six different interpretation
techniques were used to describe both prediction models at the local and global
levels. We then performed a critical analysis of merits and drawbacks of each
strategy, highlighting the implications for researchers who are interested in
applying these methodologies. The implemented methods were compared to one
another in terms of several XAI characteristics such as understandability,
fidelity, and stability. Our findings show that SHAP is the most stable with
the highest fidelity but falls short of understandability. Anchors, on the
other hand, is the most understandable approach, but it is only applicable to
tabular data and not time series data.

Food image classification serves as the foundation of image-based dietary
assessment to predict food categories. Since there are many different food
classes in real life, conventional models cannot achieve sufficiently high
accuracy. Personalized classifiers aim to largely improve the accuracy of food
image classification for each individual. However, a lack of public personal
food consumption data proves to be a challenge for training such models. To
address this issue, we propose a novel framework to simulate personal food
consumption data patterns, leveraging the use of a modified Markov chain model
and self-supervised learning. Our method is capable of creating an accurate
future data pattern from a limited amount of initial data, and our simulated
data patterns can be closely correlated with the initial data pattern.
Furthermore, we use Dynamic Time Warping distance and Kullback-Leibler
divergence as metrics to evaluate the effectiveness of our method on the public
Food-101 dataset. Our experimental results demonstrate promising performance
compared with random simulation and the original Markov chain method.

The configuration interaction approach provides a conceptually simple and
powerful approach to solve the Schr\"odinger equation for realistic molecules
and materials but is characterized by an unfavourable scaling, which strongly
limits its practical applicability. Effectively selecting only the
configurations that actually contribute to the wavefunction is a fundamental
step towards practical applications. We propose a machine learning approach
that iteratively trains a generative model to preferentially generate the
important configurations. By considering molecular applications it is shown
that convergence to chemical accuracy can be achieved much more rapidly with
respect to random sampling or the Monte Carlo configuration interaction method.
This work paves the way to a broader use of generative models to solve the
electronic structure problem.

Unmanned Aerial Vehicle (UAV) has gained significant traction in the recent
years, particularly the context of surveillance. However, video datasets that
capture violent and non-violent human activity from aerial point-of-view is
scarce. To address this issue, we propose a novel, baseline simulator which is
capable of generating sequences of photo-realistic synthetic images of crowds
engaging in various activities that can be categorized as violent or
non-violent. The crowd groups are annotated with bounding boxes that are
automatically computed using semantic segmentation. Our simulator is capable of
generating large, randomized urban environments and is able to maintain an
average of 25 frames per second on a mid-range computer with 150 concurrent
crowd agents interacting with each other. We also show that when synthetic data
from the proposed simulator is augmented with real world data, binary video
classification accuracy is improved by 5% on average across two different
models.

In the context of graphical causal discovery, we adapt the versatile
framework of linear non-Gaussian acyclic models (LiNGAMs) to propose new
algorithms to efficiently learn graphs that are polytrees. Our approach
combines the Chow--Liu algorithm, which first learns the undirected tree
structure, with novel schemes to orient the edges. The orientation schemes
assess algebraic relations among moments of the data-generating distribution
and are computationally inexpensive. We establish high-dimensional consistency
results for our approach and compare different algorithmic versions in

A recent trend in binary code analysis promotes the use of neural solutions
based on instruction embedding models. An instruction embedding model is a
neural network that transforms sequences of assembly instructions into
embedding vectors. If the embedding network is trained such that the
translation from code to vectors partially preserves the semantic, the network
effectively represents an assembly code model.
  In this paper we present BinBert, a novel assembly code model. BinBert is
built on a transformer pre-trained on a huge dataset of both assembly
instruction sequences and symbolic execution information. BinBert can be
applied to assembly instructions sequences and it is fine-tunable, i.e. it can
be re-trained as part of a neural architecture on task-specific data. Through
fine-tuning, BinBert learns how to apply the general knowledge acquired with
pre-training to the specific task.
  We evaluated BinBert on a multi-task benchmark that we specifically designed
to test the understanding of assembly code. The benchmark is composed of
several tasks, some taken from the literature, and a few novel tasks that we
designed, with a mix of intrinsic and downstream tasks.
  Our results show that BinBert outperforms state-of-the-art models for binary
instruction embedding, raising the bar for binary code understanding.

Classical false discovery rate (FDR) controlling procedures offer strong and
interpretable guarantees but often lack flexibility to work with complex data.
By contrast, machine learning-based classification algorithms have superior
performances on modern datasets but typically fall short of error-controlling
guarantees. In this paper, we make these two meet by introducing a new adaptive
novelty detection procedure with FDR control, called AdaDetect. It extends the
scope of recent works of multiple testing literature to the high dimensional
setting, notably the one in Yang et al. (2021). We prove that AdaDetect comes
with finite sample guarantees: it controls the FDR strongly and approximates
the oracle in terms of the power, with explicit remainder terms that are small
under mild conditions. In practice, AdaDetect can be used in combination with
any machine learning-based classifier, which allows the user to choose the most
relevant classification approach. We illustrate this with classical real-world
datasets, for which random forest and neural network classifiers are
particularly efficient. The versatility of our method is also shown with an
astrophysical application.

Recent counter-adversarial system design problems have motivated the
development of inverse Bayesian filters. For example, inverse Kalman filter
(I-KF) has been recently formulated to estimate the adversary's Kalman filter
tracked estimates and hence, predict the adversary's future steps. The purpose
of this paper and the companion paper (Part I) is to address the inverse
filtering problem in non-linear systems by proposing an inverse extended Kalman
filter (I-EKF). In a companion paper (Part I), we developed the theory of I-EKF
(with and without unknown inputs) and I-KF (with unknown inputs). In this
paper, we develop this theory for highly non-linear models, which employ
second-order, Gaussian sum, and dithered forward EKFs. In particular, we derive
theoretical stability guarantees for the inverse second-order EKF using the
bounded non-linearity approach. To address the limitation of the standard
I-EKFs that the system model and forward filter are perfectly known to the
defender, we propose reproducing kernel Hilbert space-based EKF to learn the
unknown system dynamics based on its observations, which can be employed as an
inverse filter to infer the adversary's estimate. Numerical experiments
demonstrate the state estimation performance of the proposed filters using
recursive Cram\'{e}r-Rao lower bound as a benchmark.

Machine learning was repeatedly proven to provide predictions with disparate
outcomes, in which subgroups of the population (e.g., defined by age, gender,
or other sensitive attributes) are systematically disadvantaged. Previous
literature has focused on detecting such disparities through statistical
procedures for when the sensitive attribute is specified a priori. However,
this limits applicability in real-world settings where datasets are high
dimensional and, on top of that, sensitive attributes may be unknown. As a
remedy, we propose a data-driven framework called Automatic Location of
Disparities (ALD) which aims at locating disparities in machine learning. ALD
meets several demands from machine learning practice: ALD (1) is applicable to
arbitrary machine learning classifiers; (2) operates on different definitions
of disparities (e.g., statistical parity or equalized odds); (3) deals with
both categorical and continuous predictors; (4) is suitable to handle
high-dimensional settings; and (5) even identifies disparities due to
intersectionality where disparities arise from complex and multi-way
interactions (e.g., age above 60 and female). ALD produces interpretable
fairness reports as output. We demonstrate the effectiveness of ALD based on
both synthetic and real-world datasets. As a result, ALD helps practitioners
and researchers of algorithmic fairness to detect disparities in machine
learning algorithms, so that disparate -- or even unfair -- outcomes can be
mitigated. Moreover, ALD supports practitioners in conducting algorithmic
audits and protecting individuals from discrimination.

To exploit high temporal correlations in video frames of the same scene, the
current frame is predicted from the already-encoded reference frames using
block-based motion estimation and compensation techniques. While this approach
can efficiently exploit the translation motion of the moving objects, it is
susceptible to other types of affine motion and object occlusion/deocclusion.
Recently, deep learning has been used to model the high-level structure of
human pose in specific actions from short videos and then generate virtual
frames in future time by predicting the pose using a generative adversarial
network (GAN). Therefore, modelling the high-level structure of human pose is
able to exploit semantic correlation by predicting human actions and
determining its trajectory. Video surveillance applications will benefit as
stored big surveillance data can be compressed by estimating human pose
trajectories and generating future frames through semantic correlation. This
paper explores a new way of video coding by modelling human pose from the
already-encoded frames and using the generated frame at the current time as an
additional forward-referencing frame. It is expected that the proposed approach
can overcome the limitations of the traditional backward-referencing frames by
predicting the blocks containing the moving objects with lower residuals.
Experimental results show that the proposed approach can achieve on average up
to 2.83 dB PSNR gain and 25.93\% bitrate savings for high motion video
sequences

Adaptive gradient algorithms borrow the moving average idea of heavy ball
acceleration to estimate accurate first- and second-order moments of gradient
for accelerating convergence. However, Nesterov acceleration which converges
faster than heavy ball acceleration in theory and also in many empirical cases
is much less investigated under the adaptive gradient setting. In this work, we
propose the ADAptive Nesterov momentum algorithm, Adan for short, to speed up
the training of deep neural networks effectively. Adan first reformulates the
vanilla Nesterov acceleration to develop a new Nesterov momentum estimation
(NME) method, which avoids the extra computation and memory overhead of
computing gradient at the extrapolation point. Then Adan adopts NME to estimate
the first- and second-order moments of the gradient in adaptive gradient
algorithms for convergence acceleration. Besides, we prove that Adan finds an
$\epsilon$-approximate first-order stationary point within $O(\epsilon^{-3.5})$
stochastic gradient complexity on the nonconvex stochastic problems (e.g., deep
learning problems), matching the best-known lower bound. Extensive experimental
results show that Adan surpasses the corresponding SoTA optimizers on both
vision transformers (ViTs) and CNNs, and sets new SoTAs for many popular
networks, e.g., ResNet, ConvNext, ViT, Swin, MAE, LSTM, Transformer-XL, and
BERT. More surprisingly, Adan can use half of the training cost (epochs) of
SoTA optimizers to achieve higher or comparable performance on ViT and ResNet,
e.t.c., and also shows great tolerance to a large range of minibatch size,
e.g., from 1k to 32k. We hope Adan can contribute to the development of deep
learning by reducing training cost and relieving engineering burden of trying
different optimizers on various architectures. Code is released at
https://github.com/sail-sg/Adan.

Modern methods in dimensionality reduction are dominated by nonlinear
attraction-repulsion force-based methods (this includes t-SNE, UMAP,
ForceAtlas2, LargeVis, and many more). The purpose of this paper is to
demonstrate that all such methods, by design, come with an additional feature
that is being automatically computed along the way, namely the vector field
associated with these forces. We show how this vector field gives additional
high-quality information and propose a general refinement strategy based on
ideas from Morse theory. The efficiency of these ideas is illustrated
specifically using t-SNE on synthetic and real-life data sets.

In recent years, supervised or unsupervised learning-based MVS methods
achieved excellent performance compared with traditional methods. However,
these methods only use the probability volume computed by cost volume
regularization to predict reference depths and this manner cannot mine enough
information from the probability volume. Furthermore, the unsupervised methods
usually try to use two-step or additional inputs for training which make the
procedure more complicated. In this paper, we propose the DS-MVSNet, an
end-to-end unsupervised MVS structure with the source depths synthesis. To mine
the information in probability volume, we creatively synthesize the source
depths by splattering the probability volume and depth hypotheses to source
views. Meanwhile, we propose the adaptive Gaussian sampling and improved
adaptive bins sampling approach that improve the depths hypotheses accuracy. On
the other hand, we utilize the source depths to render the reference images and
propose depth consistency loss and depth smoothness loss. These can provide
additional guidance according to photometric and geometric consistency in
different views without additional inputs. Finally, we conduct a series of
experiments on the DTU dataset and Tanks & Temples dataset that demonstrate the
efficiency and robustness of our DS-MVSNet compared with the state-of-the-art
methods.

We prove finite sample complexities for sequential Monte Carlo (SMC)
algorithms which require only local mixing times of the associated Markov
kernels. Our bounds are particularly useful when the target distribution is
multimodal and global mixing of the Markov kernel is slow; in such cases our
approach establishes the benefits of SMC over the corresponding Markov chain
Monte Carlo (MCMC) estimator. The lack of global mixing is addressed by
sequentially controlling the bias introduced by SMC resampling procedures. We
apply these results to obtain complexity bounds for approximating expectations
under mixtures of log-concave distributions and show that SMC provides a fully
polynomial time randomized approximation scheme for some difficult multimodal
problems where the corresponding Markov chain sampler is exponentially slow.
Finally, we compare the bounds obtained by our approach to existing bounds for
tempered Markov chains on the same problems.

Few-shot segmentation of point cloud remains a challenging task, as there is
no effective way to convert local point cloud information to global
representation, which hinders the generalization ability of point features. In
this study, we propose a bidirectional feature globalization (BFG) approach,
which leverages the similarity measurement between point features and prototype
vectors to embed global perception to local point features in a bidirectional
fashion. With point-to-prototype globalization (Po2PrG), BFG aggregates local
point features to prototypes according to similarity weights from dense point
features to sparse prototypes. With prototype-to-point globalization (Pr2PoG),
the global perception is embedded to local point features based on similarity
weights from sparse prototypes to dense point features. The sparse prototypes
of each class embedded with global perception are summarized to a single
prototype for few-shot 3D segmentation based on the metric learning framework.
Extensive experiments on S3DIS and ScanNet demonstrate that BFG significantly
outperforms the state-of-the-art methods.

With the prospect of automating a number of chemical tasks with high
fidelity, chemical language processing models are emerging at a rapid speed.
Here, we present a cloud-based real-time platform that allows users to
virtually screen molecules of interest. For this purpose, molecular embeddings
inferred from a recently proposed large chemical language model, named
MolFormer, are leveraged. The platform currently supports three tasks: nearest
neighbor retrieval, chemical space visualization, and property prediction.
Based on the functionalities of this platform and results obtained, we believe
that such a platform can play a pivotal role in automating chemistry and
chemical engineering research, as well as assist in drug discovery and material
design tasks. A demo of our platform is provided at
\url{www.ibm.biz/molecular_demo}.

This paper introduces the task of visual named entity discovery in videos
without the need for task-specific supervision or task-specific external
knowledge sources. Assigning specific names to entities (e.g. faces, scenes, or
objects) in video frames is a long-standing challenge. Commonly, this problem
is addressed as a supervised learning objective by manually annotating faces
with entity labels. To bypass the annotation burden of this setup, several
works have investigated the problem by utilizing external knowledge sources
such as movie databases. While effective, such approaches do not work when
task-specific knowledge sources are not provided and can only be applied to
movies and TV series. In this work, we take the problem a step further and
propose to discover entities in videos from videos and corresponding captions
or subtitles. We introduce a three-stage method where we (i) create bipartite
entity-name graphs from frame-caption pairs, (ii) find visual entity
agreements, and (iii) refine the entity assignment through entity-level
prototype construction. To tackle this new problem, we outline two new
benchmarks SC-Friends and SC-BBT based on the Friends and Big Bang Theory TV
series. Experiments on the benchmarks demonstrate the ability of our approach
to discover which named entity belongs to which face or scene, with an accuracy
close to a supervised oracle, just from the multimodal information present in
videos. Additionally, our qualitative examples show the potential challenges of
self-contained discovery of any visual entity for future work. The code and the
data are available on GitHub.

Category-level pose estimation is a challenging problem due to intra-class
shape variations. Recent methods deform pre-computed shape priors to map the
observed point cloud into the normalized object coordinate space and then
retrieve the pose via post-processing, i.e., Umeyama's Algorithm. The
shortcomings of this two-stage strategy lie in two aspects: 1) The surrogate
supervision on the intermediate results can not directly guide the learning of
pose, resulting in large pose error after post-processing. 2) The inference
speed is limited by the post-processing step. In this paper, to handle these
shortcomings, we propose an end-to-end trainable network SSP-Pose for
category-level pose estimation, which integrates shape priors into a direct
pose regression network. SSP-Pose stacks four individual branches on a shared
feature extractor, where two branches are designed to deform and match the
prior model with the observed instance, and the other two branches are applied
for directly regressing the totally 9 degrees-of-freedom pose and performing
symmetry reconstruction and point-wise inlier mask prediction respectively.
Consistency loss terms are then naturally exploited to align the outputs of
different branches and promote the performance. During inference, only the
direct pose regression branch is needed. In this manner, SSP-Pose not only
learns category-level pose-sensitive characteristics to boost performance but
also keeps a real-time inference speed. Moreover, we utilize the symmetry
information of each category to guide the shape prior deformation, and propose
a novel symmetry-aware loss to mitigate the matching ambiguity. Extensive
experiments on public datasets demonstrate that SSP-Pose produces superior
performance compared with competitors with a real-time inference speed at about
25Hz.

Graph neural networks (GNNs) have achieved tremendous success in the task of
graph classification and diverse downstream real-world applications. Despite
their success, existing approaches are either limited to structure attacks or
restricted to local information. This calls for a more general attack framework
on graph classification, which faces significant challenges due to the
complexity of generating local-node-level adversarial examples using the
global-graph-level information. To address this "global-to-local" problem, we
present a general framework CAMA to generate adversarial examples by
manipulating graph structure and node features in a hierarchical style.
Specifically, we make use of Graph Class Activation Mapping and its variant to
produce node-level importance corresponding to the graph classification task.
Then through a heuristic design of algorithms, we can perform both feature and
structure attacks under unnoticeable perturbation budgets with the help of both
node-level and subgraph-level importance. Experiments towards attacking four
state-of-the-art graph classification models on six real-world benchmarks
verify the flexibility and effectiveness of our framework.

Biases have marked medical history, leading to unequal care affecting
marginalised groups. The patterns of missingness in observational data often
reflect these group discrepancies, but the algorithmic fairness implications of
group-specific missingness are not well understood. Despite its potential
impact, imputation is too often an overlooked preprocessing step. When
explicitly considered, attention is placed on overall performance, ignoring how
this preprocessing can reinforce group-specific inequities. Our work questions
this choice by studying how imputation affects downstream algorithmic fairness.
First, we provide a structured view of the relationship between clinical
presence mechanisms and group-specific missingness patterns. Then, through
simulations and real-world experiments, we demonstrate that the imputation
choice influences marginalised group performance and that no imputation
strategy consistently reduces disparities. Importantly, our results show that
current practices may endanger health equity as similarly performing imputation
strategies at the population level can affect marginalised groups differently.
Finally, we propose recommendations for mitigating inequities that may stem
from a neglected step of the machine learning pipeline.

Modeling how network-level traffic flow changes in the urban environment is
useful for decision-making in transportation, public safety and urban planning.
The traffic flow system can be viewed as a dynamic process that transits
between states (e.g., traffic volumes on each road segment) over time. In the
real-world traffic system with traffic operation actions like traffic signal
control or reversible lane changing, the system's state is influenced by both
the historical states and the actions of traffic operations. In this paper, we
consider the problem of modeling network-level traffic flow under a real-world
setting, where the available data is sparse (i.e., only part of the traffic
system is observed). We present DTIGNN, an approach that can predict
network-level traffic flows from sparse data. DTIGNN models the traffic system
as a dynamic graph influenced by traffic signals, learns the transition models
grounded by fundamental transition equations from transportation, and predicts
future traffic states with imputation in the process. Through comprehensive
experiments, we demonstrate that our method outperforms state-of-the-art
methods and can better support decision-making in transportation.

Soil texture is key information in agriculture for improving soil knowledge
and crop performance, so the accurate mapping of this crucial feature is
imperative for rationally planning cultivations and for targeting
interventions. We studied the relationship between radioelements and soil
texture in the Mezzano Lowland (Italy), a 189 $km^2$ agricultural plain
investigated through a ded-icated airborne gamma-ray spectroscopy survey. The K
and Th abundances were used to retrieve the clay and sand content by means of a
multi-approach method. Linear (simple and multiple) and non-linear (machine
learning algorithms with deep neural networks) predictive models were trained
and tested adopting a 1:50,000 scale soil texture map. The comparison of these
approaches highlighted that the non-linear model introduces significant
improvements in the prediction of soil texture fractions. The predicted maps of
the clay and of the sand content were compared with the regional soil maps.
Although the macro-structures were equally present, the airborne gam-ma-ray
data permits us shedding light on finer features. Map areas with higher clay
content were coincident with paleo-channels crossing the Mezzano Lowland in
Etruscan and Roman periods, confirmed by the hydrographic setting of historical
maps and by the geo-morphological features of the study area.

Up-directed rough sets are introduced and studied by the present author in
earlier papers. This is extended by her in two different granular directions in
this research, with a surprising algebraic semantics. The granules are based on
ideas of generalized closure under up-directedness that may be read as a form
of weak consequence. This yields approximation operators that satisfy cautious
monotony, while pi-groupoidal approximations (that additionally involve
strategic choice and algebraic operators) have nicer properties. The study is
primarily motivated by possible structure of concepts in distributed cognition
perspectives, real or virtual classroom learning contexts, and student-centric
teaching. Rough clustering techniques for datasets that involve up-directed
relations (as in the study of Sentinel project image data) are additionally
proposed. This research is expected to see significant theoretical and
practical applications in related domains.

Recent years have seen the rise of extremist views in the opinion ecosystem
we call social media. Allowing online extremism to persist has dire societal
consequences, and efforts to mitigate it are continuously explored. Positive
interventions, controlled signals that add attention to the opinion ecosystem
with the aim of boosting certain opinions, are one such pathway for mitigation.
This work proposes a platform to test the effectiveness of positive
interventions, through the Opinion Market Model (OMM), a two-tier model of the
online opinion ecosystem jointly accounting for both inter-opinion interactions
and the role of positive interventions. The first tier models the size of the
opinion attention market using the multivariate discrete-time Hawkes process;
the second tier leverages the market share attraction model to model opinions
cooperating and competing for market share given limited attention. On a
synthetic dataset, we show the convergence of our proposed estimation scheme.
On a dataset of Facebook and Twitter discussions containing moderate and
far-right opinions about bushfires and climate change, we show superior
predictive performance over the state-of-the-art and the ability to uncover
latent opinion interactions. Lastly, we use OMM to demonstrate the
effectiveness of mainstream media coverage as a positive intervention in
suppressing far-right opinions.

In this paper, we propose a simple acceleration scheme for Riemannian
gradient methods by extrapolating iterates on manifolds. We show when the
iterates are generated from Riemannian gradient descent method, the accelerated
scheme achieves the optimal convergence rate asymptotically and is
computationally more favorable than the recently proposed Riemannian Nesterov
accelerated gradient methods. Our experiments verify the practical benefit of
the novel acceleration strategy.

Learning time-series representations when only unlabeled data or few labeled
samples are available can be a challenging task. Recently, contrastive
self-supervised learning has shown great improvement in extracting useful
representations from unlabeled data via contrasting different augmented views
of data. In this work, we propose a novel Time-Series representation learning
framework via Temporal and Contextual Contrasting (TS-TCC) that learns
representations from unlabeled data with contrastive learning. Specifically, we
propose time-series specific weak and strong augmentations and use their views
to learn robust temporal relations in the proposed temporal contrasting module,
besides learning discriminative representations by our proposed contextual
contrasting module. Additionally, we conduct a systematic study of time-series
data augmentation selection, which is a key part of contrastive learning. We
also extend TS-TCC to the semi-supervised learning settings and propose a
Class-Aware TS-TCC (CA-TCC) that benefits from the available few labeled data
to further improve representations learned by TS-TCC. Specifically, we leverage
robust pseudo labels produced by TS-TCC to realize class-aware contrastive
loss. Extensive experiments show that the linear evaluation of the features
learned by our proposed framework performs comparably with the fully supervised
training. Additionally, our framework shows high efficiency in few labeled data
and transfer learning scenarios. The code is publicly available at
\url{https://github.com/emadeldeen24/TS-TCC}.

Co-saliency detection (CoSOD) aims at discovering the repetitive salient
objects from multiple images. Two primary challenges are group semantics
extraction and noise object suppression. In this paper, we present a unified
Two-stage grOup semantics PropagatIon and Contrastive learning NETwork
(TopicNet) for CoSOD. TopicNet can be decomposed into two substructures,
including a two-stage group semantics propagation module (TGSP) to address the
first challenge and a contrastive learning module (CLM) to address the second
challenge. Concretely, for TGSP, we design an image-to-group propagation module
(IGP) to capture the consensus representation of intra-group similar features
and a group-to-pixel propagation module (GPP) to build the relevancy of
consensus representation. For CLM, with the design of positive samples, the
semantic consistency is enhanced. With the design of negative samples, the
noise objects are suppressed. Experimental results on three prevailing
benchmarks reveal that TopicNet outperforms other competitors in terms of
various evaluation metrics.

We present MetricBERT, a BERT-based model that learns to embed text under a
well-defined similarity metric while simultaneously adhering to the
``traditional'' masked-language task. We focus on downstream tasks of learning
similarities for recommendations where we show that MetricBERT outperforms
state-of-the-art alternatives, sometimes by a substantial margin. We conduct
extensive evaluations of our method and its different variants, showing that
our training objective is highly beneficial over a traditional contrastive
loss, a standard cosine similarity objective, and six other baselines. As an
additional contribution, we publish a dataset of video games descriptions along
with a test set of similarity annotations crafted by a domain expert.

Occupational pneumoconiosis (OP) staging is a vital task concerning the lung
healthy of a subject. The staging result of a patient is depended on the
staging standard and his chest X-ray. It is essentially an image classification
task. However, the distribution of OP data is commonly imbalanced, which
largely reduces the effect of classification models which are proposed under
the assumption that data follow a balanced distribution and causes inaccurate
staging results. To achieve accurate OP staging, we proposed an OP staging
model who is able to handle imbalance data in this work. The proposed model
adopts gray level co-occurrence matrix (GLCM) to extract texture feature of
chest X-ray and implements classification with a weighted broad learning system
(WBLS). Empirical studies on six data cases provided by a hospital indicate
that proposed model can perform better OP staging than state-of-the-art
classifiers with imbalanced data.

We consider the problem of active domain adaptation (ADA) to unlabeled target
data, of which subset is actively selected and labeled given a budget
constraint. Inspired by recent analysis on a critical issue from label
distribution mismatch between source and target in domain adaptation, we devise
a method that addresses the issue for the first time in ADA. At its heart lies
a novel sampling strategy, which seeks target data that best approximate the
entire target distribution as well as being representative, diverse, and
uncertain. The sampled target data are then used not only for supervised
learning but also for matching label distributions of source and target
domains, leading to remarkable performance improvement. On four public
benchmarks, our method substantially outperforms existing methods in every
adaptation scenario.

Extracting the latent information in high-dimensional and incomplete matrices
is an important and challenging issue. The Latent Factor Analysis (LFA) model
can well handle the high-dimensional matrices analysis. Recently, Particle
Swarm Optimization (PSO)-incorporated LFA models have been proposed to tune the
hyper-parameters adaptively with high efficiency. However, the incorporation of
PSO causes the premature problem. To address this issue, we propose a
sequential Adam-adjusting-antennae BAS (A2BAS) optimization algorithm, which
refines the latent factors obtained by the PSO-incorporated LFA model. The
A2BAS algorithm consists of two sub-algorithms. First, we design an improved
BAS algorithm which adjusts beetles' antennae and step-size with Adam; Second,
we implement the improved BAS algorithm to optimize all the row and column
latent factors sequentially. With experimental results on two real
high-dimensional matrices, we demonstrate that our algorithm can effectively
solve the premature convergence issue.

As societal concerns on data privacy recently increase, we have witnessed
data silos among multiple parties in various applications. Federated learning
emerges as a new learning paradigm that enables multiple parties to
collaboratively train a machine learning model without sharing their raw data.
Vertical federated learning, where each party owns different features of the
same set of samples and only a single party has the label, is an important and
challenging topic in federated learning. Communication costs among different
parties have been a major hurdle for practical vertical learning systems. In
this paper, we propose a novel communication-efficient vertical federated
learning algorithm named FedOnce, which requires only one-shot communication
among parties. To improve model accuracy and provide privacy guarantee, FedOnce
features unsupervised learning representations in the federated setting and
privacy-preserving techniques based on moments accountant. The comprehensive
experiments on 10 datasets demonstrate that FedOnce achieves close performance
compared to state-of-the-art vertical federated learning algorithms with much
lower communication costs. Meanwhile, our privacy-preserving technique
significantly outperforms the state-of-the-art approaches under the same
privacy budget.

A novel deep neural network (DNN) architecture is proposed wherein the
filtering and linear transform are realized solely with product quantization
(PQ). This results in a natural implementation via content addressable memory
(CAM), which transcends regular DNN layer operations and requires only simple
table lookup. Two schemes are developed for the end-to-end PQ prototype
training, namely, through angle- and distance-based similarities, which differ
in their multiplicative and additive natures with different complexity-accuracy
tradeoffs. Even more, the distance-based scheme constitutes a truly
multiplier-free DNN solution. Experiments confirm the feasibility of such
Product-Quantized Content Addressable Memory Network (PECAN), which has strong
implication on hardware-efficient deployments especially for in-memory

Backdoor attacks have been shown to be a serious security threat against deep
learning models, and detecting whether a given model has been backdoored
becomes a crucial task. Existing defenses are mainly built upon the observation
that the backdoor trigger is usually of small size or affects the activation of
only a few neurons. However, the above observations are violated in many cases
especially for advanced backdoor attacks, hindering the performance and
applicability of the existing defenses. In this paper, we propose a backdoor
defense DTInspector built upon a new observation. That is, an effective
backdoor attack usually requires high prediction confidence on the poisoned
training samples, so as to ensure that the trained model exhibits the targeted
behavior with a high probability. Based on this observation, DTInspector first
learns a patch that could change the predictions of most high-confidence data,
and then decides the existence of backdoor by checking the ratio of prediction
changes after applying the learned patch on the low-confidence data. Extensive
evaluations on five backdoor attacks, four datasets, and three advanced
attacking types demonstrate the effectiveness of the proposed defense.

Human-level AI will have significant impacts on human society. However,
estimates for the realization time are debatable. To arrive at human-level AI,
artificial general intelligence (AGI), as opposed to AI systems that are
specialized for a specific task, was set as a technically meaningful long-term
goal. But now, propelled by advances in deep learning, that achievement is
getting much closer. Considering the recent technological developments, it
would be meaningful to discuss the completion date of human-level AI through
the "comprehensive technology map approach," wherein we map human-level
capabilities at a reasonable granularity, identify the current range of
technology, and discuss the technical challenges in traversing unexplored areas
and predict when all of them will be overcome. This paper presents a new
argumentative option to view the ontological sextet, which encompasses entities
in a way that is consistent with our everyday intuition and scientific
practice, as a comprehensive technological map. Because most of the modeling of
the world, in terms of how to interpret it, by an intelligent subject is the
recognition of distal entities and the prediction of their temporal evolution,
being able to handle all distal entities is a reasonable goal. Based on the
findings of philosophy and engineering cognitive technology, we predict that in
the relatively near future, AI will be able to recognize various entities to
the same degree as humans.

We report deep learning-based design of a massively parallel broadband
diffractive neural network for all-optically performing a large group of
arbitrarily-selected, complex-valued linear transformations between an input
and output field-of-view, each with N_i and N_o pixels, respectively. This
broadband diffractive processor is composed of N_w wavelength channels, each of
which is uniquely assigned to a distinct target transformation. A large set of
arbitrarily-selected linear transformations can be individually performed
through the same diffractive network at different illumination wavelengths,
either simultaneously or sequentially (wavelength scanning). We demonstrate
that such a broadband diffractive network, regardless of its material
dispersion, can successfully approximate N_w unique complex-valued linear
transforms with a negligible error when the number of diffractive neurons (N)
in its design matches or exceeds 2 x N_w x N_i x N_o. We further report that
the spectral multiplexing capability (N_w) can be increased by increasing N;
our numerical analyses confirm these conclusions for N_w > 180, which can be
further increased to e.g., ~2000 depending on the upper bound of the
approximation error. Massively parallel, wavelength-multiplexed diffractive
networks will be useful for designing high-throughput intelligent machine
vision systems and hyperspectral processors that can perform statistical
inference and analyze objects/scenes with unique spectral properties.

In recent years, the development of robust Intelligent transportation systems
(ITS) is tackled across the globe to provide better traffic efficiency by
reducing frequent traffic problems. As an application of ITS, vehicle
re-identification has gained ample interest in the domain of computer vision
and robotics. Convolutional neural network (CNN) based methods are developed to
perform vehicle re-identification to address key challenges such as occlusion,
illumination change, scale, etc. The advancement of transformers in computer
vision has opened an opportunity to explore the re-identification process
further to enhance performance. In this paper, a framework is developed to
perform the re-identification of vehicles across CCTV cameras. To perform
re-identification, the proposed framework fuses the vehicle representation
learned using a CNN and a transformer model. The framework is tested on a
dataset that contains 81 unique vehicle identities observed across 20 CCTV
cameras. From the experiments, the fused vehicle re-identification framework
yields an mAP of 61.73% which is significantly better when compared with the
standalone CNN or transformer model.

Data imputation is an effective way to handle missing data, which is common
in practical applications. In this study, we propose and test a novel data
imputation process that achieve two important goals: (1) preserve the row-wise
similarities among observations and column-wise contextual relationships among
features in the feature matrix, and (2) tailor the imputation process to
specific downstream label prediction task. The proposed imputation process uses
Transformer network and graph structure learning to iteratively refine the
contextual relationships among features and similarities among observations.
Moreover, it uses a meta-learning framework to select features that are
influential to the downstream prediction task of interest. We conduct
experiments on real-world large data sets, and show that the proposed
imputation process consistently improves imputation and label prediction
performance over a variety of benchmark methods.

In this demo paper, we design and prototype RhythmEdge, a low-cost,
deep-learning-based contact-less system for regular HR monitoring applications.
RhythmEdge benefits over existing approaches by facilitating contact-less
nature, real-time/offline operation, inexpensive and available sensing
components, and computing devices. Our RhythmEdge system is portable and easily
deployable for reliable HR estimation in moderately controlled indoor or
outdoor environments. RhythmEdge measures HR via detecting changes in blood
volume from facial videos (Remote Photoplethysmography; rPPG) and provides
instant assessment using off-the-shelf commercially available
resource-constrained edge platforms and video cameras. We demonstrate the
scalability, flexibility, and compatibility of the RhythmEdge by deploying it
on three resource-constrained platforms of differing architectures (NVIDIA
Jetson Nano, Google Coral Development Board, Raspberry Pi) and three
heterogeneous cameras of differing sensitivity, resolution, properties (web
camera, action camera, and DSLR). RhythmEdge further stores longitudinal
cardiovascular information and provides instant notification to the users. We
thoroughly test the prototype stability, latency, and feasibility for three
edge computing platforms by profiling their runtime, memory, and power usage.

Quantum photonic neural networks are variational photonic circuits that can
be trained to implement high-fidelity quantum operations. However, work-to-date
has assumed idealized components, including a perfect $\pi$ Kerr nonlinearity.
Here, we investigate the limitations of realistic quantum photonic neural
networks that suffer from fabrication imperfections leading to photon loss and
imperfect routing, and weak nonlinearities, showing that they can learn to
overcome most of these errors. Using the example of a Bell-state analyzer, we
demonstrate that there is an optimal network size, which balances imperfections
versus the ability to compensate for lacking nonlinearities. With a sub-optimal
$\pi/10$ effective Kerr nonlinearity, we show that a network fabricated with
current state-of-the-art processes can achieve an unconditional fidelity of
0.891, that increases to 0.999999 if it is possible to precondition success on
the detection of a photon in each logical photonic qubit. Our results provide a
guide to the construction of viable, brain-inspired quantum photonic devices
for emerging quantum technologies.

The potential advantages of intelligent wireless communications with
millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) are
based on the availability of instantaneous channel state information (CSI) at
the base station (BS). However, no existence of channel reciprocity leads to
the difficult acquisition of accurate CSI at the BS in frequency division
duplex (FDD) systems. Many researchers explored effective architectures based
on deep learning (DL) to solve this problem and proved the success of DL-based
solutions. However, existing schemes focused on the acquisition of complete CSI
while ignoring the beamforming and precoding operations. In this paper, we
propose an intelligent channel feedback architecture using eigenmatrix and
eigenvector feedback neural network (EMEVNet). With the help of the attention
mechanism, the proposed EMEVNet can be considered as a dual channel
auto-encoder, which is able to jointly encode the eigenmatrix and eigenvector
into codewords. Simulation results show great performance improvement and
robustness with extremely low overhead of the proposed EMEVNet method compared
with the traditional DL-based CSI feedback methods.

Malicious software (malware) classification offers a unique challenge for
continual learning (CL) regimes due to the volume of new samples received on a
daily basis and the evolution of malware to exploit new vulnerabilities. On a
typical day, antivirus vendors receive hundreds of thousands of unique pieces
of software, both malicious and benign, and over the course of the lifetime of
a malware classifier, more than a billion samples can easily accumulate. Given
the scale of the problem, sequential training using continual learning
techniques could provide substantial benefits in reducing training and storage
overhead. To date, however, there has been no exploration of CL applied to
malware classification tasks. In this paper, we study 11 CL techniques applied
to three malware tasks covering common incremental learning scenarios,
including task, class, and domain incremental learning (IL). Specifically,
using two realistic, large-scale malware datasets, we evaluate the performance
of the CL methods on both binary malware classification (Domain-IL) and
multi-class malware family classification (Task-IL and Class-IL) tasks. To our
surprise, continual learning methods significantly underperformed naive Joint
replay of the training data in nearly all settings -- in some cases reducing
accuracy by more than 70 percentage points. A simple approach of selectively
replaying 20% of the stored data achieves better performance, with 50% of the
training time compared to Joint replay. Finally, we discuss potential reasons
for the unexpectedly poor performance of the CL techniques, with the hope that
it spurs further research on developing techniques that are more effective in
the malware classification domain.

The safety-critical scenarios of artificial intelligence (AI), such as
autonomous driving, Internet of Things, smart healthcare, etc., have raised
critical requirements of trustworthy AI to guarantee the privacy and security
with reliable decisions. As a nascent branch for trustworthy AI, federated
learning (FL) has been regarded as a promising privacy preserving framework for
training a global AI model over collaborative devices. However, security
challenges still exist in the FL framework, e.g., Byzantine attacks from
malicious devices, and model tampering attacks from malicious server, which
will degrade or destroy the accuracy of trained global AI model. In this paper,
we shall propose a decentralized blockchain based FL (B-FL) architecture by
using a secure global aggregation algorithm to resist malicious devices, and
deploying practical Byzantine fault tolerance consensus protocol with high
effectiveness and low energy consumption among multiple edge servers to prevent
model tampering from the malicious server. However, to implement B-FL system at
the network edge, multiple rounds of cross-validation in blockchain consensus
protocol will induce long training latency. We thus formulate a network
optimization problem that jointly considers bandwidth and power allocation for
the minimization of long-term average training latency consisting of
progressive learning rounds. We further propose to transform the network
optimization problem as a Markov decision process and leverage the deep
reinforcement learning based algorithm to provide high system performance with
low computational complexity. Simulation results demonstrate that B-FL can
resist malicious attacks from edge devices and servers, and the training
latency of B-FL can be significantly reduced by deep reinforcement learning
based algorithm compared with baseline algorithms.

A number of methods have been introduced for the fair ML issue, most of them
complex and many of them very specific to the underlying ML moethodology. Here
we introduce a new approach that is simple, easily explained, and potentially
applicable to a number of standard ML algorithms. Explicitly Deweighted
Features (EDF) reduces the impact of each feature among the proxies of
sensitive variables, allowing a different amount of deweighting applied to each
such feature. The user specifies the deweighting hyperparameters, to achieve a
given point in the Utility/Fairness tradeoff spectrum. We also introduce a new,
simple criterion for evaluating the degree of protection afforded by any fair
ML method.

We develop BenchPress, the first ML benchmark generator for compilers that is
steerable within feature space representations of source code. BenchPress
synthesizes compiling functions by adding new code in any part of an empty or
existing sequence by jointly observing its left and right context, achieving
excellent compilation rate. BenchPress steers benchmark generation towards
desired target features that has been impossible for state of the art
synthesizers (or indeed humans) to reach. It performs better in targeting the
features of Rodinia benchmarks in 3 different feature spaces compared with (a)
CLgen - a state of the art ML synthesizer, (b) CLSmith fuzzer, (c) SRCIROR
mutator or even (d) human-written code from GitHub. BenchPress is the first
generator to search the feature space with active learning in order to generate
benchmarks that will improve a downstream task. We show how using BenchPress,
Grewe's et al. CPU vs GPU heuristic model can obtain a higher speedup when
trained on BenchPress's benchmarks compared to other techniques. BenchPress is
a powerful code generator: Its generated samples compile at a rate of 86%,
compared to CLgen's 2.33%. Starting from an empty fixed input, BenchPress
produces 10x more unique, compiling OpenCL benchmarks than CLgen, which are
significantly larger and more feature diverse.

Existing video domain adaption (DA) methods need to store all temporal
combinations of video frames or pair the source and target videos, which are
memory cost expensive and can't scale up to long videos. To address these
limitations, we propose a memory-efficient graph-based video DA approach as
follows. At first our method models each source or target video by a graph:
nodes represent video frames and edges represent the temporal or visual
similarity relationship between frames. We use a graph attention network to
learn the weight of individual frames and simultaneously align the source and
target video into a domain-invariant graph feature space. Instead of storing a
large number of sub-videos, our method only constructs one graph with a graph
attention mechanism for one video, reducing the memory cost substantially. The
extensive experiments show that, compared with the state-of-art methods, we
achieved superior performance while reducing the memory cost significantly.

Expansion methods explore the possibility of performance bottlenecks in the
input length in Deep Learning methods. In this work, we introduce the Block
Static Expansion which distributes and processes the input over a heterogeneous
and arbitrarily big collection of sequences characterized by a different length
compared to the input one. Adopting this method we introduce a model called
ExpansionNet v2, which is trained using our novel training strategy, designed
to be not only effective but also 6 times faster compared to the standard
approach of recent works in Image Captioning. The model achieves the state of
art performance over the MS-COCO 2014 captioning challenge with a score of
143.7 CIDEr-D in the offline test split, 140.8 CIDEr-D in the online evaluation
server and 72.9 All-CIDEr on the nocaps validation set. Source code available
at: https://github.com/jchenghu/ExpansionNet_v2

The transferability of adversarial examples (AEs) across diverse models is of
critical importance for black-box adversarial attacks, where attackers cannot
access the information about black-box models. However, crafted AEs always
present poor transferability. In this paper, by regarding the transferability
of AEs as generalization ability of the model, we reveal that vanilla black-box
attacks craft AEs via solving a maximum likelihood estimation (MLE) problem.
For MLE, the results probably are model-specific local optimum when available
data is small, i.e., limiting the transferability of AEs. By contrast, we
re-formulate crafting transferable AEs as the maximizing a posteriori
probability estimation problem, which is an effective approach to boost the
generalization of results with limited available data. Because Bayes posterior
inference is commonly intractable, a simple yet effective method called
MaskBlock is developed to approximately estimate. Moreover, we show that the
formulated framework is a generalization version for various attack methods.
Extensive experiments illustrate MaskBlock can significantly improve the
transferability of crafted adversarial examples by up to about 20%.

The opacity of neural networks leads their vulnerability to backdoor attacks,
where hidden attention of infected neurons is triggered to override normal
predictions to the attacker-chosen ones. In this paper, we propose a novel
backdoor defense method to mark and purify the infected neurons in the
backdoored neural networks. Specifically, we first define a new metric, called
benign salience. By combining the first-order gradient to retain the
connections between neurons, benign salience can identify the infected neurons
with higher accuracy than the commonly used metric in backdoor defense. Then, a
new Adaptive Regularization (AR) mechanism is proposed to assist in purifying
these identified infected neurons via fine-tuning. Due to the ability to adapt
to different magnitudes of parameters, AR can provide faster and more stable
convergence than the common regularization mechanism in neuron purifying.
Extensive experimental results demonstrate that our method can erase the
backdoor in neural networks with negligible performance degradation.

Double Auction enables decentralized transfer of goods between multiple
buyers and sellers, thus underpinning functioning of many online marketplaces.
Buyers and sellers compete in these markets through bidding, but do not often
know their own valuation a-priori. As the allocation and pricing happens
through bids, the profitability of participants, hence sustainability of such
markets, depends crucially on learning respective valuations through repeated
interactions. We initiate the study of Double Auction markets under bandit
feedback on both buyers' and sellers' side. We show with confidence bound based
bidding, and `Average Pricing' there is an efficient price discovery among the
participants. In particular, the buyers and sellers exchanging goods attain
$O(\sqrt{T})$ regret in $T$ rounds. The buyers and sellers who do not benefit
from exchange in turn only experience $O(\log{T}/ \Delta)$ regret in $T$ rounds
where $\Delta$ is the minimum price gap. We augment our upper bound by showing
that even with a known fixed price of the good -- a simpler learning problem
than Double Auction -- $\omega(\sqrt{T})$ regret is unattainable in certain
markets.

With a rapidly rising number of transients detected in astronomy,
classification methods based on machine learning are increasingly being
employed. Their goals are typically to obtain a definitive classification of
transients, and for good performance they usually require the presence of a
large set of observations. However, well-designed, targeted models can reach
their classification goals with fewer computing resources. This paper presents
SNGuess, a model designed to find young extragalactic nearby transients with
high purity. SNGuess works with a set of features that can be efficiently
calculated from astronomical alert data. Some of these features are static and
associated with the alert metadata, while others must be calculated from the
photometric observations contained in the alert. Most of the features are
simple enough to be obtained or to be calculated already at the early stages in
the lifetime of a transient after its detection. We calculate these features
for a set of labeled public alert data obtained over a time span of 15 months
from the Zwicky Transient Facility (ZTF). The core model of SNGuess consists of
an ensemble of decision trees, which are trained via gradient boosting.
Approximately 88% of the candidates suggested by SNGuess from a set of alerts
from ZTF spanning from April 2020 to August 2021 were found to be true relevant
supernovae (SNe). For alerts with bright detections, this number ranges between
92% and 98%. Since April 2020, transients identified by SNGuess as potential
young SNe in the ZTF alert stream are being published to the Transient Name
Server (TNS) under the AMPEL_ZTF_NEW group identifier. SNGuess scores for any
transient observed by ZTF can be accessed via a web service. The source code of
SNGuess is publicly available.

This work explores three-player game training dynamics, under what conditions
three-player games converge and the equilibria the converge on. In contrast to
prior work, we examine a three-player game architecture in which all players
explicitly interact with each other. Prior work analyzes games in which two of
three agents interact with only one other player, constituting dual two-player
games. We explore three-player game training dynamics using an extended version
of a simplified bilinear smooth game, called a simplified trilinear smooth
game. We find that trilinear games do not converge on the Nash equilibrium in
most cases, rather converging on a fixed point which is optimal for two
players, but not for the third. Further, we explore how the order of the
updates influences convergence. In addition to alternating and simultaneous
updates, we explore a new update order--maximizer-first--which is only possible
in a three-player game. We find that three-player games can converge on a Nash
equilibrium using maximizer-first updates. Finally, we experiment with
differing momentum values for each player in a trilinear smooth game under all
three update orders and show that maximizer-first updates achieve more optimal
results in a larger set of player-specific momentum value triads than other
update orders.

Computational models are quantitative representations of systems. By
analyzing and comparing the outputs of such models, it is possible to gain a
better understanding of the system itself. Though, as the complexity of model
outputs increases, it becomes increasingly difficult to compare simulations to
each other. While it is straightforward to only compare a few specific model
outputs across multiple simulations, it is more informative to be able to
compare model simulations as a whole. However, it is difficult to holistically
compare model simulations in an unbiased manner. To address these limitations,
we use Siamese neural networks to compare model simulations as a single value,
with the neural networks capturing the relationships between the model outputs.
We provide an approach to training Siamese networks on model simulations and
display how the trained networks can then be used to provide a holistic
comparison of model outputs. This approach can be applied to a wide range of
model types, providing a quantitative method of analyzing the complex outputs
of computational models.

We develop an approach for fully Bayesian learning and calibration of
spatiotemporal dynamical mechanistic models based on noisy observations.
Calibration is achieved by melding information from observed data with
simulated computer experiments from the mechanistic system. The joint melding
makes use of both Gaussian and non-Gaussian state-space methods as well as
Gaussian process regression. Assuming the dynamical system is controlled by a
finite collection of inputs, Gaussian process regression learns the effect of
these parameters through a number of training runs, driving the stochastic
innovations of the spatiotemporal state-space component. This enables efficient
modeling of the dynamics over space and time. Through reduced-rank Gaussian
processes and a conjugate model specification, our methodology is applicable to
large-scale calibration and inverse problems. Our method is general,
extensible, and capable of learning a wide range of dynamical systems with
potential model misspecification. We demonstrate this flexibility through
solving inverse problems arising in the analysis of ordinary and partial
nonlinear differential equations and, in addition, to a black-box computer
model generating spatiotemporal dynamics across a network.

Conversational data is essential in psychology because it can help
researchers understand individuals cognitive processes, emotions, and
behaviors. Utterance labelling is a common strategy for analyzing this type of
data. The development of NLP algorithms allows researchers to automate this
task. However, psychological conversational data present some challenges to NLP
researchers, including multilabel classification, a large number of classes,
and limited available data. This study explored how automated labels generated
by NLP methods are comparable to human labels in the context of conversations
on adulthood transition. We proposed strategies to handle three common
challenges raised in psychological studies. Our findings showed that the deep
learning method with domain adaptation (RoBERTa-CON) outperformed all other
machine learning methods; and the hierarchical labelling system that we
proposed was shown to help researchers strategically analyze conversational
data. Our Python code and NLP model are available at
https://github.com/mlaricheva/automated_labeling.

In recent years, there has been tremendous progress in the field of semantic
segmentation. However, one remaining challenging problem is that segmentation
models do not generalize to unseen domains. To overcome this problem, one
either has to label lots of data covering the whole variety of domains, which
is often infeasible in practice, or apply unsupervised domain adaptation (UDA),
only requiring labeled source data. In this work, we focus on UDA and
additionally address the case of adapting not only to a single domain, but to a
sequence of target domains. This requires mechanisms preventing the model from
forgetting its previously learned knowledge. To adapt a segmentation model to a
target domain, we follow the idea of utilizing light-weight style transfer to
convert the style of labeled source images into the style of the target domain,
while retaining the source content. To mitigate the distributional shift
between the source and the target domain, the model is fine-tuned on the
transferred source images in a second step. Existing light-weight style
transfer approaches relying on adaptive instance normalization (AdaIN) or
Fourier transformation still lack performance and do not substantially improve
upon common data augmentation, such as color jittering. The reason for this is
that these methods do not focus on region- or class-specific differences, but
mainly capture the most salient style. Therefore, we propose a simple and
light-weight framework that incorporates two class-conditional AdaIN layers. To
extract the class-specific target moments needed for the transfer layers, we
use unfiltered pseudo-labels, which we show to be an effective approximation
compared to real labels. We extensively validate our approach (CACE) on a
synthetic sequence and further propose a challenging sequence consisting of
real domains. CACE outperforms existing methods visually and quantitatively.

Question answering over temporal knowledge graphs (TKGQA) has recently found
increasing interest. TKGQA requires temporal reasoning techniques to extract
the relevant information from temporal knowledge bases. The only existing TKGQA
dataset, i.e., CronQuestions, consists of temporal questions based on the facts
from a fixed time period, where a temporal knowledge graph (TKG) spanning the
same period can be fully used for answer inference, allowing the TKGQA models
to use even the future knowledge to answer the questions based on the past
facts. In real-world scenarios, however, it is also common that given the
knowledge until now, we wish the TKGQA systems to answer the questions asking
about the future. As humans constantly seek plans for the future, building
TKGQA systems for answering such forecasting questions is important.
Nevertheless, this has still been unexplored in previous research. In this
paper, we propose a novel task: forecasting question answering over temporal
knowledge graphs. We also propose a large-scale TKGQA benchmark dataset, i.e.,
ForecastTKGQuestions, for this task. It includes three types of questions,
i.e., entity prediction, yes-no, and fact reasoning questions. For every
forecasting question in our dataset, QA models can only have access to the TKG
information before the timestamp annotated in the given question for answer
inference. We find that the state-of-the-art TKGQA methods perform poorly on
forecasting questions, and they are unable to answer yes-no questions and fact
reasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model that
employs a TKG forecasting module for future inference, to answer all three
types of questions. Experimental results show that ForecastTKGQA outperforms
recent TKGQA methods on the entity prediction questions, and it also shows
great effectiveness in answering the other two types of questions.

In recent years, using orthogonal matrices has been shown to be a promising
approach in improving Recurrent Neural Networks (RNNs) with training,
stability, and convergence, particularly, to control gradients. While Gated
Recurrent Unit (GRU) and Long Short Term Memory (LSTM) architectures address
the vanishing gradient problem by using a variety of gates and memory cells,
they are still prone to the exploding gradient problem. In this work, we
analyze the gradients in GRU and propose the usage of orthogonal matrices to
prevent exploding gradient problems and enhance long-term memory. We study
where to use orthogonal matrices and we propose a Neumann series-based Scaled
Cayley transformation for training orthogonal matrices in GRU, which we call
Neumann-Cayley Orthogonal GRU, or simply NC-GRU. We present detailed
experiments of our model on several synthetic and real-world tasks, which show
that NC-GRU significantly outperforms GRU as well as several other RNNs.

The Generative Adversarial Network (GAN) is a powerful and flexible tool that
can generate high-fidelity synthesized data by learning. It has seen many
applications in simulating events in High Energy Physics (HEP), including
simulating detector responses and physics events. However, training GANs is
notoriously hard and optimizing their hyperparameters even more so. It normally
requires many trial-and-error training attempts to force a stable training and
reach a reasonable fidelity. Significant tuning work has to be done to achieve
the accuracy required by physics analyses. This work uses the physics-agnostic
and high-performance-computer-friendly hyperparameter optimization tool HYPPO
to optimize and examine the sensitivities of the hyperparameters of a GAN for
two independent HEP datasets. This work provides the first insights into
efficiently tuning GANs for Large Hadron Collider data. We show that given
proper hyperparameter tuning, we can find GANs that provide high-quality
approximations of the desired quantities. We also provide guidelines for how to
go about GAN architecture tuning using the analysis tools in HYPPO.

Sun et al. [J. Chem. Phys. 144, 191101 (2016)] suggested that common density
functional approximations (DFAs) should exhibit large energy errors for excited
states as a necessary consequence of orbital nodality. Motivated by
self-interaction corrected density functional calculations on many-electron
systems, we continue their study with the exactly solvable $1s$, $2p$, and $3d$
states of 36 hydrogenic one-electron ions (H-Kr$^{35+}$) and demonstrate with
self-consistent calculations that state-of-the-art DFAs indeed exhibit large
errors for the $2p$ and $3d$ excited states. We consider 56 functionals at the
local density approximation (LDA), generalized gradient approximation (GGA) as
well as meta-GGA levels, also including several hybrid functionals like the
recently proposed machine-learned DM21 local hybrid functional. The best
non-hybrid functional for the $1s$ ground state is revTPSS. The $2p$ and $3d$
excited states are more difficult for DFAs as Sun et al. predicted, and LDA
functionals turn out to yield the most systematic accuracy for these states
amongst non-hybrid functionals. The best performance for the three states
overall is observed with the BHandH global hybrid GGA functional, which
contains 50% Hartree-Fock exchange and 50% LDA exchange. The performance of
DM21 is found to be inconsistent, yielding good accuracy for some states and
systems and poor accuracy for others. Based on these results, we recommend
including a variety of one-electron cations in future training of
machine-learned density functionals.

Multi-shell fullerenes "buckyonions" were simulated, starting from initially
random configurations, using a density-functional-theory (DFT)-trained
machine-learning carbon potential within the Gaussian Approximation Potential
(ML-GAP) Framework [Volker L. Deringer and Gabor Csanyi, Phys. Rev. B 95,
094203 (2017)]. A large set of such fullerenes were obtained with sizes ranging
from 60 ~ 3774 atoms. The buckyonions are formed by clustering and layering
starts from the outermost shell and proceed inward. Inter-shell cohesion is
partly due to interaction between delocalized $\pi$ electrons into the gallery.
The energies of the models were validated ex post facto using density
functional codes, VASP and SIESTA, revealing an energy difference within the
range of 0.02 - 0.08 eV/atom after conjuagte gradient energy convergence of the
models were achieved with both methods.

Automatic detection of traffic accidents is an important emerging topic in
traffic monitoring systems. Nowadays many urban intersections are equipped with
surveillance cameras connected to traffic management systems. Therefore,
computer vision techniques can be viable tools for automatic accident
detection. This paper presents a new efficient framework for accident detection
at intersections for traffic surveillance applications. The proposed framework
consists of three hierarchical steps, including efficient and accurate object
detection based on the state-of-the-art YOLOv4 method, object tracking based on
Kalman filter coupled with the Hungarian algorithm for association, and
accident detection by trajectory conflict analysis. A new cost function is
applied for object association to accommodate for occlusion, overlapping
objects, and shape changes in the object tracking step. The object trajectories
are analyzed in terms of velocity, angle, and distance in order to detect
different types of trajectory conflicts including vehicle-to-vehicle,
vehicle-to-pedestrian, and vehicle-to-bicycle. Experimental results using real
traffic video data show the feasibility of the proposed method in real-time
applications of traffic surveillance. In particular, trajectory conflicts,
including near-accidents and accidents occurring at urban intersections are
detected with a low false alarm rate and a high detection rate. The robustness
of the proposed framework is evaluated using video sequences collected from
YouTube with diverse illumination conditions. The dataset is publicly available
at: http://github.com/hadi-ghnd/AccidentDetection.

Large transformer-based pre-trained language models have achieved impressive
performance on a variety of knowledge-intensive tasks and can capture factual
knowledge in their parameters. We argue that storing large amounts of knowledge
in the model parameters is sub-optimal given the ever-growing amounts of
knowledge and resource requirements. We posit that a more efficient alternative
is to provide explicit access to contextually relevant structured knowledge to
the model and train it to use that knowledge. We present LM-CORE -- a general
framework to achieve this -- that allows \textit{decoupling} of the language
model training from the external knowledge source and allows the latter to be
updated without affecting the already trained model. Experimental results show
that LM-CORE, having access to external knowledge, achieves significant and
robust outperformance over state-of-the-art knowledge-enhanced language models
on knowledge probing tasks; can effectively handle knowledge updates; and
performs well on two downstream tasks. We also present a thorough error
analysis highlighting the successes and failures of LM-CORE.

A quantum reinforcement learning protocol in the presence of thermal
dissipation is introduced and analyzed. Analytical calculations as well as
numerical simulations are carried out, obtaining evidence that decoherence and
dissipation do not significantly degrade the performance of the quantum
reinforcement learning protocol for sufficiently low temperatures, being in
some cases even beneficial. Quantum reinforcement learning under realistic
experimental conditions of decoherence and dissipation opens an avenue for the
realization of quantum agents able to interact with a changing environment, and
adapt to it, with plausible many applications inside quantum technologies and

Due to the lack of quality annotation in medical imaging community,
semi-supervised learning methods are highly valued in image semantic
segmentation tasks. In this paper, an advanced consistency-aware
pseudo-label-based self-ensembling approach is presented to fully utilize the
power of Vision Transformer(ViT) and Convolutional Neural Network(CNN) in
semi-supervised learning. Our proposed framework consists of a feature-learning
module which is enhanced by ViT and CNN mutually, and a guidance module which
is robust for consistency-aware purposes. The pseudo labels are inferred and
utilized recurrently and separately by views of CNN and ViT in the
feature-learning module to expand the data set and are beneficial to each
other. Meanwhile, a perturbation scheme is designed for the feature-learning
module, and averaging network weight is utilized to develop the guidance
module. By doing so, the framework combines the feature-learning strength of
CNN and ViT, strengthens the performance via dual-view co-training, and enables
consistency-aware supervision in a semi-supervised manner. A topological
exploration of all alternative supervision modes with CNN and ViT are detailed
validated, demonstrating the most promising performance and specific setting of
our method on semi-supervised medical image segmentation tasks. Experimental
results show that the proposed method achieves state-of-the-art performance on
a public benchmark data set with a variety of metrics. The code is publicly

Communicating useful background knowledge to reinforcement learning (RL)
agents is an important and effective method for accelerating learning. We
introduce RLang, a domain-specific language (DSL) for communicating domain
knowledge to an RL agent. Unlike other existing DSLs proposed by the RL
community that ground to single elements of a decision-making formalism (e.g.,
the reward function or policy function), RLang can specify information about
every element of a Markov decision process. We define precise syntax and
grounding semantics for RLang, and provide a parser implementation that grounds
RLang programs to an algorithm-agnostic partial world model and policy that can
be exploited by an RL agent. We provide a series of example RLang programs, and
demonstrate how different RL methods can exploit the resulting knowledge,
including model-free and model-based tabular algorithms, hierarchical
approaches, and deep RL algorithms (including both policy gradient and
value-based methods).

Cell identification within the H&E slides is an essential prerequisite that
can pave the way towards further pathology analyses including tissue
classification, cancer grading, and phenotype prediction. However, performing
such a task using deep learning techniques requires a large cell-level
annotated dataset. Although previous studies have investigated the performance
of contrastive self-supervised methods in tissue classification, the utility of
this class of algorithms in cell identification and clustering is still
unknown. In this work, we investigated the utility of Self-Supervised Learning
(SSL) in cell clustering by proposing the Contrastive Cell Representation
Learning (CCRL) model. Through comprehensive comparisons, we show that this
model can outperform all currently available cell clustering models by a large
margin across two datasets from different tissue types. More interestingly, the
results show that our proposed model worked well with a few number of cell
categories while the utility of SSL models has been mainly shown in the context
of natural image datasets with large numbers of classes (e.g., ImageNet). The
unsupervised representation learning approach proposed in this research
eliminates the time-consuming step of data annotation in cell classification
tasks, which enables us to train our model on a much larger dataset compared to
previous methods. Therefore, considering the promising outcome, this approach
can open a new avenue to automatic cell representation learning.

Unsupervised learning of latent variable models (LVMs) is widely used to
represent data in machine learning. When such models reflect the ground truth
factors and the mechanisms mapping them to observations, there is reason to
expect that they allow generalization in downstream tasks. It is however well
known that such identifiability guaranties are typically not achievable without
putting constraints on the model class. This is notably the case for nonlinear
Independent Component Analysis, in which the LVM maps statistically independent
variables to observations via a deterministic nonlinear function. Several
families of spurious solutions fitting perfectly the data, but that do not
correspond to the ground truth factors can be constructed in generic settings.
However, recent work suggests that constraining the function class of such
models may promote identifiability. Specifically, function classes with
constraints on their partial derivatives, gathered in the Jacobian matrix, have
been proposed, such as orthogonal coordinate transformations (OCT), which
impose orthogonality of the Jacobian columns. In the present work, we prove
that a subclass of these transformations, conformal maps, is identifiable and
provide novel theoretical results suggesting that OCTs have properties that
prevent families of spurious solutions to spoil identifiability in a generic
setting.

Crowdsourcing technologies rely on groups of people to input information that
may be critical for decision-making. This work examines obfuscation in the
context of reporting technologies. We show that widespread use of reporting
platforms comes with unique security and privacy implications, and introduce a
threat model and corresponding taxonomy to outline some of the many attack
vectors in this space. We then perform an empirical analysis of a dataset of
call logs from a controversial, real-world reporting hotline and identify
coordinated obfuscation strategies that are intended to hinder the platform's
legitimacy. We propose a variety of statistical measures to quantify the
strength of this obfuscation strategy with respect to the structural and
semantic characteristics of the reporting attacks in our dataset.

Empirical game-theoretic analysis (EGTA) is primarily focused on learning the
equilibria of simulation-based games. Recent approaches have tackled this
problem by learning a uniform approximation of the game's utilities, and then
applying precision-recall theorems: i.e., all equilibria of the true game are
approximate equilibria in the estimated game, and vice-versa. In this work, we
generalize this approach to all game properties that are well behaved (i.e.,
Lipschitz continuous in utilities), including regret (which defines Nash and
correlated equilibria), adversarial values, and power-mean and Gini social
welfare. Further, we introduce a novel algorithm -- progressive sampling with
pruning (PSP) -- for learning a uniform approximation and thus any well-behaved
property of a game, which prunes strategy profiles once the corresponding
players' utilities are well-estimated, and we analyze its data and query
complexities in terms of the a priori unknown utility variances. We experiment
with our algorithm extensively, showing that 1) the number of queries that PSP
saves is highly sensitive to the utility variance distribution, and 2) PSP
consistently outperforms theoretical upper bounds, achieving significantly
lower query complexities than natural baselines. We conclude with experiments
that uncover some of the remaining difficulties with learning properties of
simulation-based games, in spite of recent advances in statistical EGTA
methodology, including those developed herein.

Embedding learning is an important technique in deep recommendation models to
map categorical features to dense vectors. However, the embedding tables often
demand an extremely large number of parameters, which become the storage and
efficiency bottlenecks. Distributed training solutions have been adopted to
partition the embedding tables into multiple devices. However, the embedding
tables can easily lead to imbalances if not carefully partitioned. This is a
significant design challenge of distributed systems named embedding table
sharding, i.e., how we should partition the embedding tables to balance the
costs across devices, which is a non-trivial task because 1) it is hard to
efficiently and precisely measure the cost, and 2) the partition problem is
known to be NP-hard. In this work, we introduce our novel practice in Meta,
namely AutoShard, which uses a neural cost model to directly predict the
multi-table costs and leverages deep reinforcement learning to solve the
partition problem. Experimental results on an open-sourced large-scale
synthetic dataset and Meta's production dataset demonstrate the superiority of
AutoShard over the heuristics. Moreover, the learned policy of AutoShard can
transfer to sharding tasks with various numbers of tables and different ratios
of the unseen tables without any fine-tuning. Furthermore, AutoShard can
efficiently shard hundreds of tables in seconds. The effectiveness,
transferability, and efficiency of AutoShard make it desirable for production
use. Our algorithms have been deployed in Meta production environment. A
prototype is available at https://github.com/daochenzha/autoshard

We present an overview of the design and first proof-of-concept
implementation for AIDA, an autonomous intelligent developer agent that
develops software from scratch. AIDA takes a software requirements
specification and uses reasoning over a semantic knowledge graph to interpret
the requirements, then designs and writes software to satisfy them. AIDA uses
both declarative and procedural knowledge in the core domains of data,
algorithms, and code, plus some general knowledge. The reasoning codebase uses
this knowledge to identify needed components, then designs and builds the
necessary information structures around them that become the software. These
structures, the motivating requirements, and the resulting source code itself
are all new knowledge that are added to the knowledge graph, becoming available
for future reasoning. In this way, AIDA also learns as she writes code and
becomes more efficient when writing subsequent code.

Heating and cooling systems in buildings account for 31\% of global energy
use, much of which are regulated by Rule Based Controllers (RBCs) that neither
maximise energy efficiency nor minimise emissions by interacting optimally with
the grid. Control via Reinforcement Learning (RL) has been shown to
significantly improve building energy efficiency, but existing solutions
require access to building-specific simulators or data that cannot be expected
for every building in the world. In response, we show it is possible to obtain
emission-reducing policies without such knowledge a priori--a paradigm we call
zero-shot building control. We combine ideas from system identification and
model-based RL to create PEARL (Probabilistic Emission-Abating Reinforcement
Learning) and show that a short period of active exploration is all that is
required to build a performant model. In experiments across three varied
building energy simulations, we show PEARL outperforms an existing RBC once,
and popular RL baselines in all cases, reducing building emissions by as much
as 31\% whilst maintaining thermal comfort. Our source code is available online
via https://enjeeneer.io/projects/pearl .

This study investigates the density anomalies observed in silica glass. The
density of silica glass does not monotonically increase during cooling; it
instead shows a maximum and minimum. Despite tremendous efforts, the structural
origin of these density anomalies is not clearly understood. Our technique that
combines topological data analysis and machine learning reveals that the
density anomalies are characterized by a change in the one-dimensional topology
of the -Si-Si- network rather than that of the entire -Si-O- network. The
one-dimensional topology of the -Si-Si- network changes at the temperatures at
which the maximum and minimum densities are observed. In contrast, the
one-dimensional topologies of the -O-O- and -Si-O- networks change at lower
temperatures. Our work demonstrates the value of topological techniques in
understanding the transitions in glassy materials and sheds light on the
characterization of glass-liquid transitions.

Herein, the Hidden Markov Model is expanded to allow for Markov chain
observations. In particular, the observations are assumed to be a Markov chain
whose one step transition probabilities depend upon the hidden Markov chain. An
Expectation-Maximization analog to the Baum-Welch algorithm is developed for
this more general model to estimate the transition probabilities for both the
hidden state and for the observations as well as to estimate the probabilities
for the initial joint hidden-state-observation distribution. A believe state or
filter recursion to track the hidden state then arises from the calculations of
this Expectation-Maximization algorithm. A dynamic programming analog to the
Viterbi algorithm is also developed to estimate the most likely sequence of
hidden states given the sequence of observations.

Masked image modeling (MIM) has demonstrated impressive results in
self-supervised representation learning by recovering corrupted image patches.
However, most existing studies operate on low-level image pixels, which hinders
the exploitation of high-level semantics for representation models. In this
work, we propose to use a semantic-rich visual tokenizer as the reconstruction
target for masked prediction, providing a systematic way to promote MIM from
pixel-level to semantic-level. Specifically, we propose vector-quantized
knowledge distillation to train the tokenizer, which discretizes a continuous
semantic space to compact codes. We then pretrain vision Transformers by
predicting the original visual tokens for the masked image patches.
Furthermore, we introduce a patch aggregation strategy which associates
discrete image patches to enhance global semantic representation. Experiments
on image classification and semantic segmentation show that BEiT v2 outperforms
all compared MIM methods. On ImageNet-1K (224 size), the base-size BEiT v2
achieves 85.5% top-1 accuracy for fine-tuning and 80.1% top-1 accuracy for
linear probing. The large-size BEiT v2 obtains 87.3% top-1 accuracy for
ImageNet-1K (224 size) fine-tuning, and 56.7% mIoU on ADE20K for semantic
segmentation. The code and pretrained models are available at
https://aka.ms/beitv2.

Building reliable AI decision support systems requires a robust set of data
on which to train models; both with respect to quantity and diversity.
Obtaining such datasets can be difficult in resource limited settings, or for
applications in early stages of deployment. Sample rejection is one way to work
around this challenge, however much of the existing work in this area is
ill-suited for such scenarios. This paper substantiates that position and
proposes a simple solution as a proof of concept baseline.

Despite years of effort, the quantum machine learning community has only been
able to show quantum learning advantages for certain contrived
cryptography-inspired datasets in the case of classical data. In this note, we
discuss the challenges of finding learning problems that quantum learning
algorithms can learn much faster than any classical learning algorithm, and we
study how to identify such learning problems. Specifically, we reflect on the
main concepts in computational learning theory pertaining to this question, and
we discuss how subtle changes in definitions can mean conceptually
significantly different tasks, which can either lead to a separation or no
separation at all. Moreover, we study existing learning problems with a
provable quantum speedup to distill sets of more general and sufficient
conditions (i.e., ``checklists'') for a learning problem to exhibit a
separation between classical and quantum learners. These checklists are
intended to streamline one's approach to proving quantum speedups for learning
problems, or to elucidate bottlenecks. Finally, to illustrate its application,
we analyze examples of potential separations (i.e., when the learning problem
is build from computational separations, or when the data comes from a quantum
experiment) through the lens of our approach.

Semi-supervised learning (SSL) improves model generalization by leveraging
massive unlabeled data to augment limited labeled samples. However, currently,
popular SSL evaluation protocols are often constrained to computer vision (CV)
tasks. In addition, previous work typically trains deep neural networks from
scratch, which is time-consuming and environmentally unfriendly. To address the
above issues, we construct a Unified SSL Benchmark (USB) for classification by
selecting 15 diverse, challenging, and comprehensive tasks from CV, natural
language processing (NLP), and audio processing (Audio), on which we
systematically evaluate the dominant SSL methods, and also open-source a
modular and extensible codebase for fair evaluation of these SSL methods. We
further provide the pre-trained versions of the state-of-the-art neural models
for CV tasks to make the cost affordable for further tuning. USB enables the
evaluation of a single SSL algorithm on more tasks from multiple domains but
with less cost. Specifically, on a single NVIDIA V100, only 39 GPU days are
required to evaluate FixMatch on 15 tasks in USB while 335 GPU days (279 GPU
days on 4 CV datasets except for ImageNet) are needed on 5 CV tasks with
TorchSSL.

Probabilistic programming makes it easy to represent a probabilistic model as
a program. Building an individual model, however, is only one step of
probabilistic modeling. The broader challenge of probabilistic modeling is in
understanding and navigating spaces of alternative models. There is currently
no good way to represent these spaces of alternative models, despite their
central role. We present an extension of probabilistic programming that lets
each program represent a network of interrelated probabilistic models. We give
a formal semantics for these multi-model probabilistic programs, a collection
of efficient algorithms for network-of-model operations, and an example
implementation built on top of the popular probabilistic programming language
Stan. This network-of-models representation opens many doors, including search
and automation in model-space, tracking and communication of model development,
and explicit modeler degrees of freedom to mitigate issues like p-hacking. We
demonstrate automatic model search and model development tracking using our
Stan implementation, and we propose many more possible applications.

Artificial intelligence (AI) assisting with antimicrobial prescribing raises
significant moral questions. Utilising ethical frameworks alongside AI-driven
systems, while considering infection specific complexities, can support moral
decision making to tackle antimicrobial resistance.

Training deep graph neural networks (GNNs) poses a challenging task, as the
performance of GNNs may suffer from the number of hidden message-passing
layers. The literature has focused on the proposals of over-smoothing and
under-reaching to explain the performance deterioration of deep GNNs. In this
paper, we propose a new explanation for such deteriorated performance
phenomenon, mis-simplification, that is, mistakenly simplifying graphs by
preventing self-loops and forcing edges to be unweighted. We show that such
simplifying can reduce the potential of message-passing layers to capture the
structural information of graphs. In view of this, we propose a new framework,
edge enhanced graph neural network(EEGNN). EEGNN uses the structural
information extracted from the proposed Dirichlet mixture Poisson graph model,
a Bayesian nonparametric model for graphs, to improve the performance of
various deep message-passing GNNs. Experiments over different datasets show
that our method achieves considerable performance increase compared to

Each programming language comes with official documentation to guide
developers with APIs, methods and classes. However, in some cases, official
documentation is not an efficient way to get the needed information. As a
result, developers may consult other sources (e.g., Stack Overflow, GitHub) to
learn more about an API, its implementation, usage, and other information that
official documentation may not provide. In this research, we propose an
automatic approach to generate summaries for APIs and methods by leveraging
unofficial documentation using NLP techniques. Our findings demonstrate that
the generated summaries are competitive, and can be used as a complementary
source for guiding developers in software development and maintenance tasks.

As the global demand for data has continued to rise exponentially, some have
begun turning to the idea of semantic communication as a means of efficiently
meeting this demand. Pushing beyond the boundaries of conventional
communication systems, semantic communication focuses on the accurate recovery
of the meaning conveyed from source to receiver, as opposed to the accurate
recovery of transmitted symbols. In this survey, we aim to provide a
comprehensive view of the history and current state of semantic communication
and the techniques for engineering this higher level of communication. A survey
of the current literature reveals four broad approaches to engineering semantic
communication. We term the earliest of these approaches classical semantic
information, which seeks to extend information-theoretic results to include
semantic information. A second approach makes use of knowledge graphs to
achieve semantic communication, and a third utilizes the power of modern deep
learning techniques to facilitate this communication. The fourth approach
focuses on the significance of information, rather than its meaning, to achieve
efficient, goal-oriented communication. We discuss each of these four
approaches and their corresponding studies in detail, and provide some
challenges and opportunities that pertain to each approach. Finally, we
introduce a novel approach to semantic communication, which we term
context-based semantic communication. Inspired by the way in which humans
naturally communicate with one another, this context-based approach provides a
general, optimization-based design framework for semantic communication
systems. Together, this survey provides a useful guide for the design and
implementation of semantic communication systems.

We implemented two distinct 3-dimensional deep learning neural networks and
evaluate their ability to segment intracranial hemorrhage (ICH) seen on
non-contrast computed tomography (CT). One model, referred to as
"Voxels-Intersecting along Orthogonal Levels of Attention U-Net" (viola-Unet),
has architecture elements that are amenable to the INSTANCE 2022 Data
Challenge. A second comparison model was derived from the no-new U-Net
(nnU-Net). Input images and ground truth segmentation maps were used to train
the two networks separately in supervised manner; validation data were
subsequently used for semi-supervised training. Model predictions were compared
during 5-fold cross validation. The viola-Unet outperformed the comparison
network on two out of four performance metrics (i.e., NSD and RVD). An ensemble
model that combined viola-Unet and nnU-Net networks had the highest performance
for DSC and HD. We demonstrate there were ICH segmentation performance benefits
associated with a 3D U-Net efficiently incorporates spatially orthogonal
features during the decoding branch of the U-Net. The code base, pretrained
weights, and docker image of the viola-Unet AI tool will be publicly available
at https://github.com/samleoqh/Viola-Unet .

Impact-sounding (IS) and impact-echo (IE) are well-developed non-destructive
evaluation (NDE) methods that are widely used for inspections of concrete
structures to ensure the safety and sustainability. However, it is a tedious
work to collect IS and IE data along grid lines covering a large target area
for characterization of subsurface defects. On the other hand, data processing
is very complicated that requires domain experts to interpret the results. To
address the above problems, we present a novel robotic inspection system named
as Impact-Rover to automate the data collection process and introduce data
analytics software to visualize the inspection result allowing regular
non-professional people to understand. The system consists of three modules: 1)
a robotic platform with vertical mobility to collect IS and IE data in
hard-to-reach locations, 2) vision-based positioning module that fuses the
RGB-D camera, IMU and wheel encoder to estimate the 6-DOF pose of the robot, 3)
a data analytics software module for processing the IS data to generate defect
maps. The Impact-Rover hosts both IE and IS devices on a sliding mechanism and
can perform move-stop-sample operations to collect multiple IS and IE data at
adjustable spacing. The robot takes samples much faster than the manual data
collection method because it automatically takes the multiple measurements
along a straight line and records the locations. This paper focuses on
reporting experimental results on IS. We calculate features and use
unsupervised learning methods for analyzing the data. By combining the pose
generated by our vision-based localization module and the position of the head
of the sliding mechanism we can generate maps of possible defects. The results
on concrete slabs demonstrate that our impact-sounding system can effectively
reveal shallow defects.

Deep learning models, e.g. supervised Encoder-Decoder style networks, exhibit
promising performance in medical image segmentation, but come with a high
labelling cost. We propose TriSegNet, a semi-supervised semantic segmentation
framework. It uses triple-view feature learning on a limited amount of labelled
data and a large amount of unlabeled data. The triple-view architecture
consists of three pixel-level classifiers and a low-level shared-weight
learning module. The model is first initialized with labelled data. Label
processing, including data perturbation, confidence label voting and
unconfident label detection for annotation, enables the model to train on
labelled and unlabeled data simultaneously. The confidence of each model gets
improved through the other two views of the feature learning. This process is
repeated until each model reaches the same confidence level as its
counterparts. This strategy enables triple-view learning of generic medical
image datasets. Bespoke overlap-based and boundary-based loss functions are
tailored to the different stages of the training. The segmentation results are
evaluated on four publicly available benchmark datasets including Ultrasound,
CT, MRI, and Histology images. Repeated experiments demonstrate the
effectiveness of the proposed network compared against other semi-supervised
algorithms, across a large set of evaluation measures.

Shape metrics for objects in high dimensions remain sparse. Those that do
exist, such as hyper-volume, remain limited to objects that are better
understood such as Platonic solids and $n$-Cubes. Further, understanding
objects of ill-defined shapes in higher dimensions is ambiguous at best. Past
work does not provide a single number to give a qualitative understanding of an
object. For example, the eigenvalues from principal component analysis results
in $n$ metrics to describe the shape of an object. Therefore, we need a single
number which can discriminate objects with different shape from one another.
Previous work has developed shape metrics for specific dimensions such as two
or three dimensions. However, there is an opportunity to develop metrics for
any desired dimension. To that end, we present two new shape metrics for
objects in a given number of dimensions: hyper-Sphericity and hyper-Shape
Proportion (SP). We explore the proprieties of these metrics on a number of
different shapes including $n$-balls. We then connect these metrics to
applications of analyzing the shape of multidimensional data such as the
popular Iris dataset.

We are interested in solving linear systems arising from three applications:
(1) kernel methods in machine learning, (2) discretization of boundary integral
equations from mathematical physics, and (3) Schur complements formed in the
factorization of many large sparse matrices. The coefficient matrices are often
data-sparse in the sense that their off-diagonal blocks have low numerical
ranks; specifically, we focus on "hierarchically off-diagonal low-rank (HODLR)"
matrices. We introduce algorithms for factorizing HODLR matrices and for
applying the factorizations on a GPU. The algorithms leverage the efficiency of
batched dense linear algebra, and they scale nearly linearly with the matrix
size when the numerical ranks are fixed. The accuracy of the HODLR-matrix
approximation is a tunable parameter, so we can construct high-accuracy fast
direct solvers or low-accuracy robust preconditioners. Numerical results show
that we can solve problems with several millions of unknowns in a couple of
seconds on a single GPU.

Segmenting dental plaque from images of medical reagent staining provides
valuable information for diagnosis and the determination of follow-up treatment
plan. However, accurate dental plaque segmentation is a challenging task that
requires identifying teeth and dental plaque subjected to semantic-blur regions
(i.e., confused boundaries in border regions between teeth and dental plaque)
and complex variations of instance shapes, which are not fully addressed by
existing methods. Therefore, we propose a semantic decomposition network
(SDNet) that introduces two single-task branches to separately address the
segmentation of teeth and dental plaque and designs additional constraints to
learn category-specific features for each branch, thus facilitating the
semantic decomposition and improving the performance of dental plaque
segmentation. Specifically, SDNet learns two separate segmentation branches for
teeth and dental plaque in a divide-and-conquer manner to decouple the
entangled relation between them. Each branch that specifies a category tends to
yield accurate segmentation. To help these two branches better focus on
category-specific features, two constraint modules are further proposed: 1)
contrastive constraint module (CCM) to learn discriminative feature
representations by maximizing the distance between different category
representations, so as to reduce the negative impact of semantic-blur regions
on feature extraction; 2) structural constraint module (SCM) to provide
complete structural information for dental plaque of various shapes by the
supervision of an boundary-aware geometric constraint. Besides, we construct a
large-scale open-source Stained Dental Plaque Segmentation dataset (SDPSeg),
which provides high-quality annotations for teeth and dental plaque.
Experimental results on SDPSeg datasets show SDNet achieves state-of-the-art

"Monkey see monkey do" is an age-old adage, referring to na\"ive imitation
without a deep understanding of a system's underlying mechanics. Indeed, if a
demonstrator has access to information unavailable to the imitator (monkey),
such as a different set of sensors, then no matter how perfectly the imitator
models its perceived environment (See), attempting to reproduce the
demonstrator's behavior (Do) can lead to poor outcomes. Imitation learning in
the presence of a mismatch between demonstrator and imitator has been studied
in the literature under the rubric of causal imitation learning (Zhang et al.,
2020), but existing solutions are limited to single-stage decision-making. This
paper investigates the problem of causal imitation learning in sequential
settings, where the imitator must make multiple decisions per episode. We
develop a graphical criterion that is necessary and sufficient for determining
the feasibility of causal imitation, providing conditions when an imitator can
match a demonstrator's performance despite differing capabilities. Finally, we
provide an efficient algorithm for determining imitability and corroborate our
theory with simulations.

Contrastive representation learning seeks to acquire useful representations
by estimating the shared information between multiple views of data. Here, the
choice of data augmentation is sensitive to the quality of learned
representations: as harder the data augmentations are applied, the views share
more task-relevant information, but also task-irrelevant one that can hinder
the generalization capability of representation. Motivated by this, we present
a new robust contrastive learning scheme, coined R\'enyiCL, which can
effectively manage harder augmentations by utilizing R\'enyi divergence. Our
method is built upon the variational lower bound of R\'enyi divergence, but a
na\"ive usage of a variational method is impractical due to the large variance.
To tackle this challenge, we propose a novel contrastive objective that
conducts variational estimation of a skew R\'enyi divergence and provide a
theoretical guarantee on how variational estimation of skew divergence leads to
stable training. We show that R\'enyi contrastive learning objectives perform
innate hard negative sampling and easy positive sampling simultaneously so that
it can selectively learn useful features and ignore nuisance features. Through
experiments on ImageNet, we show that R\'enyi contrastive learning with
stronger augmentations outperforms other self-supervised methods without extra
regularization or computational overhead. Moreover, we also validate our method
on other domains such as graph and tabular, showing empirical gain over other
contrastive methods.

We propose that the machinability of hard ceramics can be improved by
reversible electrochemical doping. On the example of TiO2, we show in a
combined density functional theory-molecular dynamics computational study that
a small amount of intercalated lithium, which preserves the host structure and
can be introduced reversibly, leads to a lowering of the strength of work
materials and the cutting force. This is in spite of the fact that there are no
significant modifications of the elastic constants at room temperature, i.e.
the effect is mostly on plastic properties. This approach is expected to be
applicable to a class of ceramics exhibiting similar mechanisms of host-dopant
interactions and presents a reversible and non-destructive way of modifying
mechanical properties.

One of the common ways children learn is by mimicking adults. Imitation
learning focuses on learning policies with suitable performance from
demonstrations generated by an expert, with an unspecified performance measure,
and unobserved reward signal. Popular methods for imitation learning start by
either directly mimicking the behavior policy of an expert (behavior cloning)
or by learning a reward function that prioritizes observed expert trajectories
(inverse reinforcement learning). However, these methods rely on the assumption
that covariates used by the expert to determine her/his actions are fully
observed. In this paper, we relax this assumption and study imitation learning
when sensory inputs of the learner and the expert differ. First, we provide a
non-parametric, graphical criterion that is complete (both necessary and
sufficient) for determining the feasibility of imitation from the combinations
of demonstration data and qualitative assumptions about the underlying
environment, represented in the form of a causal model. We then show that when
such a criterion does not hold, imitation could still be feasible by exploiting
quantitative knowledge of the expert trajectories. Finally, we develop an
efficient procedure for learning the imitating policy from experts'
trajectories.

The objective of this paper is to employ machine learning (ML) and deep
learning (DL) techniques to obtain from input data (storm features) available
in or derived from the HURDAT2 database models capable of simulating important
hurricane properties such as landfall location and wind speed that are
consistent with historical records. In pursuit of this objective, a trajectory
model providing the storm center in terms of longitude and latitude, and
intensity models providing the central pressure and maximum 1-$min$ wind speed
at 10 $m$ elevation were created. The trajectory and intensity models are
coupled and must be advanced together, six hours at a time, as the features
that serve as inputs to the models at any given step depend on predictions at
the previous time steps. Once a synthetic storm database is generated,
properties of interest, such as the frequencies of large wind speeds may be
extracted from any part of the simulation domain. The coupling of the
trajectory and intensity models obviates the need for an intensity decay inland
of the coastline. Prediction results are compared to historical data, and the
efficacy of the storm simulation models is demonstrated for three examples: New
Orleans, Miami and Cape Hatteras.

The wide application of deep neural networks (DNNs) demands an increasing
amount of attention to their real-world robustness, i.e., whether a DNN resists
black-box adversarial attacks, among them score-based query attacks (SQAs) are
the most threatening ones because of their practicalities and effectiveness:
the attackers only need dozens of queries on model outputs to seriously hurt a
victim network. Defending against SQAs requires a slight but artful variation
of outputs due to the service purpose for users, who share the same output
information with attackers. In this paper, we propose a real-world defense,
called Unifying Gradients (UniG), to unify gradients of different data so that
attackers could only probe a much weaker attack direction that is similar for
different samples. Since such universal attack perturbations have been
validated as less aggressive than the input-specific perturbations, UniG
protects real-world DNNs by indicating attackers a twisted and less informative
attack direction. To enhance UniG's practical significance in real-world
applications, we implement it as a Hadamard product module that is
computationally-efficient and readily plugged into any model. According to
extensive experiments on 5 SQAs and 4 defense baselines, UniG significantly
improves real-world robustness without hurting clean accuracy on CIFAR10 and
ImageNet. For instance, UniG maintains a CIFAR-10 model of 77.80% accuracy
under 2500-query Square attack while the state-of-the-art adversarially-trained
model only has 67.34% on CIFAR10. Simultaneously, UniG greatly surpasses all
compared baselines in clean accuracy and the modification degree of outputs.
The code would be released.

In this work, we propose a sim2real method to transfer and adapt a nonlinear
model predictive controller (NMPC) from simulation to the real target system
based on executable digital twin (xDT). The xDT model is a high fidelity
vehicle dynamics simulator, executable online in the control parameter
randomization and learning process. The parameters are adapted to gradually
improve control performance and deal with changing real-world environment. In
particular, the performance metric is not required to be differentiable nor
analytical with respect to the control parameters and system dynamics are not
necessary linearized. Eventually, the proposed sim2real framework leverages
altogether online high fidelity simulator, data-driven estimations, and
simulation based optimization to transfer and adapt efficiently a controller
developed in simulation environment to the real platform. Our experiment
demonstrates that a high control performance is achieved without tedious time
and labor consuming tuning.

Nowadays, in many different fields, massive data are available and for
several reasons, it might be convenient to analyze just a subset of the data.
The application of the D-optimality criterion can be helpful to optimally
select a subsample of observations. However, it is well known that D-optimal
support points lie on the boundary of the design space and if they go hand in
hand with extreme response values, they can have a severe influence on the
estimated linear model (leverage points with high influence). To overcome this
problem, firstly, we propose an unsupervised exchange procedure that enables us
to select a nearly D-optimal subset of observations without high leverage
values. Then, we provide a supervised version of this exchange procedure, where
besides high leverage points also the outliers in the responses (that are not
associated to high leverage points) are avoided. This is possible because,
unlike other design situations, in subsampling from big datasets the response
values may be available.
  Finally, both the unsupervised and the supervised selection procedures are
generalized to I-optimality, with the goal of getting accurate predictions.

The existence of incompatible observables is a cornerstone of quantum
mechanics and a valuable resource in quantum technologies. Here we introduce a
measure of incompatibility, called the mutual eigenspace disturbance (MED),
which quantifies the amount of disturbance induced by the measurement of a
sharp observable on the eigenspaces of another. The MED is a faithful measure
of incompatibility for sharp observables and provides a metric on the space of
von Neumann measurements. It can be efficiently estimated by letting the
measurements act in an indefinite order, using a setup known as the quantum
switch. Thanks to these features, the MED can be used in quantum machine
learning tasks, such as clustering quantum measurement devices based on their
mutual compatibility. We demonstrate this application by providing an
unsupervised algorithm that clusters unknown von Neumann measurements. Our
algorithm is robust to noise can be used to identify groups of observers that
share approximately the same measurement context.

Vibrational spectra of condensed and gas-phase systems containing light
nuclei are influenced by their quantum-mechanical behaviour. The quantum
dynamics of light nuclei can be approximated by the imaginary time path
integral (PI) formulation, but still at a large computational cost that
increases sharply with decreasing temperature. By leveraging advances in
machine-learned coarse-graining, we develop a PI method with the reduced
computational cost of a classical simulation. We also propose a simple
temperature elevation scheme to significantly attenuate the artefacts of
standard PI approaches and also eliminate the unfavourable temperature scaling
of the computational cost.We illustrate the approach, by calculating
vibrational spectra using standard models of water molecules and bulk water,
demonstrating significant computational savings and dramatically improved
accuracy compared to more expensive reference approaches. We believe that our
simple, efficient and accurate method could enable routine calculations of
vibrational spectra including nuclear quantum effects for a wide range of
molecular systems.

Numerous deep learning based methods have been developed for nuclei
segmentation for H&E images and have achieved close to human performance.
However, direct application of such methods to another modality of images, such
as Immunohistochemistry (IHC) images, may not achieve satisfactory performance.
Thus, we developed a Generative Adversarial Network (GAN) based approach to
translate an IHC image to an H&E image while preserving nuclei location and
morphology and then apply pre-trained nuclei segmentation models to the virtual
H&E image. We demonstrated that the proposed methods work better than several
baseline methods including direct application of state of the art nuclei
segmentation methods such as Cellpose and HoVer-Net, trained on H&E and a
generative method, DeepLIIF, using two public IHC image datasets.

Quantum neural networks (QNN) have been proposed as a promising architecture
for quantum machine learning. There exist a number of different quantum circuit
designs being branded as QNNs, however no clear candidate has presented itself
as more suitable than the others. Rather, the search for a ``quantum
perceptron" -- the fundamental building block of a QNN -- is still underway.
One candidate is quantum perceptrons designed to emulate the nonlinear
activation functions of classical perceptrons. Such sigmoid quantum perceptrons
(SQPs) inherit the universal approximation property that guarantees that
classical neural networks can approximate any function. However, this does not
guarantee that QNNs built from SQPs will have any quantum advantage over their
classical counterparts. Here we critically investigate both the capabilities
and performance of SQP networks by computing their effective dimension and
effective capacity, as well as examining their performance on real learning
problems. The results are compared to those obtained for other candidate
networks which lack activation functions. It is found that simpler, and
apparently easier-to-implement parametric quantum circuits actually perform
better than SQPs. This indicates that the universal approximation theorem,
which a cornerstone of the theory of classical neural networks, is not a
relevant criterion for QNNs.

Pose estimation is usually tackled as either a bin classification or a
regression problem. In both cases, the idea is to directly predict the pose of
an object. This is a non-trivial task due to appearance variations between
similar poses and similarities between dissimilar poses. Instead, we follow the
key idea that comparing two poses is easier than directly predicting one.
Render-and-compare approaches have been employed to that end, however, they
tend to be unstable, computationally expensive, and slow for real-time
applications. We propose doing category-level pose estimation by learning an
alignment metric in an embedding space using a contrastive loss with a dynamic
margin and a continuous pose-label space. For efficient inference, we use a
simple real-time image retrieval scheme with a pre-rendered and pre-embedded
reference set of renderings. To achieve robustness to real-world conditions, we
employ synthetic occlusions, bounding box perturbations, and appearance
augmentations. Our approach achieves state-of-the-art performance on PASCAL3D
and OccludedPASCAL3D and surpasses the competing methods on KITTI3D in a
cross-dataset evaluation setting. The code is currently available at
https://github.com/gkouros/contrastive-pose-retrieval.

Offline reinforcement learning (RL), which aims to learn an optimal policy
using a previously collected static dataset, is an important paradigm of RL.
Standard RL methods often perform poorly in this regime due to the function
approximation errors on out-of-distribution actions. While a variety of
regularization methods have been proposed to mitigate this issue, they are
often constrained by policy classes with limited expressiveness that can lead
to highly suboptimal solutions. In this paper, we propose representing the
policy as a diffusion model, a recent class of highly-expressive deep
generative models. We introduce Diffusion Q-learning (Diffusion-QL) that
utilizes a conditional diffusion model to represent the policy. In our
approach, we learn an action-value function and we add a term maximizing
action-values into the training loss of the conditional diffusion model, which
results in a loss that seeks optimal actions that are near the behavior policy.
We show the expressiveness of the diffusion model-based policy, and the
coupling of the behavior cloning and policy improvement under the diffusion
model both contribute to the outstanding performance of Diffusion-QL. We
illustrate the superiority of our method compared to prior works in a simple 2D
bandit example with a multimodal behavior policy. We then show that our method
can achieve state-of-the-art performance on the majority of the D4RL benchmark
tasks.

Personalized federated learning (FL) facilitates collaborations between
multiple clients to learn personalized models without sharing private data. The
mechanism mitigates the statistical heterogeneity commonly encountered in the
system, i.e., non-IID data over different clients. Existing personalized
algorithms generally assume all clients volunteer for personalization. However,
potential participants might still be reluctant to personalize models since
they might not work well. In this case, clients choose to use the global model
instead. To avoid making unrealistic assumptions, we introduce the
personalization rate, measured as the fraction of clients willing to train
personalized models, into federated settings and propose DyPFL. This
dynamically personalized FL technique incentivizes clients to participate in
personalizing local models while allowing the adoption of the global model when
it performs better. We show that the algorithmic pipeline in DyPFL guarantees
good convergence performance, allowing it to outperform alternative
personalized methods in a broad range of conditions, including variation in
heterogeneity, number of clients, local epochs, and batch sizes.

Sign Language Production (SLP) aims to translate expressions in spoken
language into corresponding ones in sign language, such as skeleton-based sign
poses or videos. Existing SLP models are either AutoRegressive (AR) or
Non-Autoregressive (NAR). However, AR-SLP models suffer from regression to the
mean and error propagation during decoding. NSLP-G, a NAR-based model, resolves
these issues to some extent but engenders other problems. For example, it does
not consider target sign lengths and suffers from false decoding initiation. We
propose a novel NAR-SLP model via Knowledge Distillation (KD) to address these
problems. First, we devise a length regulator to predict the end of the
generated sign pose sequence. We then adopt KD, which distills
spatial-linguistic features from a pre-trained pose encoder to alleviate false
decoding initiation. Extensive experiments show that the proposed approach
significantly outperforms existing SLP models in both Frechet Gesture Distance
and Back-Translation evaluation.

Federated Learning (FL) is a novel framework of decentralized machine
learning. Due to the decentralized feature of FL, it is vulnerable to
adversarial attacks in the training procedure, e.g. , backdoor attacks. A
backdoor attack aims to inject a backdoor into the machine learning model such
that the model will make arbitrarily incorrect behavior on the test sample with
some specific backdoor trigger. Even though a range of backdoor attack methods
of FL has been introduced, there are also methods defending against them. Many
of the defending methods utilize the abnormal characteristics of the models
with backdoor or the difference between the models with backdoor and the
regular models. To bypass these defenses, we need to reduce the difference and
the abnormal characteristics. We find a source of such abnormality is that
backdoor attack would directly flip the label of data when poisoning the data.
However, current studies of the backdoor attack in FL are not mainly focus on
reducing the difference between the models with backdoor and the regular
models. In this paper, we propose Adversarial Knowledge Distillation(ADVKD), a
method combine knowledge distillation with backdoor attack in FL. With
knowledge distillation, we can reduce the abnormal characteristics in model
result from the label flipping, thus the model can bypass the defenses.
Compared to current methods, we show that ADVKD can not only reach a higher
attack success rate, but also successfully bypass the defenses when other
methods fails. To further explore the performance of ADVKD, we test how the
parameters affect the performance of ADVKD under different scenarios. According
to the experiment result, we summarize how to adjust the parameter for better
performance under different scenarios. We also use several methods to visualize
the effect of different attack and explain the effectiveness of ADVKD.

Graph convolutional networks (GCNs) have been the predominant methods in
skeleton-based human action recognition, including human-human interaction
recognition. However, when dealing with interaction sequences, current
GCN-based methods simply split the two-person skeleton into two discrete graphs
and perform graph convolution separately as done for single-person action
classification. Such operations ignore rich interactive information and hinder
effective spatial inter-body relationship modeling. To overcome the above
shortcoming, we introduce a novel unified two-person graph to represent
inter-body and intra-body correlations between joints. Experiments show
accuracy improvements in recognizing both interactions and individual actions
when utilizing the proposed two-person graph topology. In addition, We design
several graph labeling strategies to supervise the model to learn discriminant
spatial-temporal interactive features. Finally, we propose a two-person graph
convolutional network (2P-GCN). Our model achieves state-of-the-art results on
four benchmarks of three interaction datasets: SBU, interaction subsets of
NTU-RGB+D and NTU-RGB+D 120.

FM Synthesis is a well-known algorithm used to generate complex timbre from a
compact set of design primitives. Typically featuring a MIDI interface, it is
usually impractical to control it from an audio source. On the other hand,
Differentiable Digital Signal Processing (DDSP) has enabled nuanced audio
rendering by Deep Neural Networks (DNNs) that learn to control differentiable
synthesis layers from arbitrary sound inputs. The training process involves a
corpus of audio for supervision, and spectral reconstruction loss functions.
Such functions, while being great to match spectral amplitudes, present a lack
of pitch direction which can hinder the joint optimization of the parameters of
FM synthesizers. In this paper, we take steps towards enabling continuous
control of a well-established FM synthesis architecture from an audio input.
Firstly, we discuss a set of design constraints that ease spectral optimization
of a differentiable FM synthesizer via a standard reconstruction loss. Next, we
present Differentiable DX7 (DDX7), a lightweight architecture for neural FM
resynthesis of musical instrument sounds in terms of a compact set of
parameters. We train the model on instrument samples extracted from the URMP
dataset, and quantitatively demonstrate its comparable audio quality against
selected benchmarks.

Future wireless networks (5G and beyond) are the vision of forthcoming
cellular systems, connecting billions of devices and people together. In the
last decades, cellular networks have been dramatically growth with advanced
telecommunication technologies for high-speed data transmission, high cell
capacity, and low latency. The main goal of those technologies is to support a
wide range of new applications, such as virtual reality, metaverse, telehealth,
online education, autonomous and flying vehicles, smart cities, smart grids,
advanced manufacturing, and many more. The key motivation of NextG networks is
to meet the high demand for those applications by improving and optimizing
network functions. Artificial Intelligence (AI) has a high potential to achieve
these requirements by being integrated in applications throughout all layers of
the network. However, the security concerns on network functions of NextG using
AI-based models, i.e., model poising, have not been investigated deeply.
Therefore, it needs to design efficient mitigation techniques and secure
solutions for NextG networks using AI-based methods. This paper proposes a
comprehensive vulnerability analysis of deep learning (DL)-based channel
estimation models trained with the dataset obtained from MATLAB's 5G toolbox
for adversarial attacks and defensive distillation-based mitigation methods.
The adversarial attacks produce faulty results by manipulating trained DL-based
models for channel estimation in NextG networks, while making models more
robust against any attacks through mitigation methods. This paper also presents
the performance of the proposed defensive distillation mitigation method for
each adversarial attack against the channel estimation model. The results
indicated that the proposed mitigation method can defend the DL-based channel
estimation models against adversarial attacks in NextG networks.

Despite the development of ranking optimization techniques, the pointwise
model remains the dominating approach for click-through rate (CTR) prediction.
It can be attributed to the calibration ability of the pointwise model since
the prediction can be viewed as the click probability. In practice, a CTR
prediction model is also commonly assessed with the ranking ability, for which
prediction models based on ranking losses (e.g., pairwise or listwise loss)
usually achieve better performances than the pointwise loss. Previous studies
have experimented with a direct combination of the two losses to obtain the
benefit from both losses and observed an improved performance. However,
previous studies break the meaning of output logit as the click-through rate,
which may lead to sub-optimal solutions. To address this issue, we propose an
approach that can Jointly optimize the Ranking and Calibration abilities (JRC
for short). JRC improves the ranking ability by contrasting the logit value for
the sample with different labels and constrains the predicted probability to be
a function of the logit subtraction. We further show that JRC consolidates the
interpretation of logits, where the logits model the joint distribution. With
such an interpretation, we prove that JRC approximately optimizes the
contextualized hybrid discriminative-generative objective. Experiments on
public and industrial datasets and online A/B testing show that our approach
improves both ranking and calibration abilities. Since May 2022, JRC has been
deployed on the display advertising platform of Alibaba and has obtained
significant performance improvements.

Gradient inversion attacks on federated learning systems reconstruct client
training data from exchanged gradient information. To defend against such
attacks, a variety of defense mechanisms were proposed. However, they usually
lead to an unacceptable trade-off between privacy and model utility. Recent
observations suggest that dropout could mitigate gradient leakage and improve
model utility if added to neural networks. Unfortunately, this phenomenon has
not been systematically researched yet. In this work, we thoroughly analyze the
effect of dropout on iterative gradient inversion attacks. We find that state
of the art attacks are not able to reconstruct the client data due to the
stochasticity induced by dropout during model training. Nonetheless, we argue
that dropout does not offer reliable protection if the dropout induced
stochasticity is adequately modeled during attack optimization. Consequently,
we propose a novel Dropout Inversion Attack (DIA) that jointly optimizes for
client data and dropout masks to approximate the stochastic client model. We
conduct an extensive systematic evaluation of our attack on four seminal model
architectures and three image classification datasets of increasing complexity.
We find that our proposed attack bypasses the protection seemingly induced by
dropout and reconstructs client data with high fidelity. Our work demonstrates
that privacy inducing changes to model architectures alone cannot be assumed to
reliably protect from gradient leakage and therefore should be combined with
complementary defense mechanisms.

The crux of text-to-image synthesis stems from the difficulty of preserving
the cross-modality semantic consistency between the input text and the
synthesized image. Typical methods, which seek to model the text-to-image
mapping directly, could only capture keywords in the text that indicates common
objects or actions but fail to learn their spatial distribution patterns. An
effective way to circumvent this limitation is to generate an image layout as
guidance, which is attempted by a few methods. Nevertheless, these methods fail
to generate practically effective layouts due to the diversity of input text
and object location. In this paper we push for effective modeling in both
text-to-layout generation and layout-to-image synthesis. Specifically, we
formulate the text-to-layout generation as a sequence-to-sequence modeling
task, and build our model upon Transformer to learn the spatial relationships
between objects by modeling the sequential dependencies between them. In the
stage of layout-to-image synthesis, we focus on learning the textual-visual
semantic alignment per object in the layout to precisely incorporate the input
text into the layout-to-image synthesizing process. To evaluate the quality of
generated layout, we design a new metric specifically, dubbed Layout Quality
Score, which considers both the absolute distribution errors of bounding boxes
in the layout and the mutual spatial relationships between them. Extensive
experiments on three datasets demonstrate the superior performance of our
method over state-of-the-art methods on both predicting the layout and
synthesizing the image from the given text.

We develop two fundamental stochastic sketching techniques; Penalty Sketching
(PS) and Augmented Lagrangian Sketching (ALS) for solving consistent linear
systems. The proposed PS and ALS techniques extend and generalize the scope of
Sketch & Project (SP) method by introducing Lagrangian penalty sketches. In
doing so, we recover SP methods as special cases and furthermore develop a
family of new stochastic iterative methods. By varying sketch parameters in the
proposed PS method, we recover novel stochastic methods such as Penalty Newton
Descent, Penalty Kaczmarz, Penalty Stochastic Descent, Penalty Coordinate
Descent, Penalty Gaussian Pursuit, and Penalty Block Kaczmarz. Furthermore, the
proposed ALS method synthesizes a wide variety of new stochastic methods such
as Augmented Newton Descent, Augmented Kaczmarz, Augmented Stochastic Descent,
Augmented Coordinate Descent, Augmented Gaussian Pursuit, and Augmented Block
Kaczmarz into one framework. Moreover, we show that the developed PS and ALS
frameworks can be used to reformulate the original linear system into
equivalent stochastic optimization problems namely the Penalty Stochastic
Reformulation and Augmented Stochastic Reformulation. We prove global
convergence rates for the PS and ALS methods as well as sub-linear
$\mathcal{O}(\frac{1}{k})$ rates for the Cesaro average of iterates. The
proposed convergence results hold for a wide family of distributions of random
matrices, which provides the opportunity of fine-tuning the randomness of the
method suitable for specific applications. Finally, we perform computational
experiments that demonstrate the efficiency of our methods compared to the
existing SP methods.

We consider a global explanation of a regression or classification function
by decomposing it into the sum of main components and interaction components of
arbitrary order. When adding an identification constraint that is motivated by
a causal interpretation, we find q-interaction SHAP to be the unique solution
to that constraint. Here, q denotes the highest order of interaction present in
the decomposition. Our result provides a new perspective on SHAP values with
various practical and theoretical implications: If SHAP values are decomposed
into main and all interaction effects, they provide a global explanation with
causal interpretation. In principle, the decomposition can be applied to any
machine learning model. However, since the number of possible interactions
grows exponentially with the number of features, exact calculation is only
feasible for methods that fit low dimensional structures or ensembles of those.
We provide an algorithm and efficient implementation for gradient boosted trees
(xgboost and random planted forests that calculates this decomposition.
Conducted experiments suggest that our method provides meaningful explanations
and reveals interactions of higher orders. We also investigate further
potential of our new insights by utilizing the global explanation for
motivating a new measure of feature importance, and for reducing direct and
indirect bias by post-hoc component removal.

BERT-style models pre-trained on the general corpus (e.g., Wikipedia) and
fine-tuned on specific task corpus, have recently emerged as breakthrough
techniques in many NLP tasks: question answering, text classification, sequence
labeling and so on. However, this technique may not always work, especially for
two scenarios: a corpus that contains very different text from the general
corpus Wikipedia, or a task that learns embedding spacial distribution for a
specific purpose (e.g., approximate nearest neighbor search). In this paper, to
tackle the above two scenarios that we have encountered in an industrial
e-commerce search system, we propose customized and novel pre-training tasks
for two critical modules: user intent detection and semantic embedding
retrieval. The customized pre-trained models after fine-tuning, being less than
10% of BERT-base's size in order to be feasible for cost-efficient CPU serving,
significantly improve the other baseline models: 1) no pre-training model and
2) fine-tuned model from the official pre-trained BERT using general corpus, on
both offline datasets and online system. We have open sourced our datasets for
the sake of reproducibility and future works.

Time series are measured and analyzed across the sciences. One method of
quantifying the structure of time series is by calculating a set of summary
statistics or `features', and then representing a time series in terms of its
properties as a feature vector. The resulting feature space is interpretable
and informative, and enables conventional statistical learning approaches,
including clustering, regression, and classification, to be applied to
time-series datasets. Many open-source software packages for computing sets of
time-series features exist across multiple programming languages, including
catch22 (22 features: Matlab, R, Python, Julia), feasts (42 features: R),
tsfeatures (63 features: R), Kats (40 features: Python), tsfresh (779 features:
Python), and TSFEL (390 features: Python). However, there are several issues:
(i) a singular access point to these packages is not currently available; (ii)
to access all feature sets, users must be fluent in multiple languages; and
(iii) these feature-extraction packages lack extensive accompanying
methodological pipelines for performing feature-based time-series analysis,
such as applications to time-series classification. Here we introduce a
solution to these issues in an R software package called theft: Tools for
Handling Extraction of Features from Time series. theft is a unified and
extendable framework for computing features from the six open-source
time-series feature sets listed above. It also includes a suite of functions
for processing and interpreting the performance of extracted features,
including extensive data-visualization templates, low-dimensional projections,
and time-series classification operations. With an increasing volume and
complexity of time-series datasets in the sciences and industry, theft provides
a standardized framework for comprehensively quantifying and interpreting
informative structure in time series.

Heterogeneous graphs, which contain nodes and edges of multiple types, are
prevalent in various domains, including bibliographic networks, social media,
and knowledge graphs. As a fundamental task in analyzing heterogeneous graphs,
relevance measure aims to calculate the relevance between two objects of
different types, which has been used in many applications such as web search,
recommendation, and community detection. Most of existing relevance measures
focus on homogeneous networks where objects are of the same type, and a few
measures are developed for heterogeneous graphs, but they often need the
pre-defined meta-path. Defining meaningful meta-paths requires much domain
knowledge, which largely limits their applications, especially on schema-rich
heterogeneous graphs like knowledge graphs. Recently, the Graph Neural Network
(GNN) has been widely applied in many graph mining tasks, but it has not been
applied for measuring relevance yet. To address the aforementioned problems, we
propose a novel GNN-based relevance measure, namely GSim. Specifically, we
first theoretically analyze and show that GNN is effective for measuring the
relevance of nodes in the graph. We then propose a context path-based graph
neural network (CP-GNN) to automatically leverage the semantics in
heterogeneous graphs. Moreover, we exploit CP-GNN to support relevance measures
between two objects of any type. Extensive experiments demonstrate that GSim
outperforms existing measures.

We introduce a new implicit shape representation called Primary Ray-based
Implicit Function (PRIF). In contrast to most existing approaches based on the
signed distance function (SDF) which handles spatial locations, our
representation operates on oriented rays. Specifically, PRIF is formulated to
directly produce the surface hit point of a given input ray, without the
expensive sphere-tracing operations, hence enabling efficient shape extraction
and differentiable rendering. We demonstrate that neural networks trained to
encode PRIF achieve successes in various tasks including single shape
representation, category-wise shape generation, shape completion from sparse or
noisy observations, inverse rendering for camera pose estimation, and neural
rendering with color.

Universal style transfer (UST) infuses styles from arbitrary reference images
into content images. Existing methods, while enjoying many practical successes,
are unable of explaining experimental observations, including different
performances of UST algorithms in preserving the spatial structure of content
images. In addition, methods are limited to cumbersome global controls on
stylization, so that they require additional spatial masks for desired
stylization. In this work, we provide a systematic Fourier analysis on a
general framework for UST. We present an equivalent form of the framework in
the frequency domain. The form implies that existing algorithms treat all
frequency components and pixels of feature maps equally, except for the
zero-frequency component. We connect Fourier amplitude and phase with Gram
matrices and a content reconstruction loss in style transfer, respectively.
Based on such equivalence and connections, we can thus interpret different
structure preservation behaviors between algorithms with Fourier phase. Given
the interpretations we have, we propose two manipulations in practice for
structure preservation and desired stylization. Both qualitative and
quantitative experiments demonstrate the competitive performance of our method
against the state-of-the-art methods. We also conduct experiments to
demonstrate (1) the abovementioned equivalence, (2) the interpretability based
on Fourier amplitude and phase and (3) the controllability associated with
frequency components.

Over the past decade inter-atomic potentials based on machine-learning (ML)
techniques have become an indispensable tool in the atomic-scale modeling of
materials. Trained on energies and forces obtained from electronic-structure
calculations, they inherit their predictive accuracy, and extend greatly the
length and time scales that are accessible to explicit atomistic simulations.
Inexpensive predictions of the energetics of individual configurations have
facilitated greatly the calculation of the thermodynamics of materials,
including finite-temperature effects and disorder. More recently,
machine-learning models have been closing the gap with first-principles
calculations in another area: the prediction of arbitrarily complicated
functional properties, from vibrational and optical spectroscopies to
electronic excitations. The implementation of integrated machine-learning
models, that combine energetic and functional predictions with statistical and
dynamical sampling of atomic-scale properties is bringing the promise of
predictive, uncompromising simulations of existing and novel materials closer
to its full realisation.

A key problem in a variety of applications is that of domain adaptation from
a public source domain, for which a relatively large amount of labeled data
with no privacy constraints is at one's disposal, to a private target domain,
for which a private sample is available with very few or no labeled data. In
regression problems with no privacy constraints on the source or target data, a
discrepancy minimization algorithm based on several theoretical guarantees was
shown to outperform a number of other adaptation algorithm baselines. Building
on that approach, we design differentially private discrepancy-based algorithms
for adaptation from a source domain with public labeled data to a target domain
with unlabeled private data. The design and analysis of our private algorithms
critically hinge upon several key properties we prove for a smooth
approximation of the weighted discrepancy, such as its smoothness with respect
to the $\ell_1$-norm and the sensitivity of its gradient. Our solutions are
based on private variants of Frank-Wolfe and Mirror-Descent algorithms. We show
that our adaptation algorithms benefit from strong generalization and privacy
guarantees and report the results of experiments demonstrating their
effectiveness.

Interpretive scholars generate knowledge from text corpora by manually
sampling documents, applying codes, and refining and collating codes into
categories until meaningful themes emerge. Given a large corpus, machine
learning could help scale this data sampling and analysis, but prior research
shows that experts are generally concerned about algorithms potentially
disrupting or driving interpretive scholarship. We take a human-centered design
approach to addressing concerns around machine-assisted interpretive research
to build Scholastic, which incorporates a machine-in-the-loop clustering
algorithm to scaffold interpretive text analysis. As a scholar applies codes to
documents and refines them, the resulting coding schema serves as structured
metadata which constrains hierarchical document and word clusters inferred from
the corpus. Interactive visualizations of these clusters can help scholars
strategically sample documents further toward insights. Scholastic demonstrates
how human-centered algorithm design and visualizations employing familiar
metaphors can support inductive and interpretive research methodologies through
interactive topic modeling and document clustering.

Risk scoring systems have been widely deployed in many applications, which
assign risk scores to users according to their behavior sequences. Though many
deep learning methods with sophisticated designs have achieved promising
results, the black-box nature hinders their applications due to fairness,
explainability, and compliance consideration. Rule-based systems are considered
reliable in these sensitive scenarios. However, building a rule system is
labor-intensive. Experts need to find informative statistics from user behavior
sequences, design rules based on statistics and assign weights to each rule. In
this paper, we bridge the gap between effective but black-box models and
transparent rule models. We propose a two-stage method, RuDi, that distills the
knowledge of black-box teacher models into rule-based student models. We design
a Monte Carlo tree search-based statistics generation method that can provide a
set of informative statistics in the first stage. Then statistics are composed
into logical rules with our proposed neural logical networks by mimicking the
outputs of teacher models. We evaluate RuDi on three real-world public datasets
and an industrial dataset to demonstrate its effectiveness.

With the increasing popularity of Android in the last decade, Android is
popular among users as well as attackers. The vast number of android users
grabs the attention of attackers on android. Due to the continuous evolution of
the variety and attacking techniques of android malware, our detection methods
should need an update too. Most of the researcher's works are based on static
features, and very few focus on dynamic features. In this paper, we are filling
the literature gap by detecting android malware using System calls. We are
running the malicious app in a monitored and controlled environment using an
emulator to detect malware. Malicious behavior is activated with some simulated
events during its runtime to activate its hostile behavior. Logs collected
during the app's runtime are analyzed and fed to different machine learning
models for Detection and Family classification of Malware. The result indicates
that K-Nearest Neighbor and the Decision Tree gave the highest accuracy in
malware detection and Family Classification respectively.

Heterogeneous graph convolutional networks have gained great popularity in
tackling various network analytical tasks on heterogeneous network data,
ranging from link prediction to node classification. However, most existing
works ignore the relation heterogeneity with multiplex network between
multi-typed nodes and different importance of relations in meta-paths for node
embedding, which can hardly capture the heterogeneous structure signals across
different relations. To tackle this challenge, this work proposes a Multiplex
Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network
embedding. Our MHGCN can automatically learn the useful heterogeneous meta-path
interactions of different lengths in multiplex heterogeneous networks through
multi-layer convolution aggregation. Additionally, we effectively integrate
both multi-relation structural signals and attribute semantics into the learned
node embeddings with both unsupervised and semi-supervised learning paradigms.
Extensive experiments on five real-world datasets with various network
analytical tasks demonstrate the significant superiority of MHGCN against
state-of-the-art embedding baselines in terms of all evaluation metrics.

Audio captioning is a task that generates description of audio based on
content. Pre-trained models are widely used in audio captioning due to high
complexity. Unless a comprehensive system is re-trained, it is hard to
determine how well pre-trained models contribute to audio captioning system. To
prevent the time consuming and energy consuming process of retraining, it is
necessary to propose a preditor of performance for the pre-trained model in
audio captioning. In this paper, a series of pre-trained models are
investigated for the correlation between extracted audio features and the
performance of audio captioning. A couple of predictor is proposed based on the
experiment results.The result demonstrates that the kurtosis and skewness of
audio features extracted may act as an indicator of the performance of audio
captioning systems for pre-trained audio due to the high correlation between
kurtosis and skewness of audio features and the performance of audio captioning
systems.

Latent Factor (LF) models are effective in representing high-dimension and
sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF)
optimization is an efficient method to utilizing second-order information of an
LF model's objective function and it has been utilized to optimize second-order
LF (SLF) model. However, the low-rank representation ability of a SLF model
heavily relies on its multiple hyperparameters. Determining these
hyperparameters is time-consuming and it largely reduces the practicability of
an SLF model. To address this issue, a practical SLF (PSLF) model is proposed
in this work. It realizes hyperparameter self-adaptation with a distributed
particle swarm optimizer (DPSO), which is gradient-free and parallelized.
Experiments on real HiDS data sets indicate that PSLF model has a competitive
advantage over state-of-the-art models in data representation ability.

Gradient estimation is often necessary for fitting generative models with
discrete latent variables, in contexts such as reinforcement learning and
variational autoencoder (VAE) training. The DisARM estimator (Yin et al. 2020;
Dong, Mnih, and Tucker 2020) achieves state of the art gradient variance for
Bernoulli latent variable models in many contexts. However, DisARM and other
estimators have potentially exploding variance near the boundary of the
parameter space, where solutions tend to lie. To ameliorate this issue, we
propose a new gradient estimator \textit{bitflip}-1 that has lower variance at
the boundaries of the parameter space. As bitflip-1 has complementary
properties to existing estimators, we introduce an aggregated estimator,
\textit{unbiased gradient variance clipping} (UGC) that uses either a bitflip-1
or a DisARM gradient update for each coordinate. We theoretically prove that
UGC has uniformly lower variance than DisARM. Empirically, we observe that UGC
achieves the optimal value of the optimization objectives in toy experiments,
discrete VAE training, and in a best subset selection problem.

When sampling for Bayesian inference, one popular approach is to use
Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS)
which automatically decides the end time of the Hamiltonian trajectory.
However, HMC and NUTS can require numerous numerical gradients of the target
density, and can prove slow in practice. We propose Hamiltonian neural networks
(HNNs) with HMC and NUTS for solving Bayesian inference problems. Once trained,
HNNs do not require numerical gradients of the target density during sampling.
Moreover, they satisfy important properties such as perfect time reversibility
and Hamiltonian conservation, making them well-suited for use within HMC and
NUTS because stationarity can be shown. We also propose an HNN extension called
latent HNNs (L-HNNs), which are capable of predicting latent variable outputs.
Compared to HNNs, L-HNNs offer improved expressivity and reduced integration
errors. Finally, we employ L-HNNs in NUTS with an online error monitoring
scheme to prevent sample degeneracy in regions of low probability density. We
demonstrate L-HNNs in NUTS with online error monitoring on several examples
involving complex, heavy-tailed, and high-local-curvature probability
densities. Overall, L-HNNs in NUTS with online error monitoring satisfactorily
inferred these probability densities. Compared to traditional NUTS, L-HNNs in
NUTS with online error monitoring required 1--2 orders of magnitude fewer
numerical gradients of the target density and improved the effective sample
size (ESS) per gradient by an order of magnitude.

Quality feature representation is key to instance image retrieval. To attain
it, existing methods usually resort to a deep model pre-trained on benchmark
datasets or even fine-tune the model with a task-dependent labelled auxiliary
dataset. Although achieving promising results, this approach is restricted by
two issues: 1) the domain gap between benchmark datasets and the dataset of a
given retrieval task; 2) the required auxiliary dataset cannot be readily
obtained. In light of this situation, this work looks into a different approach
which has not been well investigated for instance image retrieval previously:
{can we learn feature representation \textit{specific to} a given retrieval
task in order to achieve excellent retrieval?} Our finding is encouraging. By
adding an object proposal generator to generate image regions for
self-supervised learning, the investigated approach can successfully learn
feature representation specific to a given dataset for retrieval. This
representation can be made even more effective by boosting it with image
similarity information mined from the dataset. As experimentally validated,
such a simple ``self-supervised learning + self-boosting'' approach can well
compete with the relevant state-of-the-art retrieval methods. Ablation study is
conducted to show the appealing properties of this approach and its limitation
on generalisation across datasets.

The Transformer has been an indispensable staple in deep learning. However,
for real-life applications, it is very challenging to deploy efficient
Transformers due to immense parameters and operations of models. To relieve
this burden, exploiting sparsity is an effective approach to accelerate
Transformers. Newly emerging Ampere GPUs leverage a 2:4 sparsity pattern to
achieve model acceleration, while it can hardly meet the diverse algorithm and
hardware constraints when deploying models. By contrast, we propose an
algorithm-hardware co-optimized framework to flexibly and efficiently
accelerate Transformers by utilizing general N:M sparsity patterns. (1) From
algorithm perspective, we propose a sparsity inheritance mechanism along with
an inherited dynamic pruning (IDP) method to obtain a series of N:M sparse
candidate Transformers rapidly. A model compression scheme is further proposed
to significantly reduce the storage requirement for deployment. (2) From
hardware perspective, we present a flexible and efficient hardware
architecture, namely STA, to achieve significant speedup when deploying N:M
sparse Transformers. STA features not only a computing engine unifying both
sparse-dense and dense-dense matrix multiplications with high computational
efficiency but also a scalable softmax module eliminating the latency from
intermediate off-chip data communication. Experimental results show that
compared to other methods, N:M sparse Transformers, generated using IDP,
achieves an average of 6.7% improvement on accuracy with high training
efficiency. Moreover, STA can achieve 14.47x and 11.33x speedup compared to
Intel i9-9900X and NVIDIA RTX 2080 Ti, respectively, and perform 2.00-19.47x
faster inference than the state-of-the-art FPGA-based accelerators for

Given data on choices made by consumers for different assortments, a key
challenge is to develop parsimonious models that describe and predict consumer
choice behavior. One such choice model is the marginal distribution model which
requires only the specification of the marginal distributions of the random
utilities of the alternatives to explain choice data. In this paper, we develop
an exact characterisation of the set of choice probabilities which are
representable by the marginal distribution model consistently across any
collection of assortments. Allowing for the possibility of alternatives to be
grouped based on the marginal distribution of their utilities, we show (a)
verifying consistency of choice probability data with this model is possible in
polynomial time and (b) finding the closest fit reduces to solving a mixed
integer convex program. Our results show that the marginal distribution model
provides much better representational power as compared to multinomial logit
and much better computational performance as compared to the random utility
model.

According to the World Health Organization(WHO), malaria is estimated to have
killed 627,000 people and infected over 241 million people in 2020 alone, a 12%
increase from 2019. Microscopic diagnosis of blood cells is the standard
testing procedure to diagnose malaria. However, this style of diagnosis is
expensive, time-consuming, and greatly subjective to human error, especially in
developing nations that lack well-trained personnel to perform high-quality
microscopy examinations. This paper proposes Mass-AI-Scope (MAIScope): a novel,
low-cost, portable device that can take microscopic images and automatically
detect malaria parasites with embedded AI. The device has two subsystems. The
first subsystem is an on-device multi-layered deep learning network, that
detects red blood cells (RBCs) from microscopic images, followed by a malaria
parasite classifier that recognizes malaria parasites in the individual RBCs.
The testing and validation demonstrated a high average accuracy of 89.9% for
classification and average precision of 61.5% for detection models using
TensorFlow Lite while addressing limited storage and computational capacity.
This system also has cloud synchronization, which sends images to the cloud
when connected to the Internet for analysis and model improvement purposes. The
second subsystem is the hardware which consists of components like Raspberry
Pi, a camera, a touch screen display, and an innovative low-cost bead
microscope. Evaluation of the bead microscope demonstrated similar image
quality with that of expensive light microscopes. The device is designed to be
portable and work in remote environments without the Internet or power. The
solution is extensible to other diseases requiring microscopy and can help
standardize automation of disease diagnosis in rural parts of developing
nations.

Contrastive learning has shown great potential in video representation
learning. However, existing approaches fail to sufficiently exploit short-term
motion dynamics, which are crucial to various down-stream video understanding
tasks. In this paper, we propose Motion Sensitive Contrastive Learning (MSCL)
that injects the motion information captured by optical flows into RGB frames
to strengthen feature learning. To achieve this, in addition to clip-level
global contrastive learning, we develop Local Motion Contrastive Learning
(LMCL) with frame-level contrastive objectives across the two modalities.
Moreover, we introduce Flow Rotation Augmentation (FRA) to generate extra
motion-shuffled negative samples and Motion Differential Sampling (MDS) to
accurately screen training samples. Extensive experiments on standard
benchmarks validate the effectiveness of the proposed method. With the
commonly-used 3D ResNet-18 as the backbone, we achieve the top-1 accuracies of
91.5\% on UCF101 and 50.3\% on Something-Something v2 for video classification,
and a 65.6\% Top-1 Recall on UCF101 for video retrieval, notably improving the

Training deep neural networks (DNNs) is becoming increasingly more resource-
and energy-intensive every year. Unfortunately, existing works primarily focus
on optimizing DNN training for faster completion, often without considering the
impact on energy efficiency.
  In this paper, we observe that common practices to improve training
performance can often lead to inefficient energy usage. More importantly, we
demonstrate that there is a tradeoff between energy consumption and performance
optimization. To this end, we propose Zeus, an optimization framework to
navigate this tradeoff by automatically finding optimal job- and GPU-level
configurations for recurring DNN training jobs. Zeus uses an online
exploration-exploitation approach in conjunction with just-in-time energy
profiling, averting the need for expensive offline measurements, while adapting
to data drifts over time. Our evaluation shows that Zeus can improve the energy
efficiency of DNN training by 15.3%-75.8% for diverse workloads.

In unsupervised domain adaptive (UDA) semantic segmentation, the distillation
based methods are currently dominant in performance. However, the distillation
technique requires complicate multi-stage process and many training tricks.
  In this paper, we propose a simple yet effective method that can achieve
competitive performance to the advanced distillation methods. Our core idea is
to fully explore the target-domain information from the views of boundaries and
features.
  First, we propose a novel mix-up strategy to generate high-quality
target-domain boundaries with ground-truth labels. Different from the
source-domain boundaries in previous works, we select the high-confidence
target-domain areas and then paste them to the source-domain images. Such a
strategy can generate the object boundaries in target domain (edge of
target-domain object areas) with the correct labels. Consequently, the boundary
information of target domain can be effectively captured by learning on the
mixed-up samples.
  Second, we design a multi-level contrastive loss to improve the
representation of target-domain data, including pixel-level and prototype-level
contrastive learning.
  By combining two proposed methods, more discriminative features can be
extracted and hard object boundaries can be better addressed for the target
domain.
  The experimental results on two commonly adopted benchmarks (\textit{i.e.},
GTA5 $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes) show that
our method achieves competitive performance to complicated distillation
methods. Notably, for the SYNTHIA$\rightarrow$ Cityscapes scenario, our method
  achieves the state-of-the-art performance with $57.8\%$ mIoU and $64.6\%$
mIoU on 16 classes and 13 classes. Code is available at
https://github.com/ljjcoder/EHTDI.

Brain network analysis for traumatic brain injury (TBI) patients is critical
for its consciousness level assessment and prognosis evaluation, which requires
the segmentation of certain consciousness-related brain regions. However, it is
difficult to construct a TBI segmentation model as manually annotated MR scans
of TBI patients are hard to collect. Data augmentation techniques can be
applied to alleviate the issue of data scarcity. However, conventional data
augmentation strategies such as spatial and intensity transformation are unable
to mimic the deformation and lesions in traumatic brains, which limits the
performance of the subsequent segmentation task. To address these issues, we
propose a novel medical image inpainting model named TBI-GAN to synthesize TBI
MR scans with paired brain label maps. The main strength of our TBI-GAN method
is that it can generate TBI images and corresponding label maps simultaneously,
which has not been achieved in the previous inpainting methods for medical
images. We first generate the inpainted image under the guidance of edge
information following a coarse-to-fine manner, and then the synthesized
intensity image is used as the prior for label inpainting. Furthermore, we
introduce a registration-based template augmentation pipeline to increase the
diversity of the synthesized image pairs and enhance the capacity of data
augmentation. Experimental results show that the proposed TBI-GAN method can
produce sufficient synthesized TBI images with high quality and valid label
maps, which can greatly improve the 2D and 3D traumatic brain segmentation
performance compared with the alternatives.

High-throughput DFT calculations are key to screening existing/novel
materials, sampling potential energy surfaces, and generating quantum
mechanical data for machine learning. By including a fraction of exact exchange
(EXX), hybrid functionals reduce the self-interaction error in semi-local DFT
and furnish a more accurate description of the underlying electronic structure,
albeit at a high computational cost that often prohibits such high-throughput
applications. To address this challenge, we have constructed SeA
(SeA=SCDM+exx+ACE), a robust, accurate, and efficient framework for
high-throughput condensed-phase hybrid DFT by combining: (1) the non-iterative
selected columns of the density matrix (SCDM) orbital localization scheme, (2)
a black-box and linear-scaling EXX algorithm (exx), and (3) adaptively
compressed exchange (ACE). By considering a diverse set of aqueous
configurations, SeA yields ~20x speedup in the rate-determining step in the
convolution-based ACE implementation in Quantum ESPRESSO, while reproducing the
EXX energy and ionic forces with high fidelity. In doing so, SeA effectively
removes the computational bottleneck that prohibits the routine use of hybrid
DFT in high-throughput applications, providing an ~6x speedup in the overall
cost of the ACE algorithm (and >100x overall speedup when compared to the
conventional EXX implementation) for systems similar in size to (H2O)64. As a
proof-of-principle high-throughput application, we used SeA to train a DNN
potential for ambient (T=300K, p=1Bar) liquid water at the hybrid (PBE0) DFT
level based on an actively learned data set of ~8,000 (H2O)64 configurations.
Using an out-of-sample test set ((H2O)512 at T=330K, p=1Bar), we confirmed the
accuracy of the SeA-trained DNN potential and showcased the capability of SeA
by directly computing the ground-truth ionic forces in this challenging system
containing >1,500 atoms.

There are many different methods in the literature for local explanation of
machine learning results. However, the methods differ in their approaches and
often do not provide same explanations. In this paper, we consider two recent
methods: Integrated Gradients (Sundararajan, Taly, & Yan, 2017) and Baseline
Shapley (Sundararajan and Najmi, 2020). The original authors have already
studied the axiomatic properties of the two methods and provided some
comparisons. Our work provides some additional insights on their comparative
behavior for tabular data. We discuss common situations where the two provide
identical explanations and where they differ. We also use simulation studies to
examine the differences when neural networks with ReLU activation function is
used to fit the models.

Recently, blockchain-based federated learning (BFL) has attracted intensive
research attention due to that the training process is auditable and the
architecture is serverless avoiding the single point failure of the parameter
server in vanilla federated learning (VFL). Nevertheless, BFL tremendously
escalates the communication traffic volume because all local model updates
(i.e., changes of model parameters) obtained by BFL clients will be transmitted
to all miners for verification and to all clients for aggregation. In contrast,
the parameter server and clients in VFL only retain aggregated model updates.
Consequently, the huge communication traffic in BFL will inevitably impair the
training efficiency and hinder the deployment of BFL in reality. To improve the
practicality of BFL, we are among the first to propose a fast blockchain-based
communication-efficient federated learning framework by compressing
communications in BFL, called BCFL. Meanwhile, we derive the convergence rate
of BCFL with non-convex loss. To maximize the final model accuracy, we further
formulate the problem to minimize the training loss of the convergence rate
subject to a limited training time with respect to the compression rate and the
block generation rate, which is a bi-convex optimization problem and can be
efficiently solved. To the end, to demonstrate the efficiency of BCFL, we carry
out extensive experiments with standard CIFAR-10 and FEMNIST datasets. Our
experimental results not only verify the correctness of our analysis, but also
manifest that BCFL can remarkably reduce the communication traffic by 95-98% or
shorten the training time by 90-95% compared with BFL.

K-means is one of the most widely used clustering models in practice. Due to
the problem of data isolation and the requirement for high model performance,
how to jointly build practical and secure K-means for multiple parties has
become an important topic for many applications in the industry. Existing work
on this is mainly of two types. The first type has efficiency advantages, but
information leakage raises potential privacy risks. The second type is provable
secure but is inefficient and even helpless for the large-scale data sparsity
scenario. In this paper, we propose a new framework for efficient
sparsity-aware K-means with three characteristics. First, our framework is
divided into a data-independent offline phase and a much faster online phase,
and the offline phase allows to pre-compute almost all cryptographic
operations. Second, we take advantage of the vectorization techniques in both
online and offline phases. Third, we adopt a sparse matrix multiplication for
the data sparsity scenario to improve efficiency further. We conduct
comprehensive experiments on three synthetic datasets and deploy our model in a
real-world fraud detection task. Our experimental results show that, compared
with the state-of-the-art solution, our model achieves competitive performance
in terms of both running time and communication size, especially on sparse
datasets.

We investigate how to modify executable files to deceive malware
classification systems. This work's main contribution is a methodology to
inject bytes across a malware file randomly and use it both as an attack to
decrease classification accuracy but also as a defensive method, augmenting the
data available for training. It respects the operating system file format to
make sure the malware will still execute after our injection and will not
change its behavior. We reproduced five state-of-the-art malware classification
approaches to evaluate our injection scheme: one based on GIST+KNN, three CNN
variations and one Gated CNN. We performed our experiments on a public dataset
with 9,339 malware samples from 25 different families. Our results show that a
mere increase of 7% in the malware size causes an accuracy drop between 25% and
40% for malware family classification. They show that a automatic malware
classification system may not be as trustworthy as initially reported in the
literature. We also evaluate using modified malwares alongside the original
ones to increase networks robustness against mentioned attacks. Results show
that a combination of reordering malware sections and injecting random data can
improve overall performance of the classification. Code available at
https://github.com/adeilsonsilva/malware-injection.

How can we accurately recommend actions for users to control their devices at
home? Action recommendation for smart home has attracted increasing attention
due to its potential impact on the markets of virtual assistants and Internet
of Things (IoT). However, designing an effective action recommender system for
smart home is challenging because it requires handling context correlations,
considering both queried contexts and previous histories of users, and dealing
with capricious intentions in history. In this work, we propose SmartSense, an
accurate action recommendation method for smart home. For individual action,
SmartSense summarizes its device control and its temporal contexts in a
self-attentive manner, to reflect the importance of the correlation between
them. SmartSense then summarizes sequences of users considering queried
contexts in a query-attentive manner to extract the query-related patterns from
the sequential actions. SmartSense also transfers the commonsense knowledge
from routine data to better handle intentions in action sequences. As a result,
SmartSense addresses all three main challenges of action recommendation for
smart home, and achieves the state-of-the-art performance giving up to 9.8%
higher mAP@1 than the best competitor.

Deep Learning has greatly advanced the performance of semantic segmentation,
however, its success relies on the availability of large amounts of annotated
data for training. Hence, many efforts have been devoted to domain adaptive
semantic segmentation that focuses on transferring semantic knowledge from a
labeled source domain to an unlabeled target domain. Existing self-training
methods typically require multiple rounds of training, while another popular
framework based on adversarial training is known to be sensitive to
hyper-parameters. In this paper, we present an easy-to-train framework that
learns domain-invariant prototypes for domain adaptive semantic segmentation.
In particular, we show that domain adaptation shares a common character with
few-shot learning in that both aim to recognize some types of unseen data with
knowledge learned from large amounts of seen data. Thus, we propose a unified
framework for domain adaptation and few-shot learning. The core idea is to use
the class prototypes extracted from few-shot annotated target images to
classify pixels of both source images and target images. Our method involves
only one-stage training and does not need to be trained on large-scale
un-annotated target images. Moreover, our method can be extended to variants of
both domain adaptation and few-shot learning. Experiments on adapting
GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes show that our method achieves
competitive performance to state-of-the-art.

Contrastive learning is commonly used as a method of self-supervised learning
with the "anchor" and "positive" being two random augmentations of a given
input image, and the "negative" is the set of all other images. However, the
requirement of large batch sizes and memory banks has made it difficult and
slow to train. This has motivated the rise of Supervised Contrasative
approaches that overcome these problems by using annotated data. We look to
further improve supervised contrastive learning by ranking classes based on
their similarity, and observe the impact of human bias (in the form of ranking)
on the learned representations. We feel this is an important question to
address, as learning good feature embeddings has been a long sought after
problem in computer vision.

Contrastive learning is commonly applied to self-supervised learning, and has
been shown to outperform traditional approaches such as the triplet loss and
N-pair loss. However, the requirement of large batch sizes and memory banks has
made it difficult and slow to train. Recently, Supervised Contrasative
approaches have been developed to overcome these problems. They focus more on
learning a good representation for each class individually, or between a
cluster of classes. In this work we attempt to rank classes based on similarity
using a user-defined ranking, to learn an efficient representation between all
classes. We observe how incorporating human bias into the learning process
could improve learning representations in the parameter space. We show that our
results are comparable to Supervised Contrastive Learning for image
classification and object detection, and discuss it's shortcomings in OOD
Detection

In order to deal with variant-length long videos, prior works extract
multi-modal features and fuse them to predict students' engagement intensity.
In this paper, we present a new end-to-end method Class Attention in Video
Transformer (CavT), which involves a single vector to process class embedding
and to uniformly perform end-to-end learning on variant-length long videos and
fixed-length short videos. Furthermore, to address the lack of sufficient
samples, we propose a binary-order representatives sampling method (BorS) to
add multiple video sequences of each video to augment the training set.
BorS+CavT not only achieves the state-of-the-art MSE (0.0495) on the EmotiW-EP
dataset, but also obtains the state-of-the-art MSE (0.0377) on the DAiSEE
dataset. The code and models have been made publicly available at
https://github.com/mountainai/cavt.

Antibody design is valuable for therapeutic usage and biological research.
Existing deep-learning-based methods encounter several key issues: 1)
incomplete context for Complementarity-Determining Regions (CDRs) generation;
2) incapable of capturing the entire 3D geometry of the input structure; 3)
inefficient prediction of the CDR sequences in an autoregressive manner. In
this paper, we propose Multi-channel Equivariant Attention Network (MEAN), an
end-to-end model that is able to co-design 1D sequences and 3D structures of
CDRs. To be specific, MEAN formulates antibody design as a conditional graph
translation problem by importing extra components including the target antigen
and the light chain of the antibody. Then, MEAN resorts to E(3)-equivariant
message passing along with a proposed attention mechanism to better capture the
geometrical correlation between different components. Finally, it outputs both
the 1D sequences and 3D structure via a multi-round progressive full-shot
scheme, which enjoys more efficiency against previous autoregressive
approaches. Our method significantly surpasses state-of-the-art models in
sequence and structure modeling, antigen-binding antibody design, and binding
affinity optimization. Specifically, the relative improvement to baselines is
about 23% in antigen-binding CDR design and 34% for affinity optimization.

Medical images come in high resolutions. A high resolution is vital for
finding malignant tissues at an early stage. Yet, this resolution presents a
challenge in terms of modeling long range dependencies. Shallow transformers
eliminate this problem, but they suffer from quadratic complexity. In this
paper, we tackle this complexity by leveraging a linear self-attention
approximation. Through this approximation, we propose an efficient vision model
called HCT that stands for High resolution Convolutional Transformer. HCT
brings transformers' merits to high resolution images at a significantly lower
cost. We evaluate HCT using a high resolution mammography dataset. HCT is
significantly superior to its CNN counterpart. Furthermore, we demonstrate
HCT's fitness for medical images by evaluating its effective receptive
field.Code available at https://bit.ly/3ykBhhf

Mixed-precision Deep Neural Networks achieve the energy efficiency and
throughput needed for hardware deployment, particularly when the resources are
limited, without sacrificing accuracy. However, the optimal per-layer bit
precision that preserves accuracy is not easily found, especially with the
abundance of models, datasets, and quantization techniques that creates an
enormous search space. In order to tackle this difficulty, a body of literature
has emerged recently, and several frameworks that achieved promising accuracy
results have been proposed. In this paper, we start by summarizing the
quantization techniques used generally in literature. Then, we present a
thorough survey of the mixed-precision frameworks, categorized according to
their optimization techniques such as reinforcement learning and quantization
techniques like deterministic rounding. Furthermore, the advantages and
shortcomings of each framework are discussed, where we present a juxtaposition.
We finally give guidelines for future mixed-precision frameworks.

Machine translation has seen rapid progress with the advent of
Transformer-based models. These models have no explicit linguistic structure
built into them, yet they may still implicitly learn structured relationships
by attending to relevant tokens. We hypothesize that this structural learning
could be made more robust by explicitly endowing Transformers with a structural
bias, and we investigate two methods for building in such a bias. One method,
the TP-Transformer, augments the traditional Transformer architecture to
include an additional component to represent structure. The second method
imbues structure at the data level by segmenting the data with morphological
tokenization. We test these methods on translating from English into
morphologically rich languages, Turkish and Inuktitut, and consider both
automatic metrics and human evaluations. We find that each of these two
approaches allows the network to achieve better performance, but this
improvement is dependent on the size of the dataset. In sum, structural
encoding methods make Transformers more sample-efficient, enabling them to
perform better from smaller amounts of data.

Solar flare prediction is a central problem in space weather forecasting and
has captivated the attention of a wide spectrum of researchers due to recent
advances in both remote sensing as well as machine learning and deep learning
approaches. The experimental findings based on both machine and deep learning
models reveal significant performance improvements for task specific datasets.
Along with building models, the practice of deploying such models to production
environments under operational settings is a more complex and often
time-consuming process which is often not addressed directly in research
settings. We present a set of new heuristic approaches to train and deploy an
operational solar flare prediction system for $\geq$M1.0-class flares with two
prediction modes: full-disk and active region-based. In full-disk mode,
predictions are performed on full-disk line-of-sight magnetograms using deep
learning models whereas in active region-based models, predictions are issued
for each active region individually using multivariate time series data
instances. The outputs from individual active region forecasts and full-disk
predictors are combined to a final full-disk prediction result with a
meta-model. We utilized an equal weighted average ensemble of two base
learners' flare probabilities as our baseline meta learner and improved the
capabilities of our two base learners by training a logistic regression model.
The major findings of this study are: (i) We successfully coupled two
heterogeneous flare prediction models trained with different datasets and model
architecture to predict a full-disk flare probability for next 24 hours, (ii)
Our proposed ensembling model, i.e., logistic regression, improves on the
predictive performance of two base learners and the baseline meta learner
measured in terms of two widely used metrics True Skill Statistic (TSS) and
Heidke Skill core (HSS), and (iii) Our result analysis suggests that the
logistic regression-based ensemble (Meta-FP) improves on the full-disk model
(base learner) by $\sim9\%$ in terms TSS and $\sim10\%$ in terms of HSS.
Similarly, it improves on the AR-based model (base learner) by $\sim17\%$ and
$\sim20\%$ in terms of TSS and HSS respectively. Finally, when compared to the
baseline meta model, it improves on TSS by $\sim10\%$ and HSS by $\sim15\%$.

Sparsity regularized loss minimization problems play an important role in
various fields including machine learning, data mining, and modern statistics.
Proximal gradient descent method and coordinate descent method are the most
popular approaches to solving the minimization problem. Although existing
methods can achieve implicit model identification, aka support set
identification, in a finite number of iterations, these methods still suffer
from huge computational costs and memory burdens in high-dimensional scenarios.
The reason is that the support set identification in these methods is implicit
and thus cannot explicitly identify the low-complexity structure in practice,
namely, they cannot discard useless coefficients of the associated features to
achieve algorithmic acceleration via dimension reduction. To address this
challenge, we propose a novel accelerated doubly stochastic gradient descent
(ADSGD) method for sparsity regularized loss minimization problems, which can
reduce the number of block iterations by eliminating inactive coefficients
during the optimization process and eventually achieve faster explicit model
identification and improve the algorithm efficiency. Theoretically, we first
prove that ADSGD can achieve a linear convergence rate and lower overall
computational complexity. More importantly, we prove that ADSGD can achieve a
linear rate of explicit model identification. Numerically, experimental results
on benchmark datasets confirm the efficiency of our proposed method.

In this paper, we present an online adaptive planning strategy for a team of
robots with heterogeneous sensors to sample from a latent spatial field using a
learned model for decision making. Current robotic sampling methods seek to
gather information about an observable spatial field. However, many
applications, such as environmental monitoring and precision agriculture,
involve phenomena that are not directly observable or are costly to measure,
called latent phenomena. In our approach, we seek to reason about the latent
phenomenon in real-time by effectively sampling the observable spatial fields
using a team of robots with heterogeneous sensors, where each robot has a
distinct sensor to measure a different observable field. The information gain
is estimated using a learned model that maps from the observable spatial fields
to the latent phenomenon. This model captures aleatoric uncertainty in the
relationship to allow for information theoretic measures. Additionally, we
explicitly consider the correlations among the observable spatial fields,
capturing the relationship between sensor types whose observations are not
independent. We show it is possible to learn these correlations, and
investigate the impact of the learned correlation models on the performance of
our sampling approach. Through our qualitative and quantitative results, we
illustrate that empirically learned correlations improve the overall sampling
efficiency of the team. We simulate our approach using a data set of sensor
measurements collected on Lac Hertel, in Quebec, which we make publicly

Self-attention based transformer models have been dominating many computer
vision tasks in the past few years. Their superb model qualities heavily depend
on the excessively large labeled image datasets. In order to reduce the
reliance on large labeled datasets, reconstruction based masked autoencoders
are gaining popularity, which learn high quality transferable representations
from unlabeled images. For the same purpose, recent weakly supervised image
pretraining methods explore language supervision from text captions
accompanying the images. In this work, we propose masked image pretraining on
language assisted representation, dubbed as MILAN. Instead of predicting raw
pixels or low level features, our pretraining objective is to reconstruct the
image features with substantial semantic signals that are obtained using
caption supervision. Moreover, to accommodate our reconstruction target, we
propose a more efficient prompting decoder architecture and a semantic aware
mask sampling mechanism, which further advance the transfer performance of the
pretrained model. Experimental results demonstrate that MILAN delivers higher
accuracy than the previous works. When the masked autoencoder is pretrained and
finetuned on ImageNet-1K dataset with an input resolution of 224x224, MILAN
achieves a top-1 accuracy of 85.4% on ViTB/16, surpassing previous
state-of-the-arts by 1%. In the downstream semantic segmentation task, MILAN
achieves 52.7 mIoU using ViT-B/16 backbone on ADE20K dataset, outperforming
previous masked pretraining results by 4 points.

Vehicle hijacking is one of the leading crimes in many cities. For instance,
in South Africa, drivers must constantly remain vigilant on the road in order
to ensure that they do not become hijacking victims. This work is aimed at
developing a map depicting hijacking spots in a city by using Twitter data.
Tweets, which include the keyword "hijacking", are obtained in a designated
city of Cape Town, in this work. In order to extract relevant tweets, these
tweets are analyzed by using the following machine learning techniques: 1) a
Multi-layer Feed-forward Neural Network (MLFNN); 2) Convolutional Neural
Network; and Bidirectional Encoder Representations from Transformers (BERT).
Through training and testing, CNN achieved an accuracy of 99.66%, while MLFNN
and BERT achieve accuracies of 98.99% and 73.99% respectively. In terms of
Recall, Precision and F1-score, CNN also achieved the best results. Therefore,
CNN was used for the identification of relevant tweets. The relevant reports
that it generates are visually presented on a points map of the City of Cape
Town. This work used a small dataset of 426 tweets. In future, the use of
evolutionary computation will be explored for purposes of optimizing the deep
learning models. A mobile application is under development to make this
information usable by the general public.

Much of software-engineering research relies on the naturalness of code, the
fact that code, in small code snippets, is repetitive and can be predicted
using statistical language models like n-gram. Although powerful, training such
models on large code corpus is tedious, time-consuming and sensitive to code
patterns (and practices) encountered during training. Consequently, these
models are often trained on a small corpora and estimate the language
naturalness that is relative to a specific style of programming or type of
project. To overcome these issues, we propose using pre-trained language models
to infer code naturalness. Pre-trained models are often built on big data, are
easy to use in an out-of-the-box way and include powerful learning associations
mechanisms. Our key idea is to quantify code naturalness through its
predictability, by using state-of-the-art generative pre-trained language
models. To this end, we infer naturalness by masking (omitting) code tokens,
one at a time, of code-sequences, and checking the models' ability to predict
them. To this end, we evaluate three different predictability metrics; a)
measuring the number of exact matches of the predictions, b) computing the
embedding similarity between the original and predicted code, i.e., similarity
at the vector space, and c) computing the confidence of the model when doing
the token completion task irrespective of the outcome. We implement this
workflow, named CodeBERT-nt, and evaluate its capability to prioritize buggy
lines over non-buggy ones when ranking code based on its naturalness. Our
results, on 2510 buggy versions of 40 projects from the SmartShark dataset,
show that CodeBERT-nt outperforms both, random-uniform and complexity-based
ranking techniques, and yields comparable results (slightly better) than the
n-gram models.

Experimental research publications provide figure form resources including
graphs, charts, and any type of images to effectively support and convey
methods and results. To describe figures, authors add captions, which are often
incomplete, and more descriptions reside in body text. This work presents a
method to extract figure descriptive text from the body of scientific articles.
We adopted ontological semantics to aid concept recognition of figure-related
information, which generates human- and machine-readable knowledge
representations from sentences. Our results show that conceptual models bring
an improvement in figure descriptive sentence classification over word-based

Despite recent advances in the accuracy of brain tumor segmentation, the
results still suffer from low reliability and robustness. Uncertainty
estimation is an efficient solution to this problem, as it provides a measure
of confidence in the segmentation results. The current uncertainty estimation
methods based on quantile regression, Bayesian neural network, ensemble, and
Monte Carlo dropout are limited by their high computational cost and
inconsistency. In order to overcome these challenges, Evidential Deep Learning
(EDL) was developed in recent work but primarily for natural image
classification. In this paper, we proposed a region-based EDL segmentation
framework that can generate reliable uncertainty maps and robust segmentation
results. We used the Theory of Evidence to interpret the output of a neural
network as evidence values gathered from input features. Following Subjective
Logic, evidence was parameterized as a Dirichlet distribution, and predicted
probabilities were treated as subjective opinions. To evaluate the performance
of our model on segmentation and uncertainty estimation, we conducted
quantitative and qualitative experiments on the BraTS 2020 dataset. The results
demonstrated the top performance of the proposed method in quantifying
segmentation uncertainty and robustly segmenting tumors. Furthermore, our
proposed new framework maintained the advantages of low computational cost and
easy implementation and showed the potential for clinical application.

To maintain a standard in a medical imaging study, images should have
necessary image quality for potential diagnostic use. Although CNN-based
approaches are used to assess the image quality, their performance can still be
improved in terms of accuracy. In this work, we approach this problem by using
Swin Transformer, which improves the poor-quality image classification
performance that causes the degradation in medical image quality. We test our
approach on Foreign Object Classification problem on Chest X-Rays (Object-CXR)
and Left Ventricular Outflow Tract Classification problem on Cardiac MRI with a
four-chamber view (LVOT). While we obtain a classification accuracy of 87.1%
and 95.48% on the Object-CXR and LVOT datasets, our experimental results
suggest that the use of Swin Transformer improves the Object-CXR classification
performance while obtaining a comparable performance for the LVOT dataset. To
the best of our knowledge, our study is the first vision transformer
application for medical image quality assessment.

Adopting reasonable strategies is challenging but crucial for an intelligent
agent with limited resources working in hazardous, unstructured, and dynamic
environments to improve the system utility, decrease the overall cost, and
increase mission success probability. Deep Reinforcement Learning (DRL) helps
organize agents' behaviors and actions based on their state and represents
complex strategies (composition of actions). This paper proposes a novel
hierarchical strategy decomposition approach based on Bayesian chaining to
separate an intricate policy into several simple sub-policies and organize
their relationships as Bayesian strategy networks (BSN). We integrate this
approach into the state-of-the-art DRL method, soft actor-critic (SAC), and
build the corresponding Bayesian soft actor-critic (BSAC) model by organizing
several sub-policies as a joint policy. We compare the proposed BSAC method
with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO
on the standard continuous control benchmarks -- Hopper-v2, Walker2d-v2, and
Humanoid-v2 -- in MuJoCo with the OpenAI Gym environment. The results
demonstrate that the promising potential of the BSAC method significantly
improves training efficiency. The open sourced codes for BSAC can be accessed
at https://github.com/herolab-uga/bsac.

Spreadsheets are widely used for table manipulation and presentation.
Stylistic formatting of these tables is an important property for both
presentation and analysis. As a result, popular spreadsheet software, such as
Excel, supports automatically formatting tables based on data-dependent rules.
Unfortunately, writing these formatting rules can be challenging for users as
that requires knowledge of the underlying rule language and data logic. In this
paper, we present CORNET, a neuro-symbolic system that tackles the novel
problem of automatically learning such formatting rules from user examples of
formatted cells. CORNET takes inspiration from inductive program synthesis and
combines symbolic rule enumeration, based on semi-supervised clustering and
iterative decision tree learning, with a neural ranker to produce conditional
formatting rules. To motivate and evaluate our approach, we extracted tables
with formatting rules from a corpus of over 40K real spreadsheets. Using this
data, we compared CORNET to a wide range of symbolic and neural baselines. Our
results show that CORNET can learn rules more accurately, across varying
conditions, compared to these baselines.

Due to the characteristics of Information and Communications Technology (ICT)
products, the critical information of ICT devices is often summarized in big
tabular data shared across supply chains. Therefore, it is critical to
automatically interpret tabular structures with the surging amount of
electronic assets. To transform the tabular data in electronic documents into a
machine-interpretable format and provide layout and semantic information for
information extraction and interpretation, we define a Table Structure
Recognition (TSR) task and a Table Cell Type Classification (CTC) task. We use
a graph to represent complex table structures for the TSR task. Meanwhile,
table cells are categorized into three groups based on their functional roles
for the CTC task, namely Header, Attribute, and Data. Subsequently, we propose
a multi-task model to solve the defined two tasks simultaneously by using the
text modal and image modal features. Our experimental results show that our
proposed method can outperform state-of-the-art methods on ICDAR2013 and UNLV
datasets.

Pharmaceutical companies operate in a strictly regulated and highly risky
environment in which a single slip can lead to serious financial implications.
Accordingly, the announcements of clinical trial results tend to determine the
future course of events, hence being closely monitored by the public. In this
work, we provide statistical evidence for the result promulgation influence on
the public pharma market value. Whereas most works focus on retrospective
impact analysis, the present research aims to predict the numerical values of
announcement-induced changes in stock prices. For this purpose, we develop a
pipeline that includes a BERT-based model for extracting sentiment polarity of
announcements, a Temporal Fusion Transformer for forecasting the expected
return, a graph convolution network for capturing event relationships, and
gradient boosting for predicting the price change. The challenge of the problem
lies in inherently different patterns of responses to positive and negative
announcements, reflected in a stronger and more pronounced reaction to the
negative news. Moreover, such phenomenon as the drop in stocks after the
positive announcements affirms the counterintuitiveness of the price behavior.
Importantly, we discover two crucial factors that should be considered while
working within a predictive framework. The first factor is the drug portfolio
size of the company, indicating the greater susceptibility to an announcement
in the case of small drug diversification. The second one is the network effect
of the events related to the same company or nosology. All findings and
insights are gained on the basis of one of the biggest FDA (the Food and Drug
Administration) announcement datasets, consisting of 5436 clinical trial
announcements from 681 companies over the last five years.

It is well known that tensor network regression models operate on an
exponentially large feature space, but questions remain as to how effectively
they are able to utilize this space. Using the polynomial featurization from
Novikov et al., we propose the interaction decomposition as a tool that can
assess the relative importance of different regressors as a function of their
polynomial degree. We apply this decomposition to tensor ring and tree tensor
network models trained on the MNIST and Fashion MNIST datasets, and find that
up to 75% of interaction degrees are contributing meaningfully to these models.
We also introduce a new type of tensor network model that is explicitly trained
on only a small subset of interaction degrees, and find that these models are
able to match or even outperform the full models using only a fraction of the
exponential feature space. This suggests that standard tensor network models
utilize their polynomial regressors in an inefficient manner, with the lower
degree terms being vastly under-utilized.

The lack of insight into deep learning systems hinders their systematic
design. In science and engineering, modeling is a methodology used to
understand complex systems whose internal processes are opaque. Modeling
replaces a complex system with a simpler surrogate that is more amenable to
interpretation. Drawing inspiration from this, we construct a class of
surrogate models for neural networks using Gaussian processes. Rather than
deriving the kernels for certain limiting cases of neural networks, we learn
the kernels of the Gaussian process empirically from the naturalistic behavior
of neural networks. We first evaluate our approach with two case studies
inspired by previous theoretical studies of neural network behavior in which we
capture neural network preferences for learning low frequencies and identify
pathological behavior in deep neural networks. In two further practical case
studies, we use the learned kernel to predict the generalization properties of

Circles of low-variance and Hawking points in the Cosmic Microwave Background
(CMB), resulting from black hole mergers and black hole evaporation,
respectively, in a previous cycle of the universe, have been predicted as
possible evidence for the Conformal Cyclic Cosmology model (CCC) introduced by
R. Penrose. We present a high-resolution search for such low-variance circles
in the Planck and WMAP CMB data, and introduce HawkingNet, our machine learning
open-source software based on a ResNet18 algorithm, to search for Hawking
points in the CMB. We find that CMB anomalies, consisting of a single or a few
bright pixels, erroneously lead to regions with many low-variance circles when
applying the search criteria used in previous works [V.G. Gurzadyan, R.
Penrose]. After removing the anomalies from the data no statistically
significant low-variance circles can be found. Concerning Hawking points, also
no statistically significant evidence is found when using a Gaussian
temperature amplitude model over 1 degree opening angle and after accounting
for CMB anomalies. That CMB anomalies themselves might be remnants of Hawking
points is not supported by low-variance and/or high-temperature circles around
them. The absence of such distinct features in the CMB does not disprove CCC
because if the density of such circles and Hawking points is large an
interference speckle pattern in the CMB might arise instead. This would lead to
non-Gaussian fluctuation in the CMB, a feature not unique to CCC. We do observe
significant local deviation of the real CMB sky from Gaussian noise on a local
scale when using best-fit Lambda-CDM and COM PowerSpect CBM TT-full power
spectra.

Temperature dependence of the neutron-nucleus interaction is known as the
Doppler broadening of the cross-sections. This is a well-known effect due to
the thermal motion of the target nuclei that occurs in the neutron-nucleus
interaction. The fast computation of such effects is crucial for any nuclear
application. Mechanisms have been developed that allow determining the Doppler
effects in the cross-section, most of them based on the numerical resolution of
the equation known as Solbrig's kernel, which is a cross-section Doppler
broadening formalism derived from a free gas atoms distribution hypothesis.
This paper explores a novel non-linear approach based on deep learning
techniques. Deep neural networks are trained on synthetic and experimental
data, serving as an alternative to the cross-section Doppler Broadening (DB).
This paper explores the possibility of using physically informed neural
networks, where the network is physically regularized to be the solution of a
partial derivative equation, inferred from Solbrig's kernel. The learning
process is demonstrated by using the fission, capture, and scattering cross
sections for $^{235}U$ in the energy range from thermal to 2250 eV.

Context: Mutation Testing (MT) is an important tool in traditional Software
Engineering (SE) white-box testing. It aims to artificially inject faults in a
system to evaluate a test suite's capability to detect them, assuming that the
test suite defects finding capability will then translate to real faults. If MT
has long been used in SE, it is only recently that it started gaining the
attention of the Deep Learning (DL) community, with researchers adapting it to
improve the testability of DL models and improve the trustworthiness of DL
systems.
  Objective: If several techniques have been proposed for MT, most of them
neglected the stochasticity inherent to DL resulting from the training phase.
Even the latest MT approaches in DL, which propose to tackle MT through a
statistical approach, might give inconsistent results. Indeed, as their
statistic is based on a fixed set of sampled training instances, it can lead to
different results across instances set when results should be consistent for
any instance.
  Methods: In this work, we propose a Probabilistic Mutation Testing (PMT)
approach that alleviates the inconsistency problem and allows for a more
consistent decision on whether a mutant is killed or not.
  Results: We show that PMT effectively allows a more consistent and informed
decision on mutations through evaluation using three models and eight mutation
operators used in previously proposed MT methods. We also analyze the trade-off
between the approximation error and the cost of our method, showing that
relatively small error can be achieved for a manageable cost.
  Conclusion: Our results showed the limitation of current MT practices in DNN
and the need to rethink them. We believe PMT is the first step in that
direction which effectively removes the lack of consistency across test
executions of previous methods caused by the stochasticity of DNN training.

The electricity consumption of buildings composes a major part of the city's
energy consumption. Electricity consumption forecasting enables the development
of home energy management systems resulting in the future design of more
sustainable houses and a decrease in total energy consumption. Energy
performance in buildings is influenced by many factors like ambient
temperature, humidity, and a variety of electrical devices. Therefore,
multivariate prediction methods are preferred rather than univariate. The Honda
Smart Home US data set was selected to compare three methods for minimizing
forecasting errors, MAE and RMSE: Artificial Neural Networks, Support Vector
Regression, and Fuzzy Rule-Based Systems for Regression by constructing many
models for each method on a multivariate data set in different time terms. The
comparison shows that SVR is a superior method over the alternatives.

Efficient defect detection in solar cell manufacturing is crucial for stable
green energy technology manufacturing. This paper presents a
deep-learning-based automatic detection model SeMaCNN for classification and
semantic segmentation of electroluminescent images for solar cell quality
evaluation and anomalies detection. The core of the model is an anomaly
detection algorithm based on Mahalanobis distance that can be trained in a
semi-supervised manner on imbalanced data with small number of digital
electroluminescence images with relevant defects. This is particularly valuable
for prompt model integration into the industrial landscape. The model has been
trained with the on-plant collected dataset consisting of 68 748
electroluminescent images of heterojunction solar cells with a busbar grid. Our
model achieves the accuracy of 92.5%, F1 score 95.8%, recall 94.8%, and
precision 96.9% within the validation subset consisting of 1049 manually
annotated images. The model was also tested on the open ELPV dataset and
demonstrates stable performance with accuracy 94.6% and F1 score 91.1%. The
SeMaCNN model demonstrates a good balance between its performance and
computational costs, which make it applicable for integrating into quality
control systems of solar cell manufacturing.

We describe a data-driven method for inferring the camera viewpoints given
multiple images of an arbitrary object. This task is a core component of
classic geometric pipelines such as SfM and SLAM, and also serves as a vital
pre-processing requirement for contemporary neural approaches (e.g. NeRF) to
object reconstruction and view synthesis. In contrast to existing
correspondence-driven methods that do not perform well given sparse views, we
propose a top-down prediction based approach for estimating camera viewpoints.
Our key technical insight is the use of an energy-based formulation for
representing distributions over relative camera rotations, thus allowing us to
explicitly represent multiple camera modes arising from object symmetries or
views. Leveraging these relative predictions, we jointly estimate a consistent
set of camera rotations from multiple images. We show that our approach
outperforms state-of-the-art SfM and SLAM methods given sparse images on both
seen and unseen categories. Further, our probabilistic approach significantly
outperforms directly regressing relative poses, suggesting that modeling
multimodality is important for coherent joint reconstruction. We demonstrate
that our system can be a stepping stone toward in-the-wild reconstruction from
multi-view datasets. The project page with code and videos can be found at
https://jasonyzhang.com/relpose.

Being able to learn an effective semantic representation directly on raw
point clouds has become a central topic in 3D understanding. Despite rapid
progress, state-of-the-art encoders are restrictive to canonicalized point
clouds, and have weaker than necessary performance when encountering geometric
transformation distortions. To overcome this challenge, we propose PointTree, a
general-purpose point cloud encoder that is robust to transformations based on
relaxed K-D trees. Key to our approach is the design of the division rule in
K-D trees by using principal component analysis (PCA). We use the structure of
the relaxed K-D tree as our computational graph, and model the features as
border descriptors which are merged with pointwise-maximum operation. In
addition to this novel architecture design, we further improve the robustness
by introducing pre-alignment -- a simple yet effective PCA-based normalization
scheme. Our PointTree encoder combined with pre-alignment consistently
outperforms state-of-the-art methods by large margins, for applications from
object classification to semantic segmentation on various transformed versions
of the widely-benchmarked datasets. Code and pre-trained models are available
at https://github.com/immortalCO/PointTree.

This paper studies the design of controllers that guarantee stability and
safety of nonlinear control affine systems with parametric uncertainty in both
the drift and control vector fields. To this end, we introduce novel classes of
robust control barrier functions (RCBF) and robust control Lyapunov functions
(RCLF) that facilitate the synthesis of safety-critical controllers in the
presence of parametric uncertainty using quadratic programming. Since the
initial bounds on the system uncertainty may be highly conservative, we present
a data-driven approach to reducing such bounds using input-output data
collected online. In particular, we leverage an integral set-membership
identification algorithm that iteratively shrinks the set of possible system
parameters online and guarantees stability and safety during learning. The
efficacy of the developed approach is illustrated on two numerical examples.

Convolutional graph networks are used in particle physics for effective event
reconstructions and classifications. However, their performances can be limited
by the considerable amount of sensors used in modern particle detectors if
applied to sensor-level data. We present a pooling scheme that uses
partitioning to create pooling kernels on graphs, similar to pooling on images.
Partition pooling can be used to adopt successful image recognition
architectures for graph neural network applications in particle physics. The
reduced computational resources allow for deeper networks and more extensive
hyperparameter optimizations. To show its applicability, we construct a
convolutional graph network with partition pooling that reconstructs simulated
interaction vertices for an idealized neutrino detector. The pooling network
yields improved performance and is less susceptible to overfitting than a
similar network without pooling. The lower resource requirements allow the
construction of a deeper network with further improved performance.

Pre-trained large language models (LLMs) such as OpenAI Codex have shown
immense potential in automating significant aspects of coding by producing
natural code from informal natural language (NL) intent. However, the code
produced does not have any correctness guarantees around satisfying user's
intent. In fact, it is hard to define a notion of correctness since natural
language can be ambiguous and lacks a formal semantics. In this paper, we take
a first step towards addressing the problem above by proposing the workflow of
test-driven user-intent formalization (TDUIF), which leverages lightweight user
feedback to jointly (a) formalize the user intent as tests (a partial
specification), and (b) generates code that meets the formal user intent. To
perform a scalable and large-scale automated evaluation of the algorithms
without requiring a user in the loop, we describe how to simulate user
interaction with high-fidelity using a reference solution. We also describe and
implement alternate implementations of several algorithmic components
(including mutating and ranking a set of tests) that can be composed for
efficient solutions to the TDUIF problem. We have developed a system TICODER
that implements several solutions to TDUIF, and compare their relative
effectiveness on the MBPP academic code generation benchmark. Our results are
promising with using the OpenAI Codex LLM on MBPP: our best algorithm improves
the pass@1 code generation accuracy metric from 48.39% to 70.49% with a single
user query, and up to 85.48% with up to 5 user queries. Second, we can generate
a non-trivial functional unit test consistent with the user intent within an
average of 1.69 user queries for 90.40% of the examples for this dataset.

Causal graph discovery and causal effect estimation are two fundamental tasks
in causal inference. While many methods have been developed for each task
individually, statistical challenges arise when applying these methods jointly:
estimating causal effects after running causal discovery algorithms on the same
data leads to "double dipping," invalidating coverage guarantees of classical
confidence intervals. To this end, we develop tools for valid
post-causal-discovery inference. One key contribution is a randomized version
of the greedy equivalence search (GES) algorithm, which permits a valid,
finite-sample correction of classical confidence intervals. Across empirical
studies, we show that a naive combination of causal discovery and subsequent
inference algorithms typically leads to highly inflated miscoverage rates; at
the same time, our noisy GES method provides reliable coverage control while
achieving more accurate causal graph recovery than data splitting.

In many clinical contexts, detecting all lesions is imperative for evaluating
disease activity. Standard approaches pose lesion detection as a segmentation
problem despite the time-consuming nature of acquiring segmentation labels. In
this paper, we present a lesion detection method which relies only on point
labels. Our model, which is trained via heatmap regression, can detect a
variable number of lesions in a probabilistic manner. In fact, our proposed
post-processing method offers a reliable way of directly estimating the lesion
existence uncertainty. Experimental results on Gad lesion detection show our
point-based method performs competitively compared to training on expensive
segmentation labels. Finally, our detection model provides a suitable
pre-training for segmentation. When fine-tuning on only 17 segmentation
samples, we achieve comparable performance to training with the full dataset.

We study the problem of finding a near-stationary point for smooth minimax
optimization. The recent proposed extra anchored gradient (EAG) methods achieve
the optimal convergence rate for the convex-concave minimax problem in
deterministic setting. However, the direct extension of EAG to stochastic
optimization is not efficient.In this paper, we design a novel stochastic
algorithm called Recursive Anchored IteratioN (RAIN). We show that the RAIN
achieves near-optimal stochastic first-order oracle (SFO) complexity for
stochastic minimax optimization in both convex-concave and
strongly-convex-strongly-concave cases. In addition, we extend the idea of RAIN
to solve structured nonconvex-nonconcave minimax problem and it also achieves
near-optimal SFO complexity.

In this paper we develop a novel regularization method for deep neural
networks by penalizing the trace of Hessian. This regularizer is motivated by a
recent guarantee bound of the generalization error. Hutchinson method is a
classical unbiased estimator for the trace of a matrix, but it is very
time-consuming on deep learning models. Hence a dropout scheme is proposed to
efficiently implements the Hutchinson method. Then we discuss a connection to
linear stability of a nonlinear dynamical system and flat/sharp minima.
Experiments demonstrate that our method outperforms existing regularizers and
data augmentation methods, such as Jacobian, confidence penalty, and label
smoothing, cutout and mixup.

With the huge growth in e-commerce domain, product recommendations have
become an increasing field of interest amongst e-commerce companies. One of the
more difficult tasks in product recommendations is size and fit predictions.
There are a lot of size related returns and refunds in e-fashion domain which
causes inconvenience to the customers as well as costs the company. Thus having
a good size and fit recommendation system, which can predict the correct sizes
for the customers will not only reduce size related returns and refunds but
also improve customer experience. Early works in this field used traditional
machine learning approaches to estimate customer and product sizes from
purchase history. These methods suffered from cold start problem due to huge
sparsity in the customer-product data. More recently, people have used deep
learning to address this problem by embedding customer and product features.
But none of them incorporates valuable customer feedback present on product
pages along with the customer and product features. We propose a novel approach
which can use information from customer reviews along with customer and product
features for size and fit predictions. We demonstrate the effectiveness of our
approach compared to using just product and customer features on 4 datasets.
Our method shows an improvement of 1.37% - 4.31% in F1 (macro) score over the
baseline across the 4 different datasets.

This paper introduces a testbed to study distributed sensing problems of
Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart
City applications, such as transport and disaster response, require efficient
collection of sensor data by a swarm of intelligent and cooperative UAVs. This
often proves to be too complex and costly to study systematically and
rigorously without compromising scale, realism and external validity. With the
proposed testbed, this paper sets a stepping stone to emulate, within small
laboratory spaces, large sensing areas of interest originated from empirical
data and simulation models. Over this sensing map, a swarm of low-cost drones
can fly allowing the study of a large spectrum of problems such as energy
consumption, charging control, navigation and collision avoidance. The
applicability of a decentralized multi-agent collective learning algorithm
(EPOS) for UAV swarm intelligence along with the assessment of power
consumption measurements provide a proof-of-concept and validate the accuracy
of the proposed testbed.

The prediction of atomistic fracture mechanisms in body-centred cubic (bcc)
iron is essential for understanding its semi-brittle nature. Existing atomistic
simulations of the crack-tip deformation mechanisms under mode-I loading based
on classical interatomic potentials yield contradicting predictions. To enable
fracture prediction with quantum accuracy, we develop a Gaussian approximation
potential (GAP) using an active learning strategy by extending a density
functional theory (DFT) database of ferromagnetic bcc iron. We apply the active
learning algorithm and obtain a Fe GAP model with a maximum predicted error of
8 meV/atom over a broad range of stress intensity factors (SIFs) and for four
crack systems. The learning efficiency of the approach is analysed, and the
predicted critical SIFs are compared with Griffith and Rice theories. The
simulations reveal that cleavage along the original crack plane is the crack
tip mechanism for {100} and {110} crack planes at T=0K, thus settling a
long-standing dispute. Our work also highlights the need for a multiscale
approach to predicting fracture and intrinsic ductility, whereby finite
temperature, finite loading rate effects and pre-existing defects (e.g.
nanovoids, dislocations) should be taken explicitly into account.

We develop a simple model for the timely monitoring of correlated sources
over a wireless network. Using this model, we study how to optimize
weighted-sum average Age of Information (AoI) in the presence of correlation.
First, we discuss how to find optimal stationary randomized policies and show
that they are at-most a factor of two away from optimal policies in general.
Then, we develop a Lyapunov drift-based max-weight policy that performs better
than randomized policies in practice and show that it is also at-most a factor
of two away from optimal. Next, we derive scaling results that show how AoI
improves in large networks in the presence of correlation. We also show that
for stationary randomized policies, the expression for average AoI is robust to
the way in which the correlation structure is modeled. Finally, for the setting
where correlation parameters are unknown and time-varying, we develop a
heuristic policy that adapts its scheduling decisions by learning the
correlation parameters in an online manner. We also provide numerical
simulations to support our theoretical results.

Recently, open radio access network (O-RAN) has become a promising technology
to provide an open environment for network vendors and operators. Coordinating
the x-applications (xAPPs) is critical to increase flexibility and guarantee
high overall network performance in O-RAN. Meanwhile, federated reinforcement
learning has been proposed as a promising technique to enhance the
collaboration among distributed reinforcement learning agents and improve
learning efficiency. In this paper, we propose a federated deep reinforcement
learning algorithm to coordinate multiple independent xAPPs in O-RAN for
network slicing. We design two xAPPs, namely a power control xAPP and a
slice-based resource allocation xAPP, and we use a federated learning model to
coordinate two xAPP agents to enhance learning efficiency and improve network
performance. Compared with conventional deep reinforcement learning, our
proposed algorithm can achieve 11% higher throughput for enhanced mobile
broadband (eMBB) slices and 33% lower delay for ultra-reliable low-latency
communication (URLLC) slices.

This work studies the classical spectral clustering algorithm which embeds
the vertices of some graph $G=(V_G, E_G)$ into $\mathbb{R}^k$ using $k$
eigenvectors of some matrix of $G$, and applies $k$-means to partition $V_G$
into $k$ clusters. Our first result is a tighter analysis on the performance of
spectral clustering, and explains why it works under some much weaker condition
than the ones studied in the literature. For the second result, we show that,
by applying fewer than $k$ eigenvectors to construct the embedding, spectral
clustering is able to produce better output for many practical instances; this
result is the first of its kind in spectral clustering. Besides its conceptual
and theoretical significance, the practical impact of our work is demonstrated
by the empirical analysis on both synthetic and real-world datasets, in which
spectral clustering produces comparable or better results with fewer than $k$
eigenvectors.

Name-based gender classification has enabled hundreds of otherwise infeasible
scientific studies of gender. Yet, the lack of standardization, proliferation
of ad hoc methods, reliance on paid services, understudied limitations, and
conceptual debates cast a shadow over many applications. To address these
problems we develop and evaluate an ensemble-based open-source method built on
publicly available data of empirical name-gender associations. Our method
integrates 36 distinct sources-spanning over 150 countries and more than a
century-via a meta-learning algorithm inspired by Cultural Consensus Theory
(CCT). We also construct a taxonomy with which names themselves can be
classified. We find that our method's performance is competitive with paid
services and that our method, and others, approach the upper limits of
performance; we show that conditioning estimates on additional metadata (e.g.
cultural context), further combining methods, or collecting additional
name-gender association data is unlikely to meaningfully improve performance.
This work definitively shows that name-based gender classification can be a
reliable part of scientific research and provides a pair of tools, a
classification method and a taxonomy of names, that realize this potential.

Extracting knowledge from unlabeled texts using machine learning algorithms
can be complex. Document categorization and information retrieval are two
applications that may benefit from unsupervised learning (e.g., text clustering
and topic modeling), including exploratory data analysis. However, the
unsupervised learning paradigm poses reproducibility issues. The initialization
can lead to variability depending on the machine learning algorithm.
Furthermore, the distortions can be misleading when regarding cluster geometry.
Amongst the causes, the presence of outliers and anomalies can be a determining
factor. Despite the relevance of initialization and outlier issues for text
clustering and topic modeling, the authors did not find an in-depth analysis of
them. This survey provides a systematic literature review (2011-2022) of these
subareas and proposes a common terminology since similar procedures have
different terms. The authors describe research opportunities, trends, and open
issues. The appendices summarize the theoretical background of the text
vectorization, the factorization, and the clustering algorithms that are
directly or indirectly related to the reviewed works.

We address the consistency of a kernel ridge regression estimate of the
conditional mean embedding (CME), which is an embedding of the conditional
distribution of $Y$ given $X$ into a target reproducing kernel Hilbert space
$\mathcal{H}_Y$. The CME allows us to take conditional expectations of target
RKHS functions, and has been employed in nonparametric causal and Bayesian
inference. We address the misspecified setting, where the target CME is in the
space of Hilbert-Schmidt operators acting from an input interpolation space
between $\mathcal{H}_X$ and $L_2$, to $\mathcal{H}_Y$. This space of operators
is shown to be isomorphic to a newly defined vector-valued interpolation space.
Using this isomorphism, we derive a novel and adaptive statistical learning
rate for the empirical CME estimator under the misspecified setting. Our
analysis reveals that our rates match the optimal $O(\log n / n)$ rates without
assuming $\mathcal{H}_Y$ to be finite dimensional. We further establish a lower
bound on the learning rate, which shows that the obtained upper bound is
optimal.

Implicit feedback is frequently used for developing personalized
recommendation services due to its ubiquity and accessibility in real-world
systems. In order to effectively utilize such information, most research adopts
the pairwise ranking method on constructed training triplets (user, positive
item, negative item) and aims to distinguish between positive items and
negative items for each user. However, most of these methods treat all the
training triplets equally, which ignores the subtle difference between
different positive or negative items. On the other hand, even though some other
works make use of the auxiliary information (e.g., dwell time) of user
behaviors to capture this subtle difference, such auxiliary information is hard
to obtain. To mitigate the aforementioned problems, we propose a novel training
framework named Triplet Importance Learning (TIL), which adaptively learns the
importance score of training triplets. We devise two strategies for the
importance score generation and formulate the whole procedure as a bilevel
optimization, which does not require any rule-based design. We integrate the
proposed training procedure with several Matrix Factorization (MF)- and Graph
Neural Network (GNN)-based recommendation models, demonstrating the
compatibility of our framework. Via a comparison using three real-world
datasets with many state-of-the-art methods, we show that our proposed method
outperforms the best existing models by 3-21\% in terms of Recall@k for the
top-k recommendation.

For responsible decision making in safety-critical settings, machine learning
models must effectively detect and process edge-case data. Although existing
works show that predictive uncertainty is useful for these tasks, it is not
evident from literature which uncertainty-aware models are best suited for a
given dataset. Thus, we compare six uncertainty-aware deep learning models on a
set of edge-case tasks: robustness to adversarial attacks as well as
out-of-distribution and adversarial detection. We find that the geometry of the
data sub-manifold is an important factor in determining the success of various
models. Our finding suggests an interesting direction in the study of
uncertainty-aware deep learning models.

To create a large amount of training labels for machine learning models
effectively and efficiently, researchers have turned to Weak Supervision (WS),
which uses programmatic labeling sources rather than manual annotation.
Existing works of WS for binary classification typically assume the presence of
labeling sources that are able to assign both positive and negative labels to
data in roughly balanced proportions. However, for many tasks of interest where
there is a minority positive class, negative examples could be too diverse for
developers to generate indicative labeling sources. Thus, in this work, we
study the application of WS on binary classification tasks with positive
labeling sources only. We propose WEAPO, a simple yet competitive WS method for
producing training labels without negative labeling sources. On 10 benchmark
datasets, we show WEAPO achieves the highest averaged performance in terms of
both the quality of synthesized labels and the performance of the final
classifier supervised with these labels. We incorporated the implementation of
\method into WRENCH, an existing benchmarking platform.

In many applications, multiple parties have private data regarding the same
set of users but on disjoint sets of attributes, and a server wants to leverage
the data to train a model. To enable model learning while protecting the
privacy of the data subjects, we need vertical federated learning (VFL)
techniques, where the data parties share only information for training the
model, instead of the private data. However, it is challenging to ensure that
the shared information maintains privacy while learning accurate models. To the
best of our knowledge, the algorithm proposed in this paper is the first
practical solution for differentially private vertical federated k-means
clustering, where the server can obtain a set of global centers with a provable
differential privacy guarantee. Our algorithm assumes an untrusted central
server that aggregates differentially private local centers and membership
encodings from local data parties. It builds a weighted grid as the synopsis of
the global dataset based on the received information. Final centers are
generated by running any k-means algorithm on the weighted grid. Our approach
for grid weight estimation uses a novel, light-weight, and differentially
private set intersection cardinality estimation algorithm based on the
Flajolet-Martin sketch. To improve the estimation accuracy in the setting with
more than two data parties, we further propose a refined version of the weights
estimation algorithm and a parameter tuning strategy to reduce the final
k-means utility to be close to that in the central private setting. We provide
theoretical utility analysis and experimental evaluation results for the
cluster centers computed by our algorithm and show that our approach performs
better both theoretically and empirically than the two baselines based on

Cyber Threat Intelligence (CTI) is information describing threat vectors,
vulnerabilities, and attacks and is often used as training data for AI-based
cyber defense systems such as Cybersecurity Knowledge Graphs (CKG). There is a
strong need to develop community-accessible datasets to train existing AI-based
cybersecurity pipelines to efficiently and accurately extract meaningful
insights from CTI. We have created an initial unstructured CTI corpus from a
variety of open sources that we are using to train and test cybersecurity
entity models using the spaCy framework and exploring self-learning methods to
automatically recognize cybersecurity entities. We also describe methods to
apply cybersecurity domain entity linking with existing world knowledge from
Wikidata. Our future work will survey and test spaCy NLP tools and create
methods for continuous integration of new information extracted from text.

Open material databases storing hundreds of thousands of material structures
and their corresponding properties have become the cornerstone of modern
computational materials science. Yet, the raw outputs of the simulations, such
as the trajectories from molecular dynamics simulations and charge densities
from density functional theory calculations, are generally not shared due to
their huge size. In this work, we describe a cloud-based platform to facilitate
the sharing of raw data and enable the fast post-processing in the cloud to
extract new properties defined by the user. As an initial demonstration, our
database currently includes 6286 molecular dynamics trajectories for amorphous
polymer electrolytes and 5.7 terabytes of data. We create a public analysis
library at https://github.com/TRI-AMDD/htp_md to extract multiple properties
from the raw data, using both expert designed functions and machine learning
models. The analysis is run automatically with computation in the cloud, and
results then populate a database that can be accessed publicly. Our platform
encourages users to contribute both new trajectory data and analysis functions
via public interfaces. Newly analyzed properties will be incorporated into the
database. Finally, we create a front-end user interface at
https://www.htpmd.matr.io for browsing and visualization of our data. We
envision the platform to be a new way of sharing raw data and new insights for
the computational materials science community.

We propose an approach to solving partial differential equations (PDEs) using
a set of neural networks which we call Neural Basis Functions (NBF). This NBF
framework is a novel variation of the POD DeepONet operator learning approach
where we regress a set of neural networks onto a reduced order Proper
Orthogonal Decomposition (POD) basis. These networks are then used in
combination with a branch network that ingests the parameters of the prescribed
PDE to compute a reduced order approximation to the PDE. This approach is
applied to the steady state Euler equations for high speed flow conditions
(mach 10-30) where we consider the 2D flow around a cylinder which develops a
shock condition. We then use the NBF predictions as initial conditions to a
high fidelity Computational Fluid Dynamics (CFD) solver (CFD++) to show faster
convergence. Lessons learned for training and implementing this algorithm will
be presented as well.

In this paper, we present a powerful differentiable surface fitting technique
to derive a compact surface representation for a given dense point cloud or
mesh, with application in the domains of graphics and CAD/CAM. We have chosen
the Loop subdivision surface, which in the limit yields the smooth surface
underlying the point cloud, and can handle complex surface topology better than
other popular compact representations, such as NURBS. The principal idea is to
fit the Loop subdivision surface not directly to the point cloud, but to the
IMLS (implicit moving least squares) surface defined over the point cloud. As
both Loop subdivision and IMLS have analytical expressions, we are able to
formulate the problem as an unconstrained minimization problem of a completely
differentiable function that can be solved with standard numerical solvers.
Differentiability enables us to integrate the subdivision surface into any deep
learning method for point clouds or meshes. We demonstrate the versatility and
potential of this approach by using it in conjunction with a differentiable
renderer to robustly reconstruct compact surface representations of
spatial-temporal sequences of dense meshes.

Properties of interest for crystals and molecules, such as band gap,
elasticity, and solubility, are generally related to each other: they are
governed by the same underlying laws of physics. However, when state-of-the-art
graph neural networks attempt to predict multiple properties simultaneously
(the multi-task learning (MTL) setting), they frequently underperform a suite
of single property predictors. This suggests graph networks may not be fully
leveraging these underlying similarities. Here we investigate a potential
explanation for this phenomenon: the curvature of each property's loss surface
significantly varies, leading to inefficient learning. This difference in
curvature can be assessed by looking at spectral properties of the Hessians of
each property's loss function, which is done in a matrix-free manner via
randomized numerical linear algebra. We evaluate our hypothesis on two
benchmark datasets (Materials Project (MP) and QM8) and consider how these
findings can inform the training of novel multi-task learning models.

The necessity for cooperation among intelligent machines has popularised
cooperative multi-agent reinforcement learning (MARL) in the artificial
intelligence (AI) research community. However, many research endeavors have
been focused on developing practical MARL algorithms whose effectiveness has
been studied only empirically, thereby lacking theoretical guarantees. As
recent studies have revealed, MARL methods often achieve performance that is
unstable in terms of reward monotonicity or suboptimal at convergence. To
resolve these issues, in this paper, we introduce a novel framework named
Heterogeneous-Agent Mirror Learning (HAML) that provides a general template for
MARL algorithmic designs. We prove that algorithms derived from the HAML
template satisfy the desired properties of the monotonic improvement of the
joint reward and the convergence to Nash equilibrium. We verify the
practicality of HAML by proving that the current state-of-the-art cooperative
MARL algorithms, HATRPO and HAPPO, are in fact HAML instances. Next, as a
natural outcome of our theory, we propose HAML extensions of two well-known RL
algorithms, HAA2C (for A2C) and HADDPG (for DDPG), and demonstrate their
effectiveness against strong baselines on StarCraftII and Multi-Agent MuJoCo
tasks.

In this work, the problem of learning Koopman operator of a discrete-time
autonomous system is considered. The learning problem is formulated as a
constrained regularized empirical loss minimization in the infinite-dimensional
space of linear operators. We show that under certain but general conditions, a
representer theorem holds for the learning problem. This allows reformulating
the problem in a finite-dimensional space without any approximation and loss of
precision. Following this, we consider various cases of regularization and
constraints in the learning problem, including the operator norm, the Frobenius
norm, rank, nuclear norm, and stability. Subsequently, we derive the
corresponding finite-dimensional problem. Furthermore, we discuss the
connection between the proposed formulation and the extended dynamic mode
decomposition. Finally, we provide an illustrative numerical example.

Artificial intelligence holds great promise in medical imaging, especially
histopathological imaging. However, artificial intelligence algorithms cannot
fully explain the thought processes during decision-making. This situation has
brought the problem of explainability, i.e., the black box problem, of
artificial intelligence applications to the agenda: an algorithm simply
responds without stating the reasons for the given images. To overcome the
problem and improve the explainability, explainable artificial intelligence
(XAI) has come to the fore, and piqued the interest of many researchers.
Against this backdrop, this study examines a new and original dataset using the
deep learning algorithm, and visualizes the output with gradient-weighted class
activation mapping (Grad-CAM), one of the XAI applications. Afterwards, a
detailed questionnaire survey was conducted with the pathologists on these
images. Both the decision-making processes and the explanations were verified,
and the accuracy of the output was tested. The research results greatly help
pathologists in the diagnosis of paratuberculosis.

Deterministic computational modeling of laser powder bed fusion (LPBF)
process fails to capture irregularities and roughness of the scan track, unless
expensive powder-scale analysis is used. In this work we developed a stochastic
computational modeling framework based on Markov Chain Monte Carlo (MCMC)
capable of capturing the irregularities of LPBF scan. The model is calibrated
against AFRL single track scan data using a specially designed tensor
decomposition method, i.e., Higher-Order Proper Generalized Decomposition
(HOPGD) that relies on non-intrusive data learning and construction of reduced
order surrogate models. Once calibrated, the stochastic model can be used to
predict the roughness and porosity at part scale at a significantly reduced
computational cost compared to detailed powder-scale deterministic simulations.
The stochastic simulation predictions are validated against AFRL multi-layer
and multitrack experiments and reported as more accurate when compared with
regular deterministic simulation results.

Most existing criteria derived from progenitor properties of core-collapse
supernovae are not very accurate in predicting explosion outcomes. We present a
novel look at identifying the explosion outcome of core-collapse supernovae
using a machine learning approach. Informed by a sample of 100 2D axisymmetric
supernova simulations evolved with Fornax, we train and evaluate a random
forest classifier as an explosion predictor. Furthermore, we examine
physics-based feature sets including the compactness parameter, the Ertl
condition, and a newly developed set that characterizes the silicon/oxygen
interface. With over 1500 supernovae progenitors from 9$-$27 M$_{\odot}$, we
additionally train an auto-encoder to extract physics-agnostic features
directly from the progenitor density profiles. We find that the density
profiles alone contain meaningful information regarding their explodability.
Both the silicon/oxygen and auto-encoder features predict explosion outcome
with $\approx$90\% accuracy. In anticipation of much larger multi-dimensional
simulation sets, we identify future directions in which machine learning
applications will be useful beyond explosion outcome prediction.

In this work we propose a stochastic primal-dual preconditioned
three-operator splitting algorithm for solving a class of convex
three-composite optimization problems. Our proposed scheme is a direct
three-operator splitting extension of the SPDHG algorithm [Chambolle et al.
2018]. We provide theoretical convergence analysis showing ergodic O(1/K)
convergence rate, and demonstrate the effectiveness of our approach in imaging
inverse problems.

Recent large-scale text-driven synthesis models have attracted much attention
thanks to their remarkable capabilities of generating highly diverse images
that follow given text prompts. Such text-based synthesis methods are
particularly appealing to humans who are used to verbally describe their
intent. Therefore, it is only natural to extend the text-driven image synthesis
to text-driven image editing. Editing is challenging for these generative
models, since an innate property of an editing technique is to preserve most of
the original image, while in the text-based models, even a small modification
of the text prompt often leads to a completely different outcome.
State-of-the-art methods mitigate this by requiring the users to provide a
spatial mask to localize the edit, hence, ignoring the original structure and
content within the masked region. In this paper, we pursue an intuitive
prompt-to-prompt editing framework, where the edits are controlled by text
only. To this end, we analyze a text-conditioned model in depth and observe
that the cross-attention layers are the key to controlling the relation between
the spatial layout of the image to each word in the prompt. With this
observation, we present several applications which monitor the image synthesis
by editing the textual prompt only. This includes localized editing by
replacing a word, global editing by adding a specification, and even delicately
controlling the extent to which a word is reflected in the image. We present
our results over diverse images and prompts, demonstrating high-quality
synthesis and fidelity to the edited prompts.

As deep neural networks (DNNs) revolutionize machine learning, energy
consumption and throughput are emerging as fundamental limitations of CMOS
electronics. This has motivated a search for new hardware architectures
optimized for artificial intelligence, such as electronic systolic arrays,
memristor crossbar arrays, and optical accelerators. Optical systems can
perform linear matrix operations at exceptionally high rate and efficiency,
motivating recent demonstrations of low latency linear algebra and optical
energy consumption below a photon per multiply-accumulate operation. However,
demonstrating systems that co-integrate both linear and nonlinear processing
units in a single chip remains a central challenge. Here we introduce such a
system in a scalable photonic integrated circuit (PIC), enabled by several key
advances: (i) high-bandwidth and low-power programmable nonlinear optical
function units (NOFUs); (ii) coherent matrix multiplication units (CMXUs); and
(iii) in situ training with optical acceleration. We experimentally demonstrate
this fully-integrated coherent optical neural network (FICONN) architecture for
a 3-layer DNN comprising 12 NOFUs and three CMXUs operating in the telecom
C-band. Using in situ training on a vowel classification task, the FICONN
achieves 92.7% accuracy on a test set, which is identical to the accuracy
obtained on a digital computer with the same number of weights. This work lends
experimental evidence to theoretical proposals for in situ training, unlocking
orders of magnitude improvements in the throughput of training data. Moreover,
the FICONN opens the path to inference at nanosecond latency and femtojoule per
operation energy efficiency.

Text-to-image models offer unprecedented freedom to guide creation through
natural language. Yet, it is unclear how such freedom can be exercised to
generate images of specific unique concepts, modify their appearance, or
compose them in new roles and novel scenes. In other words, we ask: how can we
use language-guided models to turn our cat into a painting, or imagine a new
product based on our favorite toy? Here we present a simple approach that
allows such creative freedom. Using only 3-5 images of a user-provided concept,
like an object or a style, we learn to represent it through new "words" in the
embedding space of a frozen text-to-image model. These "words" can be composed
into natural language sentences, guiding personalized creation in an intuitive
way. Notably, we find evidence that a single word embedding is sufficient for
capturing unique and varied concepts. We compare our approach to a wide range
of baselines, and demonstrate that it can more faithfully portray the concepts
across a range of applications and tasks.
  Our code, data and new words will be available at:
https://textual-inversion.github.io

The availability of large and deep electronic healthcare records (EHR)
datasets has the potential to enable a better understanding of real-world
patient journeys, and to identify novel subgroups of patients. ML-based
aggregation of EHR data is mostly tool-driven, i.e., building on available or
newly developed methods. However, these methods, their input requirements, and,
importantly, resulting output are frequently difficult to interpret, especially
without in-depth data science or statistical training. This endangers the final
step of analysis where an actionable and clinically meaningful interpretation
is needed.This study investigates approaches to perform patient stratification
analysis at scale using large EHR datasets and multiple clustering methods for
clinical research. We have developed several tools to facilitate the clinical
evaluation and interpretation of unsupervised patient stratification results,
namely pattern screening, meta clustering, surrogate modeling, and curation.
These tools can be used at different stages within the analysis. As compared to
a standard analysis approach, we demonstrate the ability to condense results
and optimize analysis time. In the case of meta clustering, we demonstrate that
the number of patient clusters can be reduced from 72 to 3 in one example. In
another stratification result, by using surrogate models, we could quickly
identify that heart failure patients were stratified if blood sodium
measurements were available. As this is a routine measurement performed for all
patients with heart failure, this indicated a data bias. By using further
cohort and feature curation, these patients and other irrelevant features could
be removed to increase the clinical meaningfulness. These examples show the
effectiveness of the proposed methods and we hope to encourage further research

Robot skills systems are meant to reduce robot setup time for new
manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often
difficult to find the right skill parameters. One strategy is to learn these
parameters by allowing the robot system to learn directly on the task. For a
learning problem, a robot operator can typically specify the type and range of
values of the parameters. Nevertheless, given their prior experience, robot
operators should be able to help the learning process further by providing
educated guesses about where in the parameter space potential optimal solutions
could be found. Interestingly, such prior knowledge is not exploited in current
robot learning frameworks. We introduce an approach that combines user priors
and Bayesian optimization to allow fast optimization of robot industrial tasks
at robot deployment time. We evaluate our method on three tasks that are
learned in simulation as well as on two tasks that are learned directly on a
real robot system. Additionally, we transfer knowledge from the corresponding
simulation tasks by automatically constructing priors from well-performing
configurations for learning on the real system. To handle potentially
contradicting task objectives, the tasks are modeled as multi-objective
problems. Our results show that operator priors, both user-specified and
transferred, vastly accelerate the discovery of rich Pareto fronts, and
typically produce final performance far superior to proposed baselines.

In this work, we evaluate how neural networks with periodic activation
functions can be leveraged to reliably compress large multidimensional medical
image datasets, with proof-of-concept application to 4D diffusion-weighted MRI
(dMRI). In the medical imaging landscape, multidimensional MRI is a key area of
research for developing biomarkers that are both sensitive and specific to the
underlying tissue microstructure. However, the high-dimensional nature of these
data poses a challenge in terms of both storage and sharing capabilities and
associated costs, requiring appropriate algorithms able to represent the
information in a low-dimensional space. Recent theoretical developments in deep
learning have shown how periodic activation functions are a powerful tool for
implicit neural representation of images and can be used for compression of 2D
images. Here we extend this approach to 4D images and show how any given 4D
dMRI dataset can be accurately represented through the parameters of a
sinusoidal activation network, achieving a data compression rate about 10 times
higher than the standard DEFLATE algorithm. Our results show that the proposed
approach outperforms benchmark ReLU and Tanh activation perceptron
architectures in terms of mean squared error, peak signal-to-noise ratio and
structural similarity index. Subsequent analyses using the tensor and spherical
harmonics representations demonstrate that the proposed lossy compression
reproduces accurately the characteristics of the original data, leading to
relative errors about 5 to 10 times lower than the benchmark JPEG2000 lossy
compression and similar to standard pre-processing steps such as MP-PCA
denosing, suggesting a loss of information within the currently accepted levels
for clinical application.

Recent years have witnessed an increased interest in recovering dynamical
laws of complex systems in a largely data-driven fashion under meaningful
hypotheses. In this work, we propose a method for scalably learning dynamical
laws of classical dynamical systems from data. As a novel ingredient, to
achieve an efficient scaling with the system size, block sparse tensor trains -
instances of tensor networks applied to function dictionaries - are used and
the self similarity of the problem is exploited. For the latter, we propose an
approach of gauge mediated weight sharing, inspired by notions of machine
learning, which significantly improves performance over previous approaches.
The practical performance of the method is demonstrated numerically on three
one-dimensional systems - the Fermi-Pasta-Ulam-Tsingou system, rotating
magnetic dipoles and classical particles interacting via modified Lennard-Jones
potentials. We highlight the ability of the method to recover these systems,
requiring 1400 samples to recover the 50 particle Fermi-Pasta-Ulam-Tsingou
system to residuum of $5\times10^{-7}$, 900 samples to recover the 50 particle
magnetic dipole chain to residuum of $1.5\times10^{-4}$ and 7000 samples to
recover the Lennard-Jones system of 10 particles to residuum
$1.5\times10^{-2}$. The robustness against additive Gaussian noise is
demonstrated for the magnetic dipole system.

Image cartoonization is recently dominated by generative adversarial networks
(GANs) from the perspective of unsupervised image-to-image translation, in
which an inherent challenge is to precisely capture and sufficiently transfer
characteristic cartoon styles (e.g., clear edges, smooth color shading,
abstract fine structures, etc.). Existing advanced models try to enhance
cartoonization effect by learning to promote edges adversarially, introducing
style transfer loss, or learning to align style from multiple representation
space. This paper demonstrates that more distinct and vivid cartoonization
effect could be easily achieved with only basic adversarial loss. Observing
that cartoon style is more evident in cartoon-texture-salient local image
regions, we build a region-level adversarial learning branch in parallel with
the normal image-level one, which constrains adversarial learning on
cartoon-texture-salient local patches for better perceiving and transferring
cartoon texture features. To this end, a novel cartoon-texture-saliency-sampler
(CTSS) module is proposed to dynamically sample cartoon-texture-salient patches
from training data. With extensive experiments, we demonstrate that texture
saliency adaptive attention in adversarial learning, as a missing ingredient of
related methods in image cartoonization, is of significant importance in
facilitating and enhancing image cartoon stylization, especially for
high-resolution input pictures.

Similar to many Machine Learning models, both accuracy and speed of the
Cluster weighted models (CWMs) can be hampered by high-dimensional data,
leading to previous works on a parsimonious technique to reduce the effect of
"Curse of dimensionality" on mixture models. In this work, we review the
background study of the cluster weighted models (CWMs). We further show that
parsimonious technique is not sufficient for mixture models to thrive in the
presence of huge high-dimensional data. We discuss a heuristic for detecting
the hidden components by choosing the initial values of location parameters
using the default values in the "FlexCWM" R package. We introduce a
dimensionality reduction technique called T-distributed stochastic neighbor
embedding (TSNE) to enhance the parsimonious CWMs in high-dimensional space.
Originally, CWMs are suited for regression but for classification purposes, all
multi-class variables are transformed logarithmically with some noise. The
parameters of the model are obtained via expectation maximization algorithm.
The effectiveness of the discussed technique is demonstrated using real data
sets from different fields.

This work addresses meta-learning (ML) by considering deep networks with
stochastic local winner-takes-all (LWTA) activations. This type of network
units results in sparse representations from each model layer, as the units are
organized into blocks where only one unit generates a non-zero output. The main
operating principle of the introduced units rely on stochastic principles, as
the network performs posterior sampling over competing units to select the
winner. Therefore, the proposed networks are explicitly designed to extract
input data representations of sparse stochastic nature, as opposed to the
currently standard deterministic representation paradigm. Our approach produces
state-of-the-art predictive accuracy on few-shot image classification and
regression experiments, as well as reduced predictive error on an active
learning setting; these improvements come with an immensely reduced

Neural operators are a type of deep architecture that learns to solve (i.e.
learns the nonlinear solution operator of) partial differential equations
(PDEs). The current state of the art for these models does not provide explicit
uncertainty quantification. This is arguably even more of a problem for this
kind of tasks than elsewhere in machine learning, because the dynamical systems
typically described by PDEs often exhibit subtle, multiscale structure that
makes errors hard to spot by humans. In this work, we first provide a
mathematically detailed Bayesian formulation of the ''shallow'' (linear)
version of neural operators in the formalism of Gaussian processes. We then
extend this analytic treatment to general deep neural operators using
approximate methods from Bayesian deep learning. We extend previous results on
neural operators by providing them with uncertainty quantification. As a
result, our approach is able to identify cases, and provide structured
uncertainty estimates, where the neural operator fails to predict well.

Online streaming feature selection (OSFS), which conducts feature selection
in an online manner, plays an important role in dealing with high-dimensional
data. In many real applications such as intelligent healthcare platform,
streaming feature always has some missing data, which raises a crucial
challenge in conducting OSFS, i.e., how to establish the uncertain relationship
between sparse streaming features and labels. Unfortunately, existing OSFS
algorithms never consider such uncertain relationship. To fill this gap, we in
this paper propose an online sparse streaming feature selection with
uncertainty (OS2FSU) algorithm. OS2FSU consists of two main parts: 1) latent
factor analysis is utilized to pre-estimate the missing data in sparse
streaming features before con-ducting feature selection, and 2) fuzzy logic and
neighborhood rough set are employed to alleviate the uncertainty between
estimated streaming features and labels during conducting feature selection. In
the experiments, OS2FSU is compared with five state-of-the-art OSFS algorithms
on six real datasets. The results demonstrate that OS2FSU outperforms its
competitors when missing data are encountered in OSFS.

This technical report describes the SurreyAudioTeam22s submission for DCASE
2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task
has two rules, (a) the ASC framework should have maximum 128K parameters, and
(b) there should be a maximum of 30 millions multiply-accumulate operations
(MACs) per inference. In this report, we present low-complexity systems for ASC
that follow the rules intended for the task.

Recently, it has been observed that a transfer learning solution might be all
we need to solve many few-shot learning benchmarks -- thus raising important
questions about when and how meta-learning algorithms should be deployed. In
this paper, we seek to clarify these questions by 1. proposing a novel metric
-- the diversity coefficient -- to measure the diversity of tasks in a few-shot
learning benchmark and 2. by comparing Model-Agnostic Meta-Learning (MAML) and
transfer learning under fair conditions (same architecture, same optimizer, and
all models trained to convergence). Using the diversity coefficient, we show
that the popular MiniImageNet and CIFAR-FS few-shot learning benchmarks have
low diversity. This novel insight contextualizes claims that transfer learning
solutions are better than meta-learned solutions in the regime of low diversity
under a fair comparison. Specifically, we empirically find that a low diversity
coefficient correlates with a high similarity between transfer learning and
MAML learned solutions in terms of accuracy at meta-test time and
classification layer similarity (using feature based distance metrics like
SVCCA, PWCCA, CKA, and OPD). To further support our claim, we find this
meta-test accuracy holds even as the model size changes. Therefore, we conclude
that in the low diversity regime, MAML and transfer learning have equivalent
meta-test performance when both are compared fairly. We also hope our work
inspires more thoughtful constructions and quantitative evaluations of
meta-learning benchmarks in the future.

Causal relationships are commonly examined in manufacturing processes to
support faults investigations, perform interventions, and make strategic
decisions. Industry 4.0 has made available an increasing amount of data that
enable data-driven Causal Discovery (CD). Considering the growing number of
recently proposed CD methods, it is necessary to introduce strict benchmarking
procedures on publicly available datasets since they represent the foundation
for a fair comparison and validation of different methods. This work introduces
two novel public datasets for CD in continuous manufacturing processes. The
first dataset employs the well-known Tennessee Eastman simulator for fault
detection and process control. The second dataset is extracted from an
ultra-processed food manufacturing plant, and it includes a description of the
plant, as well as multiple ground truths. These datasets are used to propose a
benchmarking procedure based on different metrics and evaluated on a wide
selection of CD algorithms. This work allows testing CD methods in realistic
conditions enabling the selection of the most suitable method for specific
target applications. The datasets are available at the following link:
https://github.com/giovanniMen

In this paper we explore capabilities of spiking neural networks in solving
multi-task classification problems using the approach of single-tasking of
multiple tasks. We designed and implemented a multi-task spiking neural network
(MT-SNN) that can learn two or more classification tasks while performing one
task at a time. The task to perform is selected by modulating the firing
threshold of leaky integrate and fire neurons used in this work. The network is
implemented using Intel's Lava platform for the Loihi2 neuromorphic chip. Tests
are performed on dynamic multitask classification for NMNIST data. The results
show that MT-SNN effectively learns multiple tasks by modifying its dynamics,
namely, the spiking neurons' firing threshold.

The state-of-the-art in discriminative unsupervised surface anomaly detection
relies on external datasets for synthesizing anomaly-augmented training images.
Such approaches are prone to failure on near-in-distribution anomalies since
these are difficult to be synthesized realistically due to their similarity to
anomaly-free regions. We propose an architecture based on quantized feature
space representation with dual decoders, DSR, that avoids the image-level
anomaly synthesis requirement. Without making any assumptions about the visual
properties of anomalies, DSR generates the anomalies at the feature level by
sampling the learned quantized feature space, which allows a controlled
generation of near-in-distribution anomalies. DSR achieves state-of-the-art
results on the KSDD2 and MVTec anomaly detection datasets. The experiments on
the challenging real-world KSDD2 dataset show that DSR significantly
outperforms other unsupervised surface anomaly detection methods, improving the
previous top-performing methods by 10% AP in anomaly detection and 35% AP in
anomaly localization.

Mapping near-field pollutant concentration is essential to track accidental
toxic plume dispersion in urban areas. By solving a large part of the
turbulence spectrum, large-eddy simulations (LES) have the potential to
accurately represent pollutant concentration spatial variability. Finding a way
to synthesize this large amount of information to improve the accuracy of
lower-fidelity operational models (e.g. providing better turbulence closure
terms) is particularly appealing. This is a challenge in multi-query contexts,
where LES become prohibitively costly to deploy to understand how plume flow
and tracer dispersion change with various atmospheric and source parameters. To
overcome this issue, we propose a non-intrusive reduced-order model combining
proper orthogonal decomposition (POD) and Gaussian process regression (GPR) to
predict LES field statistics of interest associated with tracer concentrations.
GPR hyperpararameters are optimized component-by-component through a maximum a
posteriori (MAP) procedure informed by POD. We provide a detailed analysis of
the reducedorder model performance on a two-dimensional case study
corresponding to a turbulent atmospheric boundary-layer flow over a
surface-mounted obstacle. We show that near-source concentration
heterogeneities upstream of the obstacle require a large number of POD modes to
be well captured. We also show that the component-by-component optimization
allows to capture the range of spatial scales in the POD modes, especially the
shorter concentration patterns in the high-order modes. The reduced-order model
predictions remain acceptable if the learning database is made of at least
fifty to hundred LES snapshot providing a first estimation of the required
budget to move towards more realistic atmospheric dispersion applications.

We tackle a new emerging problem, which is finding an optimal monopartite
matching in a weighted graph. The semi-bandit version, where a full matching is
sampled at each iteration, has been addressed by \cite{ADMA}, creating an
algorithm with an expected regret matching $O(\frac{L\log(L)}{\Delta}\log(T))$
with $2L$ players, $T$ iterations and a minimum reward gap $\Delta$. We reduce
this bound in two steps. First, as in \cite{GRAB} and \cite{UniRank} we use the
unimodality property of the expected reward on the appropriate graph to design
an algorithm with a regret in $O(L\frac{1}{\Delta}\log(T))$. Secondly, we show
that by moving the focus towards the main question `\emph{Is user $i$ better
than user $j$?}' this regret becomes
$O(L\frac{\Delta}{\tilde{\Delta}^2}\log(T))$, where $\Tilde{\Delta} > \Delta$
derives from a better way of comparing users. Some experimental results finally
show these theoretical results are corroborated in practice.

The benefit of locality is one of the major premises of LIME, one of the most
prominent methods to explain black-box machine learning models. This emphasis
relies on the postulate that the more locally we look at the vicinity of an
instance, the simpler the black-box model becomes, and the more accurately we
can mimic it with a linear surrogate. As logical as this seems, our findings
suggest that, with the current design of LIME, the surrogate model may
degenerate when the explanation is too local, namely, when the bandwidth
parameter $\sigma$ tends to zero. Based on this observation, the contribution
of this paper is twofold. Firstly, we study the impact of both the bandwidth
and the training vicinity on the fidelity and semantics of LIME explanations.
Secondly, and based on our findings, we propose \slime, an extension of LIME
that reconciles fidelity and locality.

Recent deep learning (DL) applications are mostly built on top of DL
libraries. The quality assurance of these libraries is critical to the
dependable deployment of DL applications. A few techniques have thereby been
proposed to test DL libraries by generating DL models as test inputs. Then
these techniques feed those DL models to DL libraries for making inferences, in
order to exercise DL libraries modules related to a DL model's execution.
However, the test effectiveness of these techniques is constrained by the
diversity of generated DL models. Our investigation finds that these techniques
can cover at most 11.7% of layer pairs (i.e., call sequence between two layer
APIs) and 55.8% of layer parameters (e.g., "padding" in Conv2D). As a result,
we find that many bugs arising from specific layer pairs and parameters can be
missed by existing techniques.
  In view of the limitations of existing DL library testing techniques, we
propose MEMO to efficiently generate diverse DL models by exploring layer
types, layer pairs, and layer parameters. MEMO: (1) designs an initial model
reduction technique to boost test efficiency without compromising model
diversity; and (2) designs a set of mutation operators for a customized Markov
Chain Monte Carlo (MCMC) algorithm to explore new layer types, layer pairs, and
layer parameters. We evaluate MEMO on seven popular DL libraries, including
four for model execution (TensorFlow, PyTorch and MXNet, and ONNX) and three
for model conversions (Keras-MXNet, TF2ONNX, ONNX2PyTorch). The evaluation
result shows that MEMO outperforms recent works by covering 10.3% more layer
pairs, 15.3% more layer parameters, and 2.3% library branches. Moreover, MEMO
detects 29 new bugs in the latest version of DL libraries, with 17 of them
confirmed by DL library developers, and 5 of those confirmed bugs have been
fixed.

In recent years, blockchain has been growing rapidly from a niche technology
to a promising solution for many sectors, due to its unique properties that
empower the design of innovative applications. Nevertheless, the development of
blockchain applications is still a challenge. Due to the technological novelty,
only a few developers are familiar with blockchain technologies and smart
contracts. Others might face a steep learning curve or difficulties to reuse
existing code to build blockchain applications. This study proposes a novel
approach to tackle these issues, through software product line engineering. To
support the approach, a web platform to configure and generate a blockchain
application for on-chain traceability is introduced. First, a feature model has
been designed to model core features of the chosen domain, based on the
existing literature. Then, a configurator has been implemented to support the
feature selection phase. Finally, a generator is able to ingest such
configurations to generate on-the-shelf blockchain products. The
generalizability of the contribution is validated by reproducing on-chain
traceability applications proposed in the literature by using the platform.
This work provides the first evidence that the implementation of blockchain
applications using software product lines enhances the quality of produced
applications and reduces the time to market.

Convolutional neural networks (CNN) have enabled major advances in image
classification through convolution and pooling. In particular, image pooling
transforms a connected discrete grid into a reduced grid with the same
connectivity and allows reduction functions to take into account all the pixels
of an image. However, a pooling satisfying such properties does not exist for
graphs. Indeed, some methods are based on a vertex selection step which induces
an important loss of information. Other methods learn a fuzzy clustering of
vertex sets which induces almost complete reduced graphs. We propose to
overcome both problems using a new pooling method, named MIVSPool. This method
is based on a selection of vertices called surviving vertices using a Maximal
Independent Vertex Set (MIVS) and an assignment of the remaining vertices to
the survivors. Consequently, our method does not discard any vertex information
nor artificially increase the density of the graph. Experimental results show
an increase in accuracy for graph classification on various standard datasets.

Boosting the runtime performance of deep neural networks (DNNs) is critical
due to their wide adoption in real-world tasks. Existing approaches to
optimizing the tensor algebra expression of a DNN only consider expressions
representable by a fixed set of predefined operators, missing possible
optimization opportunities between general expressions. We propose OLLIE, the
first derivation-based tensor program optimizer. OLLIE optimizes tensor
programs by leveraging transformations between general tensor algebra
expressions, enabling a significantly larger expression search space that
includes those supported by prior work as special cases. OLLIE uses a hybrid
derivation-based optimizer that effectively combines explorative and guided
derivations to quickly discover highly optimized expressions. Evaluation on
seven DNNs shows that OLLIE can outperform existing optimizers by up to
2.73$\times$ (1.46$\times$ on average) on an A100 GPU and up to 2.68$\times$
(1.51$\times$) on a V100 GPU, respectively.

This paper presents an open and comprehensive framework to systematically
evaluate state-of-the-art contributions to self-supervised monocular depth
estimation. This includes pretraining, backbone, architectural design choices
and loss functions. Many papers in this field claim novelty in either
architecture design or loss formulation. However, simply updating the backbone
of historical systems results in relative improvements of 25%, allowing them to
outperform the majority of existing systems. A systematic evaluation of papers
in this field was not straightforward. The need to compare like-with-like in
previous papers means that longstanding errors in the evaluation protocol are
ubiquitous in the field. It is likely that many papers were not only optimized
for particular datasets, but also for errors in the data and evaluation
criteria. To aid future research in this area, we release a modular codebase,
allowing for easy evaluation of alternate design decisions against corrected
data and evaluation criteria. We re-implement, validate and re-evaluate 16
state-of-the-art contributions and introduce a new dataset (SYNS-Patches)
containing dense outdoor depth maps in a variety of both natural and urban
scenes. This allows for the computation of informative metrics in complex
regions such as depth boundaries.

The automatic segmentation of blood vessels in fundus images can help analyze
the condition of retinal vasculature, which is crucial for identifying various
systemic diseases like hypertension, diabetes, etc. Despite the success of Deep
Learning-based models in this segmentation task, most of them are heavily
parametrized and thus have limited use in practical applications. This paper
proposes IterMiUnet, a new lightweight convolution-based segmentation model
that requires significantly fewer parameters and yet delivers performance
similar to existing models. The model makes use of the excellent segmentation
capabilities of Iternet architecture but overcomes its heavily parametrized
nature by incorporating the encoder-decoder structure of MiUnet model within
it. Thus, the new model reduces parameters without any compromise with the
network's depth, which is necessary to learn abstract hierarchical concepts in
deep models. This lightweight segmentation model speeds up training and
inference time and is potentially helpful in the medical domain where data is
scarce and, therefore, heavily parametrized models tend to overfit. The
proposed model was evaluated on three publicly available datasets: DRIVE,
STARE, and CHASE-DB1. Further cross-training and inter-rater variability
evaluations have also been performed. The proposed model has a lot of potential
to be utilized as a tool for the early diagnosis of many diseases.

Chaotic systems which are due to nonlinearity have attracted a great concern
in the current world and chaotic models. Systems for a wide range of operation
conditions have their application in almost all branches of engineering and
science. In the history of chaotic studies and nonlinearity, many different but
co-existent phases can be distinguished [1]. In the initial phase, chaos was
considered as a deterministic regime which, most probably, was responsible for
the variations that was regarded as noise and thus was being modeled as a
stochastic process. In the second phase, it was of great significance to
establish criteria for detecting chaotic dynamics and thus establishing
dynamical invariants which were necessary in quantifying chaos. The third step
which was to develop machine learning models which could learn the dynamics and
chaos from the data of the strange attractor [2]. With respect to this aspect,
various model structures were developed and investigated on their ability to
detect chaos from a given set of data [3]. These model structures included
radial basis functions, and local linear mapping among others. The third phase
is currently being investigated together with other issues surrounding
nonlinear dynamics and chaos, for instance in noise reduction and control,
among other issues.

Dynamic discrete choice models are widely employed to answer substantive and
policy questions in settings where individuals' current choices have future
implications. However, estimation of these models is often computationally
intensive and/or infeasible in high-dimensional settings. Indeed, even
specifying the structure for how the utilities/state transitions enter the
agent's decision is challenging in high-dimensional settings when we have no
guiding theory. In this paper, we present a semi-parametric formulation of
dynamic discrete choice models that incorporates a high-dimensional set of
state variables, in addition to the standard variables used in a parametric
utility function. The high-dimensional variable can include all the variables
that are not the main variables of interest but may potentially affect people's
choices and must be included in the estimation procedure, i.e., control
variables. We present a data-driven recursive partitioning algorithm that
reduces the dimensionality of the high-dimensional state space by taking the
variation in choices and state transition into account. Researchers can then
use the method of their choice to estimate the problem using the discretized
state space from the first stage. Our approach can reduce the estimation bias
and make estimation feasible at the same time. We present Monte Carlo
simulations to demonstrate the performance of our method compared to standard
estimation methods where we ignore the high-dimensional explanatory variable
set.

Rapid discovery of new reactions and molecules in recent years has been
facilitated by the advancements in high throughput screening, accessibility to
a much more complex chemical design space, and the development of accurate
molecular modeling frameworks. A holistic study of the growing chemistry
literature is, therefore, required that focuses on understanding the recent
trends and extrapolating them into possible future trajectories. To this end,
several network theory-based studies have been reported that use a directed
graph representation of chemical reactions. Here, we perform a study based on
representing chemical reactions as hypergraphs where the hyperedges represent
chemical reactions and nodes represent the participating molecules. We use a
standard reactions dataset to construct a hypernetwork and report its
statistics such as degree distributions, average path length, assortativity or
degree correlations, PageRank centrality, and graph-based clusters (or
communities). We also compute each statistic for an equivalent directed graph
representation of reactions to draw parallels and highlight differences between
the two. To demonstrate the AI applicability of hypergraph reaction
representation, we generate dense hypergraph embeddings and use them in the
reaction classification problem. We conclude that the hypernetwork
representation is flexible, preserves reaction context, and uncovers hidden
insights that are otherwise not apparent in a traditional directed graph
representation of chemical reactions.

Native language identification (NLI) is the task of training (via supervised
machine learning) a classifier that guesses the native language of the author
of a text. This task has been extensively researched in the last decade, and
the performance of NLI systems has steadily improved over the years. We focus
on a different facet of the NLI task, i.e., that of analysing the internals of
an NLI classifier trained by an \emph{explainable} machine learning algorithm,
in order to obtain explanations of its classification decisions, with the
ultimate goal of gaining insight into which linguistic phenomena ``give a
speaker's native language away''. We use this perspective in order to tackle
both NLI and a (much less researched) companion task, i.e., guessing whether a
text has been written by a native or a non-native speaker. Using three datasets
of different provenance (two datasets of English learners' essays and a dataset
of social media posts), we investigate which kind of linguistic traits
(lexical, morphological, syntactic, and statistical) are most effective for
solving our two tasks, namely, are most indicative of a speaker's L1. We also
present two case studies, one on Spanish and one on Italian learners of
English, in which we analyse individual linguistic traits that the classifiers
have singled out as most important for spotting these L1s. Overall, our study
shows that the use of explainable machine learning can be a valuable tool for
th

High-fidelity simulation of complex physical systems is exorbitantly
expensive and inaccessible across spatiotemporal scales. Recently, there has
been an increasing interest in leveraging deep learning to augment scientific
data based on the coarse-grained simulations, which is of cheap computational
expense and retains satisfactory solution accuracy. However, the major existing
work focuses on data-driven approaches which rely on rich training datasets and
lack sufficient physical constraints. To this end, we propose a novel and
efficient spatiotemporal super-resolution framework via physics-informed
learning, inspired by the independence between temporal and spatial derivatives
in partial differential equations (PDEs). The general principle is to leverage
the temporal interpolation for flow estimation, and then introduce
convolutional-recurrent neural networks for learning temporal refinement.
Furthermore, we employ the stacked residual blocks with wide activation and
sub-pixel layers with pixelshuffle for spatial reconstruction, where feature
extraction is conducted in a low-resolution latent space. Moreover, we consider
hard imposition of boundary conditions in the network to improve reconstruction
accuracy. Results demonstrate the superior effectiveness and efficiency of the
proposed method compared with baseline algorithms through extensive numerical

Accurately quantifying swelling of alloys that have undergone irradiation is
essential for understanding alloy performance in a nuclear reactor and critical
for the safe and reliable operation of reactor facilities. However, typical
practice is for radiation-induced defects in electron microscopy images of
alloys to be manually quantified by domain-expert researchers. Here, we employ
an end-to-end deep learning approach using the Mask Regional Convolutional
Neural Network (Mask R-CNN) model to detect and quantify nanoscale cavities in
irradiated alloys. We have assembled the largest database of labeled cavity
images to date, which includes 400 images, >34k discrete cavities, and numerous
alloy compositions and irradiation conditions. We have evaluated both
statistical (precision, recall, and F1 scores) and materials property-centric
(cavity size, density, and swelling) metrics of model performance, and
performed in-depth analysis of materials swelling assessments. We find our
model gives assessments of material swelling with an average (standard
deviation) swelling mean absolute error based on random leave-out
cross-validation of 0.30 (0.03) percent swelling. This result demonstrates our
approach can accurately provide swelling metrics on a per-image and
per-condition basis, which can provide helpful insight into material design
(e.g., alloy refinement) and impact of service conditions (e.g., temperature,
irradiation dose) on swelling. Finally, we find there are cases of test images
with poor statistical metrics, but small errors in swelling, pointing to the
need for moving beyond traditional classification-based metrics to evaluate
object detection models in the context of materials domain applications.

Method comparisons are essential to provide recommendations and guidance for
applied researchers, who often have to choose from a plethora of available
approaches. While many comparisons exist in the literature, these are often not
neutral but favour a novel method. Apart from the choice of design and a proper
reporting of the findings, there are different approaches concerning the
underlying data for such method comparison studies. Most manuscripts on
statistical methodology rely on simulation studies and provide a single
real-world data set as an example to motivate and illustrate the methodology
investigated. In the context of supervised learning, in contrast, methods are
often evaluated using so-called benchmarking data sets, i.e. real-world data
that serve as gold standard in the community. Simulation studies, on the other
hand, are much less common in this context. The aim of this paper is to
investigate differences and similarities between these approaches, to discuss
their advantages and disadvantages and ultimately to develop new approaches to
the evaluation of methods picking the best of both worlds. To this aim, we
borrow ideas from different contexts such as mixed methods research and
Clinical Scenario Evaluation.

In a recent article, Guo et al. [arXiv:2206.11228] report that adversarially
trained neural representations in deep networks may already be as robust as
corresponding primate IT neural representations. While we find the paper's
primary experiment illuminating, we have doubts about the interpretation and
phrasing of the results presented in the paper.

Autonomous robots in endovascular operations have the potential to navigate
circulatory systems safely and reliably while decreasing the susceptibility to
human errors. However, there are numerous challenges involved with the process
of training such robots such as long training duration due to sample
inefficiency of machine learning algorithms and safety issues arising from the
interaction between the catheter and the endovascular phantom. Physics
simulators have been used in the context of endovascular procedures, but they
are typically employed for staff training and generally do not conform to the
autonomous cannulation goal. Furthermore, most current simulators are
closed-source which hinders the collaborative development of safe and reliable
autonomous systems. In this work, we introduce CathSim, an open-source
simulation environment that accelerates the development of machine learning
algorithms for autonomous endovascular navigation. We first simulate the
high-fidelity catheter and aorta with the state-of-the-art endovascular robot.
We then provide the capability of real-time force sensing between the catheter
and the aorta in the simulation environment. We validate our simulator by
conducting two different catheterisation tasks within two primary arteries
using two popular reinforcement learning algorithms, Proximal Policy
Optimization (PPO) and Soft Actor-Critic (SAC). The experimental results show
that using our open-source simulator, we can successfully train the
reinforcement learning agents to perform different autonomous cannulation
tasks.

The continuous growth of data production in almost all scientific areas
raises new problems in data access and management, especially in a scenario
where the end-users, as well as the resources that they can access, are
worldwide distributed. This work is focused on the data caching management in a
Data Lake infrastructure in the context of the High Energy Physics field. We
are proposing an autonomous method, based on Reinforcement Learning techniques,
to improve the user experience and to contain the maintenance costs of the

In this work, we demonstrate that multilingual large-scale
sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising
and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners
than decoder-only models on various tasks. In particular, we train a 20 billion
parameter multilingual seq2seq model called Alexa Teacher Model (AlexaTM 20B)
and show that it achieves state-of-the-art (SOTA) performance on 1-shot
summarization tasks, outperforming a much larger 540B PaLM decoder model.
AlexaTM 20B also achieves SOTA in 1-shot machine translation, especially for
low-resource languages, across almost all language pairs supported by the model
(Arabic, English, French, German, Hindi, Italian, Japanese, Marathi,
Portuguese, Spanish, Tamil, and Telugu) on Flores-101 dataset. We also show in
zero-shot setting, AlexaTM 20B outperforms GPT3 (175B) on SuperGLUE and SQuADv2
datasets and provides SOTA performance on multilingual tasks such as XNLI,
XCOPA, Paws-X, and XWinograd. Overall, our results present a compelling case
for seq2seq models as a powerful alternative to decoder-only models for
Large-scale Language Model (LLM) training.

Viscosity in the metallurgical and glass industry plays a fundamental role in
its production processes, also in the area of geophysics. As its experimental
measurement is financially expensive, also in terms of time, several
mathematical models were built to provide viscosity results as a function of
several variables, such as chemical composition and temperature, in linear and
nonlinear models. A database was built in order to produce a nonlinear model by
artificial neural networks by variation of hyperparameters to provide reliable
predictions of viscosity in relation to chemical systems and temperatures. The
model produced named Viskositas demonstrated better statistical evaluations of
mean absolute error, standard deviation and coefficient of determination in
relation to the test database when compared to different models from literature
and 1 commercial model, offering predictions with lower errors, less
variability and less generation of outliers.

Convolutional Neural Networks (CNN) increase depth by stacking convolutional
layers, and deeper network models perform better in image recognition.
Empirical research shows that simply stacking convolutional layers does not
make the network train better, and skip connection (residual learning) can
improve network model performance. For the image classification task, models
with global densely connected architectures perform well in large datasets like
ImageNet, but are not suitable for small datasets such as CIFAR-10 and SVHN.
Different from dense connections, we propose two new algorithms to connect
layers. Baseline is a densely connected network, and the networks connected by
the two new algorithms are named ShortNet1 and ShortNet2 respectively. The
experimental results of image classification on CIFAR-10 and SVHN show that
ShortNet1 has a 5% lower test error rate and 25% faster inference time than
Baseline. ShortNet2 speeds up inference time by 40% with less loss in test
accuracy. Code and pre-trained models are available at
https://github.com/RuiyangJu/Connection_Reduction.

Unlike 2D raster images, there is no single dominant representation for 3D
visual data processing. Different formats like point clouds, meshes, or
implicit functions each have their strengths and weaknesses. Still, grid
representations such as signed distance functions have attractive properties
also in 3D. In particular, they offer constant-time random access and are
eminently suitable for modern machine learning. Unfortunately, the storage size
of a grid grows exponentially with its dimension. Hence they often exceed
memory limits even at moderate resolution. This work proposes using low-rank
tensor formats, including the Tucker, tensor train, and quantics tensor train
decompositions, to compress time-varying 3D data. Our method iteratively
computes, voxelizes, and compresses each frame's truncated signed distance
function and applies tensor rank truncation to condense all frames into a
single, compressed tensor that represents the entire 4D scene. We show that
low-rank tensor compression is extremely compact to store and query
time-varying signed distance functions. It significantly reduces the memory
footprint of 4D scenes while remarkably preserving their geometric quality.
Unlike existing, iterative learning-based approaches like DeepSDF and NeRF, our
method uses a closed-form algorithm with theoretical guarantees.

Accurate and robust abdominal multi-organ segmentation from CT imaging of
different modalities is a challenging task due to complex inter- and
intra-organ shape and appearance variations among abdominal organs. In this
paper, we propose a probabilistic multi-organ segmentation network with
hierarchical spatial-wise feature modulation to capture flexible organ semantic
variants and inject the learnt variants into different scales of feature maps
for guiding segmentation. More specifically, we design an input decomposition
module via a conditional variational auto-encoder to learn organ-specific
distributions on the low dimensional latent space and model richer organ
semantic variations that is conditioned on input images.Then by integrating
these learned variations into the V-Net decoder hierarchically via spatial
feature transformation, which has the ability to convert the variations into
conditional Affine transformation parameters for spatial-wise feature maps
modulating and guiding the fine-scale segmentation. The proposed method is
trained on the publicly available AbdomenCT-1K dataset and evaluated on two
other open datasets, i.e., 100 challenging/pathological testing patient cases
from AbdomenCT-1K fully-supervised abdominal organ segmentation benchmark and
90 cases from TCIA+&BTCV dataset. Highly competitive or superior quantitative
segmentation results have been achieved using these datasets for four abdominal
organs of liver, kidney, spleen and pancreas with reported Dice scores improved
by 7.3% for kidneys and 9.7% for pancreas, while being ~7 times faster than two
strong baseline segmentation methods(nnUNet and CoTr).

Existing gait recognition methods either directly establish Global Feature
Representation (GFR) from original gait sequences or generate Local Feature
Representation (LFR) from several local parts. However, GFR tends to neglect
local details of human postures as the receptive fields become larger in the
deeper network layers. Although LFR allows the network to focus on the detailed
posture information of each local region, it neglects the relations among
different local parts and thus only exploits limited local information of
several specific regions. To solve these issues, we propose a global-local
based gait recognition network, named GaitGL, to generate more discriminative
feature representations. To be specific, a novel Global and Local Convolutional
Layer (GLCL) is developed to take full advantage of both global visual
information and local region details in each layer. GLCL is a dual-branch
structure that consists of a GFR extractor and a mask-based LFR extractor. GFR
extractor aims to extract contextual information, e.g., the relationship among
various body parts, and the mask-based LFR extractor is presented to exploit
the detailed posture changes of local regions. In addition, we introduce a
novel mask-based strategy to improve the local feature extraction capability.
Specifically, we design pairs of complementary masks to randomly occlude
feature maps, and then train our mask-based LFR extractor on various occluded
feature maps. In this manner, the LFR extractor will learn to fully exploit
local information. Extensive experiments demonstrate that GaitGL achieves
better performance than state-of-the-art gait recognition methods. The average
rank-1 accuracy on CASIA-B, OU-MVLP, GREW and Gait3D is 93.6%, 98.7%, 68.0% and
63.8%, respectively, significantly outperforming the competing methods. The
proposed method has won the first prize in two competitions: HID 2020 and HID
2021.

Leveraging labelled data from multiple domains to enable prediction in
another domain without labels is a significant, yet challenging problem. To
address this problem, we introduce the framework DAPDAG (\textbf{D}omain
\textbf{A}daptation via \textbf{P}erturbed \textbf{DAG} Reconstruction) and
propose to learn an auto-encoder that undertakes inference on population
statistics given features and reconstructing a directed acyclic graph (DAG) as
an auxiliary task. The underlying DAG structure is assumed invariant among
observed variables whose conditional distributions are allowed to vary across
domains led by a latent environmental variable $E$. The encoder is designed to
serve as an inference device on $E$ while the decoder reconstructs each
observed variable conditioned on its graphical parents in the DAG and the
inferred $E$. We train the encoder and decoder jointly in an end-to-end manner
and conduct experiments on synthetic and real datasets with mixed variables.
Empirical results demonstrate that reconstructing the DAG benefits the
approximate inference. Furthermore, our approach can achieve competitive
performance against other benchmarks in prediction tasks, with better
adaptation ability, especially in the target domain significantly different
from the source domains.

Domain generalization (DG) aims to learn a model on one or more different but
related source domains that could be generalized into an unseen target domain.
Existing DG methods try to prompt the diversity of source domains for the
model's generalization ability, while they may have to introduce auxiliary
networks or striking computational costs. On the contrary, this work applies
the implicit semantic augmentation in feature space to capture the diversity of
source domains. Concretely, an additional loss function of distance metric
learning (DML) is included to optimize the local geometry of data distribution.
Besides, the logits from cross entropy loss with infinite augmentations is
adopted as input features for the DML loss in lieu of the deep features. We
also provide a theoretical analysis to show that the logits can approximate the
distances defined on original features well. Further, we provide an in-depth
analysis of the mechanism and rational behind our approach, which gives us a
better understanding of why leverage logits in lieu of features can help domain
generalization. The proposed DML loss with the implicit augmentation is
incorporated into a recent DG method, that is, Fourier Augmented Co-Teacher
framework (FACT). Meanwhile, our method also can be easily plugged into various
DG methods. Extensive experiments on three benchmarks (Digits-DG, PACS and
Office-Home) have demonstrated that the proposed method is able to achieve the

The advent of machine learning models that surpass human decision-making
ability in complex domains has initiated a movement towards building AI systems
that interact with humans. Many building blocks are essential for this
activity, with a central one being the algorithmic characterization of human
behavior. While much of the existing work focuses on aggregate human behavior,
an important long-range goal is to develop behavioral models that specialize to
individual people and can differentiate among them.
  To formalize this process, we study the problem of behavioral stylometry, in
which the task is to identify a decision-maker from their decisions alone. We
present a transformer-based approach to behavioral stylometry in the context of
chess, where one attempts to identify the player who played a set of games. Our
method operates in a few-shot classification framework, and can correctly
identify a player from among thousands of candidate players with 98% accuracy
given only 100 labeled games. Even when trained on amateur play, our method
generalises to out-of-distribution samples of Grandmaster players, despite the
dramatic differences between amateur and world-class players. Finally, we
consider more broadly what our resulting embeddings reveal about human style in
chess, as well as the potential ethical implications of powerful methods for
identifying individuals from behavioral data.

Network-valued time series are currently a common form of network data.
However, the study of the aggregate behavior of network sequences generated
from network-valued stochastic processes is relatively rare. Most of the
existing research focuses on the simple setup where the networks are
independent (or conditionally independent) across time, and all edges are
updated synchronously at each time step. In this paper, we study the
concentration properties of the aggregated adjacency matrix and the
corresponding Laplacian matrix associated with network sequences generated from
lazy network-valued stochastic processes, where edges update asynchronously,
and each edge follows a lazy stochastic process for its updates independent of
the other edges. We demonstrate the usefulness of these concentration results
in proving consistency of standard estimators in community estimation and
changepoint estimation problems. We also conduct a simulation study to
demonstrate the effect of the laziness parameter, which controls the extent of
temporal correlation, on the accuracy of community and changepoint estimation.

This paper seeks to answer the following question: \textit{"What can we learn
by predicting accuracy?"}.
  Indeed, classification is one of the most popular tasks in machine learning,
and many loss functions have been developed to maximize this non-differentiable
objective function.
  Unlike past work on loss function design, which was guided mainly by
intuition and theory before being validated by experimentation, here we propose
to approach this problem in the opposite way: we seek to extract knowledge by
experimentation.
  This data-driven approach is similar to that used in physics to discover
general laws from data.
  We used a symbolic regression method to automatically find a mathematical
expression highly correlated with a linear classifier's accuracy.
  The formula discovered on more than 260 datasets of embeddings has a
Pearson's correlation of 0.96 and a $r^2$ of 0.93.
  More interestingly, this formula is highly explainable and confirms insights
from various previous papers on loss design.
  We hope this work will open new perspectives in the search for new heuristics
leading to a deeper understanding of machine learning theory.

Fault injection (FI) is a powerful attack methodology allowing an adversary
to entirely break the security of a target device. As finite-state machines
(FSMs) are fundamental hardware building blocks responsible for controlling
systems, inducing faults into these controllers enables an adversary to hijack
the execution of the integrated circuit. A common defense strategy mitigating
these attacks is to manually instantiate FSMs multiple times and detect faults
using a majority voting logic. However, as each additional FSM instance only
provides security against one additional induced fault, this approach scales
poorly in a multi-fault attack scenario.
  In this paper, we present SCFI: a strong, probabilistic FSM protection
mechanism ensuring that control-flow deviations from the intended control-flow
are detected even in the presence of multiple faults. At its core, SCFI
consists of a hardened next-state function absorbing the execution history as
well as the FSM's control signals to derive the next state. When either the
absorbed inputs, the state registers, or the function itself are affected by
faults, SCFI triggers an error with no detection latency. We integrate SCFI
into a synthesis tool capable of automatically hardening arbitrary unprotected
FSMs without user interaction and open-source the tool. Our evaluation shows
that SCFI provides strong protection guarantees with a better area-time product
than FSMs protected using classical redundancy-based approaches. Finally, we
formally verify the resilience of the protected state machines using a
pre-silicon fault analysis tool.

The rapid advancement of social networks and the convenience of internet
availability have accelerated the rampant spread of false news and rumors on
social media sites. Amid the COVID 19 epidemic, this misleading information has
aggravated the situation by putting peoples mental and physical lives in
danger. To limit the spread of such inaccuracies, identifying the fake news
from online platforms could be the first and foremost step. In this research,
the authors have conducted a comparative analysis by implementing five
transformer based models such as BERT, BERT without LSTM, ALBERT, RoBERTa, and
a Hybrid of BERT & ALBERT in order to detect the fraudulent news of COVID 19
from the internet. COVID 19 Fake News Dataset has been used for training and
testing the models. Among all these models, the RoBERTa model has performed
better than other models by obtaining an F1 score of 0.98 in both real and fake
classes.

Distributed artificial intelligence (AI) has recently accomplished tremendous
breakthroughs in various communication services, ranging from fault-tolerant
factory automation to smart cities. When distributed learning is run over a set
of wireless connected devices, random channel fluctuations, and the incumbent
services simultaneously running on the same network affect the performance of
distributed learning. In this paper, we investigate the interplay between
distributed AI workflow and ultra-reliable low latency communication (URLLC)
services running concurrently over a network. Using 3GPP compliant simulations
in a factory automation use case, we show the impact of various distributed AI
settings (e.g., model size and the number of participating devices) on the
convergence time of distributed AI and the application layer performance of
URLLC. Unless we leverage the existing 5G-NR quality of service handling
mechanisms to separate the traffic from the two services, our simulation
results show that the impact of distributed AI on the availability of the URLLC
devices is significant. Moreover, with proper setting of distributed AI (e.g.,
proper user selection), we can substantially reduce network resource
utilization, leading to lower latency for distributed AI and higher
availability for the URLLC users. Our results provide important insights for
future 6G and AI standardization.

Machine learning (ML) continues to permeate all layers of academia, industry
and society. Despite its successes, mental frameworks to capture and represent
machine learning workflows in a consistent and coherent manner are lacking. For
instance, the de facto process modeling standard, Business Process Model and
Notation (BPMN), managed by the Object Management Group, is widely accepted and
applied. However, it is short of specific support to represent machine learning
workflows. Further, the number of heterogeneous tools for deployment of machine
learning solutions can easily overwhelm practitioners. Research is needed to
align the process from modeling to deploying ML workflows.
  We analyze requirements for standard based conceptual modeling for machine
learning workflows and their serverless deployment. Confronting the
shortcomings with respect to consistent and coherent modeling of ML workflows
in a technology independent and interoperable manner, we extend BPMN's
Meta-Object Facility (MOF) metamodel and the corresponding notation and
introduce BPMN4sML (BPMN for serverless machine learning). Our extension
BPMN4sML follows the same outline referenced by the Object Management Group
(OMG) for BPMN. We further address the heterogeneity in deployment by proposing
a conceptual mapping to convert BPMN4sML models to corresponding deployment
models using TOSCA.
  BPMN4sML allows technology-independent and interoperable modeling of machine
learning workflows of various granularity and complexity across the entire
machine learning lifecycle. It aids in arriving at a shared and standardized
language to communicate ML solutions. Moreover, it takes the first steps toward
enabling conversion of ML workflow model diagrams to corresponding deployment
models for serverless deployment via TOSCA.

A proper fusion of complex data is of interest to many researchers in diverse
fields, including computational statistics, computational geometry,
bioinformatics, machine learning, pattern recognition, quality management,
engineering, statistics, finance, economics, etc. It plays a crucial role in:
synthetic description of data processes or whole domains, creation of rule
bases for approximate reasoning tasks, reaching consensus and selection of the
optimal strategy in decision support systems, imputation of missing values,
data deduplication and consolidation, record linkage across heterogeneous
databases, and clustering. This open-access research monograph integrates the
spread-out results from different domains using the methodology of the
well-established classical aggregation framework, introduces researchers and
practitioners to Aggregation 2.0, as well as points out the challenges and
interesting directions for further research.

In this paper, we develop a simple yet effective screening rule strategy to
improve the computational efficiency in solving structured optimization
involving nonconvex $\ell_{q,p}$ regularization. Based on an iteratively
reweighted $\ell_1$ (IRL1) framework, the proposed screening rule works like a
preprocessing module that potentially removes the inactive groups before
starting the subproblem solver, thereby reducing the computational time in
total. This is mainly achieved by heuristically exploiting the dual subproblem
information during each iteration.Moreover, we prove that our screening rule
can remove all inactive variables in a finite number of iterations of the IRL1
method. Numerical experiments illustrate the efficiency of our screening rule
strategy compared with several state-of-the-art algorithms.

Navigating off-road with a fast autonomous vehicle depends on a robust
perception system that differentiates traversable from non-traversable terrain.
Typically, this depends on a semantic understanding which is based on
supervised learning from images annotated by a human expert. This requires a
significant investment in human time, assumes correct expert classification,
and small details can lead to misclassification. To address these challenges,
we propose a method for predicting high- and low-risk terrains from only past
vehicle experience in a self-supervised fashion. First, we develop a tool that
projects the vehicle trajectory into the front camera image. Second, occlusions
in the 3D representation of the terrain are filtered out. Third, an autoencoder
trained on masked vehicle trajectory regions identifies low- and high-risk
terrains based on the reconstruction error. We evaluated our approach with two
models and different bottleneck sizes with two different training and testing
sites with a fourwheeled off-road vehicle. Comparison with two independent test
sets of semantic labels from similar terrain as training sites demonstrates the
ability to separate the ground as low-risk and the vegetation as high-risk with
81.1% and 85.1% accuracy.

Time-domain astronomy is entering a new era as wide-field surveys with higher
cadences allow for more discoveries than ever before. The field has seen an
increased use of machine learning and deep learning for automated
classification of transients into established taxonomies. Training such
classifiers requires a large enough and representative training set, which is
not guaranteed for new future surveys such as the Vera Rubin Observatory,
especially at the beginning of operations. We present the use of Gaussian
processes to create a uniform representation of supernova light curves from
multiple surveys, obtained through the Open Supernova Catalog for supervised
classification with convolutional neural networks. We also investigate the use
of transfer learning to classify light curves from the Photometric LSST
Astronomical Time Series Classification Challenge (PLAsTiCC) dataset. Using
convolutional neural networks to classify the Gaussian process generated
representation of supernova light curves from multiple surveys, we achieve an
AUC score of 0.859 for classification into Type Ia, Ibc, and II. We find that
transfer learning improves the classification accuracy for the most
under-represented classes by up to 18% when classifying PLAsTiCC light curves,
and is able to achieve an AUC score of 0.945 when including photometric
redshifts for classification into six classes (Ia, Iax, Ia-91bg, Ibc, II,
SLSN-I). We also investigate the usefulness of transfer learning when there is
a limited labelled training set to see how this approach can be used for
training classifiers in future surveys at the beginning of operations.

Accurate tooth volume segmentation is a prerequisite for computer-aided
dental analysis. Deep learning-based tooth segmentation methods have achieved
satisfying performances but require a large quantity of tooth data with ground
truth. The dental data publicly available is limited meaning the existing
methods can not be reproduced, evaluated and applied in clinical practice. In
this paper, we establish a 3D dental CBCT dataset CTooth+, with 22 fully
annotated volumes and 146 unlabeled volumes. We further evaluate several
state-of-the-art tooth volume segmentation strategies based on fully-supervised
learning, semi-supervised learning and active learning, and define the
performance principles. This work provides a new benchmark for the tooth volume
segmentation task, and the experiment can serve as the baseline for future
AI-based dental imaging research and clinical application development.

Electronic Health Records (EHRs) exhibit a high amount of missing data due to
variations of patient conditions and treatment needs. Imputation of missing
values has been considered an effective approach to deal with this challenge.
Existing work separates imputation method and prediction model as two
independent parts of an EHR-based machine learning system. We propose an
integrated end-to-end approach by utilizing a Compound Density Network (CDNet)
that allows the imputation method and prediction model to be tuned together
within a single framework. CDNet consists of a Gated recurrent unit (GRU), a
Mixture Density Network (MDN), and a Regularized Attention Network (RAN). The
GRU is used as a latent variable model to model EHR data. The MDN is designed
to sample latent variables generated by GRU. The RAN serves as a regularizer
for less reliable imputed values. The architecture of CDNet enables GRU and MDN
to iteratively leverage the output of each other to impute missing values,
leading to a more accurate and robust prediction. We validate CDNet on the
mortality prediction task on the MIMIC-III dataset. Our model outperforms
state-of-the-art models by significant margins. We also empirically show that
regularizing imputed values is a key factor for superior prediction
performance. Analysis of prediction uncertainty shows that our model can
capture both aleatoric and epistemic uncertainties, which offers model users a
better understanding of the model results.

Causal relation extraction of biomedical entities is one of the most complex
tasks in biomedical text mining, which involves two kinds of information:
entity relations and entity functions. One feasible approach is to take
relation extraction and function detection as two independent sub-tasks.
However, this separate learning method ignores the intrinsic correlation
between them and leads to unsatisfactory performance. In this paper, we propose
a joint learning model, which combines entity relation extraction and entity
function detection to exploit their commonality and capture their
inter-relationship, so as to improve the performance of biomedical causal
relation extraction. Meanwhile, during the model training stage, different
function types in the loss function are assigned different weights.
Specifically, the penalty coefficient for negative function instances increases
to effectively improve the precision of function detection. Experimental
results on the BioCreative-V Track 4 corpus show that our joint learning model
outperforms the separate models in BEL statement extraction, achieving the F1
scores of 58.4% and 37.3% on the test set in Stage 2 and Stage 1 evaluations,
respectively. This demonstrates that our joint learning system reaches the
state-of-the-art performance in Stage 2 compared with other systems.

The combination of transfer learning (TL) a low level potential energy
surface (PES) to a higher level of electronic structure theory together with
ring-polymer instanton (RPI) theory is explored and applied to malonaldehyde.
The RPI approach provides a semiclassical approximation of the tunneling
splitting and depends sensitively on the accuracy of the PES. With second order
M{\o}ller-Plesset perturbation theory (MP2) as the low-level (LL) model and
energies and forces from coupled cluster singles, doubles and perturbative
triples (CCSD(T)) as the high-level (HL) model, it is demonstrated that CCSD(T)
information from only 25 to 50 judiciously selected structures along and around
the instanton path suffice to reach HL-accuracy for the tunneling splitting. In
addition, the global quality of the HL-PES is demonstrated through a mean
average error of 0.3 kcal/mol for energies up to 40 kcal/mol above the minimum
energy structure (a factor of 2 higher than the energies employed during TL)
and $< 2 $ cm$^{-1}$ for harmonic frequencies compared with computationally
challenging normal mode calculations at the CCSD(T) level.

PCL detection task is aimed at identifying and categorizing language that is
patronizing or condescending towards vulnerable communities in the general
media.Compared to other NLP tasks of paragraph classification, the negative
language presented in the PCL detection task is usually more implicit and
subtle to be recognized, making the performance of common text-classification
approaches disappointed. Targeting the PCL detection problem in SemEval-2022
Task 4, in this paper, we give an introduction to our team's solution, which
exploits the power of prompt-based learning on paragraph classification. We
reformulate the task as an appropriate cloze prompt and use pre-trained Masked
Language Models to fill the cloze slot. For the two subtasks, binary
classification and multi-label classification, DeBERTa model is adopted and
fine-tuned to predict masked label words of task-specific prompts. On the
evaluation dataset, for binary classification, our approach achieves an
F1-score of 0.6406; for multi-label classification, our approach achieves an
macro-F1-score of 0.4689 and ranks first in the leaderboard.

It is curious that AI increasingly outperforms human decision makers, yet
much of the public distrusts AI to make decisions affecting their lives. In
this paper we explore a novel theory that may explain one reason for this. We
propose that public distrust of AI is a moral consequence of designing systems
that prioritize reduction of costs of false positives over less tangible costs
of false negatives. We show that such systems, which we characterize as
'distrustful', are more likely to miscategorize trustworthy individuals, with
cascading consequences to both those individuals and the overall human-AI trust
relationship. Ultimately, we argue that public distrust of AI stems from
well-founded concern about the potential of being miscategorized. We propose
that restoring public trust in AI will require that systems are designed to
embody a stance of 'humble trust', whereby the moral costs of the misplaced
distrust associated with false negatives is weighted appropriately during
development and use.

Previous works on human motion prediction follow the pattern of building a
mapping relation between the sequence observed and the one to be predicted.
However, due to the inherent complexity of multivariate time series data, it
still remains a challenge to find the extrapolation relation between motion
sequences. In this paper, we present a new prediction pattern, which introduces
previously overlooked human poses, to implement the prediction task from the
view of interpolation. These poses exist after the predicted sequence, and form
the privileged sequence. To be specific, we first propose an InTerPolation
learning Network (ITP-Network) that encodes both the observed sequence and the
privileged sequence to interpolate the in-between predicted sequence, wherein
the embedded Privileged-sequence-Encoder (Priv-Encoder) learns the privileged
knowledge (PK) simultaneously. Then, we propose a Final Prediction Network
(FP-Network) for which the privileged sequence is not observable, but is
equipped with a novel PK-Simulator that distills PK learned from the previous
network. This simulator takes as input the observed sequence, but approximates
the behavior of Priv-Encoder, enabling FP-Network to imitate the interpolation
process. Extensive experimental results demonstrate that our prediction pattern
achieves state-of-the-art performance on benchmarked H3.6M, CMU-Mocap and 3DPW
datasets in both short-term and long-term predictions.

Machine Reading Comprehension with Unanswerable Questions is a difficult NLP
task, challenged by the questions which can not be answered from passages. It
is observed that subtle literal changes often make an answerable question
unanswerable, however, most MRC models fail to recognize such changes. To
address this problem, in this paper, we propose a span-based method of
Contrastive Learning (spanCL) which explicitly contrast answerable questions
with their answerable and unanswerable counterparts at the answer span level.
With spanCL, MRC models are forced to perceive crucial semantic changes from
slight literal differences. Experiments on SQuAD 2.0 dataset show that spanCL
can improve baselines significantly, yielding 0.86-2.14 absolute EM
improvements. Additional experiments also show that spanCL is an effective way
to utilize generated questions.

Recently, several studies have claimed that using class-specific feature
subsets provides certain advantages over using a single feature subset for
representing the data for a classification problem. Unlike traditional feature
selection methods, the class-specific feature selection methods select an
optimal feature subset for each class. Typically class-specific feature
selection (CSFS) methods use one-versus-all split of the data set that leads to
issues such as class imbalance, decision aggregation, and high computational
overhead. We propose a class-specific feature selection method embedded in a
fuzzy rule-based classifier, which is free from the drawbacks associated with
most existing class-specific methods. Additionally, our method can be adapted
to control the level of redundancy in the class-specific feature subsets by
adding a suitable regularizer to the learning objective. Our method results in
class-specific rules involving class-specific subsets. We also propose an
extension where different rules of a particular class are defined by different
feature subsets to model different substructures within the class. The
effectiveness of the proposed method has been validated through experiments on
three synthetic data sets.

This paper is dedicated to control theoretically explainable application of
autoencoders to optimal fault detection in nonlinear dynamic systems.
Autoencoder-based learning is a standard method of machine learning technique
and widely applied for fault (anomaly) detection and classification. In the
context of representation learning, the so-called latent (hidden) variable
plays an important role towards an optimal fault detection. In ideal case, the
latent variable should be a minimal sufficient statistic. The existing
autoencoder-based fault detection schemes are mainly application-oriented, and
few efforts have been devoted to optimal autoencoder-based fault detection and
explainable applications. The main objective of our work is to establish a
framework for learning autoencoder-based optimal fault detection in nonlinear
dynamic systems. To this aim, a process model form for dynamic systems is
firstly introduced with the aid of control and system theory, which also leads
to a clear system interpretation of the latent variable. The major efforts are
devoted to the development of a control theoretical solution to the optimal
fault detection problem, in which an analog concept to minimal sufficient
statistic, the so-called lossless information compression, is introduced for
dynamic systems and fault detection specifications. In particular, the
existence conditions for such a latent variable are derived, based on which a
loss function and further a learning algorithm are developed. This learning
algorithm enables optimally training of autoencoders to achieve an optimal
fault detection in nonlinear dynamic systems. A case study on three-tank system
is given at the end of this paper to illustrate the capability of the proposed
autoencoder-based fault detection and to explain the essential role of the
latent variable in the proposed fault detection system.

This paper aims to address a new task of image morphing under a multiview
setting, which takes two sets of multiview images as the input and generates
intermediate renderings that not only exhibit smooth transitions between the
two input sets but also ensure visual consistency across different views at any
transition state. To achieve this goal, we propose a novel approach called
Multiview Regenerative Morphing that formulates the morphing process as an
optimization to solve for rigid transformation and optimal-transport
interpolation. Given the multiview input images of the source and target
scenes, we first learn a volumetric representation that models the geometry and
appearance for each scene to enable the rendering of novel views. Then, the
morphing between the two scenes is obtained by solving optimal transport
between the two volumetric representations in Wasserstein metrics. Our approach
does not rely on user-specified correspondences or 2D/3D input meshes, and we
do not assume any predefined categories of the source and target scenes. The
proposed view-consistent interpolation scheme directly works on multiview
images to yield a novel and visually plausible effect of multiview free-form
morphing.

Reducing energy consumption is crucial to reduce the human debt's with regard
to our planet. Therefore most companies try to reduce their energetic
consumption while taking care to preserve the service delivered to their
customers. To do so, a service provider (SP) typically downscale or shutdown
part of its infrastructure in periods of low-activity where only few customers
need the service. However an SP still needs to maintain part of its
infrastructure "on", which still requires significant energy. For example a
mobile national operator (MNO) needs to maintain most of its radio access
network (RAN) active. Could an SP do better by cooperating with other SPs who
would temporarily support its users, thus allowing it to temporarily shut down
its infrastructure, and then reciprocate during another low-activity period? To
answer this question, we investigated a novel collaboration framework based on
multi-agent reinforcement learning (MARL) allowing negotiations between SPs as
well as trustful reports from a distributed ledger technology (DLT) to evaluate
the amount of energy being saved. We leveraged it to experiment three different
sets of rules (free, recommended, or imposed) regulating the negotiation
between multiple SPs (3, 4, 8, or 10). With respect to four cooperation metrics
(efficiency, safety, incentive-compatibility, and fairness), the simulations
showed that the imposed set of rules proved to be the best mode.

The insertion of through-hole components is a difficult task. As the
tolerances of the holes are very small, minor errors in the insertion will
result in failures. These failures can damage components and will require
manual intervention for recovery. Errors can occur both from imprecise object
grasps and bent pins. Therefore, it is important that a system can accurately
determine the object's position and reject components with bent pins. By
utilizing the constraints inherent in the object grasp a method using template
matching is able to obtain very precise pose estimates. Methods for
pin-checking are also implemented, compared, and a successful method is shown.
The set-up is performed automatically, with two novel contributions. A deep
learning segmentation of the pins is performed and the inspection pose is found
by simulation. From the inspection pose and the segmented pins, the templates
for pose estimation and pin check are then generated. To train the deep
learning method a dataset of segmented through-hole components is created. The
network shows a 97.3 % accuracy on the test set. The pin-segmentation network
is also tested on the insertion CAD models and successfully segment the pins.
The complete system is tested on three different objects, and experiments show
that the system is able to insert all objects successfully. Both by correcting
in-hand grasp errors and rejecting objects with bent pins.

Vehicles on the road exchange data with base station (BS) frequently through
vehicle to infrastructure (V2I) communications to ensure the normal use of
vehicular applications, where the IEEE 802.11 distributed coordination function
(DCF) is employed to allocate a minimum contention window (MCW) for channel
access. Each vehicle may change its MCW to achieve more access opportunities at
the expense of others, which results in unfair communication performance.
Moreover, the key access parameters MCW is the privacy information and each
vehicle are not willing to share it with other vehicles. In this uncertain
setting, age of information (AoI) is an important communication metric to
measure the freshness of data, we design an intelligent vehicular node to learn
the dynamic environment and predict the optimal MCW which can make it achieve
age fairness. In order to allocate the optimal MCW for the vehicular node, we
employ a learning algorithm to make a desirable decision by learning from
replay history data. In particular, the algorithm is proposed by extending the
traditional DQN training and testing method. Finally, by comparing with other
methods, it is proved that the proposed DQN method can significantly improve
the age fairness of the intelligent node.

With the rapid growth of software scale and complexity, a large number of bug
reports are submitted to the bug tracking system. In order to speed up defect
repair, these reports need to be accurately classified so that they can be sent
to the appropriate developers. However, the existing classification methods
only use the text information of the bug report, which leads to their low
performance. To solve the above problems, this paper proposes a new automatic
classification method for bug reports. The innovation is that when categorizing
bug reports, in addition to using the text information of the report, the
intention of the report (i.e. suggestion or explanation) is also considered,
thereby improving the performance of the classification. First, we collect bug
reports from four ecosystems (Apache, Eclipse, Gentoo, Mozilla) and manually
annotate them to construct an experimental data set. Then, we use Natural
Language Processing technology to preprocess the data. On this basis, BERT and
TF-IDF are used to extract the features of the intention and the multiple text
information. Finally, the features are used to train the classifiers. The
experimental result on five classifiers (including K-Nearest Neighbor, Naive
Bayes, Logistic Regression, Support Vector Machine, and Random Forest) show
that our proposed method achieves better performance and its F-Measure achieves
from 87.3% to 95.5%.

Generative Adversarial Networks have got the researchers' attention due to
their state-of-the-art performance in generating new images with only a dataset
of the target distribution. It has been shown that there is a dissimilarity
between the spectrum of authentic images and fake ones. Since the Fourier
transform is a bijective mapping, saying that the model has a significant
problem in learning the original distribution is a fair conclusion. In this
work, we investigate the possible reasons for the mentioned drawback in the
architecture and mathematical theory of the current GANs. Then we propose a new
model to reduce the discrepancies between the spectrum of the actual and fake
images. To that end, we design a brand new architecture for the frequency
domain using the blueprint of geometric deep learning. Then, we experimentally
show promising improvements in the quality of the generated images by
considering the Fourier domain representation of the original data as a
principal feature in the training process.

Internal cluster validity measures (such as the Calinski-Harabasz, Dunn, or
Davies-Bouldin indices) are frequently used for selecting the appropriate
number of partitions a dataset should be split into. In this paper we consider
what happens if we treat such indices as objective functions in unsupervised
learning activities. Is the optimal grouping with regards to, say, the
Silhouette index really meaningful? It turns out that many cluster (in)validity
indices promote clusterings that match expert knowledge quite poorly. We also
introduce a new, well-performing variant of the Dunn index that is built upon
OWA operators and the near-neighbour graph so that subspaces of higher density,
regardless of their shapes, can be separated from each other better.

By imitating biological microswimmers, microrobots can be designed to
accomplish targeted delivery of cargos and biomedical manipulations at
microscale. However, it is still a great challenge to enable microrobots to
maneuver in a complex environment. Machine learning algorithms offer a tool to
boost mobility and flexibility of a synthetic microswimmer, hence could help us
design truly smart microrobots. In this work, we investigate how a model of sea
urchin sperm cell can self-learn chemotactic motion in a chemoattractant
concentration field. We employ an artificial neural network to act as a
decision-making agent and facilitate the sperm cell to discover efficient
maneuver strategies through a deep reinforcement learning (DRL) algorithm. Our
results show that chemotactic behaviours, very similar to the realistic ones,
can be achieved by the DRL utilizing only limited environmental information. In
most cases, the DRL algorithm discovers more efficient strategies than the
human-devised one. Furthermore, the DRL can even utilize an external
disturbance to facilitate the chemotactic motion if the extra flow information
is also taken into account by the artificial neural network. Our results
provide insights to the chemotactic process of sea urchin sperm cells and also
prepare guidance for the intelligent maneuver of microrobots.

Earth Observatory is a growing research area that can capitalize on the
powers of AI for short time forecasting, a Now-casting scenario. In this work,
we tackle the challenge of weather forecasting using a video transformer
network. Vision transformer architectures have been explored in various
applications, with major constraints being the computational complexity of
Attention and the data hungry training. To address these issues, we propose the
use of Video Swin-Transformer, coupled with a dedicated augmentation scheme.
Moreover, we employ gradual spatial reduction on the encoder side and
cross-attention on the decoder. The proposed approach is tested on the
Weather4Cast2021 weather forecasting challenge data, which requires the
prediction of 8 hours ahead future frames (4 per hour) from an hourly weather
product sequence. The dataset was normalized to 0-1 to facilitate using the
evaluation metrics across different datasets. The model results in an MSE score
of 0.4750 when provided with training data, and 0.4420 during transfer learning
without using training data, respectively.

Graph-based collaborative filtering is capable of capturing the essential and
abundant collaborative signals from the high-order interactions, and thus
received increasingly research interests. Conventionally, the embeddings of
users and items are defined in the Euclidean spaces, along with the propagation
on the interaction graphs. Meanwhile, recent works point out that the
high-order interactions naturally form up the tree-likeness structures, which
the hyperbolic models thrive on. However, the interaction graphs inherently
exhibit the hybrid and nested geometric characteristics, while the existing
single geometry-based models are inadequate to fully capture such sophisticated
topological patterns. In this paper, we propose to model the user-item
interactions in a hybrid geometric space, in which the merits of Euclidean and
hyperbolic spaces are simultaneously enjoyed to learn expressive
representations. Experimental results on public datasets validate the
effectiveness of our proposal.

Vehicular edge computing (VEC) is a promising technology to support real-time
applications through caching the contents in the roadside units (RSUs), thus
vehicles can fetch the contents requested by vehicular users (VUs) from the RSU
within short time. The capacity of the RSU is limited and the contents
requested by VUs change frequently due to the high-mobility characteristics of
vehicles, thus it is essential to predict the most popular contents and cache
them in the RSU in advance. The RSU can train model based on the VUs' data to
effectively predict the popular contents. However, VUs are often reluctant to
share their data with others due to the personal privacy. Federated learning
(FL) allows each vehicle to train the local model based on VUs' data, and
upload the local model to the RSU instead of data to update the global model,
and thus VUs' privacy information can be protected. The traditional synchronous
FL must wait all vehicles to complete training and upload their local models
for global model updating, which would cause a long time to train global model.
The asynchronous FL updates the global model in time once a vehicle's local
model is received. However, the vehicles with different staying time have
different impacts to achieve the accurate global model. In this paper, we
consider the vehicle mobility and propose an Asynchronous FL based
Mobility-aware Edge Caching (AFMC) scheme to obtain an accurate global model,
and then propose an algorithm to predict the popular contents based on the
global model. Experimental results show that AFMC outperforms other baseline
caching schemes.

The sixth generation (6G) wireless networks aim to achieve ultra-high data
transmission rates, very low latency and enhanced energy-efficiency. To this
end, terahertz (THz) band is one of the key enablers of 6G to meet such
requirements. The THz-band systems are also quickly merging as high-resolution
sensing devices because of their ultra-wide bandwidth and very narrow
beamwidth. As a means to efficiently utilize spectrum and thereby save cost and
power, THz integrated sensing and communications (ISAC) paradigm envisages a
single integrated hardware platform with common signaling mechanism. However,
ISAC at THz-band entails several design challenges such as beam split,
range-dependent bandwidth, near-field beamforming, and distinct channel model.
This article examines the technologies that have the potential to bring forth
ISAC and THz transmission together. In particular, it provides an overview of
antenna and array design, hybrid beamforming, integration with reflecting
surfaces and data-driven techniques such as machine learning. These systems
also provide research opportunities in developing novel methodologies for
channel estimation, near-field beam split, waveform design and beam
misalignment.

Floods are one of nature's most catastrophic calamities which cause
irreversible and immense damage to human life, agriculture, infrastructure and
socio-economic system. Several studies on flood catastrophe management and
flood forecasting systems have been conducted. The accurate prediction of the
onset and progression of floods in real time is challenging. To estimate water
levels and velocities across a large area, it is necessary to combine data with
computationally demanding flood propagation models. This paper aims to reduce
the extreme risks of this natural disaster and also contributes to policy
suggestions by providing a prediction for floods using different machine
learning models. This research will use Binary Logistic Regression, K-Nearest
Neighbor (KNN), Support Vector Classifier (SVC) and Decision tree Classifier to
provide an accurate prediction. With the outcome, a comparative analysis will
be conducted to understand which model delivers a better accuracy.

Analytical dashboards are popular in business intelligence to facilitate
insight discovery with multiple charts. However, creating an effective
dashboard is highly demanding, which requires users to have adequate data
analysis background and be familiar with professional tools, such as Power BI.
To create a dashboard, users have to configure charts by selecting data columns
and exploring different chart combinations to optimize the communication of
insights, which is trial-and-error. Recent research has started to use deep
learning methods for dashboard generation to lower the burden of visualization
creation. However, such efforts are greatly hindered by the lack of large-scale
and high-quality datasets of dashboards. In this work, we propose using deep
reinforcement learning to generate analytical dashboards that can use
well-established visualization knowledge and the estimation capacity of
reinforcement learning. Specifically, we use visualization knowledge to
construct a training environment and rewards for agents to explore and imitate
human exploration behavior with a well-designed agent network. The usefulness
of the deep reinforcement learning model is demonstrated through ablation
studies and user studies. In conclusion, our work opens up new opportunities to
develop effective ML-based visualization recommenders without beforehand
training datasets.

Synthetic health data have the potential to mitigate privacy concerns when
sharing data to support biomedical research and the development of innovative
healthcare applications. Modern approaches for data generation based on machine
learning, generative adversarial networks (GAN) methods in particular, continue
to evolve and demonstrate remarkable potential. Yet there is a lack of a
systematic assessment framework to benchmark methods as they emerge and
determine which methods are most appropriate for which use cases. In this work,
we introduce a generalizable benchmarking framework to appraise key
characteristics of synthetic health data with respect to utility and privacy
metrics. We apply the framework to evaluate synthetic data generation methods
for electronic health records (EHRs) data from two large academic medical
centers with respect to several use cases. The results illustrate that there is
a utility-privacy tradeoff for sharing synthetic EHR data. The results further
indicate that no method is unequivocally the best on all criteria in each use
case, which makes it evident why synthetic data generation methods need to be
assessed in context.

Emerging six generation (6G) is the integration of heterogeneous wireless
networks, which can seamlessly support anywhere and anytime networking. But
high Quality-of-Trust should be offered by 6G to meet mobile user expectations.
Artificial intelligence (AI) is considered as one of the most important
components in 6G. Then AI-based trust management is a promising paradigm to
provide trusted and reliable services. In this article, a generative
adversarial learning-enabled trust management method is presented for 6G
wireless networks. Some typical AI-based trust management schemes are first
reviewed, and then a potential heterogeneous and intelligent 6G architecture is
introduced. Next, the integration of AI and trust management is developed to
optimize the intelligence and security. Finally, the presented AI-based trust
management method is applied to secure clustering to achieve reliable and
real-time communications. Simulation results have demonstrated its excellent
performance in guaranteeing network security and service quality.

Learned image compression allows achieving state-of-the-art accuracy and
compression ratios, but their relatively slow runtime performance limits their
usage. While previous attempts on optimizing learned image codecs focused more
on the neural model and entropy coding, we present an alternative method to
improving the runtime performance of various learned image compression models.
We introduce multi-threaded pipelining and an optimized memory model to enable
GPU and CPU workloads asynchronous execution, fully taking advantage of
computational resources. Our architecture alone already produces excellent
performance without any change to the neural model itself. We also demonstrate
that combining our architecture with previous tweaks to the neural models can
further improve runtime performance. We show that our implementations excel in
throughput and latency compared to the baseline and demonstrate the performance
of our implementations by creating a real-time video streaming encoder-decoder
sample application, with the encoder running on an embedded device.

There has been an increased interest in applying deep neural networks to
automatically interpret and analyze the 12-lead electrocardiogram (ECG). The
current paradigms with machine learning methods are often limited by the amount
of labeled data. This phenomenon is particularly problematic for
clinically-relevant data, where labeling at scale can be time-consuming and
costly in terms of the specialized expertise and human effort required.
Moreover, deep learning classifiers may be vulnerable to adversarial examples
and perturbations, which could have catastrophic consequences, for example,
when applied in the context of medical treatment, clinical trials, or insurance
claims. In this paper, we propose a physiologically-inspired data augmentation
method to improve performance and increase the robustness of heart disease
detection based on ECG signals. We obtain augmented samples by perturbing the
data distribution towards other classes along the geodesic in Wasserstein
space. To better utilize domain-specific knowledge, we design a ground metric
that recognizes the difference between ECG signals based on physiologically
determined features. Learning from 12-lead ECG signals, our model is able to
distinguish five categories of cardiac conditions. Our results demonstrate
improvements in accuracy and robustness, reflecting the effectiveness of our
data augmentation method.

The vehicular edge computing (VEC) can cache contents in different RSUs at
the network edge to support the real-time vehicular applications. In VEC, owing
to the high-mobility characteristics of vehicles, it is necessary to cache the
user data in advance and learn the most popular and interesting contents for
vehicular users. Since user data usually contains privacy information, users
are reluctant to share their data with others. To solve this problem,
traditional federated learning (FL) needs to update the global model
synchronously through aggregating all users' local models to protect users'
privacy. However, vehicles may frequently drive out of the coverage area of the
VEC before they achieve their local model trainings and thus the local models
cannot be uploaded as expected, which would reduce the accuracy of the global
model. In addition, the caching capacity of the local RSU is limited and the
popular contents are diverse, thus the size of the predicted popular contents
usually exceeds the cache capacity of the local RSU. Hence, the VEC should
cache the predicted popular contents in different RSUs while considering the
content transmission delay. In this paper, we consider the mobility of vehicles
and propose a cooperative Caching scheme in the VEC based on Asynchronous
Federated and deep Reinforcement learning (CAFR). We first consider the
mobility of vehicles and propose an asynchronous FL algorithm to obtain an
accurate global model, and then propose an algorithm to predict the popular
contents based on the global model. In addition, we consider the mobility of
vehicles and propose a deep reinforcement learning algorithm to obtain the
optimal cooperative caching location for the predicted popular contents in
order to optimize the content transmission delay. Extensive experimental
results have demonstrated that the CAFR scheme outperforms other baseline

Variational quantum algorithms (VQAs) have demonstrated great potentials in
the NISQ era. In the workflow of VQA, the parameters of ansatz are iteratively
updated to approximate the desired quantum states. We have seen various efforts
to draft better ansatz with less gates. In quantum computers, the gate ansatz
will eventually be transformed into control signals such as microwave pulses on
transmons. And the control pulses need elaborate calibration to minimize the
errors such as over-rotation and under-rotation. In the case of VQAs, this
procedure will introduce redundancy, but the variational properties of VQAs can
naturally handle problems of over-rotation and under-rotation by updating the
amplitude and frequency parameters. Therefore, we propose PAN, a native-pulse
ansatz generator framework for VQAs. We generate native-pulse ansatz with
trainable parameters for amplitudes and frequencies. In our proposed PAN, we
are tuning parametric pulses, which are natively supported on NISQ computers.
Considering that parameter-shift rules do not hold for native-pulse ansatz, we
need to deploy non-gradient optimizers. To constrain the number of parameters
sent to the optimizer, we adopt a progressive way to generate our native-pulse
ansatz. Experiments are conducted on both simulators and quantum devices to
validate our methods. When adopted on NISQ machines, PAN obtained improved the
performance with decreased latency by an average of 86%. PAN is able to achieve
96.482% and 99.336% accuracy for VQE tasks on H2 and HeH+ respectively, An
average accuracy of 97.27% is achieved for medium-size VQE tasks on CO2, H2O,
and NaH. PAN also demonstrates advantages on QAOA tasks even with considerable
noises in NISQ machines.

Recently, pioneer research works have proposed a large number of acoustic
features (log power spectrogram, linear frequency cepstral coefficients,
constant Q cepstral coefficients, etc.) for audio deepfake detection, obtaining
good performance, and showing that different subbands have different
contributions to audio deepfake detection. However, this lacks an explanation
of the specific information in the subband, and these features also lose
information such as phase. Inspired by the mechanism of synthetic speech, the
fundamental frequency (F0) information is used to improve the quality of
synthetic speech, while the F0 of synthetic speech is still too average, which
differs significantly from that of real speech. It is expected that F0 can be
used as important information to discriminate between bonafide and fake speech,
while this information cannot be used directly due to the irregular
distribution of F0. Insteadly, the frequency band containing most of F0 is
selected as the input feature. Meanwhile, to make full use of the phase and
full-band information, we also propose to use real and imaginary spectrogram
features as complementary input features and model the disjoint subbands
separately. Finally, the results of F0, real and imaginary spectrogram features
are fused. Experimental results on the ASVspoof 2019 LA dataset show that our
proposed system is very effective for the audio deepfake detection task,
achieving an equivalent error rate (EER) of 0.43%, which surpasses almost all
systems.

Interactive Machine Teaching (IMT) systems allow non-experts to easily create
Machine Learning (ML) models. However, existing vision-based IMT systems either
ignore annotations on the objects of interest or require users to annotate in a
post-hoc manner. Without the annotations on objects, the model may misinterpret
the objects using unrelated features. Post-hoc annotations cause additional
workload, which diminishes the usability of the overall model building process.
In this paper, we develop LookHere, which integrates in-situ object annotations
into vision-based IMT. LookHere exploits users' deictic gestures to segment the
objects of interest in real time. This segmentation information can be
additionally used for training. To achieve the reliable performance of this
object segmentation, we utilize our custom dataset called HuTics, including
2040 front-facing images of deictic gestures toward various objects by 170
people. The quantitative results of our user study showed that participants
were 16.3 times faster in creating a model with our system compared to a
standard IMT system with a post-hoc annotation process while demonstrating
comparable accuracies. Additionally, models created by our system showed a
significant accuracy improvement ($\Delta mIoU=0.466$) in segmenting the
objects of interest compared to those without annotations.

Kernel density estimation (KDE) is one of the most widely used nonparametric
density estimation methods. The fact that it is a memory-based method, i.e., it
uses the entire training data set for prediction, makes it unsuitable for most
current big data applications. Several strategies, such as tree-based or
hashing-based estimators, have been proposed to improve the efficiency of the
kernel density estimation method. The novel density kernel density estimation
method (DMKDE) uses density matrices, a quantum mechanical formalism, and
random Fourier features, an explicit kernel approximation, to produce density
estimates. This method has its roots in the KDE and can be considered as an
approximation method, without its memory-based restriction. In this paper, we
systematically evaluate the novel DMKDE algorithm and compare it with other
state-of-the-art fast procedures for approximating the kernel density
estimation method on different synthetic data sets. Our experimental results
show that DMKDE is on par with its competitors for computing density estimates
and advantages are shown when performed on high-dimensional data. We have made
all the code available as an open source software repository.

The surge in interest in Artificial Intelligence (AI) over the past decade
has been driven almost exclusively by advances in Artificial Neural Networks
(ANNs). While ANNs set state-of-the-art performance for many previously
intractable problems, the use of global gradient descent necessitates large
datasets and computational resources for training, potentially limiting their
scalability for real-world domains. Spiking Neural Networks (SNNs) are an
alternative to ANNs that use more brain-like artificial neurons and can use
local unsupervised learning to rapidly discover sparse recognizable features in
the input data. SNNs, however, struggle with dynamical stability and have
failed to match the accuracy of ANNs. Here we show how an SNN can overcome many
of the shortcomings that have been identified in the literature, including
offering a principled solution to the dynamical "vanishing spike problem", to
outperform all existing shallow SNNs and equal the performance of an ANN. It
accomplishes this while using unsupervised learning with unlabeled data and
only 1/50th of the training epochs (labeled data is used only for a simple
linear readout layer). This result makes SNNs a viable new method for fast,
accurate, efficient, explainable, and re-deployable machine learning with
unlabeled data.

We develop quantum protocols for anomaly detection and apply them to the task
of credit card fraud detection (FD). First, we establish classical benchmarks
based on supervised and unsupervised machine learning methods, where average
precision is chosen as a robust metric for detecting anomalous data. We focus
on kernel-based approaches for ease of direct comparison, basing our
unsupervised modelling on one-class support vector machines (OC-SVM). Next, we
employ quantum kernels of different type for performing anomaly detection, and
observe that quantum FD can challenge equivalent classical protocols at
increasing number of features (equal to the number of qubits for data
embedding). Performing simulations with registers up to 20 qubits, we find that
quantum kernels with re-uploading demonstrate better average precision, with
the advantage increasing with system size. Specifically, at 20 qubits we reach
the quantum-classical separation of average precision being equal to 15%. We
discuss the prospects of fraud detection with near- and mid-term quantum
hardware, and describe possible future improvements.

We present a novel gated recurrent neural network to detect when a person is
chewing on food. We implemented the neural network as a custom analog
integrated circuit in a 0.18 um CMOS technology. The neural network was trained
on 6.4 hours of data collected from a contact microphone that was mounted on
volunteers' mastoid bones. When tested on 1.6 hours of previously-unseen data,
the neural network identified chewing events at a 24-second time resolution. It
achieved a recall of 91% and an F1-score of 94% while consuming 1.1 uW of
power. A system for detecting whole eating episodes -- like meals and snacks --
that is based on the novel analog neural network consumes an estimated 18.8uW
of power.

In the traditional distributed machine learning scenario, the user's private
data is transmitted between nodes and a central server, which results in great
potential privacy risks. In order to balance the issues of data privacy and
joint training of models, federated learning (FL) is proposed as a special
distributed machine learning with a privacy protection mechanism, which can
realize multi-party collaborative computing without revealing the original
data. However, in practice, FL faces many challenging communication problems.
This review aims to clarify the relationship between these communication
problems, and focus on systematically analyzing the research progress of FL
communication work from three perspectives: communication efficiency,
communication environment, and communication resource allocation. Firstly, we
sort out the current challenges existing in the communications of FL. Secondly,
we have compiled articles related to FL communications, and then describe the
development trend of the entire field guided by the logical relationship
between them. Finally, we point out the future research directions for
communications in FL.

Multi-view clustering (MVC) optimally integrates complementary information
from different views to improve clustering performance. Although demonstrating
promising performance in various applications, most of existing approaches
directly fuse multiple pre-specified similarities to learn an optimal
similarity matrix for clustering, which could cause over-complicated
optimization and intensive computational cost. In this paper, we propose late
fusion MVC via alignment maximization to address these issues. To do so, we
first reveal the theoretical connection of existing k-means clustering and the
alignment between base partitions and the consensus one. Based on this
observation, we propose a simple but effective multi-view algorithm termed
LF-MVC-GAM. It optimally fuses multiple source information in partition level
from each individual view, and maximally aligns the consensus partition with
these weighted base ones. Such an alignment is beneficial to integrate
partition level information and significantly reduce the computational
complexity by sufficiently simplifying the optimization procedure. We then
design another variant, LF-MVC-LAM to further improve the clustering
performance by preserving the local intrinsic structure among multiple
partition spaces. After that, we develop two three-step iterative algorithms to
solve the resultant optimization problems with theoretically guaranteed
convergence. Further, we provide the generalization error bound analysis of the
proposed algorithms. Extensive experiments on eighteen multi-view benchmark
datasets demonstrate the effectiveness and efficiency of the proposed
LF-MVC-GAM and LF-MVC-LAM, ranging from small to large-scale data items. The
codes of the proposed algorithms are publicly available at
https://github.com/wangsiwei2010/latefusionalignment.

Extensive studies on Unsupervised Domain Adaptation (UDA) have propelled the
deployment of deep learning from limited experimental datasets into real-world
unconstrained domains. Most UDA approaches align features within a common
embedding space and apply a shared classifier for target prediction. However,
since a perfectly aligned feature space may not exist when the domain
discrepancy is large, these methods suffer from two limitations. First, the
coercive domain alignment deteriorates target domain discriminability due to
lacking target label supervision. Second, the source-supervised classifier is
inevitably biased to source data, thus it may underperform in target domain. To
alleviate these issues, we propose to simultaneously conduct feature alignment
in two individual spaces focusing on different domains, and create for each
space a domain-oriented classifier tailored specifically for that domain.
Specifically, we design a Domain-Oriented Transformer (DOT) that has two
individual classification tokens to learn different domain-oriented
representations, and two classifiers to preserve domain-wise discriminability.
Theoretical guaranteed contrastive-based alignment and the source-guided
pseudo-label refinement strategy are utilized to explore both domain-invariant
and specific information. Comprehensive experiments validate that our method
achieves state-of-the-art on several benchmarks.

We present a new class of structured reinforcement learning
policy-architectures, Implicit Two-Tower (ITT) policies, where the actions are
chosen based on the attention scores of their learnable latent representations
with those of the input states. By explicitly disentangling action from state
processing in the policy stack, we achieve two main goals: substantial
computational gains and better performance. Our architectures are compatible
with both: discrete and continuous action spaces. By conducting tests on 15
environments from OpenAI Gym and DeepMind Control Suite, we show that
ITT-architectures are particularly suited for blackbox/evolutionary
optimization and the corresponding policy training algorithms outperform their
vanilla unstructured implicit counterparts as well as commonly used explicit
policies. We complement our analysis by showing how techniques such as hashing
and lazy tower updates, critically relying on the two-tower structure of ITTs,
can be applied to obtain additional computational improvements.

Learning a latent embedding to understand the underlying nature of data
distribution is often formulated in Euclidean spaces with zero curvature.
However, the success of the geometry constraints, posed in the embedding space,
indicates that curved spaces might encode more structural information, leading
to better discriminative power and hence richer representations. In this work,
we investigate benefits of the curved space for analyzing anomalies or
out-of-distribution objects in data. This is achieved by considering embeddings
via three geometry constraints, namely, spherical geometry (with positive
curvature), hyperbolic geometry (with negative curvature) or mixed geometry
(with both positive and negative curvatures). Three geometric constraints can
be chosen interchangeably in a unified design given the task at hand. Tailored
for the embeddings in the curved space, we also formulate functions to compute
the anomaly score. Two types of geometric modules (i.e., Geometric-in-One and
Geometric-in-Two models) are proposed to plug in the original Euclidean
classifier, and anomaly scores are computed from the curved embeddings. We
evaluate the resulting designs under a diverse set of visual recognition
scenarios, including image detection (multi-class OOD detection and one-class
anomaly detection) and segmentation (multi-class anomaly segmentation and
one-class anomaly segmentation). The empirical results show the effectiveness
of our proposal through the consistent improvement over various scenarios.

We construct a zeroth-order gradient estimator for a smooth function defined
on the probability simplex. The proposed estimator queries the simplex only. We
prove that projected gradient descent and the exponential weights algorithm,
when run with this estimator instead of exact gradients, converge at a
$\mathcal O(T^{-1/4})$ rate.

Traditional learning-based approaches to student modeling generalize poorly
to underrepresented student groups due to biases in data availability. In this
paper, we propose a methodology for predicting student performance from their
online learning activities that optimizes inference accuracy over different
demographic groups such as race and gender. Building upon recent foundations in
federated learning, in our approach, personalized models for individual student
subgroups are derived from a global model aggregated across all student models
via meta-gradient updates that account for subgroup heterogeneity. To learn
better representations of student activity, we augment our approach with a
self-supervised behavioral pretraining methodology that leverages multiple
modalities of student behavior (e.g., visits to lecture videos and
participation on forums), and include a neural network attention mechanism in
the model aggregation stage. Through experiments on three real-world datasets
from online courses, we demonstrate that our approach obtains substantial
improvements over existing student modeling baselines in predicting student
learning outcomes for all subgroups. Visual analysis of the resulting student
embeddings confirm that our personalization methodology indeed identifies
different activity patterns within different subgroups, consistent with its
stronger inference ability compared with the baselines.

Bayesian variable selection is a powerful tool for data analysis, as it
offers a principled method for variable selection that accounts for prior
information and uncertainty. However, wider adoption of Bayesian variable
selection has been hampered by computational challenges, especially in
difficult regimes with a large number of covariates P or non-conjugate
likelihoods. To scale to the large P regime we introduce an efficient MCMC
scheme whose cost per iteration is sublinear in P. In addition we show how this
scheme can be extended to generalized linear models for count data, which are
prevalent in biology, ecology, economics, and beyond. In particular we design
efficient algorithms for variable selection in binomial and negative binomial
regression, which includes logistic regression as a special case. In
experiments we demonstrate the effectiveness of our methods, including on
cancer and maize genomic data.

Estimating 6D poses of objects is an essential computer vision task. However,
most conventional approaches rely on camera data from a single perspective and
therefore suffer from occlusions. We overcome this issue with our novel
multi-view 6D pose estimation method called MV6D which accurately predicts the
6D poses of all objects in a cluttered scene based on RGB-D images from
multiple perspectives. We base our approach on the PVN3D network that uses a
single RGB-D image to predict keypoints of the target objects. We extend this
approach by using a combined point cloud from multiple views and fusing the
images from each view with a DenseFusion layer. In contrast to current
multi-view pose detection networks such as CosyPose, our MV6D can learn the
fusion of multiple perspectives in an end-to-end manner and does not require
multiple prediction stages or subsequent fine tuning of the prediction.
Furthermore, we present three novel photorealistic datasets of cluttered scenes
with heavy occlusions. All of them contain RGB-D images from multiple
perspectives and the ground truth for instance semantic segmentation and 6D
pose estimation. MV6D significantly outperforms the state-of-the-art in
multi-view 6D pose estimation even in cases where the camera poses are known
inaccurately. Furthermore, we show that our approach is robust towards dynamic
camera setups and that its accuracy increases incrementally with an increasing
number of perspectives.

There are many general purpose benchmark datasets for Semantic Textual
Similarity but none of them are focused on technical concepts found in patents
and scientific publications. This work aims to fill this gap by presenting a
new human rated contextual phrase to phrase matching dataset. The entire
dataset contains close to $50,000$ rated phrase pairs, each with a CPC
(Cooperative Patent Classification) class as a context. This paper describes
the dataset and some baseline models.

A variety of approaches using compartmental models have been used to study
the COVID-19 pandemic and the usage of machine learning methods with these
models has had particularly notable success. We present here an approach toward
analyzing accessible data on Covid-19's U.S. development using a variation of
the "Physics Informed Neural Networks" (PINN) which is capable of using the
knowledge of the model to aid learning. We illustrate the challenges of using
the standard PINN approach, then how with appropriate and novel modifications
to the loss function the network can perform well even in our case of
incomplete information. Aspects of identifiability of the model parameters are
also assessed, as well as methods of denoising available data using a wavelet
transform. Finally, we discuss the capability of the neural network methodology
to work with models of varying parameter values, as well as a concrete
application in estimating how effectively cases are being tested for in a
population, providing a ranking of U.S. states by means of their respective
testing.

Advances in perception for self-driving cars have accelerated in recent years
due to the availability of large-scale datasets, typically collected at
specific locations and under nice weather conditions. Yet, to achieve the high
safety requirement, these perceptual systems must operate robustly under a wide
variety of weather conditions including snow and rain. In this paper, we
present a new dataset to enable robust autonomous driving via a novel data
collection process - data is repeatedly recorded along a 15 km route under
diverse scene (urban, highway, rural, campus), weather (snow, rain, sun), time
(day/night), and traffic conditions (pedestrians, cyclists and cars). The
dataset includes images and point clouds from cameras and LiDAR sensors, along
with high-precision GPS/INS to establish correspondence across routes. The
dataset includes road and object annotations using amodal masks to capture
partial occlusions and 3D bounding boxes. We demonstrate the uniqueness of this
dataset by analyzing the performance of baselines in amodal segmentation of
road and objects, depth estimation, and 3D object detection. The repeated
routes opens new research directions in object discovery, continual learning,
and anomaly detection. Link to Ithaca365: https://ithaca365.mae.cornell.edu/

Movement synchrony reflects the coordination of body movements between
interacting dyads. The estimation of movement synchrony has been automated by
powerful deep learning models such as transformer networks. However, instead of
designing a specialized network for movement synchrony estimation, previous
transformer-based works broadly adopted architectures from other tasks such as
human activity recognition. Therefore, this paper proposed a skeleton-based
graph transformer for movement synchrony estimation. The proposed model applied
ST-GCN, a spatial-temporal graph convolutional neural network for skeleton
feature extraction, followed by a spatial transformer for spatial feature
generation. The spatial transformer is guided by a uniquely designed joint
position embedding shared between the same joints of interacting individuals.
Besides, we incorporated a temporal similarity matrix in temporal attention
computation considering the periodic intrinsic of body movements. In addition,
the confidence score associated with each joint reflects the uncertainty of a
pose, while previous works on movement synchrony estimation have not
sufficiently emphasized this point. Since transformer networks demand a
significant amount of data to train, we constructed a dataset for movement
synchrony estimation using Human3.6M, a benchmark dataset for human activity
recognition, and pretrained our model on it using contrastive learning. We
further applied knowledge distillation to alleviate information loss introduced
by pose detector failure in a privacy-preserving way. We compared our method
with representative approaches on PT13, a dataset collected from autism therapy
interventions. Our method achieved an overall accuracy of 88.98% and surpassed
its counterparts by a wide margin while maintaining data privacy.

We address the problem of enabling quadrupedal robots to perform precise
shooting skills in the real world using reinforcement learning. Developing
algorithms to enable a legged robot to shoot a soccer ball to a given target is
a challenging problem that combines robot motion control and planning into one
task. To solve this problem, we need to consider the dynamics limitation and
motion stability during the control of a dynamic legged robot. Moreover, we
need to consider motion planning to shoot the hard-to-model deformable ball
rolling on the ground with uncertain friction to a desired location. In this
paper, we propose a hierarchical framework that leverages deep reinforcement
learning to train (a) a robust motion control policy that can track arbitrary
motions and (b) a planning policy to decide the desired kicking motion to shoot
a soccer ball to a target. We deploy the proposed framework on an A1
quadrupedal robot and enable it to accurately shoot the ball to random targets
in the real world.

This study focuses on exploring the use of local interpretability methods for
explaining time series clustering models. Many of the state-of-the-art
clustering models are not directly explainable. To provide explanations for
these clustering algorithms, we train classification models to estimate the
cluster labels. Then, we use interpretability methods to explain the decisions
of the classification models. The explanations are used to obtain insights into
the clustering models. We perform a detailed numerical study to test the
proposed approach on multiple datasets, clustering models, and classification
models. The analysis of the results shows that the proposed approach can be
used to explain time series clustering models, specifically when the underlying
classification model is accurate. Lastly, we provide a detailed analysis of the
results, discussing how our approach can be used in a real-life scenario.

A Cleft lip is a congenital abnormality requiring surgical repair by a
specialist. The surgeon must have extensive experience and theoretical
knowledge to perform surgery, and Artificial Intelligence (AI) method has been
proposed to guide surgeons in improving surgical outcomes. If AI can be used to
predict what a repaired cleft lip would look like, surgeons could use it as an
adjunct to adjust their surgical technique and improve results. To explore the
feasibility of this idea while protecting patient privacy, we propose a deep
learning-based image inpainting method that is capable of covering a cleft lip
and generating a lip and nose without a cleft. Our experiments are conducted on
two real-world cleft lip datasets and are assessed by expert cleft lip surgeons
to demonstrate the feasibility of the proposed method.

We investigate boosted ensemble models for off-policy learning from logged
bandit feedback. Toward this goal, we propose a new boosting algorithm that
directly optimizes an estimate of the policy's expected reward. We analyze this
algorithm and prove that the empirical risk decreases (possibly exponentially
fast) with each round of boosting, provided a "weak" learning condition is
satisfied. We further show how the base learner reduces to standard supervised
learning problems. Experiments indicate that our algorithm can outperform deep
off-policy learning and methods that simply regress on the observed rewards,
thereby demonstrating the benefits of both boosting and choosing the right
learning objective.

With the employment of smart meters, massive data on consumer behaviour can
be collected by retailers. From the collected data, the retailers may obtain
the household profile information and implement demand response. While
retailers prefer to acquire a model as accurate as possible among different
customers, there are two major challenges. First, different retailers in the
retail market do not share their consumer's electricity consumption data as
these data are regarded as their assets, which has led to the problem of data
island. Second, the electricity load data are highly heterogeneous since
different retailers may serve various consumers. To this end, a fully
distributed short-term load forecasting framework based on a consensus
algorithm and Long Short-Term Memory (LSTM) is proposed, which may protect the
customer's privacy and satisfy the accurate load forecasting requirement.
Specifically, a fully distributed learning framework is exploited for
distributed training, and a consensus technique is applied to meet confidential
privacy. Case studies show that the proposed method has comparable performance
with centralised methods regarding the accuracy, but the proposed method shows
advantages in training speed and data privacy.

Modern digital music production typically involves combining numerous
acoustic elements to compile a piece of music. Important types of such elements
are drum samples, which determine the characteristics of the percussive
components of the piece. Artists must use their aesthetic judgement to assess
whether a given drum sample fits the current musical context. However,
selecting drum samples from a potentially large library is tedious and may
interrupt the creative flow. In this work, we explore the automatic drum sample
retrieval based on aesthetic principles learned from data. As a result, artists
can rank the samples in their library by fit to some musical context at
different stages of the production process (i.e., by fit to incomplete song
mixtures). To this end, we use contrastive learning to maximize the score of
drum samples originating from the same song as the mixture. We conduct a
listening test to determine whether the human ratings match the automatic
scoring function. We also perform objective quantitative analyses to evaluate
the efficacy of our approach.

Training deep neural networks is a very demanding task, especially
challenging is how to adapt architectures to improve the performance of trained
models. We can find that sometimes, shallow networks generalize better than
deep networks, and the addition of more layers results in higher training and
test errors. The deep residual learning framework addresses this degradation
problem by adding skip connections to several neural network layers. It would
at first seem counter-intuitive that such skip connections are needed to train
deep networks successfully as the expressivity of a network would grow
exponentially with depth. In this paper, we first analyze the flow of
information through neural networks. We introduce and evaluate the
batch-entropy which quantifies the flow of information through each layer of a
neural network. We prove empirically and theoretically that a positive
batch-entropy is required for gradient descent-based training approaches to
optimize a given loss function successfully. Based on those insights, we
introduce batch-entropy regularization to enable gradient descent-based
training algorithms to optimize the flow of information through each hidden
layer individually. With batch-entropy regularization, gradient descent
optimizers can transform untrainable networks into trainable networks. We show
empirically that we can therefore train a "vanilla" fully connected network and
convolutional neural network -- no skip connections, batch normalization,
dropout, or any other architectural tweak -- with 500 layers by simply adding
the batch-entropy regularization term to the loss function. The effect of
batch-entropy regularization is not only evaluated on vanilla neural networks,
but also on residual networks, autoencoders, and also transformer models over a
wide range of computer vision as well as natural language processing tasks.

The oblique random survival forest (RSF) is an ensemble supervised learning
method for right-censored outcomes. Trees in the oblique RSF are grown using
linear combinations of predictors to create branches, whereas in the standard
RSF, a single predictor is used. Oblique RSF ensembles often have higher
prediction accuracy than standard RSF ensembles. However, assessing all
possible linear combinations of predictors induces significant computational
overhead that limits applications to large-scale data sets. In addition, few
methods have been developed for interpretation of oblique RSF ensembles, and
they remain more difficult to interpret compared to their axis-based
counterparts. We introduce a method to increase computational efficiency of the
oblique RSF and a method to estimate importance of individual predictor
variables with the oblique RSF. Our strategy to reduce computational overhead
makes use of Newton-Raphson scoring, a classical optimization technique that we
apply to the Cox partial likelihood function within each non-leaf node of
decision trees. We estimate the importance of individual predictors for the
oblique RSF by negating each coefficient used for the given predictor in linear
combinations, and then computing the reduction in out-of-bag accuracy. In
general benchmarking experiments, we find that our implementation of the
oblique RSF is approximately 450 times faster with equivalent discrimination
and superior Brier score compared to existing software for oblique RSFs. We
find in simulation studies that 'negation importance' discriminates between
relevant and irrelevant predictors more reliably than permutation importance,
Shapley additive explanations, and a previously introduced technique to measure
variable importance with oblique RSFs based on analysis of variance. Methods
introduced in the current study are available in the aorsf R package.

As machine learning (ML) models gain traction in clinical applications,
understanding the impact of clinician and societal biases on ML models is
increasingly important. While biases can arise in the labels used for model
training, the many sources from which these biases arise are not yet
well-studied. In this paper, we highlight disparate censorship (i.e.,
differences in testing rates across patient groups) as a source of label bias
that clinical ML models may amplify, potentially causing harm. Many patient
risk-stratification models are trained using the results of clinician-ordered
diagnostic and laboratory tests of labels. Patients without test results are
often assigned a negative label, which assumes that untested patients do not
experience the outcome. Since orders are affected by clinical and resource
considerations, testing may not be uniform in patient populations, giving rise
to disparate censorship. Disparate censorship in patients of equivalent risk
leads to undertesting in certain groups, and in turn, more biased labels for
such groups. Using such biased labels in standard ML pipelines could contribute
to gaps in model performance across patient groups. Here, we theoretically and
empirically characterize conditions in which disparate censorship or
undertesting affect model performance across subgroups. Our findings call
attention to disparate censorship as a source of label bias in clinical ML
models.

Heterogeneous materials such as biological tissue scatter light in random,
yet deterministic, ways. Wavefront shaping can reverse the effects of
scattering to enable deep-tissue microscopy. Such methods require either
invasive access to the internal field or the computational solving of an
inverse problem. However, calculating the coherent field on a scale relevant to
microscopy remains excessively demanding for consumer hardware. Here we show
how a recurrent neural network can mirror Maxwell's equations without training.
By harnessing public machine learning infrastructure, the light-field
throughout a $6 \, \textrm{mm}^2$ area or $110^3 \, \mu\textrm{m}^3$ volume can
be calculated in 16 minutes. The elimination of the training phase cuts the
calculation time and, importantly, it ensures a fully deterministic solution,
free of training bias. We integrated our method with an open-source
electromagnetic solver. This enables any researcher with an internet connection
to calculate complex light-scattering in volumes that are larger by two orders
of magnitude.

Recent Deep Learning (DL) advancements in solving complex real-world tasks
have led to its widespread adoption in practical applications. However, this
opportunity comes with significant underlying risks, as many of these models
rely on privacy-sensitive data for training in a variety of applications,
making them an overly-exposed threat surface for privacy violations.
Furthermore, the widespread use of cloud-based Machine-Learning-as-a-Service
(MLaaS) for its robust infrastructure support has broadened the threat surface
to include a variety of remote side-channel attacks. In this paper, we first
identify and report a novel data-dependent timing side-channel leakage (termed
Class Leakage) in DL implementations originating from non-constant time
branching operation in a widely used DL framework PyTorch. We further
demonstrate a practical inference-time attack where an adversary with user
privilege and hard-label black-box access to an MLaaS can exploit Class Leakage
to compromise the privacy of MLaaS users. DL models are vulnerable to
Membership Inference Attack (MIA), where an adversary's objective is to deduce
whether any particular data has been used while training the model. In this
paper, as a separate case study, we demonstrate that a DL model secured with
differential privacy (a popular countermeasure against MIA) is still vulnerable
to MIA against an adversary exploiting Class Leakage. We develop an
easy-to-implement countermeasure by making a constant-time branching operation
that alleviates the Class Leakage and also aids in mitigating MIA. We have
chosen two standard benchmarking image classification datasets, CIFAR-10 and
CIFAR-100 to train five state-of-the-art pre-trained DL models, over two
different computing environments having Intel Xeon and Intel i7 processors to
validate our approach.

Vaccinations against viruses have always been the need of the hour since long
past. However, it is hard to efficiently distribute the vaccines (on time) to
all the corners of a country, especially during a pandemic. Considering the
vastness of the population, diversified communities, and demands of a smart
society, it is an important task to optimize the vaccine distribution strategy
in any country/state effectively. Although there is a profusion of data (Big
Data) from various vaccine administration sites that can be mined to gain
valuable insights about mass vaccination drives, very few attempts has been
made towards revolutionizing the traditional mass vaccination campaigns to
mitigate the socio-economic crises of pandemic afflicted countries. In this
paper, we bridge this gap in studies and experimentation. We collect daily
vaccination data which is publicly available and carefully analyze it to
generate meaning-full insights and predictions. We put forward a novel
framework leveraging Supervised Learning and Reinforcement Learning (RL) which
we call VacciNet, that is capable of learning to predict the demand of
vaccination in a state of a country as well as suggest optimal vaccine
allocation in the state for minimum cost of procurement and supply. At the
present, our framework is trained and tested with vaccination data of the USA.

Recent progress in the application of color centers to nanoscale spin sensing
makes the combined use of noise spectroscopy and scanning probe imaging an
attractive route for the characterization of arbitrary material systems.
Unfortunately, the traditional approach to characterizing the environmental
magnetic field fluctuations from the measured probe signal typically requires
the experimenter's input, thus complicating the implementation of automated
imaging protocols based on spectrally resolved noise. Here, we probe the
response of color centers in diamond in the presence of externally engineered
random magnetic signals, and implement a deep neural network to methodically
extract information on their associated spectral densities. Building on a long
sequence of successive measurements under different types of stimuli, we show
that our network manages to efficiently reconstruct the spectral density of the
underlying fluctuating magnetic field with good fidelity under a broad set of
conditions and with only a minimal measured data set, even in the presence of
substantial experimental noise. These proof-of-principle results create
opportunities for the application of machine-learning methods to
color-center-based nanoscale sensing and imaging.

Many collaborative human-robot tasks require the robot to stay safe and work
efficiently around humans. Since the robot can only stay safe with respect to
its own model of the human, we want the robot to learn a good model of the
human in order to act both safely and efficiently. This paper studies methods
that enable a robot to safely explore the space of a human-robot system to
improve the robot's model of the human, which will consequently allow the robot
to access a larger state space and better work with the human. In particular,
we introduce active exploration under the framework of energy-function based
safe control, investigate the effect of different active exploration
strategies, and finally analyze the effect of safe active exploration on both
analytical and neural network human models.

Movement synchrony refers to the dynamic temporal connection between the
motions of interacting people. The applications of movement synchrony are wide
and broad. For example, as a measure of coordination between teammates,
synchrony scores are often reported in sports. The autism community also
identifies movement synchrony as a key indicator of children's social and
developmental achievements. In general, raw video recordings are often used for
movement synchrony estimation, with the drawback that they may reveal people's
identities. Furthermore, such privacy concern also hinders data sharing, one
major roadblock to a fair comparison between different approaches in autism
research. To address the issue, this paper proposes an ensemble method for
movement synchrony estimation, one of the first deep-learning-based methods for
automatic movement synchrony assessment under privacy-preserving conditions.
Our method relies entirely on publicly shareable, identity-agnostic secondary
data, such as skeleton data and optical flow. We validate our method on two
datasets: (1) PT13 dataset collected from autism therapy interventions and (2)
TASD-2 dataset collected from synchronized diving competitions. In this
context, our method outperforms its counterpart approaches, both deep neural
networks and alternatives.

Health monitoring applications increasingly rely on machine learning
techniques to learn end-user physiological and behavioral patterns in everyday
settings. Considering the significant role of wearable devices in monitoring
human body parameters, on-device learning can be utilized to build personalized
models for behavioral and physiological patterns, and provide data privacy for
users at the same time. However, resource constraints on most of these wearable
devices prevent the ability to perform online learning on them. To address this
issue, it is required to rethink the machine learning models from the
algorithmic perspective to be suitable to run on wearable devices.
Hyperdimensional computing (HDC) offers a well-suited on-device learning
solution for resource-constrained devices and provides support for
privacy-preserving personalization. Our HDC-based method offers flexibility,
high efficiency, resilience, and performance while enabling on-device
personalization and privacy protection. We evaluate the efficacy of our
approach using three case studies and show that our system improves the energy
efficiency of training by up to $45.8\times$ compared with the state-of-the-art
Deep Neural Network (DNN) algorithms while offering a comparable accuracy.

Data-driven science is an emerging paradigm where scientific discoveries
depend on the execution of computational AI models against rich,
discipline-specific datasets. With modern machine learning frameworks, anyone
can develop and execute computational models that reveal concepts hidden in the
data that could enable scientific applications. For important and widely used
datasets, computing the performance of every computational model that can run
against a dataset is cost prohibitive in terms of cloud resources. Benchmarking
approaches used in practice use representative datasets to infer performance
without actually executing models. While practicable, these approaches limit
extensive dataset profiling to a few datasets and introduce bias that favors
models suited for representative datasets. As a result, each dataset's unique
characteristics are left unexplored and subpar models are selected based on
inference from generalized datasets. This necessitates a new paradigm that
introduces dataset profiling into the model selection process. To demonstrate
the need for dataset-specific profiling, we answer two questions:(1) Can
scientific datasets significantly permute the rank order of computational
models compared to widely used representative datasets? (2) If so, could
lightweight model execution improve benchmarking accuracy? Taken together, the
answers to these questions lay the foundation for a new dataset-aware
benchmarking paradigm.

Interestingness recognition is crucial for decision making in autonomous
exploration for mobile robots. Previous methods proposed an unsupervised online
learning approach that can adapt to environments and detect interesting scenes
quickly, but lack the ability to adapt to human-informed interesting objects.
To solve this problem, we introduce a human-interactive framework,
AirInteraction, that can detect human-informed objects via few-shot online
learning. To reduce the communication bandwidth, we first apply an online
unsupervised learning algorithm on the unmanned vehicle for interestingness
recognition and then only send the potential interesting scenes to a
base-station for human inspection. The human operator is able to draw and
provide bounding box annotations for particular interesting objects, which are
sent back to the robot to detect similar objects via few-shot learning. Only
using few human-labeled examples, the robot can learn novel interesting object
categories during the mission and detect interesting scenes that contain the
objects. We evaluate our method on various interesting scene recognition
datasets. To the best of our knowledge, it is the first human-informed few-shot
object detection framework for autonomous exploration.

Deep learning models are being applied to more and more use cases with
astonishing success stories, but how do they perform in the real world? To test
a model, a specific cleaned data set is assembled. However, when deployed in
the real world, the model will face unexpected, out-of-distribution (OOD) data.
In this work, we show that the so-called "radiologist-level" CheXnet model
fails to recognize all OOD images and classifies them as having lung disease.
To address this issue, we propose in-distribution voting, a novel method to
classify out-of-distribution images for multi-label classification. Using
independent class-wise in-distribution (ID) predictors trained on ID and OOD
data we achieve, on average, 99 % ID classification specificity and 98 %
sensitivity, improving the end-to-end performance significantly compared to
previous works on the chest X-ray 14 data set. Our method surpasses other
output-based OOD detectors even when trained solely with ImageNet as OOD data
and tested with X-ray OOD images.

In-context learning refers to the ability of a model to condition on a prompt
sequence consisting of in-context examples (input-output pairs corresponding to
some task) along with a new query input, and generate the corresponding output.
Crucially, in-context learning happens only at inference time without any
parameter updates to the model. While large language models such as GPT-3
exhibit some ability to perform in-context learning, it is unclear what the
relationship is between tasks on which this succeeds and what is present in the
training data. To make progress towards understanding in-context learning, we
consider the well-defined problem of training a model to in-context learn a
function class (e.g., linear functions): that is, given data derived from some
functions in the class, can we train a model to in-context learn "most"
functions from this class? We show empirically that standard Transformers can
be trained from scratch to perform in-context learning of linear functions --
that is, the trained model is able to learn unseen linear functions from
in-context examples with performance comparable to the optimal least squares
estimator. In fact, in-context learning is possible even under two forms of
distribution shift: (i) between the training data of the model and
inference-time prompts, and (ii) between the in-context examples and the query
input during inference. We also show that we can train Transformers to
in-context learn more complex function classes -- namely sparse linear
functions, two-layer neural networks, and decision trees -- with performance
that matches or exceeds task-specific learning algorithms. Our code and models
are available at https://github.com/dtsip/in-context-learning .

Sociodemographic factors (e.g., gender or age) shape our language. Previous
work showed that incorporating specific sociodemographic factors can
consistently improve performance for various NLP tasks in traditional NLP
models. We investigate whether these previous findings still hold with
state-of-the-art pretrained Transformers. We use three common specialization
methods proven effective for incorporating external knowledge into pretrained
Transformers (e.g., domain-specific or geographic knowledge). We adapt the
language representations for the sociodemographic dimensions of gender and age,
using continuous language modeling and dynamic multi-task learning for
adaptation, where we couple language modeling with the prediction of a
sociodemographic class. Our results when employing a multilingual model show
substantial performance gains across four languages (English, German, French,
and Danish). These findings are in line with the results of previous work and
hold promise for successful sociodemographic specialization. However,
controlling for confounding factors like domain and language shows that, while
sociodemographic adaptation does improve downstream performance, the gains do
not always solely stem from sociodemographic knowledge. Our results indicate
that sociodemographic specialization, while very important, is still an
unresolved problem in NLP.

In this paper, we propose a concept learning architecture that enables a
robot to build symbols through self-exploration by interacting with a varying
number of objects. Our aim is to allow a robot to learn concepts without
constraints, such as a fixed number of interacted objects or pre-defined
symbolic structures. As such, the sought architecture should be able to build
symbols for objects such as single objects that can be grasped, object stacks
that cannot be grasped together, or other composite dynamic structures. Towards
this end, we propose a novel architecture, a self-attentive predictive
encoder-decoder network with binary activation layers. We show the validity of
the proposed network through a robotic manipulation setup involving a varying
number of rigid objects. The continuous sensorimotor experience of the robot is
used by the proposed network to form effect predictors and symbolic structures
that describe the interaction of the robot in a discrete way. We showed that
the robot acquired reasoning capabilities to encode interaction dynamics of a
varying number of objects in different configurations using the discovered
symbols. For example, the robot could reason that (possible multiple numbers
of) objects on top of another object would move together if the object below is
moved by the robot. We also showed that the discovered symbols can be used for
planning to reach goals by training a higher-level neural network that makes
pure symbolic reasoning.

The ability to reason about changes in the environment is crucial for robots
operating over extended periods of time. Agents are expected to capture changes
during operation so that actions can be followed to ensure a smooth progression
of the working session. However, varying viewing angles and accumulated
localization errors make it easy for robots to falsely detect changes in the
surrounding world due to low observation overlap and drifted object
associations. In this paper, based on the recently proposed category-level
Neural Descriptor Fields (NDFs), we develop an object-level online change
detection approach that is robust to partially overlapping observations and
noisy localization results. Utilizing the shape completion capability and
SE(3)-equivariance of NDFs, we represent objects with compact shape codes
encoding full object shapes from partial observations. The objects are then
organized in a spatial tree structure based on object centers recovered from
NDFs for fast queries of object neighborhoods. By associating objects via shape
code similarity and comparing local object-neighbor spatial layout, our
proposed approach demonstrates robustness to low observation overlap and
localization noises. We conduct experiments on both synthetic and real-world
sequences and achieve improved change detection results compared to multiple
baseline methods. Project webpage: https://yilundu.github.io/ndf_change

Prior work on language models (LMs) shows that training on a large number of
diverse tasks improves few-shot learning (FSL) performance on new tasks. We
take this to the extreme, automatically extracting 413,299 tasks from internet
tables - orders of magnitude more than the next-largest public datasets.
Finetuning on the resulting dataset leads to improved FSL performance on
Natural Language Processing (NLP) tasks, but not proportionally to dataset
scale. In fact, we find that narrow subsets of our dataset sometimes outperform
more diverse datasets. For example, finetuning on software documentation from
support.google.com raises FSL performance by a mean of +7.5% on 52 downstream
tasks, which beats training on 40 human-curated NLP datasets (+6.7%).
Finetuning on various narrow datasets leads to similar broad improvements
across test tasks, suggesting that the gains are not from domain adaptation but
adapting to FSL in general. We do not observe clear patterns between the
datasets that lead to FSL gains, leaving open questions about why certain data
helps with FSL.

Mosquito habitat ranges are projected to expand due to climate change. This
investigation aims to identify future mosquito habitats by analyzing preferred
ecological conditions of mosquito larvae. After assembling a data set with
atmospheric records and larvae observations, a neural network is trained to
predict larvae counts from ecological inputs. Time series forecasting is
conducted on these variables and climate projections are passed into the
initial deep learning model to generate location-specific larvae abundance
predictions. The results support the notion of regional ecosystem-driven
changes in mosquito spread, with high-elevation regions in particular
experiencing an increase in susceptibility to mosquito infestation.

Understanding how convolutional neural networks (CNNs) can efficiently learn
high-dimensional functions remains a fundamental challenge. A popular belief is
that these models harness the local and hierarchical structure of natural data
such as images. Yet, we lack a quantitative understanding of how such structure
affects performance, e.g. the rate of decay of the generalisation error with
the number of training samples. In this paper, we study deep CNNs in the kernel
regime. First, we show that the spectrum of the corresponding kernel inherits
the hierarchical structure of the network, and we characterise its asymptotics.
Then, we use this result together with generalisation bounds to prove that deep
CNNs adapt to the spatial scale of the target function. In particular, we find
that if the target function depends on low-dimensional subsets of adjacent
input variables, then the rate of decay of the error is controlled by the
effective dimensionality of these subsets. Conversely, if the teacher function
depends on the full set of input variables, then the error rate is inversely
proportional to the input dimension. We conclude by computing the rate when a
deep CNN is trained on the output of another deep CNN with randomly-initialised
parameters. Interestingly, we find that, despite their hierarchical structure,
the functions generated by deep CNNs are too rich to be efficiently learnable
in high dimension.

Tensor factorization has received increasing interest due to its intrinsic
ability to capture latent factors in multi-dimensional data with many
applications such as recommender systems and Electronic Health Records (EHR)
mining. PARAFAC2 and its variants have been proposed to address irregular
tensors where one of the tensor modes is not aligned, e.g., different users in
recommender systems or patients in EHRs may have different length of records.
PARAFAC2 has been successfully applied on EHRs for extracting meaningful
medical concepts (phenotypes). Despite recent advancements, current models'
predictability and interpretability are not satisfactory, which limits its
utility for downstream analysis. In this paper, we propose MULTIPAR: a
supervised irregular tensor factorization with multi-task learning. MULTIPAR is
flexible to incorporate both static (e.g. in-hospital mortality prediction) and
continuous or dynamic (e.g. the need for ventilation) tasks. By supervising the
tensor factorization with downstream prediction tasks and leveraging
information from multiple related predictive tasks, MULTIPAR can yield not only
more meaningful phenotypes but also better predictive performance for
downstream tasks. We conduct extensive experiments on two real-world temporal
EHR datasets to demonstrate that MULTIPAR is scalable and achieves better
tensor fit with more meaningful subgroups and stronger predictive performance
compared to existing state-of-the-art methods.

This paper tackles the problem of novel category discovery (NCD), which aims
to discriminate unknown categories in large-scale image collections. The NCD
task is challenging due to the closeness to the real-world scenarios, where we
have only encountered some partial classes and images. Unlike other works on
the NCD, we leverage the prototypes to emphasize the importance of category
discrimination and alleviate the issue of missing annotations of novel classes.
Concretely, we propose a novel adaptive prototype learning method consisting of
two main stages: prototypical representation learning and prototypical
self-training. In the first stage, we obtain a robust feature extractor, which
could serve for all images with base and novel categories. This ability of
instance and category discrimination of the feature extractor is boosted by
self-supervised learning and adaptive prototypes. In the second stage, we
utilize the prototypes again to rectify offline pseudo labels and train a final
parametric classifier for category clustering. We conduct extensive experiments
on four benchmark datasets and demonstrate the effectiveness and robustness of
the proposed method with state-of-the-art performance.

Large, annotated datasets are not widely available in medical image analysis
due to the prohibitive time, costs, and challenges associated with labelling
large datasets. Unlabelled datasets are easier to obtain, and in many contexts,
it would be feasible for an expert to provide labels for a small subset of
images. This work presents an information-theoretic active learning framework
that guides the optimal selection of images from the unlabelled pool to be
labeled based on maximizing the expected information gain (EIG) on an
evaluation dataset. Experiments are performed on two different medical image
classification datasets: multi-class diabetic retinopathy disease scale
classification and multi-class skin lesion classification. Results indicate
that by adapting EIG to account for class-imbalances, our proposed Adapted
Expected Information Gain (AEIG) outperforms several popular baselines
including the diversity based CoreSet and uncertainty based maximum entropy
sampling. Specifically, AEIG achieves ~95% of overall performance with only 19%
of the training data, while other active learning approaches require around
25%. We show that, by careful design choices, our model can be integrated into
existing deep learning classifiers.

We develop a penalized two-pass regression with time-varying factor loadings.
The penalization in the first pass enforces sparsity for the time-variation
drivers while also maintaining compatibility with the no-arbitrage restrictions
by regularizing appropriate groups of coefficients. The second pass delivers
risk premia estimates to predict equity excess returns. Our Monte Carlo results
and our empirical results on a large cross-sectional data set of US individual
stocks show that penalization without grouping can yield to nearly all
estimated time-varying models violating the no-arbitrage restrictions.
Moreover, our results demonstrate that the proposed method reduces the
prediction errors compared to a penalized approach without appropriate grouping
or a time-invariant factor model.

Understanding extreme events and their probability is key for the study of
climate change impacts, risk assessment, adaptation, and the protection of
living beings. In this work we develop a methodology to build forecasting
models for extreme heatwaves. These models are based on convolutional neural
networks, trained on extremely long 8,000-year climate model outputs. Because
the relation between extreme events is intrinsically probabilistic, we
emphasise probabilistic forecast and validation. We demonstrate that deep
neural networks are suitable for this purpose for long lasting 14-day heatwaves
over France, up to 15 days ahead of time for fast dynamical drivers (500 hPa
geopotential height fields), and also at much longer lead times for slow
physical drivers (soil moisture). The method is easily implemented and
versatile. We find that the deep neural network selects extreme heatwaves
associated with a North-Hemisphere wavenumber-3 pattern. We find that the 2
meter temperature field does not contain any new useful statistical information
for heatwave forecast, when added to the 500 hPa geopotential height and soil
moisture fields. The main scientific message is that training deep neural
networks for predicting extreme heatwaves occurs in a regime of drastic lack of
data. We suggest that this is likely the case for most other applications to
large scale atmosphere and climate phenomena. We discuss perspectives for
dealing with the lack of data regime, for instance rare event simulations, and
how transfer learning may play a role in this latter task.

Graph-based models have achieved great success in person re-identification
tasks recently, which compute the graph topology structure (affinities) among
different people first and then pass the information across them to achieve
stronger features. But we find existing graph-based methods in the
visible-infrared person re-identification task (VI-ReID) suffer from bad
generalization because of two issues: 1) train-test modality balance gap, which
is a property of VI-ReID task. The number of two modalities data are balanced
in the training stage, but extremely unbalanced in inference, causing the low
generalization of graph-based VI-ReID methods. 2) sub-optimal topology
structure caused by the end-to-end learning manner to the graph module. We
analyze that the well-trained input features weaken the learning of graph
topology, making it not generalized enough during the inference process. In
this paper, we propose a Counterfactual Intervention Feature Transfer (CIFT)
method to tackle these problems. Specifically, a Homogeneous and Heterogeneous
Feature Transfer (H2FT) is designed to reduce the train-test modality balance
gap by two independent types of well-designed graph modules and an unbalanced
scenario simulation. Besides, a Counterfactual Relation Intervention (CRI) is
proposed to utilize the counterfactual intervention and causal effect tools to
highlight the role of topology structure in the whole training process, which
makes the graph topology structure more reliable. Extensive experiments on
standard VI-ReID benchmarks demonstrate that CIFT outperforms the
state-of-the-art methods under various settings.

In this paper, we propose a sampling algorithm based on statistical machine
learning to obtain conditional nonlinear optimal perturbation (CNOP), which is
different from traditional deterministic optimization methods. The new approach
reduces the expensive gradient (first-order) information directly by the
objective value (zeroth-order) information and does not use the adjoint
technique that requires large amounts of storage and produces instability due
to linearization. An intuitive analysis of the sampling algorithm is shown
rigorously within the form of a concentration inequality for the approximate
gradient. The numerical experiments of a theoretical model, Burgers equation
with small viscosity, are implemented to obtain the CNOPs. The performance of
standard spatial structures demonstrates that at the cost of losing accuracy,
the sample-based method with fewer samples spends time relatively shorter than
the adjoint-based method and directly from the definition. Finally, we show
that the nonlinear time evolution of the CNOPs obtained by all the algorithms
is nearly consistent with the quantity of norm square of perturbations, their
difference and relative difference based on the definition method.

Large-scale weakly supervised product retrieval is a practically useful yet
computationally challenging problem. This paper introduces a novel solution for
the eBay Visual Search Challenge (eProduct) held at the Ninth Workshop on
Fine-Grained Visual Categorisation workshop (FGVC9) of CVPR 2022. This
competition presents two challenges: (a) E-commerce is a drastically
fine-grained domain including many products with subtle visual differences; (b)
A lacking of target instance-level labels for model training, with only coarse
category labels and product titles available. To overcome these obstacles, we
formulate a strong solution by a set of dedicated designs: (a) Instead of using
text training data directly, we mine thousands of pseudo-attributes from
product titles and use them as the ground truths for multi-label
classification. (b) We incorporate several strong backbones with advanced
training recipes for more discriminative representation learning. (c) We
further introduce a number of post-processing techniques including whitening,
re-ranking and model ensemble for retrieval enhancement. By achieving 71.53%
MAR, our solution "Involution King" achieves the second position on the
leaderboard.

Deep learning is increasingly gaining rapid adoption in healthcare to help
improve patient outcomes. This is more so in medical image analysis which
requires extensive training to gain the requisite expertise to become a trusted
practitioner. However, while deep learning techniques have continued to provide
state-of-the-art predictive performance, one of the primary challenges that
stands to hinder this progress in healthcare is the opaque nature of the
inference mechanism of these models. So, attribution has a vital role in
building confidence in stakeholders for the predictions made by deep learning
models to inform clinical decisions. This work seeks to answer the question:
what do deep neural network models learn in medical images? In that light, we
present a novel attribution framework using adaptive path-based gradient
integration techniques. Results show a promising direction of building trust in
domain experts to improve healthcare outcomes by allowing them to understand
the input-prediction correlative structures, discover new bio-markers, and
reveal potential model biases.

The recent increase in popularity of volumetric representations for scene
reconstruction and novel view synthesis has put renewed focus on animating
volumetric content at high visual quality and in real-time. While implicit
deformation methods based on learned functions can produce impressive results,
they are `black boxes' to artists and content creators, they require large
amounts of training data to generalise meaningfully, and they do not produce
realistic extrapolations outside the training data. In this work we solve these
issues by introducing a volume deformation method which is real-time, easy to
edit with off-the-shelf software and can extrapolate convincingly. To
demonstrate the versatility of our method, we apply it in two scenarios:
physics-based object deformation and telepresence where avatars are controlled
using blendshapes. We also perform thorough experiments showing that our method
compares favourably to both volumetric approaches combined with implicit
deformation and methods based on mesh deformation.

Previous methods based on 3DCNN, convLSTM, or optical flow have achieved
great success in video salient object detection (VSOD). However, they still
suffer from high computational costs or poor quality of the generated saliency
maps. To solve these problems, we design a space-time memory (STM)-based
network, which extracts useful temporal information of the current frame from
adjacent frames as the temporal branch of VSOD. Furthermore, previous methods
only considered single-frame prediction without temporal association. As a
result, the model may not focus on the temporal information sufficiently. Thus,
we initially introduce object motion prediction between inter-frame into VSOD.
Our model follows standard encoder--decoder architecture. In the encoding
stage, we generate high-level temporal features by using high-level features
from the current and its adjacent frames. This approach is more efficient than
the optical flow-based methods. In the decoding stage, we propose an effective
fusion strategy for spatial and temporal branches. The semantic information of
the high-level features is used to fuse the object details in the low-level
features, and then the spatiotemporal features are obtained step by step to
reconstruct the saliency maps. Moreover, inspired by the boundary supervision
commonly used in image salient object detection (ISOD), we design a
motion-aware loss for predicting object boundary motion and simultaneously
perform multitask learning for VSOD and object motion prediction, which can
further facilitate the model to extract spatiotemporal features accurately and
maintain the object integrity. Extensive experiments on several datasets
demonstrated the effectiveness of our method and can achieve state-of-the-art
metrics on some datasets. The proposed model does not require optical flow or
other preprocessing, and can reach a speed of nearly 100 FPS during inference.

Nuclear Magnetic Resonance (NMR) is used in structural biology to
experimentally determine the structure of proteins, which is used in many areas
of biology and is an important part of drug development. Unfortunately, NMR
data can cost thousands of dollars per sample to collect and it can take a
specialist weeks to assign the observed resonances to specific chemical groups.
There has thus been growing interest in the NMR community to use deep learning
to automate NMR data annotation. Due to similarities between NMR and audio
data, we propose that methods used in acoustic signal processing can be applied
to NMR as well. Using a simulated amino acid dataset, we show that by swapping
out filter banks with a trainable convolutional encoder, acoustic signal
embeddings from speaker verification models can be used for amino acid
classification in 2D NMR spectra by treating each amino acid as a unique
speaker. On an NMR dataset comparable in size with of 46 hours of audio, we
achieve a classification performance of 97.7% on a 20-class problem. We also
achieve a 23% relative improvement by using an acoustic embedding model
compared to an existing NMR-based model.

Open Radio Access Network (O-RAN) is an emerging paradigm, whereby
virtualized network infrastructure elements from different vendors communicate
via open, standardized interfaces. A key element therein is the RAN Intelligent
Controller (RIC), an Artificial Intelligence (AI)-based controller.
Traditionally, all data available in the network has been used to train a
single AI model to use at the RIC. In this paper we introduce, discuss, and
evaluate the creation of multiple AI model instances at different RICs,
leveraging information from some (or all) locations for their training. This
brings about a flexible relationship between gNBs, the AI models used to
control them, and the data such models are trained with. Experiments with
real-world traces show how using multiple AI model instances that choose
training data from specific locations improve the performance of traditional

The observational characteristics of a linear structural equation model can
be effectively described by polynomial constraints on the observed covariance
matrix. However, these polynomials can be exponentially large, making them
impractical for many purposes. In this paper, we present a graphical notation
for many of these polynomial constraints. The expressive power of this notation
is investigated both theoretically and empirically.

Visual-inertial localization is a key problem in computer vision and robotics
applications such as virtual reality, self-driving cars, and aerial vehicles.
The goal is to estimate an accurate pose of an object when either the
environment or the dynamics are known. Recent methods directly regress the pose
using convolutional and spatio-temporal networks. Absolute pose regression
(APR) techniques predict the absolute camera pose from an image input in a
known scene. Odometry methods perform relative pose regression (RPR) that
predicts the relative pose from a known object dynamic (visual or inertial
inputs). The localization task can be improved by retrieving information of
both data sources for a cross-modal setup, which is a challenging problem due
to contradictory tasks. In this work, we conduct a benchmark to evaluate deep
multimodal fusion based on PGO and attention networks. Auxiliary and Bayesian
learning are integrated for the APR task. We show accuracy improvements for the
RPR-aided APR task and for the RPR-RPR task for aerial vehicles and hand-held
devices. We conduct experiments on the EuRoC MAV and PennCOSYVIO datasets, and
record a novel industry dataset.

Recent research on the robustness of deep learning has shown that Vision
Transformers (ViTs) surpass the Convolutional Neural Networks (CNNs) under some
perturbations, e.g., natural corruption, adversarial attacks, etc. Some papers
argue that the superior robustness of ViT comes from the segmentation of its
input images; others say that the Multi-head Self-Attention (MSA) is the key to
preserving the robustness. In this paper, we aim to introduce a principled and
unified theoretical framework to investigate such an argument on ViT's
robustness. We first theoretically prove that, unlike Transformers in Natural
Language Processing, ViTs are Lipschitz continuous. Then we theoretically
analyze the adversarial robustness of ViTs from the perspective of the Cauchy
Problem, via which we can quantify how the robustness propagates through
layers. We demonstrate that the first and last layers are the critical factors
to affect the robustness of ViTs. Furthermore, based on our theory, we
empirically show that unlike the claims from existing research, MSA only
contributes to the adversarial robustness of ViTs under weak adversarial
attacks, e.g., FGSM, and surprisingly, MSA actually comprises the model's
adversarial robustness under stronger attacks, e.g., PGD attacks.

It's by now folklore that to understand the activity pattern of a user in an
online social network (OSN) platform, one needs to look at his friends or the
ones he follows. The common perception is that these friends exert influence on
the user, effecting his decision whether to re-share content or not. Hinging
upon this intuition, a variety of models were developed to predict how
information propagates in OSN, similar to the way infection spreads in the
population. In this paper, we revisit this world view and arrive at new
conclusions. Given a set of users $V$, we study the task of predicting whether
a user $u \in V$ will re-share content by some $v \in V$ at the following time
window given the activity of all the users in $V$ in the previous time window.
We design several algorithms for this task, ranging from a simple greedy
algorithm that only learns $u$'s conditional probability distribution, ignoring
the rest of $V$, to a convolutional neural network-based algorithm that
receives the activity of all of $V$, but does not receive explicitly the social
link structure. We tested our algorithms on four datasets that we collected
from Twitter, each revolving around a different popular topic in 2020. The best
performance, average F1-score of 0.86 over the four datasets, was achieved by
the convolutional neural network. The simple, social-link ignorant, algorithm
achieved an average F1-score of 0.78.

In minimally invasive surgery, surgical workflow segmentation from video
analysis is a well studied topic. The conventional approach defines it as a
multi-class classification problem, where individual video frames are
attributed a surgical phase label.
  We introduce a novel reinforcement learning formulation for offline phase
transition retrieval. Instead of attempting to classify every video frame, we
identify the timestamp of each phase transition. By construction, our model
does not produce spurious and noisy phase transitions, but contiguous phase
blocks. We investigate two different configurations of this model. The first
does not require processing all frames in a video (only <60% and <20% of frames
in 2 different applications), while producing results slightly under the
state-of-the-art accuracy. The second configuration processes all video frames,
and outperforms the state-of-the art at a comparable computational cost.
  We compare our method against the recent top-performing frame-based
approaches TeCNO and Trans-SVNet on the public dataset Cholec80 and also on an
in-house dataset of laparoscopic sacrocolpopexy. We perform both a frame-based
(accuracy, precision, recall and F1-score) and an event-based (event ratio)
evaluation of our algorithms.

In this paper, we propose a novel domain generalization (DG) framework based
on a new upper bound to the risk on the unseen domain. Particularly, our
framework proposes to jointly minimize both the covariate-shift as well as the
concept-shift between the seen domains for a better performance on the unseen
domain. While the proposed approach can be implemented via an arbitrary
combination of covariate-alignment and concept-alignment modules, in this work
we use well-established approaches for distributional alignment namely, Maximum
Mean Discrepancy (MMD) and covariance Alignment (CORAL), and use an Invariant
Risk Minimization (IRM)-based approach for concept alignment. Our numerical
results show that the proposed methods perform as well as or better than the
state-of-the-art for domain generalization on several data sets.

Working with causal models at different levels of abstraction is an important
feature of science. Existing work has already considered the problem of
expressing formally the relation of abstraction between causal models. In this
paper, we focus on the problem of learning abstractions. We start by defining
the learning problem formally in terms of the optimization of a standard
measure of consistency. We then point out the limitation of this approach, and
we suggest extending the objective function with a term accounting for
information loss. We suggest a concrete measure of information loss, and we
illustrate its contribution to learning new abstractions.

Inference of transfer operators from data is often formulated as a classical
problem that hinges on the Ulam method. The usual description, which we will
call the Ulam-Galerkin method, is in terms of projection onto basis functions
that are characteristic functions supported over a fine grid of rectangles. In
these terms, the usual Ulam-Galerkin approach can be understood as density
estimation by the histogram method. Here we show that the problem can be recast
in statistical density estimation formalism. This recasting of the classical
problem, is a perspective that allows for an explicit and rigorous analysis of
bias and variance, and therefore toward a discussion of the mean square error.
  Keywords: Transfer Operators; Frobenius-Perron operator; probability density
estimation; Ulam-Galerkin method;Kernel Density Estimation.

Analysing 88 sources published from 2011 to 2021, this paper presents a first
systematic review of the computer vision-based analysis of buildings and the
built environments to assess its value to architectural and urban design
studies. Following a multi-stage selection process, the types of algorithms and
data sources used are discussed in respect to architectural applications such
as a building classification, detail classification, qualitative environmental
analysis, building condition survey, and building value estimation. This
reveals current research gaps and trends, and highlights two main categories of
research aims. First, to use or optimise computer vision methods for
architectural image data, which can then help automate time-consuming,
labour-intensive, or complex tasks of visual analysis. Second, to explore the
methodological benefits of machine learning approaches to investigate new
questions about the built environment by finding patterns and relationships
between visual, statistical, and qualitative data, which can overcome
limitations of conventional manual analysis. The growing body of research
offers new methods to architectural and design studies, with the paper
identifying future challenges and directions of research.

Fundamental diagrams describe the relationship between speed, flow, and
density for some roadway (or set of roadway) configuration(s). These diagrams
typically do not reflect, however, information on how speed-flow relationships
change as a function of exogenous variables such as curb configuration, weather
or other exogenous, contextual information. In this paper we present a machine
learning methodology that respects known engineering constraints and physical
laws of roadway flux - those that are captured in fundamental diagrams - and
show how this can be used to introduce contextual information into the
generation of these diagrams. The modeling task is formulated as a probe
vehicle trajectory reconstruction problem with Neural Ordinary Differential
Equations (Neural ODEs). With the presented methodology, we extend the
fundamental diagram to non-idealized roadway segments with potentially
obstructed traffic data. For simulated data, we generalize this relationship by
introducing contextual information at the learning stage, i.e. vehicle
composition, driver behavior, curb zoning configuration, etc, and show how the
speed-flow relationship changes as a function of these exogenous factors
independent of roadway design.

Despite the recent efforts in accurate 3D annotations in hand and object
datasets, there still exist gaps in 3D hand and object reconstructions.
Existing works leverage contact maps to refine inaccurate hand-object pose
estimations and generate grasps given object models. However, they require
explicit 3D supervision which is seldom available and therefore, are limited to
constrained settings, e.g., where thermal cameras observe residual heat left on
manipulated objects. In this paper, we propose a novel semi-supervised
framework that allows us to learn contact from monocular images. Specifically,
we leverage visual and geometric consistency constraints in large-scale
datasets for generating pseudo-labels in semi-supervised learning and propose
an efficient graph-based network to infer contact. Our semi-supervised learning
framework achieves a favourable improvement over the existing supervised
learning methods trained on data with `limited' annotations. Notably, our
proposed model is able to achieve superior results with less than half the
network parameters and memory access cost when compared with the commonly-used
PointNet-based approach. We show benefits from using a contact map that rules
hand-object interactions to produce more accurate reconstructions. We further
demonstrate that training with pseudo-labels can extend contact map estimations
to out-of-domain objects and generalise better across multiple datasets.

The R-learner has been popular in causal inference as a flexible and
efficient meta-learning approach for heterogeneous treatment effect estimation.
In this article, we show the identifiability transition of the generalized
R-learning framework from a binary treatment to continuous treatment. To
resolve the non-identification issue with continuous treatment, we propose a
novel identification strategy named T-identification, acknowledging the use of
Tikhonov regularization rooted in the nonlinear functional analysis. Following
the new identification strategy, we introduce an $\ell_2$-penalized R-learner
framework to estimate the conditional average treatment effect with continuous
treatment. The new R-learner framework accommodates modern, flexible machine
learning algorithms for both nuisance function and target estimand estimation.
Asymptotic properties are studied when the target estimand is approximated by
sieve approximation, including general error bounds, asymptotic normality, and
inference. Simulations illustrate the superior performance of our proposed
estimator. An application of the new method to the medical information mart for
intensive care data reveals the heterogeneous treatment effect of oxygen
saturation on survival in sepsis patients.

This paper investigates a family of methods for defending against adversarial
attacks that owe part of their success to creating a noisy, discontinuous, or
otherwise rugged loss landscape that adversaries find difficult to navigate. A
common, but not universal, way to achieve this effect is via the use of
stochastic neural networks. We show that this is a form of gradient
obfuscation, and propose a general extension to gradient-based adversaries
based on the Weierstrass transform, which smooths the surface of the loss
function and provides more reliable gradient estimates. We further show that
the same principle can strengthen gradient-free adversaries. We demonstrate the
efficacy of our loss-smoothing method against both stochastic and
non-stochastic adversarial defences that exhibit robustness due to this type of
obfuscation. Furthermore, we provide analysis of how it interacts with
Expectation over Transformation; a popular gradient-sampling method currently
used to attack stochastic defences.

We propose a novel method enabling autocompletion of chemical flowsheets.
This idea is inspired by the autocompletion of text. We represent flowsheets as
strings using the text-based SFILES 2.0 notation and learn the grammatical
structure of the SFILES 2.0 language and common patterns in flowsheets using a
transformer-based language model. We pre-train our model on synthetically
generated flowsheets to learn the flowsheet language grammar. Then, we
fine-tune our model in a transfer learning step on real flowsheet topologies.
Finally, we use the trained model for causal language modeling to autocomplete
flowsheets. Eventually, the proposed method can provide chemical engineers with
recommendations during interactive flowsheet synthesis. The results demonstrate
a high potential of this approach for future AI-assisted process synthesis.

Video frame interpolation is a classic and challenging low-level computer
vision task. Recently, deep learning based methods have achieved impressive
results, and it has been proven that optical flow based methods can synthesize
frames with higher quality. However, most flow-based methods assume a line
trajectory with a constant velocity between two input frames. Only a little
work enforces predictions with curvilinear trajectory, but this requires more
than two frames as input to estimate the acceleration, which takes more time
and memory to execute. To address this problem, we propose an arc trajectory
based model (ATCA), which learns motion prior from only two consecutive frames
and also is lightweight. Experiments show that our approach performs better
than many SOTA methods with fewer parameters and faster inference speed.

Federated learning (FL) is an emerging promising paradigm of
privacy-preserving machine learning (ML). An important type of FL is cross-silo
FL, which enables a small scale of organizations to cooperatively train a
shared model by keeping confidential data locally and aggregating weights on a
central parameter server. However, the central server may be vulnerable to
malicious attacks or software failures in practice. To address this issue, in
this paper, we propose DeFL, a novel decentralized weight aggregation framework
for cross-silo FL. DeFL eliminates the central server by aggregating weights on
each participating node and weights of only the current training round are
maintained and synchronized among all nodes. We use Multi-Krum to enable
aggregating correct weights from honest nodes and use HotStuff to ensure the
consistency of the training round number and weights among all nodes. Besides,
we theoretically analyze the Byzantine fault tolerance, convergence, and
complexity of DeFL. We conduct extensive experiments over two widely-adopted
public datasets, i.e. CIFAR-10 and Sentiment140, to evaluate the performance of
DeFL. Results show that DeFL defends against common threat models with minimal
accuracy loss, and achieves up to 100x reduction in storage overhead and up to
12x reduction in network overhead, compared to state-of-the-art decentralized
FL approaches.

Dynamic facial expression recognition (FER) databases provide important data
support for affective computing and applications. However, most FER databases
are annotated with several basic mutually exclusive emotional categories and
contain only one modality, e.g., videos. The monotonous labels and modality
cannot accurately imitate human emotions and fulfill applications in the real
world. In this paper, we propose MAFW, a large-scale multi-modal compound
affective database with 10,045 video-audio clips in the wild. Each clip is
annotated with a compound emotional category and a couple of sentences that
describe the subjects' affective behaviors in the clip. For the compound
emotion annotation, each clip is categorized into one or more of the 11
widely-used emotions, i.e., anger, disgust, fear, happiness, neutral, sadness,
surprise, contempt, anxiety, helplessness, and disappointment. To ensure high
quality of the labels, we filter out the unreliable annotations by an
Expectation Maximization (EM) algorithm, and then obtain 11 single-label
emotion categories and 32 multi-label emotion categories. To the best of our
knowledge, MAFW is the first in-the-wild multi-modal database annotated with
compound emotion annotations and emotion-related captions. Additionally, we
also propose a novel Transformer-based expression snippet feature learning
method to recognize the compound emotions leveraging the expression-change
relations among different emotions and modalities. Extensive experiments on
MAFW database show the advantages of the proposed method over other
state-of-the-art methods for both uni- and multi-modal FER. Our MAFW database
is publicly available from https://mafw-database.github.io/MAFW.

Intelligent analysis and visualization of tables use techniques to
automatically recommend useful knowledge from data, thus freeing users from
tedious multi-dimension data mining. While many studies have succeeded in
automating recommendations through rules or machine learning, it is difficult
to generalize expert knowledge and provide explainable recommendations. In this
paper, we present the recommendation of conditional formatting for the first
time, together with chart recommendation, to exemplify intelligent table
analysis. We propose analytical semantics over tables to uncover common
analysis pattern behind user-created analyses. Here, we design analytical
semantics by separating data focus from user intent, which extract the user
motivation from data and human perspective respectively. Furthermore, the ASTA
framework is designed by us to apply analytical semantics to multiple automated
recommendations. ASTA framework extracts data features by designing signatures
based on expert knowledge, and enables data referencing at field- (chart) or
cell-level (conditional formatting) with pre-trained models. Experiments show
that our framework achieves recall at top 1 of 62.86% on public chart corpora,
outperforming the best baseline about 14%, and achieves 72.31% on the collected
corpus ConFormT, validating that ASTA framework is effective in providing
accurate and explainable recommendations.

Exploration with sparse rewards remains a challenging research problem in
reinforcement learning (RL). Especially for sequential object manipulation
tasks, the RL agent always receives negative rewards until completing all
sub-tasks, which results in low exploration efficiency. To solve these tasks
efficiently, we propose a novel self-guided continual RL framework, RelayHER
(RHER). RHER first decomposes a sequential task into new sub-tasks with
increasing complexity and ensures that the simplest sub-task can be learned
quickly by utilizing Hindsight Experience Replay (HER). Secondly, we design a
multi-goal & multi-task network to learn these sub-tasks simultaneously.
Finally, we propose a Self-Guided Exploration Strategy (SGES). With SGES, the
learned sub-task policy will guide the agent to the states that are helpful to
learn more complex sub-task with HER. By this self-guided exploration and relay
policy learning, RHER can solve these sequential tasks efficiently stage by
stage. The experimental results show that RHER significantly outperforms
vanilla-HER in sample-efficiency on five singleobject and five complex
multi-object manipulation tasks (e.g., Push, Insert, ObstaclePush, Stack,
TStack, etc.). The proposed RHER has also been applied to learn a contact-rich
push task on a physical robot from scratch, and the success rate reached 10/10
with only 250 episodes.

Locally supervised learning aims to train a neural network based on a local
estimation of the global loss function at each decoupled module of the network.
Auxiliary networks are typically appended to the modules to approximate the
gradient updates based on the greedy local losses. Despite being advantageous
in terms of parallelism and reduced memory consumption, this paradigm of
training severely degrades the generalization performance of neural networks.
In this paper, we propose Periodically Guided local Learning (PGL), which
reinstates the global objective repetitively into the local-loss based training
of neural networks primarily to enhance the model's generalization capability.
We show that a simple periodic guidance scheme begets significant performance
gains while having a low memory footprint. We conduct extensive experiments on
various datasets and networks to demonstrate the effectiveness of PGL,
especially in the configuration with numerous decoupled modules.

Due to high mutation rates, COVID-19 evolved rapidly, and several variants
such as Alpha, Gamma, Delta, Beta, and Omicron emerged with altered viral
properties like the severity of the disease caused, transmission rates, etc.
These variants burdened the medical systems worldwide and created a massive
impact on the world economy as each had to be studied and dealt with in its
specific ways. Unsupervised machine learning methods have the ability to
compress, characterize, and visualize unlabelled data. In this paper, we
present a framework that utilizes unsupervised machine learning methods to
discriminate and visualize the associations between major COVID-19 variants
based on their genome sequences. These methods comprise a combination of
selected dimensionality reduction and clustering techniques. The framework
processes the RNA sequences by performing a k-mer analysis on the data and then
compares the results from different dimensionality reduction methods including:
Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour
Embedding (t-SNE), and Uniform Manifold Approximation Projection (UMAP). Our
framework also employs agglomerative hierarchical clustering to visualize the
mutational differences among major variants of concern and country-wise
mutational differences for a particular variant (Delta and Omicron) using
dendrograms. We also provide country-wise mutational differences for selected
variants via dendrograms. We conclude that the proposed framework can
effectively distinguish between the major variants and hence can be used for
the identification of emerging variants in the future.

Extraterrestrial rovers with a general-purpose robotic arm have many
potential applications in lunar and planetary exploration. Introducing autonomy
into such systems is desirable for increasing the time that rovers can spend
gathering scientific data and collecting samples. This work investigates the
applicability of deep reinforcement learning for vision-based robotic grasping
of objects on the Moon. A novel simulation environment with
procedurally-generated datasets is created to train agents under challenging
conditions in unstructured scenes with uneven terrain and harsh illumination. A
model-free off-policy actor-critic algorithm is then employed for end-to-end
learning of a policy that directly maps compact octree observations to
continuous actions in Cartesian space. Experimental evaluation indicates that
3D data representations enable more effective learning of manipulation skills
when compared to traditionally used image-based observations. Domain
randomization improves the generalization of learned policies to novel scenes
with previously unseen objects and different illumination conditions. To this
end, we demonstrate zero-shot sim-to-real transfer by evaluating trained agents
on a real robot in a Moon-analogue facility.

Current deep learning adaptive optimizer methods adjust the step magnitude of
parameter updates by altering the effective learning rate used by each
parameter. Motivated by the known inverse relation between batch size and
learning rate on update step magnitudes, we introduce a novel training
procedure that dynamically decides the dimension and the composition of the
current update step. Our procedure, Dynamic Batch Adaptation (DBA) analyzes the
gradients of every sample and selects the subset that best improves certain
metrics such as gradient variance for each layer of the network. We present
results showing DBA significantly improves the speed of model convergence.
Additionally, we find that DBA produces an increased improvement over standard
optimizers when used in data scarce conditions where, in addition to
convergence speed, it also significantly improves model generalization,
managing to train a network with a single fully connected hidden layer using
only 1% of the MNIST dataset to reach 97.79% test accuracy. In an even more
extreme scenario, it manages to reach 97.44% test accuracy using only 10
samples per class. These results represent a relative error rate reduction of
81.78% and 88.07% respectively, compared to the standard optimizers, Stochastic
Gradient Descent (SGD) and Adam.

We perform quantum process tomography (QPT) for both discrete- and
continuous-variable quantum systems by learning a process representation using
Kraus operators. The Kraus form ensures that the reconstructed process is
completely positive. To make the process trace-preserving, we use a constrained
gradient-descent (GD) approach on the so-called Stiefel manifold during
optimization to obtain the Kraus operators. Our ansatz uses a few Kraus
operators to avoid direct estimation of large process matrices, e.g., the Choi
matrix, for low-rank quantum processes. The GD-QPT matches the performance of
both compressed-sensing (CS) and projected least-squares (PLS) QPT in
benchmarks with two-qubit random processes, but shines by combining the best
features of these two methods. Similar to CS (but unlike PLS), GD-QPT can
reconstruct a process from just a small number of random measurements, and
similar to PLS (but unlike CS) it also works for larger system sizes, up to at
least five qubits. We envisage that the data-driven approach of GD-QPT can
become a practical tool that greatly reduces the cost and computational effort
for QPT in intermediate-scale quantum systems.

Properties such as composability and automatic differentiation made
artificial neural networks a pervasive tool in applications. Tackling more
challenging problems caused neural networks to progressively become more
complex and thus difficult to define from a mathematical perspective. We
present a general definition of linear layer arising from a categorical
framework based on the notions of integration theory and parametric spans. This
definition generalizes and encompasses classical layers (e.g., dense,
convolutional), while guaranteeing existence and computability of the layer's
derivatives for backpropagation.

Cost-effective asset management is an area of interest across several
industries. Specifically, this paper develops a deep reinforcement learning
(DRL) solution to automatically determine an optimal rehabilitation policy for
continuously deteriorating water pipes. We approach the problem of
rehabilitation planning in an online and offline DRL setting. In online DRL,
the agent interacts with a simulated environment of multiple pipes with
distinct length, material, and failure rate characteristics. We train the agent
using deep Q-learning (DQN) to learn an optimal policy with minimal average
costs and reduced failure probability. In offline learning, the agent uses
static data, e.g., DQN replay data, to learn an optimal policy via a
conservative Q-learning algorithm without further interactions with the
environment. We demonstrate that DRL-based policies improve over standard
preventive, corrective, and greedy planning alternatives. Additionally,
learning from the fixed DQN replay dataset surpasses the online DQN setting.
The results warrant that the existing deterioration profiles of water pipes
consisting of large and diverse states and action trajectories provide a
valuable avenue to learn rehabilitation policies in the offline setting without
needing a simulator.

The Gaussian approximation potential (GAP) is an accurate machine-learning
interatomic potential that was recently extended to include the description of
radiation effects. In this study, we seek to validate a faster version of GAP,
known as tabulated GAP (tabGAP), by modelling primary radiation damage in 50-50
W-Mo alloys and pure W using classical molecular dynamics. We find that W-Mo
exhibits a similar number of surviving defects as in pure W. We also observe
W-Mo to possess both more efficient recombination of defects produced during
the initial phase of the cascades, and in some cases, unlike pure W,
recombination of all defects after the cascades cooled down. Furthermore, we
observe that the tabGAP is two orders of magnitude faster than GAP, but
produces a comparable number of surviving defects and cluster sizes. A small
difference is noted in the fraction of interstitials that are bound into
clusters.

With the popularity of deep learning, the hardware implementation platform of
deep learning has received increasing interest. Unlike the general purpose
devices, e.g., CPU, or GPU, where the deep learning algorithms are executed at
the software level, neural network hardware accelerators directly execute the
algorithms to achieve higher both energy efficiency and performance
improvements. However, as the deep learning algorithms evolve frequently, the
engineering effort and cost of designing the hardware accelerators are greatly
increased. To improve the design quality while saving the cost, design
automation for neural network accelerators was proposed, where design space
exploration algorithms are used to automatically search the optimized
accelerator design within a design space. Nevertheless, the increasing
complexity of the neural network accelerators brings the increasing dimensions
to the design space. As a result, the previous design space exploration
algorithms are no longer effective enough to find an optimized design. In this
work, we propose a neural network accelerator design automation framework named
GANDSE, where we rethink the problem of design space exploration, and propose a
novel approach based on the generative adversarial network (GAN) to support an
optimized exploration for high dimension large design space. The experiments
show that GANDSE is able to find the more optimized designs in negligible time
compared with approaches including multilayer perceptron and deep reinforcement
learning.

In jazz, a contrafact is a new melody composed over an existing, but often
reharmonized chord progression. Because reharmonization can introduce a wide
range of variations, detecting contrafacts is a challenging task. This paper
develops a novel vector-space model to represent chord progressions, and uses
it for contrafact detection. The process applies principles from music theory
to reduce the dimensionality of chord space, determine a common key signature
representation, and compute a chordal co-occurrence matrix. The rows of the
matrix form a basis for the vector space in which chord progressions are
represented as piecewise linear functions, and harmonic similarity is evaluated
by computing the membrane area, a novel distance metric. To illustrate our
method's effectiveness, we apply it to the Impro-Visor corpus of 2,612 chord
progressions, and present examples demonstrating its ability to account for
reharmonizations and find contrafacts.

Differentiable neural architecture search (DARTS), as a gradient-guided
search method, greatly reduces the cost of computation and speeds up the
search. In DARTS, the architecture parameters are introduced to the candidate
operations, but the parameters of some weight-equipped operations may not be
trained well in the initial stage, which causes unfair competition between
candidate operations. The weight-free operations appear in large numbers which
results in the phenomenon of performance crash. Besides, a lot of memory will
be occupied during training supernet which causes the memory utilization to be
low. In this paper, a partial channel connection based on channel attention for
differentiable neural architecture search (ADARTS) is proposed. Some channels
with higher weights are selected through the attention mechanism and sent into
the operation space while the other channels are directly contacted with the
processed channels. Selecting a few channels with higher attention weights can
better transmit important feature information into the search space and greatly
improve search efficiency and memory utilization. The instability of network
structure caused by random selection can also be avoided. The experimental
results show that ADARTS achieved 2.46% and 17.06% classification error rates
on CIFAR-10 and CIFAR-100, respectively. ADARTS can effectively solve the
problem that too many skip connections appear in the search process and obtain
network structures with better performance.

We consider the problem of navigating a mobile robot towards a target in an
unknown environment that is endowed with visual sensors, where neither the
robot nor the sensors have access to global positioning information and only
use first-person-view images. While prior work in sensor network based
navigation uses explicit mapping and planning techniques, and are often aided
by external positioning systems, we propose a vision-only based learning
approach that leverages a Graph Neural Network (GNN) to encode and communicate
relevant viewpoint information to the mobile robot. During navigation, the
robot is guided by a model that we train through imitation learning to
approximate optimal motion primitives, thereby predicting the effective
cost-to-go (to the target). In our experiments, we first demonstrate
generalizability to previously unseen environments with various sensor layouts.
Our results show that by using communication between the sensors and the robot,
we achieve up to $2.0\times$ improvement in SPL (Success weighted by Path
Length) when compared to the communication-free baseline. This is done without
requiring a global map, positioning data, nor pre-calibration of the sensor
network. Second, we perform a zero-shot transfer of our model from simulation
to the real world. To this end, we train a `translator' model that translates
between {latent encodings of} real and simulated images so that the navigation
policy (which is trained entirely in simulation) can be used directly on the
real robot, without additional fine-tuning. Physical experiments demonstrate
the feasibility of our approach in various cluttered environments.

Compared to on-policy policy gradient techniques, off-policy model-free deep
reinforcement learning (RL) that uses previously gathered data can improve
sampling efficiency. However, off-policy learning becomes challenging when the
discrepancy between the distributions of the policy of interest and the
policies that collected the data increases. Although the well-studied
importance sampling and off-policy policy gradient techniques were proposed to
compensate for this discrepancy, they usually require a collection of long
trajectories that increases the computational complexity and induce additional
problems such as vanishing/exploding gradients or discarding many useful
experiences. Moreover, their generalization to continuous action domains is
strictly limited as they require action probabilities, which is unsuitable for
deterministic policies. To overcome these limitations, we introduce a novel
policy similarity measure to mitigate the effects of such discrepancy. Our
method offers an adequate single-step off-policy correction without any
probability estimates, and theoretical results show that it can achieve a
contraction mapping with a fixed unique point, which allows "safe" off-policy
learning. An extensive set of empirical results indicate that our algorithm
substantially improves the state-of-the-art and attains higher returns in fewer
steps than the competing methods by efficiently scheduling the learning rate in
Q-learning and policy optimization.

This paper discusses creating and analysing a new dataset for data mining and
text analytics research, contributing to a joint Leeds University research
project for the Corpus of National Dialects. This report investigates machine
learning classifiers to classify samples of French dialect text across various
French-speaking countries. Following the steps of the CRISP-DM methodology,
this report explores the data collection process, data quality issues and data
conversion for text analysis. Finally, after applying suitable data mining
techniques, the evaluation methods, best overall features and classifiers and
conclusions are discussed.

Transformer-based pretrained language models (LMs) are ubiquitous across
natural language understanding, but cannot be applied to long sequences such as
stories, scientific articles and long documents, due to their quadratic
complexity. While a myriad of efficient transformer variants have been
proposed, they are typically based on custom implementations that require
expensive pretraining from scratch. In this work, we propose SLED:
SLiding-Encoder and Decoder, a simple approach for processing long sequences
that re-uses and leverages battle-tested short-text pretrained LMs.
Specifically, we partition the input into overlapping chunks, encode each with
a short-text LM encoder and use the pretrained decoder to fuse information
across chunks (fusion-in-decoder). We illustrate through controlled experiments
that SLED offers a viable strategy for long text understanding and evaluate our
approach on SCROLLS, a benchmark with seven datasets across a wide range of
language understanding tasks. We find that SLED is competitive with specialized
models that are up to 50x larger and require a dedicated and expensive
pretraining step.

Taking advantage of their data-driven and model-free features, Deep
Reinforcement Learning (DRL) algorithms have the potential to deal with the
increasing level of uncertainty due to the introduction of renewable-based
generation. To deal simultaneously with the energy systems' operational cost
and technical constraints (e.g, generation-demand power balance) DRL algorithms
must consider a trade-off when designing the reward function. This trade-off
introduces extra hyperparameters that impact the DRL algorithms' performance
and capability of providing feasible solutions. In this paper, a performance
comparison of different DRL algorithms, including DDPG, TD3, SAC, and PPO, are
presented. We aim to provide a fair comparison of these DRL algorithms for
energy systems optimal scheduling problems. Results show DRL algorithms'
capability of providing in real-time good-quality solutions, even in unseen
operational scenarios, when compared with a mathematical programming model of
the energy system optimal scheduling problem. Nevertheless, in the case of
large peak consumption, these algorithms failed to provide feasible solutions,
which can impede their practical implementation.

Safe Policy Improvement (SPI) is an important technique for offline
reinforcement learning in safety critical applications as it improves the
behavior policy with a high probability. We classify various SPI approaches
from the literature into two groups, based on how they utilize the uncertainty
of state-action pairs. Focusing on the Soft-SPIBB (Safe Policy Improvement with
Soft Baseline Bootstrapping) algorithms, we show that their claim of being
provably safe does not hold. Based on this finding, we develop adaptations, the
Adv-Soft-SPIBB algorithms, and show that they are provably safe. A heuristic
adaptation, Lower-Approx-Soft-SPIBB, yields the best performance among all
SPIBB algorithms in extensive experiments on two benchmarks. We also check the
safety guarantees of the provably safe algorithms and show that huge amounts of
data are necessary such that the safety bounds become useful in practice.

Modeling molecular potential energy surface is of pivotal importance in
science. Graph Neural Networks have shown great success in this field,
especially those using rotation-equivariant representations. However, they
either suffer from a complex mathematical form or lack theoretical support and
design principle. To avoid using equivariant representations, we introduce a
novel local frame method to molecule representation learning and analyze its
expressive power. With a frame and the projection of equivariant vectors on the
frame, GNNs can map the local environment of an atom to a scalar representation
injectively. Messages can also be passed across local environments with frames'
projection on frames. We further analyze when and how we can build such local
frames. We prove that local frames always exist when the local environments
have no symmetry, as is often the case in molecular dynamics simulations. For
symmetric molecules, though only degenerate frames can be built, we find that
the local frame method may still achieve high expressive power in some common
cases due to the reduced degrees of freedom. Using only scalar representations
allows us to adopt existing simple and powerful GNN architectures. Our model
outperforms a range of state-of-the-art baselines in experiments. Simpler
architectures also lead to higher scalability. Our model only takes about 30%
inference time compared with the fastest baseline.

Convolutional neural networks (CNNs) have been the de facto standard in a
diverse set of computer vision tasks for many years. Especially, deep neural
networks based on seminal architectures such as U-shaped models with
skip-connections or atrous convolution with pyramid pooling have been tailored
to a wide range of medical image analysis tasks. The main advantage of such
architectures is that they are prone to detaining versatile local features.
However, as a general consensus, CNNs fail to capture long-range dependencies
and spatial correlations due to the intrinsic property of confined receptive
field size of convolution operations. Alternatively, Transformer, profiting
from global information modelling that stems from the self-attention mechanism,
has recently attained remarkable performance in natural language processing and
computer vision. Nevertheless, previous studies prove that both local and
global features are critical for a deep model in dense prediction, such as
segmenting complicated structures with disparate shapes and configurations. To
this end, this paper proposes TransDeepLab, a novel DeepLab-like pure
Transformer for medical image segmentation. Specifically, we exploit
hierarchical Swin-Transformer with shifted windows to extend the DeepLabv3 and
model the Atrous Spatial Pyramid Pooling (ASPP) module. A thorough search of
the relevant literature yielded that we are the first to model the seminal
DeepLab model with a pure Transformer-based model. Extensive experiments on
various medical image segmentation tasks verify that our approach performs
superior or on par with most contemporary works on an amalgamation of Vision
Transformer and CNN-based methods, along with a significant reduction of model
complexity. The codes and trained models are publicly available at
https://github.com/rezazad68/transdeeplab

The ability to manipulate clouds of ultra-cold atoms is crucial for modern
experiments on quantum manybody systems and quantum thermodynamics as well as
future metrological applications of Bose-Einstein condensate. While optical
manipulation offers almost arbitrary flexibility, the precise control of the
resulting dipole potentials and the mitigation of unwanted disturbances is
quite involved and only heuristic algorithms with rather slow convergence rates
are available up to now. This paper thus suggests the application of iterative
learning control (ILC) methods to generate fine-tuned effective potentials in
the presence of uncertainties and external disturbances. Therefore, the given
problem is reformulated to obtain a one-dimensional tracking problem by using a
quasicontinuous input mapping which can be treated by established ILC methods.
Finally, the performance of the proposed concept is illustrated in a simulation
scenario.

The images of the supermassive black holes Sgr A* and M87* by the Event
Horizon Telescope (EHT) collaboration mark a special milestone in the history
of the subject. For the first time we are able to see the shadow of black
holes, testing basic predictions of the theory of general relativity. We are
also now learning more about the fundamental astrophysical processes close to
the event horizon that help to shape entire galaxies and even parts of our
cosmos. The ultimate result was only possible due to a large collaborative
effort of scientists and institutions around the world. The road towards these
images was the result of a long sociological and scientific process. It started
with early pathfinder experiments and a few simple ideas that were remarkably
successful in predicting the basic observational signatures to look for. This
was based on the premise that black holes are inherently simple objects. Here I
describe this journey and some lessons learned from a personal perspective.

The task of Visual Question Answering (VQA) is known to be plagued by the
issue of VQA models exploiting biases within the dataset to make its final
prediction. Many previous ensemble based debiasing methods have been proposed
where an additional model is purposefully trained to be biased in order to aid
in training a robust target model. However, these methods compute the bias for
a model from the label statistics of the training data or directly from single
modal branches. In contrast, in this work, in order to better learn the bias a
target VQA model suffers from, we propose a generative method to train the bias
model \emph{directly from the target model}, called GenB. In particular, GenB
employs a generative network to learn the bias through a combination of the
adversarial objective and knowledge distillation. We then debias our target
model with GenB as a bias model, and show through extensive experiments the
effects of our method on various VQA bias datasets including VQA-CP2, VQA-CP1,
GQA-OOD, and VQA-CE.

We adopt the neural network flow (NN flow) method to study the
Berezinskii-Kosterlitz-Thouless (BKT) phase transitions of the 2-dimensional
q-state clock model with $q\ge 4$. The NN flow consists of a sequence of the
same units to proceed the flow. This unit is a variational autoencoder (VAE)
trained by the data of Monte-Carlo configurations in the way of unsupervised
learning. To gauge the difference among the ensembles of Monte-Carlo
configurations at different temperatures and the uniqueness of the ensemble of
NN-flowed states, we adopt the Jesen-Shannon divergence (JSD) as the
information-distance measure "thermometer". This JSD thermometer compares the
probability distribution functions of the mean magnetization of two ensembles
of states. Our results show that the NN flow will flow an arbitrary spin state
to some state in a fixed-point ensemble of states. The corresponding JSD of the
fixed-point ensemble takes a unique profile with peculiar features, which can
help to identify the critical temperatures of BKT phase transitions of the
underlying Monte-Carlo configurations.

Most reinforcement learning methods for adaptive-traffic-signal-control
require training from scratch to be applied on any new intersection or after
any modification to the road network, traffic distribution, or behavioral
constraints experienced during training. Considering 1) the massive amount of
experience required to train such methods, and 2) that experience must be
gathered by interacting in an exploratory fashion with real road-network-users,
such a lack of transferability limits experimentation and applicability. Recent
approaches enable learning policies that generalize for unseen road-network
topologies and traffic distributions, partially tackling this challenge.
However, the literature remains divided between the learning of cyclic (the
evolution of connectivity at an intersection must respect a cycle) and acyclic
(less constrained) policies, and these transferable methods 1) are only
compatible with cyclic constraints and 2) do not enable coordination. We
introduce a new model-based method, MuJAM, which, on top of enabling explicit
coordination at scale for the first time, pushes generalization further by
allowing a generalization to the controllers' constraints. In a zero-shot
transfer setting involving both road networks and traffic settings never
experienced during training, and in a larger transfer experiment involving the
control of 3,971 traffic signal controllers in Manhattan, we show that MuJAM,
using both cyclic and acyclic constraints, outperforms domain-specific
baselines as well as another transferable approach.

Removing bias while keeping all task-relevant information is challenging for
fair representation learning methods since they would yield random or
degenerate representations w.r.t. labels when the sensitive attributes
correlate with labels. Existing works proposed to inject the label information
into the learning procedure to overcome such issues. However, the assumption
that the observed labels are clean is not always met. In fact, label bias is
acknowledged as the primary source inducing discrimination. In other words, the
fair pre-processing methods ignore the discrimination encoded in the labels
either during the learning procedure or the evaluation stage. This
contradiction puts a question mark on the fairness of the learned
representations. To circumvent this issue, we explore the following question:
\emph{Can we learn fair representations predictable to latent ideal fair labels
given only access to unreliable labels?} In this work, we propose a
\textbf{D}e-\textbf{B}iased \textbf{R}epresentation Learning for
\textbf{F}airness (DBRF) framework which disentangles the sensitive information
from non-sensitive attributes whilst keeping the learned representations
predictable to ideal fair labels rather than observed biased ones. We formulate
the de-biased learning framework through information-theoretic concepts such as
mutual information and information bottleneck. The core concept is that DBRF
advocates not to use unreliable labels for supervision when sensitive
information benefits the prediction of unreliable labels. Experiment results
over both synthetic and real-world data demonstrate that DBRF effectively
learns de-biased representations towards ideal labels.

The CT perfusion (CTP) is a medical exam for measuring the passage of a bolus
of contrast solution through the brain on a pixel-by-pixel basis. The objective
is to draw "perfusion maps" (namely cerebral blood volume, cerebral blood flow
and time to peak) very rapidly for ischemic lesions, and to be able to
distinguish between core and penumubra regions. A precise and quick diagnosis,
in a context of ischemic stroke, can determine the fate of the brain tissues
and guide the intervention and treatment in emergency conditions. In this work
we present UniToBrain dataset, the very first open-source dataset for CTP. It
comprises a cohort of more than a hundred of patients, and it is accompanied by
patients metadata and ground truth maps obtained with state-of-the-art
algorithms. We also propose a novel neural networks-based algorithm, using the
European library ECVL and EDDL for the image processing and developing deep
learning models respectively. The results obtained by the neural network models
match the ground truth and open the road towards potential sub-sampling of the
required number of CT maps, which impose heavy radiation doses to the patients.

Top-down connections in the biological brain has been shown to be important
in high cognitive functions. However, the function of this mechanism in machine
learning has not been defined clearly. In this study, we propose to lay out a
framework constituted by a bottom-up and a top-down network. Here, we use a
Top-down Credit Assignment Network (TDCA-network) to replace the loss function
and back propagation (BP) which serve as the feedback mechanism in traditional
bottom-up network training paradigm. Our results show that the credit given by
well-trained TDCA-network outperforms the gradient from backpropagation in
classification task under different settings on multiple datasets. In addition,
we successfully use a credit diffusing trick, which can keep training and
testing performance remain unchanged, to reduce parameter complexity of the
TDCA-network. More importantly, by comparing their trajectories in the
parameter landscape, we find that TDCA-network directly achieved a global
optimum, in contrast to that backpropagation only can gain a localized optimum.
Thus, our results demonstrate that TDCA-network not only provide a biological
plausible learning mechanism, but also has the potential to directly achieve
global optimum, indicating that top-down credit assignment can substitute
backpropagation, and provide a better learning framework for Deep Neural
Networks.

We introduce a distance-based neural network model for regression, in which
prediction uncertainty is quantified by a belief function on the real line. The
model interprets the distances of the input vector to prototypes as pieces of
evidence represented by Gaussian random fuzzy numbers (GRFN's) and combined by
the generalized product intersection rule, an operator that extends Dempster's
rule to random fuzzy sets. The network output is a GRFN that can be summarized
by three numbers characterizing the most plausible predicted value, variability
around this value, and epistemic uncertainty. Experiments with real datasets
demonstrate the very good performance of the method as compared to
state-of-the-art evidential and statistical learning algorithms.
\keywords{Evidence theory, Dempster-Shafer theory, belief functions, machine
learning, random fuzzy sets.

The accurate and consistent border segmentation plays an important role in
the tumor volume estimation and its treatment in the field of Medical Image
Segmentation. Globally, Lung cancer is one of the leading causes of death and
the early detection of lung nodules is essential for the early cancer diagnosis
and survival rate of patients. The goal of this study was to demonstrate the
feasibility of Deephealth toolkit including PyECVL and PyEDDL libraries to
precisely segment lung nodules. Experiments for lung nodules segmentation has
been carried out on UniToChest using PyECVL and PyEDDL, for data pre-processing
as well as neural network training. The results depict accurate segmentation of
lung nodules across a wide diameter range and better accuracy over a
traditional detection approach. The datasets and the code used in this paper
are publicly available as a baseline reference.

Fashion compatibility models enable online retailers to easily obtain a large
number of outfit compositions with good quality. However, effective fashion
recommendation demands precise service for each customer with a deeper
cognition of fashion. In this paper, we conduct the first study on fashion
cognitive learning, which is fashion recommendations conditioned on personal
physical information. To this end, we propose a Fashion Cognitive Network (FCN)
to learn the relationships among visual-semantic embedding of outfit
composition and appearance features of individuals. FCN contains two
submodules, namely outfit encoder and Multi-label Graph Neural Network
(ML-GCN). The outfit encoder uses a convolutional layer to encode an outfit
into an outfit embedding. The latter module learns label classifiers via
stacked GCN. We conducted extensive experiments on the newly collected O4U
dataset, and the results provide strong qualitative and quantitative evidence
that our framework outperforms alternative methods.

Real-world text applications often involve composing a wide range of text
control operations, such as editing the text w.r.t. an attribute, manipulating
keywords and structure, and generating new text of desired properties. Prior
work typically learns/finetunes a language model (LM) to perform individual or
specific subsets of operations. Recent research has studied combining
operations in a plug-and-play manner, often with costly search or optimization
in the complex sequence space. This paper proposes a new efficient approach for
composable text operations in the compact latent space of text. The
low-dimensionality and differentiability of the text latent vector allow us to
develop an efficient sampler based on ordinary differential equations (ODEs)
given arbitrary plug-in operators (e.g., attribute classifiers). By connecting
pretrained LMs (e.g., GPT2) to the latent space through efficient adaption, we
then decode the sampled vectors into desired text sequences. The flexible
approach permits diverse control operators (sentiment, tense, formality,
keywords, etc.) acquired using any relevant data from different domains.
Experiments show that composing those operators within our approach manages to
generate or edit high-quality text, substantially improving over previous
methods in terms of generation quality and efficiency.

Although pre-trained language models (PLMs) have achieved state-of-the-art
performance on various natural language processing (NLP) tasks, they are shown
to be lacking in knowledge when dealing with knowledge driven tasks. Despite
the many efforts made for injecting knowledge into PLMs, this problem remains
open. To address the challenge, we propose \textbf{DictBERT}, a novel approach
that enhances PLMs with dictionary knowledge which is easier to acquire than
knowledge graph (KG). During pre-training, we present two novel pre-training
tasks to inject dictionary knowledge into PLMs via contrastive learning:
\textit{dictionary entry prediction} and \textit{entry description
discrimination}. In fine-tuning, we use the pre-trained DictBERT as a plugin
knowledge base (KB) to retrieve implicit knowledge for identified entries in an
input sequence, and infuse the retrieved knowledge into the input to enhance
its representation via a novel extra-hop attention mechanism. We evaluate our
approach on a variety of knowledge driven and language understanding tasks,
including NER, relation extraction, CommonsenseQA, OpenBookQA and GLUE.
Experimental results demonstrate that our model can significantly improve
typical PLMs: it gains a substantial improvement of 0.5\%, 2.9\%, 9.0\%, 7.1\%
and 3.3\% on BERT-large respectively, and is also effective on RoBERTa-large.

To tackle the challenge of vehicle re-identification (Re-ID) in complex
lighting environments and diverse scenes, multi-spectral sources like visible
and infrared information are taken into consideration due to their excellent
complementary advantages.
  However, multi-spectral vehicle Re-ID suffers cross-modality discrepancy
caused by heterogeneous properties of different modalities as well as a big
challenge of the diverse appearance with different views in each identity.
  Meanwhile, diverse environmental interference leads to heavy sample
distributional discrepancy in each modality.
  In this work, we propose a novel cross-directional consistency network to
simultaneously overcome the discrepancies from both modality and sample
aspects.
  In particular, we design a new cross-directional center loss to pull the
modality centers of each identity close to mitigate cross-modality discrepancy,
while the sample centers of each identity close to alleviate the sample
discrepancy. Such strategy can generate discriminative multi-spectral feature
representations for vehicle Re-ID.
  In addition, we design an adaptive layer normalization unit to dynamically
adjust individual feature distribution to handle distributional discrepancy of
intra-modality features for robust learning.
  To provide a comprehensive evaluation platform, we create a high-quality
RGB-NIR-TIR multi-spectral vehicle Re-ID benchmark (MSVR310), including 310
different vehicles from a broad range of viewpoints, time spans and
environmental complexities.
  Comprehensive experiments on both created and public datasets demonstrate the
effectiveness of the proposed approach comparing to the state-of-the-art
methods.

Detecting out-of-distribution (OOD) data at inference time is crucial for
many applications of machine learning. We present XOOD: a novel extreme
value-based OOD detection framework for image classification that consists of
two algorithms. The first, XOOD-M, is completely unsupervised, while the second
XOOD-L is self-supervised. Both algorithms rely on the signals captured by the
extreme values of the data in the activation layers of the neural network in
order to distinguish between in-distribution and OOD instances. We show
experimentally that both XOOD-M and XOOD-L outperform state-of-the-art OOD
detection methods on many benchmark data sets in both efficiency and accuracy,
reducing false-positive rate (FPR95) by 50%, while improving the inferencing
time by an order of magnitude.

Restructuring the regional industrial structure (RIS) has the potential to
halt economic recession and achieve revitalization. Understanding the current
status and dynamics of RIS will greatly assist in studying and evaluating the
current industrial structure. Previous studies have focused on qualitative and
quantitative research to rationalize RIS from a macroscopic perspective.
Although recent studies have traced information at the industrial enterprise
level to complement existing research from a micro perspective, the ambiguity
of the underlying variables contributing to the industrial sector and its
composition, the dynamic nature, and the large number of multivariant features
of RIS records have obscured a deep and fine-grained understanding of RIS. To
this end, we propose an interactive visualization system, RISeer, which is
based on interpretable machine learning models and enhanced visualizations
designed to identify the evolutionary patterns of the RIS and facilitate
inter-regional inspection and comparison. Two case studies confirm the
effectiveness of our approach, and feedback from experts indicates that RISeer
helps them to gain a fine-grained understanding of the dynamics and evolution
of the RIS.

Influence maximization aims to select k most-influential vertices or seeds in
a network, where influence is defined by a given diffusion process. Although
computing optimal seed set is NP-Hard, efficient approximation algorithms
exist. However, even state-of-the-art parallel implementations are limited by a
sampling step that incurs large memory footprints. This in turn limits the
problem size reach and approximation quality. In this work, we study the memory
footprint of the sampling process collecting reverse reachability information
in the IMM (Influence Maximization via Martingales) algorithm over large
real-world social networks. We present a memory-efficient optimization approach
(called HBMax) based on Ripples, a state-of-the-art multi-threaded parallel
influence maximization solution. Our approach, HBMax, uses a portion of the
reverse reachable (RR) sets collected by the algorithm to learn the
characteristics of the graph. Then, it compresses the intermediate reverse
reachability information with Huffman coding or bitmap coding, and queries on
the partially decoded data, or directly on the compressed data to preserve the
memory savings obtained through compression. Considering a NUMA architecture,
we scale up our solution on 64 CPU cores and reduce the memory footprint by up
to 82.1% with average 6.3% speedup (encoding overhead is offset by performance
gain from memory reduction) without loss of accuracy. For the largest tested
graph Twitter7 (with 1.4 billion edges), HBMax achieves 5.9X compression ratio
and 2.2X speedup.

We report the discovery of four quasars with $M_{1450} \gtrsim -25.0$ mag at
$z\sim5$ and supermassive black hole mass measurement for one of the quasars.
They were selected as promising high-redshift quasar candidates via deep
learning and Bayesian information criterion, which are expected to be effective
in discriminating quasars from the late-type stars and high-redshift galaxies.
The candidates were observed by the Double Spectrograph on the Palomar 200-inch
Hale Telescope. They show clear Ly$\alpha$ breaks at about 7000-8000 \r{A},
indicating they are quasars at $4.7 < z < 5.6$. For HSC J233107-001014, we
measure the mass of its supermassive black hole (SMBH) using its
C\Romannum{4}$\lambda 1549$ emission line. The SMBH mass and Eddington ratio of
the quasar are found to be $\sim 10^8 M_{\odot}$ and $\sim 0.6$, respectively.
This suggests that this quasar possibly harbors a fast growing SMBH near the
Eddington limit despite its faintness ($L_{\rm Bol} < 10^{46}$ erg s$^{-1}$).
Our 100 $\%$ quasar identification rate supports high efficiency of our deep
learning and Bayesian information criterion selection method, which can be
applied to future surveys to increase high-redshift quasar sample.

An ideal synthetic population, a key input to activity-based models, mimics
the distribution of the individual- and household-level attributes in the
actual population. Since the entire population's attributes are generally
unavailable, household travel survey (HTS) samples are used for population
synthesis. Synthesizing population by directly sampling from HTS ignores the
attribute combinations that are unobserved in the HTS samples but exist in the
population, called 'sampling zeros'. A deep generative model (DGM) can
potentially synthesize the sampling zeros but at the expense of generating
'structural zeros' (i.e., the infeasible attribute combinations that do not
exist in the population). This study proposes a novel method to minimize
structural zeros while preserving sampling zeros. Two regularizations are
devised to customize the training of the DGM and applied to a generative
adversarial network (GAN) and a variational autoencoder (VAE). The adopted
metrics for feasibility and diversity of the synthetic population indicate the
capability of generating sampling and structural zeros -- lower structural
zeros and lower sampling zeros indicate the higher feasibility and the lower
diversity, respectively. Results show that the proposed regularizations achieve
considerable performance improvement in feasibility and diversity of the
synthesized population over traditional models. The proposed VAE additionally
generated 23.5% of the population ignored by the sample with 79.2% precision
(i.e., 20.8% structural zeros rates), while the proposed GAN generated 18.3% of
the ignored population with 89.0% precision. The proposed improvement in DGM
generates a more feasible and diverse synthetic population, which is critical
for the accuracy of an activity-based model.

This paper studies the problem of polygonal mapping of buildings by tackling
the issue of mask reversibility that leads to a notable performance gap between
the predicted masks and polygons from the learning-based methods. We addressed
such an issue by exploiting the hierarchical supervision (of bottom-level
vertices, mid-level line segments and the high-level regional masks) and
proposed a novel interaction mechanism of feature embedding sourced from
different levels of supervision signals to obtain reversible building masks for
polygonal mapping of buildings. As a result, we show that the learned
reversible building masks take all the merits of the advances of deep
convolutional neural networks for high-performing polygonal mapping of
buildings. In the experiments, we evaluated our method on the two public
benchmarks of AICrowd and Inria. On the AICrowd dataset, our proposed method
obtains unanimous improvements on the metrics of AP, APboundary and PoLiS. For
the Inria dataset, our proposed method also obtains very competitive results on
the metrics of IoU and Accuracy. The models and source code are available at
https://github.com/SarahwXU.

The ShenWei many-core series processors powering multiple cutting-edge
supercomputers are equipped with their unique on-chip heterogeneous
architecture. They have long required programmers to write separate codes for
the control part on Management Processing Element (MPE) and accelerated part on
Compute Processing Element (CPE), which is similar to open standards like
OpenCL. Such a programming model results in shattered code and bad
maintainability, and also make it hard to migrate existing projects targeting
commodity processors. Borrowing the experience from CUDA and DPC++ and
leveraging the unique unified main memory on ShenWei many-core architecture, we
propose ShenWei Universal C/C++ (SWUC), a language extension to C/C++ that
enables fluent programming acrossing the boundary of MPE and CPE. Through the
use of several new attributes and compiler directives, users are able to write
codes running on MPE and CPE in a single file. In case of C++, SWUC further
support lambda expressions on CPEs, making it possible to have the code flow
better matching the logical design. SWUC also manages to make the Athread
library interfaces available, easing the learning curve for original ShenWei
users. These powerful features together ensures SWUC to simplify the
programming on ShenWei many-core processors and migration of existing C/C++

Nearest neighbour graphs are widely used to capture the geometry or topology
of a dataset. One of the most common strategies to construct such a graph is
based on selecting a fixed number k of nearest neighbours (kNN) for each point.
However, the kNN heuristic may become inappropriate when sampling density or
noise level varies across datasets. Strategies that try to get around this
typically introduce additional parameters that need to be tuned. We propose a
simple approach to construct an adaptive neighbourhood graph from a single
parameter, based on quadratically regularised optimal transport. Our numerical
experiments show that graphs constructed in this manner perform favourably in
unsupervised and semi-supervised learning applications.

Systematic variation is a common issue in metabolomics data analysis.
Therefore, different scaling and normalization techniques are used to
preprocess the data for metabolomics data analysis. Although several scaling
methods are available in the literature, however, choice of scaling,
transformation and/or normalization technique influence the further statistical
analysis. It is challenging to choose the appropriate scaling technique for
downstream analysis to get accurate results or to make a proper decision.
Moreover, the existing scaling techniques are sensitive to outliers or extreme
values. To fill the gap, our objective is to introduce a robust scaling
approach that is not influenced by outliers as well as provides more accurate
results for downstream analysis. Here, we introduced a new weighted scaling
approach that is robust against outliers however, where no additional outlier
detection/treatment step is needed in data preprocessing and also compared it
with the conventional scaling and normalization techniques through artificial
and real metabolomics datasets. We evaluated the performance of the proposed
method in comparison to the other existing conventional scaling techniques
using metabolomics data analysis in both the absence and presence of different
percentages of outliers. Results show that in most cases, the proposed scaling
technique performs better than the traditional scaling methods in both the
absence and presence of outliers. The proposed method improves the further
downstream metabolomics analysis. The R function of the proposed robust scaling
method is available at
https://github.com/nishithkumarpaul/robustScaling/blob/main/wscaling.R

Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on
the Great Barrier Reef (GBR) and substantial surveillance and control programs
are ongoing to manage COTS populations to ecologically sustainable levels. In
this paper, we present a comprehensive real-time machine learning-based
underwater data collection and curation system on edge devices for COTS
monitoring. In particular, we leverage the power of deep learning-based object
detection techniques, and propose a resource-efficient COTS detector that
performs detection inferences on the edge device to assist marine experts with
COTS identification during the data collection phase. The preliminary results
show that several strategies for improving computational efficiency (e.g.,
batch-wise processing, frame skipping, model input size) can be combined to run
the proposed detection model on edge hardware with low resource consumption and
low information loss.

In this paper, we discuss a framework for teaching bimanual manipulation
tasks by imitation. To this end, we present a system and algorithms for
learning compliant and contact-rich robot behavior from human demonstrations.
The presented system combines insights from admittance control and machine
learning to extract control policies that can (a) recover from and adapt to a
variety of disturbances in time and space, while also (b) effectively
leveraging physical contact with the environment. We demonstrate the
effectiveness of our approach using a real-world insertion task involving
multiple simultaneous contacts between a manipulated object and insertion pegs.
We also investigate efficient means of collecting training data for such
bimanual settings. To this end, we conduct a human-subject study and analyze
the effort and mental demand as reported by the users. Our experiments show
that, while harder to provide, the additional force/torque information
available in teleoperated demonstrations is crucial for phase estimation and
task success. Ultimately, force/torque data substantially improves manipulation
robustness, resulting in a 90% success rate in a multipoint insertion task.
Code and videos can be found at https://bimanualmanipulation.com/

Stress during public speaking is common and adversely affects performance and
self-confidence. Extensive research has been carried out to develop various
models to recognize emotional states. However, minimal research has been
conducted to detect stress during public speaking in real time using voice
analysis. In this context, the current review showed that the application of
algorithms was not properly explored and helped identify the main obstacles in
creating a suitable testing environment while accounting for current
complexities and limitations. In this paper, we present our main idea and
propose a stress detection computational algorithmic model that could be
integrated into a Virtual Reality (VR) application to create an intelligent
virtual audience for improving public speaking skills. The developed model,
when integrated with VR, will be able to detect excessive stress in real time
by analysing voice features correlated to physiological parameters indicative
of stress and help users gradually control excessive stress and improve public
speaking performance

Breast cancer is one of the most serious types of cancer that can occur in
women. The automatic diagnosis of breast cancer by analyzing histological
images (HIs) is important for patients and their prognosis. The classification
of HIs provides clinicians with an accurate understanding of diseases and
allows them to treat patients more efficiently. Deep learning (DL) approaches
have been successfully employed in a variety of fields, particularly medical
imaging, due to their capacity to extract features automatically. This study
aims to classify different types of breast cancer using HIs. In this research,
we present an enhanced capsule network that extracts multi-scale features using
the Res2Net block and four additional convolutional layers. Furthermore, the
proposed method has fewer parameters due to using small convolutional kernels
and the Res2Net block. As a result, the new method outperforms the old ones
since it automatically learns the best possible features. The testing results
show that the model outperformed the previous DL methods.

Modeling the evolution of user preference is essential in recommender
systems. Recently, dynamic graph-based methods have been studied and achieved
SOTA for recommendation, majority of which focus on user's stable long-term
preference. However, in real-world scenario, user's short-term preference
evolves over time dynamically. Although there exists sequential methods that
attempt to capture it, how to model the evolution of short-term preference with
dynamic graph-based methods has not been well-addressed yet. In particular: 1)
existing methods do not explicitly encode and capture the evolution of
short-term preference as sequential methods do; 2) simply using last few
interactions is not enough for modeling the changing trend. In this paper, we
propose Long Short-Term Preference Modeling for Continuous-Time Sequential
Recommendation (LSTSR) to capture the evolution of short-term preference under
dynamic graph. Specifically, we explicitly encode short-term preference and
optimize it via memory mechanism, which has three key operations: Message,
Aggregate and Update. Our memory mechanism can not only store one-hop
information, but also trigger with new interactions online. Extensive
experiments conducted on five public datasets show that LSTSR consistently
outperforms many state-of-the-art recommendation methods across various lines.

Prostate cancer is the most dangerous cancer diagnosed in men worldwide.
Prostate diagnosis has been affected by many factors, such as lesion
complexity, observer visibility, and variability. Many techniques based on
Magnetic Resonance Imaging (MRI) have been used for prostate cancer
identification and classification in the last few decades. Developing these
techniques is crucial and has a great medical effect because they improve the
treatment benefits and the chance of patients' survival. A new technique that
depends on MRI has been proposed to improve the diagnosis. This technique
consists of two stages. First, the MRI images have been preprocessed to make
the medical image more suitable for the detection step. Second, prostate cancer
identification has been performed based on a pre-trained deep learning model,
InceptionResNetV2, that has many advantages and achieves effective results. In
this paper, the InceptionResNetV2 deep learning model used for this purpose has
average accuracy equals to 89.20%, and the area under the curve (AUC) equals to
93.6%. The experimental results of this proposed new deep learning technique
represent promising and effective results compared to other previous

Transformers have achieved remarkable success in sequence modeling and beyond
but suffer from quadratic computational and memory complexities with respect to
the length of the input sequence. Leveraging techniques include sparse and
linear attention and hashing tricks; efficient transformers have been proposed
to reduce the quadratic complexity of transformers but significantly degrade
the accuracy. In response, we first interpret the linear attention and residual
connections in computing the attention map as gradient descent steps. We then
introduce momentum into these components and propose the \emph{momentum
transformer}, which utilizes momentum to improve the accuracy of linear
transformers while maintaining linear memory and computational complexities.
Furthermore, we develop an adaptive strategy to compute the momentum value for
our model based on the optimal momentum for quadratic optimization. This
adaptive momentum eliminates the need to search for the optimal momentum value
and further enhances the performance of the momentum transformer. A range of
experiments on both autoregressive and non-autoregressive tasks, including
image generation and machine translation, demonstrate that the momentum
transformer outperforms popular linear transformers in training efficiency and
accuracy.

Understanding the faint end of quasar luminosity function at a high redshift
is important since the number density of faint quasars is a critical element in
constraining ultraviolet (UV) photon budgets for ionizing the intergalactic
medium (IGM) in the early universe. Here, we present quasar LF reaching
$M_{1450} \sim -22.0$ AB mag at $z\sim5$, about one magnitude deeper than
previous UV LFs. We select quasars at $z\sim5$ with a deep learning technique
from deep data taken by the Hyper Suprime-Cam Subaru Strategic Program
(HSC-SSP), covering a 15.5 deg$^2$ area. Beyond the traditional color selection
method, we improved the quasar selection by training an artificial neural
network for distinguishing $z\sim5$ quasars from non-quasar sources based on
their colors and adopting the Bayesian information criterion that can further
remove high-redshift galaxies from the quasar sample. When applied to a small
sample of spectroscopically identified quasars and galaxies, our method is
successful in selecting quasars at $\sim83 \%$ efficiency ($5/6$) while
minimizing the contamination rate of high-redshift galaxies ($1/8$) by up to
three times compared to the selection using color selection alone ($3/8$). The
number of our final quasar candidates with $M_{1450} < -22.0$ mag is 35. Our
quasar UV LF down to $M_{1450} = -22$ mag or even fainter ($M_{1450} = -21$
mag) suggests a rather low number density of faint quasars and the faint-end
slope of $-1.6^{+0.21}_{-0.19}$, favoring a scenario where quasars play a minor
role in ionizing the IGM at high redshift.

The electronic design automation (EDA) community has been actively exploring
machine learning (ML) for very large-scale integrated computer-aided design
(VLSI CAD). Many studies explored learning-based techniques for cross-stage
prediction tasks in the design flow to achieve faster design convergence.
Although building ML models usually requires a large amount of data, most
studies can only generate small internal datasets for validation because of the
lack of large public datasets. In this essay, we present the first open-source
dataset called CircuitNet for ML tasks in VLSI CAD.

Density estimation is a fundamental task in statistics and machine learning
applications. Kernel density estimation is a powerful tool for non-parametric
density estimation in low dimensions; however, its performance is poor in
higher dimensions. Moreover, its prediction complexity scale linearly with more
training data points. This paper presents a method for neural density
estimation that can be seen as a type of kernel density estimation, but without
the high prediction computational complexity. The method is based on density
matrices, a formalism used in quantum mechanics, and adaptive Fourier features.
The method can be trained without optimization, but it could be also integrated
with deep learning architectures and trained using gradient descent. Thus, it
could be seen as a form of neural density estimation method. The method was
evaluated in different synthetic and real datasets, and its performance
compared against state-of-the-art neural density estimation methods, obtaining
competitive results.

Backdoor Watermarking is a promising paradigm to protect the copyright of
deep neural network (DNN) models for classification tasks. In the existing
works on this subject, researchers have intensively focused on watermarking
robustness, while fidelity, which is concerned with the original functionality,
has received less attention. In this paper, we show that the existing shared
notion of the sole measurement of learning accuracy is insufficient to
characterize backdoor fidelity. Meanwhile, we show that the analogous concept
of embedding distortion in multimedia watermarking, interpreted as the total
weight loss (TWL) in DNN backdoor watermarking, is also unsuitable to measure
the fidelity. To solve this problem, we propose the concept of deep fidelity,
which states that the backdoor watermarked DNN model should preserve both the
feature representation and decision boundary of the unwatermarked host model.
Accordingly, to realize deep fidelity, we propose two loss functions termed as
penultimate feature loss (PFL) and softmax probability-distribution loss (SPL)
to preserve feature representation, while the decision boundary is preserved by
the proposed fix last layer (FixLL) treatment, inspired by the recent discovery
that deep learning with a fixed classifier causes no loss of learning accuracy.
With the above designs, both embedding from scratch and fine-tuning strategies
are implemented to evaluate deep fidelity of backdoor embedding, whose
advantages over the existing methods are verified via experiments using
ResNet18 for MNIST and CIFAR-10 classifications, and wide residual network
(i.e., WRN28_10) for CIFAR-100 task.

Unsupervised generation of clothed virtual humans with various appearance and
animatable poses is important for creating 3D human avatars and other AR/VR
applications. Existing methods are either limited to rigid object modeling, or
not generative and thus unable to synthesize high-quality virtual humans and
animate them. In this work, we propose AvatarGen, the first method that enables
not only non-rigid human generation with diverse appearance but also full
control over poses and viewpoints, while only requiring 2D images for training.
Specifically, it extends the recent 3D GANs to clothed human generation by
utilizing a coarse human body model as a proxy to warp the observation space
into a standard avatar under a canonical space. To model non-rigid dynamics, it
introduces a deformation network to learn pose-dependent deformations in the
canonical space. To improve geometry quality of the generated human avatars, it
leverages signed distance field as geometric representation, which allows more
direct regularization from the body model on the geometry learning. Benefiting
from these designs, our method can generate animatable human avatars with
high-quality appearance and geometry modeling, significantly outperforming
previous 3D GANs. Furthermore, it is competent for many applications, e.g.,
single-view reconstruction, reanimation, and text-guided synthesis. Code and
pre-trained model will be available.

Volume with the Late-Breaking Abstracts submitted to the Evo* 2022
Conference, held in Madrid (Spain), from 20 to 22 of April. These papers
present ongoing research and preliminary results investigating on the
application of different approaches of Bioinspired Methods (mainly Evolutionary
Computation) to different problems, most of them real world ones.

Recently proposed methods in data subset selection, that is active learning
and active sampling, use Fisher information, Hessians, similarity matrices
based on gradients, and gradient lengths to estimate how informative data is
for a model's training. Are these different approaches connected, and if so,
how? We revisit the fundamentals of Bayesian optimal experiment design and show
that these recently proposed methods can be understood as approximations to
information-theoretic quantities: among them, the mutual information between
predictions and model parameters, known as expected information gain or BALD in
machine learning, and the mutual information between predictions of acquisition
candidates and test samples, known as expected predictive information gain. We
develop a comprehensive set of approximations using Fisher information and
observed information and derive a unified framework that connects seemingly
disparate literature. Although Bayesian methods are often seen as separate from
non-Bayesian ones, the sometimes fuzzy notion of "informativeness" expressed in
various non-Bayesian objectives leads to the same couple of information
quantities, which were, in principle, already known by Lindley (1956) and
MacKay (1992).

The cloud-based solutions are becoming inefficient due to considerably large
time delays, high power consumption, security and privacy concerns caused by
billions of connected wireless devices and typically zillions bytes of data
they produce at the network edge. A blend of edge computing and Artificial
Intelligence (AI) techniques could optimally shift the resourceful computation
servers closer to the network edge, which provides the support for advanced AI
applications (e.g., video/audio surveillance and personal recommendation
system) by enabling intelligent decision making on computing at the point of
data generation as and when it is needed, and distributed Machine Learning (ML)
with its potential to avoid the transmission of large dataset and possible
compromise of privacy that may exist in cloud-based centralized learning.
Therefore, AI is envisioned to become native and ubiquitous in future
communication and networking systems. In this paper, we conduct a comprehensive
overview of recent advances in distributed intelligence in wireless networks
under the umbrella of native-AI wireless networks, with a focus on the basic
concepts of native-AI wireless networks, on the AI-enabled edge computing, on
the design of distributed learning architectures for heterogeneous networks, on
the communication-efficient technologies to support distributed learning, and
on the AI-empowered end-to-end communications. We highlight the advantages of
hybrid distributed learning architectures compared to the state-of-art
distributed learning techniques. We summarize the challenges of existing
research contributions in distributed intelligence in wireless networks and
identify the potential future opportunities.

Training deep neural networks for image recognition often requires
large-scale human annotated data. To reduce the reliance of deep neural
solutions on labeled data, state-of-the-art semi-supervised methods have been
proposed in the literature. Nonetheless, the use of such semi-supervised
methods has been quite rare in the field of facial expression recognition
(FER). In this paper, we present a comprehensive study on recently proposed
state-of-the-art semi-supervised learning methods in the context of FER. We
conduct comparative study on eight semi-supervised learning methods, namely
Pi-Model, Pseudo-label, Mean-Teacher, VAT, MixMatch, ReMixMatch, UDA, and
FixMatch, on three FER datasets (FER13, RAF-DB, and AffectNet), when various
amounts of labeled samples are used. We also compare the performance of these
methods against fully-supervised training. Our study shows that when training
existing semi-supervised methods on as little as 250 labeled samples per class
can yield comparable performances to that of fully-supervised methods trained
on the full labeled datasets. To facilitate further research in this area, we
make our code publicly available at: https://github.com/ShuvenduRoy/SSL_FER

Objective: Electrocardiogram (ECG) signals commonly suffer noise
interference, such as baseline wander. High-quality and high-fidelity
reconstruction of the ECG signals is of great significance to diagnosing
cardiovascular diseases. Therefore, this paper proposes a novel ECG baseline
wander and noise removal technology. Methods: We extended the diffusion model
in a conditional manner that was specific to the ECG signals, namely the Deep
Score-Based Diffusion model for Electrocardiogram baseline wander and noise
removal (DeScoD-ECG). Moreover, we deployed a multi-shots averaging strategy
that improved signal reconstructions. We conducted the experiments on the QT
Database and the MIT-BIH Noise Stress Test Database to verify the feasibility
of the proposed method. Baseline methods are adopted for comparison, including
traditional digital filter-based and deep learning-based methods. Results: The
quantities evaluation results show that the proposed method obtained
outstanding performance on four distance-based similarity metrics (the sum of
squared distance, maximum absolute square, percentage of root distance, and
cosine similarity) with 3.771 $\pm$ 5.713 au, 0.329 $\pm$ 0.258 au, 40.527
$\pm$ 26.258 \%, and 0.926 $\pm$ 0.087. This led to at least 20\% overall
improvement compared with the best baseline method. Conclusion: This paper
demonstrates the state-of-the-art performance of the DeScoD-ECG for ECG noise
removal, which has better approximations of the true data distribution and
higher stability under extreme noise corruptions. Significance: This study is
one of the first to extend the conditional diffusion-based generative model for
ECG noise removal, and the DeScoD-ECG has the potential to be widely used in
biomedical applications.

Short answer: Yes, Long answer: No! Indeed, research on adversarial
robustness has led to invaluable insights helping us understand and explore
different aspects of the problem. Many attacks and defenses have been proposed
over the last couple of years. The problem, however, remains largely unsolved
and poorly understood. Here, I argue that the current formulation of the
problem serves short term goals, and needs to be revised for us to achieve
bigger gains. Specifically, the bound on perturbation has created a somewhat
contrived setting and needs to be relaxed. This has misled us to focus on model
classes that are not expressive enough to begin with. Instead, inspired by
human vision and the fact that we rely more on robust features such as shape,
vertices, and foreground objects than non-robust features such as texture,
efforts should be steered towards looking for significantly different classes
of models. Maybe instead of narrowing down on imperceptible adversarial
perturbations, we should attack a more general problem which is finding
architectures that are simultaneously robust to perceptible perturbations,
geometric transformations (e.g. rotation, scaling), image distortions
(lighting, blur), and more (e.g. occlusion, shadow). Only then we may be able
to solve the problem of adversarial vulnerability.

Modern distributed systems are supported by fault-tolerant algorithms, like
Reliable Broadcast and Consensus, that assure the correct operation of the
system even when some of the nodes of the system fail. However, the development
of distributed algorithms is a manual and complex process, resulting in
scientific papers that usually present a single algorithm or variations of
existing ones. To automate the process of developing such algorithms, this work
presents an intelligent agent that uses Reinforcement Learning to generate
correct and efficient fault-tolerant distributed algorithms. We show that our
approach is able to generate correct fault-tolerant Reliable Broadcast
algorithms with the same performance of others available in the literature, in
only 12,000 learning episodes.

Processing 3D data efficiently has always been a challenge. Spatial
operations on large-scale point clouds, stored as sparse data, require extra
cost. Attracted by the success of transformers, researchers are using
multi-head attention for vision tasks. However, attention calculations in
transformers come with quadratic complexity in the number of inputs and miss
spatial intuition on sets like point clouds. We redesign set transformers in
this work and incorporate them into a hierarchical framework for shape
classification and part and scene segmentation. We propose our local attention
unit, which captures features in a spatial neighborhood. We also compute
efficient and dynamic global cross attentions by leveraging sampling and
grouping at each iteration. Finally, to mitigate the non-heterogeneity of point
clouds, we propose an efficient Multi-Scale Tokenization (MST), which extracts
scale-invariant tokens for attention operations. The proposed hierarchical
model achieves state-of-the-art shape classification in mean accuracy and
yields results on par with the previous segmentation methods while requiring
significantly fewer computations. Our proposed architecture predicts
segmentation labels with around half the latency and parameter count of the
previous most efficient method with comparable performance. The code is
available at https://github.com/YigeWang-WHU/CloudAttention.

The design of decentralized learning algorithms is important in the
fast-growing world in which data are distributed over participants with limited
local computation resources and communication. In this direction, we propose an
online algorithm minimizing non-convex loss functions aggregated from
individual data/models distributed over a network. We provide the theoretical
performance guarantee of our algorithm and demonstrate its utility on a real
life smart building.

To plan safe maneuvers and act with foresight, autonomous vehicles must be
capable of accurately predicting the uncertain future. In the context of
autonomous driving, deep neural networks have been successfully applied to
learning predictive models of human driving behavior from data. However, the
predictions suffer from cascading errors, resulting in large inaccuracies over
long time horizons. Furthermore, the learned models are black boxes, and thus
it is often unclear how they arrive at their predictions. In contrast,
rule-based models, which are informed by human experts, maintain long-term
coherence in their predictions and are human-interpretable. However, such
models often lack the sufficient expressiveness needed to capture complex
real-world dynamics. In this work, we begin to close this gap by embedding the
Intelligent Driver Model, a popular hand-crafted driver model, into deep neural
networks. Our model's transparency can offer considerable advantages, e.g., in
debugging the model and more easily interpreting its predictions. We evaluate
our approach on a simulated merging scenario, showing that it yields a robust
model that is end-to-end trainable and provides greater transparency at no cost
to the model's predictive accuracy.

Digital data collected over the decades and data currently being produced
with use of information technology is vastly the unlabeled data or data without
description. The unlabeled data is relatively easy to acquire but expensive to
label even with use of domain experts. Most of the recent works focus on use of
active learning with uncertainty metrics measure to address this problem.
Although most uncertainty selection strategies are very effective, they fail to
take informativeness of the unlabeled instances into account and are prone to
querying outliers. In order to address these challenges we propose an hybrid
approach of computing both the uncertainty and informativeness of an instance,
then automaticaly label the computed instances using budget annotator. To
reduce the annotation cost, we employ the state-of-the-art pre-trained models
in order to avoid querying information already contained in those models. Our
extensive experiments on different sets of datasets demonstrate the efficacy of
the proposed approach.

The metaverse has the potential to revolutionize the next generation of the
Internet by supporting highly interactive services with the help of Mixed
Reality (MR) technologies; still, to provide a satisfactory experience for
users, the synchronization between the physical world and its digital models is
crucial. This work proposes a sampling, communication and prediction co-design
framework to minimize the communication load subject to a constraint on
tracking the Mean Squared Error (MSE) between a real-world device and its
digital model in the metaverse. To optimize the sampling rate and the
prediction horizon, we exploit expert knowledge and develop a constrained Deep
Reinforcement Learning (DRL) algorithm, named Knowledge-assisted Constrained
Twin-Delayed Deep Deterministic (KC-TD3) policy gradient algorithm. We validate
our framework on a prototype composed of a real-world robotic arm and its
digital model. Compared with existing approaches: (1) When the tracking error
constraint is stringent (MSE=0.002 degrees), our policy degenerates into the
policy in the sampling-communication co-design framework. (2) When the tracking
error constraint is mild (MSE=0.007 degrees), our policy degenerates into the
policy in the prediction-communication co-design framework. (3) Our framework
achieves a better trade-off between the average MSE and the average
communication load compared with a communication system without sampling and
prediction. For example, the average communication load can be reduced up to
87% when the track error constraint is 0.002 degrees. (4) Our policy
outperforms the benchmark with the static sampling rate and prediction horizon
optimized by exhaustive search, in terms of the tail probability of the
tracking error. Furthermore, with the assistance of expert knowledge, the
proposed algorithm KC-TD3 achieves better convergence time, stability, and
final policy performance.

Recently, Stochastic Gradient Descent (SGD) and its variants have become the
dominant methods in the large-scale optimization of machine learning (ML)
problems. A variety of strategies have been proposed for tuning the step sizes,
ranging from adaptive step sizes to heuristic methods to change the step size
in each iteration. Also, momentum has been widely employed in ML tasks to
accelerate the training process. Yet, there is a gap in our theoretical
understanding of them. In this work, we start to close this gap by providing
formal guarantees to a few heuristic optimization methods and proposing
improved algorithms.
  First, we analyze a generalized version of the AdaGrad (Delayed AdaGrad) step
sizes in both convex and non-convex settings, showing that these step sizes
allow the algorithms to automatically adapt to the level of noise of the
stochastic gradients. We show for the first time sufficient conditions for
Delayed AdaGrad to achieve almost sure convergence of the gradients to zero.
Moreover, we present a high probability analysis for Delayed AdaGrad and its
momentum variant in the non-convex setting.
  Second, we analyze SGD with exponential and cosine step sizes, which are
empirically successful but lack theoretical support. We provide the very first
convergence guarantees for them in the smooth and non-convex setting, with and
without the Polyak-{\L}ojasiewicz (PL) condition. We also show their good
property of adaptivity to noise under the PL condition.
  Third, we study the last iterate of momentum methods. We prove the first
lower bound in the convex setting for the last iterate of SGD with constant
momentum. Moreover, we investigate a class of
Follow-The-Regularized-Leader-based momentum algorithms with increasing
momentum and shrinking updates. We show that their last iterate has optimal
convergence for unconstrained convex stochastic optimization problems.

DNNs are known to be vulnerable to so-called adversarial attacks that
manipulate inputs to cause incorrect results that can be beneficial to an
attacker or damaging to the victim. Recent works have proposed approximate
computation as a defense mechanism against machine learning attacks. We show
that these approaches, while successful for a range of inputs, are insufficient
to address stronger, high-confidence adversarial attacks. To address this, we
propose DNNSHIELD, a hardware-accelerated defense that adapts the strength of
the response to the confidence of the adversarial input. Our approach relies on
dynamic and random sparsification of the DNN model to achieve inference
approximation efficiently and with fine-grain control over the approximation
error. DNNSHIELD uses the output distribution characteristics of sparsified
inference compared to a dense reference to detect adversarial inputs. We show
an adversarial detection rate of 86% when applied to VGG16 and 88% when applied
to ResNet50, which exceeds the detection rate of the state of the art
approaches, with a much lower overhead. We demonstrate a
software/hardware-accelerated FPGA prototype, which reduces the performance
impact of DNNSHIELD relative to software-only CPU and GPU implementations.

As one of the most pressing challenges of the 21st century, global climate
change demands a host of changes across four critical energy infrastructures:
the electric grid, the natural gas system, the oil system, and the coal system.
Unfortunately, these four systems are often studied individually, and rarely
together as integrated systems. Instead, holistic multi-energy system models
can serve to improve the understanding of these interdependent systems and
guide policies that shape the systems as they evolve into the future. The NSF
project entitled "American Multi-Modal Energy System Synthetic \& Simulated
Data (AMES-3D)" seeks to fill this void with an open-source,
physically-informed, structural and behavioral machine-learning model of the
AMES. To that end, this paper uses a GIS-data-driven, model-based system
engineering approach to develop structural models of the American Multi-Modal
Energy System (AMES). This paper produces and reports the hetero-functional
incidence tensor, hetero-functional adjacency matrix, and the formal graph
adjacency matrix in terms of their statistics. This work compares these four
hetero-functional graph models across the states of New York (NY), California
(CA), Texas (TX), and the United States of America (USA) as a whole. From the
reported statistics, the paper finds that the geography and the sustainable
energy policies of these states are deeply reflected in the structure of their
multi-energy infrastructure systems and impact the full USA's structure.

Consumers conducting comparison shopping, researchers making sense of
competitive space, and developers looking for code snippets online all face the
challenge of capturing the information they find for later use without
interrupting their current flow. In addition, during many learning and
exploration tasks, people need to externalize their mental context, such as
estimating how urgent a topic is to follow up on, or rating a piece of evidence
as a "pro" or "con," which helps scaffold subsequent deeper exploration.
However, current approaches incur a high cost, often requiring users to select,
copy, context switch, paste, and annotate information in a separate document
without offering specific affordances that capture their mental context. In
this work, we explore a new interaction technique called "wiggling," which can
be used to fluidly collect, organize, and rate information during early
sensemaking stages with a single gesture. Wiggling involves rapid
back-and-forth movements of a pointer or up-and-down scrolling on a smartphone,
which can indicate the information to be collected and its valence, using a
single, light-weight gesture that does not interfere with other interactions
that are already available. Through implementation and user evaluation, we
found that wiggling helped participants accurately collect information and
encode their mental context with a 58% reduction in operational cost while
being 24% faster compared to a common baseline.

Timber and forest products made from wood, like furniture, are valuable
commodities, and like the global trade of many highly-valued natural resources,
face challenges of corruption, fraud, and illegal harvesting. These grey and
black market activities in the wood and forest products sector are not limited
to the countries where the wood was harvested, but extend throughout the global
supply chain and have been tied to illicit financial flows, like trade-based
money laundering, document fraud, species mislabeling, and other illegal
activities. The task of finding such fraudulent activities using trade data, in
the absence of ground truth, can be modelled as an unsupervised anomaly
detection problem. However existing approaches suffer from certain shortcomings
in their applicability towards large scale trade data. Trade data is
heterogeneous, with both categorical and numerical attributes in a tabular
format. The overall challenge lies in the complexity, volume and velocity of
data, with large number of entities and lack of ground truth labels. To
mitigate these, we propose a novel unsupervised anomaly detection --
Contrastive Learning based Heterogeneous Anomaly Detection (CHAD) that is
generally applicable for large-scale heterogeneous tabular data. We demonstrate
our model CHAD performs favorably against multiple comparable baselines for
public benchmark datasets, and outperforms them in the case of trade data. More
importantly we demonstrate our approach reduces assumptions and efforts
required hyperparameter tuning, which is a key challenging aspect in an
unsupervised training paradigm. Specifically, our overarching objective
pertains to detecting suspicious timber shipments and patterns using Bill of
Lading trade record data. Detecting anomalous transactions in shipment records
can enable further investigation by government agencies and supply chain

High-quality data has become increasingly important to software engineers in
designing and implementing today's software, for example, as an input to
machine-learning algorithms and visualisation- and analytics-based features.
Open data - i.e., data shared under a licence that gives users the right to
study, process, and distribute the data to anyone and for any purpose - offers
a mechanism to address this need. Data may originate from multiple sources,
whether crowdsourced, shared by government agencies, or shared between
commercial entities, and is undoubtedly inherent to all business and revenue
models across the public sector, business and industry today. In this guest
editorial for the Special Issue on Collaborative Aspects of Open Data in
Software Engineering, we explore the collaborative aspects of open data in
software engineering. We highlight how these aspects can benefit organisations,
what challenges may exist and how these may be addressed based on current
practice, and introduce the four papers included in this special issue.

This paper considers a setting where embedded devices are used to acquire and
classify images. Because of limited computing capacity, embedded devices rely
on a parsimonious classification model with uneven accuracy. When local
classification is deemed inaccurate, devices can decide to offload the image to
an edge server with a more accurate but resource-intensive model. Resource
constraints, e.g., network bandwidth, however, require regulating such
transmissions to avoid congestion and high latency. The paper investigates this
offloading problem when transmissions regulation is through a token bucket, a
mechanism commonly used for such purposes. The goal is to devise a lightweight,
online offloading policy that optimizes an application-specific metric (e.g.,
classification accuracy) under the constraints of the token bucket. The paper
develops a policy based on a Deep Q-Network (DQN), and demonstrates both its
efficacy and the feasibility of its deployment on embedded devices. Of note is
the fact that the policy can handle complex input patterns, including
correlation in image arrivals and classification accuracy. The evaluation is
carried out by performing image classification over a local testbed using
synthetic traces generated from the ImageNet image classification benchmark.
Implementation of this work is available at
https://github.com/qiujiaming315/edgeml-dqn.

There exists a wide variety of efficiency methods for natural language
processing (NLP) tasks, such as pruning, distillation, dynamic inference,
quantization, etc. We can consider an efficiency method as an operator applied
on a model. Naturally, we may construct a pipeline of multiple efficiency
methods, i.e., to apply multiple operators on the model sequentially. In this
paper, we study the plausibility of this idea, and more importantly, the
commutativity and cumulativeness of efficiency operators. We make two
interesting observations: (1) Efficiency operators are commutative -- the order
of efficiency methods within the pipeline has little impact on the final
results; (2) Efficiency operators are also cumulative -- the final results of
combining several efficiency methods can be estimated by combining the results
of individual methods. These observations deepen our understanding of
efficiency operators and provide useful guidelines for their real-world

Learning robotic tasks in the real world is still highly challenging and
effective practical solutions remain to be found. Traditional methods used in
this area are imitation learning and reinforcement learning, but they both have
limitations when applied to real robots. Combining reinforcement learning with
pre-collected demonstrations is a promising approach that can help in learning
control policies to solve robotic tasks. In this paper, we propose an algorithm
that uses novel techniques to leverage offline expert data using offline and
online training to obtain faster convergence and improved performance. The
proposed algorithm (AWET) weights the critic losses with a novel agent
advantage weight to improve over the expert data. In addition, AWET makes use
of an automatic early termination technique to stop and discard policy rollouts
that are not similar to expert trajectories -- to prevent drifting far from the
expert data. In an ablation study, AWET showed improved and promising
performance when compared to state-of-the-art baselines on four standard
robotic tasks.

Language modality within the vision language pretraining framework is
innately discretized, endowing each word in the language vocabulary a semantic
meaning. In contrast, visual modality is inherently continuous and
high-dimensional, which potentially prohibits the alignment as well as fusion
between vision and language modalities. We therefore propose to "discretize"
the visual representation by joint learning a codebook that imbues each visual
token a semantic. We then utilize these discretized visual semantics as
self-supervised ground-truths for building our Masked Image Modeling objective,
a counterpart of Masked Language Modeling which proves successful for language
models. To optimize the codebook, we extend the formulation of VQ-VAE which
gives a theoretic guarantee. Experiments validate the effectiveness of our
approach across common vision-language benchmarks.

Generalizability of deep learning models may be severely affected by the
difference in the distributions of the train (source domain) and the test
(target domain) sets, e.g., when the sets are produced by different hardware.
As a consequence of this domain shift, a certain model might perform well on
data from one clinic, and then fail when deployed in another. We propose a very
light and transparent approach to perform test-time domain adaptation. The idea
is to substitute the target low-frequency Fourier space components that are
deemed to reflect the style of an image. To maximize the performance, we
implement the "optimal style donor" selection technique, and use a number of
source data points for altering a single target scan appearance (Multi-Source
Transferring). We study the effect of severity of domain shift on the
performance of the method, and show that our training-free approach reaches the
state-of-the-art level of complicated deep domain adaptation models. The code
for our experiments is released.

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative
representations without labelled data and has reached comparable or even
state-of-the-art results in comparison to supervised counterparts. Contrastive
Learning (CL) is one of the most well-known approaches in SSL that attempts to
learn general, informative representations of data. CL methods have been mostly
developed for applications in computer vision and natural language processing
where only a single sensor modality is used. A majority of pervasive computing
applications, however, exploit data from a range of different sensor
modalities. While existing CL methods are limited to learning from one or two
data sources, we propose COCOA (Cross mOdality COntrastive leArning), a
self-supervised model that employs a novel objective function to learn quality
representations from multisensor data by computing the cross-correlation
between different data modalities and minimizing the similarity between
irrelevant instances. We evaluate the effectiveness of COCOA against eight
recently introduced state-of-the-art self-supervised models, and two supervised
baselines across five public datasets. We show that COCOA achieves superior
classification performance to all other approaches. Also, COCOA is far more
label-efficient than the other baselines including the fully supervised model
using only one-tenth of available labelled data.

The main challenges of using electroencephalogram (EEG) signals to make
eye-tracking (ET) predictions are the differences in distributional patterns
between benchmark data and real-world data and the noise resulting from the
unintended interference of brain signals from multiple sources. Increasing the
robustness of machine learning models in predicting eye-tracking position from
EEG data is therefore integral for both research and consumer use. In medical
research, the usage of more complicated data collection methods to test for
simpler tasks has been explored to address this very issue. In this study, we
propose a fine-grain data approach for EEG-ET data collection in order to
create more robust benchmarking. We train machine learning models utilizing
both coarse-grain and fine-grain data and compare their accuracies when tested
on data of similar/different distributional patterns in order to determine how
susceptible EEG-ET benchmarks are to differences in distributional data. We
apply a covariate distributional shift to test for this susceptibility. Results
showed that models trained on fine-grain, vector-based data were less
susceptible to distributional shifts than models trained on coarse-grain,
binary-classified data.

In the recent past, there have been many efforts to accelerate adaptive
beamforming for ultrasound (US) imaging using neural networks (NNs). However,
most of these efforts are based on static models, i.e., they are trained to
learn a single adaptive beamforming approach (e.g., minimum variance
distortionless response (MVDR)) assuming that they result in the best image
quality. Moreover, the training of such NNs is initiated only after acquiring a
large set of data that consumes several gigabytes (GBs) of storage space. In
this study, an active learning framework for beamforming is described for the
first time in the context of NNs. The best quality image chosen by the user
serves as the ground truth for the proposed technique, which trains the NN
concurrently with data acqusition. On average, the active learning approach
takes 0.5 seconds to complete a single iteration of training.

In this paper, we study the post-hoc calibration of modern neural networks, a
problem that has drawn a lot of attention in recent years. Many calibration
methods of varying complexity have been proposed for the task, but there is no
consensus about how expressive these should be. We focus on the task of
confidence scaling, specifically on post-hoc methods that generalize
Temperature Scaling, we call these the Adaptive Temperature Scaling family. We
analyse expressive functions that improve calibration and propose interpretable
methods. We show that when there is plenty of data complex models like neural
networks yield better performance, but are prone to fail when the amount of
data is limited, a common situation in certain post-hoc calibration
applications like medical diagnosis. We study the functions that expressive
methods learn under ideal conditions and design simpler methods but with a
strong inductive bias towards these well-performing functions. Concretely, we
propose Entropy-based Temperature Scaling, a simple method that scales the
confidence of a prediction according to its entropy. Results show that our
method obtains state-of-the-art performance when compared to others and, unlike
complex models, it is robust against data scarcity. Moreover, our proposed
model enables a deeper interpretation of the calibration process.

Convolutional neural networks (CNNs) have shown exceptional performance for a
range of medical imaging tasks. However, conventional CNNs are not able to
explain their reasoning process, therefore limiting their adoption in clinical
practice. In this work, we propose an inherently interpretable CNN for
regression using similarity-based comparisons (INSightR-Net) and demonstrate
our methods on the task of diabetic retinopathy grading. A prototype layer
incorporated into the architecture enables visualization of the areas in the
image that are most similar to learned prototypes. The final prediction is then
intuitively modeled as a mean of prototype labels, weighted by the
similarities. We achieved competitive prediction performance with our
INSightR-Net compared to a ResNet baseline, showing that it is not necessary to
compromise performance for interpretability. Furthermore, we quantified the
quality of our explanations using sparsity and diversity, two concepts
considered important for a good explanation, and demonstrated the effect of
several parameters on the latent space embeddings.

As an important upstream task for many medical applications, supervised
landmark localization still requires non-negligible annotation costs to achieve
desirable performance. Besides, due to cumbersome collection procedures, the
limited size of medical landmark datasets impacts the effectiveness of
large-scale self-supervised pre-training methods. To address these challenges,
we propose a two-stage framework for one-shot medical landmark localization,
which first infers landmarks by unsupervised registration from the labeled
exemplar to unlabeled targets, and then utilizes these noisy pseudo labels to
train robust detectors. To handle the significant structure variations, we
learn an end-to-end cascade of global alignment and local deformations, under
the guidance of novel loss functions which incorporate edge information. In
stage II, we explore self-consistency for selecting reliable pseudo labels and
cross-consistency for semi-supervised learning. Our method achieves
state-of-the-art performances on public datasets of different body parts, which
demonstrates its general applicability.

Blind deconvolution in low-light is one of the more challenging problems in
image restoration because of the photon shot noise. However, existing
algorithms -- both classical and deep-learning based -- are not designed for
this condition. When the shot noise is strong, conventional deconvolution
methods fail because (1) the presence of noise makes the estimation of the blur
kernel difficult; (2) generic deep-restoration models rarely model the forward
process explicitly; (3) there are currently no iterative strategies to
incorporate a non-blind solver in a kernel estimation stage. This paper
addresses these challenges by presenting an unsupervised blind deconvolution
method. At the core of this method is a reformulation of the general blind
deconvolution framework from the conventional image-kernel alternating
minimization to a purely kernel-based minimization. This kernel-based
minimization leads to a new iterative scheme that backpropagates an
unsupervised loss through a pre-trained non-blind solver to update the blur
kernel. Experimental results show that the proposed framework achieves superior
results than state-of-the-art blind deconvolution algorithms in low-light

With the development of generative-based self-supervised learning (SSL)
approaches like BeiT and MAE, how to learn good representations by masking
random patches of the input image and reconstructing the missing information
has grown in concern. However, BeiT and PeCo need a "pre-pretraining" stage to
produce discrete codebooks for masked patches representing. MAE does not
require a pre-training codebook process, but setting pixels as reconstruction
targets may introduce an optimization gap between pre-training and downstream
tasks that good reconstruction quality may not always lead to the high
descriptive capability for the model. Considering the above issues, in this
paper, we propose a simple Self-distillated masked AutoEncoder network, namely
SdAE. SdAE consists of a student branch using an encoder-decoder structure to
reconstruct the missing information, and a teacher branch producing latent
representation of masked tokens. We also analyze how to build good views for
the teacher branch to produce latent representation from the perspective of
information bottleneck. After that, we propose a multi-fold masking strategy to
provide multiple masked views with balanced information for boosting the
performance, which can also reduce the computational complexity. Our approach
generalizes well: with only 300 epochs pre-training, a vanilla ViT-Base model
achieves an 84.1% fine-tuning accuracy on ImageNet-1k classification, 48.6 mIOU
on ADE20K segmentation, and 48.9 mAP on COCO detection, which surpasses other
methods by a considerable margin. Code is available at
https://github.com/AbrahamYabo/SdAE.

This paper proposes a novel out-of-distribution (OOD) detection framework
named MoodCat for image classifiers. MoodCat masks a random portion of the
input image and uses a generative model to synthesize the masked image to a new
image conditioned on the classification result. It then calculates the semantic
difference between the original image and the synthesized one for OOD
detection. Compared to existing solutions, MoodCat naturally learns the
semantic information of the in-distribution data with the proposed mask and
conditional synthesis strategy, which is critical to identifying OODs.
Experimental results demonstrate that MoodCat outperforms state-of-the-art OOD
detection solutions by a large margin.

Applications on Medical Image Analysis suffer from acute shortage of large
volume of data properly annotated by medical experts. Supervised Learning
algorithms require a large volumes of balanced data to learn robust
representations. Often supervised learning algorithms require various
techniques to deal with imbalanced data. Self-supervised learning algorithms on
the other hand are robust to imbalance in the data and are capable of learning
robust representations. In this work, we train a 3D BYOL self-supervised model
using gradient accumulation technique to deal with the large number of samples
in a batch generally required in a self-supervised algorithm. To the best of
our knowledge, this work is one of the first of its kind in this domain. We
compare the results obtained through our experiments in the downstream task of
ACL Tear Injury detection with the contemporary self-supervised pre-training
methods and also with ResNet3D-18 initialized with the Kinetics-400 pre-trained
weights. From the downstream task experiments, it is evident that the proposed
framework outperforms the existing baselines.

Artistic text recognition is an extremely challenging task with a wide range
of applications. However, current scene text recognition methods mainly focus
on irregular text while have not explored artistic text specifically. The
challenges of artistic text recognition include the various appearance with
special-designed fonts and effects, the complex connections and overlaps
between characters, and the severe interference from background patterns. To
alleviate these problems, we propose to recognize the artistic text at three
levels. Firstly, corner points are applied to guide the extraction of local
features inside characters, considering the robustness of corner structures to
appearance and shape. In this way, the discreteness of the corner points cuts
off the connection between characters, and the sparsity of them improves the
robustness for background interference. Secondly, we design a character
contrastive loss to model the character-level feature, improving the feature
representation for character classification. Thirdly, we utilize Transformer to
learn the global feature on image-level and model the global relationship of
the corner points, with the assistance of a corner-query cross-attention
mechanism. Besides, we provide an artistic text dataset to benchmark the
performance. Experimental results verify the significant superiority of our
proposed method on artistic text recognition and also achieve state-of-the-art
performance on several blurred and perspective datasets.

Code-switching (CS) is a common linguistic phenomenon exhibited by
multilingual individuals, where they tend to alternate between languages within
one single conversation. CS is a complex phenomenon that not only encompasses
linguistic challenges, but also contains a great deal of complexity in terms of
its dynamic behaviour across speakers. Given that the factors giving rise to CS
vary from one country to the other, as well as from one person to the other, CS
is found to be a speaker-dependant behaviour, where the frequency by which the
foreign language is embedded differs across speakers. While several researchers
have looked into predicting CS behaviour from a linguistic point of view,
research is still lacking in the task of predicting user CS behaviour from
sociological and psychological perspectives. We provide an empirical user
study, where we investigate the correlations between users' CS levels and
character traits. We conduct interviews with bilinguals and gather information
on their profiles, including their demographics, personality traits, and
traveling experiences. We then use machine learning (ML) to predict users' CS
levels based on their profiles, where we identify the main influential factors
in the modeling process. We experiment with both classification as well as
regression tasks. Our results show that the CS behaviour is affected by the
relation between speakers, travel experiences as well as Neuroticism and
Extraversion personality traits.

Recently deep neural networks (DNNs) have achieved significant success in
real-world image super-resolution (SR). However, adversarial image samples with
quasi-imperceptible noises could threaten deep learning SR models. In this
paper, we propose a robust deep learning framework for real-world SR that
randomly erases potential adversarial noises in the frequency domain of input
images or features. The rationale is that on the SR task clean images or
features have a different pattern from the attacked ones in the frequency
domain. Observing that existing adversarial attacks usually add high-frequency
noises to input images, we introduce a novel random frequency mask module that
blocks out high-frequency components possibly containing the harmful
perturbations in a stochastic manner. Since the frequency masking may not only
destroys the adversarial perturbations but also affects the sharp details in a
clean image, we further develop an adversarial sample classifier based on the
frequency domain of images to determine if applying the proposed mask module.
Based on the above ideas, we devise a novel real-world image SR framework that
combines the proposed frequency mask modules and the proposed adversarial
classifier with an existing super-resolution backbone network. Experiments show
that our proposed method is more insensitive to adversarial attacks and
presents more stable SR results than existing models and defenses.

Galperin [Regul. Chaotic. Dyn. 8, 375 (2003)] introduced an interesting
method to learn the digits of $\pi $ by counting the collisions of two billiard
balls and a hard wall. This paper studies two quantum versions of the Galperin
billiards. It is shown that the digits of $\pi $ can be observed in the phase
shifts of the quantum models.

We consider matrix factorization (MF) with certain constraints, which finds
wide applications in various areas. Leveraging variational inference (VI) and
unitary approximate message passing (UAMP), we develop a Bayesian approach to
MF with an efficient message passing implementation, called UAMPMF. With proper
priors imposed on the factor matrices, UAMPMF can be used to solve many
problems that can be formulated as MF, such as non negative matrix
factorization, dictionary learning, compressive sensing with matrix
uncertainty, robust principal component analysis, and sparse matrix
factorization. Extensive numerical examples are provided to show that UAMPMF
significantly outperforms state-of-the-art algorithms in terms of recovery
accuracy, robustness and computational complexity.

Building blocks and tiles are an excellent way of learning about geometry and
mathematics in general. There are several versions of tiles that are either
snapped together or connected with magnets that can be used to introduce topics
like volume, tessellations, and Platonic solids. However, since these tiles are
made of hard plastic, they are not very suitable for creating hyperbolic
surfaces or shapes where the tiles need to bend. Curvagons are flexible regular
polygon building blocks that allow you to quickly build anything from
hyperbolic surfaces and tori to dinosaurs and shoes. They can be used to
introduce mathematical concepts from Archimedean solids to Gauss-Bonnet
theorem. You can also let your imagination run free and build whatever comes to
mind.

Active automata learning in the framework of Angluin's $L^*$ algorithm has
been applied to learning many kinds of automata models. In applications to
timed models such as timed automata, the main challenge is to determine guards
on the clock value in transitions as well as which transitions reset the clock.
In this paper, we introduce a new algorithm for active learning of
deterministic one-clock timed automata and timed Mealy machines. The algorithm
uses observation tables that do not commit to specific choices of reset, but
instead rely on constraint solving to determine reset choices that satisfy
readiness conditions. We evaluate our algorithm on randomly-generated examples
as well as practical case studies, showing that it is applicable to larger
models, and competitive with existing work for learning other forms of timed
models.

The size and complexity of deep neural networks continue to grow
exponentially, significantly increasing energy consumption for training and
inference by these models. We introduce an open-source package eco2AI to help
data scientists and researchers to track energy consumption and equivalent CO2
emissions of their models in a straightforward way. In eco2AI we put emphasis
on accuracy of energy consumption tracking and correct regional CO2 emissions
accounting. We encourage research community to search for new optimal
Artificial Intelligence (AI) architectures with a lower computational cost. The
motivation also comes from the concept of AI-based green house gases
sequestrating cycle with both Sustainable AI and Green AI pathways.

The decision-making of TBM operating parameters has an important guiding
significance for TBM safe and efficient construction, and it has been one of
the research hotpots in the field of TBM tunneling. For this purpose, this
paper introduces rock-breaking rules into machine learning method, and a
rock-machine mapping dual-driven by physical-rule and data-mining is
established with high accuracy. This dual-driven mappings are subsequently used
as objective function and constraints to build a decision-making method for TBM
operating parameters. By searching the revolution per minute and penetration
corresponding to the extremum of the objective function subject to the
constraints, the optimal operating parameters can be obtained. This method is
verified in the field of the Second Water Source Channel of Hangzhou, China,
resulting in the average penetration rate increased by 11.3%, and the total
cost decreased by 10.0%, which proves the practicability and effectiveness of
the developed decision-making model.

In ultrasound imaging the appearance of homogeneous regions of tissue is
subject to speckle, which for certain applications can make the detection of
tissue irregularities difficult. To cope with this, it is common practice to
apply speckle reduction filters to the images. Most conventional filtering
techniques are fairly hand-crafted and often need to be finely tuned to the
present hardware, imaging scheme and application. Learning based techniques on
the other hand suffer from the need for a target image for training (in case of
fully supervised techniques) or require narrow, complex physics-based models of
the speckle appearance that might not apply in all cases. With this work we
propose a deep-learning based method for speckle removal without these
limitations. To enable this, we make use of realistic ultrasound simulation
techniques that allow for instantiation of several independent speckle
realizations that represent the exact same tissue, thus allowing for the
application of image reconstruction techniques that work with pairs of
differently corrupted data. Compared to two other state-of-the-art approaches
(non-local means and the Optimized Bayesian non-local means filter) our method
performs favorably in qualitative comparisons and quantitative evaluation,
despite being trained on simulations alone, and is several orders of magnitude
faster.

Supervised deep learning methods for semantic medical image segmentation are
getting increasingly popular in the past few years.However, in resource
constrained settings, getting large number of annotated images is very
difficult as it mostly requires experts, is expensive and
time-consuming.Semi-supervised segmentation can be an attractive solution where
a very few labeled images are used along with a large number of unlabeled ones.
While the gap between supervised and semi-supervised methods have been
dramatically reduced for classification problems in the past couple of years,
there still remains a larger gap in segmentation methods. In this work, we
adapt a state-of-the-art semi-supervised classification method FixMatch to
semantic segmentation task, introducing FixMatchSeg. FixMatchSeg is evaluated
in four different publicly available datasets of different anatomy and
different modality: cardiac ultrasound, chest X-ray, retinal fundus image, and
skin images. When there are few labels, we show that FixMatchSeg performs on
par with strong supervised baselines.

Spiking neural network (SNN) is a brain-inspired model which has more
spatio-temporal information processing capacity and computational energy
efficiency. However, with the increasing depth of SNNs, the memory problem
caused by the weights of SNNs has gradually attracted attention. Inspired by
Artificial Neural Networks (ANNs) quantization technology, binarized SNN (BSNN)
is introduced to solve the memory problem. Due to the lack of suitable learning
algorithms, BSNN is usually obtained by ANN-to-SNN conversion, whose accuracy
will be limited by the trained ANNs. In this paper, we propose an ultra-low
latency adaptive local binary spiking neural network (ALBSNN) with accuracy
loss estimators, which dynamically selects the network layers to be binarized
to ensure the accuracy of the network by evaluating the error caused by the
binarized weights during the network learning process. Experimental results
show that this method can reduce storage space by more than 20 % without losing
network accuracy. At the same time, in order to accelerate the training speed
of the network, the global average pooling(GAP) layer is introduced to replace
the fully connected layers by the combination of convolution and pooling, so
that SNNs can use a small number of time steps to obtain better recognition
accuracy. In the extreme case of using only one time step, we still can achieve
92.92 %, 91.63 % ,and 63.54 % testing accuracy on three different datasets,
FashionMNIST, CIFAR-10, and CIFAR-100, respectively.

Advancements in Telemedicine as an approach to healthcare delivery have
heralded a new dawn in modern Medicine. Its fast-paced development in our
contemporary society is credence to the advances in Artificial Intelligence and
Information Technology. This paper carries out a descriptive study to broadly
explore AI's implementations in healthcare delivery with a more holistic view
of the usability of various Telemedical Innovations in enhancing Virtual
Diagnostic Solutions (VDS). This research further explores notable developments
in Deep Learning model optimizations for Virtual Diagnostic Solutions. A
further research review on the prospects of Virtual Diagnostic Solutions (VDS)
and foreseeable challenges was also highlighted. Conclusively, this research
gives a general overview of Artificial Intelligence in Telemedicine with a
central focus on Deep Learning-based approaches to Virtual Diagnostic
Solutions.

Human infants learn the names of objects and develop their own conceptual
systems without explicit supervision. In this study, we propose methods for
learning aligned vision-language conceptual systems inspired by infants' word
learning mechanisms. The proposed model learns the associations of visual
objects and words online and gradually constructs cross-modal relational graph
networks. Additionally, we also propose an aligned cross-modal representation
learning method that learns semantic representations of visual objects and
words in a self-supervised manner based on the cross-modal relational graph
networks. It allows entities of different modalities with conceptually the same
meaning to have similar semantic representation vectors. We quantitatively and
qualitatively evaluate our method, including object-to-word mapping and
zero-shot learning tasks, showing that the proposed model significantly
outperforms the baselines and that each conceptual system is topologically
aligned.

We study route choice in a repeated routing game where an uncertain state of
nature determines link latency functions, and agents receive private route
recommendation. The state is sampled in an i.i.d. manner in every round from a
publicly known distribution, and the recommendations are generated by a
randomization policy whose mapping from the state is known publicly. In a
one-shot setting, the agents are said to obey recommendation if it gives the
smallest travel time in a posteriori expectation. A plausible extension to
repeated setting is that the likelihood of following recommendation in a round
is related to regret from previous rounds. If the regret is of satisficing type
with respect to a default choice and is averaged over past rounds and over all
agents, then the asymptotic outcome under an obedient recommendation policy
coincides with the one-shot outcome. We report findings from an experiment with
one participant at a time engaged in repeated route choice decision on
computer. In every round, the participant is shown travel time distribution for
each route, a route recommendation generated by an obedient policy, and a
rating suggestive of average experience of previous participants with the
quality of recommendation. Upon entering route choice, the actual travel times
are revealed. The participant evaluates the quality of recommendation by
submitting a review. This is combined with historical reviews to update rating
for the next round. Data analysis from 33 participants each with 100 rounds
suggests moderate negative correlation between the display rating and the
average regret, and a strong positive correlation between the rating and the
likelihood of following recommendation. Overall, under obedient recommendation
policy, the rating converges close to its maximum value by the end of the
experiments in conjunction with very high frequency of following

Existing metrics used to evaluate table structure recognition algorithms have
shortcomings with regard to capturing text and empty cells alignment. In this
paper, we build on prior work and propose a new metric - TEDS based IOU
similarity (TEDS (IOU)) for table structure recognition which uses bounding
boxes instead of text while simultaneously being robust against the above
disadvantages. We demonstrate the effectiveness of our metric against previous
metrics through various examples.

Traditional multicast routing methods have some problems in constructing a
multicast tree, such as limited access to network state information, poor
adaptability to dynamic and complex changes in the network, and inflexible data
forwarding. To address these defects, the optimal multicast routing problem in
software-defined networking (SDN) is tailored as a multi-objective optimization
problem, and an intelligent multicast routing algorithm DRL-M4MR based on the
deep Q network (DQN) deep reinforcement learning (DRL) method is designed to
construct a multicast tree in SDN. First, the multicast tree state matrix, link
bandwidth matrix, link delay matrix, and link packet loss rate matrix are
designed as the state space of the DRL agent by combining the global view and
control of the SDN. Second, the action space of the agent is all the links in
the network, and the action selection strategy is designed to add the links to
the current multicast tree under four cases. Third, single-step and final
reward function forms are designed to guide the intelligence to make decisions
to construct the optimal multicast tree. The experimental results show that,
compared with existing algorithms, the multicast tree construct by DRL-M4MR can
obtain better bandwidth, delay, and packet loss rate performance after
training, and it can make more intelligent multicast routing decisions in a
dynamic network environment.

Temporal consistency is the key challenge of video depth estimation. Previous
works are based on additional optical flow or camera poses, which is
time-consuming. By contrast, we derive consistency with less information. Since
videos inherently exist with heavy temporal redundancy, a missing frame could
be recovered from neighboring ones. Inspired by this, we propose the frame
masking network (FMNet), a spatial-temporal transformer network predicting the
depth of masked frames based on their neighboring frames. By reconstructing
masked temporal features, the FMNet can learn intrinsic inter-frame
correlations, which leads to consistency. Compared with prior arts,
experimental results demonstrate that our approach achieves comparable spatial
accuracy and higher temporal consistency without any additional information.
Our work provides a new perspective on consistent video depth estimation. Our
official project page is https://github.com/RaymondWang987/FMNet.

This paper reports on progress towards building an online language learning
tool to provide learners with conversational experience by using dialog systems
as conversation practice partners. Our system can adapt to users' language
proficiency on the fly. We also provide automatic grammar error feedback to
help users learn from their mistakes. According to our first adopters, our
system is entertaining and useful. Furthermore, we will provide the learning
technology community a large-scale conversation dataset on language learning
and grammar correction. Our next step is to make our system more adaptive to
user profile information by using reinforcement learning algorithms.

Neural networks have been rapidly expanding in recent years, with novel
strategies and applications. However, challenges such as interpretability,
explainability, robustness, safety, trust, and sensibility remain unsolved in
neural network technologies, despite the fact that they will unavoidably be
addressed for critical applications. Attempts have been made to overcome the
challenges in neural network computing by representing and embedding domain
knowledge in terms of symbolic representations. Thus, the neuro-symbolic
learning (NeSyL) notion emerged, which incorporates aspects of symbolic
representation and bringing common sense into neural networks (NeSyL). In
domains where interpretability, reasoning, and explainability are crucial, such
as video and image captioning, question-answering and reasoning, health
informatics, and genomics, NeSyL has shown promising outcomes. This review
presents a comprehensive survey on the state-of-the-art NeSyL approaches, their
principles, advances in machine and deep learning algorithms, applications such
as opthalmology, and most importantly, future perspectives of this emerging
field.

The paper proposes a time-varying parameter global vector autoregressive
(TVP-GVAR) framework for predicting and analysing developed region economic
variables. We want to provide an easily accessible approach for the economy
application settings, where a variety of machine learning models can be
incorporated for out-of-sample prediction. The LASSO-type technique for
numerically efficient model selection of mean squared errors (MSEs) is
selected. We show the convincing in-sample performance of our proposed model in
all economic variables and relatively high precision out-of-sample predictions
with different-frequency economic inputs. Furthermore, the time-varying
orthogonal impulse responses provide novel insights into the connectedness of
economic variables at critical time points across developed regions. We also
derive the corresponding asymptotic bands (the confidence intervals) for
orthogonal impulse responses function under standard assumptions.

Graph convolutional network based methods that model the body-joints'
relations, have recently shown great promise in 3D skeleton-based human motion
prediction. However, these methods have two critical issues: first, deep graph
convolutions filter features within only limited graph spectrums, losing
sufficient information in the full band; second, using a single graph to model
the whole body underestimates the diverse patterns on various body-parts. To
address the first issue, we propose adaptive graph scattering, which leverages
multiple trainable band-pass graph filters to decompose pose features into
richer graph spectrum bands. To address the second issue, body-parts are
modeled separately to learn diverse dynamics, which enables finer feature
extraction along the spatial dimensions. Integrating the above two designs, we
propose a novel skeleton-parted graph scattering network (SPGSN). The cores of
the model are cascaded multi-part graph scattering blocks (MPGSBs), building
adaptive graph scattering on diverse body-parts, as well as fusing the
decomposed features based on the inferred spectrum importance and body-part
interactions. Extensive experiments have shown that SPGSN outperforms
state-of-the-art methods by remarkable margins of 13.8%, 9.3% and 2.7% in terms
of 3D mean per joint position error (MPJPE) on Human3.6M, CMU Mocap and 3DPW
datasets, respectively.

Referring Expression Comprehension (REC) is one of the most important tasks
in visual reasoning that requires a model to detect the target object referred
by a natural language expression. Among the proposed pipelines, the one-stage
Referring Expression Comprehension (OSREC) has become the dominant trend since
it merges the region proposal and selection stages. Many state-of-the-art OSREC
models adopt a multi-hop reasoning strategy because a sequence of objects is
frequently mentioned in a single expression which needs multi-hop reasoning to
analyze the semantic relation. However, one unsolved issue of these models is
that the number of reasoning steps needs to be pre-defined and fixed before
inference, ignoring the varying complexity of expressions. In this paper, we
propose a Dynamic Multi-step Reasoning Network, which allows the reasoning
steps to be dynamically adjusted based on the reasoning state and expression
complexity. Specifically, we adopt a Transformer module to memorize & process
the reasoning state and a Reinforcement Learning strategy to dynamically infer
the reasoning steps. The work achieves the state-of-the-art performance or
significant improvements on several REC datasets, ranging from RefCOCO (+, g)
with short expressions, to Ref-Reasoning, a dataset with long and complex
compositional expressions.

Heterogeneous information fusion is one of the most critical issues for
realizing vehicular cyber-physical systems (VCPSs). This work makes the first
attempt at quantitatively measuring the quality of heterogeneous information
fusion in VCPS by designing a new metric called Age of View (AoV).
Specifically, we derive a sensing model based on a multi-class M/G/1 priority
queue and a transmission model based on Shannon theory. On this basis, we
formally define AoV by modeling the timeliness, completeness, and consistency
of the heterogeneous information fusion in VCPS and formulate the problem
aiming to minimize the system's average AoV. Further, we propose a new solution
called Multi-agent Difference-Reward-based deep reinforcement learning with a
Greedy Bandwidth Allocation (MDR-GBA) to solve the problem. In particular, each
vehicle acts as an independent agent and decides the sensing frequencies and
uploading priorities of heterogeneous information. Meanwhile, the roadside unit
(RSU) decides the Vehicle-to-Infrastructure (V2I) bandwidth allocation for each
vehicle based on a greedy scheme. Finally, we build the simulation model and
compare the performance of the proposed solution with state-of-the-art
algorithms. The experimental results conclusively demonstrate the significance
of the new metric and the superiority of the proposed solution.

In the domain of face recognition, there exists a puzzling timing discrepancy
between results from macaque neurophysiology on the one hand and human
electrophysiology on the other. Single unit recordings in macaques have
demonstrated face identity specific responses in extra-striate visual cortex
within 100 milliseconds of stimulus onset. In EEG and MEG experiments with
humans, however, a consistent distinction between neural activity corresponding
to unfamiliar and familiar faces has been reported to emerge around 250 ms.
This points to the possibility that there may be a hitherto undiscovered early
correlate of face familiarity perception in human electrophysiological traces.
We report here a successful search for such a correlate in dense MEG recordings
using pattern classification techniques. Our analyses reveal markers of face
familiarity as early as 85 ms after stimulus onset. Low-level attributes of the
images, such as luminance and color distributions, are unable to account for
this early emerging response difference. These results help reconcile human and
macaque data, and provide clues regarding neural mechanisms underlying familiar
face perception.

Glass transitions are widely observed in various types of soft matter
systems. However, the physical mechanism of these transitions remains
{elusive}, despite years of ambitious research. In particular, an important
unanswered question is whether the glass transition is accompanied by a
divergence of the correlation lengths of the characteristic static structures.
In this study, we develop a deep-neural-network-based method that is used to
extract the characteristic local meso-structures solely from instantaneous
{particle} configurations without any {information} about the dynamics. We
first train a neural network to classify configurations of liquids and glasses
correctly. Then, we obtain the characteristic structures by quantifying the
grounds for the decisions made by the network using Gradient-weighted Class
Activation Mapping (Grad-CAM). We considered two qualitatively different
glass-forming binary systems, and through comparisons with several established
structural indicators, we demonstrate that our system can be used to identify
characteristic structures that depend on the details of the systems. Moreover,
the extracted structures are remarkably correlated with the nonequilibrium
aging dynamics in thermal fluctuations.

To reduce human annotations for relation extraction (RE) tasks, distantly
supervised approaches have been proposed, while struggling with low
performance. In this work, we propose a novel DSRE-NLI framework, which
considers both distant supervision from existing knowledge bases and indirect
supervision from pretrained language models for other tasks. DSRE-NLI energizes
an off-the-shelf natural language inference (NLI) engine with a semi-automatic
relation verbalization (SARV) mechanism to provide indirect supervision and
further consolidates the distant annotations to benefit multi-classification RE
models. The NLI-based indirect supervision acquires only one relation
verbalization template from humans as a semantically general template for each
relationship, and then the template set is enriched by high-quality textual
patterns automatically mined from the distantly annotated corpus. With two
simple and effective data consolidation strategies, the quality of training
data is substantially improved. Extensive experiments demonstrate that the
proposed framework significantly improves the SOTA performance (up to 7.73\% of
F1) on distantly supervised RE benchmark datasets.

In our multicultural world, affect-aware AI systems that support humans need
the ability to perceive affect across variations in emotion expression patterns
across cultures. These systems must perform well in cultural contexts without
annotated affect datasets available for training models. A standard assumption
in affective computing is that affect recognition models trained and used
within the same culture (intracultural) will perform better than models trained
on one culture and used on different cultures (intercultural). We test this
assumption and present the first systematic study of intercultural affect
recognition models using videos of real-world dyadic interactions from six
cultures. We develop an attention-based feature selection approach under
temporal causal discovery to identify behavioral cues that can be leveraged in
intercultural affect recognition models. Across all six cultures, our findings
demonstrate that intercultural affect recognition models were as effective or
more effective than intracultural models. We identify and contribute useful
behavioral features for intercultural affect recognition; facial features from
the visual modality were more useful than the audio modality in this study's
context. Our paper presents a proof-of-concept and motivation for the future
development of intercultural affect recognition systems, especially those
deployed in low-resource situations without annotated data.

Multimodal machine learning is an emerging area of research, which has
received a great deal of scholarly attention in recent years. Up to now, there
are few studies on multimodal conversational emotion recognition. Since Graph
Neural Networks (GNNs) possess the powerful capacity of relational modeling,
they have an inherent advantage in the field of multimodal learning. GNNs
leverage the graph constructed from multimodal data to perform intra- and
inter-modal information interaction, which effectively facilitates the
integration and complementation of multimodal data. In this work, we propose a
novel Graph network based Multimodal Fusion Technique (GraphMFT) for emotion
recognition in conversation. Multimodal data can be modeled as a graph, where
each data object is regarded as a node, and both intra- and inter-modal
dependencies existing between data objects can be regarded as edges. GraphMFT
utilizes multiple improved graph attention networks to capture intra-modal
contextual information and inter-modal complementary information. In addition,
the proposed GraphMFT attempts to address the challenges of existing
graph-based multimodal ERC models such as MMGCN. Empirical results on two
public multimodal datasets reveal that our model outperforms the
State-Of-The-Art (SOTA) approachs with the accuracy of 67.90% and 61.30%.

Robust quantization improves the tolerance of networks for various
implementations, allowing reliable output in different bit-widths or fragmented
low-precision arithmetic. In this work, we perform extensive analyses to
identify the sources of quantization error and present three insights to
robustify a network against quantization: reduction of error propagation, range
clamping for error minimization, and inherited robustness against quantization.
Based on these insights, we propose two novel methods called symmetry
regularization (SymReg) and saturating nonlinearity (SatNL). Applying the
proposed methods during training can enhance the robustness of arbitrary neural
networks against quantization on existing post-training quantization (PTQ) and
quantization-aware training (QAT) algorithms and enables us to obtain a single
weight flexible enough to maintain the output quality under various conditions.
We conduct extensive studies on CIFAR and ImageNet datasets and validate the

Static analysis is a mature field with applications to bug detection,
security analysis, and code optimization, etc. To facilitate these
applications, static analysis frameworks play an essential role by providing a
series of fundamental services such as program abstraction, control flow graph
construction, and points-to/alias information computation, etc. However,
despite impressive progress of static analysis, and this field has seen several
popular frameworks in the last decades, it is still not clear how a static
analysis framework should be designed in a way that analysis developers could
benefit more: for example, what a good IR (for analysis) ought to look like?
What functionalities should the module of fundamental analyses provide to ease
client analyses? How to develop and integrate new analysis conveniently? How to
manage multiple analyses?
  To answer these questions, in this work, we discuss the design trade-offs for
the crucial components of a static analysis framework, and argue for the most
appropriate design by following the HBDC (Harnessing the Best Designs of
Classics) principle: for each crucial component, we compare the design choices
made for it (possibly) by different classic frameworks such as Soot, WALA,
SpotBugs and Doop, and choose arguably the best one, but if none is good
enough, we then propose a better design. These selected or newly proposed
designs finally constitute Tai-e, a new static analysis framework for Java.
Specifically, Tai-e is novel in the designs of several aspects like IR, pointer
analysis and development of new analyses, etc., leading to an easy-to-learn,
easy-to-use and efficient system. To our knowledge, this is the first work that
systematically explores the designs and implementations of various static
analysis frameworks, and we believe it provides useful materials and viewpoints
for building better static analysis infrastructures.

The idea I propose in this paper is a method that is based on comprehensive
functions for directed and undirected rule extraction from artificial neural
network operations. Firstly, I defined comprehensive functions, then
constructed a comprehensive multilayer network (denoted as $N$). Each
activation function of $N$ is parametrized to a comprehensive function.
Following $N$ construction, I extracted rules from the network by observing
that the network output depends on probabilities of composite functions that
are comprehensive functions. This functional rule extraction method applies to
the perceptron and multilayer neural network. For any $N$ model that is trained
to predict some outcome given some event, that model behaviour can be expressed
– using the functional rule extraction method – as a formal rule or informal
rule obeyed by the network to predict that outcome. As example, figure 1
consist of a comprehensive physics function that is parameter for one of the
network hidden activation functions. Using the functional rule extraction
method, I deduced that the comprehensive multilayer network prediction depends
on probability of that physics function and probabilities of other composite
comprehensive functions in $N$. Additionally, functional rule extraction method
can aid in applied settings for generation of equations of learned phenomena.
This generation can be achieved by first training an $N$ model toward
predicting outcome of a phenomenon, then extracting the rules and assuming that
probability values of the network comprehensive functions are constants.
Finally, to simplify the generated equation, comprehensive functions with
probability $p = 0$ can be omitted.

In today's era of smart cyber-physical systems, Deep Neural Networks (DNNs)
have become ubiquitous due to their state-of-the-art performance in complex
real-world applications. The high computational complexity of these networks,
which translates to increased energy consumption, is the foremost obstacle
towards deploying large DNNs in resource-constrained systems. Fixed-Point (FP)
implementations achieved through post-training quantization are commonly used
to curtail the energy consumption of these networks. However, the uniform
quantization intervals in FP restrict the bit-width of data structures to large
values due to the need to represent most of the numbers with sufficient
resolution and avoid high quantization errors. In this paper, we leverage the
key insight that (in most of the scenarios) DNN weights and activations are
mostly concentrated near zero and only a few of them have large magnitudes. We
propose CoNLoCNN, a framework to enable energy-efficient low-precision deep
convolutional neural network inference by exploiting: (1) non-uniform
quantization of weights enabling simplification of complex multiplication
operations; and (2) correlation between activation values enabling partial
compensation of quantization errors at low cost without any run-time overheads.
To significantly benefit from non-uniform quantization, we also propose a novel
data representation format, Encoded Low-Precision Binary Signed Digit, to
compress the bit-width of weights while ensuring direct use of the encoded
weight for processing using a novel multiply-and-accumulate (MAC) unit design.

This paper studies Stochastic Shortest Path (SSP) problems in known and
unknown environments from the perspective of convex optimisation. It first
recalls results in the known parameter case, and develops understanding through
different proofs. It then focuses on the unknown parameter case, where it
studies extended value iteration (EVI) operators. This includes the existing
operators used in Rosenberg et al. [26] and Tarbouriech et al. [31] based on
the l-1 norm and supremum norm, as well as defining EVI operators corresponding
to other norms and divergences, such as the KL-divergence. This paper shows in
general how the EVI operators relate to convex programs, and the form of their
dual, where strong duality is exhibited.
  This paper then focuses on whether the bounds from finite horizon research of
Neu and Pike-Burke [21] can be applied to these extended value iteration
operators in the SSP setting. It shows that similar bounds to [21] for these
operators exist, however they lead to operators that are not in general
monotone and have more complex convergence properties. In a special case we
observe oscillating behaviour. This paper generates open questions on how
research may progress, with several examples that require further examination.

Research on Deep Neural Networks (DNNs) has focused on improving performance
and accuracy for real-world deployments, leading to new models, such as Spiking
Neural Networks (SNNs), and optimization techniques, e.g., quantization and
pruning for compressed networks. However, the deployment of these innovative
models and optimization techniques introduces possible reliability issues,
which is a pillar for DNNs to be widely used in safety-critical applications,
e.g., autonomous driving. Moreover, scaling technology nodes have the
associated risk of multiple faults happening at the same time, a possibility
not addressed in state-of-the-art resiliency analyses.
  Towards better reliability analysis for DNNs, we present enpheeph, a Fault
Injection Framework for Spiking and Compressed DNNs. The enpheeph framework
enables optimized execution on specialized hardware devices, e.g., GPUs, while
providing complete customizability to investigate different fault models,
emulating various reliability constraints and use-cases. Hence, the faults can
be executed on SNNs as well as compressed networks with minimal-to-none
modifications to the underlying code, a feat that is not achievable by other
state-of-the-art tools.
  To evaluate our enpheeph framework, we analyze the resiliency of different
DNN and SNN models, with different compression techniques. By injecting a
random and increasing number of faults, we show that DNNs can show a reduction
in accuracy with a fault rate as low as 7 x 10 ^ (-7) faults per parameter,
with an accuracy drop higher than 40%. Run-time overhead when executing
enpheeph is less than 20% of the baseline execution time when executing 100 000
faults concurrently, at least 10x lower than state-of-the-art frameworks,
making enpheeph future-proof for complex fault injection scenarios.
  We release enpheeph at https://github.com/Alexei95/enpheeph.

The performances of commonly used electrocardiogram (ECG) diagnosis models
have recently improved with the introduction of deep learning (DL). However,
the impact of various combinations of multiple DL components and/or the role of
data augmentation techniques on the diagnosis have not been sufficiently
investigated. This study proposes an ensemble-based multi-view learning
approach with an ECG augmentation technique to achieve a higher performance
than traditional automatic 12-lead ECG diagnosis methods. The data analysis
results show that the proposed model reports an F1 score of 0.840, which
outperforms existing state-ofthe-art methods in the literature.

Legged robots can traverse a wide variety of terrains, some of which may be
challenging for wheeled robots, such as stairs or highly uneven surfaces.
However, quadruped robots face stability challenges on slippery surfaces. This
can be resolved by adjusting the robot's locomotion by switching to more
conservative and stable locomotion modes, such as crawl mode (where three feet
are in contact with the ground always) or amble mode (where one foot touches
down at a time) to prevent potential falls. To tackle these challenges, we
propose an approach to learn a model from past robot experience for predictive
detection of potential failures. Accordingly, we trigger gait switching merely
based on proprioceptive sensory information. To learn this predictive model, we
propose a semi-supervised process for detecting and annotating ground truth
slip events in two stages: We first detect abnormal occurrences in the time
series sequences of the gait data using an unsupervised anomaly detector, and
then, the anomalies are verified with expert human knowledge in a replay
simulation to assert the event of a slip. These annotated slip events are then
used as ground truth examples to train an ensemble decision learner for
predicting slip probabilities across terrains for traversability. We analyze
our model on data recorded by a legged robot on multiple sites with slippery
terrain. We demonstrate that a potential slip event can be predicted up to 720
ms ahead of a potential fall with an average precision greater than 0.95 and an
average F-score of 0.82. Finally, we validate our approach in real-time by
deploying it on a legged robot and switching its gait mode based on slip event

The diversity and Zipfian frequency distribution of natural language
predicates in corpora leads to sparsity when learning Entailment Graphs. As
symbolic models for natural language inference, an EG cannot recover if missing
a novel premise or hypothesis at test-time. In this paper we approach the
problem of vertex sparsity by introducing a new method of graph smoothing,
using a Language Model to find the nearest approximations of missing
predicates. We improve recall by 25.1 and 16.3 absolute percentage points on
two difficult directional entailment datasets while exceeding average
precision, and show a complementarity with other improvements to edge sparsity.
We further analyze language model embeddings and discuss why they are naturally
suitable for premise-smoothing, but not hypothesis-smoothing. Finally, we
formalize a theory for smoothing a symbolic inference method by constructing
transitive chains to smooth both the premise and hypothesis.

There are many challenges associated with analysing gas chromatography - mass
spectrometry (GC-MS) data. Many of these challenges stem from the fact that
electron ionisation can make it difficult to recover molecular information due
to the high degree of fragmentation with concomitant loss of molecular ion
signal. With GC-MS data there are often many common fragment ions shared among
closely-eluting peaks, necessitating sophisticated methods for analysis. Some
of these methods are fully automated, but make some assumptions about the data
which can introduce artifacts during the analysis. Chemometric methods such as
Multivariate Curve Resolution, or Parallel Factor Analysis are particularly
attractive, since they are flexible and make relatively few assumptions about
the data - ideally resulting in fewer artifacts. These methods do require
expert user intervention to determine the most relevant regions of interest and
an appropriate number of components, $k$, for each region. Automated region of
interest selection is needed to permit automated batch processing of
chromatographic data with advanced signal deconvolution. Here, we propose a new
method for automated, untargeted region of interest selection that accounts for
the multivariate information present in GC-MS data to select regions of
interest based on the ratio of the squared first, and second singular values
from the Singular Value Decomposition of a window that moves across the
chromatogram. Assuming that the first singular value accounts largely for
signal, and that the second singular value accounts largely for noise, it is
possible to interpret the relationship between these two values as a
probabilistic distribution of Fisher Ratios. The sensitivity of the algorithm
was tested by investigating the concentration at which the algorithm can no
longer pick out chromatographic regions known to contain signal.

As deep learning models and datasets rapidly scale up, network training is
extremely time-consuming and resource-costly. Instead of training on the entire
dataset, learning with a small synthetic dataset becomes an efficient solution.
Extensive research has been explored in the direction of dataset condensation,
among which gradient matching achieves state-of-the-art performance. The
gradient matching method directly targets the training dynamics by matching the
gradient when training on the original and synthetic datasets. However, there
are limited deep investigations into the principle and effectiveness of this
method. In this work, we delve into the gradient matching method from a
comprehensive perspective and answer the critical questions of what, how, and
where to match. We propose to match the multi-level gradients to involve both
intra-class and inter-class gradient information. We demonstrate that the
distance function should focus on the angle, considering the magnitude
simultaneously to delay the overfitting. An overfitting-aware adaptive learning
step strategy is also proposed to trim unnecessary optimization steps for
algorithmic efficiency improvement. Ablation and comparison experiments
demonstrate that our proposed methodology shows superior accuracy, efficiency,
and generalization compared to prior work.

Training semantic segmentation models with few annotated samples has great
potential in various real-world applications. For the few-shot segmentation
task, the main challenge is how to accurately measure the semantic
correspondence between the support and query samples with limited training
data. To address this problem, we propose to aggregate the learnable covariance
matrices with a deformable 4D Transformer to effectively predict the
segmentation map. Specifically, in this work, we first devise a novel hard
example mining mechanism to learn covariance kernels for the Gaussian process.
The learned covariance kernel functions have great advantages over existing
cosine similarity-based methods in correspondence measurement. Based on the
learned covariance kernels, an efficient doubly deformable 4D Transformer
module is designed to adaptively aggregate feature similarity maps into
segmentation results. By combining these two designs, the proposed method can
not only set new state-of-the-art performance on public benchmarks, but also
converge extremely faster than existing methods. Experiments on three public
datasets have demonstrated the effectiveness of our method.

An estimated 300,000 new cases of leukemia are diagnosed each year which is
2.8 percent of all new cancer cases and the prevalence is rising day by day.
The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia
(ALL), which affects people of all age groups, including children and adults.
In this study, we propose an automated system to detect various-shaped ALL
blast cells from microscopic blood smears images using Deep Neural Networks
(DNN). The system can detect multiple subtypes of ALL cells with an accuracy of
98 percent. Moreover, we have developed a telediagnosis software to provide
real-time support to diagnose ALL subtypes from microscopic blood smears
images.

Recent efforts for improving the performance of neural network (NN)
accelerators that meet today's application requirements have given rise to a
new trend of logic-based NN inference relying on fixed function combinational
logic. Mapping such large Boolean functions with many input variables and
product terms to digital signal processors (DSPs) on Field-programmable gate
arrays (FPGAs) needs a novel framework considering the structure and the
reconfigurability of DSP blocks during this process. The proposed methodology
in this paper maps the fixed function combinational logic blocks to a set of
Boolean functions where Boolean operations corresponding to each function are
mapped to DSP devices rather than look-up tables (LUTs) on the FPGAs to take
advantage of the high performance, low latency, and parallelism of DSP blocks.
% This paper also presents an innovative design and optimization methodology
for compilation and mapping of NNs, utilizing fixed function combinational
logic to DSPs on FPGAs employing high-level synthesis flow. % Our experimental
evaluations across several \REVone{datasets} and selected NNs demonstrate the
comparable performance of our framework in terms of the inference latency and
output accuracy compared to prior art FPGA-based NN accelerators employing
DSPs.

Studies show that Studies that cardiovascular diseases (CVDs) are malignant
for human health. Thus, it is important to have an efficient way of CVD
prognosis. In response to this, the healthcare industry has adopted machine
learning-based smart solutions to alleviate the manual process of CVD
prognosis. Thus, this work proposes an information fusion technique that
combines key attributes of a person through analysis of variance (ANOVA) and
domain experts' knowledge. It also introduces a new collection of CVD data
samples for emerging research. There are thirty-eight experiments conducted
exhaustively to verify the performance of the proposed framework on four
publicly available benchmark datasets and the newly created dataset in this
work. The ablation study shows that the proposed approach can achieve a
competitive mean average accuracy (mAA) of 99.2% and a mean average AUC of
97.9%.

Temperature monitoring is critical for electrical motors to determine if
device protection measures should be executed. However, the complexity of the
internal structure of Permanent Magnet Synchronous Motors (PMSM) makes the
direct temperature measurement of the internal components difficult. This work
pragmatically develops three deep learning models to estimate the PMSMs'
internal temperature based on readily measurable external quantities. The
proposed supervised learning models exploit Long Short-Term Memory (LSTM)
modules, bidirectional LSTM, and attention mechanism to form encoder-decoder
structures to predict simultaneously the temperatures of the stator winding,
tooth, yoke, and permanent magnet. Experiments were conducted in an exhaustive
manner on a benchmark dataset to verify the proposed models' performances. The
comparative analysis shows that the proposed global attention-based
encoder-decoder (EnDec) model provides a competitive overall performance of
1.72 Mean Squared Error (MSE) and 5.34 Mean Absolute Error (MAE).

In this paper, we present a stochastic gradient algorithm for minimizing a
smooth objective function that is an expectation over noisy cost samples, and
only the latter are observed for any given parameter. Our algorithm employs a
gradient estimation scheme with random perturbations, which are formed using
the truncated Cauchy distribution from the delta sphere. We analyze the bias
and variance of the proposed gradient estimator. Our algorithm is found to be
particularly useful in the case when the objective function is non-convex, and
the parameter dimension is high. From an asymptotic convergence analysis, we
establish that our algorithm converges almost surely to the set of stationary
points of the objective function and obtains the asymptotic convergence rate.
We also show that our algorithm avoids unstable equilibria, implying
convergence to local minima. Further, we perform a non-asymptotic convergence
analysis of our algorithm. In particular, we establish here a non-asymptotic
bound for finding an epsilon-stationary point of the non-convex objective
function. Finally, we demonstrate numerically through simulations that the
performance of our algorithm outperforms GSF, SPSA, and RDSA by a significant
margin over a few non-convex settings and further validate its performance over
convex (noisy) objectives.

We explore clustering the softmax predictions of deep neural networks and
introduce a novel probabilistic clustering method, referred to as k-sBetas. In
the general context of clustering discrete distributions, the existing methods
focused on exploring distortion measures tailored to simplex data, such as the
KL divergence, as alternatives to the standard Euclidean distance. We provide a
general maximum a posteriori (MAP) perspective of clustering distributions,
which emphasizes that the statistical models underlying the existing
distortion-based methods may not be descriptive enough. Instead, we optimize a
mixed-variable objective measuring the conformity of data within each cluster
to the introduced sBeta density function, whose parameters are constrained and
estimated jointly with binary assignment variables. Our versatile formulation
approximates a variety of parametric densities for modeling simplex data, and
enables to control the cluster-balance bias. This yields highly competitive
performances for unsupervised adjustments of black-box model predictions in a
variety of scenarios. Our code and comparisons with the existing
simplex-clustering approaches along with our introduced softmax-prediction
benchmarks are publicly available:
https://github.com/fchiaroni/Clustering_Softmax_Predictions.

In this article, we propose a deep learning framework that provides a unified
approach to the problem of leg contact detection in humanoid robot walking
gaits. Our formulation accomplishes to accurately and robustly estimate the
contact state probability for each leg (i.e., stable or slip/no contact). The
proposed framework employs solely proprioceptive sensing and although it relies
on simulated ground-truth contact data for the classification process, we
demonstrate that it generalizes across varying friction surfaces and different
legged robotic platforms and, at the same time, is readily transferred from
simulation to practice. The framework is quantitatively and qualitatively
assessed in simulation via the use of ground-truth contact data and is
contrasted against state of-the-art methods with an ATLAS, a NAO, and a TALOS
humanoid robot. Furthermore, its efficacy is demonstrated in base estimation
with a real TALOS humanoid. To reinforce further research endeavors, our
implementation is offered as an open-source ROS/Python package, coined Legged
Contact Detection (LCD).

Neural Radiance Fields (NeRFs) have demonstrated amazing ability to
synthesize images of 3D scenes from novel views. However, they rely upon
specialized volumetric rendering algorithms based on ray marching that are
mismatched to the capabilities of widely deployed graphics hardware. This paper
introduces a new NeRF representation based on textured polygons that can
synthesize novel images efficiently with standard rendering pipelines. The NeRF
is represented as a set of polygons with textures representing binary opacities
and feature vectors. Traditional rendering of the polygons with a z-buffer
yields an image with features at every pixel, which are interpreted by a small,
view-dependent MLP running in a fragment shader to produce a final pixel color.
This approach enables NeRFs to be rendered with the traditional polygon
rasterization pipeline, which provides massive pixel-level parallelism,
achieving interactive frame rates on a wide range of compute platforms,
including mobile phones.

We focus on better understanding the critical factors of
augmentation-invariant representation learning. We revisit MoCo v2 and BYOL and
try to prove the authenticity of the following assumption: different frameworks
bring about representations of different characteristics even with the same
pretext task. We establish the first benchmark for fair comparisons between
MoCo v2 and BYOL, and observe: (i) sophisticated model configurations enable
better adaptation to pre-training dataset; (ii) mismatched optimization
strategies of pre-training and fine-tuning hinder model from achieving
competitive transfer performances. Given the fair benchmark, we make further
investigation and find asymmetry of network structure endows contrastive
frameworks to work well under the linear evaluation protocol, while may hurt
the transfer performances on long-tailed classification tasks. Moreover,
negative samples do not make models more sensible to the choice of data
augmentations, nor does the asymmetric network structure. We believe our
findings provide useful information for future work.

GitHub is the largest host of open source software on the Internet. This
large, freely accessible database has attracted the attention of practitioners
and researchers alike. But as GitHub's growth continues, it is becoming
increasingly hard to navigate the plethora of repositories which span a wide
range of domains. Past work has shown that taking the application domain into
account is crucial for tasks such as predicting the popularity of a repository
and reasoning about project quality. In this work, we build on a previously
annotated dataset of 5,000 GitHub repositories to design an automated
classifier for categorising repositories by their application domain. The
classifier uses state-of-the-art natural language processing techniques and
machine learning to learn from multiple data sources and catalogue repositories
according to five application domains. We contribute with (1) an automated
classifier that can assign popular repositories to each application domain with
at least 70% precision, (2) an investigation of the approach's performance on
less popular repositories, and (3) a practical application of this approach to
answer how the adoption of software engineering practices differs across
application domains. Our work aims to help the GitHub community identify
repositories of interest and opens promising avenues for future work
investigating differences between repositories from different application
domains.

Emerging vehicular systems with increasing proportions of automated
components present opportunities for optimal control to mitigate congestion and
increase efficiency. There has been a recent interest in applying deep
reinforcement learning (DRL) to these nonlinear dynamical systems for the
automatic design of effective control strategies. Despite conceptual advantages
of DRL being model-free, studies typically nonetheless rely on training setups
that are painstakingly specialized to specific vehicular systems. This is a key
challenge to efficient analysis of diverse vehicular and mobility systems. To
this end, this article contributes a streamlined methodology for vehicular
microsimulation and discovers high performance control strategies with minimal
manual design. A variable-agent, multi-task approach is presented for
optimization of vehicular Partially Observed Markov Decision Processes. The
methodology is experimentally validated on mixed autonomy traffic systems,
where fractions of vehicles are automated; empirical improvement, typically
15-60% over a human driving baseline, is observed in all configurations of six
diverse open or closed traffic systems. The study reveals numerous emergent
behaviors resembling wave mitigation, traffic signaling, and ramp metering.
Finally, the emergent behaviors are analyzed to produce interpretable control
strategies, which are validated against the learned control strategies.

In recent years, machine and quantum learning have gained considerable
momentum sustained by growth in computational power and data availability and
have shown exceptional aptness for solving recognition- and classification-type
problems, as well as problems that require complex, strategic planning. In this
work, we discuss and analyze the role machine and quantum learning are playing
in the development of diamond-based quantum technologies. This matters as
diamond and its optically-addressable spin defects are becoming prime hardware
candidates for solid state-based applications in quantum information, computing
and metrology. Through a selected number of demonstrations, we show that
machine and quantum learning are leading to both practical and fundamental
improvements in measurement speed and accuracy. This is crucial for quantum
applications, especially for those where coherence time and signal-to-noise
ratio are scarce resources. We summarize some of the most prominent machine and
quantum learning approaches that have been conducive to the presented advances
and discuss their potential for proposed and future quantum applications.

In the reinforcement learning literature, there are many algorithms developed
for either Contextual Bandit (CB) or Markov Decision Processes (MDP)
environments. However, when deploying reinforcement learning algorithms in the
real world, even with domain expertise, it is often difficult to know whether
it is appropriate to treat a sequential decision making problem as a CB or an
MDP. In other words, do actions affect future states, or only the immediate
rewards? Making the wrong assumption regarding the nature of the environment
can lead to inefficient learning, or even prevent the algorithm from ever
learning an optimal policy, even with infinite data. In this work we develop an
online algorithm that uses a Bayesian hypothesis testing approach to learn the
nature of the environment. Our algorithm allows practitioners to incorporate
prior knowledge about whether the environment is that of a CB or an MDP, and
effectively interpolate between classical CB and MDP-based algorithms to
mitigate against the effects of misspecifying the environment. We perform
simulations and demonstrate that in CB settings our algorithm achieves lower
regret than MDP-based algorithms, while in non-bandit MDP settings our
algorithm is able to learn the optimal policy, often achieving comparable
regret to MDP-based algorithms.

The history-dependent behaviors of classical plasticity models are often
driven by internal variables evolved according to phenomenological laws. The
difficulty to interpret how these internal variables represent a history of
deformation, the lack of direct measurement of these internal variables for
calibration and validation, and the weak physical underpinning of those
phenomenological laws have long been criticized as barriers to creating
realistic models. In this work, geometric machine learning on graph data (e.g.
finite element solutions) is used as a means to establish a connection between
nonlinear dimensional reduction techniques and plasticity models. Geometric
learning-based encoding on graphs allows the embedding of rich time-history
data onto a low-dimensional Euclidean space such that the evolution of plastic
deformation can be predicted in the embedded feature space. A corresponding
decoder can then convert these low-dimensional internal variables back into a
weighted graph such that the dominating topological features of plastic
deformation can be observed and analyzed.

Self-supervised learning (SSL) has achieved remarkable performance in
pretraining the models that can be further used in downstream tasks via
fine-tuning. However, these self-supervised models may not capture meaningful
semantic information since the images belonging to the same class are always
regarded as negative pairs in the contrastive loss. Consequently, the images of
the same class are often located far away from each other in learned feature
space, which would inevitably hamper the fine-tuning process. To address this
issue, we seek to provide a better initialization for the self-supervised
models by enhancing the semantic information. To this end, we propose a
Contrastive Initialization (COIN) method that breaks the standard fine-tuning
pipeline by introducing an extra initialization stage before fine-tuning.
Extensive experiments show that, with the enriched semantics, our COIN
significantly outperforms existing methods without introducing extra training
cost and sets new state-of-the-arts on multiple downstream tasks.

This paper describes the design process for the cybersecurity serious game
'PeriHack'. Publicly released under a CC (BY-NC-SA) license, PeriHack is a
board and card game for two players or teams that simulates the struggle
between a red team (attackers) and a blue team (defenders). The game requires
players to explore a sample network looking for vulnerabilities and then chain
different attacks to exploit possible weaknesses of different nature, which may
include both technical and social engineering exploits. At the same time, it
also simulates budget level constraints for the blue team by providing limited
resources to evaluate and prioritize different critical vulnerabilities. The
game is discussed via the lenses of the AGE and 6-11 Frameworks and was
primarily designed as a learning tool for students in the cybersecurity and
technology related fields.

Retrieving the missing dimension information in acoustic images from 2D
forward-looking sonar is a well-known problem in the field of underwater
robotics. There are works attempting to retrieve 3D information from a single
image which allows the robot to generate 3D maps with fly-through motion.
However, owing to the unique image formulation principle, estimating 3D
information from a single image faces severe ambiguity problems. Classical
methods of multi-view stereo can avoid the ambiguity problems, but may require
a large number of viewpoints to generate an accurate model. In this work, we
propose a novel learning-based multi-view stereo method to estimate 3D
information. To better utilize the information from multiple frames, an
elevation plane sweeping method is proposed to generate the
depth-azimuth-elevation cost volume. The volume after regularization can be
considered as a probabilistic volumetric representation of the target. Instead
of performing regression on the elevation angles, we use pseudo front depth
from the cost volume to represent the 3D information which can avoid the 2D-3D
problem in acoustic imaging. High-accuracy results can be generated with only
two or three images. Synthetic datasets were generated to simulate various
underwater targets. We also built the first real dataset with accurate ground
truth in a large scale water tank. Experimental results demonstrate the
superiority of our method, compared to other state-of-the-art methods.

Performance and scalability requirements have a fundamental role in most
large-scale software applications. To satisfy such requirements, caching is
often used at various levels and infrastructure layers. Application-level
caching -- or memoization -- is an increasingly used form of caching within the
application boundaries, which consists of storing the results of computations
in memory to avoid re-computing them. This is typically manually done by
developers, who identify caching opportunities in the code and write additional
code to manage the cache content. The task of identifying caching opportunities
is a challenge because it requires the analysis of workloads and code locations
where it is feasible and beneficial to cache objects. To aid developers in this
task, there are approaches that automatically identify cacheable methods.
Although such approaches have been individually evaluated, their effectiveness
has not been compared. We thus in this paper present an empirical evaluation to
compare the method recommendations made by the two existing application-level
caching approaches at the method level, namely APLCache and MemoizeIt, using
seven open-source web applications. We analyse the recommendations made by each
approach as well as the hits, misses and throughput achieved with their valid
caching recommendations. Our results show that the effectiveness of both
approaches largely depends on the specific application, the presence of invalid
recommendations and additional configurations, such as the time-to-live. By
inspecting the obtained results, we observed in which cases the recommendations
of each approach fail and succeed, which allowed us to derive a set of seven
lessons learned that give directions for future approaches to support
developers in the adoption of this type of caching.

Despite the progresses on pre-trained language models, there is a lack of
unified frameworks for pre-trained sentence representation. As such, it calls
for different pre-training methods for specific scenarios, and the pre-trained
models are likely to be limited by their universality and representation
quality. In this work, we extend the recently proposed MAE style pre-training
strategy, RetroMAE, such that it may effectively support a wide variety of
sentence representation tasks. The extended framework consists of two stages,
with RetroMAE conducted throughout the process. The first stage performs
RetroMAE over generic corpora, like Wikipedia, BookCorpus, etc., from which the
base model is learned. The second stage takes place on domain-specific data,
e.g., MS MARCO and NLI, where the base model is continuingly trained based on
RetroMAE and contrastive learning. The pre-training outputs at the two stages
may serve different applications, whose effectiveness are verified with
comprehensive experiments. Concretely, the base model are proved to be
effective for zero-shot retrieval, with remarkable performances achieved on
BEIR benchmark. The continuingly pre-trained models further benefit more
downstream tasks, including the domain-specific dense retrieval on MS MARCO,
Natural Questions, and the sentence embeddings' quality for standard STS and
transfer tasks in SentEval. The empirical insights of this work may inspire the
future design of sentence representation pre-training. Our pre-trained models
and source code will be released to the public communities.

Purpose: Accurate electronic stopping power data is crucial for calculating
radiation-induced effects in various applications, from dosimetry and
radiotherapy to particle physics. In this study, Stacking Ensemble Machine
Learning (EML) algorithm was developed to predict electronic stopping power for
any incident ion and target combination over a wide range of ion energies. For
this purpose, five ML models, namely BR, XGB, AdB, GB, and RF, were selected as
base and meta learners to construct the final Stacking EML. Methods: 40,044
experimental measurements, from 1928 to the present, available on the
International Atomic Energy Agency (IAEA) website were used to train machine
learning (ML) algorithms. This database consists of 593 ion-target combinations
across the energy range of 0.037 to 985 MeV. For model training, the eleven
most important features were selected. The model evaluation was performed using
several error metrics, including R-squared (R2), root-mean-squared-error
(RMSE), mean-absolute-error (MAE), and mean-absolute-percentage-error (MAPE),
on both the training and test datasets. Results: Based on model performance
evaluation tests, a stack of eXtreme Gradient Boosting (XGB) and Random Forest
(RF) via Bagging Regressor (BR) meta-learner had the highest lowest error
margin. The value of R2=0.9985 indicated a near-ideal fit to all samples in the
training data across the entire range of stopping powers. R2=0.9955 for
predictions made by the model on the unseen test data suggested that the model
accurately predicted the test data. Conclusions: The developed model resulted
in highly accurate predictions for any ion-target combination across the whole
particle energy spectrum. The associated model can serve as a universal tool to
generate the stopping power data in a wide range of cases, regardless of the
availability of experimental data or reliable theoretical equations.

LiDAR point clouds, which are usually scanned by rotating LiDAR sensors
continuously, capture precise geometry of the surrounding environment and are
crucial to many autonomous detection and navigation tasks. Though many 3D deep
architectures have been developed, efficient collection and annotation of large
amounts of point clouds remain one major challenge in the analytic and
understanding of point cloud data. This paper presents PolarMix, a point cloud
augmentation technique that is simple and generic but can mitigate the data
constraint effectively across different perception tasks and scenarios.
PolarMix enriches point cloud distributions and preserves point cloud fidelity
via two cross-scan augmentation strategies that cut, edit, and mix point clouds
along the scanning direction. The first is scene-level swapping which exchanges
point cloud sectors of two LiDAR scans that are cut along the azimuth axis. The
second is instance-level rotation and paste which crops point instances from
one LiDAR scan, rotates them by multiple angles (to create multiple copies),
and paste the rotated point instances into other scans. Extensive experiments
show that PolarMix achieves superior performance consistently across different
perception tasks and scenarios. In addition, it can work as plug-and-play for
various 3D deep architectures and also performs well for unsupervised domain

Hyperparameter optimization (HPO) is a key component of machine learning
models for achieving peak predictive performance. While numerous methods and
algorithms for HPO have been proposed over the last years, little progress has
been made in illuminating and examining the actual structure of these black-box
optimization problems. Exploratory landscape analysis (ELA) subsumes a set of
techniques that can be used to gain knowledge about properties of unknown
optimization problems. In this paper, we evaluate the performance of five
different black-box optimizers on 30 HPO problems, which consist of two-,
three- and five-dimensional continuous search spaces of the XGBoost learner
trained on 10 different data sets. This is contrasted with the performance of
the same optimizers evaluated on 360 problem instances from the black-box
optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB
problems and examine similarities and differences. A cluster analysis of the
HPO and BBOB problems in ELA feature space allows us to identify how the HPO
problems compare to the BBOB problems on a structural meta-level. We identify a
subset of BBOB problems that are close to the HPO problems in ELA feature space
and show that optimizer performance is comparably similar on these two sets of
benchmark problems. We highlight open challenges of ELA for HPO and discuss
potential directions of future research and applications.

Few-shot object detection has been extensively investigated by incorporating
meta-learning into region-based detection frameworks. Despite its success, the
said paradigm is still constrained by several factors, such as (i) low-quality
region proposals for novel classes and (ii) negligence of the inter-class
correlation among different classes. Such limitations hinder the generalization
of base-class knowledge for the detection of novel-class objects. In this work,
we design Meta-DETR, which (i) is the first image-level few-shot detector, and
(ii) introduces a novel inter-class correlational meta-learning strategy to
capture and leverage the correlation among different classes for robust and
accurate few-shot object detection. Meta-DETR works entirely at image level
without any region proposals, which circumvents the constraint of inaccurate
proposals in prevalent few-shot detection frameworks. In addition, the
introduced correlational meta-learning enables Meta-DETR to simultaneously
attend to multiple support classes within a single feedforward, which allows to
capture the inter-class correlation among different classes, thus significantly
reducing the misclassification over similar classes and enhancing knowledge
generalization to novel classes. Experiments over multiple few-shot object
detection benchmarks show that the proposed Meta-DETR outperforms
state-of-the-art methods by large margins. The implementation codes are
available at https://github.com/ZhangGongjie/Meta-DETR.

We introduce a Dimension-Reduced Second-Order Method (DRSOM) for convex and
nonconvex unconstrained optimization. Under a trust-region-like framework our
method preserves the convergence of the second-order method while using only
Hessian-vector products in two directions. Moreover, the computational overhead
remains comparable to the first-order such as the gradient descent method. We
show that the method has a complexity of $O(\epsilon^{-3/2})$ to satisfy the
first-order and second-order conditions in the subspace. The applicability and
performance of DRSOM are exhibited by various computational experiments in
logistic regression, $L_2-L_p$ minimization, sensor network localization, and
neural network training. For neural networks, our preliminary implementation
seems to gain computational advantages in terms of training accuracy and
iteration complexity over state-of-the-art first-order methods including SGD
and ADAM.

In the practical applications of computed tomography imaging, the projection
data may be acquired within a limited-angle range and corrupted by noises due
to the limitation of scanning conditions. The noisy incomplete projection data
results in the ill-posedness of the inverse problems. In this work, we
theoretically verify that the low-resolution reconstruction problem has better
numerical stability than the high-resolution problem. In what follows, a novel
low-resolution image prior based CT reconstruction model is proposed to make
use of the low-resolution image to improve the reconstruction quality. More
specifically, we build up a low-resolution reconstruction problem on the
down-sampled projection data, and use the reconstructed low-resolution image as
prior knowledge for the original limited-angle CT problem. We solve the
constrained minimization problem by the alternating direction method with all
subproblems approximated by the convolutional neural networks. Numerical
experiments demonstrate that our double-resolution network outperforms both the
variational method and popular learning-based reconstruction methods on noisy
limited-angle reconstruction problems.

Predictive and real-time inference capability for the upstream separatrix
electron density, $n_\text{e, sep}$, is essential for design and control of
core-edge integrated plasma scenarios. In this study, both supervised and
semi-supervised machine learning algorithms are explored to establish direct
mapping as well as indirect compressed representation of the pedestal profiles
for predictions and inference of $n_{\text{e, sep}}$. Based on the EUROfusion
pedestal database for JET, a tabular dataset was created, consisting of machine
parameters, fraction of ELM cycle, high resolution Thomson scattering profiles
of electron density and temperature, and $n_{\text{e, sep}}$ for 608 JET shots.
Using the tabular dataset, the direct mapping approach provides a mapping of
machine parameters and ELM percentage to $n_{\text{e, sep}}$. Through
representation learning, a compressed representation of the experimental
pedestal electron density and temperature profiles is established. By
conditioning the representation with machine control parameters, a
probabilistic generative predictive model is established. For prediction, the
machine parameters can be used to establish a conditional distribution of the
compressed pedestal profiles, and the decoder that is trained as part of the
algorithm can be used to decode the compressed representation back to full
pedestal profiles. Although, in this work, a proof-of-principle for predicting
and inferring $n_{\text{e, sep}}$ is given, such a representation learning can
be used also for many other applications as the full pedestal profile is
predicted. An implementation of this work can be found at
https://github.com/fusionby2030/moxie.

Neural architecture search (NAS) has been studied extensively and has grown
to become a research field with substantial impact. While classical
single-objective NAS searches for the architecture with the best performance,
multi-objective NAS considers multiple objectives that should be optimized
simultaneously, e.g., minimizing resource usage along the validation error.
Although considerable progress has been made in the field of multi-objective
NAS, we argue that there is some discrepancy between the actual optimization
problem of practical interest and the optimization problem that multi-objective
NAS tries to solve. We resolve this discrepancy by formulating the
multi-objective NAS problem as a quality diversity optimization (QDO) problem
and introduce three quality diversity NAS optimizers (two of them belonging to
the group of multifidelity optimizers), which search for high-performing yet
diverse architectures that are optimal for application-specific niches, e.g.,
hardware constraints. By comparing these optimizers to their multi-objective
counterparts, we demonstrate that quality diversity NAS in general outperforms
multi-objective NAS with respect to quality of solutions and efficiency. We
further show how applications and future NAS research can thrive on QDO.

Deep learning models have been successfully applied to a variety of software
engineering tasks, such as code classification, summarisation, and bug and
vulnerability detection. In order to apply deep learning to these tasks, source
code needs to be represented in a format that is suitable for input into the
deep learning model. Most approaches to representing source code, such as
tokens, abstract syntax trees (ASTs), data flow graphs (DFGs), and control flow
graphs (CFGs) only focus on the code itself and do not take into account
additional context that could be useful for deep learning models. In this
paper, we argue that it is beneficial for deep learning models to have access
to additional contextual information about the code being analysed. We present
preliminary evidence that encoding context from the call hierarchy along with
information from the code itself can improve the performance of a
state-of-the-art deep learning model for two software engineering tasks. We
outline our research agenda for adding further contextual information to source
code representations for deep learning.

Recently, the applications of the methodologies of Reinforcement Learning
(RL) to NP-Hard Combinatorial optimization problems have become a popular
topic. This is essentially due to the nature of the traditional combinatorial
algorithms, often based on a trial-and-error process. RL aims at automating
this process. At this regard, this paper focuses on the application of RL for
the Vehicle Routing Problem (VRP), a famous combinatorial problem that belongs
to the class of NP-Hard problems. In this work, first, the problem is modeled
as a Markov Decision Process (MDP) and then the PPO method (which belongs to
the Actor-Critic class of Reinforcement learning methods) is applied. In a
second phase, the neural architecture behind the Actor and Critic has been
established, choosing to adopt a neural architecture based on the Convolutional
neural networks, both for the Actor and the Critic. This choice resulted in
effectively addressing problems of different sizes. Experiments performed on a
wide range of instances show that the algorithm has good generalization
capabilities and can reach good solutions in a short time. Comparisons between
the algorithm proposed and the state-of-the-art solver OR-TOOLS show that the
latter still outperforms the Reinforcement learning algorithm. However, there
are future research perspectives, that aim to upgrade the current performance
of the algorithm proposed.

Diversity maximization is a fundamental problem with wide applications in
data summarization, web search, and recommender systems. Given a set $X$ of $n$
elements, it asks to select a subset $S$ of $k \ll n$ elements with maximum
\emph{diversity}, as quantified by the dissimilarities among the elements in
$S$. In this paper, we focus on the diversity maximization problem with
fairness constraints in the streaming setting. Specifically, we consider the
max-min diversity objective, which selects a subset $S$ that maximizes the
minimum distance (dissimilarity) between any pair of distinct elements within
it. Assuming that the set $X$ is partitioned into $m$ disjoint groups by some
sensitive attribute, e.g., sex or race, ensuring \emph{fairness} requires that
the selected subset $S$ contains $k_i$ elements from each group $i \in [1,m]$.
A streaming algorithm should process $X$ sequentially in one pass and return a
subset with maximum \emph{diversity} while guaranteeing the fairness
constraint. Although diversity maximization has been extensively studied, the
only known algorithms that can work with the max-min diversity objective and
fairness constraints are very inefficient for data streams. Since diversity
maximization is NP-hard in general, we propose two approximation algorithms for
fair diversity maximization in data streams, the first of which is
$\frac{1-\varepsilon}{4}$-approximate and specific for $m=2$, where
$\varepsilon \in (0,1)$, and the second of which achieves a
$\frac{1-\varepsilon}{3m+2}$-approximation for an arbitrary $m$. Experimental
results on real-world and synthetic datasets show that both algorithms provide
solutions of comparable quality to the state-of-the-art algorithms while
running several orders of magnitude faster in the streaming setting.

3D reconstruction of novel categories based on few-shot learning is appealing
in real-world applications and attracts increasing research interests. Previous
approaches mainly focus on how to design shape prior models for different
categories. Their performance on unseen categories is not very competitive. In
this paper, we present a Memory Prior Contrastive Network (MPCN) that can store
shape prior knowledge in a few-shot learning based 3D reconstruction framework.
With the shape memory, a multi-head attention module is proposed to capture
different parts of a candidate shape prior and fuse these parts together to
guide 3D reconstruction of novel categories. Besides, we introduce a 3D-aware
contrastive learning method, which can not only complement the retrieval
accuracy of memory network, but also better organize image features for
downstream tasks. Compared with previous few-shot 3D reconstruction methods,
MPCN can handle the inter-class variability without category annotations.
Experimental results on a benchmark synthetic dataset and the Pascal3D+
real-world dataset show that our model outperforms the current state-of-the-art
methods significantly.

Bump hunting deals with finding in sample spaces meaningful data subsets
known as bumps. These have traditionally been conceived as modal or concave
regions in the graph of the underlying density function. We define an abstract
bump construct based on curvature functionals of the probability density. Then,
we explore several alternative characterizations involving derivatives up to
second order. In particular, a suitable implementation of Good and Gaskins'
original concave bumps is proposed in the multivariate case. Moreover, we bring
to exploratory data analysis concepts like the mean curvature and the Laplacian
that have produced good results in applied domains. Our methodology addresses
the approximation of the curvature functional with a plug-in kernel density
estimator. We provide theoretical results that assure the asymptotic
consistency of bump boundaries in the Hausdorff distance with affordable
convergence rates. We also present asymptotically valid and consistent
confidence regions bounding curvature bumps. The theory is illustrated through
several use cases in sports analytics with datasets from the NBA, MLB and NFL.
We conclude that the different curvature instances effectively combine to
generate insightful visualizations.

Masked autoencoders are scalable vision learners, as the title of MAE
\cite{he2022masked}, which suggests that self-supervised learning (SSL) in
vision might undertake a similar trajectory as in NLP. Specifically, generative
pretext tasks with the masked prediction (e.g., BERT) have become a de facto
standard SSL practice in NLP. By contrast, early attempts at generative methods
in vision have been buried by their discriminative counterparts (like
contrastive learning); however, the success of mask image modeling has revived
the masking autoencoder (often termed denoising autoencoder in the past). As a
milestone to bridge the gap with BERT in NLP, masked autoencoder has attracted
unprecedented attention for SSL in vision and beyond. This work conducts a
comprehensive survey of masked autoencoders to shed insight on a promising
direction of SSL. As the first to review SSL with masked autoencoders, this
work focuses on its application in vision by discussing its historical
developments, recent progress, and implications for diverse applications.

Spina Bifida (SB) is a birth defect developed during the early stage of
pregnancy in which there is incomplete closing of the spine around the spinal
cord. The growing interest in fetoscopic Spina-Bifida repair, which is
performed in fetuses who are still in the pregnant uterus, prompts the need for
appropriate training. The learning curve for such procedures is steep and
requires excellent procedural skills. Computer-based virtual reality (VR)
simulation systems offer a safe, cost-effective, and configurable training
environment free from ethical and patient safety issues. However, to the best
of our knowledge, there are currently no commercial or experimental VR training
simulation systems available for fetoscopic SB-repair procedures. In this
paper, we propose a novel VR simulator for core manual skills training for
SB-repair. An initial simulation realism validation study was carried out by
obtaining subjective feedback (face and content validity) from 14 clinicians.
The overall simulation realism was on average marked 4.07 on a 5-point Likert
scale (1 - very unrealistic, 5 - very realistic). Its usefulness as a training
tool for SB-repair as well as in learning fundamental laparoscopic skills was
marked 4.63 and 4.80, respectively. These results indicate that VR simulation
of fetoscopic procedures may contribute to surgical training without putting
fetuses and their mothers at risk. It could also facilitate wider adaptation of
fetoscopic procedures in place of much more invasive open fetal surgeries.

In this study, we have tailored a pixel tracking method for temporal
extrapolation of the ventricular segmentation masks in cardiac magnetic
resonance images. The pixel tracking process starts from the end-diastolic
frame of the heart cycle using the available manually segmented images to
predict the end-systolic segmentation mask. The superpixels approach is used to
divide the raw images into smaller cells and in each time frame, new labels are
assigned to the image cells which leads to tracking the movement of the heart
wall elements through different frames. The tracked masks at the end of systole
are compared with the already available manually segmented masks and dice
scores are found to be between 0.81 to 0.84. Considering the fact that the
proposed method does not necessarily require a training dataset, it could be an
attractive alternative approach to deep learning segmentation methods in
scenarios where training data are limited.

In this paper, we propose a novel light field compression method based on a
Quantized Distilled Low Rank Neural Radiance Field (QDLR-NeRF) representation.
While existing compression methods encode the set of light field sub-aperture
images, our proposed method instead learns an implicit scene representation in
the form of a Neural Radiance Field (NeRF), which also enables view synthesis.
For reducing its size, the model is first learned under a Low Rank (LR)
constraint using a Tensor Train (TT) decomposition in an Alternating Direction
Method of Multipliers (ADMM) optimization framework. To further reduce the
model size, the components of the tensor train decomposition need to be
quantized. However, performing the optimization of the NeRF model by
simultaneously taking the low rank constraint and the rate-constrained weight
quantization into consideration is challenging. To deal with this difficulty,
we introduce a network distillation operation that separates the low rank
approximation and the weight quantization in the network training. The
information from the initial LR constrained NeRF (LR-NeRF) is distilled to a
model of a much smaller dimension (DLR-NeRF) based on the TT decomposition of
the LR-NeRF. An optimized global codebook is then learned to quantize all TT
components, producing the final QDLRNeRF. Experimental results show that our
proposed method yields better compression efficiency compared with
state-of-the-art methods, and it additionally has the advantage of allowing the
synthesis of any light field view with a high quality.

In this study, a method has been developed to improve the resolution of
histological human placenta images. For this purpose, a paired series of high-
and low-resolution images have been collected to train a deep neural network
model that can predict image residuals required to improve the resolution of
the input images. A modified version of the U-net neural network model has been
tailored to find the relationship between the low resolution and residual
images. After training for 900 epochs on an augmented dataset of 1000 images,
the relative mean squared error of 0.003 is achieved for the prediction of 320
test images. The proposed method has not only improved the contrast of the
low-resolution images at the edges of cells but added critical details and
textures that mimic high-resolution images of placenta villous space.

Monocular depth estimation (MDE) has attracted intense study due to its low
cost and critical functions for robotic tasks such as localization, mapping and
obstacle detection. Supervised approaches have led to great success with the
advance of deep learning, but they rely on large quantities of ground-truth
depth annotations that are expensive to acquire. Unsupervised domain adaptation
(UDA) transfers knowledge from labeled source data to unlabeled target data, so
as to relax the constraint of supervised learning. However, existing UDA
approaches may not completely align the domain gap across different datasets
because of the domain shift problem. We believe better domain alignment can be
achieved via well-designed feature decomposition. In this paper, we propose a
novel UDA method for MDE, referred to as Learning Feature Decomposition for
Adaptation (LFDA), which learns to decompose the feature space into content and
style components. LFDA only attempts to align the content component since it
has a smaller domain gap. Meanwhile, it excludes the style component which is
specific to the source domain from training the primary task. Furthermore, LFDA
uses separate feature distribution estimations to further bridge the domain
gap. Extensive experiments on three domain adaptative MDE scenarios show that
the proposed method achieves superior accuracy and lower computational cost
compared to the state-of-the-art approaches.

Effective reinforcement learning requires a proper balance of exploration and
exploitation defined by the dispersion of action distribution. However, this
balance depends on the task, the current stage of the learning process, and the
current environment state. Existing methods that designate the action
distribution dispersion require problem-dependent hyperparameters. In this
paper, we propose to automatically designate the action distribution dispersion
using the following principle: This distribution should have sufficient
dispersion to enable the evaluation of future policies. To that end, the
dispersion should be tuned to assure a sufficiently high probability
(densities) of the actions in the replay buffer and the modes of the
distributions that generated them, yet this dispersion should not be higher.
This way, a policy can be effectively evaluated based on the actions in the
buffer, but exploratory randomness in actions decreases when this policy
converges. The above principle is verified here on challenging benchmarks Ant,
HalfCheetah, Hopper, and Walker2D, with good results. Our method makes the
action standard deviations converge to values similar to those resulting from
trial-and-error optimization.

We propose a local-to-global strategy for graph machine learning and network
analysis by defining certain local features and vector representations of nodes
and then using them to learn globally defined metrics and properties of the
nodes by means of deep neural networks. By extending the notion of the degree
of a node via Breath-First Search, a general family of {\bf parametric
centrality functions} is defined which are able to reveal the importance of
nodes. We introduce the {\bf neighbors degree frequency (NDF)}, as a locally
defined embedding of nodes of undirected graphs into euclidean spaces. This
gives rise to a vectorized labeling of nodes which encodes the structure of
local neighborhoods of nodes and can be used for graph isomorphism testing. We
add flexibility to our construction so that it can handle dynamic graphs as
well. Afterwards, the Breadth-First Search is used to extend NDF vector
representations into two different matrix representations of nodes which
contain higher order information about the neighborhoods of nodes. Our matrix
representations of nodes provide us with a new way of visualizing the shape of
the neighborhood of a node. Furthermore, we use these matrix representations to
obtain feature vectors, which are suitable for typical deep learning
algorithms. To demonstrate these node embeddings actually contain some
information about the nodes, in a series of examples, we show that PageRank and
closeness centrality can be learned by applying deep learning to these local
features. Our constructions are flexible enough to handle evolving graphs.
Finally, we explain how to adapt our constructions for directed graphs.

Video shadow detection aims to generate consistent shadow predictions among
video frames. However, the current approaches suffer from inconsistent shadow
predictions across frames, especially when the illumination and background
textures change in a video. We make an observation that the inconsistent
predictions are caused by the shadow feature inconsistency, i.e., the features
of the same shadow regions show dissimilar proprieties among the nearby
frames.In this paper, we present a novel Shadow-Consistent Correspondence
method (SC-Cor) to enhance pixel-wise similarity of the specific shadow regions
across frames for video shadow detection. Our proposed SC-Cor has three main
advantages. Firstly, without requiring the dense pixel-to-pixel correspondence
labels, SC-Cor can learn the pixel-wise correspondence across frames in a
weakly-supervised manner. Secondly, SC-Cor considers intra-shadow separability,
which is robust to the variant textures and illuminations in videos. Finally,
SC-Cor is a plug-and-play module that can be easily integrated into existing
shadow detectors with no extra computational cost. We further design a new
evaluation metric to evaluate the temporal stability of the video shadow
detection results. Experimental results show that SC-Cor outperforms the prior
state-of-the-art method, by 6.51% on IoU and 3.35% on the newly introduced
temporal stability metric.

The continual appearance of new objects in the visual world poses
considerable challenges for current deep learning methods in real-world
deployments. The challenge of new task learning is often exacerbated by the
scarcity of data for the new categories due to rarity or cost. Here we explore
the important task of Few-Shot Class-Incremental Learning (FSCIL) and its
extreme data scarcity condition of one-shot. An ideal FSCIL model needs to
perform well on all classes, regardless of their presentation order or paucity
of data. It also needs to be robust to open-set real-world conditions and be
easily adapted to the new tasks that always arise in the field. In this paper,
we first reevaluate the current task setting and propose a more comprehensive
and practical setting for the FSCIL task. Then, inspired by the similarity of
the goals for FSCIL and modern face recognition systems, we propose our method
-- Augmented Angular Loss Incremental Classification or ALICE. In ALICE,
instead of the commonly used cross-entropy loss, we propose to use the angular
penalty loss to obtain well-clustered features. As the obtained features not
only need to be compactly clustered but also diverse enough to maintain
generalization for future incremental classes, we further discuss how class
augmentation, data augmentation, and data balancing affect classification
performance. Experiments on benchmark datasets, including CIFAR100,
miniImageNet, and CUB200, demonstrate the improved performance of ALICE over
the state-of-the-art FSCIL methods.

Online Gaussian processes (GPs), typically used for learning models from
time-series data, are more flexible and robust than offline GPs. Both local and
sparse approximations of GPs can efficiently learn complex models online. Yet,
these approaches assume that all signals are relatively accurate and that all
data are available for learning without misleading data. Besides, the online
learning capacity of GPs is limited for high-dimension problems and long-term
tasks in practice. This paper proposes a sparse online GP (SOGP) with a
forgetting mechanism to forget distant model information at a specific rate.
The proposed approach combines two general data deletion schemes for the basis
vector set of SOGP: The position information-based scheme and the oldest
points-based scheme. We apply our approach to learn the inverse dynamics of a
collaborative robot with 7 degrees of freedom under a two-segment trajectory
tracking problem with task switching. Both simulations and experiments have
shown that the proposed approach achieves better tracking accuracy and
predictive smoothness compared with the two general data deletion schemes.

Inferring the values and uncertainties of cosmological parameters in a
cosmology model is of paramount importance for modern cosmic observations. In
this paper, we use the simulation-based inference (SBI) approach to estimate
cosmological constraints from a simplified galaxy cluster observation analysis.
Using data generated from the Quijote simulation suite and analytical models,
we train a machine learning algorithm to learn the probability function between
cosmological parameters and the possible galaxy cluster observables. The
posterior distribution of the cosmological parameters at a given observation is
then obtained by sampling the predictions from the trained algorithm. Our
results show that the SBI method can successfully recover the truth values of
the cosmological parameters within the 2{\sigma} limit for this simplified
galaxy cluster analysis, and acquires similar posterior constraints obtained
with a likelihood-based Markov Chain Monte Carlo method, the current state-of
the-art method used in similar cosmological studies.

Computer simulation models are widely used to study complex physical systems.
A related fundamental topic is the inverse problem, also called calibration,
which aims at learning about the values of parameters in the model based on
observations. In most real applications, the parameters have specific physical
meanings, and we call them physical parameters. To recognize the true
underlying physical system, we need to effectively estimate such parameters.
However, existing calibration methods cannot do this well due to the model
identifiability problem. This paper proposes a semi-parametric model, called
the discrepancy decomposition model, to describe the discrepancy between the
physical system and the computer model. The proposed model possesses a clear
interpretation, and more importantly, it is identifiable under mild conditions.
Under this model, we present estimators of the physical parameters and the
discrepancy, and then establish their asymptotic properties. Numerical examples
show that the proposed method can better estimate the physical parameters than

Deep Metric Learning (DML) serves to learn an embedding function to project
semantically similar data into nearby embedding space and plays a vital role in
many applications, such as image retrieval and face recognition. However, the
performance of DML methods often highly depends on sampling methods to choose
effective data from the embedding space in the training. In practice, the
embeddings in the embedding space are obtained by some deep models, where the
embedding space is often with barren area due to the absence of training
points, resulting in so called "missing embedding" issue. This issue may impair
the sample quality, which leads to degenerated DML performance. In this work,
we investigate how to alleviate the "missing embedding" issue to improve the
sampling quality and achieve effective DML. To this end, we propose a
Densely-Anchored Sampling (DAS) scheme that considers the embedding with
corresponding data point as "anchor" and exploits the anchor's nearby embedding
space to densely produce embeddings without data points. Specifically, we
propose to exploit the embedding space around single anchor with Discriminative
Feature Scaling (DFS) and multiple anchors with Memorized Transformation
Shifting (MTS). In this way, by combing the embeddings with and without data
points, we are able to provide more embeddings to facilitate the sampling
process thus boosting the performance of DML. Our method is effortlessly
integrated into existing DML frameworks and improves them without bells and
whistles. Extensive experiments on three benchmark datasets demonstrate the
superiority of our method.

Existing intelligent driving technology often has a problem in balancing
smooth driving and fast obstacle avoidance, especially when the vehicle is in a
non-structural environment, and is prone to instability in emergency
situations. Therefore, this study proposed an autonomous obstacle avoidance
control strategy that can effectively guarantee vehicle stability based on
Attention-long short-term memory (Attention-LSTM) deep learning model with the
idea of humanoid driving. First, we designed the autonomous obstacle avoidance
control rules to guarantee the safety of unmanned vehicles. Second, we improved
the autonomous obstacle avoidance control strategy combined with the stability
analysis of special vehicles. Third, we constructed a deep learning obstacle
avoidance control through experiments, and the average relative error of this
system was 15%. Finally, the stability and accuracy of this control strategy
were verified numerically and experimentally. The method proposed in this study
can ensure that the unmanned vehicle can successfully avoid the obstacles while
driving smoothly.

Propensity scores are commonly used to reduce the confounding bias in
non-randomized observational studies for estimating the average treatment
effect. An important assumption underlying this approach is that all
confounders that are associated with both the treatment and the outcome of
interest are measured and included in the propensity score model. In the
absence of strong prior knowledge about potential confounders, researchers may
agnostically want to adjust for a high-dimensional set of pre-treatment
variables. As such, variable selection procedure is needed for propensity score
estimation. In addition, recent studies show that including variables related
to treatment only in the propensity score model may inflate the variance of the
treatment effect estimates, while including variables that are predictive of
only the outcome can improve efficiency. In this paper, we propose a flexible
approach to incorporating outcome-covariate relationship in the propensity
score model by including the predicted binary outcome probability (OP) as a
covariate. Our approach can be easily adapted to an ensemble of variable
selection methods, including regularization methods and modern machine learning
tools based on classification and regression trees. We evaluate our method to
estimate the treatment effects on a binary outcome, which is possibly censored,
among multiple treatment groups. Simulation studies indicate that incorporating
OP for estimating the propensity scores can improve statistical efficiency and
protect against model misspecification. The proposed methods are applied to a
cohort of advanced stage prostate cancer patients identified from a private
insurance claims database for comparing the adverse effects of four commonly
used drugs for treating castration-resistant prostate cancer.

We propose a method for estimating the 6DoF pose of a rigid object with an
available 3D model from a single RGB image. Unlike classical
correspondence-based methods which predict 3D object coordinates at pixels of
the input image, the proposed method predicts 3D object coordinates at 3D query
points sampled in the camera frustum. The move from pixels to 3D points, which
is inspired by recent PIFu-style methods for 3D reconstruction, enables
reasoning about the whole object, including its (self-)occluded parts. For a 3D
query point associated with a pixel-aligned image feature, we train a
fully-connected neural network to predict: (i) the corresponding 3D object
coordinates, and (ii) the signed distance to the object surface, with the first
defined only for query points in the surface vicinity. We call the mapping
realized by this network as Neural Correspondence Field. The object pose is
then robustly estimated from the predicted 3D-3D correspondences by the
Kabsch-RANSAC algorithm. The proposed method achieves state-of-the-art results
on three BOP datasets and is shown superior especially in challenging cases
with occlusion. The project website is at: linhuang17.github.io/NCF.

Two major causes of death in the United States and worldwide are stroke and
myocardial infarction. The underlying cause of both is thrombi released from
ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the
heart (myocardial infarction) or the brain (stroke). Clinical studies show that
plaque composition plays a more important role than lesion size in plaque
rupture or erosion events. To determine the plaque composition, various cell
types in 3D cardiovascular immunofluorescent images of plaque lesions are
counted. However, counting these cells manually is expensive, time-consuming,
and prone to human error. These challenges of manual counting motivate the need
for an automated approach to localize and count the cells in images. The
purpose of this study is to develop an automatic approach to accurately detect
and count cells in 3D immunofluorescent images with minimal annotation effort.
In this study, we used a weakly supervised learning approach to train the
HoVer-Net segmentation model using point annotations to detect nuclei in
fluorescent images. The advantage of using point annotations is that they
require less effort as opposed to pixel-wise annotation. To train the HoVer-Net
model using point annotations, we adopted a popularly used cluster labeling
approach to transform point annotations into accurate binary masks of cell
nuclei. Traditionally, these approaches have generated binary masks from point
annotations, leaving a region around the object unlabeled (which is typically
ignored during model training). However, these areas may contain important
information that helps determine the boundary between cells. Therefore, we used
the entropy minimization loss function in these areas to encourage the model to
output more confident predictions on the unlabeled areas. Our comparison
studies indicate that the HoVer-Net model trained using our weakly ...

The presence of outliers (anomalous values) in synthetic aperture radar (SAR)
data and the misspecification in statistical image models may result in
inaccurate inferences. To avoid such issues, the Rayleigh regression model
based on a robust estimation process is proposed as a more realistic approach
to model this type of data. This paper aims at obtaining Rayleigh regression
model parameter estimators robust to the presence of outliers. The proposed
approach considered the weighted maximum likelihood method and was submitted to
numerical experiments using simulated and measured SAR images. Monte Carlo
simulations were employed for the numerical assessment of the proposed robust
estimator performance in finite signal lengths, their sensitivity to outliers,
and the breakdown point. For instance, the non-robust estimators show a
relative bias value $65$-fold larger than the results provided by the robust
approach in corrupted signals. In terms of sensitivity analysis and break down
point, the robust scheme resulted in a reduction of about $96\%$ and $10\%$,
respectively, in the mean absolute value of both measures, in compassion to the
non-robust estimators. Moreover, two SAR data sets were used to compare the
ground type and anomaly detection results of the proposed robust scheme with
competing methods in the literature.

Trajectory prediction using deep neural networks (DNNs) is an essential
component of autonomous driving (AD) systems. However, these methods are
vulnerable to adversarial attacks, leading to serious consequences such as
collisions. In this work, we identify two key ingredients to defend trajectory
prediction models against adversarial attacks including (1) designing effective
adversarial training methods and (2) adding domain-specific data augmentation
to mitigate the performance degradation on clean data. We demonstrate that our
method is able to improve the performance by 46% on adversarial data and at the
cost of only 3% performance degradation on clean data, compared to the model
trained with clean data. Additionally, compared to existing robust methods, our
method can improve performance by 21% on adversarial examples and 9% on clean
data. Our robust model is evaluated with a planner to study its downstream
impacts. We demonstrate that our model can significantly reduce the severe
accident rates (e.g., collisions and off-road driving).

Occlusion poses a great threat to monocular multi-person 3D human pose
estimation due to large variability in terms of the shape, appearance, and
position of occluders. While existing methods try to handle occlusion with pose
priors/constraints, data augmentation, or implicit reasoning, they still fail
to generalize to unseen poses or occlusion cases and may make large mistakes
when multiple people are present. Inspired by the remarkable ability of humans
to infer occluded joints from visible cues, we develop a method to explicitly
model this process that significantly improves bottom-up multi-person human
pose estimation with or without occlusions. First, we split the task into two
subtasks: visible keypoints detection and occluded keypoints reasoning, and
propose a Deeply Supervised Encoder Distillation (DSED) network to solve the
second one. To train our model, we propose a Skeleton-guided human Shape
Fitting (SSF) approach to generate pseudo occlusion labels on the existing
datasets, enabling explicit occlusion reasoning. Experiments show that
explicitly learning from occlusions improves human pose estimation. In
addition, exploiting feature-level information of visible joints allows us to
reason about occluded joints more accurately. Our method outperforms both the
state-of-the-art top-down and bottom-up methods on several benchmarks.

We consider online imitation learning (OIL), where the task is to find a
policy that imitates the behavior of an expert via active interaction with the
environment. We aim to bridge the gap between the theory and practice of policy
optimization algorithms for OIL by analyzing one of the most popular OIL
algorithms, DAGGER. Specifically, if the class of policies is sufficiently
expressive to contain the expert policy, we prove that DAGGER achieves constant
regret. Unlike previous bounds that require the losses to be strongly-convex,
our result only requires the weaker assumption that the losses be
strongly-convex with respect to the policy's sufficient statistics (not its
parameterization). In order to ensure convergence for a wider class of policies
and losses, we augment DAGGER with an additional regularization term. In
particular, we propose a variant of Follow-the-Regularized-Leader (FTRL) and
its adaptive variant for OIL and develop a memory-efficient implementation,
which matches the memory requirements of FTL. Assuming that the loss functions
are smooth and convex with respect to the parameters of the policy, we also
prove that FTRL achieves constant regret for any sufficiently expressive policy
class, while retaining $O(\sqrt{T})$ regret in the worst-case. We demonstrate
the effectiveness of these algorithms with experiments on synthetic and
high-dimensional control tasks.

In this paper, we present an approach for minimizing the computational
complexity of trained Convolutional Neural Networks (ConvNet). The idea is to
approximate all elements of a given ConvNet and replace the original
convolutional filters and parameters (pooling and bias coefficients; and
activation function) with efficient approximations capable of extreme
reductions in computational complexity. Low-complexity convolution filters are
obtained through a binary (zero-one) linear programming scheme based on the
Frobenius norm over sets of dyadic rationals. The resulting matrices allow for
multiplication-free computations requiring only addition and bit-shifting
operations. Such low-complexity structures pave the way for low-power,
efficient hardware designs. We applied our approach on three use cases of
different complexity: (i) a "light" but efficient ConvNet for face detection
(with around 1000 parameters); (ii) another one for hand-written digit
classification (with more than 180000 parameters); and (iii) a significantly
larger ConvNet: AlexNet with $\approx$1.2 million matrices. We evaluated the
overall performance on the respective tasks for different levels of
approximations. In all considered applications, very low-complexity
approximations have been derived maintaining an almost equal classification

Machine learning and computer vision are dynamically growing fields, which
have proven to be able to solve very complex tasks. They could also be used for
the monitoring of the honeybee colonies and for the inspection of their health
state, which could identify potentially dangerous states before the situation
is critical, or to better plan periodic bee colony inspections and therefore
save significant costs. In this paper, we present an overview of the
state-of-the-art computer vision and machine learning applications used for bee
monitoring. We also demonstrate the potential of those methods as an example of
an automated bee counter algorithm. The paper is aimed at veterinary and
apidology professionals and experts, who might not be familiar with machine
learning to introduce to them its possibilities, therefore each family of
applications is opened by a brief theoretical introduction and motivation
related to its base method. We hope that this paper will inspire other
scientists to use the machine learning techniques for other applications in bee
monitoring.

Meta reinforcement learning (meta RL), as a combination of meta-learning
ideas and reinforcement learning (RL), enables the agent to adapt to different
tasks using a few samples. However, this sampling-based adaptation also makes
meta RL vulnerable to adversarial attacks. By manipulating the reward feedback
from sampling processes in meta RL, an attacker can mislead the agent into
building wrong knowledge from training experience, which deteriorates the
agent's performance when dealing with different tasks after adaptation. This
paper provides a game-theoretical underpinning for understanding this type of
security risk. In particular, we formally define the sampling attack model as a
Stackelberg game between the attacker and the agent, which yields a minimax
formulation. It leads to two online attack schemes: Intermittent Attack and
Persistent Attack, which enable the attacker to learn an optimal sampling
attack, defined by an $\epsilon$-first-order stationary point, within
$\mathcal{O}(\epsilon^{-2})$ iterations. These attack schemes freeride the
learning progress concurrently without extra interactions with the environment.
By corroborating the convergence results with numerical experiments, we observe
that a minor effort of the attacker can significantly deteriorate the learning
performance, and the minimax approach can also help robustify the meta RL

Creating artificial social intelligence - algorithms that can understand the
nuances of multi-person interactions - is an exciting and emerging challenge in
processing facial expressions and gestures from multimodal videos. Recent
multimodal methods have set the state of the art on many tasks, but have
difficulty modeling the complex face-to-face conversational dynamics across
speaking turns in social interaction, particularly in a self-supervised setup.
In this paper, we propose Face-to-Face Contrastive Learning (F2F-CL), a graph
neural network designed to model social interactions using factorization nodes
to contextualize the multimodal face-to-face interaction along the boundaries
of the speaking turn. With the F2F-CL model, we propose to perform contrastive
learning between the factorization nodes of different speaking turns within the
same video. We experimentally evaluated the challenging Social-IQ dataset and
show state-of-the-art results.

Optimal control problems with free terminal time present many challenges
including nonsmooth and discontinuous control laws, irregular value functions,
many local optima, and the curse of dimensionality. To overcome these issues,
we propose an adaptation of the model-based actor-critic paradigm from the
field of Reinforcement Learning via an exponential transformation to learn an
approximate feedback control and value function pair. We demonstrate the
algorithm's effectiveness on prototypical examples featuring each of the main
pathological issues present in problems of this type.

We introduce an approach to the targeted completion of lacunae in molecular
data sets which is driven by topological data analysis, such as Mapper
algorithm. Lacunae are filled in using scaffold-constrained generative models
trained with different scoring functions. The approach enables addition of
links and vertices to the skeletonized representations of the data, such as
Mapper graph, and falls in the broad category of network completion methods. We
illustrate application of the topology-driven data completion strategy by
creating a lacuna in the data set of onium cations extracted from USPTO
patents, and repairing it.

Conventional audio-visual models have independent audio and video branches.
We design a unified model for audio and video processing called Unified
Audio-Visual Model (UAVM). In this paper, we describe UAVM, report its new
state-of-the-art audio-visual event classification accuracy of 65.8% on
VGGSound, and describe the intriguing properties of the model.

In this work, we address the problem of 4D facial expressions generation.
This is usually addressed by animating a neutral 3D face to reach an expression
peak, and then get back to the neutral state. In the real world though, people
show more complex expressions, and switch from one expression to another. We
thus propose a new model that generates transitions between different
expressions, and synthesizes long and composed 4D expressions. This involves
three sub-problems: (i) modeling the temporal dynamics of expressions, (ii)
learning transitions between them, and (iii) deforming a generic mesh. We
propose to encode the temporal evolution of expressions using the motion of a
set of 3D landmarks, that we learn to generate by training a manifold-valued
GAN (Motion3DGAN). To allow the generation of composed expressions, this model
accepts two labels encoding the starting and the ending expressions. The final
sequence of meshes is generated by a Sparse2Dense mesh Decoder (S2D-Dec) that
maps the landmark displacements to a dense, per-vertex displacement of a known
mesh topology. By explicitly working with motion trajectories, the model is
totally independent from the identity. Extensive experiments on five public
datasets show that our proposed approach brings significant improvements with
respect to previous solutions, while retaining good generalization to unseen
data.

The recent introduction of gradient-based MCMC for discrete spaces holds
great promise, and comes with the tantalising possibility of new discrete
counterparts to celebrated continuous methods such as MALA and HMC. Towards
this goal, we introduce several discrete Metropolis-Hastings samplers that are
conceptually-inspired by MALA, and demonstrate their strong empirical
performance across a range of challenging sampling problems in Bayesian
inference and energy-based modelling. Methodologically, we identify why
discrete analogues to preconditioned MALA are generally intractable, motivating
us to introduce a new kind of preconditioning based on auxiliary variables and
the `Gaussian integral trick'.

Recovering the 3D motion of the heart from cine cardiac magnetic resonance
(CMR) imaging enables the assessment of regional myocardial function and is
important for understanding and analyzing cardiovascular disease. However, 3D
cardiac motion estimation is challenging because the acquired cine CMR images
are usually 2D slices which limit the accurate estimation of through-plane
motion. To address this problem, we propose a novel multi-view motion
estimation network (MulViMotion), which integrates 2D cine CMR images acquired
in short-axis and long-axis planes to learn a consistent 3D motion field of the
heart. In the proposed method, a hybrid 2D/3D network is built to generate
dense 3D motion fields by learning fused representations from multi-view
images. To ensure that the motion estimation is consistent in 3D, a shape
regularization module is introduced during training, where shape information
from multi-view images is exploited to provide weak supervision to 3D motion
estimation. We extensively evaluate the proposed method on 2D cine CMR images
from 580 subjects of the UK Biobank study for 3D motion tracking of the left
ventricular myocardium. Experimental results show that the proposed method
quantitatively and qualitatively outperforms competing methods.

SleepioTM is a digital mobile phone and web platform that uses techniques
from cognitive behavioural therapy (CBT) to improve sleep in people with sleep
difficulty. As part of this process, Sleepio captures data about the sleep
behaviour of the users that have consented to such data being processed. For
neural networks, the scale of the data is an opportunity to train meaningful
models translatable to actual clinical practice. In collaboration with Big
Health, the therapeutics company that created and utilizes Sleepio, we have
analysed data from a random sample of 401,174 sleep diaries and built a neural
network to model sleep behaviour and sleep quality of each individual in a
personalised manner. We demonstrate that this neural network is more accurate
than standard statistical methods in predicting the sleep quality of an
individual based on his/her behaviour from the last 10 days. We compare model
performance in a wide range of hyperparameter settings representing various
scenarios. We further show that the neural network can be used to produce
personalised recommendations of what sleep habits users should follow to
maximise sleep quality, and show that these recommendations are substantially
better than the ones generated by standard methods. We finally show that the
neural network can explain the recommendation given to each participant and
calculate confidence intervals for each prediction, all of which are essential
for clinicians to be able to adopt such a tool in clinical practice.

The current COVID-19 pandemic is a serious threat to humanity that directly
affects the lungs. Automatic identification of COVID-19 is a challenge for
health care officials. The standard gold method for diagnosing COVID-19 is
Reverse Transcription Polymerase Chain Reaction (RT-PCR) to collect swabs from
affected people. Some limitations encountered while collecting swabs are
related to accuracy and longtime duration. Chest CT (Computed Tomography) is
another test method that helps healthcare providers quickly identify the
infected lung areas. It was used as a supporting tool for identifying COVID-19
in an earlier stage. With the help of deep learning, the CT imaging
characteristics of COVID-19. Researchers have proven it to be highly effective
for COVID-19 CT image classification. In this study, we review the recent deep
learning techniques that can use to detect the COVID-19 disease. Relevant
studies were collected by various databases such as Web of Science, Google
Scholar, and PubMed. Finally, we compare the results of different deep learning
models, and CT image analysis is discussed.

Agriculture is the mainstay of human society because it is an essential need
for every organism. Paddy cultivation is very significant so far as humans are
concerned, largely in the Asian continent, and it is one of the staple foods.
However, plant diseases in agriculture lead to depletion in productivity. Plant
diseases are generally caused by pests, insects, and pathogens that decrease
productivity to a large scale if not controlled within a particular time.
Eventually, one cannot see an increase in paddy yield. Accurate and timely
identification of plant diseases can help farmers mitigate losses due to pests
and diseases. Recently, deep learning techniques have been used to identify
paddy diseases and overcome these problems. This paper implements a
convolutional neural network (CNN) based on a model and tests a public dataset
consisting of 636 infrared image samples with five paddy disease classes and
one healthy class. The proposed model proficiently identified and classified
paddy diseases of five different types and achieved an accuracy of 88.28%

We show that pre-trained Generative Adversarial Networks (GANs) such as
StyleGAN and BigGAN can be used as a latent bank to improve the performance of
image super-resolution. While most existing perceptual-oriented approaches
attempt to generate realistic outputs through learning with adversarial loss,
our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by
directly leveraging rich and diverse priors encapsulated in a pre-trained GAN.
But unlike prevalent GAN inversion methods that require expensive
image-specific optimization at runtime, our approach only needs a single
forward pass for restoration. GLEAN can be easily incorporated in a simple
encoder-bank-decoder architecture with multi-resolution skip connections.
Employing priors from different generative models allows GLEAN to be applied to
diverse categories (\eg~human faces, cats, buildings, and cars). We further
present a lightweight version of GLEAN, named LightGLEAN, which retains only
the critical components in GLEAN. Notably, LightGLEAN consists of only 21% of
parameters and 35% of FLOPs while achieving comparable image quality. We extend
our method to different tasks including image colorization and blind image
restoration, and extensive experiments show that our proposed models perform
favorably in comparison to existing methods. Codes and models are available at
https://github.com/open-mmlab/mmediting.

We present a new lighting estimation and editing framework to generate
high-dynamic-range (HDR) indoor panorama lighting from a single limited
field-of-view (LFOV) image captured by low-dynamic-range (LDR) cameras.
Existing lighting estimation methods either directly regress lighting
representation parameters or decompose this problem into LFOV-to-panorama and
LDR-to-HDR lighting generation sub-tasks. However, due to the partial
observation, the high-dynamic-range lighting, and the intrinsic ambiguity of a
scene, lighting estimation remains a challenging task. To tackle this problem,
we propose a coupled dual-StyleGAN panorama synthesis network (StyleLight) that
integrates LDR and HDR panorama synthesis into a unified framework. The LDR and
HDR panorama synthesis share a similar generator but have separate
discriminators. During inference, given an LDR LFOV image, we propose a
focal-masked GAN inversion method to find its latent code by the LDR panorama
synthesis branch and then synthesize the HDR panorama by the HDR panorama
synthesis branch. StyleLight takes LFOV-to-panorama and LDR-to-HDR lighting
generation into a unified framework and thus greatly improves lighting
estimation. Extensive experiments demonstrate that our framework achieves
superior performance over state-of-the-art methods on indoor lighting
estimation. Notably, StyleLight also enables intuitive lighting editing on
indoor HDR panoramas, which is suitable for real-world applications. Code is
available at https://style-light.github.io.

Handwritten Chinese text recognition (HCTR) has been an active research topic
for decades. However, most previous studies solely focus on the recognition of
cropped text line images, ignoring the error caused by text line detection in
real-world applications. Although some approaches aimed at page-level text
recognition have been proposed in recent years, they either are limited to
simple layouts or require very detailed annotations including expensive
line-level and even character-level bounding boxes. To this end, we propose
PageNet for end-to-end weakly supervised page-level HCTR. PageNet detects and
recognizes characters and predicts the reading order between them, which is
more robust and flexible when dealing with complex layouts including
multi-directional and curved text lines. Utilizing the proposed weakly
supervised learning framework, PageNet requires only transcripts to be
annotated for real data; however, it can still output detection and recognition
results at both the character and line levels, avoiding the labor and cost of
labeling bounding boxes of characters and text lines. Extensive experiments
conducted on five datasets demonstrate the superiority of PageNet over existing
weakly supervised and fully supervised page-level methods. These experimental
results may spark further research beyond the realms of existing methods based
on connectionist temporal classification or attention. The source code is
available at https://github.com/shannanyinxiang/PageNet.

The in situ synchrotron high-energy X-ray powder diffraction (XRD) technique
is highly utilized by researchers to analyze the crystallographic structures of
materials in functional devices (e.g., battery materials) or in complex sample
environments (e.g., diamond anvil cells or syntheses reactors). An atomic
structure of a material can be identified by its diffraction pattern, along
with detailed analysis such as Rietveld refinement which indicates how the
measured structure deviates from the ideal structure (e.g., internal stresses
or defects). For in situ experiments, a series of XRD images is usually
collected on the same sample at different conditions (e.g., adiabatic
conditions), yielding different states of matter, or simply collected
continuously as a function of time to track the change of a sample over a
chemical or physical process. In situ experiments are usually performed with
area detectors, collecting 2D images composed of diffraction rings for ideal
powders. Depending on the material's form, one may observe different
characteristics other than the typical Debye Scherrer rings for a realistic
sample and its environments, such as textures or preferred orientations and
single crystal diffraction spots in the 2D XRD image. In this work, we present
an investigation of machine learning methods for fast and reliable
identification and separation of the single crystal diffraction spots in XRD
images. The exclusion of artifacts during an XRD image integration process
allows a precise analysis of the powder diffraction rings of interest. We
observe that the gradient boosting method can consistently produce high
accuracy results when it is trained with small subsets of highly diverse
datasets. The method dramatically decreases the amount of time spent on
identifying and separating single crystal spots in comparison to the
conventional method.

Online and offline handwritten Chinese text recognition (HTCR) has been
studied for decades. Early methods adopted oversegmentation-based strategies
but suffered from low speed, insufficient accuracy, and high cost of character
segmentation annotations. Recently, segmentation-free methods based on
connectionist temporal classification (CTC) and attention mechanism, have
dominated the field of HCTR. However, people actually read text character by
character, especially for ideograms such as Chinese. This raises the question:
are segmentation-free strategies really the best solution to HCTR? To explore
this issue, we propose a new segmentation-based method for recognizing
handwritten Chinese text that is implemented using a simple yet efficient fully
convolutional network. A novel weakly supervised learning method is proposed to
enable the network to be trained using only transcript annotations; thus, the
expensive character segmentation annotations required by previous
segmentation-based methods can be avoided. Owing to the lack of context
modeling in fully convolutional networks, we propose a contextual
regularization method to integrate contextual information into the network
during the training stage, which can further improve the recognition
performance. Extensive experiments conducted on four widely used benchmarks,
namely CASIA-HWDB, CASIA-OLHWDB, ICDAR2013, and SCUT-HCCDoc, show that our
method significantly surpasses existing methods on both online and offline
HCTR, and exhibits a considerably higher inference speed than
CTC/attention-based approaches.

In view of its power in extracting feature representation, contrastive
self-supervised learning has been successfully integrated into the practice of
(deep) reinforcement learning (RL), leading to efficient policy learning in
various applications. Despite its tremendous empirical successes, the
understanding of contrastive learning for RL remains elusive. To narrow such a
gap, we study how RL can be empowered by contrastive learning in a class of
Markov decision processes (MDPs) and Markov games (MGs) with low-rank
transitions. For both models, we propose to extract the correct feature
representations of the low-rank model by minimizing a contrastive loss.
Moreover, under the online setting, we propose novel upper confidence bound
(UCB)-type algorithms that incorporate such a contrastive loss with online RL
algorithms for MDPs or MGs. We further theoretically prove that our algorithm
recovers the true representations and simultaneously achieves sample efficiency
in learning the optimal policy and Nash equilibrium in MDPs and MGs. We also
provide empirical studies to demonstrate the efficacy of the UCB-based
contrastive learning method for RL. To the best of our knowledge, we provide
the first provably efficient online RL algorithm that incorporates contrastive
learning for representation learning. Our codes are available at
https://github.com/Baichenjia/Contrastive-UCB.

Making progress in studying the interplay between disorder and fluctuations
is challenged by the complexity of extracting robust insights from experiments
affected by noise and finite resolution. This has hindered observations of the
Bragg glass phase, which is predicted to occur in vortex lattices and charge
density wave systems in the presence of disorder. Despite its sharp theoretical
definition in terms of diverging correlation lengths, establishing the
existence of the Bragg glass phase in a charge density wave system has been
challenging. Here, we present the first bulk probe evidence of a Bragg glass
phase in the systematically disordered CDW material Pd$_x$ErTe$_3$ using
comprehensive x-ray data and a novel machine learning data analysis tool, X-ray
Temperature Clustering (X-TEC). Using data from 20,000 Brillouin zones readily
analyzed using X-TEC, we establish a diverging correlation length in samples
with moderate intercalation over a wide temperature range. To enable such
comprehensive analysis, we introduced a new high-throughput measure of inverse
correlation length: "peak-spread". The detection of Bragg glass order and the
resulting phase diagram significantly advance our understanding of the complex
interplay between disorder and fluctuations. Moreover, using X-TEC to target
fluctuations through a high-throughput measure of "peak spread" can
revolutionize how the fluctuations are studied in scattering experiments.

Inverting seismic data to build 3D geological structures is a challenging
task due to the overwhelming amount of acquired seismic data, and the very-high
computational load due to iterative numerical solutions of the wave equation,
as required by industry-standard tools such as Full Waveform Inversion (FWI).
For example, in an area with surface dimensions of 4.5km $\times$ 4.5km,
hundreds of seismic shot-gather cubes are required for 3D model reconstruction,
leading to Terabytes of recorded data. This paper presents a deep learning
solution for the reconstruction of realistic 3D models in the presence of field
noise recorded in seismic surveys. We implement and analyze a convolutional
encoder-decoder architecture that efficiently processes the entire collection
of hundreds of seismic shot-gather cubes. The proposed solution demonstrates
that realistic 3D models can be reconstructed with a structural similarity
index measure (SSIM) of 0.8554 (out of 1.0) in the presence of field noise at
10dB signal-to-noise ratio.

Shadow tomography via classical shadows is a state-of-the-art approach for
estimating properties of a quantum state. We present a simplified,
combinatorial analysis of a recently proposed instantiation of this approach
based on the ensemble of unitaries that are both fermionic Gaussian and
Clifford. Using this analysis, we derive a corrected expression for the
variance of the estimator. We then show how this leads to efficient estimation
protocols for the fidelity with a pure fermionic Gaussian state (provably) and
for an $X$-like operator of the form ($|\mathbf 0\rangle\langle\psi|$ + h.c.)
(via numerical evidence). We also construct much smaller ensembles of
measurement bases that yield the exact same quantum channel, which may help
with compilation. We use these tools to show that an $n$-electron, $m$-mode
Slater determinant can be learned to within $\epsilon$ fidelity given $O(n^2
m^7 \log(m / \delta) / \epsilon^2)$ samples of the Slater determinant.

Explicit neural surface representations allow for exact and efficient
extraction of the encoded surface at arbitrary precision, as well as analytic
derivation of differential geometric properties such as surface normal and
curvature. Such desirable properties, which are absent in its implicit
counterpart, makes it ideal for various applications in computer vision,
graphics and robotics. However, SOTA works are limited in terms of the topology
it can effectively describe, distortion it introduces to reconstruct complex
surfaces and model efficiency. In this work, we present Minimal Neural Atlas, a
novel atlas-based explicit neural surface representation. At its core is a
fully learnable parametric domain, given by an implicit probabilistic occupancy
field defined on an open square of the parametric space. In contrast, prior
works generally predefine the parametric domain. The added flexibility enables
charts to admit arbitrary topology and boundary. Thus, our representation can
learn a minimal atlas of 3 charts with distortion-minimal parameterization for
surfaces of arbitrary topology, including closed and open surfaces with
arbitrary connected components. Our experiments support the hypotheses and show
that our reconstructions are more accurate in terms of the overall geometry,
due to the separation of concerns on topology and geometry.

Traditional datasets for the radiological diagnosis tend to only provide the
radiology image alongside the radiology report. However, radiology reading as
performed by radiologists is a complex process, and information such as the
radiologist's eye-fixations over the course of the reading has the potential to
be an invaluable data source to learn from. Nonetheless, the collection of such
data is expensive and time-consuming. This leads to the question of whether
such data is worth the investment to collect. This paper utilizes the recently
published Eye-Gaze dataset to perform an exhaustive study on the impact on
performance and explainability of deep learning (DL) classification in the face
of varying levels of input features, namely: radiology images, radiology report
text, and radiologist eye-gaze data. We find that the best classification
performance of X-ray images is achieved with a combination of radiology report
free-text and radiology image, with the eye-gaze data providing no performance
boost. Nonetheless, eye-gaze data serving as secondary ground truth alongside
the class label results in highly explainable models that generate better
attention maps compared to models trained to do classification and attention
map generation without eye-gaze data.

The application of artificial intelligence (AI) techniques to medical imaging
data has yielded promising results. As an important branch of AI pipelines in
medical imaging, radiomics faces two major challenges namely reproducibility
and accessibility. In this work, we introduce open-radiomics, a set of
radiomics datasets, and a comprehensive radiomics pipeline that investigates
the effects of radiomics feature extraction settings such as binWidth and image
normalization on the reproducibility of the radiomics results performance. To
make radiomics research more accessible and reproducible, we provide guidelines
for building machine learning (ML) models on radiomics data, introduce
Open-radiomics, an evolving collection of open-source radiomics datasets, and
publish baseline models for the datasets.

In this work, we consider the problem of procedural content generation for
video game levels. Prior approaches have relied on evolutionary search (ES)
methods capable of generating diverse levels, but this generation procedure is
slow, which is problematic in real-time settings. Reinforcement learning (RL)
has also been proposed to tackle the same problem, and while level generation
is fast, training time can be prohibitively expensive. We propose a framework
to tackle the procedural content generation problem that combines the best of
ES and RL. In particular, our approach first uses ES to generate a sequence of
levels evolved over time, and then uses behaviour cloning to distil these
levels into a policy, which can then be queried to produce new levels quickly.
We apply our approach to a maze game and Super Mario Bros, with our results
indicating that our approach does in fact decrease the time required for level
generation, especially when an increasing number of valid levels are required.

Learning-based image quality assessment (IQA) has made remarkable progress in
the past decade, but nearly all consider the two key components - model and
data - in relative isolation. Specifically, model-centric IQA focuses on
developing "better" objective quality methods on fixed and extensively reused
datasets, with a great danger of overfitting. Data-centric IQA involves
conducting psychophysical experiments to construct "better" human-annotated
datasets, which unfortunately ignores current IQA models during dataset
creation. In this paper, we first design a series of experiments to probe
computationally that such isolation of model and data impedes further progress
of IQA. We then describe a computational framework that integrates
model-centric and data-centric IQA. As a specific example, we design
computational modules to quantify the sampling-worthiness of candidate images
based on blind IQA (BIQA) model predictions and deep content-aware features.
Experimental results show that the proposed sampling-worthiness module
successfully spots diverse failures of the examined BIQA models, which are
indeed worthy samples to be included in next-generation datasets.

5G and beyond is expected to enable various emerging use cases with diverse
performance requirements from vertical industries. To serve these use cases
cost-effectively, network slicing plays a key role in dynamically creating
virtual end-to-end networks according to specific resource demands. A network
slice may have hundreds of configurable parameters over multiple technical
domains that define the performance of the network slice, which makes it
impossible to use traditional model-based solutions to orchestrate resources
for network slices. In this article, we discuss how to design and deploy deep
reinforcement learning (DRL), a model-free approach, to address the network
slicing problem. First, we analyze the network slicing problem and present a
standard-compliant system architecture that enables DRL-based solutions in 5G
and beyond networks. Second, we provide an in-depth analysis of the challenges
in designing and deploying DRL in network slicing systems. Third, we explore
multiple promising techniques, i.e., safety and distributed DRL, and imitation
learning, for automating end-to-end network slicing.

The network structure provides critical information for law enforcement
agencies to develop effective strategies to interdict illicit supply networks.
However, the complete structure of covert networks is often unavailable, thus
it is crucially important to develop approaches to infer a more complete
structure of covert networks. In this paper, we work on real-world multiplex
drug trafficking networks extracted from an investigation report. A statistical
approach built on the EM algorithm (DegEM) as well as other methods based on
structural similarity are applied to reconstruct the multiplex drug trafficking
network given different fractions of observed nodes and links. It is found that
DegEM approach achieves the best predictive performance in terms of several
accuracy metrics. Meanwhile, structural similarity-based methods perform poorly
in reconstructing the drug trafficking networks due to the sparsity of links
between nodes in the network. The inferred multiplex networks can be leveraged
to (i) inform the decision-making on monitoring covert networks as well as
allocating limited resources for collecting additional information to improve
the reconstruction accuracy and (ii) develop more effective interdiction
strategies.

User modeling is crucial to understanding user behavior and essential for
improving user experience and personalized recommendations. When users interact
with software, vast amounts of command sequences are generated through logging
and analytics systems. These command sequences contain clues to the users'
goals and intents. However, these data modalities are highly unstructured and
unlabeled, making it difficult for standard predictive systems to learn from.
We propose SimCURL, a simple yet effective contrastive self-supervised deep
learning framework that learns user representation from unlabeled command
sequences. Our method introduces a user-session network architecture, as well
as session dropout as a novel way of data augmentation. We train and evaluate
our method on a real-world command sequence dataset of more than half a billion
commands. Our method shows significant improvement over existing methods when
the learned representation is transferred to downstream tasks such as
experience and expertise classification.

Image-text matching is gaining a leading role among tasks involving the joint
understanding of vision and language. In literature, this task is often used as
a pre-training objective to forge architectures able to jointly deal with
images and texts. Nonetheless, it has a direct downstream application:
cross-modal retrieval, which consists in finding images related to a given
query text or vice-versa. Solving this task is of critical importance in
cross-modal search engines. Many recent methods proposed effective solutions to
the image-text matching problem, mostly using recent large vision-language (VL)
Transformer networks. However, these models are often computationally
expensive, especially at inference time. This prevents their adoption in
large-scale cross-modal retrieval scenarios, where results should be provided
to the user almost instantaneously. In this paper, we propose to fill in the
gap between effectiveness and efficiency by proposing an ALign And DIstill
Network (ALADIN). ALADIN first produces high-effective scores by aligning at
fine-grained level images and texts. Then, it learns a shared embedding space -
where an efficient kNN search can be performed - by distilling the relevance
scores obtained from the fine-grained alignments. We obtained remarkable
results on MS-COCO, showing that our method can compete with state-of-the-art
VL Transformers while being almost 90 times faster. The code for reproducing
our results is available at https://github.com/mesnico/ALADIN.

Optimal values and solutions of empirical approximations of stochastic
optimization problems can be viewed as statistical estimators of their true
values. From this perspective, it is important to understand the asymptotic
behavior of these estimators as the sample size goes to infinity, which is both
of theoretical as well as practical interest. This area of study has a long
tradition in stochastic programming. However, the literature is lacking
consistency analysis for problems in which the decision variables are taken
from an infinite-dimensional space, which arise in optimal control, scientific
machine learning, and statistical estimation. By exploiting the typical problem
structures found in these applications that give rise to hidden norm
compactness properties for solution sets, we prove consistency results for
nonconvex risk-averse stochastic optimization problems formulated in
infinite-dimensional space. The proof is based on several crucial results from
the theory of variational convergence. The theoretical results are demonstrated
for several important problem classes arising in the literature.

In this work, we propose a fully differentiable graph neural network
(GNN)-based architecture for channel decoding and showcase a competitive
decoding performance for various coding schemes, such as low-density
parity-check (LDPC) and BCH codes. The idea is to let a neural network (NN)
learn a generalized message passing algorithm over a given graph that
represents the forward error correction (FEC) code structure by replacing node
and edge message updates with trainable functions. Contrary to many other deep
learning-based decoding approaches, the proposed solution enjoys scalability to
arbitrary block lengths and the training is not limited by the curse of
dimensionality. We benchmark our proposed decoder against state-of-the-art in
conventional channel decoding as well as against recent deep learning-based
results. For the (63,45) BCH code, our solution outperforms weighted belief
propagation (BP) decoding by approximately 0.4 dB with significantly less
decoding iterations and even for 5G NR LDPC codes, we observe a competitive
performance when compared to conventional BP decoding. For the BCH codes, the
resulting GNN decoder can be fully parametrized with only 9640 weights.

In this paper, we present a simple seq2seq formulation for view synthesis
where we take a set of ray points as input and output colors corresponding to
the rays. Directly applying a standard transformer on this seq2seq formulation
has two limitations. First, the standard attention cannot successfully fit the
volumetric rendering procedure, and therefore high-frequency components are
missing in the synthesized views. Second, applying global attention to all rays
and pixels is extremely inefficient. Inspired by the neural radiance field
(NeRF), we propose the NeRF attention (NeRFA) to address the above problems. On
the one hand, NeRFA considers the volumetric rendering equation as a soft
feature modulation procedure. In this way, the feature modulation enhances the
transformers with the NeRF-like inductive bias. On the other hand, NeRFA
performs multi-stage attention to reduce the computational overhead.
Furthermore, the NeRFA model adopts the ray and pixel transformers to learn the
interactions between rays and pixels. NeRFA demonstrates superior performance
over NeRF and NerFormer on four datasets: DeepVoxels, Blender, LLFF, and CO3D.
Besides, NeRFA establishes a new state-of-the-art under two settings: the
single-scene view synthesis and the category-centric novel view synthesis.

Online public opinion usually spreads rapidly and widely, thus a small
incident probably evolves into a large social crisis in a very short time, and
results in a heavy loss in credit or economic aspects. We propose a method to
rate the crisis of online public opinion based on a multi-level index system to
evaluate the impact of events objectively. Firstly, the dissemination mechanism
of online public opinion is explained from the perspective of information
ecology. According to the mechanism, some evaluation indexes are selected
through correlation analysis and principal component analysis. Then, a
classification model of text emotion is created via the training by deep
learning to achieve the accurate quantification of the emotional indexes in the
index system. Finally, based on the multi-level evaluation index system and
grey correlation analysis, we propose a method to rate the crisis of online
public opinion. The experiment with the real-time incident show that this
method can objectively evaluate the emotional tendency of Internet users and
rate the crisis in different dissemination stages of online public opinion. It
is helpful to realizing the crisis warning of online public opinion and timely
blocking the further spread of the crisis.

We develop a notion of projections between sets of probability measures using
the geometric properties of the 2-Wasserstein space. It is designed for general
multivariate probability measures, is computationally efficient to implement,
and provides a unique solution in regular settings. The idea is to work on
regular tangent cones of the Wasserstein space using generalized geodesics. Its
structure and computational properties make the method applicable in a variety
of settings, from causal inference to the analysis of object data. An
application to estimating causal effects yields a generalization of the notion
of synthetic controls to multivariate data with individual-level heterogeneity,
as well as a way to estimate optimal weights jointly over all time periods.

Multivariate extreme value distributions are a common choice for modelling
multivariate extremes. In high dimensions, however, the construction of
flexible and parsimonious models is challenging. We propose to combine
bivariate extreme value distributions into a Markov random field with respect
to a tree. Although in general not an extreme value distribution itself, this
Markov tree is attracted by a multivariate extreme value distribution. The
latter serves as a tree-based approximation to an unknown extreme value
distribution with the given bivariate distributions as margins. Given data, we
learn an appropriate tree structure by Prim's algorithm with estimated pairwise
upper tail dependence coefficients or Kendall's tau values as edge weights. The
distributions of pairs of connected variables can be fitted in various ways.
The resulting tree-structured extreme value distribution allows for inference
on rare event probabilities, as illustrated on river discharge data from the
upper Danube basin.

Most reinforcement learning (RL) methods only focus on learning a single task
from scratch and are not able to use prior knowledge to learn other tasks more
effectively. Context-based meta RL techniques are recently proposed as a
possible solution to tackle this. However, they are usually less efficient than
conventional RL and may require many trial-and-errors during training. To
address this, we propose a novel meta-RL approach that achieves competitive
performance comparing to existing meta-RL algorithms, while requires
significantly fewer environmental interactions. By combining context variables
with the idea of decomposing reward in successor feature framework, our method
does not only learn high-quality policies for multiple tasks simultaneously but
also can quickly adapt to new tasks with a small amount of training. Compared
with state-of-the-art meta-RL baselines, we empirically show the effectiveness
and data efficiency of our method on several continuous control tasks.

Reward design is a critical part of the application of reinforcement
learning, the performance of which strongly depends on how well the reward
signal frames the goal of the designer and how well the signal assesses
progress in reaching that goal. In many cases, the extrinsic rewards provided
by the environment (e.g., win or loss of a game) are very sparse and make it
difficult to train agents directly. Researchers usually assist the learning of
agents by adding some auxiliary rewards in practice. However, designing
auxiliary rewards is often turned to a trial-and-error search for reward
settings that produces acceptable results. In this paper, we propose to
automatically generate goal-consistent intrinsic rewards for the agent to
learn, by maximizing which the expected accumulative extrinsic rewards can be
maximized. To this end, we introduce the concept of motivation which captures
the underlying goal of maximizing certain rewards and propose the motivation
based reward design method. The basic idea is to shape the intrinsic rewards by
minimizing the distance between the intrinsic and extrinsic motivations. We
conduct extensive experiments and show that our method performs better than the
state-of-the-art methods in handling problems of delayed reward, exploration,
and credit assignment.

We consider the Bayesian mixture of finite mixtures (MFMs) and Dirichlet
process mixture (DPM) models for clustering. Recent asymptotic theory has
established that DPMs overestimate the number of clusters for large samples and
that estimators from both classes of models are inconsistent for the number of
clusters under misspecification, but the implications for finite sample
analyses are unclear. The final reported estimate after fitting these models is
often a single representative clustering obtained using an MCMC summarisation
technique, but it is unknown how well such a summary estimates the number of
clusters. Here we investigate these practical considerations through
simulations and an application to gene expression data, and find that (i) DPMs
overestimate the number of clusters even in finite samples, but only to a
limited degree that may be correctable using appropriate summaries, and (ii)
misspecification can lead to considerable overestimation of the number of
clusters in both DPMs and MFMs, but results are nevertheless often still
interpretable. We provide recommendations on MCMC summarisation and suggest
that although the more appealing asymptotic properties of MFMs provide strong
motivation to prefer them, results obtained using MFMs and DPMs are often very
similar in practice.

Graph Neural Networks (GNNs) is a promising approach for applications with
nonEuclidean data. However, training GNNs on large scale graphs with hundreds
of millions nodes is both resource and time consuming. Different from DNNs,
GNNs usually have larger memory footprints, and thus the GPU memory capacity
and PCIe bandwidth are the main resource bottlenecks in GNN training. To
address this problem, we present BiFeat: a graph feature quantization
methodology to accelerate GNN training by significantly reducing the memory
footprint and PCIe bandwidth requirement so that GNNs can take full advantage
of GPU computing capabilities. Our key insight is that unlike DNN, GNN is less
prone to the information loss of input features caused by quantization. We
identify the main accuracy impact factors in graph feature quantization and
theoretically prove that BiFeat training converges to a network where the loss
is within $\epsilon$ of the optimal loss of uncompressed network. We perform
extensive evaluation of BiFeat using several popular GNN models and datasets,
including GraphSAGE on MAG240M, the largest public graph dataset. The results
demonstrate that BiFeat achieves a compression ratio of more than 30 and
improves GNN training speed by 200%-320% with marginal accuracy loss. In
particular, BiFeat achieves a record by training GraphSAGE on MAG240M within
one hour using only four GPUs.

When machine learning (ML) models are supplied with data outside their
training distribution, they are more likely to make inaccurate predictions; in
a cyber-physical system (CPS), this could lead to catastrophic system failure.
To mitigate this risk, an out-of-distribution (OOD) detector can run in
parallel with an ML model and flag inputs that could lead to undesirable
outcomes. Although OOD detectors have been well studied in terms of accuracy,
there has been less focus on deployment to resource constrained CPSs. In this
study, a design methodology is proposed to tune deep OOD detectors to meet the
accuracy and response time requirements of embedded applications. The
methodology uses genetic algorithms to optimize the detector's preprocessing
pipeline and selects a quantization method that balances robustness and
response time. It also identifies several candidate task graphs under the Robot
Operating System (ROS) for deployment of the selected design. The methodology
is demonstrated on two variational autoencoder based OOD detectors from the
literature on two embedded platforms. Insights into the trade-offs that occur
during the design process are provided, and it is shown that this design
methodology can lead to a drastic reduction in response time in relation to an
unoptimized OOD detector while maintaining comparable accuracy.

In this paper, we address the issue of short-term wind speed prediction at a
given site. We show that, when one uses spatiotemporal information as provided
by wind data of neighboring stations, one significantly improves the prediction
quality. Our methodology does not focus on any peculiar forecasting model but
rather considers a set of various prediction methods, from a very basic linear
regression to different machine learning models. In each case, our approach
consists in specifically and incrementally studying the benefits of using wind
data of the surrounding stations. We show that, at all horizons ranging from 1
to 6 hours ahead, the relative gain on the RMSE of the predicted wind speed can
increase up to 20 %. For all the considered forecasting methods, we show that
such a gain is far better than the one obtained by considering other kind of
information like local weather variables or seeking for an optimal deep
learning model. Moreover we provide evidence that non-linear models, as neural
networks or gradient boosting methods, significantly outperform linear
regression. These conclusions are simply interpreted as resulting from the
ability of a method to capture the transport of the information by the main
flow in the upwind direction.

Recently, learned video compression has drawn lots of attention and show a
rapid development trend with promising results. However, the previous works
still suffer from some criticial issues and have a performance gap with
traditional compression standards in terms of widely used PSNR metric. In this
paper, we propose several techniques to effectively improve the performance.
First, to address the problem of accumulative error, we introduce a
conditional-I-frame as the first frame in the GoP, which stabilizes the
reconstructed quality and saves the bit-rate. Second, to efficiently improve
the accuracy of inter prediction without increasing the complexity of decoder,
we propose a pixel-to-feature motion prediction method at encoder side that
helps us to obtain high-quality motion information. Third, we propose a
probability-based entropy skipping method, which not only brings performance
gain, but also greatly reduces the runtime of entropy coding. With these
powerful techniques, this paper proposes AlphaVC, a high-performance and
efficient learned video compression scheme. To the best of our knowledge,
AlphaVC is the first E2E AI codec that exceeds the latest compression standard
VVC on all common test datasets for both PSNR (-28.2% BD-rate saving) and
MSSSIM (-52.2% BD-rate saving), and has very fast encoding (0.001x VVC) and
decoding (1.69x VVC) speeds.

The term Big Data has been coined to refer to the gargantuan bulk of data
that cannot be dealt with by traditional data-handling techniques. Big Data is
still a novel concept, and in the following literature, we intend to elaborate
on it in a palpable fashion. It commences with the concept of the subject in
itself, along with its properties and the two general approaches to dealing
with it. Big Data provides an opportunity for educational Institutions to use
their Information Technology resources strategically to improve educational
quality, guide students to higher completion rates and improve student
persistence and outcomes. This paper explores the attributes of big data that
are relevant to educational institutions, investigates the factors influencing
the adoption of big data and analytics in learning institutions, and seeks to
establish the limiting factors hindering the use of big data in Institutions of
higher learning. A survey research design was adopted in conducting this
research, and Questionnaires were the instrument employed for data collection.

The downstream accuracy of self-supervised methods is tightly linked to the
proxy task solved during training and the quality of the gradients extracted
from it. Richer and more meaningful gradients updates are key to allow
self-supervised methods to learn better and in a more efficient manner. In a
typical self-distillation framework, the representation of two augmented images
are enforced to be coherent at the global level. Nonetheless, incorporating
local cues in the proxy task can be beneficial and improve the model accuracy
on downstream tasks. This leads to a dual objective in which, on the one hand,
coherence between global-representations is enforced and on the other,
coherence between local-representations is enforced. Unfortunately, an exact
correspondence mapping between two sets of local-representations does not exist
making the task of matching local-representations from one augmentation to
another non-trivial. We propose to leverage the spatial information in the
input images to obtain geometric matchings and compare this geometric approach
against previous methods based on similarity matchings. Our study shows that
not only 1) geometric matchings perform better than similarity based matchings
in low-data regimes but also 2) that similarity based matchings are highly
hurtful in low-data regimes compared to the vanilla baseline without local
self-distillation. The code is available at
https://github.com/tileb1/global-local-self-distillation.

Photographs captured by smartphones and mid-range cameras have limited
spatial resolution and dynamic range, with noisy response in underexposed
regions and color artefacts in saturated areas. This paper introduces the first
approach (to the best of our knowledge) to the reconstruction of
high-resolution, high-dynamic range color images from raw photographic bursts
captured by a handheld camera with exposure bracketing. This method uses a
physically-accurate model of image formation to combine an iterative
optimization algorithm for solving the corresponding inverse problem with a
learned image representation for robust alignment and a learned natural image
prior. The proposed algorithm is fast, with low memory requirements compared to
state-of-the-art learning-based approaches to image restoration, and features
that are learned end to end from synthetic yet realistic data. Extensive
experiments demonstrate its excellent performance with super-resolution factors
of up to $\times 4$ on real photographs taken in the wild with hand-held
cameras, and high robustness to low-light conditions, noise, camera shake, and
moderate object motion.

Personalised 3D vascular models are valuable for diagnosis, prognosis and
treatment planning in patients with cardiovascular disease. Traditionally, such
models have been constructed with explicit representations such as meshes and
voxel masks, or implicit representations such as radial basis functions or
atomic (tubular) shapes. Here, we propose to represent surfaces by the zero
level set of their signed distance function (SDF) in a differentiable implicit
neural representation (INR). This allows us to model complex vascular
structures with a representation that is implicit, continuous, light-weight,
and easy to integrate with deep learning algorithms. We here demonstrate the
potential of this approach with three practical examples. First, we obtain an
accurate and watertight surface for an abdominal aortic aneurysm (AAA) from CT
images and show robust fitting from as little as 200 points on the surface.
Second, we simultaneously fit nested vessel walls in a single INR without
intersections. Third, we show how 3D models of individual arteries can be
smoothly blended into a single watertight surface. Our results show that INRs
are a flexible representation with potential for minimally interactive
annotation and manipulation of complex vascular structures.

Deforestation is one of the contributing factors to climate change. Climate
change has a serious impact on human life, and it occurs due to emission of
greenhouse gases, such as carbon dioxide, to the atmosphere. It is important to
know the causes of deforestation for mitigation efforts, but there is a lack of
data-driven research studies to predict these deforestation drivers. In this
work, we propose a contrastive learning architecture, called Multimodal
SuperCon, for classifying drivers of deforestation in Indonesia using satellite
images obtained from Landsat 8. Multimodal SuperCon is an architecture which
combines contrastive learning and multimodal fusion to handle the available
deforestation dataset. Our proposed model outperforms previous work on driver
classification, giving a 7% improvement in accuracy in comparison to a
state-of-the-art rotation equivariant model for the same task.

A methodological framework for ensemble-based estimation and simulation of
high dimensional dynamical systems such as the oceanic or atmospheric flows is
proposed. To that end, the dynamical system is embedded in a family of
reproducing kernel Hilbert spaces with kernel functions driven by the dynamics.
This family is nicknamed Wonderland for its appealing properties. In Wonderland
the Koopman and Perron-Frobenius operators are unitary and uniformly
continuous. This property warrants they can be expressed in exponential series
of diagonalizable bounded infinitesimal generators. Access to Lyapunov
exponents and to exact ensemble based expressions of the tangent linear
dynamics are directly available as well. Wonderland enables us the devise of
strikingly simple ensemble data assimilation methods for trajectory
reconstructions in terms of constant-in-time linear combinations of trajectory
samples. Such an embarrassingly simple strategy is made possible through a
fully justified superposition principle ensuing from several fundamental
theorems.

Artificial intelligence (AI), machine learning, and deep learning (DL)
methods are becoming increasingly important in the field of biomedical image
analysis. However, to exploit the full potential of such methods, a
representative number of experimentally acquired images containing a
significant number of manually annotated objects is needed as training data.
Here we introduce SYNTA (synthetic data) as a novel approach for the generation
of synthetic, photo-realistic, and highly complex biomedical images as training
data for DL systems. We show the versatility of our approach in the context of
muscle fiber and connective tissue analysis in histological sections. We
demonstrate that it is possible to perform robust and expert-level segmentation
tasks on previously unseen real-world data, without the need for manual
annotations using synthetic training data alone. Being a fully parametric
technique, our approach poses an interpretable and controllable alternative to
Generative Adversarial Networks (GANs) and has the potential to significantly
accelerate quantitative image analysis in a variety of biomedical applications
in microscopy and beyond.

Discovering sequences with desired properties has long been an interesting
intellectual pursuit. In pulse compression radar (PCR), discovering phase codes
with low aperiodic autocorrelations is essential for a good estimation
performance. The design of phase code, however, is mathematically non-trivial
as the aperiodic autocorrelation properties of a sequence are intractable to
characterize. In this paper, we put forth a genetic algorithm (GA) approach to
discover new phase codes for PCR with the mismatched filter (MMF) receiver. The
developed GA, dubbed GASeq, discovers better phase codes than the state of the
art. At a code length of 59, the sequence discovered by GASeq achieves a
signal-to-clutter ratio (SCR) of 50.84, while the best-known sequence has an
SCR of 45.16. In addition, the efficiency and scalability of GASeq enable us to
search phase codes with a longer code length, which thwarts existing deep
learning-based approaches. At a code length of 100, the best phase code
discovered by GASeq exhibit an SCR of 63.23.

Vertical federated learning (VFL) is an emerging paradigm that allows
different parties (e.g., organizations or enterprises) to collaboratively build
machine learning models with privacy protection. In the training phase, VFL
only exchanges the intermediate statistics, i.e., forward activations and
backward derivatives, across parties to compute model gradients. Nevertheless,
due to its geo-distributed nature, VFL training usually suffers from the low
WAN bandwidth.
  In this paper, we introduce CELU-VFL, a novel and efficient VFL training
framework that exploits the local update technique to reduce the cross-party
communication rounds. CELU-VFL caches the stale statistics and reuses them to
estimate model gradients without exchanging the ad hoc statistics. Significant
techniques are proposed to improve the convergence performance. First, to
handle the stochastic variance problem, we propose a uniform sampling strategy
to fairly choose the stale statistics for local updates. Second, to harness the
errors brought by the staleness, we devise an instance weighting mechanism that
measures the reliability of the estimated gradients. Theoretical analysis
proves that CELU-VFL achieves a similar sub-linear convergence rate as vanilla
VFL training but requires much fewer communication rounds. Empirical results on
both public and real-world workloads validate that CELU-VFL can be up to six
times faster than the existing works.

Image restoration under adverse weather conditions has been of significant
interest for various computer vision applications. Recent successful methods
rely on the current progress in deep neural network architectural designs
(e.g., with vision transformers). Motivated by the recent progress achieved
with state-of-the-art conditional generative models, we present a novel
patch-based image restoration algorithm based on denoising diffusion
probabilistic models. Our patch-based diffusion modeling approach enables
size-agnostic image restoration by using a guided denoising process with
smoothed noise estimates across overlapping patches during inference. We
empirically evaluate our model on benchmark datasets for image desnowing,
combined deraining and dehazing, and raindrop removal. We demonstrate our
approach to achieve state-of-the-art performances on both weather-specific and
multi-weather image restoration, and qualitatively show strong generalization
to real-world test images.

Differential privacy (DP) has arisen as the gold standard in protecting an
individual's privacy in datasets by adding calibrated noise to each data
sample. While the application to categorical data is straightforward, its
usability in the context of images has been limited. Contrary to categorical
data the meaning of an image is inherent in the spatial correlation of
neighboring pixels making the simple application of noise infeasible.
Invertible Neural Networks (INN) have shown excellent generative performance
while still providing the ability to quantify the exact likelihood. Their
principle is based on transforming a complicated distribution into a simple one
e.g. an image into a spherical Gaussian. We hypothesize that adding noise to
the latent space of an INN can enable differentially private image
modification. Manipulation of the latent space leads to a modified image while
preserving important details. Further, by conditioning the INN on meta-data
provided with the dataset we aim at leaving dimensions important for downstream
tasks like classification untouched while altering other parts that potentially
contain identifying information. We term our method content-aware differential
privacy (CADP). We conduct experiments on publicly available benchmarking
datasets as well as dedicated medical ones. In addition, we show the
generalizability of our method to categorical data. The source code is publicly
available at https://github.com/Cardio-AI/CADP.

Although metals are traditionally considered hard matter, their atomic
lattices may become dynamic and undergo reconfigurations well below the melting
temperature. Understanding their complex structural dynamics is important for
many applications but is not easy. Here we report deep-potential molecular
dynamics simulations allowing us to resolve at atomistic-resolution the complex
innate dynamics of various types of copper (Cu) surfaces, here used as an
example, near the H\"uttig temperature. Unsupervised machine learning and
high-dimensional structural descriptors allow identifying and tracking the
atomic environments that emerge in the Cu surfaces and reconstructing their
equilibrium dynamics. Our results unveil the elusive identity of these metal
surfaces, which preserve their identity only in part in realistic regimes while
non-native atomic domains, typical of other surface-types, emerge within them
coexisting in equilibrium with native ones. This proposes a concept of
statistical ``equivalent identity'' for metal surfaces, which is key for
understanding their behaviors and properties.

Deep neural networks (DNN) have been widely used and play a major role in the
field of computer vision and autonomous navigation. However, these DNNs are
computationally complex and their deployment over resource-constrained
platforms is difficult without additional optimizations and customization.
  In this manuscript, we describe an overview of DNN architecture and propose
methods to reduce computational complexity in order to accelerate training and
inference speeds to fit them on edge computing platforms with low computational
resources.

Knowledge Graph (KG) completion is an important task that greatly benefits
knowledge discovery in many fields (e.g. biomedical research). In recent years,
learning KG embeddings to perform this task has received considerable
attention. Despite the success of KG embedding methods, they predominantly use
negative sampling, resulting in increased computational complexity as well as
biased predictions due to the closed world assumption. To overcome these
limitations, we propose \textbf{KG-NSF}, a negative sampling-free framework for
learning KG embeddings based on the cross-correlation matrices of embedding
vectors. It is shown that the proposed method achieves comparable link
prediction performance to negative sampling-based methods while converging much
faster.

Image-based artistic rendering can synthesize a variety of expressive styles
using algorithmic image filtering. In contrast to deep learning-based methods,
these heuristics-based filtering techniques can operate on high-resolution
images, are interpretable, and can be parameterized according to various design
aspects. However, adapting or extending these techniques to produce new styles
is often a tedious and error-prone task that requires expert knowledge. We
propose a new paradigm to alleviate this problem: implementing algorithmic
image filtering techniques as differentiable operations that can learn
parametrizations aligned to certain reference styles. To this end, we present
WISE, an example-based image-processing system that can handle a multitude of
stylization techniques, such as watercolor, oil or cartoon stylization, within
a common framework. By training parameter prediction networks for global and
local filter parameterizations, we can simultaneously adapt effects to
reference styles and image content, e.g., to enhance facial features. Our
method can be optimized in a style-transfer framework or learned in a
generative-adversarial setting for image-to-image translation. We demonstrate
that jointly training an XDoG filter and a CNN for postprocessing can achieve
comparable results to a state-of-the-art GAN-based method.

We study the problem of finding the root vertex in large growing networks. We
prove that it is possible to construct confidence sets of size independent of
the number of vertices in the network that contain the root vertex with high
probability in various models of random networks. The models include uniform
random recursive dags and uniform Cooper-Frieze random graphs.

We formulate offloading of computational tasks from a dynamic group of mobile
agents (e.g., cars) as decentralized decision making among autonomous agents.
We design an interaction mechanism that incentivizes such agents to align
private and system goals by balancing between competition and cooperation. In
the static case, the mechanism provably has Nash equilibria with optimal
resource allocation. In a dynamic environment, this mechanism's requirement of
complete information is impossible to achieve. For such environments, we
propose a novel multi-agent online learning algorithm that learns with partial,
delayed and noisy state information, thus greatly reducing information need.
Our algorithm is also capable of learning from long-term and sparse reward
signals with varying delay. Empirical results from the simulation of a V2X
application confirm that through learning, agents with the learning algorithm
significantly improve both system and individual performance, reducing up to
30% of offloading failure rate, communication overhead and load variation,
increasing computation resource utilization and fairness. Results also confirm
the algorithm's good convergence and generalization property in different

Neural schedulers based on deep reinforcement learning (DRL) have shown
considerable potential for solving real-world resource allocation problems, as
they have demonstrated significant performance gain in the domain of cluster
computing. In this paper, we investigate the feasibility of neural schedulers
for the domain of System-on-Chip (SoC) resource allocation through extensive
experiments and comparison with non-neural, heuristic schedulers. The key
finding is three-fold. First, neural schedulers designed for cluster computing
domain do not work well for SoC due to i) heterogeneity of SoC computing
resources and ii) variable action set caused by randomness in incoming jobs.
Second, our novel neural scheduler technique, Eclectic Interaction Matching
(EIM), overcomes the above challenges, thus significantly improving the
existing neural schedulers. Specifically, we rationalize the underlying reasons
behind the performance gain by the EIM-based neural scheduler. Third, we
discover that the ratio of the average processing elements (PE) switching delay
and the average PE computation time significantly impacts the performance of
neural SoC schedulers even with EIM. Consequently, future neural SoC scheduler
design must consider this metric as well as its implementation overhead for
practical utility.

A 3D caricature is an exaggerated 3D depiction of a human face. The goal of
this paper is to model the variations of 3D caricatures in a compact parameter
space so that we can provide a useful data-driven toolkit for handling 3D
caricature deformations. To achieve the goal, we propose an MLP-based framework
for building a deformable surface model, which takes a latent code and produces
a 3D surface. In the framework, a SIREN MLP models a function that takes a 3D
position on a fixed template surface and returns a 3D displacement vector for
the input position. We create variations of 3D surfaces by learning a
hypernetwork that takes a latent code and produces the parameters of the MLP.
Once learned, our deformable model provides a nice editing space for 3D
caricatures, supporting label-based semantic editing and point-handle-based
deformation, both of which produce highly exaggerated and natural 3D caricature
shapes. We also demonstrate other applications of our deformable model, such as
automatic 3D caricature creation.

Large graphs commonly appear in social networks, knowledge graphs,
recommender systems, life sciences, and decision making problems. Summarizing
large graphs by their high level properties is helpful in solving problems in
these settings. In spectral clustering, we aim to identify clusters of nodes
where most edges fall within clusters and only few edges fall between clusters.
This task is important for many downstream applications and exploratory
analysis. A core step of spectral clustering is performing an
eigendecomposition of the corresponding graph Laplacian matrix (or
equivalently, a singular value decomposition, SVD, of the incidence matrix).
The convergence of iterative singular value decomposition approaches depends on
the eigengaps of the spectrum of the given matrix, i.e., the difference between
consecutive eigenvalues. For a graph Laplacian corresponding to a
well-clustered graph, the eigenvalues will be non-negative but very small (much
less than $1$) slowing convergence. This paper introduces a parallelizable
approach to dilating the spectrum in order to accelerate SVD solvers and in
turn, spectral clustering. This is accomplished via polynomial approximations
to matrix operations that favorably transform the spectrum of a matrix without
changing its eigenvectors. Experiments demonstrate that this approach
significantly accelerates convergence, and we explain how this transformation
can be parallelized and stochastically approximated to scale with available
compute.

The introduction of electronic personal health records (EHR) enables
nationwide information exchange and curation among different health care
systems. However, the current EHR systems do not provide transparent means for
diagnosis support, medical research or can utilize the omnipresent data
produced by the personal medical devices. Besides, the EHR systems are
centrally orchestrated, which could potentially lead to a single point of
failure. Therefore, in this article, we explore novel approaches for
decentralizing machine learning over distributed ledgers to create intelligent
EHR systems that can utilize information from personal medical devices for
improved knowledge extraction. Consequently, we proposed and evaluated a
conceptual EHR to enable anonymous predictive analysis across multiple medical
institutions. The evaluation results indicate that the decentralized EHR can be
deployed over the computing continuum with reduced machine learning time of up
to 60% and consensus latency of below 8 seconds.

Zero-shot learning (ZSL) aims to recognize unseen classes by exploiting
semantic descriptions shared between seen classes and unseen classes. Current
methods show that it is effective to learn visual-semantic alignment by
projecting semantic embeddings into the visual space as class prototypes.
However, such a projection function is only concerned with seen classes. When
applied to unseen classes, the prototypes often perform suboptimally due to
domain shift. In this paper, we propose to learn prototypes via placeholders,
termed LPL, to eliminate the domain shift between seen and unseen classes.
Specifically, we combine seen classes to hallucinate new classes which play as
placeholders of the unseen classes in the visual and semantic space. Placed
between seen classes, the placeholders encourage prototypes of seen classes to
be highly dispersed. And more space is spared for the insertion of
well-separated unseen ones. Empirically, well-separated prototypes help
counteract visual-semantic misalignment caused by domain shift. Furthermore, we
exploit a novel semantic-oriented fine-tuning to guarantee the semantic
reliability of placeholders. Extensive experiments on five benchmark datasets
demonstrate the significant performance gain of LPL over the state-of-the-art
methods. Code is available at https://github.com/zaiquanyang/LPL.

This study proposes the use of generative models (GANs) for augmenting the
EuroSAT dataset for the Land Use and Land Cover (LULC) Classification task. We
used DCGAN and WGAN-GP to generate images for each class in the dataset. We
then explored the effect of augmenting the original dataset by about 10% in
each case on model performance. The choice of GAN architecture seems to have no
apparent effect on the model performance. However, a combination of geometric
augmentation and GAN-generated images improved baseline results. Our study
shows that GANs augmentation can improve the generalizability of deep
classification models on satellite images.

Multi-modal tracking gains attention due to its ability to be more accurate
and robust in complex scenarios compared to traditional RGB-based tracking. Its
key lies in how to fuse multi-modal data and reduce the gap between modalities.
However, multi-modal tracking still severely suffers from data deficiency, thus
resulting in the insufficient learning of fusion modules. Instead of building
such a fusion module, in this paper, we provide a new perspective on
multi-modal tracking by attaching importance to the multi-modal visual prompts.
We design a novel multi-modal prompt tracker (ProTrack), which can transfer the
multi-modal inputs to a single modality by the prompt paradigm. By best
employing the tracking ability of pre-trained RGB trackers learning at scale,
our ProTrack can achieve high-performance multi-modal tracking by only altering
the inputs, even without any extra training on multi-modal data. Extensive
experiments on 5 benchmark datasets demonstrate the effectiveness of the
proposed ProTrack.

ASR has been shown to achieve great performance recently. However, most of
them rely on massive paired data, which is not feasible for low-resource
languages worldwide. This paper investigates how to learn directly from
unpaired phone sequences and speech utterances. We design a two-stage iterative
framework. GAN training is adopted in the first stage to find the mapping
relationship between unpaired speech and phone sequence. In the second stage,
another HMM model is introduced to train from the generator's output, which
boosts the performance and provides a better segmentation for the next
iteration. In the experiment, we first investigate different choices of model
designs. Then we compare the framework to different types of baselines: (i)
supervised methods (ii) acoustic unit discovery based methods (iii) methods
learning from unpaired data. Our framework performs consistently better than
all acoustic unit discovery methods and previous methods learning from unpaired
data based on the TIMIT dataset.

Deep reinforcement learning with domain randomization learns a control policy
in various simulations with randomized physical and sensor model parameters to
become transferable to the real world in a zero-shot setting. However, a huge
number of samples are often required to learn an effective policy when the
range of randomized parameters is extensive due to the instability of policy
updates. To alleviate this problem, we propose a sample-efficient method named
Cyclic Policy Distillation (CPD). CPD divides the range of randomized
parameters into several small sub-domains and assigns a local policy to each
sub-domain. Then, the learning of local policies is performed while {\it
cyclically} transitioning the target sub-domain to neighboring sub-domains and
exploiting the learned values/policies of the neighbor sub-domains with a
monotonic policy-improvement scheme. Finally, all of the learned local policies
are distilled into a global policy for sim-to-real transfer. The effectiveness
and sample efficiency of CPD are demonstrated through simulations with four
tasks (Pendulum from OpenAIGym and Pusher, Swimmer, and HalfCheetah from
Mujoco), and a real-robot ball-dispersal task.

Enhanced sampling methods are indispensable in computational physics and
chemistry, where atomistic simulations cannot exhaustively sample the
high-dimensional configuration space of dynamical systems due to the sampling
problem. A class of such enhanced sampling methods works by identifying a few
slow degrees of freedom, termed collective variables (CVs), and enhancing the
sampling along these CVs. Selecting CVs to analyze and drive the sampling is
not trivial and often relies on physical and chemical intuition. Despite
routinely circumventing this issue using manifold learning to estimate CVs
directly from standard simulations, such methods cannot provide mappings to a
low-dimensional manifold from enhanced sampling simulations as the geometry and
density of the learned manifold are biased. Here, we address this crucial issue
and provide a general reweighting framework based on anisotropic diffusion maps
for manifold learning that takes into account that the learning data set is
sampled from a biased probability distribution. We consider manifold learning
methods based on constructing a Markov chain describing transition
probabilities between high-dimensional samples. We show that our framework
reverts the biasing effect yielding CVs that correctly describe the equilibrium
density. This advancement enables the construction of low-dimensional CVs using
manifold learning directly from data generated by enhanced sampling
simulations. We call our framework reweighted manifold learning. We show that
it can be used in many manifold learning techniques on data from both standard
and enhanced sampling simulations.

Recently, a variety of vision transformers have been developed as their
capability of modeling long-range dependency. In current transformer-based
backbones for medical image segmentation, convolutional layers were replaced
with pure transformers, or transformers were added to the deepest encoder to
learn global context. However, there are mainly two challenges in a scale-wise
perspective: (1) intra-scale problem: the existing methods lacked in extracting
local-global cues in each scale, which may impact the signal propagation of
small objects; (2) inter-scale problem: the existing methods failed to explore
distinctive information from multiple scales, which may hinder the
representation learning from objects with widely variable size, shape and
location. To address these limitations, we propose a novel backbone, namely
ScaleFormer, with two appealing designs: (1) A scale-wise intra-scale
transformer is designed to couple the CNN-based local features with the
transformer-based global cues in each scale, where the row-wise and column-wise
global dependencies can be extracted by a lightweight Dual-Axis MSA. (2) A
simple and effective spatial-aware inter-scale transformer is designed to
interact among consensual regions in multiple scales, which can highlight the
cross-scale dependency and resolve the complex scale variations. Experimental
results on different benchmarks demonstrate that our Scale-Former outperforms
the current state-of-the-art methods. The code is publicly available at:
https://github.com/ZJUGiveLab/ScaleFormer.

This study considers the partial monitoring problem with $k$-actions and
$d$-outcomes and provides the first best-of-both-worlds algorithms, whose
regrets are favorably bounded both in the stochastic and adversarial regimes.
In particular, we show that for non-degenerate locally observable games, the
regret is $O(m^2 k^4 \log(T) \log(k_{\Pi} T) / \Delta_{\min})$ in the
stochastic regime and $O(m k^{2/3} \sqrt{T \log(T) \log k_{\Pi}})$ in the
adversarial regime, where $T$ is the number of rounds, $m$ is the maximum
number of distinct observations per action, $\Delta_{\min}$ is the minimum
suboptimality gap, and $k_{\Pi}$ is the number of Pareto optimal actions.
Moreover, we show that for globally observable games, the regret is
$O(c_{\mathcal{G}}^2 \log(T) \log(k_{\Pi} T) / \Delta_{\min}^2)$ in the
stochastic regime and $O((c_{\mathcal{G}}^2 \log(T) \log(k_{\Pi} T))^{1/3}
T^{2/3})$ in the adversarial regime, where $c_{\mathcal{G}}$ is a
game-dependent constant. We also provide regret bounds for a stochastic regime
with adversarial corruptions. Our algorithms are based on the
follow-the-regularized-leader framework and are inspired by the approach of
exploration by optimization and the adaptive learning rate in the field of
online learning with feedback graphs.

Recently, tile pruning has been widely studied to accelerate the inference of
deep neural networks (DNNs). However, we found that the loss due to tile
pruning, which can eliminate important elements together with unimportant
elements, is large on trained DNNs. In this study, we propose a one-shot
reparameterization method, called TileTrans, to reduce the loss of tile
pruning. Specifically, we repermute the rows or columns of the weight matrix
such that the model architecture can be kept unchanged after
reparameterization. This repermutation realizes the reparameterization of the
DNN model without any retraining. The proposed reparameterization method
combines important elements into the same tile; thus, preserving the important
elements after the tile pruning. Furthermore, TileTrans can be seamlessly
integrated into existing tile pruning methods because it is a pre-processing
method executed before pruning, which is orthogonal to most existing methods.
The experimental results demonstrate that our method is essential in reducing
the loss of tile pruning on DNNs. Specifically, the accuracy is improved by up
to 17% for AlexNet while 5% for ResNet-34, where both models are pre-trained on
ImageNet.

The Internet of Things (IoT) is an emerging concept that directly links to
the billions of physical items, or "things", that are connected to the Internet
and are all gathering and exchanging information between devices and systems.
However, IoT devices were not built with security in mind, which might lead to
security vulnerabilities in a multi-device system. Traditionally, we
investigated IoT issues by polling IoT developers and specialists. This
technique, however, is not scalable since surveying all IoT developers is not
feasible. Another way to look into IoT issues is to look at IoT developer
discussions on major online development forums like Stack Overflow (SO).
However, finding discussions that are relevant to IoT issues is challenging
since they are frequently not categorized with IoT-related terms. In this
paper, we present the "IoT Security Dataset", a domain-specific dataset of 7147
samples focused solely on IoT security discussions. As there are no automated
tools to label these samples, we manually labeled them. We further employed
multiple transformer models to automatically detect security discussions.
Through rigorous investigations, we found that IoT security discussions are
different and more complex than traditional security discussions. We
demonstrated a considerable performance loss (up to 44%) of transformer models
on cross-domain datasets when we transferred knowledge from a general-purpose
dataset "Opiner", supporting our claim. Thus, we built a domain-specific IoT
security detector with an F1-Score of 0.69. We have made the dataset public in
the hope that developers would learn more about the security discussion and
vendors would enhance their concerns about product security.

Pre-training trajectory embeddings is a fundamental and critical procedure in
spatial-temporal trajectory mining, and is beneficial for a wide range of
downstream tasks. The key for generating effective trajectory embeddings is to
extract high-level travel semantics from trajectories, including movement
patterns and travel purposes, with consideration of the trajectories' long-term
spatial-temporal correlations. Despite the existing efforts, there are still
major challenges in pre-training trajectory embeddings. First, commonly used
generative pretext tasks are not suitable for extracting high-level semantics
from trajectories. Second, existing data augmentation methods fit badly on
trajectory datasets. Third, current encoder designs fail to fully incorporate
long-term spatial-temporal correlations hidden in trajectories. To tackle these
challenges, we propose a novel Contrastive Spatial-Temporal Trajectory
Embedding (CSTTE) model for learning comprehensive trajectory embeddings. CSTTE
adopts the contrastive learning framework so that its pretext task is robust to
noise. A specially designed data augmentation method for trajectories is
coupled with the contrastive pretext task to preserve the high-level travel
semantics. We also build an efficient spatial-temporal trajectory encoder to
efficiently and comprehensively model the long-term spatial-temporal
correlations in trajectories. Extensive experiments on two downstream tasks and
three real-world datasets prove the superiority of our model compared with the
existing trajectory embedding methods.

Understanding the dynamics of complex molecular processes is often linked to
the study of infrequent transitions between long-lived stable states. The
standard approach to the sampling of such rare events is to generate an
ensemble of transition paths using a random walk in trajectory space. This,
however, comes with the drawback of strong correlation between subsequently
visited paths and with an intrinsic difficulty in parallelizing the sampling
process. We propose a transition path sampling scheme based on neural-network
generated configurations. These are obtained employing normalizing flows, a
neural network class able to generate decorrelated samples from a given
distribution. With this approach, not only are correlations between visited
paths removed, but the sampling process becomes easily parallelizable.
Moreover, by conditioning the normalizing flow, the sampling of configurations
can be steered towards the regions of interest. We show that this allows for
resolving both the thermodynamics and kinetics of the transition region.

Modern artificial intelligence (AI) applications require large quantities of
training and test data. This need creates critical challenges not only
concerning the availability of such data, but also regarding its quality. For
example, incomplete, erroneous or inappropriate training data can lead to
unreliable models that produce ultimately poor decisions. Trustworthy AI
applications require high-quality training and test data along many dimensions,
such as accuracy, completeness, consistency, and uniformity.
  We explore empirically the relationship between six of the traditional data
quality dimensions and the performance of fifteen widely used machine learning
(ML) algorithms covering the tasks of classification, regression, and
clustering, with the goal of explaining their performance in terms of data
quality. Our experiments distinguish three scenarios based on the AI pipeline
steps that were fed with polluted data: polluted training data, test data, or
both. We conclude the paper with an extensive discussion of our observations.

Explanations have gained an increasing level of interest in the AI and
Machine Learning (ML) communities in order to improve model transparency and
allow users to form a mental model of a trained ML model. However, explanations
can go beyond this one way communication as a mechanism to elicit user control,
because once users understand, they can then provide feedback. The goal of this
paper is to present an overview of research where explanations are combined
with interactive capabilities as a mean to learn new models from scratch and to
edit and debug existing ones. To this end, we draw a conceptual map of the
state-of-the-art, grouping relevant approaches based on their intended purpose
and on how they structure the interaction, highlighting similarities and
differences between them. We also discuss open research issues and outline
possible directions forward, with the hope of spurring further research on this
blooming research topic.

Aligning image and text encoders from scratch using contrastive learning
requires large amounts of paired image-text data. We alleviate this need by
aligning individually pre-trained language and vision representation models
using a much smaller amount of paired data, augmented with a curriculum
learning algorithm to learn fine-grained vision-language alignments. TOnICS
(Training with Ontology-Informed Contrastive Sampling) initially samples
minibatches whose image-text pairs contain a wide variety of objects to learn
object-level alignment, and progressively samples minibatches where all
image-text pairs contain the same object to learn finer-grained contextual
alignment. Aligning pre-trained BERT and VinVL models to each other using
TOnICS outperforms CLIP on downstream zero-shot image retrieval while using
less than 1% as much training data.

Learned image compression has achieved extraordinary rate-distortion
performance in PSNR and MS-SSIM compared to traditional methods. However, it
suffers from intensive computation, which is intolerable for real-world
applications and leads to its limited industrial application for now. In this
paper, we introduce neural architecture search (NAS) to designing more
efficient networks with lower latency, and leverage quantization to accelerate
the inference process. Meanwhile, efforts in engineering like multi-threading
and SIMD have been made to improve efficiency. Optimized using a hybrid loss of
PSNR and MS-SSIM for better visual quality, we obtain much higher MS-SSIM than
JPEG, JPEG XL and AVIF over all bit rates, and PSNR between that of JPEG XL and
AVIF. Our software implementation of LIC achieves comparable or even faster
inference speed compared to jpeg-turbo while being multiple times faster than
JPEG XL and AVIF. Besides, our implementation of LIC reaches stunning
throughput of 145 fps for encoding and 208 fps for decoding on a Tesla T4 GPU
for 1080p images. On CPU, the latency of our implementation is comparable with
JPEG XL.

The mergers of neutron star-neutron star and neutron star-black hole binaries
are the most promising gravitational wave events with electromagnetic
counterparts. The rapid detection, localization and simultaneous
multi-messenger follow-up of these sources is of primary importance in the
upcoming science runs of the LIGO-Virgo-KAGRA Collaboration. While prompt
electromagnetic counterparts during binary mergers can last less than two
seconds, the time scales of existing localization methods that use Bayesian
techniques, varies from seconds to days. In this paper, we propose the first
deep learning-based approach for rapid and accurate sky localization of all
types of binary coalescences, including neutron star-neutron star and neutron
star-black hole binaries for the first time. Specifically, we train and test a
normalizing flow model on matched-filtering output from gravitational wave
searches. Our model produces sky direction posteriors in milliseconds using a
single P100 GPU, which is three to six orders of magnitude faster than Bayesian

Factorizable joint shift (FJS) was recently proposed as a type of dataset
shift for which the complete characteristics can be estimated from feature data
observations on the test dataset by a method called Joint Importance Aligning.
For the multinomial (multiclass) classification setting, we derive a
representation of factorizable joint shift in terms of the source (training)
distribution, the target (test) prior class probabilities and the target
marginal distribution of the features. On the basis of this result, we propose
alternatives to joint importance aligning and, at the same time, point out that
factorizable joint shift is not fully identifiable if no class label
information on the test dataset is available and no additional assumptions are
made. Other results of the paper include correction formulae for the posterior
class probabilities both under general dataset shift and factorizable joint
shift. In addition, we investigate the consequences of assuming factorizable
joint shift for the bias caused by sample selection.

Automatic action quality assessment (AQA) has attracted more interests due to
its wide applications. However, existing AQA methods usually employ the
multi-branch models to generate multiple scores, which is not flexible for
dealing with a variable number of judges. In this paper, we propose a novel
Uncertainty-Driven AQA (UD-AQA) model to generate multiple predictions only
using one single branch. Specifically, we design a CVAE (Conditional
Variational Auto-Encoder) based module to encode the uncertainty, where
multiple scores can be produced by sampling from the learned latent space
multiple times. Moreover, we output the estimation of uncertainty and utilize
the predicted uncertainty to re-weight AQA regression loss, which can reduce
the contributions of uncertain samples for training. We further design an
uncertainty-guided training strategy to dynamically adjust the learning order
of the samples from low uncertainty to high uncertainty. The experiments show
that our proposed method achieves new state-of-the-art results on the Olympic
events MTL-AQA and surgical skill JIGSAWS datasets.

It is common practice to reuse models initially trained on different data to
increase downstream task performance. Especially in the computer vision domain,
ImageNet-pretrained weights have been successfully used for various tasks. In
this work, we investigate the impact of transfer learning for segmentation
problems, being pixel-wise classification problems that can be tackled with
encoder-decoder architectures. We find that transfer learning the decoder does
not help downstream segmentation tasks, while transfer learning the encoder is
truly beneficial. We demonstrate that pretrained weights for a decoder may
yield faster convergence, but they do not improve the overall model performance
as one can obtain equivalent results with randomly initialized decoders.
However, we show that it is more effective to reuse encoder weights trained on
a segmentation or reconstruction task than reusing encoder weights trained on
classification tasks. This finding implicates that using ImageNet-pretrained
encoders for downstream segmentation problems is suboptimal. We also propose a
contrastive self-supervised approach with multiple self-reconstruction tasks,
which provides encoders that are suitable for transfer learning in segmentation
problems in the absence of segmentation labels.

Video stabilization is an in-camera processing commonly applied by modern
acquisition devices. While significantly improving the visual quality of the
resulting videos, it has been shown that such operation typically hinders the
forensic analysis of video signals. In fact, the correct identification of the
acquisition source usually based on Photo Response non-Uniformity (PRNU) is
subject to the estimation of the transformation applied to each frame in the
stabilization phase. A number of techniques have been proposed for dealing with
this problem, which however typically suffer from a high computational burden
due to the grid search in the space of inversion parameters. Our work attempts
to alleviate these shortcomings by exploiting the parallelization capabilities
of Graphics Processing Units (GPUs), typically used for deep learning
applications, in the framework of stabilised frames inversion. Moreover, we
propose to exploit SIFT features {to estimate the camera momentum and} %to
identify less stabilized temporal segments, thus enabling a more accurate
identification analysis, and to efficiently initialize the frame-wise parameter
search of consecutive frames. Experiments on a consolidated benchmark dataset
confirm the effectiveness of the proposed approach in reducing the required
computational time and improving the source identification accuracy. {The code
is available at \url{https://github.com/AMontiB/GPU-PRNU-SIFT}}.

Recent Language Models (LMs) achieve breakthrough performance in code
generation when trained on human-authored problems, even solving some
competitive-programming problems. Self-play has proven useful in games such as
Go, and thus it is natural to ask whether LMs can generate their own
instructive programming problems to improve their performance. We show that it
is possible for an LM to synthesize programming problems and solutions, which
are filtered for correctness by a Python interpreter. The LM's performance is
then seen to improve when it is fine-tuned on its own synthetic problems and
verified solutions; thus the model 'improves itself' using the Python
interpreter. Problems are specified formally as programming puzzles [Schuster
et al., 2021], a code-based problem format where solutions can easily be
verified for correctness by execution. In experiments on publicly-available
LMs, test accuracy more than doubles. This work demonstrates the potential for
code LMs, with an interpreter, to generate instructive problems and improve
their own performance.

Marine vessel re-identification technology is an important component of
intelligent shipping systems and an important part of the visual perception
tasks required for marine surveillance. However, unlike the situation on land,
the maritime environment is complex and variable with fewer samples, and it is
more difficult to perform vessel re-identification at sea. Therefore, this
paper proposes a transfer dynamic alignment algorithm and simulates the swaying
situation of vessels at sea, using a well-camouflaged and similar warship as
the test target to improve the recognition difficulty and thus cope with the
impact caused by complex sea conditions, and discusses the effect of different
types of vessels as transfer objects. The experimental results show that the
improved algorithm improves the mean average accuracy (mAP) by 10.2% and the
first hit rate (Rank1) by 4.9% on average.

Long-tailed datasets are very frequently encountered in real-world use cases
where few classes or categories (known as majority or head classes) have higher
number of data samples compared to the other classes (known as minority or tail
classes). Training deep neural networks on such datasets gives results biased
towards the head classes. So far, researchers have come up with multiple
weighted loss and data re-sampling techniques in efforts to reduce the bias.
However, most of such techniques assume that the tail classes are always the
most difficult classes to learn and therefore need more weightage or attention.
Here, we argue that the assumption might not always hold true. Therefore, we
propose a novel approach to dynamically measure the instantaneous difficulty of
each class during the training phase of the model. Further, we use the
difficulty measures of each class to design a novel weighted loss technique
called `class-wise difficulty based weighted (CDB-W) loss' and a novel data
sampling technique called `class-wise difficulty based sampling (CDB-S)'. To
verify the wide-scale usability of our CDB methods, we conducted extensive
experiments on multiple tasks such as image classification, object detection,
instance segmentation and video-action classification. Results verified that
CDB-W loss and CDB-S could achieve state-of-the-art results on many
class-imbalanced datasets such as ImageNet-LT, LVIS and EGTEA, that resemble

Existing learning-based image inpainting methods are still in challenge when
facing complex semantic environments and diverse hole patterns. The prior
information learned from the large scale training data is still insufficient
for these situations. Reference images captured covering the same scenes share
similar texture and structure priors with the corrupted images, which offers
new prospects for the image inpainting tasks. Inspired by this, we first build
a benchmark dataset containing 10K pairs of input and reference images for
reference-guided inpainting. Then we adopt an encoder-decoder structure to
separately infer the texture and structure features of the input image
considering their pattern discrepancy of texture and structure during
inpainting. A feature alignment module is further designed to refine these
features of the input image with the guidance of a reference image. Both
quantitative and qualitative evaluations demonstrate the superiority of our
method over the state-of-the-art methods in terms of completing complex holes.

The capacity to learn incrementally from an online stream of data is an
envied trait of human learners, as deep neural networks typically suffer from
catastrophic forgetting and stability-plasticity dilemma. Several works have
previously explored incremental few-shot learning, a task with greater
challenges due to data constraint, mostly in classification setting with mild
success. In this work, we study the underrepresented task of generative
incremental few-shot learning. To effectively handle the inherent challenges of
incremental learning and few-shot learning, we propose a novel framework named
ConPro that leverages the two-player nature of GANs. Specifically, we design a
conservative generator that preserves past knowledge in parameter and compute
efficient manner, and a progressive discriminator that learns to reason
semantic distances between past and present task samples, minimizing
overfitting with few data points and pursuing good forward transfer. We present
experiments to validate the effectiveness of ConPro.

An important technique to explore a black-box machine learning (ML) model is
called SHAP (SHapley Additive exPlanation). SHAP values decompose predictions
into contributions of the features in a fair way. We will show that for a
boosted trees model with some or all features being additively modeled, the
SHAP dependence plot of such a feature corresponds to its partial dependence
plot up to a vertical shift. We illustrate the result with XGBoost.

A novel deep learning strategy is developed for fast and accurate gas
property measurements using flame emission spectroscopy (FES). Particularly,
the short-gated fast FES is essential to resolve fast-evolving combustion
behaviors. However, as the exposure time for capturing the flame emission
spectrum gets shorter, the signal-to-noise ratio (SNR) decreases, and
characteristic spectral features indicating the gas properties become
relatively weaker. Then, the property estimation based on the short-gated
spectrum is difficult and inaccurate. Denoising convolutional neural networks
(CNN) can enhance the SNR of the short-gated spectrum. A new CNN architecture
including a reversible down- and up-sampling (DU) operator and a loss function
based on proper orthogonal decomposition (POD) coefficients is proposed. For
training and testing the CNN, flame chemiluminescence spectra were captured
from a stable methane-air flat flame using a portable spectrometer (spectral
range: 250-850 nm, resolution: 0.5 nm) with varied equivalence ratio (0.8-1.2),
pressure (1-10 bar), and exposure time (0.05, 0.2, 0.4, and 2 s). The long
exposure (2 s) spectra were used as the ground truth when training the
denoising CNN. A kriging model with POD is trained by the long-gated spectra
for calibration and then prediction of the gas properties taking the denoised
short-gated spectrum as the input. The measurement or property prediction
errors of pressure and equivalence ratio using the new technique were estimated
to be 5.7% and 1.5% with 0.2 s exposure, which are exceptionally good and
typically not achievable with such low SNR spectrum signals without a signal
amplifier.

Very little is known about the training dynamics of adaptive gradient methods
like Adam in deep learning. In this paper, we shed light on the behavior of
these algorithms in the full-batch and sufficiently large batch settings.
Specifically, we empirically demonstrate that during full-batch training, the
maximum eigenvalue of the preconditioned Hessian typically equilibrates at a
certain numerical value -- the stability threshold of a gradient descent
algorithm. For Adam with step size $\eta$ and $\beta_1 = 0.9$, this stability
threshold is $38/\eta$. Similar effects occur during minibatch training,
especially as the batch size grows. Yet, even though adaptive methods train at
the ``Adaptive Edge of Stability'' (AEoS), their behavior in this regime
differs in a significant way from that of non-adaptive methods at the EoS.
Whereas non-adaptive algorithms at the EoS are blocked from entering
high-curvature regions of the loss landscape, adaptive gradient methods at the
AEoS can keep advancing into high-curvature regions, while adapting the
preconditioner to compensate. Our findings can serve as a foundation for the
community's future understanding of adaptive gradient methods in deep learning.

The encoder-decoder model is a commonly used Deep Neural Network (DNN) model
for medical image segmentation. Conventional encoder-decoder models make
pixel-wise predictions focusing heavily on local patterns around the pixel.
This makes it challenging to give segmentation that preserves the object's
shape and topology, which often requires an understanding of the global context
of the object. In this work, we propose a Fourier Coefficient Segmentation
Network~(FCSN) -- a novel DNN-based model that segments an object by learning
the complex Fourier coefficients of the object's masks. The Fourier
coefficients are calculated by integrating over the whole contour. Therefore,
for our model to make a precise estimation of the coefficients, the model is
motivated to incorporate the global context of the object, leading to a more
accurate segmentation of the object's shape. This global context awareness also
makes our model robust to unseen local perturbations during inference, such as
additive noise or motion blur that are prevalent in medical images. When FCSN
is compared with other state-of-the-art models (UNet+, DeepLabV3+, UNETR) on 3
medical image segmentation tasks (ISIC\_2018, RIM\_CUP, RIM\_DISC), FCSN
attains significantly lower Hausdorff scores of 19.14 (6\%), 17.42 (6\%), and
9.16 (14\%) on the 3 tasks, respectively. Moreover, FCSN is lightweight by
discarding the decoder module, which incurs significant computational overhead.
FCSN only requires 22.2M parameters, 82M and 10M fewer parameters than UNETR
and DeepLabV3+. FCSN attains inference and training speeds of 1.6ms/img and
6.3ms/img, that is 8$\times$ and 3$\times$ faster than UNet and UNETR.

Deep models trained with noisy labels are prone to over-fitting and struggle
in generalization. Most existing solutions are based on an ideal assumption
that the label noise is class-conditional, i.e., instances of the same class
share the same noise model, and are independent of features. While in practice,
the real-world noise patterns are usually more fine-grained as
instance-dependent ones, which poses a big challenge, especially in the
presence of inter-class imbalance. In this paper, we propose a two-stage clean
samples identification method to address the aforementioned challenge. First,
we employ a class-level feature clustering procedure for the early
identification of clean samples that are near the class-wise prediction
centers. Notably, we address the class imbalance problem by aggregating rare
classes according to their prediction entropy. Second, for the remaining clean
samples that are close to the ground truth class boundary (usually mixed with
the samples with instance-dependent noises), we propose a novel
consistency-based classification method that identifies them using the
consistency of two classifier heads: the higher the consistency, the larger the
probability that a sample is clean. Extensive experiments on several
challenging benchmarks demonstrate the superior performance of our method
against the state-of-the-art.

Little research has been done on Neural Machine Translation (NMT) for
Azerbaijani. In this paper, we benchmark the performance of Azerbaijani-English
NMT systems on a range of techniques and datasets. We evaluate which
segmentation techniques work best on Azerbaijani translation and benchmark the
performance of Azerbaijani NMT models across several domains of text. Our
results show that while Unigram segmentation improves NMT performance and
Azerbaijani translation models scale better with dataset quality than quantity,
cross-domain generalization remains a challenge

Automatic tumor or lesion segmentation is a crucial step in medical image
analysis for computer-aided diagnosis. Although the existing methods based on
Convolutional Neural Networks (CNNs) have achieved the state-of-the-art
performance, many challenges still remain in medical tumor segmentation. This
is because, although the human visual system can detect symmetries in 2D images
effectively, regular CNNs can only exploit translation invariance, overlooking
further inherent symmetries existing in medical images such as rotations and
reflections. To solve this problem, we propose a novel group equivariant
segmentation framework by encoding those inherent symmetries for learning more
precise representations. First, kernel-based equivariant operations are devised
on each orientation, which allows it to effectively address the gaps of
learning symmetries in existing approaches. Then, to keep segmentation networks
globally equivariant, we design distinctive group layers with layer-wise
symmetry constraints. Finally, based on our novel framework, extensive
experiments conducted on real-world clinical data demonstrate that a Group
Equivariant Res-UNet (named GER-UNet) outperforms its regular CNN-based
counterpart and the state-of-the-art segmentation methods in the tasks of
hepatic tumor segmentation, COVID-19 lung infection segmentation and retinal
vessel detection. More importantly, the newly built GER-UNet also shows
potential in reducing the sample complexity and the redundancy of filters,
upgrading current segmentation CNNs and delineating organs on other medical
imaging modalities.

Our ability to track multiple objects in a dynamic environment enables us to
perform everyday tasks such as driving, playing team sports, and walking in a
crowded mall. Despite more than three decades of literature on multiple object
tracking (MOT) tasks, the underlying and intertwined neural mechanisms remain
poorly understood. Here we looked at the electroencephalography (EEG) neural
correlates and their changes across the three phases of a 3D-MOT task, namely
identification, tracking and recall. We recorded the EEG activity of 24
participants while they were performing a 3D-MOT task with either 1, 2 or 3
targets where some trials were lateralized and some were not. We observed what
seems to be a handoff between focused attention and working memory processes
when going from tracking to recall. Our findings revealed a strong inhibition
in delta and theta frequencies from the frontal region during tracking,
followed by a strong (re)activation of these same frequencies during recall.
Our results also showed contralateral delay activity (CDA) for the lateralized
trials, in both the identification and recall phases but not during tracking.

Non-orthogonal communications are expected to play a key role in future
wireless systems. In downlink transmissions, the data symbols are broadcast
from a base station to different users, which are superimposed with different
power to facilitate high-integrity detection using successive interference
cancellation (SIC). However, SIC requires accurate knowledge of both the
channel model and channel state information (CSI), which may be difficult to
acquire. We propose a deep learningaided SIC detector termed SICNet, which
replaces the interference cancellation blocks of SIC by deep neural networks
(DNNs). Explicitly, SICNet jointly trains its internal DNN-aided blocks for
inferring the soft information representing the interfering symbols in a
data-driven fashion, rather than using hard-decision decoders as in classical
SIC. As a result, SICNet reliably detects the superimposed symbols in the
downlink of non-orthogonal systems without requiring any prior knowledge of the
channel model, while being less sensitive to CSI uncertainty than its
model-based counterpart. SICNet is also robust to changes in the number of
users and to their power allocation. Furthermore, SICNet learns to produce
accurate soft outputs, which facilitates improved soft-input error correction
decoding compared to model-based SIC. Finally, we propose an online training
method for SICNet under block fading, which exploits the channel decoding for
accurately recovering online data labels for retraining, hence, allowing it to
smoothly track the fading envelope without requiring dedicated pilots. Our
numerical results show that SICNet approaches the performance of classical SIC
under perfect CSI, while outperforming it under realistic CSI uncertainty.

Transformer structure, stacked by a sequence of encoder and decoder network
layers, achieves significant development in neural machine translation.
However, vanilla Transformer mainly exploits the top-layer representation,
assuming the lower layers provide trivial or redundant information and thus
ignoring the bottom-layer feature that is potentially valuable. In this work,
we propose the Group-Transformer model (GTrans) that flexibly divides
multi-layer representations of both encoder and decoder into different groups
and then fuses these group features to generate target words. To corroborate
the effectiveness of the proposed method, extensive experiments and analytic
experiments are conducted on three bilingual translation benchmarks and two
multilingual translation tasks, including the IWLST-14, IWLST-17, LDC, WMT-14
and OPUS-100 benchmark. Experimental and analytical results demonstrate that
our model outperforms its Transformer counterparts by a consistent gain.
Furthermore, it can be successfully scaled up to 60 encoder layers and 36
decoder layers.

Fine-grained object retrieval aims to learn discriminative representation to
retrieve visually similar objects. However, existing top-performing works
usually impose pairwise similarities on the semantic embedding spaces to
continually fine-tune the entire model in limited-data regimes, thus resulting
in easily converging to suboptimal solutions. In this paper, we develop
Fine-grained Retrieval Prompt Tuning (FRPT), which steers a frozen pre-trained
model to perform the fine-grained retrieval task from the perspectives of
sample prompt and feature adaptation. Specifically, FRPT only needs to learn
fewer parameters in the prompt and adaptation instead of fine-tuning the entire
model, thus solving the convergence to suboptimal solutions caused by
fine-tuning the entire model. Technically, as sample prompts, a structure
perturbation prompt (SPP) is introduced to zoom and even exaggerate some pixels
contributing to category prediction via a content-aware inhomogeneous sampling
operation. In this way, SPP can make the fine-grained retrieval task aided by
the perturbation prompts close to the solved task during the original
pre-training. Besides, a category-specific awareness head is proposed and
regarded as feature adaptation, which removes the species discrepancies in the
features extracted by the pre-trained model using instance normalization, and
thus makes the optimized features only include the discrepancies among
subcategories. Extensive experiments demonstrate that our FRPT with fewer
learnable parameters achieves the state-of-the-art performance on three
widely-used fine-grained datasets.

Under shared autonomy, wheelchair users expect vehicles to provide safe and
comfortable rides while following users high-level navigation plans. To find
such a path, vehicles negotiate with different terrains and assess their
traversal difficulty. Most prior works model surroundings either through
geometric representations or semantic classifications, which do not reflect
perceived motion intensity and ride comfort in downstream navigation tasks. We
propose to model ride comfort explicitly in traversability analysis using
proprioceptive sensing. We develop a self-supervised learning framework to
predict traversability costmap from first-person-view images by leveraging
vehicle states as training signals. Our approach estimates how the vehicle
would feel if traversing over based on terrain appearances. We then show our
navigation system provides human-preferred ride comfort through robot
experiments together with a human evaluation study.

Occluded person re-identification (Re-ID) aims at addressing the occlusion
problem when retrieving the person of interest across multiple cameras. With
the promotion of deep learning technology and the increasing demand for
intelligent video surveillance, the frequent occlusion in real-world
applications has made occluded person Re-ID draw considerable interest from
researchers. A large number of occluded person Re-ID methods have been proposed
while there are few surveys that focus on occlusion. To fill this gap and help
boost future research, this paper provides a systematic survey of occluded
person Re-ID. Through an in-depth analysis of the occlusion in person Re-ID,
most existing methods are found to only consider part of the problems brought
by occlusion. Therefore, we review occlusion-related person Re-ID methods from
the perspective of issues and solutions. We summarize four issues caused by
occlusion in person Re-ID, i.e., position misalignment, scale misalignment,
noisy information, and missing information. The occlusion-related methods
addressing different issues are then categorized and introduced accordingly.
After that, we summarize and compare the performance of recent occluded person
Re-ID methods on four popular datasets: Partial-ReID, Partial-iLIDS,
Occluded-ReID, and Occluded-DukeMTMC. Finally, we provide insights on promising
future research directions.

The fusion of multispectral and panchromatic images is always dubbed
pansharpening. Most of the available deep learning-based pan-sharpening methods
sharpen the multispectral images through a one-step scheme, which strongly
depends on the reconstruction ability of the network. However, remote sensing
images always have large variations, as a result, these one-step methods are
vulnerable to the error accumulation and thus incapable of preserving spatial
details as well as the spectral information. In this paper, we propose a novel
two-step model for pan-sharpening that sharpens the MS image through the
progressive compensation of the spatial and spectral information. Firstly, a
deep multiscale guided generative adversarial network is used to preliminarily
enhance the spatial resolution of the MS image. Starting from the pre-sharpened
MS image in the coarse domain, our approach then progressively refines the
spatial and spectral residuals over a couple of generative adversarial networks
(GANs) that have reverse architectures. The whole model is composed of triple
GANs, and based on the specific architecture, a joint compensation loss
function is designed to enable the triple GANs to be trained simultaneously.
Moreover, the spatial-spectral residual compensation structure proposed in this
paper can be extended to other pan-sharpening methods to further enhance their
fusion results. Extensive experiments are performed on different datasets and
the results demonstrate the effectiveness and efficiency of our proposed
method.

Relations are basic building blocks of human cognition. Classic and recent
work suggests that many relations are early developing, and quickly perceived.
Machine models that aspire to human-level perception and reasoning should
reflect the ability to recognize and reason generatively about relations. We
report a systematic empirical examination of a recent text-guided image
generation model (DALL-E 2), using a set of 15 basic physical and social
relations studied or proposed in the literature, and judgements from human
participants (N = 169). Overall, we find that only ~22% of images matched basic
relation prompts. Based on a quantitative examination of people's judgments, we
suggest that current image generation models do not yet have a grasp of even
basic relations involving simple objects and agents. We examine reasons for
model successes and failures, and suggest possible improvements based on
computations observed in biological intelligence.

Learning on big data brings success for artificial intelligence (AI), but the
annotation and training costs are expensive. In future, learning on small data
is one of the ultimate purposes of AI, which requires machines to recognize
objectives and scenarios relying on small data as humans. A series of machine
learning models is going on this way such as active learning, few-shot
learning, deep clustering. However, there are few theoretical guarantees for
their generalization performance. Moreover, most of their settings are passive,
that is, the label distribution is explicitly controlled by one specified
sampling scenario. This survey follows the agnostic active sampling under a PAC
(Probably Approximately Correct) framework to analyze the generalization error
and label complexity of learning on small data using a supervised and
unsupervised fashion. With these theoretical analyses, we categorize the small
data learning models from two geometric perspectives: the Euclidean and
non-Euclidean (hyperbolic) mean representation, where their optimization
solutions are also presented and discussed. Later, some potential learning
scenarios that may benefit from small data learning are then summarized, and
their potential learning scenarios are also analyzed. Finally, some challenging
applications such as computer vision, natural language processing that may
benefit from learning on small data are also surveyed.

Future Generation Computer Systems (FGCS), published by Elsevier, is a
prestigious peer reviewed journal that started in 1984. As on date of writing
this article, the journal is in its 137th volume. Owing to its publication
quality and continued academic standards, it has been indexed by major academic
databases such as Web of Science, Scopus, DOAJ etc. It is now ranked among the
top journals in the area of Computer Science (General). Motivated by the long
history, academic reputation and prestige of the journal, the present study
attempts to do a bibliometric and text-based analysis of publications in the
journal during the period of 1984-2020 (37 years). Bibliometric analysis helped
to identify publishing and citation patterns, authorship and collaboration
structure, funding patterns of the published research, open access and
altmetric impact, gender distribution and SDG connections etc. The
text-integrated path analysis helped in identifying the thematic structure and
major concept evolution trajectories. The results of the analysis indicate that
the FGCS journal is a high-quality research journal that has grown over time in
terms of publications, citations and rankings. It publishes articles in
specified thematic areas, with cloud, grid, IoT, Blockchain etc. found as the
major themes addressed. Knowledge extension from cloud computing to
multi-domain networks is witnessed and a divergence towards service function
chains, distributed machine learning and edge computing systems is visible. The
article thus presents very informative and useful insight about research
publication trends in FGCS.

The estimation of the treatment effect is often biased in the presence of
unobserved confounding variables which are commonly referred to as hidden
variables. Although a few methods have been recently proposed to handle the
effect of hidden variables, these methods often overlook the possibility of any
interaction between the observed treatment variable and the unobserved
covariates. In this work, we address this shortcoming by studying a
multivariate response regression problem with both unobserved and heterogeneous
confounding variables of the form $Y=A^T X+ B^T Z+ \sum_{j=1}^{p} C^T_j X_j Z +
E$, where $Y \in \mathbb{R}^m$ are $m$-dimensional response variables, $X \in
\mathbb{R}^p$ are observed covariates (including the treatment variable), $Z
\in \mathbb{R}^K$ are $K$-dimensional unobserved confounders, and $E \in
\mathbb{R}^m$ is the random noise. Allowing for the interaction between $X_j$
and $Z$ induces the heterogeneous confounding effect. Our goal is to estimate
the unknown matrix $A$, the direct effect of the observed covariates or the
treatment on the responses. To this end, we propose a new debiased estimation
approach via SVD to remove the effect of unobserved confounding variables. The
rate of convergence of the estimator is established under both the
homoscedastic and heteroscedastic noises. We also present several simulation
experiments and a real-world data application to substantiate our findings.

We study the problems of quantum tomography and shadow tomography using
measurements performed on individual, identical copies of an unknown
$d$-dimensional state. We first revisit a known lower bound due to Haah et al.
(2017) on quantum tomography with accuracy $\epsilon$ in trace distance, when
the measurements choices are independent of previously observed outcomes (i.e.,
they are nonadaptive). We give a succinct proof of this result. This leads to
stronger lower bounds when the learner uses measurements with a constant number
of outcomes. In particular, this rigorously establishes the optimality of the
folklore ``Pauli tomography" algorithm in terms of its sample complexity. We
also derive novel bounds of $\Omega(r^2 d/\epsilon^2)$ and $\Omega(r^2
d^2/\epsilon^2)$ for learning rank $r$ states using arbitrary and
constant-outcome measurements, respectively, in the nonadaptive case.
  In addition to the sample complexity, a resource of practical significance
for learning quantum states is the number of different measurements used by an
algorithm. We extend our lower bounds to the case where the learner performs
possibly adaptive measurements from a fixed set of $\exp(O(d))$ measurements.
This implies in particular that adaptivity does not give us any advantage using
single-copy measurements that are efficiently implementable. We also obtain a
similar bound in the case where the goal is to predict the expectation values
of a given sequence of observables, a task known as shadow tomography. Finally,
in the case of adaptive, single-copy measurements implementable with
polynomial-size circuits, we prove that a straightforward strategy based on
computing sample means of the given observables is optimal.

The critical point for the successes of spectral-type subspace clustering
algorithms is to seek reconstruction coefficient matrices which can faithfully
reveal the subspace structures of data sets. An ideal reconstruction
coefficient matrix should have two properties: 1) it is block diagonal with
each block indicating a subspace; 2) each block is fully connected. Though
there are various spectral-type subspace clustering algorithms have been
proposed, some defects still exist in the reconstruction coefficient matrices
constructed by these algorithms. We find that a normalized membership matrix
naturally satisfies the above two conditions. Therefore, in this paper, we
devise an idempotent representation (IDR) algorithm to pursue reconstruction
coefficient matrices approximating normalized membership matrices. IDR designs
a new idempotent constraint for reconstruction coefficient matrices. And by
combining the doubly stochastic constraints, the coefficient matrices which are
closed to normalized membership matrices could be directly achieved. We present
the optimization algorithm for solving IDR problem and analyze its computation
burden as well as convergence. The comparisons between IDR and related
algorithms show the superiority of IDR. Plentiful experiments conducted on both
synthetic and real world datasets prove that IDR is an effective and efficient
subspace clustering algorithm.

This paper investigates an open research problem of generating text-image
pairs to improve the training of fine-grained image-to-text cross-modal
retrieval task, and proposes a novel framework for paired data augmentation by
uncovering the hidden semantic information of StyleGAN2 model. Specifically, we
first train a StyleGAN2 model on the given dataset. We then project the real
images back to the latent space of StyleGAN2 to obtain the latent codes. To
make the generated images manipulatable, we further introduce a latent space
alignment module to learn the alignment between StyleGAN2 latent codes and the
corresponding textual caption features. When we do online paired data
augmentation, we first generate augmented text through random token
replacement, then pass the augmented text into the latent space alignment
module to output the latent codes, which are finally fed to StyleGAN2 to
generate the augmented images. We evaluate the efficacy of our augmented data
approach on two public cross-modal retrieval datasets, in which the promising
experimental results demonstrate the augmented text-image pair data can be
trained together with the original data to boost the image-to-text cross-modal
retrieval performance.

The past decade has amply demonstrated the remarkable functionality that can
be realized by learning complex input/output relationships. Algorithmically,
one of the most important and opaque relationships is that between a problem's
structure and an effective solution method. Here, we quantitatively connect the
structure of a planning problem to the performance of a given sampling-based
motion planning (SBMP) algorithm. We demonstrate that the geometric
relationships of motion planning problems can be well captured by graph neural
networks (GNNs) to predict SBMP runtime. By using an algorithm portfolio we
show that GNN predictions of runtime on particular problems can be leveraged to
accelerate online motion planning in both navigation and manipulation tasks.
Moreover, the problem-to-runtime map can be inverted to identify subproblems
easier to solve by particular SBMPs. We provide a motivating example of how
this knowledge may be used to improve integrated task and motion planning on
simulated examples. These successes rely on the relational structure of GNNs to
capture scalable generalization from low-dimensional navigation tasks to high
degree-of-freedom manipulation tasks in 3d environments.

Reinforcement Learning (RL) and continuous nonlinear control have been
successfully deployed in multiple domains of complicated sequential
decision-making tasks. However, given the exploration nature of the learning
process and the presence of model uncertainty, it is challenging to apply them
to safety-critical control tasks due to the lack of safety guarantee. On the
other hand, while combining control-theoretical approaches with learning
algorithms has shown promise in safe RL applications, the sample efficiency of
safe data collection process for control is not well addressed. In this paper,
we propose a \emph{provably} sample efficient episodic safe learning framework
for online control tasks that leverages safe exploration and exploitation in an
unknown, nonlinear dynamical system. In particular, the framework 1) extends
control barrier functions (CBFs) in a stochastic setting to achieve provable
high-probability safety under uncertainty during model learning and 2)
integrates an optimism-based exploration strategy to efficiently guide the safe
exploration process with learned dynamics for \emph{near optimal} control
performance. We provide formal analysis on the episodic regret bound against
the optimal controller and probabilistic safety with theoretical guarantees.
Simulation results are provided to demonstrate the effectiveness and efficiency
of the proposed algorithm.

Practical success of quantum learning models hinges on having a suitable
structure for the parameterized quantum circuit. Such structure is defined both
by the types of gates employed and by the correlations of their parameters.
While much research has been devoted to devising adequate gate-sets, typically
respecting some symmetries of the problem, very little is known about how their
parameters should be structured. In this work, we show that an ideal parameter
structure naturally emerges when carefully considering spatial symmetries
(i.e., the symmetries that are permutations of parts of the system under
study). Namely, we consider the automorphism group of the problem Hamiltonian,
leading us to develop a circuit construction that is equivariant under this
symmetry group. The benefits of our novel circuit structure, called ORB, are
numerically probed in several ground-state problems. We find a consistent
improvement (in terms of circuit depth, number of parameters required, and
gradient magnitudes) compared to literature circuit constructions.

Over the last few years, convolutional neural networks (CNNs) have dominated
the field of computer vision thanks to their ability to extract features and
their outstanding performance in classification problems, for example in the
automatic analysis of X-rays. Unfortunately, these neural networks are
considered black-box algorithms, i.e. it is impossible to understand how the
algorithm has achieved the final result. To apply these algorithms in different
fields and test how the methodology works, we need to use eXplainable AI
techniques. Most of the work in the medical field focuses on binary or
multiclass classification problems. However, in many real-life situations, such
as chest X-rays, radiological signs of different diseases can appear at the
same time. This gives rise to what is known as "multilabel classification
problems". A disadvantage of these tasks is class imbalance, i.e. different
labels do not have the same number of samples. The main contribution of this
paper is a Deep Learning methodology for imbalanced, multilabel chest X-ray
datasets. It establishes a baseline for the currently underutilised PadChest
dataset and a new eXplainable AI technique based on heatmaps. This technique
also includes probabilities and inter-model matching. The results of our system
are promising, especially considering the number of labels used. Furthermore,
the heatmaps match the expected areas, i.e. they mark the areas that an expert
would use to make the decision.

The goal of this paper is to describe a system for generating synthetic
sequential data within the Synthetic data vault. To achieve this, we present
the Sequential model currently in SDV, an end-to-end framework that builds a
generative model for multi-sequence, real-world data. This includes a novel
neural network-based machine learning model, conditional probabilistic
auto-regressive (CPAR) model. The overall system and the model is available in
the open source Synthetic Data Vault (SDV) library
{https://github.com/sdv-dev/SDV}, along with a variety of other models for
different synthetic data needs.
  After building the Sequential SDV, we used it to generate synthetic data and
compared its quality against an existing, non-sequential generative adversarial
network based model called CTGAN. To compare the sequential synthetic data
against its real counterpart, we invented a new metric called Multi-Sequence
Aggregate Similarity (MSAS). We used it to conclude that our Sequential SDV
model learns higher level patterns than non-sequential models without any
trade-offs in synthetic data quality.

Ptychography is a well-studied phase imaging method that makes non-invasive
imaging possible at a nanometer scale. It has developed into a mainstream
technique with various applications across a range of areas such as material
science or the defense industry. One major drawback of ptychography is the long
data acquisition time due to the high overlap requirement between adjacent
illumination areas to achieve a reasonable reconstruction. Traditional
approaches with reduced overlap between scanning areas result in
reconstructions with artifacts. In this paper, we propose complementing
sparsely acquired or undersampled data with data sampled from a deep generative
network to satisfy the oversampling requirement in ptychography. Because the
deep generative network is pre-trained and its output can be computed as we
collect data, the experimental data and the time to acquire the data can be
reduced. We validate the method by presenting the reconstruction quality
compared to the previously proposed and traditional approaches and comment on
the strengths and drawbacks of the proposed approach.

We consider the problem where M agents collaboratively interact with an
instance of a stochastic K-armed contextual bandit, where K>>M. The goal of the
agents is to simultaneously minimize the cumulative regret over all the agents
over a time horizon T. We consider a setting where the exact context is
observed after a delay and at the time of choosing the action the agents are
unaware of the context and only a distribution on the set of contexts is
available. Such a situation arises in different applications where at the time
of the decision the context needs to be predicted (e.g., weather forecasting or
stock market prediction), and the context can be estimated once the reward is
obtained. We propose an Upper Confidence Bound (UCB)-based distributed
algorithm and prove the regret and communications bounds for linearly
parametrized reward functions. We validated the performance of our algorithm
via numerical simulations on synthetic data and real-world Movielens data.

Data-driven reduced-order models often fail to make accurate forecasts of
high-dimensional nonlinear systems that are sensitive along coordinates with
low-variance because such coordinates are often truncated, e.g., by proper
orthogonal decomposition, kernel principal component analysis, and
autoencoders. Such systems are encountered frequently in shear-dominated fluid
flows where non-normality plays a significant role in the growth of
disturbances. In order to address these issues, we employ ideas from active
subspaces to find low-dimensional systems of coordinates for model reduction
that balance adjoint-based information about the system's sensitivity with the
variance of states along trajectories. The resulting method, which we refer to
as covariance balancing reduction using adjoint snapshots (CoBRAS), is
analogous to balanced truncation with state and adjoint-based gradient
covariance matrices replacing the system Gramians and obeying the same key
transformation laws. Here, the extracted coordinates are associated with an
oblique projection that can be used to construct Petrov-Galerkin reduced-order
models. We provide an efficient snapshot-based computational method analogous
to balanced proper orthogonal decomposition. This also leads to the observation
that the reduced coordinates can be computed relying on inner products of state
and gradient samples alone, allowing us to find rich nonlinear coordinates by
replacing the inner product with a kernel function. In these coordinates,
reduced-order models can be learned using regression. We demonstrate these
techniques and compare to a variety of other methods on a simple, yet
challenging three-dimensional system and an axisymmetric jet flow simulation
with $10^5$ state variables.

Large Language Models (LLMs) have been transformative. They are pre-trained
foundational models that are self-supervised and can be adapted with fine
tuning to a wide range of natural language tasks, each of which previously
would have required a separate network model. This is one step closer to the
extraordinary versatility of human language. GPT-3 and more recently LaMDA can
carry on dialogs with humans on many topics after minimal priming with a few
examples. However, there has been a wide range of reactions and debate on
whether these LLMs understand what they are saying or exhibit signs of
intelligence. This high variance is exhibited in three interviews with LLMs
reaching wildly different conclusions. A new possibility was uncovered that
could explain this divergence. What appears to be intelligence in LLMs may in
fact be a mirror that reflects the intelligence of the interviewer, a
remarkable twist that could be considered a Reverse Turing Test. If so, then by
studying interviews we may be learning more about the intelligence and beliefs
of the interviewer than the intelligence of the LLMs. As LLMs become more
capable they may transform the way we interact with machines and how they
interact with each other. Increasingly, LLMs are being coupled with
sensorimotor devices. LLMs can talk the talk, but can they walk the walk? A
road map for achieving artificial general autonomy is outlined with seven major
improvements inspired by brain systems. LLMs could be used to uncover new
insights into brain function by downloading brain data during natural

In computer vision, fine-tuning is the de-facto approach to leverage
pre-trained vision models to perform downstream tasks. However, deploying it in
practice is quite challenging, due to adopting parameter inefficient global
update and heavily relying on high-quality downstream data. Recently,
prompt-based learning, which adds a task-relevant prompt to adapt the
downstream tasks to pre-trained models, has drastically boosted the performance
of many natural language downstream tasks. In this work, we extend this notable
transfer ability benefited from prompt into vision models as an alternative to
fine-tuning. To this end, we propose parameter-efficient Prompt tuning
(Pro-tuning) to adapt frozen vision models to various downstream vision tasks.
The key to Pro-tuning is prompt-based tuning, i.e., learning task-specific
vision prompts for downstream input images with the pre-trained model frozen.
By only training a few additional parameters, it can work on diverse CNN-based
and Transformer-based architectures. Extensive experiments evidence that
Pro-tuning outperforms fine-tuning in a broad range of vision tasks and
scenarios, including image classification (generic objects, class imbalance,
image corruption, adversarial robustness, and out-of-distribution
generalization), and dense prediction tasks such as object detection and
semantic segmentation.

Creating artificial intelligence (AI) systems capable of demonstrating
lifelong learning is a fundamental challenge, and many approaches and metrics
have been proposed to analyze algorithmic properties. However, for existing
lifelong learning metrics, algorithmic contributions are confounded by task and
scenario structure. To mitigate this issue, we introduce an algorithm-agnostic
explainable surrogate-modeling approach to estimate latent properties of
lifelong learning algorithms. We validate the approach for estimating these
properties via experiments on synthetic data. To validate the structure of the
surrogate model, we analyze real performance data from a collection of popular
lifelong learning approaches and baselines adapted for lifelong classification
and lifelong reinforcement learning.

Recent work on dissimilarity-based hierarchical clustering has led to the
introduction of global objective functions for this classical problem. Several
standard approaches, such as average linkage, as well as some new heuristics
have been shown to provide approximation guarantees. Here we introduce a broad
new class of objective functions which satisfy desirable properties studied in
prior work. Many common agglomerative and divisive clustering methods are shown
to be greedy algorithms for these objectives, which are inspired by related
concepts in phylogenetics.

We explore techniques for eye gaze estimation using machine learning. Eye
gaze estimation is a common problem for various behavior analysis and
human-computer interfaces. The purpose of this work is to discuss various model
types for eye gaze estimation and present the results from predicting gaze
direction using eye landmarks in unconstrained settings. In unconstrained
real-world settings, feature-based and model-based methods are outperformed by
recent appearance-based methods due to factors like illumination changes and
other visual artifacts. We discuss a learning-based method for eye region
landmark localization trained exclusively on synthetic data. We discuss how to
use detected landmarks as input to iterative model-fitting and lightweight
learning-based gaze estimation methods and how to use the model for
person-independent and personalized gaze estimations.

The Gini index does not give a strictly consistent scoring rule in general.
Therefore, maximizing the Gini index may lead to wrong decisions. The main
issue is that the Gini index is a rank-based score that is not
calibration-sensitive. We show that the Gini index allows for strictly
consistent scoring if we restrict to the class of auto-calibrated regression
models.

We present the first method that automatically transfers poses between
stylized 3D characters without skeletal rigging. In contrast to previous
attempts to learn pose transformations on fixed or topology-equivalent skeleton
templates, our method focuses on a novel scenario to handle skeleton-free
characters with diverse shapes, topologies, and mesh connectivities. The key
idea of our method is to represent the characters in a unified articulation
model so that the pose can be transferred through the correspondent parts. To
achieve this, we propose a novel pose transfer network that predicts the
character skinning weights and deformation transformations jointly to
articulate the target character to match the desired pose. Our method is
trained in a semi-supervised manner absorbing all existing character data with
paired/unpaired poses and stylized shapes. It generalizes well to unseen
stylized characters and inanimate objects. We conduct extensive experiments and
demonstrate the effectiveness of our method on this novel task.

The placement of art in public spaces can have a significant impact on who
feels a sense of belonging. In cities, public art communicates whose interests
and culture are being favored. In this paper, we propose a graph matching
approach with local constraints to build a curatorial tool for selecting public
art in a way that supports inclusive spaces. We develop a cost matrix by
drawing on Schelling's model of segregation. Using the cost matrix as an input,
the optimization problem is solved via projected gradient descent to obtain a
soft assignment matrix. We discuss regularization terms to set curatorial
constraints. Our optimization program allocates artwork to public spaces and
walls in a way that de-prioritizes "in-group" preferences, by satisfying
minimum representation and exposure criteria. We draw on existing literature to
develop a fairness metric for our algorithmic output. Using Tufts University as
a testbed, we assess the effectiveness of our approach and discuss its
potential pitfalls from both a curatorial and equity standpoint.

The evolution of dynamical systems is generically governed by nonlinear
partial differential equations (PDEs), whose solution, in a simulation
framework, requires vast amounts of computational resources. In this work, we
present a novel method that combines a hyper-network solver with a Fourier
Neural Operator architecture. Our method treats time and space separately. As a
result, it successfully propagates initial conditions in continuous time steps
by employing the general composition properties of the partial differential
operators. Following previous work, supervision is provided at a specific time
point. We test our method on various time evolution PDEs, including nonlinear
fluid flows in one, two, and three spatial dimensions. The results show that
the new method improves the learning accuracy at the time point of supervision
point, and is able to interpolate and the solutions to any intermediate time.

The rapid spread of COVID-19 disease has had a significant impact on the
world. In this paper, we study COVID-19 data interpretation and visualization
using open-data sources for 351 cities and towns in Massachusetts from December
6, 2020 to September 25, 2021. Because cities are embedded in rather complex
transportation networks, we construct the spatio-temporal dynamic graph model,
in which the graph attention neural network is utilized as a deep learning
method to learn the pandemic transition probability among major cities in
Massachusetts. Using the spectral graph wavelet transform (SGWT), we process
the COVID-19 data on the dynamic graph, which enables us to design effective
tools to analyze and detect spatio-temporal patterns in the pandemic spreading.
We design a new node classification method, which effectively identifies the
anomaly cities based on spectral graph wavelet coefficients. It can assist
administrations or public health organizations in monitoring the spread of the
pandemic and developing preventive measures. Unlike most work focusing on the
evolution of confirmed cases over time, we focus on the spatio-temporal
patterns of pandemic evolution among cities. Through the data analysis and
visualization, a better understanding of the epidemiological development at the
city level is obtained and can be helpful with city-specific surveillance.

Complex prediction models such as deep learning are the output from fitting
machine learning, neural networks, or AI models to a set of training data.
These are now standard tools in science. A key challenge with the current
generation of models is that they are highly parameterized, which makes
describing and interpreting the prediction strategies difficult. We use
topological data analysis to transform these complex prediction models into
pictures representing a topological view. The result is a map of the
predictions that enables inspection. The methods scale up to large datasets
across different domains and enable us to detect labeling errors in training
data, understand generalization in image classification, and inspect
predictions of likely pathogenic mutations in the BRCA1 gene.

We propose a fairness measure relaxing the equality conditions in the popular
equal odds fairness regime for classification. We design an iterative,
model-agnostic, grid-based heuristic that calibrates the outcomes per sensitive
attribute value to conform to the measure. The heuristic is designed to handle
high arity attribute values and performs a per attribute sanitization of
outcomes across different protected attribute values. We also extend our
heuristic for multiple attributes. Highlighting our motivating application,
fraud detection, we show that the proposed heuristic is able to achieve
fairness across multiple values of a single protected attribute, multiple
protected attributes. When compared to current fairness techniques, that focus
on two groups, we achieve comparable performance across several public data
sets.

In the growing field of virtual auditory display, personalized head-related
transfer functions (HRTFs) play a vital role in establishing an accurate sound
image. In this work, we propose an HRTF personalization method employing
convolutional neural networks (CNN) to predict a subject's HRTFs for all
directions from their scanned head geometry. To ease the training of the CNN
models, we propose novel pre-processing methods for both the head scans and
HRTF data to achieve compact representations. For the head scan, we use
truncated spherical cap harmonic (SCH) coefficients to represent the pinna
area, which is important in the acoustic scattering process. For the HRTF data,
we use truncated spherical harmonic (SH) coefficients to represent the HRTF
magnitudes and onsets. One CNN model is trained to predict the SH coefficients
of the HRTF magnitudes from the SCH coefficients of the scanned ear geometry
and other anthropometric measurements of the head. The other CNN model is
trained to predict SH coefficients of the HRTF onsets from only the
anthropometric measurements of the ear, head, and torso. Combining the
magnitude and onset predictions, our method is able to predict the complete and
global HRTF data. A leave-one-out validation with the log-spectral distortion
(LSD) metric is used for objective evaluation. The results show a decent LSD
level at both spatial \& temporal dimensions compared to the ground-truth HRTFs
and a lower LSD than the boundary element method (BEM) simulation of HRTFs that
the database provides. The localization simulation results with an auditory
model are also consistent with the objective evaluation metrics, showing the
localization responses with our predicted HRTFs are significantly better than
with the BEM calculated ones.

A fundamental approach in neuroscience research is to test hypotheses based
on neuropsychological and behavioral measures, i.e., whether certain factors
(e.g., related to life events) are associated with an outcome (e.g.,
depression). In recent years, deep learning has become a potential alternative
approach for conducting such analyses by predicting an outcome from a
collection of factors and identifying the most "informative" ones driving the
prediction. However, this approach has had limited impact as its findings are
not linked to statistical significance of factors supporting hypotheses. In
this article, we proposed a flexible and scalable approach based on the concept
of permutation testing that integrates hypothesis testing into the data-driven
deep learning analysis. We apply our approach to the yearly self-reported
assessments of 621 adolescent participants of the National Consortium of
Alcohol and Neurodevelopment in Adolescence (NCANDA) to predict negative
valence, a symptom of major depressive disorder according to the NIMH Research
Domain Criteria (RDoC). Our method successfully identifies categories of risk
factors that further explain the symptom.

We share our recent findings in an attempt to train a universal segmentation
network for various cell types and imaging modalities. Our method was built on
the generalized U-Net architecture, which allows the evaluation of each
component individually. We modified the traditional binary training targets to
include three classes for direct instance segmentation. Detailed experiments
were performed regarding training schemes, training settings, network
backbones, and individual modules on the segmentation performance. Our proposed
training scheme draws minibatches in turn from each dataset, and the gradients
are accumulated before an optimization step. We found that the key to training
a universal network is all-time supervision on all datasets, and it is
necessary to sample each dataset in an unbiased way. Our experiments also
suggest that there might exist common features to define cell boundaries across
cell types and imaging modalities, which could allow application of trained
models to totally unseen datasets. A few training tricks can further boost the
segmentation performance, including uneven class weights in the cross-entropy
loss function, well-designed learning rate scheduler, larger image crops for
contextual information, and additional loss terms for unbalanced classes. We
also found that segmentation performance can benefit from group normalization
layer and Atrous Spatial Pyramid Pooling module, thanks to their more reliable
statistics estimation and improved semantic understanding, respectively. We
participated in the 6th Cell Tracking Challenge (CTC) held at IEEE
International Symposium on Biomedical Imaging (ISBI) 2021 using one of the
developed variants. Our method was evaluated as the best runner up during the
initial submission for the primary track, and also secured the 3rd place in an
additional round of competition in preparation for the summary publication.

Graph Neural Networks (GNNs) have been shown as promising solutions for
collaborative filtering (CF) with the modeling of user-item interaction graphs.
The key idea of existing GNN-based recommender systems is to recursively
perform the message passing along the user-item interaction edge for refining
the encoded embeddings. Despite their effectiveness, however, most of the
current recommendation models rely on sufficient and high-quality training
data, such that the learned representations can well capture accurate user
preference. User behavior data in many practical recommendation scenarios is
often noisy and exhibits skewed distribution, which may result in suboptimal
representation performance in GNN-based models. In this paper, we propose SHT,
a novel Self-Supervised Hypergraph Transformer framework (SHT) which augments
user representations by exploring the global collaborative relationships in an
explicit way. Specifically, we first empower the graph neural CF paradigm to
maintain global collaborative effects among users and items with a hypergraph
transformer network. With the distilled global context, a cross-view generative
self-supervised learning component is proposed for data augmentation over the
user-item interaction graph, so as to enhance the robustness of recommender
systems. Extensive experiments demonstrate that SHT can significantly improve
the performance over various state-of-the-art baselines. Further ablation
studies show the superior representation ability of our SHT recommendation
framework in alleviating the data sparsity and noise issues. The source code
and evaluation datasets are available at: https://github.com/akaxlh/SHT.

In this paper, we propose the Quantum Data Center (QDC), an architecture
combining Quantum Random Access Memory (QRAM) and quantum networks. We give a
precise definition of QDC, and discuss its possible realizations and
extensions. We discuss applications of QDC in quantum computation, quantum
communication, and quantum sensing, with a primary focus on QDC for $T$-gate
resources, QDC for multi-party private quantum communication, and QDC for
distributed sensing through data compression. We show that QDC will provide
efficient, private, and fast services as a future version of data centers.

The eROSITA X-ray telescope, launched in 2019, is predicted to observe
roughly 100,000 galaxy clusters. Follow-up observations of these clusters from
Chandra, for example, will be needed to resolve outstanding questions about
galaxy cluster physics. Deep Chandra cluster observations are expensive and
follow-up of every eROSITA cluster is infeasible, therefore, objects chosen for
follow-up must be chosen with care. To address this, we have developed an
algorithm for predicting longer duration, background-free observations based on
mock eROSITA observations. We make use of the hydrodynamic cosmological
simulation Magneticum, have simulated eROSITA instrument conditions using
SIXTE, and have applied a novel convolutional neural network to output a deep
Chandra-like "super observation" of each cluster in our simulation sample. Any
follow-up merit assessment tool should be designed with a specific use case in
mind; our model produces observations that accurately and precisely reproduce
the cluster morphology, which is a critical ingredient for determining cluster
dynamical state and core type. Our model will advance our understanding of
galaxy clusters by improving follow-up selection and demonstrates that
image-to-image deep learning algorithms are a viable method for simulating
realistic follow-up observations.

Recurrent neural networks (RNNs) are a class of neural networks that have
emerged from the paradigm of artificial intelligence and has enabled lots of
interesting advances in the field of natural language processing.
Interestingly, these architectures were shown to be powerful ansatze to
approximate the ground state of quantum systems. Here, we build over the
results of [Phys. Rev. Research 2, 023358 (2020)] and construct a more powerful
RNN wave function ansatz in two dimensions. We use symmetry and annealing to
obtain accurate estimates of ground state energies of the two-dimensional (2D)
Heisenberg model, on the square lattice and on the triangular lattice. We show
that our method is superior to Density Matrix Renormalisation Group (DMRG) for
system sizes larger than or equal to $14 \times 14$ on the triangular lattice.

Visual anomaly detection is commonly used in industrial quality inspection.
In this paper, we present a new dataset as well as a new self-supervised
learning method for ImageNet pre-training to improve anomaly detection and
segmentation in 1-class and 2-class 5/10/high-shot training setups. We release
the Visual Anomaly (VisA) Dataset consisting of 10,821 high-resolution color
images (9,621 normal and 1,200 anomalous samples) covering 12 objects in 3
domains, making it the largest industrial anomaly detection dataset to date.
Both image and pixel-level labels are provided. We also propose a new
self-supervised framework - SPot-the-difference (SPD) - which can regularize
contrastive self-supervised pre-training, such as SimSiam, MoCo and SimCLR, to
be more suitable for anomaly detection tasks. Our experiments on VisA and
MVTec-AD dataset show that SPD consistently improves these contrastive
pre-training baselines and even the supervised pre-training. For example, SPD
improves Area Under the Precision-Recall curve (AU-PR) for anomaly segmentation
by 5.9% and 6.8% over SimSiam and supervised pre-training respectively in the
2-class high-shot regime. We open-source the project at
http://github.com/amazon-research/spot-diff .

Deep generative models make visual content creation more accessible to novice
users by automating the synthesis of diverse, realistic content based on a
collected dataset. However, the current machine learning approaches miss a key
element of the creative process -- the ability to synthesize things that go far
beyond the data distribution and everyday experience. To begin to address this
issue, we enable a user to "warp" a given model by editing just a handful of
original model outputs with desired geometric changes. Our method applies a
low-rank update to a single model layer to reconstruct edited examples.
Furthermore, to combat overfitting, we propose a latent space augmentation
method based on style-mixing. Our method allows a user to create a model that
synthesizes endless objects with defined geometric changes, enabling the
creation of a new generative model without the burden of curating a large-scale
dataset. We also demonstrate that edited models can be composed to achieve
aggregated effects, and we present an interactive interface to enable users to
create new models through composition. Empirical measurements on multiple test
cases suggest the advantage of our method against recent GAN fine-tuning
methods. Finally, we showcase several applications using the edited models,
including latent space interpolation and image editing.

Modern 3D computer vision leverages learning to boost geometric reasoning,
mapping image data to classical structures such as cost volumes or epipolar
constraints to improve matching. These architectures are specialized according
to the particular problem, and thus require significant task-specific tuning,
often leading to poor domain generalization performance. Recently, generalist
Transformer architectures have achieved impressive results in tasks such as
optical flow and depth estimation by encoding geometric priors as inputs rather
than as enforced constraints. In this paper, we extend this idea and propose to
learn an implicit, multi-view consistent scene representation, introducing a
series of 3D data augmentation techniques as a geometric inductive prior to
increase view diversity. We also show that introducing view synthesis as an
auxiliary task further improves depth estimation. Our Depth Field Networks
(DeFiNe) achieve state-of-the-art results in stereo and video depth estimation
without explicit geometric constraints, and improve on zero-shot domain
generalization by a wide margin.

We present Curve Distillation, CuDi, for efficient and controllable exposure
adjustment without the requirement of paired or unpaired data during training.
Our method inherits the zero-reference learning and curve-based framework from
an effective low-light image enhancement method, Zero-DCE, with further speed
up in its inference speed, reduction in its model size, and extension to
controllable exposure adjustment. The improved inference speed and lightweight
model are achieved through novel curve distillation that approximates the
time-consuming iterative operation in the conventional curve-based framework by
high-order curve's tangent line. The controllable exposure adjustment is made
possible with a new self-supervised spatial exposure control loss that
constrains the exposure levels of different spatial regions of the output to be
close to the brightness distribution of an exposure map serving as an input
condition. Different from most existing methods that can only correct either
underexposed or overexposed photos, our approach corrects both underexposed and
overexposed photos with a single model. Notably, our approach can additionally
adjust the exposure levels of a photo globally or locally with the guidance of
an input condition exposure map, which can be pre-defined or manually set in
the inference stage. Through extensive experiments, we show that our method is
appealing for its fast, robust, and flexible performance, outperforming
state-of-the-art methods in real scenes. Project page:
https://li-chongyi.github.io/CuDi_files/.

Research on Inverse Reinforcement Learning (IRL) from third-person videos has
shown encouraging results on removing the need for manual reward design for
robotic tasks. However, most prior works are still limited by training from a
relatively restricted domain of videos. In this paper, we argue that the true
potential of third-person IRL lies in increasing the diversity of videos for
better scaling. To learn a reward function from diverse videos, we propose to
perform graph abstraction on the videos followed by temporal matching in the
graph space to measure the task progress. Our insight is that a task can be
described by entity interactions that form a graph, and this graph abstraction
can help remove irrelevant information such as textures, resulting in more
robust reward functions. We evaluate our approach, GraphIRL, on
cross-embodiment learning in X-MAGICAL and learning from human demonstrations
for real-robot manipulation. We show significant improvements in robustness to
diverse video demonstrations over previous approaches, and even achieve better
results than manual reward design on a real robot pushing task. Videos are
available at https://sateeshkumar21.github.io/GraphIRL .

We study the complexity of PAC learning halfspaces in the presence of Massart
noise. In this problem, we are given i.i.d. labeled examples $(\mathbf{x}, y)
\in \mathbb{R}^N \times \{ \pm 1\}$, where the distribution of $\mathbf{x}$ is
arbitrary and the label $y$ is a Massart corruption of $f(\mathbf{x})$, for an
unknown halfspace $f: \mathbb{R}^N \to \{ \pm 1\}$, with flipping probability
$\eta(\mathbf{x}) \leq \eta < 1/2$. The goal of the learner is to compute a
hypothesis with small 0-1 error. Our main result is the first computational
hardness result for this learning problem. Specifically, assuming the (widely
believed) subexponential-time hardness of the Learning with Errors (LWE)
problem, we show that no polynomial-time Massart halfspace learner can achieve
error better than $\Omega(\eta)$, even if the optimal 0-1 error is small,
namely $\mathrm{OPT} = 2^{-\log^{c} (N)}$ for any universal constant $c \in (0,
1)$. Prior work had provided qualitatively similar evidence of hardness in the
Statistical Query model. Our computational hardness result essentially resolves
the polynomial PAC learnability of Massart halfspaces, by showing that known
efficient learning algorithms for the problem are nearly best possible.

We show convergence of the gradients of the Schr\"odinger potentials to the
Brenier map in the small-time limit under general assumptions on the marginals,
which allow for unbounded densities and supports. Furthermore, we provide novel
quantitative stability estimates for the optimal values and optimal couplings
for the Schr\"odinger problem (SP), that we express in terms of a negative
order weighted homogeneous Sobolev norm. The latter encodes the linearized
behavior of the 2-Wasserstein distance between the marginals. The proofs of
both results highlight for the first time the relevance of gradient bounds for
Schr\"odinger potentials, that we establish here in full generality, in the
analysis of the short-time behavior of Schr\"odinger bridges. Finally, we
discuss how our results translate into the framework of quadratic Entropic
Optimal Transport, that is a version of SP more suitable for applications in
machine learning and data science.

Perceptual hashes map images with identical semantic content to the same
$n$-bit hash value, while mapping semantically-different images to different
hashes. These algorithms carry important applications in cybersecurity such as
copyright infringement detection, content fingerprinting, and surveillance.
Apple's NeuralHash is one such system that aims to detect the presence of
illegal content on users' devices without compromising consumer privacy. We
make the surprising discovery that NeuralHash is approximately linear, which
inspires the development of novel black-box attacks that can (i) evade
detection of "illegal" images, (ii) generate near-collisions, and (iii) leak
information about hashed images, all without access to model parameters. These
vulnerabilities pose serious threats to NeuralHash's security goals; to address
them, we propose a simple fix using classical cryptographic standards.

We show that autoregressive language models can learn to infill text after we
apply a straightforward transformation to the dataset, which simply moves a
span of text from the middle of a document to its end. While this data
augmentation has garnered much interest in recent years, we provide extensive
evidence that training models with a large fraction of data transformed in this
way does not harm the original left-to-right generative capability, as measured
by perplexity and sampling evaluations across a wide range of scales. Given the
usefulness, simplicity, and efficiency of training models to fill-in-the-middle
(FIM), we suggest that future autoregressive language models be trained with
FIM by default. To this end, we run a series of ablations on key
hyperparameters, such as the data transformation frequency, the structure of
the transformation, and the method of selecting the infill span. We use these
ablations to prescribe strong default settings and best practices to train FIM
models. We have released our best infilling model trained with best practices
in our API, and release our infilling benchmarks to aid future research.

Robust controllers ensure stability in feedback loops designed under
uncertainty but at the cost of performance. Model uncertainty in time-invariant
systems can be reduced by recently proposed learning-based methods, which
improve the performance of robust controllers using data. However, in practice,
many systems also exhibit uncertainty in the form of changes over time, e.g.,
due to weight shifts or wear and tear, leading to decreased performance or
instability of the learning-based controller. We propose an event-triggered
learning algorithm that decides when to learn in the face of uncertainty in the
LQR problem with rare or slow changes. Our key idea is to switch between robust
and learned controllers. For learning, we first approximate the optimal length
of the learning phase via Monte-Carlo estimations using a probabilistic model.
We then design a statistical test for uncertain systems based on the
moment-generating function of the LQR cost. The test detects changes in the
system under control and triggers re-learning when control performance
deteriorates due to system changes. We demonstrate improved performance over a
robust controller baseline in a numerical example.

Person reidentification (re-ID) has been receiving increasing attention in
recent years due to its importance for both science and society. Machine
learning and particularly Deep Learning (DL) has become the main re-id tool
that allowed researches to achieve unprecedented accuracy levels on benchmark
datasets. However, there is a known problem of poor generalization of DL
models. That is, models trained to achieve high accuracy on one dataset perform
poorly on other ones and require re-training. To address this issue, we present
a model without trainable parameters which shows great potential for high
generalization. It combines a fully analytical feature extraction and
similarity ranking scheme with DL-based human parsing used to obtain the
initial subregion classification. We show that such combination to a high
extent eliminates the drawbacks of existing analytical methods. We use
interpretable color and texture features which have human-readable similarity
measures associated with them. To verify the proposed method we conduct
experiments on Market1501 and CUHK03 datasets achieving competitive rank-1
accuracy comparable with that of DL-models. Most importantly we show that our
method achieves 63.9% and 93.5% rank-1 cross-domain accuracy when applied to
transfer learning tasks. It is significantly higher than previously reported
30-50% transfer accuracy. We discuss the potential ways of adding new features
to further improve the model. We also show the advantage of interpretable
features for constructing human-generated queries from verbal description to
conduct search without a query image.

Cryogenic characterization of transition-edge sensor (TES) bolometers is a
time- and labor-intensive process. As new experiments deploy larger and larger
arrays of TES bolometers, the testing process will become more of a bottleneck.
Thus it is desirable to develop a method for evaluating detector performance at
room temperature. One possibility is using machine learning to correlate
detectors' visual appearance with their cryogenic properties. Here, we use
three engineering-grade TES bolometer wafers from the production cycle for
SPT-3G, the current receiver on the South Pole Telescope, to train and test
such an algorithm. High-resolution images of these TES bolometers were captured
and relevant features were calculated from the images. Cryogenic performance
metrics, including a detector's ability to tune and superconducting parameters
such as normal resistance, critical temperature, and transition width, were
also measured. A random forest algorithm was trained to predict these
performance metrics from the visual features. Analysis of the images proved
highly successful. While the ability of the random forest algorithm to predict
cryogenic features was limited with the chosen set of input image features, it
is possible that an increase in data volume or the addition of more image
features will solve this problem.

The LIDC-IDRI database is the most popular benchmark for lung cancer
prediction. However, with subjective assessment from radiologists, nodules in
LIDC may have entirely different malignancy annotations from the pathological
ground truth, introducing label assignment errors and subsequent supervision
bias during training. The LIDC database thus requires more objective labels for
learning-based cancer prediction. Based on an extra small dataset containing
180 nodules diagnosed by pathological examination, we propose to re-label LIDC
data to mitigate the effect of original annotation bias verified on this robust
benchmark. We demonstrate in this paper that providing new labels by similar
nodule retrieval based on metric learning would be an effective re-labeling
strategy. Training on these re-labeled LIDC nodules leads to improved model
performance, which is enhanced when new labels of uncertain nodules are added.
We further infer that re-labeling LIDC is current an expedient way for robust
lung cancer prediction while building a large pathological-proven nodule
database provides the long-term solution.

Quantum-accurate computer simulations play a central role in understanding
phase-change materials (PCMs) for advanced memory technologies. However, direct
quantum-mechanical simulations are necessarily limited to simplified models,
containing no more than a few hundred or a thousand atoms. Machine learning
(ML) based potential models that are "trained" on quantum-mechanical data are
an emerging alternative approach, currently evolving from highly specialised to
more widely applied simulation tools. Here we show that a universal,
compositionally flexible ML model can describe a wide range of flagship
Ge-Sb-Te PCMs under real device conditions, including non-isothermal heating
and chemical disorder which are relevant for memory applications. The speed of
the ML model enables atomistic simulations of multiple thermal cycles and
delicate operations for neuro-inspired computing, namely, cumulative SET and
iterative RESET. A device-scale capability demonstration (40 x 20 x 20 nm3)
shows that the new ML potential can directly describe technologically relevant
processes in PCM-based memory products. In a wider context, our work
demonstrates how ML-driven materials simulations are now entering a stage where
they can guide architecture design for high-performance electronic devices.

In this paper, we present a novel protocol of annotation and evaluation for
visual recognition. Different from traditional settings, the protocol does not
require the labeler/algorithm to annotate/recognize all targets (objects,
parts, etc.) at once, but instead raises a number of recognition instructions
and the algorithm recognizes targets by request. This mechanism brings two
beneficial properties to reduce the burden of annotation, namely, (i) variable
granularity: different scenarios can have different levels of annotation, in
particular, object parts can be labeled only in large and clear instances, (ii)
being open-domain: new concepts can be added to the database in minimal costs.
To deal with the proposed setting, we maintain a knowledge base and design a
query-based visual recognition framework that constructs queries on-the-fly
based on the requests. We evaluate the recognition system on two
mixed-annotated datasets, CPP and ADE20K, and demonstrate its promising ability
of learning from partially labeled data as well as adapting to new concepts
with only text labels.

The participation of consumers and producers in demand response programs has
increased in smart grids, which reduces investment and operation costs of power
systems. Also, with the advent of renewable energy sources, the electricity
market is becoming more complex and unpredictable. To effectively implement
demand response programs, forecasting the future price of electricity is very
crucial for producers in the electricity market. Electricity prices are very
volatile and change under the influence of various factors such as temperature,
wind speed, rainfall, intensity of commercial and daily activities, etc.
Therefore, considering the influencing factors as dependent variables can
increase the accuracy of the forecast. In this paper, a model for electricity
price forecasting is presented based on Gated Recurrent Units. The electrical
load consumption is considered as an input variable in this model. Noise in
electricity price seriously reduces the efficiency and effectiveness of
analysis. Therefore, an adaptive noise reducer is integrated into the model for
noise reduction. The SAEs are then used to extract features from the de-noised
electricity price. Finally, the de-noised features are fed into the GRU to
train predictor. Results on real dataset shows that the proposed methodology
can perform effectively in prediction of electricity price.

The exponential growth of machine learning (ML) has prompted a great deal of
interest in quantifying the uncertainty of each prediction for a user-defined
level of confidence since nowadays ML is increasingly being used in high-stakes
settings. Reliable ML via prediction intervals (PIs) that take into account
jointly the epistemic and aleatory uncertainty is therefore imperative and is a
step towards increased trust in model forecasts. Conformal prediction (CP) is a
lightweight distribution-free uncertainty quantification framework that works
for any black-box model, yielding PIs that are valid under the mild assumption
of exchangeability. CP-type methods are gaining popularity due to being easy to
implement and computationally cheap; however, the exchangeability assumption
immediately excludes time series forecasting from the stage. Although recent
papers tackle distribution shift and asymptotic versions of CP, this is not
enough for the general time series forecasting problem of producing H-step
ahead valid PIs. To attain such a goal, we propose a new method called
AEnbMIMOCQR (Adaptive ensemble batch multi-input multi-output conformalized
quantile regression), which produces valid PIs asymptotically and is
appropriate for heteroscedastic time series. We compare the proposed method
against state-of-the-art competitive methods in the NN5 forecasting competition
dataset. All the code and data to reproduce the experiments are made available.

This paper studies user attributes in light of current concerns in the
recommender system community: diversity, coverage, calibration, and data
minimization. In experiments with a conventional context-aware recommender
system that leverages side information, we show that user attributes do not
always improve recommendation. Then, we demonstrate that user attributes can
negatively impact diversity and coverage. Finally, we investigate the amount of
information about users that ``survives'' from the training data into the
recommendation lists produced by the recommender. This information is a weak
signal that could in the future be exploited for calibration or studied further
as a privacy leak.

An abundance of recent impossibility results establish that regret
minimization in Markov games with adversarial opponents is both statistically
and computationally intractable. Nevertheless, none of these results preclude
the possibility of regret minimization under the assumption that all parties
adopt the same learning procedure. In this work, we present the first (to our
knowledge) algorithm for learning in general-sum Markov games that provides
sublinear regret guarantees when executed by all agents. The bounds we obtain
are for swap regret, and thus, along the way, imply convergence to a correlated
equilibrium. Our algorithm is decentralized, computationally efficient, and
does not require any communication between agents. Our key observation is that
online learning via policy optimization in Markov games essentially reduces to
a form of weighted regret minimization, with unknown weights determined by the
path length of the agents' policy sequence. Consequently, controlling the path
length leads to weighted regret objectives for which sufficiently adaptive
algorithms provide sublinear regret guarantees.

Exemplar-free Class-incremental Learning (CIL) is a challenging problem
because rehearsing data from previous phases is strictly prohibited, causing
catastrophic forgetting of Deep Neural Networks (DNNs). In this paper, we
present iVoro, a holistic framework for CIL, derived from computational
geometry. We found Voronoi Diagram (VD), a classical model for space
subdivision, is especially powerful for solving the CIL problem, because VD
itself can be constructed favorably in an incremental manner -- the newly added
sites (classes) will only affect the proximate classes, making the
non-contiguous classes hardly forgettable. Further, in order to find a better
set of centers for VD construction, we colligate DNN with VD using Power
Diagram and show that the VD structure can be optimized by integrating local
DNN models using a divide-and-conquer algorithm. Moreover, our VD construction
is not restricted to the deep feature space, but is also applicable to multiple
intermediate feature spaces, promoting VD to be multi-centered VD (CIVD) that
efficiently captures multi-grained features from DNN. Importantly, iVoro is
also capable of handling uncertainty-aware test-time Voronoi cell assignment
and has exhibited high correlations between geometric uncertainty and
predictive accuracy (up to ~0.9). Putting everything together, iVoro achieves
up to 25.26%, 37.09%, and 33.21% improvements on CIFAR-100, TinyImageNet, and
ImageNet-Subset, respectively, compared to the state-of-the-art non-exemplar
CIL approaches. In conclusion, iVoro enables highly accurate,
privacy-preserving, and geometrically interpretable CIL that is particularly
useful when cross-phase data sharing is forbidden, e.g. in medical
applications. Our code is available at https://machunwei.github.io/ivoro.

Deep neural networks (DNNs) often have to be compressed, via pruning and/or
quantization, before they can be deployed in practical settings. In this work
we propose a new compression-aware minimizer dubbed CrAM that modifies the
optimization step in a principled way, in order to produce models whose local
loss behavior is stable under compression operations such as pruning. Thus,
dense models trained via CrAM should be compressible post-training, in a single
step, without significant accuracy loss. Experimental results on standard
benchmarks, such as residual networks for ImageNet classification and BERT
models for language modelling, show that CrAM produces dense models that can be
more accurate than the standard SGD/Adam-based baselines, but which are stable
under weight pruning: specifically, we can prune models in one-shot to 70-80%
sparsity with reasonable ($\leq 1\%$) accuracy loss, which is competitive with
gradual compression methods. Additionally, we show that CrAM produces sparse
models which perform well for transfer learning, and that it also works for
semi-structured pruning patterns supported by GPU hardware.

Human-Object Interaction (HOI) detection plays a crucial role in activity
understanding. Though significant progress has been made, interactiveness
learning remains a challenging problem in HOI detection: existing methods
usually generate redundant negative H-O pair proposals and fail to effectively
extract interactive pairs. Though interactiveness has been studied in both
whole body- and part- level and facilitates the H-O pairing, previous works
only focus on the target person once (i.e., in a local perspective) and
overlook the information of the other persons. In this paper, we argue that
comparing body-parts of multi-person simultaneously can afford us more useful
and supplementary interactiveness cues. That said, to learn body-part
interactiveness from a global perspective: when classifying a target person's
body-part interactiveness, visual cues are explored not only from
herself/himself but also from other persons in the image. We construct
body-part saliency maps based on self-attention to mine cross-person
informative cues and learn the holistic relationships between all the
body-parts. We evaluate the proposed method on widely-used benchmarks HICO-DET
and V-COCO. With our new perspective, the holistic global-local body-part
interactiveness learning achieves significant improvements over
https://github.com/enlighten0707/Body-Part-Map-for-Interactiveness.

Medical image segmentation is a fundamental and critical step in many
image-guided clinical approaches. Recent success of deep learning-based
segmentation methods usually relies on a large amount of labeled data, which is
particularly difficult and costly to obtain especially in the medical imaging
domain where only experts can provide reliable and accurate annotations.
Semi-supervised learning has emerged as an appealing strategy and been widely
applied to medical image segmentation tasks to train deep models with limited
annotations. In this paper, we present a comprehensive review of recently
proposed semi-supervised learning methods for medical image segmentation and
summarized both the technical novelties and empirical results. Furthermore, we
analyze and discuss the limitations and several unsolved problems of existing
approaches. We hope this review could inspire the research community to explore
solutions for this challenge and further promote the developments in medical
image segmentation field.

The asteroidal main belt is crossed by a web of mean-motion and secular
resonances, that occur when there is a commensurability between fundamental
frequencies of the asteroids and planets. Traditionally, these objects were
identified by visual inspection of the time evolution of their resonant
argument, which is a combination of orbital elements of the asteroid and the
perturbing planet(s). Since the population of asteroids affected by these
resonances is, in some cases, of the order of several thousand, this has become
a taxing task for a human observer. Recent works used Convolutional Neural
Networks (CNN) models to perform such task automatically. In this work, we
compare the outcome of such models with those of some of the most advanced and
publicly available CNN architectures, like the VGG, Inception and ResNet. The
performance of such models is first tested and optimized for overfitting
issues, using validation sets and a series of regularization techniques like
data augmentation, dropout, and batch normalization. The three best-performing
models were then used to predict the labels of larger testing databases
containing thousands of images. The VGG model, with and without
regularizations, proved to be the most efficient method to predict labels of
large datasets. Since the Vera C. Rubin observatory is likely to discover up to
four million new asteroids in the next few years, the use of these models might
become quite valuable to identify populations of resonant minor bodies.

Due to the very narrow beam used in millimeter wave communication (mmWave),
beam alignment (BA) is a critical issue. In this work, we investigate the issue
of mmWave BA and present a novel beam alignment scheme on the basis of a
machine learning strategy, Bayesian optimization (BO). In this context, we
consider the beam alignment issue to be a black box function and then use BO to
find the possible optimal beam pair. During the BA procedure, this strategy
exploits information from the measured beam pairs to predict the best beam
pair. In addition, we suggest a novel BO algorithm based on the gradient
boosting regression tree model. The simulation results demonstrate the spectral
efficiency performance of our proposed schemes for BA using three different
surrogate models. They also demonstrate that the proposed schemes can achieve
spectral efficiency with a small overhead when compared to the orthogonal match
pursuit (OMP) algorithm and the Thompson sampling-based multi-armed bandit
(TS-MAB) method.

In recent years, with the development of deep neural networks, end-to-end
optimized image compression has made significant progress and exceeded the
classic methods in terms of rate-distortion performance. However, most
learning-based image compression methods are unlabeled and do not consider
image semantics or content when optimizing the model. In fact, human eyes have
different sensitivities to different content, so the image content also needs
to be considered. In this paper, we propose a content-oriented image
compression method, which handles different kinds of image contents with
different strategies. Extensive experiments show that the proposed method
achieves competitive subjective results compared with state-of-the-art
end-to-end learned image compression methods or classic methods.

The acquisition and evaluation of pavement surface data play an essential
role in pavement condition evaluation. In this paper, an efficient and
effective end-to-end network for automatic pavement crack segmentation, called
RHA-Net, is proposed to improve the pavement crack segmentation accuracy. The
RHA-Net is built by integrating residual blocks (ResBlocks) and hybrid
attention blocks into the encoder-decoder architecture. The ResBlocks are used
to improve the ability of RHA-Net to extract high-level abstract features. The
hybrid attention blocks are designed to fuse both low-level features and
high-level features to help the model focus on correct channels and areas of
cracks, thereby improving the feature presentation ability of RHA-Net. An image
data set containing 789 pavement crack images collected by a self-designed
mobile robot is constructed and used for training and evaluating the proposed
model. Compared with other state-of-the-art networks, the proposed model
achieves better performance and the functionalities of adding residual blocks
and hybrid attention mechanisms are validated in a comprehensive ablation
study. Additionally, a light-weighted version of the model generated by
introducing depthwise separable convolution achieves better a performance and a
much faster processing speed with 1/30 of the number of U-Net parameters. The
developed system can segment pavement crack in real-time on an embedded device
Jetson TX2 (25 FPS). The video taken in real-time experiments is released at
https://youtu.be/3XIogk0fiG4.

In this work, we use real-world data in order to evaluate and validate a
machine learning (ML)-based algorithm for physical layer functionalities.
Specifically, we apply a recently introduced Gaussian mixture model (GMM)-based
algorithm in order to estimate uplink channels stemming from a measurement
campaign. For this estimator, there is an initial (offline) training phase,
where a GMM is fitted onto given channel (training) data. Thereafter, the
fitted GMM is used for (online) channel estimation. Our experiments suggest
that the GMM estimator learns the intrinsic characteristics of a given base
station's whole radio propagation environment. Essentially, this ambient
information is captured due to universal approximation properties of the
initially fitted GMM. For a large enough number of GMM components, the GMM
estimator was shown to approximate the (unknown) mean squared error
(MSE)-optimal channel estimator arbitrarily well. In our experiments, the GMM
estimator shows significant performance gains compared to approaches that are
not able to capture the ambient information. To validate the claim that ambient
information is learnt, we generate synthetic channel data using a
state-of-the-art channel simulator and train the GMM estimator once on these
and once on the real data, and we apply the estimator once to the synthetic and
once to the real data. We then observe how providing suitable ambient
information in the training phase beneficially impacts the later channel
estimation performance.

Pedestrian safety has become an important research topic among various
studies due to the increased number of pedestrian-involved crashes. To evaluate
pedestrian safety proactively, surrogate safety measures (SSMs) have been
widely used in traffic conflict-based studies as they do not require historical
crashes as inputs. However, most existing SSMs were developed based on the
assumption that road users would maintain constant velocity and direction. Risk
estimations based on this assumption are less unstable, more likely to be
exaggerated, and unable to capture the evasive maneuvers of drivers.
Considering the limitations among existing SSMs, this study proposes a
probabilistic framework for estimating the risk of pedestrian-vehicle conflicts
at intersections. The proposed framework loosen restrictions of constant speed
by predicting trajectories using a Gaussian Process Regression and accounts for
the different possible driver maneuvers with a Random Forest model. Real-world
LiDAR data collected at an intersection was used to evaluate the performance of
the proposed framework. The newly developed framework is able to identify all
pedestrian-vehicle conflicts. Compared to the Time-to-Collision, the proposed
framework provides a more stable risk estimation and captures the evasive
maneuvers of vehicles. Moreover, the proposed framework does not require
expensive computation resources, which makes it an ideal choice for real-time
proactive pedestrian safety solutions at intersections.

NIRPS (Near Infra-Red Planet Searcher) is an AO-assisted and fiber-fed
spectrograph for high precision radial velocity measurements in the YJH-bands.
NIRPS also has the specificity to be an SCAO assisted instrument, enabling the
use of few-mode fibers for the first time. This choice offers an excellent
trade-off by allowing to design a compact cryogenic spectrograph, while
maintaining a high coupling efficiency under bad seeing conditions and for
faint stars. The main drawback resides in a much more important modal-noise, a
problem that has to be tackled for allowing 1m/s precision radial velocity
measurements. In this paper, we present the NIRPS Front-End: an overview of its
design (opto-mechanics, control), its performance on-sky, as well as a few
lessons learned along the way.

For over a decade now, robotics and the use of artificial agents have become
a common thing.Testing the performance of new path finding or search space
optimization algorithms has also become a challenge as they require simulation
or an environment to test them.The creation of artificial environments with
artificial agents is one of the methods employed to test such algorithms.Games
have also become an environment to test them.The performance of the algorithms
can be compared by using artificial agents that will behave according to the
algorithm in the environment they are put in.The performance parameters can be,
how quickly the agent is able to differentiate between rewarding actions and
hostile actions.This can be tested by placing the agent in an environment with
different types of hurdles and the goal of the agent is to reach the farthest
by taking decisions on actions that will lead to avoiding all the obstacles.The
environment chosen is a game called "Flappy Bird".The goal of the game is to
make the bird fly through a set of pipes of random heights.The bird must go in
between these pipes and must not hit the top, the bottom, or the pipes
themselves.The actions that the bird can take are either to flap its wings or
drop down with gravity.The algorithms that are enforced on the artificial
agents are NeuroEvolution of Augmenting Topologies (NEAT) and Reinforcement
Learning.The NEAT algorithm takes an "N" initial population of artificial
agents.They follow genetic algorithms by considering an objective function,
crossover, mutation, and augmenting topologies.Reinforcement learning, on the
other hand, remembers the state, the action taken at that state, and the reward
received for the action taken using a single agent and a Deep Q-learning
Network.The performance of the NEAT algorithm improves as the initial
population of the artificial agents is increased.

Ad hoc teamwork (AHT) is the problem of creating an agent that must
collaborate with previously unseen teammates without prior coordination. Many
existing AHT methods can be categorised as type-based methods, which require a
set of predefined teammates for training. Designing teammate types for training
is a challenging issue that determines the generalisation performance of agents
when dealing with teammate types unseen during training. In this work, we
propose a method to discover diverse teammate types based on maximising best
response diversity metrics. We show that our proposed approach yields teammate
types that require a wider range of best responses from the learner during
collaboration, which potentially improves the robustness of a learner's
performance in AHT compared to alternative methods.

Brain tumor segmentation remains a challenge in medical image segmentation
tasks. With the application of transformer in various computer vision tasks,
transformer blocks show the capability of learning long-distance dependency in
global space, which is complementary with CNNs. In this paper, we proposed a
novel transformer-based generative adversarial network to automatically segment
brain tumors with multi-modalities MRI. Our architecture consists of a
generator and a discriminator, which are trained in min-max game progress. The
generator is based on a typical "U-shaped" encoder-decoder architecture, whose
bottom layer is composed of transformer blocks with resnet. Besides, the
generator is trained with deep supervision technology. The discriminator we
designed is a CNN-based network with multi-scale $L_{1}$ loss, which is proved
to be effective for medical semantic image segmentation. To validate the
effectiveness of our method, we conducted experiments on BRATS2015 dataset,
achieving comparable or better performance than previous state-of-the-art
methods.

Reservoir computing is a machine learning approach that can generate a
surrogate model of a dynamical system. It can learn the underlying dynamical
system using fewer trainable parameters and hence smaller training data sets
than competing approaches. Recently, a simpler formulation, known as
next-generation reservoir computing, removes many algorithm metaparameters and
identifies a well-performing traditional reservoir computer, thus simplifying
training even further. Here, we study a particularly challenging problem of
learning a dynamical system that has both disparate time scales and multiple
co-existing dynamical states (attractors). We compare the next-generation and
traditional reservoir computer using metrics quantifying the geometry of the
ground-truth and forecasted attractors. For the studied four-dimensional
system, the next-generation reservoir computing approach uses $\sim 1.7 \times$
less training data, requires $10^3 \times$ shorter `warm up' time, has fewer
metaparameters, and has an $\sim 100\times$ higher accuracy in predicting the
co-existing attractor characteristics in comparison to a traditional reservoir
computer. Furthermore, we demonstrate that it predicts the basin of attraction
with high accuracy. This work lends further support to the superior learning
ability of this new machine learning algorithm for dynamical systems.

In autonomous and mobile robotics, one of the main challenges is the robust
on-the-fly perception of the environment, which is often unknown and dynamic,
like in autonomous drone racing. In this work, we propose a novel deep neural
network-based perception method for racing gate detection -- PencilNet -- which
relies on a lightweight neural network backbone on top of a pencil filter. This
approach unifies predictions of the gates' 2D position, distance, and
orientation in a single pose tuple. We show that our method is effective for
zero-shot sim-to-real transfer learning that does not need any real-world
training samples. Moreover, our framework is highly robust to illumination
changes commonly seen under rapid flight compared to state-of-art methods. A
thorough set of experiments demonstrates the effectiveness of this approach in
multiple challenging scenarios, where the drone completes various tracks under
different lighting conditions.

Data-heterogeneous federated learning (FL) systems suffer from two
significant sources of convergence error: 1) client drift error caused by
performing multiple local optimization steps at clients, and 2) partial client
participation error caused by the fact that only a small subset of the edge
clients participate in every training round. We find that among these, only the
former has received significant attention in the literature. To remedy this, we
propose FedVARP, a novel variance reduction algorithm applied at the server
that eliminates error due to partial client participation. To do so, the server
simply maintains in memory the most recent update for each client and uses
these as surrogate updates for the non-participating clients in every round.
Further, to alleviate the memory requirement at the server, we propose a novel
clustering-based variance reduction algorithm ClusterFedVARP. Unlike previously
proposed methods, both FedVARP and ClusterFedVARP do not require additional
computation at clients or communication of additional optimization parameters.
Through extensive experiments, we show that FedVARP outperforms
state-of-the-art methods, and ClusterFedVARP achieves performance comparable to
FedVARP with much less memory requirements.

Predicting outcomes in sports is important for teams, leagues, bettors,
media, and fans. Given the growing amount of player tracking data, sports
analytics models are increasingly utilizing spatially-derived features built
upon player tracking data. However, player-specific information, such as
location, cannot readily be included as features themselves, since common
modeling techniques rely on vector input. Accordingly, spatially-derived
features are commonly constructed in relation to anchor objects, such as the
distance to a ball or goal, through global feature aggregations, or via
role-assignment schemes, where players are designated a distinct role in the
game. In doing so, we sacrifice inter-player and local relationships in favor
of global ones. To address this issue, we introduce a sport-agnostic
graph-based representation of game states. We then use our proposed graph
representation as input to graph neural networks to predict sports outcomes.
Our approach preserves permutation invariance and allows for flexible player
interaction weights. We demonstrate how our method provides statistically
significant improvements over the state of the art for prediction tasks in both
American football and esports, reducing test set loss by 9% and 20%,
respectively. Additionally, we show how our model can be used to answer "what
if" questions in sports and to visualize relationships between players.

We present Claim-Dissector: a novel latent variable model for fact-checking
and fact-analysis, which given a claim and a set of retrieved provenances
allows learning jointly: (i) what are the relevant provenances to this claim
(ii) what is the veracity of this claim. We propose to disentangle the
per-provenance relevance probability and its contribution to the final veracity
probability in an interpretable way - the final veracity probability is
proportional to a linear ensemble of per-provenance relevance probabilities.
This way, it can be clearly identified the relevance of which sources
contributes to what extent towards the final probability. We show that our
system achieves state-of-the-art results on FEVER dataset comparable to
two-stage systems typically used in traditional fact-checking pipelines, while
it often uses significantly less parameters and computation.
  Our analysis shows that proposed approach further allows to learn not just
which provenances are relevant, but also which provenances lead to supporting
and which toward denying the claim, without direct supervision. This not only
adds interpretability, but also allows to detect claims with conflicting
evidence automatically. Furthermore, we study whether our model can learn
fine-grained relevance cues while using coarse-grained supervision. We show
that our model can achieve competitive sentence-recall while using only
paragraph-level relevance supervision. Finally, traversing towards the finest
granularity of relevance, we show that our framework is capable of identifying
relevance at the token-level. To do this, we present a new benchmark focusing
on token-level interpretability - humans annotate tokens in relevant
provenances they considered essential when making their judgement. Then we
measure how similar are these annotations to tokens our model is focusing on.
Our code, and dataset will be released online.

FIB/SEM tomography represents an indispensable tool for the characterization
of three-dimensional nanostructures in battery research and many other fields.
However, contrast and 3D classification/reconstruction problems occur in many
cases, which strongly limits the applicability of the technique especially on
porous materials, like those used for electrode materials in batteries or fuel
cells. Distinguishing the different components like active Li storage particles
and carbon/binder materials is difficult and often prevents a reliable
quantitative analysis of image data, or may even lead to wrong conclusions
about structure-property relationships. In this contribution, we present a
novel approach for data classification in three-dimensional image data obtained
by FIB/SEM tomography and its applications to NMC battery electrode materials.
We use two different image signals, namely the signal of the angled SE2 chamber
detector and the Inlens detector signal, combine both signals and train a
random forest, i.e. a particular machine learning algorithm. We demonstrate
that this approach can overcome current limitations of existing techniques
suitable for multi-phase measurements and that it allows for quantitative data
reconstruction even where current state-of the art techniques fail, or demand
for large training sets. This approach may yield as guideline for future
research using FIB/SEM tomography.

Single-cell RNA-seq data allow the quantification of cell type differences
across a growing set of biological contexts. However, pinpointing a small
subset of genomic features explaining this variability can be ill-defined and
computationally intractable. Here we introduce MarkerMap, a generative model
for selecting minimal gene sets which are maximally informative of cell type
origin and enable whole transcriptome reconstruction. MarkerMap provides a
scalable framework for both supervised marker selection, aimed at identifying
specific cell type populations, and unsupervised marker selection, aimed at
gene expression imputation and reconstruction. We benchmark MarkerMap's
competitive performance against previously published approaches on real single
cell gene expression data sets. MarkerMap is available as a pip installable
package, as a community resource aimed at developing explainable machine
learning techniques for enhancing interpretability in single-cell studies.

Existing camouflaged object detection (COD) methods rely heavily on
large-scale datasets with pixel-wise annotations. However, due to the ambiguous
boundary, it is very time-consuming and labor-intensive to annotate camouflage
objects pixel-wisely (which takes ~ 60 minutes per image). In this paper, we
propose the first weakly-supervised camouflaged object detection (COD) method,
using scribble annotations as supervision. To achieve this, we first construct
a scribble-based camouflaged object dataset with 4,040 images and corresponding
scribble annotations. It is worth noting that annotating the scribbles used in
our dataset takes only ~ 10 seconds per image, which is 360 times faster than
per-pixel annotations. However, the network directly using scribble annotations
for supervision will fail to localize the boundary of camouflaged objects and
tend to have inconsistent predictions since scribble annotations only describe
the primary structure of objects without details. To tackle this problem, we
propose a novel consistency loss composed of two parts: a reliable cross-view
loss to attain reliable consistency over different images, and a soft
inside-view loss to maintain consistency inside a single prediction map.
Besides, we observe that humans use semantic information to segment regions
near boundaries of camouflaged objects. Therefore, we design a feature-guided
loss, which includes visual features directly extracted from images and
semantically significant features captured by models. Moreover, we propose a
novel network that detects camouflaged objects by scribble learning on
structural information and semantic relations. Experimental results show that
our model outperforms relevant state-of-the-art methods on three COD benchmarks
with an average improvement of 11.0% on MAE, 3.2% on S-measure, 2.5% on
E-measure and 4.4% on weighted F-measure.

This application paper presents a comprehensive experimental evaluation of
the suitability of Topological Data Analysis (TDA) for the quantitative
comparison of turbulent flows. Specifically, our study documents the usage of
the persistence diagram of the maxima of flow enstrophy (an established
vorticity indicator), for the topological representation of 180 ensemble
members, generated by a coarse sampling of the parameter space of five
numerical solvers. We document five main hypotheses reported by domain experts,
describing their expectations regarding the variability of the flows generated
by the distinct solver configurations. We contribute three evaluation protocols
to assess the validation of the above hypotheses by two comparison measures:
(i) a standard distance used in scientific imaging (the L2 norm) and (ii) an
established topological distance between persistence diagrams (the
L2-Wasserstein metric). Extensive experiments on the input ensemble demonstrate
the superiority of the topological distance (ii) to report as close to each
other flows which are expected to be similar by domain experts, due to the
configuration of their vortices. Overall, the insights reported by our study
bring an experimental evidence of the suitability of TDA for representing and
comparing turbulent flows, thereby providing to the fluid dynamics community
confidence for its usage in future work. Also, our flow data and evaluation
protocols provide to the TDA community an application-approved benchmark for
the evaluation and design of further topological distances.

In recent years novel activation functions have been proposed to improve the
performance of neural networks, and they show superior performance compared to
the ReLU counterpart. However, there are environments, where the availability
of complex activations is limited, and usually only the ReLU is supported. In
this paper we propose methods that can be used to improve the performance of
ReLU networks by using these efficient novel activations during model training.
More specifically, we propose ensemble activations that are composed of the
ReLU and one of these novel activations. Furthermore, the coefficients of the
ensemble are neither fixed nor learned, but are progressively updated during
the training process in a way that by the end of the training only the ReLU
activations remain active in the network and the other activations can be
removed. This means that in inference time the network contains ReLU
activations only. We perform extensive evaluations on the ImageNet
classification task using various compact network architectures and various
novel activation functions. Results show 0.2-0.8% top-1 accuracy gain, which
confirms the applicability of the proposed methods. Furthermore, we demonstrate
the proposed methods on semantic segmentation and we boost the performance of a
compact segmentation network by 0.34% mIOU on the Cityscapes dataset.

Homomorphic encryption, which enables the execution of arithmetic operations
directly on ciphertexts, is a promising solution for protecting privacy of
cloud-delegated computations on sensitive data. However, the correctness of the
computation result is not ensured. We propose two error detection encodings and
build authenticators that enable practical client-verification of cloud-based
homomorphic computations under different trade-offs and without compromising on
the features of the encryption algorithm. Our authenticators operate on top of
trending ring learning with errors based fully homomorphic encryption schemes
over the integers. We implement our solution in VERITAS, a ready-to-use system
for verification of outsourced computations executed over encrypted data. We
show that contrary to prior work VERITAS supports verification of any
homomorphic operation and we demonstrate its practicality for various
applications, such as ride-hailing, genomic-data analysis, encrypted search,
and machine-learning training and inference.

We present Neural Strands, a novel learning framework for modeling accurate
hair geometry and appearance from multi-view image inputs. The learned hair
model can be rendered in real-time from any viewpoint with high-fidelity
view-dependent effects. Our model achieves intuitive shape and style control
unlike volumetric counterparts. To enable these properties, we propose a novel
hair representation based on a neural scalp texture that encodes the geometry
and appearance of individual strands at each texel location. Furthermore, we
introduce a novel neural rendering framework based on rasterization of the
learned hair strands. Our neural rendering is strand-accurate and anti-aliased,
making the rendering view-consistent and photorealistic. Combining appearance
with a multi-view geometric prior, we enable, for the first time, the joint
learning of appearance and explicit hair geometry from a multi-view setup. We
demonstrate the efficacy of our approach in terms of fidelity and efficiency
for various hairstyles.

Termination analyses investigate the termination behavior of programs,
intending to detect nontermination, which is known to cause a variety of
program bugs (e.g. hanging programs, denial-of-service vulnerabilities). Beyond
formal approaches, various attempts have been made to estimate the termination
behavior of programs using neural networks. However, the majority of these
approaches continue to rely on formal methods to provide strong soundness
guarantees and consequently suffer from similar limitations. In this paper, we
move away from formal methods and embrace the stochastic nature of machine
learning models. Instead of aiming for rigorous guarantees that can be
interpreted by solvers, our objective is to provide an estimation of a
program's termination behavior and of the likely reason for nontermination
(when applicable) that a programmer can use for debugging purposes. Compared to
previous approaches using neural networks for program termination, we also take
advantage of the graph representation of programs by employing Graph Neural
Networks. To further assist programmers in understanding and debugging
nontermination bugs, we adapt the notions of attention and semantic
segmentation, previously used for other application domains, to programs.
Overall, we designed and implemented classifiers for program termination based
on Graph Convolutional Networks and Graph Attention Networks, as well as a
semantic segmentation Graph Neural Network that localizes AST nodes likely to
cause nontermination. We also illustrated how the information provided by
semantic segmentation can be combined with program slicing to further aid
debugging.

We show hardness of improperly learning halfspaces in the agnostic model
based on worst-case lattice problems, e.g., approximating shortest vectors
within polynomial factors. In particular, we show that under this assumption
there is no efficient algorithm that outputs any binary hypothesis, not
necessarily a halfspace, achieving misclassfication error better than $\frac 1
2 - \epsilon$ even if the optimal misclassification error is as small is as
small as $\delta$. Here, $\epsilon$ can be smaller than the inverse of any
polynomial in the dimension and $\delta$ as small as
$\mathrm{exp}\left(-\Omega\left(\log^{1-c}(d)\right)\right)$, where $0 < c < 1$
is an arbitrary constant and $d$ is the dimension.
  Previous hardness results [Daniely16] of this problem were based on
average-case complexity assumptions, specifically, variants of Feige's random
3SAT hypothesis. Our work gives the first hardness for this problem based on a
worst-case complexity assumption. It is inspired by a sequence of recent works
showing hardness of learning well-separated Gaussian mixtures based on
worst-case lattice problems.

Large-scale deployment of autonomous vehicles has been continually delayed
due to safety concerns. On the one hand, comprehensive scene understanding is
indispensable, a lack of which would result in vulnerability to rare but
complex traffic situations, such as the sudden emergence of unknown objects.
However, reasoning from a global context requires access to sensors of multiple
types and adequate fusion of multi-modal sensor signals, which is difficult to
achieve. On the other hand, the lack of interpretability in learning models
also hampers the safety with unverifiable failure causes. In this paper, we
propose a safety-enhanced autonomous driving framework, named Interpretable
Sensor Fusion Transformer(InterFuser), to fully process and fuse information
from multi-modal multi-view sensors for achieving comprehensive scene
understanding and adversarial event detection. Besides, intermediate
interpretable features are generated from our framework, which provide more
semantics and are exploited to better constrain actions to be within the safe
sets. We conducted extensive experiments on CARLA benchmarks, where our model
outperforms prior methods, ranking the first on the public CARLA Leaderboard.
Our code will be made available at https://github.com/opendilab/InterFuser

Accurately measuring the size, morphology, and structure of nanoparticles is
very important, because they are strongly dependent on their properties for
many applications. In this paper, we present a deep-learning based method for
nanoparticle measurement and classification trained from a small data set of
scanning transmission electron microscopy images. Our approach is comprised of
two stages: localization, i.e., detection of nanoparticles, and classification,
i.e., categorization of their ultrastructure. For each stage, we optimize the
segmentation and classification by analysis of the different state-of-the-art
neural networks. We show how the generation of synthetic images, either using
image processing or using various image generation neural networks, can be used
to improve the results in both stages. Finally, the application of the
algorithm to bimetallic nanoparticles demonstrates the automated data
collection of size distributions including classification of complex
ultrastructures. The developed method can be easily transferred to other
material systems and nanoparticle structures.

Generating representations that precisely reflect customers' behavior is an
important task for providing personalized skill routing experience in Alexa.
Currently, Dynamic Routing (DR) team, which is responsible for routing Alexa
traffic to providers or skills, relies on two features to be served as personal
signals: absolute traffic count and normalized traffic count of every skill
usage per customer. Neither of them considers the network based structure for
interactions between customers and skills, which contain richer information for
customer preferences. In this work, we first build a heterogeneous edge
attributed graph based customers' past interactions with the invoked skills, in
which the user requests (utterances) are modeled as edges. Then we propose a
graph convolutional network(GCN) based model, namely Personalized Dynamic
Routing Feature Encoder(PDRFE), that generates personalized customer
representations learned from the built graph. Compared with existing models,
PDRFE is able to further capture contextual information in the graph
convolutional function. The performance of our proposed model is evaluated by a
downstream task, defect prediction, that predicts the defect label from the
learned embeddings of customers and their triggered skills. We observe up to
41% improvements on the cross entropy metric for our proposed models compared
to the baselines.

This paper considers a joint multi-graph inference and clustering problem for
simultaneous inference of node centrality and association of graph signals with
their graphs. We study a mixture model of filtered low pass graph signals with
possibly non-white and low-rank excitation. While the mixture model is
motivated from practical scenarios, it presents significant challenges to prior
graph learning methods. As a remedy, we consider an inference problem focusing
on the node centrality of graphs. We design an expectation-maximization (EM)
algorithm with a unique low-rank plus sparse prior derived from low pass signal
property. We propose a novel online EM algorithm for inference from streaming
data. As an example, we extend the online algorithm to detect if the signals
are generated from an abnormal graph. We show that the proposed algorithms
converge to a stationary point of the maximum-a-posterior (MAP) problem.
Numerical experiments support our analysis.

Complex Event Processing (CEP) is a set of methods that allow efficient
knowledge extraction from massive data streams using complex and highly
descriptive patterns. Numerous applications, such as online finance, healthcare
monitoring and fraud detection use CEP technologies to capture critical alerts,
potential threats, or vital notifications in real time. As of today, in many
fields, patterns are manually defined by human experts. However, desired
patterns often contain convoluted relations that are difficult for humans to
detect, and human expertise is scarce in many domains.
  We present REDEEMER (REinforcement baseD cEp pattErn MinER), a novel
reinforcement and active learning approach aimed at mining CEP patterns that
allow expansion of the knowledge extracted while reducing the human effort
required. This approach includes a novel policy gradient method for vast
multivariate spaces and a new way to combine reinforcement and active learning
for CEP rule learning while minimizing the number of labels needed for
training.
  REDEEMER aims to enable CEP integration in domains that could not utilize it
before. To the best of our knowledge, REDEEMER is the first system that
suggests new CEP rules that were not observed beforehand, and is the first
method aimed for increasing pattern knowledge in fields where experts do not
possess sufficient information required for CEP tools.
  Our experiments on diverse data-sets demonstrate that REDEEMER is able to
extend pattern knowledge while outperforming several state-of-the-art
reinforcement learning methods for pattern mining.

Decentralized computation outsourcing should allow anyone to access the large
amounts of computational power that exists in the Internet of Things.
Unfortunately, when trusted third parties are removed to achieve this
decentralization, ensuring an outsourced computation is performed correctly
remains a significant challenge. In this paper, we provide a solution to this
problem.
  We outline Marvel DC, a fully decentralized blockchain-based
distributed-computing protocol which formally guarantees that computers are
strictly incentivized to correctly perform requested computations. Furthermore,
Marvel DC utilizes a reputation management protocol to ensure that, for any
minority of computers not performing calculations correctly, these computers
are identified and selected for computations with diminishing probability. We
then outline Privacy Marvel DC, a privacy-enhanced version of Marvel DC which
decouples results from the computers which computed them, making the protocol
suitable for computations such as Federated Learning, where results can reveal
sensitive information about that computer that computed them. We provide an
implementation of Marvel DC and analyses of both protocols, demonstrating that
they are not only the first protocols to provide the aforementioned formal
guarantees, but are also practical, competitive with prior attempts in the
field, and ready to deploy.

In this paper, we propose a data-driven skill learning approach to solve
highly dynamic manipulation tasks entirely from offline teleoperated play data.
We use a bilateral teleoperation system to continuously collect a large set of
dexterous and agile manipulation behaviors, which is enabled by providing
direct force feedback to the operator. We jointly learn the state conditional
latent skill distribution and skill decoder network in the form of
goal-conditioned policy and skill conditional state transition dynamics using a
two-stage generative modeling framework. This allows one to perform robust
model-based planning, both online and offline planning methods, in the learned
skill-space to accomplish any given downstream tasks at test time. We provide
both simulated and real-world dual-arm box manipulation experiments showing
that a sequence of force-controlled dynamic manipulation skills can be composed
in real-time to successfully configure the box to the randomly selected target
position and orientation; please refer to the supplementary video,
https://youtu.be/LA5B236ILzM.

We present an adaptive learning Intelligent Tutoring System, which uses
model-based reinforcement learning in the form of contextual bandits to assign
learning activities to students. The model is trained on the trajectories of
thousands of students in order to maximize their exercise completion rates and
continues to learn online, automatically adjusting itself to new activities. A
randomized controlled trial with students shows that our model leads to
superior completion rates and significantly improved student engagement when
compared to other approaches. Our approach is fully-automated unlocking new
opportunities for learning experience personalization.

Combining deep learning with symbolic logic reasoning aims to capitalize on
the success of both fields and is drawing increasing attention. Inspired by
DeepLogic, an end-to-end model trained to perform inference on logic programs,
we introduce IMA-GloVe-GA, an iterative neural inference network for multi-step
reasoning expressed in natural language. In our model, reasoning is performed
using an iterative memory neural network based on RNN with a gate attention
mechanism. We evaluate IMA-GloVe-GA on three datasets: PARARULES, CONCEPTRULES
V1 and CONCEPTRULES V2. Experimental results show DeepLogic with gate attention
can achieve higher test accuracy than DeepLogic and other RNN baseline models.
Our model achieves better out-of-distribution generalisation than RoBERTa-Large
when the rules have been shuffled. Furthermore, to address the issue of
unbalanced distribution of reasoning depths in the current multi-step reasoning
datasets, we develop PARARULE-Plus, a large dataset with more examples that
require deeper reasoning steps. Experimental results show that the addition of
PARARULE-Plus can increase the model's performance on examples requiring deeper
reasoning depths. The source code and data are available at
https://github.com/Strong-AI-Lab/Multi-Step-Deductive-Reasoning-Over-Natural-Language.

The study of natural and human-made processes often results in long sequences
of temporally-ordered values, aka time series (TS). Such processes often
consist of multiple states, e.g. operating modes of a machine, such that state
changes in the observed processes result in changes in the distribution of
shape of the measured values. Time series segmentation (TSS) tries to find such
changes in TS post-hoc to deduce changes in the data-generating process. TSS is
typically approached as an unsupervised learning problem aiming at the
identification of segments distinguishable by some statistical property.
Current algorithms for TSS require domain-dependent hyper-parameters to be set
by the user, make assumptions about the TS value distribution or the types of
detectable changes which limits their applicability. Common hyperparameters are
the measure of segment homogeneity and the number of change points, which are
particularly hard to tune for each data set. We present ClaSP, a novel, highly
accurate, hyper-parameter-free and domain-agnostic method for TSS. ClaSP
hierarchically splits a TS into two parts. A change point is determined by
training a binary TS classifier for each possible split point and selecting the
one split that is best at identifying subsequences to be from either of the
partitions. ClaSP learns its main two model-parameters from the data using two
novel bespoke algorithms. In our experimental evaluation using a benchmark of
115 data sets, we show that ClaSP outperforms the state of the art in terms of
accuracy and is fast and scalable. Furthermore, we highlight properties of
ClaSP using several real-world case studies.

The scarcity of available Internet of Things (IoT) datasets remains a
limiting factor in developing machine learning based security systems. Static
datasets get outdated due to evolving IoT threat landscape. Meanwhile, the
testbeds used to generate them are rarely published. This paper presents the
Gotham testbed, a reproducible and flexible network security testbed,
implemented as a middleware over the GNS3 emulator, that is extendable to
accommodate new emulated devices, services or attackers. The testbed is used to
build an IoT scenario composed of 100 emulated devices communicating via MQTT,
CoAP and RTSP protocols in a topology composed of 30 switches and 10 routers.
The scenario presents three threat actors, including the entire Mirai botnet
lifecycle and additional red-teaming tools performing DoS, scanning and various
attacks targeting the MQTT and CoAP protocols. The generated network traffic
and application logs can be used to capture datasets containing legitimate and
attacking traces. We hope that researchers can leverage the testbed and adapt
it to include other types of devices and state-of-the-art attacks to generate
new datasets that reflect the current threat landscape and IoT protocols. The
source code to reproduce the scenario is publicly accessible.

The RangL project hosted by The Alan Turing Institute aims to encourage the
wider uptake of reinforcement learning by supporting competitions relating to
real-world dynamic decision problems. This article describes the reusable code
repository developed by the RangL team and deployed for the 2022 Pathways to
Net Zero Challenge, supported by the UK Net Zero Technology Centre. The winning
solutions to this particular Challenge seek to optimize the UK's energy
transition policy to net zero carbon emissions by 2050. The RangL repository
includes an OpenAI Gym reinforcement learning environment and code that
supports both submission to, and evaluation in, a remote instance of the open
source EvalAI platform as well as all winning learning agent strategies. The
repository is an illustrative example of RangL's capability to provide a
reusable structure for future challenges.

Due to the complex layouts of documents, it is challenging to extract
information for documents. Most previous studies develop multimodal pre-trained
models in a self-supervised way. In this paper, we focus on the embedding
learning of word blocks containing text and layout information, and propose
UTel, a language model with Unified TExt and Layout pre-training. Specifically,
we propose two pre-training tasks: Surrounding Word Prediction (SWP) for the
layout learning, and Contrastive learning of Word Embeddings (CWE) for
identifying different word blocks. Moreover, we replace the commonly used 1D
position embedding with a 1D clipped relative position embedding. In this way,
the joint training of Masked Layout-Language Modeling (MLLM) and two newly
proposed tasks enables the interaction between semantic and spatial features in
a unified way. Additionally, the proposed UTel can process arbitrary-length
sequences by removing the 1D position embedding, while maintaining competitive
performance. Extensive experimental results show UTel learns better joint
representations and achieves superior performance than previous methods on
various downstream tasks, though requiring no image modality. Code is available
at \url{https://github.com/taosong2019/UTel}.

Internet of Underwater Things (IoUT) have gained rapid momentum over the past
decade with applications spanning from environmental monitoring and
exploration, defence applications, etc. The traditional IoUT systems use
machine learning (ML) approaches which cater the needs of reliability,
efficiency and timeliness. However, an extensive review of the various studies
conducted highlight the significance of data privacy and security in IoUT
frameworks as a predominant factor in achieving desired outcomes in mission
critical applications. Federated learning (FL) is a secured, decentralized
framework which is a recent development in machine learning, that will help in
fulfilling the challenges faced by conventional ML approaches in IoUT. This
paper presents an overview of the various applications of FL in IoUT, its
challenges, open issues and indicates direction of future research prospects.

The development of accurate methods for multi-label classification (MLC) of
remote sensing (RS) images is one of the most important research topics in RS.
To address MLC problems, the use of deep neural networks that require a high
number of reliable training images annotated by multiple land-cover class
labels (multi-labels) have been found popular in RS. However, collecting such
annotations is time-consuming and costly. A common procedure to obtain
annotations at zero labeling cost is to rely on thematic products or
crowdsourced labels. As a drawback, these procedures come with the risk of
label noise that can distort the learning process of the MLC algorithms. In the
literature, most label noise robust methods are designed for single label
classification (SLC) problems in computer vision (CV), where each image is
annotated by a single label. Unlike SLC, label noise in MLC can be associated
with: 1) subtractive label-noise (a land cover class label is not assigned to
an image while that class is present in the image); 2) additive label-noise (a
land cover class label is assigned to an image although that class is not
present in the given image); and 3) mixed label-noise (a combination of both).
In this paper, we investigate three different noise robust CV SLC methods and
adapt them to be robust for multi-label noise scenarios in RS. During
experiments we study the effects of different types of multi-label noise and
evaluate the adapted methods rigorously. To this end, we also introduce a
synthetic multi-label noise injection strategy that is more adequate to
simulate operational scenarios compared to the uniform label noise injection
strategy, in which the labels of absent and present classes are flipped at
uniform probability. Further, we study the relevance of different evaluation
metrics in MLC problems under noisy multi-labels.

Deepfakes are the synthesized digital media in order to create
ultra-realistic fake videos to trick the spectator. Deep generative algorithms,
such as, Generative Adversarial Networks(GAN) are widely used to accomplish
such tasks. This approach synthesizes pseudo-realistic contents that are very
difficult to distinguish by traditional detection methods. In most cases,
Convolutional Neural Network(CNN) based discriminators are being used for
detecting such synthesized media. However, it emphasise primarily on the
spatial attributes of individual video frames, thereby fail to learn the
temporal information from their inter-frame relations. In this paper, we
leveraged an optical flow based feature extraction approach to extract the
temporal features, which are then fed to a hybrid model for classification.
This hybrid model is based on the combination of CNN and recurrent neural
network (RNN) architectures. The hybrid model provides effective performance on
open source data-sets such as, DFDC, FF++ and Celeb-DF. This proposed method
shows an accuracy of 66.26%, 91.21% and 79.49% in DFDC, FF++, and Celeb-DF
respectively with a very reduced No of sample size of approx 100
samples(frames). This promises early detection of fake contents compared to
existing modalities.

Biometric systems represent valid solutions in tasks like user authentication
and verification, since they are able to analyze physical and behavioural
features with high precision. However, especially when physical biometrics are
used, as is the case of iris recognition, they require specific hardware such
as retina scanners, sensors, or HD cameras to achieve relevant results. At the
same time, they require the users to be very close to the camera to extract
high-resolution information. For this reason, in this work, we propose a novel
approach that uses long-range (LR) distance images for implementing an iris
verification system. More specifically, we present a novel methodology for
converting LR iris images into graphs and then use Graph Siamese Neural
Networks (GSNN) to predict whether two graphs belong to the same person. In
this study, we not only describe this methodology but also evaluate how the
spectral components of these images can be used for improving the graph
extraction and the final classification task. Results demonstrate the
suitability of this approach, encouraging the community to explore graph
application in biometric systems.

Work on social media rumour verification utilises signals from posts, their
propagation and users involved. Other lines of work target identifying and
fact-checking claims based on information from Wikipedia, or trustworthy news
articles without considering social media context. However works combining the
information from social media with external evidence from the wider web are
lacking. To facilitate research in this direction, we release a novel dataset,
PHEMEPlus, an extension of the PHEME benchmark, which contains social media
conversations as well as relevant external evidence for each rumour. We
demonstrate the effectiveness of incorporating such evidence in improving
rumour verification models. Additionally, as part of the evidence collection,
we evaluate various ways of query formulation to identify the most effective
method.

The most of CNN based super-resolution (SR) methods assume that the
degradation is known (\eg, bicubic). These methods will suffer a severe
performance drop when the degradation is different from their assumption.
Therefore, some approaches attempt to train SR networks with the complex
combination of multiple degradations to cover the real degradation space. To
adapt to multiple unknown degradations, introducing an explicit degradation
estimator can actually facilitate SR performance. However, previous explicit
degradation estimation methods usually predict Gaussian blur with the
supervision of groundtruth blur kernels, and estimation errors may lead to SR
failure. Thus, it is necessary to design a method that can extract implicit
discriminative degradation representation. To this end, we propose a
Meta-Learning based Region Degradation Aware SR Network (MRDA), including
Meta-Learning Network (MLN), Degradation Extraction Network (DEN), and Region
Degradation Aware SR Network (RDAN). To handle the lack of groundtruth
degradation, we use the MLN to rapidly adapt to the specific complex
degradation after several iterations and extract implicit degradation
information. Subsequently, a teacher network MRDA$_{T}$ is designed to further
utilize the degradation information extracted by MLN for SR. However, MLN
requires iterating on paired low-resolution (LR) and corresponding
high-resolution (HR) images, which is unavailable in the inference phase.
Therefore, we adopt knowledge distillation (KD) to make the student network
learn to directly extract the same implicit degradation representation (IDR) as
the teacher from LR images.

Many complex problems are naturally understood in terms of symbolic concepts.
For example, our concept of "cat" is related to our concepts of "ears" and
"whiskers" in a non-arbitrary way. Fodor (1998) proposes one theory of
concepts, which emphasizes symbolic representations related via constituency
structures. Whether neural networks are consistent with such a theory is open
for debate. We propose unit tests for evaluating whether a system's behavior is
consistent with several key aspects of Fodor's criteria. Using a simple visual
concept learning task, we evaluate several modern neural architectures against
this specification. We find that models succeed on tests of groundedness,
modularlity, and reusability of concepts, but that important questions about
causality remain open. Resolving these will require new methods for analyzing
models' internal states.

Autonomous vehicles are a growing technology that aims to enhance safety,
accessibility, efficiency, and convenience through autonomous maneuvers ranging
from lane change to overtaking. Overtaking is one of the most challenging
maneuvers for autonomous vehicles, and current techniques for autonomous
overtaking are limited to simple situations. This paper studies how to increase
safety in autonomous overtaking by allowing the maneuver to be aborted. We
propose a decision-making process based on a deep Q-Network to determine if and
when the overtaking maneuver needs to be aborted. The proposed algorithm is
empirically evaluated in simulation with varying traffic situations, indicating
that the proposed method improves safety during overtaking maneuvers.
Furthermore, the approach is demonstrated in real-world experiments using the
autonomous shuttle iseAuto.

We present RHODE, a novel system that enables privacy-preserving training of
and prediction on Recurrent Neural Networks (RNNs) in a federated learning
setting by relying on multiparty homomorphic encryption (MHE). RHODE preserves
the confidentiality of the training data, the model, and the prediction data;
and it mitigates the federated learning attacks that target the gradients under
a passive-adversary threat model. We propose a novel packing scheme,
multi-dimensional packing, for a better utilization of Single Instruction,
Multiple Data (SIMD) operations under encryption. With multi-dimensional
packing, RHODE enables the efficient processing, in parallel, of a batch of
samples. To avoid the exploding gradients problem, we also provide several
clip-by-value approximations for enabling gradient clipping under encryption.
We experimentally show that the model performance with RHODE remains similar to
non-secure solutions both for homogeneous and heterogeneous data distribution
among the data holders. Our experimental evaluation shows that RHODE scales
linearly with the number of data holders and the number of timesteps,
sub-linearly and sub-quadratically with the number of features and the number
of hidden units of RNNs, respectively. To the best of our knowledge, RHODE is
the first system that provides the building blocks for the training of RNNs and
its variants, under encryption in a federated learning setting.

Motivation: Cancer is heterogeneous, affecting the precise approach to
personalized treatment. Accurate subtyping can lead to better survival rates
for cancer patients. High-throughput technologies provide multiple omics data
for cancer subtyping. However, precise cancer subtyping remains challenging due
to the large amount and high dimensionality of omics data. Results: This study
proposed Subtype-Former, a deep learning method based on MLP and Transformer
Block, to extract the low-dimensional representation of the multi-omics data.
K-means and Consensus Clustering are also used to achieve accurate subtyping
results. We compared Subtype-Former with the other state-of-the-art subtyping
methods across the TCGA 10 cancer types. We found that Subtype-Former can
perform better on the benchmark datasets of more than 5000 tumors based on the
survival analysis. In addition, Subtype-Former also achieved outstanding
results in pan-cancer subtyping, which can help analyze the commonalities and
differences across various cancer types at the molecular level. Finally, we
applied Subtype-Former to the TCGA 10 types of cancers. We identified 50
essential biomarkers, which can be used to study targeted cancer drugs and
promote the development of cancer treatments in the era of precision medicine.

We introduce a regularization loss based on kernel mean embeddings with
rotation-invariant kernels on the hypersphere (also known as dot-product
kernels) for self-supervised learning of image representations. Besides being
fully competitive with the state of the art, our method significantly reduces
time and memory complexity for self-supervised training, making it
implementable for very large embedding dimensions on existing devices and more
easily adjustable than previous methods to settings with limited resources. Our
work follows the major paradigm where the model learns to be invariant to some
predefined image transformations (cropping, blurring, color jittering, etc.),
while avoiding a degenerate solution by regularizing the embedding
distribution. Our particular contribution is to propose a loss family promoting
the embedding distribution to be close to the uniform distribution on the
hypersphere, with respect to the maximum mean discrepancy pseudometric. We
demonstrate that this family encompasses several regularizers of former
methods, including uniformity-based and information-maximization methods, which
are variants of our flexible regularization loss with different kernels. Beyond
its practical consequences for state-of-the-art self-supervised learning with
limited resources, the proposed generic regularization approach opens
perspectives to leverage more widely the literature on kernel methods in order
to improve self-supervised learning methods.

In this work we introduce a new transformer architecture called
SparseDistilBERT (SDBERT), which is a combination of sparse attention and
knowledge distillantion (KD). We implemented sparse attention mechanism to
reduce quadratic dependency on input length to linear. In addition to reducing
computational complexity of the model, we used knowledge distillation (KD). We
were able to reduce the size of BERT model by 60% while retaining 97%
performance and it only took 40% of time to train.

AI-based protein structure prediction pipelines, such as AlphaFold2, have
achieved near-experimental accuracy. These advanced pipelines mainly rely on
Multiple Sequence Alignments (MSAs) as inputs to learn the co-evolution
information from the homologous sequences. Nonetheless, searching MSAs from
protein databases is time-consuming, usually taking dozens of minutes.
Consequently, we attempt to explore the limits of fast protein structure
prediction by using only primary sequences of proteins. HelixFold-Single is
proposed to combine a large-scale protein language model with the superior
geometric learning capability of AlphaFold2. Our proposed method,
HelixFold-Single, first pre-trains a large-scale protein language model (PLM)
with thousands of millions of primary sequences utilizing the self-supervised
learning paradigm, which will be used as an alternative to MSAs for learning
the co-evolution information. Then, by combining the pre-trained PLM and the
essential components of AlphaFold2, we obtain an end-to-end differentiable
model to predict the 3D coordinates of atoms from only the primary sequence.
HelixFold-Single is validated in datasets CASP14 and CAMEO, achieving
competitive accuracy with the MSA-based methods on the targets with large
homologous families. Furthermore, HelixFold-Single consumes much less time than
the mainstream pipelines for protein structure prediction, demonstrating its
potential in tasks requiring many predictions. The code of HelixFold-Single is
https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single,
and we also provide stable web services on
https://paddlehelix.baidu.com/app/drug/protein-single/forecast.

Modern deep neural network models are known to erroneously classify
out-of-distribution (OOD) test data into one of the in-distribution (ID)
training classes with high confidence. This can have disastrous consequences
for safety-critical applications. A popular mitigation strategy is to train a
separate classifier that can detect such OOD samples at the test time. In most
practical settings OOD examples are not known at the train time, and hence a
key question is: how to augment the ID data with synthetic OOD samples for
training such an OOD detector? In this paper, we propose a novel Compounded
Corruption technique for the OOD data augmentation termed CnC. One of the major
advantages of CnC is that it does not require any hold-out data apart from the
training set. Further, unlike current state-of-the-art (SOTA) techniques, CnC
does not require backpropagation or ensembling at the test time, making our
method much faster at inference. Our extensive comparison with 20 methods from
the major conferences in last 4 years show that a model trained using CnC based
data augmentation, significantly outperforms SOTA, both in terms of OOD
detection accuracy as well as inference time. We include a detailed post-hoc
analysis to investigate the reasons for the success of our method and identify
higher relative entropy and diversity of CnC samples as probable causes. We
also provide theoretical insights via a piece-wise decomposition analysis on a
two-dimensional dataset to reveal (visually and quantitatively) that our
approach leads to a tighter boundary around ID classes, leading to better
detection of OOD samples. Source code link: https://github.com/cnc-ood

The management of people with long-term or chronic illness is one of the
biggest challenges for national health systems. In fact, these diseases are
among the leading causes of hospitalization, especially for the elderly, and
huge amount of resources required to monitor them leads to problems with
sustainability of the healthcare systems. The increasing diffusion of portable
devices and new connectivity technologies allows the implementation of
telemonitoring system capable of providing support to health care providers and
lighten the burden on hospitals and clinics. In this paper, we present the
implementation of a telemonitoring platform for healthcare, designed to capture
several types of physiological health parameters from different consumer mobile
and custom devices. Consumer medical devices can be integrated into the
platform via the Google Fit ecosystem that supports hundreds of devices, while
custom devices can directly interact with the platform with standard
communication protocols. The platform is designed to process the acquired data
using machine learning algorithms, and to provide patients and physicians the
physiological health parameters with a user-friendly, comprehensive, and easy
to understand dashboard which monitors the parameters through time. Preliminary
usability tests show a good user satisfaction in terms of functionality and
usefulness.

Advanced music recommendation systems are being introduced along with the
development of machine learning. However, it is essential to design a music
recommendation system that can increase user satisfaction by understanding
users' music tastes, not by the complexity of models. Although several studies
related to music recommendation systems exploiting negative preferences have
shown performance improvements, there was a lack of explanation on how they led
to better recommendations. In this work, we analyze the role of negative
preference in users' music tastes by comparing music recommendation models with
contrastive learning exploiting preference (CLEP) but with three different
training strategies - exploiting preferences of both positive and negative
(CLEP-PN), positive only (CLEP-P), and negative only (CLEP-N). We evaluate the
effectiveness of the negative preference by validating each system with a small
amount of personalized data obtained via survey and further illuminate the
possibility of exploiting negative preference in music recommendations. Our
experimental results show that CLEP-N outperforms the other two in accuracy and
false positive rate. Furthermore, the proposed training strategies produced a
consistent tendency regardless of different types of front-end musical feature
extractors, proving the stability of the proposed method.

Owing to its nature of scalability and privacy by design, federated learning
(FL) has received increasing interest in decentralized deep learning. FL has
also facilitated recent research on upscaling and privatizing personalized
recommendation services, using on-device data to learn recommender models
locally. These models are then aggregated globally to obtain a more performant
model, while maintaining data privacy. Typically, federated recommender systems
(FRSs) do not consider the lack of resources and data availability at the
end-devices. In addition, they assume that the interaction data between users
and items is i.i.d. and stationary across end-devices, and that all local
recommender models can be directly averaged without considering the user's
behavioral diversity. However, in real scenarios, recommendations have to be
made on end-devices with sparse interaction data and limited resources.
Furthermore, users' preferences are heterogeneous and they frequently visit new
items. This makes their personal preferences highly skewed, and the
straightforwardly aggregated model is thus ill-posed for such non-i.i.d. data.
In this paper, we propose Resource Efficient Federated Recommender System
(ReFRS) to enable decentralized recommendation with dynamic and diversified
user preferences. On the device side, ReFRS consists of a lightweight
self-supervised local model built upon the variational autoencoder for learning
a user's temporal preference from a sequence of interacted items. On the server
side, ReFRS utilizes a semantic sampler to adaptively perform model aggregation
within each identified user cluster. The clustering module operates in an
asynchronous and dynamic manner to support efficient global model update and
cope with shifting user interests. As a result, ReFRS achieves superior
performance in terms of both accuracy and scalability, as demonstrated by
comparative experiments.

Orchard automation has attracted the attention of researchers recently due to
the shortage of global labor force. To automate tasks in orchards such as
pruning, thinning, and harvesting, a detailed understanding of the tree
structure is required. However, occlusions from foliage and fruits can make it
challenging to predict the position of occluded trunks and branches. This work
proposes a regression-based deep learning model, Hallucination of Occluded
Branch Convolutional Neural Network (HOB-CNN), for tree branch position
prediction in varying occluded conditions. We formulate tree branch position
prediction as a regression problem towards the horizontal locations of the
branch along the vertical direction or vice versa. We present comparative
experiments on Y-shaped trees with two state-of-the-art baselines, representing
common approaches to the problem. Experiments show that HOB-CNN outperform the
baselines at predicting branch position and shows robustness against varying
levels of occlusion. We further validated HOB-CNN against two different types
of 2D trees, and HOB-CNN shows generalization across different trees and
robustness under different occluded conditions.

Path-tracking control of self-driving vehicles can benefit from deep learning
for tackling longstanding challenges such as nonlinearity and uncertainty.
However, deep neural controllers lack safety guarantees, restricting their
practical use. We propose a new approach of learning almost-barrier functions,
which approximately characterizes the forward invariant set for the system
under neural controllers, to quantitatively analyze the safety of deep neural
controllers for path-tracking. We design sampling-based learning procedures for
constructing candidate neural barrier functions, and certification procedures
that utilize robustness analysis for neural networks to identify regions where
the barrier conditions are fully satisfied. We use an adversarial training loop
between learning and certification to optimize the almost-barrier functions.
The learned barrier can also be used to construct online safety monitors
through reachability analysis. We demonstrate effectiveness of our methods in
quantifying safety of neural controllers in various simulation environments,
ranging from simple kinematic models to the TORCS simulator with high-fidelity
vehicle dynamics simulation.

Training machine learning models on massive datasets incurs substantial
computational costs. To alleviate such costs, there has been a sustained effort
to develop data-efficient training methods that can carefully select subsets of
the training examples that generalize on par with the full training data.
However, existing methods are limited in providing theoretical guarantees for
the quality of the models trained on the extracted subsets, and may perform
poorly in practice. We propose AdaCore, a method that leverages the geometry of
the data to extract subsets of the training examples for efficient machine
learning. The key idea behind our method is to dynamically approximate the
curvature of the loss function via an exponentially-averaged estimate of the
Hessian to select weighted subsets (coresets) that provide a close
approximation of the full gradient preconditioned with the Hessian. We prove
rigorous guarantees for the convergence of various first and second-order
methods applied to the subsets chosen by AdaCore. Our extensive experiments
show that AdaCore extracts coresets with higher quality compared to baselines
and speeds up training of convex and non-convex machine learning models, such
as logistic regression and neural networks, by over 2.9x over the full data and
4.5x over random subsets.

While machine learning has emerged in recent years as a useful tool for rapid
prediction of materials properties, generating sufficient data to reliably
train models without overfitting is still impractical for many applications.
Towards overcoming this limitation, we present a general framework for
leveraging complementary information across different models and datasets for
accurate prediction of data scarce materials properties. Our approach, based on
a machine learning paradigm called mixture of experts, outperforms pairwise
transfer learning on 16 of 19 materials property regression tasks, performing
comparably on the remaining three. Unlike pairwise transfer learning, our
framework automatically learns to combine information from multiple source
tasks in a single training run, alleviating the need for brute-force
experiments to determine which source task to transfer from. The approach also
provides an interpretable, model-agnostic, and scalable mechanism to transfer
information from an arbitrary number of models and datasets to any downstream
property prediction task. We anticipate the performance of our framework will
further improve as better model architectures, new pre-training tasks, and
larger materials datasets are developed by the community.

Since convolutional neural networks perform well in learning generalizable
image priors from large-scale data, these models have been widely used in image
denoising tasks. However, the computational complexity increases dramatically
as well on complex model. In this paper, We propose a novel lightweight
Complementary Attention Module, which includes a density module and a sparse
module, which can cooperatively mine dense and sparse features for feature
complementary learning to build an efficient lightweight architecture.
Moreover, to reduce the loss of details caused by denoising, this paper
constructs a gradient-based structure-preserving branch. We utilize
gradient-based branches to obtain additional structural priors for denoising,
and make the model pay more attention to image geometric details through
gradient loss optimization.Based on the above, we propose an efficiently Unet
structured network with dual branch, the visual results show that can
effectively preserve the structural details of the original image, we evaluate
benchmarks including SIDD and DND, where SCANet achieves state-of-the-art
performance in PSNR and SSIM while significantly reducing computational cost.

In this work we initiate the study of the correspondence between $p$-adic
statistical field theories (SFTs) and neural networks (NNs). In general quantum
field theories over a $p$-adic spacetime can be formulated in a rigorous way.
Nowadays these theories are considered just mathematical toy models for
understanding the problems of the true theories. In this work we show these
theories are deeply connected with the deep belief networks (DBNs). Hinton et
al. constructed DBNs by stacking several restricted Boltzmann machines (RBMs).
The purpose of this construction is to obtain a network with a hierarchical
structure (a deep learning architecture). An RBM corresponds a certain spin
glass, thus a DBN should correspond to an ultrametric (hierarchical) spin
glass. A model of such system can be easily constructed by using $p$-adic
numbers. In our approach, a $p$-adic SFT corresponds to a $p$-adic continuous
DBN, and a discretization of this theory corresponds to a $p$-adic discrete
DBN. We show that these last machines are universal approximators. In the
$p$-adic framework, the correspondence between SFTs and NNs is not fully
developed. We point out several open problems.

Despite recent success, deep learning-based methods for predicting 3D garment
deformation under body motion suffer from interpenetration problems between the
garment and the body. To address this problem, we propose a novel collision
handling neural network layer called Repulsive Force Unit (ReFU). Based on the
signed distance function (SDF) of the underlying body and the current garment
vertex positions, ReFU predicts the per-vertex offsets that push any
interpenetrating vertex to a collision-free configuration while preserving the
fine geometric details. We show that ReFU is differentiable with trainable
parameters and can be integrated into different network backbones that predict
3D garment deformations. Our experiments show that ReFU significantly reduces
the number of collisions between the body and the garment and better preserves
geometric details compared to prior methods based on collision loss or
post-processing optimization.

Ultrasound imaging plays an important role in the diagnosis of vascular
lesions. Accurate segmentation of the vascular wall is important for the
prevention, diagnosis and treatment of vascular diseases. However, existing
methods have inaccurate localization of the vascular wall boundary.
Segmentation errors occur in discontinuous vascular wall boundaries and dark
boundaries. To overcome these problems, we propose a new boundary-delineation
network (BDNet). We use the boundary refinement module to re-delineate the
boundary of the vascular wall to obtain the correct boundary location. We
designed the feature extraction module to extract and fuse multi-scale features
and different receptive field features to solve the problem of dark boundaries
and discontinuous boundaries. We use a new loss function to optimize the model.
The interference of class imbalance on model optimization is prevented to
obtain finer and smoother boundaries. Finally, to facilitate clinical
applications, we design the model to be lightweight. Experimental results show
that our model achieves the best segmentation results and significantly reduces
memory consumption compared to existing models for the dataset.

Machine learning algorithms minimizing the average training loss usually
suffer from poor generalization performance due to the greedy exploitation of
correlations among the training data, which are not stable under distributional
shifts. It inspires various works for domain generalization (DG), where a
series of methods, such as Causal Matching and FISH, work by pairwise domain
operations. They would need $O(n^2)$ pairwise domain operations with $n$
domains, where each one is often highly expensive. Moreover, while a common
objective in the DG literature is to learn invariant representations against
domain-induced spurious correlations, we highlight the importance of mitigating
spurious correlations caused by objects. Based on the observation that
diversity helps mitigate spurious correlations, we propose a Diversity boosted
twO-level saMplIng framework (DOMI) utilizing Determinantal Point Processes
(DPPs) to efficiently sample the most informative ones among large number of
domains. We show that DOMI helps train robust models against spurious
correlations from both domain-side and object-side, substantially enhancing the
performance of the backbone DG algorithms on rotated MNIST, rotated Fashion
MNIST, and iwildcam datasets.

We propose application of Machine Learning (ML) and Neural Network (NN)
technique for the analysis of ultrasonic Time Reversal based Nonlinear Elastic
Wave Spectroscopy (TR-NEWS). In order to acquire topological features, we adopt
the $(2+1)D$ lattice simulation with fixed point (FP) actions. We consider 7 A
type loops which sit on $2D$ spacial plane spanned by $e_1, e_2$ and 13 B type
loops which contain links parallel to $e_1\wedge e_2$ and to -$e_1\wedge e_2$.
We consider propagation of bosonic phonons in Fermi-sea of neutral Weyl spinors
which are described by Clifford algebra. Configurations in momentum space is
transformed to real position space via Clifford Fourier Transform. We consider
A-type without hysteresis effects and B-type with hysteresis effects, and via
ML or NN technique search optimal weight of 7 A-type FP actions and 13 B-type
FP actions using the Monte-Carlo method.

Pseudo-labeling has proven to be a promising semi-supervised learning (SSL)
paradigm. Existing pseudo-labeling methods commonly assume that the class
distributions of training data are balanced. However, such an assumption is far
from realistic scenarios and existing pseudo-labeling methods suffer from
severe performance degeneration in the context of class-imbalance. In this
work, we investigate pseudo-labeling under imbalanced semi-supervised setups.
The core idea is to automatically assimilate the training bias arising from
class-imbalance, using a bias adaptive classifier that equips the original
linear classifier with a bias attractor. The bias attractor is designed to be a
light-weight residual network for adapting to the training bias. Specifically,
the bias attractor is learned through a bi-level learning framework such that
the bias adaptive classifier is able to fit imbalanced training data, while the
linear classifier can give unbiased label prediction for each class. We conduct
extensive experiments under various imbalanced semi-supervised setups, and the
results demonstrate that our method can be applicable to different
pseudo-labeling models and superior to the prior arts.

While deep neural networks are capable of achieving state-of-the-art
performance in various domains, their training typically requires iterating for
many passes over the dataset. However, due to computational and memory
constraints and potential privacy concerns, storing and accessing all the data
is impractical in many real-world scenarios where the data arrives in a stream.
In this paper, we investigate the problem of one-pass learning, in which a
model is trained on sequentially arriving data without retraining on previous
datapoints. Motivated by the increasing use of overparameterized models, we
develop Orthogonal Recursive Fitting (ORFit), an algorithm for one-pass
learning which seeks to perfectly fit every new datapoint while changing the
parameters in a direction that causes the least change to the predictions on
previous datapoints. By doing so, we bridge two seemingly distinct algorithms
in adaptive filtering and machine learning, namely the recursive least-squares
(RLS) algorithm and orthogonal gradient descent (OGD). Our algorithm uses the
memory efficiently by exploiting the structure of the streaming data via an
incremental principal component analysis (IPCA). Further, we show that, for
overparameterized linear models, the parameter vector obtained by our algorithm
is what stochastic gradient descent (SGD) would converge to in the standard
multi-pass setting. Finally, we generalize the results to the nonlinear setting
for highly overparameterized models, relevant for deep learning. Our
experiments show the effectiveness of the proposed method compared to the

Inductive link prediction for knowledge graph aims at predicting missing
links between unseen entities, those not shown in training stage. Most previous
works learn entity-specific embeddings of entities, which cannot handle unseen
entities. Recent several methods utilize enclosing subgraph to obtain inductive
ability. However, all these works only consider the enclosing part of subgraph
without complete neighboring relations, which leads to the issue that partial
neighboring relations are neglected, and sparse subgraphs are hard to be
handled. To address that, we propose Subgraph Neighboring Relations Infomax,
SNRI, which sufficiently exploits complete neighboring relations from two
aspects: neighboring relational feature for node feature and neighboring
relational path for sparse subgraph. To further model neighboring relations in
a global way, we innovatively apply mutual information (MI) maximization for
knowledge graph. Experiments show that SNRI outperforms existing state-of-art
methods by a large margin on inductive link prediction task, and verify the
effectiveness of exploring complete neighboring relations in a global way to
characterize node features and reason on sparse subgraphs.

Sparse general matrix multiplication (SpGEMM) is a fundamental building block
in numerous scientific applications. One critical task of SpGEMM is to compute
or predict the structure of the output matrix (i.e., the number of nonzero
elements per output row) for efficient memory allocation and load balance,
which impact the overall performance of SpGEMM. Existing work either precisely
calculates the output structure or adopts upper-bound or sampling-based methods
to predict the output structure. However, these methods either take much
execution time or are not accurate enough. In this paper, we propose a novel
sampling-based method with better accuracy and low costs compared to the
existing sampling-based method. The proposed method first predicts the
compression ratio of SpGEMM by leveraging the number of intermediate products
(denoted as FLOP) and the number of nonzero elements (denoted as NNZ) of the
same sampled result matrix. And then, the predicted output structure is
obtained by dividing the FLOP per output row by the predicted compression
ratio. We also propose a reference design of the existing sampling-based method
with optimized computing overheads to demonstrate the better accuracy of the
proposed method. We construct 625 test cases with various matrix dimensions and
sparse structures to evaluate the prediction accuracy. Experimental results
show that the absolute relative errors of the proposed method and the reference
design are 1.56\% and 8.12\%, respectively, on average, and 25\% and 156\%,
respectively, in the worst case.

Information retrieval from brain responses to auditory and visual stimuli has
shown success through classification of song names and image classes presented
to participants while recording EEG signals. Information retrieval in the form
of reconstructing auditory stimuli has also shown some success, but here we
improve on previous methods by reconstructing music stimuli well enough to be
perceived and identified independently. Furthermore, deep learning models were
trained on time-aligned music stimuli spectrum for each corresponding
one-second window of EEG recording, which greatly reduces feature extraction
steps needed when compared to prior studies. The NMED-Tempo and NMED-Hindi
datasets of participants passively listening to full length songs were used to
train and validate Convolutional Neural Network (CNN) regressors. The efficacy
of raw voltage versus power spectrum inputs and linear versus mel spectrogram
outputs were tested, and all inputs and outputs were converted into 2D images.
The quality of reconstructed spectrograms was assessed by training classifiers
which showed 81% accuracy for mel-spectrograms and 72% for linear spectrograms
(10% chance accuracy). Lastly, reconstructions of auditory music stimuli were
discriminated by listeners at an 85% success rate (50% chance) in a
two-alternative match-to-sample task.

In this paper, we demonstrate a unique recipe to enhance the effectiveness of
audio machine learning approaches by fusing pre-processing techniques into a
deep learning model. Our solution accelerates training and inference
performance by optimizing hyper-parameters through training instead of costly
random searches to build a reliable mosquito detector from audio signals. The
experiments and the results presented here are part of the MOS C submission of
the ACM 2022 challenge. Our results outperform the published baseline by 212%
on the unpublished test set. We believe that this is one of the best real-world
examples of building a robust bio-acoustic system that provides reliable
mosquito detection in noisy conditions.

Influenza viruses mutate rapidly and can pose a threat to public health,
especially to those in vulnerable groups. Throughout history, influenza A
viruses have caused pandemics between different species. It is important to
identify the origin of a virus in order to prevent the spread of an outbreak.
Recently, there has been increasing interest in using machine learning
algorithms to provide fast and accurate predictions for viral sequences. In
this study, real testing data sets and a variety of evaluation metrics were
used to evaluate machine learning algorithms at different taxonomic levels. As
hemagglutinin is the major protein in the immune response, only hemagglutinin
sequences were used and represented by position-specific scoring matrix and
word embedding. The results suggest that the 5-grams-transformer neural network
is the most effective algorithm for predicting viral sequence origins, with
approximately 99.54% AUCPR, 98.01% F1 score and 96.60% MCC at a higher
classification level, and approximately 94.74% AUCPR, 87.41% F1 score and
80.79% MCC at a lower classification level.

In this article, we present a new EEG signal classification framework by
integrating the complex-valued and real-valued Convolutional Neural
Network(CNN) with discrete Fourier transform (DFT). The proposed neural network
architecture consists of one complex-valued convolutional layer, two
real-valued convolutional layers, and three fully connected layers. Our method
can efficiently utilize the phase information contained in the DFT. We validate
our approach using two simulated EEG signals and a benchmark data set and
compare it with two widely used frameworks. Our method drastically reduces the
number of parameters used and improves accuracy when compared with the existing
methods in classifying benchmark data sets, and significantly improves
performance in classifying simulated EEG signals.

Uncovering the formation process that reproduces the distinct properties of
compact super-Earth exoplanet systems is a major goal of planet formation
theory. The most successful model argues that non-resonant systems begin as
resonant chains of planets that later experience a dynamical instability.
However, both the boundary of stability in resonant chains and the mechanism of
the instability itself are poorly understood. Previous work postulated that a
secondary resonance between the fastest libration frequency and a difference in
synodic frequencies destabilizes the system. Here, we use that hypothesis to
produce a simple and general criterion for resonant chain stability that
depends only on planet orbital periods and masses. We show that the criterion
accurately predicts the maximum mass of planets in synthetic resonant chains up
to six planets. More complicated resonant chains produced in population
synthesis simulations are found to be less stable than expected, although our
criterion remains useful and superior to machine learning models.

Limited computing resources of internet-of-things (IoT) nodes incur
prohibitive latency in processing input data. This triggers new research
opportunities toward task offloading systems where edge servers handle
intensive computations of IoT devices. Deploying the computing servers at
existing base stations may not be sufficient to support IoT nodes operating in
a harsh environment. This requests mobile edge servers to be mounted on
unmanned aerial vehicles (UAVs) that provide on-demand mobile edge computing
(MEC) services. Time-varying offloading demands and mobility of UAVs need a
joint design of the optimization variables for all time instances. Therefore,
an online decision mechanism is essential for UAV-aided MEC networks. This
article presents an overview of recent deep reinforcement learning (DRL)
approaches where decisions about UAVs and IoT nodes are taken in an online
manner. Specifically, joint optimization over task offloading, resource
allocation, and UAV mobility is addressed from the DRL perspective. For the
decentralized implementation, a multi-agent DRL method is proposed where
multiple intelligent UAVs cooperatively determine their computations and
communication policies without central coordination. Numerical results
demonstrate that the proposed decentralized learning strategy is superior to
existing DRL solutions. The proposed framework sheds light on the viability of
the decentralized DRL techniques in designing self-organizing IoT networks.

Network Slicing (NS) is crucial for efficiently enabling divergent network
applications in next generation networks. Nonetheless, the complex Quality of
Service (QoS) requirements and diverse heterogeneity in network services
entails high computational time for Network Slice Provisioning (NSP)
optimization. The legacy optimization methods are challenging to meet the low
latency and high reliability of network applications. To this end, we model the
real-time NSP as an Online Network Slice Provisioning (ONSP) problem.
Specifically, we formulate the ONSP problem as an online Multi-Objective
Integer Programming Optimization (MOIPO) problem. Then, we approximate the
solution of the MOIPO problem by applying the Proximal Policy Optimization
(PPO) method to the traffic demand prediction. Our simulation results show the
effectiveness of the proposed method compared to the state-of-the-art MOIPO
solvers with a lower SLA violation rate and network operation cost.

Transformer encoder architectures have recently achieved state-of-the-art
results on monocular 3D human mesh reconstruction, but they require a
substantial number of parameters and expensive computations. Due to the large
memory overhead and slow inference speed, it is difficult to deploy such models
for practical use. In this paper, we propose a novel transformer
encoder-decoder architecture for 3D human mesh reconstruction from a single
image, called FastMETRO. We identify the performance bottleneck in the
encoder-based transformers is caused by the token design which introduces high
complexity interactions among input tokens. We disentangle the interactions via
an encoder-decoder architecture, which allows our model to demand much fewer
parameters and shorter inference time. In addition, we impose the prior
knowledge of human body's morphological relationship via attention masking and
mesh upsampling operations, which leads to faster convergence with higher
accuracy. Our FastMETRO improves the Pareto-front of accuracy and efficiency,
and clearly outperforms image-based methods on Human3.6M and 3DPW. Furthermore,
we validate its generalizability on FreiHAND.

Transfer learning is an increasingly common approach for developing
performant RL agents. However, it is not well understood how to define the
relationship between the source and target tasks, and how this relationship
contributes to successful transfer. We present an algorithm called Structural
Similarity for Two MDPS, or SS2, that calculates a state similarity measure for
states in two finite MDPs based on previously developed bisimulation metrics,
and show that the measure satisfies properties of a distance metric. Then,
through empirical results with GridWorld navigation tasks, we provide evidence
that the distance measure can be used to improve transfer performance for
Q-Learning agents over previous implementations.

We present Pose-NDF, a continuous model for plausible human poses based on
neural distance fields (NDFs). Pose or motion priors are important for
generating realistic new poses and for reconstructing accurate poses from noisy
or partial observations. Pose-NDF learns a manifold of plausible poses as the
zero level set of a neural implicit function, extending the idea of modeling
implicit surfaces in 3D to the high-dimensional domain SO(3)^K, where a human
pose is defined by a single data point, represented by K quaternions. The
resulting high-dimensional implicit function can be differentiated with respect
to the input poses and thus can be used to project arbitrary poses onto the
manifold by using gradient descent on the set of 3-dimensional hyperspheres. In
contrast to previous VAE-based human pose priors, which transform the pose
space into a Gaussian distribution, we model the actual pose manifold,
preserving the distances between poses. We demonstrate that PoseNDF outperforms
existing state-of-the-art methods as a prior in various downstream tasks,
ranging from denoising real-world human mocap data, pose recovery from occluded
data to 3D pose reconstruction from images. Furthermore, we show that it can be
used to generate more diverse poses by random sampling and projection than
VAE-based methods.

In this work we introduce a novel meta-learning method for sleep scoring
based on self-supervised learning. Our approach aims at building models for
sleep scoring that can generalize across different patients and recording
facilities, but do not require a further adaptation step to the target data.
Towards this goal, we build our method on top of the Model Agnostic
Meta-Learning (MAML) framework by incorporating a self-supervised learning
(SSL) stage, and call it S2MAML. We show that S2MAML can significantly
outperform MAML. The gain in performance comes from the SSL stage, which we
base on a general purpose pseudo-task that limits the overfitting to the
subject-specific patterns present in the training dataset. We show that S2MAML
outperforms standard supervised learning and MAML on the SC, ST, ISRUC, UCD and
CAP datasets.

There has been a plethora of work towards improving robot perception and
navigation, yet their application in hazardous environments, like during a fire
or an earthquake, is still at a nascent stage. We hypothesize two key
challenges here: first, it is difficult to replicate such scenarios in the real
world, which is necessary for training and testing purposes. Second, current
systems are not fully able to take advantage of the rich multi-modal data
available in such hazardous environments. To address the first challenge, we
propose to harness the enormous amount of visual content available in the form
of movies and TV shows, and develop a dataset that can represent hazardous
environments encountered in the real world. The data is annotated with
high-level danger ratings for realistic disaster images, and corresponding
keywords are provided that summarize the content of the scene. In response to
the second challenge, we propose a multi-modal danger estimation pipeline for
collaborative human-robot escape scenarios. Our Bayesian framework improves
danger estimation by fusing information from robot's camera sensor and language
inputs from the human. Furthermore, we augment the estimation module with a
risk-aware planner that helps in identifying safer paths out of the dangerous
environment. Through extensive simulations, we exhibit the advantages of our
multi-modal perception framework that gets translated into tangible benefits
such as higher success rate in a collaborative human-robot mission.

Optical imaging is commonly used for both scientific and technological
applications across industry and academia. In image sensing, a measurement,
such as of an object's position, is performed by computational analysis of a
digitized image. An emerging image-sensing paradigm breaks this delineation
between data collection and analysis by designing optical components to perform
not imaging, but encoding. By optically encoding images into a compressed,
low-dimensional latent space suitable for efficient post-analysis, these image
sensors can operate with fewer pixels and fewer photons, allowing
higher-throughput, lower-latency operation. Optical neural networks (ONNs)
offer a platform for processing data in the analog, optical domain. ONN-based
sensors have however been limited to linear processing, but nonlinearity is a
prerequisite for depth, and multilayer NNs significantly outperform shallow NNs
on many tasks. Here, we realize a multilayer ONN pre-processor for image
sensing, using a commercial image intensifier as a parallel optoelectronic,
optical-to-optical nonlinear activation function. We demonstrate that the
nonlinear ONN pre-processor can achieve compression ratios of up to 800:1 while
still enabling high accuracy across several representative computer-vision
tasks, including machine-vision benchmarks, flow-cytometry image
classification, and identification of objects in real scenes. In all cases we
find that the ONN's nonlinearity and depth allowed it to outperform a purely
linear ONN encoder. Although our experiments are specialized to ONN sensors for
incoherent-light images, alternative ONN platforms should facilitate a range of
ONN sensors. These ONN sensors may surpass conventional sensors by
pre-processing optical information in spatial, temporal, and/or spectral
dimensions, potentially with coherent and quantum qualities, all natively in
the optical domain.

Today's Mixed Reality head-mounted displays track the user's head pose in
world space as well as the user's hands for interaction in both Augmented
Reality and Virtual Reality scenarios. While this is adequate to support user
input, it unfortunately limits users' virtual representations to just their
upper bodies. Current systems thus resort to floating avatars, whose limitation
is particularly evident in collaborative settings. To estimate full-body poses
from the sparse input sources, prior work has incorporated additional trackers
and sensors at the pelvis or lower body, which increases setup complexity and
limits practical application in mobile settings. In this paper, we present
AvatarPoser, the first learning-based method that predicts full-body poses in
world coordinates using only motion input from the user's head and hands. Our
method builds on a Transformer encoder to extract deep features from the input
signals and decouples global motion from the learned local joint orientations
to guide pose estimation. To obtain accurate full-body motions that resemble
motion capture animations, we refine the arm joints' positions using an
optimization routine with inverse kinematics to match the original tracking
input. In our evaluation, AvatarPoser achieved new state-of-the-art results in
evaluations on large motion capture datasets (AMASS). At the same time, our
method's inference speed supports real-time operation, providing a practical
interface to support holistic avatar control and representation for Metaverse

Graph neural networks (GNNs) are emerging in chemical engineering for the
end-to-end learning of physicochemical properties based on molecular graphs. A
key element of GNNs is the pooling function which combines atom feature vectors
into molecular fingerprints. Most previous works use a standard pooling
function to predict a variety of properties. However, unsuitable pooling
functions can lead to unphysical GNNs that poorly generalize. We compare and
select meaningful GNN pooling methods based on physical knowledge about the
learned properties. The impact of physical pooling functions is demonstrated
with molecular properties calculated from quantum mechanical computations. We
also compare our results to the recent set2set pooling approach. We recommend
using sum pooling for the prediction of properties that depend on molecular
size and compare pooling functions for properties that are molecular
size-independent. Overall, we show that the use of physical pooling functions
significantly enhances generalization.

Analyzing classification model performance is a crucial task for machine
learning practitioners. While practitioners often use count-based metrics
derived from confusion matrices, like accuracy, many applications, such as
weather prediction, sports betting, or patient risk prediction, rely on a
classifier's predicted probabilities rather than predicted labels. In these
instances, practitioners are concerned with producing a calibrated model, that
is, one which outputs probabilities that reflect those of the true
distribution. Model calibration is often analyzed visually, through static
reliability diagrams, however, the traditional calibration visualization may
suffer from a variety of drawbacks due to the strong aggregations it
necessitates. Furthermore, count-based approaches are unable to sufficiently
analyze model calibration. We present Calibrate, an interactive reliability
diagram that addresses the aforementioned issues. Calibrate constructs a
reliability diagram that is resistant to drawbacks in traditional approaches,
and allows for interactive subgroup analysis and instance-level inspection. We
demonstrate the utility of Calibrate through use cases on both real-world and
synthetic data. We further validate Calibrate by presenting the results of a
think-aloud experiment with data scientists who routinely analyze model
calibration.

Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks
(CNNs), aggregate the message of nodes' neighbors and structure information to
acquire expressive representations of nodes for node classification, graph
classification, and link prediction. Previous studies have indicated that GNNs
are vulnerable to Membership Inference Attacks (MIAs), which infer whether a
node is in the training data of GNNs and leak the node's private information,
like the patient's disease history. The implementation of previous MIAs takes
advantage of the models' probability output, which is infeasible if GNNs only
provide the prediction label (label-only) for the input.
  In this paper, we propose a label-only MIA against GNNs for node
classification with the help of GNNs' flexible prediction mechanism, e.g.,
obtaining the prediction label of one node even when neighbors' information is
unavailable. Our attacking method achieves around 60\% accuracy, precision, and
Area Under the Curve (AUC) for most datasets and GNN models, some of which are
competitive or even better than state-of-the-art probability-based MIAs
implemented under our environment and settings. Additionally, we analyze the
influence of the sampling method, model selection approach, and overfitting
level on the attack performance of our label-only MIA. Both of those factors
have an impact on the attack performance. Then, we consider scenarios where
assumptions about the adversary's additional dataset (shadow dataset) and extra
information about the target model are relaxed. Even in those scenarios, our
label-only MIA achieves a better attack performance in most cases. Finally, we
explore the effectiveness of possible defenses, including Dropout,
Regularization, Normalization, and Jumping knowledge. None of those four
defenses prevent our attack completely.

Objectives: The purpose is to apply a previously validated deep learning
algorithm to a new thyroid nodule ultrasound image dataset and compare its
performances with radiologists. Methods: Prior study presented an algorithm
which is able to detect thyroid nodules and then make malignancy
classifications with two ultrasound images. A multi-task deep convolutional
neural network was trained from 1278 nodules and originally tested with 99
separate nodules. The results were comparable with that of radiologists. The
algorithm was further tested with 378 nodules imaged with ultrasound machines
from different manufacturers and product types than the training cases. Four
experienced radiologists were requested to evaluate the nodules for comparison
with deep learning. Results: The Area Under Curve (AUC) of the deep learning
algorithm and four radiologists were calculated with parametric, binormal
estimation. For the deep learning algorithm, the AUC was 0.70 (95% CI: 0.64 -
0.75). The AUC of radiologists were 0.66 (95% CI: 0.61 - 0.71), 0.67 (95%
CI:0.62 - 0.73), 0.68 (95% CI: 0.63 - 0.73), and 0.66 (95%CI: 0.61 - 0.71).
Conclusion: In the new testing dataset, the deep learning algorithm achieved
similar performances with all four radiologists.

We introduce GAUDI, a generative model capable of capturing the distribution
of complex and realistic 3D scenes that can be rendered immersively from a
moving camera. We tackle this challenging problem with a scalable yet powerful
approach, where we first optimize a latent representation that disentangles
radiance fields and camera poses. This latent representation is then used to
learn a generative model that enables both unconditional and conditional
generation of 3D scenes. Our model generalizes previous works that focus on
single objects by removing the assumption that the camera pose distribution can
be shared across samples. We show that GAUDI obtains state-of-the-art
performance in the unconditional generative setting across multiple datasets
and allows for conditional generation of 3D scenes given conditioning variables
like sparse image observations or text that describes the scene.

The physics of active biological matter, such as bacterial colonies and bird
flocks, exhibiting interesting self-organizing dynamical behavior has gained
considerable importance in recent years. Recent theoretical advances use
techniques from hydrodynamics, kinetic theory, and non-equilibrium statistical
physics. However, for biological agents, these don't seem to recognize
explicitly their critical feature, namely, the role of survival-driven purpose
and the attendant pursuit of maximum utility. Here, we propose a novel
game-theoretic framework and show a surprising result that the bird-like
agents, garuds, self-organize dynamically into flocks to approach a stable
arbitrage equilibrium of equal effective utilities. While it has been
well-known for three centuries that there are constants of motion for passive
matter, it comes as a surprise to discover that the dynamics of active matter
populations could also have an invariant. This is essentially the invisible
hand mechanism of Adam Smith's in a biological context. What we demonstrate is
for ideal systems, similar to the ideal gas or Ising model in thermodynamics.
The next steps would involve examining and learning how real swarms behave
compared to their ideal versions. Our theory is not limited to just birds
flocking but can be adapted for the self-organizing dynamics of other active
matter systems.

Hawkes processes have recently gained increasing attention from the machine
learning community for their versatility in modeling event sequence data. While
they have a rich history going back decades, some of their properties, such as
sample complexity for learning the parameters and releasing differentially
private versions, are yet to be thoroughly analyzed. In this work, we study
standard Hawkes processes with background intensity $\mu$ and excitation
function $\alpha e^{-\beta t}$. We provide both non-private and differentially
private estimators of $\mu$ and $\alpha$, and obtain sample complexity results
in both settings to quantify the cost of privacy. Our analysis exploits the
strong mixing property of Hawkes processes and classical central limit theorem
results for weakly dependent random variables. We validate our theoretical
findings on both synthetic and real datasets.

Visual understanding of geometric structures with complex spatial
relationships is a fundamental component of human intelligence. As children, we
learn how to reason about structure not only from observation, but also by
interacting with the world around us -- by taking things apart and putting them
back together again. The ability to reason about structure and compositionality
allows us to not only build things, but also understand and reverse-engineer
complex systems. In order to advance research in interactive reasoning for
part-based geometric understanding, we propose a challenging new assembly
problem using LEGO bricks that we call Break and Make. In this problem an agent
is given a LEGO model and attempts to understand its structure by interactively
inspecting and disassembling it. After this inspection period, the agent must
then prove its understanding by rebuilding the model from scratch using
low-level action primitives. In order to facilitate research on this problem we
have built LTRON, a fully interactive 3D simulator that allows learning agents
to assemble, disassemble and manipulate LEGO models. We pair this simulator
with a new dataset of fan-made LEGO creations that have been uploaded to the
internet in order to provide complex scenes containing over a thousand unique
brick shapes. We take a first step towards solving this problem using
sequence-to-sequence models that provide guidance for how to make progress on
this challenging problem. Our simulator and data are available at
github.com/aaronwalsman/ltron. Additional training code and PyTorch examples
are available at github.com/aaronwalsman/ltron-torch-eccv22.

We present a new X-Ray Accretion Disk-wind Emulator (\textsc{xrade}) based on
the 2.5D Monte Carlo radiative transfer code which provides a
physically-motivated, self-consistent treatment of both absorption and emission
from a disk-wind by computing the local ionization state and velocity field
within the flow. \textsc{xrade} is then implemented through a process that
combines X-ray tracing with supervised machine learning. We develop a novel
emulation method consisting in training, validating, and testing the simulated
disk-wind spectra into a purposely built artificial neural network. The trained
emulator can generate a single synthetic spectrum for a particular parameter
set in a fraction of a second, in contrast to the few hours required by a
standard Monte Carlo radiative transfer pipeline. The emulator does not suffer
from interpolation issues with multi-dimensional spaces that are typically
faced by traditional X-ray fitting packages such as \textsc{xspec}.
\textsc{xrade} will be suitable to a wide number of sources across the
black-hole mass, ionizing luminosity, and accretion rate scales. As an example,
we demonstrate the applicability of \textsc{xrade} to the physical
interpretation of the X-ray spectra of the bright quasar PDS 456, which hosts
the best-established accretion-disk wind observed to date. We anticipate that
our emulation method will be an indispensable tool for the development of
high-resolution theoretical models, with the necessary flexibility to be
optimized for the next generation micro-calorimeters on board future missions,
like \textit{XRISM/resolve} and \textit{Athena/X-IFU}. This tool can also be
implemented across a wide variety of X-ray spectral models and beyond.

Uncertainty quantification is one of the central challenges for machine
learning in real-world applications. In reinforcement learning, an agent
confronts two kinds of uncertainty, called epistemic uncertainty and aleatoric
uncertainty. Disentangling and evaluating these uncertainties simultaneously
stands a chance of improving the agent's final performance, accelerating
training, and facilitating quality assurance after deployment. In this work, we
propose an uncertainty-aware reinforcement learning algorithm for continuous
control tasks that extends the Deep Deterministic Policy Gradient algorithm
(DDPG). It exploits epistemic uncertainty to accelerate exploration and
aleatoric uncertainty to learn a risk-sensitive policy. We conduct numerical
experiments showing that our variant of DDPG outperforms vanilla DDPG without
uncertainty estimation in benchmark tasks on robotic control and power-grid

Memristors, emerging non-volatile memory devices, have shown promising
potential in neuromorphic hardware designs, especially in spiking neural
network (SNN) hardware implementation. Memristor-based SNNs have been
successfully applied in a wide range of various applications, including image
classification and pattern recognition. However, implementing memristor-based
SNNs in text classification is still under exploration. One of the main reasons
is that training memristor-based SNNs for text classification is costly due to
the lack of efficient learning rules and memristor non-idealities. To address
these issues and accelerate the research of exploring memristor-based spiking
neural networks in text classification applications, we develop a simulation
framework with a virtual memristor array using an empirical memristor model. We
use this framework to demonstrate a sentiment analysis task in the IMDB movie
reviews dataset. We take two approaches to obtain trained spiking neural
networks with memristor models: 1) by converting a pre-trained artificial
neural network (ANN) to a memristor-based SNN, or 2) by training a
memristor-based SNN directly. These two approaches can be applied in two
scenarios: offline classification and online training. We achieve the
classification accuracy of 85.88% by converting a pre-trained ANN to a
memristor-based SNN and 84.86% by training the memristor-based SNN directly,
given that the baseline training accuracy of the equivalent ANN is 86.02%. We
conclude that it is possible to achieve similar classification accuracy in
simulation from ANNs to SNNs and from non-memristive synapses to data-driven
memristive synapses. We also investigate how global parameters such as spike
train length, the read noise, and the weight updating stop conditions affect
the neural networks in both approaches.

Our method studies the complex task of object-centric 3D understanding from a
single RGB-D observation. As it is an ill-posed problem, existing methods
suffer from low performance for both 3D shape and 6D pose and size estimation
in complex multi-object scenarios with occlusions. We present ShAPO, a method
for joint multi-object detection, 3D textured reconstruction, 6D object pose
and size estimation. Key to ShAPO is a single-shot pipeline to regress shape,
appearance and pose latent codes along with the masks of each object instance,
which is then further refined in a sparse-to-dense fashion. A novel
disentangled shape and appearance database of priors is first learned to embed
objects in their respective shape and appearance space. We also propose a
novel, octree-based differentiable optimization step, allowing us to further
improve object shape, pose and appearance simultaneously under the learned
latent space, in an analysis-by-synthesis fashion. Our novel joint implicit
textured object representation allows us to accurately identify and reconstruct
novel unseen objects without having access to their 3D meshes. Through
extensive experiments, we show that our method, trained on simulated indoor
scenes, accurately regresses the shape, appearance and pose of novel objects in
the real-world with minimal fine-tuning. Our method significantly out-performs
all baselines on the NOCS dataset with an 8% absolute improvement in mAP for 6D
pose estimation. Project page:
https://zubair-irshad.github.io/projects/ShAPO.html

Existing perceptual similarity metrics assume an image and its reference are
well aligned. As a result, these metrics are often sensitive to a small
alignment error that is imperceptible to the human eyes. This paper studies the
effect of small misalignment, specifically a small shift between the input and
reference image, on existing metrics, and accordingly develops a shift-tolerant
similarity metric. This paper builds upon LPIPS, a widely used learned
perceptual similarity metric, and explores architectural design considerations
to make it robust against imperceptible misalignment. Specifically, we study a
wide spectrum of neural network elements, such as anti-aliasing filtering,
pooling, striding, padding, and skip connection, and discuss their roles in
making a robust metric. Based on our studies, we develop a new deep neural
network-based perceptual similarity metric. Our experiments show that our
metric is tolerant to imperceptible shifts while being consistent with the
human similarity judgment.

Consider a user who wishes to store a file in multiple servers such that at
least t servers are needed to reconstruct the file, and z colluding servers
cannot learn any information about the file. Unlike traditional secret-sharing
models, where perfectly secure channels are assumed to be available at no cost
between the user and each server, we assume that the user can only send data to
the servers via a public channel, and that the user and each server share an
individual secret key with length n. For a given n, we determine the maximal
length of the file that the user can store, and thus quantify the necessary
cost to store a file of a certain length, in terms of the length of the secret
keys that the user needs to share with the servers. Additionally, for this
maximal file length, we determine (i) the optimal amount of local randomness
needed at the user, (ii) the optimal amount of public communication from the
user to the servers, and (iii) the optimal amount of storage requirement at the
servers.

Convolutional Neural Networks have become the norm in image classification.
Nevertheless, their difficulty to maintain high accuracy across datasets has
become apparent in the past few years. In order to utilize such models in
real-world scenarios and applications, they must be able to provide trustworthy
predictions on unseen data. In this paper, we argue that extracting features
from a CNN's intermediate layers can assist in the model's final prediction.
Specifically, we adapt the Hypercolumns method to a ResNet-18 and find a
significant increase in the model's accuracy, when evaluating on the NICO
dataset.

Vizier is the de-facto blackbox and hyperparameter optimization service
across Google, having optimized some of Google's largest products and research
efforts. To operate at the scale of tuning thousands of users' critical
systems, Google Vizier solved key design challenges in providing multiple
different features, while remaining fully fault-tolerant. In this paper, we
introduce Open Source (OSS) Vizier, a standalone Python-based interface for
blackbox optimization and research, based on the Google-internal Vizier
infrastructure and framework. OSS Vizier provides an API capable of defining
and solving a wide variety of optimization problems, including multi-metric,
early stopping, transfer learning, and conditional search. Furthermore, it is
designed to be a distributed system that assures reliability, and allows
multiple parallel evaluations of the user's objective function. The flexible
RPC-based infrastructure allows users to access OSS Vizier from binaries
written in any language. OSS Vizier also provides a back-end ("Pythia") API
that gives algorithm authors a way to interface new algorithms with the core
OSS Vizier system. OSS Vizier is available at https://github.com/google/vizier.

We present a fast and numerically accurate method for expanding digitized $L
\times L$ images representing functions on $[-1,1]^2$ supported on the disk
$\{x \in \mathbb{R}^2 : |x|<1\}$ in the harmonics (Dirichlet Laplacian
eigenfunctions) on the disk. Our method runs in $\mathcal{O}(L^2 \log L)$
operations. This basis is also known as the Fourier-Bessel basis and it has
several computational advantages: it is orthogonal, ordered by frequency, and
steerable in the sense that images expanded in the basis can be rotated by
applying a diagonal transform to the coefficients. Moreover, we show that
convolution with radial functions can also be efficiently computed by applying
a diagonal transform to the coefficients.

Existing video frame interpolation methods can only interpolate the frame at
a given intermediate time-step, e.g. 1/2. In this paper, we aim to explore a
more generalized kind of video frame interpolation, that at an arbitrary
time-step. To this end, we consider processing different time-steps with
adaptively generated convolutional kernels in a unified way with the help of
meta-learning. Specifically, we develop a dual meta-learned frame interpolation
framework to synthesize intermediate frames with the guidance of context
information and optical flow as well as taking the time-step as side
information. First, a content-aware meta-learned flow refinement module is
built to improve the accuracy of the optical flow estimation based on the
down-sampled version of the input frames. Second, with the refined optical flow
and the time-step as the input, a motion-aware meta-learned frame interpolation
module generates the convolutional kernels for every pixel used in the
convolution operations on the feature map of the coarse warped version of the
input frames to generate the predicted frame. Extensive qualitative and
quantitative evaluations, as well as ablation studies, demonstrate that, via
introducing meta-learning in our framework in such a well-designed way, our
method not only achieves superior performance to state-of-the-art frame
interpolation approaches but also owns an extended capacity to support the
interpolation at an arbitrary time-step.

There has been a growing number of machine learning methods for approximately
solving the travelling salesman problem. However, these methods often require
solved instances for training or use complex reinforcement learning approaches
that need a large amount of tuning. To avoid these problems, we introduce a
novel unsupervised learning approach. We use a relaxation of an integer linear
program for TSP to construct a loss function that does not require correct
instance labels. With variable discretization, its minimum coincides with the
optimal or near-optimal solution. Furthermore, this loss function is
differentiable and thus can be used to train neural networks directly. We use
our loss function with a Graph Neural Network and design controlled experiments
on both Euclidean and asymmetric TSP. Our approach has the advantage over
supervised learning of not requiring large labelled datasets. In addition, the
performance of our approach surpasses reinforcement learning for asymmetric TSP
and is comparable to reinforcement learning for Euclidean instances. Our
approach is also more stable and easier to train than reinforcement learning.

Intrusion detection systems (IDS) generate a large number of false alerts
which makes it difficult to inspect true positives. Hence, alert prioritization
plays a crucial role in deciding which alerts to investigate from an enormous
number of alerts that are generated by IDS. Recently, deep reinforcement
learning (DRL) based deep deterministic policy gradient (DDPG) off-policy
method has shown to achieve better results for alert prioritization as compared
to other state-of-the-art methods. However, DDPG is prone to the problem of
overfitting. Additionally, it also has a poor exploration capability and hence
it is not suitable for problems with a stochastic environment. To address these
limitations, we present a soft actor-critic based DRL algorithm for alert
prioritization (SAC-AP), an off-policy method, based on the maximum entropy
reinforcement learning framework that aims to maximize the expected reward
while also maximizing the entropy. Further, the interaction between an
adversary and a defender is modeled as a zero-sum game and a double oracle
framework is utilized to obtain the approximate mixed strategy Nash equilibrium
(MSNE). SAC-AP finds robust alert investigation policies and computes pure
strategy best response against opponent's mixed strategy. We present the
overall design of SAC-AP and evaluate its performance as compared to other
state-of-the art alert prioritization methods. We consider defender's loss,
i.e., the defender's inability to investigate the alerts that are triggered due
to attacks, as the performance metric. Our results show that SAC-AP achieves up
to 30% decrease in defender's loss as compared to the DDPG based alert
prioritization method and hence provides better protection against intrusions.
Moreover, the benefits are even higher when SAC-AP is compared to other
traditional alert prioritization methods including Uniform, GAIN, RIO and
Suricata.

Using a data-driven machine learning tool we report $T_{\text{eff}}$,
$\log{(g)}$, $v\sin{(i)}$, and elemental abundances for 15 elements (C, N, O,
Na, Mg, Al, Si, Ca, Ti, V, Cr, Mn, Fe, Ni, Y) for a sample of 25 exoplanet host
stars targeted by JWST's first year of observations. The chemical diversity of
these stars show that, while a number of their companion planets may have
formed in a disk with chemistry similar to Solar, some JWST targets likely
experienced different disk compositions. This sample is part of a larger
forthcoming catalog that will report homogeneous abundances of $\sim$4,500 FGK
stars derived from Keck/HIRES spectra.

It is known that representations from self-supervised pre-training can
perform on par, and often better, on various downstream tasks than
representations from fully-supervised pre-training. This has been shown in a
host of settings such as generic object classification and detection, semantic
segmentation, and image retrieval. However, some issues have recently come to
the fore that demonstrate some of the failure modes of self-supervised
representations, such as performance on non-ImageNet-like data, or complex
scenes. In this paper, we show that self-supervised representations based on
the instance discrimination objective lead to better representations of objects
that are more robust to changes in the viewpoint and perspective of the object.
We perform experiments of modern self-supervised methods against multiple
supervised baselines to demonstrate this, including approximating object
viewpoint variation through homographies, and real-world tests based on several
multi-view datasets. We find that self-supervised representations are more
robust to object viewpoint and appear to encode more pertinent information
about objects that facilitate the recognition of objects from novel views.

Conformal Prediction (CP) is a versatile nonparametric framework used to
quantify uncertainty in prediction problems. In this work, we provide an
extension of such method to the case of time series of functions defined on a
bivariate domain, by proposing for the first time a distribution-free technique
which can be applied to time-evolving surfaces. In order to obtain meaningful
and efficient prediction regions, CP must be coupled with an accurate
forecasting algorithm, for this reason, we extend the theory of autoregressive
processes in Hilbert space in order to allow for functions with a bivariate
domain. Given the novelty of the subject, we present estimation techniques for
the Functional Autoregressive model (FAR). A simulation study is implemented,
in order to investigate how different point predictors affect the resulting
prediction bands. Finally, we explore benefits and limits of the proposed
approach on a real dataset, collecting daily observations of Sea Level
Anomalies of the Black Sea in the last twenty years.

In many sequential tasks, a model needs to remember relevant events from the
distant past to make correct predictions. Unfortunately, a straightforward
application of gradient based training requires intermediate computations to be
stored for every element of a sequence. This requires prohibitively large
computing memory if a sequence consists of thousands or even millions elements,
and as a result, makes learning of very long-term dependencies infeasible.
However, the majority of sequence elements can usually be predicted by taking
into account only temporally local information. On the other hand, predictions
affected by long-term dependencies are sparse and characterized by high
uncertainty given only local information. We propose MemUP, a new training
method that allows to learn long-term dependencies without backpropagating
gradients through the whole sequence at a time. This method can be potentially
applied to any gradient based sequence learning. MemUP implementation for
recurrent architectures shows performances better or comparable to baselines
while requiring significantly less computing memory.

As technology grows and evolves rapidly, it is increasingly clear that mobile
devices are more commonly used for sensitive matters than ever before. A need
to authenticate users continuously is sought after as a single-factor or multi
factor authentication may only initially validate a user, which does not help
if an impostor can bypass this initial validation. The field of touch dynamics
emerges as a clear way to non intrusively collect data about a user and their
behaviors in order to develop and make imperative security related decisions in
real time. In this paper we present a novel dataset consisting of tracking 25
users playing two mobile games Snake.io and Minecraft each for 10 minutes,
along with their relevant gesture data. From this data, we ran machine learning
binary classifiers namely Random Forest and K Nearest Neighbor to attempt to
authenticate whether a sample of a particular users actions were genuine. Our
strongest model returned an average accuracy of roughly 93% for both games,
showing touch dynamics can differentiate users effectively and is a feasible
consideration for authentication schemes. Our dataset can be observed at
https://github.com/zderidder/MC-Snake-Results

When robots operate in real-world off-road environments with unstructured
terrains, the ability to adapt their navigational policy is critical for
effective and safe navigation. However, off-road terrains introduce several
challenges to robot navigation, including dynamic obstacles and terrain
uncertainty, leading to inefficient traversal or navigation failures. To
address these challenges, we introduce a novel approach for adaptation by
negotiation that enables a ground robot to adjust its navigational behaviors
through a negotiation process. Our approach first learns prediction models for
various navigational policies to function as a terrain-aware joint local
controller and planner. Then, through a new negotiation process, our approach
learns from various policies' interactions with the environment to agree on the
optimal combination of policies in an online fashion to adapt robot navigation
to unstructured off-road terrains on the fly. Additionally, we implement a new
optimization algorithm that offers the optimal solution for robot negotiation
in real-time during execution. Experimental results have validated that our
method for adaptation by negotiation outperforms previous methods for robot
navigation, especially over unseen and uncertain dynamic terrains.

In recent proposals of quantum circuit models for generative tasks, the
discussion about their performance has been limited to their ability to
reproduce a known target distribution. For example, expressive model families
such as Quantum Circuit Born Machines (QCBMs) have been almost entirely
evaluated on their capability to learn a given target distribution with high
accuracy. While this aspect may be ideal for some tasks, it limits the scope of
a generative model's assessment to its ability to memorize data rather than
generalize. As a result, there has been little understanding of a model's
generalization performance and the relation between such capability and the
resource requirements, e.g., the circuit depth and the amount of training data.
In this work, we leverage upon a recently proposed generalization evaluation
framework to begin addressing this knowledge gap. We first investigate the
QCBM's learning process of a cardinality-constrained distribution and see an
increase in generalization performance while increasing the circuit depth. In
the 12-qubit example presented here, we observe that with as few as 30% of the
valid patterns as the training set, the QCBM exhibits the best generalization
performance toward generating unseen and valid patterns. Lastly, we assess the
QCBM's ability to generalize not only to valid features, but to high-quality
bitstrings distributed according to an adequately biased distribution. We see
that the QCBM is able to effectively learn the bias and generate unseen samples
with higher quality than those in the training set. To the best of our
knowledge, this is the first work in the literature that presents the QCBM's
generalization performance as an integral evaluation metric for quantum
generative models, and demonstrates the QCBM's ability to generalize to
high-quality, desired novel samples.

In the recent years, social media has grown to become a major source of
information for many online users. This has given rise to the spread of
misinformation through deepfakes. Deepfakes are videos or images that replace
one persons face with another computer-generated face, often a more
recognizable person in society. With the recent advances in technology, a
person with little technological experience can generate these videos. This
enables them to mimic a power figure in society, such as a president or
celebrity, creating the potential danger of spreading misinformation and other
nefarious uses of deepfakes. To combat this online threat, researchers have
developed models that are designed to detect deepfakes. This study looks at
various deepfake detection models that use deep learning algorithms to combat
this looming threat. This survey focuses on providing a comprehensive overview
of the current state of deepfake detection models and the unique approaches
many researchers take to solving this problem. The benefits, limitations, and
suggestions for future work will be thoroughly discussed throughout this paper.

Optical flow computation is essential in the early stages of the video
processing pipeline. This paper focuses on a less explored problem in this
area, the 360$^\circ$ optical flow estimation using deep neural networks to
support increasingly popular VR applications. To address the distortions of
panoramic representations when applying convolutional neural networks, we
propose a novel multi-projection fusion framework that fuses the optical flow
predicted by the models trained using different projection methods. It learns
to combine the complementary information in the optical flow results under
different projections. We also build the first large-scale panoramic optical
flow dataset to support the training of neural networks and the evaluation of
panoramic optical flow estimation methods. The experimental results on our
dataset demonstrate that our method outperforms the existing methods and other
alternative deep networks that were developed for processing 360{\deg} content.

In predictive modeling with simulation or machine learning, it is critical to
assess the quality of estimated values through output analysis accurately. In
recent decades output analysis has become enriched with methods that quantify
the impact of input data uncertainty in the model outputs to increase
robustness. However, most developments apply when the input data can be
parametrically parameterized. We propose a unified output analysis framework
for simulation and machine learning outputs through the lens of Monte Carlo
sampling. This framework provides nonparametric quantification of the variance
and bias induced in the outputs with higher-order accuracy. Our new
bias-corrected estimation from the model outputs leverages the extension of
fast iterative bootstrap sampling and higher-order influence functions. For the
scalability of the proposed estimation methods, we devise budget-optimal rules
and leverage control variates for variance reduction. Our numerical results
demonstrate a clear advantage in building better and more robust confidence
intervals for both simulation and machine learning frameworks.

Given a set of images of a scene, the re-rendering of this scene from novel
views and lighting conditions is an important and challenging problem in
Computer Vision and Graphics. On the one hand, most existing works in Computer
Vision usually impose many assumptions regarding the image formation process,
e.g. direct illumination and predefined materials, to make scene parameter
estimation tractable. On the other hand, mature Computer Graphics tools allow
modeling of complex photo-realistic light transport given all the scene
parameters. Combining these approaches, we propose a method for scene
relighting under novel views by learning a neural precomputed radiance transfer
function, which implicitly handles global illumination effects using novel
environment maps. Our method can be solely supervised on a set of real images
of the scene under a single unknown lighting condition. To disambiguate the
task during training, we tightly integrate a differentiable path tracer in the
training process and propose a combination of a synthesized OLAT and a real
image loss. Results show that the recovered disentanglement of scene parameters
improves significantly over the current state of the art and, thus, also our
re-rendering results are more realistic and accurate.

Multi-scale learning frameworks have been regarded as a capable class of
models to boost semantic segmentation. The problem nevertheless is not trivial
especially for the real-world deployments, which often demand high efficiency
in inference latency. In this paper, we thoroughly analyze the design of
convolutional blocks (the type of convolutions and the number of channels in
convolutions), and the ways of interactions across multiple scales, all from
lightweight standpoint for semantic segmentation. With such in-depth
comparisons, we conclude three principles, and accordingly devise Lightweight
and Progressively-Scalable Networks (LPS-Net) that novelly expands the network
complexity in a greedy manner. Technically, LPS-Net first capitalizes on the
principles to build a tiny network. Then, LPS-Net progressively scales the tiny
network to larger ones by expanding a single dimension (the number of
convolutional blocks, the number of channels, or the input resolution) at one
time to meet the best speed/accuracy tradeoff. Extensive experiments conducted
on three datasets consistently demonstrate the superiority of LPS-Net over
several efficient semantic segmentation methods. More remarkably, our LPS-Net
achieves 73.4% mIoU on Cityscapes test set, with the speed of 413.5FPS on an
NVIDIA GTX 1080Ti, leading to a performance improvement by 1.5% and a 65%
speed-up against the state-of-the-art STDC. Code is available at
\url{https://github.com/YihengZhang-CV/LPS-Net}.

Fair Machine Learning endeavors to prevent unfairness arising in the context
of machine learning applications embedded in society. Despite the variety of
definitions of fairness and proposed "fair algorithms", there remain unresolved
conceptual problems regarding fairness. In this paper, we dissect the role of
statistical independence in fairness and randomness notions regularly used in
machine learning. Thereby, we are led to a suprising hypothesis: randomness and
fairness can be considered equivalent concepts in machine learning.
  In particular, we obtain a relativized notion of randomness expressed as
statistical independence by appealing to Von Mises' century-old foundations for
probability. This notion turns out to be "orthogonal" in an abstract sense to
the commonly used i.i.d.-randomness. Using standard fairness notions in machine
learning, which are defined via statistical independence, we then link the ex
ante randomness assumptions about the data to the ex post requirements for fair
predictions. This connection proves fruitful: we use it to argue that
randomness and fairness are essentially relative and that both concepts should
reflect their nature as modeling assumptions in machine learning.

In this paper we present two machine learning algorithms to identify $D_s$
mesons from radiative $W$ bosons decays at the LHC. The combined algorithm is
able to identify $D_s$ mesons via its hadronic decays with an an efficiency of
67\% while suppressing a background of quark and gluon jets by a factor of 100.

The opaque reasoning of Graph Neural Networks induces a lack of human trust.
Existing graph network explainers attempt to address this issue by providing
post-hoc explanations, however, they fail to make the model itself more
interpretable. To fill this gap, we introduce the Concept Encoder Module, the
first differentiable concept-discovery approach for graph networks. The
proposed approach makes graph networks explainable by design by first
discovering graph concepts and then using these to solve the task. Our results
demonstrate that this approach allows graph networks to: (i) attain model
accuracy comparable with their equivalent vanilla versions, (ii) discover
meaningful concepts that achieve high concept completeness and purity scores,
(iii) provide high-quality concept-based logic explanations for their
prediction, and (iv) support effective interventions at test time: these can
increase human trust as well as significantly improve model performance.

In this work, we argue that the search for Artificial General Intelligence
(AGI) should start from a much lower level than human-level intelligence. The
circumstances of intelligent behavior in nature resulted from an organism
interacting with its surrounding environment, which could change over time and
exert pressure on the organism to allow for learning of new behaviors or
environment models. Our hypothesis is that learning occurs through interpreting
sensory feedback when an agent acts in an environment. For that to happen, a
body and a reactive environment are needed. We evaluate a method to evolve a
biologically-inspired artificial neural network that learns from environment
reactions named Neuroevolution of Artificial General Intelligence (NAGI), a
framework for low-level AGI. This method allows the evolutionary
complexification of a randomly-initialized spiking neural network with adaptive
synapses, which controls agents instantiated in mutable environments. Such a
configuration allows us to benchmark the adaptivity and generality of the
controllers. The chosen tasks in the mutable environments are food foraging,
emulation of logic gates, and cart-pole balancing. The three tasks are
successfully solved with rather small network topologies and therefore it opens
up the possibility of experimenting with more complex tasks and scenarios where
curriculum learning is beneficial.

The raise of machine learning and deep learning led to significant
improvement in several domains. This change is supported by both the dramatic
rise in computation power and the collection of large datasets. Such massive
datasets often include personal data which can represent a threat to privacy.
Membership inference attacks are a novel direction of research which aims at
recovering training data used by a learning algorithm. In this paper, we
develop a mean to measure the leakage of training data leveraging a quantity
appearing as a proxy of the total variation of a trained model near its
training samples. We extend our work by providing a novel defense mechanism.
Our contributions are supported by empirical evidence through convincing

A dramatic increase in the number of outbreaks of Dengue has recently been
reported, and climate change is likely to extend the geographical spread of the
disease. In this context, this paper shows how a neural network approach can
incorporate Dengue and COVID-19 data as well as external factors (such as
social behaviour or climate variables), to develop predictive models that could
improve our knowledge and provide useful tools for health policy makers.
Through the use of neural networks with different social and natural
parameters, in this paper we define a Correlation Model through which we show
that the number of cases of COVID-19 and Dengue have very similar trends. We
then illustrate the relevance of our model by extending it to a Long short-term
memory model (LSTM) that incorporates both diseases, and using this to estimate
Dengue infections via COVID-19 data in countries that lack sufficient Dengue
data.

Mapping optimization algorithms into neural networks, deep unfolding networks
(DUNs) have achieved impressive success in compressive sensing (CS). From the
perspective of optimization, DUNs inherit a well-defined and interpretable
structure from iterative steps. However, from the viewpoint of neural network
design, most existing DUNs are inherently established based on traditional
image-domain unfolding, which takes one-channel images as inputs and outputs
between adjacent stages, resulting in insufficient information transmission
capability and inevitable loss of the image details. In this paper, to break
the above bottleneck, we first propose a generalized dual-domain optimization
framework, which is general for inverse imaging and integrates the merits of
both (1) image-domain and (2) convolutional-coding-domain priors to constrain
the feasible region in the solution space. By unfolding the proposed framework
into deep neural networks, we further design a novel Dual-Domain Deep
Convolutional Coding Network (D3C2-Net) for CS imaging with the capability of
transmitting high-throughput feature-level image representation through all the
unfolded stages. Experiments on natural and MR images demonstrate that our
D3C2-Net achieves higher performance and better accuracy-complexity trade-offs
than other state-of-the-arts.

We study optimization for data-driven decision-making when we have
observations of the uncertain parameters within the optimization model together
with concurrent observations of covariates. Given a new covariate observation,
the goal is to choose a decision that minimizes the expected cost conditioned
on this observation. We investigate three data-driven frameworks that integrate
a machine learning prediction model within a stochastic programming sample
average approximation (SAA) for approximating the solution to this problem. Two
of the SAA frameworks are new and use out-of-sample residuals of leave-one-out
prediction models for scenario generation. The frameworks we investigate are
flexible and accommodate parametric, nonparametric, and semiparametric
regression techniques. We derive conditions on the data generation process, the
prediction model, and the stochastic program under which solutions of these
data-driven SAAs are consistent and asymptotically optimal, and also derive
convergence rates and finite sample guarantees. Computational experiments
validate our theoretical results, demonstrate the potential advantages of our
data-driven formulations over existing approaches (even when the prediction
model is misspecified), and illustrate the benefits of our new data-driven
formulations in the limited data regime.

Performing joint interaction requires constant mutual monitoring of own
actions and their effects on the other's behaviour. Such an action-effect
monitoring is boosted by social cues and might result in an increasing sense of
agency. Joint actions and joint attention are strictly correlated and both of
them contribute to the formation of a precise temporal coordination. In
human-robot interaction, the robot's ability to establish joint attention with
a human partner and exploit various social cues to react accordingly is a
crucial step in creating communicative robots. Along the social component, an
effective human-robot interaction can be seen as a new method to improve and
make the robot's learning process more natural and robust for a given task. In
this work we use different social skills, such as mutual gaze, gaze following,
speech and human face recognition, to develop an effective teacher-learner
scenario tailored to visual object learning in dynamic environments.
Experiments on the iCub robot demonstrate that the system allows the robot to
learn new objects through a natural interaction with a human teacher in
presence of distractors.

As a major breakthrough in artificial intelligence and deep learning,
Convolutional Neural Networks have achieved an impressive success in solving
many problems in several fields including computer vision and image processing.
Real-time performance, robustness of algorithms and fast training processes
remain open problems in these contexts. In addition object recognition and
detection are challenging tasks for resource-constrained embedded systems,
commonly used in the industrial sector. To overcome these issues, we propose a
dimensionality reduction framework based on Proper Orthogonal Decomposition, a
classical model order reduction technique, in order to gain a reduction in the
number of hyperparameters of the net. We have applied such framework to SSD300
architecture using PASCAL VOC dataset, demonstrating a reduction of the network
dimension and a remarkable speedup in the fine-tuning of the network in a
transfer learning context.

The lack of labeled training data is the bottleneck of machine learning in
many applications. To resolve the bottleneck, one promising direction is the
data programming approach that aggregates different sources of weak supervision
signals to generate labeled data easily. Data programming encodes each weak
supervision source with a labeling function (LF), a user-provided program that
predicts noisy labels. The quality of the generated labels depends on a label
aggregation model that aggregates all noisy labels from all LFs to infer the
ground-truth labels.
  Existing label aggregation methods typically rely on various assumptions and
are not robust across datasets, as we will show empirically. We for the first
time provide an analytical label aggregation method that makes minimum
assumption and is optimal in minimizing a certain form of the averaged
prediction error. Since the complexity of the analytical form is exponential,
we train a model that learns to be the analytical method. Once trained, the
model can be used for any unseen datasets and the model predicts the
ground-truth labels for each dataset in a single forward pass in linear time.
We show the model can be trained using synthetically generated data and design
an effective architecture for the model. On 14 real-world datasets, our model
significantly outperforms the best existing methods in both accuracy (by 3.5
points on average) and efficiency (by six times on average).

Humans show high-level of abstraction capabilities in games that require
quickly communicating object information. They decompose the message content
into multiple parts and communicate them in an interpretable protocol. Toward
equipping machines with such capabilities, we propose the Primitive-based
Sketch Abstraction task where the goal is to represent sketches using a fixed
set of drawing primitives under the influence of a budget. To solve this task,
our Primitive-Matching Network (PMN), learns interpretable abstractions of a
sketch in a self supervised manner. Specifically, PMN maps each stroke of a
sketch to its most similar primitive in a given set, predicting an affine
transformation that aligns the selected primitive to the target stroke. We
learn this stroke-to-primitive mapping end-to-end with a distance-transform
loss that is minimal when the original sketch is precisely reconstructed with
the predicted primitives. Our PMN abstraction empirically achieves the highest
performance on sketch recognition and sketch-based image retrieval given a
communication budget, while at the same time being highly interpretable. This
opens up new possibilities for sketch analysis, such as comparing sketches by
extracting the most relevant primitives that define an object category. Code is
available at https://github.com/ExplainableML/sketch-primitives.

Masked image modeling (MIM) has achieved promising results on various vision
tasks. However, the limited discriminability of learned representation
manifests there is still plenty to go for making a stronger vision learner.
Towards this goal, we propose Contrastive Masked Autoencoders (CMAE), a new
self-supervised pre-training method for learning more comprehensive and capable
vision representations. By elaboratively unifying contrastive learning (CL) and
masked image model (MIM) through novel designs, CMAE leverages their respective
advantages and learns representations with both strong instance
discriminability and local perceptibility. Specifically, CMAE consists of two
branches where the online branch is an asymmetric encoder-decoder and the
target branch is a momentum updated encoder. During training, the online
encoder reconstructs original images from latent representations of masked
images to learn holistic features. The target encoder, fed with the full
images, enhances the feature discriminability via contrastive learning with its
online counterpart. To make CL compatible with MIM, CMAE introduces two new
components, i.e. pixel shift for generating plausible positive views and
feature decoder for complementing features of contrastive pairs. Thanks to
these novel designs, CMAE effectively improves the representation quality and
transfer performance over its MIM counterpart. CMAE achieves the
state-of-the-art performance on highly competitive benchmarks of image
classification, semantic segmentation and object detection. Notably, CMAE-Base
achieves $85.3\%$ top-1 accuracy on ImageNet and $52.5\%$ mIoU on ADE20k,
surpassing previous best results by $0.7\%$ and $1.8\%$ respectively. Codes
will be made publicly available.

We propose a VAE for Transformers by developing a variational information
bottleneck regulariser for Transformer embeddings. We formalise the embedding
space of Transformer encoders as mixture probability distributions, and use
Bayesian nonparametrics to derive a nonparametric variational information
bottleneck (NVIB) for such attention-based embeddings. The variable number of
mixture components supported by nonparametric methods captures the variable
number of vectors supported by attention, and the exchangeability of our
nonparametric distributions captures the permutation invariance of attention.
This allows NVIB to regularise the number of vectors accessible with attention,
as well as the amount of information in individual vectors. By regularising the
cross-attention of a Transformer encoder-decoder with NVIB, we propose a
nonparametric variational autoencoder (NVAE). Initial experiments on training a
NVAE on natural language text show that the induced embedding space has the
desired properties of a VAE for Transformers.

Star-forming galaxies can exhibit strong morphological differences between
the rest-frame far-UV and optical, reflecting inhomogeneities in star-formation
and dust attenuation. We exploit deep, high resolution NIRCAM 7-band
observations to take a first look at the morphology of galaxies in the epoch of
reionization ($z>7$), and its variation in the rest-frame wavelength range
between Lyman $\alpha$ and 6000-4000\AA, at $z=7-12$. We find no dramatic
variations in morphology with wavelength -- of the kind that would have
overturned anything we have learned from the Hubble Space Telescope. No
significant trends between morphology and wavelengths are detected using
standard quantitative morphology statistics. We detect signatures of
mergers/interactions in 4/19 galaxies. Our results are consistent with a
scenario in which Lyman Break galaxies -- observed when the universe is only
400-800 Myrs old - are growing via a combination of rapid galaxy-scale star
formation supplemented by accretion of star forming clumps and interactions.

Combinatorial optimization (CO) layers in machine learning (ML) pipelines are
a powerful tool to tackle data-driven decision tasks, but they come with two
main challenges. First, the solution of a CO problem often behaves as a
piecewise constant function of its objective parameters. Given that ML
pipelines are typically trained using stochastic gradient descent, the absence
of slope information is very detrimental. Second, standard ML losses do not
work well in combinatorial settings. A growing body of research addresses these
challenges through diverse methods. Unfortunately, the lack of well-maintained
implementations slows down the adoption of CO layers.
  In this paper, building upon previous works, we introduce a probabilistic
perspective on CO layers, which lends itself naturally to approximate
differentiation and the construction of structured losses. We recover many
approaches from the literature as special cases, and we also derive new ones.
Based on this unifying perspective, we present InferOpt.jl, an open-source
Julia package that 1) allows turning any CO oracle with a linear objective into
a differentiable layer, and 2) defines adequate losses to train pipelines
containing such layers. Our library works with arbitrary optimization
algorithms, and it is fully compatible with Julia's ML ecosystem. We
demonstrate its abilities using a pathfinding problem on video game maps.

Clinical diagnosis of the pediatric musculoskeletal system relies on the
analysis of medical imaging examinations. In the medical image processing
pipeline, semantic segmentation using deep learning algorithms enables an
automatic generation of patient-specific three-dimensional anatomical models
which are crucial for morphological evaluation. However, the scarcity of
pediatric imaging resources may result in reduced accuracy and generalization
performance of individual deep segmentation models. In this study, we propose
to design a novel multi-task, multi-domain learning framework in which a single
segmentation network is optimized over the union of multiple datasets arising
from distinct parts of the anatomy. Unlike previous approaches, we
simultaneously consider multiple intensity domains and segmentation tasks to
overcome the inherent scarcity of pediatric data while leveraging shared
features between imaging datasets. To further improve generalization
capabilities, we employ a transfer learning scheme from natural image
classification, along with a multi-scale contrastive regularization aimed at
promoting domain-specific clusters in the shared representations, and
multi-joint anatomical priors to enforce anatomically consistent predictions.
We evaluate our contributions for performing bone segmentation using three
scarce and pediatric imaging datasets of the ankle, knee, and shoulder joints.
Our results demonstrate that the proposed approach outperforms individual,
transfer, and shared segmentation schemes in Dice metric with statistically
sufficient margins. The proposed model brings new perspectives towards
intelligent use of imaging resources and better management of pediatric
musculoskeletal disorders.

Detection of fake news is crucial to ensure the authenticity of information
and maintain the news ecosystems reliability. Recently, there has been an
increase in fake news content due to the recent proliferation of social media
and fake content generation techniques such as Deep Fake. The majority of the
existing modalities of fake news detection focus on content based approaches.
However, most of these techniques fail to deal with ultra realistic synthesized
media produced by generative models. Our recent studies find that the
propagation characteristics of authentic and fake news are distinguishable,
irrespective of their modalities. In this regard, we have investigated the
auxiliary information based on social context to detect fake news. This paper
has analyzed the social context of fake news detection with a hybrid graph
neural network based approach. This hybrid model is based on integrating a
graph neural network on the propagation of news and bi directional encoder
representations from the transformers model on news content to learn the text
features. Thus this proposed approach learns the content as well as the context
features and hence able to outperform the baseline models with an f1 score of
0.91 on PolitiFact and 0.93 on the Gossipcop dataset, respectively

The usual Minimum Covariance Determinant (MCD) estimator of a covariance
matrix is robust against casewise outliers. These are cases (that is, rows of
the data matrix) that behave differently from the majority of cases, raising
suspicion that they might belong to a different population. On the other hand,
cellwise outliers are individual cells in the data matrix. When a row contains
one or more outlying cells, the other cells in the same row still contain
useful information that we wish to preserve. We propose a cellwise robust
version of the MCD method, called cellMCD. Its main building blocks are
observed likelihood and a sparsity penalty on the number of flagged cellwise
outliers. It possesses good breakdown properties. We construct a fast algorithm
for cellMCD based on concentration steps (C-steps) that always lower the
objective. The method performs well in simulations with cellwise outliers, and
has high finite-sample efficiency on clean data. It is illustrated on real data
with visualizations of the results.

Biological vision systems are unparalleled in their ability to learn visual
representations without supervision. In machine learning, contrastive learning
(CL) has led to major advances in forming object representations in an
unsupervised fashion. These systems learn representations invariant to
augmentation operations over images, like cropping or flipping. In contrast,
biological vision systems exploit the temporal structure of the visual
experience. This gives access to augmentations not commonly used in CL, like
watching the same object from multiple viewpoints or against different
backgrounds. Here, we systematically investigate and compare the potential
benefits of such time-based augmentations for learning object categories. Our
results show that time-based augmentations achieve large performance gains over
state-of-the-art image augmentations. Specifically, our analyses reveal that:
1) 3-D object rotations drastically improve the learning of object categories;
2) viewing objects against changing backgrounds is vital for learning to
discard background-related information. Overall, we conclude that time-based
augmentations can greatly improve contrastive learning, narrowing the gap
between artificial and biological vision systems.

Video transition effects are widely used in video editing to connect shots
for creating cohesive and visually appealing videos. However, it is challenging
for non-professionals to choose best transitions due to the lack of
cinematographic knowledge and design skills. In this paper, we present the
premier work on performing automatic video transitions recommendation (VTR):
given a sequence of raw video shots and companion audio, recommend video
transitions for each pair of neighboring shots. To solve this task, we collect
a large-scale video transition dataset using publicly available video templates
on editing softwares. Then we formulate VTR as a multi-modal retrieval problem
from vision/audio to video transitions and propose a novel multi-modal matching
framework which consists of two parts. First we learn the embedding of video
transitions through a video transition classification task. Then we propose a
model to learn the matching correspondence from vision/audio inputs to video
transitions. Specifically, the proposed model employs a multi-modal transformer
to fuse vision and audio information, as well as capture the context cues in
sequential transition outputs. Through both quantitative and qualitative
experiments, we clearly demonstrate the effectiveness of our method. Notably,
in the comprehensive user study, our method receives comparable scores compared
with professional editors while improving the video editing efficiency by
\textbf{300\scalebox{1.25}{$\times$}}. We hope our work serves to inspire other
researchers to work on this new task. The dataset and codes are public at
\url{https://github.com/acherstyx/AutoTransition}.

The best way to combine the results of deep learning with standard 3D
reconstruction pipelines remains an open problem. While systems that pass the
output of traditional multi-view stereo approaches to a network for
regularisation or refinement currently seem to get the best results, it may be
preferable to treat deep neural networks as separate components whose results
can be probabilistically fused into geometry-based systems. Unfortunately, the
error models required to do this type of fusion are not well understood, with
many different approaches being put forward. Recently, a few systems have
achieved good results by having their networks predict probability
distributions rather than single values. We propose using this approach to fuse
a learned single-view depth prior into a standard 3D reconstruction system.
  Our system is capable of incrementally producing dense depth maps for a set
of keyframes. We train a deep neural network to predict discrete, nonparametric
probability distributions for the depth of each pixel from a single image. We
then fuse this "probability volume" with another probability volume based on
the photometric consistency between subsequent frames and the keyframe image.
We argue that combining the probability volumes from these two sources will
result in a volume that is better conditioned. To extract depth maps from the
volume, we minimise a cost function that includes a regularisation term based
on network predicted surface normals and occlusion boundaries. Through a series
of experiments, we demonstrate that each of these components improves the
overall performance of the system.

Temporal sentence grounding (TSG) is an important yet challenging task in
multimedia information retrieval. Although previous TSG methods have achieved
decent performance, they tend to capture the selection biases of frequently
appeared video-query pairs in the dataset rather than present robust multimodal
reasoning abilities, especially for the rarely appeared pairs. In this paper,
we study the above issue of selection biases and accordingly propose a
Debiasing-TSG (D-TSG) model to filter and remove the negative biases in both
vision and language modalities for enhancing the model generalization ability.
Specifically, we propose to alleviate the issue from two perspectives: 1)
Feature distillation. We built a multi-modal debiasing branch to firstly
capture the vision and language biases, and then apply a bias identification
module to explicitly recognize the true negative biases and remove them from
the benign multi-modal representations. 2) Contrastive sample generation. We
construct two types of negative samples to enforce the model to accurately
learn the aligned multi-modal semantics and make complete semantic reasoning.
We apply the proposed model to both commonly and rarely appeared TSG cases, and
demonstrate its effectiveness by achieving the state-of-the-art performance on
three benchmark datasets (ActivityNet Caption, TACoS, and Charades-STA).

Learning in high dimensional continuous tasks is challenging, mainly when the
experience replay memory is very limited. We introduce a simple yet effective
experience sharing mechanism for deterministic policies in continuous action
domains for the future off-policy deep reinforcement learning applications in
which the allocated memory for the experience replay buffer is limited. To
overcome the extrapolation error induced by learning from other agents'
experiences, we facilitate our algorithm with a novel off-policy correction
technique without any action probability estimates. We test the effectiveness
of our method in challenging OpenAI Gym continuous control tasks and conclude
that it can achieve a safe experience sharing across multiple agents and
exhibits a robust performance when the replay memory is strictly limited.

It is challenging to use reinforcement learning (RL) in cyber-physical
systems due to the lack of safety guarantees during learning. Although there
have been various proposals to reduce undesired behaviors during learning, most
of these techniques require prior system knowledge, and their applicability is
limited. This paper aims to reduce undesired behaviors during learning without
requiring any prior system knowledge. We propose dynamic shielding: an
extension of a model-based safe RL technique called shielding using automata
learning. The dynamic shielding technique constructs an approximate system
model in parallel with RL using a variant of the RPNI algorithm and suppresses
undesired explorations due to the shield constructed from the learned model.
Through this combination, potentially unsafe actions can be foreseen before the
agent experiences them. Experiments show that our dynamic shield significantly
decreases the number of undesired events during training.

These lecture notes focus on the recent advancements in neural information
retrieval, with particular emphasis on the systems and models exploiting
transformer networks. These networks, originally proposed by Google in 2017,
have seen a large success in many natural language processing and information
retrieval tasks. While there are many fantastic textbook on information
retrieval and natural language processing as well as specialised books for a
more advanced audience, these lecture notes target people aiming at developing
a basic understanding of the main information retrieval techniques and
approaches based on deep learning. These notes have been prepared for a IR
graduate course of the MSc program in Artificial Intelligence and Data
Engineering at the University of Pisa, Italy.

Time series forecasting is at the core of important application domains
posing significant challenges to machine learning algorithms. Recently neural
network architectures have been widely applied to the problem of time series
forecasting. Most of these models are trained by minimizing a loss function
that measures predictions' deviation from the real values. Typical loss
functions include mean squared error (MSE) and mean absolute error (MAE). In
the presence of noise and uncertainty, neural network models tend to replicate
the last observed value of the time series, thus limiting their applicability
to real-world data. In this paper, we provide a formal definition of the above
problem and we also give some examples of forecasts where the problem is
observed. We also propose a regularization term penalizing the replication of
previously seen values. We evaluate the proposed regularization term both on
synthetic and real-world datasets. Our results indicate that the regularization
term mitigates to some extent the aforementioned problem and gives rise to more

Reinforcement learning shows great potential to solve complex contact-rich
robot manipulation tasks. However, the safety of using RL in the real world is
a crucial problem, since unexpected dangerous collisions might happen when the
RL policy is imperfect during training or in unseen scenarios. In this paper,
we propose a contact-safe reinforcement learning framework for contact-rich
robot manipulation, which maintains safety in both the task space and joint
space. When the RL policy causes unexpected collisions between the robot arm
and the environment, our framework is able to immediately detect the collision
and ensure the contact force to be small. Furthermore, the end-effector is
enforced to perform contact-rich tasks compliantly, while keeping robust to
external disturbances. We train the RL policy in simulation and transfer it to
the real robot. Real world experiments on robot wiping tasks show that our
method is able to keep the contact force small both in task space and joint
space even when the policy is under unseen scenario with unexpected collision,
while rejecting the disturbances on the main task.

Few-shot part segmentation aims to separate different parts of an object
given only a few annotated samples. Due to the challenge of limited data,
existing works mainly focus on learning classifiers over pre-trained features,
failing to learn task-specific features for part segmentation. In this paper,
we propose to learn task-specific features in a "pre-training"-"fine-tuning"
paradigm. We conduct prompt designing to reduce the gap between the pre-train
task (i.e., image generation) and the downstream task (i.e., part
segmentation), so that the GAN priors for generation can be leveraged for
segmentation. This is achieved by projecting part segmentation maps into the
RGB space and conducting interpolation between RGB segmentation maps and
original images. Specifically, we design a fine-tuning strategy to
progressively tune an image generator into a segmentation generator, where the
supervision of the generator varying from images to segmentation maps by
interpolation. Moreover, we propose a two-stream architecture, i.e., a
segmentation stream to generate task-specific features, and an image stream to
provide spatial constraints. The image stream can be regarded as a
self-supervised auto-encoder, and this enables our model to benefit from
large-scale support images. Overall, this work is an attempt to explore the
internal relevance between generation tasks and perception tasks by prompt
designing. Extensive experiments show that our model can achieve
state-of-the-art performance on several part segmentation datasets.

Non-local (NL) block is a popular module that demonstrates the capability to
model global contexts. However, NL block generally has heavy computation and
memory costs, so it is impractical to apply the block to high-resolution
feature maps. In this paper, to investigate the efficacy of NL block, we
empirically analyze if the magnitude and direction of input feature vectors
properly affect the attention between vectors. The results show the inefficacy
of softmax operation which is generally used to normalize the attention map of
the NL block. Attention maps normalized with softmax operation highly rely upon
magnitude of key vectors, and performance is degenerated if the magnitude
information is removed. By replacing softmax operation with the scaling factor,
we demonstrate improved performance on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
In Addition, our method shows robustness to embedding channel reduction and
embedding weight initialization. Notably, our method makes multi-head attention
employable without additional computational cost.

In this contribution, a measurement of the top quark pole mass
$m_{\text{t}}^{\text{pole}}$ is presented. Events where a top quark-antiquark
pair ($\text{t}\bar{\text{t}}$) is produced in association with at least one
additional energetic jet ($\text{t}\bar{\text{t}}$+jet) are analyzed. The data
set recorded by the CMS experiment at a center-of-mass energy of 13 TeV,
corresponding to a total integrated luminosity of 36.3fb$^{-1}$, is used.
Reconstructed events are required to contain two opposite-sign leptons in the
final state. Machine learning methods are employed for the reconstruction of
the main observable event classification. The normalized
$\text{t}\bar{\text{t}}$+jet production cross section as a function of the
inverse of the invariant mass of the $\text{t}\bar{\text{t}}$+jet system is
measured at the parton level using a likelihood unfolding method. The value for
$m_{\text{t}}^{\text{pole}}$ is extracted by comparing the measurement to
next-to-leading order theoretical predictions using two parton distribution
functions (PDFs). For a reference set of the ABMP16NLO PDF, the resulting value
is $m_{\text{t}}^{\text{pole}} = 172.94\pm1.37$ GeV.

In these proceedings, a measurement of the differential cross section of top
quark-antiquark pair ($\text{t}\bar{\text{t}}$) production in association with
one additional jet ($\text{t}\bar{\text{t}}$+jet) as a function of the inverse
of the invariant mass of the $\text{t}\bar{\text{t}}$+jet system,
$\rho=340\,\text{GeV}/m_{\text{t}\bar{\text{t}}+\text{jet}}$, is presented. The
normalized $\text{t}\bar{\text{t}}$+jet cross section is used to extract values
for the top quark pole mass $m_{\text{t}}^{\text{pole}}$ by a comparison to
theoretical predictions at next-to-leading order accuracy. The used data set
corresponds to an integrated luminosity of 36.3fb$^{-1}$ of proton--proton
collisions as collected by the CMS experiment at $\sqrt{s}=$ 13 TeV. Events
with two opposite-sign leptons are analyzed. Machine learning techniques are
employed to improve the kinematic reconstruction of the main observable and the
event classification. The unfolding to the parton level is performed using a
profiled likelihood fit. Given the ABMP16 parton distribution functions as a
reference set, a value of
$m_{\text{t}}^{\text{pole}}=172.94\pm1.37\,\text{GeV}$ is extracted using the
normalized $\text{t}\bar{\text{t}}$+jet cross section.

Many applications require high accuracy of neural networks as well as low
latency and user data privacy guaranty. Face anti-spoofing is one of such
tasks. However, a single model might not give the best results for different
device performance categories, while training multiple models is time
consuming. In this work we present Post-Train Adaptive (PTA) block. Such a
block is simple in structure and offers a drop-in replacement for the
MobileNetV2 Inverted Residual block. The PTA block has multiple branches with
different computation costs. The branch to execute can be selected on-demand
and at runtime; thus, offering different inference times and configuration
capability for multiple device tiers. Crucially, the model is trained once and
can be easily reconfigured after training, even directly on a mobile device. In
addition, the proposed approach shows substantially better overall performance
in comparison to the original MobileNetV2 as tested on CelebA-Spoof dataset.
Different PTA block configurations are sampled at training time, which also
decreases overall wall-clock time needed to train the model. While we present
computational results for the anti-spoofing problem, the MobileNetV2 with PTA
blocks is applicable to any problem solvable with convolutional neural
networks, which makes the results presented practically significant.

In a previous paper [Physical Review B \textbf{103}, 035417 (2021)], we
showed that the equilibrium molecular dynamics (EMD) method can be used to
compute the apparent thermal conductivity of finite systems. It has been shown
that the apparent thermal conductivity from EMD for a system with domain length
$2L$ is equal to that from nonequilibrium molecular dynamics (NEMD) for a
system with domain length $L$. Taking monolayer silicence with an accurate
machine learning potential as an example, here we show that the thermal
conductivity values from EMD and NEMD agree for the same domain length if the
NEMD is applied with periodic boundary conditions in the transport direction.
Our results thus establish an exact equivalence between EMD and NEMD for
thermal conductivity calculations.

This work proposes two statistical approaches for the synthesis of keystroke
biometric data based on Universal and User-dependent Models. Both approaches
are validated on the bot detection task, using the keystroke synthetic data to
better train the systems. Our experiments include a dataset with 136 million
keystroke events from 168,000 subjects. We have analyzed the performance of the
two synthesis approaches through qualitative and quantitative experiments.
Different bot detectors are considered based on two supervised classifiers
(Support Vector Machine and Long Short-Term Memory network) and a learning
framework including human and generated samples. Our results prove that the
proposed statistical approaches are able to generate realistic human-like
synthetic keystroke samples. Also, the classification results suggest that in
scenarios with large labeled data, these synthetic samples can be detected with
high accuracy. However, in few-shot learning scenarios it represents an
important challenge.

Advancements in fast electron detectors have enabled the statistically
significant sampling of crystal structures on the nanometre scale by means of
Scanning Electron Nanobeam Diffraction (SEND). Characterisation of structural
similarity across this length scale is key to bridging the gap between local
atomic structure (using atomic resolution techniques such as High Resolution
Scanning Transmission Electron Microscopy (HR-STEM)) and the macro-scale (using
bulk techniques such as powder X-ray and neutron diffraction). The use of SEND
technique allows for structural investigation of a broad range of samples, due
to the techniques ability to operate with low electron dosage and its tolerance
for sample thickness, relative to HR-STEM. This, coupled with the capacity for
data collection over a wide areas and the automation of this collection, allows
for statistically representative sampling of the microstructure. Also due to
these factors, SEND generates large datasets and as a result automated/
semi-automated data processing workflows are required to aid in maximal
extraction of useful information. As such, this paper outlines a versatile,
data-driven approach for producing domain maps, as well as a statistical
approach for assessing their applicability. The production of such domain maps
for a dataset can help highlight nuance in the microstructure, as well as
improve the manageability of that dataset for further investigation. The
workflow outlined utilises a Variational AutoEncoder to identify and learn the
sources of variance in the diffraction signal and this, in combination with
clustering techniques, is used to produce domain maps for a set of varied
example cases. This approach: is agnostic to domain crystallinity; requires no
prior knowledge of crystal structure; and does not require the, potentially
prohibitive, simulation of a library of appropriate diffraction patterns.

This paper aims to generate realistic attack samples of person
re-identification, ReID, by reading the enemy's mind (VM). In this paper, we
propose a novel inconspicuous and controllable ReID attack baseline, LCYE, to
generate adversarial query images. Concretely, LCYE first distills VM's
knowledge via teacher-student memory mimicking in the proxy task. Then this
knowledge prior acts as an explicit cipher conveying what is essential and
realistic, believed by VM, for accurate adversarial misleading. Besides,
benefiting from the multiple opposing task framework of LCYE, we further
investigate the interpretability and generalization of ReID models from the
view of the adversarial attack, including cross-domain adaption, cross-model
consensus, and online learning process. Extensive experiments on four ReID
benchmarks show that our method outperforms other state-of-the-art attackers
with a large margin in white-box, black-box, and target attacks. Our code is
now available at https://gitfront.io/r/user-3704489/mKXusqDT4ffr/LCYE/.

One of the oldest and most studied subject in scientific computing is
algorithms for solving partial differential equations (PDEs). A long list of
numerical methods have been proposed and successfully used for various
applications. In recent years, deep learning methods have shown their
superiority for high-dimensional PDEs where traditional methods fail. However,
for low dimensional problems, it remains unclear whether these methods have a
real advantage over traditional algorithms as a direct solver. In this work, we
propose the random feature method (RFM) for solving PDEs, a natural bridge
between traditional and machine learning-based algorithms. RFM is based on a
combination of well-known ideas: 1. representation of the approximate solution
using random feature functions; 2. collocation method to take care of the PDE;
3. the penalty method to treat the boundary conditions, which allows us to
treat the boundary condition and the PDE in the same footing. We find it
crucial to add several additional components including multi-scale
representation and rescaling the weights in the loss function. We demonstrate
that the method exhibits spectral accuracy and can compete with traditional
solvers in terms of both accuracy and efficiency. In addition, we find that RFM
is particularly suited for complex problems with complex geometry, where both
traditional and machine learning-based algorithms encounter difficulties.

Conventional de-noising methods rely on the assumption that all samples are
independent and identically distributed, so the resultant classifier, though
disturbed by noise, can still easily identify the noises as the outliers of
training distribution. However, the assumption is unrealistic in large-scale
data that is inevitably long-tailed. Such imbalanced training data makes a
classifier less discriminative for the tail classes, whose previously "easy"
noises are now turned into "hard" ones -- they are almost as outliers as the
clean tail samples. We introduce this new challenge as Noisy Long-Tailed
Classification (NLT). Not surprisingly, we find that most de-noising methods
fail to identify the hard noises, resulting in significant performance drop on
the three proposed NLT benchmarks: ImageNet-NLT, Animal10-NLT, and Food101-NLT.
To this end, we design an iterative noisy learning framework called
Hard-to-Easy (H2E). Our bootstrapping philosophy is to first learn a classifier
as noise identifier invariant to the class and context distributional changes,
reducing "hard" noises to "easy" ones, whose removal further improves the
invariance. Experimental results show that our H2E outperforms state-of-the-art
de-noising methods and their ablations on long-tailed settings while
maintaining a stable performance on the conventional balanced settings.
Datasets and codes are available at https://github.com/yxymessi/H2E-Framework

Predicting the origin-destination (OD) probability distribution of agent
transfer is an important problem for managing complex systems. However,
prediction accuracy of associated statistical estimators suffer from
underdetermination. While specific techniques have been proposed to overcome
this deficiency, there still lacks a general approach. Here, we propose a deep
neural network framework with gated recurrent units (DNNGRU) to address this
gap. Our DNNGRU is \emph{network-free}, as it is trained by supervised learning
with time-series data on the volume of agents passing through edges. We use it
to investigate how network topologies affect OD prediction accuracy, where
performance enhancement is observed to depend on the degree of overlap between
paths taken by different ODs. By comparing against methods that give exact
results, we demonstrate the near-optimal performance of our DNNGRU, which we
found to consistently outperform existing methods and alternative neural
network architectures, under diverse data generation scenarios.

Video deblurring is a highly under-constrained problem due to the spatially
and temporally varying blur. An intuitive approach for video deblurring
includes two steps: a) detecting the blurry region in the current frame; b)
utilizing the information from clear regions in adjacent frames for current
frame deblurring. To realize this process, our idea is to detect the pixel-wise
blur level of each frame and combine it with video deblurring. To this end, we
propose a novel framework that utilizes the motion magnitude prior (MMP) as
guidance for efficient deep video deblurring. Specifically, as the pixel
movement along its trajectory during the exposure time is positively correlated
to the level of motion blur, we first use the average magnitude of optical flow
from the high-frequency sharp frames to generate the synthetic blurry frames
and their corresponding pixel-wise motion magnitude maps. We then build a
dataset including the blurry frame and MMP pairs. The MMP is then learned by a
compact CNN by regression. The MMP consists of both spatial and temporal blur
level information, which can be further integrated into an efficient recurrent
neural network (RNN) for video deblurring. We conduct intensive experiments to
validate the effectiveness of the proposed methods on the public datasets.

Recently, haptic gloves have been extensively explored for various practical
applications, such as manipulation learning. Previous glove devices have
different force-driven systems, such as shape memory alloys, servo motors and
pneumatic actuators; however, these proposed devices may have difficulty in
fast finger movement, easy reproduction, and safety issues. In this study, we
propose MagGlove, a novel haptic glove with a movable magnet mechanism that has
a linear motor, to solve these issues. The proposed MagGlove device is a
compact system on the back of the wearer's hand with high responsiveness, ease
of use, and good safety. The proposed device is adaptive with the modification
of the magnitude of the current flowing through the coil. Based on our
evaluation study, it is verified that the proposed device can achieve finger
motion in the given tasks. Therefore, MagGlove can provide flexible support
tailored to the wearers' learning levels in manipulation learning tasks.

Current contrastive learning methods use random transformations sampled from
a large list of transformations, with fixed hyperparameters, to learn
invariance from an unannotated database. Following previous works that
introduce a small amount of supervision, we propose a framework to find optimal
transformations for contrastive learning using a differentiable transformation
network. Our method increases performances at low annotated data regime both in
supervision accuracy and in convergence speed. In contrast to previous work, no
generative model is needed for transformation optimization. Transformed images
keep relevant information to solve the supervised task, here classification.
Experiments were performed on 34000 2D slices of brain Magnetic Resonance
Images and 11200 chest X-ray images. On both datasets, with 10% of labeled
data, our model achieves better performances than a fully supervised model with
100% labels.

A deep clustering model conceptually consists of a feature extractor that
maps data points to a latent space, and a clustering head that groups data
points into clusters in the latent space. Although the two components used to
be trained jointly in an end-to-end fashion, recent works have proved it
beneficial to train them separately in two stages. In the first stage, the
feature extractor is trained via self-supervised learning, which enables the
preservation of the cluster structures among the data points. To preserve the
cluster structures even better, we propose to replace the first stage with
another model that is pretrained on a much larger dataset via self-supervised
learning. The method is simple and might suffer from domain shift. Nonetheless,
we have empirically shown that it can achieve superior clustering performance.
When a vision transformer (ViT) architecture is used for feature extraction,
our method has achieved clustering accuracy 94.0%, 55.6% and 97.9% on CIFAR-10,
CIFAR-100 and STL-10 respectively. The corresponding previous state-of-the-art
results are 84.3%, 47.7% and 80.8%. Our code will be available online with the
publication of the paper.

The ever increasing memory requirements of several applications has led to
increased demands which might not be met by embedded devices. Constraining the
usage of memory in such cases is of paramount importance. It is important that
such code size improvements should not have a negative impact on the runtime.
Improving the execution time while optimizing for code size is a non-trivial
but a significant task. The ordering of standard optimization sequences in
modern compilers is fixed, and are heuristically created by the compiler domain
experts based on their expertise. However, this ordering is sub-optimal, and
does not generalize well across all the cases. We present a reinforcement
learning based solution to the phase ordering problem, where the ordering
improves both the execution time and code size. We propose two different
approaches to model the sequences: one by manual ordering, and other based on a
graph called Oz Dependence Graph (ODG). Our approach uses minimal data as
training set, and is integrated with LLVM. We show results on x86 and AArch64
architectures on the benchmarks from SPEC-CPU 2006, SPEC-CPU 2017 and MiBench.
We observe that the proposed model based on ODG outperforms the current Oz
sequence both in terms of size and execution time by 6.19% and 11.99% in SPEC
2017 benchmarks, on an average.

Video anomaly detection is a challenging task in the computer vision
community. Most single task-based methods do not consider the independence of
unique spatial and temporal patterns, while two-stream structures lack the
exploration of the correlations. In this paper, we propose spatial-temporal
memories augmented two-stream auto-encoder framework, which learns the
appearance normality and motion normality independently and explores the
correlations via adversarial learning. Specifically, we first design two proxy
tasks to train the two-stream structure to extract appearance and motion
features in isolation. Then, the prototypical features are recorded in the
corresponding spatial and temporal memory pools. Finally, the encoding-decoding
network performs adversarial learning with the discriminator to explore the
correlations between spatial and temporal patterns. Experimental results show
that our framework outperforms the state-of-the-art methods, achieving AUCs of
98.1% and 89.8% on UCSD Ped2 and CUHK Avenue datasets.

Restless multi-armed bandits (RMAB) is a framework for allocating limited
resources under uncertainty. It is an extremely useful model for monitoring
beneficiaries and executing timely interventions to ensure maximum benefit in
public health settings (e.g., ensuring patients take medicines in tuberculosis
settings, ensuring pregnant mothers listen to automated calls about good
pregnancy practices). Due to the limited resources, typically certain
communities or regions are starved of interventions that can have follow-on
effects. To avoid starvation in the executed interventions across
individuals/regions/communities, we first provide a soft fairness constraint
and then provide an approach to enforce the soft fairness constraint in RMABs.
The soft fairness constraint requires that an algorithm never probabilistically
favor one arm over another if the long-term cumulative reward of choosing the
latter arm is higher. Our approach incorporates softmax based value iteration
method in the RMAB setting to design selection algorithms that manage to
satisfy the proposed fairness constraint. Our method, referred to as SoftFair,
also provides theoretical performance guarantees and is asymptotically optimal.
Finally, we demonstrate the utility of our approaches on simulated benchmarks
and show that the soft fairness constraint can be handled without a significant
sacrifice on value.

If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine
Learning (ML), most UQ methods suffer from disparate and inconsistent
evaluation protocols. We claim this inconsistency results from the unclear
requirements the community expects from UQ. This opinion paper offers a new
perspective by specifying those requirements through five downstream tasks
where we expect uncertainty scores to have substantial predictive power. We
design these downstream tasks carefully to reflect real-life usage of ML
models. On an example benchmark of 7 classification datasets, we did not
observe statistical superiority of state-of-the-art intrinsic UQ methods
against simple baselines. We believe that our findings question the very
rationale of why we quantify uncertainty and call for a standardized protocol
for UQ evaluation based on metrics proven to be relevant for the ML

Online stereo adaptation tackles the domain shift problem, caused by
different environments between synthetic (training) and real (test) datasets,
to promptly adapt stereo models in dynamic real-world applications such as
autonomous driving. However, previous methods often fail to counteract
particular regions related to dynamic objects with more severe environmental
changes. To mitigate this issue, we propose to incorporate an auxiliary
point-selective network into a meta-learning framework, called PointFix, to
provide a robust initialization of stereo models for online stereo adaptation.
In a nutshell, our auxiliary network learns to fix local variants intensively
by effectively back-propagating local information through the meta-gradient for
the robust initialization of the baseline model. This network is
model-agnostic, so can be used in any kind of architectures in a plug-and-play
manner. We conduct extensive experiments to verify the effectiveness of our
method under three adaptation settings such as short-, mid-, and long-term
sequences. Experimental results show that the proper initialization of the base
stereo model by the auxiliary network enables our learning paradigm to achieve
state-of-the-art performance at inference.

Active learning is an important technology for automated machine learning
systems. In contrast to Neural Architecture Search (NAS) which aims at
automating neural network architecture design, active learning aims at
automating training data selection. It is especially critical for training a
long-tailed task, in which positive samples are sparsely distributed. Active
learning alleviates the expensive data annotation issue through incrementally
training models powered with efficient data selection. Instead of annotating
all unlabeled samples, it iteratively selects and annotates the most valuable
samples. Active learning has been popular in image classification, but has not
been fully explored in object detection. Most of current approaches on object
detection are evaluated with different settings, making it difficult to fairly
compare their performance. To facilitate the research in this field, this paper
contributes an active learning benchmark framework named as ALBench for
evaluating active learning in object detection. Developed on an automatic deep
model training system, this ALBench framework is easy-to-use, compatible with
different active learning algorithms, and ensures the same training and testing
protocols. We hope this automated benchmark system help researchers to easily
reproduce literature's performance and have objective comparisons with prior
arts. The code will be release through Github.

We present automatic speech recognition (ASR) systems for Tamil and Kannada
based on subword modeling to effectively handle unlimited vocabulary due to the
highly agglutinative nature of the languages. We explore byte pair encoding
(BPE), and proposed a variant of this algorithm named extended-BPE, and
Morfessor tool to segment each word as subwords. We have effectively
incorporated maximum likelihood (ML) and Viterbi estimation techniques with
weighted finite state transducers (WFST) framework in these algorithms to learn
the subword dictionary from a large text corpus. Using the learnt subword
dictionary, the words in training data transcriptions are segmented to subwords
and we train deep neural network ASR systems which recognize subword sequence
for any given test speech utterance. The output subword sequence is then
post-processed using deterministic rules to get the final word sequence such
that the actual number of words that can be recognized is much larger. For
Tamil ASR, We use 152 hours of data for training and 65 hours for testing,
whereas for Kannada ASR, we use 275 hours for training and 72 hours for
testing. Upon experimenting with different combination of segmentation and
estimation techniques, we find that the word error rate (WER) reduces
drastically when compared to the baseline word-level ASR, achieving a maximum
absolute WER reduction of 6.24% and 6.63% for Tamil and Kannada respectively.

E-commerce has gone a long way in empowering merchants through the internet.
In order to store the goods efficiently and arrange the marketing resource
properly, it is important for them to make the accurate gross merchandise value
(GMV) prediction. However, it's nontrivial to make accurate prediction with the
deficiency of digitized data. In this article, we present a solution to better
forecast GMV inside Alipay app. Thanks to graph neural networks (GNN) which has
great ability to correlate different entities to enrich information, we propose
Gaia, a graph neural network (GNN) model with temporal shift aware attention.
Gaia leverages the relevant e-seller' sales information and learn neighbor
correlation based on temporal dependencies. By testing on Alipay's real dataset
and comparing with other baselines, Gaia has shown the best performance. And
Gaia is deployed in the simulated online environment, which also achieves great
improvement compared with baselines.

With the increasing attention in various 3D safety-critical applications,
point cloud learning models have been shown to be vulnerable to adversarial
attacks. Although existing 3D attack methods achieve high success rates, they
delve into the data space with point-wise perturbation, which may neglect the
geometric characteristics. Instead, we propose point cloud attacks from a new
perspective -- the graph spectral domain attack, aiming to perturb graph
transform coefficients in the spectral domain that corresponds to varying
certain geometric structure. Specifically, leveraging on graph signal
processing, we first adaptively transform the coordinates of points onto the
spectral domain via graph Fourier transform (GFT) for compact representation.
Then, we analyze the influence of different spectral bands on the geometric
structure, based on which we propose to perturb the GFT coefficients via a
learnable graph spectral filter. Considering the low-frequency components
mainly contribute to the rough shape of the 3D object, we further introduce a
low-frequency constraint to limit perturbations within imperceptible
high-frequency components. Finally, the adversarial point cloud is generated by
transforming the perturbed spectral representation back to the data domain via
the inverse GFT. Experimental results demonstrate the effectiveness of the
proposed attack in terms of both the imperceptibility and attack success rates.

The functionality of a deep learning (DL) model can be stolen via model
extraction where an attacker obtains a surrogate model by utilizing the
responses from a prediction API of the original model. In this work, we propose
a novel watermarking technique called DynaMarks to protect the intellectual
property (IP) of DL models against such model extraction attacks in a black-box
setting. Unlike existing approaches, DynaMarks does not alter the training
process of the original model but rather embeds watermark into a surrogate
model by dynamically changing the output responses from the original model
prediction API based on certain secret parameters at inference runtime. The
experimental outcomes on Fashion MNIST, CIFAR-10, and ImageNet datasets
demonstrate the efficacy of DynaMarks scheme to watermark surrogate models
while preserving the accuracies of the original models deployed in edge
devices. In addition, we also perform experiments to evaluate the robustness of
DynaMarks against various watermark removal strategies, thus allowing a DL
model owner to reliably prove model ownership.

Recent methods for conditional image generation benefit from dense
supervision such as segmentation label maps to achieve high-fidelity. However,
it is rarely explored to employ dense supervision for unconditional image
generation. Here we explore the efficacy of dense supervision in unconditional
generation and find generator feature maps can be an alternative of
cost-expensive semantic label maps. From our empirical evidences, we propose a
new generator-guided discriminator regularization(GGDR) in which the generator
feature maps supervise the discriminator to have rich semantic representations
in unconditional generation. In specific, we employ an U-Net architecture for
discriminator, which is trained to predict the generator feature maps given
fake images as inputs. Extensive experiments on mulitple datasets show that our
GGDR consistently improves the performance of baseline methods in terms of
quantitative and qualitative aspects. Code is available at
https://github.com/naver-ai/GGDR

Regulatory stress tests have become the primary tool for setting capital
requirements at the largest U.S. banks. The Federal Reserve uses confidential
models to evaluate bank-specific outcomes for bank-specific portfolios in
shared stress scenarios. As a matter of policy, the same models are used for
all banks, despite considerable heterogeneity across institutions; individual
banks have contended that some models are not suited to their businesses.
Motivated by this debate, we ask, what is a fair aggregation of individually
tailored models into a common model? We argue that simply pooling data across
banks treats banks equally but is subject to two deficiencies: it may distort
the impact of legitimate portfolio features, and it is vulnerable to implicit
misdirection of legitimate information to infer bank identity. We compare
various notions of regression fairness to address these deficiencies,
considering both forecast accuracy and equal treatment. In the setting of
linear models, we argue for estimating and then discarding centered bank fixed
effects as preferable to simply ignoring differences across banks. We present
evidence that the overall impact can be material. We also discuss extensions to
nonlinear models.

Nearly all existing scene graph generation (SGG) models have overlooked the
ground-truth annotation qualities of mainstream SGG datasets, i.e., they
assume: 1) all the manually annotated positive samples are equally correct; 2)
all the un-annotated negative samples are absolutely background. In this paper,
we argue that neither of the assumptions applies to SGG: there are numerous
noisy ground-truth predicate labels that break these two assumptions and harm
the training of unbiased SGG models. To this end, we propose a novel NoIsy
label CorrEction and Sample Training strategy for SGG: NICEST. Specifically, it
consists of two parts: NICE and NIST, which rule out these noisy label issues
by generating high-quality samples and the effective training strategy,
respectively. NICE first detects noisy samples and then reassigns them more
high-quality soft predicate labels. NIST is a multi-teacher knowledge
distillation based training strategy, which enables the model to learn unbiased
fusion knowledge. And a dynamic trade-off weighting strategy in NIST is
designed to penalize the bias of different teachers. Due to the model-agnostic
nature of both NICE and NIST, our NICEST can be seamlessly incorporated into
any SGG architecture to boost its performance on different predicate
categories. In addition, to better evaluate the generalization of SGG models,
we further propose a new benchmark VG-OOD, by re-organizing the prevalent VG
dataset and deliberately making the predicate distributions of the training and
test sets as different as possible for each subject-object category pair. This
new benchmark helps disentangle the influence of subject-object category based
frequency biases. Extensive ablations and results on different backbones and
tasks have attested to the effectiveness and generalization ability of each
component of NICEST.

We propose a task we name Portrait Interpretation and construct a dataset
named Portrait250K for it. Current researches on portraits such as human
attribute recognition and person re-identification have achieved many
successes, but generally, they: 1) may lack mining the interrelationship
between various tasks and the possible benefits it may bring; 2) design deep
models specifically for each task, which is inefficient; 3) may be unable to
cope with the needs of a unified model and comprehensive perception in actual
scenes. In this paper, the proposed portrait interpretation recognizes the
perception of humans from a new systematic perspective. We divide the
perception of portraits into three aspects, namely Appearance, Posture, and
Emotion, and design corresponding sub-tasks for each aspect. Based on the
framework of multi-task learning, portrait interpretation requires a
comprehensive description of static attributes and dynamic states of portraits.
To invigorate research on this new task, we construct a new dataset that
contains 250,000 images labeled with identity, gender, age, physique, height,
expression, and posture of the whole body and arms. Our dataset is collected
from 51 movies, hence covering extensive diversity. Furthermore, we focus on
representation learning for portrait interpretation and propose a baseline that
reflects our systematic perspective. We also propose an appropriate metric for
this task. Our experimental results demonstrate that combining the tasks
related to portrait interpretation can yield benefits. Code and dataset will be
made public.

A combinatorial recommender (CR) system feeds a list of items to a user at a
time in the result page, in which the user behavior is affected by both
contextual information and items. The CR is formulated as a combinatorial
optimization problem with the objective of maximizing the recommendation reward
of the whole list. Despite its importance, it is still a challenge to build a
practical CR system, due to the efficiency, dynamics, personalization
requirement in online environment. In particular, we tear the problem into two
sub-problems, list generation and list evaluation. Novel and practical model
architectures are designed for these sub-problems aiming at jointly optimizing
effectiveness and efficiency. In order to adapt to online case, a bootstrap
algorithm forming an actor-critic reinforcement framework is given to explore
better recommendation mode in long-term user interaction. Offline and online
experiment results demonstrate the efficacy of proposed JDRec framework. JDRec
has been applied in online JD recommendation, improving click through rate by
2.6% and synthetical value for the platform by 5.03%. We will publish the
large-scale dataset used in this study to contribute to the research community.

A machine learning technique is proposed for quantifying uncertainty in power
system dynamics with spatiotemporally correlated stochastic forcing. We learn
one-dimensional linear partial differential equations for the probability
density functions of real-valued quantities of interest. The method is suitable
for high-dimensional systems and helps to alleviate the curse of

In this paper, we explore a new knowledge-amalgamation problem, termed
Federated Selective Aggregation (FedSA). The goal of FedSA is to train a
student model for a new task with the help of several decentralized teachers,
whose pre-training tasks and data are different and agnostic. Our motivation
for investigating such a problem setup stems from a recent dilemma of model
sharing. Many researchers or institutes have spent enormous resources on
training large and competent networks. Due to the privacy, security, or
intellectual property issues, they are, however, not able to share their own
pre-trained models, even if they wish to contribute to the community. The
proposed FedSA offers a solution to this dilemma and makes it one step further
since, again, the learned student may specialize in a new task different from
all of the teachers. To this end, we proposed a dedicated strategy for handling
FedSA. Specifically, our student-training process is driven by a novel
saliency-based approach that adaptively selects teachers as the participants
and integrates their representative capabilities into the student. To evaluate
the effectiveness of FedSA, we conduct experiments on both single-task and
multi-task settings. Experimental results demonstrate that FedSA effectively
amalgamates knowledge from decentralized models and achieves competitive
performance to centralized baselines.

In this paper we propose an extension of the Attention Branch Network (ABN)
by using instance segmentation for generating sharper attention maps for action
recognition. Methods for visual explanation such as Grad-CAM usually generate
blurry maps which are not intuitive for humans to understand, particularly in
recognizing actions of people in videos. Our proposed method, Object-ABN,
tackles this issue by introducing a new mask loss that makes the generated
attention maps close to the instance segmentation result. Further the PC loss
and multiple attention maps are introduced to enhance the sharpness of the maps
and improve the performance of classification. Experimental results with UCF101
and SSv2 shows that the generated maps by the proposed method are much clearer
qualitatively and quantitatively than those of the original ABN.

We present Generalizable NeRF Transformer (GNT), a transformer-based
architecture that reconstructs Neural Radiance Fields (NeRFs) and learns to
renders novel views on the fly from source views. While prior works on NeRFs
optimize a scene representation by inverting a handcrafted rendering equation,
GNT achieves neural representation and rendering that generalizes across scenes
using transformers at two stages. (1) The view transformer leverages multi-view
geometry as an inductive bias for attention-based scene representation, and
predicts coordinate-aligned features by aggregating information from epipolar
lines on the neighboring views. (2) The ray transformer renders novel views
using attention to decode the features from the view transformer along the
sampled points during ray marching. Our experiments demonstrate that when
optimized on a single scene, GNT can successfully reconstruct NeRF without an
explicit rendering formula due to the learned ray renderer. When trained on
multiple scenes, GNT consistently achieves state-of-the-art performance when
transferring to unseen scenes and outperform all other methods by ~10% on
average. Our analysis of the learned attention maps to infer depth and
occlusion indicate that attention enables learning a physically-grounded
rendering. Our results show the promise of transformers as a universal modeling
tool for graphics. Please refer to our project page for video results:
https://vita-group.github.io/GNT/.

Semantic segmentation for autonomous driving should be robust against various
in-the-wild environments. Nighttime semantic segmentation is especially
challenging due to a lack of annotated nighttime images and a large domain gap
from daytime images with sufficient annotation. In this paper, we propose a
novel GPS-based training framework for nighttime semantic segmentation. Given
GPS-aligned pairs of daytime and nighttime images, we perform cross-domain
correspondence matching to obtain pixel-level pseudo supervision. Moreover, we
conduct flow estimation between daytime video frames and apply GPS-based
scaling to acquire another pixel-level pseudo supervision. Using these pseudo
supervisions with a confidence map, we train a nighttime semantic segmentation
network without any annotation from nighttime images. Experimental results
demonstrate the effectiveness of the proposed method on several nighttime
semantic segmentation datasets. Our source code is available at
https://github.com/jimmy9704/GPS-GLASS.

This study focuses on the application of a specific subfield of artificial
intelligence referred to as computer vision in the analysis of 2-dimensional
lung x-ray images for the assisted medical diagnosis of ordinary pneumonia.
  A convolutional neural network algorithm was implemented in a Python-coded,
Flask-based web application that can analyze x-ray images for the detection of
ordinary pneumonia. Since convolutional neural network algorithms rely on
machine learning for the identification and detection of patterns, a technique
referred to as transfer learning was implemented to train the neural network in
the identification and detection of patterns within the dataset. Open-source
lung x-ray images were used as training data to create a knowledge base that
served as the core element of the web application and the experimental design
employed a 5-Trial Confirmatory Test for the validation of the web application.
  The results of the 5-Trial Confirmatory Test show the calculation of
Diagnostic Precision Percentage per Trial, General Diagnostic Precision
Percentage, and General Diagnostic Error Percentage while the Confusion Matrix
further shows the relationship between the label and the corresponding
diagnosis result of the web application on each test images.
  The developed web application can be used by medical practitioners in
A.I.-assisted diagnosis of ordinary pneumonia, and by researchers in the fields
of computer science and bioinformatics.

Missing values, widely called as \textit{sparsity} in literature, is a common
characteristic of many real-world datasets. Many imputation methods have been
proposed to address this problem of data incompleteness or sparsity. However,
the accuracy of a data imputation method for a given feature or a set of
features in a dataset is highly dependent on the distribution of the feature
values and its correlation with other features. Another problem that plagues
industry deployments of machine learning (ML) solutions is concept drift
detection, which becomes more challenging in the presence of missing values.
Although data imputation and concept drift detection have been studied
extensively, little work has attempted a combined study of the two phenomena,
i.e., concept drift detection in the presence of sparsity. In this work, we
carry out a systematic study of the following: (i) different patterns of
missing values, (ii) various statistical and ML based data imputation methods
for different kinds of sparsity, (iii) several concept drift detection methods,
(iv) practical analysis of the various drift detection metrics, (v) selecting
the best concept drift detector given a dataset with missing values based on
the different metrics. We first analyze it on synthetic data and publicly
available datasets, and finally extend the findings to our deployed solution of
automated change risk assessment system. One of the major findings from our
empirical study is the absence of supremacy of any one concept drift detection
method across all the relevant metrics. Therefore, we adopt a majority voting
based ensemble of concept drift detectors for abrupt and gradual concept
drifts. Our experiments show optimal or near optimal performance can be
achieved for this ensemble method across all the metrics.

Current image-to-image translation methods formulate the task with
conditional generation models, leading to learning only the recolorization or
regional changes as being constrained by the rich structural information
provided by the conditional contexts. In this work, we propose introducing the
vector quantization technique into the image-to-image translation framework.
The vector quantized content representation can facilitate not only the
translation, but also the unconditional distribution shared among different
domains. Meanwhile, along with the disentangled style representation, the
proposed method further enables the capability of image extension with
flexibility in both intra- and inter-domains. Qualitative and quantitative
experiments demonstrate that our framework achieves comparable performance to
the state-of-the-art image-to-image translation and image extension methods.
Compared to methods for individual tasks, the proposed method, as a unified
framework, unleashes applications combining image-to-image translation,
unconditional generation, and image extension altogether. For example, it
provides style variability for image generation and extension, and equips
image-to-image translation with further extension capabilities.

In recent years, decentralized bilevel optimization problems have received
increasing attention in the networking and machine learning communities thanks
to their versatility in modeling decentralized learning problems over
peer-to-peer networks (e.g., multi-agent meta-learning, multi-agent
reinforcement learning, personalized training, and Byzantine-resilient
learning). However, for decentralized bilevel optimization over peer-to-peer
networks with limited computation and communication capabilities, how to
achieve low sample and communication complexities are two fundamental
challenges that remain under-explored so far. In this paper, we make the first
attempt to investigate the class of decentralized bilevel optimization problems
with nonconvex and strongly-convex structure corresponding to the outer and
inner subproblems, respectively. Our main contributions in this paper are
two-fold: i) We first propose a deterministic algorithm called INTERACT
(inner-gradient-descent-outer-tracked-gradient) that requires the sample
complexity of $\mathcal{O}(n \epsilon^{-1})$ and communication complexity of
$\mathcal{O}(\epsilon^{-1})$ to solve the bilevel optimization problem, where
$n$ and $\epsilon > 0$ are the number of samples at each agent and the desired
stationarity gap, respectively. ii) To relax the need for full gradient
evaluations in each iteration, we propose a stochastic variance-reduced version
of INTERACT (SVR-INTERACT), which improves the sample complexity to
$\mathcal{O}(\sqrt{n} \epsilon^{-1})$ while achieving the same communication
complexity as the deterministic algorithm. To our knowledge, this work is the
first that achieves both low sample and communication complexities for solving
decentralized bilevel optimization problems over networks. Our numerical
experiments also corroborate our theoretical findings.

In this paper, we give a cubic Goldreich-Levin algorithm which makes
polynomially-many queries to a function $f \colon \mathbb F_p^n \to \mathbb C$
and produces a decomposition of $f$ as a sum of cubic phases and a small error
term. This is a natural higher-order generalization of the classical
Goldreich-Levin algorithm. The classical (linear) Goldreich-Levin algorithm has
wide-ranging applications in learning theory, coding theory and the
construction of pseudorandom generators in cryptography, as well as being
closely related to Fourier analysis. Higher-order Goldreich-Levin algorithms on
the other hand involve central problems in higher-order Fourier analysis,
namely the inverse theory of the Gowers $U^k$ norms, which are well-studied in
additive combinatorics. The only known result in this direction prior to this
work is the quadratic Goldreich-Levin theorem, proved by Tulsiani and Wolf in
2011. The main step of their result involves an algorithmic version of the
$U^3$ inverse theorem. More complications appear in the inverse theory of the
$U^4$ and higher norms. Our cubic Goldreich-Levin algorithm is based on
algorithmizing recent work by Gowers and Mili\'cevi\'c who proved new
quantitative bounds for the $U^4$ inverse theorem.
  Our cubic Goldreich-Levin algorithm is constructed from two main tools: an
algorithmic $U^4$ inverse theorem and an arithmetic decomposition result in the
style of the Frieze-Kannan graph regularity lemma. As one application of our
main theorem we solve the problem of self-correction for cubic Reed-Muller
codes beyond the list decoding radius. Additionally we give a purely
combinatorial result: an improvement of the quantitative bounds on the $U^4$
inverse theorem.

Positive Unlabeled (PU) learning aims to learn a binary classifier from only
positive and unlabeled data, which is utilized in many real-world scenarios.
However, existing PU learning algorithms cannot deal with the real-world
challenge in an open and changing scenario, where examples from unobserved
augmented classes may emerge in the testing phase. In this paper, we propose an
unbiased risk estimator for PU learning with Augmented Classes (PUAC) by
utilizing unlabeled data from the augmented classes distribution, which can be
easily collected in many real-world scenarios. Besides, we derive the
estimation error bound for the proposed estimator, which provides a theoretical
guarantee for its convergence to the optimal solution. Experiments on multiple
realistic datasets demonstrate the effectiveness of proposed approach.

Compared with traditional model-based fault detection and classification
(FDC) methods, deep neural networks (DNN) prove to be effective for the
aerospace sensors FDC problems. However, time being consumed in training the
DNN is excessive, and explainability analysis for the FDC neural network is
still underwhelming. A concept known as imagefication-based intelligent FDC has
been studied in recent years. This concept advocates to stack the sensors
measurement data into an image format, the sensors FDC issue is then
transformed to abnormal regions detection problem on the stacked image, which
may well borrow the recent advances in the machine vision vision realm.
Although promising results have been claimed in the imagefication-based
intelligent FDC researches, due to the low size of the stacked image, small
convolutional kernels and shallow DNN layers were used, which hinders the FDC
performance. In this paper, we first propose a data augmentation method which
inflates the stacked image to a larger size (correspondent to the VGG16 net
developed in the machine vision realm). The FDC neural network is then trained
via fine-tuning the VGG16 directly. To truncate and compress the FDC net size
(hence its running time), we perform model pruning on the fine-tuned net. Class
activation mapping (CAM) method is also adopted for explainability analysis of
the FDC net to verify its internal operations. Via data augmentation,
fine-tuning from VGG16, and model pruning, the FDC net developed in this paper
claims an FDC accuracy 98.90% across 4 aircraft at 5 flight conditions (running
time 26 ms). The CAM results also verify the FDC net w.r.t. its internal

More competent learning models are demanded for data processing due to
increasingly greater amounts of data available in applications. Data that we
encounter often have certain embedded sparsity structures. That is, if they are
represented in an appropriate basis, their energies can concentrate on a small
number of basis functions. This paper is devoted to a numerical study of
adaptive approximation of solutions of nonlinear partial differential equations
whose solutions may have singularities, by deep neural networks (DNNs) with a
sparse regularization with multiple parameters. Noting that DNNs have an
intrinsic multi-scale structure which is favorable for adaptive representation
of functions, by employing a penalty with multiple parameters, we develop DNNs
with a multi-scale sparse regularization (SDNN) for effectively representing
functions having certain singularities. We then apply the proposed SDNN to
numerical solutions of the Burgers equation and the Schr\"odinger equation.
Numerical examples confirm that solutions generated by the proposed SDNN are
sparse and accurate.

Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The
disease can lead a person to death or a high level of body damage. To avoid
significant damage, patients need sugar. The research aims at implementing an
automatic system to detect hypoglycemia and perform automatic sugar injections
to save a life. Receiving the benefits of the internet of things (IoT), the
sensor data was transferred using the hypertext transfer protocol (HTTP)
protocol. To ensure the safety of health-related data, blockchain technology
was utilized. The glucose sensor and smartwatch data were processed via Fog and
sent to the cloud. A Random Forest algorithm was proposed and utilized to
decide hypoglycemic events. When the hypoglycemic event was detected, the
system sent a notification to the mobile application and auto-injection device
to push the condensed sugar into the victims body. XGBoost, k-nearest neighbors
(KNN), support vector machine (SVM), and decision tree were implemented to
compare the proposed models performance. The random forest performed 0.942
testing accuracy, better than other models in detecting hypoglycemic events.
The systems performance was measured in several conditions, and satisfactory
results were achieved. The system can benefit hypoglycemia patients to survive
this disease.

Traditional fish farming faces several challenges, including water pollution,
temperature imbalance, feed, space, cost, etc. Biofloc technology in
aquaculture transforms the manual into an advanced system that allows the reuse
of unused feed by converting them into microbial protein. The objective of the
research is to propose an IoT-based solution to aquaculture that increases
efficiency and productivity. The article presented a system that collects data
using sensors, analyzes them using a machine learning model, generates
decisions with the help of Artificial Intelligence (AI), and sends
notifications to the user. The proposed system has been implemented and tested
to validate and achieve a satisfactory result.

Serverless computing is an emerging cloud computing paradigm that has been
applied to various domains, including machine learning, scientific computing,
video processing, etc. To develop serverless computing-based software
applications (a.k.a., serverless applications), developers follow the new
cloud-based software architecture, where they develop event-driven applications
without the need for complex and error-prone server management. The great
demand for developing serverless applications poses unique challenges to
software developers. However, Software Engineering (SE) has not yet
wholeheartedly tackled these challenges. In this paper, we outline a vision for
how SE can facilitate the development of serverless applications and call for
actions by the SE research community to reify this vision. Specifically, we
discuss possible directions in which researchers and cloud providers can
facilitate serverless computing from the SE perspective, including
configuration management, data security, application migration, performance,
testing and debugging, etc.

The brain tumor is the most aggressive kind of tumor and can cause low life
expectancy if diagnosed at the later stages. Manual identification of brain
tumors is tedious and prone to errors. Misdiagnosis can lead to false treatment
and thus reduce the chances of survival for the patient. Medical resonance
imaging (MRI) is the conventional method used to diagnose brain tumors and
their types. This paper attempts to eliminate the manual process from the
diagnosis process and use machine learning instead. We proposed the use of
pretrained convolutional neural networks (CNN) for the diagnosis and
classification of brain tumors. Three types of tumors were classified with one
class of non-tumor MRI images. Networks that has been used are ResNet50,
EfficientNetB1, EfficientNetB7, EfficientNetV2B1. EfficientNet has shown
promising results due to its scalable nature. EfficientNetB1 showed the best
results with training and validation accuracy of 87.67% and 89.55%,

Nearly 30% of the people in the rural areas of Bangladesh are below the
poverty level. Moreover, due to the unavailability of modernized
healthcare-related technology, nursing and diagnosis facilities are limited for
rural people. Therefore, rural people are deprived of proper healthcare. In
this perspective, modern technology can be facilitated to mitigate their health
problems. ECG sensing tools are interfaced with the human chest, and requisite
cardiovascular data is collected through an IoT device. These data are stored
in the cloud incorporates with the MQTT and HTTP servers. An innovative
IoT-based method for ECG monitoring systems on cardiovascular or heart patients
has been suggested in this study. The ECG signal parameters P, Q, R, S, T are
collected, pre-processed, and predicted to monitor the cardiovascular
conditions for further health management. The machine learning algorithm is
used to determine the significance of ECG signal parameters and error rate. The
logistic regression model fitted the better agreements between the train and
test data. The prediction has been performed to determine the variation of
PQRST quality and its suitability in the ECG Monitoring System. Considering the
values of quality parameters, satisfactory results are obtained. The proposed
IoT-based ECG system reduces the health care cost and complexity of
cardiovascular diseases in the future.

Transformer-based methods have recently achieved great advancement on 2D
image-based vision tasks. For 3D video-based tasks such as action recognition,
however, directly applying spatiotemporal transformers on video data will bring
heavy computation and memory burdens due to the largely increased number of
patches and the quadratic complexity of self-attention computation. How to
efficiently and effectively model the 3D self-attention of video data has been
a great challenge for transformers. In this paper, we propose a Temporal Patch
Shift (TPS) method for efficient 3D self-attention modeling in transformers for
video-based action recognition. TPS shifts part of patches with a specific
mosaic pattern in the temporal dimension, thus converting a vanilla spatial
self-attention operation to a spatiotemporal one with little additional cost.
As a result, we can compute 3D self-attention using nearly the same computation
and memory cost as 2D self-attention. TPS is a plug-and-play module and can be
inserted into existing 2D transformer models to enhance spatiotemporal feature
learning. The proposed method achieves competitive performance with
state-of-the-arts on Something-something V1 & V2, Diving-48, and Kinetics400
while being much more efficient on computation and memory cost. The source code
of TPS can be found at https://github.com/MartinXM/TPS.

Knowledge distillation has emerged as a scalable and effective way for
privacy-preserving machine learning. One remaining drawback is that it consumes
privacy in a model-level (i.e., client-level) manner, every distillation query
incurs privacy loss of one client's all records. In order to attain
fine-grained privacy accountant and improve utility, this work proposes a
model-free reverse $k$-NN labeling method towards record-level private
knowledge distillation, where each record is employed for labeling at most $k$
queries. Theoretically, we provide bounds of labeling error rate under the
centralized/local/shuffle model of differential privacy (w.r.t. the number of
records per query, privacy budgets). Experimentally, we demonstrate that it
achieves new state-of-the-art accuracy with one order of magnitude lower of
privacy loss. Specifically, on the CIFAR-$10$ dataset, it reaches $82.1\%$ test
accuracy with centralized privacy budget $1.0$; on the MNIST/SVHN dataset, it
reaches $99.1\%$/$95.6\%$ accuracy respectively with budget $0.1$. It is the
first time deep learning with differential privacy achieve comparable accuracy
with reasonable data privacy protection (i.e., $\exp(\epsilon)\leq 1.5$). Our
code is available at https://github.com/liyuntong9/rknn.

Convolutional neural networks have been widely applied to medical image
segmentation and have achieved considerable performance. However, the
performance may be significantly affected by the domain gap between training
data (source domain) and testing data (target domain). To address this issue,
we propose a data manipulation based domain generalization method, called
Automated Augmentation for Domain Generalization (AADG). Our AADG framework can
effectively sample data augmentation policies that generate novel domains and
diversify the training set from an appropriate search space. Specifically, we
introduce a novel proxy task maximizing the diversity among multiple augmented
novel domains as measured by the Sinkhorn distance in a unit sphere space,
making automated augmentation tractable. Adversarial training and deep
reinforcement learning are employed to efficiently search the objectives.
Quantitative and qualitative experiments on 11 publicly-accessible fundus image
datasets (four for retinal vessel segmentation, four for optic disc and cup
(OD/OC) segmentation and three for retinal lesion segmentation) are
comprehensively performed. Two OCTA datasets for retinal vasculature
segmentation are further involved to validate cross-modality generalization.
Our proposed AADG exhibits state-of-the-art generalization performance and
outperforms existing approaches by considerable margins on retinal vessel,
OD/OC and lesion segmentation tasks. The learned policies are empirically
validated to be model-agnostic and can transfer well to other models. The
source code is available at https://github.com/CRazorback/AADG.

The prime challenge in unsupervised domain adaptation (DA) is to mitigate the
domain shift between the source and target domains. Prior DA works show that
pretext tasks could be used to mitigate this domain shift by learning domain
invariant representations. However, in practice, we find that most existing
pretext tasks are ineffective against other established techniques. Thus, we
theoretically analyze how and when a subsidiary pretext task could be leveraged
to assist the goal task of a given DA problem and develop objective subsidiary
task suitability criteria. Based on this criteria, we devise a novel process of
sticker intervention and cast sticker classification as a supervised subsidiary
DA problem concurrent to the goal task unsupervised DA. Our approach not only
improves goal task adaptation performance, but also facilitates
privacy-oriented source-free DA i.e. without concurrent source-target access.
Experiments on the standard Office-31, Office-Home, DomainNet, and VisDA
benchmarks demonstrate our superiority for both single-source and multi-source
source-free DA. Our approach also complements existing non-source-free works,
achieving leading performance.

The last decade of machine learning has seen drastic increases in scale and
capabilities. Deep neural networks (DNNs) are increasingly being deployed in
the real world. However, they are generally difficult to analyze, raising
concerns about using them without a rigorous understanding of how they
function. Effective tools for interpreting them will be important for building
more trustworthy AI by helping to identify failures, fix bugs, and improve
basic understanding. In particular, "inner" interpretability techniques, which
focus on explaining the internal components of DNNs, are well-suited for
developing a mechanistic understanding, guiding manual modifications, and
reverse engineering solutions.
  Much recent work has focused on DNN interpretability, and rapid progress has
thus far made a thorough systematization of methods difficult. In this survey,
we review over 300 works with a focus on inner interpretability tools. We
introduce a taxonomy that classifies methods by what part of the network they
help to explain (weights, neurons, subnetworks, or latent representations) and
whether they are implemented during (intrinsic) or after (post hoc) training.
To our knowledge, we are also the first to survey a number of connections
between interpretability research and work in adversarial robustness, continual
learning, modularity, network compression, and studying the human visual
system. Finally, we discuss key challenges and argue for future work
emphasizing diagnostics, benchmarking, and robustness.

Knowledge-based visual question answering (KVQA) task aims to answer
questions that require additional external knowledge as well as an
understanding of images and questions. Recent studies on KVQA inject an
external knowledge in a multi-modal form, and as more knowledge is used,
irrelevant information may be added and can confuse the question answering. In
order to properly use the knowledge, this study proposes the following: 1) we
introduce a novel semantic inconsistency measure computed from caption
uncertainty and semantic similarity; 2) we suggest a new external knowledge
assimilation method based on the semantic inconsistency measure and apply it to
integrate explicit knowledge and implicit knowledge for KVQA; 3) the proposed
method is evaluated with the OK-VQA dataset and achieves the state-of-the-art

This work presents a novel framework CISFA (Contrastive Image synthesis and
Self-supervised Feature Adaptation)that builds on image domain translation and
unsupervised feature adaptation for cross-modality biomedical image
segmentation. Different from existing works, we use a one-sided generative
model and add a weighted patch-wise contrastive loss between sampled patches of
the input image and the corresponding synthetic image, which serves as shape
constraints. Moreover, we notice that the generated images and input images
share similar structural information but are in different modalities. As such,
we enforce contrastive losses on the generated images and the input images to
train the encoder of a segmentation model to minimize the discrepancy between
paired images in the learned embedding space. Compared with existing works that
rely on adversarial learning for feature adaptation, such a method enables the
encoder to learn domain-independent features in a more explicit way. We
extensively evaluate our methods on segmentation tasks containing CT and MRI
images for abdominal cavities and whole hearts. Experimental results show that
the proposed framework not only outputs synthetic images with less distortion
of organ shapes, but also outperforms state-of-the-art domain adaptation

Using Machine Learning and Deep Learning to predict cognitive tasks from
electroencephalography (EEG) signals has been a fast-developing area in
Brain-Computer Interfaces (BCI). However, during the COVID-19 pandemic, data
collection and analysis could be more challenging than before. This paper
explored machine learning algorithms that can run efficiently on personal
computers for BCI classification tasks. Also, we investigated a way to conduct
such BCI experiments remotely via Zoom. The results showed that Random Forest
and RBF SVM performed well for EEG classification tasks. The remote experiment
during the pandemic yielded several challenges, and we discussed the possible
solutions; nevertheless, we developed a protocol that grants non-experts who
are interested a guideline for such data collection.

Facial expression is an essential factor in conveying human emotional states
and intentions. Although remarkable advancement has been made in facial
expression recognition (FER) task, challenges due to large variations of
expression patterns and unavoidable data uncertainties still remain. In this
paper, we propose mid-level representation enhancement (MRE) and graph embedded
uncertainty suppressing (GUS) addressing these issues. On one hand, MRE is
introduced to avoid expression representation learning being dominated by a
limited number of highly discriminative patterns. On the other hand, GUS is
introduced to suppress the feature ambiguity in the representation space. The
proposed method not only has stronger generalization capability to handle
different variations of expression patterns but also more robustness to capture
expression representations. Experimental evaluation on Aff-Wild2 have verified

End-to-end speech synthesis models directly convert the input characters into
an audio representation (e.g., spectrograms). Despite their impressive
performance, such models have difficulty disambiguating the pronunciations of
identically spelled words. To mitigate this issue, a separate
Grapheme-to-Phoneme (G2P) model can be employed to convert the characters into
phonemes before synthesizing the audio. This paper proposes SoundChoice, a
novel G2P architecture that processes entire sentences rather than operating at
the word level. The proposed architecture takes advantage of a weighted
homograph loss (that improves disambiguation), exploits curriculum learning
(that gradually switches from word-level to sentence-level G2P), and integrates
word embeddings from BERT (for further performance improvement). Moreover, the
model inherits the best practices in speech recognition, including multi-task
learning with Connectionist Temporal Classification (CTC) and beam search with
an embedded language model. As a result, SoundChoice achieves a Phoneme Error
Rate (PER) of 2.65% on whole-sentence transcription using data from LibriSpeech
and Wikipedia. Index Terms grapheme-to-phoneme, speech synthesis,
text-tospeech, phonetics, pronunciation, disambiguation.

Learning to derive subgoals reduces the gap between experts and students and
makes students prepared for future problem solving. Researchers have explored
subgoal labeled instructional materials with explanations in traditional
problem solving and within tutoring systems to help novices learn to subgoal.
However, only a little research is found on problem-solving strategies in
relationship with subgoal learning. Also, these strategies are under-explored
within computer-based tutors and learning environments. Backward
problem-solving strategy is closely related to the process of subgoaling, where
problem solving iteratively refines the goal into a new subgoal to reduce
difficulty. In this paper, we explore a training strategy for backward strategy
learning within an intelligent logic tutor that teaches logic proof
construction. The training session involved backward worked examples (BWE) and
problem-solving (BPS) to help students learn backward strategy towards
improving their subgoaling and problem-solving skills. To evaluate the training
strategy, we analyzed students' 1) experience with and engagement in learning
backward strategy, 2) performance, and 3) proof construction approaches in new
problems that they solved independently without tutor help after each level of
training and in post-test. Our results showed that, when new problems were
given to solve without any tutor help, students who were trained with both BWE
and BPS outperformed students who received none of the treatment or only BWE
during training. Additionally, students trained with both BWE and BPS derived
subgoals during proof construction with significantly higher efficiency than
the other two groups.

Data-driven machine learning methods have the potential to dramatically
accelerate the rate of materials design over conventional human-guided
approaches. These methods would help identify or, in the case of generative
models, even create novel crystal structures of materials with a set of
specified functional properties to then be synthesized or isolated in the
laboratory. For crystal structure generation, a key bottleneck lies in
developing suitable atomic structure fingerprints or representations for the
machine learning model, analogous to the graph-based or SMILES representations
used in molecular generation. However, finding data-efficient representations
that are invariant to translations, rotations, and permutations, while
remaining invertible to the Cartesian atomic coordinates remains an ongoing
challenge. Here, we propose an alternative approach to this problem by taking
existing non-invertible representations with the desired invariances and
developing an algorithm to reconstruct the atomic coordinates through
gradient-based optimization using automatic differentiation. This can then be
coupled to a generative machine learning model which generates new materials
within the representation space, rather than in the data-inefficient Cartesian
space. In this work, we implement this end-to-end structure generation approach
using atom-centered symmetry functions as the representation and conditional
variational autoencoders as the generative model. We are able to successfully
generate novel and valid atomic structures of sub-nanometer Pt nanoparticles as
a proof of concept. Furthermore, this method can be readily extended to any
suitable structural representation, thereby providing a powerful, generalizable
framework towards structure-based generation.

Masked language modeling (MLM) has become one of the most successful
self-supervised pre-training task. Inspired by its success, Point-BERT, as a
pioneer work in point cloud, proposed masked point modeling (MPM) to pre-train
point transformer on large scale unanotated dataset. Despite its great
performance, we find the inherent difference between language and point cloud
tends to cause ambiguous tokenization for point cloud. For point cloud, there
doesn't exist a gold standard for point cloud tokenization. Point-BERT use a
discrete Variational AutoEncoder (dVAE) as tokenizer, but it might generate
different token ids for semantically-similar patches and generate the same
token ids for semantically-dissimilar patches. To tackle above problem, we
propose our McP-BERT, a pre-training framework with multi-choice tokens.
Specifically, we ease the previous single-choice constraint on patch token ids
in Point-BERT, and provide multi-choice token ids for each patch as
supervision. Moreover, we utilitze the high-level semantics learned by
transformer to further refine our supervision signals. Extensive experiments on
point cloud classification, few-shot classification and part segmentation tasks
demonstrate the superiority of our method, e.g., the pre-trained transformer
achieves 94.1% accuracy on ModelNet40, 84.28% accuracy on the hardest setting
of ScanObjectNN and new state-of-the-art performance on few-shot learning. We
also demonstrate that our method not only improves the performance of
Point-BERT on all downstream tasks, but also incurs almost no extra
computational overhead. The code will be released in
https://github.com/fukexue/McP-BERT.

Data-driven programming feedback systems can help novices to program in the
absence of a human tutor. Prior evaluations showed that these systems improve
learning in terms of test scores, or task completion efficiency. However,
crucial aspects which can impact learning or reveal insights important for
future improvement of such systems are ignored in these evaluations. These
aspects include inherent fallibility of current state-of-the-art, students'
programming behavior in response to correct/incorrect feedback, and
effective/ineffective system components. Consequently, a great deal of
knowledge is yet to be discovered about such systems. In this paper, we apply a
multi-criteria evaluation with 5 criteria on a data-driven feedback system
integrated within a block-based novice programming environment. Each criterion
in the evaluation reveals a unique pivotal aspect of the system: 1) How
accurate the feedback system is; 2) How it guides students throughout
programming tasks; 3) How it helps students in task completion; 4) What happens
when it goes wrong; and 5) How students respond generally to the system. Our
evaluation results showed that the system was helpful to students due to its
effective design and feedback representation despite being fallible. However,
novices can be negatively impacted by this fallibility due to high reliance and
lack of self-evaluation. The negative impacts include increased working time,
implementation, or submission of incorrect/partially correct solutions. The
evaluation results reinforced the necessity of multi-criteria system
evaluations while revealing important insights helpful to ensuring proper usage
of data-driven feedback systems, designing fallibility mitigation steps, and
driving research for future improvement.

Evolution Strategy (ES) algorithms have shown promising results in training
complex robotic control policies due to their massive parallelism capability,
simple implementation, effective parameter-space exploration, and fast training
time. However, a key limitation of ES is its scalability to large capacity
models, including modern neural network architectures. In this work, we develop
Predictive Information Augmented Random Search (PI-ARS) to mitigate this
limitation by leveraging recent advancements in representation learning to
reduce the parameter search space for ES. Namely, PI-ARS combines a
gradient-based representation learning technique, Predictive Information (PI),
with a gradient-free ES algorithm, Augmented Random Search (ARS), to train
policies that can process complex robot sensory inputs and handle highly
nonlinear robot dynamics. We evaluate PI-ARS on a set of challenging
visual-locomotion tasks where a quadruped robot needs to walk on uneven
stepping stones, quincuncial piles, and moving platforms, as well as to
complete an indoor navigation task. Across all tasks, PI-ARS demonstrates
significantly better learning efficiency and performance compared to the ARS
baseline. We further validate our algorithm by demonstrating that the learned
policies can successfully transfer to a real quadruped robot, for example,
achieving a 100% success rate on the real-world stepping stone environment,
dramatically improving prior results achieving 40% success.

Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process
owing to its innate traits of irreversibility with subtle and gradual
progression. These characteristics make AD biomarker identification from
structural brain imaging (e.g., structural MRI) scans quite challenging.
Furthermore, there is a high possibility of getting entangled with normal
aging. We propose a novel deep-learning approach through eXplainable AD
Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs
using clinically-guided prototype learning. Specifically, we establish a set of
topologically-aware prototypes onto the clusters of latent clinical features,
uncovering an AD spectrum manifold. We then measure the similarities between
latent clinical features and well-established prototypes, estimating a "pseudo"
likelihood map. By considering this pseudo map as an enriched reference, we
employ an estimating network to estimate the AD likelihood map over a 3D sMRI
scan. Additionally, we promote the explainability of such a likelihood map by
revealing a comprehensible overview from two perspectives: clinical and
morphological. During the inference, this estimated likelihood map served as a
substitute over unseen sMRI scans for effectively conducting the downstream
task while providing thorough explainable states.

Understanding human behavior is an important task and has applications in
many domains such as targeted advertisement, health analytics, security, and
entertainment, etc. For this purpose, designing a system for activity
recognition (AR) is important. However, since every human can have different
behaviors, understanding and analyzing common patterns become a challenging
task. Since smartphones are easily available to every human being in the modern
world, using them to track the human activities becomes possible. In this
paper, we extracted different human activities using accelerometer,
magnetometer, and gyroscope sensors of android smartphones by building an
android mobile applications. Using different social media applications, such as
Facebook, Instagram, Whatsapp, and Twitter, we extracted the raw sensor values
along with the attributes of $29$ subjects along with their attributes (class
labels) such as age, gender, and left/right/both hands application usage. We
extract features from the raw signals and use them to perform classification
using different machine learning (ML) algorithms. Using statistical analysis,
we show the importance of different features towards the prediction of class
labels. In the end, we use the trained ML model on our data to extract unknown
features from a well known activity recognition data from UCI repository, which
highlights the potential of privacy breach using ML models. This security
analysis could help researchers in future to take appropriate steps to preserve
the privacy of human subjects.

There is a growing interest in deep model-based architectures (DMBAs) for
solving imaging inverse problems by combining physical measurement models and
learned image priors specified using convolutional neural nets (CNNs). For
example, well-known frameworks for systematically designing DMBAs include
plug-and-play priors (PnP), deep unfolding (DU), and deep equilibrium models
(DEQ). While the empirical performance and theoretical properties of DMBAs have
been widely investigated, the existing work in the area has primarily focused
on their performance when the desired image prior is known exactly. This work
addresses the gap in the prior work by providing new theoretical and numerical
insights into DMBAs under mismatched CNN priors. Mismatched priors arise
naturally when there is a distribution shift between training and testing data,
for example, due to test images being from a different distribution than images
used for training the CNN prior. They also arise when the CNN prior used for
inference is an approximation of some desired statistical estimator (MAP or
MMSE). Our theoretical analysis provides explicit error bounds on the solution
due to the mismatched CNN priors under a set of clearly specified assumptions.
Our numerical results compare the empirical performance of DMBAs under
realistic distribution shifts and approximate statistical estimators.

Recently, adversarial machine learning attacks have posed serious security
threats against practical audio signal classification systems, including speech
recognition, speaker recognition, and music copyright detection. Previous
studies have mainly focused on ensuring the effectiveness of attacking an audio
signal classifier via creating a small noise-like perturbation on the original
signal. It is still unclear if an attacker is able to create audio signal
perturbations that can be well perceived by human beings in addition to its
attack effectiveness. This is particularly important for music signals as they
are carefully crafted with human-enjoyable audio characteristics.
  In this work, we formulate the adversarial attack against music signals as a
new perception-aware attack framework, which integrates human study into
adversarial attack design. Specifically, we conduct a human study to quantify
the human perception with respect to a change of a music signal. We invite
human participants to rate their perceived deviation based on pairs of original
and perturbed music signals, and reverse-engineer the human perception process
by regression analysis to predict the human-perceived deviation given a
perturbed signal. The perception-aware attack is then formulated as an
optimization problem that finds an optimal perturbation signal to minimize the
prediction of perceived deviation from the regressed human perception model. We
use the perception-aware framework to design a realistic adversarial music
attack against YouTube's copyright detector. Experiments show that the
perception-aware attack produces adversarial music with significantly better
perceptual quality than prior work.

Win prediction is crucial to understanding skill modeling, teamwork and
matchmaking in esports. In this paper we propose GCN-WP, a semi-supervised win
prediction model for esports based on graph convolutional networks. This model
learns the structure of an esports league over the course of a season (1 year)
and makes predictions on another similar league. This model integrates over 30
features about the match and players and employs graph convolution to classify
games based on their neighborhood. Our model achieves state-of-the-art
prediction accuracy when compared to machine learning or skill rating models
for LoL. The framework is generalizable so it can easily be extended to other
multiplayer online games.

Artificial intelligence (AI) is a fast-growing field focused on modeling and
machine implementation of various cognitive functions with an increasing number
of applications in computer vision, text processing, robotics, neurotechnology,
bio-inspired computing and others. In this chapter, we describe how AI methods
can be applied in the context of intracranial electroencephalography (iEEG)
research. IEEG data is unique as it provides extremely high-quality signals
recorded directly from brain tissue. Applying advanced AI models to these data
carries the potential to further our understanding of many fundamental
questions in neuroscience. At the same time, as an invasive technique, iEEG
lends itself well to long-term, mobile brain-computer interface applications,
particularly for communication in severely paralyzed individuals. We provide a
detailed overview of these two research directions in the application of AI
techniques to iEEG. That is, (1) the development of computational models that
target fundamental questions about the neurobiological nature of cognition
(AI-iEEG for neuroscience) and (2) applied research on monitoring and
identification of event-driven brain states for the development of clinical
brain-computer interface systems (AI-iEEG for neurotechnology). We explain key
machine learning concepts, specifics of processing and modeling iEEG data and
details of state-of-the-art iEEG-based neurotechnology and brain-computer
interfaces.

The propensity model introduced by Jain et al. 2016 has become a standard
approach for dealing with missing and long-tail labels in extreme multi-label
classification (XMLC). In this paper, we critically revise this approach
showing that despite its theoretical soundness, its application in contemporary
XMLC works is debatable. We exhaustively discuss the flaws of the
propensity-based approach, and present several recipes, some of them related to
solutions used in search engines and recommender systems, that we believe
constitute promising alternatives to be followed in XMLC.

In Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in
the monochorionic placenta can produce uneven blood flow between the two
fetuses. In the current practice, TTTS is treated surgically by closing
abnormal anastomoses using laser ablation. This surgery is minimally invasive
and relies on fetoscopy. Limited field of view makes anastomosis identification
a challenging task for the surgeon. To tackle this challenge, we propose a
learning-based framework for in-vivo fetoscopy frame registration for
field-of-view expansion. The novelties of this framework relies on a
learning-based keypoint proposal network and an encoding strategy to filter (i)
irrelevant keypoints based on fetoscopic image segmentation and (ii)
inconsistent homographies. We validate of our framework on a dataset of 6
intraoperative sequences from 6 TTTS surgeries from 6 different women against
the most recent state of the art algorithm, which relies on the segmentation of
placenta vessels. The proposed framework achieves higher performance compared
to the state of the art, paving the way for robust mosaicking to provide
surgeons with context awareness during TTTS surgery.

This short review aims to make the reader familiar with state-of-the-art
works relating to planning, scheduling and learning. First, we study
state-of-the-art planning algorithms. We give a brief introduction of neural
networks. Then we explore in more detail graph neural networks, a recent
variant of neural networks suited for processing graph-structured inputs. We
describe briefly the concept of reinforcement learning algorithms and some
approaches designed to date. Next, we study some successful approaches
combining neural networks for path-planning. Lastly, we focus on temporal
planning problems with uncertainty.

What sorts of structure might enable a learner to discover classes from
unlabeled data? Traditional approaches rely on feature-space similarity and
heroic assumptions on the data. In this paper, we introduce unsupervised
learning under Latent Label Shift (LLS), where we have access to unlabeled data
from multiple domains such that the label marginals $p_d(y)$ can shift across
domains but the class conditionals $p(\mathbf{x}|y)$ do not. This work
instantiates a new principle for identifying classes: elements that shift
together group together. For finite input spaces, we establish an isomorphism
between LLS and topic modeling: inputs correspond to words, domains to
documents, and labels to topics. Addressing continuous data, we prove that when
each label's support contains a separable region, analogous to an anchor word,
oracle access to $p(d|\mathbf{x})$ suffices to identify $p_d(y)$ and
$p_d(y|\mathbf{x})$ up to permutation. Thus motivated, we introduce a practical
algorithm that leverages domain-discriminative models as follows: (i) push
examples through domain discriminator $p(d|\mathbf{x})$; (ii) discretize the
data by clustering examples in $p(d|\mathbf{x})$ space; (iii) perform
non-negative matrix factorization on the discrete data; (iv) combine the
recovered $p(y|d)$ with the discriminator outputs $p(d|\mathbf{x})$ to compute
$p_d(y|x) \; \forall d$. With semi-synthetic experiments, we show that our
algorithm can leverage domain information to improve state of the art
unsupervised classification methods. We reveal a failure mode of standard
unsupervised classification methods when feature-space similarity does not
indicate true groupings, and show empirically that our method better handles
this case. Our results establish a deep connection between distribution shift
and topic modeling, opening promising lines for future work.

Variational Inference approximates an unnormalized distribution via the
minimization of Kullback-Leibler (KL) divergence. Although this divergence is
efficient for computation and has been widely used in applications, it suffers
from some unreasonable properties. For example, it is not a proper metric,
i.e., it is non-symmetric and does not preserve the triangle inequality. On the
other hand, optimal transport distances recently have shown some advantages
over KL divergence. With the help of these advantages, we propose a new
variational inference method by minimizing sliced Wasserstein distance, a valid
metric arising from optimal transport. This sliced Wasserstein distance can be
approximated simply by running MCMC but without solving any optimization
problem. Our approximation also does not require a tractable density function
of variational distributions so that approximating families can be amortized by
generators like neural networks. Furthermore, we provide an analysis of the
theoretical properties of our method. Experiments on synthetic and real data
are illustrated to show the performance of the proposed method.

Physics-based simulations typically operate with a combination of complex
differentiable equations and many scientific and geometric inputs. Our work
involves gathering data from those simulations and seeing how well tree-based
machine learning methods can emulate desired outputs without "knowing" the
complex backing involved in the simulations. The selected physics-based
simulations included Navier-Stokes, stress analysis, and electromagnetic field
lines to benchmark performance as numerical and statistical algorithms. We
specifically focus on predicting specific spatial-temporal data between two
simulation outputs and increasing spatial resolution to generalize the physics
predictions to finer test grids without the computational costs of repeating
the numerical calculation.

One of the most popular estimation methods in Bayesian neural networks (BNN)
is mean-field variational inference (MFVI). In this work, we show that neural
networks with ReLU activation function induce posteriors, that are hard to fit
with MFVI. We provide a theoretical justification for this phenomenon, study it
empirically, and report the results of a series of experiments to investigate
the effect of activation function on the calibration of BNNs. We find that
using Leaky ReLU activations leads to more Gaussian-like weight posteriors and
achieves a lower expected calibration error (ECE) than its ReLU-based

Image captioning models aim at connecting Vision and Language by providing
natural language descriptions of input images. In the past few years, the task
has been tackled by learning parametric models and proposing visual feature
extraction advancements or by modeling better multi-modal connections. In this
paper, we investigate the development of an image captioning approach with a
kNN memory, with which knowledge can be retrieved from an external corpus to
aid the generation process. Our architecture combines a knowledge retriever
based on visual similarities, a differentiable encoder, and a kNN-augmented
attention layer to predict tokens based on the past context and on text
retrieved from the external memory. Experimental results, conducted on the COCO
dataset, demonstrate that employing an explicit external memory can aid the
generation process and increase caption quality. Our work opens up new avenues
for improving image captioning models at larger scale.

In this paper, we present a lightweight and effective change detection model,
called TinyCD. This model has been designed to be faster and smaller than
current state-of-the-art change detection models due to industrial needs.
Despite being from 13 to 140 times smaller than the compared change detection
models, and exposing at least a third of the computational complexity, our
model outperforms the current state-of-the-art models by at least $1\%$ on both
F1 score and IoU on the LEVIR-CD dataset, and more than $8\%$ on the WHU-CD
dataset. To reach these results, TinyCD uses a Siamese U-Net architecture
exploiting low-level features in a globally temporal and locally spatial way.
In addition, it adopts a new strategy to mix features in the space-time domain
both to merge the embeddings obtained from the Siamese backbones, and, coupled
with an MLP block, it forms a novel space-semantic attention mechanism, the Mix
and Attention Mask Block (MAMB). Source code, models and results are available
here: https://github.com/AndreaCodegoni/Tiny_model_4_CD

Mapping computed tomography (CT) number to material property dominates the
proton range uncertainty. This work aims to develop a physics-constrained deep
learning-based multimodal imaging (PDMI) framework to integrate physics, deep
learning, magnetic resonance imaging (MRI), and advanced dual-energy CT (DECT)
to derive accurate patient mass density maps. Seven tissue substitute MRI
phantoms were used for PDMI-based material calibration. The training inputs are
from MRI and twin-beam dual-energy images acquired at 120 kVp with gold and tin
filters. The feasibility investigation included an empirical DECT correlation
and four residual networks (ResNet) derived from different training inputs and
strategies by the PDMI framework. PRN-MR-DE and RN-MR-DE denote ResNet trained
with and without a physics constraint using MRI and DECT images. PRN-DE and
RN-DE represent ResNet trained with and without a physics constraint using
DECT-only images. For the tissue surrogate study, PRN-MR-DE, PRN-DE, and
RN-MR-DE result in mean mass density errors: -0.72%, 2.62%, -3.58% for adipose;
-0.03%, -0.61%, and -0.18% for muscle; -0.58%, -1.36%, and -4.86% for 45% HA
bone. The retrospective patient study indicated that PRN-MR-DE predicted the
densities of soft tissue and bone within expected intervals based on the
literature survey, while PRN-DE generated large density deviations. The
proposed PDMI framework can generate accurate mass density maps using MRI and
DECT images. The physics-constrained training can further enhance model
efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also
shows that the PDMI framework has the potential to improve proton range
uncertainty with accurate patient mass density maps.

Rich temporal information and variations in viewpoints make video data an
attractive choice for learning image representations using unsupervised
contrastive learning (UCL) techniques. State-of-the-art (SOTA) contrastive
learning techniques consider frames within a video as positives in the
embedding space, whereas the frames from other videos are considered negatives.
We observe that unlike multiple views of an object in natural scene videos, an
Ultrasound (US) video captures different 2D slices of an organ. Hence, there is
almost no similarity between the temporally distant frames of even the same US
video. In this paper we propose to instead utilize such frames as hard
negatives. We advocate mining both intra-video and cross-video negatives in a
hardness-sensitive negative mining curriculum in a UCL framework to learn rich
image representations. We deploy our framework to learn the representations of
Gallbladder (GB) malignancy from US videos. We also construct the first
large-scale US video dataset containing 64 videos and 15,800 frames for
learning GB representations. We show that the standard ResNet50 backbone
trained with our framework improves the accuracy of models pretrained with SOTA
UCL techniques as well as supervised pretrained models on ImageNet for the GB
malignancy detection task by 2-6%. We further validate the generalizability of
our method on a publicly available lung US image dataset of COVID-19
pathologies and show an improvement of 1.5% compared to SOTA. Source code,
dataset, and models are available at https://gbc-iitd.github.io/usucl.

We consider asset price models whose dynamics are described by linear
functions of the (time extended) signature of a primary underlying process,
which can range from a (market-inferred) Brownian motion to a general
multidimensional continuous semimartingale. The framework is universal in the
sense that classical models can be approximated arbitrarily well and that the
model's parameters can be learned from all sources of available data by simple
methods. We provide conditions guaranteeing absence of arbitrage as well as
tractable option pricing formulas for so-called sig-payoffs, exploiting the
polynomial nature of generic primary processes. One of our main focus lies on
calibration, where we consider both time-series and implied volatility surface
data, generated from classical stochastic volatility models and also from
S&P500 index market data. For both tasks the linearity of the model turns out
to be the crucial tractability feature which allows to get fast and accurate
calibrations results.

We present a hybrid industrial cooling system model that embeds analytical
solutions within a multi-physics simulation. This model is designed for
reinforcement learning (RL) applications and balances simplicity with
simulation fidelity and interpretability. The model's fidelity is evaluated
against real world data from a large scale cooling system. This is followed by
a case study illustrating how the model can be used for RL research. For this,
we develop an industrial task suite that allows specifying different problem
settings and levels of complexity, and use it to evaluate the performance of
different RL algorithms.

We propose transferability from Large Geometric Vicinity (LGV), a new
technique to increase the transferability of black-box adversarial attacks. LGV
starts from a pretrained surrogate model and collects multiple weight sets from
a few additional training epochs with a constant and high learning rate. LGV
exploits two geometric properties that we relate to transferability. First,
models that belong to a wider weight optimum are better surrogates. Second, we
identify a subspace able to generate an effective surrogate ensemble among this
wider optimum. Through extensive experiments, we show that LGV alone
outperforms all (combinations of) four established test-time transformations by
1.8 to 59.9 percentage points. Our findings shed new light on the importance of
the geometry of the weight space to explain the transferability of adversarial
examples.

We consider a Bayesian forecast aggregation model where $n$ experts, after
observing private signals about an unknown binary event, report their posterior
beliefs about the event to a principal, who then aggregates the reports into a
single prediction for the event. The signals of the experts and the outcome of
the event follow a joint distribution that is unknown to the principal, but the
principal has access to i.i.d. "samples" from the distribution, where each
sample is a tuple of experts' reports (not signals) and the realization of the
event. Using these samples, the principal aims to find an
$\varepsilon$-approximately optimal aggregator, where optimality is measured in
terms of the expected squared distance between the aggregated prediction and
the realization of the event. We show that the sample complexity of this
problem is at least $\tilde \Omega(m^{n-2} / \varepsilon)$ for arbitrary
discrete distributions, where $m$ is the size of each expert's signal space.
This sample complexity grows exponentially in the number of experts $n$. But,
if experts' signals are independent conditioned on the realization of the
event, then the sample complexity is significantly reduced, to $\tilde O(1 /
\varepsilon^2)$, which does not depend on $n$. Finally, we generalize our model
to non-binary events and obtain sample complexity bounds that depend on the
event space size.

We consider the problem of scheduling precedence-constrained jobs on
heterogenous machines in the presence of non-uniform machine-dependent
communication delays. We are given as input a set of $n$ unit size
precedence-constrained jobs, and a set of $m$ related machines each with size
$m_i$ (machine $i$ can execute at most $m_i$ jobs at any time). Each machine
$i$ also has an associated in-delay $\rho^{\mathrm{in}}_i$ and out-delay
$\rho^{\mathrm{out}}_i$. For any job $v$, machine $i$, and time $t$, we say
that $v$ is available to machine $i$ at time $t$ if $v$ is completed on $i$
before time $t$ or on any machine $j$ before time $t - (\rho^{\mathrm{in}}_i +
\rho^{\mathrm{out}}_j)$. If job $v$ is scheduled at time $t$ on machine $i$,
then all of its predecessors must be available to $i$ by time $t$. The
objective is to construct a schedule that minimizes makespan, which is the
maximum completion time over all jobs.
  We consider schedules which allow duplication of jobs as well as schedules
which do not. When duplication is allowed, we provide an asymptotic
$\mathrm{polylog}(n)$-approximation algorithm; it is a true approximation if
the makespan also accounts for the time to communicate the jobs to the machines
and for the time to communicate the results out. For no-duplication schedules,
we also obtain an asymptotic $\mathrm{polylog}(n)$-approximation via a
reduction to the case with duplication, and a true
$\mathrm{polylog}(n)$-approximation for symmetric delays ($\rho^{\text{in}}_{i}
= \rho^{\mathrm{out}}_i$ for all machines $i$). These results represent the
first polylogarithmic approximation algorithms for scheduling with non-uniform
communication delays.

This paper addresses the analysis and design of quadratic neural networks,
which have been recently introduced in the literature, and their applications
to regression, classification, system identification and control of dynamical
systems. These networks offer several advantages, the most important of which
are the fact that the architecture is a by-product of the design and is not
determined a-priori, their training can be done by solving a convex
optimization problem so that the global optimum of the weights is achieved, and
the input-output mapping can be expressed analytically by a quadratic form. It
also appears from several examples that these networks work extremely well
using only a small fraction of the training data. The results in the paper cast
regression, classification, system identification, stability and control design
as convex optimization problems, which can be solved efficiently with
polynomial-time algorithms to a global optimum. Several examples will show the
effectiveness of quadratic neural networks in applications.

An ideal quantum heat engine operates at high power, high efficiency, and
high stability (i.e. low power fluctuations). However, these three objectives
cannot be simultaneously optimized - as indicated by the so-called
thermodynamic uncertainty relations - and a systematic approach to finding
optimal balances between them has, as yet, been elusive. Here we propose such a
general framework to identify Pareto-optimal cycles for driven quantum heat
engines that trade-off power, efficiency, and fluctuations. We then employ
reinforcement learning to identify the Pareto front of a quantum dot based
engine and find abrupt changes in the form of optimal cycles when switching
between optimizing two and three objectives. We further derive analytical
results in the fast and slow-driving regimes that accurately describe different
regions of the Pareto front.

Despite the recent advances in out-of-distribution(OOD) detection, anomaly
detection, and uncertainty estimation tasks, there do not exist a task-agnostic
and post-hoc approach. To address this limitation, we design a novel
clustering-based ensembling method, called Task Agnostic and Post-hoc Unseen
Distribution Detection (TAPUDD) that utilizes the features extracted from the
model trained on a specific task. Explicitly, it comprises of TAP-Mahalanobis,
which clusters the training datasets' features and determines the minimum
Mahalanobis distance of the test sample from all clusters. Further, we propose
the Ensembling module that aggregates the computation of iterative
TAP-Mahalanobis for a different number of clusters to provide reliable and
efficient cluster computation. Through extensive experiments on synthetic and
real-world datasets, we observe that our approach can detect unseen samples
effectively across diverse tasks and performs better or on-par with the
existing baselines. To this end, we eliminate the necessity of determining the
optimal value of the number of clusters and demonstrate that our method is more
viable for large-scale classification tasks.

Leveraging many sources of offline robot data requires grappling with the
heterogeneity of such data. In this paper, we focus on one particular aspect of
heterogeneity: learning from offline data collected at different control
frequencies. Across labs, the discretization of controllers, sampling rates of
sensors, and demands of a task of interest may differ, giving rise to a mixture
of frequencies in an aggregated dataset. We study how well offline
reinforcement learning (RL) algorithms can accommodate data with a mixture of
frequencies during training. We observe that the $Q$-value propagates at
different rates for different discretizations, leading to a number of learning
challenges for off-the-shelf offline RL. We present a simple yet effective
solution that enforces consistency in the rate of $Q$-value updates to
stabilize learning. By scaling the value of $N$ in $N$-step returns with the
discretization size, we effectively balance $Q$-value propagation, leading to
more stable convergence. On three simulated robotic control problems, we
empirically find that this simple approach outperforms na\"ive mixing by 50% on
average.

We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs)
with general function approximation. Existing methods such as sequential
importance sampling estimators and fitted-Q evaluation suffer from the curse of
horizon in POMDPs. To circumvent this problem, we develop a novel model-free
OPE method by introducing future-dependent value functions that take future
proxies as inputs. Future-dependent value functions play similar roles as
classical value functions in fully-observable MDPs. We derive a new Bellman
equation for future-dependent value functions as conditional moment equations
that use history proxies as instrumental variables. We further propose a
minimax learning method to learn future-dependent value functions using the new
Bellman equation. We obtain the PAC result, which implies our OPE estimator is
consistent as long as futures and histories contain sufficient information
about latent states, and the Bellman completeness. Finally, we extend our
methods to learning of dynamics and establish the connection between our
approach and the well-known spectral learning methods in POMDPs.

Deep-learning (DL) compilers such as TVM and TensorRT are increasingly used
to optimize deep neural network (DNN) models to meet performance, resource
utilization and other requirements. Bugs in these compilers can produce
optimized models whose semantics differ from the original models, and produce
incorrect results impacting the correctness of down stream applications.
However, finding bugs in these compilers is challenging due to their
complexity. In this work, we propose a new fuzz testing approach for finding
bugs in deep-learning compilers. Our core approach uses (i) light-weight
operator specifications to generate diverse yet valid DNN models allowing us to
exercise a large part of the compiler's transformation logic; (ii) a
gradient-based search process for finding model inputs that avoid any
floating-point exceptional values during model execution, reducing the chance
of missed bugs or false alarms; and (iii) differential testing to identify
bugs. We implemented this approach in NNSmith which has found 65 new bugs in
the last seven months for TVM, TensorRT, ONNXRuntime, and PyTorch. Of these 52
have been confirmed and 44 have been fixed by project maintainers.

As tools for content editing mature, and artificial intelligence (AI) based
algorithms for synthesizing media grow, the presence of manipulated content
across online media is increasing. This phenomenon causes the spread of
misinformation, creating a greater need to distinguish between "real" and
"manipulated" content. To this end, we present VideoSham, a dataset consisting
of 826 videos (413 real and 413 manipulated). Many of the existing deepfake
datasets focus exclusively on two types of facial manipulations -- swapping
with a different subject's face or altering the existing face. VideoSham, on
the other hand, contains more diverse, context-rich, and human-centric,
high-resolution videos manipulated using a combination of 6 different spatial
and temporal attacks. Our analysis shows that state-of-the-art manipulation
detection algorithms only work for a few specific attacks and do not scale well
on VideoSham. We performed a user study on Amazon Mechanical Turk with 1200
participants to understand if they can differentiate between the real and
manipulated videos in VideoSham. Finally, we dig deeper into the strengths and
weaknesses of performances by humans and SOTA-algorithms to identify gaps that
need to be filled with better AI algorithms.

Recent self-supervised approaches have used large-scale image-text datasets
to learn powerful representations that transfer to many tasks without
finetuning. These methods often assume that there is one-to-one correspondence
between its images and their (short) captions. However, many tasks require
reasoning about multiple images and long text narratives, such as describing
news articles with visual summaries. Thus, we explore a novel setting where the
goal is to learn a self-supervised visual-language representation that is
robust to varying text length and the number of images. In addition, unlike
prior work which assumed captions have a literal relation to the image, we
assume images only contain loose illustrative correspondence with the text. To
explore this problem, we introduce a large-scale multimodal dataset containing
over 31M articles, 22M images and 1M videos. We show that state-of-the-art
image-text alignment methods are not robust to longer narratives with multiple
images. Finally, we introduce an intuitive baseline that outperforms these
methods on zero-shot image-set retrieval by 10% on the GoodNews dataset.

Transformers are powerful text representation learners, useful for all kinds
of clinical decision support tasks. Although they outperform baselines on
readmission prediction, they are not infallible. Here, we look into one such
failure case, and report patterns that lead to inferior predictive performance.

Cameras in modern devices such as smartphones, satellites and medical
equipment are capable of capturing very high resolution images and videos. Such
high-resolution data often need to be processed by deep learning models for
cancer detection, automated road navigation, weather prediction, surveillance,
optimizing agricultural processes and many other applications. Using
high-resolution images and videos as direct inputs for deep learning models
creates many challenges due to their high number of parameters, computation
cost, inference latency and GPU memory consumption. Simple approaches such as
resizing the images to a lower resolution are common in the literature,
however, they typically significantly decrease accuracy. Several works in the
literature propose better alternatives in order to deal with the challenges of
high-resolution data and improve accuracy and speed while complying with
hardware limitations and time restrictions. This survey describes such
efficient high-resolution deep learning methods, summarizes real-world
applications of high-resolution deep learning, and provides comprehensive
information about available high-resolution datasets.

We introduce the problem of domain adaptation under Open Set Label Shift
(OSLS) where the label distribution can change arbitrarily and a new class may
arrive during deployment, but the class-conditional distributions p(x|y) are
domain-invariant. OSLS subsumes domain adaptation under label shift and
Positive-Unlabeled (PU) learning. The learner's goals here are two-fold: (a)
estimate the target label distribution, including the novel class; and (b)
learn a target classifier. First, we establish necessary and sufficient
conditions for identifying these quantities. Second, motivated by advances in
label shift and PU learning, we propose practical methods for both tasks that
leverage black-box predictors. Unlike typical Open Set Domain Adaptation (OSDA)
problems, which tend to be ill-posed and amenable only to heuristics, OSLS
offers a well-posed problem amenable to more principled machinery. Experiments
across numerous semi-synthetic benchmarks on vision, language, and medical
datasets demonstrate that our methods consistently outperform OSDA baselines,
achieving 10--25% improvements in target domain accuracy. Finally, we analyze
the proposed methods, establishing finite-sample convergence to the true label
marginal and convergence to optimal classifier for linear models in a Gaussian
setup. Code is available at https://github.com/acmi-lab/Open-Set-Label-Shift.

This paper is concerned with the inverse problem of scattering of
time-harmonic acoustic waves from an inhomogeneous medium in two dimensions. We
propose a deep learning-based iterative reconstruction algorithm for recovering
the contrast of the inhomogeneous medium from the far-field data. The proposed
algorithm is given by repeated applications of the Landweber method, the
iteratively regularized Gauss-Newton method (IRGNM) and a deep neural network.
The Landweber method is used to generate initial guesses for the exact
contrast, and the IRGNM is employed to make further improvements to the
estimated contrast. Our deep neural network (called the learned projector in
this paper) mainly focuses on learning the a priori information of the shape of
the unknown contrast by using a normalization technique in the training process
and is trained to act like a projector which is expected to make the estimated
contrast obtained by the Landweber method or the IRGNM closer to the exact
contrast. It is believed that the application of the normalization technique
can release the burden of training the deep neural network and lead to good
performance of the proposed algorithm. Furthermore, the learned projector is
expected to provide good initial guesses for IRGNM and be helpful for
accelerating the proposed algorithm. Extensive numerical experiments show that
our inversion algorithm has a satisfactory reconstruction capacity and good

On neutral atom platforms, preparing specific quantum states is usually
achieved by pulse shaping, i.e., by optimizing the time-dependence of the
Hamiltonian related to the system. This process can be extremely costly, as it
requires sampling of the final state in the quantum processor many times.
Hence, determining a good pulse, as well as a good embedding, to solve specific
combinatorial graph problems is one of the most important bottlenecks of the
analog approach. In this work, we propose a novel protocol for solving hard
combinatorial graph problems that combines variational analog quantum computing
and machine learning. Our numerical simulations show that the proposed protocol
can reduce dramatically the number of iterations to be run on the quantum
device. Finally, we assess the quality of the proposed approach by estimating
the related Q-score, a recently proposed metric aimed at benchmarking QPUs.

The debate around the interpretability of attention mechanisms is centered on
whether attention scores can be used as a proxy for the relative amounts of
signal carried by sub-components of data. We propose to study the
interpretability of attention in the context of set machine learning, where
each data point is composed of an unordered collection of instances with a
global label. For classical multiple-instance-learning problems and simple
extensions, there is a well-defined "importance" ground truth that can be
leveraged to cast interpretation as a binary classification problem, which we
can quantitatively evaluate. By building synthetic datasets over several data
modalities, we perform a systematic assessment of attention-based
interpretations. We find that attention distributions are indeed often
reflective of the relative importance of individual instances, but that silent
failures happen where a model will have high classification performance but
attention patterns that do not align with expectations. Based on these
observations, we propose to use ensembling to minimize the risk of misleading
attention-based explanations.

Social influence prediction has permeated many domains, including marketing,
behavior prediction, recommendation systems, and more. However, traditional
methods of predicting social influence not only require domain expertise,they
also rely on extracting user features, which can be very tedious. Additionally,
graph convolutional networks (GCNs), which deals with graph data in
non-Euclidean space, are not directly applicable to Euclidean space. To
overcome these problems, we extended DeepInf such that it can predict the
social influence of COVID-19 via the transition probability of the page rank
domain. Furthermore, our implementation gives rise to a deep learning-based
personalized propagation algorithm, called DeepPP. The resulting algorithm
combines the personalized propagation of a neural prediction model with the
approximate personalized propagation of a neural prediction model from page
rank analysis. Four social networks from different domains as well as two
COVID-19 datasets were used to demonstrate the efficiency and effectiveness of
the proposed algorithm. Compared to other baseline methods, DeepPP provides
more accurate social influence predictions. Further, experiments demonstrate
that DeepPP can be applied to real-world prediction data for COVID-19.

We consider the sparse moment problem of learning a $k$-spike mixture in high
dimensional space from its noisy moment information in any dimension. We
measure the accuracy of the learned mixtures using transportation distance.
Previous algorithms either assume certain separation assumptions, use more
recovery moments, or run in (super) exponential time. Our algorithm for the
1-dimension problem (also called the sparse Hausdorff moment problem) is a
robust version of the classic Prony's method, and our contribution mainly lies
in the analysis. We adopt a global and much tighter analysis than previous work
(which analyzes the perturbation of the intermediate results of Prony's
method). A useful technical ingredient is a connection between the linear
system defined by the Vandermonde matrix and the Schur polynomial, which allows
us to provide tight perturbation bound independent of the separation and may be
useful in other contexts. To tackle the high dimensional problem, we first
solve the 2-dimensional problem by extending the 1-dimension algorithm and
analysis to complex numbers. Our algorithm for the high dimensional case
determines the coordinates of each spike by aligning a 1-d projection of the
mixture to a random vector and a set of 2d-projections of the mixture. Our
results have applications to learning topic models and Gaussian mixtures,
implying improved sample complexity results or running time over prior work.

In recent years, numerous automated approaches complementing the human
Prechtl's general movements assessment (GMA) were developed. Most approaches
utilised RGB or RGB-D cameras to obtain motion data, while a few employed
accelerometers or inertial measurement units. In this paper, within a
prospective longitudinal infant cohort study applying a multimodal approach for
movement tracking and analyses, we examined for the first time the performance
of pressure sensors for classifying an infant general movements pattern, the
fidgety movements. We developed an algorithm to encode movements with pressure
data from a 32x32 grid mat with 1024 sensors. Multiple neural network
architectures were investigated to distinguish presence vs. absence of the
fidgety movements, including the feed-forward networks (FFNs) with manually
defined statistical features and the convolutional neural networks (CNNs) with
learned features. The CNN with multiple convolutional layers and learned
features outperformed the FFN with manually defined statistical features, with
classification accuracy of $81.4\%$ and $75.6\%$, respectively. We compared the
pros and cons of the pressure sensing approach to the video-based and inertial
motion senor-based approaches for analysing infant movements. The
non-intrusive, extremely easy-to-use pressure sensing approach has great
potential for efficient large-scaled movement data acquisition across cites and
for application in busy daily clinical routines for evaluating infant
neuromotor functions. The pressure sensors can be combined with other sensor
modalities to enhance infant movement analyses in research and practice, as
proposed in our multimodal sensor fusion model.

This study sought to (1) expand our existing Urban Population Health
Observatory (UPHO) system by incorporating a semantics layer; (2) cohesively
employ machine learning and semantic/logical inference to provide measurable
evidence and detect pathways leading to undesirable health outcomes; (3)
provide clinical use case scenarios and design case studies to identify
socioenvironmental determinants of health associated with the prevalence of
obesity, and (4) design a dashboard that demonstrates the use of UPHO in the
context of obesity surveillance using the provided scenarios. The system design
includes a knowledge graph generation component that provides contextual
knowledge from relevant domains of interest. This system leverages semantics
using concepts, properties, and axioms from existing ontologies. In addition,
we used the publicly available US Centers for Disease Control and Prevention
500 Cities data set to perform multivariate analysis. A cohesive approach that
employs machine learning and semantic/logical inference reveals pathways
leading to diseases. In this study, we present 2 clinical case scenarios and a
proof-of-concept prototype design of a dashboard that provides warnings,
recommendations, and explanations and demonstrates the use of UPHO in the
context of obesity surveillance, treatment, and prevention. While exploring the
case scenarios using a support vector regression machine learning model, we
found that poverty, lack of physical activity, education, and unemployment were
the most important predictive variables that contribute to obesity in Memphis,
TN. The application of UPHO could help reduce health disparities and improve
urban population health. The expanded UPHO feature incorporates an additional
level of interpretable knowledge to enhance physicians, researchers, and health
officials' informed decision-making at both patient and community levels.

Product retrieval is of great importance in the ecommerce domain. This paper
introduces our 1st-place solution in eBay eProduct Visual Search Challenge
(FGVC9), which is featured for an ensemble of about 20 models from vision
models and vision-language models. While model ensemble is common, we show that
combining the vision models and vision-language models brings particular
benefits from their complementarity and is a key factor to our superiority.
Specifically, for the vision models, we use a two-stage training pipeline which
first learns from the coarse labels provided in the training set and then
conducts fine-grained self-supervised training, yielding a coarse-to-fine
metric learning manner. For the vision-language models, we use the textual
description of the training image as the supervision signals for fine-tuning
the image-encoder (feature extractor). With these designs, our solution
achieves 0.7623 MAR@10, ranking the first place among all the competitors. The
code is available at: \href{https://github.com/WangWenhao0716/V2L}{V$^2$L}.

The novel coronavirus variant, which is also widely known as COVID-19, is
currently a common threat to all humans across the world. Effective recognition
of COVID-19 using advanced machine learning methods is a timely need. Although
many sophisticated approaches have been proposed in the recent past, they still
struggle to achieve expected performances in recognizing COVID-19 using chest
X-ray images. In addition, the majority of them are involved with the complex
pre-processing task, which is often challenging and time-consuming. Meanwhile,
deep networks are end-to-end and have shown promising results in image-based
recognition tasks during the last decade. Hence, in this work, some widely used
state-of-the-art deep networks are evaluated for COVID-19 recognition with
chest X-ray images. All the deep networks are evaluated on a publicly available
chest X-ray image dataset. The evaluation results show that the deep networks
can effectively recognize COVID-19 from chest X-ray images. Further, the
comparison results reveal that the EfficientNetB7 network outperformed other
existing state-of-the-art techniques.

The blockage is a key challenge for millimeter wave communication systems,
since these systems mainly work on line-of-sight (LOS) links, and the blockage
can degrade the system performance significantly. It is recently found that
visual information, easily obtained by cameras, can be utilized to extract the
location and size information of the environmental objects, which can help to
infer the communication parameters, such as blockage status. In this paper, we
propose a novel vision-aided handover framework for UAV-assisted V2X system,
which leverages the images taken by cameras at the mobile station (MS) to
choose the direct link or UAV-assisted link to avoid blockage caused by the
vehicles on the road. We propose a deep reinforcement learning algorithm to
optimize the handover and UAV trajectory policy in order to improve the
long-term throughput. Simulations results demonstrate the effectiveness of
using visual information to deal with the blockage issues.

Lookup table (LUT) has shown its efficacy in low-level vision tasks due to
the valuable characteristics of low computational cost and hardware
independence. However, recent attempts to address the problem of single image
super-resolution (SISR) with lookup tables are highly constrained by the small
receptive field size. Besides, their frameworks of single-layer lookup tables
limit the extension and generalization capacities of the model. In this paper,
we propose a framework of series-parallel lookup tables (SPLUT) to alleviate
the above issues and achieve efficient image super-resolution. On the one hand,
we cascade multiple lookup tables to enlarge the receptive field of each
extracted feature vector. On the other hand, we propose a parallel network
which includes two branches of cascaded lookup tables which process different
components of the input low-resolution images. By doing so, the two branches
collaborate with each other and compensate for the precision loss of
discretizing input pixels when establishing lookup tables. Compared to previous
lookup table-based methods, our framework has stronger representation abilities
with more flexible architectures. Furthermore, we no longer need interpolation
methods which introduce redundant computations so that our method can achieve
faster inference speed. Extensive experimental results on five popular
benchmark datasets show that our method obtains superior SISR performance in a
more efficient way. The code is available at https://github.com/zhjy2016/SPLUT.

Deriving a good variable selection strategy in branch-and-bound is essential
for the efficiency of modern mixed-integer programming (MIP) solvers. With MIP
branching data collected during the previous solution process, learning to
branch methods have recently become superior over heuristics. As
branch-and-bound is naturally a sequential decision making task, one should
learn to optimize the utility of the whole MIP solving process instead of being
myopic on each step. In this work, we formulate learning to branch as an
offline reinforcement learning (RL) problem, and propose a long-sighted hybrid
search scheme to construct the offline MIP dataset, which values the long-term
utilities of branching decisions. During the policy training phase, we deploy a
ranking-based reward assignment scheme to distinguish the promising samples
from the long-term or short-term view, and train the branching model named
Branch Ranking via offline policy learning. Experiments on synthetic MIP
benchmarks and real-world tasks demonstrate that Branch Rankink is more
efficient and robust, and can better generalize to large scales of MIP
instances compared to the widely used heuristics and state-of-the-art
learning-based branching models.

Incremental few-shot semantic segmentation (IFSS) targets at incrementally
expanding model's capacity to segment new class of images supervised by only a
few samples. However, features learned on old classes could significantly
drift, causing catastrophic forgetting. Moreover, few samples for pixel-level
segmentation on new classes lead to notorious overfitting issues in each
learning session. In this paper, we explicitly represent class-based knowledge
for semantic segmentation as a category embedding and a hyper-class embedding,
where the former describes exclusive semantical properties, and the latter
expresses hyper-class knowledge as class-shared semantic properties. Aiming to
solve IFSS problems, we present EHNet, i.e., Embedding adaptive-update and
Hyper-class representation Network from two aspects. First, we propose an
embedding adaptive-update strategy to avoid feature drift, which maintains old
knowledge by hyper-class representation, and adaptively update category
embeddings with a class-attention scheme to involve new classes learned in
individual sessions. Second, to resist overfitting issues caused by few
training samples, a hyper-class embedding is learned by clustering all category
embeddings for initialization and aligned with category embedding of the new
class for enhancement, where learned knowledge assists to learn new knowledge,
thus alleviating performance dependence on training data scale. Significantly,
these two designs provide representation capability for classes with sufficient
semantics and limited biases, enabling to perform segmentation tasks requiring
high semantic dependence. Experiments on PASCAL-5i and COCO datasets show that
EHNet achieves new state-of-the-art performance with remarkable advantages.

Convolutional neural networks (CNN) are known for their excellent feature
extraction capabilities to enable the learning of models from data, yet are
used as black boxes. An interpretation of the convolutional filtres and
associated features can help to establish an understanding of CNN to
distinguish various classes. In this work, we focus on the explainability of a
CNN model called as cnnexplain that is used for Covid-19 and non-Covid-19
classification with a focus on the interpretability of features by the
convolutional filters, and how these features contribute to classification.
Specifically, we have used various explainable artificial intelligence (XAI)
methods, such as visualizations, SmoothGrad, Grad-CAM, and LIME to provide
interpretation of convolutional filtres, and relevant features, and their role
in classification. We have analyzed the explanation of these methods for
Covid-19 detection using dry cough spectrograms. Explanation results obtained
from the LIME, SmoothGrad, and Grad-CAM highlight important features of
different spectrograms and their relevance to classification.

Fine-tuning is widely applied in image classification tasks as a transfer
learning approach. It re-uses the knowledge from a source task to learn and
obtain a high performance in target tasks. Fine-tuning is able to alleviate the
challenge of insufficient training data and expensive labelling of new data.
However, standard fine-tuning has limited performance in complex data
distributions. To address this issue, we propose the Adaptable Multi-tuning
method, which adaptively determines each data sample's fine-tuning strategy. In
this framework, multiple fine-tuning settings and one policy network are
defined. The policy network in Adaptable Multi-tuning can dynamically adjust to
an optimal weighting to feed different samples into models that are trained
using different fine-tuning strategies. Our method outperforms the standard
fine-tuning approach by 1.69%, 2.79% on the datasets FGVC-Aircraft, and
Describable Texture, yielding comparable performance on the datasets Stanford
Cars, CIFAR-10, and Fashion-MNIST.

Background
  Clinical studies using real-world data may benefit from exploiting clinical
reports, a particularly rich albeit unstructured medium. To that end, natural
language processing can extract relevant information. Methods based on transfer
learning using pre-trained language models have achieved state-of-the-art
results in most NLP applications; however, publicly available models lack
exposure to speciality-languages, especially in the medical field.
  Objective
  We aimed to evaluate the impact of adapting a language model to French
clinical reports on downstream medical NLP tasks.
  Methods
  We leveraged a corpus of 21M clinical reports collected from August 2017 to
July 2021 at the Greater Paris University Hospitals (APHP) to produce two
CamemBERT architectures on speciality language: one retrained from scratch and
the other using CamemBERT as its initialisation. We used two French annotated
medical datasets to compare our language models to the original CamemBERT
network, evaluating the statistical significance of improvement with the
Wilcoxon test.
  Results
  Our models pretrained on clinical reports increased the average F1-score on
APMed (an APHP-specific task) by 3 percentage points to 91%, a statistically
significant improvement. They also achieved performance comparable to the
original CamemBERT on QUAERO. These results hold true for the fine-tuned and
from-scratch versions alike, starting from very few pre-training samples.
  Conclusions
  We confirm previous literature showing that adapting generalist pre-train
language models such as CamenBERT on speciality corpora improves their
performance for downstream clinical NLP tasks. Our results suggest that
retraining from scratch does not induce a statistically significant performance
gain compared to fine-tuning.

Managing finances is crucial for older adults who are retired and may rely on
savings to ensure their life quality. As digital banking platforms (e.g.,
mobile apps, electronic payment) gradually replace physical ones, it is
critical to understand how they adapt to digital banking and the potential
frictions they experience. We conducted semi-structured interviews with 16
older adults in China, where the aging population is the largest and digital
banking grows fast. We also interviewed bank employees to gain complementary
perspectives of these help givers. Our findings show that older adults used
both physical and digital platforms as an ecosystem based on perceived pros and
cons. Perceived usefulness, self-confidence, and social influence were key
motivators for learning digital banking. They experienced app-related (e.g.,
insufficient error-recovery support) and user-related challenges (e.g., trust,
security and privacy concerns, low perceived self-efficacy) and developed
coping strategies. We discuss design considerations to improve their banking
experiences.

We define edit distance for hierarchically structured data compatible with
the hierarchical multi-instance learning paradigm. Example of such data is
dataset represented in JSON format where inner Array objects are interpreted as
unordered bags of elements. We prove correct analytical properties of the
defined distance.

Small object detection (SOD) in optical images and videos is a challenging
problem that even state-of-the-art generic object detection methods fail to
accurately localize and identify such objects. Typically, small objects appear
in real-world due to large camera-object distance. Because small objects occupy
only a small area in the input image (e.g., less than 10%), the information
extracted from such a small area is not always rich enough to support decision
making. Multidisciplinary strategies are being developed by researchers working
at the interface of deep learning and computer vision to enhance the
performance of SOD deep learning based methods. In this paper, we provide a
comprehensive review of over 160 research papers published between 2017 and
2022 in order to survey this growing subject. This paper summarizes the
existing literature and provide a taxonomy that illustrates the broad picture
of current research. We investigate how to improve the performance of small
object detection in maritime environments, where increasing performance is
critical. By establishing a connection between generic and maritime SOD
research, future directions have been identified. In addition, the popular
datasets that have been used for SOD for generic and maritime applications are
discussed, and also well-known evaluation metrics for the state-of-the-art
methods on some of the datasets are provided.

We propose a framework for speeding up maximum flow computation by using
predictions. A prediction is a flow, i.e., an assignment of non-negative flow
values to edges, which satisfies the flow conservation property, but does not
necessarily respect the edge capacities of the actual instance (since these
were unknown at the time of learning). We present an algorithm that, given an
$m$-edge flow network and a predicted flow, computes a maximum flow in
$O(m\eta)$ time, where $\eta$ is the $\ell_1$ error of the prediction, i.e.,
the sum over the edges of the absolute difference between the predicted and
optimal flow values. Moreover, we prove that, given an oracle access to a
distribution over flow networks, it is possible to efficiently PAC-learn a
prediction minimizing the expected $\ell_1$ error over that distribution. Our
results fit into the recent line of research on learning-augmented algorithms,
which aims to improve over worst-case bounds of classical algorithms by using
predictions, e.g., machine-learned from previous similar instances. So far, the
main focus in this area was on improving competitive ratios for online
problems. Following Dinitz et al. (NeurIPS 2021), our results are one of the
firsts to improve the running time of an offline problem.

Recent work achieved impressive progress towards joint reconstruction of
hands and manipulated objects from monocular color images. Existing methods
focus on two alternative representations in terms of either parametric meshes
or signed distance fields (SDFs). On one side, parametric models can benefit
from prior knowledge at the cost of limited shape deformations and mesh
resolutions. Mesh models, hence, may fail to precisely reconstruct details such
as contact surfaces of hands and objects. SDF-based methods, on the other side,
can represent arbitrary details but are lacking explicit priors. In this work
we aim to improve SDF models using priors provided by parametric
representations. In particular, we propose a joint learning framework that
disentangles the pose and the shape. We obtain hand and object poses from
parametric models and use them to align SDFs in 3D space. We show that such
aligned SDFs better focus on reconstructing shape details and improve
reconstruction accuracy both for hands and objects. We evaluate our method and
demonstrate significant improvements over the state of the art on the
challenging ObMan and DexYCB benchmarks.

Using a simple and cost-effective water jet process, silicon etch depth
limitations are overcome to realize a $6\,$mm deep atomic vapor cell. While the
minimum silicon feature size was limited to a $1.5\,$mm width in these first
generation vapor cells, we successfully demonstrate a two-chamber geometry by
including a $\sim$25~mm meandering channel between the alkali pill chamber and
main interrogation chamber. We evaluate the impact of the channel conductance
on the introduction of alkali vapor density during the pill activation process,
and mitigate glass damage and pill contamination near the main chamber.
Finally, we highlight the improved signal achievable in the $6\,$mm silicon
cell compared to standard $2\,$mm path length silicon vapor cells.

The increase in use of online educational tools has led to a large amount of
educational video materials made available for students. Finding the right
video content is usually supported by the overarching learning management
system and its interface that organises video items by course, categories and
weeks, and makes them searchable. However, once a video is found, students are
left without further guidance as to what parts in that video they should focus
on. In this article, an additional timeline visualisation to augment the
conventional playback timeline is introduced which employs a novel playback
weighting strategy in which the history of different video interactions
generate scores based on the context of each playback. The resultant scores are
presented on the additional timeline, making it in effect a playback-centric
usage graph nuanced by how each playback was executed. Students can selectively
watch those portions which the contour of the usage visualisation suggests. The
visualisation was implemented and deployed in an undergraduate course at a
university for two full semesters. 270 students used the system throughout both
semesters watching 52 videos, guided by visualisations on what to watch.
Analysis of playback logs revealed students selectively watched corresponding
to the most important portions of the videos as assessed by the instructor who
created the videos. The characteristics of this as a way of guiding students as
to where to watch as well as a complementary tool for playback analysis, are
discussed. Further insights into the potential values of this visualisation and
its underlying playback weighting strategy are also discussed.

In this work, we consider the task of pairwise cross-modality image
registration, which may benefit from exploiting additional images available
only at training time from an additional modality that is different to those
being registered. As an example, we focus on aligning intra-subject
multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w)
scans and diffusion-weighted scans with high b-value (DWI$_{high-b}$). For the
application of localising tumours in mpMR images, diffusion scans with zero
b-value (DWI$_{b=0}$) are considered easier to register to T2w due to the
availability of corresponding features. We propose a learning from privileged
modality algorithm, using a training-only imaging modality DWI$_{b=0}$, to
support the challenging multi-modality registration problems. We present
experimental results based on 369 sets of 3D multiparametric MRI images from
356 prostate cancer patients and report, with statistical significance, a
lowered median target registration error of 4.34 mm, when registering the
holdout DWI$_{high-b}$ and T2w image pairs, compared with that of 7.96 mm
before registration. Results also show that the proposed learning-based
registration networks enabled efficient registration with comparable or better
accuracy, compared with a classical iterative algorithm and other tested
learning-based methods with/without the additional modality. These compared
algorithms also failed to produce any significantly improved alignment between
DWI$_{high-b}$ and T2w in this challenging application.

Proximal splitting algorithms are well suited to solving large-scale
nonsmooth optimization problems, in particular those arising in machine
learning. We propose a new primal-dual algorithm, in which the dual update is
randomized; equivalently, the proximity operator of one of the function in the
problem is replaced by a stochastic oracle. For instance, some randomly chosen
dual variables, instead of all, are updated at each iteration. Or, the
proximity operator of a function is called with some small probability only. A
nonsmooth variance-reduction technique is implemented so that the algorithm
finds an exact minimizer of the general problem involving smooth and nonsmooth
functions, possibly composed with linear operators. We derive linear
convergence results in presence of strong convexity; these results are new even
in the deterministic case, when our algorithms reverts to the recently proposed
Primal-Dual Davis-Yin algorithm. Some randomized algorithms of the literature
are also recovered as particular cases (e.g., Point-SAGA). But our
randomization technique is general and encompasses many unbiased mechanisms
beyond sampling and probabilistic updates, including compression. Since the
convergence speed depends on the slowest among the primal and dual contraction
mechanisms, the iteration complexity might remain the same when randomness is
used. On the other hand, the computation complexity can be significantly
reduced. Overall, randomness helps getting faster algorithms. This has long
been known for stochastic-gradient-type algorithms, and our work shows that
this fully applies in the more general primal-dual setting as well.

Future wireless networks are expected to support diverse mobile services,
including artificial intelligence (AI) services and ubiquitous data
transmissions. Federated learning (FL), as a revolutionary learning approach,
enables collaborative AI model training across distributed mobile edge devices.
By exploiting the superposition property of multiple-access channels,
over-the-air computation allows concurrent model uploading from massive devices
over the same radio resources, and thus significantly reduces the communication
cost of FL. In this paper, we study the coexistence of over-the-air FL and
traditional information transfer (IT) in a mobile edge network. We propose a
coexisting federated learning and information transfer (CFLIT) communication
framework, where the FL and IT devices share the wireless spectrum in an OFDM
system. Under this framework, we aim to maximize the IT data rate and guarantee
a given FL convergence performance by optimizing the long-term radio resource
allocation. A key challenge that limits the spectrum efficiency of the
coexisting system lies in the large overhead incurred by frequent communication
between the server and edge devices for FL model aggregation. To address the
challenge, we rigorously analyze the impact of the computation-to-communication
ratio on the convergence of over-the-air FL in wireless fading channels. The
analysis reveals the existence of an optimal computation-to-communication ratio
that minimizes the amount of radio resources needed for over-the-air FL to
converge to a given error tolerance. Based on the analysis, we propose a
low-complexity online algorithm to jointly optimize the radio resource
allocation for both the FL devices and IT devices. Extensive numerical
simulations verify the superior performance of the proposed design for the
coexistence of FL and IT devices in wireless cellular systems.

The robust approach for real-time analysis of the scanning transmission
electron microscopy (STEM) data streams, based on the ensemble learning and
iterative training (ELIT) of deep convolutional neural networks, is implemented
on an operational microscope, enabling the exploration of the dynamics of
specific atomic configurations under electron beam irradiation via an automated
experiment in STEM. Combined with beam control, this approach allows studying
beam effects on selected atomic groups and chemical bonds in a fully automated
mode. Here, we demonstrate atomically precise engineering of single vacancy
lines in transition metal dichalcogenides and the creation and identification
of topological defects graphene. The ELIT-based approach opens the pathway
toward the direct on-the-fly analysis of the STEM data and engendering
real-time feedback schemes for probing electron beam chemistry, atomic
manipulation, and atom by atom assembly.

Motivated by the successes of deep learning, we propose a class of neural
network-based discrete choice models, called RUMnets, which is inspired by the
random utility maximization (RUM) framework. This model formulates the agents'
random utility function using the sample average approximation (SAA) method. We
show that RUMnets sharply approximate the class of RUM discrete choice models:
any model derived from random utility maximization has choice probabilities
that can be approximated arbitrarily closely by a RUMnet. Reciprocally, any
RUMnet is consistent with the RUM principle. We derive an upper bound on the
generalization error of RUMnets fitted on choice data, and gain theoretical
insights on their ability to predict choices on new, unseen data depending on
critical parameters of the dataset and architecture. By leveraging open-source
libraries for neural networks, we find that RUMnets outperform other
state-of-the-art choice modeling and machine learning methods by a significant
margin on two real-world datasets.

We study the problem of invariant learning when the environment labels are
unknown. We focus on the invariant representation notion when the Bayes optimal
conditional label distribution is the same across different environments.
Previous work conducts Environment Inference (EI) by maximizing the penalty
term from Invariant Risk Minimization (IRM) framework. The EI step uses a
reference model which focuses on spurious correlations to efficiently reach a
good environment partition. However, it is not clear how to find such a
reference model. In this work, we propose to repeat the EI process and retrain
an ERM model on the \textit{majority} environment inferred by the previous EI
step. Under mild assumptions, we find that this iterative process helps learn a
representation capturing the spurious correlation better than the single step.
This results in better Environment Inference and better Invariant Learning. We
show that this method outperforms baselines on both synthetic and real-world
datasets.

We propose the Generalized Probabilistic U-Net, which extends the
Probabilistic U-Net by allowing more general forms of the Gaussian distribution
as the latent space distribution that can better approximate the uncertainty in
the reference segmentations. We study the effect the choice of latent space
distribution has on capturing the uncertainty in the reference segmentations
using the LIDC-IDRI dataset. We show that the choice of distribution affects
the sample diversity of the predictions and their overlap with respect to the
reference segmentations. For the LIDC-IDRI dataset, we show that using a
mixture of Gaussians results in a statistically significant improvement in the
generalized energy distance (GED) metric with respect to the standard
Probabilistic U-Net. We have made our implementation available at
https://github.com/ishaanb92/GeneralizedProbabilisticUNet

This study develops a fixed-time convergent saddle point dynamical system for
solving min-max problems under a relaxation of standard convexity-concavity
assumption. In particular, it is shown that by leveraging the dynamical systems
viewpoint of an optimization algorithm, accelerated convergence to a saddle
point can be obtained. Instead of requiring the objective function to be
strongly-convex--strongly-concave (as necessitated for accelerated convergence
of several saddle-point algorithms), uniform fixed-time convergence is
guaranteed for functions satisfying only the two-sided Polyak-{\L}ojasiewicz
(PL) inequality. A large number of practical problems, including the robust
least squares estimation, are known to satisfy the two-sided PL inequality. The
proposed method achieves arbitrarily fast convergence compared to any other
state-of-the-art method with linear or even super-linear convergence, as also
corroborated in numerical case studies.

Over the last few years, Unsupervised Domain Adaptation (UDA) techniques have
acquired remarkable importance and popularity in computer vision. However, when
compared to the extensive literature available for images, the field of videos
is still relatively unexplored. On the other hand, the performance of a model
in action recognition is heavily affected by domain shift. In this paper, we
propose a simple and novel UDA approach for video action recognition. Our
approach leverages recent advances on spatio-temporal transformers to build a
robust source model that better generalises to the target domain. Furthermore,
our architecture learns domain invariant features thanks to the introduction of
a novel alignment loss term derived from the Information Bottleneck principle.
We report results on two video action recognition benchmarks for UDA, showing
state-of-the-art performance on HMDB$\leftrightarrow$UCF, as well as on
Kinetics$\rightarrow$NEC-Drone, which is more challenging. This demonstrates
the effectiveness of our method in handling different levels of domain shift.
The source code is available at https://github.com/vturrisi/UDAVT.

Computer vision/deep learning-based 3D human pose estimation methods aim to
localize human joints from images and videos. Pose representation is normally
limited to 3D joint positional/translational degrees of freedom (3DOFs),
however, a further three rotational DOFs (6DOFs) are required for many
potential biomechanical applications. Positional DOFs are insufficient to
analytically solve for joint rotational DOFs in a 3D human skeletal model.
Therefore, we propose a temporal inverse kinematics (IK) optimization technique
to infer joint orientations throughout a biomechanically informed, and
subject-specific kinematic chain. For this, we prescribe link directions from a
position-based 3D pose estimate. Sequential least squares quadratic programming
is used to solve a minimization problem that involves both frame-based pose
terms, and a temporal term. The solution space is constrained using joint DOFs,
and ranges of motion (ROMs). We generate 3D pose motion sequences to assess the
IK approach both for general accuracy, and accuracy in boundary cases. Our
temporal algorithm achieves 6DOF pose estimates with low Mean Per Joint Angular
Separation (MPJAS) errors (3.7{\deg}/joint overall, & 1.6{\deg}/joint for lower
limbs). With frame-by-frame IK we obtain low errors in the case of bent elbows
and knees, however, motion sequences with phases of extended/straight limbs
results in ambiguity in twist angle. With temporal IK, we reduce ambiguity for
these poses, resulting in lower average errors.

Many sequential decision making problems, including pool-based active
learning and adaptive viral marketing, can be formulated as an adaptive
submodular maximization problem. Most of existing studies on adaptive
submodular optimization focus on either monotone case or non-monotone case.
Specifically, if the utility function is monotone and adaptive submodular,
\cite{golovin2011adaptive} developed a greedy policy that achieves a $(1-1/e)$
approximation ratio subject to a cardinality constraint. If the utility
function is non-monotone and adaptive submodular, \cite{tang2021beyond} showed
that a random greedy policy achieves a $1/e$ approximation ratio subject to a
cardinality constraint. In this work, we aim to generalize the above mentioned
results by studying the partial-monotone adaptive submodular maximization
problem. To this end, we introduce the notation of adaptive monotonicity ratio
$m\in[0,1]$ to measure the degree of monotonicity of a function. Our main
result is to show that a random greedy policy achieves an approximation ratio
of $m(1-1/e)+(1-m)(1/e)$ if the utility function is $m$-adaptive monotone and
adaptive submodular. Notably this result recovers the aforementioned $(1-1/e)$
and $1/e$ approximation ratios when $m = 0$ and $m = 1$, respectively. We
further extend our results to consider a knapsack constraint. We show that a
sampling-based policy achieves an approximation ratio of $(m+1)/10$ if the
utility function is $m$-adaptive monotone and adaptive submodular. One
important implication of our results is that even for a non-monotone utility
function, we still can achieve an approximation ratio close to $(1-1/e)$ if
this function is ``close'' to a monotone function. This leads to improved
performance bounds for many machine learning applications whose utility
functions are almost adaptive monotone.

In this article we demonstrate the applications of classical and quantum
machine learning in quantum transport and spintronics. With the help of a two
terminal device with magnetic impurity we show how machine learning algorithms
can predict the highly non-linear nature of conductance as well as the
non-equilibrium spin response function for any random magnetic configuration.
We finally describe the applicability of quantum machine learning which has the
capability to handle a significantly large configuration space. Our approach is
also applicable for molecular systems. These outcomes are crucial in predicting
the behaviour of large scale systems where a quantum mechanical calculation is
computationally challenging and therefore would play a crucial role in
designing nano devices.

Eye trackers can provide visual guidance to sonographers during ultrasound
(US) scanning. Such guidance is potentially valuable for less experienced
operators to improve their scanning skills on how to manipulate the probe to
achieve the desired plane. In this paper, a multimodal guidance approach
(Multimodal-GuideNet) is proposed to capture the stepwise dependency between a
real-world US video signal, synchronized gaze, and probe motion within a
unified framework. To understand the causal relationship between gaze movement
and probe motion, our model exploits multitask learning to jointly learn two
related tasks: predicting gaze movements and probe signals that an experienced
sonographer would perform in routine obstetric scanning. The two tasks are
associated by a modality-aware spatial graph to detect the co-occurrence among
the multi-modality inputs and share useful cross-modal information. Instead of
a deterministic scanning path, Multimodal-GuideNet allows for scanning
diversity by estimating the probability distribution of real scans. Experiments
performed with three typical obstetric scanning examinations show that the new
approach outperforms single-task learning for both probe motion guidance and
gaze movement prediction. Multimodal-GuideNet also provides a visual guidance
signal with an error rate of less than 10 pixels for a 224x288 US image.

In this paper, we show that the process of continually learning new tasks and
memorizing previous tasks introduces unknown privacy risks and challenges to
bound the privacy loss. Based upon this, we introduce a formal definition of
Lifelong DP, in which the participation of any data tuples in the training set
of any tasks is protected, under a consistently bounded DP protection, given a
growing stream of tasks. A consistently bounded DP means having only one fixed
value of the DP privacy budget, regardless of the number of tasks. To preserve
Lifelong DP, we propose a scalable and heterogeneous algorithm, called L2DP-ML
with a streaming batch training, to efficiently train and continue releasing
new versions of an L2M model, given the heterogeneity in terms of data sizes
and the training order of tasks, without affecting DP protection of the private
training set. An end-to-end theoretical analysis and thorough evaluations show
that our mechanism is significantly better than baseline approaches in
preserving Lifelong DP. The implementation of L2DP-ML is available at:
https://github.com/haiphanNJIT/PrivateDeepLearning.

During the COVID-19 pandemic, a number of data visualizations were created to
inform the public about the rapidly evolving crisis. Data dashboards, a form of
information dissemination used during the pandemic, have facilitated this
process by visualizing statistics regarding the number of COVID-19 cases over
time. In this research, we conducted a qualitative interview study among
dashboard creators from federal agencies, state health departments, mainstream
news media outlets, and other organizations that created (often widely-used)
COVID-19 dashboards to answer the following questions: how did visualization
creators engage in COVID-19 dashboard design, and what tensions, conflicts, and
challenges arose during this process? Our findings detail the trajectory of
design practices -- from creation to expansion, maintenance, and termination --
that are shaped by the complex interplay between design goals, tools and
technologies, labor, emerging crisis contexts, and public engagement. We
particularly examined the tensions between designers and the general public
involved in these processes. These conflicts, which often materialized due to a
divergence between public demands and standing policies, centered around the
type and amount of information to be visualized, how public perceptions shape
and are shaped by visualization design, and the strategies utilized to deal
with (potential) misinterpretations and misuse of visualizations. Our findings
and lessons learned shed light on new ways of thinking in visualization design,
focusing on the bundled activities that are invariably involved in human and
nonhuman participation throughout the entire trajectory of design practice.

Synthesizing natural interactions between virtual humans and their 3D
environments is critical for numerous applications, such as computer games and
AR/VR experiences. Our goal is to synthesize humans interacting with a given 3D
scene controlled by high-level semantic specifications as pairs of action
categories and object instances, e.g., "sit on the chair". The key challenge of
incorporating interaction semantics into the generation framework is to learn a
joint representation that effectively captures heterogeneous information,
including human body articulation, 3D object geometry, and the intent of the
interaction. To address this challenge, we design a novel transformer-based
generative model, in which the articulated 3D human body surface points and 3D
objects are jointly encoded in a unified latent space, and the semantics of the
interaction between the human and objects are embedded via positional encoding.
Furthermore, inspired by the compositional nature of interactions that humans
can simultaneously interact with multiple objects, we define interaction
semantics as the composition of varying numbers of atomic action-object pairs.
Our proposed generative model can naturally incorporate varying numbers of
atomic interactions, which enables synthesizing compositional human-scene
interactions without requiring composite interaction data. We extend the PROX
dataset with interaction semantic labels and scene instance segmentation to
evaluate our method and demonstrate that our method can generate realistic
human-scene interactions with semantic control. Our perceptual study shows that
our synthesized virtual humans can naturally interact with 3D scenes,
considerably outperforming existing methods. We name our method COINS, for
COmpositional INteraction Synthesis with Semantic Control. Code and data are
available at https://github.com/zkf1997/COINS.

State-of-the-art deep neural networks are still struggling to address the
catastrophic forgetting problem in continual learning. In this paper, we
propose one simple paradigm (named as S-Prompting) and two concrete approaches
to highly reduce the forgetting degree in one of the most typical continual
learning scenarios, i.e., domain increment learning (DIL). The key idea of the
paradigm is to learn prompts independently across domains with pre-trained
transformers, avoiding the use of exemplars that commonly appear in
conventional methods. This results in a win-win game where the prompting can
achieve the best for each domain. The independent prompting across domains only
requests one single cross-entropy loss for training and one simple K-NN
operation as a domain identifier for inference. The learning paradigm derives
an image prompt learning approach and a brand-new language-image prompt
learning approach. Owning an excellent scalability (0.03% parameter increase
per domain), the best of our approaches achieves a remarkable relative
improvement (an average of about 30%) over the best of the state-of-the-art
exemplar-free methods for three standard DIL tasks, and even surpasses the best
of them relatively by about 6% in average when they use exemplars.

It is perhaps no longer surprising that machine learning models, especially
deep neural networks, are particularly vulnerable to attacks. One such
vulnerability that has been well studied is model extraction: a phenomenon in
which the attacker attempts to steal a victim's model by training a surrogate
model to mimic the decision boundaries of the victim model. Previous works have
demonstrated the effectiveness of such an attack and its devastating
consequences, but much of this work has been done primarily for image and text
processing tasks. Our work is the first attempt to perform model extraction on
{\em audio classification models}. We are motivated by an attacker whose goal
is to mimic the behavior of the victim's model trained to identify a speaker.
This is particularly problematic in security-sensitive domains such as
biometric authentication. We find that prior model extraction techniques, where
the attacker \textit{naively} uses a proxy dataset to attack a potential
victim's model, fail. We therefore propose the use of a generative model to
create a sufficiently large and diverse pool of synthetic attack queries. We
find that our approach is able to extract a victim's model trained on
\texttt{LibriSpeech} using queries synthesized with a proxy dataset based off
of \texttt{VoxCeleb}; we achieve a test accuracy of 84.41\% with a budget of 3
million queries.

In modern dynamic constantly developing society, more and more people suffer
from chronic and serious diseases and doctors and patients need special and
sophisticated medical and health support. Accordingly, prominent health
stakeholders have recognized the importance of development of such services to
make patients life easier. Such support requires the collection of huge amount
of patients complex data like clinical, environmental, nutritional, daily
activities, variety of data from smart wearable devices, data from clothing
equipped with sensors etc. Holistic patients data must be properly aggregated,
processed, analyzed, and presented to the doctors and caregivers to recommend
adequate treatment and actions to improve patients health related parameters
and general wellbeing. Advanced artificial intelligence techniques offer the
opportunity to analyze such big data, consume them, and derive new knowledge to
support personalized medical decisions. New approaches like those based on
advanced machine learning, federated learning, transfer learning, explainable
artificial intelligence open new paths for more quality use of health and
medical data in future. In this paper, we will present some crucial aspects and
characteristic examples in the area of application of a range of artificial
intelligence approaches in personalized medical decisions.

Data augmentation for minority classes is an effective strategy for
long-tailed recognition, thus developing a large number of methods. Although
these methods all ensure the balance in sample quantity, the quality of the
augmented samples is not always satisfactory for recognition, being prone to
such problems as over-fitting, lack of diversity, semantic drift, etc. For
these issues, we propose the Class-aware Universum Inspired Re-balance
Learning(CaUIRL) for long-tailed recognition, which endows the Universum with
class-aware ability to re-balance individual minority classes from both sample
quantity and quality. In particular, we theoretically prove that the
classifiers learned by CaUIRL are consistent with those learned under the
balanced condition from a Bayesian perspective. In addition, we further develop
a higher-order mixup approach, which can automatically generate class-aware
Universum(CaU) data without resorting to any external data. Unlike the
traditional Universum, such generated Universum additionally takes the domain
similarity, class separability, and sample diversity into account. Extensive
experiments on benchmark datasets demonstrate the surprising advantages of our
method, especially the top1 accuracy in minority classes is improved by 1.9% 6%
compared to the state-of-the-art method.

The problem of how to genetically modify cells in order to maximize a certain
cellular phenotype has taken center stage in drug development over the last few
years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells
entering cancer clinical trials). Exhausting the search space for all possible
genetic edits (perturbations) or combinations thereof is infeasible due to cost
and experimental limitations. This work provides a theoretically sound
framework for iteratively exploring the space of perturbations in pooled
batches in order to maximize a target phenotype under an experimental budget.
Inspired by this application domain, we study the problem of batch query bandit
optimization and introduce the Optimistic Arm Elimination ($\mathrm{OAE}$)
principle designed to find an almost optimal arm under different functional
relationships between the queries (arms) and the outputs (rewards). We analyze
the convergence properties of $\mathrm{OAE}$ by relating it to the Eluder
dimension of the algorithm's function class and validate that $\mathrm{OAE}$
outperforms other strategies in finding optimal actions in experiments on
simulated problems, public datasets well-studied in bandit contexts, and in
genetic perturbation datasets when the regression model is a deep neural
network. OAE also outperforms the benchmark algorithms in 3 of 4 datasets in
the GeneDisco experimental planning challenge.

Explaining artificial intelligence (AI) predictions is increasingly important
and even imperative in many high-stakes applications where humans are the
ultimate decision-makers. In this work, we propose two novel architectures of
self-interpretable image classifiers that first explain, and then predict (as
opposed to post-hoc explanations) by harnessing the visual correspondences
between a query image and exemplars. Our models consistently improve (by 1 to 4
points) on out-of-distribution (OOD) datasets while performing marginally worse
(by 1 to 2 points) on in-distribution tests than ResNet-50 and a $k$-nearest
neighbor classifier (kNN). Via a large-scale, human study on ImageNet and CUB,
our correspondence-based explanations are found to be more useful to users than
kNN explanations. Our explanations help users more accurately reject AI's wrong
decisions than all other tested methods. Interestingly, for the first time, we
show that it is possible to achieve complementary human-AI team accuracy (i.e.,
that is higher than either AI-alone or human-alone), in ImageNet and CUB image
classification tasks.

With the increases in computational power and advances in machine learning,
data-driven learning-based methods have gained significant attention in solving
PDEs. Physics-informed neural networks (PINNs) have recently emerged and
succeeded in various forward and inverse PDEs problems thanks to their
excellent properties, such as flexibility, mesh-free solutions, and
unsupervised training. However, their slower convergence speed and relatively
inaccurate solutions often limit their broader applicability in many science
and engineering domains. This paper proposes a new kind of data-driven PDEs
solver, physics-informed cell representations (PIXEL), elegantly combining
classical numerical methods and learning-based approaches. We adopt a grid
structure from the numerical methods to improve accuracy and convergence speed
and overcome the spectral bias presented in PINNs. Moreover, the proposed
method enjoys the same benefits in PINNs, e.g., using the same optimization
frameworks to solve both forward and inverse PDE problems and readily enforcing
PDE constraints with modern automatic differentiation techniques. We provide
experimental results on various challenging PDEs that the original PINNs have
struggled with and show that PIXEL achieves fast convergence speed and high
accuracy.

The transformation towards intelligence in various industries is creating
more demand for intelligent and flexible products. In the field of robotics,
learning-based methods are increasingly being applied, with the purpose of
training robots to learn to deal with complex and changing external
environments through data. In this context, reinforcement learning and
imitation learning are becoming research hotspots with their respective
characteristics. However, the two have their own limitations in some cases,
such as the high cost of data acquisition for reinforcement learning. Moreover,
it is difficult for imitation learning to provide perfect demonstrations. As a
branch of imitation learning, interactive imitation learning aims at
transferring human knowledge to the agent through interactions between the
demonstrator and the robot, which alleviates the difficulty of teaching. This
thesis implements IIL algorithms in four simulation scenarios and conducts
extensive experiments, aiming at providing exhaustive information about IIL
methods both in action space and state space as well as comparison with RL
methods.

Causal analysis may be affected by selection bias, which is defined as the
systematic exclusion of data from a certain subpopulation. Previous work in
this area focused on the derivation of identifiability conditions. We propose
instead a first algorithm to address both identifiable and unidentifiable
queries. We prove that, in spite of the missingness induced by the selection
bias, the likelihood of the available data is unimodal. This enables us to use
the causal expectation-maximisation scheme to obtain the values of causal
queries in the identifiable case, and to compute bounds otherwise. Experiments
demonstrate the approach to be practically viable. Theoretical convergence
characterisations are provided.

In this paper, we propose a novel learning scheme for self-supervised video
representation learning. Motivated by how humans understand videos, we propose
to first learn general visual concepts then attend to discriminative local
areas for video understanding. Specifically, we utilize static frame and frame
difference to help decouple static and dynamic concepts, and respectively align
the concept distributions in latent space. We add diversity and fidelity
regularizations to guarantee that we learn a compact set of meaningful
concepts. Then we employ a cross-attention mechanism to aggregate detailed
local features of different concepts, and filter out redundant concepts with
low activations to perform local concept contrast. Extensive experiments
demonstrate that our method distills meaningful static and dynamic concepts to
guide video understanding, and obtains state-of-the-art results on UCF-101,
HMDB-51, and Diving-48.

Deep neural networks for image-based screening and computer-aided diagnosis
have achieved expert-level performance on various medical imaging modalities,
including chest radiographs. Recently, several works have indicated that these
state-of-the-art classifiers can be biased with respect to sensitive patient
attributes, such as race or gender, leading to growing concerns about
demographic disparities and discrimination resulting from algorithmic and
model-based decision-making in healthcare. Fair machine learning has focused on
mitigating such biases against disadvantaged or marginalised groups, mainly
concentrating on tabular data or natural images. This work presents two novel
intra-processing techniques based on fine-tuning and pruning an already-trained
neural network. These methods are simple yet effective and can be readily
applied post hoc in a setting where the protected attribute is unknown during
the model development and test time. In addition, we compare several intra- and
post-processing approaches applied to debiasing deep chest X-ray classifiers.
To the best of our knowledge, this is one of the first efforts studying
debiasing methods on chest radiographs. Our results suggest that the considered
approaches successfully mitigate biases in fully connected and convolutional
neural networks offering stable performance under various settings. The
discussed methods can help achieve group fairness of deep medical image
classifiers when deploying them in domains with different fairness
considerations and constraints.

Predictive Process Analytics is becoming an essential aid for organizations,
providing online operational support of their processes. However, process
stakeholders need to be provided with an explanation of the reasons why a given
process execution is predicted to behave in a certain way. Otherwise, they will
be unlikely to trust the predictive monitoring technology and, hence, adopt it.
This paper proposes a predictive analytics framework that is also equipped with
explanation capabilities based on the game theory of Shapley Values. The
framework has been implemented in the IBM Process Mining suite and
commercialized for business users. The framework has been tested on real-life
event data to assess the quality of the predictions and the corresponding
evaluations. In particular, a user evaluation has been performed in order to
understand if the explanations provided by the system were intelligible to
process stakeholders.

Cross-device Federated Learning is an increasingly popular machine learning
setting to train a model by leveraging a large population of client devices
with high privacy and security guarantees. However, communication efficiency
remains a major bottleneck when scaling federated learning to production
environments, particularly due to bandwidth constraints during uplink
communication. In this paper, we formalize and address the problem of
compressing client-to-server model updates under the Secure Aggregation
primitive, a core component of Federated Learning pipelines that allows the
server to aggregate the client updates without accessing them individually. In
particular, we adapt standard scalar quantization and pruning methods to Secure
Aggregation and propose Secure Indexing, a variant of Secure Aggregation that
supports quantization for extreme compression. We establish state-of-the-art
results on LEAF benchmarks in a secure Federated Learning setup with up to
40$\times$ compression in uplink communication with no meaningful loss in
utility compared to uncompressed baselines.

We develop a uniform theoretical approach towards the analysis of various
neural network connectivity architectures by introducing the notion of a quiver
neural network. Inspired by quiver representation theory in mathematics, this
approach gives a compact way to capture elaborate data flows in complex network
architectures. As an application, we use parameter space symmetries to prove a
lossless model compression algorithm for quiver neural networks with certain
non-pointwise activations known as rescaling activations. In the case of radial
rescaling activations, we prove that training the compressed model with
gradient descent is equivalent to training the original model with projected
gradient descent.

Medical image segmentation can be implemented using Deep Learning methods
with fast and efficient segmentation networks. Single-board computers (SBCs)
are difficult to use to train deep networks due to their memory and processing
limitations. Specific hardware such as Google's Edge TPU makes them suitable
for real time predictions using complex pre-trained networks. In this work, we
study the performance of two SBCs, with and without hardware acceleration for
fundus image segmentation, though the conclusions of this study can be applied
to the segmentation by deep neural networks of other types of medical images.
To test the benefits of hardware acceleration, we use networks and datasets
from a previous published work and generalize them by testing with a dataset
with ultrasound thyroid images. We measure prediction times in both SBCs and
compare them with a cloud based TPU system. The results show the feasibility of
Machine Learning accelerated SBCs for optic disc and cup segmentation obtaining
times below 25 milliseconds per image using Edge TPUs.

Context: Recent research indicates that Web queries written by software
developers are not very successful in retrieving relevant results, performing
measurably worse compared to general purpose Web queries. Most approaches up to
this point have addressed this problem with software engineering-specific
automated query reformulation techniques, which work without developer
involvement but are limited by the content of the original query. In other
words, these techniques automatically improve the existing query but can not
contribute new, previously unmentioned, concepts.
  Objective: In this paper, we propose a technique to guide software developers
in manually improving their own Web search queries. We examine a conversational
approach that follows unsuccessful queries with a clarification question aimed
at eliciting additional query terms, thus providing to the developer a clear
dimension along which the query could be improved.
  Methods: We describe a set of clarification questions derived from a corpus
of software developer queries and a neural approach to recommending them for a
newly issued query.
  Results: Our evaluation indicates that the recommendation technique is
accurate, predicting a valid clarification question 80% of the time and
outperforms simple baselines, as well as, state-of-the-art Learning To Rank
(LTR) baselines.
  Conclusion: As shown in the experimental results, the described approach is
capable at recommending appropriate clarification questions to software
developers and considered useful by a sample of developers ranging from novices
to experienced professionals.

Real-scene image super-resolution aims to restore real-world low-resolution
images into their high-quality versions. A typical RealSR framework usually
includes the optimization of multiple criteria which are designed for different
image properties, by making the implicit assumption that the ground-truth
images can provide a good trade-off between different criteria. However, this
assumption could be easily violated in practice due to the inherent contrastive
relationship between different image properties. Contrastive learning (CL)
provides a promising recipe to relieve this problem by learning discriminative
features using the triplet contrastive losses. Though CL has achieved
significant success in many computer vision tasks, it is non-trivial to
introduce CL to RealSR due to the difficulty in defining valid positive image
pairs in this case. Inspired by the observation that the contrastive
relationship could also exist between the criteria, in this work, we propose a
novel training paradigm for RealSR, named Criteria Comparative Learning
(Cria-CL), by developing contrastive losses defined on criteria instead of
image patches. In addition, a spatial projector is proposed to obtain a good
view for Cria-CL in RealSR. Our experiments demonstrate that compared with the
typical weighted regression strategy, our method achieves a significant
improvement under similar parameter settings.

Neuromorphic computing is an emerging research field that aims to develop new
intelligent systems by integrating theories and technologies from
multi-disciplines such as neuroscience and deep learning. Currently, there have
been various software frameworks developed for the related fields, but there is
a lack of an efficient framework dedicated for spike-based computing models and
algorithms. In this work, we present a Python based spiking neural network
(SNN) simulation and training framework, aka SPAIC that aims to support
brain-inspired model and algorithm researches integrated with features from
both deep learning and neuroscience. To integrate different methodologies from
the two overwhelming disciplines, and balance between flexibility and
efficiency, SPAIC is designed with neuroscience-style frontend and deep
learning backend structure. We provide a wide range of examples including
neural circuits Simulation, deep SNN learning and neuromorphic applications,
demonstrating the concise coding style and wide usability of our framework. The
SPAIC is a dedicated spike-based artificial intelligence computing platform,
which will significantly facilitate the design, prototype and validation of new
models, theories and applications. Being user-friendly, flexible and
high-performance, it will help accelerate the rapid growth and wide
applicability of neuromorphic computing research.

Thermodynamics could be seen as an expression of physics at a high epistemic
level. As such, its potential as an inductive bias to help machine learning
procedures attain accurate and credible predictions has been recently realized
in many fields. We review how thermodynamics provides helpful insights in the
learning process. At the same time, we study the influence of aspects such as
the scale at which a given phenomenon is to be described, the choice of
relevant variables for this description or the different techniques available
for the learning process.

The explainability of deep networks is becoming a central issue in the deep
learning community. It is the same for learning on graphs, a data structure
present in many real world problems. In this paper, we propose a method that is
more optimal, lighter, consistent and better exploits the topology of the
evaluated graph than the state-of-the-art methods.

To address the trade-off problem of quality-diversity for the generated
images in imbalanced classification tasks, we research on over-sampling based
methods at the feature level instead of the data level and focus on searching
the latent feature space for optimal distributions. On this basis, we propose
an iMproved Estimation Distribution Algorithm based Latent featUre Distribution
Evolution (MEDA_LUDE) algorithm, where a joint learning procedure is programmed
to make the latent features both optimized and evolved by the deep neural
networks and the evolutionary algorithm, respectively. We explore the effect of
the Large-margin Gaussian Mixture (L-GM) loss function on distribution learning
and design a specialized fitness function based on the similarities among
samples to increase diversity. Extensive experiments on benchmark based
imbalanced datasets validate the effectiveness of our proposed algorithm, which
can generate images with both quality and diversity. Furthermore, the MEDA_LUDE
algorithm is also applied to the industrial field and successfully alleviates
the imbalanced issue in fabric defect classification.

Recently, numbers of works shows that the performance of neural machine
translation (NMT) can be improved to a certain extent with using visual
information. However, most of these conclusions are drawn from the analysis of
experimental results based on a limited set of bilingual sentence-image pairs,
such as Multi30K. In these kinds of datasets, the content of one bilingual
parallel sentence pair must be well represented by a manually annotated image,
which is different with the actual translation situation. Some previous works
are proposed to addressed the problem by retrieving images from exiting
sentence-image pairs with topic model. However, because of the limited
collection of sentence-image pairs they used, their image retrieval method is
difficult to deal with the out-of-vocabulary words, and can hardly prove that
visual information enhance NMT rather than the co-occurrence of images and
sentences. In this paper, we propose an open-vocabulary image retrieval methods
to collect descriptive images for bilingual parallel corpus using image search
engine. Next, we propose text-aware attentive visual encoder to filter
incorrectly collected noise images. Experiment results on Multi30K and other
two translation datasets show that our proposed method achieves significant
improvements over strong baselines.

While deep learning has been widely used for video analytics, such as video
classification and action detection, dense action detection with fast-moving
subjects from sports videos is still challenging. In this work, we release yet
another sports video dataset $\textbf{P$^2$A}$ for $\underline{P}$ing
$\underline{P}$ong-$\underline{A}$ction detection, which consists of 2,721
video clips collected from the broadcasting videos of professional table tennis
matches in World Table Tennis Championships and Olympiads. We work with a crew
of table tennis professionals and referees to obtain fine-grained action labels
(in 14 classes) for every ping-pong action that appeared in the dataset and
formulate two sets of action detection problems - action localization and
action recognition. We evaluate a number of commonly-seen action recognition
(e.g., TSM, TSN, Video SwinTransformer, and Slowfast) and action localization
models (e.g., BSN, BSN++, BMN, TCANet), using $\textbf{P$^2$A}$ for both
problems, under various settings. These models can only achieve 48% area under
the AR-AN curve for localization and 82% top-one accuracy for recognition since
the ping-pong actions are dense with fast-moving subjects but broadcasting
videos are with only 25 FPS. The results confirm that $\textbf{P$^2$A}$ is
still a challenging task and can be used as a benchmark for action detection
from videos.

Trained ML models are commonly embedded in optimization problems. In many
cases, this leads to large-scale NLPs that are difficult to solve to global
optimality. While ML models frequently lead to large problems, they also
exhibit homogeneous structures and repeating patterns (e.g., layers in ANNs).
Thus, specialized solution strategies can be used for large problem classes.
Recently, there have been some promising works proposing specialized
reformulations using mixed-integer programming or reduced space formulations.
However, further work is needed to develop more efficient solution approaches
and keep up with the rapid development of new ML model architectures.

Quality control of apparel items is mandatory in modern textile industry, as
consumer's awareness and expectations about the highest possible standard is
constantly increasing in favor of sustainable and ethical textile products.
Such a level of quality is achieved by checking the product throughout its life
cycle, from raw materials to boxed stock. Checks may include color shading
tests, fasteners fatigue tests, fabric weigh tests, contamination tests, etc.
This work deals specifically with the automatic detection of contaminations
given by small parts in the finished product such as raw material like little
stones and plastic bits or materials from the construction process, like a
whole needle or a clip. Identification is performed by a two-level processing
of X-ray images of the items: in the first, a multi-threshold analysis
recognizes the contaminations by gray level and shape attributes; the second
level consists of a deep learning classifier that has been trained to
distinguish between true positives and false positives. The automatic detector
was successfully deployed in an actual production plant, since the results
satisfy the technical specification of the process, namely a number of false
negatives smaller than 3% and a number of false positives smaller than 15%.

We show that the derivatives of the Sinkhorn-Knopp algorithm, or iterative
proportional fitting procedure, converge towards the derivatives of the
entropic regularization of the optimal transport problem with a locally uniform
linear convergence rate.

The incidence rate for skin cancer has been steadily increasing throughout
the world, leading to it being a serious issue. Diagnosis at an early stage has
the potential to drastically reduce the harm caused by the disease, however,
the traditional biopsy is a labor-intensive and invasive procedure. In
addition, numerous rural communities do not have easy access to hospitals and
do not prefer visiting one for what they feel might be a minor issue. Using
machine learning and deep learning for skin cancer classification can increase
accessibility and reduce the discomforting procedures involved in the
traditional lesion detection process. These models can be wrapped in web or
mobile apps and serve a greater population. In this paper, two such models are
tested on the benchmark HAM10000 dataset of common skin lesions. They are
Random Forest with Stratified K-Fold Validation, and MobileNetV2 (throughout
the rest of the paper referred to as MobileNet). The MobileNet model was
trained separately using both TensorFlow and PyTorch frameworks. A side-by-side
comparison of both deep learning and machine learning models and a comparison
of the same deep learning model on different frameworks for skin lesion
diagnosis in a resource-constrained mobile environment has not been conducted
before. The results indicate that each of these models fares better at
different classification tasks. For greater overall recall, accuracy, and
detection of malignant melanoma, the TensorFlow MobileNet was the better
choice. However, for detecting noncancerous skin lesions, the PyTorch MobileNet
proved to be better. Random Forest was the better algorithm when it came to
having a low computational cost with moderate correctness.

Humans innately measure distance between instances in an unlabeled dataset
using an unknown similarity function. Distance metrics can only serve as proxy
for similarity in information retrieval of similar instances. Learning a good
similarity function from human annotations improves the quality of retrievals.
This work uses deep metric learning to learn these user-defined similarity
functions from few annotations for a large football trajectory dataset. We
adapt an entropy-based active learning method with recent work from triplet
mining to collect easy-to-answer but still informative annotations from human
participants and use them to train a deep convolutional network that
generalizes to unseen samples. Our user study shows that our approach improves
the quality of the information retrieval compared to a previous deep metric
learning approach that relies on a Siamese network. Specifically, we shed light
on the strengths and weaknesses of passive sampling heuristics and active
learners alike by analyzing the participants' response efficacy. To this end,
we collect accuracy, algorithmic time complexity, the participants' fatigue and
time-to-response, qualitative self-assessment and statements, as well as the
effects of mixed-expertise annotators and their consistency on model
performance and transfer-learning.

The numerical modeling of thin shell structures is a challenge, which has
been met by a variety of finite element (FE) and other formulations -- many of
which give rise to new challenges, from complex implementations to artificial
locking. As a potential alternative, we use machine learning and present a
Physics-Informed Neural Network (PINN) to predict the small-strain response of
arbitrarily curved shells. To this end, the shell midsurface is described by a
chart, from which the mechanical fields are derived in a curvilinear coordinate
frame by adopting Naghdi's shell theory. Unlike in typical PINN applications,
the corresponding strong or weak form must therefore be solved in a
non-Euclidean domain. We investigate the performance of the proposed PINN in
three distinct scenarios, including the well-known Scordelis-Lo roof setting
widely used to test FE shell elements against locking. Results show that the
PINN can accurately identify the solution field in all three benchmarks if the
equations are presented in their weak form, while it may fail to do so when
using the strong form. In the thin-thickness limit, where classical methods are
susceptible to locking, training time notably increases as the differences in
scaling of the membrane, shear, and bending energies lead to adverse numerical
stiffness in the gradient flow dynamics. Nevertheless, the PINN can accurately
match the ground truth and performs well in the Scordelis-Lo roof benchmark,
highlighting its potential for a drastically simplified alternative to
designing locking-free shell FE formulations.

In recent years we have explored using Haskell alongside a traditional
mathematical formalism in our large-enrolment university course on topics
including logic and formal languages, aiming to offer our students a
programming perspective on these mathematical topics. We have found it possible
to offer almost all formative and summative assessment through an interactive
learning platform, using Haskell as a lingua franca for digital exercises
across our broad syllabus. One of the hardest exercises to convert into this
format are traditional written proofs conveying constructive arguments. In this
paper we reflect on the digitisation of this kind of exercise. We share many
examples of Haskell exercises designed to target similar skills to written
proof exercises across topics in propositional logic and formal languages,
discussing various aspects of the design of such exercises. We also catalogue a
sample of student responses to such exercises. This discussion contributes to
our broader exploration of programming problems as a flexible digital medium
for learning and assessment.

Teaching programming to novices is best done with tools with simpler user
interfaces than professional IDEs that are tailored for experienced
programmers. In a distance learning situation it is also important to have a
development environment that is easy to explain and use, and that integrates
well with the variety of course material used (slides, homework, etc). In this
paper we give an experience report on teaching programming with codeBoot, an
online programming environment we designed specifically for novices.

We present a novel approach for teaching logic and the metatheory of logic to
students who have some experience with functional programming. We define
concepts in logic as a series of functional programs in the language of the
proof assistant Isabelle/HOL. This allows us to make notions which are often
unclear in textbooks precise, to experiment with definitions by executing them,
and to prove metatheoretical theorems in full detail. We have surveyed student
perceptions of our teaching approach to determine its usefulness and found that
students felt that our formalizations helped them understand concepts in logic,
and that they experimented with them as a learning tool. However, the approach
was not enough to make students feel confident in their abilities to design and
implement their own formal systems. Further studies are needed to confirm and
generalize the results of our survey, but our initial results seem promising.

Currently end-to-end deep learning based open-domain dialogue systems remain
black box models, making it easy to generate irrelevant contents with
data-driven models. Specifically, latent variables are highly entangled with
different semantics in the latent space due to the lack of priori knowledge to
guide the training. To address this problem, this paper proposes to harness the
generative model with a priori knowledge through a cognitive approach involving
mesoscopic scale feature disentanglement. Particularly, the model integrates
the macro-level guided-category knowledge and micro-level open-domain dialogue
data for the training, leveraging the priori knowledge into the latent space,
which enables the model to disentangle the latent variables within the
mesoscopic scale. Besides, we propose a new metric for open-domain dialogues,
which can objectively evaluate the interpretability of the latent space
distribution. Finally, we validate our model on different datasets and
experimentally demonstrate that our model is able to generate higher quality
and more interpretable dialogues than other models.

Accurate and fast scene understanding is one of the challenging task for
autonomous driving, which requires to take full advantage of LiDAR point clouds
for semantic segmentation. In this paper, we present a \textbf{concise} and
\textbf{efficient} image-based semantic segmentation network, named
\textbf{CENet}. In order to improve the descriptive power of learned features
and reduce the computational as well as time complexity, our CENet integrates
the convolution with larger kernel size instead of MLP, carefully-selected
activation functions, and multiple auxiliary segmentation heads with
corresponding loss functions into architecture. Quantitative and qualitative
experiments conducted on publicly available benchmarks, SemanticKITTI and
SemanticPOSS, demonstrate that our pipeline achieves much better mIoU and
inference performance compared with state-of-the-art models. The code will be
available at https://github.com/huixiancheng/CENet.

Decision trees are highly famous in machine learning and usually acquire
state-of-the-art performance. Despite that, well-known variants like CART, ID3,
random forest, and boosted trees miss a probabilistic version that encodes
prior assumptions about tree structures and shares statistical strength between
node parameters. Existing work on Bayesian decision trees depend on Markov
Chain Monte Carlo (MCMC), which can be computationally slow, especially on high
dimensional data and expensive proposals. In this study, we propose a method to
parallelise a single MCMC decision tree chain on an average laptop or personal
computer that enables us to reduce its run-time through multi-core processing
while the results are statistically identical to conventional sequential
implementation. We also calculate the theoretical and practical reduction in
run time, which can be obtained utilising our method on multi-processor
architectures. Experiments showed that we could achieve 18 times faster running
time provided that the serial and the parallel implementation are statistically
identical.

Recent findings (e.g., arXiv:2103.00065) demonstrate that modern neural
networks trained by full-batch gradient descent typically enter a regime called
Edge of Stability (EOS). In this regime, the sharpness, i.e., the maximum
Hessian eigenvalue, first increases to the value 2/(step size) (the progressive
sharpening phase) and then oscillates around this value (the EOS phase). This
paper aims to analyze the GD dynamics and the sharpness along the optimization
trajectory. Our analysis naturally divides the GD trajectory into four phases
depending on the change of the sharpness. We empirically identify the norm of
output layer weight as an interesting indicator of sharpness dynamics. Based on
this empirical observation, we attempt to theoretically and empirically explain
the dynamics of various key quantities that lead to the change of sharpness in
each phase of EOS. Moreover, based on certain assumptions, we provide a
theoretical proof of the sharpness behavior in EOS regime in two-layer
fully-connected linear neural networks. We also discuss some other empirical
findings and the limitation of our theoretical results.

Accurate prediction of roll motion in high sea state is significant for the
operability, safety and survivability of marine vehicles. This paper presents a
novel data-driven methodology for achieving the multi-step prediction of ship
roll motion in high sea states. A hybrid neural network, named ConvLSTMPNet, is
proposed to execute long short-term memory (LSTM) and one-dimensional
convolutional neural networks (CNN) in parallel to extract time-dependent and
spatio-temporal information from multidimensional inputs. Taken KCS as the
study object, the numerical solution of computational fluid dynamics method is
utilized to generate the ship motion data in sea state 7 with different wave
directions. An in-depth comparative study on the selection of feature space is
conducted, considering the effects of time history of motion states and wave
height. The comparison results demonstrate the superiority of selecting both
motion states and wave heights as the feature space for multi-step prediction.
In addition, the results demonstrate that ConvLSTMNet achieves more accurate
than LSTM and CNN methods in multi-step prediction of roll motion, validating
the efficiency of the proposed method.

Accurately detecting and identifying drivers' braking intention is the basis
of man-machine driving. In this paper, we proposed an electroencephalographic
(EEG)-based braking intention measurement strategy. We used the Car Learning to
Act (Carla) platform to build the simulated driving environment. 11 subjects
participated in our study, and each subject drove a simulated vehicle to
complete emergency braking and normal braking tasks. We compared the EEG
topographic maps in different braking situations and used three different
classifiers to predict the subjects' braking intention through EEG signals. The
experimental results showed that the average response time of subjects in
emergency braking was 762 ms; emergency braking and no braking can be well
distinguished, while normal braking and no braking were not easy to be
classified; for the two different types of braking, emergency braking and
normal braking had obvious differences in EEG topographic maps, and the
classification results also showed that the two were highly distinguishable.
This study provides a user-centered driver-assistance system and a good
framework to combine with advanced shared control algorithms, which has the
potential to be applied to achieve a more friendly interaction between the
driver and vehicle in real driving environment.

electroencephalography (EEG) signals is a rapidly advancing field in
Brain-Computer Interfaces (BCI). In contrast to the fields of computer vision
and natural language processing, the data amount of these trials is still
rather tiny. Developing a PC-based machine learning technique to increase the
participation of non-expert end-users could help solve this data collection
issue. We created a novel algorithm for machine learning called Time Majority
Voting (TMV). In our experiment, TMV performed better than cutting-edge
algorithms. It can operate efficiently on personal computers for classification
tasks involving the BCI. These interpretable data also assisted end-users and
researchers in comprehending EEG tests better.

This paper proposes a novel two-stage framework for emotion recognition using
EEG data that outperforms state-of-the-art models while keeping the model size
small and computationally efficient. The framework consists of two stages; the
first stage involves constructing efficient models named EEGNet, which is
inspired by the state-of-the-art efficient architecture and employs
inverted-residual blocks that contain depthwise separable convolutional layers.
The EEGNet models on both valence and arousal labels achieve the average
classification accuracy of 90%, 96.6%, and 99.5% with only 6.4k, 14k, and 25k
parameters, respectively. In terms of accuracy and storage cost, these models
outperform the previous state-of-the-art result by up to 9%. In the second
stage, we binarize these models to further compress them and deploy them easily
on edge devices. Binary Neural Networks (BNNs) typically degrade model
accuracy. We improve the EEGNet binarized models in this paper by introducing
three novel methods and achieving a 20\% improvement over the baseline binary
models. The proposed binarized EEGNet models achieve accuracies of 81%, 95%,
and 99% with storage costs of 0.11Mbits, 0.28Mbits, and 0.46Mbits,
respectively. Those models help deploy a precise human emotion recognition
system on the edge environment.

Large-scale multi-modal contrastive pre-training has demonstrated great
utility to learn transferable features for a range of downstream tasks by
mapping multiple modalities into a shared embedding space. Typically, this has
employed separate encoders for each modality. However, recent work suggests
that transformers can support learning across multiple modalities and allow
knowledge sharing. Inspired by this, we investigate a variety of
Modality-Shared Contrastive Language-Image Pre-training (MS-CLIP) frameworks.
More specifically, we question how many parameters of a transformer model can
be shared across modalities during contrastive pre-training, and rigorously
examine architectural design choices that position the proportion of parameters
shared along a spectrum. In studied conditions, we observe that a mostly
unified encoder for vision and language signals outperforms all other
variations that separate more parameters. Additionally, we find that
light-weight modality-specific parallel modules further improve performance.
Experimental results show that the proposed MS-CLIP approach outperforms
vanilla CLIP by up to 13\% relative in zero-shot ImageNet classification
(pre-trained on YFCC-100M), while simultaneously supporting a reduction of
parameters. In addition, our approach outperforms vanilla CLIP by 1.6 points in
linear probing on a collection of 24 downstream vision tasks. Furthermore, we
discover that sharing parameters leads to semantic concepts from different
modalities being encoded more closely in the embedding space, facilitating the
transferring of common semantic structure (e.g., attention patterns) from
language to vision. Code is available at
\href{https://github.com/Hxyou/MSCLIP}{URL}.

Implicit feedback has been widely used to build commercial recommender
systems. Because observed feedback represents users' click logs, there is a
semantic gap between true relevance and observed feedback. More importantly,
observed feedback is usually biased towards popular items, thereby
overestimating the actual relevance of popular items. Although existing studies
have developed unbiased learning methods using inverse propensity weighting
(IPW) or causal reasoning, they solely focus on eliminating the popularity bias
of items. In this paper, we propose a novel unbiased recommender learning
model, namely BIlateral SElf-unbiased Recommender (BISER), to eliminate the
exposure bias of items caused by recommender models. Specifically, BISER
consists of two key components: (i) self-inverse propensity weighting (SIPW) to
gradually mitigate the bias of items without incurring high computational
costs; and (ii) bilateral unbiased learning (BU) to bridge the gap between two
complementary models in model predictions, i.e., user- and item-based
autoencoders, alleviating the high variance of SIPW. Extensive experiments show
that BISER consistently outperforms state-of-the-art unbiased recommender
models over several datasets, including Coat, Yahoo! R3, MovieLens, and
CiteULike.

We propose a method based on deep reinforcement learning that efficiently
prepares a quantum many-body pure state in thermal or prethermal equilibrium.
The main physical intuition underlying the method is that the information on
the equilibrium states can be efficiently encoded/extracted by focusing only on
a few local observables, relying on the typicality of equilibrium states.
Instead of resorting to the expensive preparation protocol that adopts global
features such as the quantum state fidelity, we show that the equilibrium
states can be efficiently prepared only by learning the expectation values of
local observables. We demonstrate our method by preparing two illustrative
examples: Gibbs ensembles in non-integrable systems and generalized Gibbs
ensembles in integrable systems. Pure states prepared solely from local
observables are numerically shown to successfully encode the macroscopic
properties of the equilibrium states. Furthermore, we find that the preparation
errors, with respect to the system size, decay exponentially for Gibbs
ensembles and polynomially for generalized Gibbs ensembles, which are in
agreement with the finite-size fluctuation within thermodynamic ensembles. Our
method paves a new path to studying thermodynamic and statistical properties of
quantum many-body systems in quantum hardware.

Dominated point cloud-based 3D object detectors in autonomous driving
scenarios rely heavily on the huge amount of accurately labeled samples,
however, 3D annotation in the point cloud is extremely tedious, expensive and
time-consuming. To reduce the dependence on large supervision, semi-supervised
learning (SSL) based approaches have been proposed. The Pseudo-Labeling
methodology is commonly used for SSL frameworks, however, the low-quality
predictions from the teacher model have seriously limited its performance. In
this work, we propose a new Pseudo-Labeling framework for semi-supervised 3D
object detection, by enhancing the teacher model to a proficient one with
several necessary designs. First, to improve the recall of pseudo labels, a
Spatialtemporal Ensemble (STE) module is proposed to generate sufficient seed
boxes. Second, to improve the precision of recalled boxes, a Clusteringbased
Box Voting (CBV) module is designed to get aggregated votes from the clustered
seed boxes. This also eliminates the necessity of sophisticated thresholds to
select pseudo labels. Furthermore, to reduce the negative influence of wrongly
pseudo-labeled samples during the training, a soft supervision signal is
proposed by considering Box-wise Contrastive Learning (BCL). The effectiveness
of our model is verified on both ONCE and Waymo datasets. For example, on ONCE,
our approach significantly improves the baseline by 9.51 mAP. Moreover, with
half annotations, our model outperforms the oracle model with full annotations
on Waymo.

Existing approaches for unsupervised point cloud pre-training are constrained
to either scene-level or point/voxel-level instance discrimination. Scene-level
methods tend to lose local details that are crucial for recognizing the road
objects, while point/voxel-level methods inherently suffer from limited
receptive field that is incapable of perceiving large objects or context
environments. Considering region-level representations are more suitable for 3D
object detection, we devise a new unsupervised point cloud pre-training
framework, called ProposalContrast, that learns robust 3D representations by
contrasting region proposals. Specifically, with an exhaustive set of region
proposals sampled from each point cloud, geometric point relations within each
proposal are modeled for creating expressive proposal representations. To
better accommodate 3D detection properties, ProposalContrast optimizes with
both inter-cluster and inter-proposal separation, i.e., sharpening the
discriminativeness of proposal representations across semantic classes and
object instances. The generalizability and transferability of ProposalContrast
are verified on various 3D detectors (i.e., PV-RCNN, CenterPoint, PointPillars
and PointRCNN) and datasets (i.e., KITTI, Waymo and ONCE).

X-ray fluorescence spectroscopy (XRF) plays an important role for elemental
analysis in a wide range of scientific fields, especially in cultural heritage.
XRF imaging, which uses a raster scan to acquire spectra across artworks,
provides the opportunity for spatial analysis of pigment distributions based on
their elemental composition. However, conventional XRF-based pigment
identification relies on time-consuming elemental mapping by expert
interpretations of measured spectra. To reduce the reliance on manual work,
recent studies have applied machine learning techniques to cluster similar XRF
spectra in data analysis and to identify the most likely pigments.
Nevertheless, it is still challenging for automatic pigment identification
strategies to directly tackle the complex structure of real paintings, e.g.
pigment mixtures and layered pigments. In addition, pixel-wise pigment
identification based on XRF imaging remains an obstacle due to the high noise
level compared with averaged spectra. Therefore, we developed a
deep-learning-based end-to-end pigment identification framework to fully
automate the pigment identification process. In particular, it offers high
sensitivity to the underlying pigments and to the pigments with a low
concentration, therefore enabling satisfying results in mapping the pigments
based on single-pixel XRF spectrum. As case studies, we applied our framework
to lab-prepared mock-up paintings and two 19th-century paintings: Paul
Gauguin's Po\`emes Barbares (1896) that contains layered pigments with an
underlying painting, and Paul Cezanne's The Bathers (1899-1904). The pigment
identification results demonstrated that our model achieved comparable results
to the analysis by elemental mapping, suggesting the generalizability and
stability of our model.

Cross-modal hashing is a successful method to solve large-scale multimedia
retrieval issue. A lot of matrix factorization-based hashing methods are
proposed. However, the existing methods still struggle with a few problems,
such as how to generate the binary codes efficiently rather than directly relax
them to continuity. In addition, most of the existing methods choose to use an
$n\times n$ similarity matrix for optimization, which makes the memory and
computation unaffordable. In this paper we propose a novel Asymmetric Scalable
Cross-Modal Hashing (ASCMH) to address these issues. It firstly introduces a
collective matrix factorization to learn a common latent space from the
kernelized features of different modalities, and then transforms the similarity
matrix optimization to a distance-distance difference problem minimization with
the help of semantic labels and common latent space. Hence, the computational
complexity of the $n\times n$ asymmetric optimization is relieved. In the
generation of hash codes we also employ an orthogonal constraint of label
information, which is indispensable for search accuracy. So the redundancy of
computation can be much reduced. For efficient optimization and scalable to
large-scale datasets, we adopt the two-step approach rather than optimizing
simultaneously. Extensive experiments on three benchmark datasets: Wiki,
MIRFlickr-25K, and NUS-WIDE, demonstrate that our ASCMH outperforms the
state-of-the-art cross-modal hashing methods in terms of accuracy and

Skeleton-based two-person interaction recognition has been gaining increasing
attention as advancements are made in pose estimation and graph convolutional
networks. Although the accuracy has been gradually improving, the increasing
computational complexity makes it more impractical for a real-world
environment. There is still room for accuracy improvement as the conventional
methods do not fully represent the relationship between inter-body joints. In
this paper, we propose a lightweight model for accurately recognizing
two-person interactions. In addition to the architecture, which incorporates
middle fusion, we introduce a factorized convolution technique to reduce the
weight parameters of the model. We also introduce a network stream that
accounts for relative distance changes between inter-body joints to improve
accuracy. Experiments using two large-scale datasets, NTU RGB+D 60 and 120,
show that our method simultaneously achieved the highest accuracy and
relatively low computational complexity compared with the conventional methods.

Label hierarchies are often available apriori as part of biological taxonomy
or language datasets WordNet. Several works exploit these to learn hierarchy
aware features in order to improve the classifier to make semantically
meaningful mistakes while maintaining or reducing the overall error. In this
paper, we propose a novel approach for learning Hierarchy Aware Features (HAF)
that leverages classifiers at each level of the hierarchy that are constrained
to generate predictions consistent with the label hierarchy. The classifiers
are trained by minimizing a Jensen-Shannon Divergence with target soft labels
obtained from the fine-grained classifiers. Additionally, we employ a simple
geometric loss that constrains the feature space geometry to capture the
semantic structure of the label space. HAF is a training time approach that
improves the mistakes while maintaining top-1 error, thereby, addressing the
problem of cross-entropy loss that treats all mistakes as equal. We evaluate
HAF on three hierarchical datasets and achieve state-of-the-art results on the
iNaturalist-19 and CIFAR-100 datasets. The source code is available at
https://github.com/07Agarg/HAF

Deep reinforcement learning (RL) based controllers for legged robots have
demonstrated impressive robustness for walking in different environments for
several robot platforms. To enable the application of RL policies for humanoid
robots in real-world settings, it is crucial to build a system that can achieve
robust walking in any direction, on 2D and 3D terrains, and be controllable by
a user-command. In this paper, we tackle this problem by learning a policy to
follow a given step sequence. The policy is trained with the help of a set of
procedurally generated step sequences (also called footstep plans). We show
that simply feeding the upcoming 2 steps to the policy is sufficient to achieve
omnidirectional walking, turning in place, standing, and climbing stairs. Our
method employs curriculum learning on the complexity of terrains, and
circumvents the need for reference motions or pre-trained weights. We
demonstrate the application of our proposed method to learn RL policies for 2
new robot platforms - HRP5P and JVRC-1 - in the MuJoCo simulation environment.
The code for training and evaluation is available online.

Spectral computed tomography (CT) is an emerging technology, that generates a
multienergy attenuation map for the interior of an object and extends the
traditional image volume into a 4D form. Compared with traditional CT based on
energy-integrating detectors, spectral CT can make full use of spectral
information, resulting in high resolution and providing accurate material
quantification. Numerous model-based iterative reconstruction methods have been
proposed for spectral CT reconstruction. However, these methods usually suffer
from difficulties such as laborious parameter selection and expensive
computational costs. In addition, due to the image similarity of different
energy bins, spectral CT usually implies a strong low-rank prior, which has
been widely adopted in current iterative reconstruction models. Singular value
thresholding (SVT) is an effective algorithm to solve the low-rank constrained
model. However, the SVT method requires manual selection of thresholds, which
may lead to suboptimal results. To relieve these problems, in this paper, we
propose a Sparse and lOw-rank UnroLling Network for spectral CT image
reconstruction (SOUL-Net), that learns the parameters and thresholds in a
data-driven manner. Furthermore, a Taylor expansion-based neural network
backpropagation method is introduced to improve the numerical stability. The
qualitative and quantitative results demonstrate that the proposed method
outperforms several representative state-of-the-art algorithms in terms of
detail preservation and artifact reduction.

We study the problem of variance estimation in general graph-structured
problems. First, we develop a linear time estimator for the homoscedastic case
that can consistently estimate the variance in general graphs. We show that our
estimator attains minimax rates for the chain and 2D grid graphs when the mean
signal has a total variation with canonical scaling. Furthermore, we provide
general upper bounds on the mean squared error performance of the fused lasso
estimator in general graphs under a moment condition and a bound on the tail
behavior of the errors. These upper bounds allow us to generalize for broader
classes of distributions, such as sub-Exponential, many existing results on the
fused lasso that are only known to hold with the assumption that errors are
sub-Gaussian random variables. Exploiting our upper bounds, we then study a
simple total variation regularization estimator for estimating the signal of
variances in the heteroscedastic case. Our results show that the variance
estimator attains minimax rates for estimating signals of bounded variation in
grid graphs, $K$-nearest neighbor graphs with very mild assumptions, and it is
consistent for estimating the variances in any connected graph. In addition,
extensive numerical results show that our proposed estimators perform
reasonably well in a variety of graph-structured models.

Microfinance in developing areas such as Africa has been proven to improve
the local economy significantly. However, many applicants in developing areas
cannot provide adequate information required by the financial institution to
make a lending decision. As a result, it is challenging for microfinance
institutions to assign credit properly based on conventional policies. In this
paper, we formulate the decision-making of microfinance into a rigorous
optimization-based framework involving learning and control. We propose an
algorithm to explore and learn the optimal policy to approve or reject
applicants. We provide the conditions under which the algorithms are guaranteed
to converge to an optimal one. The proposed algorithm can naturally deal with
missing information and systematically tradeoff multiple objectives such as
profit maximization, financial inclusion, social benefits, and economic
development. Through extensive simulation of both real and synthetic
microfinance datasets, we showed our proposed algorithm is superior to existing
benchmarks. To the best of our knowledge, this paper is the first to make a
connection between microfinance and control and use control-theoretic tools to
optimize the policy with a provable guarantee.

Snapshot compressive imaging (SCI) encodes high-speed scene video into a
snapshot measurement and then computationally makes reconstructions, allowing
for efficient high-dimensional data acquisition. Numerous algorithms, ranging
from regularization-based optimization and deep learning, are being
investigated to improve reconstruction quality, but they are still limited by
the ill-posed and information-deficient nature of the standard SCI paradigm. To
overcome these drawbacks, we propose a new key frames assisted hybrid encoding
paradigm for compressive video sensing, termed KH-CVS, that alternatively
captures short-exposure key frames without coding and long-exposure encoded
compressive frames to jointly reconstruct photorealistic video. With the use of
optical flow and spatial warping, a deep convolutional neural network framework
is constructed to integrate the benefits of these two types of frames.
Extensive experiments on both simulations and real data from the prototype we
developed verify the superiority of the proposed method.

With the rapid growth of multimodal media data on the Web in recent years,
hash learning methods as a way to achieve efficient and flexible cross-modal
retrieval of massive multimedia data have received a lot of attention from the
current Web resource retrieval research community. Existing supervised hashing
methods simply transform label information into pairwise similarity information
to guide hash learning, leading to a potential risk of semantic error in the
face of multi-label data. In addition, most existing hash optimization methods
solve NP-hard optimization problems by employing approximate approximation
strategies based on relaxation strategies, leading to a large quantization
error. In order to address above obstacles, we present a simple yet efficient
Adaptive Asymmetric Label-guided Hashing, named A2LH, for Multimedia Search.
Specifically, A2LH is a two-step hashing method. In the first step, we design
an association representation model between the different modality
representations and semantic label representation separately, and use the
semantic label representation as an intermediate bridge to solve the semantic
gap existing between different modalities. In addition, we present an efficient
discrete optimization algorithm for solving the quantization error problem
caused by relaxation-based optimization algorithms. In the second step, we
leverage the generated hash codes to learn the hash mapping functions. The
experimental results show that our proposed method achieves optimal performance
on all compared baseline methods.

Referring video object segmentation aims to segment the object referred by a
given language expression. Existing works typically require compressed video
bitstream to be decoded to RGB frames before being segmented, which increases
computation and storage requirements and ultimately slows the inference down.
This may hamper its application in real-world computing resource limited
scenarios, such as autonomous cars and drones. To alleviate this problem, in
this paper, we explore the referring object segmentation task on compressed
videos, namely on the original video data flow. Besides the inherent difficulty
of the video referring object segmentation task itself, obtaining
discriminative representation from compressed video is also rather challenging.
To address this problem, we propose a multi-attention network which consists of
dual-path dual-attention module and a query-based cross-modal Transformer
module. Specifically, the dual-path dual-attention module is designed to
extract effective representation from compressed data in three modalities,
i.e., I-frame, Motion Vector and Residual. The query-based cross-modal
Transformer firstly models the correlation between linguistic and visual
modalities, and then the fused multi-modality features are used to guide object
queries to generate a content-aware dynamic kernel and to predict final
segmentation masks. Different from previous works, we propose to learn just one
kernel, which thus removes the complicated post mask-matching procedure of
existing methods. Extensive promising experimental results on three challenging
datasets show the effectiveness of our method compared against several
state-of-the-art methods which are proposed for processing RGB data. Source
code is available at: https://github.com/DexiangHong/MANet.

While directly fine-tuning (FT) large-scale, pretrained models on
task-specific data is well-known to induce strong in-distribution task
performance, recent works have demonstrated that different adaptation
protocols, such as linear probing (LP) prior to FT, can improve
out-of-distribution generalization. However, the design space of such
adaptation protocols remains under-explored and the evaluation of such
protocols has primarily focused on distribution shifts. Therefore, in this
work, we evaluate common adaptation protocols across distributions shifts and
machine learning safety metrics (e.g., anomaly detection, calibration,
robustness to corruptions). We find that protocols induce disparate trade-offs
that were not apparent from prior evaluation. Further, we demonstrate that
appropriate pairing of data augmentation and protocol can substantially
mitigate this trade-off. Finally, we hypothesize and empirically see that using
hardness-promoting augmentations during LP and then FT with augmentations may
be particularly effective for trade-off mitigation.

Electromagnetic (EM) imaging is widely applied in sensing for security,
biomedicine, geophysics, and various industries. It is an ill-posed inverse
problem whose solution is usually computationally expensive. Machine learning
(ML) techniques and especially deep learning (DL) show potential in fast and
accurate imaging. However, the high performance of purely data-driven
approaches relies on constructing a training set that is statistically
consistent with practical scenarios, which is often not possible in EM imaging
tasks. Consequently, generalizability becomes a major concern. On the other
hand, physical principles underlie EM phenomena and provide baselines for
current imaging techniques. To benefit from prior knowledge in big data and the
theoretical constraint of physical laws, physics embedded ML methods for EM
imaging have become the focus of a large body of recent work.
  This article surveys various schemes to incorporate physics in learning-based
EM imaging. We first introduce background on EM imaging and basic formulations
of the inverse problem. We then focus on three types of strategies combining
physics and ML for linear and nonlinear imaging and discuss their advantages
and limitations. Finally, we conclude with open challenges and possible ways
forward in this fast-developing field. Our aim is to facilitate the study of
intelligent EM imaging methods that will be efficient, interpretable and
controllable.

Medication for neurological diseases such as the Parkinson's disease usually
happens remotely at home, away from hospitals. Such out-of-lab environments
pose challenges in collecting timely and accurate health status data using the
limited professional care devices for health condition analysis, medication
adherence measurement and future dose or treatment planning. Individual
differences in behavioral signals collected from wearable sensors also lead to
difficulties in adopting current general machine learning analysis pipelines.
To address these challenges, we present a method for predicting medication
status of Parkinson's disease patients using the public mPower dataset, which
contains 62,182 remote multi-modal test records collected on smartphones from
487 patients. The proposed method shows promising results in predicting three
medication status objectively: Before Medication (AUC=0.95), After Medication
(AUC=0.958), and Another Time (AUC=0.976) by examining patient-wise historical
records with the attention weights learned through a Transformer model. We
believe our method provides an innovative way for personalized remote health
sensing in a timely and objective fashion which could benefit a broad range of
similar applications.

Constructing a differentially private (DP) estimator requires deriving the
maximum influence of an observation, which can be difficult in the absence of
exogenous bounds on the input data or the estimator, especially in high
dimensional settings. This paper shows that standard notions of statistical
depth, i.e., halfspace depth and regression depth, are particularly
advantageous in this regard, both in the sense that the maximum influence of a
single observation is easy to analyze and that this value is typically low.
This is used to motivate new approximate DP location and regression estimators
using the maximizers of these two notions of statistical depth. A more
computationally efficient variant of the approximate DP regression estimator is
also provided. Also, to avoid requiring that users specify a priori bounds on
the estimates and/or the observations, variants of these DP mechanisms are
described that satisfy random differential privacy (RDP), which is a relaxation
of differential privacy provided by Hall, Wasserman, and Rinaldo (2013). We
also provide simulations of the two DP regression methods proposed here. The
proposed estimators appear to perform favorably relative to the existing DP
regression methods we consider in these simulations when either the sample size
is at least 100-200 or the privacy-loss budget is sufficiently high.

We consider representation learning for proteins with 3D structures. We build
3D graphs based on protein structures and develop graph networks to learn their
representations. Depending on the levels of details that we wish to capture,
protein representations can be computed at different levels, \emph{e.g.}, the
amino acid, backbone, or all-atom levels. Importantly, there exist hierarchical
relations among different levels. In this work, we propose to develop a novel
hierarchical graph network, known as ProNet, to capture the relations. Our
ProNet is very flexible and can be used to compute protein representations at
different levels of granularity. We show that, given a base 3D graph network
that is complete, our ProNet representations are also complete at all levels.
To close the loop, we develop a complete and efficient 3D graph network to be
used as a base model, making our ProNet complete. We conduct experiments on
multiple downstream tasks. Results show that ProNet outperforms recent methods
on most datasets. In addition, results indicate that different downstream tasks
may require representations at different levels. Our code is available as part
of the DIG library (\url{https://github.com/divelab/DIG}).

Graph neural networks (GNNs) have demonstrated a significant boost in
prediction performance on graph data. At the same time, the predictions made by
these models are often hard to interpret. In that regard, many efforts have
been made to explain the prediction mechanisms of these models from
perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works
present systematic frameworks to interpret GNNs, a holistic review for
explainable GNNs is unavailable. In this survey, we present a comprehensive
review of explainability techniques developed for GNNs. We focus on explainable
graph neural networks and categorize them based on the use of explainable
methods. We further provide the common performance metrics for GNNs
explanations and point out several future research directions.

Classifier guidance is a recently introduced method to trade off mode
coverage and sample fidelity in conditional diffusion models post training, in
the same spirit as low temperature sampling or truncation in other types of
generative models. Classifier guidance combines the score estimate of a
diffusion model with the gradient of an image classifier and thereby requires
training an image classifier separate from the diffusion model. It also raises
the question of whether guidance can be performed without a classifier. We show
that guidance can be indeed performed by a pure generative model without such a
classifier: in what we call classifier-free guidance, we jointly train a
conditional and an unconditional diffusion model, and we combine the resulting
conditional and unconditional score estimates to attain a trade-off between
sample quality and diversity similar to that obtained using classifier
guidance.

I propose a theoretical social learning model to investigate how confirmation
bias affects opinions when agents exchange information over a social network.
For that, besides exchanging opinions with friends, individuals observe a
public sequence of potentially ambiguous signals and they interpret it
according to a rule that accounts for confirmation bias. I first show that,
regardless the level of ambiguity and both in the case of a single individual
or of a networked society, only two types of opinions might be formed and both
are biased. One opinion type, however, is necessarily less biased than the
other depending on the state of the world. The size of both biases depends on
the ambiguity level and the relative magnitude of the state and confirmation
biases. In this context, long-run learning is not attained even when
individuals interpret ambiguity impartially. Finally, since it is not trivial
to ascertain analytically the probability of emergence of the less biased
consensus when individuals are connected through a social network and have
different priors, I use simulations to analyze its determinants. Three main
results derived from this exercise are that, in expected terms, i) some network
topologies are more conducive to consensus efficiency, ii) some degree of
partisanship enhances consensus efficiency even under confirmation bias and
iii) open-mindedness, i.e. when partisans agree to exchange opinions with other
partisans with polar opposite beliefs, might harm efficiency in some cases.

The 44th International Conference on Software Engineering (ICSE 2022) was
held in person from May 22 to May 27, 2022 in Pittsburgh, PA, USA. Here, we
summarize themes of research and the direction of research in the field of
software engineering and testing that we observed at the conference.

Deep learning-based super-resolution (SR) has gained tremendous popularity in
recent years because of its high image quality performance and wide application
scenarios. However, prior methods typically suffer from large amounts of
computations and huge power consumption, causing difficulties for real-time
inference, especially on resource-limited platforms such as mobile devices. To
mitigate this, we propose a compiler-aware SR neural architecture search (NAS)
framework that conducts depth search and per-layer width search with adaptive
SR blocks. The inference speed is directly taken into the optimization along
with the SR loss to derive SR models with high image quality while satisfying
the real-time inference requirement. Instead of measuring the speed on mobile
devices at each iteration during the search process, a speed model incorporated
with compiler optimizations is leveraged to predict the inference latency of
the SR block with various width configurations for faster convergence. With the
proposed framework, we achieve real-time SR inference for implementing 720p
resolution with competitive SR performance (in terms of PSNR and SSIM) on
GPU/DSP of mobile platforms (Samsung Galaxy S21).

We study the problem of translating an image-based, step-by-step assembly
manual created by human designers into machine-interpretable instructions. We
formulate this problem as a sequential prediction task: at each step, our model
reads the manual, locates the components to be added to the current shape, and
infers their 3D poses. This task poses the challenge of establishing a 2D-3D
correspondence between the manual image and the real 3D object, and 3D pose
estimation for unseen 3D objects, since a new component to be added in a step
can be an object built from previous steps. To address these two challenges, we
present a novel learning-based framework, the Manual-to-Executable-Plan Network
(MEPNet), which reconstructs the assembly steps from a sequence of manual
images. The key idea is to integrate neural 2D keypoint detection modules and
2D-3D projection algorithms for high-precision prediction and strong
generalization to unseen components. The MEPNet outperforms existing methods on
three newly collected LEGO manual datasets and a Minecraft house dataset.

The neural boom that has sparked natural language processing (NLP) research
through the last decade has similarly led to significant innovations in
data-to-text generation (DTG). This survey offers a consolidated view into the
neural DTG paradigm with a structured examination of the approaches, benchmark
datasets, and evaluation protocols. This survey draws boundaries separating DTG
from the rest of the natural language generation (NLG) landscape, encompassing
an up-to-date synthesis of the literature, and highlighting the stages of
technological adoption from within and outside the greater NLG umbrella. With
this holistic view, we highlight promising avenues for DTG research that not
only focus on the design of linguistically capable systems but also systems
that exhibit fairness and accountability.

Comparing different AutoML frameworks is notoriously challenging and often
done incorrectly. We introduce an open and extensible benchmark that follows
best practices and avoids common mistakes when comparing AutoML frameworks. We
conduct a thorough comparison of 9 well-known AutoML frameworks across 71
classification and 33 regression tasks. The differences between the AutoML
frameworks are explored with a multi-faceted analysis, evaluating model
accuracy, its trade-offs with inference time, and framework failures. We also
use Bradley-Terry trees to discover subsets of tasks where the relative AutoML
framework rankings differ. The benchmark comes with an open-source tool that
integrates with many AutoML frameworks and automates the empirical evaluation
process end-to-end: from framework installation and resource allocation to
in-depth evaluation. The benchmark uses public data sets, can be easily
extended with other AutoML frameworks and tasks, and has a website with
up-to-date results.

In this paper, we develop four spiking neural network (SNN) models for two
static American Sign Language (ASL) hand gesture classification tasks, i.e.,
the ASL Alphabet and ASL Digits. The SNN models are deployed on Intel's
neuromorphic platform, Loihi, and then compared against equivalent deep neural
network (DNN) models deployed on an edge computing device, the Intel Neural
Compute Stick 2 (NCS2). We perform a comprehensive comparison between the two
systems in terms of accuracy, latency, power consumption, and energy. The best
DNN model achieves an accuracy of 99.93% on the ASL Alphabet dataset, whereas
the best performing SNN model has an accuracy of 99.30%. For the ASL-Digits
dataset, the best DNN model achieves an accuracy of 99.76% accuracy while the
SNN achieves 99.03%. Moreover, our obtained experimental results show that the
Loihi neuromorphic hardware implementations achieve up to 20.64x and 4.10x
reduction in power consumption and energy, respectively, when compared to NCS2.

Efficient point cloud compression is essential for applications like virtual
and mixed reality, autonomous driving, and cultural heritage. In this paper, we
propose a deep learning-based inter-frame encoding scheme for dynamic point
cloud geometry compression. We propose a lossy geometry compression scheme that
predicts the latent representation of the current frame using the previous
frame by employing a novel prediction network. Our proposed network utilizes
sparse convolutions with hierarchical multiscale 3D feature learning to encode
the current frame using the previous frame. We employ convolution on target
coordinates to map the latent representation of the previous frame to the
downsampled coordinates of the current frame to predict the current frame's
feature embedding. Our framework transmits the residual of the predicted
features and the actual features by compressing them using a learned
probabilistic factorized entropy model. At the receiver, the decoder
hierarchically reconstructs the current frame by progressively rescaling the
feature embedding. We compared our model to the state-of-the-art Video-based
Point Cloud Compression (V-PCC) and Geometry-based Point Cloud Compression
(G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG). Our
method achieves more than 91% BD-Rate Bjontegaard Delta Rate) reduction against
G-PCC, more than 62% BD-Rate reduction against V-PCC intra-frame encoding mode,
and more than 52% BD-Rate savings against V-PCC P-frame-based inter-frame
encoding mode using HEVC.

How can artificial agents learn non-reinforced preferences to continuously
adapt their behaviour to a changing environment? We decompose this question
into two challenges: ($i$) encoding diverse memories and ($ii$) selectively
attending to these for preference formation. Our proposed
\emph{no}n-\emph{re}inforced preference learning mechanism using selective
attention, \textsc{Nore}, addresses both by leveraging the agent's world model
to collect a diverse set of experiences which are interleaved with imagined
roll-outs to encode memories. These memories are selectively attended to, using
attention and gating blocks, to update agent's preferences. We validate
\textsc{Nore} in a modified OpenAI Gym FrozenLake environment (without any
external signal) with and without volatility under a fixed model of the
environment -- and compare its behaviour to \textsc{Pepper}, a Hebbian
preference learning mechanism. We demonstrate that \textsc{Nore} provides a
straightforward framework to induce exploratory preferences in the absence of
external signals.

We present an empirical dataset surveying the deep learning phenomenon on
fully-connected networks, encompassing the training and test performance of
numerous network topologies, sweeping across multiple learning tasks, depths,
numbers of free parameters, learning rates, batch sizes, and regularization
penalties. The dataset probes 178 thousand hyperparameter settings with an
average of 20 repetitions each, totaling 3.5 million training runs and 20
performance metrics for each of the 13.1 billion training epochs observed.
Accumulating this 671 GB dataset utilized 5,448 CPU core-years, 17.8 GPU-years,
and 111.2 node-years. Additionally, we provide a preliminary analysis revealing
patterns which persist across learning tasks and topologies. We aim to inspire
work empirically studying modern machine learning techniques as a catalyst for
the theoretical discoveries needed to progress the field beyond
energy-intensive and heuristic practices.

In general, large datasets enable deep learning models to perform with good
accuracy and generalizability. However, massive high-fidelity simulation
datasets (from molecular chemistry, astrophysics, computational fluid dynamics
(CFD), etc. can be challenging to curate due to dimensionality and storage
constraints. Lossy compression algorithms can help mitigate limitations from
storage, as long as the overall data fidelity is preserved. To illustrate this
point, we demonstrate that deep learning models, trained and tested on data
from a petascale CFD simulation, are robust to errors introduced during lossy
compression in a semantic segmentation problem. Our results demonstrate that
lossy compression algorithms offer a realistic pathway for exposing
high-fidelity scientific data to open-source data repositories for building
community datasets. In this paper, we outline, construct, and evaluate the
requirements for establishing a big data framework, demonstrated at
https://blastnet.github.io/, for scientific machine learning.

The lack of well-calibrated confidence estimates makes neural networks
inadequate in safety-critical domains such as autonomous driving or healthcare.
In these settings, having the ability to abstain from making a prediction on
out-of-distribution (OOD) data can be as important as correctly classifying
in-distribution data. We introduce $p$-DkNN, a novel inference procedure that
takes a trained deep neural network and analyzes the similarity structures of
its intermediate hidden representations to compute $p$-values associated with
the end-to-end model prediction. The intuition is that statistical tests
performed on latent representations can serve not only as a classifier, but
also offer a statistically well-founded estimation of uncertainty. $p$-DkNN is
scalable and leverages the composition of representations learned by hidden
layers, which makes deep representation learning successful. Our theoretical
analysis builds on Neyman-Pearson classification and connects it to recent
advances in selective classification (reject option). We demonstrate
advantageous trade-offs between abstaining from predicting on OOD inputs and
maintaining high accuracy on in-distribution inputs. We find that $p$-DkNN
forces adaptive attackers crafting adversarial examples, a form of worst-case
OOD inputs, to introduce semantically meaningful changes to the inputs.

The approval success rate of drug candidates is very low with the majority of
failure due to safety and efficacy. Increasingly available high dimensional
information on targets, drug molecules and indications provides an opportunity
for ML methods to integrate multiple data modalities and better predict
clinically promising drug targets. Notably, drug targets with human genetics
evidence are shown to have better odds to succeed. However, a recent tensor
factorization-based approach found that additional information on targets and
indications might not necessarily improve the predictive accuracy. Here we
revisit this approach by integrating different types of human genetics evidence
collated from publicly available sources to support each target-indication
pair. We use Bayesian tensor factorization to show that models incorporating
all available human genetics evidence (rare disease, gene burden, common
disease) modestly improves the clinical outcome prediction over models using
single line of genetics evidence. We provide additional insight into the
relative predictive power of different types of human genetics evidence for
predicting the success of clinical outcomes.

3D Human body pose and shape estimation within a temporal sequence can be
quite critical for understanding human behavior. Despite the significant
progress in human pose estimation in the recent years, which are often based on
single images or videos, human motion estimation on live stream videos is still
a rarely-touched area considering its special requirements for real-time output
and temporal consistency. To address this problem, we present a temporally
embedded 3D human body pose and shape estimation (TePose) method to improve the
accuracy and temporal consistency of pose estimation in live stream videos.
TePose uses previous predictions as a bridge to feedback the error for better
estimation in the current frame and to learn the correspondence between data
frames and predictions in the history. A multi-scale spatio-temporal graph
convolutional network is presented as the motion discriminator for adversarial
training using datasets without any 3D labeling. We propose a sequential data
loading strategy to meet the special start-to-end data processing requirement
of live stream. We demonstrate the importance of each proposed module with
extensive experiments. The results show the effectiveness of TePose on
widely-used human pose benchmarks with state-of-the-art performance.

Semi-supervised learning (SSL) leverages both labeled and unlabeled data to
train machine learning (ML) models. State-of-the-art SSL methods can achieve
comparable performance to supervised learning by leveraging much fewer labeled
data. However, most existing works focus on improving the performance of SSL.
In this work, we take a different angle by studying the training data privacy
of SSL. Specifically, we propose the first data augmentation-based membership
inference attacks against ML models trained by SSL. Given a data sample and the
black-box access to a model, the goal of membership inference attack is to
determine whether the data sample belongs to the training dataset of the model.
Our evaluation shows that the proposed attack can consistently outperform
existing membership inference attacks and achieves the best performance against
the model trained by SSL. Moreover, we uncover that the reason for membership
leakage in SSL is different from the commonly believed one in supervised
learning, i.e., overfitting (the gap between training and testing accuracy). We
observe that the SSL model is well generalized to the testing data (with almost
0 overfitting) but ''memorizes'' the training data by giving a more confident
prediction regardless of its correctness. We also explore early stopping as a
countermeasure to prevent membership inference attacks against SSL. The results
show that early stopping can mitigate the membership inference attack, but with
the cost of model's utility degradation.

Several recent works empirically find finetuning learning rate is critical to
the final performance in neural network structured pruning. Further researches
find that the network trainability broken by pruning answers for it, thus
calling for an urgent need to recover trainability before finetuning. Existing
attempts propose to exploit weight orthogonalization to achieve dynamical
isometry for improved trainability. However, they only work for linear MLP
networks. How to develop a filter pruning method that maintains or recovers
trainability and is scalable to modern deep networks remains elusive. In this
paper, we present trainability preserving pruning (TPP), a regularization-based
structured pruning method that can effectively maintain trainability during
sparsification. Specifically, TPP regularizes the gram matrix of convolutional
kernels so as to de-correlate the pruned filters from the kept filters. Beside
the convolutional layers, we also propose to regularize the BN parameters for
better preserving trainability. Empirically, TPP can compete with the
ground-truth dynamical isometry recovery method on linear MLP networks. On
non-linear networks (ResNet56/VGG19, CIFAR datasets), it outperforms the other
counterpart solutions by a large margin. Moreover, TPP can also work
effectively with modern deep networks (ResNets) on ImageNet, delivering
encouraging performance in comparison to many top-performing filter pruning
methods. To our best knowledge, this is the first approach that effectively
maintains trainability during pruning for the large-scale deep neural networks.

In decentralized cooperative multi-agent reinforcement learning, agents can
aggregate information from one another to learn policies that maximize a
team-average objective function. Despite the willingness to cooperate with
others, the individual agents may find direct sharing of information about
their local state, reward, and value function undesirable due to privacy
issues. In this work, we introduce a decentralized actor-critic algorithm with
TD error aggregation that does not violate privacy issues and assumes that
communication channels are subject to time delays and packet dropouts. The cost
we pay for making such weak assumptions is an increased communication burden
for every agent as measured by the dimension of the transmitted data.
Interestingly, the communication burden is only quadratic in the graph size,
which renders the algorithm applicable in large networks. We provide a
convergence analysis under diminishing step size to verify that the agents
maximize the team-average objective function.

We investigate the effect of an $\varepsilon$-room of perturbation tolerance
on symmetric tensor decomposition from an algorithmic perspective. More
precisely, we prove theorems and design algorithms for the following problem:
Suppose a real symmetric $d$-tensor $f$, a norm $||.||$ on the space of
symmetric $d$-tensors, and $\varepsilon >0$ error tolerance with respect to
$||.||$ are given. What is the smallest symmetric tensor rank in the
$\varepsilon$-neighborhood of $f$? In other words, what is the symmetric tensor
rank of $f$ after a clever $\varepsilon$-perturbation? We provide two different
theoretical bounds and three algorithms for approximate symmetric tensor rank
estimation. Our first result is a randomized energy increment algorithm for the
case of $L_p$-norms. Our second result is a simple sampling-based algorithm,
inspired by some techniques in geometric functional analysis, that works for
any norm. We also provide a supplementary algorithm in the case of the
Hilbert-Schmidt norm. All our algorithms come with rigorous complexity
estimates, which in turn yield our two main theorems on symmetric tensor rank
with $\varepsilon$-room of tolerance. We also report on our experiments with a
preliminary implementation of the energy increment algorithm.

A fully-automated deep learning algorithm matched performance of radiologists
in assessment of knee osteoarthritis severity in radiographs using the
Kellgren-Lawrence grading system.
  To develop an automated deep learning-based algorithm that jointly uses
Posterior-Anterior (PA) and Lateral (LAT) views of knee radiographs to assess
knee osteoarthritis severity according to the Kellgren-Lawrence grading system.
  We used a dataset of 9739 exams from 2802 patients from Multicenter
Osteoarthritis Study (MOST). The dataset was divided into a training set of
2040 patients, a validation set of 259 patients and a test set of 503 patients.
A novel deep learning-based method was utilized for assessment of knee OA in
two steps: (1) localization of knee joints in the images, (2) classification
according to the KL grading system. Our method used both PA and LAT views as
the input to the model. The scores generated by the algorithm were compared to
the grades provided in the MOST dataset for the entire test set as well as
grades provided by 5 radiologists at our institution for a subset of the test
set.
  The model obtained a multi-class accuracy of 71.90% on the entire test set
when compared to the ratings provided in the MOST dataset. The quadratic
weighted Kappa coefficient for this set was 0.9066. The average quadratic
weighted Kappa between all pairs of radiologists from our institution who took
a part of study was 0.748. The average quadratic-weighted Kappa between the
algorithm and the radiologists at our institution was 0.769.
  The proposed model performed demonstrated equivalency of KL classification to
MSK radiologists, but clearly superior reproducibility. Our model also agreed
with radiologists at our institution to the same extent as the radiologists
with each other. The algorithm could be used to provide reproducible assessment
of knee osteoarthritis severity.

Safe motion planning in robotics requires planning into space which has been
verified to be free of obstacles. However, obtaining such environment
representations using lidars is challenging by virtue of the sparsity of their
depth measurements. We present a learning-aided 3D lidar reconstruction
framework that upsamples sparse lidar depth measurements with the aid of
overlapping camera images so as to generate denser reconstructions with more
definitively free space than can be achieved with the raw lidar measurements
alone. We use a neural network with an encoder-decoder structure to predict
dense depth images along with depth uncertainty estimates which are fused using
a volumetric mapping system. We conduct experiments on real-world outdoor
datasets captured using a handheld sensing device and a legged robot. Using
input data from a 16-beam lidar mapping a building network, our experiments
showed that the amount of estimated free space was increased by more than 40%
with our approach. We also show that our approach trained on a synthetic
dataset generalises well to real-world outdoor scenes without additional
fine-tuning. Finally, we demonstrate how motion planning tasks can benefit from
these denser reconstructions.

The phonon density-of-states (DOS) summarizes the lattice vibrational modes
supported by a structure, and gives access to rich information about the
material's stability, thermodynamic constants, and thermal transport
coefficients. Here, we present an atomistic line graph neural network (ALIGNN)
model for the prediction of the phonon density of states and the derived
thermal and thermodynamic properties. The model is trained on a database of
over 14,000 phonon spectra included in the JARVIS-DFT (Joint Automated
Repository for Various Integrated Simulations: Density Functional Theory)
database. The model predictions are shown to capture the spectral features of
the phonon density-of-states, effectively categorize dynamical stability, and
lead to accurate predictions of DOS-derived thermal and thermodynamic
properties, including heat capacity $C_{\mathrm{V}}$, vibrational entropy
$S_{\mathrm{vib}}$, and the isotopic phonon scattering rate
$\tau^{-1}_{\mathrm{i}}$. The DOS-mediated ALIGNN model provides superior
predictions when compared to a direct deep-learning prediction of these
material properties as well as predictions based on analytic simplifications of
the phonon DOS, including the Debye or Born-von Karman models. Finally, the
ALIGNN model is used to predict the phonon spectra and properties for about
40,000 additional materials listed in the JARVIS-DFT database, which are
validated as far as possible against other open-sourced high-throughput DFT
phonon databases.

With the continuous growth of the global economy and markets, resource
imbalance has risen to be one of the central issues in real logistic scenarios.
In marine transportation, this trade imbalance leads to Empty Container
Repositioning (ECR) problems. Once the freight has been delivered from an
exporting country to an importing one, the laden will turn into empty
containers that need to be repositioned to satisfy new goods requests in
exporting countries. In such problems, the performance that any cooperative
repositioning policy can achieve strictly depends on the routes that vessels
will follow (i.e., fleet deployment). Historically, Operation Research (OR)
approaches were proposed to jointly optimize the repositioning policy along
with the fleet of vessels. However, the stochasticity of future supply and
demand of containers, together with black-box and non-linear constraints that
are present within the environment, make these approaches unsuitable for these
scenarios. In this paper, we introduce a novel framework, Configurable
Semi-POMDPs, to model this type of problems. Furthermore, we provide a
two-stage learning algorithm, "Configure & Conquer" (CC), that first configures
the environment by finding an approximation of the optimal fleet deployment
strategy, and then "conquers" it by learning an ECR policy in this tuned
environmental setting. We validate our approach in large and real-world
instances of the problem. Our experiments highlight that CC avoids the pitfalls
of OR methods and that it is successful at optimizing both the ECR policy and
the fleet of vessels, leading to superior performance in world trade

Recent work has suggested that the generalisation performance of a DNN is
related to the extent to which the Signal-to-Noise Ratio is optimised at each
of the nodes. In contrast, Gradient Descent methods do not always lead to
SNR-optimal weight configurations. One way to improve SNR performance is to
suppress large weight updates and amplify small weight updates. Such balancing
is already implicit in some common optimizers, but we propose an approach that
makes this explicit. The method applies a non-linear function to gradients
prior to making DNN parameter updates. We investigate the performance with such
non-linear approaches. The result is an adaptation to existing optimizers that
improves performance for many problem types.

The development of models for Electronic Health Record data is an area of
active research featuring a small number of public benchmark data sets.
Researchers typically write custom data processing code but this hinders
reproducibility and can introduce errors. The Python package torchtime provides
reproducible implementations of commonly used PhysioNet and UEA & UCR time
series classification repository data sets for PyTorch. Features are provided
for working with irregularly sampled and partially observed time series of
unequal length. It aims to simplify access to PhysioNet data and enable fair
comparisons of models in this exciting area of research.

The responsible use of machine learning tools in real world high-stakes
decision making demands that we audit and control for potential biases against
underrepresented groups. This process naturally requires access to the
sensitive attribute one desires to control, such as demographics, gender, or
other potentially sensitive features. Unfortunately, this information is often
unavailable. In this work we demonstrate that one can still reliably estimate,
and ultimately control, for fairness by using proxy sensitive attributes
derived from a sensitive attribute predictor. Specifically, we first show that
with just a little knowledge of the complete data distribution, one may use a
sensitive attribute predictor to obtain bounds of the classifier's true
fairness metric. Second, we demonstrate how one can provably control a
classifier's worst-case fairness violation with respect to the true sensitive
attribute by controlling for fairness with respect to the proxy sensitive
attribute. Our results hold under assumptions that are significantly milder
than previous works, and we illustrate these results with experiments on
synthetic and real datasets.

Plasma is defined as the fourth state of matter and non-thermal plasma can be
produced at atmospheric pressure under a high electrical field. The strong and
broad-spectrum antimicrobial effect of plasma-activated liquids (PALs) is now
well known. The proven applicability of machine learning (ML) in the medical
field is encouraging for its application in the field of plasma medicine as
well. Thus, ML applications on PALs could present a new perspective to better
understand the influences of various parameters on their antimicrobial effects.
In this paper, comparative supervised ML models are presented by using
previously obtained data to qualitatively predict the in vitro antimicrobial
activity of PALs. A literature search was performed and data is collected from
33 relevant articles. After the required preprocessing steps, two supervised ML
methods, namely classification, and regression are applied to data to obtain
microbial inactivation (MI) predictions. For classification, MI is labeled in
four categories and for regression, MI is used as a continuous variable. Two
different robust cross-validation strategies are conducted for classification
and regression models to evaluate the proposed method; repeated stratified
k-fold cross-validation and k-fold cross-validation, respectively. We also
investigate the effect of different features on models. The results
demonstrated that the hyperparameter-optimized Random Forest Classifier (oRFC)
and Random Forest Regressor (oRFR) provided better results than other models
for the classification and regression, respectively. Finally, the best test
accuracy of 82.68% for oRFC and R2 of 0.75 for the oRFR are obtained. ML
techniques could contribute to a better understanding of plasma parameters that
have a dominant role in the desired antimicrobial effect. Furthermore, such
findings may contribute to the definition of a plasma dose in the future.

We propose VDL-Surrogate, a view-dependent neural-network-latent-based
surrogate model for parameter space exploration of ensemble simulations that
allows high-resolution visualizations and user-specified visual mappings.
Surrogate-enabled parameter space exploration allows domain scientists to
preview simulation results without having to run a large number of
computationally costly simulations. Limited by computational resources,
however, existing surrogate models may not produce previews with sufficient
resolution for visualization and analysis. To improve the efficient use of
computational resources and support high-resolution exploration, we perform ray
casting from different viewpoints to collect samples and produce compact latent
representations. This latent encoding process reduces the cost of surrogate
model training while maintaining the output quality. In the model training
stage, we select viewpoints to cover the whole viewing sphere and train
corresponding VDL-Surrogate models for the selected viewpoints. In the model
inference stage, we predict the latent representations at previously selected
viewpoints and decode the latent representations to data space. For any given
viewpoint, we make interpolations over decoded data at selected viewpoints and
generate visualizations with user-specified visual mappings. We show the
effectiveness and efficiency of VDL-Surrogate in cosmological and ocean
simulations with quantitative and qualitative evaluations. Source code is
publicly available at https://github.com/trainsn/VDL-Surrogate.

We envision an autonomous sorbent materials foundry (SMF) for rapidly
evaluating materials for direct air capture of carbon dioxide (CO2),
specifically targeting novel metal organic framework materials. Our proposed
SMF is hierarchical, simultaneously addressing the most critical gaps in the
inter-related space of sorbent material synthesis, processing, properties, and
performance. The ability to collect these critical data streams in an agile,
coordinated, and automated fashion will enable efficient end-to-end sorbent
materials design through machine learning driven research framework.

While single-agent policy optimization in a fixed environment has attracted a
lot of research attention recently in the reinforcement learning community,
much less is known theoretically when there are multiple agents playing in a
potentially competitive environment. We take steps forward by proposing and
analyzing new fictitious play policy optimization algorithms for zero-sum
Markov games with structured but unknown transitions. We consider two classes
of transition structures: factored independent transition and single-controller
transition. For both scenarios, we prove tight
$\widetilde{\mathcal{O}}(\sqrt{K})$ regret bounds after $K$ episodes in a
two-agent competitive game scenario. The regret of each agent is measured
against a potentially adversarial opponent who can choose a single best policy
in hindsight after observing the full policy sequence. Our algorithms feature a
combination of Upper Confidence Bound (UCB)-type optimism and fictitious play
under the scope of simultaneous policy optimization in a non-stationary
environment. When both players adopt the proposed algorithms, their overall
optimality gap is $\widetilde{\mathcal{O}}(\sqrt{K})$.

Integrated Development Environments (IDEs) provide tool support to automate
many source code editing tasks. Traditionally, IDEs use only the spatial
context, i.e., the location where the developer is editing, to generate
candidate edit recommendations. However, spatial context alone is often not
sufficient to confidently predict the developer's next edit, and thus IDEs
generate many suggestions at a location. Therefore, IDEs generally do not
actively offer suggestions and instead, the developer is usually required to
click on a specific icon or menu and then select from a large list of potential
suggestions. As a consequence, developers often miss the opportunity to use the
tool support because they are not aware it exists or forget to use it.
  To better understand common patterns in developer behavior and produce better
edit recommendations, we can additionally use the temporal context, i.e., the
edits that a developer was recently performing. To enable edit recommendations
based on temporal context, we present Overwatch, a novel technique for learning
edit sequence patterns from traces of developers' edits performed in an IDE.
Our experiments show that Overwatch has 78% precision and that Overwatch not
only completed edits when developers missed the opportunity to use the IDE tool
support but also predicted new edits that have no tool support in the IDE.

We present an automatic approach to discover analytic population models for
gravitational-wave (GW) events from data. As more gravitational-wave (GW)
events are detected, flexible models such as Gaussian Mixture Models have
become more important in fitting the distribution of GW properties due to their
expressivity. However, flexible models come with many parameters that lack
physical motivation, making interpreting the implication of these models
challenging. In this work, we demonstrate symbolic regression can complement
flexible models by distilling the posterior predictive distribution of such
flexible models into interpretable analytic expressions. We recover common GW
population models such as a power-law-plus-Gaussian, and find a new empirical
population model which combines accuracy and simplicity. This demonstrates a
strategy to automatically discover interpretable population models in the
ever-growing GW catalog, which can potentially be applied to other
astrophysical phenomena.

Discriminating between quark- and gluon-initiated jets has long been a
central focus of jet substructure, leading to the introduction of numerous
observables and calculations to high perturbative accuracy. At the same time,
there have been many attempts to fully exploit the jet radiation pattern using
tools from statistics and machine learning. We propose a new approach that
combines a deep analytic understanding of jet substructure with the optimality
promised by machine learning and statistics. After specifying an approximation
to the full emission phase space, we show how to construct the optimal
observable for a given classification task. This procedure is demonstrated for
the case of quark and gluons jets, where we show how to systematically capture
sub-eikonal corrections in the splitting functions, and prove that linear
combinations of weighted multiplicity is the optimal observable. In addition to
providing a new and powerful framework for systematically improving jet
substructure observables, we demonstrate the performance of several quark
versus gluon jet tagging observables in parton-level Monte Carlo simulations,
and find that they perform at or near the level of a deep neural network
classifier. Combined with the rapid recent progress in the development of
higher order parton showers, we believe that our approach provides a basis for
systematically exploiting subleading effects in jet substructure analyses at
the Large Hadron Collider (LHC) and beyond.

Most existing studies improve the efficiency of Split learning (SL) by
compressing the transmitted features. However, most works focus on
dimension-wise compression that transforms high-dimensional features into a
low-dimensional space. In this paper, we propose circular convolution-based
batch-wise compression for SL (C3-SL) to compress multiple features into one
single feature. To avoid information loss while merging multiple features, we
exploit the quasi-orthogonality of features in high-dimensional space with
circular convolution and superposition. To the best of our knowledge, we are
the first to explore the potential of batch-wise compression under the SL
scenario. Based on the simulation results on CIFAR-10 and CIFAR-100, our method
achieves a 16x compression ratio with negligible accuracy drops compared with
the vanilla SL. Moreover, C3-SL significantly reduces 1152x memory and 2.25x
computation overhead compared to the state-of-the-art dimension-wise
compression method.

Tuning of stochastic gradient algorithms (SGAs) for optimization and sampling
is often based on heuristics and trial-and-error rather than generalizable
theory. We address this theory--practice gap by characterizing the statistical
asymptotics of SGAs via a joint step-size--sample-size scaling limit. We show
that iterate averaging with a large fixed step size is robust to the choice of
tuning parameters and asymptotically has covariance proportional to that of the
MLE sampling distribution. We also prove a Bernstein--von Mises-like theorem to
guide tuning, including for generalized posteriors that are robust to model
misspecification. Numerical experiments validate our results in realistic
finite-sample regimes.

In the recent past, several domain generalization (DG) methods have been
proposed, showing encouraging performance, however, almost all of them build on
convolutional neural networks (CNNs). There is little to no progress on
studying the DG performance of vision transformers (ViTs), which are
challenging the supremacy of CNNs on standard benchmarks, often built on i.i.d
assumption. This renders the real-world deployment of ViTs doubtful. In this
paper, we attempt to explore ViTs towards addressing the DG problem. Similar to
CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit
is overfitting to source domains. Inspired by the modular architecture of ViTs,
we propose a simple DG approach for ViTs, coined as self-distillation for ViTs.
It reduces the overfitting of source domains by easing the learning of
input-output mapping problem through curating non-zero entropy supervisory
signals for intermediate transformer blocks. Further, it does not introduce any
new parameters and can be seamlessly plugged into the modular composition of
different ViTs. We empirically demonstrate notable performance gains with
different DG baselines and various ViT backbones in five challenging datasets.
Moreover, we report favorable performance against recent state-of-the-art DG
methods. Our code along with pre-trained models are publicly available at:
https://github.com/maryam089/SDViT.

Practical real world datasets with plentiful categories introduce new
challenges for unsupervised domain adaptation like small inter-class
discriminability, that existing approaches relying on domain invariance alone
cannot handle sufficiently well. In this work we propose MemSAC, which exploits
sample level similarity across source and target domains to achieve
discriminative transfer, along with architectures that scale to a large number
of categories. For this purpose, we first introduce a memory augmented approach
to efficiently extract pairwise similarity relations between labeled source and
unlabeled target domain instances, suited to handle an arbitrary number of
classes. Next, we propose and theoretically justify a novel variant of the
contrastive loss to promote local consistency among within-class cross domain
samples while enforcing separation between classes, thus preserving
discriminative transfer from source to target. We validate the advantages of
MemSAC with significant improvements over previous state-of-the-art on multiple
challenging transfer tasks designed for large-scale adaptation, such as
DomainNet with 345 classes and fine-grained adaptation on Caltech-UCSD birds
dataset with 200 classes. We also provide in-depth analysis and insights into
the effectiveness of MemSAC.

Cardiovascular diseases (CVDs) are a group of heart and blood vessel
disorders that is one of the most serious dangers to human health, and the
number of such patients is still growing. Early and accurate detection plays a
key role in successful treatment and intervention. Electrocardiogram (ECG) is
the gold standard for identifying a variety of cardiovascular abnormalities. In
clinical practices and most of the current research, standard 12-lead ECG is
mainly used. However, using a lower number of leads can make ECG more prevalent
as it can be conveniently recorded by portable or wearable devices. In this
research, we develop a novel deep learning system to accurately identify
multiple cardiovascular abnormalities by using only three ECG leads.

Deep Learning predictions with measurable confidence are increasingly
desirable for real-world problems, especially in high-risk settings. The
Conformal Prediction (CP) framework is a versatile solution that automatically
guarantees a maximum error rate. However, CP suffers from computational
inefficiencies that limit its application to large-scale datasets. In this
paper, we propose a novel conformal loss function that approximates the
traditionally two-step CP approach in a single step. By evaluating and
penalising deviations from the stringent expected CP output distribution, a
Deep Learning model may learn the direct relationship between input data and
conformal p-values. Our approach achieves significant training time reductions
up to 86% compared to Aggregated Conformal Prediction (ACP), an accepted CP
approximation variant. In terms of approximate validity and predictive
efficiency, we carry out a comprehensive empirical evaluation to show our novel
loss function's competitiveness with ACP for binary and multi-class
classification on the well-established MNIST dataset.

Product-specific guidances (PSGs) recommended by the United States Food and
Drug Administration (FDA) are instrumental to promote and guide generic drug
product development. To assess a PSG, the FDA assessor needs to take extensive
time and effort to manually retrieve supportive drug information of absorption,
distribution, metabolism, and excretion (ADME) from the reference listed drug
labeling. In this work, we leveraged the state-of-the-art pre-trained language
models to automatically label the ADME paragraphs in the pharmacokinetics
section from the FDA-approved drug labeling to facilitate PSG assessment. We
applied a transfer learning approach by fine-tuning the pre-trained
Bidirectional Encoder Representations from Transformers (BERT) model to develop
a novel application of ADME semantic labeling, which can automatically retrieve
ADME paragraphs from drug labeling instead of manual work. We demonstrated that
fine-tuning the pre-trained BERT model can outperform the conventional machine
learning techniques, achieving up to 11.6% absolute F1 improvement. To our
knowledge, we were the first to successfully apply BERT to solve the ADME
semantic labeling task. We further assessed the relative contribution of
pre-training and fine-tuning to the overall performance of the BERT model in
the ADME semantic labeling task using a series of analysis methods such as
attention similarity and layer-based ablations. Our analysis revealed that the
information learned via fine-tuning is focused on task-specific knowledge in
the top layers of the BERT, whereas the benefit from the pre-trained BERT model
is from the bottom layers.

Neural networks are high-dimensional nonlinear dynamical systems that process
information through the coordinated activity of many interconnected units.
Understanding how biological and machine-learning networks function and learn
requires knowledge of the structure of this coordinated activity, information
contained in cross-covariances between units. Although dynamical mean field
theory (DMFT) has elucidated several features of random neural networks -- in
particular, that they can generate chaotic activity -- existing DMFT approaches
do not support the calculation of cross-covariances. We solve this longstanding
problem by extending the DMFT approach via a two-site cavity method. This
reveals, for the first time, several spatial and temporal features of activity
coordination, including the effective dimension, defined as the participation
ratio of the spectrum of the covariance matrix. Our results provide a general
analytical framework for studying the structure of collective activity in
random neural networks and, more broadly, in high-dimensional nonlinear
dynamical systems with quenched disorder.

Open Radio Access Network (RAN) architectures will enable interoperability,
openness and programmable data-driven control in next generation cellular
networks. However, developing and testing efficient solutions that generalize
across heterogeneous cellular deployments and scales, and that optimize network
performance in such diverse environments is a complex task that is still
largely unexplored. In this paper we present OpenRAN Gym, a unified, open, and
O-RAN-compliant experimental toolbox for data collection, design, prototyping
and testing of end-to-end data-driven control solutions for next generation
Open RAN systems. OpenRAN Gym extends and combines into a unique solution
several software frameworks for data collection of RAN statistics and RAN
control, and a lightweight O-RAN near-real-time RAN Intelligent Controller
(RIC) tailored to run on experimental wireless platforms. We first provide an
overview of the various architectural components of OpenRAN Gym and describe
how it is used to collect data and design, train and test artificial
intelligence and machine learning O-RAN-compliant applications (xApps) at
scale. We then describe in detail how to test the developed xApps on
softwarized RANs and provide an example of two xApps developed with OpenRAN Gym
that are used to control a network with 7 base stations and 42 users deployed
on the Colosseum testbed. Finally, we show how solutions developed with OpenRAN
Gym on Colosseum can be exported to real-world, heterogeneous wireless
platforms, such as the Arena testbed and the POWDER and COSMOS platforms of the
PAWR program. OpenRAN Gym and its software components are open-source and
publicly-available to the research community.

We revisit the classic signal-to-symbol barrier in light of the remarkable
ability of deep neural networks to generate realistic synthetic data. DeepFakes
and spoofing highlight the feebleness of the link between physical reality and
its abstract representation, whether learned by a digital computer or a
biological agent. Starting from a widely applicable definition of abstract
concept, we show that standard feed-forward architectures cannot capture but
trivial concepts, regardless of the number of weights and the amount of
training data, despite being extremely effective classifiers. On the other
hand, architectures that incorporate recursion can represent a significantly
larger class of concepts, but may still be unable to learn them from a finite
dataset. We qualitatively describe the class of concepts that can be
"understood" by modern architectures trained with variants of stochastic
gradient descent, using a (free energy) Lagrangian to measure information
complexity. Even if a concept has been understood, however, a network has no
means of communicating its understanding to an external agent, except through
continuous interaction and validation. We then characterize physical objects as
abstract concepts and use the previous analysis to show that physical objects
can be encoded by finite architectures. However, to understand physical
concepts, sensors must provide persistently exciting observations, for which
the ability to control the data acquisition process is essential (active
perception). The importance of control depends on the modality, benefiting
visual more than acoustic or chemical perception. Finally, we conclude that
binding physical entities to digital identities is possible in finite time with
finite resources, solving in principle the signal-to-symbol barrier problem,
but we highlight the need for continuous validation.

In this paper we explore cyber security defence, through the unification of a
novel cyber security simulator with models for (causal) decision-making through
optimisation. Particular attention is paid to a recently published approach:
dynamic causal Bayesian optimisation (DCBO). We propose that DCBO can act as a
blue agent when provided with a view of a simulated network and a causal model
of how a red agent spreads within that network. To investigate how DCBO can
perform optimal interventions on host nodes, in order to reduce the cost of
intrusions caused by the red agent. Through this we demonstrate a complete
cyber-simulation system, which we use to generate observational data for DCBO
and provide numerical quantitative results which lay the foundations for future
work in this space.

Deep Neural Networks (DNNs) are being heavily utilized in modern applications
and are putting energy-constraint devices to the test. To bypass high energy
consumption issues, approximate computing has been employed in DNN accelerators
to balance out the accuracy-energy reduction trade-off. However, the
approximation-induced accuracy loss can be very high and drastically degrade
the performance of the DNN. Therefore, there is a need for a fine-grain
mechanism that would assign specific DNN operations to approximation in order
to maintain acceptable DNN accuracy, while also achieving low energy
consumption. In this paper, we present an automated framework for
weight-to-approximation mapping enabling formal property exploration for
approximate DNN accelerators. At the MAC unit level, our experimental
evaluation surpassed already energy-efficient mappings by more than $\times2$
in terms of energy gains, while also supporting significantly more fine-grain
control over the introduced approximation.

Model agnostic meta-learning algorithms aim to infer priors from several
observed tasks that can then be used to adapt to a new task with few examples.
Given the inherent diversity of tasks arising in existing benchmarks, recent
methods use separate, learnable structure, such as hierarchies or graphs, for
enabling task-specific adaptation of the prior. While these approaches have
produced significantly better meta learners, our goal is to improve their
performance when the heterogeneous task distribution contains challenging
distribution shifts and semantic disparities. To this end, we introduce CAML
(Contrastive Knowledge-Augmented Meta Learning), a novel approach for
knowledge-enhanced few-shot learning that evolves a knowledge graph to
effectively encode historical experience, and employs a contrastive
distillation strategy to leverage the encoded knowledge for task-aware
modulation of the base learner. Using standard benchmarks, we evaluate the
performance of CAML in different few-shot learning scenarios. In addition to
the standard few-shot task adaptation, we also consider the more challenging
multi-domain task adaptation and few-shot dataset generalization settings in
our empirical studies. Our results shows that CAML consistently outperforms
best known approaches and achieves improved generalization.

As one of the most sophisticated attacks against power grids, coordinated
cyber-physical attacks (CCPAs) damage the power grid's physical infrastructure
and use a simultaneous cyber attack to mask its effect. This work proposes a
novel approach to detect such attacks and identify the location of the line
outages (due to the physical attack). The proposed approach consists of three
parts. Firstly, moving target defense (MTD) is applied to expose the physical
attack by actively perturbing transmission line reactance via distributed
flexible AC transmission system (D-FACTS) devices. MTD invalidates the
attackers' knowledge required to mask their physical attack. Secondly,
convolution neural networks (CNNs) are applied to localize line outage position
from the compromised measurements. Finally, model agnostic meta-learning (MAML)
is used to accelerate the training speed of CNN following the topology
reconfigurations (due to MTD) and reduce the data/retraining time requirements.
Simulations are carried out using IEEE test systems. The experimental results
demonstrate that the proposed approach can effectively localize line outages in
stealthy CCPAs.

We develop a machine learning algorithm to infer the 3D cumulative radial
profiles of total and gas mass in galaxy clusters from thermal
Sunyaev-Zel'dovich effect (SZ) maps. By using 2,522 simulated clusters from the
\thethreehundred{} project at redshift $z< 0.12$, we generate more than 73,000
mock images along several lines of sight and train a model that is a
combination of an autoencoder and a random forest. The model is able to
reconstruct the 3D gas mass profile, responsible for the SZ effect, but also
the total mass one, including the contribution of the dark matter component,
without any a priori assumption on the physics of the clusters. We show that
the recovered total and gas mass radial profiles are unbiased with a scatter of
about $10\%$, slightly increasing towards the core and the outskirts of the
cluster. We selected clusters in a wide mass range, $10^{13.5} \leq M_{200}
/(\hMsun) \leq 10^{15.5}$ and spanning different dynamical states, from very
relaxed to very disturbed halos. We verify that both the accuracy and precision
of this method show a slight dependence on the dynamical state, but not on the
cluster mass. To further check the consistency of our model, we fit the
inferred total mass profiles with a NFW analytical model and compare the
resulting concentration values with those of the true profiles. We observe that
for higher values of the concentration, the inferred profiles are estimated
without bias, reproducing a reliable mass-concentration relation. Finally, the
comparison with a more standard mass estimate method, such as the hydrostatic
equilibrium, shows that our technique recovers the total mass that is not
affected by bias due to the non thermal motions of the gas.

Neurons can code for multiple variables simultaneously and neuroscientists
are often interested in classifying neurons based on their receptive field
properties. Statistical models provide powerful tools for determining the
factors influencing neural spiking activity and classifying individual neurons.
However, as neural recording technologies have advanced to produce simultaneous
spiking data from massive populations, classical statistical methods often lack
the computational efficiency required to handle such data. Machine learning
(ML) approaches are known for enabling efficient large scale data analyses;
however, they typically require massive training sets with balanced data, along
with accurate labels to fit well. Additionally, model assessment and
interpretation are often more challenging for ML than for classical statistical
methods. To address these challenges, we develop an integrated framework,
combining statistical modeling and machine learning approaches to identify the
coding properties of neurons from large populations. In order to demonstrate
this framework, we apply these methods to data from a population of neurons
recorded from rat hippocampus to characterize the distribution of spatial
receptive fields in this region.

Due to the distributed nature of Federated Learning (FL), researchers have
uncovered that FL is vulnerable to backdoor attacks, which aim at injecting a
sub-task into the FL without corrupting the performance of the main task.
Single-shot backdoor attack achieves high accuracy on both the main task and
backdoor sub-task when injected at the FL model convergence. However, the
early-injected single-shot backdoor attack is ineffective because: (1) the
maximum backdoor effectiveness is not reached at injection because of the
dilution effect from normal local updates; (2) the backdoor effect decreases
quickly as the backdoor will be overwritten by the newcoming normal local
updates. In this paper, we strengthen the early-injected single-shot backdoor
attack utilizing FL model information leakage. We show that the FL convergence
can be expedited if the client trains on a dataset that mimics the distribution
and gradients of the whole population. Based on this observation, we proposed a
two-phase backdoor attack, which includes a preliminary phase for the
subsequent backdoor attack. In the preliminary phase, the attacker-controlled
client first launches a whole population distribution inference attack and then
trains on a locally crafted dataset that is aligned with both the gradient and
inferred distribution. Benefiting from the preliminary phase, the later
injected backdoor achieves better effectiveness as the backdoor effect will be
less likely to be diluted by the normal model updates. Extensive experiments
are conducted on MNIST dataset under various data heterogeneity settings to
evaluate the effectiveness of the proposed backdoor attack. Results show that
the proposed backdoor outperforms existing backdoor attacks in both success
rate and longevity, even when defense mechanisms are in place.

We propose a stable, parallel approach to train Wasserstein Conditional
Generative Adversarial Neural Networks (W-CGANs) under the constraint of a
fixed computational budget. Differently from previous distributed GANs training
techniques, our approach avoids inter-process communications, reduces the risk
of mode collapse and enhances scalability by using multiple generators, each
one of them concurrently trained on a single data label. The use of the
Wasserstein metric also reduces the risk of cycling by stabilizing the training
of each generator. We illustrate the approach on the CIFAR10, CIFAR100, and
ImageNet1k datasets, three standard benchmark image datasets, maintaining the
original resolution of the images for each dataset. Performance is assessed in
terms of scalability and final accuracy within a limited fixed computational
time and computational resources. To measure accuracy, we use the inception
score, the Frechet inception distance, and image quality. An improvement in
inception score and Frechet inception distance is shown in comparison to
previous results obtained by performing the parallel approach on deep
convolutional conditional generative adversarial neural networks (DC-CGANs) as
well as an improvement of image quality of the new images created by the GANs
approach. Weak scaling is attained on both datasets using up to 2,000 NVIDIA
V100 GPUs on the OLCF supercomputer Summit.

Artificial Intelligence has enabled the implementation of more accurate and
efficient solutions to problems in various areas. In the agricultural sector,
one of the main needs is to know at all times the extent of land occupied or
not by crops in order to improve production and profitability. The traditional
methods of calculation demand the collection of data manually and in person in
the field, causing high labor costs, execution times, and inaccuracy in the
results. The present work proposes a new method based on Deep Learning
techniques complemented with conventional programming for the determination of
the area of populated and unpopulated crop areas. We have considered as a case
study one of the most recognized companies in the planting and harvesting of
sugar cane in Ecuador. The strategy combines a Generative Adversarial Neural
Network (GAN) that is trained on a dataset of aerial photographs of natural and
urban landscapes to improve image resolution; a Convolutional Neural Network
(CNN) trained on a dataset of aerial photographs of sugar cane plots to
distinguish populated or unpopulated crop areas; and a standard image
processing module for the calculation of areas in a percentage manner. The
experiments performed demonstrate a significant improvement in the quality of
the aerial photographs as well as a remarkable differentiation between
populated and unpopulated crop areas, consequently, a more accurate result of
cultivated and uncultivated areas. The proposed method can be extended to the
detection of possible pests, areas of weed vegetation, dynamic crop
development, and both qualitative and quantitative quality control.

Typical methods for blind image super-resolution (SR) focus on dealing with
unknown degradations by directly estimating them or learning the degradation
representations in a latent space. A potential limitation of these methods is
that they assume the unknown degradations can be simulated by the integration
of various handcrafted degradations (e.g., bicubic downsampling), which is not
necessarily true. The real-world degradations can be beyond the simulation
scope by the handcrafted degradations, which are referred to as novel
degradations. In this work, we propose to learn a latent representation space
for degradations, which can be generalized from handcrafted (base) degradations
to novel degradations. The obtained representations for a novel degradation in
this latent space are then leveraged to generate degraded images consistent
with the novel degradation to compose paired training data for SR model.
Furthermore, we perform variational inference to match the posterior of
degradations in latent representation space with a prior distribution (e.g.,
Gaussian distribution). Consequently, we are able to sample more high-quality
representations for a novel degradation to augment the training data for SR
model. We conduct extensive experiments on both synthetic and real-world
datasets to validate the effectiveness and advantages of our method for blind
super-resolution with novel degradations.

SFILES is a text-based notation for chemical process flowsheets. It was
originally proposed by d'Anterroches (2006) who was inspired by the text-based
SMILES notation for molecules. The text-based format has several advantages
compared to flowsheet images regarding the storage format, computational
accessibility, and eventually for data analysis and processing. However, the
original SFILES version cannot describe essential flowsheet configurations
unambiguously, such as the distinction between top and bottom products. Neither
is it capable of describing the control structure required for the safe and
reliable operation of chemical processes. Also, there is no publicly available
software for decoding or encoding chemical process topologies to SFILES. We
propose the SFILES 2.0 with a complete description of the extended notation and
naming conventions. Additionally, we provide open-source software for the
automated conversion between flowsheet graphs and SFILES 2.0 strings. This way,
we hope to encourage researchers and engineers to publish their flowsheet
topologies as SFILES 2.0 strings. The ultimate goal is to set the standards for
creating a FAIR database of chemical process flowsheets, which would be of
great value for future data analysis and processing.

Despite recent advances in natural language understanding and generation, and
decades of research on the development of conversational bots, building
automated agents that can carry on rich open-ended conversations with humans
"in the wild" remains a formidable challenge. In this work we develop a
real-time, open-ended dialogue system that uses reinforcement learning (RL) to
power a bot's conversational skill at scale. Our work pairs the succinct
embedding of the conversation state generated using SOTA (supervised) language
models with RL techniques that are particularly suited to a dynamic action
space that changes as the conversation progresses. Trained using crowd-sourced
data, our novel system is able to substantially exceeds the (strong) baseline
supervised model with respect to several metrics of interest in a live
experiment with real users of the Google Assistant.

The availability of large labeled datasets is the key component for the
success of deep learning. However, annotating labels on large datasets is
generally time-consuming and expensive. Active learning is a research area that
addresses the issues of expensive labeling by selecting the most important
samples for labeling. Diversity-based sampling algorithms are known as integral
components of representation-based approaches for active learning. In this
paper, we introduce a new diversity-based initial dataset selection algorithm
to select the most informative set of samples for initial labeling in the
active learning setting. Self-supervised representation learning is used to
consider the diversity of samples in the initial dataset selection algorithm.
Also, we propose a novel active learning query strategy, which uses
diversity-based sampling on consistency-based embeddings. By considering the
consistency information with the diversity in the consistency-based embedding
scheme, the proposed method could select more informative samples for labeling
in the semi-supervised learning setting. Comparative experiments show that the
proposed method achieves compelling results on CIFAR-10 and Caltech-101
datasets compared with previous active learning approaches by utilizing the
diversity of unlabeled data.

The COVID-19 pandemic has caused devastating economic and social disruption,
straining the resources of healthcare institutions worldwide. This has led to a
nationwide call for models to predict hospitalization and severe illness in
patients with COVID-19 to inform distribution of limited healthcare resources.
We respond to one of these calls specific to the pediatric population. To
address this challenge, we study two prediction tasks for the pediatric
population using electronic health records: 1) predicting which children are
more likely to be hospitalized, and 2) among hospitalized children, which
individuals are more likely to develop severe symptoms.
  We respond to the national Pediatric COVID-19 data challenge with a novel
machine learning model, MedML. MedML extracts the most predictive features
based on medical knowledge and propensity scores from over 6 million medical
concepts and incorporates the inter-feature relationships between heterogeneous
medical features via graph neural networks (GNN). We evaluate MedML across
143,605 patients for the hospitalization prediction task and 11,465 patients
for the severity prediction task using data from the National Cohort
Collaborative (N3C) dataset. We also report detailed group-level and
individual-level feature importance analyses to evaluate the model
  MedML achieves up to a 7% higher AUROC score and up to a 14% higher AUPRC
score compared to the best baseline machine learning models and performs well
across all nine national geographic regions and over all three-month spans
since the start of the pandemic. Our cross-disciplinary research team has
developed a method of incorporating clinical domain knowledge as the framework
for a new type of machine learning model that is more predictive and
explainable than current state-of-the-art data-driven feature selection
methods.

The field of neural style transfer has experienced a surge of research
exploring different avenues ranging from optimization-based approaches and
feed-forward models to meta-learning methods. The developed techniques have not
just progressed the field of style transfer, but also led to breakthroughs in
other areas of computer vision, such as all of visual synthesis. However,
whereas quantitative evaluation and benchmarking have become pillars of
computer vision research, the reproducible, quantitative assessment of style
transfer models is still lacking. Even in comparison to other fields of visual
synthesis, where widely used metrics exist, the quantitative evaluation of
style transfer is still lagging behind. To support the automatic comparison of
different style transfer approaches and to study their respective strengths and
weaknesses, the field would greatly benefit from a quantitative measurement of
stylization performance. Therefore, we propose a method to complement the
currently mostly qualitative evaluation schemes. We provide extensive
evaluations and a large-scale user study to show that the proposed metric
strongly coincides with human judgment.

In this paper we propose an adaptive approach for clustering and
visualization of data by an orthogonalization process. Starting with the data
points being represented by a Markov process using the diffusion map framework,
the method adaptively increase the orthogonality of the clusters by applying a
feedback mechanism inspired by the Gromov-Wasserstein distance. This mechanism
iteratively increases the spectral gap and refines the orthogonality of the
data to achieve a clustering with high specificity. By using the diffusion map
framework and representing the relation between data points using transition
probabilities, the method is robust with respect to both the underlying
distance, noise in the data and random initialization. We prove that the method
converges globally to a unique fixpoint for certain parameter values. We also
propose a related approach where the transition probabilities in the Markov
process are required to be doubly stochastic, in which case the method
generates a minimizer to a nonconvex optimization problem. We apply the method
on cryo-electron microscopy image data from biopharmaceutical manufacturing
where we can confirm biologically relevant insights related to therapeutic
efficacy. We consider an example with morphological variations of gene
packaging and confirm that the method produces biologically meaningful
clustering results consistent with human expert classification.

Estimating uncertainties associated with the predictions of Machine Learning
(ML) models is of crucial importance to assess their robustness and predictive
power. In this submission, we introduce MAPIE (Model Agnostic Prediction
Interval Estimator), an open-source Python library that quantifies the
uncertainties of ML models for single-output regression and multi-class
classification tasks. MAPIE implements conformal prediction methods, allowing
the user to easily compute uncertainties with strong theoretical guarantees on
the marginal coverages and with mild assumptions on the model or on the
underlying data distribution. MAPIE is hosted on scikit-learn-contrib and is
fully "scikit-learn-compatible". As such, it accepts any type of regressor or
classifier coming with a scikit-learn API. The library is available at:
https://github.com/scikit-learn-contrib/MAPIE/.

Human-in-the-loop approaches are of great importance for robot applications.
In the presented study, we implemented a multimodal human-robot interaction
(HRI) scenario, in which a simulated robot communicates with its human partner
through speech and gestures. The robot announces its intention verbally and
selects the appropriate action using pointing gestures. The human partner, in
turn, evaluates whether the robot's verbal announcement (intention) matches the
action (pointing gesture) chosen by the robot. For cases where the verbal
announcement of the robot does not match the corresponding action choice of the
robot, we expect error-related potentials (ErrPs) in the human
electroencephalogram (EEG). These intrinsic evaluations of robot actions by
humans, evident in the EEG, were recorded in real time, continuously segmented
online and classified asynchronously. For feature selection, we propose an
approach that allows the combinations of forward and backward sliding windows
to train a classifier. We achieved an average classification performance of 91%
across 9 subjects. As expected, we also observed a relatively high variability
between the subjects. In the future, the proposed feature selection approach
will be extended to allow for customization of feature selection. To this end,
the best combinations of forward and backward sliding windows will be
automatically selected to account for inter-subject variability in
classification performance. In addition, we plan to use the intrinsic human
error evaluation evident in the error case by the ErrP in interactive
reinforcement learning to improve multimodal human-robot interaction.

The debate around misinformation and its potentially detrimental effects on
public opinion is complex and multifaceted, to the extent that even the
relevant academic research has not found unanimity on the prevalence and
consumption of misinformation compared with mainstream content. The
methodological framework presented here emphasises the importance of
considering data representative of the complexity of the phenomenon and metrics
that control for possible scale effects. By combining statistical, econometric
and machine learning models, we shed light on the real impact of misinformation
about a subject of general interest and social relevance, such as vaccines, on
both the information available to citizens and their news diet. Our results
show the prominent role achieved by misinformation sources in the news
ecosystem, but also - and above all - the inability of mainstream media to
drive the public debate over time on issues that are particularly sensitive and
emotional. Taking properly account for the temporal dynamics of public debate
seems crucial to prevent the latter from moving into uncontrolled spaces where
false narratives are more easily conveyed and entrenched.

Transfer learning through the use of pre-trained models has become a growing
trend for the machine learning community. Consequently, numerous pre-trained
models are released online to facilitate further research. However, it raises
extensive concerns on whether these pre-trained models would leak
privacy-sensitive information of their training data. Thus, in this work, we
aim to answer the following questions: "Can we effectively recover private
information from these pre-trained models? What are the sufficient conditions
to retrieve such sensitive information?" We first explore different statistical
information which can discriminate the private training distribution from other
distributions. Based on our observations, we propose a novel private data
reconstruction framework, SecretGen, to effectively recover private
information. Compared with previous methods which can recover private data with
the ground true prediction of the targeted recovery instance, SecretGen does
not require such prior knowledge, making it more practical. We conduct
extensive experiments on different datasets under diverse scenarios to compare
SecretGen with other baselines and provide a systematic benchmark to better
understand the impact of different auxiliary information and optimization
operations. We show that without prior knowledge about true class prediction,
SecretGen is able to recover private data with similar performance compared
with the ones that leverage such prior knowledge. If the prior knowledge is
given, SecretGen will significantly outperform baseline methods. We also
propose several quantitative metrics to further quantify the privacy
vulnerability of pre-trained models, which will help the model selection for
privacy-sensitive applications. Our code is available at:
https://github.com/AI-secure/SecretGen.

Sequence-to-Sequence Text-to-Speech architectures that directly generate low
level acoustic features from phonetic sequences are known to produce natural
and expressive speech when provided with adequate amounts of training data.
Such systems can learn and transfer desired speaking styles from one seen
speaker to another (in multi-style multi-speaker settings), which is highly
desirable for creating scalable and customizable Human-Computer Interaction
systems. In this work we explore one-to-many style transfer from a dedicated
single-speaker conversational corpus with style nuances and interjections. We
elaborate on the corpus design and explore the feasibility of such style
transfer when assisted with Voice-Conversion-based data augmentation. In a set
of subjective listening experiments, this approach resulted in high-fidelity
style transfer with no quality degradation. However, a certain voice persona
shift was observed, requiring further improvements in voice conversion.

Powder-based additive manufacturing has transformed the manufacturing
industry over the last decade. In Laser Powder Bed Fusion, a specific part is
built in an iterative manner in which two-dimensional cross-sections are formed
on top of each other by melting and fusing the proper areas of the powder bed.
In this process, the behavior of the melt pool and its thermal field has a very
important role in predicting the quality of the manufactured part and its
possible defects. However, the simulation of such a complex phenomenon is
usually very time-consuming and requires huge computational resources. Flow-3D
is one of the software packages capable of executing such simulations using
iterative numerical solvers. In this work, we create three datasets of
single-trail processes using Flow-3D and use them to train a convolutional
neural network capable of predicting the behavior of the three-dimensional
thermal field of the melt pool solely by taking three parameters as input:
laser power, laser velocity, and time step. The CNN achieves a relative Root
Mean Squared Error of 2% to 3% for the temperature field and an average
Intersection over Union score of 80% to 90% in predicting the melt pool area.
Moreover, since time is included as one of the inputs of the model, the thermal
field can be instantly obtained for any arbitrary time step without the need to
iterate and compute all the steps

We are interested in learning robust models from insufficient data, without
the need for any externally pre-trained checkpoints. First, compared to
sufficient data, we show why insufficient data renders the model more easily
biased to the limited training environments that are usually different from
testing. For example, if all the training swan samples are "white", the model
may wrongly use the "white" environment to represent the intrinsic class swan.
Then, we justify that equivariance inductive bias can retain the class feature
while invariance inductive bias can remove the environmental feature, leaving
the class feature that generalizes to any environmental changes in testing. To
impose them on learning, for equivariance, we demonstrate that any
off-the-shelf contrastive-based self-supervised feature learning method can be
deployed; for invariance, we propose a class-wise invariant risk minimization
(IRM) that efficiently tackles the challenge of missing environmental
annotation in conventional IRM. State-of-the-art experimental results on
real-world benchmarks (VIPriors, ImageNet100 and NICO) validate the great
potential of equivariance and invariance in data-efficient learning. The code
is available at https://github.com/Wangt-CN/EqInv

A hallmark of human intelligence is the ability to construct self-contained
chunks of knowledge and reuse them in novel combinations for solving different
problems. Learning such compositional structures has been a challenge for
artificial systems, due to the underlying combinatorial search. To date,
research into compositional learning has largely proceeded separately from work
on lifelong or continual learning. This dissertation integrated these two lines
of work to present a general-purpose framework for lifelong learning of
functionally compositional structures. The framework separates the learning
into two stages: learning how to combine existing components to assimilate a
novel problem, and learning how to adapt the existing components to accommodate
the new problem. This separation explicitly handles the trade-off between
stability and flexibility. This dissertation instantiated the framework into
various supervised and reinforcement learning (RL) algorithms. Supervised
learning evaluations found that 1) compositional models improve lifelong
learning of diverse tasks, 2) the multi-stage process permits lifelong learning
of compositional knowledge, and 3) the components learned by the framework
represent self-contained and reusable functions. Similar RL evaluations
demonstrated that 1) algorithms under the framework accelerate the discovery of
high-performing policies, and 2) these algorithms retain or improve performance
on previously learned tasks. The dissertation extended one lifelong
compositional RL algorithm to the nonstationary setting, where the task
distribution varies over time, and found that modularity permits individually
tracking changes to different elements in the environment. The final
contribution of this dissertation was a new benchmark for compositional RL,
which exposed that existing methods struggle to discover the compositional
properties of the environment.

While the keypoint-based maps created by sparse monocular simultaneous
localisation and mapping (SLAM) systems are useful for camera tracking, dense
3D reconstructions may be desired for many robotic tasks. Solutions involving
depth cameras are limited in range and to indoor spaces, and dense
reconstruction systems based on minimising the photometric error between frames
are typically poorly constrained and suffer from scale ambiguity. To address
these issues, we propose a 3D reconstruction system that leverages the output
of a convolutional neural network (CNN) to produce fully dense depth maps for
keyframes that include metric scale.
  Our system, DeepFusion, is capable of producing real-time dense
reconstructions on a GPU. It fuses the output of a semi-dense multiview stereo
algorithm with the depth and gradient predictions of a CNN in a probabilistic
fashion, using learned uncertainties produced by the network. While the network
only needs to be run once per keyframe, we are able to optimise for the depth
map with each new frame so as to constantly make use of new geometric
constraints. Based on its performance on synthetic and real-world datasets, we
demonstrate that DeepFusion is capable of performing at least as well as other
comparable systems.

While there have been increased researches using deep learning techniques for
the extraction of vascular structure from the 2D en face OCTA, for such
approach, it is known that the data annotation process on the curvilinear
structure like the retinal vasculature is very costly and time consuming,
albeit few tried to address the annotation problem.
  In this work, we propose the application of the scribble-base
weakly-supervised learning method to automate the pixel-level annotation. The
proposed method, called OCTAve, combines the weakly-supervised learning using
scribble-annotated ground truth augmented with an adversarial and a novel
self-supervised deep supervision. Our novel mechanism is designed to utilize
the discriminative outputs from the discrimination layer of a UNet-like
architecture where the Kullback-Liebler Divergence between the aggregate
discriminative outputs and the segmentation map predicate is minimized during
the training. This combined method leads to the better localization of the
vascular structure as shown in our experiments. We validate our proposed method
on the large public datasets i.e., ROSE, OCTA-500. The segmentation performance
is compared against both state-of-the-art fully-supervised and scribble-based
weakly-supervised approaches. The implementation of our work used in the
experiments is located at [LINK].

Developing semi-supervised task-oriented dialog (TOD) systems by leveraging
unlabeled dialog data has attracted increasing interests. For semi-supervised
learning of latent state TOD models, variational learning is often used, but
suffers from the annoying high-variance of the gradients propagated through
discrete latent variables and the drawback of indirectly optimizing the target
log-likelihood. Recently, an alternative algorithm, called joint stochastic
approximation (JSA), has emerged for learning discrete latent variable models
with impressive performances. In this paper, we propose to apply JSA to
semi-supervised learning of the latent state TOD models, which is referred to
as JSA-TOD. To our knowledge, JSA-TOD represents the first work in developing
JSA based semi-supervised learning of discrete latent variable conditional
models for such long sequential generation problems like in TOD systems.
Extensive experiments show that JSA-TOD significantly outperforms its
variational learning counterpart. Remarkably, semi-supervised JSA-TOD using 20%
labels performs close to the full-supervised baseline on MultiWOZ2.1.

Robots applied in therapeutic scenarios, for instance in the therapy of
individuals with Autism Spectrum Disorder, are sometimes used for imitation
learning activities in which a person needs to repeat motions by the robot. To
simplify the task of incorporating new types of motions that a robot can
perform, it is desirable that the robot has the ability to learn motions by
observing demonstrations from a human, such as a therapist. In this paper, we
investigate an approach for acquiring motions from skeleton observations of a
human, which are collected by a robot-centric RGB-D camera. Given a sequence of
observations of various joints, the joint positions are mapped to match the
configuration of a robot before being executed by a PID position controller. We
evaluate the method, in particular the reproduction error, by performing a
study with QTrobot in which the robot acquired different upper-body dance moves
from multiple participants. The results indicate the method's overall
feasibility, but also indicate that the reproduction quality is affected by
noise in the skeleton observations.

Q-learning has long been one of the most popular reinforcement learning
algorithms, and theoretical analysis of Q-learning has been an active research
topic for decades. Although researches on asymptotic convergence analysis of
Q-learning have a long tradition, non-asymptotic convergence has only recently
come under active study. The main goal of this paper is to investigate new
finite-time analysis of asynchronous Q-learning under Markovian observation
models via a control system viewpoint. In particular, we introduce a
discrete-time time-varying switching system model of Q-learning with
diminishing step-sizes for our analysis, which significantly improves recent
development of the switching system analysis with constant step-sizes, and
leads to \(\mathcal{O}\left( \sqrt{\frac{\log k}{k}} \right)\) convergence rate
that is comparable to or better than most of the state of the art results in
the literature. In the mean while, a technique using the similarly
transformation is newly applied to avoid the difficulty in the analysis posed
by diminishing step-sizes. The proposed analysis brings in additional insights,
covers different scenarios, and provides new simplified templates for analysis
to deepen our understanding on Q-learning via its unique connection to
discrete-time switching systems.

Dimensionality reduction techniques aim at representing high-dimensional data
in low-dimensional spaces to extract hidden and useful information or
facilitate visual understanding and interpretation of the data. However, few of
them take into consideration the potential cluster information contained
implicitly in the high-dimensional data. In this paper, we propose LaptSNE, a
new graph-layout nonlinear dimensionality reduction method based on t-SNE, one
of the best techniques for visualizing high-dimensional data as 2D scatter
plots. Specifically, LaptSNE leverages the eigenvalue information of the graph
Laplacian to shrink the potential clusters in the low-dimensional embedding
when learning to preserve the local and global structure from high-dimensional
space to low-dimensional space. It is nontrivial to solve the proposed model
because the eigenvalues of normalized symmetric Laplacian are functions of the
decision variable. We provide a majorization-minimization algorithm with
convergence guarantee to solve the optimization problem of LaptSNE and show how
to calculate the gradient analytically, which may be of broad interest when
considering optimization with Laplacian-composited objective. We evaluate our
method by a formal comparison with state-of-the-art methods on seven benchmark
datasets, both visually and via established quantitative measurements. The
results demonstrate the superiority of our method over baselines such as t-SNE
and UMAP. We also provide out-of-sample extension, large-scale extension and
mini-batch extension for our LaptSNE to facilitate dimensionality reduction in
various scenarios.

Subsequence anomaly detection in long sequences is an important problem with
applications in a wide range of domains. However, the approaches proposed so
far in the literature have severe limitations: they either require prior domain
knowledge used to design the anomaly discovery algorithms, or become cumbersome
and expensive to use in situations with recurrent anomalies of the same type.
In this work, we address these problems, and propose an unsupervised method
suitable for domain agnostic subsequence anomaly detection. Our method,
Series2Graph, is based on a graph representation of a novel low-dimensionality
embedding of subsequences. Series2Graph needs neither labeled instances (like
supervised techniques) nor anomaly-free data (like zero-positive learning
techniques), and identifies anomalies of varying lengths. The experimental
results, on the largest set of synthetic and real datasets used to date,
demonstrate that the proposed approach correctly identifies single and
recurrent anomalies without any prior knowledge of their characteristics,
outperforming by a large margin several competing approaches in accuracy, while
being up to orders of magnitude faster. This paper has appeared in VLDB 2020.

With the increasing penetration of distributed energy resources (DERs) in
distribution grids, their impact on grid operations can no longer be ignored.
However, the individual control of these increasingly ubiquitous devices
remains a challenge due to their numbers. One solution is to control them in
groups via virtual power plants. Previous work has typically focused on
optimally dispatching a fixed set of DERs, eschewing methods for optimally
determining set membership. Intuitively, these fixed sets may not be optimal
from a control perspective. Here, we propose a method to cluster the DERs based
on a proxy for their covariances, with the goal of minimising the maximum
variance across all DER clusters. This method does not require the enumeration
and evaluation of all DER combinations, which are required for brute force
techniques. Simulation results show that while there is typically a loss in
optimality, it is generally small. More importantly, computational tractability
is greatly improved when compared to other methods, which require some form of
enumeration and evaluation of the DER combinations.

Deep neural networks (DNNs) are found to be vulnerable to adversarial noise.
They are typically misled by adversarial samples to make wrong predictions. To
alleviate this negative effect, in this paper, we investigate the dependence
between outputs of the target model and input adversarial samples from the
perspective of information theory, and propose an adversarial defense method.
Specifically, we first measure the dependence by estimating the mutual
information (MI) between outputs and the natural patterns of inputs (called
natural MI) and MI between outputs and the adversarial patterns of inputs
(called adversarial MI), respectively. We find that adversarial samples usually
have larger adversarial MI and smaller natural MI compared with those w.r.t.
natural samples. Motivated by this observation, we propose to enhance the
adversarial robustness by maximizing the natural MI and minimizing the
adversarial MI during the training process. In this way, the target model is
expected to pay more attention to the natural pattern that contains objective
semantics. Empirical evaluations demonstrate that our method could effectively
improve the adversarial accuracy against multiple attacks.

Multiple object tracking (MOT) is an important technology in the field of
computer vision, which is widely used in automatic driving, intelligent
monitoring, behavior recognition and other directions. Among the current
popular MOT methods based on deep learning, Detection Based Tracking (DBT) is
the most widely used in industry, and the performance of them depend on their
object detection network. At present, the DBT algorithm with good performance
and the most widely used is YOLOv5-DeepSORT. Inspired by YOLOv5-DeepSORT, with
the proposal of YOLOv7 network, which performs better in object detection, we
apply YOLOv7 as the object detection part to the DeepSORT, and propose
YOLOv7-DeepSORT. After experimental evaluation, compared with the previous
YOLOv5-DeepSORT, YOLOv7-DeepSORT performances better in tracking accuracy.

Unsupervised time series anomaly detection is instrumental in monitoring and
alarming potential faults of target systems in various domains. Current
state-of-the-art time series anomaly detectors mainly focus on devising
advanced neural network structures and new reconstruction/prediction learning
objectives to learn data normality (normal patterns and behaviors) as
accurately as possible. However, these one-class learning methods can be
deceived by unknown anomalies in the training data (i.e., anomaly
contamination). Further, their normality learning also lacks knowledge about
the anomalies of interest. Consequently, they often learn a biased, inaccurate
normality boundary. This paper proposes a novel one-class learning approach,
named calibrated one-class classification, to tackle this problem. Our
one-class classifier is calibrated in two ways: (1) by adaptively penalizing
uncertain predictions, which helps eliminate the impact of anomaly
contamination while accentuating the predictions that the one-class model is
confident in, and (2) by discriminating the normal samples from native anomaly
examples that are generated to simulate genuine time series abnormal behaviors
on the basis of original data. These two calibrations result in
contamination-tolerant, anomaly-informed one-class learning, yielding a
significantly improved normality modeling. Extensive experiments on six
real-world datasets show that our model substantially outperforms twelve
state-of-the-art competitors and obtains 6% - 31% F1 score improvement. The
source code is available at \url{https://github.com/xuhongzuo/couta}.

Machine learning systems, especially the methods based on deep learning,
enjoy great success in modern computer vision tasks under experimental
settings. Generally, these classic deep learning methods are built on the
\emph{i.i.d.} assumption, supposing the training and test data are drawn from a
similar distribution independently and identically. However, the aforementioned
\emph{i.i.d.} assumption is in general unavailable in the real-world scenario,
and as a result, leads to sharp performance decay of deep learning algorithms.
Behind this, domain shift is one of the primary factors to be blamed. In order
to tackle this problem, we propose using \textbf{Po}tential \textbf{E}nergy
\textbf{R}anking (PoER) to decouple the object feature and the domain feature
(\emph{i.e.,} appearance feature) in given images, promoting the learning of
label-discriminative features while filtering out the irrelevant correlations
between the objects and the background. PoER helps the neural networks to
capture label-related features which contain the domain information first in
shallow layers and then distills the label-discriminative representations out
progressively, enforcing the neural networks to be aware of the characteristic
of objects and background which is vital to the generation of domain-invariant
features. PoER reports superior performance on domain generalization
benchmarks, improving the average top-1 accuracy by at least 1.20\% compared to
the existing methods. Moreover, we use PoER in the ECCV 2022 NICO
Challenge\footnote{https://nicochallenge.com}, achieving top place with only a
vanilla ResNet-18. The code has been made available at
https://github.com/ForeverPs/PoER.

In a number of machine learning models, an input query is searched across the
trained class vectors to find the closest feature class vector in cosine
similarity metric. However, performing the cosine similarities between the
vectors in Von-Neumann machines involves a large number of multiplications,
Euclidean normalizations and division operations, thus incurring heavy hardware
energy and latency overheads. Moreover, due to the memory wall problem that
presents in the conventional architecture, frequent cosine similarity-based
searches (CSSs) over the class vectors requires a lot of data movements,
limiting the throughput and efficiency of the system. To overcome the
aforementioned challenges, this paper introduces COSIME, an general in-memory
associative memory (AM) engine based on the ferroelectric FET (FeFET) device
for efficient CSS. By leveraging the one-transistor AND gate function of FeFET
devices, current-based translinear analog circuit and winner-take-all (WTA)
circuitry, COSIME can realize parallel in-memory CSS across all the entries in
a memory block, and output the closest word to the input query in cosine
similarity metric. Evaluation results at the array level suggest that the
proposed COSIME design achieves 333X and 90.5X latency and energy improvements,
respectively, and realizes better classification accuracy when compared with an
AM design implementing approximated CSS. The proposed in-memory computing
fabric is evaluated for an HDC problem, showcasing that COSIME can achieve on
average 47.1X and 98.5X speedup and energy efficiency improvements compared
with an GPU implementation.

Many studies have proposed methods for optimizing the dialogue performance of
an entire pipeline task-oriented dialogue system by jointly training modules in
the system using reinforcement learning. However, these methods are limited in
that they can only be applied to modules implemented using trainable
neural-based methods. To solve this problem, we propose a method for optimizing
a pipeline system composed of modules implemented with arbitrary methods for
dialogue performance. With our method, neural-based components called
post-processing networks (PPNs) are installed inside such a system to
post-process the output of each module. All PPNs are updated to improve the
overall dialogue performance of the system by using reinforcement learning, not
necessitating each module to be differentiable. Through dialogue simulation and
human evaluation on the MultiWOZ dataset, we show that our method can improve
the dialogue performance of pipeline systems consisting of various modules.

Finite State Machine is a popular modeling notation for various systems,
especially software and electronic. Test paths can be automatically generated
from the system model to test such systems using a suitable algorithm. This
paper presents a strategy that generates test paths and allows to start and end
test paths only in defined states of the finite state machine. The strategy
also simultaneously supports generating test paths only of length in a given
range. For this purpose, alternative system models, test coverage criteria, and
a set of algorithms are developed. The strategy is compared with the best
alternative based on the reduction of the test set generated by the established
N-switch coverage approach on a mix of 171 industrial and artificially
generated problem instances. The proposed strategy outperforms the compared
variant in a smaller number of test path steps. The extent varies with the used
test coverage criterion and preferred test path length range from none to two
and half fold difference. Moreover, the proposed technique detected up to 30%
more simple artificial defects inserted into experimental SUT models per one
test step than the compared alternative technique. The proposed strategy is
well applicable in situations where a possible test path starts and ends in a
state machine needs to be reflected and, concurrently, the length of the test
paths has to be in a defined range.

Data series classification is an important and challenging problem in data
science. Explaining the classification decisions by finding the discriminant
parts of the input that led the algorithm to some decisions is a real need in
many applications. Convolutional neural networks perform well for the data
series classification task; though, the explanations provided by this type of
algorithm are poor for the specific case of multivariate data series.
Addressing this important limitation is a significant challenge. In this paper,
we propose a novel method that solves this problem by highlighting both the
temporal and dimensional discriminant information. Our contribution is
two-fold: we first describe a convolutional architecture that enables the
comparison of dimensions; then, we propose a method that returns dCAM, a
Dimension-wise Class Activation Map specifically designed for multivariate time
series (and CNN-based models). Experiments with several synthetic and real
datasets demonstrate that dCAM is not only more accurate than previous
approaches, but the only viable solution for discriminant feature discovery and
classification explanation in multivariate time series. This paper has appeared
in SIGMOD'22.

Many classical and learning-based optical flow methods rely on hierarchical
concepts to improve both accuracy and robustness. However, one of the currently
most successful approaches -- RAFT -- hardly exploits such concepts. In this
work, we show that multi-scale ideas are still valuable. More precisely, using
RAFT as a baseline, we propose a novel multi-scale neural network that combines
several hierarchical concepts within a single estimation framework. These
concepts include (i) a partially shared coarse-to-fine architecture, (ii)
multi-scale features, (iii) a hierarchical cost volume and (iv) a multi-scale
multi-iteration loss. Experiments on MPI Sintel and KITTI clearly demonstrate
the benefits of our approach. They show not only substantial improvements
compared to RAFT, but also state-of-the-art results -- in particular in
non-occluded regions. Code will be available at
https://github.com/cv-stuttgart/MS_RAFT.

The self-attention mechanism, successfully employed with the transformer
structure is shown promise in many computer vision tasks including image
recognition, and object detection. Despite the surge, the use of the
transformer for the problem of stereo matching remains relatively unexplored.
In this paper, we comprehensively investigate the use of the transformer for
the problem of stereo matching, especially for laparoscopic videos, and propose
a new hybrid deep stereo matching framework (HybridStereoNet) that combines the
best of the CNN and the transformer in a unified design. To be specific, we
investigate several ways to introduce transformers to volumetric stereo
matching pipelines by analyzing the loss landscape of the designs and
in-domain/cross-domain accuracy. Our analysis suggests that employing
transformers for feature representation learning, while using CNNs for cost
aggregation will lead to faster convergence, higher accuracy and better
generalization than other options. Our extensive experiments on Sceneflow,
SCARED2019 and dVPN datasets demonstrate the superior performance of our
HybridStereoNet.

In robot-assisted therapy for individuals with Autism Spectrum Disorder, the
workload of therapists during a therapeutic session is increased if they have
to control the robot manually. To allow therapists to focus on the interaction
with the person instead, the robot should be more autonomous, namely it should
be able to interpret the person's state and continuously adapt its actions
according to their behaviour. In this paper, we develop a personalised robot
behaviour model that can be used in the robot decision-making process during an
activity; this behaviour model is trained with the help of a user model that
has been learned from real interaction data. We use Q-learning for this task,
such that the results demonstrate that the policy requires about 10,000
iterations to converge. We thus investigate policy transfer for improving the
convergence speed; we show that this is a feasible solution, but an
inappropriate initial policy can lead to a suboptimal final return.

Model-based reinforcement learning (RL) achieves higher sample efficiency in
practice than model-free RL by learning a dynamics model to generate samples
for policy learning. Previous works learn a "global" dynamics model to fit the
state-action visitation distribution for all historical policies. However, in
this paper, we find that learning a global dynamics model does not necessarily
benefit model prediction for the current policy since the policy in use is
constantly evolving. The evolving policy during training will cause
state-action visitation distribution shifts. We theoretically analyze how the
distribution of historical policies affects the model learning and model
rollouts. We then propose a novel model-based RL method, named
\textit{Policy-adaptation Model-based Actor-Critic (PMAC)}, which learns a
policy-adapted dynamics model based on a policy-adaptation mechanism. This
mechanism dynamically adjusts the historical policy mixture distribution to
ensure the learned model can continually adapt to the state-action visitation
distribution of the evolving policy. Experiments on a range of continuous
control environments in MuJoCo show that PMAC achieves state-of-the-art
asymptotic performance and almost two times higher sample efficiency than prior
model-based methods.

During the last decade, Neural Networks (NNs) have proved to be extremely
effective tools in many fields of engineering, including autonomous vehicles,
medical diagnosis and search engines, and even in art creation. Indeed, NNs
often decisively outperform traditional algorithms. One area that is only
recently attracting significant interest is using NNs for designing numerical
solvers, particularly for discretized partial differential equations. Several
recent papers have considered employing NNs for developing multigrid methods,
which are a leading computational tool for solving discretized partial
differential equations and other sparse-matrix problems. We extend these new
ideas, focusing on so-called relaxation operators (also called smoothers),
which are an important component of the multigrid algorithm that has not yet
received much attention in this context. We explore an approach for using NNs
to learn relaxation parameters for an ensemble of diffusion operators with
random coefficients, for Jacobi type smoothers and for 4Color GaussSeidel
smoothers. The latter yield exceptionally efficient and easy to parallelize
Successive Over Relaxation (SOR) smoothers. Moreover, this work demonstrates
that learning relaxation parameters on relatively small grids using a two-grid
method and Gelfand's formula as a loss function can be implemented easily.
These methods efficiently produce nearly-optimal parameters, thereby
significantly improving the convergence rate of multigrid algorithms on large
grids.

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich
source domain to a different but related fully-unlabeled target domain. To
address the problem of domain shift, more and more UDA methods adopt pseudo
labels of the target samples to improve the generalization ability on the
target domain. However, inaccurate pseudo labels of the target samples may
yield suboptimal performance with error accumulation during the optimization
process. Moreover, once the pseudo labels are generated, how to remedy the
generated pseudo labels is far from explored. In this paper, we propose a novel
approach to improve the accuracy of the pseudo labels in the target domain. It
first generates coarse pseudo labels by a conventional UDA method. Then, it
iteratively exploits the intra-class similarity of the target samples for
improving the generated coarse pseudo labels, and aligns the source and target
domains with the improved pseudo labels. The accuracy improvement of the pseudo
labels is made by first deleting dissimilar samples, and then using spanning
trees to eliminate the samples with the wrong pseudo labels in the intra-class
samples. We have applied the proposed approach to several conventional UDA
methods as an additional term. Experimental results demonstrate that the
proposed method can boost the accuracy of the pseudo labels and further lead to
more discriminative and domain invariant features than the conventional

As different people perceive others' emotional expressions differently, their
annotation in terms of arousal and valence are per se subjective. To address
this, these emotion annotations are typically collected by multiple annotators
and averaged across annotators in order to obtain labels for arousal and
valence. However, besides the average, also the uncertainty of a label is of
interest, and should also be modeled and predicted for automatic emotion
recognition. In the literature, for simplicity, label uncertainty modeling is
commonly approached with a Gaussian assumption on the collected annotations.
However, as the number of annotators is typically rather small due to resource
constraints, we argue that the Gaussian approach is a rather crude assumption.
In contrast, in this work we propose to model the label distribution using a
Student's t-distribution which allows us to account for the number of
annotations available. With this model, we derive the corresponding
Kullback-Leibler divergence based loss function and use it to train an
estimator for the distribution of emotion labels, from which the mean and
uncertainty can be inferred. Through qualitative and quantitative analysis, we
show the benefits of the t-distribution over a Gaussian distribution. We
validate our proposed method on the AVEC'16 dataset. Results reveal that our
t-distribution based approach improves over the Gaussian approach with
state-of-the-art uncertainty modeling results in speech-based emotion
recognition, along with an optimal and even faster convergence.

Rate Splitting Multiple Access (RSMA) has emerged as an effective
interference management scheme for applications that require high data rates.
Although RSMA has shown advantages in rate enhancement and spectral efficiency,
it has yet not to be ready for latency-sensitive applications such as virtual
reality streaming, which is an essential building block of future 6G networks.
Unlike conventional High-Definition streaming applications, streaming virtual
reality applications requires not only stringent latency requirements but also
the computation capability of the transmitter to quickly respond to dynamic
users' demands. Thus, conventional RSMA approaches usually fail to address the
challenges caused by computational demands at the transmitter, let alone the
dynamic nature of the virtual reality streaming applications. To overcome the
aforementioned challenges, we first formulate the virtual reality streaming
problem assisted by RSMA as a joint communication and computation optimization
problem. A novel multicast approach is then proposed to cluster users into
different groups based on a Field-of-View metric and transmit multicast streams
in a hierarchical manner. After that, we propose a deep reinforcement learning
approach to obtain the solution for the optimization problem. Extensive
simulations show that our framework can achieve the millisecond-latency
requirement, which is much lower than other baseline schemes.

Object detectors trained with weak annotations are affordable alternatives to
fully-supervised counterparts. However, there is still a significant
performance gap between them. We propose to narrow this gap by fine-tuning a
base pre-trained weakly-supervised detector with a few fully-annotated samples
automatically selected from the training set using ``box-in-box'' (BiB), a
novel active learning strategy designed specifically to address the
well-documented failure modes of weakly-supervised detectors. Experiments on
the VOC07 and COCO benchmarks show that BiB outperforms other active learning
techniques and significantly improves the base weakly-supervised detector's
performance with only a few fully-annotated images per class. BiB reaches 97%
of the performance of fully-supervised Fast RCNN with only 10% of
fully-annotated images on VOC07. On COCO, using on average 10 fully-annotated
images per class, or equivalently 1% of the training set, BiB also reduces the
performance gap (in AP) between the weakly-supervised detector and the
fully-supervised Fast RCNN by over 70%, showing a good trade-off between
performance and data efficiency. Our code is publicly available at
https://github.com/huyvvo/BiB.

Knowledge distillation (KD) is an efficient approach to transfer the
knowledge from a large "teacher" network to a smaller "student" network.
Traditional KD methods require lots of labeled training samples and a white-box
teacher (parameters are accessible) to train a good student. However, these
resources are not always available in real-world applications. The distillation
process often happens at an external party side where we do not have access to
much data, and the teacher does not disclose its parameters due to security and
privacy concerns. To overcome these challenges, we propose a black-box few-shot
KD method to train the student with few unlabeled training samples and a
black-box teacher. Our main idea is to expand the training set by generating a
diverse set of out-of-distribution synthetic images using MixUp and a
conditional variational auto-encoder. These synthetic images along with their
labels obtained from the teacher are used to train the student. We conduct
extensive experiments to show that our method significantly outperforms recent
SOTA few/zero-shot KD methods on image classification tasks. The code and
models are available at: https://github.com/nphdang/FS-BBT

Online social network platforms have a problem with misinformation. One
popular way of addressing this problem is via the use of machine learning based
automated misinformation detection systems to classify if a post is
misinformation. Instead of post hoc detection, we propose to predict if a user
will engage with misinformation in advance and design an effective graph neural
network classifier based on ego-graphs for this task. However, social networks
are highly dynamic, reflecting continual changes in user behaviour, as well as
the content being posted. This is problematic for machine learning models which
are typically trained on a static training dataset, and can thus become
outdated when the social network changes. Inspired by the success of continual
learning on such problems, we propose an ego-graphs replay strategy in
continual learning (EgoCL) using graph neural networks to effectively address
this issue. We have evaluated the performance of our method on user engagement
with misinformation on two Twitter datasets across nineteen misinformation and
conspiracy topics. Our experimental results show that our approach EgoCL has
better performance in terms of predictive accuracy and computational resources
than the state of the art.

Weakly-supervised object detection (WSOD) aims to train an object detector
only requiring the image-level annotations. Recently, some works have managed
to select the accurate boxes generated from a well-trained WSOD network to
supervise a semi-supervised detection framework for better performance.
However, these approaches simply divide the training set into labeled and
unlabeled sets according to the image-level criteria, such that sufficient
mislabeled or wrongly localized box predictions are chosen as pseudo
ground-truths, resulting in a sub-optimal solution of detection performance. To
overcome this issue, we propose a novel WSOD framework with a new paradigm that
switches from weak supervision to noisy supervision (W2N). Generally, with
given pseudo ground-truths generated from the well-trained WSOD network, we
propose a two-module iterative training algorithm to refine pseudo labels and
supervise better object detector progressively. In the localization adaptation
module, we propose a regularization loss to reduce the proportion of
discriminative parts in original pseudo ground-truths, obtaining better pseudo
ground-truths for further training. In the semi-supervised module, we propose a
two tasks instance-level split method to select high-quality labels for
training a semi-supervised detector. Experimental results on different
benchmarks verify the effectiveness of W2N, and our W2N outperforms all
existing pure WSOD methods and transfer learning methods. Our code is publicly
available at https://github.com/1170300714/w2n_wsod.

The use of Deep Learning and Computer Vision in the Cultural Heritage domain
is becoming highly relevant in the last few years with lots of applications
about audio smart guides, interactive museums and augmented reality. All these
technologies require lots of data to work effectively and be useful for the
user. In the context of artworks, such data is annotated by experts in an
expensive and time consuming process. In particular, for each artwork, an image
of the artwork and a description sheet have to be collected in order to perform
common tasks like Visual Question Answering. In this paper we propose a method
for Visual Question Answering that allows to generate at runtime a description
sheet that can be used for answering both visual and contextual questions about
the artwork, avoiding completely the image and the annotation process. For this
purpose, we investigate on the use of GPT-3 for generating descriptions for
artworks analyzing the quality of generated descriptions through captioning
metrics. Finally we evaluate the performance for Visual Question Answering and
captioning tasks.

Human interaction recognition is very important in many applications. One
crucial cue in recognizing an interaction is the interactive body parts. In
this work, we propose a novel Interaction Graph Transformer (IGFormer) network
for skeleton-based interaction recognition via modeling the interactive body
parts as graphs. More specifically, the proposed IGFormer constructs
interaction graphs according to the semantic and distance correlations between
the interactive body parts, and enhances the representation of each person by
aggregating the information of the interactive body parts based on the learned
graphs. Furthermore, we propose a Semantic Partition Module to transform each
human skeleton sequence into a Body-Part-Time sequence to better capture the
spatial and temporal information of the skeleton sequence for learning the
graphs. Extensive experiments on three benchmark datasets demonstrate that our
model outperforms the state-of-the-art with a significant margin.

Data augmentation is one of the most successful techniques to improve the
classification accuracy of machine learning models in computer vision. However,
applying data augmentation to tabular data is a challenging problem since it is
hard to generate synthetic samples with labels. In this paper, we propose an
efficient classifier with a novel data augmentation technique for tabular data.
Our method called CCRAL combines causal reasoning to learn counterfactual
samples for the original training samples and active learning to select useful
counterfactual samples based on a region of uncertainty. By doing this, our
method can maximize our model's generalization on the unseen testing data. We
validate our method analytically, and compare with the standard baselines. Our
experimental results highlight that CCRAL achieves significantly better
performance than those of the baselines across several real-world tabular
datasets in terms of accuracy and AUC. Data and source code are available at:
https://github.com/nphdang/CCRAL.

Molecular property prediction is of crucial importance in many disciplines
such as drug discovery, molecular biology, or material and process design. The
frequently employed quantitative structure-property/activity relationships
(QSPRs/QSARs) characterize molecules by descriptors which are then mapped to
the properties of interest via a linear or nonlinear model. In contrast, graph
neural networks, a novel machine learning method, directly work on the
molecular graph, i.e., a graph representation where atoms correspond to nodes
and bonds correspond to edges. GNNs allow to learn properties in an end-to-end
fashion, thereby avoiding the need for informative descriptors as in
QSPRs/QSARs. GNNs have been shown to achieve state-of-the-art prediction
performance on various property predictions tasks and represent an active field
of research. We describe the fundamentals of GNNs and demonstrate the
application of GNNs via two examples for molecular property prediction.

How can we acquire world models that veridically represent the outside world
both in terms of what is there and in terms of how our actions affect it? Can
we acquire such models by interacting with the world, and can we state
mathematical desiderata for their relationship with a hypothetical reality
existing outside our heads? As machine learning is moving towards
representations containing not just observational but also interventional
knowledge, we study these problems using tools from representation learning and
group theory. Under the assumption that our actuators act upon the world, we
propose methods to learn internal representations of not just sensory
information but also of actions that modify our sensory representations in a
way that is consistent with the actions and transitions in the world. We use an
autoencoder equipped with a group representation linearly acting on its latent
space, trained on 2-step reconstruction such as to enforce a suitable
homomorphism property on the group representation. Compared to existing work,
our approach makes fewer assumptions on the group representation and on which
transformations the agent can sample from the group. We motivate our method
theoretically, and demonstrate empirically that it can learn the correct
representation of the groups and the topology of the environment. We also
compare its performance in trajectory prediction with previous methods.

Whilst computer vision models built using self-supervised approaches are now
commonplace, some important questions remain. Do self-supervised models learn
highly redundant channel features? What if a self-supervised network could
dynamically select the important channels and get rid of the unnecessary ones?
Currently, convnets pre-trained with self-supervision have obtained comparable
performance on downstream tasks in comparison to their supervised counterparts
in computer vision. However, there are drawbacks to self-supervised models
including their large numbers of parameters, computationally expensive training
strategies and a clear need for faster inference on downstream tasks. In this
work, our goal is to address the latter by studying how a standard channel
selection method developed for supervised learning can be applied to networks
trained with self-supervision. We validate our findings on a range of target
budgets $t_{d}$ for channel computation on image classification task across
different datasets, specifically CIFAR-10, CIFAR-100, and ImageNet-100,
obtaining comparable performance to that of the original network when selecting
all channels but at a significant reduction in computation reported in terms of
FLOPs.

Model-based Reinforcement Learning and Control have demonstrated great
potential in various sequential decision making problem domains, including in
robotics settings. However, real-world robotics systems often present
challenges that limit the applicability of those methods. In particular, we
note two problems that jointly happen in many industrial systems: 1)
Irregular/asynchronous observations and actions and 2) Dramatic changes in
environment dynamics from an episode to another (e.g. varying payload inertial
properties). We propose a general framework that overcomes those difficulties
by meta-learning adaptive dynamics models for continuous-time prediction and
control. We evaluate the proposed approach on two robotic simulations, the
first of which is inspired by a real-world industrial robot.

Continual learning is a learning paradigm that learns tasks sequentially with
resources constraints, in which the key challenge is stability-plasticity
dilemma, i.e., it is uneasy to simultaneously have the stability to prevent
catastrophic forgetting of old tasks and the plasticity to learn new tasks
well. In this paper, we propose a new continual learning approach, Advanced
Null Space (AdNS), to balance the stability and plasticity without storing any
old data of previous tasks. Specifically, to obtain better stability, AdNS
makes use of low-rank approximation to obtain a novel null space and projects
the gradient onto the null space to prevent the interference on the past tasks.
To control the generation of the null space, we introduce a non-uniform
constraint strength to further reduce forgetting. Furthermore, we present a
simple but effective method, intra-task distillation, to improve the
performance of the current task. Finally, we theoretically find that null space
plays a key role in plasticity and stability, respectively. Experimental
results show that the proposed method can achieve better performance compared
to state-of-the-art continual learning approaches.

Image restoration schemes based on the pre-trained deep models have received
great attention due to their unique flexibility for solving various inverse
problems. In particular, the Plug-and-Play (PnP) framework is a popular and
powerful tool that can integrate an off-the-shelf deep denoiser for different
image restoration tasks with known observation models. However, obtaining the
observation model that exactly matches the actual one can be challenging in
practice. Thus, the PnP schemes with conventional deep denoisers may fail to
generate satisfying results in some real-world image restoration tasks. We
argue that the robustness of the PnP framework is largely limited by using the
off-the-shelf deep denoisers that are trained by deterministic optimization. To
this end, we propose a novel deep reinforcement learning (DRL) based PnP
framework, dubbed RePNP, by leveraging a light-weight DRL-based denoiser for
robust image restoration tasks. Experimental results demonstrate that the
proposed RePNP is robust to the observation model used in the PnP scheme
deviating from the actual one. Thus, RePNP can generate more reliable
restoration results for image deblurring and super resolution tasks. Compared
with several state-of-the-art deep image restoration baselines, RePNP achieves
better results subjective to model deviation with fewer model parameters.

In this paper, we investigate the transferability limitations when using deep
learning models, for semantic segmentation of pneumonia-infected areas in CT
images. The proposed approach adopts a 4 channel input; 3 channels based on
Hounsfield scale, plus one channel (binary) denoting the lung area. We used 3
different, publicly available, CT datasets. If the lung area mask was not
available, a deep learning model generates a proxy image. Experimental results
suggesting that transferability should be used carefully, when creating Covid
segmentation models; retraining the model more than one times in large sets of
data results in a decrease in segmentation accuracy.

AI has the potential to improve approaches to talent management enabling
dynamic provisions through implementing advanced automation. This study aims to
identify the new requirements for developing AI-oriented artifacts to address
talent management issues. Focusing on enhancing interactions between
professional assessment and planning attributes, the design artifact is an
intelligent employment automation solution for career guidance that is largely
dependent on a talent intelligent module and an individuals growth needs. A
design science method is adopted for conducting the experimental study with
structured machine learning techniques which is the primary element of a
comprehensive AI solution framework informed through a proposed moderation of
the technology-organization-environment theory.

Process synthesis experiences a disruptive transformation accelerated by
digitization and artificial intelligence. We propose a reinforcement learning
algorithm for chemical process design based on a state-of-the-art actor-critic
logic. Our proposed algorithm represents chemical processes as graphs and uses
graph convolutional neural networks to learn from process graphs. In
particular, the graph neural networks are implemented within the agent
architecture to process the states and make decisions. Moreover, we implement a
hierarchical and hybrid decision-making process to generate flowsheets, where
unit operations are placed iteratively as discrete decisions and corresponding
design variables are selected as continuous decisions. We demonstrate the
potential of our method to design economically viable flowsheets in an
illustrative case study comprising equilibrium reactions, azeotropic
separation, and recycles. The results show quick learning in discrete,
continuous, and hybrid action spaces. Due to the flexible architecture of the
proposed reinforcement learning agent, the method is predestined to include
large action-state spaces and an interface to process simulators in future
research.

While fine-tuning based methods for few-shot object detection have achieved
remarkable progress, a crucial challenge that has not been addressed well is
the potential class-specific overfitting on base classes and sample-specific
overfitting on novel classes. In this work we design a novel knowledge
distillation framework to guide the learning of the object detector and thereby
restrain the overfitting in both the pre-training stage on base classes and
fine-tuning stage on novel classes. To be specific, we first present a novel
Position-Aware Bag-of-Visual-Words model for learning a representative bag of
visual words (BoVW) from a limited size of image set, which is used to encode
general images based on the similarities between the learned visual words and
an image. Then we perform knowledge distillation based on the fact that an
image should have consistent BoVW representations in two different feature
spaces. To this end, we pre-learn a feature space independently from the object
detection, and encode images using BoVW in this space. The obtained BoVW
representation for an image can be considered as distilled knowledge to guide
the learning of object detector: the extracted features by the object detector
for the same image are expected to derive the consistent BoVW representations
with the distilled knowledge. Extensive experiments validate the effectiveness
of our method and demonstrate the superiority over other state-of-the-art
methods.

We study learning in periodic Markov Decision Process(MDP), a special type of
non-stationary MDP where both the state transition probabilities and reward
functions vary periodically, under the average reward maximization setting. We
formulate the problem as a stationary MDP by augmenting the state space with
the period index, and propose a periodic upper confidence bound reinforcement
learning-2 (PUCRL2) algorithm. We show that the regret of PUCRL2 varies
linearly with the period and as sub-linear with the horizon length. Numerical
results demonstrate the efficacy of PUCRL2.

Equity is widely held to be fundamental to the ethics of healthcare. In the
context of clinical decision-making, it rests on the comparative fidelity of
the intelligence -- evidence-based or intuitive -- guiding the management of
each individual patient. Though brought to recent attention by the
individuating power of contemporary machine learning, such epistemic equity
arises in the context of any decision guidance, whether traditional or
innovative. Yet no general framework for its quantification, let alone
assurance, currently exists. Here we formulate epistemic equity in terms of
model fidelity evaluated over learnt multi-dimensional representations of
identity crafted to maximise the captured diversity of the population,
introducing a comprehensive framework for Representational Ethical Model
Calibration. We demonstrate use of the framework on large-scale multimodal data
from UK Biobank to derive diverse representations of the population, quantify
model performance, and institute responsive remediation. We offer our approach
as a principled solution to quantifying and assuring epistemic equity in
healthcare, with applications across the research, clinical, and regulatory
domains.

People leverage group discussions to collaborate in order to solve complex
tasks, e.g. in project meetings or hiring panels. By doing so, they engage in a
variety of conversational strategies where they try to convince each other of
the best approach and ultimately reach a decision. In this work, we investigate
methods for detecting what makes someone change their mind. To this end, we
leverage a recently introduced dataset containing group discussions of people
collaborating to solve a task. To find out what makes someone change their
mind, we incorporate various techniques such as neural text classification and
language-agnostic change point detection. Evaluation of these methods shows
that while the task is not trivial, the best way to approach it is using a
language-aware model with learning-to-rank training. Finally, we examine the
cues that the models develop as indicative of the cause of a change of mind.

Recommender systems and search are both indispensable in facilitating
personalization and ease of browsing in online fashion platforms. However, the
two tools often operate independently, failing to combine the strengths of
recommender systems to accurately capture user tastes with search systems'
ability to process user queries. We propose a novel remedy to this problem by
automatically recommending personalized fashion items based on a user-provided
text request. Our proposed model, WhisperLite, uses contrastive learning to
capture user intent from natural language text and improves the recommendation
quality of fashion products. WhisperLite combines the strength of CLIP
embeddings with additional neural network layers for personalization, and is
trained using a composite loss function based on binary cross entropy and
contrastive loss. The model demonstrates a significant improvement in offline
recommendation retrieval metrics when tested on a real-world dataset collected
from an online retail fashion store, as well as widely used open-source
datasets in different e-commerce domains, such as restaurants, movies and TV
shows, clothing and shoe reviews. We additionally conduct a user study that
captures user judgements on the relevance of the model's recommended items,
confirming the relevancy of WhisperLite's recommendations in an online setting.

Multi-view stereo is an important research task in computer vision while
still keeping challenging. In recent years, deep learning-based methods have
shown superior performance on this task. Cost volume pyramid network-based
methods which progressively refine depth map in coarse-to-fine manner, have
yielded promising results while consuming less memory. However, these methods
fail to take fully consideration of the characteristics of the cost volumes in
each stage, leading to adopt similar range search strategies for each cost
volume stage. In this work, we present a novel cost volume pyramid based
network with different searching strategies for multi-view stereo. By choosing
different depth range sampling strategies and applying adaptive unimodal
filtering, we are able to obtain more accurate depth estimation in low
resolution stages and iteratively upsample depth map to arbitrary resolution.
We conducted extensive experiments on both DTU and BlendedMVS datasets, and
results show that our method outperforms most state-of-the-art methods.

Federated learning (FL) is a promising distributed framework for
collaborative artificial intelligence model training while protecting user
privacy. A bootstrapping component that has attracted significant research
attention is the design of incentive mechanism to stimulate user collaboration
in FL. The majority of works adopt a broker-centric approach to help the
central operator to attract participants and further obtain a well-trained
model. Few works consider forging participant-centric collaboration among
participants to pursue an FL model for their common interests, which induces
dramatic differences in incentive mechanism design from the broker-centric FL.
To coordinate the selfish and heterogeneous participants, we propose a novel
analytic framework for incentivizing effective and efficient collaborations for
participant-centric FL. Specifically, we respectively propose two novel game
models for contribution-oblivious FL (COFL) and contribution-aware FL (CAFL),
where the latter one implements a minimum contribution threshold mechanism. We
further analyze the uniqueness and existence for Nash equilibrium of both COFL
and CAFL games and design efficient algorithms to achieve equilibrium
solutions. Extensive performance evaluations show that there exists free-riding
phenomenon in COFL, which can be greatly alleviated through the adoption of
CAFL model with the optimized minimum threshold.

Deep learning has achieved great success in the past few years. However, the
performance of deep learning is likely to impede in face of non-IID situations.
Domain generalization (DG) enables a model to generalize to an unseen test
distribution, i.e., to learn domain-invariant representations. In this paper,
we argue that domain-invariant features should be originating from both
internal and mutual sides. Internal invariance means that the features can be
learned with a single domain and the features capture intrinsic semantics of
data, i.e., the property within a domain, which is agnostic to other domains.
Mutual invariance means that the features can be learned with multiple domains
(cross-domain) and the features contain common information, i.e., the
transferable features w.r.t. other domains. We then propose DIFEX for
Domain-Invariant Feature EXploration. DIFEX employs a knowledge distillation
framework to capture the high-level Fourier phase as the internally-invariant
features and learn cross-domain correlation alignment as the mutually-invariant
features. We further design an exploration loss to increase the feature
diversity for better generalization. Extensive experiments on both time-series
and visual benchmarks demonstrate that the proposed DIFEX achieves

How to aggregate information from multiple instances is a key question
multiple instance learning. Prior neural models implement different variants of
the well-known encoder-decoder strategy according to which all input features
are encoded a single, high-dimensional embedding which is then decoded to
generate an output. In this work, inspired by Choquet capacities, we propose
Capacity networks. Unlike encoder-decoders, Capacity networks generate multiple
interpretable intermediate results which can be aggregated in a semantically
meaningful space to obtain the final output. Our experiments show that
implementing this simple inductive bias leads to improvements over different
encoder-decoder architectures in a wide range of experiments. Moreover, the
interpretable intermediate results make Capacity networks interpretable by
design, which allows a semantically meaningful inspection, evaluation, and
regularization of the network internals.

One of the recent developments in deep learning is generalized zero-shot
learning (GZSL), which aims to recognize objects from both seen and unseen
classes, when only the labeled examples from seen classes are provided. Over
the past couple of years, GZSL has picked up traction and several models have
been proposed to solve this problem. Whereas an extensive amount of research on
GZSL has been carried out in fields such as computer vision and natural
language processing, no such research has been carried out to deal with time
series data. GZSL is used for applications such as detecting abnormalities from
ECG and EEG data and identifying unseen classes from sensor, spectrograph and
other devices' data. In this regard, we propose a Latent Embedding for Time
Series - GZSL (LETS-GZSL) model that can solve the problem of GZSL for time
series classification (TSC). We utilize an embedding-based approach and combine
it with attribute vectors to predict the final class labels. We report our
results on the widely popular UCR archive datasets. Our framework is able to
achieve a harmonic mean value of at least 55% on most of the datasets except
when the number of unseen classes is greater than 3 or the amount of data is
very low (less than 100 training examples).

Pansharpening enhances spatial details of high spectral resolution
multispectral images using features of high spatial resolution panchromatic
image. There are a number of traditional pansharpening approaches but producing
an image exhibiting high spectral and spatial fidelity is still an open
problem. Recently, deep learning has been used to produce promising
pansharpened images; however, most of these approaches apply similar treatment
to both multispectral and panchromatic images by using the same network for
feature extraction. In this work, we present present a novel dual
attention-based two-stream network. It starts with feature extraction using two
separate networks for both images, an encoder with attention mechanism to
recalibrate the extracted features. This is followed by fusion of the features
forming a compact representation fed into an image reconstruction network to
produce a pansharpened image. The experimental results on the Pl\'{e}iades
dataset using standard quantitative evaluation metrics and visual inspection
demonstrates that the proposed approach performs better than other approaches
in terms of pansharpened image quality.

We propose a framework that automatically transforms non-scalable GNNs into
precomputation-based GNNs which are efficient and scalable for large-scale
graphs. The advantages of our framework are two-fold; 1) it transforms various
non-scalable GNNs to scale well to large-scale graphs by separating local
feature aggregation from weight learning in their graph convolution, 2) it
efficiently executes precomputation on GPU for large-scale graphs by
decomposing their edges into small disjoint and balanced sets. Through
extensive experiments with large-scale graphs, we demonstrate that the
transformed GNNs run faster in training time than existing GNNs while achieving
competitive accuracy to the state-of-the-art GNNs. Consequently, our
transformation framework provides simple and efficient baselines for future
research on scalable GNNs.

Contrastive learning has shown great promise in the field of graph
representation learning. By manually constructing positive/negative samples,
most graph contrastive learning methods rely on the vector inner product based
similarity metric to distinguish the samples for graph representation. However,
the handcrafted sample construction (e.g., the perturbation on the nodes or
edges of the graph) may not effectively capture the intrinsic local structures
of the graph. Also, the vector inner product based similarity metric cannot
fully exploit the local structures of the graph to characterize the graph
difference well. To this end, in this paper, we propose a novel adaptive
subgraph generation based contrastive learning framework for efficient and
robust self-supervised graph representation learning, and the optimal transport
distance is utilized as the similarity metric between the subgraphs. It aims to
generate contrastive samples by capturing the intrinsic structures of the graph
and distinguish the samples based on the features and structures of subgraphs
simultaneously. Specifically, for each center node, by adaptively learning
relation weights to the nodes of the corresponding neighborhood, we first
develop a network to generate the interpolated subgraph. We then construct the
positive and negative pairs of subgraphs from the same and different nodes,
respectively. Finally, we employ two types of optimal transport distances
(i.e., Wasserstein distance and Gromov-Wasserstein distance) to construct the
structured contrastive loss. Extensive node classification experiments on
benchmark datasets verify the effectiveness of our graph contrastive learning
method.

Siamese network based trackers formulate 3D single object tracking as
cross-correlation learning between point features of a template and a search
area. Due to the large appearance variation between the template and search
area during tracking, how to learn the robust cross correlation between them
for identifying the potential target in the search area is still a challenging
problem. In this paper, we explicitly use Transformer to form a 3D Siamese
Transformer network for learning robust cross correlation between the template
and the search area of point clouds. Specifically, we develop a Siamese point
Transformer network to learn shape context information of the target. Its
encoder uses self-attention to capture non-local information of point clouds to
characterize the shape information of the object, and the decoder utilizes
cross-attention to upsample discriminative point features. After that, we
develop an iterative coarse-to-fine correlation network to learn the robust
cross correlation between the template and the search area. It formulates the
cross-feature augmentation to associate the template with the potential target
in the search area via cross attention. To further enhance the potential
target, it employs the ego-feature augmentation that applies self-attention to
the local k-NN graph of the feature space to aggregate target features.
Experiments on the KITTI, nuScenes, and Waymo datasets show that our method
achieves state-of-the-art performance on the 3D single object tracking task.

In class incremental learning (CIL) setting, groups of classes are introduced
to a model in each learning phase. The goal is to learn a unified model
performant on all the classes observed so far. Given the recent popularity of
Vision Transformers (ViTs) in conventional classification settings, an
interesting question is to study their continual learning behaviour. In this
work, we develop a Debiased Dual Distilled Transformer for CIL dubbed
$\textrm{D}^3\textrm{Former}$. The proposed model leverages a hybrid nested ViT
design to ensure data efficiency and scalability to small as well as large
datasets. In contrast to a recent ViT based CIL approach, our
$\textrm{D}^3\textrm{Former}$ does not dynamically expand its architecture when
new tasks are learned and remains suitable for a large number of incremental
tasks. The improved CIL behaviour of $\textrm{D}^3\textrm{Former}$ owes to two
fundamental changes to the ViT design. First, we treat the incremental learning
as a long-tail classification problem where the majority samples from new
classes vastly outnumber the limited exemplars available for old classes. To
avoid the bias against the minority old classes, we propose to dynamically
adjust logits to emphasize on retaining the representations relevant to old
tasks. Second, we propose to preserve the configuration of spatial attention
maps as the learning progresses across tasks. This helps in reducing
catastrophic forgetting by constraining the model to retain the attention on
the most discriminative regions. $\textrm{D}^3\textrm{Former}$ obtains
favorable results on incremental versions of CIFAR-100, MNIST, SVHN, and
ImageNet datasets.

We introduce two new classes of measures of information for statistical
experiments which generalise and subsume $\phi$-divergences, integral
probability metrics, $\mathfrak{N}$-distances (MMD), and $(f,\Gamma)$
divergences between two or more distributions. This enables us to derive a
simple geometrical relationship between measures of information and the Bayes
risk of a statistical decision problem, thus extending the variational
$\phi$-divergence representation to multiple distributions in an entirely
symmetric manner. The new families of divergence are closed under the action of
Markov operators which yields an information processing equality which is a
refinement and generalisation of the classical data processing inequality. This
equality gives insight into the significance of the choice of the hypothesis
class in classical risk minimization.

Existing self-supervised monocular depth estimation methods can get rid of
expensive annotations and achieve promising results. However, these methods
suffer from severe performance degradation when directly adopting a model
trained on a fixed resolution to evaluate at other different resolutions. In
this paper, we propose a resolution adaptive self-supervised monocular depth
estimation method (RA-Depth) by learning the scale invariance of the scene
depth. Specifically, we propose a simple yet efficient data augmentation method
to generate images with arbitrary scales for the same scene. Then, we develop a
dual high-resolution network that uses the multi-path encoder and decoder with
dense interactions to aggregate multi-scale features for accurate depth
inference. Finally, to explicitly learn the scale invariance of the scene
depth, we formulate a cross-scale depth consistency loss on depth predictions
with different scales. Extensive experiments on the KITTI, Make3D and NYU-V2
datasets demonstrate that RA-Depth not only achieves state-of-the-art
performance, but also exhibits a good ability of resolution adaptation.

Context: Differential testing is a useful approach that uses different
implementations of the same algorithms and compares the results for software
testing. In recent years, this approach was successfully used for test
campaigns of deep learning frameworks.
  Objective: There is little knowledge on the application of differential
testing beyond deep learning. Within this article, we want to close this gap
for classification algorithms.
  Method: We conduct a case study using Scikit-learn, Weka, Spark MLlib, and
Caret in which we identify the potential of differential testing by considering
which algorithms are available in multiple frameworks, the feasibility by
identifying pairs of algorithms that should exhibit the same behavior, and the
effectiveness by executing tests for the identified pairs and analyzing the
deviations.
  Results: While we found a large potential for popular algorithms, the
feasibility seems limited because often it is not possible to determine
configurations that are the same in other frameworks. The execution of the
feasible tests revealed that there is a large amount of deviations for the
scores and classes. Only a lenient approach based on statistical significance
of classes does not lead to a huge amount of test failures.
  Conclusions: The potential of differential testing beyond deep learning seems
limited for research into the quality of machine learning libraries.
Practitioners may still use the approach if they have deep knowledge about
implementations, especially if a coarse oracle that only considers significant
differences of classes is sufficient.

Compressive learning (CL) is an emerging framework that integrates signal
acquisition via compressed sensing (CS) and machine learning for inference
tasks directly on a small number of measurements. It can be a promising
alternative to classical image-domain methods and enjoys great advantages in
memory saving and computational efficiency. However, previous attempts on CL
are not only limited to a fixed CS ratio, which lacks flexibility, but also
limited to MNIST/CIFAR-like datasets and do not scale to complex real-world
high-resolution (HR) data or vision tasks. In this paper, a novel
transformer-based compressive learning framework on large-scale images with
arbitrary CS ratios, dubbed TransCL, is proposed. Specifically, TransCL first
utilizes the strategy of learnable block-based compressed sensing and proposes
a flexible linear projection strategy to enable CL to be performed on
large-scale images in an efficient block-by-block manner with arbitrary CS
ratios. Then, regarding CS measurements from all blocks as a sequence, a pure
transformer-based backbone is deployed to perform vision tasks with various
task-oriented heads. Our sufficient analysis presents that TransCL exhibits
strong resistance to interference and robust adaptability to arbitrary CS
ratios. Extensive experiments for complex HR data demonstrate that the proposed
TransCL can achieve state-of-the-art performance in image classification and
semantic segmentation tasks. In particular, TransCL with a CS ratio of $10\%$
can obtain almost the same performance as when operating directly on the
original data and can still obtain satisfying performance even with an
extremely low CS ratio of $1\%$. The source codes of our proposed TransCL is
available at \url{https://github.com/MC-E/TransCL/}.

The success of Vision Transformer (ViT) in various computer vision tasks has
promoted the ever-increasing prevalence of this convolution-free network. The
fact that ViT works on image patches makes it potentially relevant to the
problem of jigsaw puzzle solving, which is a classical self-supervised task
aiming at reordering shuffled sequential image patches back to their natural
form. Despite its simplicity, solving jigsaw puzzle has been demonstrated to be
helpful for diverse tasks using Convolutional Neural Networks (CNNs), such as
self-supervised feature representation learning, domain generalization, and
fine-grained classification.
  In this paper, we explore solving jigsaw puzzle as a self-supervised
auxiliary loss in ViT for image classification, named Jigsaw-ViT. We show two
modifications that can make Jigsaw-ViT superior to standard ViT: discarding
positional embeddings and masking patches randomly. Yet simple, we find that
Jigsaw-ViT is able to improve both in generalization and robustness over the
standard ViT, which is usually rather a trade-off. Experimentally, we show that
adding the jigsaw puzzle branch provides better generalization than ViT on
large-scale image classification on ImageNet. Moreover, the auxiliary task also
improves robustness to noisy labels on Animal-10N, Food-101N, and Clothing1M as
well as adversarial examples. Our implementation is available at
https://yingyichen-cyy.github.io/Jigsaw-ViT/.

Simulink is a widely used model-based development environment for embedded
systems. Stateflow is a component of Simulink for modeling event-driven control
via hierarchical state machines and flow charts. However, Stateflow lacks an
official formal semantics, making it difficult to formally prove properties of
its models in safety-critical applications. In this paper, we define a formal
semantics for a large subset of Stateflow, covering complex features such as
hierarchical states and transitions, event broadcasts, early return, temporal
operators, and so on. The semantics is formalized in Isabelle/HOL and proved to
be deterministic. We implement a tactic for automatic execution of the
semantics in Isabelle, as well as a translator in Python transforming Stateflow
models to the syntax in Isabelle. Using these tools, we validate the semantics
against a collection of examples illustrating the features we cover.

We propose a novel framework for target speech extraction based on semantic
information, called ConceptBeam. Target speech extraction means extracting the
speech of a target speaker in a mixture. Typical approaches have been
exploiting properties of audio signals, such as harmonic structure and
direction of arrival. In contrast, ConceptBeam tackles the problem with
semantic clues. Specifically, we extract the speech of speakers speaking about
a concept, i.e., a topic of interest, using a concept specifier such as an
image or speech. Solving this novel problem would open the door to innovative
applications such as listening systems that focus on a particular topic
discussed in a conversation. Unlike keywords, concepts are abstract notions,
making it challenging to directly represent a target concept. In our scheme, a
concept is encoded as a semantic embedding by mapping the concept specifier to
a shared embedding space. This modality-independent space can be built by means
of deep metric learning using paired data consisting of images and their spoken
captions. We use it to bridge modality-dependent information, i.e., the speech
segments in the mixture, and the specified, modality-independent concept. As a
proof of our scheme, we performed experiments using a set of images associated
with spoken captions. That is, we generated speech mixtures from these spoken
captions and used the images or speech signals as the concept specifiers. We
then extracted the target speech using the acoustic characteristics of the
identified segments. We compare ConceptBeam with two methods: one based on
keywords obtained from recognition systems and another based on sound source
separation. We show that ConceptBeam clearly outperforms the baseline methods
and effectively extracts speech based on the semantic representation.

In this paper we propose a Long Short-Term Memory Network based method to
forecast the energy consumption in public buildings, based on past
measurements. Our approach consists of three main steps: data processing step,
training and validation step, and finally the forecasting step. We tested our
method on a data set consisting of measurements taken every half an hour from
the main building of the National Archives of the United Kingdom, in Kew and as
evaluation metrics we have used Mean Absolute Error (MAE) and Mean Absolute
Percentage Error (MAPE).

As a novel deep learning model, gcForest has been widely used in various
applications. However, the current multi-grained scanning of gcForest produces
many redundant feature vectors, and this increases the time cost of the model.
To screen out redundant feature vectors, we introduce a hashing screening
mechanism for multi-grained scanning and propose a model called HW-Forest which
adopts two strategies, hashing screening and window screening. HW-Forest
employs perceptual hashing algorithm to calculate the similarity between
feature vectors in hashing screening strategy, which is used to remove the
redundant feature vectors produced by multi-grained scanning and can
significantly decrease the time cost and memory consumption. Furthermore, we
adopt a self-adaptive instance screening strategy to improve the performance of
our approach, called window screening, which can achieve higher accuracy
without hyperparameter tuning on different datasets. Our experimental results
show that HW-Forest has higher accuracy than other models, and the time cost is
also reduced.

Machine Learning (ML) models have, in contrast to their usefulness in
molecular dynamics studies, had limited success as surrogate potentials for
reaction barrier search. It is due to the scarcity of training data in relevant
transition state regions of chemical space. Currently, available datasets for
training ML models on small molecular systems almost exclusively contain
configurations at or near equilibrium. In this work, we present the dataset
Transition1x containing 9.6 million Density Functional Theory (DFT)
calculations of forces and energies of molecular configurations on and around
reaction pathways at the wB97x/6-31G(d) level of theory. The data was generated
by running Nudged Elastic Band (NEB) calculations with DFT on 10k reactions
while saving intermediate calculations. We train state-of-the-art equivariant
graph message-passing neural network models on Transition1x and cross-validate
on the popular ANI1x and QM9 datasets. We show that ML models cannot learn
features in transition-state regions solely by training on hitherto popular
benchmark datasets. Transition1x is a new challenging benchmark that will
provide an important step towards developing next-generation ML force fields
that also work far away from equilibrium configurations and reactive systems.

Time series is a collection of data instances that are ordered according to a
time stamp. Stock prices, temperature, etc are examples of time series data in
real life. Time series data are used for forecasting sales, predicting trends.
Visualization is the process of visually representing data or the relationship
between features of a data either in a two-dimensional plot or a
three-dimensional plot. Visualizing the time series data constitutes an
important part of the process for working with a time series dataset.
Visualizing the data not only helps in the modelling process but it can also be
used to identify trends and features that cause those trends. In this work, we
take a real-life time series dataset and analyse how the target feature relates
to other features of the dataset through visualization. From the work that has
been carried out, we present an effective method of visualization for time
series data which will be much useful for machine learning modelling with such
datasets.

Text detection and recognition are essential components of a modern OCR
system. Most OCR approaches attempt to obtain accurate bounding boxes of text
at the detection stage, which is used as the input of the text recognition
stage. We observe that when using tight text bounding boxes as input, a text
recognizer frequently fails to achieve optimal performance due to the
inconsistency between bounding boxes and deep representations of text
recognition. In this paper, we propose Box Adjuster, a reinforcement
learning-based method for adjusting the shape of each text bounding box to make
it more compatible with text recognition models. Additionally, when dealing
with cross-domain problems such as synthetic-to-real, the proposed method
significantly reduces mismatches in domain distribution between the source and
target domains. Experiments demonstrate that the performance of end-to-end text
recognition systems can be improved when using the adjusted bounding boxes as
the ground truths for training. Specifically, on several benchmark datasets for
scene text understanding, the proposed method outperforms state-of-the-art text
spotters by an average of 2.0% F-Score on end-to-end text recognition tasks and
4.6% F-Score on domain adaptation tasks.

Causal inference is a critical research area with multi-disciplinary origins
and applications, ranging from statistics, computer science, economics,
psychology to public health. In many scientific research, randomized
experiments provide a golden standard for estimation of causal effects for
decades. However, in many situations, randomized experiments are not feasible
in practice so that practitioners need to rely on empirical investigation for
causal reasoning. Causal inference via observational data is a challenging task
since the knowledge of the treatment assignment mechanism is missing, which
typically requires non-testable assumptions to make the inference possible. For
several years, great effort has been devoted to the research of causal
inference for binary treatments. In practice, it is also common to use
observational data on multiple treatment comparisons. Within the potential
outcomes framework, we propose a generalized cross-fitting estimator (GCF),
which generalizes the doubly robust estimator with cross-fitting for binary
treatment to multiple treatment comparisons and provides rigorous proofs on its
statistical properties. This estimator permits the use of more flexible machine
learning methods to model the nuisance parts, and based on relatively weak
assumptions, while there is still a theoretical guarantee for valid statistical
inference. We show the asymptotic properties of the GCF estimators, and provide
the asymptotic simultaneous confidence intervals that achieve the
semiparametric efficiency bound for average treatment effect. The performance
of the estimator is accessed through simulation study based on the common
evaluation metrics generally considered in the causal inference literature.

Graph neural networks (GNNs), which have emerged as an effective method for
handling machine learning tasks on graphs, bring a new approach to building
recommender systems, where the task of recommendation can be formulated as the
link prediction problem on user-item bipartite graphs. Training GNN-based
recommender systems (GNNRecSys) on large graphs incurs a large memory
footprint, easily exceeding the DRAM capacity on a typical server. Existing
solutions resort to distributed subgraph training, which is inefficient due to
the high cost of dynamically constructing subgraphs and significant redundancy
across subgraphs.
  The emerging persistent memory technologies provide a significantly larger
memory capacity than DRAMs at an affordable cost, making single-machine
GNNRecSys training feasible, which eliminates the inefficiencies in distributed
training. One major concern of using persistent memory devices for GNNRecSys is
their relatively low bandwidth compared with DRAMs. This limitation can be
particularly detrimental to achieving high performance for GNNRecSys workloads
since their dominant compute kernels are sparse and memory access intensive. To
understand whether persistent memory is a good fit for GNNRecSys training, we
perform an in-depth characterization of GNNRecSys workloads and a comprehensive
analysis of their performance on a persistent memory device, namely, Intel
Optane. Based on the analysis, we provide guidance on how to configure Optane
for GNNRecSys workloads. Furthermore, we present techniques for large-batch
training to fully realize the advantages of single-machine GNNRecSys training.
Our experiment results show that with the tuned batch size and optimal system
configuration, Optane-based single-machine GNNRecSys training outperforms
distributed training by a large margin, especially when handling deep GNN
models.

Boolean Matrix Factorization (BMF) aims to find an approximation of a given
binary matrix as the Boolean product of two low-rank binary matrices. Binary
data is ubiquitous in many fields, and representing data by binary matrices is
common in medicine, natural language processing, bioinformatics, computer
graphics, among many others. Unfortunately, BMF is computationally hard and
heuristic algorithms are used to compute Boolean factorizations. Very recently,
the theoretical breakthrough was obtained independently by two research groups.
Ban et al. (SODA 2019) and Fomin et al. (Trans. Algorithms 2020) show that BMF
admits an efficient polynomial-time approximation scheme (EPTAS). However,
despite the theoretical importance, the high double-exponential dependence of
the running times from the rank makes these algorithms unimplementable in
practice. The primary research question motivating our work is whether the
theoretical advances on BMF could lead to practical algorithms.
  The main conceptional contribution of our work is the following. While EPTAS
for BMF is a purely theoretical advance, the general approach behind these
algorithms could serve as the basis in designing better heuristics. We also use
this strategy to develop new algorithms for related $\mathbb{F}_p$-Matrix
Factorization. Here, given a matrix $A$ over a finite field GF($p$) where $p$
is a prime, and an integer $r$, our objective is to find a matrix $B$ over the
same field with GF($p$)-rank at most $r$ minimizing some norm of $A-B$. Our
empirical research on synthetic and real-world data demonstrates the advantage
of the new algorithms over previous works on BMF and $\mathbb{F}_p$-Matrix
Factorization.

The increasing scale of model size and continuous improvement of performance
herald the arrival of the Big Model era. In this report, we explore what and
how the big model training works by diving into training objectives and
training methodologies. Specifically,training objectives describe how to
leverage web-scale data to develop extremely capable and incredibly large
models based on self-supervised learning, and training methodologies which are
based on distributed training describe how to make big model training a
reality. We summarize the existing training methodologies into three main
categories: training parallelism, memory-saving technologies, and model
sparsity design. Training parallelism can be categorized into data, pipeline,
and tensor parallelism according to the dimension of parallelism that takes
place. Memory-saving technologies are orthogonal and complementary to training
parallelism. And model sparsity design further scales up the model size with a
constant computational cost. A continuously updated paper list of big model
training is provided at https://github.com/qhliu26/BM-Training.

Very recently neural implicit rendering techniques have been rapidly evolved
and shown great advantages in novel view synthesis and 3D scene reconstruction.
However, existing neural rendering methods for editing purposes offer limited
functionality, e.g., rigid transformation, or not applicable for fine-grained
editing for general objects from daily lives. In this paper, we present a novel
mesh-based representation by encoding the neural implicit field with
disentangled geometry and texture codes on mesh vertices, which facilitates a
set of editing functionalities, including mesh-guided geometry editing,
designated texture editing with texture swapping, filling and painting
operations. To this end, we develop several techniques including learnable sign
indicators to magnify spatial distinguishability of mesh-based representation,
distillation and fine-tuning mechanism to make a steady convergence, and the
spatial-aware optimization strategy to realize precise texture editing.
Extensive experiments and editing examples on both real and synthetic data
demonstrate the superiority of our method on representation quality and editing
ability. Code is available on the project webpage:
https://zju3dv.github.io/neumesh/.

There is growing interest in unifying the streaming and full-context
automatic speech recognition (ASR) networks into a single end-to-end ASR model
to simplify the model training and deployment for both use cases. While in
real-world ASR applications, the streaming ASR models typically operate under
more storage and computational constraints - e.g., on embedded devices - than
any server-side full-context models. Motivated by the recent progress in
Omni-sparsity supernet training, where multiple subnetworks are jointly
optimized in one single model, this work aims to jointly learn a compact sparse
on-device streaming ASR model, and a large dense server non-streaming model, in
a single supernet. Next, we present that, performing supernet training on both
wav2vec 2.0 self-supervised learning and supervised ASR fine-tuning can not
only substantially improve the large non-streaming model as shown in prior
works, and also be able to improve the compact sparse streaming model.

In this work, we study the problem of community detection in the stochastic
block model with adversarial node corruptions. Our main result is an efficient
algorithm that can tolerate an $\epsilon$-fraction of corruptions and achieves
error $O(\epsilon) + e^{-\frac{C}{2} (1 \pm o(1))}$ where $C = (\sqrt{a} -
\sqrt{b})^2$ is the signal-to-noise ratio and $a/n$ and $b/n$ are the
inter-community and intra-community connection probabilities respectively.
These bounds essentially match the minimax rates for the SBM without
corruptions. We also give robust algorithms for $\mathbb{Z}_2$-synchronization.
At the heart of our algorithm is a new semidefinite program that uses global
information to robustly boost the accuracy of a rough clustering. Moreover, we
show that our algorithms are doubly-robust in the sense that they work in an
even more challenging noise model that mixes adversarial corruptions with
unbounded monotone changes, from the semi-random model.

Reliable navigation systems have a wide range of applications in robotics and
autonomous driving. Current approaches employ an open-loop process that
converts sensor inputs directly into actions. However, these open-loop schemes
are challenging to handle complex and dynamic real-world scenarios due to their
poor generalization. Imitating human navigation, we add a reasoning process to
convert actions back to internal latent states, forming a two-stage closed loop
of perception, decision-making, and reasoning. Firstly, VAE-Enhanced
Demonstration Learning endows the model with the understanding of basic
navigation rules. Then, two dual processes in RL-Enhanced Interaction Learning
generate reward feedback for each other and collectively enhance obstacle
avoidance capability. The reasoning model can substantially promote
generalization and robustness, and facilitate the deployment of the algorithm
to real-world robots without elaborate transfers. Experiments show our method
is more adaptable to novel scenarios compared with state-of-the-art approaches.

"Unless and until our society recognizes cyber bullying for what it is, the
suffering of thousands of silent victims will continue." ~ Anna Maria Chavez.
There had been series of research on cyber bullying which are unable to provide
reliable solution to cyber bullying. In this research work, we were able to
provide a permanent solution to this by developing a model capable of detecting
and intercepting bullying incoming and outgoing messages with 92% accuracy. We
also developed a chatbot automation messaging system to test our model leading
to the development of Artificial Intelligence powered anti-cyber bullying
system using machine learning algorithm of Multinomial Naive Bayes (MNB) and
optimized linear Support Vector Machine (SVM). Our model is able to detect and
intercept bullying outgoing and incoming bullying messages and take immediate
action.

This paper gives the overview of the first shared task at FIRE 2020 on fake
news detection in the Urdu language. This is a binary classification task in
which the goal is to identify fake news using a dataset composed of 900
annotated news articles for training and 400 news articles for testing. The
dataset contains news in five domains: (i) Health, (ii) Sports, (iii) Showbiz,
(iv) Technology, and (v) Business. 42 teams from 6 different countries (India,
China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams
submitted their experimental results. The participants used various machine
learning methods ranging from feature-based traditional machine learning to
neural network techniques. The best performing system achieved an F-score value
of 0.90, showing that the BERT-based approach outperforms other machine
learning classifiers.

This overview paper describes the first shared task on fake news detection in
Urdu language. The task was posed as a binary classification task, in which the
goal is to differentiate between real and fake news. We provided a dataset
divided into 900 annotated news articles for training and 400 news articles for
testing. The dataset contained news in five domains: (i) Health, (ii) Sports,
(iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different
countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for
the task. 9 teams submitted their experimental results. The participants used
various machine learning methods ranging from feature-based traditional machine
learning to neural networks techniques. The best performing system achieved an
F-score value of 0.90, showing that the BERT-based approach outperforms other
machine learning techniques

Researchers have recently proposed plenty of heterogeneous graph neural
networks (HGNNs) due to the ubiquity of heterogeneous graphs in both academic
and industrial areas. Instead of pursuing a more powerful HGNN model, in this
paper, we are interested in devising a versatile plug-and-play module, which
accounts for distilling relational knowledge from pre-trained HGNNs.
  To the best of our knowledge, we are the first to propose a HIgh-order
RElational (HIRE) knowledge distillation framework on heterogeneous graphs,
which can significantly boost the prediction performance regardless of model
architectures of HGNNs. Concretely, our HIRE framework initially performs
first-order node-level knowledge distillation, which encodes the semantics of
the teacher HGNN with its prediction logits. Meanwhile, the second-order
relation-level knowledge distillation imitates the relational correlation
between node embeddings of different types generated by the teacher HGNN.
  Extensive experiments on various popular HGNNs models and three real-world
heterogeneous graphs demonstrate that our method obtains consistent and
considerable performance enhancement, proving its effectiveness and

To explore the vulnerability of deep neural networks (DNNs), many attack
paradigms have been well studied, such as the poisoning-based backdoor attack
in the training stage and the adversarial attack in the inference stage. In
this paper, we study a novel attack paradigm, which modifies model parameters
in the deployment stage. Considering the effectiveness and stealthiness goals,
we provide a general formulation to perform the bit-flip based weight attack,
where the effectiveness term could be customized depending on the attacker's
purpose. Furthermore, we present two cases of the general formulation with
different malicious purposes, i.e., single sample attack (SSA) and triggered
samples attack (TSA). To this end, we formulate this problem as a mixed integer
programming (MIP) to jointly determine the state of the binary bits (0 or 1) in
the memory and learn the sample modification. Utilizing the latest technique in
integer programming, we equivalently reformulate this MIP problem as a
continuous optimization problem, which can be effectively and efficiently
solved using the alternating direction method of multipliers (ADMM) method.
Consequently, the flipped critical bits can be easily determined through
optimization, rather than using a heuristic strategy. Extensive experiments
demonstrate the superiority of SSA and TSA in attacking DNNs.

Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo
continuous monitoring of their cardiac health. Conventional monitoring
approaches are contact-based, making the neonates prone to various nosocomial
infections. Video-based monitoring approaches have opened up potential avenues
for contactless measurement. This work presents a pipeline for remote
estimation of cardiopulmonary signals from videos in NICU setup. We have
proposed an end-to-end deep learning (DL) model that integrates a non-learning
based approach to generate surrogate ground truth (SGT) labels for supervision,
thus refraining from direct dependency on true ground truth labels. We have
performed an extended qualitative and quantitative analysis to examine the
efficacy of our proposed DL-based pipeline and achieved an overall average mean
absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2
bpm in the estimated heart rate.

Ever since the era of internet had ushered in cloud computing, there had been
increase in the demand for the unlimited data available through cloud computing
for data analysis, pattern recognition and technology advancement. With this
also bring the problem of scalability, efficiency and security threat. This
research paper focuses on how data can be dynamically mine in real time for
pattern detection in a secure cloud computing environment using combination of
decision tree algorithm and Random Forest over a restful Application
Programming Interface (API). We are able to successfully Implement data mining
on cloud computing bypassing or avoiding direct interaction with data warehouse
and without any terminal involve by using combination of IBM Cloud storage
facility, Amazing Web Service, Application Programming Interface and Window
service along with a decision tree and Random Forest algorithm for our
classifier. We were able to successfully bypass direct connection with the data
warehouse and cloud terminal with 94% accuracy in our model.

In recent years, Cross-Modal Hashing (CMH) has aroused much attention due to
its fast query speed and efficient storage. Previous literatures have achieved
promising results for Cross-Modal Retrieval (CMR) by discovering discriminative
hash codes and modality-specific hash functions. Nonetheless, most existing CMR
works are subjected to some restrictions: 1) It is assumed that data of
different modalities are fully paired, which is impractical in real
applications due to sample missing and false data alignment, and 2) binary
regression targets including the label matrix and binary codes are too rigid to
effectively learn semantic-preserving hash codes and hash functions. To address
these problems, this paper proposes an Adaptive Marginalized Semantic Hashing
(AMSH) method which not only enhances the discrimination of latent
representations and hash codes by adaptive margins, but also can be used for
both paired and unpaired CMR. As a two-step method, in the first step, AMSH
generates semantic-aware modality-specific latent representations with
adaptively marginalized labels, which enlarges the distances between different
classes, and exploits the labels to preserve the inter-modal and intra-modal
semantic similarities into latent representations and hash codes. In the second
step, adaptive margin matrices are embedded into the hash codes, and enlarge
the gaps between positive and negative bits, which improves the discrimination
and robustness of hash functions. On this basis, AMSH generates
similarity-preserving hash codes and robust hash functions without strict
one-to-one data correspondence requirement. Experiments are conducted on
several benchmark datasets to demonstrate the superiority and flexibility of
AMSH over some state-of-the-art CMR methods.

We introduce a novel multi-view stereo (MVS) method that can simultaneously
recover not just per-pixel depth but also surface normals, together with the
reflectance of textureless, complex non-Lambertian surfaces captured under
known but natural illumination. Our key idea is to formulate MVS as an
end-to-end learnable network, which we refer to as nLMVS-Net, that seamlessly
integrates radiometric cues to leverage surface normals as view-independent
surface features for learned cost volume construction and filtering. It first
estimates surface normals as pixel-wise probability densities for each view
with a novel shape-from-shading network. These per-pixel surface normal
densities and the input multi-view images are then input to a novel cost volume
filtering network that learns to recover per-pixel depth and surface normal.
The reflectance is also explicitly estimated by alternating with geometry
reconstruction. Extensive quantitative evaluations on newly established
synthetic and real-world datasets show that nLMVS-Net can robustly and
accurately recover the shape and reflectance of complex objects in natural
settings.

Visual Emotion Analysis (VEA), which aims to predict people's emotions
towards different visual stimuli, has become an attractive research topic
recently. Rather than a single label classification task, it is more rational
to regard VEA as a Label Distribution Learning (LDL) problem by voting from
different individuals. Existing methods often predict visual emotion
distribution in a unified network, neglecting the inherent subjectivity in its
crowd voting process. In psychology, the \textit{Object-Appraisal-Emotion}
model has demonstrated that each individual's emotion is affected by his/her
subjective appraisal, which is further formed by the affective memory. Inspired
by this, we propose a novel \textit{Subjectivity Appraise-and-Match Network
(SAMNet)} to investigate the subjectivity in visual emotion distribution. To
depict the diversity in crowd voting process, we first propose the
\textit{Subjectivity Appraising} with multiple branches, where each branch
simulates the emotion evocation process of a specific individual. Specifically,
we construct the affective memory with an attention-based mechanism to preserve
each individual's unique emotional experience. A subjectivity loss is further
proposed to guarantee the divergence between different individuals. Moreover,
we propose the \textit{Subjectivity Matching} with a matching loss, aiming at
assigning unordered emotion labels to ordered individual predictions in a
one-to-one correspondence with the Hungarian algorithm. Extensive experiments
and comparisons are conducted on public visual emotion distribution datasets,
and the results demonstrate that the proposed SAMNet consistently outperforms
the state-of-the-art methods. Ablation study verifies the effectiveness of our
method and visualization proves its interpretability.

In this paper we investigate Deep Learning Models using topological dynamical
systems, index theory, and computational homology. These mathematical machinery
was invented initially by Henri Poincare around 1900 and developed over time to
understand shapes and dynamical systems whose structure and behavior is too
complicated to solve for analytically but can be understood via global
relationships. In particular, we show how individual neurons in a neural
network can correspond to simplexes in a simplicial complex manifold
approximation to the decision surface learned by the NN, and how these
simplexes can be used to compute topological invariants from algebraic topology
for the decision manifold with an explicit computation of homology groups by
hand in a simple case. We also show how the gradient of the probability density
function learned by the NN creates a dynamical system, which can be analyzed by
a myriad of topological tools such as Conley Index Theory, Morse Theory, and
Stable Manifolds. We solve analytically for associated the differential
equation for a trained NN with a single hidden layer of 256 Neurons applied to
the MINST digit dataset, and approximately numerically that it a sink and basin
of attraction for each of the 10 classes, but the sinks and strong attracting
manifolds lie in regions not corresponding to images of actual digits. Index
theory implies the existence of saddles. Level sets of the probability
functions are 783-dimensional manifolds which can only change topology at
critical points of the dynamical system, and these changes in topology can be
investigated with Morse Theory.

Several studies have recently pointed that existing Visual Question Answering
(VQA) models heavily suffer from the language prior problem, which refers to
capturing superficial statistical correlations between the question type and
the answer whereas ignoring the image contents. Numerous efforts have been
dedicated to strengthen the image dependency by creating the delicate models or
introducing the extra visual annotations. However, these methods cannot
sufficiently explore how the visual cues explicitly affect the learned answer
representation, which is vital for language reliance alleviation. Moreover,
they generally emphasize the class-level discrimination of the learned answer
representation, which overlooks the more fine-grained instance-level patterns
and demands further optimization. In this paper, we propose a novel
collaborative learning scheme from the viewpoint of visual perturbation
calibration, which can better investigate the fine-grained visual effects and
mitigate the language prior problem by learning the instance-level
characteristics. Specifically, we devise a visual controller to construct two
sorts of curated images with different perturbation extents, based on which the
collaborative learning of intra-instance invariance and inter-instance
discrimination is implemented by two well-designed discriminators. Besides, we
implement the information bottleneck modulator on latent space for further bias
alleviation and representation calibration. We impose our visual
perturbation-aware framework to three orthodox baselines and the experimental
results on two diagnostic VQA-CP benchmark datasets evidently demonstrate its
effectiveness. In addition, we also justify its robustness on the balanced VQA

A particular challenge for disease progression modeling is the heterogeneity
of a disease and its manifestations in the patients. Existing approaches often
assume the presence of a single disease progression characteristics which is
unlikely for neurodegenerative disorders such as Parkinson's disease. In this
paper, we propose a hierarchical time-series model that can discover multiple
disease progression dynamics. The proposed model is an extension of an
input-output hidden Markov model that takes into account the clinical
assessments of patients' health status and prescribed medications. We
illustrate the benefits of our model using a synthetically generated dataset
and a real-world longitudinal dataset for Parkinson's disease.

Digital twins have emerged as a key technology for optimizing the performance
of engineering products and systems. High-fidelity numerical simulations
constitute the backbone of engineering design, providing an accurate insight
into the performance of complex systems. However, large-scale, dynamic,
non-linear models require significant computational resources and are
prohibitive for real-time digital twin applications. To this end, reduced order
models (ROMs) are employed, to approximate the high-fidelity solutions while
accurately capturing the dominant aspects of the physical behavior. The present
work proposes a new machine learning (ML) platform for the development of ROMs,
to handle large-scale numerical problems dealing with transient nonlinear
partial differential equations. Our framework, mentioned as
$\textit{FastSVD-ML-ROM}$, utilizes $\textit{(i)}$ a singular value
decomposition (SVD) update methodology, to compute a linear subspace of the
multi-fidelity solutions during the simulation process, $\textit{(ii)}$
convolutional autoencoders for nonlinear dimensionality reduction,
$\textit{(iii)}$ feed-forward neural networks to map the input parameters to
the latent spaces, and $\textit{(iv)}$ long short-term memory networks to
predict and forecast the dynamics of parametric solutions. The efficiency of
the $\textit{FastSVD-ML-ROM}$ framework is demonstrated for a 2D linear
convection-diffusion equation, the problem of fluid around a cylinder, and the
3D blood flow inside an arterial segment. The accuracy of the reconstructed
results demonstrates the robustness and assesses the efficiency of the proposed
approach.

Deep Learning has demonstrated a significant improvement against traditional
machine learning approaches in different domains such as image and speech
recognition. Their success on benchmark datasets is transferred to the
real-world through pretrained models by practitioners. Pretraining visual
models using supervised learning requires a significant amount of expensive
data annotation. To tackle this limitation, DeepCluster - a simple and scalable
unsupervised pretraining of visual representations - has been proposed.
However, the underlying work of the model is not yet well understood. In this
paper, we analyze DeepCluster internals and exhaustively evaluate the impact of
various hyperparameters over a wide range of values on three different
datasets. Accordingly, we propose an explanation of why the algorithm works in
practice. We also show that DeepCluster convergence and performance highly
depend on the interplay between the quality of the randomly initialized filters
of the convolutional layer and the selected number of clusters. Furthermore, we
demonstrate that continuous clustering is not critical for DeepCluster
convergence. Therefore, early stopping of the clustering phase will reduce the
training time and allow the algorithm to scale to large datasets. Finally, we
derive plausible hyperparameter selection criteria in a semi-supervised
setting.

Since early machine learning models, metrics such as accuracy and precision
have been the de facto way to evaluate and compare trained models. However, a
single metric number doesn't fully capture the similarities and differences
between models, especially in the computer vision domain. A model with high
accuracy on a certain dataset might provide a lower accuracy on another
dataset, without any further insights. To address this problem we build on a
recent interpretability technique called Dissect to introduce
\textit{inter-model interpretability}, which determines how models relate or
complement each other based on the visual concepts they have learned (such as
objects and materials). Towards this goal, we project 13 top-performing
self-supervised models into a Learned Concepts Embedding (LCE) space that
reveals proximities among models from the perspective of learned concepts. We
further crossed this information with the performance of these models on four
computer vision tasks and 15 datasets. The experiment allowed us to categorize
the models into three categories and revealed for the first time the type of
visual concepts different tasks requires. This is a step forward for designing
cross-task learning algorithms.

Graph learning models are critical tools for researchers to explore
graph-structured data. To train a capable graph learning model, a conventional
method uses sufficient training data to train a graph model on a single device.
However, it is prohibitive to do so in real-world scenarios due to privacy
concerns. Federated learning provides a feasible solution to address such
limitations via introducing various privacy-preserving mechanisms, such as
differential privacy on graph edges. Nevertheless, differential privacy in
federated graph learning secures the classified information maintained in
graphs. It degrades the performances of the graph learning models. In this
paper, we investigate how to implement differential privacy on graph edges and
observe the performances decreasing in the experiments. We also note that the
differential privacy on graph edges introduces noises to perturb graph
proximity, which is one of the graph augmentations in graph contrastive
learning. Inspired by that, we propose to leverage the advantages of graph
contrastive learning to alleviate the performance dropping caused by
differential privacy. Extensive experiments are conducted with several
representative graph models and widely-used datasets, showing that contrastive
learning indeed alleviates the models' performance dropping caused by

Machine learning interatomic potentials (MLIPs) are routinely used atomic
simulations, but generating databases of atomic configurations used in fitting
these models is a laborious process, requiring significant computational and
human effort. A computationally efficient method is presented to generate
databases of atomic configurations that contain optimal information on the
small-displacement regime of the potential energy surface of bulk crystalline
matter. Utilising non-diagonal supercell (NDSC), an automatic process is
suggested for ab initio data generation. MLIPs were fitted for Al, W, Mg and
Si, which very closely reproduce the ab initio phonon and elastic properties.
The protocol can be easily adapted to other materials and can be inserted in
the workflow of any flavour of MLIP generation.

Hyperdimensional Computing (HDC) has obtained abundant attention as an
emerging non von Neumann computing paradigm. Inspired by the way human brain
functions, HDC leverages high dimensional patterns to perform learning tasks.
Compared to neural networks, HDC has shown advantages such as energy efficiency
and smaller model size, but sub-par learning capabilities in sophisticated
applications. Recently, researchers have observed when combined with neural
network components, HDC can achieve better performance than conventional HDC
models. This motivates us to explore the deeper insights behind theoretical
foundations of HDC, particularly the connection and differences with neural
networks. In this paper, we make a comparative study between HDC and neural
network to provide a different angle where HDC can be derived from an extremely
compact neural network trained upfront. Experimental results show such neural
network-derived HDC model can achieve up to 21% and 5% accuracy increase from
conventional and learning-based HDC models respectively. This paper aims to
provide more insights and shed lights on future directions for researches on
this popular emerging learning scheme.

Graph machine learning has gained great attention in both academia and
industry recently. Most of the graph machine learning models, such as Graph
Neural Networks (GNNs), are trained over massive graph data. However, in many
real-world scenarios, such as hospitalization prediction in healthcare systems,
the graph data is usually stored at multiple data owners and cannot be directly
accessed by any other parties due to privacy concerns and regulation
restrictions. Federated Graph Machine Learning (FGML) is a promising solution
to tackle this challenge by training graph machine learning models in a
federated manner. In this survey, we conduct a comprehensive review of the
literature in FGML. Specifically, we first provide a new taxonomy to divide the
existing problems in FGML into two settings, namely, FL with structured data
and structured FL. Then, we review the mainstream techniques in each setting
and elaborate on how they address the challenges under FGML. In addition, we
summarize the real-world applications of FGML from different domains and
introduce open graph datasets and platforms adopted in FGML. Finally, we
present several limitations in the existing studies with promising research
directions in this field.

We introduce a few-shot localization dataset originating from photographers
who authentically were trying to learn about the visual content in the images
they took. It includes nearly 10,000 segmentations of 100 categories in over
4,500 images that were taken by people with visual impairments. Compared to
existing few-shot object detection and instance segmentation datasets, our
dataset is the first to locate holes in objects (e.g., found in 12.3\% of our
segmentations), it shows objects that occupy a much larger range of sizes
relative to the images, and text is over five times more common in our objects
(e.g., found in 22.4\% of our segmentations). Analysis of three modern few-shot
localization algorithms demonstrates that they generalize poorly to our new
dataset. The algorithms commonly struggle to locate objects with holes, very
small and very large objects, and objects lacking text. To encourage a larger
community to work on these unsolved challenges, we publicly share our annotated
few-shot dataset at https://vizwiz.org .

With the recent proliferation of open textual data on social media platforms,
Emotion Detection (ED) from Text has received more attention over the past
years. It has many applications, especially for businesses and online service
providers, where emotion detection techniques can help them make informed
commercial decisions by analyzing customers/users' feelings towards their
products and services. In this study, we introduce ArmanEmo, a human-labeled
emotion dataset of more than 7000 Persian sentences labeled for seven
categories. The dataset has been collected from different resources, including
Twitter, Instagram, and Digikala (an Iranian e-commerce company) comments.
Labels are based on Ekman's six basic emotions (Anger, Fear, Happiness, Hatred,
Sadness, Wonder) and another category (Other) to consider any other emotion not
included in Ekman's model. Along with the dataset, we have provided several
baseline models for emotion classification focusing on the state-of-the-art
transformer-based language models. Our best model achieves a macro-averaged F1
score of 75.39 percent across our test dataset. Moreover, we also conduct
transfer learning experiments to compare our proposed dataset's generalization
against other Persian emotion datasets. Results of these experiments suggest
that our dataset has superior generalizability among the existing Persian
emotion datasets. ArmanEmo is publicly available for non-commercial use at
https://github.com/Arman-Rayan-Sharif/arman-text-emotion.

This paper introduces a data-driven shape completion approach that focuses on
completing geometric details of missing regions of 3D shapes. We observe that
existing generative methods lack the training data and representation capacity
to synthesize plausible, fine-grained details with complex geometry and
topology. Our key insight is to copy and deform patches from the partial input
to complete missing regions. This enables us to preserve the style of local
geometric features, even if it drastically differs from the training data. Our
fully automatic approach proceeds in two stages. First, we learn to retrieve
candidate patches from the input shape. Second, we select and deform some of
the retrieved candidates to seamlessly blend them into the complete shape. This
method combines the advantages of the two most common completion methods:
similarity-based single-instance completion, and completion by learning a shape
space. We leverage repeating patterns by retrieving patches from the partial
input, and learn global structural priors by using a neural network to guide
the retrieval and deformation steps. Experimental results show our approach
considerably outperforms baselines across multiple datasets and shape
categories. Code and data are available at https://github.com/GitBoSun/PatchRD.

Anomaly detection aims at identifying deviant samples from the normal data
distribution. Contrastive learning has provided a successful way to sample
representation that enables effective discrimination on anomalies. However,
when contaminated with unlabeled abnormal samples in training set under
semi-supervised settings, current contrastive-based methods generally 1) ignore
the comprehensive relation between training data, leading to suboptimal
performance, and 2) require fine-tuning, resulting in low efficiency. To
address the above two issues, in this paper, we propose a novel hierarchical
semi-supervised contrastive learning (HSCL) framework, for
contamination-resistant anomaly detection. Specifically, HSCL hierarchically
regulates three complementary relations: sample-to-sample, sample-to-prototype,
and normal-to-abnormal relations, enlarging the discrimination between normal
and abnormal samples with a comprehensive exploration of the contaminated data.
Besides, HSCL is an end-to-end learning approach that can efficiently learn
discriminative representations without fine-tuning. HSCL achieves
state-of-the-art performance in multiple scenarios, such as one-class
classification and cross-dataset detection. Extensive ablation studies further
verify the effectiveness of each considered relation. The code is available at
https://github.com/GaoangW/HSCL.

Vertical federated learning is considered, where an active party, having
access to true class labels, wishes to build a classification model by
utilizing more features from a passive party, which has no access to the
labels, to improve the model accuracy. In the prediction phase, with logistic
regression as the classification model, several inference attack techniques are
proposed that the adversary, i.e., the active party, can employ to reconstruct
the passive party's features, regarded as sensitive information. These attacks,
which are mainly based on a classical notion of the center of a set, i.e., the
Chebyshev center, are shown to be superior to those proposed in the literature.
Moreover, several theoretical performance guarantees are provided for the
aforementioned attacks. Subsequently, we consider the minimum amount of
information that the adversary needs to fully reconstruct the passive party's
features. In particular, it is shown that when the passive party holds one
feature, and the adversary is only aware of the signs of the parameters
involved, it can perfectly reconstruct that feature when the number of
predictions is large enough. Next, as a defense mechanism, a privacy-preserving
scheme is proposed that worsen the adversary's reconstruction attacks, while
preserving the full benefits that VFL brings to the active party. Finally,
experimental results demonstrate the effectiveness of the proposed attacks and
the privacy-preserving scheme.

Aerosol particles play an important role in the climate system by absorbing
and scattering radiation and influencing cloud properties. They are also one of
the biggest sources of uncertainty for climate modeling. Many climate models do
not include aerosols in sufficient detail due to computational constraints. In
order to represent key processes, aerosol microphysical properties and
processes have to be accounted for. This is done in the ECHAM-HAM global
climate aerosol model using the M7 microphysics, but high computational costs
make it very expensive to run with finer resolution or for a longer time. We
aim to use machine learning to emulate the microphysics model at sufficient
accuracy and reduce the computational cost by being fast at inference time. The
original M7 model is used to generate data of input-output pairs to train a
neural network on it. We are able to learn the variables' tendencies achieving
an average $R^2$ score of $77.1\% $. We further explore methods to inform and
constrain the neural network with physical knowledge to reduce mass violation
and enforce mass positivity. On a GPU we achieve a speed-up of up to over 64x
compared to the original model.

Unbiased Learning to Rank~(ULTR) that learns to rank documents with biased
user feedback data is a well-known challenge in information retrieval. Existing
methods in unbiased learning to rank typically rely on click modeling or
inverse propensity weighting~(IPW). Unfortunately, the search engines are faced
with severe long-tail query distribution, where neither click modeling nor IPW
can handle well. Click modeling suffers from data sparsity problem since the
same query-document pair appears limited times on tail queries; IPW suffers
from high variance problem since it is highly sensitive to small propensity
score values. Therefore, a general debiasing framework that works well under
tail queries is in desperate need. To address this problem, we propose a
model-based unbiased learning-to-rank framework. Specifically, we develop a
general context-aware user simulator to generate pseudo clicks for unobserved
ranked lists to train rankers, which addresses the data sparsity problem. In
addition, considering the discrepancy between pseudo clicks and actual clicks,
we take the observation of a ranked list as the treatment variable and further
incorporate inverse propensity weighting with pseudo labels in a doubly robust
way. The derived bias and variance indicate that the proposed model-based
method is more robust than existing methods. Finally, extensive experiments on
benchmark datasets, including simulated datasets and real click logs,
demonstrate that the proposed model-based method consistently performs
outperforms state-of-the-art methods in various scenarios.

Central to the field of quantum machine learning is the design of quantum
perceptrons and neural network architectures. A key question in this regard is
the impact of quantum effects on the way in which such models process
information. Here, we approach this question by establishing a connection
between $(1+1)D$ quantum cellular automata, which implement a discrete
nonequilibrium quantum many-body dynamics through the successive application of
local quantum gates, and recurrent quantum neural networks, which process
information by feeding it through perceptrons interconnecting adjacent layers.
This relation allows the processing of information in quantum neural networks
to be studied in terms of the properties of their equivalent cellular automaton
dynamics. We exploit this by constructing a class of quantum gates
(perceptrons) that allow for the introduction of quantum effects, such as those
associated with a coherent Hamiltonian evolution, and establish a rigorous link
to continuous-time Lindblad dynamics. We further analyse the universal
properties of a specific quantum cellular automaton, and identify a change of
critical behavior when quantum effects are varied, demonstrating that they can
indeed affect the collective dynamical behavior underlying the processing of
information in large-scale neural networks.

Behavior prediction based on historical behavioral data have practical
real-world significance. It has been applied in recommendation, predicting
academic performance, etc. With the refinement of user data description, the
development of new functions, and the fusion of multiple data sources,
heterogeneous behavioral data which contain multiple types of behaviors become
more and more common. In this paper, we aim to incorporate heterogeneous user
behaviors and social influences for behavior predictions. To this end, this
paper proposes a variant of Long-Short Term Memory (LSTM) which can consider
context information while modeling a behavior sequence, a projection mechanism
which can model multi-faceted relationships among different types of behaviors,
and a multi-faceted attention mechanism which can dynamically find out
informative periods from different facets. Many kinds of behavioral data belong
to spatio-temporal data. An unsupervised way to construct a social behavior
graph based on spatio-temporal data and to model social influences is proposed.
Moreover, a residual learning-based decoder is designed to automatically
construct multiple high-order cross features based on social behavior
representation and other types of behavior representations. Qualitative and
quantitative experiments on real-world datasets have demonstrated the
effectiveness of this model.

With the inexorable digitalisation of the modern world, every subset in the
field of technology goes through major advancements constantly. One such subset
is digital images which are ever so popular. Images can not always be as
visually pleasing or clear as you would want them to be and are often distorted
or obscured with noise. A number of techniques to enhance images have come up
as the years passed, all with their own respective pros and cons. In this
paper, we look at one such particular technique which accomplishes this task
with the help of a neural network model commonly known as an autoencoder. We
construct different architectures for the model and compare results in order to
decide the one best suited for the task. The characteristics and working of the
model are discussed briefly knowing which can help set a path for future
research.

Talking head synthesis is an emerging technology with wide applications in
film dubbing, virtual avatars and online education. Recent NeRF-based methods
generate more natural talking videos, as they better capture the 3D structural
information of faces. However, a specific model needs to be trained for each
identity with a large dataset. In this paper, we propose Dynamic Facial
Radiance Fields (DFRF) for few-shot talking head synthesis, which can rapidly
generalize to an unseen identity with few training data. Different from the
existing NeRF-based methods which directly encode the 3D geometry and
appearance of a specific person into the network, our DFRF conditions face
radiance field on 2D appearance images to learn the face prior. Thus the facial
radiance field can be flexibly adjusted to the new identity with few reference
images. Additionally, for better modeling of the facial deformations, we
propose a differentiable face warping module conditioned on audio signals to
deform all reference images to the query space. Extensive experiments show that
with only tens of seconds of training clip available, our proposed DFRF can
synthesize natural and high-quality audio-driven talking head videos for novel
identities with only 40k iterations. We highly recommend readers view our
supplementary video for intuitive comparisons. Code is available in
https://sstzal.github.io/DFRF/.

Machine learning models are prone to making incorrect predictions on inputs
that are far from the training distribution. This hinders their deployment in
safety-critical applications such as autonomous vehicles and healthcare. The
detection of a shift from the training distribution of individual datapoints
has gained attention. A number of techniques have been proposed for such
out-of-distribution (OOD) detection. But in many applications, the inputs to a
machine learning model form a temporal sequence. Existing techniques for OOD
detection in time-series data either do not exploit temporal relationships in
the sequence or do not provide any guarantees on detection. We propose using
deviation from the in-distribution temporal equivariance as the non-conformity
measure in conformal anomaly detection framework for OOD detection in
time-series data.Computing independent predictions from multiple conformal
detectors based on the proposed measure and combining these predictions by
Fisher's method leads to the proposed detector CODiT with guarantees on false
detection in time-series data. We illustrate the efficacy of CODiT by achieving
state-of-the-art results on computer vision datasets in autonomous driving. We
also show that CODiT can be used for OOD detection in non-vision datasets by
performing experiments on the physiological GAIT sensory dataset. Code, data,
and trained models are available at
https://github.com/kaustubhsridhar/time-series-OOD.

A dialogue policy module is an essential part of task-completion dialogue
systems. Recently, increasing interest has focused on reinforcement learning
(RL)-based dialogue policy. Its favorable performance and wise action decisions
rely on an accurate estimation of action values. The overestimation problem is
a widely known issue of RL since its estimate of the maximum action value is
larger than the ground truth, which results in an unstable learning process and
suboptimal policy. This problem is detrimental to RL-based dialogue policy
learning. To mitigate this problem, this paper proposes a dynamic partial
average estimator (DPAV) of the ground truth maximum action value. DPAV
calculates the partial average between the predicted maximum action value and
minimum action value, where the weights are dynamically adaptive and
problem-dependent. We incorporate DPAV into a deep Q-network as the dialogue
policy and show that our method can achieve better or comparable results
compared to top baselines on three dialogue datasets of different domains with
a lower computational load. In addition, we also theoretically prove the
convergence and derive the upper and lower bounds of the bias compared with
those of other methods.

Heterogeneous graphs have multiple node and edge types and are semantically
richer than homogeneous graphs. To learn such complex semantics, many graph
neural network approaches for heterogeneous graphs use metapaths to capture
multi-hop interactions between nodes. Typically, features from non-target nodes
are not incorporated into the learning procedure. However, there can be
nonlinear, high-order interactions involving multiple nodes or edges. In this
paper, we present Simplicial Graph Attention Network (SGAT), a simplicial
complex approach to represent such high-order interactions by placing features
from non-target nodes on the simplices. We then use attention mechanisms and
upper adjacencies to generate representations. We empirically demonstrate the
efficacy of our approach with node classification tasks on heterogeneous graph
datasets and further show SGAT's ability in extracting structural information
by employing random node features. Numerical experiments indicate that SGAT
performs better than other current state-of-the-art heterogeneous graph
learning methods.

Data drift is a thorny challenge when deploying person re-identification
(ReID) models into real-world devices, where the data distribution is
significantly different from that of the training environment and keeps
changing. To tackle this issue, we propose a federated spatial-temporal
incremental learning approach, named FedSTIL, which leverages both lifelong
learning and federated learning to continuously optimize models deployed on
many distributed edge clients. Unlike previous efforts, FedSTIL aims to mine
spatial-temporal correlations among the knowledge learnt from different edge
clients. Specifically, the edge clients first periodically extract general
representations of drifted data to optimize their local models. Then, the
learnt knowledge from edge clients will be aggregated by centralized parameter
server, where the knowledge will be selectively and attentively distilled from
spatial- and temporal-dimension with carefully designed mechanisms. Finally,
the distilled informative spatial-temporal knowledge will be sent back to
correlated edge clients to further improve the recognition accuracy of each
edge client with a lifelong learning method. Extensive experiments on a mixture
of five real-world datasets demonstrate that our method outperforms others by
nearly 4% in Rank-1 accuracy, while reducing communication cost by 62%. All
implementation codes are publicly available on
https://github.com/MSNLAB/Federated-Lifelong-Person-ReID

We explore a new strategy for few-shot novel view synthesis based on a neural
light field representation. Given a target camera pose, an implicit neural
network maps each ray to its target pixel's color directly. The network is
conditioned on local ray features generated by coarse volumetric rendering from
an explicit 3D feature volume. This volume is built from the input images using
a 3D ConvNet. Our method achieves competitive performances on synthetic and
real MVS data with respect to state-of-the-art neural radiance field based
competition, while offering a 100 times faster rendering.

In this paper, we studied the central limit theorem (CLT) for stochastic
gradient descent (SGD) type methods, including the vanilla SGD, momentum SGD
and Nesterov accelerated SGD methods with constant or vanishing damping
parameters. By taking advantage of Lyapunov function technique and $L_p$ bound
estimates, we established the CLT under more general conditions on learning
rates for broader classes of SGD methods compared with previous results. The
CLT for the time average was also investigated, and we found that it held in
the linear case, while it was not true in general nonlinear situation.
Numerical tests were also carried out to verify our theoretical analysis.

Detecting 3D objects from point clouds is a practical yet challenging task
that has attracted increasing attention recently. In this paper, we propose a
Label-Guided auxiliary training method for 3D object detection (LG3D), which
serves as an auxiliary network to enhance the feature learning of existing 3D
object detectors. Specifically, we propose two novel modules: a
Label-Annotation-Inducer that maps annotations and point clouds in bounding
boxes to task-specific representations and a Label-Knowledge-Mapper that
assists the original features to obtain detection-critical representations. The
proposed auxiliary network is discarded in inference and thus has no extra
computational cost at test time. We conduct extensive experiments on both
indoor and outdoor datasets to verify the effectiveness of our approach. For
example, our proposed LG3D improves VoteNet by 2.5% and 3.1% mAP on the SUN
RGB-D and ScanNetV2 datasets, respectively.

The separation of single-channel underwater acoustic signals is a challenging
problem with practical significance. In view of the signal separation problem
with unknown numbers of signals, we propose a solution with a fixed number of
output channels, enabling it to avoid the dimensional disaster caused by the
permutation problem induced by the alignment of outputs to targets.
Specifically, we modify two algorithms developed for known numbers of signals
based on autoencoders, which are highly explainable. We also propose a new
performance evaluation method for situations with mute channels. Experiments
conducted on simulated mixtures of radiated ship noise show that the proposed
solution can achieve similar separation performance to that attained with a
known number of signals. The mute channel output is also good.

State of the art magnetic resonance (MR) image super-resolution methods (ISR)
using convolutional neural networks (CNNs) leverage limited contextual
information due to the limited spatial coverage of CNNs. Vision transformers
(ViT) learn better global context that is helpful in generating superior
quality HR images. We combine local information of CNNs and global information
from ViTs for image super resolution and output super resolved images that have
superior quality than those produced by state of the art methods. We include
extra constraints through multiple novel loss functions that preserve structure
and texture information from the low resolution to high resolution images.

In multi-label learning, a particular case of multi-task learning where a
single data point is associated with multiple target labels, it was widely
assumed in the literature that, to obtain best accuracy, the dependence among
the labels should be explicitly modeled. This premise led to a proliferation of
methods offering techniques to learn and predict labels together, for example
where the prediction for one label influences predictions for other labels.
Even though it is now acknowledged that in many contexts a model of dependence
is not required for optimal performance, such models continue to outperform
independent models in some of those very contexts, suggesting alternative
explanations for their performance beyond label dependence, which the
literature is only recently beginning to unravel. Leveraging and extending
recent discoveries, we turn the original premise of multi-label learning on its
head, and approach the problem of joint-modeling specifically under the absence
of any measurable dependence among task labels; for example, when task labels
come from separate problem domains. We shift insights from this study towards
building an approach for transfer learning that challenges the long-held
assumption that transferability of tasks comes from measurements of similarity
between the source and target domains or models. This allows us to design and
test a method for transfer learning, which is model driven rather than purely
data driven, and furthermore it is black box and model-agnostic (any base model
class can be considered). We show that essentially we can create
task-dependence based on source-model capacity. The results we obtain have
important implications and provide clear directions for future work, both in
the areas of multi-label and transfer learning.

Partially Observable Markov Decision Process (POMDP) is a framework
applicable to many real world problems. In this work, we propose an approach to
solve POMDPs with multimodal belief by relying on a policy that solves the
fully observable version. By defininig a new, mixture value function based on
the value function from the fully observable variant, we can use the
corresponding greedy policy to solve the POMDP itself. We develop the
mathematical framework necessary for discussion, and introduce a benchmark
built on the task of Reconnaissance Blind TicTacToe. On this benchmark, we show
that our policy outperforms policies ignoring the existence of multiple modes.

In this work, we propose an adaptive sparse learning algorithm that can be
applied to learn the physical processes and obtain a sparse representation of
the solution given a large snapshot space. Assume that there is a rich class of
precomputed basis functions that can be used to approximate the quantity of
interest. We then design a neural network architecture to learn the
coefficients of solutions in the spaces which are spanned by these basis
functions. The information of the basis functions are incorporated in the loss
function, which minimizes the differences between the downscaled reduced order
solutions and reference solutions at multiple time steps. The network contains
multiple submodules and the solutions at different time steps can be learned
simultaneously. We propose some strategies in the learning framework to
identify important degrees of freedom. To find a sparse solution
representation, a soft thresholding operator is applied to enforce the sparsity
of the output coefficient vectors of the neural network. To avoid
over-simplification and enrich the approximation space, some degrees of freedom
can be added back to the system through a greedy algorithm. In both scenarios,
that is, removing and adding degrees of freedom, the corresponding network
connections are pruned or reactivated guided by the magnitude of the solution
coefficients obtained from the network outputs. The proposed adaptive learning
process is applied to some toy case examples to demonstrate that it can achieve
a good basis selection and accurate approximation. More numerical tests are
performed on two-phase multiscale flow problems to show the capability and
interpretability of the proposed method on complicated applications.

Adversarial training and its variants have come to be the prevailing methods
to achieve adversarially robust classification using neural networks. However,
its increased computational cost together with the significant gap between
standard and robust performance hinder progress and beg the question of whether
we can do better. In this work, we take a step back and ask: Can models achieve
robustness via standard training on a suitably optimized set? To this end, we
devise a meta-learning method for robust classification, that optimizes the
dataset prior to its deployment in a principled way, and aims to effectively
remove the non-robust parts of the data. We cast our optimization method as a
multi-step PGD procedure on kernel regression, with a class of kernels that
describe infinitely wide neural nets (Neural Tangent Kernels - NTKs).
Experiments on MNIST and CIFAR-10 demonstrate that the datasets we produce
enjoy very high robustness against PGD attacks, when deployed in both kernel
regression classifiers and neural networks. However, this robustness is
somewhat fallacious, as alternative attacks manage to fool the models, which we
find to be the case for previous similar works in the literature as well. We
discuss potential reasons for this and outline further avenues of research.

Pushing back the frontiers of collaborative robots in industrial
environments, we propose a new Separable-Sparse Graph Convolutional Network
(SeS-GCN) for pose forecasting. For the first time, SeS-GCN bottlenecks the
interaction of the spatial, temporal and channel-wise dimensions in GCNs, and
it learns sparse adjacency matrices by a teacher-student framework. Compared to
the state-of-the-art, it only uses 1.72% of the parameters and it is ~4 times
faster, while still performing comparably in forecasting accuracy on Human3.6M
at 1 second in the future, which enables cobots to be aware of human operators.
As a second contribution, we present a new benchmark of Cobots and Humans in
Industrial COllaboration (CHICO). CHICO includes multi-view videos, 3D poses
and trajectories of 20 human operators and cobots, engaging in 7 realistic
industrial actions. Additionally, it reports 226 genuine collisions, taking
place during the human-cobot interaction. We test SeS-GCN on CHICO for two
important perception tasks in robotics: human pose forecasting, where it
reaches an average error of 85.3 mm (MPJPE) at 1 sec in the future with a run
time of 2.3 msec, and collision detection, by comparing the forecasted human
motion with the known cobot motion, obtaining an F1-score of 0.64.

Videos obtained by rolling-shutter (RS) cameras result in spatially-distorted
frames. These distortions become significant under fast camera/scene motions.
Undoing effects of RS is sometimes addressed as a spatial problem, where
objects need to be rectified/displaced in order to generate their correct
global shutter (GS) frame. However, the cause of the RS effect is inherently
temporal, not spatial. In this paper we propose a space-time solution to the RS
problem. We observe that despite the severe differences between their xy
frames, a RS video and its corresponding GS video tend to share the exact same
xt slices -- up to a known sub-frame temporal shift. Moreover, they share the
same distribution of small 2D xt-patches, despite the strong temporal aliasing
within each video. This allows to constrain the GS output video using
video-specific constraints imposed by the RS input video. Our algorithm is
composed of 3 main components: (i) Dense temporal upsampling between
consecutive RS frames using an off-the-shelf method, (which was trained on
regular video sequences), from which we extract GS "proposals". (ii) Learning
to correctly merge an ensemble of such GS "proposals" using a dedicated
MergeNet. (iii) A video-specific zero-shot optimization which imposes the
similarity of xt-patches between the GS output video and the RS input video.
Our method obtains state-of-the-art results on benchmark datasets, both
numerically and visually, despite being trained on a small synthetic RS/GS
dataset. Moreover, it generalizes well to new complex RS videos with motion
types outside the distribution of the training set (e.g., complex non-rigid
motions) -- videos which competing methods trained on much more data cannot
handle well. We attribute these generalization capabilities to the combination
of external and internal constraints.

In urban environments, the complex and uncertain intersection scenarios are
challenging for autonomous driving. To ensure safety, it is crucial to develop
an adaptive decision making system that can handle the interaction with other
vehicles. Manually designed model-based methods are reliable in common
scenarios. But in uncertain environments, they are not reliable, so
learning-based methods are proposed, especially reinforcement learning (RL)
methods. However, current RL methods need retraining when the scenarios change.
In other words, current RL methods cannot reuse accumulated knowledge. They
forget learned knowledge when new scenarios are given. To solve this problem,
we propose a hierarchical framework that can autonomously accumulate and reuse
knowledge. The proposed method combines the idea of motion primitives (MPs)
with hierarchical reinforcement learning (HRL). It decomposes complex problems
into multiple basic subtasks to reduce the difficulty. The proposed method and
other baseline methods are tested in a challenging intersection scenario based
on the CARLA simulator. The intersection scenario contains three different
subtasks that can reflect the complexity and uncertainty of real traffic flow.
After offline learning and testing, the proposed method is proved to have the
best performance among all methods.

Gait recognition is instrumental in crime prevention and social security, for
it can be conducted at a long distance without the cooperation of subjects.
However, existing datasets and methods cannot deal with the most challenging
problem in realistic gait recognition effectively: walking in different clothes
(CL). In order to tackle this problem, we propose two benchmarks: CASIA-BN-RCC
and OUMVLP-RCC, to simulate the cloth-changing condition in practice. The two
benchmarks can force the algorithm to realize cross-view and cross-cloth with
two sub-datasets. Furthermore, we propose a new framework that can be applied
with off-the-shelf backbones to improve its performance in the Realistic
Cloth-Changing problem with Progressive Feature Learning. Specifically, in our
framework, we design Progressive Mapping and Progressive Uncertainty to extract
the cross-view features and then extract cross-cloth features on the basis. In
this way, the features from the cross-view sub-dataset can first dominate the
feature space and relieve the uneven distribution caused by the adverse effect
from the cross-cloth sub-dataset. The experiments on our benchmarks show that
our framework can effectively improve the recognition performance in CL
conditions. Our codes and datasets will be released after accepted.

Bi-level optimization, especially the gradient-based category, has been
widely used in the deep learning community including hyperparameter
optimization and meta knowledge extraction. Bi-level optimization embeds one
problem within another and the gradient-based category solves the outer level
task by computing the hypergradient, which is much more efficient than
classical methods such as the evolutionary algorithm. In this survey, we first
give a formal definition of the gradient-based bi-level optimization. Secondly,
we illustrate how to formulate a research problem as a bi-level optimization
problem, which is of great practical use for beginners. More specifically,
there are two formulations: the single-task formulation to optimize
hyperparameters such as regularization parameters and the distilled data, and
the multi-task formulation to extract meta knowledge such as the model
initialization. With a bi-level formulation, we then discuss four bi-level
optimization solvers to update the outer variable including explicit gradient
update, proxy update, implicit function update, and closed-form update. Last
but not least, we conclude the survey by pointing out the great potential of
gradient-based bi-level optimization on science problems (AI4Science).

In a busy city street, a pedestrian surrounded by distractions can pick out a
single sign if it is relevant to their route. Artificial agents in outdoor
Vision-and-Language Navigation (VLN) are also confronted with detecting
supervisory signal on environment features and location in inputs. To boost the
prominence of relevant features in transformer-based architectures without
costly preprocessing and pretraining, we take inspiration from priority maps -
a mechanism described in neuropsychological studies. We implement a novel
priority map module and pretrain on auxiliary tasks using low-sample datasets
with high-level representations of routes and environment-related references to
urban features. A hierarchical process of trajectory planning - with subsequent
parameterised visual boost filtering on visual inputs and prediction of
corresponding textual spans - addresses the core challenges of cross-modal
alignment and feature-level localisation. The priority map module is integrated
into a feature-location framework that doubles the task completion rates of
standalone transformers and attains state-of-the-art performance on the
Touchdown benchmark for VLN. Code and data are referenced in Appendix C.

We report the largest scale deep learning with High Performance Computing
(HPC) to physics analysis with the CMS simulation data in proton-proton
collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that
takes low-level information as images considering the geometry of the CMS
detector and use this model to discriminate \textit{R}-parity violating super
symmetry (RPV SUSY) events from the background events with inelastic quantum
process from the Standard Model (QCD multi-jet). We compare the classification
performance of the CNN method with that of the widely used cut-based method.
The signal efficiency (and expected significance) of the CNN method is 1.85
(1.2) times higher than that of the cut-based method. To speed-up the training,
the model training is conducted using the Nurion HPC system at the Korea
Institute of Science and Technology Information, which is equipped with
thousands of parallel \texttt{Xeon Phi} CPUs. Notably, our CNN model shows
scalability up to 1024 nodes.

Sports field registration in broadcast videos is typically interpreted as the
task of homography estimation, which provides a mapping between a planar field
and the corresponding visible area of the image. In contrast to previous
approaches, we consider the task as a camera calibration problem. First, we
introduce a differentiable objective function that is able to learn the camera
pose and focal length from segment correspondences (e.g., lines, point clouds),
based on pixel-level annotations for segments of a known calibration object.
The calibration module iteratively minimizes the segment reprojection error
induced by the estimated camera parameters. Second, we propose a novel approach
for 3D sports field registration from broadcast soccer images. Compared to the
typical solution, which subsequently refines an initial estimation, our
solution does it in one step. The proposed method is evaluated for sports field
registration on two datasets and achieves superior results compared to two

The thesis advances the field of software security by providing knowledge and
automation support for software vulnerability assessment using data-driven
approaches. Software vulnerability assessment provides important and
multifaceted information to prevent and mitigate dangerous cyber-attacks in the
wild. The key contributions include a systematisation of knowledge, along with
a suite of novel data-driven techniques and practical recommendations for
researchers and practitioners in the area. The thesis results help improve the
understanding and inform the practice of assessing ever-increasing
vulnerabilities in real-world software systems. This in turn enables more
thorough and timely fixing prioritisation and planning of these critical
security issues.

This paper proposes a novel test-time adaptation strategy that adjusts the
model pre-trained on the source domain using only unlabeled online data from
the target domain to alleviate the performance degradation due to the
distribution shift between the source and target domains. Adapting the entire
model parameters using the unlabeled online data may be detrimental due to the
erroneous signals from an unsupervised objective. To mitigate this problem, we
propose a shift-agnostic weight regularization that encourages largely updating
the model parameters sensitive to distribution shift while slightly updating
those insensitive to the shift, during test-time adaptation. This
regularization enables the model to quickly adapt to the target domain without
performance degradation by utilizing the benefit of a high learning rate. In
addition, we present an auxiliary task based on nearest source prototypes to
align the source and target features, which helps reduce the distribution shift
and leads to further performance improvement. We show that our method exhibits
state-of-the-art performance on various standard benchmarks and even
outperforms its supervised counterpart.

Significant progress has been witnessed in learning-based Multi-view Stereo
(MVS) of supervised and unsupervised settings. To combine their respective
merits in accuracy and completeness, meantime reducing the demand for expensive
labeled data, this paper explores a novel semi-supervised setting of
learning-based MVS problem that only a tiny part of the MVS data is attached
with dense depth ground truth. However, due to huge variation of scenarios and
flexible setting in views, semi-supervised MVS problem (Semi-MVS) may break the
basic assumption in classic semi-supervised learning, that unlabeled data and
labeled data share the same label space and data distribution. To handle these
issues, we propose a novel semi-supervised MVS framework, namely SE-MVS. For
the simple case that the basic assumption works in MVS data, consistency
regularization encourages the model predictions to be consistent between
original sample and randomly augmented sample via constraints on KL divergence.
For further troublesome case that the basic assumption is conflicted in MVS
data, we propose a novel style consistency loss to alleviate the negative
effect caused by the distribution gap. The visual style of unlabeled sample is
transferred to labeled sample to shrink the gap, and the model prediction of
generated sample is further supervised with the label in original labeled
sample. The experimental results on DTU, BlendedMVS, GTA-SFM, and
Tanks\&Temples datasets show the superior performance of the proposed method.
With the same settings in backbone network, our proposed SE-MVS outperforms its
fully-supervised and unsupervised baselines.

Neuroimaging studies are often limited by the number of subjects and
cognitive processes that can be feasibly interrogated. However, a rapidly
growing number of neuroscientific studies have collectively accumulated an
extensive wealth of results. Digesting this growing literature and obtaining
novel insights remains to be a major challenge, since existing meta-analytic
tools are constrained to keyword queries. In this paper, we present Text2Brain,
an easy to use tool for synthesizing brain activation maps from open-ended text
queries. Text2Brain was built on a transformer-based neural network language
model and a coordinate-based meta-analysis of neuroimaging studies. Text2Brain
combines a transformer-based text encoder and a 3D image generator, and was
trained on variable-length text snippets and their corresponding activation
maps sampled from 13,000 published studies. In our experiments, we demonstrate
that Text2Brain can synthesize meaningful neural activation patterns from
various free-form textual descriptions. Text2Brain is available at
https://braininterpreter.com as a web-based tool for efficiently searching
through the vast neuroimaging literature and generating new hypotheses.

Online continual learning (online CL) studies the problem of learning
sequential tasks from an online data stream without task boundaries, aiming to
adapt to new data while alleviating catastrophic forgetting on the past tasks.
This paper proposes a framework Contrastive Vision Transformer (CVT), which
designs a focal contrastive learning strategy based on a transformer
architecture, to achieve a better stability-plasticity trade-off for online CL.
Specifically, we design a new external attention mechanism for online CL that
implicitly captures previous tasks' information. Besides, CVT contains
learnable focuses for each class, which could accumulate the knowledge of
previous classes to alleviate forgetting. Based on the learnable focuses, we
design a focal contrastive loss to rebalance contrastive learning between new
and past classes and consolidate previously learned representations. Moreover,
CVT contains a dual-classifier structure for decoupling learning current
classes and balancing all observed classes. The extensive experimental results
show that our approach achieves state-of-the-art performance with even fewer
parameters on online CL benchmarks and effectively alleviates the catastrophic
forgetting.

Although many methods have been proposed to enhance the transferability of
adversarial perturbations, these methods are designed in a heuristic manner,
and the essential mechanism for improving adversarial transferability is still
unclear. This paper summarizes the common mechanism shared by twelve previous
transferability-boosting methods in a unified view, i.e., these methods all
reduce game-theoretic interactions between regional adversarial perturbations.
To this end, we focus on the attacking utility of all interactions between
regional adversarial perturbations, and we first discover and prove the
negative correlation between the adversarial transferability and the attacking
utility of interactions. Based on this discovery, we theoretically prove and
empirically verify that twelve previous transferability-boosting methods all
reduce interactions between regional adversarial perturbations. More crucially,
we consider the reduction of interactions as the essential reason for the
enhancement of adversarial transferability. Furthermore, we design the
interaction loss to directly penalize interactions between regional adversarial
perturbations during attacking. Experimental results show that the interaction
loss significantly improves the transferability of adversarial perturbations.

Machine sound classification has been one of the fundamental tasks of music
technology. A major branch of sound classification is the classification of
music genres. However, though covering most genres of music, existing music
genre datasets often do not contain fine-grained labels that indicate the
detailed sub-genres of music. In consideration of the consistency of genres of
songs in a mixtape or in a DJ (live) set, we have collected and annotated a
dataset of house music that provide 4 sub-genre labels, namely future house,
bass house, progressive house and melodic house. Experiments show that our
annotations well exhibit the characteristics of different categories. Also, we
have built baseline models that classify the sub-genre based on the
mel-spectrograms of a track, achieving strongly competitive results. Besides,
we have put forward a few application scenarios of our dataset and baseline
model, with a simulated sci-fi tunnel as a short demo built and rendered in a
3D modeling software, with the colors of the lights automated by the output of
our model.

Few-shot learning performs classification tasks and regression tasks on
scarce samples. As one of the most representative few-shot learning models,
Prototypical Network represents each class as sample average, or a prototype,
and measures the similarity of samples and prototypes by Euclidean distance. In
this paper, we propose a framework of spectral filtering (shrinkage) for
measuring the difference between query samples and prototypes, or namely the
relative prototypes, in a reproducing kernel Hilbert space (RKHS). In this
framework, we further propose a method utilizing Tikhonov regularization as the
filter function for few-shot classification. We conduct several experiments to
verify our method utilizing different kernels based on the miniImageNet
dataset, tiered-ImageNet dataset and CIFAR-FS dataset. The experimental results
show that the proposed model can perform the state-of-the-art. In addition, the
experimental results show that the proposed shrinkage method can boost the
performance. Source code is available at https://github.com/zhangtao2022/DSFN.

Deep learning based semi-supervised learning (SSL) methods have achieved
strong performance in medical image segmentation, which can alleviate doctors'
expensive annotation by utilizing a large amount of unlabeled data. Unlike most
existing semi-supervised learning methods, adversarial training based methods
distinguish samples from different sources by learning the data distribution of
the segmentation map, leading the segmenter to generate more accurate
predictions. We argue that the current performance restrictions for such
approaches are the problems of feature extraction and learning preference. In
this paper, we propose a new semi-supervised adversarial method called Patch
Confidence Adversarial Training (PCA) for medical image segmentation. Rather
than single scalar classification results or pixel-level confidence maps, our
proposed discriminator creates patch confidence maps and classifies them at the
scale of the patches. The prediction of unlabeled data learns the pixel
structure and context information in each patch to get enough gradient
feedback, which aids the discriminator in convergent to an optimal state and
improves semi-supervised segmentation performance. Furthermore, at the
discriminator's input, we supplement semantic information constraints on
images, making it simpler for unlabeled data to fit the expected data
distribution. Extensive experiments on the Automated Cardiac Diagnosis
Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019
challenge dataset show that our method outperforms the state-of-the-art
semi-supervised methods, which demonstrates its effectiveness for medical image

Clustering has received much attention in Statistics and Machine learning
with the aim of developing statistical models and autonomous algorithms which
are capable of acquiring information from raw data in order to perform
exploratory analysis.Several techniques have been developed to cluster sampled
univariate vectors only considering the average value over the whole period and
as such they have not been able to explore fully the underlying distribution as
well as other features of the data, especially in presence of structured time
series. We propose a model-based clustering technique that is based on quantile
regression permitting us to cluster bivariate time series at different quantile
levels. We model the within cluster density using asymmetric Laplace
distribution allowing us to take into account asymmetry in the distribution of
the data. We evaluate the performance of the proposed technique through a
simulation study. The method is then applied to cluster time series observed
from Glob-colour satellite data related to trophic status indices with aim of
evaluating their temporal dynamics in order to identify homogeneous areas, in
terms of trophic status, in the Gulf of Gabes.

State-of-the-art parametric and non-parametric style transfer approaches are
prone to either distorted local style patterns due to global statistics
alignment, or unpleasing artifacts resulting from patch mismatching. In this
paper, we study a novel semi-parametric neural style transfer framework that
alleviates the deficiency of both parametric and non-parametric stylization.
The core idea of our approach is to establish accurate and fine-grained
content-style correspondences using graph neural networks (GNNs). To this end,
we develop an elaborated GNN model with content and style local patches as the
graph vertices. The style transfer procedure is then modeled as the
attention-based heterogeneous message passing between the style and content
nodes in a learnable manner, leading to adaptive many-to-one style-content
correlations at the local patch level. In addition, an elaborated deformable
graph convolutional operation is introduced for cross-scale style-content
matching. Experimental results demonstrate that the proposed semi-parametric
image stylization approach yields encouraging results on the challenging style
patterns, preserving both global appearance and exquisite details. Furthermore,
by controlling the number of edges at the inference stage, the proposed method
also triggers novel functionalities like diversified patch-based stylization
with a single model.

This paper studies a novel privacy-preserving anonymization problem for
pedestrian images, which preserves personal identity information (PII) for
authorized models and prevents PII from being recognized by third parties.
Conventional anonymization methods unavoidably cause semantic information loss,
leading to limited data utility. Besides, existing learned anonymization
techniques, while retaining various identity-irrelevant utilities, will change
the pedestrian identity, and thus are unsuitable for training robust
re-identification models. To explore the privacy-utility trade-off for
pedestrian images, we propose a joint learning reversible anonymization
framework, which can reversibly generate full-body anonymous images with little
performance drop on person re-identification tasks. The core idea is that we
adopt desensitized images generated by conventional methods as the initial
privacy-preserving supervision and jointly train an anonymization encoder with
a recovery decoder and an identity-invariant model. We further propose a
progressive training strategy to improve the performance, which iteratively
upgrades the initial anonymization supervision. Experiments further demonstrate
the effectiveness of our anonymized pedestrian images for privacy protection,
which boosts the re-identification performance while preserving privacy. Code
is available at \url{https://github.com/whuzjw/privacy-reid}.

Spiking Neural Networks (SNNs) are the third generation of artificial neural
networks that enable energy-efficient implementation on neuromorphic hardware.
However, the discrete transmission of spikes brings significant challenges to
the robust and high-performance learning mechanism. Most existing works focus
solely on learning between neurons but ignore the influence between synapses,
resulting in a loss of robustness and accuracy. To address this problem, we
propose a robust and effective learning mechanism by modeling the associative
plasticity between synapses (APBS) observed from the physiological phenomenon
of associative long-term potentiation (ALTP). With the proposed APBS method,
synapses of the same neuron interact through a shared factor when concurrently
stimulated by other neurons. In addition, we propose a spatiotemporal cropping
and flipping (STCF) method to improve the generalization ability of our
network. Extensive experiments demonstrate that our approaches achieve superior
performance on static CIFAR-10 datasets and state-of-the-art performance on
neuromorphic MNIST-DVS, CIFAR10-DVS datasets by a lightweight convolution
network. To our best knowledge, this is the first time to explore a learning
method between synapses and an extended approach for neuromorphic data.

Liquid metals (LM) are embedded in an elastomer matrix to obtain soft
composites with unique thermal, dielectric, and mechanical properties. They
have applications in soft robotics, biomedical engineering, and wearable
electronics. By linking the structure to the properties of these materials, it
is possible to perform material design rationally. Liquid-metal embedded
elastomers (LMEEs) have been designed for targeted electro-thermo-mechanical
properties by semi-supervised learning of structure-property (SP) links in a
variational autoencoder network (VAE). The design parameters are the
microstructural descriptors that are physically meaningful and have affine
relationships with the synthetization of the studied particulate composite. The
machine learning (ML) model is trained on a generated dataset of
microstructural descriptors with their multifunctional property quantities as
their labels. Sobol sequence is used for in-silico Design of Experiment (DoE)
by sampling the design space to generate a comprehensive dataset of 3D
microstructure realizations via a packing algorithm. The mechanical responses
of the generated microstructures are simulated using a previously developed
Finite Element (FE) model, considering the surface tension induced by LM
inclusions, while the linear thermal and dielectric constants are homogenized
with the help of our in-house Fast Fourier Transform (FFT) package. Following
the training by minimization of an appropriate loss function, the VAE encoder
acts as the surrogate of numerical solvers of the multifunctional
homogenizations, and its decoder is used for the material design. Our results
indicate the satisfactory performance of the surrogate model and the inverse
calculator with respect to high-fidelity numerical simulations validated with
LMEE experimental results.

We study the robust principal component analysis (RPCA) problem in a
distributed setting. The goal of RPCA is to find an underlying low-rank
estimation for a raw data matrix when the data matrix is subject to the
corruption of gross sparse errors. Previous studies have developed RPCA
algorithms that provide stable solutions with fast convergence. However, these
algorithms are typically hard to scale and cannot be implemented distributedly,
due to the use of either SVD or large matrix multiplication. In this paper, we
propose the first distributed robust principal analysis algorithm based on
consensus factorization, dubbed DCF-PCA. We prove the convergence of DCF-PCA
and evaluate DCF-PCA on various problem setting

Artificial intelligence (AI) technologies, and particularly deep learning
systems, are traditionally the domain of large-scale cloud servers, which have
access to high computational and energy resources. Nonetheless, in
Internet-of-Things (IoT) networks, the interface with the real-world is carried
out using edge devices that are limited in hardware and can communicate. The
conventional approach to provide AI processing to data collected by edge
devices involves sending samples to the cloud, at the cost of latency,
communication, connectivity, and privacy concerns. Consequently, recent years
have witnessed a growing interest in enabling AI-aided inference on edge
devices by leveraging their communication capabilities to establish
collaborative inference. This article reviews candidate strategies for
facilitating the transition of AI to IoT devices via collaboration. We identify
the need to operate in different mobility and connectivity constraints as a
motivating factor to consider multiple schemes, which can be roughly divided
into methods where inference is done remotely, i.e., on the cloud, and those
that infer on the edge. We identify the key characteristics of each strategy in
terms of inference accuracy, communication latency, privacy, and connectivity
requirements, providing a systematic comparison between existing approaches. We
conclude by presenting future research challenges and opportunities arising
from the concept of collaborative inference.

Event cameras are inherently suitable for spikingneural networks (SNNS) and
have great potential in challenging scenesdue to the advantages of bionics,
asynchrony, high dynamic range, and no motion blur.However, novel data
augmentations designed for event properties are required to process the
unconventional output of these cameras in order to unlock their potential.In
this paper, we explore the extraordinary influence of brightness variations due
to event properties. Along the way, two novel data augmentation methods,
lemph[EventInvert) and lemph(EventDrift) (EventID), are proposedto simulate two
basic transformations of this influence.Specifically, EventID inverts or drifts
events in the stream through transformationsin temporal and polar domains,
thereby generating samples affected by brightness variances.Extensive
experiments are carried out on the CIFAR10-DVS, N-Caltech101, and N-CARS
datasets.It turns out that this simulation improves generalization by
increasing the robustness of models against brightness variations.In addition,
EventID is broadly effective, surpassing previous state-of-the-art
performances.For example, the spiking neural network model with EventID
achieves a state-of-the-art accuracy of 83.501% on the CIFAR10-DVS dataset.

This paper proposes Federated Learning (FL) based smart healthcare system
where Medical Centers (MCs) train the local model using the data collected from
patients and send the model weights to the miners in a blockchain-based robust
framework without sharing raw data, keeping privacy preservation into
deliberation. We formulate an optimization problem by maximizing the utility
and minimizing the loss function considering energy consumption and FL process
delay of MCs for learning effective models on distributed healthcare data
underlying a blockchain-based framework. We propose a solution in two stages:
first, offer a stable matching-based association algorithm to maximize the
utility of both miners and MCs and then solve loss minimization using
Stochastic Gradient Descent (SGD) algorithm employing FL under Differential
Privacy (DP) and blockchain technology. Moreover, we incorporate blockchain
technology to provide tempered resistant and decentralized model weight sharing
in the proposed FL-based framework. The effectiveness of the proposed model is
shown through simulation on real-world healthcare data comparing other

Pavement damage segmentation has benefited enormously from deep learning. %
and large-scale datasets. However, few current public datasets limit the
potential exploration of deep learning in the application of pavement damage
segmentation. To address this problem, this study has proposed Pavementscapes,
a large-scale dataset to develop and evaluate methods for pavement damage
segmentation. Pavementscapes is comprised of 4,000 images with a resolution of
$1024 \times 2048$, which have been recorded in the real-world pavement
inspection projects with 15 different pavements. A total of 8,680 damage
instances are manually labeled with six damage classes at the pixel level. The
statistical study gives a thorough investigation and analysis of the proposed
dataset. The numeral experiments propose the top-performing deep neural
networks capable of segmenting pavement damages, which provides the baselines
of the open challenge for pavement inspection. The experiment results also
indicate the existing problems for damage segmentation using deep learning, and
this study provides potential solutions.

Model Checking is widely applied in verifying the correctness of complex and
concurrent systems against a specification. Pure symbolic approaches while
popular, still suffer from the state space explosion problem that makes them
impractical for large scale systems and/or specifications. In this paper, we
propose to use graph representation learning (GRL) for solving linear temporal
logic (LTL) model checking, where the system and the specification are
expressed by a B\"uchi automaton and an LTL formula respectively. A novel
GRL-based framework OCTAL, is designed to learn the representation of the
graph-structured system and specification, which reduces the model checking
problem to binary classification in the latent space. The empirical experiments
show that OCTAL achieves comparable accuracy against canonical SOTA model
checkers on three different datasets, with up to $5\times$ overall speedup and
above $63\times$ for satisfiability checking alone.

Scene inference under low-light is a challenging problem due to severe noise
in the captured images. One way to reduce noise is to use longer exposure
during the capture. However, in the presence of motion (scene or camera
motion), longer exposures lead to motion blur, resulting in loss of image
information. This creates a trade-off between these two kinds of image
degradations: motion blur (due to long exposure) vs. noise (due to short
exposure), also referred as a dual image corruption pair in this paper. With
the rise of cameras capable of capturing multiple exposures of the same scene
simultaneously, it is possible to overcome this trade-off. Our key observation
is that although the amount and nature of degradation varies for these
different image captures, the semantic content remains the same across all
images. To this end, we propose a method to leverage these multi exposure
captures for robust inference under low-light and motion. Our method builds on
a feature consistency loss to encourage similar results from these individual
captures, and uses the ensemble of their final predictions for robust visual
recognition. We demonstrate the effectiveness of our approach on simulated
images as well as real captures with multiple exposures, and across the tasks
of object detection and image classification.

Bayesian inference for high-dimensional inverse problems is challenged by the
computational costs of the forward operator and the selection of an appropriate
prior distribution. Amortized variational inference addresses these challenges
where a neural network is trained to approximate the posterior distribution
over existing pairs of model and data. When fed previously unseen data and
normally distributed latent samples as input, the pretrained deep neural
network -- in our case a conditional normalizing flow -- provides posterior
samples with virtually no cost. However, the accuracy of this approach relies
on the availability of high-fidelity training data, which seldom exists in
geophysical inverse problems due to the heterogeneous structure of the Earth.
In addition, accurate amortized variational inference requires the observed
data to be drawn from the training data distribution. As such, we propose to
increase the resilience of amortized variational inference when faced with data
distribution shift via a physics-based correction to the conditional
normalizing flow latent distribution. To accomplish this, instead of a standard
Gaussian latent distribution, we parameterize the latent distribution by a
Gaussian distribution with an unknown mean and diagonal covariance. These
unknown quantities are then estimated by minimizing the Kullback-Leibler
divergence between the corrected and true posterior distributions. While
generic and applicable to other inverse problems, by means of a seismic imaging
example, we show that our correction step improves the robustness of amortized
variational inference with respect to changes in number of source experiments,
noise variance, and shifts in the prior distribution. This approach provides a
seismic image with limited artifacts and an assessment of its uncertainty with
approximately the same cost as five reverse-time migrations.

Fine-Grained Visual Classification (FGVC) is a longstanding and fundamental
problem in computer vision and pattern recognition, and underpins a diverse set
of real-world applications. This paper describes our contribution at
SnakeCLEF2022 with FGVC. Firstly, we design a strong multimodal backbone to
utilize various meta-information to assist in fine-grained identification.
Secondly, we provide new loss functions to solve the long tail distribution
with dataset. Then, in order to take full advantage of unlabeled datasets, we
use self-supervised learning and supervised learning joint training to provide
pre-trained model. Moreover, some effective data process tricks also are
considered in our experiments. Last but not least, fine-tuned in downstream
task with hard mining, ensambled kinds of model performance. Extensive
experiments demonstrate that our method can effectively improve the performance
of fine-grained recognition. Our method can achieve a macro f1 score 92.7% and
89.4% on private and public dataset, respectively, which is the 1st place among
the participators on private leaderboard.

In this paper, we propose five prediction intervals for the beta
autoregressive moving average model. This model is suitable for modeling and
forecasting variables that assume values in the interval $(0,1)$. Two of the
proposed prediction intervals are based on approximations considering the
normal distribution and the quantile function of the beta distribution. We also
consider bootstrap-based prediction intervals, namely: (i) bootstrap prediction
errors (BPE) interval; (ii) bias-corrected and acceleration (BCa) prediction
interval; and (iii) percentile prediction interval based on the quantiles of
the bootstrap-predicted values for two different bootstrapping schemes. The
proposed prediction intervals were evaluated according to Monte Carlo
simulations. The BCa prediction interval offered the best performance among the
evaluated intervals, showing lower coverage rate distortion and small average
length. We applied our methodology for predicting the water level of the
Cantareira water supply system in S\~ao Paulo, Brazil.

In this paper, we propose generalized attention mechanism (GAM) by first
suggesting a new interpretation for self-attention mechanism of Vaswani et al.
. Following the interpretation, we provide description for different variants
of attention mechanism which together form GAM. Further, we propose a new
relative position representation within the framework of GAM. This
representation can be easily utilized for cases in which elements next to each
other in input sequence can be at random locations in actual dataset/corpus.

In contrast to the older writing system of the 19th century, modern Hawaiian
orthography employs characters for long vowels and glottal stops. These extra
characters account for about one-third of the phonemes in Hawaiian, so
including them makes a big difference to reading comprehension and
pronunciation. However, transliterating between older and newer texts is a
laborious task when performed manually. We introduce two related methods to
help solve this transliteration problem automatically, given that there were
not enough data to train an end-to-end deep learning model. One method is
implemented, end-to-end, using finite state transducers (FSTs). The other is a
hybrid deep learning approach which approximately composes an FST with a
recurrent neural network (RNN). We find that the hybrid approach outperforms
the end-to-end FST by partitioning the original problem into one part that can
be modelled by hand, using an FST, and into another part, which is easily
solved by an RNN trained on the available data.

Falls, highly common in the constantly increasing global aging population,
can have a variety of negative effects on their health, well-being, and quality
of life, including restricting their capabilities to conduct Activities of
Daily Living (ADLs), which are crucial for one's sustenance. Timely assistance
during falls is highly necessary, which involves tracking the indoor location
of the elderly during their diverse navigational patterns associated with ADLs
to detect the precise location of a fall. With the decreasing caregiver
population on a global scale, it is important that the future of intelligent
living environments can detect falls during ADLs while being able to track the
indoor location of the elderly in the real world. To address these challenges,
this work proposes a cost-effective and simplistic design paradigm for an
Ambient Assisted Living system that can capture multimodal components of user
behaviors during ADLs that are necessary for performing fall detection and
indoor localization in a simultaneous manner in the real world. Proof of
concept results from real-world experiments are presented to uphold the
effective working of the system. The findings from two comparison studies with
prior works in this field are also presented to uphold the novelty of this
work. The first comparison study shows how the proposed system outperforms
prior works in the areas of indoor localization and fall detection in terms of
the effectiveness of its software design and hardware design. The second
comparison study shows that the cost for the development of this system is the
least as compared to prior works in these fields, which involved real-world
development of the underlining systems, thereby upholding its cost-effective
nature.

In this work we establish an algorithm and distribution independent
non-asymptotic trade-off between the model size, excess test loss, and training
loss of linear predictors. Specifically, we show that models that perform well
on the test data (have low excess loss) are either "classical" -- have training
loss close to the noise level, or are "modern" -- have a much larger number of
parameters compared to the minimum needed to fit the training data exactly.
  We also provide a more precise asymptotic analysis when the limiting spectral
distribution of the whitened features is Marchenko-Pastur. Remarkably, while
the Marchenko-Pastur analysis is far more precise near the interpolation peak,
where the number of parameters is just enough to fit the training data, in
settings of most practical interest it differs from the distribution
independent bound by only a modest multiplicative constant.

Neural networks have shown great potential in compressing volume data for
visualization. However, due to the high cost of training and inference, such
volumetric neural representations have thus far only been applied to offline
data processing and non-interactive rendering. In this paper, we demonstrate
that by simultaneously leveraging modern GPU tensor cores, a native CUDA neural
network framework, and a well-designed rendering algorithm with macro-cell
acceleration, we can interactively ray trace volumetric neural representations
(10-60fps). Our neural representations are also high-fidelity (PSNR > 30dB) and
compact (10-1000x smaller). Additionally, we show that it is possible to fit
the entire training step inside a rendering loop and skip the pre-training
process completely. To support extreme-scale volume data, we also develop an
efficient out-of-core training strategy, which allows our volumetric neural
representation training to potentially scale up to terascale using only an
NVIDIA RTX 3090 workstation.

Motion blur of fast-moving subjects is a longstanding problem in photography
and very common on mobile phones due to limited light collection efficiency,
particularly in low-light conditions. While we have witnessed great progress in
image deblurring in recent years, most methods require significant
computational power and have limitations in processing high-resolution photos
with severe local motions. To this end, we develop a novel face deblurring
system based on the dual camera fusion technique for mobile phones. The system
detects subject motion to dynamically enable a reference camera, e.g.,
ultrawide angle camera commonly available on recent premium phones, and
captures an auxiliary photo with faster shutter settings. While the main shot
is low noise but blurry, the reference shot is sharp but noisy. We learn ML
models to align and fuse these two shots and output a clear photo without
motion blur. Our algorithm runs efficiently on Google Pixel 6, which takes 463
ms overhead per shot. Our experiments demonstrate the advantage and robustness
of our system against alternative single-image, multi-frame, face-specific, and
video deblurring algorithms as well as commercial products. To the best of our
knowledge, our work is the first mobile solution for face motion deblurring
that works reliably and robustly over thousands of images in diverse motion and
lighting conditions.

Flawed TLS certificates are not uncommon on the Internet. While they signal a
potential issue, in most cases they have benign causes (e.g., misconfiguration
or even deliberate deployment). This adds fuzziness to the decision on whether
to trust a connection or not. Little is known about perceptions of flawed
certificates by IT professionals, even though their decisions impact high
numbers of end users. Moreover, it is unclear how much the content of error
messages and documentation influences these perceptions. To shed light on these
issues, we observed 75 attendees of an industrial IT conference investigating
different certificate validation errors. We also analyzed the influence of
reworded error messages and redesigned documentation. We find that people
working in IT have very nuanced opinions, with trust decisions being far from
binary. The self-signed and the name-constrained certificates seem to be
over-trusted (the latter also being poorly understood). We show that even small
changes in existing error messages can positively influence resource use,
comprehension, and trust assessment. At the end of the article, we summarize
lessons learned from conducting usable security studies with IT professionals.

We present a non-asymptotic lower bound on the eigenspectrum of the design
matrix generated by any linear bandit algorithm with sub-linear regret when the
action set has well-behaved curvature. Specifically, we show that the minimum
eigenvalue of the expected design matrix grows as $\Omega(\sqrt{n})$ whenever
the expected cumulative regret of the algorithm is $O(\sqrt{n})$, where $n$ is
the learning horizon, and the action-space has a constant Hessian around the
optimal arm. This shows that such action-spaces force a polynomial lower bound
rather than a logarithmic lower bound, as shown by \cite{lattimore2017end}, in
discrete (i.e., well-separated) action spaces. Furthermore, while the previous
result is shown to hold only in the asymptotic regime (as $n \to \infty$), our
result for these "locally rich" action spaces is any-time. Additionally, under
a mild technical assumption, we obtain a similar lower bound on the minimum
eigen value holding with high probability.
  We apply our result to two practical scenarios -- \emph{model selection} and
\emph{clustering} in linear bandits. For model selection, we show that an
epoch-based linear bandit algorithm adapts to the true model complexity at a
rate exponential in the number of epochs, by virtue of our novel spectral
bound. For clustering, we consider a multi agent framework where we show, by
leveraging the spectral result, that no forced exploration is necessary -- the
agents can run a linear bandit algorithm and estimate their underlying
parameters at once, and hence incur a low regret.

Molecular photoswitches are the foundation of light-activated drugs. A key
photoswitch is azobenzene, which exhibits trans-cis isomerism in response to
light. The thermal half-life of the cis isomer is of crucial importance, since
it controls the duration of the light-induced biological effect. Here we
introduce a computational tool for predicting the thermal half-lives of
azobenzene derivatives. Our automated approach uses a fast and accurate machine
learning potential trained on quantum chemistry data. Building on
well-established earlier evidence, we argue that thermal isomerization proceeds
through rotation mediated by intersystem crossing, and incorporate this
mechanism into our automated workflow. We use our approach to predict the
thermal half-lives of 19,000 azobenzene derivatives. We explore trends and
tradeoffs between barriers and absorption wavelengths, and open-source our data
and software to accelerate research in photopharmacology.

Practising and honing skills forms a fundamental component of how humans
learn, yet artificial agents are rarely specifically trained to perform them.
Instead, they are usually trained end-to-end, with the hope being that useful
skills will be implicitly learned in order to maximise discounted return of
some extrinsic reward function. In this paper, we investigate how skills can be
incorporated into the training of reinforcement learning (RL) agents in complex
environments with large state-action spaces and sparse rewards. To this end, we
created SkillHack, a benchmark of tasks and associated skills based on the game
of NetHack. We evaluate a number of baselines on this benchmark, as well as our
own novel skill-based method Hierarchical Kickstarting (HKS), which is shown to
outperform all other evaluated methods. Our experiments show that learning with
a prior knowledge of useful skills can significantly improve the performance of
agents on complex problems. We ultimately argue that utilising predefined
skills provides a useful inductive bias for RL problems, especially those with
large state-action spaces and sparse rewards.

Gravitational wave astronomy is a vibrant field that leverages both classic
and modern data processing techniques for the understanding of the universe.
Various approaches have been proposed for improving the efficiency of the
detection scheme, with hierarchical matched filtering being an important
strategy. Meanwhile, deep learning methods have recently demonstrated both
consistency with matched filtering methods and remarkable statistical
performance. In this work, we propose Hierarchical Detection Network (HDN), a
novel approach to efficient detection that combines ideas from hierarchical
matching and deep learning. The network is trained using a novel loss function,
which encodes simultaneously the goals of statistical accuracy and efficiency.
We discuss the source of complexity reduction of the proposed model, and
describe a general recipe for initialization with each layer specializing in
different regions. We demonstrate the performance of HDN with experiments using
open LIGO data and synthetic injections, and observe with two-layer models a
$79\%$ efficiency gain compared with matched filtering at an equal error rate
of $0.2\%$. Furthermore, we show how training a three-layer HDN initialized
using two-layer model can further boost both accuracy and efficiency,
highlighting the power of multiple simple layers in efficient detection.

Recent advances in variational autoencoders (VAEs) have enabled learning
latent manifolds as compact Lie groups, such as $SO(d)$. Since this approach
assumes that data lies on a subspace that is homeomorphic to the Lie group
itself, we here investigate how this assumption holds in the context of images
that are generated by projecting a $d$-dimensional volume with unknown pose in
$SO(d)$. Upon examining different theoretical candidates for the group and
image space, we show that the attempt to define a group action on the data
space generally fails, as it requires more specific geometric constraints on
the volume. Using geometric VAEs, our experiments confirm that this constraint
is key to proper pose inference, and we discuss the potential of these results
for applications and future work.

Given the difficulty of obtaining high-quality labels for medical image
recognition tasks, there is a need for deep learning techniques that can be
adequately fine-tuned on small labeled data sets. Recent advances in
self-supervised learning techniques have shown that such an in-domain
representation learning approach can provide a strong initialization for
supervised fine-tuning, proving much more data-efficient than standard transfer
learning from a supervised pretraining task. However, these applications are
not adapted to applications to medical diagnostics captured in a video format.
With this progress in mind, we developed a self-supervised learning approach
catered to echocardiogram videos with the goal of learning strong
representations for downstream fine-tuning on the task of diagnosing aortic
stenosis (AS), a common and dangerous disease of the aortic valve. When
fine-tuned on 1% of the training data, our best self-supervised learning model
achieves 0.818 AUC (95% CI: 0.794, 0.840), while the standard transfer learning
approach reaches 0.644 AUC (95% CI: 0.610, 0.677). We also find that our
self-supervised model attends more closely to the aortic valve when predicting
severe AS as demonstrated by saliency map visualizations.

Deep Learning models have become dominant in tackling financial time-series
analysis problems, overturning conventional machine learning and statistical
methods. Most often, a model trained for one market or security cannot be
directly applied to another market or security due to differences inherent in
the market conditions. In addition, as the market evolves through time, it is
necessary to update the existing models or train new ones when new data is made
available. This scenario, which is inherent in most financial forecasting
applications, naturally raises the following research question: How to
efficiently adapt a pre-trained model to a new set of data while retaining
performance on the old data, especially when the old data is not accessible? In
this paper, we propose a method to efficiently retain the knowledge available
in a neural network pre-trained on a set of securities and adapt it to achieve
high performance in new ones. In our method, the prior knowledge encoded in a
pre-trained neural network is maintained by keeping existing connections fixed,
and this knowledge is adjusted for the new securities by a set of augmented
connections, which are optimized using the new data. The auxiliary connections
are constrained to be of low rank. This not only allows us to rapidly optimize
for the new task but also reduces the storage and run-time complexity during
the deployment phase. The efficiency of our approach is empirically validated
in the stock mid-price movement prediction problem using a large-scale limit
order book dataset. Experimental results show that our approach enhances
prediction performance as well as reduces the overall number of network

While early empirical evidence has supported the case for learned index
structures as having favourable average-case performance, little is known about
their worst-case performance. By contrast, classical structures are known to
achieve optimal worst-case behaviour. This work evaluates the robustness of
learned index structures in the presence of adversarial workloads. To simulate
adversarial workloads, we carry out a data poisoning attack on linear
regression models that manipulates the cumulative distribution function (CDF)
on which the learned index model is trained. The attack deteriorates the fit of
the underlying ML model by injecting a set of poisoning keys into the training
dataset, which leads to an increase in the prediction error of the model and
thus deteriorates the overall performance of the learned index structure. We
assess the performance of various regression methods and the learned index
implementations ALEX and PGM-Index. We show that learned index structures can
suffer from a significant performance deterioration of up to 20% when evaluated
on poisoned vs. non-poisoned datasets.

We propose to accelerate a high order discontinuous Galerkin solver using
neural networks. We include a corrective forcing to a low polynomial order
simulation to enhance its accuracy. The forcing is obtained by training a deep
fully connected neural network, using a high polynomial order simulation but
only for a short time frame. With this corrective forcing, we can run the low
polynomial order simulation faster (with large time steps and low cost per time
step) while improving its accuracy.
  We explored this idea for a 1D Burgers' equation in (Marique and Ferrer, CAF
2022), and we have extended this work to the 3D Navier-Stokes equations, with
and without a Large Eddy Simulation closure model. We test the methodology with
the turbulent Taylor Green Vortex case and for various Reynolds numbers (30,
200 and 1600). In addition, the Taylor Green Vortex evolves with time and
covers laminar, transitional, and turbulent regimes, as time progresses.
  The proposed methodology proves to be applicable to a variety of flows and
regimes. The results show that the corrective forcing is effective in all
Reynolds numbers and time frames (excluding the initial flow development). We
can train the corrective forcing with a polynomial order of 8, to increase the
accuracy of simulations from a polynomial order 3 to 6, when correcting outside
the training time frame. The low order correct solution is 4 to 5 times faster
than a simulation with comparable accuracy (polynomial order 6).
  Additionally, we explore changes in the hyperparameters and use transfer
learning to speed up the training. We observe that it is not useful to train a
corrective forcing using a different flow condition. However, an already
trained corrective forcing can be used to initialise a new training (at the
correct flow conditions) to obtain an effective forcing with only a few
training iterations.

Stereotypes, bias, and discrimination have been extensively documented in
Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural
Language Processing (NLP) [6], or both, in the case of large image and caption
models such as OpenAI CLIP [14]. In this paper, we evaluate how ML bias
manifests in robots that physically and autonomously act within the world. We
audit one of several recently published CLIP-powered robotic manipulation
methods, presenting it with objects that have pictures of human faces on the
surface which vary across race and gender, alongside task descriptions that
contain terms associated with common stereotypes. Our experiments definitively
show robots acting out toxic stereotypes with respect to gender, race, and
scientifically-discredited physiognomy, at scale. Furthermore, the audited
methods are less likely to recognize Women and People of Color. Our
interdisciplinary sociotechnical analysis synthesizes across fields and
applications such as Science Technology and Society (STS), Critical Studies,
History, Safety, Robotics, and AI. We find that robots powered by large
datasets and Dissolution Models (sometimes called "foundation models", e.g.
CLIP) that contain humans risk physically amplifying malignant stereotypes in
general; and that merely correcting disparities will be insufficient for the
complexity and scale of the problem. Instead, we recommend that robot learning
methods that physically manifest stereotypes or other harmful outcomes be
paused, reworked, or even wound down when appropriate, until outcomes can be
proven safe, effective, and just. Finally, we discuss comprehensive policy
changes and the potential of new interdisciplinary research on topics like
Identity Safety Assessment Frameworks and Design Justice to better understand
and address these harms.

Lemmatization is the process of grouping together the inflected forms of a
word so they can be analysed as a single item, identified by the word's lemma,
or dictionary form. In computational linguistics, lemmatisation is the
algorithmic process of determining the lemma of a word based on its intended
meaning. Unlike stemming, lemmatisation depends on correctly identifying the
intended part of speech and meaning of a word in a sentence, as well as within
the larger context surrounding that sentence. As a result, developing efficient
lemmatisation algorithm is the complex task. In recent years it can be observed
that deep learning models used for this task outperform other methods including
machine learning algorithms. In this paper the polish lemmatizer based on
Google T5 model is presented. The training was run with different context
lengths. The model achieves the best results for polish language lemmatisation
process.

The need for explainable AI (XAI) is well established but relatively little
has been published outside of the supervised learning paradigm. This paper
focuses on a principled approach to applying explainability and
interpretability to the task of unsupervised anomaly detection. We argue that
explainability is principally an algorithmic task and interpretability is
principally a cognitive task, and draw on insights from the cognitive sciences
to propose a general-purpose method for practical diagnosis using explained
anomalies. We define Attribution Error, and demonstrate, using real-world
labeled datasets, that our method based on Integrated Gradients (IG) yields
significantly lower attribution errors than alternative methods.

Fake news detection has become a major task to solve as there has been an
increasing number of fake news on the internet in recent years. Although many
classification models have been proposed based on statistical learning methods
showing good results, reasoning behind the classification performances may not
be enough. In the self-supervised learning studies, it has been highlighted
that a quality of representation (embedding) space matters and directly affects
a downstream task performance. In this study, a quality of the representation
space is analyzed visually and analytically in terms of linear separability for
different classes on a real and fake news dataset. To further add
interpretability to a classification model, a modification of Class Activation
Mapping (CAM) is proposed. The modified CAM provides a CAM score for each word
token, where the CAM score on a word token denotes a level of focus on that
word token to make the prediction. Finally, it is shown that the naive BERT
model topped with a learnable linear layer is enough to achieve robust
performance while being compatible with CAM.

Multi-view Spectral Clustering (MvSC) attracts increasing attention due to
diverse data sources. However, most existing works are prohibited in
out-of-sample predictions and overlook model interpretability and exploration
of clustering results. In this paper, a new method for MvSC is proposed via a
shared latent space from the Restricted Kernel Machine framework. Through the
lens of conjugate feature duality, we cast the weighted kernel principal
component analysis problem for MvSC and develop a modified weighted conjugate
feature duality to formulate dual variables. In our method, the dual variables,
playing the role of hidden features, are shared by all views to construct a
common latent space, coupling the views by learning projections from
view-specific spaces. Such single latent space promotes well-separated clusters
and provides straightforward data exploration, facilitating visualization and
interpretation. Our method requires only a single eigendecomposition, whose
dimension is independent of the number of views. To boost higher-order
correlations, tensor-based modelling is introduced without increasing
computational complexity. Our method can be flexibly applied with out-of-sample
extensions, enabling greatly improved efficiency for large-scale data with
fixed-size kernel schemes. Numerical experiments verify that our method is
effective regarding accuracy, efficiency, and interpretability, showing a sharp
eigenvalue decay and distinct latent variable distributions.

In recent years there has been significant progress in the field of 3D
learning on classification, detection and segmentation problems. The vast
majority of the existing studies focus on canonical closed-set conditions,
neglecting the intrinsic open nature of the real-world. This limits the
abilities of robots and autonomous systems involved in safety-critical
applications that require managing novel and unknown signals. In this context
exploiting 3D data can be a valuable asset since it provides rich information
about the geometry of perceived objects and scenes. With this paper we provide
the first broad study on 3D Open Set learning. We introduce 3DOS: a novel
testbed for semantic novelty detection that considers several settings with
increasing difficulties in terms of semantic (category) shift, and covers both
in-domain (synthetic-to-synthetic, real-to-real) and cross-domain
(synthetic-to-real) scenarios. Moreover, we investigate the related 2D Open Set
literature to understand if and how its recent improvements are effective on 3D
data. Our extensive benchmark positions several algorithms in the same coherent
picture, revealing their strengths and limitations. The results of our analysis
may serve as a reliable foothold for future tailored 3D Open Set methods.

Estimating treatment effects, especially individualized treatment effects
(ITE), using observational data is challenging due to the complex situations of
confounding bias. Existing approaches for estimating treatment effects from
longitudinal observational data are usually built upon a strong assumption of
"unconfoundedness", which is hard to fulfill in real-world practice. In this
paper, we propose the Variational Temporal Deconfounder (VTD), an approach that
leverages deep variational embeddings in the longitudinal setting using proxies
(i.e., surrogate variables that serve for unobservable variables).
Specifically, VTD leverages observed proxies to learn a hidden embedding that
reflects the true hidden confounders in the observational data. As such, our
VTD method does not rely on the "unconfoundedness" assumption. We test our VTD
method on both synthetic and real-world clinical data, and the results show
that our approach is effective when hidden confounding is the leading bias
compared to other existing models.

This work utilizes a particle swarm optimizer (PSO) for initial orbit
determination for a chief and deputy scenario in the circular restricted
three-body problem (CR3BP). The PSO is used to minimize the difference between
actual and estimated observations and knowledge of the chief's position with
known CR3BP dynamics to determine the deputy's initial state. Convergence is
achieved through limiting particle starting positions to feasible positions
based on the known chief position, and sensor constraints. Parallel and GPU
processing methods are used to improve computation time and provide an accurate
initial state estimate for a variety of cislunar orbit geometries.

Gonadotrophin-releasing hormone receptor (GnRH1R) is a promising therapeutic
target for the treatment of uterine diseases. To date, several GnRH1R
antagonists are available in clinical investigation without satisfying multiple
property constraints. To fill this gap, we aim to develop a deep learning-based
framework to facilitate the effective and efficient discovery of a new orally
active small-molecule drug targeting GnRH1R with desirable properties. In the
present work, a ligand-and-structure combined model, namely LS-MolGen, was
firstly proposed for molecular generation by fully utilizing the information on
the known active compounds and the structure of the target protein, which was
demonstrated by its superior performance than ligand- or structure-based
methods separately. Then, a in silico screening including activity prediction,
ADMET evaluation, molecular docking and FEP calculation was conducted, where
~30,000 generated novel molecules were narrowed down to 8 for experimental
synthesis and validation. In vitro and in vivo experiments showed that three of
them exhibited potent inhibition activities (compound 5 IC50 = 0.856 nM,
compound 6 IC50 = 0.901 nM, compound 7 IC50 = 2.54 nM) against GnRH1R, and
compound 5 performed well in fundamental PK properties, such as half-life, oral
bioavailability, and PPB, etc. We believed that the proposed
ligand-and-structure combined molecular generative model and the whole
computer-aided workflow can potentially be extended to similar tasks for de
novo drug design or lead optimization.

A number of products are sold in the following sequence: First a focal
product is shown, and if the customer purchases, one or more ancillary products
are displayed for purchase. A prominent example is the sale of an airline
ticket, where first the flight is shown, and when chosen, a number of
ancillaries such as cabin or hold bag options, seat selection, insurance etc.
are presented. The firm has to decide on a sale format -- whether to sell them
in sequence unbundled, or together as a bundle -- and how to price the focal
and ancillary products, separately or as a bundle. Since the ancillary is
considered by the customer only after the purchase of the focal product, the
sale strategy chosen by the firm creates an information and learning dependency
between the products: for instance, offering only a bundle would preclude
learning customers' valuation for the focal and ancillary products
individually. In this paper we study learning strategies for such focal and
ancillary item combinations under the following scenarios: (a) pure unbundling
to all customers, (b) personalized mechanism, where, depending on some observed
features of the customers, the two products are presented and priced as a
bundle or in sequence, (c) initially unbundling (for all customers), and switch
to bundling (if more profitable) permanently once during the horizon. We design
pricing and decisions algorithms for all three scenarios, with regret upper
bounded by $O(d \sqrt{T} \log T)$, and an optimal switching time for the third
scenario.

The hardness of combinatorial optimization (CO) problems hinders collecting
solutions for supervised learning. However, learning neural networks for CO
problems is notoriously difficult in lack of the labeled data as the training
is easily trapped at local optima. In this work, we propose a simple but
effective annealed training framework for CO problems. In particular, we
transform CO problems into unbiased energy-based models (EBMs). We carefully
selected the penalties terms so as to make the EBMs as smooth as possible. Then
we train graph neural networks to approximate the EBMs. To prevent the training
from being stuck at local optima near the initialization, we introduce an
annealed loss function. An experimental evaluation demonstrates that our
annealed training framework obtains substantial improvements. In four types of
CO problems, our method achieves performance substantially better than other
unsupervised neural methods on both synthetic and real-world graphs.

Automated detection of anomalous trajectories is an important problem with
considerable applications in intelligent transportation systems. Many existing
studies have focused on distinguishing anomalous trajectories from normal
trajectories, ignoring the large differences between anomalous trajectories. A
recent study has made great progress in identifying abnormal trajectory
patterns and proposed a two-stage algorithm for anomalous trajectory detection
and classification (ATDC). This algorithm has excellent performance but suffers
from a few limitations, such as high time complexity and poor interpretation.
Here, we present a careful theoretical and empirical analysis of the ATDC
algorithm, showing that the calculation of anomaly scores in both stages can be
simplified, and that the second stage of the algorithm is much more important
than the first stage. Hence, we develop a FastATDC algorithm that introduces a
random sampling strategy in both stages. Experimental results show that
FastATDC is 10 to 20 times faster than ATDC on real datasets. Moreover,
FastATDC outperforms the baseline algorithms and is comparable to the ATDC

COVID-19 has brought about many changes in social dynamics. Stay-at-home
orders and disruptions in school teaching can influence bullying behavior
in-person and online, both of which leading to negative outcomes in victims. To
study cyberbullying specifically, 1 million tweets containing keywords
associated with abuse were collected from the beginning of 2019 to the end of
2021 with the Twitter API search endpoint. A natural language processing model
pre-trained on a Twitter corpus generated probabilities for the tweets being
offensive and hateful. To overcome limitations of sampling, data was also
collected using the count endpoint. The fraction of tweets from a given daily
sample marked as abusive is multiplied to the number reported by the count
endpoint. Once these adjusted counts are assembled, a Bayesian autoregressive
Poisson model allows one to study the mean trend and lag functions of the data
and how they vary over time. The results reveal strong weekly and yearly
seasonality in hateful speech but with slight differences across years that may
be attributed to COVID-19.

A large number of robotic and human-assisted missions to the Moon and Mars
are forecast. NASA's efforts to learn about the geology and makeup of these
celestial bodies rely heavily on the use of robotic arms. The safety and
redundancy aspects will be crucial when humans will be working alongside the
robotic explorers. Additionally, robotic arms are crucial to satellite
servicing and planned orbit debris mitigation missions. The goal of this work
is to create a custom Computer Vision (CV) based Artificial Neural Network
(ANN) that would be able to rapidly identify the posture of a 7 Degree of
Freedom (DoF) robotic arm from a single (RGB-D) image - just like humans can
easily identify if an arm is pointing in some general direction. The Sawyer
robotic arm is used for developing and training this intelligent algorithm.
Since Sawyer's joint space spans 7 dimensions, it is an insurmountable task to
cover the entire joint configuration space. In this work, orthogonal arrays are
used, similar to the Taguchi method, to efficiently span the joint space with
the minimal number of training images. This ``optimally'' generated database is
used to train the custom ANN and its degree of accuracy is on average equal to
twice the smallest joint displacement step used for database generation. A
pre-trained ANN will be useful for estimating the postures of robotic
manipulators used on space stations, spacecraft, and rovers as an auxiliary
tool or for contingency plans.

Automated segmentation and volumetry of brain magnetic resonance imaging
(MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and
Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we
adopt deep learning (DL) models in brain segmentation and compared their
performance with the gold-standard non-DL method. We collected brain MRI scans
of healthy controls (n=105) and patients with PD (n=105), multiple systemic
atrophy (n=132), and progressive supranuclear palsy (n=69) at Samsung Medical
Center from January 2017 to December 2020. Using the gold-standard non-DL
model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons,
caudate, putamen, pallidum, and third ventricle, and considered them as
annotating data for DL models, the representative V-Net and UNETR. The Dice
scores and area under the curve (AUC) for differentiating normal, PD, and
P-plus cases were calculated. The segmentation times of V-Net and UNETR for the
six brain structures per patient were 3.48 +- 0.17 and 48.14 +- 0.97 s,
respectively, being at least 300 times faster than FS (15,735 +- 1.07 s). Dice
scores of both DL models were sufficiently high (>0.85), and their AUCs for
disease classification were superior to that of FS. For classification of
normal vs. P-plus and PD vs. multiple systemic atrophy (cerebellar type), the
DL models and FS showed AUCs above 0.8. DL significantly reduces the analysis
time without compromising the performance of brain segmentation and
differential diagnosis. Our findings may contribute to the adoption of DL brain
MRI segmentation in clinical settings and advance brain research.

This paper presents a low-complexity framework for acoustic scene
classification (ASC). Most of the frameworks designed for ASC use convolutional
neural networks (CNNs) due to their learning ability and improved performance
compared to hand-engineered features. However, CNNs are resource hungry due to
their large size and high computational complexity. Therefore, CNNs are
difficult to deploy on resource constrained devices. This paper addresses the
problem of reducing the computational complexity and memory requirement in
CNNs. We propose a low-complexity CNN architecture, and apply pruning and
quantization to further reduce the parameters and memory. We then propose an
ensemble framework that combines various low-complexity CNNs to improve the
overall performance. An experimental evaluation of the proposed framework is
performed on the publicly available DCASE 2022 Task 1 that focuses on ASC. The
proposed ensemble framework has approximately 60K parameters, requires 19M
multiply-accumulate operations and improves the performance by approximately
2-4 percentage points compared to the DCASE 2022 Task 1 baseline network.

Today's conflicts are becoming increasingly complex, fluid and fragmented,
often involving a host of national and international actors with multiple and
often divergent interests. This development poses significant challenges for
conflict mediation, as mediators struggle to make sense of conflict dynamics,
such as the range of conflict parties and the evolution of their political
positions, the distinction between relevant and less relevant actors in
peace-making, or the identification of key conflict issues and their
interdependence. International peace efforts appear ill-equipped to
successfully address these challenges. While technology is already being
experimented with and used in a range of conflict related fields, such as
conflict predicting or information gathering, less attention has been given to
how technology can contribute to conflict mediation. This case study
contributes to emerging research on the use of state-of-the-art machine
learning technologies and techniques in conflict mediation processes. Using
dialogue transcripts from peace negotiations in Yemen, this study shows how
machine-learning can effectively support mediating teams by providing them with
tools for knowledge management, extraction and conflict analysis. Apart from
illustrating the potential of machine learning tools in conflict mediation, the
paper also emphasises the importance of interdisciplinary and participatory,
co-creation methodology for the development of context-sensitive and targeted
tools and to ensure meaningful and responsible implementation.

Monocular vision based road detection methods are mostly based on machine
learning methods, relying on classification and feature extraction accuracy,
and suffer from appearance, illumination and weather changes. Traditional
methods introduce the predictions into conditional random fields or markov
random fields models to improve the intermediate predictions based on
structure. These methods are optimization based and therefore resource heavy
and slow, making it unsuitable for real time applications. We propose a method
to detect and segment roads with a random forest classifier of local experts
with superpixel based machine-learned features. The random forest takes in
machine learnt descriptors from a pre-trained convolutional neural network -
VGG-16. The features are also pooled into their respective superpixels,
allowing for local structure to be continuous. We compare our algorithm against
Nueral Network based methods and Traditional approaches (based on Hand-crafted
features), on both Structured Road (CamVid and Kitti) and Unstructured Road
Datasets. Finally, we introduce a Road Scene Dataset with 1000 annotated
images, and verify that our algorithm works well in non-urban and rural road

Since the onset of the COVID-19 pandemic, vaccines have been an important
topic in public discourse. The discussions around vaccines are polarized as
some see them as an important measure to end the pandemic, and others are
hesitant or find them harmful. This study investigates posts related to
COVID-19 vaccines on Twitter and focuses on those which have a negative stance
toward vaccines. A dataset of 16,713,238 English tweets related to COVID-19
vaccines was collected covering the period from March 1, 2020, to July 31,
2021. We used the Scikit-learn Python library to apply a support vector machine
(SVM) classifier to identify the tweets with a negative stance toward the
COVID-19 vaccines. A total of 5,163 tweets were used to train the classifier,
out of which a subset of 2,484 tweets were manually annotated by us and made
publicly available. We used the BERTtopic model to extract and investigate the
topics discussed within the negative tweets and how they changed over time. We
show that the negativity with respect to COVID-19 vaccines has decreased over
time along with the vaccine roll-outs. We identify 37 topics of discussion and
present their respective importance over time. We show that popular topics
consist of conspiratorial discussions such as 5G towers and microchips, but
also contain legitimate concerns around vaccination safety and side effects as
well as concerns about policies. Our study shows that even unpopular opinions
or conspiracy theories can become widespread when paired with a widely popular
discussion topic such as COVID-19 vaccines. Understanding the concerns and the
discussed topics and how they change over time is essential for policymakers
and public health authorities to provide better and in-time information and
policies, to facilitate vaccination of the population in future similar crises.

The teacher-free online Knowledge Distillation (KD) aims to train an ensemble
of multiple student models collaboratively and distill knowledge from each
other. Although existing online KD methods achieve desirable performance, they
often focus on class probabilities as the core knowledge type, ignoring the
valuable feature representational information. We present a Mutual Contrastive
Learning (MCL) framework for online KD. The core idea of MCL is to perform
mutual interaction and transfer of contrastive distributions among a cohort of
networks in an online manner. Our MCL can aggregate cross-network embedding
information and maximize the lower bound to the mutual information between two
networks. This enables each network to learn extra contrastive knowledge from
others, leading to better feature representations, thus improving the
performance of visual recognition tasks. Beyond the final layer, we extend MCL
to several intermediate layers assisted by auxiliary feature refinement
modules. This further enhances the ability of representation learning for
online KD. Experiments on image classification and transfer learning to visual
recognition tasks show that MCL can lead to consistent performance gains
against state-of-the-art online KD approaches. The superiority demonstrates
that MCL can guide the network to generate better feature representations. Our
code is publicly available at https://github.com/winycg/MCL.

Camera-captured document images usually suffer from perspective and geometric
deformations. It is of great value to rectify them when considering poor visual
aesthetics and the deteriorated performance of OCR systems. Recent
learning-based methods intensively focus on the accurately cropped document
image. However, this might not be sufficient for overcoming practical
challenges, including document images either with large marginal regions or
without margins. Due to this impracticality, users struggle to crop documents
precisely when they encounter large marginal regions. Simultaneously, dewarping
images without margins is still an insurmountable problem. To the best of our
knowledge, there is still no complete and effective pipeline for rectifying
document images in the wild. To address this issue, we propose a novel approach
called Marior (Margin Removal and \Iterative Content Rectification). Marior
follows a progressive strategy to iteratively improve the dewarping quality and
readability in a coarse-to-fine manner. Specifically, we divide the pipeline
into two modules: margin removal module (MRM) and iterative content
rectification module (ICRM). First, we predict the segmentation mask of the
input image to remove the margin, thereby obtaining a preliminary result. Then
we refine the image further by producing dense displacement flows to achieve
content-aware rectification. We determine the number of refinement iterations
adaptively. Experiments demonstrate the state-of-the-art performance of our
method on public benchmarks. The resources are available at
https://github.com/ZZZHANG-jx/Marior for further comparison.

We study open-world 3D scene understanding, a family of tasks that require
agents to reason about their 3D environment with an open-set vocabulary and
out-of-domain visual inputs - a critical skill for robots to operate in the
unstructured 3D world. Towards this end, we propose Semantic Abstraction
(SemAbs), a framework that equips 2D Vision-Language Models (VLMs) with new 3D
spatial capabilities, while maintaining their zero-shot robustness. We achieve
this abstraction using relevancy maps extracted from CLIP, and learn 3D spatial
and geometric reasoning skills on top of those abstractions in a
semantic-agnostic manner. We demonstrate the usefulness of SemAbs on two
open-world 3D scene understanding tasks: 1) completing partially observed
objects and 2) localizing hidden objects from language descriptions.
Experiments show that SemAbs can generalize to novel vocabulary,
materials/lighting, classes, and domains (i.e., real-world scans) from training
on limited 3D synthetic data. Code and data will be available at
https://semantic-abstraction.cs.columbia.edu/

Abdominal organ segmentation has many important clinical applications, such
as organ quantification, surgical planning, and disease diagnosis. However,
manually annotating organs from CT scans is time-consuming and labor-intensive.
Semi-supervised learning has shown the potential to alleviate this challenge by
learning from a large set of unlabeled images and limited labeled samples. In
this work, we follow the self-training strategy and employ a high-performance
hybrid architecture (PHTrans) consisting of CNN and Swin Transformer for the
teacher model to generate precise pseudo labels for unlabeled data. Afterward,
we introduce them with labeled data together into a two-stage segmentation
framework with lightweight PHTrans for training to improve the performance and
generalization ability of the model while remaining efficient. Experiments on
the validation set of FLARE2022 demonstrate that our method achieves excellent
segmentation performance as well as fast and low-resource model inference. The
average DSC and NSD are 0.8956 and 0.9316, respectively. Under our development
environments, the average inference time is 18.62 s, the average maximum GPU
memory is 1995.04 MB, and the area under the GPU memory-time curve and the
average area under the CPU utilization-time curve are 23196.84 and 319.67. The
code is available at https://github.com/lseventeen/FLARE22-TwoStagePHTrans.

Downsampling is widely adopted to achieve a good trade-off between accuracy
and latency for visual recognition. Unfortunately, the commonly used pooling
layers are not learned, and thus cannot preserve important information. As
another dimension reduction method, adaptive sampling weights and processes
regions that are relevant to the task, and is thus able to better preserve
useful information. However, the use of adaptive sampling has been limited to
certain layers. In this paper, we show that using adaptive sampling in the
building blocks of a deep neural network can improve its efficiency. In
particular, we propose SSBNet which is built by inserting sampling layers
repeatedly into existing networks like ResNet. Experiment results show that the
proposed SSBNet can achieve competitive image classification and object
detection performance on ImageNet and COCO datasets. For example, the
SSB-ResNet-RS-200 achieved 82.6% accuracy on ImageNet dataset, which is 0.6%
higher than the baseline ResNet-RS-152 with a similar complexity. Visualization
shows the advantage of SSBNet in allowing different layers to focus on
different positions, and ablation studies further validate the advantage of
adaptive sampling over uniform methods.

In videos, the human's actions are of three-dimensional (3D) signals. These
videos investigate the spatiotemporal knowledge of human behavior. The
promising ability is investigated using 3D convolution neural networks (CNNs).
The 3D CNNs have not yet achieved high output for their well-established
two-dimensional (2D) equivalents in still photographs. Board 3D Convolutional
Memory and Spatiotemporal fusion face training difficulty preventing 3D CNN
from accomplishing remarkable evaluation. In this paper, we implement Hybrid
Deep Learning Architecture that combines STIP and 3D CNN features to enhance
the performance of 3D videos effectively. After implementation, the more
detailed and deeper charting for training in each circle of space-time fusion.
The training model further enhances the results after handling complicated
evaluations of models. The video classification model is used in this
implemented model. Intelligent 3D Network Protocol for Multimedia Data
Classification using Deep Learning is introduced to further understand
spacetime association in human endeavors. In the implementation of the result,
the well-known dataset, i.e., UCF101 to, evaluates the performance of the
proposed hybrid technique. The results beat the proposed hybrid technique that
substantially beats the initial 3D CNNs. The results are compared with
state-of-the-art frameworks from literature for action recognition on UCF101
with an accuracy of 95%.

The sense of touch is essential for a variety of daily tasks. New advances in
event-based tactile sensors and Spiking Neural Networks (SNNs) spur the
research in event-driven tactile learning. However, SNN-enabled event-driven
tactile learning is still in its infancy due to the limited representative
abilities of existing spiking neurons and high spatio-temporal complexity in
the data. In this paper, to improve the representative capabilities of existing
spiking neurons, we propose a novel neuron model called "location spiking
neuron", which enables us to extract features of event-based data in a novel
way. Moreover, based on the classical Time Spike Response Model (TSRM), we
develop a specific location spiking neuron model - Location Spike Response
Model (LSRM) that serves as a new building block of SNNs. Furthermore, we
propose a hybrid model which combines an SNN with TSRM neurons and an SNN with
LSRM neurons to capture the complex spatio-temporal dependencies in the data.
Extensive experiments demonstrate the significant improvements of our models
over other works on event-driven tactile learning and show the superior energy
efficiency of our models and location spiking neurons, which may unlock their
potential on neuromorphic hardware.

The requirement of expensive annotations is a major burden for training a
well-performed instance segmentation model. In this paper, we present an
economic active learning setting, named active pointly-supervised instance
segmentation (APIS), which starts with box-level annotations and iteratively
samples a point within the box and asks if it falls on the object. The key of
APIS is to find the most desirable points to maximize the segmentation accuracy
with limited annotation budgets. We formulate this setting and propose several
uncertainty-based sampling strategies. The model developed with these
strategies yields consistent performance gain on the challenging MS-COCO
dataset, compared against other learning strategies. The results suggest that
APIS, integrating the advantages of active learning and point-based
supervision, is an effective learning paradigm for label-efficient instance

With the advent and popularity of big data mining and huge text analysis in
modern times, automated text summarization became prominent for extracting and
retrieving important information from documents. This research investigates
aspects of automatic text summarization from the perspectives of single and
multiple documents. Summarization is a task of condensing huge text articles
into short, summarized versions. The text is reduced in size for summarization
purpose but preserving key vital information and retaining the meaning of the
original document. This study presents the Latent Dirichlet Allocation (LDA)
approach used to perform topic modelling from summarised medical science
journal articles with topics related to genes and diseases. In this study,
PyLDAvis web-based interactive visualization tool was used to visualise the
selected topics. The visualisation provides an overarching view of the main
topics while allowing and attributing deep meaning to the prevalence individual
topic. This study presents a novel approach to summarization of single and
multiple documents. The results suggest the terms ranked purely by considering
their probability of the topic prevalence within the processed document using
extractive summarization technique. PyLDAvis visualization describes the
flexibility of exploring the terms of the topics' association to the fitted LDA
model. The topic modelling result shows prevalence within topics 1 and 2. This
association reveals that there is similarity between the terms in topic 1 and 2
in this study. The efficacy of the LDA and the extractive summarization methods
were measured using Latent Semantic Analysis (LSA) and Recall-Oriented
Understudy for Gisting Evaluation (ROUGE) metrics to evaluate the reliability
and validity of the model.

In this article gesture recognition and speech recognition applications are
implemented on embedded systems with Tiny Machine Learning (TinyML). It
features 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. The
gesture recognition,provides an innovative approach nonverbal communication. It
has wide applications in human-computer interaction and sign language. Here in
the implementation of hand gesture recognition, TinyML model is trained and
deployed from EdgeImpulse framework for hand gesture recognition and based on
the hand movements, Arduino Nano 33 BLE device having 6-axis IMU can find out
the direction of movement of hand. The Speech is a mode of communication.
Speech recognition is a way by which the statements or commands of human speech
is understood by the computer which reacts accordingly. The main aim of speech
recognition is to achieve communication between man and machine. Here in the
implementation of speech recognition, TinyML model is trained and deployed from
EdgeImpulse framework for speech recognition and based on the keywords
pronounced by human, Arduino Nano 33 BLE device having built-in microphone can
make an RGB LED glow like red, green or blue based on keyword pronounced. The
results of each application are obtained and listed in the results section and
given the analysis upon the results.

Time series prediction is often complicated by distribution shift which
demands adaptive models to accommodate time-varying distributions. We frame
time series prediction under distribution shift as a weighted empirical risk
minimisation problem. The weighting of previous observations in the empirical
risk is determined by a forgetting mechanism which controls the trade-off
between the relevancy and effective sample size that is used for the estimation
of the predictive model. In contrast to previous work, we propose a
gradient-based learning method for the parameters of the forgetting mechanism.
This speeds up optimisation and therefore allows more expressive forgetting

We propose a precise and efficient normal estimation method that can deal
with noise and nonuniform density for unstructured 3D point clouds. Unlike
existing approaches that directly take patches and ignore the local
neighborhood relationships, which make them susceptible to challenging regions
such as sharp edges, we propose to learn graph convolutional feature
representation for normal estimation, which emphasizes more local neighborhood
geometry and effectively encodes intrinsic relationships. Additionally, we
design a novel adaptive module based on the attention mechanism to integrate
point features with their neighboring features, hence further enhancing the
robustness of the proposed normal estimator against point density variations.
To make it more distinguishable, we introduce a multi-scale architecture in the
graph block to learn richer geometric features. Our method outperforms
competitors with the state-of-the-art accuracy on various benchmark datasets,
and is quite robust against noise, outliers, as well as the density variations.

Emotion recognition is involved in several real-world applications. With an
increase in available modalities, automatic understanding of emotions is being
performed more accurately. The success in Multimodal Emotion Recognition (MER),
primarily relies on the supervised learning paradigm. However, data annotation
is expensive, time-consuming, and as emotion expression and perception depends
on several factors (e.g., age, gender, culture) obtaining labels with a high
reliability is hard. Motivated by these, we focus on unsupervised feature
learning for MER. We consider discrete emotions, and as modalities text, audio
and vision are used. Our method, as being based on contrastive loss between
pairwise modalities, is the first attempt in MER literature. Our end-to-end
feature learning approach has several differences (and advantages) compared to
existing MER methods: i) it is unsupervised, so the learning is lack of data
labelling cost; ii) it does not require data spatial augmentation, modality
alignment, large number of batch size or epochs; iii) it applies data fusion
only at inference; and iv) it does not require backbones pre-trained on emotion
recognition task. The experiments on benchmark datasets show that our method
outperforms several baseline approaches and unsupervised learning methods
applied in MER. Particularly, it even surpasses a few supervised MER

We study the factorization of the numbers N = X^2+c, where c is a fixed
constant, and this independently of the value of gcd(X,c). We prove the
existence of a family of sequences with arithmetic difference (Un, Zn)
generating factorizations, i.e. such that: (Un)^2+c = ZnZn+1. The different
properties demonstrated allow us to establish new factorization methods by a
subset of prime numbers and to define a prime sieve. An algorithm is presented
on this basis and leads to empirical results which suggest a positive answer to
Landau's 4th problem.

We introduce a scalable framework for novel view synthesis from RGB-D images
with largely incomplete scene coverage. While generative neural approaches have
demonstrated spectacular results on 2D images, they have not yet achieved
similar photorealistic results in combination with scene completion where a
spatial 3D scene understanding is essential. To this end, we propose a
generative pipeline performing on a sparse grid-based neural scene
representation to complete unobserved scene parts via a learned distribution of
scenes in a 2.5D-3D-2.5D manner. We process encoded image features in 3D space
with a geometry completion network and a subsequent texture inpainting network
to extrapolate the missing area. Photorealistic image sequences can be finally
obtained via consistency-relevant differentiable rendering. Comprehensive
experiments show that the graphical outputs of our method outperform the state
of the art, especially within unobserved scene parts.

Since the inception of Bitcoin in 2009, the market of cryptocurrencies has
grown beyond initial expectations as daily trades exceed $10 billion. As
industries become automated, the need for an automated fraud detector becomes
very apparent. Detecting anomalies in real time prevents potential accidents
and economic losses. Anomaly detection in multivariate time series data poses a
particular challenge because it requires simultaneous consideration of temporal
dependencies and relationships between variables. Identifying an anomaly in
real time is not an easy task specifically because of the exact anomalistic
behavior they observe. Some points may present pointwise global or local
anomalistic behavior, while others may be anomalistic due to their frequency or
seasonal behavior or due to a change in the trend. In this paper we suggested
working on real time series of trades of Ethereum from specific accounts and
surveyed a large variety of different algorithms traditional and new. We
categorized them according to the strategy and the anomalistic behavior which
they search and showed that when bundling them together to different groups,
they can prove to be a good real-time detector with an alarm time of no longer
than a few seconds and with very high confidence.

Nonlinear state estimation (SE), with the goal of estimating complex bus
voltages based on all types of measurements available in the power system, is
usually solved using the iterative Gauss-Newton method. The nonlinear SE
presents some difficulties when considering inputs from both phasor measurement
units and supervisory control and data acquisition system. These include
numerical instabilities, convergence time depending on the starting point of
the iterative method, and the quadratic computational complexity of a single
iteration regarding the number of state variables. This paper introduces an
original graph neural network based SE implementation over the augmented factor
graph of the nonlinear power system SE, capable of incorporating measurements
on both branches and buses, as well as both phasor and legacy measurements. The
proposed regression model has linear computational complexity during the
inference time once trained, with a possibility of distributed implementation.
Since the method is noniterative and non-matrix-based, it is resilient to the
problems that the Gauss-Newton solver is prone to. Aside from prediction
accuracy on the test set, the proposed model demonstrates robustness when
simulating cyber attacks and unobservable scenarios due to communication
irregularities. In those cases, prediction errors are sustained locally, with
no effect on the rest of the power system's results.

Object placement aims to place a foreground object over a background image
with a suitable location and size. In this work, we treat object placement as a
graph completion problem and propose a novel graph completion module (GCM). The
background scene is represented by a graph with multiple nodes at different
spatial locations with various receptive fields. The foreground object is
encoded as a special node that should be inserted at a reasonable place in this
graph. We also design a dual-path framework upon the structure of GCM to fully
exploit annotated composite images. With extensive experiments on OPA dataset,
our method proves to significantly outperform existing methods in generating
plausible object placement without loss of diversity.

Privacy, security and data governance constraints rule out a brute force
process in the integration of cross-silo data, which inherits the development
of the Internet of Things. Federated learning is proposed to ensure that all
parties can collaboratively complete the training task while the data is not
out of the local. Vertical federated learning is a specialization of federated
learning for distributed features. To preserve privacy, homomorphic encryption
is applied to enable encrypted operations without decryption. Nevertheless,
together with a robust security guarantee, homomorphic encryption brings extra
communication and computation overhead. In this paper, we analyze the current
bottlenecks of vertical federated learning under homomorphic encryption
comprehensively and numerically. We propose a straggler-resilient and
computation-efficient accelerating system that reduces the communication
overhead in heterogeneous scenarios by 65.26% at most and reduces the
computation overhead caused by homomorphic encryption by 40.66% at most. Our
system can improve the robustness and efficiency of the current vertical
federated learning framework without loss of security.

Open World Object Detection (OWOD) is a challenging computer vision problem
that requires detecting unknown objects and gradually learning the identified
unknown classes. However, it cannot distinguish unknown instances as multiple
unknown classes. In this work, we propose a novel OWOD problem called
Unknown-Classified Open World Object Detection (UC-OWOD). UC-OWOD aims to
detect unknown instances and classify them into different unknown classes.
Besides, we formulate the problem and devise a two-stage object detector to
solve UC-OWOD. First, unknown label-aware proposal and unknown-discriminative
classification head are used to detect known and unknown objects. Then,
similarity-based unknown classification and unknown clustering refinement
modules are constructed to distinguish multiple unknown classes. Moreover, two
novel evaluation protocols are designed to evaluate unknown-class detection.
Abundant experiments and visualizations prove the effectiveness of the proposed
method. Code is available at https://github.com/JohnWuzh/UC-OWOD.

Finding the optimal design of a hydrodynamic or aerodynamic surface is often
impossible due to the expense of evaluating the cost functions (say, with
computational fluid dynamics) needed to determine the performances of the flows
that the surface controls. In addition, inherent limitations of the design
space itself due to imposed geometric constraints, conventional
parameterization methods, and user bias can restrict {\it all} of the designs
within a chosen design space regardless of whether traditional optimization
methods or newer, data-driven design algorithms with machine learning are used
to search the design space. We present a 2-pronged attack to address these
difficulties: we propose (1) a methodology to create the design space using
morphing that we call {\it Design-by-Morphing} (DbM); and (2) an optimization
algorithm to search that space that uses a novel Bayesian Optimization (BO)
strategy that we call {\it Mixed variable, Multi-Objective Bayesian
Optimization} (MixMOBO). We apply this shape optimization strategy to maximize
the power output of a hydrokinetic turbine. Applying these two strategies in
tandem, we demonstrate that we can create a novel, geometrically-unconstrained,
design space of a draft tube and hub shape and then optimize them
simultaneously with a {\it minimum} number of cost function calls. Our
framework is versatile and can be applied to the shape optimization of a
variety of fluid problems.

Federated learning (FL) supports distributed training of a global machine
learning model across multiple clients with the help from a central server. The
local dataset held by each client is never exchanged in FL, so the local
dataset privacy is protected. Although FL is increasingly popular, data
heterogeneity across different clients leads to the client model drift issue
and results in model performance degradation and poor model fairness. To
address the issue, we design Federated learning with global-local Knowledge
Fusion (FedKF) scheme in this paper. The key idea in FedKF is to let the server
return the global knowledge to be fused with the local knowledge in each
training round so that the local model can be regularized towards the global
optima. Thus, the client model drift issue can be mitigated. In FedKF, we first
propose the active-inactive model aggregation technique that supports a precise
global knowledge representation. Then, we propose a data-free knowledge
distillation (KD) approach to facilitate the KD from the global model to the
local model while the local model can still learn the local knowledge (embedded
in the local dataset) simultaneously, thereby realizing the global-local
knowledge fusion process. The theoretical analysis and intensive experiments
demonstrate that FedKF achieves high model performance, high fairness, and
privacy-preserving simultaneously. The project source codes will be released on
GitHub after the paper review.

Capturing the global topology of an image is essential for proposing an
accurate segmentation of its domain. However, most of existing segmentation
methods do not preserve the initial topology of the given input, which is
detrimental for numerous downstream object-based tasks. This is all the more
true for deep learning models which most work at local scales. In this paper,
we propose a new topology-preserving deep image segmentation method which
relies on a new leakage loss: the Pathloss. Our method is an extension of the
BALoss [1], in which we want to improve the leakage detection for better
recovering the closeness property of the image segmentation. This loss allows
us to correctly localize and fix the critical points (a leakage in the
boundaries) that could occur in the predictions, and is based on a
shortest-path search algorithm. This way, loss minimization enforces
connectivity only where it is necessary and finally provides a good
localization of the boundaries of the objects in the image. Moreover, according
to our research, our Pathloss learns to preserve stronger elongated structure
compared to methods without using topology-preserving loss. Training with our
topological loss function, our method outperforms state-of-the-art
topology-aware methods on two representative datasets of different natures:
Electron Microscopy and Historical Map.

Promotions have been trending in the e-commerce marketplace to build up
customer relationships and guide customers towards the desired actions. Since
incentives are effective to engage customers and customers have different
preferences for different types of incentives, the demand for personalized
promotion decision making is increasing over time.
  However, research on promotion decision making has focused specifically on
purchase conversion during the promotion period (the direct effect), while
generally disregarding the enduring effect in the post promotion period. To
achieve a better lift return on investment (lift ROI) on the enduring effect of
the promotion and improve customer retention and loyalty, we propose a
framework of multiple treatment promotion decision making by modeling each
customer's direct and enduring response. First, we propose a customer direct
and enduring effect (CDEE) model which predicts the customer direct and
enduring response. With the help of the predictions of the CDEE, we personalize
incentive allocation to optimize the enduring effect while keeping the cost
under the budget. To estimate the effect of decision making, we apply an
unbiased evaluation approach of business metrics with randomized control trial
(RCT) data. We compare our method with benchmarks using two promotions in
Mercari and achieve significantly better results.

This paper presents $\mu\text{KG}$, an open-source Python library for
representation learning over knowledge graphs. $\mu\text{KG}$ supports joint
representation learning over multi-source knowledge graphs (and also a single
knowledge graph), multiple deep learning libraries (PyTorch and TensorFlow2),
multiple embedding tasks (link prediction, entity alignment, entity typing, and
multi-source link prediction), and multiple parallel computing modes
(multi-process and multi-GPU computing). It currently implements 26 popular
knowledge graph embedding models and supports 16 benchmark datasets.
$\mu\text{KG}$ provides advanced implementations of embedding techniques with
simplified pipelines of different tasks. It also comes with high-quality
documentation for ease of use. $\mu\text{KG}$ is more comprehensive than
existing knowledge graph embedding libraries. It is useful for a thorough
comparison and analysis of various embedding models and tasks. We show that the
jointly learned embeddings can greatly help knowledge-powered downstream tasks,
such as multi-hop knowledge graph question answering. We will stay abreast of
the latest developments in the related fields and incorporate them into
$\mu\text{KG}$.

Video scene graph generation (VidSGG) aims to parse the video content into
scene graphs, which involves modeling the spatio-temporal contextual
information in the video. However, due to the long-tailed training data in
datasets, the generalization performance of existing VidSGG models can be
affected by the spatio-temporal conditional bias problem. In this work, from
the perspective of meta-learning, we propose a novel Meta Video Scene Graph
Generation (MVSGG) framework to address such a bias problem. Specifically, to
handle various types of spatio-temporal conditional biases, our framework first
constructs a support set and a group of query sets from the training data,
where the data distribution of each query set is different from that of the
support set w.r.t. a type of conditional bias. Then, by performing a novel meta
training and testing process to optimize the model to obtain good testing
performance on these query sets after training on the support set, our
framework can effectively guide the model to learn to well generalize against
biases. Extensive experiments demonstrate the efficacy of our proposed

Backpropagation through time (BPTT) is the de facto standard for training
recurrent neural networks (RNNs), but it is non-causal and non-local. Real-time
recurrent learning is a causal alternative, but it is highly inefficient.
Recently, e-prop was proposed as a causal, local, and efficient practical
alternative to these algorithms, providing an approximation of the exact
gradient by radically pruning the recurrent dependencies carried over time.
Here, we derive RTRL from BPTT using a detailed notation bringing intuition and
clarification to how they are connected. Furthermore, we frame e-prop within in
the picture, formalising what it approximates. Finally, we derive a family of
algorithms of which e-prop is a special case.

In the logic synthesis stage, structure transformations in the synthesis tool
need to be combined into optimization sequences and act on the circuit to meet
the specified circuit area and delay. However, logic synthesis optimization
sequences are time-consuming to run, and predicting the quality of the results
(QoR) against the synthesis optimization sequence for a circuit can help
engineers find a better optimization sequence faster. In this work, we propose
a deep learning method to predict the QoR of unseen circuit-optimization
sequences pairs. Specifically, the structure transformations are translated
into vectors by embedding methods and advanced natural language processing
(NLP) technology (Transformer) is used to extract the features of the
optimization sequences. In addition, to enable the prediction process of the
model to be generalized from circuit to circuit, the graph representation of
the circuit is represented as an adjacency matrix and a feature matrix. Graph
neural networks(GNN) are used to extract the structural features of the
circuits. For this problem, the Transformer and three typical GNNs are used.
Furthermore, the Transformer and GNNs are adopted as a joint learning policy
for the QoR prediction of the unseen circuit-optimization sequences. The
methods resulting from the combination of Transformer and GNNs are benchmarked.
The experimental results show that the joint learning of Transformer and
GraphSage gives the best results. The Mean Absolute Error (MAE) of the
predicted result is 0.412.

Entity alignment is a basic and vital technique in knowledge graph (KG)
integration. Over the years, research on entity alignment has resided on the
assumption that KGs are static, which neglects the nature of growth of
real-world KGs. As KGs grow, previous alignment results face the need to be
revisited while new entity alignment waits to be discovered. In this paper, we
propose and dive into a realistic yet unexplored setting, referred to as
continual entity alignment. To avoid retraining an entire model on the whole
KGs whenever new entities and triples come, we present a continual alignment
method for this task. It reconstructs an entity's representation based on
entity adjacency, enabling it to generate embeddings for new entities quickly
and inductively using their existing neighbors. It selects and replays partial
pre-aligned entity pairs to train only parts of KGs while extracting
trustworthy alignment for knowledge augmentation. As growing KGs inevitably
contain non-matchable entities, different from previous works, the proposed
method employs bidirectional nearest neighbor matching to find new entity
alignment and update old alignment. Furthermore, we also construct new datasets
by simulating the growth of multilingual DBpedia. Extensive experiments
demonstrate that our continual alignment method is more effective than
baselines based on retraining or inductive learning.

Deep learning model inference is a key service in many businesses and
scientific discovery processes. This paper introduces RIBBON, a novel deep
learning inference serving system that meets two competing objectives:
quality-of-service (QoS) target and cost-effectiveness. The key idea behind
RIBBON is to intelligently employ a diverse set of cloud computing instances
(heterogeneous instances) to meet the QoS target and maximize cost savings.
RIBBON devises a Bayesian Optimization-driven strategy that helps users build
the optimal set of heterogeneous instances for their model inference service
needs on cloud computing platforms -- and, RIBBON demonstrates its superiority
over existing approaches of inference serving systems using homogeneous
instance pools. RIBBON saves up to 16% of the inference service cost for
different learning models including emerging deep learning recommender system
models and drug-discovery enabling models.

Reinforcement learning (RL) has shown to reach super human-level performance
across a wide range of tasks. However, unlike supervised machine learning,
learning strategies that generalize well to a wide range of situations remains
one of the most challenging problems for real-world RL. Autonomous driving (AD)
provides a multi-faceted experimental field, as it is necessary to learn the
correct behavior over many variations of road layouts and large distributions
of possible traffic situations, including individual driver personalities and
hard-to-predict traffic events. In this paper we propose a challenging
benchmark for generalizable RL for AD based on a configurable, flexible, and
performant code base. Our benchmark uses a catalog of randomized scenario
generators, including multiple mechanisms for road layout and traffic
variations, different numerical and visual observation types, distinct action
spaces, diverse vehicle models, and allows for use under static scenario
definitions. In addition to purely algorithmic insights, our
application-oriented benchmark also enables a better understanding of the
impact of design decisions such as action and observation space on the
generalizability of policies. Our benchmark aims to encourage researchers to
propose solutions that are able to successfully generalize across scenarios, a
task in which current RL methods fail. The code for the benchmark is available
at https://github.com/seawee1/driver-dojo.

A two-wheeled self-balancing robot is an example of an inverse pendulum and
is an inherently non-linear, unstable system. The fundamental concept of the
proposed framework "Epersist" is to overcome the challenge of counterbalancing
an initially unstable system by delivering robust control mechanisms,
Proportional Integral Derivative(PID), and Reinforcement Learning (RL).
Moreover, the micro-controller NodeMCUESP32 and inertial sensor in the Epersist
employ fewer computational procedures to give accurate instruction regarding
the spin of wheels to the motor driver, which helps control the wheels and
balance the robot. This framework also consists of the mathematical model of
the PID controller and a novel self-trained advantage actor-critic algorithm as
the RL agent. After several experiments, control variable calibrations are made
as the benchmark values to attain the angle of static equilibrium. This
"Epersist" framework proposes PID and RL-assisted functional prototypes and
simulations for better utility.

GPU technology has been improving at an expedited pace in terms of size and
performance, empowering HPC and AI/ML researchers to advance the scientific
discovery process. However, this also leads to inefficient resource usage, as
most GPU workloads, including complicated AI/ML models, are not able to utilize
the GPU resources to their fullest extent -- encouraging support for GPU
multi-tenancy. We propose MISO, a technique to exploit the Multi-Instance GPU
(MIG) capability on the latest NVIDIA datacenter GPUs (e.g., A100, H100) to
dynamically partition GPU resources among co-located jobs. MISO's key insight
is to use the lightweight, more flexible Multi-Process Service (MPS) capability
to predict the best MIG partition allocation for different jobs, without
incurring the overhead of implementing them during exploration. Due to its
ability to utilize GPU resources more efficiently, MISO achieves 49% and 16%
lower average job completion time than the unpartitioned and optimal static GPU
partition schemes, respectively.

Numerical simulations in climate, chemistry, or astrophysics are
computationally too expensive for uncertainty quantification or
parameter-exploration at high-resolution. Reduced-order or surrogate models are
multiple orders of magnitude faster, but traditional surrogates are inflexible
or inaccurate and pure machine learning (ML)-based surrogates too data-hungry.
We propose a hybrid, flexible surrogate model that exploits known physics for
simulating large-scale dynamics and limits learning to the hard-to-model term,
which is called parametrization or closure and captures the effect of fine-
onto large-scale dynamics. Leveraging neural operators, we are the first to
learn grid-independent, non-local, and flexible parametrizations. Our
\textit{multiscale neural operator} is motivated by a rich literature in
multiscale modeling, has quasilinear runtime complexity, is more accurate or
flexible than state-of-the-art parametrizations and demonstrated on the chaotic
equation multiscale Lorenz96.

Robotic and human lunar landings are a focus of future NASA missions.
Precision landing capabilities are vital to guarantee the success of the
mission, and the safety of the lander and crew. During the approach to the
surface there are multiple challenges associated with Hazard Relative
Navigation to ensure safe landings. This paper will focus on a passive
autonomous hazard detection and avoidance sub-system to generate an initial
assessment of possible landing regions for the guidance system. The system uses
a single camera and the MobileNetV2 neural network architecture to detect and
discern between safe landing sites and hazards such as rocks, shadows, and
craters. Then a monocular structure from motion will recreate the surface to
provide slope and roughness analysis.

This work utilizes a MobileNetV2 Convolutional Neural Network (CNN) for fast,
mobile detection of satellites, and rejection of stars, in cluttered unresolved
space imagery. First, a custom database is created using imagery from a
synthetic satellite image program and labeled with bounding boxes over
satellites for "satellite-positive" images. The CNN is then trained on this
database and the inference is validated by checking the accuracy of the model
on an external dataset constructed of real telescope imagery. In doing so, the
trained CNN provides a method of rapid satellite identification for subsequent
utilization in ground-based orbit estimation.

Deep neural networks have recently succeeded in digital halftoning using
vanilla convolutional layers with high parallelism. However, existing deep
methods fail to generate halftones with a satisfying blue-noise property and
require complex training schemes. In this paper, we propose a halftoning method
based on multi-agent deep reinforcement learning, called HALFTONERS, which
learns a shared policy to generate high-quality halftone images. Specifically,
we view the decision of each binary pixel value as an action of a virtual
agent, whose policy is trained by a low-variance policy gradient. Moreover, the
blue-noise property is achieved by a novel anisotropy suppressing loss
function. Experiments show that our halftoning method produces high-quality
halftones while staying relatively fast.

We develop a new approach to drifting games, a class of two-person games with
many applications to boosting and online learning settings, including
Prediction with Expert Advice and the Hedge game. Our approach involves (a)
guessing an asymptotically optimal potential by solving an associated partial
differential equation (PDE); then (b) justifying the guess, by proving upper
and lower bounds on the final-time loss whose difference scales like a negative
power of the number of time steps. The proofs of our potential-based upper
bounds are elementary, using little more than Taylor expansion. The proofs of
our potential-based lower bounds are also rather elementary, combining Taylor
expansion with probabilistic or combinatorial arguments. Most previous work on
asymptotically optimal strategies has used potentials obtained by solving a
discrete dynamic programming principle; the arguments are complicated by their
discrete nature. Our approach is facilitated by the fact that the potentials we
use are explicit solutions of PDEs; the arguments are based on basic calculus.
Not only is our approach more elementary, but we give new potentials and derive
corresponding upper and lower bounds that match each other in the asymptotic
regime.

Most of the existing deep learning based methods for vessel segmentation
neglect two important aspects of retinal vessels, one is the orientation
information of vessels, and the other is the contextual information of the
whole fundus region. In this paper, we propose a robust Orientation and Context
Entangled Network (denoted as OCE-Net), which has the capability of extracting
complex orientation and context information of the blood vessels. To achieve
complex orientation aware, a Dynamic Complex Orientation Aware Convolution
(DCOA Conv) is proposed to extract complex vessels with multiple orientations
for improving the vessel continuity. To simultaneously capture the global
context information and emphasize the important local information, a Global and
Local Fusion Module (GLFM) is developed to simultaneously model the long-range
dependency of vessels and focus sufficient attention on local thin vessels. A
novel Orientation and Context Entangled Non-local (OCE-NL) module is proposed
to entangle the orientation and context information together. In addition, an
Unbalanced Attention Refining Module (UARM) is proposed to deal with the
unbalanced pixel numbers of background, thick and thin vessels. Extensive
experiments were performed on several commonly used datasets (DRIVE, STARE and
CHASEDB1) and some more challenging datasets (AV-WIDE, UoA-DR, RFMiD and UK
Biobank). The ablation study shows that the proposed method achieves promising
performance on maintaining the continuity of thin vessels and the comparative
experiments demonstrate that our OCE-Net can achieve state-of-the-art
performance on retinal vessel segmentation.

Computer-aided X-ray pneumonia lesion recognition is important for accurate
diagnosis of pneumonia. With the emergence of deep learning, the identification
accuracy of pneumonia has been greatly improved, but there are still some
challenges due to the fuzzy appearance of chest X-rays. In this paper, we
propose a deep learning framework named Attention-Based Contrastive Learning
for Class-Imbalanced X-Ray Pneumonia Lesion Recognition (denoted as Deep
Pneumonia). We adopt self-supervised contrastive learning strategy to pre-train
the model without using extra pneumonia data for fully mining the limited
available dataset. In order to leverage the location information of the lesion
area that the doctor has painstakingly marked, we propose mask-guided hard
attention strategy and feature learning with contrastive regulation strategy
which are applied on the attention map and the extracted features respectively
to guide the model to focus more attention on the lesion area where contains
more discriminative features for improving the recognition performance. In
addition, we adopt Class-Balanced Loss instead of traditional Cross-Entropy as
the loss function of classification to tackle the problem of serious class
imbalance between different classes of pneumonia in the dataset. The
experimental results show that our proposed framework can be used as a reliable
computer-aided pneumonia diagnosis system to assist doctors to better diagnose
pneumonia cases accurately.

In this paper, we propose the solution to the Multi-Task Learning (MTL)
Challenge of the 4th Affective Behavior Analysis in-the-wild (ABAW)
competition. The task of ABAW is to predict frame-level emotion descriptors
from videos: discrete emotional state; valence and arousal; and action units.
Although researchers have proposed several approaches and achieved promising
results in ABAW, current works in this task rarely consider interactions
between different emotion descriptors. To this end, we propose a novel end to
end architecture to achieve full integration of different types of information.
Experimental results demonstrate the effectiveness of our proposed solution.

Designing effective routing strategies for mobile wireless networks is
challenging due to the need to seamlessly adapt routing behavior to spatially
diverse and temporally changing network conditions. In this work, we use deep
reinforcement learning (DeepRL) to learn a scalable and generalizable
single-copy routing strategy for such networks. We make the following
contributions: i) we design a reward function that enables the DeepRL agent to
explicitly trade-off competing network goals, such as minimizing delay vs. the
number of transmissions per packet; ii) we propose a novel set of relational
neighborhood, path, and context features to characterize mobile wireless
networks and model device mobility independently of a specific network
topology; and iii) we use a flexible training approach that allows us to
combine data from all packets and devices into a single offline centralized
training set to train a single DeepRL agent. To evaluate generalizeability and
scalability, we train our DeepRL agent on one mobile network scenario and then
test it on other mobile scenarios, varying the number of devices and
transmission ranges. Our results show our learned single-copy routing strategy
outperforms all other strategies in terms of delay except for the optimal
strategy, even on scenarios on which the DeepRL agent was not trained.

Decision-making systems based on AI and machine learning have been used
throughout a wide range of real-world scenarios, including healthcare, law
enforcement, education, and finance. It is no longer far-fetched to envision a
future where autonomous systems will be driving entire business decisions and,
more broadly, supporting large-scale decision-making infrastructure to solve
society's most challenging problems. Issues of unfairness and discrimination
are pervasive when decisions are being made by humans, and remain (or are
potentially amplified) when decisions are made using machines with little
transparency, accountability, and fairness. In this paper, we introduce a
framework for \textit{causal fairness analysis} with the intent of filling in
this gap, i.e., understanding, modeling, and possibly solving issues of
fairness in decision-making settings. The main insight of our approach will be
to link the quantification of the disparities present on the observed data with
the underlying, and often unobserved, collection of causal mechanisms that
generate the disparity in the first place, challenge we call the Fundamental
Problem of Causal Fairness Analysis (FPCFA). In order to solve the FPCFA, we
study the problem of decomposing variations and empirical measures of fairness
that attribute such variations to structural mechanisms and different units of
the population. Our effort culminates in the Fairness Map, which is the first
systematic attempt to organize and explain the relationship between different
criteria found in the literature. Finally, we study which causal assumptions
are minimally needed for performing causal fairness analysis and propose a
Fairness Cookbook, which allows data scientists to assess the existence of
disparate impact and disparate treatment.

Medical events of interest, such as mortality, often happen at a low rate in
electronic medical records, as most admitted patients survive. Training models
with this imbalance rate (class density discrepancy) may lead to suboptimal
prediction. Traditionally this problem is addressed through ad-hoc methods such
as resampling or reweighting but performance in many cases is still limited. We
propose a framework for training models for this imbalance issue: 1) we first
decouple the feature extraction and classification process, adjusting training
batches separately for each component to mitigate bias caused by class density
discrepancy; 2) we train the network with both a density-aware loss and a
learnable cost matrix for misclassifications. We demonstrate our model's
improved performance in real-world medical datasets (TOPCAT and MIMIC-III) to
show improved AUC-ROC, AUC-PRC, Brier Skill Score compared with the baselines
in the domain.

In the past decade, deep learning-based networks have achieved unprecedented
success in numerous tasks, including image classification. Despite this
remarkable achievement, recent studies have demonstrated that such networks are
easily fooled by small malicious perturbations, also known as adversarial
examples. This security weakness led to extensive research aimed at obtaining
robust models. Beyond the clear robustness benefits of such models, it was also
observed that their gradients with respect to the input align with human
perception. Several works have identified Perceptually Aligned Gradients (PAG)
as a byproduct of robust training, but none have considered it as a standalone
phenomenon nor studied its own implications. In this work, we focus on this
trait and test whether Perceptually Aligned Gradients imply Robustness. To this
end, we develop a novel objective to directly promote PAG in training
classifiers and examine whether models with such gradients are more robust to
adversarial attacks. Extensive experiments on CIFAR-10 and STL validate that
such models have improved robust performance, exposing the surprising
bidirectional connection between PAG and robustness.

Training computer vision models usually requires collecting and labeling vast
amounts of imagery under a diverse set of scene configurations and properties.
This process is incredibly time-consuming, and it is challenging to ensure that
the captured data distribution maps well to the target domain of an application
scenario. Recently, synthetic data has emerged as a way to address both of
these issues. However, existing approaches either require human experts to
manually tune each scene property or use automatic methods that provide little
to no control; this requires rendering large amounts of random data variations,
which is slow and is often suboptimal for the target domain. We present the
first fully differentiable synthetic data pipeline that uses Neural Radiance
Fields (NeRFs) in a closed-loop with a target application's loss function. Our
approach generates data on-demand, with no human labor, to maximize accuracy
for a target task. We illustrate the effectiveness of our method on synthetic
and real-world object detection tasks. We also introduce a new
"YCB-in-the-Wild" dataset and benchmark that provides a test scenario for
object detection with varied poses in real-world environments.

First-person video highlights a camera-wearer's activities in the context of
their persistent environment. However, current video understanding approaches
reason over visual features from short video clips that are detached from the
underlying physical space and only capture what is directly seen. We present an
approach that links egocentric video and camera pose over time by learning
representations that are predictive of the camera-wearer's (potentially unseen)
local surroundings to facilitate human-centric environment understanding. We
train such models using videos from agents in simulated 3D environments where
the environment is fully observable, and test them on real-world videos of
house tours from unseen environments. We show that by grounding videos in their
physical environment, our models surpass traditional scene classification
models at predicting which room a camera-wearer is in (where frame-level
information is insufficient), and can leverage this grounding to localize video
moments corresponding to environment-centric queries, outperforming prior
methods. Project page: http://vision.cs.utexas.edu/projects/ego-scene-context/

In most sports, especially football, most coaches and analysts search for key
performance indicators using notational analysis. This method utilizes a
statistical summary of events based on video footage and numerical records of
goal scores. Unfortunately, this approach is now obsolete owing to the
continuous evolutionary increase in technology that simplifies the analysis of
more complex process variables through machine learning (ML). Machine learning,
a form of artificial intelligence (AI), uses algorithms to detect meaningful
patterns and define a structure based on positional data. This research
investigates a new method to evaluate the value of current football players,
based on establishing the machine learning models to investigate the relations
among the various features of players, the salary of players, and the market
value of players. The data of the football players used for this project is
from several football websites. The data on the salary of football players will
be the proxy for evaluating the value of players, and other features will be
used to establish and train the ML model for predicting the suitable salary for
the players. The motivation is to explore what are the relations between
different features of football players and their salaries - how each feature
affects their salaries, or which are the most important features to affect the
salary? Although many standards can reflect the value of football players, the
salary of the players is one of the most intuitive and crucial indexes, so this
study will use the salary of players as the proxy to evaluate their value.
Moreover, many features of players can affect the valuation of the football
players, but the value of players is mainly decided by three types of factors:
basic characteristics, performance on the court, and achievements at the club.

Consider a distributed quantum sensing system in which Alice and Bob are
tasked with detecting the state of a quantum system that is observed partly at
Alice and partly at Bob via local operations and classical communication
(LOCC). Prior work introduced LOCCNet, a distributed protocol that optimizes
the local operations via parameterized quantum circuits (PQCs) at Alice and
Bob. This paper presents Noise Aware-LOCCNet (NA-LOCCNet) for distributed
quantum state discrimination in the presence of noisy classical communication.
We propose specific ansatzes for the case of two observed qubit pairs, and we
describe a noise-aware training design criterion. Through experiments, we
observe that quantum, entanglement-breaking, noise on the observed quantum
system can be useful in improving the detection capacity of the system when
classical communication is noisy.

This paper proposes a supervised dimension reduction methodology for tensor
data which has two advantages over most image-based prognostic models. First,
the model does not require tensor data to be complete which expands its
application to incomplete data. Second, it utilizes time-to-failure (TTF) to
supervise the extraction of low-dimensional features which makes the extracted
features more effective for the subsequent prognostic. Besides, an optimization
algorithm is proposed for parameter estimation and closed-form solutions are
derived under certain distributions.

Deep neural networks currently provide the most advanced and accurate machine
learning models to distinguish between structural MRI scans of subjects with
Alzheimer's disease and healthy controls. Unfortunately, the subtle brain
alterations captured by these models are difficult to interpret because of the
complexity of these multi-layer and non-linear models. Several heatmap methods
have been proposed to address this issue and analyze the imaging patterns
extracted from the deep neural networks, but no quantitative comparison between
these methods has been carried out so far. In this work, we explore these
questions by deriving heatmaps from Convolutional Neural Networks (CNN) trained
using T1 MRI scans of the ADNI data set, and by comparing these heatmaps with
brain maps corresponding to Support Vector Machines (SVM) coefficients. Three
prominent heatmap methods are studied: Layer-wise Relevance Propagation (LRP),
Integrated Gradients (IG), and Guided Grad-CAM (GGC). Contrary to prior studies
where the quality of heatmaps was visually or qualitatively assessed, we
obtained precise quantitative measures by computing overlap with a ground-truth
map from a large meta-analysis that combined 77 voxel-based morphometry (VBM)
studies independently from ADNI. Our results indicate that all three heatmap
methods were able to capture brain regions covering the meta-analysis map and
achieved better results than SVM coefficients. Among them, IG produced the
heatmaps with the best overlap with the independent meta-analysis.

Following the surge of popularity of Transformers in Computer Vision, several
studies have attempted to determine whether they could be more robust to
distribution shifts and provide better uncertainty estimates than Convolutional
Neural Networks (CNNs). The almost unanimous conclusion is that they are, and
it is often conjectured more or less explicitly that the reason of this
supposed superiority is to be attributed to the self-attention mechanism. In
this paper we perform extensive empirical analyses showing that recent
state-of-the-art CNNs (particularly, ConvNeXt) can be as robust and reliable or
even sometimes more than the current state-of-the-art Transformers. However,
there is no clear winner. Therefore, although it is tempting to state the
definitive superiority of one family of architectures over another, they seem
to enjoy similar extraordinary performances on a variety of tasks while also
suffering from similar vulnerabilities such as texture, background, and
simplicity biases.

We introduce the first AI-based framework for deep, super-resolution,
wide-field radio-interferometric imaging, and demonstrate it on observations of
the ESO 137-006 radio galaxy. The algorithmic framework to solve the inverse
problem for image reconstruction builds on a recent "plug-and-play" scheme
whereby a denoising operator is injected as an image regulariser in an
optimisation algorithm, which alternates until convergence between denoising
steps and gradient-descent data-fidelity steps. We investigate handcrafted and
learned variants of high-resolution high-dynamic range denoisers. We propose a
parallel algorithm implementation relying on automated decompositions of the
image into facets, and the measurement operator into sparse low-dimensional
blocks. The resulting algorithms were deployed to form images of a wide field
of view containing ESO 137-006, from 19 gigabytes of MeerKAT data at 1053 and
1399 MHz. The recovered maps exhibit significantly more resolution and dynamic
range than CLEAN, revealing collimated synchrotron threads close to the
galactic core.

Graph Convolutional Neural Network (GCNN) is a popular class of deep learning
(DL) models in material science to predict material properties from the graph
representation of molecular structures. Training an accurate and comprehensive
GCNN surrogate for molecular design requires large-scale graph datasets and is
usually a time-consuming process. Recent advances in GPUs and distributed
computing open a path to reduce the computational cost for GCNN training
effectively. However, efficient utilization of high performance computing (HPC)
resources for training requires simultaneously optimizing large-scale data
management and scalable stochastic batched optimization techniques. In this
work, we focus on building GCNN models on HPC systems to predict material
properties of millions of molecules. We use HydraGNN, our in-house library for
large-scale GCNN training, leveraging distributed data parallelism in PyTorch.
We use ADIOS, a high-performance data management framework for efficient
storage and reading of large molecular graph data. We perform parallel training
on two open-source large-scale graph datasets to build a GCNN predictor for an
important quantum property known as the HOMO-LUMO gap. We measure the
scalability, accuracy, and convergence of our approach on two DOE
supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing
Facility (OLCF) and the Perlmutter system at the National Energy Research
Scientific Computing Center (NERSC). We present our experimental results with
HydraGNN showing i) reduction of data loading time up to 4.2 times compared
with a conventional method and ii) linear scaling performance for training up
to 1,024 GPUs on both Summit and Perlmutter.

Data lies at the core of modern deep learning. The impressive performance of
supervised learning is built upon a base of massive accurately labeled data.
However, in some real-world applications, accurate labeling might not be
viable; instead, multiple noisy labels (instead of one accurate label) are
provided by several annotators for each data sample. Learning a classifier on
such a noisy training dataset is a challenging task. Previous approaches
usually assume that all data samples share the same set of parameters related
to annotator errors, while we demonstrate that label error learning should be
both annotator and data sample dependent. Motivated by this observation, we
propose a novel learning algorithm. The proposed method displays superiority
compared with several state-of-the-art baseline methods on MNIST, CIFAR-100,
and ImageNet-100. Our code is available at:
https://github.com/zhengqigao/Learning-from-Multiple-Annotator-Noisy-Labels.

We study a simple embedding technique based on a matrix of personalized
PageRank vectors seeded on a random set of nodes. We show that the embedding
produced by the element-wise logarithm of this matrix (1) are related to the
spectral embedding for a class of graphs where spectral embeddings are
significant, and hence useful representation of the data, (2) can be done for
the entire network or a smaller part of it, which enables precise local
representation, and (3) uses a relatively small number of PageRank vectors
compared to the size of the networks. Most importantly, the general nature of
this embedding strategy opens up many emerging applications, where eigenvector
and spectral techniques may not be well established, to the PageRank-based
relatives. For instance, similar techniques can be used on PageRank vectors
from hypergraphs to get "spectral-like" embeddings.

Multi-goal policy learning for robotic manipulation is challenging. Prior
successes have used state-based representations of the objects or provided
demonstration data to facilitate learning. In this paper, by hand-coding a
high-level discrete representation of the domain, we show that policies to
reach dozens of goals can be learned with a single network using Q-learning
from pixels. The agent focuses learning on simpler, local policies which are
sequenced together by planning in the abstract space. We compare our method
against standard multi-goal RL baselines, as well as other methods that
leverage the discrete representation, on a challenging block construction
domain. We find that our method can build more than a hundred different block
structures, and demonstrate forward transfer to structures with novel objects.
Lastly, we deploy the policy learned in simulation on a real robot.

Testing of integrated circuits (IC) is a highly expensive process but also
the most important one in determining the defect level of an IC. Manufacturing
defects in the IC are modeled using stuck-at-fault models. Stuck-at-fault
models cover most of the physical faults that occur during the manufacturing
process. With decreasing feature sizes due to the advancement of semiconductor
technology, the defects are also getting smaller in size. Tests for these
hard-to-detect defects are generated using deterministic test generation (DTG)
algorithms. Our work aims at reducing the cost of Path Oriented Decision
Making: PODEM (a DTG algorithm) without compromising the test quality. We
trained a meta predictor to choose the best model given the circuit and the
target net. This ensemble chooses the best probability prediction model with a
95% accuracy. This leads to a reduced number of backtracking decisions and much
better performance of PODEM in terms of its CPU time. We show that our ML-
guided PODEM algorithm with a meta predictor outperforms the baseline PODEM by
34% and other state-of-the-art ML-guided algorithms by at least 15% for ISCAS85
benchmark circuits.

Graph neural networks (GNNs) have shown superiority in many prediction tasks
over graphs due to their impressive capability of capturing nonlinear relations
in graph-structured data. However, for node classification tasks, often, only
marginal improvement of GNNs over their linear counterparts has been observed.
Previous works provide very few understandings of this phenomenon. In this
work, we resort to Bayesian learning to deeply investigate the functions of
non-linearity in GNNs for node classification tasks. Given a graph generated
from the statistical model CSBM, we observe that the max-a-posterior estimation
of a node label given its own and neighbors' attributes consists of two types
of non-linearity, a possibly non-linear transformation of node attributes and a
ReLU-activated feature aggregation from neighbors. The latter surprisingly
matches the type of non-linearity used in many GNN models. By further imposing
Gaussian assumption on node attributes, we prove that the superiority of those
ReLU activations is only significant when the node attributes are far more
informative than the graph structure, which nicely matches many previous
empirical observations. A similar argument can be achieved when there is a
distribution shift of node attributes between the training and testing
datasets. Finally, we verify our theory on both synthetic and real-world
networks.

Agent-based modelling (ABM), simulation (ABS), and distributed computation
(ABC) are established methods. The Internet and Web-based technologies are
suitable carriers. This paper is a technical report with some tutorial aspects
of the JavaScript Agent Machine (JAM) platform and the programming of agents
with AgentJS, a sub-set of the widely used JavaScript programming language for
the programming of mobile state-based reactive agents. In addition to
explaining the motivation for particular design choices and introducing core
concepts of the architecture and the programming of agents in JavaScript, short
examples illustrate the power of the JAM platform and its components for the
deployment of large-scale multi-agent system in strong heterogeneous
environments like the Internet. JAM is suitable for the deployment in strong
heterogeneous and mobile environments. Finally, JAM can be used for ABC as well
as for ABS in an unified methodology, finally enabling mobile crowd sensing
coupled with simulation (ABS).

Purpose: To substantially shorten the acquisition time required for
quantitative 3D chemical exchange saturation transfer (CEST) and semisolid
magnetization transfer (MT) imaging and allow for rapid chemical exchange
parameter map reconstruction. Methods: Three-dimensional CEST and MT magnetic
resonance fingerprinting (MRF) datasets of L-arginine phantoms, whole-brains,
and calf muscles from healthy volunteers, cancer patients, and cardiac patients
were acquired using 3T clinical scanners at 3 different sites, using 3
different scanner models and coils. A generative adversarial network supervised
framework (GAN-CEST) was then designed and trained to learn the mapping from a
reduced input data space to the quantitative exchange parameter space, while
preserving perceptual and quantitative content. Results: The GAN-CEST 3D
acquisition time was 42-52 seconds, 70% shorter than CEST-MRF. The quantitative
reconstruction of the entire brain took 0.8 seconds. An excellent agreement was
observed between the ground truth and GAN-based L-arginine concentration and pH
values (Pearson's r > 0.97, NRMSE < 1.5%). GAN-CEST images from a brain-tumor
subject yielded a semi-solid volume fraction and exchange rate NRMSE of
3.8$\pm$1.3% and 4.6$\pm$1.3%, respectively, and SSIM of 96.3$\pm$1.6% and
95.0$\pm$2.4%, respectively. The mapping of the calf-muscle exchange parameters
in a cardiac patient, yielded NRMSE < 7% and SSIM > 94% for the semi-solid
exchange parameters. In regions with large susceptibility artifacts, GAN-CEST
has demonstrated improved performance and reduced noise compared to MRF.
Conclusion: GAN-CEST can substantially reduce the acquisition time for
quantitative semisolid MT/CEST mapping, while retaining performance even when
facing pathologies and scanner models that were not available during training.

This paper studies inference acceleration using distributed convolutional
neural networks (CNNs) in collaborative edge computing. To ensure inference
accuracy in inference task partitioning, we consider the receptive-field when
performing segment-based partitioning. To maximize the parallelization between
the communication and computing processes, thereby minimizing the total
inference time of an inference task, we design a novel task collaboration
scheme in which the overlapping zone of the sub-tasks on secondary edge servers
(ESs) is executed on the host ES, named as HALP. We further extend HALP to the
scenario of multiple tasks. Experimental results show that HALP can accelerate
CNN inference in VGG-16 by 1.7-2.0x for a single task and 1.7-1.8x for 4 tasks
per batch on GTX 1080TI and JETSON AGX Xavier, which outperforms the
state-of-the-art work MoDNN. Moreover, we evaluate the service reliability
under time-variant channel, which shows that HALP is an effective solution to
ensure high service reliability with strict service deadline.

Continuous monitoring of trained ML models to determine when their
predictions should and should not be trusted is essential for their safe
deployment. Such a framework ought to be high-performing, explainable, post-hoc
and actionable. We propose TRUST-LAPSE, a "mistrust" scoring framework for
continuous model monitoring. We assess the trustworthiness of each input
sample's model prediction using a sequence of latent-space embeddings.
Specifically, (a) our latent-space mistrust score estimates mistrust using
distance metrics (Mahalanobis distance) and similarity metrics (cosine
similarity) in the latent-space and (b) our sequential mistrust score
determines deviations in correlations over the sequence of past input
representations in a non-parametric, sliding-window based algorithm for
actionable continuous monitoring. We evaluate TRUST-LAPSE via two downstream
tasks: (1) distributionally shifted input detection and (2) data drift
detection, across diverse domains -- audio & vision using public datasets and
further benchmark our approach on challenging, real-world electroencephalograms
(EEG) datasets for seizure detection. Our latent-space mistrust scores achieve
state-of-the-art results with AUROCs of 84.1 (vision), 73.9 (audio), 77.1
(clinical EEGs), outperforming baselines by over 10 points. We expose critical
failures in popular baselines that remain insensitive to input semantic
content, rendering them unfit for real-world model monitoring. We show that our
sequential mistrust scores achieve high drift detection rates: over 90% of the
streams show < 20% error for all domains. Through extensive qualitative and
quantitative evaluations, we show that our mistrust scores are more robust and
provide explainability for easy adoption into practice.

The fundamental relationship between the mesoscopic structure of neuronal
circuits and organismic functions they subserve is one of the major challenges
in contemporary Neuroscience. Formation of structurally connected modules of
neurons enacts the conversion from single-cell firing to large-scale behaviour
of an organism, highlighting the importance of their accurate profiling in the
data. While connectomes are typically characterized by significant sparsity of
neuronal connections, recent advances in network theory and machine learning
have revealed fundamental limitations of traditionally used community detection
approaches in cases where the network is sparse. Here we studied the optimal
community structure in the structural connectome of C.elegans, for which we
exploited a non-conventional approach that is based on non-backtracking random
walks, virtually eliminating the sparsity issue. In full agreement with the
previous asymptotic results, we demonstrated that non-backtracking walks
resolve the ground true annotation into clusters on stochastic block models
(SBM) with the size and density of the connectome better than the spectral
methods related to simple random walks. Based on the cluster detectability
threshold, we determined that the optimal number of modules in the connectome
of C.elegans is seven, which, surprisingly, further provides the best agreement
with the anatomical partition (the so-called ganglia). The discovered
communities have a clear interpretation in terms of their functional role,
which allows us to discern three groups of structural clusters in the worm: the
brain group, movement controller, and information flow connector. Thus, our
work provides a robust network-based framework to reveal mesoscopic structures
in sparse connectome data, paving way to further investigations of connectome
mechanisms for different functions.

We present PanGu-Coder, a pretrained decoder-only language model adopting the
PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of
programming language solutions given a natural language problem description. We
train PanGu-Coder using a two-stage strategy: the first stage employs Causal
Language Modelling (CLM) to pre-train on raw programming language data, while
the second stage uses a combination of Causal Language Modelling and Masked
Language Modelling (MLM) training objectives that focus on the downstream task
of text-to-code generation and train on loosely curated pairs of natural
language program definitions and code functions. Finally, we discuss
PanGu-Coder-FT, which is fine-tuned on a combination of competitive programming
problems and code with continuous integration tests. We evaluate PanGu-Coder
with a focus on whether it generates functionally correct programs and
demonstrate that it achieves equivalent or better performance than similarly
sized models, such as CodeX, while attending a smaller context window and
training on less data.

Three-body interactions can eject stars from the core of a globular cluster,
causing them to enter the Galactic halo as extra-tidal stars. While finding
extra-tidal stars is imperative for understanding cluster evolution, connecting
isolated extra-tidal field stars back to their birth cluster is extremely
difficult. In this work, we present a new methodology consisting of
high-dimensional data analysis and a particle spray code to identify
extra-tidal stars of any Galactic globular cluster using M3 as a case study.
Using the t-Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold
Approximation and Projection (UMAP) machine learning dimensionality reduction
algorithms, we first identify a set of 103 extra-tidal candidates in the APOGEE
DR17 data catalogue with chemical abundances similar to M3 stars. To confirm
each candidate's extra-tidal nature, we introduce corespray - a new
Python-based three-body particle spray code that simulates extra-tidal stars
for any Galactic globular cluster. Using Gaia EDR3 proper motions and APOGEE
DR17 radial velocities, we apply multivariate Gaussian modelling and an extreme
deconvolution to identify the extra-tidal candidates that are more likely to be
associated with a distribution of corespray-simulated M3 extra-tidal stars than
the field. Through these methods, we identify 10 new high-probability
extra-tidal stars produced via three-body interactions in M3. We also explore
whether any of our extra-tidal candidates are consistent with being ejected
from M3 through different dynamical processes. Future applications of corespray
will yield better understandings of core dynamics, star formation histories and
binary fractions in globular clusters.

Existing research addresses scene graph generation (SGG) -- a critical
technology for scene understanding in images -- from a detection perspective,
i.e., objects are detected using bounding boxes followed by prediction of their
pairwise relationships. We argue that such a paradigm causes several problems
that impede the progress of the field. For instance, bounding box-based labels
in current datasets usually contain redundant classes like hairs, and leave out
background information that is crucial to the understanding of context. In this
work, we introduce panoptic scene graph generation (PSG), a new problem task
that requires the model to generate a more comprehensive scene graph
representation based on panoptic segmentations rather than rigid bounding
boxes. A high-quality PSG dataset, which contains 49k well-annotated
overlapping images from COCO and Visual Genome, is created for the community to
keep track of its progress. For benchmarking, we build four two-stage
baselines, which are modified from classic methods in SGG, and two one-stage
baselines called PSGTR and PSGFormer, which are based on the efficient
Transformer-based detector, i.e., DETR. While PSGTR uses a set of queries to
directly learn triplets, PSGFormer separately models the objects and relations
in the form of queries from two Transformer decoders, followed by a
prompting-like relation-object matching mechanism. In the end, we share
insights on open challenges and future directions.

Accurate breast cancer diagnosis through mammography has the potential to
save millions of lives around the world. Deep learning (DL) methods have shown
to be very effective for mass detection in mammograms. Additional improvements
of current DL models will further improve the effectiveness of these methods. A
critical issue in this context is how to pick the right hyperparameters for DL
models. In this paper, we present GA-E2E, a new approach for tuning the
hyperparameters of DL models for brest cancer detection using Genetic
Algorithms (GAs). Our findings reveal that differences in parameter values can
considerably alter the area under the curve (AUC), which is used to determine a
classifier's performance.

Deep neural networks perform well on prediction and classification tasks in
the canonical setting where data streams are i.i.d., labeled data is abundant,
and class labels are balanced. Challenges emerge with distribution shifts,
including non-stationary or imbalanced data streams. One powerful approach that
has addressed this challenge involves self-supervised pretraining of large
encoders on volumes of unlabeled data, followed by task-specific tuning. Given
a new task, updating the weights of these encoders is challenging as a large
number of weights needs to be fine-tuned, and as a result, they forget
information about the previous tasks. In the present work, we propose a model
architecture to address this issue, building upon a discrete bottleneck
containing pairs of separate and learnable (key, value) codes. In this setup,
we follow the encode; process the representation via a discrete bottleneck; and
decode paradigm, where the input is fed to the pretrained encoder, the output
of the encoder is used to select the nearest keys, and the corresponding values
are fed to the decoder to solve the current task. The model can only fetch and
re-use a limited number of these (key, value) pairs during inference, enabling
localized and context-dependent model updates. We theoretically investigate the
ability of the proposed model to minimize the effect of the distribution shifts
and show that such a discrete bottleneck with (key, value) pairs reduces the
complexity of the hypothesis class. We empirically verified the proposed
methods' benefits under challenging distribution shift scenarios across various
benchmark datasets and show that the proposed model reduces the common
vulnerability to non-i.i.d. and non-stationary training distributions compared
to various other baselines.

Given a partial differential equation (PDE), goal-oriented error estimation
allows us to understand how errors in a diagnostic quantity of interest (QoI),
or goal, occur and accumulate in a numerical approximation, for example using
the finite element method. By decomposing the error estimates into
contributions from individual elements, it is possible to formulate adaptation
methods, which modify the mesh with the objective of minimising the resulting
QoI error. However, the standard error estimate formulation involves the true
adjoint solution, which is unknown in practice. As such, it is common practice
to approximate it with an 'enriched' approximation (e.g. in a higher order
space or on a refined mesh). Doing so generally results in a significant
increase in computational cost, which can be a bottleneck compromising the
competitiveness of (goal-oriented) adaptive simulations. The central idea of
this paper is to develop a "data-driven" goal-oriented mesh adaptation approach
through the selective replacement of the expensive error estimation step with
an appropriately configured and trained neural network. In doing so, the error
estimator may be obtained without even constructing the enriched spaces. An
element-by-element construction is employed here, whereby local values of
various parameters related to the mesh geometry and underlying problem physics
are taken as inputs, and the corresponding contribution to the error estimator
is taken as output. We demonstrate that this approach is able to obtain the
same accuracy with a reduced computational cost, for adaptive mesh test cases
related to flow around tidal turbines, which interact via their downstream
wakes, and where the overall power output of the farm is taken as the QoI.
Moreover, we demonstrate that the element-by-element approach implies
reasonably low training costs.

Human perception reliably identifies movable and immovable parts of 3D
scenes, and completes the 3D structure of objects and background from
incomplete observations. We learn this skill not via labeled examples, but
simply by observing objects move. In this work, we propose an approach that
observes unlabeled multi-view videos at training time and learns to map a
single image observation of a complex scene, such as a street with cars, to a
3D neural scene representation that is disentangled into movable and immovable
parts while plausibly completing its 3D structure. We separately parameterize
movable and immovable scene parts via 2D neural ground plans. These ground
plans are 2D grids of features aligned with the ground plane that can be
locally decoded into 3D neural radiance fields. Our model is trained
self-supervised via neural rendering. We demonstrate that the structure
inherent to our disentangled 3D representation enables a variety of downstream
tasks in street-scale 3D scenes using simple heuristics, such as extraction of
object-centric 3D representations, novel view synthesis, instance segmentation,
and 3D bounding box prediction, highlighting its value as a backbone for
data-efficient 3D scene understanding models. This disentanglement further
enables scene editing via object manipulation such as deletion, insertion, and
rigid-body motion.

Few-shot class-incremental learning (FSCIL) has been proposed aiming to
enable a deep learning system to incrementally learn new classes with limited
data. Recently, a pioneer claims that the commonly used replay-based method in
class-incremental learning (CIL) is ineffective and thus not preferred for
FSCIL. This has, if truth, a significant influence on the fields of FSCIL. In
this paper, we show through empirical results that adopting the data replay is
surprisingly favorable. However, storing and replaying old data can lead to a
privacy concern. To address this issue, we alternatively propose using
data-free replay that can synthesize data by a generator without accessing real
data. In observing the the effectiveness of uncertain data for knowledge
distillation, we impose entropy regularization in the generator training to
encourage more uncertain examples. Moreover, we propose to relabel the
generated data with one-hot-like labels. This modification allows the network
to learn by solely minimizing the cross-entropy loss, which mitigates the
problem of balancing different objectives in the conventional knowledge
distillation approach. Finally, we show extensive experimental results and
analysis on CIFAR-100, miniImageNet and CUB-200 to demonstrate the
effectiveness of our proposed one.

Variational inference has recently emerged as a popular alternative to the
classical Markov chain Monte Carlo (MCMC) in large-scale Bayesian inference.
The core idea of variational inference is to trade statistical accuracy for
computational efficiency. It aims to approximate the posterior, reducing
computation costs but potentially compromising its statistical accuracy. In
this work, we study this statistical and computational trade-off in variational
inference via a case study in inferential model selection. Focusing on Gaussian
inferential models (a.k.a. variational approximating families) with diagonal
plus low-rank precision matrices, we initiate a theoretical study of the
trade-offs in two aspects, Bayesian posterior inference error and frequentist
uncertainty quantification error. From the Bayesian posterior inference
perspective, we characterize the error of the variational posterior relative to
the exact posterior. We prove that, given a fixed computation budget, a
lower-rank inferential model produces variational posteriors with a higher
statistical approximation error, but a lower computational error; it reduces
variances in stochastic optimization and, in turn, accelerates convergence.
From the frequentist uncertainty quantification perspective, we consider the
precision matrix of the variational posterior as an uncertainty estimate. We
find that, relative to the true asymptotic precision, the variational
approximation suffers from an additional statistical error originating from the
sampling uncertainty of the data. Moreover, this statistical error becomes the
dominant factor as the computation budget increases. As a consequence, for
small datasets, the inferential model need not be full-rank to achieve optimal
estimation error. We finally demonstrate these statistical and computational
trade-offs inference across empirical studies, corroborating the theoretical
findings.

Robotic manipulation of cloth has applications ranging from fabrics
manufacturing to handling blankets and laundry. Cloth manipulation is
challenging for robots largely due to their high degrees of freedom, complex
dynamics, and severe self-occlusions when in folded or crumpled configurations.
Prior work on robotic manipulation of cloth relies primarily on vision sensors
alone, which may pose challenges for fine-grained manipulation tasks such as
grasping a desired number of cloth layers from a stack of cloth. In this paper,
we propose to use tactile sensing for cloth manipulation; we attach a tactile
sensor (ReSkin) to one of the two fingertips of a Franka robot and train a
classifier to determine whether the robot is grasping a specific number of
cloth layers. During test-time experiments, the robot uses this classifier as
part of its policy to grasp one or two cloth layers using tactile feedback to
determine suitable grasping points. Experimental results over 180 physical
trials suggest that the proposed method outperforms baselines that do not use
tactile feedback and has better generalization to unseen cloth compared to
methods that use image classifiers. Code, data, and videos are available at
https://sites.google.com/view/reskin-cloth.

Most of existing methods for few-shot object detection follow the fine-tuning
paradigm, which potentially assumes that the class-agnostic generalizable
knowledge can be learned and transferred implicitly from base classes with
abundant samples to novel classes with limited samples via such a two-stage
training strategy. However, it is not necessarily true since the object
detector can hardly distinguish between class-agnostic knowledge and
class-specific knowledge automatically without explicit modeling. In this work
we propose to learn three types of class-agnostic commonalities between base
and novel classes explicitly: recognition-related semantic commonalities,
localization-related semantic commonalities and distribution commonalities. We
design a unified distillation framework based on a memory bank, which is able
to perform distillation of all three types of commonalities jointly and
efficiently. Extensive experiments demonstrate that our method can be readily
integrated into most of existing fine-tuning based methods and consistently
improve the performance by a large margin.

We propose a secure and lightweight key based challenge obfuscation for
strong PUFs. Our architecture is designed to be resilient against learning
attacks. Our obfuscation mechanism uses non-linear feedback shift registers
(NLFSRs). Responses are directly provided to the user, without error correction
or extra post-processing steps. We also discuss the cost of protecting our
architecture against power analysis attacks with clock randomization, and
Boolean masking. Security against learning attacks is assessed using avalanche
criterion, and deep-neural network attacks. We designed a testchip in 65 nm
CMOS. When compared to the baseline arbiter PUF implementation, the cost
increase of our proposed architecture is 1.27x, and 2.2x when using clock
randomization, and Boolean masking, respectively.

Geometric image transformations that arise in the real world, such as scaling
and rotation, have been shown to easily deceive deep neural networks (DNNs).
Hence, training DNNs to be certifiably robust to these perturbations is
critical. However, no prior work has been able to incorporate the objective of
deterministic certified robustness against geometric transformations into the
training procedure, as existing verifiers are exceedingly slow. To address
these challenges, we propose the first provable defense for deterministic
certified geometric robustness. Our framework leverages a novel GPU-optimized
verifier that can certify images between 60$\times$ to 42,600$\times$ faster
than existing geometric robustness verifiers, and thus unlike existing works,
is fast enough for use in training. Our results across multiple datasets show
that networks trained via our framework consistently achieve state-of-the-art
deterministic certified geometric robustness and clean accuracy. Furthermore,
for the first time, we verify the geometric robustness of a neural network for
the challenging, real-world setting of autonomous driving.

A 20% rise in car crashes in 2021 compared to 2020 has been observed as a
result of increased distraction and drowsiness. Drowsy and distracted driving
are the cause of 45% of all car crashes. As a means to decrease drowsy and
distracted driving, detection methods using computer vision can be designed to
be low-cost, accurate, and minimally invasive. This work investigated the use
of the vision transformer to outperform state-of-the-art accuracy from 3D-CNNs.
Two separate transformers were trained for drowsiness and distractedness. The
drowsy video transformer model was trained on the National Tsing-Hua University
Drowsy Driving Dataset (NTHU-DDD) with a Video Swin Transformer model for 10
epochs on two classes -- drowsy and non-drowsy simulated over 10.5 hours. The
distracted video transformer was trained on the Driver Monitoring Dataset (DMD)
with Video Swin Transformer for 50 epochs over 9 distraction-related classes.
The accuracy of the drowsiness model reached 44% and a high loss value on the
test set, indicating overfitting and poor model performance. Overfitting
indicates limited training data and applied model architecture lacked
quantifiable parameters to learn. The distracted model outperformed
state-of-the-art models on DMD reaching 97.5%, indicating that with sufficient
data and a strong architecture, transformers are suitable for unfit driving
detection. Future research should use newer and stronger models such as
TokenLearner to achieve higher accuracy and efficiency, merge existing datasets
to expand to detecting drunk driving and road rage to create a comprehensive
solution to prevent traffic crashes, and deploying a functioning prototype to
revolutionize the automotive safety industry.

Graph Neural Networks (GNNs) have shown remarkable effectiveness in capturing
abundant information in graph-structured data. However, the black-box nature of
GNNs hinders users from understanding and trusting the models, thus leading to
difficulties in their applications. While recent years witness the prosperity
of the studies on explaining GNNs, most of them focus on static graphs, leaving
the explanation of dynamic GNNs nearly unexplored. It is challenging to explain
dynamic GNNs, due to their unique characteristic of time-varying graph
structures. Directly using existing models designed for static graphs on
dynamic graphs is not feasible because they ignore temporal dependencies among
the snapshots. In this work, we propose DGExplainer to provide reliable
explanation on dynamic GNNs. DGExplainer redistributes the output activation
score of a dynamic GNN to the relevances of the neurons of its previous layer,
which iterates until the relevance scores of the input neuron are obtained. We
conduct quantitative and qualitative experiments on real-world datasets to
demonstrate the effectiveness of the proposed framework for identifying
important nodes for link prediction and node regression for dynamic GNNs.

Due to the beyond-classical capability of quantum computing, quantum machine
learning is applied independently or embedded in classical models for decision
making, especially in the field of finance. Fairness and other ethical issues
are often one of the main concerns in decision making. In this work, we define
a formal framework for the fairness verification and analysis of quantum
machine learning decision models, where we adopt one of the most popular
notions of fairness in the literature based on the intuition -- any two similar
individuals must be treated similarly and are thus unbiased. We show that
quantum noise can improve fairness and develop an algorithm to check whether a
(noisy) quantum machine learning model is fair. In particular, this algorithm
can find bias kernels of quantum data (encoding individuals) during checking.
These bias kernels generate infinitely many bias pairs for investigating the
unfairness of the model. Our algorithm is designed based on a highly efficient
data structure -- Tensor Networks -- and implemented on Google's TensorFlow
Quantum. The utility and effectiveness of our algorithm are confirmed by the
experimental results, including income prediction and credit scoring on
real-world data, for a class of random (noisy) quantum decision models with 27
qubits ($2^{27}$-dimensional state space) tripling ($2^{18}$ times more than)
that of the state-of-the-art algorithms for verifying quantum machine learning
models.

Most existing works on few-shot object detection (FSOD) focus on a setting
where both pre-training and few-shot learning datasets are from a similar
domain. However, few-shot algorithms are important in multiple domains; hence
evaluation needs to reflect the broad applications. We propose a Multi-dOmain
Few-Shot Object Detection (MoFSOD) benchmark consisting of 10 datasets from a
wide range of domains to evaluate FSOD algorithms. We comprehensively analyze
the impacts of freezing layers, different architectures, and different
pre-training datasets on FSOD performance. Our empirical results show several
key factors that have not been explored in previous works: 1) contrary to
previous belief, on a multi-domain benchmark, fine-tuning (FT) is a strong
baseline for FSOD, performing on par or better than the state-of-the-art (SOTA)
algorithms; 2) utilizing FT as the baseline allows us to explore multiple
architectures, and we found them to have a significant impact on down-stream
few-shot tasks, even with similar pre-training performances; 3) by decoupling
pre-training and few-shot learning, MoFSOD allows us to explore the impact of
different pre-training datasets, and the right choice can boost the performance
of the down-stream tasks significantly. Based on these findings, we list
possible avenues of investigation for improving FSOD performance and propose
two simple modifications to existing algorithms that lead to SOTA performance
on the MoFSOD benchmark. The code is available at
https://github.com/amazon-research/few-shot-object-detection-benchmark.

A data commons is a cloud-based data platform with a governance structure
that allows a community to manage, analyze and share its data. Data commons
provide a research community with the ability to manage and analyze large
datasets using the elastic scalability provided by cloud computing and to share
data securely and compliantly, and, in this way, accelerate the pace of
research. Over the past decade, a number of data commons have been developed
and we discuss some of the lessons learned from this effort.

Reducing methane emissions is essential for mitigating global warming. To
attribute methane emissions to their sources, a comprehensive dataset of
methane source infrastructure is necessary. Recent advancements with deep
learning on remotely sensed imagery have the potential to identify the
locations and characteristics of methane sources, but there is a substantial
lack of publicly available data to enable machine learning researchers and
practitioners to build automated mapping approaches. To help fill this gap, we
construct a multi-sensor dataset called METER-ML containing 86,599
georeferenced NAIP, Sentinel-1, and Sentinel-2 images in the U.S. labeled for
the presence or absence of methane source facilities including concentrated
animal feeding operations, coal mines, landfills, natural gas processing
plants, oil refineries and petroleum terminals, and wastewater treatment
plants. We experiment with a variety of models that leverage different spatial
resolutions, spatial footprints, image products, and spectral bands. We find
that our best model achieves an area under the precision recall curve of 0.915
for identifying concentrated animal feeding operations and 0.821 for oil
refineries and petroleum terminals on an expert-labeled test set, suggesting
the potential for large-scale mapping. We make METER-ML freely available at
https://stanfordmlgroup.github.io/projects/meter-ml/ to support future work on
automated methane source mapping.

Recent works in bandit problems adopted lasso convergence theory in the
sequential decision-making setting. Even with fully observed contexts, there
are technical challenges that hinder the application of existing lasso
convergence theory: 1) proving the restricted eigenvalue condition under
conditionally sub-Gaussian noise and 2) accounting for the dependence between
the context variables and the chosen actions. This paper studies the effect of
missing covariates on regret for stochastic linear bandit algorithms. Our work
provides a high-probability upper bound on the regret incurred by the proposed
algorithm in terms of covariate sampling probabilities, showing that the regret
degrades due to missingness by at most $\zeta_{min}^2$, where $\zeta_{min}$ is
the minimum probability of observing covariates in the context vector. We
illustrate our algorithm for the practical application of experimental design
for collecting gene expression data by a sequential selection of class
discriminating DNA probes.

Recent work has shown that finite mixture models with $m$ components are
identifiable, while making no assumptions on the mixture components, so long as
one has access to groups of samples of size $2m-1$ which are known to come from
the same mixture component. In this work we generalize that result and show
that, if every subset of $k$ mixture components of a mixture model are linearly
independent, then that mixture model is identifiable with only $(2m-1)/(k-1)$
samples per group. We further show that this value cannot be improved. We prove
an analogous result for a stronger form of identifiability known as
"determinedness" along with a corresponding lower bound. This independence
assumption almost surely holds if mixture components are chosen randomly from a
$k$-dimensional space. We describe some implications of our results for
multinomial mixture models and topic modeling.

Self-supervised learning has recently achieved great success in
representation learning without human annotations. The dominant method -- that
is contrastive learning, is generally based on instance discrimination tasks,
i.e., individual samples are treated as independent categories. However,
presuming all the samples are different contradicts the natural grouping of
similar samples in common visual datasets, e.g., multiple views of the same
dog. To bridge the gap, this paper proposes an adaptive method that introduces
soft inter-sample relations, namely Adaptive Soft Contrastive Learning (ASCL).
More specifically, ASCL transforms the original instance discrimination task
into a multi-instance soft discrimination task, and adaptively introduces
inter-sample relations. As an effective and concise plug-in module for existing
self-supervised learning frameworks, ASCL achieves the best performance on
several benchmarks in terms of both performance and efficiency. Code is
available at https://github.com/MrChenFeng/ASCL_ICPR2022.

This paper discusses a new approach to the fundamental problem of learning
optimal Q-functions. In this approach, optimal Q-functions are formulated as
saddle points of a nonlinear Lagrangian function derived from the classic
Bellman optimality equation. The paper shows that the Lagrangian enjoys strong
duality, in spite of its nonlinearity, which paves the way to a general
Lagrangian method to Q-function learning. As a demonstration, the paper
develops an imitation learning algorithm based on the duality theory, and
applies the algorithm to a state-of-the-art machine translation benchmark. The
paper then turns to demonstrate a symmetry breaking phenomenon regarding the
optimality of the Lagrangian saddle points, which justifies a largely
overlooked direction in developing the Lagrangian method.

In addition to maximizing the total revenue, decision-makers in lots of
industries would like to guarantee fair consumption across different resources
and avoid saturating certain resources. Motivated by these practical needs,
this paper studies the price-based network revenue management problem with both
demand learning and fairness concern about the consumption across different
resources. We introduce the regularized revenue, i.e., the total revenue with a
fairness regularization, as our objective to incorporate fairness into the
revenue maximization goal. We propose a primal-dual-type online policy with the
Upper-Confidence-Bound (UCB) demand learning method to maximize the regularized
revenue. We adopt several innovative techniques to make our algorithm a unified
and computationally efficient framework for the continuous price set and a wide
class of fairness regularizers. Our algorithm achieves a worst-case regret of
$\tilde O(N^{5/2}\sqrt{T})$, where $N$ denotes the number of products and $T$
denotes the number of time periods. Numerical experiments in a few NRM examples
demonstrate the effectiveness of our algorithm for balancing revenue and
fairness.

This paper investigates the best arm identification (BAI) problem in
stochastic multi-armed bandits in the fixed confidence setting. The general
class of the exponential family of bandits is considered. The state-of-the-art
algorithms for the exponential family of bandits face computational challenges.
To mitigate these challenges, a novel framework is proposed, which views the
BAI problem as sequential hypothesis testing, and is amenable to tractable
analysis for the exponential family of bandits. Based on this framework, a BAI
algorithm is designed that leverages the canonical sequential probability ratio
tests. This algorithm has three features for both settings: (1) its sample
complexity is asymptotically optimal, (2) it is guaranteed to be $\delta-$PAC,
and (3) it addresses the computational challenge of the state-of-the-art
approaches. Specifically, these approaches, which are focused only on the
Gaussian setting, require Thompson sampling from the arm that is deemed the
best and a challenger arm. This paper analytically shows that identifying the
challenger is computationally expensive and that the proposed algorithm
circumvents it. Finally, numerical experiments are provided to support the
analysis.

Optimal execution is a sequential decision-making problem for cost-saving in
algorithmic trading. Studies have found that reinforcement learning (RL) can
help decide the order-splitting sizes. However, a problem remains unsolved: how
to place limit orders at appropriate limit prices? The key challenge lies in
the "continuous-discrete duality" of the action space. On the one hand, the
continuous action space using percentage changes in prices is preferred for
generalization. On the other hand, the trader eventually needs to choose limit
prices discretely due to the existence of the tick size, which requires
specialization for every single stock with different characteristics (e.g., the
liquidity and the price range). So we need continuous control for
generalization and discrete control for specialization. To this end, we propose
a hybrid RL method to combine the advantages of both of them. We first use a
continuous control agent to scope an action subset, then deploy a fine-grained
agent to choose a specific limit price. Extensive experiments show that our
method has higher sample efficiency and better training stability than existing
RL algorithms and significantly outperforms previous learning-based methods for
order execution.

We present a method for learning to generate unbounded flythrough videos of
natural scenes starting from a single view, where this capability is learned
from a collection of single photographs, without requiring camera poses or even
multiple views of each scene. To achieve this, we propose a novel
self-supervised view generation training paradigm, where we sample and
rendering virtual camera trajectories, including cyclic ones, allowing our
model to learn stable view generation from a collection of single views. At
test time, despite never seeing a video during training, our approach can take
a single image and generate long camera trajectories comprised of hundreds of
new views with realistic and diverse content. We compare our approach with
recent state-of-the-art supervised view generation methods that require posed
multi-view videos and demonstrate superior performance and synthesis quality.

In shape analysis, one of the fundamental problems is to align curves or
surfaces before computing a (geodesic) distance between these shapes. To find
the optimal reparametrization realizing this alignment is a computationally
demanding task which leads to an optimization problem on the diffeomorphism
group. In this paper, we construct approximations of orientation-preserving
diffeomorphisms by composition of elementary diffeomorphisms to solve the
approximation problem. We propose a practical algorithm implemented in PyTorch
which is applicable both to unparametrized curves and surfaces. We derive
universal approximation results and obtain bounds for the Lipschitz constant of
the obtained compositions of diffeomorphisms.

We present regret minimization algorithms for stochastic contextual MDPs
under minimum reachability assumption, using an access to an offline least
square regression oracle. We analyze three different settings: where the
dynamics is known, where the dynamics is unknown but independent of the context
and the most challenging setting where the dynamics is unknown and
context-dependent. For the latter, our algorithm obtains $
  \tilde{O}\left(
\max\{H,{1}/{p_{min}}\}H|S|^{3/2}\sqrt{|A|T\log(\max\{|\mathcal{F}|,|\mathcal{P}|\}/\delta)}
  \right)$ regret bound, with probability $1-\delta$, where $\mathcal{P}$ and
$\mathcal{F}$ are finite and realizable function classes used to approximate
the dynamics and rewards respectively, $p_{min}$ is the minimum reachability
parameter, $S$ is the set of states, $A$ the set of actions, $H$ the horizon,
and $T$ the number of episodes. To our knowledge, our approach is the first
optimistic approach applied to contextual MDPs with general function
approximation (i.e., without additional knowledge regarding the function class,
such as it being linear and etc.). In addition, we present a lower bound of
$\Omega(\sqrt{T H |S| |A| \ln(|\mathcal{F}|/|S|)/\ln(|A|)})$, on the expected
regret which holds even in the case of known dynamics.

Facial Expression Recognition (FER) suffers from data uncertainties caused by
ambiguous facial images and annotators' subjectiveness, resulting in excursive
semantic and feature covariate shifting problem. Existing works usually correct
mislabeled data by estimating noise distribution, or guide network training
with knowledge learned from clean data, neglecting the associative relations of
expressions. In this work, we propose an Adaptive Graph-based Feature
Normalization (AGFN) method to protect FER models from data uncertainties by
normalizing feature distributions with the association of expressions.
Specifically, we propose a Poisson graph generator to adaptively construct
topological graphs for samples in each mini-batches via a sampling process, and
correspondingly design a coordinate descent strategy to optimize proposed
network. Our method outperforms state-of-the-art works with accuracies of
91.84% and 91.11% on the benchmark datasets FERPlus and RAF-DB, respectively,
and when the percentage of mislabeled data increases (e.g., to 20%), our
network surpasses existing works significantly by 3.38% and 4.52%.

Changing conditions or environments can cause system dynamics to vary over
time. To ensure optimal control performance, controllers should adapt to these
changes. When the underlying cause and time of change is unknown, we need to
rely on online data for this adaptation. In this paper, we will use
time-varying Bayesian optimization (TVBO) to tune controllers online in
changing environments using appropriate prior knowledge on the control
objective and its changes. Two properties are characteristic of many online
controller tuning problems: First, they exhibit incremental and lasting changes
in the objective due to changes to the system dynamics, e.g., through wear and
tear. Second, the optimization problem is convex in the tuning parameters.
Current TVBO methods do not explicitly account for these properties, resulting
in poor tuning performance and many unstable controllers through
over-exploration of the parameter space. We propose a novel TVBO forgetting
strategy using Uncertainty-Injection (UI), which incorporates the assumption of
incremental and lasting changes. The control objective is modeled as a
spatio-temporal Gaussian process (GP) with UI through a Wiener process in the
temporal domain. Further, we explicitly model the convexity assumptions in the
spatial dimension through GP models with linear inequality constraints. In
numerical experiments, we show that our model outperforms the state-of-the-art
method in TVBO, exhibiting reduced regret and fewer unstable parameter
configurations.

Semantic Web or Knowledge Graphs (KG) emerged to one of the most important
information source for intelligent systems requiring access to structured
knowledge. One of the major challenges is the extraction and processing of
unambiguous information from textual data. Following the human perception,
overlapping semantic linkages between two named entities become clear due to
our common-sense about the context a relationship lives in which is not the
case when we look at it from an automatically driven process of a machine. In
this work, we are interested in the problem of Relational Resolution within the
scope of KGs, i.e, we are investigating the inherent semantic of relationships
between entities within a network. We propose a new adaptive AutoEncoder,
called V-Coder, to identify relations inherently connecting entities from
different domains. Those relations can be considered as being ambiguous and are
candidates for disentanglement. Likewise to the Adaptive Learning Theory (ART),
our model learns new patterns from the KG by increasing units in a competitive
layer without discarding the previous observed patterns whilst learning the
quality of each relation separately. The evaluation on real-world datasets of
Freebase, Yago and NELL shows that the V-Coder is not only able to recover
links from corrupted input data, but also shows that the semantic disclosure of
relations in a KG show the tendency to improve link prediction. A semantic
evaluation wraps the evaluation up.

Fifth-Generation (5G) networks have a potential to accelerate power system
transition to a flexible, softwarized, data-driven, and intelligent grid. With
their evolving support for Machine Learning (ML)/Artificial Intelligence (AI)
functions, 5G networks are expected to enable novel data-centric Smart Grid
(SG) services. In this paper, we explore how data-driven SG services could be
integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus
on the State Estimation (SE) function as a key element of the energy management
system and focus on two main questions. Firstly, in a tutorial fashion, we
present an overview on how distributed SE can be integrated with the elements
of the 5G core network and radio access network architecture. Secondly, we
present and compare two powerful distributed SE methods based on: i) graphical
models and belief propagation, and ii) graph neural networks. We discuss their
performance and capability to support a near real-time distributed SE via 5G
network, taking into account communication delays.

Citations in science are being studied from several perspectives. On the one
hand, there are approaches such as scientometrics and the science of science,
which take a more quantitative perspective. In this chapter I briefly review
some of the literature on citations, citation distributions and models of
citations. These citations feature prominently in another part of the
literature which is dealing with research evaluation and the role of metrics
and indicators in that process. Here I briefly review part of the discussion in
research evaluation. This also touches on the subject of how citations relate
to peer review. Finally, I try to integrate the two literatures with the aim of
clarifying what I believe the two can learn from each other. The fundamental
problem in research evaluation is that research quality is unobservable. This
has consequences for conclusions that we can draw from quantitative studies of
citations and citation models. The term "indicators" is a relevant concept in
this context, which I try to clarify. Causality is important for properly
understanding indicators, especially when indicators are used in practice: when
we act on indicators, we enter causal territory. Even when an indicator might
have been valid, through its very use, the consequences of its use may
invalidate it. By combining citation models with proper causal reasoning and
acknowledging the fundamental problem about unobservable research quality, we
may hope to make progress.

Reducing speckle and limiting the variations of the physical parameters in
Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the
potential of such data. Nowadays, deep learning approaches produce state of the
art results in single-image SAR restoration. Nevertheless, huge multi-temporal
stacks are now often available and could be efficiently exploited to further
improve image quality. This paper explores two fast strategies employing a
single-image despeckling algorithm, namely SAR2SAR, in a multi-temporal
framework. The first one is based on Quegan filter and replaces the local
reflectivity pre-estimation by SAR2SAR. The second one uses SAR2SAR to suppress
speckle from a ratio image encoding the multi-temporal information under the
form of a "super-image", i.e. the temporal arithmetic mean of a time series.
Experimental results on Sentinel-1 GRD data show that these two multi-temporal
strategies provide improved filtering results while adding a limited

Previous studies have demonstrated that code intelligence models are
sensitive to program transformation among which identifier renaming is
particularly easy to apply and effective. By simply renaming one identifier in
source code, the models would output completely different results. The prior
research generally mitigates the problem by generating more training samples.
Such an approach is less than ideal since its effectiveness depends on the
quantity and quality of the generated samples. Different from these studies, we
are devoted to adjusting models for explicitly distinguishing the influence of
identifier names on the results, called naming bias in this paper, and thereby
making the models robust to identifier renaming. Specifically, we formulate the
naming bias with a structural causal model (SCM), and propose a counterfactual
reasoning based framework named CARBON for eliminating the naming bias in
neural code comprehension. CARBON explicitly captures the naming bias through
multi-task learning in the training stage, and reduces the bias by
counterfactual inference in the inference stage. We evaluate CARBON on three
neural code comprehension tasks, including function naming, defect detection
and code classification. Experiment results show that CARBON achieves
relatively better performance (e.g., +0.5% on the function naming task at F1
score) than the baseline models on the original benchmark datasets, and
significantly improvement (e.g., +37.9% on the function naming task at F1
score) on the datasets with identifiers renamed. The proposed framework
provides a causal view for improving the robustness of code intelligence
models.

Video Instance Segmentation (VIS) jointly tackles multi-object detection,
tracking, and segmentation in video sequences. In the past, VIS methods
mirrored the fragmentation of these subtasks in their architectural design,
hence missing out on a joint solution. Transformers recently allowed to cast
the entire VIS task as a single set-prediction problem. Nevertheless, the
quadratic complexity of existing Transformer-based methods requires long
training times, high memory requirements, and processing of low-single-scale
feature maps. Deformable attention provides a more efficient alternative but
its application to the temporal domain or the segmentation task have not yet
been explored.
  In this work, we present Deformable VIS (DeVIS), a VIS method which
capitalizes on the efficiency and performance of deformable Transformers. To
reason about all VIS subtasks jointly over multiple frames, we present temporal
multi-scale deformable attention with instance-aware object queries. We further
introduce a new image and video instance mask head with multi-scale features,
and perform near-online video processing with multi-cue clip tracking. DeVIS
reduces memory as well as training time requirements, and achieves
state-of-the-art results on the YouTube-VIS 2021, as well as the challenging
OVIS dataset.
  Code is available at https://github.com/acaelles97/DeVIS.

Optical coherence tomography angiography (OCTA) can non-invasively image the
eye's circulatory system. In order to reliably characterize the retinal
vasculature, there is a need to automatically extract quantitative metrics from
these images. The calculation of such biomarkers requires a precise semantic
segmentation of the blood vessels. However, deep-learning-based methods for
segmentation mostly rely on supervised training with voxel-level annotations,
which are costly to obtain. In this work, we present a pipeline to synthesize
large amounts of realistic OCTA images with intrinsically matching ground truth
labels; thereby obviating the need for manual annotation of training data. Our
proposed method is based on two novel components: 1) a physiology-based
simulation that models the various retinal vascular plexuses and 2) a suite of
physics-based image augmentations that emulate the OCTA image acquisition
process including typical artifacts. In extensive benchmarking experiments, we
demonstrate the utility of our synthetic data by successfully training retinal
vessel segmentation algorithms. Encouraged by our method's competitive
quantitative and superior qualitative performance, we believe that it
constitutes a versatile tool to advance the quantitative analysis of OCTA
images.

Moir\'e patterns made of two-dimensional (2D) materials represent highly
tunable electronic Hamiltonians, allowing a wide range of quantum phases to
emerge in a single material. Current modeling techniques for moir\'e electrons
requires significant technical work specific to each material, impeding
large-scale searches for useful moir\'e materials. In order to address this
difficulty, we have developed a material-agnostic machine learning approach and
test it here on prototypical one-dimensional (1D) moir\'e tight-binding models.
We utilize the stacking dependence of the local density of states (SD-LDOS) to
convert information about electronic bandstructure into physically relevant
images. We then train a neural network that successfully predicts moir\'e
electronic structure from the easily computed SD-LDOS of aligned bilayers. This
network can satisfactorily predict moir\'e electronic structures, even for
materials that are not included in its training data.

The recent state of the art on monocular 3D face reconstruction from image
data has made some impressive advancements, thanks to the advent of Deep
Learning. However, it has mostly focused on input coming from a single RGB
image, overlooking the following important factors: a) Nowadays, the vast
majority of facial image data of interest do not originate from single images
but rather from videos, which contain rich dynamic information. b) Furthermore,
these videos typically capture individuals in some form of verbal communication
(public talks, teleconferences, audiovisual human-computer interactions,
interviews, monologues/dialogues in movies, etc). When existing 3D face
reconstruction methods are applied in such videos, the artifacts in the
reconstruction of the shape and motion of the mouth area are often severe,
since they do not match well with the speech audio.
  To overcome the aforementioned limitations, we present the first method for
visual speech-aware perceptual reconstruction of 3D mouth expressions. We do
this by proposing a "lipread" loss, which guides the fitting process so that
the elicited perception from the 3D reconstructed talking head resembles that
of the original video footage. We demonstrate that, interestingly, the lipread
loss is better suited for 3D reconstruction of mouth movements compared to
traditional landmark losses, and even direct 3D supervision. Furthermore, the
devised method does not rely on any text transcriptions or corresponding audio,
rendering it ideal for training in unlabeled datasets. We verify the efficiency
of our method through exhaustive objective evaluations on three large-scale
datasets, as well as subjective evaluation with two web-based user studies.

The use of machine learning algorithms is an attractive way to produce very
fast detector simulations for scattering reactions that can otherwise be
computationally expensive. Here we develop a factorised approach where we deal
with each particle produced in a reaction individually: first determine if it
was detected (acceptance) and second determine its reconstructed variables such
as four momentum (reconstruction). For the acceptance we propose using a
probability classification density ratio technique to determine the probability
the particle was detected as a function of many variables. Neural Network and
Boosted Decision Tree classifiers were tested for this purpose and we found
using a combination of both, through a reweighting stage, provided the most
reliable results. For reconstruction a simple method of synthetic data
generation, based on nearest neighbour or decision trees was developed. Using a
toy parameterised detector we demonstrate that such a method can reliably and
accurately reproduce kinematic distributions from a physics reaction. The
relatively simple algorithms allow for small training overheads whilst
producing reliable results. Possible applications for such fast simulated data
include Toy-MC studies of parameter extraction, preprocessing expensive
simulations or generating templates for background distributions shapes.

In this work, we investigated the application of score-based gradient
learning in discriminative and generative classification settings. Score
function can be used to characterize data distribution as an alternative to
density. It can be efficiently learned via score matching, and used to flexibly
generate credible samples to enhance discriminative classification quality, to
recover density and to build generative classifiers. We analysed the decision
theories involving score-based representations, and performed experiments on
simulated and real-world datasets, demonstrating its effectiveness in achieving
and improving binary classification performance, and robustness to
perturbations, particularly in high dimensions and imbalanced situations.

Are intelligent machines really intelligent? Is the underlying philosophical
concept of intelligence satisfactory for describing how the present systems
work? Is understanding a necessary and sufficient condition for intelligence?
If a machine could understand, should we attribute subjectivity to it? This
paper addresses the problem of deciding whether the so-called "intelligent
machines" are capable of understanding, instead of merely processing signs. It
deals with the relationship between syntaxis and semantics. The main thesis
concerns the inevitability of semantics for any discussion about the
possibility of building conscious machines, condensed into the following two
tenets: "If a machine is capable of understanding (in the strong sense), then
it must be capable of combining rules and intuitions"; "If semantics cannot be
reduced to syntaxis, then a machine cannot understand." Our conclusion states
that it is not necessary to attribute understanding to a machine in order to
explain its exhibited "intelligent" behavior; a merely syntactic and
mechanistic approach to intelligence as a task-solving tool suffices to justify
the range of operations that it can display in the current state of
technological development.

Gathering cyber threat intelligence from open sources is becoming
increasingly important for maintaining and achieving a high level of security
as systems become larger and more complex. However, these open sources are
often subject to information overload. It is therefore useful to apply machine
learning models that condense the amount of information to what is necessary.
Yet, previous studies and applications have shown that existing classifiers are
not able to extract specific information about emerging cybersecurity events
due to their low generalization ability. Therefore, we propose a system to
overcome this problem by training a new classifier for each new incident. Since
this requires a lot of labelled data using standard training methods, we
combine three different low-data regime techniques - transfer learning, data
augmentation, and few-shot learning - to train a high-quality classifier from
very few labelled instances. We evaluated our approach using a novel dataset
derived from the Microsoft Exchange Server data breach of 2021 which was
labelled by three experts. Our findings reveal an increase in F1 score of more
than 21 points compared to standard training methods and more than 18 points
compared to a state-of-the-art method in few-shot learning. Furthermore, the
classifier trained with this method and 32 instances is only less than 5 F1
score points worse than a classifier trained with 1800 instances.

Obtaining the ground truth labels from a video is challenging since the
manual annotation of pixel-wise flow labels is prohibitively expensive and
laborious. Besides, existing approaches try to adapt the trained model on
synthetic datasets to authentic videos, which inevitably suffers from domain
discrepancy and hinders the performance for real-world applications. To solve
these problems, we propose RealFlow, an Expectation-Maximization based
framework that can create large-scale optical flow datasets directly from any
unlabeled realistic videos. Specifically, we first estimate optical flow
between a pair of video frames, and then synthesize a new image from this pair
based on the predicted flow. Thus the new image pairs and their corresponding
flows can be regarded as a new training set. Besides, we design a Realistic
Image Pair Rendering (RIPR) module that adopts softmax splatting and
bi-directional hole filling techniques to alleviate the artifacts of the image
synthesis. In the E-step, RIPR renders new images to create a large quantity of
training data. In the M-step, we utilize the generated training data to train
an optical flow network, which can be used to estimate optical flows in the
next E-step. During the iterative learning steps, the capability of the flow
network is gradually improved, so is the accuracy of the flow, as well as the
quality of the synthesized dataset. Experimental results show that RealFlow
outperforms previous dataset generation methods by a considerably large margin.
Moreover, based on the generated dataset, our approach achieves
state-of-the-art performance on two standard benchmarks compared with both
supervised and unsupervised optical flow methods. Our code and dataset are
available at https://github.com/megvii-research/RealFlow

The development of compact and energy-efficient wearable sensors has led to
an increase in the availability of biosignals. To analyze these continuously
recorded, and often multidimensional, time series at scale, being able to
conduct meaningful unsupervised data segmentation is an auspicious target. A
common way to achieve this is to identify change-points within the time series
as the segmentation basis. However, traditional change-point detection
algorithms often come with drawbacks, limiting their real-world applicability.
Notably, they generally rely on the complete time series to be available and
thus cannot be used for real-time applications. Another common limitation is
that they poorly (or cannot) handle the segmentation of multidimensional time
series. Consequently, the main contribution of this work is to propose a novel
unsupervised segmentation algorithm for multidimensional time series named
Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to
work easily with both online and batch data. When comparing LS-USS against
other state-of-the-art change-point detection algorithms on a variety of
real-world datasets, in both the offline and real-time setting, LS-USS
systematically achieves on par or better performances.

This work introduces a data-driven control approach for stabilizing
high-dimensional dynamical systems from scarce data. The proposed context-aware
controller inference approach is based on the observation that controllers need
to act locally only on the unstable dynamics to stabilize systems. This means
it is sufficient to learn the unstable dynamics alone, which are typically
confined to much lower dimensional spaces than the high-dimensional state
spaces of all system dynamics and thus few data samples are sufficient to
identify them. Numerical experiments demonstrate that context-aware controller
inference learns stabilizing controllers from orders of magnitude fewer data
samples than traditional data-driven control techniques and variants of
reinforcement learning. The experiments further show that the low data
requirements of context-aware controller inference are especially beneficial in
data-scarce engineering problems with complex physics, for which learning
complete system dynamics is often intractable in terms of data and training
costs.

In this paper, we introduce a novel method of neural network weight
compression. In our method, we store weight tensors as sparse, quantized matrix
factors, whose product is computed on the fly during inference to generate the
target model's weights. We use projected gradient descent methods to find
quantized and sparse factorization of the weight tensors. We show that this
approach can be seen as a unification of weight SVD, vector quantization, and
sparse PCA. Combined with end-to-end fine-tuning our method exceeds or is on
par with previous state-of-the-art methods in terms of the trade-off between
accuracy and model size. Our method is applicable to both moderate compression
regimes, unlike vector quantization, and extreme compression regimes.

Traffic prediction plays an important role in the realization of traffic
control and scheduling tasks in intelligent transportation systems. With the
diversification of data sources, reasonably using rich traffic data to model
the complex spatial-temporal dependence and nonlinear characteristics in
traffic flow are the key challenge for intelligent transportation system. In
addition, clearly evaluating the importance of spatial-temporal features
extracted from different data becomes a challenge. A Double Layer - Spatial
Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The
lower layer of DL-STFEE is spatial-temporal feature extraction layer. The
spatial and temporal features in traffic data are extracted by multi-graph
graph convolution and attention mechanism, and different combinations of
spatial and temporal features are generated. The upper layer of DL-STFEE is the
spatial-temporal feature evaluation layer. Through the attention score matrix
generated by the high-dimensional self-attention mechanism, the
spatial-temporal features combinations are fused and evaluated, so as to get
the impact of different combinations on prediction effect. Three sets of
experiments are performed on actual traffic datasets to show that DL-STFEE can
effectively capture the spatial-temporal features and evaluate the importance
of different spatial-temporal feature combinations.

Object detection problem solving has developed greatly within the past few
years. There is a need for lighter models in instances where hardware
limitations exist, as well as a demand for models to be tailored to mobile
devices. In this article, we will assess the methods used when creating
algorithms that address these issues. The main goal of this article is to
increase accuracy in state-of-the-art algorithms while maintaining speed and
real-time efficiency. The most significant issues in one-stage object detection
pertains to small objects and inaccurate localization. As a solution, we
created a new network by the name of MobileDenseNet suitable for embedded
systems. We also developed a light neck FCPNLite for mobile devices that will
aid with the detection of small objects. Our research revealed that very few
papers cited necks in embedded systems. What differentiates our network from
others is our use of concatenation features. A small yet significant change to
the head of the network amplified accuracy without increasing speed or limiting
parameters. In short, our focus on the challenging CoCo and Pascal VOC datasets
were 24.8 and 76.8 in percentage terms respectively - a rate higher than that
recorded by other state-of-the-art systems thus far. Our network is able to
increase accuracy while maintaining real-time efficiency on mobile devices. We
calculated operational speed on Pixel 3 (Snapdragon 845) to 22.8 fps. The
source code of this research is available on
https://github.com/hajizadeh/MobileDenseNet.

Traffic speed prediction is the key to many valuable applications, and it is
also a challenging task because of its various influencing factors. Recent work
attempts to obtain more information through various hybrid models, thereby
improving the prediction accuracy. However, the spatial information acquisition
schemes of these methods have two-level differentiation problems. Either the
modeling is simple but contains little spatial information, or the modeling is
complete but lacks flexibility. In order to introduce more spatial information
on the basis of ensuring flexibility, this paper proposes IRNet (Transferable
Intersection Reconstruction Network). First, this paper reconstructs the
intersection into a virtual intersection with the same structure, which
simplifies the topology of the road network. Then, the spatial information is
subdivided into intersection information and sequence information of traffic
flow direction, and spatiotemporal features are obtained through various
models. Third, a self-attention mechanism is used to fuse spatiotemporal
features for prediction. In the comparison experiment with the baseline, not
only the prediction effect, but also the transfer performance has obvious
advantages.

Non-terrestrial networks (NTNs) are a critical enabler of the persistent
connectivity vision of sixth-generation networks, as they can service areas
where terrestrial infrastructure falls short. However, the integration of these
networks with the terrestrial network is laden with obstacles. The dynamic
nature of NTN communication scenarios and numerous variables render
conventional model-based solutions computationally costly and impracticable for
resource allocation, parameter optimization, and other problems. Machine
learning (ML)-based solutions, thus, can perform a pivotal role due to their
inherent ability to uncover the hidden patterns in time-varying,
multi-dimensional data with superior performance and less complexity.
Centralized ML (CML) and decentralized ML (DML), named so based on the
distribution of the data and computational load, are two classes of ML that are
being studied as solutions for the various complications of terrestrial and
non-terrestrial networks (TNTN) integration. Both have their benefits and
drawbacks under different circumstances, and it is integral to choose the
appropriate ML approach for each TNTN integration issue. To this end, this
paper goes over the TNTN integration architectures as given in the 3rd
generation partnership project standard releases, proposing possible scenarios.
Then, the capabilities and challenges of CML and DML are explored from the
vantage point of these scenarios.

In this work, we propose a novel architecture for face age editing that can
produce structural modifications while maintaining relevant details present in
the original image. We disentangle the style and content of the input image and
propose a new decoder network that adopts a style-based strategy to combine the
style and content representations of the input image while conditioning the
output on the target age. We go beyond existing aging methods allowing users to
adjust the degree of structure preservation in the input image during
inference. To this purpose, we introduce a masking mechanism, the CUstom
Structure Preservation module, that distinguishes relevant regions in the input
image from those that should be discarded. CUSP requires no additional
supervision. Finally, our quantitative and qualitative analysis which include a
user study, show that our method outperforms prior art and demonstrates the
effectiveness of our strategy regarding image editing and adjustable structure
preservation. Code and pretrained models are available at
https://github.com/guillermogotre/CUSP.

Deep Neural Network (DNN) models are usually trained sequentially from one
layer to another, which causes forward, backward and update locking's problems,
leading to poor performance in terms of training time. The existing parallel
strategies to mitigate these problems provide suboptimal runtime performance.
In this work, we have proposed a novel layer-wise partitioning and merging,
forward and backward pass parallel framework to provide better training
performance. The novelty of the proposed work consists of 1) a layer-wise
partition and merging model which can minimise communication overhead between
devices without the memory cost of existing strategies during the training
process; 2) a forward pass and backward pass parallelisation and optimisation
to address the update locking problem and minimise the total training cost. The
experimental evaluation on real use cases shows that the proposed method
outperforms the state-of-the-art approaches in terms of training speed; and
achieves almost linear speedup without compromising the accuracy performance of
the non-parallel approach.

Vulnerability prediction refers to the problem of identifying system
components that are most likely to be vulnerable. Typically, this problem is
tackled by training binary classifiers on historical data. Unfortunately,
recent research has shown that such approaches underperform due to the
following two reasons: a) the imbalanced nature of the problem, and b) the
inherently noisy historical data, i.e., most vulnerabilities are discovered
much later than they are introduced. This misleads classifiers as they learn to
recognize actual vulnerable components as non-vulnerable. To tackle these
issues, we propose TROVON, a technique that learns from known vulnerable
components rather than from vulnerable and non-vulnerable components, as
typically performed. We perform this by contrasting the known vulnerable, and
their respective fixed components. This way, TROVON manages to learn from the
things we know, i.e., vulnerabilities, hence reducing the effects of noisy and
unbalanced data. We evaluate TROVON by comparing it with existing techniques on
three security-critical open source systems, i.e., Linux Kernel, OpenSSL, and
Wireshark, with historical vulnerabilities that have been reported in the
National Vulnerability Database (NVD). Our evaluation demonstrates that the
prediction capability of TROVON significantly outperforms existing
vulnerability prediction techniques such as Software Metrics, Imports, Function
Calls, Text Mining, Devign, LSTM, and LSTM-RF with an improvement of 40.84% in
Matthews Correlation Coefficient (MCC) score under Clean Training Data
Settings, and an improvement of 35.52% under Realistic Training Data Settings.

Neural Architecture Search (NAS) methods have been successfully applied to
image tasks with excellent results. However, NAS methods are often complex and
tend to converge to local minima as soon as generated architectures seem to
yield good results. This paper proposes GEA, a novel approach for guided NAS.
GEA guides the evolution by exploring the search space by generating and
evaluating several architectures in each generation at initialisation stage
using a zero-proxy estimator, where only the highest-scoring architecture is
trained and kept for the next generation. Subsequently, GEA continuously
extracts knowledge about the search space without increased complexity by
generating several off-springs from an existing architecture at each
generation. More, GEA forces exploitation of the most performant architectures
by descendant generation while simultaneously driving exploration through
parent mutation and favouring younger architectures to the detriment of older
ones. Experimental results demonstrate the effectiveness of the proposed
method, and extensive ablation studies evaluate the importance of different
parameters. Results show that GEA achieves state-of-the-art results on all data
sets of NAS-Bench-101, NAS-Bench-201 and TransNAS-Bench-101 benchmarks.

Neural networks tend to forget previously learned knowledge when continuously
learning on datasets with varying distributions, a phenomenon known as
catastrophic forgetting. More significant distribution shifts among datasets
lead to more forgetting. Recently, parameter-isolation-based approaches have
shown great potential in overcoming forgetting with significant distribution
shifts. However, they suffer from poor generalization as they fix the neural
path for each dataset during training and require dataset labels during
inference. In addition, they do not support backward knowledge transfer as they
prioritize past data over future ones. In this paper, we propose a new adaptive
learning method, named AdaptCL, that fully reuses and grows on learned
parameters to overcome catastrophic forgetting and allows the positive backward
transfer without requiring dataset labels. Our proposed technique adaptively
grows on the same neural path by allowing optimal reuse of frozen parameters.
Besides, it uses parameter-level data-driven pruning to assign equal priority
to the data. We conduct extensive experiments on MNIST Variants, DomainNet, and
Food Freshness Detection datasets under different intensities of distribution
shifts without requiring dataset labels. Results demonstrate that our proposed
method is superior to alternative baselines in minimizing forgetting and
enabling positive backward knowledge transfer.

We propose a data-centric pipeline able to generate exogenous observation
data for the New Fashion Product Performance Forecasting (NFPPF) problem, i.e.,
predicting the performance of a brand-new clothing probe with no available past
observations. Our pipeline manufactures the missing past starting from a
single, available image of the clothing probe. It starts by expanding textual
tags associated with the image, querying related fashionable or unfashionable
images uploaded on the web at a specific time in the past. A binary classifier
is robustly trained on these web images by confident learning, to learn what
was fashionable in the past and how much the probe image conforms to this
notion of fashionability. This compliance produces the POtential Performance
(POP) time series, indicating how performing the probe could have been if it
were available earlier. POP proves to be highly predictive for the probe's
future performance, ameliorating the sales forecasts of all state-of-the-art
models on the recent VISUELLE fast-fashion dataset. We also show that POP
reflects the ground-truth popularity of new styles (ensembles of clothing
items) on the Fashion Forward benchmark, demonstrating that our webly-learned
signal is a truthful expression of popularity, accessible by everyone and
generalizable to any time of analysis. Forecasting code, data and the POP time
series are available at:
https://github.com/HumaticsLAB/POP-Mining-POtential-Performance

False base stations -- IMSI catchers, Stingrays -- are devices that
impersonate legitimate base stations, as a part of malicious activities like
unauthorized surveillance or communication sabotage. Detecting them on the
network side using 3GPP standardized measurement reports is a promising
technique. While applying predetermined detection rules works well when an
attacker operates a false base station with an illegitimate Physical Cell
Identifiers (PCI), the detection will produce false negatives when a more
resourceful attacker operates the false base station with one of the legitimate
PCIs obtained by scanning the neighborhood first. In this paper, we show how
Machine Learning (ML) can be applied to alleviate such false negatives. We
demonstrate our approach by conducting experiments in a simulation setup using
the ns-3 LTE module. We propose three robust ML features (COL, DIST, XY) based
on Reference Signal Received Power (RSRP) contained in measurement reports and
cell locations. We evaluate four ML models (Regression Clustering, Anomaly
Detection Forest, Autoencoder, and RCGAN) and show that several of them have a
high precision in detection even when the false base station is using a
legitimate PCI. In our experiments with a layout of 12 cells, where one cell
acts as a moving false cell, between 75-95\% of the false positions are
detected by the best model at a cost of 0.5\% false positives.

Artificial intelligence-based analysis of lung ultrasound imaging has been
demonstrated as an effective technique for rapid diagnostic decision support
throughout the COVID-19 pandemic. However, such techniques can require days- or
weeks-long training processes and hyper-parameter tuning to develop intelligent
deep learning image analysis models. This work focuses on leveraging
'off-the-shelf' pre-trained models as deep feature extractors for scoring
disease severity with minimal training time. We propose using pre-trained
initializations of existing methods ahead of simple and compact neural networks
to reduce reliance on computational capacity. This reduction of computational
capacity is of critical importance in time-limited or resource-constrained
circumstances, such as the early stages of a pandemic. On a dataset of 49
patients, comprising over 20,000 images, we demonstrate that the use of
existing methods as feature extractors results in the effective classification
of COVID-19-related pneumonia severity while requiring only minutes of training
time. Our methods can achieve an accuracy of over 0.93 on a 4-level severity
score scale and provides comparable per-patient region and global scores
compared to expert annotated ground truths. These results demonstrate the
capability for rapid deployment and use of such minimally-adapted methods for
progress monitoring, patient stratification and management in clinical practice
for COVID-19 patients, and potentially in other respiratory diseases.

Neural networks have been proposed for medical image registration by
learning, with a substantial amount of training data, the optimal
transformations between image pairs. These trained networks can further be
optimized on a single pair of test images - known as test-time optimization.
This work formulates image registration as a meta-learning algorithm. Such
networks can be trained by aligning the training image pairs while
simultaneously improving test-time optimization efficacy; tasks which were
previously considered two independent training and optimization processes. The
proposed meta-registration is hypothesized to maximize the efficiency and
effectiveness of the test-time optimization in the "outer" meta-optimization of
the networks. For image guidance applications that often are time-critical yet
limited in training data, the potentially gained speed and accuracy are
compared with classical registration algorithms, registration networks without
meta-learning, and single-pair optimization without test-time optimization
data. Experiments are presented in this paper using clinical transrectal
ultrasound image data from 108 prostate cancer patients. These experiments
demonstrate the effectiveness of a meta-registration protocol, which yields
significantly improved performance relative to existing learning-based methods.
Furthermore, the meta-registration achieves comparable results to classical
iterative methods in a fraction of the time, owing to its rapid test-time
optimization process.

Free Point Transformer (FPT) has been proposed as a data-driven, non-rigid
point set registration approach using deep neural networks. As FPT does not
assume constraints based on point vicinity or correspondence, it may be trained
simply and in a flexible manner by minimizing an unsupervised loss based on the
Chamfer Distance. This makes FPT amenable to real-world medical imaging
applications where ground-truth deformations may be infeasible to obtain, or in
scenarios where only a varying degree of completeness in the point sets to be
aligned is available. To test the limit of the correspondence finding ability
of FPT and its dependency on training data sets, this work explores the
generalizability of the FPT from well-curated non-medical data sets to medical
imaging data sets. First, we train FPT on the ModelNet40 dataset to demonstrate
its effectiveness and the superior registration performance of FPT over
iterative and learning-based point set registration methods. Second, we
demonstrate superior performance in rigid and non-rigid registration and
robustness to missing data. Last, we highlight the interesting generalizability
of the ModelNet-trained FPT by registering reconstructed freehand ultrasound
scans of the spine and generic spine models without additional training,
whereby the average difference to the ground truth curvatures is 1.3 degrees,
across 13 patients.

Fasteners play a critical role in securing various parts of machinery.
Deformations such as dents, cracks, and scratches on the surface of fasteners
are caused by material properties and incorrect handling of equipment during
production processes. As a result, quality control is required to ensure safe
and reliable operations. The existing defect inspection method relies on manual
examination, which consumes a significant amount of time, money, and other
resources; also, accuracy cannot be guaranteed due to human error. Automatic
defect detection systems have proven impactful over the manual inspection
technique for defect analysis. However, computational techniques such as
convolutional neural networks (CNN) and deep learning-based approaches are
evolutionary methods. By carefully selecting the design parameter values, the
full potential of CNN can be realised. Using Taguchi-based design of
experiments and analysis, an attempt has been made to develop a robust
automatic system in this study. The dataset used to train the system has been
created manually for M14 size nuts having two labeled classes: Defective and
Non-defective. There are a total of 264 images in the dataset. The proposed
sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss
at 0.001 learning rate.

Implicit neural representations have shown compelling results in offline 3D
reconstruction and also recently demonstrated the potential for online SLAM
systems. However, applying them to autonomous 3D reconstruction, where robots
are required to explore a scene and plan a view path for the reconstruction,
has not been studied. In this paper, we explore for the first time the
possibility of using implicit neural representations for autonomous 3D scene
reconstruction by addressing two key challenges: 1) seeking a criterion to
measure the quality of the candidate viewpoints for the view planning based on
the new representations, and 2) learning the criterion from data that can
generalize to different scenes instead of hand-crafting one. For the first
challenge, a proxy of Peak Signal-to-Noise Ratio (PSNR) is proposed to quantify
a viewpoint quality. The proxy is acquired by treating the color of a spatial
point in a scene as a random variable under a Gaussian distribution rather than
a deterministic one; the variance of the distribution quantifies the
uncertainty of the reconstruction and composes the proxy. For the second
challenge, the proxy is optimized jointly with the parameters of an implicit
neural network for the scene. With the proposed view quality criterion, we can
then apply the new representations to autonomous 3D reconstruction. Our method
demonstrates significant improvements on various metrics for the rendered image
quality and the geometry quality of the reconstructed 3D models when compared
with variants using TSDF or reconstruction without view planning.

Estimating human poses from videos is critical in human-computer interaction.
By precisely estimating human poses, the robot can provide an appropriate
response to the human. Most existing approaches use the optical flow, RNNs, or
CNNs to extract temporal features from videos. Despite the positive results of
these attempts, most of them only straightforwardly integrate features along
the temporal dimension, ignoring temporal correlations between joints. In
contrast to previous methods, we propose a plug-and-play kinematics modeling
module (KMM) based on the domain-cross attention mechanism to model the
temporal correlation between joints across different frames explicitly.
Specifically, the proposed KMM models the temporal correlation between any two
joints by calculating their temporal similarity. In this way, KMM can learn the
motion cues of each joint. Using the motion cues (temporal domain) and
historical positions of joints (spatial domain), KMM can infer the initial
positions of joints in the current frame in advance. In addition, we present a
kinematics modeling network (KIMNet) based on the KMM for obtaining the final
positions of joints by combining pose features and initial positions of joints.
By explicitly modeling temporal correlations between joints, KIMNet can infer
the occluded joints at present according to all joints at the previous moment.
Furthermore, the KMM is achieved through an attention mechanism, which allows
it to maintain the high resolution of features. Therefore, it can transfer rich
historical pose information to the current frame, which provides effective pose
information for locating occluded joints. Our approach achieves
state-of-the-art results on two standard video-based pose estimation
benchmarks. Moreover, the proposed KIMNet shows some robustness to the
occlusion, demonstrating the effectiveness of the proposed method.

Osteoporosis is a common disease that increases fracture risk. Hip fractures,
especially in elderly people, lead to increased morbidity, decreased quality of
life and increased mortality. Being a silent disease before fracture,
osteoporosis often remains undiagnosed and untreated. Areal bone mineral
density (aBMD) assessed by dual-energy X-ray absorptiometry (DXA) is the
gold-standard method for osteoporosis diagnosis and hence also for future
fracture prediction (prognostic). However, the required special equipment is
not broadly available everywhere, in particular not to patients in developing
countries. We propose a deep learning classification model (FORM) that can
directly predict hip fracture risk from either plain radiographs (X-ray) or 2D
projection images of computed tomography (CT) data. Our method is fully
automated and therefore well suited for opportunistic screening settings,
identifying high risk patients in a broader population without additional
screening. FORM was trained and evaluated on X-rays and CT projections from the
Osteoporosis in Men (MrOS) study. 3108 X-rays (89 incident hip fractures) or
2150 CTs (80 incident hip fractures) with a 80/20 split were used. We show that
FORM can correctly predict the 10-year hip fracture risk with a validation AUC
of 81.44 +- 3.11% / 81.04 +- 5.54% (mean +- STD) including additional
information like age, BMI, fall history and health background across a 5-fold
cross validation on the X-ray and CT cohort, respectively. Our approach
significantly (p < 0.01) outperforms previous methods like Cox
Proportional-Hazards Model and \frax with 70.19 +- 6.58 and 74.72 +- 7.21
respectively on the X-ray cohort. Our model outperform on both cohorts hip aBMD
based predictions. We are confident that FORM can contribute on improving
osteoporosis diagnosis at an early stage.

The distributed subgradient method (DSG) is a widely discussed algorithm to
cope with large-scale distributed optimization problems in the arising machine
learning applications. Most exisiting works on DSG focus on ideal communication
between the cooperative agents such that the shared information between agents
is exact and perfect. This assumption, however, could lead to potential privacy
concerns and is not feasible when the wireless transmission links are not of
good quality. To overcome the challenge, a common approach is to quantize the
data locally before transmission, which avoids exposure of raw data and
significantly reduces the size of data. Compared with perfect data,
quantization poses fundamental challenges on loss of data accuracy, which
further impacts the convergence of the algorithms. To settle the problem, we
propose a generalized distributed subgradient method with random quantization,
which can be intepreted as a two time-scale stochastic approximation method. We
provide comprehensive results on the convergence of the algorithm and derive
upper bounds on the convergence rates in terms of the quantization bit,
stepsizes and the number of network agents. Our results extend the existing
results, where only special cases are considered and general conclusions for
the convergence rates are missing. Finally, numerical simulations are conducted
on linear regression problems to support our theoretical results.

This paper presents a computational framework for the Principal Geodesic
Analysis of merge trees (MT-PGA), a novel adaptation of the celebrated
Principal Component Analysis (PCA) framework [87] to the Wasserstein metric
space of merge trees [92]. We formulate MT-PGA computation as a constrained
optimization problem, aiming at adjusting a basis of orthogonal geodesic axes,
while minimizing a fitting energy. We introduce an efficient, iterative
algorithm which exploits shared-memory parallelism, as well as an analytic
expression of the fitting energy gradient, to ensure fast iterations. Our
approach also trivially extends to extremum persistence diagrams. Extensive
experiments on public ensembles demonstrate the efficiency of our approach -
with MT-PGA computations in the orders of minutes for the largest examples. We
show the utility of our contributions by extending to merge trees two typical
PCA applications. First, we apply MT-PGA to data reduction and reliably
compress merge trees by concisely representing them by their first coordinates
in the MT-PGA basis. Second, we present a dimensionality reduction framework
exploiting the first two directions of the MT-PGA basis to generate
two-dimensional layouts of the ensemble. We augment these layouts with
persistence correlation views, enabling global and local visual inspections of
the feature variability in the ensemble. In both applications, quantitative
experiments assess the relevance of our framework. Finally, we provide a C++
implementation that can be used to reproduce our results.

Human fall is one of the very critical health issues, especially for elders
and disabled people living alone. The number of elder populations is increasing
steadily worldwide. Therefore, human fall detection is becoming an effective
technique for assistive living for those people. For assistive living, deep
learning and computer vision have been used largely. In this review article, we
discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based)
fall detection techniques. We also present a survey on fall detection benchmark
datasets. For a clear understanding, we briefly discuss different metrics which
are used to evaluate the performance of the fall detection systems. This
article also gives a future direction on vision-based human fall detection

model weights as pre-training. We propose layer-wise loss normalization which
we demonstrate is key to generate high-performing models and a sampling method
based on the empirical density of hyper-representations. The models generated
using our methods are diverse, performant and capable to outperform
conventional baselines for transfer learning. Our results indicate the
potential of knowledge aggregation from model zoos to new models via
hyper-representations thereby paving the avenue for novel research directions.

Gene set collections are a common ground to study the enrichment of genes for
specific phenotypic traits. Gene set enrichment analysis aims to identify genes
that are over-represented in gene sets collections and might be associated with
a specific phenotypic trait. However, as this involves a massive number of
hypothesis testing, it is often questionable whether a pre-processing step to
reduce gene sets collections' sizes is helpful. Moreover, the often highly
overlapping gene sets and the consequent low interpretability of gene sets'
collections demand for a reduction of the included gene sets. Inspired by this
bioinformatics context, we propose a method to rank sets within a family of
sets based on the distribution of the singletons and their size. We obtain
sets' importance scores by computing Shapley values without incurring into the
usual exponential number of evaluations of the value function. Moreover, we
address the challenge of including a redundancy awareness in the rankings
obtained where, in our case, sets are redundant if they show prominent
intersections. We finally evaluate our approach for gene sets collections; the
rankings obtained show low redundancy and high coverage of the genes. The
unsupervised nature of the proposed ranking does not allow for an evident
increase in the number of significant gene sets for specific phenotypic traits
when reducing the size of the collections. However, we believe that the
rankings proposed are of use in bioinformatics to increase interpretability of
the gene sets collections and a step forward to include redundancy into Shapley
values computations.

Recent developments in self-supervised learning give us the possibility to
further reduce human intervention in multi-step pipelines where the focus
evolves around particular objects of interest. In the present paper, the focus
lays in the nuclei in histopathology images. In particular we aim at extracting
cellular information in an unsupervised manner for a downstream task. As nuclei
present themselves in a variety of sizes, we propose a new Scale-dependant
convolutional layer to bypass scaling issues when resizing nuclei. On three
nuclei datasets, we benchmark the following methods: handcrafted, pre-trained
ResNet, supervised ResNet and self-supervised features. We show that the
proposed convolution layer boosts performance and that this layer combined with
Barlows-Twins allows for better nuclei encoding compared to the supervised
paradigm in the low sample setting and outperforms all other proposed
unsupervised methods. In addition, we extend the existing TNBC dataset to
incorporate nuclei class annotation in order to enrich and publicly release a
small sample setting dataset for nuclei segmentation and classification.

Existing methods for anomaly detection based on memory-augmented autoencoder
(AE) have the following drawbacks: (1) Establishing a memory bank requires
additional memory space. (2) The fixed number of prototypes from subjective
assumptions ignores the data feature differences and diversity. To overcome
these drawbacks, we introduce DLAN-AC, a Dynamic Local Aggregation Network with
Adaptive Clusterer, for anomaly detection. First, The proposed DLAN can
automatically learn and aggregate high-level features from the AE to obtain
more representative prototypes, while freeing up extra memory space. Second,
The proposed AC can adaptively cluster video data to derive initial prototypes
with prior information. In addition, we also propose a dynamic redundant
clustering strategy (DRCS) to enable DLAN for automatically eliminating feature
clusters that do not contribute to the construction of prototypes. Extensive
experiments on benchmarks demonstrate that DLAN-AC outperforms most existing
methods, validating the effectiveness of our method. Our code is publicly
available at https://github.com/Beyond-Zw/DLAN-AC.

Prototype Generation (PG) methods are typically considered for improving the
efficiency of the $k$-Nearest Neighbour ($k$NN) classifier when tackling
high-size corpora. Such approaches aim at generating a reduced version of the
corpus without decreasing the classification performance when compared to the
initial set. Despite their large application in multiclass scenarios, very few
works have addressed the proposal of PG methods for the multilabel space. In
this regard, this work presents the novel adaptation of four multiclass PG
strategies to the multilabel case. These proposals are evaluated with three
multilabel $k$NN-based classifiers, 12 corpora comprising a varied range of
domains and corpus sizes, and different noise scenarios artificially induced in
the data. The results obtained show that the proposed adaptations are capable
of significantly improving -- both in terms of efficiency and classification
performance -- the only reference multilabel PG work in the literature as well
as the case in which no PG method is applied, also presenting a statistically
superior robustness in noisy scenarios. Moreover, these novel PG strategies
allow prioritising either the efficiency or efficacy criteria through its
configuration depending on the target scenario, hence covering a wide area in
the solution space not previously filled by other works.

Deep learning plays a more and more important role in our daily life due to
its competitive performance in multiple industrial application domains. As the
core of DL-enabled systems, deep neural networks automatically learn knowledge
from carefully collected and organized training data to gain the ability to
predict the label of unseen data. Similar to the traditional software systems
that need to be comprehensively tested, DNNs also need to be carefully
evaluated to make sure the quality of the trained model meets the demand. In
practice, the de facto standard to assess the quality of DNNs in industry is to
check their performance (accuracy) on a collected set of labeled test data.
However, preparing such labeled data is often not easy partly because of the
huge labeling effort, i.e., data labeling is labor-intensive, especially with
the massive new incoming unlabeled data every day. Recent studies show that
test selection for DNN is a promising direction that tackles this issue by
selecting minimal representative data to label and using these data to assess
the model. However, it still requires human effort and cannot be automatic. In
this paper, we propose a novel technique, named Aries, that can estimate the
performance of DNNs on new unlabeled data using only the information obtained
from the original test data. The key insight behind our technique is that the
model should have similar prediction accuracy on the data which have similar
distances to the decision boundary. We performed a large-scale evaluation of
our technique on 13 types of data transformation methods. The results
demonstrate the usefulness of our technique that the estimated accuracy by
Aries is only 0.03% -- 2.60% (on average 0.61%) off the true accuracy. Besides,
Aries also outperforms the state-of-the-art selection-labeling-based methods in
most (96 out of 128) cases.

How to handle time features shall be the core question of any time series
forecasting model. Ironically, it is often ignored or misunderstood by
deep-learning based models, even those baselines which are state-of-the-art.
This behavior makes their inefficient, untenable and unstable. In this paper,
we rigorously analyze three prevalent but deficient/unfounded deep time series
forecasting mechanisms or methods from the view of time series properties,
including normalization methods, multivariate forecasting and input sequence
length. Corresponding corollaries and solutions are given on both empirical and
theoretical basis. We thereby propose a novel time series forecasting network,
i.e. RTNet, on the basis of aforementioned analysis. It is general enough to be
combined with both supervised and self-supervised forecasting format. Thanks to
the core idea of respecting time series properties, no matter in which
forecasting format, RTNet shows obviously superior forecasting performances
compared with dozens of other SOTA time series forecasting baselines in three
real-world benchmark datasets. By and large, it even occupies less time
complexity and memory usage while acquiring better forecasting accuracy. The
source code is available at https://github.com/OrigamiSL/RTNet.

We study the performance -- and specifically the rate at which the error
probability converges to zero -- of Machine Learning (ML) classification
techniques. Leveraging the theory of large deviations, we provide the
mathematical conditions for a ML classifier to exhibit error probabilities that
vanish exponentially, say $\sim \exp\left(-n\,I + o(n) \right)$, where $n$ is
the number of informative observations available for testing (or another
relevant parameter, such as the size of the target in an image) and $I$ is the
error rate. Such conditions depend on the Fenchel-Legendre transform of the
cumulant-generating function of the Data-Driven Decision Function (D3F, i.e.,
what is thresholded before the final binary decision is made) learned in the
training phase. As such, the D3F and, consequently, the related error rate $I$,
depend on the given training set, which is assumed of finite size.
Interestingly, these conditions can be verified and tested numerically
exploiting the available dataset, or a synthetic dataset, generated according
to the available information on the underlying statistical model. In other
words, the classification error probability convergence to zero and its rate
can be computed on a portion of the dataset available for training. Coherently
with the large deviations theory, we can also establish the convergence, for
$n$ large enough, of the normalized D3F statistic to a Gaussian distribution.
This property is exploited to set a desired asymptotic false alarm probability,
which empirically turns out to be accurate even for quite realistic values of
$n$. Furthermore, approximate error probability curves $\sim \zeta_n
\exp\left(-n\,I \right)$ are provided, thanks to the refined asymptotic
derivation (often referred to as exact asymptotics), where $\zeta_n$ represents
the most representative sub-exponential terms of the error probabilities.

A sound field estimation method based on a physics-informed convolutional
neural network (PICNN) using spline interpolation is proposed. Most of the
sound field estimation methods are based on wavefunction expansion, making the
estimated function satisfy the Helmholtz equation. However, these methods rely
only on physical properties; thus, they suffer from a significant deterioration
of accuracy when the number of measurements is small. Recent learning-based
methods based on neural networks have advantages in estimating from sparse
measurements when training data are available. However, since physical
properties are not taken into consideration, the estimated function can be a
physically infeasible solution. We propose the application of PICNN to the
sound field estimation problem by using a loss function that penalizes
deviation from the Helmholtz equation. Since the output of CNN is a spatially
discretized pressure distribution, it is difficult to directly evaluate the
Helmholtz-equation loss function. Therefore, we incorporate bicubic spline
interpolation in the PICNN framework. Experimental results indicated that
accurate and physically feasible estimation from sparse measurements can be
achieved with the proposed method.

The UK, particularly London, is a global hub for money laundering, a
significant portion of which uses domestic property. However, understanding the
distribution and characteristics of offshore domestic property in the UK is
challenging due to data availability. This paper attempts to remedy that
situation by enhancing a publicly available dataset of UK property owned by
offshore companies. We create a data processing pipeline which draws on several
datasets and machine learning techniques to create a parsed set of addresses
classified into six use classes. The enhanced dataset contains 138,000
properties 44,000 more than the original dataset. The majority are domestic
(95k), with a disproportionate amount of those in London (42k). The average
offshore domestic property in London is worth 1.33 million GBP collectively
this amounts to approximately 56 Billion GBP. We perform an in-depth analysis
of the offshore domestic property in London, comparing the price, distribution
and entropy/concentration with Airbnb property, low-use/empty property and
conventional domestic property. We estimate that the total amount of offshore,
low-use and airbnb property in London is between 144,000 and 164,000 and that
they are collectively worth between 145-174 billion GBP. Furthermore, offshore
domestic property is more expensive and has higher entropy/concentration than
all other property types. In addition, we identify two different types of
offshore property, nested and individual, which have different price and
distribution characteristics. Finally, we release the enhanced offshore
property dataset, the complete low-use London dataset and the pipeline for
creating the enhanced dataset to reduce the barriers to studying this topic.

Vaccines are promising tools to control the spread of COVID-19. An effective
vaccination campaign requires government policies and community engagement,
sharing experiences for social support, and voicing concerns to vaccine safety
and efficiency. The increasing use of online social platforms allows us to
trace large-scale communication and infer public opinion in real-time. We
collected more than 100 million vaccine-related tweets posted by 8 million
users and used the Latent Dirichlet Allocation model to perform automated topic
modeling of tweet texts during the vaccination campaign in Japan. We identified
15 topics grouped into 4 themes on Personal issue, Breaking news, Politics, and
Conspiracy and humour. The evolution of the popularity of themes revealed a
shift in public opinion, initially sharing the attention over personal issues
(individual aspect), collecting information from the news (knowledge
acquisition), and government criticisms, towards personal experiences once
confidence in the vaccination campaign was established. An interrupted time
series regression analysis showed that the Tokyo Olympic Games affected public
opinion more than other critical events but not the course of the vaccination.
Public opinion on politics was significantly affected by various events,
positively shifting the attention in the early stages of the vaccination
campaign and negatively later. Tweets about personal issues were mostly
retweeted when the vaccination reached the younger population. The associations
between the vaccination campaign stages and tweet themes suggest that the
public engagement in the social platform contributed to speedup vaccine uptake
by reducing anxiety via social learning and support.

Standard molecular dynamics (MD) and Monte Carlo (MC) simulation deals with
spherical particles. Extending these standard simulation methodologies to the
non-spherical cases is non-trivial. To circumvent this problem, non-spherical
bodies are considered as a collection of constituent spherical objects. As the
number of these constituent objects becomes large, the computational burden to
simulate the system also increases. In this article, we propose an alternative
way to simulate non-circular rigid bodies in two dimensions having pairwise
repulsive interactions. Our approach is based on a machine learning (ML) based
model which predicts the overlap between two non-circular bodies. The machine
learning model is easy to train and the computation cost of its implementation
remains independent of the number of constituents disks used to represent a
non-circular rigid body. When used in MC simulation, our approach provides
significant speed up in comparison to the standard implementation where overlap
determination between two rigid bodies is done by calculating the distance of
their constituent disks. Our proposed ML based MC method provided very similar
structural features (in comparison to standard implementation) of the systems.
We believe this work is a very first step towards a time-efficient simulation
of non-spherical rigid bodies.

The Higgs boson pair production via gluon-gluon fusion and vector boson
fusion in the $b\bar{b}\mu^+\mu^-$ final state at the LHC is studied to probe
the Higgs self-coupling $\kappa_\lambda$ and the four-boson HHVV coupling
$\kappa_{\rm{2V}}$ for the first time. A cut-based analysis and a machine
learning analysis using boosted decision trees are performed with
categorizations and optimizations depending on the variations of these
couplings. The expected sensitivities are extracted with different luminosities
assumed up to the full HL-LHC runs. The expected upper limit at 95% confidence
level on the HH production is calculated as 47 (28) times the Standard Model
cross-section using the cut-based method (boosted decision trees) for the
gluon-gluon fusion production, and 928 for the vector boson fusion production,
assuming an integrated luminosity of 3000 fb$^{-1}$. The expected constraints
on the couplings at 95% confidence level are calculated to be $-13.8<
\kappa_{\lambda}< 19.1$ ($-10.0< \kappa_{\lambda}< 15.5$) and $-3.4<
\kappa_{\rm{2V}}< 5.5$ using the cut-based method (boosted decision trees),
respectively, assuming an integrated luminosity of 3000 fb$^{-1}$.

Recent years have seen the rapid development of fairness-aware machine
learning in mitigating unfairness or discrimination in decision-making in a
wide range of applications. However, much less attention has been paid to the
fairness-aware multi-objective optimization, which is indeed commonly seen in
real life, such as fair resource allocation problems and data driven
multi-objective optimization problems. This paper aims to illuminate and
broaden our understanding of multi-objective optimization from the perspective
of fairness. To this end, we start with a discussion of user preferences in
multi-objective optimization and then explore its relationship to fairness in
machine learning and multi-objective optimization. Following the above
discussions, representative cases of fairness-aware multiobjective optimization
are presented, further elaborating the importance of fairness in traditional
multi-objective optimization, data-driven optimization and federated
optimization. Finally, challenges and opportunities in fairness-aware
multi-objective optimization are addressed. We hope that this article makes a
small step forward towards understanding fairness in the context of
optimization and promote research interest in fairness-aware multi-objective

Adversarial training (AT) for robust representation learning and
self-supervised learning (SSL) for unsupervised representation learning are two
active research fields. Integrating AT into SSL, multiple prior works have
accomplished a highly significant yet challenging task: learning robust
representation without labels. A widely used framework is adversarial
contrastive learning which couples AT and SSL, and thus constitute a very
complex optimization problem. Inspired by the divide-and-conquer philosophy, we
conjecture that it might be simplified as well as improved by solving two
sub-problems: non-robust SSL and pseudo-supervised AT. This motivation shifts
the focus of the task from seeking an optimal integrating strategy for a
coupled problem to finding sub-solutions for sub-problems. With this said, this
work discards prior practices of directly introducing AT to SSL frameworks and
proposed a two-stage framework termed Decoupled Adversarial Contrastive
Learning (DeACL). Extensive experimental results demonstrate that our DeACL
achieves SOTA self-supervised adversarial robustness while significantly
reducing the training time, which validates its effectiveness and efficiency.
Moreover, our DeACL constitutes a more explainable solution, and its success
also bridges the gap with semi-supervised AT for exploiting unlabeled samples
for robust representation learning. The code is publicly accessible at
https://github.com/pantheon5100/DeACL.

Graph Machine Learning (GraphML), whereby classical machine learning is
generalized to irregular graph domains, has enjoyed a recent renaissance,
leading to a dizzying array of models and their applications in several
domains. With its growing applicability to sensitive domains and regulations by
governmental agencies for trustworthy AI systems, researchers have started
looking into the issues of transparency and privacy of graph learning.
  However, these topics have been mainly investigated independently. In this
position paper, we provide a unified perspective on the interplay of privacy
and transparency in GraphML. In particular, we describe the challenges and
possible research directions for a formal investigation of privacy-transparency
tradeoffs in GraphML.

Although deep-learning based methods for monocular pedestrian detection have
made great progress, they are still vulnerable to heavy occlusions. Using
multi-view information fusion is a potential solution but has limited
applications, due to the lack of annotated training samples in existing
multi-view datasets, which increases the risk of overfitting. To address this
problem, a data augmentation method is proposed to randomly generate 3D
cylinder occlusions, on the ground plane, which are of the average size of
pedestrians and projected to multiple views, to relieve the impact of
overfitting in the training. Moreover, the feature map of each view is
projected to multiple parallel planes at different heights, by using
homographies, which allows the CNNs to fully utilize the features across the
height of each pedestrian to infer the locations of pedestrians on the ground
plane. The proposed 3DROM method has a greatly improved performance in
comparison with the state-of-the-art deep-learning based methods for multi-view
pedestrian detection.

We present a novel unsupervised domain adaptation method for semantic
segmentation that generalizes a model trained with source images and
corresponding ground-truth labels to a target domain. A key to domain adaptive
semantic segmentation is to learn domain-invariant and discriminative features
without target ground-truth labels. To this end, we propose a bi-directional
pixel-prototype contrastive learning framework that minimizes intra-class
variations of features for the same object class, while maximizing inter-class
variations for different ones, regardless of domains. Specifically, our
framework aligns pixel-level features and a prototype of the same object class
in target and source images (i.e., positive pairs), respectively, sets them
apart for different classes (i.e., negative pairs), and performs the alignment
and separation processes toward the other direction with pixel-level features
in the source image and a prototype in the target image. The cross-domain
matching encourages domain-invariant feature representations, while the
bidirectional pixel-prototype correspondences aggregate features for the same
object class, providing discriminative features. To establish training pairs
for contrastive learning, we propose to generate dynamic pseudo labels of
target images using a non-parametric label transfer, that is, pixel-prototype
correspondences across different domains. We also present a calibration method
compensating class-wise domain biases of prototypes gradually during training.

Existing pruning techniques preserve deep neural networks' overall ability to
make correct predictions but may also amplify hidden biases during the
compression process. We propose a novel pruning method, Fairness-aware GRAdient
Pruning mEthod (FairGRAPE), that minimizes the disproportionate impacts of
pruning on different sub-groups. Our method calculates the per-group importance
of each model weight and selects a subset of weights that maintain the relative
between-group total importance in pruning. The proposed method then prunes
network edges with small importance values and repeats the procedure by
updating importance values. We demonstrate the effectiveness of our method on
four different datasets, FairFace, UTKFace, CelebA, and ImageNet, for the tasks
of face attribute classification where our method reduces the disparity in
performance degradation by up to 90% compared to the state-of-the-art pruning
algorithms. Our method is substantially more effective in a setting with a high
pruning rate (99%). The code and dataset used in the experiments are available
at https://github.com/Bernardo1998/FairGRAPE

Procedure learning involves identifying the key-steps and determining their
logical order to perform a task. Existing approaches commonly use third-person
videos for learning the procedure, making the manipulated object small in
appearance and often occluded by the actor, leading to significant errors. In
contrast, we observe that videos obtained from first-person (egocentric)
wearable cameras provide an unobstructed and clear view of the action. However,
procedure learning from egocentric videos is challenging because (a) the camera
view undergoes extreme changes due to the wearer's head motion, and (b) the
presence of unrelated frames due to the unconstrained nature of the videos. Due
to this, current state-of-the-art methods' assumptions that the actions occur
at approximately the same time and are of the same duration, do not hold.
Instead, we propose to use the signal provided by the temporal correspondences
between key-steps across videos. To this end, we present a novel
self-supervised Correspond and Cut (CnC) framework for procedure learning. CnC
identifies and utilizes the temporal correspondences between the key-steps
across multiple videos to learn the procedure. Our experiments show that CnC
outperforms the state-of-the-art on the benchmark ProceL and CrossTask datasets
by 5.2% and 6.3%, respectively. Furthermore, for procedure learning using
egocentric videos, we propose the EgoProceL dataset consisting of 62 hours of
videos captured by 130 subjects performing 16 tasks. The source code and the
dataset are available on the project page https://sid2697.github.io/egoprocel/.

With the development of 4G/5G, the rapid growth of traffic has caused a large
number of cell indicators to exceed the warning threshold, and network quality
has deteriorated. It is necessary for operators to solve the congestion in
advance and effectively to guarantee the quality of user experience. Cell-level
multi-indicator forecasting is the foundation task for proactive complex
network optimization. In this paper, we propose the 4G/5G Cell-level
multi-indicator forecasting method based on the dense-Multi-Layer Perceptron
(MLP) neural network, which adds additional fully-connected layers between
non-adjacent layers in an MLP network. The model forecasted the following
week's traffic indicators of 13000 cells according to the six-month historical
indicators of 65000 cells in the 4G&5G network, which got the highest weighted
MAPE score (0.2484) in the China Mobile problem statement in the ITU-T AI/ML in
5G Challenge 2021. Furthermore, the proposed model has been integrated into the
AsiaInfo 4G/5G energy-saving system and deployed in Jiangsu Province of China.

This paper illustrates our submission method to the fourth Affective Behavior
Analysis in-the-Wild (ABAW) Competition. The method is used for the Multi-Task
Learning Challenge. Instead of using only face information, we employ full
information from a provided dataset containing face and the context around the
face. We utilized the InceptionNet V3 model to extract deep features then we
applied the attention mechanism to refine the features. After that, we put
those features into the transformer block and multi-layer perceptron networks
to get the final multiple kinds of emotion. Our model predicts arousal and
valence, classifies the emotional expression and estimates the action units
simultaneously. The proposed system achieves the performance of 0.917 on the
MTL Challenge validation dataset.

In this work we improve forecasting of Sea Surface Height (SSH) and current
velocity (speed and direction) in oceanic scenarios. We do so by resorting to
Random Forests so as to predict the error of a numerical forecasting system
developed for the Santos Channel in Brazil. We have used the Santos Operational
Forecasting System (SOFS) and data collected in situ between the years of 2019
and 2021. In previous studies we have applied similar methods for current
velocity in the channel entrance, in this work we expand the application to
improve the SHH forecast and include four other stations in the channel. We
have obtained an average reduction of 11.9% in forecasting Root-Mean Square
Error (RMSE) and 38.7% in bias with our approach. We also obtained an increase
of Agreement (IOA) in 10 of the 14 combinations of forecasted variables and
stations.

Recent years have seen particular interest in using electronic medical
records (EMRs) for secondary purposes to enhance the quality and safety of
healthcare delivery. EMRs tend to contain large amounts of valuable clinical
notes. Learning of embedding is a method for converting notes into a format
that makes them comparable. Transformer-based representation models have
recently made a great leap forward. These models are pre-trained on large
online datasets to understand natural language texts effectively. The quality
of a learning embedding is influenced by how clinical notes are used as input
to representation models. A clinical note has several sections with different
levels of information value. It is also common for healthcare providers to use
different expressions for the same concept. Existing methods use clinical notes
directly or with an initial preprocessing as input to representation models.
However, to learn a good embedding, we identified the most essential clinical
notes section. We then mapped the extracted concepts from selected sections to
the standard names in the Unified Medical Language System (UMLS). We used the
standard phrases corresponding to the unique concepts as input for clinical
models. We performed experiments to measure the usefulness of the learned
embedding vectors in the task of hospital mortality prediction on a subset of
the publicly available Medical Information Mart for Intensive Care (MIMIC-III)
dataset. According to the experiments, clinical transformer-based
representation models produced better results with getting input generated by
standard names of extracted unique concepts compared to other input formats.
The best-performing models were BioBERT, PubMedBERT, and UmlsBERT,

High levels of noise usually exist in today's captured images due to the
relatively small sensors equipped in the smartphone cameras, where the noise
brings extra challenges to lossy image compression algorithms. Without the
capacity to tell the difference between image details and noise, general image
compression methods allocate additional bits to explicitly store the undesired
image noise during compression and restore the unpleasant noisy image during
decompression. Based on the observations, we optimize the image compression
algorithm to be noise-aware as joint denoising and compression to resolve the
bits misallocation problem. The key is to transform the original noisy images
to noise-free bits by eliminating the undesired noise during compression, where
the bits are later decompressed as clean images. Specifically, we propose a
novel two-branch, weight-sharing architecture with plug-in feature denoisers to
allow a simple and effective realization of the goal with little computational
cost. Experimental results show that our method gains a significant improvement
over the existing baseline methods on both the synthetic and real-world
datasets. Our source code is available at
https://github.com/felixcheng97/DenoiseCompression.

Contrastive self-supervised learning (CSL) has managed to match or surpass
the performance of supervised learning in image and video classification.
However, it is still largely unknown if the nature of the representation
induced by the two learning paradigms is similar. We investigate this under the
lens of adversarial robustness. Our analytical treatment of the problem reveals
intrinsic higher sensitivity of CSL over supervised learning. It identifies the
uniform distribution of data representation over a unit hypersphere in the CSL
representation space as the key contributor to this phenomenon. We establish
that this increases model sensitivity to input perturbations in the presence of
false negatives in the training data. Our finding is supported by extensive
experiments for image and video classification using adversarial perturbations
and other input corruptions. Building on the insights, we devise strategies
that are simple, yet effective in improving model robustness with CSL training.
We demonstrate up to 68% reduction in the performance gap between adversarially
attacked CSL and its supervised counterpart. Finally, we contribute to robust
CSL paradigm by incorporating our findings in adversarial self-supervised
learning. We demonstrate an average gain of about 5% over two different
state-of-the-art methods in this domain.

Particle-based systems provide a flexible and unified way to simulate physics
systems with complex dynamics. Most existing data-driven simulators for
particle-based systems adopt graph neural networks (GNNs) as their network
backbones, as particles and their interactions can be naturally represented by
graph nodes and graph edges. However, while particle-based systems usually
contain hundreds even thousands of particles, the explicit modeling of particle
interactions as graph edges inevitably leads to a significant computational
overhead, due to the increased number of particle interactions. Consequently,
in this paper we propose a novel Transformer-based method, dubbed as
Transformer with Implicit Edges (TIE), to capture the rich semantics of
particle interactions in an edge-free manner. The core idea of TIE is to
decentralize the computation involving pair-wise particle interactions into
per-particle updates. This is achieved by adjusting the self-attention module
to resemble the update formula of graph edges in GNN. To improve the
generalization ability of TIE, we further amend TIE with learnable
material-specific abstract particles to disentangle global material-wise
semantics from local particle-wise semantics. We evaluate our model on diverse
domains of varying complexity and materials. Compared with existing GNN-based
methods, without bells and whistles, TIE achieves superior performance and
generalization across all these domains. Codes and models are available at
https://github.com/ftbabi/TIE_ECCV2022.git.

Classification on long-tailed distributed data is a challenging problem,
which suffers from serious class-imbalance and hence poor performance on tail
classes with only a few samples. Owing to this paucity of samples, learning on
the tail classes is especially challenging for the fine-tuning when
transferring a pretrained model to a downstream task. In this work, we present
a simple modification of standard fine-tuning to cope with these challenges.
Specifically, we propose a two-stage fine-tuning: we first fine-tune the final
layer of the pretrained model with class-balanced reweighting loss, and then we
perform the standard fine-tuning. Our modification has several benefits: (1) it
leverages pretrained representations by only fine-tuning a small portion of the
model parameters while keeping the rest untouched; (2) it allows the model to
learn an initial representation of the specific task; and importantly (3) it
protects the learning of tail classes from being at a disadvantage during the
model updating. We conduct extensive experiments on synthetic datasets of both
two-class and multi-class tasks of text classification as well as a real-world
application to ADME (i.e., absorption, distribution, metabolism, and excretion)
semantic labeling. The experimental results show that the proposed two-stage
fine-tuning outperforms both fine-tuning with conventional loss and fine-tuning
with a reweighting loss on the above datasets.

Multi-modal learning focuses on training models by equally combining multiple
input data modalities during the prediction process. However, this equal
combination can be detrimental to the prediction accuracy because different
modalities are usually accompanied by varying levels of uncertainty. Using such
uncertainty to combine modalities has been studied by a couple of approaches,
but with limited success because these approaches are either designed to deal
with specific classification or segmentation problems and cannot be easily
translated into other tasks, or suffer from numerical instabilities. In this
paper, we propose a new Uncertainty-aware Multi-modal Learner that estimates
uncertainty by measuring feature density via Cross-modal Random Network
Prediction (CRNP). CRNP is designed to require little adaptation to translate
between different prediction tasks, while having a stable training process.
From a technical point of view, CRNP is the first approach to explore random
network prediction to estimate uncertainty and to combine multi-modal data.
Experiments on two 3D multi-modal medical image segmentation tasks and three 2D
multi-modal computer vision classification tasks show the effectiveness,
adaptability and robustness of CRNP. Also, we provide an extensive discussion
on different fusion functions and visualization to validate the proposed model.

Despite improved performances of the latest Automatic Speech Recognition
(ASR) systems, transcription errors are still unavoidable. These errors can
have a considerable impact in critical domains such as healthcare, when used to
help with clinical documentation. Therefore, detecting ASR errors is a critical
first step in preventing further error propagation to downstream applications.
To this end, we propose a novel end-to-end approach for ASR error detection
using audio-transcript entailment. To the best of our knowledge, we are the
first to frame this problem as an end-to-end entailment task between the audio
segment and its corresponding transcript segment. Our intuition is that there
should be a bidirectional entailment between audio and transcript when there is
no recognition error and vice versa. The proposed model utilizes an acoustic
encoder and a linguistic encoder to model the speech and transcript
respectively. The encoded representations of both modalities are fused to
predict the entailment. Since doctor-patient conversations are used in our
experiments, a particular emphasis is placed on medical terms. Our proposed
model achieves classification error rates (CER) of 26.2% on all transcription
errors and 23% on medical errors specifically, leading to improvements upon a
strong baseline by 12% and 15.4%, respectively.

Stability and safety are critical properties for successful deployment of
automatic control systems. As a motivating example, consider autonomous mobile
robot navigation in a complex environment. A control design that generalizes to
different operational conditions requires a model of the system dynamics,
robustness to modeling errors, and satisfaction of safety \NEWZL{constraints},
such as collision avoidance. This paper develops a neural ordinary differential
equation network to learn the dynamics of a Hamiltonian system from trajectory
data. The learned Hamiltonian model is used to synthesize an energy-shaping
passivity-based controller and analyze its \emph{robustness} to uncertainty in
the learned model and its \emph{safety} with respect to constraints imposed by
the environment. Given a desired reference path for the system, we extend our
design using a virtual reference governor to achieve tracking control. The
governor state serves as a regulation point that moves along the reference path
adaptively, balancing the system energy level, model uncertainty bounds, and
distance to safety violation to guarantee robustness and safety. Our
Hamiltonian dynamics learning and tracking control techniques are demonstrated
on \Revised{simulated hexarotor and quadrotor robots} navigating in cluttered
3D environments.

Graph structured data often possess dynamic characters in nature, e.g., the
addition of links and nodes, in many real-world applications. Recent years have
witnessed the increasing attentions paid to dynamic graph neural networks for
modelling such graph data, where almost all the existing approaches assume that
when a new link is built, the embeddings of the neighbor nodes should be
updated by learning the temporal dynamics to propagate new information.
However, such approaches suffer from the limitation that if the node introduced
by a new connection contains noisy information, propagating its knowledge to
other nodes is not reliable and even leads to the collapse of the model. In
this paper, we propose AdaNet: a robust knowledge Adaptation framework via
reinforcement learning for dynamic graph neural Networks. In contrast to
previous approaches immediately updating the embeddings of the neighbor nodes
once adding a new link, AdaNet attempts to adaptively determine which nodes
should be updated because of the new link involved. Considering that the
decision whether to update the embedding of one neighbor node will have great
impact on other neighbor nodes, we thus formulate the selection of node update
as a sequence decision problem, and address this problem via reinforcement
learning. By this means, we can adaptively propagate knowledge to other nodes
for learning robust node embedding representations. To the best of our
knowledge, our approach constitutes the first attempt to explore robust
knowledge adaptation via reinforcement learning for dynamic graph neural
networks. Extensive experiments on three benchmark datasets demonstrate that
AdaNet achieves the state-of-the-art performance. In addition, we perform the
experiments by adding different degrees of noise into the dataset,
quantitatively and qualitatively illustrating the robustness of AdaNet.

Integrated photonic neural networks (IPNNs) are emerging as promising
successors to conventional electronic AI accelerators as they offer substantial
improvements in computing speed and energy efficiency. In particular, coherent
IPNNs use arrays of Mach-Zehnder interferometers (MZIs) for unitary
transformations to perform energy-efficient matrix-vector multiplication.
However, the underlying MZI devices in IPNNs are susceptible to uncertainties
stemming from optical lithographic variations and thermal crosstalk and can
experience imprecisions due to non-uniform MZI insertion loss and quantization
errors due to low-precision encoding in the tuned phase angles. In this paper,
we, for the first time, systematically characterize the impact of such
uncertainties and imprecisions (together referred to as imperfections) in IPNNs
using a bottom-up approach. We show that their impact on IPNN accuracy can vary
widely based on the tuned parameters (e.g., phase angles) of the affected
components, their physical location, and the nature and distribution of the
imperfections. To improve reliability measures, we identify critical IPNN
building blocks that, under imperfections, can lead to catastrophic degradation
in the classification accuracy. We show that under multiple simultaneous
imperfections, the IPNN inferencing accuracy can degrade by up to 46%, even
when the imperfection parameters are restricted within a small range. Our
results also indicate that the inferencing accuracy is sensitive to
imperfections affecting the MZIs in the linear layers next to the input layer
of the IPNN.

Advancements in generative models, like Deepfake allows users to imitate a
targeted person and manipulate online interactions. It has been recognized that
disinformation may cause disturbance in society and ruin the foundation of
trust. This article presents DeFakePro, a decentralized consensus
mechanism-based Deepfake detection technique in online video conferencing
tools. Leveraging Electrical Network Frequency (ENF), an environmental
fingerprint embedded in digital media recording, affords a consensus mechanism
design called Proof-of-ENF (PoENF) algorithm. The similarity in ENF signal
fluctuations is utilized in the PoENF algorithm to authenticate the media
broadcasted in conferencing tools. By utilizing the video conferencing setup
with malicious participants to broadcast deep fake video recordings to other
participants, the DeFakePro system verifies the authenticity of the incoming
media in both audio and video channels.

Few-shot image generation and few-shot image translation are two related
tasks, both of which aim to generate new images for an unseen category with
only a few images. In this work, we make the first attempt to adapt few-shot
image translation method to few-shot image generation task. Few-shot image
translation disentangles an image into style vector and content map. An unseen
style vector can be combined with different seen content maps to produce
different images. However, it needs to store seen images to provide content
maps and the unseen style vector may be incompatible with seen content maps. To
adapt it to few-shot image generation task, we learn a compact dictionary of
local content vectors via quantizing continuous content maps into discrete
content maps instead of storing seen images. Furthermore, we model the
autoregressive distribution of discrete content map conditioned on style
vector, which can alleviate the incompatibility between content map and style
vector. Qualitative and quantitative results on three real datasets demonstrate
that our model can produce images of higher diversity and fidelity for unseen
categories than previous methods.

Accurate traffic prediction is a challenging task in intelligent
transportation systems because of the complex spatio-temporal dependencies in
transportation networks. Many existing works utilize sophisticated temporal
modeling approaches to incorporate with graph convolution networks (GCNs) for
capturing short-term and long-term spatio-temporal dependencies. However, these
separated modules with complicated designs could restrict effectiveness and
efficiency of spatio-temporal representation learning. Furthermore, most
previous works adopt the fixed graph construction methods to characterize the
global spatio-temporal relations, which limits the learning capability of the
model for different time periods and even different data scenarios. To overcome
these limitations, we propose an automated dilated spatio-temporal synchronous
graph network, named Auto-DSTSGN for traffic prediction. Specifically, we
design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG)
module to capture the short-term and long-term spatio-temporal correlations by
stacking deeper layers with dilation factors in an increasing order. Further,
we propose a graph structure search approach to automatically construct the
spatio-temporal synchronous graph that can adapt to different data scenarios.
Extensive experiments on four real-world datasets demonstrate that our model
can achieve about 10% improvements compared with the state-of-art methods.
Source codes are available at https://github.com/jinguangyin/Auto-DSTSGN.

Over-actuated systems often make it possible to achieve specific performances
by switching between different subsets of actuators. However, when the system
parameters are unknown, transferring authority to different subsets of
actuators is challenging due to stability and performance efficiency concerns.
This paper presents an efficient algorithm to tackle the so-called "learn and
control while switching between different actuating modes" problem in the
Linear Quadratic (LQ) setting. Our proposed strategy is constructed upon
Optimism in the Face of Uncertainty (OFU) based algorithm equipped with a
projection toolbox to keep the algorithm efficient, regret-wise. Along the way,
we derive an optimum duration for the warm-up phase, thanks to the existence of
a stabilizing neighborhood. The stability of the switched system is also
guaranteed by designing a minimum average dwell time. The proposed strategy is
proved to have a regret bound of
$\mathcal{\bar{O}}\big(\sqrt{T}\big)+\mathcal{O}\big(ns\sqrt{T}\big)$ in
horizon $T$ with $(ns)$ number of switches, provably outperforming naively
applying the basic OFU algorithm.

Recent works have demonstrated that deep learning models are vulnerable to
backdoor poisoning attacks, where these attacks instill spurious correlations
to external trigger patterns or objects (e.g., stickers, sunglasses, etc.). We
find that such external trigger signals are unnecessary, as highly effective
backdoors can be easily inserted using rotation-based image transformation. Our
method constructs the poisoned dataset by rotating a limited amount of objects
and labeling them incorrectly; once trained with it, the victim's model will
make undesirable predictions during run-time inference. It exhibits a
significantly high attack success rate while maintaining clean performance
through comprehensive empirical studies on image classification and object
detection tasks. Furthermore, we evaluate standard data augmentation techniques
and four different backdoor defenses against our attack and find that none of
them can serve as a consistent mitigation approach. Our attack can be easily
deployed in the real world since it only requires rotating the object, as we
show in both image classification and object detection applications. Overall,
our work highlights a new, simple, physically realizable, and highly effective
vector for backdoor attacks. Our video demo is available at
https://youtu.be/6JIF8wnX34M.

The problem of fully supervised classification is that it requires a
tremendous amount of annotated data, however, in many datasets a large portion
of data is unlabeled. To alleviate this problem semi-supervised learning (SSL)
leverages the knowledge of the classifier on the labeled domain and
extrapolates it to the unlabeled domain which has a supposedly similar
distribution as annotated data. Recent success on SSL methods crucially hinges
on thresholded pseudo labeling and thereby consistency regularization for the
unlabeled domain. However, the existing methods do not incorporate the
uncertainty of the pseudo labels or unlabeled samples in the training process
which are due to the noisy labels or out of distribution samples owing to
strong augmentations. Inspired by the recent developments in SSL, our goal in
this paper is to propose a novel unsupervised uncertainty-aware objective that
relies on aleatoric and epistemic uncertainty quantification. Complementing the
recent techniques in SSL with the proposed uncertainty-aware loss function our
approach outperforms or is on par with the state-of-the-art over standard SSL
benchmarks while being computationally lightweight. Our results outperform the
state-of-the-art results on complex datasets such as CIFAR-100 and
Mini-ImageNet.

If we want to train robots in simulation before deploying them in reality, it
seems natural and almost self-evident to presume that reducing the sim2real gap
involves creating simulators of increasing fidelity (since reality is what it
is). We challenge this assumption and present a contrary hypothesis -- sim2real
transfer of robots may be improved with lower (not higher) fidelity simulation.
We conduct a systematic large-scale evaluation of this hypothesis on the
problem of visual navigation -- in the real world, and on 2 different
simulators (Habitat and iGibson) using 3 different robots (A1, AlienGo, Spot).
Our results show that, contrary to expectation, adding fidelity does not help
with learning; performance is poor due to slow simulation speed (preventing
large-scale learning) and overfitting to inaccuracies in simulation physics.
Instead, building simple models of the robot motion using real-world data can
improve learning and generalization.

The stochastic generalised linear bandit is a well-understood model for
sequential decision-making problems, with many algorithms achieving
near-optimal regret guarantees under immediate feedback. However, in many real
world settings, the requirement that the reward is observed immediately is not
applicable. In this setting, standard algorithms are no longer theoretically
understood. We study the phenomenon of delayed rewards in a theoretical manner
by introducing a delay between selecting an action and receiving the reward.
Subsequently, we show that an algorithm based on the optimistic principle
improves on existing approaches for this setting by eliminating the need for
prior knowledge of the delay distribution and relaxing assumptions on the
decision set and the delays. This also leads to improving the regret guarantees
from $ \widetilde O(\sqrt{dT}\sqrt{d + \mathbb{E}[\tau]})$ to $ \widetilde
O(d\sqrt{T} + d^{3/2}\mathbb{E}[\tau])$, where $\mathbb{E}[\tau]$ denotes the
expected delay, $d$ is the dimension and $T$ the time horizon and we have
suppressed logarithmic terms. We verify our theoretical results through
experiments on simulated data.

Clinically significant prostate cancer has a better chance to be sampled
during ultrasound-guided biopsy procedures, if suspected lesions found in
pre-operative magnetic resonance (MR) images are used as targets. However, the
diagnostic accuracy of the biopsy procedure is limited by the
operator-dependent skills and experience in sampling the targets, a sequential
decision making process that involves navigating an ultrasound probe and
placing a series of sampling needles for potentially multiple targets. This
work aims to learn a reinforcement learning (RL) policy that optimises the
actions of continuous positioning of 2D ultrasound views and biopsy needles
with respect to a guiding template, such that the MR targets can be sampled
efficiently and sufficiently. We first formulate the task as a Markov decision
process (MDP) and construct an environment that allows the targeting actions to
be performed virtually for individual patients, based on their anatomy and
lesions derived from MR images. A patient-specific policy can thus be
optimised, before each biopsy procedure, by rewarding positive sampling in the
MDP environment. Experiment results from fifty four prostate cancer patients
show that the proposed RL-learned policies obtained a mean hit rate of 93% and
an average cancer core length of 11 mm, which compared favourably to two
alternative baseline strategies designed by humans, without hand-engineered
rewards that directly maximise these clinically relevant metrics. Perhaps more
interestingly, it is found that the RL agents learned strategies that were
adaptive to the lesion size, where spread of the needles was prioritised for
smaller lesions. Such a strategy has not been previously reported or commonly
adopted in clinical practice, but led to an overall superior targeting
performance when compared with intuitively designed strategies.

We propose an algorithm to (i) learn online a deep signed distance function
(SDF) with a LiDAR-equipped robot to represent the 3D environment geometry, and
(ii) plan collision-free trajectories given this deep learned map. Our
algorithm takes a stream of incoming LiDAR scans and continually optimizes a
neural network to represent the SDF of the environment around its current
vicinity. When the SDF network quality saturates, we cache a copy of the
network, along with a learned confidence metric, and initialize a new SDF
network to continue mapping new regions of the environment. We then concatenate
all the cached local SDFs through a confidence-weighted scheme to give a global
SDF for planning. For planning, we make use of a sequential convex model
predictive control (MPC) algorithm. The MPC planner optimizes a dynamically
feasible trajectory for the robot while enforcing no collisions with obstacles
mapped in the global SDF. We show that our online mapping algorithm produces
higher-quality maps than existing methods for online SDF training. In the
WeBots simulator, we further showcase the combined mapper and planner running
online -- navigating autonomously and without collisions in an unknown

In recent years, electricity generation has been responsible for more than a
quarter of the greenhouse gas emissions in the US. Integrating a significant
amount of renewables into a power grid is probably the most accessible way to
reduce carbon emissions from power grids and slow down climate change.
Unfortunately, the most accessible renewable power sources, such as wind and
solar, are highly fluctuating and thus bring a lot of uncertainty to power grid
operations and challenge existing optimization and control policies. The
chance-constrained alternating current (AC) optimal power flow (OPF) framework
finds the minimum cost generation dispatch maintaining the power grid
operations within security limits with a prescribed probability. Unfortunately,
the AC-OPF problem's chance-constrained extension is non-convex,
computationally challenging, and requires knowledge of system parameters and
additional assumptions on the behavior of renewable distribution. Known linear
and convex approximations to the above problems, though tractable, are too
conservative for operational practice and do not consider uncertainty in system
parameters. This paper presents an alternative data-driven approach based on
Gaussian process (GP) regression to close this gap. The GP approach learns a
simple yet non-convex data-driven approximation to the AC power flow equations
that can incorporate uncertainty inputs. The latter is then used to determine
the solution of CC-OPF efficiently, by accounting for both input and parameter
uncertainty. The practical efficiency of the proposed approach using different
approximations for GP-uncertainty propagation is illustrated over numerous IEEE
test cases.

The optimal stopping problem is a category of decision problems with a
specific constrained configuration. It is relevant to various real-world
applications such as finance and management. To solve the optimal stopping
problem, state-of-the-art algorithms in dynamic programming, such as the
least-squares Monte Carlo (LSMC), are employed. This type of algorithm relies
on path simulations using only the last price of the underlying asset as a
state representation. Also, the LSMC was thinking for option valuation where
risk-neutral probabilities can be employed to account for uncertainty. However,
the general optimal stopping problem goals may not fit the requirements of the
LSMC showing auto-correlated prices. We employ a data-driven method that uses
Monte Carlo simulation to train and test artificial neural networks (ANN) to
solve the optimal stopping problem. Using ANN to solve decision problems is not
entirely new. We propose a different architecture that uses convolutional
neural networks (CNN) to deal with the dimensionality problem that arises when
we transform the whole history of prices into a Markovian state. We present
experiments that indicate that our proposed architecture improves results over
the previous implementations under specific simulated time series function
sets. Lastly, we employ our proposed method to compare the optimal exercise of
the financial options problem with the LSMC algorithm. Our experiments show
that our method can capture more accurate exercise opportunities when compared
to the LSMC. We have outstandingly higher (above 974\% improvement) expected
payoff from these exercise policies under the many Monte Carlo simulations that
used the real-world return database on the out-of-sample (test) data.

A major goal in genomics is to properly capture the complex dynamical
behaviors of gene regulatory networks (GRNs). This includes inferring the
complex interactions between genes, which can be used for a wide range of
genomics analyses, including diagnosis or prognosis of diseases and finding
effective treatments for chronic diseases such as cancer. Boolean networks have
emerged as a successful class of models for capturing the behavior of GRNs. In
most practical settings, inference of GRNs should be achieved through limited
and temporally sparse genomics data. A large number of genes in GRNs leads to a
large possible topology candidate space, which often cannot be exhaustively
searched due to the limitation in computational resources. This paper develops
a scalable and efficient topology inference for GRNs using Bayesian
optimization and kernel-based methods. Rather than an exhaustive search over
possible topologies, the proposed method constructs a Gaussian Process (GP)
with a topology-inspired kernel function to account for correlation in the
likelihood function. Then, using the posterior distribution of the GP model,
the Bayesian optimization efficiently searches for the topology with the
highest likelihood value by optimally balancing between exploration and
exploitation. The performance of the proposed method is demonstrated through
comprehensive numerical experiments using a well-known mammalian cell-cycle
network.

Evaluating the COCO mean average precision (MaP) and COCO recall metrics as
part of the static computation graph of modern deep learning frameworks poses a
unique set of challenges. These challenges include the need for maintaining a
dynamic-sized state to compute mean average precision, reliance on global
dataset-level statistics to compute the metrics, and managing differing numbers
of bounding boxes between images in a batch. As a consequence, it is common
practice for researchers and practitioners to evaluate COCO metrics as a post
training evaluation step. With a graph-friendly algorithm to compute COCO Mean
Average Precision and recall, these metrics could be evaluated at training
time, improving visibility into the evolution of the metrics through training
curve plots, and decreasing iteration time when prototyping new model versions.
  Our contributions include an accurate approximation algorithm for Mean
Average Precision, an open source implementation of both COCO mean average
precision and COCO recall, extensive numerical benchmarks to verify the
accuracy of our implementations, and an open-source training loop that include
train-time evaluation of mean average precision and recall.

Fully Automatic License Plate Recognition (ALPR) has been a frequent research
topic due to several practical applications. However, many of the current
solutions are still not robust enough in real situations, commonly depending on
many constraints. This paper presents a robust and efficient ALPR system based
on the state-of-the-art YOLO object detector and Normalizing flows. The model
uses two new strategies. Firstly, a two-stage network using YOLO and a
normalization flow-based model for normalization to detect Licenses Plates (LP)
and recognize the LP with numbers and Arabic characters. Secondly, Multi-scale
image transformations are implemented to provide a solution to the problem of
the YOLO cropped LP detection including significant background noise.
Furthermore, extensive experiments are led on a new dataset with realistic
scenarios, we introduce a larger public annotated dataset collected from
Moroccan plates. We demonstrate that our proposed model can learn on a small
number of samples free of single or multiple characters. The dataset will also
be made publicly available to encourage further studies and research on plate
detection and recognition.

We propose a mutual information-based sufficient representation learning
(MSRL) approach, which uses the variational formulation of the mutual
information and leverages the approximation power of deep neural networks. MSRL
learns a sufficient representation with the maximum mutual information with the
response and a user-selected distribution. It can easily handle
multi-dimensional continuous or categorical response variables. MSRL is shown
to be consistent in the sense that the conditional probability density function
of the response variable given the learned representation converges to the
conditional probability density function of the response variable given the
predictor. Non-asymptotic error bounds for MSRL are also established under
suitable conditions. To establish the error bounds, we derive a generalized
Dudley's inequality for an order-two U-process indexed by deep neural networks,
which may be of independent interest. We discuss how to determine the intrinsic
dimension of the underlying data distribution. Moreover, we evaluate the
performance of MSRL via extensive numerical experiments and real data analysis
and demonstrate that MSRL outperforms some existing nonlinear sufficient
dimension reduction methods.

Spam is a serious problem plaguing web-scale digital platforms which
facilitate user content creation and distribution. It compromises platform's
integrity, performance of services like recommendation and search, and overall
business. Spammers engage in a variety of abusive and evasive behavior which
are distinct from non-spammers. Users' complex behavior can be well represented
by a heterogeneous graph rich with node and edge attributes. Learning to
identify spammers in such a graph for a web-scale platform is challenging
because of its structural complexity and size. In this paper, we propose SEINE
(Spam DEtection using Interaction NEtworks), a spam detection model over a
novel graph framework. Our graph simultaneously captures rich users' details
and behavior and enables learning on a billion-scale graph. Our model considers
neighborhood along with edge types and attributes, allowing it to capture a
wide range of spammers. SEINE, trained on a real dataset of tens of millions of
nodes and billions of edges, achieves a high performance of 80% recall with 1%
false positive rate. SEINE achieves comparable performance to the
state-of-the-art techniques on a public dataset while being pragmatic to be
used in a large-scale production system.

We develop a framework to establish benchmarks for machine learning and deep
neural networks analyses of exclusive scattering cross sections (FemtoNet).
Within this framework we present an extraction of Compton form factors for
deeply virtual Compton scattering from an unpolarized proton target. Critical
to this effort is a study of the effects of physics constraint built into
machine learning (ML) algorithms. We use the Bethe-Heitler process, which is
the QED radiative background to deeply virtual Compton scattering, to test our
ML models and, in particular, their ability to generalize information extracted
from data. We then use these techniques on the full cross section and compare
the results to analytic model calculations. We propose a quantification
technique, the random targets method, to begin understanding the separation of
aleatoric and epistemic uncertainties as they are manifest in exclusive
scattering analyses. We propose a set of both physics driven and machine
learning based benchmarks providing a stepping stone towards applying
explainable machine learning techniques with controllable uncertainties in a
wide range of deeply virtual exclusive processes.

In this paper, we study a practical space-time video super-resolution (STVSR)
problem which aims at generating a high-framerate high-resolution sharp video
from a low-framerate low-resolution blurry video. Such problem often occurs
when recording a fast dynamic event with a low-framerate and low-resolution
camera, and the captured video would suffer from three typical issues: i)
motion blur occurs due to object/camera motions during exposure time; ii)
motion aliasing is unavoidable when the event temporal frequency exceeds the
Nyquist limit of temporal sampling; iii) high-frequency details are lost
because of the low spatial sampling rate. These issues can be alleviated by a
cascade of three separate sub-tasks, including video deblurring, frame
interpolation, and super-resolution, which, however, would fail to capture the
spatial and temporal correlations among video sequences. To address this, we
propose an interpretable STVSR framework by leveraging both model-based and
learning-based methods. Specifically, we formulate STVSR as a joint video
deblurring, frame interpolation, and super-resolution problem, and solve it as
two sub-problems in an alternate way. For the first sub-problem, we derive an
interpretable analytical solution and use it as a Fourier data transform layer.
Then, we propose a recurrent video enhancement layer for the second sub-problem
to further recover high-frequency details. Extensive experiments demonstrate
the superiority of our method in terms of quantitative metrics and visual
quality.

High-resolution optical tactile sensors are increasingly used in robotic
learning environments due to their ability to capture large amounts of data
directly relating to agent-environment interaction. However, there is a high
barrier of entry to research in this area due to the high cost of tactile robot
platforms, specialised simulation software, and sim-to-real methods that lack
generality across different sensors. In this letter we extend the Tactile Gym
simulator to include three new optical tactile sensors (TacTip, DIGIT and
DigiTac) of the two most popular types, Gelsight-style (image-shading based)
and TacTip-style (marker based). We demonstrate that a single sim-to-real
approach can be used with these three different sensors to achieve strong
real-world performance despite the significant differences between real tactile
images. Additionally, we lower the barrier of entry to the proposed tasks by
adapting them to an inexpensive 4-DoF robot arm, further enabling the
dissemination of this benchmark. We validate the extended environment on three
physically-interactive tasks requiring a sense of touch: object pushing, edge
following and surface following. The results of our experimental validation
highlight some differences between these sensors, which may help future
researchers select and customize the physical characteristics of tactile
sensors for different manipulations scenarios.

We introduce TIDEE, an embodied agent that tidies up a disordered scene based
on learned commonsense object placement and room arrangement priors. TIDEE
explores a home environment, detects objects that are out of their natural
place, infers plausible object contexts for them, localizes such contexts in
the current scene, and repositions the objects. Commonsense priors are encoded
in three modules: i) visuo-semantic detectors that detect out-of-place objects,
ii) an associative neural graph memory of objects and spatial relations that
proposes plausible semantic receptacles and surfaces for object repositions,
and iii) a visual search network that guides the agent's exploration for
efficiently localizing the receptacle-of-interest in the current scene to
reposition the object. We test TIDEE on tidying up disorganized scenes in the
AI2THOR simulation environment. TIDEE carries out the task directly from pixel
and raw depth input without ever having observed the same room beforehand,
relying only on priors learned from a separate set of training houses. Human
evaluations on the resulting room reorganizations show TIDEE outperforms
ablative versions of the model that do not use one or more of the commonsense
priors. On a related room rearrangement benchmark that allows the agent to view
the goal state prior to rearrangement, a simplified version of our model
significantly outperforms a top-performing method by a large margin. Code and
data are available at the project website: https://tidee-agent.github.io/.

"Foley" refers to sound effects that are added to multimedia during
post-production to enhance its perceived acoustic properties, e.g., by
simulating the sounds of footsteps, ambient environmental sounds, or visible
objects on the screen. While foley is traditionally produced by foley artists,
there is increasing interest in automatic or machine-assisted techniques
building upon recent advances in sound synthesis and generative models. To
foster more participation in this growing research area, we propose a challenge
for automatic foley synthesis. Through case studies on successful previous
challenges in audio and machine learning, we set the goals of the proposed
challenge: rigorous, unified, and efficient evaluation of different foley
synthesis systems, with an overarching goal of drawing active participation
from the research community. We outline the details and design considerations
of a foley sound synthesis challenge, including task definition, dataset
requirements, and evaluation criteria.

Modern neural networks use building blocks such as convolutions that are
equivariant to arbitrary 2D translations. However, these vanilla blocks are not
equivariant to arbitrary 3D translations in the projective manifold. Even then,
all monocular 3D detectors use vanilla blocks to obtain the 3D coordinates, a
task for which the vanilla blocks are not designed for. This paper takes the
first step towards convolutions equivariant to arbitrary 3D translations in the
projective manifold. Since the depth is the hardest to estimate for monocular
detection, this paper proposes Depth EquiVarIAnt NeTwork (DEVIANT) built with
existing scale equivariant steerable blocks. As a result, DEVIANT is
equivariant to the depth translations in the projective manifold whereas
vanilla networks are not. The additional depth equivariance forces the DEVIANT
to learn consistent depth estimates, and therefore, DEVIANT achieves
state-of-the-art monocular 3D detection results on KITTI and Waymo datasets in
the image-only category and performs competitively to methods using extra
information. Moreover, DEVIANT works better than vanilla networks in
cross-dataset evaluation. Code and models at
https://github.com/abhi1kumar/DEVIANT

We propose a federated learning method with weighted nodes in which the
weights can be modified to optimize the model's performance on a separate
validation set. The problem is formulated as a bilevel optimization where the
inner problem is a federated learning problem with weighted nodes and the outer
problem focuses on optimizing the weights based on the validation performance
of the model returned from the inner problem. A communication-efficient
federated optimization algorithm is designed to solve this bilevel optimization
problem. Under an error-bound assumption, we analyze the generalization
performance of the output model and identify scenarios when our method is in
theory superior to training a model only locally and to federated learning with
static and evenly distributed weights.

Approximate density functional theory (DFT) has become indispensable owing to
its cost-accuracy trade-off in comparison to more computationally demanding but
accurate correlated wavefunction theory. To date, however, no single density
functional approximation (DFA) with universal accuracy has been identified,
leading to uncertainty in the quality of data generated from DFT. With electron
density fitting and transfer learning, we build a DFA recommender that selects
the DFA with the lowest expected error with respect to gold standard but
cost-prohibitive coupled cluster theory in a system-specific manner. We
demonstrate this recommender approach on vertical spin-splitting energy
evaluation for challenging transition metal complexes. Our recommender predicts
top-performing DFAs and yields excellent accuracy (ca. 2 kcal/mol) for chemical
discovery, outperforming both individual transfer learning models and the
single best functional in a set of 48 DFAs. We demonstrate the transferability
of the DFA recommender to experimentally synthesized compounds with distinct
chemistry.

As machine learning tools progress, the inevitable question arises: How can
machine learning help us write better code? With significant progress being
achieved in natural language processing with models like GPT-3 and Bert, the
applications of natural language processing techniques to code are starting to
be explored. Most of the research has been focused on automatic program repair
(APR), and while the results on synthetic or highly filtered datasets are
promising, such models are hard to apply in real-world scenarios because of
inadequate bug localization. We propose BigIssue: a benchmark for realistic bug
localization. The goal of the benchmark is two-fold. We provide (1) a general
benchmark with a diversity of real and synthetic Java bugs and (2) a motivation
to improve bug localization capabilities of models through attention to the
full repository context. With the introduction of BigIssue, we hope to advance
the state of the art in bug localization, in turn improving APR performance and
increasing its applicability to the modern development cycle.

The production, shipping, usage, and disposal of consumer goods have a
substantial impact on greenhouse gas emissions and the depletion of resources.
Machine Learning (ML) can help to foster sustainable consumption patterns by
accounting for sustainability aspects in product search or recommendations of
modern retail platforms. However, the lack of large high quality publicly
available product data with trustworthy sustainability information impedes the
development of ML technology that can help to reach our sustainability goals.
Here we present GreenDB, a database that collects products from European online
shops on a weekly basis. As proxy for the products' sustainability, it relies
on sustainability labels, which are evaluated by experts. The GreenDB schema
extends the well-known schema.org Product definition and can be readily
integrated into existing product catalogs. We present initial results
demonstrating that ML models trained with our data can reliably (F1 score 96%)
predict the sustainability label of products. These contributions can help to
complement existing e-commerce experiences and ultimately encourage users to
more sustainable consumption patterns.

Analyzing vibration data using deep neural network algorithms is an effective
way to detect damages in rotating machinery at an early stage. However, the
black-box approach of these methods often does not provide a satisfactory
solution because the cause of classifications is not comprehensible to humans.
Therefore, this work investigates the application of explainable AI (XAI)
algorithms to convolutional neural networks for vibration-based condition
monitoring. For this, various XAI algorithms are applied to classifications
based on the Fourier transform as well as the order analysis of the vibration
signal. The results are visualized as a function of the revolutions per minute
(RPM), in the shape of frequency-RPM maps and order-RPM maps. This allows to
assess the saliency given to features which depend on the rotation speed and
those with constant frequency. To compare the explanatory power of the XAI
methods, investigations are first carried out with a synthetic data set with
known class-specific characteristics. Then a real-world data set for
vibration-based imbalance classification on an electric motor, which runs at a
broad range of rotation speeds, is used. A special focus is put on the
consistency for variable periodicity of the data, which translates to a varying
rotation speed of a real-world machine. This work aims to show the different
strengths and weaknesses of the methods for this use case: GradCAM, LRP and
LIME with a new perturbation strategy.

Embedding tables dominate industrial-scale recommendation model sizes, using
up to terabytes of memory. A popular and the largest publicly available machine
learning MLPerf benchmark on recommendation data is a Deep Learning
Recommendation Model (DLRM) trained on a terabyte of click-through data. It
contains 100GB of embedding memory (25+Billion parameters). DLRMs, due to their
sheer size and the associated volume of data, face difficulty in training,
deploying for inference, and memory bottlenecks due to large embedding tables.
This paper analyzes and extensively evaluates a generic parameter sharing setup
(PSS) for compressing DLRM models. We show theoretical upper bounds on the
learnable memory requirements for achieving $(1 \pm \epsilon)$ approximations
to the embedding table. Our bounds indicate exponentially fewer parameters
suffice for good accuracy. To this end, we demonstrate a PSS DLRM reaching
10000$\times$ compression on criteo-tb without losing quality. Such a
compression, however, comes with a caveat. It requires 4.5 $\times$ more
iterations to reach the same saturation quality. The paper argues that this
tradeoff needs more investigations as it might be significantly favorable.
Leveraging the small size of the compressed model, we show a 4.3$\times$
improvement in training latency leading to similar overall training times.
Thus, in the tradeoff between system advantage of a small DLRM model vs. slower
convergence, we show that scales are tipped towards having a smaller DLRM
model, leading to faster inference, easier deployment, and similar training
times.

Given the rapidly changing machine learning environments and expensive data
labeling, semi-supervised domain adaptation (SSDA) is imperative when the
labeled data from the source domain is statistically different from the
partially labeled data from the target domain. Most prior SSDA research is
centrally performed, requiring access to both source and target data. However,
data in many fields nowadays is generated by distributed end devices. Due to
privacy concerns, the data might be locally stored and cannot be shared,
resulting in the ineffectiveness of existing SSDA research. This paper proposes
an innovative approach to achieve SSDA over multiple distributed and
confidential datasets, named by Federated Semi-Supervised Domain Adaptation
(FSSDA). FSSDA integrates SSDA with federated learning based on strategically
designed knowledge distillation techniques, whose efficiency is improved by
performing source and target training in parallel. Moreover, FSSDA controls the
amount of knowledge transferred across domains by properly selecting a key
parameter, i.e., the imitation parameter. Further, the proposed FSSDA can be
effectively generalized to multi-source domain adaptation scenarios. Extensive
experiments are conducted to demonstrate the effectiveness and efficiency of
FSSDA design.

A variety of statistical and machine learning methods are used to model crash
frequency on specific roadways with machine learning methods generally having a
higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM),
including stacking, have emerged as more accurate and robust intelligent
techniques and are often used to solve pattern recognition problems by
providing more reliable and accurate predictions. In this study, we apply one
of the key HEM methods, Stacking, to model crash frequency on five lane
undivided segments (5T) of urban and suburban arterials. The prediction
performance of Stacking is compared with parametric statistical models (Poisson
and negative binomial) and three state of the art machine learning techniques
(Decision tree, random forest, and gradient boosting), each of which is termed
as the base learner. By employing an optimal weight scheme to combine
individual base learners through stacking, the problem of biased predictions in
individual base-learners due to differences in specifications and prediction
accuracies is avoided. Data including crash, traffic, and roadway inventory
were collected and integrated from 2013 to 2017. The data are split into
training, validation, and testing datasets. Estimation results of statistical
models reveal that besides other factors, crashes increase with density (number
per mile) of different types of driveways. Comparison of out-of-sample
predictions of various models confirms the superiority of Stacking over the
alternative methods considered. From a practical standpoint, stacking can
enhance prediction accuracy (compared to using only one base learner with a
particular specification). When applied systemically, stacking can help
identify more appropriate countermeasures.

Existing adversarial example research focuses on digitally inserted
perturbations on top of existing natural image datasets. This construction of
adversarial examples is not realistic because it may be difficult, or even
impossible, for an attacker to deploy such an attack in the real-world due to
sensing and environmental effects. To better understand adversarial examples
against cyber-physical systems, we propose approximating the real-world through
simulation. In this paper we describe our synthetic dataset generation tool
that enables scalable collection of such a synthetic dataset with realistic
adversarial examples. We use the CARLA simulator to collect such a dataset and
demonstrate simulated attacks that undergo the same environmental transforms
and processing as real-world images. Our tools have been used to collect
datasets to help evaluate the efficacy of adversarial examples, and can be
found at https://github.com/carla-simulator/carla/pull/4992.

We propose \textbf{JAWS}, a series of wrapper methods for distribution-free
uncertainty quantification tasks under covariate shift, centered on our core
method \textbf{JAW}, the \textbf{JA}ckknife+ \textbf{W}eighted with
likelihood-ratio weights. JAWS also includes computationally efficient
\textbf{A}pproximations of JAW using higher-order influence functions:
\textbf{JAWA}. Theoretically, we show that JAW relaxes the jackknife+'s
assumption of data exchangeability to achieve the same finite-sample coverage
guarantee even under covariate shift. JAWA further approaches the JAW guarantee
in the limit of either the sample size or the influence function order under
mild assumptions. Moreover, we propose a general approach to repurposing any
distribution-free uncertainty quantification method and its guarantees to the
task of risk assessment: a task that generates the estimated probability that
the true label lies within a user-specified interval. We then propose
\textbf{JAW-R} and \textbf{JAWA-R} as the repurposed versions of proposed
methods for \textbf{R}isk assessment. Practically, JAWS outperform the
state-of-the-art predictive inference baselines in a variety of biased real
world data sets for both interval-generation and risk-assessment auditing
tasks.

The Majorana Demonstrator is a leading experiment searching for neutrinoless
double-beta decay with high purity germanium detectors (HPGe). Machine learning
provides a new way to maximize the amount of information provided by these
detectors, but the data-driven nature makes it less interpretable compared to
traditional analysis. An interpretability study reveals the machine's
decision-making logic, allowing us to learn from the machine to feedback to the
traditional analysis. In this work, we have presented the first machine
learning analysis of the data from the Majorana Demonstrator; this is also the
first interpretable machine learning analysis of any germanium detector
experiment. Two gradient boosted decision tree models are trained to learn from
the data, and a game-theory-based model interpretability study is conducted to
understand the origin of the classification power. By learning from data, this
analysis recognizes the correlations among reconstruction parameters to further
enhance the background rejection performance. By learning from the machine,
this analysis reveals the importance of new background categories to
reciprocally benefit the standard Majorana analysis. This model is highly
compatible with next-generation germanium detector experiments like LEGEND
since it can be simultaneously trained on a large number of detectors.

Suppose we are given integer $k \leq n$ and $n$ boxes labeled $1,\ldots, n$
by an adversary, each containing a number chosen from an unknown distribution.
We have to choose an order to sequentially open these boxes, and each time we
open the next box in this order, we learn its number. If we reject a number in
a box, the box cannot be recalled. Our goal is to accept the $k$ largest of
these numbers, without necessarily opening all boxes. This is the free order
multiple-choice secretary problem. Free order variants were studied extensively
for the secretary and prophet problems. Kesselheim, Kleinberg, and Niazadeh KKN
(STOC'15) initiated a study of randomness-efficient algorithms (with the
cheapest order in terms of used random bits) for the free order secretary
problems.
  We present an algorithm for free order multiple-choice secretary, which is
simultaneously optimal for the competitive ratio and used amount of randomness.
I.e., we construct a distribution on orders with optimal entropy
$\Theta(\log\log n)$ such that a deterministic multiple-threshold algorithm is
$1-O(\sqrt{\log k/k})$-competitive. This improves in three ways the previous
best construction by KKN, whose competitive ratio is $1 - O(1/k^{1/3}) - o(1)$.
Our competitive ratio is (near)optimal for the multiple-choice secretary
problem; it works for exponentially larger parameter $k$; and our algorithm is
a simple deterministic multiple-threshold algorithm, while that in KKN is
randomized. We also prove a corresponding lower bound on the entropy of optimal
solutions for the multiple-choice secretary problem, matching entropy of our
algorithm, where no such previous lower bound was known.
  We obtain our algorithmic results with a host of new techniques, and with
these techniques we also improve significantly the previous results of KKN
about constructing entropy-optimal distributions for the classic free order
secretary.

Advancements in deep learning are often associated with increasing model
sizes. The model size dramatically affects the deployment cost and latency of
deep models. For instance, models like BERT cannot be deployed on edge devices
and mobiles due to their sheer size. As a result, most advances in Deep
Learning are yet to reach the edge. Model compression has sought much-deserved
attention in literature across natural language processing, vision, and
recommendation domains. This paper proposes a model-agnostic, cache-friendly
model compression approach: Random Operation Access Specific Tile (ROAST)
hashing. ROAST collapses the parameters by clubbing them through a lightweight
mapping. Notably, while clubbing these parameters, ROAST utilizes cache
hierarchies by aligning the memory access pattern with the parameter access
pattern. ROAST is up to $\sim 25 \times$ faster to train and $\sim 50 \times$
faster to infer than the popular parameter sharing method HashedNet.
Additionally, ROAST introduces global weight sharing, which is empirically and
theoretically superior to local weight sharing in HashedNet, and can be of
independent interest in itself. With ROAST, we present the first compressed
BERT, which is $100\times - 1000\times$ smaller but does not result in quality
degradation. These compression levels on universal architecture like
transformers are promising for the future of SOTA model deployment on
resource-constrained devices like mobile and edge devices

We present a novel machine learning based approach for detecting galaxy-scale
gravitational lenses from interferometric data, specifically those taken with
the International LOFAR Telescope (ILT), which is observing the northern radio
sky at a frequency of 150 MHz, an angular resolution of 350 mas and a
sensitivity of 90 uJy beam-1 (1 sigma). We develop and test several
Convolutional Neural Networks to determine the probability and uncertainty of a
given sample being classified as a lensed or non-lensed event. By training and
testing on a simulated interferometric imaging data set that includes realistic
lensed and non-lensed radio sources, we find that it is possible to recover
95.3 per cent of the lensed samples (true positive rate), with a contamination
of just 0.008 per cent from non-lensed samples (false positive rate). Taking
the expected lensing probability into account results in a predicted sample
purity for lensed events of 92.2 per cent. We find that the network structure
is most robust when the maximum image separation between the lensed images is
greater than 3 times the synthesized beam size, and the lensed images have a
total flux density that is equivalent to at least a 20 sigma (point-source)
detection. For the ILT, this corresponds to a lens sample with Einstein radii
greater than 0.5 arcsec and a radio source population with 150 MHz flux
densities more than 2 mJy. By applying these criteria and our lens detection
algorithm we expect to discover the vast majority of galaxy-scale gravitational
lens systems contained within the LOFAR Two Metre Sky Survey.

Using Artificial Intelligence (AI) to create dance choreography with
intention is still at an early stage. Methods that conditionally generate dance
sequences remain limited in their ability to follow choreographer-specific
creative direction, often relying on external prompts or supervised learning.
In the same vein, fully annotated dance datasets are rare and labor intensive.
To fill this gap and help leverage deep learning as a meaningful tool for
choreographers, we propose "PirouNet", a semi-supervised conditional recurrent
variational autoencoder together with a dance labeling web application.
PirouNet allows dance professionals to annotate data with their own subjective
creative labels and subsequently generate new bouts of choreography based on
their aesthetic criteria. Thanks to the proposed semi-supervised approach,
PirouNet only requires a small portion of the dataset to be labeled, typically
on the order of 1%. We demonstrate PirouNet's capabilities as it generates
original choreography based on the "Laban Time Effort", an established dance
notion describing intention for a movement's time dynamics. We extensively
evaluate PirouNet's dance creations through a series of qualitative and
quantitative metrics, validating its applicability as a tool for
choreographers.

Recent research has shown the effectiveness of mmWave radar sensing for
object detection in low visibility environments, which makes it an ideal
technique in autonomous navigation systems. In this paper, we introduce Radar
to Point Cloud (R2P), a deep learning model that generates smooth, dense, and
highly accurate point cloud representation of a 3D object with fine geometry
details, based on rough and sparse point clouds with incorrect points obtained
from mmWave radar. These input point clouds are converted from the 2D depth
images that are generated from raw mmWave radar sensor data, characterized by
inconsistency, and orientation and shape errors. R2P utilizes an architecture
of two sequential deep learning encoder-decoder blocks to extract the essential
features of those radar-based input point clouds of an object when observed
from multiple viewpoints, and to ensure the internal consistency of a generated
output point cloud and its accurate and detailed shape reconstruction of the
original object. We implement R2P to replace Stage 2 of our recently proposed
3DRIMR (3D Reconstruction and Imaging via mmWave Radar) system. Our experiments
demonstrate the significant performance improvement of R2P over the popular
existing methods such as PointNet, PCN, and the original 3DRIMR design.

Model selection in machine learning (ML) is a crucial part of the Bayesian
learning procedure. Model choice may impose strong biases on the resulting
predictions, which can hinder the performance of methods such as Bayesian
neural networks and neural samplers. On the other hand, newly proposed
approaches for Bayesian ML exploit features of approximate inference in
function space with implicit stochastic processes (a generalization of Gaussian
processes). The approach of Sparse Implicit Processes (SIP) is particularly
successful in this regard, since it is fully trainable and achieves flexible
predictions. Here, we expand on the original experiments to show that SIP is
capable of correcting model bias when the data generating mechanism differs
strongly from the one implied by the model. We use synthetic datasets to show
that SIP is capable of providing predictive distributions that reflect the data
better than the exact predictions of the initial, but wrongly assumed model.

Identification of phases in many-body quantum states is arguably among the
most important and challenging problems of computational quantum physics. The
non-trivial phase structure of geometrically frustrated or finite-density
electron systems is the main obstacle that severely limits the applicability of
the quantum Monte Carlo, variational, and machine learning methods in many
important cases. In this paper, we focus on studying real-valued signful
ground-state wave functions of several frustrated quantum spins systems. Under
the assumption that the tasks of finding wave function amplitudes and signs can
be separated, we show that the signs of the wave functions are easily
reconstructed with almost perfect accuracy by means of combinatorial
optimization. To this end, we map the problem of finding the wave function sign
structure onto an auxiliary classical Ising model which is defined on the
Hilbert space basis. Although the parental quantum system might be highly
frustrated, we demonstrate that the Ising model does not exhibit significant
frustrations and is solvable with standard optimization algorithms such as
Simulated Annealing. In particular, given the ground state amplitudes, we
reconstruct the signs of the wave functions of a fully-connected random
Heisenberg model and the antiferromagnetic Heisenberg model on the Kagome
lattice, thereby revealing the unelaborated hidden simplicity of many-body sign

We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing
research on audiovisual fine-grained categorization. While our community has
made great strides in fine-grained visual categorization on images, the
counterparts in audio and video fine-grained categorization are relatively
unexplored. To encourage advancements in this space, we have carefully
constructed the SSW60 dataset to enable researchers to experiment with
classifying the same set of categories in three different modalities: images,
audio, and video. The dataset covers 60 species of birds and is comprised of
images from existing datasets, and brand new, expert-curated audio and video
datasets. We thoroughly benchmark audiovisual classification performance and
modality fusion experiments through the use of state-of-the-art transformer
methods. Our findings show that performance of audiovisual fusion methods is
better than using exclusively image or audio based methods for the task of
video classification. We also present interesting modality transfer
experiments, enabled by the unique construction of SSW60 to encompass three
different modalities. We hope the SSW60 dataset and accompanying baselines spur
research in this fascinating area.

Neural rendering has received tremendous attention since the advent of Neural
Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view
synthesis considerably. The recent focus has been on models that overfit to a
single scene, and the few attempts to learn models that can synthesize novel
views of unseen scenes mostly consist of combining deep convolutional features
with a NeRF-like model. We propose a different paradigm, where no deep features
and no NeRF-like volume rendering are needed. Our method is capable of
predicting the color of a target ray in a novel scene directly, just from a
collection of patches sampled from the scene. We first leverage epipolar
geometry to extract patches along the epipolar lines of each reference view.
Each patch is linearly projected into a 1D feature vector and a sequence of
transformers process the collection. For positional encoding, we parameterize
rays as in a light field representation, with the crucial difference that the
coordinates are canonicalized with respect to the target ray, which makes our
method independent of the reference frame and improves generalization. We show
that our approach outperforms the state-of-the-art on novel view synthesis of
unseen scenes even when being trained with considerably less data than prior
work.

In recent years, video instance segmentation (VIS) has been largely advanced
by offline models, while online models gradually attracted less attention
possibly due to their inferior performance. However, online methods have their
inherent advantage in handling long video sequences and ongoing videos while
offline models fail due to the limit of computational resources. Therefore, it
would be highly desirable if online models can achieve comparable or even
better performance than offline models. By dissecting current online models and
offline models, we demonstrate that the main cause of the performance gap is
the error-prone association between frames caused by the similar appearance
among different instances in the feature space. Observing this, we propose an
online framework based on contrastive learning that is able to learn more
discriminative instance embeddings for association and fully exploit history
information for stability. Despite its simplicity, our method outperforms all
online and offline methods on three benchmarks. Specifically, we achieve 49.5
AP on YouTube-VIS 2019, a significant improvement of 13.2 AP and 2.1 AP over
the prior online and offline art, respectively. Moreover, we achieve 30.2 AP on
OVIS, a more challenging dataset with significant crowding and occlusions,
surpassing the prior art by 14.8 AP. The proposed method won first place in the
video instance segmentation track of the 4th Large-scale Video Object
Segmentation Challenge (CVPR2022). We hope the simplicity and effectiveness of
our method, as well as our insight into current methods, could shed light on
the exploration of VIS models.

Recognizing scenes and objects in 3D from a single image is a longstanding
goal of computer vision with applications in robotics and AR/VR. For 2D
recognition, large datasets and scalable solutions have led to unprecedented
advances. In 3D, existing benchmarks are small in size and approaches
specialize in few object categories and specific domains, e.g. urban driving
scenes. Motivated by the success of 2D recognition, we revisit the task of 3D
object detection by introducing a large benchmark, called Omni3D. Omni3D
re-purposes and combines existing datasets resulting in 234k images annotated
with more than 3 million instances and 97 categories.3D detection at such scale
is challenging due to variations in camera intrinsics and the rich diversity of
scene and object types. We propose a model, called Cube R-CNN, designed to
generalize across camera and scene types with a unified approach. We show that
Cube R-CNN outperforms prior works on the larger Omni3D and existing
benchmarks. Finally, we prove that Omni3D is a powerful dataset for 3D object
recognition, show that it improves single-dataset performance and can
accelerate learning on new smaller datasets via pre-training.

Humans possess an innate ability to identify and differentiate instances that
they are not familiar with, by leveraging and adapting the knowledge that they
have acquired so far. Importantly, they achieve this without deteriorating the
performance on their earlier learning. Inspired by this, we identify and
formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery
without Forgetting, which tasks a machine learning model to incrementally
discover novel categories of instances from unlabeled data, while maintaining
its performance on the previously seen categories. We propose 1) a method to
generate pseudo-latent representations which act as a proxy for (no longer
available) labeled data, thereby alleviating forgetting, 2) a
mutual-information based regularizer which enhances unsupervised discovery of
novel classes, and 3) a simple Known Class Identifier which aids generalized
inference when the testing data contains instances form both seen and unseen
categories. We introduce experimental protocols based on CIFAR-10, CIFAR-100
and ImageNet-1000 to measure the trade-off between knowledge retention and
novel class discovery. Our extensive evaluations reveal that existing models
catastrophically forget previously seen categories while identifying novel
categories, while our method is able to effectively balance between the
competing objectives. We hope our work will attract further research into this
newly identified pragmatic problem setting.

As electro-optical energy from the sun propagates through the atmosphere it
is affected by radiative transfer effects including absorption, emission, and
scattering. Modeling these affects is essential for scientific remote sensing
measurements of the earth and atmosphere. For example, hyperspectral imagery is
a form of digital imagery collected with many, often hundreds, of wavelengths
of light in pixel. The amount of light measured at the sensor is the result of
emitted sunlight, atmospheric radiative transfer, and the reflectance off the
materials on the ground, all of which vary per wavelength resulting from
multiple physical phenomena. Therefore measurements of the ground spectra or
atmospheric constituents requires separating these different contributions per
wavelength. In this paper, we create an autoencoder similar to denoising
autoencoders treating the atmospheric affects as 'noise' and ground reflectance
as truth per spectrum. We generate hundreds of thousands of training samples by
taking random samples of spectra from laboratory measurements and adding
atmospheric affects using physics-based modelling via MODTRAN
(http://modtran.spectral.com/modtran\_home) by varying atmospheric inputs. This
process ideally could create an autoencoder that would separate atmospheric
effects and ground reflectance in hyperspectral imagery, a process called
atmospheric compensation which is difficult and time-consuming requiring a
combination of heuristic approximations, estimates of physical quantities, and
physical modelling. While the accuracy of our method is not as good as other
methods in the field, this an important first step in applying the growing
field of deep learning of physical principles to atmospheric compensation in
hyperspectral imagery and remote sensing.

As part of the Snowmass community planning exercise, we highlight an ongoing
program of research into the structure of scattering amplitudes in N=4
super-Yang-Mills theory, particularly in the planar limit of a large number of
colors. This theory sits at the nexus of a number of exciting topics in
high-energy particle physics, including the AdS/CFT correspondence, conformal
field theory, integrability, and string theory, and is believed to be exactly
solvable in four dimensions. In many ways, planar N=4 super-Yang-Mills theory
is the "hydrogen atom" of relativistic scattering: It has proven indispensable
for learning about new geometrical formulations of quantum field theory, for
exploring mathematical properties at high perturbative orders, and for
developing powerful new computational methods that have found applicability in
precision collider physics.

Mobile manipulator throwing is a promising method to increase the flexibility
and efficiency of dynamic manipulation in factories. Its major challenge is to
efficiently plan a feasible throw under a wide set of task specifications. We
show that the mobile manipulator throwing problem can be simplified to a planar
problem, hence greatly reducing the computational costs. Using machine learning
approaches, we build a model of the object's inverted flying dynamics and the
robot's kinematic feasibility, which enables throwing motion generation within
1 ms for given query of target position. Thanks to the computational efficiency
of our method, we show that the system is adaptive under disturbance, via
replanning on the fly for alternative solutions, instead of sticking to the
original throwing plan.

In this paper we present a new dynamical systems algorithm for clustering in
hyperspectral images. The main idea of the algorithm is that data points are
\`pushed\' in the direction of increasing density and groups of pixels that end
up in the same dense regions belong to the same class. This is essentially a
numerical solution of the differential equation defined by the gradient of the
density of data points on the data manifold. The number of classes is automated
and the resulting clustering can be extremely accurate. In addition to
providing a accurate clustering, this algorithm presents a new tool for
understanding hyperspectral data in high dimensions. We evaluate the algorithm
on the Urban (Available at www.tec.ary.mil/Hypercube/) scene comparing
performance against the k-means algorithm using pre-identified classes of
materials as ground truth.

Relying on deep supervised or self-supervised learning, previous methods for
depth completion from paired single image and sparse depth data have achieved
impressive performance in recent years. However, facing a new environment where
the test data occurs online and differs from the training data in the RGB image
content and depth sparsity, the trained model might suffer severe performance
drop. To encourage the trained model to work well in such conditions, we expect
it to be capable of adapting to the new environment continuously and
effectively. To achieve this, we propose MetaComp. It utilizes the
meta-learning technique to simulate adaptation policies during the training
phase, and then adapts the model to new environments in a self-supervised
manner in testing. Considering that the input is multi-modal data, it would be
challenging to adapt a model to variations in two modalities simultaneously,
due to significant differences in structure and form of the two modal data.
Therefore, we further propose to disentangle the adaptation procedure in the
basic meta-learning training into two steps, the first one focusing on the
depth sparsity while the second attending to the image content. During testing,
we take the same strategy to adapt the model online to new multi-modal data.
Experimental results and comprehensive ablations show that our MetaComp is
capable of adapting to the depth completion in a new environment effectively
and robust to changes in different modalities.

Recent breakthroughs in Natural Language Processing (NLP) have been driven by
language models trained on a massive amount of plain text. While powerful,
deriving supervision from textual resources is still an open question. For
example, language model pretraining often neglects the rich, freely-available
structures in textual data. In this thesis, we describe three lines of work
that seek to improve the training and evaluation of neural models using
naturally-occurring supervision.
  We first investigate self-supervised training losses to help enhance the
performance of pretrained language models for various NLP tasks. Specifically,
we alter the sentence prediction loss to make it better suited to other
pretraining losses and more challenging to solve. We design an intermediate
finetuning step that uses self-supervised training to promote models' ability
in cross-task generalization.
  Then we describe methods to leverage the structures in Wikipedia and
paraphrases. In particular, we propose training losses to exploit hyperlinks,
article structures, and article category graphs for entity-, discourse-,
entailment-related knowledge. We propose a framework that uses paraphrase pairs
to disentangle semantics and syntax in sentence representations. We extend the
framework for a novel generation task that controls the syntax of output text
with a sentential exemplar.
  Lastly, we discuss our work on tailoring textual resources for establishing
challenging evaluation tasks. We introduce three datasets by defining novel
tasks using various fan-contributed websites, including a long-form
data-to-text generation dataset, a screenplay summarization dataset, and a
long-form story generation dataset. These datasets have unique characteristics
offering challenges to future work in their respective task settings.

Accurate segmentation and motion estimation of myocardium have always been
important in clinic field, which essentially contribute to the downstream
diagnosis. However, existing methods cannot always guarantee the shape
integrity for myocardium segmentation. In addition, motion estimation requires
point correspondence on the myocardium region across different frames. In this
paper, we propose a novel end-to-end deep statistic shape model to focus on
myocardium segmentation with both shape integrity and boundary correspondence
preserving. Specifically, myocardium shapes are represented by a fixed number
of points, whose variations are extracted by Principal Component Analysis
(PCA). Deep neural network is used to predict the transformation parameters
(both affine and deformation), which are then used to warp the mean point cloud
to the image domain. Furthermore, a differentiable rendering layer is
introduced to incorporate mask supervision into the framework to learn more
accurate point clouds. In this way, the proposed method is able to consistently
produce anatomically reasonable segmentation mask without post processing.
Additionally, the predicted point cloud guarantees boundary correspondence for
sequential images, which contributes to the downstream tasks, such as the
motion estimation of myocardium. We conduct several experiments to demonstrate
the effectiveness of the proposed method on several benchmark datasets.

Pre-training has shown success in different areas of machine learning, such
as Computer Vision, Natural Language Processing (NLP), and medical imaging.
However, it has not been fully explored for clinical data analysis. An immense
amount of clinical records are recorded, but still, data and labels can be
scarce for data collected in small hospitals or dealing with rare diseases. In
such scenarios, pre-training on a larger set of unlabelled clinical data could
improve performance. In this paper, we propose novel unsupervised pre-training
techniques designed for heterogeneous, multi-modal clinical data for patient
outcome prediction inspired by masked language modeling (MLM), by leveraging
graph deep learning over population graphs. To this end, we further propose a
graph-transformer-based network, designed to handle heterogeneous clinical
data. By combining masking-based pre-training with a transformer-based network,
we translate the success of masking-based pre-training in other domains to
heterogeneous clinical data. We show the benefit of our pre-training method in
a self-supervised and a transfer learning setting, utilizing three medical
datasets TADPOLE, MIMIC-III, and a Sepsis Prediction Dataset. We find that our
proposed pre-training methods help in modeling the data at a patient and
population level and improve performance in different fine-tuning tasks on all
datasets.

Despite the success of AI-enabled onboard perception, on-ramp merging has
been one of the main challenges for autonomous driving. Due to limited sensing
range of onboard sensors, a merging vehicle can hardly observe main road
conditions and merge properly. By leveraging the wireless communications
between connected and automated vehicles (CAVs), a merging CAV has potential to
proactively obtain the intentions of nearby vehicles. However, CAVs can be
prone to inaccurate observations, such as the noisy basic safety messages (BSM)
and poor quality surveillance images. In this paper, we present a novel
approach for Robust on-ramp merge of CAVs via Augmented and Multi-modal
Reinforcement Learning, named by RAMRL. Specifically, we formulate the on-ramp
merging problem as a Markov decision process (MDP) by taking driving safety,
comfort driving behavior, and traffic efficiency into account. To provide
reliable merging maneuvers, we simultaneously leverage BSM and surveillance
images for multi-modal observation, which is used to learn a policy model
through proximal policy optimization (PPO). Moreover, to improve data
efficiency and provide better generalization performance, we train the policy
model with augmented data (e.g., noisy BSM and noisy surveillance images).
Extensive experiments are conducted with Simulation of Urban MObility (SUMO)
platform under two typical merging scenarios. Experimental results demonstrate
the effectiveness and efficiency of our robust on-ramp merging design.

Music signals are difficult to interpret from their low-level features,
perhaps even more than images: e.g. highlighting part of a spectrogram or an
image is often insufficient to convey high-level ideas that are genuinely
relevant to humans. In computer vision, concept learning was therein proposed
to adjust explanations to the right abstraction level (e.g. detect clinical
concepts from radiographs). These methods have yet to be used for MIR.
  In this paper, we adapt concept learning to the realm of music, with its
particularities. For instance, music concepts are typically non-independent and
of mixed nature (e.g. genre, instruments, mood), unlike previous work that
assumed disentangled concepts. We propose a method to learn numerous music
concepts from audio and then automatically hierarchise them to expose their
mutual relationships. We conduct experiments on datasets of playlists from a
music streaming service, serving as a few annotated examples for diverse
concepts. Evaluations show that the mined hierarchies are aligned with both
ground-truth hierarchies of concepts -- when available -- and with proxy
sources of concept similarity in the general case.

Night images suffer not only from low light, but also from uneven
distributions of light. Most existing night visibility enhancement methods
focus mainly on enhancing low-light regions. This inevitably leads to over
enhancement and saturation in bright regions, such as those regions affected by
light effects (glare, floodlight, etc). To address this problem, we need to
suppress the light effects in bright regions while, at the same time, boosting
the intensity of dark regions. With this idea in mind, we introduce an
unsupervised method that integrates a layer decomposition network and a
light-effects suppression network. Given a single night image as input, our
decomposition network learns to decompose shading, reflectance and
light-effects layers, guided by unsupervised layer-specific prior losses. Our
light-effects suppression network further suppresses the light effects and, at
the same time, enhances the illumination in dark regions. This light-effects
suppression network exploits the estimated light-effects layer as the guidance
to focus on the light-effects regions. To recover the background details and
reduce hallucination/artefacts, we propose structure and high-frequency
consistency losses. Our quantitative and qualitative evaluations on real images
show that our method outperforms state-of-the-art methods in suppressing night
light effects and boosting the intensity of dark regions.

Recent attacks on Machine Learning (ML) models such as evasion attacks with
adversarial examples and models stealing through extraction attacks pose
several security and privacy threats. Prior work proposes to use adversarial
training to secure models from adversarial examples that can evade the
classification of a model and deteriorate its performance. However, this
protection technique affects the model's decision boundary and its prediction
probabilities, hence it might raise model privacy risks. In fact, a malicious
user using only a query access to the prediction output of a model can extract
it and obtain a high-accuracy and high-fidelity surrogate model. To have a
greater extraction, these attacks leverage the prediction probabilities of the
victim model. Indeed, all previous work on extraction attacks do not take into
consideration the changes in the training process for security purposes. In
this paper, we propose a framework to assess extraction attacks on
adversarially trained models with vision datasets. To the best of our
knowledge, our work is the first to perform such evaluation. Through an
extensive empirical study, we demonstrate that adversarially trained models are
more vulnerable to extraction attacks than models obtained under natural
training circumstances. They can achieve up to $\times1.2$ higher accuracy and
agreement with a fraction lower than $\times0.75$ of the queries. We
additionally find that the adversarial robustness capability is transferable
through extraction attacks, i.e., extracted Deep Neural Networks (DNNs) from
robust models show an enhanced accuracy to adversarial examples compared to
extracted DNNs from naturally trained (i.e. standard) models.

Real-world behavior is often shaped by complex interactions between multiple
agents. To scalably study multi-agent behavior, advances in unsupervised and
self-supervised learning have enabled a variety of different behavioral
representations to be learned from trajectory data. To date, there does not
exist a unified set of benchmarks that can enable comparing methods
quantitatively and systematically across a broad set of behavior analysis
settings. We aim to address this by introducing a large-scale, multi-agent
trajectory dataset from real-world behavioral neuroscience experiments that
covers a range of behavior analysis tasks. Our dataset consists of trajectory
data from common model organisms, with 9.6 million frames of mouse data and 4.4
million frames of fly data, in a variety of experimental settings, such as
different strains, lengths of interaction, and optogenetic stimulation. A
subset of the frames also consist of expert-annotated behavior labels.
Improvements on our dataset corresponds to behavioral representations that work
across multiple organisms and is able to capture differences for common
behavior analysis tasks.

Topological data analysis (TDA) is a tool from data science and mathematics
that is beginning to make waves in environmental science. In this work, we seek
to provide an intuitive and understandable introduction to a tool from TDA that
is particularly useful for the analysis of imagery, namely persistent homology.
We briefly discuss the theoretical background but focus primarily on
understanding the output of this tool and discussing what information it can
glean. To this end, we frame our discussion around a guiding example of
classifying satellite images from the Sugar, Fish, Flower, and Gravel Dataset
produced for the study of mesocale organization of clouds by Rasp et. al. in
2020 (arXiv:1906:01906). We demonstrate how persistent homology and its
vectorization, persistence landscapes, can be used in a workflow with a simple
machine learning algorithm to obtain good results, and explore in detail how we
can explain this behavior in terms of image-level features. One of the core
strengths of persistent homology is how interpretable it can be, so throughout
this paper we discuss not just the patterns we find, but why those results are
to be expected given what we know about the theory of persistent homology. Our
goal is that a reader of this paper will leave with a better understanding of
TDA and persistent homology, be able to identify problems and datasets of their
own for which persistent homology could be helpful, and gain an understanding
of results they obtain from applying the included GitHub example code.

There have been a lot of interest in the scaling properties of Transformer
models. However, not much has been done on the front of investigating the
effect of scaling properties of different inductive biases and model
architectures. Do model architectures scale differently? If so, how does
inductive bias affect scaling behaviour? How does this influence upstream
(pretraining) and downstream (transfer)? This paper conducts a systematic study
of scaling behaviour of ten diverse model architectures such as Transformers,
Switch Transformers, Universal Transformers, Dynamic convolutions, Performers,
and recently proposed MLP-Mixers. Via extensive experiments, we show that (1)
architecture is an indeed an important consideration when performing scaling
and (2) the best performing model can fluctuate at different scales. We believe
that the findings outlined in this work has significant implications to how
model architectures are currently evaluated in the community.

Few-shot audio event detection is a task that detects the occurrence time of
a novel sound class given a few examples. In this work, we propose a system
based on segment-level metric learning for the DCASE 2022 challenge of few-shot
bioacoustic event detection (task 5). We make better utilization of the
negative data within each sound class to build the loss function, and use
transductive inference to gain better adaptation on the evaluation set. For the
input feature, we find the per-channel energy normalization concatenated with
delta mel-frequency cepstral coefficients to be the most effective combination.
We also introduce new data augmentation and post-processing procedures for this
task. Our final system achieves an f-measure of 68.74 on the DCASE task 5
validation set, outperforming the baseline performance of 29.5 by a large
margin. Our system is fully open-sourced at
https://github.com/haoheliu/DCASE_2022_Task_5.

When learning disconnected distributions, Generative adversarial networks
(GANs) are known to face model misspecification. Indeed, a continuous mapping
from a unimodal latent distribution to a disconnected one is impossible, so
GANs necessarily generate samples outside of the support of the target
distribution. This raises a fundamental question: what is the latent space
partition that minimizes the measure of these areas? Building on a recent
result of geometric measure theory, we prove that an optimal GANs must
structure its latent space as a 'simplicial cluster' - a Voronoi partition
where cells are convex cones - when the dimension of the latent space is larger
than the number of modes. In this configuration, each Voronoi cell maps to a
distinct mode of the data. We derive both an upper and a lower bound on the
optimal precision of GANs learning disconnected manifolds. Interestingly, these
two bounds have the same order of decrease: $\sqrt{\log m}$, $m$ being the
number of modes. Finally, we perform several experiments to exhibit the
geometry of the latent space and experimentally show that GANs have a geometry
with similar properties to the theoretical one.

Estimating value-at-risk on time series data with possibly heteroscedastic
dynamics is a highly challenging task. Typically, we face a small data problem
in combination with a high degree of non-linearity, causing difficulties for
both classical and machine-learning estimation algorithms. In this paper, we
propose a novel value-at-risk estimator using a long short-term memory (LSTM)
neural network and compare its performance to benchmark GARCH estimators.
  Our results indicate that even for a relatively short time series, the LSTM
could be used to refine or monitor risk estimation processes and correctly
identify the underlying risk dynamics in a non-parametric fashion. We evaluate
the estimator on both simulated and market data with a focus on
heteroscedasticity, finding that LSTM exhibits a similar performance to GARCH
estimators on simulated data, whereas on real market data it is more sensitive
towards increasing or decreasing volatility and outperforms all existing
estimators of value-at-risk in terms of exception rate and mean quantile score.

We present Assume-Guarantee-Repair (AGR) - a novel framework which verifies
that a program satisfies a set of properties and also repairs the program in
case the verification fails. We consider communicating programs - these are
simple C-like programs, extended with synchronous actions over communication
channels. Our method, which consists of a learning-based approach to
assume-guarantee reasoning, performs verification and repair simultaneously: in
every iteration, AGR either makes another step towards proving that the
(current) system satisfies the required properties, or alters the system in a
way that brings it closer to satisfying the properties. To handle
infinite-state systems we build finite abstractions, for which we check the
satisfaction of complex properties that contain first-order constraints, using
both syntactic and semantic-aware methods. We implemented AGR and evaluated it
on various communication protocols. Our experiments present compact proofs of
correctness and quick repairs.

In this paper, we propose hybrid data-driven ROM closures for fluid flows.
These new ROM closures combine two fundamentally different strategies: (i)
purely data-driven ROM closures, both for the velocity and the pressure; and
(ii) physically based, eddy viscosity data-driven closures, which model the
energy transfer in the system. The first strategy consists in the addition of
closure/correction terms to the governing equations, which are built from the
available data. The second strategy includes turbulence modeling by adding eddy
viscosity terms, which are determined by using machine learning techniques. The
two strategies are combined for the first time in this paper to investigate a
two-dimensional flow past a circular cylinder at Re=50000. Our numerical
results show that the hybrid data-driven ROM is more accurate than both the
purely data-driven ROM and the eddy viscosity ROM.

In this paper we investigate neural networks for classification in
hyperspectral imaging with a focus on connecting the architecture of the
network with the physics of the sensing and materials present. Spectroscopy is
the process of measuring light reflected or emitted by a material as a function
wavelength. Molecular bonds present in the material have vibrational
frequencies which affect the amount of light measured at each wavelength. Thus
the measured spectrum contains information about the particular chemical
constituents and types of bonds. For example, chlorophyll reflects more light
in the near-IR rage (800-900nm) than in the red (625-675nm) range, and this
difference can be measured using a normalized vegetation difference index
(NDVI), which is commonly used to detect vegetation presence, health, and type
in imagery collected at these wavelengths. In this paper we show that the
weights in a Neural Network trained on different vegetation classes learn to
measure this difference in reflectance. We then show that a Neural Network
trained on a more complex set of ten different polymer materials will learn
spectral 'features' evident in the weights for the network, and these features
can be used to reliably distinguish between the different types of polymers.
Examination of the weights provides a human-interpretable understanding of the
network.

Multi-horizon probabilistic time series forecasting has wide applicability to
real-world tasks such as demand forecasting. Recent work in neural time-series
forecasting mainly focus on the use of Seq2Seq architectures. For example,
MQTransformer - an improvement of MQCNN - has shown the state-of-the-art
performance in probabilistic demand forecasting. In this paper, we consider
incorporating cross-entity information to enhance model performance by adding a
cross-entity attention mechanism along with a retrieval mechanism to select
which entities to attend over. We demonstrate how our new neural architecture,
MQRetNN, leverages the encoded contexts from a pretrained baseline model on the
entire population to improve forecasting accuracy. Using MQCNN as the baseline
model (due to computational constraints, we do not use MQTransformer), we first
show on a small demand forecasting dataset that it is possible to achieve ~3%
improvement in test loss by adding a cross-entity attention mechanism where
each entity attends to all others in the population. We then evaluate the model
with our proposed retrieval methods - as a means of approximating an attention
over a large population - on a large-scale demand forecasting application with
over 2 million products and observe ~1% performance gain over the MQCNN
baseline.

In ophthalmological imaging, multiple imaging systems, such as color fundus,
infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT
angiography, are often involved to make a diagnosis of retinal disease.
Multi-modal retinal registration techniques can assist ophthalmologists by
providing a pixel-based comparison of aligned vessel structures in images from
different modalities or acquisition times. To this end, we propose an
end-to-end trainable deep learning method for multi-modal retinal image
registration. Our method extracts convolutional features from the vessel
structure for keypoint detection and description and uses a graph neural
network for feature matching. The keypoint detection and description network
and graph neural network are jointly trained in a self-supervised manner using
synthetic multi-modal image pairs and are guided by synthetically sampled
ground truth homographies. Our method demonstrates higher registration accuracy
as competing methods for our synthetic retinal dataset and generalizes well for
our real macula dataset and a public fundus dataset.

Previous statistical studies of plasmaspheric hiss investigated the averaged
shape of the magnetic field power spectra at various points in the
magnetosphere. However, this approach does not consider the fact that very
diverse spectral shapes exist at a given L-shell and magnetic local time.
Averaging the data together means that important features of the spectral
shapes are lost. In this paper, we use an unsupervised machine learning
technique to categorize plasmaspheric hiss. In contrast to the previous
studies, this technique allows us to identify power spectra that have "similar"
shapes and study their spatial distribution without averaging together vastly
different spectral shapes. We show that strong negative correlations exist
between the hiss frequency and bandwidth, which suggests that the observed
patterns are consistent with in situ wave growth.

Hyperspectral Imagining is a type of digital imaging in which each pixel
contains typically hundreds of wavelengths of light providing spectroscopic
information about the materials present in the pixel. In this paper we provide
classification methods for determining crop type in the USGS GHISACONUS data,
which contains around 7,000 pixel spectra from the five major U.S. agricultural
crops (winter wheat, rice, corn, soybeans, and cotton) collected by the NASA
Hyperion satellite, and includes the spectrum, geolocation, crop type, and
stage of growth for each pixel. We apply standard LDA and QDA as well as
Bayesian custom versions that compute the joint probability of crop type and
stage, and then the marginal probability for crop type, outperforming the
non-Bayesian methods. We also test a single layer neural network with dropout
on the data, which performs comparable to LDA and QDA but not as well as the
Bayesian methods.

Deep Neural Networks (DNNs) are becoming a crucial component of modern
software systems, but they are prone to fail under conditions that are
different from the ones observed during training (out-of-distribution inputs)
or on inputs that are truly ambiguous, i.e., inputs that admit multiple classes
with nonzero probability in their ground truth labels. Recent work proposed DNN
supervisors to detect high-uncertainty inputs before their possible
misclassification leads to any harm. To test and compare the capabilities of
DNN supervisors, researchers proposed test generation techniques, to focus the
testing effort on high-uncertainty inputs that should be recognized as
anomalous by supervisors. However, existing test generators can only produce
out-of-distribution inputs. No existing model- and supervisor-independent
technique supports the generation of truly ambiguous test inputs.
  In this paper, we propose a novel way to generate ambiguous inputs to test
DNN supervisors and used it to empirically compare several existing supervisor
techniques. In particular, we propose AmbiGuess to generate ambiguous samples
for image classification problems. AmbiGuess is based on gradient-guided
sampling in the latent space of a regularized adversarial autoencoder.
Moreover, we conducted what is - to the best of our knowledge - the most
extensive comparative study of DNN supervisors, considering their capabilities
to detect 4 distinct types of high-uncertainty inputs, including truly
ambiguous ones.

Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for
heart diseases. Many studies have devised ECG analysis models (e.g.,
classifiers) to assist diagnosis. As an upstream task, researches have built
generative models to synthesize ECG data, which are beneficial to providing
training samples, privacy protection, and annotation reduction. However,
previous generative methods for ECG often neither synthesized multi-view data,
nor dealt with heart disease conditions. In this paper, we propose a novel
disease-aware generative adversarial network for multi-view ECG synthesis
called ME-GAN, which attains panoptic electrocardio representations conditioned
on heart diseases and projects the representations onto multiple standard views
to yield ECG signals. Since ECG manifestations of heart diseases are often
localized in specific waveforms, we propose a new "mixup normalization" to
inject disease information precisely into suitable locations. In addition, we
propose a view discriminator to revert disordered ECG views into a
pre-determined order, supervising the generator to obtain ECG representing
correct view characteristics. Besides, a new metric, rFID, is presented to
assess the quality of the synthesized ECG signals. Comprehensive experiments
verify that our ME-GAN performs well on multi-view ECG signal synthesis with
trusty morbid manifestations.

Among random sampling methods, Markov Chain Monte Carlo algorithms are
foremost. Using a combination of analytical and numerical approaches, we study
their convergence properties towards the steady state, within a random walk
Metropolis scheme. We show that the deviations from the target steady-state
distribution feature a localization transition as a function of the
characteristic length of the attempted jumps defining the random walk. This
transition changes drastically the error which is introduced by incomplete
convergence, and discriminates two regimes where the relaxation mechanism is
limited respectively by diffusion and by rejection.

Using Bayes's theorem, we derive a unit-wise recurrence as well as a backward
recursion similar to the forward-backward algorithm. The resulting Bayesian
recurrent units can be integrated as recurrent neural networks within deep
learning frameworks, while retaining a probabilistic interpretation from the
direct correspondence with hidden Markov models. Whilst the contribution is
mainly theoretical, experiments on speech recognition indicate that adding the
derived units at the end of state-of-the-art recurrent architectures can
improve the performance at a very low cost in terms of trainable parameters.

MOTIVATION: Detection of prostate cancer during transrectal ultrasound-guided
biopsy is challenging. The highly heterogeneous appearance of cancer, presence
of ultrasound artefacts, and noise all contribute to these difficulties. Recent
advancements in high-frequency ultrasound imaging - micro-ultrasound - have
drastically increased the capability of tissue imaging at high resolution. Our
aim is to investigate the development of a robust deep learning model
specifically for micro-ultrasound-guided prostate cancer biopsy. For the model
to be clinically adopted, a key challenge is to design a solution that can
confidently identify the cancer, while learning from coarse histopathology
measurements of biopsy samples that introduce weak labels. METHODS: We use a
dataset of micro-ultrasound images acquired from 194 patients, who underwent
prostate biopsy. We train a deep model using a co-teaching paradigm to handle
noise in labels, together with an evidential deep learning method for
uncertainty estimation. We evaluate the performance of our model using the
clinically relevant metric of accuracy vs. confidence. RESULTS: Our model
achieves a well-calibrated estimation of predictive uncertainty with area under
the curve of 88$\%$. The use of co-teaching and evidential deep learning in
combination yields significantly better uncertainty estimation than either
alone. We also provide a detailed comparison against state-of-the-art in
uncertainty estimation.

In recent years, number of edge computing devices and artificial intelligence
applications on them have advanced excessively. In edge computing, decision
making processes and computations are moved from servers to edge devices.
Hence, cheap and low power devices are required. FPGAs are very low power,
inclined to do parallel operations and deeply suitable devices for running
Convolutional Neural Networks (CNN) which are the fundamental unit of an
artificial intelligence application. Face detection on surveillance systems is
the most expected application on the security market. In this work, TinyYolov3
architecture is redesigned and deployed for face detection. It is a CNN based
object detection method and developed for embedded systems. PYNQ-Z2 is selected
as a target board which has low-end Xilinx Zynq 7020 System-on-Chip (SoC) on
it. Redesigned TinyYolov3 model is defined in numerous bit width precisions
with Brevitas library which brings fundamental CNN layers and activations in
integer quantized form. Then, the model is trained in a quantized structure
with WiderFace dataset. In order to decrease latency and power consumption,
onchip memory of the FPGA is configured as a storage of whole network
parameters and the last activation function is modified as rescaled HardTanh
instead of Sigmoid. Also, high degree of parallelism is applied to logical
resources of the FPGA. The model is converted to an HLS based application with
using FINN framework and FINN-HLS library which includes the layer definitions
in C++. Later, the model is synthesized and deployed. CPU of the SoC is
employed with multithreading mechanism and responsible for preprocessing,
postprocessing and TCP/IP streaming operations. Consequently, 2.4 Watt total
board power consumption, 18 Frames-Per-Second (FPS) throughput and 0.757 mAP
accuracy rate on Easy category of the WiderFace are achieved with 4 bits
precision model.

The conventional room geometry blind inference techniques with acoustic
signals are conducted based on the prior knowledge of the environment, such as
the room impulse response (RIR) or the sound source position, which will limit
its application under unknown scenarios. To solve this problem, we have
proposed a room geometry reconstruction method in this paper by using the
geometric relation between the direct signal and first-order reflections. In
addition to the information of the compact microphone array itself, this method
does not need any precognition of the environmental parameters. Besides, the
learning-based DNN models are designed and used to improve the accuracy and
integrity of the localization results of the direct source and first-order
reflections. The direction of arrival (DOA) and time difference of arrival
(TDOA) information of the direct and reflected signals are firstly estimated
using the proposed DCNN and TD-CNN models, which have higher sensitivity and
accuracy than the conventional methods. Then the position of the sound source
is inferred by integrating the DOA, TDOA and array height using the proposed
DNN model. After that, the positions of image sources and corresponding
boundaries are derived based on the geometric relation. Experimental results of
both simulations and real measurements verify the effectiveness and accuracy of
the proposed techniques compared with the conventional methods under different
reverberant environments.

Target detection in hyperspectral imagery is the process of locating pixels
from an image which are likely to contain target, typically done by comparing
one or more spectra for the desired target material to each pixel in the image.
Target identification is the process of target detection incorporating an
additional process to identify more specifically the material that is present
in each pixel that scored high in detection. Detection is generally a 2-class
problem of target vs. background, and identification is a many class problem
including target, background, and additional know materials. The identification
process we present is probabilistic and hierarchical which provides
transparency to the process and produces trustworthy output. In this paper we
show that target identification has a much lower false alarm rate than
detection alone, and provide a detailed explanation of a robust identification
method using probabilistic hierarchical classification that handles the vague
categories of materials that depend on users which are different than the
specific physical categories of chemical constituents. Identification is often
done by comparing mixtures of materials including the target spectra to
mixtures of materials that do not include the target spectra, possibly with
other steps. (band combinations, feature checking, background removal, etc.)
Standard linear regression does not handle these problems well because the
number of regressors (identification spectra) is greater than the number of
feature variables (bands), and there are multiple correlated spectra. Our
proposed method handles these challenges efficiently and provides additional
important practical information in the form of hierarchical probabilities
computed from Bayesian model averaging.

The usage of chemical imaging technologies is becoming a routine
accompaniment to traditional methods in pathology. Significant technological
advances have developed these next generation techniques to provide rich,
spatially resolved, multidimensional chemical images. The rise of digital
pathology has significantly enhanced the synergy of these imaging modalities
with optical microscopy and immunohistochemistry, enhancing our understanding
of the biological mechanisms and progression of diseases. Techniques such as
imaging mass cytometry provide labelled multidimensional (multiplex) images of
specific components used in conjunction with digital pathology techniques.
These powerful techniques generate a wealth of high dimensional data that
create significant challenges in data analysis. Unsupervised methods such as
clustering are an attractive way to analyse these data, however, they require
the selection of parameters such as the number of clusters. Here we propose a
methodology to estimate the number of clusters in an automatic data-driven
manner using a deep sparse autoencoder to embed the data into a lower
dimensional space. We compute the density of regions in the embedded space, the
majority of which are empty, enabling the high density regions to be detected
as outliers and provide an estimate for the number of clusters. This framework
provides a fully unsupervised and data-driven method to analyse
multidimensional data. In this work we demonstrate our method using 45
multiplex imaging mass cytometry datasets. Moreover, our model is trained using
only one of the datasets and the learned embedding is applied to the remaining
44 images providing an efficient process for data analysis. Finally, we
demonstrate the high computational efficiency of our method which is two orders
of magnitude faster than estimating via computing the sum squared distances as
a function of cluster number.

While there is a general focus on predictions of values, mathematically more
appropriate is prediction of probability distributions: with additional
possibilities like prediction of uncertainty, higher moments and quantiles. For
the purpose of the computer-aided drug design field, this article applies
Hierarchical Correlation Reconstruction approach, previously applied in the
analysis of demographic, financial and astronomical data. Instead of a single
linear regression to predict values, it uses multiple linear regressions to
independently predict multiple moments, finally combining them into predicted
probability distribution, here of several ADMET properties based on
substructural fingerprint developed by Klekota\&Roth. Discussed application
example is inexpensive selection of a percentage of molecules with properties
nearly certain to be in a predicted or chosen range during virtual screening.
Such an approach can facilitate the interpretation of the results as the
predictions characterized by high rate of uncertainty are automatically
detected. In addition, for each of the investigated predictive problems, we
detected crucial structural features, which should be carefully considered when
optimizing compounds towards particular property. The whole methodology
developed in the study constitutes therefore a great support for medicinal
chemists, as it enable fast rejection of compounds with the lowest potential of
desired physicochemical/ADMET characteristic and guides the compound

Establishing visual correspondence across images is a challenging and
essential task. Recently, an influx of self-supervised methods have been
proposed to better learn representations for visual correspondence. However, we
find that these methods often fail to leverage semantic information and
over-rely on the matching of low-level features. In contrast, human vision is
capable of distinguishing between distinct objects as a pretext to tracking.
Inspired by this paradigm, we propose to learn semantic-aware fine-grained
correspondence. Firstly, we demonstrate that semantic correspondence is
implicitly available through a rich set of image-level self-supervised methods.
We further design a pixel-level self-supervised learning objective which
specifically targets fine-grained correspondence. For downstream tasks, we fuse
these two kinds of complementary correspondence representations together,
demonstrating that they boost performance synergistically. Our method surpasses
previous state-of-the-art self-supervised methods using convolutional networks
on a variety of visual correspondence tasks, including video object
segmentation, human pose tracking, and human part tracking.

Convolutional neural network (CNN) and Transformer have achieved great
success in multimedia applications. However, little effort has been made to
effectively and efficiently harmonize these two architectures to satisfy image
deraining. This paper aims to unify these two architectures to take advantage
of their learning merits for image deraining. In particular, the local
connectivity and translation equivariance of CNN and the global aggregation
ability of self-attention (SA) in Transformer are fully exploited for specific
local context and global structure representations. Based on the observation
that rain distribution reveals the degradation location and degree, we
introduce degradation prior to help background recovery and accordingly present
the association refinement deraining scheme. A novel multi-input attention
module (MAM) is proposed to associate rain perturbation removal and background
recovery. Moreover, we equip our model with effective depth-wise separable
convolutions to learn the specific feature representations and trade off
computational complexity. Extensive experiments show that our proposed method
(dubbed as ELF) outperforms the state-of-the-art approach (MPRNet) by 0.25 dB
on average, but only accounts for 11.7\% and 42.1\% of its computational cost
and parameters. The source code is available at
https://github.com/kuijiang94/Magic-ELF.

Seismic data processing involves techniques to deal with undesired effects
that occur during acquisition and pre-processing. These effects mainly comprise
coherent artefacts such as multiples, non-coherent signals such as electrical
noise, and loss of signal information at the receivers that leads to incomplete
traces. In the past years, there has been a remarkable increase of
machine-learning-based solutions that have addressed the aforementioned issues.
In particular, deep-learning practitioners have usually relied on heavily
fine-tuned, customized discriminative algorithms. Although, these methods can
provide solid results, they seem to lack semantic understanding of the provided
data. Motivated by this limitation, in this work, we employ a generative
solution, as it can explicitly model complex data distributions and hence,
yield to a better decision-making process. In particular, we introduce
diffusion models for three seismic applications: demultiple, denoising and
interpolation. To that end, we run experiments on synthetic and on real data,
and we compare the diffusion performance with standardized algorithms. We
believe that our pioneer study not only demonstrates the capability of
diffusion models, but also opens the door to future research to integrate
generative models in seismic workflows.

Excess noise is a major obstacle to high-performance continuous-variable
quantum key distribution (CVQKD), which is mainly derived from the amplitude
attenuation and phase fluctuation of quantum signals caused by channel
instability. Here, an excess noise suppression scheme based on equalization is
proposed. In this scheme, the distorted signals can be corrected through
equalization assisted by a neural network and pilot tone, relieving the
pressure on the post-processing and eliminating the hardware cost. For a
free-space channel with more intense fluctuation, a classification algorithm is
added to classify the received variables, and then the distinctive equalization
correction for different classes is carried out. The experimental results show
that the scheme can suppress the excess noise to a lower level, and has a
significant performance improvement. Moreover, the scheme also enables the
system to cope with strong turbulence. It breaks the bottleneck of
long-distance quantum communication and lays a foundation for the large-scale
application of CVQKD.

We propose a penalized nonparametric approach to estimating the quantile
regression process (QRP) in a nonseparable model using rectifier quadratic unit
(ReQU) activated deep neural networks and introduce a novel penalty function to
enforce non-crossing of quantile regression curves. We establish the
non-asymptotic excess risk bounds for the estimated QRP and derive the mean
integrated squared error for the estimated QRP under mild smoothness and
regularity conditions. To establish these non-asymptotic risk and estimation
error bounds, we also develop a new error bound for approximating $C^s$ smooth
functions with $s >0$ and their derivatives using ReQU activated neural
networks. This is a new approximation result for ReQU networks and is of
independent interest and may be useful in other problems. Our numerical
experiments demonstrate that the proposed method is competitive with or
outperforms two existing methods, including methods using reproducing kernels
and random forests, for nonparametric quantile regression.

Convolutional neural networks contain strong priors for generating natural
looking images [1]. These priors enable image denoising, super resolution, and
inpainting in an unsupervised manner. Previous attempts to demonstrate similar
ideas in audio, namely deep audio priors, (i) use hand picked architectures
such as harmonic convolutions, (ii) only work with spectrogram input, and (iii)
have been used mostly for eliminating Gaussian noise [2]. In this work we show
that existing SOTA architectures for audio source separation contain deep
priors even when working with the raw waveform. Deep priors can be discovered
by training a neural network to generate a single corrupted signal when given
white noise as input. A network with relevant deep priors is likely to generate
a cleaner version of the signal before converging on the corrupted signal. We
demonstrate this restoration effect with several corruptions: background noise,
reverberations, and a gap in the signal (audio inpainting).

Previous soft tissue manipulation studies assumed that the grasping point was
known and the target deformation can be achieved. During the operation, the
constraints are supposed to be constant, and there is no obstacles around the
soft tissue. To go beyond these assumptions, a deep reinforcement learning
framework with prior knowledge is proposed for soft tissue manipulation under
unknown constraints, such as the force applied by fascia. The prior knowledge
is represented through an intuitive manipulation strategy. As an action of the
agent, a regulator factor is used to coordinate the intuitive approach and the
deliberate network. A reward function is designed to balance the exploration
and exploitation for large deformation. Successful simulation results verify
that the proposed framework can manipulate the soft tissue while avoiding
obstacles and adding new position constraints. Compared with the soft
actor-critic (SAC) algorithm, the proposed framework can accelerate the
training procedure and improve the generalization.

Trajectory prediction has been widely pursued in many fields, and many
model-based and model-free methods have been explored. The former include
rule-based, geometric or optimization-based models, and the latter are mainly
comprised of deep learning approaches. In this paper, we propose a new method
combining both methodologies based on a new Neural Differential Equation model.
Our new model (Neural Social Physics or NSP) is a deep neural network within
which we use an explicit physics model with learnable parameters. The explicit
physics model serves as a strong inductive bias in modeling pedestrian
behaviors, while the rest of the network provides a strong data-fitting
capability in terms of system parameter estimation and dynamics stochasticity
modeling. We compare NSP with 15 recent deep learning methods on 6 datasets and
improve the state-of-the-art performance by 5.56%-70%. Besides, we show that
NSP has better generalizability in predicting plausible trajectories in
drastically different scenarios where the density is 2-5 times as high as the
testing data. Finally, we show that the physics model in NSP can provide
plausible explanations for pedestrian behaviors, as opposed to black-box deep
learning. Code is available:
https://github.com/realcrane/Human-Trajectory-Prediction-via-Neural-Social-Physics.

Shadow removal from a single image is generally still an open problem. Most
existing learning-based methods use supervised learning and require a large
number of paired images (shadow and corresponding non-shadow images) for
training. A recent unsupervised method, Mask-ShadowGAN, addresses this
limitation. However, it requires a binary mask to represent shadow regions,
making it inapplicable to soft shadows. To address the problem, in this paper,
we propose an unsupervised domain-classifier guided shadow removal network,
DC-ShadowNet. Specifically, we propose to integrate a shadow/shadow-free domain
classifier into a generator and its discriminator, enabling them to focus on
shadow regions. To train our network, we introduce novel losses based on
physics-based shadow-free chromaticity, shadow-robust perceptual features, and
boundary smoothness. Moreover, we show that our unsupervised network can be
used for test-time training that further improves the results. Our experiments
show that all these novel components allow our method to handle soft shadows,
and also to perform better on hard shadows both quantitatively and
qualitatively than the existing state-of-the-art shadow removal methods.

Traditional supervised bearing fault diagnosis methods rely on massive
labelled data, yet annotations may be very time-consuming or infeasible. The
fault diagnosis approach that utilizes limited labelled data is becoming
increasingly popular. In this paper, a Wavelet Transform (WT) and
self-supervised learning-based bearing fault diagnosis framework is proposed to
address the lack of supervised samples issue. Adopting the WT and cubic spline
interpolation technique, original measured vibration signals are converted to
the time-frequency maps (TFMs) with a fixed scale as inputs. The Vision
Transformer (ViT) is employed as the encoder for feature extraction, and the
self-distillation with no labels (DINO) algorithm is introduced in the proposed
framework for self-supervised learning with limited labelled data and
sufficient unlabeled data. Two rolling bearing fault datasets are used for
validations. In the case of both datasets only containing 1% labelled samples,
utilizing the feature vectors extracted by the trained encoder without
fine-tuning, over 90\% average diagnosis accuracy can be obtained based on the
simple K-Nearest Neighbor (KNN) classifier. Furthermore, the superiority of the
proposed method is demonstrated in comparison with other self-supervised fault
diagnosis methods.

Our theoretical understanding of deep learning has not kept pace with its
empirical success. While network architecture is known to be critical, we do
not yet understand its effect on learned representations and network behavior,
or how this architecture should reflect task structure.In this work, we begin
to address this gap by introducing the Gated Deep Linear Network framework that
schematizes how pathways of information flow impact learning dynamics within an
architecture. Crucially, because of the gating, these networks can compute
nonlinear functions of their input. We derive an exact reduction and, for
certain cases, exact solutions to the dynamics of learning. Our analysis
demonstrates that the learning dynamics in structured networks can be
conceptualized as a neural race with an implicit bias towards shared
representations, which then govern the model's ability to systematically
generalize, multi-task, and transfer. We validate our key insights on
naturalistic datasets and with relaxed assumptions. Taken together, our work
gives rise to general hypotheses relating neural architecture to learning and
provides a mathematical approach towards understanding the design of more
complex architectures and the role of modularity and compositionality in
solving real-world problems. The code and results are available at
https://www.saxelab.org/gated-dln .

Supervised multi-view stereo (MVS) methods have achieved remarkable progress
in terms of reconstruction quality, but suffer from the challenge of collecting
large-scale ground-truth depth. In this paper, we propose a novel
self-supervised training pipeline for MVS based on knowledge distillation,
termed \textit{KD-MVS}, which mainly consists of self-supervised teacher
training and distillation-based student training. Specifically, the teacher
model is trained in a self-supervised fashion using both photometric and
featuremetric consistency. Then we distill the knowledge of the teacher model
to the student model through probabilistic knowledge transferring. With the
supervision of validated knowledge, the student model is able to outperform its
teacher by a large margin. Extensive experiments performed on multiple datasets
show our method can even outperform supervised methods.

Predicting the future states of surrounding traffic participants and planning
a safe, smooth, and socially compliant trajectory accordingly is crucial for
autonomous vehicles. There are two major issues with the current autonomous
driving system: the prediction module is often decoupled from the planning
module and the cost function for planning is hard to specify and tune. To
tackle these issues, we propose an end-to-end differentiable framework that
integrates prediction and planning modules and is able to learn the cost
function from data. Specifically, we employ a differentiable nonlinear
optimizer as the motion planner, which takes the predicted trajectories of
surrounding agents given by the neural network as input and optimizes the
trajectory for the autonomous vehicle, thus enabling all operations in the
framework to be differentiable including the cost function weights. The
proposed framework is trained on a large-scale real-world driving dataset to
imitate human driving trajectories in the entire driving scene and validated in
both open-loop and closed-loop manners. The open-loop testing results reveal
that the proposed method outperforms the baseline methods across a variety of
metrics and delivers planning-centric prediction results, allowing the planning
module to output close-to-human trajectories. In closed-loop testing, the
proposed method shows the ability to handle complex urban driving scenarios and
robustness against the distributional shift that imitation learning methods
suffer from. Importantly, we find that joint training of planning and
prediction modules achieves better performance than planning with a separate
trained prediction module in both open-loop and closed-loop tests. Moreover,
the ablation study indicates that the learnable components in the framework are
essential to ensure planning stability and performance.

Optimizing noisy functions online, when evaluating the objective requires
experiments on a deployed system, is a crucial task arising in manufacturing,
robotics and many others. Often, constraints on safe inputs are unknown ahead
of time, and we only obtain noisy information, indicating how close we are to
violating the constraints. Yet, safety must be guaranteed at all times, not
only for the final output of the algorithm.
  We introduce a general approach for seeking a stationary point in high
dimensional non-linear stochastic optimization problems in which maintaining
safety during learning is crucial. Our approach called LB-SGD is based on
applying stochastic gradient descent (SGD) with a carefully chosen adaptive
step size to a logarithmic barrier approximation of the original problem. We
provide a complete convergence analysis of non-convex, convex, and
strongly-convex smooth constrained problems, with first-order and zeroth-order
feedback. Our approach yields efficient updates and scales better with
dimensionality compared to existing approaches.
  We empirically compare the sample complexity and the computational cost of
our method with existing safe learning approaches. Beyond synthetic benchmarks,
we demonstrate the effectiveness of our approach on minimizing constraint
violation in policy search tasks in safe reinforcement learning (RL).

Cosmic Ray (CR) hits are the major contaminants in astronomical imaging and
spectroscopic observations involving solid-state detectors. Correctly
identifying and masking them is a crucial part of the image processing
pipeline, since it may otherwise lead to spurious detections. For this purpose,
we have developed and tested a novel Deep Learning based framework for the
automatic detection of CR hits from astronomical imaging data from two
different imagers: Dark Energy Camera (DECam) and Las Cumbres Observatory
Global Telescope (LCOGT). We considered two baseline models namely deepCR and
Cosmic-CoNN, which are the current state-of-the-art learning based algorithms
that were trained using Hubble Space Telescope (HST) ACS/WFC and LCOGT Network
images respectively. We have experimented with the idea of augmenting the
baseline models using Attention Gates (AGs) to improve the CR detection
performance. We have trained our models on DECam data and demonstrate a
consistent marginal improvement by adding AGs in True Positive Rate (TPR) at
0.01% False Positive Rate (FPR) and Precision at 95% TPR over the
aforementioned baseline models for the DECam dataset. We demonstrate that the
proposed AG augmented models provide significant gain in TPR at 0.01% FPR when
tested on previously unseen LCO test data having images from three distinct
telescope classes. Furthermore, we demonstrate that the proposed baseline
models with and without attention augmentation outperform state-of-the-art
models such as Astro-SCRAPPY, Maximask (that is trained natively on DECam data)
and pre-trained ground-based Cosmic-CoNN. This study demonstrates that the AG
module augmentation enables us to get a better deepCR and Cosmic-CoNN models
and to improve their generalization capability on unseen data.

Drone detection has become an essential task in object detection as drone
costs have decreased and drone technology has improved. It is, however,
difficult to detect distant drones when there is weak contrast, long range, and
low visibility. In this work, we propose several sequence classification
architectures to reduce the detected false-positive ratio of drone tracks.
Moreover, we propose a new drone vs. bird sequence classification dataset to
train and evaluate the proposed architectures. 3D CNN, LSTM, and Transformer
based sequence classification architectures have been trained on the proposed
dataset to show the effectiveness of the proposed idea. As experiments show,
using sequence information, bird classification and overall F1 scores can be
increased by up to 73% and 35%, respectively. Among all sequence classification
models, R(2+1)D-based fully convolutional model yields the best transfer
learning and fine-tuning results.

Visual Question Answering (VQA) is fundamentally compositional in nature, and
many questions are simply answered by decomposing them into modular
sub-problems. The recent proposed Neural Module Network (NMN) employ this
strategy to question answering, whereas heavily rest with off-the-shelf layout
parser or additional expert policy regarding the network architecture design
instead of learning from the data. These strategies result in the
unsatisfactory adaptability to the semantically-complicated variance of the
inputs, thereby hindering the representational capacity and generalizability of
the model. To tackle this problem, we propose a Semantic-aware modUlar caPsulE
Routing framework, termed as SUPER, to better capture the instance-specific
vision-semantic characteristics and refine the discriminative representations
for prediction. Particularly, five powerful specialized modules as well as
dynamic routers are tailored in each layer of the SUPER network, and the
compact routing spaces are constructed such that a variety of customizable
routes can be sufficiently exploited and the vision-semantic representations
can be explicitly calibrated. We comparatively justify the effectiveness and
generalization ability of our proposed SUPER scheme over five benchmark
datasets, as well as the parametric-efficient advantage. It is worth
emphasizing that this work is not to pursue the state-of-the-art results in
VQA. Instead, we expect that our model is responsible to provide a novel
perspective towards architecture learning and representation calibration for
VQA.

Despite encouraging progress in deepfake detection, generalization to unseen
forgery types remains a significant challenge due to the limited forgery clues
explored during training. In contrast, we notice a common phenomenon in
deepfake: fake video creation inevitably disrupts the statistical regularity in
original videos. Inspired by this observation, we propose to boost the
generalization of deepfake detection by distinguishing the "regularity
disruption" that does not appear in real videos. Specifically, by carefully
examining the spatial and temporal properties, we propose to disrupt a real
video through a Pseudo-fake Generator and create a wide range of pseudo-fake
videos for training. Such practice allows us to achieve deepfake detection
without using fake videos and improves the generalization ability in a simple
and efficient manner. To jointly capture the spatial and temporal disruptions,
we propose a Spatio-Temporal Enhancement block to learn the regularity
disruption across space and time on our self-created videos. Through
comprehensive experiments, our method exhibits excellent performance on several
datasets.

We investigate the problem of video Referring Expression Comprehension (REC),
which aims to localize the referent objects described in the sentence to visual
regions in the video frames. Despite the recent progress, existing methods
suffer from two problems: 1) inconsistent localization results across video
frames; 2) confusion between the referent and contextual objects. To this end,
we propose a novel Dual Correspondence Network (dubbed as DCNet) which
explicitly enhances the dense associations in both the inter-frame and
cross-modal manners. Firstly, we aim to build the inter-frame correlations for
all existing instances within the frames. Specifically, we compute the
inter-frame patch-wise cosine similarity to estimate the dense alignment and
then perform the inter-frame contrastive learning to map them close in feature
space. Secondly, we propose to build the fine-grained patch-word alignment to
associate each patch with certain words. Due to the lack of this kind of
detailed annotations, we also predict the patch-word correspondence through the
cosine similarity. Extensive experiments demonstrate that our DCNet achieves
state-of-the-art performance on both video and image REC benchmarks.
Furthermore, we conduct comprehensive ablation studies and thorough analyses to
explore the optimal model designs. Notably, our inter-frame and cross-modal
contrastive losses are plug-and-play functions and are applicable to any video
REC architectures. For example, by building on top of Co-grounding, we boost
the performance by 1.48% absolute improvement on Accu.@0.5 for VID-Sentence
dataset.

The key to video inpainting is to use correlation information from as many
reference frames as possible. Existing flow-based propagation methods split the
video synthesis process into multiple steps: flow completion -> pixel
propagation -> synthesis. However, there is a significant drawback that the
errors in each step continue to accumulate and amplify in the next step. To
this end, we propose an Error Compensation Framework for Flow-guided Video
Inpainting (ECFVI), which takes advantage of the flow-based method and offsets
its weaknesses. We address the weakness with the newly designed flow completion
module and the error compensation network that exploits the error guidance map.
Our approach greatly improves the temporal consistency and the visual quality
of the completed videos. Experimental results show the superior performance of
our proposed method with the speed up of x6, compared to the state-of-the-art
methods. In addition, we present a new benchmark dataset for evaluation by
supplementing the weaknesses of existing test datasets.

The capacity sharing problem in Radio Access Network (RAN) slicing deals with
the distribution of the capacity available in each RAN node among various RAN
slices to satisfy their traffic demands and efficiently use the radio
resources. While several capacity sharing algorithmic solutions have been
proposed in the literature, their practical implementation still remains as a
gap. In this paper, the implementation of a Reinforcement Learning-based
capacity sharing algorithm over the O-RAN architecture is discussed, providing
insights into the operation of the involved interfaces and the containerization
of the solution. Moreover, the description of the testbed implemented to
validate the solution is included and some performance and validation results
are presented.

It is challenging for artificial intelligence systems to achieve accurate
video recognition under the scenario of low computation costs. Adaptive
inference based efficient video recognition methods typically preview videos
and focus on salient parts to reduce computation costs. Most existing works
focus on complex networks learning with video classification based objectives.
Taking all frames as positive samples, few of them pay attention to the
discrimination between positive samples (salient frames) and negative samples
(non-salient frames) in supervisions. To fill this gap, in this paper, we
propose a novel Non-saliency Suppression Network (NSNet), which effectively
suppresses the responses of non-salient frames. Specifically, on the frame
level, effective pseudo labels that can distinguish between salient and
non-salient frames are generated to guide the frame saliency learning. On the
video level, a temporal attention module is learned under dual video-level
supervisions on both the salient and the non-salient representations. Saliency
measurements from both two levels are combined for exploitation of
multi-granularity complementary information. Extensive experiments conducted on
four well-known benchmarks verify our NSNet not only achieves the
state-of-the-art accuracy-efficiency trade-off but also present a significantly
faster (2.4~4.3x) practical inference speed than state-of-the-art methods. Our
project page is at https://lawrencexia2008.github.io/projects/nsnet .

Machine learning (ML) holds great promise for improving healthcare, but it is
critical to ensure that its use will not propagate or amplify health
disparities. An important step is to characterize the (un)fairness of ML models
- their tendency to perform differently across subgroups of the population -
and to understand its underlying mechanisms. One potential driver of
algorithmic unfairness, shortcut learning, arises when ML models base
predictions on improper correlations in the training data. However, diagnosing
this phenomenon is difficult, especially when sensitive attributes are causally
linked with disease. Using multi-task learning, we propose the first method to
assess and mitigate shortcut learning as a part of the fairness assessment of
clinical ML systems, and demonstrate its application to clinical tasks in
radiology and dermatology. Finally, our approach reveals instances when
shortcutting is not responsible for unfairness, highlighting the need for a
holistic approach to fairness mitigation in medical AI.

Closed-loop reservoir management (CLRM), in which history matching and
production optimization are performed multiple times over the life of an asset,
can provide significant improvement in the specified objective. These
procedures are computationally expensive due to the large number of flow
simulations required for data assimilation and optimization. Existing CLRM
procedures are applied asset by asset, without utilizing information that could
be useful over a range assets. Here, we develop a CLRM framework for multiple
assets with varying numbers of wells. We use deep reinforcement learning to
train a single global control policy that is applicable for all assets
considered. The new framework is an extension of a recently introduced control
policy methodology for individual assets. Embedding layers are incorporated
into the representation to handle the different numbers of decision variables
that arise for the different assets. Because the global control policy learns a
unified representation of useful features from multiple assets, it is less
expensive to construct than asset-by-asset training (we observe about 3x
speedup in our examples). The production optimization problem includes a
relative-change constraint on the well settings, which renders the results
suitable for practical use. We apply the multi-asset CLRM framework to 2D and
3D water-flooding examples. In both cases, four assets with different well
counts, well configurations, and geostatistical descriptions are considered.
Numerical experiments demonstrate that the global control policy provides
objective function values, for both the 2D and 3D cases, that are nearly
identical to those from control policies trained individually for each asset.
This promising finding suggests that multi-asset CLRM may indeed represent a
viable practical strategy.

In this paper, we consider multiple solar-powered wireless nodes which
utilize the harvested solar energy to transmit collected data to multiple
unmanned aerial vehicles (UAVs) in the uplink. In this context, we jointly
design UAV flight trajectories, UAV-node communication associations, and uplink
power control to effectively utilize the harvested energy and manage co-channel
interference within a finite time horizon. To ensure the fairness of wireless
nodes, the design goal is to maximize the worst user rate. The joint design
problem is highly non-convex and requires causal (future) knowledge of the
instantaneous energy state information (ESI) and channel state information
(CSI), which are difficult to predict in reality. To overcome these challenges,
we propose an offline method based on convex optimization that only utilizes
the average ESI and CSI. The problem is solved by three convex subproblems with
successive convex approximation (SCA) and alternative optimization. We further
design an online convex-assisted reinforcement learning (CARL) method to
improve the system performance based on real-time environmental information. An
idea of multi-UAV regulated flight corridors, based on the optimal offline UAV
trajectories, is proposed to avoid unnecessary flight exploration by UAVs and
enables us to improve the learning efficiency and system performance, as
compared with the conventional reinforcement learning (RL) method. Computer
simulations are used to verify the effectiveness of the proposed methods. The
proposed CARL method provides 25% and 12% improvement on the worst user rate
over the offline and conventional RL methods.

Deep learning methods have been successfully applied to remote sensing
problems for several years. Among these methods, CNN based models have high
accuracy in solving the land classification problem using satellite or aerial
images. Although these models have high accuracy, this generally comes with
large memory size requirements. On the other hand, it is desirable to have
small-sized models for applications, such as the ones implemented on unmanned
aerial vehicles, with low memory space. Unfortunately, small-sized CNN models
do not provide high accuracy as with their large-sized versions. In this study,
we propose a novel method to improve the accuracy of CNN models, especially the
ones with small size, by injecting traditional features to them. To test the
effectiveness of the proposed method, we applied it to the CNN models
SqueezeNet, MobileNetV2, ShuffleNetV2, VGG16, and ResNet50V2 having size 0.5 MB
to 528 MB. We used the sample mean, gray level co-occurrence matrix features,
Hu moments, local binary patterns, histogram of oriented gradients, and color
invariants as traditional features for injection. We tested the proposed method
on the EuroSAT dataset to perform land classification. Our experimental results
show that the proposed method significantly improves the land classification
accuracy especially when applied to small-sized CNN models.

EC-KitY is a comprehensive Python library for doing evolutionary computation
(EC), licensed under GNU General Public License v3.0, and compatible with
scikit-learn. Designed with modern software engineering and machine learning
integration in mind, EC-KitY can support all popular EC paradigms, including
genetic algorithms, genetic programming, coevolution, evolutionary
multi-objective optimization, and more. This paper provides an overview of the
package, including the ease of setting up an EC experiment, the architecture,
the main features, and a comparison with other libraries.

Video-Text Pre-training (VTP) aims to learn transferable representations for
various downstream tasks from large-scale web videos. To date, almost all
existing VTP methods are limited to retrieval-based downstream tasks, e.g.,
video retrieval, whereas their transfer potentials on localization-based tasks,
e.g., temporal grounding, are under-explored. In this paper, we experimentally
analyze and demonstrate the incompatibility of current VTP methods with
localization tasks, and propose a novel Localization-oriented Video-Text
Pre-training framework, dubbed as LocVTP. Specifically, we perform the
fine-grained contrastive alignment as a complement to the coarse-grained one by
a clip-word correspondence discovery scheme. To further enhance the temporal
reasoning ability of the learned feature, we propose a context projection head
and a temporal aware contrastive loss to perceive the contextual relationships.
Extensive experiments on four downstream tasks across six datasets demonstrate
that our LocVTP achieves state-of-the-art performance on both retrieval-based
and localization-based tasks. Furthermore, we conduct comprehensive ablation
studies and thorough analyses to explore the optimum model designs and training

With the aid of hardware and software developments, there has been a surge of
interests in solving partial differential equations by deep learning
techniques, and the integration with domain decomposition strategies has
recently attracted considerable attention due to its enhanced representation
and parallelization capacity of the network solution. While there are already
several works that substitute the numerical solver of overlapping Schwarz
methods with the deep learning approach, the non-overlapping counterpart has
not been thoroughly studied yet because of the inevitable interface overfitting
problem that would propagate the errors to neighbouring subdomains and
eventually hamper the convergence of outer iteration. In this work, a novel
learning approach, i.e., the compensated deep Ritz method, is proposed to
enable the flux transmission across subregion interfaces with guaranteed
accuracy, thereby allowing us to construct effective learning algorithms for
realizing the more general non-overlapping domain decomposition methods in the
presence of overfitted interface conditions. Numerical experiments on a series
of elliptic boundary value problems including the regular and irregular
interfaces, low and high dimensions, smooth and high-contrast coefficients on
multidomains are carried out to validate the effectiveness of our proposed
domain decomposition learning algorithms.

The hardware challenges associated with light-field(LF) imaging has made it
difficult for consumers to access its benefits like applications in
post-capture focus and aperture control. Learning-based techniques which solve
the ill-posed problem of LF reconstruction from sparse (1, 2 or 4) views have
significantly reduced the requirement for complex hardware. LF video
reconstruction from sparse views poses a special challenge as acquiring
ground-truth for training these models is hard. Hence, we propose a
self-supervised learning-based algorithm for LF video reconstruction from
monocular videos. We use self-supervised geometric, photometric and temporal
consistency constraints inspired from a recent self-supervised technique for LF
video reconstruction from stereo video. Additionally, we propose three key
techniques that are relevant to our monocular video input. We propose an
explicit disocclusion handling technique that encourages the network to inpaint
disoccluded regions in a LF frame, using information from adjacent input
temporal frames. This is crucial for a self-supervised technique as a single
input frame does not contain any information about the disoccluded regions. We
also propose an adaptive low-rank representation that provides a significant
boost in performance by tailoring the representation to each input scene.
Finally, we also propose a novel refinement block that is able to exploit the
available LF image data using supervised learning to further refine the
reconstruction quality. Our qualitative and quantitative analysis demonstrates
the significance of each of the proposed building blocks and also the superior
results compared to previous state-of-the-art monocular LF reconstruction
techniques. We further validate our algorithm by reconstructing LF videos from
monocular videos acquired using a commercial GoPro camera.

The popularity of online fashion shopping continues to grow. The ability to
offer an effective recommendation to customers is becoming increasingly
important. In this work, we focus on Fashion Outfits Challenge, part of SIGIR
2022 Workshop on eCommerce. The challenge is centered around Fill in the Blank
(FITB) task that implies predicting the missing outfit, given an incomplete
outfit and a list of candidates. In this paper, we focus on applying siamese
networks on the task. More specifically, we explore how combining information
from multiple modalities (textual and visual modality) impacts the performance
of the model on the task. We evaluate our model on the test split provided by
the challenge organizers and the test split with gold assignments that we
created during the development phase. We discover that using both visual, and
visual and textual data demonstrates promising results on the task. We conclude
by suggesting directions for further improvement of our method.

There are inevitably many mislabeled data in real-world datasets. Because
deep neural networks (DNNs) have an enormous capacity to memorize noisy labels,
a robust training scheme is required to prevent labeling errors from degrading
the generalization performance of DNNs. Current state-of-the-art methods
present a co-training scheme that trains dual networks using samples associated
with small losses. In practice, however, training two networks simultaneously
can burden computing resources. In this study, we propose a simple yet
effective robust training scheme that operates by training only a single
network. During training, the proposed method generates temporal self-ensemble
by sampling intermediate network parameters from the weight trajectory formed
by stochastic gradient descent optimization. The loss sum evaluated with these
self-ensembles is used to identify incorrectly labeled samples. In parallel,
our method generates multi-view predictions by transforming an input data into
various forms and considers their agreement to identify incorrectly labeled
samples. By combining the aforementioned metrics, we present the proposed {\it
self-ensemble-based robust training} (SRT) method, which can filter the samples
with noisy labels to reduce their influence on training. Experiments on
widely-used public datasets demonstrate that the proposed method achieves a
state-of-the-art performance in some categories without training the dual
networks.

Automated data augmentation, which aims at engineering augmentation policy
automatically, recently draw a growing research interest. Many previous
auto-augmentation methods utilized a Density Matching strategy by evaluating
policies in terms of the test-time augmentation performance. In this paper, we
theoretically and empirically demonstrated the inconsistency between the train
and validation set of small-scale medical image datasets, referred to as
in-domain sampling bias. Next, we demonstrated that the in-domain sampling bias
might cause the inefficiency of Density Matching. To address the problem, an
improved augmentation search strategy, named Augmented Density Matching, was
proposed by randomly sampling policies from a prior distribution for training.
Moreover, an efficient automatical machine learning(AutoML) algorithm was
proposed by unifying the search on data augmentation and neural architecture.
Experimental results indicated that the proposed methods outperformed
state-of-the-art approaches on MedMNIST, a pioneering benchmark designed for
AutoML in medical image analysis.

Recent breakthroughs in deep learning (DL) techniques have stimulated
significant growth in developing DL-enabled applications. These DL
applications, built upon a heterogeneous and complex DL stack (e.g., Nvidia
GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to
software and hardware dependencies across the DL stack. A persistent challenge
in dependency management across the entire engineering lifecycle is posed by
the asynchronous and radical evolution as well as the complex version
constraints among dependencies. Developers might introduce dependency bugs
(DBs) in selecting, using and maintaining dependencies. However, the
characteristics of DBs in DL stack is still under-investigated, hindering
practical solutions to dependency management in DL stack. To fill this gap,
this paper presents the first comprehensive study to characterize symptoms,
root causes and fix patterns of DBs across the whole DL stack with 326 DBs
collected from StackOverflow posts. For each DB, we first investigate the
symptom as well as the lifecyle stage and dependency where the symptom is
exposed. Then, we analyze the root cause as well as the lifecycle stage and
dependency where the root cause is introduced. Finally, we explore the fix
pattern as well as the knowledge sources that are used to fix it. Our findings
from this study shed light on the implications on dependency management, e.g.,
constructing dependency knowledge graph for the entire DL stack, recommending
dependencies, detecting, localizing and fixing dependency bugs, and upgrading
and migrating dependencies.

Prompted models have demonstrated impressive few-shot learning abilities.
Repeated interactions at test-time with a single model, or the composition of
multiple models together, further expands capabilities. These compositions are
probabilistic models, and may be expressed in the language of graphical models
with random variables whose values are complex data types such as strings.
Cases with control flow and dynamic structure require techniques from
probabilistic programming, which allow implementing disparate model structures
and inference strategies in a unified language. We formalize several existing
techniques from this perspective, including scratchpads / chain of thought,
verifiers, STaR, selection-inference, and tool use. We refer to the resulting
programs as language model cascades.

This paper proposes a novel Unified Feature Optimization (UFO) paradigm for
training and deploying deep models under real-world and large-scale scenarios,
which requires a collection of multiple AI functions. UFO aims to benefit each
single task with a large-scale pretraining on all tasks. Compared with the well
known foundation model, UFO has two different points of emphasis, i.e.,
relatively smaller model size and NO adaptation cost: 1) UFO squeezes a wide
range of tasks into a moderate-sized unified model in a multi-task learning
manner and further trims the model size when transferred to down-stream tasks.
2) UFO does not emphasize transfer to novel tasks. Instead, it aims to make the
trimmed model dedicated for one or more already-seen task. With these two
characteristics, UFO provides great convenience for flexible deployment, while
maintaining the benefits of large-scale pretraining. A key merit of UFO is that
the trimming process not only reduces the model size and inference consumption,
but also even improves the accuracy on certain tasks. Specifically, UFO
considers the multi-task training and brings two-fold impact on the unified
model: some closely related tasks have mutual benefits, while some tasks have
conflicts against each other. UFO manages to reduce the conflicts and to
preserve the mutual benefits through a novel Network Architecture Search (NAS)
method. Experiments on a wide range of deep representation learning tasks
(i.e., face recognition, person re-identification, vehicle re-identification
and product retrieval) show that the model trimmed from UFO achieves higher
accuracy than its single-task-trained counterpart and yet has smaller model
size, validating the concept of UFO. Besides, UFO also supported the release of
17 billion parameters computer vision (CV) foundation model which is the
largest CV model in the industry.

Neural architecture search (NAS) aims to automate architecture design
processes and improve the performance of deep neural networks. Platform-aware
NAS methods consider both performance and complexity and can find
well-performing architectures with low computational resources. Although
ordinary NAS methods result in tremendous computational costs owing to the
repetition of model training, one-shot NAS, which trains the weights of a
supernetwork containing all candidate architectures only once during the search
process, has been reported to result in a lower search cost. This study focuses
on the architecture complexity-aware one-shot NAS that optimizes the objective
function composed of the weighted sum of two metrics, such as the predictive
performance and number of parameters. In existing methods, the architecture
search process must be run multiple times with different coefficients of the
weighted sum to obtain multiple architectures with different complexities. This
study aims at reducing the search cost associated with finding multiple
architectures. The proposed method uses multiple distributions to generate
architectures with different complexities and updates each distribution using
the samples obtained from multiple distributions based on importance sampling.
The proposed method allows us to obtain multiple architectures with different
complexities in a single architecture search, resulting in reducing the search
cost. The proposed method is applied to the architecture search of
convolutional neural networks on the CIAFR-10 and ImageNet datasets.
Consequently, compared with baseline methods, the proposed method finds
multiple architectures with varying complexities while requiring less
computational effort.

In this paper, we demonstrated the benefit of using pre-trained model to
extract acoustic embedding to jointly predict (multitask learning) three tasks:
emotion, age, and native country. The pre-trained model was trained with
wav2vec 2.0 large robust model on the speech emotion corpus. The emotion and
age tasks were regression problems, while country prediction was a
classification task. A single harmonic mean from three metrics was used to
evaluate the performance of multitask learning. The classifier was a linear
network with two independent layers and shared layers, including the output
layers. This study explores multitask learning on different acoustic features
(including the acoustic embedding extracted from a model trained on an
affective speech dataset), seed numbers, batch sizes, and normalizations for
predicting paralinguistic information from speech.

Depression is a serious mental illness that impacts the way people
communicate, especially through their emotions, and, allegedly, the way they
interact with others. This work examines depression signals in dialogs, a less
studied setting that suffers from data sparsity. We hypothesize that depression
and emotion can inform each other, and we propose to explore the influence of
dialog structure through topic and dialog act prediction. We investigate a
Multi-Task Learning (MTL) approach, where all tasks mentioned above are learned
jointly with dialog-tailored hierarchical modeling. We experiment on the DAIC
and DailyDialog corpora-both contain dialogs in English-and show important
improvements over state-ofthe-art on depression detection (at best 70.6% F 1),
which demonstrates the correlation of depression with emotion and dialog
organization and the power of MTL to leverage information from different
sources.

Current rapid changes in climate increase the urgency to change energy
production and consumption management, to reduce carbon and other green-house
gas production. In this context, the French electricity network management
company RTE (R{\'e}seau de Transport d'{\'E}lectricit{\'e}) has recently
published the results of an extensive study outlining various scenarios for
tomorrow's French power management. We propose a challenge that will test the
viability of such a scenario. The goal is to control electricity transportation
in power networks, while pursuing multiple objectives: balancing production and
consumption, minimizing energetic losses, and keeping people and equipment safe
and particularly avoiding catastrophic failures. While the importance of the
application provides a goal in itself, this challenge also aims to push the
state-of-the-art in a branch of Artificial Intelligence (AI) called
Reinforcement Learning (RL), which offers new possibilities to tackle control
problems. In particular, various aspects of the combination of Deep Learning
and RL called Deep Reinforcement Learning remain to be harnessed in this
application domain. This challenge belongs to a series started in 2019 under
the name "Learning to run a power network" (L2RPN). In this new edition, we
introduce new more realistic scenarios proposed by RTE to reach carbon
neutrality by 2050, retiring fossil fuel electricity production, increasing
proportions of renewable and nuclear energy and introducing batteries.
Furthermore, we provide a baseline using state-of-the-art reinforcement
learning algorithm to stimulate the future participants.

Image translation based on a generative adversarial network (GAN-IT) is a
promising method for the precise localization of abnormal regions in chest
X-ray images (AL-CXR) even without pixel-level annotation. However,
heterogeneous unpaired datasets undermine existing methods to extract key
features and distinguish normal from abnormal cases, resulting in inaccurate
and unstable AL-CXR. To address this problem, we propose an improved two-stage
GAN-IT involving registration and data augmentation. For the first stage, we
introduce an advanced deep-learning-based registration technique that virtually
and reasonably converts unpaired data into paired data for learning
registration maps, by sequentially utilizing linear-based global and uniform
coordinate transformation and AI-based non-linear coordinate fine-tuning. This
approach enables the independent and complex coordinate transformation of each
detailed location of the lung while recognizing the entire lung structure,
thereby achieving higher registration performance with resolving inherent
artifacts caused by unpaired conditions. For the second stage, we apply data
augmentation to diversify anomaly locations by swapping the left and right lung
regions on the uniform registered frames, further improving the performance by
alleviating imbalance in data distribution showing left and right lung lesions.
The proposed method is model agnostic and shows consistent AL-CXR performance
improvement in representative AI models. Therefore, we believe GAN-IT for
AL-CXR can be clinically implemented by using our basis framework, even if
learning data are scarce or difficult for the pixel-level disease annotation.

We address the task of person search, that is, localizing and re-identifying
query persons from a set of raw scene images. Recent approaches are typically
built upon OIMNet, a pioneer work on person search, that learns joint person
representations for performing both detection and person re-identification
(reID) tasks. To obtain the representations, they extract features from
pedestrian proposals, and then project them on a unit hypersphere with L2
normalization. These methods also incorporate all positive proposals, that
sufficiently overlap with the ground truth, equally to learn person
representations for reID. We have found that 1) the L2 normalization without
considering feature distributions degenerates the discriminative power of
person representations, and 2) positive proposals often also depict background
clutter and person overlaps, which could encode noisy features to person
representations. In this paper, we introduce OIMNet++ that addresses the
aforementioned limitations. To this end, we introduce a novel normalization
layer, dubbed ProtoNorm, that calibrates features from pedestrian proposals,
while considering a long-tail distribution of person IDs, enabling L2
normalized person representations to be discriminative. We also propose a
localization-aware feature learning scheme that encourages better-aligned
proposals to contribute more in learning discriminative representations.
Experimental results and analysis on standard person search benchmarks
demonstrate the effectiveness of OIMNet++.

Changing the encoding parameters, in particular the video resolution, is a
common practice before transcoding. To this end, streaming and broadcast
platforms benefit from so-called bitrate ladders to determine the optimal
resolution for given bitrates. However, the task of determining the bitrate
ladder can usually be challenging as, on one hand, so-called fit-for-all static
ladders would waste bandwidth, and on the other hand, fully specialized ladders
are often not affordable in terms of computational complexity. In this paper,
we propose an ML-based scheme for predicting the bitrate ladder based on the
content of the video. The baseline of our solution predicts the bitrate ladder
using two constituent methods, which require no encoding passes. To further
enhance the performance of the constituent methods, we integrate a conditional
ensemble method to aggregate their decisions, with a negligibly limited number
of encoding passes. The experiment, carried out on the optimized software
encoder implementation of the VVC standard, called VVenC, shows significant
performance improvement. When compared to static bitrate ladder, the proposed
method can offer about 13% bitrate reduction in terms of BD-BR with a
negligible additional computational overhead. Conversely, when compared to the
fully specialized bitrate ladder method, the proposed method can offer about
86% to 92% complexity reduction, at cost the of only 0.8% to 0.9% coding
efficiency drop in terms of BD-BR.

A training pipeline for optical flow CNNs consists of a pretraining stage on
a synthetic dataset followed by a fine tuning stage on a target dataset.
However, obtaining ground truth flows from a target video requires a tremendous
effort. This paper proposes a practical fine tuning method to adapt a
pretrained model to a target dataset without ground truth flows, which has not
been explored extensively. Specifically, we propose a flow supervisor for
self-supervision, which consists of parameter separation and a student output
connection. This design is aimed at stable convergence and better accuracy over
conventional self-supervision methods which are unstable on the fine tuning
task. Experimental results show the effectiveness of our method compared to
different self-supervision methods for semi-supervised learning. In addition,
we achieve meaningful improvements over state-of-the-art optical flow models on
Sintel and KITTI benchmarks by exploiting additional unlabeled datasets. Code
is available at https://github.com/iwbn/flow-supervisor.

Novel view synthesis has recently been revolutionized by learning neural
radiance fields directly from sparse observations. However, rendering images
with this new paradigm is slow due to the fact that an accurate quadrature of
the volume rendering equation requires a large number of samples for each ray.
Previous work has mainly focused on speeding up the network evaluations that
are associated with each sample point, e.g., via caching of radiance values
into explicit spatial data structures, but this comes at the expense of model
compactness. In this paper, we propose a novel dual-network architecture that
takes an orthogonal direction by learning how to best reduce the number of
required sample points. To this end, we split our network into a sampling and
shading network that are jointly trained. Our training scheme employs fixed
sample positions along each ray, and incrementally introduces sparsity
throughout training to achieve high quality even at low sample counts. After
fine-tuning with the target number of samples, the resulting compact neural
representation can be rendered in real-time. Our experiments demonstrate that
our approach outperforms concurrent compact neural representations in terms of
quality and frame rate and performs on par with highly efficient hybrid
representations. Code and supplementary material is available at
https://thomasneff.github.io/adanerf.

Generative adversarial networks (GANs) have drawn enormous attention due to
the simple yet effective training mechanism and superior image generation
quality. With the ability to generate photo-realistic high-resolution (e.g.,
$1024\times1024$) images, recent GAN models have greatly narrowed the gaps
between the generated images and the real ones. Therefore, many recent works
show emerging interest to take advantage of pre-trained GAN models by
exploiting the well-disentangled latent space and the learned GAN priors. In
this paper, we briefly review recent progress on leveraging pre-trained
large-scale GAN models from three aspects, i.e., 1) the training of large-scale
generative adversarial networks, 2) exploring and understanding the pre-trained
GAN models, and 3) leveraging these models for subsequent tasks like image
restoration and editing. More information about relevant methods and
repositories can be found at https://github.com/csmliu/pretrained-GANs.

Federated Learning (FL) has become a practical and popular paradigm in
machine learning. However, currently, there is no systematic solution that
covers diverse use cases. Practitioners often face the challenge of how to
select a matching FL framework for their use case. In this work, we present
UniFed, the first unified benchmark for standardized evaluation of the existing
open-source FL frameworks. With 15 evaluation scenarios, we present both
qualitative and quantitative evaluation results of nine existing popular
open-sourced FL frameworks, from the perspectives of functionality, usability,
and system performance. We also provide suggestions on framework selection
based on the benchmark conclusions and point out future improvement directions.

Recent studies have shown that deep neural networks-based recommender systems
are vulnerable to adversarial attacks, where attackers can inject carefully
crafted fake user profiles (i.e., a set of items that fake users have
interacted with) into a target recommender system to achieve malicious
purposes, such as promote or demote a set of target items. Due to the security
and privacy concerns, it is more practical to perform adversarial attacks under
the black-box setting, where the architecture/parameters and training data of
target systems cannot be easily accessed by attackers. However, generating
high-quality fake user profiles under black-box setting is rather challenging
with limited resources to target systems. To address this challenge, in this
work, we introduce a novel strategy by leveraging items' attribute information
(i.e., items' knowledge graph), which can be publicly accessible and provide
rich auxiliary knowledge to enhance the generation of fake user profiles. More
specifically, we propose a knowledge graph-enhanced black-box attacking
framework (KGAttack) to effectively learn attacking policies through deep
reinforcement learning techniques, in which knowledge graph is seamlessly
integrated into hierarchical policy networks to generate fake user profiles for
performing adversarial black-box attacks. Comprehensive experiments on various
real-world datasets demonstrate the effectiveness of the proposed attacking
framework under the black-box setting.

Recent advances have shown the success of using reinforcement learning and
search to solve NP-hard graph-related tasks, such as Traveling Salesman
Optimization, Graph Edit Distance computation, etc. However, it remains unclear
how one can efficiently and accurately detect the occurrences of a small query
graph in a large target graph, which is a core operation in graph database
search, biomedical analysis, social group finding, etc. This task is called
Subgraph Matching which essentially performs subgraph isomorphism check between
a query graph and a large target graph. One promising approach to this
classical problem is the "learning-to-search" paradigm, where a reinforcement
learning (RL) agent is designed with a learned policy to guide a search
algorithm to quickly find the solution without any solved instances for
supervision. However, for the specific task of Subgraph Matching, though the
query graph is usually small given by the user as input, the target graph is
often orders-of-magnitude larger. It poses challenges to the neural network
design and can lead to solution and reward sparsity. In this paper, we propose
NSUBS with two innovations to tackle the challenges: (1) A novel
encoder-decoder neural network architecture to dynamically compute the matching
information between the query and the target graphs at each search state; (2) A
novel look-ahead loss function for training the policy network. Experiments on
six large real-world target graphs show that NSUBS can significantly improve
the subgraph matching performance.

Noisy label Facial Expression Recognition (FER) is more challenging than
traditional noisy label classification tasks due to the inter-class similarity
and the annotation ambiguity. Recent works mainly tackle this problem by
filtering out large-loss samples. In this paper, we explore dealing with noisy
labels from a new feature-learning perspective. We find that FER models
remember noisy samples by focusing on a part of the features that can be
considered related to the noisy labels instead of learning from the whole
features that lead to the latent truth. Inspired by that, we propose a novel
Erasing Attention Consistency (EAC) method to suppress the noisy samples during
the training process automatically. Specifically, we first utilize the flip
semantic consistency of facial images to design an imbalanced framework. We
then randomly erase input images and use flip attention consistency to prevent
the model from focusing on a part of the features. EAC significantly
outperforms state-of-the-art noisy label FER methods and generalizes well to
other tasks with a large number of classes like CIFAR100 and Tiny-ImageNet. The
code is available at
https://github.com/zyh-uaiaaaa/Erasing-Attention-Consistency.

Currently, deep learning-based visual inspection has been highly successful
with the help of supervised learning methods. However, in real industrial
scenarios, the scarcity of defect samples, the cost of annotation, and the lack
of a priori knowledge of defects may render supervised-based methods
ineffective. In recent years, unsupervised anomaly localization algorithms have
become more widely used in industrial inspection tasks. This paper aims to help
researchers in this field by comprehensively surveying recent achievements in
unsupervised anomaly localization in industrial images using deep learning. The
survey reviews more than 120 significant publications covering different
aspects of anomaly localization, mainly covering various concepts, challenges,
taxonomies, benchmark datasets, and quantitative performance comparisons of the
methods reviewed. In reviewing the achievements to date, this paper provides
detailed predictions and analysis of several future research directions. This
review provides detailed technical information for researchers interested in
industrial anomaly localization and who wish to apply it to the localization of
anomalies in other fields.

Multiplayer Online Battle Arena (MOBA) is one of the most successful game
genres. MOBA games such as League of Legends have competitive environments
where players race for their rank. In most MOBA games, a player's rank is
determined by the match result (win or lose). It seems natural because of the
nature of team play, but in some sense, it is unfair because the players who
put a lot of effort lose their rank just in case of loss and some players even
get free-ride on teammates' efforts in case of a win. To reduce the
side-effects of the team-based ranking system and evaluate a player's
performance impartially, we propose a novel embedding model that converts a
player's actions into quantitative scores based on the actions' respective
contribution to the team's victory. Our model is built using a sequence-based
deep learning model with a novel loss function working on the team match. The
sequence-based deep learning model process the action sequence from the game
start to the end of a player in a team play using a GRU unit that takes a
hidden state from the previous step and the current input selectively. The loss
function is designed to help the action score to reflect the final score and
the success of the team. We showed that our model can evaluate a player's
individual performance fairly and analyze the contributions of the player's
respective actions.

Predictive coding (PC) is an influential theory in computational
neuroscience, which argues that the cortex forms unsupervised world models by
implementing a hierarchical process of prediction error minimization. PC
networks (PCNs) are trained in two phases. First, neural activities are updated
to optimize the network's response to external stimuli. Second, synaptic
weights are updated to consolidate this change in activity -- an algorithm
called \emph{prospective configuration}. While previous work has shown how in
various limits, PCNs can be found to approximate backpropagation (BP), recent
work has demonstrated that PCNs operating in this standard regime, which does
not approximate BP, nevertheless obtain competitive training and generalization
performance to BP-trained networks while outperforming them on tasks such as
online, few-shot, and continual learning, where brains are known to excel.
Despite this promising empirical performance, little is understood
theoretically about the properties and dynamics of PCNs in this regime. In this
paper, we provide a comprehensive theoretical analysis of the properties of
PCNs trained with prospective configuration. We first derive analytical results
concerning the inference equilibrium for PCNs and a previously unknown close
connection relationship to target propagation (TP). Secondly, we provide a
theoretical analysis of learning in PCNs as a variant of generalized
expectation-maximization and use that to prove the convergence of PCNs to
critical points of the BP loss function, thus showing that deep PCNs can, in
theory, achieve the same generalization performance as BP, while maintaining
their unique advantages.

Impressive results in natural language processing (NLP) based on the
Transformer neural network architecture have inspired researchers to explore
viewing offline reinforcement learning (RL) as a generic sequence modeling
problem. Recent works based on this paradigm have achieved state-of-the-art
results in several of the mostly deterministic offline Atari and D4RL
benchmarks. However, because these methods jointly model the states and actions
as a single sequencing problem, they struggle to disentangle the effects of the
policy and world dynamics on the return. Thus, in adversarial or stochastic
environments, these methods lead to overly optimistic behavior that can be
dangerous in safety-critical systems like autonomous driving. In this work, we
propose a method that addresses this optimism bias by explicitly disentangling
the policy and world models, which allows us at test time to search for
policies that are robust to multiple possible futures in the environment. We
demonstrate our method's superior performance on a variety of autonomous
driving tasks in simulation.

Facial behavior analysis is a broad topic with various categories such as
facial emotion recognition, age, and gender recognition. Many studies focus on
individual tasks while the multi-task learning approach is still an open
research issue and requires more research. In this paper, we present our
solution and experiment result for the Multi-Task Learning challenge of the
Affective Behavior Analysis in-the-wild competition. The challenge is a
combination of three tasks: action unit detection, facial expression
recognition, and valance-arousal estimation. To address this challenge, we
introduce a cross-attentive module to improve multi-task learning performance.
Additionally, a facial graph is applied to capture the association among action
units. As a result, we achieve the evaluation measure of 128.8 on the
validation data provided by the organizers, which outperforms the baseline
result of 30.

Physics-informed neural networks (PINNs) have shown to be an effective tool
for solving forward and inverse problems of partial differential equations
(PDEs). PINNs embed the PDEs into the loss of the neural network, and this PDE
loss is evaluated at a set of scattered residual points. The distribution of
these points are highly important to the performance of PINNs. However, in the
existing studies on PINNs, only a few simple residual point sampling methods
have mainly been used. Here, we present a comprehensive study of two categories
of sampling: non-adaptive uniform sampling and adaptive nonuniform sampling. We
consider six uniform sampling, including (1) equispaced uniform grid, (2)
uniformly random sampling, (3) Latin hypercube sampling, (4) Halton sequence,
(5) Hammersley sequence, and (6) Sobol sequence. We also consider a resampling
strategy for uniform sampling. To improve the sampling efficiency and the
accuracy of PINNs, we propose two new residual-based adaptive sampling methods:
residual-based adaptive distribution (RAD) and residual-based adaptive
refinement with distribution (RAR-D), which dynamically improve the
distribution of residual points based on the PDE residuals during training.
Hence, we have considered a total of 10 different sampling methods, including
six non-adaptive uniform sampling, uniform sampling with resampling, two
proposed adaptive sampling, and an existing adaptive sampling. We extensively
tested the performance of these sampling methods for four forward problems and
two inverse problems in many setups. Our numerical results presented in this
study are summarized from more than 6000 simulations of PINNs. We show that the
proposed adaptive sampling methods of RAD and RAR-D significantly improve the
accuracy of PINNs with fewer residual points. The results obtained in this
study can also be used as a practical guideline in choosing sampling methods.

As the central nerve of the intelligent vehicle control system, the
in-vehicle network bus is crucial to the security of vehicle driving. One of
the best standards for the in-vehicle network is the Controller Area Network
(CAN bus) protocol. However, the CAN bus is designed to be vulnerable to
various attacks due to its lack of security mechanisms. To enhance the security
of in-vehicle networks and promote the research in this area, based upon a
large scale of CAN network traffic data with the extracted valuable features,
this study comprehensively compared fully-supervised machine learning with
semi-supervised machine learning methods for CAN message anomaly detection.
Both traditional machine learning models (including single classifier and
ensemble models) and neural network based deep learning models are evaluated.
Furthermore, this study proposed a deep autoencoder based semi-supervised
learning method applied for CAN message anomaly detection and verified its
superiority over other semi-supervised methods. Extensive experiments show that
the fully-supervised methods generally outperform semi-supervised ones as they
are using more information as inputs. Typically the developed XGBoost based
model obtained state-of-the-art performance with the best accuracy (98.65%),
precision (0.9853), and ROC AUC (0.9585) beating other methods reported in the

Reducing the representational discrepancy between source and target domains
is a key component to maximize the model generalization. In this work, we
advocate for leveraging natural language supervision for the domain
generalization task. We introduce two modules to ground visual representations
with texts containing typical reasoning of humans: (1) Visual and Textual Joint
Embedder and (2) Textual Explanation Generator. The former learns the
image-text joint embedding space where we can ground high-level
class-discriminative information into the model. The latter leverages an
explainable model and generates explanations justifying the rationale behind
its decision. To the best of our knowledge, this is the first work to leverage
the vision-and-language cross-modality approach for the domain generalization
task. Our experiments with a newly created CUB-DG benchmark dataset demonstrate
that cross-modality supervision can be successfully used to ground
domain-invariant visual representations and improve the model generalization.
Furthermore, in the large-scale DomainBed benchmark, our proposed method
achieves state-of-the-art results and ranks 1st in average performance for five
multi-domain datasets. The dataset and codes are available at
https://github.com/mswzeus/GVRT.

Transformers have emerged as a preferred model for many tasks in natural
langugage processing and vision. Recent efforts on training and deploying
Transformers more efficiently have identified many strategies to approximate
the self-attention matrix, a key module in a Transformer architecture.
Effective ideas include various prespecified sparsity patterns, low-rank basis
expansions and combinations thereof. In this paper, we revisit classical
Multiresolution Analysis (MRA) concepts such as Wavelets, whose potential value
in this setting remains underexplored thus far. We show that simple
approximations based on empirical feedback and design choices informed by
modern hardware and implementation challenges, eventually yield a MRA-based
approach for self-attention with an excellent performance profile across most
criteria of interest. We undertake an extensive set of experiments and
demonstrate that this multi-resolution scheme outperforms most efficient
self-attention proposals and is favorable for both short and long sequences.
Code is available at \url{https://github.com/mlpen/mra-attention}.

Adversarial training is a promising method to improve the robustness against
adversarial attacks. To enhance its performance, recent methods impose high
weights on the cross-entropy loss for important data points near the decision
boundary. However, these importance-aware methods are vulnerable to
sophisticated attacks, e.g., Auto-Attack. In this paper, we experimentally
investigate the cause of their vulnerability via margins between logits for the
true label and the other labels because they should be large enough to prevent
the largest logit from being flipped by the attacks. Our experiments reveal
that the histogram of the logit margins of na\"ive adversarial training has two
peaks. Thus, the levels of difficulty in increasing logit margins are roughly
divided into two: difficult samples (small logit margins) and easy samples
(large logit margins). On the other hand, only one peak near zero appears in
the histogram of importance-aware methods, i.e., they reduce the logit margins
of easy samples. To increase logit margins of difficult samples without
reducing those of easy samples, we propose switching one-versus-the-rest loss
(SOVR), which switches from cross-entropy to one-versus-the-rest loss (OVR) for
difficult samples. We derive trajectories of logit margins for a simple problem
and prove that OVR increases logit margins two times larger than the weighted
cross-entropy loss. Thus, SOVR increases logit margins of difficult samples,
unlike existing methods. We experimentally show that SOVR achieves better
robustness against Auto-Attack than importance-aware methods.

The classification of airborne laser scanning (ALS) point clouds is a
critical task of remote sensing and photogrammetry fields. Although recent deep
learning-based methods have achieved satisfactory performance, they have
ignored the unicity of the receptive field, which makes the ALS point cloud
classification remain challenging for the distinguishment of the areas with
complex structures and extreme scale variations. In this article, for the
objective of configuring multi-receptive field features, we propose a novel
receptive field fusion-and-stratification network (RFFS-Net). With a novel
dilated graph convolution (DGConv) and its extension annular dilated
convolution (ADConv) as basic building blocks, the receptive field fusion
process is implemented with the dilated and annular graph fusion (DAGFusion)
module, which obtains multi-receptive field feature representation through
capturing dilated and annular graphs with various receptive regions. The
stratification of the receptive fields with point sets of different resolutions
as the calculation bases is performed with Multi-level Decoders nested in
RFFS-Net and driven by the multi-level receptive field aggregation loss
(MRFALoss) to drive the network to learn in the direction of the supervision
labels with different resolutions. With receptive field
fusion-and-stratification, RFFS-Net is more adaptable to the classification of
regions with complex structures and extreme scale variations in large-scale ALS
point clouds. Evaluated on the ISPRS Vaihingen 3D dataset, our RFFS-Net
significantly outperforms the baseline approach by 5.3% on mF1 and 5.4% on
mIoU, accomplishing an overall accuracy of 82.1%, an mF1 of 71.6%, and an mIoU
of 58.2%. Furthermore, experiments on the LASDU dataset and the 2019 IEEE-GRSS
Data Fusion Contest dataset show that RFFS-Net achieves a new state-of-the-art
classification performance.

The ability to train deep neural networks under label noise is appealing, as
imperfectly annotated data are relatively cheaper to obtain. State-of-the-art
approaches are based on semi-supervised learning(SSL), which selects small loss
examples as clean and then applies SSL techniques for boosted performance.
However, the selection step mostly provides a medium-sized and decent-enough
clean subset, which overlooks a rich set of clean samples. In this work, we
propose a novel noisy label learning framework ProMix that attempts to maximize
the utility of clean samples for boosted performance. Key to our method, we
propose a matched high-confidence selection technique that selects those
examples having high confidence and matched prediction with its given labels.
Combining with the small-loss selection, our method is able to achieve a
precision of 99.27 and a recall of 98.22 in detecting clean samples on the
CIFAR-10N dataset. Based on such a large set of clean data, ProMix improves the
best baseline method by +2.67% on CIFAR-10N and +1.61% on CIFAR-100N datasets.
The code and data are available at https://github.com/Justherozen/ProMix

Learning to generate new images for a novel category based on only a few
images, named as few-shot image generation, has attracted increasing research
interest. Several state-of-the-art works have yielded impressive results, but
the diversity is still limited. In this work, we propose a novel Delta
Generative Adversarial Network (DeltaGAN), which consists of a reconstruction
subnetwork and a generation subnetwork. The reconstruction subnetwork captures
intra-category transformation, i.e., delta, between same-category pairs. The
generation subnetwork generates sample-specific delta for an input image, which
is combined with this input image to generate a new image within the same
category. Besides, an adversarial delta matching loss is designed to link the
above two subnetworks together. Extensive experiments on six benchmark datasets
demonstrate the effectiveness of our proposed method. Our code is available at
https://github.com/bcmi/DeltaGAN-Few-Shot-Image-Generation.

A channel should be built to transmit information from one place to another.
Imaging is 2 or higher dimensional information communication. Conventionally,
an imaging channel comprises a lens and free spaces of its both sides. The
transfer function of each part is known; thus, the response of a conventional
imaging channel is known as well. Replacing the lens with a scattering layer,
the image can still be extracted from the detection plane. That is to say, the
scattering medium reconstructs the channel for imaging. Aided by deep learning,
we find that different from the lens there are different channels in a
scattering medium, i.e., the same scattering medium can construct different
channels to match different manners of source encoding. Moreover, we found that
without a valid channel the convolution law for a shift-invariant system, i.e.,
the output is the convolution of its point spread function (PSF) and the input
object, is broken, and information cannot be transmitted onto the detection
plane. In other words, valid channels are essential to transmit image
information through even a shift-invariant system.

Federated learning (FL) provides an effective collaborative training
paradigm, allowing local agents to train a global model jointly without sharing
their local data to protect privacy. However, due to the heterogeneous nature
of local data, it is challenging to optimize or even define the fairness of the
trained global model for the agents. For instance, existing work usually
considers accuracy equity as fairness for different agents in FL, which is
limited, especially under the heterogeneous setting, since it is intuitively
"unfair" to enforce agents with high-quality data to achieve similar accuracy
to those who contribute low-quality data. In this work, we aim to address such
limitations and propose a formal fairness definition in FL, fairness via
agent-awareness (FAA), which takes different contributions of heterogeneous
agents into account. Under FAA, the performance of agents with high-quality
data will not be sacrificed just due to the existence of large amounts of
agents with low-quality data. In addition, we propose a fair FL training
algorithm based on agent clustering (FOCUS) to achieve fairness in FL measured
by FAA. Theoretically, we prove the convergence and optimality of FOCUS under
mild conditions for linear and general convex loss functions with bounded
smoothness. We also prove that FOCUS always achieves higher fairness in terms
of FAA compared with standard FedAvg under both linear and general convex loss
functions. Empirically, we evaluate FOCUS on four datasets, including synthetic
data, images, and texts under different settings, and we show that FOCUS
achieves significantly higher fairness in terms of FAA while maintaining
similar or even higher prediction accuracy compared with FedAvg and other
existing fair FL algorithms.

Human activity recognition requires the efforts to build a generalizable
model using the training datasets with the hope to achieve good performance in
test datasets. However, in real applications, the training and testing datasets
may have totally different distributions due to various reasons such as
different body shapes, acting styles, and habits, damaging the model's
generalization performance. While such a distribution gap can be reduced by
existing domain adaptation approaches, they typically assume that the test data
can be accessed in the training stage, which is not realistic. In this paper,
we consider a more practical and challenging scenario: domain-generalized
activity recognition (DGAR) where the test dataset \emph{cannot} be accessed
during training. To this end, we propose \emph{Adaptive Feature Fusion for
Activity Recognition~(AFFAR)}, a domain generalization approach that learns to
fuse the domain-invariant and domain-specific representations to improve the
model's generalization performance. AFFAR takes the best of both worlds where
domain-invariant representations enhance the transferability across domains and
domain-specific representations leverage the model discrimination power from
each domain. Extensive experiments on three public HAR datasets show its
effectiveness. Furthermore, we apply AFFAR to a real application, i.e., the
diagnosis of Children's Attention Deficit Hyperactivity Disorder~(ADHD), which
also demonstrates the superiority of our approach.

Scattering of light in complex media scrambles optical wavefronts and breaks
the principles of conventional imaging methods. For decades, researchers have
endeavored to conquer the problem by inventing approaches such as adaptive
optics, iterative wavefront shaping, and transmission matrix measurement. That
said, imaging through/into thick scattering media remains challenging to date.
With the rapid development of computing power, deep learning has been
introduced and shown potentials to reconstruct target information through
complex media or from rough surfaces. But it also fails once coming to
optically thick media where ballistic photons become negligible. Here, instead
of treating deep learning only as an image extraction method, whose
best-selling advantage is to avoid complicate physical models, we exploit it as
a tool to explore the underlying physical principles. By adjusting the weights
of ballistic and scattered photons through a random phasemask, it is found that
although deep learning can extract images from both scattered and ballistic
light, the mechanisms are different: scattering may function as an encryption
key and decryption from scattered light is key sensitive, while extraction from
ballistic light is stable. Based on this finding, it is hypothesized and
experimentally confirmed that the foundation of the generalization capability
of trained neural networks for different diffusers can trace back to the
contribution of ballistic photons, even though their weights of photon counting
in detection are not that significant. Moreover, the study may pave an avenue
for using deep learning as a probe in exploring the unknown physical principles
in various fields.

Scene text recognition is a challenging task due to the complex backgrounds
and diverse variations of text instances. In this paper, we propose a novel
Semantic GAN and Balanced Attention Network (SGBANet) to recognize the texts in
scene images. The proposed method first generates the simple semantic feature
using Semantic GAN and then recognizes the scene text with the Balanced
Attention Module. The Semantic GAN aims to align the semantic feature
distribution between the support domain and target domain. Different from the
conventional image-to-image translation methods that perform at the image
level, the Semantic GAN performs the generation and discrimination on the
semantic level with the Semantic Generator Module (SGM) and Semantic
Discriminator Module (SDM). For target images (scene text images), the Semantic
Generator Module generates simple semantic features that share the same feature
distribution with support images (clear text images). The Semantic
Discriminator Module is used to distinguish the semantic features between the
support domain and target domain. In addition, a Balanced Attention Module is
designed to alleviate the problem of attention drift. The Balanced Attention
Module first learns a balancing parameter based on the visual glimpse vector
and semantic glimpse vector, and then performs the balancing operation for
obtaining a balanced glimpse vector. Experiments on six benchmarks, including
regular datasets, i.e., IIIT5K, SVT, ICDAR2013, and irregular datasets, i.e.,
ICDAR2015, SVTP, CUTE80, validate the effectiveness of our proposed method.

We present SplitMixer, a simple and lightweight isotropic MLP-like
architecture, for visual recognition. It contains two types of interleaving
convolutional operations to mix information across spatial locations (spatial
mixing) and channels (channel mixing). The first one includes sequentially
applying two depthwise 1D kernels, instead of a 2D kernel, to mix spatial
information. The second one is splitting the channels into overlapping or
non-overlapping segments, with or without shared parameters, and applying our
proposed channel mixing approaches or 3D convolution to mix channel
information. Depending on design choices, a number of SplitMixer variants can
be constructed to balance accuracy, the number of parameters, and speed. We
show, both theoretically and experimentally, that SplitMixer performs on par
with the state-of-the-art MLP-like models while having a significantly lower
number of parameters and FLOPS. For example, without strong data augmentation
and optimization, SplitMixer achieves around 94% accuracy on CIFAR-10 with only
0.28M parameters, while ConvMixer achieves the same accuracy with about 0.6M
parameters. The well-known MLP-Mixer achieves 85.45% with 17.1M parameters. On
CIFAR-100 dataset, SplitMixer achieves around 73% accuracy, on par with
ConvMixer, but with about 52% fewer parameters and FLOPS. We hope that our
results spark further research towards finding more efficient vision
architectures and facilitate the development of MLP-like models. Code is
available at https://github.com/aliborji/splitmixer.

Facial forgery by deepfakes has raised severe societal concerns. Several
solutions have been proposed by the vision community to effectively combat the
misinformation on the internet via automated deepfake detection systems. Recent
studies have demonstrated that facial analysis-based deep learning models can
discriminate based on protected attributes. For the commercial adoption and
massive roll-out of the deepfake detection technology, it is vital to evaluate
and understand the fairness (the absence of any prejudice or favoritism) of
deepfake detectors across demographic variations such as gender and race. As
the performance differential of deepfake detectors between demographic
subgroups would impact millions of people of the deprived sub-group. This paper
aims to evaluate the fairness of the deepfake detectors across males and
females. However, existing deepfake datasets are not annotated with demographic
labels to facilitate fairness analysis. To this aim, we manually annotated
existing popular deepfake datasets with gender labels and evaluated the
performance differential of current deepfake detectors across gender. Our
analysis on the gender-labeled version of the datasets suggests (a) current
deepfake datasets have skewed distribution across gender, and (b) commonly
adopted deepfake detectors obtain unequal performance across gender with mostly
males outperforming females. Finally, we contributed a gender-balanced and
annotated deepfake dataset, GBDF, to mitigate the performance differential and
to promote research and development towards fairness-aware deep fake detectors.
The GBDF dataset is publicly available at: https://github.com/aakash4305/GBDF

Solutions for rapid prioritization of different ransomware have been raised
to formulate fast response plans to minimize socioeconomic damage from the
massive growth of ransomware attacks in recent years. To address this concern,
we propose a ransomware triage approach that can rapidly classify and
prioritize different ransomware classes. Our Siamese Neural Network (SNN) based
approach utilizes a pre-trained ResNet18 network in a meta-learning fashion to
reduce the biases in weight and parameter calculations typically associated
with a machine learning model trained with a limited number of training
samples. Instead of image features typically used as inputs to many existing
machine learning-based triage applications, our approach uses the entropy
features directly obtained from the ransomware binary files to improve feature
representation, resilient to obfuscation noise, and computationally less
expensive. Our triage approach can classify ransomware samples into the correct
classes if the ransomware features significantly match known ransomware
profiles. Our evaluation shows that this classification part of our proposed
approach achieves the accuracy exceeding 88% and outperforms other similar
classification only machine learning-based approaches. In addition, we offer a
new triage strategy based on the normalized and regularized weight ratios that
evaluate the level of similarity matching across ransomware classes to identify
any risky and unknown ransomware (e.g., zero-day attacks) so that a rapid
further analysis can be conducted

Combining many cross-sectional stock return predictors, as in machine
learning, often requires imputing missing values. We compare imputation using
the expectation-maximization algorithm with simple ad-hoc methods.
Surprisingly, expectation-maximization and ad-hoc methods lead to similar
results. This similarity happens because predictors are largely independent:
Correlations cluster near zero and more than 10 principal components are
required to span 50% of total variance. Independence implies observed
predictors are uninformative about missing predictors, making ad-hoc methods
valid. In an out-of-sample principal components (PC) regression test, 50 PCs
are required to capture equal-weighted long-short expected returns (30 PCs
value-weighted), regardless of the imputation method.

Machine learning methods have been lately used to solve differential
equations and dynamical systems. These approaches have been developed into a
novel research field known as scientific machine learning in which techniques
such as deep neural networks and statistical learning are applied to classical
problems of applied mathematics. Because neural networks provide an
approximation capability, computational parameterization through machine
learning and optimization methods achieve noticeable performance when solving
various partial differential equations (PDEs). In this paper, we develop a
novel numerical algorithm that incorporates machine learning and artificial
intelligence to solve PDEs. In particular, we propose an unsupervised machine
learning algorithm based on the Legendre-Galerkin neural network to find an
accurate approximation to the solution of different types of PDEs. The proposed
neural network is applied to the general 1D and 2D PDEs as well as singularly
perturbed PDEs that possess boundary layer behavior.

We study the convergence properties, in Hellinger and related distances, of
nonparametric density estimators based on measure transport. These estimators
represent the measure of interest as the pushforward of a chosen reference
distribution under a transport map, where the map is chosen via a maximum
likelihood objective (equivalently, minimizing an empirical Kullback-Leibler
loss) or a penalized version thereof. We establish concentration inequalities
for a general class of penalized measure transport estimators, by combining
techniques from M-estimation with analytical properties of the transport-based
density representation. We then demonstrate the implications of our theory for
the case of triangular Knothe-Rosenblatt (KR) transports on the $d$-dimensional
unit cube, and show that both penalized and unpenalized versions of such
estimators achieve minimax optimal convergence rates over H\"older classes of
densities. Specifically, we establish optimal rates for unpenalized
nonparametric maximum likelihood estimation over bounded H\"older-type balls,
and then for certain Sobolev-penalized estimators and sieved wavelet
estimators.

During the Covid, online meetings have become an indispensable part of our
lives. This trend is likely to continue due to their convenience and broad
reach. However, background noise from other family members, roommates,
office-mates not only degrades the voice quality but also raises serious
privacy issues. In this paper, we develop a novel system, called Spatial Aware
Multi-task learning-based Separation (SAMS), to extract audio signals from the
target user during teleconferencing. Our solution consists of three novel
components: (i) generating fine-grained location embeddings from the user's
voice and inaudible tracking sound, which contains the user's position and rich
multipath information, (ii) developing a source separation neural network using
multi-task learning to jointly optimize source separation and location, and
(iii) significantly speeding up inference to provide a real-time guarantee. Our
testbed experiments demonstrate the effectiveness of our approach

Recently, self-supervised pre-training has advanced Vision Transformers on
various tasks w.r.t. different data modalities, e.g., image and 3D point cloud
data. In this paper, we explore this learning paradigm for 3D mesh data
analysis based on Transformers. Since applying Transformer architectures to new
modalities is usually non-trivial, we first adapt Vision Transformer to 3D mesh
data processing, i.e., Mesh Transformer. In specific, we divide a mesh into
several non-overlapping local patches with each containing the same number of
faces and use the 3D position of each patch's center point to form positional
embeddings. Inspired by MAE, we explore how pre-training on 3D mesh data with
the Transformer-based structure benefits downstream 3D mesh analysis tasks. We
first randomly mask some patches of the mesh and feed the corrupted mesh into
Mesh Transformers. Then, through reconstructing the information of masked
patches, the network is capable of learning discriminative representations for
mesh data. Therefore, we name our method MeshMAE, which can yield
state-of-the-art or comparable performance on mesh analysis tasks, i.e.,
classification and segmentation. In addition, we also conduct comprehensive
ablation studies to show the effectiveness of key designs in our method.

Extreme value statistics is the max analogue of classical statistics, while
tropical geometry is the max analogue of classical geometry. In this paper, we
review recent work where insights from tropical geometry were used to develop
new, efficient learning algorithms with leading performance on benchmark
datasets in extreme value statistics. We give intuition, backed by performances
on benchmark datasets, for why and when causal inference for extremes should be
employed over classical methods. Finally, we list some open problems at the
intersection of causal inference, tropical geometry and deep learning.

Federated learning (FL) enables distributed devices to jointly train a shared
model while keeping the training data local. Different from the horizontal FL
(HFL) setting where each client has partial data samples, vertical FL (VFL),
which allows each client to collect partial features, has attracted intensive
research efforts recently. In this paper, we identified two challenges that
state-of-the-art VFL frameworks are facing: (1) some works directly average the
learned feature embeddings and therefore might lose the unique properties of
each local feature set; (2) server needs to communicate gradients with the
clients for each training step, incurring high communication cost that leads to
rapid consumption of privacy budgets. In this paper, we aim to address the
above challenges and propose an efficient VFL with multiple linear heads (VIM)
framework, where each head corresponds to local clients by taking the separate
contribution of each client into account. In addition, we propose an
Alternating Direction Method of Multipliers (ADMM)-based method to solve our
optimization problem, which reduces the communication cost by allowing multiple
local updates in each step, and thus leads to better performance under
differential privacy. We consider various settings including VFL with model
splitting and without model splitting. For both settings, we carefully analyze
the differential privacy mechanism for our framework. Moreover, we show that a
byproduct of our framework is that the weights of learned linear heads reflect
the importance of local clients. We conduct extensive evaluations and show that
on four real-world datasets, VIM achieves significantly higher performance and
faster convergence compared with state-of-the-arts. We also explicitly evaluate
the importance of local clients and show that VIM enables functionalities such
as client-level explanation and client denoising.

Weakly supervised object localization (WSOL) aims to learn representations
that encode object location using only image-level category labels. However,
many objects can be labeled at different levels of granularity. Is it an
animal, a bird, or a great horned owl? Which image-level labels should we use?
In this paper we study the role of label granularity in WSOL. To facilitate
this investigation we introduce iNatLoc500, a new large-scale fine-grained
benchmark dataset for WSOL. Surprisingly, we find that choosing the right
training label granularity provides a much larger performance boost than
choosing the best WSOL algorithm. We also show that changing the label
granularity can significantly improve data efficiency.

Direct localization (DLOC) methods, which use the observed data to localize a
source at an unknown position in a one-step procedure, generally outperform
their indirect two-step counterparts (e.g., using time-difference of arrivals).
However, underwater acoustic DLOC methods require prior knowledge of the
environment, and are computationally costly, hence slow. We propose, what is to
the best of our knowledge, the first data-driven DLOC method. Inspired by
classical and contemporary optimal model-based DLOC solutions, and leveraging
the capabilities of convolutional neural networks (CNNs), we devise a holistic
CNN-based solution. Our method includes a specifically-tailored input
structure, architecture, loss function, and a progressive training procedure,
which are of independent interest in the broader context of machine learning.
We demonstrate that our method outperforms attractive alternatives, and
asymptotically matches the performance of an oracle optimal model-based
solution.

Quantum federated learning (QFL) has recently received increasing attention,
(FL). In contrast to the existing static QFL methods, we propose slimmable QFL
(SlimQFL) in this article, which is a dynamic QFL framework that can cope with
time-varying communication channels and computing energy limitations. This is
made viable by leveraging the unique nature of a QNN where its angle parameters
and pole parameters can be separately trained and dynamically exploited.
Simulation results corroborate that SlimQFL achieves higher classification
accuracy than Vanilla QFL, particularly under poor channel conditions on
average.

As machine learning (ML) is more tightly woven into society, it is imperative
that we better characterize ML's strengths and limitations if we are to employ
it responsibly. Existing benchmark environments for ML, such as board and video
games, offer well-defined benchmarks for progress, but constituent tasks are
often complex, and it is frequently unclear how task characteristics contribute
to overall difficulty for the machine learner. Likewise, without a systematic
assessment of how task characteristics influence difficulty, it is challenging
to draw meaningful connections between performance in different benchmark
environments. We introduce a novel benchmark environment that offers an
enormous range of ML challenges and enables precise examination of how task
elements influence practical difficulty. The tool frames learning tasks as a
"board-clearing game," which we call the Game of Hidden Rules (GOHR). The
environment comprises an expressive rule language and a captive server
environment that can be installed locally. We propose a set of benchmark
rule-learning tasks and plan to support a performance leader-board for
researchers interested in attempting to learn our rules. GOHR complements
existing environments by allowing fine, controlled modifications to tasks,
enabling experimenters to better understand how each facet of a given learning
task contributes to its practical difficulty for an arbitrary ML algorithm.

With the growing complexity of big data workloads that require abundant data
and computation, data centers consume a tremendous amount of power daily. In an
effort to minimize data center power consumption, several studies developed
power models that can be used for job scheduling either reducing the number of
active servers or balancing workloads across servers at their peak energy
efficiency points. Due to increasing software and hardware heterogeneity, we
observed that there is no single power model that works the best for all server
conditions. Some complicated machine learning models themselves incur
performance and power overheads and hence it is not desirable to use them
frequently. There are no power models that consider containerized workload
execution. In this paper, we propose a hybrid server power model, Hydra, that
considers both prediction accuracy and performance overhead. Hydra dynamically
chooses the best power model for the given server conditions. Compared with
state-of-the-art solutions, Hydra outperforms across all compute-intensity
levels on heterogeneous servers.

In recent years, significant progress has been achieved for 3D object
detection on point clouds thanks to the advances in 3D data collection and deep
learning techniques. Nevertheless, 3D scenes exhibit a lot of variations and
are prone to sensor inaccuracies as well as information loss during
pre-processing. Thus, it is crucial to design techniques that are robust
against these variations. This requires a detailed analysis and understanding
of the effect of such variations. This work aims to analyze and benchmark
popular point-based 3D object detectors against several data corruptions. To
the best of our knowledge, we are the first to investigate the robustness of
point-based 3D object detectors. To this end, we design and evaluate
corruptions that involve data addition, reduction, and alteration. We further
study the robustness of different modules against local and global variations.
Our experimental results reveal several intriguing findings. For instance, we
show that methods that integrate Transformers at a patch or object level lead
to increased robustness, compared to using Transformers at the point level.

This paper describes our submission to the fourth Affective Behavior Analysis
(ABAW) competition. We proposed a hybrid CNN-Transformer model for the
Multi-Task-Learning (MTL) and Learning from Synthetic Data (LSD) task.
Experimental results on validation dataset shows that our method achieves
better performance than baseline model, which verifies that the effectiveness
of proposed network.

An important unresolved challenge in the theory of regularization is to set
the regularization coefficients of popular techniques like the ElasticNet with
general provable guarantees. We consider the problem of tuning the
regularization parameters of Ridge regression, LASSO, and the ElasticNet across
multiple problem instances, a setting that encompasses both cross-validation
and multi-task hyperparameter optimization. We obtain a novel structural result
for the ElasticNet which characterizes the loss as a function of the tuning
parameters as a piecewise-rational function with algebraic boundaries. We use
this to bound the structural complexity of the regularized loss functions and
show generalization guarantees for tuning the ElasticNet regression
coefficients in the statistical setting. We also consider the more challenging
online learning setting, where we show vanishing average expected regret
relative to the optimal parameter pair. We further extend our results to tuning
classification algorithms obtained by thresholding regression fits regularized
by Ridge, LASSO, or ElasticNet. Our results are the first general
learning-theoretic guarantees for this important class of problems that avoid
strong assumptions on the data distribution. Furthermore, our guarantees hold
for both validation and popular information criterion objectives.

Deep neural network quantization with adaptive bitwidths has gained
increasing attention due to the ease of model deployment on various platforms
with different resource budgets. In this paper, we propose a meta-learning
approach to achieve this goal. Specifically, we propose MEBQAT, a simple yet
effective way of bitwidth-adaptive quantization aware training (QAT) where
meta-learning is effectively combined with QAT by redefining meta-learning
tasks to incorporate bitwidths. After being deployed on a platform, MEBQAT
allows the (meta-)trained model to be quantized to any candidate bitwidth then
helps to conduct inference without much accuracy drop from quantization.
Moreover, with a few-shot learning scenario, MEBQAT can also adapt a model to
any bitwidth as well as any unseen target classes by adding conventional
optimization or metric-based meta-learning. We design variants of MEBQAT to
support both (1) a bitwidth-adaptive quantization scenario and (2) a new
few-shot learning scenario where both quantization bitwidths and target classes
are jointly adapted. We experimentally demonstrate their validity in multiple
QAT schemes. By comparing their performance to (bitwidth-dedicated) QAT,
existing bitwidth adaptive QAT and vanilla meta-learning, we find that merging
bitwidths into meta-learning tasks achieves a higher level of robustness.

This work in progress aims to provide a unified introduction to statistical
learning, building up slowly from classical models like the GMM and HMM to
modern neural networks like the VAE and diffusion models. There are today many
internet resources that explain this or that new machine-learning algorithm in
isolation, but they do not (and cannot, in so brief a space) connect these
algorithms with each other or with the classical literature on statistical
models, out of which the modern algorithms emerged. Also conspicuously lacking
is a single notational system which, although unfazing to those already
familiar with the material (like the authors of these posts), raises a
significant barrier to the novice's entry. Likewise, I have aimed to assimilate
the various models, wherever possible, to a single framework for inference and
learning, showing how (and why) to change one model into another with minimal
alteration (some of them novel, others from the literature).
  Some background is of course necessary. I have assumed the reader is familiar
with basic multivariable calculus, probability and statistics, and linear
algebra. The goal of this book is certainly not completeness, but rather to
draw a more or less straight-line path from the basics to the extremely
powerful new models of the last decade. The goal then is to complement, not
replace, such comprehensive texts as Bishop's \emph{Pattern Recognition and
Machine Learning}, which is now 15 years old.

Recent research has shown that controllable image generation based on
pre-trained GANs can benefit a wide range of computer vision tasks. However,
less attention has been devoted to 3D vision tasks. In light of this, we
propose a novel image-conditioned neural implicit field, which can leverage 2D
supervisions from GAN-generated multi-view images and perform the single-view
reconstruction of generic objects. Firstly, a novel offline StyleGAN-based
generator is presented to generate plausible pseudo images with full control
over the viewpoint. Then, we propose to utilize a neural implicit function,
along with a differentiable renderer to learn 3D geometry from pseudo images
with object masks and rough pose initializations. To further detect the
unreliable supervisions, we introduce a novel uncertainty module to predict
uncertainty maps, which remedy the negative effect of uncertain regions in
pseudo images, leading to a better reconstruction performance. The
effectiveness of our approach is demonstrated through superior single-view 3D
reconstruction results of generic objects.

In this work, we develop a neural network-based method to convert a noisy
motion signal generated from segmenting rebinned list-mode cardiac SPECT
images, to that of a high-quality surrogate signal, such as those seen from
external motion tracking systems (EMTs). This synthetic surrogate will be used
as input to our pre-existing motion correction technique developed for EMT
surrogate signals. In our method, we test two families of neural networks to
translate noisy internal motion to external surrogate: 1) fully connected
networks and 2) convolutional neural networks. Our dataset consists of cardiac
perfusion SPECT acquisitions for which cardiac motion was estimated (input:
center-of-count-mass - COM signals) in conjunction with a respiratory surrogate
motion signal acquired using a commercial Vicon Motion Tracking System (GT: EMT
signals). We obtained an average R-score of 0.76 between the predicted
surrogate and the EMT signal. Our goal is to lay a foundation to guide the
optimization of neural networks for respiratory motion correction from SPECT
without the need for an EMT.

Magnetic Resonance Spectroscopic Imaging (MRSI) is an essential tool for
quantifying metabolites in the body, but the low spatial resolution limits its
clinical applications. Deep learning-based super-resolution methods provided
promising results for improving the spatial resolution of MRSI, but the
super-resolved images are often blurry compared to the experimentally-acquired
high-resolution images. Attempts have been made with the generative adversarial
networks to improve the image visual quality. In this work, we consider another
type of generative model, the flow-based model, of which the training is more
stable and interpretable compared to the adversarial networks. Specifically, we
propose a flow-based enhancer network to improve the visual quality of
super-resolution MRSI. Different from previous flow-based models, our enhancer
network incorporates anatomical information from additional image modalities
(MRI) and uses a learnable base distribution. In addition, we impose a guide
loss and a data-consistency loss to encourage the network to generate images
with high visual quality while maintaining high fidelity. Experiments on a
1H-MRSI dataset acquired from 25 high-grade glioma patients indicate that our
enhancer network outperforms the adversarial networks and the baseline
flow-based methods. Our method also allows visual quality adjustment and

Although significant advances have been made in face recognition (FR), FR in
unconstrained environments remains challenging due to the domain gap between
the semi-constrained training datasets and unconstrained testing scenarios. To
address this problem, we propose a controllable face synthesis model (CFSM)
that can mimic the distribution of target datasets in a style latent space.
CFSM learns a linear subspace with orthogonal bases in the style latent space
with precise control over the diversity and degree of synthesis. Furthermore,
the pre-trained synthesis model can be guided by the FR model, making the
resulting images more beneficial for FR model training. Besides, target dataset
distributions are characterized by the learned orthogonal bases, which can be
utilized to measure the distributional similarity among face datasets. Our
approach yields significant performance gains on unconstrained benchmarks, such
as IJB-B, IJB-C, TinyFace and IJB-S (+5.76% Rank1).

Scene classification has established itself as a challenging research
problem. Compared to images of individual objects, scene images could be much
more semantically complex and abstract. Their difference mainly lies in the
level of granularity of recognition. Yet, image recognition serves as a key
pillar for the good performance of scene recognition as the knowledge attained
from object images can be used for accurate recognition of scenes. The existing
scene recognition methods only take the category label of the scene into
consideration. However, we find that the contextual information that contains
detailed local descriptions are also beneficial in allowing the scene
recognition model to be more discriminative. In this paper, we aim to improve
scene recognition using attribute and category label information encoded in
objects. Based on the complementarity of attribute and category labels, we
propose a Multi-task Attribute-Scene Recognition (MASR) network which learns a
category embedding and at the same time predicts scene attributes. Attribute
acquisition and object annotation are tedious and time consuming tasks. We
tackle the problem by proposing a partially supervised annotation strategy in
which human intervention is significantly reduced. The strategy provides a much
more cost-effective solution to real world scenarios, and requires considerably
less annotation efforts. Moreover, we re-weight the attribute predictions
considering the level of importance indicated by the object detected scores.
Using the proposed method, we efficiently annotate attribute labels for four
large-scale datasets, and systematically investigate how scene and attribute
recognition benefit from each other. The experimental results demonstrate that
MASR learns a more discriminative representation and achieves competitive
recognition performance compared to the state-of-the-art methods

The information content of atomic resolution scanning transmission electron
microscopy (STEM) images can often be reduced to a handful of parameters
describing each atomic column, chief amongst which is the column position.
Neural networks (NNs) are a high performance, computationally efficient method
to automatically locate atomic columns in images, which has led to a profusion
of NN models and associated training datasets. We have developed a benchmark
dataset of simulated and experimental STEM images and used it to evaluate the
performance of two sets of recent NN models for atom location in STEM images.
Both models exhibit high performance for images of varying quality from several
different crystal lattices. However, there are important differences in
performance as a function of image quality, and both models perform poorly for
images outside the training data, such as interfaces with large difference in
background intensity. Both the benchmark dataset and the models are available
using the Foundry service for dissemination, discovery, and reuse of machine

Video Anomaly Detection (VAD) is an important topic in computer vision.
Motivated by the recent advances in self-supervised learning, this paper
addresses VAD by solving an intuitive yet challenging pretext task, i.e.,
spatio-temporal jigsaw puzzles, which is cast as a multi-label fine-grained
classification problem. Our method exhibits several advantages over existing
works: 1) the spatio-temporal jigsaw puzzles are decoupled in terms of spatial
and temporal dimensions, responsible for capturing highly discriminative
appearance and motion features, respectively; 2) full permutations are used to
provide abundant jigsaw puzzles covering various difficulty levels, allowing
the network to distinguish subtle spatio-temporal differences between normal
and abnormal events; and 3) the pretext task is tackled in an end-to-end manner
without relying on any pre-trained models. Our method outperforms
state-of-the-art counterparts on three public benchmarks. Especially on
ShanghaiTech Campus, the result is superior to reconstruction and
prediction-based methods by a large margin.

Autonomous intelligent agents deployed to the real-world need to be robust
against adversarial attacks on sensory inputs. Existing work in reinforcement
learning focuses on minimum-norm perturbation attacks, which were originally
introduced to mimic a notion of perceptual invariance in computer vision. In
this paper, we note that such minimum-norm perturbation attacks can be
trivially detected by victim agents, as these result in observation sequences
that are not consistent with the victim agent's actions. Furthermore, many
real-world agents, such as physical robots, commonly operate under human
supervisors, which are not susceptible to such perturbation attacks. As a
result, we propose to instead focus on illusionary attacks, a novel form of
attack that is consistent with the world model of the victim agent. We provide
a formal definition of this novel attack framework, explore its characteristics
under a variety of conditions, and conclude that agents must seek realism
feedback to be robust to illusionary attacks.

Bone age assessment (BAA) is a standard method for determining the age
difference between skeletal and chronological age. Manual processes are
complicated and necessitate the expertise of experts. This is where deep
learning comes into play. In this study, pre-trained models like VGG-16,
InceptionV3, XceptionNet, and MobileNet are used to assess the bone age of the
input data, and their mean average errors are compared and evaluated to see
which model predicts the best.

Machine learning models and applications in materials design and discovery
typically involve the use of feature representations or "descriptors" followed
by a learning algorithm that maps them to a user-desired property of interest.
Most popular mathematical formulation-based descriptors are not unique across
atomic environments or suffer from transferability issues across different
application domains and/or material classes. In this work, we introduce the
Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph
attention-based architecture to learn unique feature representations and
perform classification of materials across multiple scales (from atomic to
mesoscale) and diverse classes ranging from metals, oxides, non-metals and even
hierarchical materials such as zeolites and semi ordered materials such as
mesophases. We first demonstrate a case study where the classification is based
on a global, structure-level representation such as space group and structural
dimensionality (e.g., bulk, 2D, clusters etc.). Using representative materials
such as polycrystals and zeolites, we next demonstrate the transferability of
our network in successfully performing local atom-level classification tasks,
such as grain boundary identification and other heterointerfaces. We also
demonstrate classification in (thermal) noisy dynamical environments using a
representative example of crystal nucleation and growth of a zeolite polymorph
from an amorphous synthesis mixture. Finally, we characterize the formation of
a binary mesophase and its phase transitions and the growth of ice,
demonstrating the performance of CEGANN in systems with thermal noise and
compositional diversity. Overall, our approach is agnostic to the material type
and allows for multiscale classification of features ranging from atomic-scale
crystal structures to heterointerfaces to microscale grain boundaries.

Model-based reconstruction employing the time separation technique (TST) was
found to improve dynamic perfusion imaging of the liver using C-arm cone-beam
computed tomography (CBCT). To apply TST using prior knowledge extracted from
CT perfusion data, the liver should be accurately segmented from the CT scans.
Reconstructions of primary and model-based CBCT data need to be segmented for
proper visualisation and interpretation of perfusion maps. This research
proposes Turbolift learning, which trains a modified version of the multi-scale
Attention UNet on different liver segmentation tasks serially, following the
order of the trainings CT, CBCT, CBCT TST - making the previous trainings act
as pre-training stages for the subsequent ones - addressing the problem of
limited number of datasets for training. For the final task of liver
segmentation from CBCT TST, the proposed method achieved an overall Dice scores
of 0.874$\pm$0.031 and 0.905$\pm$0.007 in 6-fold and 4-fold cross-validation
experiments, respectively - securing statistically significant improvements
over the model, which was trained only for that task. Experiments revealed that
Turbolift not only improves the overall performance of the model but also makes
it robust against artefacts originating from the embolisation materials and
truncation artefacts. Additionally, in-depth analyses confirmed the order of
the segmentation tasks. This paper shows the potential of segmenting the liver
from CT, CBCT, and CBCT TST, learning from the available limited training data,
which can possibly be used in the future for the visualisation and evaluation
of the perfusion maps for the treatment evaluation of liver diseases.

With the abundance of available data, many enterprises seek to implement
data-driven prescriptive analytics to help them make informed decisions. These
prescriptive policies need to satisfy operational constraints, and proactively
eliminate rule conflicts, both of which are ubiquitous in practice. It is also
desirable for them to be simple and interpretable, so they can be easily
verified and implemented. Existing approaches from the literature center around
constructing variants of prescriptive decision trees to generate interpretable
policies. However, none of the existing methods are able to handle constraints.
In this paper, we propose a scalable method that solves the constrained
prescriptive policy generation problem. We introduce a novel path-based
mixed-integer program (MIP) formulation which identifies a (near) optimal
policy efficiently via column generation. The policy generated can be
represented as a multiway-split tree which is more interpretable and
informative than a binary-split tree due to its shorter rules. We demonstrate
the efficacy of our method with extensive experiments on both synthetic and

Clustering is a ubiquitous tool in unsupervised learning. Most of the
existing self-supervised representation learning methods typically cluster
samples based on visually dominant features. While this works well for
image-based self-supervision, it often fails for videos, which require
understanding motion rather than focusing on background. Using optical flow as
complementary information to RGB can alleviate this problem. However, we
observe that a naive combination of the two views does not provide meaningful
gains. In this paper, we propose a principled way to combine two views.
Specifically, we propose a novel clustering strategy where we use the initial
cluster assignment of each view as prior to guide the final cluster assignment
of the other view. This idea will enforce similar cluster structures for both
views, and the formed clusters will be semantically abstract and robust to
noisy inputs coming from each individual view. Additionally, we propose a novel
regularization strategy to address the feature collapse problem, which is
common in cluster-based self-supervised learning methods. Our extensive
evaluation shows the effectiveness of our learned representations on downstream
tasks, e.g., video retrieval and action recognition. Specifically, we
outperform the state of the art by 7% on UCF and 4% on HMDB for video
retrieval, and 5% on UCF and 6% on HMDB for video classification

Each year, thousands of people learn new visual categorization tasks --
radiologists learn to recognize tumors, birdwatchers learn to distinguish
similar species, and crowd workers learn how to annotate valuable data for
applications like autonomous driving. As humans learn, their brain updates the
visual features it extracts and attend to, which ultimately informs their final
classification decisions. In this work, we propose a novel task of tracing the
evolving classification behavior of human learners as they engage in
challenging visual classification tasks. We propose models that jointly extract
the visual features used by learners as well as predicting the classification
functions they utilize. We collect three challenging new datasets from real
human learners in order to evaluate the performance of different visual
knowledge tracing methods. Our results show that our recurrent models are able
to predict the classification behavior of human learners on three challenging
medical image and species identification tasks.

This paper considers the problem of unsupervised 3D object reconstruction
from in-the-wild single-view images. Due to ambiguity and intrinsic
ill-posedness, this problem is inherently difficult to solve and therefore
requires strong regularization to achieve disentanglement of different latent
factors. Unlike existing works that introduce explicit regularizations into
objective functions, we look into a different space for implicit regularization
-- the structure of latent space. Specifically, we restrict the structure of
latent space to capture a topological causal ordering of latent factors (i.e.,
representing causal dependency as a directed acyclic graph). We first show that
different causal orderings matter for 3D reconstruction, and then explore
several approaches to find a task-dependent causal factor ordering. Our
experiments demonstrate that the latent space structure indeed serves as an
implicit regularization and introduces an inductive bias beneficial for

Accurate force/torque estimation is essential for applications such as
powered exoskeletons, robotics, and rehabilitation. However, force/torque
estimation under dynamic conditions is a challenging due to changing joint
angles, force levels, muscle lengths, and movement speeds. We propose a novel
method to accurately model the generated force under isotonic, isokinetic
(quasi-dynamic), and fully dynamic conditions. Our solution uses a deep
multimodal CNN to learn from multimodal EMG-IMU data and estimate the generated
force for elbow flexion and extension, for both intra- and inter-subject
schemes. The proposed deep multimodal CNN extracts representations from EMG (in
time and frequency domains) and IMU (in time domain) and aggregates them to
obtain an effective embedding for force estimation. We describe a new dataset
containing EMG, IMU, and output force data, collected under a number of
different experimental conditions, and use this dataset to evaluate our
proposed method. The results show the robustness of our approach in comparison
to other baseline methods as well as those in the literature, in different
experimental setups and validation schemes. The obtained $R^2$ values are
0.91$\pm$0.034, 0.87$\pm$0.041, and 0.81$\pm$0.037 for the intra-subject and
0.81$\pm$0.048, 0.64$\pm$0.037, and 0.59$\pm$0.042 for the inter-subject
scheme, during isotonic, isokinetic, and dynamic contractions, respectively.
Additionally, our results indicate that force estimation improves significantly
when the kinematic information (IMU data) is included. Average improvements of
13.95\%, 118.18\%, and 50.0\% (intra-subject) and 28.98\%, 41.18\%, and
137.93\% (inter-subject) for isotonic, isokinetic, and dynamic contractions
respectively are achieved.

Machine learning models fail to perform well on real-world applications when
1) the category distribution P(Y) of the training dataset suffers from
long-tailed distribution and 2) the test data is drawn from different
conditional distributions P(X|Y). Existing approaches cannot handle the
scenario where both issues exist, which however is common for real-world
applications. In this study, we took a step forward and looked into the problem
of long-tailed classification under domain shifts. We designed three novel core
functional blocks including Distribution Calibrated Classification Loss,
Visual-Semantic Mapping and Semantic-Similarity Guided Augmentation.
Furthermore, we adopted a meta-learning framework which integrates these three
blocks to improve domain generalization on unseen target domains. Two new
datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS. We
evaluated our method on the two datasets and extensive experimental results
demonstrate that our proposed method can achieve superior performance over
state-of-the-art long-tailed/domain generalization approaches and the
combinations. Source codes and datasets can be found at our project page
https://xiaogu.site/LTDS.

Structural node embeddings, vectors capturing local connectivity information
for each node in a graph, have many applications in data mining and machine
learning, e.g., network alignment and node classification, clustering and
anomaly detection. For the analysis of directed graphs, e.g., transactions
graphs, communication networks and social networks, the capability to capture
directional information in the structural node embeddings is highly desirable,
as is scalability of the embedding extraction method. Most existing methods are
nevertheless only designed for undirected graph. Therefore, we present
Digraphwave -- a scalable algorithm for extracting structural node embeddings
on directed graphs. The Digraphwave embeddings consist of compressed diffusion
pattern signatures, which are twice enhanced to increase their discriminate
capacity. By proving a lower bound on the heat contained in the local vicinity
of a diffusion initialization node, theoretically justified diffusion timescale
values are established, and Digraphwave is left with only two easy-to-interpret
hyperparameters: the embedding dimension and a neighbourhood resolution
specifier. In our experiments, the two embedding enhancements, named
transposition and aggregation, are shown to lead to a significant increase in
macro F1 score for classifying automorphic identities, with Digraphwave
outperforming all other structural embedding baselines. Moreover, Digraphwave
either outperforms or matches the performance of all baselines on real graph
datasets, displaying a particularly large performance gain in a network
alignment task, while also being scalable to graphs with millions of nodes and
edges, running up to 30x faster than a previous diffusion pattern based method
and with a fraction of the memory consumption.

We present a novel Learning from Demonstration (LfD) method, Deformable
Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks
using states or images as inputs, given expert demonstrations. Our method uses
demonstrations in three different ways, and balances the trade-off between
exploring the environment online and using guidance from experts to explore
high dimensional spaces effectively. We test DMfD on a set of representative
manipulation tasks for a 1-dimensional rope and a 2-dimensional cloth from the
SoftGym suite of tasks, each with state and image observations. Our method
exceeds baseline performance by up to 12.9% for state-based tasks and up to
33.44% on image-based tasks, with comparable or better robustness to
randomness. Additionally, we create two challenging environments for folding a
2D cloth using image-based observations, and set a performance benchmark for
them. We deploy DMfD on a real robot with a minimal loss in normalized
performance during real-world execution compared to simulation (~6%). Source
code is on github.com/uscresl/dmfd

The investigation of finite temperature properties using Monte-Carlo (MC)
methods requires a large number of evaluations of the system's Hamiltonian to
sample the phase space needed to obtain physical observables as function of
temperature. DFT calculations can provide accurate evaluations of the energies,
but they are too computationally expensive for routine simulations. To
circumvent this problem, machine-learning (ML) based surrogate models have been
developed and implemented on high-performance computing (HPC) architectures. In
this paper, we describe two ML methods (linear mixing model and HydraGNN) as
surrogates for first principles density functional theory (DFT) calculations
with classical MC simulations. These two surrogate models are used to learn the
dependence of target physical properties from complex compositions and
interactions of their constituents. We present the predictive performance of
these two surrogate models with respect to their complexity while avoiding the
danger of overfitting the model. An important aspect of our approach is the
periodic retraining with newly generated first principles data based on the
progressive exploration of the system's phase space by the MC simulation. The
numerical results show that HydraGNN model attains superior predictive
performance compared to the linear mixing model for magnetic alloy materials.

This paper presents the findings of an exploratory study on the continuously
generating Big Data on Twitter related to the sharing of information, news,
views, opinions, ideas, feedback, and experiences about the COVID-19 pandemic,
with a specific focus on the Omicron variant, which is the globally dominant
variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron
variant were studied, and the specific characteristics of tweets that were
analyzed include - sentiment, language, source, type, and embedded URLs. The
findings of this study are manifold. First, from sentiment analysis, it was
observed that 50.5% of tweets had a neutral emotion. The other emotions - bad,
good, terrible, and great were found in 15.6%, 14.0%, 12.5%, and 7.5% of the
tweets, respectively. Second, the findings of language interpretation showed
that 65.9% of the tweets were posted in English. It was followed by Spanish,
French, Italian, and other languages. Third, the findings from source tracking
showed that Twitter for Android was associated with 35.2% of tweets. It was
followed by Twitter Web App, Twitter for iPhone, Twitter for iPad, and other
sources. Fourth, studying the type of tweets revealed that retweets accounted
for 60.8% of the tweets, it was followed by original tweets and replies that
accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of
embedded URL analysis, the most common domain embedded in the tweets was found
to be twitter.com, which was followed by biorxiv.org, nature.com, and other
domains. Finally, to support similar research in this field, we have developed
a Twitter dataset that comprises more than 500,000 tweets about the SARS-CoV-2
omicron variant since the first detected case of this variant on November 24,
2021.

We introduce AudioScopeV2, a state-of-the-art universal audio-visual
on-screen sound separation system which is capable of learning to separate
sounds and associate them with on-screen objects by looking at in-the-wild
videos. We identify several limitations of previous work on audio-visual
on-screen sound separation, including the coarse resolution of spatio-temporal
attention, poor convergence of the audio separation model, limited variety in
training and evaluation data, and failure to account for the trade off between
preservation of on-screen sounds and suppression of off-screen sounds. We
provide solutions to all of these issues. Our proposed cross-modal and
self-attention network architectures capture audio-visual dependencies at a
finer resolution over time, and we also propose efficient separable variants
that are capable of scaling to longer videos without sacrificing much
performance. We also find that pre-training the separation model only on audio
greatly improves results. For training and evaluation, we collected new human
annotations of onscreen sounds from a large database of in-the-wild videos
(YFCC100M). This new dataset is more diverse and challenging. Finally, we
propose a calibration procedure that allows exact tuning of on-screen
reconstruction versus off-screen suppression, which greatly simplifies
comparing performance between models with different operating points. Overall,
our experimental results show marked improvements in on-screen separation
performance under much more general conditions than previous methods with
minimal additional computational complexity.

This paper examines whether one can learn to play an optimal action while
only knowing part of true specification of the environment. We choose the
optimal pricing problem as our laboratory, where the monopolist is endowed with
an underspecified model of the market demand, but can observe market outcomes.
In contrast to conventional learning models where the model specification is
complete and exogenously fixed, the monopolist has to learn the specification
and the parameters of the demand curve from the data. We formulate the learning
dynamics as an algorithm that forecast the optimal price based on the data,
following the machine learning literature (Shalev-Shwartz and Ben-David
(2014)). Inspired by PAC learnability, we develop a new notion of learnability
by requiring that the algorithm must produce an accurate forecast with a
reasonable amount of data uniformly over the class of models consistent with
the part of the true specification. In addition, we assume that the monopolist
has a lexicographic preference over the payoff and the complexity cost of the
algorithm, seeking an algorithm with a minimum number of parameters subject to
PAC-guaranteeing the optimal solution (Rubinstein (1986)). We show that for the
set of demand curves with strictly decreasing uniformly Lipschitz continuous
marginal revenue curve, the optimal algorithm recursively estimates the slope
and the intercept of the linear demand curve, even if the actual demand curve
is not linear. The monopolist chooses a misspecified model to save
computational cost, while learning the true optimal decision uniformly over the
set of underspecified demand curves.

Wind energy is expected to be one of the leading ways to achieve the goals of
the Paris Agreement but it in turn heavily depends on effective management of
its operations and maintenance (O&M) costs. Blade failures account for
one-third of all O&M costs thus making accurate detection of blade damages,
especially cracks, very important for sustained operations and cost savings.
Traditionally, damage inspection has been a completely manual process thus
making it subjective, error-prone, and time-consuming. Hence in this work, we
bring more objectivity, scalability, and repeatability in our damage inspection
process, using deep learning, to miss fewer cracks. We build a deep learning
model trained on a large dataset of blade damages, collected by our drone-based
inspection, to correctly detect cracks. Our model is already in production and
has processed more than a million damages with a recall of 0.96. We also focus
on model interpretability using class activation maps to get a peek into the
model workings. The model not only performs as good as human experts but also
better in certain tricky cases. Thus, in this work, we aim to increase wind
energy adoption by decreasing one of its major hurdles - the O\&M costs
resulting from missing blade failures like cracks.

The canonical technique for nonlinear modeling of spatial and other
point-referenced data is known as kriging in the geostatistics literature, and
by Gaussian Process (GP) regression in surrogate modeling and machine learning
communities. There are many similarities shared between kriging and GPs, but
also some important differences. One is that GPs impose a process on the
data-generating mechanism that can be used to automate kernel/variogram
inference, thus removing the human from the loop in a conventional
semivariogram analysis. The GP framework also suggests a probabilistically
valid means of scaling to handle a large corpus of training data, i.e., an
alternative to so-called ordinary kriging. Finally, recent GP implementations
are tailored to make the most of modern computing architectures such as
multi-core workstations and multi-node supercomputers. Ultimately, we use this
discussion as a springboard for an empirics-based advocacy of state-of-the-art
GP technology in the geospatial modeling of a large corpus of borehole data
involved in mining for gold and other minerals. Our out-of-sample validation
exercise quantifies how GP methods (as implemented by open source libraries)
can be both more economical (fewer human and compute resources), more accurate
and offer better uncertainty quantification than kriging-based alternatives.
Once in the GP framework, several possible extensions benefit from a fully
generative modeling apparatus. In particular, we showcase a simple imputation
scheme that copes with left-censoring of small measurements, which is a common
feature in borehole assays.

Due to their inference, data representation and reconstruction properties,
Variational Autoencoders (VAE) have been successfully used in continual
learning classification tasks. However, their ability to generate images with
specifications corresponding to the classes and databases learned during
Continual Learning (CL) is not well understood and catastrophic forgetting
remains a significant challenge. In this paper, we firstly analyze the
forgetting behaviour of VAEs by developing a new theoretical framework that
formulates CL as a dynamic optimal transport problem. This framework proves
approximate bounds to the data likelihood without requiring the task
information and explains how the prior knowledge is lost during the training
process. We then propose a novel memory buffering approach, namely the Online
Cooperative Memorization (OCM) framework, which consists of a Short-Term Memory
(STM) that continually stores recent samples to provide future information for
the model, and a Long-Term Memory (LTM) aiming to preserve a wide diversity of
samples. The proposed OCM transfers certain samples from STM to LTM according
to the information diversity selection criterion without requiring any
supervised signals. The OCM framework is then combined with a dynamic VAE
expansion mixture network for further enhancing its performance.

Predictive uncertainty estimation is essential for deploying Deep Neural
Networks in real-world autonomous systems. However, most successful approaches
are computationally intensive. In this work, we attempt to address these
challenges in the context of autonomous driving perception tasks. Recently
proposed Deterministic Uncertainty Methods (DUM) can only partially meet such
requirements as their scalability to complex computer vision tasks is not
obvious. In this work we advance a scalable and effective DUM for
high-resolution semantic segmentation, that relaxes the Lipschitz constraint
typically hindering practicality of such architectures. We learn a discriminant
latent space by leveraging a distinction maximization layer over an
arbitrarily-sized set of trainable prototypes. Our approach achieves
competitive results over Deep Ensembles, the state-of-the-art for uncertainty
prediction, on image classification, segmentation and monocular depth
estimation tasks. Our code is available at https://github.com/ENSTA-U2IS/LDU

Despite the prevalence of recent success in learning from static graphs,
learning from time-evolving graphs remains an open challenge. In this work, we
design new, more stringent evaluation procedures for link prediction specific
to dynamic graphs, which reflect real-world considerations, to better compare
the strengths and weaknesses of methods. First, we create two visualization
techniques to understand the reoccurring patterns of edges over time and show
that many edges reoccur at later time steps. Based on this observation, we
propose a pure memorization baseline called EdgeBank. EdgeBank achieves
surprisingly strong performance across multiple settings because easy negative
edges are often used in the current evaluation setting. To evaluate against
more difficult negative edges, we introduce two more challenging negative
sampling strategies that improve robustness and better match real-world
applications. Lastly, we introduce six new dynamic graph datasets from a
diverse set of domains missing from current benchmarks, providing new
challenges and opportunities for future research. Our code repository is
accessible at https://github.com/fpour/DGB.git.

We study the challenging problem of recovering detailed motion from a single
motion-blurred image. Existing solutions to this problem estimate a single
image sequence without considering the motion ambiguity for each region.
Therefore, the results tend to converge to the mean of the multi-modal
possibilities. In this paper, we explicitly account for such motion ambiguity,
allowing us to generate multiple plausible solutions all in sharp detail. The
key idea is to introduce a motion guidance representation, which is a compact
quantization of 2D optical flow with only four discrete motion directions.
Conditioned on the motion guidance, the blur decomposition is led to a
specific, unambiguous solution by using a novel two-stage decomposition
network. We propose a unified framework for blur decomposition, which supports
various interfaces for generating our motion guidance, including human input,
motion information from adjacent video frames, and learning from a video
dataset. Extensive experiments on synthesized datasets and real-world data show
that the proposed framework is qualitatively and quantitatively superior to
previous methods, and also offers the merit of producing physically plausible
and diverse solutions. Code is available at
https://github.com/zzh-tech/Animation-from-Blur.

Generative models for audio-conditioned dance motion synthesis map music
features to dance movements. Models are trained to associate motion patterns to
audio patterns, usually without an explicit knowledge of the human body. This
approach relies on a few assumptions: strong music-dance correlation,
controlled motion data and relatively simple poses and movements. These
characteristics are found in all existing datasets for dance motion synthesis,
and indeed recent methods can achieve good results.We introduce a new dataset
aiming to challenge these common assumptions, compiling a set of dynamic dance
sequences displaying complex human poses. We focus on breakdancing which
features acrobatic moves and tangled postures. We source our data from the Red
Bull BC One competition videos. Estimating human keypoints from these videos is
difficult due to the complexity of the dance, as well as the multiple moving
cameras recording setup. We adopt a hybrid labelling pipeline leveraging deep
estimation models as well as manual annotations to obtain good quality keypoint
sequences at a reduced cost. Our efforts produced the BRACE dataset, which
contains over 3 hours and 30 minutes of densely annotated poses. We test
state-of-the-art methods on BRACE, showing their limitations when evaluated on
complex sequences. Our dataset can readily foster advance in dance motion
synthesis. With intricate poses and swift movements, models are forced to go
beyond learning a mapping between modalities and reason more effectively about
body structure and movements.

The COVID-19 Omicron variant, reported to be the most immune evasive variant
of COVID-19, is resulting in a surge of COVID-19 cases globally. This has
caused schools, colleges, and universities in different parts of the world to
transition to online learning. As a result, social media platforms such as
Twitter are seeing an increase in conversations related to online learning in
the form of tweets. Mining such tweets to develop a dataset can serve as a data
resource for different applications and use-cases related to the analysis of
interest, views, opinions, perspectives, attitudes, and feedback towards online
learning during the current surge of COVID-19 cases caused by the Omicron
variant. Therefore, this work presents a large-scale open-access Twitter
dataset of conversations about online learning from different parts of the
world since the first detected case of the COVID-19 Omicron variant in November
2021. The dataset is compliant with the privacy policy, developer agreement,
and guidelines for content redistribution of Twitter, as well as with the FAIR
principles (Findability, Accessibility, Interoperability, and Reusability)
principles for scientific data management. The paper also briefly outlines some
potential applications in the fields of Big Data, Data Mining, Natural Language
Processing, and their related disciplines, with a specific focus on online
learning during this Omicron wave that may be studied, explored, and
investigated by using this dataset.

Tidying up a household environment using a mobile manipulator poses various
challenges in robotics, such as adaptation to large real-world environmental
variations, and safe and robust deployment in the presence of humans.The
Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global
competition held in September 2021, benchmarked tidying tasks in the real home
environments, and importantly, tested for full system performances.For this
challenge, we developed an entire household service robot system, which
leverages a data-driven approach to adapt to numerous edge cases that occur
during the execution, instead of classical manual pre-programmed solutions. In
this paper, we describe the core ingredients of the proposed robot system,
including visual recognition, object manipulation, and motion planning. Our
robot system won the second prize, verifying the effectiveness and potential of
data-driven robot systems for mobile manipulation in home environments.

Context. Determining properties of dust formed in and around supernovae from
observations remains challenging. This may be due to either incomplete coverage
of data in wavelength or time but also due to often inconspicuous signatures of
dust in the observed data. Aims. Here we address this challenge using modern
machine learning methods to determine the amount, composition and temperature
of dust from a large set of simulated data. We aim to determine whether such
methods are suitable to infer these properties from future observations of
supernovae. Methods. We calculate spectral energy distributions (SEDs) of dusty
shells around supernovae. We develop a neural network consisting of eight fully
connected layers and an output layer with specified activation functions that
allow us to predict the dust mass, temperature and composition and their
respective uncertainties from each SED. We conduct a feature importance
analysis via SHapley Additive exPlanations (SHAP) to find the minimum set of
JWST filters required to accurately predict these properties. Results. We find
that our neural network predicts dust masses and temperatures with a
root-mean-square error (RMSE) of $\sim$ 0.12 dex and $\sim$ 38 K, respectively.
Moreover, our neural network can well distinguish between the different dust
species included in our work, reaching a classification accuracy of up to 95\%
for carbon and 99\% for silicate dust. Conclusions. Our analysis shows that the
JWST filters NIRCam F070W, F140M, F356W, F480M and MIRI F560W, F770W, F1000W,
F1130W, F1500W, F1800W are likely the most important needed to determine the
properties of dust formed in and around supernovae from future observations. We
tested this on selected optical to infrared data of SN 1987A at 615 days past
explosion and find good agreement with dust masses and temperatures inferred
with standard fitting methods in the literature.

Deep image classifiers have been found to learn biases from datasets. To
mitigate the biases, most previous methods require labels of protected
attributes (e.g., age, skin tone) as full-supervision, which has two
limitations: 1) it is infeasible when the labels are unavailable; 2) they are
incapable of mitigating unknown biases -- biases that humans do not
preconceive. To resolve those problems, we propose Debiasing Alternate Networks
(DebiAN), which comprises two networks -- a Discoverer and a Classifier. By
training in an alternate manner, the discoverer tries to find multiple unknown
biases of the classifier without any annotations of biases, and the classifier
aims at unlearning the biases identified by the discoverer. While previous
works evaluate debiasing results in terms of a single bias, we create
Multi-Color MNIST dataset to better benchmark mitigation of multiple biases in
a multi-bias setting, which not only reveals the problems in previous methods
but also demonstrates the advantage of DebiAN in identifying and mitigating
multiple biases simultaneously. We further conduct extensive experiments on
real-world datasets, showing that the discoverer in DebiAN can identify unknown
biases that may be hard to be found by humans. Regarding debiasing, DebiAN
achieves strong bias mitigation performance.

The objective of this work is to learn an object-centric video
representation, with the aim of improving transferability to novel tasks, i.e.,
tasks different from the pre-training task of action classification. To this
end, we introduce a new object-centric video recognition model based on a
transformer architecture. The model learns a set of object-centric summary
vectors for the video, and uses these vectors to fuse the visual and
spatio-temporal trajectory 'modalities' of the video clip. We also introduce a
novel trajectory contrast loss to further enhance objectness in these summary
vectors. With experiments on four datasets -- SomethingSomething-V2,
SomethingElse, Action Genome and EpicKitchens -- we show that the
object-centric model outperforms prior video representations (both
object-agnostic and object-aware), when: (1) classifying actions on unseen
objects and unseen environments; (2) low-shot learning of novel classes; (3)
linear probe to other downstream tasks; as well as (4) for standard action

Meaningful uncertainty quantification in computer vision requires reasoning
about semantic information -- say, the hair color of the person in a photo or
the location of a car on the street. To this end, recent breakthroughs in
generative modeling allow us to represent semantic information in disentangled
latent spaces, but providing uncertainties on the semantic latent variables has
remained challenging. In this work, we provide principled uncertainty intervals
that are guaranteed to contain the true semantic factors for any underlying
generative model. The method does the following: (1) it uses quantile
regression to output a heuristic uncertainty interval for each element in the
latent space (2) calibrates these uncertainties such that they contain the true
value of the latent for a new, unseen input. The endpoints of these calibrated
intervals can then be propagated through the generator to produce interpretable
uncertainty visualizations for each semantic factor. This technique reliably
communicates semantically meaningful, principled, and instance-adaptive
uncertainty in inverse problems like image super-resolution and image

Machine learning (ML) research has generally focused on models, while the
most prominent datasets have been employed for everyday ML tasks without regard
for the breadth, difficulty, and faithfulness of these datasets to the
underlying problem. Neglecting the fundamental importance of datasets has
caused major problems involving data cascades in real-world applications and
saturation of dataset-driven criteria for model quality, hindering research
growth. To solve this problem, we present DataPerf, a benchmark package for
evaluating ML datasets and dataset-working algorithms. We intend it to enable
the "data ratchet," in which training sets will aid in evaluating test sets on
the same problems, and vice versa. Such a feedback-driven strategy will
generate a virtuous loop that will accelerate development of data-centric AI.
The MLCommons Association will maintain DataPerf.

We study the problem of offline Imitation Learning (IL) where an agent aims
to learn an optimal expert behavior policy without additional online
environment interactions. Instead, the agent is provided with a supplementary
offline dataset from suboptimal behaviors. Prior works that address this
problem either require that expert data occupies the majority proportion of the
offline dataset, or need to learn a reward function and perform offline
reinforcement learning (RL) afterwards. In this paper, we aim to address the
problem without additional steps of reward learning and offline RL training for
the case when demonstrations contain a large proportion of suboptimal data.
Built upon behavioral cloning (BC), we introduce an additional discriminator to
distinguish expert and non-expert data. We propose a cooperation framework to
boost the learning of both tasks, Based on this framework, we design a new IL
algorithm, where the outputs of discriminator serve as the weights of the BC
loss. Experimental results show that our proposed algorithm achieves higher
returns and faster training speed compared to baseline algorithms.

Training large neural networks is possible by training a smaller hypernetwork
that predicts parameters for the large ones. A recently released Graph
HyperNetwork (GHN) trained this way on one million smaller ImageNet
architectures is able to predict parameters for large unseen networks such as
ResNet-50. While networks with predicted parameters lose performance on the
source task, the predicted parameters have been found useful for fine-tuning on
other tasks. We study if fine-tuning based on the same GHN is still useful on
novel strong architectures that were published after the GHN had been trained.
We found that for recent architectures such as ConvNeXt, GHN initialization
becomes less useful than for ResNet-50. One potential reason is the increased
distribution shift of novel architectures from those used to train the GHN. We
also found that the predicted parameters lack the diversity necessary to
successfully fine-tune parameters with gradient descent. We alleviate this
limitation by applying simple post-processing techniques to predicted
parameters before fine-tuning them on a target task and improve fine-tuning of
ResNet-50 and ConvNeXt.

Compressed Stochastic Gradient Descent (SGD) algorithms have been recently
proposed to address the communication bottleneck in distributed and
decentralized optimization problems, such as those that arise in federated
machine learning. Existing compressed SGD algorithms assume the use of
non-adaptive step-sizes(constant or diminishing) to provide theoretical
convergence guarantees. Typically, the step-sizes are fine-tuned in practice to
the dataset and the learning algorithm to provide good empirical performance.
Such fine-tuning might be impractical in many learning scenarios, and it is
therefore of interest to study compressed SGD using adaptive step-sizes.
Motivated by prior work on adaptive step-size methods for SGD to train neural
networks efficiently in the uncompressed setting, we develop an adaptive
step-size method for compressed SGD. In particular, we introduce a scaling
technique for the descent step in compressed SGD, which we use to establish
order-optimal convergence rates for convex-smooth and strong convex-smooth
objectives under an interpolation condition and for non-convex objectives under
a strong growth condition. We also show through simulation examples that
without this scaling, the algorithm can fail to converge. We present
experimental results on deep neural networks for real-world datasets, and
compare the performance of our proposed algorithm with previously proposed
compressed SGD methods in literature, and demonstrate improved performance on
ResNet-18, ResNet-34 and DenseNet architectures for CIFAR-100 and CIFAR-10
datasets at various levels of compression.

It is generally assumed that the brain uses something akin to sparse
distributed representations. These representations, however, are
high-dimensional and consequently they affect classification performance of
traditional Machine Learning models due to "the curse of dimensionality". In
tasks for which there is a vast amount of labeled data, Deep Networks seem to
solve this issue with many layers and a non-Hebbian backpropagation algorithm.
The brain, however, seems to be able to solve the problem with few layers. In
this work, we hypothesize that this happens by using Hebbian learning.
Actually, the Hebbian-like learning rule of Restricted Boltzmann Machines
learns the input patterns asymmetrically. It exclusively learns the correlation
between non-zero values and ignores the zeros, which represent the vast
majority of the input dimensionality. By ignoring the zeros "the curse of
dimensionality" problem can be avoided. To test our hypothesis, we generated
several sparse datasets and compared the performance of a Restricted Boltzmann
Machine classifier with some Backprop-trained networks. The experiments using
these codes confirm our initial intuition as the Restricted Boltzmann Machine
shows a good generalization performance, while the Neural Networks trained with
the backpropagation algorithm overfit the training data.

Action quality assessment (AQA) is an active research problem in video-based
applications that is a challenging task due to the score variance per frame.
Existing methods address this problem via convolutional-based approaches but
suffer from its limitation of effectively capturing long-range dependencies.
With the recent advancements in Transformers, we show that they are a suitable
alternative to the conventional convolutional-based architectures.
Specifically, can transformer-based models solve the task of AQA by effectively
capturing long-range dependencies, parallelizing computation, and providing a
wider receptive field for diving videos? To demonstrate the effectiveness of
our proposed architectures, we conducted comprehensive experiments and achieved
a competitive Spearman correlation score of 0.9317. Additionally, we explore
the hyperparameters effect on the model's performance and pave a new path for
exploiting Transformers in AQA.

While the Vision Transformer (VT) architecture is becoming trendy in computer
vision, pure VT models perform poorly on tiny datasets. To address this issue,
this paper proposes the locality guidance for improving the performance of VTs
on tiny datasets. We first analyze that the local information, which is of
great importance for understanding images, is hard to be learned with limited
data due to the high flexibility and intrinsic globality of the self-attention
mechanism in VTs. To facilitate local information, we realize the locality
guidance for VTs by imitating the features of an already trained convolutional
neural network (CNN), inspired by the built-in local-to-global hierarchy of
CNN. Under our dual-task learning paradigm, the locality guidance provided by a
lightweight CNN trained on low-resolution images is adequate to accelerate the
convergence and improve the performance of VTs to a large extent. Therefore,
our locality guidance approach is very simple and efficient, and can serve as a
basic performance enhancement method for VTs on tiny datasets. Extensive
experiments demonstrate that our method can significantly improve VTs when
training from scratch on tiny datasets and is compatible with different kinds
of VTs and datasets. For example, our proposed method can boost the performance
of various VTs on tiny datasets (e.g., 13.07% for DeiT, 8.98% for T2T and 7.85%
for PVT), and enhance even stronger baseline PVTv2 by 1.86% to 79.30%, showing
the potential of VTs on tiny datasets. The code is available at
https://github.com/lkhl/tiny-transformers.

Facial expression in-the-wild is essential for various interactive computing
domains. Especially, "Learning from Synthetic Data" (LSD) is an important topic
in the facial expression recognition task. In this paper, we propose a
multi-task learning-based facial expression recognition approach which consists
of emotion and appearance learning branches that can share all face
information, and present preliminary results for the LSD challenge introduced
in the 4th affective behavior analysis in-the-wild (ABAW) competition. Our
method achieved the mean F1 score of 0.71.

Recently, it is shown that deploying a proper self-supervision is a
prospective way to enhance the performance of supervised learning. Yet, the
benefits of self-supervision are not fully exploited as previous pretext tasks
are specialized for unsupervised representation learning. To this end, we begin
by presenting three desirable properties for such auxiliary tasks to assist the
supervised objective. First, the tasks need to guide the model to learn rich
features. Second, the transformations involved in the self-supervision should
not significantly alter the training distribution. Third, the tasks are
preferred to be light and generic for high applicability to prior arts.
Subsequently, to show how existing pretext tasks can fulfill these and be
tailored for supervised learning, we propose a simple auxiliary
self-supervision task, predicting localizable rotation (LoRot). Our exhaustive
experiments validate the merits of LoRot as a pretext task tailored for
supervised learning in terms of robustness and generalization capability. Our
code is available at https://github.com/wjun0830/Localizable-Rotation.

Event cameras respond to scene dynamics and offer advantages to estimate
motion. Following recent image-based deep-learning achievements, optical flow
estimation methods for event cameras have rushed to combine those image-based
methods with event data. However, it requires several adaptations (data
conversion, loss function, etc.) as they have very different properties. We
develop a principled method to extend the Contrast Maximization framework to
estimate optical flow from events alone. We investigate key elements: how to
design the objective function to prevent overfitting, how to warp events to
deal better with occlusions, and how to improve convergence with multi-scale
raw events. With these key elements, our method ranks first among unsupervised
methods on the MVSEC benchmark, and is competitive on the DSEC benchmark.
Moreover, our method allows us to expose the issues of the ground truth flow in
those benchmarks, and produces remarkable results when it is transferred to
unsupervised learning settings. Our code is available at
https://github.com/tub-rip/event_based_optical_flow

Combining the preferences of many rankers into one single consensus ranking
is critical for consequential applications from hiring and admissions to
lending. While group fairness has been extensively studied for classification,
group fairness in rankings and in particular rank aggregation remains in its
infancy. Recent work introduced the concept of fair rank aggregation for
combining rankings but restricted to the case when candidates have a single
binary protected attribute, i.e., they fall into two groups only. Yet it
remains an open problem how to create a consensus ranking that represents the
preferences of all rankers while ensuring fair treatment for candidates with
multiple protected attributes such as gender, race, and nationality. In this
work, we are the first to define and solve this open Multi-attribute Fair
Consensus Ranking (MFCR) problem. As a foundation, we design novel group
fairness criteria for rankings, called MANI-RANK, ensuring fair treatment of
groups defined by individual protected attributes and their intersection.
Leveraging the MANI-RANK criteria, we develop a series of algorithms that for
the first time tackle the MFCR problem. Our experimental study with a rich
variety of consensus scenarios demonstrates our MFCR methodology is the only
approach to achieve both intersectional and protected attribute fairness while
also representing the preferences expressed through many base rankings. Our
real-world case study on merit scholarships illustrates the effectiveness of
our MFCR methods to mitigate bias across multiple protected attributes and
their intersections. This is an extended version of "MANI-Rank: Multiple
Attribute and Intersectional Group Fairness for Consensus Ranking", to appear
in ICDE 2022.

Existing work on fairness modeling commonly assumes that sensitive attributes
for all instances are fully available, which may not be true in many real-world
applications due to the high cost of acquiring sensitive information. When
sensitive attributes are not disclosed or available, it is needed to manually
annotate a small part of the training data to mitigate bias. However, the
skewed distribution across different sensitive groups preserves the skewness of
the original dataset in the annotated subset, which leads to non-optimal bias
mitigation. To tackle this challenge, we propose Active Penalization Of
Discrimination (APOD), an interactive framework to guide the limited
annotations towards maximally eliminating the effect of algorithmic bias. The
proposed APOD integrates discrimination penalization with active instance
selection to efficiently utilize the limited annotation budget, and it is
theoretically proved to be capable of bounding the algorithmic bias. According
to the evaluation on five benchmark datasets, APOD outperforms the
state-of-the-arts baseline methods under the limited annotation budget, and
shows comparable performance to fully annotated bias mitigation, which
demonstrates that APOD could benefit real-world applications when sensitive
information is limited.

The automation and digitalization of business processes has resulted in large
amounts of data captured in information systems, which can aid businesses in
understanding their processes better, improve workflows, or provide operational
support. By making predictions about ongoing processes, bottlenecks can be
identified and resources reallocated, as well as insights gained into the state
of a process instance (case). Traditionally, data is extracted from systems in
the form of an event log with a single identifying case notion, such as an
order id for an Order to Cash (O2C) process. However, real processes often have
multiple object types, for example, order, item, and package, so a format that
forces the use of a single case notion does not reflect the underlying
relations in the data. The Object-Centric Event Log (OCEL) format was
introduced to correctly capture this information. The state-of-the-art
predictive methods have been tailored to only traditional event logs. This
thesis shows that a prediction method utilizing Generative Adversarial Networks
(GAN), Long Short-Term Memory (LSTM) architectures, and Sequence to Sequence
models (Seq2seq), can be augmented with the rich data contained in OCEL.
Objects in OCEL can have attributes that are useful in predicting the next
event and timestamp, such as a priority class attribute for an object type
package indicating slower or faster processing. In the metrics of sequence
similarity of predicted remaining events and mean absolute error (MAE) of the
timestamp, the approach in this thesis matches or exceeds previous research,
depending on whether selected object attributes are useful features for the
model. Additionally, this thesis provides a web interface to predict the next
sequence of activities from user input.

We consider the inverse problem of determining the geometry of penetrable
objects from scattering data generated by one incident wave at a fixed
frequency. We first study an orthogonality sampling type method which is fast,
simple to implement, and robust against noise in the data. This sampling method
has a new imaging functional that is applicable to data measured in near field
or far field regions. The resolution analysis of the imaging functional is
analyzed where the explicit decay rate of the functional is established. A
connection with the orthogonality sampling method by Potthast is also studied.
The sampling method is then combined with a deep neural network to solve the
inverse scattering problem. This combined method can be understood as a network
using the image computed by the sampling method for the first layer and
followed by the U-net architecture for the rest of the layers. The fast
computation and the knowledge from the results of the sampling method help
speed up the training of the network. The combination leads to a significant
improvement in the reconstruction results initially obtained by the sampling
method. The combined method is also able to invert some limited aperture
experimental data without any additional transfer training.

Simulating general quantum processes that describe realistic interactions of
quantum systems following a non-unitary evolution is challenging for
conventional quantum computers that directly implement unitary gates. We
analyze complexities for promising methods such as the Sz.-Nagy dilation and
linear combination of unitaries that can simulate open systems by the
probabilistic realization of non-unitary operators, requiring multiple calls to
both the encoding and state preparation oracles. We propose a quantum
two-unitary decomposition (TUD) algorithm to decompose a $d$-dimensional
operator $A$ with non-zero singular values as $A=(U_1+U_2)/2$ using the quantum
singular value transformation algorithm, avoiding classically expensive
singular value decomposition (SVD) with an $\mathcal{O}(d^3)$ overhead in time.
The two unitaries can be deterministically implemented, thus requiring only a
single call to the state preparation oracle for each. The calls to the encoding
oracle can also be reduced significantly at the expense of an acceptable error
in measurements. Since the TUD method can be used to implement non-unitary
operators as only two unitaries, it also has potential applications in linear
algebra and quantum machine learning.

Attention mechanisms have emerged as important tools that boost the
performance of deep models by allowing them to focus on key parts of learned
embeddings. However, current attention mechanisms used in speaker recognition
tasks fail to consider fine-grained information items such as frequency bins in
input spectral representations used by the deep networks. To address this
issue, we propose the novel Fine-grained Early Frequency Attention (FEFA) for
speaker recognition in-the-wild. Once integrated into a deep neural network,
our proposed mechanism works by obtaining queries from early layers of the
network and generating learnable weights to attend to information items as
small as the frequency bins in the input spectral representations. To evaluate
the performance of FEFA, we use several well-known deep models as backbone
networks and integrate our attention module in their pipelines. The overall
performance of these networks (with and without FEFA) are evaluated on the
VoxCeleb1 dataset, where we observe considerable improvements when FEFA is
used.

With the improved performance of deep learning, the number of studies trying
to apply deep learning to human emotion analysis is increasing rapidly. But
even with this trend going on, it is still difficult to obtain high-quality
images and annotations. For this reason, the Learning from Synthetic Data (LSD)
Challenge, which learns from synthetic images and infers from real images, is
one of the most interesting areas. In general, Domain Adaptation methods are
widely used to address LSD challenges, but there is a limitation that target
domains (real images) are still needed. Focusing on these limitations, we
propose a framework Bootstrap Your Emotion Latent (BYEL), which uses only
synthetic images in training. BYEL is implemented by adding Emotion Classifiers
and Emotion Vector Subtraction to the BYOL framework that performs well in
Self-Supervised Representation Learning. We train our framework using synthetic
images generated from the Aff-wild2 dataset and evaluate it using real images
from the Aff-wild2 dataset. The result shows that our framework (0.3084)
performs 2.8% higher than the baseline (0.3) on the macro F1 score metric.

Shortcut learning occurs when a deep neural network overly relies on spurious
correlations in the training dataset in order to solve downstream tasks. Prior
works have shown how this impairs the compositional generalization capability
of deep learning models. To address this problem, we propose a novel approach
to mitigate shortcut learning in uncontrolled target domains. Our approach
extends the training set with an additional dataset (the source domain), which
is specifically designed to facilitate learning independent representations of
basic visual factors. We benchmark our idea on synthetic target domains where
we explicitly control shortcut opportunities as well as real-world target
domains. Furthermore, we analyze the effect of different specifications of the
source domain and the network architecture on compositional generalization. Our
main finding is that leveraging data from a source domain is an effective way
to mitigate shortcut learning. By promoting independence across different
factors of variation in the learned representations, networks can learn to
consider only predictive factors and ignore potential shortcut factors during

Differential privacy is known to protect against threats to validity incurred
due to adaptive, or exploratory, data analysis -- even when the analyst
adversarially searches for a statistical estimate that diverges from the true
value of the quantity of interest on the underlying population. The cost of
this protection is the accuracy loss incurred by differential privacy. In this
work, inspired by standard models in the genomics literature, we consider data
models in which individuals are represented by a sequence of attributes with
the property that where distant attributes are only weakly correlated. We show
that, under this assumption, it is possible to "re-use" privacy budget on
different portions of the data, significantly improving accuracy without
increasing the risk of overfitting.

Digital twins have recently gained significant interest in simulation,
optimization, and predictive maintenance of Industrial Control Systems (ICS).
Recent studies discuss the possibility of using digital twins for intrusion
detection in industrial systems. Accordingly, this study contributes to a
digital twin-based security framework for industrial control systems, extending
its capabilities for simulation of attacks and defense mechanisms. Four types
of process-aware attack scenarios are implemented on a standalone open-source
digital twin of an industrial filling plant: command injection, network Denial
of Service (DoS), calculated measurement modification, and naive measurement
modification. A stacked ensemble classifier is proposed as the real-time
intrusion detection, based on the offline evaluation of eight supervised
machine learning algorithms. The designed stacked model outperforms previous
methods in terms of F1-Score and accuracy, by combining the predictions of
various algorithms, while it can detect and classify intrusions in near
real-time (0.1 seconds). This study also discusses the practicality and
benefits of the proposed digital twin-based security framework.

Unsupervised domain adaptation in semantic segmentation has been raised to
alleviate the reliance on expensive pixel-wise annotations. It leverages a
labeled source domain dataset as well as unlabeled target domain images to
learn a segmentation network. In this paper, we observe two main issues of the
existing domain-invariant learning framework. (1) Being distracted by the
feature distribution alignment, the network cannot focus on the segmentation
task. (2) Fitting source domain data well would compromise the target domain
performance. To address these issues, we propose DecoupleNet that alleviates
source domain overfitting and enables the final model to focus more on the
segmentation task. Furthermore, we put forward Self-Discrimination (SD) and
introduce an auxiliary classifier to learn more discriminative target domain
features with pseudo labels. Finally, we propose Online Enhanced Self-Training
(OEST) to contextually enhance the quality of pseudo labels in an online
manner. Experiments show our method outperforms existing state-of-the-art
methods, and extensive ablation studies verify the effectiveness of each
component. Code is available at https://github.com/dvlab-research/DecoupleNet.

Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring
success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph
Neural Networks (GNNs). However, unlike GNNs, FMs struggle to incorporate node
features and generalise to unseen nodes in inductive settings. Our work bridges
the gap between FMs and GNNs by proposing ReFactor GNNs. This new architecture
draws upon both modelling paradigms, which previously were largely thought of
as disjoint. Concretely, using a message-passing formalism, we show how FMs can
be cast as GNNs by reformulating the gradient descent procedure as
message-passing operations, which forms the basis of our ReFactor GNNs. Across
a multitude of well-established KGC benchmarks, our ReFactor GNNs achieve
comparable transductive performance to FMs, and state-of-the-art inductive
performance while using an order of magnitude fewer parameters.

We introduce a method for instance proposal generation for 3D point clouds.
Existing techniques typically directly regress proposals in a single
feed-forward step, leading to inaccurate estimation. We show that this serves
as a critical bottleneck, and propose a method based on iterative bilateral
filtering with learned kernels. Following the spirit of bilateral filtering, we
consider both the deep feature embeddings of each point, as well as their
locations in the 3D space. We show via synthetic experiments that our method
brings drastic improvements when generating instance proposals for a given
point of interest. We further validate our method on the challenging ScanNet
benchmark, achieving the best instance segmentation performance amongst the
sub-category of top-down methods.

Quantum mechanical methods like Density Functional Theory (DFT) are used with
great success alongside efficient search algorithms for studying kinetics of
reactive systems. However, DFT is prohibitively expensive for large scale
exploration. Machine Learning (ML) models have turned out to be excellent
emulators of small molecule DFT calculations and could possibly replace DFT in
such tasks. For kinetics, success relies primarily on the models capability to
accurately predict the Potential Energy Surface (PES) around transition-states
and Minimal Energy Paths (MEPs). Previously this has not been possible due to
scarcity of relevant data in the literature. In this paper we train state of
the art equivariant Graph Neural Network (GNN)-based models on around 10.000
elementary reactions from the Transition1x dataset. We apply the models as
potentials for the Nudged Elastic Band (NEB) algorithm and achieve a Mean
Average Error (MAE) of 0.13+/-0.03 eV on barrier energies on unseen reactions.
We compare the results against equivalent models trained on QM9 and ANI1x. We
also compare with and outperform Density Functional based Tight Binding (DFTB)
on both accuracy and computational resource. The implication is that ML models,
given relevant data, are now at a level where they can be applied for
downstream tasks in quantum chemistry transcending prediction of simple
molecular features.

Audio-visual generalised zero-shot learning for video classification requires
understanding the relations between the audio and visual information in order
to be able to recognise samples from novel, previously unseen classes at test
time. The natural semantic and temporal alignment between audio and visual data
in video data can be exploited to learn powerful representations that
generalise to unseen classes at test time. We propose a multi-modal and
Temporal Cross-attention Framework (\modelName) for audio-visual generalised
zero-shot learning. Its inputs are temporally aligned audio and visual features
that are obtained from pre-trained networks. Encouraging the framework to focus
on cross-modal correspondence across time instead of self-attention within the
modalities boosts the performance significantly. We show that our proposed
framework that ingests temporal features yields state-of-the-art performance on
the \ucf, \vgg, and \activity benchmarks for (generalised) zero-shot learning.
Code for reproducing all results is available at
\url{https://github.com/ExplainableML/TCAF-GZSL}.

Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning
novel classes from a few labeled samples by avoiding the overfitting and
catastrophic forgetting simultaneously. The current protocol of FSCIL is built
by mimicking the general class-incremental learning setting, while it is not
totally appropriate due to the different data configuration, i.e., novel
classes are all in the limited data regime. In this paper, we rethink the
configuration of FSCIL with the open-set hypothesis by reserving the
possibility in the first session for incoming categories. To assign better
performances on both close-set and open-set recognition to the model,
Hyperbolic Reciprocal Point Learning module (Hyper-RPL) is built on Reciprocal
Point Learning (RPL) with hyperbolic neural networks. Besides, for learning
novel categories from limited labeled data, we incorporate a hyperbolic metric
learning (Hyper-Metric) module into the distillation-based framework to
alleviate the overfitting issue and better handle the trade-off issue between
the preservation of old knowledge and the acquisition of new knowledge. The
comprehensive assessments of the proposed configuration and modules on three
benchmark datasets are executed to validate the effectiveness concerning three
evaluation indicators.

As learning machines increase their influence on decisions concerning human
lives, analyzing their fairness properties becomes a subject of central
importance. Yet, our best tools for measuring the fairness of learning systems
are rigid fairness metrics encapsulated as mathematical one-liners, offer
limited power to the stakeholders involved in the prediction task, and are easy
to manipulate when we exhort excessive pressure to optimize them. To advance
these issues, we propose to shift focus from shaping fairness metrics to
curating the distributions of examples under which these are computed. In
particular, we posit that every claim about fairness should be immediately
followed by the tagline "Fair under what examples, and collected by whom?". By
highlighting connections to the literature in domain generalization, we propose
to measure fairness as the ability of the system to generalize under multiple
stress tests -- distributions of examples with social relevance. We encourage
each stakeholder to curate one or multiple stress tests containing examples
reflecting their (possibly conflicting) interests. The machine passes or fails
each stress test by falling short of or exceeding a pre-defined metric value.
The test results involve all stakeholders in a discussion about how to improve
the learning system, and provide flexible assessments of fairness dependent on
context and based on interpretable data. We provide full implementation
guidelines for stress testing, illustrate both the benefits and shortcomings of
this framework, and introduce a cryptographic scheme to enable a degree of
prediction accountability from system providers.

We showcase a variety of functions and classes that implement sampling
procedures with improved exploration of the parameter space assisted by machine
learning. Special attention is paid to setting sane defaults with the objective
that adjustments required by different problems remain minimal. This collection
of routines can be employed for different types of analysis, from finding
bounds on the parameter space to accumulating samples in areas of interest. In
particular, we discuss two methods assisted by incorporating different machine
learning models: regression and classification. We show that a machine learning
classifier can provide higher efficiency for exploring the parameter space.
Also, we introduce a boosting technique to improve the slow convergence at the
start of the process. The use of these routines is better explained with the
help of a few examples that illustrate the type of results one can obtain. We
also include examples of the code used to obtain the examples as well as
descriptions of the adjustments that can be made to adapt the calculation to
other problems. We finalize by showing the impact of these techniques when
exploring the parameter space of the two Higgs doublet model that matches the
measured Higgs Boson signal strength. The code used for this paper and
instructions on how to use it are available on the web.

Deep Learning approaches based on Convolutional Neural Networks (CNNs) are
extensively utilized and very successful in a wide range of application areas,
including image classification and speech recognition. For the execution of
trained CNNs, i.e. model inference, we nowadays witness a shift from the Cloud
to the Edge. Unfortunately, deploying and inferring large, compute and memory
intensive CNNs on edge devices is challenging because these devices typically
have limited power budgets and compute/memory resources. One approach to
address this challenge is to leverage all available resources across multiple
edge devices to deploy and execute a large CNN by properly partitioning the CNN
and running each CNN partition on a separate edge device. Although such
distribution, deployment, and execution of large CNNs on multiple edge devices
is a desirable and beneficial approach, there currently does not exist a design
and programming framework that takes a trained CNN model, together with a CNN
partitioning specification, and fully automates the CNN model splitting and
deployment on multiple edge devices to facilitate distributed CNN inference at
the Edge. Therefore, in this paper, we propose a novel framework, called
AutoDiCE, for automated splitting of a CNN model into a set of sub-models and
automated code generation for distributed and collaborative execution of these
sub-models on multiple, possibly heterogeneous, edge devices, while supporting
the exploitation of parallelism among and within the edge devices. Our
experimental results show that AutoDiCE can deliver distributed CNN inference
with reduced energy consumption and memory usage per edge device, and improved
overall system throughput at the same time.

Machine learning models are typically deployed in a test setting that differs
from the training setting, potentially leading to decreased model performance
because of domain shift. If we could estimate the performance that a
pre-trained model would achieve on data from a specific deployment setting, for
example a certain clinic, we could judge whether the model could safely be
deployed or if its performance degrades unacceptably on the specific data.
Existing approaches estimate this based on the confidence of predictions made
on unlabeled test data from the deployment's domain. We find existing methods
struggle with data that present class imbalance, because the methods used to
calibrate confidence do not account for bias induced by class imbalance,
consequently failing to estimate class-wise accuracy. Here, we introduce
class-wise calibration within the framework of performance estimation for
imbalanced datasets. Specifically, we derive class-specific modifications of
state-of-the-art confidence-based model evaluation methods including
temperature scaling (TS), difference of confidences (DoC), and average
thresholded confidence (ATC). We also extend the methods to estimate Dice
similarity coefficient (DSC) in image segmentation. We conduct experiments on
four tasks and find the proposed modifications consistently improve the
estimation accuracy for imbalanced datasets. Our methods improve accuracy
estimation by 18\% in classification under natural domain shifts, and double
the estimation accuracy on segmentation tasks, when compared with prior
methods.

Video conferencing, which includes both video and audio content, has
contributed to dramatic increases in Internet traffic, as the COVID-19 pandemic
forced millions of people to work and learn from home. Global Internet traffic
of video conferencing has dramatically increased Because of this, efficient and
accurate video quality tools are needed to monitor and perceptually optimize
telepresence traffic streamed via Zoom, Webex, Meet, etc. However, existing
models are limited in their prediction capabilities on multi-modal, live
streaming telepresence content. Here we address the significant challenges of
Telepresence Video Quality Assessment (TVQA) in several ways. First, we
mitigated the dearth of subjectively labeled data by collecting ~2k
telepresence videos from different countries, on which we crowdsourced ~80k
subjective quality labels. Using this new resource, we created a
first-of-a-kind online video quality prediction framework for live streaming,
using a multi-modal learning framework with separate pathways to compute visual
and audio quality predictions. Our all-in-one model is able to provide accurate
quality predictions at the patch, frame, clip, and audiovisual levels. Our
model achieves state-of-the-art performance on both existing quality databases
and our new TVQA database, at a considerably lower computational expense,
making it an attractive solution for mobile and embedded systems.

As Graph Neural Networks (GNNs) increase in popularity for scientific machine
learning, their training and inference efficiency is becoming increasingly
critical. Additionally, the deep learning field as a whole is trending towards
wider and deeper networks, and ever increasing data sizes, to the point where
hard hardware bottlenecks are often encountered. Emerging specialty hardware
platforms provide an exciting solution to this problem. In this paper, we
systematically profile and select low-level operations pertinent to GNNs for
scientific computing implemented in the Pytorch Geometric software framework.
These are then rigorously benchmarked on NVIDIA A100 GPUs for several various
combinations of input values, including tensor sparsity. We then analyze these
results for each operation. At a high level, we conclude that on NVIDIA
systems: (1) confounding bottlenecks such as memory inefficiency often dominate
runtime costs moreso than data sparsity alone, (2) native Pytorch operations
are often as or more competitive than their Pytorch Geometric equivalents,
especially at low to moderate levels of input data sparsity, and (3) many
operations central to state-of-the-art GNN architectures have little to no
optimization for sparsity. We hope that these results serve as a baseline for
those developing these operations on specialized hardware and that our
subsequent analysis helps to facilitate future software and hardware based
optimizations of these operations and thus scalable GNN performance as a whole.

Modeling the dynamics of people walking is a problem of long-standing
interest in computer vision. Many previous works involving pedestrian
trajectory prediction define a particular set of individual actions to
implicitly model group actions. In this paper, we present a novel architecture
named GP-Graph which has collective group representations for effective
pedestrian trajectory prediction in crowded environments, and is compatible
with all types of existing approaches. A key idea of GP-Graph is to model both
individual-wise and group-wise relations as graph representations. To do this,
GP-Graph first learns to assign each pedestrian into the most likely behavior
group. Using this assignment information, GP-Graph then forms both intra- and
inter-group interactions as graphs, accounting for human-human relations within
a group and group-group relations, respectively. To be specific, for the
intra-group interaction, we mask pedestrian graph edges out of an associated
group. We also propose group pooling&unpooling operations to represent a group
with multiple pedestrians as one graph node. Lastly, GP-Graph infers a
probability map for socially-acceptable future trajectories from the integrated
features of both group interactions. Moreover, we introduce a group-level
latent vector sampling to ensure collective inferences over a set of possible
future trajectories. Extensive experiments are conducted to validate the
effectiveness of our architecture, which demonstrates consistent performance
improvements with publicly available benchmarks. Code is publicly available at
https://github.com/inhwanbae/GPGraph.

The stochastic control problem of optimal market making is among the central
problems in quantitative finance. In this paper, a deep reinforcement
learning-based controller is trained on a weakly consistent, multivariate
Hawkes process-based limit order book simulator to obtain market making
controls. The proposed approach leverages the advantages of Monte Carlo
backtesting and contributes to the line of research on market making under
weakly consistent limit order book models. The ensuing deep reinforcement
learning controller is compared to multiple market making benchmarks, with the
results indicating its superior performance with respect to various risk-reward
metrics, even under significant transaction costs.

While monocular 3D pose estimation seems to have achieved very accurate
results on the public datasets, their generalization ability is largely
overlooked. In this work, we perform a systematic evaluation of the existing
methods and find that they get notably larger errors when tested on different
cameras, human poses and appearance. To address the problem, we introduce
VirtualPose, a two-stage learning framework to exploit the hidden "free lunch"
specific to this task, i.e. generating infinite number of poses and cameras for
training models at no cost. To that end, the first stage transforms images to
abstract geometry representations (AGR), and then the second maps them to 3D
poses. It addresses the generalization issue from two aspects: (1) the first
stage can be trained on diverse 2D datasets to reduce the risk of over-fitting
to limited appearance; (2) the second stage can be trained on diverse AGR
synthesized from a large number of virtual cameras and poses. It outperforms
the SOTA methods without using any paired images and 3D poses from the
benchmarks, which paves the way for practical applications. Code is available
at https://github.com/wkom/VirtualPose.

We derive conditions for the existence of fixed points of cone mappings
without assuming scalability of functions. Monotonicity and scalability are
often inseparable in the literature in the context of searching for fixed
points of interference mappings. In applications, such mappings are
approximated by non-negative neural networks. It turns out, however, that the
process of training non-negative networks requires imposing an artificial
constraint on the weights of the model. However, in the case of specific
non-negative data, it cannot be said that if the mapping is non-negative, it
has only non-negative weights. Therefore, we considered the problem of the
existence of fixed points for general neural networks, assuming the conditions
of tangency conditions with respect to specific cones. This does not relax the
physical assumptions, because even assuming that the input and output are to be
non-negative, the weights can have (small, but) less than zero values. Such
properties (often found in papers on the interpretability of weights of neural
networks) lead to the weakening of the assumptions about the monotonicity or
scalability of the mapping associated with the neural network. To the best of
our knowledge, this paper is the first to study this phenomenon.

Domain generalization (DG) seeks predictors which perform well on unseen test
distributions by leveraging data drawn from multiple related training
distributions or domains. To achieve this, DG is commonly formulated as an
average- or worst-case problem over the set of possible domains. However,
predictors that perform well on average lack robustness while predictors that
perform well in the worst case tend to be overly-conservative. To address this,
we propose a new probabilistic framework for DG where the goal is to learn
predictors that perform well with high probability. Our key idea is that
distribution shifts seen during training should inform us of probable shifts at
test time, which we realize by explicitly relating training and test domains as
draws from the same underlying meta-distribution. To achieve probable DG, we
propose a new optimization problem called Quantile Risk Minimization (QRM). By
minimizing the $\alpha$-quantile of predictor's risk distribution over domains,
QRM seeks predictors that perform well with probability $\alpha$. To solve QRM
in practice, we propose the Empirical QRM (EQRM) algorithm, and prove: (i) a
generalization bound for EQRM; and (ii) that EQRM recovers the causal predictor
as $\alpha \to 1$. In our experiments, we introduce a more holistic
quantile-focused evaluation protocol for DG, and demonstrate that EQRM
outperforms state-of-the-art baselines on CMNIST and several datasets from
WILDS and DomainBed.

Seismic data processing heavily relies on the solution of physics-driven
inverse problems. In the presence of unfavourable data acquisition conditions
(e.g., regular or irregular coarse sampling of sources and/or receivers), the
underlying inverse problem becomes very ill-posed and prior information is
required to obtain a satisfactory solution. Sparsity-promoting inversion,
coupled with fixed-basis sparsifying transforms, represent the go-to approach
for many processing tasks due to its simplicity of implementation and proven
successful application in a variety of acquisition scenarios. Leveraging the
ability of deep neural networks to find compact representations of complex,
multi-dimensional vector spaces, we propose to train an AutoEncoder network to
learn a direct mapping between the input seismic data and a representative
latent manifold. The trained decoder is subsequently used as a nonlinear
preconditioner for the physics-driven inverse problem at hand. Synthetic and
field data are presented for a variety of seismic processing tasks and the
proposed nonlinear, learned transformations are shown to outperform fixed-basis
transforms and convergence faster to the sought solution.

In clinical procedures of angioplasty (i.e., open clogged coronary arteries),
devices such as balloons and stents need to be placed and expanded in arteries
under the guidance of X-ray fluoroscopy. Due to the limitation of X-ray dose,
the resulting images are often noisy. To check the correct placement of these
devices, typically multiple motion-compensated frames are averaged to enhance
the view. Therefore, device tracking is a necessary procedure for this purpose.
Even though angioplasty devices are designed to have radiopaque markers for the
ease of tracking, current methods struggle to deliver satisfactory results due
to the small marker size and complex scenes in angioplasty. In this paper, we
propose an end-to-end deep learning framework for single stent tracking, which
consists of three hierarchical modules: U-Net based landmark detection, ResNet
based stent proposal and feature extraction, and graph convolutional neural
network (GCN) based stent tracking that temporally aggregates both spatial
information and appearance features. The experiments show that our method
performs significantly better in detection compared with the state-of-the-art
point-based tracking models. In addition, its fast inference speed satisfies
clinical requirements.

This work examines secret key rates of key distribution based on quantum
repeaters in a broad parameter space of the communication distance and
coherence time of the quantum memories. As the first step in this task, a
Markov decision process modeling the distribution of entangled quantum states
via quantum repeaters is developed. Based on this model, a simulation is
implemented, which is employed to determine secret key rates under naively
controlled, limited memory storage times for a wide range of parameters. The
complexity of the quantum state evolution in a multiple-segment quantum
repeater chain motivates the use of deep reinforcement learning to search for
optimal solutions for the memory storage time limits - the so-called memory
cut-offs. The novel contribution in this work is to explore very general
cut-off strategies which dynamically adapt to the state of the quantum
repeater. An implementation of this approach is presented, with a particular
focus on four-segment quantum repeaters, achieving proof of concept of its
validity by finding exemplary solutions that outperform the naive strategies.

In this paper a pure-attention bottom-up approach, called ViGAT, that
utilizes an object detector together with a Vision Transformer (ViT) backbone
network to derive object and frame features, and a head network to process
these features for the task of event recognition and explanation in video, is
proposed. The ViGAT head consists of graph attention network (GAT) blocks
factorized along the spatial and temporal dimensions in order to capture
effectively both local and long-term dependencies between objects or frames.
Moreover, using the weighted in-degrees (WiDs) derived from the adjacency
matrices at the various GAT blocks, we show that the proposed architecture can
identify the most salient objects and frames that explain the decision of the
network. A comprehensive evaluation study is performed, demonstrating that the
proposed approach provides state-of-the-art results on three large, publicly
available video datasets (FCVID, Mini-Kinetics, ActivityNet).

Temporal action segmentation (TAS) aims to classify and locate actions in the
long untrimmed action sequence. With the success of deep learning, many deep
models for action segmentation have emerged. However, few-shot TAS is still a
challenging problem. This study proposes an efficient framework for the
few-shot skeleton-based TAS, including a data augmentation method and an
improved model. The data augmentation approach based on motion interpolation is
presented here to solve the problem of insufficient data, and can increase the
number of samples significantly by synthesizing action sequences. Besides, we
concatenate a Connectionist Temporal Classification (CTC) layer with a network
designed for skeleton-based TAS to obtain an optimized model. Leveraging CTC
can enhance the temporal alignment between prediction and ground truth and
further improve the segment-wise metrics of segmentation results. Extensive
experiments on both public and self-constructed datasets, including two
small-scale datasets and one large-scale dataset, show the effectiveness of two
proposed methods in improving the performance of the few-shot skeleton-based
TAS task.

Existing datasets used to train deep learning models for narrowband radio
frequency (RF) signal classification lack enough diversity in signal types and
channel impairments to sufficiently assess model performance in the real world.
We introduce the Sig53 dataset consisting of 5 million synthetically-generated
samples from 53 different signal classes and expertly chosen impairments. We
also introduce TorchSig, a signals processing machine learning toolkit that can
be used to generate this dataset. TorchSig incorporates data handling
principles that are common to the vision domain, and it is meant to serve as an
open-source foundation for future signals machine learning research. Initial
experiments using the Sig53 dataset are conducted using state of the art (SoTA)
convolutional neural networks (ConvNets) and Transformers. These experiments
reveal Transformers outperform ConvNets without the need for additional
regularization or a ConvNet teacher, which is contrary to results from the
vision domain. Additional experiments demonstrate that TorchSig's
domain-specific data augmentations facilitate model training, which ultimately
benefits model performance. Finally, TorchSig supports on-the-fly synthetic
data creation at training time, thus enabling massive scale training sessions
with virtually unlimited datasets.

Completely decentralized Multi-Player Bandit models have demonstrated high
localization accuracy at the cost of long convergence times in cognitive radar
networks. Rather than model each radar node as an independent learner, entirely
unable to swap information with other nodes in a network, in this work we
construct a "central coordinator" to facilitate the exchange of information
between radar nodes. We show that in interference-limited spectrum, where the
signal to interference plus noise (SINR) ratio for the available bands may vary
by location, a cognitive radar network (CRN) is able to use information from a
central coordinator to reduce the number of time steps required to attain a
given localization error. Importantly, each node is still able to learn
separately. We provide a description of a network which has hybrid cognition in
both a central coordinator and in each of the cognitive radar nodes, and
examine the online machine learning algorithms which can be implemented in this

Recent work has uncovered close links between between classical reinforcement
learning algorithms, Bayesian filtering, and Active Inference which lets us
understand value functions in terms of Bayesian posteriors. An alternative, but
less explored, model-free RL algorithm is the successor representation, which
expresses the value function in terms of a successor matrix of expected future
state occupancies. In this paper, we derive the probabilistic interpretation of
the successor representation in terms of Bayesian filtering and thus design a
novel active inference agent architecture utilizing successor representations
instead of model-based planning. We demonstrate that active inference successor
representations have significant advantages over current active inference
agents in terms of planning horizon and computational cost. Moreover, we
demonstrate how the successor representation agent can generalize to changing
reward functions such as variants of the expected free energy.

This work presents a systematic review of recent efforts (since 2010) aimed
at automatic analysis of nonverbal cues displayed in face-to-face co-located
human-human social interactions. The main reason for focusing on nonverbal cues
is that these are the physical, machine detectable traces of social and
psychological phenomena. Therefore, detecting and understanding nonverbal cues
means, at least to a certain extent, to detect and understand social and
psychological phenomena. The covered topics are categorized into three as: a)
modeling social traits, such as leadership, dominance, personality traits, b)
social role recognition and social relations detection and c) interaction
dynamics analysis in terms of group cohesion, empathy, rapport and so forth. We
target the co-located interactions, in which the interactants are always
humans. The survey covers a wide spectrum of settings and scenarios, including
free-standing interactions, meetings, indoor and outdoor social exchanges,
dyadic conversations, and crowd dynamics. For each of them, the survey
considers the three main elements of nonverbal cues analysis, namely data,
sensing approaches and computational methodologies. The goal is to highlight
the main advances of the last decade, to point out existing limitations, and to
outline future directions.

The proliferation of automated data collection schemes and the advances in
sensorics are increasing the amount of data we are able to monitor in
real-time. However, given the high annotation costs and the time required by
quality inspections, data is often available in an unlabeled form. This is
fostering the use of active learning for the development of soft sensors and
predictive models. In production, instead of performing random inspections to
obtain product information, labels are collected by evaluating the information
content of the unlabeled data. Several query strategy frameworks for regression
have been proposed in the literature but most of the focus has been dedicated
to the static pool-based scenario. In this work, we propose a new strategy for
the stream-based scenario, where instances are sequentially offered to the
learner, which must instantaneously decide whether to perform the quality check
to obtain the label or discard the instance. The approach is inspired by the
optimal experimental design theory and the iterative aspect of the
decision-making process is tackled by setting a threshold on the
informativeness of the unlabeled data points. The proposed approach is
evaluated using numerical simulations and the Tennessee Eastman Process
simulator. The results confirm that selecting the examples suggested by the
proposed algorithm allows for a faster reduction in the prediction error.

Training neural networks to perform 3D object detection for autonomous
driving requires a large amount of diverse annotated data. However, obtaining
training data with sufficient quality and quantity is expensive and sometimes
impossible due to human and sensor constraints. Therefore, a novel solution is
needed for extending current training methods to overcome this limitation and
enable accurate 3D object detection. Our solution for the above-mentioned
problem combines semi-pseudo-labeling and novel 3D augmentations. For
demonstrating the applicability of the proposed method, we have designed a
convolutional neural network for 3D object detection which can significantly
increase the detection range in comparison with the training data distribution.

With various face presentation attacks emerging continually, face
anti-spoofing (FAS) approaches based on domain generalization (DG) have drawn
growing attention. Existing DG-based FAS approaches always capture the
domain-invariant features for generalizing on the various unseen domains.
However, they neglect individual source domains' discriminative characteristics
and diverse domain-specific information of the unseen domains, and the trained
model is not sufficient to be adapted to various unseen domains. To address
this issue, we propose an Adaptive Mixture of Experts Learning (AMEL)
framework, which exploits the domain-specific information to adaptively
establish the link among the seen source domains and unseen target domains to
further improve the generalization. Concretely, Domain-Specific Experts (DSE)
are designed to investigate discriminative and unique domain-specific features
as a complement to common domain-invariant features. Moreover, Dynamic Expert
Aggregation (DEA) is proposed to adaptively aggregate the complementary
information of each source expert based on the domain relevance to the unseen
target domain. And combined with meta-learning, these modules work
collaboratively to adaptively aggregate meaningful domain-specific information
for the various unseen target domains. Extensive experiments and visualizations
demonstrate the effectiveness of our method against the state-of-the-art

Data-driven perception approaches are well-established in automated driving
systems. In many fields even super-human performance is reached. Unlike
prediction and planning approaches, mainly supervised learning algorithms are
used for the perception domain. Therefore, a major remaining challenge is the
efficient generation of ground truth data. As perception modules are positioned
close to the sensor, they typically run on raw sensor data of high bandwidth.
Due to that, the generation of ground truth labels typically causes a
significant manual effort, which leads to high costs for the labelling itself
and the necessary quality control. In this contribution, we propose an
automatic labeling approach for semantic segmentation of the drivable ego
corridor that reduces the manual effort by a factor of 150 and more. The
proposed holistic approach could be used in an automated data loop, allowing a
continuous improvement of the depending perception modules.

Vehicle Routing Problems (VRPs) in real-world applications often come with
various constraints, therefore bring additional computational challenges to
exact solution methods or heuristic search approaches. The recent idea to learn
heuristic move patterns from sample data has become increasingly promising to
reduce solution developing costs. However, using learning-based approaches to
address more types of constrained VRP remains a challenge. The difficulty lies
in controlling for constraint violations while searching for optimal solutions.
To overcome this challenge, we propose a Reinforcement Learning based method to
solve soft-constrained VRPs by incorporating the Lagrangian relaxation
technique and using constrained policy optimization. We apply the method on
three common types of VRPs, the Travelling Salesman Problem with Time Windows
(TSPTW), the Capacitated VRP (CVRP) and the Capacitated VRP with Time Windows
(CVRPTW), to show the generalizability of the proposed method. After comparing
to existing RL-based methods and open-source heuristic solvers, we demonstrate
its competitive performance in finding solutions with a good balance in travel
distance, constraint violations and inference speed.

form of medical data from distinct EHR systems. Our framework significantly
outperforms baseline models in multi-source learning tasks, including transfer
and pooled learning, while also showing comparable results when trained on a
single medical dataset. To empirically demonstrate the efficacy of our work, we
conducted extensive experiments using various datasets, model structures, and
tasks. We believe that our findings can provide helpful insights for further
research on the multi-source learning of EHRs.

Deep neural networks (DNNs) offer a real-time solution for the inversion of
borehole resistivity measurements to approximate forward and inverse operators.
It is possible to use extremely large DNNs to approximate the operators, but it
demands a considerable training time. Moreover, evaluating the network after
training also requires a significant amount of memory and processing power. In
addition, we may overfit the model. In this work, we propose a scoring function
that accounts for the accuracy and size of the DNNs compared to a reference DNN
that provides a good approximation for the operators. Using this scoring
function, we use DNN architecture search algorithms to obtain a quasi-optimal
DNN smaller than the reference network; hence, it requires less computational
effort during training and evaluation. The quasi-optimal DNN delivers
comparable accuracy to the original large DNN.

Integrating low-power wearable Internet of Things (IoT) systems into routine
health monitoring is an ongoing challenge. Recent advances in the computation
capabilities of wearables make it possible to target complex scenarios by
exploiting multiple biosignals and using high-performance algorithms, such as
Deep Neural Networks (DNNs). There is, however, a trade-off between performance
of the algorithms and the low-power requirements of IoT platforms with limited
resources. Besides, physically larger and multi-biosignal-based wearables bring
significant discomfort to the patients. Consequently, reducing power
consumption and discomfort is necessary for patients to use IoT devices
continuously during everyday life. To overcome these challenges, in the context
of epileptic seizure detection, we propose a many-to-one signals knowledge
distillation approach targeting single-biosignal processing in IoT wearable
systems. The starting point is to get a highly-accurate multi-biosignal DNN,
then apply our approach to develop a single-biosignal DNN solution for IoT
systems that achieves an accuracy comparable to the original multi-biosignal
DNN. To assess the practicality of our approach to real-life scenarios, we
perform a comprehensive simulation experiment analysis on several
state-of-the-art edge computing platforms, such as Kendryte K210 and Raspberry
Pi Zero.

Reinforcement learning (RL) has become widely adopted in robot control.
Despite many successes, one major persisting problem can be very low data
efficiency. One solution is interactive feedback, which has been shown to speed
up RL considerably. As a result, there is an abundance of different strategies,
which are, however, primarily tested on discrete grid-world and small scale
optimal control scenarios. In the literature, there is no consensus about which
feedback frequency is optimal or at which time the feedback is most beneficial.
To resolve these discrepancies we isolate and quantify the effect of feedback
frequency in robotic tasks with continuous state and action spaces. The
experiments encompass inverse kinematics learning for robotic manipulator arms
of different complexity. We show that seemingly contradictory reported
phenomena occur at different complexity levels. Furthermore, our results
suggest that no single ideal feedback frequency exists. Rather that feedback
frequency should be changed as the agent's proficiency in the task increases.

In this paper, we present end-to-end and speech embedding based systems
trained in a self-supervised fashion to participate in the ACM Multimedia 2022
ComParE Challenge, specifically the stuttering sub-challenge. In particular, we
exploit the embeddings from the pre-trained Wav2Vec2.0 model for stuttering
detection (SD) on the KSoF dataset. After embedding extraction, we benchmark
with several methods for SD. Our proposed self-supervised based SD system
achieves a UAR of 36.9% and 41.0% on validation and test sets respectively,
which is 31.32% (validation set) and 1.49% (test set) higher than the best
(DeepSpectrum) challenge baseline (CBL). Moreover, we show that concatenating
layer embeddings with Mel-frequency cepstral coefficients (MFCCs) features
further improves the UAR of 33.81% and 5.45% on validation and test sets
respectively over the CBL. Finally, we demonstrate that the summing information
across all the layers of Wav2Vec2.0 surpasses the CBL by a relative margin of
45.91% and 5.69% on validation and test sets respectively. Grand-challenge:
Computational Paralinguistics ChallengE

We propose united implicit functions (UNIF), a part-based method for clothed
human reconstruction and animation with raw scans and skeletons as the input.
Previous part-based methods for human reconstruction rely on ground-truth part
labels from SMPL and thus are limited to minimal-clothed humans. In contrast,
our method learns to separate parts from body motions instead of part
supervision, thus can be extended to clothed humans and other articulated
objects. Our Partition-from-Motion is achieved by a bone-centered
initialization, a bone limit loss, and a section normal loss that ensure stable
part division even when the training poses are limited. We also present a
minimal perimeter loss for SDF to suppress extra surfaces and part overlapping.
Another core of our method is an adjacent part seaming algorithm that produces
non-rigid deformations to maintain the connection between parts which
significantly relieves the part-based artifacts. Under this algorithm, we
further propose "Competing Parts", a method that defines blending weights by
the relative position of a point to bones instead of the absolute position,
avoiding the generalization problem of neural implicit functions with inverse
LBS (linear blend skinning). We demonstrate the effectiveness of our method by
clothed human body reconstruction and animation on the CAPE and the ClothSeq
datasets.

This paper summarizes and evaluates various approaches, methods, and
techniques for pursuing fairness in artificial intelligence (AI) systems. It
examines the merits and shortcomings of these measures and proposes practical
guidelines for defining, measuring, and preventing bias in AI. In particular,
it cautions against some of the simplistic, yet common, methods for evaluating
bias in AI systems, and offers more sophisticated and effective alternatives.
The paper also addresses widespread controversies and confusions in the field
by providing a common language among different stakeholders of high-impact AI
systems. It describes various trade-offs involving AI fairness, and provides
practical recommendations for balancing them. It offers techniques for
evaluating the costs and benefits of fairness targets, and defines the role of
human judgment in setting these targets. This paper provides discussions and
guidelines for AI practitioners, organization leaders, and policymakers, as
well as various links to additional materials for a more technical audience.
Numerous real-world examples are provided to clarify the concepts, challenges,
and recommendations from a practical perspective.

Human motion trajectory prediction, an essential task for autonomous systems
in many domains, has been on the rise in recent years. With a multitude of new
methods proposed by different communities, the lack of standardized benchmarks
and objective comparisons is increasingly becoming a major limitation to assess
progress and guide further research. Existing benchmarks are limited in their
scope and flexibility to conduct relevant experiments and to account for
contextual cues of agents and environments. In this paper we present Atlas, a
benchmark to systematically evaluate human motion trajectory prediction
algorithms in a unified framework. Atlas offers data preprocessing functions,
hyperparameter optimization, comes with popular datasets and has the
flexibility to setup and conduct underexplored yet relevant experiments to
analyze a method's accuracy and robustness. In an example application of Atlas,
we compare five popular model- and learning-based predictors and find that,
when properly applied, early physics-based approaches are still remarkably
competitive. Such results confirm the necessity of benchmarks like Atlas.

The journal impact factor (JIF) is often equated with journal quality and the
quality of the peer review of the papers submitted to the journal. We examined
the association between the content of peer review and JIF by analysing 10,000
peer review reports submitted to 1,644 medical and life sciences journals. Two
researchers hand-coded a random sample of 2,000 sentences. We then trained
machine learning models to classify all 187,240 sentences as contributing or
not contributing to content categories. We examined the association between ten
groups of journals defined by JIF deciles and the content of peer reviews using
linear mixed-effects models, adjusting for the length of the review. The JIF
ranged from 0.21 to 74.70. The length of peer reviews increased from the lowest
(median number of words 185) to the JIF group (387 words). The proportion of
sentences allocated to different content categories varied widely, even within
JIF groups. For thoroughness, sentences on 'Materials and Methods' were more
common in the highest JIF journals than in the lowest JIF group (difference of
7.8 percentage points; 95% CI 4.9 to 10.7%). The trend for 'Presentation and
Reporting' went in the opposite direction, with the highest JIF journals giving
less emphasis to such content (difference -8.9%; 95% CI -11.3 to -6.5%). For
helpfulness, reviews for higher JIF journals devoted less attention to
'Suggestion and Solution' and provided fewer Examples than lower impact factor
journals. No, or only small differences were evident for other content
categories. In conclusion, peer review in journals with higher JIF tends to be
more thorough in discussing the methods used but less helpful in terms of
suggesting solutions and providing examples. Differences were modest and
variability high, indicating that the JIF is a bad predictor for the quality of
peer review of an individual manuscript.

The increasing penetration of distributed energy resources in low-voltage
networks is turning end-users from consumers to prosumers. However, the
incomplete smart meter rollout and paucity of smart meter data due to the
regulatory separation between retail and network service provision make active
distribution network management difficult. Furthermore, distribution network
operators oftentimes do not have access to real-time smart meter data, which
creates an additional challenge. For the lack of better solutions, they use
blanket rooftop solar export limits, leading to suboptimal outcomes. To address
this, we designed a conditional generative adversarial network (CGAN)-based
model to forecast household solar generation and electricity demand, which
serves as an input to chance-constrained optimal power flow used to compute
fair operating envelopes under uncertainty.

In this paper, we present NUWA-Infinity, a generative model for infinite
visual synthesis, which is defined as the task of generating arbitrarily-sized
high-resolution images or long-duration videos. An autoregressive over
autoregressive generation mechanism is proposed to deal with this variable-size
generation task, where a global patch-level autoregressive model considers the
dependencies between patches, and a local token-level autoregressive model
considers dependencies between visual tokens within each patch. A Nearby
Context Pool (NCP) is introduced to cache-related patches already generated as
the context for the current patch being generated, which can significantly save
computation costs without sacrificing patch-level dependency modeling. An
Arbitrary Direction Controller (ADC) is used to decide suitable generation
orders for different visual synthesis tasks and learn order-aware positional
embeddings. Compared to DALL-E, Imagen and Parti, NUWA-Infinity can generate
high-resolution images with arbitrary sizes and support long-duration video
generation additionally. Compared to NUWA, which also covers images and videos,
NUWA-Infinity has superior visual synthesis capabilities in terms of resolution
and variable-size generation. The GitHub link is
https://github.com/microsoft/NUWA. The homepage link is
https://nuwa-infinity.microsoft.com.

Based on the model's resilience to computational noise, model quantization is
important for compressing models and improving computing speed. Existing
quantization techniques rely heavily on experience and "fine-tuning" skills. In
the majority of instances, the quantization model has a larger loss than a full
precision model. This study provides a methodology for acquiring a
mixed-precise quantization model with a lower loss than the full precision
model. In addition, the analysis demonstrates that, throughout the inference
process, the loss function is mostly affected by the noise of the layer inputs.
In particular, we will demonstrate that neural networks with massive identity
mappings are resistant to the quantization method. It is also difficult to
improve the performance of these networks using quantization.

Machine learning is transforming the video editing industry. Recent advances
in computer vision have leveled-up video editing tasks such as intelligent
reframing, rotoscoping, color grading, or applying digital makeups. However,
most of the solutions have focused on video manipulation and VFX. This work
introduces the Anatomy of Video Editing, a dataset, and benchmark, to foster
research in AI-assisted video editing. Our benchmark suite focuses on video
editing tasks, beyond visual effects, such as automatic footage organization
and assisted video assembling. To enable research on these fronts, we annotate
more than 1.5M tags, with relevant concepts to cinematography, from 196176
shots sampled from movie scenes. We establish competitive baseline methods and
detailed analyses for each of the tasks. We hope our work sparks innovative
research towards underexplored areas of AI-assisted video editing.

It is hard to collect enough flaw images for training deep learning network
in industrial production. Therefore, existing industrial anomaly detection
methods prefer to use CNN-based unsupervised detection and localization network
to achieve this task. However, these methods always fail when there are
varieties happened in new signals since traditional end-to-end networks suffer
barriers of fitting nonlinear model in high-dimensional space. Moreover, they
have a memory library by clustering the feature of normal images essentially,
which cause it is not robust to texture change. To this end, we propose the
Vision Transformer based (VIT-based) unsupervised anomaly detection network. It
utilizes a hierarchical task learning and human experience to enhance its
interpretability. Our network consists of pattern generation and comparison
networks. Pattern generation network uses two VIT-based encoder modules to
extract the feature of two consecutive image patches, then uses VIT-based
decoder module to learn the human designed style of these features and predict
the third image patch. After this, we use the Siamese-based network to compute
the similarity of the generation image patch and original image patch. Finally,
we refine the anomaly localization by the bi-directional inference strategy.
Comparison experiments on public dataset MVTec dataset show our method achieves
99.8% AUC, which surpasses previous state-of-the-art methods. In addition, we
give a qualitative illustration on our own leather and cloth datasets. The
accurate segment results strongly prove the accuracy of our method in anomaly

Algorithms for solving nonconvex, nonsmooth, finite-sum optimization problems
are proposed and tested. In particular, the algorithms are proposed and tested
in the context of an optimization problem formulation arising in
semi-supervised machine learning. The common feature of all algorithms is that
they employ an incremental quasi-Newton (IQN) strategy, specifically an
incremental BFGS (IBFGS) strategy. One applies an IBFGS strategy to the problem
directly, whereas the others apply an IBFGS strategy to a difference-of-convex
reformulation, smoothed approximation, or (strongly) convex local
approximation. Experiments show that all IBFGS approaches fare well in
practice, and all outperform a state-of-the-art bundle method.

Multiple modalities for certain information provide a variety of perspectives
on that information, which can improve the understanding of the information.
Thus, it may be crucial to generate data of different modality from the
existing data to enhance the understanding. In this paper, we investigate the
cross-modal audio-to-image generation problem and propose Cross-Modal
Contrastive Representation Learning (CMCRL) to extract useful features from
audios and use it in the generation phase. Experimental results show that CMCRL
enhances quality of images generated than previous research.

Diffusion models have emerged as one of the most promising frameworks for
deep generative modeling. In this work, we explore the potential of non-uniform
diffusion models. We show that non-uniform diffusion leads to multi-scale
diffusion models which have similar structure to this of multi-scale
normalizing flows. We experimentally find that in the same or less training
time, the multi-scale diffusion model achieves better FID score than the
standard uniform diffusion model. More importantly, it generates samples $4.4$
times faster in $128\times 128$ resolution. The speed-up is expected to be
higher in higher resolutions where more scales are used. Moreover, we show that
non-uniform diffusion leads to a novel estimator for the conditional score
function which achieves on par performance with the state-of-the-art
conditional denoising estimator. Our theoretical and experimental findings are
accompanied by an open source library MSDiff which can facilitate further
research of non-uniform diffusion models.

In this work, a method for unsupervised energy disaggregation in private
households equipped with smart meters is proposed. This method aims to classify
power consumption as active or passive, granting the ability to report on the
residents' activity and presence without direct interaction. This lays the
foundation for applications like non-intrusive health monitoring of private
homes.
  The proposed method is based on minimizing a suitable energy functional, for
which the iPALM (inertial proximal alternating linearized minimization)
algorithm is employed, demonstrating that various conditions guaranteeing
convergence are satisfied.
  In order to confirm feasibility of the proposed method, experiments on
semi-synthetic test data sets and a comparison to existing, supervised methods
are provided.

A novel smart metering technique capable of anomaly detection was proposed
for real-time home power management system. Smart meter data generated in
real-time was obtained from 900 households of single apartments. To detect
outliers and missing values in smart meter data, a deep learning model, the
autoencoder, consisting of a graph convolutional network and bidirectional long
short-term memory network, was applied to the smart metering technique. Power
management based on the smart metering technique was performed by
multi-objective optimization in the presence of a battery storage system and an
electric vehicle. The results of the power management employing the proposed
smart metering technique indicate a reduction in electricity cost and amount of
power supplied by the grid compared to the results of power management without
anomaly detection.

Defining and separating cancer subtypes is essential for facilitating
personalized therapy modality and prognosis of patients. The definition of
subtypes has been constantly recalibrated as a result of our deepened
understanding. During this recalibration, researchers often rely on clustering
of cancer data to provide an intuitive visual reference that could reveal the
intrinsic characteristics of subtypes. The data being clustered are often omics
data such as transcriptomics that have strong correlations to the underlying
biological mechanism. However, while existing studies have shown promising
results, they suffer from issues associated with omics data: sample scarcity
and high dimensionality. As such, existing methods often impose unrealistic
assumptions to extract useful features from the data while avoiding overfitting
to spurious correlations. In this paper, we propose to leverage a recent strong
generative model, Vector Quantized Variational AutoEncoder (VQ-VAE), to tackle
the data issues and extract informative latent features that are crucial to the
quality of subsequent clustering by retaining only information relevant to
reconstructing the input. VQ-VAE does not impose strict assumptions and hence
its latent features are better representations of the input, capable of
yielding superior clustering performance with any mainstream clustering method.
Extensive experiments and medical analysis on multiple datasets comprising 10
distinct cancers demonstrate the VQ-VAE clustering results can significantly
and robustly improve prognosis over prevalent subtyping systems.

3D LiDAR semantic segmentation is fundamental for autonomous driving. Several
Unsupervised Domain Adaptation (UDA) methods for point cloud data have been
recently proposed to improve model generalization for different sensors and
environments. Researchers working on UDA problems in the image domain have
shown that sample mixing can mitigate domain shift. We propose a new approach
of sample mixing for point cloud UDA, namely Compositional Semantic Mix
(CoSMix), the first UDA approach for point cloud segmentation based on sample
mixing. CoSMix consists of a two-branch symmetric network that can process
labelled synthetic data (source) and real-world unlabelled point clouds
(target) concurrently. Each branch operates on one domain by mixing selected
pieces of data from the other one, and by using the semantic information
derived from source labels and target pseudo-labels. We evaluate CoSMix on two
large-scale datasets, showing that it outperforms state-of-the-art methods by a
large margin. Our code is available at
https://github.com/saltoricristiano/cosmix-uda.

The paper describes our proposed methodology for the six basic expression
classification track of Affective Behavior Analysis in-the-wild (ABAW)
Competition 2022. In Learing from Synthetic Data(LSD) task, facial expression
recognition (FER) methods aim to learn the representation of expression from
the artificially generated data and generalise to real data. Because of the
ambiguous of the synthetic data and the objectivity of the facial Action Unit
(AU), we resort to the AU information for performance boosting, and make
contributions as follows. First, to adapt the model to synthetic scenarios, we
use the knowledge from pre-trained large-scale face recognition data. Second,
we propose a conceptually-new framework, termed as AU-Supervised Convolutional
Vision Transformers (AU-CVT), which clearly improves the performance of FER by
jointly training auxiliary datasets with AU or pseudo AU labels. Our AU-CVT
achieved F1 score as $0.6863$, accuracy as $0.7433$ on the validation set. The
source code of our work is publicly available online:
https://github.com/msy1412/ABAW4

Wireless Capsule Endoscopy is one of the most advanced non-invasive methods
for the examination of gastrointestinal tracts. An intelligent computer-aided
diagnostic system for detecting gastrointestinal abnormalities like polyp,
bleeding, inflammation, etc. is highly exigent in wireless capsule endoscopy
image analysis. Abnormalities greatly differ in their shape, size, color, and
texture, and some appear to be visually similar to normal regions. This poses a
challenge in designing a binary classifier due to intra-class variations. In
this study, a hybrid convolutional neural network is proposed for abnormality
detection that extracts a rich pool of meaningful features from wireless
capsule endoscopy images using a variety of convolution operations. It consists
of three parallel convolutional neural networks, each with a distinctive
feature learning capability. The first network utilizes depthwise separable
convolution, while the second employs cosine normalized convolution operation.
A novel meta-feature extraction mechanism is introduced in the third network,
to extract patterns from the statistical information drawn over the features
generated from the first and second networks and its own previous layer. The
network trio effectively handles intra-class variance and efficiently detects
gastrointestinal abnormalities. The proposed hybrid convolutional neural
network model is trained and tested on two widely used publicly available
datasets. The test results demonstrate that the proposed model outperforms six
state-of-the-art methods with 97\% and 98\% classification accuracy on KID and
Kvasir-Capsule datasets respectively. Cross dataset evaluation results also
demonstrate the generalization performance of the proposed model.

We propose a method to learn predictors that are invariant under
counterfactual changes of certain covariates. This method is useful when the
prediction target is causally influenced by covariates that should not affect
the predictor output. For instance, an object recognition model may be
influenced by position, orientation, or scale of the object itself. We address
the problem of training predictors that are explicitly counterfactually
invariant to changes of such covariates. We propose a model-agnostic
regularization term based on conditional kernel mean embeddings, to enforce
counterfactual invariance during training. We prove the soundness of our
method, which can handle mixed categorical and continuous multi-variate
attributes. Empirical results on synthetic and real-world data demonstrate the
efficacy of our method in a variety of settings.

In this work, we consider the problem of cross-domain 3D action recognition
in the open-set setting, which has been rarely explored before. Specifically,
there is a source domain and a target domain that contain the skeleton
sequences with different styles and categories, and our purpose is to cluster
the target data by utilizing the labeled source data and unlabeled target data.
For such a challenging task, this paper presents a novel approach dubbed CoDT
to collaboratively cluster the domain-shared features and target-specific
features. CoDT consists of two parallel branches. One branch aims to learn
domain-shared features with supervised learning in the source domain, while the
other is to learn target-specific features using contrastive learning in the
target domain. To cluster the features, we propose an online clustering
algorithm that enables simultaneous promotion of robust pseudo label generation
and feature clustering. Furthermore, to leverage the complementarity of
domain-shared features and target-specific features, we propose a novel
collaborative clustering strategy to enforce pair-wise relationship consistency
between the two branches. We conduct extensive experiments on multiple
cross-domain 3D action recognition datasets, and the results demonstrate the

Profile hidden Markov models (pHMMs) are widely used in many bioinformatics
applications to accurately identify similarities between biological sequences
(e.g., DNA or protein sequences). PHMMs use a commonly-adopted and
highly-accurate method, called the Baum-Welch algorithm, to calculate these
similarities. However, the Baum-Welch algorithm is computationally expensive,
and existing works provide either software- or hardware-only solutions for a
fixed pHMM design. When we analyze the state-of-the-art works, we find that
there is a pressing need for a flexible, high-performant, and energy-efficient
hardware-software co-design to efficiently and effectively solve all the major
inefficiencies in the Baum-Welch algorithm for pHMMs.
  We propose ApHMM, the first flexible acceleration framework that can
significantly reduce computational and energy overheads of the Baum-Welch
algorithm for pHMMs. ApHMM leverages hardware-software co-design to solve the
major inefficiencies in the Baum-Welch algorithm by 1) designing a flexible
hardware to support different pHMMs designs, 2) exploiting the predictable data
dependency pattern in an on-chip memory with memoization techniques, 3) quickly
eliminating negligible computations with a hardware-based filter, and 4)
minimizing the redundant computations. We implement our 1) hardware-software
optimizations on a specialized hardware and 2) software optimizations for GPUs
to provide the first flexible Baum-Welch accelerator for pHMMs. ApHMM provides
significant speedups of 15.55x-260.03x, 1.83x-5.34x, and 27.97x compared to
CPU, GPU, and FPGA implementations of the Baum-Welch algorithm, respectively.
ApHMM outperforms the state-of-the-art CPU implementations of three important
bioinformatics applications, 1) error correction, 2) protein family search, and
3) multiple sequence alignment, by 1.29x-59.94x, 1.03x-1.75x, and 1.03x-1.95x,

3D point cloud semantic segmentation is fundamental for autonomous driving.
Most approaches in the literature neglect an important aspect, i.e., how to
deal with domain shift when handling dynamic scenes. This can significantly
hinder the navigation capabilities of self-driving vehicles. This paper
advances the state of the art in this research field. Our first contribution
consists in analysing a new unexplored scenario in point cloud segmentation,
namely Source-Free Online Unsupervised Domain Adaptation (SF-OUDA). We
experimentally show that state-of-the-art methods have a rather limited ability
to adapt pre-trained deep network models to unseen domains in an online manner.
Our second contribution is an approach that relies on adaptive self-training
and geometric-feature propagation to adapt a pre-trained source model online
without requiring either source data or target labels. Our third contribution
is to study SF-OUDA in a challenging setup where source data is synthetic and
target data is point clouds captured in the real world. We use the recent
SynLiDAR dataset as a synthetic source and introduce two new synthetic (source)
datasets, which can stimulate future synthetic-to-real autonomous driving
research. Our experiments show the effectiveness of our segmentation approach
on thousands of real-world point clouds. Code and synthetic datasets are
available at https://github.com/saltoricristiano/gipso-sfouda.

A primary challenge faced in few-shot action recognition is inadequate video
data for training. To address this issue, current methods in this field mainly
focus on devising algorithms at the feature level while little attention is
paid to processing input video data. Moreover, existing frame sampling
strategies may omit critical action information in temporal and spatial
dimensions, which further impacts video utilization efficiency. In this paper,
we propose a novel video frame sampler for few-shot action recognition to
address this issue, where task-specific spatial-temporal frame sampling is
achieved via a temporal selector (TS) and a spatial amplifier (SA).
Specifically, our sampler first scans the whole video at a small computational
cost to obtain a global perception of video frames. The TS plays its role in
selecting top-T frames that contribute most significantly and subsequently. The
SA emphasizes the discriminative information of each frame by amplifying
critical regions with the guidance of saliency maps. We further adopt
task-adaptive learning to dynamically adjust the sampling strategy according to
the episode task at hand. Both the implementations of TS and SA are
differentiable for end-to-end optimization, facilitating seamless integration
of our proposed sampler with most few-shot action recognition methods.
Extensive experiments show a significant boost in the performances on various
benchmarks including long-term videos.

Spiking Neural Networks (SNNs) have emerged as a hardware efficient
architecture for classification tasks. The penalty of spikes-based encoding has
been the lack of a universal training mechanism performed entirely using
spikes. There have been several attempts to adopt the powerful backpropagation
(BP) technique used in non-spiking artificial neural networks (ANN): (1) SNNs
can be trained by externally computed numerical gradients. (2) A major
advancement toward native spike-based learning has been the use of approximate
Backpropagation using spike-time-dependent plasticity (STDP) with phased
forward/backward passes. However, the transfer of information between such
phases necessitates external memory and computational access. This is a
challenge for neuromorphic hardware implementations. In this paper, we propose
a stochastic SNN-based Back-Prop (SSNN-BP) algorithm that utilizes a composite
neuron to simultaneously compute the forward pass activations and backward pass
gradients explicitly with spikes. Although signed gradient values are a
challenge for spike-based representation, we tackle this by splitting the
gradient signal into positive and negative streams. The composite neuron
encodes information in the form of stochastic spike-trains and converts
Backpropagation weight updates into temporally and spatially local discrete
STDP-like spike coincidence updates compatible with hardware-friendly Resistive
Processing Units (RPUs). Furthermore, our method approaches BP ANN baseline
with sufficiently long spike-trains. Finally, we show that softmax
cross-entropy loss function can be implemented through inhibitory lateral
connections enforcing a Winner Take All (WTA) rule. Our SNN shows excellent
generalization through comparable performance to ANNs on the MNIST,
Fashion-MNIST and Extended MNIST datasets. Thus, SSNN-BP enables BP compatible
with purely spike-based neuromorphic hardware.

Facial affect analysis remains a challenging task with its setting
transitioned from lab-controlled to in-the-wild situations. In this paper, we
present novel frameworks to handle the two challenges in the 4th Affective
Behavior Analysis In-The-Wild (ABAW) competition: i) Multi-Task-Learning (MTL)
Challenge and ii) Learning from Synthetic Data (LSD) Challenge. For MTL
challenge, we adopt the SMM-EmotionNet with a better ensemble strategy of
feature vectors. For LSD challenge, we propose respective methods to combat the
problems of single labels, imbalanced distribution, fine-tuning limitations,
and choice of model architectures. Experimental results on the official
validation sets from the competition demonstrated that our proposed approaches
outperformed baselines by a large margin. The code is available at
https://github.com/sylyoung/ABAW4-HUST-ANT.

Automatic speech recognition (ASR) has progressed significantly in recent
years due to the emergence of large-scale datasets and the self-supervised
learning (SSL) paradigm. However, as its counterpart problem in the singing
domain, the development of automatic lyric transcription (ALT) suffers from
limited data and degraded intelligibility of sung lyrics. To fill in the
performance gap between ALT and ASR, we attempt to exploit the similarities
between speech and singing. In this work, we propose a transfer-learning-based
ALT solution that takes advantage of these similarities by adapting wav2vec
2.0, an SSL ASR model, to the singing domain. We maximize the effectiveness of
transfer learning by exploring the influence of different transfer starting
points. We further enhance the performance by extending the original CTC model
to a hybrid CTC/attention model. Our method surpasses previous approaches by a
large margin on various ALT benchmark datasets. Further experiments show that,
with even a tiny proportion of training data, our method still achieves

To improve the modeling resilience of silicon strong physical unclonable
functions (PUFs), in particular, the APUFs, that yield a very large number of
challenge response pairs (CRPs), a number of composited APUF variants such as
XOR-APUF, interpose-PUF (iPUF), feed-forward APUF (FF-APUF),and OAX-APUF have
been devised. When examining their security in terms of modeling resilience,
utilizing multiple information sources such as power side channel information
(SCI) or/and reliability SCI given a challenge is under-explored, which poses a
challenge to their supposed modeling resilience in practice. Building upon
multi-label/head deep learning model architecture,this work proposes
Multi-Label Multi-Side-channel-information enabled deep learning Attacks
(MLMSA) to thoroughly evaluate the modeling resilience of aforementioned APUF
variants. Despite its simplicity, MLMSA can successfully break large-scaled
APUF variants, which has not previously been achieved. More precisely, the
MLMSA breaks 128-stage 30-XOR-APUF, (9, 9)- and (2, 18)-iPUFs, and (2, 2,
30)-OAX-APUF when CRPs, power SCI and reliability SCI are concurrently used. It
breaks 128-stage 12-XOR-APUF and (2, 2, 9)-OAX-APUF even when only the
easy-to-obtain reliability SCI and CRPs are exploited. The 128-stage six-loop
FF-APUF and one-loop 20-XOR-FF-APUF can be broken by simultaneously using
reliability SCI and CRPs. All these attacks are normally completed within an
hour with a standard personalcomputer. Therefore, MLMSA is a useful technique
for evaluating other existing or any emerging strong PUF designs.

Generative models such as Generative Adversarial Networks (GANs) and
Variational Autoencoders (VAEs) play an increasingly important role in medical
image analysis. The latent spaces of these models often show semantically
meaningful directions corresponding to human-interpretable image
transformations. However, until now, their exploration for medical images has
been limited due to the requirement of supervised data. Several methods for
unsupervised discovery of interpretable directions in GAN latent spaces have
shown interesting results on natural images. This work explores the potential
of applying these techniques on medical images by training a GAN and a VAE on
thoracic CT scans and using an unsupervised method to discover interpretable
directions in the resulting latent space. We find several directions
corresponding to non-trivial image transformations, such as rotation or breast
size. Furthermore, the directions show that the generative models capture 3D
structure despite being presented only with 2D data. The results show that
unsupervised methods to discover interpretable directions in GANs generalize to
VAEs and can be applied to medical images. This opens a wide array of future
work using these methods in medical image analysis.

We propose CrossHuman, a novel method that learns cross-guidance from
parametric human model and multi-frame RGB images to achieve high-quality 3D
human reconstruction. To recover geometry details and texture even in invisible
regions, we design a reconstruction pipeline combined with tracking-based
methods and tracking-free methods. Given a monocular RGB sequence, we track the
parametric human model in the whole sequence, the points (voxels) corresponding
to the target frame are warped to reference frames by the parametric body
motion. Guided by the geometry priors of the parametric body and spatially
aligned features from RGB sequence, the robust implicit surface is fused.
Moreover, a multi-frame transformer (MFT) and a self-supervised warp refinement
module are integrated to the framework to relax the requirements of parametric
body and help to deal with very loose cloth. Compared with previous works, our
CrossHuman enables high-fidelity geometry details and texture in both visible
and invisible regions and improves the accuracy of the human reconstruction
even under estimated inaccurate parametric human models. The experiments
demonstrate that our method achieves state-of-the-art (SOTA) performance.

The amount of audio data available on public websites is growing rapidly, and
an efficient mechanism for accessing the desired data is necessary. We propose
a content-based audio retrieval method that can retrieve a target audio that is
similar to but slightly different from the query audio by introducing auxiliary
textual information which describes the difference between the query and target
audio. While the range of conventional content-based audio retrieval is limited
to audio that is similar to the query audio, the proposed method can adjust the
retrieval range by adding an embedding of the auxiliary text query-modifier to
the embedding of the query sample audio in a shared latent space. To evaluate
our method, we built a dataset comprising two different audio clips and the
text that describes the difference. The experimental results show that the
proposed method retrieves the paired audio more accurately than the baseline.
We also confirmed based on visualization that the proposed method obtains the
shared latent space in which the audio difference and the corresponding text
are represented as similar embedding vectors.

Subspace clustering is the classical problem of clustering a collection of
data samples that approximately lie around several low-dimensional subspaces.
The current state-of-the-art approaches for this problem are based on the
self-expressive model which represents the samples as linear combination of
other samples. However, these approaches require sufficiently well-spread
samples for accurate representation which might not be necessarily accessible
in many applications. In this paper, we shed light on this commonly neglected
issue and argue that data distribution within each subspace plays a critical
role in the success of self-expressive models. Our proposed solution to tackle
this issue is motivated by the central role of data augmentation in the
generalization power of deep neural networks. We propose two subspace
clustering frameworks for both unsupervised and semi-supervised settings that
use augmented samples as an enlarged dictionary to improve the quality of the
self-expressive representation. We present an automatic augmentation strategy
using a few labeled samples for the semi-supervised problem relying on the fact
that the data samples lie in the union of multiple linear subspaces.
Experimental results confirm the effectiveness of data augmentation, as it
significantly improves the performance of general self-expressive models.

Current supervised cross-domain image retrieval methods can achieve excellent
performance. However, the cost of data collection and labeling imposes an
intractable barrier to practical deployment in real applications. In this
paper, we investigate the unsupervised cross-domain image retrieval task, where
class labels and pairing annotations are no longer a prerequisite for training.
This is an extremely challenging task because there is no supervision for both
in-domain feature representation learning and cross-domain alignment. We
address both challenges by introducing: 1) a new cluster-wise contrastive
learning mechanism to help extract class semantic-aware features, and 2) a
novel distance-of-distance loss to effectively measure and minimize the domain
discrepancy without any external supervision. Experiments on the Office-Home
and DomainNet datasets consistently show the superior image retrieval
accuracies of our framework over state-of-the-art approaches. Our source code
can be found at https://github.com/conghuihu/UCDIR.

Facial valence/arousal, expression and action unit are related tasks in
facial affective analysis. However, the tasks only have limited performance in
the wild due to the various collected conditions. The 4th competition on
affective behavior analysis in the wild (ABAW) provided images with
valence/arousal, expression and action unit labels. In this paper, we introduce
multi-task learning framework to enhance the performance of three related tasks
in the wild. Feature sharing and label fusion are used to utilize their
relations. We conduct experiments on the provided training and validating data.

Mechanistic simulators are an indispensable tool for epidemiology to explore
the behavior of complex, dynamic infections under varying conditions and
navigate uncertain environments. ODE-based models are the dominant paradigm
that enable fast simulations and are tractable to gradient-based optimization,
but make simplifying assumptions about population homogeneity. Agent-based
models (ABMs) are an increasingly popular alternative paradigm that can
represent the heterogeneity of contact interactions with granular detail and
agency of individual behavior. However, conventional ABM frameworks are not
differentiable and present challenges in scalability; due to which it is
non-trivial to connect them to auxiliary data sources easily. In this paper we
introduce GradABM which is a new scalable, fast and differentiable design for
ABMs. GradABM runs simulations in few seconds on commodity hardware and enables
fast forward and differentiable inverse simulations. This makes it amenable to
be merged with deep neural networks and seamlessly integrate heterogeneous data
sources to help with calibration, forecasting and policy evaluation. We
demonstrate the efficacy of GradABM via extensive experiments with real
COVID-19 and influenza datasets. We are optimistic this work will bring ABM and
AI communities closer together.

The number of mobile robots with constrained computing resources that need to
execute complex machine learning models has been increasing during the past
decade. Commonly, these robots rely on edge infrastructure accessible over
wireless communication to execute heavy computational complex tasks. However,
the edge might become unavailable and, consequently, oblige the execution of
the tasks on the robot. This work focuses on making it possible to execute the
tasks on the robots by reducing the complexity and the total number of
parameters of pre-trained computer vision models. This is achieved by using
model compression techniques such as Pruning and Knowledge Distillation. These
compression techniques have strong theoretical and practical foundations, but
their combined usage has not been widely explored in the literature. Therefore,
this work especially focuses on investigating the effects of combining these
two compression techniques. The results of this work reveal that up to 90% of
the total number of parameters of a computer vision model can be removed
without any considerable reduction in the model's accuracy.

The key challenge of sequence representation learning is to capture the
long-range temporal dependencies. Typical methods for supervised sequence
representation learning are built upon recurrent neural networks to capture
temporal dependencies. One potential limitation of these methods is that they
only model one-order information interactions explicitly between adjacent time
steps in a sequence, hence the high-order interactions between nonadjacent time
steps are not fully exploited. It greatly limits the capability of modeling the
long-range temporal dependencies since the temporal features learned by
one-order interactions cannot be maintained for a long term due to temporal
information dilution and gradient vanishing. To tackle this limitation, we
propose the Non-local Recurrent Neural Memory (NRNM) for supervised sequence
representation learning, which performs non-local operations \MR{by means of
self-attention mechanism} to learn full-order interactions within a sliding
temporal memory block and models global interactions between memory blocks in a
gated recurrent manner. Consequently, our model is able to capture long-range
dependencies. Besides, the latent high-level features contained in high-order
interactions can be distilled by our model. We validate the effectiveness and
generalization of our NRNM on three types of sequence applications across
different modalities, including sequence classification, step-wise sequential
prediction and sequence similarity learning. Our model compares favorably
against other state-of-the-art methods specifically designed for each of these
sequence applications.

Imitation learning is a widely used policy learning method that enables
intelligent agents to acquire complex skills from expert demonstrations. The
input to the imitation learning algorithm is usually composed of both the
current observation and historical observations since the most recent
observation might not contain enough information. This is especially the case
with image observations, where a single image only includes one view of the
scene, and it suffers from a lack of motion information and object occlusions.
In theory, providing multiple observations to the imitation learning agent will
lead to better performance. However, surprisingly people find that sometimes
imitation from observation histories performs worse than imitation from the
most recent observation. In this paper, we explain this phenomenon from the
information flow within the neural network perspective. We also propose a novel
imitation learning neural network architecture that does not suffer from this
issue by design. Furthermore, our method scales to high-dimensional image
observations. Finally, we benchmark our approach on two widely used simulators,
CARLA and MuJoCo, and it successfully alleviates the copycat problem and
surpasses the existing solutions.

Learning accurate object detectors often requires large-scale training data
with precise object bounding boxes. However, labeling such data is expensive
and time-consuming. As the crowd-sourcing labeling process and the ambiguities
of the objects may raise noisy bounding box annotations, the object detectors
will suffer from the degenerated training data. In this work, we aim to address
the challenge of learning robust object detectors with inaccurate bounding
boxes. Inspired by the fact that localization precision suffers significantly
from inaccurate bounding boxes while classification accuracy is less affected,
we propose leveraging classification as a guidance signal for refining
localization results. Specifically, by treating an object as a bag of
instances, we introduce an Object-Aware Multiple Instance Learning approach
(OA-MIL), featured with object-aware instance selection and object-aware
instance extension. The former aims to select accurate instances for training,
instead of directly using inaccurate box annotations. The latter focuses on
generating high-quality instances for selection. Extensive experiments on
synthetic noisy datasets (i.e., noisy PASCAL VOC and MS-COCO) and a real noisy
wheat head dataset demonstrate the effectiveness of our OA-MIL. Code is
available at https://github.com/cxliu0/OA-MIL.

Recent studies have utilized sparse classifications to predict categorical
variables from high-dimensional brain activity signals to expose human's
intentions and mental states, selecting the relevant features automatically in
the model training process. However, existing sparse classification models will
likely be prone to the performance degradation which is caused by noise
inherent in the brain recordings. To address this issue, we aim to propose a
new robust and sparse classification algorithm in this study. To this end, we
introduce the correntropy learning framework into the automatic relevance
determination based sparse classification model, proposing a new
correntropy-based robust sparse logistic regression algorithm. To demonstrate
the superior brain activity decoding performance of the proposed algorithm, we
evaluate it on a synthetic dataset, an electroencephalogram (EEG) dataset, and
a functional magnetic resonance imaging (fMRI) dataset. The extensive
experimental results confirm that not only the proposed method can achieve
higher classification accuracy in a noisy and high-dimensional classification
task, but also it would select those more informative features for the decoding
scenarios. Integrating the correntropy learning approach with the automatic
relevance determination technique will significantly improve the robustness
with respect to the noise, leading to more adequate robust sparse brain
decoding algorithm. It provides a more powerful approach in the real-world
brain activity decoding and the brain-computer interfaces.

Background \& purpose: The recent emergence of neural networks models for the
analysis of breast images has been a breakthrough in computer aided diagnostic.
This approach was not yet developed in Contrast Enhanced Spectral Mammography
(CESM) where access to large databases is complex. This work proposes a
deep-learning-based Computer Aided Diagnostic development for CESM recombined
images able to detect lesions and classify cases. Material \& methods: A large
CESM diagnostic dataset with biopsy-proven lesions was collected from various
hospitals and different acquisition systems. The annotated data were split on a
patient level for the training (55%), validation (15%) and test (30%) of a deep
neural network with a state-of-the-art detection architecture. Free Receiver
Operating Characteristic (FROC) was used to evaluate the model for the
detection of 1) all lesions, 2) biopsied lesions and 3) malignant lesions. ROC
curve was used to evaluate breast cancer classification. The metrics were
finally compared to clinical results. Results: For the evaluation of the
malignant lesion detection, at high sensitivity (Se>0.95), the false positive
rate was at 0.61 per image. For the classification of malignant cases, the
model reached an Area Under the Curve (AUC) in the range of clinical CESM
diagnostic results. Conclusion: This CAD is the first development of a lesion
detection and classification model for CESM images. Trained on a large dataset,
it has the potential to be used for helping the management of biopsy decision
and for helping the radiologist detecting complex lesions that could modify the
clinical treatment.

Recently, Deep Neural Networks (DNNs) are utilized to reduce the bandwidth
and improve the quality of Internet video delivery. Existing methods train
corresponding content-aware super-resolution (SR) model for each video chunk on
the server, and stream low-resolution (LR) video chunks along with SR models to
the client. Although they achieve promising results, the huge computational
cost of network training limits their practical applications. In this paper, we
present a method named Efficient Meta-Tuning (EMT) to reduce the computational
cost. Instead of training from scratch, EMT adapts a meta-learned model to the
first chunk of the input video. As for the following chunks, it fine-tunes the
partial parameters selected by gradient masking of previous adapted model. In
order to achieve further speedup for EMT, we propose a novel sampling strategy
to extract the most challenging patches from video frames. The proposed
strategy is highly efficient and brings negligible additional cost. Our method
significantly reduces the computational cost and achieves even better
performance, paving the way for applying neural video delivery techniques to
practical applications. We conduct extensive experiments based on various
efficient SR architectures, including ESPCN, SRCNN, FSRCNN and EDSR-1,
demonstrating the generalization ability of our work. The code is released at
\url{https://github.com/Neural-video-delivery/EMT-Pytorch-ECCV2022}.

A main challenge faced in the deep learning-based Underwater Image
Enhancement (UIE) is that the ground truth high-quality image is unavailable.
Most of the existing methods first generate approximate reference maps and then
train an enhancement network with certainty. This kind of method fails to
handle the ambiguity of the reference map. In this paper, we resolve UIE into
distribution estimation and consensus process. We present a novel probabilistic
network to learn the enhancement distribution of degraded underwater images.
Specifically, we combine conditional variational autoencoder with adaptive
instance normalization to construct the enhancement distribution. After that,
we adopt a consensus process to predict a deterministic result based on a set
of samples from the distribution. By learning the enhancement distribution, our
method can cope with the bias introduced in the reference map labeling to some
extent. Additionally, the consensus process is useful to capture a robust and
stable result. We examined the proposed method on two widely used real-world
underwater image enhancement datasets. Experimental results demonstrate that
our approach enables sampling possible enhancement predictions. Meanwhile, the
consensus estimate yields competitive performance compared with
state-of-the-art UIE methods. Code available at
https://github.com/zhenqifu/PUIE-Net.

Real world-datasets characterized by discrete features are ubiquitous: from
categorical surveys to clinical questionnaires, from unweighted networks to DNA
sequences. Nevertheless, the most common unsupervised dimensional reduction
methods are designed for continuous spaces, and their use for discrete spaces
can lead to errors and biases. In this letter we introduce an algorithm to
infer the intrinsic dimension (ID) of datasets embedded in discrete spaces. We
demonstrate its accuracy on benchmark datasets, and we apply it to analyze a
metagenomic dataset for species fingerprinting, finding a surprisingly small
ID, of order 2. This suggests that evolutive pressure acts on a low-dimensional
manifold despite the high-dimensionality of sequences' space.

The neural implicit representation has shown its effectiveness in novel view
synthesis and high-quality 3D reconstruction from multi-view images. However,
most approaches focus on holistic scene representation yet ignore individual
objects inside it, thus limiting potential downstream applications. In order to
learn object-compositional representation, a few works incorporate the 2D
semantic map as a cue in training to grasp the difference between objects. But
they neglect the strong connections between object geometry and instance
semantic information, which leads to inaccurate modeling of individual
instance. This paper proposes a novel framework, ObjectSDF, to build an
object-compositional neural implicit representation with high fidelity in 3D
reconstruction and object representation. Observing the ambiguity of
conventional volume rendering pipelines, we model the scene by combining the
Signed Distance Functions (SDF) of individual object to exert explicit surface
constraint. The key in distinguishing different instances is to revisit the
strong association between an individual object's SDF and semantic label.
Particularly, we convert the semantic information to a function of object SDF
and develop a unified and compact representation for scene and objects.
Experimental results show the superiority of ObjectSDF framework in
representing both the holistic object-compositional scene and the individual
instances. Code can be found at https://qianyiwu.github.io/objectsdf/

For realistic and vivid colorization, generative priors have recently been
exploited. However, such generative priors often fail for in-the-wild complex
images due to their limited representation space. In this paper, we propose
BigColor, a novel colorization approach that provides vivid colorization for
diverse in-the-wild images with complex structures. While previous generative
priors are trained to synthesize both image structures and colors, we learn a
generative color prior to focus on color synthesis given the spatial structure
of an image. In this way, we reduce the burden of synthesizing image structures
from the generative prior and expand its representation space to cover diverse
images. To this end, we propose a BigGAN-inspired encoder-generator network
that uses a spatial feature map instead of a spatially-flattened BigGAN latent
code, resulting in an enlarged representation space. Our method enables robust
colorization for diverse inputs in a single forward pass, supports arbitrary
input resolutions, and provides multi-modal colorization results. We
demonstrate that BigColor significantly outperforms existing methods especially
on in-the-wild images with complex structures.

Comparing the functional behavior of neural network models, whether it is a
single network over time or two (or more networks) during or post-training, is
an essential step in understanding what they are learning (and what they are
not), and for identifying strategies for regularization or efficiency
improvements. Despite recent progress, e.g., comparing vision transformers to
CNNs, systematic comparison of function, especially across different networks,
remains difficult and is often carried out layer by layer. Approaches such as
canonical correlation analysis (CCA) are applicable in principle, but have been
sparingly used so far. In this paper, we revisit a (less widely known) from
statistics, called distance correlation (and its partial variant), designed to
evaluate correlation between feature spaces of different dimensions. We
describe the steps necessary to carry out its deployment for large scale models
-- this opens the door to a surprising array of applications ranging from
conditioning one deep model w.r.t. another, learning disentangled
representations as well as optimizing diverse models that would directly be
more robust to adversarial attacks. Our experiments suggest a versatile
regularizer (or constraint) with many advantages, which avoids some of the
common difficulties one faces in such analyses. Code is at
https://github.com/zhenxingjian/Partial_Distance_Correlation.

Recent years have witnessed significant success in Gradient Boosting Decision
Trees (GBDT) for a wide range of machine learning applications. Generally, a
consensus about GBDT's training algorithms is gradients and statistics are
computed based on high-precision floating points. In this paper, we investigate
an essentially important question which has been largely ignored by the
previous literature: how many bits are needed for representing gradients in
training GBDT? To solve this mystery, we propose to quantize all the
high-precision gradients in a very simple yet effective way in the GBDT's
training algorithm. Surprisingly, both our theoretical analysis and empirical
studies show that the necessary precisions of gradients without hurting any
performance can be quite low, e.g., 2 or 3 bits. With low-precision gradients,
most arithmetic operations in GBDT training can be replaced by integer
operations of 8, 16, or 32 bits. Promisingly, these findings may pave the way
for much more efficient training of GBDT from several aspects: (1) speeding up
the computation of gradient statistics in histograms; (2) compressing the
communication cost of high-precision statistical information during distributed
training; (3) the inspiration of utilization and development of hardware
architectures which well support low-precision computation for GBDT training.
Benchmarked on CPU, GPU, and distributed clusters, we observe up to 2$\times$
speedup of our simple quantization strategy compared with SOTA GBDT systems on
extensive datasets, demonstrating the effectiveness and potential of the
low-precision training of GBDT. The code will be released to the official
repository of LightGBM.

This paper aims to interpret how deepfake detection models learn artifact
features of images when just supervised by binary labels. To this end, three
hypotheses from the perspective of image matching are proposed as follows. 1.
Deepfake detection models indicate real/fake images based on visual concepts
that are neither source-relevant nor target-relevant, that is, considering such
visual concepts as artifact-relevant. 2. Besides the supervision of binary
labels, deepfake detection models implicitly learn artifact-relevant visual
concepts through the FST-Matching (i.e. the matching fake, source, target
images) in the training set. 3. Implicitly learned artifact visual concepts
through the FST-Matching in the raw training set are vulnerable to video
compression. In experiments, the above hypotheses are verified among various
DNNs. Furthermore, based on this understanding, we propose the FST-Matching
Deepfake Detection Model to boost the performance of forgery detection on
compressed videos. Experiment results show that our method achieves great
performance, especially on highly-compressed (e.g. c40) videos.

Early action prediction aims to successfully predict the class label of an
action before it is completely performed. This is a challenging task because
the beginning stages of different actions can be very similar, with only minor
subtle differences for discrimination. In this paper, we propose a novel Expert
Retrieval and Assembly (ERA) module that retrieves and assembles a set of
experts most specialized at using discriminative subtle differences, to
distinguish an input sample from other highly similar samples. To encourage our
model to effectively use subtle differences for early action prediction, we
push experts to discriminate exclusively between samples that are highly
similar, forcing these experts to learn to use subtle differences that exist
between those samples. Additionally, we design an effective Expert Learning
Rate Optimization method that balances the experts' optimization and leads to
better performance. We evaluate our ERA module on four public action datasets
and achieve state-of-the-art performance.

Inverse text normalization (ITN) is used to convert the spoken form output of
an automatic speech recognition (ASR) system to a written form. Traditional
handcrafted ITN rules can be complex to transcribe and maintain. Meanwhile
neural modeling approaches require quality large-scale spoken-written pair
examples in the same or similar domain as the ASR system (in-domain data), to
train. Both these approaches require costly and complex annotations. In this
paper, we present a data augmentation technique that effectively generates rich
spoken-written numeric pairs from out-of-domain textual data with minimal human
annotation. We empirically demonstrate that ITN model trained using our data
augmentation technique consistently outperform ITN model trained using only
in-domain data across all numeric surfaces like cardinal, currency, and
fraction, by an overall accuracy of 14.44%.

To drive health innovation that meets the needs of all and democratize
healthcare, there is a need to assess the generalization performance of deep
learning (DL) algorithms across various distribution shifts to ensure that
these algorithms are robust. This retrospective study is, to the best of our
knowledge, the first to develop and assess the generalization performance of a
deep learning (DL) model for AF events detection from long term beat-to-beat
intervals across ethnicities, ages and sexes. The new recurrent DL model,
denoted ArNet2, was developed on a large retrospective dataset of 2,147
patients totaling 51,386 hours of continuous electrocardiogram (ECG). The
models generalization was evaluated on manually annotated test sets from four
centers (USA, Israel, Japan and China) totaling 402 patients. The model was
further validated on a retrospective dataset of 1,730 consecutives Holter
recordings from the Rambam Hospital Holter clinic, Haifa, Israel. The model
outperformed benchmark state-of-the-art models and generalized well across
ethnicities, ages and sexes. Performance was higher for female than male and
young adults (less than 60 years old) and showed some differences across
ethnicities. The main finding explaining these variations was an impairment in
performance in groups with a higher prevalence of atrial flutter (AFL). Our
findings on the relative performance of ArNet2 across groups may have clinical
implications on the choice of the preferred AF examination method to use
relative to the group of interest.

Exoplanet detection opens the door to the discovery of new habitable worlds
and helps us understand how planets were formed. With the objective of finding
earth-like habitable planets, NASA launched Kepler space telescope and its
follow up mission K2. The advancement of observation capabilities has increased
the range of fresh data available for research, and manually handling them is
both time-consuming and difficult. Machine learning and deep learning
techniques can greatly assist in lowering human efforts to process the vast
array of data produced by the modern instruments of these exoplanet programs in
an economical and unbiased manner. However, care should be taken to detect all
the exoplanets precisely while simultaneously minimizing the misclassification
of non-exoplanet stars. In this paper, we utilize two variations of generative
adversarial networks, namely semi-supervised generative adversarial networks
and auxiliary classifier generative adversarial networks, to detect transiting
exoplanets in K2 data. We find that the usage of these models can be helpful
for the classification of stars with exoplanets. Both of our techniques are
able to categorize the light curves with a recall and precision of 1.00 on the
test data. Our semi-supervised technique is beneficial to solve the cumbersome
task of creating a labeled dataset.

Surgical scene segmentation is fundamentally crucial for prompting cognitive
assistance in robotic surgery. However, pixel-wise annotating surgical video in
a frame-by-frame manner is expensive and time consuming. To greatly reduce the
labeling burden, in this work, we study semi-supervised scene segmentation from
robotic surgical video, which is practically essential yet rarely explored
before. We consider a clinically suitable annotation situation under the
equidistant sampling. We then propose PGV-CL, a novel pseudo-label guided
cross-video contrast learning method to boost scene segmentation. It
effectively leverages unlabeled data for a trusty and global model
regularization that produces more discriminative feature representation.
Concretely, for trusty representation learning, we propose to incorporate
pseudo labels to instruct the pair selection, obtaining more reliable
representation pairs for pixel contrast. Moreover, we expand the representation
learning space from previous image-level to cross-video, which can capture the
global semantics to benefit the learning process. We extensively evaluate our
method on a public robotic surgery dataset EndoVis18 and a public cataract
dataset CaDIS. Experimental results demonstrate the effectiveness of our
method, consistently outperforming the state-of-the-art semi-supervised methods
under different labeling ratios, and even surpassing fully supervised training
on EndoVis18 with 10.1% labeling.

Learning from synthetic images plays an important role in facial expression
recognition task due to the difficulties of labeling the real images, and it is
challenging because of the gap between the synthetic images and real images.
The fourth Affective Behavior Analysis in-the-wild Competition raises the
challenge and provides the synthetic images generated from Aff-Wild2 dataset.
In this paper, we propose a hand-assisted expression recognition method to
reduce the gap between the synthetic data and real data. Our method consists of
two parts: expression recognition module and hand prediction module. Expression
recognition module extracts expression information and hand prediction module
predicts whether the image contains hands. Decision mode is used to combine the
results of two modules, and post-pruning is used to improve the result. F1
score is used to verify the effectiveness of our method.

We consider the problem of estimating the factors of a rank-$1$ matrix with
i.i.d. Gaussian, rank-$1$ measurements that are nonlinearly transformed and
corrupted by noise. Considering two prototypical choices for the nonlinearity,
we study the convergence properties of a natural alternating update rule for
this nonconvex optimization problem starting from a random initialization. We
show sharp convergence guarantees for a sample-split version of the algorithm
by deriving a deterministic recursion that is accurate even in high-dimensional
problems. Notably, while the infinite-sample population update is uninformative
and suggests exact recovery in a single step, the algorithm -- and our
deterministic prediction -- converges geometrically fast from a random
initialization. Our sharp, non-asymptotic analysis also exposes several other
fine-grained properties of this problem, including how the nonlinearity and
noise level affect convergence behavior.
  On a technical level, our results are enabled by showing that the empirical
error recursion can be predicted by our deterministic sequence within
fluctuations of the order $n^{-1/2}$ when each iteration is run with $n$
observations. Our technique leverages leave-one-out tools originating in the
literature on high-dimensional $M$-estimation and provides an avenue for
sharply analyzing higher-order iterative algorithms from a random
initialization in other high-dimensional optimization problems with random
data.

For better photography, most recent commercial cameras including smartphones
have either adopted large-aperture lens to collect more light or used a burst
mode to take multiple images within short times. These interesting features
lead us to examine depth from focus/defocus.
  In this work, we present a convolutional neural network-based depth
estimation from single focal stacks. Our method differs from relevant
state-of-the-art works with three unique features. First, our method allows
depth maps to be inferred in an end-to-end manner even with image alignment.
Second, we propose a sharp region detection module to reduce blur ambiguities
in subtle focus changes and weakly texture-less regions. Third, we design an
effective downsampling module to ease flows of focal information in feature
extractions. In addition, for the generalization of the proposed network, we
develop a simulator to realistically reproduce the features of commercial
cameras, such as changes in field of view, focal length and principal points.
  By effectively incorporating these three unique features, our network
achieves the top rank in the DDFF 12-Scene benchmark on most metrics. We also
demonstrate the effectiveness of the proposed method on various quantitative
evaluations and real-world images taken from various off-the-shelf cameras
compared with state-of-the-art methods. Our source code is publicly available
at https://github.com/wcy199705/DfFintheWild.

Cross-silo federated learning utilizes a few hundred reliable data silos with
high-speed access links to jointly train a model. While this approach becomes a
popular setting in federated learning, designing a robust topology to reduce
the training time is still an open problem. In this paper, we present a new
multigraph topology for cross-silo federated learning. We first construct the
multigraph using the overlay graph. We then parse this multigraph into
different simple graphs with isolated nodes. The existence of isolated nodes
allows us to perform model aggregation without waiting for other nodes, hence
reducing the training time. We further propose a new distributed learning
algorithm to use with our multigraph topology. The intensive experiments on
public datasets show that our proposed method significantly reduces the
training time compared with recent state-of-the-art topologies while ensuring
convergence and maintaining the model's accuracy.

Evaluation of researchers' output is vital for hiring committees and funding
bodies, and it is usually measured via their scientific productivity,
citations, or a combined metric such as h-index. Assessing young researchers is
more critical because it takes a while to get citations and increment of
h-index. Hence, predicting the h-index can help to discover the researchers'
scientific impact. In addition, identifying the influential factors to predict
the scientific impact is helpful for researchers seeking solutions to improve
it. This study investigates the effect of author, paper and venue-specific
features on the future h-index. For this purpose, we used machine learning
methods to predict the h-index and feature analysis techniques to advance the
understanding of feature impact. Utilizing the bibliometric data in Scopus, we
defined and extracted two main groups of features. The first relates to prior
scientific impact, and we name it 'prior impact-based features' and includes
the number of publications, received citations, and h-index. The second group
is 'non-impact-based features' and contains the features related to author,
co-authorship, paper, and venue characteristics. We explored their importance
in predicting h-index for researchers in three different career phases. Also,
we examine the temporal dimension of predicting performance for different
feature categories to find out which features are more reliable for long- and
short-term prediction. We referred to the gender of the authors to examine the
role of this author's characteristics in the prediction task. Our findings
showed that gender has a very slight effect in predicting the h-index. We found
that non-impact-based features are more robust predictors for younger scholars
than seniors in the short term. Also, prior impact-based features lose their
power to predict more than other features in the long-term.

Deep learning methods have achieved impressive performance for multi-class
medical image segmentation. However, they are limited in their ability to
encode topological interactions among different classes (e.g., containment and
exclusion). These constraints naturally arise in biomedical images and can be
crucial in improving segmentation quality. In this paper, we introduce a novel
topological interaction module to encode the topological interactions into a
deep neural network. The implementation is completely convolution-based and
thus can be very efficient. This empowers us to incorporate the constraints
into end-to-end training and enrich the feature representation of neural
networks. The efficacy of the proposed method is validated on different types
of interactions. We also demonstrate the generalizability of the method on both
proprietary and public challenge datasets, in both 2D and 3D settings, as well
as across different modalities such as CT and Ultrasound. Code is available at:
https://github.com/TopoXLab/TopoInteraction

Federated learning~(FL) has recently attracted increasing attention from
academia and industry, with the ultimate goal of achieving collaborative
training under privacy and communication constraints. Existing iterative model
averaging based FL algorithms require a large number of communication rounds to
obtain a well-performed model due to extremely unbalanced and non-i.i.d data
partitioning among different clients. Thus, we propose FedDM to build the
global training objective from multiple local surrogate functions, which
enables the server to gain a more global view of the loss landscape. In detail,
we construct synthetic sets of data on each client to locally match the loss
landscape from original data through distribution matching. FedDM reduces
communication rounds and improves model quality by transmitting more
informative and smaller synthesized data compared with unwieldy model weights.
We conduct extensive experiments on three image classification datasets, and
results show that our method can outperform other FL counterparts in terms of
efficiency and model performance. Moreover, we demonstrate that FedDM can be
adapted to preserve differential privacy with Gaussian mechanism and train a
better model under the same privacy budget.

In this paper, we examine self-supervised learning methods, particularly
VICReg, to provide an information-theoretical understanding of their
construction. As a first step, we demonstrate how information-theoretic
quantities can be obtained for a deterministic network, offering a possible
alternative to prior work that relies on stochastic models. This enables us to
demonstrate how VICReg can be (re)discovered from first principles and its
assumptions about data distribution. Furthermore, we empirically demonstrate
the validity of our assumptions, confirming our novel understanding of VICReg.
Finally, we believe that the derivation and insights we obtain can be
generalized to many other SSL methods, opening new avenues for theoretical and
practical understanding of SSL and transfer learning.

Automatic modulation recognition (AMR) detects the modulation scheme of the
received signals for further signal processing without needing prior
information, and provides the essential function when such information is
missing. Recent breakthroughs in deep learning (DL) have laid the foundation
for developing high-performance DL-AMR approaches for communications systems.
Comparing with traditional modulation detection methods, DL-AMR approaches have
achieved promising performance including high recognition accuracy and low
false alarms due to the strong feature extraction and classification abilities
of deep neural networks. Despite the promising potential, DL-AMR approaches
also bring concerns to complexity and explainability, which affect the
practical deployment in wireless communications systems. This paper aims to
present a review of the current DL-AMR research, with a focus on appropriate DL
models and benchmark datasets. We further provide comprehensive experiments to
compare the state of the art models for single-input-single-output (SISO)
systems from both accuracy and complexity perspectives, and propose to apply
DL-AMR in the new multiple-input-multiple-output (MIMO) scenario with
precoding. Finally, existing challenges and possible future research directions
are discussed.

Despite the success of fully-supervised human skeleton sequence modeling,
utilizing self-supervised pre-training for skeleton sequence representation
learning has been an active field because acquiring task-specific skeleton
annotations at large scales is difficult. Recent studies focus on learning
video-level temporal and discriminative information using contrastive learning,
but overlook the hierarchical spatial-temporal nature of human skeletons.
Different from such superficial supervision at the video level, we propose a
self-supervised hierarchical pre-training scheme incorporated into a
hierarchical Transformer-based skeleton sequence encoder (Hi-TRS), to
explicitly capture spatial, short-term, and long-term temporal dependencies at
frame, clip, and video levels, respectively. To evaluate the proposed
self-supervised pre-training scheme with Hi-TRS, we conduct extensive
experiments covering three skeleton-based downstream tasks including action
recognition, action detection, and motion prediction. Under both supervised and
semi-supervised evaluation protocols, our method achieves the state-of-the-art
performance. Additionally, we demonstrate that the prior knowledge learned by
our model in the pre-training stage has strong transfer capability for
different downstream tasks.

Test-time adaptation (TTA) refers to adapting neural networks to distribution
shifts, with access to only the unlabeled test samples from the new domain at
test-time. Prior TTA methods optimize over unsupervised objectives such as the
entropy of model predictions in TENT [Wang et al., 2021], but it is unclear
what exactly makes a good TTA loss. In this paper, we start by presenting a
surprising phenomenon: if we attempt to meta-learn the best possible TTA loss
over a wide class of functions, then we recover a function that is remarkably
similar to (a temperature-scaled version of) the softmax-entropy employed by
TENT. This only holds, however, if the classifier we are adapting is trained
via cross-entropy; if trained via squared loss, a different best TTA loss
emerges. To explain this phenomenon, we analyze TTA through the lens of the
training losses's convex conjugate. We show that under natural conditions, this
(unsupervised) conjugate function can be viewed as a good local approximation
to the original supervised loss and indeed, it recovers the best losses found
by meta-learning. This leads to a generic recipe that can be used to find a
good TTA loss for any given supervised training loss function of a general
class. Empirically, our approach consistently dominates other baselines over a
wide range of benchmarks. Our approach is particularly of interest when applied
to classifiers trained with novel loss functions, e.g., the recently-proposed
PolyLoss, where it differs substantially from (and outperforms) an
entropy-based loss. Further, we show that our approach can also be interpreted
as a kind of self-training using a very specific soft label, which we refer to
as the conjugate pseudolabel. Overall, our method provides a broad framework
for better understanding and improving test-time adaptation. Code is available
at https://github.com/locuslab/tta_conjugate.

Dataset Condensation is a newly emerging technique aiming at learning a tiny
dataset that captures the rich information encoded in the original dataset. As
the size of datasets contemporary machine learning models rely on becomes
increasingly large, condensation methods become a prominent direction for
accelerating network training and reducing data storage. Despite numerous
methods have been proposed in this rapidly growing field, evaluating and
comparing different condensation methods is non-trivial and still remains an
open issue. The quality of condensed dataset are often shadowed by many
critical contributing factors to the end performance, such as data augmentation
and model architectures. The lack of a systematic way to evaluate and compare
condensation methods not only hinders our understanding of existing techniques,
but also discourages practical usage of the synthesized datasets. This work
provides the first large-scale standardized benchmark on Dataset Condensation.
It consists of a suite of evaluations to comprehensively reflect the
generability and effectiveness of condensation methods through the lens of
their generated dataset. Leveraging this benchmark, we conduct a large-scale
study of current condensation methods, and report many insightful findings that
open up new possibilities for future development. The benchmark library,
including evaluators, baseline methods, and generated datasets, is open-sourced
to facilitate future research and application.

Over-parameterized models, typically pretrained language models (LMs), have
shown an appealing expressive power due to their small learning bias. However,
the huge learning capacity of LMs can also lead to large learning variance. In
a pilot study, we find that, when faced with multiple domains, a critical
portion of parameters behave unexpectedly in a domain-specific manner while
others behave in a domain-general one. Motivated by this phenomenon, we for the
first time posit that domain-general parameters can underpin a domain-general
LM that can be derived from the original LM. To uncover the domain-general LM,
we propose to identify domain-general parameters by playing lottery tickets
(dubbed doge tickets). In order to intervene the lottery, we propose a
domain-general score, which depicts how domain-invariant a parameter is by
associating it with the variance. Comprehensive experiments are conducted on
the Amazon, Mnli and OntoNotes datasets. The results show that the doge tickets
obtains an improved out-of-domain generalization in comparison with a range of
competitive baselines. Analysis results further hint the existence of
domain-general parameters and the performance consistency of doge tickets.

The fast development of self-supervised learning lowers the bar learning
feature representation from massive unlabeled data and has triggered a series
of research on change detection of remote sensing images. Challenges in
adapting self-supervised learning from natural images classification to remote
sensing images change detection arise from difference between the two tasks.
The learned patch-level feature representations are not satisfying for the
pixel-level precise change detection. In this paper, we proposed a novel
pixel-level self-supervised hyperspectral spatial-spectral understanding
network (HyperNet) to accomplish pixel-wise feature representation for
effective hyperspectral change detection. Concretely, not patches but the whole
images are fed into the network and the multi-temporal spatial-spectral
features are compared pixel by pixel. Instead of processing the two-dimensional
imaging space and spectral response dimension in hybrid style, a powerful
spatial-spectral attention module is put forward to explore the spatial
correlation and discriminative spectral features of multi-temporal
hyperspectral images (HSIs), separately. Only the positive samples at the same
location of bi-temporal HSIs are created and forced to be aligned, aiming at
learning the spectral difference-invariant features. Moreover, a new similarity
loss function named focal cosine is proposed to solve the problem of imbalanced
easy and hard positive samples comparison, where the weights of those hard
samples are enlarged and highlighted to promote the network training. Six
hyperspectral datasets have been adopted to test the validity and
generalization of proposed HyperNet. The extensive experiments demonstrate the
superiority of HyperNet over the state-of-the-art algorithms on downstream
hyperspectral change detection tasks.

Due to the ever-growing demands for electronic chips in different sectors the
semiconductor companies have been mandated to offshore their manufacturing
processes. This unwanted matter has made security and trustworthiness of their
fabricated chips concerning and caused creation of hardware attacks. In this
condition, different entities in the semiconductor supply chain can act
maliciously and execute an attack on the design computing layers, from devices
to systems. Our attack is a hardware Trojan that is inserted during mask
generation/fabrication in an untrusted foundry. The Trojan leaves a footprint
in the fabricated through addition, deletion, or change of design cells. In
order to tackle this problem, we propose Explainable Vision System for Hardware
Testing and Assurance (EVHA) in this work that can detect the smallest possible
change to a design in a low-cost, accurate, and fast manner. The inputs to this
system are Scanning Electron Microscopy (SEM) images acquired from the
Integrated Circuits (ICs) under examination. The system output is determination
of IC status in terms of having any defect and/or hardware Trojan through
addition, deletion, or change in the design cells at the cell-level. This
article provides an overview on the design, development, implementation, and
analysis of our defense system.

Deep learning has seen tremendous interest in medical imaging, particularly
in the use of convolutional neural networks (CNNs) for developing automated
diagnostic tools. The facility of its non-invasive acquisition makes retinal
fundus imaging amenable to such automated approaches. Recent work in analyzing
fundus images using CNNs relies on access to massive data for training and
validation - hundreds of thousands of images. However, data residency and data
privacy restrictions stymie the applicability of this approach in medical
settings where patient confidentiality is a mandate. Here, we showcase results
for the performance of DL on small datasets to classify patient sex from fundus
images - a trait thought not to be present or quantifiable in fundus images
until recently. We fine-tune a Resnet-152 model whose last layer has been
modified for binary classification. In several experiments, we assess
performance in the small dataset context using one private (DOVS) and one
public (ODIR) data source. Our models, developed using approximately 2500
fundus images, achieved test AUC scores of up to 0.72 (95% CI: [0.67, 0.77]).
This corresponds to a mere 25% decrease in performance despite a nearly
1000-fold decrease in the dataset size compared to prior work in the
literature. Even with a hard task like sex categorization from retinal images,
we find that classification is possible with very small datasets. Additionally,
we perform domain adaptation experiments between DOVS and ODIR; explore the
effect of data curation on training and generalizability; and investigate model
ensembling to maximize CNN classifier performance in the context of small
development datasets.

To construct effective teaming strategies between humans and AI systems in
complex, risky situations requires an understanding of individual preferences
and behaviors of humans. Previously this problem has been treated in
case-specific or data-agnostic ways. In this paper, we build a framework
capable of capturing a compact latent representation of the human in terms of
their behavior and preferences based on data from a simulated population of
drivers. Our framework leverages, to the extent available, knowledge of
individual preferences and types from samples within the population to deploy
interaction policies appropriate for specific drivers. We then build a
lightweight simulation environment, HMIway-env, for modelling one form of
distracted driving behavior, and use it to generate data for different driver
types and train intervention policies. We finally use this environment to
quantify both the ability to discriminate drivers and the effectiveness of
intervention policies.

Concept-based interpretability methods aim to explain deep neural network
model predictions using a predefined set of semantic concepts. These methods
evaluate a trained model on a new, "probe" dataset and correlate model
predictions with the visual concepts labeled in that dataset. Despite their
popularity, they suffer from limitations that are not well-understood and
articulated by the literature. In this work, we analyze three commonly
overlooked factors in concept-based explanations. First, the choice of the
probe dataset has a profound impact on the generated explanations. Our analysis
reveals that different probe datasets may lead to very different explanations,
and suggests that the explanations are not generalizable outside the probe
dataset. Second, we find that concepts in the probe dataset are often less
salient and harder to learn than the classes they claim to explain, calling
into question the correctness of the explanations. We argue that only visually
salient concepts should be used in concept-based explanations. Finally, while
existing methods use hundreds or even thousands of concepts, our human studies
reveal a much stricter upper bound of 32 concepts or less, beyond which the
explanations are much less practically useful. We make suggestions for future
development and analysis of concept-based interpretability methods. Code for
our analysis and user interface can be found at
\url{https://github.com/princetonvisualai/OverlookedFactors}

Domain adaptation for object detection (DAOD) has recently drawn much
attention owing to its capability of detecting target objects without any
annotations. To tackle the problem, previous works focus on aligning features
extracted from partial levels (e.g., image-level, instance-level, RPN-level) in
a two-stage detector via adversarial training. However, individual levels in
the object detection pipeline are closely related to each other and this
inter-level relation is unconsidered yet. To this end, we introduce a novel
framework for DAOD with three proposed components: Multi-scale-aware
Uncertainty Attention (MUA), Transferable Region Proposal Network (TRPN), and
Dynamic Instance Sampling (DIS). With these modules, we seek to reduce the
negative transfer effect during training while maximizing transferability as
well as discriminability in both domains. Finally, our framework implicitly
learns domain invariant regions for object detection via exploiting the
transferable information and enhances the complementarity between different
detection levels by collaboratively utilizing their domain information. Through
ablation studies and experiments, we show that the proposed modules contribute
to the performance improvement in a synergic way, demonstrating the
effectiveness of our method. Moreover, our model achieves a new
state-of-the-art performance on various benchmarks.

Federated learning (FL) and split learning (SL) are two emerging
collaborative learning methods that may greatly facilitate ubiquitous
intelligence in Internet of Things (IoT). Federated learning enables machine
learning (ML) models locally trained using private data to be aggregated into a
global model. Split learning allows different portions of an ML model to be
collaboratively trained on different workers in a learning framework. Federated
learning and split learning, each has unique advantages and respective
limitations, may complement each other toward ubiquitous intelligence in IoT.
Therefore, combination of federated learning and split learning recently became
an active research area attracting extensive interest. In this article, we
review the latest developments in federated learning and split learning and
present a survey on the state-of-the-art technologies for combining these two
learning methods in an edge computing-based IoT environment. We also identify
some open problems and discuss possible directions for future research in this
area with a hope to further arouse the research community's interest in this
emerging field.

3D shape matching is a long-standing problem in computer vision and computer
graphics. While deep neural networks were shown to lead to state-of-the-art
results in shape matching, existing learning-based approaches are limited in
the context of multi-shape matching: (i) either they focus on matching pairs of
shapes only and thus suffer from cycle-inconsistent multi-matchings, or (ii)
they require an explicit template shape to address the matching of a collection
of shapes. In this paper, we present a novel approach for deep multi-shape
matching that ensures cycle-consistent multi-matchings while not depending on
an explicit template shape. To this end, we utilise a shape-to-universe
multi-matching representation that we combine with powerful functional map
regularisation, so that our multi-shape matching neural network can be trained
in a fully unsupervised manner. While the functional map regularisation is only
considered during training time, functional maps are not computed for
predicting correspondences, thereby allowing for fast inference. We demonstrate
that our method achieves state-of-the-art results on several challenging
benchmark datasets, and, most remarkably, that our unsupervised method even
outperforms recent supervised methods.

Image classification with deep neural networks has seen a surge of
technological breakthroughs with promising applications in areas such as face
recognition, medical imaging, and autonomous driving. In engineering problems,
however, such as high-speed imaging of engine fuel injector sprays or body
paint sprays, deep neural networks face a fundamental challenge related to the
availability of adequate and diverse data. Typically, only thousands or
sometimes even hundreds of samples are available for training. In addition, the
transition between different spray classes is a continuum and requires a high
level of domain expertise to label the images accurately. In this work, we used
Mixup as an approach to systematically deal with the data scarcity and
ambiguous class boundaries found in industrial spray applications. We show that
data augmentation can mitigate the over-fitting problem of large neural
networks on small data sets, to a certain level, but cannot fundamentally
resolve the issue. We discuss how a convex linear interpolation of different
classes naturally aligns with the continuous transition between different
classes in our application. Our experiments demonstrate Mixup as a simple yet
effective method to train an accurate and robust deep neural network classifier
with only a few hundred samples.

Higgs boson pair production is well known to probe the structure of the
electroweak symmetry breaking sector. We illustrate using the gluon-fusion
process $pp \to H \to h h \to (b\bar b) (b\bar b)$ in the framework of
two-Higgs-doublet models and how the machine learning approach (three-stream
convolutional neural network) can substantially improve the signal-background
discrimination and thus improves the sensitivity coverage of the relevant
parameter space. We show that such $gg \to hh \to b \bar b b\bar b$ process can
further probe the currently allowed parameter space by HiggsSignals and
HiggsBounds at the HL-LHC. The results for Types I to IV are shown.

Three-dimensional volumetric imaging of cells allows for in situ
visualization, thus preserving contextual insights into cellular processes.
Despite recent advances in machine learning methods, morphological analysis of
sub-nuclear structures have proven challenging due to both the shallow contrast
profile and the technical limitation in feature detection. Here, we present a
convolutional neural network, supervised deep learning-based approach which can
identify sub-nuclear structures with 90% accuracy. We develop and apply this
model to C. elegans gonads imaged using focused ion beam milling combined with
scanning electron microscopy resulting in the accurate identification and
segmentation of all sub-nuclear structures including entire chromosomes. We
discuss in depth the architecture, parameterization, and optimization of the
deep learning model, as well as provide evaluation metrics to assess the
quality of the network prediction. Lastly, we highlight specific aspects of the
model that can be optimized for its broad application to other volumetric
imaging data as well as in situ cryo-electron tomography.

Robust reinforcement learning (RL) considers the problem of learning policies
that perform well in the worst case among a set of possible environment
parameter values. In real-world environments, choosing the set of possible
values for robust RL can be a difficult task. When that set is specified too
narrowly, the agent will be left vulnerable to reasonable parameter values
unaccounted for. When specified too broadly, the agent will be too cautious. In
this paper, we propose Feasible Adversarial Robust RL (FARR), a novel problem
formulation and objective for automatically determining the set of environment
parameter values over which to be robust. FARR implicitly defines the set of
feasible parameter values as those on which an agent could achieve a benchmark
reward given enough training resources. By formulating this problem as a
two-player zero-sum game, optimizing the FARR objective jointly produces an
adversarial distribution over parameter values with feasible support and a
policy robust over this feasible parameter set. We demonstrate that approximate
Nash equilibria for this objective can be found using a variation of the PSRO
algorithm. Furthermore, we show that an optimal agent trained with FARR is more
robust to feasible adversarial parameter selection than with existing minimax,
domain-randomization, and regret objectives in a parameterized gridworld and
three MuJoCo control environments.

Closed-loop architecture is widely utilized in automatic control systems and
attain distinguished performance. However, classical compressive sensing
systems employ open-loop architecture with separated sampling and
reconstruction units. Therefore, a method of iterative compensation recovery
for image compressive sensing (ICRICS) is proposed by introducing closed-loop
framework into traditional compresses sensing systems. The proposed method
depends on any existing approaches and upgrades their reconstruction
performance by adding negative feedback structure. Theory analysis on negative
feedback of compressive sensing systems is performed. An approximate
mathematical proof of the effectiveness of the proposed method is also
provided. Simulation experiments on more than 3 image datasets show that the
proposed method is superior to 10 competition approaches in reconstruction
performance. The maximum increment of average peak signal-to-noise ratio is
4.36 dB and the maximum increment of average structural similarity is 0.034 on
one dataset. The proposed method based on negative feedback mechanism can
efficiently correct the recovery error in the existing systems of image
compressive sensing.

We consider some classical optimization problems in path planning and network
transport, and we introduce new auction-based algorithms for their optimal and
suboptimal solution. The algorithms are based on mathematical ideas that are
related to competitive bidding by persons for objects and the attendant market
equilibrium, which underlie auction processes. However, the starting point of
our algorithms is different, namely weighted and unweighted path construction
in directed graphs, rather than assignment of persons to objects. The new
algorithms have several potential advantages over existing methods: they are
empirically faster in some important contexts, such as max-flow, they are
well-suited for on-line replanning, and they can be adapted to distributed
asynchronous operation. Moreover, they allow arbitrary initial prices, without
complementary slackness restrictions, and thus are better-suited to take
advantage of reinforcement learning methods that use off-line training with
data, as well as on-line training during real-time operation. The new
algorithms may also find use in reinforcement learning contexts involving
approximation, such as multistep lookahead and tree search schemes, and/or
rollout algorithms.

In this paper, we take an optimization theory-based approach for data-driven
control. We first show that if the basis functions are known, there is a direct
data representation of the system model for linear systems, switched-linear
systems and nonlinear systems, which provides some insight on in what scenarios
the optimization theory-based methods are more suitable as opposed to the
current trend of applying machine learning by default. We next derive the
bounds on modeling error originated from noisy data. Similar to many earlier
results, condition number of the data matrix determines the tightness of the
bounds. We therefore review some practically useful methods to reduce the
condition number. Built on satisfactory tight bounds, robust controllers are
next developed. Numerical results confirm the effectiveness of the proposed
data-driven robust control.

One of the goals of learning algorithms is to complement and reduce the
burden on human decision makers. The expert deferral setting wherein an
algorithm can either predict on its own or defer the decision to a downstream
expert helps accomplish this goal. A fundamental aspect of this setting is the
need to learn complementary predictors that improve on the human's weaknesses
rather than learning predictors optimized for average error. In this work, we
provide the first theoretical analysis of the benefit of learning complementary
predictors in expert deferral. To enable efficiently learning such predictors,
we consider a family of consistent surrogate loss functions for expert deferral
and analyze their theoretical properties. Finally, we design active learning
schemes that require minimal amount of data of human expert predictions in
order to learn accurate deferral systems.

3D image segmentation is a recent and crucial step in many medical analysis
and recognition schemes. In fact, it represents a relevant research subject and
a fundamental challenge due to its importance and influence. This paper
provides a multi-phase Deep Learning-based system that hybridizes various
efficient methods in order to get the best 3D segmentation output. First, to
reduce the amount of data and accelerate the processing time, the application
of Decimate compression technique is suggested and justified. We then use a CNN
model to segment dental images into fifteen separated classes. In the end, a
special KNN-based transformation is applied for the purpose of removing
isolated meshes and of correcting dental forms. Experimentations demonstrate
the precision and the robustness of the selected framework applied to 3D dental
images within a private clinical benchmark.

This paper presents a frequency-velocity convolutional neural network (CNN)
for rapid, non-invasive 2D shear wave velocity (Vs) imaging of near-surface
geo-materials. Operating in the frequency-velocity domain allows for
significant flexibility in the linear-array, active-source experimental testing
configurations used for generating the CNN input, which are normalized
dispersion images. Unlike wavefield images, normalized dispersion images are
relatively insensitive to the experimental testing configuration, accommodating
various source types, source offsets, numbers of receivers, and receiver
spacings. We demonstrate the effectiveness of the frequency-velocity CNN by
applying it to a classic near-surface geophysics problem, namely, imaging a
two-layer, undulating, soil-over-bedrock interface. This problem was recently
investigated in our group by developing a time-distance CNN, which showed great
promise but lacked flexibility in utilizing different field-testing
configurations. Herein, the new frequency-velocity CNN is shown to have
comparable accuracy to the time-distance CNN while providing greater
flexibility to handle varied field applications. The frequency-velocity CNN was
trained, validated, and tested using 100,000 synthetic near-surface models. The
ability of the proposed frequency-velocity CNN to generalize across various
acquisition configurations is first tested using synthetic near-surface models
with different acquisition configurations from that of the training set, and
then applied to experimental field data collected at the Hornsby Bend site in
Austin, Texas, USA. When fully developed for a wider range of geological
conditions, the proposed CNN may ultimately be used as a rapid, end-to-end
alternative for current pseudo-2D surface wave imaging techniques or to develop
starting models for full waveform inversion.

As deep learning models have gradually become the main workhorse of time
series forecasting, the potential vulnerability under adversarial attacks to
forecasting and decision system accordingly has emerged as a main issue in
recent years. Albeit such behaviors and defense mechanisms started to be
investigated for the univariate time series forecasting, there are still few
studies regarding the multivariate forecasting which is often preferred due to
its capacity to encode correlations between different time series. In this
work, we study and design adversarial attack on multivariate probabilistic
forecasting models, taking into consideration attack budget constraints and the
correlation architecture between multiple time series. Specifically, we
investigate a sparse indirect attack that hurts the prediction of an item (time
series) by only attacking the history of a small number of other items to save
attacking cost. In order to combat these attacks, we also develop two defense
strategies. First, we adopt randomized smoothing to multivariate time series
scenario and verify its effectiveness via empirical experiments. Second, we
leverage a sparse attacker to enable end-to-end adversarial training that
delivers robust probabilistic forecasters. Extensive experiments on real
dataset confirm that our attack schemes are powerful and our defend algorithms
are more effective compared with other baseline defense mechanisms.

Federated learning (FL) is a recently developed area of machine learning, in
which the private data of a large number of distributed clients is used to
develop a global model under the coordination of a central server without
explicitly exposing the data. The standard FL strategy has a number of
significant bottlenecks including large communication requirements and high
impact on the clients' resources. Several strategies have been described in the
literature trying to address these issues. In this paper, a novel scheme based
on the notion of "model growing" is proposed. Initially, the server deploys a
small model of low complexity, which is trained to capture the data complexity
during the initial set of rounds. When the performance of such a model
saturates, the server switches to a larger model with the help of
function-preserving transformations. The model complexity increases as more
data is processed by the clients, and the overall process continues until the
desired performance is achieved. Therefore, the most complex model is broadcast
only at the final stage in our approach resulting in substantial reduction in
communication cost and client computational requirements. The proposed approach
is tested extensively on three standard benchmarks and is shown to achieve
substantial reduction in communication and client computation while achieving
comparable accuracy when compared to the current most effective strategies.

The design of data-driven formulations for machine learning and
decision-making with good out-of-sample performance is a key challenge. The
observation that good in-sample performance does not guarantee good
out-of-sample performance is generally known as overfitting. Practical
overfitting can typically not be attributed to a single cause but instead is
caused by several factors all at once. We consider here three overfitting
sources: (i) statistical error as a result of working with finite sample data,
(ii) data noise which occurs when the data points are measured only with finite
precision, and finally (iii) data misspecification in which a small fraction of
all data may be wholly corrupted. We argue that although existing data-driven
formulations may be robust against one of these three sources in isolation they
do not provide holistic protection against all overfitting sources
simultaneously. We design a novel data-driven formulation which does guarantee
such holistic protection and is furthermore computationally viable. Our
distributionally robust optimization formulation can be interpreted as a novel
combination of a Kullback-Leibler and Levy-Prokhorov robust optimization
formulation. Finally, we show how in the context of classification and
regression problems several popular regularized and robust formulations reduce
to a particular case of our proposed more general formulation.

Collaborative Filtering(CF) recommender is a crucial application in the
online market and ecommerce. However, CF recommender has been proven to suffer
from persistent problems related to sparsity of the user rating that will
further lead to a cold-start issue. Existing methods address the data sparsity
issue by applying token-level sentiment analysis that translate text review
into sentiment scores as a complement of the user rating. In this paper, we
attempt to optimize the sentiment analysis with advanced NLP models including
BERT and RoBERTa, and experiment on whether the CF recommender has been further
enhanced. We build the recommenders on the Amazon US Reviews dataset, and tune
the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as
well as the new prompt-based learning paradigm. Experimental result shows that
the recommender enhanced with the sentiment ratings predicted by the fine-tuned
RoBERTa has the best performance, and achieved 30.7% overall gain by comparing
MAP, NDCG and precision at K to the baseline recommender. Prompt-based learning
paradigm, although superior to traditional fine-tune paradigm in pure sentiment
analysis, fail to further improve the CF recommender.

Designing and generating new data under targeted properties has been
attracting various critical applications such as molecule design, image editing
and speech synthesis. Traditional hand-crafted approaches heavily rely on
expertise experience and intensive human efforts, yet still suffer from the
insufficiency of scientific knowledge and low throughput to support effective
and efficient data generation. Recently, the advancement of deep learning
induces expressive methods that can learn the underlying representation and
properties of data. Such capability provides new opportunities in figuring out
the mutual relationship between the structural patterns and functional
properties of the data and leveraging such relationship to generate structural
data given the desired properties. This article provides a systematic review of
this promising research area, commonly known as controllable deep data
generation. Firstly, the potential challenges are raised and preliminaries are
provided. Then the controllable deep data generation is formally defined, a
taxonomy on various techniques is proposed and the evaluation metrics in this
specific domain are summarized. After that, exciting applications of
controllable deep data generation are introduced and existing works are
experimentally analyzed and compared. Finally, the promising future directions
of controllable deep data generation are highlighted and five potential
challenges are identified.

The user persona is a communication tool for designers to generate a mental
model that describes the archetype of users. Developing building occupant
personas is proven to be an effective method for human-centered smart building
design, which considers occupant comfort, behavior, and energy consumption.
Optimization of building energy consumption also requires a deep understanding
of occupants' preferences and behaviors. The current approaches to developing
building occupant personas face a major obstruction of manual data processing
and analysis. In this study, we propose and evaluate a machine learning-based
semi-automated approach to generate building occupant personas. We investigate
the 2015 Residential Energy Consumption Dataset with five machine learning
techniques - Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree
(Random Forest), Support Vector Machine, and AdaBoost classifier - for the
prediction of 16 occupant characteristics, such as age, education, and, thermal
comfort. The models achieve an average accuracy of 61% and accuracy over 90%
for attributes including the number of occupants in the household, their age
group, and preferred usage of heating or cooling equipment. The results of the
study show the feasibility of using machine learning techniques for the
development of building occupant persona to minimize human effort.

With the growing burden of training deep learning models with large data
sets, transfer-learning has been widely adopted in many emerging deep learning
algorithms. Transformer models such as BERT are the main player in natural
language processing and use transfer-learning as a de facto standard training
method. A few big data companies release pre-trained models that are trained
with a few popular datasets with which end users and researchers fine-tune the
model with their own datasets. Transfer-learning significantly reduces the time
and effort of training models. However, it comes at the cost of security
concerns. In this paper, we show a new observation that pre-trained models and
fine-tuned models have significantly high similarities in weight values. Also,
we demonstrate that there exist vendor-specific computing patterns even for the
same models. With these new findings, we propose a new model extraction attack
that reveals the model architecture and the pre-trained model used by the
black-box victim model with vendor-specific computing patterns and then
estimates the entire model weights based on the weight value similarities
between the fine-tuned model and pre-trained model. We also show that the
weight similarity can be leveraged for increasing the model extraction
feasibility through a novel weight extraction pruning.

Variational autoencoders (VAEs) suffer from posterior collapse, where the
powerful neural networks used for modeling and inference optimize the objective
without meaningfully using the latent representation. We introduce inference
critics that detect and incentivize against posterior collapse by requiring
correspondence between latent variables and the observations. By connecting the
critic's objective to the literature in self-supervised contrastive
representation learning, we show both theoretically and empirically that
optimizing inference critics increases the mutual information between
observations and latents, mitigating posterior collapse. This approach is
straightforward to implement and requires significantly less training time than
prior methods, yet obtains competitive results on three established datasets.
Overall, the approach lays the foundation to bridge the previously disconnected
frameworks of contrastive learning and probabilistic modeling with variational
autoencoders, underscoring the benefits both communities may find at their
intersection.

Graphical perception studies are a key element of visualization research,
forming the basis of design recommendations and contributing to our
understanding of how people make sense of visualizations. However, graphical
perception studies typically include only brief training sessions, and the
impact of longer and more in-depth feedback remains unclear. In this paper, we
explore the design and evaluation of feedback for graphical perception tasks,
called VisQuiz. Using a quiz-like metaphor, we design feedback for a typical
visualization comparison experiment, showing participants their answer
alongside the correct answer in an animated sequence in each trial. We extend
this quiz metaphor to include summary feedback after each stage of the
experiment, providing additional moments for participants to reflect on their
performance. To evaluate VisQuiz, we conduct a between-subjects experiment,
including three stages of 40 trials each with a control condition that included
only summary feedback. Results from n = 80 participants show that once
participants started receiving trial feedback (Stage 2) they performed
significantly better with bubble charts than those in the control condition.
This effect carried over when feedback was removed (Stage 3). Results also
suggest an overall trend of improved performance due to feedback. We discuss
these findings in the context of other visualization literacy efforts, and
possible future work at the intersection of visualization, feedback, and
learning. Experiment data and analysis scripts are available at the following
repository https://osf.io/jys5d/

The success of CNN-based architecture on image classification in learning and
extracting features made them so popular these days, but the task of image
classification becomes more challenging when we apply state of art models to
classify noisy and low-quality images. It is still difficult for models to
extract meaningful features from this type of image due to its low-resolution
and the lack of meaningful global features. Moreover, high-resolution images
need more layers to train which means they take more time and computational
power to train. Our method also addresses the problem of vanishing gradients as
the layers become deeper in deep neural networks that we mentioned earlier. In
order to address all these issues, we developed a novel image classification
architecture, composed of blocks that are designed to learn both low level and
global features from blurred and noisy low-resolution images. Our design of the
blocks was heavily influenced by Residual Connections and Inception modules in
order to increase performance and reduce parameter sizes. We also assess our
work using the MNIST family datasets, with a particular emphasis on the
Oracle-MNIST dataset, which is the most difficult to classify due to its
low-quality and noisy images. We have performed in-depth tests that demonstrate
the presented architecture is faster and more accurate than existing
cutting-edge convolutional neural networks. Furthermore, due to the unique
properties of our model, it can produce a better result with fewer parameters.

Prevalence of gastrointestinal (GI) cancer is growing alarmingly every year
leading to a substantial increase in the mortality rate. Endoscopic detection
is providing crucial diagnostic support, however, subtle lesions in upper and
lower GI are quite hard to detect and cause considerable missed detection. In
this work, we leverage deep learning to develop a framework to improve the
localization of difficult to detect lesions and minimize the missed detection
rate. We propose an end to end student-teacher learning setup where class
probabilities of a trained teacher model on one class with larger dataset are
used to penalize multi-class student network. Our model achieves higher
performance in terms of mean average precision (mAP) on both endoscopic disease
detection (EDD2020) challenge and Kvasir-SEG datasets. Additionally, we show
that using such learning paradigm, our model is generalizable to unseen test
set giving higher APs for clinically crucial neoplastic and polyp categories

Monitoring of prevalent airborne diseases such as COVID-19 characteristically
involve respiratory assessments. While auscultation is a mainstream method for
symptomatic monitoring, its diagnostic utility is hampered by the need for
dedicated hospital visits. Continual remote monitoring based on recordings of
respiratory sounds on portable devices is a promising alternative, which can
assist in screening of COVID-19. In this study, we introduce a novel deep
learning approach to distinguish patients with COVID-19 from healthy controls
given audio recordings of cough or breathing sounds. The proposed approach
leverages a novel hierarchical spectrogram transformer (HST) on spectrogram
representations of respiratory sounds. HST embodies self-attention mechanisms
over local windows in spectrograms, and window size is progressively grown over
model stages to capture local to global context. HST is compared against
state-of-the-art conventional and deep-learning baselines. Comprehensive
demonstrations on a multi-national dataset indicate that HST outperforms
competing methods, achieving over 97% area under the receiver operating
characteristic curve (AUC) in detecting COVID-19 cases.

Albeit the Dice loss is one of the dominant loss functions in medical image
segmentation, most research omits a closer look at its derivative, i.e. the
real motor of the optimization when using gradient descent. In this paper, we
highlight the peculiar action of the Dice loss in the presence of missing or
empty labels. First, we formulate a theoretical basis that gives a general
description of the Dice loss and its derivative. It turns out that the choice
of the reduction dimensions $\Phi$ and the smoothing term $\epsilon$ is
non-trivial and greatly influences its behavior. We find and propose heuristic
combinations of $\Phi$ and $\epsilon$ that work in a segmentation setting with
either missing or empty labels. Second, we empirically validate these findings
in a binary and multiclass segmentation setting using two publicly available
datasets. We confirm that the choice of $\Phi$ and $\epsilon$ is indeed
pivotal. With $\Phi$ chosen such that the reductions happen over a single batch
(and class) element and with a negligible $\epsilon$, the Dice loss deals with
missing labels naturally and performs similarly compared to recent adaptations
specific for missing labels. With $\Phi$ chosen such that the reductions happen
over multiple batch elements or with a heuristic value for $\epsilon$, the Dice
loss handles empty labels correctly. We believe that this work highlights some
essential perspectives and hope that it encourages researchers to better
describe their exact implementation of the Dice loss in future work.

Working with big data using data mining tools is rapidly becoming a trend in
education industry. The combination of the current capacity to collect, store,
manage and process data in a timely manner, and data from online educational
platforms represents an unprecedented opportunity for educational institutes,
learners, educators, and researchers. In this position paper, we consider some
basic concepts as well as most popular tools, methods and techniques regarding
Educational Data Mining and Learning Analytics, and discuss big data
applications in language learning, in particular.

Contrastive Vision-Language Pre-training, known as CLIP, has provided a new
paradigm for learning visual representations using large-scale image-text
pairs. It shows impressive performance on downstream tasks by zero-shot
knowledge transfer. To further enhance CLIP's adaption capability, existing
methods proposed to fine-tune additional learnable modules, which significantly
improves the few-shot performance but introduces extra training time and
computational resources. In this paper, we propose a training-free adaption
method for CLIP to conduct few-shot classification, termed as Tip-Adapter,
which not only inherits the training-free advantage of zero-shot CLIP but also
performs comparably to those training-required approaches. Tip-Adapter
constructs the adapter via a key-value cache model from the few-shot training
set, and updates the prior knowledge encoded in CLIP by feature retrieval. On
top of that, the performance of Tip-Adapter can be further boosted to be
state-of-the-art on ImageNet by fine-tuning the cache model for 10$\times$
fewer epochs than existing methods, which is both effective and efficient. We
conduct extensive experiments of few-shot classification on 11 datasets to
demonstrate the superiority of our proposed methods. Code is released at
https://github.com/gaopengcuhk/Tip-Adapter.

This paper presents recent progress on integrating speech separation and
enhancement (SSE) into the ESPnet toolkit. Compared with the previous ESPnet-SE
work, numerous features have been added, including recent state-of-the-art
speech enhancement models with their respective training and evaluation
recipes. Importantly, a new interface has been designed to flexibly combine
speech enhancement front-ends with other tasks, including automatic speech
recognition (ASR), speech translation (ST), and spoken language understanding
(SLU). To showcase such integration, we performed experiments on carefully
designed synthetic datasets for noisy-reverberant multi-channel ST and SLU
tasks, which can be used as benchmark corpora for future research. In addition
to these new tasks, we also use CHiME-4 and WSJ0-2Mix to benchmark multi- and
single-channel SE approaches. Results show that the integration of SE
front-ends with back-end tasks is a promising research direction even for tasks
besides ASR, especially in the multi-channel scenario. The code is available
online at https://github.com/ESPnet/ESPnet. The multi-channel ST and SLU
datasets, which are another contribution of this work, are released on
HuggingFace.

The goal of this survey is to present an explanatory review of the
approximation properties of deep neural networks. Specifically, we aim at
understanding how and why deep neural networks outperform other classical
linear and nonlinear approximation methods. This survey consists of three
chapters. In Chapter 1 we review the key ideas and concepts underlying deep
networks and their compositional nonlinear structure. We formalize the neural
network problem by formulating it as an optimization problem when solving
regression and classification problems. We briefly discuss the stochastic
gradient descent algorithm and the back-propagation formulas used in solving
the optimization problem and address a few issues related to the performance of
neural networks, including the choice of activation functions, cost functions,
overfitting issues, and regularization. In Chapter 2 we shift our focus to the
approximation theory of neural networks. We start with an introduction to the
concept of density in polynomial approximation and in particular study the
Stone-Weierstrass theorem for real-valued continuous functions. Then, within
the framework of linear approximation, we review a few classical results on the
density and convergence rate of feedforward networks, followed by more recent
developments on the complexity of deep networks in approximating Sobolev
functions. In Chapter 3, utilizing nonlinear approximation theory, we further
elaborate on the power of depth and approximation superiority of deep ReLU
networks over other classical methods of nonlinear approximation.

We investigate the contributions of three important features of the human
visual system (HVS)~ -- ~shape, texture, and color ~ -- ~to object
classification. We build a humanoid vision engine (HVE) that explicitly and
separately computes shape, texture, and color features from images. The
resulting feature vectors are then concatenated to support the final
classification. We show that HVE can summarize and rank-order the contributions
of the three features to object recognition. We use human experiments to
confirm that both HVE and humans predominantly use some specific features to
support the classification of specific classes (e.g., texture is the dominant
feature to distinguish a zebra from other quadrupeds, both for humans and HVE).
With the help of HVE, given any environment (dataset), we can summarize the
most important features for the whole task (task-specific; e.g., color is the
most important feature overall for classification with the CUB dataset), and
for each class (class-specific; e.g., shape is the most important feature to
recognize boats in the iLab-20M dataset). To demonstrate more usefulness of
HVE, we use it to simulate the open-world zero-shot learning ability of humans
with no attribute labeling. Finally, we show that HVE can also simulate human
imagination ability with the combination of different features. We will
open-source the HVE engine and corresponding datasets.

In this paper, we present the results of the HSE-NN team in the 4th
competition on Affective Behavior Analysis in-the-wild (ABAW). The novel
multi-task EfficientNet model is trained for simultaneous recognition of facial
expressions and prediction of valence and arousal on static photos. The
resulting MT-EmotiEffNet extracts visual features that are fed into simple
feed-forward neural networks in the multi-task learning challenge. We obtain
performance measure 1.3 on the validation set, which is significantly greater
when compared to either performance of baseline (0.3) or existing models that
are trained only on the s-Aff-Wild2 database. In the learning from synthetic
data challenge, the quality of the original synthetic training set is increased
by using the super-resolution techniques, such as Real-ESRGAN. Next, the
MT-EmotiEffNet is fine-tuned on the new training set. The final prediction is a
simple blending ensemble of pre-trained and fine-tuned MT-EmotiEffNets. Our
average validation F1 score is 18% greater than the baseline convolutional
neural network.

Due to the current COVID-19 virus, which has already been declared a pandemic
by the World Health Organization (WHO), we are witnessing the greatest pandemic
of the decade. Millions of people are being infected, resulting in thousands of
deaths every day across the globe. Even it was difficult for the best
healthcare-providing countries could not handle the pandemic because of the
strain of treating thousands of patients at a time. The count of infections and
deaths is increasing at an alarming rate because of the spread of the virus. We
believe that innovative technologies could help reduce pandemics to a certain
extent until we find a definite solution from the medical field to handle and
treat such pandemic situations. Technology innovation has the potential to
introduce new technologies that could support people and society during these
difficult times. Therefore, this paper proposes the idea of using drones as a
companion to tackle current and future pandemics. Our COROID drone is based on
the principle of crowdsourcing sensors data of the public's smart devices,
which can correlate the reading of the infrared cameras equipped on the COROID
drones. To the best of our knowledge, this concept has yet to be investigated
either as a concept or as a product. Therefore, we believe that the COROID
drone is innovative and has a huge potential to tackle COVID-19 and future
pandemics.

This work presents SeasoNet, a new large-scale multi-label land cover and
land use scene understanding dataset. It includes $1\,759\,830$ images from
Sentinel-2 tiles, with 12 spectral bands and patch sizes of up to $ 120 \
\mathrm{px} \times 120 \ \mathrm{px}$. Each image is annotated with large scale
pixel level labels from the German land cover model LBM-DE2018 with land cover
classes based on the CORINE Land Cover database (CLC) 2018 and a five times
smaller minimum mapping unit (MMU) than the original CLC maps. We provide pixel
synchronous examples from all four seasons, plus an additional snowy set. These
properties make SeasoNet the currently most versatile and biggest remote
sensing scene understanding dataset with possible applications ranging from
scene classification over land cover mapping to content-based cross season
image retrieval and self-supervised feature learning. We provide baseline
results by evaluating state-of-the-art deep networks on the new dataset in
scene classification and semantic segmentation scenarios.

Existing long-tailed classification (LT) methods only focus on tackling the
class-wise imbalance that head classes have more samples than tail classes, but
overlook the attribute-wise imbalance. In fact, even if the class is balanced,
samples within each class may still be long-tailed due to the varying
attributes. Note that the latter is fundamentally more ubiquitous and
challenging than the former because attributes are not just implicit for most
datasets, but also combinatorially complex, thus prohibitively expensive to be
balanced. Therefore, we introduce a novel research problem: Generalized
Long-Tailed classification (GLT), to jointly consider both kinds of imbalances.
By "generalized", we mean that a GLT method should naturally solve the
traditional LT, but not vice versa. Not surprisingly, we find that most
class-wise LT methods degenerate in our proposed two benchmarks: ImageNet-GLT
and MSCOCO-GLT. We argue that it is because they over-emphasize the adjustment
of class distribution while neglecting to learn attribute-invariant features.
To this end, we propose an Invariant Feature Learning (IFL) method as the first
strong baseline for GLT. IFL first discovers environments with divergent
intra-class distributions from the imperfect predictions and then learns
invariant features across them. Promisingly, as an improved feature backbone,
IFL boosts all the LT line-up: one/two-stage re-balance, augmentation, and
ensemble. Codes and benchmarks are available on Github:
https://github.com/KaihuaTang/Generalized-Long-Tailed-Benchmarks.pytorch

Prostate cancer is the second-most frequently diagnosed cancer and the sixth
leading cause of cancer death in males worldwide. The main problem that
specialists face during the diagnosis of prostate cancer is the localization of
Regions of Interest (ROI) containing a tumor tissue. Currently, the
segmentation of this ROI in most cases is carried out manually by expert
doctors, but the procedure is plagued with low detection rates (of about
27-44%) or overdiagnosis in some patients. Therefore, several research works
have tackled the challenge of automatically segmenting and extracting features
of the ROI from magnetic resonance images, as this process can greatly
facilitate many diagnostic and therapeutic applications. However, the lack of
clear prostate boundaries, the heterogeneity inherent to the prostate tissue,
and the variety of prostate shapes makes this process very difficult to
automate.In this work, six deep learning models were trained and analyzed with
a dataset of MRI images obtained from the Centre Hospitalaire de Dijon and
Universitat Politecnica de Catalunya. We carried out a comparison of multiple
deep learning models (i.e. U-Net, Attention U-Net, Dense-UNet, Attention
Dense-UNet, R2U-Net, and Attention R2U-Net) using categorical cross-entropy
loss function. The analysis was performed using three metrics commonly used for
image segmentation: Dice score, Jaccard index, and mean squared error. The
model that give us the best result segmenting all the zones was R2U-Net, which
achieved 0.869, 0.782, and 0.00013 for Dice, Jaccard and mean squared error,

We approach the problem of learning by watching humans in the wild. While
traditional approaches in Imitation and Reinforcement Learning are promising
for learning in the real world, they are either sample inefficient or are
constrained to lab settings. Meanwhile, there has been a lot of success in
processing passive, unstructured human data. We propose tackling this problem
via an efficient one-shot robot learning algorithm, centered around learning
from a third-person perspective. We call our method WHIRL: In-the-Wild Human
Imitating Robot Learning. WHIRL extracts a prior over the intent of the human
demonstrator, using it to initialize our agent's policy. We introduce an
efficient real-world policy learning scheme that improves using interactions.
Our key contributions are a simple sampling-based policy optimization approach,
a novel objective function for aligning human and robot videos as well as an
exploration method to boost sample efficiency. We show one-shot generalization
and success in real-world settings, including 20 different manipulation tasks
in the wild. Videos and talk at https://human2robot.github.io

Many-body physics of electron-electron correlations plays a central role in
condensed mater physics, it governs a wide range of phenomena, stretching from
superconductivity to magnetism, and is behind numerous technological
applications. To explore this rich interaction-driven physics, two-dimensional
(2D) materials with flat electronic bands provide a natural playground thanks
to their highly localised electrons. Currently, thousands of 2D materials with
computed electronic bands are available in open science databases, awaiting
such exploration. Here we used a new machine learning algorithm combining both
supervised and unsupervised machine intelligence to automate the otherwise
daunting task of materials search and classification, to build a genome of 2D
materials hosting flat electronic bands. To this end, a feedforward artificial
neural network was employed to identify 2D flat band materials, which were then
classified by a bilayer unsupervised learning algorithm. Such a hybrid approach
of exploring materials databases allowed us to reveal completely new material
classes outside the known flat band paradigms, offering new systems for
in-depth study on their electronic interactions.

We present Theseus, an efficient application-agnostic open source library for
differentiable nonlinear least squares (DNLS) optimization built on PyTorch,
providing a common framework for end-to-end structured learning in robotics and
vision. Existing DNLS implementations are application specific and do not
always incorporate many ingredients important for efficiency. Theseus is
application-agnostic, as we illustrate with several example applications that
are built using the same underlying differentiable components, such as
second-order optimizers, standard costs functions, and Lie groups. For
efficiency, Theseus incorporates support for sparse solvers, automatic
vectorization, batching, GPU acceleration, and gradient computation with
implicit differentiation and direct loss minimization. We do extensive
performance evaluation in a set of applications, demonstrating significant
efficiency gains and better scalability when these features are incorporated.
Project page: https://sites.google.com/view/theseus-ai

One of the brightest objects in the universe, supernovae (SNe) are powerful
explosions marking the end of a star's lifetime. Supernova (SN) type is defined
by spectroscopic emission lines, but obtaining spectroscopy is often
logistically unfeasible. Thus, the ability to identify SNe by type using
time-series image data alone is crucial, especially in light of the increasing
breadth and depth of upcoming telescopes. We present a convolutional neural
network method for fast supernova time-series classification, with observed
brightness data smoothed in both the wavelength and time directions with
Gaussian process regression. We apply this method to full duration and
truncated SN time-series, to simulate retrospective as well as real-time
classification performance. Retrospective classification is used to
differentiate cosmologically useful Type Ia SNe from other SN types, and this
method achieves >99% accuracy on this task. We are also able to differentiate
between 6 SN types with 60% accuracy given only two nights of data and 98%
accuracy retrospectively.

In a typical optimization problem, the task is to pick one of a number of
options with the lowest cost or the highest value. In practice, these
cost/value quantities often come through processes such as measurement or
machine learning, which are noisy, with quantifiable noise distributions. To
take these noise distributions into account, one approach is to assume a prior
for the values, use it to build a posterior, and then apply standard stochastic
optimization to pick a solution. However, in many practical applications, such
prior distributions may not be available. In this paper, we study such
scenarios using a regret minimization model.
  In our model, the task is to pick the highest one out of $n$ values. The
values are unknown and chosen by an adversary, but can be observed through
noisy channels, where additive noises are stochastically drawn from known
distributions. The goal is to minimize the regret of our selection, defined as
the expected difference between the highest and the selected value on the
worst-case choices of values. We show that the na\"ive algorithm of picking the
highest observed value has regret arbitrarily worse than the optimum, even when
$n = 2$ and the noises are unbiased in expectation. On the other hand, we
propose an algorithm which gives a constant-approximation to the optimal regret
for any $n$. Our algorithm is conceptually simple, computationally efficient,
and requires only minimal knowledge of the noise distributions.

Deep equilibrium networks (DEQs) are a promising way to construct models
which trade off memory for compute. However, theoretical understanding of these
models is still lacking compared to traditional networks, in part because of
the repeated application of a single set of weights. We show that DEQs are
sensitive to the higher order statistics of the matrix families from which they
are initialized. In particular, initializing with orthogonal or symmetric
matrices allows for greater stability in training. This gives us a practical
prescription for initializations which allow for training with a broader range
of initial weight scales.

Two-dimensionally extended amorphous carbon ("amorphous graphene") is a
prototype system for disorder in 2D, showing a rich and complex configurational
space that is yet to be fully understood. Here we explore the nature of
amorphous graphene with an atomistic machine-learning (ML) model. We create
structural models by introducing defects into ordered graphene through
Monte-Carlo bond switching, defining acceptance criteria using the
machine-learned local, atomic energies associated with a defect, as well as the
nearest-neighbor (NN) environments. We find that physically meaningful
structural models arise from ML atomic energies in this way, ranging from
continuous random networks to paracrystalline structures. Our results show that
ML atomic energies can be used to guide Monte-Carlo structural searches in
principle, and that their predictions of local stability can be linked to
short- and medium-range order in amorphous graphene. We expect that the former
point will be relevant more generally to the study of amorphous materials, and
that the latter has wider implications for the interpretation of ML potential
models.

The hydrodynamic forces on a slender rod in a fluid medium at low Reynolds
number can be modeled using resistive force theories (RFTs) or slender body
theories (SBTs). The former represent the forces by local drag coefficients and
are computationally cheap; however, they are physically inaccurate when
long-range hydrodynamic interaction is involved. The later are physically
accurate but require solving integral equations and, therefore, are
computationally expensive. This paper investigates RFTs in comparison with
state-of-the art SBT methods. During the process, a neural network-based
hydrodynamic model that -- similar to RFTs -- relies on local drag coefficients
for computational efficiency was developed. However, the network is trained
using data from an SBT (regularized stokeslet segments method).The $R^2$ value
of the trained coefficients were $\sim 0.99$ with mean absolute error of
$1.6\times10^{-2}$. The machine learning resistive force theory (MLRFT)
accounts for local hydrodynamic forces distribution, the dependence on
rotational and translational speeds and directions, and geometric parameters of
the slender object. We show that, when classical RFT fails to accurately
predict the forces, torques, and drags on slender rods under low Reynolds
number flows, MLRFT exhibits good agreement with physically accurate SBT
simulations. In terms of computational speed, MLRFT forgoes the need of solving
an inverse problem and, therefore, requires negligible computation time in
comparison with SBT. MLRFT presents a computationally inexpensive hydrodynamic
model for flagellar propulsion can be used in the design and optimization of
biomimetic flagellated robots and analysis of bacterial locomotion.

The group synchronization problem involves estimating a collection of group
elements from noisy measurements of their pairwise ratios. This task is a key
component in many computational problems, including the molecular
reconstruction problem in single-particle cryo-electron microscopy (cryo-EM).
The standard methods to estimate the group elements are based on iteratively
applying linear and non-linear operators. Motivated by the structural
similarity to deep neural networks, we adopt the concept of algorithm
unrolling, where training data is used to optimize the algorithm. We design
unrolled algorithms for several group synchronization instances, including
synchronization over the group of 3-D rotations: the synchronization problem in
cryo-EM. We also apply a similar approach to the multi-reference alignment
problem. We show by numerical experiments that the unrolling strategy
outperforms existing synchronization algorithms in a wide variety of scenarios.

Federated Learning aims at training a global model from multiple
decentralized devices (i.e. clients) without exchanging their private local
data. A key challenge is the handling of non-i.i.d. (independent identically
distributed) data across multiple clients that may induce disparities of their
local features. We introduce the Hyperspherical Federated Learning (SphereFed)
framework to address the non-i.i.d. issue by constraining learned
representations of data points to be on a unit hypersphere shared by clients.
Specifically, all clients learn their local representations by minimizing the
loss with respect to a fixed classifier whose weights span the unit
hypersphere. After federated training in improving the global model, this
classifier is further calibrated with a closed-form solution by minimizing a
mean squared loss. We show that the calibration solution can be computed
efficiently and distributedly without direct access of local data. Extensive
experiments indicate that our SphereFed approach is able to improve the
accuracy of multiple existing federated learning algorithms by a considerable
margin (up to 6% on challenging datasets) with enhanced computation and
communication efficiency across datasets and model architectures.

Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) is an
important task that often relies on availability of held-out data. The ability
to better predict GE based on a single training set may yield overarching DNN
design principles to reduce a reliance on trial-and-error, along with other
performance assessment advantages. In search of a quantity relevant to GE, we
investigate the Mutual Information (MI) between the input and final layer
representations, using the infinite-width DNN limit to bound MI. An existing
input compression-based GE bound is used to link MI and GE. To the best of our
knowledge, this represents the first empirical study of this bound. In our
attempt to empirically falsify the theoretical bound, we find that it is often
tight for best-performing models. Furthermore, it detects randomization of
training labels in many cases, reflects test-time perturbation robustness, and
works well given only few training samples. These results are promising given
that input compression is broadly applicable where MI can be estimated with
confidence.

Reinforcement learning (RL) offers the potential for training generally
capable agents that can interact autonomously in the real world. However, one
key limitation is the brittleness of RL algorithms to core hyperparameters and
network architecture choice. Furthermore, non-stationarities such as evolving
training data and increased agent complexity mean that different
hyperparameters and architectures may be optimal at different points of
training. This motivates AutoRL, a class of methods seeking to automate these
design choices. One prominent class of AutoRL methods is Population-Based
Training (PBT), which have led to impressive performance in several large scale
settings. In this paper, we introduce two new innovations in PBT-style methods.
First, we employ trust-region based Bayesian Optimization, enabling full
coverage of the high-dimensional mixed hyperparameter search space. Second, we
show that using a generational approach, we can also learn both architectures
and hyperparameters jointly on-the-fly in a single training run. Leveraging the
new highly parallelizable Brax physics engine, we show that these innovations
lead to large performance gains, significantly outperforming the tuned baseline
while learning entire configurations on the fly. Code is available at
https://github.com/xingchenwan/bgpbt.

Data-driven distributionally robust optimization is a recently emerging
paradigm aimed at finding a solution that is driven by sample data but is
protected against sampling errors. An increasingly popular approach, known as
Wasserstein distributionally robust optimization (DRO), achieves this by
applying the Wasserstein metric to construct a ball centred at the empirical
distribution and finding a solution that performs well against the most
adversarial distribution from the ball. In this paper, we present a general
framework for studying different choices of a Wasserstein metric and point out
the limitation of the existing choices. In particular, while choosing a
Wasserstein metric of a higher order is desirable from a data-driven
perspective, given its less conservative nature, such a choice comes with a
high price from a robustness perspective - it is no longer applicable to many
heavy-tailed distributions of practical concern. We show that this seemingly
inevitable trade-off can be resolved by our framework, where a new class of
Wasserstein metrics, called coherent Wasserstein metrics, is introduced. Like
Wasserstein DRO, distributionally robust optimization using the coherent
Wasserstein metrics, termed generalized Wasserstein distributionally robust
optimization (GW-DRO), has all the desirable performance guarantees:
finite-sample guarantee, asymptotic consistency, and computational
tractability. The worst-case expectation problem in GW-DRO is in general a
nonconvex optimization problem, yet we provide new analysis to prove its
tractability without relying on the common duality scheme. Our framework, as
shown in this paper, offers a fruitful opportunity to design novel Wasserstein
DRO models that can be applied in various contexts such as operations
management, finance, and machine learning.

We propose a greedy algorithm to select $N$ important features among $P$
input features for a non-linear prediction problem. The features are selected
one by one sequentially, in an iterative loss minimization procedure. We use
neural networks as predictors in the algorithm to compute the loss and hence,
we refer to our method as neural greedy pursuit (NGP). NGP is efficient in
selecting $N$ features when $N \ll P$, and it provides a notion of feature
importance in a descending order following the sequential selection procedure.
We experimentally show that NGP provides better performance than several
feature selection methods such as DeepLIFT and Drop-one-out loss. In addition,
we experimentally show a phase transition behavior in which perfect selection
of all $N$ features without false positives is possible when the training data
size exceeds a threshold.

Lung nodule detection in chest X-ray (CXR) images is common to early
screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis
(CAD) systems can support radiologists for nodule screening in CXR. However, it
requires large-scale and diverse medical data with high-quality annotations to
train such robust and accurate CADs. To alleviate the limited availability of
such datasets, lung nodule synthesis methods are proposed for the sake of data
augmentation. Nevertheless, previous methods lack the ability to generate
nodules that are realistic with the size attribute desired by the detector. To
address this issue, we introduce a novel lung nodule synthesis framework in
this paper, which decomposes nodule attributes into three main aspects
including shape, size, and texture, respectively. A GAN-based Shape Generator
firstly models nodule shapes by generating diverse shape masks. The following
Size Modulation then enables quantitative control on the diameters of the
generated nodule shapes in pixel-level granularity. A coarse-to-fine gated
convolutional Texture Generator finally synthesizes visually plausible nodule
textures conditioned on the modulated shape masks. Moreover, we propose to
synthesize nodule CXR images by controlling the disentangled nodule attributes
for data augmentation, in order to better compensate for the nodules that are
easily missed in the detection task. Our experiments demonstrate the enhanced
image quality, diversity, and controllability of the proposed lung nodule
synthesis framework. We also validate the effectiveness of our data
augmentation on greatly improving nodule detection performance.

In this paper, a green, quantized FL framework, which represents data with a
finite precision level in both local training and uplink transmission, is
proposed. Here, the finite precision level is captured through the use of
quantized neural networks (QNNs) that quantize weights and activations in
fixed-precision format. In the considered FL model, each device trains its QNN
and transmits a quantized training result to the base station. Energy models
for the local training and the transmission with quantization are rigorously
derived. To minimize the energy consumption and the number of communication
rounds simultaneously, a multi-objective optimization problem is formulated
with respect to the number of local iterations, the number of selected devices,
and the precision levels for both local training and transmission while
ensuring convergence under a target accuracy constraint. To solve this problem,
the convergence rate of the proposed FL system is analytically derived with
respect to the system control variables. Then, the Pareto boundary of the
problem is characterized to provide efficient solutions using the normal
boundary inspection method. Design insights on balancing the tradeoff between
the two objectives are drawn from using the Nash bargaining solution and
analyzing the derived convergence rate. Simulation results show that the
proposed FL framework can reduce energy consumption until convergence by up to
52% compared to a baseline FL algorithm that represents data with full
precision.

Automatic segmentation of multiple organs and tumors from 3D medical images
such as magnetic resonance imaging (MRI) and computed tomography (CT) scans
using deep learning methods can aid in diagnosing and treating cancer. However,
organs often overlap and are complexly connected, characterized by extensive
anatomical variation and low contrast. In addition, the diversity of tumor
shape, location, and appearance, coupled with the dominance of background
voxels, makes accurate 3D medical image segmentation difficult. In this paper,
a novel large-kernel (LK) attention module is proposed to address these
problems to achieve accurate multi-organ segmentation and tumor segmentation.
The advantages of convolution and self-attention are combined in the proposed
LK attention module, including local contextual information, long-range
dependence, and channel adaptation. The module also decomposes the LK
convolution to optimize the computational cost and can be easily incorporated
into FCNs such as U-Net. Comprehensive ablation experiments demonstrated the
feasibility of convolutional decomposition and explored the most efficient and
effective network design. Among them, the best Mid-type LK attention-based
U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving
state-of-the-art segmentation performance. The performance improvement due to
the proposed LK attention module was also statistically validated.

This paper presents our system for the Multi-Task Learning (MTL) Challenge in
the 4th Affective Behavior Analysis in-the-wild (ABAW) competition. We explore
the research problems of this challenge from three aspects: 1) For obtaining
efficient and robust visual feature representations, we propose MAE-based
unsupervised representation learning and IResNet/DenseNet-based supervised
representation learning methods; 2) Considering the importance of temporal
information in videos, we explore three types of sequential encoders to capture
the temporal information, including the encoder based on transformer, the
encoder based on LSTM, and the encoder based on GRU; 3) For modeling the
correlation between these different tasks (i.e., valence, arousal, expression,
and AU) for multi-task affective analysis, we first explore the dependency
between these different tasks and propose three multi-task learning frameworks
to model the correlations effectively. Our system achieves the performance of
$1.7607$ on the validation dataset and $1.4361$ on the test dataset, ranking
first in the MTL Challenge. The code is available at
https://github.com/AIM3-RUC/ABAW4.

Federated Learning (FL) allows for collaboratively aggregating learned
information across several computing devices and sharing the same amongst them,
thereby tackling issues of privacy and the need of huge bandwidth. FL
techniques generally use a central server or cloud for aggregating the models
received from the devices. Such centralized FL techniques suffer from inherent
problems such as failure of the central node and bottlenecks in channel
bandwidth. When FL is used in conjunction with connected robots serving as
devices, a failure of the central controlling entity can lead to a chaotic
situation. This paper describes a mobile agent based paradigm to decentralize
FL in multi-robot scenarios. Using Webots, a popular free open-source robot
simulator, and Tartarus, a mobile agent platform, we present a methodology to
decentralize federated learning in a set of connected robots. With Webots
running on different connected computing systems, we show how mobile agents can
perform the task of Decentralized Federated Reinforcement Learning (dFRL).
Results obtained from experiments carried out using Q-learning and SARSA by
aggregating their corresponding Q-tables, show the viability of using
decentralized FL in the domain of robotics. Since the proposed work can be used
in conjunction with other learning algorithms and also real robots, it can act
as a vital tool for the study of decentralized FL using heterogeneous learning
algorithms concurrently in multi-robot scenarios.

The COVID-19 pandemic has brought forth the importance of epidemic
forecasting for decision makers in multiple domains, ranging from public health
to the economy as a whole. While forecasting epidemic progression is frequently
conceptualized as being analogous to weather forecasting, however it has some
key differences and remains a non-trivial task. The spread of diseases is
subject to multiple confounding factors spanning human behavior, pathogen
dynamics, weather and environmental conditions. Research interest has been
fueled by the increased availability of rich data sources capturing previously
unobservable facets and also due to initiatives from government public health
and funding agencies. This has resulted, in particular, in a spate of work on
'data-centered' solutions which have shown potential in enhancing our
forecasting capabilities by leveraging non-traditional data sources as well as
recent innovations in AI and machine learning. This survey delves into various
data-driven methodological and practical advancements and introduces a
conceptual framework to navigate through them. First, we enumerate the large
number of epidemiological datasets and novel data streams that are relevant to
epidemic forecasting, capturing various factors like symptomatic online
surveys, retail and commerce, mobility, genomics data and more. Next, we
discuss methods and modeling paradigms focusing on the recent data-driven
statistical and deep-learning based methods as well as on the novel class of
hybrid models that combine domain knowledge of mechanistic models with the
effectiveness and flexibility of statistical approaches. We also discuss
experiences and challenges that arise in real-world deployment of these
forecasting systems including decision-making informed by forecasts. Finally,
we highlight some challenges and open problems found across the forecasting
pipeline.

From the 1970s up to now, Mastermind, a classic two-player game, has
attracted plenty of attention, not only from the public as a popular game, but
also from the academic community as a scientific issue. Mastermind with n
positions and k colors is formally described as: the codemaker privately
chooses a secret $s\in [k]^n$, and the coderbreaker want to determine $s$ in as
few queries like $f_s(x)$ as possible to the codemaker, where $f_s(x)$
indicates how x is close to s. The complexity of a strategy is measured by the
number of queries used. In this work we have a systematic study on quantum
strategies for playing Mastermind, obtaining a complete characterization of the
quantum complexity and constructing optimal quantum algorithms in both
non-adaptive and adaptive settings. Technically, we develop a framework for
designing quantum algorithms for the general string learning problem, by
discovering a new structure that not only allows quantum speedup to be
maximized on Mastermind in the non-adaptive setting, but also is very likely
helpful for addressing other string learning problems with different kinds of
query oracles.

Communication systems to date primarily aim at reliably communicating bit
sequences. Such an approach provides efficient engineering designs that are
agnostic to the meanings of the messages or to the goal that the message
exchange aims to achieve. Next generation systems, however, can be potentially
enriched by folding message semantics and goals of communication into their
design. Further, these systems can be made cognizant of the context in which
communication exchange takes place, providing avenues for novel design
insights. This tutorial summarizes the efforts to date, starting from its early
adaptations, semantic-aware and task-oriented communications, covering the
foundations, algorithms and potential implementations. The focus is on
approaches that utilize information theory to provide the foundations, as well
as the significant role of learning in semantics and task-aware communications.

This work considers optimization of composition of functions in a nested form
over Riemannian manifolds where each function contains an expectation. This
type of problems is gaining popularity in applications such as policy
evaluation in reinforcement learning or model customization in meta-learning.
The standard Riemannian stochastic gradient methods for non-compositional
optimization cannot be directly applied as stochastic approximation of inner
functions create bias in the gradients of the outer functions. For two-level
composition optimization, we present a Riemannian Stochastic Composition
Gradient Descent (R-SCGD) method that finds an approximate stationary point,
with expected squared Riemannian gradient smaller than $\epsilon$, in
$O(\epsilon^{-2})$ calls to the stochastic gradient oracle of the outer
function and stochastic function and gradient oracles of the inner function.
Furthermore, we generalize the R-SCGD algorithms for problems with multi-level
nested compositional structures, with the same complexity of $O(\epsilon^{-2})$
for the first-order stochastic oracle. Finally, the performance of the R-SCGD
method is numerically evaluated over a policy evaluation problem in
reinforcement learning.

In healthcare, detecting stress and enabling individuals to monitor their
mental health and wellbeing is challenging. Advancements in wearable technology
now enable continuous physiological data collection. This data can provide
insights into mental health and behavioural states through psychophysiological
analysis. However, automated analysis is required to provide timely results due
to the quantity of data collected. Machine learning has shown efficacy in
providing an automated classification of physiological data for health
applications in controlled laboratory environments. Ambulatory uncontrolled
environments, however, provide additional challenges requiring further
modelling to overcome. This work empirically assesses several approaches
utilising machine learning classifiers to detect stress using physiological
data recorded in an ambulatory setting with self-reported stress annotations. A
subset of the training portion SMILE dataset enables the evaluation of
approaches before submission. The optimal stress detection approach achieves
90.77% classification accuracy, 91.24 F1-Score, 90.42 Sensitivity and 91.08
Specificity, utilising an ExtraTrees classifier and feature imputation methods.
Meanwhile, accuracy on the challenge data is much lower at 59.23% (submission
#54 from BEaTS-MTU, username ZacDair). The cause of the performance disparity
is explored in this work.

In this work, we consider the task of improving the accuracy of dynamic
models for model predictive control (MPC) in an online setting. Even though
prediction models can be learned and applied to model-based controllers, these
models are often learned offline. In this offline setting, training data is
first collected and a prediction model is learned through an elaborated
training procedure. After the model is trained to a desired accuracy, it is
then deployed in a model predictive controller. However, since the model is
learned offline, it does not adapt to disturbances or model errors observed
during deployment. To improve the adaptiveness of the model and the controller,
we propose an online dynamics learning framework that continually improves the
accuracy of the dynamic model during deployment. We adopt knowledge-based
neural ordinary differential equations (KNODE) as the dynamic models, and use
techniques inspired by transfer learning to continually improve the model
accuracy. We demonstrate the efficacy of our framework with a quadrotor robot,
and verify the framework in both simulations and physical experiments. Results
show that the proposed approach is able to account for disturbances that are
possibly time-varying, while maintaining good trajectory tracking performance.

In Bora et al. (2017), a mathematical framework was developed for compressed
sensing guarantees in the setting where the measurement matrix is Gaussian and
the signal structure is the range of a generative neural network (GNN). The
problem of compressed sensing with GNNs has since been extensively analyzed
when the measurement matrix and/or network weights follow a subgaussian
distribution. We move beyond the subgaussian assumption, to measurement
matrices that are derived by sampling uniformly at random rows of a unitary
matrix (including subsampled Fourier measurements as a special case).
Specifically, we prove the first known restricted isometry guarantee for
generative compressed sensing with subsampled isometries and provide recovery
bounds, addressing an open problem of Scarlett et al. (2022, p. 10). Recovery
efficacy is characterized by the coherence, a new parameter, which measures the
interplay between the range of the network and the measurement matrix. Our
approach relies on subspace counting arguments and ideas central to
high-dimensional probability. Furthermore, we propose a regularization strategy
for training GNNs to have favourable coherence with the measurement operator.
We provide compelling numerical simulations that support this regularized
training strategy: our strategy yields low coherence networks that require
fewer measurements for signal recovery. This, together with our theoretical
results, supports coherence as a natural quantity for characterizing generative
compressed sensing with subsampled isometries.

Visual representation learning is the key of solving various vision problems.
Relying on the seminal grid structure priors, convolutional neural networks
(CNNs) have been the de facto standard architectures of most deep vision
models. For instance, classical semantic segmentation methods often adopt a
fully-convolutional network (FCN) with an encoder-decoder architecture. The
encoder progressively reduces the spatial resolution and learns more abstract
visual concepts with larger receptive fields. Since context modeling is
critical for segmentation, the latest efforts have been focused on increasing
the receptive field, through either dilated (i.e., atrous) convolutions or
inserting attention modules. However, the FCN-based architecture remains
unchanged. In this paper, we aim to provide an alternative perspective by
treating visual representation learning generally as a sequence-to-sequence
prediction task. Specifically, we deploy a pure Transformer to encode an image
as a sequence of patches, without local convolution and resolution reduction.
With the global context modeled in every layer of the Transformer, stronger
visual representation can be learned for better tackling vision tasks. In
particular, our segmentation model, termed as SEgmentation TRansformer (SETR),
excels on ADE20K (50.28% mIoU, the first position in the test leaderboard on
the day of submission), Pascal Context (55.83% mIoU) and reaches competitive
results on Cityscapes. Further, we formulate a family of Hierarchical
Local-Global (HLG) Transformers characterized by local attention within windows
and global-attention across windows in a hierarchical and pyramidal
architecture. Extensive experiments show that our method achieves appealing
performance on a variety of visual recognition tasks (e.g., image
classification, object detection and instance segmentation and semantic
segmentation).

The superior performance of some of today's state-of-the-art deep learning
models is to some extent owed to extensive (self-)supervised contrastive
pretraining on large-scale datasets. In contrastive learning, the network is
presented with pairs of positive (similar) and negative (dissimilar) datapoints
and is trained to find an embedding vector for each datapoint, i.e., a
representation, which can be further fine-tuned for various downstream tasks.
In order to safely deploy these models in critical decision-making systems, it
is crucial to equip them with a measure of their uncertainty or reliability.
However, due to the pairwise nature of training a contrastive model, and the
lack of absolute labels on the output (an abstract embedding vector), adapting
conventional uncertainty estimation techniques to such models is non-trivial.
In this work, we study whether the uncertainty of such a representation can be
quantified for a single datapoint in a meaningful way. In other words, we
explore if the downstream performance on a given datapoint is predictable,
directly from its pre-trained embedding. We show that this goal can be achieved
by directly estimating the distribution of the training data in the embedding
space and accounting for the local consistency of the representations. Our
experiments show that this notion of uncertainty for an embedding vector often
strongly correlates with its downstream accuracy.

Since Intersection-over-Union (IoU) based optimization maintains the
consistency of the final IoU prediction metric and losses, it has been widely
used in both regression and classification branches of single-stage 2D object
detectors. Recently, several 3D object detection methods adopt IoU-based
optimization and directly replace the 2D IoU with 3D IoU. However, such a
direct computation in 3D is very costly due to the complex implementation and
inefficient backward operations. Moreover, 3D IoU-based optimization is
sub-optimal as it is sensitive to rotation and thus can cause training
instability and detection performance deterioration. In this paper, we propose
a novel Rotation-Decoupled IoU (RDIoU) method that can mitigate the
rotation-sensitivity issue, and produce more efficient optimization objectives
compared with 3D IoU during the training stage. Specifically, our RDIoU
simplifies the complex interactions of regression parameters by decoupling the
rotation variable as an independent term, yet preserving the geometry of 3D
IoU. By incorporating RDIoU into both the regression and classification
branches, the network is encouraged to learn more precise bounding boxes and
concurrently overcome the misalignment issue between classification and
regression. Extensive experiments on the benchmark KITTI and Waymo Open Dataset
validate that our RDIoU method can bring substantial improvement for the
single-stage 3D object detection.

Enhancing existing transmission lines is a useful tool to combat transmission
congestion and guarantee transmission security with increasing demand and
boosting the renewable energy source. This study concerns the selection of
lines whose capacity should be expanded and by how much from the perspective of
independent system operator (ISO) to minimize the system cost with the
consideration of transmission line constraints and electricity generation and
demand balance conditions, and incorporating ramp-up and startup ramp rates,
shutdown ramp rates, ramp-down rate limits and minimum up and minimum down
times. For that purpose, we develop the ISO unit commitment and economic
dispatch model and show it as a right-hand side uncertainty multiple parametric
analysis for the mixed integer linear programming (MILP) problem. We first
relax the binary variable to continuous variables and employ the Lagrange
method and Karush-Kuhn-Tucker conditions to obtain optimal solutions (optimal
decision variables and objective function) and critical regions associated with
active and inactive constraints. Further, we extend the traditional branch and
bound method for the large-scale MILP problem by determining the upper bound of
the problem at each node, then comparing the difference between the upper and
lower bounds and reaching the approximate optimal solution within the decision
makers' tolerated error range. In additional, the objective function's first
derivative on the parameters of each line is used to inform the selection of
lines to ease congestion and maximize social welfare. Finally, the amount of
capacity upgrade will be chosen by balancing the cost-reduction rate of the
objective function on parameters and the cost of the line upgrade. Our findings
are supported by numerical simulation and provide transmission line planners
with decision-making guidance.

Forecast reconciliation is an important topic of research. Although some
recent works studied probabilistic forecast reconciliation, there is currently
no method for the probabilistic reconciliation of count time series, which are
very common. In this paper we make two main contributions. First we propose a
formal definition of coherent and reconciled probabilistic forecast for count
variables. Secondly, we propose a principled method for reconciling count time
series, based on a generalization of Bayes' rule. We carry out experiments
regarding the application of temporal hierarchies to count time series,
obtaining major improvements compared to probabilistic reconciliation based on
the Gaussian or the truncated Gaussian distribution.

Machine learning (ML) practitioners and organizations are building model zoos
of pre-trained models, containing metadata describing properties of the ML
models and datasets that are useful for reporting, auditing, reproducibility,
and interpretability purposes. The metatada is currently not standardised; its
expressivity is limited; and there is no interoperable way to store and query
it. Consequently, model search, reuse, comparison, and composition are
hindered. In this paper, we advocate for standardized ML model meta-data
representation and management, proposing a toolkit supported to help
practitioners manage and query that metadata.

Instance segmentation with unseen objects is a challenging problem in
unstructured environments. To solve this problem, we propose a robot learning
approach to actively interact with novel objects and collect each object's
training label for further fine-tuning to improve the segmentation model
performance, while avoiding the time-consuming process of manually labeling a
dataset. The Singulation-and-Grasping (SaG) policy is trained through
end-to-end reinforcement learning. Given a cluttered pile of objects, our
approach chooses pushing and grasping motions to break the clutter and conducts
object-agnostic grasping for which the SaG policy takes as input the visual
observations and imperfect segmentation. We decompose the problem into three
subtasks: (1) the object singulation subtask aims to separate the objects from
each other, which creates more space that alleviates the difficulty of (2) the
collision-free grasping subtask; (3) the mask generation subtask to obtain the
self-labeled ground truth masks by using an optical flow-based binary
classifier and motion cue post-processing for transfer learning. Our system
achieves 70% singulation success rate in simulated cluttered scenes. The
interactive segmentation of our system achieves 87.8%, 73.9%, and 69.3% average
precision for toy blocks, YCB objects in simulation and real-world novel
objects, respectively, which outperforms several baselines.

To more efficiently address image compressed sensing (CS) problems, we
present a novel content-aware scalable network dubbed CASNet which collectively
achieves adaptive sampling rate allocation, fine granular scalability and
high-quality reconstruction. We first adopt a data-driven saliency detector to
evaluate the importances of different image regions and propose a
saliency-based block ratio aggregation (BRA) strategy for sampling rate
allocation. A unified learnable generating matrix is then developed to produce
sampling matrix of any CS ratio with an ordered structure. Being equipped with
the optimization-inspired recovery subnet guided by saliency information and a
multi-block training scheme preventing blocking artifacts, CASNet jointly
reconstructs the image blocks sampled at various sampling rates with one single
model. To accelerate training convergence and improve network robustness, we
propose an SVD-based initialization scheme and a random transformation
enhancement (RTE) strategy, which are extensible without introducing extra
parameters. All the CASNet components can be combined and learned end-to-end.
We further provide a four-stage implementation for evaluation and practical
deployments. Experiments demonstrate that CASNet outperforms other CS networks
by a large margin, validating the collaboration and mutual supports among its
components and strategies. Codes are available at
https://github.com/Guaishou74851/CASNet.

Building AI models with trustworthiness is important especially in regulated
areas such as healthcare. In tackling COVID-19, previous work uses
convolutional neural networks as the backbone architecture, which has shown to
be prone to over-caution and overconfidence in making decisions, rendering them
less trustworthy -- a crucial flaw in the context of medical imaging. In this
study, we propose a feature learning approach using Vision Transformers, which
use an attention-based mechanism, and examine the representation learning
capability of Transformers as a new backbone architecture for medical imaging.
Through the task of classifying COVID-19 chest radiographs, we investigate into
whether generalization capabilities benefit solely from Vision Transformers'
architectural advances. Quantitative and qualitative evaluations are conducted
on the trustworthiness of the models, through the use of "trust score"
computation and a visual explainability technique. We conclude that the
attention-based feature learning approach is promising in building trustworthy
deep learning models for healthcare.

In a desired environmental protection system, groundwater may not be
excluded. In addition to the problem of over-exploitation, in total
disagreement with the concept of sustainable development, another not
negligible issue concerns the groundwater contamination. Mainly, this aspect is
due to intensive agricultural activities or industrialized areas. In
literature, several papers have dealt with transport problem, especially for
inverse problems in which the release history or the source location are
identified. The innovative aim of the paper is to develop a data-driven model
that is able to analyze multiple scenarios, even strongly non-linear, in order
to solve forward and inverse transport problems, preserving the reliability of
the results and reducing the uncertainty. Furthermore, this tool has the
characteristic of providing extremely fast responses, essential to identify
remediation strategies immediately. The advantages produced by the model were
compared with literature studies. In this regard, a feedforward artificial
neural network, which has been trained to handle different cases, represents
the data-driven model. Firstly, to identify the concentration of the pollutant
at specific observation points in the study area (forward problem); secondly,
to deal with inverse problems identifying the release history at known source
location; then, in case of one contaminant source, identifying the release
history and, at the same time, the location of the source in a specific
sub-domain of the investigated area. At last, the observation error is
investigated and estimated. The results are satisfactorily achieved,
highlighting the capability of the ANN to deal with multiple scenarios by
approximating nonlinear functions without the physical point of view that
describes the phenomenon, providing reliable results, with very low
computational burden and uncertainty.

Around one third of the point-like sources in the Fermi-LAT catalogs remain
as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear,
univocal association with a known astrophysical source. If dark matter (DM) is
composed of weakly interacting massive particles (WIMPs), there is the exciting
possibility that some of these unIDs may actually be DM sources, emitting gamma
rays from WIMPs annihilation. We propose a new approach to solve the standard,
Machine Learning (ML) binary classification problem of disentangling
prospective DM sources (simulated data) from astrophysical sources (observed
data) among the unIDs of the 4FGL Fermi-LAT catalogue. Concretely, we
artificially build two systematic features for the DM data which are originally
inherent to observed data: the detection significance and the uncertainty on
the spectral curvature. We do it by sampling from the observed population of
unIDs, assuming that the DM distributions would, if any, follow the latter. We
consider different ML models: Logistic Regression, Neural Network (NN), Naive
Bayes and Gaussian Process, out of which the best, in terms of classification
accuracy, is the NN, achieving around 93% performance. Applying the NN to the
unIDs sample, we find that the degeneracy between some astrophysical and DM
sources can be partially solved within this methodology. Nonetheless, we
conclude that there are no DM source candidates among the pool of 4FGL
Fermi-LAT unIDs.

We establish a sharp uniform-in-time error estimate for the Stochastic
Gradient Langevin Dynamics (SGLD), which is a popular sampling algorithm. Under
mild assumptions, we obtain a uniform-in-time $O(\eta^2)$ bound for the
KL-divergence between the SGLD iteration and the Langevin diffusion, where
$\eta$ is the step size (or learning rate). Our analysis is also valid for
varying step sizes. Based on this, we are able to obtain an $O(\eta)$ bound for
the distance between the SGLD iteration and the invariant distribution of the
Langevin diffusion, in terms of Wasserstein or total variation distances.

The ultimate aim of image restoration like denoising is to find an exact
correlation between the noisy and clear image domains. But the optimization of
end-to-end denoising learning like pixel-wise losses is performed in a
sample-to-sample manner, which ignores the intrinsic correlation of images,
especially semantics. In this paper, we introduce the Deep Semantic Statistics
Matching (D2SM) Denoising Network. It exploits semantic features of pretrained
classification networks, then it implicitly matches the probabilistic
distribution of clear images at the semantic feature space. By learning to
preserve the semantic distribution of denoised images, we empirically find our
method significantly improves the denoising capabilities of networks, and the
denoised results can be better understood by high-level vision tasks.
Comprehensive experiments conducted on the noisy Cityscapes dataset demonstrate
the superiority of our method on both the denoising performance and semantic
segmentation accuracy. Moreover, the performance improvement observed on our
extended tasks including super-resolution and dehazing experiments shows its
potentiality as a new general plug-and-play component.

We propose the novel few-shot teamwork (FST) problem, where skilled agents
trained in a team to complete one task are combined with skilled agents from
different tasks, and together must learn to adapt to an unseen but related
task. We discuss how the FST problem can be seen as addressing two separate
problems: one of reducing the experience required to train a team of agents to
complete a complex task; and one of collaborating with unfamiliar teammates to
complete a new task. Progress towards solving FST could lead to progress in
both multi-agent reinforcement learning and ad hoc teamwork.

A deep learning approach for the approximation of the Hamilton-Jacobi-Bellman
partial differential equation (HJB PDE) associated to the Nonlinear Quadratic
Regulator (NLQR) problem. A state-dependent Riccati equation control law is
first used to generate a gradient-augmented synthetic dataset for supervised
learning. The resulting model becomes a warm start for the minimization of a
loss function based on the residual of the HJB PDE. The combination of
supervised learning and residual minimization avoids spurious solutions and
mitigate the data inefficiency of a supervised learning-only approach.
Numerical tests validate the different advantages of the proposed methodology.

Distributed file systems are widely used nowadays, yet using their default
configurations is often not optimal. At the same time, tuning configuration
parameters is typically challenging and time-consuming. It demands expertise
and tuning operations can also be expensive. This is especially the case for
static parameters, where changes take effect only after a restart of the system
or workloads. We propose a novel approach, Magpie, which utilizes deep
reinforcement learning to tune static parameters by strategically exploring and
exploiting configuration parameter spaces. To boost the tuning of the static
parameters, our method employs both server and client metrics of distributed
file systems to understand the relationship between static parameters and
performance. Our empirical evaluation results show that Magpie can noticeably
improve the performance of the distributed file system Lustre, where our
approach on average achieves 91.8% throughput gains against default
configuration after tuning towards single performance indicator optimization,
while it reaches 39.7% more throughput gains against the baseline.

We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for
detecting, tracking, and counting fish in sonar videos. We identify sonar
videos as a rich source of data for advancing low signal-to-noise computer
vision applications and tackling domain generalization in multiple-object
tracking (MOT) and counting. In comparison to existing MOT and counting
datasets, which are largely restricted to videos of people and vehicles in
cities, CFC is sourced from a natural-world domain where targets are not easily
resolvable and appearance features cannot be easily leveraged for target
re-identification. With over half a million annotations in over 1,500 videos
sourced from seven different sonar cameras, CFC allows researchers to train MOT
and counting algorithms and evaluate generalization performance at unseen test
locations. We perform extensive baseline experiments and identify key
challenges and opportunities for advancing the state of the art in
generalization in MOT and counting.

With the ever growing amount of data collected by the ATLAS and CMS
experiments at the CERN LHC, fiducial and differential measurements of the
Higgs boson production cross section have become important tools to test the
standard model predictions with an unprecedented level of precision, as well as
seeking deviations that can manifest the presence of physics beyond the
standard model. These measurements are in general designed for being easily
comparable to any present or future theoretical prediction, and to achieve this
goal it is important to keep the model dependence to a minimum. Nevertheless,
the reduction of the model dependence usually comes at the expense of the
measurement precision, preventing to exploit the full potential of the signal
extraction procedure. In this paper a novel methodology based on the machine
learning concept of domain adaptation is proposed, which allows using a complex
deep neural network in the signal extraction procedure while ensuring a minimal
dependence of the measurements on the theoretical modelling of the signal.

Significant geometric structures can be compactly described by global
wireframes in the estimation of 3D room layout from a single panoramic image.
Based on this observation, we present an alternative approach to estimate the
walls in 3D space by modeling long-range geometric patterns in a learnable
Hough Transform block. We transform the image feature from a cubemap tile to
the Hough space of a Manhattan world and directly map the feature to the
geometric output. The convolutional layers not only learn the local
gradient-like line features, but also utilize the global information to
successfully predict occluded walls with a simple network structure. Unlike
most previous work, the predictions are performed individually on each cubemap
tile, and then assembled to get the layout estimation. Experimental results
show that we achieve comparable results with recent state-of-the-art in
prediction accuracy and performance. Code is available at
https://github.com/Starrah/DMH-Net.

Disorders of voice production have severe effects on the quality of life of
the affected individuals. A simulation approach is used to investigate the
cause-effect chain in voice production showing typical characteristics of voice
such as sub-glottal pressure and of functional voice disorders as glottal
closure insufficiency and left-right asymmetry. Therewith, 24 different voice
configurations are simulated in a parameter study using a previously published
hybrid aeroacoustic simulation model. Based on these 24 simulation
configurations, selected acoustic parameters (HNR, CPP, ...) at simulation
evaluation points are correlated with these simulation configuration details to
derive characteristic insight in the flow-induced sound generation of human
phonation based on simulation results. Recently, several institutions studied
experimental data, of flow and acoustic properties and correlated it with
healthy and disordered voice signals. Upon this, the study is a next step
towards a detailed dataset definition, the dataset is small, but the definition
of relevant characteristics are precise based on the existing simulation
methodology of simVoice. The small datasets are studied by correlation
analysis, and a Support Vector Machine classifier with RBF kernel is used to
classify the representations. With the use of Linear Discriminant Analysis the
dimensions of the individual studies are visualized. This allows to draw
correlations and determine the most important features evaluated from the
acoustic signals in front of the mouth. The GC type can be best discriminated
based on CPP and boxplot visualizations. Furthermore and using the
LDA-dimensionality-reduced feature space, one can best classify subglottal
pressure with 91.7\% accuracy, independent of healthy or disordered voice
simulation parameters.

In molecular discovery and drug design, structure-property relationships and
activity landscapes are often qualitatively or quantitatively analyzed to guide
the navigation of chemical space. The roughness (or smoothness) of these
molecular property landscapes is one of their most studied geometric
attributes, as it can characterize the presence of activity cliffs, with
rougher landscapes generally expected to pose tougher optimization challenges.
Here, we introduce a general, quantitative measure for describing the roughness
of molecular property landscapes. The proposed roughness index (ROGI) is
loosely inspired by the concept of fractal dimension and strongly correlates
with the out-of-sample error achieved by machine learning models on numerous
regression tasks.

The Contrastive Language-Image Pre-training (CLIP) Model is a recently
proposed large-scale pre-train model which attracts increasing attention in the
computer vision community. Benefiting from its gigantic image-text training
set, the CLIP model has learned outstanding capabilities in zero-shot learning
and image-text matching. To boost the recognition performance of CLIP on some
target visual concepts, it is often desirable to further update the CLIP model
by fine-tuning some classes-of-interest on extra training data. This operation,
however, raises an important concern: will the update hurt the zero-shot
learning or image-text matching capability of the CLIP, i.e., the catastrophic
forgetting issue? If yes, could existing continual learning algorithms be
adapted to alleviate the risk of catastrophic forgetting? To answer these
questions, this work conducts a systemic study on the continual learning issue
of the CLIP model. We construct evaluation protocols to measure the impact of
fine-tuning updates and explore different ways to upgrade existing continual
learning methods to mitigate the forgetting issue of the CLIP model. Our study
reveals the particular challenges of CLIP continual learning problem and lays a
foundation for further researches. Moreover, we propose a new algorithm, dubbed
Learning without Forgetting via Replayed Vocabulary (VR-LwF), which shows exact
effectiveness for alleviating the forgetting issue of the CLIP model.

Although Deep Reinforcement Learning (DRL) has been popular in many
disciplines including robotics, state-of-the-art DRL algorithms still struggle
to learn long-horizon, multi-step and sparse reward tasks, such as stacking
several blocks given only a task-completion reward signal. To improve learning
efficiency for such tasks, this paper proposes a DRL exploration technique,
termed A^2, which integrates two components inspired by human experiences:
Abstract demonstrations and Adaptive exploration. A^2 starts by decomposing a
complex task into subtasks, and then provides the correct orders of subtasks to
learn. During training, the agent explores the environment adaptively, acting
more deterministically for well-mastered subtasks and more stochastically for
ill-learnt subtasks. Ablation and comparative experiments are conducted on
several grid-world tasks and three robotic manipulation tasks. We demonstrate
that A^2 can aid popular DRL algorithms (DQN, DDPG, and SAC) to learn more
efficiently and stably in these environments.

Since out-of-distribution generalization is a generally ill-posed problem,
various proxy targets (e.g., calibration, adversarial robustness, algorithmic
corruptions, invariance across shifts) were studied across different research
programs resulting in different recommendations. While sharing the same
aspirational goal, these approaches have never been tested under the same
experimental conditions on real data. In this paper, we take a unified view of
previous work, highlighting message discrepancies that we address empirically,
and providing recommendations on how to measure the robustness of a model and
how to improve it. To this end, we collect 172 publicly available dataset pairs
for training and out-of-distribution evaluation of accuracy, calibration error,
adversarial attacks, environment invariance, and synthetic corruptions. We
fine-tune over 31k networks, from nine different architectures in the many- and
few-shot setting. Our findings confirm that in- and out-of-distribution
accuracies tend to increase jointly, but show that their relation is largely
dataset-dependent, and in general more nuanced and more complex than posited by
previous, smaller scale studies.

This document aims to be a self-contained, mathematically precise overview of
transformer architectures and algorithms (*not* results). It covers what
transformers are, how they are trained, what they are used for, their key
architectural components, and a preview of the most prominent models. The
reader is assumed to be familiar with basic ML terminology and simpler neural
network architectures such as MLPs.

Semi-supervised learning (SSL) is a common approach to learning predictive
models using not only labeled examples, but also unlabeled examples. While SSL
for the simple tasks of classification and regression has received a lot of
attention from the research community, this is not properly investigated for
complex prediction tasks with structurally dependent variables. This is the
case of multi-label classification and hierarchical multi-label classification
tasks, which may require additional information, possibly coming from the
underlying distribution in the descriptive space provided by unlabeled
examples, to better face the challenging task of predicting simultaneously
multiple class labels.
  In this paper, we investigate this aspect and propose a (hierarchical)
multi-label classification method based on semi-supervised learning of
predictive clustering trees. We also extend the method towards ensemble
learning and propose a method based on the random forest approach. Extensive
experimental evaluation conducted on 23 datasets shows significant advantages
of the proposed method and its extension with respect to their supervised
counterparts. Moreover, the method preserves interpretability and reduces the
time complexity of classical tree-based models.

We examine federated learning (FL) with over-the-air (OTA) aggregation, where
mobile users (MUs) aim to reach a consensus on a global model with the help of
a parameter server (PS) that aggregates the local gradients. In OTA FL, MUs
train their models using local data at every training round and transmit their
gradients simultaneously using the same frequency band in an uncoded fashion.
Based on the received signal of the superposed gradients, the PS performs a
global model update. While the OTA FL has a significantly decreased
communication cost, it is susceptible to adverse channel effects and noise.
Employing multiple antennas at the receiver side can reduce these effects, yet
the path-loss is still a limiting factor for users located far away from the
PS. To ameliorate this issue, in this paper, we propose a wireless-based
hierarchical FL scheme that uses intermediate servers (ISs) to form clusters at
the areas where the MUs are more densely located. Our scheme utilizes OTA
cluster aggregations for the communication of the MUs with their corresponding
IS, and OTA global aggregations from the ISs to the PS. We present a
convergence analysis for the proposed algorithm, and show through numerical
evaluations of the derived analytical expressions and experimental results that
utilizing ISs results in a faster convergence and a better performance than the
OTA FL alone while using less transmit power. We also validate the results on
the performance using different number of cluster iterations with different
datasets and data distributions. We conclude that the best choice of cluster
aggregations depends on the data distribution among the MUs and the clusters.

Since the first success of Dong et al., the deep-learning-based approach has
become dominant in the field of single-image super-resolution. This replaces
all the handcrafted image processing steps of traditional sparse-coding-based
methods with a deep neural network. In contrast to sparse-coding-based methods,
which explicitly create high/low-resolution dictionaries, the dictionaries in
deep-learning-based methods are implicitly acquired as a nonlinear combination
of multiple convolutions. One disadvantage of deep-learning-based methods is
that their performance is degraded for images created differently from the
training dataset (out-of-domain images). We propose an end-to-end
super-resolution network with a deep dictionary (SRDD), where a high-resolution
dictionary is explicitly learned without sacrificing the advantages of deep
learning. Extensive experiments show that explicit learning of high-resolution
dictionary makes the network more robust for out-of-domain test images while
maintaining the performance of the in-domain test images.

In transfer learning, only the last part of the networks - the so-called head
- is often fine-tuned. Representation similarity analysis shows that the most
significant change still occurs in the head even if all weights are updatable.
However, recent results from few-shot learning have shown that representation
change in the early layers, which are mostly convolutional, is beneficial,
especially in the case of cross-domain adaption. In our paper, we find out
whether that also holds true for transfer learning. In addition, we analyze the
change of representation in transfer learning, both during pre-training and
fine-tuning, and find out that pre-trained structure is unlearned if not
usable.

With an increasing emphasis on driving down the costs of Operations and
Maintenance (O&M) in the Offshore Wind (OSW) sector, comes the requirement to
explore new methodology and applications of Deep Learning (DL) to the domain.
Condition-based monitoring (CBM) has been at the forefront of recent research
developing alarm-based systems and data-driven decision making. This paper
provides a brief insight into the research being conducted in this area, with a
specific focus on alarm sequence modelling and the associated challenges faced
in its implementation. The paper proposes a novel idea to predict a set of
relevant repair actions from an input sequence of alarm sequences, comparing
Long Short-term Memory (LSTM) and Bidirectional LSTM (biLSTM) models. Achieving
training accuracy results of up to 80.23%, and test accuracy results of up to
76.01% with biLSTM gives a strong indication to the potential benefits of the
proposed approach that can be furthered in future research. The paper
introduces a framework that integrates the proposed approach into O$\&$M
procedures and discusses the potential benefits which include the reduction of
a confusing plethora of alarms, as well as unnecessary vessel transfers to the
turbines for fault diagnosis and correction.

Federated learning (FL) is vulnerable to model poisoning attacks, in which
malicious clients corrupt the global model via sending manipulated model
updates to the server. Existing defenses mainly rely on Byzantine-robust FL
methods, which aim to learn an accurate global model even if some clients are
malicious. However, they can only resist a small number of malicious clients in
practice. It is still an open challenge how to defend against model poisoning
attacks with a large number of malicious clients. Our FLDetector addresses this
challenge via detecting malicious clients. FLDetector aims to detect and remove
the majority of the malicious clients such that a Byzantine-robust FL method
can learn an accurate global model using the remaining clients. Our key
observation is that, in model poisoning attacks, the model updates from a
client in multiple iterations are inconsistent. Therefore, FLDetector detects
malicious clients via checking their model-updates consistency. Roughly
speaking, the server predicts a client's model update in each iteration based
on its historical model updates using the Cauchy mean value theorem and L-BFGS,
and flags a client as malicious if the received model update from the client
and the predicted model update are inconsistent in multiple iterations. Our
extensive experiments on three benchmark datasets show that FLDetector can
accurately detect malicious clients in multiple state-of-the-art model
poisoning attacks. After removing the detected malicious clients, existing
Byzantine-robust FL methods can learn accurate global models.Our code is
available at https://github.com/zaixizhang/FLDetector.

We present VoloGAN, an adversarial domain adaptation network that translates
synthetic RGB-D images of a high-quality 3D model of a person, into RGB-D
images that could be generated with a consumer depth sensor. This system is
especially useful to generate high amount training data for single-view 3D
reconstruction algorithms replicating the real-world capture conditions, being
able to imitate the style of different sensor types, for the same high-end 3D
model database. The network uses a CycleGAN framework with a U-Net architecture
for the generator and a discriminator inspired by SIV-GAN. We use different
optimizers and learning rate schedules to train the generator and the
discriminator. We further construct a loss function that considers image
channels individually and, among other metrics, evaluates the structural
similarity. We demonstrate that CycleGANs can be used to apply adversarial
domain adaptation of synthetic 3D data to train a volumetric video generator
model having only few training samples.

Resource-constrained classification tasks are common in real-world
applications such as allocating tests for disease diagnosis, hiring decisions
when filling a limited number of positions, and defect detection in
manufacturing settings under a limited inspection budget. Typical
classification algorithms treat the learning process and the resource
constraints as two separate and sequential tasks. Here we design an adaptive
learning approach that considers resource constraints and learning jointly by
iteratively fine-tuning misclassification costs. Via a structured experimental
study using a publicly available data set, we evaluate a decision tree
classifier that utilizes the proposed approach. The adaptive learning approach
performs significantly better than alternative approaches, especially for
difficult classification problems in which the performance of common approaches
may be unsatisfactory. We envision the adaptive learning approach as an
important addition to the repertoire of techniques for handling
resource-constrained classification problems.

We propose Neural Deformable Fields (NDF), a new representation for dynamic
human digitization from a multi-view video. Recent works proposed to represent
a dynamic human body with shared canonical neural radiance fields which links
to the observation space with deformation fields estimations. However, the
learned canonical representation is static and the current design of the
deformation fields is not able to represent large movements or detailed
geometry changes. In this paper, we propose to learn a neural deformable field
wrapped around a fitted parametric body model to represent the dynamic human.
The NDF is spatially aligned by the underlying reference surface. A neural
network is then learned to map pose to the dynamics of NDF. The proposed NDF
representation can synthesize the digitized performer with novel views and
novel poses with a detailed and reasonable dynamic appearance. Experiments show
that our method significantly outperforms recent human synthesis methods.

Real-world databases are complex and usually require dealing with
heterogeneous and mixed data types making the exploitation of shared
information between views a critical issue. For this purpose, recent studies
based on deep generative models merge all views into a nonlinear complex latent
space, which can share information among views. However, this solution limits
the model's interpretability, flexibility, and modularity. We propose a novel
method to overcome these limitations by combining multiple Variational
AutoEncoders (VAE) with a Factor Analysis latent space (FA-VAE). We use VAEs to
learn a private representation of each heterogeneous view in a continuous
latent space. Then, we share the information between views by a low-dimensional
latent space using a linear projection matrix. This way, we create a flexible
and modular hierarchical dependency between private and shared information in
which new views can be incorporated afterwards. Beyond that, we can condition
pre-trained models, cross-generate data from different domains, and perform
transfer learning between generative models.

Recent advances in data processing have stimulated the demand for learning
graphs of very large scales. Graph Neural Networks (GNNs), being an emerging
and powerful approach in solving graph learning tasks, are known to be
difficult to scale up. Most scalable models apply node-based techniques in
simplifying the expensive graph message-passing propagation procedure of GNN.
However, we find such acceleration insufficient when applied to million- or
even billion-scale graphs. In this work, we propose SCARA, a scalable GNN with
feature-oriented optimization for graph computation. SCARA efficiently computes
graph embedding from node features, and further selects and reuses feature
computation results to reduce overhead. Theoretical analysis indicates that our
model achieves sub-linear time complexity with a guaranteed precision in
propagation process as well as GNN training and inference. We conduct extensive
experiments on various datasets to evaluate the efficacy and efficiency of
SCARA. Performance comparison with baselines shows that SCARA can reach up to
100x graph propagation acceleration than current state-of-the-art methods with
fast convergence and comparable accuracy. Most notably, it is efficient to
process precomputation on the largest available billion-scale GNN dataset
Papers100M (111M nodes, 1.6B edges) in 100 seconds.

Existing few-shot learning (FSL) methods rely on training with a large
labeled dataset, which prevents them from leveraging abundant unlabeled data.
From an information-theoretic perspective, we propose an effective unsupervised
FSL method, learning representations with self-supervision. Following the
InfoMax principle, our method learns comprehensive representations by capturing
the intrinsic structure of the data. Specifically, we maximize the mutual
information (MI) of instances and their representations with a low-bias MI
estimator to perform self-supervised pre-training. Rather than supervised
pre-training focusing on the discriminable features of the seen classes, our
self-supervised model has less bias toward the seen classes, resulting in
better generalization for unseen classes. We explain that supervised
pre-training and self-supervised pre-training are actually maximizing different
MI objectives. Extensive experiments are further conducted to analyze their FSL
performance with various training settings. Surprisingly, the results show that
self-supervised pre-training can outperform supervised pre-training under the
appropriate conditions. Compared with state-of-the-art FSL methods, our
approach achieves comparable performance on widely used FSL benchmarks without
any labels of the base classes.

In this paper, how to successfully and efficiently condition a target
population of agents towards consensus is discussed. To overcome the curse of
dimensionality, the mean field formulation of the consensus control problem is
considered. Although such formulation is designed to be independent of the
number of agents, it is feasible to solve only for moderate intrinsic
dimensions of the agents space. For this reason, the solution is approached by
means of a Boltzmann procedure, i.e. quasi-invariant limit of controlled binary
interactions as approximation of the mean field PDE. The need for an efficient
solver for the binary interaction control problem motivates the use of a
supervised learning approach to encode a binary feedback map to be sampled at a
very high rate. A gradient augmented feedforward neural network for the Value
function of the binary control problem is considered and compared with direct
approximation of the feedback law.

The semantic segmentation task aims at dense classification at the pixel-wise
level. Deep models exhibited progress in tackling this task. However, one
remaining problem with these approaches is the loss of spatial precision, often
produced at the segmented objects' boundaries. Our proposed model addresses
this problem by providing an internal structure for the feature representations
while extracting a global representation that supports the former. To fit the
internal structure, during training, we predict a Gaussian Mixture Model from
the data, which, merged with the skip connections and the decoding stage, helps
avoid wrong inductive biases. Furthermore, our results show that we can improve
semantic segmentation by providing both learning representations (global and
local) with a clustering behavior and combining them. Finally, we present
results demonstrating our advances in Cityscapes and Synthia datasets.

This paper presents FedX, an unsupervised federated learning framework. Our
model learns unbiased representation from decentralized and heterogeneous local
data. It employs a two-sided knowledge distillation with contrastive learning
as a core component, allowing the federated system to function without
requiring clients to share any data features. Furthermore, its adaptable
architecture can be used as an add-on module for existing unsupervised
algorithms in federated settings. Experiments show that our model improves
performance significantly (1.58--5.52pp) on five unsupervised algorithms.

Supervised deep learning-based approaches have been applied to task-oriented
dialog and have proven to be effective for limited domain and language
applications when a sufficient number of training examples are available. In
practice, these approaches suffer from the drawbacks of domain-driven design
and under-resourced languages. Domain and language models are supposed to grow
and change as the problem space evolves. On one hand, research on transfer
learning has demonstrated the cross-lingual ability of multilingual
Transformers-based models to learn semantically rich representations. On the
other, in addition to the above approaches, meta-learning have enabled the
development of task and language learning algorithms capable of far
generalization. Through this context, this article proposes to investigate the
cross-lingual transferability of using synergistically few-shot learning with
prototypical neural networks and multilingual Transformers-based models.
Experiments in natural language understanding tasks on MultiATIS++ corpus shows
that our approach substantially improves the observed transfer learning
performances between the low and the high resource languages. More generally
our approach confirms that the meaningful latent space learned in a given
language can be can be generalized to unseen and under-resourced ones using
meta-learning.

Self-supervised cross-modal super-resolution (SR) can overcome the difficulty
of acquiring paired training data, but is challenging because only
low-resolution (LR) source and high-resolution (HR) guide images from different
modalities are available. Existing methods utilize pseudo or weak supervision
in LR space and thus deliver results that are blurry or not faithful to the
source modality. To address this issue, we present a mutual modulation SR
(MMSR) model, which tackles the task by a mutual modulation strategy, including
a source-to-guide modulation and a guide-to-source modulation. In these
modulations, we develop cross-domain adaptive filters to fully exploit
cross-modal spatial dependency and help induce the source to emulate the
resolution of the guide and induce the guide to mimic the modality
characteristics of the source. Moreover, we adopt a cycle consistency
constraint to train MMSR in a fully self-supervised manner. Experiments on
various tasks demonstrate the state-of-the-art performance of our MMSR.

Bayesian optimisation provides an effective method to optimise expensive
black box functions. It has recently been applied to problems in fluid
dynamics. This paper studies and compares common Bayesian optimisation
algorithms empirically on a range of synthetic test functions. It investigates
the choice of acquisition function and number of training samples, exact
calculation of acquisition functions and Monte Carlo based approaches and both
single-point and multi-point optimisation. The test functions considered cover
a wide selection of challenges and therefore serve as an ideal test bed to
understand the performance of Bayesian optimisation and to identify general
situations where Bayesian optimisation performs well and poorly. This knowledge
can be utilised in applications, including those in fluid dynamics, where
objective functions are unknown. The results of this investigation show that
the choices to be made are less relevant for relatively simple functions, while
optimistic acquisition functions such as Upper Confidence Bound should be
preferred for more complex objective functions. Furthermore, results from the
Monte Carlo approach are comparable to results from analytical acquisition
functions. In instances where the objective function allows parallel
evaluations, the multi-point approach offers a quicker alternative, yet it may
potentially require more objective function evaluations.

For many tasks, state-of-the-art results have been achieved with
Transformer-based architectures, resulting in a paradigmatic shift in practices
from the use of task-specific architectures to the fine-tuning of pre-trained
language models. The ongoing trend consists in training models with an
ever-increasing amount of data and parameters, which requires considerable
resources. It leads to a strong search to improve resource efficiency based on
algorithmic and hardware improvements evaluated only for English. This raises
questions about their usability when applied to small-scale learning problems,
for which a limited amount of training data is available, especially for
under-resourced languages tasks. The lack of appropriately sized corpora is a
hindrance to applying data-driven and transfer learning-based approaches with
strong instability cases. In this paper, we establish a state-of-the-art of the
efforts dedicated to the usability of Transformer-based models and propose to
evaluate these improvements on the question-answering performances of French
language which have few resources. We address the instability relating to data
scarcity by investigating various training strategies with data augmentation,
hyperparameters optimization and cross-lingual transfer. We also introduce a
new compact model for French FrALBERT which proves to be competitive in
low-resource settings.

Most machine learning models for audio tasks are dealing with a handcrafted
feature, the spectrogram. However, it is still unknown whether the spectrogram
could be replaced with deep learning based features. In this paper, we answer
this question by comparing the different learnable neural networks extracting
features with a successful spectrogram model and proposed a General Audio
Feature eXtractor (GAFX) based on a dual U-Net (GAFX-U), ResNet (GAFX-R), and
Attention (GAFX-A) modules. We design experiments to evaluate this model on the
music genre classification task on the GTZAN dataset and perform a detailed
ablation study of different configurations of our framework and our model
GAFX-U, following the Audio Spectrogram Transformer (AST) classifier achieves

A superconducting radio-frequency (SRF) photo injector is in operation at the
electron linac for beams with high brilliance and low emittance (ELBE)
radiation center and generates continuous wave (CW) electron beams with high
average current and high brightness for user operation since 2018. The speed of
emittance measurement at the SRF gun beamline can be increased by improving the
slit-scan system, thus the measurement time for one phase space mapping can be
shortened from about 15 minutes to 90 seconds. A parallel algorithm and machine
learning have been used to reduce the beamlet image noise. In order to estimate
the uncertainty in the calculation of normalized emittance, we analyze the main
error contributions such as slit position uncertainty, image noise, space
charge effects and energy measurement inaccuracy.

A permanently increasing number of on-board automotive control systems
requires new approaches to their digital mapping that improves functionality in
terms of adaptability and robustness as well as enables their easier on-line
software update. As it can be concluded from many recent studies, various
methods applying neural networks (NN) can be good candidates for relevant
digital twin (DT) tools in automotive control system design, for example, for
controller parameterization and condition monitoring. However, the NN-based DT
has strong requirements to an adequate amount of data to be used in training
and design. In this regard, the paper presents an approach, which demonstrates
how the regression tasks can be efficiently handled by the modeling of a
semi-active shock absorber within the DT framework. The approach is based on
the adaptation of time series augmentation techniques to the stationary data
that increases the variance of the latter. Such a solution gives a background
to elaborate further data engineering methods for the data preparation of
sophisticated databases.

A new method for estimating the conditional average treatment effect is
proposed in the paper. It is called TNW-CATE (the Trainable Nadaraya-Watson
regression for CATE) and based on the assumption that the number of controls is
rather large whereas the number of treatments is small. TNW-CATE uses the
Nadaraya-Watson regression for predicting outcomes of patients from the control
and treatment groups. The main idea behind TNW-CATE is to train kernels of the
Nadaraya-Watson regression by using a weight sharing neural network of a
specific form. The network is trained on controls, and it replaces standard
kernels with a set of neural subnetworks with shared parameters such that every
subnetwork implements the trainable kernel, but the whole network implements
the Nadaraya-Watson estimator. The network memorizes how the feature vectors
are located in the feature space. The proposed approach is similar to the
transfer learning when domains of source and target data are similar, but tasks
are different. Various numerical simulation experiments illustrate TNW-CATE and
compare it with the well-known T-learner, S-learner and X-learner for several
types of the control and treatment outcome functions. The code of proposed
algorithms implementing TNW-CATE is available in
https://github.com/Stasychbr/TNW-CATE.

Estimating the pose of a moving camera from monocular video is a challenging
problem, especially due to the presence of moving objects in dynamic
environments, where the performance of existing camera pose estimation methods
are susceptible to pixels that are not geometrically consistent. To tackle this
challenge, we present a robust dense indirect structure-from-motion method for
videos that is based on dense correspondence initialized from pairwise optical
flow. Our key idea is to optimize long-range video correspondence as dense
point trajectories and use it to learn robust estimation of motion
segmentation. A novel neural network architecture is proposed for processing
irregular point trajectory data. Camera poses are then estimated and optimized
with global bundle adjustment over the portion of long-range point trajectories
that are classified as static. Experiments on MPI Sintel dataset show that our
system produces significantly more accurate camera trajectories compared to
existing state-of-the-art methods. In addition, our method is able to retain
reasonable accuracy of camera poses on fully static scenes, which consistently
outperforms strong state-of-the-art dense correspondence based methods with
end-to-end deep learning, demonstrating the potential of dense indirect methods
based on optical flow and point trajectories. As the point trajectory
representation is general, we further present results and comparisons on
in-the-wild monocular videos with complex motion of dynamic objects. Code is
available at https://github.com/bytedance/particle-sfm.

Clustering traffic scenarios and detecting novel scenario types are required
for scenario-based testing of autonomous vehicles. These tasks benefit from
either good similarity measures or good representations for the traffic
scenarios. In this work, an expert-knowledge aided representation learning for
traffic scenarios is presented. The latent space so formed is used for
successful clustering and novel scenario type detection. Expert-knowledge is
used to define objectives that the latent representations of traffic scenarios
shall fulfill. It is presented, how the network architecture and loss is
designed from these objectives, thereby incorporating expert-knowledge. An
automatic mining strategy for traffic scenarios is presented, such that no
manual labeling is required. Results show the performance advantage compared to
baseline methods. Additionally, extensive analysis of the latent space is

Recent advances in deep learning optimization showed that, with some
a-posteriori information on fully-trained models, it is possible to match the
same performance by simply training a subset of their parameters. Such a
discovery has a broad impact from theory to applications, driving the research
towards methods to identify the minimum subset of parameters to train without
look-ahead information exploitation. However, the methods proposed do not match
the state-of-the-art performance, and rely on unstructured sparsely connected
models. In this work we shift our focus from the single parameters to the
behavior of the whole neuron, exploiting the concept of neuronal equilibrium
(NEq). When a neuron is in a configuration at equilibrium (meaning that it has
learned a specific input-output relationship), we can halt its update; on the
contrary, when a neuron is at non-equilibrium, we let its state evolve towards
an equilibrium state, updating its parameters. The proposed approach has been
tested on different state-of-the-art learning strategies and tasks, validating
NEq and observing that the neuronal equilibrium depends on the specific
learning setup.

The success of today's AI applications requires not only model training
(Model-centric) but also data engineering (Data-centric). In data-centric AI,
active learning (AL) plays a vital role, but current AL tools 1) require users
to manually select AL strategies, and 2) can not perform AL tasks efficiently.
To this end, this paper presents an automatic and efficient MLOps system for
AL, named ALaaS (Active-Learning-as-a-Service). Specifically, 1) ALaaS
implements an AL agent, including a performance predictor and a workflow
controller, to decide the most suitable AL strategies given users' datasets and
budgets. We call this a predictive-based successive halving early-stop (PSHEA)
procedure. 2) ALaaS adopts a server-client architecture to support an AL
pipeline and implements stage-level parallelism for high efficiency. Meanwhile,
caching and batching techniques are employed to further accelerate the AL
process. In addition to efficiency, ALaaS ensures accessibility with the help
of the design philosophy of configuration-as-a-service. Extensive experiments
show that ALaaS outperforms all other baselines in terms of latency and
throughput. Also, guided by the AL agent, ALaaS can automatically select and
run AL strategies for non-expert users under different datasets and budgets.
Our code is available at
\url{https://github.com/MLSysOps/Active-Learning-as-a-Service}.

Digital Twins (DT) are essentially Dynamic Data-driven models that serve as
real-time symbiotic "virtual replicas" of real-world systems. DT can leverage
fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional
symbiotic sensing feedback loops for its continuous updates. Sensing loops can
consequently steer measurement, analysis and reconfiguration aimed at more
accurate modelling and analysis in DT. The reconfiguration decisions can be
autonomous or interactive, keeping human-in-the-loop. The trustworthiness of
these decisions can be hindered by inadequate explainability of the rationale,
and utility gained in implementing the decision for the given situation among
alternatives. Additionally, different decision-making algorithms and models
have varying complexity, quality and can result in different utility gained for
the model. The inadequacy of explainability can limit the extent to which
humans can evaluate the decisions, often leading to updates which are unfit for
the given situation, erroneous, compromising the overall accuracy of the model.
The novel contribution of this paper is an approach to harnessing
explainability in human-in-the-loop DDDAS and DT systems, leveraging
bidirectional symbiotic sensing feedback. The approach utilises interpretable
machine learning and goal modelling to explainability, and considers trade-off
analysis of utility gained. We use examples from smart warehousing to
demonstrate the approach.

We study the identity testing problem for high-dimensional distributions.
Given as input an explicit distribution $\mu$, an $\varepsilon>0$, and access
to sampling oracle(s) for a hidden distribution $\pi$, the goal in identity
testing is to distinguish whether the two distributions $\mu$ and $\pi$ are
identical or are at least $\varepsilon$-far apart. When there is only access to
full samples from the hidden distribution $\pi$, it is known that exponentially
many samples (in the dimension) may be needed for identity testing, and hence
previous works have studied identity testing with additional access to various
"conditional" sampling oracles. We consider a significantly weaker conditional
sampling oracle, which we call the $\mathsf{Coordinate\ Oracle}$, and provide a
computational and statistical characterization of the identity testing problem
in this new model.
  We prove that if an analytic property known as approximate tensorization of
entropy holds for an $n$-dimensional visible distribution $\mu$, then there is
an efficient identity testing algorithm for any hidden distribution $\pi$ using
$\tilde{O}(n/\varepsilon)$ queries to the $\mathsf{Coordinate\ Oracle}$.
Approximate tensorization of entropy is a pertinent condition as recent works
have established it for a large class of high-dimensional distributions. We
also prove a computational phase transition: for a well-studied class of
$n$-dimensional distributions, specifically sparse antiferromagnetic Ising
models over $\{+1,-1\}^n$, we show that in the regime where approximate
tensorization of entropy fails, there is no efficient identity testing
algorithm unless $\mathsf{RP}=\mathsf{NP}$. We complement our results with a
matching $\Omega(n/\varepsilon)$ statistical lower bound for the sample
complexity of identity testing in the $\mathsf{Coordinate\ Oracle}$ model.

At present, the identification of agricultural pests and diseases has the
problem that the model is not lightweight enough and difficult to apply. Based
on MobileNetV3, this paper introduces the Coordinate Attention block. The
parameters of MobileNetV3-large are reduced by 22%, the model size is reduced
by 19.7%, and the accuracy is improved by 0.92%. The parameters of
MobileNetV3-small are reduced by 23.4%, the model size is reduced by 18.3%, and
the accuracy is increased by 0.40%. In addition, the improved MobileNetV3-small
was migrated to Jetson Nano for testing. The accuracy increased by 2.48% to
98.31%, and the inference speed increased by 7.5%. It provides a reference for
deploying the agricultural pest identification model to embedded devices.

Vision-language navigation is the task of directing an embodied agent to
navigate in 3D scenes with natural language instructions. For the agent,
inferring the long-term navigation target from visual-linguistic clues is
crucial for reliable path planning, which, however, has rarely been studied
before in literature. In this article, we propose a Target-Driven Structured
Transformer Planner (TD-STP) for long-horizon goal-guided and room layout-aware
navigation. Specifically, we devise an Imaginary Scene Tokenization mechanism
for explicit estimation of the long-term target (even located in unexplored
environments). In addition, we design a Structured Transformer Planner which
elegantly incorporates the explored room layout into a neural attention
architecture for structured and global planning. Experimental results
demonstrate that our TD-STP substantially improves previous best methods'
success rate by 2% and 5% on the test set of R2R and REVERIE benchmarks,
respectively. Our code is available at https://github.com/YushengZhao/TD-STP .

Modern language models leverage increasingly large numbers of parameters to
achieve performance on natural language understanding tasks. Ensembling these
models in specific configurations for downstream tasks show even further
performance improvements. In this paper, we perform an analysis of bagging
language models and compare single language models to bagged ensembles that are
roughly equivalent in terms of final model size. We explore an array of model
bagging configurations for natural language understanding tasks with final
ensemble sizes ranging from 300M parameters to 1.5B parameters and determine
that our ensembling methods are at best roughly equivalent to single LM
baselines. We note other positive effects of bagging and pruning in specific
scenarios according to findings in our experiments such as variance reduction
and minor performance improvements.

In this paper, we study a one-shot distributed learning algorithm via
refitting Bootstrap samples, which we refer to as ReBoot. Given the local
models that are fit on multiple independent subsamples, ReBoot refits a new
model on the union of the Bootstrap samples drawn from these local models. The
whole procedure requires only one round of communication of model parameters.
Theoretically, we analyze the statistical rate of ReBoot for generalized linear
models (GLM) and noisy phase retrieval, which represent convex and non-convex
problems respectively. In both cases, ReBoot provably achieves the full-sample
statistical rate whenever the subsample size is not too small. In particular,
we show that the systematic bias of ReBoot, the error that is independent of
the number of subsamples, is $O(n ^ {-2})$ in GLM, where n is the subsample
size. This rate is sharper than that of model parameter averaging and its
variants, implying the higher tolerance of ReBoot with respect to data splits
to maintain the full-sample rate. Simulation study exhibits the statistical
advantage of ReBoot over competing methods including averaging and CSL
(Communication-efficient Surrogate Likelihood) with up to two rounds of
gradient communication. Finally, we propose FedReBoot, an iterative version of
ReBoot, to aggregate convolutional neural networks for image classification,
which exhibits substantial superiority over FedAve within early rounds of
communication.

As opaque predictive models increasingly impact many areas of modern life,
interest in quantifying the importance of a given input variable for making a
specific prediction has grown. Recently, there has been a proliferation of
model-agnostic methods to measure variable importance (VI) that analyze the
difference in predictive power between a full model trained on all variables
and a reduced model that excludes the variable(s) of interest. A bottleneck
common to these methods is the estimation of the reduced model for each
variable (or subset of variables), which is an expensive process that often
does not come with theoretical guarantees. In this work, we propose a fast and
flexible method for approximating the reduced model with important inferential
guarantees. We replace the need for fully retraining a wide neural network by a
linearization initialized at the full model parameters. By adding a ridge-like
penalty to make the problem convex, we prove that when the ridge penalty
parameter is sufficiently large, our method estimates the variable importance
measure with an error rate of $O(\frac{1}{\sqrt{n}})$ where $n$ is the number
of training samples. We also show that our estimator is asymptotically normal,
enabling us to provide confidence bounds for the VI estimates. We demonstrate
through simulations that our method is fast and accurate under several
data-generating regimes, and we demonstrate its real-world applicability on a
seasonal climate forecasting example.

Sparsely Mixture of Experts (MoE) has received great interest due to its
promising scaling capability with affordable computational overhead. MoE
converts dense layers into sparse experts, and utilizes a gated routing network
to make experts conditionally activated. However, as the number of experts
grows, MoE with outrageous parameters suffers from overfitting and sparse data
allocation. Such problems are especially severe on tasks with limited data,
thus hindering the progress for MoE models to improve performance by scaling
up. In this work, we propose Mixture of Expert Clusters - a general approach to
enable expert layers to learn more diverse and appropriate knowledge by
imposing variance-based constraints on the routing stage. We further propose a
cluster-level expert dropout strategy specifically designed for the expert
cluster structure. Our experiments reveal that MoEC could improve performance
on machine translation and natural language understanding tasks, and raise the
performance upper bound for scaling up experts under limited data. We also
verify that MoEC plays a positive role in mitigating overfitting and sparse
data allocation.

As the importance of intrusion detection and prevention systems (IDPSs)
increases, great costs are incurred to manage the signatures that are generated
by malicious communication pattern files. Experts in network security need to
classify signatures by importance for an IDPS to work. We propose and evaluate
a machine learning signature classification model with a reject option (RO) to
reduce the cost of setting up an IDPS. To train the proposed model, it is
essential to design features that are effective for signature classification.
Experts classify signatures with predefined if-then rules. An if-then rule
returns a label of low, medium, high, or unknown importance based on keyword
matching of the elements in the signature. Therefore, we first design two types
of features, symbolic features (SFs) and keyword features (KFs), which are used
in keyword matching for the if-then rules. Next, we design web information and
message features (WMFs) to capture the properties of signatures that do not
match the if-then rules. The WMFs are extracted as term frequency-inverse
document frequency (TF-IDF) features of the message text in the signatures. The
features are obtained by web scraping from the referenced external attack
identification systems described in the signature. Because failure needs to be
minimized in the classification of IDPS signatures, as in the medical field, we
consider introducing a RO in our proposed model. The effectiveness of the
proposed classification model is evaluated in experiments with two real
datasets composed of signatures labeled by experts: a dataset that can be
classified with if-then rules and a dataset with elements that do not match an
if-then rule. In the experiment, the proposed model is evaluated. In both
cases, the combined SFs and WMFs performed better than the combined SFs and
KFs. In addition, we also performed feature analysis.

We consider an improper reinforcement learning setting where a learner is
given $M$ base controllers for an unknown Markov decision process, and wishes
to combine them optimally to produce a potentially new controller that can
outperform each of the base ones. This can be useful in tuning across
controllers, learnt possibly in mismatched or simulated environments, to obtain
a good controller for a given target environment with relatively few trials.
  Towards this, we propose two algorithms: (1) a Policy Gradient-based
approach; and (2) an algorithm that can switch between a simple Actor-Critic
(AC) based scheme and a Natural Actor-Critic (NAC) scheme depending on the
available information. Both algorithms operate over a class of improper
mixtures of the given controllers. For the first case, we derive convergence
rate guarantees assuming access to a gradient oracle. For the AC-based approach
we provide convergence rate guarantees to a stationary point in the basic AC
case and to a global optimum in the NAC case. Numerical results on (i) the
standard control theoretic benchmark of stabilizing an cartpole; and (ii) a
constrained queueing task show that our improper policy optimization algorithm
can stabilize the system even when the base policies at its disposal are
unstable.

In this paper, we propose XG-BoT, an explainable deep graph neural network
model for botnet node detection. The proposed model is mainly composed of a
botnet detector and an explainer for automatic forensics. The XG-BoT detector
can effectively detect malicious botnet nodes under large-scale networks.
Specifically, it utilizes a grouped reversible residual connection with a graph
isomorphism network to learn expressive node representations from the botnet
communication graphs. The explainer in XG-BoT can perform automatic network
forensics by highlighting suspicious network flows and related botnet nodes. We
evaluated XG-BoT based on real-world, large-scale botnet network graph
datasets. Overall, XG-BoT is able to outperform the state-of-the-art in terms
of key evaluation metrics. In addition, we show that the XG-BoT explainer can
generate useful explanations based on GNNExplainer and saliency map for
automatic network forensics.

We consider vertical logistic regression (VLR) trained with mini-batch
gradient descent -- a setting which has attracted growing interest among
industries and proven to be useful in a wide range of applications including
finance and medical research. We provide a comprehensive and rigorous privacy
analysis of VLR in a class of open-source Federated Learning frameworks, where
the protocols might differ between one another, yet a procedure of obtaining
local gradients is implicitly shared. We first consider the honest-but-curious
threat model, in which the detailed implementation of protocol is neglected and
only the shared procedure is assumed, which we abstract as an oracle. We find
that even under this general setting, single-dimension feature and label can
still be recovered from the other party under suitable constraints of batch
size, thus demonstrating the potential vulnerability of all frameworks
following the same philosophy. Then we look into a popular instantiation of the
protocol based on Homomorphic Encryption (HE). We propose an active attack that
significantly weaken the constraints on batch size in the previous analysis via
generating and compressing auxiliary ciphertext. To address the privacy leakage
within the HE-based protocol, we develop a simple-yet-effective countermeasure
based on Differential Privacy (DP), and provide both utility and privacy
guarantees for the updated algorithm. Finally, we empirically verify the
effectiveness of our attack and defense on benchmark datasets. Altogether, our
findings suggest that all vertical federated learning frameworks that solely
depend on HE might contain severe privacy risks, and DP, which has already
demonstrated its power in horizontal federated learning, can also play a
crucial role in the vertical setting, especially when coupled with HE or secure
multi-party computation (MPC) techniques.

This paper is our attempt at answering a twofold question covering the areas
of ethics and authorship analysis. Firstly, since the methods used for
performing authorship analysis imply that an author can be recognized by the
content he or she creates, we were interested in finding out whether it would
be possible for an author identification system to correctly attribute works to
authors if in the course of years they have undergone a major psychological
transition. Secondly, and from the point of view of the evolution of an
author's ethical values, we checked what it would mean if the authorship
attribution system encounters difficulties in detecting single authorship. We
set out to answer those questions through performing a binary authorship
analysis task using a text classifier based on a pre-trained transformer model
and a baseline method relying on conventional similarity metrics. For the test
set, we chose works of Arata Osada, a Japanese educator and specialist in the
history of education, with half of them being books written before the World
War II and another half in the 1950s, in between which he underwent a
transformation in terms of political opinions. As a result, we were able to
confirm that in the case of texts authored by Arata Osada in a time span of
more than 10 years, while the classification accuracy drops by a large margin
and is substantially lower than for texts by other non-fiction writers,
confidence scores of the predictions remain at a similar level as in the case
of a shorter time span, indicating that the classifier was in many instances
tricked into deciding that texts written over a time span of multiple years
were actually written by two different people, which in turn leads us to
believe that such a change can affect authorship analysis, and that historical
events have great impact on a person's ethical outlook as expressed in their
writings.

As a pivotal component to attaining generalizable solutions in human
intelligence, reasoning provides great potential for reinforcement learning
(RL) agents' generalization towards varied goals by summarizing part-to-whole
arguments and discovering cause-and-effect relations. However, how to discover
and represent causalities remains a huge gap that hinders the development of
causal RL. In this paper, we augment Goal-Conditioned RL (GCRL) with Causal
Graph (CG), a structure built upon the relation between objects and events. We
novelly formulate the GCRL problem into variational likelihood maximization
with CG as latent variables. To optimize the derived objective, we propose a
framework with theoretical performance guarantees that alternates between two
steps: using interventional data to estimate the posterior of CG; using CG to
learn generalizable models and interpretable policies. Due to the lack of
public benchmarks that verify generalization capability under reasoning, we
design nine tasks and then empirically show the effectiveness of the proposed
method against five baselines on these tasks. Further theoretical analysis
shows that our performance improvement is attributed to the virtuous cycle of
causal discovery, transition modeling, and policy training, which aligns with
the experimental evidence in extensive ablation studies.

Federated Learning (FL), a distributed machine learning paradigm, has been
adapted to mitigate privacy concerns for customers. Despite their appeal, there
are various inference attacks that can exploit shared-plaintext model updates
to embed traces of customer private information, leading to serious privacy
concerns. To alleviate this privacy issue, cryptographic techniques such as
Secure Multi-Party Computation and Homomorphic Encryption have been used for
privacy-preserving FL. However, such security issues in privacy-preserving FL
are poorly elucidated and underexplored. This work is the first attempt to
elucidate the triviality of performing model corruption attacks on
privacy-preserving FL based on lightweight secret sharing. We consider
scenarios in which model updates are quantized to reduce communication overhead
in this case, where an adversary can simply provide local parameters outside
the legal range to corrupt the model. We then propose the MUD-PQFed protocol,
which can precisely detect malicious clients performing attacks and enforce
fair penalties. By removing the contributions of detected malicious clients,
the global model utility is preserved to be comparable to the baseline global
model without the attack. Extensive experiments validate effectiveness in
maintaining baseline accuracy and detecting malicious clients in a fine-grained
manner

Incremental learning is one paradigm to enable model building and updating at
scale with streaming data. For end-to-end automatic speech recognition (ASR)
tasks, the absence of human annotated labels along with the need for privacy
preserving policies for model building makes it a daunting challenge. Motivated
by these challenges, in this paper we use a cloud based framework for
production systems to demonstrate insights from privacy preserving incremental
learning for automatic speech recognition (ILASR). By privacy preserving, we
mean, usage of ephemeral data which are not human annotated. This system is a
step forward for production levelASR models for incremental/continual learning
that offers near real-time test-bed for experimentation in the cloud for
end-to-end ASR, while adhering to privacy-preserving policies. We show that the
proposed system can improve the production models significantly(3%) over a new
time period of six months even in the absence of human annotated labels with
varying levels of weak supervision and large batch sizes in incremental
learning. This improvement is 20% over test sets with new words and phrases in
the new time period. We demonstrate the effectiveness of model building in a
privacy-preserving incremental fashion for ASR while further exploring the
utility of having an effective teacher model and use of large batch sizes.

Some Transformer-based models can perform cross-lingual transfer learning:
those models can be trained on a specific task in one language and give
relatively good results on the same task in another language, despite having
been pre-trained on monolingual tasks only. But, there is no consensus yet on
whether those transformer-based models learn universal patterns across
languages. We propose a word-level task-agnostic method to evaluate the
alignment of contextualized representations built by such models. We show that
our method provides more accurate translated word pairs than previous methods
to evaluate word-level alignment. And our results show that some inner layers
of multilingual Transformer-based models outperform other explicitly aligned
representations, and even more so according to a stricter definition of
multilingual alignment.

Incremental Task learning (ITL) is a category of continual learning that
seeks to train a single network for multiple tasks (one after another), where
training data for each task is only available during the training of that task.
Neural networks tend to forget older tasks when they are trained for the newer
tasks; this property is often known as catastrophic forgetting. To address this
issue, ITL methods use episodic memory, parameter regularization, masking and
pruning, or extensible network structures. In this paper, we propose a new
incremental task learning framework based on low-rank factorization. In
particular, we represent the network weights for each layer as a linear
combination of several rank-1 matrices. To update the network for a new task,
we learn a rank-1 (or low-rank) matrix and add that to the weights of every
layer. We also introduce an additional selector vector that assigns different
weights to the low-rank matrices learned for the previous tasks. We show that
our approach performs better than the current state-of-the-art methods in terms
of accuracy and forgetting. Our method also offers better memory efficiency
compared to episodic memory- and mask-based approaches. Our code will be
available at https://github.com/CSIPlab/task-increment-rank-update.git

We explore the capability of deep learning to classify cosmic structures. In
cosmological simulations, cosmic volumes are segmented into voids, sheets,
filaments and knots, according to the distribution and kinematics of dark
matter (DM), and galaxies are also classified according to the segmentation.
However, observational studies cannot adopt this classification method using
DM. In this study, we demonstrate that deep learning can bridge the gap between
simulations and observations. Our models are based on three-dimensional
convolutional neural networks and trained with data of the distribution of
galaxies in a simulation to deduce the structure classes from the galaxies
rather than DM. Our model can predict the class labels as accurate as a
previous study using DM distribution for the training and prediction. This
means that galaxy distribution can be a substitution for DM for the
cosmic-structure classification, and our models using galaxies can be directly
applied to wide-field survey observations. When observational restrictions are
ignored, our model can classify simulated galaxies into the four classes with
an accuracy (macro-averaged $F_{\rm 1}$-score) of 64 per cent. If restrictions
such as limiting magnitude are considered, our model can classify SDSS galaxies
at $\sim100~{\rm Mpc}$ with an accuracy of 60 per cent. In the binary
classification distinguishing void galaxies from the others, our model can
achieve an accuracy of 88 per cent.

Meta reinforcement learning (meta-RL) aims to learn a policy solving a set of
training tasks simultaneously and quickly adapting to new tasks. It requires
massive amounts of data drawn from training tasks to infer the common structure
shared among tasks. Without heavy reward engineering, the sparse rewards in
long-horizon tasks exacerbate the problem of sample efficiency in meta-RL.
Another challenge in meta-RL is the discrepancy of difficulty level among
tasks, which might cause one easy task dominating learning of the shared policy
and thus preclude policy adaptation to new tasks. This work introduces a novel
objective function to learn an action translator among training tasks. We
theoretically verify that the value of the transferred policy with the action
translator can be close to the value of the source policy and our objective
function (approximately) upper bounds the value difference. We propose to
combine the action translator with context-based meta-RL algorithms for better
data collection and more efficient exploration during meta-training. Our
approach empirically improves the sample efficiency and performance of meta-RL
algorithms on sparse-reward tasks.

Since BERT (Devlin et al., 2018), learning contextualized word embeddings has
been a de-facto standard in NLP. However, the progress of learning
contextualized phrase embeddings is hindered by the lack of a human-annotated,
phrase-in-context benchmark. To fill this gap, we propose PiC - a dataset of
~28K of noun phrases accompanied by their contextual Wikipedia pages and a
suite of three tasks of increasing difficulty for evaluating the quality of
phrase embeddings. We find that training on our dataset improves ranking
models' accuracy and remarkably pushes Question Answering (QA) models to
near-human accuracy which is 95% Exact Match (EM) on semantic search given a
query phrase and a passage. Interestingly, we find evidence that such
impressive performance is because the QA models learn to better capture the
common meaning of a phrase regardless of its actual context. That is, on our
Phrase Sense Disambiguation (PSD) task, SotA model accuracy drops substantially
(60% EM), failing to differentiate between two different senses of the same
phrase under two different contexts. Further results on our 3-task PiC
benchmark reveal that learning contextualized phrase embeddings remains an
interesting, open challenge.

Understanding temporal dynamics of video is an essential aspect of learning
better video representations. Recently, transformer-based architectural designs
have been extensively explored for video tasks due to their capability to
capture long-term dependency of input sequences. However, we found that these
Video Transformers are still biased to learn spatial dynamics rather than
temporal ones, and debiasing the spurious correlation is critical for their
performance. Based on the observations, we design simple yet effective
self-supervised tasks for video models to learn temporal dynamics better.
Specifically, for debiasing the spatial bias, our method learns the temporal
order of video frames as extra self-supervision and enforces the randomly
shuffled frames to have low-confidence outputs. Also, our method learns the
temporal flow direction of video tokens among consecutive frames for enhancing
the correlation toward temporal dynamics. Under various video action
recognition tasks, we demonstrate the effectiveness of our method and its
compatibility with state-of-the-art Video Transformers.

Feature selection is an important process in machine learning. It builds an
interpretable and robust model by selecting the features that contribute the
most to the prediction target. However, most mature feature selection
algorithms, including supervised and semi-supervised, fail to fully exploit the
complex potential structure between features. We believe that these structures
are very important for the feature selection process, especially when labels
are lacking and data is noisy.
  To this end, we innovatively introduce a deep learning-based self-supervised
mechanism into feature selection problems, namely batch-Attention-based
Self-supervision Feature Selection(A-SFS). Firstly, a multi-task
self-supervised autoencoder is designed to uncover the hidden structure among
features with the support of two pretext tasks. Guided by the integrated
information from the multi-self-supervised learning model, a batch-attention
mechanism is designed to generate feature weights according to batch-based
feature selection patterns to alleviate the impacts introduced by a handful of
noisy data. This method is compared to 14 major strong benchmarks, including
LightGBM and XGBoost. Experimental results show that A-SFS achieves the highest
accuracy in most datasets. Furthermore, this design significantly reduces the
reliance on labels, with only 1/10 labeled data needed to achieve the same
performance as those state of art baselines. Results show that A-SFS is also
most robust to the noisy and missing data.

The growing role of data science (DS) and machine learning (ML) in
high-energy physics (HEP) is well established and pertinent given the complex
detectors, large data, sets and sophisticated analyses at the heart of HEP
research. Moreover, exploiting symmetries inherent in physics data have
inspired physics-informed ML as a vibrant sub-field of computer science
research. HEP researchers benefit greatly from materials widely available
materials for use in education, training and workforce development. They are
also contributing to these materials and providing software to DS/ML-related
fields. Increasingly, physics departments are offering courses at the
intersection of DS, ML and physics, often using curricula developed by HEP
researchers and involving open software and data used in HEP. In this white
paper, we explore synergies between HEP research and DS/ML education, discuss
opportunities and challenges at this intersection, and propose community
activities that will be mutually beneficial.

Few-shot open-set recognition aims to classify both seen and novel images
given only limited training data of seen classes. The challenge of this task is
that the model is required not only to learn a discriminative classifier to
classify the pre-defined classes with few training data but also to reject
inputs from unseen classes that never appear at training time. In this paper,
we propose to solve the problem from two novel aspects. First, instead of
learning the decision boundaries between seen classes, as is done in standard
close-set classification, we reserve space for unseen classes, such that images
located in these areas are recognized as the unseen classes. Second, to
effectively learn such decision boundaries, we propose to utilize the
background features from seen classes. As these background regions do not
significantly contribute to the decision of close-set classification, it is
natural to use them as the pseudo unseen classes for classifier learning. Our
extensive experiments show that our proposed method not only outperforms
multiple baselines but also sets new state-of-the-art results on three popular
benchmarks, namely tieredImageNet, miniImageNet, and Caltech-USCD
Birds-200-2011 (CUB).

Quantum dissipation arises from the unavoidable coupling between a quantum
system and its surrounding environment, which is known as a major obstacle in
the quantum processing of information. Apart from its existence, how to trace
the dissipation from observational data is a crucial topic that may stimulate
manners to suppress the dissipation. In this paper, we propose to learn the
quantum dissipation from dynamical observations using the neural ordinary
differential equation, and then demonstrate this method concretely on two open
quantum-spin systems -- a large spin system and a spin-1/2 chain. We also
investigate the learning efficiency of the dataset, which provides useful
guidance for data acquisition in experiments. Our work promisingly facilitates
effective modeling and decoherence suppression in open quantum systems.

In contrast to the fully supervised methods using pixel-wise mask labels,
box-supervised instance segmentation takes advantage of the simple box
annotations, which has recently attracted a lot of research attentions. In this
paper, we propose a novel single-shot box-supervised instance segmentation
approach, which integrates the classical level set model with deep neural
network delicately. Specifically, our proposed method iteratively learns a
series of level sets through a continuous Chan-Vese energy-based function in an
end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict
the instance-aware mask map as the level set for each instance. Both the input
image and its deep features are employed as the input data to evolve the level
set curves, where a box projection function is employed to obtain the initial
boundary. By minimizing the fully differentiable energy function, the level set
for each instance is iteratively optimized within its corresponding bounding
box annotation. The experimental results on four challenging benchmarks
demonstrate the leading performance of our proposed approach to robust instance
segmentation in various scenarios. The code is available at:
https://github.com/LiWentomng/boxlevelset.

Few-Shot Classification(FSC) aims to generalize from base classes to novel
classes given very limited labeled samples, which is an important step on the
path toward human-like machine learning. State-of-the-art solutions involve
learning to find a good metric and representation space to compute the distance
between samples. Despite the promising accuracy performance, how to model
uncertainty for metric-based FSC methods effectively is still a challenge. To
model uncertainty, We place a distribution over class probability based on the
theory of evidence. As a result, uncertainty modeling and metric learning can
be decoupled. To reduce the uncertainty of classification, we propose a
Bayesian evidence fusion theorem. Given observed samples, the network learns to
get posterior distribution parameters given the prior parameters produced by
the pre-trained network. Detailed gradient analysis shows that our method
provides a smooth optimization target and can capture the uncertainty. The
proposed method is agnostic to metric learning strategies and can be
implemented as a plug-and-play module. We integrate our method into several
newest FSC methods and demonstrate the improved accuracy and uncertainty
quantification on standard FSC benchmarks.

Real-world data typically follow a long-tailed distribution, where a few
majority categories occupy most of the data while most minority categories
contain a limited number of samples. Classification models minimizing
cross-entropy struggle to represent and classify the tail classes. Although the
problem of learning unbiased classifiers has been well studied, methods for
representing imbalanced data are under-explored. In this paper, we focus on
representation learning for imbalanced data. Recently, supervised contrastive
learning has shown promising performance on balanced data recently. However,
through our theoretical analysis, we find that for long-tailed data, it fails
to form a regular simplex which is an ideal geometric configuration for
representation learning. To correct the optimization behavior of SCL and
further improve the performance of long-tailed visual recognition, we propose a
novel loss for balanced contrastive learning (BCL). Compared with SCL, we have
two improvements in BCL: class-averaging, which balances the gradient
contribution of negative classes; class-complement, which allows all classes to
appear in every mini-batch. The proposed balanced contrastive learning (BCL)
method satisfies the condition of forming a regular simplex and assists the
optimization of cross-entropy. Equipped with BCL, the proposed two-branch
framework can obtain a stronger feature representation and achieve competitive
performance on long-tailed benchmark datasets such as CIFAR-10-LT,
CIFAR-100-LT, ImageNet-LT, and iNaturalist2018. Our code is available at
https://github.com/FlamieZhu/BCL .

Considering the prevalence of the power-law distribution in user-item
networks, hyperbolic space has attracted considerable attention and achieved
impressive performance in the recommender system recently. The advantage of
hyperbolic recommendation lies in that its exponentially increasing capacity is
well-suited to describe the power-law distributed user-item network whereas the
Euclidean equivalent is deficient. Nonetheless, it remains unclear which kinds
of items can be effectively recommended by the hyperbolic model and which
cannot. To address the above concerns, we take the most basic recommendation
technique, collaborative filtering, as a medium, to investigate the behaviors
of hyperbolic and Euclidean recommendation models. The results reveal that (1)
tail items get more emphasis in hyperbolic space than that in Euclidean space,
but there is still ample room for improvement; (2) head items receive modest
attention in hyperbolic space, which could be considerably improved; (3) and
nonetheless, the hyperbolic models show more competitive performance than
Euclidean models. Driven by the above observations, we design a novel learning
method, named hyperbolic informative collaborative filtering (HICF), aiming to
compensate for the recommendation effectiveness of the head item while at the
same time improving the performance of the tail item. The main idea is to adapt
the hyperbolic margin ranking learning, making its pull and push procedure
geometric-aware, and providing informative guidance for the learning of both
head and tail items. Extensive experiments back up the analytic findings and
also show the effectiveness of the proposed method. The work is valuable for
personalized recommendations since it reveals that the hyperbolic space
facilitates modeling the tail item, which often represents user-customized
preferences or new products.

Assistive robots operating in household environments would require items to
be available in the house to perform assistive tasks. However, when these items
run out, the assistive robot must remind its user to buy the missing items. In
this paper, we present a computational architecture that can allow a robot to
learn personalized contextual knowledge of a household through interactions
with its user. The architecture can then use the learned knowledge to make
predictions about missing items from the household over a long period of time.
The architecture integrates state-of-the-art perceptual learning algorithms,
cognitive models of memory encoding and learning, a reasoning module for
predicting missing items from the household, and a graphical user interface
(GUI) to interact with the user. The architecture is integrated with the Fetch
mobile manipulator robot and validated in a large indoor environment with
multiple contexts and objects. Our experimental results show that the robot can
adapt to an environment by learning contextual knowledge through interactions
with its user. The robot can also use the learned knowledge to correctly
predict missing items over multiple weeks and it is robust against sensory and
perceptual errors.

Binary neural network (BNN) is an extreme quantization version of
convolutional neural networks (CNNs) with all features and weights mapped to
just 1-bit. Although BNN saves a lot of memory and computation demand to make
CNN applicable on edge or mobile devices, BNN suffers the drop of network
performance due to the reduced representation capability after binarization. In
this paper, we propose a new replaceable and easy-to-use convolution module
RepConv, which enhances feature maps through replicating input or output along
channel dimension by $\beta$ times without extra cost on the number of
parameters and convolutional computation. We also define a set of RepTran rules
to use RepConv throughout BNN modules like binary convolution, fully connected
layer and batch normalization. Experiments demonstrate that after the RepTran
transformation, a set of highly cited BNNs have achieved universally better
performance than the original BNN versions. For example, the Top-1 accuracy of
Rep-ReCU-ResNet-20, i.e., a RepBconv enhanced ReCU-ResNet-20, reaches 88.97% on
CIFAR-10, which is 1.47% higher than that of the original network. And
Rep-AdamBNN-ReActNet-A achieves 71.342% Top-1 accuracy on ImageNet, a fresh
state-of-the-art result of BNNs. Code and models are available
at:https://github.com/imfinethanks/Rep_AdamBNN.

Video frame interpolation (VFI) aims to synthesize an intermediate frame
between two consecutive frames. State-of-the-art approaches usually adopt a
two-step solution, which includes 1) generating locally-warped pixels by
flow-based motion estimations, 2) blending the warped pixels to form a full
frame through deep neural synthesis networks. However, due to the inconsistent
warping from the two consecutive frames, the warped features for new frames are
usually not aligned, which leads to distorted and blurred frames, especially
when large and complex motions occur. To solve this issue, in this paper we
propose a novel Trajectory-aware Transformer for Video Frame Interpolation
(TTVFI). In particular, we formulate the warped features with inconsistent
motions as query tokens, and formulate relevant regions in a motion trajectory
from two original consecutive frames into keys and values. Self-attention is
learned on relevant tokens along the trajectory to blend the pristine features
into intermediate frames through end-to-end training. Experimental results
demonstrate that our method outperforms other state-of-the-art methods in four
widely-used VFI benchmarks. Both code and pre-trained models will be released
soon.

Predictive coding is a promising theoretical framework in neuroscience for
understanding information transmission and perception. It posits that the brain
perceives the external world through internal models and updates these models
under the guidance of prediction errors. Previous studies on predictive coding
emphasized top-down feedback interactions in hierarchical multi-layered
networks but largely ignored lateral recurrent interactions. We perform
analytical and numerical investigations in this work on the effects of
single-layer lateral interactions. We consider a simple predictive response
dynamics and run it on the MNIST dataset of hand-written digits. We find that
learning will generally break the interaction symmetry between peer neurons,
and that high input correlation between two neurons does not necessarily bring
strong direct interactions between them. The optimized network responds to
familiar input signals much faster than to novel or random inputs, and it
significantly reduces the correlations between the output states of pairs of
neurons.

Open compound domain adaptation (OCDA) considers the target domain as the
compound of multiple unknown homogeneous subdomains. The goal of OCDA is to
minimize the domain gap between the labeled source domain and the unlabeled
compound target domain, which benefits the model generalization to the unseen
domains. Current OCDA for semantic segmentation methods adopt manual domain
separation and employ a single model to simultaneously adapt to all the target
subdomains. However, adapting to a target subdomain might hinder the model from
adapting to other dissimilar target subdomains, which leads to limited
performance. In this work, we introduce a multi-teacher framework with
bidirectional photometric mixing to separately adapt to every target subdomain.
First, we present an automatic domain separation to find the optimal number of
subdomains. On this basis, we propose a multi-teacher framework in which each
teacher model uses bidirectional photometric mixing to adapt to one target
subdomain. Furthermore, we conduct an adaptive distillation to learn a student
model and apply consistency regularization to improve the student
generalization. Experimental results on benchmark datasets show the efficacy of
the proposed approach for both the compound domain and the open domains against
existing state-of-the-art approaches.

Artificial intelligence has made great progresses in medical data analysis,
but the lack of robustness and interpretability has kept these methods from
being widely deployed. In particular, data-driven models are vulnerable to
adversarial attacks, which are small, targeted perturbations that dramatically
degrade model performance. As a recent example, while deep learning has shown
impressive performance in electrocardiogram (ECG) classification, Han et al.
crafted realistic perturbations that fooled the network 74% of the time [2020].
Current adversarial defense paradigms are computationally intensive and
impractical for many high dimensional problems. Previous research indicates
that a network vulnerability is related to the features learned during
training. We propose a novel approach based on ensemble decorrelation and
Fourier partitioning for training parallel network arms into a decorrelated
architecture to learn complementary features, significantly reducing the chance
of a perturbation fooling all arms of the deep learning model. We test our
approach in ECG classification, demonstrating a much-improved 77.2% chance of
at least one correct network arm on the strongest adversarial attack tested, in
contrast to a 21.7% chance from a comparable ensemble. Our approach does not
require expensive optimization with adversarial samples, and thus can be scaled
to large problems. These methods can easily be applied to other tasks for
improved network robustness.

5G radio access network (RAN) slicing aims to logically split an
infrastructure into a set of self-contained programmable RAN slices, with each
slice built on top of the underlying physical RAN (substrate) is a separate
logical mobile network, which delivers a set of services with similar
characteristics. Each RAN slice is constituted by various virtual network
functions (VNFs) distributed geographically in numerous substrate nodes. A key
challenge in building a robust RAN slicing is, therefore, designing a RAN
slicing (RS)-configuration scheme that can utilize information such as resource
availability in substrate networks as well as the interdependent relationships
among slices to map (embed) VNFs onto live substrate nodes. With such
motivation, we propose a machine-learning-powered RAN slicing scheme that aims
to accommodate maximum numbers of slices (a set of connected Virtual Network
Functions - VNFs) within a given request set. More specifically, we present a
deep reinforcement scheme that is called Deep Allocation Agent (DAA). In short,
DAA utilizes an empirically designed deep neural network that observes the
current states of the substrate network and the requested slices to schedule
the slices of which VNFs are then mapped to substrate nodes using an
optimization algorithm. DAA is trained towards the goal of maximizing the
number of accommodated slices in the given set by using an explicitly designed
reward function. Our experiment study shows that, on average, DAA is able to
maintain a rate of successfully routed slices above 80% in a resource-limited
substrate network, and about 60% in extreme conditions, i.e., the available
resources are much less than the demands.

Richtmyer-Meshkov Instability (RMI) is a complicated phenomenon that occurs
when a shockwave passes through a perturbed interface. Over a thousand
hydrodynamic simulations were performed to study the formation of RMI for a
parameterized high velocity impact. Deep learning was used to learn the
temporal mapping of initial geometric perturbations to the full-field
hydrodynamic solutions of density and velocity. The continuity equation was
used to include physical information into the loss function, however only
resulted in very minor improvements at the cost of additional training
complexity. Predictions from the deep learning model appear to accurately
capture temporal RMI formations for a variety of geometric conditions within
the domain. First principle physical laws were investigated to infer the
accuracy of the model's predictive capability. While the continuity equation
appeared to show no correlation with the accuracy of the model, conservation of
mass and momentum were weakly correlated with accuracy. Since conservation laws
can be quickly calculated from the deep learning model, they may be useful in
applications where a relative accuracy measure is needed.

This paper presents a multifunctional interdisciplinary framework that makes
four scientific contributions towards the development of personalized ambient
assisted living, with a specific focus to address the different and dynamic
needs of the diverse aging population in the future of smart living
environments. First, it presents a probabilistic reasoning-based mathematical
approach to model all possible forms of user interactions for any activity
arising from the user diversity of multiple users in such environments. Second,
it presents a system that uses this approach with a machine learning method to
model individual user profiles and user-specific user interactions for
detecting the dynamic indoor location of each specific user. Third, to address
the need to develop highly accurate indoor localization systems for increased
trust, reliance, and seamless user acceptance, the framework introduces a novel
methodology where two boosting approaches Gradient Boosting and the AdaBoost
algorithm are integrated and used on a decision tree-based learning model to
perform indoor localization. Fourth, the framework introduces two novel
functionalities to provide semantic context to indoor localization in terms of
detecting each user's floor-specific location as well as tracking whether a
specific user was located inside or outside a given spatial region in a
multi-floor-based indoor setting. These novel functionalities of the proposed
framework were tested on a dataset of localization-related Big Data collected
from 18 different users who navigated in 3 buildings consisting of 5 floors and
254 indoor spatial regions. The results show that this approach of indoor
localization for personalized AAL that models each specific user always
achieves higher accuracy as compared to the traditional approach of modeling an
average user.

With the rapid increasing number of open source software (OSS), the majority
of the software vulnerabilities in the open source components are fixed
silently, which leads to the deployed software that integrated them being
unable to get a timely update. Hence, it is critical to design a security patch
identification system to ensure the security of the utilized software. However,
most of the existing works for security patch identification just consider the
changed code and the commit message of a commit as a flat sequence of tokens
with simple neural networks to learn its semantics, while the structure
information is ignored. To address these limitations, in this paper, we propose
our well-designed approach E-SPI, which extracts the structure information
hidden in a commit for effective identification. Specifically, it consists of
the code change encoder to extract the syntactic of the changed code with the
BiLSTM to learn the code representation and the message encoder to construct
the dependency graph for the commit message with the graph neural network (GNN)
to learn the message representation. We further enhance the code change encoder
by embedding contextual information related to the changed code. To demonstrate
the effectiveness of our approach, we conduct the extensive experiments against
six state-of-the-art approaches on the existing dataset and from the real
deployment environment. The experimental results confirm that our approach can
significantly outperform current state-of-the-art baselines.

Fault localization is challenging in an online service system due to its
monitoring data's large volume and variety and complex dependencies across or
within its components (e.g., services or databases). Furthermore, engineers
require fault localization solutions to be actionable and interpretable, which
existing research approaches cannot satisfy. Therefore, the common industry
practice is that, for a specific online service system, its experienced
engineers focus on localization for recurring failures based on the knowledge
accumulated about the system and historical failures. Although the above common
practice is actionable and interpretable, it is largely manual, thus slow and
sometimes inaccurate. In this paper, we aim to automate this practice through
machine learning. That is, we propose an actionable and interpretable fault
localization approach, DejaVu, for recurring failures in online service
systems. For a specific online service system, DejaVu takes historical failures
and dependencies in the system as input and trains a localization model
offline; for an incoming failure, the trained model online recommends where the
failure occurs (i.e., the faulty components) and which kind of failure occurs
(i.e., the indicative group of metrics) (thus actionable), which are further
interpreted by both global and local interpretation methods (thus
interpretable). Based on the evaluation on 601 failures from three production
systems and one open-source benchmark, in less than one second, DejaVu can on
average rank the ground truths at 1.66-th to 5.03-th among a long candidate
list, outperforming baselines by at least 51.51%.

Morphable models are essential for the statistical modeling of 3D faces.
Previous works on morphable models mostly focus on large-scale facial geometry
but ignore facial details. This paper augments morphable models in representing
facial details by learning a Structure-aware Editable Morphable Model (SEMM).
SEMM introduces a detail structure representation based on the distance field
of wrinkle lines, jointly modeled with detail displacements to establish better
correspondences and enable intuitive manipulation of wrinkle structure.
Besides, SEMM introduces two transformation modules to translate expression
blendshape weights and age values into changes in latent space, allowing
effective semantic detail editing while maintaining identity. Extensive
experiments demonstrate that the proposed model compactly represents facial
details, outperforms previous methods in expression animation qualitatively and
quantitatively, and achieves effective age editing and wrinkle line editing of
facial details. Code and model are available at
https://github.com/gerwang/facial-detail-manipulation.

Historically used in settings where the outcome is rare or data collection is
expensive, outcome-dependent sampling is relevant to many modern settings where
data is readily available for a biased sample of the target population, such as
public administrative data. Under outcome-dependent sampling, common effect
measures such as the average risk difference and the average risk ratio are not
identified, but the conditional odds ratio is. Aggregation of the conditional
odds ratio is challenging since summary measures are generally not identified.
Furthermore, the marginal odds ratio can be larger (or smaller) than all
conditional odds ratios. This so-called non-collapsibility of the odds ratio is
avoidable if we use an alternative aggregation to the standard arithmetic mean.
We provide a new definition of collapsibility that makes this choice of
aggregation method explicit, and we demonstrate that the odds ratio is
collapsible under geometric aggregation. We describe how to partially identify,
estimate, and do inference on the geometric odds ratio under outcome-dependent
sampling. Our proposed estimator is based on the efficient influence function
and therefore has doubly robust-style properties.

Automatic affect recognition has applications in many areas such as
education, gaming, software development, automotives, medical care, etc. but it
is non trivial task to achieve appreciable performance on in-the-wild data
sets. In-the-wild data sets though represent real-world scenarios better than
synthetic data sets, the former ones suffer from the problem of incomplete
labels. Inspired by semi-supervised learning, in this paper, we introduce our
submission to the Multi-Task-Learning Challenge at the 4th Affective Behavior
Analysis in-the-wild (ABAW) 2022 Competition. The three tasks that are
considered in this challenge are valence-arousal(VA) estimation, classification
of expressions into 6 basic (anger, disgust, fear, happiness, sadness,
surprise), neutral, and the 'other' category and 12 action units(AU) numbered
AU-{1,2,4,6,7,10,12,15,23,24,25,26}. Our method Semi-supervised Multi-task
Facial Affect Recognition titled SS-MFAR uses a deep residual network with task
specific classifiers for each of the tasks along with adaptive thresholds for
each expression class and semi-supervised learning for the incomplete labels.
Source code is available at https://github.com/1980x/ABAW2022DMACS.

We employ a machine-learning force field, trained by a neural network (NN)
with bispectrum coefficients as descriptors, to investigate the short-range
order (SRO) influences on the BCC NbMoTaW alloy strengthening mechanism. The NN
interatomic potential provides a transferable force field with density
functional theory accuracy. This novel NN potential is applied to elucidate the
SRO effects on the elasticity, vibrational modes, plasticity, and strength in
the NbMoTaW multi-principal element alloy (MPEA). The results show the strong
attraction among Mo-Ta pairs forming the local ordered B2 structures, which
could be tuned via temperature and improved by Nb content. SRO increases the
elastic constants and high-frequency phonon modes as well as introduces extra
lattice friction of dislocation motion. This approach enables a rapid
compositional screening, paves the way for computation-guided materials design
of new MPEAs with better performance, and opens avenues for tuning the
mechanical properties by processing optimization.

Causal Inference has wide applications in various areas such as E-commerce
and precision medicine, and its performance heavily relies on the accurate
estimation of the Individual Treatment Effect (ITE). Conventionally, ITE is
predicted by modeling the treated and control response functions separately in
their individual sample spaces. However, such an approach usually encounters
two issues in practice, i.e. divergent distribution between treated and control
groups due to treatment bias, and significant sample imbalance of their
population sizes. This paper proposes Deep Entire Space Cross Networks (DESCN)
to model treatment effects from an end-to-end perspective. DESCN captures the
integrated information of the treatment propensity, the response, and the
hidden treatment effect through a cross network in a multi-task learning
manner. Our method jointly learns the treatment and response functions in the
entire sample space to avoid treatment bias and employs an intermediate pseudo
treatment effect prediction network to relieve sample imbalance. Extensive
experiments are conducted on a synthetic dataset and a large-scaled production
dataset from the E-commerce voucher distribution business. The results indicate
that DESCN can successfully enhance the accuracy of ITE estimation and improve
the uplift ranking performance. A sample of the production dataset and the
source code are released to facilitate future research in the community, which
is, to the best of our knowledge, the first large-scale public biased treatment
dataset for causal inference.

Despite recent advances in autonomous driving systems, accidents such as the
fatal Uber crash in 2018 show these systems are still susceptible to edge
cases. Such systems must be thoroughly tested and validated before being
deployed in the real world to avoid such events. Testing in open-world
scenarios can be difficult, time-consuming, and expensive. These challenges can
be addressed by using driving simulators such as CARLA instead. A key part of
such tests is adversarial testing, in which the goal is to find scenarios that
lead to failures of the given system. While several independent efforts in
testing have been made, a well-established testing framework that enables
adversarial testing has yet to be made available for CARLA. We therefore
propose ANTI-CARLA, an automated testing framework in CARLA for simulating
adversarial weather conditions (e.g., heavy rain) and sensor faults (e.g.,
camera occlusion) that fail the system. The operating conditions in which a
given system should be tested are specified in a scenario description language.
The framework offers an efficient search mechanism that searches for
adversarial operating conditions that will fail the tested system. In this way,
ANTI-CARLA extends the CARLA simulator with the capability of performing
adversarial testing on any given driving pipeline. We use ANTI-CARLA to test
the driving pipeline trained with Learning By Cheating (LBC) approach. The
simulation results demonstrate that ANTI-CARLA can effectively and
automatically find a range of failure cases despite LBC reaching an accuracy of
100% in the CARLA benchmark.

Mobile technology creates value on three fundamental pillars: productivity,
coordination, and transformation. Mobile apps are becoming an increasingly
important aspect of teaching and learning in many countries. The use of mobile
applications in education is not only beneficial, but also provides students
with an enjoyable and interactive experience. For a mobile product launch to be
as successful as it can be, it is imperative that a systematic, precise,
controlled, and well-established process is in place, which is controlled,
efficient, and well-established. Many educational organisation's find
themselves in situations where they have to get all departments working
effectively and together in order to meet a specific deadline, including
marketing, production, and operations, after the organization's product
clearance board approves the new product. In many ways, the situation is
similar to the software crisis that took place in the middle of the 1970's. As
a result of globalisation and communication, oftentimes the effects of
globalisation are amplified because of the vast amount of information that must
be shared among project team members. Every educational organization has a
unique style and way of doing things, and the project management team is no
exception. As a consequence, it is commonplace to see that every education
entity has its own particular style and way of doing things. In regards to the
creation of a mobile application that is going to function efficiently, it is
important to remember that it is extremely important to stick to the strategies
and requirements that will yield the best results for the education sector.

External eye photos were recently shown to reveal signs of diabetic retinal
disease and elevated HbA1c. In this paper, we evaluate if external eye photos
contain information about additional systemic medical conditions. We developed
a deep learning system (DLS) that takes external eye photos as input and
predicts multiple systemic parameters, such as those related to the liver
(albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI
creatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH);
and blood count (Hgb, WBC, platelets). Development leveraged 151,237 images
from 49,015 patients with diabetes undergoing diabetic eye screening in 11
sites across Los Angeles county, CA. Evaluation focused on 9 pre-specified
systemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869
patients with and without diabetes undergoing eye screening in 3 independent
sites in Los Angeles County, CA, and the greater Atlanta area, GA. We compared
against baseline models incorporating available clinicodemographic variables
(e.g. age, sex, race/ethnicity, years with diabetes). Relative to the baseline,
the DLS achieved statistically significant superior performance at detecting
AST>36, calcium<8.6, eGFR<60, Hgb<11, platelets<150, ACR>=300, and WBC<4 on
validation set A (a patient population similar to the development sets), where
the AUC of DLS exceeded that of the baseline by 5.2-19.4%. On validation sets B
and C, with substantial patient population differences compared to the
development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by
7.3-13.2%. Our findings provide further evidence that external eye photos
contain important biomarkers of systemic health spanning multiple organ
systems. Further work is needed to investigate whether and how these biomarkers
can be translated into clinical impact.

Articulated objects are abundant in daily life. Discovering their parts,
joints, and kinematics is crucial for robots to interact with these objects. We
introduce Structure from Action (SfA), a framework that discovers the 3D part
geometry and joint parameters of unseen articulated objects via a sequence of
inferred interactions. Our key insight is that 3D interaction and perception
should be considered in conjunction to construct 3D articulated CAD models,
especially in the case of categories not seen during training. By selecting
informative interactions, SfA discovers parts and reveals initially occluded
surfaces, like the inside of a closed drawer. By aggregating visual
observations in 3D, SfA accurately segments multiple parts, reconstructs part
geometry, and infers all joint parameters in a canonical coordinate frame. Our
experiments demonstrate that a single SfA model trained in simulation can
generalize to many unseen object categories with unknown kinematic structures
and to real-world objects. Code and data will be publicly available.

We consider a multi-source hybrid automatic repeat request (HARQ) based
system, where a transmitter sends status update packets of random arrival
(i.e., uncontrollable sampling) and generate-atwill (i.e., controllable
sampling) sources to a destination through an error-prone channel. We develop
transmission scheduling policies to minimize the average number of
transmissions subject to an average age of information (AoI) constraint. First,
we consider known environment (i.e., known system statistics) and develop a
near-optimal deterministic transmission policy and a low-complexity dynamic
transmission (LC-DT) policy. The former policy is derived by casting the main
problem into a constrained Markov decision process (CMDP) problem, which is
then solved using the Lagrangian relaxation, relative value iteration
algorithm, and bisection. The LC-DT policy is developed via the
drift-plus-penalty (DPP) method by transforming the main problem into a
sequence of per-slot problems. Finally, we consider unknown environment and
devise a learning-based transmission policy by relaxing the CMDP problem into
an MDP problem using the DPP method and then adopting the deep Q-learning
algorithm. Numerical results show that the proposed policies achieve
near-optimal performance and illustrate the benefits of HARQ in status
updating.

The constrained-search formulation of Levy and Lieb provides a concrete
mapping from N-representable densities to the space of N-particle wavefunctions
and explicitly defines the universal functional of density functional theory.
We numerically implement the Levy-Lieb procedure for a paradigmatic lattice
system, the Hubbard dimer, using a modified variational quantum eigensolver
approach. We demonstrate density variational minimization using the resulting
hybrid quantum-classical scheme featuring real-time computation of the
Levy-Lieb functional along the search trajectory. We further illustrate a
fidelity based quantum kernel associated with the density to pure-state
embedding implied by the Levy-Lieb procedure and employ the kernel for learning
observable functionals of the density. We study the kernel's ability to
generalize with high accuracy through numerical experiments on the Hubbard
dimer.

While sequential recommender systems achieve significant improvements on
capturing user dynamics, we argue that sequential recommenders are vulnerable
against substitution-based profile pollution attacks. To demonstrate our
hypothesis, we propose a substitution-based adversarial attack algorithm, which
modifies the input sequence by selecting certain vulnerable elements and
substituting them with adversarial items. In both untargeted and targeted
attack scenarios, we observe significant performance deterioration using the
proposed profile pollution algorithm. Motivated by such observations, we design
an efficient adversarial defense method called Dirichlet neighborhood sampling.
Specifically, we sample item embeddings from a convex hull constructed by
multi-hop neighbors to replace the original items in input sequences. During
sampling, a Dirichlet distribution is used to approximate the probability
distribution in the neighborhood such that the recommender learns to combat
local perturbations. Additionally, we design an adversarial training method
tailored for sequential recommender systems. In particular, we represent
selected items with one-hot encodings and perform gradient ascent on the
encodings to search for the worst case linear combination of item embeddings in
training. As such, the embedding function learns robust item representations
and the trained recommender is resistant to test-time adversarial examples.
Extensive experiments show the effectiveness of both our attack and defense
methods, which consistently outperform baselines by a significant margin across
model architectures and datasets.

Since the late '50s, when the first artificial satellite was launched, the
number of resident space objects (RSOs) has steadily increased. It is estimated
that around 1 Million objects larger than 1 cm are currently orbiting the
Earth, with only 30,000, larger than 10 cm, presently being tracked. To avert a
chain reaction of collisions, termed Kessler Syndrome, it is indispensable to
accurately track and predict space debris and satellites' orbit alike. Current
physics-based methods have errors in the order of kilometres for 7 days
predictions, which is insufficient when considering space debris that have
mostly less than 1 meter. Typically, this failure is due to uncertainty around
the state of the space object at the beginning of the trajectory, forecasting
errors in environmental conditions such as atmospheric drag, as well as
specific unknown characteristics such as mass or geometry of the RSO.
Leveraging data-driven techniques, namely machine learning, the orbit
prediction accuracy can be enhanced: by deriving unmeasured objects'
characteristics, improving non-conservative forces' effects, and by the
superior abstraction capacity that Deep Learning models have of modelling
highly complex non-linear systems. In this survey, we provide an overview of
the current work being done in this field.

Rendering programs have changed the design process completely as they permit
to see how the products will look before they are fabricated. However, the
rendering process is complicated and takes a significant amount of time, not
only in the rendering itself but in the setting of the scene as well.
Materials, lights and cameras need to be set in order to get the best quality
results. Nevertheless, the optimal output may not be obtained in the first
render. This all makes the rendering process a tedious process. Since
Goodfellow et al. introduced Generative Adversarial Networks (GANs) in 2014
[1], they have been used to generate computer-assigned synthetic data, from
non-existing human faces to medical data analysis or image style transfer. GANs
have been used to transfer image textures from one domain to another. However,
paired data from both domains was needed. When Zhu et al. introduced the
CycleGAN model, the elimination of this expensive constraint permitted
transforming one image from one domain into another, without the need for
paired data. This work validates the applicability of CycleGANs on style
transfer from an initial sketch to a final render in 2D that represents a 3D
design, a step that is paramount in every product design process. We inquiry
the possibilities of including CycleGANs as part of the design pipeline, more
precisely, applied to the rendering of ring designs. Our contribution entails a
crucial part of the process as it allows the customer to see the final product
before buying. This work sets a basis for future research, showing the
possibilities of GANs in design and establishing a starting point for novel
applications to approach crafts design.

Federated Learning (FL) is a technique to train models using data distributed
across devices. Differential Privacy (DP) provides a formal privacy guarantee
for sensitive data. Our goal is to train a large neural network language model
(NNLM) on compute-constrained devices while preserving privacy using FL and DP.
However, the DP-noise introduced to the model increases as the model size
grows, which often prevents convergence. We propose Partial Embedding Updates
(PEU), a novel technique to decrease noise by decreasing payload size.
Furthermore, we adopt Low Rank Adaptation (LoRA) and Noise Contrastive
Estimation (NCE) to reduce the memory demands of large models on
compute-constrained devices. This combination of techniques makes it possible
to train large-vocabulary language models while preserving accuracy and
privacy.

In this work we show how large language models (LLMs) can learn statistical
dependencies between otherwise unconditionally independent variables due to
dataset selection bias. To demonstrate the effect, we developed a masked gender
task that can be applied to BERT-family models to reveal spurious correlations
between predicted gender pronouns and a variety of seemingly gender-neutral
variables like date and location, on pre-trained (unmodified) BERT and RoBERTa
large models. Finally, we provide an online demo, inviting readers to
experiment further.

Nonlinear iris texture deformations due to pupil size variations are one of
the main factors responsible for within-class variance of genuine comparison
scores in iris recognition. In dominant approaches to iris recognition, the
size of a ring-shaped iris region is linearly scaled to a canonical rectangle,
used further in encoding and matching. However, the biological complexity of
the iris sphincter and dilator muscles causes the movements of iris features to
be nonlinear in a function of pupil size, and not solely organized along radial
paths. Alternatively to the existing theoretical models based on the
biomechanics of iris musculature, in this paper we propose a novel deep
autoencoder-based model that can effectively learn complex movements of iris
texture features directly from the data. The proposed model takes two inputs,
(a) an ISO-compliant near-infrared iris image with initial pupil size, and (b)
the binary mask defining the target shape of the iris. The model makes all the
necessary nonlinear deformations to the iris texture to match the shape of the
iris in an image (a) with the shape provided by the target mask (b). The
identity-preservation component of the loss function helps the model in finding
deformations that preserve identity and not only the visual realism of the
generated samples. We also demonstrate two immediate applications of this
model: better compensation for iris texture deformations in iris recognition
algorithms, compared to linear models, and the creation of a generative
algorithm that can aid human forensic examiners, who may need to compare iris
images with a large difference in pupil dilation. We offer the source codes and
model weights available along with this paper.

We often see undesirable tradeoffs in robust machine learning where
out-of-distribution (OOD) accuracy is at odds with in-distribution (ID)
accuracy: a robust classifier obtained via specialized techniques such as
removing spurious features often has better OOD but worse ID accuracy compared
to a standard classifier trained via ERM. In this paper, we find that
ID-calibrated ensembles -- where we simply ensemble the standard and robust
models after calibrating on only ID data -- outperforms prior state-of-the-art
(based on self-training) on both ID and OOD accuracy. On eleven natural
distribution shift datasets, ID-calibrated ensembles obtain the best of both
worlds: strong ID accuracy and OOD accuracy. We analyze this method in stylized
settings, and identify two important conditions for ensembles to perform well
both ID and OOD: (1) we need to calibrate the standard and robust models (on ID
data, because OOD data is unavailable), (2) OOD has no anticorrelated spurious
features.

Diffusion MRI tractography is an advanced imaging technique that enables in
vivo mapping of the brain's white matter connections. White matter parcellation
classifies tractography streamlines into clusters or anatomically meaningful
tracts. It enables quantification and visualization of whole-brain
tractography. Currently, most parcellation methods focus on the deep white
matter (DWM), whereas fewer methods address the superficial white matter (SWM)
due to its complexity. We propose a novel two-stage deep-learning-based
framework, Superficial White Matter Analysis (SupWMA), that performs an
efficient and consistent parcellation of 198 SWM clusters from whole-brain
tractography. A point-cloud-based network is adapted to our SWM parcellation
task, and supervised contrastive learning enables more discriminative
representations between plausible streamlines and outliers for SWM. We train
our model on a large-scale tractography dataset including streamline samples
from labeled SWM clusters and anatomically implausible streamline samples, and
we perform testing on six independently acquired datasets of different ages and
health conditions (including neonates and patients with space-occupying brain
tumors). Compared to several state-of-the-art methods, SupWMA obtains highly
consistent and accurate SWM parcellation results on all datasets, showing good
generalization across the lifespan in health and disease. In addition, the
computational speed of SupWMA is much faster than other methods.

Typical educational robotics approaches rely on imperative programming for
robot navigation. However, with the increasing presence of AI in everyday life,
these approaches miss an opportunity to introduce machine learning (ML)
techniques grounded in an authentic and engaging learning context. Furthermore,
the needs for costly specialized equipment and ample physical space are
barriers that limit access to robotics experiences for all learners. We propose
ARtonomous, a relatively low-cost, virtual alternative to physical,
programming-only robotics kits. With ARtonomous, students employ reinforcement
learning (RL) alongside code to train and customize virtual autonomous robotic
vehicles. Through a study evaluating ARtonomous, we found that middle-school
students developed an understanding of RL, reported high levels of engagement,
and demonstrated curiosity for learning more about ML. This research
demonstrates the feasibility of an approach like ARtonomous for 1) eliminating
barriers to robotics education and 2) promoting student learning and interest
in RL and ML.

This article describes an implementation of low-cost portable touch screen
technology, applied to university teaching, using as a base the remote control
of the Nintendo Wii console (known as Wiimote), a normal projector, a computer
and free software. The purpose is to show the feasibility of such
implementation to improve teaching/learning processes, without incurring high
costs associated with unaffordable technological equipment, special
infrastructure in classrooms, or expensive computer programs. Also included is
a summary of a test of the system in two college courses.

The ever-increasing computational complexity of deep learning models makes
their training and deployment difficult on various cloud and edge platforms.
Replacing floating-point arithmetic with low-bit integer arithmetic is a
promising approach to save energy, memory footprint, and latency of deep
learning models. As such, quantization has attracted the attention of
researchers in recent years. However, using integer numbers to form a fully
functional integer training pipeline including forward pass, back-propagation,
and stochastic gradient descent is not studied in detail. Our empirical and
mathematical results reveal that integer arithmetic seems to be enough to train
deep learning models. Unlike recent proposals, instead of quantization, we
directly switch the number representation of computations. Our novel training
method forms a fully integer training pipeline that does not change the
trajectory of the loss and accuracy compared to floating-point, nor does it
need any special hyper-parameter tuning, distribution adjustment, or gradient
clipping. Our experimental results show that our proposed method is effective
in a wide variety of tasks such as classification (including vision
transformers), object detection, and semantic segmentation.

Existing literature on constructing optimal regimes often focuses on
intention-to-treat analyses that completely ignore the compliance behavior of
individuals. Instrumental variable-based methods have been developed for
learning optimal regimes under endogeneity. However, when there are two active
treatment arms, the average causal effects of treatments cannot be identified
using a binary instrument, and thus the existing methods will not be
applicable. To fill this gap, we provide a procedure that identifies an optimal
regime and the corresponding value function as a function of a vector of
sensitivity parameters. We also derive the canonical gradient of the target
parameter and propose a multiply robust classification-based estimator of the
optimal regime. Our simulations highlight the need for and usefulness of the
proposed method in practice. We implement our method on the Adaptive Treatment
for Alcohol and Cocaine Dependence randomized trial.

Directed acyclic graph (DAG) models have become widely studied and applied in
statistics and machine learning -- indeed, their simplicity facilitates
efficient procedures for learning and inference. Unfortunately, these models
are not closed under marginalization, making them poorly equipped to handle
systems with latent confounding. Acyclic directed mixed graph (ADMG) models
characterize margins of DAG models, making them far better suited to handle
such systems. However, ADMG models have not seen wide-spread use due to their
complexity and a shortage of statistical tools for their analysis. In this
paper, we introduce the m-connecting imset which provides an alternative
representation for the independence models induced by ADMGs. Furthermore, we
define the m-connecting factorization criterion for ADMG models, characterized
by a single equation, and prove its equivalence to the global Markov property.
The m-connecting imset and factorization criterion provide two new statistical
tools for learning and inference with ADMG models. We demonstrate the
usefulness of these tools by formulating and evaluating a consistent scoring
criterion with a closed form solution.

We study the problem of online learning in adversarial bandit problems under
a partial observability model called off-policy feedback. In this sequential
decision making problem, the learner cannot directly observe its rewards, but
instead sees the ones obtained by another unknown policy run in parallel
(behavior policy). Instead of a standard exploration-exploitation dilemma, the
learner has to face another challenge in this setting: due to limited
observations outside of their control, the learner may not be able to estimate
the value of each policy equally well. To address this issue, we propose a set
of algorithms that guarantee regret bounds that scale with a natural notion of
mismatch between any comparator policy and the behavior policy, achieving
improved performance against comparators that are well-covered by the
observations. We also provide an extension to the setting of adversarial linear
contextual bandits, and verify the theoretical guarantees via a set of
experiments. Our key algorithmic idea is adapting the notion of pessimistic
reward estimators that has been recently popular in the context of off-policy

Vector-symbolic architectures (VSAs) provide methods for computing which are
highly flexible and carry unique advantages. Concepts in VSAs are represented
by 'symbols,' long vectors of values which utilize properties of
high-dimensional spaces to represent and manipulate information. In this new
work, we combine efficiency of the operations provided within the framework of
the Fourier Holographic Reduced Representation (FHRR) VSA with the power of
deep networks to construct novel VSA based residual and attention-based neural
network architectures. Using an attentional FHRR architecture, we demonstrate
that the same network architecture can address problems from different domains
(image classification and molecular toxicity prediction) by encoding different
information into the network's inputs, similar to the Perceiver model. This
demonstrates a novel application of VSAs and a potential path to implementing
state-of-the-art neural models on neuromorphic hardware.

We propose the novel framework for generative modelling using hybrid
energy-based models. In our method we combine the interpretable input gradients
of the robust classifier and Langevin Dynamics for sampling. Using the
adversarial training we improve not only the training stability, but robustness
and generative modelling of the joint energy-based models.

Adversarial examples are transferable between different models. In our paper,
we propose to use this property for multi-step domain adaptation. In
unsupervised domain adaptation settings, we demonstrate that replacing the
source domain with adversarial examples to $\mathcal{H} \Delta
\mathcal{H}$-divergence can improve source classifier accuracy on the target
domain. Our method can be connected to most domain adaptation techniques. We
conducted a range of experiments and achieved improvement in accuracy on Digits
and Office-Home datasets.

We present e3nn, a generalized framework for creating E(3) equivariant
trainable functions, also known as Euclidean neural networks. e3nn naturally
operates on geometry and geometric tensors that describe systems in 3D and
transform predictably under a change of coordinate system. The core of e3nn are
equivariant operations such as the TensorProduct class or the spherical
harmonics functions that can be composed to create more complex modules such as
convolutions and attention mechanisms. These core operations of e3nn can be
used to efficiently articulate Tensor Field Networks, 3D Steerable CNNs,
Clebsch-Gordan Networks, SE(3) Transformers and other E(3) equivariant
networks.

This work presents gauge-equivariant architectures for flow-based sampling in
fermionic lattice field theories using pseudofermions as stochastic estimators
for the fermionic determinant. This is the default approach in state-of-the-art
lattice field theory calculations, making this development critical to the
practical application of flow models to theories such as QCD. Methods by which
flow-based sampling approaches can be improved via standard techniques such as
even/odd preconditioning and the Hasenbusch factorization are also outlined.
Numerical demonstrations in two-dimensional U(1) and SU(3) gauge theories with
$N_f=2$ flavors of fermions are provided.

We introduce the initial release of our software Robustar, which aims to
improve the robustness of vision classification machine learning models through
a data-driven perspective. Building upon the recent understanding that the lack
of machine learning model's robustness is the tendency of the model's learning
of spurious features, we aim to solve this problem from its root at the data
perspective by removing the spurious features from the data before training. In
particular, we introduce a software that helps the users to better prepare the
data for training image classification models by allowing the users to annotate
the spurious features at the pixel level of images. To facilitate this process,
our software also leverages recent advances to help identify potential images
and pixels worthy of attention and to continue the training with newly
annotated data. Our software is hosted at the GitHub Repository
https://github.com/HaohanWang/Robustar.

Many recent neural models have shown remarkable empirical results in Machine
Reading Comprehension, but evidence suggests sometimes the models take
advantage of dataset biases to predict and fail to generalize on out-of-sample
data. While many other approaches have been proposed to address this issue from
the computation perspective such as new architectures or training procedures,
we believe a method that allows researchers to discover biases, and adjust the
data or the models in an earlier stage will be beneficial. Thus, we introduce
MRCLens, a toolkit that detects whether biases exist before users train the
full model. For the convenience of introducing the toolkit, we also provide a
categorization of common biases in MRC.

We study the implicit regularization effects of deep learning in tensor
factorization. While implicit regularization in deep matrix and 'shallow'
tensor factorization via linear and certain type of non-linear neural networks
promotes low-rank solutions with at most quadratic growth, we show that its
effect in deep tensor factorization grows polynomially with the depth of the
network. This provides a remarkably faithful description of the observed
experimental behaviour. Using numerical experiments, we demonstrate the
benefits of this implicit regularization in yielding a more accurate estimation
and better convergence properties.

It was shown that adversarial examples improve object recognition. But what
about their opposite side, easy examples? Easy examples are samples that the
machine learning model classifies correctly with high confidence. In our paper,
we are making the first step toward exploring the potential benefits of using
easy examples in the training procedure of neural networks. We propose to use
an auxiliary batch normalization for easy examples for the standard and robust
accuracy improvement.

We present a method for supervised learning of sparsity-promoting
regularizers for denoising signals and images. Sparsity-promoting
regularization is a key ingredient in solving modern signal reconstruction
problems; however, the operators underlying these regularizers are usually
either designed by hand or learned from data in an unsupervised way. The recent
success of supervised learning (mainly convolutional neural networks) in
solving image reconstruction problems suggests that it could be a fruitful
approach to designing regularizers. Towards this end, we propose to denoise
signals using a variational formulation with a parametric, sparsity-promoting
regularizer, where the parameters of the regularizer are learned to minimize
the mean squared error of reconstructions on a training set of ground truth
image and measurement pairs. Training involves solving a challenging bilievel
optimization problem; we derive an expression for the gradient of the training
loss using the closed-form solution of the denoising problem and provide an
accompanying gradient descent algorithm to minimize it. Our experiments with
structured 1D signals and natural images show that the proposed method can
learn an operator that outperforms well-known regularizers (total variation,
DCT-sparsity, and unsupervised dictionary learning) and collaborative filtering
for denoising. While the approach we present is specific to denoising, we
believe that it could be adapted to the larger class of inverse problems with
linear measurement models, giving it applicability in a wide range of signal
reconstruction settings.

We consider the Influence Maximization (IM) problem: 'if we can try to
convince a subset of individuals in a social network to adopt a new product or
innovation, and the goal is to trigger a large cascade of further adoptions,
which set of individuals should we target'? Formally, it is the task of
selecting $k$ seed nodes in a social network such that the expected number of
influenced nodes in the network (under some influence propagation model) is
maximized. This problem has been widely studied in the literature and several
solution approaches have been proposed. However, most simulation-based
approaches involve time-consuming Monte-Carlo simulations to compute the
influence of the seed nodes in the entire network. This limits the
applicability of these methods on large social networks. In the paper, we are
interested in solving the problem of influence maximization in a time-efficient
manner. We propose a community-aware divide-and-conquer strategy that involves
(i) learning the inherent community structure of the social network, (ii)
generating candidate solutions by solving the influence maximization problem
for each community, and (iii) selecting the final set of individuals from the
candidate solutions using a novel progressive budgeting scheme.
  We provide experiments on real-world social networks, showing that the
proposed algorithm outperforms the simulation-based algorithms in terms of
empirical run-time and the heuristic algorithms in terms of influence. We also
study the effect of the community structure on the performance of our
algorithm. Our experiments show that the community structures with higher
modularity lead the proposed algorithm to perform better in terms of run-time
and influence.

Reasoning about 3D objects based on 2D images is challenging due to
variations in appearance caused by viewing the object from different
orientations. Tasks such as object classification are invariant to 3D rotations
and other such as pose estimation are equivariant. However, imposing
equivariance as a model constraint is typically not possible with 2D image
input because we do not have an a priori model of how the image changes under
out-of-plane object rotations. The only $\mathrm{SO}(3)$-equivariant models
that currently exist require point cloud or voxel input rather than 2D images.
In this paper, we propose a novel architecture based on icosahedral group
convolutions that reasons in $\mathrm{SO(3)}$ by learning a projection of the
input image onto an icosahedron. The resulting model is approximately
equivariant to rotation in $\mathrm{SO}(3)$. We apply this model to object pose
estimation and shape classification tasks and find that it outperforms
reasonable baselines. Project website:
\url{https://dmklee.github.io/image2icosahedral}

In this paper, we are interested in Detection Transformer (DETR), an
end-to-end object detection approach based on a transformer encoder-decoder
architecture without hand-crafted postprocessing, such as NMS. Inspired by
Conditional DETR, an improved DETR with fast training convergence, that
presented box queries (originally called spatial queries) for internal decoder
layers, we reformulate the object query into the format of the box query that
is a composition of the embeddings of the reference point and the
transformation of the box with respect to the reference point. This
reformulation indicates the connection between the object query in DETR and the
anchor box that is widely studied in Faster R-CNN. Furthermore, we learn the
box queries from the image content, further improving the detection quality of
Conditional DETR still with fast training convergence. In addition, we adopt
the idea of axial self-attention to save the memory cost and accelerate the
encoder. The resulting detector, called Conditional DETR V2, achieves better
results than Conditional DETR, saves the memory cost and runs more efficiently.
For example, for the DC$5$-ResNet-$50$ backbone, our approach achieves $44.8$
AP with $16.4$ FPS on the COCO $val$ set and compared to Conditional DETR, it
runs $1.6\times$ faster, saves $74$\% of the overall memory cost, and improves
$1.0$ AP score.

Deep Learning (DL) methods have dramatically increased in popularity in
recent years, with significant growth in their application to supervised
learning problems in the biomedical sciences. However, the greater prevalence
and complexity of missing data in modern biomedical datasets present
significant challenges for DL methods. Here, we provide a formal treatment of
missing data in the context of deeply learned generalized linear models, a
supervised DL architecture for regression and classification problems. We
propose a new architecture, \textit{dlglm}, that is one of the first to be able
to flexibly account for both ignorable and non-ignorable patterns of
missingness in input features and response at training time. We demonstrate
through statistical simulation that our method outperforms existing approaches
for supervised learning tasks in the presence of missing not at random (MNAR)
missingness. We conclude with a case study of a Bank Marketing dataset from the
UCI Machine Learning Repository, in which we predict whether clients subscribed
to a product based on phone survey data.

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented
amount of sequence data of the SARS-CoV-2 genome -- millions of sequences and
counting. This amount of data, while being orders of magnitude beyond the
capacity of traditional approaches to understanding the diversity, dynamics,
and evolution of viruses is nonetheless a rich resource for machine learning
(ML) approaches as alternatives for extracting such important information from
these data. It is of hence utmost importance to design a framework for testing
and benchmarking the robustness of these ML models.
  This paper makes the first effort (to our knowledge) to benchmark the
robustness of ML models by simulating biological sequences with errors. In this
paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to
mimic the error profiles of common sequencing platforms such as Illumina and
PacBio. We show from experiments on a wide array of ML models that some
simulation-based approaches are more robust (and accurate) than others for
specific embedding methods to certain adversarial attacks to the input
sequences. Our benchmarking framework may assist researchers in properly
assessing different ML models and help them understand the behavior of the
SARS-CoV-2 virus or avoid possible future pandemics.

As a general type of machine learning approach, artificial neural networks
have established state-of-art benchmarks in many pattern recognition and data
analysis tasks. Among various kinds of neural networks architectures,
polynomial neural networks (PNNs) have been recently shown to be analyzable by
spectrum analysis via neural tangent kernel, and particularly effective at
image generation and face recognition. However, acquiring theoretical insight
into the computation and sample complexity of PNNs remains an open problem. In
this paper, we extend the analysis in previous literature to PNNs and obtain
novel results on sample complexity of PNNs, which provides some insights in
explaining the generalization ability of PNNs.

This paper proposes novel, end-to-end deep reinforcement learning algorithms
for learning two-player zero-sum Markov games. Different from prior efforts on
training agents to beat a fixed set of opponents, our objective is to find the
Nash equilibrium policies that are free from exploitation by even the
adversarial opponents. We propose (1) Nash DQN algorithm, which integrates DQN
with a Nash finding subroutine for the joint value functions; and (2) Nash DQN
Exploiter algorithm, which additionally adopts an exploiter for guiding agent's
exploration. Our algorithms are the practical variants of theoretical
algorithms which are guaranteed to converge to Nash equilibria in the basic
tabular setting. Experimental evaluation on both tabular examples and
two-player Atari games demonstrates the robustness of the proposed algorithms
against adversarial opponents, as well as their advantageous performance over

This paper develops a Distributed Differentiable Dynamic Game (DDDG)
framework, which enables learning multi-robot coordination from demonstrations.
We represent multi-robot coordination as a dynamic game, where the behavior of
a robot is dictated by its own dynamics and objective that also depends on
others' behavior. The coordination thus can be adapted by tuning the objective
and dynamics of each robot. The proposed DDDG enables each robot to
automatically tune its individual dynamics and objectives in a distributed
manner by minimizing the mismatch between its trajectory and demonstrations.
This process requires a new distributed design of the forward-pass, where all
robots collaboratively seek Nash equilibrium behavior, and a backward-pass,
where gradients are propagated via the communication graph. We test the DDDG in
simulation with a team of quadrotors given different task configurations. The
results demonstrate the capability of DDDG for learning multi-robot
coordination from demonstrations

Neural representations are popular for representing shapes, as they can be
learned form sensor data and used for data cleanup, model completion, shape
editing, and shape synthesis. Current neural representations can be categorized
as either overfitting to a single object instance, or representing a collection
of objects. However, neither allows accurate editing of neural scene
representations: on the one hand, methods that overfit objects achieve highly
accurate reconstructions, but do not generalize to unseen object configurations
and thus cannot support editing; on the other hand, methods that represent a
family of objects with variations do generalize but produce only approximate
reconstructions. We propose NEUFORM to combine the advantages of both
overfitted and generalizable representations by adaptively using the one most
appropriate for each shape region: the overfitted representation where reliable
data is available, and the generalizable representation everywhere else. We
achieve this with a carefully designed architecture and an approach that blends
the network weights of the two representations, avoiding seams and other
artifacts. We demonstrate edits that successfully reconfigure parts of
human-designed shapes, such as chairs, tables, and lamps, while preserving
semantic integrity and the accuracy of an overfitted shape representation. We
compare with two state-of-the-art competitors and demonstrate clear
improvements in terms of plausibility and fidelity of the resultant edits.

Developing deterministic neighborhood-informed point-particle closure models
using machine learning has garnered interest in recent times from dispersed
multiphase flow community. The robustness of neural models for this complex
multi-body problem is hindered by the availability of particle-resolved data.
The present work addresses this unavoidable limitation of data paucity by
implementing two strategies: (i) by using a rotation and reflection equivariant
neural network and (ii) by pursuing a physics-based hierarchical machine
learning approach. The resulting machine learned models are observed to achieve
a maximum accuracy of 85% and 96% in the prediction of neighbor-induced force
and torque fluctuations, respectively, for a wide range of Reynolds number and
volume fraction conditions considered. Furthermore, we pursue force and torque
network architectures that provide universal prediction spanning a wide range
of Reynolds number ($0.25 \leq Re \leq 250$) and particle volume fraction ($0
\leq \phi \leq 0.4$). The hierarchical nature of the approach enables improved
prediction of quantities such as streamwise torque, by going beyond binary
interactions to include trinary interactions.

The exponential growth of data generated on the Internet in the current
information age is a driving force for the digital economy. Extraction of
information is the major value in an accumulated big data. Big data dependency
on statistical analysis and hand-engineered rules machine learning algorithms
are overwhelmed with vast complexities inherent in human languages. Natural
Language Processing (NLP) is equipping machines to understand these human
diverse and complicated languages. Text Classification is an NLP task which
automatically identifies patterns based on predefined or undefined labeled
sets. Common text classification application includes information retrieval,
modeling news topic, theme extraction, sentiment analysis, and spam detection.
In texts, some sequences of words depend on the previous or next word sequences
to make full meaning; this is a challenging dependency task that requires the
machine to be able to store some previous important information to impact
future meaning. Sequence models such as RNN, GRU, and LSTM is a breakthrough
for tasks with long-range dependencies. As such, we applied these models to
Binary and Multi-class classification. Results generated were excellent with
most of the models performing within the range of 80% and 94%. However, this
result is not exhaustive as we believe there is room for improvement if
machines are to compete with humans.

Computer vision-based damage detection using remote cameras and unmanned
aerial vehicles (UAVs) enables efficient and low-cost bridge health monitoring
that reduces labor costs and the needs for sensor installation and maintenance.
By leveraging recent semantic image segmentation approaches, we are able to
find regions of critical structural components and recognize damage at the
pixel level using images as the only input. However, existing methods perform
poorly when detecting small damages (e.g., cracks and exposed rebars) and thin
objects with limited image samples, especially when the components of interest
are highly imbalanced. To this end, this paper introduces a semantic
segmentation framework that imposes the hierarchical semantic relationship
between component category and damage types. For example, certain concrete
cracks only present on bridge columns and therefore the non-column region will
be masked out when detecting such damages. In this way, the damage detection
model could focus on learning features from possible damaged regions only and
avoid the effects of other irrelevant regions. We also utilize multi-scale
augmentation that provides views with different scales that preserves
contextual information of each image without losing the ability of handling
small and thin objects. Furthermore, the proposed framework employs important
sampling that repeatedly samples images containing rare components (e.g.,
railway sleeper and exposed rebars) to provide more data samples, which
addresses the imbalanced data challenge.

The waive of labels in the target domain makes Unsupervised Domain Adaptation
(UDA) an attractive technique in many real-world applications, though it also
brings great challenges as model adaptation becomes harder without labeled
target data. In this paper, we address this issue by seeking compensation from
target domain prior knowledge, which is often (partially) available in
practice, e.g., from human expertise. This leads to a novel yet practical
setting where in addition to the training data, some prior knowledge about the
target class distribution are available. We term the setting as
Knowledge-guided Unsupervised Domain Adaptation (KUDA). In particular, we
consider two specific types of prior knowledge about the class distribution in
the target domain: Unary Bound that describes the lower and upper bounds of
individual class probabilities, and Binary Relationship that describes the
relations between two class probabilities. We propose a general rectification
module that uses such prior knowledge to refine model generated pseudo labels.
The module is formulated as a Zero-One Programming problem derived from the
prior knowledge and a smooth regularizer. It can be easily plugged into
self-training based UDA methods, and we combine it with two state-of-the-art
methods, SHOT and DINE. Empirical results on four benchmarks confirm that the
rectification module clearly improves the quality of pseudo labels, which in
turn benefits the self-training stage. With the guidance from prior knowledge,
the performances of both methods are substantially boosted. We expect our work
to inspire further investigations in integrating prior knowledge in UDA. Code
is available at https://github.com/tsun/KUDA.

Top-$k$ classification is a generalization of multiclass classification used
widely in information retrieval, image classification, and other extreme
classification settings. Several hinge-like (piecewise-linear) surrogates have
been proposed for the problem, yet all are either non-convex or inconsistent.
For the proposed hinge-like surrogates that are convex (i.e., polyhedral), we
apply the recent embedding framework of Finocchiaro et al. (2019; 2022) to
determine the prediction problem for which the surrogate is consistent. These
problems can all be interpreted as variants of top-$k$ classification, which
may be better aligned with some applications. We leverage this analysis to
derive constraints on the conditional label distributions under which these
proposed surrogates become consistent for top-$k$. It has been further
suggested that every convex hinge-like surrogate must be inconsistent for
top-$k$. Yet, we use the same embedding framework to give the first consistent
polyhedral surrogate for this problem.

Cross-device federated learning is an emerging machine learning (ML) paradigm
where a large population of devices collectively train an ML model while the
data remains on the devices. This research field has a unique set of practical
challenges, and to systematically make advances, new datasets curated to be
compatible with this paradigm are needed. Existing federated learning
benchmarks in the image domain do not accurately capture the scale and
heterogeneity of many real-world use cases. We introduce FLAIR, a challenging
large-scale annotated image dataset for multi-label classification suitable for
federated learning. FLAIR has 429,078 images from 51,414 Flickr users and
captures many of the intricacies typically encountered in federated learning,
such as heterogeneous user data and a long-tailed label distribution. We
implement multiple baselines in different learning setups for different tasks
on this dataset. We believe FLAIR can serve as a challenging benchmark for
advancing the state-of-the art in federated learning. Dataset access and the
code for the benchmark are available at
\url{https://github.com/apple/ml-flair}.

In this paper, we introduce MCTensor, a library based on PyTorch for
providing general-purpose and high-precision arithmetic for DL training.
MCTensor is used in the same way as PyTorch Tensor: we implement multiple
basic, matrix-level computation operators and NN modules for MCTensor with
identical PyTorch interface. Our algorithms achieve high precision computation
and also benefits from heavily-optimized PyTorch floating-point arithmetic. We
evaluate MCTensor arithmetic against PyTorch native arithmetic for a series of
tasks, where models using MCTensor in float16 would match or outperform the
PyTorch model with float32 or float64 precision.

Due to the high performance and safety requirements of self-driving
applications, the complexity of modern autonomous driving systems (ADS) has
been growing, instigating the need for more sophisticated hardware which could
add to the energy footprint of the ADS platform. Addressing this, edge
computing is poised to encompass self-driving applications, enabling the
compute-intensive autonomy-related tasks to be offloaded for processing at
compute-capable edge servers. Nonetheless, the intricate hardware architecture
of ADS platforms, in addition to the stringent robustness demands, set forth
complications for task offloading which are unique to autonomous driving.
Hence, we present $ROMANUS$, a methodology for robust and efficient task
offloading for modular ADS platforms with multi-sensor processing pipelines.
Our methodology entails two phases: (i) the introduction of efficient
offloading points along the execution path of the involved deep learning
models, and (ii) the implementation of a runtime solution based on Deep
Reinforcement Learning to adapt the operating mode according to variations in
the perceived road scene complexity, network connectivity, and server load.
Experiments on the object detection use case demonstrated that our approach is
14.99% more energy-efficient than pure local execution while achieving a 77.06%
reduction in risky behavior from a robust-agnostic offloading baseline.

Fast adversarial training (FAT) effectively improves the efficiency of
standard adversarial training (SAT). However, initial FAT encounters
catastrophic overfitting, i.e.,the robust accuracy against adversarial attacks
suddenly and dramatically decreases. Though several FAT variants spare no
effort to prevent overfitting, they sacrifice much calculation cost. In this
paper, we explore the difference between the training processes of SAT and FAT
and observe that the attack success rate of adversarial examples (AEs) of FAT
gets worse gradually in the late training stage, resulting in overfitting. The
AEs are generated by the fast gradient sign method (FGSM) with a zero or random
initialization. Based on the observation, we propose a prior-guided FGSM
initialization method to avoid overfitting after investigating several
initialization strategies, improving the quality of the AEs during the whole
training process. The initialization is formed by leveraging historically
generated AEs without additional calculation cost. We further provide a
theoretical analysis for the proposed initialization method. We also propose a
simple yet effective regularizer based on the prior-guided initialization,i.e.,
the currently generated perturbation should not deviate too much from the
prior-guided initialization. The regularizer adopts both historical and current
adversarial perturbations to guide the model learning. Evaluations on four
datasets demonstrate that the proposed method can prevent catastrophic
overfitting and outperform state-of-the-art FAT methods. The code is released
at https://github.com/jiaxiaojunQAQ/FGSM-PGI.

CyberPhysical systems (CPS) must be closely monitored to identify and
potentially mitigate emergent problems that arise during their routine
operations. However, the multivariate time-series data which they typically
produce can be complex to understand and analyze. While formal product
documentation often provides example data plots with diagnostic suggestions,
the sheer diversity of attributes, critical thresholds, and data interactions
can be overwhelming to non-experts who subsequently seek help from discussion
forums to interpret their data logs. Deep learning models, such as Long
Short-term memory (LSTM) networks can be used to automate these tasks and to
provide clear explanations of diverse anomalies detected in real-time
multivariate data-streams. In this paper we present RESAM, a requirements
process that integrates knowledge from domain experts, discussion forums, and
formal product documentation, to discover and specify requirements and design
definitions in the form of time-series attributes that contribute to the
construction of effective deep learning anomaly detectors. We present a
case-study based on a flight control system for small Uncrewed Aerial Systems
and demonstrate that its use guides the construction of effective anomaly
detection models whilst also providing underlying support for explainability.
RESAM is relevant to domains in which open or closed online forums provide
discussion support for log analysis.

This paper proposes TaDaa: Ticket Assignment Deep learning Auto Advisor,
which leverages the latest Transformers models and machine learning techniques
quickly assign issues within an organization, like customer support, help desk
and alike issue ticketing systems. The project provides functionality to 1)
assign an issue to the correct group, 2) assign an issue to the best resolver,
and 3) provide the most relevant previously solved tickets to resolvers. We
leverage one ticketing system sample dataset, with over 3k+ groups and over
10k+ resolvers to obtain a 95.2% top 3 accuracy on group suggestions and a
79.0% top 5 accuracy on resolver suggestions. We hope this research will
greatly improve average issue resolution time on customer support, help desk,
and issue ticketing systems.

The resonant frequencies of three-dimensional microwave cavities are
explicitly dependent on the dielectric constant of the material filling the
cavity, making them an ideal system for probing material properties. In
particular, dielectric constant measurements allow one to extract the helium
density through the Clausius-Mossotti relation. By filling a cylindrical
aluminum cavity with superfluid helium, we make precision measurements of the
dielectric constant of liquid $^4$He at saturated vapor pressure for range of
temperatures 30 -- 300 mK and at pressures of 0-25.0 bar at 30 mK, essentially
the zero temperature limit for the properties of $^4$He. After reviewing
previous measurements, we find systematic discrepancy between low and high
frequency determination of the dielectric constant in the zero-temperature
limit and moderate discrepancy with previously reported values of
pressure-dependent density. Our precision measurements suggest 3D microwave
cavities are a promising choice for refining previously measured values in
helium, with potential applications in metrology.

There is mounting empirical evidence of emergent phenomena in the
capabilities of deep learning methods as we scale up datasets, model sizes, and
training times. While there are some accounts of how these resources modulate
statistical capacity, far less is known about their effect on the computational
problem of model training. This work conducts such an exploration through the
lens of learning $k$-sparse parities of $n$ bits, a canonical family of
problems which pose theoretical computational barriers. In this setting, we
find that neural networks exhibit surprising phase transitions when scaling up
dataset size and running time. In particular, we demonstrate empirically that
with standard training, a variety of architectures learn sparse parities with
$n^{O(k)}$ examples, with loss (and error) curves abruptly dropping after
$n^{O(k)}$ iterations. These positive results nearly match known SQ lower
bounds, even without an explicit sparsity-promoting prior. We elucidate the
mechanisms of these phenomena with a theoretical analysis: we find that the
phase transition in performance is not due to SGD "stumbling in the dark" until
it finds the hidden set of features (a natural algorithm which also runs in
$n^{O(k)}$ time); instead, we show that SGD gradually amplifies a Fourier gap
in the population gradient.

A learning-based THz multi-layer imaging has been recently used for
contactless three-dimensional (3D) positioning and encoding. We show a
proof-of-concept demonstration of an emerging quantum machine learning (QML)
framework to deal with depth variation, shadow effect, and double-sided content
recognition, through an experimental validation.

This paper introduces and proves asymptotic normality for a new
semi-parametric estimator of continuous treatment effects in panel data.
Specifically, we estimate an average derivative of the regression function. Our
estimator uses the panel structure of data to account for unobservable
time-invariant heterogeneity and machine learning methods to flexibly estimate
functions of high-dimensional inputs. We construct our estimator using tools
from double de-biased machine learning (DML) literature. We show the
performance of our method in Monte Carlo simulations and also apply our
estimator to real-world data and measure the impact of extreme heat in United
States (U.S.) agriculture. We use the estimator on a county-level dataset of
corn yields and weather variation, measuring the elasticity of yield with
respect to a marginal increase in extreme heat exposure. In our preferred
specification, the difference between the estimates from OLS and our method is
statistically significant and economically significant. We find a significantly
higher degree of impact, corresponding to an additional $1.18 billion in annual
damages by the year 2050 under median climate scenarios. We find little
evidence that this elasticity is changing over time.

The objective functions used in spectral clustering are usually composed of
two terms: i) a term that minimizes the local quadratic variation of the
cluster assignments on the graph and; ii) a term that balances the clustering
partition and helps avoiding degenerate solutions. This paper shows that a
graph neural network, equipped with suitable message passing layers, can
generate good cluster assignments by optimizing only a balancing term. Results
on attributed graph datasets show the effectiveness of the proposed approach in
terms of clustering performance and computation time.

This report presents the open-source package which implements the series of
our boosting works in the past years. In particular, the package includes
mainly three lines of techniques, among which the following two are already the
standard implementations in popular boosted tree platforms:
  (i) The histogram-based (feature-binning) approach makes the tree
implementation convenient and efficient. In Li et al (2007), a simple
fixed-length adaptive binning algorithm was developed. In this report, we
demonstrate that such a simple algorithm is still surprisingly effective
compared to more sophisticated variants in popular tree platforms.
  (ii) The explicit gain formula in Li (20010) for tree splitting based on
second-order derivatives of the loss function typically improves, often
considerably, over the first-order methods. Although the gain formula in Li
(2010) was derived for logistic regression loss, it is a generic formula for
loss functions with second-derivatives. For example, the open-source package
also includes $L_p$ regression for $p\geq 1$.
  The main contribution of this package is the ABC-Boost (adaptive base class
boosting) for multi-class classification. The initial work in Li (2008) derived
a new set of derivatives of the classical multi-class logistic regression by
specifying a "base class". The accuracy can be substantially improved if the
base class is chosen properly. The major technical challenge is to design a
search strategy to select the base class. The prior published works implemented
an exhaustive search procedure to find the base class which is computationally
too expensive. Recently, a new report (Li and Zhao, 20022) presents a unified
framework of "Fast ABC-Boost" which allows users to efficiently choose the
proper search space for the base class.
  The package provides interfaces for linux, windows, mac, matlab, R, python.

Recent works have revealed an essential paradigm in designing loss functions
that differentiate individual losses vs. aggregate losses. The individual loss
measures the quality of the model on a sample, while the aggregate loss
combines individual losses/scores over each training sample. Both have a common
procedure that aggregates a set of individual values to a single numerical
value. The ranking order reflects the most fundamental relation among
individual values in designing losses. In addition, decomposability, in which a
loss can be decomposed into an ensemble of individual terms, becomes a
significant property of organizing losses/scores. This survey provides a
systematic and comprehensive review of rank-based decomposable losses in
machine learning. Specifically, we provide a new taxonomy of loss functions
that follows the perspectives of aggregate loss and individual loss. We
identify the aggregator to form such losses, which are examples of set
functions. We organize the rank-based decomposable losses into eight
categories. Following these categories, we review the literature on rank-based
aggregate losses and rank-based individual losses. We describe general formulas
for these losses and connect them with existing research topics. We also
suggest future research directions spanning unexplored, remaining, and emerging
issues in rank-based decomposable losses.

Language models like OpenAI's Generative Pre-Trained Transformers (GPT-2/3)
capture the long-term correlations needed to generate text in a variety of
domains (such as language translators) and recently in gameplay (chess, Go, and
checkers). The present research applies both the larger (GPT-3) and smaller
(GPT-2) language models to explore the complex strategies for the game of
Othello (or Reverses). Given the game rules for rapid reversals of fortune, the
language model not only represents a candidate predictor of the next move based
on previous game moves but also avoids sparse rewards in gameplay. The language
model automatically captures or emulates championship-level strategies. The
fine-tuned GPT-2 model generates Othello games ranging from 13-71% completion,
while the larger GPT-3 model reaches 41% of a complete game. Like previous work
with chess and Go, these language models offer a novel way to generate
plausible game archives, particularly for comparing opening moves across a
larger sample than humanly possible to explore. A primary contribution of these
models magnifies (by two-fold) the previous record for player archives (120,000
human games over 45 years from 1977-2022), thus supplying the research
community with more diverse and original strategies for sampling with other
reinforcement learning techniques.

Machine hearing of the environmental sound is one of the important issues in
the audio recognition domain. It gives the machine the ability to discriminate
between the different input sounds that guides its decision making. In this
work we exploit the self-supervised contrastive technique and a shallow 1D CNN
to extract the distinctive audio features (audio representations) without using
any explicit annotations.We generate representations of a given audio using
both its raw audio waveform and spectrogram and evaluate if the proposed
learner is agnostic to the type of audio input. We further use canonical
correlation analysis (CCA) to fuse representations from the two types of input
of a given audio and demonstrate that the fused global feature results in
robust representation of the audio signal as compared to the individual
representations. The evaluation of the proposed technique is done on both
ESC-50 and UrbanSound8K. The results show that the proposed technique is able
to extract most features of the environmental audio and gives an improvement of
12.8% and 0.9% on the ESC-50 and UrbanSound8K datasets respectively.

Electron density irregularities present within the ionosphere induce
significant fluctuations in global navigation satellite system (GNSS) signals.
Fluctuations in signal power are referred to as amplitude scintillation and can
be monitored through the S4 index. Forecasting the severity of amplitude
scintillation based on historical S4 index data is beneficial when real-time
data is unavailable. In this work, we study the possibility of using historical
data from a single GPS scintillation monitoring receiver to train a machine
learning (ML) model to forecast the severity of amplitude scintillation, either
weak, moderate, or severe, with respect to temporal and spatial parameters. Six
different ML models were evaluated and the bagged trees model was the most
accurate among them, achieving a forecasting accuracy of $81\%$ using a
balanced dataset, and $97\%$ using an imbalanced dataset.

Graph convolution (GConv) is a widely used technique that has been
demonstrated to be extremely effective for graph learning applications, most
notably node categorization. On the other hand, many GConv-based models do not
quantify the effect of graph topology and node features on performance, and are
even surpassed by some models that do not consider graph structure or node
properties. We quantify the information overlap between graph topology, node
features, and labels in order to determine graph convolution's representation
power in the node classification task. In this work, we first determine the
linear separability of graph convoluted features using analysis of variance.
Mutual information is used to acquire a better understanding of the possible
non-linear relationship between graph topology, node features, and labels. Our
theoretical analysis demonstrates that a simple and efficient graph operation
that concatenates only graph topology and node properties consistently
outperforms conventional graph convolution, especially in the heterophily case.
Extensive empirical research utilizing a synthetic dataset and real-world
benchmarks demonstrates that graph concatenation is a simple but more flexible
alternative to graph convolution.

Building on the framework introduced by Xu and Raginksy [1] for supervised
learning problems, we study the best achievable performance for model-based
Bayesian reinforcement learning problems. With this purpose, we define minimum
Bayesian regret (MBR) as the difference between the maximum expected cumulative
reward obtainable either by learning from the collected data or by knowing the
environment and its dynamics. We specialize this definition to reinforcement
learning problems modeled as Markov decision processes (MDPs) whose kernel
parameters are unknown to the agent and whose uncertainty is expressed by a
prior distribution. One method for deriving upper bounds on the MBR is
presented and specific bounds based on the relative entropy and the Wasserstein
distance are given. We then focus on two particular cases of MDPs, the
multi-armed bandit problem (MAB) and the online optimization with partial
feedback problem. For the latter problem, we show that our bounds can recover
from below the current information-theoretic bounds by Russo and Van Roy [2].

Pretraining molecular representation models without labels is fundamental to
various applications. Conventional methods mainly process 2D molecular graphs
and focus solely on 2D tasks, making their pretrained models incapable of
characterizing 3D geometry and thus defective for downstream 3D tasks. In this
work, we tackle 3D molecular pretraining in a complete and novel sense. In
particular, we first propose to adopt an equivariant energy-based model as the
backbone for pretraining, which enjoys the merits of fulfilling the symmetry of
3D space. Then we develop a node-level pretraining loss for force prediction,
where we further exploit the Riemann-Gaussian distribution to ensure the loss
to be E(3)-invariant, enabling more robustness. Moreover, a graph-level noise
scale prediction task is also leveraged to further promote the eventual
performance. We evaluate our model pretrained from a large-scale 3D dataset
GEOM-QM9 on two challenging 3D benchmarks: MD17 and QM9. Experimental results
demonstrate the efficacy of our method against current state-of-the-art
pretraining approaches, and verify the validity of our design for each proposed

One of the significant challenges linked with the massive integration of
distributed energy resources (DER) in the active distribution grids is the
uncertainty it brings along. The grid operation becomes more arduous to avoid
voltage or thermal violations. While the Optimal Power Flow (OPF) algorithm is
vastly discussed in the literature, little attention has been given to the
robustness of such centralised implementation, such as the provision of
redundant control solutions during a communication failure. This paper aims to
implement a machine learning-based algorithm at each Intelligent Electronic
Device (IED) that mimics the centralised OPF used during communication failures
using IEC 61850 data models. Under normal circumstances, the IEDs communicate
for centralised OPF. In addition, the system is trained offline for all
operational conditions and the individual look-up tables linking the actual
voltages to the DER setpoints are sent to the respective controllers. The
regression models allow for the local reconstruction of the DER setpoints,
emulating the overall OPF, in case of a communication failure. In addition to
the regression control, the paper also explains an offline learning approach
for periodic re-training of the regression models. The implementation is
experimentally verified using a Hardware-in-the-loop test setup. The tests
showed promising results compared to conventional control strategies during
communication failures. When properly trained and coordinated, such an
intuitive local control approach for each DER could be very beneficial for the
bulk power system. This machine learning-based approach could also replace the
existing Q(V) control strategies, to better support the bulk power system.

Legal Judgment Prediction is one of the most acclaimed fields for the
combined area of NLP, AI, and Law. By legal prediction we mean an intelligent
systems capable to predict specific judicial characteristics, such as judicial
outcome, a judicial class, predict an specific case. In this research, we have
used AI classifiers to predict judicial outcomes in the Brazilian legal system.
For this purpose, we developed a text crawler to extract data from the official
Brazilian electronic legal systems. These texts formed a dataset of
second-degree murder and active corruption cases. We applied different
classifiers, such as Support Vector Machines and Neural Networks, to predict
judicial outcomes by analyzing textual features from the dataset. Our research
showed that Regression Trees, Gated Recurring Units and Hierarchical Attention
Networks presented higher metrics for different subsets. As a final goal, we
explored the weights of one of the algorithms, the Hierarchical Attention
Networks, to find a sample of the most important words used to absolve or
convict defendants.

Online reinforcement learning is concerned with training an agent on-the-fly
via dynamic interaction with the environment. Here, due to the specifics of the
application, it is not generally possible to perform long pre-training, as it
is commonly done in off-line, model-free approaches, which are akin to dynamic
programming. Such applications may be found more frequently in industry, rather
than in pure digital fields, such as cloud services, video games, database
management, etc., where reinforcement learning has been demonstrating success.
Online reinforcement learning, in contrast, is more akin to classical control,
which utilizes some model knowledge about the environment. Stability of the
closed-loop (agent plus the environment) is a major challenge for such online
approaches. In this paper, we tackle this problem by a special fusion of online
reinforcement learning with elements of classical control, namely, based on the
Lyapunov theory of stability. The idea is to start the agent at once, without
pre-training, and learn approximately optimal policy under specially designed
constraints, which guarantee stability. The resulting approach was tested in an
extensive experimental study with a mobile robot. A nominal parking controller
was used as a baseline. It was observed that the suggested agent could always
successfully park the robot, while significantly improving the cost. While many
approaches may be exploited for mobile robot control, we suggest that the
experiments showed the promising potential of online reinforcement learning
agents based on Lyapunov-like constraints. The presented methodology may be
utilized in safety-critical, industrial applications where stability is
necessary.

This article proposes a novel methodology to learn a stable robot control law
driven by dynamical systems. The methodology requires a single demonstration
and can deduce a stable dynamics in arbitrary high dimensions. The method
relies on the idea that there exists a latent space in which the nonlinear
dynamics appears quasi linear. The original nonlinear dynamics is mapped into a
stable linear DS, by leveraging on the properties of graph embeddings. We show
that the eigendecomposition of the Graph Laplacian results in linear embeddings
in two dimensions and quasi-linear in higher dimensions. The nonlinear terms
vanish, exponentially as the number of datapoints increase, and for large
density of points, the embedding appears linear. We show that this new
embedding enables to model highly nonlinear dynamics in high dimension and
overcomes alternative techniques in both precision of reconstruction and number
of parameters required for the embedding. We demonstrate its applicability to
control real robot tasked to perform complex free motion in space.

Semantic novelty detection aims at discovering unknown categories in the test
data. This task is particularly relevant in safety-critical applications, such
as autonomous driving or healthcare, where it is crucial to recognize unknown
objects at deployment time and issue a warning to the user accordingly. Despite
the impressive advancements of deep learning research, existing models still
need a finetuning stage on the known categories in order to recognize the
unknown ones. This could be prohibitive when privacy rules limit data access,
or in case of strict memory and computational constraints (e.g. edge
computing). We claim that a tailored representation learning strategy may be
the right solution for effective and efficient semantic novelty detection.
Besides extensively testing state-of-the-art approaches for this task, we
propose a novel representation learning paradigm based on relational reasoning.
It focuses on learning how to measure semantic similarity rather than
recognizing known categories. Our experiments show that this knowledge is
directly transferable to a wide range of scenarios, and it can be exploited as
a plug-and-play module to convert closed-set recognition models into reliable
open-set ones.

Grafting polymer chains on nanoparticles surfaces is a well-known route to
control their self assembly and distribution in a polymer matrix. A wide
variety of self assembled structures are achieved by changing the grafting
patterns on an individual nanoparticle surface. However, accurate estimation of
the effective potential of mean force between a pair of grafted nanoparticles
that determines their assembly and distribution in a polymer matrix is an
outstanding challenge in nanoscience. Here, we propose a new deep learning
method that learns the interaction between a pair of grafted nanoparticles from
the molecular dynamics trajectory of a cluster of polymer-grafted
nanoparticles. Subsequently, we carry out the deep learning potential of mean
force-based molecular simulation that predicts the self-assembly of a large
number of polymer grafted nanoparticles into various anisotropic
superstructures, including percolating networks and bilayers depending on
nanoparticles concentration in 3D. The deep learning potential of mean
force-predicted self-assembled superstructures are consistent with the actual
superstructures of polymer grafted nanoparticles. This deep learning framework
is very generic and can accelerate the characterization and prediction of the
self-assembly and phase behaviour of polymer-grafted and unfunctionalized
nanoparticles in free space or a polymer matrix.

Objects are usually associated with multiple attributes, and these attributes
often exhibit high correlations. Modeling complex relationships between
attributes poses a great challenge for multi-attribute learning. This paper
proposes a simple yet generic framework named Label2Label to exploit the
complex attribute correlations. Label2Label is the first attempt for
multi-attribute prediction from the perspective of language modeling.
Specifically, it treats each attribute label as a "word" describing the sample.
As each sample is annotated with multiple attribute labels, these "words" will
naturally form an unordered but meaningful "sentence", which depicts the
semantic information of the corresponding sample. Inspired by the remarkable
success of pre-training language models in NLP, Label2Label introduces an
image-conditioned masked language model, which randomly masks some of the
"word" tokens from the label "sentence" and aims to recover them based on the
masked "sentence" and the context conveyed by image features. Our intuition is
that the instance-wise attribute relations are well grasped if the neural net
can infer the missing attributes based on the context and the remaining
attribute hints. Label2Label is conceptually simple and empirically powerful.
Without incorporating task-specific prior knowledge and highly specialized
network designs, our approach achieves state-of-the-art results on three
different multi-attribute learning tasks, compared to highly customized
domain-specific methods. Code is available at
https://github.com/Li-Wanhua/Label2Label.

We introduce a practical method to enforce linear partial differential
equation (PDE) constraints for functions defined by neural networks (NNs), up
to a desired tolerance. By combining methods in differentiable physics and
applications of the implicit function theorem to NN models, we develop a
differentiable PDE-constrained NN layer. During training, our model learns a
family of functions, each of which defines a mapping from PDE parameters to PDE
solutions. At inference time, the model finds an optimal linear combination of
the functions in the learned family by solving a PDE-constrained optimization
problem. Our method provides continuous solutions over the domain of interest
that exactly satisfy desired physical constraints. Our results show that
incorporating hard constraints directly into the NN architecture achieves much
lower test error, compared to training on an unconstrained objective.

Learning from previously collected datasets of expert data offers the promise
of acquiring robotic policies without unsafe and costly online explorations.
However, a major challenge is a distributional shift between the states in the
training dataset and the ones visited by the learned policy at the test time.
While prior works mainly studied the distribution shift caused by the policy
during the offline training, the problem of recovering from out-of-distribution
states at the deployment time is not very well studied yet. We alleviate the
distributional shift at the deployment time by introducing a recovery policy
that brings the agent back to the training manifold whenever it steps out of
the in-distribution states, e.g., due to an external perturbation. The recovery
policy relies on an approximation of the training data density and a learned
equivariant mapping that maps visual observations into a latent space in which
translations correspond to the robot actions. We demonstrate the effectiveness
of the proposed method through several manipulation experiments on a real
robotic platform. Our results show that the recovery policy enables the agent
to complete tasks while the behavioral cloning alone fails because of the
distributional shift problem.

Neural networks have seen an explosion of usage and research in the past
decade, particularly within the domains of computer vision and natural language
processing. However, only recently have advancements in neural networks yielded
performance improvements beyond narrow applications and translated to expanded
multitask models capable of generalizing across multiple data types and
modalities. Simultaneously, it has been shown that neural networks are
overparameterized to a high degree, and pruning techniques have proved capable
of significantly reducing the number of active weights within the network while
largely preserving performance. In this work, we identify a methodology and
network representational structure which allows a pruned network to employ
previously unused weights to learn subsequent tasks. We employ these
methodologies on well-known benchmarking datasets for testing purposes and show
that networks trained using our approaches are able to learn multiple tasks,
which may be related or unrelated, in parallel or in sequence without
sacrificing performance on any task or exhibiting catastrophic forgetting.

In this work, we demonstrate the advantage of the pGMM (``powered generalized
min-max'') kernel in the context of (ridge) regression. In recent prior
studies, the pGMM kernel has been extensively evaluated for classification
tasks, for logistic regression, support vector machines, as well as deep neural
networks. In this paper, we provide an experimental study on ridge regression,
to compare the pGMM kernel regression with the ordinary ridge linear regression
as well as the RBF kernel ridge regression. Perhaps surprisingly, even without
a tuning parameter (i.e., $p=1$ for the power parameter of the pGMM kernel),
the pGMM kernel already performs well. Furthermore, by tuning the parameter
$p$, this (deceptively simple) pGMM kernel even performs quite comparably to
boosted trees.
  Boosting and boosted trees are very popular in machine learning practice. For
regression tasks, typically, practitioners use $L_2$ boost, i.e., for
minimizing the $L_2$ loss. Sometimes for the purpose of robustness, the $L_1$
boost might be a choice. In this study, we implement $L_p$ boost for $p\geq 1$
and include it in the package of ``Fast ABC-Boost''. Perhaps also surprisingly,
the best performance (in terms of $L_2$ regression loss) is often attained at
$p>2$, in some cases at $p\gg 2$. This phenomenon has already been demonstrated
by Li et al (UAI 2010) in the context of k-nearest neighbor classification
using $L_p$ distances. In summary, the implementation of $L_p$ boost provides
practitioners the additional flexibility of tuning boosting algorithms for
potentially achieving better accuracy in regression applications.

Deep Learning has been one of the most disruptive technological advancements
in recent times. The high performance of deep learning models comes at the
expense of high computational, storage and power requirements. Sensing the
immediate need for accelerating and compressing these models to improve
on-device performance, we introduce Deeplite Neutrino for production-ready
optimization of the models and Deeplite Runtime for deployment of ultra-low bit
quantized models on Arm-based platforms. We implement low-level quantization
kernels for Armv7 and Armv8 architectures enabling deployment on the vast array
of 32-bit and 64-bit Arm-based devices. With efficient implementations using
vectorization, parallelization, and tiling, we realize speedups of up to 2x and
2.2x compared to TensorFlow Lite with XNNPACK backend on classification and
detection models, respectively. We also achieve significant speedups of up to
5x and 3.2x compared to ONNX Runtime for classification and detection models,

Unknown class distributions in unlabelled astrophysical training data have
previously been shown to detrimentally affect model performance due to dataset
shift between training and validation sets. For radio galaxy classification, we
demonstrate in this work that removing low angular extent sources from the
unlabelled data before training produces qualitatively different training
dynamics for a contrastive model. By applying the model on an unlabelled
data-set with unknown class balance and sub-population distribution to generate
a representation space of radio galaxies, we show that with an appropriate cut
threshold we can find a representation with FRI/FRII class separation
approaching that of a supervised baseline explicitly trained to separate radio
galaxies into these two classes. Furthermore we show that an excessively
conservative cut threshold blocks any increase in validation accuracy. We then
use the learned representation for the downstream task of performing a
similarity search on rare hybrid sources, finding that the contrastive model
can reliably return semantically similar samples, with the added bonus of
finding duplicates which remain after pre-processing.

In recent decades, there has been an increase in polypharmacy, the concurrent
administration of multiple drugs per patient. Studies have shown that
polypharmacy is linked to adverse patient outcomes and there is interest in
elucidating the exact causes behind this observation. In this paper, we are
studying the relationship between drug prescriptions, drug-drug interactions
(DDIs) and patient mortality. Our focus is not so much on the number of
prescribed drugs, the typical metric in polypharmacy research, but rather on
the specific combinations of drugs leading to a DDI. To learn the space of
real-world drug combinations, we first assessed the drug prescription landscape
of the UK Biobank, a large patient data registry. We observed distinct drug
constellation patterns driven by the UK Biobank participants' disease status.
We show that these drug prescription clusters matter in terms of the number and
types of expected DDIs, and may possibly explain observed differences in health
outcomes.

Trajectory prediction is an essential task for successful human robot
interaction, such as in autonomous driving. In this work, we address the
problem of predicting future pedestrian trajectories in a first person view
setting with a moving camera. To that end, we propose a novel action-based
contrastive learning loss, that utilizes pedestrian action information to
improve the learned trajectory embeddings. The fundamental idea behind this new
loss is that trajectories of pedestrians performing the same action should be
closer to each other in the feature space than the trajectories of pedestrians
with significantly different actions. In other words, we argue that behavioral
information about pedestrian action influences their future trajectory.
Furthermore, we introduce a novel sampling strategy for trajectories that is
able to effectively increase negative and positive contrastive samples.
Additional synthetic trajectory samples are generated using a trained
Conditional Variational Autoencoder (CVAE), which is at the core of several
models developed for trajectory prediction. Results show that our proposed
contrastive framework employs contextual information about pedestrian behavior,
i.e. action, effectively, and it learns a better trajectory representation.
Thus, integrating the proposed contrastive framework within a trajectory
prediction model improves its results and outperforms state-of-the-art methods
on three trajectory prediction benchmarks [31, 32, 26].

Vibrational spectroscopy clearly is one of the key techniques to elucidate
microscopic structure and dynamics. Without the aid of theoretical approaches,
it is however, often difficult to understand such spectra at a microscopic
level. Therefore, there is a demand to calculate accurate, and thus predictive,
vibrational spectra from ab initio techniques at corresponding experimental
thermodynamic conditions. Ab initio molecular dynamics have repeatedly proved
to be suitable for this purpose, however, the computational demand is huge,
especially if expensive electronic structure methods are used. In the last
decades, machine learning of potential energy surfaces has become a popular way
to accelerate such simulations. This way the information on the electronic
structure is usually lost and calculating IR or Raman spectra with proper
selection rules is therefore still difficult. Here, the E(3)-equivariant neural
network e3nn is used to fit the atomic polar tensor a posteriori on top of
existing molecular dynamics simulations. The introduced approach is general and
thus transferable to any system, with or without periodic boundary conditions.
As an exemplary case study, the new approach is applied to calculate the IR
spectrum of ambient liquid water. It is demonstrated, that the machine learned
IR spectrum exactly matches the explicitly calculated one and excellently
reproduces the experimentally measured spectrum in shape and intensity. It is
foreshadowed that this approach may enable one to calculate accurate spectra at
even higher levels of theory, such as hybrid DFT and beyond.

The improvement of traffic efficiency at urban intersections receives strong
research interest in the field of automated intersection management. So far,
mostly non-learning algorithms like reservation or optimization-based ones were
proposed to solve the underlying multi-agent planning problem. At the same
time, automated driving functions for a single ego vehicle are increasingly
implemented using machine learning methods. In this work, we build upon a
previously presented graph-based scene representation and graph neural network
to approach the problem using reinforcement learning. The scene representation
is improved in key aspects by using edge features in addition to the existing
node features for the vehicles. This leads to an increased representation
quality that is leveraged by an updated network architecture. The paper
provides an in-depth evaluation of the proposed method against baselines that
are commonly used in automatic intersection management. Compared to a
traditional signalized intersection and an enhanced first-in-first-out scheme,
a significant reduction of induced delay is observed at varying traffic
densities. Finally, the generalization capability of the graph-based
representation is evaluated by testing the policy on intersection layouts not
seen during training. The model generalizes virtually without restrictions to
smaller intersection layouts and within certain limits to larger ones.

We present a semi-supervised learning approach to the temporal action
segmentation task. The goal of the task is to temporally detect and segment
actions in long, untrimmed procedural videos, where only a small set of videos
are densely labelled, and a large collection of videos are unlabelled. To this
end, we propose two novel loss functions for the unlabelled data: an action
affinity loss and an action continuity loss. The action affinity loss guides
the unlabelled samples learning by imposing the action priors induced from the
labelled set. Action continuity loss enforces the temporal continuity of
actions, which also provides frame-wise classification supervision. In
addition, we propose an Adaptive Boundary Smoothing (ABS) approach to build
coarser action boundaries for more robust and reliable learning. The proposed
loss functions and ABS were evaluated on three benchmarks. Results show that
they significantly improved action segmentation performance with a low amount
(5% and 10%) of labelled data and achieved comparable results to full
supervision with 50% labelled data. Furthermore, ABS succeeded in boosting
performance when integrated into fully-supervised learning.

Explainability of Reinforcement Learning (RL) policies remains a challenging
research problem, particularly when considering RL in a safety context.
Understanding the decisions and intentions of an RL policy offer avenues to
incorporate safety into the policy by limiting undesirable actions. We propose
the use of a Boolean Decision Rules model to create a post-hoc rule-based
summary of an agent's policy. We evaluate our proposed approach using a DQN
agent trained on an implementation of a lava gridworld and show that, given a
hand-crafted feature representation of this gridworld, simple generalised rules
can be created, giving a post-hoc explainable summary of the agent's policy. We
discuss possible avenues to introduce safety into a RL agent's policy by using
rules generated by this rule-based model as constraints imposed on the agent's
policy, as well as discuss how creating simple rule summaries of an agent's
policy may help in the debugging process of RL agents.

Upper limb movement classification, which maps input signals to the target
activities, is a key building block in the control of rehabilitative robotics.
Classifiers are trained for the rehabilitative system to comprehend the desires
of the patient whose upper limbs do not function properly. Electromyography
(EMG) signals and Electroencephalography (EEG) signals are used widely for
upper limb movement classification. By analysing the classification results of
the real-time EEG and EMG signals, the system can understand the intention of
the user and predict the events that one would like to carry out. Accordingly,
it will provide external help to the user. However, the noise in the real-time
EEG and EMG data collection process contaminates the effectiveness of the data,
which undermines classification performance. Moreover, not all patients process
strong EMG signals due to muscle damage and neuromuscular disorder. To address
these issues, this paper explores different feature extraction techniques and
machine learning and deep learning models for EEG and EMG signals
classification and proposes a novel decision-level multisensor fusion technique
to integrate EEG signals with EMG signals. This system retrieves effective
information from both sources to understand and predict the desire of the user,
and thus aid. By testing out the proposed technique on a publicly available
WAY-EEG-GAL dataset, which contains EEG and EMG signals that were recorded
simultaneously, we manage to conclude the feasibility and effectiveness of the
novel system.

There has been a long history of works showing that neural networks have hard
time extrapolating beyond the training set. A recent study by Balestriero et
al. (2021) challenges this view: defining interpolation as the state of
belonging to the convex hull of the training set, they show that the test set,
either in input or neural space, cannot lie for the most part in this convex
hull, due to the high dimensionality of the data, invoking the well known curse
of dimensionality. Neural networks are then assumed to necessarily work in
extrapolative mode. We here study the neural activities of the last hidden
layer of typical neural networks. Using an autoencoder to uncover the intrinsic
space underlying the neural activities, we show that this space is actually
low-dimensional, and that the better the model, the lower the dimensionality of
this intrinsic space. In this space, most samples of the test set actually lie
in the convex hull of the training set: under the convex hull definition, the
models thus happen to work in interpolation regime. Moreover, we show that
belonging to the convex hull does not seem to be the relevant criteria.
Different measures of proximity to the training set are actually better related
to performance accuracy. Thus, typical neural networks do seem to operate in
interpolation regime. Good generalization performances are linked to the
ability of a neural network to operate well in such a regime.

Two-dimensional (2D) materials have been a central focus of recent research
because they host a variety of properties, making them attractive both for
fundamental science and for applications. It is thus crucial to be able to
identify accurately and efficiently if bulk three-dimensional (3D) materials
are formed by layers held together by a weak binding energy that, thus, can be
potentially exfoliated into 2D materials. In this work, we develop a
machine-learning (ML) approach that, combined with a fast preliminary
geometrical screening, is able to efficiently identify potentially exfoliable
materials. Starting from a combination of descriptors for crystal structures,
we work out a subset of them that are crucial for accurate predictions. Our
final ML model, based on a random forest classifier, has a very high recall of
98\%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an
intuitive explanation of the five most important variables of the model.
Finally, we compare the performance of our best ML model with a deep neural
network architecture using the same descriptors. To make our algorithms and
models easily accessible, we publish an online tool on the Materials Cloud
portal that only requires a bulk 3D crystal structure as input. Our tool thus
provides a practical yet straightforward approach to assess whether any 3D
compound can be exfoliated into 2D layers.

Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferring a
reward function from expert demonstrations. Many IRL algorithms require a known
transition model and sometimes even a known expert policy, or they at least
require access to a generative model. However, these assumptions are too strong
for many real-world applications, where the environment can be accessed only
through sequential interaction. We propose a novel IRL algorithm: Active
exploration for Inverse Reinforcement Learning (AceIRL), which actively
explores an unknown environment and expert policy to quickly learn the expert's
reward function and identify a good policy. AceIRL uses previous observations
to construct confidence intervals that capture plausible reward functions and
find exploration policies that focus on the most informative regions of the
environment. AceIRL is the first approach to active IRL with sample-complexity
bounds that does not require a generative model of the environment. AceIRL
matches the sample complexity of active IRL with a generative model in the
worst case. Additionally, we establish a problem-dependent bound that relates
the sample complexity of AceIRL to the suboptimality gap of a given IRL
problem. We empirically evaluate AceIRL in simulations and find that it
significantly outperforms more naive exploration strategies.

We present a novel quantum algorithm for estimating Gibbs partition functions
in sublinear time with respect to the logarithm of the size of the state space.
This is the first speed-up of this type to be obtained over the seminal
nearly-linear time algorithm of \v{S}tefankovi\v{c}, Vempala and Vigoda [JACM,
2009]. Our result also preserves the quadratic speed-up in precision and
spectral gap achieved in previous work by exploiting the properties of quantum
Markov chains. As an application, we obtain new polynomial improvements over
the best-known algorithms for computing the partition function of the Ising
model, and counting the number of $k$-colorings, matchings or independent sets
of a graph.
  Our approach relies on developing new variants of the quantum phase and
amplitude estimation algorithms that return nearly unbiased estimates with low
variance and without destroying their initial quantum state. We extend these
subroutines into a nearly unbiased quantum mean estimator that reduces the
variance quadratically faster than the classical empirical mean. No such
estimator was known to exist prior to our work. These properties, which are of
general interest, lead to better convergence guarantees within the paradigm of
simulated annealing for computing partition functions.

Monitoring the behavior of automated real-time stream processing systems has
become one of the most relevant problems in real world applications. Such
systems have grown in complexity relying heavily on high dimensional input
data, and data hungry Machine Learning (ML) algorithms. We propose a flexible
system, Feature Monitoring (FM), that detects data drifts in such data sets,
with a small and constant memory footprint and a small computational cost in
streaming applications. The method is based on a multi-variate statistical test
and is data driven by design (full reference distributions are estimated from
the data). It monitors all features that are used by the system, while
providing an interpretable features ranking whenever an alarm occurs (to aid in
root cause analysis). The computational and memory lightness of the system
results from the use of Exponential Moving Histograms. In our experimental
study, we analyze the system's behavior with its parameters and, more
importantly, show examples where it detects problems that are not directly
related to a single feature. This illustrates how FM eliminates the need to add
custom signals to detect specific types of problems and that monitoring the
available space of features is often enough.

Deep implicit functions have shown remarkable shape modeling ability in
various 3D computer vision tasks. One drawback is that it is hard for them to
represent a 3D shape as multiple parts. Current solutions learn various
primitives and blend the primitives directly in the spatial space, which still
struggle to approximate the 3D shape accurately. To resolve this problem, we
introduce a novel implicit representation to represent a single 3D shape as a
set of parts in the latent space, towards both highly accurate and plausibly
interpretable shape modeling. Our insight here is that both the part learning
and the part blending can be conducted much easier in the latent space than in
the spatial space. We name our method Latent Partition Implicit (LPI), because
of its ability of casting the global shape modeling into multiple local part
modeling, which partitions the global shape unity. LPI represents a shape as
Signed Distance Functions (SDFs) using surface codes. Each surface code is a
latent code representing a part whose center is on the surface, which enables
us to flexibly employ intrinsic attributes of shapes or additional surface
properties. Eventually, LPI can reconstruct both the shape and the parts on the
shape, both of which are plausible meshes. LPI is a multi-level representation,
which can partition a shape into different numbers of parts after training. LPI
can be learned without ground truth signed distances, point normals or any
supervision for part partition. LPI outperforms the latest methods under the
widely used benchmarks in terms of reconstruction accuracy and modeling
interpretability. Our code, data and models are available at
https://github.com/chenchao15/LPI.

Data-Efficient GANs (DE-GANs), which aim to learn generative models with a
limited amount of training data, encounter several challenges for generating
high-quality samples. Since data augmentation strategies have largely
alleviated the training instability, how to further improve the generative
performance of DE-GANs becomes a hotspot. Recently, contrastive learning has
shown the great potential of increasing the synthesis quality of DE-GANs, yet
related principles are not well explored. In this paper, we revisit and compare
different contrastive learning strategies in DE-GANs, and identify (i) the
current bottleneck of generative performance is the discontinuity of latent
space; (ii) compared to other contrastive learning strategies,
Instance-perturbation works towards latent space continuity, which brings the
major improvement to DE-GANs. Based on these observations, we propose FakeCLR,
which only applies contrastive learning on perturbed fake samples, and devises
three related training techniques: Noise-related Latent Augmentation,
Diversity-aware Queue, and Forgetting Factor of Queue. Our experimental results
manifest the new state of the arts on both few-shot generation and limited-data
generation. On multiple datasets, FakeCLR acquires more than 15% FID
improvement compared to existing DE-GANs. Code is available at
https://github.com/iceli1007/FakeCLR.

Graph Neural Networks (GNNs) tend to suffer from high computation costs due
to the exponentially increasing scale of graph data and the number of model
parameters, which restricts their utility in practical applications. To this
end, some recent works focus on sparsifying GNNs with the lottery ticket
hypothesis (LTH) to reduce inference costs while maintaining performance
levels. However, the LTH-based methods suffer from two major drawbacks: 1) they
require exhaustive and iterative training of dense models, resulting in an
extremely large training computation cost, and 2) they only trim graph
structures and model parameters but ignore the node feature dimension, where
significant redundancy exists. To overcome the above limitations, we propose a
comprehensive graph gradual pruning framework termed CGP. This is achieved by
designing a during-training graph pruning paradigm to dynamically prune GNNs
within one training process. Unlike LTH-based methods, the proposed CGP
approach requires no re-training, which significantly reduces the computation
costs. Furthermore, we design a co-sparsifying strategy to comprehensively trim
all three core elements of GNNs: graph structures, node features, and model
parameters. Meanwhile, aiming at refining the pruning operation, we introduce a
regrowth process into our CGP framework, in order to re-establish the pruned
but important connections. The proposed CGP is evaluated by using a node
classification task across 6 GNN architectures, including shallow models (GCN
and GAT), shallow-but-deep-propagation models (SGC and APPNP), and deep models
(GCNII and ResGCN), on a total of 14 real-world graph datasets, including
large-scale graph datasets from the challenging Open Graph Benchmark.
Experiments reveal that our proposed strategy greatly improves both training
and inference efficiency while matching or even exceeding the accuracy of

Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of
the aorta enlarges, weakening its walls and potentially rupturing the vessel.
Abdominal ultrasound has been utilized for diagnostics, but due to its limited
image quality and operator dependency, CT scans are usually required for
monitoring and treatment planning. Recently, abdominal CT datasets have been
successfully utilized to train deep neural networks for automatic aorta
segmentation. Knowledge gathered from this solved task could therefore be
leveraged to improve US segmentation for AAA diagnosis and monitoring. To this
end, we propose CACTUSS: a common anatomical CT-US space, which acts as a
virtual bridge between CT and US modalities to enable automatic AAA screening
sonography. CACTUSS makes use of publicly available labelled data to learn to
segment based on an intermediary representation that inherits properties from
both US and CT. We train a segmentation network in this new representation and
employ an additional image-to-image translation network which enables our model
to perform on real B-mode images. Quantitative comparisons against fully
supervised methods demonstrate the capabilities of CACTUSS in terms of Dice
Score and diagnostic metrics, showing that our method also meets the clinical
requirements for AAA scanning and diagnosis.

Representation learning in recent years has been addressed with
self-supervised learning methods. The input data is augmented into two
distorted views and an encoder learns the representations that are invariant to
distortions -- cross-view prediction. Augmentation is one of the key components
in cross-view self-supervised learning frameworks to learn visual
representations. This paper presents ExAgt, a novel method to include expert
knowledge for augmenting traffic scenarios, to improve the learnt
representations without any human annotation. The expert-guided augmentations
are generated in an automated fashion based on the infrastructure, the
interactions between the EGO and the traffic participants and an ideal sensor
model. The ExAgt method is applied in two state-of-the-art cross-view
prediction methods and the representations learnt are tested in downstream
tasks like classification and clustering. Results show that the ExAgt method
improves representation learning compared to using only standard augmentations
and it provides a better representation space stability. The code is available
at https://github.com/lab176344/ExAgt.

We study the new task of class-incremental Novel Class Discovery
(class-iNCD), which refers to the problem of discovering novel categories in an
unlabelled data set by leveraging a pre-trained model that has been trained on
a labelled data set containing disjoint yet related categories. Apart from
discovering novel classes, we also aim at preserving the ability of the model
to recognize previously seen base categories. Inspired by rehearsal-based
incremental learning methods, in this paper we propose a novel approach for
class-iNCD which prevents forgetting of past information about the base classes
by jointly exploiting base class feature prototypes and feature-level knowledge
distillation. We also propose a self-training clustering strategy that
simultaneously clusters novel categories and trains a joint classifier for both
the base and novel classes. This makes our method able to operate in a
class-incremental setting. Our experiments, conducted on three common
benchmarks, demonstrate that our method significantly outperforms
state-of-the-art approaches. Code is available at
https://github.com/OatmealLiu/class-iNCD

Structural causal models (SCMs) are a widespread formalism to deal with
causal systems. A recent direction of research has considered the problem of
relating formally SCMs at different levels of abstraction, by defining maps
between SCMs and imposing a requirement of interventional consistency. This
paper offers a review of the solutions proposed so far, focusing on the formal
properties of a map between SCMs, and highlighting the different layers
(structural, distributional) at which these properties may be enforced. This
allows us to distinguish families of abstractions that may or may not be
permitted by choosing to guarantee certain properties instead of others. Such
an understanding not only allows to distinguish among proposal for causal
abstraction with more awareness, but it also allows to tailor the definition of
abstraction with respect to the forms of abstraction relevant to specific

The process of fusing a high spatial resolution (HR) panchromatic (PAN) image
and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS
image is known as pansharpening. With the development of convolutional neural
networks, the performance of pansharpening methods has been improved, however,
the blurry effects and the spectral distortion still exist in their fusion
results due to the insufficiency in details learning and the mismatch between
the high-frequency (HF) and low-frequency (LF) components. Therefore, the
improvements of spatial details at the premise of reducing spectral distortion
is still a challenge. In this paper, we propose a frequency-aware network (FAN)
together with a novel latent-space similarity loss to address above mentioned
problems. FAN is composed of three modules, where the frequency feature
extraction module aims to extract features in the frequency domain with the
help of discrete wavelet transform (DWT) layers, and the inverse DWT (IDWT)
layers are then utilized in the frequency feature fusion module to reconstruct
the features. Finally, the fusion results are obtained through the
reconstruction module. In order to learn the correspondency, we also propose a
latent-space similarity loss to constrain the LF features derived from PAN and
MS branches, so that HF features of PAN can reasonably be used to supplement
that of MS. Experimental results on three datasets at both reduced- and
full-resolution demonstrate the superiority of the proposed method compared
with several state-of-the-art pansharpening models, especially for the fusion
at full resolution.

Learning expressive molecular representations is crucial to facilitate the
accurate prediction of molecular properties. Despite the significant
advancement of graph neural networks (GNNs) in molecular representation
learning, they generally face limitations such as neighbors-explosion,
under-reaching, over-smoothing, and over-squashing. Also, GNNs usually have
high computational costs because of the large-scale number of parameters.
Typically, such limitations emerge or increase when facing relatively
large-size graphs or using a deeper GNN model architecture. An idea to overcome
these problems is to simplify a molecular graph into a small, rich, and
informative one, which is more efficient and less challenging to train GNNs. To
this end, we propose a novel molecular graph coarsening framework named FunQG
utilizing Functional groups, as influential building blocks of a molecule to
determine its properties, based on a graph-theoretic concept called Quotient
Graph. By experiments, we show that the resulting informative graphs are much
smaller than the molecular graphs and thus are good candidates for training
GNNs. We apply the FunQG on popular molecular property prediction benchmarks
and then compare the performance of some popular baseline GNNs on the obtained
datasets with the performance of several state-of-the-art baselines on the
original datasets. By experiments, this method significantly outperforms
previous baselines on various datasets, besides its dramatic reduction in the
number of parameters and low computational costs. Therefore, the FunQG can be
used as a simple, cost-effective, and robust method for solving the molecular
representation learning problem.

Self-triggered control, a well-documented technique for reducing the
communication overhead while ensuring desired system performance, is gaining
increasing popularity. However, existing methods for self-triggered control
require explicit system models that are assumed perfectly known a priori. An
end-to-end control paradigm known as data-driven control learns control laws
directly from data, and offers a competing alternative to the routine system
identification-then-control method. In this context, the present paper puts
forth data-driven self-triggered control schemes for unknown linear systems
using data collected offline. Specifically, for output feedback control
systems, a data-driven model predictive control (MPC) scheme is proposed, which
computes a sequence of control inputs while generating a predicted system
trajectory. A data-driven self-triggering law is designed using the predicted
trajectory, to determine the next triggering time once a new measurement
becomes available. For state feedback control systems, instead of capitalizing
on MPC to predict the trajectory, a data-fitting problem using the
pre-collected input-state data is solved, whose solution is employed to
construct the self-triggering mechanism. Both feasibility and stability are
established for the proposed self-triggered controllers, which are validated
using numerical examples.

The classical $\textit{Procrustes}$ problem is to find a rigid motion
(orthogonal transformation and translation) that best aligns two given
point-sets in the least-squares sense. The $\textit{Robust Procrustes}$ problem
is an important variant, in which a power-1 objective is used instead of least
squares to improve robustness to outliers. While the optimal solution of the
least-squares problem can be easily computed in closed form, dating back to
Sch\"onemann (1966), no such solution is known for the power-1 problem. In this
paper we propose a novel convex relaxation for the Robust Procrustes problem.
Our relaxation enjoys several theoretical and practical advantages:
Theoretically, we prove that our method provides a $\sqrt{2}$-factor
approximation to the Robust Procrustes problem, and that, under appropriate
assumptions, it exactly recovers the true rigid motion from point
correspondences contaminated by outliers. In practice, we find in numerical
experiments on both synthetic and real robust Procrustes problems, that our
method performs similarly to the standard Iteratively Reweighted Least Squares
(IRLS). However the convexity of our algorithm allows incorporating additional
convex penalties, which are not readily amenable to IRLS. This turns out to be
a substantial advantage, leading to improved results in high-dimensional
problems, including non-rigid shape alignment and semi-supervised interlingual
word translation.

Decoding images from brain activity has been a challenge. Owing to the
development of deep learning, there are available tools to solve this problem.
The decoded image, which aims to map neural spike trains to low-level visual
features and high-level semantic information space. Recently, there are a few
studies of decoding from spike trains, however, these studies pay less
attention to the foundations of neuroscience and there are few studies that
merged receptive field into visual image reconstruction. In this paper, we
propose a deep learning neural network architecture with biological properties
to reconstruct visual image from spike trains. As far as we know, we
implemented a method that integrated receptive field property matrix into loss
function at the first time. Our model is an end-to-end decoder from neural
spike trains to images. We not only merged Gabor filter into auto-encoder which
used to generate images but also proposed a loss function with receptive field
properties. We evaluated our decoder on two datasets which contain macaque
primary visual cortex neural spikes and salamander retina ganglion cells (RGCs)
spikes. Our results show that our method can effectively combine receptive
field features to reconstruct images, providing a new approach to visual
reconstruction based on neural information.

We introduce a new distributed policy gradient algorithm and show that it
outperforms existing reward-aware training procedures such as REINFORCE,
minimum risk training (MRT) and proximal policy optimization (PPO) in terms of
training stability and generalization performance when optimizing machine
translation models. Our algorithm, which we call MAD (on account of using the
mean absolute deviation in the importance weighting calculation), has
distributed data generators sampling multiple candidates per source sentence on
worker nodes, while a central learner updates the policy. MAD depends crucially
on two variance reduction strategies: (1) a conditional reward normalization
method that ensures each source sentence has both positive and negative reward
translation examples and (2) a new robust importance weighting scheme that acts
as a conditional entropy regularizer. Experiments on a variety of translation
tasks show that policies learned using the MAD algorithm perform very well when
using both greedy decoding and beam search, and that the learned policies are
sensitive to the specific reward used during training.

Adaptive learning aims to stimulate and meet the needs of individual
learners, which requires sophisticated system-level coordination of diverse
tasks, including modeling learning resources, estimating student states, and
making personalized recommendations. Existing deep learning methods have
achieved great success over statistical models; however, they still lack
generalization for diverse tasks and suffer from insufficient capacity since
they are composed of highly-coupled task-specific architectures and rely on
small-scale, coarse-grained recommendation scenarios. To realize the idea of
general adaptive systems proposed in pedagogical theory, with the emerging
pre-training techniques in NLP, we try to conduct a practical exploration on
applying pre-training to adaptive learning, to propose a unified framework
based on data observation and learning style analysis, properly leveraging
heterogeneous learning elements. Through a series of downstream tasks of
Learning Recommendation, Learning Resource Evaluation, Knowledge Tracing, and
Dropout Prediction, we find that course structures, text, and knowledge are
helpful for modeling and inherently coherent to student non-sequential learning
behaviors and that indirectly relevant information included in the pre-training
foundation can be shared across downstream tasks to facilitate effectiveness.
We finally build a simplified systematic application of adaptive learning and
reflect on the insights brought back to pedagogy. The source code and dataset

Federated learning is a data decentralization privacy-preserving technique
used to perform machine or deep learning in a secure way. In this paper we
present theoretical aspects about federated learning, such as the presentation
of an aggregation operator, different types of federated learning, and issues
to be taken into account in relation to the distribution of data from the
clients, together with the exhaustive analysis of a use case where the number
of clients varies. Specifically, a use case of medical image analysis is
proposed, using chest X-Ray images obtained from an open data repository. In
addition to the advantages related to privacy, improvements in predictions (in
terms of accuracy, loss and area under the curve) and reduction of execution
times will be studied with respect to the classical case (the centralized
approach). Different clients will be simulated from the training data, selected
in an unbalanced manner. The results of considering three or ten clients are
exposed and compared between them and against the centralized case. Two
different problems related to intermittent clients are discussed, together with
two approaches to be followed for each of them. Specifically, this type of
problems may occur because in a real scenario some clients may leave the
training, and others enter it, and on the other hand because of client
technical or connectivity problems. Finally, improvements and future work in
the field are proposed.

Internet of Things (IoT) is transforming the industry by bridging the gap
between Information Technology (IT) and Operational Technology (OT). Machines
are being integrated with connected sensors and managed by intelligent
analytics applications, accelerating digital transformation and business
operations. Bringing Machine Learning (ML) to industrial devices is an
advancement aiming to promote the convergence of IT and OT. However, developing
an ML application in industrial IoT (IIoT) presents various challenges,
including hardware heterogeneity, non-standardized representations of ML
models, device and ML model compatibility issues, and slow application
development. Successful deployment in this area requires a deep understanding
of hardware, algorithms, software tools, and applications. Therefore, this
paper presents a framework called Semantic Low-Code Engineering for ML
Applications (SeLoC-ML), built on a low-code platform to support the rapid
development of ML applications in IIoT by leveraging Semantic Web technologies.
SeLoC-ML enables non-experts to easily model, discover, reuse, and matchmake ML
models and devices at scale. The project code can be automatically generated
for deployment on hardware based on the matching results. Developers can
benefit from semantic application templates, called recipes, to fast prototype
end-user applications. The evaluations confirm an engineering effort reduction
by a factor of at least three compared to traditional approaches on an
industrial ML classification case study, showing the efficiency and usefulness
of SeLoC-ML. We share the code and welcome any contributions.

In this paper, we present a new method for few-sample supervised feature
selection (FS). Our method first learns the manifold of the feature space of
each class using kernels capturing multi-feature associations. Then, based on
Riemannian geometry, a composite kernel is computed, extracting the differences
between the learned feature associations. Finally, a FS score based on spectral
analysis is proposed. Considering multi-feature associations makes our method
multivariate by design. This in turn allows for the extraction of the hidden
manifold underlying the features and avoids overfitting, facilitating
few-sample FS. We showcase the efficacy of our method on illustrative examples
and several benchmarks, where our method demonstrates higher accuracy in
selecting the informative features compared to competing methods. In addition,
we show that our FS leads to improved classification and better generalization
when applied to test data.

In the field of representation learning on knowledge graphs (KGs), a
hyper-relational fact consists of a main triple and several auxiliary attribute
value descriptions, which is considered to be more comprehensive and specific
than a triple-based fact. However, the existing hyper-relational KG embedding
methods in a single view are limited in application due to weakening the
hierarchical structure representing the affiliation between entities. To break
this limitation, we propose a dual-view hyper-relational KG (DH-KG) structure
which contains a hyper-relational instance view for entities and a
hyper-relational ontology view for concepts abstracted hierarchically from
entities to jointly model hyper-relational and hierarchical information. In
this paper, we first define link prediction and entity typing tasks on DH-KG
and construct two DH-KG datasets, JW44K-6K extracted from Wikidata and HTDM
based on medical data. Furthermore, We propose a DH-KG embedding model DHGE,
based on GRAN encoder, HGNN, and joint learning. Experimental results show that
DHGE outperforms baseline models on DH-KG. We also provide an example of the
application of this technology in the field of hypertension medication. Our
model and datasets are publicly available.

Collaborative perception has recently shown great potential to improve
perception capabilities over single-agent perception. Existing collaborative
perception methods usually consider an ideal communication environment.
However, in practice, the communication system inevitably suffers from latency
issues, causing potential performance degradation and high risks in
safety-critical applications, such as autonomous driving. To mitigate the
effect caused by the inevitable latency, from a machine learning perspective,
we present the first latency-aware collaborative perception system, which
actively adapts asynchronous perceptual features from multiple agents to the
same time stamp, promoting the robustness and effectiveness of collaboration.
To achieve such a feature-level synchronization, we propose a novel latency
compensation module, called SyncNet, which leverages feature-attention
symbiotic estimation and time modulation techniques. Experiments results show
that the proposed latency aware collaborative perception system with SyncNet
can outperforms the state-of-the-art collaborative perception method by 15.6%
in the communication latency scenario and keep collaborative perception being
superior to single agent perception under severe latency.

Online presence on social media platforms such as Facebook and Twitter has
become a daily habit for internet users. Despite the vast amount of services
the platforms offer for their users, users suffer from cyber-bullying, which
further leads to mental abuse and may escalate to cause physical harm to
individuals or targeted groups. In this paper, we present our submission to the
Arabic Hate Speech 2022 Shared Task Workshop (OSACT5 2022) using the associated
Arabic Twitter dataset. The shared task consists of 3 sub-tasks, sub-task A
focuses on detecting whether the tweet is offensive or not. Then, For offensive
Tweets, sub-task B focuses on detecting whether the tweet is hate speech or
not. Finally, For hate speech Tweets, sub-task C focuses on detecting the
fine-grained type of hate speech among six different classes. Transformer
models proved their efficiency in classification tasks, but with the problem of
over-fitting when fine-tuned on a small or an imbalanced dataset. We overcome
this limitation by investigating multiple training paradigms such as
Contrastive learning and Multi-task learning along with Classification
fine-tuning and an ensemble of our top 5 performers. Our proposed solution
achieved 0.841, 0.817, and 0.476 macro F1-average in sub-tasks A, B, and C

Self-driving cars (SDC) commonly implement the perception pipeline to detect
the surrounding obstacles and track their moving trajectories, which lays the
ground for the subsequent driving decision making process. Although the
security of obstacle detection in SDC is intensively studied, not until very
recently the attackers start to exploit the vulnerability of the tracking
module. Compared with solely attacking the object detectors, this new attack
strategy influences the driving decision more effectively with less attack
budgets. However, little is known on whether the revealed vulnerability remains
effective in end-to-end self-driving systems and, if so, how to mitigate the
threat.
  In this paper, we present the first systematic research on the security of
object tracking in SDC. Through a comprehensive case study on the full
perception pipeline of a popular open-sourced self-driving system, Baidu's
Apollo, we prove the mainstream multi-object tracker (MOT) based on Kalman
Filter (KF) is unsafe even with an enabled multi-sensor fusion mechanism. Our
root cause analysis reveals, the vulnerability is innate to the design of
KF-based MOT, which shall error-handle the prediction results from the object
detectors yet the adopted KF algorithm is prone to trust the observation more
when its deviation from the prediction is larger. To address this design flaw,
we propose a simple yet effective security patch for KF-based MOT, the core of
which is an adaptive strategy to balance the focus of KF on observations and
predictions according to the anomaly index of the observation-prediction
deviation, and has certified effectiveness against a generalized hijacking
attack model. Extensive evaluation on $4$ KF-based existing MOT implementations
(including 2D and 3D, academic and Apollo ones) validate the defense
effectiveness and the trivial performance overhead of our approach.

We investigate deterministic non-preemptive online scheduling with delayed
commitment for total completion time minimization on parallel identical
machines. In this problem, jobs arrive one-by-one and their processing times
are revealed upon arrival. An online algorithm can assign a job to a machine at
any time after its arrival. We neither allow preemption nor a restart of the
job, that is, once started, the job occupies the assigned machine until its
completion. Our objective is the minimization of the sum of the completion
times of all jobs. In the more general weighted version of the problem, we
multiply the completion time of a job by the individual weight of the job. We
apply competitive analysis to evaluate our algorithms.
  We improve 25-year-old lower bounds for the competitive ratio of this problem
by optimizing a simple job pattern. These lower bounds decrease with growing
numbers of machines. Based on the job pattern, we develop an online algorithm
which is an extension of the delayed-SPT (Shortest Processing Time first)
approach to the parallel machine environment. We show that the competitive
ratio is at most 1.546 for any even machine number which is a significant
improvement over the best previously known competitive ratio of 1.791. For the
two-machine environment, this algorithm achieves a tight competitive ratio.
This is the first algorithm which optimally solves an online total completion
time problem in a parallel machine environment. Finally, we give the first
separation between the weighted and unweighted versions of the problem by
showing that in the two-machine environment, the competitive ratio of the
weighted completion time objective is strictly larger than 1.546.

We propose a novel high-performance, parameter and computationally efficient
deep learning architecture for tabular data, Gated Additive Tree
Ensemble(GATE). GATE uses a gating mechanism, inspired from GRU, as a feature
representation learning unit with an in-built feature selection mechanism. We
combine it with an ensemble of differentiable, non-linear decision trees,
re-weighted with simple self-attention to predict our desired output. We
demonstrate that GATE is a competitive alternative to SOTA approaches like
GBDTs, NODE, FT Transformers, etc. by experiments on several public datasets
(both classification and regression). The code will be uploaded as soon as the
paper comes out of review.

Knowledge graph embedding research has mainly focused on learning continuous
representations of knowledge graphs towards the link prediction problem.
Recently developed frameworks can be effectively applied in research related
applications. Yet, these frameworks do not fulfill many requirements of
real-world applications. As the size of the knowledge graph grows, moving
computation from a commodity computer to a cluster of computers in these
frameworks becomes more challenging. Finding suitable hyperparameter settings
w.r.t. time and computational budgets are left to practitioners. In addition,
the continual learning aspect in knowledge graph embedding frameworks is often
ignored, although continual learning plays an important role in many real-world
(deep) learning-driven applications. Arguably, these limitations explain the
lack of publicly available knowledge graph embedding models for large knowledge
graphs. We developed a framework based on the frameworks DASK, Pytorch
Lightning and Hugging Face to compute embeddings for large-scale knowledge
graphs in a hardware-agnostic manner, which is able to address real-world
challenges pertaining to the scale of real application. We provide an
open-source version of our framework along with a hub of pre-trained models
having more than 11.4 B parameters.

Variance reduction techniques such as SPIDER/SARAH/STORM have been
extensively studied to improve the convergence rates of stochastic non-convex
optimization, which usually maintain and update a sequence of estimators for a
single function across iterations. What if we need to track multiple functional
mappings across iterations but only with access to stochastic samples of
$\mathcal{O}(1)$ functional mappings at each iteration? There is an important
application in solving an emerging family of coupled compositional optimization
problems in the form of $\sum_{i=1}^m f_i(g_i(\mathbf{w}))$, where $g_i$ is
accessible through a stochastic oracle. The key issue is to track and estimate
a sequence of $\mathbf g(\mathbf{w})=(g_1(\mathbf{w}), \ldots,
g_m(\mathbf{w}))$ across iterations, where $\mathbf g(\mathbf{w})$ has $m$
blocks and it is only allowed to probe $\mathcal{O}(1)$ blocks to attain their
stochastic values and Jacobians. To improve the complexity for solving these
problems, we propose a novel stochastic method named Multi-block-Single-probe
Variance Reduced (MSVR) estimator to track the sequence of $\mathbf
g(\mathbf{w})$. It is inspired by STORM but introduces a customized error
correction term to alleviate the noise not only in stochastic samples for the
selected blocks but also in those blocks that are not sampled. With the help of
the MSVR estimator, we develop several algorithms for solving the
aforementioned compositional problems with improved complexities across a
spectrum of settings with non-convex/convex/strongly
convex/Polyak-{\L}ojasiewicz (PL) objectives. Our results improve upon prior
ones in several aspects, including the order of sample complexities and
dependence on the strong convexity parameter. Empirical studies on multi-task
deep AUC maximization demonstrate the better performance of using the new
estimator.

Pairwise debiasing is one of the most effective strategies in reducing
position bias in learning-to-rank (LTR) models. However, limiting the scope of
this strategy, are the underlying assumptions required by many pairwise
debiasing approaches. In this paper, we develop an approach based on a
minimalistic set of assumptions that can be applied to a much broader range of
user browsing patterns and arbitrary presentation layouts. We implement the
approach as a simplified version of the Unbiased LambdaMART and demonstrate
that it retains the underlying unbiasedness property in a wider variety of
settings than the original algorithm. Finally, using simulations with "golden"
relevance labels, we will show that the simplified version compares favourably
with the original Unbiased LambdaMART when the examination of different
positions in a ranked list is not assumed to be independent.

Background - Social anxiety (SA) is a common and debilitating condition,
negatively affecting life quality even at sub-diagnostic thresholds. We sought
to characterize SA's acoustic signature using hypothesis-testing and machine
learning (ML) approaches. Methods - Participants formed spontaneous utterances
responding to instructions to refuse or consent to commands of alleged peers.
Vocal properties (e.g., intensity and duration) of these utterances were
analyzed. Results - Our prediction that, as compared to low-SA (n=31), high-SA
(n=32) individuals exhibit a less confident vocal speech signature, especially
with respect to refusal utterances, was only partially supported by the
classical hypothesis-testing approach. However, the results of the ML analyses
and specifically the decision tree classifier were consistent with such speech
patterns in SA. Using a Gaussian Process (GP) classifier, we were able to
distinguish between high- and low-SA individuals with high (75.6%) accuracy and
good (.83 AUC) separability. We also expected and found that vocal properties
differentiated between refusal and consent utterances. Conclusions - Our
findings provide further support for the usefulness of ML approach for the
study of psychopathology, highlighting the utility of developing automatic
techniques to create behavioral markers of SAD. Clinically, the simplicity and
accessibility of these procedures may encourage people to seek professional
help.

Spiking neural networks (SNNs) have attracted extensive attentions in
Brain-inspired Artificial Intelligence and computational neuroscience. They can
be used to simulate biological information processing in the brain at multiple
scales. More importantly, SNNs serve as an appropriate level of abstraction to
bring inspirations from brain and cognition to Artificial Intelligence. In this
paper, we present the Brain-inspired Cognitive Intelligence Engine (BrainCog)
for creating brain-inspired AI and brain simulation models. BrainCog
incorporates different types of spiking neuron models, learning rules, brain
areas, etc., as essential modules provided by the platform. Based on these
easy-to-use modules, BrainCog supports various brain-inspired cognitive
functions, including Perception and Learning, Decision Making, Knowledge
Representation and Reasoning, Motor Control, and Social Cognition. These
brain-inspired AI models have been effectively validated on various supervised,
unsupervised, and reinforcement learning tasks, and they can be used to enable
AI models to be with multiple brain-inspired cognitive functions. For brain
simulation, BrainCog realizes the function simulation of decision-making,
working memory, the structure simulation of the Neural Circuit, and whole brain
structure simulation of Mouse brain, Macaque brain, and Human brain. An AI
engine named BORN is developed based on BrainCog, and it demonstrates how the
components of BrainCog can be integrated and used to build AI models and
applications. To enable the scientific quest to decode the nature of biological
intelligence and create AI, BrainCog aims to provide essential and easy-to-use
building blocks, and infrastructural support to develop brain-inspired spiking
neural network based AI, and to simulate the cognitive brains at multiple
scales. The online repository of BrainCog can be found at
https://github.com/braincog-x.

Monocular 3D detection has drawn much attention from the community due to its
low cost and setup simplicity. It takes an RGB image as input and predicts 3D
boxes in the 3D space. The most challenging sub-task lies in the instance depth
estimation. Previous works usually use a direct estimation method. However, in
this paper we point out that the instance depth on the RGB image is
non-intuitive. It is coupled by visual depth clues and instance attribute
clues, making it hard to be directly learned in the network. Therefore, we
propose to reformulate the instance depth to the combination of the instance
visual surface depth (visual depth) and the instance attribute depth (attribute
depth). The visual depth is related to objects' appearances and positions on
the image. By contrast, the attribute depth relies on objects' inherent
attributes, which are invariant to the object affine transformation on the
image. Correspondingly, we decouple the 3D location uncertainty into visual
depth uncertainty and attribute depth uncertainty. By combining different types
of depths and associated uncertainties, we can obtain the final instance depth.
Furthermore, data augmentation in monocular 3D detection is usually limited due
to the physical nature, hindering the boost of performance. Based on the
proposed instance depth disentanglement strategy, we can alleviate this
problem. Evaluated on KITTI, our method achieves new state-of-the-art results,
and extensive ablation studies validate the effectiveness of each component in
our method. The codes are released at https://github.com/SPengLiang/DID-M3D.

Curriculum learning needs example difficulty to proceed from easy to hard.
However, the credibility of image difficulty is rarely investigated, which can
seriously affect the effectiveness of curricula. In this work, we propose
Angular Gap, a measure of difficulty based on the difference in angular
distance between feature embeddings and class-weight embeddings built by
hyperspherical learning. To ascertain difficulty estimation, we introduce
class-wise model calibration, as a post-training technique, to the learnt
hyperbolic space. This bridges the gap between probabilistic model calibration
and angular distance estimation of hyperspherical learning. We show the
superiority of our calibrated Angular Gap over recent difficulty metrics on
CIFAR10-H and ImageNetV2. We further propose Angular Gap based curriculum
learning for unsupervised domain adaptation that can translate from learning
easy samples to mining hard samples. We combine this curriculum with a
state-of-the-art self-training method, Cycle Self Training (CST). The proposed
Curricular CST learns robust representations and outperforms recent baselines
on Office31 and VisDA 2017.

In the Machine Learning research community, there is a consensus regarding
the relationship between model complexity and the required amount of data and
computation power. In real world applications, these computational requirements
are not always available, motivating research on regularization methods. In
addition, current and past research have shown that simpler classification
algorithms can reach state-of-the-art performance on computer vision tasks
given a robust method to artificially augment the training dataset. Because of
this, data augmentation techniques became a popular research topic in recent
years. However, existing data augmentation methods are generally less
transferable than other regularization methods. In this paper we identify the
main areas of application of data augmentation algorithms, the types of
algorithms used, significant research trends, their progression over time and
research gaps in data augmentation literature. To do this, the related
literature was collected through the Scopus database. Its analysis was done
following network science, text mining and exploratory analysis approaches. We
expect readers to understand the potential of data augmentation, as well as
identify future research directions and open questions within data augmentation
research.

The most difficult task in machine learning is to interpret trained shallow
neural networks. Deep neural networks (DNNs) provide impressive results on a
larger number of tasks, but it is generally still unclear how decisions are
made by such a trained deep neural network. Providing feature importance is the
most important and popular interpretation technique used in shallow and deep
neural networks. In this paper, we develop an algorithm extending the idea of
Garson Algorithm to explain Deep Belief Network based Auto-encoder (DBNA). It
is used to determine the contribution of each input feature in the DBN. It can
be used for any kind of neural network with many hidden layers. The
effectiveness of this method is tested on both classification and regression
datasets taken from literature. Important features identified by this method
are compared against those obtained by Wald chi square (\c{hi}2). For 2 out of
4 classification datasets and 2 out of 5 regression datasets, our proposed
methodology resulted in the identification of better-quality features leading
to statistically more significant results vis-\`a-vis Wald \c{hi}2.

The application of Federated Learning (FL) is steadily increasing, especially
in privacy-aware applications, such as healthcare. However, its applications
have been limited by security concerns due to various adversarial attacks, such
as poisoning attacks (model and data poisoning). Such attacks attempt to poison
the local models and data to manipulate the global models in order to obtain
undue benefits and malicious use. Traditional methods of data auditing to
mitigate poisoning attacks find their limited applications in FL because the
edge devices never share their raw data directly due to privacy concerns, and
are globally distributed with no insight into their training data. Thereafter,
it is challenging to develop appropriate strategies to address such attacks and
minimize their impact on the global model in federated learning. In order to
address such challenges in FL, we proposed a novel framework to detect
poisoning attacks using deep neural networks and support vector machines, in
the form of anomaly without acquiring any direct access or information about
the underlying training data of local edge devices. We illustrate and evaluate
the proposed framework using different state of art poisoning attacks for two
different healthcare applications: Electrocardiograph classification and human
activity recognition. Our experimental analysis shows that the proposed method
can efficiently detect poisoning attacks and can remove the identified poisoned
updated from the global aggregation. Thereafter can increase the performance of
the federated global.

The automatic, sensor-based assessment of challenging behavior of persons
with dementia is an important task to support the selection of interventions.
However, predicting behaviors like apathy and agitation is challenging due to
the large inter- and intra-patient variability. Goal of this paper is to
improve the recognition performance by making use of the observation that
patients tend to show specific behaviors at certain times of the day or week.
We propose to identify such segments of similar behavior via clustering the
distributions of annotations of the time segments. All time segments within a
cluster then consist of similar behaviors and thus indicate a behavioral
predisposition (BPD). We utilize BPDs by training a classifier for each BPD.
Empirically, we demonstrate that when the BPD per time segment is known,
activity recognition performance can be substantially improved.

Physics informed neural networks (PINNs) require regularity of solutions of
the underlying PDE to guarantee accurate approximation. Consequently, they may
fail at approximating discontinuous solutions of PDEs such as nonlinear
hyperbolic equations. To ameliorate this, we propose a novel variant of PINNs,
termed as weak PINNs (wPINNs) for accurate approximation of entropy solutions
of scalar conservation laws. wPINNs are based on approximating the solution of
a min-max optimization problem for a residual, defined in terms of Kruzkhov
entropies, to determine parameters for the neural networks approximating the
entropy solution as well as test functions. We prove rigorous bounds on the
error incurred by wPINNs and illustrate their performance through numerical
experiments to demonstrate that wPINNs can approximate entropy solutions

The increasingly sophisticated and growing number of threat actors along with
the sheer speed at which cyber attacks unfold, make timely identification of
attacks imperative to an organisations' security. Consequently, persons
responsible for security employ a large variety of information sources
concerning emerging attacks, attackers' course of actions or indicators of
compromise. However, a vast amount of the needed security information is
available in unstructured textual form, which complicates the automated and
timely extraction of attackers' Tactics, Techniques and Procedures (TTPs). In
order to address this problem we systematically evaluate and compare different
Natural Language Processing (NLP) and machine learning techniques used for
security information extraction in research. Based on our investigations we
propose a data processing pipeline that automatically classifies unstructured
text according to attackers' tactics and techniques derived from a knowledge
base of adversary tactics, techniques and procedures.

This paper presents a novel feature selection method based on the conditional
mutual information (CMI). The proposed High Order Conditional Mutual
Information Maximization (HOCMIM) incorporates high order dependencies into the
feature selection procedure and has a straightforward interpretation due to its
bottom-up derivation. The HOCMIM is derived from the CMI's chain expansion and
expressed as a maximization optimization problem. The maximization problem is
solved using a greedy search procedure, which speeds up the entire feature
selection process. The experiments are run on a set of benchmark datasets (20
in total). The HOCMIM is compared with eighteen state-of-the-art feature
selection algorithms, from the results of two supervised learning classifiers
(Support Vector Machine and K-Nearest Neighbor). The HOCMIM achieves the best
results in terms of accuracy and shows to be faster than high order feature
selection counterparts.

Automated segmentation of white matter hyperintensities (WMHs) is an
essential step in neuroimaging analysis of Magnetic Resonance Imaging (MRI).
Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is
particularly useful to visualize and quantify WMHs, a hallmark of cerebral
small vessel disease and Alzheimer's disease (AD). Clinical MRI protocols
migrate to a three-dimensional (3D) FLAIR-weighted acquisition to enable high
spatial resolution in all three voxel dimensions. The current study details the
deployment of deep learning tools to enable automated WMH segmentation and
characterization from 3D FLAIR-weighted images acquired as part of a national
AD imaging initiative.
  Among 642 participants (283 male, mean age: (65.18 +/- 9.33) years) from the
DDI study, two in-house networks were trained and validated across five
national collection sites. Three models were tested on a held-out subset of the
internal data from the 642 participants and an external dataset with 29 cases
from an international collaborator. These test sets were evaluated
independently. Five established WMH performance metrics were used for
comparison against ground truth human-in-the-loop segmentation.
  Results of the three networks tested, the 3D nnU-Net had the best performance
with an average dice similarity coefficient score of 0.78 +/- 0.10, performing
better than both the in-house developed 2.5D model and the SOTA Deep Bayesian
network.
  With the increasing use of 3D FLAIR-weighted images in MRI protocols, our
results suggest that WMH segmentation models can be trained on 3D data and
yield WMH segmentation performance that is comparable to or better than
state-of-the-art without the need for including T1-weighted image series.

In the field of source code processing, the transformer-based representation
models have shown great powerfulness and have achieved state-of-the-art (SOTA)
performance in many tasks. Although the transformer models process the
sequential source code, pieces of evidence show that they may capture the
structural information (\eg, in the syntax tree, data flow, control flow, \etc)
as well. We propose the aggregated attention score, a method to investigate the
structural information learned by the transformer. We also put forward the
aggregated attention graph, a new way to extract program graphs from the
pre-trained models automatically. We measure our methods from multiple
perspectives. Furthermore, based on our empirical findings, we use the
automatically extracted graphs to replace those ingenious manual designed
graphs in the Variable Misuse task. Experimental results show that the semantic
graphs we extracted automatically are greatly meaningful and effective, which
provide a new perspective for us to understand and use the information
contained in the model.

Urban region function recognition plays a vital character in monitoring and
managing the limited urban areas. Since urban functions are complex and full of
social-economic properties, simply using remote sensing~(RS) images equipped
with physical and optical information cannot completely solve the
classification task. On the other hand, with the development of mobile
communication and the internet, the acquisition of geospatial big data~(GBD)
becomes possible. In this paper, we propose a Multi-dimension Feature Learning
Model~(MDFL) using high-dimensional GBD data in conjunction with RS images for
urban region function recognition. When extracting multi-dimension features,
our model considers the user-related information modeled by their activity, as
well as the region-based information abstracted from the region graph.
Furthermore, we propose a decision fusion network that integrates the decisions
from several neural networks and machine learning classifiers, and the final
decision is made considering both the visual cue from the RS images and the
social information from the GBD data. Through quantitative evaluation, we
demonstrate that our model achieves overall accuracy at 92.75, outperforming
the state-of-the-art by 10 percent.

Causal discovery is a major task with the utmost importance for machine
learning since causal structures can enable models to go beyond pure
correlation-based inference and significantly boost their performance. However,
finding causal structures from data poses a significant challenge both in
computational effort and accuracy, let alone its impossibility without
interventions in general. In this paper, we develop a meta-reinforcement
learning algorithm that performs causal discovery by learning to perform
interventions such that it can construct an explicit causal graph. Apart from
being useful for possible downstream applications, the estimated causal graph
also provides an explanation for the data-generating process. In this article,
we show that our algorithm estimates a good graph compared to the SOTA
approaches, even in environments whose underlying causal structure is
previously unseen. Further, we make an ablation study that shows how learning
interventions contribute to the overall performance of our approach. We
conclude that interventions indeed help boost the performance, efficiently
yielding an accurate estimate of the causal structure of a possibly unseen

Noninvasive brain stimulation and neuroimaging have revolutionized human
neuroscience, with a multitude of applications including diagnostic subtyping,
treatment optimization, and relapse prediction. It is therefore particularly
relevant to identify robust and clinically valuable brain biomarkers linking
symptoms to their underlying neural mechanisms. Brain biomarkers must be
reproducible (i.e., have internal reliability) across similar experiments
within a laboratory and be generalizable (i.e., have external reliability)
across experimental setups, laboratories, brain regions, and disease states.
However, reliability (internal and external) is not alone sufficient;
biomarkers also must have validity. Validity describes closeness to a true
measure of the underlying neural signal or disease state. We propose that these
two metrics, reliability and validity, should be evaluated and optimized before
any biomarker is used to inform treatment decisions. Here, we discuss these
metrics with respect to causal brain connectivity biomarkers from coupling
transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We
discuss controversies around TMS-EEG stemming from the multiple large
off-target components (noise) and relatively weak genuine brain responses
(signal), as is unfortunately often the case with human neuroscience. We review
the current state of TMS-EEG recordings, which consist of a mix of reliable
noise and unreliable signal. We describe methods for evaluating TMS-EEG
biomarkers, including how to assess internal and external reliability across
facilities, cognitive states, brain networks, and disorders, and how to
validate these biomarkers using invasive neural recordings or treatment
response. We provide recommendations to increase reliability and validity,
discuss lessons learned, and suggest future directions for the field.

To bridge the gap between supervised semantic segmentation and real-world
applications that acquires one model to recognize arbitrary new concepts,
recent zero-shot segmentation attracts a lot of attention by exploring the
relationships between unseen and seen object categories, yet requiring large
amounts of densely-annotated data with diverse base classes. In this paper, we
propose a new open-world semantic segmentation pipeline that makes the first
attempt to learn to segment semantic objects of various open-world categories
without any efforts on dense annotations, by purely exploiting the
image-caption data that naturally exist on the Internet. Our method,
Vision-language-driven Semantic Segmentation (ViL-Seg), employs an image and a
text encoder to generate visual and text embeddings for the image-caption data,
with two core components that endow its segmentation ability: First, the image
encoder is jointly trained with a vision-based contrasting and a cross-modal
contrasting, which encourage the visual embeddings to preserve both
fine-grained semantics and high-level category information that are crucial for
the segmentation task. Furthermore, an online clustering head is devised over
the image encoder, which allows to dynamically segment the visual embeddings
into distinct semantic groups such that they can be classified by comparing
with various text embeddings to complete our segmentation pipeline. Experiments
show that without using any data with dense annotations, our method can
directly segment objects of arbitrary categories, outperforming zero-shot
segmentation methods that require data labeling on three benchmark datasets.

Speech Emotion Recognition (SER) has become a growing focus of research in
human-computer interaction. An essential challenge in SER is to extract common
attributes from different speakers or languages, especially when a specific
source corpus has to be trained to recognize the unknown data coming from
another speech corpus. To address this challenge, a Capsule Network (CapsNet)
and Transfer Learning based Mixed Task Net (CTLMTNet) are proposed to deal with
both the singlecorpus and cross-corpus SER tasks simultaneously in this paper.
For the single-corpus task, the combination of Convolution-Pooling and
Attention CapsNet module CPAC) is designed by embedding the self-attention
mechanism to the CapsNet, guiding the module to focus on the important features
that can be fed into different capsules. The extracted high-level features by
CPAC provide sufficient discriminative ability. Furthermore, to handle the
cross-corpus task, CTL-MTNet employs a Corpus Adaptation Adversarial Module
(CAAM) by combining CPAC with Margin Disparity Discrepancy (MDD), which can
learn the domain-invariant emotion representations through extracting the
strong emotion commonness. Experiments including ablation studies and
visualizations on both singleand cross-corpus tasks using four well-known SER
datasets in different languages are conducted for performance evaluation and
comparison. The results indicate that in both tasks the CTL-MTNet showed better
performance in all cases compared to a number of state-of-the-art methods. The
source code and the supplementary materials are available at:
https://github.com/MLDMXM2017/CTLMTNet

Training semantic segmentation models on multiple datasets has sparked a lot
of recent interest in the computer vision community. This interest has been
motivated by expensive annotations and a desire to achieve proficiency across
multiple visual domains. However, established datasets have mutually
incompatible labels which disrupt principled inference in the wild. We address
this issue by automatic construction of universal taxonomies through iterative
dataset integration. Our method detects subset-superset relationships between
dataset-specific labels, and supports learning of sub-class logits by treating
super-classes as partial labels. We present experiments on collections of
standard datasets and demonstrate competitive generalization performance with
respect to previous work.

Simulation-based inference (SBI) is rapidly establishing itself as a standard
machine learning technique for analyzing data in cosmological surveys. Despite
continual improvements to the quality of density estimation by learned models,
applications of such techniques to real data are entirely reliant on the
generalization power of neural networks far outside the training distribution,
which is mostly unconstrained. Due to the imperfections in scientist-created
simulations, and the large computational expense of generating all possible
parameter combinations, SBI methods in cosmology are vulnerable to such
generalization issues. Here, we discuss the effects of both issues, and show
how using a Bayesian neural network framework for training SBI can mitigate
biases, and result in more reliable inference outside the training set. We
introduce cosmoSWAG, the first application of Stochastic Weight Averaging to
cosmology, and apply it to SBI trained for inference on the cosmic microwave

While deep learning has enabled tremendous progress on text and image
datasets, its superiority on tabular data is not clear. We contribute extensive
benchmarks of standard and novel deep learning methods as well as tree-based
models such as XGBoost and Random Forests, across a large number of datasets
and hyperparameter combinations. We define a standard set of 45 datasets from
varied domains with clear characteristics of tabular data and a benchmarking
methodology accounting for both fitting models and finding good
hyperparameters. Results show that tree-based models remain state-of-the-art on
medium-sized data ($\sim$10K samples) even without accounting for their
superior speed. To understand this gap, we conduct an empirical investigation
into the differing inductive biases of tree-based models and Neural Networks
(NNs). This leads to a series of challenges which should guide researchers
aiming to build tabular-specific NNs: 1. be robust to uninformative features,
2. preserve the orientation of the data, and 3. be able to easily learn
irregular functions. To stimulate research on tabular architectures, we
contribute a standard benchmark and raw data for baselines: every point of a 20
000 compute hours hyperparameter search for each learner.

The study of learning in games has thus far focused primarily on normal form
games. In contrast, our understanding of learning in extensive form games
(EFGs) and particularly in EFGs with many agents lags far behind, despite them
being closer in nature to many real world applications. We consider the natural
class of Network Zero-Sum Extensive Form Games, which combines the global
zero-sum property of agent payoffs, the efficient representation of graphical
games as well the expressive power of EFGs. We examine the convergence
properties of Optimistic Gradient Ascent (OGA) in these games. We prove that
the time-average behavior of such online learning dynamics exhibits $O(1/T)$
rate convergence to the set of Nash Equilibria. Moreover, we show that the
day-to-day behavior also converges to Nash with rate $O(c^{-t})$ for some
game-dependent constant $c>0$.

Video text spotting(VTS) is the task that requires simultaneously detecting,
tracking and recognizing text in the video. Existing video text spotting
methods typically develop sophisticated pipelines and multiple models, which is
not friend for real-time applications. Here we propose a real-time end-to-end
video text spotter with Contrastive Representation learning (CoText). Our
contributions are three-fold: 1) CoText simultaneously address the three tasks
(e.g., text detection, tracking, recognition) in a real-time end-to-end
trainable framework. 2) With contrastive learning, CoText models long-range
dependencies and learning temporal information across multiple frames. 3) A
simple, lightweight architecture is designed for effective and accurate
performance, including GPU-parallel detection post-processing, CTC-based
recognition head with Masked RoI. Extensive experiments show the superiority of
our method. Especially, CoText achieves an video text spotting IDF1 of 72.0% at
41.0 FPS on ICDAR2015video, with 10.5% and 32.0 FPS improvement the previous
best method. The code can be found at github.com/weijiawu/CoText.

Outlier explanation is the task of identifying a set of features that
distinguish a sample from normal data, which is important for downstream
(human) decision-making. Existing methods are based on beam search in the space
of feature subsets. They quickly becomes computationally expensive, as they
require to run an outlier detection algorithm from scratch for each feature
subset. To alleviate this problem, we propose a novel outlier explanation
algorithm based on Sum-Product Networks (SPNs), a class of probabilistic
circuits. Our approach leverages the tractability of marginal inference in SPNs
to compute outlier scores in feature subsets. By using SPNs, it becomes
feasible to perform backwards elimination instead of the usual forward beam
search, which is less susceptible to missing relevant features in an
explanation, especially when the number of features is large. We empirically
show that our approach achieves state-of-the-art results for outlier
explanation, outperforming recent search-based as well as deep learning-based
explanation methods

Global correlations are widely seen in human anatomical structures due to
similarity across tissues and bones. These correlations are reflected in
magnetic resonance imaging (MRI) scans as a result of close-range proton
density and T1/T2 parameter. Furthermore, to achieve accelerated MRI, k-space
data are undersampled which causes global aliasing artifacts. Convolutional
neural network (CNN) models are widely utilized for accelerated MRI
reconstruction, but those models are limited in capturing global correlations
due to the intrinsic locality of the convolution operation. The
self-attention-based transformer models are capable of capturing global
correlations among image features, however, the current contributions of
transformer models for MRI reconstruction are minute. The existing
contributions mostly provide CNN-transformer hybrid solutions and rarely
leverage the physics of MRI. In this paper, we propose a physics-based
stand-alone (convolution free) transformer model titled, the Multi-head
Cascaded Swin Transformers (McSTRA) for accelerated MRI reconstruction. McSTRA
combines several interconnected MRI physics-related concepts with the
transformer networks: it exploits global MR features via the shifted window
self-attention mechanism; it extracts MR features belonging to different
spectral components separately using a multi-head setup; it iterates between
intermediate de-aliasing and k-space correction via a cascaded network with
data consistency in k-space and intermediate loss computations; furthermore, we
propose a novel positional embedding generation mechanism to guide
self-attention utilizing the point spread function corresponding to the
undersampling mask. Our model significantly outperforms state-of-the-art MRI
reconstruction methods both visually and quantitatively while depicting
improved resolution and removal of aliasing artifacts.

We introduce FewGAN, a generative model for generating novel, high-quality
and diverse images whose patch distribution lies in the joint patch
distribution of a small number of N>1 training samples. The method is, in
essence, a hierarchical patch-GAN that applies quantization at the first coarse
scale, in a similar fashion to VQ-GAN, followed by a pyramid of residual fully
convolutional GANs at finer scales. Our key idea is to first use quantization
to learn a fixed set of patch embeddings for training images. We then use a
separate set of side images to model the structure of generated images using an
autoregressive model trained on the learned patch embeddings of training
images. Using quantization at the coarsest scale allows the model to generate
both conditional and unconditional novel images. Subsequently, a patch-GAN
renders the fine details, resulting in high-quality images. In an extensive set
of experiments, it is shown that FewGAN outperforms baselines both

CutMix is a popular augmentation technique commonly used for training modern
convolutional and transformer vision networks. It was originally designed to
encourage Convolution Neural Networks (CNNs) to focus more on an image's global
context instead of local information, which greatly improves the performance of
CNNs. However, we found it to have limited benefits for transformer-based
architectures that naturally have a global receptive field. In this paper, we
propose a novel data augmentation technique TokenMix to improve the performance
of vision transformers. TokenMix mixes two images at token level via
partitioning the mixing region into multiple separated parts. Besides, we show
that the mixed learning target in CutMix, a linear combination of a pair of the
ground truth labels, might be inaccurate and sometimes counter-intuitive. To
obtain a more suitable target, we propose to assign the target score according
to the content-based neural activation maps of the two images from a
pre-trained teacher model, which does not need to have high performance. With
plenty of experiments on various vision transformer architectures, we show that
our proposed TokenMix helps vision transformers focus on the foreground area to
infer the classes and enhances their robustness to occlusion, with consistent
performance gains. Notably, we improve DeiT-T/S/B with +1% ImageNet top-1
accuracy. Besides, TokenMix enjoys longer training, which achieves 81.2% top-1
accuracy on ImageNet with DeiT-S trained for 400 epochs. Code is available at
https://github.com/Sense-X/TokenMix.

Prompt tuning has been an extremely effective tool to adapt a pre-trained
model to downstream tasks. However, standard prompt-based methods mainly
consider the case of sufficient data of downstream tasks. It is still unclear
whether the advantage can be transferred to the few-shot regime, where only
limited data are available for each downstream task. Although some works have
demonstrated the potential of prompt-tuning under the few-shot setting, the
main stream methods via searching discrete prompts or tuning soft prompts with
limited data are still very challenging. Through extensive empirical studies,
we find that there is still a gap between prompt tuning and fully fine-tuning
for few-shot learning. To bridge the gap, we propose a new prompt-tuning
framework, called Soft Template Tuning (STT). STT combines manual and auto
prompts, and treats downstream classification tasks as a masked language
modeling task. Comprehensive evaluation on different settings suggests STT can
close the gap between fine-tuning and prompt-based methods without introducing
additional parameters. Significantly, it can even outperform the time- and
resource-consuming fine-tuning method on sentiment classification tasks.

In this work, we present formulations for regularized Kullback-Leibler and
R\'enyi divergences via the Alpha Log-Determinant (Log-Det) divergences between
positive Hilbert-Schmidt operators on Hilbert spaces in two different settings,
namely (i) covariance operators and Gaussian measures defined on reproducing
kernel Hilbert spaces (RKHS); and (ii) Gaussian processes with squared
integrable sample paths. For characteristic kernels, the first setting leads to
divergences between arbitrary Borel probability measures on a complete,
separable metric space. We show that the Alpha Log-Det divergences are
continuous in the Hilbert-Schmidt norm, which enables us to apply laws of large
numbers for Hilbert space-valued random variables. As a consequence of this, we
show that, in both settings, the infinite-dimensional divergences can be
consistently and efficiently estimated from their finite-dimensional versions,
using finite-dimensional Gram matrices/Gaussian measures and finite sample
data, with {\it dimension-independent} sample complexities in all cases. RKHS
methodology plays a central role in the theoretical analysis in both settings.
The mathematical formulation is illustrated by numerical experiments.

Macro placement is the problem of placing memory blocks on a chip canvas. It
can be formulated as a combinatorial optimization problem over sequence pairs,
a representation which describes the relative positions of macros. Solving this
problem is particularly challenging since the objective function is expensive
to evaluate. In this paper, we develop a novel approach to macro placement
using Bayesian optimization (BO) over sequence pairs. BO is a machine learning
technique that uses a probabilistic surrogate model and an acquisition function
that balances exploration and exploitation to efficiently optimize a black-box
objective function. BO is more sample-efficient than reinforcement learning and
therefore can be used with more realistic objectives. Additionally, the ability
to learn from data and adapt the algorithm to the objective function makes BO
an appealing alternative to other black-box optimization methods such as
simulated annealing, which relies on problem-dependent heuristics and
parameter-tuning. We benchmark our algorithm on the fixed-outline macro
placement problem with the half-perimeter wire length objective and demonstrate

Unrolled neural networks have recently achieved state-of-the-art accelerated
MRI reconstruction. These networks unroll iterative optimization algorithms by
alternating between physics-based consistency and neural-network based
regularization. However, they require several iterations of a large neural
network to handle high-dimensional imaging tasks such as 3D MRI. This limits
traditional training algorithms based on backpropagation due to prohibitively
large memory and compute requirements for calculating gradients and storing
intermediate activations. To address this challenge, we propose Greedy LEarning
for Accelerated MRI (GLEAM) reconstruction, an efficient training strategy for
high-dimensional imaging settings. GLEAM splits the end-to-end network into
decoupled network modules. Each module is optimized in a greedy manner with
decoupled gradient updates, reducing the memory footprint during training. We
show that the decoupled gradient updates can be performed in parallel on
multiple graphical processing units (GPUs) to further reduce training time. We
present experiments with 2D and 3D datasets including multi-coil knee, brain,
and dynamic cardiac cine MRI. We observe that: i) GLEAM generalizes as well as
state-of-the-art memory-efficient baselines such as gradient checkpointing and
invertible networks with the same memory footprint, but with 1.3x faster
training; ii) for the same memory footprint, GLEAM yields 1.1dB PSNR gain in 2D
and 1.8 dB in 3D over end-to-end baselines.

Federated learning (FL) is an emerging technique used to collaboratively
train a global machine learning model while keeping the data localized on the
user devices. The main obstacle to FL's practical implementation is the
Non-Independent and Identical (Non-IID) data distribution across users, which
slows convergence and degrades performance. To tackle this fundamental issue,
we propose a method (ComFed) that enhances the whole training process on both
the client and server sides. The key idea of ComFed is to simultaneously
utilize client-variance reduction techniques to facilitate server aggregation
and global adaptive update techniques to accelerate learning. Our experiments
on the Cifar-10 classification task show that ComFed can improve
state-of-the-art algorithms dedicated to Non-IID data.

In this paper, two methodologies are used to speed up the maximization of the
breakdown volt-age (BV) of a vertical GaN diode that has a theoretical maximum
BV of ~2100V. Firstly, we demonstrated a 5X faster accurate simulation method
in Technology Computer-Aided-Design (TCAD). This allows us to find 50% more
numbers of high BV (>1400V) designs at a given simulation time. Secondly, a
machine learning (ML) model is developed using TCAD-generated data and used as
a surrogate model for differential evolution optimization. It can inversely
design an out-of-the-training-range structure with BV as high as 1887V (89% of
the ideal case) compared to ~1100V designed with human domain expertise.

For the past 25 years, we have witnessed an extensive application of Machine
Learning to the Compiler space; the selection and the phase-ordering problem.
However, limited works have been upstreamed into the state-of-the-art
compilers, i.e., LLVM, to seamlessly integrate the former into the optimization
pipeline of a compiler to be readily deployed by the user. MLGO was among the
first of such projects and it only strives to reduce the code size of a binary
with an ML-based Inliner using Reinforcement Learning.
  This paper presents MLGOPerf; the first end-to-end framework capable of
optimizing performance using LLVM's ML-Inliner. It employs a secondary ML model
to generate rewards used for training a retargeted Reinforcement learning
agent, previously used as the primary model by MLGO. It does so by predicting
the post-inlining speedup of a function under analysis and it enables a fast
training framework for the primary model which otherwise wouldn't be practical.
The experimental results show MLGOPerf is able to gain up to 1.8% and 2.2% with
respect to LLVM's optimization at O3 when trained for performance on SPEC
CPU2006 and Cbench benchmarks, respectively. Furthermore, the proposed approach
provides up to 26% increased opportunities to autotune code regions for our
benchmarks which can be translated into an additional 3.7% speedup value.

High-end vehicles have been furnished with a number of electronic control
units (ECUs), which provide upgrading functions to enhance the driving
experience. The controller area network (CAN) is a well-known protocol that
connects these ECUs because of its modesty and efficiency. However, the CAN bus
is vulnerable to various types of attacks. Although the intrusion detection
system (IDS) is proposed to address the security problem of the CAN bus, most
previous studies only provide alerts when attacks occur without knowing the
specific type of attack. Moreover, an IDS is designed for a specific car model
due to diverse car manufacturers. In this study, we proposed a novel deep
learning model called supervised contrastive (SupCon) ResNet, which can handle
multiple attack identification on the CAN bus. Furthermore, the model can be
used to improve the performance of a limited-size dataset using a transfer
learning technique. The capability of the proposed model is evaluated on two
real car datasets. When tested with the car hacking dataset, the experiment
results show that the SupCon ResNet model improves the overall false-negative
rates of four types of attack by four times on average, compared to other
models. In addition, the model achieves the highest F1 score at 0.9994 on the
survival dataset by utilizing transfer learning. Finally, the model can adapt
to hardware constraints in terms of memory size and running time.

Designing smart home services is a complex task when multiple services with a
large number of sensors and actuators are deployed simultaneously. It may rely
on knowledge-based or data-driven approaches. The former can use rule-based
methods to design services statically, and the latter can use learning methods
to discover inhabitants' preferences dynamically. However, neither of these
approaches is entirely satisfactory because rules cannot cover all possible
situations that may change, and learning methods may make decisions that are
sometimes incomprehensible to the inhabitant. In this paper, PBRE (Pedagogic
Based Rule Extractor) is proposed to extract rules from learning methods to
realize dynamic rule generation for smart home systems. The expected advantage
is that both the explainability of rule-based methods and the dynamicity of
learning methods are adopted. We compare PBRE with an existing rule extraction
method, and the results show better performance of PBRE. We also apply PBRE to
extract rules from a smart home service represented by an NRL (Neural
Network-based Reinforcement Learning). The results show that PBRE can help the
NRL-simulated service to make understandable suggestions to the inhabitant.

Realistic fake videos are a potential tool for spreading harmful
misinformation given our increasing online presence and information intake.
This paper presents a multimodal learning-based method for detection of real
and fake videos. The method combines information from three modalities - audio,
video, and physiology. We investigate two strategies for combining the video
and physiology modalities, either by augmenting the video with information from
the physiology or by novelly learning the fusion of those two modalities with a
proposed Graph Convolutional Network architecture. Both strategies for
combining the two modalities rely on a novel method for generation of visual
representations of physiological signals. The detection of real and fake videos
is then based on the dissimilarity between the audio and modified video
modalities. The proposed method is evaluated on two benchmark datasets and the
results show significant increase in detection performance compared to previous
methods.

Deep reinforcement learning (DRL) has attracted much attention in automated
game testing. Early attempts rely on game internal information for game space
exploration, thus requiring deep integration with games, which is inconvenient
for practical applications. In this work, we propose using only
screenshots/pixels as input for automated game testing and build a general game
testing agent, Inspector, that can be easily applied to different games without
deep integration with games. In addition to covering all game space for
testing, our agent tries to take human-like behaviors to interact with key
objects in a game, since some bugs usually happen in player-object
interactions. Inspector is based on purely pixel inputs and comprises three key
modules: game space explorer, key object detector, and human-like object
investigator. Game space explorer aims to explore the whole game space by using
a curiosity-based reward function with pixel inputs. Key object detector aims
to detect key objects in a game, based on a small number of labeled
screenshots. Human-like object investigator aims to mimic human behaviors for
investigating key objects via imitation learning. We conduct experiments on two
popular video games: Shooter Game and Action RPG Game. Experiment results
demonstrate the effectiveness of Inspector in exploring game space, detecting
key objects, and investigating objects. Moreover, Inspector successfully
discovers two potential bugs in those two games. The demo video of Inspector is
available at https://github.com/Inspector-GameTesting/Inspector-GameTesting.

Admittedly, Graph Convolution Network (GCN) has achieved excellent results on
graph datasets such as social networks, citation networks, etc. However,
softmax used as the decision layer in these frameworks is generally optimized
with thousands of iterations via gradient descent. Furthermore, due to ignoring
the inner distribution of the graph nodes, the decision layer might lead to an
unsatisfactory performance in semi-supervised learning with less label support.
To address the referred issues, we propose a novel graph deep model with a
non-gradient decision layer for graph mining. Firstly, manifold learning is
unified with label local-structure preservation to capture the topological
information of the nodes. Moreover, owing to the non-gradient property,
closed-form solutions is achieved to be employed as the decision layer for GCN.
Particularly, a joint optimization method is designed for this graph model,
which extremely accelerates the convergence of the model. Finally, extensive
experiments show that the proposed model has achieved state-of-the-art
performance compared to the current models.

Contrastive learning (CL) has recently been applied to adversarial learning
tasks. Such practice considers adversarial samples as additional positive views
of an instance, and by maximizing their agreements with each other, yields
better adversarial robustness. However, this mechanism can be potentially
flawed, since adversarial perturbations may cause instance-level identity
confusion, which can impede CL performance by pulling together different
instances with separate identities. To address this issue, we propose to treat
adversarial samples unequally when contrasted, with an asymmetric InfoNCE
objective ($A-InfoNCE$) that allows discriminating considerations of
adversarial samples. Specifically, adversaries are viewed as inferior positives
that induce weaker learning signals, or as hard negatives exhibiting higher
contrast to other negative samples. In the asymmetric fashion, the adverse
impacts of conflicting objectives between CL and adversarial learning can be
effectively mitigated. Experiments show that our approach consistently
outperforms existing Adversarial CL methods across different finetuning schemes
without additional computational cost. The proposed A-InfoNCE is also a generic
form that can be readily extended to other CL methods. Code is available at
https://github.com/yqy2001/A-InfoNCE.

Debugging performance anomalies in real-world databases is challenging.
Causal inference techniques enable qualitative and quantitative root cause
analysis of performance downgrade. Nevertheless, causality analysis is
practically challenging, particularly due to limited observability. Recently,
chaos engineering has been applied to test complex real-world software systems.
Chaos frameworks like Chaos Mesh mutate a set of chaos variables to inject
catastrophic events (e.g., network slowdowns) to "stress" software systems. The
systems under chaos stress are then tested using methods like differential
testing to check if they retain their normal functionality (e.g., SQL query
output is always correct under stress). Despite its ubiquity in the industry,
chaos engineering is now employed mostly to aid software testing rather for
performance debugging.
  This paper identifies novel usage of chaos engineering on helping developers
diagnose performance anomalies in databases. Our presented framework, PERFCE,
comprises an offline phase and an online phase. The offline phase learns the
statistical models of the target database system, whilst the online phase
diagnoses the root cause of monitored performance anomalies on the fly. During
the offline phase, PERFCE leverages both passive observations and proactive
chaos experiments to constitute accurate causal graphs and structural equation
models (SEMs). When observing performance anomalies during the online phase,
causal graphs enable qualitative root cause identification (e.g., high CPU
usage) and SEMs enable quantitative counterfactual analysis (e.g., determining
"when CPU usage is reduced to 45\%, performance returns to normal"). PERFCE
notably outperforms prior works on common synthetic datasets, and our
evaluation on real-world databases, MySQL and TiDB, shows that PERFCE is highly
accurate and moderately expensive.

Alongside the rapid development of data collection and analysis techniques in
recent years, there is increasingly an emphasis on the need to address
information leakage associated with such usage of data. To this end, much work
in the privacy literature is devoted to the protection of individual users and
contributors of data. However, some situations instead require a different
notion of data confidentiality involving global properties aggregated over the
records of a dataset. Such notions of information protection are particularly
applicable for business and organization data, where global properties may
reflect trade secrets, or demographic data, which can be harmful if mishandled.
Recent work on property inference attacks furthermore shows how data analysis
algorithms can be susceptible to leaking these global properties of data,
highlighting the importance of developing mechanisms that can protect such
  In this work, we demonstrate how a distribution privacy framework can be
applied to formalize the problem of protecting global properties of datasets.
Given this framework, we investigate several mechanisms and their tradeoffs for
providing this notion of data confidentiality. We analyze the theoretical
protection guarantees offered by these mechanisms under various data
assumptions, then implement and empirically evaluate these mechanisms for
several data analysis tasks. The results of our experiments show that our
mechanisms can indeed reduce the effectiveness of practical property inference
attacks while providing utility substantially greater than a crude group
differential privacy baseline. Our work thus provides groundwork for
theoretically supported mechanisms for protecting global properties of
datasets.

This paper presents a simple method for speech videos generation based on
audio: given a piece of audio, we can generate a video of the target face
speaking this audio. We propose Generative Adversarial Networks (GAN) with cut
speech audio input as condition and use Convolutional Gate Recurrent Unit (GRU)
in generator and discriminator. Our model is trained by exploiting the short
audio and the frames in this duration. For training, we cut the audio and
extract the face in the corresponding frames. We designed a simple encoder and
compare the generated frames using GAN with and without GRU. We use GRU for
temporally coherent frames and the results show that short audio can produce
relatively realistic output results.

Neural audio/speech coding has shown its capability to deliver a high quality
at much lower bitrates than traditional methods recently. However, existing
neural audio/speech codecs employ either acoustic features or learned blind
features with a convolutional neural network for encoding, by which there are
still temporal redundancies inside encoded features. This paper introduces
latent-domain predictive coding into the VQ-VAE framework to fully remove such
redundancies and proposes the TF-Codec for low-latency neural speech coding in
an end-to-end way. Specifically, the extracted features are encoded conditioned
on a prediction from past quantized latent frames so that temporal correlations
are further removed. What's more, we introduce a learnable compression on the
time-frequency input to adaptively adjust the attention paid on main
frequencies and details at different bitrates. A differentiable vector
quantization scheme based on distance-to-soft mapping and Gumbel-Softmax is
proposed to better model the latent distributions with rate constraint.
Subjective results on multilingual speech datasets show that with a latency of
40ms, the proposed TF-Codec at 1kbps can achieve a much better quality than
Opus 9kbps and TF-Codec at 3kbps outperforms both EVS 9.6kbps and Opus 12kbps.
Numerous studies are conducted to show the effectiveness of these techniques.

Knowledge distillation (KD), which can efficiently transfer knowledge from a
cumbersome network (teacher) to a compact network (student), has demonstrated
its advantages in some computer vision applications. The representation of
knowledge is vital for knowledge transferring and student learning, which is
generally defined in hand-crafted manners or uses the intermediate features
directly. In this paper, we propose a model-agnostic meta knowledge
distillation method under the teacher-student architecture for the single image
super-resolution task. It provides a more flexible and accurate way to help the
teachers transmit knowledge in accordance with the abilities of students via
knowledge representation networks (KRNets) with learnable parameters. In order
to improve the perception ability of knowledge representation to students'
requirements, we propose to solve the transformation process from intermediate
outputs to transferred knowledge by employing the student features and the
correlation between teacher and student in the KRNets. Specifically, the
texture-aware dynamic kernels are generated and then extract texture features
to be improved and the corresponding teacher guidance so as to decompose the
distillation problem into texture-wise supervision for further promoting the
recovery quality of high-frequency details. In addition, the KRNets are
optimized in a meta-learning manner to ensure the knowledge transferring and
the student learning are beneficial to improving the reconstructed quality of
the student. Experiments conducted on various single image super-resolution
datasets demonstrate that our proposed method outperforms existing defined
knowledge representation related distillation methods, and can help
super-resolution algorithms achieve better reconstruction quality without
introducing any inference complexity.

Estimating the political leanings of social media users is a challenging and
ever more pressing problem given the increase in social media consumption. We
introduce Retweet-BERT, a simple and scalable model to estimate the political
leanings of Twitter users. Retweet-BERT leverages the retweet network structure
and the language used in users' profile descriptions. Our assumptions stem from
patterns of networks and linguistics homophily among people who share similar
ideologies. Retweet-BERT demonstrates competitive performance against other
state-of-the-art baselines, achieving 96%-97% macro-F1 on two recent Twitter
datasets (a COVID-19 dataset and a 2020 United States presidential elections
dataset). We also perform manual validation to validate the performance of
Retweet-BERT on users not in the training data. Finally, in a case study of
COVID-19, we illustrate the presence of political echo chambers on Twitter and
show that it exists primarily among right-leaning users. Our code is
open-sourced and our data is publicly available.

We propose a new framework for differentially private optimization of convex
functions which are Lipschitz in an arbitrary norm $\|\cdot\|$. Our algorithms
are based on a regularized exponential mechanism which samples from the density
$\propto \exp(-k(F+\mu r))$ where $F$ is the empirical loss and $r$ is a
regularizer which is strongly convex with respect to $\|\cdot\|$, generalizing
a recent work of [Gopi, Lee, Liu '22] to non-Euclidean settings. We show that
this mechanism satisfies Gaussian differential privacy and solves both DP-ERM
(empirical risk minimization) and DP-SCO (stochastic convex optimization) by
using localization tools from convex geometry. Our framework is the first to
apply to private convex optimization in general normed spaces and directly
recovers non-private SCO rates achieved by mirror descent as the privacy
parameter $\epsilon \to \infty$. As applications, for Lipschitz optimization in
$\ell_p$ norms for all $p \in (1, 2)$, we obtain the first optimal
privacy-utility tradeoffs; for $p = 1$, we improve tradeoffs obtained by the
recent works [Asi, Feldman, Koren, Talwar '21, Bassily, Guzman, Nandi '21] by
at least a logarithmic factor. Our $\ell_p$ norm and Schatten-$p$ norm
optimization frameworks are complemented with polynomial-time samplers whose
query complexity we explicitly bound.

The current paper studies sample-efficient Reinforcement Learning (RL) in
settings where only the optimal value function is assumed to be
linearly-realizable. It has recently been understood that, even under this
seemingly strong assumption and access to a generative model, worst-case sample
complexities can be prohibitively (i.e., exponentially) large. We investigate
the setting where the learner additionally has access to interactive
demonstrations from an expert policy, and we present a statistically and
computationally efficient algorithm (Delphi) for blending exploration with
expert queries. In particular, Delphi requires $\tilde{\mathcal{O}}(d)$ expert
queries and a $\texttt{poly}(d,H,|\mathcal{A}|,1/\varepsilon)$ amount of
exploratory samples to provably recover an $\varepsilon$-suboptimal policy.
Compared to pure RL approaches, this corresponds to an exponential improvement
in sample complexity with surprisingly-little expert input. Compared to prior
imitation learning (IL) approaches, our required number of expert
demonstrations is independent of $H$ and logarithmic in $1/\varepsilon$,
whereas all prior work required at least linear factors of both in addition to
the same dependence on $d$. Towards establishing the minimal amount of expert
queries needed, we show that, in the same setting, any learner whose
exploration budget is polynomially-bounded (in terms of $d,H,$ and
$|\mathcal{A}|$) will require at least $\tilde\Omega(\sqrt{d})$ oracle calls to
recover a policy competing with the expert's value function. Under the weaker
assumption that the expert's policy is linear, we show that the lower bound
increases to $\tilde\Omega(d)$.

Machine learning models have demonstrated promising performance in many
areas. However, the concerns that they can be biased against specific groups
hinder their adoption in high-stake applications. Thus it is essential to
ensure fairness in machine learning models. Most of the previous efforts
require access to sensitive attributes for mitigating bias. Nonetheless, it is
often infeasible to obtain large scale of data with sensitive attributes due to
people's increasing awareness of privacy and the legal compliance. Therefore,
an important research question is how to make fair predictions under privacy?
In this paper, we study a novel problem on fair classification in a
semi-private setting, where most of the sensitive attributes are private and
only a small amount of clean sensitive attributes are available. To this end,
we propose a novel framework FairSP that can first learn to correct the noisy
sensitive attributes under privacy guarantee via exploiting the limited clean
sensitive attributes. Then, it jointly models the corrected and clean data in
an adversarial way for debiasing and prediction. Theoretical analysis shows
that the proposed model can ensure fairness when most of the sensitive
attributes are private. Experimental results on real-world datasets demonstrate
the effectiveness of the proposed model for making fair predictions under
privacy and maintaining high accuracy.

Generative Adversarial Network (GAN) is widely adopted in numerous
application areas, such as data preprocessing, image editing, and creativity
support. However, GAN's 'black box' nature prevents non-expert users from
controlling what data a model generates, spawning a plethora of prior work that
focused on algorithm-driven approaches to extract editing directions to control
GAN. Complementarily, we propose a GANzilla: a user-driven tool that empowers a
user with the classic scatter/gather technique to iteratively discover
directions to meet their editing goals. In a study with 12 participants,
GANzilla users were able to discover directions that (i) edited images to match
provided examples (closed-ended tasks) and that (ii) met a high-level goal,
e.g., making the face happier, while showing diversity across individuals
(open-ended tasks).

Surface defect detection is an extremely crucial step to ensure the quality
of industrial products. Nowadays, convolutional neural networks (CNNs) based on
encoder-decoder architecture have achieved tremendous success in various defect
detection tasks. However, due to the intrinsic locality of convolution, they
commonly exhibit a limitation in explicitly modeling long-range interactions,
critical for pixel-wise defect detection in complex cases, e.g., cluttered
background and illegible pseudo-defects. Recent transformers are especially
skilled at learning global image dependencies but with limited local structural
information necessary for detailed defect location. To overcome the above
limitations, we propose an efficient hybrid transformer architecture, termed
Defect Transformer (DefT), for surface defect detection, which incorporates CNN
and transformer into a unified model to capture local and non-local
relationships collaboratively. Specifically, in the encoder module, a
convolutional stem block is firstly adopted to retain more detailed spatial
information. Then, the patch aggregation blocks are used to generate
multi-scale representation with four hierarchies, each of them is followed by a
series of DefT blocks, which respectively include a locally position-aware
block for local position encoding, a lightweight multi-pooling self-attention
to model multi-scale global contextual relationships with good computational
efficiency, and a convolutional feed-forward network for feature transformation
and further location information learning. Finally, a simple but effective
decoder module is proposed to gradually recover spatial details from the skip
connections in the encoder. Extensive experiments on three datasets demonstrate
the superiority and efficiency of our method compared with other CNN- and
transformer-based networks.

We introduce a novel machine learning strategy, kernel addition Gaussian
process regression (KA-GPR), in molecular-orbital-based machine learning
(MOB-ML) to learn the total correlation energies of general electronic
structure theories for closed- and open-shell systems by introducing a machine
learning strategy. The learning efficiency of MOB-ML (KA-GPR) is the same as
the original MOB-ML method for the smallest criegee molecule, which is a
closed-shell molecule with multi-reference characters. In addition, the
prediction accuracies of different small free radicals could reach the chemical
accuracy of 1 kcal/mol by training on one example structure. Accurate potential
energy surfaces for the H10 chain (closed-shell) and water OH bond dissociation
(open-shell) could also be generated by MOB-ML (KA-GPR). To explore the breadth
of chemical systems that KA-GPR can describe, we further apply MOB-ML to
accurately predict the large benchmark datasets for closed- (QM9, QM7b-T,
GDB-13-T) and open-shell (QMSpin) molecules.

We introduce CULT (Continual Unsupervised Representation Learning with
Typicality-Based Environment Detection), a new algorithm for continual
unsupervised learning with variational auto-encoders. CULT uses a simple
typicality metric in the latent space of a VAE to detect distributional shifts
in the environment, which is used in conjunction with generative replay and an
auxiliary environmental classifier to limit catastrophic forgetting in
unsupervised representation learning. In our experiments, CULT significantly
outperforms baseline continual unsupervised learning approaches. Code for this
paper can be found here: https://github.com/oliveradk/cult

We consider regression estimation with modified ReLU neural networks in which
network weight matrices are first modified by a function $\alpha$ before being
multiplied by input vectors. We give an example of continuous, piecewise linear
function $\alpha$ for which the empirical risk minimizers over the classes of
modified ReLU networks with $l_1$ and squared $l_2$ penalties attain, up to a
logarithmic factor, the minimax rate of prediction of unknown $\beta$-smooth
function.

Providing invariances in a given learning task conveys a key inductive bias
that can lead to sample-efficient learning and good generalisation, if
correctly specified. However, the ideal invariances for many problems of
interest are often not known, which has led both to a body of engineering lore
as well as attempts to provide frameworks for invariance learning. However,
invariance learning is expensive and data intensive for popular neural
architectures. We introduce the notion of amortizing invariance learning. In an
up-front learning phase, we learn a low-dimensional manifold of feature
extractors spanning invariance to different transformations using a
hyper-network. Then, for any problem of interest, both model and invariance
learning are rapid and efficient by fitting a low-dimensional invariance
descriptor an output head. Empirically, this framework can identify appropriate
invariances in different downstream tasks and lead to comparable or better test
performance than conventional approaches. Our HyperInvariance framework is also
theoretically appealing as it enables generalisation-bounds that provide an
interesting new operating point in the trade-off between model fit and

Tensor decomposition has become an essential tool in many data science
applications. Sparse Matricized Tensor Times Khatri-Rao Product (MTTKRP) is the
pivotal kernel in tensor decomposition algorithms that decompose higher-order
real-world large tensors into multiple matrices. Accelerating MTTKRP can speed
up the tensor decomposition process immensely. Sparse MTTKRP is a challenging
kernel to accelerate due to its irregular memory access characteristics.
Implementing accelerators on Field Programmable Gate Array (FPGA) for kernels
such as MTTKRP is attractive due to the energy efficiency and the inherent
parallelism of FPGA. This paper explores the opportunities, key challenges, and
an approach for designing a custom memory controller on FPGA for MTTKRP while
exploring the parameter space of such a custom memory controller.

We develop a learning-based algorithm for the distributed formation control
of networked multi-agent systems governed by unknown, nonlinear dynamics. Most
existing algorithms either assume certain parametric forms for the unknown
dynamic terms or resort to unnecessarily large control inputs in order to
provide theoretical guarantees. The proposed algorithm avoids these drawbacks
by integrating neural network-based learning with adaptive control in a
two-step procedure. In the first step of the algorithm, each agent learns a
controller, represented as a neural network, using training data that
correspond to a collection of formation tasks and agent parameters. These
parameters and tasks are derived by varying the nominal agent parameters and a
user-defined formation task to be achieved, respectively. In the second step of
the algorithm, each agent incorporates the trained neural network into an
online and adaptive control policy in such a way that the behavior of the
multi-agent closed-loop system satisfies the user-defined formation task. Both
the learning phase and the adaptive control policy are distributed, in the
sense that each agent computes its own actions using only local information
from its neighboring agents. The proposed algorithm does not use any a priori
information on the agents' unknown dynamic terms or any approximation schemes.
We provide formal theoretical guarantees on the achievement of the formation
task.

This paper presents a comprehensive analysis of the social and environmental
determinants of solar photovoltaic (PV) deployment rates in Colorado, USA.
Using 652,795 satellite imagery and computer vision frameworks based on a
convolutional neural network, we estimated the proportion of households with
solar PV systems and the roof areas covered by solar panels. At the census
block group level, 7% of Coloradan households have a rooftop PV system, and
2.5% of roof areas in Colorado are covered by solar panels as of 2021. Our
machine learning models predict solar PV deployment based on 43 natural and
social characteristics of neighborhoods. Using four algorithms (Random Forest,
CATBoost, LightGBM, XGBoost), we find that the share of Democratic party votes,
hail risks, strong wind risks, median home value, and solar PV permitting
timelines are the most important predictors of solar PV count per household. In
addition to the size of the houses, PV-to-roof area ratio is highly dependent
on solar PV permitting timelines, proportion of renters and multifamily
housing, and winter weather risks. We also find racial and ethnic disparities
in rooftop solar deployment. The average marginal effects of median household
income on solar deployment are lower in communities with a greater proportion
of African American and Hispanic residents and are higher in communities with a
greater proportion of White and Asian residents. In the ongoing energy
transition, knowing the key predictors of solar deployment can better inform
business and policy decision making for more efficient and equitable grid
infrastructure investment and distributed energy resource management.

Due to the promising future of Automated Program Repair (APR), researchers
have proposed various APR techniques, including heuristic-based,
template-based, and constraint-based techniques. Among such classic APR
techniques, template-based techniques have been widely recognized as state of
the art. However, such template-based techniques require predefined templates
to perform repair, and their effectiveness is thus limited. To this end,
researchers leveraged the recent advances in Deep Learning to further improve
APR. Such learning-based techniques view APR as a Neural Machine Translation
problem, using the buggy/fixed code snippets as the source/target languages for
translation. In this way, such techniques heavily rely on large numbers of
high-quality bug-fixing commits, which can be extremely costly and challenging
to construct. Furthermore, the edit variety of these learning-based techniques
are limited to the available bug-fixes within their training datasets.
Therefore, in this paper, we aim to revisit the learning-based APR problem, and
propose AlphaRepair, to leverage zero-shot learning directly using large
pre-trained code models for APR. Our main insight is instead of modeling what a
repair edit should look like, we can directly predict what the correct code is
based on the context information. We have implemented AlphaRepair as a
practical multilingual APR tool based on the recent CodeBERT model. Our results
on the widely used Defects4J benchmark show that AlphaRepair can substantially
outperform state-of-the-art APR tools. We also studied the impact of different
design choices and show that AlphaRepair performs even better on a newer
version of Defects4J (2.0) with 3.3X more fixes than best performing baseline,
indicating that AlphaRepair can potentially avoid the dataset-overfitting issue
of existing learning-based techniques.

In operations of multi-agent teams ranging from homogeneous robot swarms to
heterogeneous human-autonomy teams, unexpected events might occur. While
efficiency of operation for multi-agent task allocation problems is the primary
objective, it is essential that the decision-making framework is intelligent
enough to manage unexpected task load with limited resources. Otherwise,
operation effectiveness would drastically plummet with overloaded agents facing
unforeseen risks. In this work, we present a decision-making framework for
multi-agent teams to learn task allocation with the consideration of load
management through decentralized reinforcement learning, where idling is
encouraged and unnecessary resource usage is avoided. We illustrate the effect
of load management on team performance and explore agent behaviors in example
scenarios. Furthermore, a measure of agent importance in collaboration is
developed to infer team resilience when facing handling potential overload

Deep Learning approaches are becoming the go-to methods for data analysis in
High Energy Physics (HEP). Nonetheless, most physics-inspired modern
architectures are computationally inefficient and lack interpretability. This
is especially the case with jet tagging algorithms, where computational
efficiency is crucial considering the large amounts of data produced by modern
particle detectors. In this work, we present a novel, versatile and transparent
framework for jet representation; invariant to Lorentz group boosts, which
achieves high accuracy on jet tagging benchmarks while being orders of
magnitudes faster to train and evaluate than other modern approaches for both
supervised and unsupervised schemes.

The seminal work of Cohen and Peng introduced Lewis weight sampling to the
theoretical computer science community, yielding fast row sampling algorithms
for approximating $d$-dimensional subspaces of $\ell_p$ up to $(1+\epsilon)$
error. Several works have extended this important primitive to other settings,
including the online coreset, sliding window, and adversarial streaming models.
However, these results are only for $p\in\{1,2\}$, and results for $p=1$
require a suboptimal $\tilde O(d^2/\epsilon^2)$ samples.
  In this work, we design the first nearly optimal $\ell_p$ subspace embeddings
for all $p\in(0,\infty)$ in the online coreset, sliding window, and the
adversarial streaming models. In all three models, our algorithms store $\tilde
O(d^{1\lor(p/2)}/\epsilon^2)$ rows. This answers a substantial generalization
of the main open question of [BDMMUWZ2020], and gives the first results for all
$p\notin\{1,2\}$.
  Towards our result, we give the first analysis of "one-shot'' Lewis weight
sampling of sampling rows proportionally to their Lewis weights, with sample
complexity $\tilde O(d^{p/2}/\epsilon^2)$ for $p>2$. Previously, this scheme
was only known to have sample complexity $\tilde O(d^{p/2}/\epsilon^5)$,
whereas $\tilde O(d^{p/2}/\epsilon^2)$ is known if a more sophisticated
recursive sampling is used. The recursive sampling cannot be implemented
online, thus necessitating an analysis of one-shot Lewis weight sampling. Our
analysis uses a novel connection to online numerical linear algebra.
  As an application, we obtain the first one-pass streaming coreset algorithms
for $(1+\epsilon)$ approximation of important generalized linear models, such
as logistic regression and $p$-probit regression. Our upper bounds are
parameterized by a complexity parameter $\mu$ introduced by [MSSW2018], and we
show the first lower bounds showing that a linear dependence on $\mu$ is

Histopathology whole slide images (WSIs) play a very important role in
clinical studies and serve as the gold standard for many cancer diagnoses.
However, generating automatic tools for processing WSIs is challenging due to
their enormous sizes. Currently, to deal with this issue, conventional methods
rely on a multiple instance learning (MIL) strategy to process a WSI at patch
level. Although effective, such methods are computationally expensive, because
tiling a WSI into patches takes time and does not explore the spatial relations
between these tiles. To tackle these limitations, we propose a locally
supervised learning framework which processes the entire slide by exploring the
entire local and global information that it contains. This framework divides a
pre-trained network into several modules and optimizes each module locally
using an auxiliary model. We also introduce a random feature reconstruction
unit (RFR) to preserve distinguishing features during training and improve the
performance of our method by 1% to 3%. Extensive experiments on three publicly
available WSI datasets: TCGA-NSCLC, TCGA-RCC and LKS, highlight the superiority
of our method on different classification tasks. Our method outperforms the
state-of-the-art MIL methods by 2% to 5% in accuracy, while being 7 to 10 times
faster. Additionally, when dividing it into eight modules, our method requires
as little as 20% of the total gpu memory required by end-to-end training. Our
code is available at https://github.com/cvlab-stonybrook/local_learning_wsi.

Brain vessel image segmentation can be used as a promising biomarker for
better prevention and treatment of different diseases. One successful approach
is to consider the segmentation as an image-to-image translation task and
perform a conditional Generative Adversarial Network (cGAN) to learn a
transformation between two distributions. In this paper, we present a novel
multi-view approach, MLP-GAN, which splits a 3D volumetric brain vessel image
into three different dimensional 2D images (i.e., sagittal, coronal, axial) and
then feed them into three different 2D cGANs. The proposed MLP-GAN not only
alleviates the memory issue which exists in the original 3D neural networks but
also retains 3D spatial information. Specifically, we utilize U-Net as the
backbone for our generator and redesign the pattern of skip connection
integrated with the MLP-Mixer which has attracted lots of attention recently.
Our model obtains the ability to capture cross-patch information to learn
global information with the MLP-Mixer. Extensive experiments are performed on
the public brain vessel dataset that show our MLP-GAN outperforms other
state-of-the-art methods. We release our code at
https://github.com/bxie9/MLP-GAN

We propose a novel framework for multitask reinforcement learning based on
the minimum description length (MDL) principle. In this approach, which we term
MDL-control (MDL-C), the agent learns the common structure among the tasks with
which it is faced and then distills it into a simpler representation which
facilitates faster convergence and generalization to new tasks. In doing so,
MDL-C naturally balances adaptation to each task with epistemic uncertainty
about the task distribution. We motivate MDL-C via formal connections between
the MDL principle and Bayesian inference, derive theoretical performance
guarantees, and demonstrate MDL-C's empirical effectiveness on both discrete
and high-dimensional continuous control tasks.

We consider the problem of designing uniformly stable first-order
optimization algorithms for empirical risk minimization. Uniform stability is
often used to obtain generalization error bounds for optimization algorithms,
and we are interested in a general approach to achieve it. For Euclidean
geometry, we suggest a black-box conversion which given a smooth optimization
algorithm, produces a uniformly stable version of the algorithm while
maintaining its convergence rate up to logarithmic factors. Using this
reduction we obtain a (nearly) optimal algorithm for smooth optimization with
convergence rate $\widetilde{O}(1/T^2)$ and uniform stability $O(T^2/n)$,
resolving an open problem of Chen et al. (2018); Attia and Koren (2021). For
more general geometries, we develop a variant of Mirror Descent for smooth
optimization with convergence rate $\widetilde{O}(1/T)$ and uniform stability
$O(T/n)$, leaving open the question of devising a general conversion method as
in the Euclidean case.

Image schema is a recurrent pattern of reasoning where one entity is mapped
into another. Image schema is similar to conceptual metaphor and is also
related to metaphoric gesture. Our main goal is to generate metaphoric gestures
for an Embodied Conversational Agent.
  We propose a technique to learn the vector representation of image schemas.
As far as we are aware of, this is the first work which addresses that problem.
Our technique uses Ravenet et al's algorithm which we use to compute the image
schemas from the text input and also BERT and SenseBERT which we use as the
base word embedding technique to calculate the final vector representation of
the image schema. Our representation learning technique works by clustering:
word embedding vectors which belong to the same image schema should be
relatively closer to each other, and thus form a cluster.
  With the image schemas representable as vectors, it also becomes possible to
have a notion that some image schemas are closer or more similar to each other
than to the others because the distance between the vectors is a proxy of the
dissimilarity between the corresponding image schemas. Therefore, after
obtaining the vector representation of the image schemas, we calculate the
distances between those vectors. Based on these, we create visualizations to
illustrate the relative distances between the different image schemas.

The action governor is an add-on scheme to a nominal control loop that
monitors and adjusts the control actions to enforce safety specifications
expressed as pointwise-in-time state and control constraints. In this paper, we
introduce the Robust Action Governor (RAG) for systems the dynamics of which
can be represented using discrete-time Piecewise Affine (PWA) models with both
parametric and additive uncertainties and subject to non-convex constraints. We
develop the theoretical properties and computational approaches for the RAG.
After that, we introduce the use of the RAG for realizing safe Reinforcement
Learning (RL), i.e., ensuring all-time constraint satisfaction during online RL
exploration-and-exploitation process. This development enables safe real-time
evolution of the control policy and adaptation to changes in the operating
environment and system parameters (due to aging, damage, etc.). We illustrate
the effectiveness of the RAG in constraint enforcement and safe RL using the
RAG by considering their applications to a soft-landing problem of a
mass-spring-damper system.

In recent years, numerous troll accounts that manipulate social media
sentiment have emerged. Due to the use of fake and automated accounts by
businesses, abusers, and nation-state-sponsored troll farms, detecting and
eliminating trolling is a crucial issue for social networking platforms.
Various NLP techniques are used to extract information from social networking
text, such as tweets on Twitter, to identify the messages originating from fake
accounts. In this context, this paper implements and compares nine deep
learning-based architectures for troll tweet detection, with three models for
each BERT, ELMo, and GloVe word embedding model. The majority of BERT-based
architectures improve the detection of trolling tweets, as demonstrated by
experimental results. An ELMo-based architecture with a GRU classifier has the
highest AUC for detecting troll messages. Future socially-based systems can
utilize the proposed architectures to identify troll messages.

A person walking along a city street who tries to model all aspects of the
world would quickly be overwhelmed by a multitude of shops, cars, and people
moving in and out of view, following their own complex and inscrutable
dynamics. Exploration and navigation in such an environment is an everyday
task, requiring no vast exertion of mental resources. Is it possible to turn
this fire hose of sensory information into a minimal latent state which is
necessary and sufficient for an agent to successfully act in the world? We
formulate this question concretely, and propose the Agent-Controllable State
Discovery algorithm (AC-State), which has theoretical guarantees and is
practically demonstrated to discover the \textit{minimal controllable latent
state} which contains all of the information necessary for controlling the
agent, while fully discarding all irrelevant information. This algorithm
consists of a multi-step inverse model (predicting actions from distant
observations) with an information bottleneck. AC-State enables localization,
exploration, and navigation without reward or demonstrations. We demonstrate
the discovery of controllable latent state in three domains: localizing a robot
arm with distractions (e.g., changing lighting conditions and background),
exploring in a maze alongside other agents, and navigating in the Matterport
house simulator.

Existing multimodal stress/pain recognition approaches generally extract
features from different modalities independently and thus ignore cross-modality
correlations. This paper proposes a novel geometric framework for multimodal
stress/pain detection utilizing Symmetric Positive Definite (SPD) matrices as a
representation that incorporates the correlation relationship of physiological
and behavioural signals from covariance and cross-covariance. Considering the
non-linearity of the Riemannian manifold of SPD matrices, well-known machine
learning techniques are not suited to classify these matrices. Therefore, a
tangent space mapping method is adopted to map the derived SPD matrix sequences
to the vector sequences in the tangent space where the LSTM-based network can
be applied for classification. The proposed framework has been evaluated on two
public multimodal datasets, achieving both the state-of-the-art results for
stress and pain detection tasks.

This paper introduces a system that learns to sing new tunes by listening to
examples. It extracts sequencing rules from input music and uses these rules to
generate new tunes, which are sung by a vocal synthesiser. We developed a
method to represent rules for musical composition as quantum circuits. We claim
that such musical rules are quantum native: they are naturally encodable in the
amplitudes of quantum states. To evaluate a rule to generate a subsequent
event, the system builds the respective quantum circuit dynamically and
measures it. After a brief discussion about the vocal synthesis methods that we
have been experimenting with, the paper introduces our novel generative music
method through a practical example. The paper shows some experiments and
concludes with a discussion about harnessing the creative potential of the
system.

Life-long learning aims at learning a sequence of tasks without forgetting
the previously acquired knowledge. However, the involved training data may not
be life-long legitimate due to privacy or copyright reasons. In practical
scenarios, for instance, the model owner may wish to enable or disable the
knowledge of specific tasks or specific samples from time to time. Such
flexible control over knowledge transfer, unfortunately, has been largely
overlooked in previous incremental or decremental learning methods, even at a
problem-setup level. In this paper, we explore a novel learning scheme, termed
as Learning wIth Recoverable Forgetting (LIRF), that explicitly handles the
task- or sample-specific knowledge removal and recovery. Specifically, LIRF
brings in two innovative schemes, namely knowledge deposit and withdrawal,
which allow for isolating user-designated knowledge from a pre-trained network
and injecting it back when necessary. During the knowledge deposit process, the
specified knowledge is extracted from the target network and stored in a
deposit module, while the insensitive or general knowledge of the target
network is preserved and further augmented. During knowledge withdrawal, the
taken-off knowledge is added back to the target network. The deposit and
withdraw processes only demand for a few epochs of finetuning on the removal
data, ensuring both data and time efficiency. We conduct experiments on several
datasets, and demonstrate that the proposed LIRF strategy yields encouraging
results with gratifying generalization capability.

Contrastive-based self-supervised learning methods achieved great success in
recent years. However, self-supervision requires extremely long training epochs
(e.g., 800 epochs for MoCo v3) to achieve promising results, which is
unacceptable for the general academic community and hinders the development of
this topic. This work revisits the momentum-based contrastive learning
frameworks and identifies the inefficiency in which two augmented views
generate only one positive pair. We propose Fast-MoCo - a novel framework that
utilizes combinatorial patches to construct multiple positive pairs from two
augmented views, which provides abundant supervision signals that bring
significant acceleration with neglectable extra computational cost. Fast-MoCo
trained with 100 epochs achieves 73.5% linear evaluation accuracy, similar to
MoCo v3 (ResNet-50 backbone) trained with 800 epochs. Extra training (200
epochs) further improves the result to 75.1%, which is on par with
state-of-the-art methods. Experiments on several downstream tasks also confirm
the effectiveness of Fast-MoCo.

We propose an algorithm to estimate the path-gradient of both the reverse and
forward Kullback-Leibler divergence for an arbitrary manifestly invertible
normalizing flow. The resulting path-gradient estimators are straightforward to
implement, have lower variance, and lead not only to faster convergence of
training but also to better overall approximation results compared to standard
total gradient estimators. We also demonstrate that path-gradient training is
less susceptible to mode-collapse. In light of our results, we expect that
path-gradient estimators will become the new standard method to train
normalizing flows for variational inference.

Inference by means of mathematical modeling from a collection of observations
remains a crucial tool for scientific discovery and is ubiquitous in
application areas such as signal compression, imaging restoration, and
supervised machine learning. The inference problems may be solved using
variational formulations that provide theoretically proven methods and
algorithms. With ever-increasing model complexities and growing data size, new
specially designed methods are urgently needed to recover meaningful quantifies
of interest. We consider the broad spectrum of linear inverse problems where
the aim is to reconstruct quantities with a sparse representation on some
vector space; often solved using the (generalized) least absolute shrinkage and
selection operator (lasso). The associated optimization problems have received
significant attention, in particular in the early 2000's, because of their
connection to compressed sensing and the reconstruction of solutions with
favorable sparsity properties using augmented Lagrangians, alternating
directions and splitting methods. We provide a new perspective on the
underlying l1 regularized inverse problem by exploring the generalized lasso
problem through variable projection methods. We arrive at our proposed variable
projected augmented Lagrangian (vpal) method. We analyze this method and
provide an approach for automatic regularization parameter selection based on a
degrees of freedom argument. Further, we provide numerical examples
demonstrating the computational efficiency for various imaging problems.

In this paper, we presented a data-driven framework to optimize the
out-of-plane stiffness for soft grippers to achieve mechanical properties as
hard-to-twist and easy-to-bend. The effectiveness of this method is
demonstrated in the design of a soft pneumatic bending actuator (SPBA). First,
a new objective function is defined to quantitatively evaluate the out-of-plane
stiffness as well as the bending performance. Then, sensitivity analysis is
conducted on the parametric model of an SPBA design to determine the optimized
design parameters with the help of Finite Element Analysis (FEA). To enable the
computation of numerical optimization, a data-driven approach is employed to
learn a cost function that directly represents the out-of-plane stiffness as a
differentiable function of the design variables. A gradient-based method is
used to maximize the out-of-plane stiffness of the SPBA while ensuring specific
bending performance. The effectiveness of our method has been demonstrated in
physical experiments taken on 3D-printed grippers.

Neural networks are known to produce over-confident predictions on input
images, even when these images are out-of-distribution (OOD) samples. This
limits the applications of neural network models in real-world scenarios, where
OOD samples exist. Many existing approaches identify the OOD instances via
exploiting various cues, such as finding irregular patterns in the feature
space, logits space, gradient space or the raw space of images. In contrast,
this paper proposes a simple Test-time Linear Training (ETLT) method for OOD
detection. Empirically, we find that the probabilities of input images being
out-of-distribution are surprisingly linearly correlated to the features
extracted by neural networks. To be specific, many state-of-the-art OOD
algorithms, although designed to measure reliability in different ways,
actually lead to OOD scores mostly linearly related to their image features.
Thus, by simply learning a linear regression model trained from the paired
image features and inferred OOD scores at test-time, we can make a more precise
OOD prediction for the test instances. We further propose an online variant of
the proposed method, which achieves promising performance and is more practical
in real-world applications. Remarkably, we improve FPR95 from $51.37\%$ to
$12.30\%$ on CIFAR-10 datasets with maximum softmax probability as the base OOD
detector. Extensive experiments on several benchmark datasets show the efficacy
of ETLT for OOD detection task.

As a prevalent distributed learning paradigm, Federated Learning (FL) trains
a global model on a massive amount of devices with infrequent communication.
This paper investigates a class of composite optimization and statistical
recovery problems in the FL setting, whose loss function consists of a
data-dependent smooth loss and a non-smooth regularizer. Examples include
sparse linear regression using Lasso, low-rank matrix recovery using nuclear
norm regularization, etc. In the existing literature, federated composite
optimization algorithms are designed only from an optimization perspective
without any statistical guarantees. In addition, they do not consider commonly
used (restricted) strong convexity in statistical recovery problems. We advance
the frontiers of this problem from both optimization and statistical
perspectives. From optimization upfront, we propose a new algorithm named
\textit{Fast Federated Dual Averaging} for strongly convex and smooth loss and
establish state-of-the-art iteration and communication complexity in the
composite setting. In particular, we prove that it enjoys a fast rate, linear
speedup, and reduced communication rounds. From statistical upfront, for
restricted strongly convex and smooth loss, we design another algorithm, namely
\textit{Multi-stage Federated Dual Averaging}, and prove a high probability
complexity bound with linear speedup up to optimal statistical precision.
Experiments in both synthetic and real data demonstrate that our methods
perform better than other baselines. To the best of our knowledge, this is the
first work providing fast optimization algorithms and statistical recovery
guarantees for composite problems in FL.

As we move away from the data, the predictive uncertainty should increase,
since a great variety of explanations are consistent with the little available
information. We introduce Distance-Aware Prior (DAP) calibration, a method to
correct overconfidence of Bayesian deep learning models outside of the training
domain. We define DAPs as prior distributions over the model parameters that
depend on the inputs through a measure of their distance from the training set.
DAP calibration is agnostic to the posterior inference method, and it can be
performed as a post-processing step. We demonstrate its effectiveness against
several baselines in a variety of classification and regression problems,
including benchmarks designed to test the quality of predictive distributions
away from the data.

We introduce SPIRAL, a SuPerlinearly convergent Incremental pRoximal
ALgorithm, for solving nonconvex regularized finite sum problems under a
relative smoothness assumption. In the spirit of SVRG and SARAH, each iteration
of SPIRAL consists of an inner and an outer loop. It combines incremental and
full (proximal) gradient updates with a linesearch. It is shown that when using
quasi-Newton directions, superlinear convergence is attained under mild
assumptions at the limit points. More importantly, thanks to said linesearch,
global convergence is ensured while it is shown that unit stepsize will be
eventually always accepted. Simulation results on different convex, nonconvex,
and non-Lipschitz differentiable problems show that our algorithm as well as
its adaptive variant are competitive to the state of the art.

We introduce BusyBoard, a toy-inspired robot learning environment that
leverages a diverse set of articulated objects and inter-object functional
relations to provide rich visual feedback for robot interactions. Based on this
environment, we introduce a learning framework, BusyBot, which allows an agent
to jointly acquire three fundamental capabilities (interaction, reasoning, and
planning) in an integrated and self-supervised manner. With the rich sensory
feedback provided by BusyBoard, BusyBot first learns a policy to efficiently
interact with the environment; then with data collected using the policy,
BusyBot reasons the inter-object functional relations through a causal
discovery network; and finally by combining the learned interaction policy and
relation reasoning skill, the agent is able to perform goal-conditioned
manipulation tasks. We evaluate BusyBot in both simulated and real-world
environments, and validate its generalizability to unseen objects and
relations. Video is available at https://youtu.be/EJ98xBJZ9ek.

Combinatorial optimization problems can be solved by heuristic algorithms
such as simulated annealing (SA) which aims to find the global minima solution
within a large search space through thermal fluctuations. The algorithm
generates new solutions through Markov-chain Monte Carlo techniques. The latter
can result in severe limitations, such as slow convergence and a tendency to
stay within the same local search space at small temperatures. To overcome
these shortcomings, we use the variational classical annealing (VCA) framework
that combines autoregressive recurrent neural networks (RNNs) with traditional
annealing to sample solutions independent of each other. In this paper, we
demonstrate the potential of using VCA as an approach to solving real-world
optimization problems. We explore VCA's performance in comparison with SA at
solving three popular optimization problems: the maximum cut problem (Max-Cut),
the nurse scheduling problem (NSP), and the traveling salesman problem (TSP).
For all three problems, we find that VCA outperforms SA on average in the
asymptotic limit. Interestingly, we reach large system sizes up to $256$ cities
for the TSP. We conclude that in the best-case scenario, VCA can serve as a
great alternative when SA fails to find the optimal solution.

Federated Learning is a new machine learning paradigm dealing with
distributed model learning on independent devices. One of the many advantages
of federated learning is that training data stay on devices (such as
smartphones), and only learned models are shared with a centralized server. In
the case of supervised learning, labeling is entrusted to the clients. However,
acquiring such labels can be prohibitively expensive and error-prone for many
tasks, such as human activity recognition. Hence, a wealth of data remains
unlabelled and unexploited. Most existing federated learning approaches that
focus mainly on supervised learning have mostly ignored this mass of unlabelled
data. Furthermore, it is unclear whether standard federated Learning approaches
are suited to self-supervised learning. The few studies that have dealt with
the problem have limited themselves to the favorable situation of homogeneous
datasets. This work lays the groundwork for a reference evaluation of federated
Learning with Semi-Supervised Learning in a realistic setting. We show that
standard lightweight autoencoder and standard Federated Averaging fail to learn
a robust representation for Human Activity Recognition with several realistic
heterogeneous datasets. These findings advocate for a more intensive research
effort in Federated Self Supervised Learning to exploit the mass of
heterogeneous unlabelled data present on mobile devices.

Exploiting pseudo labels (e.g., categories and bounding boxes) of unannotated
objects produced by a teacher detector have underpinned much of recent progress
in semi-supervised object detection (SSOD). However, due to the limited
generalization capacity of the teacher detector caused by the scarce
annotations, the produced pseudo labels often deviate from ground truth,
especially those with relatively low classification confidences, thus limiting
the generalization performance of SSOD. To mitigate this problem, we propose a
dual pseudo-label polishing framework for SSOD. Instead of directly exploiting
the pseudo labels produced by the teacher detector, we take the first attempt
at reducing their deviation from ground truth using dual polishing learning,
where two differently structured polishing networks are elaborately developed
and trained using synthesized paired pseudo labels and the corresponding ground
truth for categories and bounding boxes on the given annotated objects,
respectively. By doing this, both polishing networks can infer more accurate
pseudo labels for unannotated objects through sufficiently exploiting their
context knowledge based on the initially produced pseudo labels, and thus
improve the generalization performance of SSOD. Moreover, such a scheme can be
seamlessly plugged into the existing SSOD framework for joint end-to-end
learning. In addition, we propose to disentangle the polished pseudo categories
and bounding boxes of unannotated objects for separate category classification
and bounding box regression in SSOD, which enables introducing more unannotated
objects during model training and thus further improve the performance.
Experiments on both PASCAL VOC and MS COCO benchmarks demonstrate the
superiority of the proposed method over existing state-of-the-art baselines.

In supervised learning, the presence of noise can have a significant impact
on decision making. Since many classifiers do not take label noise into account
in the derivation of the loss function, including the loss functions of
logistic regression, SVM, and AdaBoost, especially the AdaBoost iterative
algorithm, whose core idea is to continuously increase the weight value of the
misclassified samples, the weight of samples in many presence of label noise
will be increased, leading to a decrease in model accuracy. In addition, the
learning process of BP neural network and decision tree will also be affected
by label noise. Therefore, solving the label noise problem is an important
element of maintaining the robustness of the network model, which is of great
practical significance. Granular ball computing is an important modeling method
developed in the field of granular computing in recent years, which is an
efficient, robust and scalable learning method. In this paper, we pioneered a
granular ball neural network algorithm model, which adopts the idea of
multi-granular to filter label noise samples during model training, solving the
current problem of model instability caused by label noise in the field of deep
learning, greatly reducing the proportion of label noise in training samples
and improving the robustness of neural network models.

Federated Learning has been introduced as a new machine learning paradigm
enhancing the use of local devices. At a server level, FL regularly aggregates
models learned locally on distributed clients to obtain a more general model.
Current solutions rely on the availability of large amounts of stored data at
the client side in order to fine-tune the models sent by the server. Such
setting is not realistic in mobile pervasive computing where data storage must
be kept low and data characteristic can change dramatically. To account for
this variability, a solution is to use the data regularly collected by the
client to progressively adapt the received model. But such naive approach
exposes clients to the well-known problem of catastrophic forgetting. To
address this problem, we have defined a Federated Continual Learning approach
which is mainly based on distillation. Our approach allows a better use of
resources, eliminating the need to retrain from scratch at the arrival of new
data and reducing memory usage by limiting the amount of data to be stored.
This proposal has been evaluated in the Human Activity Recognition (HAR) domain
and has shown to effectively reduce the catastrophic forgetting effect.

In this way, no private data is sent over the network, and the communication
cost is reduced. However, current solutions rely on the availability of large
amounts of stored data at the client side in order to fine-tune the models sent
by the server. Such setting is not realistic in mobile pervasive computing
where data storage must be kept low and data characteristic (distribution) can
change dramatically. To account for this variability, a solution is to use the
data regularly collected by the client to progressively adapt the received
model. But such naive approach exposes clients to the well-known problem of
catastrophic forgetting. The purpose of this paper is to demonstrate this
problem in the mobile human activity recognition context on smartphones.

As a common security tool, visible watermarking has been widely applied to
protect copyrights of digital images. However, recent works have shown that
visible watermarks can be removed by DNNs without damaging their host images.
Such watermark-removal techniques pose a great threat to the ownership of
images. Inspired by the vulnerability of DNNs on adversarial perturbations, we
propose a novel defence mechanism by adversarial machine learning for good.
From the perspective of the adversary, blind watermark-removal networks can be
posed as our target models; then we actually optimize an imperceptible
adversarial perturbation on the host images to proactively attack against
watermark-removal networks, dubbed Watermark Vaccine. Specifically, two types
of vaccines are proposed. Disrupting Watermark Vaccine (DWV) induces to ruin
the host image along with watermark after passing through watermark-removal
networks. In contrast, Inerasable Watermark Vaccine (IWV) works in another
fashion of trying to keep the watermark not removed and still noticeable.
Extensive experiments demonstrate the effectiveness of our DWV/IWV in
preventing watermark removal, especially on various watermark removal networks.

Many successful methods developed for medical image analysis that are based
on machine learning use supervised learning approaches, which often require
large datasets annotated by experts to achieve high accuracy. However, medical
data annotation is time-consuming and expensive, especially for segmentation
tasks. To solve the problem of learning with limited labeled medical image
data, an alternative deep learning training strategy based on self-supervised
pretraining on unlabeled MRI scans is proposed in this work. Our pretraining
approach first, randomly applies different distortions to random areas of
unlabeled images and then predicts the type of distortions and loss of
information. To this aim, an improved version of Mask-RCNN architecture has
been adapted to localize the distortion location and recover the original image
pixels. The effectiveness of the proposed method for segmentation tasks in
different pre-training and fine-tuning scenarios is evaluated based on the
Osteoarthritis Initiative dataset. Using this self-supervised pretraining
method improved the Dice score by 20% compared to training from scratch. The
proposed self-supervised learning is simple, effective, and suitable for
different ranges of medical image analysis tasks including anomaly detection,
segmentation, and classification.

Recently the "SP" (Stochastic Polyak step size) method has emerged as a
competitive adaptive method for setting the step sizes of SGD. SP can be
interpreted as a method specialized to interpolated models, since it solves the
interpolation equations. SP solves these equation by using local linearizations
of the model. We take a step further and develop a method for solving the
interpolation equations that uses the local second-order approximation of the
model. Our resulting method SP2 uses Hessian-vector products to speed-up the
convergence of SP. Furthermore, and rather uniquely among second-order methods,
the design of SP2 in no way relies on positive definite Hessian matrices or
convexity of the objective function. We show SP2 is very competitive on matrix
completion, non-convex test problems and logistic regression. We also provide a
convergence theory on sums-of-quadratics.

In the last decades, global awareness towards the importance of diverse
representation has been increasing. Lack of diversity and discrimination toward
minorities did not skip the film industry. Here, we examine ethnic bias in the
film industry through commercial posters, the industry's primary advertisement
medium for decades. Movie posters are designed to establish the viewer's
initial impression. We developed a novel approach for evaluating ethnic bias in
the film industry by analyzing nearly 125,000 posters using state-of-the-art
deep learning models. Our analysis shows that while ethnic biases still exist,
there is a trend of reduction of bias, as seen by several parameters.
Particularly in English-speaking movies, the ethnic distribution of characters
on posters from the last couple of years is reaching numbers that are
approaching the actual ethnic composition of US population. An automatic
approach to monitor ethnic diversity in the film industry, potentially
integrated with financial value, may be of significant use for producers and
policymakers.

Heart rate variability (HRV) indices describe properties of interbeat
intervals in electrocardiogram (ECG). Usually HRV is measured exclusively in
normal sinus rhythm (NSR) excluding any form of paroxysmal rhythm. Atrial
fibrillation (AF) is the most widespread cardiac arrhythmia in human
population. Usually such abnormal rhythm is not analyzed and assumed to be
chaotic and unpredictable. Nonetheless, ranges of HRV indices differ between
patients with AF, yet physiological characteristics which influence them are
poorly understood. In this study, we propose a statistical model that describes
relationship between HRV indices in NSR and AF. The model is based on
Mahalanobis distance, the k-Nearest neighbour approach and multivariate normal
distribution framework. Verification of the method was performed using 10 min
intervals of NSR and AF that were extracted from long-term Holter ECGs. For
validation we used Bhattacharyya distance and Kolmogorov-Smirnov 2-sample test
in a k-fold procedure. The model is able to predict at least 7 HRV indices with
high precision.

We propose a novel framework, On-Demand MOtion Generation (ODMO), for
generating realistic and diverse long-term 3D human motion sequences
conditioned only on action types with an additional capability of
customization. ODMO shows improvements over SOTA approaches on all traditional
motion evaluation metrics when evaluated on three public datasets (HumanAct12,
UESTC, and MoCap). Furthermore, we provide both qualitative evaluations and
quantitative metrics demonstrating several first-known customization
capabilities afforded by our framework, including mode discovery,
interpolation, and trajectory customization. These capabilities significantly
widen the spectrum of potential applications of such motion generation models.
The novel on-demand generative capabilities are enabled by innovations in both
the encoder and decoder architectures: (i) Encoder: Utilizing contrastive
learning in low-dimensional latent space to create a hierarchical embedding of
motion sequences, where not only the codes of different action types form
different groups, but within an action type, codes of similar inherent patterns
(motion styles) cluster together, making them readily discoverable; (ii)
Decoder: Using a hierarchical decoding strategy where the motion trajectory is
reconstructed first and then used to reconstruct the whole motion sequence.
Such an architecture enables effective trajectory control. Our code is released
on the Github page: https://github.com/roychowdhuryresearch/ODMO

Time series analysis has achieved great success in diverse applications such
as network security, environmental monitoring, and medical informatics.
Learning similarities among different time series is a crucial problem since it
serves as the foundation for downstream analysis such as clustering and anomaly
detection. It often remains unclear what kind of distance metric is suitable
for similarity learning due to the complex temporal dynamics of the time series
generated from event-triggered sensing, which is common in diverse
applications, including automated driving, interactive healthcare, and smart
home automation. The overarching goal of this paper is to develop an
unsupervised learning framework that is capable of learning task-aware
similarities among unlabeled event-triggered time series. From the machine
learning vantage point, the proposed framework harnesses the power of both
hierarchical multi-scale sequence autoencoders and Gaussian Mixture Model (GMM)
to effectively learn the low-dimensional representations from the time series.
Finally, the obtained similarity measure can be easily visualized for
explaining. The proposed framework aspires to offer a stepping stone that gives
rise to a systematic approach to model and learn similarities among a multitude
of event-triggered time series. Through extensive qualitative and quantitative
experiments, it is revealed that the proposed method outperforms
state-of-the-art methods considerably.

Although principles of neuroscience like reinforcement learning, visual
perception and attention have been applied in machine learning models, there is
a huge gap between machine learning and mammalian learning. Based on the
advances in neuroscience, we propose the context sequence theory to give a
common explanation for multiple types of learning in mammals and hope that can
provide a new insight into the construct of machine learning models.

Over the last decade, Neural Networks (NNs) have been widely used in numerous
applications including safety-critical ones such as autonomous systems. Despite
their emerging adoption, it is well known that NNs are susceptible to
Adversarial Attacks. Hence, it is highly important to provide guarantees that
such systems work correctly. To remedy these issues we introduce a framework
for repairing unsafe NNs w.r.t. safety specification, that is by utilizing
satisfiability modulo theories (SMT) solvers. Our method is able to search for
a new, safe NN representation, by modifying only a few of its weight values. In
addition, our technique attempts to maximize the similarity to original network
with regard to its decision boundaries. We perform extensive experiments which
demonstrate the capability of our proposed framework to yield safe NNs w.r.t.
the Adversarial Robustness property, with only a mild loss of accuracy (in
terms of similarity). Moreover, we compare our method with a naive baseline to
empirically prove its effectiveness. To conclude, we provide an algorithm to
automatically repair NNs given safety properties, and suggest a few heuristics
to improve its computational performance. Currently, by following this approach
we are capable of producing small-sized (i.e., with up to few hundreds of
parameters) correct NNs, composed of the piecewise linear ReLU activation
function. Nevertheless, our framework is general in the sense that it can
synthesize NNs w.r.t. any decidable fragment of first-order logic

Large-scale Vision-and-Language (V+L) pre-training for representation
learning has proven to be effective in boosting various downstream V+L tasks.
However, when it comes to the fashion domain, existing V+L methods are
inadequate as they overlook the unique characteristics of both the fashion V+L
data and downstream tasks. In this work, we propose a novel fashion-focused V+L
representation learning framework, dubbed as FashionViL. It contains two novel
fashion-specific pre-training tasks designed particularly to exploit two
intrinsic attributes with fashion V+L data. First, in contrast to other domains
where a V+L data point contains only a single image-text pair, there could be
multiple images in the fashion domain. We thus propose a Multi-View Contrastive
Learning task for pulling closer the visual representation of one image to the
compositional multimodal representation of another image+text. Second, fashion
text (e.g., product description) often contains rich fine-grained concepts
(attributes/noun phrases). To exploit this, a Pseudo-Attributes Classification
task is introduced to encourage the learned unimodal (visual/textual)
representations of the same concept to be adjacent. Further, fashion V+L tasks
uniquely include ones that do not conform to the common one-stream or
two-stream architectures (e.g., text-guided image retrieval). We thus propose a
flexible, versatile V+L model architecture consisting of a modality-agnostic
Transformer so that it can be flexibly adapted to any downstream tasks.
Extensive experiments show that our FashionViL achieves a new state of the art
across five downstream tasks. Code is available at
https://github.com/BrandonHanx/mmf.

The deep learning literature is continuously updated with new architectures
and training techniques. However, weight initialization is overlooked by most
recent research, despite some intriguing findings regarding random weights. On
the other hand, recent works have been approaching Network Science to
understand the structure and dynamics of Artificial Neural Networks (ANNs)
after training. Therefore, in this work, we analyze the centrality of neurons
in randomly initialized networks. We show that a higher neuronal strength
variance may decrease performance, while a lower neuronal strength variance
usually improves it. A new method is then proposed to rewire neuronal
connections according to a preferential attachment (PA) rule based on their
strength, which significantly reduces the strength variance of layers
initialized by common methods. In this sense, PA rewiring only reorganizes
connections, while preserving the magnitude and distribution of the weights. We
show through an extensive statistical analysis in image classification that
performance is improved in most cases, both during training and testing, when
using both simple and complex architectures and learning schedules. Our results
show that, aside from the magnitude, the organization of the weights is also
relevant for better initialization of deep ANNs.

Federated learning enables many applications benefiting distributed and
private datasets of a large number of potential data-holding clients. However,
different clients usually have their own particular objectives in terms of the
tasks to be learned from the data. So, supporting federated learning with
meta-learning tools such as multi-task learning and transfer learning will help
enlarge the set of potential applications of federated learning by letting
clients of different but related tasks share task-agnostic models that can be
then further updated and tailored by each individual client for its particular
task. In a federated multi-task learning problem, the trained deep neural
network model should be fine-tuned for the respective objective of each client
while sharing some parameters for more generalizability. We propose to train a
deep neural network model with more generalized layers closer to the input and
more personalized layers to the output. We achieve that by introducing layer
types such as pre-trained, common, task-specific, and personal layers. We
provide simulation results to highlight particular scenarios in which
meta-learning-based federated learning proves to be useful.

In contrast to a standard closed-set domain adaptation task, partial domain
adaptation setup caters to a realistic scenario by relaxing the identical label
set assumption. The fact of source label set subsuming the target label set,
however, introduces few additional obstacles as training on private source
category samples thwart relevant knowledge transfer and mislead the
classification process. To mitigate these issues, we devise a mechanism for
strategic selection of highly-confident target samples essential for the
estimation of class-importance weights. Furthermore, we capture
class-discriminative and domain-invariant features by coupling the process of
achieving compact and distinct class distributions with an adversarial
objective. Experimental findings over numerous cross-domain classification
tasks demonstrate the potential of the proposed technique to deliver superior
and comparable accuracy over existing methods.

Although large language models (LLMs) often produce impressive outputs, they
also fail to reason and be factual. We set out to investigate how these
limitations affect the LLM's ability to answer and reason about difficult
real-world based questions. We applied the human-aligned GPT-3 (InstructGPT) to
answer multiple-choice medical exam questions (USMLE and MedMCQA) and medical
research questions (PubMedQA). We investigated Chain-of-thought (think step by
step) prompts, grounding (augmenting the prompt with search results) and
few-shot (prepending the question with question-answer exemplars). For a subset
of the USMLE questions, a medical domain expert reviewed and annotated the
model's reasoning. Overall, GPT-3 achieved a substantial improvement in
state-of-the-art machine learning performance. We observed that GPT-3 is often
knowledgeable and can reason about medical questions. GPT-3, when confronted
with a question it cannot answer, will still attempt to answer, often resulting
in a biased predictive distribution. LLMs are not on par with human performance
but our results suggest the emergence of reasoning patterns that are compatible
with medical problem-solving. We speculate that scaling model and data,
enhancing prompt alignment and allowing for better contextualization of the
completions will be sufficient for LLMs to reach human-level performance on
this type of task.

Recently, for few-shot or even zero-shot learning, the new paradigm
"pre-train, prompt, and predict" has achieved remarkable achievements compared
with the "pre-train, fine-tune" paradigm. After the success of prompt-based
GPT-3, a series of masked language model (MLM)-based (e.g., BERT, RoBERTa)
prompt learning methods became popular and widely used. However, another
efficient pre-trained discriminative model, ELECTRA, has probably been
neglected. In this paper, we attempt to accomplish several NLP tasks in the
zero-shot scenario using a novel our proposed replaced token detection
(RTD)-based prompt learning method. Experimental results show that ELECTRA
model based on RTD-prompt learning achieves surprisingly state-of-the-art
zero-shot performance. Numerically, compared to MLM-RoBERTa-large and
MLM-BERT-large, our RTD-ELECTRA-large has an average of about 8.4% and 13.7%
improvement on all 15 tasks. Especially on the SST-2 task, our
RTD-ELECTRA-large achieves an astonishing 90.1% accuracy without any training
data. Overall, compared to the pre-trained masked language models, the
pre-trained replaced token detection model performs better in zero-shot
learning. The source code is available at:
https://github.com/nishiwen1214/RTD-ELECTRA.

Deep Convolutional Neural Networks (CNNs) have recently reached
state-of-the-art Handwritten Text Recognition (HTR) performance. However,
recent research has shown that typical CNNs' learning performance is limited
since they are homogeneous networks with a simple (linear) neuron model. With
their heterogeneous network structure incorporating non-linear neurons,
Operational Neural Networks (ONNs) have recently been proposed to address this
drawback. Self-ONNs are self-organized variations of ONNs with the generative
neuron model that can generate any non-linear function using the Taylor
approximation. In this study, in order to improve the state-of-the-art
performance level in HTR, the 2D Self-organized ONNs (Self-ONNs) in the core of
a novel network model are proposed. Moreover, deformable convolutions, which
have recently been demonstrated to tackle variations in the writing styles
better, are utilized in this study. The results over the IAM English dataset
and HADARA80P Arabic dataset show that the proposed model with the operational
layers of Self-ONNs significantly improves Character Error Rate (CER) and Word
Error Rate (WER). Compared with its counterpart CNNs, Self-ONNs reduce CER and
WER by 1.2% and 3.4 % in the HADARA80P and 0.199% and 1.244% in the IAM
dataset. The results over the benchmark IAM demonstrate that the proposed model
with the operational layers of Self-ONNs outperforms recent deep CNN models by
a significant margin while the use of Self-ONNs with deformable convolutions
demonstrates exceptional results.

Adversarial deep learning is to train robust DNNs against adversarial
attacks, which is one of the major research focuses of deep learning. Game
theory has been used to answer some of the basic questions about adversarial
deep learning such as the existence of a classifier with optimal robustness and
the existence of optimal adversarial samples for a given class of classifiers.
In most previous work, adversarial deep learning was formulated as a
simultaneous game and the strategy spaces are assumed to be certain probability
distributions in order for the Nash equilibrium to exist. But, this assumption
is not applicable to the practical situation. In this paper, we give answers to
these basic questions for the practical case where the classifiers are DNNs
with a given structure, by formulating the adversarial deep learning as
sequential games. The existence of Stackelberg equilibria for these games are
proved. Furthermore, it is shown that the equilibrium DNN has the largest
adversarial accuracy among all DNNs with the same structure, when
Carlini-Wagner's margin loss is used. Trade-off between robustness and accuracy
in adversarial deep learning is also studied from game theoretical aspect.

We propose a novel approach for planning agents to compose abstract skills
via observing and learning from historical interactions with the world. Our
framework operates in a Markov state-space model via a set of actions under
unknown pre-conditions. We formulate skills as high-level abstract policies
that propose action plans based on the current state. Each policy learns new
plans by observing the states' transitions while the agent interacts with the
world. Such an approach automatically learns new plans to achieve specific
intended effects, but the success of such plans is often dependent on the
states in which they are applicable. Therefore, we formulate the evaluation of
such plans as infinitely many multi-armed bandit problems, where we balance the
allocation of resources on evaluating the success probability of existing arms
and exploring new options. The result is a planner capable of automatically
learning robust high-level skills under a noisy environment; such skills
implicitly learn the action pre-condition without explicit knowledge. We show
that this planning approach is experimentally very competitive in
high-dimensional state space domains.

Recently, speech enhancement technologies that are based on deep learning
have received considerable research attention. If the spatial information in
microphone signals is exploited, microphone arrays can be advantageous under
some adverse acoustic conditions compared with single-microphone systems.
However, multichannel speech enhancement is often performed in the short-time
Fourier transform (STFT) domain, which renders the enhancement approach
computationally expensive. To remedy this problem, we propose a novel
equivalent rectangular bandwidth (ERB)-scaled spatial coherence feature that is
dependent on the target speaker activity between two ERB bands. Experiments
conducted using a four-microphone array in a reverberant environment, which
involved speech interference, demonstrated the efficacy of the proposed system.
This study also demonstrated that a network that was trained with the
ERB-scaled spatial feature was robust against variations in the geometry and
number of the microphones in the array.

This extended abstract introduces the initial steps taken to develop a system
for Rapid Internal Simulation of Knowledge (RISK). RISK aims to enable more
transparency in artificial intelligence systems, especially those created by
deep learning networks by allowing real-time simulation of what the system
knows. By looking at hypothetical situations based on these simulations a
system may make more informed decisions, and produce them for non-expert
observers to understand the reasoning behind a given action.

Existing learning-based methods for blind image quality assessment (BIQA) are
heavily dependent on large amounts of annotated training data, and usually
suffer from a severe performance degradation when encountering the
domain/distribution shift problem. Thanks to the development of unsupervised
domain adaptation (UDA), some works attempt to transfer the knowledge from a
label-sufficient source domain to a label-free target domain under domain shift
with UDA. However, it requires the coexistence of source and target data, which
might be impractical for source data due to the privacy or storage issues. In
this paper, we take the first step towards the source-free unsupervised domain
adaptation (SFUDA) in a simple yet efficient manner for BIQA to tackle the
domain shift without access to the source data. Specifically, we cast the
quality assessment task as a rating distribution prediction problem. Based on
the intrinsic properties of BIQA, we present a group of well-designed
self-supervised objectives to guide the adaptation of the BN affine parameters
towards the target domain. Among them, minimizing the prediction entropy and
maximizing the batch prediction diversity aim to encourage more confident
results while avoiding the trivial solution. Besides, based on the observation
that the IQA rating distribution of single image follows the Gaussian
distribution, we apply Gaussian regularization to the predicted rating
distribution to make it more consistent with the nature of human scoring.
Extensive experimental results under cross-domain scenarios demonstrated the
effectiveness of our proposed method to mitigate the domain shift.

Purpose: To create and evaluate the accuracy of an artificial intelligence
Deep learning platform (ORAiCLE) capable of using only retinal fundus images to
predict both an individuals overall 5 year cardiovascular risk (CVD) and the
relative contribution of the component risk factors that comprise this risk.
Methods: We used 165,907 retinal images from a database of 47,236 patient
visits. Initially, each image was paired with biometric data age, ethnicity,
sex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD
event wtihin 5 years of the retinal image acquisition. A risk score based on
Framingham equations was calculated. The real CVD event rate was also
determined for the individuals and overall population. Finally, ORAiCLE was
trained using only age, ethnicity, sex plus retinal images. Results: Compared
to Framingham-based score, ORAiCLE was up to 12% more accurate in prediciting
cardiovascular event in he next 5-years, especially for the highest risk group
of people. The reliability and accuracy of each of the restrictive models was
suboptimal to ORAiCLE performance ,indicating that it was using data from both
sets of data to derive its final results. Conclusion: Retinal photography is
inexpensive and only minimal training is required to acquire them as fully
automated, inexpensive camera systems are now widely available. As such,
AI-based CVD risk algorithms such as ORAiCLE promise to make CV health
screening more accurate, more afforadable and more accessible for all.
Furthermore, ORAiCLE unique ability to assess the relative contribution of the
components that comprise an individuals overall risk would inform treatment
decisions based on the specific needs of an individual, thereby increasing the
likelihood of positive health outcomes.

Video frame interpolation (VFI) serves as a useful tool for many video
processing applications. Recently, it has also been applied in the video
compression domain for enhancing both conventional video codecs and
learning-based compression architectures. While there has been an increased
focus on the development of enhanced frame interpolation algorithms in recent
years, the perceptual quality assessment of interpolated content remains an
open field of research. In this paper, we present a bespoke full reference
video quality metric for VFI, FloLPIPS, that builds on the popular perceptual
image quality metric, LPIPS, which captures the perceptual degradation in
extracted image feature space. In order to enhance the performance of LPIPS for
evaluating interpolated content, we re-designed its spatial feature aggregation
step by using the temporal distortion (through comparing optical flows) to
weight the feature difference maps. Evaluated on the BVI-VFI database, which
contains 180 test sequences with various frame interpolation artefacts,
FloLPIPS shows superior correlation performance (with statistical significance)
with subjective ground truth over 12 popular quality assessors. To facilitate
further research in VFI quality assessment, our code is publicly available at
https://danielism97.github.io/FloLPIPS.

The spread of COVID-19 has brought a huge disaster to the world, and the
automatic segmentation of infection regions can help doctors to make diagnosis
quickly and reduce workload. However, there are several challenges for the
accurate and complete segmentation, such as the scattered infection area
distribution, complex background noises, and blurred segmentation boundaries.
To this end, in this paper, we propose a novel network for automatic COVID-19
lung infection segmentation from CT images, named BCS-Net, which considers the
boundary, context, and semantic attributes. The BCS-Net follows an
encoder-decoder architecture, and more designs focus on the decoder stage that
includes three progressively Boundary-Context-Semantic Reconstruction (BCSR)
blocks. In each BCSR block, the attention-guided global context (AGGC) module
is designed to learn the most valuable encoder features for decoder by
highlighting the important spatial and boundary locations and modeling the
global context dependence. Besides, a semantic guidance (SG) unit generates the
semantic guidance map to refine the decoder features by aggregating multi-scale
high-level features at the intermediate resolution. Extensive experiments
demonstrate that our proposed framework outperforms the existing competitors
both qualitatively and quantitatively.

We investigate the conditional colour-magnitude distribution (CCMD), namely
the colour-magnitude distribution at fixed halo mass, of the central galaxies
in semi-analytic galaxy formation model (SAM) and hydrodynamic simulations. We
analyse the CCMD of central galaxies in each halo mass bin with the Gaussian
mixture model and find that it can be decomposed into red and blue components
nearly orthogonal to each other, a red component narrow in colour and extended
in magnitude and a blue component narrow in magnitude and extended in colour.
We focus on the SAM galaxies to explore the origin of the CCMD components by
studying the relation between central galaxy colour and halo or galaxy
properties. Central galaxy colour is correlated with halo assembly properties
for low mass haloes and independent of them for high mass haloes. Galaxy
properties such as central supermassive black hole mass, cold gas mass, and gas
specific angular momentum can all impact central galaxy colour. These results
are corroborated by an alternative machine learning analysis in which we
attempt to predict central galaxy colour with halo and galaxy properties. We
find that the prediction for colours of central galaxies can be significantly
improved using both halo and galaxy properties as input compared to using halo
properties alone. With the halo and galaxy properties considered here, we find
that subtle discrepancies remain between predicted and original colour
distribution for low mass haloes and that no significant determining properties
are identified in massive haloes, suggesting modulations by additional
stochastic processes in galaxy formation.

The security of learnable image encryption schemes for image classification
using deep neural networks against several attacks has been discussed. On the
other hand, block scrambling image encryption using the vision transformer has
been proposed, which applies to lossless compression methods such as JPEG
standard by dividing an image into permuted blocks. Although robustness of the
block scrambling image encryption against jigsaw puzzle solver attacks that
utilize a correlation among the blocks has been evaluated under the condition
of a large number of encrypted blocks, the security of encrypted images with a
small number of blocks has never been evaluated. In this paper, the security of
the block scrambling image encryption against ciphertext-only attacks is
evaluated by using jigsaw puzzle solver attacks.

Multimodal emotion recognition in conversations (mERC) is an active research
topic in natural language processing (NLP), which aims to predict human's
emotional states in communications of multiple modalities, e,g., natural
language and facial gestures. Innumerable implicit prejudices and
preconceptions fill human language and conversations, leading to the question
of whether the current data-driven mERC approaches produce a biased error. For
example, such approaches may offer higher emotional scores on the utterances by
females than males. In addition, the existing debias models mainly focus on
gender or race, where multibias mitigation is still an unexplored task in mERC.
In this work, we take the first step to solve these issues by proposing a
series of approaches to mitigate five typical kinds of bias in textual
utterances (i.e., gender, age, race, religion and LGBTQ+) and visual
representations (i.e, gender and age), followed by a Multibias-Mitigated and
sentiment Knowledge Enriched bi-modal Transformer (MMKET). Comprehensive
experimental results show the effectiveness of the proposed model and prove
that the debias operation has a great impact on the classification performance
for mERC. We hope our study will benefit the development of bias mitigation in
mERC and related emotion studies.

Aspect-based sentiment analysis (ABSA) aims at predicting sentiment polarity
(SC) or extracting opinion span (OE) expressed towards a given aspect. Previous
work in ABSA mostly relies on rather complicated aspect-specific feature
induction. Recently, pretrained language models (PLMs), e.g., BERT, have been
used as context modeling layers to simplify the feature induction structures
and achieve state-of-the-art performance. However, such PLM-based context
modeling can be not that aspect-specific. Therefore, a key question is left
under-explored: how the aspect-specific context can be better modeled through
PLMs? To answer the question, we attempt to enhance aspect-specific context
modeling with PLM in a non-intrusive manner. We propose three aspect-specific
input transformations, namely aspect companion, aspect prompt, and aspect
marker. Informed by these transformations, non-intrusive aspect-specific PLMs
can be achieved to promote the PLM to pay more attention to the aspect-specific
context in a sentence. Additionally, we craft an adversarial benchmark for ABSA
(advABSA) to see how aspect-specific modeling can impact model robustness.
Extensive experimental results on standard and adversarial benchmarks for SC
and OE demonstrate the effectiveness and robustness of the proposed method,
yielding new state-of-the-art performance on OE and competitive performance on
SC.

The propagation of rumours on social media poses an important threat to
societies, so that various techniques for rumour detection have been proposed
recently. Yet, existing work focuses on \emph{what} entities constitute a
rumour, but provides little support to understand \emph{why} the entities have
been classified as such. This prevents an effective evaluation of the detected
rumours as well as the design of countermeasures. In this work, we argue that
explanations for detected rumours may be given in terms of examples of related
rumours detected in the past. A diverse set of similar rumours helps users to
generalize, i.e., to understand the properties that govern the detection of
rumours. Since the spread of rumours in social media is commonly modelled using
feature-annotated graphs, we propose a query-by-example approach that, given a
rumour graph, extracts the $k$ most similar and diverse subgraphs from past
rumours. The challenge is that all of the computations require fast assessment
of similarities between graphs. To achieve an efficient and adaptive
realization of the approach in a streaming setting, we present a novel graph
representation learning technique and report on implementation considerations.
Our evaluation experiments show that our approach outperforms baseline
techniques in delivering meaningful explanations for various rumour propagation
behaviours.

Rapid progress and superior performance have been achieved for skeleton-based
action recognition recently. In this article, we investigate this problem under
a cross-dataset setting, which is a new, pragmatic, and challenging task in
real-world scenarios. Following the unsupervised domain adaptation (UDA)
paradigm, the action labels are only available on a source dataset, but
unavailable on a target dataset in the training stage. Different from the
conventional adversarial learning-based approaches for UDA, we utilize a
self-supervision scheme to reduce the domain shift between two skeleton-based
action datasets. Our inspiration is drawn from Cubism, an art genre from the
early 20th century, which breaks and reassembles the objects to convey a
greater context. By segmenting and permuting temporal segments or human body
parts, we design two self-supervised learning classification tasks to explore
the temporal and spatial dependency of a skeleton-based action and improve the
generalization ability of the model. We conduct experiments on six datasets for
skeleton-based action recognition, including three large-scale datasets (NTU
RGB+D, PKU-MMD, and Kinetics) where new cross-dataset settings and benchmarks
are established. Extensive results demonstrate that our method outperforms
state-of-the-art approaches. The source codes of our model and all the compared
methods are available at https://github.com/shanice-l/st-cubism.

We propose a novel image retouching method by modeling the retouching process
as performing a sequence of newly introduced trainable neural color operators.
The neural color operator mimics the behavior of traditional color operators
and learns pixelwise color transformation while its strength is controlled by a
scalar. To reflect the homomorphism property of color operators, we employ
equivariant mapping and adopt an encoder-decoder structure which maps the
non-linear color transformation to a much simpler transformation (i.e.,
translation) in a high dimensional space. The scalar strength of each neural
color operator is predicted using CNN based strength predictors by analyzing
global image statistics. Overall, our method is rather lightweight and offers
flexible controls. Experiments and user studies on public datasets show that
our method consistently achieves the best results compared with SOTA methods in
both quantitative measures and visual qualities. The code and pretrained models
are provided at https://github.com/amberwangyili/neurop

ImageNet trained PyTorch models are generally preferred as the off-the-shelf
models for direct use or for initialisation in most computer vision tasks. In
this paper, we simply test a representative set of these convolution and
transformer based models under many simple image transformations like
horizontal shifting, vertical shifting, scaling, rotation, presence of Gaussian
noise, cutout, horizontal flip and vertical flip and report the performance
drop caused by such transformations. We find that even simple transformations
like rotating the image by 10{\deg} or zooming in by 20% can reduce the top-1
accuracy of models like ResNet152 by 1%+. The code is available at
https://github.com/harshm121/imagenet-transformation-degradation.

This paper considers reconfigurable intelligent surface (RIS)-assisted
point-to-point multiple-input multiple-output (MIMO) communication systems,
where a transmitter communicates with a receiver through an RIS. Based on the
main target of reducing the bit error rate (BER) and therefore enhancing the
communication reliability, we study different model-based and data-driven
(autoencoder) approaches. In particular, we consider a model-based approach
that optimizes both active and passive optimization variables. We further
propose a novel end-to-end data-driven framework, which leverages the recent
advances in machine learning. The neural networks presented for conventional
signal processing modules are jointly trained with the channel effects to
minimize the bit error detection. Numerical results demonstrate that the
proposed data-driven approach can learn to encode the transmitted signal via
different channel realizations dynamically. In addition, the data-driven
approach not only offers a significant gain in the BER performance compared to
the other state-of-the-art benchmarks but also guarantees the performance when
perfect channel information is unavailable.

Variational inference (VI) provides an appealing alternative to traditional
sampling-based approaches for implementing Bayesian inference due to its
conceptual simplicity, statistical accuracy and computational scalability.
However, common variational approximation schemes, such as the mean-field (MF)
approximation, require certain conjugacy structure to facilitate efficient
computation, which may add unnecessary restrictions to the viable prior
distribution family and impose further constraints on the variational
approximation family. In this work, we develop a general computational
framework for implementing MF-VI via Wasserstein gradient flow (WGF), a
gradient flow over the space of probability measures. When specialized to
Bayesian latent variable models, we analyze the algorithmic convergence of an
alternating minimization scheme based on a time-discretized WGF for
implementing the MF approximation. In particular, the proposed algorithm
resembles a distributional version of EM algorithm, consisting of an E-step of
updating the latent variable variational distribution and an M-step of
conducting steepest descent over the variational distribution of parameters.
Our theoretical analysis relies on optimal transport theory and subdifferential
calculus in the space of probability measures. We prove the exponential
convergence of the time-discretized WGF for minimizing a generic objective
functional given strict convexity along generalized geodesics. We also provide
a new proof of the exponential contraction of the variational distribution
obtained from the MF approximation by using the fixed-point equation of the
time-discretized WGF. We apply our method and theory to two classic Bayesian
latent variable models, the Gaussian mixture model and the mixture of
regression model. Numerical experiments are also conducted to compliment the
theoretical findings under these two models.

This work introduces a novel knowledge distillation framework for
classification tasks where information on existing subclasses is available and
taken into consideration. In classification tasks with a small number of
classes or binary detection, the amount of information transferred from the
teacher to the student is restricted, thus limiting the utility of knowledge
distillation. Performance can be improved by leveraging information of possible
subclasses within the classes. To that end, we propose the so-called Subclass
Knowledge Distillation (SKD), a process of transferring the knowledge of
predicted subclasses from a teacher to a smaller student. Meaningful
information that is not in the teacher's class logits but exists in subclass
logits (e.g., similarities within classes) will be conveyed to the student
through the SKD, which will then boost the student's performance. Analytically,
we measure how much extra information the teacher can provide the student via
the SKD to demonstrate the efficacy of our work. The framework developed is
evaluated in clinical application, namely colorectal polyp binary
classification. It is a practical problem with two classes and a number of
subclasses per class. In this application, clinician-provided annotations are
used to define subclasses based on the annotation label's variability in a
curriculum style of learning. A lightweight, low-complexity student trained
with the SKD framework achieves an F1-score of 85.05%, an improvement of 1.47%,
and a 2.10% gain over the student that is trained with and without conventional
knowledge distillation, respectively. The 2.10% F1-score gap between students
trained with and without the SKD can be explained by the extra subclass
knowledge, i.e., the extra 0.4656 label bits per sample that the teacher can
transfer in our experiment.

Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling
errors. In recent years, related researches focus on introducing the character
similarity from confusion set to enhance the CSC models, ignoring the context
of characters that contain richer information. To make better use of contextual
similarity, we propose a simple yet effective curriculum learning framework for
the CSC task. With the help of our designed model-agnostic framework, existing
CSC models will be trained from easy to difficult as humans learn Chinese
characters and achieve further performance improvements. Extensive experiments
and detailed analyses on widely used SIGHAN datasets show that our method
outperforms previous state-of-the-art methods.

Over-the-air federated learning (AirFL) allows devices to train a learning
model in parallel and synchronize their local models using over-the-air
computation. The integrity of AirFL is vulnerable due to the obscurity of the
local models aggregated over-the-air. This paper presents a novel framework to
balance the accuracy and integrity of AirFL, where multi-antenna devices and
base station (BS) are jointly optimized with a reconfigurable intelligent
surface (RIS). The key contributions include a new and non-trivial problem
jointly considering the model accuracy and integrity of AirFL, and a new
framework that transforms the problem into tractable subproblems. Under perfect
channel state information (CSI), the new framework minimizes the aggregated
model's distortion and retains the local models' recoverability by optimizing
the transmit beamformers of the devices, the receive beamformers of the BS, and
the RIS configuration in an alternating manner. Under imperfect CSI, the new
framework delivers a robust design of the beamformers and RIS configuration to
combat non-negligible channel estimation errors. As corroborated
experimentally, the novel framework can achieve comparable accuracy to the
ideal FL while preserving local model recoverability under perfect CSI, and
improve the accuracy when the number of receive antennas is small or moderate
under imperfect CSI.

Indoor multi-robot communications face two key challenges: one is the severe
signal strength degradation caused by blockages (e.g., walls) and the other is
the dynamic environment caused by robot mobility. To address these issues, we
consider the reconfigurable intelligent surface (RIS) to overcome the signal
blockage and assist the trajectory design among multiple robots. Meanwhile, the
non-orthogonal multiple access (NOMA) is adopted to cope with the scarcity of
spectrum and enhance the connectivity of robots. Considering the limited
battery capacity of robots, we aim to maximize the energy efficiency by jointly
optimizing the transmit power of the access point (AP), the phase shifts of the
RIS, and the trajectory of robots. A novel federated deep reinforcement
learning (F-DRL) approach is developed to solve this challenging problem with
one dynamic long-term objective. Through each robot planning its path and
downlink power, the AP only needs to determine the phase shifts of the RIS,
which can significantly save the computation overhead due to the reduced
training dimension. Simulation results reveal the following findings: I) the
proposed F-DRL can reduce at least 86% convergence time compared to the
centralized DRL; II) the designed algorithm can adapt to the increasing number
of robots; III) compared to traditional OMA-based benchmarks, NOMA-enhanced
schemes can achieve higher energy efficiency.

Unsupervised pre-training methods for large vision models have shown to
enhance performance on downstream supervised tasks. Developing similar
techniques for satellite imagery presents significant opportunities as
unlabelled data is plentiful and the inherent temporal and multi-spectral
structure provides avenues to further improve existing pre-training strategies.
In this paper, we present SatMAE, a pre-training framework for temporal or
multi-spectral satellite imagery based on Masked Autoencoder (MAE). To leverage
temporal information, we include a temporal embedding along with independently
masking image patches across time. In addition, we demonstrate that encoding
multi-spectral data as groups of bands with distinct spectral positional
encodings is beneficial. Our approach yields strong improvements over previous
state-of-the-art techniques, both in terms of supervised learning performance
on benchmark datasets (up to $\uparrow$ 7%), and transfer learning performance
on downstream remote sensing tasks, including land cover classification (up to
$\uparrow$ 14%) and semantic segmentation.

Data cleaning often comprises outlier detection and data repair. Systematic
errors result from nearly deterministic transformations that occur repeatedly
in the data, e.g. specific image pixels being set to default values or
watermarks. Consequently, models with enough capacity easily overfit to these
errors, making detection and repair difficult. Seeing as a systematic outlier
is a combination of patterns of a clean instance and systematic error patterns,
our main insight is that inliers can be modelled by a smaller representation
(subspace) in a model than outliers. By exploiting this, we propose Clean
Subspace Variational Autoencoder (CLSVAE), a novel semi-supervised model for
detection and automated repair of systematic errors. The main idea is to
partition the latent space and model inlier and outlier patterns separately.
CLSVAE is effective with much less labelled data compared to previous related
models, often with less than 2% of the data. We provide experiments using three
image datasets in scenarios with different levels of corruption and labelled
set sizes, comparing to relevant baselines. CLSVAE provides superior repairs
without human intervention, e.g. with just 0.25% of labelled data we see a
relative error decrease of 58% compared to the closest baseline.

Optimizing management practices and selecting the best cultivar for planting
play a significant role in increasing agricultural food production and
decreasing environmental footprint. In this study, we develop optimization
frameworks under uncertainty using conditional value-at-risk in the stochastic
programming objective function. We integrate the crop model, APSIM, and a
parallel Bayesian optimization algorithm to optimize the management practices
and select the best cultivar at different levels of risk aversion. This
approach integrates the power of optimization in determining the best decisions
and crop model in simulating nature's output corresponding to various
decisions. As a case study, we set up the crop model for 25 locations across
the US Corn Belt. We optimized the management options (planting date, N
fertilizer amount, fertilizing date, and plant density in the farm) and
cultivar options (cultivars with different maturity days) three times: a)
before, b) at planting and c) after a growing season with known weather.
Results indicated that the proposed model produced meaningful connections
between weather and optima decisions. Also, we found risk-tolerance farmers get
more expected yield than risk-averse ones in wet and non-wet weathers.

The Class Activation Map (CAM) lookup of a neural network tells us to which
regions the neural network focuses when it makes a decision. In the past, the
CAM search method was dependent upon a specific internal module of the network.
It has specific constraints on the structure of the neural network. To make the
search of CAM have generality and high performance. We propose a learning-based
algorithm, namely Multiple Dynamic Masks (MDM). It is based on a public
cognition that only active features of a picture related to classification will
affect the classification results of the neural network, and other features
will hardly affect the classification results of the network. The mask
generated by MDM conforms to the above cognition. It trains mask vectors of
different sizes by constraining mask values and activating consistency, then it
uses stacking masks of different scale to generate CAM that can balance spatial
information and semantic information. Comparing the results of MDM with those
of the recent advanced CAM search method, the performance of MDM has reached
the state of the art results. We applied the MDM method to the interpretable
neural networks ProtoPNet and XProtoNet, which improved the performance of
model in the explainable prototype search. Finally, we visualized the CAM
generation effect of MDM on neural networks of different architectures,
verifying the generality of the MDM method.

The adversarial attack can force a CNN-based model to produce an incorrect
output by craftily manipulating human-imperceptible input. Exploring such
perturbations can help us gain a deeper understanding of the vulnerability of
neural networks, and provide robustness to deep learning against miscellaneous
adversaries. Despite extensive studies focusing on the robustness of image,
audio, and NLP, works on adversarial examples of visual object tracking --
especially in a black-box manner -- are quite lacking. In this paper, we
propose a novel adversarial attack method to generate noises for single object
tracking under black-box settings, where perturbations are merely added on
initial frames of tracking sequences, which is difficult to be noticed from the
perspective of a whole video clip. Specifically, we divide our algorithm into
three components and exploit reinforcement learning for localizing important
frame patches precisely while reducing unnecessary computational queries
overhead. Compared to existing techniques, our method requires fewer queries on
initialized frames of a video to manipulate competitive or even better attack
performance. We test our algorithm in both long-term and short-term datasets,
including OTB100, VOT2018, UAV123, and LaSOT. Extensive experiments demonstrate
the effectiveness of our method on three mainstream types of trackers:
discrimination, Siamese-based, and reinforcement learning-based trackers.

In this paper, we tackle a significant challenge in PCA: heterogeneity. When
data are collected from different sources with heterogeneous trends while still
sharing some congruency, it is critical to extract shared knowledge while
retaining unique features of each source. To this end, we propose personalized
PCA (PerPCA), which uses mutually orthogonal global and local principal
components to encode both unique and shared features. We show that, under mild
conditions, both unique and shared features can be identified and recovered by
a constrained optimization problem, even if the covariance matrices are
immensely different. Also, we design a fully federated algorithm inspired by
distributed Stiefel gradient descent to solve the problem. The algorithm
introduces a new group of operations called generalized retractions to handle
orthogonality constraints, and only requires global PCs to be shared across
sources. We prove the linear convergence of the algorithm under suitable
assumptions. Comprehensive numerical experiments highlight PerPCA's superior
performance in feature extraction and prediction from heterogeneous datasets.
As a systematic approach to decouple shared and unique features from
heterogeneous datasets, PerPCA finds applications in several tasks including
video segmentation, topic extraction, and distributed clustering.

There has been considerable interest in leveraging RL and stochastic control
methods to learn optimal treatment strategies for critically ill patients,
directly from observational data. However, there is significant ambiguity on
the control objective and on the best reward choice for the standard RL
objective. In this work, we propose a clinically motivated control objective
for critically ill patients, for which the value functions have a simple
medical interpretation. Further, we present theoretical results and adapt our
method to a practical Deep RL algorithm, which can be used alongside any value
based Deep RL method. We experiment on a large sepsis cohort and show that our
method produces results consistent with clinical knowledge.

We study a matrix that arises in a singular formulation of the Woodbury
matrix identity when the Woodbury identity no longer holds. We present
generalized inverse and pseudo-determinant identities for such matrix that have
direct applications to the Gaussian process regression, in particular, its
likelihood representation and its precision matrix. We also provide an
efficient algorithm and numerical analysis for the presented determinant
identities and demonstrate their advantages in certain conditions which are
applicable to computing log-determinant terms in likelihood functions of
Gaussian process regression.

Spatial resolution of medical images can be improved using super-resolution
methods. Real Enhanced Super Resolution Generative Adversarial Network
(Real-ESRGAN) is one of the recent effective approaches utilized to produce
higher resolution images, given input images of lower resolution. In this
paper, we apply this method to enhance the spatial resolution of 2D MR images.
In our proposed approach, we slightly modify the structure of the Real-ESRGAN
to train 2D Magnetic Resonance images (MRI) taken from the Brain Tumor
Segmentation Challenge (BraTS) 2018 dataset. The obtained results are validated
qualitatively and quantitatively by computing SSIM (Structural Similarity Index
Measure), NRMSE (Normalized Root Mean Square Error), MAE (Mean Absolute Error),
and VIF (Visual Information Fidelity) values.

When users exchange data with Unmanned Aerial vehicles - (UAVs) over
air-to-ground (A2G) wireless communication networks, they expose the link to
attacks that could increase packet loss and might disrupt connectivity. For
example, in emergency deliveries, losing control information (i.e data related
to the UAV control communication) might result in accidents that cause UAV
destruction and damage to buildings or other elements in a city. To prevent
these problems, these issues must be addressed in 5G and 6G scenarios. This
research offers a deep learning (DL) approach for detecting attacks in UAVs
equipped with orthogonal frequency division multiplexing (OFDM) receivers on
Clustered Delay Line (CDL) channels in highly complex scenarios involving
authenticated terrestrial users, as well as attackers in unknown locations. We
use the two observable parameters available in 5G UAV connections: the Received
Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise
Ratio (SINR). The prospective algorithm is generalizable regarding attack
identification, which does not occur during training. Further, it can identify
all the attackers in the environment with 20 terrestrial users. A deeper
investigation into the timing requirements for recognizing attacks show that
after training, the minimum time necessary after the attack begins is 100 ms,
and the minimum attack power is 2 dBm, which is the same power that the
authenticated UAV uses. Our algorithm also detects moving attackers from a
distance of 500 m.

Information over-squashing is a phenomenon of inefficient information
propagation between distant nodes on networks. It is an important problem that
is known to significantly impact the training of graph neural networks (GNNs),
as the receptive field of a node grows exponentially. To mitigate this problem,
a preprocessing procedure known as rewiring is often applied to the input
network. In this paper, we investigate the use of discrete analogues of
classical geometric notions of curvature to model information flow on networks
and rewire them. We show that these classical notions achieve state-of-the-art
performance in GNN training accuracy on a variety of real-world network
datasets. Moreover, compared to the current state-of-the-art, these classical
notions exhibit a clear advantage in computational runtime by several orders of

Generating representations of video data is of key importance in advancing
the field of machine perception. Most current techniques rely on hand-annotated
data, which can be difficult to work with, expensive to generate, and hard to
scale. In this work, we propose a novel learning approach based on contrastive
learning, LAVA, which is capable of learning joint language, audio, and video
representations in a self-supervised manner. We pre-train LAVA on the Kinetics
700 dataset using transformer encoders to learn representations for each
modality. We then demonstrate that LAVA performs competitively with the current
state-of-the-art self-supervised and weakly-supervised pretraining techniques
on UCF-101 and HMDB-51 video action recognition while using a fraction of the

Deep learning for molecular science has so far mainly focused on 2D molecular
graphs. Recently, however, there has been work to extend it to 3D molecular
geometry, due to its scientific significance and critical importance in
real-world applications. The 3D distance-geometric graph representation (DG-GR)
adopts a unified scheme (distance) for representing the geometry of 3D graphs.
It is invariant to rotation and translation of the graph, and it reflects
pair-wise node interactions and their generally local nature, particularly
relevant for 3D molecular geometry. To facilitate the incorporation of 3D
molecular geometry in deep learning for molecular science, we adopt the new
graph attention network with dynamic attention (GATv2) for use with DG-GR and
propose the 3D distance-geometric graph attention network (DG-GAT). GATv2 is a
great fit for DG-GR since the attention can vary by node and by distance
between nodes. Experimental results of DG-GAT for the ESOL and FreeSolv
datasets show major improvement (31% and 38%, respectively) over those of the
standard graph convolution network based on 2D molecular graphs. The same is
true for the QM9 dataset. Our work demonstrates the utility and value of DG-GAT
for deep learning based on 3D molecular geometry.

Deep reinforcement learning approaches have been a popular method for visual
navigation tasks in the computer vision and robotics community of late. In most
cases, the reward function has a binary structure, i.e., a large positive
reward is provided when the agent reaches goal state, and a negative step
penalty is assigned for every other state in the environment. A sparse signal
like this makes the learning process challenging, specially in big
environments, where a large number of sequential actions need to be taken to
reach the target. We introduce a reward shaping mechanism which gradually
adjusts the reward signal based on distance to the goal. Detailed experiments
conducted using the AI2-THOR simulation environment demonstrate the efficacy of
the proposed approach for object-goal navigation tasks.

In this paper, we study an online sampling problem of the Wiener process. The
goal is to minimize the mean squared error (MSE) of the remote estimator under
a sampling frequency constraint when the transmission delay distribution is
unknown. The sampling problem is reformulated into a renewal reward
optimization problem, and we propose an online sampling algorithm that can
adaptively learn the optimal sampling policy through stochastic approximation.
We show that the cumulative MSE regret grows with rate $\mathcal{O}(\ln k)$,
where $k$ is the number of samples. Through Le Cam's two point method, we show
that the worst-case cumulative MSE regret of any online sampling algorithm is
lower bounded by $\Omega(\ln k)$. Hence, the proposed online sampling algorithm
is minimax order-optimal. Finally, we validate the performance of the proposed
algorithm via numerical simulations.

In this paper, we study the tradeoffs between the time speedup and the round
complexity in the collaborative learning model with non-IID data, where
multiple agents interact with possibly different environments and they want to
learn an objective in the aggregated environment. We use a basic problem in
bandit theory called best arm identification in multi-armed bandits as a
vehicle to deliver the following conceptual message: collaborative learning on
non-IID data is provably more difficult than that on IID data. In particular,
we show the following: 1) Learning time speedup in the non-IID data setting can
be much smaller than $1$ (that is, a slowdown). When the number of rounds $R =
O(1)$, we will need at least a polynomial number of agents (in terms of the
number of arms) to achieve a speedup $\tilde{\Omega}(1)$. This is in stark
contrast to the IID data setting, where the speedup is always
$\tilde{\Omega}(1)$ regardless of $R$ and the number of agents $K$. 2) Local
adaptivity of the agents cannot help much in the non-IID data setting. This is
in contrast with the IID data setting, in which to achieve the same speedup,
the best non-adaptive algorithm requires a significantly larger number of
rounds than the best adaptive algorithm.

Human beings use compositionality to generalise from past experiences to
actual or fictive, novel experiences. To do so, we separate our experiences
into fundamental atomic components. These atomic components can then be
recombined in novel ways to support our ability to imagine and engage with
novel experiences. We frame this as the ability to learn to generalise
compositionally. And, we will refer to behaviours making use of this ability as
compositional learning behaviours (CLBs).
  A central problem to learning CLBs is the resolution of a binding problem
(BP) (by learning to, firstly, segregate the supportive stimulus components
from the observation of multiple stimuli, and then, combine them in a single
episodic experience). While it is another feat of intelligence that human
beings perform with ease, it is not the case for state-of-the-art artificial
agents.
  Thus, in order to build artificial agents able to collaborate with human
beings, we propose to develop a novel benchmark to investigate agents'
abilities to exhibit CLBs by solving a domain-agnostic version of the BP. We
take inspiration from the language emergence and grounding framework of
referential games and propose a meta-learning extension of referential games,
entitled Meta-Referential Games, and use this framework to build our benchmark,
that we name Symbolic Behaviour Benchmark (S2B).
  While it has the potential to test for more symbolic behaviours, rather than
solely CLBs, in the present paper, though, we solely focus on the single-agent
language grounding task that tests for CLBs. We provide baseline results for
it, using state-of-the-art RL agents, and show that our proposed benchmark is a
compelling challenge that we hope will spur the research community towards
developing more capable artificial agents.

Exploiting sparsity underlying neural networks has become one of the most
potential methodologies to reduce the memory footprint, I/O cost, and
computation workloads during inference. And the degree of sparsity one can
exploit has become higher as larger model sizes have been considered along with
the trend of pre-training giant models. On the other hand, compared with
quantization that has been a widely supported option, acceleration through
high-degree sparsity is not supported in most computing platforms. In this
work, we introduce the first commercial hardware platform supporting
high-degree sparsity acceleration up to 32 times -- S4. Combined with
state-of-the-art sparse pruning techniques, we demonstrate several-times
practical inference speedup on S4 over mainstream inference platforms such as
Nvidia T4. We also show that in practice a sparse model of larger size can
achieve both higher accuracy and higher throughput on S4 than a dense model of
smaller size.

A self-supervised multi-task learning (SSMTL) framework for video anomaly
detection was recently introduced in literature. Due to its highly accurate
results, the method attracted the attention of many researchers. In this work,
we revisit the self-supervised multi-task learning framework, proposing several
updates to the original method. First, we study various detection methods, e.g.
based on detecting high-motion regions using optical flow or background
subtraction, since we believe the currently used pre-trained YOLOv3 is
suboptimal, e.g. objects in motion or objects from unknown classes are never
detected. Second, we modernize the 3D convolutional backbone by introducing
multi-head self-attention modules, inspired by the recent success of vision
transformers. As such, we alternatively introduce both 2D and 3D convolutional
vision transformer (CvT) blocks. Third, in our attempt to further improve the
model, we study additional self-supervised learning tasks, such as predicting
segmentation maps through knowledge distillation, solving jigsaw puzzles,
estimating body pose through knowledge distillation, predicting masked regions
(inpainting), and adversarial learning with pseudo-anomalies. We conduct
experiments to assess the performance impact of the introduced changes. Upon
finding more promising configurations of the framework, dubbed SSMTL++v1 and
SSMTL++v2, we extend our preliminary experiments to more data sets,
demonstrating that our performance gains are consistent across all data sets.
In most cases, our results on Avenue, ShanghaiTech and UBnormal raise the
state-of-the-art performance bar to a new level.

There is a growing need for sparse representational formats of human
affective states that can be utilized in scenarios with limited computational
memory resources. We explore whether representing neural data, in response to
emotional stimuli, in a latent vector space can serve to both predict emotional
states as well as generate synthetic EEG data that are participant- and/or
emotion-specific. We propose a conditional variational autoencoder based
framework, EEG2Vec, to learn generative-discriminative representations from EEG
data. Experimental results on affective EEG recording datasets demonstrate that
our model is suitable for unsupervised EEG modeling, classification of three
distinct emotion categories (positive, neutral, negative) based on the latent
representation achieves a robust performance of 68.49%, and generated synthetic
EEG sequences resemble real EEG data inputs to particularly reconstruct
low-frequency signal components. Our work advances areas where affective EEG
representations can be useful in e.g., generating artificial (labeled) training
data or alleviating manual feature extraction, and provide efficiency for
memory constrained edge computing applications.

In this work, we focus on generating graphical representations of noisy,
instructional videos for video understanding. We propose a self-supervised,
interpretable approach that does not require any annotations for graphical
representations, which would be expensive and time consuming to collect. We
attempt to overcome "black box" learning limitations by presenting Semantic
Video Graph or SVGraph, a multi-modal approach that utilizes narrations for
semantic interpretability of the learned graphs. SVGraph 1) relies on the
agreement between multiple modalities to learn a unified graphical structure
with the help of cross-modal attention and 2) assigns semantic interpretation
with the help of Semantic-Assignment, which captures the semantics from video
narration. We perform experiments on multiple datasets and demonstrate the
interpretability of SVGraph in semantic graph learning.

We describe a method for signal parameter estimation using the signed
cumulative distribution transform (SCDT), a recently introduced signal
representation tool based on optimal transport theory. The method builds upon
signal estimation using the cumulative distribution transform (CDT) originally
introduced for positive distributions. Specifically, we show that
Wasserstein-type distance minimization can be performed simply using linear
least squares techniques in SCDT space for arbitrary signal classes, thus
providing a global minimizer for the estimation problem even when the
underlying signal is a nonlinear function of the unknown parameters.
Comparisons to current signal estimation methods using $L_p$ minimization shows
the advantage of the method.

Memristors have shown promising features for enhancing neuromorphic computing
concepts and AI hardware accelerators. In this paper, we present a
user-friendly software infrastructure that allows emulating a wide range of
neuromorphic architectures with memristor models. This tool empowers studies
that exploit memristors for online learning and online classification tasks,
predicting memristor resistive state changes during the training process. The
versatility of the tool is showcased through the capability for users to
customise parameters in the employed memristor and neuronal models as well as
the employed learning rules. This further allows users to validate concepts and
their sensitivity across a wide range of parameters. We demonstrate the use of
the tool via an MNIST classification task. Finally, we show how this tool can
also be used to emulate the concepts under study in-silico with practical
memristive devices via appropriate interfacing with commercially available
characterisation tools.

Time series forecasting has always been a thought-provoking topic in the
field of machine learning. Machine learning scientists define a time series as
a set of observations recorded over consistent time steps. And, time series
forecasting is a way of analyzing the data and finding how variables change
over time and hence, predicting the future value. Time is of great essence in
this forecasting as it shows how the data coordinates over the dataset and the
final result. It also requires a large dataset to ascertain the regularity and
reliability. Quantum computers may prove to be a better option for perceiving
the trends in the time series by exploiting quantum mechanical phenomena like
superposition and entanglement. Here, we consider one-step time series
forecasting using variational quantum circuits, and record observations for
different datasets.

In this relatively informal discussion-paper we summarise issues in the
domains of safety and security in machine learning that will affect industry
sectors in the next five to ten years. Various products using neural network
classification, most often in vision related applications but also in
predictive maintenance, have been researched and applied in real-world
applications in recent years. Nevertheless, reports of underlying problems in
both safety and security related domains, for instance adversarial attacks have
unsettled early adopters and are threatening to hinder wider scale adoption of
this technology. The problem for real-world applicability lies in being able to
assess the risk of applying these technologies. In this discussion-paper we
describe the process of arriving at a machine-learnt neural network classifier
pointing out safety and security vulnerabilities in that workflow, citing
relevant research where appropriate.

We provide the first sub-linear space and sub-linear regret algorithm for
online learning with expert advice (against an oblivious adversary), addressing
an open question raised recently by Srinivas, Woodruff, Xu and Zhou (STOC
2022). We also demonstrate a separation between oblivious and (strong) adaptive
adversaries by proving a linear memory lower bound of any sub-linear regret
algorithm against an adaptive adversary. Our algorithm is based on a novel pool
selection procedure that bypasses the traditional wisdom of leader selection
for online learning, and a generic reduction that transforms any weakly
sub-linear regret $o(T)$ algorithm to $T^{1-\alpha}$ regret algorithm, which
may be of independent interest. Our lower bound utilizes the connection of
no-regret learning and equilibrium computation in zero-sum games, leading to a
proof of a strong lower bound against an adaptive adversary.

Most existing compound facial expression recognition (FER) methods rely on
large-scale labeled compound expression data for training. However, collecting
such data is labor-intensive and time-consuming. In this paper, we address the
compound FER task in the cross-domain few-shot learning (FSL) setting, which
requires only a few samples of compound expressions in the target domain.
Specifically, we propose a novel cascaded decomposition network (CDNet), which
cascades several learn-to-decompose modules with shared parameters based on a
sequential decomposition mechanism, to obtain a transferable feature space. To
alleviate the overfitting problem caused by limited base classes in our task, a
partial regularization strategy is designed to effectively exploit the best of
both episodic training and batch training. By training across similar tasks on
multiple basic expression datasets, CDNet learns the ability of
learn-to-decompose that can be easily adapted to identify unseen compound
expressions. Extensive experiments on both in-the-lab and in-the-wild compound
expression datasets demonstrate the superiority of our proposed CDNet against
several state-of-the-art FSL methods. Code is available at:
https://github.com/zouxinyi0625/CDNet.

Watermarking is a commonly used strategy to protect creators' rights to
digital images, videos and audio. Recently, watermarking methods have been
extended to deep learning models -- in principle, the watermark should be
preserved when an adversary tries to copy the model. However, in practice,
watermarks can often be removed by an intelligent adversary. Several papers
have proposed watermarking methods that claim to be empirically resistant to
different types of removal attacks, but these new techniques often fail in the
face of new or better-tuned adversaries. In this paper, we propose a
certifiable watermarking method. Using the randomized smoothing technique
proposed in Chiang et al., we show that our watermark is guaranteed to be
unremovable unless the model parameters are changed by more than a certain l2
threshold. In addition to being certifiable, our watermark is also empirically
more robust compared to previous watermarking methods. Our experiments can be
reproduced with code at https://github.com/arpitbansal297/Certified_Watermarks

Recently, diffusion models have shown remarkable results in image synthesis
by gradually removing noise and amplifying signals. Although the simple
generative process surprisingly works well, is this the best way to generate
image data? For instance, despite the fact that human perception is more
sensitive to the low frequencies of an image, diffusion models themselves do
not consider any relative importance of each frequency component. Therefore, to
incorporate the inductive bias for image data, we propose a novel generative
process that synthesizes images in a coarse-to-fine manner. First, we
generalize the standard diffusion models by enabling diffusion in a rotated
coordinate system with different velocities for each component of the vector.
We further propose a blur diffusion as a special case, where each frequency
component of an image is diffused at different speeds. Specifically, the
proposed blur diffusion consists of a forward process that blurs an image and
adds noise gradually, after which a corresponding reverse process deblurs an
image and removes noise progressively. Experiments show that the proposed model
outperforms the previous method in FID on LSUN bedroom and church datasets.
Code is available at https://github.com/sangyun884/blur-diffusion.

A datathon is a time-constrained competition involving data science applied
to a specific problem. In the past decade, datathons have been shown to be a
valuable bridge between fields and expertise . Biomedical data analysis
represents a challenging area requiring collaboration between engineers,
biologists and physicians to gain a better understanding of patient physiology
and of guide decision processes for diagnosis, prognosis and therapeutic
interventions to improve care practice. Here, we reflect on the outcomes of an
event that we organized in Israel at the end of March 2022 between the MIT
Critical Data group, Rambam Health Care Campus (Rambam) and the Technion Israel
Institute of Technology (Technion) in Haifa. Participants were asked to
complete a survey about their skills and interests, which enabled us to
identify current needs in machine learning training for medical problem
applications. This work describes opportunities and limitations in medical data
science in the Israeli context.

Applications of machine learning (ML) are growing by the day for many unique
and challenging scientific applications. However, a crucial challenge facing
these applications is their need for ultra low-latency and on-detector ML
capabilities. Given the slowdown in Moore's law and Dennard scaling, coupled
with the rapid advances in scientific instrumentation that is resulting in
growing data rates, there is a need for ultra-fast ML at the extreme edge. Fast
ML at the edge is essential for reducing and filtering scientific data in
real-time to accelerate science experimentation and enable more profound
insights. To accelerate real-time scientific edge ML hardware and software
solutions, we need well-constrained benchmark tasks with enough specifications
to be generically applicable and accessible. These benchmarks can guide the
design of future edge ML hardware for scientific applications capable of
meeting the nanosecond and microsecond level latency requirements. To this end,
we present an initial set of scientific ML benchmarks, covering a variety of ML
and embedded system techniques.

Cloud computing has become the backbone of the computing industry and offers
subscription-based on-demand services. Through virtualization, which produces a
virtual instance of a computer system running in an abstracted hardware layer,
it has made it possible for us to share resources among many users. Contrary to
early distributed computing models, it guarantees limitless computing resources
through its expansive cloud datacenters, and it has been immensely popular in
recent years because to its constantly expanding infrastructure, user base, and
hosted data volume. These datacenters enormous and sophisticated workloads
present a number of problems, including issues with resource use, power
consumption, scalability, operational expense, security and many more. In this
context, the article provides a comprehensive overview of the most prevalent
problems with sharing and communication in organizations of all sizes. There is
discussion of a taxonomy of security, load balancing, and the main difficulties
encountered in protecting sensitive data. A complete examination of current
state-of-the-art contributions, resource management analysis methodologies,
load balancing solutions, empowering heuristics, and multi-objective
learning-based approaches are required. In its final section, the paper
examines, reviews, and suggests future research directions in the area of
secure VM placements.

The famous $k$-means++ algorithm of Arthur and Vassilvitskii [SODA 2007] is
the most popular way of solving the $k$-means problem in practice. The
algorithm is very simple: it samples the first center uniformly at random and
each of the following $k-1$ centers is then always sampled proportional to its
squared distance to the closest center so far. Afterward, Lloyd's iterative
algorithm is run. The $k$-means++ algorithm is known to return a $\Theta(\log
k)$ approximate solution in expectation.
  In their seminal work, Arthur and Vassilvitskii [SODA 2007] asked about the
guarantees for its following \emph{greedy} variant: in every step, we sample
$\ell$ candidate centers instead of one and then pick the one that minimizes
the new cost. This is also how $k$-means++ is implemented in e.g. the popular
Scikit-learn library [Pedregosa et al.; JMLR 2011].
  We present nearly matching lower and upper bounds for the greedy $k$-means++:
We prove that it is an $O(\ell^3 \log^3 k)$-approximation algorithm. On the
other hand, we prove a lower bound of $\Omega(\ell^3 \log^3 k / \log^2(\ell\log
k))$. Previously, only an $\Omega(\ell \log k)$ lower bound was known
[Bhattacharya, Eube, R\"oglin, Schmidt; ESA 2020] and there was no known upper
bound.

We consider federated learning with personalization, where in addition to a
global objective, each client is also interested in maximizing a personalized
local objective. We consider this problem under a general continuous action
space setting where the objective functions belong to a reproducing kernel
Hilbert space. We propose algorithms based on surrogate Gaussian process (GP)
models that achieve the optimal regret order (up to polylogarithmic factors).
Furthermore, we show that the sparse approximations of the GP models
significantly reduce the communication cost across clients.

When a high-resolution (HR) image is degraded into a low-resolution (LR)
image, the image loses some of the existing information. Consequently, multiple
HR images can correspond to the LR image. Most of the existing methods do not
consider the uncertainty caused by the stochastic attribute, which can only be
probabilistically inferred. Therefore, the predicted HR images are often blurry
because the network tries to reflect all possibilities in a single output
image. To overcome this limitation, this paper proposes a novel face
super-resolution (SR) scheme to take into the uncertainty by stochastic
modeling. Specifically, the information in LR images is separately encoded into
deterministic and stochastic attributes. Furthermore, an Input Conditional
Attribute Predictor is proposed and separately trained to predict the partially
alive stochastic attributes from only the LR images. Extensive evaluation shows
that the proposed method successfully reduces the uncertainty in the learning
process and outperforms the existing state-of-the-art approaches.

Implementations of SGD on distributed systems create new vulnerabilities,
which can be identified and misused by one or more adversarial agents.
Recently, it has been shown that well-known Byzantine-resilient gradient
aggregation schemes are indeed vulnerable to informed attackers that can tailor
the attacks (Fang et al., 2020; Xie et al., 2020b). We introduce MixTailor, a
scheme based on randomization of the aggregation strategies that makes it
impossible for the attacker to be fully informed. Deterministic schemes can be
integrated into MixTailor on the fly without introducing any additional
hyperparameters. Randomization decreases the capability of a powerful adversary
to tailor its attacks, while the resulting randomized aggregation scheme is
still competitive in terms of performance. For both iid and non-iid settings,
we establish almost sure convergence guarantees that are both stronger and more
general than those available in the literature. Our empirical studies across
various datasets, attacks, and settings, validate our hypothesis and show that
MixTailor successfully defends when well-known Byzantine-tolerant schemes fail.

Perception of auditory events is inherently multimodal relying on both audio
and visual cues. A large number of existing multimodal approaches process each
modality using modality-specific models and then fuse the embeddings to encode
the joint information. In contrast, we employ heterogeneous graphs to
explicitly capture the spatial and temporal relationships between the
modalities and represent detailed information about the underlying signal.
Using heterogeneous graph approaches to address the task of visually-aware
acoustic event classification, which serves as a compact, efficient and
scalable way to represent data in the form of graphs. Through heterogeneous
graphs, we show efficiently modelling of intra- and inter-modality
relationships both at spatial and temporal scales. Our model can easily be
adapted to different scales of events through relevant hyperparameters.
Experiments on AudioSet, a large benchmark, shows that our model achieves

Text response generation for multimodal task-oriented dialog systems, which
aims to generate the proper text response given the multimodal context, is an
essential yet challenging task. Although existing efforts have achieved
compelling success, they still suffer from two pivotal limitations: 1) overlook
the benefit of generative pre-training, and 2) ignore the textual context
related knowledge. To address these limitations, we propose a novel dual
knowledge-enhanced generative pretrained language model for multimodal
task-oriented dialog systems (DKMD), consisting of three key components: dual
knowledge selection, dual knowledge-enhanced context learning, and
knowledge-enhanced response generation. To be specific, the dual knowledge
selection component aims to select the related knowledge according to both
textual and visual modalities of the given context. Thereafter, the dual
knowledge-enhanced context learning component targets seamlessly integrating
the selected knowledge into the multimodal context learning from both global
and local perspectives, where the cross-modal semantic relation is also
explored. Moreover, the knowledge-enhanced response generation component
comprises a revised BART decoder, where an additional dot-product
knowledge-decoder attention sub-layer is introduced for explicitly utilizing
the knowledge to advance the text response generation. Extensive experiments on
a public dataset verify the superiority of the proposed DKMD over
state-of-the-art competitors.

Recently, deep convolutional neural networks (CNNs) have achieved many
eye-catching results. However, deploying CNNs on resource-constrained edge
devices is constrained by limited memory bandwidth for transmitting large
intermediated data during inference, i.e., activation. Existing research
utilizes mixed-precision and dimension reduction to reduce computational
complexity but pays less attention to its application for activation
compression. To further exploit the redundancy in activation, we propose a
learnable mixed-precision and dimension reduction co-design system, which
separates channels into groups and allocates specific compression policies
according to their importance. In addition, the proposed dynamic searching
technique enlarges search space and finds out the optimal bit-width allocation
automatically. Our experimental results show that the proposed methods improve
3.54%/1.27% in accuracy and save 0.18/2.02 bits per value over existing
mixed-precision methods on ResNet18 and MobileNetv2, respectively.

Deep neural networks have exhibited remarkable performance in image
super-resolution (SR) tasks by learning a mapping from low-resolution (LR)
images to high-resolution (HR) images. However, the SR problem is typically an
ill-posed problem and existing methods would come with several limitations.
First, the possible mapping space of SR can be extremely large since there may
exist many different HR images that can be downsampled to the same LR image. As
a result, it is hard to directly learn a promising SR mapping from such a large
space. Second, it is often inevitable to develop very large models with
extremely high computational cost to yield promising SR performance. In
practice, one can use model compression techniques to obtain compact models by
reducing model redundancy. Nevertheless, it is hard for existing model
compression methods to accurately identify the redundant components due to the
extremely large SR mapping space. To alleviate the first challenge, we propose
a dual regression learning scheme to reduce the space of possible SR mappings.
Specifically, in addition to the mapping from LR to HR images, we learn an
additional dual regression mapping to estimate the downsampling kernel and
reconstruct LR images. In this way, the dual mapping acts as a constraint to
reduce the space of possible mappings. To address the second challenge, we
propose a lightweight dual regression compression method to reduce model
redundancy in both layer-level and channel-level based on channel pruning.
Specifically, we first develop a channel number search method that minimizes
the dual regression loss to determine the redundancy of each layer. Given the
searched channel numbers, we further exploit the dual regression manner to
evaluate the importance of channels and prune the redundant ones. Extensive
experiments show the effectiveness of our method in obtaining accurate and
efficient SR models.

Quantum computing has been moving from a theoretical phase to practical one,
presenting daunting challenges in implementing physical qubits, which are
subjected to noises from the surrounding environment. These quantum noises are
ubiquitous in quantum devices and generate adverse effects in the quantum
computational model, leading to extensive research on their correction and
mitigation techniques. But do these quantum noises always provide
disadvantages? We tackle this issue by proposing a framework called quantum
noise-induced reservoir computing and show that some abstract quantum noise
models can induce useful information processing capabilities for temporal input
data. We demonstrate this ability in several typical benchmarks and investigate
the information processing capacity to clarify the framework's processing
mechanism and memory profile. We verified our perspective by implementing the
framework in a number of IBM quantum processors and obtained similar
characteristic memory profiles with model analyses. As a surprising result,
information processing capacity increased with quantum devices' higher noise
levels and error rates. Our study opens up a novel path for diverting useful
information from quantum computer noises into a more sophisticated information
processor.

Recently, several spatial-temporal memory-based methods have verified that
storing intermediate frames and their masks as memory are helpful to segment
target objects in videos. However, they mainly focus on better matching between
the current frame and the memory frames without explicitly paying attention to
the quality of the memory. Therefore, frames with poor segmentation masks are
prone to be memorized, which leads to a segmentation mask error accumulation
problem and further affect the segmentation performance. In addition, the
linear increase of memory frames with the growth of frame number also limits
the ability of the models to handle long videos. To this end, we propose a
Quality-aware Dynamic Memory Network (QDMN) to evaluate the segmentation
quality of each frame, allowing the memory bank to selectively store accurately
segmented frames to prevent the error accumulation problem. Then, we combine
the segmentation quality with temporal consistency to dynamically update the
memory bank to improve the practicability of the models. Without any bells and
whistles, our QDMN achieves new state-of-the-art performance on both DAVIS and
YouTube-VOS benchmarks. Moreover, extensive experiments demonstrate that the
proposed Quality Assessment Module (QAM) can be applied to memory-based methods
as generic plugins and significantly improves performance. Our source code is
available at https://github.com/workforai/QDMN.

Euler's elastica constitute an appealing variational image inpainting model.
It minimises an energy that involves the total variation as well as the level
line curvature. These components are transparent and make it attractive for
shape completion tasks. However, its gradient flow is a singular, anisotropic,
and nonlinear PDE of fourth order, which is numerically challenging: It is
difficult to find efficient algorithms that offer sharp edges and good rotation
invariance. As a remedy, we design the first neural algorithm that simulates
inpainting with Euler's Elastica. We use the deep energy concept which employs
the variational energy as neural network loss. Furthermore, we pair it with a
deep image prior where the network architecture itself acts as a prior. This
yields better inpaintings by steering the optimisation trajectory closer to the
desired solution. Our results are qualitatively on par with state-of-the-art
algorithms on elastica-based shape completion. They combine good rotation
invariance with sharp edges. Moreover, we benefit from the high efficiency and
effortless parallelisation within a neural framework. Our neural elastica
approach only requires 3x3 central difference stencils. It is thus much simpler
than other well-performing algorithms for elastica inpainting. Last but not
least, it is unsupervised as it requires no ground truth training data.

Non-holonomic vehicle motion has been studied extensively using physics-based
models. Common approaches when using these models interpret the wheel/ground
interactions using a linear tire model and thus may not fully capture the
nonlinear and complex dynamics under various environments. On the other hand,
neural network models have been widely employed in this domain, demonstrating
powerful function approximation capabilities. However, these black-box learning
strategies completely abandon the existing knowledge of well-known physics. In
this paper, we seamlessly combine deep learning with a fully differentiable
physics model to endow the neural network with available prior knowledge. The
proposed model shows better generalization performance than the vanilla neural
network model by a large margin. We also show that the latent features of our
model can accurately represent lateral tire forces without the need for any
additional training. Lastly, We develop a risk-aware model predictive
controller using proprioceptive information derived from the latent features.
We validate our idea in two autonomous driving tasks under unknown friction,
outperforming the baseline control framework.

Plant diseases are the primary cause of crop losses globally, with an impact
on the world economy. To deal with these issues, smart agriculture solutions
are evolving that combine the Internet of Things and machine learning for early
disease detection and control. Many such systems use vision-based machine
learning methods for real-time disease detection and diagnosis. With the
advancement in deep learning techniques, new methods have emerged that employ
convolutional neural networks for plant disease detection and identification.
Another trend in vision-based deep learning is the use of vision transformers,
which have proved to be powerful models for classification and other problems.
However, vision transformers have rarely been investigated for plant pathology
applications. In this study, a Vision Transformer enabled Convolutional Neural
Network model called "PlantXViT" is proposed for plant disease identification.
The proposed model combines the capabilities of traditional convolutional
neural networks with the Vision Transformers to efficiently identify a large
number of plant diseases for several crops. The proposed model has a
lightweight structure with only 0.8 million trainable parameters, which makes
it suitable for IoT-based smart agriculture services. The performance of
PlantXViT is evaluated on five publicly available datasets. The proposed
PlantXViT network performs better than five state-of-the-art methods on all
five datasets. The average accuracy for recognising plant diseases is shown to
exceed 93.55%, 92.59%, and 98.33% on Apple, Maize, and Rice datasets,
respectively, even under challenging background conditions. The efficiency in
terms of explainability of the proposed model is evaluated using
gradient-weighted class activation maps and Local Interpretable Model Agnostic
Explanation.

The current studies of Scene Graph Generation (SGG) focus on solving the
long-tailed problem for generating unbiased scene graphs. However, most
de-biasing methods overemphasize the tail predicates and underestimate head
ones throughout training, thereby wrecking the representation ability of head
predicate features. Furthermore, these impaired features from head predicates
harm the learning of tail predicates. In fact, the inference of tail predicates
heavily depends on the general patterns learned from head ones, e.g., "standing
on" depends on "on". Thus, these de-biasing SGG methods can neither achieve
excellent performance on tail predicates nor satisfying behaviors on head ones.
To address this issue, we propose a Dual-branch Hybrid Learning network (DHL)
to take care of both head predicates and tail ones for SGG, including a
Coarse-grained Learning Branch (CLB) and a Fine-grained Learning Branch (FLB).
Specifically, the CLB is responsible for learning expertise and robust features
of head predicates, while the FLB is expected to predict informative tail
predicates. Furthermore, DHL is equipped with a Branch Curriculum Schedule
(BCS) to make the two branches work well together. Experiments show that our
approach achieves a new state-of-the-art performance on VG and GQA datasets and
makes a trade-off between the performance of tail predicates and head ones.
Moreover, extensive experiments on two downstream tasks (i.e., Image Captioning
and Sentence-to-Graph Retrieval) further verify the generalization and
practicability of our method.

The inference of causal structures from observed data plays a key role in
unveiling the underlying dynamics of the system. This paper exposes a novel
method, named Multiscale-Causal Structure Learning (MS-CASTLE), to estimate the
structure of linear causal relationships occurring at different time scales.
Differently from existing approaches, MS-CASTLE takes explicitly into account
instantaneous and lagged inter-relations between multiple time series,
represented at different scales, hinging on stationary wavelet transform and
non-convex optimization. MS-CASTLE incorporates, as a special case, a
single-scale version named SS-CASTLE, which compares favorably in terms of
computational efficiency, performance and robustness with respect to the state
of the art onto synthetic data. We used MS-CASTLE to study the multiscale
causal structure of the risk of 15 global equity markets, during covid-19
pandemic, illustrating how MS-CASTLE can extract meaningful information thanks
to its multiscale analysis, outperforming SS-CASTLE. We found that the most
persistent and strongest interactions occur at mid-term time resolutions.
Moreover, we identified the stock markets that drive the risk during the
considered period: Brazil, Canada and Italy. The proposed approach can be
exploited by financial investors who, depending to their investment horizon,
can manage the risk within equity portfolios from a causal perspective.

The Internet of Things (IoT) has altered living by controlling devices/things
over the Internet. IoT has specified many smart solutions for daily problems,
transforming cyber-physical systems (CPS) and other classical fields into smart
regions. Most of the edge devices that make up the Internet of Things have very
minimal processing power. To bring down the IoT network, attackers can utilise
these devices to conduct a variety of network attacks. In addition, as more and
more IoT devices are added, the potential for new and unknown threats grows
exponentially. For this reason, an intelligent security framework for IoT
networks must be developed that can identify such threats. In this paper, we
have developed an unsupervised ensemble learning model that is able to detect
new or unknown attacks in an IoT network from an unlabelled dataset. The
system-generated labelled dataset is used to train a deep learning model to
detect IoT network attacks. Additionally, the research presents a feature
selection mechanism for identifying the most relevant aspects in the dataset
for detecting attacks. The study shows that the suggested model is able to
identify the unlabelled IoT network datasets and DBN (Deep Belief Network)
outperform the other models with a detection accuracy of 97.5% and a false
alarm rate of 2.3% when trained using labelled dataset supplied by the proposed
approach.

Inter-person occlusion and depth ambiguity make estimating the 3D poses of
monocular multiple persons as camera-centric coordinates a challenging problem.
Typical top-down frameworks suffer from high computational redundancy with an
additional detection stage. By contrast, the bottom-up methods enjoy low
computational costs as they are less affected by the number of humans. However,
most existing bottom-up methods treat camera-centric 3D human pose estimation
as two unrelated subtasks: 2.5D pose estimation and camera-centric depth
estimation. In this paper, we propose a unified model that leverages the mutual
benefits of both these subtasks. Within the framework, a robust structured 2.5D
pose estimation is designed to recognize inter-person occlusion based on depth
relationships. Additionally, we develop an end-to-end geometry-aware depth
reasoning method that exploits the mutual benefits of both 2.5D pose and
camera-centric root depths. This method first uses 2.5D pose and geometry
information to infer camera-centric root depths in a forward pass, and then
exploits the root depths to further improve representation learning of 2.5D
pose estimation in a backward pass. Further, we designed an adaptive fusion
scheme that leverages both visual perception and body geometry to alleviate
inherent depth ambiguity issues. Extensive experiments demonstrate the
superiority of our proposed model over a wide range of bottom-up methods. Our
accuracy is even competitive with top-down counterparts. Notably, our model
runs much faster than existing bottom-up and top-down methods.

RGB-D salient object detection (SOD) has been in the spotlight recently
because it is an important preprocessing operation for various vision tasks.
However, despite advances in deep learning-based methods, RGB-D SOD is still
challenging due to the large domain gap between an RGB image and the depth map
and low-quality depth maps. To solve this problem, we propose a novel
superpixel prototype sampling network (SPSN) architecture. The proposed model
splits the input RGB image and depth map into component superpixels to generate
component prototypes. We design a prototype sampling network so that the
network only samples prototypes corresponding to salient objects. In addition,
we propose a reliance selection module to recognize the quality of each RGB and
depth feature map and adaptively weight them in proportion to their
reliability. The proposed method makes the model robust to inconsistencies
between RGB images and depth maps and eliminates the influence of non-salient
objects. Our method is evaluated on five popular datasets, achieving
state-of-the-art performance. We prove the effectiveness of the proposed method
through comparative experiments.

Uncertainty control and scalability to large datasets are the two main issues
for the deployment of Gaussian process models into the autonomous material and
chemical space exploration pipelines. One way to address both of these issues
is by introducing the latent inducing variables and choosing the right
approximation for the marginal log-likelihood objective. Here, we show that
variational learning of the inducing points in the high-dimensional molecular
descriptor space significantly improves both the prediction quality and
uncertainty estimates on test configurations from a sample molecular dynamics
dataset. Additionally, we show that inducing points can learn to represent the
configurations of the molecules of different types that were not present within
the initialization set of inducing points. Among several evaluated approximate
marginal log-likelihood objectives, we show that the predictive log-likelihood
provides both the predictive quality comparable to the exact Gaussian process
model and excellent uncertainty control. Finally, we comment on whether a
machine learning model makes predictions by interpolating the molecular
configurations in high-dimensional descriptor space. We show that despite our
intuition, and even for densely sampled molecular dynamics datasets, most of
the predictions are done in the extrapolation regime.

In this paper, we propose an innovative Transfer learning for Time series
classification method. Instead of using an existing dataset from the UCR
archive as the source dataset, we generated a 15,000,000 synthetic univariate
time series dataset that was created using our unique synthetic time series
generator algorithm which can generate data with diverse patterns and angles
and different sequence lengths. Furthermore, instead of using classification
tasks provided by the UCR archive as the source task as previous studies did,we
used our own 55 regression tasks as the source tasks, which produced better
results than selecting classification tasks from the UCR archive

Human identification is a key requirement for many applications in everyday
life, such as personalized services, automatic surveillance, continuous
authentication, and contact tracing during pandemics, etc. This work studies
the problem of cross-modal human re-identification (ReID), in response to the
regular human movements across camera-allowed regions (e.g., streets) and
camera-restricted regions (e.g., offices) deployed with heterogeneous sensors.
By leveraging the emerging low-cost RGB-D cameras and mmWave radars, we propose
the first-of-its-kind vision-RF system for cross-modal multi-person ReID at the
same time. Firstly, to address the fundamental inter-modality discrepancy, we
propose a novel signature synthesis algorithm based on the observed specular
reflection model of a human body. Secondly, an effective cross-modal deep
metric learning model is introduced to deal with interference caused by
unsynchronized data across radars and cameras. Through extensive experiments in
both indoor and outdoor environments, we demonstrate that our proposed system
is able to achieve ~92.5% top-1 accuracy and ~97.5% top-5 accuracy out of 56
volunteers. We also show that our proposed system is able to robustly
reidentify subjects even when multiple subjects are present in the sensors'
field of view.

Depth estimation, visual odometry (VO), and bird's-eye-view (BEV) scene
layout estimation present three critical tasks for driving scene perception,
which is fundamental for motion planning and navigation in autonomous driving.
Though they are complementary to each other, prior works usually focus on each
individual task and rarely deal with all three tasks together. A naive way is
to accomplish them independently in a sequential or parallel manner, but there
are many drawbacks, i.e., 1) the depth and VO results suffer from the inherent
scale ambiguity issue; 2) the BEV layout is directly predicted from the
front-view image without using any depth-related information, although the
depth map contains useful geometry clues for inferring scene layouts. In this
paper, we address these issues by proposing a novel joint perception framework
named JPerceiver, which can simultaneously estimate scale-aware depth and VO as
well as BEV layout from a monocular video sequence. It exploits the cross-view
geometric transformation (CGT) to propagate the absolute scale from the road
layout to depth and VO based on a carefully-designed scale loss. Meanwhile, a
cross-view and cross-modal transfer (CCT) module is devised to leverage the
depth clues for reasoning road and vehicle layout through an attention
mechanism. JPerceiver can be trained in an end-to-end multi-task learning way,
where the CGT scale loss and CCT module promote inter-task knowledge transfer
to benefit feature learning of each task. Experiments on Argoverse, Nuscenes
and KITTI show the superiority of JPerceiver over existing methods on all the
above three tasks in terms of accuracy, model size, and inference speed. The
code and models are available
at~\href{https://github.com/sunnyHelen/JPerceiver}{https://github.com/sunnyHelen/JPerceiver}.

Data-driven approaches to assist operating room (OR) workflow analysis depend
on large curated datasets that are time consuming and expensive to collect. On
the other hand, we see a recent paradigm shift from supervised learning to
self-supervised and/or unsupervised learning approaches that can learn
representations from unlabeled datasets. In this paper, we leverage the
unlabeled data captured in robotic surgery ORs and propose a novel way to fuse
the multi-modal data for a single video frame or image. Instead of producing
different augmentations (or 'views') of the same image or video frame which is
a common practice in self-supervised learning, we treat the multi-modal data as
different views to train the model in an unsupervised manner via clustering. We
compared our method with other state of the art methods and results show the
superior performance of our approach on surgical video activity recognition and

In the past few years, graph neural networks (GNNs) have become the de facto
model of choice for graph classification. While, from the theoretical
viewpoint, most GNNs can operate on graphs of any size, it is empirically
observed that their classification performance degrades when they are applied
on graphs with sizes that differ from those in the training data. Previous
works have tried to tackle this issue in graph classification by providing the
model with inductive biases derived from assumptions on the generative process
of the graphs, or by requiring access to graphs from the test domain. The first
strategy is tied to the quality of the assumptions made for the generative
process, and requires the use of specific models designed after the explicit
definition of the generative process of the data, leaving open the question of
how to improve the performance of generic GNN models in general settings. On
the other hand, the second strategy can be applied to any GNN, but requires
access to information that is not always easy to obtain. In this work we
consider the scenario in which we only have access to the training data, and we
propose a regularization strategy that can be applied to any GNN to improve its
generalization capabilities from smaller to larger graphs without requiring
access to the test data. Our regularization is based on the idea of simulating
a shift in the size of the training graphs using coarsening techniques, and
enforcing the model to be robust to such a shift. Experimental results on
standard datasets show that popular GNN models, trained on the 50% smallest
graphs in the dataset and tested on the 10% largest graphs, obtain performance
improvements of up to 30% when trained with our regularization strategy.

Training machine learning (ML) algorithms is a computationally intensive
process, which is frequently memory-bound due to repeatedly accessing large
training datasets. As a result, processor-centric systems (e.g., CPU, GPU)
suffer from costly data movement between memory units and processing units,
which consumes large amounts of energy and execution cycles. Memory-centric
computing systems, i.e., with processing-in-memory (PIM) capabilities, can
alleviate this data movement bottleneck.
  Our goal is to understand the potential of modern general-purpose PIM
architectures to accelerate ML training. To do so, we (1) implement several
representative classic ML algorithms (namely, linear regression, logistic
regression, decision tree, K-Means clustering) on a real-world general-purpose
PIM architecture, (2) rigorously evaluate and characterize them in terms of
accuracy, performance and scaling, and (3) compare to their counterpart
implementations on CPU and GPU. Our evaluation on a real memory-centric
computing system with more than 2500 PIM cores shows that general-purpose PIM
architectures can greatly accelerate memory-bound ML workloads, when the
necessary operations and datatypes are natively supported by PIM hardware. For
example, our PIM implementation of decision tree is $27\times$ faster than a
state-of-the-art CPU version on an 8-core Intel Xeon, and $1.34\times$ faster
than a state-of-the-art GPU version on an NVIDIA A100. Our K-Means clustering
on PIM is $2.8\times$ and $3.2\times$ than state-of-the-art CPU and GPU
versions, respectively.
  To our knowledge, our work is the first one to evaluate ML training on a
real-world PIM architecture. We conclude with key observations, takeaways, and
recommendations that can inspire users of ML workloads, programmers of PIM
architectures, and hardware designers & architects of future memory-centric
computing systems.

Building a universal video-language model for solving various video
understanding tasks (e.g., text-video retrieval, video question answering) is
an open challenge to the machine learning field. Towards this goal, most recent
attempts train the models, usually consisting of uni-modal and cross-modal
feature encoders, with supervised or pair-wise contrastive pre-text tasks.
Though offering attractive generality, the resulted models have to compromise
between efficiency and performance. We argue the flaws are caused by their
pre-training strategies\textemdash they cannot well align and fuse features
from different modalities simultaneously. We then introduce Clover -- a
Correlated Video-Language pre-training method -- towards a universal
video-language model for solving multiple video understanding tasks with
neither performance nor efficiency compromise. It improves cross-modal feature
alignment and fusion via a novel tri-modal alignment pre-training task.
Additionally, we propose to enhance the tri-modal alignment via incorporating
learning from masked samples and a novel pair-wise ranking loss. It establishes
new state-of-the-arts on multiple downstream tasks, including three retrieval
tasks for both zero-shot and fine-tuning settings, and eight video question
answering tasks. Codes and pre-trained models will be released at
https://github.com/LeeYN-43/Clover.

The order/dimension of models derived on the basis of data is commonly
restricted by the number of observations, or in the context of monitored
systems, sensing nodes. This is particularly true for structural systems (e.g.
civil or mechanical structures), which are typically high-dimensional in
nature. In the scope of physics-informed machine learning, this paper proposes
a framework - termed Neural Modal ODEs - to integrate physics-based modeling
with deep learning (particularly, Neural Ordinary Differential Equations --
Neural ODEs) for modeling the dynamics of monitored and high-dimensional
engineered systems. In this initiating exploration, we restrict ourselves to
linear or mildly nonlinear systems. We propose an architecture that couples a
dynamic version of variational autoencoders with physics-informed Neural ODEs
(Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the abstract
mappings from the first few items of observational data to the initial values
of the latent variables, which drive the learning of embedded dynamics via
physics-informed Neural ODEs, imposing a \textit{modal model} structure to that
latent space. The decoder of the proposed model adopts the eigenmodes derived
from an eigen-analysis applied to the linearized portion of a physics-based
model: a process implicitly carrying the spatial relationship between
degrees-of-freedom (DOFs). The framework is validated on a numerical example,
and an experimental dataset of a scaled cable-stayed bridge, where the learned
hybrid model is shown to outperform a purely physics-based approach to
modeling. We further show the functionality of the proposed scheme within the
context of virtual sensing, i.e., the recovery of generalized response
quantities in unmeasured DOFs from spatially sparse data.

Self-Supervised Learning (SSL) has become a very active area of Deep Learning
research where it is heavily used as a pre-training method for classification
and other tasks. However, the rapid pace of advancements in this area comes at
a price: training pipelines vary significantly across papers, which presents a
potentially crucial confounding factor. Here, we show that, indeed, the choice
of hyperparameters and data augmentation strategies can have a dramatic impact
on performance. To shed light on these neglected factors and help maximize the
power of SSL, we hyperparameterize these components and optimize them with
Bayesian optimization, showing improvements across multiple datasets for the
SimSiam SSL approach. Realizing the importance of data augmentations for SSL,
we also introduce a new automated data augmentation algorithm, GroupAugment,
which considers groups of augmentations and optimizes the sampling across
groups. In contrast to algorithms designed for supervised learning,
GroupAugment achieved consistently high linear evaluation accuracy across all
datasets we considered. Overall, our results indicate the importance and likely
underestimated role of data augmentation for SSL.

Contrastive learning (CL) has achieved remarkable success in learning
transferable representations. It has been identified that the temperature $
\tau $ of CL loss plays an essential role in automatically concentrating on
hard negative samples. However, recent work also indicates a
uniformity-tolerance dilemma (UTD) connected to $ \tau $, which will lead to
unexpected performance degradation. We argue that it is the fixity of
temperature that is inextricably linked to UTD and suboptimal embedding space.
To tackle the challenge of UTD, we enrich the CL loss family by presenting a
Model-Aware Contrastive Learning (MACL) strategy. In MACL, the temperature
parameter is adaptive to the magnitude of alignment that reflects the basic
confidence of the instance discrimination task. Lower alignment implies poor
discrimination for the undertrained phase, then there is less possibility that
the high similarity region contains latent positive samples (LPs). Thus, a
small $ \tau $ can impose larger penalties on hard negative samples to learn
uniformly informative embeddings. Instead, a larger $ \tau $ in the
well-trained phase facilitates the exploration of semantic structures due to
its increased tolerance for LPs. Besides, theoretically, we uncover why
contrastive learning requires a large number of negative samples from a unified
gradient reduction perspective. Based on MACL and these analyses, a new CL loss
is proposed. Experimental results validate the effectiveness of our approach to
escape UTD, which can achieve state-of-the-art performance and training with
fewer negative samples.

Since RANSAC, a great deal of research has been devoted to improving both its
accuracy and run-time. Still, only a few methods aim at recognizing invalid
minimal samples early, before the often expensive model estimation and quality
calculation are done. To this end, we propose NeFSAC, an efficient algorithm
for neural filtering of motion-inconsistent and poorly-conditioned minimal
samples. We train NeFSAC to predict the probability of a minimal sample leading
to an accurate relative pose, only based on the pixel coordinates of the image
correspondences. Our neural filtering model learns typical motion patterns of
samples which lead to unstable poses, and regularities in the possible motions
to favour well-conditioned and likely-correct samples. The novel lightweight
architecture implements the main invariants of minimal samples for pose
estimation, and a novel training scheme addresses the problem of extreme class
imbalance. NeFSAC can be plugged into any existing RANSAC-based pipeline. We
integrate it into USAC and show that it consistently provides strong speed-ups
even under extreme train-test domain gaps - for example, the model trained for
the autonomous driving scenario works on PhotoTourism too. We tested NeFSAC on
more than 100k image pairs from three publicly available real-world datasets
and found that it leads to one order of magnitude speed-up, while often finding
more accurate results than USAC alone. The source code is available at
https://github.com/cavalli1234/NeFSAC.

The ability to handle objects in cluttered environment has been long
anticipated by robotic community. However, most of works merely focus on
manipulation instead of rendering hidden semantic information in cluttered
objects. In this work, we introduce the scene graph for embodied exploration in
cluttered scenarios to solve this problem. To validate our method in cluttered
scenario, we adopt the Manipulation Question Answering (MQA) tasks as our test
benchmark, which requires an embodied robot to have the active exploration
ability and semantic understanding ability of vision and language.As a general
solution framework to the task, we propose an imitation learning method to
generate manipulations for exploration. Meanwhile, a VQA model based on dynamic
scene graph is adopted to comprehend a series of RGB frames from wrist camera
of manipulator along with every step of manipulation is conducted to answer
questions in our framework.The experiments on of MQA dataset with different
interaction requirements demonstrate that our proposed framework is effective
for MQA task a representative of tasks in cluttered scenario.

One-shot Neural Architecture Search (NAS) has been widely used to discover
architectures due to its efficiency. However, previous studies reveal that
one-shot performance estimations of architectures might not be well correlated
with their performances in stand-alone training because of the excessive
sharing of operation parameters (i.e., large sharing extent) between
architectures. Thus, recent methods construct even more over-parameterized
supernets to reduce the sharing extent. But these improved methods introduce a
large number of extra parameters and thus cause an undesirable trade-off
between the training costs and the ranking quality. To alleviate the above
issues, we propose to apply Curriculum Learning On Sharing Extent (CLOSE) to
train the supernet both efficiently and effectively. Specifically, we train the
supernet with a large sharing extent (an easier curriculum) at the beginning
and gradually decrease the sharing extent of the supernet (a harder
curriculum). To support this training strategy, we design a novel supernet
(CLOSENet) that decouples the parameters from operations to realize a flexible
sharing scheme and adjustable sharing extent. Extensive experiments demonstrate
that CLOSE can obtain a better ranking quality across different computational
budget constraints than other one-shot supernets, and is able to discover
superior architectures when combined with various search strategies. Code is
available at https://github.com/walkerning/aw_nas.

Financial forensics has an important role in the field of finance to detect
and investigate the occurrence of finance related crimes like money laundering.
However, as with other forms of criminal activities, the forensics analysis of
such activities is a complex undertaking with attempts by the adversaries to
constantly upgrade their ability to evade detection. Also, the extent of the
volume and complexity of financial activities or transactions further
complicates the task of performing financial forensics. Machine Learning or
Artificial Intelligence algorithms could be used to deal with such
complexities. However, the challenge of limitedly available labeled datasets
especially with fraudulent activities limits the means to develop efficient
algorithms. Additionally, the complexity of defining precise search patterns of
evasive fraudulent transactions further complicates this challenge. In this
paper, we developed a novel deep set classifier algorithm based on meta
learning and applied it to deal with the complexity deriving patterns of
interest with sample of limitedly labelled transactions to detect fraudulent
cryptocurrency money laundering transactions. We a unique approach to train our
model with progressive provision of samples and the test result exceeds leading
research algorithms.

WiFi sensing has been evolving rapidly in recent years. Empowered by
propagation models and deep learning methods, many challenging applications are
realized such as WiFi-based human activity recognition and gesture recognition.
However, in contrast to deep learning for visual recognition and natural
language processing, no sufficiently comprehensive public benchmark exists. In
this paper, we highlight the recent progress on deep learning enabled WiFi
sensing, and then propose a benchmark, SenseFi, to study the effectiveness of
various deep learning models for WiFi sensing. These advanced models are
compared in terms of distinct sensing tasks, WiFi platforms, recognition
accuracy, model size, computational complexity, feature transferability, and
adaptability of unsupervised learning. It is also regarded as a tutorial for
deep learning based WiFi sensing, starting from CSI hardware platform to
sensing algorithms. The extensive experiments provide us with experiences in
deep model design, learning strategy skills and training techniques for
real-world applications. To the best of our knowledge, this is the first
benchmark with an open-source library for deep learning in WiFi sensing
research. The benchmark codes are available at
https://github.com/CHENXINYAN-sg/WiFi-CSI-Sensing-Benchmark.

Recently many plug-and-play self-attention modules (SAMs) are proposed to
enhance the model generalization by exploiting the internal information of deep
convolutional neural networks (CNNs). In general, previous works ignore where
to plug in the SAMs since they connect the SAMs individually with each block of
the entire CNN backbone for granted, leading to incremental computational cost
and the number of parameters with the growth of network depth. However, we
empirically find and verify some counterintuitive phenomena that: (a)
Connecting the SAMs to all the blocks may not always bring the largest
performance boost, and connecting to partial blocks would be even better; (b)
Adding the SAMs to a CNN may not always bring a performance boost, and instead
it may even harm the performance of the original CNN backbone. Therefore, we
articulate and demonstrate the Lottery Ticket Hypothesis for Self-attention
Networks: a full self-attention network contains a subnetwork with sparse
self-attention connections that can (1) accelerate inference, (2) reduce extra
parameter increment, and (3) maintain accuracy. In addition to the empirical
evidence, this hypothesis is also supported by our theoretical evidence.
Furthermore, we propose a simple yet effective reinforcement-learning-based
method to search the ticket, i.e., the connection scheme that satisfies the
three above-mentioned conditions. Extensive experiments on widely-used
benchmark datasets and popular self-attention networks show the effectiveness
of our method. Besides, our experiments illustrate that our searched ticket has
the capacity of transferring to some vision tasks, e.g., crowd counting and

Text-Video retrieval is a task of great practical value and has received
increasing attention, among which learning spatial-temporal video
representation is one of the research hotspots. The video encoders in the
state-of-the-art video retrieval models usually directly adopt the pre-trained
vision backbones with the network structure fixed, they therefore can not be
further improved to produce the fine-grained spatial-temporal video
representation. In this paper, we propose Token Shift and Selection Network
(TS2-Net), a novel token shift and selection transformer architecture, which
dynamically adjusts the token sequence and selects informative tokens in both
temporal and spatial dimensions from input video samples. The token shift
module temporally shifts the whole token features back-and-forth across
adjacent frames, to preserve the complete token representation and capture
subtle movements. Then the token selection module selects tokens that
contribute most to local spatial semantics. Based on thorough experiments, the
proposed TS2-Net achieves state-of-the-art performance on major text-video
retrieval benchmarks, including new records on MSRVTT, VATEX, LSMDC,
ActivityNet, and DiDeMo.

Convex model predictive controls (MPCs) with a single rigid body model have
demonstrated strong performance on real legged robots. However, convex MPCs are
limited by their assumptions such as small rotation angle and pre-defined gait,
limiting the richness of potential solutions. We remove those assumptions and
solve the complete mixed-integer non-convex programming with single rigid body
model. We first collect datasets of pre-solved problems offline, then learn the
problem-solution map to solve this optimization fast for MPC. If warm-starts
can be found, offline problems can be solved close to the global optimality.
The proposed controller is tested by generating various gaits and behaviors
depending on the initial conditions. Hardware test demonstrates online gait
generation and adaptation running at more than 50 Hz based on sensor feedback.

Existing class-incremental lifelong learning studies only the data is with
single-label, which limits its adaptation to multi-label data. This paper
studies Lifelong Multi-Label (LML) classification, which builds an online
class-incremental classifier in a sequential multi-label classification data
stream. Training on the data with Partial Labels in LML classification may
result in more serious Catastrophic Forgetting in old classes. To solve the
problem, the study proposes an Augmented Graph Convolutional Network (AGCN)
with a built Augmented Correlation Matrix (ACM) across sequential partial-label
tasks. The results of two benchmarks show that the method is effective for LML
classification and reducing forgetting.

Positioning benefits from channel models that capture geometric effects and,
in particular, from the signal properties of the first arriving path and the
spatial consistency of the propagation condition of multiple links. The models
that capture the physical effects observed in a realistic deployment scenario
are essential for assessing the potential benefits of enhancements in
positioning methods. Channel models based on ray-tracing simulations and
statistical channel models, which are current state-of-the-art methods employed
to evaluate performance of positioning in 3GPP systems, do not fully capture
important aspects applicable to positioning. Hence, we propose an extension of
existing statistical channel models with semi-deterministic clusters (SDCs).
SDCs allow channels to be simulated using three types of clusters: fixed-,
specular-, and random-clusters. Our results show that the proposed model aligns
with measurements obtained in a real deployment scenario. Thus, our channel
models can be used to develop advanced positioning solutions based on machine
learning, which enable positioning with centimeter level accuracy in NLOS and
multipath scenarios.

For legged robots to match the athletic capabilities of humans and animals,
they must not only produce robust periodic walking and running, but also
seamlessly switch between nominal locomotion gaits and more specialized
transient maneuvers. Despite recent advancements in controls of bipedal robots,
there has been little focus on producing highly dynamic behaviors. Recent work
utilizing reinforcement learning to produce policies for control of legged
robots have demonstrated success in producing robust walking behaviors.
However, these learned policies have difficulty expressing a multitude of
different behaviors on a single network. Inspired by conventional
optimization-based control techniques for legged robots, this work applies a
recurrent policy to execute four-step, 90 degree turns trained using reference
data generated from optimized single rigid body model trajectories. We present
a novel training framework using epilogue terminal rewards for learning
specific behaviors from pre-computed trajectory data and demonstrate a
successful transfer to hardware on the bipedal robot Cassie.

Due to its vast application on online social networks, Influence Maximization
(IM) has garnered considerable attention over the last couple of decades.
Current IM research lacks human-comprehensible explanations of how the seed set
results in the influence effect, hence reducing the trustworthiness of existing
solutions despite their applicability. Due to the intricacy of IM, the majority
of current research concentrate on estimating first-order spreading power and
often is regard the interplay between flows dispersed from different seeds.
This study uses Sobol indices, the cornerstone of variance-based sensitivity
analysis, to decompose the influence effect to individual seeds and their
interactions. The Sobol indices are tailored for IM contexts by modeling the
seed selection as binary variables. This explanation method is universally
applicable to all network types, IM techniques, and diffusion models. Based on
the explanation method, a general framework dubbed SobolIM is proposed to
improve the performance of current IM studies by over-selecting nodes followed
by an elimination strategy. Experiments on synthetic and real-world graphs
demonstrate that the explanation of the impact effect can dependably identify
the key high-order interaction between seeds across a variety of networks and
IM methods. SobolIM is empirically proved to be superior on effectiveness and
competitive on efficiency.

Standard artificial neural networks (ANNs) use sum-product or
multiply-accumulate node operations with a memoryless nonlinear activation.
These neural networks are known to have universal function approximation
capabilities. Previously proposed morphological perceptrons use max-sum, in
place of sum-product, node processing and have promising properties for circuit
implementations. In this paper we show that these max-sum ANNs do not have
universal approximation capabilities. Furthermore, we consider proposed
signed-max-sum and max-star-sum generalizations of morphological ANNs and show
that these variants also do not have universal approximation capabilities. We
contrast these variations to log-number system (LNS) implementations which also
avoid multiplications, but do exhibit universal approximation capabilities.

There has been significant progress in sensing, perception, and localization
for automated driving, However, due to the wide spectrum of traffic/road
structure scenarios and the long tail distribution of human driver behavior, it
has remained an open challenge for an intelligent vehicle to always know how to
make and execute the best decision on road given available sensing / perception
/ localization information. In this chapter, we talk about how artificial
intelligence and more specifically, reinforcement learning, can take advantage
of operational knowledge and safety reflex to make strategical and tactical
decisions. We discuss some challenging problems related to the robustness of
reinforcement learning solutions and their implications to the practical design
of driving strategies for autonomous vehicles. We focus on automated driving on
highway and the integration of reinforcement learning, vehicle motion control,
and control barrier function, leading to a robust AI driving strategy that can
learn and adapt safely.

We propose an effective Structural Prior guided Generative Adversarial
Transformer (SPGAT) to solve low-light image enhancement. Our SPGAT mainly
contains a generator with two discriminators and a structural prior estimator
(SPE). The generator is based on a U-shaped Transformer which is used to
explore non-local information for better clear image restoration. The SPE is
used to explore useful structures from images to guide the generator for better
structural detail estimation. To generate more realistic images, we develop a
new structural prior guided adversarial learning method by building the skip
connections between the generator and discriminators so that the discriminators
can better discriminate between real and fake features. Finally, we propose a
parallel windows-based Swin Transformer block to aggregate different level
hierarchical features for high-quality image restoration. Experimental results
demonstrate that the proposed SPGAT performs favorably against recent
state-of-the-art methods on both synthetic and real-world datasets.

Multivariate long sequence time-series forecasting (M-LSTF) is a practical
but challenging problem. Unlike traditional timer-series forecasting tasks,
M-LSTF tasks are more challenging from two aspects: 1) M-LSTF models need to
learn time-series patterns both within and between multiple time features; 2)
Under the rolling forecasting setting, the similarity between two consecutive
training samples increases with the increasing prediction length, which makes
models more prone to overfitting. In this paper, we propose a generalizable
memory-driven Transformer to target M-LSTF problems. Specifically, we first
propose a global-level memory component to drive the forecasting procedure by
integrating multiple time-series features. In addition, we adopt a progressive
fashion to train our model to increase its generalizability, in which we
gradually introduce Bernoulli noises to training samples. Extensive experiments
have been performed on five different datasets across multiple fields.
Experimental results demonstrate that our approach can be seamlessly plugged
into varying Transformer-based models to improve their performances up to
roughly 30%. Particularly, this is the first work to specifically focus on the
M-LSTF tasks to the best of our knowledge.

Few-shot learning (FSL) aims to recognize novel queries with only a few
support samples through leveraging prior knowledge from a base dataset. In this
paper, we consider the domain shift problem in FSL and aim to address the
domain gap between the support set and the query set. Different from previous
cross-domain FSL work (CD-FSL) that considers the domain shift between base and
novel classes, the new problem, termed cross-domain cross-set FSL (CDSC-FSL),
requires few-shot learners not only to adapt to the new domain, but also to be
consistent between different domains within each novel class. To this end, we
propose a novel approach, namely stabPA, to learn prototypical compact and
cross-domain aligned representations, so that the domain shift and few-shot
learning can be addressed simultaneously. We evaluate our approach on two new
CDCS-FSL benchmarks built from the DomainNet and Office-Home datasets
respectively. Remarkably, our approach outperforms multiple elaborated
baselines by a large margin, e.g., improving 5-shot accuracy by 6.0 points on
average on DomainNet. Code is available at
https://github.com/WentaoChen0813/CDCS-FSL

In reinforcement learning, agents have successfully used environments modeled
with Markov decision processes (MDPs). However, in many problem domains, an
agent may suffer from noisy observations or random times until its subsequent
decision. While partially observable Markov decision processes (POMDPs) have
dealt with noisy observations, they have yet to deal with the unknown time
aspect. Of course, one could discretize the time, but this leads to Bellman's
Curse of Dimensionality. To incorporate continuous sojourn-time distributions
in the agent's decision making, we propose that partially observable
semi-Markov decision processes (POSMDPs) can be helpful in this regard. We
extend \citet{Spaan2005a} randomized point-based value iteration (PBVI)
\textsc{Perseus} algorithm used for POMDP to POSMDP by incorporating continuous
sojourn time distributions and using importance sampling to reduce the solver
complexity. We call this new PBVI algorithm with importance sampling for
POSMDPs -- \textsc{ChronosPerseus}. This further allows for compressed complex
POMDPs requiring temporal state information by moving this information into
state sojourn time of a POMSDP. The second insight is that keeping a set of
sampled times and weighting it by its likelihood can be used in a single
backup; this helps further reduce the algorithm complexity. The solver also
works on episodic and non-episodic problems. We conclude our paper with two
examples, an episodic bus problem and a non-episodic maintenance problem.

This paper considers the problem where a group of mobile robots subject to
unknown external disturbances aim to safely reach goal regions. We develop a
distributed safe learning and planning algorithm that allows the robots to
learn about the external unknown disturbances and safely navigate through the
environment via their single trajectories. We use Gaussian process regression
for online learning where variance is adopted to quantify the learning
uncertainty. By leveraging set-valued analysis, the developed algorithm enables
fast adaptation to newly learned models while avoiding collision against the
learning uncertainty. Active learning is then applied to return a control
policy such that the robots are able to actively explore the unknown
disturbances and reach their goal regions in time. Sufficient conditions are
established to guarantee the safety of the robots. A set of simulations are
conducted for evaluation.

We introduce data structures for solving robust regression through stochastic
gradient descent (SGD) by sampling gradients with probability proportional to
their norm, i.e., importance sampling. Although SGD is widely used for large
scale machine learning, it is well-known for possibly experiencing slow
convergence rates due to the high variance from uniform sampling. On the other
hand, importance sampling can significantly decrease the variance but is
usually difficult to implement because computing the sampling probabilities
requires additional passes over the data, in which case standard gradient
descent (GD) could be used instead. In this paper, we introduce an algorithm
that approximately samples $T$ gradients of dimension $d$ from nearly the
optimal importance sampling distribution for a robust regression problem over
$n$ rows. Thus our algorithm effectively runs $T$ steps of SGD with importance
sampling while using sublinear space and just making a single pass over the
data. Our techniques also extend to performing importance sampling for
second-order optimization.

Powered by advances in deep learning (DL) techniques, machine learning and
artificial intelligence have achieved astonishing successes. However, the
rapidly growing needs for DL also led to communication- and resource-intensive
distributed training jobs for large-scale DL training, which are typically
deployed over GPU clusters. To sustain the ever-increasing demand for DL
training, the so-called "ring-all-reduce" (RAR) technologies have recently
emerged as a favorable computing architecture to efficiently process network
communication and computation load in GPU clusters. The most salient feature of
RAR is that it removes the need for dedicated parameter servers, thus
alleviating the potential communication bottleneck. However, when multiple
RAR-based DL training jobs are deployed over GPU clusters, communication
bottlenecks could still occur due to contentions between DL training jobs. So
far, there remains a lack of theoretical understanding on how to design
contention-aware resource scheduling algorithms for RAR-based DL training jobs,
which motivates us to fill this gap in this work. Our main contributions are
three-fold: i) We develop a new analytical model that characterizes both
communication overhead related to the worker distribution of the job and
communication contention related to the co-location of different jobs; ii)
Based on the proposed analytical model, we formulate the problem as a
non-convex integer program to minimize the makespan of all RAR-based DL
training jobs. To address the unique structure in this problem that is not
amenable for optimization algorithm design, we reformulate the problem into an
integer linear program that enables provable approximation algorithm design
called SJF-BCO (Smallest Job First with Balanced Contention and Overhead); and
iii) We conduct extensive experiments to show the superiority of SJF-BCO over
existing schedulers.

Speech data is expensive to collect, and incredibly sensitive to its sources.
It is often the case that organizations independently collect small datasets
for their own use, but often these are not performant for the demands of
machine learning. Organizations could pool these datasets together and jointly
build a strong ASR system; sharing data in the clear, however, comes with
tremendous risk, in terms of intellectual property loss as well as loss of
privacy of the individuals who exist in the dataset. In this paper, we offer a
potential solution for learning an ML model across multiple organizations where
we can provide mathematical guarantees limiting privacy loss. We use a
Federated Learning approach built on a strong foundation of Differential
Privacy techniques. We apply these to a senone classification prototype and
demonstrate that the model improves with the addition of private data while
still respecting privacy.

With the vigorous development of artificial intelligence (AI), the
intelligent applications based on deep neural network (DNN) change people's
lifestyles and the production efficiency. However, the huge amount of
computation and data generated from the network edge becomes the major
bottleneck, and traditional cloud-based computing mode has been unable to meet
the requirements of real-time processing tasks. To solve the above problems, by
embedding AI model training and inference capabilities into the network edge,
edge intelligence (EI) becomes a cutting-edge direction in the field of AI.
Furthermore, collaborative DNN inference among the cloud, edge, and end device
provides a promising way to boost the EI. Nevertheless, at present, EI oriented
collaborative DNN inference is still in its early stage, lacking a systematic
classification and discussion of existing research efforts. Thus motivated, we
have made a comprehensive investigation on the recent studies about EI oriented
collaborative DNN inference. In this paper, we firstly review the background
and motivation of EI. Then, we classify four typical collaborative DNN
inference paradigms for EI, and analyze the characteristics and key
technologies of them. Finally, we summarize the current challenges of
collaborative DNN inference, discuss the future development trend and provide
the future research direction.

Accurate ab initio calculations are of fundamental importance in physics,
chemistry, biology, and materials science, which have witnessed rapid
development in the last couple of years with the help of machine learning
computational techniques such as neural networks. Most of the recent efforts
applying neural networks to ab initio calculation have been focusing on the
energy of the system. In this study, we take a step forward and look at the
interatomic force obtained with neural network wavefunction methods by
implementing and testing several commonly used force estimators in variational
quantum Monte Carlo (VMC). Our results show that neural network ansatz can
improve the calculation of interatomic force upon traditional VMC. The relation
between the force error and the quality of neural network, the contribution of
different force terms, and the computational cost of each term are also
discussed to provide guidelines for future applications. Our work demonstrates
that it is promising to apply neural network wavefunction methods in simulating
structures/dynamics of molecules/materials and provide training data for
developing accurate force fields.

In this paper, we report a hierarchical deep learning model for
classification of complex human activities using motion sensors. In contrast to
traditional Human Activity Recognition (HAR) models used for event-based
activity recognition, such as step counting, fall detection, and gesture
identification, this new deep learning model, which we refer to as CHARM
(Complex Human Activity Recognition Model), is aimed for recognition of
high-level human activities that are composed of multiple different low-level
activities in a non-deterministic sequence, such as meal preparation, house
chores, and daily routines. CHARM not only quantitatively outperforms
state-of-the-art supervised learning approaches for high-level activity
recognition in terms of average accuracy and F1 scores, but also automatically
learns to recognize low-level activities, such as manipulation gestures and
locomotion modes, without any explicit labels for such activities. This opens
new avenues for Human-Machine Interaction (HMI) modalities using wearable
sensors, where the user can choose to associate an automated task with a
high-level activity, such as controlling home automation (e.g., robotic vacuum
cleaners, lights, and thermostats) or presenting contextually relevant
information at the right time (e.g., reminders, status updates, and
weather/news reports). In addition, the ability to learn low-level user
activities when trained using only high-level activity labels may pave the way
to semi-supervised learning of HAR tasks that are inherently difficult to
label.

The GammaTPC is an MeV-scale single-phase liquid argon
time-projection-chamber gamma-ray telescope concept with a novel dual-scale
pixel-based charge-readout system. It promises to enable a significant
improvement in sensitivity to MeV-scale gamma-rays over previous telescopes.
The novel pixel-based charge readout allows for imaging of the tracks of
electrons scattered by Compton interactions of incident gamma-rays. The two
primary contributors to the accuracy of a Compton telescope in reconstructing
an incident gamma-ray's original direction are its energy and position
resolution. In this work, we focus on using deep learning to optimize the
reconstruction of the initial position and direction of electrons scattered in
Compton interactions, including using probabilistic models to estimate
predictive uncertainty. We show that the deep learning models are able to
predict locations of Compton scatters of MeV-scale gamma-rays from simulated
pixel-based data to better than 0.6 mm RMS error, and are sensitive to the
initial direction of the scattered electron. We compare and contrast different
deep learning uncertainty estimation algorithms for reconstruction
applications. Additionally, we show that event-by-event estimates of the
uncertainty of the locations of the Compton scatters can be used to select
those events that were reconstructed most accurately, leading to improvement in
locating the origin of gamma-ray sources on the sky.

Deep learning methodology contributes a lot to the development of
hyperspectral image (HSI) analysis community. However, it also makes HSI
analysis systems vulnerable to adversarial attacks. To this end, we propose a
masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised
learning theory, for enhancing the robustness of HSI analysis systems. First, a
masked sequence attention learning module is conducted to promote the inherent
robustness of HSI analysis systems along spectral channel. Then, we develop a
graph convolutional network with learnable graph structure to establish global
pixel-wise combinations.In this way, the attack effect would be dispersed by
all the related pixels among each combination, and a better defense performance
is achievable in spatial aspect.Finally, to improve the defense transferability
and address the problem of limited labelled samples, MSSA employs spectra
reconstruction as a pretext task and fits the datasets in a self-supervised
manner.Comprehensive experiments over three benchmarks verify the effectiveness
of MSSA in comparison with the state-of-the-art hyperspectral classification
methods and representative adversarial defense strategies.

Text-to-image person re-identification (ReID) aims to search for pedestrian
images of an interested identity via textual descriptions. It is challenging
due to both rich intra-modal variations and significant inter-modal gaps.
Existing works usually ignore the difference in feature granularity between the
two modalities, i.e., the visual features are usually fine-grained while
textual features are coarse, which is mainly responsible for the large
inter-modal gaps. In this paper, we propose an end-to-end framework based on
transformers to learn granularity-unified representations for both modalities,
denoted as LGUR. LGUR framework contains two modules: a Dictionary-based
Granularity Alignment (DGA) module and a Prototype-based Granularity
Unification (PGU) module. In DGA, in order to align the granularities of two
modalities, we introduce a Multi-modality Shared Dictionary (MSD) to
reconstruct both visual and textual features. Besides, DGA has two important
factors, i.e., the cross-modality guidance and the foreground-centric
reconstruction, to facilitate the optimization of MSD. In PGU, we adopt a set
of shared and learnable prototypes as the queries to extract diverse and
semantically aligned features for both modalities in the granularity-unified
feature space, which further promotes the ReID performance. Comprehensive
experiments show that our LGUR consistently outperforms state-of-the-arts by
large margins on both CUHK-PEDES and ICFG-PEDES datasets. Code will be released
at https://github.com/ZhiyinShao-H/LGUR.

Robustness of quantum operations or controls is important to build reliable
quantum devices. The robustness-infidelity measure (RIM$_p$) is introduced to
statistically quantify the robustness and fidelity of a controller as the
p-order Wasserstein distance between the fidelity distribution of the
controller under any uncertainty and an ideal fidelity distribution. The
RIM$_p$ is the p-th root of the p-th raw moment of the infidelity distribution.
Using a metrization argument, we justify why RIM$_1$ (the average infidelity)
suffices as a practical robustness measure. Based on the RIM$_p$, an
algorithmic robustness-infidelity measure (ARIM) is developed to quantify the
expected robustness and fidelity of controllers found by a control algorithm.
The utility of the RIM and ARIM is demonstrated by considering the problem of
robust control of spin- 12 networks using energy landscape shaping subject to
Hamiltonian uncertainty. The robustness and fidelity of individual control
solutions as well as the expected robustness and fidelity of controllers found
by different popular quantum control algorithms are characterized. For
algorithm comparisons, stochastic and non-stochastic optimization objectives
are considered, with the goal of effective RIM optimization in the latter.
Although high fidelity and robustness are often conflicting objectives, some
high fidelity, robust controllers can usually be found, irrespective of the
choice of the quantum control algorithm. However, for noisy optimization
objectives, adaptive sequential decision making approaches such as
reinforcement learning have a cost advantage compared to standard control
algorithms and, in contrast, the infidelities obtained are more consistent with
higher RIM values for low noise levels.

Adversarial training, as one of the most effective defense methods against
adversarial attacks, tends to learn an inclusive decision boundary to increase
the robustness of deep learning models. However, due to the large and
unnecessary increase in the margin along adversarial directions, adversarial
training causes heavy cross-over between natural examples and adversarial
examples, which is not conducive to balancing the trade-off between robustness
and natural accuracy. In this paper, we propose a novel adversarial training
scheme to achieve a better trade-off between robustness and natural accuracy.
It aims to learn a moderate-inclusive decision boundary, which means that the
margins of natural examples under the decision boundary are moderate. We call
this scheme Moderate-Margin Adversarial Training (MMAT), which generates
finer-grained adversarial examples to mitigate the cross-over problem. We also
take advantage of logits from a teacher model that has been well-trained to
guide the learning of our model. Finally, MMAT achieves high natural accuracy
and robustness under both black-box and white-box attacks. On SVHN, for
example, state-of-the-art robustness and natural accuracy are achieved.

Driver identification is a momentous field of modern decorated vehicles in
the controller area network (CAN-BUS) perspective. Many conventional systems
are used to identify the driver. One step ahead, most of the researchers use
sensor data of CAN-BUS but there are some difficulties because of the variation
of the protocol of different models of vehicle. Our aim is to identify the
driver through supervised learning algorithms based on driving behavior
analysis. To determine the driver, a driver verification technique is proposed
that evaluate driving pattern using the measurement of CAN sensor data. In this
paper on-board diagnostic (OBD-II) is used to capture the data from the CAN-BUS
sensor and the sensors are listed under SAE J1979 statement. According to the
service of OBD-II, drive identification is possible. However, we have gained
two types of accuracy on a complete data set with 10 drivers and a partial data
set with two drivers. The accuracy is good with less number of drivers compared
to the higher number of drivers. We have achieved statistically significant
results in terms of accuracy in contrast to the baseline algorithm

Experience replay is an essential component in deep reinforcement learning
(DRL), which stores the experiences and generates experiences for the agent to
learn in real time. Recently, prioritized experience replay (PER) has been
proven to be powerful and widely deployed in DRL agents. However, implementing
PER on traditional CPU or GPU architectures incurs significant latency overhead
due to its frequent and irregular memory accesses. This paper proposes a
hardware-software co-design approach to design an associative memory (AM) based
PER, AMPER, with an AM-friendly priority sampling operation. AMPER replaces the
widely-used time-costly tree-traversal-based priority sampling in PER while
preserving the learning performance. Further, we design an in-memory computing
hardware architecture based on AM to support AMPER by leveraging parallel
in-memory search operations. AMPER shows comparable learning performance while
achieving 55x to 270x latency improvement when running on the proposed hardware
compared to the state-of-the-art PER running on GPU.

We utilize an offline reinforcement learning (RL) model for sequential
targeted promotion in the presence of budget constraints in a real-world
business environment. In our application, the mobile app aims to boost customer
retention by sending cash bonuses to customers and control the costs of such
cash bonuses during each time period. To achieve the multi-task goal, we
propose the Budget Constrained Reinforcement Learning for Sequential Promotion
(BCRLSP) framework to determine the value of cash bonuses to be sent to users.
We first find out the target policy and the associated Q-values that maximizes
the user retention rate using an RL model. A linear programming (LP) model is
then added to satisfy the constraints of promotion costs. We solve the LP
problem by maximizing the Q-values of actions learned from the RL model given
the budget constraints. During deployment, we combine the offline RL model with
the LP model to generate a robust policy under the budget constraints. Using
both online and offline experiments, we demonstrate the efficacy of our
approach by showing that BCRLSP achieves a higher long-term customer retention
rate and a lower cost than various baselines. Taking advantage of the near
real-time cost control method, the proposed framework can easily adapt to data
with a noisy behavioral policy and/or meet flexible budget constraints.

Accuracy and stability are common requirements for Quadrotor trajectory
tracking systems. Designing an accurate and stable tracking controller remains
challenging, particularly in unknown and dynamic environments with complex
aerodynamic disturbances. We propose a Quantile-approximation-based
Distributional-reinforced Uncertainty Estimator (QuaDUE) to accurately identify
the effects of aerodynamic disturbances, i.e., the uncertainties between the
true and estimated Control Contraction Metrics (CCMs). Taking inspiration from
contraction theory and integrating the QuaDUE for uncertainties, our novel
CCM-based trajectory tracking framework tracks any feasible reference
trajectory precisely whilst guaranteeing exponential convergence. More
importantly, the convergence and training acceleration of the distributional RL
are guaranteed and analyzed, respectively, from theoretical perspectives. We
also demonstrate our system under unknown and diverse aerodynamic forces. Under
large aerodynamic forces (>2m/s^2), compared with the classic data-driven
approach, our QuaDUE-CCM achieves at least a 56.6% improvement in tracking
error. Compared with QuaDRED-MPC, a distributional RL-based approach,
QuaDUE-CCM achieves at least a 3 times improvement in contraction rate.

Forecasts help businesses allocate resources and achieve objectives. At
LinkedIn, product owners use forecasts to set business targets, track outlook,
and monitor health. Engineers use forecasts to efficiently provision hardware.
Developing a forecasting solution to meet these needs requires accurate and
interpretable forecasts on diverse time series with sub-hourly to quarterly
frequencies. We present Greykite, an open-source Python library for forecasting
that has been deployed on over twenty use cases at LinkedIn. Its flagship
algorithm, Silverkite, provides interpretable, fast, and highly flexible
univariate forecasts that capture effects such as time-varying growth and
seasonality, autocorrelation, holidays, and regressors. The library enables
self-serve accuracy and trust by facilitating data exploration, model
configuration, execution, and interpretation. Our benchmark results show
excellent out-of-the-box speed and accuracy on datasets from a variety of
domains. Over the past two years, Greykite forecasts have been trusted by
Finance, Engineering, and Product teams for resource planning and allocation,
target setting and progress tracking, anomaly detection and root cause
analysis. We expect Greykite to be useful to forecast practitioners with
similar applications who need accurate, interpretable forecasts that capture
complex dynamics common to time series related to human activity.

Active speaker detection (ASD) in videos with multiple speakers is a
challenging task as it requires learning effective audiovisual features and
spatial-temporal correlations over long temporal windows. In this paper, we
present SPELL, a novel spatial-temporal graph learning framework that can solve
complex tasks such as ASD. To this end, each person in a video frame is first
encoded in a unique node for that frame. Nodes corresponding to a single person
across frames are connected to encode their temporal dynamics. Nodes within a
frame are also connected to encode inter-person relationships. Thus, SPELL
reduces ASD to a node classification task. Importantly, SPELL is able to reason
over long temporal contexts for all nodes without relying on computationally
expensive fully connected graph neural networks. Through extensive experiments
on the AVA-ActiveSpeaker dataset, we demonstrate that learning graph-based
representations can significantly improve the active speaker detection
performance owing to its explicit spatial and temporal structure. SPELL
outperforms all previous state-of-the-art approaches while requiring
significantly lower memory and computational resources. Our code is publicly
available at https://github.com/SRA2/SPELL

Subgroup discovery is a descriptive and exploratory data mining technique to
identify subgroups in a population that exhibit interesting behavior with
respect to a variable of interest. Subgroup discovery has numerous applications
in knowledge discovery and hypothesis generation, yet it remains inapplicable
for unstructured, high-dimensional data such as images. This is because
subgroup discovery algorithms rely on defining descriptive rules based on
(attribute, value) pairs, however, in unstructured data, an attribute is not
well defined. Even in cases where the notion of attribute intuitively exists in
the data, such as a pixel in an image, due to the high dimensionality of the
data, these attributes are not informative enough to be used in a rule. In this
paper, we introduce the subgroup-aware variational autoencoder, a novel
variational autoencoder that learns a representation of unstructured data which
leads to subgroups with higher quality. Our experimental results demonstrate
the effectiveness of the method at learning subgroups with high quality while
supporting the interpretability of the concepts.

Federated learning has emerged as a privacy-preserving machine learning
approach where multiple parties can train a single model without sharing their
raw training data. Federated learning typically requires the utilization of
multi-party computation techniques to provide strong privacy guarantees by
ensuring that an untrusted or curious aggregator cannot obtain isolated replies
from parties involved in the training process, thereby preventing potential
inference attacks. Until recently, it was thought that some of these secure
aggregation techniques were sufficient to fully protect against inference
attacks coming from a curious aggregator. However, recent research has
demonstrated that a curious aggregator can successfully launch a disaggregation
attack to learn information about model updates of a target party. This paper
presents DeTrust-FL, an efficient privacy-preserving federated learning
framework for addressing the lack of transparency that enables isolation
attacks, such as disaggregation attacks, during secure aggregation by assuring
that parties' model updates are included in the aggregated model in a private
and secure manner. DeTrust-FL proposes a decentralized trust consensus
mechanism and incorporates a recently proposed decentralized functional
encryption (FE) scheme in which all parties agree on a participation matrix
before collaboratively generating decryption key fragments, thereby gaining
control and trust over the secure aggregation process in a decentralized
setting. Our experimental evaluation demonstrates that DeTrust-FL outperforms
state-of-the-art FE-based secure multi-party aggregation solutions in terms of
training time and reduces the volume of data transferred. In contrast to
existing approaches, this is achieved without creating any trust dependency on
external trusted entities.

We address performance fairness for speaker verification using the
adversarial reweighting (ARW) method. ARW is reformulated for speaker
verification with metric learning, and shown to improve results across
different subgroups of gender and nationality, without requiring annotation of
subgroups in the training data. An adversarial network learns a weight for each
training sample in the batch so that the main learner is forced to focus on
poorly performing instances. Using a min-max optimization algorithm, this
method improves overall speaker verification fairness. We present three
different ARWformulations: accumulated pairwise similarity, pseudo-labeling,
and pairwise weighting, and measure their performance in terms of equal error
rate (EER) on the VoxCeleb corpus. Results show that the pairwise weighting
method can achieve 1.08% overall EER, 1.25% for male and 0.67% for female
speakers, with relative EER reductions of 7.7%, 10.1% and 3.0%, respectively.
For nationality subgroups, the proposed algorithm showed 1.04% EER for US
speakers, 0.76% for UK speakers, and 1.22% for all others. The absolute EER gap
between gender groups was reduced from 0.70% to 0.58%, while the standard
deviation over nationality groups decreased from 0.21 to 0.19.

Few-shot bioacoustic event detection is a task that detects the occurrence
time of a novel sound given a few examples. Previous methods employ metric
learning to build a latent space with the labeled part of different sound
classes, also known as positive events. In this study, we propose a
segment-level few-shot learning framework that utilizes both the positive and
negative events during model optimization. Training with negative events, which
are larger in volume than positive events, can increase the generalization
ability of the model. In addition, we use transductive inference on the
validation set during training for better adaptation to novel classes. We
conduct ablation studies on our proposed method with different setups on input
features, training data, and hyper-parameters. Our final system achieves an
F-measure of 62.73 on the DCASE 2022 challenge task 5 (DCASE2022-T5) validation
set, outperforming the performance of the baseline prototypical network 34.02
by a large margin. Using the proposed method, our submitted system ranks 2nd in
DCASE2022-T5. The code of this paper is fully open-sourced at

Loss-gradients are used to interpret the decision making process of deep
learning models. In this work, we evaluate loss-gradient based attribution
methods by occluding parts of the input and comparing the performance of the
occluded input to the original input. We observe that the occluded input has
better performance than the original across the test dataset under certain
conditions. Similar behaviour is observed in sound and image recognition tasks.
We explore different loss-gradient attribution methods, occlusion levels and
replacement values to explain the phenomenon of performance improvement under
occlusion.

Lung cancer tends to be detected at an advanced stage, resulting in a high
patient mortality rate. Thus, recent research has focused on early disease
detection. Lung cancer generally first appears as lesions developing within the
bronchial epithelium of the airway walls. Bronchoscopy is the procedure of
choice for effective noninvasive bronchial lesion detection. In particular,
autofluorescence bronchoscopy (AFB) discriminates the autofluorescence
properties of normal and diseased tissue, whereby lesions appear reddish brown
in AFB video frames, while normal tissue appears green. Because recent studies
show AFB's ability for high lesion sensitivity, it has become a potentially
pivotal method during the standard bronchoscopic airway exam for early-stage
lung cancer detection. Unfortunately, manual inspection of AFB video is
extremely tedious and error-prone, while limited effort has been expended
toward potentially more robust automatic AFB lesion detection and segmentation.
We propose a real-time deep learning architecture ESFPNet for robust detection
and segmentation of bronchial lesions from an AFB video stream. The
architecture features an encoder structure that exploits pretrained Mix
Transformer (MiT) encoders and a stage-wise feature pyramid (ESFP) decoder
structure. Results from AFB videos derived from lung cancer patient airway
exams indicate that our approach gives mean Dice index and IOU values of 0.782
and 0.658, respectively, while having a processing throughput of 27 frames/sec.
These values are superior to results achieved by other competing architectures
that use Mix transformers or CNN-based encoders. Moreover, the superior
performance on the ETIS-LaribPolypDB dataset demonstrates its potential
applicability to other domains.

Estimation of conditional average treatment effects (CATEs) plays an
essential role in modern medicine by informing treatment decision-making at a
patient level. Several metalearners have been proposed recently to estimate
CATEs in an effective and flexible way by re-purposing predictive machine
learning models for causal estimation. In this chapter, we summarize the
literature on metalearners and provide concrete guidance for their application
for treatment heterogeneity estimation from randomized controlled trials' data
with survival outcomes. The guidance we provide is supported by a comprehensive
simulation study in which we vary the complexity of the underlying baseline
risk and CATE functions, the magnitude of the heterogeneity in the treatment
effect, the censoring mechanism, and the balance in treatment assignment. To
demonstrate the applicability of our findings, we reanalyze the data from the
Systolic Blood Pressure Intervention Trial (SPRINT) and the Action to Control
Cardiovascular Risk in Diabetes (ACCORD) study. While recent literature reports
the existence of heterogeneous effects of intensive blood pressure treatment
with multiple treatment effect modifiers, our results suggest that many of
these modifiers may be spurious discoveries. This chapter is accompanied by
survlearners, an R package that provides well-documented implementations of the
CATE estimation strategies described in this work, to allow easy use of our
recommendations as well as the reproduction of our numerical study.

Physics-Informed Neural Networks (PINNs) are machine learning tools that
approximate the solution of general partial differential equations (PDEs) by
adding them in some form as terms of the loss/cost function of a Neural
Network. Most pieces of work in the area of PINNs tackle non-linear PDEs.
Nevertheless, many interesting problems involving linear PDEs may benefit from
PINNs; these include parametric studies, multi-query problems, and parabolic
(transient) PDEs. The purpose of this paper is to explore PINNs for linear PDEs
whose solutions may present one or more boundary layers. More specifically, we
analyze the steady-state reaction-advection-diffusion equation in regimes in
which the diffusive coefficient is small in comparison with the reactive or
advective coefficients. We show that adding information about these
coefficients as predictor variables in a PINN results in better prediction
models than in a PINN that only uses spatial information as predictor
variables. This finding may be instrumental in multiscale problems where the
coefficients of the PDEs present high variability in small spatiotemporal
regions of the domain, and therefore PINNs may be employed together with domain
decomposition techniques to efficiently approximate the PDEs locally at each
partition of the spatiotemporal domain, without resorting to different learned
PINN models at each of these partitions.

Over the last few years, research in automatic sleep scoring has mainly
focused on developing increasingly complex deep learning architectures.
However, recently these approaches achieved only marginal improvements, often
at the expense of requiring more data and more expensive training procedures.
Despite all these efforts and their satisfactory performance, automatic sleep
staging solutions are not widely adopted in a clinical context yet. We argue
that most deep learning solutions for sleep scoring are limited in their
real-world applicability as they are hard to train, deploy, and reproduce.
Moreover, these solutions lack interpretability and transparency, which are
often key to increase adoption rates. In this work, we revisit the problem of
sleep stage classification using classical machine learning. Results show that
state-of-the-art performance can be achieved with a conventional machine
learning pipeline consisting of preprocessing, feature extraction, and a simple
machine learning model. In particular, we analyze the performance of a linear
model and a non-linear (gradient boosting) model. Our approach surpasses
state-of-the-art (that uses the same data) on two public datasets: Sleep-EDF
SC-20 (MF1 0.810) and Sleep-EDF ST (MF1 0.795), while achieving competitive
results on Sleep-EDF SC-78 (MF1 0.775) and MASS SS3 (MF1 0.817). We show that,
for the sleep stage scoring task, the expressiveness of an engineered feature
vector is on par with the internally learned representations of deep learning
models. This observation opens the door to clinical adoption, as a
representative feature vector allows to leverage both the interpretability and
successful track record of traditional machine learning models.

The multi-robot adaptive sampling problem aims at finding trajectories for a
team of robots to efficiently sample the phenomenon of interest within a given
endurance budget of the robots. In this paper, we propose a robust and scalable
approach using Multi-Agent Reinforcement Learning for cooperated Adaptive
Sampling (MARLAS) of quasi-static environmental processes. Given a prior on the
field being sampled, the proposed method learns decentralized policies for a
team of robots to sample high-utility regions within a fixed budget. The
multi-robot adaptive sampling problem requires the robots to coordinate with
each other to avoid overlapping sampling trajectories. Therefore, we encode the
estimates of neighbor positions and intermittent communication between robots
into the learning process. We evaluated MARLAS over multiple performance
metrics and found it to outperform other baseline multi-robot sampling
techniques. Additionally, we demonstrate scalability with both the size of the
robot team and the size of the region being sampled. We further demonstrate
robustness to communication failures and robot failures. The experimental
evaluations are conducted both in simulations on real data and in real robot
experiments on demo environmental setup.

Deep Reinforcement Learning (RL) agents often overfit the training
environment, leading to poor generalization performance. In this paper, we
propose Thinker, a bootstrapping method to remove adversarial effects of
confounding features from the observation in an unsupervised way, and thus, it
improves RL agents' generalization. Thinker first clusters experience
trajectories into several clusters. These trajectories are then bootstrapped by
applying a style transfer generator, which translates the trajectories from one
cluster's style to another while maintaining the content of the observations.
The bootstrapped trajectories are then used for policy learning. Thinker has
wide applicability among many RL settings. Experimental results reveal that
Thinker leads to better generalization capability in the Procgen benchmark
environments compared to base algorithms and several data augmentation

Although spatial index structures shorten the query response time, they rely
on complex tree structures to narrow down the search space. Such structures in
turn yield additional storage overhead and take a toll on index maintenance.
Recently, there has been a flurry on works attempting to leverage
machine-Learning(ML) models to simplify the index structures. Some follow-up
works extend the idea to support geospatial point data. These approaches
partition the multidimensional space to cells and assign IDs to these cells
using space-filling curve(e.g., Z-order curve) or mathematical equations. These
approaches work well for geospatial points but are not able to handle complex
geometries such as polygons and trajectories which are widely available in
geospatial data.
  This paper introduces GLIN, a lightweight learned index for spatial range
queries on complex geometries. To achieve that, GLIN transforms geometries to
Z-address intervals, and builds a hierarchical model to learn the cumulative
distribution function between these intervals and the record positions. The
lightweight hierarchical model greatly shortens the index probing time.
Furthermore, GLIN augments spatial query windows using an add-on function to
guarantee the query accuracy for both Contains and Intersects spatial
relationships. Our experiments on real-world and synthetic datasets show that
GLIN occupies 40-70 times less storage overhead than popular spatial indexes
such as Quad-Tree while still showing similar query response time in medium
selectivity queries. Moreover, GLIN's maintenance speed is around 1.5 times
higher on insertion and 3-5 times higher on deletion.

Minimum redundancy among different elements of an embedding in a latent space
is a fundamental requirement or major preference in representation learning to
capture intrinsic informational structures. Current self-supervised learning
methods minimize a pair-wise covariance matrix to reduce the feature redundancy
and produce promising results. However, such representation features of
multiple variables may contain the redundancy among more than two feature
variables that cannot be minimized via the pairwise regularization. Here we
propose the High-Order Mixed-Moment-based Embedding (HOME) strategy to reduce
the redundancy between any sets of feature variables, which is to our best
knowledge the first attempt to utilize high-order statistics/information in
this context. Multivariate mutual information is minimum if and only if
multiple variables are mutually independent, which suggests the necessary
conditions of factorized mixed moments among multiple variables. Based on these
statistical and information theoretic principles, our general HOME framework is
presented for self-supervised representation learning. Our initial experiments
show that a simple version in the form of a three-order HOME scheme already
significantly outperforms the current two-order baseline method (i.e., Barlow
Twins) in terms of the linear evaluation on representation features.

Focal Loss has reached incredible popularity as it uses a simple technique to
identify and utilize hard examples to achieve better performance on
classification. However, this method does not easily generalize outside of
classification tasks, such as in keypoint detection. In this paper, we propose
a novel adaptation of Focal Loss for keypoint detection tasks, called
Adversarial Focal Loss (AFL). AFL not only is semantically analogous to Focal
loss, but also works as a plug-and-chug upgrade for arbitrary loss functions.
While Focal Loss requires output from a classifier, AFL leverages a separate
adversarial network to produce a difficulty score for each input. This
difficulty score can then be used to dynamically prioritize learning on hard
examples, even in absence of a classifier. In this work, we show AFL's
effectiveness in enhancing existing methods in keypoint detection and verify
its capability to re-weigh examples based on difficulty.

Reinforcement learning (RL) workloads take a notoriously long time to train
due to the large number of samples collected at run-time from simulators.
Unfortunately, cluster scale-up approaches remain expensive, and commonly used
CPU implementations of simulators induce high overhead when switching back and
forth between GPU computations. We explore two optimizations that increase RL
data collection efficiency by increasing GPU utilization: (1) GPU
vectorization: parallelizing simulation on the GPU for increased hardware
parallelism, and (2) simulator kernel fusion: fusing multiple simulation steps
to run in a single GPU kernel launch to reduce global memory bandwidth
requirements. We find that GPU vectorization can achieve up to $1024\times$
speedup over commonly used CPU simulators. We profile the performance of
different implementations and show that for a simple simulator, ML compiler
implementations (XLA) of GPU vectorization outperform a DNN framework (PyTorch)
by $13.4\times$ by reducing CPU overhead from repeated Python to DL backend API
calls. We show that simulator kernel fusion speedups with a simple simulator
are $11.3\times$ and increase by up to $1024\times$ as simulator complexity
increases in terms of memory bandwidth requirements. We show that the speedups
from simulator kernel fusion are orthogonal and combinable with GPU
vectorization, leading to a multiplicative speedup.

Disentanglement via mechanism sparsity was introduced recently as a
principled approach to extract latent factors without supervision when the
causal graph relating them in time is sparse, and/or when actions are observed
and affect them sparsely. However, this theory applies only to ground-truth
graphs satisfying a specific criterion. In this work, we introduce a
generalization of this theory which applies to any ground-truth graph and
specifies qualitatively how disentangled the learned representation is expected
to be, via a new equivalence relation over models we call consistency. This
equivalence captures which factors are expected to remain entangled and which
are not based on the specific form of the ground-truth graph. We call this
weaker form of identifiability partial disentanglement. The graphical criterion
that allows complete disentanglement, proposed in an earlier work, can be
derived as a special case of our theory. Finally, we enforce graph sparsity
with constrained optimization and illustrate our theory and algorithm in

This paper considers the problem of characterizing the stability region of a
large-scale networked system comprised of dissipative nonlinear subsystems, in
a distributed and computationally tractable way. One standard approach to
estimate the stability region of a general nonlinear system is to first find a
Lyapunov function for the system and characterize its region of attraction as
the stability region. However, classical approaches, such as sum-of-squares
methods and quadratic approximation, for finding a Lyapunov function either do
not scale to large systems or give very conservative estimates for the
stability region. In this context, we propose a new distributed learning based
approach by exploiting the dissipativity structure of the subsystems. Our
approach has two parts: the first part is a distributed approach to learn the
storage functions (similar to the Lyapunov functions) for all the subsystems,
and the second part is a distributed optimization approach to find the Lyapunov
function for the networked system using the learned storage functions of the
subsystems. We demonstrate the superior performance of our proposed approach
through extensive case studies in microgrid networks.

A major goal of artificial intelligence (AI) is to create an agent capable of
acquiring a general understanding of the world. Such an agent would require the
ability to continually accumulate and build upon its knowledge as it encounters
new experiences. Lifelong or continual learning addresses this setting, whereby
an agent faces a continual stream of problems and must strive to capture the
knowledge necessary for solving each new task it encounters. If the agent is
capable of accumulating knowledge in some form of compositional representation,
it could then selectively reuse and combine relevant pieces of knowledge to
construct novel solutions. Despite the intuitive appeal of this simple idea,
the literatures on lifelong learning and compositional learning have proceeded
largely separately. In an effort to promote developments that bridge between
the two fields, this article surveys their respective research landscapes and
discusses existing and future connections between them.

The COVID-19 pandemic has revealed the importance of digital fabrication to
enable online learning, which remains a challenge for robotics courses. We
introduce a teaching methodology that allows students to participate remotely
in a hands-on robotics course involving the design and fabrication of robots.
Our methodology employs 3D printing techniques with flexible filaments to
create innovative soft robots; robots are made from flexible, as opposed to
rigid, materials. Students design flexible robotic components such as
actuators, sensors, and controllers using CAD software, upload their designs to
a remote 3D printing station, monitor the print with a web camera, and inspect
the components with lab staff before being mailed for testing and assembly. At
the end of the course, students will have iterated through several designs and
created fluidically-driven soft robots. Our remote teaching methodology enables
educators to utilize 3D printing resources to teach soft robotics and cultivate
creativity among students to design novel and innovative robots. Our
methodology seeks to democratize robotics engineering by decoupling hands-on
learning experiences from expensive equipment in the learning environment.

In the early twentieth century, many scholars focused on the preparation of
editions and translations of texts previously available only to the few
specialists able to read archaic hands and privileged enough to travel to work
in person with them in manuscript. Valuable scholarship in its own right, the
preparation of these editions and translations for particular texts deemed
important enough to justify the effort and time, laid the foundation for
generations of scholarship in medieval studies. On the other hand, for many
materials in historical archival collections, including already digitised
collections, medievalists have only had the time to create partial
transcriptions, if any at all. Access to textual material from the medieval
period has increased greatly in recent years with digitisation, and we are able
to imagine many new research projects in decades to come. What challenges do
new frontiers of automation in the archives raise with respect to medieval
studies and in particular to the ways we transcribe? In this article, we argue
that if medievalists hope to pursue the kinds of analysis that goes on in
advanced computational research, we will need new kinds of transcriptions,
intentionally theorized not only for human reading, but also for machine
processing. We already have mature methods for remediating generations of
editions of medieval works such as Optical Character Recognition (OCR), but we
can ask ourselves if these are the kinds of text we want to use for future
computational analysis. We suggest instead that one way forward is by going
back to the scriptorium.

An increased awareness concerning risks of algorithmic bias has driven a
surge of efforts around bias mitigation strategies. A vast majority of the
proposed approaches fall under one of two categories: (1) imposing algorithmic
fairness constraints on predictive models, and (2) collecting additional
training samples. Most recently and at the intersection of these two
categories, methods that propose active learning under fairness constraints
have been developed. However, proposed bias mitigation strategies typically
overlook the bias presented in the observed labels. In this work, we study
fairness considerations of active data collection strategies in the presence of
label bias. We first present an overview of different types of label bias in
the context of supervised learning systems. We then empirically show that, when
overlooking label bias, collecting more data can aggravate bias, and imposing
fairness constraints that rely on the observed labels in the data collection
process may not address the problem. Our results illustrate the unintended
consequences of deploying a model that attempts to mitigate a single type of
bias while neglecting others, emphasizing the importance of explicitly
differentiating between the types of bias that fairness-aware algorithms aim to
address, and highlighting the risks of neglecting label bias during data
collection.

We suggest a novel classification algorithm that is based on local
approximations and explain its connections with Artificial Neural Networks
(ANNs) and Nearest Neighbour classifiers. We illustrate it on the datasets
MNIST and EMNIST of images of handwritten digits. We use the dataset MNIST to
find parameters of our algorithm and apply it with these parameters to the
challenging EMNIST dataset. It is demonstrated that the algorithm misclassifies
0.42% of the images of EMNIST and therefore significantly outperforms
predictions by humans and shallow artificial neural networks (ANNs with few
hidden layers) that both have more than 1.3% of errors

Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to
approximate the action of the Koopman operator on a linear function space
spanned by a dictionary of functions. The accuracy of EDMD model critically
depends on the quality of the particular dictionary's span, specifically on how
close it is to being invariant under the Koopman operator. Motivated by the
observation that the residual error of EDMD, typically used for dictionary
learning, does not encode the quality of the function space and is sensitive to
the choice of basis, we introduce the novel concept of consistency index. We
show that this measure, based on using EDMD forward and backward in time,
enjoys a number of desirable qualities that make it suitable for data-driven
modeling of dynamical systems: it measures the quality of the function space,
it is invariant under the choice of basis, can be computed in closed form from
the data, and provides a tight upper-bound for the relative root mean square
error of all function predictions on the entire span of the dictionary.

We use machine learning to predict the dimension of a lattice polytope
directly from its Ehrhart series. This is highly effective, achieving almost
100% accuracy. We also use machine learning to recover the volume of a lattice
polytope from its Ehrhart series, and to recover the dimension, volume, and
quasi-period of a rational polytope from its Ehrhart series. In each case we
achieve very high accuracy, and we propose mathematical explanations for why
this should be so.

We present a novel generative method for producing unseen and plausible
counterfactual examples for reinforcement learning (RL) agents based upon
outcome variables that characterize agent behavior. Our approach uses a
variational autoencoder to train a latent space that jointly encodes
information about the observations and outcome variables pertaining to an
agent's behavior. Counterfactuals are generated using traversals in this latent
space, via gradient-driven updates as well as latent interpolations against
cases drawn from a pool of examples. These include updates to raise the
likelihood of generated examples, which improves the plausibility of generated
counterfactuals. From experiments in three RL environments, we show that these
methods produce counterfactuals that are more plausible and proximal to their
queries compared to purely outcome-driven or case-based baselines. Finally, we
show that a latent jointly trained to reconstruct both the input observations
and behavioral outcome variables produces higher-quality counterfactuals over
latents trained solely to reconstruct the observation inputs.

Federated learning (FL) was proposed to facilitate the training of models in
a distributed environment. It supports the protection of (local) data privacy
and uses local resources for model training. Until now, the majority of
research has been devoted to "core issues", such as adaptation of machine
learning algorithms to FL, data privacy protection, or dealing with the effects
of uneven data distribution between clients. This contribution is anchored in a
practical use case, where FL is to be actually deployed within an Internet of
Things ecosystem. Hence, somewhat different issues that need to be considered,
beyond popular considerations found in the literature, are identified.
Moreover, an architecture that enables the building of flexible, and adaptable,
FL solutions is introduced.

Fine-tuning models on edge devices like mobile phones would enable
privacy-preserving personalization over sensitive data. However, edge training
has historically been limited to relatively small models with simple
architectures because training is both memory and energy intensive. We present
POET, an algorithm to enable training large neural networks on memory-scarce
battery-operated edge devices. POET jointly optimizes the integrated search
search spaces of rematerialization and paging, two algorithms to reduce the
memory consumption of backpropagation. Given a memory budget and a run-time
constraint, we formulate a mixed-integer linear program (MILP) for
energy-optimal training. Our approach enables training significantly larger
models on embedded devices while reducing energy consumption while not
modifying mathematical correctness of backpropagation. We demonstrate that it
is possible to fine-tune both ResNet-18 and BERT within the memory constraints
of a Cortex-M class embedded device while outperforming current edge training
methods in energy efficiency. POET is an open-source project available at
https://github.com/ShishirPatil/poet

We algorithmically determine the regions and facets of all dimensions of the
canonical polyhedral complex, the universal object into which a ReLU network
decomposes its input space. We show that the locations of the vertices of the
canonical polyhedral complex along with their signs with respect to layer maps
determine the full facet structure across all dimensions. We present an
algorithm which calculates this full combinatorial structure, making use of our
theorems that the dual complex to the canonical polyhedral complex is cubical
and it possesses a multiplication compatible with its facet structure. The
resulting algorithm is numerically stable, polynomial time in the number of
intermediate neurons, and obtains accurate information across all dimensions.
This permits us to obtain, for example, the true topology of the decision
boundaries of networks with low-dimensional inputs. We run empirics on such
networks at initialization, finding that width alone does not increase observed
topology, but width in the presence of depth does. Source code for our
algorithms is accessible online at https://github.com/mmasden/canonicalpoly.

At a fundamental level most physical equations are time reversible. In this
paper we propose an integrator that preserves this property at the discrete
computational level. Our simulations can be run forward and backwards and trace
the same path exactly bitwise. We achieve this by implementing theoretically
reversible integrators using a mix of fixed and floating point arithmetic. Our
main application is in efficiently implementing the reverse step in the adjoint
method used in optimization. Our integrator has applications in differential
simulations and machine learning (backpropagation).

We present a series of two studies conducted to understand user's affective
states during voice-based human-machine interactions. Emphasis is placed on the
cases of communication errors or failures. In particular, we are interested in
understanding "confusion" in relation with other affective states. The studies
consist of two types of tasks: (1) related to communication with a voice-based
virtual agent: speaking to the machine and understanding what the machine says,
(2) non-communication related, problem-solving tasks where the participants
solve puzzles and riddles but are asked to verbally explain the answers to the
machine. We collected audio-visual data and self-reports of affective states of
the participants. We report results of two studies and analysis of the
collected data. The first study was analyzed based on the annotator's
observation, and the second study was analyzed based on the self-report.

The classical hinge-loss support vector machines (SVMs) model is sensitive to
outlier observations due to the unboundedness of its loss function. To
circumvent this issue, recent studies have focused on non-convex loss
functions, such as the hard-margin loss, which associates a constant penalty to
any misclassified or within-margin sample. Applying this loss function yields
much-needed robustness for critical applications but it also leads to an
NP-hard model that makes training difficult, since current exact optimization
algorithms show limited scalability, whereas heuristics are not able to find
high-quality solutions consistently. Against this background, we propose new
integer programming strategies that significantly improve our ability to train
the hard-margin SVM model to global optimality. We introduce an iterative
sampling and decomposition approach, in which smaller subproblems are used to
separate combinatorial Benders' cuts. Those cuts, used within a branch-and-cut
algorithm, permit to converge much more quickly towards a global optimum.
Through extensive numerical analyses on classical benchmark data sets, our
solution algorithm solves, for the first time, 117 new data sets to optimality
and achieves a reduction of 50% in the average optimality gap for the hardest
datasets of the benchmark.

Currently, the evolution of Covid-19 allows researchers to gather the
datasets accumulated over 2 years and to use them in predictive analysis. In
turn, this makes it possible to assess the efficiency potential of more complex
predictive models, including neural networks with different forecast horizons.
In this paper, we present the results of a consistent comparative study of
different types of methods for predicting the dynamics of the spread of
Covid-19 based on regional data for two countries: the United States and
Russia. We used well-known statistical methods (e.g., Exponential Smoothing), a
"tomorrow-as-today" approach, as well as a set of classic machine learning
models trained on data from individual regions. Along with them, a neural
network model based on Long short-term memory (LSTM) layers was considered, the
training samples of which aggregate data from all regions of two countries: the
United States and Russia. Efficiency evaluation was carried out using
cross-validation according to the MAPE metric. It is shown that for complicated
periods characterized by a large increase in the number of confirmed daily
cases, the best results are shown by the LSTM model trained on all regions of
both countries, showing an average Mean Absolute Percentage Error (MAPE) of
18%, 30%, 37% for Russia and 31%, 41%, 50% for US for predictions at forecast
horizons of 14, 28, and 42 days, respectively.

We present a Bayesian hierarchical framework to analyze photometric galaxy
survey data with stellar population synthesis (SPS) models. Our method couples
robust modeling of spectral energy distributions with a population model and a
noise model to characterize the statistical properties of the galaxy
populations and real observations, respectively. By self-consistently inferring
all model parameters, from high-level hyper-parameters to SPS parameters of
individual galaxies, one can separate sources of bias and uncertainty in the
data.We demonstrate the strengths and flexibility of this approach by deriving
accurate photometric redshifts for a sample of spectroscopically-confirmed
galaxies in the COSMOS field, all with 26-band photometry and spectroscopic
redshifts. We achieve a performance competitive with publicly-released
photometric redshift catalogs based on the same data. Prior to this work, this
approach was computationally intractable in practice due to the heavy
computational load of SPS model calls; we overcome this challenge using with
neural emulators. We find that the largest photometric residuals are associated
with poor calibration for emission line luminosities and thus build a framework
to mitigate these effects. This combination of physics-based modeling
accelerated with machine learning paves the path towards meeting the stringent
requirements on the accuracy of photometric redshift estimation imposed by
upcoming cosmological surveys. The approach also has the potential to create
new links between cosmology and galaxy evolution through the analysis of
photometric datasets.

Gambling agencies forbid late bets, placed after the winning horse crosses
the finish line. A time-traveling gambler could cheat the system. We construct
a gamble that one can win by simulating time travel with experimentally
feasible entanglement manipulation. Our gamble echoes a common metrology
protocol: A gambler must prepare probes to input into a metrology experiment.
The goal is to infer as much information per probe as possible about a
parameter's value. If the input is optimal, the information gained per probe
can exceed any value achievable classically. The gambler chooses the input
state analogously to choosing a horse. However, only after the probes are
measured does the gambler learn which input would have been optimal. The
gambler can "place a late bet" by effectively teleporting the optimal input
back in time, via entanglement manipulation. Our Gedankenexperiment
demonstrates that not only true time travel, but even a simulation offers a
quantum advantage in metrology.

Utilizing vision and language models (VLMs) pre-trained on large-scale
image-text pairs is becoming a promising paradigm for open-vocabulary visual
recognition. In this work, we extend this paradigm by leveraging motion and
audio that naturally exist in video. We present \textbf{MOV}, a simple yet
effective method for \textbf{M}ultimodal \textbf{O}pen-\textbf{V}ocabulary
video classification. In MOV, we directly use the vision encoder from
pre-trained VLMs with minimal modifications to encode video, optical flow and
audio spectrogram. We design a cross-modal fusion mechanism to aggregate
complimentary multimodal information. Experiments on Kinetics-700 and VGGSound
show that introducing flow or audio modality brings large performance gains
over the pre-trained VLM and existing methods. Specifically, MOV greatly
improves the accuracy on base classes, while generalizes better on novel
classes. MOV achieves state-of-the-art results on UCF and HMDB zero-shot video
classification benchmarks, significantly outperforming both traditional
zero-shot methods and recent methods based on VLMs. Code and models will be
released.

We construct a physically-parameterized probabilistic autoencoder (PAE) to
learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set
of spectral time series. The PAE is a two-stage generative model, composed of
an Auto-Encoder (AE) which is interpreted probabilistically after training
using a Normalizing Flow (NF). We demonstrate that the PAE learns a
low-dimensional latent space that captures the nonlinear range of features that
exists within the population, and can accurately model the spectral evolution
of SNe Ia across the full range of wavelength and observation times directly
from the data. By introducing a correlation penalty term and multi-stage
training setup alongside our physically-parameterized network we show that
intrinsic and extrinsic modes of variability can be separated during training,
removing the need for the additional models to perform magnitude
standardization. We then use our PAE in a number of downstream tasks on SNe Ia
for increasingly precise cosmological analyses, including automatic detection
of SN outliers, the generation of samples consistent with the data
distribution, and solving the inverse problem in the presence of noisy and
incomplete data to constrain cosmological distance measurements. We find that
the optimal number of intrinsic model parameters appears to be three, in line
with previous studies, and show that we can standardize our test sample of SNe
Ia with an RMS of $0.091 \pm 0.010$ mag, which corresponds to $0.074 \pm 0.010$
mag if peculiar velocity contributions are removed. Trained models and codes
are released at
\href{https://github.com/georgestein/suPAErnova}{github.com/georgestein/suPAErnova}

The development of CLIP [Radford et al., 2021] has sparked a debate on
whether language supervision can result in vision models with more transferable
representations than traditional image-only methods. Our work studies this
question through a carefully controlled comparison of two approaches in terms
of their ability to learn representations that generalize to downstream
classification tasks. We find that when the pre-training dataset meets certain
criteria -- it is sufficiently large and contains descriptive captions with low
variability -- image-only methods do not match CLIP's transfer performance,
even when they are trained with more image data. However, contrary to what one
might expect, there are practical settings in which these criteria are not met,
wherein added supervision through captions is actually detrimental. Motivated
by our findings, we devise simple prescriptions to enable CLIP to better
leverage the language information present in existing pre-training datasets.

Supervised and unsupervised deep trackers that rely on deep learning
technologies are popular in recent years. Yet, they demand high computational
complexity and a high memory cost. A green unsupervised single-object tracker,
called GUSOT, that aims at object tracking for long videos under a
resource-constrained environment is proposed in this work. Built upon a
baseline tracker, UHP-SOT++, which works well for short-term tracking, GUSOT
contains two additional new modules: 1) lost object recovery, and 2)
color-saliency-based shape proposal. They help resolve the tracking loss
problem and offer a more flexible object proposal, respectively. Thus, they
enable GUSOT to achieve higher tracking accuracy in the long run. We conduct
experiments on the large-scale dataset LaSOT with long video sequences, and
show that GUSOT offers a lightweight high-performance tracking solution that
finds applications in mobile and edge computing platforms.

Computing-in-Memory (CiM) architectures based on emerging non-volatile memory
(NVM) devices have demonstrated great potential for deep neural network (DNN)
acceleration thanks to their high energy efficiency. However, NVM devices
suffer from various non-idealities, especially device-to-device variations due
to fabrication defects and cycle-to-cycle variations due to the stochastic
behavior of devices. As such, the DNN weights actually mapped to NVM devices
could deviate significantly from the expected values, leading to large
performance degradation. To address this issue, most existing works focus on
maximizing average performance under device variations. This objective would
work well for general-purpose scenarios. But for safety-critical applications,
the worst-case performance must also be considered. Unfortunately, this has
been rarely explored in the literature. In this work, we formulate the problem
of determining the worst-case performance of CiM DNN accelerators under the
impact of device variations. We further propose a method to effectively find
the specific combination of device variation in the high-dimensional space that
leads to the worst-case performance. We find that even with very small device
variations, the accuracy of a DNN can drop drastically, causing concerns when
deploying CiM accelerators in safety-critical applications. Finally, we show
that surprisingly none of the existing methods used to enhance average DNN
performance in CiM accelerators are very effective when extended to enhance the
worst-case performance, and further research down the road is needed to address
this problem.

We study the highly practical but comparatively under-studied problem of
latent-domain adaptation, where a source model should be adapted to a target
dataset that contains a mixture of unlabelled domain-relevant and
domain-irrelevant examples. Furthermore, motivated by the requirements for data
privacy and the need for embedded and resource-constrained devices of all kinds
to adapt to local data distributions, we focus on the setting of feed-forward
source-free domain adaptation, where adaptation should not require access to
the source dataset, and also be back propagation-free. Our solution is to
meta-learn a network capable of embedding the mixed-relevance target dataset
and dynamically adapting inference for target examples using cross-attention.
The resulting framework leads to consistent improvement on strong ERM
baselines. We also show that our framework sometimes even improves on the upper
bound of domain-supervised adaptation, where only domain-relevant instances are
provided for adaptation. This suggests that human annotated domain labels may
not always be optimal, and raises the possibility of doing better through
automated instance selection.

This work characterizes the effect of depth on the optimization landscape of
linear regression, showing that, despite their nonconvexity, deeper models have
more desirable optimization landscape. We consider a robust and
over-parameterized setting, where a subset of measurements are grossly
corrupted with noise and the true linear model is captured via an $N$-layer
linear neural network. On the negative side, we show that this problem
\textit{does not} have a benign landscape: given any $N\geq 1$, with constant
probability, there exists a solution corresponding to the ground truth that is
neither local nor global minimum. However, on the positive side, we prove that,
for any $N$-layer model with $N\geq 2$, a simple sub-gradient method becomes
oblivious to such ``problematic'' solutions; instead, it converges to a
balanced solution that is not only close to the ground truth but also enjoys a
flat local landscape, thereby eschewing the need for "early stopping". Lastly,
we empirically verify that the desirable optimization landscape of deeper
models extends to other robust learning tasks, including deep matrix recovery
and deep ReLU networks with $\ell_1$-loss.

Transformers have gained increasing popularity in a wide range of
applications, including Natural Language Processing (NLP), Computer Vision and
Speech Recognition, because of their powerful representational capacity.
However, harnessing this representational capacity effectively requires a large
amount of data, strong regularization, or both, to mitigate overfitting.
Recently, the power of the Transformer has been unlocked by self-supervised
pretraining strategies based on masked autoencoders which rely on
reconstructing masked inputs, directly, or contrastively from unmasked content.
This pretraining strategy which has been used in BERT models in NLP, Wav2Vec
models in Speech and, recently, in MAE models in Vision, forces the model to
learn about relationships between the content in different parts of the input
using autoencoding related objectives. In this paper, we propose a novel, but
surprisingly simple alternative to content reconstruction~-- that of predicting
locations from content, without providing positional information for it. Doing
so requires the Transformer to understand the positional relationships between
different parts of the input, from their content alone. This amounts to an
efficient implementation where the pretext task is a classification problem
among all possible positions for each input token. We experiment on both Vision
and Speech benchmarks, where our approach brings improvements over strong
supervised training baselines and is comparable to modern
unsupervised/self-supervised pretraining methods. Our method also enables
Transformers trained without position embeddings to outperform ones trained
with full position information.

Vehicle-to-Everything (V2X) network has enabled collaborative perception in
autonomous driving, which is a promising solution to the fundamental defect of
stand-alone intelligence including blind zones and long-range perception.
However, the lack of datasets has severely blocked the development of
collaborative perception algorithms. In this work, we release DOLPHINS: Dataset
for cOllaborative Perception enabled Harmonious and INterconnected
Self-driving, as a new simulated large-scale various-scenario multi-view
multi-modality autonomous driving dataset, which provides a ground-breaking
benchmark platform for interconnected autonomous driving. DOLPHINS outperforms
current datasets in six dimensions: temporally-aligned images and point clouds
from both vehicles and Road Side Units (RSUs) enabling both Vehicle-to-Vehicle
(V2V) and Vehicle-to-Infrastructure (V2I) based collaborative perception; 6
typical scenarios with dynamic weather conditions make the most various
interconnected autonomous driving dataset; meticulously selected viewpoints
providing full coverage of the key areas and every object; 42376 frames and
292549 objects, as well as the corresponding 3D annotations, geo-positions, and
calibrations, compose the largest dataset for collaborative perception; Full-HD
images and 64-line LiDARs construct high-resolution data with sufficient
details; well-organized APIs and open-source codes ensure the extensibility of
DOLPHINS. We also construct a benchmark of 2D detection, 3D detection, and
multi-view collaborative perception tasks on DOLPHINS. The experiment results
show that the raw-level fusion scheme through V2X communication can help to
improve the precision as well as to reduce the necessity of expensive LiDAR
equipment on vehicles when RSUs exist, which may accelerate the popularity of
interconnected self-driving vehicles. DOLPHINS is now available on
https://dolphins-dataset.net/.

Feature attributions based on the Shapley value are popular for explaining
machine learning models; however, their estimation is complex from both a
theoretical and computational standpoint. We disentangle this complexity into
two factors: (1)~the approach to removing feature information, and (2)~the
tractable estimation strategy. These two factors provide a natural lens through
which we can better understand and compare 24 distinct algorithms. Based on the
various feature removal approaches, we describe the multiple types of Shapley
value feature attributions and methods to calculate each one. Then, based on
the tractable estimation strategies, we characterize two distinct families of
approaches: model-agnostic and model-specific approximations. For the
model-agnostic approximations, we benchmark a wide class of estimation
approaches and tie them to alternative yet equivalent characterizations of the
Shapley value. For the model-specific approximations, we clarify the
assumptions crucial to each method's tractability for linear, tree, and deep
models. Finally, we identify gaps in the literature and promising future
research directions.

In this study, a novel multiple-frame based image and texture independent
convolutional Neural Network (CNN) noise estimator is introduced. The estimator
works.

Many existing autonomous driving paradigms involve a multi-stage discrete
pipeline of tasks. To better predict the control signals and enhance user
safety, an end-to-end approach that benefits from joint spatial-temporal
feature learning is desirable. While there are some pioneering works on
LiDAR-based input or implicit design, in this paper we formulate the problem in
an interpretable vision-based setting. In particular, we propose a
spatial-temporal feature learning scheme towards a set of more representative
features for perception, prediction and planning tasks simultaneously, which is
called ST-P3. Specifically, an egocentric-aligned accumulation technique is
proposed to preserve geometry information in 3D space before the bird's eye
view transformation for perception; a dual pathway modeling is devised to take
past motion variations into account for future prediction; a temporal-based
refinement unit is introduced to compensate for recognizing vision-based
elements for planning. To the best of our knowledge, we are the first to
systematically investigate each part of an interpretable end-to-end
vision-based autonomous driving system. We benchmark our approach against
previous state-of-the-arts on both open-loop nuScenes dataset as well as
closed-loop CARLA simulation. The results show the effectiveness of our method.
Source code, model and protocol details are made publicly available at
https://github.com/OpenPerceptionX/ST-P3.

Among user authentication methods, behavioural biometrics has proven to be
effective against identity theft as well as user-friendly and unobtrusive. One
of the most popular traits in the literature is keystroke dynamics due to the
large deployment of computers and mobile devices in our society. This paper
focuses on improving keystroke biometric systems on the free-text scenario.
This scenario is characterised as very challenging due to the uncontrolled text
conditions, the influence of the user's emotional and physical state, and the
in-use application. To overcome these drawbacks, methods based on deep learning
such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks
(RNNs) have been proposed in the literature, outperforming traditional machine
learning methods. However, these architectures still have aspects that need to
be reviewed and improved. To the best of our knowledge, this is the first study
that proposes keystroke biometric systems based on Transformers. The proposed
Transformer architecture has achieved Equal Error Rate (EER) values of 3.84\%
in the popular Aalto mobile keystroke database using only 5 enrolment sessions,
outperforming by a large margin other state-of-the-art approaches in the

By the end of 2021, the renewable energy share of the global electricity
capacity reached 38.3% and the new installations are dominated by wind and
solar energy, showing global increases of 12.7% and 18.5%, respectively.
However, both wind and photovoltaic energy sources are highly volatile making
planning difficult for grid operators, so accurate forecasts of the
corresponding weather variables are essential for reliable electricity
predictions. The most advanced approach in weather prediction is the ensemble
method, which opens the door for probabilistic forecasting; though ensemble
forecast are often underdispersive and subject to systematic bias. Hence, they
require some form of statistical post-processing, where parametric models
provide full predictive distributions of the weather variables at hand. We
propose a general two-step machine learning-based approach to calibrating
ensemble weather forecasts, where in the first step improved point forecasts
are generated, which are then together with various ensemble statistics serve
as input features of the neural network estimating the parameters of the
predictive distribution. In two case studies based of 100m wind speed and
global horizontal irradiance forecasts of the operational ensemble pre diction
system of the Hungarian Meteorological Service, the predictive performance of
this novel method is compared with the forecast skill of the raw ensemble and
the state-of-the-art parametric approaches. Both case studies confirm that at
least up to 48h statistical post-processing substantially improves the
predictive performance of the raw ensemble for all considered forecast
horizons. The investigated variants of the proposed two-step method outperform
in skill their competitors and the suggested new approach is well applicable
for different weather quantities and for a fair range of predictive
distributions.

One of the main challenges in autonomous racing is to design algorithms for
motion planning at high speed, and across complex racing courses. End-to-end
trajectory synthesis has been previously proposed where the trajectory for the
ego vehicle is computed based on camera images from the racecar. This is done
in a supervised learning setting using behavioral cloning techniques. In this
paper, we address the limitations of behavioral cloning methods for trajectory
synthesis by introducing Differential Bayesian Filtering (DBF), which uses
probabilistic B\'ezier curves as a basis for inferring optimal autonomous
racing trajectories based on Bayesian inference. We introduce a trajectory
sampling mechanism and combine it with a filtering process which is able to
push the car to its physical driving limits. The performance of DBF is
evaluated on the DeepRacing Formula One simulation environment and compared
with several other trajectory synthesis approaches as well as human driving
performance. DBF achieves the fastest lap time, and the fastest speed, by
pushing the racecar closer to its limits of control while always remaining
inside track bounds.

Machine learning systems typically apply the same model to both easy and
tough cases. This is in stark contrast with humans, who tend to evoke either
fast (instinctive) or slow (analytical) thinking depending on the problem
difficulty, a property called the dual-process theory of mind. We present
FLOWGEN, a graph-generation model inspired by the dual-process theory of mind
that generates large graphs incrementally. Depending on the difficulty of
completing the graph at the current step, graph generation is routed to either
a fast (weaker) or a slow (stronger) model. These modules have identical
architectures, but vary in the number of parameters and consequently differ in
generative power. Experiments on real-world graphs show that ours can
successfully generate graphs similar to those generated by a single large
model, while being up to 2x faster.

There is growing interest in continuous wearable vital sign sensors for
monitoring patients remotely at home. These monitors are usually coupled to an
alerting system, which is triggered when vital sign measurements fall outside a
predefined normal range. Trends in vital signs, such as an increasing heart
rate, are often indicative of deteriorating health, but are rarely incorporated
into alerting systems. In this work, we present a novel outlier detection
algorithm to identify such abnormal vital sign trends. We introduce a
distance-based measure to compare vital sign trajectories. For each patient in
our dataset, we split vital sign time series into 180 minute, non-overlapping
epochs. We then calculated a distance between all pairs of epochs using the
dynamic time warp distance. Each epoch was characterized by its mean pairwise
distance (average link distance) to all other epochs, with large distances
considered as outliers. We applied this method to a pilot dataset collected
over 1561 patient-hours from 8 patients who had recently been discharged from
hospital after contracting COVID-19. We show that outlier epochs correspond
well with patients who were subsequently readmitted to hospital. We also show,
descriptively, how epochs transition from normal to abnormal for one such
patient.

We study the multi-step off-policy learning approach to distributional RL.
Despite the apparent similarity between value-based RL and distributional RL,
our study reveals intriguing and fundamental differences between the two cases
in the multi-step setting. We identify a novel notion of path-dependent
distributional TD error, which is indispensable for principled multi-step
distributional RL. The distinction from the value-based case bears important
implications on concepts such as backward-view algorithms. Our work provides
the first theoretical guarantees on multi-step off-policy distributional RL
algorithms, including results that apply to the small number of existing
approaches to multi-step distributional RL. In addition, we derive a novel
algorithm, Quantile Regression-Retrace, which leads to a deep RL agent
QR-DQN-Retrace that shows empirical improvements over QR-DQN on the Atari-57
benchmark. Collectively, we shed light on how unique challenges in multi-step
distributional RL can be addressed both in theory and practice.

Model-based reinforcement learning (RL) is a sample-efficient way of learning
complex behaviors by leveraging a learned single-step dynamics model to plan
actions in imagination. However, planning every action for long-horizon tasks
is not practical, akin to a human planning out every muscle movement. Instead,
humans efficiently plan with high-level skills to solve complex tasks. From
this intuition, we propose a Skill-based Model-based RL framework (SkiMo) that
enables planning in the skill space using a skill dynamics model, which
directly predicts the skill outcomes, rather than predicting all small details
in the intermediate states, step by step. For accurate and efficient long-term
planning, we jointly learn the skill dynamics model and a skill repertoire from
prior experience. We then harness the learned skill dynamics model to
accurately simulate and plan over long horizons in the skill space, which
enables efficient downstream learning of long-horizon, sparse reward tasks.
Experimental results in navigation and manipulation domains show that SkiMo
extends the temporal horizon of model-based approaches and improves the sample
efficiency for both model-based RL and skill-based RL. Code and videos are
available at \url{https://clvrai.com/skimo}

Artificial intelligence solutions for Autonomous Vehicles (AVs) have been
developed using publicly available datasets such as Argoverse, ApolloScape,
Level5, and NuScenes. One major limitation of these datasets is the absence of
infrastructure and/or pooled vehicle information like lane line type, vehicle
speed, traffic signs, and intersections. Such information is necessary and not
complementary to eliminating high-risk edge cases. The rapid advancements in
Vehicle-to-Infrastructure and Vehicle-to-Vehicle technologies show promise that
infrastructure and pooled vehicle information will soon be accessible in near
real-time. Taking a leap in the future, we introduce the first comprehensive
synthetic dataset with intelligent infrastructure and pooled vehicle
information for advancing the next generation of AVs, named VTrackIt. We also
introduce the first deep learning model (InfraGAN) for trajectory predictions
that considers such information. Our experiments with InfraGAN show that the
comprehensive information offered by VTrackIt reduces the number of high-risk
edge cases. The VTrackIt dataset is available upon request under the Creative
Commons CC BY-NC-SA 4.0 license at http://vtrackit.irda.club.

Deep learning models used in medical image analysis are prone to raising
reliability concerns due to their black-box nature. To shed light on these
black-box models, previous works predominantly focus on identifying the
contribution of input features to the diagnosis, i.e., feature attribution. In
this work, we explore counterfactual explanations to identify what patterns the
models rely on for diagnosis. Specifically, we investigate the effect of
changing features within chest X-rays on the classifier's output to understand
its decision mechanism. We leverage a StyleGAN-based approach (StyleEx) to
create counterfactual explanations for chest X-rays by manipulating specific
latent directions in their latent space. In addition, we propose EigenFind to
significantly reduce the computation time of generated explanations. We
clinically evaluate the relevancy of our counterfactual explanations with the
help of radiologists. Our code is publicly available.

In decentralized optimization environments, each agent $i$ in a network of
$n$ nodes has its own private function $f_i$, and nodes communicate with their
neighbors to cooperatively minimize the aggregate objective $\sum_{i=1}^n f_i$.
In this setting, synchronizing the nodes' updates incurs significant
communication overhead and computational costs, so much of the recent
literature has focused on the analysis and design of asynchronous optimization
algorithms, where agents activate and communicate at arbitrary times without
needing a global synchronization enforcer. However, most works assume that when
a node activates, it selects the neighbor to contact based on a fixed
probability (e.g., uniformly at random), a choice that ignores the optimization
landscape at the moment of activation. Instead, in this work we introduce an
optimization-aware selection rule that chooses the neighbor providing the
highest dual cost improvement (a quantity related to a dualization of the
problem based on consensus). This scheme is related to the coordinate descent
(CD) method with the Gauss-Southwell (GS) rule for coordinate updates; in our
setting however, only a subset of coordinates is accessible at each iteration
(because each node can communicate only with its neighbors), so the existing
literature on GS methods does not apply. To overcome this difficulty, we
develop a new analytical framework for smooth and strongly convex $f_i$ that
covers the class of set-wise CD algorithms -- a class that directly applies to
decentralized scenarios, but is not limited to them -- and we show that the
proposed set-wise GS rule achieves a speedup factor of up to the maximum degree
in the network (which is in the order of $\Theta(n)$ for highly connected
graphs). The speedup predicted by our analysis is validated in numerical
experiments with synthetic data.

People who need robots are often not the same as people who can program them.
This key observation in human-robot interaction (HRI) has lead to a number of
challenges when developing robotic applications, since developers must
understand the exact needs of end-users.
  Participatory Design (PD), the process of including stakeholders such as end
users early in the robot design process, has been used with noteworthy success
in HRI, but typically remains limited to the early phases of development.
Resulting robot behaviors are often then hardcoded by engineers or utilized in
Wizard-of-Oz (WoZ) systems that rarely achieve autonomy. End-User Programming
(EUP), i.e., the research of tools allowing end users with limited computer
knowledge to program systems, has been widely applied to the design of robot
behaviors for interaction with humans, but these tools risk being used solely
as research demonstrations only existing for the amount of time required for
them to be evaluated and published.
  In the PD/EUP Workshop, we aim to facilitate mutual learning between these
communities and to create communication opportunities that could help the
larger HRI community work towards end-user personalized and adaptable
interactions. Both PD and EUP will be key requirements if we want robots to be
useful for wider society. From this workshop, we expect new collaboration
opportunities to emerge and we aim to formalize new methodologies that
integrate PD and EUP approaches.

3D point cloud models are widely applied in safety-critical scenes, which
delivers an urgent need to obtain more solid proofs to verify the robustness of
models. Existing verification method for point cloud model is time-expensive
and computationally unattainable on large networks. Additionally, they cannot
handle the complete PointNet model with joint alignment network (JANet) that
contains multiplication layers, which effectively boosts the performance of 3D
models. This motivates us to design a more efficient and general framework to
verify various architectures of point cloud models. The key challenges in
verifying the large-scale complete PointNet models are addressed as dealing
with the cross-non-linearity operations in the multiplication layers and the
high computational complexity of high-dimensional point cloud inputs and added
layers. Thus, we propose an efficient verification framework, 3DVerifier, to
tackle both challenges by adopting a linear relaxation function to bound the
multiplication layer and combining forward and backward propagation to compute
the certified bounds of the outputs of the point cloud models. Our
comprehensive experiments demonstrate that 3DVerifier outperforms existing
verification algorithms for 3D models in terms of both efficiency and accuracy.
Notably, our approach achieves an orders-of-magnitude improvement in
verification efficiency for the large network, and the obtained certified
bounds are also significantly tighter than the state-of-the-art verifiers. We
release our tool 3DVerifier via https://github.com/TrustAI/3DVerifier for use
by the community.

We consider an expected-value ranking and selection problem where all k
solutions' simulation outputs depend on a common uncertain input model. Given
that the uncertainty of the input model is captured by a probability simplex on
a finite support, we define the most probable best (MPB) to be the solution
whose probability of being optimal is the largest. To devise an efficient
sampling algorithm to find the MPB, we first derive a lower bound to the large
deviation rate of the probability of falsely selecting the MPB, then formulate
an optimal computing budget allocation (OCBA) problem to find the optimal
static sampling ratios for all solution-input model pairs that maximize the
lower bound. We devise a series of sequential algorithms that apply
interpretable and computationally efficient sampling rules and prove their
sampling ratios achieve the optimality conditions for the OCBA problem as the
simulation budget increases. The algorithms are benchmarked against a
state-of-the-art sequential sampling algorithm designed for contextual ranking
and selection problems and demonstrated to have superior empirical performances
at finding the MPB.

The recently increased complexity of Machine Learning (ML) methods, led to
the necessity to lighten both the research and industry development processes.
ML pipelines have become an essential tool for experts of many domains, data
scientists and researchers, allowing them to easily put together several ML
models to cover the full analytic process starting from raw datasets. Over the
years, several solutions have been proposed to automate the building of ML
pipelines, most of them focused on semantic aspects and characteristics of the
input dataset. However, an approach taking into account the new quality
concerns needed by ML systems (like fairness, interpretability, privacy, etc.)
is still missing. In this paper, we first identify, from the literature, key
quality attributes of ML systems. Further, we propose a new engineering
approach for quality ML pipeline by properly extending the Feature Models
meta-model. The presented approach allows to model ML pipelines, their quality
requirements (on the whole pipeline and on single phases), and quality
characteristics of algorithms used to implement each pipeline phase. Finally,
we demonstrate the expressiveness of our model considering the classification
problem.

In both industrial and service domains, a central benefit of the use of
robots is their ability to quickly and reliably execute repetitive tasks.
However, even relatively simple peg-in-hole tasks are typically subject to
stochastic variations, requiring search motions to find relevant features such
as holes. While search improves robustness, it comes at the cost of increased
runtime: More exhaustive search will maximize the probability of successfully
executing a given task, but will significantly delay any downstream tasks. This
trade-off is typically resolved by human experts according to simple
heuristics, which are rarely optimal. This paper introduces an automatic,
data-driven and heuristic-free approach to optimize robot search strategies. By
training a neural model of the search strategy on a large set of simulated
stochastic environments, conditioning it on few real-world examples and
inverting the model, we can infer search strategies which adapt to the
time-variant characteristics of the underlying probability distributions, while
requiring very few real-world measurements. We evaluate our approach on two
different industrial robots in the context of spiral and probe search for THT
electronics assembly.

Scene flow estimation, which extracts point-wise motion between scenes, is
becoming a crucial task in many computer vision tasks. However, all of the
existing estimation methods utilize only the unidirectional features,
restricting the accuracy and generality. This paper presents a novel scene flow
estimation architecture using bidirectional flow embedding layers. The proposed
bidirectional layer learns features along both forward and backward directions,
enhancing the estimation performance. In addition, hierarchical feature
extraction and warping improve the performance and reduce computational
overhead. Experimental results show that the proposed architecture achieved a
new state-of-the-art record by outperforming other approaches with large margin
in both FlyingThings3D and KITTI benchmarks. Codes are available at
https://github.com/cwc1260/BiFlow.

Full-immersive multiuser Virtual Reality (VR) envisions supporting
unconstrained mobility of the users in the virtual worlds, while at the same
time constraining their physical movements inside VR setups through redirected
walking. For enabling delivery of high data rate video content in real-time,
the supporting wireless networks will leverage highly directional communication
links that will "track" the users for maintaining the Line-of-Sight (LoS)
connectivity. Recurrent Neural Networks (RNNs) and in particular Long
Short-Term Memory (LSTM) networks have historically presented themselves as a
suitable candidate for near-term movement trajectory prediction for natural
human mobility, and have also recently been shown as applicable in predicting
VR users' mobility under the constraints of redirected walking. In this work,
we extend these initial findings by showing that Gated Recurrent Unit (GRU)
networks, another candidate from the RNN family, generally outperform the
traditionally utilized LSTMs. Second, we show that context from a virtual world
can enhance the accuracy of the prediction if used as an additional input
feature in comparison to the more traditional utilization of solely the
historical physical movements of the VR users. Finally, we show that the
prediction system trained on a static number of coexisting VR users be scaled
to a multi-user system without significant accuracy degradation.

The ability to detect Out-of-Distribution (OOD) data is important in
safety-critical applications of deep learning. The aim is to separate
In-Distribution (ID) data drawn from the training distribution from OOD data
using a measure of uncertainty extracted from a deep neural network. Deep
Ensembles are a well-established method of improving the quality of uncertainty
estimates produced by deep neural networks, and have been shown to have
superior OOD detection performance compared to single models. An existing
intuition in the literature is that the diversity of Deep Ensemble predictions
indicates distributional shift, and so measures of diversity such as Mutual
Information (MI) should be used for OOD detection. We show experimentally that
this intuition is not valid on ImageNet-scale OOD detection -- using MI leads
to 30-40% worse %FPR@95 compared to single-model entropy on some OOD datasets.
We suggest an alternative explanation for Deep Ensembles' better OOD detection
performance -- OOD detection is binary classification and we are ensembling
diverse classifiers. As such we show that practically, even better OOD
detection performance can be achieved for Deep Ensembles by averaging
task-specific detection scores such as Energy over the ensemble.

We investigate the composability of soft-rules learned by relational neural
architectures when operating over object-centric (slot-based) representations,
under a variety of sparsity-inducing constraints. We find that increasing
sparsity, especially on features, improves the performance of some models and
leads to simpler relations. Additionally, we observe that object-centric
representations can be detrimental when not all objects are fully captured; a
failure mode to which CNNs are less prone. These findings demonstrate the
trade-offs between interpretability and performance, even for models designed
to tackle relational tasks.

Detecting out-of-distribution (OOD) data is a task that is receiving an
increasing amount of research attention in the domain of deep learning for
computer vision. However, the performance of detection methods is generally
evaluated on the task in isolation, rather than also considering potential
downstream tasks in tandem. In this work, we examine selective classification
in the presence of OOD data (SCOD). That is to say, the motivation for
detecting OOD samples is to reject them so their impact on the quality of
predictions is reduced. We show under this task specification, that existing
post-hoc methods perform quite differently compared to when evaluated only on
OOD detection. This is because it is no longer an issue to conflate
in-distribution (ID) data with OOD data if the ID data is going to be
misclassified. However, the conflation within ID data of correct and incorrect
predictions becomes undesirable. We also propose a novel method for SCOD,
Softmax Information Retaining Combination (SIRC), that augments softmax-based
confidence scores with feature-agnostic information such that their ability to
identify OOD samples is improved without sacrificing separation between correct
and incorrect ID predictions. Experiments on a wide variety of ImageNet-scale
datasets and convolutional neural network architectures show that SIRC is able
to consistently match or outperform the baseline for SCOD, whilst existing OOD
detection methods fail to do so.

Deep neural networks (DNN) have achieved impressive success in multiple
domains. Over the years, the accuracy of these models has increased with the
proliferation of deeper and more complex architectures. Thus, state-of-the-art
solutions are often computationally expensive, which makes them unfit to be
deployed on edge computing platforms. In order to mitigate the high
computation, memory, and power requirements of inferring convolutional neural
networks (CNNs), we propose the use of power-of-two quantization, which
quantizes continuous parameters into low-bit power-of-two values. This reduces
computational complexity by removing expensive multiplication operations and
with the use of low-bit weights. ResNet is adopted as the building block of our
solution and the proposed model is evaluated on a spoken language understanding
(SLU) task. Experimental results show improved performance for shift neural
network architectures, with our low-bit quantization achieving 98.76 \% on the
test set which is comparable performance to its full-precision counterpart and
state-of-the-art solutions.

Federated learning (FL) is a novel learning paradigm that addresses the
privacy leakage challenge of centralized learning. However, in FL, users with
non-independent and identically distributed (non-IID) characteristics can
deteriorate the performance of the global model. Specifically, the global model
suffers from the weight divergence challenge owing to non-IID data. To address
the aforementioned challenge, we propose a novel diffusion strategy of the
machine learning (ML) model (FedDif) to maximize the FL performance with
non-IID data. In FedDif, users spread local models to neighboring users over
D2D communications. FedDif enables the local model to experience different
distributions before parameter aggregation. Furthermore, we theoretically
demonstrate that FedDif can circumvent the weight divergence challenge. On the
theoretical basis, we propose the communication-efficient diffusion strategy of
the ML model, which can determine the trade-off between the learning
performance and communication cost based on auction theory. The performance
evaluation results show that FedDif improves the test accuracy of the global
model by 10.37% compared to the baseline FL with non-IID settings. Moreover,
FedDif improves the number of consumed sub-frames by 1.28 to 2.85 folds to the
latest methods except for the model compression scheme. FedDif also improves
the number of transmitted models by 1.43 to 2.67 folds to the latest methods.

Within ``orbital-free'' density functional theory, it is essential to develop
general kinetic energy density (KED), denoted as $t(\mathbf{r})$. This is
usually done by empirical corrections and enhancements, gradient expansions,
machine learning, or axiomatic approaches to find forms that satisfy physical
necessities. In all cases, it is crucial to determine the largest spatial
density derivative order, $m$ in, $t(\mathbf{r})$. There have been many efforts
to do so, but none have proven general or conclusive and there is no clear
guide on how to set $m$. In this work, we found that, by imposing KED finitude,
$m=D+1$ for systems of dimension $D$. This is consistent with observations and
provides a needed guide for systematically developing more accurate KEDs.

In the recent years, Distributed Denial of Service (DDoS) attacks on Internet
of Things (IoT) devices have become one of the prime concerns to Internet users
around the world. One of the sources of the attacks on IoT ecosystems are
botnets. Intruders force IoT devices to become unavailable for its legitimate
users by sending large number of messages within a short interval. This study
proposes NFDLM, a lightweight and optimised Artificial Neural Network (ANN)
based Distributed Denial of Services (DDoS) attack detection framework with
mutual correlation as feature selection method which produces a superior result
when compared with Long Short Term Memory (LSTM) and simple ANN. Overall, the
detection performance achieves approximately 99\% accuracy for the detection of
attacks from botnets. In this work, we have designed and compared four
different models where two are based on ANN and the other two are based on LSTM
to detect the attack types of DDoS.

BERT4Rec is an effective model for sequential recommendation based on the
Transformer architecture. In the original publication, BERT4Rec claimed
superiority over other available sequential recommendation approaches (e.g.
SASRec), and it is now frequently being used as a state-of-the art baseline for
sequential recommendations. However, not all subsequent publications confirmed
this result and proposed other models that were shown to outperform BERT4Rec in
effectiveness. In this paper we systematically review all publications that
compare BERT4Rec with another popular Transformer-based model, namely SASRec,
and show that BERT4Rec results are not consistent within these publications. To
understand the reasons behind this inconsistency, we analyse the available
implementations of BERT4Rec and show that we fail to reproduce results of the
original BERT4Rec publication when using their default configuration
parameters. However, we are able to replicate the reported results with the
original code if training for a much longer amount of time (up to 30x) compared
to the default configuration. We also propose our own implementation of
BERT4Rec based on the Hugging Face Transformers library, which we demonstrate
replicates the originally reported results on 3 out 4 datasets, while requiring
up to 95% less training time to converge. Overall, from our systematic review
and detailed experiments, we conclude that BERT4Rec does indeed exhibit
state-of-the-art effectiveness for sequential recommendation, but only when
trained for a sufficient amount of time. Additionally, we show that our
implementation can further benefit from adapting other Transformer
architectures that are available in the Hugging Face Transformers library (e.g.
using disentangled attention, as provided by DeBERTa, or larger hidden layer
size cf. ALBERT).

In this paper the Mechanical Neural Network(MNN) is introduced, a physical
implementation of a multilayer perceptron(MLP) with ReLU activation functions,
two input neurons, four hidden neurons and two output neurons. This physical
model of a MLP is used in education to give a hands on experience and allow
students to experience the effect of changing the parameters of the network on
the output. Neurons are small wooden levers which are connected by threads.
Students can adapt the weights between the neurons by moving the clamps
connecting a neuron via a thread to the next. The MNN can model real valued
functions and logical operators including XOR.

Weight-tied models have attracted attention in the modern development of
neural networks. The deep equilibrium model (DEQ) represents infinitely deep
neural networks with weight-tying, and recent studies have shown the potential
of this type of approach. DEQs are needed to iteratively solve root-finding
problems in training and are built on the assumption that the underlying
dynamics determined by the models converge to a fixed point. In this paper, we
present the stable invariant model (SIM), a new class of deep models that in
principle approximates DEQs under stability and extends the dynamics to more
general ones converging to an invariant set (not restricted in a fixed point).
The key ingredient in deriving SIMs is a representation of the dynamics with
the spectra of the Koopman and Perron--Frobenius operators. This perspective
approximately reveals stable dynamics with DEQs and then derives two variants
of SIMs. We also propose an implementation of SIMs that can be learned in the
same way as feedforward models. We illustrate the empirical performance of SIMs
with experiments and demonstrate that SIMs achieve comparative or superior
performance against DEQs in several learning tasks.

Modern non-autoregressive~(NAR) speech recognition systems aim to accelerate
the inference speed; however, they suffer from performance degradation compared
with autoregressive~(AR) models as well as the huge model size issue. We
propose a novel knowledge transfer and distillation architecture that leverages
knowledge from AR models to improve the NAR performance while reducing the
model's size. Frame- and sequence-level objectives are well-designed for
transfer learning. To further boost the performance of NAR, a beam search
method on Mask-CTC is developed to enlarge the search space during the
inference stage. Experiments show that the proposed NAR beam search relatively
reduces CER by over 5% on AISHELL-1 benchmark with a tolerable
real-time-factor~(RTF) increment. By knowledge transfer, the NAR student who
has the same size as the AR teacher obtains relative CER reductions of 8/16% on
AISHELL-1 dev/test sets, and over 25% relative WER reductions on LibriSpeech
test-clean/other sets. Moreover, the ~9x smaller NAR models achieve ~25%
relative CER/WER reductions on both AISHELL-1 and LibriSpeech benchmarks with
the proposed knowledge transfer and distillation.

In this paper we propose USegScene, a framework for semantically guided
unsupervised learning of depth, optical flow and ego-motion estimation for
stereo camera images using convolutional neural networks. Our framework
leverages semantic information for improved regularization of depth and optical
flow maps, multimodal fusion and occlusion filling considering dynamic rigid
object motions as independent SE(3) transformations. Furthermore, complementary
to pure photo-metric matching, we propose matching of semantic features,
pixel-wise classes and object instance borders between the consecutive images.
In contrast to previous methods, we propose a network architecture that jointly
predicts all outputs using shared encoders and allows passing information
across the task-domains, e.g., the prediction of optical flow can benefit from
the prediction of the depth. Furthermore, we explicitly learn the depth and
optical flow occlusion maps inside the network, which are leveraged in order to
improve the predictions in therespective regions. We present results on the
popular KITTI dataset and show that our approach outperforms other methods by a

We present a method for finding optimal hedging policies for arbitrary
initial portfolios and market states. We develop a novel actor-critic algorithm
for solving general risk-averse stochastic control problems and use it to learn
hedging strategies across multiple risk aversion levels simultaneously. We
demonstrate the effectiveness of the approach with a numerical example in a
stochastic volatility environment.

Photovoltaic (PV) energy is rapidly growing and key to mitigating the energy
crisis. However, distributed PV generation, which amounts to half of the PV
installed capacity, is typically unavailable to transmission system operators
(TSOs), making it increasingly difficult to balance the load and supply and
avoid grid congestions. To assess distributed PV generation, TSOs need precise
knowledge regarding the metadata of distributed PV installations. Many remote
sensing-based approaches have been proposed to map these installations in
recent years. However, to use these methods in industrial processes, assessing
their accuracy over the mapping area, i.e., the area covered by the model
during deployment, is necessary. We define the downstream task accuracy (DTA)
as the accuracy over the mapping area, automatically computed using publicly
available data sources and the model's outputs and expressed in an
interpretable way for operators. We benchmark existing models for distributed
PV mapping and show how they perform in terms of DTA. We show that the accuracy
computed on the test set overestimates by about 30 percentage points the
accuracy on the mapping area. Our approach paves the way for safer integration
of deep-learning-based pipelines for remote PV mapping. Code is available at
https://github.com/gabrielkasmi/dsfrance.

Modern Artificial Intelligence (AI) enabled Intrusion Detection Systems (IDS)
are complex black boxes. This means that a security analyst will have little to
no explanation or clarification on why an IDS model made a particular
prediction. A potential solution to this problem is to research and develop
Explainable Intrusion Detection Systems (X-IDS) based on current capabilities
in Explainable Artificial Intelligence (XAI). In this paper, we create a Self
Organizing Maps (SOMs) based X-IDS system that is capable of producing
explanatory visualizations. We leverage SOM's explainability to create both
global and local explanations. An analyst can use global explanations to get a
general idea of how a particular IDS model computes predictions. Local
explanations are generated for individual datapoints to explain why a certain
prediction value was computed. Furthermore, our SOM based X-IDS was evaluated
on both explanation generation and traditional accuracy tests using the NSL-KDD
and the CIC-IDS-2017 datasets.

Bacterial infections are responsible for high mortality worldwide.
Antimicrobial resistance underlying the infection, and multifaceted patient's
clinical status can hamper the correct choice of antibiotic treatment.
Randomized clinical trials provide average treatment effect estimates but are
not ideal for risk stratification and optimization of therapeutic choice, i.e.,
individualized treatment effects (ITE). Here, we leverage large-scale
electronic health record data, collected from Southern US academic clinics, to
emulate a clinical trial, i.e., 'target trial', and develop a machine learning
model of mortality prediction and ITE estimation for patients diagnosed with
acute bacterial skin and skin structure infection (ABSSSI) due to
methicillin-resistant Staphylococcus aureus (MRSA). ABSSSI-MRSA is a
challenging condition with reduced treatment options - vancomycin is the
preferred choice, but it has non-negligible side effects. First, we use
propensity score matching to emulate the trial and create a treatment
randomized (vancomycin vs. other antibiotics) dataset. Next, we use this data
to train various machine learning methods (including boosted/LASSO logistic
regression, support vector machines, and random forest) and choose the best
model in terms of area under the receiver characteristic (AUC) through
bootstrap validation. Lastly, we use the models to calculate ITE and identify
possible averted deaths by therapy change. The out-of-bag tests indicate that
SVM and RF are the most accurate, with AUC of 81% and 78%, respectively, but
BLR/LASSO is not far behind (76%). By calculating the counterfactuals using the
BLR/LASSO, vancomycin increases the risk of death, but it shows a large
variation (odds ratio 1.2, 95% range 0.4-3.8) and the contribution to outcome
probability is modest. Instead, the RF exhibits stronger changes in ITE,
suggesting more complex treatment heterogeneity.

Graphs offer a natural way to formulate Multiple Object Tracking (MOT) and
Multiple Object Tracking and Segmentation (MOTS) within the
tracking-by-detection paradigm. However, they also introduce a major challenge
for learning methods, as defining a model that can operate on such structured
domain is not trivial. In this work, we exploit the classical network flow
formulation of MOT to define a fully differentiable framework based on Message
Passing Networks (MPNs). By operating directly on the graph domain, our method
can reason globally over an entire set of detections and exploit contextual
features. It then jointly predicts both final solutions for the data
association problem and segmentation masks for all objects in the scene while
exploiting synergies between the two tasks. We achieve state-of-the-art results
for both tracking and segmentation in several publicly available datasets. Our
code is available at github.com/ocetintas/MPNTrackSeg.

This study aims to establish an essential milestone by leveraging an
exhaustive performance analysis of common Neural networks' (NNs) capability to
decipher mental representations from electroencephalogram (EEG) signals
recorded in representative classification tasks. Methods: In this study, 14
mechanism-wise different, typical NN types and their variants were first
constructed to decode EEG signals to compare their information representation
capability based on four brain-computer interfaces BCI paradigms. Based on
throughout literature review and the result, we gradually develop improvements
to propose a novel, pure convolutional NN architecture: EEGNeX. We tested
EEGNeX with existing advanced approaches and Mother of All BCI Benchmarks
(MOABB), a large-scale test benchmark containing experiments in 11 different
EEG motor imagination (MI) datasets. Results: The result reveals vital insights
regarding the reliable development of the network architecture in BCI signal
feature extraction and representation learning. EEGNeX outperforms other
current state-of-the-art methods. Modifications are proven beneficial in
universal NN improvement with statistical significance over EEGNet and common
approaches. Four pillars are summarized to design NNs for efficient design EEG
signal decoding. Significance: The study lightens a pathway based on the
analysis results to gradually develop general improvements for analogy
investigations between the brain bioelectric signal generation process and NN
architecture. A novel NN architecture: EEGNeX, is proposed. We open-sourced all
models in an out-of-the-box status (https://github.com/chenxiachan/EEGNeX) to
support further interdisciplinary studies.

Handling various robot action-language translation tasks flexibly is an
essential requirement for natural interaction between a robot and a human.
Previous approaches require change in the configuration of the model
architecture per task during inference, which undermines the premise of
multi-task learning. In this work, we propose the paired gated autoencoders
(PGAE) for flexible translation between robot actions and language descriptions
in a tabletop object manipulation scenario. We train our model in an end-to-end
fashion by pairing each action with appropriate descriptions that contain a
signal informing about the translation direction. During inference, our model
can flexibly translate from action to language and vice versa according to the
given language signal. Moreover, with the option to use a pretrained language
model as the language encoder, our model has the potential to recognise unseen
natural language input. Another capability of our model is that it can
recognise and imitate actions of another agent by utilising robot
demonstrations. The experiment results highlight the flexible bidirectional
translation capabilities of our approach alongside with the ability to
generalise to the actions of the opposite-sitting agent.

Continuously learning new classes without catastrophic forgetting is a
challenging problem for on-device environmental sound classification given the
restrictions on computation resources (e.g., model size, running memory). To
address this issue, we propose a simple and efficient continual learning
method. Our method selects the historical data for the training by measuring
the per-sample classification uncertainty. Specifically, we measure the
uncertainty by observing how the classification probability of data fluctuates
against the parallel perturbations added to the classifier embedding. In this
way, the computation cost can be significantly reduced compared with adding
perturbation to the raw data. Experimental results on the DCASE 2019 Task 1 and
ESC-50 dataset show that our proposed method outperforms baseline continual
learning methods on classification accuracy and computational efficiency,
indicating our method can efficiently and incrementally learn new classes
without the catastrophic forgetting problem for on-device environmental sound

Equaixed dendrites are frequently encountered in solidification. They
typically form in large numbers, which makes their detection, localization, and
tracking practically impossible for a human eye. In this paper, we show how
recent progress in the field of machine learning can be leveraged to tackle
this problem and we present computer vision neural network to automatically
detect equiaxed dendrites. Our network is trained using phase-field simulation
results, and proper data augmentation allows to perform the detection task in
solidification conditions entirely different from those simulated for training.
For example, here we show how they can successfully detect dendrites of various
sizes in a microgravity solidification experiment. We discuss challenges in
training such a network along with our solutions for them, and compare the
performance of neural network with traditional methods of shapes detection.

The semantic segmentation of surgical scenes is a prerequisite for task
automation in robot assisted interventions. We propose LapSeg3D, a novel
DNN-based approach for the voxel-wise annotation of point clouds representing
surgical scenes. As the manual annotation of training data is highly time
consuming, we introduce a semi-autonomous clustering-based pipeline for the
annotation of the gallbladder, which is used to generate segmented labels for
the DNN. When evaluated against manually annotated data, LapSeg3D achieves an
F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo
porcine livers. We show LapSeg3D to generalize accurately across different
gallbladders and datasets recorded with different RGB-D camera systems.

We study the problem of approximating a given tensor with $q$ modes $A \in
\mathbb{R}^{n \times \ldots \times n}$ with an arbitrary tensor network of rank
$k$ -- that is, a graph $G = (V, E)$, where $|V| = q$, together with a
collection of tensors $\{U_v \mid v \in V\}$ which are contracted in the manner
specified by $G$ to obtain a tensor $T$. For each mode of $U_v$ corresponding
to an edge incident to $v$, the dimension is $k$, and we wish to find $U_v$
such that the Frobenius norm distance between $T$ and $A$ is minimized. This
generalizes a number of well-known tensor network decompositions, such as the
Tensor Train, Tensor Ring, Tucker, and PEPS decompositions. We approximate $A$
by a binary tree network $T'$ with $O(q)$ cores, such that the dimension on
each edge of this network is at most $\widetilde{O}(k^{O(dt)} \cdot
q/\varepsilon)$, where $d$ is the maximum degree of $G$ and $t$ is its
treewidth, such that $\|A - T'\|_F^2 \leq (1 + \varepsilon) \|A - T\|_F^2$. The
running time of our algorithm is $O(q \cdot \text{nnz}(A)) + n \cdot
\text{poly}(k^{dt}q/\varepsilon)$, where $\text{nnz}(A)$ is the number of
nonzero entries of $A$. Our algorithm is based on a new dimensionality
reduction technique for tensor decomposition which may be of independent
interest.
  We also develop fixed-parameter tractable $(1 + \varepsilon)$-approximation
algorithms for Tensor Train and Tucker decompositions, improving the running
time of Song, Woodruff and Zhong (SODA, 2019) and avoiding the use of generic
polynomial system solvers. We show that our algorithms have a nearly optimal
dependence on $1/\varepsilon$ assuming that there is no $O(1)$-approximation
algorithm for the $2 \to 4$ norm with better running time than brute force.
Finally, we give additional results for Tucker decomposition with robust loss
functions, and fixed-parameter tractable CP decomposition.

A recent trend in artificial intelligence is the use of pretrained models for
language and vision tasks, which have achieved extraordinary performance but
also puzzling failures. Probing these models' abilities in diverse ways is
therefore critical to the field. In this paper, we explore the reliability of
models, where we define a reliable model as one that not only achieves strong
predictive performance but also performs well consistently over many
decision-making tasks involving uncertainty (e.g., selective prediction, open
set recognition), robust generalization (e.g., accuracy and proper scoring
rules such as log-likelihood on in- and out-of-distribution datasets), and
adaptation (e.g., active learning, few-shot uncertainty). We devise 10 types of
tasks over 40 datasets in order to evaluate different aspects of reliability on
both vision and language domains. To improve reliability, we developed ViT-Plex
and T5-Plex, pretrained large model extensions for vision and language
modalities, respectively. Plex greatly improves the state-of-the-art across
reliability tasks, and simplifies the traditional protocol as it improves the
out-of-the-box performance and does not require designing scores or tuning the
model for each task. We demonstrate scaling effects over model sizes up to 1B
parameters and pretraining dataset sizes up to 4B examples. We also demonstrate
Plex's capabilities on challenging tasks including zero-shot open set
recognition, active learning, and uncertainty in conversational language

Graph Convolutional Networks (GCNs), similarly to Convolutional Neural
Networks (CNNs), are typically based on two main operations - spatial and
point-wise convolutions. In the context of GCNs, differently from CNNs, a
pre-determined spatial operator based on the graph Laplacian is often chosen,
allowing only the point-wise operations to be learnt. However, learning a
meaningful spatial operator is critical for developing more expressive GCNs for
improved performance. In this paper we propose pathGCN, a novel approach to
learn the spatial operator from random paths on the graph. We analyze the
convergence of our method and its difference from existing GCNs. Furthermore,
we discuss several options of combining our learnt spatial operator with
point-wise convolutions. Our extensive experiments on numerous datasets suggest
that by properly learning both the spatial and point-wise convolutions,
phenomena like over-smoothing can be inherently avoided, and new
state-of-the-art performance is achieved.

We introduce PodcastMix, a dataset formalizing the task of separating
background music and foreground speech in podcasts. We aim at defining a
benchmark suitable for training and evaluating (deep learning) source
separation models. To that end, we release a large and diverse training dataset
based on programatically generated podcasts. However, current (deep learning)
models can incur into generalization issues, specially when trained on
synthetic data. To target potential generalization issues, we release an
evaluation set based on real podcasts for which we design objective and
subjective tests. Out of our experiments with real podcasts, we find that
current (deep learning) models may have generalization issues. Yet, these can
perform competently, e.g., our best baseline separates speech with a mean
opinion score of 3.84 (rating "overall separation quality" from 1 to 5). The
dataset and baselines are accessible online.

Complex networks are graphs representing real-life systems that exhibit
unique characteristics not found in purely regular or completely random graphs.
The study of such systems is vital but challenging due to the complexity of the
underlying processes. This task has nevertheless been made easier in recent
decades thanks to the availability of large amounts of networked data. Link
prediction in complex networks aims to estimate the likelihood that a link
between two nodes is missing from the network. Links can be missing due to
imperfections in data collection or simply because they are yet to appear.
Discovering new relationships between entities in networked data has attracted
researchers' attention in various domains such as sociology, computer science,
physics, and biology. Most existing research focuses on link prediction in
undirected complex networks. However, not all real-life systems can be
faithfully represented as undirected networks. This simplifying assumption is
often made when using link prediction algorithms but inevitably leads to loss
of information about relations among nodes and degradation in prediction
performance. This paper introduces a link prediction method designed explicitly
for directed networks. It is based on the similarity-popularity paradigm, which
has recently proven successful in undirected networks. The presented algorithms
handle the asymmetry in node relationships by modeling it as asymmetry in
similarity and popularity. Given the observed network topology, the algorithms
approximate the hidden similarities as shortest path distances using edge
weights that capture and factor out the links' asymmetry and nodes' popularity.
The proposed approach is evaluated on real-life networks, and the experimental
results demonstrate its effectiveness in predicting missing links across a
broad spectrum of networked data types and sizes.

Point cloud video transmission is challenging due to high encoding/decoding
complexity, high video bitrate, and low latency requirement. Consequently,
conventional adaptive streaming methodologies often find themselves
unsatisfactory to meet the requirements in threefold: 1) current algorithms
reuse existing quality of experience (QoE) definitions while overlooking the
unique features of point cloud video thus failing to provide optimal user
experience, 2) most deep learning approaches require long-span data collections
to learn sufficiently varied network conditions and result in long training
period and capacity occupation, 3) cloud training approaches pose privacy risks
caused by leakage of user reported service usage and networking conditions.
  To overcome the limitations, we present FRAS, the first federated
reinforcement learning framework, to the best of our knowledge, for adaptive
point cloud video streaming. We define a new QoE model which takes the unique
features of point cloud video into account. Each client uses reinforcement
learning (RL) to train encoding rate selection with the objective of optimizing
the user's QoE under multiple constraints. Then, a federated learning framework
is integrated with the RL algorithm to enhance training performance with
privacy preservation. Extensive simulations using real point cloud videos and
network traces reveal the superiority of the proposed scheme over baseline
schemes. We also implement a prototype that demonstrates the performance of
FRAS via real-world tests.

Multiple studies have now demonstrated that machine learning (ML) can give
improved skill for predicting or simulating fairly typical weather events, for
tasks such as short-term and seasonal weather forecasting, downscaling
simulations to higher resolution and emulating and speeding up expensive model
parameterisations. Many of these used ML methods with very high numbers of
parameters, such as neural networks, which are the focus of the discussion
here. Not much attention has been given to the performance of these methods for
extreme event severities of relevance for many critical weather and climate
prediction applications, with return periods of more than a few years. This
leaves a lot of uncertainty about the usefulness of these methods, particularly
for general purpose prediction systems that must perform reliably in extreme
situations. ML models may be expected to struggle to predict extremes due to
there usually being few samples of such events. However, there are some studies
that do indicate that ML models can have reasonable skill for extreme weather,
and that it is not hopeless to use them in situations requiring extrapolation.
This article reviews these studies and argues that this is an area that needs
researching more. Ways to get a better understanding of how well ML models
perform at predicting extreme weather events are discussed.

Some of the most relevant future applications of multi-agent systems like
autonomous driving or factories as a service display mixed-motive scenarios,
where agents might have conflicting goals. In these settings agents are likely
to learn undesirable outcomes in terms of cooperation under independent
learning, such as overly greedy behavior. Motivated from real world societies,
in this work we propose to utilize market forces to provide incentives for
agents to become cooperative. As demonstrated in an iterated version of the
Prisoner's Dilemma, the proposed market formulation can change the dynamics of
the game to consistently learn cooperative policies. Further we evaluate our
approach in spatially and temporally extended settings for varying numbers of
agents. We empirically find that the presence of markets can improve both the
overall result and agent individual returns via their trading activities.

To generate dance that temporally and aesthetically matches the music is a
challenging problem, as the following factors need to be considered. First, the
aesthetic styles and messages conveyed by the motion and music should be
consistent. Second, the beats of the generated motion should be locally aligned
to the musical features. And finally, basic choreomusical rules should be
observed, and the motion generated should be diverse. To address these
challenges, we propose ChoreoGraph, which choreographs high-quality dance
motion for a given piece of music over a Dynamic Graph. A data-driven learning
strategy is proposed to evaluate the aesthetic style and rhythmic connections
between music and motion in a progressively learned cross-modality embedding
space. The motion sequences will be beats-aligned based on the music segments
and then incorporated as nodes of a Dynamic Motion Graph. Compatibility factors
such as the style and tempo consistency, motion context connection, action
completeness, and transition smoothness are comprehensively evaluated to
determine the node transition in the graph. We demonstrate that our
repertoire-based framework can generate motions with aesthetic consistency and
robustly extensible in diversity. Both quantitative and qualitative experiment
results show that our proposed model outperforms other baseline models.

The music streaming service Deezer extensively relies on its Flow algorithm,
which generates personalized radio-style playlists of songs, to help users
discover musical content. Nonetheless, despite promising results over the past
years, Flow used to ignore the moods of users when providing recommendations.
In this paper, we present Flow Moods, an improved version of Flow that
addresses this limitation. Flow Moods leverages collaborative filtering, audio
content analysis, and mood annotations from professional music curators to
generate personalized mood-specific playlists at scale. We detail the
motivations, the development, and the deployment of this system on Deezer.
Since its release in 2021, Flow Moods has been recommending music by moods to
millions of users every day.

Low-dose computed tomography (CT) denoising algorithms aim to enable reduced
patient dose in routine CT acquisitions while maintaining high image quality.
Recently, deep learning~(DL)-based methods were introduced, outperforming
conventional denoising algorithms on this task due to their high model
capacity. However, for the transition of DL-based denoising to clinical
practice, these data-driven approaches must generalize robustly beyond the seen
training data. We, therefore, propose a hybrid denoising approach consisting of
a set of trainable joint bilateral filters (JBFs) combined with a convolutional
DL-based denoising network to predict the guidance image. Our proposed
denoising pipeline combines the high model capacity enabled by DL-based feature
extraction with the reliability of the conventional JBF. The pipeline's ability
to generalize is demonstrated by training on abdomen CT scans without metal
implants and testing on abdomen scans with metal implants as well as on head CT
data. When embedding two well-established DL-based denoisers (RED-CNN/QAE) in
our pipeline, the denoising performance is improved by $10\,\%$/$82\,\%$ (RMSE)
and $3\,\%$/$81\,\%$ (PSNR) in regions containing metal and by $6\,\%$/$78\,\%$
(RMSE) and $2\,\%$/$4\,\%$ (PSNR) on head CT data, compared to the respective
vanilla model. Concluding, the proposed trainable JBFs limit the error bound of
deep neural networks to facilitate the applicability of DL-based denoisers in
low-dose CT pipelines.

We derive rigorous bounds on the error resulting from the approximation of
the solution of parametric hyperbolic scalar conservation laws with ReLU neural
networks. We show that the approximation error can be made as small as desired
with ReLU neural networks that overcome the curse of dimensionality. In
addition, we provide an explicit upper bound on the generalization error in
terms of the training error, number of training samples and the neural network
size. The theoretical results are illustrated by numerical experiments.

This paper considers few-shot anomaly detection (FSAD), a practical yet
under-studied setting for anomaly detection (AD), where only a limited number
of normal images are provided for each category at training. So far, existing
FSAD studies follow the one-model-per-category learning paradigm used for
standard AD, and the inter-category commonality has not been explored. Inspired
by how humans detect anomalies, i.e., comparing an image in question to normal
images, we here leverage registration, an image alignment task that is
inherently generalizable across categories, as the proxy task, to train a
category-agnostic anomaly detection model. During testing, the anomalies are
identified by comparing the registered features of the test image and its
corresponding support (normal) images. As far as we know, this is the first
FSAD method that trains a single generalizable model and requires no
re-training or parameter fine-tuning for new categories. Experimental results
have shown that the proposed method outperforms the state-of-the-art FSAD
methods by 3%-8% in AUC on the MVTec and MPDD benchmarks.

Fast production of large area patterns with nanometre resolution is crucial
for the established semiconductor industry and for enabling industrial-scale
production of next-generation quantum devices. Metastable atom lithography with
binary holography masks has been suggested as a higher resolution/low-cost
alternative to the current state of the art: extreme ultraviolet (EUV)
lithography. However, it was recently shown that the interaction of the
metastable atoms with the mask material (SiN) leads to a strong perturbation of
the wavefront, not included in existing mask generation theory, which is based
on classical scalar waves. This means that the inverse problem (creating a mask
based on the desired pattern) cannot be solved analytically even in 1D. Here we
present a machine learning approach to mask generation targeted for metastable
atoms. Our algorithm uses a combination of genetic optimisation and deep
learning to obtain the mask. A novel deep neural architecture is trained to
produce an initial approximation of the mask. This approximation is then used
to generate the initial population of the genetic optimisation algorithm that
can converge to arbitrary precision. We demonstrate the generation of arbitrary
1D patterns for system dimensions within the Fraunhofer approximation limit.

Diverse human motion prediction aims at predicting multiple possible future
pose sequences from a sequence of observed poses. Previous approaches usually
employ deep generative networks to model the conditional distribution of data,
and then randomly sample outcomes from the distribution. While different
results can be obtained, they are usually the most likely ones which are not
diverse enough. Recent work explicitly learns multiple modes of the conditional
distribution via a deterministic network, which however can only cover a fixed
number of modes within a limited range. In this paper, we propose a novel
sampling strategy for sampling very diverse results from an imbalanced
multimodal distribution learned by a deep generative model. Our method works by
generating an auxiliary space and smartly making randomly sampling from the
auxiliary space equivalent to the diverse sampling from the target
distribution. We propose a simple yet effective network architecture that
implements this novel sampling strategy, which incorporates a Gumbel-Softmax
coefficient matrix sampling method and an aggressive diversity promoting hinge
loss function. Extensive experiments demonstrate that our method significantly
improves both the diversity and accuracy of the samplings compared with
previous state-of-the-art sampling approaches. Code and pre-trained models are
available at https://github.com/Droliven/diverse_sampling.

Standard approaches for adversarial patch generation lead to noisy
conspicuous patterns, which are easily recognizable by humans. Recent research
has proposed several approaches to generate naturalistic patches using
generative adversarial networks (GANs), yet only a few of them were evaluated
on the object detection use case. Moreover, the state of the art mostly focuses
on suppressing a single large bounding box in input by overlapping it with the
patch directly. Suppressing objects near the patch is a different, more complex
task. In this work, we have evaluated the existing approaches to generate
inconspicuous patches. We have adapted methods, originally developed for
different computer vision tasks, to the object detection use case with YOLOv3
and the COCO dataset. We have evaluated two approaches to generate naturalistic
patches: by incorporating patch generation into the GAN training process and by
using the pretrained GAN. For both cases, we have assessed a trade-off between
performance and naturalistic patch appearance. Our experiments have shown, that
using a pre-trained GAN helps to gain realistic-looking patches while
preserving the performance similar to conventional adversarial patches.

There is ample neurobiological evidence that context-sensitive neocortical
neurons use their apical inputs as context to amplify the transmission of
coherent feedforward (FF) inputs. However, it has not been demonstrated until
now how this mechanism can provide useful neural computation. Here we show for
the first time that the processing and learning capabilities of this form of
neural information processing are well-matched to the abilities of mammalian
neocortex. Specifically, we show that a network composed of such local
processors restricts the transmission of conflicting information to higher
levels and greatly reduces the amount of activity required to process large
amounts of heterogeneous real-world data e.g., when processing audiovisual
speech, these processors use seen lip movements to selectively amplify FF
transmission of the auditory information that those movements generate and vice
versa. As this mechanism is shown to be over 1250X more efficient (per FF
transmission) than the best available forms of deep neural nets, it offers a
step-change in understanding the brain's mysterious energy-saving mechanism and
inspires advances in designing enhanced forms of biologically plausible machine

Under stereo settings, the performance of image JPEG artifacts removal can be
further improved by exploiting the additional information provided by a second
view. However, incorporating this information for stereo image JPEG artifacts
removal is a huge challenge, since the existing compression artifacts make
pixel-level view alignment difficult. In this paper, we propose a novel
parallax transformer network (PTNet) to integrate the information from stereo
image pairs for stereo image JPEG artifacts removal. Specifically, a
well-designed symmetric bi-directional parallax transformer module is proposed
to match features with similar textures between different views instead of
pixel-level view alignment. Due to the issues of occlusions and boundaries, a
confidence-based cross-view fusion module is proposed to achieve better feature
fusion for both views, where the cross-view features are weighted with
confidence maps. Especially, we adopt a coarse-to-fine design for the
cross-view interaction, leading to better performance. Comprehensive
experimental results demonstrate that our PTNet can effectively remove
compression artifacts and achieves superior performance than other testing

Remote sensing of rainfall events is critical for both operational and
scientific needs, including for example weather forecasting, extreme flood
mitigation, water cycle monitoring, etc. Ground-based weather radars, such as
NOAA's Next-Generation Radar (NEXRAD), provide reflectivity and precipitation
measurements of rainfall events. However, the observation range of such radars
is limited to a few hundred kilometers, prompting the exploration of other
remote sensing methods, paricularly over the open ocean, that represents large
areas not covered by land-based radars. For a number of decades, C-band SAR
imagery such a such as Sentinel-1 imagery has been known to exhibit rainfall
signatures over the sea surface. However, the development of SAR-derived
rainfall products remains a challenge. Here we propose a deep learning approach
to extract rainfall information from SAR imagery. We demonstrate that a
convolutional neural network, such as U-Net, trained on a colocated and
preprocessed Sentinel-1/NEXRAD dataset clearly outperforms state-of-the-art
filtering schemes. Our results indicate high performance in segmenting
precipitation regimes, delineated by thresholds at 1, 3, and 10 mm/h. Compared
to current methods that rely on Koch filters to draw binary rainfall maps,
these multi-threshold learning-based models can provide rainfall estimation for
higher wind speeds and thus may be of great interest for data assimilation
weather forecasting or for improving the qualification of SAR-derived wind
field data.

A session-based news recommender system recommends the next news to a user by
modeling the potential interests embedded in a sequence of news read/clicked by
her/him in a session. Generally, a user's interests are diverse, namely there
are multiple interests corresponding to different types of news, e.g., news of
distinct topics, within a session. %Modeling such multiple interests is
critical for precise news recommendation. However, most of existing methods
typically overlook such important characteristic and thus fail to distinguish
and model the potential multiple interests of a user, impeding accurate
recommendation of the next piece of news. Therefore, this paper proposes
multi-interest news sequence (MINS) model for news recommendation. In MINS, a
news encoder based on self-attention is devised on learn an informative
embedding for each piece of news, and then a novel parallel interest network is
devised to extract the potential multiple interests embedded in the news
sequence in preparation for the subsequent next-news recommendations. The
experimental results on a real-world dataset demonstrate that our model can
achieve better performance than the state-of-the-art compared models.

Plug-and-Play (PnP) and Regularization by Denoising (RED) are recent
paradigms for image reconstruction that can leverage the power of modern
denoisers for image regularization. In particular, these algorithms have been
shown to deliver state-of-the-art reconstructions using CNN denoisers. Since
the regularization is performed in an ad-hoc manner in PnP and RED,
understanding their convergence has been an active research area. Recently, it
was observed in many works that iterate convergence of PnP and RED can be
guaranteed if the denoiser is averaged or nonexpansive. However, integrating
nonexpansivity with gradient-based learning is a challenging task -- checking
nonexpansivity is known to be computationally intractable. Using numerical
examples, we show that existing CNN denoisers violate the nonexpansive property
and can cause the PnP iterations to diverge. In fact, algorithms for training
nonexpansive denoisers either cannot guarantee nonexpansivity of the final
denoiser or are computationally intensive. In this work, we propose to
construct averaged (contractive) image denoisers by unfolding ISTA and ADMM
iterations applied to wavelet denoising and demonstrate that their
regularization capacity for PnP and RED can be matched with CNN denoisers. To
the best of our knowledge, this is the first work to propose a simple framework
for training provably averaged (contractive) denoisers using unfolding
networks.

Expert forecast combination -- the aggregation of individual forecasts from
multiple subject-matter experts -- is a proven approach to economic
forecasting. To date, research in this area has exclusively concentrated on
local combination methods, which handle separate but related forecasting tasks
in isolation. Yet, it has been known for over two decades in the machine
learning community that global methods, which exploit task-relatedness, can
improve on local methods that ignore it. Motivated by the possibility for
improvement, this paper introduces a framework for globally combining expert
forecasts. Through our framework, we develop global versions of several
existing forecast combinations. To evaluate the efficacy of these new global
forecast combinations, we conduct extensive comparisons using synthetic and
real data. Our real data comparisons, which involve expert forecasts of core
economic indicators in the Eurozone, are the first empirical evidence that the
accuracy of global combinations of expert forecasts can surpass local

Face recognition technology has been used in many fields due to its high
recognition accuracy, including the face unlocking of mobile devices, community
access control systems, and city surveillance. As the current high accuracy is
guaranteed by very deep network structures, facial images often need to be
transmitted to third-party servers with high computational power for inference.
However, facial images visually reveal the user's identity information. In this
process, both untrusted service providers and malicious users can significantly
increase the risk of a personal privacy breach. Current privacy-preserving
approaches to face recognition are often accompanied by many side effects, such
as a significant increase in inference time or a noticeable decrease in
recognition accuracy. This paper proposes a privacy-preserving face recognition
method using differential privacy in the frequency domain. Due to the
utilization of differential privacy, it offers a guarantee of privacy in
theory. Meanwhile, the loss of accuracy is very slight. This method first
converts the original image to the frequency domain and removes the direct
component termed DC. Then a privacy budget allocation method can be learned
based on the loss of the back-end face recognition network within the
differential privacy framework. Finally, it adds the corresponding noise to the
frequency domain features. Our method performs very well with several classical
face recognition test sets according to the extensive experiments.

Bitcoin is the first and highest valued cryptocurrency that stores
transactions in a publicly distributed ledger called the blockchain.
Understanding the activity and behavior of Bitcoin actors is a crucial research
topic as they are pseudonymous in the transaction network. In this article, we
propose a method based on taint analysis to extract taint flows --dynamic
networks representing the sequence of Bitcoins transferred from an initial
source to other actors until dissolution. Then, we apply graph embedding
methods to characterize taint flows. We evaluate our embedding method with
taint flows from top mining pools and show that it can classify mining pools
with high accuracy. We also found that taint flows from the same period show
high similarity. Our work proves that tracing the money flows can be a
promising approach to classifying source actors and characterizing different
money flow patterns

The wide use of social media and digital technologies facilitates sharing
various news and information about events and activities. Despite sharing
positive information misleading and false information is also spreading on
social media. There have been efforts in identifying such misleading
information both manually by human experts and automatic tools. Manual effort
does not scale well due to the high volume of information, containing factual
claims, are appearing online. Therefore, automatically identifying check-worthy
claims can be very useful for human experts. In this study, we describe our
participation in Subtask-1A: Check-worthiness of tweets (English, Dutch and
Spanish) of CheckThat! lab at CLEF 2022. We performed standard preprocessing
steps and applied different models to identify whether a given text is worthy
of fact checking or not. We use the oversampling technique to balance the
dataset and applied SVM and Random Forest (RF) with TF-IDF representations. We
also used BERT multilingual (BERT-m) and XLM-RoBERTa-base pre-trained models
for the experiments. We used BERT-m for the official submissions and our
systems ranked as 3rd, 5th, and 12th in Spanish, Dutch, and English,
respectively. In further experiments, our evaluation shows that transformer
models (BERT-m and XLM-RoBERTa-base) outperform the SVM and RF in Dutch and
English languages where a different scenario is observed for Spanish.

Recent neural network based Direction of Arrival (DoA) estimation algorithms
have performed well on unknown number of sound sources scenarios. These
algorithms are usually achieved by mapping the multi-channel audio input to the
single output (i.e. overall spatial pseudo-spectrum (SPS) of all sources), that
is called MISO. However, such MISO algorithms strongly depend on empirical
threshold setting and the angle assumption that the angles between the sound
sources are greater than a fixed angle. To address these limitations, we
propose a novel multi-channel input and multiple outputs DoA network called
MIMO-DoAnet. Unlike the general MISO algorithms, MIMO-DoAnet predicts the SPS
coding of each sound source with the help of the informative spatial covariance
matrix. By doing so, the threshold task of detecting the number of sound
sources becomes an easier task of detecting whether there is a sound source in
each output, and the serious interaction between sound sources disappears
during inference stage. Experimental results show that MIMO-DoAnet achieves
relative 18.6% and absolute 13.3%, relative 34.4% and absolute 20.2% F1 score
improvement compared with the MISO baseline system in 3, 4 sources scenes. The
results also demonstrate MIMO-DoAnet alleviates the threshold setting problem
and solves the angle assumption problem effectively.

Deep learning-based melanoma classification with dermoscopic images has
recently shown great potential in automatic early-stage melanoma diagnosis.
However, limited by the significant data imbalance and obvious extraneous
artifacts, i.e., the hair and ruler markings, discriminative feature extraction
from dermoscopic images is very challenging. In this study, we seek to resolve
these problems respectively towards better representation learning for lesion
features. Specifically, a GAN-based data augmentation (GDA) strategy is adapted
to generate synthetic melanoma-positive images, in conjunction with the
proposed implicit hair denoising (IHD) strategy. Wherein the hair-related
representations are implicitly disentangled via an auxiliary classifier network
and reversely sent to the melanoma-feature extraction backbone for better
melanoma-specific representation learning. Furthermore, to train the IHD
module, the hair noises are additionally labeled on the ISIC2020 dataset,
making it the first large-scale dermoscopic dataset with annotation of
hair-like artifacts. Extensive experiments demonstrate the superiority of the
proposed framework as well as the effectiveness of each component. The improved
dataset publicly avaliable at https://github.com/kirtsy/DermoscopicDataset.

Existing deep compressive sensing (CS) methods either ignore adaptive online
optimization or depend on costly iterative optimizer during reconstruction.
This work explores a novel image CS framework with recurrent-residual
structural constraint, termed as R$^2$CS-NET. The R$^2$CS-NET first
progressively optimizes the acquired samplings through a novel recurrent neural
network. The cascaded residual convolutional network then fully reconstructs
the image from optimized latent representation. As the first deep CS framework
efficiently bridging adaptive online optimization, the R$^2$CS-NET integrates
the robustness of online optimization with the efficiency and nonlinear
capacity of deep learning methods. Signal correlation has been addressed
through the network architecture. The adaptive sensing nature further makes it
an ideal candidate for color image CS via leveraging channel correlation.
Numerical experiments verify the proposed recurrent latent optimization design
not only fulfills the adaptation motivation, but also outperforms classic long
short-term memory (LSTM) architecture in the same scenario. The overall
framework demonstrates hardware implementation feasibility, with leading
robustness and generalization capability among existing deep CS benchmarks.

This paper describes the practical response- and performance-aware
development of online speech enhancement for an augmented reality (AR) headset
that helps a user understand conversations made in real noisy echoic
environments (e.g., cocktail party). One may use a state-of-the-art blind
source separation method called fast multichannel nonnegative matrix
factorization (FastMNMF) that works well in various environments thanks to its
unsupervised nature. Its heavy computational cost, however, prevents its
application to real-time processing. In contrast, a supervised beamforming
method that uses a deep neural network (DNN) for estimating spatial information
of speech and noise readily fits real-time processing, but suffers from drastic
performance degradation in mismatched conditions. Given such complementary
characteristics, we propose a dual-process robust online speech enhancement
method based on DNN-based beamforming with FastMNMF-guided adaptation. FastMNMF
(back end) is performed in a mini-batch style and the noisy and enhanced speech
pairs are used together with the original parallel training data for updating
the direction-aware DNN (front end) with backpropagation at a
computationally-allowable interval. This method is used with a blind
dereverberation method called weighted prediction error (WPE) for transcribing
the noisy reverberant speech of a speaker, which can be detected from video or
selected by a user's hand gesture or eye gaze, in a streaming manner and
spatially showing the transcriptions with an AR technique. Our experiment
showed that the word error rate was improved by more than 10 points with the
run-time adaptation using only twelve minutes of observation.

Federated Learning (FL) as a secure distributed learning frame gains interest
in Internet of Things (IoT) due to its capability of protecting private data of
participants. However, traditional FL systems are vulnerable to attacks such as
Free-Rider (FR) attack, which causes not only unfairness but also privacy
leakage and inferior performance to FL systems. The existing defense mechanisms
against FR attacks only concern the scenarios where the adversaries declare
less than 50% of the total amount of clients. Moreover, they lose effectiveness
in resisting selfish FR (SFR) attacks. In this paper, we propose a Parameter
Audit-based Secure and fair federated learning Scheme (PASS) against FR
attacks. The PASS has the following key features: (a) works well in the
scenario where adversaries are more than 50% of the total amount of clients;
(b) is effective in countering anonymous FR attacks and SFR attacks; (c)
prevents from privacy leakage without accuracy loss. Extensive experimental
results verify the data protecting capability in mean square error against
privacy leakage and reveal the effectiveness of PASS in terms of a higher
defense success rate and lower false positive rate against anonymous SFR
attacks. Note in addition, PASS produces no effect on FL accuracy when there is
no FR adversary.

A proportional iterative learning control (P-ILC) for linear models of an
existing hybrid stroke rehabilitation scheme is implemented for elbow
extension/flexion during a rehabilitative task. Owing to transient error growth
problem of P-ILC, a learning derivative constraint controller was included to
ensure that the controlled system does not exceed a predefined velocity limit
at every trial. To achieve this, linear transfer function models of the robot
end-effector interaction with a stroke subject (plant) and muscle response to
stimulation controllers were developed. A straight-line point-point trajectory
of 0 - 0.3 m range served as the reference task space trajectory for the plant,
feedforward, and feedback stimulation controllers. At each trial, a SAT-based
bounded error derivative ILC algorithm served as the learning constraint
controller. Three control configurations were developed and simulated. The
system performance was evaluated using the root means square error (RMSE) and
normalized RMSE. At different ILC gains over 16 iterations, a displacement
error of 0.0060 m was obtained when control configurations were combined.

This paper studies large-scale optimization problems on Riemannian manifolds
whose objective function is a finite sum of negative log-probability losses.
Such problems arise in various machine learning and signal processing
applications. By introducing the notion of Fisher information matrix in the
manifold setting, we propose a novel Riemannian natural gradient method, which
can be viewed as a natural extension of the natural gradient method from the
Euclidean setting to the manifold setting. We establish the almost-sure global
convergence of our proposed method under standard assumptions. Moreover, we
show that if the loss function satisfies certain convexity and smoothness
conditions and the input-output map satisfies a Riemannian Jacobian stability
condition, then our proposed method enjoys a local linear -- or, under the
Lipschitz continuity of the Riemannian Jacobian of the input-output map, even
quadratic -- rate of convergence. We then prove that the Riemannian Jacobian
stability condition will be satisfied by a two-layer fully connected neural
network with batch normalization with high probability, provided that the width
of the network is sufficiently large. This demonstrates the practical relevance
of our convergence rate result. Numerical experiments on applications arising
from machine learning demonstrate the advantages of the proposed method over
state-of-the-art ones.

Video-text retrieval has been a crucial and fundamental task in multi-modal
research. The development of video-text retrieval has been considerably
promoted by large-scale multi-modal contrastive pre-training, which primarily
focuses on coarse-grained or fine-grained contrast. However, cross-grained
contrast, which is the contrast between coarse-grained representations and
fine-grained representations, has rarely been explored in prior research.
Compared with fine-grained or coarse-grained contrasts, cross-grained contrast
calculate the correlation between coarse-grained features and each fine-grained
feature, and is able to filter out the unnecessary fine-grained features guided
by the coarse-grained feature during similarity calculation, thus improving the
accuracy of retrieval. To this end, this paper presents a novel multi-grained
contrastive model, namely X-CLIP, for video-text retrieval. However, another
challenge lies in the similarity aggregation problem, which aims to aggregate
fine-grained and cross-grained similarity matrices to instance-level
similarity. To address this challenge, we propose the Attention Over Similarity
Matrix (AOSM) module to make the model focus on the contrast between essential
frames and words, thus lowering the impact of unnecessary frames and words on
retrieval results. With multi-grained contrast and the proposed AOSM module,
X-CLIP achieves outstanding performance on five widely-used video-text
retrieval datasets, including MSR-VTT (49.3 R@1), MSVD (50.4 R@1), LSMDC (26.1
R@1), DiDeMo (47.8 R@1) and ActivityNet (46.2 R@1). It outperforms the previous
state-of-theart by +6.3%, +6.6%, +11.1%, +6.7%, +3.8% relative improvements on
these benchmarks, demonstrating the superiority of multi-grained contrast and
AOSM.

With the prosperity of e-commerce industry, various modalities, e.g., vision
and language, are utilized to describe product items. It is an enormous
challenge to understand such diversified data, especially via extracting the
attribute-value pairs in text sequences with the aid of helpful image regions.
Although a series of previous works have been dedicated to this task, there
remain seldomly investigated obstacles that hinder further improvements: 1)
Parameters from up-stream single-modal pretraining are inadequately applied,
without proper jointly fine-tuning in a down-stream multi-modal task. 2) To
select descriptive parts of images, a simple late fusion is widely applied,
regardless of priori knowledge that language-related information should be
encoded into a common linguistic embedding space by stronger encoders. 3) Due
to diversity across products, their attribute sets tend to vary greatly, but
current approaches predict with an unnecessary maximal range and lead to more
potential false positives. To address these issues, we propose in this paper a
novel approach to boost multi-modal e-commerce attribute value extraction via
unified learning scheme and dynamic range minimization: 1) Firstly, a unified
scheme is designed to jointly train a multi-modal task with pretrained
single-modal parameters. 2) Secondly, a text-guided information range
minimization method is proposed to adaptively encode descriptive parts of each
modality into an identical space with a powerful pretrained linguistic model.
3) Moreover, a prototype-guided attribute range minimization method is proposed
to first determine the proper attribute set of the current product, and then
select prototypes to guide the prediction of the chosen attributes. Experiments
on the popular multi-modal e-commerce benchmarks show that our approach
achieves superior performance over the other state-of-the-art techniques.

The clinical spectrum of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2), the strain of coronavirus that caused the COVID-19 pandemic, is
broad, extending from asymptomatic infection to severe immunopulmonary
reactions that, if not categorized properly, may be life-threatening.
Researchers rate COVID-19 patients on a scale from 1 to 8 according to the
severity level of COVID-19, 1 being healthy and 8 being extremely sick, based
on a multitude of factors including number of clinic visits, days since the
first sign of symptoms, and more. However, there are two issues with the
current state of severity level designation. Firstly, there exists variation
among researchers in determining these patient scores, which may lead to
improper treatment. Secondly, researchers use a variety of metrics to determine
patient severity level, including metrics involving plasma collection that
require invasive procedures. This project aims to remedy both issues by
introducing a machine learning framework that unifies severity level
designations based on noninvasive saliva biomarkers. Our results show that we
can successfully use machine learning on salivaomics data to predict the
severity level of COVID-19 patients, indicating the presence of viral load
using saliva biomarkers.

This paper describes noisy speech recognition for an augmented reality
headset that helps verbal communication within real multiparty conversational
environments. A major approach that has actively been studied in simulated
environments is to sequentially perform speech enhancement and automatic speech
recognition (ASR) based on deep neural networks (DNNs) trained in a supervised
manner. In our task, however, such a pretrained system fails to work due to the
mismatch between the training and test conditions and the head movements of the
user. To enhance only the utterances of a target speaker, we use beamforming
based on a DNN-based speech mask estimator that can adaptively extract the
speech components corresponding to a head-relative particular direction. We
propose a semi-supervised adaptation method that jointly updates the mask
estimator and the ASR model at run-time using clean speech signals with
ground-truth transcriptions and noisy speech signals with highly-confident
estimated transcriptions. Comparative experiments using the state-of-the-art
distant speech recognition system show that the proposed method significantly
improves the ASR performance.

This paper analyzes finite state Markov Decision Processes (MDPs) with
uncertain parameters in compact sets and re-examines results from robust MDP
via set-based fixed point theory. We generalize the Bellman and policy
evaluation operators to operators that contract on the space of value functions
and denote them as \emph{value operators}. We generalize these value operators
to act on the space of value function sets and denote them as \emph{set-based
value operators}. We prove that these set-based value operators are
contractions in the space of compact value function sets. Leveraging insights
from set theory, we generalize the rectangularity condition for the Bellman
operator from classic robust MDP literature to a \emph{containment condition}
for a generic value operator, which is weaker and can be applied to a larger
set of parameter-uncertain MDPs and contractive operators in dynamic
programming and reinforcement learning. We prove that both the rectangularity
condition and the containment condition sufficiently ensure that the set-based
value operator's fixed point set contains its own supremum and infimum
elements. For convex and compact sets of uncertain MDP parameters, we show
equivalence between the classic robust value function and the supremum of the
fixed point set of the set-based Bellman operator. Under dynamically changing
MDP parameters in compact sets, we prove a set convergence result for value
iteration, which otherwise may not converge to a single value function.

Recent advances in vision transformers (ViTs) have achieved great performance
in visual recognition tasks. Convolutional neural networks (CNNs) exploit
spatial inductive bias to learn visual representations, but these networks are
spatially local. ViTs can learn global representations with their
self-attention mechanism, but they are usually heavy-weight and unsuitable for
mobile devices. In this paper, we propose cross feature attention (XFA) to
bring down computation cost for transformers, and combine efficient mobile CNNs
to form a novel efficient light-weight CNN-ViT hybrid model, XFormer, which can
serve as a general-purpose backbone to learn both global and local
representation. Experimental results show that XFormer outperforms numerous CNN
and ViT-based models across different tasks and datasets. On ImageNet1K
dataset, XFormer achieves top-1 accuracy of 78.5% with 5.5 million parameters,
which is 2.2% and 6.3% more accurate than EfficientNet-B0 (CNN-based) and DeiT
(ViT-based) for similar number of parameters. Our model also performs well when
transferring to object detection and semantic segmentation tasks. On MS COCO
dataset, XFormer exceeds MobileNetV2 by 10.5 AP (22.7 -> 33.2 AP) in YOLOv3
framework with only 6.3M parameters and 3.8G FLOPs. On Cityscapes dataset, with
only a simple all-MLP decoder, XFormer achieves mIoU of 78.5 and FPS of 15.3,
surpassing state-of-the-art lightweight segmentation networks.

Recently, community has paid increasing attention on model scaling and
contributed to developing a model family with a wide spectrum of scales.
Current methods either simply resort to a one-shot NAS manner to construct a
non-structural and non-scalable model family or rely on a manual yet fixed
scaling strategy to scale an unnecessarily best base model. In this paper, we
bridge both two components and propose ScaleNet to jointly search base model
and scaling strategy so that the scaled large model can have more promising
performance. Concretely, we design a super-supernet to embody models with
different spectrum of sizes (e.g., FLOPs). Then, the scaling strategy can be
learned interactively with the base model via a Markov chain-based evolution
algorithm and generalized to develop even larger models. To obtain a decent
super-supernet, we design a hierarchical sampling strategy to enhance its
training sufficiency and alleviate the disturbance. Experimental results show
our scaled networks enjoy significant performance superiority on various FLOPs,
but with at least 2.53x reduction on search cost. Codes are available at
https://github.com/luminolx/ScaleNet.

Visualizing the uncertainty of ensemble simulations is challenging due to the
large size and multivariate and temporal features of ensemble data sets. One
popular approach to studying the uncertainty of ensembles is analyzing the
positional uncertainty of the level sets. Probabilistic marching cubes is a
technique that performs Monte Carlo sampling of multivariate Gaussian noise
distributions for positional uncertainty visualization of level sets. However,
the technique suffers from high computational time, making interactive
visualization and analysis impossible to achieve. This paper introduces a
deep-learning-based approach to learning the level-set uncertainty for
two-dimensional ensemble data with a multivariate Gaussian noise assumption. We
train the model using the first few time steps from time-varying ensemble data
in our workflow. We demonstrate that our trained model accurately infers
uncertainty in level sets for new time steps and is up to 170X faster than that
of the original probabilistic model with serial computation and 10X faster than
that of the original parallel computation.

Task-free continual learning (CL) aims to learn a non-stationary data stream
without explicit task definitions and not forget previous knowledge. The widely
adopted memory replay approach could gradually become less effective for long
data streams, as the model may memorize the stored examples and overfit the
memory buffer. Second, existing methods overlook the high uncertainty in the
memory data distribution since there is a big gap between the memory data
distribution and the distribution of all the previous data examples. To address
these problems, for the first time, we propose a principled memory evolution
framework to dynamically evolve the memory data distribution by making the
memory buffer gradually harder to be memorized with distributionally robust
optimization (DRO). We then derive a family of methods to evolve the memory
buffer data in the continuous probability measure space with Wasserstein
gradient flow (WGF). The proposed DRO is w.r.t the worst-case evolved memory
data distribution, thus guarantees the model performance and learns
significantly more robust features than existing memory-replay-based methods.
Extensive experiments on existing benchmarks demonstrate the effectiveness of
the proposed methods for alleviating forgetting. As a by-product of the
proposed framework, our method is more robust to adversarial examples than
existing task-free CL methods. Code is available on GitHub
\url{https://github.com/joey-wang123/DRO-Task-free}

Investigating cooperativity of interlocutors is central in studying
pragmatics of dialogue. Models of conversation that only assume cooperative
agents fail to explain the dynamics of strategic conversations. Thus, we
investigate the ability of agents to identify non-cooperative interlocutors
while completing a concurrent visual-dialogue task. Within this novel setting,
we study the optimality of communication strategies for achieving this
multi-task objective. We use the tools of learning theory to develop a
theoretical model for identifying non-cooperative interlocutors and apply this
theory to analyze different communication strategies. We also introduce a
corpus of non-cooperative conversations about images in the GuessWhat?! dataset
proposed by De Vries et al. (2017). We use reinforcement learning to implement
multiple communication strategies in this context and find empirical results
validate our theory.

We introduce a framework of the equivariant convolutional algorithms which is
tailored for a number of machine-learning tasks on physical systems with
arbitrary SU($d$) symmetries. It allows us to enhance a natural model of
quantum computation--permutational quantum computing (PQC) [Quantum Inf.
Comput., 10, 470-497 (2010)] --and defines a more powerful model: PQC+. While
PQC was shown to be effectively classically simulatable, we exhibit a problem
which can be efficiently solved on PQC+ machine, whereas the best known
classical algorithms runs in $O(n!n^2)$ time, thus providing strong evidence
against PQC+ being classically simulatable. We further discuss practical
quantum machine learning algorithms which can be carried out in the paradigm of
PQC+.

Recent studies have demonstrated that smart grids are vulnerable to stealthy
false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad
data detection mechanisms. The SFDIA detection has become one of the focuses of
smart grid research. Methods based on deep learning technology have shown
promising accuracy in the detection of SFDIAs. However, most existing methods
rely on the temporal structure of a sequence of measurements but do not take
account of the spatial structure between buses and transmission lines. To
address this issue, we propose a spatiotemporal deep network, PowerFDNet, for
the SFDIA detection in AC-model power grids. The PowerFDNet consists of two
sub-architectures: spatial architecture (SA) and temporal architecture (TA).
The SA is aimed at extracting representations of bus/line measurements and
modeling the spatial structure based on their representations. The TA is aimed
at modeling the temporal structure of a sequence of measurements. Therefore,
the proposed PowerFDNet can effectively model the spatiotemporal structure of
measurements. Case studies on the detection of SFDIAs on the benchmark smart
grids show that the PowerFDNet achieved significant improvement compared with
the state-of-the-art SFDIA detection methods. In addition, an IoT-oriented
lightweight prototype of size 52 MB is implemented and tested for mobile
devices, which demonstrates the potential applications on mobile devices. The
trained model will be available at
\textit{https://github.com/FrankYinXF/PowerFDNet}.

Collaboration among multiple data-owning entities (e.g., hospitals) can
accelerate the training process and yield better machine learning models due to
the availability and diversity of data. However, privacy concerns make it
challenging to exchange data while preserving confidentiality. Federated
Learning (FL) is a promising solution that enables collaborative training
through exchange of model parameters instead of raw data. However, most
existing FL solutions work under the assumption that participating clients are
\emph{honest} and thus can fail against poisoning attacks from malicious
parties, whose goal is to deteriorate the global model performance. In this
work, we propose a robust aggregation rule called Distance-based Outlier
Suppression (DOS) that is resilient to byzantine failures. The proposed method
computes the distance between local parameter updates of different clients and
obtains an outlier score for each client using Copula-based Outlier Detection
(COPOD). The resulting outlier scores are converted into normalized weights
using a softmax function, and a weighted average of the local parameters is
used for updating the global model. DOS aggregation can effectively suppress
parameter updates from malicious clients without the need for any
hyperparameter selection, even when the data distributions are heterogeneous.
Evaluation on two medical imaging datasets (CheXpert and HAM10000) demonstrates
the higher robustness of DOS method against a variety of poisoning attacks in
comparison to other state-of-the-art methods. The code can be found here
https://github.com/Naiftt/SPAFD.

Inverse scattering problems, such as those in electromagnetic imaging using
phaseless data (PD-ISPs), involve imaging objects using phaseless measurements
of wave scattering. Such inverse problems can be highly non-linear and
ill-posed under extremely strong scattering conditions such as when the objects
have very high permittivity or are large in size. In this work, we propose an
end-to-end reconstruction framework using unrolled optimization with deep
priors to solve PD-ISPs under very strong scattering conditions. We incorporate
an approximate linear physics-based model into our optimization framework along
with a deep learning-based prior and solve the resulting problem using an
iterative algorithm which is unfolded into a deep network. This network not
only learns data-driven regularization, but also overcomes the shortcomings of
approximate linear models and learns non-linear features. More important,
unlike existing PD-ISP methods, the proposed framework learns optimum values of
all tunable parameters (including multiple regularization parameters) as a part
of the framework. Results from simulations and experiments are shown for the
use case of indoor imaging using 2.4 GHz phaseless Wi-Fi measurements, where
the objects exhibit extremely strong scattering and low-absorption. Results
show that the proposed framework outperforms existing model-driven and
data-driven techniques by a significant margin and provides up to 20 times
higher validity range.

Data visualization captions help readers understand the purpose of a
visualization and are crucial for individuals with visual impairments. The
prevalence of poor figure captions and the successful application of deep
learning approaches to image captioning motivate the use of similar techniques
for automated figure captioning. However, research in this field has been
stunted by the lack of suitable datasets. We introduce LineCap, a novel figure
captioning dataset of 3,528 figures, and we provide insights from curating this
dataset and using end-to-end deep learning models for automated figure
captioning.

Bark beetle outbreaks can dramatically impact forest ecosystems and services
around the world. For the development of effective forest policies and
management plans, the early detection of infested trees is essential. Despite
the visual symptoms of bark beetle infestation, this task remains challenging,
considering overlapping tree crowns and non-homogeneity in crown foliage
discolouration. In this work, a deep learning based method is proposed to
effectively classify different stages of bark beetle attacks at the individual
tree level. The proposed method uses RetinaNet architecture (exploiting a
robust feature extraction backbone pre-trained for tree crown detection) to
train a shallow subnetwork for classifying the different attack stages of
images captured by unmanned aerial vehicles (UAVs). Moreover, various data
augmentation strategies are examined to address the class imbalance problem,
and consequently, the affine transformation is selected to be the most
effective one for this purpose. Experimental evaluations demonstrate the
effectiveness of the proposed method by achieving an average accuracy of
98.95%, considerably outperforming the baseline method by approximately 10%.

Creating agents that can both appropriately respond to conversations and
understand complex human linguistic tendencies and social cues has been a long
standing challenge in the NLP community. A recent pillar of research revolves
around emotion recognition in conversation (ERC); a sub-field of emotion
recognition that focuses on conversations or dialogues that contain two or more
utterances. In this work, we explore an approach to ERC that exploits the use
of neural embeddings along with complex structures in dialogues. We implement
our approach in a framework called Probabilistic Soft Logic (PSL), a
declarative templating language that uses first-order like logical rules, that
when combined with data, define a particular class of graphical model.
Additionally, PSL provides functionality for the incorporation of results from
neural models into PSL models. This allows our model to take advantage of
advanced neural methods, such as sentence embeddings, and logical reasoning
over the structure of a dialogue. We compare our method with state-of-the-art
purely neural ERC systems, and see almost a 20% improvement. With these
results, we provide an extensive qualitative and quantitative analysis over the
DailyDialog conversation dataset.

We are interested in estimating the uncertainties of deep neural networks,
which play an important role in many scientific and engineering problems. In
this paper, we present a striking new finding that an ensemble of neural
networks with the same weight initialization, trained on datasets that are
shifted by a constant bias gives rise to slightly inconsistent trained models,
where the differences in predictions are a strong indicator of epistemic
uncertainties. Using the neural tangent kernel (NTK), we demonstrate that this
phenomena occurs in part because the NTK is not shift-invariant. Since this is
achieved via a trivial input transformation, we show that it can therefore be
approximated using just a single neural network -- using a technique that we
call $\Delta-$UQ -- that estimates uncertainty around prediction by
marginalizing out the effect of the biases. We show that $\Delta-$UQ's
uncertainty estimates are superior to many of the current methods on a variety
of benchmarks -- outlier rejection, calibration under distribution shift, and
sequential design optimization of black box functions.

Lipschitz Bound Estimation is an effective method of regularizing deep neural
networks to make them robust against adversarial attacks. This is useful in a
variety of applications ranging from reinforcement learning to autonomous
systems. In this paper, we highlight the significant gap in obtaining a
non-trivial Lipschitz bound certificate for Convolutional Neural Networks
(CNNs) and empirically support it with extensive graphical analysis. We also
show that unrolling Convolutional layers or Toeplitz matrices can be employed
to convert Convolutional Neural Networks (CNNs) to a Fully Connected Network.
Further, we propose a simple algorithm to show the existing 20x-50x gap in a
particular data distribution between the actual lipschitz constant and the
obtained tight bound. We also ran sets of thorough experiments on various
network architectures and benchmark them on datasets like MNIST and CIFAR-10.
All these proposals are supported by extensive testing, graphs, histograms and
comparative analysis.

The federated learning (FL) framework enables edge clients to collaboratively
learn a shared inference model while keeping privacy of training data on
clients. Recently, many heuristics efforts have been made to generalize
centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc.,
to federated settings for improving convergence and accuracy. However, there is
still a paucity of theoretical principles on where to and how to design and
utilize adaptive optimization methods in federated settings. This work aims to
develop novel adaptive optimization methods for FL from the perspective of
dynamics of ordinary differential equations (ODEs). First, an analytic
framework is established to build a connection between federated optimization
methods and decompositions of ODEs of corresponding centralized optimizers.
Second, based on this analytic framework, a momentum decoupling adaptive
optimization method, FedDA, is developed to fully utilize the global momentum
on each local iteration and accelerate the training convergence. Last but not
least, full batch gradients are utilized to mimic centralized optimization in
the end of the training process to ensure the convergence and overcome the
possible inconsistency caused by adaptive optimization methods.

Crowdfunding, which is the act of raising funds from a large number of
people's contributions, is among the most popular research topics in economic
theory. Due to the fact that crowdfunding platforms (CFPs) have facilitated the
process of raising funds by offering several features, we should take their
existence and survival in the marketplace into account. In this study, we
investigated the significant role of platform features in a customer behavioral
choice model. In particular, we proposed a multinomial logit model to describe
the customers' (backers') behavior in a crowdfunding setting. We proceed by
discussing the revenue-sharing model in these platforms. For this purpose, we
conclude that an assortment optimization problem could be of major importance
in order to maximize the platforms' revenue. We were able to derive a
reasonable amount of data in some cases and implement two well-known machine
learning methods such as multivariate regression and classification problems to
predict the best assortments the platform could offer to every arriving
customer. We compared the results of these two methods and investigated how
well they perform in all cases.

While classical scaling, just like principal component analysis, is
parameter-free, most other methods for embedding multivariate data require the
selection of one or several parameters. This tuning can be difficult due to the
unsupervised nature of the situation. We propose a simple, almost obvious,
approach to supervise the choice of tuning parameter(s): minimize a notion of
stress. We substantiate this choice by reference to rigidity theory. We extend
a result by Aspnes et al. (IEEE Mobile Computing, 2006), showing that general
random geometric graphs are trilateration graphs with high probability. And we
provide a stability result \`a la Anderson et al. (SIAM Discrete Mathematics,
2010). We illustrate this approach in the context of the MDS-MAP(P) algorithm
of Shang and Ruml (IEEE INFOCOM, 2004). As a prototypical patch-stitching
method, it requires the choice of patch size, and we use the stress to make
that choice data-driven. In this context, we perform a number of experiments to
illustrate the validity of using the stress as the basis for tuning parameter
selection. In so doing, we uncover a bias-variance tradeoff, which is a
phenomenon which may have been overlooked in the multidimensional scaling
literature. By turning MDS-MAP(P) into a method for manifold learning, we
obtain a local version of Isomap for which the minimization of the stress may
also be used for parameter tuning.

Machine Learning (ML) methods have become a useful tool for tackling vehicle
routing problems, either in combination with popular heuristics or as
standalone models. However, current methods suffer from poor generalization
when tackling problems of different sizes or different distributions. As a
result, ML in vehicle routing has witnessed an expansion phase with new
methodologies being created for particular problem instances that become
infeasible at larger problem sizes.
  This paper aims at encouraging the consolidation of the field through
understanding and improving current existing models, namely the attention model
by Kool et al. We identify two discrepancy categories for VRP generalization.
The first is based on the differences that are inherent to the problems
themselves, and the second relates to architectural weaknesses that limit the
model's ability to generalize. Our contribution becomes threefold: We first
target model discrepancies by adapting the Kool et al. method and its loss
function for Sparse Dynamic Attention based on the alpha-entmax activation. We
then target inherent differences through the use of a mixed instance training
method that has been shown to outperform single instance training in certain
scenarios. Finally, we introduce a framework for inference level data
augmentation that improves performance by leveraging the model's lack of
invariance to rotation and dilation changes.

Randomized smoothing is sound when using infinite precision. However, we show
that randomized smoothing is no longer sound for limited floating-point
precision. We present a simple example where randomized smoothing certifies a
radius of $1.26$ around a point, even though there is an adversarial example in
the distance $0.8$ and extend this example further to provide false
certificates for CIFAR10. We discuss the implicit assumptions of randomized
smoothing and show that they do not apply to generic image classification
models whose smoothed versions are commonly certified. In order to overcome
this problem, we propose a sound approach to randomized smoothing when using
floating-point precision with essentially equal speed and matching the
certificates of the standard, unsound practice for standard classifiers tested
so far. Our only assumption is that we have access to a fair coin.

Nearest prototype classifiers (NPCs) assign to each input point the label of
the nearest prototype with respect to a chosen distance metric. A direct
advantage of NPCs is that the decisions are interpretable. Previous work could
provide lower bounds on the minimal adversarial perturbation in the
$\ell_p$-threat model when using the same $\ell_p$-distance for the NPCs. In
this paper we provide a complete discussion on the complexity when using
$\ell_p$-distances for decision and $\ell_q$-threat models for certification
for $p,q \in \{1,2,\infty\}$. In particular we provide scalable algorithms for
the \emph{exact} computation of the minimal adversarial perturbation when using
$\ell_2$-distance and improved lower bounds in other cases. Using efficient
improved lower bounds we train our Provably adversarially robust NPC (PNPC),
for MNIST which have better $\ell_2$-robustness guarantees than neural
networks. Additionally, we show up to our knowledge the first certification
results w.r.t. to the LPIPS perceptual metric which has been argued to be a
more realistic threat model for image classification than $\ell_p$-balls. Our
PNPC has on CIFAR10 higher certified robust accuracy than the empirical robust
accuracy reported in (Laidlaw et al., 2021). The code is available in our

IoT devices are increasingly being implemented with neural network models to
enable smart applications. Energy harvesting (EH) technology that harvests
energy from ambient environment is a promising alternative to batteries for
powering those devices due to the low maintenance cost and wide availability of
the energy sources. However, the power provided by the energy harvester is low
and has an intrinsic drawback of instability since it varies with the ambient
environment. This paper proposes EVE, an automated machine learning (autoML)
co-exploration framework to search for desired multi-models with shared weights
for energy harvesting IoT devices. Those shared models incur significantly
reduced memory footprint with different levels of model sparsity, latency, and
accuracy to adapt to the environmental changes. An efficient on-device
implementation architecture is further developed to efficiently execute each
model on device. A run-time model extraction algorithm is proposed that
retrieves individual model with negligible overhead when a specific model mode
is triggered.Experimental results show that the neural networks models
generated by EVE is on average 2.5X times faster than the baseline models
without pruning and shared weights.

This paper studies queueing problems with an endogenous number of machines
with and without an initial queue, the novelty being that coalitions not only
choose how to queue, but also on how many machines. For a given problem, agents
can (de)activate as many machines as they want, at a cost. After minimizing the
total cost (processing costs and machine costs), we use a game theoretical
approach to share to proceeds of this cooperation, and study the existence of
stable allocations. First, we study queueing problems with an endogenous number
of machines, and examine how to share the total cost. We provide an upper bound
and a lower bound on the cost of a machine to guarantee the non-emptiness of
the core (the set of stable allocations). Next, we study requeueing problems
with an endogenous number of machines, where there is an existing queue. We
examine how to share the cost savings compared to the initial situation, when
optimally requeueing/changing the number of machines. Although, in general,
stable allocation may not exist, we guarantee the existence of stable
allocations when all machines are considered public goods, and we start with an
initial schedule that might not have the optimal number of machines, but in
which agents with large waiting costs are processed first.

We present 'AiTLAS: Benchmark Arena' -- an open-source benchmark framework
for evaluating state-of-the-art deep learning approaches for image
classification in Earth Observation (EO). To this end, we present a
comprehensive comparative analysis of more than 400 models derived from nine
different state-of-the-art architectures, and compare them to a variety of
multi-class and multi-label classification tasks from 22 datasets with
different sizes and properties. In addition to models trained entirely on these
datasets, we also benchmark models trained in the context of transfer learning,
leveraging pre-trained model variants, as it is typically performed in
practice. All presented approaches are general and can be easily extended to
many other remote sensing image classification tasks not considered in this
study. To ensure reproducibility and facilitate better usability and further
developments, all of the experimental resources including the trained models,
model configurations and processing details of the datasets (with their
corresponding splits used for training and evaluating the models) are publicly
available on the repository: https://github.com/biasvariancelabs/aitlas-arena.

The rise of deep neural networks provides an important driver in optimizing
recommender systems. However, the success of recommender systems lies in
delicate architecture fabrication, and thus calls for Neural Architecture
Search (NAS) to further improve its modeling. We propose NASRec, a paradigm
that trains a single supernet and efficiently produces abundant
models/sub-architectures by weight sharing. To overcome the data multi-modality
and architecture heterogeneity challenges in recommendation domain, NASRec
establishes a large supernet (i.e., search space) to search the full
architectures, with the supernet incorporating versatile operator choices and
dense connectivity minimizing human prior for flexibility. The scale and
heterogeneity in NASRec impose challenges in search, such as training
inefficiency, operator-imbalance, and degraded rank correlation. We tackle
these challenges by proposing single-operator any-connection sampling,
operator-balancing interaction modules, and post-training fine-tuning. Our
results on three Click-Through Rates (CTR) prediction benchmarks show that
NASRec can outperform both manually designed models and existing NAS methods,

Recently brain networks have been widely adopted to study brain dynamics,
brain development and brain diseases. Graph representation learning techniques
on brain functional networks can facilitate the discovery of novel biomarkers
for clinical phenotypes and neurodegenerative diseases. However, current graph
learning techniques have several issues on brain network mining. Firstly, most
current graph learning models are designed for unsigned graph, which hinders
the analysis of many signed network data (e.g., brain functional networks).
Meanwhile, the insufficiency of brain network data limits the model performance
on clinical phenotypes predictions. Moreover, few of current graph learning
model is interpretable, which may not be capable to provide biological insights
for model outcomes. Here, we propose an interpretable hierarchical signed graph
representation learning model to extract graph-level representations from brain
functional networks, which can be used for different prediction tasks. In order
to further improve the model performance, we also propose a new strategy to
augment functional brain network data for contrastive learning. We evaluate
this framework on different classification and regression tasks using the data
from HCP and OASIS. Our results from extensive experiments demonstrate the
superiority of the proposed model compared to several state-of-the-art
techniques. Additionally, we use graph saliency maps, derived from these
prediction tasks, to demonstrate detection and interpretation of phenotypic
biomarkers.

Understanding non-linear relationships among financial instruments has
various applications in investment processes ranging from risk management,
portfolio construction and trading strategies. Here, we focus on
interconnectedness among stocks based on their correlation matrix which we
represent as a network with the nodes representing individual stocks and the
weighted links between pairs of nodes representing the corresponding pair-wise
correlation coefficients. The traditional network science techniques, which are
extensively utilized in financial literature, require handcrafted features such
as centrality measures to understand such correlation networks. However,
manually enlisting all such handcrafted features may quickly turn out to be a
daunting task. Instead, we propose a new approach for studying nuances and
relationships within the correlation network in an algorithmic way using a
graph machine learning algorithm called Node2Vec. In particular, the algorithm
compresses the network into a lower dimensional continuous space, called an
embedding, where pairs of nodes that are identified as similar by the algorithm
are placed closer to each other. By using log returns of S&P 500 stock data, we
show that our proposed algorithm can learn such an embedding from its
correlation network. We define various domain specific quantitative (and
objective) and qualitative metrics that are inspired by metrics used in the
field of Natural Language Processing (NLP) to evaluate the embeddings in order
to identify the optimal one. Further, we discuss various applications of the
embeddings in investment management.

While large pretrained foundation models (FMs) have shown remarkable
zero-shot classification robustness to dataset-level distribution shifts, their
robustness to subpopulation or group shifts is relatively underexplored. We
study this problem, and find that FMs such as CLIP may not be robust to various
group shifts. Across 9 robustness benchmarks, zero-shot classification with
their embeddings results in gaps of up to 80.7 percentage points (pp) between
average and worst-group accuracy. Unfortunately, existing methods to improve
robustness require retraining, which can be prohibitively expensive on large
foundation models. We also find that efficient ways to improve model inference
(e.g., via adapters, lightweight networks with FM embeddings as inputs) do not
consistently improve and can sometimes hurt group robustness compared to
zero-shot (e.g., increasing the accuracy gap by 50.1 pp on CelebA). We thus
develop an adapter training strategy to effectively and efficiently improve FM
group robustness. Our motivating observation is that while poor robustness
results from groups in the same class being embedded far apart in the
foundation model "embedding space," standard adapter training may not bring
these points closer together. We thus propose contrastive adapting, which
trains adapters with contrastive learning to bring sample embeddings close to
both their ground-truth class embeddings and other sample embeddings in the
same class. Across the 9 benchmarks, our approach consistently improves group
robustness, raising worst-group accuracy by 8.5 to 56.0 pp over zero-shot. Our
approach is also efficient, doing so without any FM finetuning and only a fixed
set of frozen FM embeddings. On benchmarks such as Waterbirds and CelebA, this
leads to worst-group accuracy comparable to state-of-the-art methods that
retrain entire models, while only training $\leq$1% of the model parameters.

Training generative models that capture rich semantics of the data and
interpreting the latent representations encoded by such models are very
important problems in unsupervised learning. In this work, we provide a simple
algorithm that relies on perturbation experiments on latent codes of a
pre-trained generative autoencoder to uncover a causal graph that is implied by
the generative model. We leverage pre-trained attribute classifiers and perform
perturbation experiments to check for influence of a given latent variable on a
subset of attributes. Given this, we show that one can fit an effective causal
graph that models a structural equation model between latent codes taken as
exogenous variables and attributes taken as observed variables. One interesting
aspect is that a single latent variable controls multiple overlapping subsets
of attributes unlike conventional approach that tries to impose full
independence. Using a pre-trained RNN-based generative autoencoder trained on a
dataset of peptide sequences, we demonstrate that the learnt causal graph from
our algorithm between various attributes and latent codes can be used to
predict a specific property for sequences which are unseen. We compare
prediction models trained on either all available attributes or only the ones
in the Markov blanket and empirically show that in both the unsupervised and
supervised regimes, typically, using the predictor that relies on Markov
blanket attributes generalizes better for out-of-distribution sequences.

Deep clustering has recently attracted significant attention. Despite the
remarkable progress, most of the previous deep clustering works still suffer
from two limitations. First, many of them focus on some distribution-based
clustering loss, lacking the ability to exploit sample-wise (or
augmentation-wise) relationships via contrastive learning. Second, they often
neglect the indirect sample-wise structure information, overlooking the rich
possibilities of multi-scale neighborhood structure learning. In view of this,
this paper presents a new deep clustering approach termed Image clustering with
contrastive learning and multi-scale Graph Convolutional Networks (IcicleGCN),
which bridges the gap between convolutional neural network (CNN) and graph
convolutional network (GCN) as well as the gap between contrastive learning and
multi-scale neighborhood structure learning for the image clustering task. The
proposed IcicleGCN framework consists of four main modules, namely, the
CNN-based backbone, the Instance Similarity Module (ISM), the Joint Cluster
Structure Learning and Instance reconstruction Module (JC-SLIM), and the
Multi-scale GCN module (M-GCN). Specifically, with two random augmentations
performed on each image, the backbone network with two weight-sharing views is
utilized to learn the representations for the augmented samples, which are then
fed to ISM and JC-SLIM for instance-level and cluster-level contrastive
learning, respectively. Further, to enforce multi-scale neighborhood structure
learning, two streams of GCNs and an auto-encoder are simultaneously trained
via (i) the layer-wise interaction with representation fusion and (ii) the
joint self-adaptive learning that ensures their last-layer output distributions
to be consistent. Experiments on multiple image datasets demonstrate the
superior clustering performance of IcicleGCN over the state-of-the-art.

In this work, we propose a Machine Learning model that generates an
adjustable 3D dose distribution for external beam radiation therapy for
head-and-neck cancer treatments. In contrast to existing Machine Learning
methods that provide a single model, we create pairs of models for each
organ-at-risk, namely lower-extreme and upper-extreme models. These model pairs
for an organ-at-risk propose doses that give lower and higher doses to that
organ-at-risk, while also encapsulating the dose trade-off to other
organs-at-risk. By weighting and combining the model pairs for all
organs-at-risk, we are able to dynamically create adjustable dose distributions
that can be used, in real-time, to move doses between organs-at-risk, thereby
customizing the dose distribution to the needs of a particular patient. We
leverage a key observation that the training data set inherently contains the
clinical trade-offs. We show that the adjustable distributions are able to
provide reasonable clinical dose latitude in the trade-off of doses between
organs-at-risk.

The standard problem setting in cooperative multi-agent settings is self-play
(SP), where the goal is to train a team of agents that works well together.
However, optimal SP policies commonly contain arbitrary conventions
("handshakes") and are not compatible with other, independently trained agents
or humans. This latter desiderata was recently formalized by Hu et al. 2020 as
the zero-shot coordination (ZSC) setting and partially addressed with their
Other-Play (OP) algorithm, which showed improved ZSC and human-AI performance
in the card game Hanabi. OP assumes access to the symmetries of the environment
and prevents agents from breaking these in a mutually incompatible way during
training. However, as the authors point out, discovering symmetries for a given
environment is a computationally hard problem. Instead, we show that through a
simple adaption of k-level reasoning (KLR) Costa Gomes et al. 2006,
synchronously training all levels, we can obtain competitive ZSC and ad-hoc
teamplay performance in Hanabi, including when paired with a human-like proxy
bot. We also introduce a new method, synchronous-k-level reasoning with a best
response (SyKLRBR), which further improves performance on our synchronous KLR
by co-training a best response.

We present a computational approach for estimating emotion contagion on
social media networks. Built on a foundation of psychology literature, our
approach estimates the degree to which the perceivers' emotional states
(positive or negative) start to match those of the expressors, based on the
latter's content. We use a combination of deep learning and social network
analysis to model emotion contagion as a diffusion process in dynamic social
network graphs, taking into consideration key aspects like causality,
homophily, and interference. We evaluate our approach on user behavior data
obtained from a popular social media platform for sharing short videos. We
analyze the behavior of 48 users over a span of 8 weeks (over 200k audio-visual
short posts analyzed) and estimate how contagious the users with whom they
engage with are on social media. As per the theory of diffusion, we account for
the videos a user watches during this time (inflow) and the daily engagements;
liking, sharing, downloading or creating new videos (outflow) to estimate
contagion. To validate our approach and analysis, we obtain human feedback on
these 48 social media platform users with an online study by collecting
responses of about 150 participants. We report users who interact with more
number of creators on the platform are 12% less prone to contagion, and those
who consume more content of `negative' sentiment are 23% more prone to
contagion. We will publicly release our code upon acceptance.

Over 60,000 songs are released on Spotify every day, and the competition for
the listener's attention is immense. In that regard, the importance of
captivating and inviting cover art cannot be underestimated, because it is
deeply entangled with a song's character and the artist's identity, and remains
one of the most important gateways to lead people to discover music. However,
designing cover art is a highly creative, lengthy and sometimes expensive
process that can be daunting, especially for non-professional artists. For this
reason, we propose a novel deep-learning framework to generate cover art guided
by audio features. Inspired by VQGAN-CLIP, our approach is highly flexible
because individual components can easily be replaced without the need for any
retraining. This paper outlines the architectural details of our models and
discusses the optimization challenges that emerge from them. More specifically,
we will exploit genetic algorithms to overcome bad local minima and adversarial
examples. We find that our framework can generate suitable cover art for most
genres, and that the visual features adapt themselves to audio feature changes.
Given these results, we believe that our framework paves the road for
extensions and more advanced applications in audio-guided visual generation
tasks.

One of the crucial tasks in computer science is the processing time reduction
of various data types, i.e., images, which is important for different fields --
from medicine and logistics to virtual shopping. Compared to classical
computers, quantum computers are capable of parallel data processing, which
reduces the data processing time. This quality of quantum computers inspired
intensive research of the potential of quantum technologies applicability to
real-life tasks. Some progress has already revealed on a smaller volumes of the
input data. In this research effort, I aimed to increase the amount of input
data (I used images from 2 x 2 to 8 x 8), while reducing the processing time,
by way of skipping intermediate measurement steps. The hypothesis was that, for
increased input data, the omitting of intermediate measurement steps after each
quantum convolution layer will improve output metric results and accelerate
data processing. To test the hypothesis, I performed experiments to chose the
best activation function and its derivative in each network. The hypothesis was
partly confirmed in terms of output mean squared error (MSE) -- it dropped from
0.25 in the result of classical convolutional neural network (CNN) training to
0.23 in the result of quantum convolutional neural network (QCNN) training. In
terms of the training time, however, which was 1.5 minutes for CNN and 4 hours
37 minutes in the least lengthy training iteration, the hypothesis was
rejected.

Chemical kinetics plays an important role in governing the thermal evolution
in reactive flows problems. The possible interactions between chemical species
increase drastically with the number of species considered in the system.
Various ways have been proposed before to simplify chemical networks with an
aim to reduce the computational complexity of the chemical network. These
techniques oftentimes require domain-knowledge experts to handcraftedly
identify important reaction pathways and possible simplifications. Here, we
propose a combination of autoencoder and neural ordinary differential equation
to model the temporal evolution of chemical kinetics in a reduced subspace. We
demonstrated that our model has achieved a close-to 10-fold speed-up compared
to commonly used astro-chemistry solver for a 9-species primordial network,
while maintaining 1 percent accuracy across a wide-range of density and
temperature.

It is common to address the curse of dimensionality in Markov decision
processes (MDPs) by exploiting low-rank representations. This motivates much of
the recent theoretical study on linear MDPs. However, most approaches require a
given representation under unrealistic assumptions about the normalization of
the decomposition or introduce unresolved computational challenges in practice.
Instead, we consider an alternative definition of linear MDPs that
automatically ensures normalization while allowing efficient representation
learning via contrastive estimation. The framework also admits
confidence-adjusted index algorithms, enabling an efficient and principled
approach to incorporating optimism or pessimism in the face of uncertainty. To
the best of our knowledge, this provides the first practical representation
learning method for linear MDPs that achieves both strong theoretical
guarantees and empirical performance. Theoretically, we prove that the proposed
algorithm is sample efficient in both the online and offline settings.
Empirically, we demonstrate superior performance over existing state-of-the-art
model-based and model-free algorithms on several benchmarks.

We propose bootstrapped masked autoencoders (BootMAE), a new approach for
vision BERT pretraining. BootMAE improves the original masked autoencoders
(MAE) with two core designs: 1) momentum encoder that provides online feature
as extra BERT prediction targets; 2) target-aware decoder that tries to reduce
the pressure on the encoder to memorize target-specific information in BERT
pretraining. The first design is motivated by the observation that using a
pretrained MAE to extract the features as the BERT prediction target for masked
tokens can achieve better pretraining performance. Therefore, we add a momentum
encoder in parallel with the original MAE encoder, which bootstraps the
pretraining performance by using its own representation as the BERT prediction
target. In the second design, we introduce target-specific information (e.g.,
pixel values of unmasked patches) from the encoder directly to the decoder to
reduce the pressure on the encoder of memorizing the target-specific
information. Thus, the encoder focuses on semantic modeling, which is the goal
of BERT pretraining, and does not need to waste its capacity in memorizing the
information of unmasked tokens related to the prediction target. Through
extensive experiments, our BootMAE achieves $84.2\%$ Top-1 accuracy on
ImageNet-1K with ViT-B backbone, outperforming MAE by $+0.8\%$ under the same
pre-training epochs. BootMAE also gets $+1.0$ mIoU improvements on semantic
segmentation on ADE20K and $+1.3$ box AP, $+1.4$ mask AP improvement on object
detection and segmentation on COCO dataset. Code is released at
https://github.com/LightDXY/BootMAE.

We propose a new method for fine-grained few-shot recognition via deep object
parsing. In our framework, an object is made up of K distinct parts and for
each part, we learn a dictionary of templates, which is shared across all
instances and categories. An object is parsed by estimating the locations of
these K parts and a set of active templates that can reconstruct the part
features. We recognize test instances by comparing its active templates and the
relative geometry of its part locations against those of the presented few-shot
instances. Our method is end-to-end trainable to learn part templates on-top of
a convolutional backbone. To combat visual distortions such as orientation,
pose and size, we learn templates at multiple scales, and at test-time parse
and match instances across these scales. We show that our method is competitive
with the state-of-the-art, and by virtue of parsing enjoys interpretability as
well.

Algorithms that solve zero-sum games, multi-objective agent objectives, or,
more generally, variational inequality (VI) problems are notoriously unstable
on general problems. Owing to the increasing need for solving such problems in
machine learning, this instability has been highlighted in recent years as a
significant research challenge. In this paper, we provide an overview of recent
progress in the use of continuous-time perspectives in the analysis and design
of methods targeting the broad VI problem class. Our presentation draws
parallels between single-objective problems and multi-objective problems,
highlighting the challenges of the latter. We also formulate various desiderata
for algorithms that apply to general VIs and we argue that achieving these
desiderata may profit from an understanding of the associated continuous-time
dynamics.

Though impressive performance has been achieved in specific visual realms
(e.g. faces, dogs, and places), an omni-vision representation generalizing to
many natural visual domains is highly desirable. But, existing benchmarks are
biased and inefficient to evaluate the omni-vision representation -- these
benchmarks either only include several specific realms, or cover most realms at
the expense of subsuming numerous datasets that have extensive realm
overlapping. In this paper, we propose Omni-Realm Benchmark (OmniBenchmark). It
includes 21 realm-wise datasets with 7,372 concepts and 1,074,346 images.
Without semantic overlapping, these datasets cover most visual realms
comprehensively and meanwhile efficiently. In addition, we propose a new
supervised contrastive learning framework, namely Relational Contrastive
learning (ReCo), for a better omni-vision representation. Beyond pulling two
instances from the same concept closer -- the typical supervised contrastive
learning framework -- ReCo also pulls two instances from the same semantic
realm closer, encoding the semantic relation between concepts, and facilitating
omni-vision representation learning. We benchmark ReCo and other advances in
omni-vision representation studies that are different in architectures (from
CNNs to transformers) and in learning paradigms (from supervised learning to
self-supervised learning) on OmniBenchmark. We illustrate the superior of ReCo
to other supervised contrastive learning methods and reveal multiple practical
observations to facilitate future research.

Human relighting is a highly desirable yet challenging task. Existing works
either require expensive one-light-at-a-time (OLAT) captured data using light
stage or cannot freely change the viewpoints of the rendered body. In this
work, we propose a principled framework, Relighting4D, that enables
free-viewpoints relighting from only human videos under unknown illuminations.
Our key insight is that the space-time varying geometry and reflectance of the
human body can be decomposed as a set of neural fields of normal, occlusion,
diffuse, and specular maps. These neural fields are further integrated into
reflectance-aware physically based rendering, where each vertex in the neural
field absorbs and reflects the light from the environment. The whole framework
can be learned from videos in a self-supervised manner, with physically
informed priors designed for regularization. Extensive experiments on both real
and synthetic datasets demonstrate that our framework is capable of relighting
dynamic human actors with free-viewpoints.

This paper proposes a low-cost and highly accurate ECG-monitoring system
intended for personalized early arrhythmia detection for wearable mobile
sensors. Earlier supervised approaches for personalized ECG monitoring require
both abnormal and normal heartbeats for the training of the dedicated
classifier. However, in a real-world scenario where the personalized algorithm
is embedded in a wearable device, such training data is not available for
healthy people with no cardiac disorder history. In this study, (i) we propose
a null space analysis on the healthy signal space obtained via sparse
dictionary learning, and investigate how a simple null space projection or
alternatively regularized least squares-based classification methods can reduce
the computational complexity, without sacrificing the detection accuracy, when
compared to sparse representation-based classification. (ii) Then we introduce
a sparse representation-based domain adaptation technique in order to project
other existing users' abnormal and normal signals onto the new user's signal
space, enabling us to train the dedicated classifier without having any
abnormal heartbeat of the new user. Therefore, zero-shot learning can be
achieved without the need for synthetic abnormal heartbeat generation. An
extensive set of experiments performed on the benchmark MIT-BIH ECG dataset
shows that when this domain adaptation-based training data generator is used
with a simple 1-D CNN classifier, the method outperforms the prior work by a
significant margin. (iii) Then, by combining (i) and (ii), we propose an
ensemble classifier that further improves the performance. This approach for
zero-shot arrhythmia detection achieves an average accuracy level of 98.2% and
an F1-Score of 92.8%. Finally, a personalized energy-efficient ECG monitoring
scheme is proposed using the above-mentioned innovations.

Human drivers have limited and time-varying cognitive resources when making
decisions in real-world traffic scenarios, which often leads to unique and
stochastic behaviors that can not be explained by perfect rationality
assumption, a widely accepted premise in modeling driving behaviors that
presume drivers rationally make decisions to maximize their own rewards under
all circumstances. To explicitly address this disadvantage, this study presents
a novel driver behavior model that aims to capture the resource rationality and
stochasticity of the human driver's behaviors in realistic longitudinal driving
scenarios. The resource rationality principle can provide a theoretic framework
to better understand the human cognition processes by modeling human's internal
cognitive mechanisms as utility maximization subject to cognitive resource
limitations, which can be represented as finite and time-varying preview
horizons in the context of driving. An inverse resource rational-based
stochastic inverse reinforcement learning approach (IRR-SIRL) is proposed to
learn a distribution of the planning horizon and cost function of the human
driver with a given series of human demonstrations. A nonlinear model
predictive control (NMPC) with a time-varying horizon approach is used to
generate driver-specific trajectories by using the learned distributions of the
planning horizon and the cost function of the driver. The simulation
experiments are carried out using human demonstrations gathered from the
driver-in-the-loop driving simulator. The results reveal that the proposed
inverse resource rational-based stochastic driver model can address the
resource rationality and stochasticity of human driving behaviors in a variety
of realistic longitudinal driving scenarios.

Prompt tuning attempts to update few task-specific parameters in pre-trained
models. It has achieved comparable performance to fine-tuning of the full
parameter set on both language understanding and generation tasks. In this
work, we study the problem of prompt tuning for neural text retrievers. We
introduce parameter-efficient prompt tuning for text retrieval across
in-domain, cross-domain, and cross-topic settings. Through an extensive
analysis, we show that the strategy can mitigate the two issues --
parameter-inefficiency and weak generalizability -- faced by fine-tuning based
retrieval methods. Notably, it can significantly improve the out-of-domain
zero-shot generalization of the retrieval models. By updating only 0.1% of the
model parameters, the prompt tuning strategy can help retrieval models achieve
better generalization performance than traditional methods in which all
parameters are updated. Finally, to facilitate research on retrievers'
cross-topic generalizability, we curate and release an academic retrieval
dataset with 18K query-results pairs in 87 topics, making it the largest
topic-specific one to date.

In this article we study the possibilities of recovering the structure of
port-Hamiltonian systems starting from ``unlabelled'' ordinary differential
equations describing mechanical systems. The algorithm we suggest solves the
problem in two phases. It starts by constructing the connectivity structure of
the system using machine learning methods -- producing thus a graph of
interconnected subsystems. Then this graph is enhanced by recovering the
Hamiltonian structure of each subsystem as well as the corresponding ports.
This second phase relies heavily on results from symplectic and Poisson
geometry that we briefly sketch. And the precise solutions can be constructed
using methods of computer algebra and symbolic computations. The algorithm
permits to extend the port-Hamiltonian formalism to generic ordinary
differential equations, hence introducing eventually a new concept of normal
forms of ODEs.

Contrastive learning is a representation learning method performed by
contrasting a sample to other similar samples so that they are brought closely
together, forming clusters in the feature space. The learning process is
typically conducted using a two-stage training architecture, and it utilizes
the contrastive loss (CL) for its feature learning. Contrastive learning has
been shown to be quite successful in handling imbalanced datasets, in which
some classes are overrepresented while some others are underrepresented.
However, previous studies have not specifically modified CL for imbalanced
datasets. In this work, we introduce an asymmetric version of CL, referred to
as ACL, in order to directly address the problem of class imbalance. In
addition, we propose the asymmetric focal contrastive loss (AFCL) as a further
generalization of both ACL and focal contrastive loss (FCL). Results on the
FMNIST and ISIC 2018 imbalanced datasets show that AFCL is capable of
outperforming CL and FCL in terms of both weighted and unweighted
classification accuracies. In the appendix, we provide a full axiomatic
treatment on entropy, along with complete proofs.

We present a unified method, termed Unicorn, that can simultaneously solve
four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the
same model parameters. Due to the fragmented definitions of the object tracking
problem itself, most existing trackers are developed to address a single or
part of tasks and overspecialize on the characteristics of specific tasks. By
contrast, Unicorn provides a unified solution, adopting the same input,
backbone, embedding, and head across all tracking tasks. For the first time, we
accomplish the great unification of the tracking network architecture and
learning paradigm. Unicorn performs on-par or better than its task-specific
counterparts in 8 tracking datasets, including LaSOT, TrackingNet, MOT17,
BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS. We believe that Unicorn will
serve as a solid step towards the general vision model. Code is available at
https://github.com/MasterBin-IIAU/Unicorn.

In this paper, we study a problem of egocentric scene understanding, i.e.,
predicting depths and surface normals from an egocentric image. Egocentric
scene understanding poses unprecedented challenges: (1) due to large head
movements, the images are taken from non-canonical viewpoints (i.e., tilted
images) where existing models of geometry prediction do not apply; (2) dynamic
foreground objects including hands constitute a large proportion of visual
scenes. These challenges limit the performance of the existing models learned
from large indoor datasets, such as ScanNet and NYUv2, which comprise
predominantly upright images of static scenes. We present a multimodal spatial
rectifier that stabilizes the egocentric images to a set of reference
directions, which allows learning a coherent visual representation. Unlike
unimodal spatial rectifier that often produces excessive perspective warp for
egocentric images, the multimodal spatial rectifier learns from multiple
directions that can minimize the impact of the perspective warp. To learn
visual representations of the dynamic foreground objects, we present a new
dataset called EDINA (Egocentric Depth on everyday INdoor Activities) that
comprises more than 500K synchronized RGBD frames and gravity directions.
Equipped with the multimodal spatial rectifier and the EDINA dataset, our
proposed method on single-view depth and surface normal estimation
significantly outperforms the baselines not only on our EDINA dataset, but also
on other popular egocentric datasets, such as First Person Hand Action (FPHA)
and EPIC-KITCHENS.

Classical univariate isotonic regression involves nonparametric estimation
under a monotonicity constraint of the true signal. We consider a variation of
this generating process, which we term adversarial sign-corrupted isotonic
(\texttt{ASCI}) regression. Under this \texttt{ASCI} setting, the adversary has
full access to the true isotonic responses, and is free to sign-corrupt them.
Estimating the true monotonic signal given these sign-corrupted responses is a
highly challenging task. Notably, the sign-corruptions are designed to violate
monotonicity, and possibly induce heavy dependence between the corrupted
response terms. In this sense, \texttt{ASCI} regression may be viewed as an
adversarial stress test for isotonic regression. Our motivation is driven by
understanding whether efficient robust estimation of the monotone signal is
feasible under this adversarial setting. We develop \texttt{ASCIFIT}, a
three-step estimation procedure under the \texttt{ASCI} setting. The
\texttt{ASCIFIT} procedure is conceptually simple, easy to implement with
existing software, and consists of applying the \texttt{PAVA} with crucial pre-
and post-processing corrections. We formalize this procedure, and demonstrate
its theoretical guarantees in the form of sharp high probability upper bounds
and minimax lower bounds. We illustrate our findings with detailed simulations.

Spiking Neural Networks (SNN) are known to be very effective for neuromorphic
processor implementations, achieving orders of magnitude improvements in energy
efficiency and computational latency over traditional deep learning approaches.
Comparable algorithmic performance was recently made possible as well with the
adaptation of supervised training algorithms to the context of SNN. However,
information including audio, video, and other sensor-derived data are typically
encoded as real-valued signals that are not well-suited to SNN, preventing the
network from leveraging spike timing information. Efficient encoding from
real-valued signals to spikes is therefore critical and significantly impacts
the performance of the overall system. To efficiently encode signals into
spikes, both the preservation of information relevant to the task at hand as
well as the density of the encoded spikes must be considered. In this paper, we
study four spike encoding methods in the context of a speaker independent digit
classification system: Send on Delta, Time to First Spike, Leaky Integrate and
Fire Neuron and Bens Spiker Algorithm. We first show that all encoding methods
yield higher classification accuracy using significantly fewer spikes when
encoding a bio-inspired cochleagram as opposed to a traditional short-time
Fourier transform. We then show that two Send On Delta variants result in
classification results comparable with a state of the art deep convolutional
neural network baseline, while simultaneously reducing the encoded bit rate.
Finally, we show that several encoding methods result in improved performance
over the conventional deep learning baseline in certain cases, further
demonstrating the power of spike encoding algorithms in the encoding of
real-valued signals and that neuromorphic implementation has the potential to
outperform state of the art techniques.

We design an algorithm for finding counterfactuals with strong theoretical
guarantees on its performance. For any monotone model $f : X^d \to \{0,1\}$ and
instance $x^\star$, our algorithm makes \[ {S(f)^{O(\Delta_f(x^\star))}\cdot
\log d}\] queries to $f$ and returns {an {\sl optimal}} counterfactual for
$x^\star$: a nearest instance $x'$ to $x^\star$ for which $f(x')\ne
f(x^\star)$. Here $S(f)$ is the sensitivity of $f$, a discrete analogue of the
Lipschitz constant, and $\Delta_f(x^\star)$ is the distance from $x^\star$ to
its nearest counterfactuals. The previous best known query complexity was
$d^{\,O(\Delta_f(x^\star))}$, achievable by brute-force local search. We
further prove a lower bound of $S(f)^{\Omega(\Delta_f(x^\star))} + \Omega(\log
d)$ on the query complexity of any algorithm, thereby showing that the
guarantees of our algorithm are essentially optimal.

This paper provides a comprehensive survey of bias mitigation methods for
achieving fairness in Machine Learning (ML) models. We collect a total of 341
publications concerning bias mitigation for ML classifiers. These methods can
be distinguished based on their intervention procedure (i.e., pre-processing,
in-processing, post-processing) and the technology they apply. We investigate
how existing bias mitigation methods are evaluated in the literature. In
particular, we consider datasets, metrics and benchmarking. Based on the
gathered insights (e.g., what is the most popular fairness metric? How many
datasets are used for evaluating bias mitigation methods?). We hope to support
practitioners in making informed choices when developing and evaluating new
bias mitigation methods.

Generalization and invariance are two essential properties of any machine
learning model. Generalization captures a model's ability to classify unseen
data while invariance measures consistency of model predictions on
transformations of the data. Existing research suggests a positive
relationship: a model generalizing well should be invariant to certain visual
factors. Building on this qualitative implication we make two contributions.
First, we introduce effective invariance (EI), a simple and reasonable measure
of model invariance which does not rely on image labels. Given predictions on a
test image and its transformed version, EI measures how well the predictions
agree and with what level of confidence. Second, using invariance scores
computed by EI, we perform large-scale quantitative correlation studies between
generalization and invariance, focusing on rotation and grayscale
transformations. From a model-centric view, we observe generalization and
invariance of different models exhibit a strong linear relationship, on both
in-distribution and out-of-distribution datasets. From a dataset-centric view,
we find a certain model's accuracy and invariance linearly correlated on
different test sets. Apart from these major findings, other minor but
interesting insights are also discussed.

Recent advances in Transformer-based large language models (LLMs) have led to
significant performance improvements across many tasks. These gains come with a
drastic increase in the models' size, potentially leading to slow and costly
use at inference time. In practice, however, the series of generations made by
LLMs is composed of varying levels of difficulty. While certain predictions
truly benefit from the models' full capacity, other continuations are more
trivial and can be solved with reduced compute. In this work, we introduce
Confident Adaptive Language Modeling (CALM), a framework for dynamically
allocating different amounts of compute per input and generation timestep.
Early exit decoding involves several challenges that we address here, such as:
(1) what confidence measure to use; (2) connecting sequence-level constraints
to local per-token exit decisions; and (3) attending back to missing hidden
representations due to early exits in previous tokens. Through theoretical
analysis and empirical experiments on three diverse text generation tasks, we
demonstrate the efficacy of our framework in reducing compute -- potential
speedup of up to $\times 3$ -- while provably maintaining high performance.

Existing temporal action detection (TAD) methods rely on a large number of
training data with segment-level annotations. Collecting and annotating such a
training set is thus highly expensive and unscalable. Semi-supervised TAD
(SS-TAD) alleviates this problem by leveraging unlabeled videos freely
available at scale. However, SS-TAD is also a much more challenging problem
than supervised TAD, and consequently much under-studied. Prior SS-TAD methods
directly combine an existing proposal-based TAD method and a SSL method. Due to
their sequential localization (e.g, proposal generation) and classification
design, they are prone to proposal error propagation. To overcome this
limitation, in this work we propose a novel Semi-supervised Temporal action
detection model based on PropOsal-free Temporal mask (SPOT) with a parallel
localization (mask generation) and classification architecture. Such a novel
design effectively eliminates the dependence between localization and
classification by cutting off the route for error propagation in-between. We
further introduce an interaction mechanism between classification and
localization for prediction refinement, and a new pretext task for
self-supervised model pre-training. Extensive experiments on two standard
benchmarks show that our SPOT outperforms state-of-the-art alternatives, often
by a large margin. The PyTorch implementation of SPOT is available at
https://github.com/sauradip/SPOT

Abstract reasoning is a key ability for an intelligent system. Large language
models achieve above-chance performance on abstract reasoning tasks, but
exhibit many imperfections. However, human abstract reasoning is also
imperfect, and depends on our knowledge and beliefs about the content of the
reasoning problem. For example, humans reason much more reliably about logical
rules that are grounded in everyday situations than arbitrary rules about
abstract attributes. The training experiences of language models similarly
endow them with prior expectations that reflect human knowledge and beliefs. We
therefore hypothesized that language models would show human-like content
effects on abstract reasoning problems. We explored this hypothesis across
three logical reasoning tasks: natural language inference, judging the logical
validity of syllogisms, and the Wason selection task (Wason, 1968). We find
that state of the art large language models (with 7 or 70 billion parameters;
Hoffman et al., 2022) reflect many of the same patterns observed in humans
across these tasks -- like humans, models reason more effectively about
believable situations than unrealistic or abstract ones. Our findings have
implications for understanding both these cognitive effects, and the factors
that contribute to language model performance.

Based on the data gathered by echo-sounder buoys attached to drifting Fish
Aggregating Devices (dFADs) across tropical oceans, the current study applies a
Machine Learning protocol to examine the temporal trends of tuna schools'
association to drifting objects. Using a binary output, metrics typically used
in the literature were adapted to account for the fact that the entire tuna
aggregation under the dFAD was considered. The median time it took tuna to
colonize the dFADs for the first time varied between 25 and 43 days, depending
on the ocean, and the longest soak and colonization times were registered in
the Pacific Ocean. The tuna schools' Continuous Residence Times were generally
shorter than Continuous Absence Times (median values between 5 and 7 days, and
9 and 11 days, respectively), in line with the results found by previous
studies. Using a regression output, two novel metrics, namely aggregation time
and disaggregation time, were estimated to obtain further insight into the
symmetry of the aggregation process. Across all oceans, the time it took for
the tuna aggregation to depart from the dFADs was not significantly longer than
the time it took for the aggregation to form. The value of these results in the
context of the "ecological trap" hypothesis is discussed, and further analyses
to enrich and make use of this data source are proposed.

The use of machine learning (ML) methods for prediction and forecasting has
become widespread across the quantitative sciences. However, there are many
known methodological pitfalls, including data leakage, in ML-based science. In
this paper, we systematically investigate reproducibility issues in ML-based
science. We show that data leakage is indeed a widespread problem and has led
to severe reproducibility failures. Specifically, through a survey of
literature in research communities that adopted ML methods, we find 17 fields
where errors have been found, collectively affecting 329 papers and in some
cases leading to wildly overoptimistic conclusions. Based on our survey, we
present a fine-grained taxonomy of 8 types of leakage that range from textbook
errors to open research problems.
  We argue for fundamental methodological changes to ML-based science so that
cases of leakage can be caught before publication. To that end, we propose
model info sheets for reporting scientific claims based on ML models that would
address all types of leakage identified in our survey. To investigate the
impact of reproducibility errors and the efficacy of model info sheets, we
undertake a reproducibility study in a field where complex ML models are
believed to vastly outperform older statistical models such as Logistic
Regression (LR): civil war prediction. We find that all papers claiming the
superior performance of complex ML models compared to LR models fail to
reproduce due to data leakage, and complex ML models don't perform
substantively better than decades-old LR models. While none of these errors
could have been caught by reading the papers, model info sheets would enable
the detection of leakage in each case.

An electric vehicle charging station (EVCS) infrastructure is the backbone of
transportation electrification. However, the EVCS has myriads of exploitable
vulnerabilities in software, hardware, supply chain, and incumbent legacy
technologies such as network, communication, and control. These standalone or
networked EVCS open up large attack surfaces for the local or state-funded
adversaries. The state-of-the-art approaches are not agile and intelligent
enough to defend against and mitigate advanced persistent threats (APT). We
propose the data-driven model-free distributed intelligence based on multiagent
Deep Reinforcement Learning (MADRL)-- Twin Delayed Deep Deterministic Policy
Gradient (TD3) -- that efficiently learns the control policy to mitigate the
cyberattacks on the controllers of EVCS. Also, we have proposed two additional
mitigation methods: the manual/Bruteforce mitigation and the controller
clone-based mitigation. The attack model considers the APT designed to
malfunction the duty cycles of the EVCS controllers with Type-I low-frequency
attack and Type-II constant attack. The proposed model restores the EVCS
operation under threat incidence in any/all controllers by correcting the
control signals generated by the legacy controllers. Also, the TD3 algorithm
provides higher granularity by learning nonlinear control policies as compared
to the other two mitigation methods. Index Terms: Cyberattack, Deep
Reinforcement Learning(DRL), Electric Vehicle Charging Station, Mitigation.

The pretrain-then-finetune paradigm has been widely adopted in computer
vision. But as the size of Vision Transformer (ViT) grows exponentially, the
full finetuning becomes prohibitive in view of the heavier storage overhead.
Motivated by parameter-efficient transfer learning (PETL) on language
transformers, recent studies attempt to insert lightweight adaptation modules
(e.g., adapter layers or prompt tokens) to pretrained ViT and only finetune
these modules while the pretrained weights are frozen. However, these modules
were originally proposed to finetune language models and did not take into
account the prior knowledge specifically for visual tasks. In this paper, we
propose to construct Convolutional Bypasses (Convpass) in ViT as adaptation
modules, introducing only a small amount (less than 0.5% of model parameters)
of trainable parameters to adapt the large ViT. Different from other PETL
methods, Convpass benefits from the hard-coded inductive bias of convolutional
layers and thus is more suitable for visual tasks, especially in the low-data
regime. Experimental results on VTAB-1K benchmark and few-shot learning
datasets show that Convpass outperforms current language-oriented adaptation
modules, demonstrating the necessity to tailor vision-oriented adaptation
modules for adapting vision models.

The complex nature of artificial neural networks raises concerns on their
reliability, trustworthiness, and fairness in real-world scenarios. The Shapley
value -- a solution concept from game theory -- is one of the most popular
explanation methods for machine learning models. More traditionally, from the
perspective of statistical learning, feature importance is defined in terms of
conditional independence. So far, these two approaches to interpretability and
feature importance have been considered separate and distinct. In this work, we
show that Shapley-based explanation methods and conditional independence
testing are closely related. We introduce the $\textbf{SHAP}$ley
$\textbf{L}$ocal $\textbf{I}$ndependence $\textbf{T}$est ($\textbf{SHAPLIT}$),
a novel testing procedure inspired by the Conditional Randomization Test (CRT)
for a specific notion of local (i.e., on a sample) conditional independence.
With it, we prove that for binary classification problems, each marginal
contribution in the Shapley value is an upper bound to the $p$-value of this
conditional independence test. Furthermore, we show that the Shapley value
itself provides an upper bound to the $p$-value of a global SHAPLIT null
hypothesis. As a result, we grant the Shapley value with a precise statistical
sense of importance with false positive rate control.

Advances in deep learning have resulted in steady progress in computer vision
with improved accuracy on tasks such as object detection and semantic
segmentation. Nevertheless, deep neural networks are vulnerable to adversarial
attacks, thus presenting a challenge in reliable deployment. Two of the
prominent tasks in 3D scene-understanding for robotics and advanced drive
assistance systems are monocular depth and pose estimation, often learned
together in an unsupervised manner. While studies evaluating the impact of
adversarial attacks on monocular depth estimation exist, a systematic
demonstration and analysis of adversarial perturbations against pose estimation
are lacking. We show how additive imperceptible perturbations can not only
change predictions to increase the trajectory drift but also catastrophically
alter its geometry. We also study the relation between adversarial
perturbations targeting monocular depth and pose estimation networks, as well
as the transferability of perturbations to other networks with different
architectures and losses. Our experiments show how the generated perturbations
lead to notable errors in relative rotation and translation predictions and
elucidate vulnerabilities of the networks.

Accurate and efficient detection of ovarian cancer at early stages is
critical to ensure proper treatments for patients. Among the first-line
modalities investigated in studies of early diagnosis are features distilled
from protein mass spectra. This method, however, considers only a specific
subset of spectral responses and ignores the interplay among protein expression
levels, which can also contain diagnostic information. We propose a new
modality that automatically searches protein mass spectra for discriminatory
features by considering the self-similar nature of the spectra. Self-similarity
is assessed by taking a wavelet decomposition of protein mass spectra and
estimating the rate of level-wise decay in the energies of the resulting
wavelet coefficients. Level-wise energies are estimated in a robust manner
using distance variance, and rates are estimated locally via a rolling window
approach. This results in a collection of rates that can be used to
characterize the interplay among proteins, which can be indicative of cancer
presence. Discriminatory descriptors are then selected from these evolutionary
rates and used as classifying features. The proposed wavelet-based features are
used in conjunction with features proposed in the existing literature for early
stage diagnosis of ovarian cancer using two datasets published by the American
National Cancer Institute. Including the wavelet-based features from the new
modality results in improvements in diagnostic performance for early-stage
ovarian cancer detection. This demonstrates the ability of the proposed
modality to characterize new ovarian cancer diagnostic information.

Multi-modal fusion approaches aim to integrate information from different
data sources. Unlike natural datasets, such as in audio-visual applications,
where samples consist of "paired" modalities, data in healthcare is often
collected asynchronously. Hence, requiring the presence of all modalities for a
given sample is not realistic for clinical tasks and significantly limits the
size of the dataset during training. In this paper, we propose MedFuse, a
conceptually simple yet promising LSTM-based fusion module that can accommodate
uni-modal as well as multi-modal input. We evaluate the fusion method and
introduce new benchmark results for in-hospital mortality prediction and
phenotype classification, using clinical time-series data in the MIMIC-IV
dataset and corresponding chest X-ray images in MIMIC-CXR. Compared to more
complex multi-modal fusion strategies, MedFuse provides a performance
improvement by a large margin on the fully paired test set. It also remains
robust across the partially paired test set containing samples with missing
chest X-ray images. We release our code for reproducibility and to enable the
evaluation of competing models in the future.

We formulate and test a technique to use Emergent Communication (EC) with a
pretrained multilingual model to improve on modern Unsupervised NMT systems,
especially for low-resource languages. It has been argued that the currently
dominant paradigm in NLP of pretraining on text-only corpora will not yield
robust natural language understanding systems, and the need for grounded,
goal-oriented, and interactive language learning has been highlighted. In our
approach, we embed a modern multilingual model (mBART, Liu et. al. 2020) into
an EC image-reference game, in which the model is incentivized to use
multilingual generations to accomplish a vision-grounded task, with the
hypothesis that this will align multiple languages to a shared task space. We
present two variants of EC Fine-Tuning (Steinert-Threlkeld et. al. 2022), one
of which outperforms a backtranslation-based baseline in 6/8 translation
settings, and proves especially beneficial for the very low-resource languages
of Nepali and Sinhala.

Accurate three-dimensional perception is a fundamental task in several
computer vision applications. Recently, commercial RGB-depth (RGB-D) cameras
have been widely adopted as single-view depth-sensing devices owing to their
efficient depth-sensing abilities. However, the depth quality of most RGB-D
sensors remains insufficient owing to the inherent noise from a single-view
environment. Recently, several studies have focused on the single-view depth
enhancement of RGB-D cameras. Recent research has proposed deep-learning-based
approaches that typically train networks using high-quality supervised depth
datasets, which indicates that the quality of the ground-truth (GT) depth
dataset is a top-most important factor for accurate system; however, such
high-quality GT datasets are difficult to obtain. In this study, we developed a
novel method for high-quality GT depth generation based on an RGB-D stream
dataset. First, we defined consecutive depth frames in a local spatial region
as a local frame set. Then, the depth frames were aligned to a certain frame in
the local frame set using an unsupervised point cloud registration scheme. The
registration parameters were trained based on an overfit-training scheme, which
was primarily used to construct a single GT depth image for each frame set. The
final GT depth dataset was constructed using several local frame sets, and each
local frame set was trained independently. The primary advantage of this study
is that a high-quality GT depth dataset can be constructed under various
scanning environments using only the RGB-D stream dataset. Moreover, our
proposed method can be used as a new benchmark GT dataset for accurate
performance evaluations. We evaluated our GT dataset on previously benchmarked
GT depth datasets and demonstrated that our method is superior to
state-of-the-art depth enhancement frameworks.

This response to the RFI considers the potential for biometrics to help or
harm disabled people2. Biometrics are already integrated into many aspects of
daily life, from airport travel to mobile phone use. Yet many of these systems
are not accessible to people who experience different kinds of disability
exclusion . Different personal characteristics may impact any or all of the
physical (DNA, fingerprints, face or retina) and behavioral (gesture, gait,
voice) characteristics listed in the RFI as examples of biometric signals.

Language models are defined over a finite set of inputs, which creates a
vocabulary bottleneck when we attempt to scale the number of supported
languages. Tackling this bottleneck results in a trade-off between what can be
represented in the embedding matrix and computational issues in the output
layer. This paper introduces PIXEL, the Pixel-based Encoder of Language, which
suffers from neither of these issues. PIXEL is a pretrained language model that
renders text as images, making it possible to transfer representations across
languages based on orthographic similarity or the co-activation of pixels.
PIXEL is trained to reconstruct the pixels of masked patches, instead of
predicting a distribution over tokens. We pretrain the 86M parameter PIXEL
model on the same English data as BERT and evaluate on syntactic and semantic
tasks in typologically diverse languages, including various non-Latin scripts.
We find that PIXEL substantially outperforms BERT on syntactic and semantic
processing tasks on scripts that are not found in the pretraining data, but
PIXEL is slightly weaker than BERT when working with Latin scripts.
Furthermore, we find that PIXEL is more robust to noisy text inputs than BERT,
further confirming the benefits of modelling language with pixels.

In this paper, we mainly focus on the problem of how to learn additional
feature representations for few-shot image classification through pretext tasks
(e.g., rotation or color permutation and so on). This additional knowledge
generated by pretext tasks can further improve the performance of few-shot
learning (FSL) as it differs from human-annotated supervision (i.e., class
labels of FSL tasks). To solve this problem, we present a plug-in Hierarchical
Tree Structure-aware (HTS) method, which not only learns the relationship of
FSL and pretext tasks, but more importantly, can adaptively select and
aggregate feature representations generated by pretext tasks to maximize the
performance of FSL tasks. A hierarchical tree constructing component and a
gated selection aggregating component is introduced to construct the tree
structure and find richer transferable knowledge that can rapidly adapt to
novel classes with a few labeled images. Extensive experiments show that our
HTS can significantly enhance multiple few-shot methods to achieve new
state-of-the-art performance on four benchmark datasets. The code is available
at: https://github.com/remiMZ/HTS-ECCV22.

We present ObjectBox, a novel single-stage anchor-free and highly
generalizable object detection approach. As opposed to both existing
anchor-based and anchor-free detectors, which are more biased toward specific
object scales in their label assignments, we use only object center locations
as positive samples and treat all objects equally in different feature levels
regardless of the objects' sizes or shapes. Specifically, our label assignment
strategy considers the object center locations as shape- and size-agnostic
anchors in an anchor-free fashion, and allows learning to occur at all scales
for every object. To support this, we define new regression targets as the
distances from two corners of the center cell location to the four sides of the
bounding box. Moreover, to handle scale-variant objects, we propose a tailored
IoU loss to deal with boxes with different sizes. As a result, our proposed
object detector does not need any dataset-dependent hyperparameters to be tuned
across datasets. We evaluate our method on MS-COCO 2017 and PASCAL VOC 2012
datasets, and compare our results to state-of-the-art methods. We observe that
ObjectBox performs favorably in comparison to prior works. Furthermore, we
perform rigorous ablation experiments to evaluate different components of our
method. Our code is available at: https://github.com/MohsenZand/ObjectBox.

Existing approaches for generating multitrack music with transformer models
have been limited in terms of the number of instruments, the length of the
music segments and slow inference. This is partly due to the memory
requirements of the lengthy input sequences necessitated by existing
representations. In this work, we propose a new multitrack music representation
that allows a diverse set of instruments while keeping a short sequence length.
Our proposed Multitrack Music Transformer (MMT) achieves comparable performance
with state-of-the-art systems, landing in between two recently proposed models
in a subjective listening test, while achieving substantial speedups and memory
reductions over both, making the method attractive for real time improvisation
or near real time creative applications. Further, we propose a new measure for
analyzing musical self-attention and show that the trained model attends more
to notes that form a consonant interval with the current note and to notes that
are 4N beats away from the current step.

Data imbalance between common and rare diseases during model training often
causes intelligent diagnosis systems to have biased predictions towards common
diseases. The state-of-the-art approaches apply a two-stage learning framework
to alleviate the class-imbalance issue, where the first stage focuses on
training of a general feature extractor and the second stage focuses on
fine-tuning the classifier head for class rebalancing. However, existing
two-stage approaches do not consider the fine-grained property between
different diseases, often causing the first stage less effective for medical
image classification than for natural image classification tasks. In this
study, we propose embedding metric learning into the first stage of the
two-stage framework specially to help the feature extractor learn to extract
more discriminative feature representations. Extensive experiments mainly on
three medical image datasets show that the proposed approach consistently
outperforms existing onestage and two-stage approaches, suggesting that metric
learning can be used as an effective plug-in component in the two-stage
framework for fine-grained class-imbalanced image classification tasks.

Context-aware STR methods typically use internal autoregressive (AR) language
models (LM). Inherent limitations of AR models motivated two-stage methods
which employ an external LM. The conditional independence of the external LM on
the input image may cause it to erroneously rectify correct predictions,
leading to significant inefficiencies. Our method, PARSeq, learns an ensemble
of internal AR LMs with shared weights using Permutation Language Modeling. It
unifies context-free non-AR and context-aware AR inference, and iterative
refinement using bidirectional context. Using synthetic training data, PARSeq
achieves state-of-the-art (SOTA) results in STR benchmarks (91.9% accuracy) and
more challenging datasets. It establishes new SOTA results (96.0% accuracy)
when trained on real data. PARSeq is optimal on accuracy vs parameter count,
FLOPS, and latency because of its simple, unified structure and parallel token
processing. Due to its extensive use of attention, it is robust on
arbitrarily-oriented text which is common in real-world images. Code,
pretrained weights, and data are available at: https://github.com/baudm/parseq.

Asset allocation is an investment strategy that aims to balance risk and
reward by constantly redistributing the portfolio's assets according to certain
goals, risk tolerance, and investment horizon. Unfortunately, there is no
simple formula that can find the right allocation for every individual. As a
result, investors may use different asset allocations' strategy to try to
fulfil their financial objectives. In this work, we conduct an extensive
benchmark study to determine the efficacy and reliability of a number of
optimization techniques. In particular, we focus on traditional approaches
based on Modern Portfolio Theory, and on machine-learning approaches based on
deep reinforcement learning. We assess the model's performance under different
market tendency, i.e., both bullish and bearish markets. For reproducibility,
we provide the code implementation code in this repository.

Popular models such as Transformers and LSTMs use tokens as its unit of
information. That is, each token is encoded into a vector representation, and
those vectors are used directly in a computation. However, humans frequently
consider spans of tokens (i.e., phrases) instead of their constituent tokens.
In this paper we introduce Treeformer, an architecture inspired by the CKY
algorithm and Transformer which learns a composition operator and pooling
function in order to construct hierarchical encodings for phrases and
sentences. Our extensive experiments demonstrate the benefits of incorporating
a hierarchical structure into the Transformer, and show significant
improvements compared to a baseline Transformer in machine translation,
abstractive summarization, and various natural language understanding tasks.

Demystifying the delay propagation mechanisms among multiple airports is
fundamental to precise and interpretable delay prediction, which is crucial
during decision-making for all aviation industry stakeholders. The principal
challenge lies in effectively leveraging the spatiotemporal dependencies and
exogenous factors related to the delay propagation. However, previous works
only consider limited spatiotemporal patterns with few factors. To promote more
comprehensive propagation modeling for delay prediction, we propose
SpatioTemporal Propagation Network (STPN), a space-time separable graph
convolutional network, which is novel in spatiotemporal dependency capturing.
From the aspect of spatial relation modeling, we propose a multi-graph
convolution model considering both geographic proximity and airline schedule.
From the aspect of temporal dependency capturing, we propose a multi-head
self-attentional mechanism that can be learned end-to-end and explicitly reason
multiple kinds of temporal dependency of delay time series. We show that the
joint spatial and temporal learning models yield a sum of the Kronecker
product, which factors the spatiotemporal dependence into the sum of several
spatial and temporal adjacency matrices. By this means, STPN allows cross-talk
of spatial and temporal factors for modeling delay propagation. Furthermore, a
squeeze and excitation module is added to each layer of STPN to boost
meaningful spatiotemporal features. To this end, we apply STPN to multi-step
ahead arrival and departure delay prediction in large-scale airport networks.
To validate the effectiveness of our model, we experiment with two real-world
delay datasets, including U.S and China flight delays; and we show that STPN
outperforms state-of-the-art methods. In addition, counterfactuals produced by
STPN show that it learns explainable delay propagation patterns.

This is the Proceedings of the ICML Expressive Vocalization (ExVo)
Competition. The ExVo competition focuses on understanding and generating vocal
bursts: laughs, gasps, cries, and other non-verbal vocalizations that are
central to emotional expression and communication. ExVo 2022, included three
competition tracks using a large-scale dataset of 59,201 vocalizations from
1,702 speakers. The first, ExVo-MultiTask, requires participants to train a
multi-task model to recognize expressed emotions and demographic traits from
vocal bursts. The second, ExVo-Generate, requires participants to train a
generative model that produces vocal bursts conveying ten different emotions.
The third, ExVo-FewShot, requires participants to leverage few-shot learning
incorporating speaker identity to train a model for the recognition of 10
emotions conveyed by vocal bursts.

We present FITE, a First-Implicit-Then-Explicit framework for modeling human
avatars in clothing. Our framework first learns implicit surface templates
representing the coarse clothing topology, and then employs the templates to
guide the generation of point sets which further capture pose-dependent
clothing deformations such as wrinkles. Our pipeline incorporates the merits of
both implicit and explicit representations, namely, the ability to handle
varying topology and the ability to efficiently capture fine details. We also
propose diffused skinning to facilitate template training especially for loose
clothing, and projection-based pose-encoding to extract pose information from
mesh templates without predefined UV map or connectivity. Our code is publicly
available at https://github.com/jsnln/fite.

Low-order functional ANOVA (fANOVA) models have been rediscovered in the
machine learning (ML) community under the guise of inherently interpretable
machine learning. Explainable Boosting Machines or EBM (Lou et al. 2013) and
GAMI-Net (Yang et al. 2021) are two recently proposed ML algorithms for fitting
functional main effects and second-order interactions. We propose a new
algorithm, called GAMI-Tree, that is similar to EBM, but has a number of
features that lead to better performance. It uses model-based trees as base
learners and incorporates a new interaction filtering method that is better at
capturing the underlying interactions. In addition, our iterative training
method converges to a model with better predictive performance, and the
embedded purification ensures that interactions are hierarchically orthogonal
to main effects. The algorithm does not need extensive tuning, and our
implementation is fast and efficient. We use simulated and real datasets to
compare the performance and interpretability of GAMI-Tree with EBM and
GAMI-Net.

Clustering is an unsupervised machine learning methodology where unlabeled
elements/objects are grouped together aiming to the construction of
well-established clusters that their elements are classified according to their
similarity. The goal of this process is to provide a useful aid to the
researcher that will help her/him to identify patterns among the data. Dealing
with large databases, such patterns may not be easily detectable without the
contribution of a clustering algorithm. This article provides a deep
description of the most widely used clustering methodologies accompanied by
useful presentations concerning suitable parameter selection and
initializations. Simultaneously, this article not only represents a review
highlighting the major elements of examined clustering techniques but
emphasizes the comparison of these algorithms' clustering efficiency based on 3
datasets, revealing their existing weaknesses and capabilities through accuracy
and complexity, during the confrontation of discrete and continuous
observations. The produced results help us extract valuable conclusions about
the appropriateness of the examined clustering techniques in accordance with
the dataset's size.

Despite a growing recognition of the importance of insurgent group structure
on conflict outcomes, there is very little empirical research thereon. Though
this problem is rooted in the inaccessibility of data on militant group
structure, insurgents frequently publish large volumes of image data on the
internet. In this paper, I develop a new methodology that leverages this
abundant but underutilized source of data by automating the creation of a
social network graph based on co-appearance in photographs using deep learning.
Using a trove of 19,115 obituary images published online by the PKK, a Kurdish
militant group in Turkey, I demonstrate that an individual's centrality in the
resulting co-appearance network is closely correlated with their rank in the
insurgent group.

Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of
graph representations such as node ranking, labeling, and graph embedding.
However, while data privacy is one of the most important recent concerns,
existing PPR algorithms are not designed to protect user privacy. PPR is highly
sensitive to the input graph edges: the difference of only one edge may cause a
big change in the PPR vector, potentially leaking private user data.
  In this work, we propose an algorithm which outputs an approximate PPR and
has provably bounded sensitivity to input edges. In addition, we prove that our
algorithm achieves similar accuracy to non-private algorithms when the input
graph has large degrees. Our sensitivity-bounded PPR directly implies private
algorithms for several tools of graph learning, such as, differentially private
(DP) PPR ranking, DP node classification, and DP node embedding. To complement
our theoretical analysis, we also empirically verify the practical performances
of our algorithms.

Hyperparameter optimization (HPO) and neural architecture search (NAS) are
methods of choice to obtain the best-in-class machine learning models, but in
practice they can be costly to run. When models are trained on large datasets,
tuning them with HPO or NAS rapidly becomes prohibitively expensive for
practitioners, even when efficient multi-fidelity methods are employed. We
propose an approach to tackle the challenge of tuning machine learning models
trained on large datasets with limited computational resources. Our approach,
named PASHA, is able to dynamically allocate maximum resources for the tuning
procedure depending on the need. The experimental comparison shows that PASHA
identifies well-performing hyperparameter configurations and architectures
while consuming significantly fewer computational resources than solutions like
ASHA.

A critical challenge of federated learning is data heterogeneity and
imbalance across clients, which leads to inconsistency between local networks
and unstable convergence of global models. To alleviate the limitations, we
propose a novel architectural regularization technique that constructs multiple
auxiliary branches in each local model by grafting local and global subnetworks
at several different levels and that learns the representations of the main
pathway in the local model congruent to the auxiliary hybrid pathways via
online knowledge distillation. The proposed technique is effective to robustify
the global model even in the non-iid setting and is applicable to various
federated learning frameworks conveniently without incurring extra
communication costs. We perform comprehensive empirical studies and demonstrate
remarkable performance gains in terms of accuracy and efficiency compared to
existing methods. The source code is available at our project page.

The value of uncertainty quantification on predictions for trained neural
networks (NNs) on quantum chemical reference data is quantitatively explored.
For this, the architecture of the PhysNet NN was suitably modified and the
resulting model was evaluated with different metrics to quantify calibration,
quality of predictions, and whether prediction error and the predicted
uncertainty can be correlated. The results from training on the QM9 database
and evaluating data from the test set within and outside the distribution
indicate that error and uncertainty are not linearly related. The results
clarify that noise and redundancy complicate property prediction for molecules
even in cases for which changes - e.g. double bond migration in two otherwise
identical molecules - are small. The model was then applied to a real database
of tautomerization reactions. Analysis of the distance between members in
feature space combined with other parameters shows that redundant information
in the training dataset can lead to large variances and small errors whereas
the presence of similar but unspecific information returns large errors but
small variances. This was, e.g., observed for nitro-containing aliphatic chains
for which predictions were difficult although the training set contained
several examples for nitro groups bound to aromatic molecules. This underlines
the importance of the composition of the training data and provides chemical
insight into how this affects the prediction capabilities of a ML model.
Finally, the approach put forward can be used for information-based improvement
of chemical databases for target applications through active learning

Language models learn and represent language differently than humans; they
learn the form and not the meaning. Thus, to assess the success of language
model explainability, we need to consider the impact of its divergence from a
user's mental model of language. In this position paper, we argue that in order
to avoid harmful rationalization and achieve truthful understanding of language
models, explanation processes must satisfy three main conditions: (1)
explanations have to truthfully represent the model behavior, i.e., have a high
fidelity; (2) explanations must be complete, as missing information distorts
the truth; and (3) explanations have to take the user's mental model into
account, progressively verifying a person's knowledge and adapting their
understanding. We introduce a decision tree model to showcase potential reasons
why current explanations fail to reach their objectives. We further emphasize
the need for human-centered design to explain the model from multiple
perspectives, progressively adapting explanations to changing user
expectations.

The manifold hypothesis (real world data concentrates near low-dimensional
manifolds) is suggested as the principle behind the effectiveness of machine
learning algorithms in very high dimensional problems that are common in
domains such as vision and speech. Multiple methods have been proposed to
explicitly incorporate the manifold hypothesis as a prior in modern Deep Neural
Networks (DNNs), with varying success. In this paper, we propose a new method,
Distance Learner, to incorporate this prior for DNN-based classifiers. Distance
Learner is trained to predict the distance of a point from the underlying
manifold of each class, rather than the class label. For classification,
Distance Learner then chooses the class corresponding to the closest predicted
class manifold. Distance Learner can also identify points as being out of
distribution (belonging to neither class), if the distance to the closest
manifold is higher than a threshold. We evaluate our method on multiple
synthetic datasets and show that Distance Learner learns much more meaningful
classification boundaries compared to a standard classifier. We also evaluate
our method on the task of adversarial robustness, and find that it not only
outperforms standard classifier by a large margin, but also performs at par
with classifiers trained via state-of-the-art adversarial training.

We leverage pre-trained language models to solve the task of complex NER for
two low-resource languages: Chinese and Spanish. We use the technique of Whole
Word Masking(WWM) to boost the performance of masked language modeling
objective on large and unsupervised corpora. We experiment with multiple neural
network architectures, incorporating CRF, BiLSTMs, and Linear Classifiers on
top of a fine-tuned BERT layer. All our models outperform the baseline by a
significant margin and our best performing model obtains a competitive position
on the evaluation leaderboard for the blind test set.

Transformer-based models show their effectiveness across multiple domains and
tasks. The self-attention allows to combine information from all sequence
elements into context-aware representations. However, global and local
information has to be stored mostly in the same element-wise representations.
Moreover, the length of an input sequence is limited by quadratic computational
complexity of self-attention.
  In this work, we propose and study a memory-augmented segment-level recurrent
Transformer (Recurrent Memory Transformer). Memory allows to store and process
local and global information as well as to pass information between segments of
the long sequence with the help of recurrence. We implement a memory mechanism
with no changes to Transformer model by adding special memory tokens to the
input or output sequence. Then Transformer is trained to control both memory
operations and sequence representations processing.
  Results of experiments show that our model performs on par with the
Transformer-XL on language modeling for smaller memory sizes and outperforms it
for tasks that require longer sequence processing. We show that adding memory
tokens to Tr-XL is able to improve it performance. This makes Recurrent Memory
Transformer a promising architecture for applications that require learning of
long-term dependencies and general purpose in memory processing, such as
algorithmic tasks and reasoning.

High-quality calibrated uncertainty estimates are crucial for numerous
real-world applications, especially for deep learning-based deployed ML
systems. While Bayesian deep learning techniques allow uncertainty estimation,
training them with large-scale datasets is an expensive process that does not
always yield models competitive with non-Bayesian counterparts. Moreover, many
of the high-performing deep learning models that are already trained and
deployed are non-Bayesian in nature and do not provide uncertainty estimates.
To address these issues, we propose BayesCap that learns a Bayesian identity
mapping for the frozen model, allowing uncertainty estimation. BayesCap is a
memory-efficient method that can be trained on a small fraction of the original
dataset, enhancing pretrained non-Bayesian computer vision models by providing
calibrated uncertainty estimates for the predictions without (i) hampering the
performance of the model and (ii) the need for expensive retraining the model
from scratch. The proposed method is agnostic to various architectures and
tasks. We show the efficacy of our method on a wide variety of tasks with a
diverse set of architectures, including image super-resolution, deblurring,
inpainting, and crucial application such as medical image translation.
Moreover, we apply the derived uncertainty estimates to detect
out-of-distribution samples in critical scenarios like depth estimation in
autonomous driving. Code is available at
https://github.com/ExplainableML/BayesCap.

Automatic Speech Recognition (ASR) is a key element in new services that
helps users to interact with an automated system. Deep learning methods have
made it possible to deploy systems with word error rates below 5% for ASR of
English. However, the use of these methods is only available for languages with
hundreds or thousands of hours of audio and their corresponding transcriptions.
For the so-called low-resource languages to speed up the availability of
resources that can improve the performance of their ASR systems, methods of
creating new resources on the basis of existing ones are being investigated. In
this paper we describe our data augmentation approach to improve the results of
ASR models for low-resource and agglutinative languages. We carry out
experiments developing an ASR for Quechua using the wav2letter++ model. We
reduced WER by 8.73% through our approach to the base model. The resulting ASR
model obtained 22.75% WER and was trained with 99 hours of original resources
and 99 hours of synthetic data obtained with a combination of text augmentation
and synthetic speech generati

Self-supervised learning (SSL) is seen as a very promising approach with high
performance for several speech downstream tasks. Since the parameters of SSL
models are generally so large that training and inference require a lot of
memory and computational cost, it is desirable to produce compact SSL models
without a significant performance degradation by applying compression methods
such as knowledge distillation (KD). Although the KD approach is able to shrink
the depth and/or width of SSL model structures, there has been little research
on how varying the depth and width impacts the internal representation of the
small-footprint model. This paper provides an empirical study that addresses
the question. We investigate the performance on SUPERB while varying the
structure and KD methods so as to keep the number of parameters constant; this
allows us to analyze the contribution of the representation introduced by
varying the model architecture. Experiments demonstrate that a certain depth is
essential for solving content-oriented tasks (e.g. automatic speech
recognition) accurately, whereas a certain width is necessary for achieving
high performance on several speaker-oriented tasks (e.g. speaker
identification). Based on these observations, we identify, for SUPERB, a more
compressed model with better performance than previous studies.

Nonunion is one of the challenges faced by orthopedics clinics for the
technical difficulties and high costs in photographing interosseous
capillaries. Segmenting vessels and filling capillaries are critical in
understanding the obstacles encountered in capillary growth. However, existing
datasets for blood vessel segmentation mainly focus on the large blood vessels
of the body, and the lack of labeled capillary image datasets greatly limits
the methodological development and applications of vessel segmentation and
capillary filling. Here, we present a benchmark dataset, named IFCIS-155,
consisting of 155 2D capillary images with segmentation boundaries and vessel
fillings annotated by biomedical experts, and 19 large-scale, high-resolution
3D capillary images. To obtain better images of interosseous capillaries, we
leverage state-of-the-art immunofluorescence imaging techniques to highlight
the rich vascular morphology of interosseous capillaries. We conduct
comprehensive experiments to verify the effectiveness of the dataset and the
benchmarking deep learning models (\eg UNet/UNet++ and the modified
UNet/UNet++). Our work offers a benchmark dataset for training deep learning
models for capillary image segmentation and provides a potential tool for
future capillary research. The IFCIS-155 dataset and code are all publicly
available at \url{https://github.com/ncclabsustech/IFCIS-55}.

A Quantum Self-Attention Network (QSAN) that can be achieved on near-term
quantum devices is investigated. First, the theoretical basis of QSAN, a
linearized and reversible Quantum Self-Attention Mechanism (QSAM) including
Quantum Logic Similarity (QLS) and Quantum Bit Self-Attention Score Matrix
(QBSASM), is explored to solve the storage problem of Self-Attention Mechanism
(SAM) due to quadratic complexity. More importantly, QLS uses logical
operations instead of inner product operations to enable QSAN to be fully
deployed on quantum computers and meanwhile saves quantum bits by avoiding
numerical operations, and QBSASM is a by-product generated with the evolution
of QSAN, reflecting the output attention distribution in the form of a density
matrix. Then, the framework and the quantum circuit of QSAN are designed with 9
execution steps and 5 special functional sub-modules, which can acquire QBSASM
effectively in the intermediate process, as well as compressing the number of
measurements. In addition, a quantum coordinate prototype is proposed to
describe the mathematical connection between the control and output bits in
order to realize programming and model optimization conveniently. Finally, a
miniaturized experiment is implemented and it demonstrates that QSAN can be
trained faster in the presence of quantum natural gradient descent method, as
well as produce quantum characteristic attention distribution QBSASM. QSAN has
great potential to be embedded in classical or quantum machine learning
frameworks to lay the foundation for quantum enhanced Natural Language
Processing (NLP).

This paper introduces a new synthesis-based defense algorithm for
counteracting with a varieties of adversarial attacks developed for challenging
the performance of the cutting-edge speech-to-text transcription systems. Our
algorithm implements a Sobolev-based GAN and proposes a novel regularizer for
effectively controlling over the functionality of the entire generative model,
particularly the discriminator network during training. Our achieved results
upon carrying out numerous experiments on the victim DeepSpeech, Kaldi, and
Lingvo speech transcription systems corroborate the remarkable performance of
our defense approach against a comprehensive range of targeted and non-targeted
adversarial attacks.

Low-precision arithmetic has had a transformative effect on the training of
neural networks, reducing computation, memory and energy requirements. However,
despite its promise, low-precision arithmetic has received little attention for
Gaussian processes (GPs), largely because GPs require sophisticated linear
algebra routines that are unstable in low-precision. We study the different
failure modes that can occur when training GPs in half precision. To circumvent
these failure modes, we propose a multi-faceted approach involving conjugate
gradients with re-orthogonalization, mixed precision, and preconditioning. Our
approach significantly improves the numerical stability and practical
performance of conjugate gradients in low-precision over a wide range of
settings, enabling GPs to train on $1.8$ million data points in $10$ hours on a
single GPU, without any sparse approximations.

This paper introduces a compound vision system that enables robots to
localize people up to 15m away using a cheap camera. And, it proposes a robust
navigation stack that combines Deep Reinforcement Learning (DRL) and a
probabilistic localization method. To test the efficacy of these systems, we
prototyped a low-cost mobile robot that we call Covy. Covy can be used for
applications such as promoting social distancing during pandemics or estimating
the density of a crowd. We evaluated Covy's performance through extensive sets
of experiments both in simulated and realistic environments. Our results show
that Covy's compound vision algorithm doubles the range of the used depth
camera, and its hybrid navigation stack is more robust than a pure DRL-based
one.

In recent years, deep dictionary learning (DDL)has attracted a great amount
of attention due to its effectiveness for representation learning and visual
recognition.~However, most existing methods focus on unsupervised deep
dictionary learning, failing to further explore the category information.~To
make full use of the category information of different samples, we propose a
novel deep dictionary learning model with an intra-class constraint (DDLIC) for
visual classification. Specifically, we design the intra-class compactness
constraint on the intermediate representation at different levels to encourage
the intra-class representations to be closer to each other, and eventually the
learned representation becomes more discriminative.~Unlike the traditional DDL
methods, during the classification stage, our DDLIC performs a layer-wise
greedy optimization in a similar way to the training stage. Experimental
results on four image datasets show that our method is superior to the

This study discusses the effect of semi-supervised learning in combination
with pretrained language models for data-to-text generation. It is not known
whether semi-supervised learning is still helpful when a large-scale language
model is also supplemented. This study aims to answer this question by
comparing a data-to-text system only supplemented with a language model, to two
data-to-text systems that are additionally enriched by a data augmentation or a
pseudo-labeling semi-supervised learning approach.
  Results show that semi-supervised learning results in higher scores on
diversity metrics. In terms of output quality, extending the training set of a
data-to-text system with a language model using the pseudo-labeling approach
did increase text quality scores, but the data augmentation approach yielded
similar scores to the system without training set extension. These results
indicate that semi-supervised learning approaches can bolster output quality
and diversity, even when a language model is also present.

Business analytics and machine learning have become essential success factors
for various industries - with the downside of cost-intensive gathering and
labeling of data. Few-shot learning addresses this challenge and reduces data
gathering and labeling costs by learning novel classes with very few labeled
data. In this paper, we design a human-in-the-loop (HITL) system for few-shot
learning and analyze an extensive range of mechanisms that can be used to
acquire human expert knowledge for instances that have an uncertain prediction
outcome. We show that the acquisition of human expert knowledge significantly
accelerates the few-shot model performance given a negligible labeling effort.
We validate our findings in various experiments on a benchmark dataset in
computer vision and real-world datasets. We further demonstrate the
cost-effectiveness of HITL systems for few-shot learning. Overall, our work
aims at supporting researchers and practitioners in effectively adapting
machine learning models to novel classes at reduced costs.

Molecular representation learning has attracted much attention recently. A
molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds,
and can also be represented by a 3D conformation with 3-dimensional coordinates
of all atoms. We note that most previous work handles 2D and 3D information
separately, while jointly leveraging these two sources may foster a more
informative representation. In this work, we explore this appealing idea and
propose a new representation learning method based on a unified 2D and 3D
pre-training. Atom coordinates and interatomic distances are encoded and then
fused with atomic representations through graph neural networks. The model is
pre-trained on three tasks: reconstruction of masked atoms and coordinates, 3D
conformation generation conditioned on 2D graph, and 2D graph generation
conditioned on 3D conformation. We evaluate our method on 11 downstream
molecular property prediction tasks: 7 with 2D information only and 4 with both
2D and 3D information. Our method achieves state-of-the-art results on 10
tasks, and the average improvement on 2D-only tasks is 8.3%. Our method also
achieves significant improvement on two 3D conformation generation tasks.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that
results in a variety of motor dysfunction symptoms, including tremors,
bradykinesia, rigidity and postural instability. The diagnosis of PD mainly
relies on clinical experience rather than a definite medical test, and the
diagnostic accuracy is only about 73-84% since it is challenged by the
subjective opinions or experiences of different medical experts. Therefore, an
efficient and interpretable automatic PD diagnosis system is valuable for
supporting clinicians with more robust diagnostic decision-making. To this end,
we propose to classify Parkinson's tremor since it is one of the most
predominant symptoms of PD with strong generalizability. Different from other
computer-aided time and resource-consuming Parkinson's Tremor (PT)
classification systems that rely on wearable sensors, we propose SPAPNet, which
only requires consumer-grade non-intrusive video recording of camera-facing
human movements as input to provide undiagnosed patients with low-cost PT
classification results as a PD warning sign. For the first time, we propose to
use a novel attention module with a lightweight pyramidal
channel-squeezing-fusion architecture to extract relevant PT information and
filter the noise efficiently. This design aids in improving both classification
performance and system interpretability. Experimental results show that our
system outperforms state-of-the-arts by achieving a balanced accuracy of 90.9%
and an F1-score of 90.6% in classifying PT with the non-PT class.

Object detection using single point supervision has received increasing
attention over the years. However, the performance gap between point supervised
object detection (PSOD) and bounding box supervised detection remains large. In
this paper, we attribute such a large performance gap to the failure of
generating high-quality proposal bags which are crucial for multiple instance
learning (MIL). To address this problem, we introduce a lightweight alternative
to the off-the-shelf proposal (OTSP) method and thereby create the Point-to-Box
Network (P2BNet), which can construct an inter-objects balanced proposal bag by
generating proposals in an anchor-like way. By fully investigating the accurate
position information, P2BNet further constructs an instance-level bag, avoiding
the mixture of multiple objects. Finally, a coarse-to-fine policy in a cascade
fashion is utilized to improve the IoU between proposals and ground-truth (GT).
Benefiting from these strategies, P2BNet is able to produce high-quality
instance-level bags for object detection. P2BNet improves the mean average
precision (AP) by more than 50% relative to the previous best PSOD method on
the MS COCO dataset. It also demonstrates the great potential to bridge the
performance gap between point supervised and bounding-box supervised detectors.
The code will be released at github.com/ucas-vg/P2BNet.

In this paper, we propose DEXTER, an end to end system to extract information
from tables present in medical health documents, such as electronic health
records (EHR) and explanation of benefits (EOB). DEXTER consists of four
sub-system stages: i) table detection ii) table type classification iii) cell
detection; and iv) cell content extraction. We propose a two-stage transfer
learning-based approach using CDeC-Net architecture along with Non-Maximal
suppression for table detection. We design a conventional computer vision-based
approach for table type classification and cell detection using parameterized
kernels based on image size for detecting rows and columns. Finally, we extract
the text from the detected cells using pre-existing OCR engine Tessaract. To
evaluate our system, we manually annotated a sample of the real-world medical
dataset (referred to as Meddata) consisting of wide variations of documents (in
terms of appearance) covering different table structures, such as bordered,
partially bordered, borderless, or coloured tables. We experimentally show that
DEXTER outperforms the commercially available Amazon Textract and Microsoft
Azure Form Recognizer systems on the annotated real-world medical dataset

The idea of using computers to read medical scans was introduced as early as
1966. However, limits to machine learning technology meant progress was slow
initially. The Alexnet breakthrough in 2012 sparked new interest in the topic,
which resulted in the release of 100s of medical AI solutions on the market. In
spite of success for some diseases and modalities, many challenges remain.
Research typically focuses on the development of specific applications or
techniques, clinical evaluation, or meta analysis of clinical studies or
techniques through surveys or challenges. However, limited attention has been
given to the development process of improving real world performance. In this
tutorial, we address the latter and discuss some techniques to conduct the
development process in order to make this as efficient as possible.

This paper investigates Graph Neural Networks (GNNs) application for
self-supervised network intrusion and anomaly detection. GNNs are a deep
learning approach for graph-based data that incorporate graph structures into
learning to generalise graph representations and output embeddings. As network
flows are naturally graph-based, GNNs are a suitable fit for analysing and
learning network behaviour. The majority of current implementations of
GNN-based Network Intrusion Detection Systems (NIDSs) rely heavily on labelled
network traffic which can not only restrict the amount and structure of input
traffic, but also the NIDSs potential to adapt to unseen attacks. To overcome
these restrictions, we present Anomal-E, a GNN approach to intrusion and
anomaly detection that leverages edge features and graph topological structure
in a self-supervised process. This approach is, to the best our knowledge, the
first successful and practical approach to network intrusion detection that
utilises network flows in a self-supervised, edge leveraging GNN. Experimental
results on two modern benchmark NIDS datasets not only clearly display the
improvement of using Anomal-E embeddings rather than raw features, but also the
potential Anomal-E has for detection on wild network traffic.

Most existing few-shot learning (FSL) methods require a large amount of
labeled data in meta-training, which is a major limit. To reduce the
requirement of labels, a semi-supervised meta-training setting has been
proposed for FSL, which includes only a few labeled samples and numbers of
unlabeled samples in base classes. However, existing methods under this setting
require class-aware sample selection from the unlabeled set, which violates the
assumption of unlabeled set. In this paper, we propose a practical
semi-supervised meta-training setting with truly unlabeled data. Under the new
setting, the performance of existing methods drops notably. To better utilize
both the labeled and truly unlabeled data, we propose a simple and effective
meta-training framework, called pseudo-labeling based on meta-learning (PLML).
Firstly, we train a classifier via common semi-supervised learning (SSL) and
use it to obtain the pseudo-labels of unlabeled data. Then we build few-shot
tasks from labeled and pseudo-labeled data and run meta-learning over the
constructed tasks to learn the FSL model. Surprisingly, through extensive
experiments across two FSL datasets, we find that this simple meta-training
framework effectively prevents the performance degradation of FSL under limited
labeled data. Besides, benefiting from meta-training, the proposed method
improves the classifiers learned by two representative SSL algorithms as well.

Different encodings of datapoints in the latent space of latent-vector
generative models may result in more or less effective and disentangled
characterizations of the different explanatory factors of variation behind the
data. Many works have been recently devoted to the explorationof the latent
space of specific models, mostly focused on the study of how features are
disentangled and of how trajectories producing desired alterations of data in
the visible space can be found. In this work we address the more general
problem of comparing the latent spaces of different models, looking for
transformations between them. We confined the investigation to the familiar and
largely investigated case of generative models for the data manifold of human
faces. The surprising, preliminary result reported in this article is that
(provided models have not been taught or explicitly conceived to act
differently) a simple linear mapping is enough to pass from a latent space to
another while preserving most of the information.

Continually learning new classes from a few training examples without
forgetting previous old classes demands a flexible architecture with an
inevitably growing portion of storage, in which new examples and classes can be
incrementally stored and efficiently retrieved. One viable architectural
solution is to tightly couple a stationary deep neural network to a dynamically
evolving explicit memory (EM). As the centerpiece of this architecture, we
propose an EM unit that leverages energy-efficient in-memory compute (IMC)
cores during the course of continual learning operations. We demonstrate for
the first time how the EM unit can physically superpose multiple training
examples, expand to accommodate unseen classes, and perform similarity search
during inference, using operations on an IMC core based on phase-change memory
(PCM). Specifically, the physical superposition of a few encoded training
examples is realized via in-situ progressive crystallization of PCM devices.
The classification accuracy achieved on the IMC core remains within a range of
1.28%--2.5% compared to that of the state-of-the-art full-precision baseline
software model on both the CIFAR-100 and miniImageNet datasets when continually
learning 40 novel classes (from only five examples per class) on top of 60 old
classes.

Automated medical coding is a process of codifying clinical notes to
appropriate diagnosis and procedure codes automatically from the standard
taxonomies such as ICD (International Classification of Diseases) and CPT
(Current Procedure Terminology). The manual coding process involves the
identification of entities from the clinical notes followed by querying a
commercial or non-commercial medical codes Information Retrieval (IR) system
that follows the Centre for Medicare and Medicaid Services (CMS) guidelines. We
propose to automate this manual process by automatically constructing a query
for the IR system using the entities auto-extracted from the clinical notes. We
propose \textbf{GrabQC}, a \textbf{Gra}ph \textbf{b}ased \textbf{Q}uery
\textbf{C}ontextualization method that automatically extracts queries from the
clinical text, contextualizes the queries using a Graph Neural Network (GNN)
model and obtains the ICD Codes using an external IR system. We also propose a
method for labelling the dataset for training the model. We perform experiments
on two datasets of clinical text in three different setups to assert the
effectiveness of our approach. The experimental results show that our proposed
method is better than the compared baselines in all three settings.

Knowledge Graphs (KGs) are becoming increasingly essential infrastructures in
many applications while suffering from incompleteness issues. The KG completion
task (KGC) automatically predicts missing facts based on an incomplete KG.
However, existing methods perform unsatisfactorily in real-world scenarios. On
the one hand, their performance will dramatically degrade along with the
increasing sparsity of KGs. On the other hand, the inference procedure for
prediction is an untrustworthy black box.
  This paper proposes a novel explainable model for sparse KGC, compositing
high-order reasoning into a graph convolutional network, namely HoGRN. It can
not only improve the generalization ability to mitigate the information
insufficiency issue but also provide interpretability while maintaining the
model's effectiveness and efficiency. There are two main components that are
seamlessly integrated for joint optimization. First, the high-order reasoning
component learns high-quality relation representations by capturing endogenous
correlation among relations. This can reflect logical rules to justify a
broader of missing facts. Second, the entity updating component leverages a
weight-free Graph Convolutional Network (GCN) to efficiently model KG
structures with interpretability. Unlike conventional methods, we conduct
entity aggregation and design composition-based attention in the relational
space without additional parameters. The lightweight design makes HoGRN better
suitable for sparse settings. For evaluation, we have conducted extensive
experiments-the results of HoGRN on several sparse KGs present impressive
improvements (9% MRR gain on average). Further ablation and case studies
demonstrate the effectiveness of the main components. Our codes will be
released upon acceptance.

Quasars can be used to measure baryon acoustic oscillations at high redshift,
which are considered as direct tracers of the most distant large-scale
structures in the Universe. It is fundamental to select quasars from
observations before implementing the above research. This work focuses on
creating a catalog of quasar candidates based on photometric data to provide
primary priors for further object classification with spectroscopic data in the
future, such as The Dark Energy Spectroscopic Instrument (DESI) Survey. We
adopt a machine learning algorithm (Random Forest, RF) for quasar
identification. The training set includes $651,073$ positives and $1,227,172$
negatives, in which the photometric information are from DESI Legacy Imaging
Surveys (DESI-LIS) \& Wide-field Infrared Survey Explore (WISE), and the labels
are from a database of spectroscopically confirmed quasars based on Sloan
Digital Sky Survey (SDSS) and the Set of Identifications \& Measurements and
Bibliography for Astronomical Data (SIMBAD). The trained RF model is applied to
point-like sources in DESI-LIS Data Release 9. To quantify the classifier's
performance, we also inject a testing set into the to-be-applied data.
Eventually, we obtained $1,953,932$ Grade-A quasar candidates and $22,486, 884$
Grade-B quasar candidates out of $425,540,269$ sources ($\sim 5.7\%$). The
catalog covers $\sim 99\%$ of quasars in the to-be-applied data by evaluating
the completeness of the classification on the testing set. The statistical
properties of the candidates agree with that given by the method of color-cut
selection. Our catalog can intensely decrease the workload for confirming
quasars with the upcoming DESI data by eliminating enormous non-quasars but
remaining high completeness. All data in this paper is publicly available
online.

We propose a novel approach to multimodal sensor fusion for Ambient Assisted
Living (AAL) which takes advantage of learning using privileged information
(LUPI). We address two major shortcomings of standard multimodal approaches,
limited area coverage and reduced reliability. Our new framework fuses the
concept of modality hallucination with triplet learning to train a model with
different modalities to handle missing sensors at inference time. We evaluate
the proposed model on inertial data from a wearable accelerometer device, using
RGB videos and skeletons as privileged modalities, and show an improvement of
accuracy of an average 6.6% on the UTD-MHAD dataset and an average 5.5% on the
Berkeley MHAD dataset, reaching a new state-of-the-art for inertial-only
classification accuracy on these datasets. We validate our framework through
several ablation studies.

We present an efficient algorithm that, given a discrete random variable $X$
and a number $m$, computes a random variable whose support is of size at most
$m$ and whose Kolmogorov distance from $X$ is minimal, also for the one-sided
Kolmogorov approximation. We present some variants of the algorithm, analyse
their correctness and computational complexity, and present a detailed
empirical evaluation that shows how they performs in practice. The main
application that we examine, which is our motivation for this work, is
estimation of the probability missing deadlines in series-parallel schedules.
Since exact computation of these probabilities is NP-hard, we propose to use
the algorithms described in this paper to obtain an approximation.

We introduce L-hydra (landmarked hyperbolic distance recovery and
approximation), a method for embedding network- or distance-based data into
hyperbolic space, which requires only the distance measurements to a few
'landmark nodes'. This landmark heuristic makes L-hydra applicable to
large-scale graphs and improves upon previously introduced methods. As a
mathematical justification, we show that a point configuration in d-dimensional
hyperbolic space can be perfectly recovered (up to isometry) from distance
measurements to just d+1 landmarks. We also show that L-hydra solves a
two-stage strain-minimization problem, similar to our previous (unlandmarked)
method 'hydra'. Testing on real network data, we show that L-hydra is an order
of magnitude faster than existing hyperbolic embedding methods and scales
linearly in the number of nodes. While the embedding error of L-hydra is higher
than the error of existing methods, we introduce an extension, L-hydra+, which
outperforms existing methods in both runtime and embedding quality.

Few-shot sound event detection is the task of detecting sound events, despite
having only a few labelled examples of the class of interest. This framework is
particularly useful in bioacoustics, where often there is a need to annotate
very long recordings but the expert annotator time is limited. This paper
presents an overview of the second edition of the few-shot bioacoustic sound
event detection task included in the DCASE 2022 challenge. A detailed
description of the task objectives, dataset, and baselines is presented,
together with the main results obtained and characteristics of the submitted
systems. This task received submissions from 15 different teams from which 13
scored higher than the baselines. The highest F-score was of 60% on the
evaluation set, which leads to a huge improvement over last year's edition.
Highly-performing methods made use of prototypical networks, transductive
learning, and addressed the variable length of events from all target classes.
Furthermore, by analysing results on each of the subsets we can identify the
main difficulties that the systems face, and conclude that few-show bioacoustic
sound event detection remains an open challenge.

For decades, manufacturers have attempted to reduce or eliminate the optical
aberrations that appear on the progressive addition lens' surfaces during
manufacturing. Besides every effort made, some of these distortions are
inevitable given how lenses are fabricated, where in fact, astigmatism appears
on the surface and cannot be entirely removed or where non-uniform
magnification becomes inherent to the power change across the lens. Some
presbyopes may refer to certain discomfort when wearing these lenses for the
first time, and a subset of them might never adapt. Developing, prototyping,
testing and purveying those lenses into the market come at a cost, which is
usually reflected in the retail price. This study aims to test the feasibility
of virtual reality for testing customers' satisfaction with these lenses, even
before getting them onto production. VR offers a controlled environment where
different parameters affecting progressive lens comforts, such as distortions,
image displacement or optical blurring, can be analysed separately. In this
study, the focus was set on the distortions and image displacement, not taking
blur into account. Behavioural changes (head and eye movements) were recorded
using the built-in eye tracker. Participants were significantly more displeased
in the presence of highly distorted lens simulations. In addition, a gradient
boosting regressor was fitted to the data, so predictors of discomfort could be
unveiled, and ratings could be predicted without performing additional
measurements.

Speech emotion recognition (SER) on a single language has achieved remarkable
results through deep learning approaches over the last decade. However,
cross-lingual SER remains a challenge in real-world applications due to (i) a
large difference between the source and target domain distributions, (ii) the
availability of few labeled and many unlabeled utterances for the new language.
Taking into account previous aspects, we propose a Semi-Supervised Learning
(SSL) method for cross-lingual emotion recognition when a few labels from the
new language are available. Based on a Convolutional Neural Network (CNN), our
method adapts to a new language by exploiting a pseudo-labeling strategy for
the unlabeled utterances. In particular, the use of a hard and soft
pseudo-labels approach is investigated. We thoroughly evaluate the performance
of the method in a speaker-independent setup on both the source and the new
language and show its robustness across five languages belonging to different
linguistic strains.

Semantic segmentation of point clouds, aiming to assign each point a semantic
category, is critical to 3D scene understanding.Despite of significant advances
in recent years, most of existing methods still suffer from either the
object-level misclassification or the boundary-level ambiguity. In this paper,
we present a robust semantic segmentation network by deeply exploring the
geometry of point clouds, dubbed GeoSegNet. Our GeoSegNet consists of a
multi-geometry based encoder and a boundary-guided decoder. In the encoder, we
develop a new residual geometry module from multi-geometry perspectives to
extract object-level features. In the decoder, we introduce a contrastive
boundary learning module to enhance the geometric representation of boundary
points. Benefiting from the geometric encoder-decoder modeling, our GeoSegNet
can infer the segmentation of objects effectively while making the
intersections (boundaries) of two or more objects clear. Experiments show
obvious improvements of our method over its competitors in terms of the overall
segmentation accuracy and object boundary clearness. Code is available at
https://github.com/Chen-yuiyui/GeoSegNet.

This work in progress paper introduces robustness verification for
autoencoder-based regression neural network (NN) models, following
state-of-the-art approaches for robustness verification of image classification
NNs. Despite the ongoing progress in developing verification methods for safety
and robustness in various deep neural networks (DNNs), robustness checking of
autoencoder models has not yet been considered. We explore this open space of
research and check ways to bridge the gap between existing DNN verification
methods by extending existing robustness analysis methods for such autoencoder
networks. While classification models using autoencoders work more or less
similar to image classification NNs, the functionality of regression models is
distinctly different. We introduce two definitions of robustness evaluation
metrics for autoencoder-based regression models, specifically the percentage
robustness and un-robustness grade. We also modified the existing Imagestar
approach, adjusting the variables to take care of the specific input types for
regression networks. The approach is implemented as an extension of NNV, then
applied and evaluated on a dataset, with a case study experiment shown using
the same dataset. As per the authors' understanding, this work in progress
paper is the first to show possible reachability analysis of autoencoder-based
NNs.

This paper presents an approach for verifying the behaviour of nonlinear
Artificial Neural Networks (ANNs) found in cyber-physical safety-critical
systems. We implement a dedicated interval constraint propagator for the
sigmoid function into the SMT solver iSAT and compare this approach with a
compositional approach encoding the sigmoid function by basic arithmetic
features available in iSAT and an approximating approach. Our experimental
results show that the dedicated and the compositional approach clearly
outperform the approximating approach. Throughout all our benchmarks, the
dedicated approach showed an equal or better performance compared to the
compositional approach.

Expandable networks have demonstrated their advantages in dealing with
catastrophic forgetting problem in incremental learning. Considering that
different tasks may need different structures, recent methods design dynamic
structures adapted to different tasks via sophisticated skills. Their routine
is to search expandable structures first and then train on the new tasks,
which, however, breaks tasks into multiple training stages, leading to
suboptimal or overmuch computational cost. In this paper, we propose an
end-to-end trainable adaptively expandable network named E2-AEN, which
dynamically generates lightweight structures for new tasks without any accuracy
drop in previous tasks. Specifically, the network contains a serial of powerful
feature adapters for augmenting the previously learned representations to new
tasks, and avoiding task interference. These adapters are controlled via an
adaptive gate-based pruning strategy which decides whether the expanded
structures can be pruned, making the network structure dynamically changeable
according to the complexity of the new tasks. Moreover, we introduce a novel
sparsity-activation regularization to encourage the model to learn
discriminative features with limited parameters. E2-AEN reduces cost and can be
built upon any feed-forward architectures in an end-to-end manner. Extensive
experiments on both classification (i.e., CIFAR and VDD) and detection (i.e.,
COCO, VOC and ICCV2021 SSLAD challenge) benchmarks demonstrate the
effectiveness of the proposed method, which achieves the new remarkable
results.

Single-pixel imaging (SPI) is a novel imaging technique whose working
principle is based on the compressive sensing (CS) theory. In SPI, data is
obtained through a series of compressive measurements and the corresponding
image is reconstructed. Typically, the reconstruction algorithm such as basis
pursuit relies on the sparsity assumption in images. However, recent advances
in deep learning have found its uses in reconstructing CS images. Despite
showing a promising result in simulations, it is often unclear how such an
algorithm can be implemented in an actual SPI setup. In this paper, we
demonstrate the use of deep learning on the reconstruction of SPI images in
conjunction with block compressive sensing (BCS). We also proposed a novel
reconstruction model based on convolutional neural networks that outperforms
other competitive CS reconstruction algorithms. Besides, by incorporating BCS
in our deep learning model, we were able to reconstruct images of any size
above a certain smallest image size. In addition, we show that our model is
capable of reconstructing images obtained from an SPI setup while being priorly
trained on natural images, which can be vastly different from the SPI images.
This opens up opportunity for the feasibility of pretrained deep learning
models for CS reconstructions of images from various domain areas.

The rising popularity of neural networks (NNs) in recent years and their
increasing prevalence in real-world applications have drawn attention to the
importance of their verification. While verification is known to be
computationally difficult theoretically, many techniques have been proposed for
solving it in practice. It has been observed in the literature that by default
neural networks rarely satisfy logical constraints that we want to verify. A
good course of action is to train the given NN to satisfy said constraint prior
to verifying them. This idea is sometimes referred to as continuous
verification, referring to the loop between training and verification. Usually
training with constraints is implemented by specifying a translation for a
given formal logic language into loss functions. These loss functions are then
used to train neural networks. Because for training purposes these functions
need to be differentiable, these translations are called differentiable logics
(DL). This raises several research questions. What kind of differentiable
logics are possible? What difference does a specific choice of DL make in the
context of continuous verification? What are the desirable criteria for a DL
viewed from the point of view of the resulting loss function? In this extended
abstract we will discuss and answer these questions.

Detectingandsegmentingobjectswithinwholeslideimagesis essential in
computational pathology workflow. Self-supervised learning (SSL) is appealing
to such annotation-heavy tasks. Despite the extensive benchmarks in natural
images for dense tasks, such studies are, unfortunately, absent in current
works for pathology. Our paper intends to narrow this gap. We first benchmark
representative SSL methods for dense prediction tasks in pathology images.
Then, we propose concept contrastive learning (ConCL), an SSL framework for
dense pre-training. We explore how ConCL performs with concepts provided by
different sources and end up with proposing a simple dependency-free concept
generating method that does not rely on external segmentation algorithms or
saliency detection models. Extensive experiments demonstrate the superiority of
ConCL over previous state-of-the-art SSL methods across different settings.
Along our exploration, we distll several important and intriguing components
contributing to the success of dense pre-training for pathology images. We hope
this work could provide useful data points and encourage the community to
conduct ConCL pre-training for problems of interest. Code is available.

Modeling users' dynamic preferences from historical behaviors lies at the
core of modern recommender systems. Due to the diverse nature of user
interests, recent advances propose the multi-interest networks to encode
historical behaviors into multiple interest vectors. In real scenarios, the
corresponding items of captured interests are usually retrieved together to get
exposure and collected into training data, which produces dependencies among
interests. Unfortunately, multi-interest networks may incorrectly concentrate
on subtle dependencies among captured interests. Misled by these dependencies,
the spurious correlations between irrelevant interests and targets are
captured, resulting in the instability of prediction results when training and
test distributions do not match. In this paper, we introduce the widely used
Hilbert-Schmidt Independence Criterion (HSIC) to measure the degree of
independence among captured interests and empirically show that the continuous
increase of HSIC may harm model performance. Based on this, we propose a novel
multi-interest network, named DEep Stable Multi-Interest Learning (DESMIL),
which tries to eliminate the influence of subtle dependencies among captured
interests via learning weights for training samples and make model concentrate
more on underlying true causation. We conduct extensive experiments on public
recommendation datasets, a large-scale industrial dataset and the synthetic
datasets which simulate the out-of-distribution data. Experimental results
demonstrate that our proposed DESMIL outperforms state-of-the-art models by a
significant margin. Besides, we also conduct comprehensive model analysis to
reveal the reason why DESMIL works to a certain extent.

The classification problem's complexity assessment is an essential element of
many topics in the supervised learning domain. It plays a significant role in
meta-learning -- becoming the basis for determining meta-attributes or
multi-criteria optimization -- allowing the evaluation of the training set
resampling without needing to rebuild the recognition model. The tools
currently available for the academic community, which would enable the
calculation of problem complexity measures, are available only as libraries of
the C++ and R languages. This paper describes the software module that allows
for the estimation of 22 complexity measures for the Python language --
compatible with the scikit-learn programming interface -- allowing for the
implementation of research using them in the most popular programming
environment of the machine learning community.

Attention-based models have been widely used in many areas, such as computer
vision and natural language processing. However, relevant applications in time
series classification (TSC) have not been explored deeply yet, causing a
significant number of TSC algorithms still suffer from general problems of
attention mechanism, like quadratic complexity. In this paper, we promote the
efficiency and performance of the attention mechanism by proposing our flexible
multi-head linear attention (FMLA), which enhances locality awareness by
layer-wise interactions with deformable convolutional blocks and online
knowledge distillation. What's more, we propose a simple but effective mask
mechanism that helps reduce the noise influence in time series and decrease the
redundancy of the proposed FMLA by masking some positions of each given series
proportionally. To stabilize this mechanism, samples are forwarded through the
model with random mask layers several times and their outputs are aggregated to
teach the same model with regular mask layers. We conduct extensive experiments
on 85 UCR2018 datasets to compare our algorithm with 11 well-known ones and the
results show that our algorithm has comparable performance in terms of top-1
accuracy. We also compare our model with three Transformer-based models with
respect to the floating-point operations per second and number of parameters
and find that our algorithm achieves significantly better efficiency with lower

We present MDEAW, a multimodal database consisting of Electrodermal Activity
(EDA) and Photoplethysmography (PPG) signals recorded during the exams for the
course taught by the teacher at Eurecat Academy, Sabadell, Barcelona in order
to elicit the emotional reactions to the students in a classroom scenario.
Signals from 10 students were recorded along with the students' self-assessment
of their affective state after each stimulus, in terms of 6 basic emotion
states. All the signals were captured using portable, wearable, wireless,
low-cost, and off-the-shelf equipment that has the potential to allow the use
of affective computing methods in everyday applications. A baseline for
student-wise affect recognition using EDA and PPG-based features, as well as
their fusion, was established through ReMECS, Fed-ReMECS, and Fed-ReMECS-U.
These results indicate the prospects of using low-cost devices for affective
state recognition applications. The proposed database will be made publicly
available in order to allow researchers to achieve a more thorough evaluation
of the suitability of these capturing devices for emotion state recognition

Recently, generalization on out-of-distribution (OOD) data with correlation
shift has attracted great attention. The correlation shift is caused by the
spurious attributes that correlate to the class label, as the correlation
between them may vary in training and test data. For such a problem, we show
that given the class label, the conditionally independent models of spurious
attributes are OOD generalizable. Based on this, a metric Conditional Spurious
Variation (CSV) which controls OOD generalization error, is proposed to measure
such conditional independence. To improve the OOD generalization, we regularize
the training process with the proposed CSV. Under mild assumptions, our
training objective can be formulated as a nonconvex-concave mini-max problem.
An algorithm with provable convergence rate is proposed to solve the problem.
Extensive empirical results verify our algorithm's efficacy in improving OOD

Small subgraphs (graphlets) are important features to describe fundamental
units of a large network. The calculation of the subgraph frequency
distributions has a wide application in multiple domains including biology and
engineering. Unfortunately due to the inherent complexity of this task, most of
the existing methods are computationally intensive and inefficient. In this
work, we propose GNNS, a novel representational learning framework that
utilizes graph neural networks to sample subgraphs efficiently for estimating
their frequency distribution. Our framework includes an inference model and a
generative model that learns hierarchical embeddings of nodes, subgraphs, and
graph types. With the learned model and embeddings, subgraphs are sampled in a
highly scalable and parallel way and the frequency distribution estimation is
then performed based on these sampled subgraphs. Eventually, our methods
achieve comparable accuracy and a significant speedup by three orders of
magnitude compared to existing methods.

Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs
provide a promising way to model higher-order relations in data and further
solve relevant prediction tasks built upon such higher-order relations.
However, higher-order relations in practice contain complex patterns and are
often highly irregular. So, it is often challenging to design an HNN that
suffices to express those relations while keeping computational efficiency.
Inspired by hypergraph diffusion algorithms, this work proposes a new HNN
architecture named ED-HNN, which provably represents any continuous equivariant
hypergraph diffusion operators that can model a wide range of higher-order
relations. ED-HNN can be implemented efficiently by combining star expansions
of hypergraphs with standard message passing neural networks. ED-HNN further
shows great superiority in processing heterophilic hypergraphs and constructing
deep models. We evaluate ED-HNN for node classification on nine real-world
hypergraph datasets. ED-HNN uniformly outperforms the best baselines over these
nine datasets and achieves more than 2\%$\uparrow$ in prediction accuracy over
four datasets therein.

Automatic theorem proving with deep learning methods has attracted attentions
recently. In this paper, we construct an automatic proof system for
trigonometric identities. We define the normalized form of trigonometric
identities, design a set of rules for the proof and put forward a method which
can generate theoretically infinite trigonometric identities. Our goal is not
only to complete the proof, but to complete the proof in as few steps as
possible. For this reason, we design a model to learn proof data generated by
random BFS (rBFS), and it is proved theoretically and experimentally that the
model can outperform rBFS after a simple imitation learning. After further
improvement through reinforcement learning, we get AutoTrig, which can give
proof steps for identities in almost as short steps as BFS (theoretically
shortest method), with a time cost of only one-thousandth. In addition,
AutoTrig also beats Sympy, Matlab and human in the synthetic dataset, and
performs well in many generalization tasks.

Composed of amino acid chains that influence how they fold and thus dictating
their function and features, proteins are a class of macromolecules that play a
central role in major biological processes and are required for the structure,
function, and regulation of the body's tissues. Understanding protein functions
is vital to the development of therapeutics and precision medicine, and hence
the ability to classify proteins and their functions based on measurable
features is crucial; indeed, the automatic inference of a protein's properties
from its sequence of amino acids, known as its primary structure, remains an
important open problem within the field of bioinformatics, especially given the
recent advancements in sequencing technologies and the extensive number of
known but uncategorized proteins with unknown properties. In this work, we
demonstrate and compare the performance of several deep learning frameworks,
including novel bi-directional LSTM and convolutional models, on widely
available sequencing data from the Protein Data Bank (PDB) of the Research
Collaboratory for Structural Bioinformatics (RCSB), as well as benchmark this
performance against classical machine learning approaches, including k-nearest
neighbors and multinomial regression classifiers, trained on experimental data.
Our results show that our deep learning models deliver superior performance to
classical machine learning methods, with the convolutional architecture
providing the most impressive inference performance.

A rapidly growing area of research is the use of machine learning approaches
such as autoencoders for dimensionality reduction of data and models in
scientific applications. We show that the canonical formulation of autoencoders
suffers from several deficiencies that can hinder their performance. Using a
meta-learning approach, we reformulate the autoencoder problem as a bi-level
optimization procedure that explicitly solves the dimensionality reduction
task. We prove that the new formulation corrects the identified deficiencies
with canonical autoencoders, provide a practical way to solve it, and showcase
the strength of this formulation with a simple numerical illustration.

Counterfactual explanations interpret the recommendation mechanism via
exploring how minimal alterations on items or users affect the recommendation
decisions. Existing counterfactual explainable approaches face huge search
space and their explanations are either action-based (e.g., user click) or
aspect-based (i.e., item description). We believe item attribute-based
explanations are more intuitive and persuadable for users since they explain by
fine-grained item demographic features (e.g., brand). Moreover, counterfactual
explanation could enhance recommendations by filtering out negative items.
  In this work, we propose a novel Counterfactual Explainable Recommendation
(CERec) to generate item attribute-based counterfactual explanations meanwhile
to boost recommendation performance. Our CERec optimizes an explanation policy
upon uniformly searching candidate counterfactuals within a reinforcement
learning environment. We reduce the huge search space with an adaptive path
sampler by using rich context information of a given knowledge graph. We also
deploy the explanation policy to a recommendation model to enhance the
recommendation. Extensive explainability and recommendation evaluations
demonstrate CERec's ability to provide explanations consistent with user
preferences and maintain improved recommendations. We release our code at
https://github.com/Chrystalii/CERec.

The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the
U.S. cotton industry that has cost more than 16 billion USD in damages since it
entered the United States from Mexico in the late 1800s. This pest has been
nearly eradicated; however, southern part of Texas still faces this issue and
is always prone to the pest reinfestation each year due to its sub-tropical
climate where cotton plants can grow year-round. Volunteer cotton (VC) plants
growing in the fields of inter-seasonal crops, like corn, can serve as hosts to
these pests once they reach pin-head square stage (5-6 leaf stage) and
therefore need to be detected, located, and destroyed or sprayed . In this
paper, we present a study to detect VC plants in a corn field using YOLOv3 on
three band aerial images collected by unmanned aircraft system (UAS). The
two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be
used for VC detection in a corn field using RGB (red, green, and blue) aerial
images collected by UAS and (ii) to investigate the behavior of YOLOv3 on
images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512,
S3 pixels) based on average precision (AP), mean average precision (mAP) and
F1-score at 95% confidence level. No significant differences existed for mAP
among the three scales, while a significant difference was found for AP between
S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was
also found for F1-score between S2 and S3 (p = 0.02). The lack of significant
differences of mAP at all the three scales indicated that the trained YOLOv3
model can be used on a computer vision-based remotely piloted aerial
application system (RPAAS) for VC detection and spray application in near
real-time.

Although more layers and more parameters generally improve the accuracy of
the models, such big models generally have high computational complexity and
require big memory, which exceed the capacity of small devices for inference
and incurs long training time. In addition, it is difficult to afford long
training time and inference time of big models even in high performance
servers, as well. As an efficient approach to compress a large deep model (a
teacher model) to a compact model (a student model), knowledge distillation
emerges as a promising approach to deal with the big models. Existing knowledge
distillation methods cannot exploit the elastic available computing resources
and correspond to low efficiency. In this paper, we propose an Elastic Deep
Learning framework for knowledge Distillation, i.e., EDL-Dist. The advantages
of EDL-Dist are three-fold. First, the inference and the training process is
separated. Second, elastic available computing resources can be utilized to
improve the efficiency. Third, fault-tolerance of the training and inference
processes is supported. We take extensive experimentation to show that the
throughput of EDL-Dist is up to 3.125 times faster than the baseline method
(online knowledge distillation) while the accuracy is similar or higher.

We present a new Integrated Finite Element Neural Network framework (I-FENN),
with the objective to accelerate the numerical solution of nonlinear
computational mechanics problems. We leverage the swift predictive capability
of neural networks (NNs) and we embed them inside the finite element stiffness
function, to compute element-level state variables and their derivatives within
a nonlinear, iterative numerical solution. This process is conducted jointly
with conventional finite element methods that involve shape functions: the NN
receives input data that resembles the material point deformation and its
output is used to construct element-level field variables such as the element
Jacobian matrix and residual vector. Here we introduce I-FENN to the continuum
damage analysis of quasi-brittle materials, and we establish a new non-local
gradient-based damage framework which operates at the cost of a local damage
approach. First, we develop a physics informed neural network (PINN) to
resemble the non-local gradient model and then we train the neural network
offline. The network learns to predict the non-local equivalent strain at each
material point, as well as its derivative with respect to the local strain.
Then, the PINN is integrated in the element stiffness definition and conducts
the local to non-local strain transformation, whereas the two PINN outputs are
used to construct the element Jacobian matrix and residual vector. This process
is carried out within the nonlinear solver, until numerical convergence is
achieved. The resulting method bears the computational cost of the conventional
local damage approach, but ensures mesh-independent results and a diffused
non-local strain and damage profile. As a result, the proposed method tackles
the vital drawbacks of both the local and non-local gradient method,
respectively being the mesh-dependence and additional computational cost.

Traditional algorithms of point set registration minimizing point-to-plane
distances often achieve a better estimation of rigid transformation than those
minimizing point-to-point distances. Nevertheless, recent deep-learning-based
methods minimize the point-to-point distances. In contrast to these methods,
this paper proposes the first deep-learning-based approach to point-to-plane
registration. A challenging part of this problem is that a typical solution for
point-to-plane registration requires an iterative process of accumulating small
transformations obtained by minimizing a linearized energy function. The
iteration significantly increases the size of the computation graph needed for
backpropagation and can slow down both forward and backward network
evaluations. To solve this problem, we consider the estimated rigid
transformation as a function of input point clouds and derive its analytic
gradients using the implicit function theorem. The analytic gradient that we
introduce is independent of how the error minimizing function (i.e., the rigid
transformation) is obtained, thus allowing us to calculate both the rigid
transformation and its gradient efficiently. We implement the proposed
point-to-plane registration module over several previous methods that minimize
point-to-point distances and demonstrate that the extensions outperform the
base methods even with point clouds with noise and low-quality point normals
estimated with local point distributions.

With video-level labels, weakly supervised temporal action localization
(WTAL) applies a localization-by-classification paradigm to detect and classify
the action in untrimmed videos. Due to the characteristic of classification,
class-specific background snippets are inevitably mis-activated to improve the
discriminability of the classifier in WTAL. To alleviate the disturbance of
background, existing methods try to enlarge the discrepancy between action and
background through modeling background snippets with pseudo-snippet-level
annotations, which largely rely on artificial hypotheticals. Distinct from the
previous works, we present an adversarial learning strategy to break the
limitation of mining pseudo background snippets. Concretely, the background
classification loss forces the whole video to be regarded as the background by
a background gradient reinforcement strategy, confusing the recognition model.
Reversely, the foreground(action) loss guides the model to focus on action
snippets under such conditions. As a result, competition between the two
classification losses drives the model to boost its ability for action
modeling. Simultaneously, a novel temporal enhancement network is designed to
facilitate the model to construct temporal relation of affinity snippets based
on the proposed strategy, for further improving the performance of action
localization. Finally, extensive experiments conducted on THUMOS14 and
ActivityNet1.2 demonstrate the effectiveness of the proposed method.

Existing automatic data augmentation (DA) methods either ignore updating DA's
parameters according to the target model's state during training or adopt
update strategies that are not effective enough. In this work, we design a
novel data augmentation strategy called "Universal Adaptive Data Augmentation"
(UADA). Different from existing methods, UADA would adaptively update DA's
parameters according to the target model's gradient information during
training: given a pre-defined set of DA operations, we randomly decide types
and magnitudes of DA operations for every data batch during training, and
adaptively update DA's parameters along the gradient direction of the loss
concerning DA's parameters. In this way, UADA can increase the training loss of
the target networks, and the target networks would learn features from harder
samples to improve the generalization. Moreover, UADA is very general and can
be utilized in numerous tasks, e.g., image classification, semantic
segmentation and object detection. Extensive experiments with various models
are conducted on CIFAR-10, CIFAR-100, ImageNet, tiny-ImageNet, Cityscapes, and
VOC07+12 to prove the significant performance improvements brought by our
proposed adaptive augmentation.

With the wide availability of large pre-trained language models such as GPT-2
and BERT, the recent trend has been to fine-tune a pre-trained model to achieve
state-of-the-art performance on a downstream task. One natural example is the
"Smart Reply" application where a pre-trained model is tuned to provide
suggested responses for a given query message. Since these models are often
tuned using sensitive data such as emails or chat transcripts, it is important
to understand and mitigate the risk that the model leaks its tuning data. We
investigate potential information leakage vulnerabilities in a typical Smart
Reply pipeline and introduce a new type of active extraction attack that
exploits canonical patterns in text containing sensitive data. We show
experimentally that it is possible for an adversary to extract sensitive user
information present in the training data. We explore potential mitigation
strategies and demonstrate empirically how differential privacy appears to be
an effective defense mechanism to such pattern extraction attacks.

Likelihood-free methods are an essential tool for performing inference for
implicit models which can be simulated from, but for which the corresponding
likelihood is intractable. However, common likelihood-free methods do not scale
well to a large number of model parameters. A promising approach to
high-dimensional likelihood-free inference involves estimating low-dimensional
marginal posteriors by conditioning only on summary statistics believed to be
informative for the low-dimensional component, and then combining the
low-dimensional approximations in some way. In this paper, we demonstrate that
such low-dimensional approximations can be surprisingly poor in practice for
seemingly intuitive summary statistic choices. We describe an idealized
low-dimensional summary statistic that is, in principle, suitable for marginal
estimation. However, a direct approximation of the idealized choice is
difficult in practice. We thus suggest an alternative approach to marginal
estimation which is easier to implement and automate. Given an initial choice
of low-dimensional summary statistic that might only be informative about a
marginal posterior location, the new method improves performance by first
crudely localising the posterior approximation using all the summary statistics
to ensure global identifiability, followed by a second step that hones in on an
accurate low-dimensional approximation using the low-dimensional summary
statistic. We show that the posterior this approach targets can be represented
as a logarithmic pool of posterior distributions based on the low-dimensional
and full summary statistics, respectively. The good performance of our method
is illustrated in several examples.

Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the
labeled source domain to an unlabeled target domain. In this paper, we present
Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive
learning method for unsupervised domain adaptive semantic segmentation.
Previous domain adaptation methods merely consider the alignment of the
intra-class representational distributions across various domains, while the
inter-class structural relationship is insufficiently explored, resulting in
the aligned representations on the target domain might not be as easily
discriminated as done on the source domain anymore. Instead, ProCA incorporates
inter-class information into class-wise prototypes, and adopts the
class-centered distribution alignment for adaptation. By considering the same
class prototypes as positives and other class prototypes as negatives to
achieve class-centered distribution alignment, ProCA achieves state-of-the-art
performance on classical domain adaptation tasks, {\em i.e., GTA5 $\to$
Cityscapes \text{and} SYNTHIA $\to$ Cityscapes}. Code is available at
\href{https://github.com/jiangzhengkai/ProCA}{ProCA}

User embeddings (vectorized representations of a user) are essential in
recommendation systems. Numerous approaches have been proposed to construct a
representation for the user in order to find similar items for retrieval tasks,
and they have been proven effective in industrial recommendation systems as
well. Recently people have discovered the power of using multiple embeddings to
represent a user, with the hope that each embedding represents the user's
interest in a certain topic. With multi-interest representation, it's important
to model the user's preference over the different topics and how the preference
change with time. However, existing approaches either fail to estimate the
user's affinity to each interest or unreasonably assume every interest of every
user fades with an equal rate with time, thus hurting the recall of candidate
retrieval. In this paper, we propose the Multi-Interest Preference (MIP) model,
an approach that not only produces multi-interest for users by using the user's
sequential engagement more effectively but also automatically learns a set of
weights to represent the preference over each embedding so that the candidates
can be retrieved from each interest proportionally. Extensive experiments have
been done on various industrial-scale datasets to demonstrate the effectiveness

The model selection procedure is usually a single-criterion decision making
in which we select the model that maximizes a specific metric in a specific
set, such as the Validation set performance. We claim this is very naive and
can perform poor selections of over-fitted models due to the over-searching
phenomenon, which over-estimates the performance on that specific set.
Futhermore, real world data contains noise that should not be ignored by the
model selection procedure and must be taken into account when performing model
selection. Also, we have defined four theoretical optimality conditions that we
can pursue to better select the models and analyze them by using a
multi-criteria decision-making algorithm (TOPSIS) that considers proxies to the
optimality conditions to select reasonable models.

In this paper, we propose the physics informed adversarial training (PIAT) of
neural networks for solving nonlinear differential equations (NDE). It is
well-known that the standard training of neural networks results in non-smooth
functions. Adversarial training (AT) is an established defense mechanism
against adversarial attacks, which could also help in making the solution
smooth. AT include augmenting the training mini-batch with a perturbation that
makes the network output mismatch the desired output adversarially. Unlike
formal AT, which relies only on the training data, here we encode the governing
physical laws in the form of nonlinear differential equations using automatic
differentiation in the adversarial network architecture. We compare PIAT with
PINN to indicate the effectiveness of our method in solving NDEs for up to 10
dimensions. Moreover, we propose weight decay and Gaussian smoothing to
demonstrate the PIAT advantages. The code repository is available at
https://github.com/rohban-lab/PIAT.

Modern deep neural networks require a significant amount of computing time
and power to train and deploy, which limits their usage on edge devices.
Inspired by the iterative weight pruning in the Lottery Ticket Hypothesis, we
propose DropNet, an iterative pruning method which prunes nodes/filters to
reduce network complexity. DropNet iteratively removes nodes/filters with the
lowest average post-activation value across all training samples. Empirically,
we show that DropNet is robust across diverse scenarios, including MLPs and
CNNs using the MNIST, CIFAR-10 and Tiny ImageNet datasets. We show that up to
90% of the nodes/filters can be removed without any significant loss of
accuracy. The final pruned network performs well even with reinitialization of
the weights and biases. DropNet also has similar accuracy to an oracle which
greedily removes nodes/filters one at a time to minimise training loss,
highlighting its effectiveness.

Deep learning-based source dehazing methods trained on synthetic datasets
have achieved remarkable performance but suffer from dramatic performance
degradation on real hazy images due to domain shift. Although certain Domain
Adaptation (DA) dehazing methods have been presented, they inevitably require
access to the source dataset to reduce the gap between the source synthetic and
target real domains. To address these issues, we present a novel Source-Free
Unsupervised Domain Adaptation (SFUDA) image dehazing paradigm, in which only a
well-trained source model and an unlabeled target real hazy dataset are
available. Specifically, we devise the Domain Representation Normalization
(DRN) module to make the representation of real hazy domain features match that
of the synthetic domain to bridge the gaps. With our plug-and-play DRN module,
unlabeled real hazy images can adapt existing well-trained source networks.
Besides, the unsupervised losses are applied to guide the learning of the DRN
module, which consists of frequency losses and physical prior losses. Frequency
losses provide structure and style constraints, while the prior loss explores
the inherent statistic property of haze-free images. Equipped with our DRN
module and unsupervised loss, existing source dehazing models are able to
dehaze unlabeled real hazy images. Extensive experiments on multiple baselines
demonstrate the validity and superiority of our method visually and
quantitatively.

A covert attack method often used by APT organizations is the DNS tunnel,
which is used to pass information by constructing C2 networks. And they often
use the method of frequently changing domain names and server IP addresses to
evade monitoring, which makes it extremely difficult to detect them. However,
they carry DNS tunnel information traffic in normal DNS communication, which
inevitably brings anomalies in some statistical characteristics of DNS traffic,
so that it would provide security personnel with the opportunity to find them.
Based on the above considerations, this paper studies the statistical discovery
methodology of typical DNS tunnel high-frequency query behavior. Firstly, we
analyze the distribution of the DNS domain name length and times and finds that
the DNS domain name length and times follow the normal distribution law.
Secondly, based on this distribution law, we propose a method for detecting and
discovering high-frequency DNS query behaviors of non-single domain names based
on the statistical rules of domain name length and frequency and we also give
three theorems as theoretical support. Thirdly, we design a sliding window
difference scheme based on the above method. Experimental results show that our
method has a higher detection rate. At the same time, since our method does not
need to construct a data set, it has better practicability in detecting unknown
DNS tunnels. This also shows that our detection method based on mathematical
models can effectively avoid the dilemma for machine learning methods that must
have useful training data sets, and has strong practical significance.

Improved understanding of the relation between the behavior of RAS and RAF
proteins and the local lipid environment in the cell membrane is critical for
getting insights into the mechanisms underlying cancer formation. In this work,
we employ deep learning (DL) to learn this relationship by predicting protein
orientational states of RAS and RAS-RAF protein complexes with respect to the
lipid membrane based on the lipid densities around the protein domains from
coarse-grained (CG) molecular dynamics (MD) simulations. Our DL model can
predict six protein states with an overall accuracy of over 80%. The findings
of this work offer new insights into how the proteins modulate the lipid
environment, which in turn may assist designing novel therapies to regulate
such interactions in the mechanisms associated with cancer development.

Recent studies of deep learning based stereo image super-resolution
(StereoSR) have promoted the development of StereoSR. However, existing
StereoSR models mainly concentrate on improving quantitative evaluation metrics
and neglect the visual quality of super-resolved stereo images. To improve the
perceptual performance, this paper proposes the first perception-oriented
stereo image super-resolution approach by exploiting the feedback, provided by
the evaluation on the perceptual quality of StereoSR results. To provide
accurate guidance for the StereoSR model, we develop the first special stereo
image super-resolution quality assessment (StereoSRQA) model, and further
construct a StereoSRQA database. Extensive experiments demonstrate that our
StereoSR approach significantly improves the perceptual quality and enhances
the reliability of stereo images for disparity estimation.

Computational antibody design seeks to automatically create an antibody that
binds to an antigen. The binding affinity is governed by the 3D binding
interface where antibody residues (paratope) closely interact with antigen
residues (epitope). Thus, predicting 3D paratope-epitope complex (docking) is
the key to finding the best paratope. In this paper, we propose a new model
called Hierarchical Equivariant Refinement Network (HERN) for paratope docking
and design. During docking, HERN employs a hierarchical message passing network
to predict atomic forces and use them to refine a binding complex in an
iterative, equivariant manner. During generation, its autoregressive decoder
progressively docks generated paratopes and builds a geometric representation
of the binding interface to guide the next residue choice. Our results show
that HERN significantly outperforms prior state-of-the-art on paratope docking
and design benchmarks.

In a single qubit system, a universal quantum classifier can be realised
using the data-reuploading technique. In this study, we propose a new quantum
classifier applying this technique to bosonic systems and successfully
demonstrated it using silicon optical integrated quantum circuits. We
established a theory of quantum machine learning algorithm applicable to
bosonic systems and implemented a programmable optical circuit combined with an
interferometer. Learning and classification using part of the implemented
optical quantum circuit with uncorrelated two-photons resulted in a
classification with a reproduction rate of approximately 94\% in the proof of
principle experiment. As this method can be applied to arbitrary two-mod
N-photon system, further development of optical quantum classifiers, such as
extensions to quantum entangled and multi-photon states, is expected in the
future.

Deploying Machine learning (ML) on the milliwatt-scale edge devices (tinyML)
is gaining popularity due to recent breakthroughs in ML and IoT. However, the
capabilities of tinyML are restricted by strict power and compute constraints.
The majority of the contemporary research in tinyML focuses on model
compression techniques such as model pruning and quantization to fit ML models
on low-end devices. Nevertheless, the improvements in energy consumption and
inference time obtained by existing techniques are limited because aggressive
compression quickly shrinks model capacity and accuracy. Another approach to
improve inference time and/or reduce power while preserving its model capacity
is through early-exit networks. These networks place intermediate classifiers
along a baseline neural network that facilitate early exit from neural network
computation if an intermediate classifier exhibits sufficient confidence in its
prediction. Previous work on early-exit networks have focused on large
networks, beyond what would typically be used for tinyML applications. In this
paper, we discuss the challenges of adding early-exits to state-of-the-art
tiny-CNNs and devise an early-exit architecture, T-RECX, that addresses these
challenges. In addition, we develop a method to alleviate the effect of network
overthinking at the final exit by leveraging the high-level representations
learned by the early-exit. We evaluate T-RECX on three CNNs from the MLPerf
tiny benchmark suite for image classification, keyword spotting and visual wake
word detection tasks. Our results demonstrate that T-RECX improves the accuracy
of baseline network and significantly reduces the average inference time of
tiny-CNNs. T-RECX achieves 32.58% average reduction in FLOPS in exchange for 1%
accuracy across all evaluated models. Also, our techniques increase the
accuracy of baseline network in two out of three models we evaluate

Robots are increasingly expected to manipulate objects in ever more
unstructured environments where the object properties have high perceptual
uncertainty from any single sensory modality. This directly impacts successful
object manipulation. In this work, we propose a reinforcement learning-based
motion planning framework for object manipulation which makes use of both
on-the-fly multisensory feedback and a learned attention-guided deep affordance
model as perceptual states. The affordance model is learned from multiple
sensory modalities, including vision and touch (tactile and force/torque),
which is designed to predict and indicate the manipulable regions of multiple
affordances (i.e., graspability and pushability) for objects with similar
appearances but different intrinsic properties (e.g., mass distribution). A
DQN-based deep reinforcement learning algorithm is then trained to select the
optimal action for successful object manipulation. To validate the performance
of the proposed framework, our method is evaluated and benchmarked using both
an open dataset and our collected dataset. The results show that the proposed
method and overall framework outperform existing methods and achieve better
accuracy and higher efficiency.

Deep neural networks have greatly promoted the performance of single image
super-resolution (SISR). Conventional methods still resort to restoring the
single high-resolution (HR) solution only based on the input of image modality.
However, the image-level information is insufficient to predict adequate
details and photo-realistic visual quality facing large upscaling factors (x8,
x16). In this paper, we propose a new perspective that regards the SISR as a
semantic image detail enhancement problem to generate semantically reasonable
HR image that are faithful to the ground truth. To enhance the semantic
accuracy and the visual quality of the reconstructed image, we explore the
multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution
(TGSR) framework, which can effectively utilize the information from the text
and image modalities. Different from existing methods, the proposed TGSR could
generate HR image details that match the text descriptions through a
coarse-to-fine process. Extensive experiments and ablation studies demonstrate
the effect of the TGSR, which exploits the text reference to recover realistic
images.

We investigate the problem of testing whether a discrete probability
distribution over an ordered domain is a histogram on a specified number of
bins. One of the most common tools for the succinct approximation of data,
$k$-histograms over $[n]$, are probability distributions that are piecewise
constant over a set of $k$ intervals. The histogram testing problem is the
following: Given samples from an unknown distribution $\mathbf{p}$ on $[n]$, we
want to distinguish between the cases that $\mathbf{p}$ is a $k$-histogram
versus $\varepsilon$-far from any $k$-histogram, in total variation distance.
Our main result is a sample near-optimal and computationally efficient
algorithm for this testing problem, and a nearly-matching (within logarithmic
factors) sample complexity lower bound. Specifically, we show that the
histogram testing problem has sample complexity $\widetilde \Theta (\sqrt{nk} /
\varepsilon + k / \varepsilon^2 + \sqrt{n} / \varepsilon^2)$.

Specific emitter identification (SEI) is a highly potential technology for
physical layer authentication that is one of the most critical supplement for
the upper-layer authentication. SEI is based on radio frequency (RF) features
from circuit difference, rather than cryptography. These features are inherent
characteristic of hardware circuits, which difficult to counterfeit. Recently,
various deep learning (DL)-based conventional SEI methods have been proposed,
and achieved advanced performances. However, these methods are proposed for
close-set scenarios with massive RF signal samples for training, and they
generally have poor performance under the condition of limited training
samples. Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification
via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel
FS-SEI method is proposed, based on deep metric ensemble learning (DMEL).
Specifically, the proposed method consists of feature embedding and
classification. The former is based on metric learning with complex-valued
convolutional neural network (CVCNN) for extracting discriminative features
with compact intra-category distance and separable inter-category distance,
while the latter is realized by an ensemble classifier. Simulation results show
that if the number of samples per category is more than 5, the average accuracy
of our proposed method is higher than 98\%. Moreover, feature visualization
demonstrates the advantages of our proposed method in both discriminability and
generalization. The codes of this paper can be downloaded from
GitHub(https://github.com/BeechburgPieStar/Few-Shot-Specific-Emitter-Identification-via-Deep-Metric-Ensemble-Learning)

Automated Program Repair (APR) techniques have drawn wide attention from both
academia and industry. Meanwhile, one main limitation with the current
state-of-the-art APR tools is that patches passing all the original tests are
not necessarily the correct ones wanted by developers, i.e., the plausible
patch problem. To date,various Patch-Correctness Checking (PCC) techniques have
been proposed to address this important issue. However, they are only evaluated
on very limited datasets as the APR tools used for generating such patches can
only explore a small subset of the search space of possible patches, posing
serious threats to external validity to existing PCC studies. In this paper, we
construct an extensive PCC dataset (the largest labeled PCC dataset to our
knowledge) to revisit all state-of-the-art PCC techniques. More specifically,
our PCC dataset includes 1,988 patches generated from the recent PraPR APR
tool, which leverages highly-optimized bytecode-level patch executions and can
exhaustively explore all possible plausible patches within its large predefined
search space (including wellknown fixing patterns from various prior APR
tools). Our extensive study of representative PCC techniques on the new dataset
has revealed various surprising findings, including: 1) the assumption made by
existing static PCC techniques that correct patches are more similar to buggy
code than incorrect plausible patches no longer holds, 2) state-of-the-art
learning-based techniques tend to suffer from the dataset overfitting problem,
and 3) while dynamic techniques overall retain their effectiveness on our new
dataset, their performance drops substantially on patches with more complicated
changes. Based on our findings, we also provide various guidelines/suggestions
for advancing PCC in the near future.

We develop a method ('Galactic Archaeology Neural Network', GANN) based on
neural network models (NNMs) to identify accreted stars in galactic discs by
only their chemical fingerprint and age, using a suite of simulated galaxies
from the Auriga Project. We train the network on the target galaxy's own local
environment defined by the stellar halo and the surviving satellites. We
demonstrate that this approach allows the detection of accreted stars that are
spatially mixed into the disc. Two performance measures are defined - recovery
fraction of accreted stars, and the probability that a star with a positive
(accreted) classification is a true-positive result, P(TP). As the NNM output
is akin to an assigned probability, we are able to determine positivity based
on flexible threshold values that can be adjusted easily to refine the
selection of presumed-accreted stars. We find that GANN identifies accreted
disc stars within simulated galaxies, with high recovery fraction and/or high
P(TP). We also find that stars in Gaia-Enceladus-Sausage (GES) mass systems are
over 50% recovered by our NNMs in the majority (18/24) of cases. Additionally,
nearly every individual source of accreted stars is detected at 10% or more of
its peak stellar mass in the disc. We also demonstrate that a conglomerated
NNM, trained on the halo and satellite stars from all of the Auriga galaxies
provides the most consistent results, and could prove to be an intriguing
future approach as our observational capabilities expand.

Existing temporal action detection (TAD) methods rely on generating an
overwhelmingly large number of proposals per video. This leads to complex model
designs due to proposal generation and/or per-proposal action instance
evaluation and the resultant high computational cost. In this work, for the
first time, we propose a proposal-free Temporal Action detection model with
Global Segmentation mask (TAGS). Our core idea is to learn a global
segmentation mask of each action instance jointly at the full video length. The
TAGS model differs significantly from the conventional proposal-based methods
by focusing on global temporal representation learning to directly detect local
start and end points of action instances without proposals. Further, by
modeling TAD holistically rather than locally at the individual proposal level,
TAGS needs a much simpler model architecture with lower computational cost.
Extensive experiments show that despite its simpler design, TAGS outperforms
existing TAD methods, achieving new state-of-the-art performance on two
benchmarks. Importantly, it is ~ 20x faster to train and ~1.6x more efficient
for inference. Our PyTorch implementation of TAGS is available at
https://github.com/sauradip/TAGS .

Soil erosion is a significant threat to the environment and long-term land
management around the world. Accelerated soil erosion by human activities
inflicts extreme changes in terrestrial and aquatic ecosystems, which is not
fully surveyed/predicted for the present and probable future at field-scales
(30-m). Here, we estimate/predict soil erosion rates by water erosion, (sheet
and rill erosion), using three alternative (2.6, 4.5, and 8.5) Shared
Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP)
scenarios across the contiguous United States. Field Scale Soil Erosion Model
(FSSLM) estimations rely on a high resolution (30-m) G2 erosion model
integrated by satellite- and imagery-based estimations of land use and land
cover (LULC), gauge observations of long-term precipitation, and scenarios of
the Coupled Model Intercomparison Project Phase 6 (CMIP6). The baseline model
(2020) estimates soil erosion rates of 2.32 Mg ha 1 yr 1 with current
agricultural conservation practices (CPs). Future scenarios with current CPs
indicate an increase between 8% to 21% under different combinations of SSP-RCP
scenarios of climate and LULC changes. The soil erosion forecast for 2050
suggests that all the climate and LULC scenarios indicate either an increase in
extreme events or a change in the spatial location of extremes largely from the
southern to the eastern and northeastern regions of the United States.

Pathological diagnosis relies on the visual inspection of histologically
stained thin tissue specimens, where different types of stains are applied to
bring contrast to and highlight various desired histological features. However,
the destructive histochemical staining procedures are usually irreversible,
making it very difficult to obtain multiple stains on the same tissue section.
Here, we demonstrate a virtual stain transfer framework via a cascaded deep
neural network (C-DNN) to digitally transform hematoxylin and eosin (H&E)
stained tissue images into other types of histological stains. Unlike a single
neural network structure which only takes one stain type as input to digitally
output images of another stain type, C-DNN first uses virtual staining to
transform autofluorescence microscopy images into H&E and then performs stain
transfer from H&E to the domain of the other stain in a cascaded manner. This
cascaded structure in the training phase allows the model to directly exploit
histochemically stained image data on both H&E and the target special stain of
interest. This advantage alleviates the challenge of paired data acquisition
and improves the image quality and color accuracy of the virtual stain transfer
from H&E to another stain. We validated the superior performance of this C-DNN
approach using kidney needle core biopsy tissue sections and successfully
transferred the H&E-stained tissue images into virtual PAS (periodic
acid-Schiff) stain. This method provides high-quality virtual images of special
stains using existing, histochemically stained slides and creates new
opportunities in digital pathology by performing highly accurate stain-to-stain
transformations.

Sim-to-real transfer is a powerful paradigm for robotic reinforcement
learning. The ability to train policies in simulation enables safe exploration
and large-scale data collection quickly at low cost. However, prior works in
sim-to-real transfer of robotic policies typically do not involve any
human-robot interaction because accurately simulating human behavior is an open
problem. In this work, our goal is to leverage the power of simulation to train
robotic policies that are proficient at interacting with humans upon
deployment. But there is a chicken and egg problem -- how do we gather examples
of a human interacting with a physical robot so as to model human behavior in
simulation without already having a robot that is able to interact with a
human? Our proposed method, Iterative-Sim-to-Real (i-S2R), attempts to address
this. i-S2R bootstraps from a simple model of human behavior and alternates
between training in simulation and deploying in the real world. In each
iteration, both the human behavior model and the policy are refined. We
evaluate our method on a real world robotic table tennis setting, where the
objective for the robot is to play cooperatively with a human player for as
long as possible. Table tennis is a high-speed, dynamic task that requires the
two players to react quickly to each other's moves, making a challenging test
bed for research on human-robot interaction. We present results on an
industrial robotic arm that is able to cooperatively play table tennis with
human players, achieving rallies of 22 successive hits on average and 150 at
best. Further, for 80% of players, rally lengths are 70% to 175% longer
compared to the sim-to-real (S2R) baseline. For videos of our system in action,
please see https://sites.google.com/view/is2r.

The practical success of overparameterized neural networks has motivated the
recent scientific study of interpolating methods, which perfectly fit their
training data. Certain interpolating methods, including neural networks, can
fit noisy training data without catastrophically bad test performance, in
defiance of standard intuitions from statistical learning theory. Aiming to
explain this, a body of recent work has studied benign overfitting, a
phenomenon where some interpolating methods approach Bayes optimality, even in
the presence of noise. In this work we argue that while benign overfitting has
been instructive and fruitful to study, many real interpolating methods like
neural networks do not fit benignly: modest noise in the training set causes
nonzero (but non-infinite) excess risk at test time, implying these models are
neither benign nor catastrophic but rather fall in an intermediate regime. We
call this intermediate regime tempered overfitting, and we initiate its
systematic study. We first explore this phenomenon in the context of kernel
(ridge) regression (KR) by obtaining conditions on the ridge parameter and
kernel eigenspectrum under which KR exhibits each of the three behaviors. We
find that kernels with powerlaw spectra, including Laplace kernels and ReLU
neural tangent kernels, exhibit tempered overfitting. We then empirically study
deep neural networks through the lens of our taxonomy, and find that those
trained to interpolation are tempered, while those stopped early are benign. We
hope our work leads to a more refined understanding of overfitting in modern
learning.

Difference-in-differences (or DiD) is a commonly used method for estimating
treatment effects, and parallel trends is its main identifying assumption: the
trend in mean untreated outcomes must be independent of the observed treatment
status. In observational settings, treatment is often a dynamic choice made or
influenced by rational actors, such as policy-makers, firms, or individual
agents. This paper relates the parallel trends assumption to economic models of
dynamic choice, which allow for dynamic selection motives such as learning or
optimal stopping. In these cases, we clarify the implications of parallel
trends on agent behavior, and we show how such features can lead to violations
of parallel trends even in simple settings where anticipation concerns are
ruled out or mean untreated outcomes are stationary. Finally, we consider some
identification results under alternative assumptions that accommodate these
features of dynamic choice.

The improved diagnostic accuracy of ultrasound breast examinations remains an
important goal. In this study, we propose a biophysical feature based machine
learning method for breast cancer detection to improve the performance beyond a
benchmark deep learning algorithm and to furthermore provide a color overlay
visual map of the probability of malignancy within a lesion. This overall
framework is termed disease specific imaging. Previously, 150 breast lesions
were segmented and classified utilizing a modified fully convolutional network
and a modified GoogLeNet, respectively. In this study multiparametric analysis
was performed within the contoured lesions. Features were extracted from
ultrasound radiofrequency, envelope, and log compressed data based on
biophysical and morphological models. The support vector machine with a
Gaussian kernel constructed a nonlinear hyperplane, and we calculated the
distance between the hyperplane and data point of each feature in
multiparametric space. The distance can quantitatively assess a lesion, and
suggest the probability of malignancy that is color coded and overlaid onto B
mode images. Training and evaluation were performed on in vivo patient data.
The overall accuracy for the most common types and sizes of breast lesions in
our study exceeded 98.0% for classification and 0.98 for an area under the
receiver operating characteristic curve, which is more precise than the
performance of radiologists and a deep learning system. Further, the
correlation between the probability and BI RADS enables a quantitative
guideline to predict breast cancer. Therefore, we anticipate that the proposed
framework can help radiologists achieve more accurate and convenient breast
cancer classification and detection.

Reinforcement learning algorithms require a large amount of samples; this
often limits their real-world applications on even simple tasks. Such a
challenge is more outstanding in multi-agent tasks, as each step of operation
is more costly requiring communications or shifting or resources. This work
aims to improve data efficiency of multi-agent control by model-based learning.
We consider networked systems where agents are cooperative and communicate only
locally with their neighbors, and propose the decentralized model-based policy
optimization framework (DMPO). In our method, each agent learns a dynamic model
to predict future states and broadcast their predictions by communication, and
then the policies are trained under the model rollouts. To alleviate the bias
of model-generated data, we restrain the model usage for generating myopic
rollouts, thus reducing the compounding error of model generation. To pertain
the independence of policy update, we introduce extended value function and
theoretically prove that the resulting policy gradient is a close approximation
to true policy gradients. We evaluate our algorithm on several benchmarks for
intelligent transportation systems, which are connected autonomous vehicle
control tasks (Flow and CACC) and adaptive traffic signal control (ATSC).
Empirically results show that our method achieves superior data efficiency and
matches the performance of model-free methods using true models.

The goal of attribute manipulation is to control specified attribute(s) in
given images. Prior work approaches this problem by learning disentangled
representations for each attribute that enables it to manipulate the encoded
source attributes to the target attributes. However, encoded attributes are
often correlated with relevant image content. Thus, the source attribute
information can often be hidden in the disentangled features, leading to
unwanted image editing effects. In this paper, we propose an Attribute
Information Removal and Reconstruction (AIRR) network that prevents such
information hiding by learning how to remove the attribute information
entirely, creating attribute excluded features, and then learns to directly
inject the desired attributes in a reconstructed image. We evaluate our
approach on four diverse datasets with a variety of attributes including
DeepFashion Synthesis, DeepFashion Fine-grained Attribute, CelebA and
CelebA-HQ, where our model improves attribute manipulation accuracy and top-k
retrieval rate by 10% on average over prior work. A user study also reports
that AIRR manipulated images are preferred over prior work in up to 76% of
cases.

D3 is arguably the most popular tool for implementing web based
visualizations. Yet D3 has a steep learning curve that may hinder its adoption
and continued use. To simplify the process of programming D3 visualizations, we
must first understand the space of implementation practices that D3 users
engage in. We present a qualitative analysis of 2500 D3 visualizations and
their corresponding implementations. We find that 5 visualization types (Bar
Charts, Geomaps, Line Charts, Scatterplots, and Force Directed Graphs) account
for 80% of D3 visualizations found in our corpus. While implementation styles
vary slightly across designs, the underlying code structure for all
visualization types remains the same; presenting an opportunity for code reuse.
Using our corpus of D3 examples, we synthesize reusable code templates for
eight popular D3 visualization types and share them in our open source
repository. Based on our results, we discuss design considerations for
leveraging users' implementation patterns to reduce visualization design effort
through design templates and auto-generated code recommendations.

A broad class of stochastic volatility models are defined by systems of
stochastic differential equations. While these models have seen widespread
success in domains such as finance and statistical climatology, they typically
lack an ability to condition on historical data to produce a true posterior
distribution. To address this fundamental limitation, we show how to re-cast a
class of stochastic volatility models as a hierarchical Gaussian process (GP)
model with specialized covariance functions. This GP model retains the
inductive biases of the stochastic volatility model while providing the
posterior predictive distribution given by GP inference. Within this framework,
we take inspiration from well studied domains to introduce a new class of
models, Volt and Magpie, that significantly outperform baselines in stock and
wind speed forecasting, and naturally extend to the multitask setting.

Continual learning requires incremental compatibility with a sequence of
tasks. However, the design of model architecture remains an open question: In
general, learning all tasks with a shared set of parameters suffers from severe
interference between tasks; while learning each task with a dedicated parameter
subspace is limited by scalability. In this work, we theoretically analyze the
generalization errors for learning plasticity and memory stability in continual
learning, which can be uniformly upper-bounded by (1) discrepancy between task
distributions, (2) flatness of loss landscape and (3) cover of parameter space.
Then, inspired by the robust biological learning system that processes
sequential experiences with multiple parallel compartments, we propose
Cooperation of Small Continual Learners (CoSCL) as a general strategy for
continual learning. Specifically, we present an architecture with a fixed
number of narrower sub-networks to learn all incremental tasks in parallel,
which can naturally reduce the two errors through improving the three
components of the upper bound. To strengthen this advantage, we encourage to
cooperate these sub-networks by penalizing the difference of predictions made
by their feature representations. With a fixed parameter budget, CoSCL can
improve a variety of representative continual learning approaches by a large
margin (e.g., up to 10.64% on CIFAR-100-SC, 9.33% on CIFAR-100-RS, 11.45% on
CUB-200-2011 and 6.72% on Tiny-ImageNet) and achieve the new state-of-the-art

In competitive two-agent environments, deep reinforcement learning (RL)
methods based on the \emph{Double Oracle (DO)} algorithm, such as \emph{Policy
Space Response Oracles (PSRO)} and \emph{Anytime PSRO (APSRO)}, iteratively add
RL best response policies to a population. Eventually, an optimal mixture of
these population policies will approximate a Nash equilibrium. However, these
methods might need to add all deterministic policies before converging. In this
work, we introduce \emph{Self-Play PSRO (SP-PSRO)}, a method that adds an
approximately optimal stochastic policy to the population in each iteration.
Instead of adding only deterministic best responses to the opponent's least
exploitable population mixture, SP-PSRO also learns an approximately optimal
stochastic policy and adds it to the population as well. As a result, SP-PSRO
empirically tends to converge much faster than APSRO and in many games
converges in just a few iterations.

Relying on the premise that the performance of a binary neural network can be
largely restored with eliminated quantization error between full-precision
weight vectors and their corresponding binary vectors, existing works of
network binarization frequently adopt the idea of model robustness to reach the
aforementioned objective. However, robustness remains to be an ill-defined
concept without solid theoretical support. In this work, we introduce the
Lipschitz continuity, a well-defined functional property, as the rigorous
criteria to define the model robustness for BNN. We then propose to retain the
Lipschitz continuity as a regularization term to improve the model robustness.
Particularly, while the popular Lipschitz-involved regularization methods often
collapse in BNN due to its extreme sparsity, we design the Retention Matrices
to approximate spectral norms of the targeted weight matrices, which can be
deployed as the approximation for the Lipschitz constant of BNNs without the
exact Lipschitz constant computation (NP-hard). Our experiments prove that our
BNN-specific regularization method can effectively strengthen the robustness of
BNN (testified on ImageNet-C), achieving state-of-the-art performance on CIFAR
and ImageNet.

Performance of vehicle-to-vehicle (V2V) communications depends highly on the
employed scheduling approach. While centralized network schedulers offer high
V2V communication reliability, their operation is conventionally restricted to
areas with full cellular network coverage. In contrast, in
out-of-cellular-coverage areas, comparatively inefficient distributed radio
resource management is used. To exploit the benefits of the centralized
approach for enhancing the reliability of V2V communications on roads lacking
cellular coverage, we propose VRLS (Vehicular Reinforcement Learning
Scheduler), a centralized scheduler that proactively assigns resources for
out-of-coverage V2V communications \textit{before} vehicles leave the cellular
network coverage. By training in simulated vehicular environments, VRLS can
learn a scheduling policy that is robust and adaptable to environmental
changes, thus eliminating the need for targeted (re-)training in complex
real-life environments. We evaluate the performance of VRLS under varying
mobility, network load, wireless channel, and resource configurations. VRLS
outperforms the state-of-the-art distributed scheduling algorithm in zones
without cellular network coverage by reducing the packet error rate by half in
highly loaded conditions and achieving near-maximum reliability in low-load

Continuous deep learning models, referred to as Neural Ordinary Differential
Equations (Neural ODEs), have received considerable attention over the last
several years. Despite their burgeoning impact, there is a lack of formal
analysis techniques for these systems. In this paper, we consider a general
class of neural ODEs with varying architectures and layers, and introduce a
novel reachability framework that allows for the formal analysis of their
behavior. The methods developed for the reachability analysis of neural ODEs
are implemented in a new tool called NNVODE. Specifically, our work extends an
existing neural network verification tool to support neural ODEs. We
demonstrate the capabilities and efficacy of our methods through the analysis
of a set of benchmarks that include neural ODEs used for classification, and in
control and dynamical systems, including an evaluation of the efficacy and
capabilities of our approach with respect to existing software tools within the
continuous-time systems reachability literature, when it is possible to do so.

This paper investigates the post-hoc calibration of confidence for
"exploratory" machine learning classification problems. The difficulty in these
problems stems from the continuing desire to push the boundaries of which
categories have enough examples to generalize from when curating datasets, and
confusion regarding the validity of those categories. We argue that for such
problems the "one-versus-all" approach (top-label calibration) must be used
rather than the "calibrate-the-full-response-matrix" approach advocated
elsewhere in the literature. We introduce and test four new algorithms designed
to handle the idiosyncrasies of category-specific confidence estimation. Chief
among these methods is the use of kernel density ratios for confidence
calibration including a novel, bulletproof algorithm for choosing the
bandwidth. We test our claims and explore the limits of calibration on a
bioinformatics application (PhANNs) as well as the classic MNIST benchmark.
Finally, our analysis argues that post-hoc calibration should always be
performed, should be based only on the test dataset, and should be
sanity-checked visually.

The forward full-wave modeling of ground-penetrating radar (GPR) facilitates
the understanding and interpretation of GPR data. Traditional forward solvers
require excessive computational resources, especially when their repetitive
executions are needed in signal processing and/or machine learning algorithms
for GPR data inversion. To alleviate the computational burden, a deep
learning-based 2D GPR forward solver is proposed to predict the GPR B-scans of
subsurface objects buried in the heterogeneous soil. The proposed solver is
constructed as a bimodal encoder-decoder neural network. Two encoders followed
by an adaptive feature fusion module are designed to extract informative
features from the subsurface permittivity and conductivity maps. The decoder
subsequently constructs the B-scans from the fused feature representations. To
enhance the network's generalization capability, transfer learning is employed
to fine-tune the network for new scenarios vastly different from those in
training set. Numerical results show that the proposed solver achieves a mean
relative error of 1.28%. For predicting the B-scan of one subsurface object,
the proposed solver requires 12 milliseconds, which is 22,500x less than the
time required by a classical physics-based solver.

Automatic differentiation is an invaluable feature of machine learning and
quantum machine learning software libraries. In this work it is shown how
quantum automatic differentiation can be used to solve the condensed-matter
problem of computing fidelity susceptibility, a quantity whose value may be
indicative of a phase transition in a system. Results are presented using
simulations including hardware noise for small instances of the
transverse-field Ising model, and a number of optimizations that can be applied
are highlighted. Error mitigation (zero-noise extrapolation) is applied within
the autodifferentiation framework to a number of gradient values required for
computation of fidelity susceptibility and a related quantity, the second
derivative of the energy. Such computations are found to be highly sensitive to
the additional statistical noise incurred by the error mitigation method.

Fully cooperative, partially observable multi-agent problems are ubiquitous
in the real world. In this paper, we focus on a specific subclass of
coordination problems in which humans are able to discover self-explaining
deviations (SEDs). SEDs are actions that deviate from the common understanding
of what reasonable behavior would be in normal circumstances. They are taken
with the intention of causing another agent or other agents to realize, using
theory of mind, that the circumstance must be abnormal. We first motivate SED
with a real world example and formalize its definition. Next, we introduce a
novel algorithm, improvement maximizing self-explaining deviations
(IMPROVISED), to perform SEDs. Lastly, we evaluate IMPROVISED both in an
illustrative toy setting and the popular benchmark setting Hanabi, where it is
the first method to produce so called finesse plays, which are regarded as one
of the more iconic examples of human theory of mind.

In this paper, we propose a feature-free method for detecting phishing
websites using the Normalized Compression Distance (NCD), a parameter-free
similarity measure which computes the similarity of two websites by compressing
them, thus eliminating the need to perform any feature extraction. It also
removes any dependence on a specific set of website features. This method
examines the HTML of webpages and computes their similarity with known phishing
websites, in order to classify them. We use the Furthest Point First algorithm
to perform phishing prototype extractions, in order to select instances that
are representative of a cluster of phishing webpages. We also introduce the use
of an incremental learning algorithm as a framework for continuous and adaptive
detection without extracting new features when concept drift occurs. On a large
dataset, our proposed method significantly outperforms previous methods in
detecting phishing websites, with an AUC score of 98.68%, a high true positive
rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of
0.58%. Our approach uses prototypes, eliminating the need to retain long term
data in the future, and is feasible to deploy in real systems with a processing
time of roughly 0.3 seconds.

Detecting performance issues and identifying their root causes in the runtime
is a challenging task. Typically, developers use methods such as logging and
tracing to identify bottlenecks. These solutions are, however, not ideal as
they are time-consuming and require manual effort. In this paper, we propose a
method to automate the task of detecting latency outliers using system-level
traces and then comparing them to identify the root cause(s). Our method makes
use of dependency graphs to show internal interactions between threads and
system resources. With these graphs, one can pinpoint where performance issues
occur. However, a single trace can be composed of a large number of requests,
each generating one graph. To automate the task of identifying outliers within
the dataset, we use machine learning density-based models and statistical
calculations such as -score. Our evaluation shows an accuracy greater than 97 %
on outlier detection, making them appropriate for in-production servers and
industry-level use cases.

Microstructures, i.e., architected materials, are designed today, typically,
by maximizing an objective, such as bulk modulus, subject to a volume
constraint. However, in many applications, it is often more appropriate to
impose constraints on other physical quantities of interest. In this paper, we
consider such generalized microstructural optimization problems where any of
the microstructural quantities, namely, bulk, shear, Poisson ratio, or volume,
can serve as the objective, while the remaining can serve as constraints. In
particular, we propose here a neural-network (NN) framework to solve such
problems. The framework relies on the classic density formulation of
microstructural optimization, but the density field is represented through the
NN's weights and biases. The main characteristics of the proposed NN framework
are: (1) it supports automatic differentiation, eliminating the need for manual
sensitivity derivations, (2) smoothing filters are not required due to implicit
filtering, (3) the framework can be easily extended to multiple-materials, and
(4) a high-resolution microstructural topology can be recovered through a
simple post-processing step. The framework is illustrated through a variety of
microstructural optimization problems.

Multi-institutional collaborations are key for learning generalizable MRI
synthesis models that translate source- onto target-contrast images. To
facilitate collaboration, federated learning (FL) adopts decentralized training
and mitigates privacy concerns by avoiding sharing of imaging data. However,
FL-trained synthesis models can be impaired by the inherent heterogeneity in
the data distribution, with domain shifts evident when common or variable
translation tasks are prescribed across sites. Here we introduce the first
personalized FL method for MRI Synthesis (pFLSynth) to improve reliability
against domain shifts. pFLSynth is based on an adversarial model that produces
latents specific to individual sites and source-target contrasts, and leverages
novel personalization blocks to adaptively tune the statistics and weighting of
feature maps across the generator stages given latents. To further promote site
specificity, partial model aggregation is employed over downstream layers of
the generator while upstream layers are retained locally. As such, pFLSynth
enables training of a unified synthesis model that can reliably generalize
across multiple sites and translation tasks. Comprehensive experiments on
multi-site datasets clearly demonstrate the enhanced performance of pFLSynth
against prior federated methods in multi-contrast MRI synthesis.

The theory of learning in games has extensively studied situations where
agents respond dynamically to each other by optimizing a fixed utility
function. However, in real situations, the strategic environment varies as a
result of past agent choices. Unfortunately, the analysis techniques that
enabled a rich characterization of the emergent behavior in static environment
games fail to cope with dynamic environment games. To address this, we develop
a general framework using probabilistic couplings to extend the analysis of
static environment games to dynamic ones. Using this approach, we obtain
sufficient conditions under which traditional characterizations of Nash
equilibria with best response dynamics and stochastic stability with log-linear
learning can be extended to dynamic environment games. As a case study, we pose
a model of cyber threat intelligence sharing between firms and a simple dynamic
game-theoretic model of social precautions in an epidemic, both of which
feature dynamic environments. For both examples, we obtain conditions under
which the emergent behavior is characterized in the dynamic game by performing
the traditional analysis on a reference static environment game.

Randomly pivoted Cholesky (RPCholesky) is a natural algorithm for computing a
rank-k approximation of an N x N positive semidefinite (psd) matrix. RPCholesky
can be implemented with just a few lines of code. It requires only (k+1)N entry
evaluations and O(k^2 N) additional arithmetic operations. This paper offers
the first serious investigation of its experimental and theoretical behavior.
Empirically, RPCholesky matches or improves on the performance of alternative
algorithms for low-rank psd approximation. Furthermore, RPCholesky provably
achieves near-optimal approximation guarantees. The simplicity, effectiveness,
and robustness of this algorithm strongly support its use in scientific
computing and machine learning applications.

We investigate Nash equilibrium learning in a competitive Markov Game (MG)
environment, where multiple agents compete, and multiple Nash equilibria can
exist. In particular, for an oligopolistic dynamic pricing environment, exact
Nash equilibria are difficult to obtain due to the curse-of-dimensionality. We
develop a new model-free method to find approximate Nash equilibria.
Gradient-free black box optimization is then applied to estimate $\epsilon$,
the maximum reward advantage of an agent unilaterally deviating from any joint
policy, and to also estimate the $\epsilon$-minimizing policy for any given
state. The policy-$\epsilon$ correspondence and the state to
$\epsilon$-minimizing policy are represented by neural networks, the latter
being the Nash Policy Net. During batch update, we perform Nash Q learning on
the system, by adjusting the action probabilities using the Nash Policy Net. We
demonstrate that an approximate Nash equilibrium can be learned, particularly
in the dynamic pricing domain where exact solutions are often intractable.

The current study of cell architecture of inflammation in histopathology
images commonly performed for diagnosis and research purposes excludes a lot of
information available on the biopsy slide. In autoimmune diseases, major
outstanding research questions remain regarding which cell types participate in
inflammation at the tissue level, and how they interact with each other. While
these questions can be partially answered using traditional methods, artificial
intelligence approaches for segmentation and classification provide a much more
efficient method to understand the architecture of inflammation in autoimmune
disease, holding great promise for novel insights. In this paper, we
empirically develop deep learning approaches that use dermatomyositis biopsies
of human tissue to detect and identify inflammatory cells. Our approach
improves classification performance by 26% and segmentation performance by 5%.
We also propose a novel post-processing autoencoder architecture that improves
segmentation performance by an additional 3%.

Single-image haze removal is a long-standing hurdle for computer vision
applications. Several works have been focused on transferring advances from
image classification, detection, and segmentation to the niche of image
dehazing, primarily focusing on contrastive learning and knowledge
distillation. However, these approaches prove computationally expensive,
raising concern regarding their applicability to on-the-edge use-cases. This
work introduces a simple, lightweight, and efficient framework for single-image
haze removal, exploiting rich "dark-knowledge" information from a lightweight
pre-trained super-resolution model via the notion of heterogeneous knowledge
distillation. We designed a feature affinity module to maximize the flow of
rich feature semantics from the super-resolution teacher to the student
dehazing network. In order to evaluate the efficacy of our proposed framework,
its performance as a plug-and-play setup to a baseline model is examined. Our
experiments are carried out on the RESIDE-Standard dataset to demonstrate the
robustness of our framework to the synthetic and real-world domains. The
extensive qualitative and quantitative results provided establish the
effectiveness of the framework, achieving gains of upto 15\% (PSNR) while
reducing the model size by $\sim$20 times.

The efficient resolution of optimization problems is one of the key issues in
today's industry. This task relies mainly on classical algorithms that present
scalability problems and processing limitations. Quantum computing has emerged
to challenge these types of problems. In this paper, we focus on the
Metropolis-Hastings quantum algorithm that is based on quantum walks. We use
this algorithm to build a quantum software tool called Quantum Metropolis
Solver (QMS). We validate QMS with the N-Queen problem to show a potential
quantum advantage in an example that can be easily extrapolated to an
Artificial Intelligence domain. We carry out different simulations to validate
the performance of QMS and its configuration.

This paper proposes a data-driven machine learning framework for parameter
estimation and uncertainty quantification in epidemic models based on two key
ingredients: (i) prior parameters learning via the cross-entropy method and
(ii) update of the model calibration and uncertainty propagation through
approximate Bayesian computation. The effectiveness of the new methodology is
illustrated with the aid of actual data from COVID-19 epidemic at Rio de
Janeiro city in Brazil, employing an ordinary differential equation-based model
with a generalized SEIR-type mechanistic structure that includes time-dependent
transmission rate, asymptomatics, and hospitalizations. A minimization problem
with two cost terms (number of hospitalizations and deaths) is formulated, and
twelve parameters are identified. The calibrated model provides a consistent
description of the available data, able to extrapolate forecasts over a few
weeks, which makes the proposed methodology very appealing for use in the
context of real-time epidemic modeling.

We consider the bandit optimization problem with the reward function defined
over graph-structured data. This problem has important applications in molecule
design and drug discovery, where the reward is naturally invariant to graph
permutations. The key challenges in this setting are scaling to large domains,
and to graphs with many nodes. We resolve these challenges by embedding the
permutation invariance into our model. In particular, we show that graph neural
networks (GNNs) can be used to estimate the reward function, assuming it
resides in the Reproducing Kernel Hilbert Space of a permutation-invariant
additive kernel. By establishing a novel connection between such kernels and
the graph neural tangent kernel (GNTK), we introduce the first GNN confidence
bound and use it to design a phased-elimination algorithm with sublinear
regret. Our regret bound depends on the GNTK's maximum information gain, which
we also provide a bound for. While the reward function depends on all $N$ node
features, our guarantees are independent of the number of graph nodes $N$.
Empirically, our approach exhibits competitive performance and scales well on
graph-structured domains.

Moving Object Detection (MOD) is a fundamental step for many computer vision
applications. MOD becomes very challenging when a video sequence captured from
a static or moving camera suffers from the challenges: camouflage, shadow,
dynamic backgrounds, and lighting variations, to name a few. Deep learning
methods have been successfully applied to address MOD with competitive
performance. However, in order to handle the overfitting problem, deep learning
methods require a large amount of labeled data which is a laborious task as
exhaustive annotations are always not available. Moreover, some MOD deep
learning methods show performance degradation in the presence of unseen video
sequences because the testing and training splits of the same sequences are
involved during the network learning process. In this work, we pose the problem
of MOD as a node classification problem using Graph Convolutional Neural
Networks (GCNNs). Our algorithm, dubbed as GraphMOD-Net, encompasses instance
segmentation, background initialization, feature extraction, and graph
construction. GraphMOD-Net is tested on unseen videos and outperforms
state-of-the-art methods in unsupervised, semi-supervised, and supervised
learning in several challenges of the Change Detection 2014 (CDNet2014) and
UCSD background subtraction datasets.

Graph Signal Processing (GSP) is an emerging research field that extends the
concepts of digital signal processing to graphs. GSP has numerous applications
in different areas such as sensor networks, machine learning, and image
processing. The sampling and reconstruction of static graph signals have played
a central role in GSP. However, many real-world graph signals are inherently
time-varying and the smoothness of the temporal differences of such graph
signals may be used as a prior assumption. In the current work, we assume that
the temporal differences of graph signals are smooth, and we introduce a novel
algorithm based on the extension of a Sobolev smoothness function for the
reconstruction of time-varying graph signals from discrete samples. We explore
some theoretical aspects of the convergence rate of our Time-varying Graph
signal Reconstruction via Sobolev Smoothness (GraphTRSS) algorithm by studying
the condition number of the Hessian associated with our optimization problem.
Our algorithm has the advantage of converging faster than other methods that
are based on Laplacian operators without requiring expensive eigenvalue
decomposition or matrix inversions. The proposed GraphTRSS is evaluated on
several datasets including two COVID-19 datasets and it has outperformed many
existing state-of-the-art methods for time-varying graph signal reconstruction.
GraphTRSS has also shown excellent performance on two environmental datasets
for the recovery of particulate matter and sea surface temperature signals.

This paper studies a simple extension of image-based Masked Autoencoders
(MAE) to self-supervised representation learning from audio spectrograms.
Following the Transformer encoder-decoder design in MAE, our Audio-MAE first
encodes audio spectrogram patches with a high masking ratio, feeding only the
non-masked tokens through encoder layers. The decoder then re-orders and
decodes the encoded context padded with mask tokens, in order to reconstruct
the input spectrogram. We find it beneficial to incorporate local window
attention in the decoder, as audio spectrograms are highly correlated in local
time and frequency bands. We then fine-tune the encoder with a lower masking
ratio on target datasets. Empirically, Audio-MAE sets new state-of-the-art
performance on six audio and speech classification tasks, outperforming other
recent models that use external supervised pre-training. The code and models
will be at https://github.com/facebookresearch/AudioMAE.

In this paper, we address the challenging problem of 3D concept grounding
(i.e. segmenting and learning visual concepts) by looking at RGBD images and
reasoning about paired questions and answers. Existing visual reasoning
approaches typically utilize supervised methods to extract 2D segmentation
masks on which concepts are grounded. In contrast, humans are capable of
grounding concepts on the underlying 3D representation of images. However,
traditionally inferred 3D representations (e.g., point clouds, voxelgrids, and
meshes) cannot capture continuous 3D features flexibly, thus making it
challenging to ground concepts to 3D regions based on the language description
of the object being referred to. To address both issues, we propose to leverage
the continuous, differentiable nature of neural fields to segment and learn
concepts. Specifically, each 3D coordinate in a scene is represented as a
high-dimensional descriptor. Concept grounding can then be performed by
computing the similarity between the descriptor vector of a 3D coordinate and
the vector embedding of a language concept, which enables segmentations and
concept learning to be jointly learned on neural fields in a differentiable
fashion. As a result, both 3D semantic and instance segmentations can emerge
directly from question answering supervision using a set of defined neural
operators on top of neural fields (e.g., filtering and counting). Experimental
results show that our proposed framework outperforms
unsupervised/language-mediated segmentation models on semantic and instance
segmentation tasks, as well as outperforms existing models on the challenging
3D aware visual reasoning tasks. Furthermore, our framework can generalize well
to unseen shape categories and real scans.

It has been recently shown that a state generated by a one-dimensional noisy
quantum computer is well approximated by a matrix product operator with a
finite bond dimension independent of the number of qubits. We show that full
quantum state tomography can be performed for such a state with a minimal
number of measurement settings using a method known as tensor train cross
approximation. The method works for reconstructing full rank density matrices
and only requires measuring local operators, which are routinely performed in
state-of-art experimental quantum platforms. Our method requires exponentially
fewer state copies than the best known tomography method for unstructured
states and local measurements. The fidelity of our reconstructed state can be
further improved via supervised machine learning, without demanding more
experimental data. Scalable tomography is achieved if the full state can be
reconstructed from local reductions.

We propose a novel 2-stage sub 8-bit quantization aware training algorithm
for all components of a 250K parameter feedforward, streaming, state-free
keyword spotting model. For the 1st-stage, we adapt a recently proposed
quantization technique using a non-linear transformation with tanh(.) on dense
layer weights. In the 2nd-stage, we use linear quantization methods on the rest
of the network, including other parameters (bias, gain, batchnorm), inputs, and
activations. We conduct large scale experiments, training on 26,000 hours of
de-identified production, far-field and near-field audio data (evaluating on
4,000 hours of data). We organize our results in two embedded chipset settings:
a) with commodity ARM NEON instruction set and 8-bit containers, we present
accuracy, CPU, and memory results using sub 8-bit weights (4, 5, 8-bit) and
8-bit quantization of rest of the network; b) with off-the-shelf neural network
accelerators, for a range of weight bit widths (1 and 5-bit), while presenting
accuracy results, we project reduction in memory utilization. In both
configurations, our results show that the proposed algorithm can achieve: a)
parity with a full floating point model's operating point on a detection error
tradeoff (DET) curve in terms of false detection rate (FDR) at false rejection
rate (FRR); b) significant reduction in compute and memory, yielding up to 3
times improvement in CPU consumption and more than 4 times improvement in
memory consumption.

Denoising diffusion probabilistic models (DDPMs) have recently achieved
leading performances in many generative tasks. However, the inherited iterative
sampling process costs hinder their applications to text-to-speech deployment.
Through the preliminary study on diffusion model parameterization, we find that
previous gradient-based TTS models require hundreds or thousands of iterations
to guarantee high sample quality, which poses a challenge for accelerating
sampling. In this work, we propose ProDiff, on progressive fast diffusion model
for high-quality text-to-speech. Unlike previous work estimating the gradient
for data density, ProDiff parameterizes the denoising model by directly
predicting clean data to avoid distinct quality degradation in accelerating
sampling. To tackle the model convergence challenge with decreased diffusion
iterations, ProDiff reduces the data variance in the target site via knowledge
distillation. Specifically, the denoising model uses the generated
mel-spectrogram from an N-step DDIM teacher as the training target and distills
the behavior into a new model with N/2 steps. As such, it allows the TTS model
to make sharp predictions and further reduces the sampling time by orders of
magnitude. Our evaluation demonstrates that ProDiff needs only 2 iterations to
synthesize high-fidelity mel-spectrograms, while it maintains sample quality
and diversity competitive with state-of-the-art models using hundreds of steps.
ProDiff enables a sampling speed of 24x faster than real-time on a single
NVIDIA 2080Ti GPU, making diffusion models practically applicable to
text-to-speech synthesis deployment for the first time. Our extensive ablation
studies demonstrate that each design in ProDiff is effective, and we further
show that ProDiff can be easily extended to the multi-speaker setting. Audio
samples are available at \url{https://ProDiff.github.io/.}

We propose a novel approach for semi-supervised learning (SSL) designed to
overcome distribution shifts between training and real-world data arising in
the keyword spotting (KWS) task. Shifts from training data distribution are a
key challenge for real-world KWS tasks: when a new model is deployed on device,
the gating of the accepted data undergoes a shift in distribution, making the
problem of timely updates via subsequent deployments hard. Despite the shift,
we assume that the marginal distributions on labels do not change. We utilize a
modified teacher/student training framework, where labeled training data is
augmented with unlabeled data. Note that the teacher does not have access to
the new distribution as well. To train effectively with a mix of human and
teacher labeled data, we develop a teacher labeling strategy based on
confidence heuristics to reduce entropy on the label distribution from the
teacher model; the data is then sampled to match the marginal distribution on
the labels. Large scale experimental results show that a convolutional neural
network (CNN) trained on far-field audio, and evaluated on far-field audio
drawn from a different distribution, obtains a 14.3% relative improvement in
false discovery rate (FDR) at equal false reject rate (FRR), while yielding a
5% improvement in FDR under no distribution shift. Under a more severe
distribution shift from far-field to near-field audio with a smaller fully
connected network (FCN) our approach achieves a 52% relative improvement in FDR
at equal FRR, while yielding a 20% relative improvement in FDR on the original

This paper concerns quasi-stochastic approximation (QSA) to solve root
finding problems commonly found in applications to optimization and
reinforcement learning. The general constant gain algorithm may be expressed as
the time-inhomogeneous ODE $ \frac{d}{dt} \Theta_t= \alpha f_t (\Theta_t)$,
with state process $\Theta$ evolving on $\mathbb{R}^n$. Theory is based on an
almost periodic vector field, so that in particular the time average of
$f_t(\theta)$ defines the time-homogeneous mean vector field $\bar{f} \colon
\mathbb{R}^d \to \mathbb{R}^d$ with $\bar{f}(\theta^*)=0$. Under smoothness
assumptions on the functions involved, the following exact representation is
obtained: \[
  \frac{d}{dt} \Theta_t= \alpha [\bar{f}(\Theta_t) -\alpha \bar \Upsilon_t +
\alpha^2 \mathcal{W}_t^0 + \alpha \frac{d}{dt} \mathcal{W}_t^1 +
\frac{d^2}{dt^2} \mathcal{W}_t^2] \] along with formulae for the smooth signals
$\{\bar \Upsilon_t , \mathcal{W}_t^i : i=0, 1, 2\}$. This new representation,
combined with new conditions for ultimate boundedness, has many applications
for furthering the theory of QSA and its applications, including the following
implications developed in this paper:
  (i) A proof that the estimation error $\| \Theta_t - \theta^* \|$ is of order
$O(\alpha)$, but can be reduced to $O(\alpha^2)$ using a second order linear
filter.
  (ii) In application to extremum seeking control, it is found that the results
do not apply because the standard algorithms are not Lipschitz continuous. A
new approach is presented to ensure that the required Lipschitz bounds hold,
and from this we obtain stability, transient bounds, and asymptotic bias of
order $O(\alpha^2)$, and asymptotic variance of order $O(\alpha^4)$.
  (iii) It is in general possible to obtain better than $O(\alpha)$ bounds on
error in traditional stochastic approximation when there is Markovian noise.

Transformer models have recently emerged as one of the foundational models in
natural language processing, and as a byproduct, there is significant recent
interest and investment in scaling these models. However, the training and
inference costs of these large Transformer language models are prohibitive,
thus necessitating more research in identifying more efficient variants. In
this work, we propose a simple yet effective modification to the Transformer
architecture inspired by the literature in statistical language modeling, by
augmenting the model with n-grams that are constructed from a discrete latent
representation of the text sequence. We evaluate our model, the N-Grammer on
language modeling on the C4 data-set as well as text classification on the
SuperGLUE data-set, and find that it outperforms several strong baselines such
as the Transformer and the Primer. We open-source our model for reproducibility
purposes in Jax.

Importance Sampling (IS) is a method for approximating expectations under a
target distribution using independent samples from a proposal distribution and
the associated importance weights. In many applications, the target
distribution is known only up to a normalization constant, in which case
self-normalized IS (SNIS) can be used. While the use of self-normalization can
have a positive effect on the dispersion of the estimator, it introduces bias.
In this work, we propose a new method, BR-SNIS, whose complexity is essentially
the same as that of SNIS and which significantly reduces bias without
increasing the variance. This method is a wrapper in the sense that it uses the
same proposal samples and importance weights as SNIS, but makes clever use of
iterated sampling--importance resampling (ISIR) to form a bias-reduced version
of the estimator. We furnish the proposed algorithm with rigorous theoretical
results, including new bias, variance and high-probability bounds, and these
are illustrated by numerical examples.

We present the implementation of nonlinear control algorithms based on linear
and quadratic approximations of the objective from a functional viewpoint. We
present a gradient descent, a Gauss-Newton method, a Newton method,
differential dynamic programming approaches with linear quadratic or quadratic
approximations, various line-search strategies, and regularized variants of
these algorithms. We derive the computational complexities of all algorithms in
a differentiable programming framework and present sufficient optimality
conditions. We compare the algorithms on several benchmarks, such as autonomous
car racing using a bicycle model of a car. The algorithms are coded in a
differentiable programming language in a publicly available package.

When working with user data providing well-defined privacy guarantees is
paramount. In this work we aim to manipulate and share an entire sparse dataset
with a third party privately. In fact, differential privacy has emerged as the
gold standard of privacy, however, when it comes to sharing sparse datasets, as
one of our main results, we prove that \emph{any} differentially private
mechanism that maintains a reasonable similarity with the initial dataset is
doomed to have a very weak privacy guarantee. Hence we need to opt for other
privacy notions such as $k$-anonymity are better at preserving utility in this
context. In this work we present a variation of $k$-anonymity, which we call
smooth $k$-anonymity and design simple algorithms that efficiently provide
smooth $k$-anonymity. We further perform an empirical evaluation to back our
theoretical guarantees, and show that our algorithm improves the performance in
downstream machine learning tasks on anonymized data.

We consider shrinkage estimation of higher order Hilbert space valued Bochner
integrals in a non-parametric setting. We propose estimators that shrink the
$U$-statistic estimator of the Bochner integral towards a pre-specified target
element in the Hilbert space. Depending on the degeneracy of the kernel of the
$U$-statistic, we construct consistent shrinkage estimators with fast rates of
convergence, and develop oracle inequalities comparing the risks of the the
$U$-statistic estimator and its shrinkage version. Surprisingly, we show that
the shrinkage estimator designed by assuming complete degeneracy of the kernel
of the $U$-statistic is a consistent estimator even when the kernel is not
complete degenerate. This work subsumes and improves upon Krikamol et al.,
2016, JMLR and Zhou et al., 2019, JMVA, which only handle mean element and
covariance operator estimation in a reproducing kernel Hilbert space. We also
specialize our results to normal mean estimation and show that for $d\ge 3$,
the proposed estimator strictly improves upon the sample mean in terms of the
mean squared error.

Focusing on Random Forests, we propose a multi-armed contextual bandit
recommendation framework for feature-based selection of a single shallow tree
of the learned ensemble. The trained system, which works on top of the Random
Forest, dynamically identifies a base predictor that is responsible for
providing the final output. In this way, we obtain local interpretations by
observing the rules of the recommended tree. The carried out experiments reveal
that our dynamic method is superior to an independent fitted CART decision tree
and comparable to the whole black-box Random Forest in terms of predictive
performances.

Supervised learning depth estimation methods can achieve good performance
when trained on high-quality ground-truth, like LiDAR data. However, LiDAR can
only generate sparse 3D maps which causes losing information. Obtaining
high-quality ground-truth depth data per pixel is difficult to acquire. In
order to overcome this limitation, we propose a novel approach combining
structure information from a promising Plane and Parallax geometry pipeline
with depth information into a U-Net supervised learning network, which results
in quantitative and qualitative improvement compared to existing popular
learning-based methods. In particular, the model is evaluated on two
large-scale and challenging datasets: KITTI Vision Benchmark and Cityscapes
dataset and achieve the best performance in terms of relative error. Compared
with pure depth supervision models, our model has impressive performance on
depth prediction of thin objects and edges, and compared to structure
prediction baseline, our model performs more robustly.

One hour before sunrise, one can experience the dawn chorus where birds from
different species sing together. In this scenario, high levels of polyphony, as
in the number of overlapping sound sources, are prone to happen resulting in a
complex acoustic outcome. Sound Event Detection (SED) tasks analyze acoustic
scenarios in order to identify the occurring events and their respective
temporal information. However, highly dense scenarios can be hard to process
and have not been studied in depth. Here we show, using a Convolutional
Recurrent Neural Network (CRNN), how birdsong polyphonic scenarios can be
detected when dealing with higher polyphony and how effectively this type of
model can face a very dense scene with up to 10 overlapping birds. We found
that models trained with denser examples (i.e., higher polyphony) learn at a
similar rate as models that used simpler samples in their training set.
Additionally, the model trained with the densest samples maintained a
consistent score for all polyphonies, while the model trained with the least
dense samples degraded as the polyphony increased. Our results demonstrate that
highly dense acoustic scenarios can be dealt with using CRNNs. We expect that
this study serves as a starting point for working on highly populated bird
scenarios such as dawn chorus or other dense acoustic problems.

Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates
high-quality videos with higher resolution and higher frame rates. Existing
advanced methods accomplish ST-VSR tasks through the association of Spatial and
Temporal video super-resolution (S-VSR and T-VSR). These methods require two
alignments and fusions in S-VSR and T-VSR, which is obviously redundant and
fails to sufficiently explore the information flow of consecutive spatial LR
frames. Although bidirectional learning (future-to-past and past-to-future) was
introduced to cover all input frames, the direct fusion of final predictions
fails to sufficiently exploit intrinsic correlations of bidirectional motion
learning and spatial information from all frames. We propose an effective yet
efficient recurrent network with bidirectional interaction for ST-VSR, where
only one alignment and fusion is needed. Specifically, it first performs
backward inference from future to past, and then follows forward inference to
super-resolve intermediate frames. The backward and forward inferences are
assigned to learn structures and details to simplify the learning task with
joint optimizations. Furthermore, a Hybrid Fusion Module (HFM) is designed to
aggregate and distill information to refine spatial information and reconstruct
high-quality video frames. Extensive experiments on two public datasets
demonstrate that our method outperforms state-of-the-art methods in efficiency,
and reduces calculation cost by about 22%.

State-of-the-art federated learning methods can perform far worse than their
centralized counterparts when clients have dissimilar data distributions. For
neural networks, even when centralized SGD easily finds a solution that is
simultaneously performant for all clients, current federated optimization
methods fail to converge to a comparable solution. We show that this
performance disparity can largely be attributed to optimization challenges
presented by nonconvexity. Specifically, we find that the early layers of the
network do learn useful features, but the final layers fail to make use of
them. That is, federated optimization applied to this non-convex problem
distorts the learning of the final layers. Leveraging this observation, we
propose a Train-Convexify-Train (TCT) procedure to sidestep this issue: first,
learn features using off-the-shelf methods (e.g., FedAvg); then, optimize a
convexified problem obtained from the network's empirical neural tangent kernel
approximation. Our technique yields accuracy improvements of up to +36% on
FMNIST and +37% on CIFAR10 when clients have dissimilar data.

Randomized algorithms based on sketching have become a workhorse tool in
low-rank matrix approximation. To use these algorithms safely in applications,
they should be coupled with diagnostics to assess the quality of approximation.
To meet this need, this paper proposes a jackknife resampling method to
estimate the variability of the output of a randomized matrix computation. The
variability estimate can recognize that a computation requires additional data
or that the computation is intrinsically unstable. As examples, the paper
studies jackknife estimates for two randomized low-rank matrix approximation
algorithms. In each case, the operation count for the jackknife estimate is
independent of the dimensions of the target matrix. In numerical experiments,
the estimator accurately assesses variability and also provides an
order-of-magnitude estimate of the mean-square error.

In this work, we revisit the marking decisions made in the standard adaptive
finite element method (AFEM). Experience shows that a na\"{i}ve marking policy
leads to inefficient use of computational resources for adaptive mesh
refinement (AMR). Consequently, using AFEM in practice often involves ad-hoc or
time-consuming offline parameter tuning to set appropriate parameters for the
marking subroutine. To address these practical concerns, we recast AMR as a
Markov decision process in which refinement parameters can be selected
on-the-fly at run time, without the need for pre-tuning by expert users. In
this new paradigm, the refinement parameters are also chosen adaptively via a
marking policy that can be optimized using methods from reinforcement learning.
We use the Poisson equation to demonstrate our techniques on $h$- and
$hp$-refinement benchmark problems, and our experiments suggest that superior
marking policies remain undiscovered for many classical AFEM applications.
Furthermore, an unexpected observation from this work is that marking policies
trained on one family of PDEs are sometimes robust enough to perform well on
problems far outside the training family. For illustration, we show that a
simple $hp$-refinement policy trained on 2D domains with only a single
re-entrant corner can be deployed on far more complicated 2D domains, and even
3D domains, without significant performance loss. For reproduction and broader
adoption, we accompany this work with an open-source implementation of our
methods.

In order to promote the use of machine learning in 5G, the International
Telecommunication Union (ITU) proposed in 2021 the second edition of the ITU
AI/ML in 5G challenge, with over 1600 participants from 82 countries. This work
details the second place solution overall, which is also the winning solution
of the Graph Neural Networking Challenge 2021. We tackle the problem of
generalization when applying a model to a 5G network that may have longer paths
and larger link capacities than the ones observed in training. To achieve this,
we propose to first extract robust features related to Queueing Theory (QT),
and then fine-tune the analytical baseline prediction using a modification of
the Routenet Graph Neural Network (GNN) model. The proposed solution
generalizes much better than simply using Routenet, and manages to reduce the
analytical baseline's 10.42 mean absolute percent error to 1.45 (1.27 with an
ensemble). This suggests that making small changes to an approximate model that
is known to be robust can be an effective way to improve accuracy without
compromising generalization.

Deep learning has been shown to accurately assess 'hidden' phenotypes and
predict biomarkers from medical imaging beyond traditional clinician
interpretation of medical imaging. Given the black box nature of artificial
intelligence (AI) models, caution should be exercised in applying models to
healthcare as prediction tasks might be short-cut by differences in
demographics across disease and patient populations. Using large
echocardiography datasets from two healthcare systems, we test whether it is
possible to predict age, race, and sex from cardiac ultrasound images using
deep learning algorithms and assess the impact of varying confounding
variables. We trained video-based convolutional neural networks to predict age,
sex, and race. We found that deep learning models were able to identify age and
sex, while unable to reliably predict race. Without considering confounding
differences between categories, the AI model predicted sex with an AUC of 0.85
(95% CI 0.84 - 0.86), age with a mean absolute error of 9.12 years (95% CI 9.00
- 9.25), and race with AUCs ranging from 0.63 - 0.71. When predicting race, we
show that tuning the proportion of a confounding variable (sex) in the training
data significantly impacts model AUC (ranging from 0.57 to 0.84), while in
training a sex prediction model, tuning a confounder (race) did not
substantially change AUC (0.81 - 0.83). This suggests a significant proportion
of the model's performance on predicting race could come from confounding
features being detected by AI. Further work remains to identify the particular
imaging features that associate with demographic information and to better
understand the risks of demographic identification in medical AI as it pertains
to potentially perpetuating bias and disparities.

Generative Adversarial Networks (GANs) have gained significant attention in
several computer vision tasks for generating high-quality synthetic data.
Various medical applications including diagnostic imaging and radiation therapy
can benefit greatly from synthetic data generation due to data scarcity in the
domain. However, medical image data is typically kept in 3D space, and
generative models suffer from the curse of dimensionality issues in generating
such synthetic data. In this paper, we investigate the potential of GANs for
generating connected 3D volumes. We propose an improved version of 3D
$\alpha$-GAN by incorporating various architectural enhancements. On a
synthetic dataset of connected 3D spheres and ellipsoids, our model can
generate fully connected 3D shapes with similar geometrical characteristics to
that of training data. We also show that our 3D GAN model can successfully
generate high-quality 3D tumor volumes and associated treatment specifications
(e.g., isocenter locations). Similar moment invariants to the training data as
well as fully connected 3D shapes confirm that improved 3D $\alpha$-GAN
implicitly learns the training data distribution, and generates
realistic-looking samples. The capability of improved 3D $\alpha$-GAN makes it
a valuable source for generating synthetic medical image data that can help
future research in this domain.

High energy physics experiments essentially rely on simulated data for
physics analyses. However, running detailed simulation models requires a
tremendous amount of computation resources. New approaches to speed up detector
simulation are therefore needed. The generation of calorimeter responses is
often the most expensive component of the simulation chain for HEP experiments.
It was shown that deep learning techniques, especially Generative Adversarial
Networks, may be used to reproduce the calorimeter response. However, those
applications are challenging, as the generated responses need evaluation not
only in terms of image consistency: different physics-based quality metrics
should be also taken into consideration. In our work, we develop a multitask
GAN-based framework with the goal to speed up the response generation of the
Electromagnetic Calorimeter (ECAL) of the LHCb detector at LHC. We introduce
the Auxiliary Regressor as a second task to evaluate a proxy metric of the
given input that is used by the Discriminator of the GAN. We show that this
approach improves the stability of GAN and the model produces samples with
better physics distributions.

Bayesian optimization is a popular framework for the optimization of black
box functions. Multifidelity methods allows to accelerate Bayesian optimization
by exploiting low-fidelity representations of expensive objective functions.
Popular multifidelity Bayesian strategies rely on sampling policies that
account for the immediate reward obtained evaluating the objective function at
a specific input, precluding greater informative gains that might be obtained
looking ahead more steps. This paper proposes a non-myopic multifidelity
Bayesian framework to grasp the long-term reward from future steps of the
optimization. Our computational strategy comes with a two-step lookahead
multifidelity acquisition function that maximizes the cumulative reward
obtained measuring the improvement in the solution over two steps ahead. We
demonstrate that the proposed algorithm outperforms a standard multifidelity
Bayesian framework on popular benchmark optimization problems.

Consider the regression problem where the response $Y\in\mathbb{R}$ and the
covariate $X\in\mathbb{R}^d$ for $d\geq 1$ are \textit{unmatched}. Under this
scenario, we do not have access to pairs of observations from the distribution
of $(X, Y)$, but instead, we have separate datasets $\{Y_i\}_{i=1}^n$ and
$\{X_j\}_{j=1}^m$, possibly collected from different sources. We study this
problem assuming that the regression function is linear and the noise
distribution is known or can be estimated. We introduce an estimator of the
regression vector based on deconvolution and demonstrate its consistency and
asymptotic normality under an identifiability assumption. In the general case,
we show that our estimator (DLSE: Deconvolution Least Squared Estimator) is
consistent in terms of an extended $\ell_2$ norm. Using this observation, we
devise a method for semi-supervised learning, i.e., when we have access to a
small sample of matched pairs $(X_k, Y_k)$. Several applications with synthetic
and real datasets are considered to illustrate the theory.

A major challenge for ridesharing platforms is to guarantee profit and
fairness simultaneously, especially in the presence of misaligned incentives of
drivers and riders. We focus on the dispatching-pricing problem to maximize the
total revenue while keeping both drivers and riders satisfied. We study the
computational complexity of the problem, provide a novel two-phased pricing
solution with revenue and fairness guarantees, extend it to stochastic settings
and develop a dynamic (a.k.a., learning-while-doing) algorithm that actively
collects data to learn the demand distribution during the scheduling process.
We also conduct extensive experiments to demonstrate the effectiveness of our

This article introduces new multiplicative updates for nonnegative matrix
factorization with the $\beta$-divergence and sparse regularization of one of
the two factors (say, the activation matrix). It is well known that the norm of
the other factor (the dictionary matrix) needs to be controlled in order to
avoid an ill-posed formulation. Standard practice consists in constraining the
columns of the dictionary to have unit norm, which leads to a nontrivial
optimization problem. Our approach leverages a reparametrization of the
original problem into the optimization of an equivalent scale-invariant
objective function. From there, we derive block-descent
majorization-minimization algorithms that result in simple multiplicative
updates for either $\ell_{1}$-regularization or the more "aggressive"
log-regularization. In contrast with other state-of-the-art methods, our
algorithms are universal in the sense that they can be applied to any
$\beta$-divergence (i.e., any value of $\beta$) and that they come with
convergence guarantees. We report numerical comparisons with existing heuristic
and Lagrangian methods using various datasets: face images, an audio
spectrogram, hyperspectral data, and song play counts. We show that our methods
obtain solutions of similar quality at convergence (similar objective values)
but with significantly reduced CPU times.

Variational quantum algorithms such as the Quantum Approximation Optimization
Algorithm (QAOA) in recent years have gained popularity as they provide the
hope of using NISQ devices to tackle hard combinatorial optimization problems.
It is, however, known that at low depth, certain locality constraints of QAOA
limit its performance. To go beyond these limitations, a non-local variant of
QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of
approximate solutions. The RQAOA has been studied comparatively less than QAOA,
and it is less understood, for instance, for what family of instances it may
fail to provide high quality solutions. However, as we are tackling
$\mathsf{NP}$-hard problems (specifically, the Ising spin model), it is
expected that RQAOA does fail, raising the question of designing even better
quantum algorithms for combinatorial optimization. In this spirit, we identify
and analyze cases where RQAOA fails and, based on this, propose a reinforcement
learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show
that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly
better on these identified instances where RQAOA underperforms, and is
similarly performing on instances where RQAOA is near-optimal. Our work
exemplifies the potentially beneficial synergy between reinforcement learning
and quantum (inspired) optimization in the design of new, even better
heuristics for hard problems.

Methods allowing the synthesis of realistic cell shapes could help generate
training data sets to improve cell tracking and segmentation in biomedical
images. Deep generative models for cell shape synthesis require a light-weight
and flexible representation of the cell shape. However, commonly used
voxel-based representations are unsuitable for high-resolution shape synthesis,
and polygon meshes have limitations when modeling topology changes such as cell
growth or mitosis. In this work, we propose to use level sets of signed
distance functions (SDFs) to represent cell shapes. We optimize a neural
network as an implicit neural representation of the SDF value at any point in a
3D+time domain. The model is conditioned on a latent code, thus allowing the
synthesis of new and unseen shape sequences. We validate our approach
quantitatively and qualitatively on C. elegans cells that grow and divide, and
lung cancer cells with growing complex filopodial protrusions. Our results show
that shape descriptors of synthetic cells resemble those of real cells, and
that our model is able to generate topologically plausible sequences of complex
cell shapes in 3D+time.

Quantization is one of the most applied Deep Neural Network (DNN) compression
strategies, when deploying a trained DNN model on an embedded system or a cell
phone. This is owing to its simplicity and adaptability to a wide range of
applications and circumstances, as opposed to specific Artificial Intelligence
(AI) accelerators and compilers that are often designed only for certain
specific hardware (e.g., Google Coral Edge TPU). With the growing demand for
quantization, ensuring the reliability of this strategy is becoming a critical
challenge. Traditional testing methods, which gather more and more genuine data
for better assessment, are often not practical because of the large size of the
input space and the high similarity between the original DNN and its quantized
counterpart. As a result, advanced assessment strategies have become of
paramount importance. In this paper, we present DiverGet, a search-based
testing framework for quantization assessment. DiverGet defines a space of
metamorphic relations that simulate naturally-occurring distortions on the
inputs. Then, it optimally explores these relations to reveal the disagreements
among DNNs of different arithmetic precision. We evaluate the performance of
DiverGet on state-of-the-art DNNs applied to hyperspectral remote sensing
images. We chose the remote sensing DNNs as they're being increasingly deployed
at the edge (e.g., high-lift drones) in critical domains like climate change
research and astronomy. Our results show that DiverGet successfully challenges
the robustness of established quantization techniques against
naturally-occurring shifted data, and outperforms its most recent concurrent,
DiffChaser, with a success rate that is (on average) four times higher.

We propose a novel deep learning model named ACLNet, for cloud segmentation
from ground images. ACLNet uses both deep neural network and machine learning
(ML) algorithm to extract complementary features. Specifically, it uses
EfficientNet-B0 as the backbone, "`a trous spatial pyramid pooling" (ASPP) to
learn at multiple receptive fields, and "global attention module" (GAM) to
extract finegrained details from the image. ACLNet also uses k-means clustering
to extract cloud boundaries more precisely. ACLNet is effective for both
daytime and nighttime images. It provides lower error rate, higher recall and
higher F1-score than state-of-art cloud segmentation models. The source-code of
ACLNet is available here: https://github.com/ckmvigil/ACLNet.

In recent years, machine learning algorithms have become ubiquitous in a
multitude of high-stakes decision-making applications. The unparalleled ability
of machine learning algorithms to learn patterns from data also enables them to
incorporate biases embedded within. A biased model can then make decisions that
disproportionately harm certain groups in society -- limiting their access to
financial services, for example. The awareness of this problem has given rise
to the field of Fair ML, which focuses on studying, measuring, and mitigating
unfairness in algorithmic prediction, with respect to a set of protected groups
(e.g., race or gender). However, the underlying causes for algorithmic
unfairness still remain elusive, with researchers divided between blaming
either the ML algorithms or the data they are trained on. In this work, we
maintain that algorithmic unfairness stems from interactions between models and
biases in the data, rather than from isolated contributions of either of them.
To this end, we propose a taxonomy to characterize data bias and we study a set
of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML
algorithms exhibit under different data bias settings. On our real-world
account-opening fraud use case, we find that each setting entails specific
trade-offs, affecting fairness in expected value and variance -- the latter
often going unnoticed. Moreover, we show how algorithms compare differently in
terms of accuracy and fairness, depending on the biases affecting the data.
Finally, we note that under specific data bias conditions, simple
pre-processing interventions can successfully balance group-wise error rates,
while the same techniques fail in more complex settings.

We develop a reinforcement learning (RL) framework for applications that deal
with sequential decisions and exogenous uncertainty, such as resource
allocation and inventory management. In these applications, the uncertainty is
only due to exogenous variables like future demands. A popular approach is to
predict the exogenous variables using historical data and then plan with the
predictions. However, this indirect approach requires high-fidelity modeling of
the exogenous process to guarantee good downstream decision-making, which can
be impractical when the exogenous process is complex. In this work we propose
an alternative approach based on hindsight learning which sidesteps modeling
the exogenous process. Our key insight is that, unlike Sim2Real RL, we can
revisit past decisions in the historical data and derive counterfactual
consequences for other actions in these applications. Our framework uses
hindsight-optimal actions as the policy training signal and has strong
theoretical guarantees on decision-making performance. We develop an algorithm
using our framework to allocate compute resources for real-world Microsoft
Azure workloads. The results show our approach learns better policies than
domain-specific heuristics and Sim2Real RL baselines.

We develop a Reinforcement Learning (RL) framework for improving an existing
behavior policy via sparse, user-interpretable changes. Our goal is to make
minimal changes while gaining as much benefit as possible. We define a minimal
change as having a sparse, global contrastive explanation between the original
and proposed policy. We improve the current policy with the constraint of
keeping that global contrastive explanation short. We demonstrate our framework
with a discrete MDP and a continuous 2D navigation domain.

Continual learning (CL) over non-stationary data streams remains one of the
long-standing challenges in deep neural networks (DNNs) as they are prone to
catastrophic forgetting. CL models can benefit from self-supervised
pre-training as it enables learning more generalizable task-agnostic features.
However, the effect of self-supervised pre-training diminishes as the length of
task sequences increases. Furthermore, the domain shift between pre-training
data distribution and the task distribution reduces the generalizability of the
learned representations. To address these limitations, we propose Task Agnostic
Representation Consolidation (TARC), a two-stage training paradigm for CL that
intertwines task-agnostic and task-specific learning whereby self-supervised
training is followed by supervised learning for each task. To further restrict
the deviation from the learned representations in the self-supervised stage, we
employ a task-agnostic auxiliary loss during the supervised stage. We show that
our training paradigm can be easily added to memory- or regularization-based
approaches and provides consistent performance gain across more challenging CL
settings. We further show that it leads to more robust and well-calibrated
models.

Text readability assessment has a wide range of applications for different
target people, from language learners to people with disabilities. The fast
pace of textual content production on the web makes it impossible to measure
text complexity without the benefit of machine learning and natural language
processing techniques. Although various research addressed the readability
assessment of English text in recent years, there is still room for improvement
of the models for other languages. In this paper, we proposed a new model for
text complexity assessment for German text based on transfer learning. Our
results show that the model outperforms more classical solutions based on
linguistic features extraction from input text. The best model is based on the
BERT pre-trained language model achieved the Root Mean Square Error (RMSE) of
0.483.

Intuition might suggest that motion and dynamic information are key to
video-based action recognition. In contrast, there is evidence that
state-of-the-art deep-learning video understanding architectures are biased
toward static information available in single frames. Presently, a methodology
and corresponding dataset to isolate the effects of dynamic information in
video are missing. Their absence makes it difficult to understand how well
contemporary architectures capitalize on dynamic vs. static information. We
respond with a novel Appearance Free Dataset (AFD) for action recognition. AFD
is devoid of static information relevant to action recognition in a single
frame. Modeling of the dynamics is necessary for solving the task, as the
action is only apparent through consideration of the temporal dimension. We
evaluated 11 contemporary action recognition architectures on AFD as well as
its related RGB video. Our results show a notable decrease in performance for
all architectures on AFD compared to RGB. We also conducted a complimentary
study with humans that shows their recognition accuracy on AFD and RGB is very
similar and much better than the evaluated architectures on AFD. Our results
motivate a novel architecture that revives explicit recovery of optical flow,
within a contemporary design for best performance on AFD and RGB.

Fairness has become an essential problem in many domains of Machine Learning
(ML), such as classification, natural language processing, and Generative
Adversarial Networks (GANs). In this research effort, we study the unfairness
of GANs. We formally define a new fairness notion for generative models in
terms of the distribution of generated samples sharing the same protected
attributes (gender, race, etc.). The defined fairness notion (representational
fairness) requires the distribution of the sensitive attributes at the test
time to be uniform, and, in particular for GAN model, we show that this
fairness notion is violated even when the dataset contains equally represented
groups, i.e., the generator favors generating one group of samples over the
others at the test time. In this work, we shed light on the source of this
representation bias in GANs along with a straightforward method to overcome
this problem. We first show on two widely used datasets (MNIST, SVHN) that when
the norm of the gradient of one group is more important than the other during
the discriminator's training, the generator favours sampling data from one
group more than the other at test time. We then show that controlling the
groups' gradient norm by performing group-wise gradient norm clipping in the
discriminator during the training leads to a more fair data generation in terms
of representational fairness compared to existing models while preserving the
quality of generated samples.

The training of artificial neural networks (ANNs) is nowadays a highly
relevant algorithmic procedure with many applications in science and industry.
Roughly speaking, ANNs can be regarded as iterated compositions between affine
linear functions and certain fixed nonlinear functions, which are usually
multidimensional versions of a one-dimensional so-called activation function.
The most popular choice of such a one-dimensional activation function is the
rectified linear unit (ReLU) activation function which maps a real number to
its positive part $ \mathbb{R} \ni x \mapsto \max\{ x, 0 \} \in \mathbb{R} $.
In this article we propose and analyze a modified variant of the standard
training procedure of such ReLU ANNs in the sense that we propose to restrict
the negative gradient flow dynamics to a large submanifold of the ANN parameter
space, which is a strict $ C^{ \infty } $-submanifold of the entire ANN
parameter space that seems to enjoy better regularity properties than the
entire ANN parameter space but which is also sufficiently large and
sufficiently high dimensional so that it can represent all ANN realization
functions that can be represented through the entire ANN parameter space. In
the special situation of shallow ANNs with just one-dimensional ANN layers we
also prove for every Lipschitz continuous target function that every gradient
flow trajectory on this large submanifold of the ANN parameter space is
globally bounded. For the standard gradient flow on the entire ANN parameter
space with Lipschitz continuous target functions it remains an open problem of
research to prove or disprove the global boundedness of gradient flow
trajectories even in the situation of shallow ANNs with just one-dimensional
ANN layers.

The availability of genomic data has grown exponentially in the last decade,
mainly due to the development of new sequencing technologies. Based on the
interactions between genes (and gene products) extracted from the increasing
genomic data, numerous studies have focused on the identification of
associations between genes and functions. While these studies have shown great
promise, the problem of annotating genes with functions remains an open
challenge. In this work, we present a method to detect missing annotations in
hierarchical multi-label classification datasets. We propose a method that
exploits the class hierarchy by computing aggregated probabilities to the paths
of classes from the leaves to the root for each instance. The proposed method
is presented in the context of predicting missing gene function annotations,
where these aggregated probabilities are further used to select a set of
annotations to be verified through in vivo experiments. The experiments on
Oriza sativa Japonica, a variety of rice, showcase that incorporating the
hierarchy of classes into the method often improves the predictive performance
and our proposed method yields superior results when compared to competitor
methods from the literature.

The application of Artificial Intelligence (AI) and Machine Learning (ML) to
cybersecurity challenges has gained traction in industry and academia,
partially as a result of widespread malware attacks on critical systems such as
cloud infrastructures and government institutions. Intrusion Detection Systems
(IDS), using some forms of AI, have received widespread adoption due to their
ability to handle vast amounts of data with a high prediction accuracy. These
systems are hosted in the organizational Cyber Security Operation Center (CSoC)
as a defense tool to monitor and detect malicious network flow that would
otherwise impact the Confidentiality, Integrity, and Availability (CIA). CSoC
analysts rely on these systems to make decisions about the detected threats.
However, IDSs designed using Deep Learning (DL) techniques are often treated as
black box models and do not provide a justification for their predictions. This
creates a barrier for CSoC analysts, as they are unable to improve their
decisions based on the model's predictions. One solution to this problem is to
design explainable IDS (X-IDS).
  This survey reviews the state-of-the-art in explainable AI (XAI) for IDS, its
current challenges, and discusses how these challenges span to the design of an
X-IDS. In particular, we discuss black box and white box approaches
comprehensively. We also present the tradeoff between these approaches in terms
of their performance and ability to produce explanations. Furthermore, we
propose a generic architecture that considers human-in-the-loop which can be
used as a guideline when designing an X-IDS. Research recommendations are given
from three critical viewpoints: the need to define explainability for IDS, the
need to create explanations tailored to various stakeholders, and the need to
design metrics to evaluate explanations.

Analyzing the planet at scale with satellite imagery and machine learning is
a dream that has been constantly hindered by the cost of difficult-to-access
highly-representative high-resolution imagery. To remediate this, we introduce
here the WorldStrat dataset. The largest and most varied such publicly
available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5
m/pixel, empowered by European Space Agency's Phi-Lab as part of the ESA-funded
QueryPlanet project, we curate nearly 10,000 sqkm of unique locations to ensure
stratified representation of all types of land-use across the world: from
agriculture to ice caps, from forests to multiple urbanization densities. We
also enrich those with locations typically under-represented in ML datasets:
sites of humanitarian interest, illegal mining sites, and settlements of
persons at risk. We temporally-match each high-resolution image with multiple
low-resolution images from the freely accessible lower-resolution Sentinel-2
satellites at 10 m/pixel. We accompany this dataset with an open-source Python
package to: rebuild or extend the WorldStrat dataset, train and infer baseline
algorithms, and learn with abundant tutorials, all compatible with the popular
EO-learn toolbox. We hereby hope to foster broad-spectrum applications of ML to
satellite imagery, and possibly develop from free public low-resolution
Sentinel2 imagery the same power of analysis allowed by costly private
high-resolution imagery. We illustrate this specific point by training and
releasing several highly compute-efficient baselines on the task of Multi-Frame
Super-Resolution. High-resolution Airbus imagery is CC BY-NC, while the labels
and Sentinel2 imagery are CC BY, and the source code and pre-trained models
under BSD. The dataset is available at https://zenodo.org/record/6810792 and
the software package at https://github.com/worldstrat/worldstrat .

High-quality data is a key aspect of modern machine learning. However, labels
generated by humans suffer from issues like label noise and class ambiguities.
We raise the question of whether hard labels are sufficient to represent the
underlying ground truth distribution in the presence of these inherent
imprecision. Therefore, we compare the disparity of learning with hard and soft
labels quantitatively and qualitatively for a synthetic and a real-world
dataset. We show that the application of soft labels leads to improved
performance and yields a more regular structure of the internal feature space.

Tackling new machine learning problems with neural networks always means
optimizing numerous hyperparameters that define their structure and strongly
impact their performances. In this work, we study the use of goal-oriented
sensitivity analysis, based on the Hilbert-Schmidt Independence Criterion
(HSIC), for hyperparameter analysis and optimization. Hyperparameters live in
spaces that are often complex and awkward. They can be of different natures
(categorical, discrete, boolean, continuous), interact, and have
inter-dependencies. All this makes it non-trivial to perform classical
sensitivity analysis. We alleviate these difficulties to obtain a robust
analysis index that is able to quantify hyperparameters' relative impact on a
neural network's final error. This valuable tool allows us to better understand
hyperparameters and to make hyperparameter optimization more interpretable. We
illustrate the benefits of this knowledge in the context of hyperparameter
optimization and derive an HSIC-based optimization algorithm that we apply on
MNIST and Cifar, classical machine learning data sets, but also on the
approximation of Runge function and Bateman equations solution, of interest for
scientific machine learning. This method yields neural networks that are both
competitive and cost-effective.

Microscopy imaging techniques are instrumental for characterization and
analysis of biological structures. As these techniques typically render 3D
visualization of cells by stacking 2D projections, issues such as out-of-plane
excitation and low resolution in the $z$-axis may pose challenges (even for
human experts) to detect individual cells in 3D volumes as these
non-overlapping cells may appear as overlapping. In this work, we introduce a
comprehensive method for accurate 3D instance segmentation of cells in the
brain tissue. The proposed method combines the 2D YOLO detection method with a
multi-view fusion algorithm to construct a 3D localization of the cells. Next,
the 3D bounding boxes along with the data volume are input to a 3D U-Net
network that is designed to segment the primary cell in each 3D bounding box,
and in turn, to carry out instance segmentation of cells in the entire volume.
The promising performance of the proposed method is shown in comparison with
some current deep learning-based 3D instance segmentation methods.

High-quality data is necessary for modern machine learning. However, the
acquisition of such data is difficult due to noisy and ambiguous annotations of
humans. The aggregation of such annotations to determine the label of an image
leads to a lower data quality. We propose a data-centric image classification
benchmark with ten real-world datasets and multiple annotations per image to
allow researchers to investigate and quantify the impact of such data quality
issues. With the benchmark we can study the impact of annotation costs and
(semi-)supervised methods on the data quality for image classification by
applying a novel methodology to a range of different algorithms and diverse
datasets. Our benchmark uses a two-phase approach via a data label improvement
method in the first phase and a fixed evaluation model in the second phase.
Thereby, we give a measure for the relation between the input labeling effort
and the performance of (semi-)supervised algorithms to enable a deeper insight
into how labels should be created for effective model training. Across
thousands of experiments, we show that one annotation is not enough and that
the inclusion of multiple annotations allows for a better approximation of the
real underlying class distribution. We identify that hard labels can not
capture the ambiguity of the data and this might lead to the common issue of
overconfident models. Based on the presented datasets, benchmarked methods, and
analysis, we create multiple research opportunities for the future directed at
the improvement of label noise estimation approaches, data annotation schemes,
realistic (semi-)supervised learning, or more reliable image collection.

This article presents two model-free controllers for wind-turbine torque and
pitch control. These controllers are based on reinforcement learning (RL) and
Bayesian optimization (BO) and do not rely on any mathematical model of the
wind-turbine dynamics, in contrast to classical approaches designed on
linearized models. The model-free controllers were benchmarked against a
proportional-integral-derivative (PID) regulator in a numerical environment
using Blade Element Momentum theory for computing the aerodynamic torque and
the blade loads. The results showed that the model-free approaches could
increase power harvesting while reducing wind turbine loads.

The contribution of galactic supernova remnants (SNRs) to the origin of
cosmic rays (CRs) is an important open question in modern astrophysics.
Broadband non-thermal emission is a useful proxy for probing the energy budget
and production history of CRs in SNRs. We conduct hydrodynamic simulations to
model the long-term SNR evolution from explosion all the way to the radiative
phase (or $3\times10^5$ yrs at maximum), and compute the time evolution of the
broadband non-thermal spectrum to explore its potential applications on
constraining the surrounding environments as well as the natures and mass-loss
histories of the SNR progenitors. A parametric survey is performed on the
ambient environments separated into two main groups, namely a homogeneous
medium with a uniform gas density and one with the presence of a circumstellar
structure created by the stellar wind of a massive red-supergiant (RSG)
progenitor star. Our results reveal a highly diverse evolution history of the
non-thermal emission closely correlated to the environmental characteristics of
a SNR. Up to the radiative phase, the roles of CR re-acceleration and
ion-neutral wave damping on the spectral evolution are investigated. Finally,
we make an assessment of the future prospect of SNR observations by the
next-generation hard X-ray space observatory FORCE and predict what we can
learn from their comparison with our evolution models.

Object detection, as a fundamental computer vision task, has achieved a
remarkable progress with the emergence of deep neural networks. Nevertheless,
few works explore the adversarial robustness of object detectors to resist
adversarial attacks for practical applications in various real-world scenarios.
Detectors have been greatly challenged by unnoticeable perturbation, with sharp
performance drop on clean images and extremely poor performance on adversarial
images. In this work, we empirically explore the model training for adversarial
robustness in object detection, which greatly attributes to the conflict
between learning clean images and adversarial images. To mitigate this issue,
we propose a Robust Detector (RobustDet) based on adversarially-aware
convolution to disentangle gradients for model learning on clean and
adversarial images. RobustDet also employs the Adversarial Image Discriminator
(AID) and Consistent Features with Reconstruction (CFR) to ensure a reliable
robustness. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that
our model effectively disentangles gradients and significantly enhances the
detection robustness with maintaining the detection ability on clean images.

Machine learning has proved useful in many software disciplines, including
computer vision, speech and audio processing, natural language processing,
robotics and some other fields. However, its applicability has been
significantly hampered due its black-box nature and significant resource
consumption. Performance is achieved at the expense of enormous computational
resource and usually compromising the robustness and trustworthiness of the
model. Recent researches have been identifying a lack of interactivity as the
prime source of these machine learning problems. Consequently, interactive
machine learning (iML) has acquired increased attention of researchers on
account of its human-in-the-loop modality and relatively efficient resource
utilization. Thereby, a state-of-the-art review of interactive machine learning
plays a vital role in easing the effort toward building human-centred models.
In this paper, we provide a comprehensive analysis of the state-of-the-art of
iML. We analyze salient research works using merit-oriented and
application/task oriented mixed taxonomy. We use a bottom-up clustering
approach to generate a taxonomy of iML research works. Research works on
adversarial black-box attacks and corresponding iML based defense system,
exploratory machine learning, resource constrained learning, and iML
performance evaluation are analyzed under their corresponding theme in our
merit-oriented taxonomy. We have further classified these research works into
technical and sectoral categories. Finally, research opportunities that we
believe are inspiring for future work in iML are discussed thoroughly.

Recently, large, high-quality public datasets have led to the development of
convolutional neural networks that can detect lymph node metastases of breast
cancer at the level of expert pathologists. Many cancers, regardless of the
site of origin, can metastasize to lymph nodes. However, collecting and
annotating high-volume, high-quality datasets for every cancer type is
challenging. In this paper we investigate how to leverage existing high-quality
datasets most efficiently in multi-task settings for closely related tasks.
Specifically, we will explore different training and domain adaptation
strategies, including prevention of catastrophic forgetting, for colon and
head-and-neck cancer metastasis detection in lymph nodes.
  Our results show state-of-the-art performance on both cancer metastasis
detection tasks. Furthermore, we show the effectiveness of repeated adaptation
of networks from one cancer type to another to obtain multi-task metastasis
detection networks. Last, we show that leveraging existing high-quality
datasets can significantly boost performance on new target tasks and that
catastrophic forgetting can be effectively mitigated using regularization.

Neural approaches for combinatorial optimization (CO) equip a learning
mechanism to discover powerful heuristics for solving complex real-world
problems. While neural approaches capable of high-quality solutions in a single
shot are emerging, state-of-the-art approaches are often unable to take full
advantage of the solving time available to them. In contrast, hand-crafted
heuristics perform highly effective search well and exploit the computation
time given to them, but contain heuristics that are difficult to adapt to a
dataset being solved. With the goal of providing a powerful search procedure to
neural CO approaches, we propose simulation-guided beam search (SGBS), which
examines candidate solutions within a fixed-width tree search that both a
neural net-learned policy and a simulation (rollout) identify as promising. We
further hybridize SGBS with efficient active search (EAS), where SGBS enhances
the quality of solutions backpropagated in EAS, and EAS improves the quality of
the policy used in SGBS. We evaluate our methods on well-known CO benchmarks
and show that SGBS significantly improves the quality of the solutions found
under reasonable runtime assumptions.

Image registration is useful for quantifying morphological changes in
longitudinal MR images from prostate cancer patients. This paper describes a
development in improving the learning-based registration algorithms, for this
challenging clinical application often with highly variable yet limited
training data. First, we report that the latent space can be clustered into a
much lower dimensional space than that commonly found as bottleneck features at
the deep layer of a trained registration network. Based on this observation, we
propose a hierarchical quantization method, discretizing the learned feature
vectors using a jointly-trained dictionary with a constrained size, in order to
improve the generalisation of the registration networks. Furthermore, a novel
collaborative dictionary is independently optimised to incorporate additional
prior information, such as the segmentation of the gland or other regions of
interest, in the latent quantized space. Based on 216 real clinical images from
86 prostate cancer patients, we show the efficacy of both the designed
components. Improved registration accuracy was obtained with statistical
significance, in terms of both Dice on gland and target registration error on
corresponding landmarks, the latter of which achieved 5.46 mm, an improvement
of 28.7\% from the baseline without quantization. Experimental results also
show that the difference in performance was indeed minimised between training
and testing data.

Failure to timely diagnose and effectively treat depression leads to over 280
million people suffering from this psychological disorder worldwide. The
information cues of depression can be harvested from diverse heterogeneous
resources, e.g., audio, visual, and textual data, raising demand for new
effective multi-modal fusion approaches for automatic estimation. In this work,
we tackle the task of automatically identifying depression from multi-modal
data and introduce a sub-attention mechanism for linking heterogeneous
information while leveraging Convolutional Bidirectional LSTM as our backbone.
To validate this idea, we conduct extensive experiments on the public DAIC-WOZ
benchmark for depression assessment featuring different evaluation modes and
taking gender-specific biases into account. The proposed model yields effective
results with 0.89 precision and 0.70 F1-score in detecting major depression and
4.92 MAE in estimating the severity. Our attention-based fusion module
consistently outperforms conventional late fusion approaches and achieves
competitive performance compared to the previously published depression
estimation frameworks, while learning to diagnose the disorder end-to-end and
relying on far fewer preprocessing steps.

Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g.,
artifacts and noise), which severely compromise the exact diagnosis of
cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality
Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error
diagnosis. The primary challenges of CCTA VIQA are that the local part of
coronary that determines final quality is hard to locate. To tackle the
challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL)
problem, and exploit Transformer-based MIL backbone (termed as T-MIL) to
aggregate the multiple instances along the coronary centerline into the final
quality. However, not all instances are informative for final quality. There
are some quality-irrelevant/negative instances intervening the exact quality
assessment(e.g., instances covering only background or the coronary in
instances is not identifiable). Therefore, we propose a Progressive
Reinforcement learning based Instance Discarding module (termed as PRID) to
progressively remove quality-irrelevant/negative instances for CCTA VIQA. Based
on the above two modules, we propose a Reinforced Transformer Network (RTN) for
automatic CCTA VIQA based on end-to-end optimization. Extensive experimental
results demonstrate that our proposed method achieves the state-of-the-art
performance on the real-world CCTA dataset, exceeding previous MIL methods by a

Machine learning (ML) is increasingly used to automate networking tasks, in a
paradigm known as zero-touch network and service management (ZSM). In
particular, Deep Reinforcement Learning (DRL) techniques have recently gathered
much attention for their ability to learn taking complex decisions in different
fields. In the ZSM context, DRL is an appealing candidate for tasks such as
dynamic resource allocation, that is generally formulated as hard optimization
problems. At the same time, successful training and deployment of DRL agents in
real-world scenarios faces a number of challenges that we outline and address
in this paper. Tackling the case of Wireless Local Area Network (WLAN) radio
resource management, we report guidelines that extend to other usecases and
more general contexts.

Learning the evolution of real-time strategy (RTS) game is a challenging
problem in artificial intelligent (AI) system. In this paper, we present a
novel Hebbian learning method to extract the global feature of point sets in
StarCraft II game units, and its application to predict the movement of the
points. Our model includes encoder, LSTM, and decoder, and we train the encoder
with the unsupervised learning method. We introduce the concept of neuron
activity aware learning combined with k-Winner-Takes-All. The optimal value of
neuron activity is mathematically derived, and experiments support the
effectiveness of the concept over the downstream task. Our Hebbian learning
rule benefits the prediction with lower loss compared to self-supervised
learning. Also, our model significantly saves the computational cost such as
activations and FLOPs compared to a frame-based approach.

UNet [27] is widely used in semantic segmentation due to its simplicity and
effectiveness. However, its manually-designed architecture is applied to a
large number of problem settings, either with no architecture optimizations, or
with manual tuning, which is time consuming and can be sub-optimal. In this
work, firstly, we propose Markov Random Field Neural Architecture Search
(MRF-NAS) that extends and improves the recent Adaptive and Optimal Network
Width Search (AOWS) method [4] with (i) a more general MRF framework (ii)
diverse M-best loopy inference (iii) differentiable parameter learning. This
provides the necessary NAS framework to efficiently explore network
architectures that induce loopy inference graphs, including loops that arise
from skip connections. With UNet as the backbone, we find an architecture,
MRF-UNet, that shows several interesting characteristics. Secondly, through the
lens of these characteristics, we identify the sub-optimality of the original
UNet architecture and further improve our results with MRF-UNetV2. Experiments
show that our MRF-UNets significantly outperform several benchmarks on three
aerial image datasets and two medical image datasets while maintaining low
computational costs. The code is available at:
https://github.com/zifuwanggg/MRF-UNets.

In this paper, we propose a genuine group-level contrastive visual
representation learning method whose linear evaluation performance on ImageNet
surpasses the vanilla supervised learning. Two mainstream unsupervised learning
schemes are the instance-level contrastive framework and clustering-based
schemes. The former adopts the extremely fine-grained instance-level
discrimination whose supervisory signal is not efficient due to the false
negatives. Though the latter solves this, they commonly come with some
restrictions affecting the performance. To integrate their advantages, we
design the SMoG method. SMoG follows the framework of contrastive learning but
replaces the contrastive unit from instance to group, mimicking
clustering-based methods. To achieve this, we propose the momentum grouping
scheme which synchronously conducts feature grouping with representation
learning. In this way, SMoG solves the problem of supervisory signal hysteresis
which the clustering-based method usually faces, and reduces the false
negatives of instance contrastive methods. We conduct exhaustive experiments to
show that SMoG works well on both CNN and Transformer backbones. Results prove
that SMoG has surpassed the current SOTA unsupervised representation learning
methods. Moreover, its linear evaluation results surpass the performances
obtained by vanilla supervised learning and the representation can be well
transferred to downstream tasks.

An important task in visualization is the extraction and highlighting of
dominant features in data to support users in their analysis process.
Topological methods are a well-known means of identifying such features in
deterministic fields. However, many real-world phenomena studied today are the
result of a chaotic system that cannot be fully described by a single
simulation. Instead, the variability of such systems is usually captured with
ensemble simulations that produce a variety of possible outcomes of the
simulated process. The topological analysis of such ensemble data sets and
uncertain data, in general, is less well studied. In this work, we present an
approach for the computation and visual representation of confidence intervals
for the occurrence probabilities of critical points in ensemble data sets. We
demonstrate the added value of our approach over existing methods for critical
point prediction in uncertain data on a synthetic data set and show its
applicability to a data set from climate research.

The growing adoption of IoT devices for healthcare has enabled researchers to
build intelligence using all the data produced by these devices. Monitoring and
diagnosing health have been the two most common scenarios where such devices
have proven beneficial. Achieving high prediction accuracy was a top priority
initially, but the focus has slowly shifted to efficiency and higher
throughput, and processing the data from these devices in a distributed manner
has proven to help achieve both. Since the field of machine learning is vast
with numerous state-of-the-art algorithms in play, it has been a challenge to
identify the algorithms that perform best in different scenarios. In this
literature review, we explored the distributed machine learning algorithms
tested by the authors of the selected studies and identified the ones that
achieved the best prediction accuracy in each healthcare scenario. While no
algorithm performed consistently, Random Forest performed the best in a few
studies. This could serve as a good starting point for future studies on
collaborative machine learning on IoMT data.

Vulnerability to adversarial attacks is one of the principal hurdles to the
adoption of deep learning in safety-critical applications. Despite significant
efforts, both practical and theoretical, training deep learning models robust
to adversarial attacks is still an open problem. In this paper, we analyse the
geometry of adversarial attacks in the large-data, overparameterized limit for
Bayesian Neural Networks (BNNs). We show that, in the limit, vulnerability to
gradient-based attacks arises as a result of degeneracy in the data
distribution, i.e., when the data lies on a lower-dimensional submanifold of
the ambient space. As a direct consequence, we demonstrate that in this limit
BNN posteriors are robust to gradient-based adversarial attacks. Crucially, we
prove that the expected gradient of the loss with respect to the BNN posterior
distribution is vanishing, even when each neural network sampled from the
posterior is vulnerable to gradient-based attacks. Experimental results on the
MNIST, Fashion MNIST, and half moons datasets, representing the finite data
regime, with BNNs trained with Hamiltonian Monte Carlo and Variational
Inference, support this line of arguments, showing that BNNs can display both
high accuracy on clean data and robustness to both gradient-based and
gradient-free based adversarial attacks.

Completely blind video quality assessment (VQA) refers to a class of quality
assessment methods that do not use any reference videos, human opinion scores
or training videos from the target database to learn a quality model. The
design of this class of methods is particularly important since it can allow
for superior generalization in performance across various datasets. We consider
the design of completely blind VQA for user generated content. While several
deep feature extraction methods have been considered in supervised and weakly
supervised settings, such approaches have not been studied in the context of
completely blind VQA. We bridge this gap by presenting a self-supervised
multiview contrastive learning framework to learn spatio-temporal quality
representations. In particular, we capture the common information between frame
differences and frames by treating them as a pair of views and similarly obtain
the shared representations between frame differences and optical flow. The
resulting features are then compared with a corpus of pristine natural video
patches to predict the quality of the distorted video. Detailed experiments on
multiple camera captured VQA datasets reveal the superior performance of our
method over other features when evaluated without training on human scores.

As an important framework for safe Reinforcement Learning, the Constrained
Markov Decision Process (CMDP) has been extensively studied in the recent
literature. However, despite the rich results under various on-policy learning
settings, there still lacks some essential understanding of the offline CMDP
problems, in terms of both the algorithm design and the information theoretic
sample complexity lower bound. In this paper, we focus on solving the CMDP
problems where only offline data are available. By adopting the concept of the
single-policy concentrability coefficient $C^*$, we establish an
$\Omega\left(\frac{\min\left\{|\mathcal{S}||\mathcal{A}|,|\mathcal{S}|+I\right\}
C^*}{(1-\gamma)^3\epsilon^2}\right)$ sample complexity lower bound for the
offline CMDP problem, where $I$ stands for the number of constraints. By
introducing a simple but novel deviation control mechanism, we propose a
near-optimal primal-dual learning algorithm called DPDL. This algorithm
provably guarantees zero constraint violation and its sample complexity matches
the above lower bound except for an $\tilde{\mathcal{O}}((1-\gamma)^{-1})$
factor. Comprehensive discussion on how to deal with the unknown constant $C^*$
and the potential asynchronous structure on the offline dataset are also
included.

Recent model-based Recommender Systems (RecSys) algorithms emphasize on the
use of features, also called side information, in their design similar to
algorithms in Machine Learning (ML). In contrast, some of the most popular and
traditional algorithms for RecSys solely focus on a given user-item-rating
relation without including side information. The goal of this case study is to
provide a performance comparison and assessment of RecSys and ML algorithms
when side information is included. We chose the Movielens-100K data set since
it is a standard for comparing RecSys algorithms. We compared six different
feature sets with varying quantities of features which were generated from the
baseline data and evaluated on a total of 19 RecSys algorithms, baseline ML
algorithms, Automated Machine Learning (AutoML) pipelines, and state-of-the-art
RecSys algorithms that incorporate side information. The results show that
additional features benefit all algorithms we evaluated. However, the
correlation between feature quantity and performance is not monotonous for
AutoML and RecSys. In these categories, an analysis of feature importance
revealed that the quality of features matters more than quantity. Throughout
our experiments, the average performance on the feature set with the lowest
number of features is about 6% worse compared to that with the highest in terms
of the Root Mean Squared Error. An interesting observation is that AutoML
outperforms matrix factorization-based RecSys algorithms when additional
features are used. Almost all algorithms that can include side information have
higher performance when using the highest quantity of features. In the other
cases, the performance difference is negligible (<1%). The results show a clear
positive trend for the effect of feature quantity as well as the important
effects of feature quality on the evaluated algorithms.

One aim of representation learning is to recover the original latent code
that generated the data, a task which requires additional information or
inductive biases. A recently proposed approach termed Independent Mechanism
Analysis (IMA) postulates that each latent source should influence the observed
mixtures independently, complementing standard nonlinear independent component
analysis, and taking inspiration from the principle of independent causal
mechanisms. While it was shown in theory and experiments that IMA helps
recovering the true latents, the method's performance was so far only
characterized when the modeling assumptions are exactly satisfied. Here, we
test the method's robustness to violations of the underlying assumptions. We
find that the benefits of IMA-based regularization for recovering the true
sources extend to mixing functions with various degrees of violation of the IMA
principle, while standard regularizers do not provide the same merits.
Moreover, we show that unregularized maximum likelihood recovers mixing
functions which systematically deviate from the IMA principle, and provide an
argument elucidating the benefits of IMA-based regularization.

Inside the NLP community there is a considerable amount of language resources
created, annotated and released every day with the aim of studying specific
linguistic phenomena. Despite a variety of attempts in order to organize such
resources has been carried on, a lack of systematic methods and of possible
interoperability between resources are still present. Furthermore, when storing
linguistic information, still nowadays, the most common practice is the concept
of "gold standard", which is in contrast with recent trends in NLP that aim at
stressing the importance of different subjectivities and points of view when
training machine learning and deep learning methods. In this paper we present
O-Dang!: The Ontology of Dangerous Speech Messages, a systematic and
interoperable Knowledge Graph (KG) for the collection of linguistic annotated
data. O-Dang! is designed to gather and organize Italian datasets into a
structured KG, according to the principles shared within the Linguistic Linked
Open Data community. The ontology has also been designed to account for a
perspectivist approach, since it provides a model for encoding both gold
standard and single-annotator labels in the KG. The paper is structured as
follows. In Section 1 the motivations of our work are outlined. Section 2
describes the O-Dang! Ontology, that provides a common semantic model for the
integration of datasets in the KG. The Ontology Population stage with
information about corpora, users, and annotations is presented in Section 3.
Finally, in Section 4 an analysis of offensiveness across corpora is provided
as a first case study for the resource.

We use tensor network techniques to obtain high order perturbative
diagrammatic expansions for the quantum many-body problem at very high
precision. The approach is based on a tensor train parsimonious representation
of the sum of all Feynman diagrams, obtained in a controlled and accurate way
with the tensor cross interpolation algorithm. It yields the full time
evolution of physical quantities in the presence of any arbitrary time
dependent interaction. Our benchmarks on the Anderson quantum impurity problem,
within the real time non-equilibrium Schwinger-Keldysh formalism, demonstrate
that this technique supersedes diagrammatic Quantum Monte Carlo by orders of
magnitude in precision and speed, with convergence rates $1/N^2$ or faster,
where N is the number of function evaluations. The method also works in
parameter regimes characterized by strongly oscillatory integrals in high
dimension, which suffer from a catastrophic sign problem in Quantum
Monte-Carlo. Finally, we also present two exploratory studies showing that the
technique generalizes to more complex situations: a double quantum dot and a
single impurity embedded in a two dimensional lattice.

Unmanned aerial base stations (UABSs) can be deployed in vehicular wireless
networks to support applications such as extended sensing via
vehicle-to-everything (V2X) services. A key problem in such systems is
designing algorithms that can efficiently optimize the trajectory of the UABS
in order to maximize coverage. In existing solutions, such optimization is
carried out from scratch for any new traffic configuration, often by means of
conventional reinforcement learning (RL). In this paper, we propose the use of
continual meta-RL as a means to transfer information from previously
experienced traffic configurations to new conditions, with the goal of reducing
the time needed to optimize the UABS's policy. Adopting the Continual Meta
Policy Search (CoMPS) strategy, we demonstrate significant efficiency gains as
compared to conventional RL, as well as to naive transfer learning methods.

The past several years have witnessed Variational Auto-Encoder's superiority
in various text generation tasks. However, due to the sequential nature of the
text, auto-regressive decoders tend to ignore latent variables and then reduce
to simple language models, known as the KL vanishing problem, which would
further deteriorate when VAE is combined with Transformer-based structures. To
ameliorate this problem, we propose DELLA, a novel variational Transformer
framework. DELLA learns a series of layer-wise latent variables with each
inferred from those of lower layers and tightly coupled with the hidden states
by low-rank tensor product. In this way, DELLA forces these posterior latent
variables to be fused deeply with the whole computation path and hence
incorporate more information. We theoretically demonstrate that our method can
be regarded as entangling latent variables to avoid posterior information
decrease through layers, enabling DELLA to get higher non-zero KL values even
without any annealing or thresholding tricks. Experiments on four unconditional
and three conditional generation tasks show that DELLA could better alleviate
KL vanishing and improve both quality and diversity compared to several strong

The free energy principle, and its corollary active inference, constitute a
bio-inspired theory that assumes biological agents act to remain in a
restricted set of preferred states of the world, i.e., they minimize their free
energy. Under this principle, biological agents learn a generative model of the
world and plan actions in the future that will maintain the agent in an
homeostatic state that satisfies its preferences. This framework lends itself
to being realized in silico, as it comprehends important aspects that make it
computationally affordable, such as variational inference and amortized
planning. In this work, we investigate the tool of deep learning to design and
realize artificial agents based on active inference, presenting a deep-learning
oriented presentation of the free energy principle, surveying works that are
relevant in both machine learning and active inference areas, and discussing
the design choices that are involved in the implementation process. This
manuscript probes newer perspectives for the active inference framework,
grounding its theoretical aspects into more pragmatic affairs, offering a
practical guide to active inference newcomers and a starting point for deep
learning practitioners that would like to investigate implementations of the
free energy principle.

Indoor Positioning based on Machine Learning has drawn increasing attention
both in the academy and the industry as meaningful information from the
reference data can be extracted. Many researchers are using supervised,
semi-supervised, and unsupervised Machine Learning models to reduce the
positioning error and offer reliable solutions to the end-users. In this
article, we propose a new architecture by combining Convolutional Neural
Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network
(GAN) in order to increase the training data and thus improve the position
accuracy. The proposed combination of supervised and unsupervised models was
tested in 17 public datasets, providing an extensive analysis of its
performance. As a result, the positioning error has been reduced in more than
70% of them.

Weighted Majority Voting (WMV) is a well-known optimal decision rule for
collective decision making, given the probability of sources to provide
accurate information (trustworthiness). However, in reality, the
trustworthiness is not a known quantity to the decision maker - they have to
rely on an estimate called trust. A (machine learning) algorithm that computes
trust is called unbiased when it has the property that it does not
systematically overestimate or underestimate the trustworthiness. To formally
analyse the uncertainty to the decision process, we introduce and analyse two
important properties of such unbiased trust values: stability of correctness
and stability of optimality. Stability of correctness means that the decision
accuracy that the decision maker believes they achieved is equal to the actual
accuracy. We prove stability of correctness holds. Stability of optimality
means that the decisions made based on trust, are equally good as they would
have been if they were based on trustworthiness. Stability of optimality does
not hold. We analyse the difference between the two, and bounds thereon. We
also present an overview of how sensitive decision correctness is to changes in
trust and trustworthiness.

We present the classical coordinate-free formalism for forward and backward
mode ad in the real and complex setting. We show how to formally derive the
forward and backward formulae for a number of matrix functions starting from
basic principles.

One of the major barriers to the training of algorithms on knowledge graph
schemas, such as vocabularies or ontologies, is the difficulty that scientists
have in finding the best input resource to address the target prediction tasks.
In addition to this, a key challenge is to determine how to manipulate (and
embed) these data, which are often in the form of particular triples (i.e.,
subject, predicate, object), to enable the learning process. In this paper, we
describe the LiveSchema initiative, namely a gateway that offers a family of
services to easily access, analyze, transform and exploit knowledge graph
schemas, with the main goal of facilitating the reuse of these resources in
machine learning use cases. As an early implementation of the initiative, we
also advance an online catalog, which relies on more than 800 resources, with
the first set of example services.

Conventional biometrics have been employed in high security user
authentication systems for over 20 years now. However, some of these modalities
face low security issues in common practice. Brain wave based user
authentication has emerged as a promising alternative method, as it overcomes
some of these drawbacks and allows for continuous user authentication. In the
present study we address the problem of individual user variability, by
proposing a data-driven Electroencephalography (EEG) based authentication
method. We introduce machine learning techniques, in order to reveal the
optimal classification algorithm that best fits the data of each individual
user, in a fast and efficient manner. A set of 15 power spectral features
(delta, theta, lower alpha, higher alpha, and alpha) is extracted from the
three EEG channels. The results show that our approach can reliably grant or
deny access to the user (mean accuracy 95,6%), while at the same time poses as
a viable option for real time applications, as the total time of the training
procedure was kept under one minute.

Progress in reinforcement learning (RL) research is often driven by the
design of new, challenging environments -- a costly undertaking requiring
skills orthogonal to that of a typical machine learning researcher. The
complexity of environment development has only increased with the rise of
procedural-content generation (PCG) as the prevailing paradigm for producing
varied environments capable of testing the robustness and generalization of RL
agents. Moreover, existing environments often require complex build processes,
making reproducing results difficult. To address these issues, we introduce
GriddlyJS, a web-based Integrated Development Environment (IDE) based on the
Griddly engine. GriddlyJS allows researchers to visually design and debug
arbitrary, complex PCG grid-world environments using a convenient graphical
interface, as well as visualize, evaluate, and record the performance of
trained agent models. By connecting the RL workflow to the advanced
functionality enabled by modern web standards, GriddlyJS allows publishing
interactive agent-environment demos that reproduce experimental results
directly to the web. To demonstrate the versatility of GriddlyJS, we use it to
quickly develop a complex compositional puzzle-solving environment alongside
arbitrary human-designed environment configurations and their solutions for use
in automatic curriculum learning and offline RL. The GriddlyJS IDE is open
source and freely available at https://griddly.ai.

In this work, we for the first time present a method for detecting label
errors in image datasets with semantic segmentation, i.e., pixel-wise class
labels. Annotation acquisition for semantic segmentation datasets is
time-consuming and requires plenty of human labor. In particular, review
processes are time consuming and label errors can easily be overlooked by
humans. The consequences are biased benchmarks and in extreme cases also
performance degradation of deep neural networks (DNNs) trained on such
datasets. DNNs for semantic segmentation yield pixel-wise predictions, which
makes detection of label errors via uncertainty quantification a complex task.
Uncertainty is particularly pronounced at the transitions between connected
components of the prediction. By lifting the consideration of uncertainty to
the level of predicted components, we enable the usage of DNNs together with
component-level uncertainty quantification for the detection of label errors.
We present a principled approach to benchmarking the task of label error
detection by dropping labels from the Cityscapes dataset as well from a dataset
extracted from the CARLA driving simulator, where in the latter case we have
the labels under control. Our experiments show that our approach is able to
detect the vast majority of label errors while controlling the number of false
label error detections. Furthermore, we apply our method to semantic
segmentation datasets frequently used by the computer vision community and
present a collection of label errors along with sample statistics.

Low-light raw denoising is an important and valuable task in computational
photography where learning-based methods trained with paired real data are
mainstream. However, the limited data volume and complicated noise distribution
have constituted a learnability bottleneck for paired real data, which limits
the denoising performance of learning-based methods. To address this issue, we
present a learnability enhancement strategy to reform paired real data
according to noise modeling. Our strategy consists of two efficient techniques:
shot noise augmentation (SNA) and dark shading correction (DSC). Through noise
model decoupling, SNA improves the precision of data mapping by increasing the
data volume and DSC reduces the complexity of data mapping by reducing the
noise complexity. Extensive results on the public datasets and real imaging
scenarios collectively demonstrate the state-of-the-art performance of our
method. Our code is available at: https://github.com/megvii-research/PMN.

We propose a new transformer model for the task of unsupervised learning of
skeleton motion sequences. The existing transformer model utilized for
unsupervised skeleton-based action learning is learned the instantaneous
velocity of each joint from adjacent frames without global motion information.
Thus, the model has difficulties in learning the attention globally over
whole-body motions and temporally distant joints. In addition, person-to-person
interactions have not been considered in the model. To tackle the learning of
whole-body motion, long-range temporal dynamics, and person-to-person
interactions, we design a global and local attention mechanism, where, global
body motions and local joint motions pay attention to each other. In addition,
we propose a novel pretraining strategy, multi-interval pose displacement
prediction, to learn both global and local attention in diverse time ranges.
The proposed model successfully learns local dynamics of the joints and
captures global context from the motion sequences. Our model outperforms
state-of-the-art models by notable margins in the representative benchmarks.
Codes are available at https://github.com/Boeun-Kim/GL-Transformer.

The rapid development of artificial intelligence (AI) technology has enabled
large-scale AI applications to land in the market and practice. However, while
AI technology has brought many conveniences to people in the productization
process, it has also exposed many security issues. Especially, attacks against
online learning vulnerabilities of chatbots occur frequently. Therefore, this
paper proposes a semantics censorship chatbot system based on reinforcement
learning, which is mainly composed of two parts: the Offensive semantics
censorship model and the semantics purification model. Offensive semantics
review can combine the context of user input sentences to detect the rapid
evolution of Offensive semantics and respond to Offensive semantics responses.
The semantics purification model For the case of chatting robot models, it has
been contaminated by large numbers of offensive semantics, by strengthening the
offensive reply learned by the learning algorithm, rather than rolling back to
the early versions. In addition, by integrating a once-through learning
approach, the speed of semantics purification is accelerated while reducing the
impact on the quality of replies. The experimental results show that our
proposed approach reduces the probability of the chat model generating
offensive replies and that the integration of the few-shot learning algorithm
improves the training speed rapidly while effectively slowing down the decline
in BLEU values.

Objective: Machine learning techniques have been used extensively for 12-lead
electrocardiogram (ECG) analysis. For physiological time series, deep learning
(DL) superiority to feature engineering (FE) approaches based on domain
knowledge is still an open question. Moreover, it remains unclear whether
combining DL with FE may improve performance. Methods: We considered three
tasks intending to address these research gaps: cardiac arrhythmia diagnosis
(multiclass-multilabel classification), atrial fibrillation risk prediction
(binary classification), and age estimation (regression). We used an overall
dataset of 2.3M 12-lead ECG recordings to train the following models for each
task: i) a random forest taking the FE as input was trained as a classical
machine learning approach; ii) an end-to-end DL model; and iii) a merged model
of FE+DL. Results: FE yielded comparable results to DL while necessitating
significantly less data for the two classification tasks and it was
outperformed by DL for the regression task. For all tasks, merging FE with DL
did not improve performance over DL alone. Conclusion: We found that for
traditional 12-lead ECG based diagnosis tasks DL did not yield a meaningful
improvement over FE, while it improved significantly the nontraditional
regression task. We also found that combining FE with DL did not improve over
DL alone which suggests that the FE were redundant with the features learned by
DL. Significance: Our findings provides important recommendations on what
machine learning strategy and data regime to chose with respect to the task at
hand for the development of new machine learning models based on the 12-lead
ECG.

Pulse shaping for coherent optical fiber communication has been an active
area of research for the past decade. Most of the early schemes are based on
classic Nyquist pulse shaping that was originally intended for linear channels.
The best known classic scheme, the split digital back-propagation (DBP), uses
joint pre-distortion and post equalization and hence, a nonlinear transmitter
(TX); it, however, suffers from spectral broadening on the fiber due to the
Kerr-effect. With the advent of deep learning in communications, it has been
realized that an Autoencoder can learn to communicate efficiently over the
optical fiber channel, jointly optimizing geometric constellations and pulse
shaping - while also taking into account linear and nonlinear impairments such
as chromatic dispersion and Kerr-nonlinearity. E.g., arXiv:2006.15027 shows how
an Autoencoder can learn to mitigate spectral broadening due to the Kerr-effect
using a trainable linear TX. In this paper, we extend this linear architectural
template to a scalable nonlinear pulse shaping consisting of a Convolutional
Neural Network at both transmitter and receiver. By introducing a novel
$\gamma$-lifting training procedure tailored to the nonlinear optical fiber
channel, we achieve stable Autoencoder convergence to pulse shapes reaching
information rates outperforming the classic split DBP reference at high input
powers.

Assessing the blurriness of an object image is fundamentally important to
improve the performance for object recognition and retrieval. The main
challenge lies in the lack of abundant images with reliable labels and
effective learning strategies. Current datasets are labeled with limited and
confused quality levels. To overcome this limitation, we propose to label the
rank relationships between pairwise images rather their quality levels, since
it is much easier for humans to label, and establish a large-scale realistic
face image blur assessment dataset with reliable labels. Based on this dataset,
we propose a method to obtain the blur scores only with the pairwise rank
labels as supervision. Moreover, to further improve the performance, we propose
a self-supervised method based on quadruplet ranking consistency to leverage
the unlabeled data more effectively. The supervised and self-supervised methods
constitute a final semi-supervised learning framework, which can be trained
end-to-end. Experimental results demonstrate the effectiveness of our method.

Machine learning (ML) is playing an increasingly important role in rendering
decisions that affect a broad range of groups in society. ML models inform
decisions in criminal justice, the extension of credit in banking, and the
hiring practices of corporations. This posits the requirement of model
fairness, which holds that automated decisions should be equitable with respect
to protected features (e.g., gender, race, or age) that are often
under-represented in the data. We postulate that this problem of
under-representation has a corollary to the problem of imbalanced data
learning. This class imbalance is often reflected in both classes and protected
features. For example, one class (those receiving credit) may be
over-represented with respect to another class (those not receiving credit) and
a particular group (females) may be under-represented with respect to another
group (males). A key element in achieving algorithmic fairness with respect to
protected groups is the simultaneous reduction of class and protected group
imbalance in the underlying training data, which facilitates increases in both
model accuracy and fairness. We discuss the importance of bridging imbalanced
learning and group fairness by showing how key concepts in these fields overlap
and complement each other; and propose a novel oversampling algorithm, Fair
Oversampling, that addresses both skewed class distributions and protected
features. Our method: (i) can be used as an efficient pre-processing algorithm
for standard ML algorithms to jointly address imbalance and group equity; and
(ii) can be combined with fairness-aware learning algorithms to improve their
robustness to varying levels of class imbalance. Additionally, we take a step
toward bridging the gap between fairness and imbalanced learning with a new
metric, Fair Utility, that combines balanced accuracy with fairness.

Deep learning models tend to memorize training data, which hurts their
ability to generalize to under-represented classes. We empirically study a
convolutional neural network's internal representation of imbalanced image data
and measure the generalization gap between a model's feature embeddings in the
training and test sets, showing that the gap is wider for minority classes.
This insight enables us to design an efficient three-phase CNN training
framework for imbalanced data. The framework involves training the network
end-to-end on imbalanced data to learn accurate feature embeddings, performing
data augmentation in the learned embedded space to balance the train
distribution, and fine-tuning the classifier head on the embedded balanced
training data. We propose Expansive Over-Sampling (EOS) as a data augmentation
technique to utilize in the training framework. EOS forms synthetic training
instances as convex combinations between the minority class samples and their
nearest enemies in the embedded space to reduce the generalization gap. The
proposed framework improves the accuracy over leading cost-sensitive and
resampling methods commonly used in imbalanced learning. Moreover, it is more
computationally efficient than standard data pre-processing methods, such as
SMOTE and GAN-based oversampling, as it requires fewer parameters and less
training time.

Self-supervised learning (SSL) has achieved promising downstream performance.
However, when facing various resource budgets in real-world applications, it
costs a huge computation burden to pretrain multiple networks of various sizes
one by one. In this paper, we propose Discriminative-SSL-based Slimmable
Pretrained Networks (DSPNet), which can be trained at once and then slimmed to
multiple sub-networks of various sizes, each of which faithfully learns good
representation and can serve as good initialization for downstream tasks with
various resource budgets. Specifically, we extend the idea of slimmable
networks to a discriminative SSL paradigm, by integrating SSL and knowledge
distillation gracefully. We show comparable or improved performance of DSPNet
on ImageNet to the networks individually pretrained one by one under the linear
evaluation and semi-supervised evaluation protocols, while reducing large
training cost. The pretrained models also generalize well on downstream
detection and segmentation tasks. Code will be made public.

We study the estimation of the reach, an ubiquitous regularity parameter in
manifold estimation and geometric data analysis. Given an i.i.d. sample over an
unknown $d$-dimensional $\mathcal{C}^k$-smooth submanifold of $\mathbb{R}^D$,
we provide optimal nonasymptotic bounds for the estimation of its reach. We
build upon a formulation of the reach in terms of maximal curvature on one
hand, and geodesic metric distortion on the other hand. The derived rates are
adaptive, with rates depending on whether the reach of $M$ arises from
curvature or from a bottleneck structure. In the process, we derive optimal
geodesic metric estimation bounds.

Traffic sign detection is a vital task in the visual system of self-driving
cars and the automated driving system. Recently, novel Transformer-based models
have achieved encouraging results for various computer vision tasks. We still
observed that vanilla ViT could not yield satisfactory results in traffic sign
detection because the overall size of the datasets is very small and the class
distribution of traffic signs is extremely unbalanced. To overcome this
problem, a novel Pyramid Transformer with locality mechanisms is proposed in
this paper. Specifically, Pyramid Transformer has several spatial pyramid
reduction layers to shrink and embed the input image into tokens with rich
multi-scale context by using atrous convolutions. Moreover, it inherits an
intrinsic scale invariance inductive bias and is able to learn local feature
representation for objects at various scales, thereby enhancing the network
robustness against the size discrepancy of traffic signs. The experiments are
conducted on the German Traffic Sign Detection Benchmark (GTSDB). The results
demonstrate the superiority of the proposed model in the traffic sign detection
tasks. More specifically, Pyramid Transformer achieves 77.8% mAP on GTSDB when
applied to the Cascade RCNN as the backbone, which surpasses most well-known
and widely-used state-of-the-art models.

Recent work by Xia et al. leveraged the continuous-limit of the classical
momentum accelerated gradient descent and proposed heavy-ball neural ODEs.
While this model offers computational efficiency and high utility over vanilla
neural ODEs, this approach often causes the overshooting of internal dynamics,
leading to unstable training of a model. Prior work addresses this issue by
using ad-hoc approaches, e.g., bounding the internal dynamics using specific
activation functions, but the resulting models do not satisfy the exact
heavy-ball ODE. In this work, we propose adaptive momentum estimation neural
ODEs (AdamNODEs) that adaptively control the acceleration of the classical
momentum-based approach. We find that its adjoint states also satisfy AdamODE
and do not require ad-hoc solutions that the prior work employs. In evaluation,
we show that AdamNODEs achieve the lowest training loss and efficacy over
existing neural ODEs. We also show that AdamNODEs have better training
stability than classical momentum-based neural ODEs. This result sheds some
light on adapting the techniques proposed in the optimization community to
improving the training and inference of neural ODEs further. Our code is
available at https://github.com/pmcsh04/AdamNODE.

Spherically-symmetric atom-centered descriptors of atomic environments have
been widely used for constructing potential or free energy surfaces of
atomistic and colloidal systems and to characterize local structures using
machine learning techniques. However, when particle shapes are non-spherical,
as in the case of rods and ellipsoids, standard spherically-symmetric structure
functions alone produce imprecise descriptions of local environments. In order
to account for the effects of orientation, we introduce two- and three-body
orientation-dependent particle-centered descriptors for systems composed of
rod-like particles. To demonstrate the suitability of the proposed functions,
we use an efficient feature selection scheme and simple linear regression to
construct coarse-grained many-body interaction potentials for
computationally-efficient simulations of model systems consisting of colloidal
particles with anisotropic shape: mixtures of colloidal rods and nonadsorbing
polymer, hard rods enclosed by an elastic microgel shell, and ligand-stabilized
nanorods. We validate the machine-learning (ML) effective many-body potentials
based on orientation-dependent symmetry functions by using them in direct
coexistence simulations to map out the phase behavior of colloidal rods and
non-adsorbing polymer. We find good agreement with results obtained from
simulations of the true binary mixture, demonstrating that the effective
interactions are well-described by the orientation-dependent ML potentials.

This paper defines the problem of optimizing the downlink multi-user multiple
input, single output (MU-MISO) sum-rate for ground users served by an aerial
reconfigurable intelligent surface (ARIS) that acts as a relay to the
terrestrial base station. The deep deterministic policy gradient (DDPG) is
proposed to calculate the optimal active beamforming matrix at the base station
and the phase shifts of the reflecting elements at the ARIS to maximize the
data rate. Simulation results show the superiority of the proposed scheme when
compared to deep Q-learning (DQL) and baseline approaches.

Building models for health prediction based on Electronic Health Records
(EHR) has become an active research area. EHR patient journey data consists of
patient time-ordered clinical events/visits from patients. Most existing
studies focus on modeling long-term dependencies between visits, without
explicitly taking short-term correlations between consecutive visits into
account, where irregular time intervals, incorporated as auxiliary information,
are fed into health prediction models to capture latent progressive patterns of
patient journeys. We present a novel deep neural network with four modules to
take into account the contributions of various variables for health prediction:
i) the Stacked Attention module strengthens the deep semantics in clinical
events within each patient journey and generates visit embeddings, ii) the
Short-Term Temporal Attention module models short-term correlations between
consecutive visit embeddings while capturing the impact of time intervals
within those visit embeddings, iii) the Long-Term Temporal Attention module
models long-term dependencies between visit embeddings while capturing the
impact of time intervals within those visit embeddings, iv) and finally, the
Coupled Attention module adaptively aggregates the outputs of Short-Term
Temporal Attention and Long-Term Temporal Attention modules to make health
predictions. Experimental results on MIMIC-III demonstrate superior predictive
accuracy of our model compared to existing state-of-the-art methods, as well as
the interpretability and robustness of this approach. Furthermore, we found
that modeling short-term correlations contributes to local priors generation,
leading to improved predictive modeling of patient journeys.

Control of linear dynamics with multiplicative noise naturally introduces
robustness against dynamical uncertainty. Moreover, many physical systems are
subject to multiplicative disturbances. In this work we show how these dynamics
can be identified from state trajectories. The least-squares scheme enables
exploitation of prior information and comes with practical data-driven
confidence bounds and sample complexity guarantees. We complement this scheme
with an associated control synthesis procedure for LQR which robustifies
against distributional uncertainty, guaranteeing stability with high
probability and converging to the true optimum at a rate inversely proportional
with the sample count. Throughout we exploit the underlying multi-linear
problem structure through tensor algebra and completely positive operators. The
scheme is validated through numerical experiments.

Voice conversion is to generate a new speech with the source content and a
target voice style. In this paper, we focus on one general setting, i.e.,
non-parallel many-to-many voice conversion, which is close to the real-world
scenario. As the name implies, non-parallel many-to-many voice conversion does
not require the paired source and reference speeches and can be applied to
arbitrary voice transfer. In recent years, Generative Adversarial Networks
(GANs) and other techniques such as Conditional Variational Autoencoders
(CVAEs) have made considerable progress in this field. However, due to the
sophistication of voice conversion, the style similarity of the converted
speech is still unsatisfactory. Inspired by the inherent structure of
mel-spectrogram, we propose a new voice conversion framework, i.e.,
Subband-based Generative Adversarial Network for Voice Conversion (SGAN-VC).
SGAN-VC converts each subband content of the source speech separately by
explicitly utilizing the spatial characteristics between different subbands.
SGAN-VC contains one style encoder, one content encoder, and one decoder. In
particular, the style encoder network is designed to learn style codes for
different subbands of the target speaker. The content encoder network can
capture the content information on the source speech. Finally, the decoder
generates particular subband content. In addition, we propose a pitch-shift
module to fine-tune the pitch of the source speaker, making the converted tone
more accurate and explainable. Extensive experiments demonstrate that the
proposed approach achieves state-of-the-art performance on VCTK Corpus and
AISHELL3 datasets both qualitatively and quantitatively, whether on seen or
unseen data. Furthermore, the content intelligibility of SGAN-VC on unseen data
even exceeds that of StarGANv2-VC with ASR network assistance.

Great progress has been achieved in the community of autonomous driving in
the past few years. As a safety-critical problem, however, anomaly detection is
a huge hurdle towards a large-scale deployment of autonomous vehicles in the
real world. While many approaches, such as uncertainty estimation or
segmentation-based image resynthesis, are extremely promising, there is more to
be explored. Especially inspired by works on anomaly detection based on image
resynthesis, we propose a novel approach for anomaly detection through style
transfer. We leverage generative models to map an image from its original style
domain of road traffic to an arbitrary one and back to generate pixelwise
anomaly scores. However, our experiments have proven our hypothesis wrong, and
we were unable to produce significant results. Nevertheless, we want to share
our findings, so that others can learn from our experiments.

In this work, we propose an efficient method for channel state information
(CSI) adaptive quantization and feedback in frequency division duplexing (FDD)
systems. Existing works mainly focus on the implementation of autoencoder (AE)
neural networks (NNs) for CSI compression, and consider straightforward
quantization methods, e.g., uniform quantization, which are generally not
optimal. With this strategy, it is hard to achieve a low reconstruction error,
especially, when the available number of bits reserved for the latent space
quantization is small. To address this issue, we recommend two different
methods: one based on a post training quantization and the second one in which
the codebook is found during the training of the AE. Both strategies achieve
better reconstruction accuracy compared to standard quantization techniques.

This paper analyses both nonlinear activation functions and spatial
max-pooling for Deep Convolutional Neural Networks (DCNNs) by means of the
algebraic basis of mathematical morphology. Additionally, a general family of
activation functions is proposed by considering both max-pooling and nonlinear
operators in the context of morphological representations. Experimental section
validates the goodness of our approach on classical benchmarks for supervised
learning by DCNN.

Advances in I.T. infrastructure has led to the collection of longer sequences
of time-series. Such sequences are typically non-stationary, exhibiting
distribution shifts over time -- a challenging scenario for the forecasting
task, due to the problems of covariate shift, and conditional distribution
shift. In this paper, we show that deep time-index models possess strong
synergies with a meta-learning formulation of forecasting, displaying
significant advantages over existing neural forecasting methods in tackling the
problems arising from non-stationarity. These advantages include having a
stronger smoothness prior, avoiding the problem of covariate shift, and having
better sample efficiency. To this end, we propose DeepTime, a deep time-index
model trained via meta-learning. Extensive experiments on real-world datasets
in the long sequence time-series forecasting setting demonstrate that our
approach achieves competitive results with state-of-the-art methods, and is
highly efficient. Code is available at https://github.com/salesforce/DeepTime.

Clustering is a representative unsupervised method widely applied in
multi-modal and multi-view scenarios. Multiple kernel clustering (MKC) aims to
group data by integrating complementary information from base kernels. As a
representative, late fusion MKC first decomposes the kernels into orthogonal
partition matrices, then learns a consensus one from them, achieving promising
performance recently. However, these methods fail to consider the noise inside
the partition matrix, preventing further improvement of clustering performance.
We discover that the noise can be disassembled into separable dual parts, i.e.
N-noise and C-noise (Null space noise and Column space noise). In this paper,
we rigorously define dual noise and propose a novel parameter-free MKC
algorithm by minimizing them. To solve the resultant optimization problem, we
design an efficient two-step iterative strategy. To our best knowledge, it is
the first time to investigate dual noise within the partition in the kernel
space. We observe that dual noise will pollute the block diagonal structures
and incur the degeneration of clustering performance, and C-noise exhibits
stronger destruction than N-noise. Owing to our efficient mechanism to minimize
dual noise, the proposed algorithm surpasses the recent methods by large
margins.

Deep learning-based face recognition models are vulnerable to adversarial
attacks. To curb these attacks, most defense methods aim to improve the
robustness of recognition models against adversarial perturbations. However,
the generalization capacities of these methods are quite limited. In practice,
they are still vulnerable to unseen adversarial attacks. Deep learning models
are fairly robust to general perturbations, such as Gaussian noises. A
straightforward approach is to inactivate the adversarial perturbations so that
they can be easily handled as general perturbations. In this paper, a
plug-and-play adversarial defense method, named perturbation inactivation
(PIN), is proposed to inactivate adversarial perturbations for adversarial
defense. We discover that the perturbations in different subspaces have
different influences on the recognition model. There should be a subspace,
called the immune space, in which the perturbations have fewer adverse impacts
on the recognition model than in other subspaces. Hence, our method estimates
the immune space and inactivates the adversarial perturbations by restricting
them to this subspace. The proposed method can be generalized to unseen
adversarial perturbations since it does not rely on a specific kind of
adversarial attack method. This approach not only outperforms several
state-of-the-art adversarial defense methods but also demonstrates a superior
generalization capacity through exhaustive experiments. Moreover, the proposed
method can be successfully applied to four commercial APIs without additional
training, indicating that it can be easily generalized to existing face
recognition systems. The source code is available at
https://github.com/RenMin1991/Perturbation-Inactivate

Given an image of a white shoe drawn on a blackboard, how are the white
pixels deemed (say by human minds) to be informative for recognizing the shoe
without any labeling information on the pixels? Here we investigate such a
``white shoe'' recognition problem from the perspective of tensor network (TN)
machine learning and quantum entanglement. Utilizing a generative TN that
captures the probability distribution of the features as quantum amplitudes, we
propose an unsupervised recognition scheme of informative features with the
variations of entanglement entropy (EE) caused by designed measurements. In
this way, a given sample, where the values of its features are statistically
meaningless, is mapped to the variations of EE that statistically characterize
the gain of information. We show that the EE variations identify the features
that are critical to recognize this specific sample, and the EE itself reveals
the information distribution of the probabilities represented by the TN model.
The signs of the variations further reveal the entanglement structures among
the features. We test the validity of our scheme on a toy dataset of strip
images, the MNIST dataset of hand-drawn digits, the fashion-MNIST dataset of
the pictures of fashion articles, and the images of brain cells. Our scheme
opens the avenue to the quantum-inspired and interpreted unsupervised learning,
which can be applied to, e.g., image segmentation and object detection.

How can we collect the most useful labels to learn a model selection policy,
when presented with arbitrary heterogeneous data streams? In this paper, we
formulate this task as an online contextual active model selection problem,
where at each round the learner receives an unlabeled data point along with a
context. The goal is to output the best model for any given context without
obtaining an excessive amount of labels. In particular, we focus on the task of
selecting pre-trained classifiers, and propose a contextual active model
selection algorithm (CAMS), which relies on a novel uncertainty sampling query
criterion defined on a given policy class for adaptive model selection. In
comparison to prior art, our algorithm does not assume a globally optimal
model. We provide rigorous theoretical analysis for the regret and query
complexity under both adversarial and stochastic settings. Our experiments on
several benchmark classification datasets demonstrate the algorithm's
effectiveness in terms of both regret and query complexity. Notably, to achieve
the same accuracy, CAMS incurs less than 10% of the label cost when compared to
the best online model selection baselines on CIFAR10.

Aiming at two molecular graph datasets and one protein association subgraph
dataset in OGB graph classification task, we design a graph neural network
framework for graph classification task by introducing PAS(Pooling Architecture
Search). At the same time, we improve it based on the GNN topology design
method F2GNN to further design the feature selection and fusion strategies, so
as to further improve the performance of the model in the graph property
prediction task while overcoming the over smoothing problem of deep GNN
training. Finally, a performance breakthrough is achieved on these three
datasets, which is significantly better than other methods with fixed aggregate
function. It is proved that the NAS method has high generalization ability for
multiple tasks and the advantage of our method in processing graph property
prediction tasks.

Hyperparameters in machine learning (ML) have received a fair amount of
attention, and hyperparameter tuning has come to be regarded as an important
step in the ML pipeline. But just how useful is said tuning? While
smaller-scale experiments have been previously conducted, herein we carry out a
large-scale investigation, specifically, one involving 26 ML algorithms, 250
datasets (regression and both binary and multinomial classification), 6 score
metrics, and 28,857,600 algorithm runs. Analyzing the results we conclude that
for many ML algorithms we should not expect considerable gains from
hyperparameter tuning on average, however, there may be some datasets for which
default hyperparameters perform poorly, this latter being truer for some
algorithms than others. By defining a single hp_score value, which combines an
algorithm's accumulated statistics, we are able to rank the 26 ML algorithms
from those expected to gain the most from hyperparameter tuning to those
expected to gain the least. We believe such a study may serve ML practitioners
at large.

Safety and security issues for Critical Infrastructures (CI) are growing as
attackers increasingly adopt drones as an attack vector flying in sensitive
airspace, such as airports, military bases, city centres, and crowded places.
The rapid proliferation of drones for merchandise, shipping recreations
activities, and other commercial applications poses severe concerns on the CI
operators due to the violations and the invasions of the restricted airspaces.
A cost-effective framework is needed to detect, classify and identify the
presence of drones in such cases. In this paper, we demonstrate that CI
operators can detect, classify and identify timely and efficiently drones
(multi-copter and fixed-wings) invading no-drone zones, with an inexpensive
RF-based detection framework named URANUS. Our experiments show that by using
Random Forest classifier, we achieved a classification accuracy of 93.4% in the
classification of one or multiple specific drones. The tracking performance
achieves an accuracy with an average of MAE=0.3650, MSE=0.9254 and R2 = 0.7502.
Our framework has been released as open-source, to enable the community to
verify our findings and use URANUS as a ready-to-use basis for further
analysis.

This paper addresses two major challenges in terahertz (THz) channel
estimation: the beam-split phenomenon, i.e., beam misalignment because of
frequency-independent analog beamformers, and computational complexity because
of the usage of ultra-massive number of antennas to compensate propagation
losses. Data-driven techniques are known to mitigate the complexity of this
problem but usually require the transmission of the datasets from the users to
a central server entailing huge communication overhead. In this work, we
introduce a federated multi-task learning (FMTL), wherein the users transmit
only the model parameters instead of the whole dataset, for THz channel and
user direction-of-arrival (DoA) estimation to improve the
communications-efficiency. We first propose a novel beamspace support alignment
technique for channel estimation with beam-split correction. Then, the channel
and DoA information are used as labels to train an FMTL model. By exploiting
the sparsity of the THz channel, the proposed approach is implemented with
fewer pilot signals than the traditional techniques. Compared to the previous
works, our FMTL approach provides higher channel estimation accuracy as well as
approximately 25 (32) times lower model (channel) training overhead,

Knowledge graph embedding is a representation learning technique that
projects entities and relations in a knowledge graph to continuous vector
spaces. Embeddings have gained a lot of uptake and have been heavily used in
link prediction and other downstream prediction tasks. Most approaches are
evaluated on a single task or a single group of tasks to determine their
overall performance. The evaluation is then assessed in terms of how well the
embedding approach performs on the task at hand. Still, it is hardly evaluated
(and often not even deeply understood) what information the embedding
approaches are actually learning to represent.
  To fill this gap, we present the DLCC (Description Logic Class Constructors)
benchmark, a resource to analyze embedding approaches in terms of which kinds
of classes they can represent. Two gold standards are presented, one based on
the real-world knowledge graph DBpedia and one synthetic gold standard. In
addition, an evaluation framework is provided that implements an experiment
protocol so that researchers can directly use the gold standard. To demonstrate
the use of DLCC, we compare multiple embedding approaches using the gold
standards. We find that many DL constructors on DBpedia are actually learned by
recognizing different correlated patterns than those defined in the gold
standard and that specific DL constructors, such as cardinality constraints,
are particularly hard to be learned for most embedding approaches.

The mapping of text to speech (TTS) is non-deterministic, letters may be
pronounced differently based on context, or phonemes can vary depending on
various physiological and stylistic factors like gender, age, accent, emotions,
etc. Neural speaker embeddings, trained to identify or verify speakers are
typically used to represent and transfer such characteristics from reference
speech to synthesized speech. Speech separation on the other hand is the
challenging task of separating individual speakers from an overlapping mixed
signal of various speakers. Speaker attractors are high-dimensional embedding
vectors that pull the time-frequency bins of each speaker's speech towards
themselves while repelling those belonging to other speakers. In this work, we
explore the possibility of using these powerful speaker attractors for
zero-shot speaker adaptation in multi-speaker TTS synthesis and propose speaker
attractor text to speech (SATTS). Through various experiments, we show that
SATTS can synthesize natural speech from text from an unseen target speaker's
reference signal which might have less than ideal recording conditions, i.e.
reverberations or mixed with other speakers.

Extracting informative representations of molecules using Graph neural
networks (GNNs) is crucial in AI-driven drug discovery. Recently, the graph
research community has been trying to replicate the success of self-supervised
pretraining in natural language processing, with several successes claimed.
However, we find the benefit brought by self-supervised pretraining on small
molecular data can be negligible in many cases. We conduct thorough ablation
studies on the key components of GNN pretraining, including pretraining
objectives, data splitting methods, input features, pretraining dataset scales,
and GNN architectures, to see how they affect the accuracy of the downstream
tasks. Our first important finding is, self-supervised graph pretraining do not
always have statistically significant advantages over non-pretraining methods
in many settings. Secondly, although noticeable improvement can be observed
with additional supervised pretraining, the improvement may diminish with
richer features or more balanced data splits. Thirdly, hyper-parameters could
have larger impacts on accuracy of downstream tasks than the choice of
pretraining tasks, especially when the scales of downstream tasks are small.
Finally, we provide our conjectures where the complexity of some pretraining
methods on small molecules might be insufficient, followed by empirical
evidences on different pretraining datasets.

Expressive text-to-speech has shown improved performance in recent years.
However, the style control of synthetic speech is often restricted to discrete
emotion categories and requires training data recorded by the target speaker in
the target style. In many practical situations, users may not have reference
speech recorded in target emotion but still be interested in controlling speech
style just by typing text description of desired emotional style. In this
paper, we propose a text-based interface for emotional style control and
cross-speaker style transfer in multi-speaker TTS. We propose the bi-modal
style encoder which models the semantic relationship between text description
embedding and speech style embedding with a pretrained language model. To
further improve cross-speaker style transfer on disjoint, multi-style datasets,
we propose the novel style loss. The experimental results show that our model
can generate high-quality expressive speech even in unseen style.

Traditional reinforcement learning (RL) environments typically are the same
for both the training and testing phases. Hence, current RL methods are largely
not generalizable to a test environment which is conceptually similar but
different from what the method has been trained on, which we term the novel
test environment. As an effort to push RL research towards algorithms which can
generalize to novel test environments, we introduce the Brick Tic-Tac-Toe
(BTTT) test bed, where the brick position in the test environment is different
from that in the training environment. Using a round-robin tournament on the
BTTT environment, we show that traditional RL state-search approaches such as
Monte Carlo Tree Search (MCTS) and Minimax are more generalizable to novel test
environments than AlphaZero is. This is surprising because AlphaZero has been
shown to achieve superhuman performance in environments such as Go, Chess and
Shogi, which may lead one to think that it performs well in novel test
environments. Our results show that BTTT, though simple, is rich enough to
explore the generalizability of AlphaZero. We find that merely increasing MCTS
lookahead iterations was insufficient for AlphaZero to generalize to some novel
test environments. Rather, increasing the variety of training environments
helps to progressively improve generalizability across all possible starting
brick configurations.

Using machine learning to solve combinatorial optimization (CO) problems is
challenging, especially when the data is unlabeled. This work proposes an
unsupervised learning framework for CO problems. Our framework follows a
standard relaxation-plus-rounding approach and adopts neural networks to
parameterize the relaxed solutions so that simple back-propagation can train
the model end-to-end. Our key contribution is the observation that if the
relaxed objective satisfies entry-wise concavity, a low optimization loss
guarantees the quality of the final integral solutions. This observation
significantly broadens the applicability of the previous framework inspired by
Erdos' probabilistic method. In particular, this observation can guide the
design of objective models in applications where the objectives are not given
explicitly while requiring being modeled in prior. We evaluate our framework by
solving a synthetic graph optimization problem, and two real-world applications
including resource allocation in circuit design and approximate computing. Our
framework largely outperforms the baselines based on na\"{i}ve relaxation,
reinforcement learning, and Gumbel-softmax tricks.

In federated learning (FL), a set of participants share updates computed on
their local data with an aggregator server that combines updates into a global
model. However, reconciling accuracy with privacy and security is a challenge
to FL. On the one hand, good updates sent by honest participants may reveal
their private local information, whereas poisoned updates sent by malicious
participants may compromise the model's availability and/or integrity. On the
other hand, enhancing privacy via update distortion damages accuracy, whereas
doing so via update aggregation damages security because it does not allow the
server to filter out individual poisoned updates. To tackle the
accuracy-privacy-security conflict, we propose {\em fragmented federated
learning} (FFL), in which participants randomly exchange and mix fragments of
their updates before sending them to the server. To achieve privacy, we design
a lightweight protocol that allows participants to privately exchange and mix
encrypted fragments of their updates so that the server can neither obtain
individual updates nor link them to their originators. To achieve security, we
design a reputation-based defense tailored for FFL that builds trust in
participants and their mixed updates based on the quality of the fragments they
exchange and the mixed updates they send. Since the exchanged fragments'
parameters keep their original coordinates and attackers can be neutralized,
the server can correctly reconstruct a global model from the received mixed
updates without accuracy loss. Experiments on four real data sets show that FFL
can prevent semi-honest servers from mounting privacy attacks, can effectively
counter poisoning attacks and can keep the accuracy of the global model.

Purpose: Registration and segmentation of magnetic resonance (MR) and
ultrasound (US) images play an essential role in surgical planning and
resection of brain tumors. However, validating these techniques is challenging
due to the scarcity of publicly accessible sources with high-quality ground
truth information. To this end, we propose a unique annotation dataset of tumor
tissues and resection cavities from the previously published RESECT dataset
(Xiao et al. 2017) to encourage a more rigorous assessments of image processing
techniques. Acquisition and validation methods: The RESECT database consists of
MR and intraoperative US (iUS) images of 23 patients who underwent resection
surgeries. The proposed dataset contains tumor tissues and resection cavity
annotations of the iUS images. The quality of annotations were validated by two
highly experienced neurosurgeons through several assessment criteria. Data
format and availability: Annotations of tumor tissues and resection cavities
are provided in 3D NIFTI formats. Both sets of annotations are accessible
online in the \url{https://osf.io/6y4db}. Discussion and potential
applications: The proposed database includes tumor tissue and resection cavity
annotations from real-world clinical ultrasound brain images to evaluate
segmentation and registration methods. These labels could also be used to train
deep learning approaches. Eventually, this dataset should further improve the
quality of image guidance in neurosurgery.

This paper studies the multi-modal recommendation problem, where the item
multi-modality information (eg. images and textual descriptions) is exploited
to improve the recommendation accuracy. Besides the user-item interaction
graph, existing state-of-the-art methods usually use auxiliary graphs (eg.
user-user or item-item relation graph) to augment the learned representations
of users and/or items. These representations are often propagated and
aggregated on auxiliary graphs using graph convolutional networks, which can be
prohibitively expensive in computation and memory, especially for large graphs.
Moreover, existing multi-modal recommendation methods usually leverage randomly
sampled negative examples in Bayesian Personalized Ranking (BPR) loss to guide
the learning of user/item representations, which increases the computational
cost on large graphs and may also bring noisy supervision signals into the
training process. To tackle the above issues, we propose a novel
self-supervised multi-modal recommendation model, dubbed BM3, which requires
neither augmentations from auxiliary graphs nor negative samples. Specifically,
BM3 first bootstraps latent contrastive views from the representations of users
and items with a simple dropout augmentation. It then jointly optimizes three
multi-modal objectives to learn the representations of users and items by
reconstructing the user-item interaction graph and aligning modality features
under both inter- and intra-modality perspectives. BM3 alleviates both the need
for contrasting with negative examples and the complex graph augmentation from
an additional target network for contrastive view generation. We show BM3
outperforms prior recommendation models on three datasets with number of nodes
ranging from 20K to 200K, while achieving a 2-9X reduction in training time.
Our code is available at https://github.com/enoche/BM3.

Collaborative filtering (CF) is a widely searched problem in recommender
systems. Linear autoencoder is a kind of well-established method for CF, which
estimates item-item relations through encoding user-item interactions. Despite
the excellent performance of linear autoencoders, the rapidly increasing
computational and storage costs caused by the growing number of items limit
their scalabilities in large-scale real-world scenarios. Recently, graph-based
approaches have achieved success on CF with high scalability, and have been
shown to have commonalities with linear autoencoders in user-item interaction
modeling. Motivated by this, we propose an efficient and scalable
recommendation via item-item graph partitioning (ERGP), aiming to address the
limitations of linear autoencoders. In particular, a recursive graph
partitioning strategy is proposed to ensure that the item set is divided into
several partitions of finite size. Linear autoencoders encode user-item
interactions within partitions while preserving global information across the
entire item set. This allows ERGP to have guaranteed efficiency and high
scalability when the number of items increases. Experiments conducted on 3
public datasets and 3 open benchmarking datasets demonstrate the effectiveness
of ERGP, which outperforms state-of-the-art models with lower training time and
storage costs.

Open-set recognition (OSR) aims to simultaneously detect unknown-class
samples and classify known-class samples. Most of the existing OSR methods are
inductive methods, which generally suffer from the domain shift problem that
the learned model from the known-class domain might be unsuitable for the
unknown-class domain. Addressing this problem, inspired by the success of
transductive learning for alleviating the domain shift problem in many other
visual tasks, we propose an Iterative Transductive OSR framework, called
IT-OSR, which implements three explored modules iteratively, including a
reliability sampling module, a feature generation module, and a baseline update
module. Specifically, at each iteration, a dual-space consistent sampling
approach is presented in the explored reliability sampling module for selecting
some relatively more reliable ones from the test samples according to their
pseudo labels assigned by a baseline method, which could be an arbitrary
inductive OSR method. Then, a conditional dual-adversarial generative network
under an orthogonal coding condition is designed in the feature generation
module to generate discriminative sample features of both known and unknown
classes according to the selected test samples with their pseudo labels.
Finally, the baseline method is updated for sample re-prediction in the
baseline update module by jointly utilizing the generated features, the
selected test samples with pseudo labels, and the training samples. Extensive
experimental results on both the standard-dataset and the cross-dataset
settings demonstrate that the derived transductive methods, by introducing two
typical inductive OSR methods into the proposed IT-OSR framework, achieve
better performances than 15 state-of-the-art methods in most cases.

It is important to understand how the popular regularization method dropout
helps the neural network training find a good generalization solution. In this
work, we theoretically derive the implicit regularization of dropout and study
the relation between the Hessian matrix of the loss function and the covariance
matrix of the dropout noise, supported by a series of experiments. We then
numerically study two implications of the implicit regularization of dropout,
which intuitively rationalize why dropout helps generalization. First, we find
that the training with dropout finds the neural network with a flatter minimum
compared with standard gradient descent training in experiments, and the
implicit regularization is the key for finding flat solutions. Second, trained
with dropout, input weights of hidden neurons (the input weight of a hidden
neuron consists of the weight from its input layer to the hidden neuron and its
bias term) would tend to condense on isolated orientations. Condensation is a
feature in non-linear learning process, which makes the neural network low
complexity. Although our theory mainly focuses on the dropout used in the last
hidden layer, our experiments apply for general dropout in training neural
networks. This work points out the distinct characteristics of dropout compared
with stochastic gradient descent and serves as an important basis for fully
understanding dropout.

During the radiotherapy treatment of patients with lung cancer, the radiation
delivered to healthy tissue around the tumor needs to be minimized, which is
difficult because of respiratory motion and the latency of linear accelerator
systems. In the proposed study, we first use the Lucas-Kanade pyramidal optical
flow algorithm to perform deformable image registration of chest computed
tomography scan images of four patients with lung cancer. We then track three
internal points close to the lung tumor based on the previously computed
deformation field and predict their position with a recurrent neural network
(RNN) trained using real-time recurrent learning (RTRL) and gradient clipping.
The breathing data is quite regular, sampled at approximately 2.5Hz, and
includes artificial drift in the spine direction. The amplitude of the motion
of the tracked points ranged from 12.0mm to 22.7mm. Finally, we propose a
simple method for recovering and predicting 3D tumor images from the tracked
points and the initial tumor image based on a linear correspondence model and
Nadaraya-Watson non-linear regression. The root-mean-square error, maximum
error, and jitter corresponding to the RNN prediction on the test set were
smaller than the same performance measures obtained with linear prediction and
least mean squares (LMS). In particular, the maximum prediction error
associated with the RNN, equal to 1.51mm, is respectively 16.1% and 5.0% lower
than the maximum error associated with linear prediction and LMS. The average
prediction time per time step with RTRL is equal to 119ms, which is less than
the 400ms marker position sampling time. The tumor position in the predicted
images appears visually correct, which is confirmed by the high mean
cross-correlation between the original and predicted images, equal to 0.955.

Analog/mixed-signal circuit design is one of the most complex and
time-consuming stages in the whole chip design process. Due to various process,
voltage, and temperature (PVT) variations from chip manufacturing, analog
circuits inevitably suffer from performance degradation. Although there has
been plenty of work on automating analog circuit design under the typical
condition, limited research has been done on exploring robust designs under
real and unpredictable silicon variations. Automatic analog design against
variations requires prohibitive computation and time costs. To address the
challenge, we present RobustAnalog, a robust circuit design framework that
involves the variation information in the optimization process. Specifically,
circuit optimizations under different variations are considered as a set of
tasks. Similarities among tasks are leveraged and competitions are alleviated
to realize a sample-efficient multi-task training. Moreover, RobustAnalog
prunes the task space according to the current performance in each iteration,
leading to a further simulation cost reduction. In this way, RobustAnalog can
rapidly produce a set of circuit parameters that satisfies diverse constraints
(e.g. gain, bandwidth, noise...) across variations. We compare RobustAnalog
with Bayesian optimization, Evolutionary algorithm, and Deep Deterministic
Policy Gradient (DDPG) and demonstrate that RobustAnalog can significantly
reduce required optimization time by 14-30 times. Therefore, our study provides
a feasible method to handle various real silicon conditions.

The rewriting method for text summarization combines extractive and
abstractive approaches, improving the conciseness and readability of extractive
summaries using an abstractive model. Exiting rewriting systems take each
extractive sentence as the only input, which is relatively focused but can lose
necessary background knowledge and discourse context. In this paper, we
investigate contextualized rewriting, which consumes the entire document and
considers the summary context. We formalize contextualized rewriting as a
seq2seq with group-tag alignments, introducing group-tag as a solution to model
the alignments, identifying extractive sentences through content-based
addressing. Results show that our approach significantly outperforms
non-contextualized rewriting systems without requiring reinforcement learning,
achieving strong improvements on ROUGE scores upon multiple extractors.

With the wide spread of deep learning and gradient descent inspired
optimization algorithms, differentiable programming has gained traction.
Nowadays it has found applications in many different areas as well, such as
scientific computing, robotics, computer graphics and others. One of its
notoriously difficult problems consists in interpreting programs that are not
differentiable everywhere.
  In this work we define $\lambda_\delta$, a core calculus for non-smooth
differentiable programs and define its semantics using concepts from
distribution theory, a well-established area of functional analysis. We also
show how $\lambda_\delta$ presents better equational properties than other
existing semantics and use our semantics to reason about a simplified ray
tracing algorithm. Further, we relate our semantics to existing differentiable
languages by providing translations to and from other existing differentiable
semantic models. Finally, we provide a proof-of-concept implementation in
PyTorch of the novel constructions in this paper.

Active regression considers a linear regression problem where the learner
receives a large number of data points but can only observe a small number of
labels. Since online algorithms can deal with incremental training data and
take advantage of low computational cost, we consider an online extension of
the active regression problem: the learner receives data points one by one and
immediately decides whether it should collect the corresponding labels. The
goal is to efficiently maintain the regression of received data points with a
small budget of label queries. We propose novel algorithms for this problem
under $\ell_p$ loss where $p\in[1,2]$. To achieve a $(1+\epsilon)$-approximate
solution, our proposed algorithms only require
$\tilde{\mathcal{O}}(\epsilon^{-1} d \log(n\kappa))$ queries of labels, where
$n$ is the number of data points and $\kappa$ is a quantity, called the
condition number, of the data points. The numerical results verify our
theoretical results and show that our methods have comparable performance with
offline active regression algorithms.

Machine learning models in the wild have been shown to be vulnerable to
Trojan attacks during training. Although many detection mechanisms have been
proposed, strong adaptive attackers have been shown to be effective against
them. In this paper, we aim to answer the questions considering an intelligent
and adaptive adversary: (i) What is the minimal amount of instances required to
be Trojaned by a strong attacker? and (ii) Is it possible for such an attacker
to bypass strong detection mechanisms?
  We provide an analytical characterization of adversarial capability and
strategic interactions between the adversary and detection mechanism that take
place in such models. We characterize adversary capability in terms of the
fraction of the input dataset that can be embedded with a Trojan trigger. We
show that the loss function has a submodular structure, which leads to the
design of computationally efficient algorithms to determine this fraction with
provable bounds on optimality. We propose a Submodular Trojan algorithm to
determine the minimal fraction of samples to inject a Trojan trigger. To evade
detection of the Trojaned model, we model strategic interactions between the
adversary and Trojan detection mechanism as a two-player game. We show that the
adversary wins the game with probability one, thus bypassing detection. We
establish this by proving that output probability distributions of a Trojan
model and a clean model are identical when following the Min-Max (MM) Trojan
  We perform extensive evaluations of our algorithms on MNIST, CIFAR-10, and
EuroSAT datasets. The results show that (i) with Submodular Trojan algorithm,
the adversary needs to embed a Trojan trigger into a very small fraction of
samples to achieve high accuracy on both Trojan and clean samples, and (ii) the
MM Trojan algorithm yields a trained Trojan model that evades detection with
probability 1.

Stochastic gradient descent (SGD) with momentum is widely used for training
modern deep learning architectures. While it is well-understood that using
momentum can lead to faster convergence rate in various settings, it has also
been observed that momentum yields higher generalization. Prior work argue that
momentum stabilizes the SGD noise during training and this leads to higher
generalization. In this paper, we adopt another perspective and first
empirically show that gradient descent with momentum (GD+M) significantly
improves generalization compared to gradient descent (GD) in some deep learning
problems. From this observation, we formally study how momentum improves
generalization. We devise a binary classification setting where a one-hidden
layer (over-parameterized) convolutional neural network trained with GD+M
provably generalizes better than the same network trained with GD, when both
algorithms are similarly initialized. The key insight in our analysis is that
momentum is beneficial in datasets where the examples share some feature but
differ in their margin. Contrary to GD that memorizes the small margin data,
GD+M still learns the feature in these data thanks to its historical gradients.
Lastly, we empirically validate our theoretical findings.

Automatic speaker verification has achieved remarkable progress in recent
years. However, there is little research on cross-age speaker verification
(CASV) due to insufficient relevant data. In this paper, we mine cross-age test
sets based on the VoxCeleb dataset and propose our age-invariant speaker
representation(AISR) learning method. Since the VoxCeleb is collected from the
YouTube platform, the dataset consists of cross-age data inherently. However,
the meta-data does not contain the speaker age label. Therefore, we adopt the
face age estimation method to predict the speaker age value from the associated
visual data, then label the audio recording with the estimated age. We
construct multiple Cross-Age test sets on VoxCeleb (Vox-CA), which deliberately
select the positive trials with large age-gap. Also, the effect of nationality
and gender is considered in selecting negative pairs to align with Vox-H cases.
The baseline system performance drops from 1.939\% EER on the Vox-H test set to
10.419\% on the Vox-CA20 test set, which indicates how difficult the cross-age
scenario is. Consequently, we propose an age-decoupling adversarial learning
(ADAL) method to alleviate the negative effect of the age gap and reduce
intra-class variance. Our method outperforms the baseline system by over 10\%
related EER reduction on the Vox-CA20 test set. The source code and trial
resources are available on
https://github.com/qinxiaoyi/Cross-Age_Speaker_Verification

Existing Deep-Learning-based (DL-based) Unsupervised Salient Object Detection
(USOD) methods learn saliency information in images based on the prior
knowledge of traditional saliency methods and pretrained deep networks.
However, these methods employ a simple learning strategy to train deep networks
and therefore cannot properly incorporate the "hidden" information of the
training samples into the learning process. Moreover, appearance information,
which is crucial for segmenting objects, is only used as post-process after the
network training process. To address these two issues, we propose a novel
appearance-guided attentive self-paced learning framework for unsupervised
salient object detection. The proposed framework integrates both self-paced
learning (SPL) and appearance guidance into a unified learning framework.
Specifically, for the first issue, we propose an Attentive Self-Paced Learning
(ASPL) paradigm that organizes the training samples in a meaningful order to
excavate gradually more detailed saliency information. Our ASPL facilitates our
framework capable of automatically producing soft attention weights that
measure the learning difficulty of training samples in a purely self-learning
way. For the second issue, we propose an Appearance Guidance Module (AGM),
which formulates the local appearance contrast of each pixel as the probability
of saliency boundary and finds the potential boundary of the target objects by
maximizing the probability. Furthermore, we further extend our framework to
other multi-modality SOD tasks by aggregating the appearance vectors of other
modality data, such as depth map, thermal image or optical flow. Extensive
experiments on RGB, RGB-D, RGB-T and video SOD benchmarks prove that our
framework achieves state-of-the-art performance against existing USOD methods
and is comparable to the latest supervised SOD methods.

Bayesian inference allows the transparent communication of uncertainty in
material flow analyses (MFAs), and a systematic update of uncertainty as new
data become available. However, the method is undermined by the difficultly of
defining proper priors for the MFA parameters and quantifying the noise in the
collected data. We start to address these issues by first deriving and
implementing an expert elicitation procedure suitable for generating MFA
parameter priors. Second, we propose to learn the data noise concurrent with
the parametric uncertainty. These methods are demonstrated using a case study
on the 2012 U.S. steel flow. Eight experts are interviewed to elicit
distributions on steel flow uncertainty from raw materials to intermediate
goods. The experts' distributions are combined and weighted according to the
expertise demonstrated in response to seeding questions. These aggregated
distributions form our model parameters' prior. A sensible, weakly-informative
prior is also adopted for learning the data noise. Bayesian inference is then
performed to update the parametric and data noise uncertainty given MFA data
collected from the United States Geological Survey (USGS) and the World Steel
Association (WSA). The results show a reduction in MFA parametric uncertainty
when incorporating the collected data. Only a modest reduction in data noise
uncertainty was observed; however, greater reductions were achieved when using
data from multiple years in the inference. These methods generate transparent
MFA and data noise uncertainties learned from data rather than pre-assumed data
noise levels, providing a more robust basis for decision-making that affects
the system.

Studies suggest that one in three US adults use the Internet to diagnose or
learn about a health concern. However, such access to health information online
could exacerbate the disparities in health information availability and use.
Health information seeking behavior (HISB) refers to the ways in which
individuals seek information about their health, risks, illnesses, and
health-protective behaviors. For patients engaging in searches for health
information on digital media platforms, health literacy divides can be
exacerbated both by their own lack of knowledge and by algorithmic
recommendations, with results that disproportionately impact disadvantaged
populations, minorities, and low health literacy users. This study reports on
an exploratory investigation of the above challenges by examining whether
responsible and representative recommendations can be generated using advanced
analytic methods applied to a large corpus of videos and their metadata on a
chronic condition (diabetes) from the YouTube social media platform. The paper
focusses on biases associated with demographic characters of actors using
videos on diabetes that were retrieved and curated for multiple criteria such
as encoded medical content and their understandability to address patient
education and population health literacy needs. This approach offers an immense
opportunity for innovation in human-in-the-loop, augmented-intelligence,
bias-aware and responsible algorithmic recommendations by combining the
perspectives of health professionals and patients into a scalable and
generalizable machine learning framework for patient empowerment and improved
health outcomes.

In this paper, we investigate the opportunities of automating the judgment
process in online one-on-one math classes. We build a Wide & Deep framework to
learn fine-grained predictive representations from a limited amount of noisy
classroom conversation data that perform better student judgments. We conducted
experiments on the task of predicting students' levels of mastery of example
questions and the results demonstrate the superiority and availability of our
model in terms of various evaluation metrics.

Over the decades, the Markowitz framework has been used extensively in
portfolio analysis though it puts too much emphasis on the analysis of the
market uncertainty rather than on the trend prediction. While generative
adversarial network (GAN), conditional GAN (CGAN), and autoencoding CGAN
(ACGAN) have been explored to generate financial time series and extract
features that can help portfolio analysis. The limitation of the CGAN or ACGAN
framework stands in putting too much emphasis on generating series and finding
the internal trends of the series rather than predicting the future trends. In
this paper, we introduce a hybrid approach on conditional GAN based on deep
generative models that learns the internal trend of historical data while
modeling market uncertainty and future trends. We evaluate the model on several
real-world datasets from both the US and Europe markets, and show that the
proposed HybridCGAN and HybridACGAN models lead to better portfolio allocation
compared to the existing Markowitz, CGAN, and ACGAN approaches.

Existing keyframe-based motion synthesis mainly focuses on the generation of
cyclic actions or short-term motion, such as walking, running, and transitions
between close postures. However, these methods will significantly degrade the
naturalness and diversity of the synthesized motion when dealing with complex
and impromptu movements, e.g., dance performance and martial arts. In addition,
current research lacks fine-grained control over the generated motion, which is
essential for intelligent human-computer interaction and animation creation. In
this paper, we propose a novel keyframe-based motion generation network based
on multiple constraints, which can achieve diverse dance synthesis via learned
knowledge. Specifically, the algorithm is mainly formulated based on the
recurrent neural network (RNN) and the Transformer architecture. The backbone
of our network is a hierarchical RNN module composed of two long short-term
memory (LSTM) units, in which the first LSTM is utilized to embed the posture
information of the historical frames into a latent space, and the second one is
employed to predict the human posture for the next frame. Moreover, our
framework contains two Transformer-based controllers, which are used to model
the constraints of the root trajectory and the velocity factor respectively, so
as to better utilize the temporal context of the frames and achieve
fine-grained motion control. We verify the proposed approach on a dance dataset
containing a wide range of contemporary dance. The results of three
quantitative analyses validate the superiority of our algorithm. The video and
qualitative experimental results demonstrate that the complex motion sequences
generated by our algorithm can achieve diverse and smooth motion transitions
between keyframes, even for long-term synthesis.

Recent works on machine learning for combinatorial optimization have shown
that learning based approaches can outperform heuristic methods in terms of
speed and performance. In this paper, we consider the problem of finding an
optimal topological order on a directed acyclic graph with focus on the memory
minimization problem which arises in compilers. We propose an end-to-end
machine learning based approach for topological ordering using an
encoder-decoder framework. Our encoder is a novel attention based graph neural
network architecture called \emph{Topoformer} which uses different topological
transforms of a DAG for message passing. The node embeddings produced by the
encoder are converted into node priorities which are used by the decoder to
generate a probability distribution over topological orders. We train our model
on a dataset of synthetically generated graphs called layered graphs. We show
that our model outperforms, or is on-par, with several topological ordering
baselines while being significantly faster on synthetic graphs with up to 2k
nodes. We also train and test our model on a set of real-world computation
graphs, showing performance improvements.

We consider the problem of testing and learning quantum $k$-juntas: $n$-qubit
unitary matrices which act non-trivially on just $k$ of the $n$ qubits and as
the identity on the rest. As our main algorithmic results, we give (a) a
$\widetilde{O}(\sqrt{k})$-query quantum algorithm that can distinguish quantum
$k$-juntas from unitary matrices that are "far" from every quantum $k$-junta;
and (b) a $O(4^k)$-query algorithm to learn quantum $k$-juntas. We complement
our upper bounds for testing quantum $k$-juntas and learning quantum $k$-juntas
with near-matching lower bounds of $\Omega(\sqrt{k})$ and
$\Omega(\frac{4^k}{k})$, respectively. Our techniques are Fourier-analytic and
make use of a notion of influence of qubits on unitaries.

Continual learning -- accumulating knowledge from a sequence of learning
experiences -- is an important yet challenging problem. In this paradigm, the
model's performance for previously encountered instances may substantially drop
as additional data are seen. When dealing with class-imbalanced data,
forgetting is further exacerbated. Prior work has proposed replay-based
approaches which aim at reducing forgetting by intelligently storing instances
for future replay. Although Class-Balancing Reservoir Sampling (CBRS) has been
successful in dealing with imbalanced data, the intra-class diversity has not
been accounted for, implicitly assuming that each instance of a class is
equally informative. We present Diverse-CBRS (D-CBRS), an algorithm that allows
us to consider within class diversity when storing instances in the memory. Our
results show that D-CBRS outperforms state-of-the-art memory management
continual learning algorithms on data sets with considerable intra-class

Point cloud semantic segmentation has attracted attentions due to its
robustness to light condition. This makes it an ideal semantic solution for
autonomous driving. However, considering the large computation burden and
bandwidth demanding of neural networks, putting all the computing into vehicle
Electronic Control Unit (ECU) is not efficient or practical. In this paper, we
proposed a light weighted point cloud semantic segmentation network based on
range view. Due to its simple pre-processing and standard convolution, it is
efficient when running on deep learning accelerator like DPU. Furthermore, a
near sensor computing system is built for autonomous vehicles. In this system,
a FPGA-based deep learning accelerator core (DPU) is placed next to the LiDAR
sensor, to perform point cloud pre-processing and segmentation neural network.
By leaving only the post-processing step to ECU, this solution heavily
alleviate the computation burden of ECU and consequently shortens the decision
making and vehicles reaction latency. Our semantic segmentation network
achieved 10 frame per second (fps) on Xilinx DPU with computation efficiency
42.5 GOP/W.

By recursively summing node features over entire neighborhoods, spatial graph
convolution operators have been heralded as key to the success of Graph Neural
Networks (GNNs). Yet, despite the multiplication of GNN methods across tasks
and applications, the impact of this aggregation operation on their performance
still has yet to be extensively analysed. In fact, while efforts have mostly
focused on optimizing the architecture of the neural network, fewer works have
attempted to characterize (a) the different classes of spatial convolution
operators, (b) how the choice of a particular class relates to properties of
the data , and (c) its impact on the geometry of the embedding space. In this
paper, we propose to answer all three questions by dividing existing operators
into two main classes ( symmetrized vs. row-normalized spatial convolutions),
and show how these translate into different implicit biases on the nature of
the data. Finally, we show that this aggregation operator is in fact tunable,
and explicit regimes in which certain choices of operators -- and therefore,
embedding geometries -- might be more appropriate.

In this work, we integrate `social' interactions into the MARL setup through
a user-defined relational network and examine the effects of agent-agent
relations on the rise of emergent behaviors. Leveraging insights from sociology
and neuroscience, our proposed framework models agent relationships using the
notion of Reward-Sharing Relational Networks (RSRN), where network edge weights
act as a measure of how much one agent is invested in the success of (or `cares
about') another. We construct relational rewards as a function of the RSRN
interaction weights to collectively train the multi-agent system via a
multi-agent reinforcement learning algorithm. The performance of the system is
tested for a 3-agent scenario with different relational network structures
(e.g., self-interested, communitarian, and authoritarian networks). Our results
indicate that reward-sharing relational networks can significantly influence
learned behaviors. We posit that RSRN can act as a framework where different
relational networks produce distinct emergent behaviors, often analogous to the
intuited sociological understanding of such networks.

The immune response is a dynamic process by which the body determines whether
an antigen is self or nonself. The state of this dynamic process is defined by
the relative balance and population of inflammatory and regulatory actors which
comprise this decision making process. The goal of immunotherapy as applied to,
e.g. Rheumatoid Arthritis (RA), then, is to bias the immune state in favor of
the regulatory actors - thereby shutting down autoimmune pathways in the
response. While there are several known approaches to immunotherapy, the
effectiveness of the therapy will depend on how this intervention alters the
evolution of this state. Unfortunately, this process is determined not only by
the dynamics of the process, but the state of the system at the time of
intervention - a state which is difficult if not impossible to determine prior
to application of the therapy.

Deep MRI reconstruction is commonly performed with conditional models that
de-alias undersampled acquisitions to recover images consistent with
fully-sampled data. Since conditional models are trained with knowledge of the
imaging operator, they can show poor generalization across variable operators.
Unconditional models instead learn generative image priors decoupled from the
imaging operator to improve reliability against domain shifts. Recent diffusion
models are particularly promising given their high sample fidelity.
Nevertheless, inference with a static image prior can perform suboptimally.
Here we propose the first adaptive diffusion prior for MRI reconstruction,
AdaDiff, to improve performance and reliability against domain shifts. AdaDiff
leverages an efficient diffusion prior trained via adversarial mapping over
large reverse diffusion steps. A two-phase reconstruction is executed following
training: a rapid-diffusion phase that produces an initial reconstruction with
the trained prior, and an adaptation phase that further refines the result by
updating the prior to minimize reconstruction loss on acquired data.
Demonstrations on multi-contrast brain MRI clearly indicate that AdaDiff
outperforms competing conditional and unconditional methods under domain
shifts, and achieves superior or on par within-domain performance.

Global environment monitoring is a task that requires additional attention in
the contemporary rapid climate change environment. This includes monitoring the
rate of deforestation and areas affected by flooding. Satellite imaging has
greatly helped monitor the earth, and deep learning techniques have helped to
automate this monitoring process. This paper proposes a solution for observing
the area covered by the forest and water. To achieve this task UNet model has
been proposed, which is an image segmentation model. The model achieved a
validation accuracy of 82.55% and 82.92% for the segmentation of areas covered
by forest and water, respectively.

Reservoir computers (RCs) are among the fastest to train of all neural
networks, especially when they are compared to other recurrent neural networks.
RC has this advantage while still handling sequential data exceptionally well.
However, RC adoption has lagged other neural network models because of the
model's sensitivity to its hyper-parameters (HPs). A modern unified software
package that automatically tunes these parameters is missing from the
literature. Manually tuning these numbers is very difficult, and the cost of
traditional grid search methods grows exponentially with the number of HPs
considered, discouraging the use of the RC and limiting the complexity of the
RC models which can be devised. We address these problems by introducing
RcTorch, a PyTorch based RC neural network package with automated HP tuning.
Herein, we demonstrate the utility of RcTorch by using it to predict the
complex dynamics of a driven pendulum being acted upon by varying forces. This
work includes coding examples. Example Python Jupyter notebooks can be found on
our GitHub repository https://github.com/blindedjoy/RcTorch and documentation
can be found at https://rctorch.readthedocs.io/.

Machine learning is considered to be one of the most promising applications
of quantum computing. Therefore, the search for quantum advantage of the
quantum analogues of machine learning models is a key research goal. Here, we
show that variational quantum classifiers (VQC) and support vector machines
with quantum kernels (QSVM) can solve a classification problem based on the
k-Forrelation problem, which is known to be PromiseBQP-complete. Because the
PromiseBQP complexity class includes all Bounded-Error Quantum Polynomial-Time
(BQP) decision problems, our results imply that there exists a feature map and
a quantum kernel that make VQC and QSVM efficient solvers for any BQP problem.
This means that the feature map of VQC or the quantum kernel of QSVM can be
designed to have quantum advantage for any classification problem that cannot
be classically solved in polynomial time but contrariwise by a quantum
computer.

We provide the first $\mathit{constant}$-$\mathit{round}$ construction of
post-quantum non-malleable commitments under the minimal assumption that
$\mathit{post}$-$\mathit{quantum}$ $\mathit{one}$-$\mathit{way}$
$\mathit{functions}$ exist. We achieve the standard notion of non-malleability
with respect to commitments. Prior constructions required
$\Omega(\log^*\lambda)$ rounds under the same assumption.
  We achieve our results through a new technique for constant-round
non-malleable commitments which is easier to use in the post-quantum setting.
The technique also yields an almost elementary proof of security for
constant-round non-malleable commitments in the classical setting, which may be
of independent interest.
  As an application, when combined with existing work, our results yield the
first constant-round post-quantum secure multiparty computation under the
$\mathit{polynomial}$ hardness of quantum fully-homomorphic encryption and
quantum learning with errors.

3D multi-object tracking aims to uniquely and consistently identify all
mobile entities through time. Despite the rich spatiotemporal information
available in this setting, current 3D tracking methods primarily rely on
abstracted information and limited history, e.g. single-frame object bounding
boxes. In this work, we develop a holistic representation of traffic scenes
that leverages both spatial and temporal information of the actors in the
scene. Specifically, we reformulate tracking as a spatiotemporal problem by
representing tracked objects as sequences of time-stamped points and bounding
boxes over a long temporal history. At each timestamp, we improve the location
and motion estimates of our tracked objects through learned refinement over the
full sequence of object history. By considering time and space jointly, our
representation naturally encodes fundamental physical priors such as object
permanence and consistency across time. Our spatiotemporal tracking framework
achieves state-of-the-art performance on the Waymo and nuScenes benchmarks.

Human services systems make key decisions that impact individuals in the
society. The U.S. child welfare system makes such decisions, from screening-in
hotline reports of suspected abuse or neglect for child protective
investigations, placing children in foster care, to returning children to
permanent home settings. These complex and impactful decisions on children's
lives rely on the judgment of child welfare decisionmakers. Child welfare
agencies have been exploring ways to support these decisions with empirical,
data-informed methods that include machine learning (ML). This paper describes
a conceptual framework for ML to support child welfare decisions. The ML
framework guides how child welfare agencies might conceptualize a target
problem that ML can solve; vet available administrative data for building ML;
formulate and develop ML specifications that mirror relevant populations and
interventions the agencies are undertaking; deploy, evaluate, and monitor ML as
child welfare context, policy, and practice change over time. Ethical
considerations, stakeholder engagement, and avoidance of common pitfalls
underpin the framework's impact and success. From abstract to concrete, we
describe one application of this framework to support a child welfare decision.
This ML framework, though child welfare-focused, is generalizable to solving
other public policy problems.

Motion control algorithms in the presence of pedestrians are critical for the
development of safe and reliable Autonomous Vehicles (AVs). Traditional motion
control algorithms rely on manually designed decision-making policies which
neglect the mutual interactions between AVs and pedestrians. On the other hand,
recent advances in Deep Reinforcement Learning allow for the automatic learning
of policies without manual designs. To tackle the problem of decision-making in
the presence of pedestrians, the authors introduce a framework based on Social
Value Orientation and Deep Reinforcement Learning (DRL) that is capable of
generating decision-making policies with different driving styles. The policy
is trained using state-of-the-art DRL algorithms in a simulated environment. A
novel computationally-efficient pedestrian model that is suitable for DRL
training is also introduced. We perform experiments to validate our framework
and we conduct a comparative analysis of the policies obtained with two
different model-free Deep Reinforcement Learning Algorithms. Simulations
results show how the developed model exhibits natural driving behaviours, such
as short-stopping, to facilitate the pedestrian's crossing.

Optimistic algorithms have been extensively studied for regret minimization
in episodic tabular MDPs, both from a minimax and an instance-dependent view.
However, for the PAC RL problem, where the goal is to identify a near-optimal
policy with high probability, little is known about their instance-dependent
sample complexity. A negative result of Wagenmaker et al. (2021) suggests that
optimistic sampling rules cannot be used to attain the (still elusive) optimal
instance-dependent sample complexity. On the positive side, we provide the
first instance-dependent bound for an optimistic algorithm for PAC RL,
BPI-UCRL, for which only minimax guarantees were available (Kaufmann et al.,
2021). While our bound features some minimal visitation probabilities, it also
features a refined notion of sub-optimality gap compared to the value gaps that
appear in prior work. Moreover, in MDPs with deterministic transitions, we show
that BPI-UCRL is actually near-optimal. On the technical side, our analysis is
very simple thanks to a new "target trick" of independent interest. We
complement these findings with a novel hardness result explaining why the
instance-dependent complexity of PAC RL cannot be easily related to that of
regret minimization, unlike in the minimax regime.

Sockeye 3 is the latest version of the Sockeye toolkit for Neural Machine
Translation (NMT). Now based on PyTorch, Sockeye 3 provides faster model
implementations and more advanced features with a further streamlined codebase.
This enables broader experimentation with faster iteration, efficient training
of stronger and faster models, and the flexibility to move new ideas quickly
from research to production. When running comparable models, Sockeye 3 is up to
126% faster than other PyTorch implementations on GPUs and up to 292% faster on
CPUs. Sockeye 3 is open source software released under the Apache 2.0 license.

We study policy optimization problems for deterministic Markov decision
processes (MDPs) with metric state and action spaces, which we refer to as
Metric Policy Optimization Problems (MPOPs). Our goal is to establish
theoretical results on the well-posedness of MPOPs that can characterize
practically relevant continuous control systems. To do so, we define a special
class of MPOPs called Compactly Restrictable MPOPs (CR-MPOPs), which are
flexible enough to capture the complex behavior of robotic systems but specific
enough to admit solutions using dynamic programming methods such as value
iteration. We show how to arrive at CR-MPOPs using forward-invariance. We
further show that our theoretical results on CR-MPOPs can be used to
characterize feedback linearizable control affine systems.

Designing efficient general-purpose contextual bandit algorithms that work
with large -- or even continuous -- action spaces would facilitate application
to important scenarios such as information retrieval, recommendation systems,
and continuous control. While obtaining standard regret guarantees can be
hopeless, alternative regret notions have been proposed to tackle the large
action setting. We propose a smooth regret notion for contextual bandits, which
dominates previously proposed alternatives. We design a statistically and
computationally efficient algorithm -- for the proposed smooth regret -- that
works with general function approximation under standard supervised oracles. We
also present an adaptive algorithm that automatically adapts to any smoothness
level. Our algorithms can be used to recover the previous minimax/Pareto
optimal guarantees under the standard regret, e.g., in bandit problems with
multiple best arms and Lipschitz/H{\"o}lder bandits. We conduct large-scale
empirical evaluations demonstrating the efficacy of our proposed algorithms.

Analyzing sports performance or preventing injuries requires capturing ground
reaction forces (GRFs) exerted by the human body during certain movements.
Standard practice uses physical markers paired with force plates in a
controlled environment, but this is marred by high costs, lengthy
implementation time, and variance in repeat experiments; hence, we propose GRF
inference from video. While recent work has used LSTMs to estimate GRFs from 2D
viewpoints, these can be limited in their modeling and representation capacity.
First, we propose using a transformer architecture to tackle the GRF from video
task, being the first to do so. Then we introduce a new loss to minimize high
impact peaks in regressed curves. We also show that pre-training and multi-task
learning on 2D-to-3D human pose estimation improves generalization to unseen
motions. And pre-training on this different task provides good initial weights
when finetuning on smaller (rarer) GRF datasets. We evaluate on LAAS Parkour
and a newly collected ForcePose dataset; we show up to 19% decrease in error
compared to prior approaches.

The progress of EEG-based emotion recognition has received widespread
attention from the fields of human-machine interactions and cognitive science
in recent years. However, how to recognize emotions with limited labels has
become a new research and application bottleneck. To address the issue, this
paper proposes a Self-supervised Group Meiosis Contrastive learning framework
(SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC,
a novel genetics-inspired data augmentation method, named Meiosis, is
developed. It takes advantage of the alignment of stimuli among the EEG samples
in a group for generating augmented groups by pairing, cross exchanging, and
separating. And the model adopts a group projector to extract group-level
feature representations from group EEG samples triggered by the same emotion
video stimuli. Then contrastive learning is employed to maximize the similarity
of group-level representations of augmented groups with the same stimuli. The
SGMC achieves the state-of-the-art emotion recognition results on the publicly
available DEAP dataset with an accuracy of 94.72% and 95.68% in valence and
arousal dimensions, and also reaches competitive performance on the public SEED
dataset with an accuracy of 94.04%. It is worthy of noting that the SGMC shows
significant performance even when using limited labels. Moreover, the results
of feature visualization suggest that the model might have learned video-level
emotion-related feature representations to improve emotion recognition. And the
effects of group size are further evaluated in the hyper parametric analysis.
Finally, a control experiment and ablation study are carried out to examine the
rationality of architecture. The code is provided publicly online.

Generalizing machine learning (ML) models for network traffic dynamics tends
to be considered a lost cause. Hence for every new task, we design new models
and train them on model-specific datasets closely mimicking the deployment
environments. Yet, an ML architecture called_Transformer_ has enabled
previously unimaginable generalization in other domains. Nowadays, one can
download a model pre-trained on massive datasets and only fine-tune it for a
specific task and context with comparatively little time and data. These
fine-tuned models are now state-of-the-art for many benchmarks.
  We believe this progress could translate to networking and propose a Network
Traffic Transformer (NTT), a transformer adapted to learn network dynamics from
packet traces. Our initial results are promising: NTT seems able to generalize
to new prediction tasks and environments. This study suggests there is still
hope for generalization, though it calls for a lot of future research.

The long-tail effect is a common issue that limits the performance of deep
learning models on real-world datasets. Character image datasets are also
affected by such unbalanced data distribution due to differences in character
usage frequency. Thus, current character recognition methods are limited when
applied in the real world, especially for the categories in the tail which are
lacking training samples, e.g., uncommon characters. In this paper, we propose
a zero-shot character recognition framework via radical extraction (REZCR) to
improve the recognition performance of few-sample character categories in the
tail. Specifically, we exploit radicals, the graphical units of characters, by
decomposing and reconstructing characters according to orthography. REZCR
consists of an attention-based radical information extractor (RIE) and a
knowledge graph-based character reasoner (KGR). The RIE aims to recognize
candidate radicals and their possible structural relations from character
images. The results are then fed into KGR to recognize the target character by
reasoning with a pre-designed knowledge graph. We validate our method on
multiple datasets, and REZCR shows promising experimental results, especially
on few-sample character datasets.

We study representation learning for Offline Reinforcement Learning (RL),
focusing on the important task of Offline Policy Evaluation (OPE). Recent work
shows that, in contrast to supervised learning, realizability of the Q-function
is not enough for learning it. Two sufficient conditions for sample-efficient
OPE are Bellman completeness and coverage. Prior work often assumes that
representations satisfying these conditions are given, with results being
mostly theoretical in nature. In this work, we propose BCRL, which directly
learns from data an approximately linear Bellman complete representation with
good coverage. With this learned representation, we perform OPE using Least
Square Policy Evaluation (LSPE) with linear functions in our learned
representation. We present an end-to-end theoretical analysis, showing that our
two-stage algorithm enjoys polynomial sample complexity provided some
representation in the rich class considered is linear Bellman complete.
Empirically, we extensively evaluate our algorithm on challenging, image-based
continuous control tasks from the Deepmind Control Suite. We show our
representation enables better OPE compared to previous representation learning
methods developed for off-policy RL (e.g., CURL, SPR). BCRL achieve competitive
OPE error with the state-of-the-art method Fitted Q-Evaluation (FQE), and beats
FQE when evaluating beyond the initial state distribution. Our ablations show
that both linear Bellman complete and coverage components of our method are
crucial.

A central problem in sequential decision making is to develop algorithms that
are practical and computationally efficient, yet support the use of flexible,
general-purpose models. Focusing on the contextual bandit problem, recent
progress provides provably efficient algorithms with strong empirical
performance when the number of possible alternatives ("actions") is small, but
guarantees for decision making in large, continuous action spaces have remained
elusive, leading to a significant gap between theory and practice. We present
the first efficient, general-purpose algorithm for contextual bandits with
continuous, linearly structured action spaces. Our algorithm makes use of
computational oracles for (i) supervised learning, and (ii) optimization over
the action space, and achieves sample complexity, runtime, and memory
independent of the size of the action space. In addition, it is simple and
practical. We perform a large-scale empirical evaluation, and show that our
approach typically enjoys superior performance and efficiency compared to
standard baselines.

The problem of travel time estimation is widely considered as the fundamental
challenge of modern logistics. The complex nature of interconnections between
spatial aspects of roads and temporal dynamics of ground transport still
preserves an area to experiment with. However, the total volume of currently
accumulated data encourages the construction of the learning models which have
the perspective to significantly outperform earlier solutions. In order to
address the problems of travel time estimation, we propose a new method based
on transformer architecture - TransTTE.

Conventionally, Earth system (e.g., weather and climate) forecasting relies
on numerical simulation with complex physical models and are hence both
expensive in computation and demanding on domain expertise. With the explosive
growth of the spatiotemporal Earth observation data in the past decade,
data-driven models that apply Deep Learning (DL) are demonstrating impressive
potential for various Earth system forecasting tasks. The Transformer as an
emerging DL architecture, despite its broad success in other domains, has
limited adoption in this area. In this paper, we propose Earthformer, a
space-time Transformer for Earth system forecasting. Earthformer is based on a
generic, flexible and efficient space-time attention block, named Cuboid
Attention. The idea is to decompose the data into cuboids and apply
cuboid-level self-attention in parallel. These cuboids are further connected
with a collection of global vectors. We conduct experiments on the MovingMNIST
dataset and a newly proposed chaotic N-body MNIST dataset to verify the
effectiveness of cuboid attention and figure out the best design of
Earthformer. Experiments on two real-world benchmarks about precipitation
nowcasting and El Nino/Southern Oscillation (ENSO) forecasting show Earthformer

In this paper, we study the problem of global reward maximization with only
partial distributed feedback. This problem is motivated by several real-world
applications (e.g., cellular network configuration, dynamic pricing, and policy
selection) where an action taken by a central entity influences a large
population that contributes to the global reward. However, collecting such
reward feedback from the entire population not only incurs a prohibitively high
cost but often leads to privacy concerns. To tackle this problem, we consider
differentially private distributed linear bandits, where only a subset of users
from the population are selected (called clients) to participate in the
learning process and the central server learns the global model from such
partial feedback by iteratively aggregating these clients' local feedback in a
differentially private fashion. We then propose a unified algorithmic learning
framework, called differentially private distributed phased elimination
(DP-DPE), which can be naturally integrated with popular differential privacy
(DP) models (including central DP, local DP, and shuffle DP). Furthermore, we
prove that DP-DPE achieves both sublinear regret and sublinear communication
cost. Interestingly, DP-DPE also achieves privacy protection "for free" in the
sense that the additional cost due to privacy guarantees is a lower-order
additive term. In addition, as a by-product of our techniques, the same results
of "free" privacy can also be achieved for the standard differentially private
linear bandits. Finally, we conduct simulations to corroborate our theoretical
results and demonstrate the effectiveness of DP-DPE.

We present our findings in the gap between theory and practice of using
conditional energy-based models (EBM) as an implicit representation for
behavior-cloned policies. We also clarify several subtle, and potentially
confusing, details in previous work in an attempt to help future research in
this area. We point out key differences between unconditional and conditional
EBMs, and warn that blindly applying training methods for one to the other
could lead to undesirable results that do not generalize well. Finally, we
emphasize the importance of the Maximum Mutual Information principle as a
necessary condition to achieve good generalization in conditional EBMs as
implicit models for regression tasks.

Data visualisation helps understanding data represented by multiple
variables, also called features, stored in a large matrix where individuals are
stored in lines and variable values in columns. These data structures are
frequently called multidimensional spaces.In this paper, we illustrate ways of
employing the visual results of multidimensional projection algorithms to
understand and fine-tune the parameters of their mathematical framework. Some
of the common mathematical common to these approaches are Laplacian matrices,
Euclidian distance, Cosine distance, and statistical methods such as
Kullback-Leibler divergence, employed to fit probability distributions and
reduce dimensions. Two of the relevant algorithms in the data visualisation
field are t-distributed stochastic neighbourhood embedding (t-SNE) and
Least-Square Projection (LSP). These algorithms can be used to understand
several ranges of mathematical functions including their impact on datasets. In
this article, mathematical parameters of underlying techniques such as
Principal Component Analysis (PCA) behind t-SNE and mesh reconstruction methods
behind LSP are adjusted to reflect the properties afforded by the mathematical
formulation. The results, supported by illustrative methods of the processes of
LSP and t-SNE, are meant to inspire students in understanding the mathematics
behind such methods, in order to apply them in effective data analysis tasks in
multiple applications.

Emotion prediction plays an essential role in mental health and emotion-aware
computing. The complex nature of emotion resulting from its dependency on a
person's physiological health, mental state, and his surroundings makes its
prediction a challenging task. In this work, we utilize mobile sensing data to
predict happiness and stress. In addition to a person's physiological features,
we also incorporate the environment's impact through weather and social
network. To this end, we leverage phone data to construct social networks and
develop a machine learning architecture that aggregates information from
multiple users of the graph network and integrates it with the temporal
dynamics of data to predict emotion for all the users. The construction of
social networks does not incur additional cost in terms of EMAs or data
collection from users and doesn't raise privacy concerns. We propose an
architecture that automates the integration of a user's social network affect
prediction, is capable of dealing with the dynamic distribution of real-life
social networks, making it scalable to large-scale networks. Our extensive
evaluation highlights the improvement provided by the integration of social
networks. We further investigate the impact of graph topology on model's

Identifying spans in medical texts that correspond to medical entities is one
of the core steps for many healthcare NLP tasks such as ICD coding, medical
finding extraction, medical note contextualization, to name a few. Existing
entity extraction methods rely on a fixed and limited vocabulary of medical
entities and have difficulty with extracting entities represented by disjoint
spans. In this paper, we present a new transformer-based architecture called
OSLAT, Open Set Label Attention Transformer, that addresses many of the
limitations of the previous methods. Our approach uses the label-attention
mechanism to implicitly learn spans associated with entities of interest. These
entities can be provided as free text, including entities not seen during
OSLAT's training, and the model can extract spans even when they are disjoint.
To test the generalizability of our method, we train two separate models on two
different datasets, which have very low entity overlap: (1) a public discharge
notes dataset from hNLP, and (2) a much more challenging proprietary patient
text dataset "Reasons for Encounter" (RFE). We find that OSLAT models trained
on either dataset outperform rule-based and fuzzy string matching baselines
when applied to the RFE dataset as well as to the portion of hNLP dataset where
entities are represented by disjoint spans. Our code can be found at
https://github.com/curai/curai-research/tree/main/OSLAT.

The popularity of machine learning has increased the risk of unfair models
getting deployed in high-stake applications, such as justice system,
drug/vaccination design, and medical diagnosis. Although there are effective
methods to train fair models from scratch, how to automatically reveal and
explain the unfairness of a trained model remains a challenging task. Revealing
unfairness of machine learning models in interpretable fashion is a critical
step towards fair and trustworthy AI. In this paper, we systematically tackle
the novel task of revealing unfair models by mining interpretable evidence
(RUMIE). The key idea is to find solid evidence in the form of a group of data
instances discriminated most by the model. To make the evidence interpretable,
we also find a set of human-understandable key attributes and decision rules
that characterize the discriminated data instances and distinguish them from
the other non-discriminated data. As demonstrated by extensive experiments on
many real-world data sets, our method finds highly interpretable and solid
evidence to effectively reveal the unfairness of trained models. Moreover, it
is much more scalable than all of the baseline methods.

We propose a general, flexible, and scalable framework dpart, an open source
Python library for differentially private synthetic data generation. Central to
the approach is autoregressive modelling -- breaking the joint data
distribution to a sequence of lower-dimensional conditional distributions,
captured by various methods such as machine learning models (logistic/linear
regression, decision trees, etc.), simple histogram counts, or custom
techniques. The library has been created with a view to serve as a quick and
accessible baseline as well as to accommodate a wide audience of users, from
those making their first steps in synthetic data generation, to more
experienced ones with domain expertise who can configure different aspects of
the modelling and contribute new methods/mechanisms. Specific instances of
dpart include Independent, an optimized version of PrivBayes, and a newly
proposed model, dp-synthpop.
  Code: https://github.com/hazy/dpart

Fast approximations to matrix multiplication have the potential to
dramatically reduce the cost of neural network inference. Recent work on
approximate matrix multiplication proposed to replace costly multiplications
with table-lookups by fitting a fast hash function from training data. In this
work, we propose improvements to this previous work, targeted to the deep
learning inference setting, where one has access to both training data and
fixed (already learned) model weight matrices. We further propose a fine-tuning
procedure for accelerating entire neural networks while minimizing loss in
accuracy. Finally, we analyze the proposed method on a simple image
classification task. While we show improvements to prior work, overall
classification accuracy remains substantially diminished compared to exact
matrix multiplication. Our work, despite this negative result, points the way
towards future efforts to accelerate inner products with fast nonlinear hashing
methods.

Dam reservoirs play an important role in meeting sustainable development
goals and global climate targets. However, particularly for small dam
reservoirs, there is a lack of consistent data on their geographical location.
To address this data gap, a promising approach is to perform automated dam
reservoir extraction based on globally available remote sensing imagery. It can
be considered as a fine-grained task of water body extraction, which involves
extracting water areas in images and then separating dam reservoirs from
natural water bodies. We propose a novel deep neural network (DNN) based
pipeline that decomposes dam reservoir extraction into water body segmentation
and dam reservoir recognition. Water bodies are firstly separated from
background lands in a segmentation model and each individual water body is then
predicted as either dam reservoir or natural water body in a classification
model. For the former step, point-level metric learning with triplets across
images is injected into the segmentation model to address contour ambiguities
between water areas and land regions. For the latter step, prior-guided metric
learning with triplets from clusters is injected into the classification model
to optimize the image embedding space in a fine-grained level based on
reservoir clusters. To facilitate future research, we establish a benchmark
dataset with earth imagery data and human labelled reservoirs from river basins
in West Africa and India. Extensive experiments were conducted on this
benchmark in the water body segmentation task, dam reservoir recognition task,
and the joint dam reservoir extraction task. Superior performance has been
observed in the respective tasks when comparing our method with state of the
art approaches.

As a long-term threat to the privacy of training data, membership inference
attacks (MIAs) emerge ubiquitously in machine learning models. Existing works
evidence strong connection between the distinguishability of the training and
testing loss distributions and the model's vulnerability to MIAs. Motivated by
existing results, we propose a novel training framework based on a relaxed loss
with a more achievable learning target, which leads to narrowed generalization
gap and reduced privacy leakage. RelaxLoss is applicable to any classification
model with added benefits of easy implementation and negligible overhead.
Through extensive evaluations on five datasets with diverse modalities (images,
medical data, transaction records), our approach consistently outperforms
state-of-the-art defense mechanisms in terms of resilience against MIAs as well
as model utility. Our defense is the first that can withstand a wide range of
attacks while preserving (or even improving) the target model's utility. Source
code is available at https://github.com/DingfanChen/RelaxLoss

Following work on joint object-action representation, functional
object-oriented networks (FOON) were introduced as a knowledge representation
for robots. A FOON contains symbolic (high-level) concepts useful to a robot's
understanding of tasks and its environment for object-level planning. Prior to
this work, little has been done to show how plans acquired from FOON can be
executed by a robot, as the concepts in a FOON are too abstract for immediate
execution. We propose a hierarchical task planning approach that translates a
FOON graph into a PDDL-based representation of domain knowledge for
manipulation planning. As a result of this process, a manipulation plan can be
acquired, which can be executed by a robot from start to end, leveraging the
use of action contexts and skills as dynamic movement primitives (DMPs). We
demonstrate the entire pipeline from planning to execution using CoppeliaSim
and show how learned action contexts can be extended to never-before-seen

Estimating the test performance of software AI-based medical devices under
distribution shifts is crucial for evaluating the safety, efficiency, and
usability prior to clinical deployment. Due to the nature of regulated medical
device software and the difficulty in acquiring large amounts of labeled
medical datasets, we consider the task of predicting the test accuracy of an
arbitrary black-box model on an unlabeled target domain without modification to
the original training process or any distributional assumptions of the original
source data (i.e. we treat the model as a "black-box" and only use the
predicted output responses). We propose a "black-box" test estimation technique
based on conformal prediction and evaluate it against other methods on three
medical imaging datasets (mammography, dermatology, and histopathology) under
several clinically relevant types of distribution shift (institution, hardware
scanner, atlas, hospital). We hope that by promoting practical and effective
estimation techniques for black-box models, manufacturers of medical devices
will develop more standardized and realistic evaluation procedures to improve
the robustness and trustworthiness of clinical AI tools.

Inpainting has recently been proposed as a successful deep learning technique
for unsupervised medical image model discovery. The masks used for inpainting
are generally independent of the dataset and are not tailored to perform on
different given classes of anatomy. In this work, we introduce a method for
generating shape-aware masks for inpainting, which aims at learning the
statistical shape prior. We hypothesize that although the variation of masks
improves the generalizability of inpainting models, the shape of the masks
should follow the topology of the organs of interest. Hence, we propose an
unsupervised guided masking approach based on an off-the-shelf inpainting model
and a superpixel over-segmentation algorithm to generate a wide range of
shape-dependent masks. Experimental results on abdominal MR image
reconstruction show the superiority of our proposed masking method over
standard methods using square-shaped or dataset of irregular shape masks.

In theory, the success of unsupervised domain adaptation (UDA) largely relies
on domain gap estimation. However, for source free UDA, the source domain data
can not be accessed during adaptation, which poses great challenge of measuring
the domain gap. In this paper, we propose to use many classifiers to learn the
source domain decision boundaries, which provides a tighter upper bound of the
domain gap, even if both of the domain data can not be simultaneously accessed.
The source model is trained to push away each pair of classifiers whilst
ensuring the correctness of the decision boundaries. In this sense, our many
classifiers model separates the source different categories as far as possible
which induces the maximum disagreement of many classifiers in the target
domain, thus the transferable source domain knowledge is maximized. For
adaptation, the source model is adapted to maximize the agreement among pairs
of the classifiers. Thus the target features are pushed away from the decision
boundaries. Experiments on several datasets of UDA show that our approach
achieves state of the art performance among source free UDA approaches and can
even compete to source available UDA methods.

This work introduces BRILLsson, a novel binary neural network-based
representation learning model for a broad range of non-semantic speech tasks.
We train the model with knowledge distillation from a large and real-valued
TRILLsson model with only a fraction of the dataset used to train TRILLsson.
The resulting BRILLsson models are only 2MB in size with a latency less than
8ms, making them suitable for deployment in low-resource devices such as
wearables. We evaluate BRILLsson on eight benchmark tasks (including but not
limited to spoken language identification, emotion recognition, health
condition diagnosis, and keyword spotting), and demonstrate that our proposed
ultra-light and low-latency models perform as well as large-scale models.

We present an Equation/Variable free machine learning (EVFML) framework for
the control of the collective dynamics of complex/multiscale systems modelled
via microscopic/agent-based simulators. The approach obviates the need for
construction of surrogate, reduced-order models.~The proposed implementation
consists of three steps: (A) from high-dimensional agent-based simulations,
machine learning (in particular, non-linear manifold learning (Diffusion Maps
(DMs)) helps identify a set of coarse-grained variables that parametrize the
low-dimensional manifold on which the emergent/collective dynamics evolve. The
out-of-sample extension and pre-image problems, i.e. the construction of
non-linear mappings from the high-dimensional input space to the
low-dimensional manifold and back, are solved by coupling DMs with the Nystrom
extension and Geometric Harmonics, respectively; (B) having identified the
manifold and its coordinates, we exploit the Equation-free approach to perform
numerical bifurcation analysis of the emergent dynamics; then (C) based on the
previous steps, we design data-driven embedded wash-out controllers that drive
the agent-based simulators to their intrinsic, imprecisely known, emergent
open-loop unstable steady-states, thus demonstrating that the scheme is robust
against numerical approximation errors and modelling uncertainty.~The
efficiency of the framework is illustrated by controlling emergent unstable (i)
traveling waves of a deterministic agent-based model of traffic dynamics, and
(ii) equilibria of a stochastic financial market agent model with mimesis.

Fairness researchers in machine learning (ML) have coalesced around several
fairness criteria which provide formal definitions of what it means for an ML
model to be fair. However, these criteria have some serious limitations. We
identify four key shortcomings of these formal fairness criteria, and aim to
help to address them by extending performative prediction to include a
distributionally robust objective.

Modeling the mass distribution of galaxy-scale strong gravitational lenses is
a task of increasing difficulty. The high-resolution and depth of imaging data
now available render simple analytical forms ineffective at capturing lens
structures spanning a large range in spatial scale, mass scale, and morphology.
In this work, we address the problem with a novel multi-scale method based on
wavelets. We test our method on simulated Hubble Space Telescope imaging data
of strong lenses containing different types of mass substructures making them
deviate from smooth models: (1) a localized small dark matter subhalo, (2) a
Gaussian random field that mimics a non-localized population of subhalos along
the line of sight, (3) galaxy-scale multipoles that break elliptical symmetry.
We show that wavelets are able to recover all of these structures accurately.
This is made technically possible by using gradient-informed optimization based
on automatic differentiation over thousands of parameters, also allowing us to
sample the posterior distributions of all model parameters simultaneously. By
construction, our method merges all current modeling paradigms - analytical,
pixelated, and deep learning - into a single modular framework. It is also
well-suited for the fast modeling of large samples of lenses. All methods
presented here are publicly available in our new Herculens package.

In lifelong learning, an agent learns throughout its entire life without
resets, in a constantly changing environment, as we humans do. Consequently,
lifelong learning comes with a plethora of research problems such as continual
domain shifts, which result in non-stationary rewards and environment dynamics.
These non-stationarities are difficult to detect and cope with due to their
continuous nature. Therefore, exploration strategies and learning methods are
required that are capable of tracking the steady domain shifts, and adapting to
them. We propose Reactive Exploration to track and react to continual domain
shifts in lifelong reinforcement learning, and to update the policy
correspondingly. To this end, we conduct experiments in order to investigate
different exploration strategies. We empirically show that representatives of
the policy-gradient family are better suited for lifelong learning, as they
adapt more quickly to distribution shifts than Q-learning. Thereby,
policy-gradient methods profit the most from Reactive Exploration and show good
results in lifelong learning with continual domain shifts. Our code is
available at: https://github.com/ml-jku/reactive-exploration.

The d-separation criterion detects the compatibility of a joint probability
distribution with a directed acyclic graph through certain conditional
independences. In this work, we study this problem in the context of
categorical probability theory by introducing a categorical definition of
causal models, a categorical notion of d-separation, and proving an abstract
version of the d-separation criterion. This approach has two main benefits.
First, categorical d-separation is a very intuitive criterion based on
topological connectedness. Second, our results apply in measure-theoretic
probability (with standard Borel spaces), and therefore provide a clean proof
of the equivalence of local and global Markov properties with causal
compatibility for continuous and mixed variables.

It is commonly believed that in transfer learning including more pre-training
data translates into better performance. However, recent evidence suggests that
removing data from the source dataset can actually help too. In this work, we
take a closer look at the role of the source dataset's composition in transfer
learning and present a framework for probing its impact on downstream
performance. Our framework gives rise to new capabilities such as pinpointing
transfer learning brittleness as well as detecting pathologies such as
data-leakage and the presence of misleading examples in the source dataset. In
particular, we demonstrate that removing detrimental datapoints identified by
our framework improves transfer learning performance from ImageNet on a variety
of target tasks. Code is available at https://github.com/MadryLab/data-transfer

In this paper we study online Reinforcement Learning (RL) in partially
observable dynamical systems. We focus on the Predictive State Representations
(PSRs) model, which is an expressive model that captures other well-known
models such as Partially Observable Markov Decision Processes (POMDP). PSR
represents the states using a set of predictions of future observations and is
defined entirely using observable quantities. We develop a novel model-based
algorithm for PSRs that can learn a near optimal policy in sample complexity
scaling polynomially with respect to all the relevant parameters of the
systems. Our algorithm naturally works with function approximation to extend to
systems with potentially large state and observation spaces. We show that given
a realizable model class, the sample complexity of learning the near optimal
policy only scales polynomially with respect to the statistical complexity of
the model class, without any explicit polynomial dependence on the size of the
state and observation spaces. Notably, our work is the first work that shows
polynomial sample complexities to compete with the globally optimal policy in
PSRs. Finally, we demonstrate how our general theorem can be directly used to
derive sample complexity bounds for special models including $m$-step weakly
revealing and $m$-step decodable tabular POMDPs, POMDPs with low-rank latent
transition, and POMDPs with linear emission and latent transition.

NLP systems typically require support for more than one language. As
different languages have different amounts of supervision, cross-lingual
transfer benefits languages with little to no training data by transferring
from other languages. From an engineering perspective, multilingual NLP
benefits development and maintenance by serving multiple languages with a
single system. Both cross-lingual transfer and multilingual NLP rely on
cross-lingual representations serving as the foundation. As BERT revolutionized
representation learning and NLP, it also revolutionized cross-lingual
representations and cross-lingual transfer. Multilingual BERT was released as a
replacement for single-language BERT, trained with Wikipedia data in 104
languages.
  Surprisingly, without any explicit cross-lingual signal, multilingual BERT
learns cross-lingual representations in addition to representations for
individual languages. This thesis first shows such surprising cross-lingual
effectiveness compared against prior art on various tasks. Naturally, it raises
a set of questions, most notably how do these multilingual encoders learn
cross-lingual representations. In exploring these questions, this thesis will
analyze the behavior of multilingual models in a variety of settings on high
and low resource languages. We also look at how to inject different
cross-lingual signals into multilingual encoders, and the optimization behavior
of cross-lingual transfer with these models. Together, they provide a better
understanding of multilingual encoders on cross-lingual transfer. Our findings
will lead us to suggested improvements to multilingual encoders and
cross-lingual transfer.

Although neural radiance fields (NeRF) have shown impressive advances for
novel view synthesis, most methods typically require multiple input images of
the same scene with accurate camera poses. In this work, we seek to
substantially reduce the inputs to a single unposed image. Existing approaches
condition on local image features to reconstruct a 3D object, but often render
blurry predictions at viewpoints that are far away from the source view. To
address this issue, we propose to leverage both the global and local features
to form an expressive 3D representation. The global features are learned from a
vision transformer, while the local features are extracted from a 2D
convolutional network. To synthesize a novel view, we train a multilayer
perceptron (MLP) network conditioned on the learned 3D representation to
perform volume rendering. This novel 3D representation allows the network to
reconstruct unseen regions without enforcing constraints like symmetry or
canonical coordinate systems. Our method can render novel views from only a
single input image and generalize across multiple object categories using a
single model. Quantitative and qualitative evaluations demonstrate that the
proposed method achieves state-of-the-art performance and renders richer
details than existing approaches.

A new attention-based model for the gradient boosting machine (GBM) called
AGBoost (the attention-based gradient boosting) is proposed for solving
regression problems. The main idea behind the proposed AGBoost model is to
assign attention weights with trainable parameters to iterations of GBM under
condition that decision trees are base learners in GBM. Attention weights are
determined by applying properties of decision trees and by using the Huber's
contamination model which provides an interesting linear dependence between
trainable parameters of the attention and the attention weights. This
peculiarity allows us to train the attention weights by solving the standard
quadratic optimization problem with linear constraints. The attention weights
also depend on the discount factor as a tuning parameter, which determines how
much the impact of the weight is decreased with the number of iterations.
Numerical experiments performed for two types of base learners, original
decision trees and extremely randomized trees with various regression datasets
illustrate the proposed model.

Learning predictors that do not rely on spurious correlations involves
building causal representations. However, learning such a representation is
very challenging. We, therefore, formulate the problem of learning a causal
representation from high dimensional data and study causal recovery with
synthetic data. This work introduces a latent variable decoder model, Decoder
BCD, for Bayesian causal discovery and performs experiments in mildly
supervised and unsupervised settings. We present a series of synthetic
experiments to characterize important factors for causal discovery and show
that using known intervention targets as labels helps in unsupervised Bayesian
inference over structure and parameters of linear Gaussian additive noise
latent structural causal models.

Software fault-proneness prediction is an active research area, with many
factors affecting prediction performance extensively studied. However, the
impact of the learning approach (i.e., the specifics of the data used for
training and the target variable being predicted) on the prediction performance
has not been studied, except for one initial work. This paper explores the
effects of two learning approaches, useAllPredictAll and usePrePredictPost, on
the performance of software fault-proneness prediction, both within-release and
across-releases. The empirical results are based on data extracted from 64
releases of twelve open-source projects. Results show that the learning
approach has a substantial, and typically unacknowledged, impact on the
classification performance. Specifically, using useAllPredictAll leads to
significantly better performance than using usePrePredictPost learning
approach, both within-release and across-releases. Furthermore, this paper
uncovers that, for within-release predictions, this difference in
classification performance is due to different levels of class imbalance in the
two learning approaches. When class imbalance is addressed, the performance
difference between the learning approaches is eliminated. Our findings imply
that the learning approach should always be explicitly identified and its
impact on software fault-proneness prediction considered. The paper concludes
with a discussion of potential consequences of our results for both research
and practice.

Irregular time series data are prevalent in the real world and are
challenging to model with a simple recurrent neural network (RNN). Hence, a
model that combines the use of ordinary differential equations (ODE) and RNN
was proposed (ODE-RNN) to model irregular time series with higher accuracy, but
it suffers from high computational costs. In this paper, we propose an
improvement in the runtime on ODE-RNNs by using a different efficient batching
strategy. Our experiments show that the new models reduce the runtime of
ODE-RNN significantly ranging from 2 times up to 49 times depending on the
irregularity of the data while maintaining comparable accuracy. Hence, our
model can scale favorably for modeling larger irregular data sets.

The convergence of stochastic interacting particle systems in the mean-field
limit to solutions to conservative stochastic partial differential equations is
shown, with optimal rate of convergence. As a second main result, a
quantitative central limit theorem for such SPDEs is derived, again with
optimal rate of convergence.
  The results apply in particular to the convergence in the mean-field scaling
of stochastic gradient descent dynamics in overparametrized, shallow neural
networks to solutions to SPDEs. It is shown that the inclusion of fluctuations
in the limiting SPDE improves the rate of convergence, and retains information
about the fluctuations of stochastic gradient descent in the continuum limit.

We propose and practically demonstrate a joint detection and decoding scheme
for short-packet wireless communications in scenarios that require to first
detect the presence of a message before actually decoding it. For this, we
extend the recently proposed serial Turbo-autoencoder neural network (NN)
architecture and train it to find short messages that can be, all "at once",
detected, synchronized, equalized and decoded when sent over an unsynchronized
channel with memory. The conceptional advantage of the proposed system stems
from a holistic message structure with superimposed pilots for joint detection
and decoding without the need of relying on a dedicated preamble. This results
not only in higher spectral efficiency, but also translates into the
possibility of shorter messages compared to using a dedicated preamble. We
compare the detection error rate (DER), bit error rate (BER) and block error
rate (BLER) performance of the proposed system with a hand-crafted
state-of-the-art conventional baseline and our simulations show a significant
advantage of the proposed autoencoder-based system over the conventional
baseline in every scenario up to messages conveying k = 96 information bits.
Finally, we practically evaluate and confirm the improved performance of the
proposed system over-the-air (OTA) using a software-defined radio (SDR)-based
measurement testbed.

In this paper we consider finding an approximate second-order stationary
point (SOSP) of nonconvex conic optimization that minimizes a twice
differentiable function over the intersection of an affine subspace and a
convex cone. In particular, we propose a Newton-conjugate gradient (Newton-CG)
based barrier method for finding an $(\epsilon,\sqrt{\epsilon})$-SOSP of this
problem. Our method is not only implementable, but also achieves an iteration
complexity of ${\cal O}(\epsilon^{-3/2})$, which matches the best known
iteration complexity of second-order methods for finding an
$(\epsilon,\sqrt{\epsilon})$-SOSP of unconstrained nonconvex optimization. The
operation complexity, consisting of ${\cal O}(\epsilon^{-3/2})$ Cholesky
factorizations and $\widetilde{\cal
O}(\epsilon^{-3/2}\min\{n,\epsilon^{-1/4}\})$ other fundamental operations, is
also established for our method.

Real-estate image tagging is one of the essential use-cases to save efforts
involved in manual annotation and enhance the user experience. This paper
proposes an end-to-end pipeline (referred to as RE-Tagger) for the real-estate
image classification problem. We present a two-stage transfer learning approach
using custom InceptionV3 architecture to classify images into different
categories (i.e., bedroom, bathroom, kitchen, balcony, hall, and others).
Finally, we released the application as REST API hosted as a web application
running on 2 cores machine with 2 GB RAM. The demo video is available here.

Nuclear fusion represents one of the best alternatives for a sustainable
source of clean energy. Tokamaks allow to confine fusion plasma with magnetic
fields and one of the main challenges in the control of the magnetic
configuration is the prediction/reconstruction of the Last Closed-Flux Surface
(LCFS). The evolution in time of the LCFS is determined by the interaction of
the actuator coils and the internal tokamak plasma. This task requires
real-time capable tools able to deal with high-dimensional data as well as with
high resolution in time, where the interaction between a wide range of input
actuator coils with internal plasma state responses add additional layer of
complexity. In this work, we present the application of a novel state of the
art machine learning model to the LCFS reconstruction in the Experimental
Advanced Superconducting Tokamak (EAST) that learns automatically from the
experimental data of EAST. This architecture allows not only offline simulation
and testing of a particular control strategy, but can also be embedded in the
real-time control system for online magnetic equilibrium reconstruction and
prediction. In the real-time modeling test, our approach achieves very high
accuracies, with over 99% average similarity in LCFS reconstruction of the
entire discharge process.

Lyric-to-melody generation, which generates melody according to given lyrics,
is one of the most important automatic music composition tasks. With the rapid
development of deep learning, previous works address this task with end-to-end
neural network models. However, deep learning models cannot well capture the
strict but subtle relationships between lyrics and melodies, which compromises
the harmony between lyrics and generated melodies. In this paper, we propose
ReLyMe, a method that incorporates Relationships between Lyrics and Melodies
from music theory to ensure the harmony between lyrics and melodies.
Specifically, we first introduce several principles that lyrics and melodies
should follow in terms of tone, rhythm, and structure relationships. These
principles are then integrated into neural network lyric-to-melody models by
adding corresponding constraints during the decoding process to improve the
harmony between lyrics and melodies. We use a series of objective and
subjective metrics to evaluate the generated melodies. Experiments on both
English and Chinese song datasets show the effectiveness of ReLyMe,
demonstrating the superiority of incorporating lyric-melody relationships from
the music domain into neural lyric-to-melody generation.

Multiclass neural networks are a common tool in modern unsupervised domain
adaptation, yet an appropriate theoretical description for their non-uniform
sample complexity is lacking in the adaptation literature. To fill this gap, we
propose the first PAC-Bayesian adaptation bounds for multiclass learners. We
facilitate practical use of our bounds by also proposing the first
approximation techniques for the multiclass distribution divergences we
consider. For divergences dependent on a Gibbs predictor, we propose additional
PAC-Bayesian adaptation bounds which remove the need for inefficient
Monte-Carlo estimation. Empirically, we test the efficacy of our proposed
approximation techniques as well as some novel design-concepts which we include
in our bounds. Finally, we apply our bounds to analyze a common adaptation
algorithm that uses neural networks.

The volume of fluid (VoF) method is widely used in multi-phase flow
simulations to track and locate the interface between two immiscible fluids. A
major bottleneck of the VoF method is the interface reconstruction step due to
its high computational cost and low accuracy on unstructured grids. We propose
a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to
accelerate the interface reconstruction on general unstructured meshes. We
first develop a methodology to generate a synthetic dataset based on paraboloid
surfaces discretized on unstructured meshes. We then train a GNN based model
and perform generalization tests. Our results demonstrate the efficiency of a
GNN based approach for interface reconstruction in multi-phase flow simulations
in the industrial context.

The current inventory of recent (fresh) impacts on Mars shows a strong bias
towards areas of low thermal inertia. These areas are generally visually
bright, and impacts create dark scours and rays that make them easier to
detect. It is expected that impacts occur at a similar rate in areas of higher
thermal inertia, but those impacts are under-detected. This study investigates
the use of a trained machine learning classifier to increase the detection of
fresh impacts on Mars using CTX data. This approach discovered 69 new fresh
impacts that have been confirmed with follow-up HiRISE images. We found that
examining candidates partitioned by thermal inertia (TI) values, which is only
possible due to the large number of machine learning candidates, helps reduce
the observational bias and increase the number of known high-TI impacts.

Various structures in human physiology follow a treelike morphology, which
often expresses complexity at very fine scales. Examples of such structures are
intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large
collections of 2D and 3D images have been made available by medical imaging
modalities such as magnetic resonance imaging (MRI), computed tomography (CT),
Optical coherence tomography (OCT) and ultrasound in which the spatial
arrangement can be observed. Segmentation of these structures in medical
imaging is of great importance since the analysis of the structure provides
insights into disease diagnosis, treatment planning, and prognosis. Manually
labelling extensive data by radiologists is often time-consuming and
error-prone. As a result, automated or semi-automated computational models have
become a popular research field of medical imaging in the past two decades, and
many have been developed to date. In this survey, we aim to provide a
comprehensive review of currently publicly available datasets, segmentation
algorithms, and evaluation metrics. In addition, current challenges and future
research directions are discussed.

Polypharmacy, defined as the use of multiple drugs together, is a standard
treatment method, especially for severe and chronic diseases. However, using
multiple drugs together may cause interactions between drugs. Drug-drug
interaction (DDI) is the activity that occurs when the impact of one drug
changes when combined with another. DDIs may obstruct, increase, or decrease
the intended effect of either drug or, in the worst-case scenario, create
adverse side effects. While it is critical to detect DDIs on time, it is
timeconsuming and expensive to identify them in clinical trials due to their
short duration and many possible drug pairs to be considered for testing. As a
result, computational methods are needed for predicting DDIs. In this paper, we
present a novel heterogeneous graph attention model, HAN-DDI to predict
drug-drug interactions. We create a heterogeneous network of drugs with
different biological entities. Then, we develop a heterogeneous graph attention
network to learn DDIs using relations of drugs with other entities. It consists
of an attention-based heterogeneous graph node encoder for obtaining drug node
representations and a decoder for predicting drug-drug interactions. Further,
we utilize comprehensive experiments to evaluate of our model and to compare it
with state-of-the-art models. Experimental results show that our proposed
method, HAN-DDI, outperforms the baselines significantly and accurately
predicts DDIs, even for new drugs.

Graph Convolutional Networks (GCNs) have been shown to be a powerful concept
that has been successfully applied to a large variety of tasks across many
domains over the past years. In this work we study the theory that paved the
way to the definition of GCN, including related parts of classical graph
theory. We also discuss and experimentally demonstrate key properties and
limitations of GCNs such as those caused by the statistical dependency of
samples, introduced by the edges of the graph, which causes the estimates of
the full gradient to be biased. Another limitation we discuss is the negative
impact of minibatch sampling on the model performance. As a consequence, during
parameter update, gradients are computed on the whole dataset, undermining
scalability to large graphs. To account for this, we research alternative
methods which allow to safely learn good parameters while sampling only a
subset of data per iteration. We reproduce the results reported in the work of
Kipf et al. and propose an implementation inspired to SIGN, which is a
sampling-free minibatch method. Eventually we compare the two implementations
on a benchmark dataset, proving that they are comparable in terms of prediction
accuracy for the task of semi-supervised node classification.

Initially introduced by Peter Hammer, Logical Analysis of Data is a
methodology that aims at computing a logical justification for dividing a group
of data in two groups of observations, usually called the positive and negative
groups. Consider this partition into positive and negative groups as the
description of a partially defined Boolean function; the data is then processed
to identify a subset of attributes, whose values may be used to characterize
the observations of the positive groups against those of the negative group.
  LAD constitutes an interesting rule-based learning alternative to classic
statistical learning techniques and has many practical applications.
Nevertheless, the computation of group characterization may be costly,
depending on the properties of the data instances. A major aim of our work is
to provide effective tools for speeding up the computations, by computing some
\emph{a priori} probability that a given set of attributes does characterize
the positive and negative groups. To this effect, we propose several models for
representing the data set of observations, according to the information we have
on it. These models, and the probabilities they allow us to compute, are also
helpful for quickly assessing some properties of the real data at hand;
furthermore they may help us to better analyze and understand the computational
difficulties encountered by solving methods.
  Once our models have been established, the mathematical tools for computing
probabilities come from Analytic Combinatorics. They allow us to express the
desired probabilities as ratios of generating functions coefficients, which
then provide a quick computation of their numerical values. A further,
long-range goal of this paper is to show that the methods of Analytic
Combinatorics can help in analyzing the performance of various algorithms in
LAD and related fields.

We study the backward compatible problem for person re-identification
(Re-ID), which aims to constrain the features of an updated new model to be
comparable with the existing features from the old model in galleries. Most of
the existing works adopt distillation-based methods, which focus on pushing new
features to imitate the distribution of the old ones. However, the
distillation-based methods are intrinsically sub-optimal since it forces the
new feature space to imitate the inferior old feature space. To address this
issue, we propose the Ranking-based Backward Compatible Learning (RBCL), which
directly optimizes the ranking metric between new features and old features.
Different from previous methods, RBCL only pushes the new features to find
best-ranking positions in the old feature space instead of strictly alignment,
and is in line with the ultimate goal of backward retrieval. However, the sharp
sigmoid function used to make the ranking metric differentiable also incurs the
gradient vanish issue, therefore stems the ranking refinement during the later
period of training. To address this issue, we propose the Dynamic Gradient
Reactivation (DGR), which can reactivate the suppressed gradients by adding
dynamic computed constant during forward step. To further help targeting the
best-ranking positions, we include the Neighbor Context Agents (NCAs) to
approximate the entire old feature space during training. Unlike previous works
which only test on the in-domain settings, we make the first attempt to
introduce the cross-domain settings (including both supervised and
unsupervised), which are more meaningful and difficult. The experimental
results on all five settings show that the proposed RBCL outperforms previous
state-of-the-art methods by large margins under all settings.

Nonconvex constrained optimization problems can be used to model a number of
machine learning problems, such as multi-class Neyman-Pearson classification
and constrained Markov decision processes. However, such kinds of problems are
challenging because both the objective and constraints are possibly nonconvex,
so it is difficult to balance the reduction of the loss value and reduction of
constraint violation. Although there are a few methods that solve this class of
problems, all of them are double-loop or triple-loop algorithms, and they
require oracles to solve some subproblems up to certain accuracy by tuning
multiple hyperparameters at each iteration. In this paper, we propose a novel
gradient descent and perturbed ascent (GDPA) algorithm to solve a class of
smooth nonconvex inequality constrained problems. The GDPA is a primal-dual
algorithm, which only exploits the first-order information of both the
objective and constraint functions to update the primal and dual variables in
an alternating way. The key feature of the proposed algorithm is that it is a
single-loop algorithm, where only two step-sizes need to be tuned. We show that
under a mild regularity condition GDPA is able to find Karush-Kuhn-Tucker (KKT)
points of nonconvex functional constrained problems with convergence rate
guarantees. To the best of our knowledge, it is the first single-loop algorithm
that can solve the general nonconvex smooth problems with nonconvex inequality
constraints. Numerical results also showcase the superiority of GDPA compared
with the best-known algorithms (in terms of both stationarity measure and
feasibility of the obtained solutions).

Recommendation systems have been widely used in various domains such as
music, films, e-shopping etc. After mostly avoiding digitization, the art world
has recently reached a technological turning point due to the pandemic, making
online sales grow significantly as well as providing quantitative online data
about artists and artworks. In this work, we present a content-based
recommendation system on contemporary art relying on images of artworks and
contextual metadata of artists. We gathered and annotated artworks with
advanced and art-specific information to create a completely unique database
that was used to train our models. With this information, we built a proximity
graph between artworks. Similarly, we used NLP techniques to characterize the
practices of the artists and we extracted information from exhibitions and
other event history to create a proximity graph between artists. The power of
graph analysis enables us to provide an artwork recommendation system based on
a combination of visual and contextual information from artworks and artists.
After an assessment by a team of art specialists, we get an average final
rating of 75% of meaningful artworks when compared to their professional

We present a new probabilistic method for detecting, deblending, and
cataloging astronomical sources called the Bayesian Light Source Separator
(BLISS). BLISS is based on deep generative models, which embed neural networks
within a Bayesian model. For posterior inference, BLISS uses a new form of
variational inference known as Forward Amortized Variational Inference. The
BLISS inference routine is fast, requiring a single forward pass of the encoder
networks on a GPU once the encoder networks are trained. BLISS can perform
fully Bayesian inference on megapixel images in seconds, and produces highly
accurate catalogs. BLISS is highly extensible, and has the potential to
directly answer downstream scientific questions in addition to producing
probabilistic catalogs.

Crowd counting is a regression task that estimates the number of people in a
scene image, which plays a vital role in a range of safety-critical
applications, such as video surveillance, traffic monitoring and flow control.
In this paper, we investigate the vulnerability of deep learning based crowd
counting models to backdoor attacks, a major security threat to deep learning.
A backdoor attack implants a backdoor trigger into a target model via data
poisoning so as to control the model's predictions at test time. Different from
image classification models on which most of existing backdoor attacks have
been developed and tested, crowd counting models are regression models that
output multi-dimensional density maps, thus requiring different techniques to
manipulate.
  In this paper, we propose two novel Density Manipulation Backdoor Attacks
(DMBA$^{-}$ and DMBA$^{+}$) to attack the model to produce arbitrarily large or
small density estimations. Experimental results demonstrate the effectiveness
of our DMBA attacks on five classic crowd counting models and four types of
datasets. We also provide an in-depth analysis of the unique challenges of
backdooring crowd counting models and reveal two key elements of effective
attacks: 1) full and dense triggers and 2) manipulation of the ground truth
counts or density maps. Our work could help evaluate the vulnerability of crowd
counting models to potential backdoor attacks.

Simulation-Based Inference (SBI) is a promising Bayesian inference framework
that alleviates the need for analytic likelihoods to estimate posterior
distributions. Recent advances using neural density estimators in SBI
algorithms have demonstrated the ability to achieve high-fidelity posteriors,
at the expense of a large number of simulations ; which makes their application
potentially very time-consuming when using complex physical simulations. In
this work we focus on boosting the sample-efficiency of posterior density
estimation using the gradients of the simulator. We present a new method to
perform Neural Posterior Estimation (NPE) with a differentiable simulator. We
demonstrate how gradient information helps constrain the shape of the posterior
and improves sample-efficiency.

Puzzle solving is a combinatorial challenge due to the difficulty of matching
adjacent pieces. Instead, we infer a mental image from all pieces, which a
given piece can then be matched against avoiding the combinatorial explosion.
Exploiting advancements in Generative Adversarial methods, we learn how to
reconstruct the image given a set of unordered pieces, allowing the model to
learn a joint embedding space to match an encoding of each piece to the cropped
layer of the generator. Therefore we frame the problem as a R@1 retrieval task,
and then solve the linear assignment using differentiable Hungarian attention,
making the process end-to-end. In doing so our model is puzzle size agnostic,
in contrast to prior deep learning methods which are single size. We evaluate
on two new large-scale datasets, where our model is on par with deep learning
methods, while generalizing to multiple puzzle sizes.

Recent algorithms designed for reinforcement learning tasks focus on finding
a single optimal solution. However, in many practical applications, it is
important to develop reasonable agents with diverse strategies. In this paper,
we propose Diversity-Guided Policy Optimization (DGPO), an on-policy framework
for discovering multiple strategies for the same task. Our algorithm uses
diversity objectives to guide a latent code conditioned policy to learn a set
of diverse strategies in a single training procedure. Specifically, we
formalize our algorithm as the combination of a diversity-constrained
optimization problem and an extrinsic-reward constrained optimization problem.
And we solve the constrained optimization as a probabilistic inference task and
use policy iteration to maximize the derived lower bound. Experimental results
show that our method efficiently finds diverse strategies in a wide variety of
reinforcement learning tasks. We further show that DGPO achieves a higher
diversity score and has similar sample complexity and performance compared to
other baselines.

We study Online Continual Learning with missing labels and propose SemiCon, a
new contrastive loss designed for partly labeled data. We demonstrate its
efficiency by devising a memory-based method trained on an unlabeled data
stream, where every data added to memory is labeled using an oracle. Our
approach outperforms existing semi-supervised methods when few labels are
available, and obtain similar results to state-of-the-art supervised methods
while using only 2.6% of labels on Split-CIFAR10 and 10% of labels on
Split-CIFAR100.

Recent works have shown how the reasoning capabilities of Large Language
Models (LLMs) can be applied to domains beyond natural language processing,
such as planning and interaction for robots. These embodied problems require an
agent to understand many semantic aspects of the world: the repertoire of
skills available, how these skills influence the world, and how changes to the
world map back to the language. LLMs planning in embodied environments need to
consider not just what skills to do, but also how and when to do them - answers
that change over time in response to the agent's own choices. In this work, we
investigate to what extent LLMs used in such embodied contexts can reason over
sources of feedback provided through natural language, without any additional
training. We propose that by leveraging environment feedback, LLMs are able to
form an inner monologue that allows them to more richly process and plan in
robotic control scenarios. We investigate a variety of sources of feedback,
such as success detection, scene description, and human interaction. We find
that closed-loop language feedback significantly improves high-level
instruction completion on three domains, including simulated and real table top
rearrangement tasks and long-horizon mobile manipulation tasks in a kitchen
environment in the real world.

We present a novel application of the machine learning / artificial
intelligence method called boosted decision trees to estimate physical
quantities on field programmable gate arrays (FPGA). The software package
fwXmachina features a new architecture called parallel decision paths that
allows for deep decision trees with arbitrary number of input variables. It
also features a new optimization scheme to use different numbers of bits for
each input variable, which produces optimal physics results and ultraefficient
FPGA resource utilization. Problems in high energy physics of proton collisions
at the Large Hadron Collider (LHC) are considered. Estimation of missing
transverse momentum (ETmiss) at the first level trigger system at the High
Luminosity LHC (HL-LHC) experiments, with a simplified detector modeled by
Delphes, is used to benchmark and characterize the firmware performance. The
firmware implementation with a maximum depth of up to 10 using eight input
variables of 16-bit precision gives a latency value of O(10) ns, independent of
the clock speed, and O(0.1)% of the available FPGA resources without using
digital signal processors.

Learning dynamic user preference has become an increasingly important
component for many online platforms (e.g., video-sharing sites, e-commerce
systems) to make sequential recommendations. Previous works have made many
efforts to model item-item transitions over user interaction sequences, based
on various architectures, e.g., recurrent neural networks and self-attention
mechanism. Recently emerged graph neural networks also serve as useful backbone
models to capture item dependencies in sequential recommendation scenarios.
Despite their effectiveness, existing methods have far focused on item sequence
representation with singular type of interactions, and thus are limited to
capture dynamic heterogeneous relational structures between users and items
(e.g., page view, add-to-favorite, purchase). To tackle this challenge, we
design a Multi-Behavior Hypergraph-enhanced Transformer framework (MBHT) to
capture both short-term and long-term cross-type behavior dependencies.
Specifically, a multi-scale Transformer is equipped with low-rank
self-attention to jointly encode behavior-aware sequential patterns from
fine-grained and coarse-grained levels. Additionally, we incorporate the global
multi-behavior dependency into the hypergraph neural architecture to capture
the hierarchical long-range item correlations in a customized manner.
Experimental results demonstrate the superiority of our MBHT over various
state-of-the-art recommendation solutions across different settings. Further
ablation studies validate the effectiveness of our model design and benefits of
the new MBHT framework. Our implementation code is released at:
https://github.com/yuh-yang/MBHT-KDD22.

Achieving at least some level of explainability requires complex analyses for
many machine learning systems, such as common black-box models. We recently
proposed a new rule-based learning system, SupRB, to construct compact,
interpretable and transparent models by utilizing separate optimizers for the
model selection tasks concerning rule discovery and rule set composition.This
allows users to specifically tailor their model structure to fulfil use-case
specific explainability requirements. From an optimization perspective, this
allows us to define clearer goals and we find that -- in contrast to many state
of the art systems -- this allows us to keep rule fitnesses independent. In
this paper we investigate this system's performance thoroughly on a set of
regression problems and compare it against XCSF, a prominent rule-based
learning system. We find the overall results of SupRB's evaluation comparable
to XCSF's while allowing easier control of model structure and showing a
substantially smaller sensitivity to random seeds and data splits. This
increased control can aid in subsequently providing explanations for both
training and final structure of the model.

Human affect and mental state estimation in an automated manner, face a
number of difficulties, including learning from labels with poor or no temporal
resolution, learning from few datasets with little data (often due to
confidentiality constraints) and, (very) long, in-the-wild videos. For these
reasons, deep learning methodologies tend to overfit, that is, arrive at latent
representations with poor generalisation performance on the final regression
task. To overcome this, in this work, we introduce two complementary
contributions. First, we introduce a novel relational loss for multilabel
regression and ordinal problems that regularises learning and leads to better
generalisation. The proposed loss uses label vector inter-relational
information to learn better latent representations by aligning batch label
distances to the distances in the latent feature space. Second, we utilise a
two-stage attention architecture that estimates a target for each clip by using
features from the neighbouring clips as temporal context. We evaluate the
proposed methodology on both continuous affect and schizophrenia severity
estimation problems, as there are methodological and contextual parallels
between the two. Experimental results demonstrate that the proposed methodology
outperforms all baselines. In the domain of schizophrenia, the proposed
methodology outperforms previous state-of-the-art by a large margin, achieving
a PCC of up to 78% performance close to that of human experts (85%) and much
higher than previous works (uplift of up to 40%). In the case of affect
recognition, we outperform previous vision-based methods in terms of CCC on
both the OMG and the AMIGOS datasets. Specifically for AMIGOS, we outperform
previous SoTA CCC for both arousal and valence by 9% and 13% respectively, and
in the OMG dataset we outperform previous vision works by up to 5% for both
arousal and valence.

Explainable artificial intelligence (XAI) methods lack ground truth. In its
place, method developers have relied on axioms to determine desirable
properties for their explanations' behavior. For high stakes uses of machine
learning that require explainability, it is not sufficient to rely on axioms as
the implementation, or its usage, can fail to live up to the ideal. As a
result, there exists active research on validating the performance of XAI
methods. The need for validation is especially magnified in domains with a
reliance on XAI. A procedure frequently used to assess their utility, and to
some extent their fidelity, is an ablation study. By perturbing the input
variables in rank order of importance, the goal is to assess the sensitivity of
the model's performance. Perturbing important variables should correlate with
larger decreases in measures of model capability than perturbing less important
features. While the intent is clear, the actual implementation details have not
been studied rigorously for tabular data. Using five datasets, three XAI
methods, four baselines, and three perturbations, we aim to show 1) how varying
perturbations and adding simple guardrails can help to avoid potentially flawed
conclusions, 2) how treatment of categorical variables is an important
consideration in both post-hoc explainability and ablation studies, and 3) how
to identify useful baselines for XAI methods and viable perturbations for
ablation studies.

Video coding is a mathematical optimization problem of rate and distortion
essentially. To solve this complex optimization problem, two popular video
coding frameworks have been developed: block-based hybrid video coding and
end-to-end learned video coding. If we rethink video coding from the
perspective of optimization, we find that the existing two frameworks represent
two directions of optimization solutions. Block-based hybrid coding represents
the discrete optimization solution because those irrelevant coding modes are
discrete in mathematics. It searches for the best one among multiple starting
points (i.e. modes). However, the search is not efficient enough. On the other
hand, end-to-end learned coding represents the continuous optimization solution
because the gradient descent is based on a continuous function. It optimizes a
group of model parameters efficiently by the numerical algorithm. However,
limited by only one starting point, it is easy to fall into the local optimum.
To better solve the optimization problem, we propose to regard video coding as
a hybrid of the discrete and continuous optimization problem, and use both
search and numerical algorithm to solve it. Our idea is to provide multiple
discrete starting points in the global space and optimize the local optimum
around each point by numerical algorithm efficiently. Finally, we search for
the global optimum among those local optimums. Guided by the hybrid
optimization idea, we design a hybrid optimization video coding framework,
which is built on continuous deep networks entirely and also contains some
discrete modes. We conduct a comprehensive set of experiments. Compared to the
continuous optimization framework, our method outperforms pure learned video
coding methods. Meanwhile, compared to the discrete optimization framework, our
method achieves comparable performance to HEVC reference software HM16.10 in
PSNR.

Vision transformers (ViTs) are usually considered to be less light-weight
than convolutional neural networks (CNNs) due to the lack of inductive bias.
Recent works thus resort to convolutions as a plug-and-play module and embed
them in various ViT counterparts. In this paper, we argue that the
convolutional kernels perform information aggregation to connect all tokens;
however, they would be actually unnecessary for light-weight ViTs if this
explicit aggregation could function in a more homogeneous way. Inspired by
this, we present LightViT as a new family of light-weight ViTs to achieve
better accuracy-efficiency balance upon the pure transformer blocks without
convolution. Concretely, we introduce a global yet efficient aggregation scheme
into both self-attention and feed-forward network (FFN) of ViTs, where
additional learnable tokens are introduced to capture global dependencies; and
bi-dimensional channel and spatial attentions are imposed over token
embeddings. Experiments show that our model achieves significant improvements
on image classification, object detection, and semantic segmentation tasks. For
example, our LightViT-T achieves 78.7% accuracy on ImageNet with only 0.7G
FLOPs, outperforming PVTv2-B0 by 8.2% while 11% faster on GPU. Code is
available at https://github.com/hunto/LightViT.

While machine learning is vulnerable to adversarial examples, it still lacks
systematic procedures and tools for evaluating its security in different
application contexts. In this article, we discuss how to develop automated and
scalable security evaluations of machine learning using practical attacks,
reporting a use case on Windows malware detection.

Structural manipulation at the nanoscale breaks the intrinsic correlations
among different energy carrier transport properties, achieving high
thermoelectric performance. However, the coupled multifunctional (phonon and
electron) transport in the design of nanomaterials makes the optimization of
thermoelectric properties challenging. Machine learning brings convenience to
the design of nanostructures with large degree of freedom. Herein, we conducted
comprehensive thermoelectric optimization of isotopic armchair graphene
nanoribbons (AGNRs) with antidots and interfaces by combining Green's function
approach with machine learning algorithms. The optimal AGNR with ZT of 0.894 by
manipulating antidots was obtained at the interfaces of the aperiodic isotope
superlattices, which is 5.69 times larger than that of the pristine structure.
The proposed optimal structure via machine learning provides physical insights
that the carbon-13 atoms tend to form a continuous interface barrier
perpendicular to the carrier transport direction to suppress the propagation of
phonons through isotope AGNRs. The antidot effect is more effective than
isotope substitution in improving the thermoelectric properties of AGNRs. The
proposed approach coupling energy carrier transport property analysis with
machine learning algorithms offers highly efficient guidance on enhancing the
thermoelectric properties of low-dimensional nanomaterials, as well as to
explore and gain non-intuitive physical insights.

Deep generative models are widely used for modelling high-dimensional time
series, such as video animations, audio and climate data. Sequential
variational autoencoders have been successfully considered for many
applications, with many variant models relying on discrete-time methods and
recurrent neural networks (RNNs). On the other hand, continuous-time methods
have recently gained attraction, especially in the context of
irregularly-sampled time series, where they can better handle the data than
discrete-time methods. One such class are Gaussian process variational
autoencoders (GPVAEs), where the VAE prior is set as a Gaussian process (GPs),
allowing inductive biases to be explicitly encoded via the kernel function and
interpretability of the latent space. However, a major limitation of GPVAEs is
that it inherits the same cubic computational cost as GPs. In this work, we
leverage the equivalent discrete state space representation of Markovian GPs to
enable a linear-time GP solver via Kalman filtering and smoothing. We show via
corrupt and missing frames tasks that our method performs favourably,
especially on the latter where it outperforms RNN-based models.

The Mean-Teacher (MT) scheme is widely adopted in semi-supervised object
detection (SSOD). In MT, the sparse pseudo labels, offered by the final
predictions of the teacher (e.g., after Non Maximum Suppression (NMS)
post-processing), are adopted for the dense supervision for the student via
hand-crafted label assignment. However, the sparse-to-dense paradigm
complicates the pipeline of SSOD, and simultaneously neglects the powerful
direct, dense teacher supervision. In this paper, we attempt to directly
leverage the dense guidance of teacher to supervise student training, i.e., the
dense-to-dense paradigm. Specifically, we propose the Inverse NMS Clustering
(INC) and Rank Matching (RM) to instantiate the dense supervision, without the
widely used, conventional sparse pseudo labels. INC leads the student to group
candidate boxes into clusters in NMS as the teacher does, which is implemented
by learning grouping information revealed in NMS procedure of the teacher.
After obtaining the same grouping scheme as the teacher via INC, the student
further imitates the rank distribution of the teacher over clustered candidates
through Rank Matching. With the proposed INC and RM, we integrate Dense Teacher
Guidance into Semi-Supervised Object Detection (termed DTG-SSOD), successfully
abandoning sparse pseudo labels and enabling more informative learning on
unlabeled data. On COCO benchmark, our DTG-SSOD achieves state-of-the-art
performance under various labelling ratios. For example, under 10% labelling
ratio, DTG-SSOD improves the supervised baseline from 26.9 to 35.9 mAP,
outperforming the previous best method Soft Teacher by 1.9 points.

Smart wearables have played an integral part in our day to day life. From
recording ECG signals to analysing body fat composition, the smart wearables
can do it all. The smart devices encompass various sensors which can be
employed to derive meaningful information regarding the user's physical and
psychological conditions. Our approach focuses on employing such sensors to
identify and obtain the variations in the mood of a user at a given instance
through the use of supervised machine learning techniques. The study examines
the performance of various supervised learning models such as Decision Trees,
Random Forests, XGBoost, LightGBM on the dataset. With our proposed model, we
obtained a high recognition rate of 92.5% using XGBoost and LightGBM for 9
different emotion classes. By utilizing this, we aim to improvise and suggest
methods to aid emotion recognition for better mental health analysis and mood

In this work, we suggest Kernel Filtering Linear Overparameterization (KFLO),
where a linear cascade of filtering layers is used during training to improve
network performance in test time. We implement this cascade in a kernel
filtering fashion, which prevents the trained architecture from becoming
unnecessarily deeper. This also allows using our approach with almost any
network architecture and let combining the filtering layers into a single layer
in test time. Thus, our approach does not add computational complexity during
inference. We demonstrate the advantage of KFLO on various network models and
datasets in supervised learning.

A growing body of research has been dedicated to DL model testing. However,
there is still limited work on testing DL libraries, which serve as the
foundations for building, training, and running DL models. Prior work on
fuzzing DL libraries can only generate tests for APIs which have been invoked
by documentation examples, developer tests, or DL models, leaving a large
number of APIs untested. In this paper, we propose DeepREL, the first approach
to automatically inferring relational APIs for more effective DL library
fuzzing. Our basic hypothesis is that for a DL library under test, there may
exist a number of APIs sharing similar input parameters and outputs; in this
way, we can easily "borrow" test inputs from invoked APIs to test other
relational APIs. Furthermore, we formalize the notion of value equivalence and
status equivalence for relational APIs to serve as the oracle for effective bug
finding. We have implemented DeepREL as a fully automated end-to-end relational
API inference and fuzzing technique for DL libraries, which 1) automatically
infers potential API relations based on API syntactic or semantic information,
2) synthesizes concrete test programs for invoking relational APIs, 3)
validates the inferred relational APIs via representative test inputs, and
finally 4) performs fuzzing on the verified relational APIs to find potential
inconsistencies. Our evaluation on two of the most popular DL libraries,
PyTorch and TensorFlow, demonstrates that DeepREL can cover 157% more APIs than
state-of-the-art FreeFuzz. To date, DeepREL has detected 162 bugs in total,
with 106 already confirmed by the developers as previously unknown bugs.
Surprisingly, DeepREL has detected 13.5% of the high-priority bugs for the
entire PyTorch issue-tracking system in a three-month period. Also, besides the
162 code bugs, we have also detected 14 documentation bugs (all confirmed).

Absolute pose regressor (APR) networks are trained to estimate the pose of
the camera given a captured image. They compute latent image representations
from which the camera position and orientation are regressed. APRs provide a
different tradeoff between localization accuracy, runtime, and memory, compared
to structure-based localization schemes that provide state-of-the-art accuracy.
In this work, we introduce Camera Pose Auto-Encoders (PAEs), multilayer
perceptrons that are trained via a Teacher-Student approach to encode camera
poses using APRs as their teachers. We show that the resulting latent pose
representations can closely reproduce APR performance and demonstrate their
effectiveness for related tasks. Specifically, we propose a light-weight
test-time optimization in which the closest train poses are encoded and used to
refine camera position estimation. This procedure achieves a new
state-of-the-art position accuracy for APRs, on both the CambridgeLandmarks and
7Scenes benchmarks. We also show that train images can be reconstructed from
the learned pose encoding, paving the way for integrating visual information
from the train set at a low memory cost. Our code and pre-trained models are
available at https://github.com/yolish/camera-pose-auto-encoders.

The increasing deployment of end use power resources in distribution systems
created active distribution systems. Uncontrolled active distribution systems
exhibit wide variations of voltage and loading throughout the day as some of
these resources operate under max power tracking control of highly variable
wind and solar irradiation while others exhibit random variations and/or
dependency on weather conditions. It is necessary to control the system to
provide power reliably and securely under normal voltages and frequency.
Classical optimization approaches to control the system towards this goal
suffer from the dimensionality of the problem and the need for a global
optimization approach to coordinate a huge number of small resources.
Artificial Intelligence (AI) methods offer an alternative that can provide a
practical approach to this problem. We suggest that neural networks with
self-attention mechanisms have the potential to aid in the optimization of the
system. In this paper, we present this approach and provide promising
preliminary results.

Deep hashing has been widely applied in large-scale data retrieval due to its
superior retrieval efficiency and low storage cost. However, data are often
scattered in data silos with privacy concerns, so performing centralized data
storage and retrieval is not always possible. Leveraging the concept of
federated learning (FL) to perform deep hashing is a recent research trend.
However, existing frameworks mostly rely on the aggregation of the local deep
hashing models, which are trained by performing similarity learning with local
skewed data only. Therefore, they cannot work well for non-IID clients in a
real federated environment. To overcome these challenges, we propose a novel
federated hashing framework that enables participating clients to jointly train
the shared deep hashing model by leveraging the prototypical hash codes for
each class. Globally, the transmission of global prototypes with only one
prototypical hash code per class will minimize the impact of communication cost
and privacy risk. Locally, the use of global prototypes are maximized by
jointly training a discriminator network and the local hashing network.
Extensive experiments on benchmark datasets are conducted to demonstrate that
our method can significantly improve the performance of the deep hashing model
in the federated environments with non-IID data distributions.

A counter-intuitive property of convolutional neural networks (CNNs) is their
inherent susceptibility to adversarial examples, which severely hinders the
application of CNNs in security-critical fields. Adversarial examples are
similar to original examples but contain malicious perturbations. Adversarial
training is a simple and effective defense method to improve the robustness of
CNNs to adversarial examples. The mechanisms behind adversarial examples and
adversarial training are worth exploring. Therefore, this work investigates
similarities and differences between normally trained CNNs (NT-CNNs) and
adversarially trained CNNs (AT-CNNs) in information extraction from the mutual
information perspective. We show that 1) whether NT-CNNs or AT-CNNs, for
original and adversarial examples, the trends towards mutual information are
almost similar throughout training; 2) compared with normal training,
adversarial training is more difficult and the amount of information that
AT-CNNs extract from the input is less; 3) the CNNs trained with different
methods have different preferences for certain types of information; NT-CNNs
tend to extract texture-based information from the input, while AT-CNNs prefer
to shape-based information. The reason why adversarial examples mislead CNNs
may be that they contain more texture-based information about other classes.
Furthermore, we also analyze the mutual information estimators used in this
work and find that they outline the geometric properties of the middle layer's
output.

With privacy legislation empowering users with the right to be forgotten, it
has become essential to make a model forget about some of its training data. We
explore the problem of removing any client's contribution in federated learning
(FL). During FL rounds, each client performs local training to learn a model
that minimizes the empirical loss on their private data. We propose to perform
unlearning at the client (to be erased) by reversing the learning process,
i.e., training a model to \emph{maximize} the local empirical loss. In
particular, we formulate the unlearning problem as a constrained maximization
problem by restricting to an $\ell_2$-norm ball around a suitably chosen
reference model to help retain some knowledge learnt from the other clients'
data. This allows the client to use projected gradient descent to perform
unlearning. The method does neither require global access to the data used for
training nor the history of the parameter updates to be stored by the
aggregator (server) or any of the clients. Experiments on the MNIST dataset
show that the proposed unlearning method is efficient and effective.

Multi-object tracking (MOT) requires detecting and associating objects
through frames. Unlike tracking via detected bounding boxes or tracking objects
as points, we propose tracking objects as pixel-wise distributions. We
instantiate this idea on a transformer-based architecture, P3AFormer, with
pixel-wise propagation, prediction, and association. P3AFormer propagates
pixel-wise features guided by flow information to pass messages between frames.
Furthermore, P3AFormer adopts a meta-architecture to produce multi-scale object
feature maps. During inference, a pixel-wise association procedure is proposed
to recover object connections through frames based on the pixel-wise
prediction. P3AFormer yields 81.2\% in terms of MOTA on the MOT17 benchmark --
the first among all transformer networks to reach 80\% MOTA in literature.
P3AFormer also outperforms state-of-the-arts on the MOT20 and KITTI benchmarks.

Automatic Identification System (AIS) messages are useful for tracking vessel
activity across oceans worldwide using radio links and satellite transceivers.
Such data plays a significant role in tracking vessel activity and mapping
mobility patterns such as those found in fishing. Accordingly, this paper
proposes a geometric-driven semi-supervised approach for fishing activity
detection from AIS data. Through the proposed methodology we show how to
explore the information included in the messages to extract features describing
the geometry of the vessel route. To this end, we leverage the unsupervised
nature of cluster analysis to label the trajectory geometry highlighting the
changes in the vessel's moving pattern which tends to indicate fishing
activity. The labels obtained by the proposed unsupervised approach are used to
detect fishing activities, which we approach as a time-series classification
task. In this context, we propose a solution using recurrent neural networks on
AIS data streams with roughly 87% of the overall $F$-score on the whole
trajectories of 50 different unseen fishing vessels. Such results are
accompanied by a broad benchmark study assessing the performance of different
Recurrent Neural Network (RNN) architectures. In conclusion, this work
contributes by proposing a thorough process that includes data preparation,
labeling, data modeling, and model validation. Therefore, we present a novel
solution for mobility pattern detection that relies upon unfolding the
trajectory in time and observing their inherent geometry.

We propose two novel transferability metrics F-OTCE (Fast Optimal Transport
based Conditional Entropy) and JC-OTCE (Joint Correspondence OTCE) to evaluate
how much the source model (task) can benefit the learning of the target task
and to learn more transferable representations for cross-domain cross-task
transfer learning. Unlike the existing metric that requires evaluating the
empirical transferability on auxiliary tasks, our metrics are auxiliary-free
such that they can be computed much more efficiently. Specifically, F-OTCE
estimates transferability by first solving an Optimal Transport (OT) problem
between source and target distributions, and then uses the optimal coupling to
compute the Negative Conditional Entropy between source and target labels. It
can also serve as a loss function to maximize the transferability of the source
model before finetuning on the target task. Meanwhile, JC-OTCE improves the
transferability robustness of F-OTCE by including label distances in the OT
problem, though it may incur additional computation cost. Extensive experiments
demonstrate that F-OTCE and JC-OTCE outperform state-of-the-art auxiliary-free
metrics by 18.85% and 28.88%, respectively in correlation coefficient with the
ground-truth transfer accuracy. By eliminating the training cost of auxiliary
tasks, the two metrics reduces the total computation time of the previous
method from 43 minutes to 9.32s and 10.78s, respectively, for a pair of tasks.
When used as a loss function, F-OTCE shows consistent improvements on the
transfer accuracy of the source model in few-shot classification experiments,
with up to 4.41% accuracy gain.

We present a new scheme to compensate for the small-scales approximations
resulting from Particle-Mesh (PM) schemes for cosmological N-body simulations.
This kind of simulations are fast and low computational cost realizations of
the large scale structures, but lack resolution on small scales. To improve
their accuracy, we introduce an additional effective force within the
differential equations of the simulation, parameterized by a Fourier-space
Neural Network acting on the PM-estimated gravitational potential. We compare
the results for the matter power spectrum obtained to the ones obtained by the
PGD scheme (Potential gradient descent scheme). We notice a similar improvement
in term of power spectrum, but we find that our approach outperforms PGD for
the cross-correlation coefficients, and is more robust to changes in simulation
settings (different resolutions, different cosmologies).

In audio classification, differentiable auditory filterbanks with few
parameters cover the middle ground between hard-coded spectrograms and raw
audio. LEAF (arXiv:2101.08596), a Gabor-based filterbank combined with
Per-Channel Energy Normalization (PCEN), has shown promising results, but is
computationally expensive. With inhomogeneous convolution kernel sizes and
strides, and by replacing PCEN with better parallelizable operations, we can
reach similar results more efficiently. In experiments on six audio
classification tasks, our frontend matches the accuracy of LEAF at 3% of the
cost, but both fail to consistently outperform a fixed mel filterbank. The
quest for learnable audio frontends is not solved.

State-of-the-art speaker verification systems are inherently dependent on
some kind of human supervision as they are trained on massive amounts of
labeled data. However, manually annotating utterances is slow, expensive and
not scalable to the amount of data available today. In this study, we explore
self-supervised learning for speaker verification by learning representations
directly from raw audio. The objective is to produce robust speaker embeddings
that have small intra-speaker and large inter-speaker variance. Our approach is
based on recent information maximization learning frameworks and an intensive
data augmentation pre-processing step. We evaluate the ability of these methods
to work without contrastive samples before showing that they achieve better
performance when combined with a contrastive loss. Furthermore, we conduct
experiments to show that our method reaches competitive results compared to
existing techniques and can get better performances compared to a supervised
baseline when fine-tuned with a small portion of labeled data.

Weakly-supervised audio-visual violence detection aims to distinguish
snippets containing multimodal violence events with video-level labels. Many
prior works perform audio-visual integration and interaction in an early or
intermediate manner, yet overlooking the modality heterogeneousness over the
weakly-supervised setting. In this paper, we analyze the modality asynchrony
and undifferentiated instances phenomena of the multiple instance learning
(MIL) procedure, and further investigate its negative impact on
weakly-supervised audio-visual learning. To address these issues, we propose a
modality-aware contrastive instance learning with self-distillation (MACIL-SD)
strategy. Specifically, we leverage a lightweight two-stream network to
generate audio and visual bags, in which unimodal background, violent, and
normal instances are clustered into semi-bags in an unsupervised way. Then
audio and visual violent semi-bag representations are assembled as positive
pairs, and violent semi-bags are combined with background and normal instances
in the opposite modality as contrastive negative pairs. Furthermore, a
self-distillation module is applied to transfer unimodal visual knowledge to
the audio-visual model, which alleviates noises and closes the semantic gap
between unimodal and multimodal features. Experiments show that our framework
outperforms previous methods with lower complexity on the large-scale
XD-Violence dataset. Results also demonstrate that our proposed approach can be
used as plug-in modules to enhance other networks. Codes are available at
https://github.com/JustinYuu/MACIL_SD.

3D object detection task from lidar or camera sensors is essential for
autonomous driving. Pioneer attempts at multi-modality fusion complement the
sparse lidar point clouds with rich semantic texture information from images at
the cost of extra network designs and overhead. In this work, we propose a
novel semantic passing framework, named SPNet, to boost the performance of
existing lidar-based 3D detection models with the guidance of rich context
painting, with no extra computation cost during inference. Our key design is to
first exploit the potential instructive semantic knowledge within the
ground-truth labels by training a semantic-painted teacher model and then guide
the pure-lidar network to learn the semantic-painted representation via
knowledge passing modules at different granularities: class-wise passing,
pixel-wise passing and instance-wise passing. Experimental results show that
the proposed SPNet can seamlessly cooperate with most existing 3D detection
frameworks with 1~5% AP gain and even achieve new state-of-the-art 3D detection
performance on the KITTI test benchmark. Code is available at:
https://github.com/jb892/SPNet.

Reinforcement Learning (RL) agents are often unable to generalise well to
environment variations in the state space that were not observed during
training. This issue is especially problematic for image-based RL, where a
change in just one variable, such as the background colour, can change many
pixels in the image, which can lead to drastic changes in the agent's latent
representation of the image, causing the learned policy to fail. To learn more
robust representations, we introduce TEmporal Disentanglement (TED), a
self-supervised auxiliary task that leads to disentangled image representations
exploiting the sequential nature of RL observations. We find empirically that
RL algorithms utilising TED as an auxiliary task adapt more quickly to changes
in environment variables with continued training compared to state-of-the-art
representation learning methods. Since TED enforces a disentangled structure of
the representation, we also find that policies trained with TED generalise
better to unseen values of variables irrelevant to the task (e.g.\ background
colour) as well as unseen values of variables that affect the optimal policy
(e.g.\ goal positions).

Accurate protein structure prediction can significantly accelerate the
development of life science. The accuracy of AlphaFold2, a frontier end-to-end
structure prediction system, is already close to that of the experimental
determination techniques. Due to the complex model architecture and large
memory consumption, it requires lots of computational resources and time to
implement the training and inference of AlphaFold2 from scratch. The cost of
running the original AlphaFold2 is expensive for most individuals and
institutions. Therefore, reducing this cost could accelerate the development of
life science. We implement AlphaFold2 using PaddlePaddle, namely HelixFold, to
improve training and inference speed and reduce memory consumption. The
performance is improved by operator fusion, tensor fusion, and hybrid
parallelism computation, while the memory is optimized through Recompute,
BFloat16, and memory read/write in-place. Compared with the original AlphaFold2
(implemented with Jax) and OpenFold (implemented with PyTorch), HelixFold needs
only 7.5 days to complete the full end-to-end training and only 5.3 days when
using hybrid parallelism, while both AlphaFold2 and OpenFold take about 11
days. HelixFold saves 1x training time. We verified that HelixFold's accuracy
could be on par with AlphaFold2 on the CASP14 and CAMEO datasets. HelixFold's
code is available on GitHub for free download:
https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold,
https://paddlehelix.baidu.com/app/drug/protein/forecast.

Deep belief networks (DBNs) are stochastic neural networks that can extract
rich internal representations of the environment from the sensory data. DBNs
had a catalytic effect in triggering the deep learning revolution,
demonstrating for the very first time the feasibility of unsupervised learning
in networks with many layers of hidden neurons. These hierarchical
architectures incorporate plausible biological and cognitive properties, making
them particularly appealing as computational models of human perception and
cognition. However, learning in DBNs is usually carried out in a greedy,
layer-wise fashion, which does not allow to simulate the holistic maturation of
cortical circuits and prevents from modeling cognitive development. Here we
present iDBN, an iterative learning algorithm for DBNs that allows to jointly
update the connection weights across all layers of the model. We evaluate the
proposed iterative algorithm on two different sets of visual stimuli, measuring
the generative capabilities of the learned model and its potential to support
supervised downstream tasks. We also track network development in terms of
graph theoretical properties and investigate the potential extension of iDBN to
continual learning scenarios. DBNs trained using our iterative approach achieve
a final performance comparable to that of the greedy counterparts, at the same
time allowing to accurately analyze the gradual development of internal
representations in the deep network and the progressive improvement in task
performance. Our work paves the way to the use of iDBN for modeling
neurocognitive development.

Learning against label noise is a vital topic to guarantee a reliable
performance for deep neural networks. Recent research usually refers to dynamic
noise modeling with model output probabilities and loss values, and then
separates clean and noisy samples. These methods have gained notable success.
However, unlike cherry-picked data, existing approaches often cannot perform
well when facing imbalanced datasets, a common scenario in the real world. We
thoroughly investigate this phenomenon and point out two major issues that
hinder the performance, i.e., \emph{inter-class loss distribution discrepancy}
and \emph{misleading predictions due to uncertainty}. The first issue is that
existing methods often perform class-agnostic noise modeling. However, loss
distributions show a significant discrepancy among classes under class
imbalance, and class-agnostic noise modeling can easily get confused with noisy
samples and samples in minority classes. The second issue refers to that models
may output misleading predictions due to epistemic uncertainty and aleatoric
uncertainty, thus existing methods that rely solely on the output probabilities
may fail to distinguish confident samples. Inspired by our observations, we
propose an Uncertainty-aware Label Correction framework~(ULC) to handle label
noise on imbalanced datasets. First, we perform epistemic uncertainty-aware
class-specific noise modeling to identify trustworthy clean samples and
refine/discard highly confident true/corrupted labels. Then, we introduce
aleatoric uncertainty in the subsequent learning process to prevent noise
accumulation in the label noise modeling process. We conduct experiments on
several synthetic and real-world datasets. The results demonstrate the
effectiveness of the proposed method, especially on imbalanced datasets.

De-identification of data used for automatic speech recognition modeling is a
critical component in protecting privacy, especially in the medical domain.
However, simply removing all personally identifiable information (PII) from
end-to-end model training data leads to a significant performance degradation
in particular for the recognition of names, dates, locations, and words from
similar categories. We propose and evaluate a two-step method for partially
recovering this loss. First, PII is identified, and each occurrence is replaced
with a random word sequence of the same category. Then, corresponding audio is
produced via text-to-speech or by splicing together matching audio fragments
extracted from the corpus. These artificial audio/label pairs, together with
speaker turns from the original data without PII, are used to train models. We
evaluate the performance of this method on in-house data of medical
conversations and observe a recovery of almost the entire performance
degradation in the general word error rate while still maintaining a strong
diarization performance. Our main focus is the improvement of recall and
precision in the recognition of PII-related words. Depending on the PII
category, between $50\% - 90\%$ of the performance degradation can be recovered
using our proposed method.

Despite their advantages, normalizing flows generally suffer from several
shortcomings including their tendency to generate unrealistic data (e.g.,
images) and their failing to detect out-of-distribution data. One reason for
these deficiencies lies in the training strategy which traditionally exploits a
maximum likelihood principle only. This paper proposes a new training paradigm
based on a hybrid objective function combining the maximum likelihood principle
(MLE) and a sliced-Wasserstein distance. Results obtained on synthetic toy
examples and real image data sets show better generative abilities in terms of
both likelihood and visual aspects of the generated samples. Reciprocally, the
proposed approach leads to a lower likelihood of out-of-distribution data,
demonstrating a greater data fidelity of the resulting flows.

The paper describes the MetroPT data set, an outcome of a eXplainable
Predictive Maintenance (XPM) project with an urban metro public transportation
service in Porto, Portugal. The data was collected in 2022 that aimed to
evaluate machine learning methods for online anomaly detection and failure
prediction. By capturing several analogic sensor signals (pressure,
temperature, current consumption), digital signals (control signals, discrete
signals), and GPS information (latitude, longitude, and speed), we provide a
dataset that can be easily used to evaluate online machine learning methods.
This dataset contains some interesting characteristics and can be a good
benchmark for predictive maintenance models.

Energy disaggregation estimates appliance-by-appliance electricity
consumption from a single meter that measures the whole home's electricity
demand. Compared with intrusive load monitoring, NILM (Non-intrusive load
monitoring) is low cost, easy to deploy, and flexible. In this paper, we
propose a new method, coined IMG-NILM, that utilises convolutional neural
networks (CNN) to disaggregate electricity data represented as images. Instead
of the traditional approach of dealing with electricity data as time series,
IMG-NILM transforms time series into heatmaps with higher electricity readings
portrayed as 'hotter' colours. The image representation is then used in CNN to
detect the signature of an appliance from aggregated data. IMG-NILM is robust
and flexible with consistent performance on various types of appliances;
including single and multiple states. It attains a test accuracy of up to 93%
on the UK-Dale dataset within a single house, where a substantial number of
appliances are present. In more challenging settings where electricity data is
collected from different houses, IMG-NILM attains also a very good average
accuracy of 85%.

This paper presents a kernel-based adaptive filter that is applied for the
digital domain self-interference cancellation (SIC) in a transceiver operating
in full-duplex (FD) mode. In FD, the benefit of simultaneous transmission and
receiving of signals comes at the price of strong self-interference (SI). In
this work, we are primarily interested in suppressing the SI using an adaptive
filter namely adaptive projected subgradient method (APSM) in a reproducing
kernel Hilbert space (RKHS) of functions. Using the projection concept as a
powerful tool, APSM is used to model and consequently remove the SI. A
low-complexity and fast-tracking algorithm is provided taking advantage of
parallel projections as well as the kernel trick in RKHS. The performance of
the proposed method is evaluated on real measurement data. The method
illustrates the good performance of the proposed adaptive filter, compared to
the known popular benchmarks. They demonstrate that the kernel-based algorithm
achieves a favorable level of digital SIC while enabling parallel
computation-based implementation within a rich and nonlinear function space,
thanks to the employed adaptive filtering method.

Face attribute evaluation plays an important role in video surveillance and
face analysis. Although methods based on convolution neural networks have made
great progress, they inevitably only deal with one local neighborhood with
convolutions at a time. Besides, existing methods mostly regard face attribute
evaluation as the individual multi-label classification task, ignoring the
inherent relationship between semantic attributes and face identity
information. In this paper, we propose a novel \textbf{trans}former-based
representation for \textbf{f}ace \textbf{a}ttribute evaluation method
(\textbf{TransFA}), which could effectively enhance the attribute
discriminative representation learning in the context of attention mechanism.
The multiple branches transformer is employed to explore the inter-correlation
between different attributes in similar semantic regions for attribute feature
learning. Specially, the hierarchical identity-constraint attribute loss is
designed to train the end-to-end architecture, which could further integrate
face identity discriminative information to boost performance. Experimental
results on multiple face attribute benchmarks demonstrate that the proposed
TransFA achieves superior performances compared with state-of-the-art methods.

NeuroEvolution automates the generation of Artificial Neural Networks through
the application of techniques from Evolutionary Computation. The main goal of
these approaches is to build models that maximize predictive performance,
sometimes with an additional objective of minimizing computational complexity.
Although the evolved models achieve competitive results performance-wise, their
robustness to adversarial examples, which becomes a concern in
security-critical scenarios, has received limited attention. In this paper, we
evaluate the adversarial robustness of models found by two prominent
NeuroEvolution approaches on the CIFAR-10 image classification task: DENSER and
NSGA-Net. Since the models are publicly available, we consider white-box
untargeted attacks, where the perturbations are bounded by either the L2 or the
Linfinity-norm. Similarly to manually-designed networks, our results show that
when the evolved models are attacked with iterative methods, their accuracy
usually drops to, or close to, zero under both distance metrics. The DENSER
model is an exception to this trend, showing some resistance under the L2
threat model, where its accuracy only drops from 93.70% to 18.10% even with
iterative attacks. Additionally, we analyzed the impact of pre-processing
applied to the data before the first layer of the network. Our observations
suggest that some of these techniques can exacerbate the perturbations added to
the original inputs, potentially harming robustness. Thus, this choice should
not be neglected when automatically designing networks for applications where
adversarial attacks are prone to occur.

It is difficult to precisely annotate object instances and their semantics in
3D space, and as such, synthetic data are extensively used for these tasks,
e.g., category-level 6D object pose and size estimation. However, the easy
annotations in synthetic domains bring the downside effect of synthetic-to-real
(Sim2Real) domain gap. In this work, we aim to address this issue in the task
setting of Sim2Real, unsupervised domain adaptation for category-level 6D
object pose and size estimation. We propose a method that is built upon a novel
Deep Prior Deformation Network, shortened as DPDN. DPDN learns to deform
features of categorical shape priors to match those of object observations, and
is thus able to establish deep correspondence in the feature space for direct
regression of object poses and sizes. To reduce the Sim2Real domain gap, we
formulate a novel self-supervised objective upon DPDN via consistency learning;
more specifically, we apply two rigid transformations to each object
observation in parallel, and feed them into DPDN respectively to yield dual
sets of predictions; on top of the parallel learning, an inter-consistency term
is employed to keep cross consistency between dual predictions for improving
the sensitivity of DPDN to pose changes, while individual intra-consistency
ones are used to enforce self-adaptation within each learning itself. We train
DPDN on both training sets of the synthetic CAMERA25 and real-world REAL275
datasets; our results outperform the existing methods on REAL275 test set under
both the unsupervised and supervised settings. Ablation studies also verify the
efficacy of our designs. Our code is released publicly at
https://github.com/JiehongLin/Self-DPDN.

We propose a new auto-regressive model for the statistical analysis of
multivariate distributional time series. The data of interest consist of a
collection of multiple series of probability measures supported over a bounded
interval of the real line, and that are indexed by distinct time instants. The
probability measures are modelled as random objects in the Wasserstein space.
We establish the auto-regressive model in the tangent space at the Lebesgue
measure by first centering all the raw measures so that their Fr\'echet means
turn to be the Lebesgue measure. Using the theory of iterated random function
systems, results on the existence, uniqueness and stationarity of the solution
of such a model are provided. We also propose a consistent estimator for the
model coefficient. In addition to the analysis of simulated data, the proposed
model is illustrated with two real data sets made of observations from age
distribution in different countries and bike sharing network in Paris. Finally,
due to the positive and boundedness constraints that we impose on the model
coefficients, the proposed estimator that is learned under these constraints,
naturally has a sparse structure. The sparsity allows furthermore the
application of the proposed model in learning a graph of temporal dependency
from the multivariate distributional time series.

Abstract knowledge is deeply grounded in many computer-based applications. An
important research area of Artificial Intelligence (AI) deals with the
automatic derivation of knowledge from data. Machine learning offers the
according algorithms. One area of research focuses on the development of
biologically inspired learning algorithms. The respective machine learning
methods are based on neurological concepts so that they can systematically
derive knowledge from data and store it. One type of machine learning
algorithms that can be categorized as "deep learning" model is referred to as
Deep Neural Networks (DNNs). DNNs consist of multiple artificial neurons
arranged in layers that are trained by using the backpropagation algorithm.
These deep learning methods exhibit amazing capabilities for inferring and
storing complex knowledge from high-dimensional data. However, DNNs are
affected by a problem that prevents new knowledge from being added to an
existing base. The ability to continuously accumulate knowledge is an important
factor that contributed to evolution and is therefore a prerequisite for the
development of strong AIs. The so-called "catastrophic forgetting" (CF) effect
causes DNNs to immediately loose already derived knowledge after a few training
iterations on a new data distribution. Only an energetically expensive
retraining with the joint data distribution of past and new data enables the
abstraction of the entire new set of knowledge. In order to counteract the
effect, various techniques have been and are still being developed with the
goal to mitigate or even solve the CF problem. These published CF avoidance
studies usually imply the effectiveness of their approaches for various
continual learning tasks. This dissertation is set in the context of continual
machine learning with deep learning methods. The first part deals with the
development of an ...

Online learning to rank (OLTR) interactively learns to choose lists of items
from a large collection based on certain click models that describe users'
click behaviors. Most recent works for this problem focus on the stochastic
environment where the item attractiveness is assumed to be invariant during the
learning process. In many real-world scenarios, however, the environment could
be dynamic or even arbitrarily changing. This work studies the OLTR problem in
both stochastic and adversarial environments under the position-based model
(PBM). We propose a method based on the follow-the-regularized-leader (FTRL)
framework with Tsallis entropy and develop a new self-bounding constraint
especially designed for PBM. We prove the proposed algorithm simultaneously
achieves $O(\log{T})$ regret in the stochastic environment and $O(m\sqrt{nT})$
regret in the adversarial environment, where $T$ is the number of rounds, $n$
is the number of items and $m$ is the number of positions. We also provide a
lower bound of order $\Omega(m\sqrt{nT})$ for adversarial PBM, which matches
our upper bound and improves over the state-of-the-art lower bound. The
experiments show that our algorithm could simultaneously learn in both
stochastic and adversarial environments and is competitive compared to existing
methods that are designed for a single environment.

It is challenging for a security analyst to detect or defend against
cyber-attacks. Moreover, traditional defense deployment methods require the
security analyst to manually enforce the defenses in the presence of
uncertainties about the defense to deploy. As a result, it is essential to
develop an automated and resilient defense deployment mechanism to thwart the
new generation of attacks. In this paper, we propose a framework based on
Markov Decision Process (MDP) and Q-learning to automatically generate optimal
defense solutions for networked system states. The framework consists of four
phases namely; the model initialization phase, model generation phase,
Q-learning phase, and the conclusion phase. The proposed model collects real
network information as inputs and then builds them into structural data. We
implement a Q-learning process in the model to learn the quality of a defense
action in a particular state. To investigate the feasibility of the proposed
model, we perform simulation experiments and the result reveals that the model
can reduce the risk of network systems from cyber attacks. Furthermore, the
experiment shows that the model has shown a certain level of flexibility when
different parameters are used for Q-learning.

We propose a concept of quantum extensive-form games, which is a quantum
extension of classical extensive-form games. Extensive-form games is a general
concept of games such as Go, Shogi, and chess, which have triggered the recent
AI revolution, and is the basis for many important game theoretic models in
economics. Quantum transitions allow for pairwise annihilation of paths in the
quantum game tree, resulting in a probability distribution that is more likely
to produce a particular outcome. This is similar in principle to the mechanism
of speed-up by quantum computation represented by Grover's algorithm. A quantum
extensive-form game is also a generalization of quantum learning, including
Quantum Generative Adversarial Networks. Therefore it will become new theoretic
basis of quantum machine learning, as well as a basis for a new game theoretic
foundation for microeconomics. We propose the quantum angel problem as a new
example of quantum extensive-form games. This is a quantum version of angel
problem proposed by Conway in 1996. His original problem has already been
solved, but by quantizing it, it becomes a non-trivial problem. In the quantum
angel problem, Angel moves on a general graph as a quantum walker. By not only
changing the dimensions and geometry of the graph, but also by adding/relaxing
restrictions to the quantum resources available to Angel and Devil, the
difficulty and complexity of the game is diversified in a way that is not
possible in the traditional angel problem.

We propose and demonstrate a generative deep learning approach for the shape
recognition of an arbitrary object from its acoustic scattering properties. The
strategy exploits deep neural networks to learn the mapping between the latent
space of a two-dimensional acoustic object and the far-field scattering
amplitudes. A neural network is designed as an Adversarial autoencoder and
trained via unsupervised learning to determine the latent space of the acoustic
object. Important structural features of the object are embedded in
lower-dimensional latent space which supports the modeling of a shape generator
and accelerates the learning in the inverse design process.The proposed inverse
design uses the variational inference approach with encoder and decoder-like
architecture where the decoder is composed of two pretrained neural networks,
the generator and the forward model. The data-driven framework finds an
accurate solution to the ill-posed inverse scattering problem, where non-unique
solution space is overcome by the multifrequency phaseless far-field patterns.
This inverse method is a powerful design tool that does not require complex
analytical calculation and opens up new avenues for practical realization,
automatic recognition of arbitrary shaped submarines or large fish, and other
underwater applications.

With the success of self-supervised learning (SSL), it has become a
mainstream paradigm to fine-tune from self-supervised pretrained models to
boost the performance on downstream tasks. However, we find that current SSL
models suffer severe accuracy drops when performing low-bit quantization,
prohibiting their deployment in resource-constrained applications. In this
paper, we propose a method called synergistic self-supervised and quantization
learning (SSQL) to pretrain quantization-friendly self-supervised models
facilitating downstream deployment. SSQL contrasts the features of the
quantized and full precision models in a self-supervised fashion, where the
bit-width for the quantized model is randomly selected in each step. SSQL not
only significantly improves the accuracy when quantized to lower bit-widths,
but also boosts the accuracy of full precision models in most cases. By only
training once, SSQL can then benefit various downstream tasks at different
bit-widths simultaneously. Moreover, the bit-width flexibility is achieved
without additional storage overhead, requiring only one copy of weights during
training and inference. We theoretically analyze the optimization process of
SSQL, and conduct exhaustive experiments on various benchmarks to further
demonstrate the effectiveness of our method. Our code is available at
https://github.com/megvii-research/SSQL-ECCV2022.

The rapid growth of the electric vehicle (EV) sector is giving rise to many
infrastructural challenges. One such challenge is its requirement for the
widespread development of EV charging stations which must be able to provide
large amounts of power in an on-demand basis. This can cause large stresses on
the electrical and electronic components of the charging infrastructure -
negatively affecting its reliability as well as leading to increased
maintenance and operation costs. This paper proposes a human-interpretable
data-driven method for anomaly detection in EV charging stations, aiming to
provide information for the condition monitoring and predictive maintenance of
power converters within such a station. To this end, a model of a
high-efficiency EV charging station is used to simulate the thermal behaviour
of EV charger power converter modules, creating a data set for the training of
neural network models. These machine learning models are then employed for the
identification of anomalous performance.

Image retouching, aiming to regenerate the visually pleasing renditions of
given images, is a subjective task where the users are with different aesthetic
sensations. Most existing methods deploy a deterministic model to learn the
retouching style from a specific expert, making it less flexible to meet
diverse subjective preferences. Besides, the intrinsic diversity of an expert
due to the targeted processing on different images is also deficiently
described. To circumvent such issues, we propose to learn diverse image
retouching with normalizing flow-based architectures. Unlike current flow-based
methods which directly generate the output image, we argue that learning in a
style domain could (i) disentangle the retouching styles from the image
content, (ii) lead to a stable style presentation form, and (iii) avoid the
spatial disharmony effects. For obtaining meaningful image tone style
representations, a joint-training pipeline is delicately designed, which is
composed of a style encoder, a conditional RetouchNet, and the image tone style
normalizing flow (TSFlow) module. In particular, the style encoder predicts the
target style representation of an input image, which serves as the conditional
information in the RetouchNet for retouching, while the TSFlow maps the style
representation vector into a Gaussian distribution in the forward pass. After
training, the TSFlow can generate diverse image tone style vectors by sampling
from the Gaussian distribution. Extensive experiments on MIT-Adobe FiveK and
PPR10K datasets show that our proposed method performs favorably against
state-of-the-art methods and is effective in generating diverse results to
satisfy different human aesthetic preferences. Source code and pre-trained
models are publicly available at https://github.com/SSRHeart/TSFlow.

During a pandemic, early prognostication of patient infected rates can reduce
the death by ensuring treatment facility and proper resource allocation. In
recent months, the number of death and infected rates has increased more
distinguished than before in Bangladesh. The country is struggling to provide
moderate medical treatment to many patients. This study distinguishes machine
learning models and creates a prediction system to anticipate the infected and
death rate for the coming days. Equipping a dataset with data from March 1,
2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The
data was managed from a trusted government website and concocted manually for
training purposes. Several test cases determine the model's accuracy and
prediction capability. The comparison between specific models assumes that the
MLP model has more reliable prediction capability than the support vector
regression (SVR) and linear regression model. The model presents a report about
the risky situation and impending coronavirus disease (COVID-19) attack.
According to the prediction produced by the model, Bangladesh may suffer
another COVID-19 attack, where the number of infected cases can be between 929
to 2443 and death cases between 19 to 57.

Designing an automatic checkout system for retail stores at the human level
accuracy is challenging due to similar appearance products and their various
poses. This paper addresses the problem by proposing a method with a two-stage
pipeline. The first stage detects class-agnostic items, and the second one is
dedicated to classify product categories. We also track the objects across
video frames to avoid duplicated counting. One major challenge is the domain
gap because the models are trained on synthetic data but tested on the real
images. To reduce the error gap, we adopt domain generalization methods for the
first-stage detector. In addition, model ensemble is used to enhance the
robustness of the 2nd-stage classifier. The method is evaluated on the AI City
challenge 2022 -- Track 4 and gets the F1 score $40\%$ on the test A set. Code
is released at the link https://github.com/cybercore-co-ltd/aicity22-track4.

Recently, transformer and multi-layer perceptron (MLP) architectures have
achieved impressive results on various vision tasks. However, how to
effectively combine those operators to form high-performance hybrid visual
architectures still remains a challenge. In this work, we study the learnable
combination of convolution, transformer, and MLP by proposing a novel unified
architecture search approach. Our approach contains two key designs to achieve
the search for high-performance networks. First, we model the very different
searchable operators in a unified form, and thus enable the operators to be
characterized with the same set of configuration parameters. In this way, the
overall search space size is significantly reduced, and the total search cost
becomes affordable. Second, we propose context-aware downsampling modules
(DSMs) to mitigate the gap between the different types of operators. Our
proposed DSMs are able to better adapt features from different types of
operators, which is important for identifying high-performance hybrid
architectures. Finally, we integrate configurable operators and DSMs into a
unified search space and search with a Reinforcement Learning-based search
algorithm to fully explore the optimal combination of the operators. To this
end, we search a baseline network and scale it up to obtain a family of models,
named UniNets, which achieve much better accuracy and efficiency than previous
ConvNets and Transformers. In particular, our UniNet-B5 achieves 84.9% top-1
accuracy on ImageNet, outperforming EfficientNet-B7 and BoTNet-T7 with 44% and
55% fewer FLOPs respectively. By pretraining on the ImageNet-21K, our UniNet-B6
achieves 87.4%, outperforming Swin-L with 51% fewer FLOPs and 41% fewer
parameters. Code is available at https://github.com/Sense-X/UniNet.

Image captioning research achieved breakthroughs in recent years by
developing neural models that can generate diverse and high-quality
descriptions for images drawn from the same distribution as training images.
However, when facing out-of-distribution (OOD) images, such as corrupted
images, or images containing unknown objects, the models fail in generating
relevant captions.
  In this paper, we consider the problem of OOD detection in image captioning.
We formulate the problem and suggest an evaluation setup for assessing the
model's performance on the task. Then, we analyze and show the effectiveness of
the caption's likelihood score at detecting and rejecting OOD images, which
implies that the relatedness between the input image and the generated caption
is encapsulated within the score.

Knowledge Distillation (KD) transfers the knowledge from a high-capacity
teacher network to strengthen a smaller student. Existing methods focus on
excavating the knowledge hints and transferring the whole knowledge to the
student. However, the knowledge redundancy arises since the knowledge shows
different values to the student at different learning stages. In this paper, we
propose Knowledge Condensation Distillation (KCD). Specifically, the knowledge
value on each sample is dynamically estimated, based on which an
Expectation-Maximization (EM) framework is forged to iteratively condense a
compact knowledge set from the teacher to guide the student learning. Our
approach is easy to build on top of the off-the-shelf KD methods, with no extra
training parameters and negligible computation overhead. Thus, it presents one
new perspective for KD, in which the student that actively identifies teacher's
knowledge in line with its aptitude can learn to learn more effectively and
efficiently. Experiments on standard benchmarks manifest that the proposed KCD
can well boost the performance of student model with even higher distillation
efficiency. Code is available at https://github.com/dzy3/KCD.

The finance industry is producing an increasing amount of datasets that
investment professionals can consider to be influential on the price of
financial assets. These datasets were initially mainly limited to exchange
data, namely price, capitalization and volume. Their coverage has now
considerably expanded to include, for example, macroeconomic data, supply and
demand of commodities, balance sheet data and more recently extra-financial
data such as ESG scores. This broadening of the factors retained as influential
constitutes a serious challenge for statistical modeling. Indeed, the
instability of the correlations between these factors makes it practically
impossible to identify the joint laws needed to construct scenarios.
Fortunately, spectacular advances in Deep Learning field in recent years have
given rise to GANs. GANs are a type of generative machine learning models that
produce new data samples with the same characteristics as a training data
distribution in an unsupervised way, avoiding data assumptions and human
induced biases. In this work, we are exploring the use of GANs for synthetic
financial scenarios generation. This pilot study is the result of a
collaboration between Fujitsu and Advestis and it will be followed by a
thorough exploration of the use cases that can benefit from the proposed
solution. We propose a GANs-based algorithm that allows the replication of
multivariate data representing several properties (including, but not limited
to, price, market capitalization, ESG score, controversy score,. . .) of a set
of stocks. This approach differs from examples in the financial literature,
which are mainly focused on the reproduction of temporal asset price scenarios.
We also propose several metrics to evaluate the quality of the data generated
by the GANs. This approach is well fit for the generation of scenarios, the
time direction simply arising as a subsequent (eventually conditioned)
generation of data points drawn from the learned distribution. Our method will
allow to simulate high dimensional scenarios (compared to $\lesssim 10$
features currently employed in most recent use cases) where network complexity
is reduced thanks to a wisely performed feature engineering and selection.
Complete results will be presented in a forthcoming study.

Time is one of the most significant characteristics of time-series, yet has
received insufficient attention. Prior time-series forecasting research has
mainly focused on mapping a past subseries (lookback window) to a future series
(forecast window), and time of series often just play an auxiliary role even
completely ignored in most cases. Due to the point-wise processing within these
windows, extrapolating series to longer-term future is tough in the pattern. To
overcome this barrier, we propose a brand-new time-series forecasting framework
named Dateformer who turns attention to modeling time instead of following the
above practice. Specifically, time-series are first split into patches by day
to supervise the learning of dynamic date-representations with Date Encoder
Representations from Transformers (DERT). These representations are then fed
into a simple decoder to produce a coarser (or global) prediction, and used to
help the model seek valuable information from the lookback window to learn a
refined (or local) prediction. Dateformer obtains the final result by summing
the above two parts. Our empirical studies on seven benchmarks show that the
time-modeling method is more efficient for long-term series forecasting
compared with sequence modeling methods. Dateformer yields state-of-the-art
accuracy with a 40% remarkable relative improvement, and broadens the maximum
credible forecasting range to a half-yearly level.

The deployment of an expert system running over a wireless acoustic sensors
network made up of bioacoustic monitoring devices that recognise bird species
from their sounds would enable the automation of many tasks of ecological
value, including the analysis of bird population composition or the detection
of endangered species in areas of environmental interest. Endowing these
devices with accurate audio classification capabilities is possible thanks to
the latest advances in artificial intelligence, among which deep learning
techniques excel. However, a key issue to make bioacoustic devices affordable
is the use of small footprint deep neural networks that can be embedded in
resource and battery constrained hardware platforms. For this reason, this work
presents a critical comparative analysis between two heavy and large footprint
deep neural networks (VGG16 and ResNet50) and a lightweight alternative,
MobileNetV2. Our experimental results reveal that MobileNetV2 achieves an
average F1-score less than a 5\% lower than ResNet50 (0.789 vs. 0.834),
performing better than VGG16 with a footprint size nearly 40 times smaller.
Moreover, to compare the models, we have created and made public the Western
Mediterranean Wetland Birds dataset, consisting of 201.6 minutes and 5,795
audio excerpts of 20 endemic bird species of the Aiguamolls de l'Empord\`a
Natural Park.

Wound image segmentation is a critical component for the clinical diagnosis
and in-time treatment of wounds. Recently, deep learning has become the
mainstream methodology for wound image segmentation. However, the
pre-processing of the wound image, such as the illumination correction, is
required before the training phase as the performance can be greatly improved.
The correction procedure and the training of deep models are independent of
each other, which leads to sub-optimal segmentation performance as the fixed
illumination correction may not be suitable for all images. To address
aforementioned issues, an end-to-end dual-view segmentation approach was
proposed in this paper, by incorporating a learn-able illumination correction
module into the deep segmentation models. The parameters of the module can be
learned and updated during the training stage automatically, while the
dual-view fusion can fully employ the features from both the raw images and the
enhanced ones. To demonstrate the effectiveness and robustness of the proposed
framework, the extensive experiments are conducted on the benchmark datasets.
The encouraging results suggest that our framework can significantly improve
the segmentation performance, compared to the state-of-the-art methods.

Shadows are essential for realistic image compositing. Physics-based shadow
rendering methods require 3D geometries, which are not always available. Deep
learning-based shadow synthesis methods learn a mapping from the light
information to an object's shadow without explicitly modeling the shadow
geometry. Still, they lack control and are prone to visual artifacts. We
introduce pixel heigh, a novel geometry representation that encodes the
correlations between objects, ground, and camera pose. The pixel height can be
calculated from 3D geometries, manually annotated on 2D images, and can also be
predicted from a single-view RGB image by a supervised approach. It can be used
to calculate hard shadows in a 2D image based on the projective geometry,
providing precise control of the shadows' direction and shape. Furthermore, we
propose a data-driven soft shadow generator to apply softness to a hard shadow
based on a softness input parameter. Qualitative and quantitative evaluations
demonstrate that the proposed pixel height significantly improves the quality
of the shadow generation while allowing for controllability.

Transfer learning, or domain adaptation, is concerned with machine learning
problems in which training and testing data come from possibly different
probability distributions. In this work, we give an information-theoretic
analysis on the generalization error and excess risk of transfer learning
algorithms, following a line of work initiated by Russo and Xu. Our results
suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence
$D(\mu||\mu')$ plays an important role in the characterizations where $\mu$ and
$\mu'$ denote the distribution of the training data and the testing test,
respectively. Specifically, we provide generalization error upper bounds for
the empirical risk minimization (ERM) algorithm where data from both
distributions are available in the training phase. We further apply the
analysis to approximated ERM methods such as the Gibbs algorithm and the
stochastic gradient descent method. We then generalize the mutual information
bound with $\phi$-divergence and Wasserstein distance. These generalizations
lead to tighter bounds and can handle the case when $\mu$ is not absolutely
continuous with respect to $\mu'$. Furthermore, we apply a new set of
techniques to obtain an alternative upper bound which gives a fast (and
optimal) learning rate for some learning problems. Finally, inspired by the
derived bounds, we propose the InfoBoost algorithm in which the importance
weights for source and target data are adjusted adaptively in accordance to
information measures. The empirical results show the effectiveness of the
proposed algorithm.

Motivated by many applications, we study clustering with a faulty oracle. In
this problem, there are $n$ items belonging to $k$ unknown clusters, and the
algorithm is allowed to ask the oracle whether two items belong to the same
cluster or not. However, the answer from the oracle is correct only with
probability $\frac{1}{2}+\frac{\delta}{2}$. The goal is to recover the hidden
clusters with minimum number of noisy queries. Previous works have shown that
the problem can be solved with $O(\frac{nk\log n}{\delta^2} +
\text{poly}(k,\frac{1}{\delta}, \log n))$ queries, while
$\Omega(\frac{nk}{\delta^2})$ queries is known to be necessary. So, for any
values of $k$ and $\delta$, there is still a non-trivial gap between upper and
lower bounds. In this work, we obtain the first matching upper and lower bounds
for a wide range of parameters. In particular, a new polynomial time algorithm
with $O(\frac{n(k+\log n)}{\delta^2} + \text{poly}(k,\frac{1}{\delta}, \log
n))$ queries is proposed. Moreover, we prove a new lower bound of
$\Omega(\frac{n\log n}{\delta^2})$, which, combined with the existing
$\Omega(\frac{nk}{\delta^2})$ bound, matches our upper bound up to an additive
$\text{poly}(k,\frac{1}{\delta},\log n)$ term. To obtain the new results, our
main ingredient is an interesting connection between our problem and
multi-armed bandit, which might provide useful insights for other similar
problems.

In this work we evaluate the applicability of an ensemble of population
models and machine learning models to predict the near future evolution of the
COVID-19 pandemic, with a particular use case in Spain. We rely solely in open
and public datasets, fusing incidence, vaccination, human mobility and weather
data to feed our machine learning models (Random Forest, Gradient Boosting,
k-Nearest Neighbours and Kernel Ridge Regression). We use the incidence data to
adjust classic population models (Gompertz, Logistic, Richards, Bertalanffy) in
order to be able to better capture the trend of the data. We then ensemble
these two families of models in order to obtain a more robust and accurate
prediction. Furthermore, we have observed an improvement in the predictions
obtained with machine learning models as we add new features (vaccines,
mobility, climatic conditions), analyzing the importance of each of them using
Shapley Additive Explanation values. As in any other modelling work, data and
predictions quality have several limitations and therefore they must be seen
from a critical standpoint, as we discuss in the text. Our work concludes that
the ensemble use of these models improves the individual predictions (using
only machine learning models or only population models) and can be applied,
with caution, in cases when compartmental models cannot be utilized due to the
lack of relevant data.

Although significant progress has been achieved on monocular maker-less human
motion capture in recent years, it is still hard for state-of-the-art methods
to obtain satisfactory results in occlusion scenarios. There are two main
reasons: the one is that the occluded motion capture is inherently ambiguous as
various 3D poses can map to the same 2D observations, which always results in
an unreliable estimation. The other is that no sufficient occluded human data
can be used for training a robust model. To address the obstacles, our key-idea
is to employ non-occluded human data to learn a joint-level spatial-temporal
motion prior for occluded human with a self-supervised strategy. To further
reduce the gap between synthetic and real occlusion data, we build the first 3D
occluded motion dataset~(OcMotion), which can be used for both training and
testing. We encode the motions in 2D maps and synthesize occlusions on
non-occluded data for the self-supervised training. A spatial-temporal layer is
then designed to learn joint-level correlations. The learned prior reduces the
ambiguities of occlusions and is robust to diverse occlusion types, which is
then adopted to assist the occluded human motion capture. Experimental results
show that our method can generate accurate and coherent human motions from
occluded videos with good generalization ability and runtime efficiency. The
dataset and code are publicly available at
\url{https://github.com/boycehbz/CHOMP}.

Deep learning (DL) techniques have been intensively studied for the
optimization of multi-user multiple-input single-output (MU-MISO) downlink
systems owing to the capability of handling nonconvex formulations. However,
the fixed computation structure of existing deep neural networks (DNNs) lacks
flexibility with respect to the system size, i.e., the number of antennas or
users. This paper develops a bipartite graph neural network (BGNN) framework, a
scalable DL solution designed for multi-antenna beamforming optimization. The
MU-MISO system is first characterized by a bipartite graph where two disjoint
vertex sets, each of which consists of transmit antennas and users, are
connected via pairwise edges. These vertex interconnection states are modeled
by channel fading coefficients. Thus, a generic beamforming optimization
process is interpreted as a computation task over a weight bipartite graph.
This approach partitions the beamforming optimization procedure into multiple
suboperations dedicated to individual antenna vertices and user vertices.
Separated vertex operations lead to scalable beamforming calculations that are
invariant to the system size. The vertex operations are realized by a group of
DNN modules that collectively form the BGNN architecture. Identical DNNs are
reused at all antennas and users so that the resultant learning structure
becomes flexible to the network size. Component DNNs of the BGNN are trained
jointly over numerous MU-MISO configurations with randomly varying network
sizes. As a result, the trained BGNN can be universally applied to arbitrary
MU-MISO systems. Numerical results validate the advantages of the BGNN
framework over conventional methods.

Recently vision transformer models have become prominent models for a range
of vision tasks. These models, however, are usually opaque with weak feature
interpretability. Moreover, there is no method currently built for an
intrinsically interpretable transformer, which is able to explain its reasoning
process and provide a faithful explanation. To close these crucial gaps, we
propose a novel vision transformer dubbed the eXplainable Vision Transformer
(eX-ViT), an intrinsically interpretable transformer model that is able to
jointly discover robust interpretable features and perform the prediction.
Specifically, eX-ViT is composed of the Explainable Multi-Head Attention
(E-MHA) module, the Attribute-guided Explainer (AttE) module and the
self-supervised attribute-guided loss. The E-MHA tailors explainable attention
weights that are able to learn semantically interpretable representations from
local patches in terms of model decisions with noise robustness. Meanwhile,
AttE is proposed to encode discriminative attribute features for the target
object through diverse attribute discovery, which constitutes faithful evidence
for the model's predictions. In addition, a self-supervised attribute-guided
loss is developed for our eX-ViT, which aims at learning enhanced
representations through the attribute discriminability mechanism and attribute
diversity mechanism, to localize diverse and discriminative attributes and
generate more robust explanations. As a result, we can uncover faithful and
robust interpretations with diverse attributes through the proposed eX-ViT.

With decentralized optimization having increased applications in various
domains ranging from machine learning, control, sensor networks, to robotics,
its privacy is also receiving increased attention. Existing privacy-preserving
approaches for decentralized optimization achieve privacy preservation by
patching decentralized optimization with information-technology privacy
mechanisms such as differential privacy or homomorphic encryption, which either
sacrifices optimization accuracy or incurs heavy computation/communication
overhead. We propose an inherently privacy-preserving decentralized
optimization algorithm by exploiting the robustness of decentralized
optimization to uncertainties in optimization dynamics. More specifically, we
present a general decentralized optimization framework, based on which we show
that privacy can be enabled in decentralized optimization by adding randomness
in optimization parameters. We further show that the added randomness has no
influence on the accuracy of optimization, and prove that our inherently
privacy-preserving algorithm has $R$-linear convergence when the global
objective function is smooth and strongly convex. We also rigorously prove that
the proposed algorithm can avoid the gradient of a node from being inferable by
other nodes. Numerical simulation results confirm the theoretical predictions.

Policy learning in multi-agent reinforcement learning (MARL) is challenging
due to the exponential growth of joint state-action space with respect to the
number of agents. To achieve higher scalability, the paradigm of centralized
training with decentralized execution (CTDE) is broadly adopted with factorized
structure in MARL. However, we observe that existing CTDE algorithms in
cooperative MARL cannot achieve optimality even in simple matrix games. To
understand this phenomenon, we introduce a framework of Generalized Multi-Agent
Actor-Critic with Policy Factorization (GPF-MAC), which characterizes the
learning of factorized joint policies, i.e., each agent's policy only depends
on its own observation-action history. We show that most popular CTDE MARL
algorithms are special instances of GPF-MAC and may be stuck in a suboptimal
joint policy. To address this issue, we present a novel transformation
framework that reformulates a multi-agent MDP as a special "single-agent" MDP
with a sequential structure and can allow employing off-the-shelf single-agent
reinforcement learning (SARL) algorithms to efficiently learn corresponding
multi-agent tasks. This transformation retains the optimality guarantee of SARL
algorithms into cooperative MARL. To instantiate this transformation framework,
we propose a Transformed PPO, called T-PPO, which can theoretically perform
optimal policy learning in the finite multi-agent MDPs and shows significant
outperformance on a large set of cooperative multi-agent tasks.

In recent years, Generative Adversarial Networks (GANs) have become a hot
topic among researchers and engineers that work with deep learning. It has been
a ground-breaking technique which can generate new pieces of content of data in
a consistent way. The topic of GANs has exploded in popularity due to its
applicability in fields like image generation and synthesis, and music
production and composition. GANs have two competing neural networks: a
generator and a discriminator. The generator is used to produce new samples or
pieces of content, while the discriminator is used to recognize whether the
piece of content is real or generated. What makes it different from other
generative models is its ability to learn unlabeled samples. In this review
paper, we will discuss the evolution of GANs, several improvements proposed by
the authors and a brief comparison between the different models. Index Terms
generative adversarial networks, unsupervised learning, deep learning.

Reconstructing the state of many-body quantum systems is of fundamental
importance in quantum information tasks, but extremely challenging due to the
curse of dimensionality. In this work, we present a quantum tomography approach
based on neural networks to achieve the ultrafast reconstruction of multi-qubit
states. Particularly, we propose a simple 3-layer feed-forward network to
process the experimental data generated from measuring each qubit with a
positive operator-valued measure, which is able to reduce the storage cost and
computational complexity. Moreover, the techniques of state decomposition and
$P$-order absolute projection are jointly introduced to ensure the positivity
of state matrices learned in the maximum likelihood function and to improve the
convergence speed and robustness of the above network. Finally, it is tested on
a large number of states with a wide range of purity to show that we can
faithfully tomography 11-qubit states on a laptop within 2 minutes under noise.
Our numerical results also demonstrate that more state samples are required to
achieve the given tomography fidelity for the low-purity states, and the
increased depolarizing noise induces a linear decrease in the tomography
fidelity.

Contrastive learning has shown promising potential in self-supervised
spatio-temporal representation learning. Most works naively sample different
clips to construct positive and negative pairs. However, we observe that this
formulation inclines the model towards the background scene bias. The
underlying reasons are twofold. First, the scene difference is usually more
noticeable and easier to discriminate than the motion difference. Second, the
clips sampled from the same video often share similar backgrounds but have
distinct motions. Simply regarding them as positive pairs will draw the model
to the static background rather than the motion pattern. To tackle this
challenge, this paper presents a novel dual contrastive formulation.
Concretely, we decouple the input RGB video sequence into two complementary
modes, static scene and dynamic motion. Then, the original RGB features are
pulled closer to the static features and the aligned dynamic features,
respectively. In this way, the static scene and the dynamic motion are
simultaneously encoded into the compact RGB representation. We further conduct
the feature space decoupling via activation maps to distill static- and
dynamic-related features. We term our method as \textbf{D}ual
\textbf{C}ontrastive \textbf{L}earning for spatio-temporal
\textbf{R}epresentation (DCLR). Extensive experiments demonstrate that DCLR
learns effective spatio-temporal representations and obtains state-of-the-art
or comparable performance on UCF-101, HMDB-51, and Diving-48 datasets.

Vision-Language Pre-training (VLP) with large-scale image-text pairs has
demonstrated superior performance in various fields. However, the image-text
pairs co-occurrent on the Internet typically lack explicit alignment
information, which is suboptimal for VLP. Existing methods proposed to adopt an
off-the-shelf object detector to utilize additional image tag information.
However, the object detector is time-consuming and can only identify the
pre-defined object categories, limiting the model capacity. Inspired by the
observation that the texts incorporate incomplete fine-grained image
information, we introduce IDEA, which stands for increasing text diversity via
online multi-label recognition for VLP. IDEA shows that multi-label learning
with image tags extracted from the texts can be jointly optimized during VLP.
Moreover, IDEA can identify valuable image tags online to provide more explicit
textual supervision. Comprehensive experiments demonstrate that IDEA can
significantly boost the performance on multiple downstream datasets with a
small extra computational cost.

In this paper, we present a motion-based robotic communication framework that
enables non-verbal communication among autonomous underwater vehicles (AUVs)
and human divers. We design a gestural language for AUV-to-AUV communication
which can be easily understood by divers observing the conversation unlike
typical radio frequency, light, or audio based AUV communication. To allow AUVs
to visually understand a gesture from another AUV, we propose a deep network
(RRCommNet) which exploits a self-attention mechanism to learn to recognize
each message by extracting maximally discriminative spatio-temporal features.
We train this network on diverse simulated and real-world data. Our
experimental evaluations, both in simulation and in closed-water robot trials,
demonstrate that the proposed RRCommNet architecture is able to decipher
gesture-based messages with an average accuracy of 88-94% on simulated data,
73-83% on real data (depending on the version of the model used). Further, by
performing a message transcription study with human participants, we also show
that the proposed language can be understood by humans, with an overall
transcription accuracy of 88%. Finally, we discuss the inference runtime of
RRCommNet on embedded GPU hardware, for real-time use on board AUVs in the
field.

Deep neural networks (DNNs) are reshaping the field of information
processing. With their exponential growth challenging existing electronic
hardware, optical neural networks (ONNs) are emerging to process DNN tasks in
the optical domain with high clock rates, parallelism and low-loss data
transmission. However, to explore the potential of ONNs, it is necessary to
investigate the full-system performance incorporating the major DNN elements,
including matrix algebra and nonlinear activation. Existing challenges to ONNs
are high energy consumption due to low electro-optic (EO) conversion
efficiency, low compute density due to large device footprint and channel
crosstalk, and long latency due to the lack of inline nonlinearity. Here we
experimentally demonstrate an ONN system that simultaneously overcomes all
these challenges. We exploit neuron encoding with volume-manufactured
micron-scale vertical-cavity surface-emitting laser (VCSEL) transmitter arrays
that exhibit high EO conversion (<5 attojoule/symbol with $V_\pi$=4 mV), high
operation bandwidth (up to 25 GS/s), and compact footprint (<0.01 mm$^2$ per
device). Photoelectric multiplication allows low-energy matrix operations at
the shot-noise quantum limit. Homodyne detection-based nonlinearity enables
nonlinear activation with instantaneous response. The full-system energy
efficiency and compute density reach 7 femtojoules per operation (fJ/OP) and 25
TeraOP/(mm$^2\cdot$ s), both representing a >100-fold improvement over
state-of-the-art digital computers, with substantially several more orders of
magnitude for future improvement. Beyond neural network inference, its feature
of rapid weight updating is crucial for training deep learning models. Our
technique opens an avenue to large-scale optoelectronic processors to
accelerate machine learning tasks from data centers to decentralized edge
devices.

Translation, rotation, and scaling are three commonly used geometric
manipulation operations in image processing. Besides, some of them are
successfully used in developing effective knowledge graph embedding (KGE)
models such as TransE and RotatE. Inspired by the synergy, we propose a new KGE
model by leveraging all three operations in this work. Since translation,
rotation, and scaling operations are cascaded to form a compound one, the new
model is named CompoundE. By casting CompoundE in the framework of group
theory, we show that quite a few scoring-function-based KGE models are special
cases of CompoundE. CompoundE extends the simple distance-based relation to
relation-dependent compound operations on head and/or tail entities. To
demonstrate the effectiveness of CompoundE, we conduct experiments on three
popular KG completion datasets. Experimental results show that CompoundE
consistently achieves the state of-the-art performance.

Most pregnancies and births result in a good outcome, but complications are
not uncommon and when they do occur, they can be associated with serious
implications for mothers and babies. Predictive modeling has the potential to
improve outcomes through better understanding of risk factors, heightened
surveillance, and more timely and appropriate interventions, thereby helping
obstetricians deliver better care. For three types of complications we identify
and study the most important risk factors using Explainable Boosting Machine
(EBM), a glass box model, in order to gain intelligibility: (i) Severe Maternal
Morbidity (SMM), (ii) shoulder dystocia, and (iii) preterm preeclampsia. While
using the interpretability of EBM's to reveal surprising insights into the
features contributing to risk, our experiments show EBMs match the accuracy of
other black-box ML methods such as deep neural nets and random forests.

Deep neural networks have been found vulnerable to adversarial attacks, thus
raising potentially concerns in security-sensitive contexts. To address this
problem, recent research has investigated the adversarial robustness of deep
neural networks from the architectural point of view. However, searching for
architectures of deep neural networks is computationally expensive,
particularly when coupled with adversarial training process. To meet the above
challenge, this paper proposes a bi-fidelity multiobjective neural architecture
search approach. First, we formulate the NAS problem for enhancing adversarial
robustness of deep neural networks into a multiobjective optimization problem.
Specifically, in addition to a low-fidelity performance predictor as the first
objective, we leverage an auxiliary-objective -- the value of which is the
output of a surrogate model trained with high-fidelity evaluations. Secondly,
we reduce the computational cost by combining three performance estimation
methods, i.e., parameter sharing, low-fidelity evaluation, and surrogate-based
predictor. The effectiveness of the proposed approach is confirmed by extensive
experiments conducted on CIFAR-10, CIFAR-100 and SVHN datasets.

This paper presents an end-to-end learning-based video compression system,
termed CANF-VC, based on conditional augmented normalizing flows (CANF). Most
learned video compression systems adopt the same hybrid-based coding
architecture as the traditional codecs. Recent research on conditional coding
has shown the sub-optimality of the hybrid-based coding and opens up
opportunities for deep generative models to take a key role in creating new
coding frameworks. CANF-VC represents a new attempt that leverages the
conditional ANF to learn a video generative model for conditional inter-frame
coding. We choose ANF because it is a special type of generative model, which
includes variational autoencoder as a special case and is able to achieve
better expressiveness. CANF-VC also extends the idea of conditional coding to
motion coding, forming a purely conditional coding framework. Extensive
experimental results on commonly used datasets confirm the superiority of
CANF-VC to the state-of-the-art methods. The source code of CANF-VC is
available at https://github.com/NYCU-MAPL/CANF-VC.

Image outpainting, which is well studied with Convolution Neural Network
(CNN) based framework, has recently drawn more attention in computer vision.
However, CNNs rely on inherent inductive biases to achieve effective sample
learning, which may degrade the performance ceiling. In this paper, motivated
by the flexible self-attention mechanism with minimal inductive biases in
transformer architecture, we reframe the generalised image outpainting problem
as a patch-wise sequence-to-sequence autoregression problem, enabling
query-based image outpainting. Specifically, we propose a novel hybrid
vision-transformer-based encoder-decoder framework, named \textbf{Query}
\textbf{O}utpainting \textbf{TR}ansformer (\textbf{QueryOTR}), for
extrapolating visual context all-side around a given image. Patch-wise mode's
global modeling capacity allows us to extrapolate images from the attention
mechanism's query standpoint. A novel Query Expansion Module (QEM) is designed
to integrate information from the predicted queries based on the encoder's
output, hence accelerating the convergence of the pure transformer even with a
relatively small dataset. To further enhance connectivity between each patch,
the proposed Patch Smoothing Module (PSM) re-allocates and averages the
overlapped regions, thus providing seamless predicted images. We experimentally
show that QueryOTR could generate visually appealing results smoothly and
realistically against the state-of-the-art image outpainting approaches.

The success of deep learning is usually accompanied by the growth in neural
network depth. However, the traditional training method only supervises the
neural network at its last layer and propagates the supervision layer-by-layer,
which leads to hardship in optimizing the intermediate layers. Recently, deep
supervision has been proposed to add auxiliary classifiers to the intermediate
layers of deep neural networks. By optimizing these auxiliary classifiers with
the supervised task loss, the supervision can be applied to the shallow layers
directly. However, deep supervision conflicts with the well-known observation
that the shallow layers learn low-level features instead of task-biased
high-level semantic features. To address this issue, this paper proposes a
novel training framework named Contrastive Deep Supervision, which supervises
the intermediate layers with augmentation-based contrastive learning.
Experimental results on nine popular datasets with eleven models demonstrate
its effects on general image classification, fine-grained image classification
and object detection in supervised learning, semi-supervised learning and
knowledge distillation. Codes have been released in Github.

Recent work highlights the role of causality in designing equitable
decision-making algorithms. It is not immediately clear, however, how existing
causal conceptions of fairness relate to one another, or what the consequences
are of using these definitions as design principles. Here, we first assemble
and categorize popular causal definitions of algorithmic fairness into two
broad families: (1) those that constrain the effects of decisions on
counterfactual disparities; and (2) those that constrain the effects of legally
protected characteristics, like race and gender, on decisions. We then show,
analytically and empirically, that both families of definitions \emph{almost
always} -- in a measure theoretic sense -- result in strongly Pareto dominated
decision policies, meaning there is an alternative, unconstrained policy
favored by every stakeholder with preferences drawn from a large, natural
class. For example, in the case of college admissions decisions, policies
constrained to satisfy causal fairness definitions would be disfavored by every
stakeholder with neutral or positive preferences for both academic preparedness
and diversity. Indeed, under a prominent definition of causal fairness, we
prove the resulting policies require admitting all students with the same
probability, regardless of academic qualifications or group membership. Our
results highlight formal limitations and potential adverse consequences of
common mathematical notions of causal fairness.

The graph-theoretical task of determining most likely inter-community edges
based on disconnected subgraphs' intra-community connectivity is proposed. An
algorithm is developed for this edge augmentation task, based on elevating the
zero eigenvalues of graph's spectrum. Upper bounds for eigenvalue elevation
amplitude and for the corresponding augmented edge density are derived and are
authenticated with simulation on random graphs. The algorithm works
consistently across synthetic and real networks, yielding desirable performance
at connecting graph components. Edge augmentation reverse-engineers graph
partition under different community detection methods (Girvan-Newman method,
greedy modularity maximization, label propagation, Louvain method, and fluid
community), in most cases producing inter-community edges at >50% frequency.

Due to the short wavelength and large attenuation of millimeter-wave
(mmWave), mmWave BSs are densely distributed and require beamforming with high
directivity. When the user moves out of the coverage of the current BS or is
severely blocked, the mmWave BS must be switched to ensure the communication
quality. In this paper, we proposed a multi-camera view based proactive BS
selection and beam switching that can predict the optimal BS of the user in the
future frame and switch the corresponding beam pair. Specifically, we extract
the features of multi-camera view images and a small part of channel state
information (CSI) in historical frames, and dynamically adjust the weight of
each modality feature. Then we design a multi-task learning module to guide the
network to better understand the main task, thereby enhancing the accuracy and
the robustness of BS selection and beam switching. Using the outputs of all
tasks, a prior knowledge based fine tuning network is designed to further
increase the BS switching accuracy. After the optimal BS is obtained, a beam
pair switching network is proposed to directly predict the optimal beam pair of
the corresponding BS. Simulation results in an outdoor intersection environment
show the superior performance of our proposed solution under several metrics
such as predicting accuracy, achievable rate, harmonic mean of precision and
recall.

Despite the recent progress in speech emotion recognition (SER),
state-of-the-art systems lack generalisation across different conditions. A key
underlying reason for poor generalisation is the scarcity of emotion datasets,
which is a significant roadblock to designing robust machine learning (ML)
models. Recent works in SER focus on utilising multitask learning (MTL) methods
to improve generalisation by learning shared representations. However, most of
these studies propose MTL solutions with the requirement of meta labels for
auxiliary tasks, which limits the training of SER systems. This paper proposes
an MTL framework (MTL-AUG) that learns generalised representations from
augmented data. We utilise augmentation-type classification and unsupervised
reconstruction as auxiliary tasks, which allow training SER systems on
augmented data without requiring any meta labels for auxiliary tasks. The
semi-supervised nature of MTL-AUG allows for the exploitation of the abundant
unlabelled data to further boost the performance of SER. We comprehensively
evaluate the proposed framework in the following settings: (1) within corpus,
(2) cross-corpus and cross-language, (3) noisy speech, (4) and adversarial
attacks. Our evaluations using the widely used IEMOCAP, MSP-IMPROV, and EMODB
datasets show improved results compared to existing state-of-the-art methods.

Federated Learning (FL) is a Machine Learning (ML) technique that aims to
reduce the threats to user data privacy. Training is done using the raw data on
the users' device, called clients, and only the training results, called
gradients, are sent to the server to be aggregated and generate an updated
model. However, we cannot assume that the server can be trusted with private
information, such as metadata related to the owner or source of the data. So,
hiding the client information from the server helps reduce privacy-related
attacks. Therefore, the privacy of the client's identity, along with the
privacy of the client's data, is necessary to make such attacks more difficult.
This paper proposes an efficient and privacy-preserving protocol for FL based
on group signature. A new group signature for federated learning, called GSFL,
is designed to not only protect the privacy of the client's data and identity
but also significantly reduce the computation and communication costs
considering the iterative process of federated learning. We show that GSFL
outperforms existing approaches in terms of computation, communication, and
signaling costs. Also, we show that the proposed protocol can handle various
security attacks in the federated learning environment.

Near-term quantum systems tend to be noisy. Crosstalk noise has been
recognized as one of several major types of noises in superconducting Noisy
Intermediate-Scale Quantum (NISQ) devices. Crosstalk arises from the concurrent
execution of two-qubit gates on nearby qubits, such as \texttt{CX}. It might
significantly raise the error rate of gates in comparison to running them
individually. Crosstalk can be mitigated through scheduling or hardware machine
tuning. Prior scientific studies, however, manage crosstalk at a really late
phase in the compilation process, usually after hardware mapping is done. It
may miss great opportunities of optimizing algorithm logic, routing, and
crosstalk at the same time. In this paper, we push the envelope by considering
all these factors simultaneously at the very early compilation stage. We
propose a crosstalk-aware quantum program compilation framework called CQC that
can enhance crosstalk mitigation while achieving satisfactory circuit depth.
Moreover, we identify opportunities for translation from intermediate
representation to the circuit for application-specific crosstalk mitigation,
for instance, the \texttt{CX} ladder construction in variational quantum
eigensolvers (VQE). Evaluations through simulation and on real IBM-Q devices
show that our framework can significantly reduce the error rate by up to
6$\times$, with only $\sim$60\% circuit depth compared to state-of-the-art gate
scheduling approaches. In particular, for VQE, we demonstrate 49\% circuit
depth reduction with 9.6\% fidelity improvement over prior art on the H4
molecule using IBMQ Guadalupe. Our CQC framework will be released on GitHub.

Synthetic tabular data generation becomes crucial when real data is limited,
expensive to collect, or simply cannot be used due to privacy concerns.
However, producing good quality synthetic data is challenging. Several
probabilistic, statistical, and generative adversarial networks (GANs) based
approaches have been presented for synthetic tabular data generation. Once
generated, evaluating the quality of the synthetic data is quite challenging.
Some of the traditional metrics have been used in the literature but there is
lack of a common, robust, and single metric. This makes it difficult to
properly compare the effectiveness of different synthetic tabular data
generation methods. In this paper we propose a new universal metric, TabSynDex,
for robust evaluation of synthetic data. TabSynDex assesses the similarity of
synthetic data with real data through different component scores which evaluate
the characteristics that are desirable for "high quality" synthetic data. Being
a single score metric, TabSynDex can also be used to observe and evaluate the
training of neural network based approaches. This would help in obtaining
insights that was not possible earlier. Further, we present several baseline
models for comparative analysis of the proposed evaluation metric with existing
generative models.

As a typical data-driven method, deep learning becomes a natural choice for
analysing astronomical data nowadays. In this study, we built a deep
convolutional neural network to estimate basic stellar parameters
$T\rm{_{eff}}$, log g, metallicity ([M/H] and [Fe/H]) and [$\alpha$/M] along
with nine individual elemental abundances ([C/Fe], [N/Fe], [O/Fe], [Mg/Fe],
[Al/Fe], [Si/Fe], [Ca/Fe], [Mn/Fe], [Ni/Fe]). The neural network is trained
using common stars between the APOGEE survey and the LAMOST survey. We used
low-resolution spectra from LAMOST survey as input, and measurements from
APOGEE as labels. For stellar spectra with the signal-to-noise ratio in g band
larger than 10 in the test set, the mean absolute error (MAE) is 29 K for
$T\rm{_{eff}}$, 0.07 dex for log g, 0.03 dex for both [Fe/H] and [M/H], and
0.02 dex for [$\alpha$/M]. The MAE of most elements is between 0.02 dex and
0.04 dex. The trained neural network was applied to 1,210,145 giants, including
sub-giants, from LAMOST DR8 within the range of stellar parameters 3500 K <
$T\rm{_{eff}}$ < 5500 K, 0.0 dex < log g < 4.0 dex, -2.5 dex < [Fe/H] < 0.5
dex. The distribution of our results in the chemical spaces is highly
consistent with APOGEE labels and stellar parameters show consistency with
external high-resolution measurements from GALAH. The results in this study
allow us to further studies based on LAMOST data and deepen our understanding
of the accretion and evolution history of the Milky Way. The electronic version
of the value added catalog is available at
http://www.lamost.org/dr8/v1.1/doc/vac.

Multi-state survival analysis (MSA) uses multi-state models for the analysis
of time-to-event data. In medical applications, MSA can provide insights about
the complex disease progression in patients. A key challenge in MSA is the
accurate subject-specific prediction of multi-state model quantities such as
transition probability and state occupation probability in the presence of
censoring. Traditional multi-state methods such as Aalen-Johansen (AJ)
estimators and Cox-based methods are respectively limited by Markov and
proportional hazards assumptions and are infeasible for making subject-specific
predictions. Neural ordinary differential equations for MSA relax these
assumptions but are computationally expensive and do not directly model the
transition probabilities. To address these limitations, we propose a new class
of pseudo-value-based deep learning models for multi-state survival analysis,
where we show that pseudo values - designed to handle censoring - can be a
natural replacement for estimating the multi-state model quantities when
derived from a consistent estimator. In particular, we provide an algorithm to
derive pseudo values from consistent estimators to directly predict the
multi-state survival quantities from the subject's covariates. Empirical
results on synthetic and real-world datasets show that our proposed models
achieve state-of-the-art results under various censoring settings.

Different people age in different ways. Learning a personalized age estimator
for each person is a promising direction for age estimation given that it
better models the personalization of aging processes. However, most existing
personalized methods suffer from the lack of large-scale datasets due to the
high-level requirements: identity labels and enough samples for each person to
form a long-term aging pattern. In this paper, we aim to learn personalized age
estimators without the above requirements and propose a meta-learning method
named MetaAge for age estimation. Unlike most existing personalized methods
that learn the parameters of a personalized estimator for each person in the
training set, our method learns the mapping from identity information to age
estimator parameters. Specifically, we introduce a personalized estimator
meta-learner, which takes identity features as the input and outputs the
parameters of customized estimators. In this way, our method learns the meta
knowledge without the above requirements and seamlessly transfers the learned
meta knowledge to the test set, which enables us to leverage the existing
large-scale age datasets without any additional annotations. Extensive
experimental results on three benchmark datasets including MORPH II, ChaLearn
LAP 2015 and ChaLearn LAP 2016 databases demonstrate that our MetaAge
significantly boosts the performance of existing personalized methods and
outperforms the state-of-the-art approaches.

Accurately detecting out-of-distribution (OOD) data with varying levels of
semantic and covariate shifts with respect to the in-distribution (ID) data is
critical for deployment of safe and reliable models. This is particularly the
case when dealing with highly consequential applications (e.g. medical imaging,
self-driving cars, etc). The goal is to design a detector that can accept
meaningful variations of the ID data, while also rejecting examples from OOD
regimes. In practice, this dual objective can be realized by enforcing
consistency using an appropriate scoring function (e.g., energy) and
calibrating the detector to reject a curated set of OOD data (referred to as
outlier exposure or shortly OE). While OE methods are widely adopted,
assembling representative OOD datasets is both costly and challenging due to
the unpredictability of real-world scenarios, hence the recent trend of
designing OE-free detectors. In this paper, we make a surprising finding that
controlled generalization to ID variations and exposure to diverse (synthetic)
outlier examples are essential to simultaneously improving semantic and
modality shift detection. In contrast to existing methods, our approach samples
inliers in the latent space, and constructs outlier examples via negative data
augmentation. Through a rigorous empirical study on medical imaging benchmarks
(MedMNIST, ISIC2019 and NCT), we demonstrate significant performance gains
($15\% - 35\%$ in AUROC) over existing OE-free, OOD detection approaches under
both semantic and modality shifts.

Offline reinforcement learning (Offline RL) is an emerging field that has
recently begun gaining attention across various application domains due to its
ability to learn behavior from earlier collected datasets. Using logged data is
imperative when further interaction with the environment is expensive
(computationally or otherwise), unsafe, or entirely unfeasible. Offline RL
proved very successful, paving a path to solving previously intractable
real-world problems, and we aim to generalize this paradigm to a multi-agent or
multiplayer-game setting. Very little research has been done in this area, as
the progress is hindered by the lack of standardized datasets and meaningful
benchmarks. In this work, we coin the term offline equilibrium finding (OEF) to
describe this area and construct multiple datasets consisting of strategies
collected across a wide range of games using several established methods. We
also propose a benchmark method -- an amalgamation of a behavior-cloning and a
model-based algorithm. Our two model-based algorithms -- OEF-PSRO and OEF-CFR
-- are adaptations of the widely-used equilibrium finding algorithms Deep CFR
and PSRO in the context of offline learning. In the empirical part, we evaluate
the performance of the benchmark algorithms on the constructed datasets. We
hope that our efforts may help to accelerate research in large-scale
equilibrium finding. Datasets and code are available at
https://github.com/SecurityGames/oef.

Entity linking aims to link ambiguous mentions to their corresponding
entities in a knowledge base, which is significant and fundamental for various
downstream applications, e.g., knowledge base completion, question answering,
and information extraction. While great efforts have been devoted to this task,
most of these studies follow the assumption that large-scale labeled data is
available. However, when the labeled data is insufficient for specific domains
due to labor-intensive annotation work, the performance of existing algorithms
will suffer an intolerable decline. In this paper, we endeavor to solve the
problem of few-shot entity linking, which only requires a minimal amount of
in-domain labeled data and is more practical in real situations. Specifically,
we firstly propose a novel weak supervision strategy to generate non-trivial
synthetic entity-mention pairs based on mention rewriting. Since the quality of
the synthetic data has a critical impact on effective model training, we
further design a meta-learning mechanism to assign different weights to each
synthetic entity-mention pair automatically. Through this way, we can
profoundly exploit rich and precious semantic information to derive a
well-trained entity linking model under the few-shot setting. The experiments
on real-world datasets show that the proposed method can extensively improve
the state-of-the-art few-shot entity linking model and achieve impressive
performance when only a small amount of labeled data is available. Moreover, we
also demonstrate the outstanding ability of the model's transferability.

Photonic Microring Resonator (MRR) based hardware accelerators have been
shown to provide disruptive speedup and energy-efficiency improvements for
processing deep Convolutional Neural Networks (CNNs). However, previous
MRR-based CNN accelerators fail to provide efficient adaptability for CNNs with
mixed-sized tensors. One example of such CNNs is depthwise separable CNNs.
Performing inferences of CNNs with mixed-sized tensors on such inflexible
accelerators often leads to low hardware utilization, which diminishes the
achievable performance and energy efficiency from the accelerators. In this
paper, we present a novel way of introducing reconfigurability in the MRR-based
CNN accelerators, to enable dynamic maximization of the size compatibility
between the accelerator hardware components and the CNN tensors that are
processed using the hardware components. We classify the state-of-the-art
MRR-based CNN accelerators from prior works into two categories, based on the
layout and relative placements of the utilized hardware components in the
accelerators. We then use our method to introduce reconfigurability in
accelerators from these two classes, to consequently improve their parallelism,
the flexibility of efficiently mapping tensors of different sizes, speed, and
overall energy efficiency. We evaluate our reconfigurable accelerators against
three prior works for the area proportionate outlook (equal hardware area for
all accelerators). Our evaluation for the inference of four modern CNNs
indicates that our designed reconfigurable CNN accelerators provide
improvements of up to 1.8x in Frames-Per-Second (FPS) and up to 1.5x in FPS/W,
compared to an MRR-based accelerator from prior work.

Monotone functions and data sets arise in a variety of applications. We study
the interpolation problem for monotone data sets: The input is a monotone data
set with $n$ points, and the goal is to find a size and depth efficient
monotone neural network, with non negative parameters and threshold units, that
interpolates the data set. We show that there are monotone data sets that
cannot be interpolated by a monotone network of depth $2$. On the other hand,
we prove that for every monotone data set with $n$ points in $\mathbb{R}^d$,
there exists an interpolating monotone network of depth $4$ and size $O(nd)$.
Our interpolation result implies that every monotone function over $[0,1]^d$
can be approximated arbitrarily well by a depth-4 monotone network, improving
the previous best-known construction of depth $d+1$. Finally, building on
results from Boolean circuit complexity, we show that the inductive bias of
having positive parameters can lead to a super-polynomial blow-up in the number
of neurons when approximating monotone functions.

Transformer attracts much attention because of its ability to learn global
relations and superior performance. In order to achieve higher performance, it
is natural to distill complementary knowledge from Transformer to convolutional
neural network (CNN). However, most existing knowledge distillation methods
only consider homologous-architecture distillation, such as distilling
knowledge from CNN to CNN. They may not be suitable when applying to
cross-architecture scenarios, such as from Transformer to CNN. To deal with
this problem, a novel cross-architecture knowledge distillation method is
proposed. Specifically, instead of directly mimicking output/intermediate
features of the teacher, partially cross attention projector and group-wise
linear projector are introduced to align the student features with the
teacher's in two projected feature spaces. And a multi-view robust training
scheme is further presented to improve the robustness and stability of the
framework. Extensive experiments show that the proposed method outperforms 14
state-of-the-arts on both small-scale and large-scale datasets.

Game consists of multiple types of content, while the harmony of different
content types play an essential role in game design. However, most works on
procedural content generation consider only one type of content at a time. In
this paper, we propose and formulate online level generation from music, in a
way of matching a level feature to a music feature in real-time, while adapting
to players' play speed. A generic framework named online player-adaptive
procedural content generation via reinforcement learning, OPARL for short, is
built upon the experience-driven reinforcement learning and controllable
reinforcement learning, to enable online level generation from music.
Furthermore, a novel control policy based on local search and k-nearest
neighbours is proposed and integrated into OPARL to control the level generator
considering the play data collected online. Results of simulation-based
experiments show that our implementation of OPARL is competent to generate
playable levels with difficulty degree matched to the ``energy'' dynamic of
music for different artificial players in an online fashion.

With the worldwide ravaging of the covid-19 epidemic, the traditional
face-to-face education systems have been interrupted frequently. It is demanded
to develop high-quality online education modalities. The webcasting based
online classroom is one of the popular education modalities but suffers from
poor teacher-student interactions and and low immersive learning experiences.
This thesis aims to improve the online education quality by using the virtual
reality (VR) technology. For the purpose of automobile engine education, we
develop a VR based engine maintenance learning system. The system includes many
teaching and learning components in VR enabled by the Unity engine. Users can
immersively experience the complete engine disassembly process through the
wearable VR display and interactive devices. The system is designed with an
interactive layer, a control layer, and a physical data layer. Such a system
architecture effectively separates the specific implementations of different
domains and improves the R&D efficiency. Once new object models and process
profiles are provided, the proposed system architecture requires no
modification of codes for changed learning objects and processes. The
efficiency and effictiveness of the proposed method are verfied by various
experiments. The developed techniques can be useful for many other

Virtual reality(VR) is a hot research topic, and it has been effectively
applied in military, education and other fields. The application prospect of
virtual reality in education is very broad. It can effectively reduce labor
cost, resource consumption, stimulate students' interest in learning, and
improve students' knowledge level. New energy vehicles have also been widely
promoted in recent years, and the production of new energy vehicles has played
a key role in it. However, the teaching of car engine disassembly and assembly
still retains a more traditional way. That's why applying VR technology has
high significance. This project uses the Unity 3D engine to develop a VR-based
engine teaching software, which aims to allow users to use VR headsets, handles
and other accessories to simulate the disassembly and assembly of car engines
in a virtual environment. We design a modular system framework and divided the
software into two layers, the system layer and the function layer. The system
layer includes a message system and a data configuration system. The functional
layer includes the user interface system, disassembly and assembly function,
and data module. In addition to fulfilling functional requirements , we used
the Unity UPR tool to check out performance issues, and optimized product
performance by turning off vertical sync and turning on static switches for
some scene objects.

We present an efficient framework of corpus for sign language translation.
Aided with a simple but dramatic data augmentation technique, our method
converts text into annotated forms with minimum information loss. Sign
languages are composed of manual signals, non-manual signals, and iconic
features. According to professional sign language interpreters, non-manual
signals such as facial expressions and gestures play an important role in
conveying exact meaning. By considering the linguistic features of sign
language, our proposed framework is a first and unique attempt to build a
multimodal sign language augmentation corpus (hereinafter referred to as the
KoSLA corpus) containing both manual and non-manual modalities. The corpus we
built demonstrates confident results in the hospital context, showing improved
performance with augmented datasets. To overcome data scarcity, we resorted to
data augmentation techniques such as synonym replacement to boost the
efficiency of our translation model and available data, while maintaining
grammatical and semantic structures of sign language. For the experimental
support, we verify the effectiveness of data augmentation technique and
usefulness of our corpus by performing a translation task between normal
sentences and sign language annotations on two tokenizers. The result was
convincing, proving that the BLEU scores with the KoSLA corpus were
significant.

Consider the finite state graph that results from a simple, discrete,
dynamical system in which an agent moves in a rectangular grid picking up and
dropping packages. Can the state variables of the problem, namely, the agent
location and the package locations, be recovered from the structure of the
state graph alone without having access to information about the objects, the
structure of the states, or any background knowledge? We show that this is
possible provided that the dynamics is learned over a suitable
domain-independent first-order causal language that makes room for objects and
relations that are not assumed to be known. The preference for the most compact
representation in the language that is compatible with the data provides a
strong and meaningful learning bias that makes this possible. The language of
structured causal models (SCMs) is the standard language for representing
(static) causal models but in dynamic worlds populated by objects, first-order
causal languages such as those used in "classical AI planning" are required.
While "classical AI" requires handcrafted representations, similar
representations can be learned from unstructured data over the same languages.
Indeed, it is the languages and the preference for compact representations in
those languages that provide structure to the world, uncovering objects,
relations, and causes.

The real-world testing of decisions made using causal machine learning models
is an essential prerequisite for their successful application. We focus on
evaluating and improving contextual treatment assignment decisions: these are
personalised treatments applied to e.g. customers, each with their own
contextual information, with the aim of maximising a reward. In this paper we
introduce a model-agnostic framework for gathering data to evaluate and improve
contextual decision making through Bayesian Experimental Design. Specifically,
our method is used for the data-efficient evaluation of the regret of past
treatment assignments. Unlike approaches such as A/B testing, our method avoids
assigning treatments that are known to be highly sub-optimal, whilst engaging
in some exploration to gather pertinent information. We achieve this by
introducing an information-based design objective, which we optimise
end-to-end. Our method applies to discrete and continuous treatments. Comparing
our information-theoretic approach to baselines in several simulation studies
demonstrates the superior performance of our proposed approach.

Survival analysis, time-to-event analysis, is an important problem in
healthcare since it has a wide-ranging impact on patients and palliative care.
Many survival analysis methods have assumed that the survival data is centrally
available either from one medical center or by data sharing from multi-centers.
However, the sensitivity of the patient attributes and the strict privacy laws
have increasingly forbidden sharing of healthcare data. To address this
challenge, the research community has looked at the solution of decentralized
training and sharing of model parameters using the Federated Learning (FL)
paradigm. In this paper, we study the utilization of FL for performing survival
analysis on distributed healthcare datasets. Recently, the popular Cox
proportional hazard (CPH) models have been adapted for FL settings; however,
due to its linearity and proportional hazards assumptions, CPH models result in
suboptimal performance, especially for non-linear, non-iid, and heavily
censored survival datasets. To overcome the challenges of existing federated
survival analysis methods, we leverage the predictive accuracy of the deep
learning models and the power of pseudo values to propose a first-of-its-kind,
pseudo value-based deep learning model for federated survival analysis (FSA)
called FedPseudo. Furthermore, we introduce a novel approach of deriving pseudo
values for survival probability in the FL settings that speeds up the
computation of pseudo values. Extensive experiments on synthetic and real-world
datasets show that our pseudo valued-based FL framework achieves similar
performance as the best centrally trained deep survival analysis model.
Moreover, our proposed FL approach obtains the best results for various
censoring settings.

Responding to the current urgent need for low carbon emissions and high
efficiency in manufacturing processes, the relationships between three
different machining factors (depth of cut, feed rate, and spindle rate) on
power consumption and surface finish (roughness) were analysed by applying a
Bayesian seemingly unrelated regressions (SUR) model. For the analysis, an
optimization criterion was established and minimized by using an optimization
algorithm that combines evolutionary algorithm methods with a derivative-based
(quasi-Newton) method to find the optimal conditions for energy consumption
that obtains a good surface finish quality. A Bayesian ANOVA was also performed
to identify the most important factors in terms of variance explanation of the
observed outcomes. The data were obtained from a factorial experimental design
performed in two computerized numerical control (CNC) vertical machining
centers (Haas UMC-750 and Leadwell V-40iT). Some results from this study show
that the feed rate is the most influential factor in power consumption, and the
depth of cut is the factor with the stronger influence on roughness values. An
optimal operational point is found for the three factors with a predictive
error of less than 0.01% and 0.03% for the Leadwell V-40iT machine and the Haas
UMC-750 machine, respectively.

We investigate the unsupervised learning of non-invertible observation
functions in nonlinear state-space models. Assuming abundant data of the
observation process along with the distribution of the state process, we
introduce a nonparametric generalized moment method to estimate the observation
function via constrained regression. The major challenge comes from the
non-invertibility of the observation function and the lack of data pairs
between the state and observation. We address the fundamental issue of
identifiability from quadratic loss functionals and show that the function
space of identifiability is the closure of a RKHS that is intrinsic to the
state process. Numerical results show that the first two moments and temporal
correlations, along with upper and lower bounds, can identify functions ranging
from piecewise polynomials to smooth functions, leading to convergent
estimators. The limitations of this method, such as non-identifiability due to
symmetry and stationarity, are also discussed.

K-nearest neighbor search is one of the fundamental tasks in various
applications and the hierarchical navigable small world (HNSW) has recently
drawn attention in large-scale cloud services, as it easily scales up the
database while offering fast search. On the other hand, a computational storage
device (CSD) that combines programmable logic and storage modules on a single
board becomes popular to address the data bandwidth bottleneck of modern
computing systems. In this paper, we propose a computational storage platform
that can accelerate a large-scale graph-based nearest neighbor search algorithm
based on SmartSSD CSD. To this end, we modify the algorithm more amenable on
the hardware and implement two types of accelerators using HLS- and RTL-based
methodology with various optimization methods. In addition, we scale up the
proposed platform to have 4 SmartSSDs and apply graph parallelism to boost the
system performance further. As a result, the proposed computational storage
platform achieves 75.59 query per second throughput for the SIFT1B dataset at
258.66W power dissipation, which is 12.83x and 17.91x faster and 10.43x and
24.33x more energy efficient than the conventional CPU-based and GPU-based
server platform, respectively. With multi-terabyte storage and custom
acceleration capability, we believe that the proposed computational storage
platform is a promising solution for cost-sensitive cloud datacenters.

The USPTO disseminates one of the largest publicly accessible repositories of
scientific, technical, and commercial data worldwide. USPTO data has
historically seen frequent use in fields such as patent analytics, economics,
and prosecution & litigation tools. This article highlights an emerging class
of usecases directed to the research, development, and application of
artificial intelligence technology. Such usecases contemplate both the delivery
of artificial intelligence capabilities for practical IP applications and the
enablement of future state-of-the-art artificial intelligence research via
USPTO data products. Examples from both within and beyond the USPTO are offered
as case studies.

Regression plays an essential role in many medical imaging applications for
estimating various clinical risk or measurement scores. While training
strategies and loss functions have been studied for the deep neural networks in
medical image classification tasks, options for regression tasks are very
limited. One of the key challenges is that the high-dimensional feature
representation learned by existing popular loss functions like Mean Squared
Error or L1 loss is hard to interpret. In this paper, we propose a novel
Regression Metric Loss (RM-Loss), which endows the representation space with
the semantic meaning of the label space by finding a representation manifold
that is isometric to the label space. Experiments on two regression tasks, i.e.
coronary artery calcium score estimation and bone age assessment, show that
RM-Loss is superior to the existing popular regression losses on both
performance and interpretability. Code is available at
https://github.com/DIAL-RPI/Regression-Metric-Loss.

Safe and reliable autonomy solutions are a critical component of
next-generation intelligent transportation systems. Autonomous vehicles in such
systems must reason about complex and dynamic driving scenes in real time and
anticipate the behavior of nearby drivers. Human driving behavior is highly
nuanced and specific to individual traffic participants. For example, drivers
might display cooperative or non-cooperative behaviors in the presence of
merging vehicles. These behaviors must be estimated and incorporated in the
planning process for safe and efficient driving. In this work, we present a
framework for estimating the cooperation level of drivers on a freeway and plan
merging maneuvers with the drivers' latent behaviors explicitly modeled. The
latent parameter estimation problem is solved using a particle filter to
approximate the probability distribution over the cooperation level. A
partially observable Markov decision process (POMDP) that includes the latent
state estimate is solved online to extract a policy for a merging vehicle. We
evaluate our method in a high-fidelity automotive simulator against methods
that are agnostic to latent states or rely on $\textit{a priori}$ assumptions
about actor behavior.

The recent advances in continual (incremental or lifelong) learning have
concentrated on the prevention of forgetting that can lead to catastrophic
consequences, but there are two outstanding challenges that must be addressed.
The first is the evaluation of the robustness of the proposed methods. The
second is ensuring the security of learned tasks remains largely unexplored.
This paper presents a comprehensive study of the susceptibility of the
continually learned tasks (including both current and previously learned tasks)
that are vulnerable to forgetting. Such vulnerability of tasks against
adversarial attacks raises profound issues in data integrity and privacy. We
consider all three scenarios (i.e, task-incremental leaning, domain-incremental
learning and class-incremental learning) of continual learning and explore
three regularization-based experiments, three replay-based experiments, and one
hybrid technique based on the reply and exemplar approach. We examine the
robustness of these methods. In particular, we consider cases where we
demonstrate that any class belonging to the current or previously learned tasks
is prone to misclassification. Our observations, we identify potential
limitations in continual learning approaches against adversarial attacks. Our
empirical study recommends that the research community consider the robustness
of the proposed continual learning approaches and invest extensive efforts in
mitigating catastrophic forgetting.

We present TacoBot, a task-oriented dialogue system built for the inaugural
Alexa Prize TaskBot Challenge, which assists users in completing multi-step
cooking and home improvement tasks. TacoBot is designed with a user-centered
principle and aspires to deliver a collaborative and accessible dialogue
experience. Towards that end, it is equipped with accurate language
understanding, flexible dialogue management, and engaging response generation.
Furthermore, TacoBot is backed by a strong search engine and an automated
end-to-end test suite. In bootstrapping the development of TacoBot, we explore
a series of data augmentation strategies to train advanced neural language
processing models and continuously improve the dialogue experience with
collected real conversations. At the end of the semifinals, TacoBot achieved an
average rating of 3.55/5.0.

We study whether language models can evaluate the validity of their own
claims and predict which questions they will be able to answer correctly. We
first show that larger models are well-calibrated on diverse multiple choice
and true/false questions when they are provided in the right format. Thus we
can approach self-evaluation on open-ended sampling tasks by asking models to
first propose answers, and then to evaluate the probability "P(True)" that
their answers are correct. We find encouraging performance, calibration, and
scaling for P(True) on a diverse array of tasks. Performance at self-evaluation
further improves when we allow models to consider many of their own samples
before predicting the validity of one specific possibility. Next, we
investigate whether models can be trained to predict "P(IK)", the probability
that "I know" the answer to a question, without reference to any particular
proposed answer. Models perform well at predicting P(IK) and partially
generalize across tasks, though they struggle with calibration of P(IK) on new
tasks. The predicted P(IK) probabilities also increase appropriately in the
presence of relevant source materials in the context, and in the presence of
hints towards the solution of mathematical word problems. We hope these
observations lay the groundwork for training more honest models, and for
investigating how honesty generalizes to cases where models are trained on
objectives other than the imitation of human writing.

Adaptive curricula in reinforcement learning (RL) have proven effective for
producing policies robust to discrepancies between the train and test
environment. Recently, the Unsupervised Environment Design (UED) framework
generalized RL curricula to generating sequences of entire environments,
leading to new methods with robust minimax regret properties. Problematically,
in partially-observable or stochastic settings, optimal policies may depend on
the ground-truth distribution over aleatoric parameters of the environment in
the intended deployment setting, while curriculum learning necessarily shifts
the training distribution. We formalize this phenomenon as curriculum-induced
covariate shift (CICS), and describe how its occurrence in aleatoric parameters
can lead to suboptimal policies. Directly sampling these parameters from the
ground-truth distribution avoids the issue, but thwarts curriculum learning. We
propose SAMPLR, a minimax regret UED method that optimizes the ground-truth
utility function, even when the underlying training data is biased due to CICS.
We prove, and validate on challenging domains, that our approach preserves
optimality under the ground-truth distribution, while promoting robustness
across the full range of environment settings.

Shapley-related techniques have gained attention as both global and local
interpretation tools because of their desirable properties. However, their
computation using conditional expectations is computationally expensive.
Approximation methods suggested in the literature have limitations. This paper
proposes the use of a surrogate model-based tree to compute Shapley and SHAP
values based on conditional expectation. Simulation studies show that the
proposed algorithm provides improvements in accuracy, unifies global Shapley
and SHAP interpretation, and the thresholding method provides a way to
trade-off running time and accuracy.

Deep learning surrogate models have shown promise in solving partial
differential equations (PDEs). Among them, the Fourier neural operator (FNO)
achieves good accuracy, and is significantly faster compared to numerical
solvers, on a variety of PDEs, such as fluid flows. However, the FNO uses the
Fast Fourier transform (FFT), which is limited to rectangular domains with
uniform grids. In this work, we propose a new framework, viz., geo-FNO, to
solve PDEs on arbitrary geometries. Geo-FNO learns to deform the input
(physical) domain, which may be irregular, into a latent space with a uniform
grid. The FNO model with the FFT is applied in the latent space. The resulting
geo-FNO model has both the computation efficiency of FFT and the flexibility of
handling arbitrary geometries. Our geo-FNO is also flexible in terms of its
input formats, viz., point clouds, meshes, and design parameters are all valid
inputs. We consider a variety of PDEs such as the Elasticity, Plasticity,
Euler's, and Navier-Stokes equations, and both forward modeling and inverse
design problems. Geo-FNO is $10^5$ times faster than the standard numerical
solvers and twice more accurate compared to direct interpolation on existing
ML-based PDE solvers such as the standard FNO.

Weakly supervised object detection (WSOD) enables object detectors to be
trained using image-level class labels. However, the practical application of
current WSOD models is limited, as they operate at small scales and require
extensive training and refinement. We propose the Weakly Supervised Detection
Transformer, which enables efficient knowledge transfer from a large-scale
pretraining dataset to WSOD finetuning on hundreds of novel objects. We
leverage pretrained knowledge to improve the multiple instance learning
framework used in WSOD, and experiments show our approach outperforms the
state-of-the-art on datasets with twice the novel classes than previously
shown.

Low inertia, nonlinearity and a high level of uncertainty (varying topologies
and operating conditions) pose challenges to microgrid (MG) systemwide
operation. This paper proposes an online adaptive Koopman operator optimal
control (AKOOC) method for MG secondary voltage and frequency control. Unlike
typical data-driven methods that are data-hungry and lack guaranteed stability,
the proposed AKOOC requires no warm-up training yet with guaranteed
bounded-input-bounded-output (BIBO) stability and even asymptotical stability
under some mild conditions. The proposed AKOOC is developed based on an
ensemble Koopman state space modeling with full basis functions that combines
both linear and nonlinear bases without the need of event detection or
switching. An iterative learning method is also developed to exploit model
parameters, ensuring the effectiveness and the adaptiveness of the designed
control. Simulation studies in the 4-bus (with detailed inner-loop control) MG
system and the 34-bus MG system showed improved modeling accuracy and control,
verifying the effectiveness of the proposed method subject to various changes
of operating conditions even with time delay, measurement noise, and missing

We present an implicit likelihood approach to quantifying cosmological
information over discrete catalogue data, assembled as graphs. To do so, we
explore cosmological inference using mock dark matter halo catalogues. We
employ Information Maximising Neural Networks (IMNNs) to quantify Fisher
information extraction as a function of graph representation. We a) demonstrate
the high sensitivity of modular graph structure to the underlying cosmology in
the noise-free limit, b) show that networks automatically combine mass and
clustering information through comparisons to traditional statistics, c)
demonstrate that graph neural networks can still extract information when
catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear
IMNN summaries can be used as asymptotically optimal compressed statistics for
Bayesian implicit likelihood inference. We reduce the area of joint $\Omega_m,
\sigma_8$ parameter constraints with small ($\sim$100 object) halo catalogues
by a factor of 42 over the two-point correlation function, and demonstrate that
the networks automatically combine mass and clustering information. This work
utilises a new IMNN implementation over graph data in Jax, which can take
advantage of either numerical or auto-differentiability. We also show that
graph IMNNs successfully compress simulations far from the fiducial model at
which the network is fitted, indicating a promising alternative to $n$-point
statistics in catalogue-based analyses.

This work aims to address the challenges in autonomous driving by focusing on
the 3D perception of the environment using roadside LiDARs. We design a 3D
object detection model that can detect traffic participants in roadside LiDARs
in real-time. Our model uses an existing 3D detector as a baseline and improves
its accuracy. To prove the effectiveness of our proposed modules, we train and
evaluate the model on three different vehicle and infrastructure datasets. To
show the domain adaptation ability of our detector, we train it on an
infrastructure dataset from China and perform transfer learning on a different
dataset recorded in Germany. We do several sets of experiments and ablation
studies for each module in the detector that show that our model outperforms
the baseline by a significant margin, while the inference speed is at 45 Hz (22
ms). We make a significant contribution with our LiDAR-based 3D detector that
can be used for smart city applications to provide connected and automated
vehicles with a far-reaching view. Vehicles that are connected to the roadside
sensors can get information about other vehicles around the corner to improve
their path and maneuver planning and to increase road traffic safety.

In multi-modal multi-agent trajectory forecasting, two major challenges have
not been fully tackled: 1) how to measure the uncertainty brought by the
interaction module that causes correlations among the predicted trajectories of
multiple agents; 2) how to rank the multiple predictions and select the optimal
predicted trajectory. In order to handle these challenges, this work first
proposes a novel concept, collaborative uncertainty (CU), which models the
uncertainty resulting from interaction modules. Then we build a general
CU-aware regression framework with an original permutation-equivariant
uncertainty estimator to do both tasks of regression and uncertainty
estimation. Further, we apply the proposed framework to current SOTA
multi-agent multi-modal forecasting systems as a plugin module, which enables
the SOTA systems to 1) estimate the uncertainty in the multi-agent multi-modal
trajectory forecasting task; 2) rank the multiple predictions and select the
optimal one based on the estimated uncertainty. We conduct extensive
experiments on a synthetic dataset and two public large-scale multi-agent
trajectory forecasting benchmarks. Experiments show that: 1) on the synthetic
dataset, the CU-aware regression framework allows the model to appropriately
approximate the ground-truth Laplace distribution; 2) on the multi-agent
trajectory forecasting benchmarks, the CU-aware regression framework steadily
helps SOTA systems improve their performances. Specially, the proposed
framework helps VectorNet improve by 262 cm regarding the Final Displacement
Error of the chosen optimal prediction on the nuScenes dataset; 3) for
multi-agent multi-modal trajectory forecasting systems, prediction uncertainty
is positively correlated with future stochasticity; and 4) the estimated CU
values are highly related to the interactive information among agents.

Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is
graded by endoscopists and this assessment is the basis for risk stratification
and therapy monitoring. Presently, endoscopic characterisation is largely
operator dependant leading to sometimes undesirable clinical outcomes for
patients with IBD. We focus on the Mayo Endoscopic Scoring (MES) system which
is widely used but requires the reliable identification of subtle changes in
mucosal inflammation. Most existing deep learning classification methods cannot
detect these fine-grained changes which make UC grading such a challenging
task. In this work, we introduce a novel patch-level instance-group
discrimination with pretext-invariant representation learning (PLD-PIRL) for
self-supervised learning (SSL). Our experiments demonstrate both improved
accuracy and robustness compared to the baseline supervised network and several
state-of-the-art SSL methods. Compared to the baseline (ResNet50) supervised
classification our proposed PLD-PIRL obtained an improvement of 4.75% on
hold-out test data and 6.64% on unseen center test data for top-1 accuracy.

A research division plays an important role of driving innovation in an
organization. Drawing insights, following trends, keeping abreast of new
research, and formulating strategies are increasingly becoming more challenging
for both researchers and executives as the amount of information grows in both
velocity and volume. In this paper we present a use case of how a corporate
research community, IBM Research, utilizes Semantic Web technologies to induce
a unified Knowledge Graph from both structured and textual data obtained by
integrating various applications used by the community related to research
projects, academic papers, datasets, achievements and recognition. In order to
make the Knowledge Graph more accessible to application developers, we
identified a set of common patterns for exploiting the induced knowledge and
exposed them as APIs. Those patterns were born out of user research which
identified the most valuable use cases or user pain points to be alleviated. We
outline two distinct scenarios: recommendation and analytics for business use.
We will discuss these scenarios in detail and provide an empirical evaluation
on entity recommendation specifically. The methodology used and the lessons
learned from this work can be applied to other organizations facing similar

Machine learning methods are increasingly used to build computationally
inexpensive surrogates for complex physical models. The predictive capability
of these surrogates suffers when data are noisy, sparse, or time-dependent. As
we are interested in finding a surrogate that provides valid predictions of any
potential future model evaluations, we introduce an online learning method
empowered by optimizer-driven sampling. The method has two advantages over
current approaches. First, it ensures that all turning points on the model
response surface are included in the training data. Second, after any new model
evaluations, surrogates are tested and "retrained" (updated) if the "score"
drops below a validity threshold. Tests on benchmark functions reveal that
optimizer-directed sampling generally outperforms traditional sampling methods
in terms of accuracy around local extrema, even when the scoring metric favors
overall accuracy. We apply our method to simulations of nuclear matter to
demonstrate that highly accurate surrogates for the nuclear equation of state
can be reliably auto-generated from expensive calculations using a few model

Image denoising is still a challenging issue in many computer vision
sub-domains. Recent studies show that significant improvements are made
possible in a supervised setting. However, few challenges, such as spatial
fidelity and cartoon-like smoothing remain unresolved or decisively overlooked.
Our study proposes a simple yet efficient architecture for the denoising
problem that addresses the aforementioned issues. The proposed architecture
revisits the concept of modular concatenation instead of long and deeper
cascaded connections, to recover a cleaner approximation of the given image. We
find that different modules can capture versatile representations, and
concatenated representation creates a richer subspace for low-level image
restoration. The proposed architecture's number of parameters remains smaller
than the number for most of the previous networks and still achieves
significant improvements over the current state-of-the-art networks.

Machine learning is becoming a powerful tool to predict temperature-dependent
yield strengths (YS) of structural materials, particularly for
multi-principal-element systems. However, successful machine-learning
predictions depend on the use of reasonable machine-learning models. Here, we
present a comprehensive and up-to-date overview of a bilinear log model for
predicting temperature-dependent YS of medium-entropy or high-entropy alloys
(MEAs or HEAs). In this model, a break temperature, Tbreak, is introduced,
which can guide the design of MEAs or HEAs with attractive high-temperature
properties. Unlike assuming black-box structures, our model is based on the
underlying physics, incorporated in form of a priori information. A technique
of global optimization is employed to enable the concurrent optimization of
model parameters over low- and high-temperature regimes, showing that the break
temperature is consistent across YS and ultimate strength for a variety of HEA
compositions. A high-level comparison between YS of MEAs/HEAs and those of
nickel-based superalloys reveal superior strength properties of selected
refractory HEAs. For reliable operations, the temperature of a structural
component, such as a turbine blade, made from refractory alloys may need to
stay below Tbreak. Once above Tbreak, phase transformations may start taking
place, and the alloy may begin losing structural integrity.

We introduce the challenges of multi-party quantum entanglement and explain a
recent success in learning to take its measure. Given the widely accepted
reputation of entanglement as a counter-intuitive feature of quantum theory, we
first describe pure-state entanglement itself. We restrict attention to
multi-party qubit states. Then we introduce the features that have made it
challenging for several decades to extend an entanglement measure beyond the
2-qubit case of Bell states. We finish with a description of the current
understanding that solves the 3-qubit entanglement challenge. This necessarily
takes into account the fundamental division of the 3-qubit state space into two
completely independent sectors identified with the so-called GHZ and $W$
states.

Despite the large body of academic work on machine learning security, little
is known about the occurrence of attacks on machine learning systems in the
wild. In this paper, we report on a quantitative study with 139 industrial
practitioners. We analyze attack occurrence and concern and evaluate
statistical hypotheses on factors influencing threat perception and exposure.
Our results shed light on real-world attacks on deployed machine learning. On
the organizational level, while we find no predictors for threat exposure in
our sample, the amount of implement defenses depends on exposure to threats or
expected likelihood to become a target. We also provide a detailed analysis of
practitioners' replies on the relevance of individual machine learning attacks,
unveiling complex concerns like unreliable decision making, business
information leakage, and bias introduction into models. Finally, we find that
on the individual level, prior knowledge about machine learning security
influences threat perception. Our work paves the way for more research about
adversarial machine learning in practice, but yields also insights for
regulation and auditing.

Uncertainty quantification (UQ) is essential for creating trustworthy machine
learning models. Recent years have seen a steep rise in UQ methods that can
flag suspicious examples, however, it is often unclear what exactly these
methods identify. In this work, we propose an assumption-light method for
interpreting UQ models themselves. We introduce the confusion density matrix --
a kernel-based approximation of the misclassification density -- and use this
to categorize suspicious examples identified by a given UQ method into three
classes: out-of-distribution (OOD) examples, boundary (Bnd) examples, and
examples in regions of high in-distribution misclassification (IDM). Through
extensive experiments, we shed light on existing UQ methods and show that the
cause of the uncertainty differs across models. Additionally, we show how the
proposed framework can make use of the categorized examples to improve
predictive performance.

We propose Deep Lossless Image Coding (DLIC), a full resolution learned
lossless image compression algorithm. Our algorithm is based on a neural
network combined with an entropy encoder. The neural network performs a density
estimation on each pixel of the source image. The density estimation is then
used to code the target pixel, beating FLIF in terms of compression rate.
Similar approaches have been attempted. However, long run times make them
unfeasible for real world applications. We introduce a parallelized GPU based
implementation, allowing for encoding and decoding of grayscale, 8-bit images
in less than one second. Because DLIC uses a neural network to estimate the
probabilities used for the entropy coder, DLIC can be trained on domain
specific image data. We demonstrate this capability by adapting and training
DLIC with Magnet Resonance Imaging (MRI) images.

Many real-world applications of image recognition require multi-label
learning, whose goal is to find all labels in an image. Thus, robustness of
such systems to adversarial image perturbations is extremely important.
However, despite a large body of recent research on adversarial attacks, the
scope of the existing works is mainly limited to the multi-class setting, where
each image contains a single label. We show that the naive extensions of
multi-class attacks to the multi-label setting lead to violating label
relationships, modeled by a knowledge graph, and can be detected using a
consistency verification scheme. Therefore, we propose a graph-consistent
multi-label attack framework, which searches for small image perturbations that
lead to misclassifying a desired target set while respecting label hierarchies.
By extensive experiments on two datasets and using several multi-label
recognition models, we show that our method generates extremely successful
attacks that, unlike naive multi-label perturbations, can produce model
predictions consistent with the knowledge graph.

Automatic detection of fake news is a highly important task in the
contemporary world. This study reports the 2nd shared task called
UrduFake@FIRE2021 on identifying fake news detection in Urdu. The goal of the
shared task is to motivate the community to come up with efficient methods for
solving this vital problem, particularly for the Urdu language. The task is
posed as a binary classification problem to label a given news article as a
real or a fake news article. The organizers provide a dataset comprising news
in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and
(v) Business, split into training and testing sets. The training set contains
1300 annotated news articles -- 750 real news, 550 fake news, while the testing
set contains 300 news articles -- 200 real, 100 fake news. 34 teams from 7
different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE)
registered to participate in the UrduFake@FIRE2021 shared task. Out of those,
18 teams submitted their experimental results, and 11 of those submitted their
technical reports, which is substantially higher compared to the UrduFake
shared task in 2020 when only 6 teams submitted their technical reports. The
technical reports submitted by the participants demonstrated different data
representation techniques ranging from count-based BoW features to word vector
embeddings as well as the use of numerous machine learning algorithms ranging
from traditional SVM to various neural network architectures including
Transformers such as BERT and RoBERTa. In this year's competition, the best
performing system obtained an F1-macro score of 0.679, which is lower than the
past year's best result of 0.907 F1-macro. Admittedly, while training sets from
the past and the current years overlap to a large extent, the testing set
provided this year is completely different.

Accurate assessment of the domain expertise of developers is important for
assigning the proper candidate to contribute to a project or to attend a job
role. Since the potential candidate can come from a large pool, the automated
assessment of this domain expertise is a desirable goal. While previous methods
have had some success within a single software project, the assessment of a
developer's domain expertise from contributions across multiple projects is
more challenging. In this paper, we employ doc2vec to represent the domain
expertise of developers as embedding vectors. These vectors are derived from
different sources that contain evidence of developers' expertise, such as the
description of repositories that they contributed, their issue resolving
history, and API calls in their commits. We name it dev2vec and demonstrate its
effectiveness in representing the technical specialization of developers. Our
results indicate that encoding the expertise of developers in an embedding
vector outperforms state-of-the-art methods and improves the F1-score up to
21%. Moreover, our findings suggest that ``issue resolving history'' of
developers is the most informative source of information to represent the
domain expertise of developers in embedding spaces.

In the past decade, with the development of big data technology, an
increasing amount of patient information has been stored as electronic health
records (EHRs). Leveraging these data, various doctor recommendation systems
have been proposed. Typically, such studies process the EHR data in a
flat-structured manner, where each encounter was treated as an unordered set of
features. Nevertheless, the heterogeneous structured information such as
service sequence stored in claims shall not be ignored. This paper presents a
doctor recommendation system with time embedding to reconstruct the potential
connections between patients and doctors using heterogeneous graph attention
network. Besides, to address the privacy issue of patient data sharing crossing
hospitals, a federated decentralized learning method based on a minimization
optimization model is also proposed. The graph-based recommendation system has
been validated on a EHR dataset. Compared to baseline models, the proposed
method improves the AUC by up to 6.2%. And our proposed federated-based
algorithm not only yields the fictitious fusion center's performance but also
enjoys a convergence rate of O(1/T).

Recently, adversarial training has been incorporated in self-supervised
contrastive pre-training to augment label efficiency with exciting adversarial
robustness. However, the robustness came at a cost of expensive adversarial
training. In this paper, we show a surprising fact that contrastive
pre-training has an interesting yet implicit connection with robustness, and
such natural robustness in the pre trained representation enables us to design
a powerful robust algorithm against adversarial attacks, RUSH, that combines
the standard contrastive pre-training and randomized smoothing. It boosts both
standard accuracy and robust accuracy, and significantly reduces training costs
as compared with adversarial training. We use extensive empirical studies to
show that the proposed RUSH outperforms robust classifiers from adversarial
training, by a significant margin on common benchmarks (CIFAR-10, CIFAR-100,
and STL-10) under first-order attacks. In particular, under
$\ell_{\infty}$-norm perturbations of size 8/255 PGD attack on CIFAR-10, our
model using ResNet-18 as backbone reached 77.8% robust accuracy and 87.9%
standard accuracy. Our work has an improvement of over 15% in robust accuracy
and a slight improvement in standard accuracy, compared to the

In this paper, we derive a new method for determining shared features of
datasets by employing joint non-negative matrix factorization and analyzing the
resulting factorizations. Our approach uses the joint factorization of two
dataset matrices $X_1,X_2$ into non-negative matrices $X_1 = AS_1, X_2 = AS_2$
to derive a similarity measure that determines how well a shared basis for
$X_1, X_2$ approximates each dataset. We also propose a dataset distance
measure built upon this method and the learned factorization. Our method is
able to successfully identity differences in structure in both image and text
datasets. Potential applications include classification, detecting plagiarism
or other manipulation, and learning relationships between data sets.

We introduce a novel machine learning framework for estimating the Bayesian
posteriors of morphological parameters for arbitrarily large numbers of
galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates
values and uncertainties for a galaxy's bulge-to-total light ratio ($L_B/L_T$),
effective radius ($R_e$), and flux ($F$). To estimate posteriors, GaMPEN uses
the Monte Carlo Dropout technique and incorporates the full covariance matrix
between the output parameters in its loss function. GaMPEN also uses a Spatial
Transformer Network (STN) to automatically crop input galaxy frames to an
optimal size before determining their morphology. This will allow it to be
applied to new data without prior knowledge of galaxy size. Training and
testing GaMPEN on galaxies simulated to match $z < 0.25$ galaxies in Hyper
Suprime-Cam Wide $g$-band images, we demonstrate that GaMPEN achieves typical
errors of $0.1$ in $L_B/L_T$, $0.17$ arcsec ($\sim 7\%$) in $R_e$, and
$6.3\times10^4$ nJy ($\sim 1\%$) in $F$. GaMPEN's predicted uncertainties are
well-calibrated and accurate ($<5\%$ deviation) -- for regions of the parameter
space with high residuals, GaMPEN correctly predicts correspondingly large
uncertainties. We also demonstrate that we can apply categorical labels (i.e.,
classifications such as "highly bulge-dominated") to predictions in regions
with high residuals and verify that those labels are $\gtrsim 97\%$ accurate.
To the best of our knowledge, GaMPEN is the first machine learning framework
for determining joint posterior distributions of multiple morphological
parameters and is also the first application of an STN to optical imaging in

We explore semantic correspondence estimation through the lens of
unsupervised learning. We thoroughly evaluate several recently proposed
unsupervised methods across multiple challenging datasets using a standardized
evaluation protocol where we vary factors such as the backbone architecture,
the pre-training strategy, and the pre-training and finetuning datasets. To
better understand the failure modes of these methods, and in order to provide a
clearer path for improvement, we provide a new diagnostic framework along with
a new performance metric that is better suited to the semantic matching task.
Finally, we introduce a new unsupervised correspondence approach which utilizes
the strength of pre-trained features while encouraging better matches during
training. This results in significantly better matching performance compared to
current state-of-the-art methods.

We propose to learn to generate grasping motion for manipulation with a
dexterous hand using implicit functions. With continuous time inputs, the model
can generate a continuous and smooth grasping plan. We name the proposed model
Continuous Grasping Function (CGF). CGF is learned via generative modeling with
a Conditional Variational Autoencoder using 3D human demonstrations. We will
first convert the large-scale human-object interaction trajectories to robot
demonstrations via motion retargeting, and then use these demonstrations to
train CGF. During inference, we perform sampling with CGF to generate different
grasping plans in the simulator and select the successful ones to transfer to
the real robot. By training on diverse human data, our CGF allows
generalization to manipulate multiple objects. Compared to previous planning
algorithms, CGF is more efficient and achieves significant improvement on
success rate when transferred to grasping with the real Allegro Hand. Our
project page is at https://jianglongye.com/cgf .

Recent research has shown the potential for neural networks to improve upon
classical survival models such as the Cox model, which is widely used in
clinical practice. Neural networks, however, typically rely on data that are
centrally available, whereas healthcare data are frequently held in secure
silos. We present a federated Cox model that accommodates this data setting and
also relaxes the proportional hazards assumption, allowing time-varying
covariate effects. In this latter respect, our model does not require explicit
specification of the time-varying effects, reducing upfront organisational
costs compared to previous works. We experiment with publicly available
clinical datasets and demonstrate that the federated model is able to perform
as well as a standard model.

MATSim (Multi-Agent Transport Simulation Toolkit) is an open source
large-scale agent-based transportation planning project applied to various
areas like road transport, public transport, freight transport, regional
evacuation, etc. BEAM (Behavior, Energy, Autonomy, and Mobility) framework
extends MATSim to enable powerful and scalable analysis of urban transportation
systems. The agents from the BEAM simulation exhibit 'mode choice' behavior
based on multinomial logit model. In our study, we consider eight mode choices
viz. bike, car, walk, ride hail, driving to transit, walking to transit, ride
hail to transit, and ride hail pooling. The 'alternative specific constants'
for each mode choice are critical hyperparameters in a configuration file
related to a particular scenario under experimentation. We use the
'Urbansim-10k' BEAM scenario (with 10,000 population size) for all our
experiments. Since these hyperparameters affect the simulation in complex ways,
manual calibration methods are time consuming. We present a parallel Bayesian
optimization method with early stopping rule to achieve fast convergence for
the given multi-in-multi-out problem to its optimal configurations. Our model
is based on an open source HpBandSter package. This approach combines hierarchy
of several 1D Kernel Density Estimators (KDE) with a cheap evaluator
(Hyperband, a single multidimensional KDE). Our model has also incorporated
extrapolation based early stopping rule. With our model, we could achieve a 25%
L1 norm for a large-scale BEAM simulation in fully autonomous manner. To the
best of our knowledge, our work is the first of its kind applied to large-scale
multi-agent transportation simulations. This work can be useful for surrogate
modeling of scenarios with very large populations.

In this paper, we show that recent advances in self-supervised feature
learning enable unsupervised object discovery and semantic segmentation with a
performance that matches the state of the field on supervised semantic
segmentation 10 years ago. We propose a methodology based on unsupervised
saliency masks and self-supervised feature clustering to kickstart object
discovery followed by training a semantic segmentation network on pseudo-labels
to bootstrap the system on images with multiple objects. We present results on
PASCAL VOC that go far beyond the current state of the art (47.3 mIoU), and we
report for the first time results on MS COCO for the whole set of 81 classes:
our method discovers 34 categories with more than $20\%$ IoU, while obtaining
an average IoU of 19.6 for all 81 categories.

We introduce a new synthetic data generator PSP-HDRI$+$ that proves to be a
superior pre-training alternative to ImageNet and other large-scale synthetic
data counterparts. We demonstrate that pre-training with our synthetic data
will yield a more general model that performs better than alternatives even
when tested on out-of-distribution (OOD) sets. Furthermore, using ablation
studies guided by person keypoint estimation metrics with an off-the-shelf
model architecture, we show how to manipulate our synthetic data generator to
further improve model performance.

Cross-modal retrieval has drawn much attention in both computer vision and
natural language processing domains. With the development of convolutional and
recurrent neural networks, the bottleneck of retrieval across image-text
modalities is no longer the extraction of image and text features but an
efficient loss function learning in embedding space. Many loss functions try to
closer pairwise features from heterogeneous modalities. This paper proposes a
method for learning joint embedding of images and texts using an intra-modal
constraint loss function to reduce the violation of negative pairs from the
same homogeneous modality. Experimental results show that our approach
outperforms state-of-the-art bi-directional image-text retrieval methods on
Flickr30K and Microsoft COCO datasets. Our code is publicly available:
https://github.com/CanonChen/IMC.

With the long-term goal of reverse-architecting the computational brain from
the bottom up, the focus of this document is the macrocolumn abstraction layer.
A basic macrocolumn architecture is developed by first describing its operation
with a state machine model. Then state machine functions are implemented with
spiking neurons that support temporal computation. The neuron model is based on
active spiking dendrites and mirrors the Hawkins/Numenta neuron model. The
architecture is demonstrated with a research benchmark in which an agent uses a
macrocolumn to first learn and then navigate 2-d environments containing
randomly placed features. Environments are represented in the macrocolumn as
labeled directed graphs where edges connect features and labels indicate the
relative displacements between them.

Natural Language Processing (NLP) inference is seeing increasing adoption by
mobile applications, where on-device inference is desirable for crucially
preserving user data privacy and avoiding network roundtrips. Yet, the
unprecedented size of an NLP model stresses both latency and memory, the two
key resources of a mobile device. To meet a target latency, holding the whole
model in memory launches execution as soon as possible but increases one app's
memory footprints by several times, limiting its benefits to only a few
inferences before being recycled by mobile memory management. On the other
hand, loading the model from storage on demand incurs a few seconds long IO,
far exceeding the delay range satisfying to a user; pipelining layerwise model
loading and execution does not hide IO either, due to the large skewness
between IO and computation delays.
  To this end, we propose WRX. Built on the key idea of maximizing IO/compute
resource utilization on the most important parts of a model, WRX reconciles the
latency/memory tension via two novel techniques. First, model sharding. WRX
manages model parameters as independently tunable shards and profiles their
importance to accuracy. Second, elastic pipeline planning with a preload
buffer. WRX instantiates an IO/computation pipeline and uses a small buffer for
preload shards to bootstrap execution without stalling in early stages; it
judiciously selects, tunes, and assembles shards per their importance for
resource-elastic execution, which maximizes inference accuracy.
  Atop two commodity SoCs, we build WRX and evaluate it against a wide range of
NLP tasks, under a practical range of target latencies, and on both CPU and
GPU. We demonstrate that, WRX delivers high accuracies with 1--2 orders of
magnitude lower memory, outperforming competitive baselines.

This work explores the usage of a supplementary controller for improving the
transient performance of inverter$\unicode{x2013}$based resources (IBR) in
microgrids. The supplementary controller is trained using a reinforcement
learning (RL)$\unicode{x2013}$based algorithm to minimize transients in a power
converter connected to a microgrid. The controller works autonomously to issue
adaptive, intermediate set points based on the current state and trajectory of
the observed or tracked variable. The ability of the designed controller to
mitigate transients is verified on a medium voltage test system using
PSCAD/EMTDC.

Problems which require both long-horizon planning and continuous control
capabilities pose significant challenges to existing reinforcement learning
agents. In this paper we introduce a novel hierarchical reinforcement learning
agent which links temporally extended skills for continuous control with a
forward model in a symbolic discrete abstraction of the environment's state for
planning. We term our agent SEADS for Symbolic Effect-Aware Diverse Skills. We
formulate an objective and corresponding algorithm which leads to unsupervised
learning of a diverse set of skills through intrinsic motivation given a known
state abstraction. The skills are jointly learned with the symbolic forward
model which captures the effect of skill execution in the state abstraction.
After training, we can leverage the skills as symbolic actions using the
forward model for long-horizon planning and subsequently execute the plan using
the learned continuous-action control skills. The proposed algorithm learns
skills and forward models that can be used to solve complex tasks which require
both continuous control and long-horizon planning capabilities with high
success rate. It compares favorably with other flat and hierarchical
reinforcement learning baseline agents and is successfully demonstrated with a

Digital cognitive tests offer several potential advantages over established
paper-pencil tests but have not yet been fully evaluated for the clinical
evaluation of mild cognitive impairment. The NeuroCognitive Performance Test
(NCPT) is a web-based, self-directed, modular battery intended for repeated
assessments of multiple cognitive domains. Our objective was to examine its
relationship with the ADAS-Cog and MMSE as well as with established
paper-pencil tests of cognition and daily functioning in MCI. We used Spearman
correlations, regressions and principal components analysis followed by a
factor analysis (varimax rotated) to examine our objectives. In MCI subjects,
the NCPT composite is significantly correlated with both a composite measure of
established tests (r=0.78, p<0.0001) as well as with the ADAS-Cog (r=0.55,
p<0.0001). Both NCPT and paper-pencil test batteries had a similar factor
structure that included a large g component with a high eigenvalue. The
correlation for the analogous tests (e.g. Trails A and B, learning memory
tests) were significant (p<0.0001). Further, both the NCPT and established
tests significantly (p< 0.01) predicted the University of California San Diego
Performance-Based Skills Assessment and Functional Activities Questionnaire,
measures of daily functioning. The NCPT, a web-based, self-directed,
computerized test, shows high concurrent validity with established tests and
hence offers promise for use as a research or clinical tool in MCI. Despite
limitations such as a relatively small sample, absence of control group and
cross-sectional nature, these findings are consistent with the growing
literature on the promise of self-directed, web-based cognitive assessments for
MCI.

The discovery of new catalysts is one of the significant topics of
computational chemistry as it has the potential to accelerate the adoption of
renewable energy sources. Recently developed deep learning approaches such as
graph neural networks (GNNs) open new opportunity to significantly extend scope
for modelling novel high-performance catalysts. Nevertheless, the graph
representation of particular crystal structure is not a straightforward task
due to the ambiguous connectivity schemes and numerous embeddings of nodes and
edges. Here we present embedding improvement for GNN that has been modified by
Voronoi tesselation and is able to predict the energy of catalytic systems
within Open Catalyst Project dataset. Enrichment of the graph was calculated
via Voronoi tessellation and the corresponding contact solid angles and types
(direct or indirect) were considered as features of edges and Voronoi volumes
were used as node characteristics. The auxiliary approach was enriching node
representation by intrinsic atomic properties (electronegativity, period and
group position). Proposed modifications allowed us to improve the mean absolute
error of the original model and the final error equals to 651 meV per atom on
the Open Catalyst Project dataset and 6 meV per atom on the intermetallics
dataset. Also, by consideration of additional dataset, we show that a sensible
choice of data can decrease the error to values above physically-based 20 meV
per atom threshold.

Positivity, the assumption that every unique combination of confounding
variables that occurs in a population has a non-zero probability of an action,
can be further delineated as deterministic positivity and stochastic
positivity. Here, we revisit this distinction, examine its relation to
nonparametric identifiability and estimability, and discuss how to address
violations of positivity assumptions. Finally, we relate positivity to recent
interest in machine learning, as well as the limitations of data-adaptive
algorithms for causal inference. Positivity may often be overlooked, but it
remains important for inference.

We propose an efficient method for rendering complex luminaires using a
high-quality octree-based representation of the luminaire emission. Complex
luminaires are a particularly challenging problem in rendering, due to their
caustic light paths inside the luminaire. We reduce the geometric complexity of
luminaires by using a simple proxy geometry and encode the visually-complex
emitted light field by using a neural radiance field. We tackle the multiple
challenges of using NeRFs for representing luminaires, including their high
dynamic range, high-frequency content and null-emission areas, by proposing a
specialized loss function. For rendering, we distill our luminaires' NeRF into
a Plenoctree, which we can be easily integrated into traditional rendering
systems. Our approach allows for speed-ups of up to 2 orders of magnitude in
scenes containing complex luminaires introducing minimal error.

3D scene graphs (3DSGs) are an emerging description; unifying symbolic,
topological, and metric scene representations. However, typical 3DSGs contain
hundreds of objects and symbols even for small environments; rendering task
planning on the full graph impractical. We construct TASKOGRAPHY, the first
large-scale robotic task planning benchmark over 3DSGs. While most benchmarking
efforts in this area focus on vision-based planning, we systematically study
symbolic planning, to decouple planning performance from visual representation
learning. We observe that, among existing methods, neither classical nor
learning-based planners are capable of real-time planning over full 3DSGs.
Enabling real-time planning demands progress on both (a) sparsifying 3DSGs for
tractable planning and (b) designing planners that better exploit 3DSG
hierarchies. Towards the former goal, we propose SCRUB, a task-conditioned 3DSG
sparsification method; enabling classical planners to match and in some cases
surpass state-of-the-art learning-based planners. Towards the latter goal, we
propose SEEK, a procedure enabling learning-based planners to exploit 3DSG
structure, reducing the number of replanning queries required by current best
approaches by an order of magnitude. We will open-source all code and baselines
to spur further research along the intersections of robot task planning,
learning and 3DSGs.

Natural cellular materials, such as honeycombs, woods, foams, trabecular
bones, plant parenchyma, and sponges, may benefit from the disorderliness
within their internal microstructures to achieve damage tolerant behaviours.
Inspired by this, we have created quasi-disordered truss metamaterials (QTMs)
via introducing spatial coordinate perturbations or strut thickness variations
to the perfect, periodic truss lattices. Numerical studies have suggested that
the QTMs can exhibit either ductile, damage tolerant behaviours or sudden,
catastrophic failure mode, depending on the distribution of the introduced
disorderliness. A data-driven approach has been developed, combining
deep-learning and global optimization algorithms, to tune the distribution of
the disorderliness to achieve the damage tolerant QTM designs. A case study on
the QTMs created from a periodic Face Centred Cubic (FCC) lattice has
demonstrated that the optimised QTMs can achieve up to 100% increase in
ductility at the expense of less than 5% stiffness and less than 10% tensile
strength. Our results suggest a novel design pathway for architected materials
to improve damage tolerance.

Deep neural networks struggle to continually learn multiple sequential tasks
due to catastrophic forgetting of previously learned tasks. Rehearsal-based
methods which explicitly store previous task samples in the buffer and
interleave them with the current task samples have proven to be the most
effective in mitigating forgetting. However, Experience Replay (ER) does not
perform well under low-buffer regimes and longer task sequences as its
performance is commensurate with the buffer size. Consistency in predictions of
soft-targets can assist ER in preserving information pertaining to previous
tasks better as soft-targets capture the rich similarity structure of the data.
Therefore, we examine the role of consistency regularization in ER framework
under various continual learning scenarios. We also propose to cast consistency
regularization as a self-supervised pretext task thereby enabling the use of a
wide variety of self-supervised learning methods as regularizers. While
simultaneously enhancing model calibration and robustness to natural
corruptions, regularizing consistency in predictions results in lesser
forgetting across all continual learning scenarios. Among the different
families of regularizers, we find that stricter consistency constraints
preserve previous task information in ER better.

Self-supervised pre-training for 3D vision has drawn increasing research
interest in recent years. In order to learn informative representations, a lot
of previous works exploit invariances of 3D features, e.g.,
perspective-invariance between views of the same scene, modality-invariance
between depth and RGB images, format-invariance between point clouds and
voxels. Although they have achieved promising results, previous researches lack
a systematic and fair comparison of these invariances. To address this issue,
our work, for the first time, introduces a unified framework, under which
various pre-training methods can be investigated. We conduct extensive
experiments and provide a closer look at the contributions of different
invariances in 3D pre-training. Also, we propose a simple but effective method
that jointly pre-trains a 3D encoder and a depth map encoder using contrastive
learning. Models pre-trained with our method gain significant performance boost
in downstream tasks. For instance, a pre-trained VoteNet outperforms previous
methods on SUN RGB-D and ScanNet object detection benchmarks with a clear
margin.

Data-driven design shows the promise of accelerating materials discovery but
is challenging due to the prohibitive cost of searching the vast design space
of chemistry, structure, and synthesis methods. Bayesian Optimization (BO)
employs uncertainty-aware machine learning models to select promising designs
to evaluate, hence reducing the cost. However, BO with mixed numerical and
categorical variables, which is of particular interest in materials design, has
not been well studied. In this work, we survey frequentist and Bayesian
approaches to uncertainty quantification of machine learning with mixed
variables. We then conduct a systematic comparative study of their performances
in BO using a popular representative model from each group, the random
forest-based Lolo model (frequentist) and the latent variable Gaussian process
model (Bayesian). We examine the efficacy of the two models in the optimization
of mathematical functions, as well as properties of structural and functional
materials, where we observe performance differences as related to problem
dimensionality and complexity. By investigating the machine learning models'
predictive and uncertainty estimation capabilities, we provide interpretations
of the observed performance differences. Our results provide practical guidance
on choosing between frequentist and Bayesian uncertainty-aware machine learning
models for mixed-variable BO in materials design.

Since the 1980s, machine learning has been widely used for horse-racing
predictions, gradually expanding to where algorithms are now playing a huge
role in the betting market. Machine learning has changed the horse-racing
betting market over the last ten years, but main changes are still to come. The
paradigm shift of neural networks (deep learning) may not only improve our
ability to simply predict the outcome of a race, but it will also certainly
shake our entire way of thinking about horse-racing - and maybe more generally
about horses. Since 2012, deep learning provided more and more state-of-the-art
results in computer vision and now statistical learning or game theory. We
describe how the convergence of the three machine learning fields (computer
vision, statistical learning, and game theory) will be game-changers in the
next decade in our ability to predict and understand horse-racing. We consider
that horse-racing is a real world laboratory where we can work on the
animal-human interaction and build a non-anthropocentric Artificial
Intelligence. We believe that this will lead us to understand the horses better
and the interactions between animals and humans in general.

Knowledge graph embedding (KGE) models are an effective and popular approach
to represent and reason with multi-relational data. Prior studies have shown
that KGE models are sensitive to hyperparameter settings, however, and that
suitable choices are dataset-dependent. In this paper, we explore
hyperparameter optimization (HPO) for very large knowledge graphs, where the
cost of evaluating individual hyperparameter configurations is excessive. Prior
studies often avoided this cost by using various heuristics; e.g., by training
on a subgraph or by using fewer epochs. We systematically discuss and evaluate
the quality and cost savings of such heuristics and other low-cost
approximation techniques. Based on our findings, we introduce GraSH, an
efficient multi-fidelity HPO algorithm for large-scale KGEs that combines both
graph and epoch reduction techniques and runs in multiple rounds of increasing
fidelities. We conducted an experimental study and found that GraSH obtains
state-of-the-art results on large graphs at a low cost (three complete training
runs in total).

Physics Informed Neural Networks (PINNs) have gained immense popularity as an
alternate method for numerically solving PDEs. Despite their empirical success
we are still building an understanding of the convergence properties of
training on such constraints with gradient descent. It is known that, in the
absence of an explicit inductive bias, Neural Networks can struggle to learn or
approximate even simple and well known functions in a sample efficient manner.
Thus the numerical approximation induced from few collocation points may not
generalize over the entire domain. Meanwhile, a symbolic form can exhibit good
generalization, with interpretability as a useful byproduct. However, symbolic
approximations can struggle to simultaneously be concise and accurate.
Therefore in this work we explore a NeuroSymbolic approach to approximate the
solution for PDEs. We observe that our approach work for several simple cases.
We illustrate the efficacy of our approach on Navier Stokes: Kovasznay flow
where there are multiple physical quantities of interest governed with
non-linear coupled PDE system. Domain splitting is now becoming a popular trick
to help PINNs approximate complex functions. We observe that a NeuroSymbolic
approach can help such complex functions as well. We demonstrate
Domain-splitting assisted NeuroSymbolic approach on a temporally varying
two-dimensional Burger's equation. Finally we consider the scenario where PINNs
have to be solved for parameterized PDEs, for changing Initial-Boundary
Conditions and changes in the coefficient of the PDEs. Hypernetworks have shown
to hold promise to overcome these challenges. We show that one can design
Hyper-NeuroSymbolic Networks which can combine the benefits of speed and
increased accuracy. We observe that that the NeuroSymbolic approximations are
consistently 1-2 order of magnitude better than just the neural or symbolic
approximations.

Multi-scale Vision Transformer (ViT) has emerged as a powerful backbone for
computer vision tasks, while the self-attention computation in Transformer
scales quadratically w.r.t. the input patch number. Thus, existing solutions
commonly employ down-sampling operations (e.g., average pooling) over
keys/values to dramatically reduce the computational cost. In this work, we
argue that such over-aggressive down-sampling design is not invertible and
inevitably causes information dropping especially for high-frequency components
in objects (e.g., texture details). Motivated by the wavelet theory, we
construct a new Wavelet Vision Transformer (\textbf{Wave-ViT}) that formulates
the invertible down-sampling with wavelet transforms and self-attention
learning in a unified way. This proposal enables self-attention learning with
lossless down-sampling over keys/values, facilitating the pursuing of a better
efficiency-vs-accuracy trade-off. Furthermore, inverse wavelet transforms are
leveraged to strengthen self-attention outputs by aggregating local contexts
with enlarged receptive field. We validate the superiority of Wave-ViT through
extensive experiments over multiple vision tasks (e.g., image recognition,
object detection and instance segmentation). Its performances surpass
state-of-the-art ViT backbones with comparable FLOPs. Source code is available
at \url{https://github.com/YehLi/ImageNetModel}.

Prior works have proposed several strategies to reduce the computational cost
of self-attention mechanism. Many of these works consider decomposing the
self-attention procedure into regional and local feature extraction procedures
that each incurs a much smaller computational complexity. However, regional
information is typically only achieved at the expense of undesirable
information lost owing to down-sampling. In this paper, we propose a novel
Transformer architecture that aims to mitigate the cost issue, named Dual
Vision Transformer (Dual-ViT). The new architecture incorporates a critical
semantic pathway that can more efficiently compress token vectors into global
semantics with reduced order of complexity. Such compressed global semantics
then serve as useful prior information in learning finer pixel level details,
through another constructed pixel pathway. The semantic pathway and pixel
pathway are then integrated together and are jointly trained, spreading the
enhanced self-attention information in parallel through both of the pathways.
Dual-ViT is henceforth able to reduce the computational complexity without
compromising much accuracy. We empirically demonstrate that Dual-ViT provides
superior accuracy than SOTA Transformer architectures with reduced training
complexity. Source code is available at
\url{https://github.com/YehLi/ImageNetModel}.

Recently, continual learning (CL) has gained significant interest because it
enables deep learning models to acquire new knowledge without forgetting
previously learnt information. However, most existing works require knowing the
task identities and boundaries, which is not realistic in a real context. In
this paper, we address a more challenging and realistic setting in CL, namely
the Task-Free Continual Learning (TFCL) in which a model is trained on
non-stationary data streams with no explicit task information. To address TFCL,
we introduce an evolved mixture model whose network architecture is dynamically
expanded to adapt to the data distribution shift. We implement this expansion
mechanism by evaluating the probability distance between the knowledge stored
in each mixture model component and the current memory buffer using the Hilbert
Schmidt Independence Criterion (HSIC). We further introduce two simple dropout
mechanisms to selectively remove stored examples in order to avoid memory
overload while preserving memory diversity. Empirical results demonstrate that
the proposed approach achieves excellent performance.

Binary neural networks (BNNs) have demonstrated their ability to solve
complex tasks with comparable accuracy as full-precision deep neural networks
(DNNs), while also reducing computational power and storage requirements and
increasing the processing speed. These properties make them an attractive
alternative for the development and deployment of DNN-based applications in
Internet-of-Things (IoT) devices. Despite the recent improvements, they suffer
from a fixed and limited compression factor that may result insufficient for
certain devices with very limited resources. In this work, we propose sparse
binary neural networks (SBNNs), a novel model and training scheme which
introduces sparsity in BNNs and a new quantization function for binarizing the
network's weights. The proposed SBNN is able to achieve high compression
factors and it reduces the number of operations and parameters at inference
time. We also provide tools to assist the SBNN design, while respecting
hardware resource constraints. We study the generalization properties of our
method for different compression factors through a set of experiments on linear
and convolutional networks on three datasets. Our experiments confirm that
SBNNs can achieve high compression rates, without compromising generalization,
while further reducing the operations of BNNs, making SBNNs a viable option for
deploying DNNs in cheap, low-cost, limited-resources IoT devices and sensors.

Explainable AI (XAI) is a necessity in safety-critical systems such as in
clinical diagnostics due to a high risk for fatal decisions. Currently,
however, XAI resembles a loose collection of methods rather than a well-defined
process. In this work, we elaborate on conceptual similarities between the
largest subgroup of XAI, interpretable machine learning (IML), and classical
statistics. Based on these similarities, we present a formalization of IML
along the lines of a statistical process. Adopting this statistical view allows
us to interpret machine learning models and IML methods as sophisticated
statistical tools. Based on this interpretation, we infer three key questions,
which we identify as crucial for the success and adoption of IML in
safety-critical settings. By formulating these questions, we further aim to
spark a discussion about what distinguishes IML from classical statistics and
what our perspective implies for the future of the field.

Networked dynamical systems are common throughout science in engineering;
e.g., biological networks, reaction networks, power systems, and the like. For
many such systems, nonlinearity drives populations of identical (or
near-identical) units to exhibit a wide range of nontrivial behaviors, such as
the emergence of coherent structures (e.g., waves and patterns) or otherwise
notable dynamics (e.g., synchrony and chaos). In this work, we seek to infer
(i) the intrinsic physics of a base unit of a population, (ii) the underlying
graphical structure shared between units, and (iii) the coupling physics of a
given networked dynamical system given observations of nodal states. These
tasks are formulated around the notion of the Universal Differential Equation,
whereby unknown dynamical systems can be approximated with neural networks,
mathematical terms known a priori (albeit with unknown parameterizations), or
combinations of the two. We demonstrate the value of these inference tasks by
investigating not only future state predictions but also the inference of
system behavior on varied network topologies. The effectiveness and utility of
these methods is shown with their application to canonical networked nonlinear
coupled oscillators.

Categorization of mutual funds or Exchange-Traded-funds (ETFs) have long
served the financial analysts to perform peer analysis for various purposes
starting from competitor analysis, to quantifying portfolio diversification.
The categorization methodology usually relies on fund composition data in the
structured format extracted from the Form N-1A. Here, we initiate a study to
learn the categorization system directly from the unstructured data as depicted
in the forms using natural language processing (NLP). Positing as a multi-class
classification problem with the input data being only the investment strategy
description as reported in the form and the target variable being the Lipper
Global categories, and using various NLP models, we show that the
categorization system can indeed be learned with high accuracy. We discuss
implications and applications of our findings as well as limitations of
existing pre-trained architectures in applying them to learn fund

The opacity of machine learning data is a significant threat to ethical data
work and intelligible systems. Previous research has addressed this issue by
proposing standardized checklists to document datasets. This paper expands that
field of inquiry by proposing a shift of perspective: from documenting datasets
toward documenting data production. We draw on participatory design and
collaborate with data workers at two companies located in Bulgaria and
Argentina, where the collection and annotation of data for machine learning are
outsourced. Our investigation comprises 2.5 years of research, including 33
semi-structured interviews, five co-design workshops, the development of
prototypes, and several feedback instances with participants. We identify key
challenges and requirements related to the integration of documentation
practices in real-world data production scenarios. Our findings comprise
important design considerations and highlight the value of designing data
documentation based on the needs of data workers. We argue that a view of
documentation as a boundary object, i.e., an object that can be used
differently across organizations and teams but holds enough immutable content
to maintain integrity, can be useful when designing documentation to retrieve
heterogeneous, often distributed, contexts of data production.

Approximation rates are analyzed for deep surrogates of maps between
infinite-dimensional function spaces, arising e.g. as data-to-solution maps of
linear and nonlinear partial differential equations. Specifically, we study
approximation rates for Deep Neural Operator and Generalized Polynomial Chaos
(gpc) Operator surrogates for nonlinear, holomorphic maps between
infinite-dimensional, separable Hilbert spaces. Operator in- and outputs from
function spaces are assumed to be parametrized by stable, affine representation
systems. Admissible representation systems comprise orthonormal bases, Riesz
bases or suitable tight frames of the spaces under consideration. Algebraic
expression rate bounds are established for both, deep neural and gpc operator
surrogates acting in scales of separable Hilbert spaces containing domain and
range of the map to be expressed, with finite Sobolev or Besov regularity. We
illustrate the abstract concepts by expression rate bounds for the
coefficient-to-solution map for a linear elliptic PDE on the torus.

Social media is often utilized as a lifeline for communication during natural
disasters. Traditionally, natural disaster tweets are filtered from the Twitter
stream using the name of the natural disaster and the filtered tweets are sent
for human annotation. The process of human annotation to create labeled sets
for machine learning models is laborious, time consuming, at times inaccurate,
and more importantly not scalable in terms of size and real-time use. In this
work, we curate a silver standard dataset using weak supervision. In order to
validate its utility, we train machine learning models on the weakly supervised
data to identify three different types of natural disasters i.e earthquakes,
hurricanes and floods. Our results demonstrate that models trained on the
silver standard dataset achieved performance greater than 90% when classifying
a manually curated, gold-standard dataset. To enable reproducible research and
additional downstream utility, we release the silver standard dataset for the
scientific community.

We develop a multiscale approach to estimate high-dimensional probability
distributions from a dataset of physical fields or configurations observed in
experiments or simulations. In this way we can estimate energy functions (or
Hamiltonians) and efficiently generate new samples of many-body systems in
various domains, from statistical physics to cosmology. Our method -- the
Wavelet Conditional Renormalization Group (WC-RG) -- proceeds scale by scale,
estimating models for the conditional probabilities of "fast degrees of
freedom" conditioned by coarse-grained fields. These probability distributions
are modeled by energy functions associated with scale interactions, and are
represented in an orthogonal wavelet basis. WC-RG decomposes the microscopic
energy function as a sum of interaction energies at all scales and can
efficiently generate new samples by going from coarse to fine scales. Near
phase transitions, it avoids the "critical slowing down" of direct estimation
and sampling algorithms. This is explained theoretically by combining results
from RG and wavelet theories, and verified numerically for the Gaussian and
$\varphi^4$ field theories. We show that multiscale WC-RG energy-based models
are more general than local potential models and can capture the physics of
complex many-body interacting systems at all length scales. This is
demonstrated for weak-gravitational-lensing fields reflecting dark matter
distributions in cosmology, which include long-range interactions with
long-tail probability distributions. WC-RG has a large number of potential
applications in non-equilibrium systems, where the underlying distribution is
not known {\it a priori}. Finally, we discuss the connection between WC-RG and
deep network architectures.

Stochastic gradient descent (SGD), a widely used algorithm in deep-learning
neural networks has attracted continuing studies for the theoretical principles
behind its success. A recent work uncovered a generic inverse variance-flatness
(IVF) relation between the variance of neural weights and the landscape
flatness of loss function near solutions under SGD [Feng & Tu, PNAS 118,0027
(2021)]. To investigate this seemly violation of statistical principle, we
deploy a stochastic decomposition to analyze the dynamical properties of SGD.
The method constructs the true "energy" function which can be used by Boltzmann
distribution. The new energy differs from the usual cost function and explains
the IVF relation under SGD. We further verify the scaling relation identified
in Feng's work. Our approach may bridge the gap between the classical
statistical mechanics and the emerging discipline of artificial intelligence,
with potential for better algorithm to the latter.

The latest LHC collaborations results on $\sigma_{H^{\pm}}BR(H^{\pm}
\rightarrow \tau^{\pm}\nu)$ and $\sigma_{H^{\pm}}BR(H^{+} \rightarrow
t\bar{b})$ are used to impose constraints on the charged Higgs $H^{\pm}$
parameters within the Two Higgs Doublet Model (2HDM). But it leaves $1.5 \leq
tan \beta \leq 3$ window unexplored where the $BR(H^{\pm} \rightarrow
W^{\pm}h)$ becomes sizable for $m_{H^{\pm}} > m_{t}$. In this manuscript
$H^{\pm} \rightarrow W^{\pm}h$ is investigated with neutral Higgs boson
decaying to a pair of b-quarks and the discovery prospects of charged Higgs
boson is discussed. In particular, the analysis is optimized by putting the
kinematic cuts and prospects of using Machine Learning Technique to derive
values of $\sigma\times BR$ needed for a $5 \sigma$ discovery at the LHC.

The diffusion approximation of stochastic gradient descent (SGD) in current
literature is only valid on a finite time interval. In this paper, we establish
the uniform-in-time diffusion approximation of SGD, by only assuming that the
expected loss is strongly convex and some other mild conditions, without
assuming the convexity of each random loss function. The main technique is to
establish the exponential decay rates of the derivatives of the solution to the
backward Kolmogorov equation. The uniform-in-time approximation allows us to
study asymptotic behaviors of SGD via the continuous stochastic differential
equation (SDE) even when the random objective function $f(\cdot;\xi)$ is not
strongly convex.

This article presents the CERBERUS robotic system-of-systems, which won the
DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge
was organized by DARPA with the vision to facilitate the novel technologies
necessary to reliably explore diverse underground environments despite the
grueling challenges they present for robotic autonomy. Due to their geometric
complexity, degraded perceptual conditions combined with lack of GPS support,
austere navigation conditions, and denied communications, subterranean settings
render autonomous operations particularly demanding. In response to this
challenge, we developed the CERBERUS system which exploits the synergy of
legged and flying robots, coupled with robust control especially for overcoming
perilous terrain, multi-modal and multi-robot perception for localization and
mapping in conditions of sensor degradation, and resilient autonomy through
unified exploration path planning and local motion planning that reflects
robot-specific limitations. Based on its ability to explore diverse underground
environments and its high-level command and control by a single human
supervisor, CERBERUS demonstrated efficient exploration, reliable detection of
objects of interest, and accurate mapping. In this article, we report results
from both the preliminary runs and the final Prize Round of the DARPA
Subterranean Challenge, and discuss highlights and challenges faced, alongside
lessons learned for the benefit of the community.

Domain generalization aims at learning a universal model that performs well
on unseen target domains, incorporating knowledge from multiple source domains.
In this research, we consider the scenario where different domain shifts occur
among conditional distributions of different classes across domains. When
labeled samples in the source domains are limited, existing approaches are not
sufficiently robust. To address this problem, we propose a novel domain
generalization framework called Wasserstein Distributionally Robust Domain
Generalization (WDRDG), inspired by the concept of distributionally robust
optimization. We encourage robustness over conditional distributions within
class-specific Wasserstein uncertainty sets and optimize the worst-case
performance of a classifier over these uncertainty sets. We further develop a
test-time adaptation module leveraging optimal transport to quantify the
relationship between the unseen target domain and source domains to make
adaptive inference for target data. Experiments on the Rotated MNIST, PACS and
the VLCS datasets demonstrate that our method could effectively balance the
robustness and discriminability in challenging generalization scenarios.

Deep neural networks are known to be susceptible to adversarial perturbations
-- small perturbations that alter the output of the network and exist under
strict norm limitations. While such perturbations are usually discussed as
tailored to a specific input, a universal perturbation can be constructed to
alter the model's output on a set of inputs. Universal perturbations present a
more realistic case of adversarial attacks, as awareness of the model's exact
input is not required. In addition, the universal attack setting raises the
subject of generalization to unseen data, where given a set of inputs, the
universal perturbations aim to alter the model's output on out-of-sample data.
In this work, we study physical passive patch adversarial attacks on visual
odometry-based autonomous navigation systems. A visual odometry system aims to
infer the relative camera motion between two corresponding viewpoints, and is
frequently used by vision-based autonomous navigation systems to estimate their
state. For such navigation systems, a patch adversarial perturbation poses a
severe security issue, as it can be used to mislead a system onto some
collision course. To the best of our knowledge, we show for the first time that
the error margin of a visual odometry model can be significantly increased by
deploying patch adversarial attacks in the scene. We provide evaluation on
synthetic closed-loop drone navigation data and demonstrate that a comparable
vulnerability exists in real data. A reference implementation of the proposed
method and the reported experiments is provided at
https://github.com/patchadversarialattacks/patchadversarialattacks.

LiDAR sensors used in autonomous driving applications are negatively affected
by adverse weather conditions. One common, but understudied effect, is the
condensation of vehicle gas exhaust in cold weather. This everyday phenomenon
can severely impact the quality of LiDAR measurements, resulting in a less
accurate environment perception by creating artifacts like ghost object
detections. In the literature, the semantic segmentation of adverse weather
effects like rain and fog is achieved using learning-based approaches. However,
such methods require large sets of labeled data, which can be extremely
expensive and laborious to get. We address this problem by presenting a
two-step approach for the detection of condensed vehicle gas exhaust. First, we
identify for each vehicle in a scene its emission area and detect gas exhaust
if present. Then, isolated clouds are detected by modeling through time the
regions of space where gas exhaust is likely to be present. We test our method
on real urban data, showing that our approach can reliably detect gas exhaust
in different scenarios, making it appealing for offline pre-labeling and online
applications such as ghost object detection.

Multilingual neural machine translation (MNMT) trained in multiple language
pairs has attracted considerable attention due to fewer model parameters and
lower training costs by sharing knowledge among multiple languages.
Nonetheless, multilingual training is plagued by language interference
degeneration in shared parameters because of the negative interference among
different translation directions, especially on high-resource languages. In
this paper, we propose the multilingual translation model with the
high-resource language-specific training (HLT-MT) to alleviate the negative
interference, which adopts the two-stage training with the language-specific
selection mechanism. Specifically, we first train the multilingual model only
with the high-resource pairs and select the language-specific modules at the
top of the decoder to enhance the translation quality of high-resource
directions. Next, the model is further trained on all available corpora to
transfer knowledge from high-resource languages (HRLs) to low-resource
languages (LRLs). Experimental results show that HLT-MT outperforms various
strong baselines on WMT-10 and OPUS-100 benchmarks. Furthermore, the analytic
experiments validate the effectiveness of our method in mitigating the negative
interference in multilingual training.

Computer vision models for image quality assessment (IQA) predict the
subjective effect of generic image degradation, such as artefacts, blurs, bad
exposure, or colors. The scarcity of face images in existing IQA datasets
(below 10\%) is limiting the precision of IQA required for accurately filtering
low-quality face images or guiding CV models for face image processing, such as
super-resolution, image enhancement, and generation. In this paper, we first
introduce the largest annotated IQA database to date that contains 20,000 human
faces (an order of magnitude larger than all existing rated datasets of faces),
of diverse individuals, in highly varied circumstances, quality levels, and
distortion types. Based on the database, we further propose a novel deep
learning model, which re-purposes generative prior features for predicting
subjective face quality. By exploiting rich statistics encoded in well-trained
generative models, we obtain generative prior information of the images and
serve them as latent references to facilitate the blind IQA task. Experimental
results demonstrate the superior prediction accuracy of the proposed model on
the face IQA task.

The ability to extrapolate from short problem instances to longer ones is an
important form of out-of-distribution generalization in reasoning tasks, and is
crucial when learning from datasets where longer problem instances are rare.
These include theorem proving, solving quantitative mathematics problems, and
reading/summarizing novels. In this paper, we run careful empirical studies
exploring the length generalization capabilities of transformer-based language
models. We first establish that naively finetuning transformers on length
generalization tasks shows significant generalization deficiencies independent
of model scale. We then show that combining pretrained large language models'
in-context learning abilities with scratchpad prompting (asking the model to
output solution steps before producing an answer) results in a dramatic
improvement in length generalization. We run careful failure analyses on each
of the learning modalities and identify common sources of mistakes that
highlight opportunities in equipping language models with the ability to
generalize to longer problems.

Most translation tasks among languages belong to the zero-resource
translation problem where parallel corpora are unavailable. Multilingual neural
machine translation (MNMT) enables one-pass translation using shared semantic
space for all languages compared to the two-pass pivot translation but often
underperforms the pivot-based method. In this paper, we propose a novel method,
named as Unified Multilingual Multiple teacher-student Model for NMT (UM4). Our
method unifies source-teacher, target-teacher, and pivot-teacher models to
guide the student model for the zero-resource translation. The source teacher
and target teacher force the student to learn the direct source to target
translation by the distilled knowledge on both source and target sides. The
monolingual corpus is further leveraged by the pivot-teacher model to enhance
the student model. Experimental results demonstrate that our model of 72
directions significantly outperforms previous methods on the WMT benchmark.

In this work, we present a learning-based goal-tracking control method for
soft robot snakes. Inspired by biological snakes, our controller is composed of
two key modules: A reinforcement learning (RL) module for learning
goal-tracking behaviors given stochastic dynamics of the soft snake robot, and
a central pattern generator (CPG) system with the Matsuoka oscillators for
generating stable and diverse locomotion patterns. Based on the proposed
framework, we comprehensively discuss the maneuverability of the soft snake
robot, including steering and speed control during its serpentine locomotion.
Such maneuverability can be mapped into the control of oscillation patterns of
the CPG system. Through theoretical analysis of the oscillating properties of
the Matsuoka CPG system, this work shows that the key to realizing the free
mobility of our soft snake robot is to properly constrain and control certain
coefficients of the Matsuoka CPG system, including the tonic inputs and the
frequency ratio. Based on this analysis, we systematically formulate the
controllable coefficients of the CPG system for the RL agent to operate. With
experimental validation, we show that our control policy learned in the
simulated environment can be directly applied to control our real snake robot
to perform goal-tracking tasks, regardless of the physical environment gap
between simulation and the real world. The experiment results also show that
our method's adaptability and robustness to the sim-to-real transition are
significantly improved compared to our previous approach and a baseline RL
method (PPO).

Bottleneck problems are an important class of optimization problems that have
recently gained increasing attention in the domain of machine learning and
information theory. They are widely used in generative models, fair machine
learning algorithms, design of privacy-assuring mechanisms, and appear as
information-theoretic performance bounds in various multi-user communication
problems. In this work, we propose a general family of optimization problems,
termed as complexity-leakage-utility bottleneck (CLUB) model, which (i)
provides a unified theoretical framework that generalizes most of the
state-of-the-art literature for the information-theoretic privacy models, (ii)
establishes a new interpretation of the popular generative and discriminative
models, (iii) constructs new insights to the generative compression models, and
(iv) can be used in the fair generative models. We first formulate the CLUB
model as a complexity-constrained privacy-utility optimization problem. We then
connect it with the closely related bottleneck problems, namely information
bottleneck (IB), privacy funnel (PF), deterministic IB (DIB), conditional
entropy bottleneck (CEB), and conditional PF (CPF). We show that the CLUB model
generalizes all these problems as well as most other information-theoretic
privacy models. Then, we construct the deep variational CLUB (DVCLUB) models by
employing neural networks to parameterize variational approximations of the
associated information quantities. Building upon these information quantities,
we present unified objectives of the supervised and unsupervised DVCLUB models.
Leveraging the DVCLUB model in an unsupervised setup, we then connect it with
state-of-the-art generative models, such as variational auto-encoders (VAEs),
generative adversarial networks (GANs), as well as the Wasserstein GAN (WGAN),
Wasserstein auto-encoder (WAE), and adversarial auto-encoder (AAE) models
through the optimal transport (OT) problem. We then show that the DVCLUB model
can also be used in fair representation learning problems, where the goal is to
mitigate the undesired bias during the training phase of a machine learning
model. We conduct extensive quantitative experiments on colored-MNIST and
CelebA datasets, with a public implementation available, to evaluate and
analyze the CLUB model.

With the advent of big data era and the development of artificial
intelligence and other technologies, data security and privacy protection have
become more important. Recommendation systems have many applications in our
society, but the model construction of recommendation systems is often
inseparable from users' data. Especially for deep learning-based recommendation
systems, due to the complexity of the model and the characteristics of deep
learning itself, its training process not only requires long training time and
abundant computational resources but also needs to use a large amount of user
data, which poses a considerable challenge in terms of data security and
privacy protection. How to train a distributed recommendation system while
ensuring data security has become an urgent problem to be solved. In this
paper, we implement two schemes, Horizontal Federated Learning and Secure
Distributed Training, based on Intel SGX(Software Guard Extensions), an
implementation of a trusted execution environment, and TensorFlow framework, to
achieve secure, distributed recommendation system-based learning schemes in
different scenarios. We experiment on the classical Deep Learning
Recommendation Model (DLRM), which is a neural network-based machine learning
model designed for personalization and recommendation, and the results show
that our implementation introduces approximately no loss in model performance.
The training speed is within acceptable limits.

In this paper, we consider the problem of domain generalization in semantic
segmentation, which aims to learn a robust model using only labeled synthetic
(source) data. The model is expected to perform well on unseen real (target)
domains. Our study finds that the image style variation can largely influence
the model's performance and the style features can be well represented by the
channel-wise mean and standard deviation of images. Inspired by this, we
propose a novel adversarial style augmentation (AdvStyle) approach, which can
dynamically generate hard stylized images during training and thus can
effectively prevent the model from overfitting on the source domain.
Specifically, AdvStyle regards the style feature as a learnable parameter and
updates it by adversarial training. The learned adversarial style feature is
used to construct an adversarial image for robust model training. AdvStyle is
easy to implement and can be readily applied to different models. Experiments
on two synthetic-to-real semantic segmentation benchmarks demonstrate that
AdvStyle can significantly improve the model performance on unseen real domains
and show that we can achieve the state of the art. Moreover, AdvStyle can be
employed to domain generalized image classification and produces a clear
improvement on the considered datasets.

Machine Learning~(ML) has provided promising results in recent years across
different applications and domains. However, in many cases, qualities such as
reliability or even safety need to be ensured. To this end, one important
aspect is to determine whether or not ML components are deployed in situations
that are appropriate for their application scope. For components whose
environments are open and variable, for instance those found in autonomous
vehicles, it is therefore important to monitor their operational situation to
determine its distance from the ML components' trained scope. If that distance
is deemed too great, the application may choose to consider the ML component
outcome unreliable and switch to alternatives, e.g. using human operator input
instead. SafeML is a model-agnostic approach for performing such monitoring,
using distance measures based on statistical testing of the training and
operational datasets. Limitations in setting SafeML up properly include the
lack of a systematic approach for determining, for a given application, how
many operational samples are needed to yield reliable distance information as
well as to determine an appropriate distance threshold. In this work, we
address these limitations by providing a practical approach and demonstrate its
use in a well known traffic sign recognition problem, and on an example using
the CARLA open-source automotive simulator.

Owen and Hoyt recently showed that the effective dimension offers key
structural information about the input-output mapping underlying an artificial
neural network. Along this line of research, this work proposes an estimation
procedure that allows the calculation of the mean dimension from a given
dataset, without resampling from external distributions. The design yields
total indices when features are independent and a variant of total indices when
features are correlated. We show that this variant possesses the zero
independence property. With synthetic datasets, we analyse how the mean
dimension evolves layer by layer and how the activation function impacts the
magnitude of interactions. We then use the mean dimension to study some of the
most widely employed convolutional architectures for image recognition (LeNet,
ResNet, DenseNet). To account for pixel correlations, we propose calculating
the mean dimension after the addition of an inverse PCA layer that allows one
to work on uncorrelated PCA-transformed features, without the need to retrain
the neural network. We use the generalized total indices to produce heatmaps
for post-hoc explanations, and we employ the mean dimension on the
PCA-transformed features for cross comparisons of the artificial neural
networks structures. Results provide several insights on the difference in
magnitude of interactions across the architectures, as well as indications on
how the mean dimension evolves during training.

Video-text retrieval is a class of cross-modal representation learning
problems, where the goal is to select the video which corresponds to the text
query between a given text query and a pool of candidate videos. The
contrastive paradigm of vision-language pretraining has shown promising success
with large-scale datasets and unified transformer architecture, and
demonstrated the power of a joint latent space. Despite this, the intrinsic
divergence between the visual domain and textual domain is still far from being
eliminated, and projecting different modalities into a joint latent space might
result in the distorting of the information inside the single modality. To
overcome the above issue, we present a novel mechanism for learning the
translation relationship from a source modality space $\mathcal{S}$ to a target
modality space $\mathcal{T}$ without the need for a joint latent space, which
bridges the gap between visual and textual domains. Furthermore, to keep cycle
consistency between translations, we adopt a cycle loss involving both forward
translations from $\mathcal{S}$ to the predicted target space $\mathcal{T'}$,
and backward translations from $\mathcal{T'}$ back to $\mathcal{S}$. Extensive
experiments conducted on MSR-VTT, MSVD, and DiDeMo datasets demonstrate the
superiority and effectiveness of our LaT approach compared with vanilla

The internet-of-Vehicle (IoV) can facilitate seamless connectivity between
connected vehicles (CV), autonomous vehicles (AV), and other IoV entities.
Intrusion Detection Systems (IDSs) for IoV networks can rely on machine
learning (ML) to protect the in-vehicle network from cyber-attacks.
Blockchain-based Federated Forests (BFFs) could be used to train ML models
based on data from IoV entities while protecting the confidentiality of the
data and reducing the risks of tampering with the data. However, ML models
created this way are still vulnerable to evasion, poisoning, and exploratory
attacks using adversarial examples. This paper investigates the impact of
various possible adversarial examples on the BFF-IDS. We proposed integrating a
statistical detector to detect and extract unknown adversarial samples. By
including the unknown detected samples into the dataset of the detector, we
augment the BFF-IDS with an additional model to detect original known attacks
and the new adversarial inputs. The statistical adversarial detector
confidently detected adversarial examples at the sample size of 50 and 100
input samples. Furthermore, the augmented BFF-IDS (BFF-IDS(AUG)) successfully
mitigates the adversarial examples with more than 96% accuracy. With this
approach, the model will continue to be augmented in a sandbox whenever an
adversarial sample is detected and subsequently adopt the BFF-IDS(AUG) as the
active security model. Consequently, the proposed integration of the
statistical adversarial detector and the subsequent augmentation of the BFF-IDS
with detected adversarial samples provides a sustainable security framework
against adversarial examples and other unknown attacks.

Speech synthesis for poetry is challenging due to specific intonation
patterns inherent to poetic speech. In this work, we propose an approach to
synthesise poems with almost human like naturalness in order to enable literary
scholars to systematically examine hypotheses on the interplay between text,
spoken realisation, and the listener's perception of poems. To meet these
special requirements for literary studies, we resynthesise poems by cloning
prosodic values from a human reference recitation, and afterwards make use of
fine-grained prosody control to manipulate the synthetic speech in a
human-in-the-loop setting to alter the recitation w.r.t. specific phenomena. We
find that finetuning our TTS model on poetry captures poetic intonation
patterns to a large extent which is beneficial for prosody cloning and
manipulation and verify the success of our approach both in an objective
evaluation as well as in human studies.

This paper describes the RRMSE (Relative Root Mean Square Error) based
weights to weight the occurrences of predictive values before averaging for the
ensemble voting regression. The core idea behind ensemble regression is to
combine several base regression models in order to improve the prediction
performance in learning problems with a numeric continuous target variable. The
default weights setting for the ensemble voting regression is uniform weights,
and without domain knowledge of learning task, assigning weights for
predictions are impossible, which makes it very difficult to improve the
predictions. This work attempts to improve the prediction of voting regression
by implementing the RRMSE based weighting function. Experiments show that RRMSE
voting regressor produces significantly better predictions than other
state-of-the-art ensemble regression algorithms on six popular regression
learning datasets.

Massive vector field datasets are common in multi-spectral optical and radar
sensors, among many other emerging areas of application. In this paper we
develop a novel stochastic functional (data) analysis approach for detecting
anomalies based on the covariance structure of nominal stochastic behavior
across a domain. An optimal vector field Karhunen-Loeve expansion is applied to
such random field data. A series of multilevel orthogonal functional subspaces
is constructed from the geometry of the domain, adapted from the KL expansion.
Detection is achieved by examining the projection of the random field on the
multilevel basis. In addition, reliable hypothesis tests are formed that do not
require prior assumptions on probability distributions of the data. The method
is applied to the important problem of deforestation and degradation in the
Amazon forest. This is a complex non-monotonic process, as forests can degrade
and recover. Using multi-spectral satellite data from Sentinel-2, the
multilevel filter is constructed and anomalies are treated as deviations from
the initial state of the forest. Forest anomalies are quantified with robust
hypothesis tests. Our approach shows the advantage of using multiple bands of
data in a vectorized complex, leading to better anomaly detection beyond the
capabilities of scalar-based methods.

In this work, we propose a speaker anonymization pipeline that leverages high
quality automatic speech recognition and synthesis systems to generate speech
conditioned on phonetic transcriptions and anonymized speaker embeddings. Using
phones as the intermediate representation ensures near complete elimination of
speaker identity information from the input while preserving the original
phonetic content as much as possible. Our experimental results on LibriSpeech
and VCTK corpora reveal two key findings: 1) although automatic speech
recognition produces imperfect transcriptions, our neural speech synthesis
system can handle such errors, making our system feasible and robust, and 2)
combining speaker embeddings from different resources is beneficial and their
appropriate normalization is crucial. Overall, our final best system
outperforms significantly the baselines provided in the Voice Privacy Challenge
2020 in terms of privacy robustness against a lazy-informed attacker while
maintaining high intelligibility and naturalness of the anonymized speech.

Recordings of complex neural population responses provide a unique
opportunity for advancing our understanding of neural information processing at
multiple scales and improving performance of brain computer interfaces.
However, most existing analytical techniques fall short of capturing the
complexity of interactions within the concerted population activity. Vine
copula-based approaches have shown to be successful at addressing complex
high-order dependencies within the population, disentangled from the
single-neuron statistics. However, most applications have focused on parametric
copulas which bear the risk of misspecifying dependence structures. In order to
avoid this risk, we adopted a fully non-parametric approach for the
single-neuron margins and copulas by using Neural Spline Flows (NSF). We
validated the NSF framework on simulated data of continuous and discrete type
with various forms of dependency structures and with different dimensionality.
Overall, NSFs performed similarly to existing non-parametric estimators, while
allowing for considerably faster and more flexible sampling which also enables
faster Monte Carlo estimation of copula entropy. Moreover, our framework was
able to capture low and higher order heavy tail dependencies in neuronal
responses recorded in the mouse primary visual cortex during a visual learning
task while the animal was navigating a virtual reality environment. These
findings highlight an often ignored aspect of complexity in coordinated
neuronal activity which can be important for understanding and deciphering
collective neural dynamics for neurotechnological applications.

Drawing from memory the face of a friend you have not seen in years is a
difficult task. However, if you happen to cross paths, you would easily
recognize each other. The biological memory is equipped with an impressive
compression algorithm that can store the essential, and then infer the details
to match perception. Willshaw's model of Associative memory is a likely
candidate for a computational model of this brain function, but its application
on real-world data is hindered by the so-called Sparse Coding Problem. Due to a
recently proposed sparse encoding prescription [31], which maps visual patterns
into binary feature maps, we were able to analyze the behavior of the Willshaw
Network (WN) on real-world data and gain key insights into the strengths of the
model. To further enhance the capabilities of the WN, we propose the
Multiple-Modality architecture. In this new setting, the memory stores several
modalities (e.g., visual, or textual) simultaneously. After training, the model
can be used to infer missing modalities when just a subset is perceived, thus
serving as a flexible framework for learning tasks. We evaluated the model on
the MNIST dataset. By storing both the images and labels as modalities, we were
able to successfully perform pattern completion, classification, and generation

Behavioral models enable the analysis of the functionality of software
product lines (SPL), e.g., model checking and model-based testing. Model
learning aims at constructing behavioral models for software systems in some
form of a finite state machine. Due to the commonalities among the products of
an SPL, it is possible to reuse the previously learned models during the model
learning process. In this paper, an adaptive approach (the $\text{PL}^*$
method) for learning the product models of an SPL is presented based on the
well-known $L^*$ algorithm. In this method, after model learning of each
product, the sequences in the final observation table are stored in a
repository which will be used to initialize the observation table of the
remaining products to be learned. The proposed algorithm is evaluated on two
open-source SPLs and the total learning cost is measured in terms of the number
of rounds, the total number of resets and input symbols. The results show that
for complex SPLs, the total learning cost for the $\text{PL}^*$ method is
significantly lower than that of the non-adaptive learning method in terms of
all three metrics. Furthermore, it is observed that the order in which the
products are learned affects the efficiency of the $\text{PL}^*$ method. Based
on this observation, we introduced a heuristic to determine an ordering which
reduces the total cost of adaptive learning in both case studies.

We investigate adaptive design based on a single sparse pilot scan for
generating effective scanning strategies for computed tomography
reconstruction. We propose a novel approach using the linearised deep image
prior. It allows incorporating information from the pilot measurements into the
angle selection criteria, while maintaining the tractability of a conjugate
Gaussian-linear model. On a synthetically generated dataset with preferential
directions, linearised DIP design allows reducing the number of scans by up to
30% relative to an equidistant angle baseline.

It is a sad reflection of modern academia that code is often ignored after
publication -- there is no academic 'kudos' for bug fixes / maintenance. Code
is often unavailable or, if available, contains bugs, is incomplete, or relies
on out-of-date / unavailable libraries. This has a significant impact on
reproducibility and general scientific progress. Neural Architecture Search
(NAS) is no exception to this, with some prior work in reproducibility.
However, we argue that these do not consider long-term reproducibility issues.
We therefore propose a checklist for long-term NAS reproducibility. We evaluate
our checklist against common NAS approaches along with proposing how we can
retrospectively make these approaches more long-term reproducible.

Radiology report generation (RRG) aims to describe automatically a radiology
image with human-like language and could potentially support the work of
radiologists, reducing the burden of manual reporting. Previous approaches
often adopt an encoder-decoder architecture and focus on single-modal feature
learning, while few studies explore cross-modal feature interaction. Here we
propose a Cross-modal PROtotype driven NETwork (XPRONET) to promote cross-modal
pattern learning and exploit it to improve the task of radiology report
generation. This is achieved by three well-designed, fully differentiable and
complementary modules: a shared cross-modal prototype matrix to record the
cross-modal prototypes; a cross-modal prototype network to learn the
cross-modal prototypes and embed the cross-modal information into the visual
and textual features; and an improved multi-label contrastive loss to enable
and enhance multi-label prototype learning. XPRONET obtains substantial
improvements on the IU-Xray and MIMIC-CXR benchmarks, where its performance
exceeds recent state-of-the-art approaches by a large margin on IU-Xray and
comparable performance on MIMIC-CXR.

Deep learning-based approaches for content-based image retrieval (CBIR) of CT
liver images is an active field of research, but suffers from some critical
limitations. First, they are heavily reliant on labeled data, which can be
challenging and costly to acquire. Second, they lack transparency and
explainability, which limits the trustworthiness of deep CBIR systems. We
address these limitations by (1) proposing a self-supervised learning framework
that incorporates domain-knowledge into the training procedure and (2)
providing the first representation learning explainability analysis in the
context of CBIR of CT liver images. Results demonstrate improved performance
compared to the standard self-supervised approach across several metrics, as
well as improved generalisation across datasets. Further, we conduct the first
representation learning explainability analysis in the context of CBIR, which
reveals new insights into the feature extraction process. Lastly, we perform a
case study with cross-examination CBIR that demonstrates the usability of our
proposed framework. We believe that our proposed framework could play a vital
role in creating trustworthy deep CBIR systems that can successfully take
advantage of unlabeled data.

Prediction failures of machine learning models often arise from deficiencies
in training data, such as incorrect labels, outliers, and selection biases.
However, such data points that are responsible for a given failure mode are
generally not known a priori, let alone a mechanism for repairing the failure.
This work draws on the Bayesian view of continual learning, and develops a
generic framework for both, identifying training examples that have given rise
to the target failure, and fixing the model through erasing information about
them. This framework naturally allows leveraging recent advances in continual
learning to this new problem of model repairment, while subsuming the existing
works on influence functions and data deletion as specific instances.
Experimentally, the proposed approach outperforms the baselines for both
identification of detrimental training data and fixing model failures in a
generalisable manner.

Entity Matching (EM), which aims to identify whether two entity records from
two relational tables refer to the same real-world entity, is one of the
fundamental problems in data management. Traditional EM assumes that two tables
are homogeneous with the aligned schema, while it is common that entity records
of different formats (e.g., relational, semi-structured, or textual types)
involve in practical scenarios. It is not practical to unify their schemas due
to the different formats. To support EM on format-different entity records,
Generalized Entity Matching (GEM) has been proposed and gained much attention
recently. To do GEM, existing methods typically perform in a supervised
learning way, which relies on a large amount of high-quality labeled examples.
However, the labeling process is extremely labor-intensive, and frustrates the
use of GEM. Low-resource GEM, i.e., GEM that only requires a small number of
labeled examples, becomes an urgent need. To this end, this paper, for the
first time, focuses on the low-resource GEM and proposes a novel low-resource
GEM method, termed as PromptEM. PromptEM has addressed three challenging issues
(i.e., designing GEM-specific prompt-tuning, improving pseudo-labels quality,
and running efficient self-training) in low-resource GEM. Extensive
experimental results on eight real benchmarks demonstrate the superiority of
PromptEM in terms of effectiveness and efficiency.

Image color harmonization algorithm aims to automatically match the color
distribution of foreground and background images captured in different
conditions. Previous deep learning based models neglect two issues that are
critical for practical applications, namely high resolution (HR) image
processing and model comprehensibility. In this paper, we propose a novel Deep
Comprehensible Color Filter (DCCF) learning framework for high-resolution image
harmonization. Specifically, DCCF first downsamples the original input image to
its low-resolution (LR) counter-part, then learns four human comprehensible
neural filters (i.e. hue, saturation, value and attentive rendering filters) in
an end-to-end manner, finally applies these filters to the original input image
to get the harmonized result. Benefiting from the comprehensible neural
filters, we could provide a simple yet efficient handler for users to cooperate
with deep model to get the desired results with very little effort when
necessary. Extensive experiments demonstrate the effectiveness of DCCF learning
framework and it outperforms state-of-the-art post-processing method on
iHarmony4 dataset on images' full-resolutions by achieving 7.63% and 1.69%
relative improvements on MSE and PSNR respectively.

Currently deployed public-key cryptosystems will be vulnerable to attacks by
full-scale quantum computers. Consequently, "quantum resistant" cryptosystems
are in high demand, and lattice-based cryptosystems, based on a hard problem
known as Learning With Errors (LWE), have emerged as strong contenders for
standardization. In this work, we train transformers to perform modular
arithmetic and combine half-trained models with statistical cryptanalysis
techniques to propose SALSA: a machine learning attack on LWE-based
cryptographic schemes. SALSA can fully recover secrets for small-to-mid size
LWE instances with sparse binary secrets, and may scale to attack real-world
LWE-based cryptosystems.

As the number of authors is increasing exponentially over years, the number
of authors sharing the same names is increasing proportionally. This makes it
challenging to assign newly published papers to their adequate authors.
Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in
digital libraries. This paper proposes an Author Name Disambiguation (AND)
approach that links author names to their real-world entities by leveraging
their co-authors and domain of research. To this end, we use a collection from
the DBLP repository that contains more than 5 million bibliographic records
authored by around 2.6 million co-authors. Our approach first groups authors
who share the same last names and same first name initials. The author within
each group is identified by capturing the relation with his/her co-authors and
area of research, which is represented by the titles of the validated
publications of the corresponding author. To this end, we train a neural
network model that learns from the representations of the co-authors and
titles. We validated the effectiveness of our approach by conducting extensive
experiments on a large dataset.

Important problems in causal inference, economics, and, more generally,
robust machine learning can be expressed as conditional moment restrictions,
but estimation becomes challenging as it requires solving a continuum of
unconditional moment restrictions. Previous works addressed this problem by
extending the generalized method of moments (GMM) to continuum moment
restrictions. In contrast, generalized empirical likelihood (GEL) provides a
more general framework and has been shown to enjoy favorable small-sample
properties compared to GMM-based estimators. To benefit from recent
developments in machine learning, we provide a functional reformulation of GEL
in which arbitrary models can be leveraged. Motivated by a dual formulation of
the resulting infinite dimensional optimization problem, we devise a practical
method and explore its asymptotic properties. Finally, we provide kernel- and
neural network-based implementations of the estimator, which achieve
state-of-the-art empirical performance on two conditional moment restriction
problems.

Single-image human relighting aims to relight a target human under new
lighting conditions by decomposing the input image into albedo, shape and
lighting. Although plausible relighting results can be achieved, previous
methods suffer from both the entanglement between albedo and lighting and the
lack of hard shadows, which significantly decrease the realism. To tackle these
two problems, we propose a geometry-aware single-image human relighting
framework that leverages single-image geometry reconstruction for joint
deployment of traditional graphics rendering and neural rendering techniques.
For the de-lighting, we explore the shortcomings of UNet architecture and
propose a modified HRNet, achieving better disentanglement between albedo and
lighting. For the relighting, we introduce a ray tracing-based per-pixel
lighting representation that explicitly models high-frequency shadows and
propose a learning-based shading refinement module to restore realistic shadows
(including hard cast shadows) from the ray-traced shading maps. Our framework
is able to generate photo-realistic high-frequency shadows such as cast shadows
under challenging lighting conditions. Extensive experiments demonstrate that
our proposed method outperforms previous methods on both synthetic and real
images.

All scientific claims of gravitational wave discovery to date rely on the
offline statistical analysis of candidate observations in order to quantify
significance relative to background processes. The current foundation in such
offline detection pipelines in experiments at LIGO is the matched-filter
algorithm, which produces a signal-to-noise-ratio-based statistic for ranking
candidate observations. Existing deep-learning-based attempts to detect
gravitational waves, which have shown promise in both signal sensitivity and
computational efficiency, output probability scores. However, probability
scores are not easily integrated into discovery workflows, limiting the use of
deep learning thus far to non-discovery-oriented applications. In this paper,
the Deep Learning Signal-to-Noise Ratio (DeepSNR) detection pipeline, which
uses a novel method for generating a signal-to-noise ratio ranking statistic
from deep learning classifiers, is introduced, providing the first foundation
for the use of deep learning algorithms in discovery-oriented pipelines. The
performance of DeepSNR is demonstrated by identifying binary black hole merger
candidates versus noise sources in open LIGO data from the first observation
run. High-fidelity simulations of the LIGO detector responses are used to
present the first sensitivity estimates of deep learning models in terms of
physical observables. The robustness of DeepSNR under various experimental
considerations is also investigated. The results pave the way for DeepSNR to be
used in the scientific discovery of gravitational waves and rare signals in
broader contexts, potentially enabling the detection of fainter signals and
never-before-observed phenomena.

Despite the progress observed with model-agnostic explainable AI (XAI), it is
the case that model-agnostic XAI can produce incorrect explanations. One
alternative are the so-called formal approaches to XAI, that include
PI-explanations. Unfortunately, PI-explanations also exhibit important
drawbacks, the most visible of which is arguably their size. The computation of
relevant features serves to trade off probabilistic precision for the number of
features in an explanation. However, even for very simple classifiers, the
complexity of computing sets of relevant features is prohibitive. This paper
investigates the computation of relevant sets for Naive Bayes Classifiers
(NBCs), and shows that, in practice, these are easy to compute. Furthermore,
the experiments confirm that succinct sets of relevant features can be obtained
with NBCs.

This paper looks at the task of network topology inference, where the goal is
to learn an unknown graph from nodal observations. One of the novelties of the
approach put forth is the consideration of prior information about the density
of motifs of the unknown graph to enhance the inference of classical Gaussian
graphical models. Dealing with the density of motifs directly constitutes a
challenging combinatorial task. However, we note that if two graphs have
similar motif densities, one can show that the expected value of a polynomial
applied to their empirical spectral distributions will be similar. Guided by
this, we first assume that we have a reference graph that is related to the
sought graph (in the sense of having similar motif densities) and then, we
exploit this relation by incorporating a similarity constraint and a
regularization term in the network topology inference optimization problem. The
(non-)convexity of the optimization problem is discussed and a computational
efficient alternating majorization-minimization algorithm is designed. We
assess the performance of the proposed method through exhaustive numerical
experiments where different constraints are considered and compared against
popular baselines algorithms on both synthetic and real-world datasets.

The latest $\textit{Fermi}$-LAT gamma-ray catalog, 4FGL-DR3, presents a large
fraction of sources without clear association to known counterparts, i.e.,
unidentified sources (unIDs). In this paper, we aim to classify them using
machine learning algorithms, which are trained with the spectral
characteristics of associated sources to predict the class of the unID
population. With the state-of-the-art $\texttt{CatBoost}$ algorithm, based on
gradient boosting decision trees, we are able to reach a 67% accuracy on a
23-class dataset. Removing a single of these classes -- blazars of uncertain
type -- increases the accuracy to 81%. If interested only in a binary
AGN/pulsar distinction, the model accuracy is boosted up to 99%. Additionally,
we perform an unsupervised search among both known and unID population, and try
to predict the number of clusters of similar sources, without prior knowledge
of their classes. The full code used to perform all calculations is provided as
an interactive Python notebook.

Observation is an essential tool for understanding and studying human
behavior and mental states. However, coding human behavior is a time-consuming,
expensive task, in which reliability can be difficult to achieve and bias is a
risk. Machine learning (ML) methods offer ways to improve reliability, decrease
cost, and scale up behavioral coding for application in clinical and research
settings. Here, we use computer vision to derive behavioral codes or concepts
of a gold standard behavioral rating system, offering familiar interpretation
for mental health professionals. Features were extracted from videos of
clinical diagnostic interviews of children and adolescents with and without
obsessive-compulsive disorder. Our computationally-derived ratings were
comparable to human expert ratings for negative emotions,
activity-level/arousal and anxiety. For the attention and positive affect
concepts, our ML ratings performed reasonably. However, results for gaze and
vocalization indicate a need for improved data quality or additional data
modalities.

In this work we introduce a biologically inspired long-range skip connection
for the UNet architecture that relies on the perceptual illusion of hybrid
images, being images that simultaneously encode two images. The fusion of early
encoder features with deeper decoder ones allows UNet models to produce
finer-grained dense predictions. While proven in segmentation tasks, the
network's benefits are down-weighted for dense regression tasks as these
long-range skip connections additionally result in texture transfer artifacts.
Specifically for depth estimation, this hurts smoothness and introduces false
positive edges which are detrimental to the task due to the depth maps'
piece-wise smooth nature. The proposed HybridSkip connections show improved
performance in balancing the trade-off between edge preservation, and the
minimization of texture transfer artifacts that hurt smoothness. This is
achieved by the proper and balanced exchange of information that Hybrid-Skip
connections offer between the high and low frequency, encoder and decoder
features, respectively.

Deep learning has substantially boosted the performance of Monocular Depth
Estimation (MDE), a critical component in fully vision-based autonomous driving
(AD) systems (e.g., Tesla and Toyota). In this work, we develop an attack
against learning-based MDE. In particular, we use an optimization-based method
to systematically generate stealthy physical-object-oriented adversarial
patches to attack depth estimation. We balance the stealth and effectiveness of
our attack with object-oriented adversarial design, sensitive region
localization, and natural style camouflage. Using real-world driving scenarios,
we evaluate our attack on concurrent MDE models and a representative downstream
task for AD (i.e., 3D object detection). Experimental results show that our
method can generate stealthy, effective, and robust adversarial patches for
different target objects and models and achieves more than 6 meters mean depth
estimation error and 93% attack success rate (ASR) in object detection with a
patch of 1/9 of the vehicle's rear area. Field tests on three different driving
routes with a real vehicle indicate that we cause over 6 meters mean depth
estimation error and reduce the object detection rate from 90.70% to 5.16% in
continuous video frames.

Continuous Normalizing Flows (CNFs) are a class of generative models that
transform a prior distribution to a model distribution by solving an ordinary
differential equation (ODE). We propose to train CNFs on manifolds by
minimizing probability path divergence (PPD), a novel family of divergences
between the probability density path generated by the CNF and a target
probability density path. PPD is formulated using a logarithmic mass
conservation formula which is a linear first order partial differential
equation relating the log target probabilities and the CNF's defining vector
field. PPD has several key benefits over existing methods: it sidesteps the
need to solve an ODE per iteration, readily applies to manifold data, scales to
high dimensions, and is compatible with a large family of target paths
interpolating pure noise and data in finite time. Theoretically, PPD is shown
to bound classical probability divergences. Empirically, we show that CNFs
learned by minimizing PPD achieve state-of-the-art results in likelihoods and
sample quality on existing low-dimensional manifold benchmarks, and is the
first example of a generative model to scale to moderately high dimensional
manifolds.

Ridesharing has received global popularity due to its convenience and cost
efficiency for both drivers and passengers and its strong potential to
contribute to the implementation of the UN Sustainable Development Goals. As a
result, recent years have witnessed an explosion of research interest in the
RSODP (Origin-Destination Prediction for Ridesharing) problem with the goal of
predicting the future ridesharing requests and providing schedules for vehicles
ahead of time. Most of the existing prediction models utilise Deep Learning.
However, they fail to effectively consider both spatial and temporal dynamics.
In this paper the Baselined Gated Attention Recurrent Network (BGARN), is
proposed, which uses graph convolution with multi-head gated attention to
extract spatial features, a recurrent module to extract temporal features, and
a baselined transferring layer to calculate the final results. The model is
implemented with PyTorch and DGL (Deep Graph Library) and is experimentally
evaluated using the New York Taxi Demand Dataset. The results show that BGARN
outperforms all the other existing models in terms of prediction accuracy.

Reinforcement learning (RL) algorithms hold the promise of enabling
autonomous skill acquisition for robotic systems. However, in practice,
real-world robotic RL typically requires time consuming data collection and
frequent human intervention to reset the environment. Moreover, robotic
policies learned with RL often fail when deployed beyond the carefully
controlled setting in which they were learned. In this work, we study how these
challenges can all be tackled by effective utilization of diverse offline
datasets collected from previously seen tasks. When faced with a new task, our
system adapts previously learned skills to quickly learn to both perform the
new task and return the environment to an initial state, effectively performing
its own environment reset. Our empirical results demonstrate that incorporating
prior data into robotic reinforcement learning enables autonomous learning,
substantially improves sample-efficiency of learning, and enables better
generalization. Project website: https://sites.google.com/view/ariel-berkeley/

The research and applications of multimodal emotion recognition have become
increasingly popular recently. However, multimodal emotion recognition faces
the challenge of lack of data. To solve this problem, we propose to use
transfer learning which leverages state-of-the-art pre-trained models including
wav2vec 2.0 and BERT for this task. Multi-level fusion approaches including
coattention-based early fusion and late fusion with the models trained on both
embeddings are explored. Also, a multi-granularity framework which extracts not
only frame-level speech embeddings but also segment-level embeddings including
phone, syllable and word-level speech embeddings is proposed to further boost
the performance. By combining our coattention-based early fusion model and late
fusion model with the multi-granularity feature extraction framework, we obtain
result that outperforms best baseline approaches by 1.3% unweighted accuracy
(UA) on the IEMOCAP dataset.

As the demand for highly secure and dependable lightweight systems increases
in the modern world, Physically Unclonable Functions (PUFs) continue to promise
a lightweight alternative to high-cost encryption techniques and secure key
storage. While the security features promised by PUFs are highly attractive for
secure system designers, they have been shown to be vulnerable to various
sophisticated attacks - most notably Machine Learning (ML) based modelling
attacks (ML-MA) which attempt to digitally clone the PUF behaviour and thus
undermine their security. More recent ML-MA have even exploited publicly known
helper data required for PUF error correction in order to predict PUF responses
without requiring knowledge of response data. In response to this, research is
beginning to emerge regarding the authentication of PUF devices with the
assistance of ML as opposed to traditional PUF techniques of storage and
comparison of pre-known Challenge-Response pairs (CRPs). In this article, we
propose a classification system using ML based on a novel `PUF-Phenotype'
concept to accurately identify the origin and determine the validity of noisy
memory derived (DRAM) PUF responses as an alternative to helper data-reliant
denoising techniques. To our best knowledge, we are the first to perform
classification over multiple devices per model to enable a group-based PUF
authentication scheme. We achieve up to 98\% classification accuracy using a
modified deep convolutional neural network (CNN) for feature extraction in
conjunction with several well-established classifiers. We also experimentally
verified the performance of our model on a Raspberry Pi device to determine the
suitability of deploying our proposed model in a resource-constrained

Throttling is one of the most popular budget control methods in today's
online advertising markets. When a budget-constrained advertiser employs
throttling, she can choose whether or not to participate in an auction after
the advertising platform recommends a bid. This paper focuses on the dynamic
budget throttling process in repeated second-price auctions from a theoretical
view. An essential feature of the underlying problem is that the advertiser
does not know the distribution of the highest competing bid upon entering the
market. To model the difficulty of eliminating such uncertainty, we consider
two different information structures. The advertiser could obtain the highest
competing bid in each round with full-information feedback. Meanwhile, with
partial information feedback, the advertiser could only have access to the
highest competing bid in the auctions she participates in. We propose the
OGD-CB algorithm, which involves simultaneous distribution learning and revenue
optimization. In both settings, we demonstrate that this algorithm guarantees
an $O(\sqrt{T\log T})$ regret with probability $1 - O(1/T)$ relative to the
fluid adaptive throttling benchmark. By proving a lower bound of
$\Omega(\sqrt{T})$ on the minimal regret for even the hindsight optimum, we
establish the near optimality of our algorithm. Finally, we compare the fluid
optimum of throttling to that of pacing, another widely adopted budget control
method. The numerical relationship of these benchmarks sheds new light on the
understanding of different online algorithms for revenue maximization under
budget constraints.

We study the problem of differentially private linear regression where each
data point is sampled from a fixed sub-Gaussian style distribution. We propose
and analyze a one-pass mini-batch stochastic gradient descent method
(DP-AMBSSGD) where points in each iteration are sampled without replacement.
Noise is added for DP but the noise standard deviation is estimated online.
Compared to existing $(\epsilon, \delta)$-DP techniques which have sub-optimal
error bounds, DP-AMBSSGD is able to provide nearly optimal error bounds in
terms of key parameters like dimensionality $d$, number of points $N$, and the
standard deviation $\sigma$ of the noise in observations. For example, when the
$d$-dimensional covariates are sampled i.i.d. from the normal distribution,
then the excess error of DP-AMBSSGD due to privacy is $\frac{\sigma^2
d}{N}(1+\frac{d}{\epsilon^2 N})$, i.e., the error is meaningful when number of
samples $N= \Omega(d \log d)$ which is the standard operative regime for linear
regression. In contrast, error bounds for existing efficient methods in this
setting are: $\mathcal{O}\big(\frac{d^3}{\epsilon^2 N^2}\big)$, even for
$\sigma=0$. That is, for constant $\epsilon$, the existing techniques require
$N=\Omega(d\sqrt{d})$ to provide a non-trivial result.

Storing information in DNA molecules is of great interest because of its
advantages in longevity, high storage density, and low maintenance cost. A key
step in the DNA storage pipeline is to efficiently cluster the retrieved DNA
sequences according to their similarities. Levenshtein distance is the most
suitable metric on the similarity between two DNA sequences, but it is inferior
in terms of computational complexity and less compatible with mature clustering
algorithms. In this work, we propose a novel deep squared Euclidean embedding
for DNA sequences using Siamese neural network, squared Euclidean embedding,
and chi-squared regression. The Levenshtein distance is approximated by the
squared Euclidean distance between the embedding vectors, which is fast
calculated and clustering algorithm friendly. The proposed approach is analyzed
theoretically and experimentally. The results show that the proposed embedding
is efficient and robust.

Unsupervised monocular depth and ego-motion estimation has drawn extensive
research attention in recent years. Although current methods have reached a
high up-to-scale accuracy, they usually fail to learn the true scale metric due
to the inherent scale ambiguity from training with monocular sequences. In this
work, we tackle this problem and propose DynaDepth, a novel scale-aware
framework that integrates information from vision and IMU motion dynamics.
Specifically, we first propose an IMU photometric loss and a cross-sensor
photometric consistency loss to provide dense supervision and absolute scales.
To fully exploit the complementary information from both sensors, we further
drive a differentiable camera-centric extended Kalman filter (EKF) to update
the IMU preintegrated motions when observing visual measurements. In addition,
the EKF formulation enables learning an ego-motion uncertainty measure, which
is non-trivial for unsupervised methods. By leveraging IMU during training,
DynaDepth not only learns an absolute scale, but also provides a better
generalization ability and robustness against vision degradation such as
illumination change and moving objects. We validate the effectiveness of
DynaDepth by conducting extensive experiments and simulations on the KITTI and
Make3D datasets.

Research community has witnessed substantial growth in the detection of
mental health issues and their associated reasons from analysis of social
media. We introduce a new dataset for Causal Analysis of Mental health issues
in Social media posts (CAMS). Our contributions for causal analysis are
two-fold: causal interpretation and causal categorization. We introduce an
annotation schema for this task of causal analysis. We demonstrate the efficacy
of our schema on two different datasets: (i) crawling and annotating 3155
Reddit posts and (ii) re-annotating the publicly available SDCNL dataset of
1896 instances for interpretable causal analysis. We further combine these into
the CAMS dataset and make this resource publicly available along with
associated source code: https://github.com/drmuskangarg/CAMS. We present
experimental results of models learned from CAMS dataset and demonstrate that a
classic Logistic Regression model outperforms the next best (CNN-LSTM) model by
4.9\% accuracy.

LiDAR-based 3D scene perception is a fundamental and important task for
autonomous driving. Most state-of-the-art methods on LiDAR-based 3D recognition
tasks focus on single frame 3D point cloud data, and the temporal information
is ignored in those methods. We argue that the temporal information across the
frames provides crucial knowledge for 3D scene perceptions, especially in the
driving scenario. In this paper, we focus on spatial and temporal variations to
better explore the temporal information across the 3D frames. We design a
temporal variation-aware interpolation module and a temporal voxel-point
refiner to capture the temporal variation in the 4D point cloud. The temporal
variation-aware interpolation generates local features from the previous and
current frames by capturing spatial coherence and temporal variation
information. The temporal voxel-point refiner builds a temporal graph on the 3D
point cloud sequences and captures the temporal variation with a graph
convolution module. The temporal voxel-point refiner also transforms the coarse
voxel-level predictions into fine point-level predictions. With our proposed
modules, the new network TVSN achieves state-of-the-art performance on
SemanticKITTI and SemantiPOSS. Specifically, our method achieves 52.5\% in mIoU
(+5.5% against previous best approaches) on the multiple scan segmentation task
on SemanticKITTI, and 63.0% on SemanticPOSS (+2.8% against previous best
approaches).

Driven by the goal of eradicating language barriers on a global scale,
machine translation has solidified itself as a key focus of artificial
intelligence research today. However, such efforts have coalesced around a
small subset of languages, leaving behind the vast majority of mostly
low-resource languages. What does it take to break the 200 language barrier
while ensuring safe, high quality results, all while keeping ethical
considerations in mind? In No Language Left Behind, we took on this challenge
by first contextualizing the need for low-resource language translation support
through exploratory interviews with native speakers. Then, we created datasets
and models aimed at narrowing the performance gap between low and high-resource
languages. More specifically, we developed a conditional compute model based on
Sparsely Gated Mixture of Experts that is trained on data obtained with novel
and effective data mining techniques tailored for low-resource languages. We
propose multiple architectural and training improvements to counteract
overfitting while training on thousands of tasks. Critically, we evaluated the
performance of over 40,000 different translation directions using a
human-translated benchmark, Flores-200, and combined human evaluation with a
novel toxicity benchmark covering all languages in Flores-200 to assess
translation safety. Our model achieves an improvement of 44% BLEU relative to
the previous state-of-the-art, laying important groundwork towards realizing a
universal translation system. Finally, we open source all contributions
described in this work, accessible at
https://github.com/facebookresearch/fairseq/tree/nllb.

The deep learning (DL) technology has been widely used for image
classification in many scenarios, e.g., face recognition and suspect tracking.
Such a highly commercialized application has given rise to intellectual
property protection of its DL model. To combat that, the mainstream method is
to embed a unique watermark into the target model during the training process.
However, existing efforts focus on detecting copyright infringement for a given
model, while rarely consider the problem of traitors tracking. Moreover, the
watermark embedding process can incur privacy issues for the training data in a
distributed manner. In this paper, we propose SECUREMARK-DL, a novel
fingerprinting framework to address the above two problems in a distributed
learning environment. It embeds a unique fingerprint into the target model for
each customer, which can be extracted and verified from any suspicious model
once a dispute arises. In addition, it adopts a new privacy partitioning
technique in the training process to protect the training data privacy.
Extensive experiments demonstrate the robustness of SECUREMARK-DL against
various attacks, and its high classification accuracy (> 95%) even if a
long-bit (304-bit) fingerprint is embedded into an input image.

Tiny machine learning (TinyML), executing AI workloads on resource and power
strictly restricted systems, is an important and challenging topic. This brief
firstly presents an extremely tiny backbone to construct high efficiency CNN
models for various visual tasks. Then, a specially designed neural co-processor
(NCP) is interconnected with MCU to build an ultra-low power TinyML system,
which stores all features and weights on chip and completely removes both of
latency and power consumption in off-chip memory access. Furthermore, an
application specific instruction-set is further presented for realizing agile
development and rapid deployment. Extensive experiments demonstrate that the
proposed TinyML system based on our model, NCP and instruction set yields
considerable accuracy and achieves a record ultra-low power of 160mW while
implementing object detection and recognition at 30FPS. The demo video is
available on \url{https://www.youtube.com/watch?v=mIZPxtJ-9EY}.

Medical image segmentation under federated learning (FL) is a promising
direction by allowing multiple clinical sites to collaboratively learn a global
model without centralizing datasets. However, using a single model to adapt to
various data distributions from different sites is extremely challenging.
Personalized FL tackles this issue by only utilizing partial model parameters
shared from global server, while keeping the rest to adapt to its own data
distribution in the local training of each site. However, most existing methods
concentrate on the partial parameter splitting, while do not consider the
\textit{inter-site in-consistencies} during the local training, which in fact
can facilitate the knowledge communication over sites to benefit the model
learning for improving the local accuracy. In this paper, we propose a
personalized federated framework with \textbf{L}ocal \textbf{C}alibration
(LC-Fed), to leverage the inter-site in-consistencies in both \textit{feature-
and prediction- levels} to boost the segmentation. Concretely, as each local
site has its alternative attention on the various features, we first design the
contrastive site embedding coupled with channel selection operation to
calibrate the encoded features. Moreover, we propose to exploit the knowledge
of prediction-level in-consistency to guide the personalized modeling on the
ambiguous regions, e.g., anatomical boundaries. It is achieved by computing a
disagreement-aware map to calibrate the prediction. Effectiveness of our method
has been verified on three medical image segmentation tasks with different
modalities, where our method consistently shows superior performance to the
state-of-the-art personalized FL methods. Code is available at
https://github.com/jcwang123/FedLC.

Learning user sequence behaviour embedding is very sophisticated and
challenging due to the complicated feature interactions over time and high
dimensions of user features. Recent emerging foundation models, e.g., BERT and
its variants, encourage a large body of researchers to investigate in this
field. However, unlike natural language processing (NLP) tasks, the parameters
of user behaviour model come mostly from user embedding layer, which makes most
existing works fail in training a universal user embedding of large scale.
Furthermore, user representations are learned from multiple downstream tasks,
and the past research work do not address the seesaw phenomenon. In this paper,
we propose SUPERMOE, a generic framework to obtain high quality user
representation from multiple tasks. Specifically, the user behaviour sequences
are encoded by MoE transformer, and we can thus increase the model capacity to
billions of parameters, or even to trillions of parameters. In order to deal
with seesaw phenomenon when learning across multiple tasks, we design a new
loss function with task indicators. We perform extensive offline experiments on
public datasets and online experiments on private real-world business
scenarios. Our approach achieves the best performance over state-of-the-art
models, and the results demonstrate the effectiveness of our framework.

Current text to speech (TTS) systems usually leverage a cascaded acoustic
model and vocoder pipeline with mel-spectrograms as the intermediate
representations, which suffer from two limitations: 1) the acoustic model and
vocoder are separately trained instead of jointly optimized, which incurs
cascaded errors; 2) the intermediate speech representations (e.g.,
mel-spectrogram) are pre-designed and lose phase information, which are
sub-optimal. To solve these problems, in this paper, we develop DelightfulTTS
2, a new end-to-end speech synthesis system with automatically learned speech
representations and jointly optimized acoustic model and vocoder. Specifically,
1) we propose a new codec network based on vector-quantized auto-encoders with
adversarial training (VQ-GAN) to extract intermediate frame-level speech
representations (instead of traditional representations like mel-spectrograms)
and reconstruct speech waveform; 2) we jointly optimize the acoustic model
(based on DelightfulTTS) and the vocoder (the decoder of VQ-GAN), with an
auxiliary loss on the acoustic model to predict intermediate speech
representations. Experiments show that DelightfulTTS 2 achieves a CMOS gain
+0.14 over DelightfulTTS, and more method analyses further verify the
effectiveness of the developed system.

Photonic Ising machine is a new paradigm of optical computing, which is based
on the characteristics of light wave propagation, parallel processing and low
loss transmission. Thus, the process of solving the combinatorial optimization
problems can be accelerated through photonic/optoelectronic devices. In this
work, we have proposed and demonstrated the so-called Phase-Encoding and
Intensity Detection Ising Annealer (PEIDIA) to solve arbitrary Ising problems
on demand. The PEIDIA is based on the simulated annealing algorithm and
requires only one step of optical linear transformation with simplified
Hamiltonian calculation. With PEIDIA, the Ising spins are encoded on the phase
term of the optical field and only intensity detection is required during the
solving process. As a proof of principle, several 20-dimensional Ising problems
have been solved with high ground state probability (0.98 within 1000
iterations for the antiferromagnetic cubic model, >0.99 within 4000 iterations
for two random spin-glass models, respectively). It should be mentioned that
our proposal is also potential to be implemented with integrated photonic
devices such as tunable metasurfaces to achieve large-scale and on-demand
photonic Ising machines.

In this paper, we aim to improve the Quality-of-Service (QoS) of
Ultra-Reliability and Low-Latency Communications (URLLC) in
interference-limited wireless networks. To obtain time diversity within the
channel coherence time, we first put forward a random repetition scheme that
randomizes the interference power. Then, we optimize the number of reserved
slots and the number of repetitions for each packet to minimize the QoS
violation probability, defined as the percentage of users that cannot achieve
URLLC. We build a cascaded Random Edge Graph Neural Network (REGNN) to
represent the repetition scheme and develop a model-free unsupervised learning
method to train it. We analyze the QoS violation probability using stochastic
geometry in a symmetric scenario and apply a model-based Exhaustive Search (ES)
method to find the optimal solution. Simulation results show that in the
symmetric scenario, the QoS violation probabilities achieved by the model-free
learning method and the model-based ES method are nearly the same. In more
general scenarios, the cascaded REGNN generalizes very well in wireless
networks with different scales, network topologies, cell densities, and
frequency reuse factors. It outperforms the model-based ES method in the
presence of the model mismatch.

Deformable object manipulation has many applications such as cooking and
laundry folding in our daily lives. Manipulating elastoplastic objects such as
dough is particularly challenging because dough lacks a compact state
representation and requires contact-rich interactions. We consider the task of
flattening a piece of dough into a specific shape from RGB-D images. While the
task is seemingly intuitive for humans, there exist local optima for common
approaches such as naive trajectory optimization. We propose a novel trajectory
optimizer that optimizes through a differentiable "reset" module, transforming
a single-stage, fixed-initialization trajectory into a multistage,
multi-initialization trajectory where all stages are optimized jointly. We then
train a closed-loop policy on the demonstrations generated by our trajectory
optimizer. Our policy receives partial point clouds as input, allowing ease of
transfer from simulation to the real world. We show that our policy can perform
real-world dough manipulation, flattening a ball of dough into a target shape.

Continual Learning, also known as Lifelong Learning, aims to continually
learn from new data as it becomes available. While prior research on continual
learning in automatic speech recognition has focused on the adaptation of
models across multiple different speech recognition tasks, in this paper we
propose an experimental setting for \textit{online continual learning} for
automatic speech recognition of a single task. Specifically focusing on the
case where additional training data for the same task becomes available
incrementally over time, we demonstrate the effectiveness of performing
incremental model updates to end-to-end speech recognition models with an
online Gradient Episodic Memory (GEM) method. Moreover, we show that with
online continual learning and a selective sampling strategy, we can maintain an
accuracy that is similar to retraining a model from scratch while requiring
significantly lower computation costs. We have also verified our method with
self-supervised learning (SSL) features.

In this paper, we study the problem of secure ML inference against a
malicious client and a semi-trusted server such that the client only learns the
inference output while the server learns nothing. This problem is first
formulated by Lehmkuhl \textit{et al.} with a solution (MUSE, Usenix
Security'21), whose performance is then substantially improved by Chandran et
al.'s work (SIMC, USENIX Security'22). However, there still exists a nontrivial
gap in these efforts towards practicality, giving the challenges of overhead
reduction and secure inference acceleration in an all-round way.
  We propose SIMC 2.0, which complies with the underlying structure of SIMC,
but significantly optimizes both the linear and non-linear layers of the model.
Specifically, (1) we design a new coding method for homomorphic parallel
computation between matrices and vectors. It is custom-built through the
insight into the complementarity between cryptographic primitives in SIMC. As a
result, it can minimize the number of rotation operations incurred in the
calculation process, which is very computationally expensive compared to other
homomorphic operations e.g., addition, multiplication). (2) We reduce the size
of the garbled circuit (GC) (used to calculate nonlinear activation functions,
e.g., ReLU) in SIMC by about two thirds. Then, we design an alternative
lightweight protocol to perform tasks that are originally allocated to the
expensive GCs. Compared with SIMC, our experiments show that SIMC 2.0 achieves
a significant speedup by up to $17.4\times $ for linear layer computation, and
at least $1.3\times$ reduction of both the computation and communication
overheads in the implementation of non-linear layers under different data
dimensions. Meanwhile, SIMC 2.0 demonstrates an encouraging runtime boost by
$2.3\sim 4.3\times$ over SIMC on different state-of-the-art ML models.

How neural networks in the human brain represent commonsense knowledge, and
complete related reasoning tasks is an important research topic in
neuroscience, cognitive science, psychology, and artificial intelligence.
Although the traditional artificial neural network using fixed-length vectors
to represent symbols has gained good performance in some specific tasks, it is
still a black box that lacks interpretability, far from how humans perceive the
world. Inspired by the grandmother-cell hypothesis in neuroscience, this work
investigates how population encoding and spiking timing-dependent plasticity
(STDP) mechanisms can be integrated into the learning of spiking neural
networks, and how a population of neurons can represent a symbol via guiding
the completion of sequential firing between different neuron populations. The
neuron populations of different communities together constitute the entire
commonsense knowledge graph, forming a giant graph spiking neural network.
Moreover, we introduced the Reward-modulated spiking timing-dependent
plasticity (R-STDP) mechanism to simulate the biological reinforcement learning
process and completed the related reasoning tasks accordingly, achieving
comparable accuracy and faster convergence speed than the graph convolutional
artificial neural networks. For the fields of neuroscience and cognitive
science, the work in this paper provided the foundation of computational
modeling for further exploration of the way the human brain represents
commonsense knowledge. For the field of artificial intelligence, this paper
indicated the exploration direction for realizing a more robust and
interpretable neural network by constructing a commonsense knowledge
representation and reasoning spiking neural networks with solid biological
plausibility.

We present SkexGen, a novel autoregressive generative model for
computer-aided design (CAD) construction sequences containing
sketch-and-extrude modeling operations. Our model utilizes distinct Transformer
architectures to encode topological, geometric, and extrusion variations of
construction sequences into disentangled codebooks. Autoregressive Transformer
decoders generate CAD construction sequences sharing certain properties
specified by the codebook vectors. Extensive experiments demonstrate that our
disentangled codebook representation generates diverse and high-quality CAD
models, enhances user control, and enables efficient exploration of the design
space. The code is available at https://samxuxiang.github.io/skexgen.

Imbalanced data is a frequently encountered problem in machine learning.
Despite a vast amount of literature on sampling techniques for imbalanced data,
there is a limited number of studies that address the issue of the optimal
sampling ratio. In this paper, we attempt to fill the gap in the literature by
conducting a large scale study of the effects of sampling ratio on
classification accuracy. We consider 10 popular sampling methods and evaluate
their performance over a range of ratios based on 20 datasets. The results of
the numerical experiments suggest that the optimal sampling ratio is between
0.7 and 0.8 albeit the exact ratio varies depending on the dataset.
Furthermore, we find that while factors such the original imbalance ratio or
the number of features do not play a discernible role in determining the
optimal ratio, the number of samples in the dataset may have a tangible effect.

Ten years into the revival of deep networks and artificial intelligence, we
propose a theoretical framework that sheds light on understanding deep networks
within a bigger picture of Intelligence in general. We introduce two
fundamental principles, Parsimony and Self-consistency, that address two
fundamental questions regarding Intelligence: what to learn and how to learn,
respectively. We believe the two principles are the cornerstones for the
emergence of Intelligence, artificial or natural. While these two principles
have rich classical roots, we argue that they can be stated anew in entirely
measurable and computable ways. More specifically, the two principles lead to
an effective and efficient computational framework, compressive closed-loop
transcription, that unifies and explains the evolution of modern deep networks
and many artificial intelligence practices. While we mainly use modeling of
visual data as an example, we believe the two principles will unify
understanding of broad families of autonomous intelligent systems and provide a
framework for understanding the brain.

By providing highly efficient one-sided communication with globally shared
memory space, Partitioned Global Address Space (PGAS) has become one of the
most promising parallel computing models in high-performance computing (HPC).
Meanwhile, FPGA is getting attention as an alternative compute platform for HPC
systems with the benefit of custom computing and design flexibility. However,
the exploration of PGAS has not been conducted on FPGAs, unlike the traditional
message passing interface. This paper proposes FSHMEM, a software/hardware
framework that enables the PGAS programming model on FPGAs. We implement the
core functions of GASNet specification on FPGA for native PGAS integration in
hardware, while its programming interface is designed to be highly compatible
with legacy software. Our experiments show that FSHMEM achieves the peak
bandwidth of 3813 MB/s, which is more than 95% of the theoretical maximum,
outperforming the prior works by 9.5$\times$. It records 0.35$us$ and 0.59$us$
latency for remote write and read operations, respectively. Finally, we conduct
a case study on the two Intel D5005 FPGA nodes integrating Intel's deep
learning accelerator. The two-node system programmed by FSHMEM achieves
1.94$\times$ and 1.98$\times$ speedup for matrix multiplication and convolution
operation, respectively, showing its scalability potential for HPC

In this paper, we present a deep neural network based adaptive learning
(DNN-AL) approach for switched systems. Currently, deep neural network based
methods are actively developed for learning governing equations in unknown
dynamic systems, but their efficiency can degenerate for switching systems,
where structural changes exist at discrete time instants. In this new DNN-AL
strategy, observed datasets are adaptively decomposed into subsets, such that
no structural changes within each subset. During the adaptive procedures, DNNs
are hierarchically constructed, and unknown switching time instants are
gradually identified. Especially, network parameters at previous iteration
steps are reused to initialize networks for the later iteration steps, which
gives efficient training procedures for the DNNs. For the DNNs obtained through
our DNN-AL, bounds of the prediction error are established. Numerical studies
are conducted to demonstrate the efficiency of DNN-AL.

In this paper, we address the problem of privacy-preserving federated neural
network training with $N$ users. We present Hercules, an efficient and
high-precision training framework that can tolerate collusion of up to $N-1$
users. Hercules follows the POSEIDON framework proposed by Sav et al.
(NDSS'21), but makes a qualitative leap in performance with the following
contributions: (i) we design a novel parallel homomorphic computation method
for matrix operations, which enables fast Single Instruction and Multiple Data
(SIMD) operations over ciphertexts. For the multiplication of two $h\times h$
dimensional matrices, our method reduces the computation complexity from
$O(h^3)$ to $O(h)$. This greatly improves the training efficiency of the neural
network since the ciphertext computation is dominated by the convolution
operations; (ii) we present an efficient approximation on the sign function
based on the composite polynomial approximation. It is used to approximate
non-polynomial functions (i.e., ReLU and max), with the optimal asymptotic
complexity. Extensive experiments on various benchmark datasets (BCW, ESR,
CREDIT, MNIST, SVHN, CIFAR-10 and CIFAR-100) show that compared with POSEIDON,
Hercules obtains up to 4% increase in model accuracy, and up to 60$\times$
reduction in the computation and communication cost.

A typical multi-source domain adaptation (MSDA) approach aims to transfer
knowledge learned from a set of labeled source domains, to an unlabeled target
domain. Nevertheless, prior works strictly assume that each source domain
shares the identical group of classes with the target domain, which could
hardly be guaranteed as the target label space is not observable. In this
paper, we consider a more versatile setting of MSDA, namely Generalized
Multi-source Domain Adaptation, wherein the source domains are partially
overlapped, and the target domain is allowed to contain novel categories that
are not presented in any source domains. This new setting is more elusive than
any existing domain adaptation protocols due to the coexistence of the domain
and category shifts across the source and target domains. To address this
issue, we propose a variational domain disentanglement (VDD) framework, which
decomposes the domain representations and semantic features for each instance
by encouraging dimension-wise independence. To identify the target samples of
unknown classes, we leverage online pseudo labeling, which assigns the
pseudo-labels to unlabeled target data based on the confidence scores.
Quantitative and qualitative experiments conducted on two benchmark datasets
demonstrate the validity of the proposed framework.

Turbulence is notoriously difficult to model due to its multi-scale nature
and sensitivity to small perturbations. Classical solvers of turbulence
simulation generally operate on finer grids and are computationally
inefficient. In this paper, we propose the Turbulence Neural Transformer (TNT),
which is a learned simulator based on the transformer architecture, to predict
turbulent dynamics on coarsened grids. TNT extends the positional embeddings of
vanilla transformers to a spatiotemporal setting to learn the representation in
the 3D time-series domain, and applies Temporal Mutual Self-Attention (TMSA),
which captures adjacent dependencies, to extract deep and dynamic features. TNT
is capable of generating comparatively long-range predictions stably and
accurately, and we show that TNT outperforms the state-of-the-art U-net
simulator on several metrics. We also test the model performance with different
components removed and evaluate robustness to different initial conditions.
Although more experiments are needed, we conclude that TNT has great potential
to outperform existing solvers and generalize to additional simulation
datasets.

This paper formulates a new problem, instance shadow detection, which aims to
detect shadow instance and the associated object instance that cast each shadow
in the input image. To approach this task, we first compile a new dataset with
the masks for shadow instances, object instances, and shadow-object
associations. We then design an evaluation metric for quantitative evaluation
of the performance of instance shadow detection. Further, we design a
single-stage detector to perform instance shadow detection in an end-to-end
manner, where the bidirectional relation learning module and the deformable
maskIoU head are proposed in the detector to directly learn the relation
between shadow instances and object instances and to improve the accuracy of
the predicted masks. Finally, we quantitatively and qualitatively evaluate our
method on the benchmark dataset of instance shadow detection and show the
applicability of our method on light direction estimation and photo editing.

We introduce a novel framework for nonlinear sufficient dimension reduction
where both the predictor and the response are distributional data, which are
modeled as members of a metric space. Our key step to achieving the nonlinear
sufficient dimension reduction is to build universal kernels on the metric
spaces, which results in reproducing kernel Hilbert spaces for the predictor
and response that are rich enough to characterize the conditional independence
that determines sufficient dimension reduction. For univariate distributions,
we use the well-known quantile representation of the Wasserstein distance to
construct the universal kernel; for multivariate distributions, we resort to
the recently developed sliced Wasserstein distance to achieve this purpose.
Since the sliced Wasserstein distance can be computed by aggregation of
quantile representation of the univariate Wasserstein distance, the computation
of multivariate Wasserstein distance is kept at a manageable level. The method
is applied to several data sets, including fertility and mortality distribution
data and Calgary temperature data.

Although learning in high dimensions is commonly believed to suffer from the
curse of dimensionality, modern machine learning methods often exhibit an
astonishing power to tackle a wide range of challenging real-world learning
problems without using abundant amounts of data. How exactly these methods
break this curse remains a fundamental open question in the theory of deep
learning. While previous efforts have investigated this question by studying
the data (D), model (M), and inference algorithm (I) as independent modules, in
this paper, we analyze the triplet (D, M, I) as an integrated system and
identify important synergies that help mitigate the curse of dimensionality. We
first study the basic symmetries associated with various learning algorithms
(M, I), focusing on four prototypical architectures in deep learning:
fully-connected networks (FCN), locally-connected networks (LCN), and
convolutional networks with and without pooling (GAP/VEC). We find that
learning is most efficient when these symmetries are compatible with those of
the data distribution and that performance significantly deteriorates when any
member of the (D, M, I) triplet is inconsistent or suboptimal.

Translating machine learning algorithms into clinical applications requires
addressing challenges related to interpretability, such as accounting for the
effect of confounding variables (or metadata). Confounding variables affect the
relationship between input training data and target outputs. When we train a
model on such data, confounding variables will bias the distribution of the
learned features. A recent promising solution, MetaData Normalization (MDN),
estimates the linear relationship between the metadata and each feature based
on a non-trainable closed-form solution. However, this estimation is confined
by the sample size of a mini-batch and thereby may cause the approach to be
unstable during training. In this paper, we extend the MDN method by applying a
Penalty approach (referred to as PDMN). We cast the problem into a bi-level
nested optimization problem. We then approximate this optimization problem
using a penalty method so that the linear parameters within the MDN layer are
trainable and learned on all samples. This enables PMDN to be plugged into any
architectures, even those unfit to run batch-level operations, such as
transformers and recurrent models. We show improvement in model accuracy and
greater independence from confounders using PMDN over MDN in a synthetic
experiment and a multi-label, multi-site dataset of magnetic resonance images
(MRIs).

Sparse tensors are rapidly becoming critical components of modern deep
learning workloads. However, developing high-performance sparse operators can
be difficult and tedious, and existing vendor libraries cannot satisfy the
escalating demands from new operators. Sparse tensor compilers simplify the
development of operators, but efficient sparse compilation for deep learning
remains challenging because a single sparse format cannot maximize hardware
efficiency, and single-shot compilers cannot keep up with latest hardware and
system advances. We show that the key to addressing both challenges is two
forms of composability. In this paper, we propose SparseTIR, a sparse tensor
compilation abstraction that offers composable formats and composable
transformations for deep learning workloads. SparseTIR constructs a search
space over these composable components for performance tuning. With these
improvements, SparseTIR obtains consistent performance speedups vs vendor
libraries on GPUs for single operators: 1.1-3.3x for GNN operators, 1.1-3.3x
for sparse attention operators, and 0.6-2.2x for sparse convolution operators.
SparseTIR also accelerates end-to-end GNNs by 1.1-2.2x for GraphSAGE training,
and 4.2-16.8x for RGCN inference.

Decentralized deep learning plays a key role in collaborative model training
due to its attractive properties, including tolerating high network latency and
less prone to single-point failures. Unfortunately, such a training mode is
more vulnerable to data privacy leaks compared to other distributed training
frameworks. Existing efforts exclusively use differential privacy as the
cornerstone to alleviate the data privacy threat. However, it is still not
clear whether differential privacy can provide a satisfactory utility-privacy
trade-off for model training, due to its inherent contradictions. To address
this problem, we propose D-MHE, the first secure and efficient decentralized
training framework with lossless precision. Inspired by the latest developments
in the homomorphic encryption technology, we design a multiparty version of
Brakerski-Fan-Vercauteren (BFV), one of the most advanced cryptosystems, and
use it to implement private gradient updates of users'local models. D-MHE can
reduce the communication complexity of general Secure Multiparty Computation
(MPC) tasks from quadratic to linear in the number of users, making it very
suitable and scalable for large-scale decentralized learning systems. Moreover,
D-MHE provides strict semantic security protection even if the majority of
users are dishonest with collusion. We conduct extensive experiments on MNIST
and CIFAR-10 datasets to demonstrate the superiority of D-MHE in terms of model
accuracy, computation and communication cost compared with existing schemes.

The performance of current Scene Graph Generation (SGG) models is severely
hampered by hard-to-distinguish predicates, e.g., woman-on/standing on/walking
on-beach. As general SGG models tend to predict head predicates and
re-balancing strategies prefer tail categories, none of them can appropriately
handle hard-to-distinguish predicates. To tackle this issue, inspired by
fine-grained image classification, which focuses on differentiating
hard-to-distinguish objects, we propose an Adaptive Fine-Grained Predicates
Learning (FGPL-A) which aims at differentiating hard-to-distinguish predicates
for SGG. First, we introduce an Adaptive Predicate Lattice (PL-A) to figure out
hard-to-distinguish predicates, which adaptively explores predicate
correlations in keeping with model's dynamic learning pace. Practically, PL-A
is initialized from SGG dataset, and gets refined by exploring model's
predictions of current mini-batch. Utilizing PL-A, we propose an Adaptive
Category Discriminating Loss (CDL-A) and an Adaptive Entity Discriminating Loss
(EDL-A), which progressively regularize model's discriminating process with
fine-grained supervision concerning model's dynamic learning status, ensuring
balanced and efficient learning process. Extensive experimental results show
that our proposed model-agnostic strategy significantly boosts performance of
benchmark models on VG-SGG and GQA-SGG datasets by up to 175% and 76% on Mean
Recall@100, achieving new state-of-the-art performance. Moreover, experiments
on Sentence-to-Graph Retrieval and Image Captioning tasks further demonstrate

This paper investigates the computational and statistical limits in
clustering matrix-valued observations. We propose a low-rank mixture model
(LrMM), adapted from the classical Gaussian mixture model (GMM) to treat
matrix-valued observations, which assumes low-rankness for population center
matrices. A computationally efficient clustering method is designed by
integrating Lloyd algorithm and low-rank approximation. Once well-initialized,
the algorithm converges fast and achieves an exponential-type clustering error
rate that is minimax optimal. Meanwhile, we show that a tensor-based spectral
method delivers a good initial clustering. Comparable to GMM, the minimax
optimal clustering error rate is decided by the separation strength, i.e, the
minimal distance between population center matrices. By exploiting
low-rankness, the proposed algorithm is blessed with a weaker requirement on
separation strength. Unlike GMM, however, the statistical and computational
difficulty of LrMM is characterized by the signal strength, i.e, the smallest
non-zero singular values of population center matrices. Evidences are provided
showing that no polynomial-time algorithm is consistent if the signal strength
is not strong enough, even though the separation strength is strong. The
performance of our low-rank Lloyd algorithm is further demonstrated under
sub-Gaussian noise. Intriguing differences between estimation and clustering
under LrMM are discussed. The merits of low-rank Lloyd algorithm are confirmed
by comprehensive simulation experiments. Finally, our method outperforms others
in the literature on real-world datasets.

Considering the problem of resource allocation for potentially complex and
diverse streaming (e.g., query processing) or long-running iterative (e.g.,
deep learning) workloads in the public cloud, we argue that a framework based
on simulated annealing is suitable for navigating performance/cost trade-offs
when selecting from among heterogeneous service offerings. Annealing is
particularly useful when the complex workload and heterogeneous service
offerings may vary over time. Based on a macroscopic objective that combines
both performance and cost terms, annealing facilitates light-weight and
coherent policies of exploration and exploitation when considering the service
suite offered by a cloud provider. In this paper, we first give some background
on simulated annealing and then demonstrate through experiments the usefulness
of a particular resource management framework based on it: selecting the types
and numbers of virtual machines for a particular job stream.

Learned video compression has recently emerged as an essential research topic
in developing advanced video compression technologies, where motion
compensation is considered one of the most challenging issues. In this paper,
we propose a learned video compression framework via heterogeneous deformable
compensation strategy (HDCVC) to tackle the problems of unstable compression
performance caused by single-size deformable kernels in downsampled feature
domain. More specifically, instead of utilizing optical flow warping or
single-size-kernel deformable alignment, the proposed algorithm extracts
features from the two adjacent frames to estimate content-adaptive
heterogeneous deformable (HetDeform) kernel offsets. Then we transform the
reference features with the HetDeform convolution to accomplish motion
compensation. Moreover, we design a Spatial-Neighborhood-Conditioned Divisive
Normalization (SNCDN) to achieve more effective data Gaussianization combined
with the Generalized Divisive Normalization. Furthermore, we propose a
multi-frame enhanced reconstruction module for exploiting context and temporal
information for final quality enhancement. Experimental results indicate that
HDCVC achieves superior performance than the recent state-of-the-art learned
video compression approaches.

Cross-study replicability is a powerful model evaluation criterion that
emphasizes generalizability of predictions. When training cross-study
replicable prediction models, it is critical to decide between merging and
treating the studies separately. We study boosting algorithms in the presence
of potential heterogeneity in predictor-outcome relationships across studies
and compare two multi-study learning strategies: 1) merging all the studies and
training a single model, and 2) multi-study ensembling, which involves training
a separate model on each study and ensembling the resulting predictions. In the
regression setting, we provide theoretical guidelines based on an analytical
transition point to determine whether it is more beneficial to merge or to
ensemble for boosting with linear learners. In addition, we characterize a
bias-variance decomposition of estimation error for boosting with
component-wise linear learners. We verify the theoretical transition point
result in simulation and illustrate how it can guide the decision on merging
vs. ensembling in an application to breast cancer gene expression data.

The effectiveness of unsupervised domain adaptation degrades when there is a
large discrepancy between the source and target domains. Gradual domain
adaptation (GDA) is one promising way to mitigate such an issue, by leveraging
additional unlabeled data that gradually shift from the source to the target.
Through sequentially adapting the model along the "indexed" intermediate
domains, GDA substantially improves the overall adaptation performance. In
practice, however, the extra unlabeled data may not be separated into
intermediate domains and indexed properly, limiting the applicability of GDA.
In this paper, we investigate how to discover the sequence of intermediate
domains when it is not already available. Concretely, we propose a
coarse-to-fine framework, which starts with a coarse domain discovery step via
progressive domain discriminator training. This coarse domain sequence then
undergoes a fine indexing step via a novel cycle-consistency loss, which
encourages the next intermediate domain to preserve sufficient discriminative
knowledge of the current intermediate domain. The resulting domain sequence can
then be used by a GDA algorithm. On benchmark data sets of GDA, we show that
our approach, which we name Intermediate DOmain Labeler (IDOL), can lead to
comparable or even better adaptation performance compared to the pre-defined
domain sequence, making GDA more applicable and robust to the quality of domain
sequences. Codes are available at https://github.com/hongyouc/IDOL.

In recent years, deep learning has shown potential and efficiency in a wide
area including computer vision, image and signal processing. Yet, translational
challenges remain for user applications due to a lack of interpretability of
algorithmic decisions and results. This black box problem is particularly
problematic for high-risk applications such as medical-related decision-making.
The current study goal was to design an interpretable deep learning system for
time series classification of electroencephalogram (EEG) for sleep stage
scoring as a step toward designing a transparent system. We have developed an
interpretable deep neural network that includes a kernel-based layer based on a
set of principles used for sleep scoring by human experts in the visual
analysis of polysomnographic records. A kernel-based convolutional layer was
defined and used as the first layer of the system and made available for user
interpretation. The trained system and its results were interpreted in four
levels from the microstructure of EEG signals, such as trained kernels and the
effect of each kernel on the detected stages, to macrostructures, such as the
transition between stages. The proposed system demonstrated greater performance
than prior studies and the results of interpretation showed that the system
learned information which was consistent with expert knowledge.

With the introduction of machine learning in high-stakes decision making,
ensuring algorithmic fairness has become an increasingly important problem to
solve. In response to this, many mathematical definitions of fairness have been
proposed, and a variety of optimisation techniques have been developed, all
designed to maximise a defined notion of fairness. However, fair solutions are
reliant on the quality of the training data, and can be highly sensitive to
noise. Recent studies have shown that robustness (the ability for a model to
perform well on unseen data) plays a significant role in the type of strategy
that should be used when approaching a new problem and, hence, measuring the
robustness of these strategies has become a fundamental problem. In this work,
we therefore propose a new criterion to measure the robustness of various
fairness optimisation strategies - the robustness ratio. We conduct multiple
extensive experiments on five bench mark fairness data sets using three of the
most popular fairness strategies with respect to four of the most popular
definitions of fairness. Our experiments empirically show that fairness methods
that rely on threshold optimisation are very sensitive to noise in all the
evaluated data sets, despite mostly outperforming other methods. This is in
contrast to the other two methods, which are less fair for low noise scenarios
but fairer for high noise ones. To the best of our knowledge, we are the first
to quantitatively evaluate the robustness of fairness optimisation strategies.
This can potentially can serve as a guideline in choosing the most suitable
fairness strategy for various data sets.

Upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space
and Time (LSST) will detect up to 10 million time-varying sources in the sky
every night for ten years. This information will be transmitted in a continuous
stream to brokers that will select the most promising events for a variety of
science cases using machine learning algorithms. We study the benefits and
challenges of Bayesian Neural Networks (BNNs) for this type of classification
tasks. BNNs are found to be accurate classifiers which also provide additional
information: they quantify the classification uncertainty which can be
harnessed to analyse this upcoming data avalanche more efficiently.

Semantic relation prediction aims to mine the implicit relationships between
objects in heterogeneous graphs, which consist of different types of objects
and different types of links. In real-world scenarios, new semantic relations
constantly emerge and they typically appear with only a few labeled data. Since
a variety of semantic relations exist in multiple heterogeneous graphs, the
transferable knowledge can be mined from some existing semantic relations to
help predict the new semantic relations with few labeled data. This inspires a
novel problem of few-shot semantic relation prediction across heterogeneous
graphs. However, the existing methods cannot solve this problem because they
not only require a large number of labeled samples as input, but also focus on
a single graph with a fixed heterogeneity. Targeting this novel and challenging
problem, in this paper, we propose a Meta-learning based Graph neural network
for Semantic relation prediction, named MetaGS. Firstly, MetaGS decomposes the
graph structure between objects into multiple normalized subgraphs, then adopts
a two-view graph neural network to capture local heterogeneous information and
global structure information of these subgraphs. Secondly, MetaGS aggregates
the information of these subgraphs with a hyper-prototypical network, which can
learn from existing semantic relations and adapt to new semantic relations.
Thirdly, using the well-initialized two-view graph neural network and
hyper-prototypical network, MetaGS can effectively learn new semantic relations
from different graphs while overcoming the limitation of few labeled data.
Extensive experiments on three real-world datasets have demonstrated the
superior performance of MetaGS over the state-of-the-art methods.

We study the generalised rock-paper-scissors game with five species whose
organisms face local epidemic outbreaks. As an evolutionary behavioural
survival strategy, organisms of one out of the species move in the direction
with more enemies of their enemies to benefit from protection against
selection. We consider that each organism scans the environment, performing
social distancing instead of agglomerating when perceiving that the density of
sick organisms is higher than a tolerable threshold. Running stochastic
simulations, we study the interference of the adaptive movement survival
strategy in spatial pattern formation, calculating the characteristic length
scale of the typical spatial domains inhabited by organisms of each species. We
compute how social distancing trigger impacts the chances of an individual
being killed in the cyclic game and contaminated by the disease. The outcomes
show that the species predominates in the cyclic game because of the organisms'
local adaptation. The territory occupied by the species grows with the
proportion of individuals learning to trigger the social distancing tactic. We
also show that organisms that perceive large distances more properly execute
the adaptive strategy, promptly triggering the social distancing tactic and
choosing the correct direction to move. Our findings may contribute to
understanding the role of adaptive behaviour when environmental changes
threaten biodiversity.

The supervised machine learning (ML) approach is applied to realize the
trajectory-based nonadiabatic dynamics within the framework of the symmetrical
quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian
(MM-SQC). After the construction of the long short-term memory recurrent neural
network (LSTM-RNN) model, it is used to perform the entire trajectory
evolutions from initial sampling conditions. The proposed idea is proven to be
reliable and accurate in the simulations of the dynamics of several
site-exciton electron-phonon coupling models, which cover two-site and
three-site systems with biased and unbiased energy levels, as well as include a
few or many phonon modes. The LSTM-RNN approach also shows the powerful ability
to obtain the accurate and stable results for the long-time evolutions. It
indicates that the LSTM-RNN model perfectly captures of dynamical correction
information in the trajectory evolution in the MM-SQC dynamics. Our work
provides the possibility to employ the ML methods in the simulation of the
trajectory-based nonadiabatic dynamic of complex systems with a large number of
degrees of freedoms.

In the field of waste copper granules recycling, engineers should be able to
identify all different sorts of impurities in waste copper granules and
estimate their mass proportion relying on experience before rating. This manual
rating method is costly, lacking in objectivity and comprehensiveness. To
tackle this problem, we propose a waste copper granules rating system based on
machine vision and deep learning. We firstly formulate the rating task into a
2D image recognition and purity regression task. Then we design a two-stage
convolutional rating network to compute the mass purity and rating level of
waste copper granules. Our rating network includes a segmentation network and a
purity regression network, which respectively calculate the semantic
segmentation heatmaps and purity results of the waste copper granules. After
training the rating network on the augmented datasets, experiments on real
waste copper granules demonstrate the effectiveness and superiority of the
proposed network. Specifically, our system is superior to the manual method in
terms of accuracy, effectiveness, robustness, and objectivity.

We propose a novel framework for Alzheimer's disease (AD) detection using
brain MRIs. The framework starts with a data augmentation method called
Brain-Aware Replacements (BAR), which leverages a standard brain parcellation
to replace medically-relevant 3D brain regions in an anchor MRI from a randomly
picked MRI to create synthetic samples. Ground truth "hard" labels are also
linearly mixed depending on the replacement ratio in order to create "soft"
labels. BAR produces a great variety of realistic-looking synthetic MRIs with
higher local variability compared to other mix-based methods, such as CutMix.
On top of BAR, we propose using a soft-label-capable supervised contrastive
loss, aiming to learn the relative similarity of representations that reflect
how mixed are the synthetic MRIs using our soft labels. This way, we do not
fully exhaust the entropic capacity of our hard labels, since we only use them
to create soft labels and synthetic MRIs through BAR. We show that a model
pre-trained using our framework can be further fine-tuned with a cross-entropy
loss using the hard labels that were used to create the synthetic samples. We
validated the performance of our framework in a binary AD detection task
against both from-scratch supervised training and state-of-the-art
self-supervised training plus fine-tuning approaches. Then we evaluated BAR's
individual performance compared to another mix-based method CutMix by
integrating it within our framework. We show that our framework yields superior
results in both precision and recall for the AD detection task.

Federated learning provides the ability to learn over heterogeneous user data
in a distributed manner, while preserving user privacy. However, its current
clients selection technique is a source of bias as it discriminates against
slow clients. For starters, it selects clients that satisfy certain network and
system specific criteria, thus not selecting slow clients. Even when such
clients are included in the training process, they either straggle the training
or are altogether dropped from the round for being too slow. Our proposed idea
looks to find a sweet spot between fast convergence and heterogeneity by
looking at smart clients selection and scheduling techniques.

Papilledema is an ophthalmic neurologic disorder in which increased
intracranial pressure leads to swelling of the optic nerves. Undiagnosed
papilledema in children may lead to blindness and may be a sign of
life-threatening conditions, such as brain tumors. Robust and accurate clinical
diagnosis of this syndrome can be facilitated by automated analysis of fundus
images using deep learning, especially in the presence of challenges posed by
pseudopapilledema that has similar fundus appearance but distinct clinical
implications. We present a deep learning-based algorithm for the automatic
detection of pediatric papilledema. Our approach is based on optic disc
localization and detection of explainable papilledema indicators through data
augmentation. Experiments on real-world clinical data demonstrate that our
proposed method is effective with a diagnostic accuracy comparable to expert
ophthalmologists.

In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging
self-supervised approach. One of the difficulties is how to learn task
discrimination in the absence of target labels. Unlike previous literature
which directly aligns cross-domain distributions or leverages reverse gradient,
we propose Domain Confused Contrastive Learning (DCCL) to bridge the source and
the target domains via domain puzzles, and retain discriminative
representations after adaptation. Technically, DCCL searches for a most
domain-challenging direction and exquisitely crafts domain confused
augmentations as positive pairs, then it contrastively encourages the model to
pull representations towards the other domain, thus learning more stable and
effective domain invariances. We also investigate whether contrastive learning
necessarily helps with UDA when performing other data augmentations. Extensive
experiments demonstrate that DCCL significantly outperforms baselines.

Federated learning is typically considered a beneficial technology which
allows multiple agents to collaborate with each other, improve the accuracy of
their models, and solve problems which are otherwise too data-intensive /
expensive to be solved individually. However, under the expectation that other
agents will share their data, rational agents may be tempted to engage in
detrimental behavior such as free-riding where they contribute no data but
still enjoy an improved model. In this work, we propose a framework to analyze
the behavior of such rational data generators. We first show how a naive scheme
leads to catastrophic levels of free-riding where the benefits of data sharing
are completely eroded. Then, using ideas from contract theory, we introduce
accuracy shaping based mechanisms to maximize the amount of data generated by
each agent. These provably prevent free-riding without needing any payment
mechanism.

Decentralized optimization is a common paradigm used in distributed signal
processing and sensing as well as privacy-preserving and large-scale machine
learning. It is assumed that several computational entities locally hold
objective functions and are connected by a network. The agents aim to commonly
minimize the sum of the local objectives subject by making gradient updates and
exchanging information with their immediate neighbors. Theory of decentralized
optimization is pretty well-developed in the literature. In particular, it
includes lower bounds and optimal algorithms. In this paper, we assume that
along with an objective, each node also holds affine constraints. We discuss
several primal and dual approaches to decentralized optimization problem with
affine constraints.

We consider a stochastic lost-sales inventory control system with a lead time
$L$ over a planning horizon $T$. Supply is uncertain, and is a function of the
order quantity (due to random yield/capacity, etc). We aim to minimize the
$T$-period cost, a problem that is known to be computationally intractable even
under known distributions of demand and supply. In this paper, we assume that
both the demand and supply distributions are unknown and develop a
computationally efficient online learning algorithm. We show that our algorithm
achieves a regret (i.e. the performance gap between the cost of our algorithm
and that of an optimal policy over $T$ periods) of $O(L+\sqrt{T})$ when
$L\geq\log(T)$. We do so by 1) showing our algorithm cost is higher by at most
$O(L+\sqrt{T})$ for any $L\geq 0$ compared to an optimal constant-order policy
under complete information (a well-known and widely-used algorithm) and 2)
leveraging its known performance guarantee from the existing literature. To the
best of our knowledge, a finite-sample $O(\sqrt{T})$ (and polynomial in $L$)
regret bound when benchmarked against an optimal policy is not known before in
the online inventory control literature. A key challenge in this learning
problem is that both demand and supply data can be censored; hence only
truncated values are observable. We circumvent this challenge by showing that
the data generated under an order quantity $q^2$ allows us to simulate the
performance of not only $q^2$ but also $q^1$ for all $q^1<q^2$, a key
observation to obtain sufficient information even under data censoring. By
establishing a high probability coupling argument, we are able to evaluate and
compare the performance of different order policies at their steady state
within a finite time horizon. Since the problem lacks convexity, we develop an
active elimination method that adaptively rules out suboptimal solutions.

Large pre-trained language models are successfully being used in a variety of
tasks, across many languages. With this ever-increasing usage, the risk of
harmful side effects also rises, for example by reproducing and reinforcing
stereotypes. However, detecting and mitigating these harms is difficult to do
in general and becomes computationally expensive when tackling multiple
languages or when considering different biases. To address this, we present
FairDistillation: a cross-lingual method based on knowledge distillation to
construct smaller language models while controlling for specific biases. We
found that our distillation method does not negatively affect the downstream
performance on most tasks and successfully mitigates stereotyping and
representational harms. We demonstrate that FairDistillation can create fairer
language models at a considerably lower cost than alternative approaches.

Standard gradient descent algorithms applied to sequences of tasks are known
to produce catastrophic forgetting in deep neural networks. When trained on a
new task in a sequence, the model updates its parameters on the current task,
forgetting past knowledge.
  This article explores scenarios where we scale the number of tasks in a
finite environment. Those scenarios are composed of a long sequence of tasks
with reoccurring data.
  We show that in such setting, stochastic gradient descent can learn,
progress, and converge to a solution that according to existing literature
needs a continual learning algorithm. In other words, we show that the model
performs knowledge retention and accumulation without specific memorization
  We propose a new experimentation framework, SCoLe (Scaling Continual
Learning), to study the knowledge retention and accumulation of algorithms in
potentially infinite sequences of tasks. To explore this setting, we performed
a large number of experiments on sequences of 1,000 tasks to better understand
this new family of settings.
  We also propose a slight modifications to the vanilla stochastic gradient
descent to facilitate continual learning in this setting.
  The SCoLe framework represents a good simulation of practical training
environments with reoccurring situations and allows the study of convergence
behavior in long sequences. Our experiments show that previous results on short
scenarios cannot always be extrapolated to longer scenarios.

Recently, attention mechanisms have been applied successfully in neural
network-based speaker verification systems. Incorporating the
Squeeze-and-Excitation block into convolutional neural networks has achieved
remarkable performance. However, it uses global average pooling (GAP) to simply
average the features along time and frequency dimensions, which is incapable of
preserving sufficient speaker information in the feature maps. In this study,
we show that GAP is a special case of a discrete cosine transform (DCT) on
time-frequency domain mathematically using only the lowest frequency component
in frequency decomposition. To strengthen the speaker information extraction
ability, we propose to utilize multi-frequency information and design two novel
and effective attention modules, called Single-Frequency Single-Channel (SFSC)
attention module and Multi-Frequency Single-Channel (MFSC) attention module.
The proposed attention modules can effectively capture more speaker information
from multiple frequency components on the basis of DCT. We conduct
comprehensive experiments on the VoxCeleb datasets and a probe evaluation on
the 1st 48-UTD forensic corpus. Experimental results demonstrate that our
proposed SFSC and MFSC attention modules can efficiently generate more
discriminative speaker representations and outperform ResNet34-SE and
ECAPA-TDNN systems with relative 20.9% and 20.2% reduction in EER, without
adding extra network parameters.

Corporate credit ratings issued by third-party rating agencies are quantified
assessments of a company's creditworthiness. Credit Ratings highly correlate to
the likelihood of a company defaulting on its debt obligations. These ratings
play critical roles in investment decision-making as one of the key risk
factors. They are also central to the regulatory framework such as BASEL II in
calculating necessary capital for financial institutions. Being able to predict
rating changes will greatly benefit both investors and regulators alike. In
this paper, we consider the corporate credit rating migration early prediction
problem, which predicts the credit rating of an issuer will be upgraded,
unchanged, or downgraded after 12 months based on its latest financial
reporting information at the time. We investigate the effectiveness of
different standard machine learning algorithms and conclude these models
deliver inferior performance. As part of our contribution, we propose a new
Multi-task Envisioning Transformer-based Autoencoder (META) model to tackle
this challenging problem. META consists of Positional Encoding,
Transformer-based Autoencoder, and Multi-task Prediction to learn effective
representations for both migration prediction and rating prediction. This
enables META to better explore the historical data in the training stage for
one-year later prediction. Experimental results show that META outperforms all
baseline models.

Semantic scene understanding is essential for mobile agents acting in various
environments. Although semantic segmentation already provides a lot of
information, details about individual objects as well as the general scene are
missing but required for many real-world applications. However, solving
multiple tasks separately is expensive and cannot be accomplished in real time
given limited computing and battery capabilities on a mobile platform. In this
paper, we propose an efficient multi-task approach for RGB-D scene
analysis~(EMSANet) that simultaneously performs semantic and instance
segmentation~(panoptic segmentation), instance orientation estimation, and
scene classification. We show that all tasks can be accomplished using a single
neural network in real time on a mobile platform without diminishing
performance - by contrast, the individual tasks are able to benefit from each
other. In order to evaluate our multi-task approach, we extend the annotations
of the common RGB-D indoor datasets NYUv2 and SUNRGB-D for instance
segmentation and orientation estimation. To the best of our knowledge, we are
the first to provide results in such a comprehensive multi-task setting for
indoor scene analysis on NYUv2 and SUNRGB-D.

Image-based machine learning models can be used to make the sorting and
grading of agricultural products more efficient. In many regions, implementing
such systems can be difficult due to the lack of centralization and automation
of postharvest supply chains. Stakeholders are often too small to specialize in
machine learning, and large training data sets are unavailable. We propose a
machine learning procedure for images based on pre-trained Vision Transformers.
It is easier to implement than the current standard approach of training
Convolutional Neural Networks (CNNs) as we do not (re-)train deep neural
networks. We evaluate our approach based on two data sets for apple defect
detection and banana ripeness estimation. Our model achieves a competitive
classification accuracy equal to or less than one percent below the
best-performing CNN. At the same time, it requires three times fewer training
samples to achieve a 90% accuracy.

For a broader adoption of AI in industrial production, adequate
infrastructure capabilities are crucial. This includes easing the integration
of AI with industrial devices, support for distributed deployment, monitoring,
and consistent system configuration. Existing IIoT platforms still lack
required capabilities to flexibly integrate reusable AI services and relevant
standards such as Asset Administration Shells or OPC UA in an open,
ecosystem-based manner. This is exactly what our next level Intelligent
Industrial Production Ecosphere (IIP-Ecosphere) platform addresses, employing a
highly configurable low-code based approach. In this paper, we introduce the
design of this platform and discuss an early evaluation in terms of a
demonstrator for AI-enabled visual quality inspection. This is complemented by
insights and lessons learned during this early evaluation activity.

In nature, the collective behavior of animals, such as flying birds is
dominated by the interactions between individuals of the same species. However,
the study of such behavior among the bird species is a complex process that
humans cannot perform using conventional visual observational techniques such
as focal sampling in nature. For social animals such as birds, the mechanism of
group formation can help ecologists understand the relationship between social
cues and their visual characteristics over time (e.g., pose and shape). But,
recovering the varying pose and shapes of flying birds is a highly challenging
problem. A widely-adopted solution to tackle this bottleneck is to extract the
pose and shape information from 2D image to 3D correspondence. Recent advances
in 3D vision have led to a number of impressive works on the 3D shape and pose
estimation, each with different pros and cons. To the best of our knowledge,
this work is the first attempt to provide an overview of recent advances in 3D
bird reconstruction based on monocular vision, give both computer vision and
biology researchers an overview of existing approaches, and compare their
characteristics.

Our surrounding environment impacts our cognitive-emotional processes on a
daily basis and shapes our physical, psychological and social wellbeing.
Although the effects of the built environment on our psycho-physiological
processes are well studied, virtual environment design with a potentially
similar impact on the user, has received limited attention. Based on the
influence of space design on a user and combining that with the dynamic
affordances of virtual spaces, we present the idea of adaptive virtual
neuroarchitecture (AVN), where virtual environments respond to the user and the
user's real world context while simultaneously influencing them both in
realtime. To show how AVN has been explored in current research, we present a
sampling of recent work that demonstrates reciprocal relationships using
physical affordances (space, objects), the user's state (physiological,
cognitive, emotional), and the virtual world used in the design of novel
virtual reality experiences. We believe AVN has the potential to help us learn
how to design spaces and environments that can enhance the wellbeing of their
inhabitants.

The rapid growth of technology has led to the creation of computing networks.
The applications of the Internet of Things are becoming more and more visible
with the expansion and development of sensors and the use of a series of
equipment to connect to the Internet. Of course, the growth of any network will
also provide some challenges. The main challenge of IoT like any other network
is its security. In the field of security, there are issues such as attack
detection, authentication, encryption and the so on. One of the most important
attack is cyber-attacks that disrupt the network usage. One of the most
important attacks on the IoT is BotNet attack. The most important challenges of
this topic include very high computational complexity, lack of comparison with
previous methods, lack of scalability, high execution time, lack of review of
the proposed approach in terms of accuracy to detect and classify attacks and
intrusions. Using intrusion detection systems for the IoT is an important step
in identifying and detecting various attacks. Therefore, an algorithm that can
solve these challenges has provided a near-optimal method. Using training-based
models and algorithms such as Deep Dearning-Reinforcement Learning and XGBoost
learning in combination (DRL-XGBoost) models can be an interesting approach to
overcoming previous weaknesses. The data of this research is Bot-IoT-2018.

Errors might not have the same consequences depending on the task at hand.
Nevertheless, there is limited research investigating the impact of imbalance
in the contribution of different features in an error vector. Therefore, we
propose the Feature Impact Balance (FIB) score. It measures whether there is a
balanced impact of features in the discrepancies between two vectors. We
designed the FIB score to lie in [0, 1]. Scores close to 0 indicate that a
small number of features contribute to most of the error, and scores close to 1
indicate that most features contribute to the error equally. We experimentally
study the FIB on different datasets, using AutoEncoders and Variational
AutoEncoders. We show how the feature impact balance varies during training and
showcase its usability to support model selection for single output and
multi-output tasks.

We propose a supervised learning approach to detect people interested in
Non-Suicidal Self-Injury (NSSI). We treat the task as a binary classification
problem, and build classifiers based upon features extracted from people
self-declared interests. Experimental evaluation on a real-world dataset, the
LiveJournal social blogging networking platform, demonstrates the effectiveness
of our proposed model.

Causal background knowledge about the existence or the absence of causal
edges and paths is frequently encountered in observational studies. The shared
directed edges and links of a subclass of Markov equivalent DAGs refined due to
background knowledge can be represented by a causal maximally partially
directed acyclic graph (MPDAG). In this paper, we first provide a sound and
complete graphical characterization of causal MPDAGs and give a minimal
representation of a causal MPDAG. Then, we introduce a novel representation
called direct causal clause (DCC) to represent all types of causal background
knowledge in a unified form. Using DCCs, we study the consistency and
equivalency of causal background knowledge and show that any causal background
knowledge set can be equivalently decomposed into a causal MPDAG plus a minimal
residual set of DCCs. Polynomial-time algorithms are also provided for checking
the consistency, equivalency, and finding the decomposed MPDAG and residual
DCCs. Finally, with causal background knowledge, we prove a sufficient and
necessary condition to identify causal effects and surprisingly find that the
identifiability of causal effects only depends on the decomposed MPDAG. We also
develop a local IDA-type algorithm to estimate the possible values of an
unidentifiable effect. Simulations suggest that causal background knowledge can
significantly improve the identifiability of causal effects.

Recent studies show that despite achieving high accuracy on a number of
real-world applications, deep neural networks (DNNs) can be backdoored: by
injecting triggered data samples into the training dataset, the adversary can
mislead the trained model into classifying any test data to the target class as
long as the trigger pattern is presented. To nullify such backdoor threats,
various methods have been proposed. Particularly, a line of research aims to
purify the potentially compromised model. However, one major limitation of this
line of work is the requirement to access sufficient original training data:
the purifying performance is a lot worse when the available training data is
limited. In this work, we propose Adversarial Weight Masking (AWM), a novel
method capable of erasing the neural backdoors even in the one-shot setting.
The key idea behind our method is to formulate this into a min-max optimization
problem: first, adversarially recover the trigger patterns and then (soft) mask
the network weights that are sensitive to the recovered patterns. Comprehensive
evaluations of several benchmark datasets suggest that AWM can largely improve
the purifying effects over other state-of-the-art methods on various available
training dataset sizes.

Optimizing over the stationary distribution of stochastic differential
equations (SDEs) is computationally challenging. A new forward propagation
algorithm has been recently proposed for the online optimization of SDEs. The
algorithm solves an SDE, derived using forward differentiation, which provides
a stochastic estimate for the gradient. The algorithm continuously updates the
SDE model's parameters and the gradient estimate simultaneously. This paper
studies the convergence of the forward propagation algorithm for nonlinear
dissipative SDEs. We leverage the ergodicity of this class of nonlinear SDEs to
characterize the convergence rate of the transition semi-group and its
derivatives. Then, we prove bounds on the solution of a Poisson partial
differential equation (PDE) for the expected time integral of the algorithm's
stochastic fluctuations around the direction of steepest descent. We then
re-write the algorithm using the PDE solution, which allows us to characterize
the parameter evolution around the direction of steepest descent. Our main
result is a convergence theorem for the forward propagation algorithm for
nonlinear dissipative SDEs.

Universal domain adaptation (UniDA) is a general unsupervised domain
adaptation setting, which addresses both domain and label shifts in adaptation.
Its main challenge lies in how to identify target samples in unshared or
unknown classes. Previous methods commonly strive to depict sample "confidence"
along with a threshold for rejecting unknowns, and align feature distributions
of shared classes across domains. However, it is still hard to pre-specify a
"confidence" criterion and threshold which are adaptive to various real tasks,
and a mis-prediction of unknowns further incurs misalignment of features in
shared classes. In this paper, we propose a new UniDA method with adaptive
Unknown Authentication by Classifier Paradox (UACP), considering that samples
with paradoxical predictions are probably unknowns belonging to none of the
source classes. In UACP, a composite classifier is jointly designed with two
types of predictors. That is, a multi-class (MC) predictor classifies samples
to one of the multiple source classes, while a binary one-vs-all (OVA)
predictor further verifies the prediction by MC predictor. Samples with
verification failure or paradox are identified as unknowns. Further, instead of
feature alignment for shared classes, implicit domain alignment is conducted in
output space such that samples across domains share the same decision boundary,
though with feature discrepancy. Empirical results validate UACP under both
open-set and universal UDA settings.

A computational revolution unleashed the power of artificial neural networks.
At the heart of that revolution is automatic differentiation, which calculates
the derivative of a performance measure relative to a large number of
parameters. Differentiation enhances the discovery of improved performance in
large models, an achievement that was previously difficult or impossible.
Recently, a second computational advance optimizes the temporal trajectories
traced by differential equations. Optimization requires differentiating a
measure of performance over a trajectory, such as the closeness of tracking the
environment, with respect to the parameters of the differential equations.
Because model trajectories are usually calculated numerically by multistep
algorithms, such as Runge-Kutta, the automatic differentiation must be passed
through the numerical algorithm. This article explains how such automatic
differentiation of trajectories is achieved. It also discusses why such
computational breakthroughs are likely to advance theoretical and statistical
studies of biological problems, in which one can consider variables as dynamic
paths over time and space. Many common problems arise between improving success
in computational learning models over performance landscapes, improving
evolutionary fitness over adaptive landscapes, and improving statistical fits
to data over information landscapes.

A popular approach for sequential decision-making is to perform
simulator-based search guided with Machine Learning (ML) methods like policy
learning. On the other hand, model-relaxation heuristics can guide the search
effectively if a full declarative model is available. In this work, we consider
how a practitioner can improve ML-based black-box planning on settings where a
complete symbolic model is not available. We show that specifying an incomplete
STRIPS model that describes only part of the problem enables the use of
relaxation heuristics. Our findings on several planning domains suggest that
this is an effective way to improve ML-based black-box planning beyond
collecting more data or tuning ML architectures.

It is a challenge to design a equalizer for complex time-frequency
doubly-spread channels. In this paper, we employ the deep learning (DL)
architecture by that unfolding an existing iterative algorithm to build an
equalizer named underwater deep network (UDNet) for underwater acoustic (UWA)
orthogonal frequency division multiplexing (OFDM) signal. Considering
constellation recognition is a classification issue, the one-hot coding and
softmax layer are adopted in the proposed network to achieve the minimum
Kullback-Leibler (KL) criterion. Simultaneously, we introduce a sliding
structure based on the banded approximation of the channel matrix to reduce
computational complexity and aid UDNet performs well for different length
signals without changing the network structure. Furthermore, we apply the
environment of the true UWA channel as much as possible, including utilize
measured doubly-spread UWA channel and offshore background noise to evaluate
the UDNet. Experimental results show that in the case of 10-35dB SNR, UDNet
achieves better performance with low computational complexity.

This paper provides a finite-time analysis of linear stochastic approximation
(LSA) algorithms with fixed step size, a core method in statistics and machine
learning. LSA is used to compute approximate solutions of a $d$-dimensional
linear system $\bar{\mathbf{A}} \theta = \bar{\mathbf{b}}$, for which
$(\bar{\mathbf{A}}, \bar{\mathbf{b}})$ can only be estimated through
(asymptotically) unbiased observations
$\{(\mathbf{A}(Z_n),\mathbf{b}(Z_n))\}_{n \in \mathbb{N}}$. We consider here
the case where $\{Z_n\}_{n \in \mathbb{N}}$ is an i.i.d. sequence or a
uniformly geometrically ergodic Markov chain, and derive $p$-moments inequality
and high probability bounds for the iterates defined by LSA and its
Polyak-Ruppert averaged version. More precisely, we establish bounds of order
$(p \alpha t_{\operatorname{mix}})^{1/2}d^{1/p}$ on the $p$-th moment of the
last iterate of LSA. In this formula $\alpha$ is the step size of the procedure
and $t_{\operatorname{mix}}$ is the mixing time of the underlying chain
($t_{\operatorname{mix}}=1$ in the i.i.d. setting). We then prove finite-time
instance-dependent bounds on the Polyak-Ruppert averaged sequence of iterates.
These results are sharp in the sense that the leading term we obtain matches
the local asymptotic minimax limit, including tight dependence on the
parameters $(d,t_{\operatorname{mix}})$ in the higher order terms.

The common research goal of self-supervised learning is to extract a general
representation which an arbitrary downstream task would benefit from. In this
work, we investigate music audio representation learned from different
contrastive self-supervised learning schemes and empirically evaluate the
embedded vectors on various music information retrieval (MIR) tasks where
different levels of the music perception are concerned. We analyze the results
to discuss the proper direction of contrastive learning strategies for
different MIR tasks. We show that these representations convey a comprehensive
information about the auditory characteristics of music in general, although
each of the self-supervision strategies has its own effectiveness in certain
aspect of information.

Network Morphism based Neural Architecture Search (NAS) is one of the most
efficient methods, however, knowing where and when to add new neurons or remove
dis-functional ones is generally left to black-box Reinforcement Learning
models. In this paper, we present a new Network Morphism based NAS called Noisy
Heuristics NAS which uses heuristics learned from manually developing neural
network models and inspired by biological neuronal dynamics. Firstly, we add
new neurons randomly and prune away some to select only the best fitting
neurons. Secondly, we control the number of layers in the network using the
relationship of hidden units to the number of input-output connections. Our
method can increase or decrease the capacity or non-linearity of models online
which is specified with a few meta-parameters by the user. Our method
generalizes both on toy datasets and on real-world data sets such as MNIST,
CIFAR-10, and CIFAR-100. The performance is comparable to the hand-engineered
architecture ResNet-18 with the similar parameters.

This paper presents a Progressively-connected Light Field network (ProLiF),
for the novel view synthesis of complex forward-facing scenes. ProLiF encodes a
4D light field, which allows rendering a large batch of rays in one training
step for image- or patch-level losses. Directly learning a neural light field
from images has difficulty in rendering multi-view consistent images due to its
unawareness of the underlying 3D geometry. To address this problem, we propose
a progressive training scheme and regularization losses to infer the underlying
geometry during training, both of which enforce the multi-view consistency and
thus greatly improves the rendering quality. Experiments demonstrate that our
method is able to achieve significantly better rendering quality than the
vanilla neural light fields and comparable results to NeRF-like rendering
methods on the challenging LLFF dataset and Shiny Object dataset. Moreover, we
demonstrate better compatibility with LPIPS loss to achieve robustness to
varying light conditions and CLIP loss to control the rendering style of the
scene. Project page: https://totoro97.github.io/projects/prolif.

Predicting clinical outcomes to anti-cancer drugs on a personalized basis is
challenging in cancer treatment due to the heterogeneity of tumors. Traditional
computational efforts have been made to model the effect of drug response on
individual samples depicted by their molecular profile, yet overfitting occurs
because of the high dimension for omics data, hindering models from clinical
application. Recent research shows that deep learning is a promising approach
to build drug response models by learning alignment patterns between drugs and
samples. However, existing studies employed the simple feature fusion strategy
and only considered the drug features as a whole representation while ignoring
the substructure information that may play a vital role when aligning drugs and
genes. Hereby in this paper, we propose TCR (Transformer based network for
Cancer drug Response) to predict anti-cancer drug response. By utilizing an
attention mechanism, TCR is able to learn the interactions between drug
atom/sub-structure and molecular signatures efficiently in our study.
Furthermore, a dual loss function and cross sampling strategy were designed to
improve the prediction power of TCR. We show that TCR outperformed all other
methods under various data splitting strategies on all evaluation matrices
(some with significant improvement). Extensive experiments demonstrate that TCR
shows significantly improved generalization ability on independent in-vitro
experiments and in-vivo real patient data. Our study highlights the prediction
power of TCR and its potential value for cancer drug repurpose and precision
oncology treatment.

Extreme Classification (XC) seeks to tag data points with the most relevant
subset of labels from an extremely large label set. Performing deep XC with
dense, learnt representations for data points and labels has attracted much
attention due to its superiority over earlier XC methods that used sparse,
hand-crafted features. Negative mining techniques have emerged as a critical
component of all deep XC methods that allow them to scale to millions of
labels. However, despite recent advances, training deep XC models with large
encoder architectures such as transformers remains challenging. This paper
identifies that memory overheads of popular negative mining techniques often
force mini-batch sizes to remain small and slow training down. In response,
this paper introduces NGAME, a light-weight mini-batch creation technique that
offers provably accurate in-batch negative samples. This allows training with
larger mini-batches offering significantly faster convergence and higher
accuracies than existing negative sampling techniques. NGAME was found to be up
to 16% more accurate than state-of-the-art methods on a wide array of benchmark
datasets for extreme classification, as well as 3% more accurate at retrieving
search engine queries in response to a user webpage visit to show personalized
ads. In live A/B tests on a popular search engine, NGAME yielded up to 23%
gains in click-through-rates.

Monocular 3D object detection is an essential perception task for autonomous
driving. However, the high reliance on large-scale labeled data make it costly
and time-consuming during model optimization. To reduce such over-reliance on
human annotations, we propose Mix-Teaching, an effective semi-supervised
learning framework applicable to employ both labeled and unlabeled images in
training stage. Mix-Teaching first generates pseudo-labels for unlabeled images
by self-training. The student model is then trained on the mixed images
possessing much more intensive and precise labeling by merging instance-level
image patches into empty backgrounds or labeled images. This is the first to
break the image-level limitation and put high-quality pseudo labels from multi
frames into one image for semi-supervised training. Besides, as a result of the
misalignment between confidence score and localization quality, it's hard to
discriminate high-quality pseudo-labels from noisy predictions using only
confidence-based criterion. To that end, we further introduce an
uncertainty-based filter to help select reliable pseudo boxes for the above
mixing operation. To the best of our knowledge, this is the first unified SSL
framework for monocular 3D object detection. Mix-Teaching consistently improves
MonoFlex and GUPNet by significant margins under various labeling ratios on
KITTI dataset. For example, our method achieves around +6.34% AP@0.7
improvement against the GUPNet baseline on validation set when using only 10%
labeled data. Besides, by leveraging full training set and the additional 48K
raw images of KITTI, it can further improve the MonoFlex by +4.65% improvement
on AP@0.7 for car detection, reaching 18.54% AP@0.7, which ranks the 1st place
among all monocular based methods on KITTI test leaderboard. The code and
pretrained models will be released at
https://github.com/yanglei18/Mix-Teaching.

We investigate the morphology and size distribution of satellite droplets
resulting from the interaction of a freely falling water droplet with a
swirling airstream of different strengths by employing shadowgraphy and
deep-learning-based digital in-line holography techniques. We found that the
droplet exhibits vibrational, retracting bag and normal breakup phenomena for
the no swirl, low and high swirl strengths for the same aerodynamic field. In
the high swirl scenario, the disintegrations of the nodes, rim, and bag-film
contribute to the number mean diameter, resulting in smaller satellite
droplets. In contrast, in the low swirl case, the breakup of the rim and nodes
only contributes to the size distribution, resulting in larger droplets. The
temporal variation of the Sauter mean diameter reveals that for a given
aerodynamic force, a high swirl strength produces more surface area and surface
energy than a low swirl strength. The theoretical prediction of the number-mean
probability density of tiny satellite droplets under swirl conditions agrees
with experimental data. However, for the low swirl, the predictions differ from
the experimental results, particularly due to the presence of large satellite
droplets. Our results reveal that the volume-weighted droplet size distribution
exhibits two (bi-modal) and three (multi-model) peaks for low and high swirl
strengths, respectively. The analytical model that takes into account various
mechanisms, such as the nodes, rim, and bag breakups, accurately predicts the
shape and characteristic sizes of each mode for the case of high swirl
strength.

Recently, physiological signal-based biometric systems have received wide
attention. Unlike traditional biometric features, physiological signals can not
be easily compromised (usually unobservable to human eyes).
Photoplethysmography (PPG) signal is easy to measure, making it more attractive
than many other physiological signals for biometric authentication. However,
with the advent of remote PPG (rPPG), unobservability has been challenged when
the attacker can remotely steal the rPPG signals by monitoring the victim's
face, subsequently posing a threat to PPG-based biometrics. In PPG-based
biometric authentication, current attack approaches mandate the victim's PPG
signal, making rPPG-based attacks neglected. In this paper, we firstly analyze
the security of PPG-based biometrics, including user authentication and
communication protocols. We evaluate the signal waveforms, heart rate and
inter-pulse-interval information extracted by five rPPG methods, including four
traditional optical computing methods (CHROM, POS, LGI, PCA) and one deep
learning method (CL_rPPG). We conducted experiments on five datasets (PURE,
UBFC_rPPG, UBFC_Phys, LGI_PPGI, and COHFACE) to collect a comprehensive set of
results. Our empirical studies show that rPPG poses a serious threat to the
authentication system. The success rate of the rPPG signal spoofing attack in
the user authentication system reached 0.35. The bit hit rate is 0.6 in
inter-pulse-interval-based security protocols. Further, we propose an active
defence strategy to hide the physiological signals of the face to resist the
attack. It reduces the success rate of rPPG spoofing attacks in user
authentication to 0.05. The bit hit rate was reduced to 0.5, which is at the
level of a random guess. Our strategy effectively prevents the exposure of PPG
signals to protect users' sensitive physiological data.

The continuous growth of data complexity requires methods and models that
adequately account for non-trivial structures, as any simplification may induce
loss of information. Many analytical tools have been introduced to work with
complex data objects in their original form, but such tools can typically deal
with single-type variables only. In this work, we propose Energy Trees as a
model for regression and classification tasks where covariates are potentially
both structured and of different types. Energy Trees incorporate Energy
Statistics to generalize Conditional Trees, from which they inherit
statistically sound foundations, interpretability, scale invariance, and lack
of distributional assumptions. We focus on functions and graphs as structured
covariates and we show how the model can be easily adapted to work with almost
any other type of variable. Through an extensive simulation study, we highlight
the good performance of our proposal in terms of variable selection and
robustness to overfitting. Finally, we validate the model's predictive ability
through two empirical analyses with human biological data.

Goal-conditioned policies for robotic navigation can be trained on large,
unannotated datasets, providing for good generalization to real-world settings.
However, particularly in vision-based settings where specifying goals requires
an image, this makes for an unnatural interface. Language provides a more
convenient modality for communication with robots, but contemporary methods
typically require expensive supervision, in the form of trajectories annotated
with language descriptions. We present a system, LM-Nav, for robotic navigation
that enjoys the benefits of training on unannotated large datasets of
trajectories, while still providing a high-level interface to the user. Instead
of utilizing a labeled instruction following dataset, we show that such a
system can be constructed entirely out of pre-trained models for navigation
(ViNG), image-language association (CLIP), and language modeling (GPT-3),
without requiring any fine-tuning or language-annotated robot data. We
instantiate LM-Nav on a real-world mobile robot and demonstrate long-horizon
navigation through complex, outdoor environments from natural language
instructions. For videos of our experiments, code release, and an interactive
Colab notebook that runs in your browser, please check out our project page
https://sites.google.com/view/lmnav

Previous unsupervised domain adaptation (UDA) methods aim to promote target
learning via a single-directional knowledge transfer from label-rich source
domain to unlabeled target domain, while its reverse adaption from target to
source has not jointly been considered yet so far. In fact, in some real
teaching practice, a teacher helps students learn while also gets promotion
from students to some extent, which inspires us to explore a dual-directional
knowledge transfer between domains, and thus propose a Dual-Correction
Adaptation Network (DualCAN) in this paper. However, due to the asymmetrical
label knowledge across domains, transfer from unlabeled target to labeled
source poses a more difficult challenge than the common source-to-target
counterpart. First, the target pseudo-labels predicted by source commonly
involve noises due to model bias, hence in the reverse adaptation, they may
hurt the source performance and bring a negative target-to-source transfer.
Secondly, source domain usually contains innate noises, which will inevitably
aggravate the target noises, leading to noise amplification across domains. To
this end, we further introduce a Noise Identification and Correction (NIC)
module to correct and recycle noises in both domains. To our best knowledge,
this is the first naive attempt of dual-directional adaptation for noisy UDA,
and naturally applicable to noise-free UDA. A theory justification is given to
state the rationality of our intuition. Empirical results confirm the
effectiveness of DualCAN with remarkable performance gains over
state-of-the-arts, particularly for extreme noisy tasks (e.g., ~+ 15% on Pw->Pr
and Pr->Rw of Office-Home).

In this report, we describe the technical details of our submission to the
EPIC-Kitchens Action Anticipation Challenge 2022. In this competition, we
develop the following two approaches. 1) Anticipation Time Knowledge
Distillation using the soft labels learned by the teacher model as knowledge to
guide the student network to learn the information of anticipation time; 2)
Verb-Noun Relation Module for building the relationship between verbs and
nouns. Our method achieves state-of-the-art results on the testing set of
EPIC-Kitchens Action Anticipation Challenge 2022.

In this paper, we focus on exploring effective methods for faster, accurate,
and domain agnostic semantic segmentation. Inspired by the Optical Flow for
motion alignment between adjacent video frames, we propose a Flow Alignment
Module (FAM) to learn \textit{Semantic Flow} between feature maps of adjacent
levels, and broadcast high-level features to high resolution features
effectively and efficiently. Furthermore, integrating our FAM to a common
feature pyramid structure exhibits superior performance over other real-time
methods even on light-weight backbone networks, such as ResNet-18 and DFNet.
Then to further speed up the inference procedure, we also present a novel Gated
Dual Flow Alignment Module to directly align high resolution feature maps and
low resolution feature maps where we term improved version network as
SFNet-Lite. Extensive experiments are conducted on several challenging
datasets, where results show the effectiveness of both SFNet and SFNet-Lite. In
particular, the proposed SFNet-Lite series achieve 80.1 mIoU while running at
60 FPS using ResNet-18 backbone and 78.8 mIoU while running at 120 FPS using
STDC backbone on RTX-3090. Moreover, we unify four challenging driving datasets
(i.e., Cityscapes, Mapillary, IDD and BDD) into one large dataset, which we
named Unified Driving Segmentation (UDS) dataset. It contains diverse domain
and style information. We benchmark several representative works on UDS. Both
SFNet and SFNet-Lite still achieve the best speed and accuracy trade-off on UDS
which serves as a strong baseline in such a new challenging setting. All the
code and models are publicly available at https://github.com/lxtGH/SFSegNets.

Scene segmentation in images is a fundamental yet challenging problem in
visual content understanding, which is to learn a model to assign every image
pixel to a categorical label. One of the challenges for this learning task is
to consider the spatial and semantic relationships to obtain descriptive
feature representations, so learning the feature maps from multiple scales is a
common practice in scene segmentation. In this paper, we explore the effective
use of self-attention within multi-scale image windows to learn descriptive
visual features, then propose three different strategies to aggregate these
feature maps to decode the feature representation for dense prediction. Our
design is based on the recently proposed Swin Transformer models, which totally
discards convolution operations. With the simple yet effective multi-scale
feature learning and aggregation, our models achieve very promising performance
on four public scene segmentation datasets, PASCAL VOC2012, COCO-Stuff 10K,
ADE20K and Cityscapes.

Transformers are built upon multi-head scaled dot-product attention and
positional encoding, which aim to learn the feature representations and token
dependencies. In this work, we focus on enhancing the distinctive
representation by learning to augment the feature maps with the self-attention
mechanism in Transformers. Specifically, we propose the horizontal attention to
re-weight the multi-head output of the scaled dot-product attention before
dimensionality reduction, and propose the vertical attention to adaptively
re-calibrate channel-wise feature responses by explicitly modelling
inter-dependencies among different channels. We demonstrate the Transformer
models equipped with the two attentions have a high generalization capability
across different supervised learning tasks, with a very minor additional
computational cost overhead. The proposed horizontal and vertical attentions
are highly modular, which can be inserted into various Transformer models to
further improve the performance. Our code is available in the supplementary
material.

We present a self-supervised learning (SSL) method suitable for semi-global
tasks such as object detection and semantic segmentation. We enforce local
consistency between self-learned features, representing corresponding image
locations of transformed versions of the same image, by minimizing a
pixel-level local contrastive (LC) loss during training. LC-loss can be added
to existing self-supervised learning methods with minimal overhead. We evaluate
our SSL approach on two downstream tasks -- object detection and semantic
segmentation, using COCO, PASCAL VOC, and CityScapes datasets. Our method
outperforms the existing state-of-the-art SSL approaches by 1.9% on COCO object
detection, 1.4% on PASCAL VOC detection, and 0.6% on CityScapes segmentation.

As camera and LiDAR sensors capture complementary information used in
autonomous driving, great efforts have been made to develop semantic
segmentation algorithms through multi-modality data fusion. However,
fusion-based approaches require paired data, i.e., LiDAR point clouds and
camera images with strict point-to-pixel mappings, as the inputs in both
training and inference, which seriously hinders their application in practical
scenarios. Thus, in this work, we propose the 2D Priors Assisted Semantic
Segmentation (2DPASS), a general training scheme, to boost the representation
learning on point clouds, by fully taking advantage of 2D images with rich
appearance. In practice, by leveraging an auxiliary modal fusion and
multi-scale fusion-to-single knowledge distillation (MSFSKD), 2DPASS acquires
richer semantic and structural information from the multi-modal data, which are
then online distilled to the pure 3D network. As a result, equipped with
2DPASS, our baseline shows significant improvement with only point cloud
inputs. Specifically, it achieves the state-of-the-arts on two large-scale
benchmarks (i.e. SemanticKITTI and NuScenes), including top-1 results in both
single and multiple scan(s) competitions of SemanticKITTI.

As the field of Graph Neural Networks (GNN) continues to grow, it experiences
a corresponding increase in the need for large, real-world datasets to train
and test new GNN models on challenging, realistic problems. Unfortunately, such
graph datasets are often generated from online, highly privacy-restricted
ecosystems, which makes research and development on these datasets hard, if not
impossible. This greatly reduces the amount of benchmark graphs available to
researchers, causing the field to rely only on a handful of publicly-available
datasets. To address this dilemma, we introduce a novel graph generative model,
Computation Graph Transformer (CGT) that can learn and reproduce the
distribution of real-world graphs in a privacy-enhanced way. Our proposed model
(1) generates effective benchmark graphs on which GNNs show similar task
performance as on the source graphs, (2) scales to process large-scale
real-world graphs, (3) guarantees privacy for end-users. Extensive experiments
across a vast body of graph generative models show that only our model can
successfully generate privacy-controlled, synthetic substitutes of large-scale
real-world graphs that can be effectively used to evaluate GNN models.

Before the recent success of deep learning methods for automated medical
image analysis, practitioners used handcrafted radiomic features to
quantitatively describe local patches of medical images. However, extracting
discriminative radiomic features relies on accurate pathology localization,
which is difficult to acquire in real-world settings. Despite advances in
disease classification and localization from chest X-rays, many approaches fail
to incorporate clinically-informed domain knowledge. For these reasons, we
propose a Radiomics-Guided Transformer (RGT) that fuses \textit{global} image
information with \textit{local} knowledge-guided radiomics information to
provide accurate cardiopulmonary pathology localization and classification
\textit{without any bounding box annotations}. RGT consists of an image
Transformer branch, a radiomics Transformer branch, and fusion layers that
aggregate image and radiomic information. Using the learned self-attention of
its image branch, RGT extracts a bounding box for which to compute radiomic
features, which are further processed by the radiomics branch; learned image
and radiomic features are then fused and mutually interact via cross-attention
layers. Thus, RGT utilizes a novel end-to-end feedback loop that can bootstrap
accurate pathology localization only using image-level disease labels.
Experiments on the NIH ChestXRay dataset demonstrate that RGT outperforms prior
works in weakly supervised disease localization (by an average margin of 3.6\%
over various intersection-over-union thresholds) and classification (by 1.1\%
in average area under the receiver operating characteristic curve). We publicly
release our codes and pre-trained models at
\url{https://github.com/VITA-Group/chext}.

We propose efficient Langevin Monte Carlo algorithms for sampling
distributions with nonsmooth convex composite potentials, which is the sum of a
continuously differentiable function and a possibly nonsmooth function. We
devise such algorithms leveraging recent advances in convex analysis and
optimization methods involving Bregman divergences, namely the Bregman--Moreau
envelopes and the Bregman proximity operators, and in the Langevin Monte Carlo
algorithms reminiscent of mirror descent. The proposed algorithms extend
existing Langevin Monte Carlo algorithms in two aspects -- the ability to
sample nonsmooth distributions with mirror descent-like algorithms, and the use
of the more general Bregman--Moreau envelope in place of the Moreau envelope as
a smooth approximation of the nonsmooth part of the potential. A particular
case of the proposed scheme is reminiscent of the Bregman proximal gradient
algorithm. The efficiency of the proposed methodology is illustrated with
various sampling tasks at which existing Langevin Monte Carlo methods are known
to perform poorly.

We introduce a new algorithm for numerical composition of privacy random
variables, useful for computing the accurate differential privacy parameters
for composition of mechanisms. Our algorithm achieves a running time and memory
usage of $\mathrm{polylog}(k)$ for the task of self-composing a mechanism, from
a broad class of mechanisms, $k$ times; this class, e.g., includes the
sub-sampled Gaussian mechanism, that appears in the analysis of differentially
private stochastic gradient descent. By comparison, recent work by Gopi et al.
(NeurIPS 2021) has obtained a running time of $\widetilde{O}(\sqrt{k})$ for the
same task. Our approach extends to the case of composing $k$ different
mechanisms in the same class, improving upon their running time and memory
usage from $\widetilde{O}(k^{1.5})$ to $\widetilde{O}(k)$.

The privacy loss distribution (PLD) provides a tight characterization of the
privacy loss of a mechanism in the context of differential privacy (DP). Recent
work has shown that PLD-based accounting allows for tighter $(\varepsilon,
\delta)$-DP guarantees for many popular mechanisms compared to other known
methods. A key question in PLD-based accounting is how to approximate any
(potentially continuous) PLD with a PLD over any specified discrete support.
  We present a novel approach to this problem. Our approach supports both
pessimistic estimation, which overestimates the hockey-stick divergence (i.e.,
$\delta$) for any value of $\varepsilon$, and optimistic estimation, which
underestimates the hockey-stick divergence. Moreover, we show that our
pessimistic estimate is the best possible among all pessimistic estimates.
Experimental evaluation shows that our approach can work with much larger
discretization intervals while keeping a similar error bound compared to
previous approaches and yet give a better approximation than existing methods.

We study the task of node classification for graph neural networks (GNNs) and
establish a connection between group fairness, as measured by statistical
parity and equal opportunity, and local assortativity, i.e., the tendency of
linked nodes to have similar attributes. Such assortativity is often induced by
homophily, the tendency for nodes of similar properties to connect. Homophily
can be common in social networks where systemic factors have forced individuals
into communities which share a sensitive attribute. Through synthetic graphs,
we study the interplay between locally occurring homophily and fair
predictions, finding that not all node neighborhoods are equal in this respect
-- neighborhoods dominated by one category of a sensitive attribute often
struggle to obtain fair treatment, especially in the case of diverging local
class and sensitive attribute homophily. After determining that a relationship
between local homophily and fairness exists, we investigate if the issue of
unfairness can be associated to the design of the applied GNN model. We show
that by adopting heterophilous GNN designs capable of handling disassortative
group labels, group fairness in locally heterophilous neighborhoods can be
improved by up to 25% over homophilous designs in real and synthetic datasets.

Coronavirus disease or COVID-19 is an infectious disease caused by the
SARS-CoV-2 virus. The first confirmed case caused by this virus was found at
the end of December 2019 in Wuhan City, China. This case then spread throughout
the world, including Indonesia. Therefore, the COVID-19 case was designated as
a global pandemic by WHO. The growth of COVID-19 cases, especially in
Indonesia, can be predicted using several approaches, such as the Deep Neural
Network (DNN). One of the DNN models that can be used is Deep Transformer which
can predict time series. The model is trained with several test scenarios to
get the best model. The evaluation is finding the best hyperparameters. Then,
further evaluation was carried out using the best hyperparameters setting of
the number of prediction days, the optimizer, the number of features, and
comparison with the former models of the Long Short-Term Memory (LSTM) and
Recurrent Neural Network (RNN). All evaluations used metric of the Mean
Absolute Percentage Error (MAPE). Based on the results of the evaluations, Deep
Transformer produces the best results when using the Pre-Layer Normalization
and predicting one day ahead with a MAPE value of 18.83. Furthermore, the model
trained with the Adamax optimizer obtains the best performance among other
tested optimizers. The performance of the Deep Transformer also exceeds other
test models, which are LSTM and RNN.

Humans have a remarkable ability to quickly and effectively learn new
concepts in a continuous manner without forgetting old knowledge. Though deep
learning has made tremendous successes on various computer vision tasks, it
faces challenges for achieving such human-level intelligence. In this paper, we
define a new problem called continual few-shot learning, in which tasks arrive
sequentially and each task is associated with a few training samples. We
propose Continual Meta-Learner (CML) to solve this problem. CML integrates
metric-based classification and a memory-based mechanism along with adversarial
learning into a meta-learning framework, which leads to the desirable
properties: 1) it can quickly and effectively learn to handle a new task; 2) it
overcomes catastrophic forgetting; 3) it is model-agnostic. We conduct
extensive experiments on two image datasets, MiniImageNet and CIFAR100.
Experimental results show that CML delivers state-of-the-art performance in
terms of classification accuracy on few-shot learning tasks without
catastrophic forgetting.

Financial literature consists of ample research on similarity and comparison
of financial assets and securities such as stocks, bonds, mutual funds, etc.
However, going beyond correlations or aggregate statistics has been arduous
since financial datasets are noisy, lack useful features, have missing data and
often lack ground truth or annotated labels. However, though similarity
extrapolated from these traditional models heuristically may work well on an
aggregate level, such as risk management when looking at large portfolios, they
often fail when used for portfolio construction and trading which require a
local and dynamic measure of similarity on top of global measure. In this paper
we propose a supervised similarity framework for corporate bonds which allows
for inference based on both local and global measures. From a machine learning
perspective, this paper emphasis that random forest (RF), which is usually
viewed as a supervised learning algorithm, can also be used as a similarity
learning (more specifically, a distance metric learning) algorithm. In
addition, this framework proposes a novel metric to evaluate similarities, and
analyses other metrics which further demonstrate that RF outperforms all other
methods experimented with, in this work.

Increasingly, human behavior is captured on mobile devices, leading to an
increased interest in automated human activity recognition. However, existing
datasets typically consist of scripted movements. Our long-term goal is to
perform mobile activity recognition in natural settings. We collect a dataset
to support this goal with activity categories that are relevant for downstream
tasks such as health monitoring and intervention. Because of the large
variations present in human behavior, we collect data from many participants
across two different age groups. Because human behavior can change over time,
we also collect data from participants over a month's time to capture the
temporal drift. We hypothesize that mobile activity recognition can benefit
from unsupervised domain adaptation algorithms. To address this need and test
this hypothesis, we analyze the performance of domain adaptation across people
and across time. We then enhance unsupervised domain adaptation with
contrastive learning and with weak supervision when label proportions are
available. The dataset is available at
https://github.com/WSU-CASAS/smartwatch-data

Traversing intersections is a challenging problem for autonomous vehicles,
especially when the intersections do not have traffic control. Recently deep
reinforcement learning has received massive attention due to its success in
dealing with autonomous driving tasks. In this work, we address the problem of
traversing unsignalized intersections using a novel curriculum for deep
reinforcement learning. The proposed curriculum leads to: 1) A faster training
process for the reinforcement learning agent, and 2) Better performance
compared to an agent trained without curriculum. Our main contribution is
two-fold: 1) Presenting a unique curriculum for training deep reinforcement
learning agents, and 2) showing the application of the proposed curriculum for
the unsignalized intersection traversal task. The framework expects processed
observations of the surroundings from the perception system of the autonomous
vehicle. We test our method in the CommonRoad motion planning simulator on
T-intersections and four-way intersections.

Considering that acoustic scenes and sound events are closely related to each
other, in some previous papers, a joint analysis of acoustic scenes and sound
events utilizing multitask learning (MTL)-based neural networks was proposed.
In conventional methods, a strongly supervised scheme is applied to sound event
detection in MTL models, which requires strong labels of sound events in model
training; however, annotating strong event labels is quite time-consuming. In
this paper, we thus propose a method for the joint analysis of acoustic scenes
and sound events based on the MTL framework with weak labels of sound events.
In particular, in the proposed method, we introduce the multiple-instance
learning scheme for weakly supervised training of sound event detection and
evaluate four pooling functions, namely, max pooling, average pooling,
exponential softmax pooling, and attention pooling. Experimental results
obtained using parts of the TUT Acoustic Scenes 2016/2017 and TUT Sound Events
2016/2017 datasets show that the proposed MTL-based method with weak labels
outperforms the conventional single-task-based scene classification and event
detection models with weak labels in terms of both the scene classification and
event detection performances.

We present a large-scale comparative study of self-supervised speech
representation (S3R)-based voice conversion (VC). In the context of
recognition-synthesis VC, S3Rs are attractive owing to their potential to
replace expensive supervised representations such as phonetic posteriorgrams
(PPGs), which are commonly adopted by state-of-the-art VC systems. Using
S3PRL-VC, an open-source VC software we previously developed, we provide a
series of in-depth objective and subjective analyses under three VC settings:
intra-/cross-lingual any-to-one (A2O) and any-to-any (A2A) VC, using the voice
conversion challenge 2020 (VCC2020) dataset. We investigated S3R-based VC in
various aspects, including model type, multilinguality, and supervision. We
also studied the effect of a post-discretization process with k-means
clustering and showed how it improves in the A2A setting. Finally, the
comparison with state-of-the-art VC systems demonstrates the competitiveness of
S3R-based VC and also sheds light on the possible improving directions.

This primer is an attempt to provide a detailed summary of the different
facets of lifelong learning. We start with Chapter 2 which provides a
high-level overview of lifelong learning systems. In this chapter, we discuss
prominent scenarios in lifelong learning (Section 2.4), provide 8 Introduction
a high-level organization of different lifelong learning approaches (Section
2.5), enumerate the desiderata for an ideal lifelong learning system (Section
2.6), discuss how lifelong learning is related to other learning paradigms
(Section 2.7), describe common metrics used to evaluate lifelong learning
systems (Section 2.8). This chapter is more useful for readers who are new to
lifelong learning and want to get introduced to the field without focusing on
specific approaches or benchmarks. The remaining chapters focus on specific
aspects (either learning algorithms or benchmarks) and are more useful for
readers who are looking for specific approaches or benchmarks. Chapter 3
focuses on regularization-based approaches that do not assume access to any
data from previous tasks. Chapter 4 discusses memory-based approaches that
typically use a replay buffer or an episodic memory to save subset of data
across different tasks. Chapter 5 focuses on different architecture families
(and their instantiations) that have been proposed for training lifelong
learning systems. Following these different classes of learning algorithms, we
discuss the commonly used evaluation benchmarks and metrics for lifelong
learning (Chapter 6) and wrap up with a discussion of future challenges and
important research directions in Chapter 7.

Most deep learning algorithms lack explanations for their predictions, which
limits their deployment in clinical practice. Approaches to improve
explainability, especially in medical imaging, have often been shown to convey
limited information, be overly reassuring, or lack robustness. In this work, we
introduce the task of generating natural language explanations (NLEs) to
justify predictions made on medical images. NLEs are human-friendly and
comprehensive, and enable the training of intrinsically explainable models. To
this goal, we introduce MIMIC-NLE, the first, large-scale, medical imaging
dataset with NLEs. It contains over 38,000 NLEs, which explain the presence of
various thoracic pathologies and chest X-ray findings. We propose a general
approach to solve the task and evaluate several architectures on this dataset,
including via clinician assessment.

We study distributed optimization methods based on the {\em local training
(LT)} paradigm: achieving communication efficiency by performing richer local
gradient-based training on the clients before parameter averaging. Looking back
at the progress of the field, we {\em identify 5 generations of LT methods}: 1)
heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The
5${}^{\rm th}$ generation, initiated by the ProxSkip method of Mishchenko,
Malinovsky, Stich and Richt\'{a}rik (2022) and its analysis, is characterized
by the first theoretical confirmation that LT is a communication acceleration
mechanism. Inspired by this recent progress, we contribute to the 5${}^{\rm
th}$ generation of LT methods by showing that it is possible to enhance them
further using {\em variance reduction}. While all previous theoretical results
for LT methods ignore the cost of local work altogether, and are framed purely
in terms of the number of communication rounds, we show that our methods can be
substantially faster in terms of the {\em total training cost} than the
state-of-the-art method ProxSkip in theory and practice in the regime when
local computation is sufficiently expensive. We characterize this threshold
theoretically, and confirm our theoretical predictions with empirical results.

Capturing intricate biological phenomena often requires multiscale modeling
where coarse and inexpensive models are developed using limited components of
expensive and high-fidelity models. Here, we consider such a multiscale
framework in the context of cancer biology and address the challenge of
evaluating the descriptive capabilities of a continuum model developed using
1-dimensional statistics from a molecular dynamics model. Using deep learning,
we develop a highly predictive classification model that identifies complex and
emergent behavior from the continuum model. With over 99.9% accuracy
demonstrated for two simulations, our approach confirms the existence of
protein-specific "lipid fingerprints", i.e. spatial rearrangements of lipids in
response to proteins of interest. Through this demonstration, our model also
provides external validation of the continuum model, affirms the value of such
multiscale modeling, and can foster new insights through further analysis of
these fingerprints.

Federated Learning (FL) is a variant of distributed learning where edge
devices collaborate to learn a model without sharing their data with the
central server or each other. We refer to the process of training multiple
independent models simultaneously in a federated setting using a common pool of
clients as multi-model FL. In this work, we propose two variants of the popular
FedAvg algorithm for multi-model FL, with provable convergence guarantees. We
further show that for the same amount of computation, multi-model FL can have
better performance than training each model separately. We supplement our
theoretical results with experiments in strongly convex, convex, and non-convex
settings.

Tensor train decomposition is widely used in machine learning and quantum
physics due to its concise representation of high-dimensional tensors,
overcoming the curse of dimensionality. Cross approximation-originally
developed for representing a matrix from a set of selected rows and columns-is
an efficient method for constructing a tensor train decomposition of a tensor
from few of its entries. While tensor train cross approximation has achieved
remarkable performance in practical applications, its theoretical analysis, in
particular regarding the error of the approximation, is so far lacking. To our
knowledge, existing results only provide element-wise approximation accuracy
guarantees, which lead to a very loose bound when extended to the entire
tensor. In this paper, we bridge this gap by providing accuracy guarantees in
terms of the entire tensor for both exact and noisy measurements. Our results
illustrate how the choice of selected subtensors affects the quality of the
cross approximation and that the approximation error caused by model error
and/or measurement error may not grow exponentially with the order of the
tensor. These results are verified by numerical experiments, and may have
important implications for the usefulness of cross approximations for
high-order tensors, such as those encountered in the description of quantum
many-body states.

Traffic flow forecasting on graphs has real-world applications in many
fields, such as transportation system and computer networks. Traffic
forecasting can be highly challenging due to complex spatial-temporal
correlations and non-linear traffic patterns. Existing works mostly model such
spatial-temporal dependencies by considering spatial correlations and temporal
correlations separately and fail to model the direct spatial-temporal
correlations. Inspired by the recent success of transformers in the graph
domain, in this paper, we propose to directly model the cross-spatial-temporal
correlations on the spatial-temporal graph using local multi-head
self-attentions. To reduce the time complexity, we set the attention receptive
field to the spatially neighboring nodes, and we also introduce an adaptive
graph to capture the hidden spatial-temporal dependencies. Based on these
attention mechanisms, we propose a novel Adaptive Graph Spatial-Temporal
Transformer Network (ASTTN), which stacks multiple spatial-temporal attention
layers to apply self-attention on the input graph, followed by linear layers
for predictions. Experimental results on public traffic network datasets,
METR-LA PEMS-BAY, PeMSD4, and PeMSD7, demonstrate the superior performance of
our model.

Unsupervised image transfer enables intra- and inter-modality image
translation in applications where a large amount of paired training data is not
abundant. To ensure a structure-preserving mapping from the input to the target
domain, existing methods for unpaired image transfer are commonly based on
cycle-consistency, causing additional computational resources and instability
due to the learning of an inverse mapping. This paper presents a novel method
for uni-directional domain mapping that does not rely on any paired training
data. A proper transfer is achieved by using a GAN architecture and a novel
generator loss based on patch invariance. To be more specific, the generator
outputs are evaluated and compared at different scales, also leading to an
increased focus on high-frequency details as well as an implicit data
augmentation. This novel patch loss also offers the possibility to accurately
predict aleatoric uncertainty by modeling an input-dependent scale map for the
patch residuals. The proposed method is comprehensively evaluated on three
well-established medical databases. As compared to four state-of-the-art
methods, we observe significantly higher accuracy on these datasets, indicating
great potential of the proposed method for unpaired image transfer with
uncertainty taken into account. Implementation of the proposed framework is
released here:
\url{https://github.com/anger-man/unsupervised-image-transfer-and-uq}.

We propose in this paper a new paradigm for facial video compression. We
leverage the generative capacity of GANs such as StyleGAN to represent and
compress a video, including intra and inter compression. Each frame is inverted
in the latent space of StyleGAN, from which the optimal compression is learned.
To do so, a diffeomorphic latent representation is learned using a normalizing
flows model, where an entropy model can be optimized for image coding. In
addition, we propose a new perceptual loss that is more efficient than other
counterparts. Finally, an entropy model for video inter coding with residual is
also learned in the previously constructed latent representation. Our method
(SGANC) is simple, faster to train, and achieves better results for image and
video coding compared to state-of-the-art codecs such as VTM, AV1, and recent
deep learning techniques. In particular, it drastically minimizes perceptual
distortion at low bit rates.

Multi-person pose understanding from RGB videos includes three complex tasks:
pose estimation, tracking and motion forecasting. Among these three tasks, pose
estimation and tracking are correlated, and tracking is crucial to motion
forecasting. Most existing works either focus on a single task or employ
cascaded methods to solve each individual task separately. In this paper, we
propose Snipper, a framework to perform multi-person 3D pose estimation,
tracking and motion forecasting simultaneously in a single inference.
Specifically, we first propose a deformable attention mechanism to aggregate
spatiotemporal information from video snippets. Building upon this deformable
attention, a visual transformer is learned to encode the spatiotemporal
features from multi-frame images and to decode informative pose features to
update multi-person pose queries. Last, these queries are regressed to predict
multi-person pose trajectories and future motions in one forward pass. In the
experiments, we show the effectiveness of Snipper on three challenging public
datasets where a generic model rivals specialized state-of-art baselines for
pose estimation, tracking, and forecasting. Code is available at
https://github.com/JimmyZou/Snipper

Recommender systems employ machine learning models to learn from historical
data to predict the preferences of users. Deep neural network (DNN) models such
as neural collaborative filtering (NCF) are increasingly popular. However, the
tangibility and trustworthiness of the recommendations are questionable due to
the complexity and lack of explainability of the models. To enable
explainability, recent techniques such as ACCENT and FIA are looking for
counterfactual explanations that are specific historical actions of a user, the
removal of which leads to a change to the recommendation result. In this work,
we present a general framework for both DNN and non-DNN models so that the
counterfactual explainers all belong to it with specific choices of components.
This framework first estimates the influence of a certain historical action
after its removal and then uses search algorithms to find the minimal set of
such actions for the counterfactual explanation. With this framework, we are
able to investigate the relationship between the explainers and recommenders.
We empirically study two recommender models (NCF and Factorization Machine) and
two datasets (MovieLens and Yelp). We analyze the relationship between the
performance of the recommender and the quality of the explainer. We observe
that with standard evaluation metrics, the explainers deliver worse performance
when the recommendations are more accurate. This indicates that having good
explanations to correct predictions is harder than having them to wrong
predictions. The community needs more fine-grained evaluation metrics to
measure the quality of counterfactual explanations to recommender systems.

Diffusion models are a powerful class of generative models that iteratively
denoise samples to produce data. While many works have focused on the number of
iterations in this sampling procedure, few have focused on the cost of each
iteration. We find that adding a simple ViT-style patching transformation can
considerably reduce a diffusion model's sampling time and memory usage. We
justify our approach both through an analysis of the diffusion model objective,
and through empirical experiments on LSUN Church, ImageNet 256, and FFHQ 1024.
We provide implementations in Tensorflow and Pytorch.

MIT wanted to commission a large scale artwork that would serve to
'illuminate a new campus gateway, inaugurate a space of exchange between MIT
and Cambridge, and inspire our students, faculty, visitors, and the surrounding
community to engage with art in new ways and to have art be part of their daily
lives.' Among other things, the art was to reflect the fact that scientific
discovery is often the result of many individual contributions, both
acknowledged and unacknowledged. In this work, a group of computer scientists
collaborated with a conceptual artist to produce a collective signature, or a
signature learned from contributions of an entire community. After collecting
signatures from two communities -- the university, and the surrounding city --
the computer scientists developed generative models and a human-in-the-loop
feedback process to work with the artist create an original signature-like
structure representative of each community. These signatures are now
large-scale steel, LED and neon light sculptures that appear to sign two new
buildings in Cambridge, MA.

Despite the rapid progress on research in adversarial robustness of deep
neural networks (DNNs), there is little principled work for the time-series
domain. Since time-series data arises in diverse applications including mobile
health, finance, and smart grid, it is important to verify and improve the
robustness of DNNs for the time-series domain. In this paper, we propose a
novel framework for the time-series domain referred as {\em Dynamic Time
Warping for Adversarial Robustness (DTW-AR)} using the dynamic time warping
measure. Theoretical and empirical evidence is provided to demonstrate the
effectiveness of DTW over the standard Euclidean distance metric employed in
prior methods for the image domain. We develop a principled algorithm justified
by theoretical analysis to efficiently create diverse adversarial examples
using random alignment paths. Experiments on diverse real-world benchmarks show
the effectiveness of DTW-AR to fool DNNs for time-series data and to improve
their robustness using adversarial training. The source code of DTW-AR
algorithms is available at https://github.com/tahabelkhouja/DTW-AR

Time-series data arises in many real-world applications (e.g., mobile health)
and deep neural networks (DNNs) have shown great success in solving them.
Despite their success, little is known about their robustness to adversarial
attacks. In this paper, we propose a novel adversarial framework referred to as
Time-Series Attacks via STATistical Features (TSA-STAT)}. To address the unique
challenges of time-series domain, TSA-STAT employs constraints on statistical
features of the time-series data to construct adversarial examples. Optimized
polynomial transformations are used to create attacks that are more effective
(in terms of successfully fooling DNNs) than those based on additive
perturbations. We also provide certified bounds on the norm of the statistical
features for constructing adversarial examples. Our experiments on diverse
real-world benchmark datasets show the effectiveness of TSA-STAT in fooling
DNNs for time-series domain and in improving their robustness. The source code
of TSA-STAT algorithms is available at
https://github.com/tahabelkhouja/Time-Series-Attacks-via-STATistical-Features

Safe deployment of time-series classifiers for real-world applications relies
on the ability to detect the data which is not generated from the same
distribution as training data. This task is referred to as out-of-distribution
(OOD) detection. We consider the novel problem of OOD detection for the
time-series domain. We discuss the unique challenges posed by time-series data
and explain why prior methods from the image domain will perform poorly.
Motivated by these challenges, this paper proposes a novel {\em Seasonal Ratio
Scoring (SRS)} approach. SRS consists of three key algorithmic steps. First,
each input is decomposed into class-wise semantic component and remainder.
Second, this decomposition is employed to estimate the class-wise conditional
likelihoods of the input and remainder using deep generative models. The
seasonal ratio score is computed from these estimates. Third, a threshold
interval is identified from the in-distribution data to detect OOD examples.
Experiments on diverse real-world benchmarks demonstrate that the SRS method is
well-suited for time-series OOD detection when compared to baseline methods.
Open-source code for SRS method is provided at
https://github.com/tahabelkhouja/SRS

Despite the success of deep neural networks (DNNs) for real-world
applications over time-series data such as mobile health, little is known about
how to train robust DNNs for time-series domain due to its unique
characteristics compared to images and text data. In this paper, we propose a
novel algorithmic framework referred as RObust Training for Time-Series (RO-TS)
to create robust DNNs for time-series classification tasks. Specifically, we
formulate a min-max optimization problem over the model parameters by
explicitly reasoning about the robustness criteria in terms of additive
perturbations to time-series inputs measured by the global alignment kernel
(GAK) based distance. We also show the generality and advantages of our
formulation using the summation structure over time-series alignments by
relating both GAK and dynamic time warping (DTW). This problem is an instance
of a family of compositional min-max optimization problems, which are
challenging and open with unclear theoretical guarantee. We propose a
principled stochastic compositional alternating gradient descent ascent
(SCAGDA) algorithm for this family of optimization problems. Unlike traditional
methods for time-series that require approximate computation of distance
measures, SCAGDA approximates the GAK based distance on-the-fly using a moving
average approach. We theoretically analyze the convergence rate of SCAGDA and
provide strong theoretical support for the estimation of GAK based distance.
Our experiments on real-world benchmarks demonstrate that RO-TS creates more
robust DNNs when compared to adversarial training using prior methods that rely
on data augmentation or new definitions of loss functions. We also demonstrate
the importance of GAK for time-series data over the Euclidean distance. The
source code of RO-TS algorithms is available at
https://github.com/tahabelkhouja/Robust-Training-for-Time-Series

In actual industrial production, the assessment of the steel plate welding
effect is an important task, and the segmentation of the weld section is the
basis of the assessment. This paper proposes an industrial weld segmentation
network based on a deep learning semantic segmentation algorithm fused with
heatmap detail guidance and Image Matting to solve the automatic segmentation
problem of weld regions. In the existing semantic segmentation networks, the
boundary information can be preserved by fusing the features of both high-level
and low-level layers. However, this method can lead to insufficient expression
of the spatial information in the low-level layer, resulting in inaccurate
segmentation boundary positioning. We propose a detailed guidance module based
on heatmaps to fully express the segmented region boundary information in the
low-level network to address this problem. Specifically, the expression of
boundary information can be enhanced by adding a detailed branch to predict
segmented boundary and then matching it with the boundary heat map generated by
mask labels to calculate the mean square error loss. In addition, although deep
learning has achieved great success in the field of semantic segmentation, the
precision of the segmentation boundary region is not high due to the loss of
detailed information caused by the classical segmentation network in the
process of encoding and decoding process. This paper introduces a matting
algorithm to calibrate the boundary of the segmentation region of the semantic
segmentation network to solve this problem. Through many experiments on
industrial weld data sets, the effectiveness of our method is demonstrated, and
the MIOU reaches 97.93%. It is worth noting that this performance is comparable
to human manual segmentation ( MIOU 97.96%).

Deploying deep learning models on various devices has become an important
topic. The wave of hardware specialization brings a diverse set of acceleration
primitives for multi-dimensional tensor computations. These new acceleration
primitives, along with the emerging machine learning models, bring tremendous
engineering challenges. In this paper, we present TensorIR, a compiler
abstraction for optimizing programs with these tensor computation primitives.
TensorIR generalizes the loop nest representation used in existing machine
learning compilers to bring tensor computation as the first-class citizen.
Finally, we build an end-to-end framework on top of our abstraction to
automatically optimize deep learning models for given tensor computation
primitives. Experimental results show that TensorIR compilation automatically
uses the tensor computation primitives for given hardware backends and delivers
performance that is competitive to state-of-art hand-optimized systems across

Artificial intelligence has become pervasive across disciplines and fields,
and biomedical image and signal processing is no exception. The growing and
widespread interest on the topic has triggered a vast research activity that is
reflected in an exponential research effort. Through study of massive and
diverse biomedical data, machine and deep learning models have revolutionized
various tasks such as modeling, segmentation, registration, classification and
synthesis, outperforming traditional techniques. However, the difficulty in
translating the results into biologically/clinically interpretable information
is preventing their full exploitation in the field. Explainable AI (XAI)
attempts to fill this translational gap by providing means to make the models
interpretable and providing explanations. Different solutions have been
proposed so far and are gaining increasing interest from the community. This
paper aims at providing an overview on XAI in biomedical data processing and
points to an upcoming Special Issue on Deep Learning in Biomedical Image and
Signal Processing of the IEEE Signal Processing Magazine that is going to
appear in March 2022.

New models of random forests jointly using the attention and self-attention
mechanisms are proposed for solving the regression problem. The models can be
regarded as extensions of the attention-based random forest whose idea stems
from applying a combination of the Nadaraya-Watson kernel regression and the
Huber's contamination model to random forests. The self-attention aims to
capture dependencies of the tree predictions and to remove noise or anomalous
predictions in the random forest. The self-attention module is trained jointly
with the attention module for computing weights. It is shown that the training
process of attention weights is reduced to solving a single quadratic or linear
optimization problem. Three modifications of the general approach are proposed
and compared. A specific multi-head self-attention for the random forest is
also considered. Heads of the self-attention are obtained by changing its
tuning parameters including the kernel parameters and the contamination
parameter of models. Numerical experiments with various datasets illustrate the
proposed models and show that the supplement of the self-attention improves the
model performance for many datasets.

Considering the user mobility and unpredictable mobile edge computing (MEC)
environments, this paper studies the intelligent task offloading problem in
unmanned aerial vehicle (UAV)-enabled MEC with the assistance of digital twin
(DT). We aim at minimizing the energy consumption of the entire MEC system by
jointly optimizing mobile terminal users (MTUs) association, UAV trajectory,
transmission power distribution and computation capacity allocation while
respecting the constraints of mission maximum processing delays. Specifically,
double deep Q-network (DDQN) algorithm stemming from deep reinforcement
learning is first proposed to effectively solve the problem of MTUs association
and UAV trajectory. Then, the closed-form expression is employed to handle the
problem of transmission power distribution and the computation capacity
allocation problem is further addressed via an iterative algorithm. Numerical
results show that our proposed scheme is able to converge and significantly
reduce the total energy consumption of the MEC system compared to the benchmark
schemes.

Transformer-based models have demonstrated state-of-the-art performance in
many intelligent coding tasks such as code comment generation and code
completion. Previous studies show that deep learning models are sensitive to
the input variations, but few studies have systematically studied the
robustness of Transformer under perturbed input code. In this work, we
empirically study the effect of semantic-preserving code transformation on the
performance of Transformer. Specifically, 24 and 27 code transformation
strategies are implemented for two popular programming languages, Java and
Python, respectively. For facilitating analysis, the strategies are grouped
into five categories: block transformation, insertion/deletion transformation,
grammatical statement transformation, grammatical token transformation, and
identifier transformation. Experiments on three popular code intelligence
tasks, including code completion, code summarization and code search,
demonstrate insertion/deletion transformation and identifier transformation
show the greatest impact on the performance of Transformer. Our results also
suggest that Transformer based on abstract syntax trees (ASTs) shows more
robust performance than the model based on only code sequence under most code
transformations. Besides, the design of positional encoding can impact the
robustness of Transformer under code transformation. Based on our findings, we
distill some insights about the challenges and opportunities for
Transformer-based code intelligence.

This paper tackles the challenging problem of hyperspectral (HS) image
denoising. Unlike existing deep learning-based methods usually adopting
complicated network architectures or empirically stacking off-the-shelf modules
to pursue performance improvement, we focus on the efficient and effective
feature extraction manner for capturing the high-dimensional characteristics of
HS images. To be specific, based on the theoretical analysis that increasing
the rank of the matrix formed by the unfolded convolutional kernels can promote
feature diversity, we propose rank-enhanced low-dimensional convolution set
(Re-ConvSet), which separately performs 1-D convolution along the three
dimensions of an HS image side-by-side, and then aggregates the resulting
spatial-spectral embeddings via a learnable compression layer. Re-ConvSet not
only learns the diverse spatial-spectral features of HS images, but also
reduces the parameters and complexity of the network. We then incorporate
Re-ConvSet into the widely-used U-Net architecture to construct an HS image
denoising method. Surprisingly, we observe such a concise framework outperforms
the most recent method to a large extent in terms of quantitative metrics,
visual results, and efficiency. We believe our work may shed light on deep
learning-based HS image processing and analysis.

This article illustrates a novel Quantum Secure Aggregation (QSA) scheme that
is designed to provide highly secure and efficient aggregation of local model
parameters for federated learning. The scheme is secure in protecting private
model parameters from being disclosed to semi-honest attackers by utilizing
quantum bits i.e. qubits to represent model parameters. The proposed security
mechanism ensures that any attempts to eavesdrop private model parameters can
be immediately detected and stopped. The scheme is also efficient in terms of
the low computational complexity of transmitting and aggregating model
parameters through entangled qubits. Benefits of the proposed QSA scheme are
showcased in a horizontal federated learning setting in which both a
centralized and decentralized architectures are taken into account. It was
empirically demonstrated that the proposed QSA can be readily applied to
aggregate different types of local models including logistic regression (LR),
convolutional neural networks (CNN) as well as quantum neural network (QNN),
indicating the versatility of the QSA scheme. Performances of global models are
improved to various extents with respect to local models obtained by individual
participants, while no private model parameters are disclosed to semi-honest
adversaries.

In recommender systems, a common problem is the presence of various biases in
the collected data, which deteriorates the generalization ability of the
recommendation models and leads to inaccurate predictions. Doubly robust (DR)
learning has been studied in many tasks in RS, with the advantage that unbiased
learning can be achieved when either a single imputation or a single propensity
model is accurate. In this paper, we propose a multiple robust (MR) estimator
that can take the advantage of multiple candidate imputation and propensity
models to achieve unbiasedness. Specifically, the MR estimator is unbiased when
any of the imputation or propensity models, or a linear combination of these
models is accurate. Theoretical analysis shows that the proposed MR is an
enhanced version of DR when only having a single imputation and propensity
model, and has a smaller bias. Inspired by the generalization error bound of
MR, we further propose a novel multiple robust learning approach with
stabilization. We conduct extensive experiments on real-world and
semi-synthetic datasets, which demonstrates the superiority of the proposed
approach over state-of-the-art methods.

Schizophrenia (SZ) is a serious mental disorder that could seriously affect
the patient's quality of life. In recent years, detection of SZ based on deep
learning (DL) using electroencephalogram (EEG) has received increasing
attention. In this paper, we proposed an end-to-end recurrent auto-encoder
(RAE) model to detect SZ. In the RAE model, the raw data was input into one
auto-encoder block, and the reconstructed data were recurrently input into the
same block. The extracted code by auto-encoder block was simultaneously served
as an input of a classifier block to discriminate SZ patients from healthy
controls (HC). Evaluated on the dataset containing 14 SZ patients and 14 HC
subjects, and the proposed method achieved an average classification accuracy
of 81.81% in subject-independent experiment scenario. This study demonstrated
that the structure of RAE is able to capture the differential features between
SZ patients and HC subjects.

We propose a novel method for building fuzzy clusters of large data sets,
using a smoothing numerical approach. The usual sum-of-squares criterion is
relaxed so the search for good fuzzy partitions is made on a continuous space,
rather than a combinatorial space as in classical methods \cite{Hartigan}. The
smoothing allows a conversion from a strongly non-differentiable problem into
differentiable subproblems of optimization without constraints of low
dimension, by using a differentiable function of infinite class. For the
implementation of the algorithm we used the statistical software $R$ and the
results obtained were compared to the traditional fuzzy $C$--means method,
proposed by Bezdek.

Both in the domains of Feature Selection and Interpretable AI, there exists a
desire to `rank' features based on their importance. Such feature importance
rankings can then be used to either: (1) reduce the dataset size or (2)
interpret the Machine Learning model. In the literature, however, such Feature
Rankers are not evaluated in a systematic, consistent way. Many papers have a
different way of arguing which feature importance ranker works best. This paper
fills this gap, by proposing a new evaluation methodology. By making use of
synthetic datasets, feature importance scores can be known beforehand, allowing
more systematic evaluation. To facilitate large-scale experimentation using the
new methodology, a benchmarking framework was built in Python, called fseval.
The framework allows running experiments in parallel and distributed over
machines on HPC systems. By integrating with an online platform called Weights
and Biases, charts can be interactively explored on a live dashboard. The
software was released as open-source software, and is published as a package on
the PyPi platform. The research concludes by exploring one such large-scale
experiment, to find the strengths and weaknesses of the participating
algorithms, on many fronts.

Feedforward neural networks (FNNs) can be viewed as non-linear regression
models, where covariates enter the model through a combination of weighted
summations and non-linear functions. Although these models have some
similarities to the models typically used in statistical modelling, the
majority of neural network research has been conducted outside of the field of
statistics. This has resulted in a lack of statistically-based methodology,
and, in particular, there has been little emphasis on model parsimony.
Determining the input layer structure is analogous to variable selection, while
the structure for the hidden layer relates to model complexity. In practice,
neural network model selection is often carried out by comparing models using
out-of-sample performance. However, in contrast, the construction of an
associated likelihood function opens the door to information-criteria-based
variable and architecture selection. A novel model selection method, which
performs both input- and hidden-node selection, is proposed using the Bayesian
information criterion (BIC) for FNNs. The choice of BIC over out-of-sample
performance as the model selection objective function leads to an increased
probability of recovering the true model, while parsimoniously achieving
favourable out-of-sample performance. Simulation studies are used to evaluate
and justify the proposed method, and applications on real data are
investigated.

For semantic-guided cross-view image translation, it is crucial to learn
where to sample pixels from the source view image and where to reallocate them
guided by the target view semantic map, especially when there is little overlap
or drastic view difference between the source and target images. Hence, one not
only needs to encode the long-range dependencies among pixels in both the
source view image and target view the semantic map but also needs to translate
these learned dependencies. To this end, we propose a novel generative
adversarial network, PI-Trans, which mainly consists of a novel
Parallel-ConvMLP module and an Implicit Transformation module at multiple
semantic levels. Extensive experimental results show that the proposed PI-Trans
achieves the best qualitative and quantitative performance by a large margin
compared to the state-of-the-art methods on two challenging datasets. The code
will be made available at https://github.com/Amazingren/PI-Trans.

Deep reinforcement learning (DRL) is a machine learning-based method suited
for complex and high-dimensional control problems. In this study, a real-time
control system based on DRL is developed for long-term voltage stability
events. The possibility of using system services from demand response (DR) and
energy storage systems (ESS) as control measures to stabilize the system is
investigated. The performance of the DRL control is evaluated on a modified
Nordic32 test system. The results show that the DRL control quickly learns an
effective control policy that can handle the uncertainty involved when using DR
and ESS. The DRL control is compared to a rule-based load shedding scheme and
the DRL control is shown to stabilize the system both significantly faster and
with lesser load curtailment. Finally, when testing and evaluating the
performance on load and disturbance scenarios that were not included in the
training data, the robustness and generalization capability of the control were
shown to be effective.

Very large language models (LLMs), such as GPT-3 and Codex have achieved
state-of-the-art performance on several natural-language tasks, and show great
promise also for code. A particularly exciting aspect of LLMs is their knack
for few-shot and zero-shot learning: they can learn to perform a task with very
few examples. Few-shotting has particular synergies in software engineering,
where there are a lot of phenomena (identifier names, APIs, terminology, coding
patterns) that are known to be highly project-specific. However,
project-specific data can be quite limited, especially early in the history of
a project; thus the few-shot learning capacity of LLMs might be very relevant.
In this paper, we investigate the use few-shot training with the very large GPT
(Generative Pre-trained Transformer) Codex model, and find evidence suggesting
that one can significantly surpass state-of-the-art models for
code-summarization, leveraging project-specific training.

Neural networks are essential components of learning-based software systems.
However, their high compute, memory, and power requirements make using them in
low resources domains challenging. For this reason, neural networks are often
quantized before deployment. Existing quantization techniques tend to degrade
the network accuracy. We propose Counter-Example Guided Neural Network
Quantization Refinement (CEG4N). This technique combines search-based
quantization and equivalence verification: the former minimizes the
computational requirements, while the latter guarantees that the network's
output does not change after quantization. We evaluate CEG4N~on a diverse set
of benchmarks, including large and small networks. Our technique successfully
quantizes the networks in our evaluation while producing models with up to 72%
better accuracy than state-of-the-art techniques.

EigenDecomposition (ED) is at the heart of many computer vision algorithms
and applications. One crucial bottleneck limiting its usage is the expensive
computation cost, particularly for a mini-batch of matrices in the deep neural
networks. In this paper, we propose a QR-based ED method dedicated to the
application scenarios of computer vision. Our proposed method performs the ED
entirely by batched matrix/vector multiplication, which processes all the
matrices simultaneously and thus fully utilizes the power of GPUs. Our
technique is based on the explicit QR iterations by Givens rotation with double
Wilkinson shifts. With several acceleration techniques, the time complexity of
QR iterations is reduced from $O{(}n^5{)}$ to $O{(}n^3{)}$. The numerical test
shows that for small and medium batched matrices (\emph{e.g.,} $dim{<}32$) our
method can be much faster than the Pytorch SVD function. Experimental results
on visual recognition and image generation demonstrate that our methods also
achieve competitive performances.

The robustness and anomaly detection capability of neural networks are
crucial topics for their safe adoption in the real-world. Moreover, the
over-parameterization of recent networks comes with high computational costs
and raises questions about its influence on robustness and anomaly detection.
In this work, we show that sparsity can make networks more robust and better
anomaly detectors. To motivate this even further, we show that a pre-trained
neural network contains, within its parameter space, sparse subnetworks that
are better at these tasks without any further training. We also show that
structured sparsity greatly helps in reducing the complexity of expensive
robustness and detection methods, while maintaining or even improving their
results on these tasks. Finally, we introduce a new method, SensNorm, which
uses the sensitivity of weights derived from an appropriate pruning method to
detect anomalous samples in the input.

Over the past decades, the incidence of thyroid cancer has been increasing
globally. Accurate and early diagnosis allows timely treatment and helps to
avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both
transverse and longitudinal views using thyroid ultrasound. However, the
appearance of the thyroid gland and lesions can vary dramatically across
individuals. Identifying key diagnostic information from both views requires
specialized expertise. Furthermore, finding an optimal way to integrate
multi-view information also relies on the experience of clinicians and adds
further difficulty to accurate diagnosis. To address these, we propose a
personalized diagnostic tool that can customize its decision-making process for
different patients. It consists of a multi-view classification module for
feature extraction and a personalized weighting allocation network that
generates optimal weighting for different views. It is also equipped with a
self-supervised view-aware contrastive loss to further improve the model
robustness towards different patient groups. Experimental results show that the
proposed framework can better utilize multi-view information and outperform the

Measuring semantic similarity between job titles is an essential
functionality for automatic job recommendations. This task is usually
approached using supervised learning techniques, which requires training data
in the form of equivalent job title pairs. In this paper, we instead propose an
unsupervised representation learning method for training a job title similarity
model using noisy skill labels. We show that it is highly effective for tasks
such as text ranking and job normalization.

Financial time series simulation is a central topic since it extends the
limited real data for training and evaluation of trading strategies. It is also
challenging because of the complex statistical properties of the real financial
data. We introduce two generative adversarial networks (GANs), which utilize
the convolutional networks with attention and the transformers, for financial
time series simulation. The GANs learn the statistical properties in a
data-driven manner and the attention mechanism helps to replicate the
long-range dependencies. The proposed GANs are tested on the S&P 500 index and
option data, examined by scores based on the stylized facts and are compared
with the pure convolutional GAN, i.e. QuantGAN. The attention-based GANs not
only reproduce the stylized facts, but also smooth the autocorrelation of
returns.

Robots are good at performing repetitive tasks in modern manufacturing
industries. However, robot motions are mostly planned and preprogrammed with a
notable lack of adaptivity to task changes. Even for slightly changed tasks,
the whole system must be reprogrammed by robotics experts. Therefore, it is
highly desirable to have a flexible motion planning method, with which robots
can adapt to specific task changes in unstructured environments, such as
production systems or warehouses, with little or no intervention from
non-expert personnel. In this paper, we propose a user-guided motion planning
algorithm in combination with the reinforcement learning (RL) method to enable
robots automatically generate their motion plans for new tasks by learning from
a few kinesthetic human demonstrations. To achieve adaptive motion plans for a
specific application environment, e.g., desk assembly or warehouse
loading/unloading, a library is built by abstracting features of common human
demonstrated tasks. The definition of semantical similarity between features in
the library and features of a new task is proposed and further used to
construct the reward function in RL. The RL policy can automatically generate
motion plans for a new task if it determines that new task constraints can be
satisfied with the current library and request additional human demonstrations.
Multiple experiments conducted on common tasks and scenarios demonstrate that
the proposed user-guided RL-assisted motion planning method is effective.

The growing availability of data about online information behaviour enables
new possibilities for political communication research. However, the volume and
variety of these data makes them difficult to analyse and prompts the need for
developing automated content approaches relying on a broad range of natural
language processing techniques (e.g. machine learning- or neural network-based
ones). In this paper, we discuss how these techniques can be used to detect
political content across different platforms. Using three validation datasets,
which include a variety of political and non-political textual documents from
online platforms, we systematically compare the performance of three groups of
detection techniques relying on dictionaries, supervised machine learning, or
neural networks. We also examine the impact of different modes of data
preprocessing (e.g. stemming and stopword removal) on the low-cost
implementations of these techniques using a large set (n = 66) of detection
models. Our results show the limited impact of preprocessing on model
performance, with the best results for less noisy data being achieved by neural
network- and machine-learning-based models, in contrast to the more robust
performance of dictionary-based models on noisy data.

A determinantal point process (DPP) is an elegant model that assigns a
probability to every subset of a collection of $n$ items. While conventionally
a DPP is parameterized by a symmetric kernel matrix, removing this symmetry
constraint, resulting in nonsymmetric DPPs (NDPPs), leads to significant
improvements in modeling power and predictive performance. Recent work has
studied an approximate Markov chain Monte Carlo (MCMC) sampling algorithm for
NDPPs restricted to size-$k$ subsets (called $k$-NDPPs). However, the runtime
of this approach is quadratic in $n$, making it infeasible for large-scale
settings. In this work, we develop a scalable MCMC sampling algorithm for
$k$-NDPPs with low-rank kernels, thus enabling runtime that is sublinear in
$n$. Our method is based on a state-of-the-art NDPP rejection sampling
algorithm, which we enhance with a novel approach for efficiently constructing
the proposal distribution. Furthermore, we extend our scalable $k$-NDPP
sampling algorithm to NDPPs without size constraints. Our resulting sampling
method has polynomial time complexity in the rank of the kernel, while the
existing approach has runtime that is exponential in the rank. With both a
theoretical analysis and experiments on real-world datasets, we verify that our
scalable approximate sampling algorithms are orders of magnitude faster than
existing sampling approaches for $k$-NDPPs and NDPPs.

Human intention prediction provides an augmented solution for the design of
assistants and collaboration between the human driver and intelligent vehicles.
In this study, a multi-task sequential learning framework is developed to
predict future steering torques and steering postures based on the upper limb
neuromuscular Electromyography (EMG) signals. A single-right-hand driving mode
is particularly studied. For this driving mode, three different driving
postures are also evaluated. Then, a multi-task time-series transformer network
(MTS-Trans) is developed to predict the steering torques and driving postures.
To evaluate the multi-task learning performance, four different frameworks are
assessed. Twenty-one participants are involved in the driving simulator-based
experiment. The proposed model achieved accurate prediction results on the
future steering torque prediction and driving postures recognition for
single-hand driving modes. The proposed system can contribute to the
development of advanced driver steering assistant systems and ensure mutual
understanding between human drivers and intelligent vehicles.

Bayesian optimization (BO) is a widely used approach for computationally
expensive black-box optimization such as simulator calibration and
hyperparameter optimization of deep learning methods. In BO, a dynamically
updated computationally cheap surrogate model is employed to learn the
input-output relationship of the black-box function; this surrogate model is
used to explore and exploit the promising regions of the input space.
Multipoint BO methods adopt a single manager/multiple workers strategy to
achieve high-quality solutions in shorter time. However, the computational
overhead in multipoint generation schemes is a major bottleneck in designing BO
methods that can scale to thousands of workers. We present an
asynchronous-distributed BO (ADBO) method wherein each worker runs a search and
asynchronously communicates the input-output values of black-box evaluations
from all other workers without the manager. We scale our method up to 4,096
workers and demonstrate improvement in the quality of the solution and faster
convergence. We demonstrate the effectiveness of our approach for tuning the
hyperparameters of neural networks from the Exascale computing project CANDLE

Future airports are becoming more complex and congested with the increasing
number of travellers. While the airports are more likely to become hotspots for
potential conflicts to break out which can cause serious delays to flights and
several safety issues. An intelligent algorithm which renders security
surveillance more effective in detecting conflicts would bring many benefits to
the passengers in terms of their safety, finance, and travelling efficiency.
This paper details the development of a machine learning model to classify
conflicting behaviour in a crowd. HRNet is used to segment the images and then
two approaches are taken to classify the poses of people in the frame via
multiple classifiers. Among them, it was found that the support vector machine
(SVM) achieved the most performant achieving precision of 94.37%. Where the
model falls short is against ambiguous behaviour such as a hug or losing track
of a subject in the frame. The resulting model has potential for deployment
within an airport if improvements are made to cope with the vast number of
potential passengers in view as well as training against further ambiguous
behaviours which will arise in an airport setting. In turn, will provide the
capability to enhance security surveillance and improve airport safety.

Deep segmentation models often face the failure risks when the testing image
presents unseen distributions. Improving model robustness against these risks
is crucial for the large-scale clinical application of deep models. In this
study, inspired by human learning cycle, we propose a novel online reflective
learning framework (RefSeg) to improve segmentation robustness. Based on the
reflection-on-action conception, our RefSeg firstly drives the deep model to
take action to obtain semantic segmentation. Then, RefSeg triggers the model to
reflect itself. Because making deep models realize their segmentation failures
during testing is challenging, RefSeg synthesizes a realistic proxy image from
the semantic mask to help deep models build intuitive and effective
reflections. This proxy translates and emphasizes the segmentation flaws. By
maximizing the structural similarity between the raw input and the proxy, the
reflection-on-action loop is closed with segmentation robustness improved.
RefSeg runs in the testing phase and is general for segmentation models.
Extensive validation on three medical image segmentation tasks with a public
cardiac MR dataset and two in-house large ultrasound datasets show that our
RefSeg remarkably improves model robustness and reports state-of-the-art
performance over strong competitors.

Standard plane (SP) localization is essential in routine clinical ultrasound
(US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in
one scan and provide complete anatomy with the addition of coronal plane.
However, manually navigating SPs in 3D US is laborious and biased due to the
orientation variability and huge search space. In this study, we introduce a
novel reinforcement learning (RL) framework for automatic SP localization in 3D
US. Our contribution is three-fold. First, we formulate SP localization in 3D
US as a tangent-point-based problem in RL to restructure the action space and
significantly reduce the search space. Second, we design an auxiliary task
learning strategy to enhance the model's ability to recognize subtle
differences crossing Non-SPs and SPs in plane search. Finally, we propose a
spatial-anatomical reward to effectively guide learning trajectories by
exploiting spatial and anatomical information simultaneously. We explore the
efficacy of our approach on localizing four SPs on uterus and fetal brain
datasets. The experiments indicate that our approach achieves a high
localization accuracy as well as robust performance.

Ultrasound (US) is widely used for its advantages of real-time imaging,
radiation-free and portability. In clinical practice, analysis and diagnosis
often rely on US sequences rather than a single image to obtain dynamic
anatomical information. This is challenging for novices to learn because
practicing with adequate videos from patients is clinically unpractical. In
this paper, we propose a novel framework to synthesize high-fidelity US videos.
Specifically, the synthesis videos are generated by animating source content
images based on the motion of given driving videos. Our highlights are
three-fold. First, leveraging the advantages of self- and fully-supervised
learning, our proposed system is trained in weakly-supervised manner for
keypoint detection. These keypoints then provide vital information for handling
complex high dynamic motions in US videos. Second, we decouple content and
texture learning using the dual decoders to effectively reduce the model
learning difficulty. Last, we adopt the adversarial training strategy with GAN
losses for further improving the sharpness of the generated videos, narrowing
the gap between real and synthesis videos. We validate our method on a large
in-house pelvic dataset with high dynamic motion. Extensive evaluation metrics
and user study prove the effectiveness of our proposed method.

Learning task-oriented dialog policies via reinforcement learning typically
requires large amounts of interaction with users, which in practice renders
such methods unusable for real-world applications. In order to reduce the data
requirements, we propose to leverage data from across different dialog domains,
thereby reducing the amount of data required from each given domain. In
particular, we propose to learn domain-agnostic action embeddings, which
capture general-purpose structure that informs the system how to act given the
current dialog context, and are then specialized to a specific domain. We show
how this approach is capable of learning with significantly less interaction
with users, with a reduction of 35% in the number of dialogs required to learn,
and to a higher level of proficiency than training separate policies for each
domain on a set of simulated domains.

Methods for learning from demonstration (LfD) have shown success in acquiring
behavior policies by imitating a user. However, even for a single task, LfD may
require numerous demonstrations. For versatile agents that must learn many
tasks via demonstration, this process would substantially burden the user if
each task were learned in isolation. To address this challenge, we introduce
the novel problem of lifelong learning from demonstration, which allows the
agent to continually build upon knowledge learned from previously demonstrated
tasks to accelerate the learning of new tasks, reducing the amount of
demonstrations required. As one solution to this problem, we propose the first
lifelong learning approach to inverse reinforcement learning, which learns
consecutive tasks via demonstration, continually transferring knowledge between
tasks to improve performance.

Inverse problems consist in reconstructing signals from incomplete sets of
measurements and their performance is highly dependent on the quality of the
prior knowledge encoded via regularization. While traditional approaches focus
on obtaining a unique solution, an emerging trend considers exploring multiple
feasibile solutions. In this paper, we propose a method to generate multiple
reconstructions that fit both the measurements and a data-driven prior learned
by a generative adversarial network. In particular, we show that, starting from
an initial solution, it is possible to find directions in the latent space of
the generative model that are null to the forward operator, and thus keep
consistency with the measurements, while inducing significant perceptual
change. Our exploration approach allows to generate multiple solutions to the
inverse problem an order of magnitude faster than existing approaches; we show
results on image super-resolution and inpainting problems.

Optical coherence tomography (OCT) is a non-invasive 3D modality widely used
in ophthalmology for imaging the retina. Achieving automated, anatomically
coherent retinal layer segmentation on OCT is important for the detection and
monitoring of different retinal diseases, like Age-related Macular Disease
(AMD) or Diabetic Retinopathy. However, the majority of state-of-the-art layer
segmentation methods are based on purely supervised deep-learning, requiring a
large amount of pixel-level annotated data that is expensive and hard to
obtain. With this in mind, we introduce a semi-supervised paradigm into the
retinal layer segmentation task that makes use of the information present in
large-scale unlabeled datasets as well as anatomical priors. In particular, a
novel fully differentiable approach is used for converting surface position
regression into a pixel-wise structured segmentation, allowing to use both 1D
surface and 2D layer representations in a coupled fashion to train the model.
In particular, these 2D segmentations are used as anatomical factors that,
together with learned style factors, compose disentangled representations used
for reconstructing the input image. In parallel, we propose a set of anatomical
priors to improve network training when a limited amount of labeled data is
available. We demonstrate on the real-world dataset of scans with intermediate
and wet-AMD that our method outperforms state-of-the-art when using our full
training set, but more importantly largely exceeds state-of-the-art when it is
trained with a fraction of the labeled data.

The Fe14Nd2B-based permanent magnets are technologically sought-after for
energy conversion due to their unparalleled high energy product (520 kJ/m3).
For such 14:2:1 phases of different compositions, determining the magnetization
from the measured magnetic moment is often bottlenecked by lack of mass
density. We present a machine learning (ML) mass density model for 14:2:1
phases using chemical composition-based features (representing 33 elements) and
optionally lattice parameter (LP) features. The datasets for training and
testing contain 190 phases (177 compositionally different) with their
literature reported densities and LP. With an ML model with merely
compositional features, we achieved a low mean-absolute-error of 0.51% on an
unseen test-dataset.

The field of surgical computer vision has undergone considerable
breakthroughs in recent years with the rising popularity of deep neural
network-based methods. However, standard fully-supervised approaches for
training such models require vast amounts of annotated data, imposing a
prohibitively high cost; especially in the clinical domain. Self-Supervised
Learning (SSL) methods, which have begun to gain traction in the general
computer vision community, represent a potential solution to these annotation
costs, allowing to learn useful representations from only unlabeled data.
Still, the effectiveness of SSL methods in more complex and impactful domains,
such as medicine and surgery, remains limited and unexplored. In this work, we
address this critical need by investigating four state-of-the-art SSL methods
(MoCo v2, SimCLR, DINO, SwAV) in the context of surgical computer vision. We
present an extensive analysis of the performance of these methods on the
Cholec80 dataset for two fundamental and popular tasks in surgical context
understanding, phase recognition and tool presence detection. We examine their
parameterization, then their behavior with respect to training data quantities
in semi-supervised settings. Correct transfer of these methods to surgery, as
described and conducted in this work, leads to substantial performance gains
over generic uses of SSL - up to 7% on phase recognition and 20% on tool
presence detection - as well as state-of-the-art semi-supervised phase
recognition approaches by up to 14%. The code will be made available at
https://github.com/CAMMA-public/SelfSupSurg.

Decision-making is critical for lane change in autonomous driving.
Reinforcement learning (RL) algorithms aim to identify the values of behaviors
in various situations and thus they become a promising pathway to address the
decision-making problem. However, poor runtime safety hinders RL-based
decision-making strategies from complex driving tasks in practice. To address
this problem, human demonstrations are incorporated into the RL-based
decision-making strategy in this paper. Decisions made by human subjects in a
driving simulator are treated as safe demonstrations, which are stored into the
replay buffer and then utilized to enhance the training process of RL. A
complex lane change task in an off-ramp scenario is established to examine the
performance of the developed strategy. Simulation results suggest that human
demonstrations can effectively improve the safety of decisions of RL. And the
proposed strategy surpasses other existing learning-based decision-making
strategies with respect to multiple driving performances.

Machine learning (ML) has been used to accelerate the closure of functional
coverage in simulation-based verification. A supervised ML algorithm, as a
prevalent option in the previous work, is used to bias the test generation or
filter the generated tests. However, for missing coverage events, these
algorithms lack the positive examples to learn from in the training phase.
Therefore, the tests generated or filtered by the algorithms cannot effectively
fill the coverage holes. This is more severe when verifying large-scale design
because the coverage space is larger and the functionalities are more complex.
This paper presents a configurable framework of test selection based on neural
networks (NN), which can achieve a similar coverage gain as random simulation
with far less simulation effort under three configurations of the framework.
Moreover, the performance of the framework is not limited by the number of
coverage events being hit. A commercial signal processing unit is used in the
experiment to demonstrate the effectiveness of the framework. Compared to the
random simulation, the framework can reduce up to 53.74% of simulation time to
reach 99% coverage level.

Considering high-temperature heating, the equations of transient heat
conduction model require an adaptation, i.e. the dependence of thermophysical
parameters of the model on the temperature is to be identified for each
specific material to be heated. This problem is most often solved by
approximation of the tabular data on the measurements of the required
parameters, which can be found in the literature, by means of regression
equations. But, for example, considering the steel heating process, this
approach is difficult to be implemented due to the lack of tabular discrete
measurements for many grades of steel, such as alloyed ones. In this paper, the
new approach is proposed, which is based on a solution of a related variational
problem. Its main idea is to substitute the adaptation process in the classical
sense (i.e., to find the dependencies of thermophysical parameters on
temperature) with 'supervised learning' of a mesh model on the basis of the
technological data received from the plant. The equations to adjust the
parameters of the transient heat conduction model, which are related to the
thermophysical coefficients, have been derived. A numerical experiment is
conducted for steel of a particular group of grades, for which enough both
technological as well as tabular data are available. As a result, the 'trained'
mesh model, which has not received explicitly any information about the
physical and chemical properties of the heated substance, demonstrated an
average error of 18.820 C, which is quite close to the average error of the
model adapted classically on the basis of the tabular data (18.10 C).

The finance industry has adopted machine learning (ML) as a form of
quantitative research to support better investment decisions, yet there are
several challenges often overlooked in practice. (1) ML code tends to be
unstructured and ad hoc, which hinders cooperation with others. (2) Resource
requirements and dependencies vary depending on which algorithm is used, so a
flexible and scalable system is needed. (3) It is difficult for domain experts
in traditional finance to apply their experience and knowledge in ML-based
strategies unless they acquire expertise in recent technologies. This paper
presents Shai-am, an ML platform integrated with our own Python framework. The
platform leverages existing modern open-source technologies, managing
containerized pipelines for ML-based strategies with unified interfaces to
solve the aforementioned issues. Each strategy implements the interface defined
in the core framework. The framework is designed to enhance reusability and
readability, facilitating collaborative work in quantitative research. Shai-am
aims to be a pure AI asset manager for solving various tasks in financial
markets.

Priming and antipriming can be modelled with error-driven learning (Marsolek,
2008), by assuming that the learning of the prime influences processing of the
target stimulus. This implies that participants are continuously learning in
priming studies, and predicts that they are also learning in each trial of
other psycholinguistic experiments. This study investigates whether
trial-to-trial learning can be detected in lexical decision experiments. We
used the Discriminative Lexicon Model (DLM; Baayen et al., 2019), a model of
the mental lexicon with meaning representations from distributional semantics,
which models incremental learning with the Widrow-Hoff rule. We used data from
the British Lexicon Project (BLP; Keuleers et al., 2012) and simulated the
lexical decision experiment with the DLM on a trial-by-trial basis for each
subject individually. Then, reaction times for words and nonwords were
predicted with Generalised Additive Models, using measures derived from the DLM
simulations as predictors. Models were developed with the data of two subjects
and tested on all other subjects. We extracted measures from two simulations
for each subject (one with learning updates between trials and one without),
and used them as input to two GAMs. Learning-based models showed better model
fit than the non-learning ones for the majority of subjects. Our measures also
provided insights into lexical processing and enabled us to explore individual
differences with Linear Mixed Models. This demonstrates the potential of the
DLM to model behavioural data and leads to the conclusion that trial-to-trial
learning can indeed be detected in psycholinguistic experiments.

Humans commonly solve complex problems by decomposing them into easier
subproblems and then combining the subproblem solutions. This type of
compositional reasoning permits reuse of the subproblem solutions when tackling
future tasks that share part of the underlying compositional structure. In a
continual or lifelong reinforcement learning (RL) setting, this ability to
decompose knowledge into reusable components would enable agents to quickly
learn new RL tasks by leveraging accumulated compositional structures. We
explore a particular form of composition based on neural modules and present a
set of RL problems that intuitively admit compositional solutions. Empirically,
we demonstrate that neural composition indeed captures the underlying structure
of this space of problems. We further propose a compositional lifelong RL
method that leverages accumulated neural components to accelerate the learning
of future tasks while retaining performance on previous tasks via off-line RL
over replayed experiences.

Navigation inside luminal organs is an arduous task that requires
non-intuitive coordination between the movement of the operator's hand and the
information obtained from the endoscopic video. The development of tools to
automate certain tasks could alleviate the physical and mental load of doctors
during interventions, allowing them to focus on diagnosis and decision-making
tasks. In this paper, we present a synergic solution for intraluminal
navigation consisting of a 3D printed endoscopic soft robot that can move
safely inside luminal structures. Visual servoing, based on Convolutional
Neural Networks (CNNs) is used to achieve the autonomous navigation task. The
CNN is trained with phantoms and in-vivo data to segment the lumen, and a
model-less approach is presented to control the movement in constrained
environments. The proposed robot is validated in anatomical phantoms in
different path configurations. We analyze the movement of the robot using
different metrics such as task completion time, smoothness, error in the
steady-state, and mean and maximum error. We show that our method is suitable
to navigate safely in hollow environments and conditions which are different
than the ones the network was originally trained on.

Deep learning based solutions are being succesfully implemented for a wide
variety of applications. Most notably, clinical use-cases have gained an
increased interest and have been the main driver behind some of the
cutting-edge data-driven algorithms proposed in the last years. For
applications like sparse-view tomographic reconstructions, where the amount of
measurement data is small in order to keep acquisition times short and
radiation dose low, reduction of the streaking artifacts has prompted the
development of data-driven denoising algorithms with the main goal of obtaining
diagnostically viable images with only a subset of a full-scan data. We propose
WNet, a data-driven dual-domain denoising model which contains a trainable
reconstruction layer for sparse-view artifact denoising. Two encoder-decoder
networks perform denoising in both sinogram- and reconstruction-domain
simultaneously, while a third layer implementing the Filtered Backprojection
algorithm is sandwiched between the first two and takes care of the
reconstruction operation. We investigate the performance of the network on
sparse-view chest CT scans, and we highlight the added benefit of having a
trainable reconstruction layer over the more conventional fixed ones. We train
and test our network on two clinically relevant datasets and we compare the
obtained results with three different types of sparse-view CT denoising and
reconstruction algorithms.

Federated learning (FL) is an emerging machine learning paradigm involving
multiple clients, e.g., mobile phone devices, with an incentive to collaborate
in solving a machine learning problem coordinated by a central server. FL was
proposed in 2016 by Kone\v{c}n\'{y} et al. and McMahan et al. as a viable
privacy-preserving alternative to traditional centralized machine learning
since, by construction, the training data points are decentralized and never
transferred by the clients to a central server. Therefore, to a certain degree,
FL mitigates the privacy risks associated with centralized data collection.
  Unfortunately, optimization for FL faces several specific issues that
centralized optimization usually does not need to handle. In this thesis, we
identify several of these challenges and propose new methods and algorithms to
address them, with the ultimate goal of enabling practical FL solutions
supported with mathematically rigorous guarantees.

Data imbalance is a common problem in the machine learning literature that
can have a critical effect on the performance of a model. Various solutions
exist - such as the ones that focus on resampling or data generation - but
their impact on the convergence of gradient-based optimizers used in deep
learning is not understood. We here elucidate the significant negative impact
of data imbalance on learning, showing that the learning curves for minority
and majority classes follow sub-optimal trajectories when training with a
gradient-based optimizer. The reason is not only that the gradient signal
neglects the minority classes, but also that the minority classes are subject
to a larger directional noise, which slows their learning by an amount related
to the imbalance ratio. To address this problem, we propose a new algorithmic
solution, for which we provide a detailed analysis of its convergence behavior.
We show both theoretically and empirically that this new algorithm exhibits a
better behavior with more stable learning curves for each class, as well as a
better generalization performance.

In this paper we provide a novel approach to the analysis of kinetic models
for label switching, which are used for particle systems that can randomly
switch between gradient flows in different energy landscapes. Besides problems
in biology and physics, we also demonstrate that stochastic gradient descent,
the most popular technique in machine learning, can be understood in this
setting, when considering a time-continuous variant. Our analysis is focusing
on the case of evolution in a collection of external potentials, for which we
provide analytical and numerical results about the evolution as well as the
stationary problem.

Semblance velocity analysis is a crucial step in seismic data processing. To
avoid the huge time-cost when performed manually, some deep learning methods
are proposed for automatic semblance velocity picking. However, the application
of existing deep learning methods is still restricted by the shortage of labels
in practice. In this letter, we propose an attention neural network combined
with a point-to-point regression velocity picking strategy to mitigate this
problem. In our method, semblance patch and velocity value are served as
network input and output, respectively. In this way, global and local features
hidden in semblance patch can be effectively extracted by attention neural
network. A down-sampling strategy based on horizontal layer extraction is also
designed to improve the picking efficiency in prediction process. Tests on
synthetic and field datasets demonstrate that the proposed method can produce
reasonable results and maintain global velocity trend consistent with labels.
Besides, robustness against random noise is also tested on the field data.

We consider the control of decentralized learning dynamics for agents in an
anti-coordination network game. In the anti-coordination network game, there is
a preferred action in the absence of neighbors' actions, and the utility an
agent receives from the preferred action decreases as more of its neighbors
select the preferred action, potentially causing the agent to select a less
desirable action. The decentralized dynamics that is based on the iterated
elimination of dominated strategies converge for the considered game. Given a
convergent action profile, we measure anti-coordination by the number of edges
in the underlying graph that have at least one agent in either end of the edge
not taking the preferred action. The maximum anti-coordination (MAC) problem
seeks to find an optimal set of agents to control under a finite budget so that
the overall network disconnect is maximized on game convergence as a result of
the dynamics. We show that the MAC is submodular in expectation in dense
bipartite networks for any realization of the utility constants in the
population. Utilizing this result, we obtain a performance guarantee for the
greedy agent selection algorithm for MAC. Finally, we provide a computational
study to show the effectiveness of greedy node selection strategies to solve
MAC on general bipartite networks.

Time series prediction is the crucial task for many human activities e.g.
weather forecasts or predicting stock prices. One solution to this problem is
to use Recurrent Neural Networks (RNNs). Although they can yield accurate
predictions, their learning process is slow and complex. Here we propose a
Quantum Recurrent Neural Network (QRNN) to address these obstacles. The design
of the network is based on the continuous-variable quantum computing paradigm.
We demonstrate that the network is capable of learning time dependence of a few
types of temporal data. Our numerical simulations show that the QRNN converges
to optimal weights in fewer epochs than the classical network. Furthermore, for
a small number of trainable parameters it can achieve lower loss than the
latter.

There has been a growing interest in the use of Deep Neural Networks (DNNs)
to solve Partial Differential Equations (PDEs). Despite the promise that such
approaches hold, there are various aspects where they could be improved. Two
such shortcomings are (i) their computational inefficiency relative to
classical numerical methods, and (ii) the non-interpretability of a trained DNN
model. In this work we present ASPINN, an anisotropic extension of our earlier
work called SPINN--Sparse, Physics-informed, and Interpretable Neural
Networks--to solve PDEs that addresses both these issues. ASPINNs generalize
radial basis function networks. We demonstrate using a variety of examples
involving elliptic and hyperbolic PDEs that the special architecture we propose
is more efficient than generic DNNs, while at the same time being directly
interpretable. Further, they improve upon the SPINN models we proposed earlier
in that fewer nodes are require to capture the solution using ASPINN than using
SPINN, thanks to the anisotropy of the local zones of influence of each node.
The interpretability of ASPINN translates to a ready visualization of their
weights and biases, thereby yielding more insight into the nature of the
trained model. This in turn provides a systematic procedure to improve the
architecture based on the quality of the computed solution. ASPINNs thus serve
as an effective bridge between classical numerical algorithms and modern DNN
based methods to solve PDEs. In the process, we also streamline the training of
ASPINNs into a form that is closer to that of supervised learning algorithms.

Recent deep learning-based methods for medical image registration achieve
results that are competitive with conventional optimization algorithms at
reduced run times. However, deep neural networks generally require plenty of
labeled training data and are vulnerable to domain shifts between training and
test data. While typical intensity shifts can be mitigated by keypoint-based
registration, these methods still suffer from geometric domain shifts, for
instance, due to different fields of view. As a remedy, in this work, we
present a novel approach to geometric domain adaptation for image registration,
adapting a model from a labeled source to an unlabeled target domain. We build
on a keypoint-based registration model, combining graph convolutions for
geometric feature learning with loopy belief optimization, and propose to
reduce the domain shift through self-ensembling. To this end, we embed the
model into the Mean Teacher paradigm. We extend the Mean Teacher to this
context by 1) adapting the stochastic augmentation scheme and 2) combining
learned feature extraction with differentiable optimization. This enables us to
guide the learning process in the unlabeled target domain by enforcing
consistent predictions of the learning student and the temporally averaged
teacher model. We evaluate the method for exhale-to-inhale lung CT registration
under two challenging adaptation scenarios (DIR-Lab 4D CT to COPD, COPD to
Learn2Reg). Our method consistently improves on the baseline model by 50%/47%
while even matching the accuracy of models trained on target data. Source code
is available at
https://github.com/multimodallearning/registration-da-mean-teacher.

Many real-world problems contain multiple objectives and agents, where a
trade-off exists between objectives. Key to solving such problems is to exploit
sparse dependency structures that exist between agents. For example, in wind
farm control a trade-off exists between maximising power and minimising stress
on the systems components. Dependencies between turbines arise due to the wake
effect. We model such sparse dependencies between agents as a multi-objective
coordination graph (MO-CoG). In multi-objective reinforcement learning a
utility function is typically used to model a users preferences over
objectives, which may be unknown a priori. In such settings a set of optimal
policies must be computed. Which policies are optimal depends on which
optimality criterion applies. If the utility function of a user is derived from
multiple executions of a policy, the scalarised expected returns (SER) must be
optimised. If the utility of a user is derived from a single execution of a
policy, the expected scalarised returns (ESR) criterion must be optimised. For
example, wind farms are subjected to constraints and regulations that must be
adhered to at all times, therefore the ESR criterion must be optimised. For
MO-CoGs, the state-of-the-art algorithms can only compute a set of optimal
policies for the SER criterion, leaving the ESR criterion understudied. To
compute a set of optimal polices under the ESR criterion, also known as the ESR
set, distributions over the returns must be maintained. Therefore, to compute a
set of optimal policies under the ESR criterion for MO-CoGs, we present a novel
distributional multi-objective variable elimination (DMOVE) algorithm. We
evaluate DMOVE in realistic wind farm simulations. Given the returns in
real-world wind farm settings are continuous, we utilise a model known as
real-NVP to learn the continuous return distributions to calculate the ESR set.

Outlier or anomaly detection is an important task in data analysis. We
discuss the problem from a geometrical perspective and provide a framework that
exploits the metric structure of a data set. Our approach rests on the manifold
assumption, i.e., that the observed, nominally high-dimensional data lie on a
much lower dimensional manifold and that this intrinsic structure can be
inferred with manifold learning methods. We show that exploiting this structure
significantly improves the detection of outlying observations in
high-dimensional data. We also suggest a novel, mathematically precise, and
widely applicable distinction between distributional and structural outliers
based on the geometry and topology of the data manifold that clarifies
conceptual ambiguities prevalent throughout the literature. Our experiments
focus on functional data as one class of structured high-dimensional data, but
the framework we propose is completely general and we include image and graph
data applications. Our results show that the outlier structure of
high-dimensional and non-tabular data can be detected and visualized using
manifold learning methods and quantified using standard outlier scoring methods
applied to the manifold embedding vectors.

In this work, we propose a deep U-Net based model to tackle the challenging
task of prostate cancer segmentation by aggressiveness in MRI based on weak
scribble annotations. This model extends the size constraint loss proposed by
Kervadec et al. 1 in the context of multiclass detection and segmentation task.
This model is of high clinical interest as it allows training on prostate
biopsy samples and avoids time-consuming full annotation process. Performance
is assessed on a private dataset (219 patients) where the full ground truth is
available as well as on the ProstateX-2 challenge database, where only biopsy
results at different localisations serve as reference. We show that we can
approach the fully-supervised baseline in grading the lesions by using only
6.35% of voxels for training. We report a lesion-wise Cohen's kappa score of
0.29 $\pm$ 0.07 for the weak model versus 0.32 $\pm$ 0.05 for the baseline. We
also report a kappa score (0.276 $\pm$ 0.037) on the ProstateX-2 challenge
dataset with our weak U-Net trained on a combination of ProstateX-2 and our
dataset, which is the highest reported value on this challenge dataset for a
segmentation task to our knowledge.

Recent advances in spoken language understanding benefited from
Self-Supervised models trained on large speech corpora. For French, the
LeBenchmark project has made such models available and has led to impressive
progress on several tasks including spoken language understanding. These
advances have a non-negligible cost in terms of computation time and energy
consumption. In this paper, we compare several learning strategies trying to
reduce such cost while keeping competitive performance. At the same time we
propose an extensive analysis where we measure the cost of our models in terms
of training time and electric energy consumption, hopefully promoting a
comprehensive evaluation procedure. The experiments are performed on the FSC
and MEDIA corpora, and show that it is possible to reduce the learning cost
while maintaining state-of-the-art performance and using SSL models.

Radio Frequency Interference (RFI) corrupts astronomical measurements, thus
affecting the performance of radio telescopes. To address this problem,
supervised segmentation models have been proposed as candidate solutions to RFI
detection. However, the unavailability of large labelled datasets, due to the
prohibitive cost of annotating, makes these solutions unusable. To solve these
shortcomings, we focus on the inverse problem; training models on only
uncontaminated emissions thereby learning to discriminate RFI from all known
astronomical signals and system noise. We use Nearest-Latent-Neighbours (NLN) -
an algorithm that utilises both the reconstructions and latent distances to the
nearest-neighbours in the latent space of generative autoencoding models for
novelty detection. The uncontaminated regions are selected using weak-labels in
the form of RFI flags (generated by classical RFI flagging methods) available
from most radio astronomical data archives at no additional cost. We evaluate
performance on two independent datasets, one simulated from the HERA telescope
and another consisting of real observations from LOFAR telescope. Additionally,
we provide a small expert-labelled LOFAR dataset (i.e., strong labels) for
evaluation of our and other methods. Performance is measured using AUROC, AUPRC
and the maximum F1-score for a fixed threshold. For the simulated data we
outperform the current state-of-the-art by approximately 1% in AUROC and 3% in
AUPRC for the HERA dataset. Furthermore, our algorithm offers both a 4%
increase in AUROC and AUPRC at a cost of a degradation in F1-score performance
for the LOFAR dataset, without any manual labelling.

Recommender systems are used in many different applications and contexts,
however their main goal can always be summarised as "connecting relevant
content to interested users". Personalized recommendation algorithms achieve
this goal by first building a profile of the user, either implicitly or
explicitly, and then matching items with this profile to find relevant content.
The more interpretable the profile and this "matching function" are, the easier
it is to provide users with accurate and intuitive explanations, and also to
let them interact with the system. Indeed, for a user to see what the system
has already learned about her interests is of key importance for her to provide
feedback to the system and to guide it towards better understanding her
preferences.
  To this end, we propose a linear collaborative filtering recommendation model
that builds user profiles within the domain of item metadata, which is arguably
the most interpretable domain for end users. Our method is hence inherently
transparent and explainable. Moreover, since recommendations are computed as a
linear function of item metadata and the interpretable user profile, our method
seamlessly supports interactive recommendation. In other words, users can
directly tweak the weights of the learned profile for more fine-grained
browsing and discovery of content based on their current interests.
  We demonstrate the interactive aspect of this model in an online application
for discovering cultural events in Belgium. Additionally, the performance of
the model is evaluated with offline experiments, both static and with simulated
feedback, and compared to several state-of-the-art and state-of-practice

consumption. In this paper, we compare several learning strategies aiming at
reducing such cost while keeping competitive performances. The experiments are
performed on the MEDIA corpus, and show that it is possible to reduce the
learning cost while maintaining state-of-the-art performances.

Measurements of Higgs boson production cross-sections are carried out in the
diphoton decay channel using 139 fb$^{-1}$ of $pp$ collision data at $\sqrt{s}
= 13$ TeV collected by the ATLAS experiment at the LHC. The analysis is based
on the definition of 101 distinct signal regions using machine-learning
techniques. The inclusive Higgs boson signal strength in the diphoton channel
is measured to be $1.04^{+0.10}_{-0.09}$. Cross-sections for gluon-gluon
fusion, vector-boson fusion, associated production with a $W$ or $Z$ boson, and
top associated production processes are reported. An upper limit of 10 times
the Standard Model prediction is set for the associated production process of a
Higgs boson with a single top quark, which has a unique sensitivity to the sign
of the top quark Yukawa coupling. Higgs boson production is further
characterized through measurements of Simplified Template Cross-Sections
(STXS). In total, cross-sections of 28 STXS regions are measured. The measured
STXS cross-sections are compatible with their Standard Model predictions, with
a $p$-value of 93%. The measurements are also used to set constraints on Higgs
boson coupling strengths, as well as on new interactions beyond the Standard
Model in an effective field theory approach. No significant deviations from the
Standard Model predictions are observed in these measurements, which provide
significant sensitivity improvements compared to the previous ATLAS results.

Regression learning is classic and fundamental for medical image analysis. It
provides the continuous mapping for many critical applications, like the
attribute estimation, object detection, segmentation and non-rigid
registration. However, previous studies mainly took the case-wise criteria,
like the mean square errors, as the optimization objectives. They ignored the
very important population-wise correlation criterion, which is exactly the
final evaluation metric in many tasks. In this work, we propose to revisit the
classic regression tasks with novel investigations on directly optimizing the
fine-grained correlation losses. We mainly explore two complementary
correlation indexes as learnable losses: Pearson linear correlation (PLC) and
Spearman rank correlation (SRC). The contributions of this paper are two folds.
First, for the PLC on global level, we propose a strategy to make it robust
against the outliers and regularize the key distribution factors. These efforts
significantly stabilize the learning and magnify the efficacy of PLC. Second,
for the SRC on local level, we propose a coarse-to-fine scheme to ease the
learning of the exact ranking order among samples. Specifically, we convert the
learning for the ranking of samples into the learning of similarity
relationships among samples. We extensively validate our method on two typical
ultrasound image regression tasks, including the image quality assessment and
bio-metric measurement. Experiments prove that, with the fine-grained guidance
in directly optimizing the correlation, the regression performances are
significantly improved. Our proposed correlation losses are general and can be
extended to more important applications.

We introduce a new automatic evaluation method for speaker similarity
assessment, that is consistent with human perceptual scores. Modern neural
text-to-speech models require a vast amount of clean training data, which is
why many solutions switch from single speaker models to solutions trained on
examples from many different speakers. Multi-speaker models bring new
possibilities, such as a faster creation of new voices, but also a new problem
- speaker leakage, where the speaker identity of a synthesized example might
not match those of the target speaker. Currently, the only way to discover this
issue is through costly perceptual evaluations. In this work, we propose an
automatic method for assessment of speaker similarity. For that purpose, we
extend the recent work on speaker verification systems and evaluate how
different metrics and speaker embeddings models reflect Multiple Stimuli with
Hidden Reference and Anchor (MUSHRA) scores. Our experiments show that we can
train a model to predict speaker similarity MUSHRA scores from speaker
embeddings with 0.96 accuracy and significant correlation up to 0.78 Pearson
score at the utterance level.

In post-silicon validation, tuning is to find the values for the tuning
knobs, potentially as a function of process parameters and/or known operating
conditions. In this sense, an more efficient tuning requires identifying the
most critical tuning knobs and process parameters in terms of a given
figure-of-merit for a Device Under Test (DUT). This is often manually conducted
by experienced experts. However, with increasingly complex chips, manual
inspection on a large amount of raw variables has become more challenging. In
this work, we leverage neural networks to efficiently select the most relevant
variables and present a corresponding deep-learning-aided pipeline for
efficient tuning.

Intelligent test requires efficient and effective analysis of
high-dimensional data in a large scale. Traditionally, the analysis is often
conducted by human experts, but it is not scalable in the era of big data. To
tackle this challenge, variable selection has been recently introduced to
intelligent test. However, in practice, we encounter scenarios where certain
variables (e.g. some specific processing conditions for a device under test)
must be maintained after variable selection. We call this conditional variable
selection, which has not been well investigated for embedded or
deep-learning-based variable selection methods. In this paper, we discuss a
novel conditional variable selection framework that can select the most
important candidate variables given a set of preselected variables.

Skeleton based recognition systems are gaining popularity and machine
learning models focusing on points or joints in a skeleton have proved to be
computationally effective and application in many areas like Robotics. It is
easy to track points and thereby preserving spatial and temporal information,
which plays an important role in abstracting the required information,
classification becomes an easy task. In this paper, we aim to study these
points but using a cloud mechanism, where we define a cloud as collection of
points. However, when we add temporal information, it may not be possible to
retrieve the coordinates of a point in each frame and hence instead of focusing
on a single point, we can use k-neighbors to retrieve the state of the point
under discussion. Our focus is to gather such information using weight sharing
but making sure that when we try to retrieve the information from neighbors, we
do not carry noise with it. LSTM which has capability of long-term modelling
and can carry both temporal and spatial information. In this article we tried
to summarise graph based gesture recognition method.

The stock market offers a platform where people buy and sell shares of
publicly listed companies. Generally, stock prices are quite volatile; hence
predicting them is a daunting task. There is still much research going to
develop more accuracy in stock price prediction. Portfolio construction refers
to the allocation of different sector stocks optimally to achieve a maximum
return by taking a minimum risk. A good portfolio can help investors earn
maximum profit by taking a minimum risk. Beginning with Dow Jones Theory a lot
of advancement has happened in the area of building efficient portfolios. In
this project, we have tried to predict the future value of a few stocks from
six important sectors of the Indian economy and also built a portfolio. As part
of the project, our team has conducted a study of the performance of various
Time series, machine learning, and deep learning models in stock price
prediction on selected stocks from the chosen six important sectors of the
economy. As part of building an efficient portfolio, we have studied multiple
portfolio optimization theories beginning with the Modern Portfolio theory. We
have built a minimum variance portfolio and optimal risk portfolio for all the
six chosen sectors by using the daily stock prices over the past five years as
training data and have also conducted back testing to check the performance of
the portfolio. We look forward to continuing our study in the area of stock
price prediction and asset allocation and consider this project as the first
stepping stone.

The electroencephalogram (EEG) is a powerful method to understand how the
brain processes speech. Linear models have recently been replaced for this
purpose with deep neural networks and yield promising results. In related EEG
classification fields, it is shown that explicitly modeling subject-invariant
features improves generalization of models across subjects and benefits
classification accuracy. In this work, we adapt factorized hierarchical
variational autoencoders to exploit parallel EEG recordings of the same
stimuli. We model EEG into two disentangled latent spaces. Subject accuracy
reaches 98.96% and 1.60% on respectively the subject and content latent space,
whereas binary content classification experiments reach an accuracy of 51.51%
and 62.91% on respectively the subject and content latent space.

In this work, we address the task of SDR videos to HDR
videos(SDRTV-to-HDRTV). Previous approaches use global feature modulation for
SDRTV-to-HDRTV. Feature modulation scales and shifts the features in the
original feature space, which has limited mapping capability. In addition, the
global image mapping cannot restore detail in HDR frames due to the luminance
differences in different regions of SDR frames. To resolve the appeal, we
propose a two-stage solution. The first stage is a hierarchical Dynamic Context
feature mapping (HDCFM) model. HDCFM learns the SDR frame to HDR frame mapping
function via hierarchical feature modulation (HME and HM ) module and a dynamic
context feature transformation (DCT) module. The HME estimates the feature
modulation vector, HM is capable of hierarchical feature modulation, consisting
of global feature modulation in series with local feature modulation, and is
capable of adaptive mapping of local image features. The DCT module constructs
a feature transformation module in conjunction with the context, which is
capable of adaptively generating a feature transformation matrix for feature
mapping. Compared with simple feature scaling and shifting, the DCT module can
map features into a new feature space and thus has a more excellent feature
mapping capability. In the second stage, we introduce a patch
discriminator-based context generation model PDCG to obtain subjective quality
enhancement of over-exposed regions. PDCG can solve the problem that the model
is challenging to train due to the proportion of overexposed regions of the
image. The proposed method can achieve state-of-the-art objective and
subjective quality results. Specifically, HDCFM achieves a PSNR gain of 0.81 dB
at a parameter of about 100K. The number of parameters is 1/14th of the
previous state-of-the-art methods. The test code will be released soon.

We demonstrate the application of the YOLOv5 model, a general purpose
convolution-based single-shot object detection model, in the task of detecting
binary neutron star (BNS) coalescence events from gravitational-wave data of
current generation interferometer detectors. We also present a thorough
explanation of the synthetic data generation and preparation tasks based on
approximant waveform models used for the model training, validation and testing
steps. Using this approach, we achieve mean average precision
($\text{mAP}_{[0.50]}$) values of 0.945 for a single class validation dataset
and as high as 0.978 for test datasets. Moreover, the trained model is
successful in identifying the GW170817 event in the LIGO H1 detector data. The
identification of this event is also possible for the LIGO L1 detector data
with an additional pre-processing step, without the need of removing the large
glitch in the final stages of the inspiral. The detection of the GW190425 event
is less successful, which attests to performance degradation with the
signal-to-noise ratio. Our study indicates that the YOLOv5 model is an
interesting approach for first-stage detection alarm pipelines and, when
integrated in more complex pipelines, for real-time inference of physical
source parameters.

Integrative learning of multiple datasets has the potential to mitigate the
challenge of small $n$ and large $p$ that is often encountered in analysis of
big biomedical data such as genomics data. Detection of weak yet important
signals can be enhanced by jointly selecting features for all datasets.
However, the set of important features may not always be the same across all
datasets. Although some existing integrative learning methods allow
heterogeneous sparsity structure where a subset of datasets can have zero
coefficients for some selected features, they tend to yield reduced efficiency,
reinstating the problem of losing weak important signals. We propose a new
integrative learning approach which can not only aggregate important signals
well in homogeneous sparsity structure, but also substantially alleviate the
problem of losing weak important signals in heterogeneous sparsity structure.
Our approach exploits a priori known graphical structure of features and
encourages joint selection of features that are connected in the graph.
Integrating such prior information over multiple datasets enhances the power,
while also accounting for the heterogeneity across datasets. Theoretical
properties of the proposed method are investigated. We also demonstrate the
limitations of existing approaches and the superiority of our method using a
simulation study and analysis of gene expression data from ADNI.

Electronic health records (EHRs) offer great promises for advancing precision
medicine and, at the same time, present significant analytical challenges.
Particularly, it is often the case that patient-level data in EHRs cannot be
shared across institutions (data sources) due to government regulations and/or
institutional policies. As a result, there are growing interests about
distributed learning over multiple EHRs databases without sharing patient-level
data. To tackle such challenges, we propose a novel communication efficient
method that aggregates the local optimal estimates, by turning the problem into
a missing data problem. In addition, we propose incorporating posterior samples
of remote sites, which can provide partial information on the missing
quantities and improve efficiency of parameter estimates while having the
differential privacy property and thus reducing the risk of information
leaking. The proposed approach, without sharing the raw patient level data,
allows for proper statistical inference and can accommodate sparse regressions.
We provide theoretical investigation for the asymptotic properties of the
proposed method for statistical inference as well as differential privacy, and
evaluate its performance in simulations and real data analyses in comparison
with several recently developed methods.

Real bug fixes found in open source repositories seem to be the perfect
source for learning to localize and repair real bugs. However, the absence of
large scale bug fix collections has made it difficult to effectively exploit
real bug fixes in the training of larger neural models in the past. In
contrast, artificial bugs -- produced by mutating existing source code -- can
be easily obtained at a sufficient scale and are therefore often preferred in
the training of existing approaches. Still, localization and repair models that
are trained on artificial bugs usually underperform when faced with real bugs.
This raises the question whether bug localization and repair models trained on
real bug fixes are more effective in localizing and repairing real bugs.
  We address this question by introducing RealiT, a pre-train-and-fine-tune
approach for effectively learning to localize and repair real bugs from real
bug fixes. RealiT is first pre-trained on a large number of artificial bugs
produced by traditional mutation operators and then fine-tuned on a smaller set
of real bug fixes. Fine-tuning does not require any modifications of the
learning algorithm and hence can be easily adopted in various training
scenarios for bug localization or repair (even when real training data is
scarce). In addition, we found that training on real bug fixes with RealiT is
empirically powerful by nearly doubling the localization performance of an
existing model on real bugs while maintaining or even improving the repair

This work takes a critical look at the application of conventional machine
learning methods to wireless communication problems through the lens of
reliability and robustness. Deep learning techniques adopt a frequentist
framework, and are known to provide poorly calibrated decisions that do not
reproduce the true uncertainty caused by limitations in the size of the
training data. Bayesian learning, while in principle capable of addressing this
shortcoming, is in practice impaired by model misspecification and by the
presence of outliers. Both problems are pervasive in wireless communication
settings, in which the capacity of machine learning models is subject to
resource constraints and training data is affected by noise and interference.
In this context, we explore the application of the framework of robust Bayesian
learning. After a tutorial-style introduction to robust Bayesian learning, we
showcase the merits of robust Bayesian learning on several important wireless
communication problems in terms of accuracy, calibration, and robustness to
outliers and misspecification.

Quantum technologies hold the promise to revolutionise our society with
ground-breaking applications in secure communication, high-performance
computing and ultra-precise sensing. One of the main features in scaling up
quantum technologies is that the complexity of quantum systems scales
exponentially with their size. This poses severe challenges in the efficient
calibration, benchmarking and validation of quantum states and their dynamical
control. While the complete simulation of large-scale quantum systems may only
be possible with a quantum computer, classical characterisation and
optimisation methods (supported by cutting edge numerical techniques) can still
play an important role.
  Here, we review classical approaches to learning quantum systems, their
correlation properties, their dynamics and their interaction with the
environment. We discuss theoretical proposals and successful implementations in
different physical platforms such as spin qubits, trapped ions, photonic and
atomic systems, and superconducting circuits. This review provides a brief
background for key concepts recurring across many of these approaches, such as
the Bayesian formalism or Neural Networks, and outlines open questions.

Computational Law has begun taking the role in society which has been
predicted for some time. Automated decision-making and systems which assist
users are now used in various jurisdictions, but with this maturity come
certain caveats. Computational Law exists on the platforms which enable it, in
this case digital systems, which means that it inherits the same flaws.
Cybersecurity addresses these potential weaknesses. In this paper we go through
known issues and discuss them in the various levels, from design to the
physical realm. We also look at machine-learning specific adversarial problems.
Additionally, we make certain considerations regarding computational law and
existing and future legislation. Finally, we present three recommendations
which are necessary for computational law to function globally, and which
follow ideas in safety and security engineering. As indicated, we find that
computational law must seriously consider that not only does it face the same
risks as other types of software and computer systems, but that failures within
it may cause financial or physical damage, as well as injustice. Consequences
of Computational Legal systems failing are greater than if they were merely
software and hardware. If the system employs machine-learning, it must take
note of the very specific dangers which this brings, of which data poisoning is
the classic example. Computational law must also be explicitly legislated for,
which we show is not the case currently in the EU, and this is also true for
the cybersecurity aspects that will be relevant to it. But there is great hope
in EU's proposed AI Act, which makes an important attempt at taking the
specific problems which Computational Law bring into the legal sphere. Our
recommendations for Computational Law and Cybersecurity are: Accommodation of
threats, adequate use, and that humans remain in the centre of their
deployment.

Deep learning has proved particularly useful for semantic segmentation, a
fundamental image analysis task. However, the standard deep learning methods
need many training images with ground-truth pixel-wise annotations, which are
usually laborious to obtain and, in some cases (e.g., medical images), require
domain expertise. Therefore, instead of pixel-wise annotations, we focus on
image annotations that are significantly easier to acquire but still
informative, namely the size of foreground objects. We define the object size
as the maximum Chebyshev distance between a foreground and the nearest
background pixel. We propose an algorithm for training a deep segmentation
network from a dataset of a few pixel-wise annotated images and many images
with known object sizes. The algorithm minimizes a discrete
(non-differentiable) loss function defined over the object sizes by sampling
the gradient and then using the standard back-propagation algorithm.
Experiments show that the new approach improves the segmentation performance.

Due to its high sample complexity, simulation is, as of today, critical for
the successful application of reinforcement learning. Many real-world problems,
however, exhibit overly complex dynamics, which makes their full-scale
simulation computationally slow. In this paper, we show how to decompose large
networked systems of many agents into multiple local components such that we
can build separate simulators that run independently and in parallel. To
monitor the influence that the different local components exert on one another,
each of these simulators is equipped with a learned model that is periodically
trained on real trajectories. Our empirical results reveal that distributing
the simulation among different processes not only makes it possible to train
large multi-agent systems in just a few hours but also helps mitigate the
negative effects of simultaneous learning.

Deeply learned representations have achieved superior image retrieval
performance in a retrieve-then-rerank manner. Recent state-of-the-art single
stage model, which heuristically fuses local and global features, achieves
promising trade-off between efficiency and effectiveness. However, we notice
that efficiency of existing solutions is still restricted because of their
multi-scale inference paradigm. In this paper, we follow the single stage art
and obtain further complexity-effectiveness balance by successfully getting rid
of multi-scale testing. To achieve this goal, we abandon the widely-used
convolution network giving its limitation in exploring diverse visual patterns,
and resort to fully attention based framework for robust representation
learning motivated by the success of Transformer. Besides applying Transformer
for global feature extraction, we devise a local branch composed of
window-based multi-head attention and spatial attention to fully exploit local
image patterns. Furthermore, we propose to combine the hierarchical local and
global features via a cross-attention module, instead of using heuristically
fusion as previous art does. With our Deep Attentive Local and Global modeling
framework (DALG), extensive experimental results show that efficiency can be
significantly improved while maintaining competitive results with the state of
the arts.

We propose a novel machine learning method for sampling from the
high-dimensional probability distributions of Lattice Quantum Field Theories.
Instead of the deep architectures used so far for this task, our proposal is
based on a single neural ODE layer and incorporates the full symmetries of the
problem. We test our model on the $\phi^4$ theory, showing that it
systematically outperforms previously proposed flow-based methods in sampling
efficiency, and the improvement is especially pronounced for larger lattices.
Compared to the previous baseline model, we improve a key metric, the effective
sample size, from 1% to 91% on a lattice of size $32\times 32$. We also
demonstrate that our model can successfully learn a continuous family of
theories at once, and the results of learning can be transferred to larger
lattices. Such generalization capacities further accentuate the potential
advantages of machine learning methods compared to traditional MCMC-based
methods.

Grapheme-to-phoneme (G2P) conversion is an indispensable part of the Chinese
Mandarin text-to-speech (TTS) system, and the core of G2P conversion is to
solve the problem of polyphone disambiguation, which is to pick up the correct
pronunciation for several candidates for a Chinese polyphonic character. In
this paper, we propose a Chinese polyphone BERT model to predict the
pronunciations of Chinese polyphonic characters. Firstly, we create 741 new
Chinese monophonic characters from 354 source Chinese polyphonic characters by
pronunciation. Then we get a Chinese polyphone BERT by extending a pre-trained
Chinese BERT with 741 new Chinese monophonic characters and adding a
corresponding embedding layer for new tokens, which is initialized by the
embeddings of source Chinese polyphonic characters. In this way, we can turn
the polyphone disambiguation task into a pre-training task of the Chinese
polyphone BERT. Experimental results demonstrate the effectiveness of the
proposed model, and the polyphone BERT model obtain 2% (from 92.1% to 94.1%)
improvement of average accuracy compared with the BERT-based classifier model,
which is the prior state-of-the-art in polyphone disambiguation.

Cross-modal representation learning has become a new normal for bridging the
semantic gap between text and visual data. Learning modality agnostic
representations in a continuous latent space, however, is often treated as a
black-box data-driven training process. It is well-known that the effectiveness
of representation learning depends heavily on the quality and scale of training
data. For video representation learning, having a complete set of labels that
annotate the full spectrum of video content for training is highly difficult if
not impossible. These issues, black-box training and dataset bias, make
representation learning practically challenging to be deployed for video
understanding due to unexplainable and unpredictable results. In this paper, we
propose two novel training objectives, likelihood and unlikelihood functions,
to unroll semantics behind embeddings while addressing the label sparsity
problem in training. The likelihood training aims to interpret semantics of
embeddings beyond training labels, while the unlikelihood training leverages
prior knowledge for regularization to ensure semantically coherent
interpretation. With both training objectives, a new encoder-decoder network,
which learns interpretable cross-modal representation, is proposed for ad-hoc
video search. Extensive experiments on TRECVid and MSR-VTT datasets show the
proposed network outperforms several state-of-the-art retrieval models with a
statistically significant performance margin.

Due to its powerful feature learning capability and high efficiency, deep
hashing has achieved great success in large-scale image retrieval. Meanwhile,
extensive works have demonstrated that deep neural networks (DNNs) are
susceptible to adversarial examples, and exploring adversarial attack against
deep hashing has attracted many research efforts. Nevertheless, backdoor
attack, another famous threat to DNNs, has not been studied for deep hashing
yet. Although various backdoor attacks have been proposed in the field of image
classification, existing approaches failed to realize a truly imperceptive
backdoor attack that enjoys invisible triggers and clean label setting
simultaneously, and they also cannot meet the intrinsic demand of image
retrieval backdoor. In this paper, we propose BadHash, the first
generative-based imperceptible backdoor attack against deep hashing, which can
effectively generate invisible and input-specific poisoned images with clean
label. Specifically, we first propose a new conditional generative adversarial
network (cGAN) pipeline to effectively generate poisoned samples. For any given
benign image, it seeks to generate a natural-looking poisoned counterpart with
a unique invisible trigger. In order to improve the attack effectiveness, we
introduce a label-based contrastive learning network LabCLN to exploit the
semantic characteristics of different labels, which are subsequently used for
confusing and misleading the target model to learn the embedded trigger. We
finally explore the mechanism of backdoor attacks on image retrieval in the
hash space. Extensive experiments on multiple benchmark datasets verify that
BadHash can generate imperceptible poisoned samples with strong attack ability
and transferability over state-of-the-art deep hashing schemes.

In many reacting flow systems, the thermo-chemical state-space is known or
assumed to evolve close to a low-dimensional manifold (LDM). Various approaches
are available to obtain those manifolds and subsequently express the original
high-dimensional space with fewer parameterizing variables. Principal component
analysis (PCA) is one of the dimensionality reduction methods that can be used
to obtain LDMs. PCA does not make prior assumptions about the parameterizing
variables and retrieves them empirically from the training data. In this paper,
we show that PCA applied in local clusters of data (local PCA) is capable of
detecting the intrinsic parameterization of the thermo-chemical state-space. We
first demonstrate that utilizing three common combustion models of varying
complexity: the Burke-Schumann model, the chemical equilibrium model and the
homogeneous reactor. Parameterization of these models is known a priori which
allows for benchmarking with the local PCA approach. We further extend the
application of local PCA to a more challenging case of a turbulent non-premixed
$n$-heptane/air jet flame for which the parameterization is no longer obvious.
Our results suggest that meaningful parameterization can be obtained also for
more complex datasets. We show that local PCA finds variables that can be
linked to local stoichiometry, reaction progress and soot formation processes.

Text-based simulated environments have proven to be a valid testbed for
machine learning approaches. The process of affordance extraction can be used
to generate possible actions for interaction within such an environment. In
this paper the capabilities and challenges for utilizing external knowledge
databases (in particular ConceptNet) in the process of affordance extraction
are studied. An algorithm for automated affordance extraction is introduced and
evaluated on the Interactive Fiction (IF) platforms TextWorld and Jericho. For
this purpose, the collected affordances are translated into text commands for
IF agents. To probe the quality of the automated evaluation process, an
additional human baseline study is conducted. The paper illustrates that,
despite some challenges, external databases can in principle be used for
affordance extraction. The paper concludes with recommendations for further
modification and improvement of the process.

Future industrial applications will encompass compelling new use cases
requiring stringent performance guarantees over multiple key performance
indicators (KPI) such as reliability, dependability, latency, time
synchronization, security, etc. Achieving such stringent and diverse service
requirements necessitates the design of a special-purpose Industrial Internet
of Things (IIoT) network comprising a multitude of specialized functionalities
and technological enablers. This article proposes an innovative architecture
for such a special-purpose 6G IIoT network incorporating seven functional
building blocks categorized into: special-purpose functionalities and enabling
technologies. The former consists of Wireless Environment Control,
Traffic/Channel Prediction, Proactive Resource Management and End-to-End
Optimization functions; whereas the latter includes Synchronization and
Coordination, Machine Learning and Artificial Intelligence Algorithms, and
Auxiliary Functions. The proposed architecture aims at providing a
resource-efficient and holistic solution for the complex and dynamically
challenging requirements imposed by future 6G industrial use cases. Selected
test scenarios are provided and assessed to illustrate cross-functional
collaboration and demonstrate the applicability of the proposed architecture in
a wireless IIoT network.

A Generative Adversarial Network (GAN) is a deep-learning generative model in
the field of Machine Learning (ML) that involves training two Neural Networks
(NN) using a sizable data set. In certain fields, such as medicine, the
training data may be hospital patient records that are stored across different
hospitals. The classic centralized approach would involve sending the data to a
centralized server where the model would be trained. However, that would
involve breaching the privacy and confidentiality of the patients and their
data, which would be unacceptable. Therefore, Federated Learning (FL), an ML
technique that trains ML models in a distributed setting without data ever
leaving the host device, would be a better alternative to the centralized
option. In this ML technique, only parameters and certain metadata would be
communicated. In spite of that, there still exist attacks that can infer user
data using the parameters and metadata. A fully privacy-preserving solution
involves homomorphically encrypting (HE) the data communicated. This paper will
focus on the performance loss of training an FL-GAN with three different types
of Homomorphic Encryption: Partial Homomorphic Encryption (PHE), Somewhat
Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE). We will
also test the performance loss of Multi-Party Computations (MPC), as it has
homomorphic properties. The performances will be compared to the performance of
training an FL-GAN without encryption as well. Our experiments show that the
more complex the encryption method is, the longer it takes, with the extra time
taken for HE is quite significant in comparison to the base case of FL.

Current RGB-based 6D object pose estimation methods have achieved noticeable
performance on datasets and real world applications. However, predicting 6D
pose from single 2D image features is susceptible to disturbance from changing
of environment and textureless or resemblant object surfaces. Hence, RGB-based
methods generally achieve less competitive results than RGBD-based methods,
which deploy both image features and 3D structure features. To narrow down this
performance gap, this paper proposes a framework for 6D object pose estimation
that learns implicit 3D information from 2 RGB images. Combining the learned 3D
information and 2D image features, we establish more stable correspondence
between the scene and the object models. To seek for the methods best utilizing
3D information from RGB inputs, we conduct an investigation on three different
approaches, including Early- Fusion, Mid-Fusion, and Late-Fusion. We ascertain
the Mid- Fusion approach is the best approach to restore the most precise 3D
keypoints useful for object pose estimation. The experiments show that our
method outperforms state-of-the-art RGB-based methods, and achieves comparable
results with RGBD-based methods.

Our main goal in this study is to propose a transfer learning based method
for COVID-19 detection from Computed Tomography (CT) images. The transfer
learning model used for the task is a pretrained Xception model. Both model
architecture and pre-trained weights on ImageNet were used. The resulting
modified model was trained with 128 batch size and 224x224, 3 channeled input
images, converted from original 512x512, grayscale images. The dataset used is
a the COV19-CT-DB. Labels in the dataset include COVID-19 cases and
Non-COVID-19 cases for COVID-1919 detection. Firstly, a accuracy and loss on
the validation partition of the dataset as well as precision recall and macro
F1 score were used to measure the performance of the proposed method. The
resulting Macro F1 score on the validation set exceeded the baseline model.

This paper proposes a gaze correction and animation method for
high-resolution, unconstrained portrait images, which can be trained without
the gaze angle and the head pose annotations. Common gaze-correction methods
usually require annotating training data with precise gaze, and head pose
information. Solving this problem using an unsupervised method remains an open
problem, especially for high-resolution face images in the wild, which are not
easy to annotate with gaze and head pose labels. To address this issue, we
first create two new portrait datasets: CelebGaze and high-resolution
CelebHQGaze. Second, we formulate the gaze correction task as an image
inpainting problem, addressed using a Gaze Correction Module (GCM) and a Gaze
Animation Module (GAM). Moreover, we propose an unsupervised training strategy,
i.e., Synthesis-As-Training, to learn the correlation between the eye region
features and the gaze angle. As a result, we can use the learned latent space
for gaze animation with semantic interpolation in this space. Moreover, to
alleviate both the memory and the computational costs in the training and the
inference stage, we propose a Coarse-to-Fine Module (CFM) integrated with GCM
and GAM. Extensive experiments validate the effectiveness of our method for
both the gaze correction and the gaze animation tasks in both low and
high-resolution face datasets in the wild and demonstrate the superiority of
our method with respect to the state of the arts. Code is available at
https://github.com/zhangqianhui/GazeAnimationV2

In socio-technical settings, operators are increasingly assisted by decision
support systems. By employing these, important properties of socio-technical
systems such as self-adaptation and self-optimization are expected to improve
further. To be accepted by and engage efficiently with operators, decision
support systems need to be able to provide explanations regarding the reasoning
behind specific decisions. In this paper, we propose the usage of Learning
Classifier Systems, a family of rule-based machine learning methods, to
facilitate transparent decision making and highlight some techniques to improve
that. We then present a template of seven questions to assess
application-specific explainability needs and demonstrate their usage in an
interview-based case study for a manufacturing scenario. We find that the
answers received did yield useful insights for a well-designed LCS model and
requirements to have stakeholders actively engage with an intelligent agent.

Predicting future locations of agents in the scene is an important problem in
self-driving. In recent years, there has been a significant progress in
representing the scene and the agents in it. The interactions of agents with
the scene and with each other are typically modeled with a Graph Neural
Network. However, the graph structure is mostly static and fails to represent
the temporal changes in highly dynamic scenes. In this work, we propose a
temporal graph representation to better capture the dynamics in traffic scenes.
We complement our representation with two types of memory modules; one focusing
on the agent of interest and the other on the entire scene. This allows us to
learn temporally-aware representations that can achieve good results even with
simple regression of multiple futures. When combined with goal-conditioned
prediction, we show better results that can reach the state-of-the-art
performance on the Argoverse benchmark.

Active Simultaneous Localization and Mapping (SLAM) is the problem of
planning and controlling the motion of a robot to build the most accurate and
complete model of the surrounding environment. Since the first foundational
work in active perception appeared, more than three decades ago, this field has
received increasing attention across different scientific communities. This has
brought about many different approaches and formulations, and makes a review of
the current trends necessary and extremely valuable for both new and
experienced researchers. In this work, we survey the state-of-the-art in active
SLAM and take an in-depth look at the open challenges that still require
attention to meet the needs of modern applications. % in order to achieve
real-world deployment. After providing a historical perspective, we present a
unified problem formulation and review the classical solution scheme, which
decouples the problem into three stages that identify, select, and execute
potential navigation actions. We then analyze alternative approaches, including
belief-space planning and modern techniques based on deep reinforcement
learning, and review related work on multi-robot coordination. The manuscript
concludes with a discussion of new research directions, addressing reproducible
research, active spatial perception, and practical applications, among other
topics.

Although deep learning algorithms have been intensively developed for
computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully
annotated datasets, leading to much time and resource consumption. Weakly
supervised learning (WSL), which leverages coarse-grained labels to accomplish
fine-grained tasks, has the potential to solve this problem. In this paper, we
first propose a new large-scale tuberculosis (TB) chest X-ray dataset, namely
the tuberculosis chest X-ray attribute dataset (TBX-Att), and then establish an
attribute-assisted weakly-supervised framework to classify and localize TB by
leveraging the attribute information to overcome the insufficiency of
supervision in WSL scenarios. Specifically, first, the TBX-Att dataset contains
2000 X-ray images with seven kinds of attributes for TB relational reasoning,
which are annotated by experienced radiologists. It also includes the public
TBX11K dataset with 11200 X-ray images to facilitate weakly supervised
detection. Second, we exploit a multi-scale feature interaction model for TB
area classification and detection with attribute relational reasoning. The
proposed model is evaluated on the TBX-Att dataset and will serve as a solid
baseline for future research. The code and data will be available at
https://github.com/GangmingZhao/tb-attribute-weak-localization.

Offline imitation learning (IL) is a powerful method to solve decision-making
problems from expert demonstrations without reward labels. Existing offline IL
methods suffer from severe performance degeneration under limited expert data
due to covariate shift. Including a learned dynamics model can potentially
improve the state-action space coverage of expert data, however, it also faces
challenging issues like model approximation/generalization errors and
suboptimality of rollout data. In this paper, we propose the
Discriminator-guided Model-based offline Imitation Learning (DMIL) framework,
which introduces a discriminator to simultaneously distinguish the dynamics
correctness and suboptimality of model rollout data against real expert
demonstrations. DMIL adopts a novel cooperative-yet-adversarial learning
strategy, which uses the discriminator to guide and couple the learning process
of the policy and dynamics model, resulting in improved model performance and
robustness. Our framework can also be extended to the case when demonstrations
contain a large proportion of suboptimal data. Experimental results show that
DMIL and its extension achieve superior performance and robustness compared to
state-of-the-art offline IL methods under small datasets.

As the need for miniaturized structural and functional materials has
increased,the need for precise materials characterizaton has also expanded.
Nanoindentation is a popular method that can be used to measure material
mechanical behavior which enables high-throughput experiments and, in some
cases, can also provide images of the indented area through scanning. Both
indenting and scanning can cause tip wear that can influence the measurements.
Therefore, precise characterization of tip radii is needed to improve data
evaluation. A data fusion method is introduced which uses finite element
simulations and experimental data to estimate the tip radius in situ in a
meaningful way using an interpretable multi-fidelity deep learning approach. By
interpreting the machine learning models, it is shown that the approaches are
able to accurately capture physical indentation phenomena.

While deep learning based speech enhancement systems have made rapid progress
in improving the quality of speech signals, they can still produce outputs that
contain artifacts and can sound unnatural. We propose a novel approach to
speech enhancement aimed at improving perceptual quality and naturalness of
enhanced signals by optimizing for key characteristics of speech. We first
identify key acoustic parameters that have been found to correlate well with
voice quality (e.g. jitter, shimmer, and spectral flux) and then propose
objective functions which are aimed at reducing the difference between clean
speech and enhanced speech with respect to these features. The full set of
acoustic features is the extended Geneva Acoustic Parameter Set (eGeMAPS),
which includes 25 different attributes associated with perception of speech.
Given the non-differentiable nature of these feature computation, we first
build differentiable estimators of the eGeMAPS and then use them to fine-tune
existing speech enhancement systems. Our approach is generic and can be applied
to any existing deep learning based enhancement systems to further improve the
enhanced speech signals. Experimental results conducted on the Deep Noise
Suppression (DNS) Challenge dataset shows that our approach can improve the
state-of-the-art deep learning based enhancement systems.

This article seeks for a distributed learning solution for the visual
transformer (ViT) architectures. Compared to convolutional neural network (CNN)
architectures, ViTs often have larger model sizes, and are computationally
expensive, making federated learning (FL) ill-suited. Split learning (SL) can
detour this problem by splitting a model and communicating the hidden
representations at the split-layer, also known as smashed data.
Notwithstanding, the smashed data of ViT are as large as and as similar as the
input data, negating the communication efficiency of SL while violating data
privacy. To resolve these issues, we propose a new form of CutSmashed data by
randomly punching and compressing the original smashed data. Leveraging this,
we develop a novel SL framework for ViT, coined CutMixSL, communicating
CutSmashed data. CutMixSL not only reduces communication costs and privacy
leakage, but also inherently involves the CutMix data augmentation, improving
accuracy and scalability. Simulations corroborate that CutMixSL outperforms
baselines such as parallelized SL and SplitFed that integrates FL with SL.

When adapting Simultaneous Mapping and Localization (SLAM) to real-world
applications, such as autonomous vehicles, drones, and augmented reality
devices, its memory footprint and computing cost are the two main factors
limiting the performance and the range of applications. In sparse feature based
SLAM algorithms, one efficient way for this problem is to limit the map point
size by selecting the points potentially useful for local and global bundle
adjustment (BA). This study proposes an efficient graph optimization for
sparsifying map points in SLAM systems. Specifically, we formulate a maximum
pose-visibility and maximum spatial diversity problem as a minimum-cost
maximum-flow graph optimization problem. The proposed method works as an
additional step in existing SLAM systems, so it can be used in both
conventional or learning based SLAM systems. By extensive experimental
evaluations we demonstrate the proposed method achieves even more accurate
camera poses with approximately 1/3 of the map points and 1/2 of the

Vision-Language Pretraining (VLP) models have recently successfully
facilitated many cross-modal downstream tasks. Most existing works evaluated
their systems by comparing the fine-tuned downstream task performance. However,
only average downstream task accuracy provides little information about the
pros and cons of each VLP method, let alone provides insights on how the
community can improve the systems in the future. Inspired by the CheckList for
testing natural language processing, we introduce VL-CheckList, a novel
framework to understand the capabilities of VLP models. The proposed method
divides the image-texting ability of a VLP model into three categories:
objects, attributes, and relations, and uses a novel taxonomy to further break
down these three aspects. We conduct comprehensive studies to analyze seven
recently popular VLP models via the proposed framework. Results confirm the
effectiveness of the proposed method by revealing fine-grained differences
among the compared models that were not visible from downstream task-only
evaluation. Further results show promising research direction in building
better VLP models. Data and Code: https://github.com/om-ai-lab/VL-CheckList

One concern with the rise of large language models lies with their potential
for significant harm, particularly from pretraining on biased, obscene,
copyrighted, and private information. Emerging ethical approaches have
attempted to filter pretraining material, but such approaches have been ad hoc
and failed to take into account context. We offer an approach to filtering
grounded in law, which has directly addressed the tradeoffs in filtering
material. First, we gather and make available the Pile of Law, a 256GB (and
growing) dataset of open-source English-language legal and administrative data,
covering court opinions, contracts, administrative rules, and legislative
records. Pretraining on the Pile of Law may potentially help with legal tasks
that have the promise to improve access to justice. Second, we distill the
legal norms that governments have developed to constrain the inclusion of toxic
or private content into actionable lessons for researchers and discuss how our
dataset reflects these norms. Third, we show how the Pile of Law offers
researchers the opportunity to learn such filtering rules directly from the
data, providing an exciting new research direction in model-based processing.

Solving large-scale Mixed Integer Programs (MIP) can be difficult without
advanced algorithms such as decomposition based techniques. Even if a
decomposition technique might be appropriate, there are still many possible
decompositions for any large MIP and it may not be obvious which will be the
most effective. This paper presents a comprehensive analysis of the predictive
capabilities of a Machine Learning ranking (ML) function for predicting the
quality of Mixed Integer Programming (MIP) decompositions created via
constraint relaxation. In this analysis, the role of instance similarity and ML
prediction quality is explored, as well as the benchmarking of a ML ranking
function against existing heuristic functions. For this analysis, a new dataset
consisting of over 40000 unique decompositions sampled from across 24 instances
from the MIPLIB2017 library has been established. These decompostions have been
created by both a greedy relaxation algorithm as well as a population based
multi-objective algorithm, which has previously been shown to produce high
quality decompositions. In this paper, we demonstrate that a ML ranking
function is able to provide state-of-the-art predictions when benchmarked
against existing heuristic ranking functions. Additionally, we demonstrate that
by only considering a small set of features related to the relaxed constraints
in each decomposition, a ML ranking function is still able to be competitive
with heuristic techniques. Such a finding is promising for future constraint
relaxation approaches, as these features can be used to guide decomposition
creation. Finally, we highlight where a ML ranking function would be beneficial
in a decomposition creation framework.

Polarized color photography provides both visual textures and object
surficial information in one single snapshot. However, the use of the
directional polarizing filter array causes extremely lower photon count and SNR
compared to conventional color imaging. Thus, the feature essentially leads to
unpleasant noisy images and destroys polarization analysis performance. It is a
challenge for traditional image processing pipelines owing to the fact that the
physical constraints exerted implicitly in the channels are excessively
complicated. To address this issue, we propose a learning-based approach to
simultaneously restore clean signals and precise polarization information. A
real-world polarized color image dataset of paired raw short-exposed noisy and
long-exposed reference images are captured to support the learning-based
pipeline. Moreover, we embrace the development of vision Transformer and
propose a hybrid transformer model for the Polarized Color image denoising,
namely PoCoformer, for a better restoration performance. Abundant experiments
demonstrate the effectiveness of proposed method and key factors that affect
results are analyzed.

Understanding vision and language representations of product content is vital
for search and recommendation applications in e-commerce. As a backbone for
online shopping platforms and inspired by the recent success in representation
learning research, we propose a contrastive learning framework that aligns
language and visual models using unlabeled raw product text and images. We
present techniques we used to train large-scale representation learning models
and share solutions that address domain-specific challenges. We study the
performance using our pre-trained model as backbones for diverse downstream
tasks, including category classification, attribute extraction, product
matching, product clustering, and adult product recognition. Experimental
results show that our proposed method outperforms the baseline in each
downstream task regarding both single modality and multiple modalities.

Collision detection plays an important role in simulation, control, and
learning for robotic systems. However, no existing method is differentiable
with respect to the configurations of the objects, greatly limiting the sort of
algorithms that can be built on top of collision detection. In this work, we
propose a set of differentiable collision detection algorithms between capsules
and padded polygons by formulating these problems as differentiable convex
quadratic programs. The resulting algorithms are able to return a proximity
value indicating if a collision has taken place, as well as the closest points
between objects, all of which are differentiable. As a result, they can be used
reliably within other gradient-based optimization methods, including trajectory
optimization, state estimation, and reinforcement learning methods.

We study the impact of different pruning techniques on the representation
learned by deep neural networks trained with contrastive loss functions. Our
work finds that at high sparsity levels, contrastive learning results in a
higher number of misclassified examples relative to models trained with
traditional cross-entropy loss. To understand this pronounced difference, we
use metrics such as the number of PIEs (Hooker et al., 2019), Q-Score (Kalibhat
et al., 2022), and PD-Score (Baldock et al., 2021) to measure the impact of
pruning on the learned representation quality. Our analysis suggests the
schedule of the pruning method implementation matters. We find that the
negative impact of sparsity on the quality of the learned representation is the
highest when pruning is introduced early on in the training phase.

To fully utilize the versatility of a multi-finger dexterous robotic hand for
object grasping, one must satisfy complex physical constraints introduced by
hand-object interaction and object geometry during grasp planning. We propose
an integrative approach of combining a generative model and a bilevel
optimization to compute diverse grasps for novel unseen objects. First, a grasp
prediction is obtained from a conditional variational autoencoder trained on
merely six YCB objects. The prediction is then projected onto the manifold of
kinematically and dynamically feasible grasps by jointly solving
collision-aware inverse kinematics, force closure, and friction constraints as
one nonconvex bilevel optimization. We demonstrate the effectiveness of our
method on hardware by successfully grasping a wide range of unseen household
objects, including adversarial shapes challenging to other types of robotic
grippers. A video summary of our results is available at
https://youtu.be/9DTrImbN99I.

Existing text recognition methods usually need large-scale training data.
Most of them rely on synthetic training data due to the lack of annotated real
images. However, there is a domain gap between the synthetic data and real
data, which limits the performance of the text recognition models. Recent
self-supervised text recognition methods attempted to utilize unlabeled real
images by introducing contrastive learning, which mainly learns the
discrimination of the text images. Inspired by the observation that humans
learn to recognize the texts through both reading and writing, we propose to
learn discrimination and generation by integrating contrastive learning and
masked image modeling in our self-supervised method. The contrastive learning
branch is adopted to learn the discrimination of text images, which imitates
the reading behavior of humans. Meanwhile, masked image modeling is firstly
introduced for text recognition to learn the context generation of the text
images, which is similar to the writing behavior. The experimental results show
that our method outperforms previous self-supervised text recognition methods
by 10.2%-20.2% on irregular scene text recognition datasets. Moreover, our
proposed text recognizer exceeds previous state-of-the-art text recognition
methods by averagely 5.3% on 11 benchmarks, with similar model size. We also
demonstrate that our pre-trained model can be easily applied to other
text-related tasks with obvious performance gain.

Recent advancements in computer graphics technology allow more realistic
ren-dering of car driving environments. They have enabled self-driving car
simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large
amounts of synthetic data that can complement the existing real-world dataset
in training autonomous car perception. Furthermore, since self-driving car
simulators allow full control of the environment, they can generate dangerous
driving scenarios that the real-world dataset lacks such as bad weather and
accident scenarios. In this paper, we will demonstrate the effectiveness of
combining data gathered from the real world with data generated in the
simulated world to train perception systems on object detection and
localization task. We will also propose a multi-level deep learning perception
framework that aims to emulate a human learning experience in which a series of
tasks from the simple to more difficult ones are learned in a certain domain.
The autonomous car perceptron can learn from easy-to-drive scenarios to more
challenging ones customized by simulation software.

Natural language (NL) toolkits enable visualization developers, who may not
have a background in natural language processing (NLP), to create natural
language interfaces (NLIs) for end-users to flexibly specify and interact with
visualizations. However, these toolkits currently only support one-off
utterances, with minimal capability to facilitate a multi-turn dialog between
the user and the system. Developing NLIs with such conversational interaction
capabilities remains a challenging task, requiring implementations of low-level
NLP techniques to process a new query as an intent to follow-up on an older
query. We extend an existing Python-based toolkit, NL4DV, that processes an NL
query about a tabular dataset and returns an analytic specification containing
data attributes, analytic tasks, and relevant visualizations, modeled as a JSON
object. Specifically, NL4DV now enables developers to facilitate multiple
simultaneous conversations about a dataset and resolve associated ambiguities,
augmenting new conversational information into the output JSON object. We
demonstrate these capabilities through three examples: (1) an NLI to learn
aspects of the Vega-Lite grammar, (2) a mind mapping application to create
free-flowing conversations, and (3) a chatbot to answer questions and resolve
ambiguities.

Although existing machine reading comprehension models are making rapid
progress on many datasets, they are far from robust. In this paper, we propose
an understanding-oriented machine reading comprehension model to address three
kinds of robustness issues, which are over sensitivity, over stability and
generalization. Specifically, we first use a natural language inference module
to help the model understand the accurate semantic meanings of input questions
so as to address the issues of over sensitivity and over stability. Then in the
machine reading comprehension module, we propose a memory-guided multi-head
attention method that can further well understand the semantic meanings of
input questions and passages. Third, we propose a multilanguage learning
mechanism to address the issue of generalization. Finally, these modules are
integrated with a multi-task learning based method. We evaluate our model on
three benchmark datasets that are designed to measure models robustness,
including DuReader (robust) and two SQuAD-related datasets. Extensive
experiments show that our model can well address the mentioned three kinds of
robustness issues. And it achieves much better results than the compared
state-of-the-art models on all these datasets under different evaluation
metrics, even under some extreme and unfair evaluations. The source code of our
work is available at: https://github.com/neukg/RobustMRC.

Freehand 3D ultrasound (US) has important clinical value due to its low cost
and unrestricted field of view. Recently deep learning algorithms have removed
its dependence on bulky and expensive external positioning devices. However,
improving reconstruction accuracy is still hampered by difficult elevational
displacement estimation and large cumulative drift. In this context, we propose
a novel deep motion network (MoNet) that integrates images and a lightweight
sensor known as the inertial measurement unit (IMU) from a velocity perspective
to alleviate the obstacles mentioned above. Our contribution is two-fold.
First, we introduce IMU acceleration for the first time to estimate elevational
displacements outside the plane. We propose a temporal and multi-branch
structure to mine the valuable information of low signal-to-noise ratio (SNR)
acceleration. Second, we propose a multi-modal online self-supervised strategy
that leverages IMU information as weak labels for adaptive optimization to
reduce drift errors and further ameliorate the impacts of acceleration noise.
Experiments show that our proposed method achieves the superior reconstruction
performance, exceeding state-of-the-art methods across the board.

Reliable quantitative analysis of immunohistochemical staining images
requires accurate and robust cell detection and classification. Recent
weakly-supervised methods usually estimate probability density maps for cell
recognition. However, in dense cell scenarios, their performance can be limited
by pre- and post-processing as it is impossible to find a universal parameter
setting. In this paper, we introduce an end-to-end framework that applies
direct regression and classification for preset anchor points. Specifically, we
propose a pyramidal feature aggregation strategy to combine low-level features
and high-level semantics simultaneously, which provides accurate cell
recognition for our purely point-based model. In addition, an optimized cost
function is designed to adapt our multi-task learning framework by matching
ground truth and predicted points. The experimental results demonstrate the
superior accuracy and efficiency of the proposed method, which reveals the high
potentiality in assisting pathologist assessments.

Large pretrained models can be privately fine-tuned to achieve performance
approaching that of non-private models. A common theme in these results is the
surprising observation that high-dimensional models can achieve favorable
privacy-utility trade-offs. This seemingly contradicts known results on the
model-size dependence of differentially private convex learning and raises the
following research question: When does the performance of differentially
private learning not degrade with increasing model size? We identify that the
magnitudes of gradients projected onto subspaces is a key factor that
determines performance. To precisely characterize this for private convex
learning, we introduce a condition on the objective that we term
\emph{restricted Lipschitz continuity} and derive improved bounds for the
excess empirical and population risks that are dimension-independent under
additional conditions. We empirically show that in private fine-tuning of large
language models, gradients obtained during fine-tuning are mostly controlled by
a few principal components. This behavior is similar to conditions under which
we obtain dimension-independent bounds in convex settings. Our theoretical and
empirical results together provide a possible explanation for recent successes
in large-scale private fine-tuning. Code to reproduce our results can be found
at
\url{https://github.com/lxuechen/private-transformers/tree/main/examples/classification/spectral_analysis}.

We aim to quantitatively measure the practical usability of medical image
segmentation models: to what extent, how often, and on which samples a model's
predictions can be used/trusted. We first propose a measure,
Correctness-Confidence Rank Correlation (CCRC), to capture how predictions'
confidence estimates correlate with their correctness scores in rank. A model
with a high value of CCRC means its prediction confidences reliably suggest
which samples' predictions are more likely to be correct. Since CCRC does not
capture the actual prediction correctness, it alone is insufficient to indicate
whether a prediction model is both accurate and reliable to use in practice.
Therefore, we further propose another method, Usable Region Estimate (URE),
which simultaneously quantifies predictions' correctness and reliability of
confidence assessments in one estimate. URE provides concrete information on to
what extent a model's predictions are usable. In addition, the sizes of usable
regions (UR) can be utilized to compare models: A model with a larger UR can be
taken as a more usable and hence better model. Experiments on six datasets
validate that the proposed evaluation methods perform well, providing a
concrete and concise measure for the practical usability of medical image
segmentation models. Code is made available at
https://github.com/yizhezhang2000/ure.

The ongoing COVID-19 pandemic has already taken millions of lives and damaged
economies across the globe. Most COVID-19 deaths and economic losses are
reported from densely crowded cities. It is comprehensible that the effective
control and prevention of epidemic/pandemic infectious diseases is vital.
According to WHO, testing and diagnosis is the best strategy to control
pandemics. Scientists worldwide are attempting to develop various innovative
and cost-efficient methods to speed up the testing process. This paper
comprehensively evaluates the applicability of the recent top ten
state-of-the-art Deep Convolutional Neural Networks (CNNs) for automatically
detecting COVID-19 infection using chest X-ray images. Moreover, it provides a
comparative analysis of these models in terms of accuracy. This study
identifies the effective methodologies to control and prevent infectious
respiratory diseases. Our trained models have demonstrated outstanding results
in classifying the COVID-19 infected chest x-rays. In particular, our trained
models MobileNet, EfficentNet, and InceptionV3 achieved a classification
average accuracy of 95\%, 95\%, and 94\% test set for COVID-19 class
classification, respectively. Thus, it can be beneficial for clinical
practitioners and radiologists to speed up the testing, detection, and
follow-up of COVID-19 cases.

Platooning and coordination are two implementation strategies that are
frequently proposed for traffic control of connected and autonomous vehicles
(CAVs) at signal-free intersections instead of using conventional traffic
signals. However, few studies have attempted to integrate both strategies to
better facilitate the CAV control at signal-free intersections. To this end,
this study proposes a hierarchical control model, named COOR-PLT, to coordinate
adaptive CAV platoons at a signal-free intersection based on deep reinforcement
learning (DRL). COOR-PLT has a two-layer framework. The first layer uses a
centralized control strategy to form adaptive platoons. The optimal size of
each platoon is determined by considering multiple objectives (i.e.,
efficiency, fairness and energy saving). The second layer employs a
decentralized control strategy to coordinate multiple platoons passing through
the intersection. Each platoon is labeled with coordinated status or
independent status, upon which its passing priority is determined. As an
efficient DRL algorithm, Deep Q-network (DQN) is adopted to determine platoon
sizes and passing priorities respectively in the two layers. The model is
validated and examined on the simulator Simulation of Urban Mobility (SUMO).
The simulation results demonstrate that the model is able to: (1) achieve
satisfactory convergence performances; (2) adaptively determine platoon size in
response to varying traffic conditions; and (3) completely avoid deadlocks at
the intersection. By comparison with other control methods, the model manifests
its superiority of adopting adaptive platooning and DRL-based coordination
strategies. Also, the model outperforms several state-of-the-art methods on
reducing travel time and fuel consumption in different traffic conditions.

Graph contrastive learning has emerged as a powerful tool for unsupervised
graph representation learning. The key to the success of graph contrastive
learning is to acquire high-quality positive and negative samples as
contrasting pairs for the purpose of learning underlying structural semantics
of the input graph. Recent works usually sample negative samples from the same
training batch with the positive samples, or from an external irrelevant graph.
However, a significant limitation lies in such strategies, which is the
unavoidable problem of sampling false negative samples. In this paper, we
propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate
artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive
learning, namely \textbf{CGC}, which has a different perspective compared to
those sampling-based strategies. We utilize counterfactual mechanism to produce
hard negative samples, which ensures that the generated samples are similar to,
but have labels that different from the positive sample. The proposed method
achieves satisfying results on several datasets compared to some traditional
unsupervised graph learning methods and some SOTA graph contrastive learning
methods. We also conduct some supplementary experiments to give an extensive
illustration of the proposed method, including the performances of CGC with
different hard negative samples and evaluations for hard negative samples
generated with different similarity measurements.

Breast lesion detection in ultrasound is critical for breast cancer
diagnosis. Existing methods mainly rely on individual 2D ultrasound images or
combine unlabeled video and labeled 2D images to train models for breast lesion
detection. In this paper, we first collect and annotate an ultrasound video
dataset (188 videos) for breast lesion detection. Moreover, we propose a
clip-level and video-level feature aggregated network (CVA-Net) for addressing
breast lesion detection in ultrasound videos by aggregating video-level lesion
classification features and clip-level temporal features. The clip-level
temporal features encode local temporal information of ordered video frames and
global temporal information of shuffled video frames. In our CVA-Net, an
inter-video fusion module is devised to fuse local features from original video
frames and global features from shuffled video frames, and an intra-video
fusion module is devised to learn the temporal information among adjacent video
frames. Moreover, we learn video-level features to classify the breast lesions
of the original video as benign or malignant lesions to further enhance the
final breast lesion detection performance in ultrasound videos. Experimental
results on our annotated dataset demonstrate that our CVA-Net clearly
outperforms state-of-the-art methods. The corresponding code and dataset are
publicly available at \url{https://github.com/jhl-Det/CVA-Net}.

Recent work introduced the epinet as a new approach to uncertainty modeling
in deep learning. An epinet is a small neural network added to traditional
neural networks, which, together, can produce predictive distributions. In
particular, using an epinet can greatly improve the quality of joint
predictions across multiple inputs, a measure of how well a neural network
knows what it does not know. In this paper, we examine whether epinets can
offer similar advantages under distributional shifts. We find that, across
ImageNet-A/O/C, epinets generally improve robustness metrics. Moreover, these
improvements are more significant than those afforded by even very large
ensembles at orders of magnitude lower computational costs. However, these
improvements are relatively small compared to the outstanding issues in
distributionally-robust deep learning. Epinets may be a useful tool in the
toolbox, but they are far from the complete solution.

This paper is a cursory study on how topological features are preserved
within the internal representations of neural network layers. Using techniques
from topological data analysis, namely persistent homology, the topological
features of a simple feedforward neural network's layer representations of a
modified torus with a Klein bottle-like twist were computed. The network
appeared to approximate homeomorphisms in early layers, before significantly
changing the topology of the data in deeper layers. The resulting noise
hampered the ability of persistent homology to compute these features, however
similar topological features seemed to persist longer in a network with a
bijective activation function.

Quantum algorithms based on variational approaches are one of the most
promising methods to construct quantum solutions and have found a myriad of
applications in the last few years. Despite the adaptability and simplicity,
their scalability and the selection of suitable ans\"atzs remain key
challenges. In this work, we report an algorithmic framework based on nested
Monte-Carlo Tree Search (MCTS) coupled with the combinatorial multi-armed
bandit (CMAB) model for the automated design of quantum circuits. Through
numerical experiments, we demonstrated our algorithm applied to various kinds
of problems, including the ground energy problem in quantum chemistry, quantum
optimisation on a graph, solving systems of linear equations, and finding
encoding circuit for quantum error detection codes. Compared to the existing
approaches, the results indicate that our circuit design algorithm can explore
larger search spaces and optimise quantum circuits for larger systems, showing
both versatility and scalability.

Unsupervised and semi-supervised ML methods such as variational autoencoders
(VAE) have become widely adopted across multiple areas of physics, chemistry,
and materials sciences due to their capability in disentangling representations
and ability to find latent manifolds for classification and regression of
complex experimental data. Like other ML problems, VAEs require hyperparameter
tuning, e.g., balancing the Kullback Leibler (KL) and reconstruction terms.
However, the training process and resulting manifold topology and connectivity
depend not only on hyperparameters, but also their evolution during training.
Because of the inefficiency of exhaustive search in a high-dimensional
hyperparameter space for the expensive to train models, here we explored a
latent Bayesian optimization (zBO) approach for the hyperparameter trajectory
optimization for the unsupervised and semi-supervised ML and demonstrate for
joint-VAE with rotational invariances. We demonstrate an application of this
method for finding joint discrete and continuous rotationally invariant
representations for MNIST and experimental data of a plasmonic nanoparticles
material system. The performance of the proposed approach has been discussed
extensively, where it allows for any high dimensional hyperparameter tuning or
trajectory optimization of other ML models.

We introduce Proteus, a novel self-designing approximate range filter, which
configures itself based on sampled data in order to optimize its false positive
rate (FPR) for a given space requirement. Proteus unifies the probabilistic and
deterministic design spaces of state-of-the-art range filters to achieve robust
performance across a larger variety of use cases. At the core of Proteus lies
our Contextual Prefix FPR (CPFPR) model - a formal framework for the FPR of
prefix-based filters across their design spaces. We empirically demonstrate the
accuracy of our model and Proteus' ability to optimize over both synthetic
workloads and real-world datasets. We further evaluate Proteus in RocksDB and
show that it is able to improve end-to-end performance by as much as 5.3x over
more brittle state-of-the-art methods such as SuRF and Rosetta. Our experiments
also indicate that the cost of modeling is not significant compared to the
end-to-end performance gains and that Proteus is robust to workload shifts.

There is a family of label modification approaches including self and
non-self label correction (LC), and output regularisation. They are widely used
for training robust deep neural networks (DNNs), but have not been
mathematically and thoroughly analysed together. We study them and discover
three key issues: (1) We are more interested in adopting Self LC as it
leverages its own knowledge and requires no auxiliary models. However, it is
unclear how to adaptively trust a learner as the training proceeds. (2) Some
methods penalise while the others reward low-entropy (i.e., high-confidence)
predictions, prompting us to ask which one is better. (3) Using the standard
training setting, a learned model becomes less confident when severe noise
exists. Self LC using high-entropy knowledge would generate high-entropy
targets.
  To resolve the issue (1), inspired by a well-accepted finding, i.e., deep
neural networks learn meaningful patterns before fitting noise, we propose a
novel end-to-end method named ProSelfLC, which is designed according to the
learning time and prediction entropy. Concretely, for any data point, we
progressively and adaptively trust its predicted probability distribution
versus its annotated one if a network has been trained for a relatively long
time and the prediction is of low entropy. For the issue (2), the effectiveness
of ProSelfLC defends entropy minimisation. By ProSelfLC, we empirically prove
that it is more effective to redefine a semantic low-entropy state and optimise
the learner toward it. To address the issue (3), we decrease the entropy of
self knowledge using a low temperature before exploiting it to correct labels,
so that the revised labels redefine low-entropy target probability
  We demonstrate the effectiveness of ProSelfLC through extensive experiments
in both clean and noisy settings, and on both image and protein datasets.

In the growing world of machine learning and data analytics, scholars are
finding new and innovative ways to solve real-world problems. One solution
comes by way of an intersection between healthcare, sports statistics, and data
sciences. Within the realm of Major League Baseball (MLB), pitchers are
regarded as the most important roster position. They often are among the
highest paid players and are crucial to a franchise's success, but they are
more at risk to suffer an injury that sidelines them for over a complete
season. The ulnar collateral ligament (UCL) is a small ligament in the elbow
that controls the strength and stability of a pitcher's throwing arm. Due to
repetitive strain, it is not uncommon for pitchers to tear it partially or
completely during their careers. Repairing this injury requires UCL
reconstruction surgery, as known informally as Tommy John surgery. In this
podium abstract, we want to investigate whether we can use machine learning
techniques to predict UCL injury by analyzing online pitcher data.

Formal XAI (explainable AI) is a growing area that focuses on computing
explanations with mathematical guarantees for the decisions made by ML models.
Inside formal XAI, one of the most studied cases is that of explaining the
choices taken by decision trees, as they are traditionally deemed as one of the
most interpretable classes of models. Recent work has focused on studying the
computation of "sufficient reasons", a kind of explanation in which given a
decision tree $T$ and an instance $x$, one explains the decision $T(x)$ by
providing a subset $y$ of the features of $x$ such that for any other instance
$z$ compatible with $y$, it holds that $T(z) = T(x)$, intuitively meaning that
the features in $y$ are already enough to fully justify the classification of
$x$ by $T$. It has been argued, however, that sufficient reasons constitute a
restrictive notion of explanation, and thus the community has started to study
their probabilistic counterpart, in which one requires that the probability of
$T(z) = T(x)$ must be at least some value $\delta \in (0, 1]$, where $z$ is a
random instance that is compatible with $y$. Our paper settles the
computational complexity of $\delta$-sufficient-reasons over decision trees,
showing that both (1) finding $\delta$-sufficient-reasons that are minimal in
size, and (2) finding $\delta$-sufficient-reasons that are minimal
inclusion-wise, do not admit polynomial-time algorithms (unless P=NP). This is
in stark contrast with the deterministic case ($\delta = 1$) where
inclusion-wise minimal sufficient-reasons are easy to compute. By doing this,
we answer two open problems originally raised by Izza et al. On the positive
side, we identify structural restrictions of decision trees that make the
problem tractable, and show how SAT solvers might be able to tackle these
problems in practical settings.

Factorizing a large matrix into small matrices is a popular strategy for
model compression. Singular value decomposition (SVD) plays a vital role in
this compression strategy, approximating a learned matrix with fewer
parameters. However, SVD minimizes the squared error toward reconstructing the
original matrix without gauging the importance of the parameters, potentially
giving a larger reconstruction error for those who affect the task accuracy
more. In other words, the optimization objective of SVD is not aligned with the
trained model's task accuracy. We analyze this previously unexplored problem,
make observations, and address it by introducing Fisher information to weigh
the importance of parameters affecting the model prediction. This idea leads to
our method: Fisher-Weighted SVD (FWSVD). Although the factorized matrices from
our approach do not result in smaller reconstruction errors, we find that our
resulting task accuracy is much closer to the original model's performance. We
perform analysis with the transformer-based language models, showing our
weighted SVD largely alleviates the mismatched optimization objectives and can
maintain model performance with a higher compression rate. Our method can
directly compress a task-specific model while achieving better performance than
other compact model strategies requiring expensive model pre-training.
Moreover, the evaluation of compressing an already compact model shows our
method can further reduce 9% to 30% parameters with an insignificant impact on
task accuracy.

Growing evidence suggests that YouTube's recommendation algorithm plays a
role in online radicalization via surfacing extreme content. Radical Islamist
groups, in particular, have been profiting from the global appeal of YouTube to
disseminate hate and jihadist propaganda. In this quantitative, data-driven
study, we investigate the prevalence of religiously intolerant Arabic YouTube
videos, the tendency of the platform to recommend such videos, and how these
recommendations are affected by demographics and watch history. Based on our
deep learning classifier developed to detect hateful videos and a large-scale
dataset of over 350K videos, we find that Arabic videos targeting religious
minorities are particularly prevalent in search results (30%) and first-level
recommendations (21%), and that 15% of overall captured recommendations point
to hateful videos. Our personalized audit experiments suggest that gender and
religious identity can substantially affect the extent of exposure to hateful
content. Our results contribute vital insights into the phenomenon of online
radicalization and facilitate curbing online harmful content.

We study a ranking problem in the contextual multi-armed bandit setting. A
learning agent selects an ordered list of items at each time step and observes
stochastic outcomes for each position. In online recommendation systems,
showing an ordered list of the most attractive items would not be the best
choice since both position and item dependencies result in a complicated reward
function. A very naive example is the lack of diversity when all the most
attractive items are from the same category. We model position and item
dependencies in the ordered list and design UCB and Thompson Sampling type
algorithms for this problem. We prove that the regret bound over $T$ rounds and
$L$ positions is $\Tilde{O}(L\sqrt{d T})$, which has the same order as the
previous works with respect to $T$ and only increases linearly with $L$. Our
work generalizes existing studies in several directions, including position
dependencies where position discount is a particular case, and proposes a more
general contextual bandit model.

Machine learning algorithms are routinely used for business decisions that
may directly affect individuals, for example, because a credit scoring
algorithm refuses them a loan. It is then relevant from an ethical (and legal)
point of view to ensure that these algorithms do not discriminate based on
sensitive attributes (like sex or race), which may occur unwittingly and
unknowingly by the operator and the management. Statistical tools and methods
are then required to detect and eliminate such potential biases.

Graph convolution is a recent scalable method for performing deep feature
learning on attributed graphs by aggregating local node information over
multiple layers. Such layers only consider attribute information of node
neighbors in the forward model and do not incorporate knowledge of global
network structure in the learning task. In particular, the modularity function
provides a convenient source of information about the community structure of
networks. In this work we investigate the effect on the quality of learned
representations by the incorporation of community structure preservation
objectives of networks in the graph convolutional model. We incorporate the
objectives in two ways, through an explicit regularization term in the cost
function in the output layer and as an additional loss term computed via an
auxiliary layer. We report the effect of community structure preserving terms
in the graph convolutional architectures. Experimental evaluation on two
attributed bibilographic networks showed that the incorporation of the
community-preserving objective improves semi-supervised node classification
accuracy in the sparse label regime.

Parkinson's disease (PD) is a neurological disorder that has a variety of
observable motor-related symptoms such as slow movement, tremor, muscular
rigidity, and impaired posture. PD is typically diagnosed by evaluating the
severity of motor impairments according to scoring systems such as the Movement
Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).
Automated severity prediction using video recordings of individuals provides a
promising route for non-intrusive monitoring of motor impairments. However, the
limited size of PD gait data hinders model ability and clinical potential.
Because of this clinical data scarcity and inspired by the recent advances in
self-supervised large-scale language models like GPT-3, we use human motion
forecasting as an effective self-supervised pre-training task for the
estimation of motor impairment severity. We introduce GaitForeMer, Gait
Forecasting and impairment estimation transforMer, which is first pre-trained
on public datasets to forecast gait movements and then applied to clinical data
to predict MDS-UPDRS gait impairment severity. Our method outperforms previous
approaches that rely solely on clinical data by a large margin, achieving an F1
score of 0.76, precision of 0.79, and recall of 0.75. Using GaitForeMer, we
show how public human movement data repositories can assist clinical use cases
through learning universal motion representations. The code is available at
https://github.com/markendo/GaitForeMer .

Machine learning models exhibit two seemingly contradictory phenomena:
training data memorization and various forms of forgetting. In memorization,
models overfit specific training examples and become susceptible to privacy
attacks. In forgetting, examples which appeared early in training are forgotten
by the end. In this work, we connect these phenomena. We propose a technique to
measure to what extent models ``forget'' the specifics of training examples,
becoming less susceptible to privacy attacks on examples they have not seen
recently. We show that, while non-convexity can prevent forgetting from
happening in the worst-case, standard image and speech models empirically do
forget examples over time. We identify nondeterminism as a potential
explanation, showing that deterministically trained models do not forget. Our
results suggest that examples seen early when training with extremely large
datasets -- for instance those examples used to pre-train a model -- may
observe privacy benefits at the expense of examples seen later.

Current approaches for classification of whole slide images (WSI) in digital
pathology predominantly utilize a two-stage learning pipeline. The first stage
identifies areas of interest (e.g. tumor tissue), while the second stage
processes cropped tiles from these areas in a supervised fashion. During
inference, a large number of tiles are combined into a unified prediction for
the entire slide. A major drawback of such approaches is the requirement for
task-specific auxiliary labels which are not acquired in clinical routine. We
propose a novel learning pipeline for WSI classification that is trainable
end-to-end and does not require any auxiliary annotations. We apply our
approach to predict molecular alterations for a number of different use-cases,
including detection of microsatellite instability in colorectal tumors and
prediction of specific mutations for colon, lung, and breast cancer cases from
The Cancer Genome Atlas. Results reach AUC scores of up to 94% and are shown to
be competitive with state of the art two-stage pipelines. We believe our
approach can facilitate future research in digital pathology and contribute to
solve a large range of problems around the prediction of cancer phenotypes,
hopefully enabling personalized therapies for more patients in future.

Machine learning is a field of artificial intelligence (AI) that is becoming
essential for several critical systems, making it a good target for threat
actors. Threat actors exploit different Tactics, Techniques, and Procedures
(TTPs) against the confidentiality, integrity, and availability of Machine
Learning (ML) systems. During the ML cycle, they exploit adversarial TTPs to
poison data and fool ML-based systems. In recent years, multiple security
practices have been proposed for traditional systems but they are not enough to
cope with the nature of ML-based systems. In this paper, we conduct an
empirical study of threats reported against ML-based systems with the aim to
understand and characterize the nature of ML threats and identify common
mitigation strategies. The study is based on 89 real-world ML attack scenarios
from the MITRE's ATLAS database, the AI Incident Database, and the literature;
854 ML repositories from the GitHub search and the Python Packaging Advisory
database, selected based on their reputation. Attacks from the AI Incident
Database and the literature are used to identify vulnerabilities and new types
of threats that were not documented in ATLAS. Results show that convolutional
neural networks were one of the most targeted models among the attack
scenarios. ML repositories with the largest vulnerability prominence include
TensorFlow, OpenCV, and Notebook. In this paper, we also report the most
frequent vulnerabilities in the studied ML repositories, the most targeted ML
phases and models, the most used TTPs in ML phases and attack scenarios. This
information is particularly important for red/blue teams to better conduct
attacks/defenses, for practitioners to prevent threats during ML development,
and for researchers to develop efficient defense mechanisms.

Plaque assay is the gold standard method for quantifying the concentration of
replication-competent lytic virions. Expediting and automating viral plaque
assays will significantly benefit clinical diagnosis, vaccine development, and
the production of recombinant proteins or antiviral agents. Here, we present a
rapid and stain-free quantitative viral plaque assay using lensfree holographic
imaging and deep learning. This cost-effective, compact, and automated device
significantly reduces the incubation time needed for traditional plaque assays
while preserving their advantages over other virus quantification methods. This
device captures ~0.32 Giga-pixel/hour phase information of the objects per test
well, covering an area of ~30x30 mm^2, in a label-free manner, eliminating
staining entirely. We demonstrated the success of this computational method
using Vero E6 cells and vesicular stomatitis virus. Using a neural network,
this stain-free device automatically detected the first cell lysing events due
to the viral replication as early as 5 hours after the incubation, and achieved
>90% detection rate for the plaque-forming units (PFUs) with 100% specificity
in <20 hours, providing major time savings compared to the traditional plaque
assays that take ~48 hours or more. This data-driven plaque assay also offers
the capability of quantifying the infected area of the cell monolayer,
performing automated counting and quantification of PFUs and virus-infected
areas over a 10-fold larger dynamic range of virus concentration than standard
viral plaque assays. This compact, low-cost, automated PFU quantification
device can be broadly used in virology research, vaccine development, and
clinical applications

Privacy and security-related concerns are growing as machine learning reaches
diverse application domains. The data holders want to train or infer with
private data while exploiting accelerators, such as GPUs, that are hosted in
the cloud. Cloud systems are vulnerable to attackers that compromise the
privacy of data and integrity of computations. Tackling such a challenge
requires unifying theoretical privacy algorithms with hardware security
capabilities. This paper presents DarKnight, a framework for large DNN training
while protecting input privacy and computation integrity. DarKnight relies on
cooperative execution between trusted execution environments (TEE) and
accelerators, where the TEE provides privacy and integrity verification, while
accelerators perform the bulk of the linear algebraic computation to optimize
the performance. In particular, DarKnight uses a customized data encoding
strategy based on matrix masking to create input obfuscation within a TEE. The
obfuscated data is then offloaded to GPUs for fast linear algebraic
computation. DarKnight's data obfuscation strategy provides provable data
privacy and computation integrity in the cloud servers. While prior works
tackle inference privacy and cannot be utilized for training, DarKnight's
encoding scheme is designed to support both training and inference.

The demand for computing is continuing to grow exponentially. This growth
will translate to exponential growth in computing's energy consumption unless
improvements in its energy-efficiency can outpace increases in its demand. Yet,
after decades of research, further improving energy-efficiency is becoming
increasingly challenging, as it is already highly optimized. As a result, at
some point, increases in computing demand are likely to outpace increases in
its energy-efficiency, potentially by a wide margin. Such exponential growth,
if left unchecked, will position computing as a substantial contributor to
global carbon emissions. While prominent technology companies have recognized
the problem and sought to reduce their carbon emissions, they understandably
focus on their successes, which has the potential to inadvertently convey the
false impression that this is now, or will soon be, a solved problem. Such
false impressions can be counterproductive if they serve to discourage further
research in this area, since, as we discuss, eliminating computing's, and more
generally society's, carbon emissions is far from a solved problem. To better
understand the problem's scope, this paper distills the fundamental trends that
determine computing's carbon footprint and their implications for achieving
sustainable computing.

Modern high-throughput single-cell immune profiling technologies, such as
flow and mass cytometry and single-cell RNA sequencing can readily measure the
expression of a large number of protein or gene features across the millions of
cells in a multi-patient cohort. While bioinformatics approaches can be used to
link immune cell heterogeneity to external variables of interest, such as,
clinical outcome or experimental label, they often struggle to accommodate such
a large number of profiled cells. To ease this computational burden, a limited
number of cells are typically \emph{sketched} or subsampled from each patient.
However, existing sketching approaches fail to adequately subsample rare cells
from rare cell-populations, or fail to preserve the true frequencies of
particular immune cell-types. Here, we propose a novel sketching approach based
on Kernel Herding that selects a limited subsample of all cells while
preserving the underlying frequencies of immune cell-types. We tested our
approach on three flow and mass cytometry datasets and on one single-cell RNA
sequencing dataset and demonstrate that the sketched cells (1) more accurately
represent the overall cellular landscape and (2) facilitate increased
performance in downstream analysis tasks, such as classifying patients
according to their clinical outcome. An implementation of sketching with Kernel
Herding is publicly available at
\url{https://github.com/vishalathreya/Set-Summarization}.

We study the ranking of individuals, teams, or objects on the basis of
pairwise comparisons using the Bradley-Terry model. Maximum-likelihood
estimates of rankings within this model are commonly made using a simple
iterative algorithm first introduced by Zermelo almost a century ago. Here we
describe an alternative and similarly simple iteration that solves the same
problem much faster -- over a hundred times faster in some cases. We
demonstrate this algorithm with applications to a range of example data sets
and derive some results regarding its convergence.

30-day hospital readmission is a long standing medical problem that affects
patients' morbidity and mortality and costs billions of dollars annually.
Recently, machine learning models have been created to predict risk of
inpatient readmission for patients with specific diseases, however no model
exists to predict this risk across all patients. We developed a bi-directional
Long Short Term Memory (LSTM) Network that is able to use readily available
insurance data (inpatient visits, outpatient visits, and drug prescriptions) to
predict 30 day re-admission for any admitted patient, regardless of reason. The
top-performing model achieved an ROC AUC of 0.763 (0.011) when using
historical, inpatient, and post-discharge data. The LSTM model significantly
outperformed a baseline random forest classifier, indicating that understanding
the sequence of events is important for model prediction. Incorporation of
30-days of historical data also significantly improved model performance
compared to inpatient data alone, indicating that a patients clinical history
prior to admission, including outpatient visits and pharmacy data is a strong
contributor to readmission. Our results demonstrate that a machine learning
model is able to predict risk of inpatient readmission with reasonable accuracy
for all patients using structured insurance billing data. Because billing data
or equivalent surrogates can be extracted from sites, such a model could be
deployed to identify patients at risk for readmission before they are
discharged, or to assign more robust follow up (closer follow up, home health,
mailed medications) to at-risk patients after discharge.

Radar must adapt to changing environments, and we propose changepoint
detection as a method to do so. In the world of increasingly congested radio
frequencies, radars must adapt to avoid interference. Many radar systems employ
the prediction action cycle to proactively determine transmission mode while
spectrum sharing. This method constructs and implements a model of the
environment to predict unused frequencies, and then transmits in this predicted
availability. For these selection strategies, performance is directly reliant
on the quality of the underlying environmental models. In order to keep up with
a changing environment, these models can employ changepoint detection.
Changepoint detection is the identification of sudden changes, or changepoints,
in the distribution from which data is drawn. This information allows the
models to discard "garbage" data from a previous distribution, which has no
relation to the current state of the environment. In this work, bayesian online
changepoint detection (BOCD) is applied to the sense and predict algorithm to
increase the accuracy of its models and improve its performance. In the context
of spectrum sharing, these changepoints represent interferers leaving and
entering the spectral environment. The addition of changepoint detection allows
for dynamic and robust spectrum sharing even as interference patterns change
dramatically. BOCD is especially advantageous because it enables online
changepoint detection, allowing models to be updated continuously as data are
collected. This strategy can also be applied to many other predictive
algorithms that create models in a changing environment.

The promise of multimodal models for real-world applications has inspired
research in visualizing and understanding their internal mechanics with the end
goal of empowering stakeholders to visualize model behavior, perform model
debugging, and promote trust in machine learning models. However, modern
multimodal models are typically black-box neural networks, which makes it
challenging to understand their internal mechanics. How can we visualize the
internal modeling of multimodal interactions in these models? Our paper aims to
fill this gap by proposing MultiViz, a method for analyzing the behavior of
multimodal models by scaffolding the problem of interpretability into 4 stages:
(1) unimodal importance: how each modality contributes towards downstream
modeling and prediction, (2) cross-modal interactions: how different modalities
relate with each other, (3) multimodal representations: how unimodal and
cross-modal interactions are represented in decision-level features, and (4)
multimodal prediction: how decision-level features are composed to make a
prediction. MultiViz is designed to operate on diverse modalities, models,
tasks, and research areas. Through experiments on 8 trained models across 6
real-world tasks, we show that the complementary stages in MultiViz together
enable users to (1) simulate model predictions, (2) assign interpretable
concepts to features, (3) perform error analysis on model misclassifications,
and (4) use insights from error analysis to debug models. MultiViz is publicly
available, will be regularly updated with new interpretation tools and metrics,
and welcomes inputs from the community.

We present the design and experimental realization of a device that acts like
a spin-wave lens i.e., it focuses spin waves to a specified location. The
structure of the lens does not resemble any conventional lens design, it is a
nonintuitive pattern produced by a machine learning algorithm. As a spin-wave
design tool, we used our custom micromagnetic solver "SpinTorch" that has
built-in automatic gradient calculation and can perform backpropagation through
time for spin-wave propagation. The training itself is performed with the
saturation magnetization of a YIG film as a variable parameter, with the goal
to guide spin waves to a predefined location. We verified the operation of the
device in the widely used mumax3 micromagnetic solver, and by experimental
realization. For the experimental implementation, we developed a technique to
create effective saturation-magnetization landscapes in YIG by direct
focused-ion-beam irradiation. This allows us to rapidly transfer the nanoscale
design patterns to the YIG medium, without patterning the material by etching.
We measured the effective saturation magnetization corresponding to the FIB
dose levels in advance and used this mapping to translate the designed
scatterer to the required dose levels. Our demonstration serves as a proof of
concept for a workflow that can be used to realize more sophisticated spin-wave
devices with complex functionality, e.g., spin-wave signal processors, or
neuromorphic devices.

A powerful paradigm for sensorimotor control is to predict actions from
observations directly. Training such an end-to-end system allows
representations that are useful for the downstream tasks to emerge
automatically. In visual navigation, an agent can learn to navigate without any
manual designs by correlating how its views change with the actions being
taken. However, the lack of inductive bias makes this system data-inefficient
and impractical in scenarios like search and rescue, where interacting with the
environment to collect data is costly. We hypothesize a sufficient
representation of the current view and the goal view for a navigation policy
can be learned by predicting the location and size of a crop of the current
view that corresponds to the goal. We further show that training such random
crop prediction in a self-supervised fashion purely on random noise images
transfers well to natural home images. The learned representation can then be
bootstrapped to learn a navigation policy efficiently with little interaction
data. Code is available at https://github.com/yanweiw/noise2ptz.

Directly motivated by security-related applications from the Homeland
Security Enterprise, we focus on the privacy-preserving analysis of graph data,
which provides the crucial capacity to represent rich attributes and
relationships. In particular, we discuss two directions, namely
privacy-preserving graph generation and federated graph learning, which can
jointly enable the collaboration among multiple parties each possessing private
graph data. For each direction, we identify both "quick wins" and "hard
problems". Towards the end, we demonstrate a user interface that can facilitate
model explanation, interpretation, and visualization. We believe that the
techniques developed in these directions will significantly enhance the
capabilities of the Homeland Security Enterprise to tackle and mitigate the
various security risks.

We introduce the framework of performative reinforcement learning where the
policy chosen by the learner affects the underlying reward and transition
dynamics of the environment. Following the recent literature on performative
prediction~\cite{Perdomo et. al., 2020}, we introduce the concept of
performatively stable policy. We then consider a regularized version of the
reinforcement learning problem and show that repeatedly optimizing this
objective converges to a performatively stable policy under reasonable
assumptions on the transition dynamics. Our proof utilizes the dual perspective
of the reinforcement learning problem and may be of independent interest in
analyzing the convergence of other algorithms with decision-dependent
  We then extend our results for the setting where the learner just performs
gradient ascent steps instead of fully optimizing the objective, and for the
setting where the learner has access to a finite number of trajectories from
the changed environment. For both the settings, we leverage the dual
formulation of performative reinforcement learning and establish convergence to
a stable solution. Finally, through extensive experiments on a grid-world
environment, we demonstrate the dependence of convergence on various parameters
e.g. regularization, smoothness, and the number of samples.

Non-intrusive load monitoring (NILM), which usually utilizes machine learning
methods and is effective in disaggregating smart meter readings from the
household-level into appliance-level consumption, can help analyze electricity
consumption behaviours of users and enable practical smart energy and smart
grid applications. Recent studies have proposed many novel NILM frameworks
based on federated deep learning (FL). However, there lacks comprehensive
research exploring the utility optimization schemes and the privacy-preserving
schemes in different FL-based NILM application scenarios. In this paper, we
make the first attempt to conduct FL-based NILM focusing on both the utility
optimization and the privacy-preserving by developing a distributed and
privacy-preserving NILM (DP2-NILM) framework and carrying out comparative
experiments on practical NILM scenarios based on real-world smart meter
datasets. Specifically, two alternative federated learning strategies are
examined in the utility optimization schemes, i.e., the FedAvg and the FedProx.
Moreover, different levels of privacy guarantees, i.e., the local differential
privacy federated learning and the global differential privacy federated
learning are provided in the DP2-NILM. Extensive comparison experiments are
conducted on three real-world datasets to evaluate the proposed framework.

We present an approach to clustering time series data using a model-based
generalization of the K-Means algorithm which we call K-Models. We prove the
convergence of this general algorithm and relate it to the hard-EM algorithm
for mixture modeling. We then apply our method first with an AR($p$) clustering
example and show how the clustering algorithm can be made robust to outliers
using a least-absolute deviations criteria. We then build our clustering
algorithm up for ARMA($p,q$) models and extend this to ARIMA($p,d,q$) models.
We develop a goodness of fit statistic for the models fitted to clusters based
on the Ljung-Box statistic. We perform experiments with simulated data to show
how the algorithm can be used for outlier detection, detecting distributional
drift, and discuss the impact of initialization method on empty clusters. We
also perform experiments on real data which show that our method is competitive
with other existing methods for similar time series clustering tasks.

After the COVID pandemic, student leadership development within the brigade
of midshipmen at the United States Naval Academy (USNA) was severely degraded
due to reduced student interaction from isolation and remote learning. Brigade
leadership decided that reassignment of some students from their previous
companies to new companies could facilitate new leadership bonds for the next
academic year. All students in the classes of 2023 and 2024 are reassigned from
their previous companies to new companies with the Brigade Reassignment Problem
(BRP). The first goal of BRP is to minimize the number of students reassigned
to their current company. The second goal seeks to improve the homogeneity of
each company in terms of average metrics defined by brigade leadership. We
create three similar mathematical programming models to achieve different goals
of the BRP. The first model focuses on the first goal and quickly obtains an
optimal solution. A second model focuses on the second goal and obtains
high-quality feasible solutions. A third model combines the two goals and
obtains an optimal solution within seven hours. USNA leadership implemented
results from the first model to reassign the classes of 2023 and 2024 to new
companies for the next academic year.

The past several years have witnessed the success of transformer-based
models, and their scale and application scenarios continue to grow
aggressively. The current landscape of transformer models is increasingly
diverse: the model size varies drastically with the largest being of
hundred-billion parameters; the model characteristics differ due to the
sparsity introduced by the Mixture-of-Experts; the target application scenarios
can be latency-critical or throughput-oriented; the deployment hardware could
be single- or multi-GPU systems with different types of memory and storage,
etc. With such increasing diversity and the fast-evolving pace of transformer
models, designing a highly performant and efficient inference system is
extremely challenging. In this paper, we present DeepSpeed Inference, a
comprehensive system solution for transformer model inference to address the
above-mentioned challenges. DeepSpeed Inference consists of (1) a multi-GPU
inference solution to minimize latency while maximizing the throughput of both
dense and sparse transformer models when they fit in aggregate GPU memory, and
(2) a heterogeneous inference solution that leverages CPU and NVMe memory in
addition to the GPU memory and compute to enable high inference throughput with
large models which do not fit in aggregate GPU memory. DeepSpeed Inference
reduces latency by up to 7.3X over the state-of-the-art for latency-oriented
scenarios and increases throughput by over 1.5x for throughput-oriented
scenarios. Moreover, it enables trillion parameter scale inference under
real-time latency constraints by leveraging hundreds of GPUs, an unprecedented
scale for inference. It can inference 25x larger models than with GPU-only
solutions, while delivering a high throughput of 84 TFLOPS (over $50\%$ of
A6000 peak).

Densely annotating LiDAR point clouds is costly, which restrains the
scalability of fully-supervised learning methods. In this work, we study the
underexplored semi-supervised learning (SSL) in LiDAR segmentation. Our core
idea is to leverage the strong spatial cues of LiDAR point clouds to better
exploit unlabeled data. We propose LaserMix to mix laser beams from different
LiDAR scans, and then encourage the model to make consistent and confident
predictions before and after mixing. Our framework has three appealing
properties: 1) Generic: LaserMix is agnostic to LiDAR representations (e.g.,
range view and voxel), and hence our SSL framework can be universally applied.
2) Statistically grounded: We provide a detailed analysis to theoretically
explain the applicability of the proposed framework. 3) Effective:
Comprehensive experimental analysis on popular LiDAR segmentation datasets
(nuScenes, SemanticKITTI, and ScribbleKITTI) demonstrates our effectiveness and
superiority. Notably, we achieve competitive results over fully-supervised
counterparts with 2x to 5x fewer labels and improve the supervised-only
baseline significantly by 10.8% on average. We hope this concise yet
high-performing framework could facilitate future research in semi-supervised
LiDAR segmentation. Code will be publicly available.

Our world is full of asymmetries. Gravity and wind can make reaching a place
easier than coming back. Social artifacts such as genealogy charts and citation
graphs are inherently directed. In reinforcement learning and control, optimal
goal-reaching strategies are rarely reversible (symmetrical). Distance
functions supported on these asymmetrical structures are called quasimetrics.
Despite their common appearance, little research has been done on the learning
of quasimetrics.
  Our theoretical analysis reveals that a common class of learning algorithms,
including unconstrained multilayer perceptrons (MLPs), provably fails to learn
a quasimetric consistent with training data. In contrast, our proposed Poisson
Quasimetric Embedding (PQE) is the first quasimetric learning formulation that
both is learnable with gradient-based optimization and enjoys strong
performance guarantees. Experiments on random graphs, social graphs, and
offline Q-learning demonstrate its effectiveness over many common baselines.

The ability to separate signal from noise, and reason with clean
abstractions, is critical to intelligence. With this ability, humans can
efficiently perform real world tasks without considering all possible nuisance
factors.How can artificial agents do the same? What kind of information can
agents safely discard as noises?
  In this work, we categorize information out in the wild into four types based
on controllability and relation with reward, and formulate useful information
as that which is both controllable and reward-relevant. This framework
clarifies the kinds information removed by various prior work on representation
learning in reinforcement learning (RL), and leads to our proposed approach of
learning a Denoised MDP that explicitly factors out certain noise distractors.
Extensive experiments on variants of DeepMind Control Suite and RoboDesk
demonstrate superior performance of our denoised world model over using raw
observations alone, and over prior works, across policy optimization control
tasks as well as the non-control task of joint position regression.

Analyzing the distribution shift of data is a growing research direction in
nowadays Machine Learning (ML), leading to emerging new benchmarks that focus
on providing a suitable scenario for studying the generalization properties of
ML models. The existing benchmarks are focused on supervised learning, and to
the best of our knowledge, there is none for unsupervised learning. Therefore,
we introduce an unsupervised anomaly detection benchmark with data that shifts
over time, built over Kyoto-2006+, a traffic dataset for network intrusion
detection. This type of data meets the premise of shifting the input
distribution: it covers a large time span ($10$ years), with naturally
occurring changes over time (eg users modifying their behavior patterns, and
software updates). We first highlight the non-stationary nature of the data,
using a basic per-feature analysis, t-SNE, and an Optimal Transport approach
for measuring the overall distribution distances between years. Next, we
propose AnoShift, a protocol splitting the data in IID, NEAR, and FAR testing
splits. We validate the performance degradation over time with diverse models,
ranging from classical approaches to deep learning. Finally, we show that by
acknowledging the distribution shift problem and properly addressing it, the
performance can be improved compared to the classical training which assumes
independent and identically distributed data (on average, by up to $3\%$ for
our approach). Dataset and code are available at
https://github.com/bit-ml/AnoShift/.

Causal Machine Learning (CausalML) is an umbrella term for machine learning
methods that formalize the data-generation process as a structural causal model
(SCM). This perspective enables us to reason about the effects of changes to
this process (interventions) and what would have happened in hindsight
(counterfactuals). We categorize work in CausalML into five groups according to
the problems they address: (1) causal supervised learning, (2) causal
generative modeling, (3) causal explanations, (4) causal fairness, and (5)
causal reinforcement learning. We systematically compare the methods in each
category and point out open problems. Further, we review data-modality-specific
applications in computer vision, natural language processing, and graph
representation learning. Finally, we provide an overview of causal benchmarks
and a critical discussion of the state of this nascent field, including
recommendations for future work.

Forecasting future world events is a challenging but valuable task. Forecasts
of climate, geopolitical conflict, pandemics and economic indicators help shape
policy and decision making. In these domains, the judgment of expert humans
contributes to the best forecasts. Given advances in language modeling, can
these forecasts be automated? To this end, we introduce Autocast, a dataset
containing thousands of forecasting questions and an accompanying news corpus.
Questions are taken from forecasting tournaments, ensuring high quality,
real-world importance, and diversity. The news corpus is organized by date,
allowing us to precisely simulate the conditions under which humans made past
forecasts (avoiding leakage from the future). Motivated by the difficulty of
forecasting numbers across orders of magnitude (e.g. global cases of COVID-19
in 2022), we also curate IntervalQA, a dataset of numerical questions and
metrics for calibration. We test language models on our forecasting task and
find that performance is far below a human expert baseline. However,
performance improves with increased model size and incorporation of relevant
information from the news corpus. In sum, Autocast poses a novel challenge for
large language models and improved performance could bring large practical
benefits.

On-device training enables the model to adapt to new data collected from the
sensors by fine-tuning a pre-trained model. Users can benefit from customized
AI models without having to transfer the data to the cloud, protecting the
privacy. However, the training memory consumption is prohibitive for IoT
devices that have tiny memory resources. We propose an algorithm-system
co-design framework to make on-device training possible with only 256KB of
memory. On-device training faces two unique challenges: (1) the quantized
graphs of neural networks are hard to optimize due to low bit-precision and the
lack of normalization; (2) the limited hardware resource does not allow full
back-propagation. To cope with the optimization difficulty, we propose
Quantization-Aware Scaling to calibrate the gradient scales and stabilize 8-bit
quantized training. To reduce the memory footprint, we propose Sparse Update to
skip the gradient computation of less important layers and sub-tensors. The
algorithm innovation is implemented by a lightweight training system, Tiny
Training Engine, which prunes the backward computation graph to support sparse
updates and offload the runtime auto-differentiation to compile time. Our
framework is the first solution to enable tiny on-device training of
convolutional neural networks under 256KB SRAM and 1MB Flash without auxiliary
memory, using less than 1/1000 of the memory of PyTorch and TensorFlow while
matching the accuracy on tinyML application VWW. Our study enables IoT devices
not only to perform inference but also to continuously adapt to new data for
on-device lifelong learning. A video demo can be found here:
https://youtu.be/XaDCO8YtmBw.

Despite recent progress in developing animatable full-body avatars, realistic
modeling of clothing - one of the core aspects of human self-expression -
remains an open challenge. State-of-the-art physical simulation methods can
generate realistically behaving clothing geometry at interactive rates.
Modeling photorealistic appearance, however, usually requires physically-based
rendering which is too expensive for interactive applications. On the other
hand, data-driven deep appearance models are capable of efficiently producing
realistic appearance, but struggle at synthesizing geometry of highly dynamic
clothing and handling challenging body-clothing configurations. To this end, we
introduce pose-driven avatars with explicit modeling of clothing that exhibit
both photorealistic appearance learned from real-world data and realistic
clothing dynamics. The key idea is to introduce a neural clothing appearance
model that operates on top of explicit geometry: at training time we use
high-fidelity tracking, whereas at animation time we rely on physically
simulated geometry. Our core contribution is a physically-inspired appearance
network, capable of generating photorealistic appearance with view-dependent
and dynamic shadowing effects even for unseen body-clothing configurations. We
conduct a thorough evaluation of our model and demonstrate diverse animation
results on several subjects and different types of clothing. Unlike previous
work on photorealistic full-body avatars, our approach can produce much richer
dynamics and more realistic deformations even for many examples of loose
clothing. We also demonstrate that our formulation naturally allows clothing to
be used with avatars of different people while staying fully animatable, thus
enabling, for the first time, photorealistic avatars with novel clothing.

Imitation learning holds tremendous promise in learning policies efficiently
for complex decision making problems. Current state-of-the-art algorithms often
use inverse reinforcement learning (IRL), where given a set of expert
demonstrations, an agent alternatively infers a reward function and the
associated optimal policy. However, such IRL approaches often require
substantial online interactions for complex control problems. In this work, we
present Regularized Optimal Transport (ROT), a new imitation learning algorithm
that builds on recent advances in optimal transport based trajectory-matching.
Our key technical insight is that adaptively combining trajectory-matching
rewards with behavior cloning can significantly accelerate imitation even with
only a few demonstrations. Our experiments on 20 visual control tasks across
the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World
Benchmark demonstrate an average of 7.8X faster imitation to reach 90% of
expert performance compared to prior state-of-the-art methods. On real-world
robotic manipulation, with just one demonstration and an hour of online
training, ROT achieves an average success rate of 90.1% across 14 tasks.

Machine learning (ML) interpretability techniques can reveal undesirable
patterns in data that models exploit to make predictions--potentially causing
harms once deployed. However, how to take action to address these patterns is
not always clear. In a collaboration between ML and human-computer interaction
researchers, physicians, and data scientists, we develop GAM Changer, the first
interactive system to help domain experts and data scientists easily and
responsibly edit Generalized Additive Models (GAMs) and fix problematic
patterns. With novel interaction techniques, our tool puts interpretability
into action--empowering users to analyze, validate, and align model behaviors
with their knowledge and values. Physicians have started to use our tool to
investigate and fix pneumonia and sepsis risk prediction models, and an
evaluation with 7 data scientists working in diverse domains highlights that
our tool is easy to use, meets their model editing needs, and fits into their
current workflows. Built with modern web technologies, our tool runs locally in
users' web browsers or computational notebooks, lowering the barrier to use.
GAM Changer is available at the following public demo link:
https://interpret.ml/gam-changer.

We study the problem of learning the parameters for the Hamiltonian of a
quantum many-body system, given limited access to the system. In this work, we
build upon recent approaches to Hamiltonian learning via derivative estimation.
We propose a protocol that improves the scaling dependence of prior works,
particularly with respect to parameters relating to the structure of the
Hamiltonian (e.g., its locality $k$). Furthermore, by deriving exact bounds on
the performance of our protocol, we are able to provide a precise numerical
prescription for theoretically optimal settings of hyperparameters in our
learning protocol, such as the maximum evolution time (when learning with
unitary dynamics) or minimum temperature (when learning with Gibbs states).
Thanks to these improvements, our protocol is practical for large problems: we
demonstrate this with a numerical simulation of our protocol on an 80-qubit
system.

As the machine learning and systems communities strive to achieve higher
energy-efficiency through custom deep neural network (DNN) accelerators, varied
precision or quantization levels, and model compression techniques, there is a
need for design space exploration frameworks that incorporate
quantization-aware processing elements into the accelerator design space while
having accurate and fast power, performance, and area models. In this work, we
present QUIDAM, a highly parameterized quantization-aware DNN accelerator and
model co-exploration framework. Our framework can facilitate future research on
design space exploration of DNN accelerators for various design choices such as
bit precision, processing element type, scratchpad sizes of processing
elements, global buffer size, number of total processing elements, and DNN
configurations. Our results show that different bit precisions and processing
element types lead to significant differences in terms of performance per area
and energy. Specifically, our framework identifies a wide range of design
points where performance per area and energy varies more than 5x and 35x,
respectively. With the proposed framework, we show that lightweight processing
elements achieve on par accuracy results and up to 5.7x more performance per
area and energy improvement when compared to the best INT16 based
implementation. Finally, due to the efficiency of the pre-characterized power,
performance, and area models, QUIDAM can speed up the design exploration
process by 3-4 orders of magnitude as it removes the need for expensive
synthesis and characterization of each design.

We propose a margin-based loss for vision-language model pretraining that
encourages gradient-based explanations that are consistent with region-level
annotations. We refer to this objective as Attention Mask Consistency (AMC) and
demonstrate that it produces superior visual grounding performance compared to
models that rely instead on region-level annotations for explicitly training an
object detector such as Faster R-CNN. AMC works by encouraging gradient-based
explanation masks that focus their attention scores mostly within annotated
regions of interest for images that contain such annotations. Particularly, a
model trained with AMC on top of standard vision-language modeling objectives
obtains a state-of-the-art accuracy of 86.59% in the Flickr30k visual grounding
benchmark, an absolute improvement of 5.48% when compared to the best previous
model. Our approach also performs exceedingly well on established benchmarks
for referring expression comprehension and offers the added benefit by design
of gradient-based explanations that better align with human annotations.

Traditional approaches based on finite element analyses have been
successfully used to predict the macro-scale behavior of heterogeneous
materials (composites, multicomponent alloys, and polycrystals) widely used in
industrial applications. However, this necessitates the mesh size to be smaller
than the characteristic length scale of the microstructural heterogeneities in
the material leading to computationally expensive and time-consuming
calculations. The recent advances in deep learning based image super-resolution
(SR) algorithms open up a promising avenue to tackle this computational
challenge by enabling researchers to enhance the spatio-temporal resolution of
data obtained from coarse mesh simulations. However, technical challenges still
remain in developing a high-fidelity SR model for application to computational
solid mechanics, especially for materials undergoing large deformation. This
work aims at developing a physics-informed deep learning based super-resolution
framework (PhySRNet) which enables reconstruction of high-resolution
deformation fields (displacement and stress) from their low-resolution
counterparts without requiring high-resolution labeled data. We design a
synthetic case study to illustrate the effectiveness of the proposed framework
and demonstrate that the super-resolved fields match the accuracy of an
advanced numerical solver running at 400 times the coarse mesh resolution while
simultaneously satisfying the (highly nonlinear) governing laws. The approach
opens the door to applying machine learning and traditional numerical
approaches in tandem to reduce computational complexity accelerate scientific
discovery and engineering design.

It is believed that one of the first useful applications for a quantum
computer will be the preparation of groundstates of molecular Hamiltonians. A
crucial task involving state preparation and readout is obtaining physical
observables of such states, which are typically estimated using projective
measurements on the qubits. At present, measurement data is costly and
time-consuming to obtain on any quantum computing architecture, which has
significant consequences for the statistical errors of estimators. In this
paper, we adapt common neural network models (restricted Boltzmann machines and
recurrent neural networks) to learn complex groundstate wavefunctions for
several prototypical molecular qubit Hamiltonians from typical measurement
data. By relating the accuracy $\varepsilon$ of the reconstructed groundstate
energy to the number of measurements, we find that using a neural network model
provides a robust improvement over using single-copy measurement outcomes alone
to reconstruct observables. This enhancement yields an asymptotic scaling near
$\varepsilon^{-1}$ for the model-based approaches, as opposed to
$\varepsilon^{-2}$ in the case of classical shadow tomography.

Deep learning has excelled on complex pattern recognition tasks such as image
classification and object recognition. However, it struggles with tasks
requiring nontrivial reasoning, such as algorithmic computation. Humans are
able to solve such tasks through iterative reasoning -- spending more time
thinking about harder tasks. Most existing neural networks, however, exhibit a
fixed computational budget controlled by the neural network architecture,
preventing additional computational processing on harder tasks. In this work,
we present a new framework for iterative reasoning with neural networks. We
train a neural network to parameterize an energy landscape over all outputs,
and implement each step of the iterative reasoning as an energy minimization
step to find a minimal energy solution. By formulating reasoning as an energy
minimization problem, for harder problems that lead to more complex energy
landscapes, we may then adjust our underlying computational budget by running a
more complex optimization procedure. We empirically illustrate that our
iterative reasoning approach can solve more accurate and generalizable
algorithmic reasoning tasks in both graph and continuous domains. Finally, we
illustrate that our approach can recursively solve algorithmic problems
requiring nested reasoning

Sleep is particularly important to the health of infants, children, and
adolescents, and sleep scoring is the first step to accurate diagnosis and
treatment of potentially life-threatening conditions. But pediatric sleep is
severely under-researched compared to adult sleep in the context of machine
learning for health, and sleep scoring algorithms developed for adults usually
perform poorly on infants. Here, we present the first automated sleep scoring
results on a recent large-scale pediatric sleep study dataset that was
collected during standard clinical care. We develop a transformer-based model
that learns to classify five sleep stages from millions of multi-channel
electroencephalogram (EEG) sleep epochs with 78% overall accuracy. Further, we
conduct an in-depth analysis of the model performance based on patient
demographics and EEG channels. The results point to the growing need for
machine learning research on pediatric sleep.

Accurate infarct segmentation in non-contrast CT (NCCT) images is a crucial
step toward computer-aided acute ischemic stroke (AIS) assessment. In clinical
practice, bilateral symmetric comparison of brain hemispheres is usually used
to locate pathological abnormalities. Recent research has explored asymmetries
to assist with AIS segmentation. However, most previous symmetry-based work
mixed different types of asymmetries when evaluating their contribution to AIS.
In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to
automatically separate pathological asymmetries and intrinsic anatomical
asymmetries in NCCTs for more effective and interpretable AIS segmentation. ADN
first performs asymmetry disentanglement based on input NCCTs, which produces
different types of 3D asymmetry maps. Then a synthetic,
intrinsic-asymmetry-compensated and pathology-asymmetry-salient NCCT volume is
generated and later used as input to a segmentation network. The training of
ADN incorporates domain knowledge and adopts a tissue-type aware regularization
loss function to encourage clinically-meaningful pathological asymmetry
extraction. Coupled with an unsupervised 3D transformation network, ADN
achieves state-of-the-art AIS segmentation performance on a public NCCT
dataset. In addition to the superior performance, we believe the learned
clinically-interpretable asymmetry maps can also provide insights towards a
better understanding of AIS assessment. Our code is available at
https://github.com/nihaomiao/MICCAI22_ADN.

We propose a general deep architecture for learning functions on multiple
permutation-invariant sets. We also show how to generalize this architecture to
sets of elements of any dimension by dimension equivariance. We demonstrate
that our architecture is a universal approximator of these functions, and show
superior results to existing methods on a variety of tasks including counting
tasks, alignment tasks, distinguishability tasks and statistical distance
measurements. This last task is quite important in Machine Learning. Although
our approach is quite general, we demonstrate that it can generate approximate
estimates of KL divergence and mutual information that are more accurate than
previous techniques that are specifically designed to approximate those
statistical distances.

Model-based treatment planning for transcranial ultrasound therapy typically
involves mapping the acoustic properties of the skull from an x-ray computed
tomography (CT) image of the head. Here, three methods for generating pseudo-CT
images from magnetic resonance (MR) images were compared as an alternative to
CT. A convolutional neural network (U-Net) was trained on paired MR-CT images
to generate pseudo-CT images from either T1-weighted or zero-echo time (ZTE) MR
images (denoted tCT and zCT, respectively). A direct mapping from ZTE to
pseudo-CT was also implemented (denoted cCT). When comparing the pseudo-CT and
ground truth CT images for the test set, the mean absolute error was 133, 83,
and 145 Hounsfield units (HU) across the whole head, and 398, 222, and 336 HU
within the skull for the tCT, zCT, and cCT images, respectively. Ultrasound
simulations were also performed using the generated pseudo-CT images and
compared to simulations based on CT. An annular array transducer was used
targeting the visual or motor cortex. The mean differences in the simulated
focal pressure, focal position, and focal volume were 9.9%, 1.5 mm, and 15.1%
for simulations based on the tCT images, 5.7%, 0.6 mm, and 5.7% for the zCT,
and 6.7%, 0.9 mm, and 12.1% for the cCT. The improved results for images mapped
from ZTE highlight the advantage of using imaging sequences which improve
contrast of the skull bone. Overall, these results demonstrate that acoustic
simulations based on MR images can give comparable accuracy to those based on
CT.

A variety of fairness constraints have been proposed in the literature to
mitigate group-level statistical bias. Their impacts have been largely
evaluated for different groups of populations corresponding to a set of
sensitive attributes, such as race or gender. Nonetheless, the community has
not observed sufficient explorations for how imposing fairness constraints fare
at an instance level. Building on the concept of influence function, a measure
that characterizes the impact of a training example on the target model and its
predictive performance, this work studies the influence of training examples
when fairness constraints are imposed. We find out that under certain
assumptions, the influence function with respect to fairness constraints can be
decomposed into a kernelized combination of training examples. One promising
application of the proposed fairness influence function is to identify
suspicious training examples that may cause model discrimination by ranking
their influence scores. We demonstrate with extensive experiments that training
on a subset of weighty data examples leads to lower fairness violations with a
trade-off of accuracy.

6D object pose estimation is one of the fundamental problems in computer
vision and robotics research. While a lot of recent efforts have been made on
generalizing pose estimation to novel object instances within the same
category, namely category-level 6D pose estimation, it is still restricted in
constrained environments given the limited number of annotated data. In this
paper, we collect Wild6D, a new unlabeled RGBD object video dataset with
diverse instances and backgrounds. We utilize this data to generalize
category-level 6D object pose estimation in the wild with semi-supervised
learning. We propose a new model, called Rendering for Pose estimation network
RePoNet, that is jointly trained using the free ground-truths with the
synthetic data, and a silhouette matching objective function on the real-world
data. Without using any 3D annotations on real data, our method outperforms
state-of-the-art methods on the previous dataset and our Wild6D test set (with
manual annotations for evaluation) by a large margin. Project page with Wild6D
data: https://oasisyang.github.io/semi-pose .

We present a single-stage casual waveform-to-waveform multichannel model that
can separate moving sound sources based on their broad spatial locations in a
dynamic acoustic scene. We divide the scene into two spatial regions
containing, respectively, the target and the interfering sound sources. The
model is trained end-to-end and performs spatial processing implicitly, without
any components based on traditional processing or use of hand-crafted spatial
features. We evaluate the proposed model on a real-world dataset and show that
the model matches the performance of an oracle beamformer followed by a
state-of-the-art single-channel enhancement network.

UMPIRE provides seamless meeting interaction among remote and local
participants. It uses the BFCP, an IETF standard for moderation.BFCP introduces
automated floor control functions to a centralized conferencing environment.
This article discusses the design and implementation of the UMPIRE system and
highlights the most notable solutions we devised to handle variegated
requirements and constraints. We also discuss the lessons learned while
experiencing in the first person how the application of research results that
have eventually led to new standards still must confront a number of minor yet
concrete issues that might completely undermine the overall process of wide
adoption by the community.

Detection of adversarial examples has been a hot topic in the last years due
to its importance for safely deploying machine learning algorithms in critical
applications. However, the detection methods are generally validated by
assuming a single implicitly known attack strategy, which does not necessarily
account for real-life threats. Indeed, this can lead to an overoptimistic
assessment of the detectors' performance and may induce some bias in the
comparison between competing detection schemes. We propose a novel multi-armed
framework, called MEAD, for evaluating detectors based on several attack
strategies to overcome this limitation. Among them, we make use of three new
objectives to generate attacks. The proposed performance metric is based on the
worst-case scenario: detection is successful if and only if all different
attacks are correctly recognized. Empirically, we show the effectiveness of our
approach. Moreover, the poor performance obtained for state-of-the-art
detectors opens a new exciting line of research.

Distributional shift, or the mismatch between training and deployment data,
is a significant obstacle to the usage of machine learning in high-stakes
industrial applications, such as autonomous driving and medicine. This creates
a need to be able to assess how robustly ML models generalize as well as the
quality of their uncertainty estimates. Standard ML baseline datasets do not
allow these properties to be assessed, as the training, validation and test
data are often identically distributed. Recently, a range of dedicated
benchmarks have appeared, featuring both distributionally matched and shifted
data. Among these benchmarks, the Shifts dataset stands out in terms of the
diversity of tasks as well as the data modalities it features. While most of
the benchmarks are heavily dominated by 2D image classification tasks, Shifts
contains tabular weather forecasting, machine translation, and vehicle motion
prediction tasks. This enables the robustness properties of models to be
assessed on a diverse set of industrial-scale tasks and either universal or
directly applicable task-specific conclusions to be reached. In this paper, we
extend the Shifts Dataset with two datasets sourced from industrial, high-risk
applications of high societal importance. Specifically, we consider the tasks
of segmentation of white matter Multiple Sclerosis lesions in 3D magnetic
resonance brain images and the estimation of power consumption in marine cargo
vessels. Both tasks feature ubiquitous distributional shifts and a strict
safety requirement due to the high cost of errors. These new datasets will
allow researchers to further explore robust generalization and uncertainty
estimation in new situations. In this work, we provide a description of the
dataset and baseline results for both tasks.

In this paper, we propose a novel end-to-end user-defined keyword spotting
method that utilizes linguistically corresponding patterns between speech and
text sequences. Unlike previous approaches requiring speech keyword enrollment,
our method compares input queries with an enrolled text keyword sequence. To
place the audio and text representations within a common latent space, we adopt
an attention-based cross-modal matching approach that is trained in an
end-to-end manner with monotonic matching loss and keyword classification loss.
We also utilize a de-noising loss for the acoustic embedding network to improve
robustness in noisy environments. Additionally, we introduce the LibriPhrase
dataset, a new short-phrase dataset based on LibriSpeech for efficiently
training keyword spotting models. Our proposed method achieves competitive
results on various evaluation sets compared to other single-modal and
cross-modal baselines.

I discuss here three important roles where machine intelligence, brain and
behaviour studies together may facilitate criminal law. First, predictive
modelling using brain and behaviour data may support legal investigations by
predicting categorical, continuous, and longitudinal legal outcomes of
interests related to brain injury and mental illnesses. Second, psychological,
psychiatric, and behavioural studies supported by machine learning algorithms
may help predict human behaviour and actions, such as lies, biases, and visits
to crime scenes. Third, machine learning models have been used to predict
recidivism using clinical and criminal data whereas brain decoding is beginning
to uncover one's thoughts and intentions based on brain imaging data. Having
dispensed with achievements and promises, I examine concerns regarding the
accuracy, reliability, and reproducibility of the brain- and behaviour-based
assessments in criminal law, as well as questions regarding data possession,
ethics, free will (and automatism), privacy, and security. Further, I will
discuss issues related to predictability vs. explainability, population-level
prediction vs. personalised prediction, and predicting future actions, and
outline three potential scenarios where brain and behaviour data may be used as
court evidence. Taken together, brain and behaviour decoding in legal
exploration and decision-making at present is promising but primitive. The
derived evidence is limited and should not be used to generate definitive
conclusions, although it can be potentially used in addition, or parallel, to
existing evidence. Finally, I suggest that there needs to be (more precise)
definitions and regulations regarding when and when not brain and behaviour
data can be used in a predictive manner in legal cases.

3D object detection in autonomous driving aims to reason "what" and "where"
the objects of interest present in a 3D world. Following the conventional
wisdom of previous 2D object detection, existing methods often adopt the
canonical Cartesian coordinate system with perpendicular axis. However, we
conjugate that this does not fit the nature of the ego car's perspective, as
each onboard camera perceives the world in shape of wedge intrinsic to the
imaging geometry with radical (non-perpendicular) axis. Hence, in this paper we
advocate the exploitation of the Polar coordinate system and propose a new
Polar Transformer (PolarFormer) for more accurate 3D object detection in the
bird's-eye-view (BEV) taking as input only multi-camera 2D images.
Specifically, we design a cross attention based Polar detection head without
restriction to the shape of input structure to deal with irregular Polar grids.
For tackling the unconstrained object scale variations along Polar's distance
dimension, we further introduce a multi-scalePolar representation learning
strategy. As a result, our model can make best use of the Polar representation
rasterized via attending to the corresponding image observation in a
sequence-to-sequence fashion subject to the geometric constraints. Thorough
experiments on the nuScenes dataset demonstrate that our PolarFormer
outperforms significantly state-of-the-art 3D object detection alternatives, as
well as yielding competitive performance on BEV semantic segmentation task.

K-FAC is a successful tractable implementation of Natural Gradient for Deep
Learning, which nevertheless suffers from the requirement to compute the
inverse of the Kronecker factors (through an eigen-decomposition). This can be
very time-consuming (or even prohibitive) when these factors are large. In this
paper, we theoretically show that, owing to the exponential-average
construction paradigm of the Kronecker factors that is typically used, their
eigen-spectrum must decay. We show numerically that in practice this decay is
very rapid, leading to the idea that we could save substantial computation by
only focusing on the first few eigen-modes when inverting the
Kronecker-factors. Randomized Numerical Linear Algebra provides us with the
necessary tools to do so. Numerical results show we obtain $\approx2.5\times$
reduction in per-epoch time and $\approx3.3\times$ reduction in time to target
accuracy. We compare our proposed K-FAC sped-up versions with a more
computationally efficient NG implementation, SENG, and observe we perform on
par with it.

On 11 February 2016, the LIGO and Virgo scientific collaborations announced
the first direct detection of gravitational waves, a signal caught by the LIGO
interferometers on 14 September 2015, and produced by the coalescence of two
stellar-mass black holes. The discovery represented the beginning of an
entirely new way to investigate the Universe. The latest gravitational-wave
catalog by LIGO, Virgo and KAGRA brings the total number of gravitational-wave
events to 90, and the count is expected to significantly increase in the next
years, when additional ground-based and space-born interferometers will be
operational. From the theoretical point of view, we have only fuzzy ideas about
where the detected events came from, and the answers to most of the five Ws and
How for the astrophysics of compact binary coalescences are still unknown. In
this work, we review our current knowledge and uncertainties on the
astrophysical processes behind merging compact-object binaries. Furthermore, we
discuss the astrophysical lessons learned through the latest gravitational-wave
detections, paying specific attention to the theoretical challenges coming from
exceptional events (e.g., GW190521 and GW190814).

We present an open-source differentiable acoustic simulator, j-Wave, which
can solve time-varying and time-harmonic acoustic problems. It supports
automatic differentiation, which is a program transformation technique that has
many applications, especially in machine learning and scientific computing.
j-Wave is composed of modular components that can be easily customized and
reused. At the same time, it is compatible with some of the most popular
machine learning libraries, such as JAX and TensorFlow. The accuracy of the
simulation results for known configurations is evaluated against the widely
used k-Wave toolbox and a cohort of acoustic simulation software. j-Wave is
available from https://github.com/ucl-bug/jwave.

An oft-cited challenge of federated learning is the presence of data
heterogeneity -- the data at different clients may follow very different
distributions. Several federated optimization methods have been proposed to
address these challenges. In the literature, empirical evaluations usually
start federated training from a random initialization. However, in many
practical applications of federated learning, the server has access to proxy
data for the training task which can be used to pre-train a model before
starting federated training. We empirically study the impact of starting from a
pre-trained model in federated learning using four common federated learning
benchmark datasets. Unsurprisingly, starting from a pre-trained model reduces
the training time required to reach a target error rate and enables training
more accurate models (by up to 40\%) than is possible than when starting from a
random initialization. Surprisingly, we also find that the effect of data
heterogeneity is much less significant when starting federated training from a
pre-trained initialization. Rather, when starting from a pre-trained model,
using an adaptive optimizer at the server, such as \textsc{FedAdam},
consistently leads to the best accuracy. We recommend that future work
proposing and evaluating federated optimization methods consider the
performance when starting both random and pre-trained initializations. We also
believe this study raises several questions for further work on understanding
the role of heterogeneity in federated optimization.

We introduce DeepNash, an autonomous agent capable of learning to play the
imperfect information game Stratego from scratch, up to a human expert level.
Stratego is one of the few iconic board games that Artificial Intelligence (AI)
has not yet mastered. This popular game has an enormous game tree on the order
of $10^{535}$ nodes, i.e., $10^{175}$ times larger than that of Go. It has the
additional complexity of requiring decision-making under imperfect information,
similar to Texas hold'em poker, which has a significantly smaller game tree (on
the order of $10^{164}$ nodes). Decisions in Stratego are made over a large
number of discrete actions with no obvious link between action and outcome.
Episodes are long, with often hundreds of moves before a player wins, and
situations in Stratego can not easily be broken down into manageably-sized
sub-problems as in poker. For these reasons, Stratego has been a grand
challenge for the field of AI for decades, and existing AI methods barely reach
an amateur level of play. DeepNash uses a game-theoretic, model-free deep
reinforcement learning method, without search, that learns to master Stratego
via self-play. The Regularised Nash Dynamics (R-NaD) algorithm, a key component
of DeepNash, converges to an approximate Nash equilibrium, instead of 'cycling'
around it, by directly modifying the underlying multi-agent learning dynamics.
DeepNash beats existing state-of-the-art AI methods in Stratego and achieved a
yearly (2022) and all-time top-3 rank on the Gravon games platform, competing
with human expert players.

We study two problems related to recovering causal graphs from interventional
data: (i) $\textit{verification}$, where the task is to check if a purported
causal graph is correct, and (ii) $\textit{search}$, where the task is to
recover the correct causal graph. For both, we wish to minimize the number of
interventions performed. For the first problem, we give a characterization of a
minimal sized set of atomic interventions that is necessary and sufficient to
check the correctness of a claimed causal graph. Our characterization uses the
notion of $\textit{covered edges}$, which enables us to obtain simple proofs
and also easily reason about earlier known results. We also generalize our
results to the settings of bounded size interventions and node-dependent
interventional costs. For all the above settings, we provide the first known
provable algorithms for efficiently computing (near)-optimal verifying sets on
general graphs. For the second problem, we give a simple adaptive algorithm
based on graph separators that produces an atomic intervention set which fully
orients any essential graph while using $\mathcal{O}(\log n)$ times the optimal
number of interventions needed to $\textit{verify}$ (verifying size) the
underlying DAG on $n$ vertices. This approximation is tight as $\textit{any}$
search algorithm on an essential line graph has worst case approximation ratio
of $\Omega(\log n)$ with respect to the verifying size. With bounded size
interventions, each of size $\leq k$, our algorithm gives an $\mathcal{O}(\log
n \cdot \log k)$ factor approximation. Our result is the first known algorithm
that gives a non-trivial approximation guarantee to the verifying size on
general unweighted graphs and with bounded size interventions.

With the recent rapid progress in the machine-learning (ML), there have
emerged a new approach using the ML methods to the exchange-correlation
functional of density functional theory. In this chapter, we review how the ML
tools are used for this and the performances achieved recently. It is revealed
that the ML, not being opposed to the analytical methods, complements the human
intuition and advance the development toward the first-principles calculation
with desired accuracy.

We consider the problem of training a deep neural network on a given
classification task, e.g., ImageNet-1K (IN1K), so that it excels at that task
as well as at other (future) transfer tasks. These two seemingly contradictory
properties impose a trade-off between improving the model's generalization
while maintaining its performance on the original task. Models trained with
self-supervised learning (SSL) tend to generalize better than their supervised
counterparts for transfer learning; yet, they still lag behind supervised
models on IN1K. In this paper, we propose a supervised learning setup that
leverages the best of both worlds. We enrich the common supervised training
framework using two key components of recent SSL models: multi-scale crops for
data augmentation and the use of an expendable projector head. We replace the
last layer of class weights with class prototypes computed on the fly using a
memory bank. We show that these three improvements lead to a more favorable
trade-off between the IN1K training task and 13 transfer tasks. Over all the
explored configurations, we single out two models: t-ReX that achieves a new
state of the art for transfer learning and outperforms top methods such as DINO
and PAWS on IN1K, and t-ReX* that matches the highly optimized RSB-A1 model on
IN1K while performing better on transfer tasks. Project page and pretrained
models: https://europe.naverlabs.com/t-rex

We investigate estimation of causal effects of multiple competing
(multi-valued) treatments in the absence of randomization. Our work is
motivated by an intention-to-treat study of the relative metabolic risk of
assignment to one of six commonly prescribed antipsychotic drugs in a cohort of
adults with serious mental illness. Doubly-robust estimators of multi-level
treatment effects with observational data, such as targeted minimum loss-based
estimation (TMLE), require that either the treatment model or outcome model is
correctly specified to ensure consistent estimation. However, common TMLE
implementations estimate treatment probabilities using multiple binomial
regressions rather than a single multinomial regression. We implement a TMLE
estimator that uses multinomial treatment assignment and ensemble machine
learning to estimate average treatment effects. Our implementation achieves
superior coverage probability relative to the binomial implementation in
simulation experiments with varying treatment propensity overlap and event
rates. An evaluation of the causal effects of six antipsychotic drugs on the
risk of diabetes or death illustrates our approach. We find a relative safety
benefit of moving from a second-generation antipyschotic thought to have more
favorable metabolic risk profile relative to other second-generation drugs to a
less commonly prescribed first-generation antipyschotic known for having a low
rate of metabolic disturbance.

We initiate the study of online routing problems with predictions, inspired
by recent exciting results in the area of learning-augmented algorithms. A
learning-augmented online algorithm which incorporates predictions in a
black-box manner to outperform existing algorithms if the predictions are
accurate while otherwise maintaining theoretical guarantees even when the
predictions are extremely erroneous is a popular framework for overcoming
pessimistic worst-case competitive analysis.
  In this study, we particularly begin investigating the classical online
traveling salesman problem (OLTSP), where future requests are augmented with
predictions. Unlike the prediction models in other previous studies, each
actual request in the OLTSP, associated with its arrival time and position, may
not coincide with the predicted ones, which, as imagined, leads to a
troublesome situation. Our main result is to study different prediction models
and design algorithms to improve the best-known results in the different
settings. Moreover, we generalize the proposed results to the online
dial-a-ride problem.

The recent spike in certified Artificial Intelligence (AI) tools for
healthcare has renewed the debate around adoption of this technology. One
thread of such debate concerns Explainable AI and its promise to render AI
devices more transparent and trustworthy. A few voices active in the medical AI
space have expressed concerns on the reliability of Explainable AI techniques,
questioning their use and inclusion in guidelines and standards. Revisiting
such criticisms, this article offers a balanced and comprehensive perspective
on the utility of Explainable AI, focusing on the specificity of clinical
applications of AI and placing them in the context of healthcare interventions.
Against its detractors and despite valid concerns, we argue that the
Explainable AI research program is still central to human-machine interaction
and ultimately our main tool against loss of control, a danger that cannot be
prevented by rigorous clinical validation alone.

The COVID-19 pandemic has caused globally significant impacts since the
beginning of 2020. This brought a lot of confusion to society, especially due
to the spread of misinformation through social media. Although there were
already several studies related to the detection of misinformation in social
media data, most studies focused on the English dataset. Research on COVID-19
misinformation detection in Indonesia is still scarce. Therefore, through this
research, we collect and annotate datasets for Indonesian and build prediction
models for detecting COVID-19 misinformation by considering the tweet's
relevance. The dataset construction is carried out by a team of annotators who
labeled the relevance and misinformation of the tweet data. In this study, we
propose the two-stage classifier model using IndoBERT pre-trained language
model for the Tweet misinformation detection task. We also experiment with
several other baseline models for text classification. The experimental results
show that the combination of the BERT sequence classifier for relevance
prediction and Bi-LSTM for misinformation detection outperformed other machine
learning models with an accuracy of 87.02%. Overall, the BERT utilization
contributes to the higher performance of most prediction models. We release a
high-quality COVID-19 misinformation Tweet corpus in the Indonesian language,
indicated by the high inter-annotator agreement.

Reinforcement learning is applied to the development of control strategies in
order to reduce skin friction drag in a fully developed turbulent channel flow
at a low Reynolds number. Motivated by the so-called opposition control (Choi
et al. 1993), in which a control input is applied so as to cancel the
wall-normal velocity fluctuation on a detection plane at a certain distance
from the wall, we consider wall blowing and suction as a control input, and its
spatial distribution is determined by the instantaneous streamwise and
wall-normal velocity fluctuations at the distance of 15 wall units above the
wall. Deep neural network is used to express the complex relationship between
the sensing information and the control input, and it is trained so as to
maximize the expected long-term reward, i.e., drag reduction. When only the
wall-normal velocity fluctuation is measured and a linear network is used, the
present framework successfully reproduces the optimal linear weight for the
opposition control reported in the previous study (Chung & Talha 2011). In
contrast, when a non-linear network is used, more complex control strategies
based on the instantaneous streamwise and wall-normal velocity fluctuations are
obtained. Specifically, the obtained control strategies abruptly switch between
strong wall blowing and suction for downwelling of a high-speed fluid toward
the wall and upwelling of a low-speed fluid away from the wall, respectively.
The obtained control policies lead to drag reduction rates as high as 37 %,
which is higher than 23 % achieved by the conventional opposition control at
the same Reynolds number. The present results indicate that reinforcement
learning can be a novel framework for the development of effective control
strategies through systematic learning based on a large number of trials.

Using decentralized data for federated training is one promising emerging
research direction for alleviating data scarcity in the medical domain.
However, in contrast to large-scale fully labeled data commonly seen in general
object recognition tasks, the local medical datasets are more likely to only
have images annotated for a subset of classes of interest due to high
annotation costs. In this paper, we consider a practical yet under-explored
problem, where underrepresented classes only have few labeled instances
available and only exist in a few clients of the federated system. We show that
standard federated learning approaches fail to learn robust multi-label
classifiers with extreme class imbalance and address it by proposing a novel
federated learning framework, FedFew. FedFew consists of three stages, where
the first stage leverages federated self-supervised learning to learn
class-agnostic representations. In the second stage, the decentralized
partially labeled data are exploited to learn an energy-based multi-label
classifier for the common classes. Finally, the underrepresented classes are
detected based on the energy and a prototype-based nearest-neighbor model is
proposed for few-shot matching. We evaluate FedFew on multi-label thoracic
disease classification tasks and demonstrate that it outperforms the federated
baselines by a large margin.

The recent proliferation of real-world human mobility datasets has catalyzed
geospatial and transportation research in trajectory prediction, demand
forecasting, travel time estimation, and anomaly detection. However, these
datasets also enable, more broadly, a descriptive analysis of intricate systems
of human mobility. We formally define patterns of life analysis as a natural,
explainable extension of online unsupervised anomaly detection, where we not
only monitor a data stream for anomalies but also explicitly extract normal
patterns over time. To learn patterns of life, we adapt Grow When Required
(GWR) episodic memory from research in computational biology and neurorobotics
to a new domain of geospatial analysis. This biologically-inspired neural
network, related to self-organizing maps (SOM), constructs a set of "memories"
or prototype traffic patterns incrementally as it iterates over the GPS stream.
It then compares each new observation to its prior experiences, inducing an
online, unsupervised clustering and anomaly detection on the data. We mine
patterns-of-interest from the Porto taxi dataset, including both major public
holidays and newly-discovered transportation anomalies, such as festivals and
concerts which, to our knowledge, have not been previously acknowledged or
reported in prior work. We anticipate that the capability to incrementally
learn normal and abnormal road transportation behavior will be useful in many
domains, including smart cities, autonomous vehicles, and urban planning and

The remarkable progress in computer vision over the last few years is, by and
large, attributed to deep learning, fueled by the availability of huge sets of
labeled data, and paired with the explosive growth of the GPU paradigm. While
subscribing to this view, this book criticizes the supposed scientific progress
in the field and proposes the investigation of vision within the framework of
information-based laws of nature. Specifically, the present work poses
fundamental questions about vision that remain far from understood, leading the
reader on a journey populated by novel challenges resonating with the
foundations of machine learning. The central thesis is that for a deeper
understanding of visual computational processes, it is necessary to look beyond
the applications of general purpose machine learning algorithms and focus
instead on appropriate learning theories that take into account the
spatiotemporal nature of the visual signal.

The competitive Coding approach (CompCode) is one of the most promising
methods for palmprint recognition. Due to its high performance and simple
formulation, it has been continuously studied for many years. However, although
numerous variations of CompCode have been proposed, a detailed analysis of the
method is still absent. In this paper, we provide a detailed analysis of
CompCode from the perspective of linear discriminant analysis (LDA) for the
first time. A non-trivial sufficient condition under which the CompCode is
optimal in the sense of Fisher's criterion is presented. Based on our analysis,
we examined the statistics of palmprints and concluded that CompCode deviates
from the optimal condition. To mitigate the deviation, we propose a new method
called Class-Specific CompCode that improves CompCode by excluding
non-palm-line areas from matching. A nonlinear mapping of the competitive code
is also applied in this method to further enhance accuracy. Experiments on two
public databases demonstrate the effectiveness of the proposed method.

Automatic program synthesis is a long-lasting dream in software engineering.
Recently, a promising Deep Learning (DL) based solution, called Copilot, has
been proposed by Open AI and Microsoft as an industrial product. Although some
studies evaluate the correctness of Copilot solutions and report its issues,
more empirical evaluations are necessary to understand how developers can
benefit from it effectively. In this paper, we study the capabilities of
Copilot in two different programming tasks: (1) generating (and reproducing)
correct and efficient solutions for fundamental algorithmic problems, and (2)
comparing Copilot's proposed solutions with those of human programmers on a set
of programming tasks. For the former, we assess the performance and
functionality of Copilot in solving selected fundamental problems in computer
science, like sorting and implementing basic data structures. In the latter, a
dataset of programming problems with human-provided solutions is used. The
results show that Copilot is capable of providing solutions for almost all
fundamental algorithmic problems, however, some solutions are buggy and
non-reproducible. Moreover, Copilot has some difficulties in combining multiple
methods to generate a solution. Comparing Copilot to humans, our results show
that the correct ratio of human solutions is greater than Copilot's correct
ratio, while the buggy solutions generated by Copilot require less effort to be
repaired. While Copilot shows limitations as an assistant for developers
especially in advanced programming tasks, as highlighted in this study and
previous ones, it can generate preliminary solutions for basic programming
tasks.

The human annotations are imperfect, especially when produced by junior
practitioners. Multi-expert consensus is usually regarded as golden standard,
while this annotation protocol is too expensive to implement in many real-world
projects. In this study, we propose a method to refine human annotation, named
Neural Annotation Refinement (NeAR). It is based on a learnable implicit
function, which decodes a latent vector into represented shape. By integrating
the appearance as an input of implicit functions, the appearance-aware NeAR
fixes the annotation artefacts. Our method is demonstrated on the application
of adrenal gland analysis. We first show that the NeAR can repair distorted
golden standards on a public adrenal gland segmentation dataset. Besides, we
develop a new Adrenal gLand ANalysis (ALAN) dataset with the proposed NeAR,
where each case consists of a 3D shape of adrenal gland and its diagnosis label
(normal vs. abnormal) assigned by experts. We show that models trained on the
shapes repaired by the NeAR can diagnose adrenal glands better than the
original ones. The ALAN dataset will be open-source, with 1,584 shapes for
adrenal gland diagnosis, which serves as a new benchmark for medical shape
analysis. Code and dataset are available at https://github.com/M3DV/NeAR.

We propose a novel anomaly detection method for echocardiogram videos. The
introduced method takes advantage of the periodic nature of the heart cycle to
learn different variants of a variational latent trajectory model (TVAE). The
models are trained on the healthy samples of an in-house dataset of infant
echocardiogram videos consisting of multiple chamber views to learn a normative
prior of the healthy population. During inference, maximum a posteriori (MAP)
based anomaly detection is performed to detect out-of-distribution samples in
our dataset. The proposed method reliably identifies severe congenital heart
defects, such as Ebstein's Anomaly or Shonecomplex. Moreover, it achieves
superior performance over MAP-based anomaly detection with standard variational
autoencoders on the task of detecting pulmonary hypertension and right
ventricular dilation. Finally, we demonstrate that the proposed method provides
interpretable explanations of its output through heatmaps which highlight the
regions corresponding to anomalous heart structures.

A data-driven model augmentation framework, referred to as Weakly-coupled
Integrated Inference and Machine Learning (IIML), is presented to improve the
predictive accuracy of physical models. In contrast to parameter calibration,
this work seeks corrections to the structure of the model by a) inferring
augmentation fields that are consistent with the underlying model, and b)
transforming these fields into corrective model forms. The proposed approach
couples the inference and learning steps in a weak sense via an alternating
optimization approach. This coupling ensures that the augmentation fields
remain learnable and maintain consistent functional relationships with local
modeled quantities across the training dataset. An iterative solution procedure
is presented in this paper, removing the need to embed the augmentation
function during the inference process. This framework is used to infer an
augmentation introduced within a Polymer electrolyte membrane fuel cell (PEMFC)
model using a small amount of training data (from only 14 training cases.)
These training cases belong to a dataset consisting of high-fidelity simulation
data obtained from a high-fidelity model of a first generation Toyota Mirai.
All cases in this dataset are characterized by different inflow and outflow
conditions on the same geometry. When tested on 1224 different configurations,
the inferred augmentation significantly improves the predictive accuracy for a
wide range of physical conditions. Predictions and available data for the
current density distribution are also compared to demonstrate the predictive
capability of the model for quantities of interest which were not involved in
the inference process. The results demonstrate that the weakly-coupled IIML
framework offers sophisticated and robust model augmentation capabilities
without requiring extensive changes to the numerical solver.

Prompt tuning is an emerging way of adapting pre-trained language models to
downstream tasks. However, the existing studies are mainly to add prompts to
the input sequence. This way would not work as expected due to the intermediate
multi-head self-attention and feed-forward network computation, making model
optimization not very smooth. Hence, we propose a novel tuning way called layer
tuning, aiming to add learnable parameters in Transformer layers. Specifically,
we focus on layer tuning for feed-forward network in the Transformer, namely
FL-tuning. It introduces additional units into the hidden layer of each
feed-forward network. We conduct extensive experiments on the public CLUE
benchmark. The results show that: 1) Our FL-tuning outperforms prompt tuning
methods under both full-data and few-shot settings in almost all cases. In
particular, it improves accuracy by 17.93% (full-data setting) on WSC 1.0 and
F1 by 16.142% (few-shot setting) on CLUENER over P-tuning v2. 2) Our FL-tuning
is more stable and converges about 1.17 times faster than P-tuning v2. 3) With
only about 3% of Transformer's parameters to be trained, FL-tuning is
comparable with fine-tuning on most datasets, and significantly outperforms
fine-tuning (e.g., accuracy improved by 12.9% on WSC 1.1) on several datasets.
The source codes are available at https://github.com/genggui001/FL-Tuning.

Recent work on deep learning for tabular data demonstrates the strong
performance of deep tabular models, often bridging the gap between gradient
boosted decision trees and neural networks. Accuracy aside, a major advantage
of neural models is that they learn reusable features and are easily fine-tuned
in new domains. This property is often exploited in computer vision and natural
language applications, where transfer learning is indispensable when
task-specific training data is scarce. In this work, we demonstrate that
upstream data gives tabular neural networks a decisive advantage over widely
used GBDT models. We propose a realistic medical diagnosis benchmark for
tabular transfer learning, and we present a how-to guide for using upstream
data to boost performance with a variety of tabular neural network
architectures. Finally, we propose a pseudo-feature method for cases where the
upstream and downstream feature sets differ, a tabular-specific problem
widespread in real-world applications. Our code is available at
https://github.com/LevinRoman/tabular-transfer-learning .

In this paper, we leverage low-level compiler intermediate representations
(IR) to improve code translation. Traditional transpilers rely on syntactic
information and handcrafted rules, which limits their applicability and
produces unnatural-looking code. Applying neural machine translation (NMT)
approaches to code has successfully broadened the set of programs on which one
can get a natural-looking translation. However, they treat the code as
sequences of text tokens, and still do not differentiate well enough between
similar pieces of code which have different semantics in different languages.
The consequence is low quality translation, reducing the practicality of NMT,
and stressing the need for an approach significantly increasing its accuracy.
Here we propose to augment code translation with IRs, specifically LLVM IR,
with results on the C++, Java, Rust, and Go languages. Our method improves upon
the state of the art for unsupervised code translation, increasing the number
of correct translations by 11% on average, and up to 79% for the Java -> Rust
pair with greedy decoding. With beam search, it increases the number of correct
translations by 5.5% in average. We extend previous test sets for code
translation, by adding hundreds of Go and Rust functions. Additionally, we
train models with high performance on the problem of IR decompilation,
generating programming source code from IR, and study using IRs as intermediary
pivot for translation.

In the last few years, there have been many new developments and significant
accomplishments in the research of bionic robot fishes. However, in terms of
swimming performance, existing bionic robot fishes lag far behind fish,
prompting researchers to constantly develop innovative designs of various
bionic robot fishes. In this paper, the latest designs of robot fishes are
presented in detail, distinguished by the propulsion mode. New robot fishes
mainly include soft robot fishes and rigid-soft coupled robot fishes. The
latest progress in the study of the swimming mechanism is analyzed on the basis
of summarizing the main swimming theories of fish. The current state-of-the-art
research in the new field of motion coordination and communication of multiple
robot fishes is summarized. The general research trend in robot fishes is to
utilize more efficient and robust methods to best mimic real fish while
exhibiting superior swimming performance. The current challenges and potential
future research directions are discussed. Various methods are needed to narrow
the gap in swimming performance between robot fishes and fish. This paper is a
first step to bring together roboticists and marine biologists interested in
learning state-of-the-art research on bionic robot fishes.

The use of machine learning in Structural Health Monitoring is becoming more
common, as many of the inherent tasks (such as regression and classification)
in developing condition-based assessment fall naturally into its remit. This
chapter introduces the concept of physics-informed machine learning, where one
adapts ML algorithms to account for the physical insight an engineer will often
have of the structure they are attempting to model or assess. The chapter will
demonstrate how grey-box models, that combine simple physics-based models with
data-driven ones, can improve predictive capability in an SHM setting. A
particular strength of the approach demonstrated here is the capacity of the
models to generalise, with enhanced predictive capability in different regimes.
This is a key issue when life-time assessment is a requirement, or when
monitoring data do not span the operational conditions a structure will
undergo.
  The chapter will provide an overview of physics-informed ML, introducing a
number of new approaches for grey-box modelling in a Bayesian setting. The main
ML tool discussed will be Gaussian process regression, we will demonstrate how
physical assumptions/models can be incorporated through constraints, through
the mean function and kernel design, and finally in a state-space setting. A
range of SHM applications will be demonstrated, from loads monitoring tasks for
off-shore and aerospace structures, through to performance monitoring for
long-span bridges.

A distributed massive MIMO channel sounder for acquiring large CSI datasets,
dubbed DICHASUS, is presented. The measured data has potential applications in
the study of various machine learning algorithms for user localization, JCAS,
channel charting, enabling massive MIMO in FDD operation, and many others. The
proposed channel sounder architecture is distinct from similar previous designs
in that each individual single-antenna receiver is completely autonomous,
enabling arbitrary, spatially distributed antenna deployments, and offering
virtually unlimited scalability in the number of antennas. Optionally,
extracted channel coefficient vectors can be tagged with ground truth position
data, obtained either through a GNSS receiver (for outdoor operation) or
through various indoor positioning techniques.

Plasma simulation is an important and sometimes only approach to
investigating plasma behavior. In this work, we propose two general AI-driven
frameworks for low-temperature plasma simulation: Coefficient-Subnet
Physics-Informed Neural Network (CS-PINN) and Runge-Kutta Physics-Informed
Neural Network (RK-PINN). The CS-PINN uses either a neural network or an
interpolation function (e.g. spline function) as the subnet to approximate
solution-dependent coefficients (e.g. electron-impact cross sections,
thermodynamic properties, transport coefficients, et al.) in plasma equations.
On the basis of this, the RK-PINN incorporates the implicit Runge-Kutta
formalism in neural networks to achieve a large-time-step prediction of
transient plasmas. Both CS-PINN and RK-PINN learn the complex non-linear
relationship mapping from spatio-temporal space to equation's solution. Based
on these two frameworks, we demonstrate preliminary applications by four cases
covering plasma kinetic and fluid modeling. The results verify that both
CS-PINN and RK-PINN have good performance in solving plasma equations.
Moreover, the RK-PINN has ability of yielding a good solution for transient
plasma simulation with not only large time step but also limited noisy sensing
data.

Machine learning (ML) may improve and automate quality control (QC) in
injection moulding manufacturing. As the labelling of extensive, real-world
process data is costly, however, the use of simulated process data may offer a
first step towards a successful implementation. In this study, simulated data
was used to develop a predictive model for the product quality of an injection
moulded sorting container. The achieved accuracy, specificity and sensitivity
on the test set was $99.4\%$, $99.7\%$ and $94.7\%$, respectively. This study
thus shows the potential of ML towards automated QC in injection moulding and
encourages the extension to ML models trained on real-world data.

QuASK is a quantum machine learning software written in Python that supports
researchers in designing, experimenting, and assessing different quantum and
classical kernels performance. This software is package agnostic and can be
integrated with all major quantum software packages (e.g. IBM Qiskit, Xanadu's
Pennylane, Amazon Braket). QuASK guides the user through a simple preprocessing
of input data, definition and calculation of quantum and classical kernels,
either custom or pre-defined ones. From this evaluation the package provides an
assessment about potential quantum advantage and prediction bounds on
generalization error. Moreover, it allows for the generation of parametric
quantum kernels that can be trained using gradient-descent-based optimization,
grid search, or genetic algorithms. Projected quantum kernels, an effective
solution to mitigate the curse of dimensionality induced by the exponential
scaling dimension of large Hilbert spaces, are also calculated. QuASK can
furthermore generate the observable values of a quantum model and use them to
study the prediction capabilities of the quantum and classical kernels.

Recent contrastive learning methods achieved state-of-the-art in low label
regimes. However, the training requires large batch sizes and heavy
augmentations to create multiple views of an image. With non-contrastive
methods, the negatives are implicitly incorporated in the loss, allowing
different images and modalities as pairs. Although the meta-information (i.e.,
age, sex) in medical imaging is abundant, the annotations are noisy and prone
to class imbalance. In this work, we exploited already existing temporal
information (different visits from a patient) in a longitudinal optical
coherence tomography (OCT) dataset using temporally informed non-contrastive
loss (TINC) without increasing complexity and need for negative pairs.
Moreover, our novel pair-forming scheme can avoid heavy augmentations and
implicitly incorporates the temporal information in the pairs. Finally, these
representations learned from the pretraining are more successful in predicting
disease progression where the temporal information is crucial for the
downstream task. More specifically, our model outperforms existing models in
predicting the risk of conversion within a time frame from intermediate
age-related macular degeneration (AMD) to the late wet-AMD stage.

This paper introduces R-MelNet, a two-part autoregressive architecture with a
frontend based on the first tier of MelNet and a backend WaveRNN-style audio
decoder for neural text-to-speech synthesis. Taking as input a mixed sequence
of characters and phonemes, with an optional audio priming sequence, this model
produces low-resolution mel-spectral features which are interpolated and used
by a WaveRNN decoder to produce an audio waveform. Coupled with half precision
training, R-MelNet uses under 11 gigabytes of GPU memory on a single commodity
GPU (NVIDIA 2080Ti). We detail a number of critical implementation details for
stable half precision training, including an approximate, numerically stable
mixture of logistics attention. Using a stochastic, multi-sample per step
inference scheme, the resulting model generates highly varied audio, while
enabling text and audio based controls to modify output waveforms. Qualitative
and quantitative evaluations of an R-MelNet system trained on a single speaker
TTS dataset demonstrate the effectiveness of our approach.

The natural gradient field is a vector field that lives on a model equipped
with a distinguished Riemannian metric, e.g. the Fisher-Rao metric, and
represents the direction of steepest ascent of an objective function on the
model with respect to this metric. In practice, one tries to obtain the
corresponding direction on the parameter space by multiplying the ordinary
gradient by the inverse of the Gram matrix associated with the metric. We refer
to this vector on the parameter space as the natural parameter gradient. In
this paper we study when the pushforward of the natural parameter gradient is
equal to the natural gradient. Furthermore we investigate the invariance
properties of the natural parameter gradient. Both questions are addressed in
an overparametrised setting.

Transformers are neural network models that utilize multiple layers of
self-attention heads. Attention is implemented in transformers as the
contextual embeddings of the 'key' and 'query'. Transformers allow the
re-combination of attention information from different layers and the
processing of all inputs at once, which are more convenient than recurrent
neural networks when dealt with a large number of data. Transformers have
exhibited great performances on natural language processing tasks in recent
years. Meanwhile, there have been tremendous efforts to adapt transformers into
other fields of machine learning, such as Swin Transformer and Decision
Transformer. Swin Transformer is a promising neural network architecture that
splits image pixels into small patches and applies local self-attention
operations inside the (shifted) windows of fixed sizes. Decision Transformer
has successfully applied transformers to off-line reinforcement learning and
showed that random-walk samples from Atari games are sufficient to let an agent
learn optimized behaviors. However, it is considerably more challenging to
combine online reinforcement learning with transformers. In this article, we
further explore the possibility of not modifying the reinforcement learning
policy, but only replacing the convolutional neural network architecture with
the self-attention architecture from Swin Transformer. Namely, we target at
changing how an agent views the world, but not how an agent plans about the
world. We conduct our experiment on 49 games in Arcade Learning Environment.
The results show that using Swin Transformer in reinforcement learning achieves
significantly higher evaluation scores across the majority of games in Arcade
Learning Environment. Thus, we conclude that online reinforcement learning can
benefit from exploiting self-attentions with spatial token embeddings.

The aim of this thesis is to bridge the gap between the world of physics
research and secondary education on contemporary quantum physics. We fostered
the creation of a generative learning environment formed by quantum physics
researchers, high school physics teachers and student. The result was the
development of an teaching approach to the core ideas of quantum physics
(quantum states, superposition and measurement) that has been used to design
and implement teaching-learning activities in regular high school lessons.

Reconstruction of the soft tissues in robotic surgery from endoscopic stereo
videos is important for many applications such as intra-operative navigation
and image-guided robotic surgery automation. Previous works on this task mainly
rely on SLAM-based approaches, which struggle to handle complex surgical
scenes. Inspired by recent progress in neural rendering, we present a novel
framework for deformable tissue reconstruction from binocular captures in
robotic surgery under the single-viewpoint setting. Our framework adopts
dynamic neural radiance fields to represent deformable surgical scenes in MLPs
and optimize shapes and deformations in a learning-based manner. In addition to
non-rigid deformations, tool occlusion and poor 3D clues from a single
viewpoint are also particular challenges in soft tissue reconstruction. To
overcome these difficulties, we present a series of strategies of tool
mask-guided ray casting, stereo depth-cueing ray marching and stereo
depth-supervised optimization. With experiments on DaVinci robotic surgery
videos, our method significantly outperforms the current state-of-the-art
reconstruction method for handling various complex non-rigid deformations. To
our best knowledge, this is the first work leveraging neural rendering for
surgical scene 3D reconstruction with remarkable potential demonstrated. Code
is available at: https://github.com/med-air/EndoNeRF.

Actuaries use predictive modeling techniques to assess the loss cost on a
contract as a function of observable risk characteristics. State-of-the-art
statistical and machine learning methods are not well equipped to handle
hierarchically structured risk factors with a large number of levels. In this
paper, we demonstrate the construction of a data-driven insurance pricing model
when hierarchically structured risk factors, contract-specific as well as
externally collected risk factors are available. We examine the pricing of a
workers' compensation insurance product with a hierarchical credibility model
(Jewell, 1975), Ohlsson's combination of a generalized linear and a
hierarchical credibility model (Ohlsson, 2008) and mixed models. We compare the
predictive performance of these models and evaluate the effect of the
distributional assumption on the target variable by comparing linear mixed
models with Tweedie generalized linear mixed models. For our case-study the
Tweedie distribution is well suited to model and predict the loss cost on a
contract. Moreover, incorporating contract-specific risk factors in the
predictive model improves the performance and allows for a improved risk
differentiation in our workers' compensation insurance portfolio.

Precipitation forecasting is an important scientific challenge that has
wide-reaching impacts on society. Historically, this challenge has been tackled
using numerical weather prediction (NWP) models, grounded on physics-based
simulations. Recently, many works have proposed an alternative approach, using
end-to-end deep learning (DL) models to replace physics-based NWP models. While
these DL methods show improved performance and computational efficiency, they
exhibit limitations in long-term forecasting and lack the explainability. In
this work, we present a hybrid NWP-DL workflow to fill the gap between
standalone NWP and DL approaches. Under this workflow, the outputs of NWP
models are fed into a deep neural network, which post-processes the data to
yield a refined precipitation forecast. The deep model is trained with
supervision, using Automatic Weather Station (AWS) observations as ground-truth
labels. This can achieve the best of both worlds, and can even benefit from
future improvements in NWP technology. To facilitate study in this direction,
we present a novel dataset focused on the Korean Peninsula, termed KoMet (Korea
Meteorological Dataset), comprised of NWP outputs and AWS observations. For the
NWP model, the Global Data Assimilation and Prediction Systems-Korea Integrated
Model (GDAPS-KIM) is utilized. We provide analysis on a comprehensive set of
baseline methods aimed at addressing the challenges of KoMet, including the
sparsity of AWS observations and class imbalance. To lower the barrier to entry
and encourage further study, we also provide an extensive open-source Python
package for data processing and model development. Our benchmark data and code
are available at https://github.com/osilab-kaist/KoMet-Benchmark-Dataset.

The three packages libACA, pyACA, and ACA-Code provide reference
implementations for basic approaches and algorithms for the analysis of musical
audio signals in three different languages: C++, Python, and Matlab. All three
packages cover the same algorithms, such as extraction of low level audio
features, fundamental frequency estimation, as well as simple approaches to
chord recognition, musical key detection, and onset detection. In addition, it
implementations of more generic algorithms useful in audio content analysis
such as dynamic time warping and the Viterbi algorithm are provided. The three
packages thus provide a practical cross-language and cross-platform reference
to students and engineers implementing audio analysis algorithms and enable
implementation-focused learning of algorithms for audio content analysis and
music information retrieval.

Learning nonparametric systems of Ordinary Differential Equations (ODEs)
$\dot x = f(t,x)$ from noisy and sparse data is an emerging machine learning
topic. We use the well-developed theory of Reproducing Kernel Hilbert Spaces
(RKHS) to define candidates for $f$ for which the solution of the ODE exists
and is unique. Learning $f$ consists of solving a constrained optimization
problem in an RKHS. We propose a penalty method that iteratively uses the
Representer theorem and Euler approximations to provide a numerical solution.
We prove a generalization bound for the $L^2$ distance between $x$ and its
estimator. Experiments are provided for the FitzHugh Nagumo oscillator and for
the prediction of the Amyloid level in the cortex of aging subjects. In both
cases, we show competitive results when compared with the state of the art.

Reinforcement Learning (RL) has presented an impressive performance in video
games through raw pixel imaging and continuous control tasks. However, RL
performs poorly with high-dimensional observations such as raw pixel images. It
is generally accepted that physical state-based RL policies such as laser
sensor measurements give a more sample-efficient result than learning by
pixels. This work presents a new approach that extracts information from a
depth map estimation to teach an RL agent to perform the mapless navigation of
Unmanned Aerial Vehicle (UAV). We propose the Depth-Imaged Contrastive
Unsupervised Prioritized Representations in Reinforcement Learning(Depth-CUPRL)
that estimates the depth of images with a prioritized replay memory. We used a
combination of RL and Contrastive Learning to lead with the problem of RL based
on images. From the analysis of the results with Unmanned Aerial Vehicles
(UAVs), it is possible to conclude that our Depth-CUPRL approach is effective
for the decision-making and outperforms state-of-the-art pixel-based approaches
in the mapless navigation capability.

Active learning maximizes the hypothesis updates to find those desired
unlabeled data. An inherent assumption is that this learning manner can derive
those updates into the optimal hypothesis. However, its convergence may not be
guaranteed well if those incremental updates are negative and disordered. In
this paper, we introduce a machine teacher who provides a black-box teaching
hypothesis for an active learner, where the teaching hypothesis is an effective
approximation for the optimal hypothesis. Theoretically, we prove that, under
the guidance of this teaching hypothesis, the learner can converge into a
tighter generalization error and label complexity bound than those non-educated
learners who do not receive any guidance from a teacher. We further consider
two teaching scenarios: teaching a white-box and black-box learner, where
self-improvement of teaching is firstly proposed to improve the teaching
performance. Experiments verify this idea and show better performance than the
fundamental active learning strategies, such as IWAL, IWAL-D, etc.

Deep learning on large-scale data is dominant nowadays. The unprecedented
scale of data has been arguably one of the most important driving forces for
the success of deep learning. However, there still exist scenarios where
collecting data or labels could be extremely expensive, e.g., medical imaging
and robotics. To fill up this gap, this paper considers the problem of
data-efficient learning from scratch using a small amount of representative
data. First, we characterize this problem by active learning on homeomorphic
tubes of spherical manifolds. This naturally generates feasible hypothesis
class. With homologous topological properties, we identify an important
connection -- finding tube manifolds is equivalent to minimizing hyperspherical
energy (MHE) in physical geometry. Inspired by this connection, we propose a
MHE-based active learning (MHEAL) algorithm, and provide comprehensive
theoretical guarantees for MHEAL, covering convergence and generalization
analysis. Finally, we demonstrate the empirical performance of MHEAL in a wide
range of applications on data-efficient learning, including deep clustering,
distribution matching, version space sampling and deep active learning.

We provide a simple and general solution for the discovery of scarce topics
in unbalanced short-text datasets, namely, a word co-occurrence network-based
model CWIBTD, which can simultaneously address the sparsity and unbalance of
short-text topics and attenuate the effect of occasional pairwise occurrences
of words, allowing the model to focus more on the discovery of scarce topics.
Unlike previous approaches, CWIBTD uses co-occurrence word networks to model
the topic distribution of each word, which improves the semantic density of the
data space and ensures its sensitivity in identify-ing rare topics by improving
the way node activity is calculated and normal-izing scarce topics and large
topics to some extent. In addition, using the same Gibbs sampling as LDA makes
CWIBTD easy to be extended to vari-ous application scenarios. Extensive
experimental validation in the unbal-anced short text dataset confirms the
superiority of CWIBTD over the base-line approach in discovering rare topics.
Our model can be used for early and accurate discovery of emerging topics or
unexpected events on social platforms.

We study how to utilize (possibly erroneous) predictions in a model for
computing under uncertainty in which an algorithm can query unknown data. Our
aim is to minimize the number of queries needed to solve the minimum spanning
tree problem, a fundamental combinatorial optimization problem that has been
central also to the research area of explorable uncertainty. For all integral
$\gamma\ge 2$, we present algorithms that are $\gamma$-robust and
$(1+\frac{1}{\gamma})$-consistent, meaning that they use at most $\gamma OPT$
queries if the predictions are arbitrarily wrong and at most
$(1+\frac{1}{\gamma})OPT$ queries if the predictions are correct, where $OPT$
is the optimal number of queries for the given instance. Moreover, we show that
this trade-off is best possible. Furthermore, we argue that a suitably defined
hop distance is a useful measure for the amount of prediction error and design
algorithms with performance guarantees that degrade smoothly with the hop
distance. We also show that the predictions are PAC-learnable in our model. Our
results demonstrate that untrusted predictions can circumvent the known lower
bound of~$2$, without any degradation of the worst-case ratio. To obtain our
results, we provide new structural insights for the minimum spanning tree
problem that might be useful in the context of query-based algorithms
regardless of predictions. In particular, we generalize the concept of witness
sets -- the key to lower-bounding the optimum -- by proposing novel global
witness set structures and completely new ways of adaptively using those.

Efficient search is a critical component for an e-commerce platform with an
innumerable number of products. Every day millions of users search for products
pertaining to their needs. Thus, showing the relevant products on the top will
enhance the user experience. In this work, we propose a novel approach of
fusing a transformer-based model with various listwise loss functions for
ranking e-commerce products, given a user query. We pre-train a RoBERTa model
over a fashion e-commerce corpus and fine-tune it using different listwise loss
functions. Our experiments indicate that the RoBERTa model fine-tuned with an
NDCG based surrogate loss function(approxNDCG) achieves an NDCG improvement of
13.9% compared to other popular listwise loss functions like ListNET and
ListMLE. The approxNDCG based RoBERTa model also achieves an NDCG improvement
of 20.6% compared to the pairwise RankNet based RoBERTa model. We call our
methodology of directly optimizing the RoBERTa model in an end-to-end manner
with a listwise surrogate loss function as ListBERT. Since there is a low
latency requirement in a real-time search setting, we show how these models can
be easily adopted by using a knowledge distillation technique to learn a
representation-focused student model that can be easily deployed and leads to
~10 times lower ranking latency.

In recent years, the introduction of the Transformer models sparked a
revolution in natural language processing (NLP). BERT was one of the first text
encoders using only the attention mechanism without any recurrent parts to
achieve state-of-the-art results on many NLP tasks.
  This paper introduces a text classifier using topological data analysis. We
use BERT's attention maps transformed into attention graphs as the only input
to that classifier. The model can solve tasks such as distinguishing spam from
ham messages, recognizing whether a sentence is grammatically correct, or
evaluating a movie review as negative or positive. It performs comparably to
the BERT baseline and outperforms it on some tasks.
  Additionally, we propose a new method to reduce the number of BERT's
attention heads considered by the topological classifier, which allows us to
prune the number of heads from 144 down to as few as ten with no reduction in
performance. Our work also shows that the topological model displays higher
robustness against adversarial attacks than the original BERT model, which is
maintained during the pruning process. To the best of our knowledge, this work
is the first to confront topological-based models with adversarial attacks in
the context of NLP.

Load forecasting is very essential in the analysis and grid planning of power
systems. For this reason, we first propose a household load forecasting method
based on federated deep learning and non-intrusive load monitoring (NILM). For
all we know, this is the first research on federated learning (FL) in household
load forecasting based on NILM. In this method, the integrated power is
decomposed into individual device power by non-intrusive load monitoring, and
the power of individual appliances is predicted separately using a federated
deep learning model. Finally, the predicted power values of individual
appliances are aggregated to form the total power prediction. Specifically, by
separately predicting the electrical equipment to obtain the predicted power,
it avoids the error caused by the strong time dependence in the power signal of
a single device. And in the federated deep learning prediction model, the
household owners with the power data share the parameters of the local model
instead of the local power data, guaranteeing the privacy of the household user
data. The case results demonstrate that the proposed approach provides a better
prediction effect than the traditional methodology that directly predicts the
aggregated signal as a whole. In addition, experiments in various federated
learning environments are designed and implemented to validate the validity of
this methodology.

Sequences arise in many real-world scenarios; thus, identifying the
mechanisms behind symbol generation is essential to understanding many complex
systems. This paper analyzes sequences generated by agents walking on a
networked topology. Given that in many real scenarios, the underlying processes
generating the sequence is hidden, we investigate whether the reconstruction of
the network via the co-occurrence method is useful to recover both the network
topology and agent dynamics generating sequences. We found that the
characterization of reconstructed networks provides valuable information
regarding the process and topology used to create the sequences. In a machine
learning approach considering 16 combinations of network topology and agent
dynamics as classes, we obtained an accuracy of 87% with sequences generated
with less than 40% of nodes visited. Larger sequences turned out to generate
improved machine learning models. Our findings suggest that the proposed
methodology could be extended to classify sequences and understand the
mechanisms behind sequence generation.

Deep learning models suffer from catastrophic forgetting when learning new
tasks incrementally. Incremental learning has been proposed to retain the
knowledge of old classes while learning to identify new classes. A typical
approach is to use a few exemplars to avoid forgetting old knowledge. In such a
scenario, data imbalance between old and new classes is a key issue that leads
to performance degradation of the model. Several strategies have been designed
to rectify the bias towards the new classes due to data imbalance. However,
they heavily rely on the assumptions of the bias relation between old and new
classes. Therefore, they are not suitable for complex real-world applications.
In this study, we propose an assumption-agnostic method, Multi-Granularity
Regularized re-Balancing (MGRB), to address this problem. Re-balancing methods
are used to alleviate the influence of data imbalance; however, we empirically
discover that they would under-fit new classes. To this end, we further design
a novel multi-granularity regularization term that enables the model to
consider the correlations of classes in addition to re-balancing the data. A
class hierarchy is first constructed by grouping the semantically or visually
similar classes. The multi-granularity regularization then transforms the
one-hot label vector into a continuous label distribution, which reflects the
relations between the target class and other classes based on the constructed
class hierarchy. Thus, the model can learn the inter-class relational
information, which helps enhance the learning of both old and new classes.
Experimental results on both public datasets and a real-world fault diagnosis
dataset verify the effectiveness of the proposed method.

The human brain can recognize objects hidden in even severely degraded images
after observing them for a while, which is known as a type of Eureka effect,
possibly associated with human creativity. A previous psychological study
suggests that the basis of this "Eureka recognition" is neural processes of
coincidence of multiple stochastic activities. Here we constructed an
artificial-neural-network-based model that simulated the characteristics of the
human Eureka recognition.

Recent years have witnessed a rapid development of automated methods for skin
lesion diagnosis and classification. Due to an increasing deployment of such
systems in clinics, it has become important to develop a more robust system
towards various Out-of-Distribution(OOD) samples (unknown skin lesions and
conditions). However, the current deep learning models trained for skin lesion
classification tend to classify these OOD samples incorrectly into one of their
learned skin lesion categories. To address this issue, we propose a simple yet
strategic approach that improves the OOD detection performance while
maintaining the multi-class classification accuracy for the known categories of
skin lesion. To specify, this approach is built upon a realistic scenario of a
long-tailed and fine-grained OOD detection task for skin lesion images. Through
this approach, 1) First, we target the mixup amongst middle and tail classes to
address the long-tail problem. 2) Later, we combine the above mixup strategy
with prototype learning to address the fine-grained nature of the dataset. The
unique contribution of this paper is two-fold, justified by extensive
experiments. First, we present a realistic problem setting of OOD task for skin
lesion. Second, we propose an approach to target the long-tailed and
fine-grained aspects of the problem setting simultaneously to increase the OOD

Depth maps are needed by various graphics rendering and processing
operations. Depth map streaming is often necessary when such operations are
performed in a distributed system and it requires in most cases fast performing
compression, which is why video codecs are often used. Hardware implementations
of standard video codecs enable relatively high resolution and framerate
combinations, even on resource constrained devices, but unfortunately those
implementations do not currently support RGB+depth extensions. However, they
can be used for depth compression by first packing the depth maps into RGB or
YUV frames. We investigate depth map compression using a combination of depth
map packing followed by encoding with a standard video codec. We show that the
precision at which depth maps are packed has a large and nontrivial impact on
the resulting error caused by the combination of the packing scheme and lossy
compression when bitrate is constrained. Consequently, we propose a variable
precision packing scheme assisted by a neural network model that predicts the
optimal precision for each depth map given a bitrate constraint. We demonstrate
that the model yields near optimal predictions and that it can be integrated
into a game engine with very low overhead using modern hardware.

We analyze algorithmic and computational aspects of biological phenomena,
such as replication and programmed death, in the context of machine learning.
We use two different measures of neuron efficiency to develop machine learning
algorithms for adding neurons to the system (i.e. replication algorithm) and
removing neurons from the system (i.e. programmed death algorithm). We argue
that the programmed death algorithm can be used for compression of neural
networks and the replication algorithm can be used for improving performance of
the already trained neural networks. We also show that a combined algorithm of
programmed death and replication can improve the learning efficiency of
arbitrary machine learning systems. The computational advantages of the
bio-inspired algorithms are demonstrated by training feedforward neural
networks on the MNIST dataset of handwritten images.

New medical datasets are now more open to the public, allowing for better and
more extensive research. Although prepared with the utmost care, new datasets
might still be a source of spurious correlations that affect the learning
process. Moreover, data collections are usually not large enough and are often
unbalanced. One approach to alleviate the data imbalance is using data
augmentation with Generative Adversarial Networks (GANs) to extend the dataset
with high-quality images. GANs are usually trained on the same biased datasets
as the target data, resulting in more biased instances. This work explored
unconditional and conditional GANs to compare their bias inheritance and how
the synthetic data influenced the models. We provided extensive manual data
annotation of possibly biasing artifacts on the well-known ISIC dataset with
skin lesions. In addition, we examined classification models trained on both
real and synthetic data with counterfactual bias explanations. Our experiments
showed that GANs inherited biases and sometimes even amplified them, leading to
even stronger spurious correlations. Manual data annotation and synthetic
images are publicly available for reproducible scientific research.

Since MDLatLRR only considers detailed parts (salient features) of input
images extracted by latent low-rank representation (LatLRR), it doesn't use
base parts (principal features) extracted by LatLRR effectively. Therefore, we
proposed an improved multi-level decomposition method called MDLatLRRv2 which
effectively analyzes and utilizes all the image features obtained by LatLRR.
Then we apply MDLatLRRv2 to medical image fusion. The base parts are fused by
average strategy and the detail parts are fused by nuclear-norm operation. The
comparison with the existing methods demonstrates that the proposed method can
achieve state-of-the-art fusion performance in objective and subjective

Finding equilibria points in continuous minimax games has become a key
problem within machine learning, in part due to its connection to the training
of generative adversarial networks. Because of existence and robustness issues,
recent developments have shifted from pure equilibria to focusing on mixed
equilibria points. In this note we consider a method proposed by Domingo-Enrich
et al. for finding mixed equilibria in two-layer zero-sum games. The method is
based on entropic regularisation and the two competing strategies are
represented by two sets of interacting particles. We show that the sequence of
empirical measures of the particle system satisfies a large deviation principle
as the number of particles grows to infinity, and how this implies convergence
of the empirical measure and the associated Nikaid\^o-Isoda error,
complementing existing law of large numbers results.

Devising and analyzing learning models for spatiotemporal network data is of
importance for tasks including forecasting, anomaly detection, and multi-agent
coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an
established approach to learn from time-invariant network data. The graph
convolution operation offers a principled approach to aggregate multiresolution
information. However, extending the convolution principled learning and
respective analysis to the spatiotemporal domain is challenging because
spatiotemporal data have more intrinsic dependencies. Hence, a higher
flexibility to capture jointly the spatial and the temporal dependencies is
required to learn meaningful higher-order representations. Here, we leverage
product graphs to represent the spatiotemporal dependencies in the data and
introduce Graph-Time Convolutional Neural Networks (GTCNNs) as a principled
architecture to aid learning. The proposed approach can work with any type of
product graph and we also introduce a parametric product graph to learn also
the spatiotemporal coupling. The convolution principle further allows a similar
mathematical tractability as for GCNNs. In particular, the stability result
shows GTCNNs are stable to spatial perturbations but there is an implicit
trade-off between discriminability and robustness; i.e., the more complex the
model, the less stable. Extensive numerical results on benchmark datasets
corroborate our findings and show the GTCNN compares favorably with
state-of-the-art solutions. We anticipate the GTCNN to be a starting point for
more sophisticated models that achieve good performance but are also
fundamentally grounded.

Benefiting from the message passing mechanism, Graph Neural Networks (GNNs)
have been successful on flourish tasks over graph data. However, recent studies
have shown that attackers can catastrophically degrade the performance of GNNs
by maliciously modifying the graph structure. A straightforward solution to
remedy this issue is to model the edge weights by learning a metric function
between pairwise representations of two end nodes, which attempts to assign low
weights to adversarial edges. The existing methods use either raw features or
representations learned by supervised GNNs to model the edge weights. However,
both strategies are faced with some immediate problems: raw features cannot
represent various properties of nodes (e.g., structure information), and
representations learned by supervised GNN may suffer from the poor performance
of the classifier on the poisoned graph. We need representations that carry
both feature information and as mush correct structure information as possible
and are insensitive to structural perturbations. To this end, we propose an
unsupervised pipeline, named STABLE, to optimize the graph structure. Finally,
we input the well-refined graph into a downstream classifier. For this part, we
design an advanced GCN that significantly enhances the robustness of vanilla
GCN without increasing the time complexity. Extensive experiments on four
real-world graph benchmarks demonstrate that STABLE outperforms the
state-of-the-art methods and successfully defends against various attacks.

Machine-matter, of which mechanical metamaterials and meta-devices are
important sub-categories, is emerging as a major paradigm for designing
advanced functional materials. Various exciting applications of these concepts
have been recently demonstrated, ranging from exotic mechanical properties to
device-like and adaptive functionalities. The vast majority of the studies
published to date have, however, focused on the quasi-static behavior of such
devices, neglecting their rich dynamic behavior. Recently, we proposed a new
class of strain rate-dependent mechanical metamaterials that are made from
bi-beams (i.e., viscoelastic bilayer beams). The buckling direction of such
bi-beams can be controlled with the applied strain rate. The proposed approach,
however, suffers from a major limitation: 3D printing of such bi-beams with
such a 'strong' differential strain rate-dependent response is very
challenging. Here, we propose an alternative approach that only requires a
'weak' differential response and a rationally designed geometric artifact to
control the buckling direction of bi-beams. We present an analytical model that
describes the landscape of all possible combinations of geometric designs and
hyperelastic as well as viscoelastic properties that lead to the desired strain
rate-dependent switching of the buckling direction. We also demonstrate how
multi- and single-material 3D printing techniques can be used to fabricate the
proposed bi-beams with microscale and submicron resolutions. More importantly,
we show how the requirement for a weak differential response eliminates the
need for multi-material 3D printing, as the change in the laser processing
parameters is sufficient to achieve effective differential responses. Finally,
we use the same 3D printing techniques to produce strain rate-dependent gripper
mechanisms as showcases of potential applications.

Candidate generation is a crucial module in entity linking. It also plays a
key role in multiple NLP tasks that have been proven to beneficially leverage
knowledge bases. Nevertheless, it has often been overlooked in the monolingual
English entity linking literature, as naive approaches obtain very good
performance. Unfortunately, the existing approaches for English cannot be
successfully transferred to poorly resourced languages. This paper constitutes
an in-depth analysis of the candidate generation problem in the context of
cross-lingual entity linking with a focus on low-resource languages. Among
other contributions, we point out limitations in the evaluation conducted in
previous works. We introduce a characterization of queries into types based on
their difficulty, which improves the interpretability of the performance of
different methods. We also propose a light-weight and simple solution based on
the construction of indexes whose design is motivated by more complex transfer
learning based neural approaches. A thorough empirical analysis on 9 real-world
datasets under 2 evaluation settings shows that our simple solution outperforms
the state-of-the-art approach in terms of both quality and efficiency for
almost all datasets and query types.

Today, machine learning is applied in almost any field. In machine learning,
where there are numerous methods, classification is one of the most basic and
crucial ones. Various problems can be solved by classification. The feature
selection for model setup is extremely important, and producing new features
via feature engineering also has a vital place in the success of the model. In
our study, fraud detection classification models are built on a labeled and
imbalanced dataset as a case-study. Although it is a natural language
processing method, a customer space has been created with word embedding, which
has been used in different areas, especially for recommender systems. The
customer vectors in the created space are fed to the classification model as a
feature. Moreover, to increase the number of positive labels, rows with similar
characteristics are re-labeled as positive by using customer similarity
determined by embedding. The model in which embedding methods are included in
the classification, which provides a better representation of customers, has
been compared with other models. Considering the results, it is observed that
the customer embedding method had a positive effect on the success of the
classification models.

Autonomous grasping of novel objects that are previously unseen to a robot is
an ongoing challenge in robotic manipulation. In the last decades, many
approaches have been presented to address this problem for specific robot
hands. The UniGrasp framework, introduced recently, has the ability to
generalize to different types of robotic grippers; however, this method does
not work on grippers with closed-loop constraints and is data-inefficient when
applied to robot hands with multigrasp configurations. In this paper, we
present EfficientGrasp, a generalized grasp synthesis and gripper control
method that is independent of gripper model specifications. EfficientGrasp
utilizes a gripper workspace feature rather than UniGrasp's gripper attribute
inputs. This reduces memory use by 81.7% during training and makes it possible
to generalize to more types of grippers, such as grippers with closed-loop
constraints. The effectiveness of EfficientGrasp is evaluated by conducting
object grasping experiments both in simulation and real-world; results show
that the proposed method also outperforms UniGrasp when considering only
grippers without closed-loop constraints. In these cases, EfficientGrasp shows
9.85% higher accuracy in generating contact points and 3.10% higher grasping
success rate in simulation. The real-world experiments are conducted with a
gripper with closed-loop constraints, which UniGrasp fails to handle while
EfficientGrasp achieves a success rate of 83.3%. The main causes of grasping
failures of the proposed method are analyzed, highlighting ways of enhancing
grasp performance.

Conversational recommender systems (CRS) aim to capture user's current
intentions and provide recommendations through real-time multi-turn
conversational interactions. As a human-machine interactive system, it is
essential for CRS to improve the user experience. However, most CRS methods
neglect the importance of user experience. In this paper, we propose two key
points for CRS to improve the user experience: (1) Speaking like a human, human
can speak with different styles according to the current dialogue context. (2)
Identifying fine-grained intentions, even for the same utterance, different
users have diverse finegrained intentions, which are related to users' inherent
preference. Based on the observations, we propose a novel CRS model, coined
Customized Conversational Recommender System (CCRS), which customizes CRS model
for users from three perspectives. For human-like dialogue services, we propose
multi-style dialogue response generator which selects context-aware speaking
style for utterance generation. To provide personalized recommendations, we
extract user's current fine-grained intentions from dialogue context with the
guidance of user's inherent preferences. Finally, to customize the model
parameters for each user, we train the model from the meta-learning
perspective. Extensive experiments and a series of analyses have shown the
superiority of our CCRS on both the recommendation and dialogue services.

Besides standard cameras, autonomous vehicles typically include multiple
additional sensors, such as lidars and radars, which help acquire richer
information for perceiving the content of the driving scene. While several
recent works focus on fusing certain pairs of sensors - such as camera and
lidar or camera and radar - by using architectural components specific to the
examined setting, a generic and modular sensor fusion architecture is missing
from the literature. In this work, we focus on 2D object detection, a
fundamental high-level task which is defined on the 2D image domain, and
propose HRFuser, a multi-resolution sensor fusion architecture that scales
straightforwardly to an arbitrary number of input modalities. The design of
HRFuser is based on state-of-the-art high-resolution networks for image-only
dense prediction and incorporates a novel multi-window cross-attention block as
the means to perform fusion of multiple modalities at multiple resolutions.
Even though cameras alone provide very informative features for 2D detection,
we demonstrate via extensive experiments on the nuScenes and Seeing Through Fog
datasets that our model effectively leverages complementary features from
additional modalities, substantially improving upon camera-only performance and
consistently outperforming state-of-the-art fusion methods for 2D detection
both in normal and adverse conditions. The source code will be made publicly

An essential metric for the quality of a particle-identification experiment
is its statistical power to discriminate between signal and background. Pulse
shape discrimination (PSD) is a basic method for this purpose in many nuclear,
high-energy, and rare-event search experiments where scintillator detectors are
used. Conventional techniques exploit the difference between decay-times of the
pulse from signal and background events or pulse signals caused by different
types of radiation quanta to achieve good discrimination. However, such
techniques are efficient only when the total light-emission is sufficient to
get a proper pulse profile. This is only possible when there is significant
recoil energy due to the incident particle in the detector. But, rare-event
search experiments like neutrino or dark-matter direct search experiments don't
always satisfy these conditions. Hence, it becomes imperative to have a method
that can deliver very efficient discrimination in these scenarios. Neural
network-based machine-learning algorithms have been used for classification
problems in many areas of physics, especially in high-energy experiments, and
have given better results compared to conventional techniques. We present the
results of our investigations of two network-based methods viz. Dense Neural
Network and Recurrent Neural Network, for pulse shape discrimination and
compare the same with conventional methods.

Significant theoretical work has established that in specific regimes, neural
networks trained by gradient descent behave like kernel methods. However, in
practice, it is known that neural networks strongly outperform their associated
kernels. In this work, we explain this gap by demonstrating that there is a
large class of functions which cannot be efficiently learned by kernel methods
but can be easily learned with gradient descent on a two layer neural network
outside the kernel regime by learning representations that are relevant to the
target task. We also demonstrate that these representations allow for efficient
transfer learning, which is impossible in the kernel regime.
  Specifically, we consider the problem of learning polynomials which depend on
only a few relevant directions, i.e. of the form $f^\star(x) = g(Ux)$ where $U:
\R^d \to \R^r$ with $d \gg r$. When the degree of $f^\star$ is $p$, it is known
that $n \asymp d^p$ samples are necessary to learn $f^\star$ in the kernel
regime. Our primary result is that gradient descent learns a representation of
the data which depends only on the directions relevant to $f^\star$. This
results in an improved sample complexity of $n\asymp d^2 r + dr^p$.
Furthermore, in a transfer learning setup where the data distributions in the
source and target domain share the same representation $U$ but have different
polynomial heads we show that a popular heuristic for transfer learning has a
target sample complexity independent of $d$.

The second-order optimization methods, notably the D-KFAC (Distributed
Kronecker Factored Approximate Curvature) algorithms, have gained traction on
accelerating deep neural network (DNN) training on GPU clusters. However,
existing D-KFAC algorithms require to compute and communicate a large volume of
second-order information, i.e., Kronecker factors (KFs), before preconditioning
gradients, resulting in large computation and communication overheads as well
as a high memory footprint. In this paper, we propose DP-KFAC, a novel
distributed preconditioning scheme that distributes the KF constructing tasks
at different DNN layers to different workers. DP-KFAC not only retains the
convergence property of the existing D-KFAC algorithms but also enables three
benefits: reduced computation overhead in constructing KFs, no communication of
KFs, and low memory footprint. Extensive experiments on a 64-GPU cluster show
that DP-KFAC reduces the computation overhead by 1.55x-1.65x, the communication
cost by 2.79x-3.15x, and the memory footprint by 1.14x-1.47x in each
second-order update compared to the state-of-the-art D-KFAC methods.

Risk-based authentication (RBA) aims to protect users against attacks
involving stolen passwords. RBA monitors features during login, and requests
re-authentication when feature values widely differ from previously observed
ones. It is recommended by various national security organizations, and users
perceive it more usable and equally secure than equivalent two-factor
authentication. Despite that, RBA is still only used by very few online
services. Reasons for this include a lack of validated open resources on RBA
properties, implementation, and configuration. This effectively hinders the RBA
research, development, and adoption progress.
  To close this gap, we provide the first long-term RBA analysis on a
real-world large-scale online service. We collected feature data of 3.3 million
users and 31.3 million login attempts over more than one year. Based on the
data, we provide (i) studies on RBA's real-world characteristics, and its
configurations and enhancements to balance usability, security, and privacy,
(ii) a machine learning based RBA parameter optimization method to support
administrators finding an optimal configuration for their own use case
scenario, (iii) an evaluation of the round-trip time feature's potential to
replace the IP address for enhanced user privacy, and (iv) a synthesized RBA
data set to reproduce this research and to foster future RBA research. Our
results provide insights on selecting an optimized RBA configuration so that
users profit from RBA after just a few logins. The open data set enables
researchers to study, test, and improve RBA for widespread deployment in the
wild.

Nuclei Segmentation from histology images is a fundamental task in digital
pathology analysis. However, deep-learning-based nuclei segmentation methods
often suffer from limited annotations. This paper proposes a realistic data
augmentation method for nuclei segmentation, named InsMix, that follows a
Copy-Paste-Smooth principle and performs morphology-constrained generative
instance augmentation. Specifically, we propose morphology constraints that
enable the augmented images to acquire luxuriant information about nuclei while
maintaining their morphology characteristics (e.g., geometry and location). To
fully exploit the pixel redundancy of the background and improve the model's
robustness, we further propose a background perturbation method, which randomly
shuffles the background patches without disordering the original nuclei
distribution. To achieve contextual consistency between original and template
instances, a smooth-GAN is designed with a foreground similarity encoder (FSE)
and a triplet loss. We validated the proposed method on two datasets, i.e.,
Kumar and CPS datasets. Experimental results demonstrate the effectiveness of
each component and the superior performance achieved by our method to the

Novel techniques are indispensable to process the flood of data from the new
generation of radio telescopes. In particular, the classification of
astronomical sources in images is challenging. Morphological classification of
radio galaxies could be automated with deep learning models that require large
sets of labelled training data. Here, we demonstrate the use of generative
models, specifically Wasserstein GANs (wGAN), to generate artificial data for
different classes of radio galaxies. Subsequently, we augment the training data
with images from our wGAN. We find that a simple fully-connected neural network
for classification can be improved significantly by including generated images
into the training set.

Cognitive biases are mental shortcuts humans use in dealing with information
and the environment, and which result in biased actions and behaviors (or,
actions), unbeknownst to themselves. Biases take many forms, with cognitive
biases occupying a central role that inflicts fairness, accountability,
transparency, ethics, law, medicine, and discrimination. Detection of biases is
considered a necessary step toward their mitigation. Herein, we focus on two
cognitive biases - anchoring and recency. The recognition of cognitive bias in
computer science is largely in the domain of information retrieval, and bias is
identified at an aggregate level with the help of annotated data. Proposing a
different direction for bias detection, we offer a principled approach along
with Machine Learning to detect these two cognitive biases from Web logs of
users' actions. Our individual user level detection makes it truly
personalized, and does not rely on annotated data. Instead, we start with two
basic principles established in cognitive psychology, use modified training of
an attention network, and interpret attention weights in a novel way according
to those principles, to infer and distinguish between these two biases. The
personalized approach allows detection for specific users who are susceptible
to these biases when performing their tasks, and can help build awareness among
them so as to undertake bias mitigation.

Deep neural networks (DNNs) have been shown to be vulnerable against
adversarial examples (AEs) which are maliciously designed to fool target
models. The normal examples (NEs) added with imperceptible adversarial
perturbation, can be a security threat to DNNs. Although the existing AEs
detection methods have achieved a high accuracy, they failed to exploit the
information of the AEs detected. Thus, based on high-dimension perturbation
extraction, we propose a model-free AEs detection method, the whole process of
which is free from querying the victim model. Research shows that DNNs are
sensitive to the high-dimension features. The adversarial perturbation hiding
in the adversarial example belongs to the high-dimension feature which is
highly predictive and non-robust. DNNs learn more details from high-dimension
data than others. In our method, the perturbation extractor can extract the
adversarial perturbation from AEs as high-dimension feature, then the trained
AEs discriminator determines whether the input is an AE. Experimental results
show that the proposed method can not only detect the adversarial examples with
high accuracy, but also detect the specific category of the AEs. Meanwhile, the
extracted perturbation can be used to recover the AEs to NEs.

Recently, the learning by confusion (LBC) approach has been proposed as a
machine learning tool to determine the critical temperature Tc of phase
transitions without any prior knowledge of its even approximate value. However,
the effectiveness of the method has been demonstrated only for continuous phase
transitions, where confusion can result only from a deliberate incorrect
labeling of the data and not from the coexistence of different phases. To
verify whether the confusion scheme can also be used for discontinuous phase
transitions, in this work, we apply the LBC method to three microscopic models,
the Blume-Capel, the q-state Potts, and the Falicov-Kimball models, which
undergo continuous or discontinuous phase transitions depending on model
parameters. With the help of a simple model, we predict that the phase
coexistence present in discontinuous phase transitions can make the neural
network more confused and thus decrease its performance. However, numerical
calculations performed for the models mentioned above indicate that other
aspects of this kind of phase transition are more important and can render the
LBC method less effective. Nevertheless, we demonstrate that in some cases the
same aspects allow us to use the LBC method to identify the order of a phase
transition

We develop and implement an automated experiment in multimodal imaging to
probe structural, chemical, and functional behaviors in complex materials and
elucidate the dominant physical mechanisms that control device function. Here
the emergence of non-linear electromechanical responses in piezoresponse force
microscopy (PFM) is explored. Non-linear responses in PFM can originate from
multiple mechanisms, including intrinsic material responses often controlled by
domain structure, surface topography that affects the mechanical phenomena at
the tip-surface junction, and, potentially, the presence of surface
contaminants. Using an automated experiment to probe the origins of non-linear
behavior in model ferroelectric lead titanate (PTO) and ferroelectric
Al0.93B0.07N films, it was found that PTO showed asymmetric nonlinear behavior
across a/c domain walls and a broadened high nonlinear response region around
c/c domain walls. In contrast, for Al0.93B0.07N, well-poled regions showed high
linear piezoelectric responses paired with low non-linear responses and regions
that were multidomain indicated low linear responses and high nonlinear
responses. We show that formulating dissimilar exploration strategies in deep
kernel learning as alternative hypotheses allows for establishing the
preponderant physical mechanisms behind the non-linear behaviors, suggesting
that this approach automated experiments can potentially discern between
competing physical mechanisms. This technique can also be extended to electron,
probe, and chemical imaging.

Link prediction aims to predict links of a network that are not directly
visible, with profound applications in biological and social systems. Despite
intensive utilization of the topological feature in this task, it is unclear to
what extent a particular feature can be leveraged to infer missing links. Here,
we show that the maximum capability of a topological feature follows a simple
mathematical expression, which is independent of how an index gauges the
feature. Hence, a family of indexes associated with one topological feature
shares the same performance limit. A feature's capability is lifted in the
supervised prediction, which in general gives rise to better results compared
with unsupervised prediction. The universality of the pattern uncovered is
empirically verified by 550 structurally diverse networks, which can be applied
to feature selection and the analysis of network characteristics associated
with a topological feature in link prediction.

Human brains lie at the core of complex neurobiological systems, where the
neurons, circuits, and subsystems interact in enigmatic ways. Understanding the
structural and functional mechanisms of the brain has long been an intriguing
pursuit for neuroscience research and clinical disorder therapy. Mapping the
connections of the human brain as a network is one of the most pervasive
paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged
as a potential method for modeling complex network data. Deep models, on the
other hand, have low interpretability, which prevents their usage in
decision-critical contexts like healthcare. To bridge this gap, we propose an
interpretable framework to analyze disorder-specific Regions of Interest (ROIs)
and prominent connections. The proposed framework consists of two modules: a
brain-network-oriented backbone model for disease prediction and a globally
shared explanation generator that highlights disorder-specific biomarkers
including salient ROIs and important connections. We conduct experiments on
three real-world datasets of brain disorders. The results verify that our
framework can obtain outstanding performance and also identify meaningful
biomarkers. All code for this work is available at
https://github.com/HennyJie/IBGNN.git.

The problem of beam alignment and tracking in high mobility scenarios such as
high-speed railway (HSR) becomes extremely challenging, since large overhead
cost and significant time delay are introduced for fast time-varying channel
estimation. To tackle this challenge, we propose a learning-aided beam
prediction scheme for HSR networks, which predicts the beam directions and the
channel amplitudes within a period of future time with fine time granularity,
using a group of observations. Concretely, we transform the problem of
high-dimensional beam prediction into a two-stage task, i.e., a low-dimensional
parameter estimation and a cascaded hybrid beamforming operation. In the first
stage, the location and speed of a certain terminal are estimated by maximum
likelihood criterion, and a data-driven data fusion module is designed to
improve the final estimation accuracy and robustness. Then, the probable future
beam directions and channel amplitudes are predicted, based on the HSR scenario
priors including deterministic trajectory, motion model, and channel model.
Furthermore, we incorporate a learnable non-linear mapping module into the
overall beam prediction to allow non-linear tracks. Both of the proposed
learnable modules are model-based and have a good interpretability. Compared to
the existing beam management scheme, the proposed beam prediction has (near)
zero overhead cost and time delay. Simulation results verify the effectiveness
of the proposed scheme.

Flexible endoscopes for colonoscopy present several limitations due to their
inherent complexity, resulting in patient discomfort and lack of intuitiveness
for clinicians. Robotic devices together with autonomous control represent a
viable solution to reduce the workload of endoscopists and the training time
while improving the overall procedure outcome. Prior works on autonomous
endoscope control use heuristic policies that limit their generalisation to the
unstructured and highly deformable colon environment and require frequent human
intervention. This work proposes an image-based control of the endoscope using
Deep Reinforcement Learning, called Deep Visuomotor Control (DVC), to exhibit
adaptive behaviour in convoluted sections of the colon tract. DVC learns a
mapping between the endoscopic images and the control signal of the endoscope.
A first user study of 20 expert gastrointestinal endoscopists was carried out
to compare their navigation performance with DVC policies using a realistic
virtual simulator. The results indicate that DVC shows equivalent performance
on several assessment parameters, being more safer. Moreover, a second user
study with 20 novice participants was performed to demonstrate easier human
supervision compared to a state-of-the-art heuristic control policy. Seamless
supervision of colonoscopy procedures would enable interventionists to focus on
the medical decision rather than on the control problem of the endoscope.

Skeleton-based action recognition aims to project skeleton sequences to
action categories, where skeleton sequences are derived from multiple forms of
pre-detected points. Compared with earlier methods that focus on exploring
single-form skeletons via Graph Convolutional Networks (GCNs), existing methods
tend to improve GCNs by leveraging multi-form skeletons due to their
complementary cues. However, these methods (either adapting structure of GCNs
or model ensemble) require the co-existence of all forms of skeletons during
both training and inference stages, while a typical situation in real life is
the existence of only partial forms for inference. To tackle this issue, we
present Adaptive Cross-Form Learning (ACFL), which empowers well-designed GCNs
to generate complementary representation from single-form skeletons without
changing model capacity. Specifically, each GCN model in ACFL not only learns
action representation from the single-form skeletons, but also adaptively
mimics useful representations derived from other forms of skeletons. In this
way, each GCN can learn how to strengthen what has been learned, thus
exploiting model potential and facilitating action recognition as well.
Extensive experiments conducted on three challenging benchmarks, i.e.,
NTU-RGB+D 120, NTU-RGB+D 60 and UAV-Human, demonstrate the effectiveness and
generalizability of the proposed method. Specifically, the ACFL significantly
improves various GCN models (i.e., CTR-GCN, MS-G3D, and Shift-GCN), achieving a
new record for skeleton-based action recognition.

Procrastination, the irrational delay of tasks, is a common occurrence in
online learning. Potential negative consequences include higher risk of
drop-outs, increased stress, and reduced mood. Due to the rise of learning
management systems and learning analytics, indicators of such behavior can be
detected, enabling predictions of future procrastination and other dilatory
behavior. However, research focusing on such predictions is scarce. Moreover,
studies involving different types of predictors and comparisons between the
predictive performance of various methods are virtually non-existent. In this
study, we aim to fill these research gaps by analyzing the performance of
multiple machine learning algorithms when predicting the delayed or timely
submission of online assignments in a higher education setting with two
categories of predictors: subjective, questionnaire-based variables and
objective, log-data based indicators extracted from a learning management
system. The results show that models with objective predictors consistently
outperform models with subjective predictors, and a combination of both
variable types perform slightly better. For each of these three options, a
different approach prevailed (Gradient Boosting Machines for the subjective,
Bayesian multilevel models for the objective, and Random Forest for the
combined predictors). We conclude that careful attention should be paid to the
selection of predictors and algorithms before implementing such models in
learning management systems.

Established methods for unsupervised representation learning such as
variational autoencoders produce none or poorly calibrated uncertainty
estimates making it difficult to evaluate if learned representations are stable
and reliable. In this work, we present a Bayesian autoencoder for unsupervised
representation learning, which is trained using a novel variational lower-bound
of the autoencoder evidence. This is maximized using Monte Carlo EM with a
variational distribution that takes the shape of a Laplace approximation. We
develop a new Hessian approximation that scales linearly with data size
allowing us to model high-dimensional data. Empirically, we show that our
Laplacian autoencoder estimates well-calibrated uncertainties in both latent
and output space. We demonstrate that this results in improved performance
across a multitude of downstream tasks.

Training and evaluating language models increasingly requires the
construction of meta-datasets --diverse collections of curated data with clear
provenance. Natural language prompting has recently lead to improved zero-shot
generalization by transforming existing, supervised datasets into a diversity
of novel pretraining tasks, highlighting the benefits of meta-dataset curation.
While successful in general-domain text, translating these data-centric
approaches to biomedical language modeling remains challenging, as labeled
biomedical datasets are significantly underrepresented in popular data hubs. To
address this challenge, we introduce BigBIO a community library of 126+
biomedical NLP datasets, currently covering 12 task categories and 10+
languages. BigBIO facilitates reproducible meta-dataset curation via
programmatic access to datasets and their metadata, and is compatible with
current platforms for prompt engineering and end-to-end few/zero shot language
model evaluation. We discuss our process for task schema harmonization, data
auditing, contribution guidelines, and outline two illustrative use cases:
zero-shot evaluation of biomedical prompts and large-scale, multi-task
learning. BigBIO is an ongoing community effort and is available at
https://github.com/bigscience-workshop/biomedical

Topological data analysis refers to approaches for systematically and
reliably computing abstract ``shapes'' of complex data sets. There are various
applications of topological data analysis in life and data sciences, with
growing interest among physicists. We present a concise yet (we hope)
comprehensive review of applications of topological data analysis to physics
and machine learning problems in physics including the detection of phase
transitions. We finish with a preview of anticipated directions for future
research.

State-of-the-art schemes for performance analysis and optimization of
multiple-input multiple-output systems generally experience degradation or even
become invalid in dynamic complex scenarios with unknown interference and
channel state information (CSI) uncertainty. To adapt to the challenging
settings and better accomplish these network auto-tuning tasks, we propose a
generic learnable model-driven framework in this paper. To explain how the
proposed framework works, we consider regularized zero-forcing precoding as a
usage instance and design a light-weight neural network for refined prediction
of sum rate and detection error based on coarse model-driven approximations.
Then, we estimate the CSI uncertainty on the learned predictor in an iterative
manner and, on this basis, optimize the transmit regularization term and
subsequent receive power scaling factors. A deep unfolded projected gradient
descent based algorithm is proposed for power scaling, which achieves favorable
trade-off between convergence rate and robustness.

Massive machine type communication (mMTC) has attracted new coding schemes
optimized for reliable short message transmission. In this paper, a novel deep
learning-based near-orthogonal superposition (NOS) coding scheme is proposed to
transmit short messages in multiple-input multiple-output (MIMO) channels for
mMTC applications. In the proposed MIMO-NOS scheme, a neural network-based
encoder is optimized via end-to-end learning with a corresponding neural
network-based detector/decoder in a superposition-based auto-encoder framework
including a MIMO channel. The proposed MIMO-NOS encoder spreads the information
bits to multiple near-orthogonal high dimensional vectors to be combined
(superimposed) into a single vector and reshaped for the space-time
transmission. For the receiver, we propose a novel looped K-best tree-search
algorithm with cyclic redundancy check (CRC) assistance to enhance the error
correcting ability in the block-fading MIMO channel. Simulation results show
the proposed MIMO-NOS scheme outperforms maximum likelihood (ML) MIMO detection
combined with a polar code with CRC-assisted list decoding by 1-2 dB in various
MIMO systems for short (32-64 bit) message transmission.

Randomly initialized wide neural networks transition to linear functions of
weights as the width grows, in a ball of radius $O(1)$ around initialization. A
necessary condition for this result is that all layers of the network are wide
enough, i.e., all widths tend to infinity. However, the transition to linearity
breaks down when this infinite width assumption is violated. In this work we
show that linear networks with a bottleneck layer learn bilinear functions of
the weights, in a ball of radius $O(1)$ around initialization. In general, for
$B-1$ bottleneck layers, the network is a degree $B$ multilinear function of
weights. Importantly, the degree only depends on the number of bottlenecks and
not the total depth of the network.

Self-supervised learning representations (SSLR) have resulted in robust
features for downstream tasks in many fields. Recently, several SSLRs have
shown promising results on automatic speech recognition (ASR) benchmark
corpora. However, previous studies have only shown performance for solitary
SSLRs as an input feature for ASR models. In this study, we propose to
investigate the effectiveness of diverse SSLR combinations using various fusion
methods within end-to-end (E2E) ASR models. In addition, we will show there are
correlations between these extracted SSLRs. As such, we further propose a
feature refinement loss for decorrelation to efficiently combine the set of
input features. For evaluation, we show that the proposed 'FeaRLESS learning
features' perform better than systems without the proposed feature refinement
loss for both the WSJ and Fearless Steps Challenge (FSC) corpora.

The latency reduction between the discovery of vulnerabilities, the build-up
and dissemination of cyber-attacks has put significant pressure on
cybersecurity professionals. For that, security researchers have increasingly
resorted to collective action in order to reduce the time needed to
characterize and tame outstanding threats. Here, we investigate how joining and
contributions dynamics on MISP, an open source threat intelligence sharing
platform, influence the time needed to collectively complete threat
descriptions. We find that performance, defined as the capacity to characterize
quickly a threat event, is influenced by (i) its own complexity (negatively),
by (ii) collective action (positively), and by (iii) learning, information
integration and modularity (positively). Our results inform on how collective
action can be organized at scale and in a modular way to overcome a large
number of time-critical tasks, such as cybersecurity threats.

Invariance has recently proven to be a powerful inductive bias in machine
learning models. One such class of predictive or generative models are tensor
networks. We introduce a new numerical algorithm to construct a basis of
tensors that are invariant under the action of normal matrix representations of
an arbitrary discrete group. This method can be up to several orders of
magnitude faster than previous approaches. The group-invariant tensors are then
combined into a group-invariant tensor train network, which can be used as a
supervised machine learning model. We applied this model to a protein binding
classification problem, taking into account problem-specific invariances, and
obtained prediction accuracy in line with state-of-the-art deep learning

Humans have the remarkable ability to recognize and acquire novel visual
concepts in a zero-shot manner. Given a high-level, symbolic description of a
novel concept in terms of previously learned visual concepts and their
relations, humans can recognize novel concepts without seeing any examples.
Moreover, they can acquire new concepts by parsing and communicating symbolic
structures using learned visual concepts and relations. Endowing these
capabilities in machines is pivotal in improving their generalization
capability at inference time. In this work, we introduce Zero-shot Concept
Recognition and Acquisition (ZeroC), a neuro-symbolic architecture that can
recognize and acquire novel concepts in a zero-shot way. ZeroC represents
concepts as graphs of constituent concept models (as nodes) and their relations
(as edges). To allow inference time composition, we employ energy-based models
(EBMs) to model concepts and relations. We design ZeroC architecture so that it
allows a one-to-one mapping between a symbolic graph structure of a concept and
its corresponding EBM, which for the first time, allows acquiring new concepts,
communicating its graph structure, and applying it to classification and
detection tasks (even across domains) at inference time. We introduce
algorithms for learning and inference with ZeroC. We evaluate ZeroC on a
challenging grid-world dataset which is designed to probe zero-shot concept
recognition and acquisition, and demonstrate its capability.

Ensembles of deep neural networks have demonstrated superior performance, but
their heavy computational cost hinders applying them for resource-limited
environments. It motivates distilling knowledge from the ensemble teacher into
a smaller student network, and there are two important design choices for this
ensemble distillation: 1) how to construct the student network, and 2) what
data should be shown during training. In this paper, we propose a weight
averaging technique where a student with multiple subnetworks is trained to
absorb the functional diversity of ensemble teachers, but then those
subnetworks are properly averaged for inference, giving a single student
network with no additional inference cost. We also propose a perturbation
strategy that seeks inputs from which the diversities of teachers can be better
transferred to the student. Combining these two, our method significantly
improves upon previous methods on various image classification tasks.

With the increase in world population, food resources have to be modified to
be more productive, resistive, and reliable. Wheat is one of the most important
food resources in the world, mainly because of the variety of wheat-based
products. Wheat crops are threatened by three main types of diseases which
cause large amounts of annual damage in crop yield. These diseases can be
eliminated by using pesticides at the right time. While the task of manually
spraying pesticides is burdensome and expensive, agricultural robotics can aid
farmers by increasing the speed and decreasing the amount of chemicals. In this
work, a smart autonomous system has been implemented on an unmanned aerial
vehicle to automate the task of monitoring wheat fields. First, an image-based
deep learning approach is used to detect and classify disease-infected wheat
plants. To find the most optimal method, different approaches have been
studied. Because of the lack of a public wheat-disease dataset, a custom
dataset has been created and labeled. Second, an efficient mapping and
navigation system is presented using a simulation in the robot operating system
and Gazebo environments. A 2D simultaneous localization and mapping algorithm
is used for mapping the workspace autonomously with the help of a
frontier-based exploration method.

Associative memory has been a prominent candidate for the computation
performed by the massively recurrent neocortical networks. Attractor networks
implementing associative memory have offered mechanistic explanation for many
cognitive phenomena. However, attractor memory models are typically trained
using orthogonal or random patterns to avoid interference between memories,
which makes them unfeasible for naturally occurring complex correlated stimuli
like images. We approach this problem by combining a recurrent attractor
network with a feedforward network that learns distributed representations
using an unsupervised Hebbian-Bayesian learning rule. The resulting network
model incorporates many known biological properties: unsupervised learning,
Hebbian plasticity, sparse distributed activations, sparse connectivity,
columnar and laminar cortical architecture, etc. We evaluate the synergistic
effects of the feedforward and recurrent network components in complex pattern
recognition tasks on the MNIST handwritten digits dataset. We demonstrate that
the recurrent attractor component implements associative memory when trained on
the feedforward-driven internal (hidden) representations. The associative
memory is also shown to perform prototype extraction from the training data and
make the representations robust to severely distorted input. We argue that
several aspects of the proposed integration of feedforward and recurrent
computations are particularly attractive from a machine learning perspective.

Automatic modulation classification is of crucial importance in wireless
communication networks. Deep learning based automatic modulation classification
schemes have attracted extensive attention due to the superior accuracy.
However, the data-driven method relies on a large amount of training samples
and the classification accuracy is poor in the low signal-to-noise radio (SNR).
In order to tackle these problems, a novel data-and-knowledge dual-driven
automatic modulation classification scheme based on radio frequency machine
learning is proposed by exploiting the attribute features of different
modulations. The visual model is utilized to extract visual features. The
attribute learning model is used to learn the attribute semantic
representations. The transformation model is proposed to convert the attribute
representation into the visual space. Extensive simulation results demonstrate
that our proposed automatic modulation classification scheme can achieve better
performance than the benchmark schemes in terms of the classification accuracy,
especially in the low SNR. Moreover, the confusion among high-order modulations
is reduced by using our proposed scheme compared with other traditional
schemes.

Detecting anomalies and the corresponding root causes in multivariate time
series plays an important role in monitoring the behaviors of various
real-world systems, e.g., IT system operations or manufacturing industry.
Previous anomaly detection approaches model the joint distribution without
considering the underlying mechanism of multivariate time series, making them
computationally hungry and hard to identify root causes. In this paper, we
formulate the anomaly detection problem from a causal perspective and view
anomalies as instances that do not follow the regular causal mechanism to
generate the multivariate data. We then propose a causality-based framework for
detecting anomalies and root causes. It first learns the causal structure from
data and then infers whether an instance is an anomaly relative to the local
causal mechanism whose conditional distribution can be directly estimated from
data. In light of the modularity property of causal systems (the causal
processes to generate different variables are irrelevant modules), the original
problem is divided into a series of separate, simpler, and low-dimensional
anomaly detection problems so that where an anomaly happens (root causes) can
be directly identified. We evaluate our approach with both simulated and public
datasets as well as a case study on real-world AIOps applications, showing its
efficacy, robustness, and practical feasibility.

Despite the astonishing success of COVID-19 vaccines against the virus, a
substantial proportion of the population is still hesitant to be vaccinated,
undermining governmental efforts to control the virus. To address this problem,
we need to understand the different factors giving rise to such a behavior,
including social media discourses, news media propaganda, government responses,
demographic and socioeconomic statuses, and COVID-19 statistics, etc. However,
existing datasets fail to cover all these aspects, making it difficult to form
a complete picture in inferencing about the problem of vaccine hesitancy. In
this paper, we construct a multi-source, multi-modal, and multi-feature
online-offline data repository CoVaxNet. We provide descriptive analyses and
insights to illustrate critical patterns in CoVaxNet. Moreover, we propose a
novel approach for connecting online and offline data so as to facilitate the
inference tasks that exploit complementary information sources.

We introduce a novel approach for temporal activity segmentation with
timestamp supervision. Our main contribution is a graph convolutional network,
which is learned in an end-to-end manner to exploit both frame features and
connections between neighboring frames to generate dense framewise labels from
sparse timestamp labels. The generated dense framewise labels can then be used
to train the segmentation model. In addition, we propose a framework for
alternating learning of both the segmentation model and the graph convolutional
model, which first initializes and then iteratively refines the learned models.
Detailed experiments on four public datasets, including 50 Salads, GTEA,
Breakfast, and Desktop Assembly, show that our method is superior to the
multi-layer perceptron baseline, while performing on par with or better than
the state of the art in temporal activity segmentation with timestamp
supervision.

Question Answering (QA) is one of the most important natural language
processing (NLP) tasks. It aims using NLP technologies to generate a
corresponding answer to a given question based on the massive unstructured
corpus. With the development of deep learning, more and more challenging QA
datasets are being proposed, and lots of new methods for solving them are also
emerging. In this paper, we investigate influential QA datasets that have been
released in the era of deep learning. Specifically, we begin with introducing
two of the most common QA tasks - textual question answer and visual question
answering - separately, covering the most representative datasets, and then
give some current challenges of QA research.

Bilevel optimization has been applied to a wide variety of machine learning
models. Numerous stochastic bilevel optimization algorithms have been developed
in recent years. However, most of them restrict their focus on the
single-machine setting so that they are incapable of handling the distributed
data. To address this issue, under the setting where all participants compose a
network and perform the peer-to-peer communication in this network, we
developed two novel distributed stochastic bilevel optimization algorithms
based on the gradient tracking communication mechanism and two different
gradient estimators. Additionally, we show that they can achieve
$O(\frac{1}{\epsilon^{2}(1-\lambda)^2})$ and
$O(\frac{1}{\epsilon^{3/2}(1-\lambda)^2})$ convergence rate respectively to
obtain the $\epsilon$-accuracy solution, where $1-\lambda$ denotes the spectral
gap of the communication network. To our knowledge, this is the first work
achieving these theoretical results. Finally, we applied our algorithms to
practical machine learning models, and the experimental results confirmed the
efficacy of our algorithms.

Data augmentation has recently emerged as an essential component of modern
training recipes for visual recognition tasks. However, data augmentation for
video recognition has been rarely explored despite its effectiveness. Few
existing augmentation recipes for video recognition naively extend the image
augmentation methods by applying the same operations to the whole video frames.
Our main idea is that the magnitude of augmentation operations for each frame
needs to be changed over time to capture the real-world video's temporal
variations. These variations should be generated as diverse as possible using
fewer additional hyper-parameters during training. Through this motivation, we
propose a simple yet effective video data augmentation framework, DynaAugment.
The magnitude of augmentation operations on each frame is changed by an
effective mechanism, Fourier Sampling that parameterizes diverse, smooth, and
realistic temporal variations. DynaAugment also includes an extended search
space suitable for video for automatic data augmentation methods. DynaAugment
experimentally demonstrates that there are additional performance rooms to be
improved from static augmentations on diverse video models. Specifically, we
show the effectiveness of DynaAugment on various video datasets and tasks:
large-scale video recognition (Kinetics-400 and Something-Something-v2),
small-scale video recognition (UCF- 101 and HMDB-51), fine-grained video
recognition (Diving-48 and FineGym), video action segmentation on Breakfast,
video action localization on THUMOS'14, and video object detection on MOT17Det.
DynaAugment also enables video models to learn more generalized representation
to improve the model robustness on the corrupted videos.

Autonomous racing represents a uniquely challenging control environment where
agents must act while on the limits of a vehicle's capability in order to set
competitive lap times. This places the agent on a knife's edge, with a very
small margin between success and loss of control. Pushing towards this limit
leads to a practical tension: we want agents to explore the limitations of
vehicle control to maximise speed, but inadvertently going past that limit and
losing control can cause irreparable damage to the vehicle itself. We provide a
model predictive control (MPC) baseline that is able to, in a single lap,
safely adapt to an unseen racetrack and achieve competitive lap times. Our
approaches efficacy is demonstrated in simulation using the Learn To Race
Challenge's environment and metrics.

Few-shot transfer often shows substantial gain over zero-shot
transfer~\cite{lauscher2020zero}, which is a practically useful trade-off
between fully supervised and unsupervised learning approaches for multilingual
pretrained model-based systems. This paper explores various strategies for
selecting data for annotation that can result in a better few-shot transfer.
The proposed approaches rely on multiple measures such as data entropy using
$n$-gram language model, predictive entropy, and gradient embedding. We propose
a loss embedding method for sequence labeling tasks, which induces diversity
and uncertainty sampling similar to gradient embedding. The proposed data
selection strategies are evaluated and compared for POS tagging, NER, and NLI
tasks for up to 20 languages. Our experiments show that the gradient and loss
embedding-based strategies consistently outperform random data selection
baselines, with gains varying with the initial performance of the zero-shot
transfer. Furthermore, the proposed method shows similar trends in improvement
even when the model is fine-tuned using a lower proportion of the original
task-specific labeled training data for zero-shot transfer.

Helping end users comprehend the abstract distribution shifts can greatly
facilitate AI deployment. Motivated by this, we propose a novel task, dataset
explanation. Given two image data sets, dataset explanation aims to
automatically point out their dataset-level distribution shifts with natural
language. Current techniques for monitoring distribution shifts provide
inadequate information to understand datasets with the goal of improving data
quality. Therefore, we introduce GSCLIP, a training-free framework to solve the
dataset explanation task. In GSCLIP, we propose the selector as the first
quantitative evaluation method to identify explanations that are proper to
summarize dataset shifts. Furthermore, we leverage this selector to demonstrate
the superiority of a generator based on language model generation. Systematic
evaluation on natural data shift verifies that GSCLIP, a combined system of a
hybrid generator group and an efficient selector is not only easy-to-use but
also powerful for dataset explanation at scale.

Independence testing plays a central role in statistical and causal inference
from observational data. Standard independence tests assume that the data
samples are independent and identically distributed (i.i.d.) but that
assumption is violated in many real-world datasets and applications centered on
relational systems. This work examines the problem of estimating independence
in data drawn from relational systems by defining sufficient representations
for the sets of observations influencing individual instances. Specifically, we
define marginal and conditional independence tests for relational data by
considering the kernel mean embedding as a flexible aggregation function for
relational variables. We propose a consistent, non-parametric, scalable kernel
test to operationalize the relational independence test for non-i.i.d.
observational data under a set of structural assumptions. We empirically
evaluate our proposed method on a variety of synthetic and semi-synthetic
networks and demonstrate its effectiveness compared to state-of-the-art
kernel-based independence tests.

Traffic demand forecasting by deep neural networks has attracted widespread
interest in both academia and industry society. Among them, the pairwise
Origin-Destination (OD) demand prediction is a valuable but challenging problem
due to several factors: (i) the large number of possible OD pairs, (ii)
implicitness of spatial dependence, and (iii) complexity of traffic states. To
address the above issues, this paper proposes a Continuous-time and Multi-level
dynamic graph representation learning method for Origin-Destination demand
prediction (CMOD). Firstly, a continuous-time dynamic graph representation
learning framework is constructed, which maintains a dynamic state vector for
each traffic node (metro stations or taxi zones). The state vectors keep
historical transaction information and are continuously updated according to
the most recently happened transactions. Secondly, a multi-level structure
learning module is proposed to model the spatial dependency of station-level
nodes. It can not only exploit relations between nodes adaptively from data,
but also share messages and representations via cluster-level and area-level
virtual nodes. Lastly, a cross-level fusion module is designed to integrate
multi-level memories and generate comprehensive node representations for the
final prediction. Extensive experiments are conducted on two real-world
datasets from Beijing Subway and New York Taxi, and the results demonstrate the
superiority of our model against the state-of-the-art approaches.

As a network-based functional approximator, we have proposed a "Lagrangian
Density Space-Time Deep Neural Networks" (LDDNN) topology. It is qualified for
unsupervised training and learning to predict the dynamics of underlying
physical science governed phenomena. The prototypical network respects the
fundamental conservation laws of nature through the succinctly described
Lagrangian and Hamiltonian density of the system by a given data-set of
generalized nonlinear partial differential equations. The objective is to
parameterize the Lagrangian density over a neural network and directly learn
from it through data instead of hand-crafting an exact time-dependent "Action
solution" of Lagrangian density for the physical system. With this novel
approach, can understand and open up the information inference aspect of the
"Black-box deep machine learning representation" for the physical dynamics of
nature by constructing custom-tailored network interconnect topologies,
activation, and loss/cost functions based on the underlying physical
differential operators. This article will discuss statistical physics
interpretation of neural networks in the Lagrangian and Hamiltonian domains.

Human action recognition is a quite hugely investigated area where most
remarkable action recognition networks usually use large-scale coarse-grained
action datasets of daily human actions as inputs to state the superiority of
their networks. We intend to recognize our small-scale fine-grained Tai Chi
action dataset using neural networks and propose a transfer-learning method
using NTU RGB+D dataset to pre-train our network. More specifically, the
proposed method first uses a large-scale NTU RGB+D dataset to pre-train the
Transformer-based network for action recognition to extract common features
among human motion. Then we freeze the network weights except for the fully
connected (FC) layer and take our Tai Chi actions as inputs only to train the
initialized FC weights. Experimental results show that our general model
pipeline can reach a high accuracy of small-scale fine-grained Tai Chi action
recognition with even few inputs and demonstrate that our method achieves the
state-of-the-art performance compared with previous Tai Chi action recognition
methods.

We present a robot learning and planning framework that produces an effective
tool-use strategy with the least joint efforts, capable of handling objects
different from training. Leveraging a Finite Element Method (FEM)-based
simulator that reproduces fine-grained, continuous visual and physical effects
given observed tool-use events, the essential physical properties contributing
to the effects are identified through the proposed Iterative Deepening Symbolic
Regression (IDSR) algorithm. We further devise an optimal control-based motion
planning scheme to integrate robot- and tool-specific kinematics and dynamics
to produce an effective trajectory that enacts the learned properties. In
simulation, we demonstrate that the proposed framework can produce more
effective tool-use strategies, drastically different from the observed ones in
two exemplar tasks.

Detection models trained by one party (server) may face severe performance
degradation when distributed to other users (clients). For example, in
autonomous driving scenarios, different driving environments may bring obvious
domain shifts, which lead to biases in model predictions. Federated learning
that has emerged in recent years can enable multi-party collaborative training
without leaking client data. In this paper, we focus on a special cross-domain
scenario where the server contains large-scale data and multiple clients only
contain a small amount of data; meanwhile, there exist differences in data
distributions among the clients. In this case, traditional federated learning
techniques cannot take into account the learning of both the global knowledge
of all participants and the personalized knowledge of a specific client. To
make up for this limitation, we propose a cross-domain federated object
detection framework, named FedOD. In order to learn both the global knowledge
and the personalized knowledge in different domains, the proposed framework
first performs the federated training to obtain a public global aggregated
model through multi-teacher distillation, and sends the aggregated model back
to each client for finetuning its personalized local model. After very few
rounds of communication, on each client we can perform weighted ensemble
inference on the public global model and the personalized local model. With the
ensemble, the generalization performance of the client-side model can
outperform a single model with the same parameter scale. We establish a
federated object detection dataset which has significant background differences
and instance differences based on multiple public autonomous driving datasets,
and then conduct extensive experiments on the dataset. The experimental results
validate the effectiveness of the proposed method.

Mean-field games (MFGs) are a modeling framework for systems with a large
number of interacting agents. They have applications in economics, finance, and
game theory. Normalizing flows (NFs) are a family of deep generative models
that compute data likelihoods by using an invertible mapping, which is
typically parameterized by using neural networks. They are useful for density
modeling and data generation. While active research has been conducted on both
models, few noted the relationship between the two. In this work, we unravel
the connections between MFGs and NFs by contextualizing the training of an NF
as solving the MFG. This is achieved by reformulating the MFG problem in terms
of agent trajectories and parameterizing a discretization of the resulting MFG
with flow architectures. With this connection, we explore two research
directions. First, we employ expressive NF architectures to accurately solve
high-dimensional MFGs, sidestepping the curse of dimensionality in traditional
numerical methods. Compared with other deep learning approaches, our
trajectory-based formulation encodes the continuity equation in the neural
network, resulting in a better approximation of the population dynamics.
Second, we regularize the training of NFs with transport costs and show the
effectiveness on controlling the model's Lipschitz bound, resulting in better
generalization performance. We demonstrate numerical results through
comprehensive experiments on a variety of synthetic and real-life datasets.

Data privacy and class imbalance are the norm rather than the exception in
many machine learning tasks. Recent attempts have been launched to, on one
side, address the problem of learning from pervasive private data, and on the
other side, learn from long-tailed data. However, both assumptions might hold
in practical applications, while an effective method to simultaneously
alleviate both issues is yet under development. In this paper, we focus on
learning with long-tailed (LT) data distributions under the context of the
popular privacy-preserved federated learning (FL) framework. We characterize
three scenarios with different local or global long-tailed data distributions
in the FL framework, and highlight the corresponding challenges. The
preliminary results under different scenarios reveal that substantial future
work are of high necessity to better resolve the characterized federated
long-tailed learning tasks.

The expressive and computationally inexpensive bipartite Graph Neural
Networks (GNN) have been shown to be an important component of deep learning
based Mixed-Integer Linear Program (MILP) solvers. Recent works have
demonstrated the effectiveness of such GNNs in replacing the branching
(variable selection) heuristic in branch-and-bound (B&B) solvers. These GNNs
are trained, offline and on a collection of MILPs, to imitate a very good but
computationally expensive branching heuristic, strong branching. Given that B&B
results in a tree of sub-MILPs, we ask (a) whether there are strong
dependencies exhibited by the target heuristic among the neighboring nodes of
the B&B tree, and (b) if so, whether we can incorporate them in our training
procedure. Specifically, we find that with the strong branching heuristic, a
child node's best choice was often the parent's second-best choice. We call
this the "lookback" phenomenon. Surprisingly, the typical branching GNN of
Gasse et al. (2019) often misses this simple "answer". To imitate the target
behavior more closely by incorporating the lookback phenomenon in GNNs, we
propose two methods: (a) target smoothing for the standard cross-entropy loss
function, and (b) adding a Parent-as-Target (PAT) Lookback regularizer term.
Finally, we propose a model selection framework to incorporate
harder-to-formulate objectives such as solving time in the final models.
Through extensive experimentation on standard benchmark instances, we show that
our proposal results in up to 22% decrease in the size of the B&B tree and up
to 15% improvement in the solving times.

Recent advances in synthetic speech quality have enabled us to train
text-to-speech (TTS) systems by using synthetic corpora. However, merely
increasing the amount of synthetic data is not always advantageous for
improving training efficiency. Our aim in this study is to selectively choose
synthetic data that are beneficial to the training process. In the proposed
method, we first adopt a variational autoencoder whose posterior distribution
is utilized to extract latent features representing acoustic similarity between
the recorded and synthetic corpora. By using those learned features, we then
train a ranking support vector machine (RankSVM) that is well known for
effectively ranking relative attributes among binary classes. By setting the
recorded and synthetic ones as two opposite classes, RankSVM is used to
determine how the synthesized speech is acoustically similar to the recorded
data. Then, synthetic TTS data, whose distribution is close to the recorded
data, are selected from large-scale synthetic corpora. By using these data for
retraining the TTS model, the synthetic quality can be significantly improved.
Objective and subjective evaluation results show the superiority of the
proposed method over the conventional methods.

This work seeks to center validity considerations in deliberations around
whether and how to build data-driven algorithms in high-stakes domains. Toward
this end, we translate key concepts from validity theory to predictive
algorithms. We describe common challenges in problem formulation and data
issues that jeopardize the validity of predictive algorithms. We distill these
issues into a series of high-level questions intended to promote and document
reflections on the legitimacy of the predictive task and the suitability of the
data. This contribution lays the foundation for co-designing a validity
protocol, in collaboration with real-world stakeholders, including
decision-makers, modelers, and members of potentially impacted communities, to
critically evaluate the justifiability of specific designs and uses of
data-driven algorithmic systems.

Multilingual Neural Machine Translation (MNMT) enables one system to
translate sentences from multiple source languages to multiple target
languages, greatly reducing deployment costs compared with conventional
bilingual systems. The MNMT training benefit, however, is often limited to
many-to-one directions. The model suffers from poor performance in one-to-many
and many-to-many with zero-shot setup. To address this issue, this paper
discusses how to practically build MNMT systems that serve arbitrary X-Y
translation directions while leveraging multilinguality with a two-stage
training strategy of pretraining and finetuning. Experimenting with the WMT'21
multilingual translation task, we demonstrate that our systems outperform the
conventional baselines of direct bilingual models and pivot translation models
for most directions, averagely giving +6.0 and +4.1 BLEU, without the need for
architecture change or extra data collection. Moreover, we also examine our
proposed approach in an extremely large-scale data setting to accommodate
practical deployment scenarios.

Nonsmooth optimization finds wide applications in many engineering fields. In
this work, we propose to utilize the {Randomized Coordinate Subgradient Method}
(RCS) for solving both nonsmooth convex and nonsmooth nonconvex (nonsmooth
weakly convex) optimization problems. At each iteration, RCS randomly selects
one block coordinate rather than all the coordinates to update. Motivated by
practical applications, we consider the {linearly bounded subgradients
assumption} for the objective function, which is much more general than the
Lipschitz continuity assumption. Under such a general assumption, we conduct
thorough convergence analysis for RCS in both convex and nonconvex cases and
establish both expected convergence rate and almost sure asymptotic convergence
results. In order to derive these convergence results, we establish a
convergence lemma and the relationship between the global metric subregularity
properties of a weakly convex function and its Moreau envelope, which are
fundamental and of independent interests. Finally, we conduct several
experiments to show the possible superiority of RCS over the subgradient
method.

Physiological measurements involves observing variables that attribute to the
normative functioning of human systems and subsystems directly or indirectly.
The measurements can be used to detect affective states of a person with aims
such as improving human-computer interactions. There are several methods of
collecting physiological data, but wearable sensors are a common, non-invasive
tool for accurate readings. However, valuable information is hard to extract
from the raw physiological data, especially for affective state detection.
Machine Learning techniques are used to detect the affective state of a person
through labeled physiological data. A clear problem with using labeled data is
creating accurate labels. An expert is needed to analyze a form of recording of
participants and mark sections with different states such as stress and calm.
While expensive, this method delivers a complete dataset with labeled data that
can be used in any number of supervised algorithms. An interesting question
arises from the expensive labeling: how can we reduce the cost while
maintaining high accuracy? Semi-Supervised learning (SSL) is a potential
solution to this problem. These algorithms allow for machine learning models to
be trained with only a small subset of labeled data (unlike unsupervised which
use no labels). They provide a way of avoiding expensive labeling. This paper
compares a fully supervised algorithm to a SSL on the public WESAD (Wearable
Stress and Affect Detection) Dataset for stress detection. This paper shows
that Semi-Supervised algorithms are a viable method for inexpensive affective
state detection systems with accurate results.

Nowadays, Breast cancer has risen to become one of the most prominent causes
of death in recent years. Among all malignancies, this is the most frequent and
the major cause of death for women globally. Manually diagnosing this disease
requires a good amount of time and expertise. Breast cancer detection is
time-consuming, and the spread of the disease can be reduced by developing
machine-based breast cancer predictions. In Machine learning, the system can
learn from prior instances and find hard-to-detect patterns from noisy or
complicated data sets using various statistical, probabilistic, and
optimization approaches. This work compares several machine learning
algorithm's classification accuracy, precision, sensitivity, and specificity on
a newly collected dataset. In this work Decision tree, Random Forest, Logistic
Regression, Naive Bayes, and XGBoost, these five machine learning approaches
have been implemented to get the best performance on our dataset. This study
focuses on finding the best algorithm that can forecast breast cancer with
maximum accuracy in terms of its classes. This work evaluated the quality of
each algorithm's data classification in terms of efficiency and effectiveness.
And also compared with other published work on this domain. After implementing
the model, this study achieved the best model accuracy, 94% on Random Forest
and XGBoost.

This paper presents a new approach to boost a single-modality (LiDAR) 3D
object detector by teaching it to simulate features and responses that follow a
multi-modality (LiDAR-image) detector. The approach needs LiDAR-image data only
when training the single-modality detector, and once well-trained, it only
needs LiDAR data at inference. We design a novel framework to realize the
approach: response distillation to focus on the crucial response samples and
avoid the background samples; sparse-voxel distillation to learn voxel
semantics and relations from the estimated crucial voxels; a fine-grained
voxel-to-point distillation to better attend to features of small and distant
objects; and instance distillation to further enhance the deep-feature
consistency. Experimental results on the nuScenes dataset show that our
approach outperforms all SOTA LiDAR-only 3D detectors and even surpasses the
baseline LiDAR-image detector on the key NDS metric, filling 72% mAP gap
between the single- and multi-modality detectors.

Existing explanation models generate only text for recommendations but still
struggle to produce diverse contents. In this paper, to further enrich
explanations, we propose a new task named personalized showcases, in which we
provide both textual and visual information to explain our recommendations.
Specifically, we first select a personalized image set that is the most
relevant to a user's interest toward a recommended item. Then, natural language
explanations are generated accordingly given our selected images. For this new
task, we collect a large-scale dataset from Google Local (i.e.,~maps) and
construct a high-quality subset for generating multi-modal explanations. We
propose a personalized multi-modal framework which can generate diverse and
visually-aligned explanations via contrastive learning. Experiments show that
our framework benefits from different modalities as inputs, and is able to
produce more diverse and expressive explanations compared to previous methods
on a variety of evaluation metrics.

Previous Part-Of-Speech (POS) induction models usually assume certain
independence assumptions (e.g., Markov, unidirectional, local dependency) that
do not hold in real languages. For example, the subject-verb agreement can be
both long-term and bidirectional. To facilitate flexible dependency modeling,
we propose a Masked Part-of-Speech Model (MPoSM), inspired by the recent
success of Masked Language Models (MLM). MPoSM can model arbitrary tag
dependency and perform POS induction through the objective of masked POS
reconstruction. We achieve competitive results on both the English Penn WSJ
dataset as well as the universal treebank containing 10 diverse languages.
Though modeling the long-term dependency should ideally help this task, our
ablation study shows mixed trends in different languages. To better understand
this phenomenon, we design a novel synthetic experiment that can specifically
diagnose the model's ability to learn tag agreement. Surprisingly, we find that
even strong baselines fail to solve this problem consistently in a very
simplified setting: the agreement between adjacent words. Nonetheless, MPoSM
achieves overall better performance. Lastly, we conduct a detailed error
analysis to shed light on other remaining challenges. Our code is available at
https://github.com/owenzx/MPoSM

Audio-visual speech enhancement system is regarded as one of promising
solutions for isolating and enhancing speech of desired speaker. Typical
methods focus on predicting clean speech spectrum via a naive convolution
neural network based encoder-decoder architecture, and these methods a) are not
adequate to use data fully, b) are unable to effectively balance audio-visual
features. The proposed model alleviates these drawbacks by a) applying a model
that fuses audio and visual features layer by layer in encoding phase, and that
feeds fused audio-visual features to each corresponding decoder layer, and more
importantly, b) introducing a 2-stage multi-head cross attention (MHCA)
mechanism to infer audio-visual speech enhancement for balancing the fused
audio-visual features and eliminating irrelevant features. This paper proposes
attentional audio-visual multi-layer feature fusion model, in which MHCA units
are applied to feature mapping at every layer of decoder. The proposed model
demonstrates the superior performance of the network against the

For monaural speech enhancement, contextual information is important for
accurate speech estimation. However, commonly used convolution neural networks
(CNNs) are weak in capturing temporal contexts since they only build blocks
that process one local neighborhood at a time. To address this problem, we
learn from human auditory perception to introduce a two-stage trainable
reasoning mechanism, referred as global-local dependency (GLD) block. GLD
blocks capture long-term dependency of time-frequency bins both in global level
and local level from the noisy spectrogram to help detecting correlations among
speech part, noise part, and whole noisy input. What is more, we conduct a
monaural speech enhancement network called GLD-Net, which adopts
encoder-decoder architecture and consists of speech object branch, interference
branch, and global noisy branch. The extracted speech feature at global-level
and local-level are efficiently reasoned and aggregated in each of the
branches. We compare the proposed GLD-Net with existing state-of-art methods on
WSJ0 and DEMAND dataset. The results show that GLD-Net outperforms the
state-of-the-art methods in terms of PESQ and STOI.

Automated analysis of optical colonoscopy (OC) video frames (to assist
endoscopists during OC) is challenging due to variations in color, lighting,
texture, and specular reflections. Previous methods either remove some of these
variations via preprocessing (making pipelines cumbersome) or add diverse
training data with annotations (but expensive and time-consuming). We present
CLTS-GAN, a new deep learning model that gives fine control over color,
lighting, texture, and specular reflection synthesis for OC video frames. We
show that adding these colonoscopy-specific augmentations to the training data
can improve state-of-the-art polyp detection/segmentation methods as well as
drive next generation of OC simulators for training medical students. The code
and pre-trained models for CLTS-GAN are available on Computational Endoscopy
Platform GitHub (https://github.com/nadeemlab/CEP).

Electromyography signals can be used as training data by machine learning
models to classify various gestures. We seek to produce a model that can
classify six different hand gestures with a limited number of samples that
generalizes well to a wider audience while comparing the effect of our feature
extraction results on model accuracy to other more conventional methods such as
the use of AR parameters on a sliding window across the channels of a signal.
We appeal to a set of more elementary methods such as the use of random bounds
on a signal, but desire to show the power these methods can carry in an online
setting where EMG classification is being conducted, as opposed to more
complicated methods such as the use of the Fourier Transform. To augment our
limited training data, we used a standard technique, known as jitter, where
random noise is added to each observation in a channel wise manner. Once all
datasets were produced using the above methods, we performed a grid search with
Random Forest and XGBoost to ultimately create a high accuracy model. For human
computer interface purposes, high accuracy classification of EMG signals is of
particular importance to their functioning and given the difficulty and cost of
amassing any sort of biomedical data in a high volume, it is valuable to have
techniques that can work with a low amount of high-quality samples with less
expensive feature extraction methods that can reliably be carried out in an
online application.

Next generation telescopes, such as Euclid, Rubin/LSST, and Roman, will open
new windows on the Universe, allowing us to infer physical properties for tens
of millions of galaxies. Machine learning methods are increasingly becoming the
most efficient tools to handle this enormous amount of data, not only as they
are faster to apply to data samples than traditional methods, but because they
are also often more accurate. Properly understanding their applications and
limitations for the exploitation of these data is of utmost importance. In this
paper we present an exploration of this topic by investigating how well
redshifts, stellar masses, and star-formation rates can be measured with deep
learning algorithms for galaxies within data that mimics the Euclid and
Rubin/LSST surveys. We find that Deep Learning Neural Networks and
Convolutional Neutral Networks (CNN), which are dependent on the parameter
space of the sample used for training, perform well in measuring the properties
of these galaxies and have an accuracy which is better than traditional methods
based on spectral energy distribution fitting. CNNs allow the processing of
multi-band magnitudes together with $H_{E}$-band images. We find that the
estimates of stellar masses improve with the use of an image, but those of
redshift and star-formation rates do not. Our best machine learning results are
deriving i) the redshift within a normalised error of less than 0.15 for 99.9%
of the galaxies in the sample with S/N>3 in the $H_{E}$-band; ii) the stellar
mass within a factor of two ($\sim$0.3 dex) for 99.5% of the considered
galaxies; iii) the star-formation rates within a factor of two ($\sim$0.3 dex)
for $\sim$70% of the sample. We discuss the implications of our work for
application to surveys, mainly but not limited to Euclid and Rubin/LSST, and
how measurements of these galaxy parameters can be improved with deep learning.

In plant breeding the presence of a genotype by environment (GxE) interaction
has a strong impact on cultivation decision making and the introduction of new
crop cultivars. The combination of linear and bilinear terms has been shown to
be very useful in modelling this type of data. A widely-used approach to
identify GxE is the Additive Main Effects and Multiplicative Interaction
Effects (AMMI) model. However, as data frequently can be high-dimensional,
Markov chain Monte Carlo (MCMC) approaches can be computationally infeasible.
In this article, we consider a variational inference approach for such a model.
We derive variational approximations for estimating the parameters and we
compare the approximations to MCMC using both simulated and real data. The new
inferential framework we propose is on average two times faster whilst
maintaining the same predictive performance as MCMC.

X-ray ptychography allows for large fields to be imaged at high resolution at
the cost of additional computational expense due to the large volume of data.
Given limited information regarding the object, the acquired data often has an
excessive amount of information that is outside the region of interest (RoI).
In this work we propose a physics-inspired unsupervised learning algorithm to
identify the RoI of an object using only diffraction patterns from a
ptychography dataset before committing computational resources to
reconstruction. Obtained diffraction patterns that are automatically identified
as not within the RoI are filtered out, allowing efficient reconstruction by
focusing only on important data within the RoI while preserving image quality.

Neural radiance fields, or NeRF, represent a breakthrough in the field of
novel view synthesis and 3D modeling of complex scenes from multi-view image
collections. Numerous recent works have been focusing on making the models more
robust, by means of regularization, so as to be able to train with possibly
inconsistent and/or very sparse data. In this work, we scratch the surface of
how differential geometry can provide regularization tools for robustly
training NeRF-like models, which are modified so as to represent continuous and
infinitely differentiable functions. In particular, we show how these tools
yield a direct mathematical formalism of previously proposed NeRF variants
aimed at improving the performance in challenging conditions (i.e. RegNeRF).
Based on this, we show how the same formalism can be used to natively encourage
the regularity of surfaces (by means of Gaussian and Mean Curvatures) making it
possible, for example, to learn surfaces from a very limited number of views.

We construct a classically verifiable succinct interactive argument for
quantum computation (BQP) with communication complexity and verifier runtime
that are poly-logarithmic in the runtime of the BQP computation (and polynomial
in the security parameter). Our protocol is secure assuming the post-quantum
security of indistinguishability obfuscation (iO) and Learning with Errors
(LWE). This is the first succinct argument for quantum computation in the plain
model; prior work (Chia-Chung-Yamakawa, TCC '20) requires both a long common
reference string and non-black-box use of a hash function modeled as a random
oracle.
  At a technical level, we revisit the framework for constructing classically
verifiable quantum computation (Mahadev, FOCS '18). We give a self-contained,
modular proof of security for Mahadev's protocol, which we believe is of
independent interest. Our proof readily generalizes to a setting in which the
verifier's first message (which consists of many public keys) is compressed.
Next, we formalize this notion of compressed public keys; we view the object as
a generalization of constrained/programmable PRFs and instantiate it based on
indistinguishability obfuscation.
  Finally, we compile the above protocol into a fully succinct argument using a
(sufficiently composable) succinct argument of knowledge for NP. Using our
framework, we achieve several additional results, including
  - Succinct arguments for QMA (given multiple copies of the witness),
  - Succinct non-interactive arguments for BQP (or QMA) in the quantum random
oracle model, and
  - Succinct batch arguments for BQP (or QMA) assuming post-quantum LWE
(without iO).

We present a method called Manifold Interpolating Optimal-Transport Flow
(MIOFlow) that learns stochastic, continuous population dynamics from static
snapshot samples taken at sporadic timepoints. MIOFlow combines dynamic models,
manifold learning, and optimal transport by training neural ordinary
differential equations (Neural ODE) to interpolate between static population
snapshots as penalized by optimal transport with manifold ground distance.
Further, we ensure that the flow follows the geometry by operating in the
latent space of an autoencoder that we call a geodesic autoencoder (GAE). In
GAE the latent space distance between points is regularized to match a novel
multiscale geodesic distance on the data manifold that we define. We show that
this method is superior to normalizing flows, Schr\"odinger bridges and other
generative models that are designed to flow from noise to data in terms of
interpolating between populations. Theoretically, we link these trajectories
with dynamic optimal transport. We evaluate our method on simulated data with
bifurcations and merges, as well as scRNA-seq data from embryoid body
differentiation, and acute myeloid leukemia treatment.

In this paper, we design, analyze the convergence properties and address the
implementation aspects of AFAFed. This is a novel Asynchronous Fair Adaptive
Federated learning framework for stream-oriented IoT application environments,
which are featured by time-varying operating conditions, heterogeneous
resource-limited devices (i.e., coworkers), non-i.i.d. local training data and
unreliable communication links. The key new of AFAFed is the synergic co-design
of: (i) two sets of adaptively tuned tolerance thresholds and fairness
coefficients at the coworkers and central server, respectively; and, (ii) a
distributed adaptive mechanism, which allows each coworker to adaptively tune
own communication rate. The convergence properties of AFAFed under (possibly)
non-convex loss functions is guaranteed by a set of new analytical bounds,
which formally unveil the impact on the resulting AFAFed convergence rate of a
number of Federated Learning (FL) parameters, like, first and second moments of
the per-coworker number of consecutive model updates, data skewness,
communication packet-loss probability, and maximum/minimum values of the
(adaptively tuned) mixing coefficient used for model aggregation.

Real-world knowledge graphs (KG) are mostly incomplete. The problem of
recovering missing relations, called KG completion, has recently become an
active research area. Knowledge graph (KG) embedding, a low-dimensional
representation of entities and relations, is the crucial technique for KG
completion. Convolutional neural networks in models such as ConvE, SACN,
InteractE, and RGCN achieve recent successes. This paper takes a different
architectural view and proposes ComDensE which combines relation-aware and
common features using dense neural networks. In the relation-aware feature
extraction, we attempt to create relational inductive bias by applying an
encoding function specific to each relation. In the common feature extraction,
we apply the common encoding function to all input embeddings. These encoding
functions are implemented using dense layers in ComDensE. ComDensE achieves the
state-of-the-art performance in the link prediction in terms of MRR, HIT@1 on
FB15k-237 and HIT@1 on WN18RR compared to the previous baseline approaches. We
conduct an extensive ablation study to examine the effects of the
relation-aware layer and the common layer of the ComDensE. Experimental results
illustrate that the combined dense architecture as implemented in ComDensE
achieves the best performance.

In recent years, creating and managing knowledge bases have become crucial to
the retail product and enterprise domains. We present an automatic knowledge
base construction system that mines data from documents. This system can
generate training data during the training process without human intervention.
Therefore, it is domain-agnostic trainable using only the target domain text
corpus and a pre-defined knowledge base. This system is called OASYS and is the
first system built with the Korean language in mind. In addition, we also have
constructed a new human-annotated benchmark dataset of the Korean Wikipedia
corpus paired with a Korean DBpedia to aid system evaluation. The system
performance results on human-annotated benchmark test dataset are meaningful
and show that the generated knowledge base from OASYS trained on only
auto-generated data is useful. We provide both a human-annotated test dataset
and an auto-generated dataset.

Semi-Supervised Learning (SSL) is fundamentally a missing label problem, in
which the label Missing Not At Random (MNAR) problem is more realistic and
challenging, compared to the widely-adopted yet naive Missing Completely At
Random assumption where both labeled and unlabeled data share the same class
distribution. Different from existing SSL solutions that overlook the role of
"class" in causing the non-randomness, e.g., users are more likely to label
popular classes, we explicitly incorporate "class" into SSL. Our method is
three-fold: 1) We propose Class-Aware Propensity (CAP) that exploits the
unlabeled data to train an improved classifier using the biased labeled data.
2) To encourage rare class training, whose model is low-recall but
high-precision that discards too many pseudo-labeled data, we propose
Class-Aware Imputation (CAI) that dynamically decreases (or increases) the
pseudo-label assignment threshold for rare (or frequent) classes. 3) Overall,
we integrate CAP and CAI into a Class-Aware Doubly Robust (CADR) estimator for
training an unbiased SSL model. Under various MNAR settings and ablations, our
method not only significantly outperforms existing baselines but also surpasses
other label bias removal SSL methods. Please check our code at:
https://github.com/JoyHuYY1412/CADR-FixMatch.

Exploration of bias has significant impact on the transparency and
applicability of deep learning pipelines in medical settings, yet is so far
woefully understudied. In this paper, we consider two separate groups for which
training data is only available at differing image resolutions. For group H,
available images and labels are at the preferred high resolution while for
group L only deprecated lower resolution data exist. We analyse how this
resolution-bias in the data distribution propagates to systematically biased
predictions for group L at higher resolutions. Our results demonstrate that
single-resolution training settings result in significant loss of volumetric
group differences that translate to erroneous segmentations as measured by DSC
and subsequent classification failures on the low resolution group. We further
explore how training data across resolutions can be used to combat this
systematic bias. Specifically, we investigate the effect of image resampling,
scale augmentation and resolution independence and demonstrate that biases can
effectively be reduced with multi-resolution approaches.

There has been a massive increase in research interest towards applying data
driven methods to problems in mechanics. While traditional machine learning
(ML) methods have enabled many breakthroughs, they rely on the assumption that
the training (observed) data and testing (unseen) data are independent and
identically distributed (i.i.d). Thus, traditional ML approaches often break
down when applied to real world mechanics problems with unknown test
environments and data distribution shifts. In contrast, out-of-distribution
(OOD) generalization assumes that the test data may shift (i.e., violate the
i.i.d. assumption). To date, multiple methods have been proposed to improve the
OOD generalization of ML methods. However, because of the lack of benchmark
datasets for OOD regression problems, the efficiency of these OOD methods on
regression problems, which dominate the mechanics field, remains unknown. To
address this, we investigate the performance of OOD generalization methods for
regression problems in mechanics. Specifically, we identify three OOD problems:
covariate shift, mechanism shift, and sampling bias. For each problem, we
create two benchmark examples that extend the Mechanical MNIST dataset
collection, and we investigate the performance of popular OOD generalization
methods on these mechanics-specific regression problems. Our numerical
experiments show that in most cases, while the OOD generalization algorithms
perform better compared to traditional ML methods on these OOD problems, there
is a compelling need to develop more robust OOD generalization methods that are
effective across multiple OOD scenarios. Overall, we expect that this study, as
well as the associated open access benchmark datasets, will enable further
development of OOD generalization methods for mechanics specific regression
problems.

Mixed microbial communities, usually composed of various bacterial and fungal
species, are fundamental in a plethora of environments, from soil to human gut
and skin. Their evolution is a paradigmatic example of intertwined dynamics,
where not just the relations among species plays a role, but also the
opportunities -- and possible harms -- that each species presents to the
others. These opportunities are in fact \textit{affordances}, which can be
seized by heritable variation and selection. In this paper, starting from a
systemic viewpoint of mixed microbial communities, we focus on the pivotal role
of affordances in evolution and we contrast it to the artificial evolution of
programs and robots. We maintain that the two realms are neatly separated, in
that natural evolution proceeds by extending the space of its possibilities in
a completely open way, while the latter is inherently limited by the
algorithmic framework it is defined. This discrepancy characterises also an
envisioned setting in which robots evolve in the physical world. We present
arguments supporting our claim and we propose an experimental setting for
assessing our statements. Rather than just discussing the limitations of the
artificial evolution of machines, the aim of this contribution is to emphasize
the tremendous potential of the evolution of the biosphere, beautifully
represented by the evolution of communities of microbes.

One of the most pressing societal issues is the fight against false news. The
false claims, as difficult as they are to expose, create a lot of damage. To
tackle the problem, fact verification becomes crucial and thus has been a topic
of interest among diverse research communities. Using only the textual form of
data we propose our solution to the problem and achieve competitive results
with other approaches. We present our solution based on two approaches - PLM
(pre-trained language model) based method and Prompt based method. The
PLM-based approach uses the traditional supervised learning, where the model is
trained to take 'x' as input and output prediction 'y' as P(y|x). Whereas,
Prompt-based learning reflects the idea to design input to fit the model such
that the original objective may be re-framed as a problem of (masked) language
modeling. We may further stimulate the rich knowledge provided by PLMs to
better serve downstream tasks by employing extra prompts to fine-tune PLMs. Our
experiments showed that the proposed method performs better than just
fine-tuning PLMs. We achieved an F1 score of 0.6946 on the FACTIFY dataset and
a 7th position on the competition leader-board.

We study the problem of model selection in bandit scenarios in the presence
of nested policy classes, with the goal of obtaining simultaneous adversarial
and stochastic ("best of both worlds") high-probability regret guarantees. Our
approach requires that each base learner comes with a candidate regret bound
that may or may not hold, while our meta algorithm plays each base learner
according to a schedule that keeps the base learner's candidate regret bounds
balanced until they are detected to violate their guarantees. We develop
careful mis-specification tests specifically designed to blend the above model
selection criterion with the ability to leverage the (potentially benign)
nature of the environment. We recover the model selection guarantees of the
CORRAL algorithm for adversarial environments, but with the additional benefit
of achieving high probability regret bounds, specifically in the case of nested
adversarial linear bandits. More importantly, our model selection results also
hold simultaneously in stochastic environments under gap assumptions. These are
the first theoretical results that achieve best of both world (stochastic and
adversarial) guarantees while performing model selection in (linear) bandit

We present a modified tuning of the algorithm of Zimmert and Seldin [2020]
for adversarial multiarmed bandits with delayed feedback, which in addition to
the minimax optimal adversarial regret guarantee shown by Zimmert and Seldin
simultaneously achieves a near-optimal regret guarantee in the stochastic
setting with fixed delays. Specifically, the adversarial regret guarantee is
$\mathcal{O}(\sqrt{TK} + \sqrt{dT\log K})$, where $T$ is the time horizon, $K$
is the number of arms, and $d$ is the fixed delay, whereas the stochastic
regret guarantee is $\mathcal{O}\left(\sum_{i \neq i^*}(\frac{1}{\Delta_i}
\log(T) + \frac{d}{\Delta_{i}\log K}) + d K^{1/3}\log K\right)$, where
$\Delta_i$ are the suboptimality gaps. We also present an extension of the
algorithm to the case of arbitrary delays, which is based on an oracle
knowledge of the maximal delay $d_{max}$ and achieves $\mathcal{O}(\sqrt{TK} +
\sqrt{D\log K} + d_{max}K^{1/3} \log K)$ regret in the adversarial regime,
where $D$ is the total delay, and $\mathcal{O}\left(\sum_{i \neq
i^*}(\frac{1}{\Delta_i} \log(T) + \frac{\sigma_{max}}{\Delta_{i}\log K}) +
d_{max}K^{1/3}\log K\right)$ regret in the stochastic regime, where
$\sigma_{max}$ is the maximal number of outstanding observations. Finally, we
present a lower bound that matches regret upper bound achieved by the skipping
technique of Zimmert and Seldin [2020] in the adversarial setting.

Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules,
are good predictors of lung cancer malignancy and hence, are routinely assessed
and reported by radiologists as part of the standardized Lung-RADS clinical
scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice
assessment by radiologists, manual spiculation/lobulation annotation is a
tedious task and thus no public datasets exist to date for probing the
importance of these clinically-reported features in the SOTA malignancy
prediction algorithms. As part of this paper, we release a large-scale
Clinically-Interpretable Radiomics Dataset, CIRDataset, containing 956
radiologist QA/QC'ed spiculation/lobulation annotations on segmented lung
nodules from two public datasets, LIDC-IDRI (N=883) and LUNGx (N=73). We also
present an end-to-end deep learning model based on multi-class Voxel2Mesh
extension to segment nodules (while preserving spikes), classify spikes
(sharp/spiculation and curved/lobulation), and perform malignancy prediction.
Previous methods have performed malignancy prediction for LIDC and LUNGx
datasets but without robust attribution to any clinically reported/actionable
features (due to known hyperparameter sensitivity issues with general
attribution schemes). With the release of this comprehensively-annotated
CIRDataset and end-to-end deep learning baseline, we hope that malignancy
prediction methods can validate their explanations, benchmark against our
baseline, and provide clinically-actionable insights. Dataset, code, pretrained
models, and docker containers are available at
https://github.com/nadeemlab/CIR.

Recently, a family of locally balanced (LB) samplers has demonstrated
excellent performance at sampling and learning energy-based models (EBMs) in
discrete spaces. However, the theoretical understanding of this success is
limited. In this work, we show how LB functions give rise to LB dynamics
corresponding to Wasserstein gradient flow in a discrete space. From first
principles, previous LB samplers can then be seen as discretizations of the LB
dynamics with respect to Hamming distance. Based on this observation, we
propose a new algorithm, the Locally Balanced Jump (LBJ), by discretizing the
LB dynamics with respect to simulation time. As a result, LBJ has a
location-dependent "velocity" that allows it to make proposals with larger
distances. Additionally, LBJ decouples each dimension into independent
sub-processes, enabling convenient parallel implementation. We demonstrate the
advantages of LBJ for sampling and learning in various binary and categorical

Generative adversarial networks (GANs) have proven to be surprisingly
efficient for image editing by inverting and manipulating the latent code
corresponding to an input real image. This editing property emerges from the
disentangled nature of the latent space. In this paper, we identify that the
facial attribute disentanglement is not optimal, thus facial editing relying on
linear attribute separation is flawed. We thus propose to improve semantic
disentanglement with supervision. Our method consists in learning a proxy
latent representation using normalizing flows, and we show that this leads to a
more efficient space for face image editing.

Most of the existing methods for estimating the local intrinsic dimension of
a data distribution do not scale well to high-dimensional data. Many of them
rely on a non-parametric nearest neighbors approach which suffers from the
curse of dimensionality. We attempt to address that challenge by proposing a
novel approach to the problem: Local Intrinsic Dimension estimation using
approximate Likelihood (LIDL). Our method relies on an arbitrary density
estimation method as its subroutine and hence tries to sidestep the
dimensionality challenge by making use of the recent progress in parametric
neural methods for likelihood estimation. We carefully investigate the
empirical properties of the proposed method, compare them with our theoretical
predictions, and show that LIDL yields competitive results on the standard
benchmarks for this problem and that it scales to thousands of dimensions. What
is more, we anticipate this approach to improve further with the continuing
advances in the density estimation literature.

If code is law, then the language of law is a programming language. Lawyers
and legal scholars can learn about law by studying programming-language theory,
and programming-language tools can be usefully applied to legal problems. This
article surveys the history of research on programming languages and law and
presents ten promising avenues for future efforts. Its goals are to explain how
the combination of programming languages and law is distinctive within the
broader field of computer science and law, and to demonstrate with concrete
examples the remarkable power of programming-language concepts in this new
domain.

On October 30th, 2021, Rebellion Research's CEO announced in a Q3 2021 Letter
to Investors that Rebellion's AI Global Equity strategy returned +6.8% gross
for the first three quarters of 2021. "It's no surprise", Alex told us, "Our
Machine Learning global strategy has a history of outperforming the S&P 500 for
14 years". In 2021, Rebellion's brokerage accounts can be opened in over 70
countries, and Rebellion's research covers over 50 countries. Besides being an
AI asset management company, Rebellion also defines itself as a top-tier,
global machine learning think tank. Alex planned to build a Rebellion ML & AI
ecosystem. Should Rebellion stay in the asset management area or jump into
other areas? How could the Rebellion strategically move towards a more broad
area? What were Rebellion's new or alternative business models?

Continual Learning (CL) on time series data represents a promising but
under-studied avenue for real-world applications. We propose two new CL
benchmarks for Human State Monitoring. We carefully designed the benchmarks to
mirror real-world environments in which new subjects are continuously added. We
conducted an empirical evaluation to assess the ability of popular CL
strategies to mitigate forgetting in our benchmarks. Our results show that,
possibly due to the domain-incremental properties of our benchmarks, forgetting
can be easily tackled even with a simple finetuning and that existing
strategies struggle in accumulating knowledge over a fixed, held-out, test
subject.

Mixup refers to interpolation-based data augmentation, originally motivated
as a way to go beyond empirical risk minimization (ERM). Yet, its extensions
focus on the definition of interpolation and the space where it takes place,
while the augmentation itself is less studied: For a mini-batch of size $m$,
most methods interpolate between $m$ pairs with a single scalar interpolation
factor $\lambda$.
  In this work, we make progress in this direction by introducing MultiMix,
which interpolates an arbitrary number $n$ of tuples, each of length $m$, with
one vector $\lambda$ per tuple. On sequence data, we further extend to dense
interpolation and loss computation over all spatial positions. Overall, we
increase the number of tuples per mini-batch by orders of magnitude at little
additional cost. This is possible by interpolating at the very last layer
before the classifier. Finally, to address inconsistencies due to linear target
interpolation, we introduce a self-distillation approach to generate and
interpolate synthetic targets.
  We empirically show that our contributions result in significant improvement
over state-of-the-art mixup methods on four benchmarks. By analyzing the
embedding space, we observe that the classes are more tightly clustered and
uniformly spread over the embedding space, thereby explaining the improved
behavior.

Physics-informed neural network (PINN) algorithms have shown promising
results in solving a wide range of problems involving partial differential
equations (PDEs). However, they often fail to converge to desirable solutions
when the target function contains high-frequency features, due to a phenomenon
known as spectral bias. In the present work, we exploit neural tangent kernels
(NTKs) to investigate the training dynamics of PINNs evolving under stochastic
gradient descent with momentum (SGDM). This demonstrates SGDM significantly
reduces the effect of spectral bias. We have also examined why training a model
via the Adam optimizer can accelerate the convergence while reducing the
spectral bias. Moreover, our numerical experiments have confirmed that
wide-enough networks using SGDM still converge to desirable solutions, even in
the presence of high-frequency features. In fact, we show that the width of a
network plays a critical role in convergence.

Language models have achieved remarkable performance on a wide range of tasks
that require natural language understanding. Nevertheless, state-of-the-art
models have generally struggled with tasks that require quantitative reasoning,
such as solving mathematics, science, and engineering problems at the college
level. To help close this gap, we introduce Minerva, a large language model
pretrained on general natural language data and further trained on technical
content. The model achieves state-of-the-art performance on technical
benchmarks without the use of external tools. We also evaluate our model on
over two hundred undergraduate-level problems in physics, biology, chemistry,
economics, and other sciences that require quantitative reasoning, and find
that the model can correctly answer nearly a third of them.

The success of DNNs is driven by the counter-intuitive ability of
over-parameterized networks to generalize, even when they perfectly fit the
training data. In practice, test error often continues to decrease with
increasing over-parameterization, referred to as double descent. This allows
practitioners to instantiate large models without having to worry about
over-fitting. Despite its benefits, however, prior work has shown that
over-parameterization can exacerbate bias against minority subgroups. Several
fairness-constrained DNN training methods have been proposed to address this
concern. Here, we critically examine MinDiff, a fairness-constrained training
procedure implemented within TensorFlow's Responsible AI Toolkit, that aims to
achieve Equality of Opportunity. We show that although MinDiff improves
fairness for under-parameterized models, it is likely to be ineffective in the
over-parameterized regime. This is because an overfit model with zero training
loss is trivially group-wise fair on training data, creating an "illusion of
fairness," thus turning off the MinDiff optimization (this will apply to any
disparity-based measures which care about errors or accuracy. It won't apply to
demographic parity). Within specified fairness constraints, under-parameterized
MinDiff models can even have lower error compared to their over-parameterized
counterparts (despite baseline over-parameterized models having lower error).
We further show that MinDiff optimization is very sensitive to choice of batch
size in the under-parameterized regime. Thus, fair model training using MinDiff
requires time-consuming hyper-parameter searches. Finally, we suggest using
previously proposed regularization techniques, viz. L2, early stopping and
flooding in conjunction with MinDiff to train fair over-parameterized models.

Small bowel path tracking is a challenging problem considering its many folds
and contact along its course. For the same reason, it is very costly to achieve
the ground-truth (GT) path of the small bowel in 3D. In this work, we propose
to train a deep reinforcement learning tracker using datasets with different
types of annotations. Specifically, we utilize CT scans that have only GT small
bowel segmentation as well as ones with the GT path. It is enabled by designing
a unique environment that is compatible for both, including a reward definable
even without the GT path. The performed experiments proved the validity of the
proposed method. The proposed method holds a high degree of usability in this
problem by being able to utilize the scans with weak annotations, and thus by
possibly reducing the required annotation cost.

Online influence maximization aims to maximize the influence spread of a
content in a social network with unknown network model by selecting a few seed
nodes. Recent studies followed a non-adaptive setting, where the seed nodes are
selected before the start of the diffusion process and network parameters are
updated when the diffusion stops. We consider an adaptive version of
content-dependent online influence maximization problem where the seed nodes
are sequentially activated based on real-time feedback. In this paper, we
formulate the problem as an infinite-horizon discounted MDP under a linear
diffusion process and present a model-based reinforcement learning solution.
Our algorithm maintains a network model estimate and selects seed users
adaptively, exploring the social network while improving the optimal policy
optimistically. We establish $\widetilde O(\sqrt{T})$ regret bound for our
algorithm. Empirical evaluations on synthetic network demonstrate the
efficiency of our algorithm.

There have been several post-hoc explanation approaches developed to explain
pre-trained black-box neural networks. However, there is still a gap in
research efforts toward designing neural networks that are inherently
explainable. In this paper, we utilize a recently proposed instance-wise
post-hoc causal explanation method to make an existing transformer architecture
inherently explainable. Once trained, our model provides an explanation in the
form of top-$k$ regions in the input space of the given instance contributing
to its decision. We evaluate our method on binary classification tasks using
three image datasets: MNIST, FMNIST, and CIFAR. Our results demonstrate that
compared to the causality-based post-hoc explainer model, our inherently
explainable model achieves better explainability results while eliminating the
need of training a separate explainer model. Our code is available at
https://github.com/mvrl/CAT-XPLAIN.

In 1933, Fritz Zwicky's famous investigations of the mass of the Coma cluster
led him to infer the existence of dark matter \cite{1933AcHPh...6..110Z}. His
fundamental discoveries have proven to be foundational to modern cosmology; as
we now know such dark matter makes up 85\% of the matter and 25\% of the
mass-energy content in the universe. Galaxy clusters like Coma are massive,
complex systems of dark matter in addition to hot ionized gas and thousands of
galaxies, and serve as excellent probes of the dark matter distribution.
However, empirical studies show that the total mass of such systems remains
elusive and difficult to precisely constrain. Here, we present new estimates
for the dynamical mass of the Coma cluster based on Bayesian deep learning
methodologies developed in recent years. Using our novel data-driven approach,
we predict Coma's $\mthc$ mass to be $10^{15.10 \pm 0.15}\ \hmsun$ within a
radius of $1.78 \pm 0.03\ h^{-1}\mathrm{Mpc}$ of its center. We show that our
predictions are rigorous across multiple training datasets and statistically
consistent with historical estimates of Coma's mass. This measurement
reinforces our understanding of the dynamical state of the Coma cluster and
advances rigorous analyses and verification methods for empirical applications
of machine learning in astronomy.

Evaluating loop amplitudes is a time-consuming part of LHC event generation.
For di-photon production with jets we show that simple, Bayesian networks can
learn such amplitudes and model their uncertainties reliably. A boosted
training of the Bayesian network further improves the uncertainty estimate and
the network precision in critical phase space regions. In general, boosted
network training of Bayesian networks allows us to move between fit-like and
interpolation-like regimes of network training.

From the nature of dark matter to the rate of expansion of our Universe,
observations of distant galaxies distorted through strong gravitational lensing
have the potential to answer some of the major open questions in astrophysics.
Modeling galaxy-galaxy strong lensing observations presents a number of
challenges as the exact configuration of both the background source and
foreground lens galaxy is unknown. A timely call, prompted by a number of
upcoming surveys anticipating high-resolution lensing images, demands methods
that can efficiently model lenses at their full complexity. In this work, we
introduce a method that uses continuous neural fields to non-parametrically
reconstruct the complex morphology of a source galaxy while simultaneously
inferring a distribution over foreground lens galaxy configurations. We
demonstrate the efficacy of our method through experiments on simulated data
targeting high-resolution lensing images similar to those anticipated in
near-future astrophysical surveys.

Machine learning has become widely used in astronomy. Gaussian Process (GP)
regression in particular has been employed a number of times to fit or
re-sample supernova (SN) light-curves, however by their nature typical GP
models are not suited to fit SN photometric data and they will be prone to
over-fitting. Recently GP re-sampling was used in the context of studying the
morphologies of type II and IIb SNe and they were found to be clearly distinct
with respect to four parameters: the rise time (t$_{\rm rise}$), the magnitude
difference between 40 and 30 days post explosion ($\Delta m_{\rm 40-30}$), and
the earliest maxima of the first and second derivative of the light curve (dm1
and dm2). Here we take a close look at Gaussian process regression and its
limitations in the context of SNe light-curves in general, and we also discuss
the uncertainties on these specific parameters, finding that dm1 and dm2 cannot
give reliable astrophysical information. We do reproduce the clustering in
t$_{\rm rise}$--$\Delta m_{\rm 40-30}$ space although it is not as clear cut as
previously presented. The best strategy to accurately populate the t$_{\rm
rise}$-- $\Delta m_{\rm 40-30}$ space will be to use an expanded sample of high
quality light-curves (such as those in the ATLAS transient survey) and
analytical fitting methods. Finally, using the BPASS fiducial models, we
predict that future photometric studies will reveal clear clustering of the
type IIb and II light curve morphologies with a distinct continuum of
transitional events.

Model-free policy learning has been shown to be capable of learning
manipulation policies which can solve long-time horizon tasks using single-step
manipulation primitives. However, training these policies is a time-consuming
process requiring large amounts of data. We propose the Local Dynamics Model
(LDM) which efficiently learns the state-transition function for these
manipulation primitives. By combining the LDM with model-free policy learning,
we can learn policies which can solve complex manipulation tasks using one-step
lookahead planning. We show that the LDM is both more sample-efficient and
outperforms other model architectures. When combined with planning, we can
outperform other model-based and model-free policies on several challenging
manipulation tasks in simulation.

A need to understand and predict vehicles' behavior underlies both public and
private goals in the transportation domain, including urban planning and
management, ride-sharing services, and intelligent transportation systems.
Individuals' preferences and intended destinations vary throughout the day,
week, and year: for example, bars are most popular in the evenings, and beaches
are most popular in the summer. Despite this principle, we note that recent
studies on a popular benchmark dataset from Porto, Portugal have found, at
best, only marginal improvements in predictive performance from incorporating
temporal information. We propose an approach based on hypernetworks, a variant
of meta-learning ("learning to learn") in which a neural network learns to
change its own weights in response to an input. In our case, the weights
responsible for destination prediction vary with the metadata, in particular
the time, of the input trajectory. The time-conditioned weights notably improve
the model's error relative to ablation studies and comparable prior work, and
we confirm our hypothesis that knowledge of time should improve prediction of a
vehicle's intended destination.

We study the mutual information between (certain summaries of) the output of
a learning algorithm and its $n$ training data, conditional on a supersample of
$n+1$ i.i.d. data from which the training data is chosen at random without
replacement. These leave-one-out variants of the conditional mutual information
(CMI) of an algorithm (Steinke and Zakynthinou, 2020) are also seen to control
the mean generalization error of learning algorithms with bounded loss
functions. For learning algorithms achieving zero empirical risk under 0-1 loss
(i.e., interpolating algorithms), we provide an explicit connection between
leave-one-out CMI and the classical leave-one-out error estimate of the risk.
Using this connection, we obtain upper and lower bounds on risk in terms of the
(evaluated) leave-one-out CMI. When the limiting risk is constant or decays
polynomially, the bounds converge to within a constant factor of two. As an
application, we analyze the population risk of the one-inclusion graph
algorithm, a general-purpose transductive learning algorithm for VC classes in
the realizable setting. Using leave-one-out CMI, we match the optimal bound for
learning VC classes in the realizable setting, answering an open challenge
raised by Steinke and Zakynthinou (2020). Finally, in order to understand the
role of leave-one-out CMI in studying generalization, we place leave-one-out
CMI in a hierarchy of measures, with a novel unconditional mutual information
at the root. For 0-1 loss and interpolating learning algorithms, this mutual
information is observed to be precisely the risk.

In this paper we establish a bridge between Topological Data Analysis and
Geometric Deep Learning, adapting the topological theory of group equivariant
non-expansive operators (GENEOs) to act on the space of all graphs weighted on
vertices or edges. This is done by showing how the general concept of GENEO can
be used to transform graphs and to give information about their structure. This
requires the introduction of the new concepts of generalized permutant and
generalized permutant measure and the mathematical proof that these concepts
allow us to build GENEOs between graphs. An experimental section concludes the
paper, illustrating the possible use of our operators to extract information
from graphs. This paper is part of a line of research devoted to developing a
compositional and geometric theory of GENEOs for Geometric Deep Learning.

Generative models have emerged as an essential building block for many image
synthesis and editing tasks. Recent advances in this field have also enabled
high-quality 3D or video content to be generated that exhibits either
multi-view or temporal consistency. With our work, we explore 4D generative
adversarial networks (GANs) that learn unconditional generation of 3D-aware
videos. By combining neural implicit representations with time-aware
discriminator, we develop a GAN framework that synthesizes 3D video supervised
only with monocular videos. We show that our method learns a rich embedding of
decomposable 3D structures and motions that enables new visual effects of
spatio-temporal renderings while producing imagery with quality comparable to
that of existing 3D or video GANs.

Most works on modeling the conversation history in Conversational Question
Answering (CQA) report a single main result on a common CQA benchmark. While
existing models show impressive results on CQA leaderboards, it remains unclear
whether they are robust to shifts in setting (sometimes to more realistic
ones), training data size (e.g. from large to small sets) and domain. In this
work, we design and conduct the first large-scale robustness study of history
modeling approaches for CQA. We find that high benchmark scores do not
necessarily translate to strong robustness, and that various methods can
perform extremely differently under different settings. Equipped with the
insights from our study, we design a novel prompt-based history modeling
approach, and demonstrate its strong robustness across various settings. Our
approach is inspired by existing methods that highlight historic answers in the
passage. However, instead of highlighting by modifying the passage token
embeddings, we add textual prompts directly in the passage text. Our approach
is simple, easy-to-plug into practically any model, and highly effective, thus
we recommend it as a starting point for future model developers. We also hope
that our study and insights will raise awareness to the importance of
robustness-focused evaluation, in addition to obtaining high leaderboard
scores, leading to better CQA systems.

Post-processing ensemble prediction systems can improve the reliability of
weather forecasting, especially for extreme event prediction. In recent years,
different machine learning models have been developed to improve the quality of
weather post-processing. However, these models require a comprehensive dataset
of weather simulations to produce high-accuracy results, which comes at a high
computational cost to generate. This paper introduces the ENS-10 dataset,
consisting of ten ensemble members spanning 20 years (1998-2017). The ensemble
members are generated by perturbing numerical weather simulations to capture
the chaotic behavior of the Earth. To represent the three-dimensional state of
the atmosphere, ENS-10 provides the most relevant atmospheric variables at 11
distinct pressure levels and the surface at 0.5-degree resolution for forecast
lead times T=0, 24, and 48 hours (two data points per week). We propose the
ENS-10 prediction correction task for improving the forecast quality at a
48-hour lead time through ensemble post-processing. We provide a set of
baselines and compare their skill at correcting the predictions of three
important atmospheric variables. Moreover, we measure the baselines' skill at
improving predictions of extreme weather events using our dataset. The ENS-10
dataset is available under the Creative Commons Attribution 4.0 International
(CC BY 4.0) license.

Recent works have tried to increase the verifiability of adversarially
trained networks by running the attacks over domains larger than the original
perturbations and adding various regularization terms to the objective.
However, these algorithms either underperform or require complex and expensive
stage-wise training procedures, hindering their practical applicability. We
present IBP-R, a novel verified training algorithm that is both simple and
effective. IBP-R induces network verifiability by coupling adversarial attacks
on enlarged domains with a regularization term, based on inexpensive interval
bound propagation, that minimizes the gap between the non-convex verification
problem and its approximations. By leveraging recent branch-and-bound
frameworks, we show that IBP-R obtains state-of-the-art verified
robustness-accuracy trade-offs for small perturbations on CIFAR-10 while
training significantly faster than relevant previous work. Additionally, we
present UPB, a novel branching strategy that, relying on a simple heuristic
based on $\beta$-CROWN, reduces the cost of state-of-the-art branching
algorithms while yielding splits of comparable quality.

In this work, we propose a hybrid variant of the level-based learning swarm
optimizer (LLSO) for solving large-scale portfolio optimization problems. Our
goal is to maximize a modified formulation of the Sharpe ratio subject to
cardinality, box and budget constraints. The algorithm involves a projection
operator to deal with these three constraints simultaneously and we implicitly
control transaction costs thanks to a rebalancing constraint. We also introduce
a suitable exact penalty function to manage the turnover constraint. In
addition, we develop an ad hoc mutation operator to modify candidate exemplars
in the highest level of the swarm. The experimental results, using three
large-scale data sets, show that the inclusion of this procedure improves the
accuracy of the solutions. Then, a comparison with other variants of the LLSO
algorithm and two state-of-the-art swarm optimizers points out the outstanding
performance of the proposed solver in terms of exploration capabilities and
solution quality. Finally, we assess the profitability of the portfolio
allocation strategy in the last five years using an investible pool of 1119
constituents from the MSCI World Index.

Existing methods for isolating hard subpopulations and spurious correlations
in datasets often require human intervention. This can make these methods
labor-intensive and dataset-specific. To address these shortcomings, we present
a scalable method for automatically distilling a model's failure modes.
Specifically, we harness linear classifiers to identify consistent error
patterns, and, in turn, induce a natural representation of these failure modes
as directions within the feature space. We demonstrate that this framework
allows us to discover and automatically caption challenging subpopulations
within the training dataset, and intervene to improve the model's performance
on these subpopulations. Code available at
https://github.com/MadryLab/failure-directions

We investigate an auto-regressive formulation for the problem of smoothing
time-series by manipulating the inherent objective function of the traditional
moving mean smoothers. Not only the auto-regressive smoothers enforce a higher
degree of smoothing, they are just as efficient as the traditional moving means
and can be optimized accordingly with respect to the input dataset.
Interestingly, the auto-regressive models result in moving means with
exponentially tapered windows.

Automatic segmentation of the placenta in fetal ultrasound (US) is
challenging due to the (i) high diversity of placenta appearance, (ii) the
restricted quality in US resulting in highly variable reference annotations,
and (iii) the limited field-of-view of US prohibiting whole placenta assessment
at late gestation. In this work, we address these three challenges with a
multi-task learning approach that combines the classification of placental
location (e.g., anterior, posterior) and semantic placenta segmentation in a
single convolutional neural network. Through the classification task the model
can learn from larger and more diverse datasets while improving the accuracy of
the segmentation task in particular in limited training set conditions. With
this approach we investigate the variability in annotations from multiple
raters and show that our automatic segmentations (Dice of 0.86 for anterior and
0.83 for posterior placentas) achieve human-level performance as compared to
intra- and inter-observer variability. Lastly, our approach can deliver whole
placenta segmentation using a multi-view US acquisition pipeline consisting of
three stages: multi-probe image acquisition, image fusion and image
segmentation. This results in high quality segmentation of larger structures
such as the placenta in US with reduced image artifacts which are beyond the
field-of-view of single probes.

For many wireless communication applications, traffic pattern modeling of
radio signals combined with channel effects is much needed. While analytical
models are used to capture these phenomena, real world non-linear effects (e.g.
device responses, interferences, distortions, noise) and especially the
combination of such effects can be difficult to capture by these models. This
is simply due to their complexity and degrees of freedom which can be hard to
explicitize in compact expressions. In this paper, we propose a more model-free
approach to jointly approximate an end-to-end black-boxed wireless
communication scenario using software-defined radio platforms and optimize for
an efficient synthesis of subsequently similar 'pseudo-radio-signals'. More
precisely, we implement a generative adversarial network based solution that
automatically learns radio properties from recorded prototypes in specific
scenarios. This allows for a high degree of expressive freedom. Numerical
results show that the prototypes' traffic patterns jointly with channel effects
are learned without the introduction of assumptions about the scenario or the
simplification to a parametric model.

The large majority of human activities require collaborations within and
across formal or informal teams. Our understanding of how the collaborative
efforts spent by teams relate to their performance is still a matter of debate.
Teamwork results in a highly interconnected ecosystem of potentially
overlapping components where tasks are performed in interaction with team
members and across other teams. To tackle this problem, we propose a graph
neural network model designed to predict a team's performance while identifying
the drivers that determine such an outcome. In particular, the model is based
on three architectural channels: topological, centrality, and contextual which
capture different factors potentially shaping teams' success. We endow the
model with two attention mechanisms to boost model performance and allow
interpretability. A first mechanism allows pinpointing key members inside the
team. A second mechanism allows us to quantify the contributions of the three
driver effects in determining the outcome performance. We test model
performance on a wide range of domains outperforming most of the classical and
neural baselines considered. Moreover, we include synthetic datasets
specifically designed to validate how the model disentangles the intended
properties on which our model vastly outperforms baselines.

We present GO-Surf, a direct feature grid optimization method for accurate
and fast surface reconstruction from RGB-D sequences. We model the underlying
scene with a learned hierarchical feature voxel grid that encapsulates
multi-level geometric and appearance local information. Feature vectors are
directly optimized such that after being tri-linearly interpolated, decoded by
two shallow MLPs into signed distance and radiance values, and rendered via
surface volume rendering, the discrepancy between synthesized and observed
RGB/depth values is minimized. Our supervision signals -- RGB, depth and
approximate SDF -- can be obtained directly from input images without any need
for fusion or post-processing. We formulate a novel SDF gradient regularization
term that encourages surface smoothness and hole filling while maintaining high
frequency details. GO-Surf can optimize sequences of $1$-$2$K frames in
$15$-$45$ minutes, a speedup of $\times60$ over NeuralRGB-D, the most related
approach based on an MLP representation, while maintaining on par performance
on standard benchmarks. Project page: https://jingwenwang95.github.io/go_surf/

Privacy and interpretability are two of the important ingredients for
achieving trustworthy machine learning. We study the interplay of these two
aspects in graph machine learning through graph reconstruction attacks. The
goal of the adversary here is to reconstruct the graph structure of the
training data given access to model explanations. Based on the different kinds
of auxiliary information available to the adversary, we propose several graph
reconstruction attacks. We show that additional knowledge of post-hoc feature
explanations substantially increases the success rate of these attacks.
Further, we investigate in detail the differences between attack performance
with respect to three different classes of explanation methods for graph neural
networks: gradient-based, perturbation-based, and surrogate model-based
methods. While gradient-based explanations reveal the most in terms of the
graph structure, we find that these explanations do not always score high in
utility. For the other two classes of explanations, privacy leakage increases
with an increase in explanation utility. Finally, we propose a defense based on
a randomized response mechanism for releasing the explanations which
substantially reduces the attack success rate. Our anonymized code is

In contemporary popular music production, drum sound design is commonly
performed by cumbersome browsing and processing of pre-recorded samples in
sound libraries. One can also use specialized synthesis hardware, typically
controlled through low-level, musically meaningless parameters. Today, the
field of Deep Learning offers methods to control the synthesis process via
learned high-level features and allows generating a wide variety of sounds. In
this paper, we present DrumGAN VST, a plugin for synthesizing drum sounds using
a Generative Adversarial Network. DrumGAN VST operates on 44.1 kHz sample-rate
audio, offers independent and continuous instrument class controls, and
features an encoding neural network that maps sounds into the GAN's latent
space, enabling resynthesis and manipulation of pre-existing drum sounds. We
provide numerous sound examples and a demo of the proposed VST plugin.

Clinical trials are essential for drug development but are extremely
expensive and time-consuming to conduct. It is beneficial to study similar
historical trials when designing a clinical trial. However, lengthy trial
documents and lack of labeled data make trial similarity search difficult. We
propose a zero-shot clinical trial retrieval method, Trial2Vec, which learns
through self-supervision without annotating similar clinical trials.
Specifically, the meta-structure of trial documents (e.g., title, eligibility
criteria, target disease) along with clinical knowledge (e.g., UMLS knowledge
base https://www.nlm.nih.gov/research/umls/index.html) are leveraged to
automatically generate contrastive samples. Besides, Trial2Vec encodes trial
documents considering meta-structure thus producing compact embeddings
aggregating multi-aspect information from the whole document. We show that our
method yields medically interpretable embeddings by visualization and it gets a
15% average improvement over the best baselines on precision/recall for trial
retrieval, which is evaluated on our labeled 1600 trial pairs. In addition, we
prove the pre-trained embeddings benefit the downstream trial outcome
prediction task over 240k trials. Software ias available at
https://github.com/RyanWangZf/Trial2Vec.

Deep learning has been widely used in medical image segmentation and other
aspects. However, the performance of existing medical image segmentation models
has been limited by the challenge of obtaining sufficient number of
high-quality data with the high cost of data annotation. To overcome the
limitation, we propose a new vision-language medical image segmentation model
LViT (Language meets Vision Transformer). In our model, medical text annotation
is introduced to compensate for the quality deficiency in image data. In
addition, the text information can guide the generation of pseudo labels to a
certain extent and further guarantee the quality of pseudo labels in
semi-supervised learning. We also propose the Exponential Pseudo label
Iteration mechanism (EPI) to help extend the semi-supervised version of LViT
and the Pixel-Level Attention Module (PLAM) to preserve local features of
images. In our model, LV (Language-Vision) loss is designed to supervise the
training of unlabeled images using text information directly. To validate the
performance of LViT, we construct multimodal medical segmentation datasets
(image + text) containing pathological images, X-rays,etc. Experimental results
show that our proposed LViT has better segmentation performance in both fully
and semi-supervised conditions. Code and datasets are available at
https://github.com/HUANGLIZI/LViT.

Perceptual video quality assessment (VQA) is an integral component of many
streaming and video sharing platforms. Here we consider the problem of learning
perceptually relevant video quality representations in a self-supervised
manner. Distortion type identification and degradation level determination is
employed as an auxiliary task to train a deep learning model containing a deep
Convolutional Neural Network (CNN) that extracts spatial features, as well as a
recurrent unit that captures temporal information. The model is trained using a
contrastive loss and we therefore refer to this training framework and
resulting model as CONtrastive VIdeo Quality EstimaTor (CONVIQT). During
testing, the weights of the trained model are frozen, and a linear regressor
maps the learned features to quality scores in a no-reference (NR) setting. We
conduct comprehensive evaluations of the proposed model on multiple VQA
databases by analyzing the correlations between model predictions and
ground-truth quality ratings, and achieve competitive performance when compared
to state-of-the-art NR-VQA models, even though it is not trained on those
databases. Our ablation experiments demonstrate that the learned
representations are highly robust and generalize well across synthetic and
realistic distortions. Our results indicate that compelling representations
with perceptual bearing can be obtained using self-supervised learning. The
implementations used in this work have been made available at
https://github.com/pavancm/CONVIQT.

Recent progress in \emph{Geometric Deep Learning} (GDL) has shown its
potential to provide powerful data-driven models. This gives momentum to
explore new methods for learning physical systems governed by \emph{Partial
Differential Equations} (PDEs) from Graph-Mesh data. However, despite the
efforts and recent achievements, several research directions remain unexplored
and progress is still far from satisfying the physical requirements of
real-world phenomena. One of the major impediments is the absence of
benchmarking datasets and common physics evaluation protocols. In this paper,
we propose a 2-D graph-mesh dataset to study the airflow over airfoils at high
Reynolds regime (from $10^6$ and beyond). We also introduce metrics on the
stress forces over the airfoil in order to evaluate GDL models on important
physical quantities. Moreover, we provide extensive GDL baselines.

We formalize and study the natural approach of designing convex surrogate
loss functions via embeddings, for problems such as classification, ranking, or
structured prediction. In this approach, one embeds each of the finitely many
predictions (e.g. rankings) as a point in $R^d$, assigns the original loss
values to these points, and "convexifies" the loss in some way to obtain a
surrogate. We establish a strong connection between this approach and
polyhedral (piecewise-linear convex) surrogate losses: every discrete loss is
embedded by some polyhedral loss, and every polyhedral loss embeds some
discrete loss. Moreover, an embedding gives rise to a consistent link function
as well as linear surrogate regret bounds. Our results are constructive, as we
illustrate with several examples. In particular, our framework gives succinct
proofs of consistency or inconsistency for various polyhedral surrogates in the
literature, and for inconsistent surrogates, it further reveals the discrete
losses for which these surrogates are consistent. We go on to show additional
structure of embeddings, such as the equivalence of embedding and matching
Bayes risks, and the equivalence of various notions of non-redudancy. Using
these results, we establish that indirect elicitation, a necessary condition
for consistency, is also sufficient when working with polyhedral surrogates.

Contrastive learning (CL)-based self-supervised learning models learn visual
representations in a pairwise manner. Although the prevailing CL model has
achieved great progress, in this paper, we uncover an ever-overlooked
phenomenon: When the CL model is trained with full images, the performance
tested in full images is better than that in foreground areas; when the CL
model is trained with foreground areas, the performance tested in full images
is worse than that in foreground areas. This observation reveals that
backgrounds in images may interfere with the model learning semantic
information and their influence has not been fully eliminated. To tackle this
issue, we build a Structural Causal Model (SCM) to model the background as a
confounder. We propose a backdoor adjustment-based regularization method,
namely Interventional Contrastive Learning with Meta Semantic Regularizer
(ICL-MSR), to perform causal intervention towards the proposed SCM. ICL-MSR can
be incorporated into any existing CL methods to alleviate background
distractions from representation learning. Theoretically, we prove that ICL-MSR
achieves a tighter error bound. Empirically, our experiments on multiple
benchmark datasets demonstrate that ICL-MSR is able to improve the performances
of different state-of-the-art CL methods.

Recurrent State-space models (RSSMs) are highly expressive models for
learning patterns in time series data and system identification. However, these
models assume that the dynamics are fixed and unchanging, which is rarely the
case in real-world scenarios. Many control applications often exhibit tasks
with similar but not identical dynamics which can be modeled as a latent
variable. We introduce the Hidden Parameter Recurrent State Space Models
(HiP-RSSMs), a framework that parametrizes a family of related dynamical
systems with a low-dimensional set of latent factors. We present a simple and
effective way of learning and performing inference over this Gaussian graphical
model that avoids approximations like variational inference. We show that
HiP-RSSMs outperforms RSSMs and competing multi-task models on several
challenging robotic benchmarks both on real-world systems and simulations.

Representing physical signals at different scales is among the most
challenging problems in engineering. Several multi-scale modeling tools have
been developed to describe physical systems governed by \emph{Partial
Differential Equations} (PDEs). These tools are at the crossroad of principled
physical models and numerical schema. Recently, data-driven models have been
introduced to speed-up the approximation of PDE solutions compared to numerical
solvers. Among these recent data-driven methods, neural integral operators are
a class that learn a mapping between function spaces. These functions are
discretized on graphs (meshes) which are appropriate for modeling interactions
in physical phenomena. In this work, we study three multi-resolution schema
with integral kernel operators that can be approximated with \emph{Message
Passing Graph Neural Networks} (MPGNNs). To validate our study, we make
extensive MPGNNs experiments with well-chosen metrics considering steady and
unsteady PDEs.

Computer-aided methods have shown added value for diagnosing and predicting
brain disorders and can thus support decision making in clinical care and
treatment planning. This chapter will provide insight into the type of methods,
their working, their input data - such as cognitive tests, imaging and genetic
data - and the types of output they provide. We will focus on specific use
cases for diagnosis, i.e. estimating the current 'condition' of the patient,
such as early detection and diagnosis of dementia, differential diagnosis of
brain tumours, and decision making in stroke. Regarding prediction, i.e.
estimation of the future 'condition' of the patient, we will zoom in on use
cases such as predicting the disease course in multiple sclerosis and
predicting patient outcomes after treatment in brain cancer. Furthermore, based
on these use cases, we will assess the current state-of-the-art methodology and
highlight current efforts on benchmarking of these methods and the importance
of open science therein. Finally, we assess the current clinical impact of
computer-aided methods and discuss the required next steps to increase clinical
impact.

We employ a machine learning-enabled approach to quantum state engineering
based on evolutionary algorithms. In particular, we focus on superconducting
platforms and consider a network of qubits -- encoded in the states of
artificial atoms with no direct coupling -- interacting via a common
single-mode driven microwave resonator. The qubit-resonator couplings are
assumed to be in the resonant regime and tunable in time. A genetic algorithm
is used in order to find the functional time-dependence of the couplings that
optimise the fidelity between the evolved state and a variety of targets,
including three-qubit GHZ and Dicke states and four-qubit graph states. We
observe high quantum fidelities (above 0.96 in the worst case setting of a
system of effective dimension 96) and resilience to noise, despite the
algorithm being trained in the ideal noise-free setting. These results show
that the genetic algorithms represent an effective approach to control quantum
systems of large dimensions.

Fetal growth assessment from ultrasound is based on a few biometric
measurements that are performed manually and assessed relative to the expected
gestational age. Reliable biometry estimation depends on the precise detection
of landmarks in standard ultrasound planes. Manual annotation can be
time-consuming and operator dependent task, and may results in high
measurements variability. Existing methods for automatic fetal biometry rely on
initial automatic fetal structure segmentation followed by geometric landmark
detection. However, segmentation annotations are time-consuming and may be
inaccurate, and landmark detection requires developing measurement-specific
geometric methods. This paper describes BiometryNet, an end-to-end landmark
regression framework for fetal biometry estimation that overcomes these
limitations. It includes a novel Dynamic Orientation Determination (DOD) method
for enforcing measurement-specific orientation consistency during network
training. DOD reduces variabilities in network training, increases landmark
localization accuracy, thus yields accurate and robust biometric measurements.
To validate our method, we assembled a dataset of 3,398 ultrasound images from
1,829 subjects acquired in three clinical sites with seven different ultrasound
devices. Comparison and cross-validation of three different biometric
measurements on two independent datasets shows that BiometryNet is robust and
yields accurate measurements whose errors are lower than the clinically
permissible errors, outperforming other existing automated biometry estimation
methods. Code is available at
https://github.com/netanellavisdris/fetalbiometry.

Signature-based techniques give mathematical insight into the interactions
between complex streams of evolving data. These insights can be quite naturally
translated into numerical approaches to understanding streamed data, and
perhaps because of their mathematical precision, have proved useful in
analysing streamed data in situations where the data is irregular, and not
stationary, and the dimension of the data and the sample sizes are both
moderate.
  Understanding streamed multi-modal data is exponential: a word in $n$ letters
from an alphabet of size $d$ can be any one of $d^n$ messages. Signatures
remove the exponential amount of noise that arises from sampling irregularity,
but an exponential amount of information still remain. This survey aims to stay
in the domain where that exponential scaling can be managed directly.
Scalability issues are an important challenge in many problems but would
require another survey article and further ideas. This survey describes a range
of contexts where the data sets are small enough to remove the possibility of
massive machine learning, and the existence of small sets of context free and
principled features can be used effectively.
  The mathematical nature of the tools can make their use intimidating to
non-mathematicians. The examples presented in this article are intended to
bridge this communication gap and provide tractable working examples drawn from
the machine learning context. Notebooks are available online for several of
these examples. This survey builds on the earlier paper of Ilya Chevryev and
Andrey Kormilitzin which had broadly similar aims at an earlier point in the
development of this machinery. This article illustrates how the theoretical
insights offered by signatures are simply realised in the analysis of
application data in a way that is largely agnostic to the data type.

Attenuation and scatter correction (AC) is crucial for quantitative Positron
Emission Tomography (PET) imaging. Recently, direct application of AC in the
image domain using deep learning approaches has been proposed for the hybrid
PET/MR and dedicated PET systems that lack accompanying transmission or
anatomical imaging. This study set out to investigate deep learning-based AC in
the image domain using different input settings.

Since the appearance of Covid-19 in late 2019, Covid-19 has become an active
research topic for the artificial intelligence (AI) community. One of the most
interesting AI topics is Covid-19 analysis of medical imaging. CT-scan imaging
is the most informative tool about this disease. This work is part of the 2nd
COV19D competition, where two challenges are set: Covid-19 Detection and
Covid-19 Severity Detection from the CT-scans. For Covid-19 detection from
CT-scans, we proposed an ensemble of 2D Convolution blocks with Densenet-161
models. Here, each 2D convolutional block with Densenet-161 architecture is
trained separately and in testing phase, the ensemble model is based on the
average of their probabilities. On the other hand, we proposed an ensemble of
Convolutional Layers with Inception models for Covid-19 severity detection. In
addition to the Convolutional Layers, three Inception variants were used,
namely Inception-v3, Inception-v4 and Inception-Resnet. Our proposed approaches
outperformed the baseline approach in the validation data of the 2nd COV19D
competition by 11% and 16% for Covid-19 detection and Covid-19 severity
detection, respectively.

Recent hype surrounding the increasing sophistication of language processing
models has renewed optimism regarding machines achieving a human-like command
of natural language. Research in the area of natural language understanding
(NLU) in artificial intelligence claims to have been making great strides in
this area, however, the lack of conceptual clarity/consistency in how
'understanding' is used in this and other disciplines makes it difficult to
discern how close we actually are. In this interdisciplinary research thesis, I
integrate insights from cognitive science/psychology, philosophy of mind, and
cognitive linguistics, and evaluate it against a critical review of current
approaches in NLU to explore the basic requirements--and remaining
challenges--for developing artificially intelligent systems with human-like
capacities for language use and comprehension.

Spin-orbit torque characterizations on magnetic heterostructures with
perpendicular anisotropy are demonstrated on a projected vector field magnet
via hysteresis loop shift measurement and harmonic Hall measurement with planar
Hall correction. Accurate magnetic field calibration of the vector magnet is
realized with the help of deep learning models, which are able to capture the
nonlinear behavior between the generated magnetic field and the currents
applied to the magnet. The trained models can successfully predict the applied
current combinations under the circumstances of magnetic field scans, angle
scans, and hysteresis loop shift measurements. The validity of the models is
further verified, complemented by the comparison of the spin-orbit torque
characterization results obtained from the deep-learning-trained vector magnet
system with those obtained from a conventional setup comprised of two separated
electromagnets. The damping-like spin-orbit torque (DL-SOT) efficiencies
(|$\xi_{DL}$|) extracted from the vector magnet and the traditional measurement
configuration are consistent, where |$\xi_{DL}$| $\approx$ 0.22 for amorphous W
and |$\xi_{DL}$| $\approx$ 0.02 for $\alpha$-W. Our work provides an advanced
method to meticulously control a vector magnet and to conveniently perform
various spin-orbit torque characterizations.

When observations are truncated, we are limited to an incomplete picture of
our dataset. Recent methods deal with truncated density estimation problems by
turning to score matching, where the access to the intractable normalising
constant is not required. We present a novel extension to truncated score
matching for a Riemannian manifold. Applications are presented for the von
Mises-Fisher and Kent distributions on a two dimensional sphere in $\R^3$, as
well as a real-world application of extreme storm observations in the USA. In
simulated data experiments, our score matching estimator is able to approximate
the true parameter values with a low estimation error and shows improvements
over a maximum likelihood estimator.

We propose a novel framework to solve risk-sensitive reinforcement learning
(RL) problems where the agent optimises time-consistent dynamic spectral risk
measures. Based on the notion of conditional elicitability, our methodology
constructs (strictly consistent) scoring functions that are used as penalizers
in the estimation procedure. Our contribution is threefold: we (i) devise an
efficient approach to estimate a class of dynamic spectral risk measures with
deep neural networks, (ii) prove that these dynamic spectral risk measures may
be approximated to any arbitrary accuracy using deep neural networks, and (iii)
develop a risk-sensitive actor-critic algorithm that uses full episodes and
does not require any additional nested transitions. We compare our conceptually
improved reinforcement learning algorithm with the nested simulation approach
and illustrate its performance in two settings: statistical arbitrage and
portfolio allocation on both simulated and real data.

Physics simulators have shown great promise for conveniently learning
reinforcement learning policies in safe, unconstrained environments. However,
transferring the acquired knowledge to the real world can be challenging due to
the reality gap. To this end, several methods have been recently proposed to
automatically tune simulator parameters with posterior distributions given real
data, for use with domain randomization at training time. These approaches have
been shown to work for various robotic tasks under different settings and
assumptions. Nevertheless, existing literature lacks a thorough comparison of
existing adaptive domain randomization methods with respect to transfer
performance and real-data efficiency. In this work, we present an open
benchmark for both offline and online methods (SimOpt, BayRn, DROID, DROPO), to
shed light on which are most suitable for each setting and task at hand. We
found that online methods are limited by the quality of the currently learned
policy for the next iteration, while offline methods may sometimes fail when
replaying trajectories in simulation with open-loop commands. The code used
will be released at https://github.com/gabrieletiboni/adr-benchmark.

This paper describes the THUEE team's speech recognition system for the IARPA
Open Automatic Speech Recognition Challenge (OpenASR21), with further
experiment explorations. We achieve outstanding results under both the
Constrained and Constrained-plus training conditions. For the Constrained
training condition, we construct our basic ASR system based on the standard
hybrid architecture. To alleviate the Out-Of-Vocabulary (OOV) problem, we
extend the pronunciation lexicon using Grapheme-to-Phoneme (G2P) techniques for
both OOV and potential new words. Standard acoustic model structures such as
CNN-TDNN-F and CNN-TDNN-F-A are adopted. In addition, multiple data
augmentation techniques are applied. For the Constrained-plus training
condition, we use the self-supervised learning framework wav2vec2.0. We
experiment with various fine-tuning techniques with the Connectionist Temporal
Classification (CTC) criterion on top of the publicly available pre-trained
model XLSR-53. We find that the frontend feature extractor plays an important
role when applying the wav2vec2.0 pre-trained model to the encoder-decoder
based CTC/Attention ASR architecture. Extra improvements can be achieved by
using the CTC model finetuned in the target language as the frontend feature
extractor.

Pruning effectively compresses overparameterized models. Despite the success
of pruning methods for discriminative models, applying them for generative
models has been relatively rarely approached. This study conducts structured
pruning on U-Net generators of conditional GANs. A per-layer sensitivity
analysis confirms that many unnecessary filters exist in the innermost layers
near the bottleneck and can be substantially pruned. Based on this observation,
we prune these filters from multiple inner layers or suggest alternative
architectures by completely eliminating the layers. We evaluate our approach
with Pix2Pix for image-to-image translation and Wav2Lip for speech-driven
talking face generation. Our method outperforms global pruning baselines,
demonstrating the importance of properly considering where to prune for U-Net
generators.

Diadochokinetic speech tasks (DDK), in which participants repeatedly produce
syllables, are commonly used as part of the assessment of speech motor
impairments. These studies rely on manual analyses that are time-intensive,
subjective, and provide only a coarse-grained picture of speech. This paper
presents two deep neural network models that automatically segment consonants
and vowels from unannotated, untranscribed speech. Both models work on the raw
waveform and use convolutional layers for feature extraction. The first model
is based on an LSTM classifier followed by fully connected layers, while the
second model adds more convolutional layers followed by fully connected layers.
These segmentations predicted by the models are used to obtain measures of
speech rate and sound duration. Results on a young healthy individuals dataset
show that our LSTM model outperforms the current state-of-the-art systems and
performs comparably to trained human annotators. Moreover, the LSTM model also
presents comparable results to trained human annotators when evaluated on
unseen older individuals with Parkinson's Disease dataset.

This is an exciting era for exo-planetary exploration. In the past two
decades, astronomers have harvested data from all the observatories at their
disposal. Those collective efforts allowed us to have a glimpse at the
convoluted process of planetary formation and evolution and its connections to
atmospheric compositions, but nevertheless remained limited by the low quality
and scarcity of exo-atmospheric data. Now, the recently launched JWST, and
other upcoming space missions such as Ariel, Twinkle and ELTs are set to anew
the landscape, bringing fresh insights to these remote worlds. However, with
new opportunities come new challenges. The field of exoplanet atmospheres is
already struggling with the incoming volume and quality of data, and machine
learning (ML) techniques lands itself as a promising alternative. Developing
techniques of this kind is an inter-disciplinary task, one that requires domain
knowledge of the field, access to relevant tools and expert insights on the
capability and limitations of current ML models. These stringent requirements
have so far limited the developments of ML in the field to a few isolated
initiatives. As part of the data product of the NeurIPS 2022 Data challenge, we
would like to present the Ariel Big Challenge Database (ABC Database), a
carefully designed, organised and publicly available database. With 105,887
forward models, 26,109 complementary posterior distributions and an
easy-to-understand documentation, this represents an unprecedented effort to
invite cross-disciplinary experts to the study of the inverse problem in the
context of exoplanetary studies.

We report the discovery of a new short period pulsating variable in the field
of exoplanet host star XO-2. Variable has been identified while it was being
used as a comparison star. In order to verify the variability of the candidate,
a follow-up program was carried out. Period analysis of multi-band light curves
revealed a very prominent and consistent pulsation periodicity of $P\sim0.95$
hours. Given the variability period, amplitude and the color index, the object
is most likely a \emph{Delta Scuti} type variable. Absolute magnitude ($M_{v}$)
and the color index $(B-V)_{0}$ of the star determined as $2.76$ and $0.22$,
respectively. This $(B-V)_{0}$ of the star corresponds to A7 spectral type with
an approximate effective temperature of 7725 K. Machine-learning analysis of
the time-series data also revealed that the object is of variable type DSCT
with a probability of 78\%.

The minimization of a data-fidelity term and an additive regularization
functional gives rise to a powerful framework for supervised learning. In this
paper, we present a unifying regularization functional that depends on an
operator and on a generic Radon-domain norm. We establish the existence of a
minimizer and give the parametric form of the solution(s) under very mild
assumptions. When the norm is Hilbertian, the proposed formulation yields a
solution that involves radial-basis functions and is compatible with the
classical methods of machine learning. By contrast, for the total-variation
norm, the solution takes the form of a two-layer neural network with an
activation function that is determined by the regularization operator. In
particular, we retrieve the popular ReLU networks by letting the operator be
the Laplacian. We also characterize the solution for the intermediate
regularization norms $\|\cdot\|=\|\cdot\|_{L_p}$ with $p\in(1,2]$. Our
framework offers guarantees of universal approximation for a broad family of
regularization operators or, equivalently, for a wide variety of shallow neural
networks, including the cases (such as ReLU) where the activation function is
increasing polynomially. It also explains the favorable role of bias and skip
connections in neural architectures.

Causal inference methods for treatment effect estimation usually assume
independent experimental units. However, this assumption is often questionable
because experimental units may interact. We develop augmented inverse
probability weighting (AIPW) for estimation and inference of causal treatment
effects on dependent observational data. Our framework covers very general
cases of spillover effects induced by units interacting in networks. We use
plugin machine learning to estimate infinite-dimensional nuisance components
leading to a consistent treatment effect estimator that converges at the
parametric rate and asymptotically follows a Gaussian distribution. We apply
our AIPW method to the Swiss StudentLife Study data to investigate the effect
of hours spent studying on exam performance accounting for the students' social
network.

Numerical reasoning is required when solving most problems in our life, but
it has been neglected in previous artificial intelligence researches. FinQA
challenge has been organized to strengthen the study on numerical reasoning
where the participants are asked to predict the numerical reasoning program to
solve financial question. The result of FinQA will be evaluated by both
execution accuracy and program accuracy. In this paper, we present our approach
to tackle the task objective by developing models with different specialized
capabilities and fusing their strength. Overall, our approach achieves the 1st
place in FinQA challenge, with 71.93% execution accuracy and 67.03% program
accuracy.

Reliable scene understanding is indispensable for modern autonomous systems.
Current learning-based methods typically try to maximize their performance
based on segmentation metrics that only consider the quality of the
segmentation. However, for the safe operation of a system in the real world it
is crucial to consider the uncertainty in the prediction as well. In this work,
we introduce the novel task of uncertainty-aware panoptic segmentation, which
aims to predict per-pixel semantic and instance segmentations, together with
per-pixel uncertainty estimates. We define two novel metrics to facilitate its
quantitative analysis, the uncertainty-aware Panoptic Quality (uPQ) and the
panoptic Expected Calibration Error (pECE). We further propose the novel
top-down Evidential Panoptic Segmentation Network (EvPSNet) to solve this task.
Our architecture employs a simple yet effective probabilistic fusion module
that leverages the predicted uncertainties. Additionally, we propose a new
Lov\'asz evidential loss function to optimize the IoU for the segmentation
utilizing the probabilities provided by deep evidential learning. Furthermore,
we provide several strong baselines combining state-of-the-art panoptic
segmentation networks with sampling-free uncertainty estimation techniques.
Extensive evaluations show that our EvPSNet achieves the new state-of-the-art
for the standard Panoptic Quality (PQ), as well as for our uncertainty-aware
panoptic metrics.

The attention mechanisms of transformers effectively extract pertinent
information from the input sequence. However, the quadratic complexity of
self-attention w.r.t the sequence length incurs heavy computational and memory
burdens, especially for tasks with long sequences. Existing accelerators face
performance degradation in these tasks. To this end, we propose SALO to enable
hybrid sparse attention mechanisms for long sequences. SALO contains a data
scheduler to map hybrid sparse attention patterns onto hardware and a spatial
accelerator to perform the efficient attention computation. We show that SALO
achieves 17.66x and 89.33x speedup on average compared to GPU and CPU
implementations, respectively, on typical workloads, i.e., Longformer and ViL.

In this paper, we propose a probabilistic model with automatic relevance
determination (ARD) for learning interpolative decomposition (ID), which is
commonly used for low-rank approximation, feature selection, and identifying
associated with each data dimension. Prior densities with support on the
specified subspace are used to address the constraint for the magnitude of the
factored component of the observed matrix. Bayesian inference procedure based
on Gibbs sampling is employed. We evaluate the model on a variety of real-world
datasets including CCLE $EC50$, CCLE $IC50$, Gene Body Methylation, and
Promoter Methylation datasets with different sizes, and dimensions, and show
that the proposed Bayesian ID algorithms with automatic relevance determination
lead to smaller reconstructive errors even compared to vanilla Bayesian ID
algorithms with fixed latent dimension set to matrix rank.

Rights provisioned within data protection regulations, permit patients to
request that knowledge about their information be eliminated by data holders.
With the advent of AI learned on data, one can imagine that such rights can
extent to requests for forgetting knowledge of patient's data within AI models.
However, forgetting patients' imaging data from AI models, is still an
under-explored problem. In this paper, we study the influence of patient data
on model performance and formulate two hypotheses for a patient's data: either
they are common and similar to other patients or form edge cases, i.e. unique
and rare cases. We show that it is not possible to easily forget patient data.
We propose a targeted forgetting approach to perform patient-wise forgetting.
Extensive experiments on the benchmark Automated Cardiac Diagnosis Challenge
dataset showcase the improved performance of the proposed targeted forgetting
approach as opposed to a state-of-the-art method.

Identification of cyber threats is one of the essential tasks for security
teams. Currently, cyber threats can be identified using knowledge organized
into various formats, enumerations, and knowledge bases. This paper studies the
current challenges of identifying vulnerabilities and threats in cyberspace
using enumerations and data about assets. Although enumerations are used in
practice, we point out several issues that still decrease the quality of
vulnerability and threat identification. Since vulnerability identification
methods are based on network monitoring and agents, the issues are related to
the asset discovery, the precision of vulnerability discovery, and the amount
of data. On the other hand, threat identification utilizes graph-based,
nature-language, machine-learning, and ontological approaches. The current
trend is to propose methods that utilize tactics, techniques, and procedures
instead of low-level indicators of compromise to make cyber threat
identification more mature. Cooperation between standards from threat,
vulnerability, and asset management is also an unresolved issue confirmed by
analyzing relationships between public enumerations and knowledge bases. Last,
we studied the usability of techniques from the MITRE ATT&CK knowledge base for
threat modeling using network monitoring to capture data. Although network
traffic is not the most used data source, it allows the modeling of almost all
tactics from the MITRE ATT&CK.

Training medical image segmentation models usually requires a large amount of
labeled data. By contrast, humans can quickly learn to accurately recognise
anatomy of interest from medical (e.g. MRI and CT) images with some limited
guidance. Such recognition ability can easily generalise to new images from
different clinical centres. This rapid and generalisable learning ability is
mostly due to the compositional structure of image patterns in the human brain,
which is less incorporated in medical image segmentation. In this paper, we
model the compositional components (i.e. patterns) of human anatomy as
learnable von-Mises-Fisher (vMF) kernels, which are robust to images collected
from different domains (e.g. clinical centres). The image features can be
decomposed to (or composed by) the components with the composing operations,
i.e. the vMF likelihoods. The vMF likelihoods tell how likely each anatomical
part is at each position of the image. Hence, the segmentation mask can be
predicted based on the vMF likelihoods. Moreover, with a reconstruction module,
unlabeled data can also be used to learn the vMF kernels and likelihoods by
recombining them to reconstruct the input image. Extensive experiments show
that the proposed vMFNet achieves improved generalisation performance on two
benchmarks, especially when annotations are limited. Code is publicly available
at: https://github.com/vios-s/vMFNet.

By inferring latent groups in the training data, recent works introduce
invariant learning to the case where environment annotations are unavailable.
Typically, learning group invariance under a majority/minority split is
empirically shown to be effective in improving out-of-distribution
generalization on many datasets. However, theoretical guarantee for these
methods on learning invariant mechanisms is lacking. In this paper, we reveal
the insufficiency of existing group invariant learning methods in preventing
classifiers from depending on spurious correlations in the training set.
Specifically, we propose two criteria on judging such sufficiency.
Theoretically and empirically, we show that existing methods can violate both
criteria and thus fail in generalizing to spurious correlation shifts.
Motivated by this, we design a new group invariant learning method, which
constructs groups with statistical independence tests, and reweights samples by
group label proportion to meet the criteria. Experiments on both synthetic and
real data demonstrate that the new method significantly outperforms existing
group invariant learning methods in generalizing to spurious correlation
shifts.

This work investigates the compatibility between label smoothing (LS) and
knowledge distillation (KD). Contemporary findings addressing this thesis
statement take dichotomous standpoints: Muller et al. (2019) and Shen et al.
(2021b). Critically, there is no effort to understand and resolve these
contradictory findings, leaving the primal question -- to smooth or not to
smooth a teacher network? -- unanswered. The main contributions of our work are
the discovery, analysis and validation of systematic diffusion as the missing
concept which is instrumental in understanding and resolving these
contradictory findings. This systematic diffusion essentially curtails the
benefits of distilling from an LS-trained teacher, thereby rendering KD at
increased temperatures ineffective. Our discovery is comprehensively supported
by large-scale experiments, analyses and case studies including image
classification, neural machine translation and compact student distillation
tasks spanning across multiple datasets and teacher-student architectures.
Based on our analysis, we suggest practitioners to use an LS-trained teacher
with a low-temperature transfer to achieve high performance students. Code and
models are available at
https://keshik6.github.io/revisiting-ls-kd-compatibility/

Case-based Reasoning (CBR) on high-dimensional and heterogeneous data is a
trending yet challenging and computationally expensive task in the real world.
A promising approach is to obtain low-dimensional hash codes representing cases
and perform a similarity retrieval of cases in Hamming space. However, previous
methods based on data-independent hashing rely on random projections or manual
construction, inapplicable to address specific data issues (e.g.,
high-dimensionality and heterogeneity) due to their insensitivity to data
characteristics. To address these issues, this work introduces a novel deep
hashing network to learn similarity-preserving compact hash codes for efficient
case retrieval and proposes a deep-hashing-enabled CBR model HeCBR.
Specifically, we introduce position embedding to represent heterogeneous
features and utilize a multilinear interaction layer to obtain case embeddings,
which effectively filtrates zero-valued features to tackle high-dimensionality
and sparsity and captures inter-feature couplings. Then, we feed the case
embeddings into fully-connected layers, and subsequently a hash layer generates
hash codes with a quantization regularizer to control the quantization loss
during relaxation. To cater to incremental learning of CBR, we further propose
an adaptive learning strategy to update the hash function. Extensive
experiments on public datasets show that HeCBR greatly reduces storage and
significantly accelerates case retrieval. HeCBR achieves desirable performance
compared with the state-of-the-art CBR methods and performs significantly
better than hashing-based CBR methods in classification.

The Pandora Software Development Kit and algorithm libraries provide
pattern-recognition logic essential to the reconstruction of particle
interactions in liquid argon time projection chamber detectors. Pandora is the
primary event reconstruction software used at ProtoDUNE-SP, a prototype for the
Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at
CERN, is exposed to a charged-particle test beam. This paper gives an overview
of the Pandora reconstruction algorithms and how they have been tailored for
use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam
background particles, the simulated reconstruction and identification
efficiency for triggered test-beam particles is above 80% for the majority of
particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$
charged pions and protons are correctly reconstructed and identified with
efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies
measured for test-beam data are shown to be within 5% of those predicted by the

The time series captured by a single scalp electrode (plus the reference
electrode) of refractory epileptic patients is used to forecast seizures
susceptibility. The time series is preprocessed, segmented, and each segment
transformed into an image, using three different known methods: Recurrence
Plot, Gramian Angular Field, Markov Transition Field. The likelihood of the
occurrence of a seizure in a future predefined time window is computed by
averaging the output of the softmax layer of a CNN, differently from the usual
consideration of the output of the classification layer. By thresholding this
likelihood, seizure forecasting has better performance. Interestingly, for
almost every patient, the best threshold was different from 50%. The results
show that this technique can predict with good results for some seizures and
patients. However, more tests, namely more patients and more seizures, are
needed to better understand the real potential of this technique.

The emergence of new organizational forms--such as virtual teams--has brought
forward some challenges for teams. One of the most relevant challenges is
coordinating the decisions of team members who work from different time zones.
Intuition suggests that task performance should improve if the team members'
decisions are coordinated. However, previous research suggests that the effect
of coordination on task performance is ambiguous. Specifically, the effect of
coordination on task performance depends on aspects such as the team members'
learning and the changes in team composition over time. This paper aims to
understand how individual learning and team composition moderate the
relationship between coordination and task performance. We implement an
agent-based modeling approach based on the NK-framework to fulfill our research
objective. Our results suggest that both factors have moderating effects.
Specifically, we find that excessively increasing individual learning is
harmful for the task performance of fully autonomous teams, but less
detrimental for teams that coordinate their decisions. In addition, we find
that teams that coordinate their decisions benefit from changing their
composition in the short-term, but fully autonomous teams do not. In
conclusion, teams that coordinate their decisions benefit more from individual
learning and dynamic composition than teams that do not coordinate.
Nevertheless, we should note that the existence of moderating effects does not
imply that coordination improves task performance. Whether coordination
improves task performance depends on the interdependencies between the team
members' decisions.

A kernel-based quantum classifier is the most interesting and powerful
quantum machine learning technique for hyperlinear classification of complex
data, which can be easily realized in shallow-depth quantum circuits such as a
SWAP test classifier. A variational quantum approximate support vector machine
(VQASVM) can be realized inherently and explicitly on these circuits by
introduction of a variational scheme to map the quadratic optimization problem
of the support vector machine theory to a quantum-classical variational
optimization problem. Probability weight modulation in index qubits of a
classifier can designate support vectors among training vectors, which can be
achieved with a parameterized quantum circuit (PQC). The classical parameters
of PQC is then transferred to many copies of other decision inference circuits.
Our VQASVM algorithm is experimented with toy example data sets on cloud-based
quantum machines for feasibility evaluation, and numerically investigated to
evaluate its performance on a standard iris flower and MNIST data set. The
empirical run-time complexity of VQASVM is estimated to be sub-quadratic on the
training data set size, while that of the classical solver is quadratic.

We present a statistical model for German medical natural language processing
trained for named entity recognition (NER) as an open, publicly available
model. The work serves as a refined successor to our first GERNERMED model
which is substantially outperformed by our work. We demonstrate the
effectiveness of combining multiple techniques in order to achieve strong
results in entity recognition performance by the means of transfer-learning on
pretrained deep language models (LM), word-alignment and neural machine
translation. Due to the sparse situation on open, public medical entity
recognition models for German texts, this work offers benefits to the German
research community on medical NLP as a baseline model. Since our model is based
on public English data, its weights are provided without legal restrictions on
usage and distribution. The sample code and the statistical model is available
at: https://github.com/frankkramer-lab/GERNERMED-pp

We show that the effectiveness of the well celebrated Mixup [Zhang et al.,
2018] can be further improved if instead of using it as the sole learning
objective, it is utilized as an additional regularizer to the standard
cross-entropy loss. This simple change not only provides much improved accuracy
but also significantly improves the quality of the predictive uncertainty
estimation of Mixup in most cases under various forms of covariate shifts and
out-of-distribution detection experiments. In fact, we observe that Mixup
yields much degraded performance on detecting out-of-distribution samples
possibly, as we show empirically, because of its tendency to learn models that
exhibit high-entropy throughout; making it difficult to differentiate
in-distribution samples from out-distribution ones. To show the efficacy of our
approach (RegMixup), we provide thorough analyses and experiments on vision
datasets (ImageNet & CIFAR-10/100) and compare it with a suite of recent
approaches for reliable uncertainty estimation.

An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is
proposed to predict the NOx emission concentration based on the combination of
mutual information algorithm (MI), AE, and ELM. First, the importance of
practical variables is computed by the MI algorithm, and the mechanism is
analyzed to determine the variables related to the NOx emission concentration.
Then, the time delay correlations between the selected variables and NOx
emission concentration are further analyzed to reconstruct the modeling data.
Subsequently, the AE is applied to extract hidden features within the input
variables. Finally, an ELM algorithm establishes the relationship between the
NOx emission concentration and deep features. The experimental results on
practical data indicate that the proposed model shows promising performance
compared to state-of-art models.

Widely observed neural scaling laws, in which error falls off as a power of
the training set size, model size, or both, have driven substantial performance
improvements in deep learning. However, these improvements through scaling
alone require considerable costs in compute and energy. Here we focus on the
scaling of error with dataset size and show how in theory we can break beyond
power law scaling and potentially even reduce it to exponential scaling instead
if we have access to a high-quality data pruning metric that ranks the order in
which training examples should be discarded to achieve any pruned dataset size.
We then test this improved scaling prediction with pruned dataset size
empirically, and indeed observe better than power law scaling in practice on
ResNets trained on CIFAR-10, SVHN, and ImageNet. Next, given the importance of
finding high-quality pruning metrics, we perform the first large-scale
benchmarking study of ten different data pruning metrics on ImageNet. We find
most existing high performing metrics scale poorly to ImageNet, while the best
are computationally intensive and require labels for every image. We therefore
developed a new simple, cheap and scalable self-supervised pruning metric that
demonstrates comparable performance to the best supervised metrics. Overall,
our work suggests that the discovery of good data-pruning metrics may provide a
viable path forward to substantially improved neural scaling laws, thereby
reducing the resource costs of modern deep learning.

Multispectral optoacoustic tomography (MSOT) is a high-resolution functional
imaging modality that can non-invasively access a broad range of
pathophysiological phenomena by quantifying the contrast of endogenous
chromophores in tissue. Real-time imaging is imperative to translate MSOT into
clinical imaging, visualize dynamic pathophysiological changes associated with
disease progression, and enable in situ diagnoses. Model-based reconstruction
affords state-of-the-art optoacoustic images; however, the advanced image
quality provided by model-based reconstruction remains inaccessible during
real-time imaging because the algorithm is iterative and computationally
demanding. Deep-learning may afford faster reconstructions for real-time
optoacoustic imaging, but existing approaches only support oversimplified
imaging settings and fail to generalize to in vivo data. In this work, we
introduce a novel deep-learning framework, termed DeepMB, to learn the
model-based reconstruction operator and infer optoacoustic images with
state-of-the-art quality in 31 ms per image. DeepMB accurately generalizes to
in vivo data after training on synthesized sinograms that are derived from
real-world images. The framework affords in-focus images for a broad range of
anatomical locations because it supports dynamic adjustment of the
reconstruction speed of sound during imaging. Furthermore, DeepMB is compatible
with the data rates and image sizes of modern multispectral optoacoustic
tomography scanners. We evaluate DeepMB on a diverse dataset of in vivo images
and demonstrate that the framework reconstructs images 1000 times faster than
the iterative model-based reference method while affording near-identical image
qualities. Accurate and real-time image reconstructions with DeepMB can enable
full access to the high-resolution and multispectral contrast of handheld
optoacoustic tomography.

Objective: The use of deep learning for electroencephalography (EEG)
classification tasks has been rapidly growing in the last years, yet its
application has been limited by the relatively small size of EEG datasets. Data
augmentation, which consists in artificially increasing the size of the dataset
during training, can be employed to alleviate this problem. While a few
augmentation transformations for EEG data have been proposed in the literature,
their positive impact on performance is often evaluated on a single dataset and
compared to one or two competing augmentation methods. This work proposes to
better validate the existing data augmentation approaches through a unified and
exhaustive analysis. Approach: We compare quantitatively 13 different
augmentations with two different predictive tasks, datasets and models, using
three different types of experiments. Main results: We demonstrate that
employing the adequate data augmentations can bring up to 45% accuracy
improvements in low data regimes compared to the same model trained without any
augmentation. Our experiments also show that there is no single best
augmentation strategy, as the good augmentations differ on each task.
Significance: Our results highlight the best data augmentations to consider for
sleep stage classification and motor imagery brain-computer interfaces. More
broadly, it demonstrates that EEG classification tasks benefit from adequate
data augmentation

Training an ensemble of different sub-models has empirically proven to be an
effective strategy to improve deep neural networks' adversarial robustness.
Current ensemble training methods for image recognition usually encode the
image labels by one-hot vectors, which neglect dependency relationships between
the labels. Here we propose a novel adversarial ensemble training approach to
jointly learn the label dependencies and the member models. Our approach
adaptively exploits the learned label dependencies to promote the diversity of
the member models. We test our approach on widely used datasets MNIST,
FasionMNIST, and CIFAR-10. Results show that our approach is more robust
against black-box attacks compared with the state-of-the-art methods. Our code
is available at https://github.com/ZJLAB-AMMI/LSD.

Many generative models synthesize data by transforming a standard Gaussian
random variable using a deterministic neural network. Among these models are
the Variational Autoencoders and the Generative Adversarial Networks. In this
work, we call them "push-forward" models and study their expressivity. We show
that the Lipschitz constant of these generative networks has to be large in
order to fit multimodal distributions. More precisely, we show that the total
variation distance and the Kullback-Leibler divergence between the generated
and the data distribution are bounded from below by a constant depending on the
mode separation and the Lipschitz constant. Since constraining the Lipschitz
constants of neural networks is a common way to stabilize generative models,
there is a provable trade-off between the ability of push-forward models to
approximate multimodal distributions and the stability of their training. We
validate our findings on one-dimensional and image datasets and empirically
show that generative models consisting of stacked networks with stochastic
input at each step, such as diffusion models do not suffer of such limitations.

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions
formed from seen state and object during training. Since the same state may be
various in the visual appearance while entangled with different objects, CZSL
is still a challenging task. Some methods recognize state and object with two
trained classifiers, ignoring the impact of the interaction between object and
state; the other methods try to learn the joint representation of the
state-object compositions, leading to the domain gap between seen and unseen
composition sets. In this paper, we propose a novel Siamese Contrastive
Embedding Network (SCEN) (Code: https://github.com/XDUxyLi/SCEN-master) for
unseen composition recognition. Considering the entanglement between state and
object, we embed the visual feature into a Siamese Contrastive Space to capture
prototypes of them separately, alleviating the interaction between state and
object. In addition, we design a State Transition Module (STM) to increase the
diversity of training compositions, improving the robustness of the recognition
model. Extensive experiments indicate that our method significantly outperforms
the state-of-the-art approaches on three challenging benchmark datasets,
including the recent proposed C-QGA dataset.

Conversational recommendation system (CRS) is emerging as a user-friendly way
to capture users' dynamic preferences over candidate items and attributes.
Multi-shot CRS is designed to make recommendations multiple times until the
user either accepts the recommendation or leaves at the end of their patience.
Existing works are trained with reinforcement learning (RL), which may suffer
from unstable learning and prohibitively high demands for computing. In this
work, we propose a simple and efficient CRS, MInimalist Non-reinforced
Interactive COnversational Recommender Network (MINICORN). MINICORN models the
epistemic uncertainty of the estimated user preference and queries the user for
the attribute with the highest uncertainty. The system employs a simple network
architecture and makes the query-vs-recommendation decision using a single
rule. Somewhat surprisingly, this minimalist approach outperforms
state-of-the-art RL methods on three real-world datasets by large margins. We
hope that MINICORN will serve as a valuable baseline for future research.

Domain generalization typically requires data from multiple source domains
for model learning. However, such strong assumption may not always hold in
practice, especially in medical field where the data sharing is highly
concerned and sometimes prohibitive due to privacy issue. This paper studies
the important yet challenging single domain generalization problem, in which a
model is learned under the worst-case scenario with only one source domain to
directly generalize to different unseen target domains. We present a novel
approach to address this problem in medical image segmentation, which extracts
and integrates the semantic shape prior information of segmentation that are
invariant across domains and can be well-captured even from single domain data
to facilitate segmentation under distribution shifts. Besides, a test-time
adaptation strategy with dual-consistency regularization is further devised to
promote dynamic incorporation of these shape priors under each unseen domain to
improve model generalizability. Extensive experiments on two medical image
segmentation tasks demonstrate the consistent improvements of our method across
various unseen domains, as well as its superiority over state-of-the-art
approaches in addressing domain generalization under the worst-case scenario.

Dynamic community detection has been prospered as a powerful tool for
quantifying changes in dynamic brain network connectivity patterns by
identifying strongly connected sets of nodes. However, as the network science
problems and network data to be processed become gradually more sophisticated,
it awaits a better method to efficiently learn low dimensional representation
from dynamic network data and reveal its latent function that changes over time
in the brain network. In this work, an adversarial temporal graph
representation learning (ATGRL) framework is proposed to detect dynamic
communities from a small sample of brain network data. It adopts a novel
temporal graph attention network as an encoder to capture more efficient
spatio-temporal features by attention mechanism in both spatial and temporal
dimensions. In addition, the framework employs adversarial training to guide
the learning of temporal graph representation and optimize the measurable
modularity loss to maximize the modularity of community. Experiments on the
real-world brain networks datasets are demonstrated to show the effectiveness
of this new method.

Score-based generative models (SGMs) are a recently proposed paradigm for
deep generative tasks and now show the state-of-the-art sampling performance.
It is known that the original SGM design solves the two problems of the
generative trilemma: i) sampling quality, and ii) sampling diversity. However,
the last problem of the trilemma was not solved, i.e., their training/sampling
complexity is notoriously high. To this end, distilling SGMs into simpler
models, e.g., generative adversarial networks (GANs), is gathering much
attention currently. We present an enhanced distillation method, called
straight-path interpolation GAN (SPI-GAN), which can be compared to the
state-of-the-art shortcut-based distillation method, called denoising diffusion
GAN (DD-GAN). However, our method corresponds to an extreme method that does
not use any intermediate shortcut information of the reverse SDE path, in which
case DD-GAN fails to obtain good results. Nevertheless, our straight-path
interpolation method greatly stabilizes the overall training process. As a
result, SPI-GAN is one of the best models in terms of the sampling
quality/diversity/time for CIFAR-10, CelebA-HQ-256, and LSUN-Church-256.

Modern search systems use several large ranker models with transformer
architectures. These models require large computational resources and are not
suitable for usage on devices with limited computational resources. Knowledge
distillation is a popular compression technique that can reduce the resource
needs of such models, where a large teacher model transfers knowledge to a
small student model. To drastically reduce memory requirements and energy
consumption, we propose two extensions for a popular sentence-transformer
distillation procedure: generation of an optimal size vocabulary and
dimensionality reduction of the embedding dimension of teachers prior to
distillation. We evaluate these extensions on two different types of ranker
models. This results in extremely compressed student models whose analysis on a
test dataset shows the significance and utility of our proposed extensions.

A major source of information can be taken from financial news articles,
which have some correlations about the fluctuation of stock trends. In this
paper, we investigate the influences of financial news on the stock trends,
from a multi-instance view. The intuition behind this is based on the news
uncertainty of varying intervals of news occurrences and the lack of annotation
in every single financial news. Under the scenario of Multiple Instance
Learning (MIL) where training instances are arranged in bags, and a label is
assigned for the entire bag instead of instances, we develop a flexible and
adaptive multi-instance learning model and evaluate its ability in directional
movement forecast of Standard & Poors 500 index on financial news dataset.
Specifically, we treat each trading day as one bag, with certain amounts of
news happening on each trading day as instances in each bag. Experiment results
demonstrate that our proposed multi-instance-based framework gains outstanding
results in terms of the accuracy of trend prediction, compared with other
state-of-art approaches and baselines.

Detection and tracking of moving objects (DATMO) is an essential component in
environmental perception for autonomous driving. In the flourishing field of
multi-view 3D camera-based detectors, different transformer-based pipelines are
designed to learn queries in 3D space from 2D feature maps of perspective
views, but the dominant dense cross-attention mechanism between queries to
values is computationally inefficient. This paper proposes Sparse R-CNN 3D
(SRCN3D), a novel two-stage fully-sparse detector with sparse queries, sparse
attention and sparse prediction for surround-view camera detection and
tracking. SRCN3D adopts a cascade structure with twin-track update of both
fixed number of proposal boxes and latent proposal features. Compared to prior
arts, our novel sparse feature sampling module only utilizes local 2D region of
interest (RoI) features calculated by projection of 3D proposal boxes for
further box refinement, leading to an effective, fast and lightweight pipeline.
For multi-object tracking, motion features, proposal features and RoI features
are comprehensively utilized in multi-hypotheses data association. Extensive
experiments on nuScenes dataset demonstrate that SRCN3D achieves competitive
performance in object detection and surpasses previous best arts before
2022.08.09 in camera-only multi-object tracking by more than 10 points in terms
of AMOTA metric. Code is available at https://github.com/synsin0/SRCN3D.

We consider a general non-linear model where the signal is a finite mixture
of an unknown, possibly increasing, number of features issued from a continuous
dictionary parameterized by a real nonlinear parameter. The signal is observed
with Gaussian (possibly correlated) noise in either a continuous or a discrete
setup. We propose an off-the-grid optimization method, that is, a method which
does not use any discretization scheme on the parameter space, to estimate both
the non-linear parameters of the features and the linear parameters of the
mixture. We use recent results on the geometry of off-the-grid methods to give
minimal separation on the true underlying non-linear parameters such that
interpolating certificate functions can be constructed. Using also tail bounds
for suprema of Gaussian processes we bound the prediction error with high
probability. Assuming that the certificate functions can be constructed, our
prediction error bound is up to log --factors similar to the rates attained by
the Lasso predictor in the linear regression model. We also establish
convergence rates that quantify with high probability the quality of estimation
for both the linear and the non-linear parameters.

Users have the right to have their data deleted by third-party learned
systems, as codified by recent legislation such as the General Data Protection
Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Such data
deletion can be accomplished by full re-training, but this incurs a high
computational cost for modern machine learning models. To avoid this cost, many
approximate data deletion methods have been developed for supervised learning.
Unsupervised learning, in contrast, remains largely an open problem when it
comes to (approximate or exact) efficient data deletion. In this paper, we
propose a density-ratio-based framework for generative models. Using this
framework, we introduce a fast method for approximate data deletion and a
statistical test for estimating whether or not training points have been
deleted. We provide theoretical guarantees under various learner assumptions
and empirically demonstrate our methods across a variety of generative methods.

In this work, we propose a mutual information (MI) based unsupervised domain
adaptation (UDA) method for the cross-domain nuclei segmentation. Nuclei vary
substantially in structure and appearances across different cancer types,
leading to a drop in performance of deep learning models when trained on one
cancer type and tested on another. This domain shift becomes even more critical
as accurate segmentation and quantification of nuclei is an essential
histopathology task for the diagnosis/ prognosis of patients and annotating
nuclei at the pixel level for new cancer types demands extensive effort by
medical experts. To address this problem, we maximize the MI between labeled
source cancer type data and unlabeled target cancer type data for transferring
nuclei segmentation knowledge across domains. We use the Jensen-Shanon
divergence bound, requiring only one negative pair per positive pair for MI
maximization. We evaluate our set-up for multiple modeling frameworks and on
different datasets comprising of over 20 cancer-type domain shifts and
demonstrate competitive performance. All the recently proposed approaches
consist of multiple components for improving the domain adaptation, whereas our
proposed module is light and can be easily incorporated into other methods
(Implementation: https://github.com/YashSharma/MaNi ).

The authors recently gave an $n^{O(\log\log n)}$ time membership query
algorithm for properly learning decision trees under the uniform distribution
(Blanc et al., 2021). The previous fastest algorithm for this problem ran in
$n^{O(\log n)}$ time, a consequence of Ehrenfeucht and Haussler (1989)'s
classic algorithm for the distribution-free setting. In this article we
highlight the natural open problem of obtaining a polynomial-time algorithm,
discuss possible avenues towards obtaining it, and state intermediate
milestones that we believe are of independent interest.

To accurately study chemical reactions in the condensed phase or within
enzymes, both a quantum-mechanical description and sufficient configurational
sampling is required to reach converged estimates. Here, quantum
mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations play
an important role, providing QM accuracy for the region of interest at a
decreased computational cost. However, QM/MM simulations are still too
expensive to study large systems on longer time scales. Recently, machine
learning (ML) models have been proposed to replace the QM description. The main
limitation of these models lies in the accurate description of long-range
interactions present in condensed-phase systems. To overcome this issue, a
recent workflow has been introduced combining a semi-empirical method (i.e.
density functional tight binding (DFTB)) and a high-dimensional neural network
potential (HDNNP) in a $\Delta$-learning scheme. This approach has been shown
to be capable of correctly incorporating long-range interactions within a
cutoff of 1.4 nm. One of the promising alternative approaches to efficiently
take long-range effects into account is the development of graph convolutional
neural networks (GCNN) for the prediction of the potential-energy surface. In
this work, we investigate the use of GCNN models -- with and without a
$\Delta$-learning scheme -- for (QM)ML/MM MD simulations. We show that the
$\Delta$-learning approach using a GCNN and DFTB and as baseline achieves
competitive performance on our benchmarking set of solutes and chemical
reactions in water. The method is additionally validated by performing
prospective (QM)ML/MM MD simulations of retinoic acid in water and
S-adenoslymethioniat interacting with cytosine in water. The results indicate
that the $\Delta$-learning GCNN model is a valuable alternative for (QM)ML/MM
MD simulations of condensed-phase systems.

We present a new class of neurons, ARNs, which give a cross entropy on test
data that is up to three times lower than the one achieved by carefully
optimized LSTM neurons. The explanations for the huge improvements that often
are achieved are elaborate skip connections through time, up to four internal
memory states per neuron and a number of novel activation functions including
small quadratic forms. The new neurons were generated using automatic
programming and are formulated as pure functional programs that easily can be
transformed. We present experimental results for eight datasets and found
excellent improvements for seven of them, but LSTM remained the best for one
dataset. The results are so promising that automatic programming to generate
new neurons should become part of the standard operating procedure for any
machine learning practitioner who works on time series data such as sensor
signals.

We propose cKAM, cyclical Kernel Adaptive Metropolis, which incorporates a
cyclical stepsize scheme to allow control for exploration and sampling. We show
that on a crafted bimodal distribution, existing Adaptive Metropolis type
algorithms would fail to converge to the true posterior distribution. We point
out that this is because adaptive samplers estimates the local/global
covariance structure using past history of the chain, which will lead to
adaptive algorithms be trapped in a local mode. We demonstrate that cKAM
encourages exploration of the posterior distribution and allows the sampler to
escape from a local mode, while maintaining the high performance of adaptive
methods.

The current quantum reinforcement learning control models often assume that
the quantum states are known a priori for control optimization. However, full
observation of quantum state is experimentally infeasible due to the
exponential scaling of the number of required quantum measurements on the
number of qubits. In this paper, we investigate a robust reinforcement learning
method using partial observations to overcome this difficulty. This control
scheme is compatible with near-term quantum devices, where the noise is
prevalent and predetermining the dynamics of quantum state is practically
impossible. We show that this simplified control scheme can achieve similar or
even better performance when compared to the conventional methods relying on
full observation. We demonstrate the effectiveness of this scheme on examples
of quantum state control and quantum approximate optimization algorithm. It has
been shown that high-fidelity state control can be achieved even if the noise
amplitude is at the same level as the control amplitude. Besides, an acceptable
level of optimization accuracy can be achieved for QAOA with noisy control
Hamiltonian. This robust control optimization model can be trained to
compensate the uncertainties in practical quantum computing.

Due to the over-smoothing issue, most existing graph neural networks can only
capture limited dependencies with their inherently finite aggregation layers.
To overcome this limitation, we propose a new kind of graph convolution, called
Graph Implicit Nonlinear Diffusion (GIND), which implicitly has access to
infinite hops of neighbors while adaptively aggregating features with nonlinear
diffusion to prevent over-smoothing. Notably, we show that the learned
representation can be formalized as the minimizer of an explicit convex
optimization objective. With this property, we can theoretically characterize
the equilibrium of our GIND from an optimization perspective. More
interestingly, we can induce new structural variants by modifying the
corresponding optimization objective. To be specific, we can embed prior
properties to the equilibrium, as well as introducing skip connections to
promote training stability. Extensive experiments show that GIND is good at
capturing long-range dependencies, and performs well on both homophilic and
heterophilic graphs with nonlinear diffusion. Moreover, we show that the
optimization-induced variants of our models can boost the performance and
improve training stability and efficiency as well. As a result, our GIND
obtains significant improvements on both node-level and graph-level tasks.

Vision transformers have recently set off a new wave in the field of medical
image analysis due to their remarkable performance on various computer vision
tasks. However, recent hybrid-/transformer-based approaches mainly focus on the
benefits of transformers in capturing long-range dependency while ignoring the
issues of their daunting computational complexity, high training costs, and
redundant dependency. In this paper, we propose to employ adaptive pruning to
transformers for medical image segmentation and propose a lightweight and
effective hybrid network APFormer. To our best knowledge, this is the first
work on transformer pruning for medical image analysis tasks. The key features
of APFormer mainly are self-supervised self-attention (SSA) to improve the
convergence of dependency establishment, Gaussian-prior relative position
embedding (GRPE) to foster the learning of position information, and adaptive
pruning to eliminate redundant computations and perception information.
Specifically, SSA and GRPE consider the well-converged dependency distribution
and the Gaussian heatmap distribution separately as the prior knowledge of
self-attention and position embedding to ease the training of transformers and
lay a solid foundation for the following pruning operation. Then, adaptive
transformer pruning, both query-wise and dependency-wise, is performed by
adjusting the gate control parameters for both complexity reduction and
performance improvement. Extensive experiments on two widely-used datasets
demonstrate the prominent segmentation performance of APFormer against the
state-of-the-art methods with much fewer parameters and lower GFLOPs. More
importantly, we prove, through ablation studies, that adaptive pruning can work
as a plug-n-play module for performance improvement on other
hybrid-/transformer-based methods. Code is available at
https://github.com/xianlin7/APFormer.

Convolutional neural networks (CNN), the most prevailing architecture for
deep-learning based medical image analysis, are still functionally limited by
their intrinsic inductive biases and inadequate receptive fields. Transformer,
born to address this issue, has drawn explosive attention in natural language
processing and computer vision due to its remarkable ability in capturing
long-range dependency. However, most recent transformer-based methods for
medical image segmentation directly apply vanilla transformers as an auxiliary
module in CNN-based methods, resulting in severe detail loss due to the rigid
patch partitioning scheme in transformers. To address this problem, we propose
C2FTrans, a novel multi-scale architecture that formulates medical image
segmentation as a coarse-to-fine procedure. C2FTrans mainly consists of a
cross-scale global transformer (CGT) which addresses local contextual
similarity in CNN and a boundary-aware local transformer (BLT) which overcomes
boundary uncertainty brought by rigid patch partitioning in transformers.
Specifically, CGT builds global dependency across three different small-scale
feature maps to obtain rich global semantic features with an acceptable
computational cost, while BLT captures mid-range dependency by adaptively
generating windows around boundaries under the guidance of entropy to reduce
computational complexity and minimize detail loss based on large-scale feature
maps. Extensive experimental results on three public datasets demonstrate the
superior performance of C2FTrans against state-of-the-art CNN-based and
transformer-based methods with fewer parameters and lower FLOPs. We believe the
design of C2FTrans would further inspire future work on developing efficient
and lightweight transformers for medical image segmentation. The source code of
this paper is publicly available at https://github.com/xianlin7/C2FTrans.

The Dihedral Coset Problem (DCP) in $Z_N$ is an important quantumly defined
problem to which for instance the learning with errors problem reduces. There
are several algorithms for solving it. On one hand, we have algorithms \`a la
Ettinger-H{\o}yer running in $O(2^{c_{SS}\log{N}})$ time but using $O(\log{N})$
queries, for some constant $c_{SS}$. On the other hand, algorithms \`a la
Kuperberg need $\widetilde{O}(2^{\sqrt{c_{DCP}\log{N}}})$ time and queries, for
some constant $c_{DCP}$. Despite being the subject of a lot of studies, no
algorithm using an intermediate number of queries was known up till now. It
means that an attacker who has, for cost or time reasons, limited queries, will
be forced to use a linear query algorithm running in exponential time. We
propose an algorithm taking as a parameter the number $Q$ of queries we are
allowed to do, therefore interpolating between existing algorithms. We thus
explicitly show that it takes
$\widetilde{O}(2^{c_{SS}\left(\log{N}-\frac{1}{c_{DCP}}\log^2{Q}\right)})$
time. Our algorithm improves over algorithms \`a la Ettinger-H{\o}yer when $Q$
is minimal and matches algorithms \`a la Kuperberg when $Q$ is maximal, while
offering new possibilities in between. The key point of our algorithm is to
give a degree of freedom in the way coset states are preprocessed, that we then
link to the parameter $Q$ to obtain an optimal algorithm with respect to $Q$.
We also introduce a new sieving procedure to assemble the constructed coset
states, allowing us to retrieve the secret $s$.

Benefiting from the digitization of healthcare data and the development of
computing power, machine learning methods are increasingly used in the
healthcare domain. Fairness problems have been identified in machine learning
for healthcare, resulting in an unfair allocation of limited healthcare
resources or excessive health risks for certain groups. Therefore, addressing
the fairness problems has recently attracted increasing attention from the
healthcare community. However, the intersection of machine learning for
healthcare and fairness in machine learning remains understudied. In this
review, we build the bridge by exposing fairness problems, summarizing possible
biases, sorting out mitigation methods and pointing out challenges along with
opportunities for the future.

The Regression Tree (RT) sorts the samples using a specific feature and finds
the split point that produces the maximum variance reduction from a node to its
children. Our key observation is that the best factor to use (in terms of MSE
drop) is always the target itself, as this most clearly separates the target.
Thus using the target as the splitting factor provides an upper bound on MSE
drop (or lower bound on the residual children MSE). Based on this observation,
we define the k-bit lepto-variance ${\lambda}k^2$ of a target variable (or
equivalently the lepto-variance at a specific depth k) as the variance that
cannot be removed by any regression tree of a depth equal to k. As the upper
bound performance for any feature, we believe ${\lambda}k^2$ to be an
interesting statistical concept related to the underlying structure of the
sample as it quantifies the resolving power of the RT for the sample. The max
variance that may be explained using RTs of depth up to k is called the sample
k-bit macro-variance. At any depth, total sample variance is thus decomposed
into lepto-variance ${\lambda}^2$ and macro-variance ${\mu}^2$. We demonstrate
the concept, by performing 1- and 2-bit RT based lepto-structure analysis for
daily IBM stock returns.

Large pre-trained generative models are known to occasionally provide samples
that may be undesirable for various reasons. The standard way to mitigate this
is to re-train the models differently. In this work, we take a different, more
compute-friendly approach and investigate how to post-edit a model after
training so that it forgets certain kinds of samples. We provide three
different algorithms for GANs that differ on how the samples to be forgotten
are described. Extensive evaluations on real-world image datasets show that our
algorithms are capable of forgetting data while retaining high generation
quality at a fraction of the cost of full re-training.

Word embedding is a fundamental natural language processing task which can
learn feature of words. However, most word embedding methods assign only one
vector to a word, even if polysemous words have multi-senses. To address this
limitation, we propose SememeWSD Synonym (SWSDS) model to assign a different
vector to every sense of polysemous words with the help of word sense
disambiguation (WSD) and synonym set in OpenHowNet. We use the SememeWSD model,
an unsupervised word sense disambiguation model based on OpenHowNet, to do word
sense disambiguation and annotate the polysemous word with sense id. Then, we
obtain top 10 synonyms of the word sense from OpenHowNet and calculate the
average vector of synonyms as the vector of the word sense. In experiments, We
evaluate the SWSDS model on semantic similarity calculation with Gensim's
wmdistance method. It achieves improvement of accuracy. We also examine the
SememeWSD model on different BERT models to find the more effective model.

The problem of algorithmic recourse has been explored for supervised machine
learning models, to provide more interpretable, transparent and robust outcomes
from decision support systems. An unexplored area is that of algorithmic
recourse for anomaly detection, specifically for tabular data with only
discrete feature values. Here the problem is to present a set of
counterfactuals that are deemed normal by the underlying anomaly detection
model so that applications can utilize this information for explanation
purposes or to recommend countermeasures. We present an approach -- Context
preserving Algorithmic Recourse for Anomalies in Tabular data (CARAT), that is
effective, scalable, and agnostic to the underlying anomaly detection model.
CARAT uses a transformer based encoder-decoder model to explain an anomaly by
finding features with low likelihood. Subsequently semantically coherent
counterfactuals are generated by modifying the highlighted features, using the
overall context of features in the anomalous instance(s). Extensive experiments
help demonstrate the efficacy of CARAT.

Many performance gains achieved by massive multiple-input and multiple-output
depend on the accuracy of the downlink channel state information (CSI) at the
transmitter (base station), which is usually obtained by estimating at the
receiver (user terminal) and feeding back to the transmitter. The overhead of
CSI feedback occupies substantial uplink bandwidth resources, especially when
the number of the transmit antennas is large. Deep learning (DL)-based CSI
feedback refers to CSI compression and reconstruction by a DL-based autoencoder
and can greatly reduce feedback overhead. In this paper, a comprehensive
overview of state-of-the-art research on this topic is provided, beginning with
basic DL concepts widely used in CSI feedback and then categorizing and
describing some existing DL-based feedback works. The focus is on novel neural
network architectures and utilization of communication expert knowledge to
improve CSI feedback accuracy. Works on bit-level CSI feedback and joint design
of CSI feedback with other communication modules are also introduced, and some
practical issues, including training dataset collection, online training,
complexity, generalization, and standardization effect, are discussed. At the
end of the paper, some challenges and potential research directions associated
with DL-based CSI feedback in future wireless communication systems are
identified.

In this report, we present our approach for EPIC-KITCHENS-100 Multi-Instance
Retrieval Challenge 2022. We first parse sentences into semantic roles
corresponding to verbs and nouns; then utilize self-attentions to exploit
semantic role contextualized video features along with textual features via
triplet losses in multiple embedding spaces. Our method overpasses the strong
baseline in normalized Discounted Cumulative Gain (nDCG), which is more
valuable for semantic similarity. Our submission is ranked 3rd for nDCG and
ranked 4th for mAP.

In recent years, there have been significant advances in the use of deep
learning methods in inverse problems such as denoising, compressive sensing,
inpainting, and super-resolution. While this line of works has predominantly
been driven by practical algorithms and experiments, it has also given rise to
a variety of intriguing theoretical problems. In this paper, we survey some of
the prominent theoretical developments in this line of works, focusing in
particular on generative priors, untrained neural network priors, and unfolding
algorithms. In addition to summarizing existing results in these topics, we
highlight several ongoing challenges and open problems.

In this paper, we present a novel insider attack called Matryoshka, which
employs an irrelevant scheduled-to-publish DNN model as a carrier model for
covert transmission of multiple secret models which memorize the functionality
of private ML data stored in local data centers. Instead of treating the
parameters of the carrier model as bit strings and applying conventional
steganography, we devise a novel parameter sharing approach which exploits the
learning capacity of the carrier model for information hiding. Matryoshka
simultaneously achieves: (i) High Capacity -- With almost no utility loss of
the carrier model, Matryoshka can hide a 26x larger secret model or 8 secret
models of diverse architectures spanning different application domains in the
carrier model, neither of which can be done with existing steganography
techniques; (ii) Decoding Efficiency -- once downloading the published carrier
model, an outside colluder can exclusively decode the hidden models from the
carrier model with only several integer secrets and the knowledge of the hidden
model architecture; (iii) Effectiveness -- Moreover, almost all the recovered
models have similar performance as if it were trained independently on the
private data; (iv) Robustness -- Information redundancy is naturally
implemented to achieve resilience against common post-processing techniques on
the carrier before its publishing; (v) Covertness -- A model inspector with
different levels of prior knowledge could hardly differentiate a carrier model
from a normal model.

Voltage control generally requires accurate information about the grid's
topology in order to guarantee network stability. However, accurate topology
identification is a challenging problem for existing methods, especially as the
grid is subject to increasingly frequent reconfiguration due to the adoption of
renewable energy. Further, running existing control mechanisms with incorrect
network information may lead to unstable control. In this work, we combine a
nested convex body chasing algorithm with a robust predictive controller to
achieve provably finite-time convergence to safe voltage limits in the online
setting where the network topology is initially unknown. Specifically, the
online controller does not know the true network topology and line parameters,
but instead must learn them over time by narrowing down the set of network
topologies and line parameters that are consistent with its observations and
adjusting reactive power generation accordingly to keep voltages within desired
safety limits. We demonstrate the effectiveness of the approach using a case
study, which shows that in practical settings the controller is indeed able to
narrow the set of consistent topologies quickly enough to make control
decisions that ensure stability.

Nowadays, graph becomes an increasingly popular model in many real
applications. The efficiency of graph storage is crucial for these
applications. Generally speaking, the tune tasks of graph storage rely on the
database administrators (DBAs) to find the best graph storage. However, DBAs
make the tune decisions by mainly relying on their experiences and intuition.
Due to the limitations of DBAs's experiences, the tunes may have an uncertain
performance and conduct worse efficiency. In this paper, we observe that an
estimator of graph workload has the potential to guarantee the performance of
tune operations. Unfortunately, because of the complex characteristics of graph
evaluation task, there exists no mature estimator for graph workload. We
formulate the evaluation task of graph workload as a classification task and
carefully design the feature engineering process, including graph data
features, graph workload features and graph storage features. Considering the
complex features of graph and the huge time consumption in graph workload
execution, it is difficult for the graph workload estimator to obtain enough
training set. So, we propose an active auto-estimator (AAE) for the graph
workload evaluation by combining the active learning and deep learning. AAE
could achieve good evaluation efficiency with limited training set. We test the
time efficiency and evaluation accuracy of AAE with two open source graph data,
LDBC and Freebase. Experimental results show that our estimator could
efficiently complete the graph workload evaluation in milliseconds.

It is common practice to collect observations of feature and response pairs
from different environments. A natural question is how to identify features
that have consistent prediction power across environments. The invariant causal
prediction framework proposes to approach this problem through invariance,
assuming a linear model that is invariant under different environments. In this
work, we make an attempt to shed light on this framework by connecting it to
the Gaussian multiple access channel problem. Specifically, we incorporate
optimal code constructions and decoding methods to provide lower bounds on the
error probability. We illustrate our findings by various simulation settings.

Tree Ensemble (TE) models (e.g. Gradient Boosted Trees and Random Forests)
often provide higher prediction performance compared to single decision trees.
However, TE models generally lack transparency and interpretability, as humans
have difficulty understanding their decision logic. This paper presents a novel
approach to convert a TE trained for a binary classification task, to a rule
list (RL) that is a global equivalent to the TE and is comprehensible for a
human. This RL captures all necessary and sufficient conditions for decision
making by the TE. Experiments on benchmark datasets demonstrate that, compared
to state-of-the-art methods, (i) predictions from the RL generated by TE2Rules
have high fidelity with respect to the original TE, (ii) the RL from TE2Rules
has high interpretability measured by the number and the length of the decision
rules, (iii) the run-time of TE2Rules algorithm can be reduced significantly at
the cost of a slightly lower fidelity, and (iv) the RL is a fast alternative to
the state-of-the-art rule-based instance-level outcome explanation techniques.

Understanding public discourse on emergency use of unproven therapeutics is
essential to monitor safe use and combat misinformation. We developed a natural
language processing (NLP)-based pipeline to understand public perceptions of
and stances on COVID-19-related drugs on Twitter across time. This
retrospective study included 609,189 US-based tweets between January 29th, 2020
and November 30th, 2021 on four drugs that gained wide public attention during
the COVID-19 pandemic: 1) Hydroxychloroquine and Ivermectin, drug therapies
with anecdotal evidence; and 2) Molnupiravir and Remdesivir, FDA-approved
treatment options for eligible patients. Time-trend analysis was used to
understand the popularity and related events. Content and demographic analyses
were conducted to explore potential rationales of people's stances on each
drug. Time-trend analysis revealed that Hydroxychloroquine and Ivermectin
received much more discussion than Molnupiravir and Remdesivir, particularly
during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly
politicized, related to conspiracy theories, hearsay, celebrity effects, etc.
The distribution of stance between the two major US political parties was
significantly different (p<0.001); Republicans were much more likely to support
Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with
healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the
general population; in contrast, the general population was more likely to
support Ivermectin (+14%). We make all the data, code, and models available at
https://github.com/ningkko/COVID-drug.

The availability of clean and diverse labeled data is a major roadblock for
training models on complex tasks such as visual question answering (VQA). The
extensive work on large vision-and-language models has shown that
self-supervised learning is effective for pretraining multimodal interactions.
In this technical report, we focus on visual representations. We review and
evaluate self-supervised methods to leverage unlabeled images and pretrain a
model, which we then fine-tune on a custom VQA task that allows controlled
evaluation and diagnosis. We compare energy-based models (EBMs) with
contrastive learning (CL). While EBMs are growing in popularity, they lack an
evaluation on downstream tasks. We find that both EBMs and CL can learn
representations from unlabeled images that enable training a VQA model on very
little annotated data. In a simple setting similar to CLEVR, we find that CL
representations also improve systematic generalization, and even match the
performance of representations from a larger, supervised, ImageNet-pretrained
model. However, we find EBMs to be difficult to train because of instabilities
and high variability in their results. Although EBMs prove useful for OOD
detection, other results on supervised energy-based training and uncertainty
calibration are largely negative. Overall, CL currently seems a preferable
option over EBMs.

We explore algorithms and limitations for sparse optimization problems such
as sparse linear regression and robust linear regression. The goal of the
sparse linear regression problem is to identify a small number of key features,
while the goal of the robust linear regression problem is to identify a small
number of erroneous measurements. Specifically, the sparse linear regression
problem seeks a $k$-sparse vector $x\in\mathbb{R}^d$ to minimize $\|Ax-b\|_2$,
given an input matrix $A\in\mathbb{R}^{n\times d}$ and a target vector
$b\in\mathbb{R}^n$, while the robust linear regression problem seeks a set $S$
that ignores at most $k$ rows and a vector $x$ to minimize $\|(Ax-b)_S\|_2$.
  We first show bicriteria, NP-hardness of approximation for robust regression
building on the work of [OWZ15] which implies a similar result for sparse
regression. We further show fine-grained hardness of robust regression through
a reduction from the minimum-weight $k$-clique conjecture. On the positive
side, we give an algorithm for robust regression that achieves arbitrarily
accurate additive error and uses runtime that closely matches the lower bound
from the fine-grained hardness result, as well as an algorithm for sparse
regression with similar runtime. Both our upper and lower bounds rely on a
general reduction from robust linear regression to sparse regression that we
introduce. Our algorithms, inspired by the 3SUM problem, use approximate
nearest neighbor data structures and may be of independent interest for solving
sparse optimization problems. For instance, we demonstrate that our techniques
can also be used for the well-studied sparse PCA problem.

Due to the massive explanation of artificial intelligence, machine learning
technology is being used in various areas of our day-to-day life. In the world,
there are a lot of scenarios where a simple crime can be prevented before it
may even happen or find the person responsible for it. A face is one
distinctive feature that we have and can differentiate easily among many other
species. But not just different species, it also plays a significant role in
determining someone from the same species as us, humans. Regarding this
critical feature, a single problem occurs most often nowadays. When the camera
is pointed, it cannot detect a person's face, and it becomes a poor image. On
the other hand, where there was a robbery and a security camera installed, the
robber's identity is almost indistinguishable due to the low-quality camera.
But just making an excellent algorithm to work and detecting a face reduces the
cost of hardware, and it doesn't cost that much to focus on that area. Facial
recognition, widget control, and such can be done by detecting the face
correctly. This study aims to create and enhance a machine learning model that
correctly recognizes faces. Total 627 Data have been collected from different
Bangladeshi people's faces on four angels. In this work, CNN, Harr Cascade,
Cascaded CNN, Deep CNN & MTCNN are these five machine learning approaches
implemented to get the best accuracy of our dataset. After creating and running
the model, Multi-Task Convolutional Neural Network (MTCNN) achieved 96.2% best
model accuracy with training data rather than other machine learning models.

Commercial and industrial deployments of robot fleets at Amazon, Nimble, Plus
One, Waymo, and Zoox query remote human teleoperators when robots are at risk
or unable to make task progress. With continual learning, interventions from
the remote pool of humans can also be used to improve the robot fleet control
policy over time. A central question is how to effectively allocate limited
human attention. Prior work addresses this in the single-robot, single-human
setting; we formalize the Interactive Fleet Learning (IFL) setting, in which
multiple robots interactively query and learn from multiple human supervisors.
We propose Return on Human Effort (ROHE) as a new metric and Fleet-DAgger, a
family of IFL algorithms. We present an open-source IFL benchmark suite of
GPU-accelerated Isaac Gym environments for standardized evaluation and
development of IFL algorithms. We compare a novel Fleet-DAgger algorithm to 4
baselines with 100 robots in simulation. We also perform a physical
block-pushing experiment with 4 ABB YuMi robot arms and 2 remote humans.
Experiments suggest that the allocation of humans to robots significantly
affects the performance of the fleet, and that the novel Fleet-DAgger algorithm
can achieve up to 8.8x higher ROHE than baselines. See
https://tinyurl.com/fleet-dagger for supplemental material.

Performance in natural language processing, and specifically for the
question-answer task, is typically measured by comparing a model\'s most
confident (primary) prediction to golden answers (the ground truth). We are
making the case that it is also useful to quantify how close a model came to
predicting a correct answer even for examples that failed. We define the Golden
Rank (GR) of an example as the rank of its most confident prediction that
exactly matches a ground truth, and show why such a match always exists. For
the 16 transformer models we analyzed, the majority of exactly matched golden
answers in secondary prediction space hover very close to the top rank. We
refer to secondary predictions as those ranking above 0 in descending
confidence probability order. We demonstrate how the GR can be used to classify
questions and visualize their spectrum of difficulty, from persistent near
successes to persistent extreme failures. We derive a new aggregate statistic
over entire test sets, named the Golden Rank Interpolated Median (GRIM) that
quantifies the proximity of failed predictions to the top choice made by the
model. To develop some intuition and explore the applicability of these metrics
we use the Stanford Question Answering Dataset (SQuAD-2) and a few popular
transformer models from the Hugging Face hub. We first demonstrate that the
GRIM is not directly correlated with the F1 and exact match (EM) scores. We
then calculate and visualize these scores for various transformer
architectures, probe their applicability in error analysis by clustering failed
predictions, and compare how they relate to other training diagnostics such as
the EM and F1 scores. We finally suggest various research goals, such as
broadening data collection for these metrics and their possible use in

In recent years, deep learning has shown itself to be an incredibly valuable
tool in cybersecurity as it helps network intrusion detection systems to
classify attacks and detect new ones. Adversarial learning is the process of
utilizing machine learning to generate a perturbed set of inputs to then feed
to the neural network to misclassify it. Much of the current work in the field
of adversarial learning has been conducted in image processing and natural
language processing with a wide variety of algorithms. Two algorithms of
interest are the Elastic-Net Attack on Deep Neural Networks and TextAttack. In
our experiment the EAD and TextAttack algorithms are applied to a Domain Name
System amplification classifier. The algorithms are used to generate malicious
Distributed Denial of Service adversarial examples to then feed as inputs to
the network intrusion detection systems neural network to classify as valid
traffic. We show in this work that both image processing and natural language
processing adversarial learning algorithms can be applied against a network
intrusion detection neural network.

Human skeleton data has received increasing attention in action recognition
due to its background robustness and high efficiency. In skeleton-based action
recognition, graph convolutional network (GCN) has become the mainstream
method. This paper analyzes the fundamental factor for GCN-based models -- the
adjacency matrix. We notice that most GCN-based methods conduct their adjacency
matrix based on the human natural skeleton structure. Based on our former work
and analysis, we propose that the human natural skeleton structure adjacency
matrix is not proper for skeleton-based action recognition. We propose a new
adjacency matrix that abandons all rigid neighbor connections but lets the
model adaptively learn the relationships of joints. We conduct extensive
experiments and analysis with a validation model on two skeleton-based action
recognition datasets (NTURGBD60 and FineGYM). Comprehensive experimental
results and analysis reveals that 1) the most widely used human natural
skeleton structure adjacency matrix is unsuitable in skeleton-based action
recognition; 2) The proposed adjacency matrix is superior in model performance,
noise robustness and transferability.

We introduce a novel, practically relevant variation of the anomaly detection
problem in multi-variate time series: intrinsic anomaly detection. It appears
in diverse practical scenarios ranging from DevOps to IoT, where we want to
recognize failures of a system that operates under the influence of a
surrounding environment. Intrinsic anomalies are changes in the functional
dependency structure between time series that represent an environment and time
series that represent the internal state of a system that is placed in said
environment. We formalize this problem, provide under-studied public and new
purpose-built data sets for it, and present methods that handle intrinsic
anomaly detection. These address the short-coming of existing anomaly detection
methods that cannot differentiate between expected changes in the system's
state and unexpected ones, i.e., changes in the system that deviate from the
environment's influence. Our most promising approach is fully unsupervised and
combines adversarial learning and time series representation learning, thereby
addressing problems such as label sparsity and subjectivity, while allowing to
navigate and improve notoriously problematic anomaly detection data sets.

The need for secure Internet of Things (IoT) devices is growing as IoT
devices are becoming more integrated into vital networks. Many systems rely on
these devices to remain available and provide reliable service. Denial of
service attacks against IoT devices are a real threat due to the fact these low
power devices are very susceptible to denial-of-service attacks. Machine
learning enabled network intrusion detection systems are effective at
identifying new threats, but they require a large amount of data to work well.
There are many network traffic data sets but very few that focus on IoT network
traffic. Within the IoT network data sets there is a lack of CoAP denial of
service data. We propose a novel data set covering this gap. We develop a new
data set by collecting network traffic from real CoAP denial of service attacks
and compare the data on multiple different machine learning classifiers. We
show that the data set is effective on many classifiers.

Transformer-based models have recently shown success in representation
learning on graph-structured data beyond natural language processing and
computer vision. However, the success is limited to small-scale graphs due to
the drawbacks of full dot-product attention on graphs such as the quadratic
complexity with respect to the number of nodes and message aggregation from
enormous irrelevant nodes. To address these issues, we propose Deformable Graph
Transformer (DGT) that performs sparse attention via dynamically sampled
relevant nodes for efficiently handling large-scale graphs with a linear
complexity in the number of nodes. Specifically, our framework first constructs
multiple node sequences with various criteria to consider both structural and
semantic proximity. Then, combining with our learnable Katz Positional
Encodings, the sparse attention is applied to the node sequences for learning
node representations with a significantly reduced computational cost. Extensive
experiments demonstrate that our DGT achieves state-of-the-art performance on 7
graph benchmark datasets with 2.5 - 449 times less computational cost compared
to transformer-based graph models with full attention.

Given the ever-increasing number of time-domain astronomical surveys,
employing robust, interpretative, and automated data-driven classification
schemes is pivotal. This work presents new data-driven classification
heuristics for spectral data based on graph theory. As a case in point, we
devise a spectral classification scheme of Type II supernova (SNe II) as a
function of the phase relative to the $V$-band maximum light and the end of the
plateau phase. We use compiled optical data sets comprising 145 SNe and 1595
optical spectra in 4000-9000 angstrom. Our classification method naturally
identifies outliers and arranges the different SNe in terms of their major
spectral features. We compare our approach to the off-the-shelf UMAP manifold
learning and show that both strategies are consistent with a continuous
variation of spectral types rather than discrete families. The automated
classification naturally reflects the fast evolution of Type II SNe around the
maximum light while showcasing their homogeneity close to the end of the
plateau phase. The scheme we develop could be more widely applicable to
unsupervised time series classification or characterisation of other functional
data.

A key challenge in science and engineering is to design experiments to learn
about some unknown quantity of interest. Classical experimental design
optimally allocates the experimental budget to maximize a notion of utility
(e.g., reduction in uncertainty about the unknown quantity). We consider a rich
setting, where the experiments are associated with states in a {\em Markov
chain}, and we can only choose them by selecting a {\em policy} controlling the
state transitions. This problem captures important applications, from
exploration in reinforcement learning to spatial monitoring tasks. We propose
an algorithm -- \textsc{markov-design} -- that efficiently selects policies
whose measurement allocation \emph{provably converges to the optimal one}. The
algorithm is sequential in nature, adapting its choice of policies
(experiments) informed by past measurements. In addition to our theoretical
analysis, we showcase our framework on applications in ecological surveillance
and pharmacology.

Modeling the energy and forces of atomic systems is a fundamental problem in
computational chemistry with the potential to help address many of the world's
most pressing problems, including those related to energy scarcity and climate
change. These calculations are traditionally performed using Density Functional
Theory, which is computationally very expensive. Machine learning has the
potential to dramatically improve the efficiency of these calculations from
days or hours to seconds. We propose the Spherical Channel Network (SCN) to
model atomic energies and forces. The SCN is a graph neural network where nodes
represent atoms and edges their neighboring atoms. The atom embeddings are a
set of spherical functions, called spherical channels, represented using
spherical harmonics. We demonstrate, that by rotating the embeddings based on
the 3D edge orientation, more information may be utilized while maintaining the
rotational equivariance of the messages. While equivariance is a desirable
property, we find that by relaxing this constraint in both message passing and
aggregation, improved accuracy may be achieved. We demonstrate state-of-the-art
results on the large-scale Open Catalyst dataset in both energy and force
prediction for numerous tasks and metrics.

The R\'{e}nyi cross-entropy measure between two distributions, a
generalization of the Shannon cross-entropy, was recently used as a loss
function for the improved design of deep learning generative adversarial
networks. In this work, we examine the properties of this measure and derive
closed-form expressions for it when one of the distributions is fixed and when
both distributions belong to the exponential family. We also analytically
determine a formula for the cross-entropy rate for stationary Gaussian
processes and for finite-alphabet Markov sources.

There is an increase in deploying Deep Learning (DL)-based software systems
in real-world applications. Usually DL models are developed and trained using
DL frameworks that have their own internal mechanisms/formats to represent and
train DL models, and usually those formats cannot be recognized by other
frameworks. Moreover, trained models are usually deployed in environments
different from where they were developed. To solve the interoperability issue
and make DL models compatible with different frameworks/environments, some
exchange formats are introduced for DL models, like ONNX and CoreML. However,
ONNX and CoreML were never empirically evaluated by the community to reveal
their prediction accuracy, performance, and robustness after conversion. Poor
accuracy or non-robust behavior of converted models may lead to poor quality of
deployed DL-based software systems. We conduct, in this paper, the first
empirical study to assess ONNX and CoreML for converting trained DL models. In
our systematic approach, two popular DL frameworks, Keras and PyTorch, are used
to train five widely used DL models on three popular datasets. The trained
models are then converted to ONNX and CoreML and transferred to two runtime
environments designated for such formats, to be evaluated. We investigate the
prediction accuracy before and after conversion. Our results unveil that the
prediction accuracy of converted models are at the same level of originals. The
performance (time cost and memory consumption) of converted models are studied
as well. The size of models are reduced after conversion, which can result in
optimized DL-based software deployment. Converted models are generally assessed
as robust at the same level of originals. However, obtained results show that
CoreML models are more vulnerable to adversarial attacks compared to ONNX.

End-to-end spoken language understanding (SLU) systems benefit from
pretraining on large corpora, followed by fine-tuning on application-specific
data. The resulting models are too large for on-edge applications. For
instance, BERT-based systems contain over 110M parameters. Observing the model
is overparameterized, we propose lean transformer structure where the dimension
of the attention mechanism is automatically reduced using group sparsity. We
propose a variant where the learned attention subspace is transferred to an
attention bottleneck layer. In a low-resource setting and without pre-training,
the resulting compact SLU model achieves accuracies competitive with
pre-trained large models.

Unsupervised learning of 3D-aware generative adversarial networks (GANs)
using only collections of single-view 2D photographs has very recently made
much progress. These 3D GANs, however, have not been demonstrated for human
bodies and the generated radiance fields of existing frameworks are not
directly editable, limiting their applicability in downstream tasks. We propose
a solution to these challenges by developing a 3D GAN framework that learns to
generate radiance fields of human bodies or faces in a canonical pose and warp
them using an explicit deformation field into a desired body pose or facial
expression. Using our framework, we demonstrate the first high-quality radiance
field generation results for human bodies. Moreover, we show that our
deformation-aware training procedure significantly improves the quality of
generated bodies or faces when editing their poses or facial expressions
compared to a 3D GAN that is not trained with explicit deformations.

Machine learning (ML) for diagnosis of thyroid nodules on ultrasound is an
active area of research. However, ML tools require large, well-labelled
datasets, the curation of which is time-consuming and labor-intensive. The
purpose of our study was to develop and test a deep-learning-based tool to
facilitate and automate the data annotation process for thyroid nodules; we
named our tool Multistep Automated Data Labelling Procedure (MADLaP). MADLaP
was designed to take multiple inputs included pathology reports, ultrasound
images, and radiology reports. Using multiple step-wise modules including
rule-based natural language processing, deep-learning-based imaging
segmentation, and optical character recognition, MADLaP automatically
identified images of a specific thyroid nodule and correctly assigned a
pathology label. The model was developed using a training set of 378 patients
across our health system and tested on a separate set of 93 patients. Ground
truths for both sets were selected by an experienced radiologist. Performance
metrics including yield (how many labeled images the model produced) and
accuracy (percentage correct) were measured using the test set. MADLaP achieved
a yield of 63% and an accuracy of 83%. The yield progressively increased as the
input data moved through each module, while accuracy peaked part way through.
Error analysis showed that inputs from certain examination sites had lower
accuracy (40%) than the other sites (90%, 100%). MADLaP successfully created
curated datasets of labeled ultrasound images of thyroid nodules. While
accurate, the relatively suboptimal yield of MADLaP exposed some challenges
when trying to automatically label radiology images from heterogeneous sources.
The complex task of image curation and annotation could be automated, allowing
for enrichment of larger datasets for use in machine learning development.

In this paper, we study the Bayesian multi-task variable selection problem,
where the goal is to select activated variables for multiple related data sets
simultaneously. Our proposed method generalizes the spike-and-slab prior to
multiple data sets, and we prove its posterior consistency in high-dimensional
regimes. To calculate the posterior distribution, we propose a novel
variational Bayes algorithm based on the recently developed "sum of single
effects" model of Wang et al. (2020). Finally, motivated by differential gene
network analysis in biology, we extend our method to joint learning of multiple
directed acyclic graphical models. Both simulation studies and real gene
expression data analysis are conducted to show the effectiveness of the

Motivation: Medical image analysis involves tasks to assist physicians in
qualitative and quantitative analysis of lesions or anatomical structures,
significantly improving the accuracy and reliability of diagnosis and
prognosis. Traditionally, these tasks are finished by physicians or medical
physicists and lead to two major problems: (i) low efficiency; (ii) biased by
personal experience. In the past decade, many machine learning methods have
been applied to accelerate and automate the image analysis process. Compared to
the enormous deployments of supervised and unsupervised learning models,
attempts to use reinforcement learning in medical image analysis are scarce.
This review article could serve as the stepping-stone for related research.
Significance: From our observation, though reinforcement learning has gradually
gained momentum in recent years, many researchers in the medical analysis field
find it hard to understand and deploy in clinics. One cause is lacking
well-organized review articles targeting readers lacking professional computer
science backgrounds. Rather than providing a comprehensive list of all
reinforcement learning models in medical image analysis, this paper may help
the readers to learn how to formulate and solve their medical image analysis
research as reinforcement learning problems. Approach & Results: We selected
published articles from Google Scholar and PubMed. Considering the scarcity of
related articles, we also included some outstanding newest preprints. The
papers are carefully reviewed and categorized according to the type of image
analysis task. We first review the basic concepts and popular models of
reinforcement learning. Then we explore the applications of reinforcement
learning models in landmark detection. Finally, we conclude the article by
discussing the reviewed reinforcement learning approaches' limitations and
possible improvements.

Despite the success of practical solvers in various NP-complete domains such
as SAT and CSP as well as using deep reinforcement learning to tackle
two-player games such as Go, certain classes of PSPACE-hard planning problems
have remained out of reach. Even carefully designed domain-specialized solvers
can fail quickly due to the exponential search space on hard instances. Recent
works that combine traditional search methods, such as best-first search and
Monte Carlo tree search, with Deep Neural Networks' (DNN) heuristics have shown
promising progress and can solve a significant number of hard planning
instances beyond specialized solvers. To better understand why these approaches
work, we studied the interplay of the policy and value networks of DNN-based
best-first search on Sokoban and show the surprising effectiveness of the
policy network, further enhanced by the value network, as a guiding heuristic
for the search. To further understand the phenomena, we studied the cost
distribution of the search algorithms and found that Sokoban instances can have
heavy-tailed runtime distributions, with tails both on the left and right-hand
sides. In particular, for the first time, we show the existence of \textit{left
heavy tails} and propose an abstract tree model that can empirically explain
the appearance of these tails. The experiments show the critical role of the
policy network as a powerful heuristic guiding the search, which can lead to
left heavy tails with polynomial scaling by avoiding exploring exponentially
sized subtrees. Our results also demonstrate the importance of random restarts,
as are widely used in traditional combinatorial solvers, for DNN-based search
methods to avoid left and right heavy tails.

Forward modeling approaches in cosmology have made it possible to reconstruct
the initial conditions at the beginning of the Universe from the observed
survey data. However the high dimensionality of the parameter space still poses
a challenge to explore the full posterior, with traditional algorithms such as
Hamiltonian Monte Carlo (HMC) being computationally inefficient due to
generating correlated samples and the performance of variational inference
being highly dependent on the choice of divergence (loss) function. Here we
develop a hybrid scheme, called variational self-boosted sampling (VBS) to
mitigate the drawbacks of both these algorithms by learning a variational
approximation for the proposal distribution of Monte Carlo sampling and combine
it with HMC. The variational distribution is parameterized as a normalizing
flow and learnt with samples generated on the fly, while proposals drawn from
it reduce auto-correlation length in MCMC chains. Our normalizing flow uses
Fourier space convolutions and element-wise operations to scale to high
dimensions. We show that after a short initial warm-up and training phase, VBS
generates better quality of samples than simple VI approaches and reduces the
correlation length in the sampling phase by a factor of 10-50 over using only
HMC to explore the posterior of initial conditions in 64$^3$ and 128$^3$
dimensional problems, with larger gains for high signal-to-noise data

A novel way of using neural networks to learn the dynamics of time delay
systems from sequential data is proposed. A neural network with trainable
delays is used to approximate the right hand side of a delay differential
equation. We relate the delay differential equation to an ordinary differential
equation by discretizing the time history and train the corresponding neural
ordinary differential equation (NODE) to learn the dynamics. An example on
learning the dynamics of the Mackey-Glass equation using data from chaotic
behavior is given. After learning both the nonlinearity and the time delay, we
demonstrate that the bifurcation diagram of the neural network matches that of
the original system.

This paper presents a novel nearest neighbor search algorithm achieving TPU
(Google Tensor Processing Unit) peak performance, outperforming
state-of-the-art GPU algorithms with similar level of recall. The design of the
proposed algorithm is motivated by an accurate accelerator performance model
that takes into account both the memory and instruction bottlenecks. Our
algorithm comes with an analytical guarantee of recall in expectation and does
not require maintaining sophisticated index data structure or tuning, making it
suitable for applications with frequent updates. Our work is available in the
open-source package of Jax and Tensorflow on TPU.

Hybrid MPI+threads programming is gaining prominence, but, in practice,
applications perform slower with it compared to the MPI everywhere model. The
most critical challenge to the parallel efficiency of MPI+threads applications
is slow MPI_THREAD_MULTIPLE performance. MPI libraries have recently made
significant strides on this front, but to exploit their capabilities, users
must expose the communication parallelism in their MPI+threads applications.
Recent studies show that MPI 4.0 provides users with new performance-oriented
options to do so, but our evaluation of these new mechanisms shows that they
pose several challenges. An alternative design is MPI Endpoints. In this paper,
we present a comparison of the different designs from the perspective of MPI's
end-users: domain scientists and application developers. We evaluate the
mechanisms on metrics beyond performance such as usability, scope, and
portability. Based on the lessons learned, we make a case for a future

This paper studies the problem of forecasting general stochastic processes
using an extension of the Neural Jump ODE (NJ-ODE) framework. While NJ-ODE was
the first framework to establish convergence guarantees for the prediction of
irregularly observed time series, these results were limited to data stemming
from It\^o-diffusions with complete observations, in particular Markov
processes where all coordinates are observed simultaneously. In this work, we
generalise these results to generic, possibly non-Markovian or discontinuous,
stochastic processes with incomplete observations, by utilising the
reconstruction properties of the signature transform. These theoretical results
are supported by empirical studies, where it is shown that the path-dependent
NJ-ODE outperforms the original NJ-ODE framework in the case of non-Markovian
data. Moreover, we show that PD-NJ-ODE can be applied successfully to limit
order book (LOB) data.

Modeling continuous dynamical systems from discretely sampled observations is
a fundamental problem in data science. Often, such dynamics are the result of
non-local processes that present an integral over time. As such, these systems
are modeled with Integro-Differential Equations (IDEs); generalizations of
differential equations that comprise both an integral and a differential
component. For example, brain dynamics are not accurately modeled by
differential equations since their behavior is non-Markovian, i.e. dynamics are
in part dictated by history. Here, we introduce the Neural IDE (NIDE), a novel
deep learning framework based on the theory of IDEs where integral operators
are learned using neural networks. We test NIDE on several toy and brain
activity datasets and demonstrate that NIDE outperforms other models. These
tasks include time extrapolation as well as predicting dynamics from unseen
initial conditions, which we test on whole-cortex activity recordings in freely
behaving mice. Further, we show that NIDE can decompose dynamics into their
Markovian and non-Markovian constituents via the learned integral operator,
which we test on fMRI brain activity recordings of people on ketamine. Finally,
the integrand of the integral operator provides a latent space that gives
insight into the underlying dynamics, which we demonstrate on wide-field brain
imaging recordings. Altogether, NIDE is a novel approach that enables modeling
of complex non-local dynamics with neural networks.

This paper derives a perturbation bound on the optimal subspace estimator
obtained from a subset of its canonical projections contaminated by noise. This
fundamental result has important implications in matrix completion, subspace
clustering, and related problems.

Scientists increasingly rely on Python tools to perform scalable distributed
memory array operations using rich, NumPy-like expressions. However, many of
these tools rely on dynamic schedulers optimized for abstract task graphs,
which often encounter memory and network bandwidth-related bottlenecks due to
sub-optimal data and operator placement decisions. Tools built on the message
passing interface (MPI), such as ScaLAPACK and SLATE, have better scaling
properties, but these solutions require specialized knowledge to use. In this
work, we present NumS, an array programming library which optimizes NumPy-like
expressions on task-based distributed systems. This is achieved through a novel
scheduler called Load Simulated Hierarchical Scheduling (LSHS). LSHS is a local
search method which optimizes operator placement by minimizing maximum memory
and network load on any given node within a distributed system. Coupled with a
heuristic for load balanced data layouts, our approach is capable of attaining
communication lower bounds on some common numerical operations, and our
empirical study shows that LSHS enhances performance on Ray by decreasing
network load by a factor of 2x, requiring 4x less memory, and reducing
execution time by 10x on the logistic regression problem. On terabyte-scale
data, NumS achieves competitive performance to SLATE on DGEMM, up to 20x
speedup over Dask on a key operation for tensor factorization, and a 2x speedup
on logistic regression compared to Dask ML and Spark's MLlib.

Within the framework of Gaussian graphical models, a prior distribution for
the underlying graph is introduced to induce a block structure in the adjacency
matrix of the graph and learning relationships between fixed groups of
variables. A novel sampling strategy named Double Reversible Jumps Markov chain
Monte Carlo is developed for block structural learning, under the conjugate
G-Wishart prior. The algorithm proposes moves that add or remove not just a
single link but an entire group of edges. The method is then applied to smooth
functional data. The classical smoothing procedure is improved by placing a
graphical model on the basis expansion coefficients, providing an estimate of
their conditional independence structure. Since the elements of a B-Spline
basis have compact support, the independence structure is reflected on
well-defined portions of the domain. A known partition of the functional domain
is exploited to investigate relationships among the substances within the
compound.

We present our experience as annotators in the creation of high-quality,
adversarial machine-reading-comprehension data for extractive QA for Task 1 of
the First Workshop on Dynamic Adversarial Data Collection (DADC). DADC is an
emergent data collection paradigm with both models and humans in the loop. We
set up a quasi-experimental annotation design and perform quantitative analyses
across groups with different numbers of annotators focusing on successful
adversarial attacks, cost analysis, and annotator confidence correlation. We
further perform a qualitative analysis of our perceived difficulty of the task
given the different topics of the passages in our dataset and conclude with
recommendations and suggestions that might be of value to people working on
future DADC tasks and related annotation interfaces.

This paper presents a Double Deep Q-Network algorithm for trading single
assets, namely the E-mini S&P 500 continuous futures contract. We use a proven
setup as the foundation for our environment with multiple extensions. The
features of our trading agent are constantly being expanded to include
additional assets such as commodities, resulting in four models. We also
respond to environmental conditions, including costs and crises. Our trading
agent is first trained for a specific time period and tested on new data and
compared with the long-and-hold strategy as a benchmark (market). We analyze
the differences between the various models and the in-sample/out-of-sample
performance with respect to the environment. The experimental results show that
the trading agent follows an appropriate behavior. It can adjust its policy to
different circumstances, such as more extensive use of the neutral position
when trading costs are present. Furthermore, the net asset value exceeded that
of the benchmark, and the agent outperformed the market in the test set. We
provide initial insights into the behavior of an agent in a financial domain
using a DDQN algorithm. The results of this study can be used for further

The growing number of IoT devices and their use to monitor the operation of
machines and equipment increases interest in anomaly detection algorithms
running on devices. However, the difficulty is the limitations of the available
computational and memory resources on the devices. In the case of
microcontrollers (MCUs), these are single megabytes of program and several
hundred kilobytes of working memory. Consequently, algorithms must be
appropriately matched to the capabilities of the devices. In the paper, we
analyse the processing pipeline for anomaly detection and implementation of the
Local Outliner Factor (LOF) algorithm on a MCU. We also show that it is
possible to train such an algorithm directly on the device, which gives great
potential to use the solution in real devices.

Optimal transport (OT) theory describes general principles to define and
select, among many possible choices, the most efficient way to map a
probability measure onto another. That theory has been mostly used to estimate,
given a pair of source and target probability measures $(\mu,\nu)$, a
parameterized map $T_\theta$ that can efficiently map $\mu$ onto $\nu$. In many
applications, such as predicting cell responses to treatments, the data
measures $\mu,\nu$ (features of untreated/treated cells) that define optimal
transport problems do not arise in isolation but are associated with a context
$c$ (the treatment). To account for and incorporate that context in OT
estimation, we introduce CondOT, an approach to estimate OT maps conditioned on
a context variable, using several pairs of measures $(\mu_i, \nu_i)$ tagged
with a context label $c_i$. Our goal is to % extract from a dataset of labeled
pairs $\{(c_i, (\mu_i, \nu_i))\}$ learn a global map $\mathcal{T}_{\theta}$
which is not only expected to fit em all pairs in the dataset $\{(c_i, (\mu_i,
\nu_i))\}$, i.e., $\mathcal{T}_{\theta}(c_i) \sharp\mu_i \approx \nu_i$, but
should generalize to produce meaningful maps
$\mathcal{T}_{\theta}(c_{\text{new}})$ conditioned on unseen contexts
$c_{\text{new}}$. Our approach harnesses and provides a novel usage for
partially input convex neural networks, for which we introduce a robust and
efficient initialization strategy inspired by Gaussian approximations. We
demonstrate the ability of CondOT to infer the effect of an arbitrary
combination of genetic or therapeutic perturbations on single cells, using only
observations of the effects of said perturbations separately.

Semi-supervised learning is the problem of training an accurate predictive
model by combining a small labeled dataset with a presumably much larger
unlabeled dataset. Many methods for semi-supervised deep learning have been
developed, including pseudolabeling, consistency regularization, and
contrastive learning techniques. Pseudolabeling methods however are highly
susceptible to confounding, in which erroneous pseudolabels are assumed to be
true labels in early iterations, thereby causing the model to reinforce its
prior biases and thereby fail to generalize to strong predictive performance.
We present a new approach to suppress confounding errors through a method we
describe as Semi-supervised Contrastive Outlier removal for Pseudo Expectation
Maximization (SCOPE). Like basic pseudolabeling, SCOPE is related to
Expectation Maximization (EM), a latent variable framework which can be
extended toward understanding cluster-assumption deep semi-supervised
algorithms. However, unlike basic pseudolabeling which fails to adequately take
into account the probability of the unlabeled samples given the model, SCOPE
introduces an outlier suppression term designed to improve the behavior of EM
iteration given a discrimination DNN backbone in the presence of outliers. Our
results show that SCOPE greatly improves semi-supervised classification
accuracy over a baseline, and furthermore when combined with consistency
regularization achieves the highest reported accuracy for the semi-supervised
CIFAR-10 classification task using 250 and 4000 labeled samples. Moreover, we
show that SCOPE reduces the prevalence of confounding errors during
pseudolabeling iterations by pruning erroneous high-confidence pseudolabeled
samples that would otherwise contaminate the labeled set in subsequent
retraining iterations.

In this study, we address the problem of efficient exploration in
reinforcement learning. Most common exploration approaches depend on random
action selection, however these approaches do not work well in environments
with sparse or no rewards. We propose Generative Adversarial Network-based
Intrinsic Reward Module that learns the distribution of the observed states and
sends an intrinsic reward that is computed as high for states that are out of
distribution, in order to lead agent to unexplored states. We evaluate our
approach in Super Mario Bros for a no reward setting and in Montezuma's Revenge
for a sparse reward setting and show that our approach is indeed capable of
exploring efficiently. We discuss a few weaknesses and conclude by discussing
future works.

Kernel ridge regression (KRR) has recently attracted renewed interest due to
its potential for explaining the transient effects, such as double descent,
that emerge during neural network training. In this work, we study how the
alignment between the target function and the kernel affects the performance of
the KRR. We focus on the truncated KRR (TKRR) which utilizes an additional
parameter that controls the spectral truncation of the kernel matrix. We show
that for polynomial alignment, there is an \emph{over-aligned} regime, in which
TKRR can achieve a faster rate than what is achievable by full KRR. The rate of
TKRR can improve all the way to the parametric rate, while that of full KRR is
capped at a sub-optimal value. This shows that target alignemnt can be better
leveraged by utilizing spectral truncation in kernel methods. We also consider
the bandlimited alignment setting and show that the regularization surface of
TKRR can exhibit transient effects including multiple descent and non-monotonic
behavior. Our results show that there is a strong and quantifable relation
between the shape of the \emph{alignment spectrum} and the generalization
performance of kernel methods, both in terms of rates and in finite samples.

Imputation allows datasets to be used with algorithms that cannot handle
missing values by themselves. However, missing values may in principle
contribute useful information that is lost through imputation. The
missing-indicator approach can be used to preserve this information. There are
several theoretical considerations why missing-indicators may or may not be
beneficial, but there has not been any large-scale practical experiment on
real-life datasets to test this question for machine learning predictions. We
perform this experiment for three imputation strategies and a range of
different classification algorithms, on the basis of twenty real-life datasets.
We find that missing-indicators generally increase classification performance,
and that nearest neighbour and iterative imputation do not lead to better
performance than simple mean/mode imputation. Therefore, we recommend the use
of missing-indicators with mean/mode imputation as a safe default, with the
caveat that for decision trees, pruning is necessary to prevent overfitting.

Visual model-based reinforcement learning (RL) has the potential to enable
sample-efficient robot learning from visual observations. Yet the current
approaches typically train a single model end-to-end for learning both visual
representations and dynamics, making it difficult to accurately model the
interaction between robots and small objects. In this work, we introduce a
visual model-based RL framework that decouples visual representation learning
and dynamics learning. Specifically, we train an autoencoder with convolutional
layers and vision transformers (ViT) to reconstruct pixels given masked
convolutional features, and learn a latent dynamics model that operates on the
representations from the autoencoder. Moreover, to encode task-relevant
information, we introduce an auxiliary reward prediction objective for the
autoencoder. We continually update both autoencoder and dynamics model using
online samples collected from environment interaction. We demonstrate that our
decoupling approach achieves state-of-the-art performance on a variety of
visual robotic tasks from Meta-world and RLBench, e.g., we achieve 81.7%
success rate on 50 visual robotic manipulation tasks from Meta-world, while the
baseline achieves 67.9%. Code is available on the project website:
https://sites.google.com/view/mwm-rl.

Software Defined Network (SDN) is the next generation network that decouples
the control plane from the data plane of forwarding devices by utilizing the
OpenFlow protocol as a communication link between the data plane and the
control plane. However, there are some security issues might be in actions on
SDN that the attackers can take control over the SDN control plane. Thus,
traffic measurement is a fundamental technique of protecting SDN against the
high-security threats such as DDoS, heavy hitter, superspreader as well as live
video calling, QoS control, high bandwidth requirement, resource management are
also inevitable in SDN/Software Defined Cellular Network (SDCN). In such a
scenario, we survey SDN traffic measurement solutions, in order to assess how
these solutions can make a secured, efficient and robust SDN/SDCN architecture.
In this paper, various types of SDN traffic measurement solutions have been
categorized based on network applications behaviour. Furthermore, we find out
the challenges related to SDN/SDCN traffic measurement and future scope of
research, which will guide to design and develop more advanced traffic
measurement solutions for a scalable, heterogeneous, hierarchical and widely
deployed SDN/SDCN in future prospects. More in details, we list out kinds of
practical machine learning (ML) approaches to analyze how we can make
improvement in the traffic measurement performances. We conclude that using ML
in SDN traffic measurement solutions will give benefit to get secured SDN/SDCN
network in complementary ways.

In deterministic optimization, it is typically assumed that all parameters of
the problem are fixed and known. In practice, however, some parameters may be a
priori unknown but can be estimated from historical data. A typical
predict-then-optimize approach separates predictions and optimization into two
stages. Recently, end-to-end predict-then-optimize has become an attractive
alternative. In this work, we present the PyEPO package, a PyTorch-based
end-to-end predict-then-optimize library in Python. To the best of our
knowledge, PyEPO (pronounced like "pineapple" with a silent "n") is the first
such generic tool for linear and integer programming with predicted objective
function coefficients. It provides two base algorithms: the first is based on
the convex surrogate loss function from the seminal work of Elmachtoub & Grigas
(2021), and the second is based on the differentiable black-box solver approach
of Vlastelica et al. (2019). PyEPO provides a simple interface for the
definition of new optimization problems, the implementation of state-of-the-art
predict-then-optimize training algorithms, the use of custom neural network
architectures, and the comparison of end-to-end approaches with the two-stage
approach. PyEPO enables us to conduct a comprehensive set of experiments
comparing a number of end-to-end and two-stage approaches along axes such as
prediction accuracy, decision quality, and running time on problems such as
Shortest Path, Multiple Knapsack, and the Traveling Salesperson Problem. We
discuss some empirical insights from these experiments which could guide future
research. PyEPO and its documentation are available at
https://github.com/khalil-research/PyEPO.

In this article we propose and validate an unsupervised probabilistic model,
Gaussian Latent Dirichlet Allocation (GLDA), for the problem of discrete state
discovery from repeated, multivariate psychophysiological samples collected
from multiple, inherently distinct, individuals. Psychology and medical
research heavily involves measuring potentially related but individually
inconclusive variables from a cohort of participants to derive diagnosis,
necessitating clustering analysis. Traditional probabilistic clustering models
such as Gaussian Mixture Model (GMM) assume a global mixture of component
distributions, which may not be realistic for observations from different
patients. The GLDA model borrows the individual-specific mixture structure from
a popular topic model Latent Dirichlet Allocation (LDA) in Natural Language
Processing and merges it with the Gaussian component distributions of GMM to
suit continuous type data. We implemented GLDA using STAN (a probabilistic
modeling language) and applied it on two datasets, one containing Ecological
Momentary Assessments (EMA) and the other heart measures from electrocardiogram
and impedance cardiograph. We found that in both datasets the GLDA-learned
class weights achieved significantly higher correlations with clinically
assessed depression, anxiety, and stress scores than those produced by the
baseline GMM. Our findings demonstrate the advantage of GLDA over conventional
finite mixture models for human state discovery from repeated multivariate
data, likely due to better characterization of potential underlying
between-participant differences. Future work is required to validate the
utility of this model on a broader range of applications.

The Astropy Project supports and fosters the development of open-source and
openly-developed Python packages that provide commonly needed functionality to
the astronomical community. A key element of the Astropy Project is the core
package $\texttt{astropy}$, which serves as the foundation for more specialized
projects and packages. In this article, we summarize key features in the core
package as of the recent major release, version 5.0, and provide major updates
for the Project. We then discuss supporting a broader ecosystem of
interoperable packages, including connections with several astronomical
observatories and missions. We also revisit the future outlook of the Astropy
Project and the current status of Learn Astropy. We conclude by raising and
discussing the current and future challenges facing the Project.

We develop a set of machine-learning based cosmological emulators, to obtain
fast model predictions for the $C(\ell)$ angular power spectrum coefficients
characterising tomographic observations of galaxy clustering and weak
gravitational lensing from multi-band photometric surveys (and their
cross-correlation). A set of neural networks are trained to map cosmological
parameters into the coefficients, achieving a speed-up $\mathcal{O}(10^3)$ in
computing the required statistics for a given set of cosmological parameters,
with respect to standard Boltzmann solvers, with an accuracy better than
$0.175\%$ ($<0.1\%$ for the weak lensing case). This corresponds to $\sim 2\%$
or less of the statistical error bars expected from a typical Stage IV
photometric surveys. Such overall improvement in speed and accuracy is obtained
through ($\textit{i}$) a specific pre-processing optimisation, ahead of the
training phase, and ($\textit{ii}$) a more effective neural network
architecture, compared to previous implementations.

Safety is still the main issue of autonomous driving, and in order to be
globally deployed, they need to predict pedestrians' motions sufficiently in
advance. While there is a lot of research on coarse-grained (human center
prediction) and fine-grained predictions (human body keypoints prediction), we
focus on 3D bounding boxes, which are reasonable estimates of humans without
modeling complex motion details for autonomous vehicles. This gives the
flexibility to predict in longer horizons in real-world settings. We suggest
this new problem and present a simple yet effective model for pedestrians' 3D
bounding box prediction. This method follows an encoder-decoder architecture
based on recurrent neural networks, and our experiments show its effectiveness
in both the synthetic (JTA) and real-world (NuScenes) datasets. The learned
representation has useful information to enhance the performance of other
tasks, such as action anticipation. Our code is available online:
https://github.com/vita-epfl/bounding-box-prediction

Heating and cooling systems in buildings account for 31% of global energy
as 31% whilst maintaining thermal comfort. Our source code is available online
via https://enjeeneer.io/projects/pearl/

We present an approach for generating playable games that blend a given set
of games in a desired combination using deep generative latent variable models.
We refer to this approach as latent combinational game design -- latent since
we use learned latent representations to perform blending, combinational since
game blending is a combinational creativity process and game design since the
approach generates novel, playable games. We use Gaussian Mixture Variational
Autoencoders (GMVAEs), which use a mixture of Gaussians to model the VAE latent
space. Through supervised training, each component learns to encode levels from
one game and lets us define new, blended games as linear combinations of these
learned components. This enables generating new games that blend the input
games as well as control the relative proportions of each game in the blend. We
also extend prior work using conditional VAEs to perform blending and compare
against the GMVAE. Our results show that both models can generate playable
blended games that blend the input games in the desired proportions.

A long-held objective in AI is to build systems that understand concepts in a
humanlike way. Setting aside the difficulty of building such a system, even
trying to evaluate one is a challenge, due to present-day AI's relative opacity
and its proclivity for finding shortcut solutions. This is exacerbated by
humans' tendency to anthropomorphize, assuming that a system that can recognize
one instance of a concept must also understand other instances, as a human
would. In this paper, we argue that understanding a concept requires the
ability to use it in varied contexts. Accordingly, we propose systematic
evaluations centered around concepts, by probing a system's ability to use a
given concept in many different instantiations. We present case studies of such
an evaluations on two domains -- RAVEN (inspired by Raven's Progressive
Matrices) and the Abstraction and Reasoning Corpus (ARC) -- that have been used
to develop and assess abstraction abilities in AI systems. Our concept-based
approach to evaluation reveals information about AI systems that conventional
test sets would have left hidden.

In many computational problems in engineering and science, function or model
differentiation is essential, but also integration is needed. An important
class of computational problems include so-called integro-differential
equations which include both integrals and derivatives of a function. In
another example, stochastic differential equations can be written in terms of a
partial differential equation of a probability density function of the
stochastic variable. To learn characteristics of the stochastic variable based
on the density function, specific integral transforms, namely moments, of the
density function need to be calculated. Recently, the machine learning paradigm
of Physics-Informed Neural Networks emerged with increasing popularity as a
method to solve differential equations by leveraging automatic differentiation.
In this work, we propose to augment the paradigm of Physics-Informed Neural
Networks with automatic integration in order to compute complex integral
transforms on trained solutions, and to solve integro-differential equations
where integrals are computed on-the-fly during training. Furthermore, we
showcase the techniques in various application settings, numerically simulating
quantum computer-based neural networks as well as classical neural networks.

To solve tasks in complex environments, robots need to learn from experience.
Deep reinforcement learning is a common approach to robot learning but requires
a large amount of trial and error to learn, limiting its deployment in the
physical world. As a consequence, many advances in robot learning rely on
simulators. On the other hand, learning inside of simulators fails to capture
the complexity of the real world, is prone to simulator inaccuracies, and the
resulting behaviors do not adapt to changes in the world. The Dreamer algorithm
has recently shown great promise for learning from small amounts of interaction
by planning within a learned world model, outperforming pure reinforcement
learning in video games. Learning a world model to predict the outcomes of
potential actions enables planning in imagination, reducing the amount of trial
and error needed in the real environment. However, it is unknown whether
Dreamer can facilitate faster learning on physical robots. In this paper, we
apply Dreamer to 4 robots to learn online and directly in the real world,
without simulators. Dreamer trains a quadruped robot to roll off its back,
stand up, and walk from scratch and without resets in only 1 hour. We then push
the robot and find that Dreamer adapts within 10 minutes to withstand
perturbations or quickly roll over and stand back up. On two different robotic
arms, Dreamer learns to pick and place multiple objects directly from camera
images and sparse rewards, approaching human performance. On a wheeled robot,
Dreamer learns to navigate to a goal position purely from camera images,
automatically resolving ambiguity about the robot orientation. Using the same
hyperparameters across all experiments, we find that Dreamer is capable of
online learning in the real world, establishing a strong baseline. We release
our infrastructure for future applications of world models to robot learning.

Distributional reinforcement learning demonstrates state-of-the-art
performance in continuous and discrete control settings with the features of
variance and risk, which can be used to explore. However, the exploration
method employing the risk property is hard to find, although numerous
exploration methods in Distributional RL employ the variance of return
distribution per action. In this paper, we present risk scheduling approaches
that explore risk levels and optimistic behaviors from a risk perspective. We
demonstrate the performance enhancement of the DMIX algorithm using risk
scheduling in a multi-agent setting with comprehensive experiments.

When used in autonomous driving, goal recognition allows the future behaviour
of other vehicles to be more accurately predicted. A recent goal recognition
method for autonomous vehicles, GRIT, has been shown to be fast, accurate,
interpretable and verifiable. In autonomous driving, vehicles can encounter
novel scenarios that were unseen during training, and the environment is
partially observable due to occlusions. However, GRIT can only operate in fixed
frame scenarios, with full observability. We present a novel goal recognition
method named Goal Recognition with Interpretable Trees under Occlusion (OGRIT),
which solves these shortcomings of GRIT. We demonstrate that OGRIT can
generalise between different scenarios and handle missing data due to
occlusions, while still being fast, accurate, interpretable and verifiable.

There is abundant medical data on the internet, most of which are unlabeled.
Traditional supervised learning algorithms are often limited by the amount of
labeled data, especially in the medical domain, where labeling is costly in
terms of human processing and specialized experts needed to label them. They
are also prone to human error and biased as a select few expert annotators
label them. These issues are mitigated by Self-supervision, where we generate
pseudo-labels from unlabelled data by seeing the data itself. This paper
presents various self-supervision strategies to enhance the performance of a
time-series based Diffusion convolution recurrent neural network (DCRNN) model.
The learned weights in the self-supervision pretraining phase can be
transferred to the supervised training phase to boost the model's prediction
capability. Our techniques are tested on an extension of a Diffusion
Convolutional Recurrent Neural network (DCRNN) model, an RNN with graph
diffusion convolutions, which models the spatiotemporal dependencies present in
EEG signals. When the learned weights from the pretraining stage are
transferred to a DCRNN model to determine whether an EEG time window has a
characteristic seizure signal associated with it, our method yields an AUROC
score $1.56\%$ than the current state-of-the-art models on the TUH EEG seizure
corpus.

Differentiable simulation is a promising toolkit for fast gradient-based
policy optimization and system identification. However, existing approaches to
differentiable simulation have largely tackled scenarios where obtaining smooth
gradients has been relatively easy, such as systems with mostly smooth
dynamics. In this work, we study the challenges that differentiable simulation
presents when it is not feasible to expect that a single descent reaches a
global optimum, which is often a problem in contact-rich scenarios. We analyze
the optimization landscapes of diverse scenarios that contain both rigid bodies
and deformable objects. In dynamic environments with highly deformable objects
and fluids, differentiable simulators produce rugged landscapes with
nonetheless useful gradients in some parts of the space. We propose a method
that combines Bayesian optimization with semi-local 'leaps' to obtain a global
search method that can use gradients effectively, while also maintaining robust
performance in regions with noisy gradients. We show that our approach
outperforms several gradient-based and gradient-free baselines on an extensive
set of experiments in simulation, and also validate the method using
experiments with a real robot and deformables. Videos and supplementary
materials are available at https://tinyurl.com/globdiff

Traffic congestion anomaly detection is of paramount importance in
intelligent traffic systems. The goals of transportation agencies are two-fold:
to monitor the general traffic conditions in the area of interest and to locate
road segments under abnormal congestion states. Modeling congestion patterns
can achieve these goals for citywide roadways, which amounts to learning the
distribution of multivariate time series (MTS). However, existing works are
either not scalable or unable to capture the spatial-temporal information in
MTS simultaneously. To this end, we propose a principled and comprehensive
framework consisting of a data-driven generative approach that can perform
tractable density estimation for detecting traffic anomalies. Our approach
first clusters segments in the feature space and then uses conditional
normalizing flow to identify anomalous temporal snapshots at the cluster level
in an unsupervised setting. Then, we identify anomalies at the segment level by
using a kernel density estimator on the anomalous cluster. Extensive
experiments on synthetic datasets show that our approach significantly
outperforms several state-of-the-art congestion anomaly detection and diagnosis
methods in terms of Recall and F1-Score. We also use the generative model to
sample labeled data, which can train classifiers in a supervised setting,
alleviating the lack of labeled data for anomaly detection in sparse settings.

The rise of vehicle automation has generated significant interest in the
potential role of future automated vehicles (AVs). In particular, in highly
dense traffic settings, AVs are expected to serve as congestion-dampeners,
mitigating the presence of instabilities that arise from various sources.
However, in many applications, such maneuvers rely heavily on non-local sensing
or coordination by interacting AVs, thereby rendering their adaptation to
real-world settings a particularly difficult challenge. To address this
challenge, this paper examines the role of imitation learning in bridging the
gap between such control strategies and realistic limitations in communication
and sensing. Treating one such controller as an "expert", we demonstrate that
imitation learning can succeed in deriving policies that, if adopted by 5% of
vehicles, may boost the energy-efficiency of networks with varying traffic
conditions by 15% using only local observations. Results and code are available
online at https://sites.google.com/view/il-traffic/home.

Thermal comfort in indoor environments has an enormous impact on the health,
well-being, and performance of occupants. Given the focus on energy efficiency
and Internet-of-Things enabled smart buildings, machine learning (ML) is being
increasingly used for data-driven thermal comfort (TC) prediction. Generally,
ML-based solutions are proposed for air-conditioned or HVAC ventilated
buildings and the models are primarily designed for adults. On the other hand,
naturally ventilated (NV) buildings are the norm in most countries. They are
also ideal for energy conservation and long-term sustainability goals. However,
the indoor environment of NV buildings lacks thermal regulation and varies
significantly across spatial contexts. These factors make TC prediction
extremely challenging. Thus, determining the impact of the building environment
on the performance of TC models is important. Further, the generalization
capability of TC prediction models across different NV indoor spaces needs to
be studied. This work addresses these problems. Data is gathered through
month-long field experiments conducted in 5 naturally ventilated school
buildings, involving 512 primary school students. The impact of spatial
variability on student comfort is demonstrated through variation in prediction
accuracy (by as much as 71%). The influence of building environment on TC
prediction is also demonstrated through variation in feature importance.
Further, a comparative analysis of spatial variability in model performance is
done for children (our dataset) and adults (ASHRAE-II database). Finally, the
generalization capability of thermal comfort models in NV classrooms is
assessed and major challenges are highlighted.

Model stealing attacks present a dilemma for public machine learning APIs. To
protect financial investments, companies may be forced to withhold important
information about their models that could facilitate theft, including
uncertainty estimates and prediction explanations. This compromise is harmful
not only to users but also to external transparency. Model stealing defenses
seek to resolve this dilemma by making models harder to steal while preserving
utility for benign users. However, existing defenses have poor performance in
practice, either requiring enormous computational overheads or severe utility
trade-offs. To meet these challenges, we present a new approach to model
stealing defenses called gradient redirection. At the core of our approach is a
provably optimal, efficient algorithm for steering an adversary's training
updates in a targeted manner. Combined with improvements to surrogate networks
and a novel coordinated defense strategy, our gradient redirection defense,
called GRAD${}^2$, achieves small utility trade-offs and low computational
overhead, outperforming the best prior defenses. Moreover, we demonstrate how
gradient redirection enables reprogramming the adversary with arbitrary
behavior, which we hope will foster work on new avenues of defense.

Precision agriculture is rapidly attracting research to efficiently introduce
automation and robotics solutions to support agricultural activities. Robotic
navigation in vineyards and orchards offers competitive advantages in
autonomously monitoring and easily accessing crops for harvesting, spraying and
performing time-consuming necessary tasks. Nowadays, autonomous navigation
algorithms exploit expensive sensors which also require heavy computational
cost for data processing. Nonetheless, vineyard rows represent a challenging
outdoor scenario where GPS and Visual Odometry techniques often struggle to
provide reliable positioning information. In this work, we combine Edge AI with
Deep Reinforcement Learning to propose a cutting-edge lightweight solution to
tackle the problem of autonomous vineyard navigation without exploiting precise
localization data and overcoming task-tailored algorithms with a flexible
learning-based approach. We train an end-to-end sensorimotor agent which
directly maps noisy depth images and position-agnostic robot state information
to velocity commands and guides the robot to the end of a row, continuously
adjusting its heading for a collision-free central trajectory. Our extensive
experimentation in realistic simulated vineyards demonstrates the effectiveness
of our solution and the generalization capabilities of our agent.

Software packages like TensorFlow and PyTorch are designed to support linear
algebra operations, and their speed and usability determine their success.
However, by prioritising speed, they often neglect memory requirements. As a
consequence, the implementations of memory-intensive algorithms that are
convenient in terms of software design can often not be run for large problems
due to memory overflows. Memory-efficient solutions require complex programming
approaches with significant logic outside the computational framework. This
impairs the adoption and use of such algorithms. To address this, we developed
an XLA compiler extension that adjusts the computational data-flow
representation of an algorithm according to a user-specified memory limit. We
show that k-nearest neighbour and sparse Gaussian process regression methods
can be run at a much larger scale on a single device, where standard
implementations would have failed. Our approach leads to better use of hardware
resources. We believe that further focus on removing memory constraints at a
compiler level will widen the range of machine learning methods that can be
developed in the future.

We analyze the current trends in higher education and discuss its impact on
physics enrollment in US institutions. The pandemic, lockdowns, unemployment,
and healthcare problems have led to unique social and economic conditions.
These conditions have modified the latest trends in education. COVID-19 has had
great impact on the academic culture due to online teaching and learning
methods. We identify some of the key factors including economic problems,
changes in job market, modifications in family obligations, physical and mental
health conditions, and overall insecurity and uncertainty in life. These key
factors are causing a shift in educational preferences. A few recommendations
are made to get out of the current dilemma. We use all the data collected by
the American Physical Society statistics department [1].

The recent development of novel aerial vehicles capable of physically
interacting with the environment leads to new applications such as
contact-based inspection. These tasks require the robotic system to exchange
forces with partially-known environments, which may contain uncertainties
including unknown spatially-varying friction properties and discontinuous
variations of the surface geometry. Finding a control strategy that is robust
against these environmental uncertainties remains an open challenge. This paper
presents a learning-based adaptive control strategy for aerial sliding tasks.
In particular, the gains of a standard impedance controller are adjusted in
real-time by a policy based on the current control signals, proprioceptive
measurements, and tactile sensing. This policy is trained in simulation with
simplified actuator dynamics in a student-teacher learning setup. The
real-world performance of the proposed approach is verified using a tilt-arm
omnidirectional flying vehicle. The proposed controller structure combines
data-driven and model-based control methods, enabling our approach to
successfully transfer directly and without adaptation from simulation to the
real platform. Compared to fine-tuned state of the art interaction control
methods we achieve reduced tracking error and improved disturbance rejection.

Self-supervised learning (SSL) is an emerging technique that has been
successfully employed to train convolutional neural networks (CNNs) and graph
neural networks (GNNs) for more transferable, generalizable, and robust
representation learning. However its potential in motion forecasting for
autonomous driving has rarely been explored. In this study, we report the first
systematic exploration and assessment of incorporating self-supervision into
motion forecasting. We first propose to investigate four novel self-supervised
learning tasks for motion forecasting with theoretical rationale and
quantitative and qualitative comparisons on the challenging large-scale
Argoverse dataset. Secondly, we point out that our auxiliary SSL-based learning
setup not only outperforms forecasting methods which use transformers,
complicated fusion mechanisms and sophisticated online dense goal candidate
optimization algorithms in terms of performance accuracy, but also has low
inference time and architectural complexity. Lastly, we conduct several
experiments to understand why SSL improves motion forecasting. Code is
open-sourced at \url{https://github.com/AutoVision-cloud/SSL-Lanes}.

Quantum neural networks are promising for a wide range of applications in the
Noisy Intermediate-Scale Quantum era. As such, there is an increasing demand
for automatic quantum neural architecture search. We tackle this challenge by
designing a quantum circuits metric for Bayesian optimization with Gaussian
process. To this goal, we propose a new quantum gates distance that
characterizes the gates' action over every quantum state and provide a
theoretical perspective on its geometrical properties. Our approach
significantly outperforms the benchmark on three empirical quantum machine
learning problems including training a quantum generative adversarial network,
solving combinatorial optimization in the MaxCut problem, and simulating
quantum Fourier transform. Our method can be extended to characterize behaviors
of various quantum machine learning models.

We train an LSTM network based on a pooled dataset made of hundreds of liquid
stocks aiming to forecast the next daily realized volatility for all stocks.
Showing the consistent outperformance of this universal LSTM relative to other
asset-specific parametric models, we uncover nonparametric evidences of a
universal volatility formation mechanism across assets relating past market
realizations, including daily returns and volatilities, to current
volatilities. A parsimonious parametric forecasting device combining the rough
fractional stochastic volatility and quadratic rough Heston models with fixed
parameters results in the same level of performance as the universal LSTM,
which confirms the universality of the volatility formation process from a
parametric perspective.

This work introduces the RevSilo, the first reversible module for
bidirectional multi-scale feature fusion. Like other reversible methods,
RevSilo eliminates the need to store hidden activations by recomputing them.
Existing reversible methods, however, do not apply to multi-scale feature
fusion and are therefore not applicable to a large class of networks.
Bidirectional multi-scale feature fusion promotes local and global coherence
and has become a de facto design principle for networks targeting spatially
sensitive tasks e.g. HRNet and EfficientDet. When paired with high-resolution
inputs, these networks achieve state-of-the-art results across various computer
vision tasks, but training them requires substantial accelerator memory for
saving large, multi-resolution activations. These memory requirements cap
network size and limit progress. Using reversible recomputation, the RevSilo
alleviates memory issues while still operating across resolution scales.
Stacking RevSilos, we create RevBiFPN, a fully reversible bidirectional feature
pyramid network. For classification, RevBiFPN is competitive with networks such
as EfficientNet while using up to 19.8x lesser training memory. When fine-tuned
on COCO, RevBiFPN provides up to a 2.5% boost in AP over HRNet using fewer MACs
and a 2.4x reduction in training-time memory.

As an alternative to classical numerical solvers for partial differential
equations (PDEs) subject to boundary value constraints, there has been a surge
of interest in investigating neural networks that can solve such problems
efficiently. In this work, we design a general solution operator for two
different time-independent PDEs using graph neural networks (GNNs) and spectral
graph convolutions. We train the networks on simulated data from a finite
elements solver on a variety of shapes and inhomogeneities. In contrast to
previous works, we focus on the ability of the trained operator to generalize
to previously unseen scenarios. Specifically, we test generalization to meshes
with different shapes and superposition of solutions for a different number of
inhomogeneities. We find that training on a diverse dataset with lots of
variation in the finite element meshes is a key ingredient for achieving good
generalization results in all cases. With this, we believe that GNNs can be
used to learn solution operators that generalize over a range of properties and
produce solutions much faster than a generic solver. Our dataset, which we make
publicly available, can be used and extended to verify the robustness of these
models under varying conditions.

Compiler optimization decisions are often based on hand-crafted heuristics
centered around a few established benchmark suites. Alternatively, they can be
learned from feature and performance data produced during compilation. However,
data-driven compiler optimizations based on machine learning models require
large sets of quality data for training in order to match or even outperform
existing human-crafted heuristics. In static compilation setups, related work
has addressed this problem with iterative compilation. However, a dynamic
compiler may produce different data depending on dynamically-chosen compilation
strategies, which aggravates the generation of comparable data. We propose
compilation forking, a technique for generating consistent feature and
performance data from arbitrary, dynamically-compiled programs. Different
versions of program parts with the same profiling and compilation history are
executed within single program runs to minimize noise stemming from dynamic
compilation and the runtime environment. Our approach facilitates large-scale
performance evaluations of compiler optimization decisions. Additionally,
compilation forking supports creating domain-specific compilation strategies
based on machine learning by providing the data for model training. We
implemented compilation forking in the GraalVM compiler in a
programming-language-agnostic way. To assess the quality of the generated data,
we trained several machine learning models to replace compiler heuristics for
loop-related optimizations. The trained models perform equally well to the
highly-tuned compiler heuristics when comparing the geometric means of
benchmark suite performances. Larger impacts on few single benchmarks range
from speedups of 20% to slowdowns of 17%. The presented approach can be
implemented in any dynamic compiler. We believe that it can help to analyze
compilation decisions and leverage the use of machine learning into dynamic
compilation.

A timeline provides one of the most effective ways to visualize the important
historical facts that occurred over a period of time, presenting the insights
that may not be so apparent from reading the equivalent information in textual
form. By leveraging generative adversarial learning for important sentence
classification and by assimilating knowledge based tags for improving the
performance of event coreference resolution we introduce a two staged system
for event timeline generation from multiple (historical) text documents. We
demonstrate our results on two manually annotated historical text documents.
Our results can be extremely helpful for historians, in advancing research in
history and in understanding the socio-political landscape of a country as
reflected in the writings of famous personas.

The electron density of a molecule or material has recently received major
attention as a target quantity of machine-learning models. A natural choice to
construct a model that yields transferable and linear-scaling predictions is to
represent the scalar field using a multi-centered atomic basis analogous to
that routinely used in density fitting approximations. However, the
non-orthogonality of the basis poses challenges for the learning exercise, as
it requires accounting for all the atomic density components at once. We devise
a gradient-based approach to directly minimize the loss function of the
regression problem in an optimized and highly sparse feature space. In so
doing, we overcome the limitations associated with adopting an atom-centered
model to learn the electron density over arbitrarily complex datasets,
obtaining extremely accurate predictions. The enhanced framework is tested on
32-molecule periodic cells of liquid water, presenting enough complexity to
require an optimal balance between accuracy and computational efficiency. We
show that starting from the predicted density a single Kohn-Sham
diagonalization step can be performed to access total energy components that
carry an error of just 0.1 meV/atom with respect to the reference density
functional calculations. Finally, we test our method on the highly
heterogeneous QM9 benchmark dataset, showing that a small fraction of the
training data is enough to derive ground-state total energies within chemical
accuracy.

In many real-world scenarios, data to train machine learning models become
available over time. However, neural network models struggle to continually
learn new concepts without forgetting what has been learnt in the past. This
phenomenon is known as catastrophic forgetting and it is often difficult to
prevent due to practical constraints, such as the amount of data that can be
stored or the limited computation sources that can be used. Moreover, training
large neural networks, such as Transformers, from scratch is very costly and
requires a vast amount of training data, which might not be available in the
application domain of interest. A recent trend indicates that dynamic
architectures based on an expansion of the parameters can reduce catastrophic
forgetting efficiently in continual learning, but this needs complex tuning to
balance the growing number of parameters and barely share any information
across tasks. As a result, they struggle to scale to a large number of tasks
without significant overhead. In this paper, we validate in the computer vision
domain a recent solution called Adaptive Distillation of Adapters (ADA), which
is developed to perform continual learning using pre-trained Transformers and
Adapters on text classification tasks. We empirically demonstrate on different
classification tasks that this method maintains a good predictive performance
without retraining the model or increasing the number of model parameters over
the time. Besides it is significantly faster at inference time compared to the

Dedicated surveys searching for Fast Radio Bursts (FRBs) are subject to
selection effects which bias the observed population of events. Software
injection systems are one method of correcting for these biases by injecting a
mock population of synthetic FRBs directly into the realtime search pipeline.
The injected population may then be used to map intrinsic burst properties onto
an expected signal-to-noise ratio (SNR), so long as telescope characteristics
such as the beam model and calibration factors are properly accounted for. This
paper presents an injection system developed for the Canadian Hydrogen
Intensity Mapping Experiment Fast Radio Burst project (CHIME/FRB). The system
was tested to ensure high detection efficiency, and the pulse calibration
method was verified. Using an injection population of ~85,000 synthetic FRBs,
we found that the correlation between fluence and SNR for injected FRBs was
consistent with that of CHIME/FRB detections in the first CHIME/FRB catalog. We
also noted that the sensitivity of the telescope varied strongly as a function
of the broadened burst width, but not as a function of the dispersion measure.
We conclude that some of the machine-learning based Radio Frequency
Interference (RFI) mitigation methods used by CHIME/FRB can be re-trained using
injection data to increase sensitivity to wide events, and that planned
upgrades to the presented injection system will allow for determining a more
accurate CHIME/FRB selection function in the near future.

In compressed sensing, the goal is to reconstruct the signal from an
underdetermined system of linear measurements. Thus, prior knowledge about the
signal of interest and its structure is required. Additionally, in many
scenarios, the signal has an unknown orientation prior to measurements. To
address such recovery problems, we propose using equivariant generative models
as a prior, which encapsulate orientation information in their latent space.
Thereby, we show that signals with unknown orientations can be recovered with
iterative gradient descent on the latent space of these models and provide
additional theoretical recovery guarantees. We construct an equivariant
variational autoencoder and use the decoder as generative prior for compressed
sensing. We discuss additional potential gains of the proposed approach in
terms of convergence and latency.

Reward-free reinforcement learning (RF-RL), a recently introduced RL
paradigm, relies on random action-taking to explore the unknown environment
without any reward feedback information. While the primary goal of the
exploration phase in RF-RL is to reduce the uncertainty in the estimated model
with minimum number of trajectories, in practice, the agent often needs to
abide by certain safety constraint at the same time. It remains unclear how
such safe exploration requirement would affect the corresponding sample
complexity in order to achieve the desired optimality of the obtained policy in
planning. In this work, we make a first attempt to answer this question. In
particular, we consider the scenario where a safe baseline policy is known
beforehand, and propose a unified Safe reWard-frEe ExploraTion (SWEET)
framework. We then particularize the SWEET framework to the tabular and the
low-rank MDP settings, and develop algorithms coined Tabular-SWEET and
Low-rank-SWEET, respectively. Both algorithms leverage the concavity and
continuity of the newly introduced truncated value functions, and are
guaranteed to achieve zero constraint violation during exploration with high
probability. Furthermore, both algorithms can provably find a near-optimal
policy subject to any constraint in the planning phase. Remarkably, the sample
complexities under both algorithms match or even outperform the state of the
art in their constraint-free counterparts up to some constant factors, proving
that safety constraint hardly increases the sample complexity for RF-RL.

In Machine Learning, Artificial Neural Networks (ANNs) are a very powerful
tool, broadly used in many applications. Often, the selected (deep)
architectures include many layers, and therefore a large amount of parameters,
which makes training, storage and inference expensive. This motivated a stream
of research about compressing the original networks into smaller ones without
excessively sacrificing performances. Among the many proposed compression
approaches, one of the most popular is \emph{pruning}, whereby entire elements
of the ANN (links, nodes, channels, \ldots) and the corresponding weights are
deleted. Since the nature of the problem is inherently combinatorial (what
elements to prune and what not), we propose a new pruning method based on
Operational Research tools. We start from a natural Mixed-Integer-Programming
model for the problem, and we use the Perspective Reformulation technique to
strengthen its continuous relaxation. Projecting away the indicator variables
from this reformulation yields a new regularization term, which we call the
Structured Perspective Regularization, that leads to structured pruning of the
initial architecture. We test our method on some ResNet architectures applied
to CIFAR-10, CIFAR-100 and ImageNet datasets, obtaining competitive
performances w.r.t.~the state of the art for structured pruning.

Business Process Simulation (BPS) is a common approach to estimate the impact
of changes to a business process on its performance measures. For example, BPS
allows us to estimate what would be the cycle time of a process if we automated
one of its activities. The starting point of BPS is a business process model
annotated with simulation parameters (a BPS model). Several studies have
proposed methods to automatically discover BPS models from event logs via
process mining. However, current techniques in this space discover BPS models
that only capture waiting times caused by resource contention or resource
unavailability. Oftentimes, a considerable portion of the waiting time in a
business process is caused by extraneous delays, e.g. a resource waits for the
customer to return a phone call. This paper proposes a method that discovers
extraneous delays from input data, and injects timer events into a BPS model to
capture the discovered delays. An empirical evaluation involving synthetic and
real-life logs shows that the approach produces BPS models that better reflect
the temporal dynamics of the process, relative to BPS models that do not
capture extraneous delays.

The method discussed in this paper uses fuzzy logic and image processing to
determine the eye of tropical cyclones from images of IR channels from
geostationary satellites. This technique has been used to analyze images of
four tropical cyclones: Amphan, Nivar, Tauktae, and Yaas. Fuzzy logic provides
the ability to model the dynamic behavior of the storm and determines the
location of the best eye in an area of interest. It provides an effective
method for describing the uncertain behavior of complicated systems. After
that, image processing is applied to enable accurate eye positioning based on
the search results. A comparison has been made between this technique's
experimental results and those of the Indian Meteorological Department and
Cooperative Institute of Meteorological Satellite Studies. As a result, the
storm's eye location can be found to improve performance significantly. Using
the present technique, it is possible to determine the eye entirely
automatically, thus replacing the manual method that has been employed in the
past.

Short-term plasticity (STP) is a mechanism that stores decaying memories in
synapses of the cerebral cortex. In computing practice, STP has been used, but
mostly in the niche of spiking neurons, even though theory predicts that it is
the optimal solution to certain dynamic tasks. Here we present a new type of
recurrent neural unit, the STP Neuron (STPN), which indeed turns out strikingly
powerful. Its key mechanism is that synapses have a state, propagated through
time by a self-recurrent connection-within-the-synapse. This formulation
enables training the plasticity with backpropagation through time, resulting in
a form of learning to learn and forget in the short term. The STPN outperforms
all tested alternatives, i.e. RNNs, LSTMs, other models with fast weights, and
differentiable plasticity. We confirm this in both supervised and reinforcement
learning (RL), and in tasks such as Associative Retrieval, Maze Exploration,
Atari video games, and MuJoCo robotics. Moreover, we calculate that, in
neuromorphic or biological circuits, the STPN minimizes energy consumption
across models, as it depresses individual synapses dynamically. Based on these,
biological STP may have been a strong evolutionary attractor that maximizes
both efficiency and computational power. The STPN now brings these neuromorphic
advantages also to a broad spectrum of machine learning practice. Code is
available at https://github.com/NeuromorphicComputing/stpn

The recent developments of machine learning have enabled accurate predictions
of glassy dynamics. However, existing machine-learning models are designed so
that they learn a single dynamic quantity and related it to the structural
features of glassy liquids. In this Letter, we propose a graph neural network
model which learns relative motion between neighboring pairs of particles, in
addition to self motion of particles. By relating the structural features to
these two different dynamical variables, the model autonomously acquires the
ability to discern how two different types of dynamic processes, strain
fluctuations and particle rearrangements, affect self motion of particles
undergoing slow relaxation. Our model affords a methodology to treat these two
at the same time and realizes highly precise predictions of glassy structural
relaxation over the entire temporal range.

In this paper, we introduce RISP, a reduced instruction spiking processor.
While most spiking neuroprocessors are based on the brain, or notions from the
brain, we present the case for a spiking processor that simplifies rather than
complicates. As such, it features discrete integration cycles, configurable
leak, and little else. We present the computing model of RISP and highlight the
benefits of its simplicity. We demonstrate how it aids in developing hand built
neural networks for simple computational tasks, detail how it may be employed
to simplify neural networks built with more complicated machine learning
techniques, and demonstrate how it performs similarly to other spiking
neurprocessors.

Molecular and morphological characters, as important parts of biological
taxonomy, are contradictory but need to be integrated. Organism's image
recognition and bioinformatics are emerging and hot problems nowadays but with
a gap between them. In this work, a multi-branching recognition framework
mediated by genetic information bridges this barrier, which establishes the
link between macro-morphology and micro-molecular information of mushrooms. The
novel multi-perspective structure is proposed to fuse the feature images from
three branching models, which significantly improves the accuracy of
recognition by about 10% and up to more than 90%. Further, genetic information
is implemented to the mushroom image recognition task by using genetic distance
embeddings as the representation space for predicting image distance and
species identification. Semantic overfitting of traditional classification
tasks and the granularity of fine-grained image recognition are also discussed
in depth for the first time. The generalizability of the model was investigated
in fine-grained scenarios using zero-shot learning tasks, which could predict
the taxonomic and evolutionary information of unseen samples. We presented the
first method to map images to DNA, namely used an encoder mapping image to
genetic distances, and then decoded DNA through a pre-trained decoder, where
the total test accuracy on 37 species for DNA prediction is 87.45%. This study
creates a novel recognition framework by systematically studying the mushroom
image recognition problem, bridging the gap between macroscopic biological
information and microscopic molecular information, which will provide a new
reference for intelligent biometrics in the future.

When we speak, the prosody and content of the speech can be inferred from the
movement of our lips. In this work, we explore the task of lip to speech
synthesis, i.e., learning to generate speech given only the lip movements of a
speaker where we focus on learning accurate lip to speech mappings for multiple
speakers in unconstrained, large vocabulary settings. We capture the speaker's
voice identity through their facial characteristics, i.e., age, gender,
ethnicity and condition them along with the lip movements to generate speaker
identity aware speech. To this end, we present a novel method "Lip2Speech",
with key design choices to achieve accurate lip to speech synthesis in
unconstrained scenarios. We also perform various experiments and extensive
evaluation using quantitative, qualitative metrics and human evaluation.

A variety of explanation methods have been proposed in recent years to help
users gain insights into the results returned by neural networks, which are
otherwise complex and opaque black-boxes. However, explanations give rise to
potential side-channels that can be leveraged by an adversary for mounting
attacks on the system. In particular, post-hoc explanation methods that
highlight input dimensions according to their importance or relevance to the
result also leak information that weakens security and privacy. In this work,
we perform the first systematic characterization of the privacy and security
risks arising from various popular explanation techniques. First, we propose
novel explanation-guided black-box evasion attacks that lead to 10 times
reduction in query count for the same success rate. We show that the
adversarial advantage from explanations can be quantified as a reduction in the
total variance of the estimated gradient. Second, we revisit the membership
information leaked by common explanations. Contrary to observations in prior
studies, via our modified attacks we show significant leakage of membership
information (above 100% improvement over prior results), even in a much
stricter black-box setting. Finally, we study explanation-guided model
extraction attacks and demonstrate adversarial gains through a large reduction
in query count.

SATNet is a differentiable constraint solver with a custom backpropagation
algorithm, which can be used as a layer in a deep-learning system. It is a
promising proposal for bridging deep learning and logical reasoning. In fact,
SATNet has been successfully applied to learn, among others, the rules of a
complex logical puzzle, such as Sudoku, just from input and output pairs where
inputs are given as images. In this paper, we show how to improve the learning
of SATNet by exploiting symmetries in the target rules of a given but unknown
logical puzzle or more generally a logical formula. We present SymSATNet, a
variant of SATNet that translates the given symmetries of the target rules to a
condition on the parameters of SATNet and requires that the parameters should
have a particular parametric form that guarantees the condition. The
requirement dramatically reduces the number of parameters to learn for the
rules with enough symmetries, and makes the parameter learning of SymSATNet
much easier than that of SATNet. We also describe a technique for automatically
discovering symmetries of the target rules from examples. Our experiments with
Sudoku and Rubik's cube show the substantial improvement of SymSATNet over the
baseline SATNet.

Unsupervised and unpaired domain translation using generative adversarial
neural networks, and more precisely CycleGAN, is state of the art for the stain
translation of histopathology images. It often, however, suffers from the
presence of cycle-consistent but non structure-preserving errors. We propose an
alternative approach to the set of methods which, relying on segmentation
consistency, enable the preservation of pathology structures. Focusing on
immunohistochemistry (IHC) and multiplexed immunofluorescence (mIF), we
introduce a simple yet effective guidance scheme as a loss function that
leverages the consistency of stain translation with stain isolation.
Qualitative and quantitative experiments show the ability of the proposed
approach to improve translation between the two domains.

Hundreds of defenses have been proposed to make deep neural networks robust
against minimal (adversarial) input perturbations. However, only a handful of
these defenses held up their claims because correctly evaluating robustness is
extremely challenging: Weak attacks often fail to find adversarial examples
even if they unknowingly exist, thereby making a vulnerable network look
robust. In this paper, we propose a test to identify weak attacks, and thus
weak defense evaluations. Our test slightly modifies a neural network to
guarantee the existence of an adversarial example for every sample.
Consequentially, any correct attack must succeed in breaking this modified
network. For eleven out of thirteen previously-published defenses, the original
evaluation of the defense fails our test, while stronger attacks that break
these defenses pass it. We hope that attack unit tests - such as ours - will be
a major component in future robustness evaluations and increase confidence in
an empirical field that is currently riddled with skepticism.

One of the main focuses in distributed learning is communication efficiency,
since model aggregation at each round of training can consist of millions to
billions of parameters. Several model compression methods, such as gradient
quantization and sparsification, have been proposed to improve the
communication efficiency of model aggregation. However, the
information-theoretic minimum communication cost for a given distortion of
gradient estimators is still unknown. In this paper, we study the fundamental
limit of communication cost of model aggregation in distributed learning from a
rate-distortion perspective. By formulating the model aggregation as a vector
Gaussian CEO problem, we derive the rate region bound and sum-rate-distortion
function for the model aggregation problem, which reveals the minimum
communication rate at a particular gradient distortion upper bound. We also
analyze the communication cost at each iteration and total communication cost
based on the sum-rate-distortion function with the gradient statistics of
real-world datasets. It is found that the communication gain by exploiting the
correlation between worker nodes is significant for SignSGD, and a high
distortion of gradient estimator can achieve low total communication cost in
gradient compression.

The problem of interpreting the decisions of machine learning is a
well-researched and important. We are interested in a specific type of machine
learning model that deals with graph data called graph neural networks.
Evaluating interpretability approaches for graph neural networks (GNN)
specifically are known to be challenging due to the lack of a commonly accepted
benchmark. Given a GNN model, several interpretability approaches exist to
explain GNN models with diverse (sometimes conflicting) evaluation
methodologies. In this paper, we propose a benchmark for evaluating the
explainability approaches for GNNs called Bagel. In Bagel, we firstly propose
four diverse GNN explanation evaluation regimes -- 1) faithfulness, 2)
sparsity, 3) correctness. and 4) plausibility. We reconcile multiple evaluation
metrics in the existing literature and cover diverse notions for a holistic
evaluation. Our graph datasets range from citation networks, document graphs,
to graphs from molecules and proteins. We conduct an extensive empirical study
on four GNN models and nine post-hoc explanation approaches for node and graph
classification tasks. We open both the benchmarks and reference implementations
and make them available at https://github.com/Mandeep-Rathee/Bagel-benchmark.

As deep learning is widely used in the radiology field, the explainability of
such models is increasingly becoming essential to gain clinicians' trust when
using the models for diagnosis. In this research, three experiment sets were
conducted with a U-Net architecture to improve the classification performance
while enhancing the heatmaps corresponding to the model's focus through
incorporating heatmap generators during training. All of the experiments used
the dataset that contained chest radiographs, associated labels from one of the
three conditions ("normal", "congestive heart failure (CHF)", and "pneumonia"),
and numerical information regarding a radiologist's eye-gaze coordinates on the
images. The paper (A. Karargyris and Moradi, 2021) that introduced this dataset
developed a U-Net model, which was treated as the baseline model for this
research, to show how the eye-gaze data can be used in multi-modal training for
explainability improvement. To compare the classification performances, the 95%
confidence intervals (CI) of the area under the receiver operating
characteristic curve (AUC) were measured. The best method achieved an AUC of
0.913 (CI: 0.860-0.966). The greatest improvements were for the "pneumonia" and
"CHF" classes, which the baseline model struggled most to classify, resulting
in AUCs of 0.859 (CI: 0.732-0.957) and 0.962 (CI: 0.933-0.989), respectively.
The proposed method's decoder was also able to produce probability masks that
highlight the determining image parts in model classifications, similarly as
the radiologist's eye-gaze data. Hence, this work showed that incorporating
heatmap generators and eye-gaze information into training can simultaneously
improve disease classification and provide explainable visuals that align well
with how the radiologist viewed the chest radiographs when making diagnosis.

Generate-then-rank is a widely used mechanism for text generation, where a
generator produces multiple candidates and a ranker chooses the best one.
However, existing methods usually train the generator and the ranker
separately, which causes a lack of mutual feedback and a misalignment of their
objectives. This results in suboptimal generation quality. To address this
issue, we propose JGR, a novel joint training algorithm that integrates the
generator and the ranker in a single framework. JGR optimizes the generator
with a hybrid objective that combines data likelihood and ranker reward, and
trains the ranker with a contrastive loss that compares the generator outputs.
By alternately updating the generator and the ranker, JGR can effectively
harmonize their learning and enhance their quality jointly. We evaluate JGR on
various text generation tasks and demonstrate that it surpasses existing
methods on four public datasets across three common generation scenarios. Our
code, data, and models are available at https://github.com/microsoft/AdvNLG.

There exist well-developed frameworks for causal modelling, but these require
rather a lot of human domain expertise to define causal variables and perform
interventions. In order to enable autonomous agents to learn abstract causal
models through interactive experience, the existing theoretical foundations
need to be extended and clarified. Existing frameworks give no guidance
regarding variable choice / representation, and more importantly, give no
indication as to which behaviour policies or physical transformations of state
space shall count as interventions. The framework sketched in this paper
describes actions as transformations of state space, for instance induced by an
agent running a policy. This makes it possible to describe in a uniform way
both transformations of the micro-state space and abstract models thereof, and
say when the latter is veridical / grounded / natural. We then introduce
(causal) variables, define a mechanism as an invariant predictor, and say when
an action can be viewed as a ``surgical intervention'', thus bringing the
objective of causal representation \& intervention skill learning into clearer
focus.

Timely and reliable detection of falls is a large and rapidly growing field
of research due to the medical and financial demand of caring for a constantly
growing elderly population. Within the past 2 decades, the availability of
high-quality hardware (high-quality sensors and AI microchips) and software
(machine learning algorithms) technologies has served as a catalyst for this
research by giving developers the capabilities to develop such systems. This
study developed multiple application components in order to investigate the
development challenges and choices for fall detection systems, and provide
materials for future research. The smart application developed using this
methodology was validated by the results from fall detection modelling
experiments and model mobile deployment. The best performing model overall was
the ResNet152 on a standardised, and shuffled dataset with a 2s window size
which achieved 92.8% AUC, 7.28% sensitivity, and 98.33% specificity. Given
these results it is evident that accelerometer and ECG sensors are beneficial
for fall detection, and allow for the discrimination between falls and other
activities. This study leaves a significant amount of room for improvement due
to weaknesses identified in the resultant dataset. These improvements include
using a labelling protocol for the critical phase of a fall, increasing the
number of dataset samples, improving the test subject representation, and
experimenting with frequency domain preprocessing.

We propose a new approach to the optimal placement of sensors for the problem
of reconstructing geophysical fields from sparse measurements. Our method
consists of two stages. In the first stage, we estimate the variability of the
physical field as a function of spatial coordinates by approximating its
information entropy through the Conditional PixelCNN network. To calculate the
entropy, a new ordering of a two-dimensional data array (spiral ordering) is
proposed, which makes it possible to obtain the entropy of a physical field
simultaneously for several spatial scales. In the second stage, the entropy of
the physical field is used to initialize the distribution of optimal sensor
locations. This distribution is further optimized with the Concrete Autoencoder
architecture with the straight-through gradient estimator and adversarial loss
to simultaneously minimize the number of sensors and maximize reconstruction
accuracy. Our method scales linearly with data size, unlike commonly used
Principal Component Analysis. We demonstrate our method on the two examples:
(a) temperature and (b) salinity fields around the Barents Sea and the Svalbard
group of islands. For these examples, we compute the reconstruction error of
our method and a few baselines. We test our approach against two baselines (1)
PCA with QR factorization and (2) climatology. We find out that the obtained
optimal sensor locations have clear physical interpretation and correspond to
the boundaries between sea currents.

Reinforcement learning is a promising method for robotic grasping as it can
learn effective reaching and grasping policies in difficult scenarios. However,
achieving human-like manipulation capabilities with sophisticated robotic hands
is challenging because of the problem's high dimensionality. Although remedies
such as reward shaping or expert demonstrations can be employed to overcome
this issue, they often lead to oversimplified and biased policies. We present
Dext-Gen, a reinforcement learning framework for Dexterous Grasping in sparse
reward ENvironments that is applicable to a variety of grippers and learns
unbiased and intricate policies. Full orientation control of the gripper and
object is achieved through smooth orientation representation. Our approach has
reasonable training durations and provides the option to include desired prior
knowledge. The effectiveness and adaptability of the framework to different
scenarios is demonstrated in simulated experiments.

Second language learning can be enabled by tandem collaboration where
students are grouped into video conference calls while learning the native
language of other student(s) on the calls. This places students in an online
environment where the more outgoing can actively contribute and engage in
dialogue while those more shy and unsure of their second language skills can
sit back and coast through the calls. We have built and deployed the L2L system
which records timings of conversational utterances from all participants in a
call. We generate visualisations including participation rates and timelines
for each student in each call and present these on a dashboard. We have
recently developed a measure called personal conversational volatility for how
dynamic has been each student's contribution to the dialogue in each call. We
present an analysis of conversational volatility measures for a sample of 19
individual English-speaking students from our University who are learning
Frenchm, in each of 86 tandem telecollaboration calls over one teaching
semester. Our analysis shows there is a need to look into the nature of the
interactions and see if the choices of discussion topics assigned to them were
too difficult for some students and that may have influenced their engagement
in some way.

Gait depicts individuals' unique and distinguishing walking patterns and has
become one of the most promising biometric features for human identification.
As a fine-grained recognition task, gait recognition is easily affected by many
factors and usually requires a large amount of completely annotated data that
is costly and insatiable. This paper proposes a large-scale self-supervised
benchmark for gait recognition with contrastive learning, aiming to learn the
general gait representation from massive unlabelled walking videos for
practical applications via offering informative walking priors and diverse
real-world variations. Specifically, we collect a large-scale unlabelled gait
dataset GaitLU-1M consisting of 1.02M walking sequences and propose a
conceptually simple yet empirically powerful baseline model GaitSSB.
Experimentally, we evaluate the pre-trained model on four widely-used gait
benchmarks, CASIA-B, OU-MVLP, GREW and Gait3D with or without transfer
learning. The unsupervised results are comparable to or even better than the
early model-based and GEI-based methods. After transfer learning, our method
outperforms existing methods by a large margin in most cases. Theoretically, we
discuss the critical issues for gait-specific contrastive framework and present
some insights for further study. As far as we know, GaitLU-1M is the first
large-scale unlabelled gait dataset, and GaitSSB is the first method that
achieves remarkable unsupervised results on the aforementioned benchmarks. The
source code of GaitSSB will be integrated into OpenGait which is available at
https://github.com/ShiqiYu/OpenGait.

Large-scale vector mapping is important for transportation, city planning,
and survey and census. We propose GraphMapper, a unified framework for
end-to-end vector map extraction from satellite images. Our key idea is a novel
unified representation of shapes of different topologies named "primitive
graph", which is a set of shape primitives and their pairwise relationship
matrix. Then, we convert vector shape prediction, regularization, and topology
reconstruction into a unique primitive graph learning problem. Specifically,
GraphMapper is a generic primitive graph learning network based on global shape
context modelling through multi-head-attention. An embedding space sorting
method is developed for accurate primitive relationship modelling. We
empirically demonstrate the effectiveness of GraphMapper on two challenging
mapping tasks, building footprint regularization and road network topology
reconstruction. Our model outperforms state-of-the-art methods in both tasks on
public benchmarks. All code will be publicly available.

Blind Face Restoration (BFR) aims to recover high-quality face images from
low-quality ones and usually resorts to facial priors for improving restoration
performance. However, current methods still suffer from two major difficulties:
1) how to derive a powerful network architecture without extensive hand tuning;
2) how to capture complementary information from multiple facial priors in one
network to improve restoration performance. To this end, we propose a Face
Restoration Searching Network (FRSNet) to adaptively search the suitable
feature extraction architecture within our specified search space, which can
directly contribute to the restoration quality. On the basis of FRSNet, we
further design our Multiple Facial Prior Searching Network (MFPSNet) with a
multi-prior learning scheme. MFPSNet optimally extracts information from
diverse facial priors and fuses the information into image features, ensuring
that both external guidance and internal features are reserved. In this way,
MFPSNet takes full advantage of semantic-level (parsing maps), geometric-level
(facial heatmaps), reference-level (facial dictionaries) and pixel-level
(degraded images) information and thus generates faithful and realistic images.
Quantitative and qualitative experiments show that MFPSNet performs favorably
on both synthetic and real-world datasets against the state-of-the-art BFR
methods. The codes are publicly available at:
https://github.com/YYJ1anG/MFPSNet.

Real-world applications of reinforcement learning for recommendation and
experimentation faces a practical challenge: the relative reward of different
bandit arms can evolve over the lifetime of the learning agent. To deal with
these non-stationary cases, the agent must forget some historical knowledge, as
it may no longer be relevant to minimise regret. We present a solution to
handling non-stationarity that is suitable for deployment at scale, to provide
business operators with automated adaptive optimisation. Our solution aims to
provide interpretable learning that can be trusted by humans, whilst responding
to non-stationarity to minimise regret. To this end, we develop an adaptive
Bayesian learning agent that employs a novel form of dynamic memory. It enables
interpretability through statistical hypothesis testing, by targeting a set
point of statistical power when comparing rewards and adjusting its memory
dynamically to achieve this power. By design, the agent is agnostic to
different kinds of non-stationarity. Using numerical simulations, we compare
its performance against an existing proposal and show that, under multiple
non-stationary scenarios, our agent correctly adapts to real changes in the
true rewards. In all bandit solutions, there is an explicit trade-off between
learning and achieving maximal performance. Our solution sits on a different
point on this trade-off when compared to another similarly robust approach: we
prioritise interpretability, which relies on more learning, at the cost of some
regret. We describe the architecture of a large-scale deployment of automatic
optimisation-as-a-service where our agent achieves interpretability whilst
adapting to changing circumstances.

Graph-Convolution-based methods have been successfully applied to
representation learning on homophily graphs where nodes with the same label or
similar attributes tend to connect with one another. Due to the homophily
assumption of Graph Convolutional Networks (GCNs) that these methods use, they
are not suitable for heterophily graphs where nodes with different labels or
dissimilar attributes tend to be adjacent. Several methods have attempted to
address this heterophily problem, but they do not change the fundamental
aggregation mechanism of GCNs because they rely on summation operators to
aggregate information from neighboring nodes, which is implicitly subject to
the homophily assumption. Here, we introduce a novel aggregation mechanism and
develop a RAndom Walk Aggregation-based Graph Neural Network (called RAW-GNN)
method. The proposed approach integrates the random walk strategy with graph
neural networks. The new method utilizes breadth-first random walk search to
capture homophily information and depth-first search to collect heterophily
information. It replaces the conventional neighborhoods with path-based
neighborhoods and introduces a new path-based aggregator based on Recurrent
Neural Networks. These designs make RAW-GNN suitable for both homophily and
heterophily graphs. Extensive experimental results showed that the new method
achieved state-of-the-art performance on a variety of homophily and heterophily
graphs.

Vision Transformer (ViT) is becoming more popular in image processing.
Specifically, we investigate the effectiveness of test-time adaptation (TTA) on
ViT, a technique that has emerged to correct its prediction during test-time by
itself. First, we benchmark various test-time adaptation approaches on ViT-B16
and ViT-L16. It is shown that the TTA is effective on ViT and the
prior-convention (sensibly selecting modulation parameters) is not necessary
when using proper loss function. Based on the observation, we propose a new
test-time adaptation method called class-conditional feature alignment (CFA),
which minimizes both the class-conditional distribution differences and the
whole distribution differences of the hidden representation between the source
and target in an online manner. Experiments of image classification tasks on
common corruption (CIFAR-10-C, CIFAR-100-C, and ImageNet-C) and domain
adaptation (digits datasets and ImageNet-Sketch) show that CFA stably
outperforms the existing baselines on various datasets. We also verify that CFA
is model agnostic by experimenting on ResNet, MLP-Mixer, and several ViT
variants (ViT-AugReg, DeiT, and BeiT). Using BeiT backbone, CFA achieves 19.8%
top-1 error rate on ImageNet-C, outperforming the existing test-time adaptation
baseline 44.0%. This is a state-of-the-art result among TTA methods that do not
need to alter training phase.

This paper presents the AIVC submission to the CLIC 2022 video track. AIVC is
a fully-learned video codec based on conditional autoencoders. The flexibility
of the AIVC models is leveraged to implement rate allocation and frame
structure competition to select the optimal coding configuration per-sequence.
This competition yields compelling compression performance, offering a rate
reduction of -26 % compared with the absence of competition.

Deep neural networks present impressive performance, yet they cannot reliably
estimate their predictive confidence, limiting their applicability in high-risk
domains. We show that applying a multi-label one-vs-all loss reveals
classification ambiguity and reduces model overconfidence. The introduced SLOVA
(Single Label One-Vs-All) model redefines typical one-vs-all predictive
probabilities to a single label situation, where only one class is the correct
answer. The proposed classifier is confident only if a single class has a high
probability and other probabilities are negligible. Unlike the typical softmax
function, SLOVA naturally detects out-of-distribution samples if the
probabilities of all other classes are small. The model is additionally
fine-tuned with exponential calibration, which allows us to precisely align the
confidence score with model accuracy. We verify our approach on three tasks.
First, we demonstrate that SLOVA is competitive with the state-of-the-art on
in-distribution calibration. Second, the performance of SLOVA is robust under
dataset shifts. Finally, our approach performs extremely well in the detection
of out-of-distribution samples. Consequently, SLOVA is a tool that can be used
in various applications where uncertainty modeling is required.

The introduction of pattern languages in the seminal work [Angluin, ``Finding
Patterns Common to a Set of Strings'', JCSS 1980] has revived the classical
model of inductive inference (learning in the limit, gold-style learning). In
[Shinohara, ``Polynomial Time Inference of Pattern Languages and Its
Application'', 7th IBM Symposium on Mathematical Foundations of Computer
Science 1982] a simple and elegant algorithm has been introduced that, based on
membership queries, computes a pattern that is descriptive for a given sample
of input strings (and, consequently, can be employed in strategies for
inductive inference). In this paper, we give a brief survey of the recent work
[Kleest-Mei{\ss}ner et al., ``Discovering Event Queries from Traces: Laying
Foundations for Subsequence-Queries with Wildcards and Gap-Size Constraints'',
ICDT 2022], where the classical concepts of Angluin-style (descriptive)
patterns and the respective Shinohara's algorithm are extended to a query class
with applications in complex event recognition -- a modern topic from
databases.

Greedy algorithms for NLP such as transition based parsing are prone to error
propagation. One way to overcome this problem is to allow the algorithm to
backtrack and explore an alternative solution in cases where new evidence
contradicts the solution explored so far. In order to implement such a
behavior, we use reinforcement learning and let the algorithm backtrack in
cases where such an action gets a better reward than continuing to explore the
current solution. We test this idea on both POS tagging and dependency parsing
and show that backtracking is an effective means to fight against error
propagation.

This technical report describes the details of our TASK1A submission of the
DCASE2021 challenge. The goal of the task is to design an audio scene
classification system for device-imbalanced datasets under the constraints of
model complexity. This report introduces four methods to achieve the goal.
First, we propose Residual Normalization, a novel feature normalization method
that uses instance normalization with a shortcut path to discard unnecessary
device-specific information without losing useful information for
classification. Second, we design an efficient architecture, BC-ResNet-Mod, a
modified version of the baseline architecture with a limited receptive field.
Third, we exploit spectrogram-to-spectrogram translation from one to multiple
devices to augment training data. Finally, we utilize three model compression
schemes: pruning, quantization, and knowledge distillation to reduce model
complexity. The proposed system achieves an average test accuracy of 76.3% in
TAU Urban Acoustic Scenes 2020 Mobile, development dataset with 315k
parameters, and average test accuracy of 75.3% after compression to 61.0KB of
non-zero parameters. We extend this work to [1].

Recently, introspective models like IntroVAE and S-IntroVAE have excelled in
image generation and reconstruction tasks. The principal characteristic of
introspective models is the adversarial learning of VAE, where the encoder
attempts to distinguish between the real and the fake (i.e., synthesized)
images. However, due to the unavailability of an effective metric to evaluate
the difference between the real and the fake images, the posterior collapse and
the vanishing gradient problem still exist, reducing the fidelity of the
synthesized images. In this paper, we propose a new variation of IntroVAE
called Adversarial Similarity Distance Introspective Variational Autoencoder
(AS-IntroVAE). We theoretically analyze the vanishing gradient problem and
construct a new Adversarial Similarity Distance (AS-Distance) using the
2-Wasserstein distance and the kernel trick. With weight annealing on
AS-Distance and KL-Divergence, the AS-IntroVAE are able to generate stable and
high-quality images. The posterior collapse problem is addressed by making
per-batch attempts to transform the image so that it better fits the prior
distribution in the latent space. Compared with the per-image approach, this
strategy fosters more diverse distributions in the latent space, allowing our
model to produce images of great diversity. Comprehensive experiments on
benchmark datasets demonstrate the effectiveness of AS-IntroVAE on image
generation and reconstruction tasks.

This paper proposes a new accelerated proximal Markov chain Monte Carlo
(MCMC) methodology to perform Bayesian computation efficiently in imaging
inverse problems. The proposed methodology is derived from the Langevin
diffusion process and stems from tightly integrating two state-of-the-art
proximal Langevin MCMC samplers, SK-ROCK and split Gibbs sampling (SGS), which
employ distinctively different strategies to improve convergence speed. More
precisely, we show how to integrate, at the level of the Langevin diffusion
process, the proximal SK-ROCK sampler which is based on a stochastic
Runge-Kutta-Chebyshev approximation of the diffusion, with the model
augmentation and relaxation strategy that SGS exploits to speed up Bayesian
computation at the expense of asymptotic bias. This leads to a new and faster
proximal SK-ROCK sampler that combines the accelerated quality of the original
SK-ROCK sampler with the computational benefits of augmentation and relaxation.
Moreover, rather than viewing the augmented and relaxed model as an
approximation of the target model, positioning relaxation in a bias-variance
trade-off, we propose to regard the augmented and relaxed model as a
generalisation of the target model. This then allows us to carefully calibrate
the amount of relaxation in order to simultaneously improve the accuracy of the
model (as measured by the model evidence) and the sampler's convergence speed.
To achieve this, we derive an empirical Bayesian method to automatically
estimate the optimal amount of relaxation by maximum marginal likelihood
estimation. The proposed methodology is demonstrated with a range of numerical
experiments related to image deblurring and inpainting, as well as with
comparisons with alternative approaches from the state of the art.

Dimensionality reduction (DR) plays a vital role in the visual analysis of
high-dimensional data. One main aim of DR is to reveal hidden patterns that lie
on intrinsic low-dimensional manifolds. However, DR often overlooks important
patterns when the manifolds are distorted or hidden by certain influential data
attributes. This paper presents a feature learning framework, FEALM, designed
to generate an optimized set of data projections for nonlinear DR in order to
capture important patterns in the hidden manifolds. These projections produce
maximally different nearest-neighbor graphs so that resultant DR outcomes are
significantly different. To achieve such a capability, we design an
optimization algorithm as well as introduce a new graph dissimilarity measure,
named neighbor-shape dissimilarity. Additionally, we develop interactive
visualizations to assist comparison of obtained DR results and interpretation
of each DR result. We demonstrate FEALM's effectiveness through experiments and
case studies using synthetic and real-world datasets.

Machine learning algorithms are widely used in the area of malware detection.
With the growth of sample amounts, training of classification algorithms
becomes more and more expensive. In addition, training data sets may contain
redundant or noisy instances. The problem to be solved is how to select
representative instances from large training data sets without reducing the
accuracy. This work presents a new parallel instance selection algorithm called
Parallel Instance Filtering (PIF). The main idea of the algorithm is to split
the data set into non-overlapping subsets of instances covering the whole data
set and apply a filtering process for each subset. Each subset consists of
instances that have the same nearest enemy. As a result, the PIF algorithm is
fast since subsets are processed independently of each other using parallel
computation. We compare the PIF algorithm with several state-of-the-art
instance selection algorithms on a large data set of 500,000 malicious and
benign samples. The feature set was extracted using static analysis, and it
includes metadata from the portable executable file format. Our experimental
results demonstrate that the proposed instance selection algorithm reduces the
size of a training data set significantly with the only slightly decreased
accuracy. The PIF algorithm outperforms existing instance selection methods
used in the experiments in terms of the ratio between average classification
accuracy and storage percentage.

These days more companies are shifting towards using cloud environments to
provide their services to their client. While it is easy to set up a cloud
environment, it is equally important to monitor the system's runtime behaviour
and identify anomalous behaviours that occur during its operation. In recent
years, the utilisation of \ac{rnn} and \ac{dnn} to detect anomalies that might
occur during runtime has been a trending approach. However, it is unclear how
to explain the decisions made by these networks and how these networks should
be interpreted to understand the runtime behaviour that they model. On the
contrary, state machine models provide an easier manner to interpret and
understand the behaviour that they model. In this work, we propose an approach
that learns state machine models to model the runtime behaviour of a cloud
environment that runs multiple microservice applications. To the best of our
knowledge, this is the first work that tries to apply state machine models to
microservice architectures. The state machine model is used to detect the
different types of attacks that we launch on the cloud environment. From our
experiment results, our approach can detect the attacks very well, achieving a
balanced accuracy of 99.2% and an F1 score of 0.982.

An increasing ubiquity of machine learning (ML) motivates research on
algorithms to explain ML models and their predictions -- so-called eXplainable
Artificial Intelligence (XAI). Despite many survey papers and discussions, the
goals and capabilities of XAI algorithms are far from being well understood. We
argue that this is because of a problematic reasoning scheme in XAI literature:
XAI algorithms are said to complement ML models with desired properties, such
as "interpretability", or "explainability". These properties are in turn
assumed to contribute to a goal, like "trust" in an ML system. But most
properties lack precise definitions and their relationship to such goals is far
from obvious. The result is a reasoning scheme that obfuscates research results
and leaves an important question unanswered: What can one expect from XAI
algorithms? In this article, we clarify the goals and capabilities of XAI
algorithms from a concrete perspective: that of their users. Explaining ML
models is only necessary if users have questions about them. We show that users
can ask diverse questions, but that only one of them can be answered by current
XAI algorithms. Answering this core question can be trivial, difficult or even
impossible, depending on the ML application. Based on these insights, we
outline which capabilities policymakers, researchers and society can reasonably
expect from XAI algorithms.

6-DoF visual localization systems utilize principled approaches rooted in 3D
geometry to perform accurate camera pose estimation of images to a map. Current
techniques use hierarchical pipelines and learned 2D feature extractors to
improve scalability and increase performance. However, despite gains in typical
recall@0.25m type metrics, these systems still have limited utility for
real-world applications like autonomous vehicles because of their `worst' areas
of performance - the locations where they provide insufficient recall at a
certain required error tolerance. Here we investigate the utility of using
`place specific configurations', where a map is segmented into a number of
places, each with its own configuration for modulating the pose estimation
step, in this case selecting a camera within a multi-camera system. On the Ford
AV benchmark dataset, we demonstrate substantially improved worst-case
localization performance compared to using off-the-shelf pipelines - minimizing
the percentage of the dataset which has low recall at a certain error
tolerance, as well as improved overall localization performance. Our proposed
approach is particularly applicable to the crowdsharing model of autonomous
vehicle deployment, where a fleet of AVs are regularly traversing a known
route.

In this paper, we describe an approach to guide drivers searching for a
parking space (PS). The proposed system suggests a sequence of routes that
drivers should traverse in order to maximise the expected likelihood of finding
a PS and minimise the travel distance. This system is built on our recent
architecture SPToken, which combines both Distributed Ledger Technology (DLT)
and Reinforcement Learning (RL) to realise a system for the estimation of an
unknown distribution without disturbing the environment. For this, we use a
number of virtual tokens that are passed from vehicle to vehicle to enable a
massively parallelised RL system that estimates the best route for a given
origin-destination (OD) pair, using crowdsourced information from participant
vehicles. Additionally, a moving window with reward memory mechanism is
included to better cope with non-stationary environments. Simulation results
are given to illustrate the efficacy of our system.

Interest in understanding and factorizing embedding spaces learned by deep
encoders is growing. Concept discovery methods search the embedding spaces for
interpretable latent components like object shape or color and disentangle them
into individual axes in the embedding space. Yet, the applicability of modern
disentanglement learning techniques or independent component analysis (ICA) is
limited when it comes to vision tasks: They either require training a model of
the complex image-generating process or their rigid stochastic independence
assumptions on the component distribution are violated in practice. In this
work, we identify components in encoder embedding spaces without distributional
assumptions and without training a generator. Instead, we utilize functional
compositionality properties of image-generating processes. We derive two novel
post-hoc component discovery methods and prove theoretical identifiability
guarantees. We study them in realistic visual disentanglement tasks with
correlated components and violated functional assumptions. Our approaches
stably maintain superior performance against 300+ state-of-the-art
disentanglement and component analysis models.

Spiking neural networks (SNNs) are largely inspired by biology and
neuroscience and leverage ideas and theories to create fast and efficient
learning systems. Spiking neuron models are adopted as core processing units in
neuromorphic systems because they enable event-based processing. The
integrate-and-fire (I&F) models are often adopted, with the simple Leaky I&F
(LIF) being the most used. The reason for adopting such models is their
efficiency and/or biological plausibility. Nevertheless, rigorous justification
for adopting LIF over other neuron models for use in artificial learning
systems has not yet been studied. This work considers various neuron models in
the literature and then selects computational neuron models that are
single-variable, efficient, and display different types of complexities. From
this selection, we make a comparative study of three simple I&F neuron models,
namely the LIF, the Quadratic I&F (QIF) and the Exponential I&F (EIF), to
understand whether the use of more complex models increases the performance of
the system and whether the choice of a neuron model can be directed by the task
to be completed. Neuron models are tested within an SNN trained with
Spike-Timing Dependent Plasticity (STDP) on a classification task on the
N-MNIST and DVS Gestures datasets. Experimental results reveal that more
complex neurons manifest the same ability as simpler ones to achieve high
levels of accuracy on a simple dataset (N-MNIST), albeit requiring comparably
more hyper-parameter tuning. However, when the data possess richer
Spatio-temporal features, the QIF and EIF neuron models steadily achieve better
results. This suggests that accurately selecting the model based on the
richness of the feature spectrum of the data could improve the whole system's
performance. Finally, the code implementing the spiking neurons in the
SpykeTorch framework is made publicly available.

Deep learning is highly pervasive in today's data-intensive era. In
particular, convolutional neural networks (CNNs) are being widely adopted in a
variety of fields for superior accuracy. However, computing deep CNNs on
traditional CPUs and GPUs brings several performance and energy pitfalls.
Several novel approaches based on ASIC, FPGA, and resistive-memory devices have
been recently demonstrated with promising results. Most of them target only the
inference (testing) phase of deep learning. There have been very limited
attempts to design a full-fledged deep learning accelerator capable of both
training and inference. It is due to the highly compute and memory-intensive
nature of the training phase. In this paper, we propose LiteCON, a novel analog
photonics CNN accelerator. LiteCON uses silicon microdisk-based convolution,
memristor-based memory, and dense-wavelength-division-multiplexing for
energy-efficient and ultrafast deep learning. We evaluate LiteCON using a
commercial CAD framework (IPKISS) on deep learning benchmark models including
LeNet and VGG-Net. Compared to the state-of-the-art, LiteCON improves the CNN
throughput, energy efficiency, and computational efficiency by up to 32x, 37x,
and 5x respectively with trivial accuracy degradation.

Distributed dataflow systems like Apache Spark and Apache Hadoop enable
data-parallel processing of large datasets on clusters. Yet, selecting
appropriate computational resources for dataflow jobs -- that neither lead to
bottlenecks nor to low resource utilization -- is often challenging, even for
expert users such as data engineers. Further, existing automated approaches to
resource selection rely on the assumption that a job is recurring to learn from
previous runs or to warrant the cost of full test runs to learn from. However,
this assumption often does not hold since many jobs are too unique.
  Therefore, we present Crispy, a method for optimizing data processing cluster
configurations based on job profiling runs with small samples of the dataset on
just a single machine. Crispy attempts to extrapolate the memory usage for the
full dataset to then choose a cluster configuration with enough total memory.
In our evaluation on a dataset with 1031 Spark and Hadoop jobs, we see a
reduction of job execution costs by 56% compared to the baseline, while on
average spending less than ten minutes on profiling runs per job on a
consumer-grade laptop.

Self-supervised deep learning methods for joint depth and ego-motion
estimation can yield accurate trajectories without needing ground-truth
training data. However, as they typically use photometric losses, their
performance can degrade significantly when the assumptions these losses make
(e.g. temporal illumination consistency, a static scene, and the absence of
noise and occlusions) are violated. This limits their use for e.g. nighttime
sequences, which tend to contain many point light sources (including on dynamic
objects) and low signal-to-noise ratio (SNR) in darker image regions. In this
paper, we show how to use a combination of three techniques to allow the
existing photometric losses to work for both day and nighttime images. First,
we introduce a per-pixel neural intensity transformation to compensate for the
light changes that occur between successive frames. Second, we predict a
per-pixel residual flow map that we use to correct the reprojection
correspondences induced by the estimated ego-motion and depth from the
networks. And third, we denoise the training images to improve the robustness
and accuracy of our approach. These changes allow us to train a single model
for both day and nighttime images without needing separate encoders or extra
feature networks like existing methods. We perform extensive experiments and
ablation studies on the challenging Oxford RobotCar dataset to demonstrate the
efficacy of our approach for both day and nighttime sequences.

We use a multi-agent system to model how agents (representing firms) may
collaborate and adapt in a business 'landscape' where some, more influential,
firms are given the power to shape the landscape of other firms. The landscapes
we study are based on the well-known NK model of Kauffman, with the addition of
'shapers', firms that can change the landscape's features for themselves and
all other players. Our work investigates how firms that are additionally
endowed with cognitive and experiential search, and the ability to form
collaborations with other firms, can use these capabilities to adapt more
quickly and adeptly. We find that, in a collaborative group, firms must still
have a mind of their own and resist direct mimicry of stronger partners to
attain better heights collectively. Larger groups and groups with more
influential members generally do better, so targeted intelligent cooperation is
beneficial. These conclusions are tentative, and our results show a sensitivity
to landscape ruggedness and "malleability" (i.e. the capacity of the landscape
to be changed by the shaper firms). Overall, our work demonstrates the
potential of computer science, evolution, and machine learning to contribute to
business strategy in these complex environments.

Continual Learning aims to bring machine learning into a more realistic
scenario, where tasks are learned sequentially and the i.i.d. assumption is not
preserved. Although this setting is natural for biological systems, it proves
very difficult for machine learning models such as artificial neural networks.
To reduce this performance gap, we investigate the question whether
biologically inspired Hebbian learning is useful for tackling continual
challenges. In particular, we highlight a realistic and often overlooked
unsupervised setting, where the learner has to build representations without
any supervision. By combining sparse neural networks with Hebbian learning
principle, we build a simple yet effective alternative (HebbCL) to typical
neural network models trained via the gradient descent. Due to Hebbian
learning, the network have easily interpretable weights, which might be
essential in critical application such as security or healthcare. We
demonstrate the efficacy of HebbCL in an unsupervised learning setting applied
to MNIST and Omniglot datasets. We also adapt the algorithm to the supervised
scenario and obtain promising results in the class-incremental learning.

With the development of learning-based embedding models, embedding vectors
are widely used for analyzing and searching unstructured data. As vector
collections exceed billion-scale, fully managed and horizontally scalable
vector databases are necessary. In the past three years, through interaction
with our 1200+ industry users, we have sketched a vision for the features that
next-generation vector databases should have, which include long-term
evolvability, tunable consistency, good elasticity, and high performance. We
present Manu, a cloud native vector database that implements these features. It
is difficult to integrate all these features if we follow traditional DBMS
design rules. As most vector data applications do not require complex data
models and strong data consistency, our design philosophy is to relax the data
model and consistency constraints in exchange for the aforementioned features.
Specifically, Manu firstly exposes the write-ahead log (WAL) and binlog as
backbone services. Secondly, write components are designed as log publishers
while all read-only analytic and search components are designed as independent
subscribers to the log services. Finally, we utilize multi-version concurrency
control (MVCC) and a delta consistency model to simplify the communication and
cooperation among the system components. These designs achieve a low coupling
among the system components, which is essential for elasticity and evolution.
We also extensively optimize Manu for performance and usability with
hardware-aware implementations and support for complex search semantics.

Deepfake Generation Techniques are evolving at a rapid pace, making it
possible to create realistic manipulated images and videos and endangering the
serenity of modern society. The continual emergence of new and varied
techniques brings with it a further problem to be faced, namely the ability of
deepfake detection models to update themselves promptly in order to be able to
identify manipulations carried out using even the most recent methods. This is
an extremely complex problem to solve, as training a model requires large
amounts of data, which are difficult to obtain if the deepfake generation
method is too recent. Moreover, continuously retraining a network would be
unfeasible. In this paper, we ask ourselves if, among the various deep learning
techniques, there is one that is able to generalise the concept of deepfake to
such an extent that it does not remain tied to one or more specific deepfake
generation methods used in the training set. We compared a Vision Transformer
with an EfficientNetV2 on a cross-forgery context based on the ForgeryNet
dataset. From our experiments, It emerges that EfficientNetV2 has a greater
tendency to specialize often obtaining better results on training methods while
Vision Transformers exhibit a superior generalization ability that makes them
more competent even on images generated with new methodologies.

Automatic methods to predict Mean Opinion Score (MOS) of listeners have been
researched to assure the quality of Text-to-Speech systems. Many previous
studies focus on architectural advances (e.g. MBNet, LDNet, etc.) to capture
relations between spectral features and MOS in a more effective way and
achieved high accuracy. However, the optimal representation in terms of
generalization capability still largely remains unknown. To this end, we
compare the performance of Self-Supervised Learning (SSL) features obtained by
the wav2vec framework to that of spectral features such as magnitude of
spectrogram and melspectrogram. Moreover, we propose to combine the SSL
features and features which we believe to retain essential information to the
automatic MOS to compensate each other for their drawbacks. We conduct
comprehensive experiments on a large-scale listening test corpus collected from
past Blizzard and Voice Conversion Challenges. We found that the wav2vec
feature set showed the best generalization even though the given ground-truth
was not always reliable. Furthermore, we found that the combinations performed
the best and analyzed how they bridged the gap between spectral and the wav2vec
feature sets.

Recent studies have shown great promise in applying graph neural networks for
multivariate time series forecasting, where the interactions of time series are
described as a graph structure and the variables are represented as the graph
nodes. Along this line, existing methods usually assume that the graph
structure (or the adjacency matrix), which determines the aggregation manner of
graph neural network, is fixed either by definition or self-learning. However,
the interactions of variables can be dynamic and evolutionary in real-world
scenarios. Furthermore, the interactions of time series are quite different if
they are observed at different time scales. To equip the graph neural network
with a flexible and practical graph structure, in this paper, we investigate
how to model the evolutionary and multi-scale interactions of time series. In
particular, we first provide a hierarchical graph structure cooperated with the
dilated convolution to capture the scale-specific correlations among time
series. Then, a series of adjacency matrices are constructed under a recurrent
manner to represent the evolving correlations at each layer. Moreover, a
unified neural network is provided to integrate the components above to get the
final prediction. In this way, we can capture the pair-wise correlations and
temporal dependency simultaneously. Finally, experiments on both single-step
and multi-step forecasting tasks demonstrate the superiority of our method over

Social networks are often modeled using signed graphs, where vertices
correspond to users and edges have a sign that indicates whether an interaction
between users was positive or negative. The arising signed graphs typically
contain a clear community structure in the sense that the graph can be
partitioned into a small number of polarized communities, each defining a
sparse cut and indivisible into smaller polarized sub-communities. We provide a
local clustering oracle for signed graphs with such a clear community
structure, that can answer membership queries, i.e., "Given a vertex $v$, which
community does $v$ belong to?", in sublinear time by reading only a small
portion of the graph. Formally, when the graph has bounded maximum degree and
the number of communities is at most $O(\log n)$, then with
$\tilde{O}(\sqrt{n}\operatorname{poly}(1/\varepsilon))$ preprocessing time, our
oracle can answer each membership query in
$\tilde{O}(\sqrt{n}\operatorname{poly}(1/\varepsilon))$ time, and it correctly
classifies a $(1-\varepsilon)$-fraction of vertices w.r.t. a set of hidden
planted ground-truth communities. Our oracle is desirable in applications where
the clustering information is needed for only a small number of vertices.
Previously, such local clustering oracles were only known for unsigned graphs;
our generalization to signed graphs requires a number of new ideas and gives a
novel spectral analysis of the behavior of random walks with signs. We evaluate
our algorithm for constructing such an oracle and answering membership queries
on both synthetic and real-world datasets, validating its performance in
practice.

Verifying the identity of a speaker is crucial in modern human-machine
interfaces, e.g., to ensure privacy protection or to enable biometric
authentication. Classical speaker verification (SV) approaches estimate a
fixed-dimensional embedding from a speech utterance that encodes the speaker's
voice characteristics. A speaker is verified if his/her voice embedding is
sufficiently similar to the embedding of the claimed speaker. However, such
approaches assume that only a single speaker exists in the input. The presence
of concurrent speakers is likely to have detrimental effects on the
performance. To address SV in a multi-speaker environment, we propose an
end-to-end deep learning-based SV system that detects whether the target
speaker exists within an input or not. First, an embedding is estimated from a
reference utterance to represent the target's characteristics. Second,
frame-level features are estimated from the input mixture. The reference
embedding is then fused frame-wise with the mixture's features to allow
distinguishing the target from other speakers on a frame basis. Finally, the
fused features are used to predict whether the target speaker is active in the
speech segment or not. Experimental evaluation shows that the proposed method
outperforms the x-vector in multi-speaker conditions.

Federated learning (FL), enabling different medical institutions or clients
to train a model collaboratively without data privacy leakage, has drawn great
attention in medical imaging communities recently. Though inter-client data
heterogeneity has been thoroughly studied, the class imbalance problem due to
the existence of rare diseases still is under-explored. In this paper, we
propose a novel FL framework FedRare for medical image classification
especially on dealing with data heterogeneity with the existence of rare
diseases. In FedRare, each client trains a model locally to extract
highly-separable latent features for classification via intra-client supervised
contrastive learning. Considering the limited data on rare diseases, we build
positive sample queues for augmentation (i.e. data re-sampling). The server in
FedRare would collect the latent features from clients and automatically select
the most reliable latent features as guidance sent back to clients. Then, each
client is jointly trained by an inter-client contrastive loss to align its
latent features to the federated latent features of full classes. In this way,
the parameter/feature variances across clients are effectively minimized,
leading to better convergence and performance improvements. Experimental
results on the publicly-available dataset for skin lesion diagnosis demonstrate
FedRare's superior performance. Under the 10-client federated setting where
four clients have no rare disease samples, FedRare achieves an average increase
of 9.60% and 5.90% in balanced accuracy compared to the baseline framework
FedAvg and the state-of-the-art approach FedIRM respectively. Considering the
board existence of rare diseases in clinical scenarios, we believe FedRare
would benefit future FL framework design for medical image classification. The
source code of this paper is publicly available at
https://github.com/wnn2000/FedRare.

Despite the remarkable success of Generative Adversarial Networks (GANs) on
text, images, and videos, generating high-quality tabular data is still under
development owing to some unique challenges such as capturing dependencies in
imbalanced data, optimizing the quality of synthetic patient data while
preserving privacy. In this paper, we propose DP-CGANS, a differentially
private conditional GAN framework consisting of data transformation, sampling,
conditioning, and networks training to generate realistic and
privacy-preserving tabular data. DP-CGANS distinguishes categorical and
continuous variables and transforms them to latent space separately. Then, we
structure a conditional vector as an additional input to not only presents the
minority class in the imbalanced data, but also capture the dependency between
variables. We inject statistical noise to the gradients in the networking
training process of DP-CGANS to provide a differential privacy guarantee. We
extensively evaluate our model with state-of-the-art generative models on three
public datasets and two real-world personal health datasets in terms of
statistical similarity, machine learning performance, and privacy measurement.
We demonstrate that our model outperforms other comparable models, especially
in capturing dependency between variables. Finally, we present the balance
between data utility and privacy in synthetic data generation considering the
different data structure and characteristics of real-world datasets such as
imbalance variables, abnormal distributions, and sparsity of data.

We propose a novel approach for joint 3D multi-object tracking and
reconstruction from RGB-D sequences in indoor environments. To this end, we
detect and reconstruct objects in each frame while predicting dense
correspondences mappings into a normalized object space. We leverage those
correspondences to inform a graph neural network to solve for the optimal,
temporally-consistent 7-DoF pose trajectories of all objects. The novelty of
our method is two-fold: first, we propose a new graph-based approach for
differentiable pose estimation over time to learn optimal pose trajectories;
second, we present a joint formulation of reconstruction and pose estimation
along the time axis for robust and geometrically consistent multi-object
tracking. In order to validate our approach, we introduce a new synthetic
dataset comprising 2381 unique indoor sequences with a total of 60k rendered
RGB-D images for multi-object tracking with moving objects and camera positions
derived from the synthetic 3D-FRONT dataset. We demonstrate that our method
improves the accumulated MOTA score for all test sequences by 24.8% over
existing state-of-the-art methods. In several ablations on synthetic and
real-world sequences, we show that our graph-based, fully end-to-end-learnable
approach yields a significant boost in tracking performance.

The mixed quantum-classical dynamical simulation is essential to study
nonadiabatic phenomena in photophysics and photochemistry. In recent years,
many machine learning models have been developed to accelerate the time
evolution of the nuclear subsystem. Herein, we implement long short-term memory
(LSTM) networks as a propagator to accelerate the time evolution of the
electronic subsystem during the fewest-switches surface hopping (FSSH)
simulations. A small number of reference trajectories are generated using the
original FSSH method, and then the LSTM networks can be built, accompanied by
careful examination of typical LSTM-FSSH trajectories that employ the same
initial condition and random numbers as the corresponding reference. The
constructed network is applied to FSSH to further produce a trajectory ensemble
to reveal the mechanism of nonadiabatic processes. Taking Tully's three models
as test systems, the collective results can be reproduced qualitatively. This
work demonstrates that LSTM is applicable to the most popular surface hopping

Detecting beneficial feature interactions is essential in recommender
systems, and existing approaches achieve this by examining all the possible
feature interactions. However, the cost of examining all the possible
higher-order feature interactions is prohibitive (exponentially growing with
the order increasing). Hence existing approaches only detect limited order
(e.g., combinations of up to four features) beneficial feature interactions,
which may miss beneficial feature interactions with orders higher than the
limitation. In this paper, we propose a hypergraph neural network based model
named HIRS. HIRS is the first work that directly generates beneficial feature
interactions of arbitrary orders and makes recommendation predictions
accordingly. The number of generated feature interactions can be specified to
be much smaller than the number of all the possible interactions and hence, our
model admits a much lower running time. To achieve an effective algorithm, we
exploit three properties of beneficial feature interactions, and propose
deep-infomax-based methods to guide the interaction generation. Our
experimental results show that HIRS outperforms state-of-the-art algorithms by
up to 5% in terms of recommendation accuracy.

Recently, the application of deep learning models to diagnose
neuropsychiatric diseases from brain imaging data has received more and more
attention. However, in practice, exploring interactions in brain functional
connectivity based on operational magnetic resonance imaging data is critical
for studying mental illness. Since Attention-Deficit and Hyperactivity Disorder
(ADHD) is a type of chronic disease that is very difficult to diagnose in the
early stages, it is necessary to improve the diagnosis accuracy of such illness
using machine learning models treating patients before the critical condition.
In this study, we utilize the dynamics of brain functional connectivity to
model features from medical imaging data, which can extract the differences in
brain function interactions between Normal Control (NC) and ADHD. To meet that
requirement, we employ the Bayesian connectivity change-point model to detect
brain dynamics using the local binary encoding approach and kernel hierarchical
extreme learning machine for classifying features. To verify our model, we
experimented with it on several real-world children's datasets, and our results
achieved superior classification rates compared to the state-of-the-art models.

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating
preventive care and delay progression. Speech based automatic AD screening
systems provide a non-intrusive and more scalable alternative to other clinical
screening techniques. Scarcity of such specialist data leads to uncertainty in
both model selection and feature learning when developing such systems. To this
end, this paper investigates the use of feature and model combination
approaches to improve the robustness of domain fine-tuning of BERT and Roberta
pre-trained text encoders on limited data, before the resulting embedding
features being fed into an ensemble of backend classifiers to produce the final
AD detection decision via majority voting. Experiments conducted on the
ADReSS20 Challenge dataset suggest consistent performance improvements were
obtained using model and feature combination in system development.
State-of-the-art AD detection accuracies of 91.67 percent and 93.75 percent
were obtained using manual and ASR speech transcripts respectively on the
ADReSS20 test set consisting of 48 elderly speakers.

Electrocardiogram (ECG) is a simple non-invasive measure to identify
heart-related issues such as irregular heartbeats known as arrhythmias. While
artificial intelligence and machine learning is being utilized in a wide range
of healthcare related applications and datasets, many arrhythmia classifiers
using deep learning methods have been proposed in recent years. However, sizes
of the available datasets from which to build and assess machine learning
models is often very small and the lack of well-annotated public ECG datasets
is evident. In this paper, we propose a deep transfer learning framework that
is aimed to perform classification on a small size training dataset. The
proposed method is to fine-tune a general-purpose image classifier ResNet-18
with MIT-BIH arrhythmia dataset in accordance with the AAMI EC57 standard. This
paper further investigates many existing deep learning models that have failed
to avoid data leakage against AAMI recommendations. We compare how different
data split methods impact the model performance. This comparison study implies
that future work in arrhythmia classification should follow the AAMI EC57
standard when using any including MIT-BIH arrhythmia dataset.

While notable progress has been made in specifying and learning objectives
for general cyber-physical systems, applying these methods to distributed
multi-agent systems still pose significant challenges. Among these are the need
to (a) craft specification primitives that allow expression and interplay of
both local and global objectives, (b) tame explosion in the state and action
spaces to enable effective learning, and (c) minimize coordination frequency
and the set of engaged participants for global objectives. To address these
challenges, we propose a novel specification framework that allows natural
composition of local and global objectives used to guide training of a
multi-agent system. Our technique enables learning expressive policies that
allow agents to operate in a coordination-free manner for local objectives,
while using a decentralized communication protocol for enforcing global ones.
Experimental results support our claim that sophisticated multi-agent
distributed planning problems can be effectively realized using
specification-guided learning.

Spectral camera based on ghost imaging via sparsity constraints (GISC
spectral camera) obtains three-dimensional (3D) hyperspectral information with
two-dimensional (2D) compressive measurements in a single shot, which has
attracted much attention in recent years. However, its imaging quality and
real-time performance of reconstruction still need to be further improved.
Recently, deep learning has shown great potential in improving the
reconstruction quality and reconstruction speed for computational imaging. When
applying deep learning into GISC spectral camera, there are several challenges
need to be solved: 1) how to deal with the large amount of 3D hyperspectral
data, 2) how to reduce the influence caused by the uncertainty of the random
reference measurements, 3) how to improve the reconstructed image quality as
far as possible. In this paper, we present an end-to-end V-DUnet for the
reconstruction of 3D hyperspectral data in GISC spectral camera. To reduce the
influence caused by the uncertainty of the measurement matrix and enhance the
reconstructed image quality, both differential ghost imaging results and the
detected measurements are sent into the network's inputs. Compared with
compressive sensing algorithm, such as PICHCS and TwIST, it not only
significantly improves the imaging quality with high noise immunity, but also
speeds up the reconstruction time by more than two orders of magnitude.

On e-commerce platforms, predicting if two products are compatible with each
other is an important functionality to achieve trustworthy product
recommendation and search experience for consumers. However, accurately
predicting product compatibility is difficult due to the heterogeneous product
data and the lack of manually curated training data. We study the problem of
discovering effective labeling rules that can enable weakly-supervised product
compatibility prediction. We develop AMRule, a multi-view rule discovery
framework that can (1) adaptively and iteratively discover novel rulers that
can complement the current weakly-supervised model to improve compatibility
prediction; (2) discover interpretable rules from both structured attribute
tables and unstructured product descriptions. AMRule adaptively discovers
labeling rules from large-error instances via a boosting-style strategy, the
high-quality rules can remedy the current model's weak spots and refine the
model iteratively. For rule discovery from structured product attributes, we
generate composable high-order rules from decision trees; and for rule
discovery from unstructured product descriptions, we generate prompt-based
rules from a pre-trained language model. Experiments on 4 real-world datasets
show that AMRule outperforms the baselines by 5.98% on average and improves
rule quality and rule proposal efficiency.

Search engines in e-commerce settings allow users to search, browse, and
select items from a wide range of products available online including
children's items. Children's products such as toys, books, and learning
materials often have stereotype-based gender associations. Both academic
research and public campaigns are working to promote stereotype-free childhood
development. However, to date, e-commerce search engines have not received as
much attention as physical stores, product design, or marketing as a potential
channel of gender stereotypes. To fill this gap, in this paper, we study the
manifestations of gender stereotypes in e-commerce sites when responding to
queries related to children's products by exploring query suggestions and
search results. We have three primary contributions. First, we provide an
aggregated list of children's products with associated gender stereotypes from
the existing body of research. Second, we provide preliminary methods for
identifying and quantifying gender stereotypes in system's responses. Third, to
show the importance of attending this problem, we identify the existence of
gender stereotypes in query suggestions and search results across multiple
e-commerce sites.

We study the problem of few-shot Fine-grained Entity Typing (FET), where only
a few annotated entity mentions with contexts are given for each entity type.
Recently, prompt-based tuning has demonstrated superior performance to standard
fine-tuning in few-shot scenarios by formulating the entity type classification
task as a ''fill-in-the-blank'' problem. This allows effective utilization of
the strong language modeling capability of Pre-trained Language Models (PLMs).
Despite the success of current prompt-based tuning approaches, two major
challenges remain: (1) the verbalizer in prompts is either manually designed or
constructed from external knowledge bases, without considering the target
corpus and label hierarchy information, and (2) current approaches mainly
utilize the representation power of PLMs, but have not explored their
generation power acquired through extensive general-domain pre-training. In
this work, we propose a novel framework for few-shot FET consisting of two
modules: (1) an entity type label interpretation module automatically learns to
relate type labels to the vocabulary by jointly leveraging few-shot instances
and the label hierarchy, and (2) a type-based contextualized instance generator
produces new instances based on given instances to enlarge the training set for
better generalization. On three benchmark datasets, our model outperforms
existing methods by significant margins. Code can be found at
https://github.com/teapot123/Fine-Grained-Entity-Typing.

An organ segmentation method that can generalize to unseen contrasts and
scanner settings can significantly reduce the need for retraining of deep
learning models. Domain Generalization (DG) aims to achieve this goal. However,
most DG methods for segmentation require training data from multiple domains
during training. We propose a novel adversarial domain generalization method
for organ segmentation trained on data from a \emph{single} domain. We
synthesize the new domains via learning an adversarial domain synthesizer (ADS)
and presume that the synthetic domains cover a large enough area of plausible
distributions so that unseen domains can be interpolated from synthetic
domains. We propose a mutual information regularizer to enforce the semantic
consistency between images from the synthetic domains, which can be estimated
by patch-level contrastive learning. We evaluate our method for various organ
segmentation for unseen modalities, scanning protocols, and scanner sites.

Deep recommender systems jointly leverage the retrieval and ranking
operations to generate the recommendation result. The retriever targets
selecting a small set of relevant candidates from the entire items with high
efficiency; while the ranker, usually more precise but time-consuming, is
supposed to identify the best items out of the retrieved candidates with high
precision. However, the retriever and ranker are usually trained in
poorly-cooperative ways, leading to limited recommendation performances when
working as an entirety. In this work, we propose a novel DRS training framework
CoRR(short for Cooperative Retriever and Ranker), where the retriever and
ranker can be mutually reinforced. On one hand, the retriever is learned from
recommendation data and the ranker via knowledge distillation; knowing that the
ranker is more precise, the knowledge distillation may provide extra
weak-supervision signals for the improvement of retrieval quality. On the other
hand, the ranker is trained by learning to discriminate the truth positive
items from hard negative candidates sampled from the retriever. With the
iteration going on, the ranker may become more precise, which in return gives
rise to informative training signals for the retriever; meanwhile, with the
improvement of retriever, harder negative candidates can be sampled, which
contributes to a higher discriminative capability of the ranker. To facilitate
the effective conduct of CoRR, an asymptotic-unbiased approximation of KL
divergence is introduced for the knowledge distillation over sampled items;
besides, a scalable and adaptive strategy is developed to efficiently sample
from the retriever. Comprehensive experimental studies are performed over four
large-scale benchmark datasets, where CoRR improves the overall recommendation
quality resulting from the cooperation between retriever and ranker.

Graph Neural Networks (GNNs) have drawn tremendous attention due to their
unique capability to extend Machine Learning (ML) approaches to applications
broadly-defined as having unstructured data, especially graphs. Compared with
other Machine Learning (ML) modalities, the acceleration of Graph Neural
Networks (GNNs) is more challenging due to the irregularity and heterogeneity
derived from graph typologies. Existing efforts, however, have focused mainly
on handling graphs' irregularity and have not studied their heterogeneity.
  To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine)
based hybrid accelerator that leverages the emerging heterogeneity of Xilinx
Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve
high-performance GNN inference. In particular, H-GCN partitions each graph into
three subgraphs based on its inherent heterogeneity, and processes them using
PL and AIE, respectively. To further improve performance, we explore the
sparsity support of AIE and develop an efficient density-aware method to
automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the
systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN
achieves, on average, speedups of 1.1~2.3X.

Gait recognition aims at identifying a person at a distance through visual
cameras. With the emergence of deep learning, significant advancements in gait
recognition have achieved inspiring success in many scenarios by utilizing deep
learning techniques. Nevertheless, the increasing need for video surveillance
introduces more challenges, including robust recognition under various
variances, modeling motion information in gait sequences, unfair performance
comparison due to protocol variances, biometrics security, and privacy
prevention. This paper provides a comprehensive survey of deep learning for
gait recognition. We first present the odyssey of gait recognition from
traditional algorithms to deep models, providing explicit knowledge of the
whole workflow of a gait recognition system. Then deep learning for gait
recognition is discussed from the perspective of deep representations and
architecture with an in-depth summary. Specifically, deep gait representations
are categorized into static and dynamic features, while deep architectures
include single-stream and multi-stream architecture. Following our proposed
taxonomy with novelty, it can be beneficial for providing inspiration and
promoting the perception of deep gait recognition. Besides, we also present a
comprehensive summary of all vision-based gait datasets and the performance
analysis. Finally, the article discusses some open issues with significant
potential prospects.

Constructing efficient descriptors that represent atomic configurations is
crucial for developing a superior machine-learning potential. Widely used
conventional descriptors are based on two- or three-body correlations of atomic
distribution. Recently, several limitations of these many-body descriptors in
classifying different configurations were revealed, which have detrimental
effects on the prediction of physical properties. We proposed a new class of
descriptors based on persistent homology. We focused on the two-dimensional
visualization of persistent homology, that is, a persistence diagram, as a
descriptor of atomic configurations in the form of an image. We demonstrated
that convolutional neural network models based on this descriptor provide
sufficient accuracy in predicting the mean energies per atom of amorphous
graphene and amorphous carbon. Our results provide an avenue for improving
machine-learning potential using descriptors that depict both topological and
geometric information.

Click-through rate (CTR) prediction, whose goal is to predict the probability
of the user to click on an item, has become increasingly significant in the
recommender systems. Recently, some deep learning models with the ability to
automatically extract the user interest from his/her behaviors have achieved
great success. In these work, the attention mechanism is used to select the
user interested items in historical behaviors, improving the performance of the
CTR predictor. Normally, these attentive modules can be jointly trained with
the base predictor by using gradient descents. In this paper, we regard user
interest modeling as a feature selection problem, which we call user interest
selection. For such a problem, we propose a novel approach under the framework
of the wrapper method, which is named Meta-Wrapper. More specifically, we use a
differentiable module as our wrapping operator and then recast its learning
problem as a continuous bilevel optimization. Moreover, we use a meta-learning
algorithm to solve the optimization and theoretically prove its convergence.
Meanwhile, we also provide theoretical analysis to show that our proposed
method 1) efficiencies the wrapper-based feature selection, and 2) achieves
better resistance to overfitting. Finally, extensive experiments on three
public datasets manifest the superiority of our method in boosting the
performance of CTR prediction.

With increasing applications in areas such as biomedical information
extraction pipelines and social media analytics, Named Entity Recognition (NER)
has become an indispensable tool for knowledge extraction. However, with the
gradual shift in language structure and vocabulary, NERs are plagued with
distribution shifts, making them redundant or not as profitable without
re-training. Re-training NERs based on Large Language Models (LLMs) from
scratch over newly acquired data poses economic disadvantages. In contrast,
re-training only with newly acquired data will result in Catastrophic
Forgetting of previously acquired knowledge. Therefore, we propose NERDA-Con, a
pipeline for training NERs with LLM bases by incorporating the concept of
Elastic Weight Consolidation (EWC) into the NER fine-tuning NERDA pipeline. As
we believe our work has implications to be utilized in the pipeline of
continual learning and NER, we open-source our code as well as provide the
fine-tuning library of the same name NERDA-Con at
https://github.com/SupritiVijay/NERDA-Con and
https://pypi.org/project/NERDA-Con/.

Vertical federated learning (VFL) is attracting much attention because it
enables cross-silo data cooperation in a privacy-preserving manner. While most
research works in VFL focus on linear and tree models, deep models (e.g.,
neural networks) are not well studied in VFL. In this paper, we focus on
SplitNN, a well-known neural network framework in VFL, and identify a trade-off
between data security and model performance in SplitNN. Briefly, SplitNN trains
the model by exchanging gradients and transformed data. On the one hand,
SplitNN suffers from the loss of model performance since multiply parties
jointly train the model using transformed data instead of raw data, and a large
amount of low-level feature information is discarded. On the other hand, a
naive solution of increasing the model performance through aggregating at lower
layers in SplitNN (i.e., the data is less transformed and more low-level
feature is preserved) makes raw data vulnerable to inference attacks. To
mitigate the above trade-off, we propose a new neural network protocol in VFL
called Security Forward Aggregation (SFA). It changes the way of aggregating
the transformed data and adopts removable masks to protect the raw data.
Experiment results show that networks with SFA achieve both data security and
high model performance.

While deep neural networks (DNNs) have achieved impressive classification
performance in closed-world learning scenarios, they typically fail to
generalize to unseen categories in dynamic open-world environments, in which
the number of concepts is unbounded. In contrast, human and animal learners
have the ability to incrementally update their knowledge by recognizing and
adapting to novel observations. In particular, humans characterize concepts via
exclusive (unique) sets of essential features, which are used for both
recognizing known classes and identifying novelty. Inspired by natural
learners, we introduce a Sparse High-level-Exclusive, Low-level-Shared feature
representation (SHELS) that simultaneously encourages learning exclusive sets
of high-level features and essential, shared low-level features. The
exclusivity of the high-level features enables the DNN to automatically detect
out-of-distribution (OOD) data, while the efficient use of capacity via sparse
low-level features permits accommodating new knowledge. The resulting approach
uses OOD detection to perform class-incremental continual learning without
known class boundaries. We show that using SHELS for novelty detection results
in statistically significant improvements over state-of-the-art OOD detection
approaches over a variety of benchmark datasets. Further, we demonstrate that
the SHELS model mitigates catastrophic forgetting in a class-incremental
learning setting,enabling a combined novelty detection and accommodation
framework that supports learning in open-world settings

In cloud data center (CDC), reducing energy consumption while maintaining
performance has always been a hot issue. In server consolidation, the
traditional solution is to divide the problem into multiple small problems such
as host overloading detection, virtual machine (VM) selection and VM placement
and solve them step by step. However, the design of host overloading detection
strategies and VM selection strategies cannot be directly linked to the
ultimate goal of reducing energy consumption and ensuring performance. This
paper proposes a learning-based VM selection strategy that selects appropriate
VMs for migration without direct host overloading detection. Thereby reducing
the generation of SLAV, ensuring the performance and reducing the energy
consumption of CDC. Simulations driven by real VM workload traces show that our
method outperforms the existing methods in reducing SLAV generation and CDC
energy consumption.

Federated Recommendation can mitigate the systematical privacy risks of
traditional recommendation since it allows the model training and online
inferring without centralized user data collection. Most existing works assume
that all user devices are available and adequate to participate in the
Federated Learning. However, in practice, the complex recommendation models
designed for accurate prediction and massive item data cause a high computation
and communication cost to the resource-constrained user device, resulting in
poor performance or training failure. Therefore, how to effectively compress
the computation and communication overhead to achieve efficient federated
recommendations across ubiquitous mobile devices remains a significant
challenge. This paper introduces split learning into the two-tower
recommendation models and proposes STTFedRec, a privacy-preserving and
efficient cross-device federated recommendation framework. STTFedRec achieves
local computation reduction by splitting the training and computation of the
item model from user devices to a performance-powered server. The server with
the item model provides low-dimensional item embeddings instead of raw item
data to the user devices for local training and online inferring, achieving
server broadcast compression. The user devices only need to perform similarity
calculations with cached user embeddings to achieve efficient online inferring.
We also propose an obfuscated item request strategy and multi-party circular
secret sharing chain to enhance the privacy protection of model training. The
experiments conducted on two public datasets demonstrate that STTFedRec
improves the average computation time and communication size of the baseline
models by about 40 times and 42 times in the best-case scenario with balanced
recommendation accuracy.

Real-world sequential decision making requires data-driven algorithms that
provide practical guarantees on performance throughout training while also
making efficient use of data. Model-free deep reinforcement learning represents
a framework for such data-driven decision making, but existing algorithms
typically only focus on one of these goals while sacrificing performance with
respect to the other. On-policy algorithms guarantee policy improvement
throughout training but suffer from high sample complexity, while off-policy
algorithms make efficient use of data through sample reuse but lack theoretical
guarantees. In order to balance these competing goals, we develop a class of
Generalized Policy Improvement algorithms that combines the policy improvement
guarantees of on-policy methods with the efficiency of theoretically supported
sample reuse. We demonstrate the benefits of this new class of algorithms
through extensive experimental analysis on a variety of continuous control
tasks from the DeepMind Control Suite.

Keyword spotting (KWS) plays an essential role in enabling speech-based user
interaction on smart devices, and conventional KWS (C-KWS) approaches have
concentrated on detecting user-agnostic pre-defined keywords. However, in
practice, most user interactions come from target users enrolled in the device
which motivates to construct personalized keyword spotting. We design two
personalized KWS tasks; (1) Target user Biased KWS (TB-KWS) and (2) Target user
Only KWS (TO-KWS). To solve the tasks, we propose personalized keyword spotting
through multi-task learning (PK-MTL) that consists of multi-task learning and
task-adaptation. First, we introduce applying multi-task learning on keyword
spotting and speaker verification to leverage user information to the keyword
spotting system. Next, we design task-specific scoring functions to adapt to
the personalized KWS tasks thoroughly. We evaluate our framework on
conventional and personalized scenarios, and the results show that PK-MTL can
dramatically reduce the false alarm rate, especially in various practical

Deep Reinforcement Learning (DRL) uses diverse, unstructured data and makes
RL capable of learning complex policies in high dimensional environments.
Intelligent Transportation System (ITS) based on Autonomous Vehicles (AVs)
offers an excellent playground for policy-based DRL. Deep learning
architectures solve computational challenges of traditional algorithms while
helping in real-world adoption and deployment of AVs. One of the main
challenges in AVs implementation is that it can worsen traffic congestion on
roads if not reliably and efficiently managed. Considering each vehicle's
holistic effect and using efficient and reliable techniques could genuinely
help optimise traffic flow management and congestion reduction. For this
purpose, we proposed a intelligent traffic control system that deals with
complex traffic congestion scenarios at intersections and behind the
intersections. We proposed a DRL-based signal control system that dynamically
adjusts traffic signals according to the current congestion situation on
intersections. To deal with the congestion on roads behind the intersection, we
used re-routing technique to load balance the vehicles on road networks. To
achieve the actual benefits of the proposed approach, we break down the data
silos and use all the data coming from sensors, detectors, vehicles and roads
in combination to achieve sustainable results. We used SUMO micro-simulator for
our simulations. The significance of our proposed approach is manifested from
the results.

Deep learning models for verification systems often fail to generalize to new
users and new environments, even though they learn highly discriminative
features. To address this problem, we propose a few-shot domain generalization
framework that learns to tackle distribution shift for new users and new
domains. Our framework consists of domain-specific and domain-aggregation
networks, which are the experts on specific and combined domains, respectively.
By using these networks, we generate episodes that mimic the presence of both
novel users and novel domains in the training phase to eventually produce
better generalization. To save memory, we reduce the number of domain-specific
networks by clustering similar domains together. Upon extensive evaluation on
artificially generated noise domains, we can explicitly show generalization
ability of our framework. In addition, we apply our proposed methods to the
existing competitive architecture on the standard benchmark, which shows
further performance improvements.

Graph neural networks (GNNs) enable the analysis of graphs using deep
learning, with promising results in capturing structured information in graphs.
This paper focuses on creating a small graph to represent the original graph,
so that GNNs trained on the size-reduced graph can make accurate predictions.
We view the original graph as a distribution of receptive fields and aim to
synthesize a small graph whose receptive fields share a similar distribution.
Thus, we propose Graph Condesation via Receptive Field Distribution Matching
(GCDM), which is accomplished by optimizing the synthetic graph through the use
of a distribution matching loss quantified by maximum mean discrepancy (MMD).
Additionally, we demonstrate that the synthetic graph generated by GCDM is
highly generalizable to a variety of models in evaluation phase and that the
condensing speed is significantly improved using this framework.

Galaxy photometric redshift (photo-$z$) is crucial in cosmological studies,
such as weak gravitational lensing and galaxy angular clustering measurements.
In this work, we try to extract photo-$z$ information and construct its
probability distribution function (PDF) using the Bayesian neural networks
(BNN) from both galaxy flux and image data expected to be obtained by the China
Space Station Telescope (CSST). The mock galaxy images are generated from the
Advanced Camera for Surveys of Hubble Space Telescope ($HST$-ACS) and COSMOS
catalog, in which the CSST instrumental effects are carefully considered. And
the galaxy flux data are measured from galaxy images using aperture photometry.
We construct Bayesian multilayer perceptron (B-MLP) and Bayesian convolutional
neural network (B-CNN) to predict photo-$z$ along with the PDFs from fluxes and
images, respectively. We combine the B-MLP and B-CNN together, and construct a
hybrid network and employ the transfer learning techniques to investigate the
improvement of including both flux and image data. For galaxy samples with
SNR$>$10 in $g$ or $i$ band, we find the accuracy and outlier fraction of
photo-$z$ can achieve $\sigma_{\rm NMAD}=0.022$ and $\eta=2.35\%$ for the B-MLP
using flux data only, and $\sigma_{\rm NMAD}=0.022$ and $\eta=1.32\%$ for the
B-CNN using image data only. The Bayesian hybrid network can achieve
$\sigma_{\rm NMAD}=0.021$ and $\eta=1.23\%$, and utilizing transfer learning
technique can improve results to $\sigma_{\rm NMAD}=0.019$ and $\eta=1.17\%$,
which can provide the most confident predictions with the lowest average

Keyword spotting is the task of detecting a keyword in streaming audio.
Conventional keyword spotting targets predefined keywords classification, but
there is growing attention in few-shot (query-by-example) keyword spotting,
e.g., N-way classification given M-shot support samples. Moreover, in
real-world scenarios, there can be utterances from unexpected categories
(open-set) which need to be rejected rather than classified as one of the N
classes. Combining the two needs, we tackle few-shot open-set keyword spotting
with a new benchmark setting, named splitGSC. We propose episode-known dummy
prototypes based on metric learning to detect an open-set better and introduce
a simple and powerful approach, Dummy Prototypical Networks (D-ProtoNets). Our
D-ProtoNets shows clear margins compared to recent few-shot open-set
recognition (FSOSR) approaches in the suggested splitGSC. We also verify our
method on a standard benchmark, miniImageNet, and D-ProtoNets shows the
state-of-the-art open-set detection rate in FSOSR.

Out-of-distribution (OOD) detection is indispensable for machine learning
models deployed in the open world. Recently, the use of an auxiliary outlier
dataset during training (also known as outlier exposure) has shown promising
performance. As the sample space for potential OOD data can be prohibitively
large, sampling informative outliers is essential. In this work, we propose a
novel posterior sampling-based outlier mining framework, POEM, which
facilitates efficient use of outlier data and promotes learning a compact
decision boundary between ID and OOD data for improved detection. We show that
POEM establishes state-of-the-art performance on common benchmarks. Compared to
the current best method that uses a greedy sampling strategy, POEM improves the
relative performance by 42.0% and 24.2% (FPR95) on CIFAR-10 and CIFAR-100,
respectively. We further provide theoretical insights on the effectiveness of
POEM for OOD detection.

Our prior experiments show that humans and machines seem to employ different
approaches to speaker discrimination, especially in the presence of speaking
style variability. The experiments examined read versus conversational speech.
Listeners focused on speaker-specific idiosyncrasies while "telling speakers
together", and on relative distances in a shared acoustic space when "telling
speakers apart". However, automatic speaker verification (ASV) systems use the
same loss function irrespective of target or non-target trials. To improve ASV
performance in the presence of style variability, insights learnt from human
perception are used to design a new training loss function that we refer to as
"CllrCE loss". CllrCE loss uses both speaker-specific idiosyncrasies and
relative acoustic distances between speakers to train the ASV system. When
using the UCLA speaker variability database, in the x-vector and conditioning
setups, CllrCE loss results in significant relative improvements in EER by
1-66%, and minDCF by 1-31% and 1-56%, respectively, when compared to the
x-vector baseline. Using the SITW evaluation tasks, which involve different
conversational speech tasks, the proposed loss combined with self-attention
conditioning results in significant relative improvements in EER by 2-5% and
minDCF by 6-12% over baseline. In the SITW case, performance improvements were
consistent only with conditioning.

We propose an approach to extract speaker embeddings that are robust to
speaking style variations in text-independent speaker verification. Typically,
speaker embedding extraction includes training a DNN for speaker classification
and using the bottleneck features as speaker representations. Such a network
has a pooling layer to transform frame-level to utterance-level features by
calculating statistics over all utterance frames, with equal weighting.
However, self-attentive embeddings perform weighted pooling such that the
weights correspond to the importance of the frames in a speaker classification
task. Entropy can capture acoustic variability due to speaking style
variations. Hence, an entropy-based variable frame rate vector is proposed as
an external conditioning vector for the self-attention layer to provide the
network with information that can address style effects. This work explores
five different approaches to conditioning. The best conditioning approach,
concatenation with gating, provided statistically significant improvements over
the x-vector baseline in 12/23 tasks and was the same as the baseline in 11/23
tasks when using the UCLA speaker variability database. It also significantly
outperformed self-attention without conditioning in 9/23 tasks and was worse in
1/23. The method also showed significant improvements in multi-speaker
scenarios of SITW.

There is a lack of data on the location, condition, and accessibility of
sidewalks across the world, which not only impacts where and how people travel
but also fundamentally limits interactive mapping tools and urban analytics. In
this paper, we describe initial work in semi-automatically building a sidewalk
network topology from satellite imagery using hierarchical multi-scale
attention models, inferring surface materials from street-level images using
active learning-based semantic segmentation, and assessing sidewalk condition
and accessibility features using Crowd+AI. We close with a call to create a
database of labeled satellite and streetscape scenes for sidewalks and sidewalk
accessibility issues along with standardized benchmarks.

Signal measurement appearing in the form of time series is one of the most
common types of data used in medical machine learning applications. Such
datasets are often small in size, expensive to collect and annotate, and might
involve privacy issues, which hinders our ability to train large,
state-of-the-art deep learning models for biomedical applications. For
time-series data, the suite of data augmentation strategies we can use to
expand the size of the dataset is limited by the need to maintain the basic
properties of the signal. Generative Adversarial Networks (GANs) can be
utilized as another data augmentation tool. In this paper, we present TTS-CGAN,
a transformer-based conditional GAN model that can be trained on existing
multi-class datasets and generate class-specific synthetic time-series
sequences of arbitrary length. We elaborate on the model architecture and
design strategies. Synthetic sequences generated by our model are
indistinguishable from real ones, and can be used to complement or replace real
signals of the same type, thus achieving the goal of data augmentation. To
evaluate the quality of the generated data, we modify the wavelet coherence
metric to be able to compare the similarity between two sets of signals, and
also conduct a case study where a mix of synthetic and real data are used to
train a deep learning model for sequence classification. Together with other
visualization techniques and qualitative evaluation approaches, we demonstrate
that TTS-CGAN generated synthetic data are similar to real data, and that our
model performs better than the other state-of-the-art GAN models built for
time-series data generation.

The generalization of machine learning models has a complex dependence on the
data, model and learning algorithm. We study train and test performance, as
well as the generalization gap given by the mean of their difference over
different data set samples to understand their ``typical" behavior. We derive
an expression for the gap as a function of the covariance between the model
parameter distribution and the train loss, and another expression for the
average test performance, showing test generalization only depends on
data-averaged parameter distribution and the data-averaged loss. We show that
for a large class of model parameter distributions a modified generalization
gap is always non-negative. By specializing further to parameter distributions
produced by stochastic gradient descent (SGD), along with a few approximations
and modeling considerations, we are able to predict some aspects about how the
generalization gap and model train and test performance vary as a function of
SGD noise. We evaluate these predictions empirically on the Cifar10
classification task based on a ResNet architecture.

We develop an algebraic framework for sequential data assimilation of
partially observed dynamical systems. In this framework, Bayesian data
assimilation is embedded in a non-abelian operator algebra, which provides a
representation of observables by multiplication operators and probability
densities by density operators (quantum states). In the algebraic approach, the
forecast step of data assimilation is represented by a quantum operation
induced by the Koopman operator of the dynamical system. Moreover, the analysis
step is described by a quantum effect, which generalizes the Bayesian
observational update rule. Projecting this formulation to finite-dimensional
matrix algebras leads to new computational data assimilation schemes that are
(i) automatically positivity-preserving; and (ii) amenable to consistent
data-driven approximation using kernel methods for machine learning. Moreover,
these methods are natural candidates for implementation on quantum computers.
Applications to data assimilation of the Lorenz 96 multiscale system and the El
Nino Southern Oscillation in a climate model show promising results in terms of
forecast skill and uncertainty quantification.

Deep learning combined with high-resolution tactile sensing could lead to
highly capable dexterous robots. However, progress is slow because of the
specialist equipment and expertise. The DIGIT tactile sensor offers low-cost
entry to high-resolution touch using GelSight-type sensors. Here we customize
the DIGIT to have a 3D-printed sensing surface based on the TacTip family of
soft biomimetic optical tactile sensors. The DIGIT-TacTip (DigiTac) enables
direct comparison between these distinct tactile sensor types. For this
comparison, we introduce a tactile robot system comprising a desktop arm,
mounts and 3D-printed test objects. We use tactile servo control with a PoseNet
deep learning model to compare the DIGIT, DigiTac and TacTip for edge- and
surface-following over 3D-shapes. All three sensors performed similarly at pose
prediction, but their constructions led to differing performances at servo
control, offering guidance for researchers selecting or innovating tactile
sensors. All hardware and software for reproducing this study will be openly
released. Project website: www.lepora.com/digitac. Project repository:
www.github.com/nlepora/digitac-design.

The rapid increase in serendipitous X-rays source detections requires the
development of novel approaches to efficiently explore the nature of X-ray
sources. If even a fraction of these sources could be reliably classified, it
would enable population studies for various astrophysical source types on a
much larger scale than currently possible. Classification of large numbers of
sources from multiple classes characterized by multiple properties (features)
must be done automatically and supervised machine learning (ML) seems to
provide the only feasible approach. We perform classification of Chandra Source
Catalog version 2 (CSCv2) sources to explore the potential of the ML approach
and identify various biases, limitations, and bottlenecks that present
themselves in these kinds of studies. We establish the framework and present a
flexible and expandable Python pipeline, which can be used and improved by
others. We also release the training dataset of 2,941 X-ray sources with
confidently established classes. In addition to providing probabilistic
classifications of 66,369 CSCv2 sources (21% of the entire CSCv2 catalog), we
perform several narrower-focused case studies (high-mass X-ray binary
candidates and X-ray sources within the extent of the H.E.S.S. TeV sources) to
demonstrate some possible applications of our ML approach. We also discuss
future possible modifications of the presented pipeline, which are expected to
lead to substantial improvements in classification confidences.

This project aims to explore the process of deploying Machine learning models
on Kubernetes using an open-source tool called Kubeflow [1] - an end-to-end ML
Stack orchestration toolkit. We create end-to-end Machine Learning models on
Kubeflow in the form of pipelines and analyze various points including the ease
of setup, deployment models, performance, limitations and features of the tool.
We hope that our project acts almost like a seminar/introductory report that
can help vanilla cloud/Kubernetes users with zero knowledge on Kubeflow use
Kubeflow to deploy ML models. From setup on different clouds to serving our
trained model over the internet - we give details and metrics detailing the
performance of Kubeflow.

Self-supervised learning (SSL) of speech representations has received much
attention over the last few years but most work has focused on languages and
domains with an abundance of unlabeled data. However, for many languages there
is a shortage even in the unlabeled data which limits the effectiveness of SSL.
In this work, we focus on the problem of applying SSL to domains with limited
available data by leveraging data augmentation for Wav2Vec 2.0 pretraining.
Further, we propose improvements to each component of the model which result in
a combined relative word error rate (WER) improvement of up to 13% compared to
Wav2Vec 2.0 on Librispeech test-clean / other.

Standard uniform convergence results bound the generalization gap of the
expected loss over a hypothesis class. The emergence of risk-sensitive learning
requires generalization guarantees for functionals of the loss distribution
beyond the expectation. While prior works specialize in uniform convergence of
particular functionals, our work provides uniform convergence for a general
class of H\"older risk functionals for which the closeness in the Cumulative
Distribution Function (CDF) entails closeness in risk. We establish the first
uniform convergence results for estimating the CDF of the loss distribution,
yielding guarantees that hold simultaneously both over all H\"older risk
functionals and over all hypotheses. Thus licensed to perform empirical risk
minimization, we develop practical gradient-based methods for minimizing
distortion risks (widely studied subset of H\"older risks that subsumes the
spectral risks, including the mean, conditional value at risk, cumulative
prospect theory risks, and others) and provide convergence guarantees. In
experiments, we demonstrate the efficacy of our learning procedure, both in
settings where uniform convergence results hold and in high-dimensional
settings with deep networks.

It is an elementary fact in the scientific literature that the Lipschitz norm
of the realization function of a feedforward fully-connected rectified linear
unit (ReLU) artificial neural network (ANN) can, up to a multiplicative
constant, be bounded from above by sums of powers of the norm of the ANN
parameter vector. Roughly speaking, in this work we reveal in the case of
shallow ANNs that the converse inequality is also true. More formally, we prove
that the norm of the equivalence class of ANN parameter vectors with the same
realization function is, up to a multiplicative constant, bounded from above by
the sum of powers of the Lipschitz norm of the ANN realization function (with
the exponents $ 1/2 $ and $ 1 $). Moreover, we prove that this upper bound only
holds when employing the Lipschitz norm but does neither hold for H\"older
norms nor for Sobolev-Slobodeckij norms. Furthermore, we prove that this upper
bound only holds for sums of powers of the Lipschitz norm with the exponents $
1/2 $ and $ 1 $ but does not hold for the Lipschitz norm alone.

There is broad agreement that Artificial Intelligence (AI) systems,
particularly those using Machine Learning (ML), should be able to "explain"
their behavior. Unfortunately, there is little agreement as to what constitutes
an "explanation." This has caused a disconnect between the explanations that
systems produce in service of explainable Artificial Intelligence (XAI) and
those explanations that users and other audiences actually need, which should
be defined by the full spectrum of functional roles, audiences, and
capabilities for explanation. In this paper, we explore the features of
explanations and how to use those features in evaluating their utility. We
focus on the requirements for explanations defined by their functional role,
the knowledge states of users who are trying to understand them, and the
availability of the information needed to generate them. Further, we discuss
the risk of XAI enabling trust in systems without establishing their
trustworthiness and define a critical next step for the field of XAI to
establish metrics to guide and ground the utility of system-generated
explanations.

The von Neumann-Morgenstern (VNM) utility theorem shows that under certain
axioms of rationality, decision-making is reduced to maximizing the expectation
of some utility function. We extend these axioms to increasingly structured
sequential decision making settings and identify the structure of the
corresponding utility functions. In particular, we show that memoryless
preferences lead to a utility in the form of a per transition reward and
multiplicative factor on the future return. This result motivates a
generalization of Markov Decision Processes (MDPs) with this structure on the
agent's returns, which we call Affine-Reward MDPs. A stronger constraint on
preferences is needed to recover the commonly used cumulative sum of scalar
rewards in MDPs. A yet stronger constraint simplifies the utility function for
goal-seeking agents in the form of a difference in some function of states that
we call potential functions. Our necessary and sufficient conditions demystify
the reward hypothesis that underlies the design of rational agents in
reinforcement learning by adding an axiom to the VNM rationality axioms and
motivates new directions for AI research involving sequential decision making.

Comprehensive semantic segmentation on renal pathological images is
challenging due to the heterogeneous scales of the objects. For example, on a
whole slide image (WSI), the cross-sectional areas of glomeruli can be 64 times
larger than that of the peritubular capillaries, making it impractical to
segment both objects on the same patch, at the same scale. To handle this
scaling issue, prior studies have typically trained multiple segmentation
networks in order to match the optimal pixel resolution of heterogeneous tissue
types. This multi-network solution is resource-intensive and fails to model the
spatial relationship between tissue types. In this paper, we propose the
Omni-Seg+ network, a scale-aware dynamic neural network that achieves
multi-object (six tissue types) and multi-scale (5X to 40X scale) pathological
image segmentation via a single neural network. The contribution of this paper
is three-fold: (1) a novel scale-aware controller is proposed to generalize the
dynamic neural network from single-scale to multi-scale; (2) semi-supervised
consistency regularization of pseudo-labels is introduced to model the
inter-scale correlation of unannotated tissue types into a single end-to-end
learning paradigm; and (3) superior scale-aware generalization is evidenced by
directly applying a model trained on human kidney images to mouse kidney
images, without retraining. By learning from ~150,000 human pathological image
patches from six tissue types at three different resolutions, our approach
achieved superior segmentation performance according to human visual assessment
and evaluation of image-omics (i.e., spatial transcriptomics). The official
implementation is available at https://github.com/ddrrnn123/Omni-Seg.

The semantics of the environment, such as the terrain type and property,
reveals important information for legged robots to adjust their behaviors. In
this work, we present a framework that learns semantics-aware locomotion skills
from perception for quadrupedal robots, such that the robot can traverse
through complex offroad terrains with appropriate speeds and gaits using
perception information. Due to the lack of high-fidelity outdoor simulation,
our framework needs to be trained directly in the real world, which brings
unique challenges in data efficiency and safety. To ensure sample efficiency,
we pre-train the perception model with an off-road driving dataset. To avoid
the risks of real-world policy exploration, we leverage human demonstration to
train a speed policy that selects a desired forward speed from camera image.
For maximum traversability, we pair the speed policy with a gait selector,
which selects a robust locomotion gait for each forward speed. Using only 40
minutes of human demonstration data, our framework learns to adjust the speed
and gait of the robot based on perceived terrain semantics, and enables the
robot to walk over 6km without failure at close-to-optimal speed.

Knowledge of search-landscape features of BlackBox Optimization (BBO)
problems offers valuable information in light of the Algorithm Selection and/or
Configuration problems. Exploratory Landscape Analysis (ELA) models have gained
success in identifying predefined human-derived features and in facilitating
portfolio selectors to address those challenges. Unlike ELA approaches, the
current study proposes to transform the identification problem into an image
recognition problem, with a potential to detect conception-free, machine-driven
landscape features. To this end, we introduce the notion of Landscape Images,
which enables us to generate imagery instances per a benchmark function, and
then target the classification challenge over a diverse generalized dataset of
functions. We address it as a supervised multi-class image recognition problem
and apply basic artificial neural network models to solve it. The efficacy of
our approach is numerically validated on the noise free BBOB and IOHprofiler
benchmarking suites. This evident successful learning is another step toward
automated feature extraction and local structure deduction of BBO problems. By
using this definition of landscape images, and by capitalizing on existing
capabilities of image recognition algorithms, we foresee the construction of an
ImageNet-like library of functions for training generalized detectors that rely
on machine-driven features.

The paper introduces a method to construct confidence bands for bounded,
band-limited functions based on a finite sample of input-output pairs. The
approach is distribution-free w.r.t. the observation noises and only the
knowledge of the input distribution is assumed. It is nonparametric, that is,
it does not require a parametric model of the regression function and the
regions have non-asymptotic guarantees. The algorithm is based on the theory of
Paley-Wiener reproducing kernel Hilbert spaces. The paper first studies the
fully observable variant, when there are no noises on the observations and only
the inputs are random; then it generalizes the ideas to the noisy case using
gradient-perturbation methods. Finally, numerical experiments demonstrating
both cases are presented.

In this paper, we present a comprehensive point cloud semantic segmentation
network that aggregates both local and global multi-scale information. First,
we propose an Angle Correlation Point Convolution (ACPConv) module to
effectively learn the local shapes of points. Second, based upon ACPConv, we
introduce a local multi-scale split (MSS) block that hierarchically connects
features within one single block and gradually enlarges the receptive field
which is beneficial for exploiting the local context. Third, inspired by HRNet
which has excellent performance on 2D image vision tasks, we build an HRNet
customized for point cloud to learn global multi-scale context. Lastly, we
introduce a point-wise attention fusion approach that fuses multi-resolution
predictions and further improves point cloud semantic segmentation performance.
Our experimental results and ablations on several benchmark datasets show that
our proposed method is effective and able to achieve state-of-the-art
performances compared to existing methods.

Understanding structure-property relations is essential to optimally design
materials for specific applications. Two-scale simulations are often employed
to analyze the effect of the microstructure on a component's macroscopic
properties. However, they are typically computationally expensive and
infeasible in multi-query contexts such as optimization and material design. To
make such analyses amenable, the microscopic simulations can be replaced by
surrogate models that must be able to handle a wide range of microstructural
parameters. This work focuses on extending the methodology of a previous work,
where an accurate surrogate model was constructed for microstructures under
varying loading and material parameters using proper orthogonal decomposition
and Gaussian process regression, to treat geometrical parameters. To this end,
a method that transforms different geometries onto a parent domain is
presented. We propose to solve an auxiliary problem based on linear elasticity
to obtain the geometrical transformations. Using these transformations,
combined with the nonlinear microscopic problem, we derive a fast-to-evaluate
surrogate model with the following key features: (1) the predictions of the
effective quantities are independent of the auxiliary problem, (2) the
predicted stress fields fulfill the microscopic balance laws and are periodic,
(3) the method is non-intrusive, (4) the stress field for all geometries can be
recovered, and (5) the sensitivities are available and can be readily used for
optimization and material design. The proposed methodology is tested on several
composite microstructures, where rotations and large variations in the shape of
inclusions are considered. Finally, a two-scale example is shown, where the
surrogate model achieves a high accuracy and significant speed up,
demonstrating its potential in two-scale shape optimization and material design
problems.

In medical image processing, the most important information is often located
on small parts of the image. Patch-based approaches aim at using only the most
relevant parts of the image. Finding ways to automatically select the patches
is a challenge. In this paper, we investigate two criteria to choose patches:
entropy and a spectral similarity criterion. We perform experiments at
different levels of patch size. We train a Convolutional Neural Network on the
subsets of patches and analyze the training time. We find that, in addition to
requiring less preprocessing time, the classifiers trained on the datasets of
patches selected based on entropy converge faster than on those selected based
on the spectral similarity criterion and, furthermore, lead to higher accuracy.
Moreover, patches of high entropy lead to faster convergence and better
accuracy than patches of low entropy.

Recent performance breakthroughs in Artificial intelligence (AI) and Machine
learning (ML), especially advances in Deep learning (DL), the availability of
powerful, easy-to-use ML libraries (e.g., scikit-learn, TensorFlow, PyTorch.),
and increasing computational power have led to unprecedented interest in AI/ML
among nuclear engineers. For physics-based computational models, Verification,
Validation and Uncertainty Quantification (VVUQ) have been very widely
investigated and a lot of methodologies have been developed. However, VVUQ of
ML models has been relatively less studied, especially in nuclear engineering.
In this work, we focus on UQ of ML models as a preliminary step of ML VVUQ,
more specifically, Deep Neural Networks (DNNs) because they are the most widely
used supervised ML algorithm for both regression and classification tasks. This
work aims at quantifying the prediction, or approximation uncertainties of DNNs
when they are used as surrogate models for expensive physical models. Three
techniques for UQ of DNNs are compared, namely Monte Carlo Dropout (MCD), Deep
Ensembles (DE) and Bayesian Neural Networks (BNNs). Two nuclear engineering
examples are used to benchmark these methods, (1) time-dependent fission gas
release data using the Bison code, and (2) void fraction simulation based on
the BFBT benchmark using the TRACE code. It was found that the three methods
typically require different DNN architectures and hyperparameters to optimize
their performance. The UQ results also depend on the amount of training data
available and the nature of the data. Overall, all these three methods can
provide reasonable estimations of the approximation uncertainties. The
uncertainties are generally smaller when the mean predictions are close to the
test data, while the BNN methods usually produce larger uncertainties than MCD
and DE.

Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the
need for large human-authored data-sets and allows agents to train explicitly
on functional constraints, using computable, user-defined measures of quality
instead of target output. We explore the application of PCGRL to 3D domains, in
which content-generation tasks naturally have greater complexity and potential
pertinence to real-world applications. Here, we introduce several PCGRL tasks
for the 3D domain, Minecraft (Mojang Studios, 2009). These tasks will challenge
RL-based generators using affordances often found in 3D environments, such as
jumping, multiple dimensional movement, and gravity. We train an agent to
optimize each of these tasks to explore the capabilities of previous research
in PCGRL. This agent is able to generate relatively complex and diverse levels,
and generalize to random initial states and control targets. Controllability
tests in the presented tasks demonstrate their utility to analyze success and
failure for 3D generators.

Improving software performance is an important yet challenging part of the
software development cycle. Today, the majority of performance inefficiencies
are identified and patched by performance experts. Recent advancements in deep
learning approaches and the wide-spread availability of open source data
creates a great opportunity to automate the identification and patching of
performance problems. In this paper, we present DeepPERF, a transformer-based
approach to suggest performance improvements for C# applications. We pretrain
DeepPERF on English and Source code corpora and followed by finetuning for the
task of generating performance improvement patches for C# applications. Our
evaluation shows that our model can generate the same performance improvement
suggestion as the developer fix in ~53% of the cases, getting ~34% of them
verbatim in our expert-verified dataset of performance changes made by C#
developers. Additionally, we evaluate DeepPERF on 50 open source C#
repositories on GitHub using both benchmark and unit tests and find that our
model is able to suggest valid performance improvements that can improve both
CPU usage and Memory allocations. So far we've submitted 19 pull-requests with
28 different performance optimizations and 11 of these PRs have been approved
by the project owners.

Network honeypots are often used by information security teams to measure the
threat landscape in order to secure their networks. With the advancement of
honeypot development, today's medium-interaction honeypots provide a way for
security teams and researchers to deploy these active defense tools that
require little maintenance on a variety of protocols. In this work, we deploy
such honeypots on five different protocols on the public Internet and study the
intent and sophistication of the attacks we observe. We then use the
information gained to develop a clustering approach that identifies
correlations in attacker behavior to discover IPs that are highly likely to be
controlled by a single operator, illustrating the advantage of using these
honeypots for data collection.

This paper presents improvements and novel additions to our recent work on
end-to-end optimized hierarchical bi-directional video compression to further
advance the state-of-the-art in learned video compression. As an improvement,
we combine motion estimation and prediction modules and compress refined
residual motion vectors for improved rate-distortion performance. As novel
addition, we adapted the gain unit proposed for image compression to
flexible-rate video compression in two ways: first, the gain unit enables a
single encoder model to operate at multiple rate-distortion operating points;
second, we exploit the gain unit to control bit allocation among intra-coded
vs. bi-directionally coded frames by fine tuning corresponding models for truly
flexible-rate learned video coding. Experimental results demonstrate that we
obtain state-of-the-art rate-distortion performance exceeding those of all
prior art in learned video coding.

We present ClearBuds, the first hardware and software system that utilizes a
neural network to enhance speech streamed from two wireless earbuds. Real-time
speech enhancement for wireless earbuds requires high-quality sound separation
and background cancellation, operating in real-time and on a mobile phone.
Clear-Buds bridges state-of-the-art deep learning for blind audio source
separation and in-ear mobile systems by making two key technical contributions:
1) a new wireless earbud design capable of operating as a synchronized,
binaural microphone array, and 2) a lightweight dual-channel speech enhancement
neural network that runs on a mobile device. Our neural network has a novel
cascaded architecture that combines a time-domain conventional neural network
with a spectrogram-based frequency masking neural network to reduce the
artifacts in the audio output. Results show that our wireless earbuds achieve a
synchronization error less than 64 microseconds and our network has a runtime
of 21.4 milliseconds on an accompanying mobile phone. In-the-wild evaluation
with eight users in previously unseen indoor and outdoor multipath scenarios
demonstrates that our neural network generalizes to learn both spatial and
acoustic cues to perform noise suppression and background speech removal. In a
user-study with 37 participants who spent over 15.4 hours rating 1041 audio
samples collected in-the-wild, our system achieves improved mean opinion score
and background noise suppression.
  Project page with demos: https://clearbuds.cs.washington.edu

Feature-based self-explanatory methods explain their classification in terms
of human-understandable features. In the medical imaging community, this
semantic matching of clinical knowledge adds significantly to the
trustworthiness of the AI. However, the cost of additional annotation of
features remains a pressing issue. We address this problem by proposing
cRedAnno, a data-/annotation-efficient self-explanatory approach for lung
nodule diagnosis. cRedAnno considerably reduces the annotation need by
introducing self-supervised contrastive learning to alleviate the burden of
learning most parameters from annotation, replacing end-to-end training with
two-stage training. When training with hundreds of nodule samples and only 1%
of their annotations, cRedAnno achieves competitive accuracy in predicting
malignancy, meanwhile significantly surpassing most previous works in
predicting nodule attributes. Visualisation of the learned space further
indicates that the correlation between the clustering of malignancy and nodule
attributes coincides with clinical knowledge. Our complete code is open-source
available: https://github.com/diku-dk/credanno.

Test-time augmentation -- the aggregation of predictions across transformed
examples of test inputs -- is an established technique to improve the
performance of image classification models. Importantly, TTA can be used to
improve model performance post-hoc, without additional training. Although
test-time augmentation (TTA) can be applied to any data modality, it has seen
limited adoption in NLP due in part to the difficulty of identifying
label-preserving transformations. In this paper, we present augmentation
policies that yield significant accuracy improvements with language models. A
key finding is that augmentation policy design -- for instance, the number of
samples generated from a single, non-deterministic augmentation -- has a
considerable impact on the benefit of TTA. Experiments across a binary
classification task and dataset show that test-time augmentation can deliver
consistent improvements over current state-of-the-art approaches.

We study stochastic online resource allocation: a decision maker needs to
allocate limited resources to stochastically-generated sequentially-arriving
requests in order to maximize reward. At each time step, requests are drawn
independently from a distribution that is unknown to the decision maker. Online
resource allocation and its special cases have been studied extensively in the
past, but prior results crucially and universally rely on the strong assumption
that the total number of requests (the horizon) is known to the decision maker
in advance. In many applications, such as revenue management and online
advertising, the number of requests can vary widely because of fluctuations in
demand or user traffic intensity. In this work, we develop online algorithms
that are robust to horizon uncertainty. In sharp contrast to the known-horizon
setting, no algorithm can achieve even a constant asymptotic competitive ratio
that is independent of the horizon uncertainty. We introduce a novel
generalization of dual mirror descent which allows the decision maker to
specify a schedule of time-varying target consumption rates, and prove
corresponding performance guarantees. We go on to give a fast algorithm for
computing a schedule of target consumption rates that leads to near-optimal
performance in the unknown-horizon setting. In particular, our competitive
ratio attains the optimal rate of growth (up to logarithmic factors) as the
horizon uncertainty grows large. Finally, we also provide a way to incorporate
machine-learned predictions about the horizon which interpolates between the
known and unknown horizon settings.

Synaptic memory consolidation has been heralded as one of the key mechanisms
for supporting continual learning in neuromorphic Artificial Intelligence (AI)
systems. Here we report that a Fowler-Nordheim (FN) quantum-tunneling device
can implement synaptic memory consolidation similar to what can be achieved by
algorithmic consolidation models like the cascade and the elastic weight
consolidation (EWC) models. The proposed FN-synapse not only stores the
synaptic weight but also stores the synapse's historical usage statistic on the
device itself. We also show that the operation of the FN-synapse is
near-optimal in terms of the synaptic lifetime and we demonstrate that a
network comprising FN-synapses outperforms a comparable EWC network for a small
benchmark continual learning task. With an energy footprint of femtojoules per
synaptic update, we believe that the proposed FN-synapse provides an
ultra-energy-efficient approach for implementing both synaptic memory
consolidation and persistent learning.

Autonomous underwater vehicles (AUV) perform various applications such as
seafloor mapping and underwater structure health monitoring. Commonly, an
inertial navigation system aided by a Doppler velocity log (DVL) is used to
provide the vehicle's navigation solution. In such fusion, the DVL provides the
velocity vector of the AUV, which determines the navigation solution's accuracy
and helps estimate the navigation states. This paper proposes BeamsNet, an
end-to-end deep learning framework to regress the estimated DVL velocity vector
that improves the accuracy of the velocity vector estimate, and could replace
the model-based approach. Two versions of BeamsNet, differing in their input to
the network, are suggested. The first uses the current DVL beam measurements
and inertial sensors data, while the other utilizes only DVL data, taking the
current and past DVL measurements for the regression process. Both simulation
and sea experiments were made to validate the proposed learning approach
relative to the model-based approach. Sea experiments were made with the Snapir
AUV in the Mediterranean Sea, collecting approximately four hours of DVL and
inertial sensor data. Our results show that the proposed approach achieved an
improvement of more than 60% in estimating the DVL velocity vector.

Pretraining molecular representations is critical in a variety of
applications in drug and material discovery due to the limited number of
labeled molecules, yet most of existing work focuses on pretraining on 2D
molecular graphs. The power of pretraining on 3D geometric structures, however,
has been less explored, owning to the difficulty of finding a sufficient proxy
task to empower the pretraining to effectively extract essential features from
the geometric structures. Motivated by the dynamic nature of 3D molecules,
where the continuous motion of a molecule in the 3D Euclidean space forms a
smooth potential energy surface, we propose a 3D coordinate denoising
pretraining framework to model such an energy landscape. Leveraging a
SE(3)-invariant score matching method, we propose SE(3)-DDM where the
coordinate denoising proxy task is effectively boiled down to the denoising of
the pairwise atomic distances in a molecule. Our comprehensive experiments
confirm the effectiveness and robustness of our proposed method.

Embedded machine learning (ML) systems have now become the dominant platform
for deploying ML serving tasks and are projected to become of equal importance
for training ML models. With this comes the challenge of overall efficient
deployment, in particular low power and high throughput implementations, under
stringent memory constraints. In this context, non-volatile memory (NVM)
technologies such as STT-MRAM and SOT-MRAM have significant advantages compared
to conventional SRAM due to their non-volatility, higher cell density, and
scalability features. While prior work has investigated several architectural
implications of NVM for generic applications, in this work we present
DeepNVM++, a comprehensive framework to characterize, model, and analyze
NVM-based caches in GPU architectures for deep learning (DL) applications by
combining technology-specific circuit-level models and the actual memory
behavior of various DL workloads. DeepNVM++ relies on iso-capacity and iso-area
performance and energy models for last-level caches implemented using
conventional SRAM and emerging STT-MRAM and SOT-MRAM technologies. In the
iso-capacity case, STT-MRAM and SOT-MRAM provide up to 3.8x and 4.7x
energy-delay product (EDP) reduction and 2.4x and 2.8x area reduction compared
to conventional SRAM, respectively. Under iso-area assumptions, STT-MRAM and
SOT-MRAM provide up to 2.2x and 2.4x EDP reduction and accommodate 2.3x and
3.3x cache capacity when compared to SRAM, respectively. We also perform a
scalability analysis and show that STT-MRAM and SOT-MRAM achieve orders of
magnitude EDP reduction when compared to SRAM for large cache capacities.
DeepNVM++ is demonstrated on STT-/SOT-MRAM technologies and can be used for the
characterization, modeling, and analysis of any NVM technology for last-level
caches in GPUs for DL applications.

We review current solutions and technical challenges for automatic speech
recognition, keyword spotting, device arbitration, speech enhancement, and
source localization in multidevice home environments to provide context for the
INTERSPEECH 2022 special session, "Challenges and opportunities for signal
processing and machine learning for multiple smart devices". We also identify
the datasets needed to support these research areas. Based on the review and
our research experience in the multi-device domain, we conclude with an outlook
on the future evolution

OPF problems are formulated and solved for power system operations,
especially for determining generation dispatch points in real-time. For large
and complex power system networks with large numbers of variables and
constraints, finding the optimal solution for real-time OPF in a timely manner
requires a massive amount of computing power. This paper presents a new method
to reduce the number of constraints in the original OPF problem using a graph
neural network (GNN). GNN is an innovative machine learning model that utilizes
features from nodes, edges, and network topology to maximize its performance.
In this paper, we proposed a GNN model to predict which lines would be heavily
loaded or congested with given load profiles and generation capacities. Only
these critical lines will be monitored in an OPF problem, creating a reduced
OPF (ROPF) problem. Significant saving in computing time is expected from the
proposed ROPF model. A comprehensive analysis of predictions from the GNN model
was also made. It is concluded that the application of GNN for ROPF is able to
reduce computing time while retaining solution quality.

Koopman operators model nonlinear dynamics as a linear dynamic system acting
on a nonlinear function as the state. This nonstandard state is often called a
Koopman observable and is usually approximated numerically by a superposition
of functions drawn from a \textit{dictionary}. A widely used algorithm, is
\textit{Extended Dynamic Mode Decomposition}, where the dictionary functions
are drawn from a fixed, homogeneous class of functions. Recently, deep learning
combined with EDMD has been used to learn novel dictionary functions in an
algorithm called deep dynamic mode decomposition (deepDMD). The learned
representation both (1) accurately models and (2) scales well with the
dimension of the original nonlinear system. In this paper we analyze the
learned dictionaries from deepDMD and explore the theoretical basis for their
strong performance. We discover a novel class of dictionary functions to
approximate Koopman observables. Error analysis of these dictionary functions
show they satisfy a property of subspace approximation, which we define as
uniform finite approximate closure. We discover that structured mixing of
heterogeneous dictionary functions drawn from different classes of nonlinear
functions achieve the same accuracy and dimensional scaling as deepDMD. This
mixed dictionary does so with an order of magnitude reduction in parameters,
while maintaining geometric interpretability. Our results provide a hypothesis
to explain the success of deep neural networks in learning numerical
approximations to Koopman operators.

We pursue a line of research that seeks to regularize the spectral norm of
the Jacobian of the input-output mapping for deep neural networks. While
previous work rely on upper bounding techniques, we provide a scheme that
targets the exact spectral norm. We showcase that our algorithm achieves an
improved generalization performance compared to previous spectral
regularization techniques while simultaneously maintaining a strong safeguard
against natural and adversarial noise. Moreover, we further explore some
previous reasoning concerning the strong adversarial protection that Jacobian
regularization provides and show that it can be misleading.

The task of ranking individuals or teams, based on a set of comparisons
between pairs, arises in various contexts, including sporting competitions and
the analysis of dominance hierarchies among animals and humans. Given data on
which competitors beat which others, the challenge is to rank the competitors
from best to worst. Here we study the problem of computing rankings when there
are multiple, potentially conflicting modes of comparison, such as multiple
types of dominance behaviors among animals. We assume that we do not know a
priori what information each behavior conveys about the ranking, or even
whether they convey any information at all. Nonetheless we show that it is
possible to compute a ranking in this situation and present a fast method for
doing so, based on a combination of an expectation-maximization algorithm and a
modified Bradley-Terry model. We give a selection of example applications to
both animal and human competition.

Pre-trained transformer language models on large unlabeled corpus have
produced state-of-the-art results in natural language processing, organic
molecule design, and protein sequence generation. However, no such models have
been applied to learn the composition patterns of inorganic materials. Here we
train a series of seven modern transformer language models (GPT, GPT-2,
GPT-Neo, GPT-J, BLMM, BART, and RoBERTa) using the expanded formulas from
material deposited in the ICSD, OQMD, and Materials Projects databases. Six
different datasets with/out non-charge-neutral or balanced electronegativity
samples are used to benchmark the performances and uncover the generation
biases of modern transformer models for the generative design of materials
compositions. Our extensive experiments showed that the causal language models
based materials transformers can generate chemically valid materials
compositions with as high as 97.54\% to be charge neutral and 91.40\% to be
electronegativity balanced, which has more than 6 times higher enrichment
compared to a baseline pseudo-random sampling algorithm. These models also
demonstrate high novelty and their potential in new materials discovery has
been proved by their capability to recover the leave-out materials. We also
find that the properties of the generated samples can be tailored by training
the models with selected training sets such as high-bandgap materials. Our
experiments also showed that different models each have their own preference in
terms of the properties of the generated samples and their running time
complexity varies a lot. We have applied our materials transformer models to
discover a set of new materials as validated using DFT calculations.

Yoga is a globally acclaimed and widely recommended practice for a healthy
living. Maintaining correct posture while performing a Yogasana is of utmost
importance. In this work, we employ transfer learning from Human Pose
Estimation models for extracting 136 key-points spread all over the body to
train a Random Forest classifier which is used for estimation of the Yogasanas.
The results are evaluated on an in-house collected extensive yoga video
database of 51 subjects recorded from 4 different camera angles. We propose a 3
step scheme for evaluating the generalizability of a Yoga classifier by testing
it on 1) unseen frames, 2) unseen subjects, and 3) unseen camera angles. We
argue that for most of the applications, validation accuracies on unseen
subjects and unseen camera angles would be most important. We empirically
analyze over three public datasets, the advantage of transfer learning and the
possibilities of target leakage. We further demonstrate that the classification
accuracies critically depend on the cross validation method employed and can
often be misleading. To promote further research, we have made key-points
dataset and code publicly available.

Good initialization is essential for training Deep Neural Networks (DNNs).
Oftentimes such initialization is found through a trial and error approach,
which has to be applied anew every time an architecture is substantially
modified, or inherited from smaller size networks leading to sub-optimal
initialization. In this work we introduce a new and cheap algorithm, that
allows one to find a good initialization automatically, for general
feed-forward DNNs. The algorithm utilizes the Jacobian between adjacent network
blocks to tune the network hyperparameters to criticality. We solve the
dynamics of the algorithm for fully connected networks with ReLU and derive
conditions for its convergence. We then extend the discussion to more general
architectures with BatchNorm and residual connections. Finally, we apply our
method to ResMLP and VGG architectures, where the automatic one-shot
initialization found by our method shows good performance on vision tasks.

Internet of Things systems carry substantial engineering risks including
catastrophic physical failures. To aid software engineers in developing
reliable IoT systems, we conducted an experiment to evaluate the influence of
learning treatments on design decisions. Specifically, we compared the
influence of a set of design guidelines (current practice) and failure stories
(proposed learning treatment) on developers' design rationale. We conducted an
experiment with 21 computer engineering students using a questionnaire. We
observed that both treatments helped subjects reason about criticality as a
part of their design rationale. However, failure stories had a greater effect
at enabling subjects to reason about safety as a part of their design
rationale. We share our results illustrating the effects of a failure-aware
design process and propose new research directions to enable a Failure-Aware
Software Development Life Cycle for IoT development.

Capitalizing on large pre-trained models for various downstream tasks of
interest have recently emerged with promising performance. Due to the
ever-growing model size, the standard full fine-tuning based task adaptation
strategy becomes prohibitively costly in terms of model training and storage.
This has led to a new research direction in parameter-efficient transfer
learning. However, existing attempts typically focus on downstream tasks from
the same modality (e.g., image understanding) of the pre-trained model. This
creates a limit because in some specific modalities, (e.g., video
understanding) such a strong pre-trained model with sufficient knowledge is
less or not available. In this work, we investigate such a novel cross-modality
transfer learning setting, namely parameter-efficient image-to-video transfer
learning. To solve this problem, we propose a new Spatio-Temporal Adapter
(ST-Adapter) for parameter-efficient fine-tuning per video task. With a
built-in spatio-temporal reasoning capability in a compact design, ST-Adapter
enables a pre-trained image model without temporal knowledge to reason about
dynamic video content at a small (~8%) per-task parameter cost, requiring
approximately 20 times fewer updated parameters compared to previous work.
Extensive experiments on video action recognition tasks show that our
ST-Adapter can match or even outperform the strong full fine-tuning strategy
and state-of-the-art video models, whilst enjoying the advantage of parameter
efficiency. The code and model are available at
https://github.com/linziyi96/st-adapter

The multiscale entropy assesses the complexity of a signal across different
timescales. It originates from the biomedical domain and was recently
successfully used to characterize light curves as part of a supervised machine
learning framework to classify stellar variability. We explore the behavior of
the multiscale entropy in detail by studying its algorithmic properties in a
stellar variability context and by linking it with traditional astronomical
time series analysis methods. We subsequently use the multiscale entropy as the
basis for an interpretable clustering framework that can distinguish hybrid
pulsators with both p- and g-modes from stars with only p-mode pulsations, such
as $\delta$ Sct stars, or from stars with only g-mode pulsations, such as
$\gamma$ Dor stars. We find that the multiscale entropy is a powerful tool for
capturing variability patterns in stellar light curves. The multiscale entropy
provides insights into the pulsation structure of a star and reveals how short-
and long-term variability interact with each other based on time-domain
information only. We also show that the multiscale entropy is correlated to the
frequency content of a stellar signal and in particular to the near-core
rotation rates of g-mode pulsators. We find that our new clustering framework
can successfully identify the hybrid pulsators with both p- and g-modes in sets
of $\delta$ Sct and $\gamma$ Dor stars, respectively. The benefit of our
clustering framework is that it is unsupervised. It therefore does not require
previously labeled data and hence is not biased by previous knowledge.

We introduce Programmatic Motion Concepts, a hierarchical motion
representation for human actions that captures both low-level motion and
high-level description as motion concepts. This representation enables human
motion description, interactive editing, and controlled synthesis of novel
video sequences within a single framework. We present an architecture that
learns this concept representation from paired video and action sequences in a
semi-supervised manner. The compactness of our representation also allows us to
present a low-resource training recipe for data-efficient learning. By
outperforming established baselines, especially in the small data regime, we
demonstrate the efficiency and effectiveness of our framework for multiple

Unpaired image translation algorithms can be used for sim2real tasks, but
many fail to generate temporally consistent results. We present a new approach
that combines differentiable rendering with image translation to achieve
temporal consistency over indefinite timescales, using surface consistency
losses and \emph{neural neural textures}. We call this algorithm TRITON
(Texture Recovering Image Translation Network): an unsupervised, end-to-end,
stateless sim2real algorithm that leverages the underlying 3D geometry of input
scenes by generating realistic-looking learnable neural textures. By settling
on a particular texture for the objects in a scene, we ensure consistency
between frames statelessly. Unlike previous algorithms, TRITON is not limited
to camera movements -- it can handle the movement of objects as well, making it
useful for downstream tasks such as robotic manipulation.

Humans can leverage prior experience and learn novel tasks from a handful of
demonstrations. In contrast to offline meta-reinforcement learning, which aims
to achieve quick adaptation through better algorithm design, we investigate the
effect of architecture inductive bias on the few-shot learning capability. We
propose a Prompt-based Decision Transformer (Prompt-DT), which leverages the
sequential modeling ability of the Transformer architecture and the prompt
framework to achieve few-shot adaptation in offline RL. We design the
trajectory prompt, which contains segments of the few-shot demonstrations, and
encodes task-specific information to guide policy generation. Our experiments
in five MuJoCo control benchmarks show that Prompt-DT is a strong few-shot
learner without any extra finetuning on unseen target tasks. Prompt-DT
outperforms its variants and strong meta offline RL baselines by a large margin
with a trajectory prompt containing only a few timesteps. Prompt-DT is also
robust to prompt length changes and can generalize to out-of-distribution (OOD)

Transparency methods such as model visualizations provide information that
outputs alone might miss, since they describe the internals of neural networks.
But can we trust that model explanations reflect model behavior? For instance,
can they diagnose abnormal behavior such as backdoors or shape bias? To
evaluate model explanations, we define a model as anomalous if it differs from
a reference set of normal models, and we test whether transparency methods
assign different explanations to anomalous and normal models. We find that
while existing methods can detect stark anomalies such as shape bias or
adversarial training, they struggle to identify more subtle anomalies such as
models trained on incomplete data. Moreover, they generally fail to distinguish
the inputs that induce anomalous behavior, e.g. images containing a backdoor
trigger. These results reveal new blind spots in existing model explanations,
pointing to the need for further method development.

This paper proves that robustness implies generalization via data-dependent
generalization bounds. As a result, robustness and generalization are shown to
be connected closely in a data-dependent manner. Our bounds improve previous
bounds in two directions, to solve an open problem that has seen little
development since 2010. The first is to reduce the dependence on the covering
number. The second is to remove the dependence on the hypothesis space. We
present several examples, including ones for lasso and deep learning, in which
our bounds are provably preferable. The experiments on real-world data and
theoretical models demonstrate near-exponential improvements in various
situations. To achieve these improvements, we do not require additional
assumptions on the unknown distribution; instead, we only incorporate an
observable and computable property of the training samples. A key technical
innovation is an improved concentration bound for multinomial random variables
that is of independent interest beyond robustness and generalization.

Attention-based models trained on protein sequences have demonstrated
incredible success at classification and generation tasks relevant for
artificial intelligence-driven protein design. However, we lack a sufficient
understanding of how very large-scale models and data play a role in effective
protein model development. We introduce a suite of protein language models,
named ProGen2, that are scaled up to 6.4B parameters and trained on different
sequence datasets drawn from over a billion proteins from genomic, metagenomic,
and immune repertoire databases. ProGen2 models show state-of-the-art
performance in capturing the distribution of observed evolutionary sequences,
generating novel viable sequences, and predicting protein fitness without
additional finetuning. As large model sizes and raw numbers of protein
sequences continue to become more widely accessible, our results suggest that a
growing emphasis needs to be placed on the data distribution provided to a
protein sequence model. We release the ProGen2 models and code at
https://github.com/salesforce/progen.

Stars grazing supermassive black holes (SMBHs) on bound orbits may survive
tidal disruption, causing periodic flares. Inspired by the recent discovery of
the periodic nuclear transient ASASSN-14ko, a promising candidate for a
repeating tidal disruption event (TDE), we study the tidal deformation of stars
approaching SMBHs on eccentric orbits. With both analytical and hydrodynamics
methods, we show the overall tidal deformation of a star is similar to that in
a parabolic orbit provided that the eccentricity is above a critical value.
This allows one to make use of existing simulation libraries from parabolic
encounters to calculate the mass fallback rate in eccentric TDEs. We find the
flare structures of eccentric TDEs show a complicated dependence on both the
SMBH mass and the orbital period. For stars orbiting SMBHs with relatively
short periods, we predict significantly shorter-lived duration flares than
those in parabolic TDEs, which can be used to predict repeating events if the
mass of the SMBH can be independently measured. Using an adiabatic mass loss
model, we study the flare evolution over multiple passages, and show the
evolved stars can survive many more passages than main sequence stars. We apply
this theoretical framework to the repeating TDE candidate ASASSN-14ko and
suggest that its recurrent flares originate from a moderately massive
($M\gtrsim 1\,\mathrm{M_\odot}$), extended ($\approx$ a few
$\mathrm{R_\odot}$), evolved star on a grazing, bound orbit around the SMBH.
Future hydrodynamics simulations of multiple tidal interactions will enable
realistic models on the individual flare structure and the evolution over
multiple flares.

Ensembling is a popular and effective method for improving machine learning
(ML) models. It proves its value not only in classical ML but also for deep
learning. Ensembles enhance the quality and trustworthiness of ML solutions,
and allow uncertainty estimation. However, they come at a price: training
ensembles of deep learning models eat a huge amount of computational resources.
  A snapshot ensembling collects models in the ensemble along a single training
path. As it runs training only one time, the computational time is similar to
the training of one model. However, the quality of models along the training
path is different: typically, later models are better if no overfitting occurs.
So, the models are of varying utility.
  Our method improves snapshot ensembling by selecting and weighting ensemble
members along the training path. It relies on training-time likelihoods without
looking at validation sample errors that standard stacking methods do.
Experimental evidence for Fashion MNIST, CIFAR-10, and CIFAR-100 datasets
demonstrates the superior quality of the proposed weighted ensembles c.t.
vanilla ensembling of deep learning models.

Digital recommender systems such as Spotify and Netflix affect not only
consumer behavior but also producer incentives: producers seek to supply
content that will be recommended by the system. But what content will be
produced? In this paper, we investigate the supply-side equilibria in content
recommender systems. We model users and content as $D$-dimensional vectors, and
recommend the content that has the highest dot product with each user. The main
features of our model are that the producer decision space is high-dimensional
and the user base is heterogeneous. This gives rise to new qualitative
phenomena at equilibrium: First, the formation of genres, where producers
specialize to compete for subsets of users. Using a duality argument, we derive
necessary and sufficient conditions for this specialization to occur. Second,
we show that producers can achieve positive profit at equilibrium, which is
typically impossible under perfect competition. We derive sufficient conditions
for this to occur, and show it is closely connected to specialization of
content. In both results, the interplay between the geometry of the users and
the structure of producer costs influences the structure of the supply-side
equilibria. At a conceptual level, our work serves as a starting point to
investigate how recommender systems shape supply-side competition between
producers.

We introduce the Gram-Hadamard Density Operator (GHDO), a new deep
neural-network architecture that can encode positive semi-definite density
operators of exponential rank with polynomial resources. We then show how to
embed an autoregressive structure in the GHDO to allow direct sampling of the
probability distribution. These properties are especially important when
representing and variationally optimizing the mixed quantum state of a system
interacting with an environment. Finally, we benchmark this architecture by
simulating the steady state of the dissipative transverse-field Ising model.
Estimating local observables and the R\'enyi entropy, we show significant
improvements over previous state-of-the-art variational approaches.

Meta learning has demonstrated tremendous success in few-shot learning with
limited supervised data. In those settings, the meta model is usually
overparameterized. While the conventional statistical learning theory suggests
that overparameterized models tend to overfit, empirical evidence reveals that
overparameterized meta learning methods still work well -- a phenomenon often
called "benign overfitting." To understand this phenomenon, we focus on the
meta learning settings with a challenging bilevel structure that we term the
gradient-based meta learning, and analyze its generalization performance under
an overparameterized meta linear regression model. While our analysis uses the
relatively tractable linear models, our theory contributes to understanding the
delicate interplay among data heterogeneity, model adaptation and benign
overfitting in gradient-based meta learning tasks. We corroborate our
theoretical claims through numerical simulations.

If capable AI agents are generally incentivized to seek power in service of
the objectives we specify for them, then these systems will pose enormous
risks, in addition to enormous benefits. In fully observable environments, most
reward functions have an optimal policy which seeks power by keeping options
open and staying alive. However, the real world is neither fully observable,
nor must trained agents be even approximately reward-optimal. We consider a
range of models of AI decision-making, from optimal, to random, to choices
informed by learning and interacting with an environment. We discover that many
decision-making functions are retargetable, and that retargetability is
sufficient to cause power-seeking tendencies. Our functional criterion is
simple and broad. We show that a range of qualitatively dissimilar
decision-making procedures incentivize agents to seek power. We demonstrate the
flexibility of our results by reasoning about learned policy incentives in
Montezuma's Revenge. These results suggest a safety risk: Eventually,
retargetable training procedures may train real-world agents which seek power
over humans.

Acoustic events are sounds with well-defined spectro-temporal characteristics
which can be associated with the physical objects generating them. Acoustic
scenes are collections of such acoustic events in no specific temporal order.
Given this natural linkage between events and scenes, a common belief is that
the ability to classify events must help in the classification of scenes. This
has led to several efforts attempting to do well on Acoustic Event Tagging
(AET) and Acoustic Scene Classification (ASC) using a multi-task network.
However, in these efforts, improvement in one task does not guarantee an
improvement in the other, suggesting a tension between ASC and AET. It is
unclear if improvements in AET translates to improvements in ASC. We explore
this conundrum through an extensive empirical study and show that under certain
conditions, using AET as an auxiliary task in the multi-task network
consistently improves ASC performance. Additionally, ASC performance further
improves with the AET data-set size and is not sensitive to the choice of
events or the number of events in the AET data-set. We conclude that this
improvement in ASC performance comes from the regularization effect of using
AET and not from the network's improved ability to discern between acoustic
events.

Over the past few years, different types of data-driven Artificial
Intelligence (AI) techniques have been widely adopted in various domains of
science for generating predictive black-box models. However, because of their
black-box nature, it is crucial to establish trust in these models before
accepting them as accurate. One way of achieving this goal is through the
implementation of a post-hoc interpretation scheme that can put forward the
reasons behind a black-box model prediction. In this work, we propose a
classical thermodynamics inspired approach for this purpose: Thermodynamically
Explainable Representations of AI and other black-box Paradigms (TERP). TERP
works by constructing a linear, local surrogate model that approximates the
behaviour of the black-box model within a small neighborhood around the
instance being explained. By employing a simple forward feature selection Monte
Carlo algorithm, TERP assigns an interpretability free energy score to all the
possible surrogate models in order to choose an optimal interpretation.
Additionally, we validate TERP as a generally applicable method by successfully
interpreting four different classes of black-box models trained on datasets
coming from relevant domains, including classifying images, predicting heart
disease and classifying biomolecular conformations.

Measuring customer experience on mobile data is of utmost importance for
global mobile operators. The reference signal received power (RSRP) is one of
the important indicators for current mobile network management, evaluation and
monitoring. Radio data gathered through the minimization of drive test (MDT), a
3GPP standard technique, is commonly used for radio network analysis.
Collecting MDT data in different geographical areas is inefficient and
constrained by the terrain conditions and user presence, hence is not an
adequate technique for dynamic radio environments. In this paper, we study a
generative model for RSRP prediction, exploiting MDT data and a digital twin
(DT), and propose a data-driven, two-tier neural network (NN) model. In the
first tier, environmental information related to user equipment (UE), base
stations (BS) and network key performance indicators (KPI) are extracted
through a variational autoencoder (VAE). The second tier is designed as a
likelihood model. Here, the environmental features and real MDT data features
are adopted, formulating an integrated training process. On validation, our
proposed model that uses real-world data demonstrates an accuracy improvement
of about 20% or more compared with the empirical model and about 10% when
compared with a fully connected prediction network.

In most data-scientific approaches, the principle of Maximum Entropy (MaxEnt)
is used to a posteriori justify some parametric model which has been already
chosen based on experience, prior knowledge or computational simplicity. In a
perpendicular formulation to conventional model building, we start from the
linear system of phenomenological constraints and asymptotically derive the
distribution over all viable distributions that satisfy the provided set of
constraints. The MaxEnt distribution plays a special role, as it is the most
typical among all phenomenologically viable distributions representing a good
expansion point for large-N techniques. This enables us to consistently
formulate hypothesis testing in a fully-data driven manner. The appropriate
parametric model which is supported by the data can be always deduced at the
end of model selection. In the MaxEnt framework, we recover major scores and
selection procedures used in multiple applications and assess their ability to
capture associations in the data-generating process and identify the most
generalizable model. This data-driven counterpart of standard model selection
demonstrates the unifying prospective of the deductive logic advocated by
MaxEnt principle, while potentially shedding new insights to the inverse
problem.

We study the sample complexity of entropic optimal transport in high
dimensions using computationally efficient plug-in estimators. We significantly
advance the state of the art by establishing dimension-free, parametric rates
for estimating various quantities of interest, including the entropic
regression function which is a natural analog to the optimal transport map. As
an application, we propose a practical model for transfer learning based on
entropic optimal transport and establish parametric rates of convergence for
nonparametric regression and classification.

Brain graph representation learning serves as the fundamental technique for
brain diseases diagnosis. Great efforts from both the academic and industrial
communities have been devoted to brain graph representation learning in recent
years. The isomorphic neural network (IsoNN) introduced recently can
automatically learn the existence of sub-graph patterns in brain graphs, which
is also the state-of-the-art brain graph representation learning method by this
context so far. However, IsoNN fails to capture the orientations of sub-graph
patterns, which may render the learned representations to be useless for many
cases. In this paper, we propose a new Iso-CapsNet (Isomorphic Capsule Net)
model by introducing the graph isomorphic capsules for effective brain graph
representation learning. Based on the capsule dynamic routing, besides the
subgraph pattern existence confidence scores, Iso-CapsNet can also learn other
sub-graph rich properties, including position, size and orientation, for
calculating the class-wise digit capsules. We have compared Iso-CapsNet with
both classic and state-of-the-art brain graph representation approaches with
extensive experiments on four brain graph benchmark datasets. The experimental
results also demonstrate the effectiveness of Iso-CapsNet, which can
out-perform the baseline methods with significant improvements.

Learning effective reinforcement learning (RL) policies to solve real-world
complex tasks can be quite challenging without a high-fidelity simulation
environment. In most cases, we are only given imperfect simulators with
simplified dynamics, which inevitably lead to severe sim-to-real gaps in RL
policy learning. The recently emerged field of offline RL provides another
possibility to learn policies directly from pre-collected historical data.
However, to achieve reasonable performance, existing offline RL algorithms need
impractically large offline data with sufficient state-action space coverage
for training. This brings up a new question: is it possible to combine learning
from limited real data in offline RL and unrestricted exploration through
imperfect simulators in online RL to address the drawbacks of both approaches?
In this study, we propose the Dynamics-Aware Hybrid Offline-and-Online
Reinforcement Learning (H2O) framework to provide an affirmative answer to this
question. H2O introduces a dynamics-aware policy evaluation scheme, which
adaptively penalizes the Q function learning on simulated state-action pairs
with large dynamics gaps, while also simultaneously allowing learning from a
fixed real-world dataset. Through extensive simulation and real-world tasks, as
well as theoretical analysis, we demonstrate the superior performance of H2O
against other cross-domain online and offline RL algorithms. H2O provides a
brand new hybrid offline-and-online RL paradigm, which can potentially shed
light on future RL algorithm design for solving practical real-world tasks.

The visual system of a robot has different requirements depending on the
application: it may require high accuracy or reliability, be constrained by
limited resources or need fast adaptation to dynamically changing environments.
In this work, we focus on the instance segmentation task and provide a
comprehensive study of different techniques that allow adapting an object
segmentation model in presence of novel objects or different domains. We
propose a pipeline for fast instance segmentation learning designed for robotic
applications where data come in stream. It is based on an hybrid method
leveraging on a pre-trained CNN for feature extraction and fast-to-train
Kernel-based classifiers. We also propose a training protocol that allows to
shorten the training time by performing feature extraction during the data
acquisition. We benchmark the proposed pipeline on two robotics datasets and we
deploy it on a real robot, i.e. the iCub humanoid. To this aim, we adapt our
method to an incremental setting in which novel objects are learned on-line by
the robot. The code to reproduce the experiments is publicly available on
GitHub.

To address the vaccine hesitancy which impairs the efforts of the COVID-19
vaccination campaign, it is imperative to understand public vaccination
attitudes and timely grasp their changes. In spite of reliability and
trustworthiness, conventional attitude collection based on surveys is
time-consuming and expensive, and cannot follow the fast evolution of
vaccination attitudes. We leverage the textual posts on social media to extract
and track users' vaccination stances in near real time by proposing a deep
learning framework. To address the impact of linguistic features such as
sarcasm and irony commonly used in vaccine-related discourses, we integrate
into the framework the recent posts of a user's social network neighbours to
help detect the user's genuine attitude. Based on our annotated dataset from
Twitter, the models instantiated from our framework can increase the
performance of attitude extraction by up to 23% compared to state-of-the-art
text-only models. Using this framework, we successfully validate the
feasibility of using social media to track the evolution of vaccination
attitudes in real life. We further show one practical use of our framework by
validating the possibility to forecast a user's vaccine hesitancy changes with
information perceived from social media.

Learning dynamics models accurately is an important goal for Model-Based
Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics
model which is vulnerable to spurious correlations and therefore generalizes
poorly to unseen states. In this paper, we introduce Causal Dynamics Learning
for Task-Independent State Abstraction (CDL), which first learns a
theoretically proved causal dynamics model that removes unnecessary
dependencies between state variables and the action, thus generalizing well to
unseen states. A state abstraction can then be derived from the learned
dynamics, which not only improves sample efficiency but also applies to a wider
range of tasks than existing state abstraction methods. Evaluated on two
simulated environments and downstream tasks, both the dynamics model and
policies learned by the proposed method generalize well to unseen states and
the derived state abstraction improves sample efficiency compared to learning
without it.

Despite extensive theoretical work on biologically plausible learning rules,
clear evidence about whether and how such rules are implemented in the brain
has been difficult to obtain. We consider biologically plausible supervised-
and reinforcement-learning rules and ask whether changes in network activity
during learning can be used to determine which learning rule is being used.
Supervised learning requires a credit-assignment model estimating the mapping
from neural activity to behavior, and, in a biological organism, this model
will inevitably be an imperfect approximation of the ideal mapping, leading to
a bias in the direction of the weight updates relative to the true gradient.
Reinforcement learning, on the other hand, requires no credit-assignment model
and tends to make weight updates following the true gradient direction. We
derive a metric to distinguish between learning rules by observing changes in
the network activity during learning, given that the mapping from brain to
behavior is known by the experimenter. Because brain-machine interface (BMI)
experiments allow for precise knowledge of this mapping, we model a
cursor-control BMI task using recurrent neural networks, showing that learning
rules can be distinguished in simulated experiments using only observations
that a neuroscience experimenter would plausibly have access to.

This is a collection of (mostly) pen-and-paper exercises in machine learning.
The exercises are on the following topics: linear algebra, optimisation,
directed graphical models, undirected graphical models, expressive power of
graphical models, factor graphs and message passing, inference for hidden
Markov models, model-based learning (including ICA and unnormalised models),
sampling and Monte-Carlo integration, and variational inference.

In this paper, we present CopyCat2 (CC2), a novel model capable of: a)
synthesizing speech with different speaker identities, b) generating speech
with expressive and contextually appropriate prosody, and c) transferring
prosody at fine-grained level between any pair of seen speakers. We do this by
activating distinct parts of the network for different tasks. We train our
model using a novel approach to two-stage training. In Stage I, the model
learns speaker-independent word-level prosody representations from speech which
it uses for many-to-many fine-grained prosody transfer. In Stage II, we learn
to predict these prosody representations using the contextual information
available in text, thereby, enabling multi-speaker TTS with contextually
appropriate prosody. We compare CC2 to two strong baselines, one in TTS with
contextually appropriate prosody, and one in fine-grained prosody transfer. CC2
reduces the gap in naturalness between our baseline and copy-synthesised speech
by $22.79\%$. In fine-grained prosody transfer evaluations, it obtains a
relative improvement of $33.15\%$ in target speaker similarity.

Emergency vehicles (EMVs) play a crucial role in responding to time-critical
calls such as medical emergencies and fire outbreaks in urban areas. Existing
methods for EMV dispatch typically optimize routes based on historical
traffic-flow data and design traffic signal pre-emption accordingly; however,
we still lack a systematic methodology to address the coupling between EMV
routing and traffic signal control. In this paper, we propose EMVLight, a
decentralized reinforcement learning (RL) framework for joint dynamic EMV
routing and traffic signal pre-emption. We adopt the multi-agent advantage
actor-critic method with policy sharing and spatial discounted factor. This
framework addresses the coupling between EMV navigation and traffic signal
control via an innovative design of multi-class RL agents and a novel
pressure-based reward function. The proposed methodology enables EMVLight to
learn network-level cooperative traffic signal phasing strategies that not only
reduce EMV travel time but also shortens the travel time of non-EMVs.
Simulation-based experiments indicate that EMVLight enables up to a $42.6\%$
reduction in EMV travel time as well as an $23.5\%$ shorter average travel time
compared with existing approaches.

Establishing voxelwise semantic correspondence across distinct imaging
modalities is a foundational yet formidable computer vision task. Current
multi-modality registration techniques maximize hand-crafted inter-domain
similarity functions, are limited in modeling nonlinear intensity-relationships
and deformations, and may require significant re-engineering or underperform on
new tasks, datasets, and domain pairs. This work presents ContraReg, an
unsupervised contrastive representation learning approach to multi-modality
deformable registration. By projecting learned multi-scale local patch features
onto a jointly learned inter-domain embedding space, ContraReg obtains
representations useful for non-rigid multi-modality alignment. Experimentally,
ContraReg achieves accurate and robust results with smooth and invertible
deformations across a series of baselines and ablations on a neonatal T1-T2
brain MRI registration task with all methods validated over a wide range of
deformation regularization strengths.

An open research question in deep reinforcement learning is how to focus the
policy learning of key decisions within a sparse domain. This paper emphasizes
combining the advantages of inputoutput hidden Markov models and reinforcement
learning towards interpretable maintenance decisions. We propose a novel
hierarchical-modeling methodology that, at a high level, detects and interprets
the root cause of a failure as well as the health degradation of the turbofan
engine, while, at a low level, it provides the optimal replacement policy. It
outperforms the baseline performance of deep reinforcement learning methods
applied directly to the raw data or when using a hidden Markov model without
such a specialized hierarchy. It also provides comparable performance to prior
work, however, with the additional benefit of interpretability.

Kinematic variables have been playing an important role in collider
phenomenology, as they expedite discoveries of new particles by separating
signal events from unwanted background events and allow for measurements of
particle properties such as masses, couplings, spins, etc. For the past 10
years, an enormous number of kinematic variables have been designed and
proposed, primarily for the experiments at the Large Hadron Collider, allowing
for a drastic reduction of high-dimensional experimental data to
lower-dimensional observables, from which one can readily extract underlying
features of phase space and develop better-optimized data-analysis strategies.
We review these recent developments in the area of phase space kinematics,
summarizing the new kinematic variables with important phenomenological
implications and physics applications. We also review recently proposed
analysis methods and techniques specifically designed to leverage the new
kinematic variables. As machine learning is nowadays percolating through many
fields of particle physics including collider phenomenology, we discuss the
interconnection and mutual complementarity of kinematic variables and machine
learning techniques. We finally discuss how the utilization of kinematic
variables originally developed for colliders can be extended to other
high-energy physics experiments including neutrino experiments.

Given the democratic nature of open source development, code review and issue
discussions may be uncivil. Incivility, defined as features of discussion that
convey an unnecessarily disrespectful tone, can have negative consequences to
open source communities. To prevent or minimize these negative consequences,
open source platforms have included mechanisms for removing uncivil language
from the discussions. However, such approaches require manual inspection, which
can be overwhelming given the large number of discussions. To help open source
communities deal with this problem, in this paper, we aim to compare six
classical machine learning models with BERT to detect incivility in open source
code review and issue discussions. Furthermore, we assess if adding contextual
information improves the models' performance and how well the models perform in
a cross-platform setting. We found that BERT performs better than classical
machine learning models, with a best F1-score of 0.95. Furthermore, classical
machine learning models tend to underperform to detect non-technical and civil
discussions. Our results show that adding the contextual information to BERT
did not improve its performance and that none of the analyzed classifiers had
an outstanding performance in a cross-platform setting. Finally, we provide
insights into the tones that the classifiers misclassify.

applications. Recently, the usage of multi-rotor unmanned autonomous vehicles
(UAV) for marine applications is receiving more attention in the literature.
Usually, both platforms employ an inertial navigation system (INS), and aiding
sensors for an accurate navigation solution. In AUV navigation, Doppler
velocity log (DVL) is mainly used to aid the INS, while for UAVs, it is common
to use global navigation satellite systems (GNSS) receivers. The fusion between
the aiding sensor and the INS requires a definition of step size parameter in
the estimation process. It is responsible for the solution frequency update
and, eventually, its accuracy. The choice of the step size poses a tradeoff
between computational load and navigation performance. Generally, the aiding
sensors update frequency is considered much slower compared to the INS
operating frequency (hundreds Hertz). Such high rate is unnecessary for most
platforms, specifically for low dynamics AUVs. In this work, a supervised
machine learning based adaptive tuning scheme to select the proper INS step
size is proposed. To that end, a velocity error bound is defined, allowing the
INS/DVL or the INS/GNSS to act in a sub-optimal working conditions, and yet
minimize the computational load. Results from simulations and field experiment
show the benefits of using the proposed approach. In addition, the proposed
framework can be applied to any other fusion scenarios between any type of
sensors or platforms.

Numerical validation is at the core of machine learning research as it allows
to assess the actual impact of new methods, and to confirm the agreement
between theory and practice. Yet, the rapid development of the field poses
several challenges: researchers are confronted with a profusion of methods to
compare, limited transparency and consensus on best practices, as well as
tedious re-implementation work. As a result, validation is often very partial,
which can lead to wrong conclusions that slow down the progress of research. We
propose Benchopt, a collaborative framework to automate, reproduce and publish
optimization benchmarks in machine learning across programming languages and
hardware architectures. Benchopt simplifies benchmarking for the community by
providing an off-the-shelf tool for running, sharing and extending experiments.
To demonstrate its broad usability, we showcase benchmarks on three standard
learning tasks: $\ell_2$-regularized logistic regression, Lasso, and ResNet18
training for image classification. These benchmarks highlight key practical
findings that give a more nuanced view of the state-of-the-art for these
problems, showing that for practical evaluation, the devil is in the details.
We hope that Benchopt will foster collaborative work in the community hence
improving the reproducibility of research findings.

Light-sheet fluorescence microscopy (LSFM) is a cutting-edge volumetric
imaging technique that allows for three-dimensional imaging of mesoscopic
samples with decoupled illumination and detection paths. Although the selective
excitation scheme of such a microscope provides intrinsic optical sectioning
that minimizes out-of-focus fluorescence background and sample photodamage, it
is prone to light absorption and scattering effects, which results in uneven
illumination and striping artifacts in the images adversely. To tackle this
issue, in this paper, we propose a blind stripe artifact removal algorithm in
LSFM, called DeStripe, which combines a self-supervised spatio-spectral graph
neural network with unfolded Hessian prior. Specifically, inspired by the
desirable properties of Fourier transform in condensing striping information
into isolated values in the frequency domain, DeStripe firstly localizes the
potentially corrupted Fourier coefficients by exploiting the structural
difference between unidirectional stripe artifacts and more isotropic
foreground images. Affected Fourier coefficients can then be fed into a graph
neural network for recovery, with a Hessian regularization unrolled to further
ensure structures in the standard image space are well preserved. Since in
realistic, stripe-free LSFM barely exists with a standard image acquisition
protocol, DeStripe is equipped with a Self2Self denoising loss term, enabling
artifact elimination without access to stripe-free ground truth images.
Competitive experimental results demonstrate the efficacy of DeStripe in
recovering corrupted biomarkers in LSFM with both synthetic and real stripe
artifacts.

Cutting planes are essential for solving mixed-integer linear problems
(MILPs), because they facilitate bound improvements on the optimal solution
value. For selecting cuts, modern solvers rely on manually designed heuristics
that are tuned to gauge the potential effectiveness of cuts. We show that a
greedy selection rule explicitly looking ahead to select cuts that yield the
best bound improvement delivers strong decisions for cut selection - but is too
expensive to be deployed in practice. In response, we propose a new neural
architecture (NeuralCut) for imitation learning on the lookahead expert. Our
model outperforms standard baselines for cut selection on several synthetic
MILP benchmarks. Experiments with a B&C solver for neural network verification
further validate our approach, and exhibit the potential of learning methods in
this setting.

Optimal Transport, a theory for optimal allocation of resources, is widely
used in various fields such as astrophysics, machine learning, and imaging
science. However, many applications impose elementwise constraints on the
transport plan which traditional optimal transport cannot enforce. Here we
introduce Supervised Optimal Transport (sOT) that formulates a constrained
optimal transport problem where couplings between certain elements are
prohibited according to specific applications. sOT is proved to be equivalent
to an $l^1$ penalized optimization problem, from which efficient algorithms are
designed to solve its entropy regularized formulation. We demonstrate the
capability of sOT by comparing it to other variants and extensions of
traditional OT in color transfer problem. We also study the barycenter problem
in sOT formulation, where we discover and prove a unique reverse and portion
selection (control) mechanism. Supervised optimal transport is broadly
applicable to applications in which constrained transport plan is involved and
the original unit should be preserved by avoiding normalization.

In agriculture, the majority of vision systems perform still image
classification. Yet, recent work has highlighted the potential of spatial and
temporal cues as a rich source of information to improve the classification
performance. In this paper, we propose novel approaches to explicitly capture
both spatial and temporal information to improve the classification of deep
convolutional neural networks. We leverage available RGB-D images and robot
odometry to perform inter-frame feature map spatial registration. This
information is then fused within recurrent deep learnt models, to improve their
accuracy and robustness. We demonstrate that this can considerably improve the
classification performance with our best performing spatial-temporal model
(ST-Atte) achieving absolute performance improvements for
intersection-over-union (IoU[%]) of 4.7 for crop-weed segmentation and 2.6 for
fruit (sweet pepper) segmentation. Furthermore, we show that these approaches
are robust to variable framerates and odometry errors, which are frequently
observed in real-world applications.

Robustness is a fundamental pillar of Machine Learning (ML) classifiers,
substantially determining their reliability. Methods for assessing classifier
robustness are therefore essential. In this work, we address the challenge of
evaluating corruption robustness in a way that allows comparability and
interpretability on a given dataset. We propose a test data augmentation method
that uses a robustness distance $\epsilon$ derived from the datasets minimal
class separation distance. The resulting MSCR (mean statistical corruption
robustness) metric allows a dataset-specific comparison of different
classifiers with respect to their corruption robustness. The MSCR value is
interpretable, as it represents the classifiers avoidable loss of accuracy due
to statistical corruptions. On 2D and image data, we show that the metric
reflects different levels of classifier robustness. Furthermore, we observe
unexpected optima in classifiers robust accuracy through training and testing
classifiers with different levels of noise. While researchers have frequently
reported on a significant tradeoff on accuracy when training robust models, we
strengthen the view that a tradeoff between accuracy and corruption robustness
is not inherent. Our results indicate that robustness training through simple
data augmentation can already slightly improve accuracy.

It has been demonstrated that deep neural networks outperform traditional
machine learning. However, deep networks lack generalisability, that is, they
will not perform as good as in a new (testing) set drawn from a different
distribution due to the domain shift. In order to tackle this known issue,
several transfer learning approaches have been proposed, where the knowledge of
a trained model is transferred into another to improve performance with
different data. However, most of these approaches require additional training
steps, or they suffer from catastrophic forgetting that occurs when a trained
model has overwritten previously learnt knowledge. We address both problems
with a novel transfer learning approach that uses network aggregation. We train
dataset-specific networks together with an aggregation network in a unified
framework. The loss function includes two main components: a task-specific loss
(such as cross-entropy) and an aggregation loss. The proposed aggregation loss
allows our model to learn how trained deep network parameters can be aggregated
with an aggregation operator. We demonstrate that the proposed approach learns
model aggregation at test time without any further training step, reducing the
burden of transfer learning to a simple arithmetical operation. The proposed
approach achieves comparable performance w.r.t. the baseline. Besides, if the
aggregation operator has an inverse, we will show that our model also
inherently allows for selective forgetting, i.e., the aggregated model can
forget one of the datasets it was trained on, retaining information on the
others.

One unexpected technique that emerged in recent years consists in training a
Deep Network (DN) with a Self-Supervised Learning (SSL) method, and using this
network on downstream tasks but with its last few layers entirely removed. This
usually skimmed-over trick is actually critical for SSL methods to display
competitive performances. For example, on ImageNet classification, more than 30
points of percentage can be gained that way. This is a little vexing, as one
would hope that the network layer at which invariance is explicitly enforced by
the SSL criterion during training (the last layer) should be the one to use for
best generalization performance downstream. But it seems not to be, and this
study sheds some light on why. This trick, which we name Guillotine
Regularization (GR), is in fact a generically applicable form of regularization
that has also been used to improve generalization performance in transfer
learning scenarios. In this work, through theory and experiments, we formalize
GR and identify the underlying reasons behind its success in SSL methods. Our
study shows that the use of this trick is essential to SSL performance for two
main reasons: (i) improper data-augmentations to define the positive pairs used
during training, and/or (ii) suboptimal selection of the hyper-parameters of
the SSL loss.

We propose a framework for the stability verification of Mixed-Integer Linear
Programming (MILP) representable control policies. This framework compares a
fixed candidate policy, which admits an efficient parameterization and can be
evaluated at a low computational cost, against a fixed baseline policy, which
is known to be stable but expensive to evaluate. We provide sufficient
conditions for the closed-loop stability of the candidate policy in terms of
the worst-case approximation error with respect to the baseline policy, and we
show that these conditions can be checked by solving a Mixed-Integer Quadratic
Program (MIQP). Additionally, we demonstrate that an outer approximation of the
stability region of the candidate policy can be computed by solving an MILP.
The proposed framework is sufficiently general to accommodate a broad range of
candidate policies including ReLU Neural Networks (NNs), optimal solution maps
of parametric quadratic programs, and Model Predictive Control (MPC) policies.
We also present an open-source toolbox in Python based on the proposed
framework, which allows for the easy verification of custom NN architectures
and MPC formulations. We showcase the flexibility and reliability of our
framework in the context of a DC-DC power convertor case study and investigate
the computational complexity.

In this paper, we describe an approach for representation learning of audio
signals for the task of COVID-19 detection. The raw audio samples are processed
with a bank of 1-D convolutional filters that are parameterized as cosine
modulated Gaussian functions. The choice of these kernels allows the
interpretation of the filterbanks as smooth band-pass filters. The filtered
outputs are pooled, log-compressed and used in a self-attention based relevance
weighting mechanism. The relevance weighting emphasizes the key regions of the
time-frequency decomposition that are important for the downstream task. The
subsequent layers of the model consist of a recurrent architecture and the
models are trained for a COVID-19 detection task. In our experiments on the
Coswara data set, we show that the proposed model achieves significant
performance improvements over the baseline system as well as other
representation learning approaches. Further, the approach proposed is shown to
be uniformly applicable for speech and breathing signals and for transfer
learning from a larger data set.

In this paper we provide a latent-variable formulation and solution to the
recommender system (RS) problem in terms of a fundamental property that any
reasonable solution should be expected to satisfy. Specifically, we examine a
novel tensor completion method to efficiently and accurately learn parameters
of a model for the unobservable personal preferences that underly user ratings.
By regularizing the tensor decomposition with a single latent invariant, we
achieve three properties for a reliable recommender system: (1) uniqueness of
the tensor completion result with minimal assumptions, (2) unit consistency
that is independent of arbitrary preferences of users, and (3) a consensus
ordering guarantee that provides consistent ranking between observed and
unobserved rating scores. Our algorithm leads to a simple and elegant
recommendation framework that has linear computational complexity and with no
hyperparameter tuning. We provide empirical results demonstrating that the
approach significantly outperforms current state-of-the-art methods.

In the nonparametric regression setting, we construct an estimator which is a
continuous function interpolating the data points with high probability, while
attaining minimax optimal rates under mean squared risk on the scale of
H\"older classes adaptively to the unknown smoothness.

Machine learning models deployed on medical imaging tasks must be equipped
with out-of-distribution detection capabilities in order to avoid erroneous
predictions. It is unsure whether out-of-distribution detection models reliant
on deep neural networks are suitable for detecting domain shifts in medical
imaging. Gaussian Processes can reliably separate in-distribution data points
from out-of-distribution data points via their mathematical construction.
Hence, we propose a parameter efficient Bayesian layer for hierarchical
convolutional Gaussian Processes that incorporates Gaussian Processes operating
in Wasserstein-2 space to reliably propagate uncertainty. This directly
replaces convolving Gaussian Processes with a distance-preserving affine
operator on distributions. Our experiments on brain tissue-segmentation show
that the resulting architecture approaches the performance of well-established
deterministic segmentation algorithms (U-Net), which has not been achieved with
previous hierarchical Gaussian Processes. Moreover, by applying the same
segmentation model to out-of-distribution data (i.e., images with pathology
such as brain tumors), we show that our uncertainty estimates result in
out-of-distribution detection that outperforms the capabilities of previous
Bayesian networks and reconstruction-based approaches that learn normative
distributions. To facilitate future work our code is publicly available.

The machine learning (ML) and deep learning (DL) techniques are widely
recognized to be powerful tools for virtual drug screening. The recently
reported ML- or DL-based scoring functions have shown exciting performance in
predicting protein-ligand binding affinities with fruitful application
prospects. However, the differentiation between highly similar ligand
conformations, including the native binding pose (the global energy minimum
state), remains challenging which could greatly enhance the docking. In this
work, we propose a fully differentiable framework for ligand pose optimization
based on a hybrid scoring function (SF) combined with a multi-layer perceptron
(DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which
combines (1) the root mean square deviation (RMSD) of the docking pose with
respect to the native pose and (2) the AutoDock Vina score, is fully
differentiable thus is capable of optimizing the ligand binding pose to the
energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset,
the DeepRMSD+Vina reaches a success rate of 95.4%, which is by far the best
reported SF to date. Based on this SF, an end-to-end ligand pose optimization
framework was implemented to improve the docking pose quality. We demonstrated
that this method significantly improves the docking success rate (by 15%) in
redocking and crossdocking tasks, revealing the high potentialities of this
framework in drug design and discovery.

Machine learning recently proved efficient in learning differential equations
and dynamical systems from data. However, the data is commonly assumed to
originate from a single never-changing system. In contrast, when modeling
real-world dynamical processes, the data distribution often shifts due to
changes in the underlying system dynamics. Continual learning of these
processes aims to rapidly adapt to abrupt system changes without forgetting
previous dynamical regimes. This work proposes an approach to continual
learning based on reservoir computing, a state-of-the-art method for training
recurrent neural networks on complex spatiotemporal dynamical systems.
Reservoir computing fixes the recurrent network weights - hence these cannot be
forgotten - and only updates linear projection heads to the output. We propose
to train multiple competitive prediction heads concurrently. Inspired by
neuroscience's predictive coding, only the most predictive heads activate,
laterally inhibiting and thus protecting the inactive heads from forgetting
induced by interfering parameter updates. We show that this multi-head
reservoir minimizes interference and catastrophic forgetting on several
dynamical systems, including the Van-der-Pol oscillator, the chaotic Lorenz
attractor, and the high-dimensional Lorenz-96 weather model. Our results
suggest that reservoir computing is a promising candidate framework for the
continual learning of dynamical systems. We provide our code for data
generation, method, and comparisons at
\url{https://github.com/leonardbereska/multiheadreservoir}.

The worldwide increase of antimicrobial resistance (AMR) is a serious threat
to human health. To avert the spread of AMR, fast reliable diagnostics tools
that facilitate optimal antibiotic stewardship are an unmet need. In this
regard, Raman spectroscopy promises rapid label- and culture-free
identification and antimicrobial susceptibility testing (AST) in a single step.
However, even though many Raman-based bacteria-identification and AST studies
have demonstrated impressive results, some shortcomings must be addressed. To
bridge the gap between proof-of-concept studies and clinical application, we
have developed machine learning techniques in combination with a novel
data-augmentation algorithm, for fast identification of minimally prepared
bacteria phenotypes and the distinctions of methicillin-resistant (MR) from
methicillin-susceptible (MS) bacteria. For this we have implemented a spectral
transformer model for hyper-spectral Raman images of bacteria. We show that our
model outperforms the standard convolutional neural network models on a
multitude of classification problems, both in terms of accuracy and in terms of
training time. We attain more than 96$\%$ classification accuracy on a dataset
consisting of 15 different classes and 95.6$\%$ classification accuracy for six
MR-MS bacteria species. More importantly, our results are obtained using only
fast and easy-to-produce training and test data

We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch
acquisition function that enables multi-objective Bayesian optimisation methods
to efficiently exploit parallel processing resources. Multi-Objective Bayesian
Optimisation (MOBO) is a very efficient tool for tackling expensive black-box
problems. However, most MOBO algorithms are designed as purely sequential
strategies, and existing batch approaches are prohibitively expensive for all
but the smallest of batch sizes. We show that by encouraging batch diversity
through penalising evaluations with similar predicted objective values, HIPPO
is able to cheaply build large batches of informative points. Our extensive
experimental validation demonstrates that HIPPO is at least as efficient as
existing alternatives whilst incurring an order of magnitude lower
computational overhead and scaling easily to batch sizes considerably higher
than currently supported in the literature. Additionally, we demonstrate the
application of HIPPO to a challenging heat exchanger design problem, stressing
the real-world utility of our highly parallelisable approach to MOBO.

Developers use shell commands for many tasks, such as file system management,
network control, and process management. Bash is one of the most commonly used
shells and plays an important role in Linux system development and maintenance.
Due to the language flexibility of Bash code, developers who are not familiar
with Bash often have difficulty understanding the purpose and functionality of
Bash code. In this study, we study Bash code comment generation problem and
proposed an automatic method BashExplainer based on two-stage training
strategy. In the first stage, we train a Bash encoder by fine-tuning CodeBERT
on our constructed Bash code corpus. In the second stage, we first retrieve the
most similar code from the code repository for the target code based on
semantic and lexical similarity. Then we use the trained Bash encoder to
generate two vector representations. Finally, we fuse these two vector
representations via the fusion layer and generate the code comment through the
decoder. To show the competitiveness of our proposed method, we construct a
high-quality corpus by combining the corpus shared in the previous NL2Bash
study and the corpus shared in the NLC2CMD competition. This corpus contains
10,592 Bash codes and corresponding comments. Then we selected ten baselines
from previous studies on automatic code comment generation, which cover
information retrieval methods, deep learning methods, and hybrid methods.

We present a case study applying learning-based distributionally robust model
predictive control to highway motion planning under stochastic uncertainty of
the lane change behavior of surrounding road users. The dynamics of road users
are modelled using Markov jump systems, in which the switching variable
describes the desired lane of the vehicle under consideration and the
continuous state describes the pose and velocity of the vehicles. We assume the
switching probabilities of the underlying Markov chain to be unknown. As the
vehicle is observed and thus, samples from the Markov chain are drawn, the
transition probabilities are estimated along with an ambiguity set which
accounts for misestimations of these probabilities. Correspondingly, a
distributionally robust optimal control problem is formulated over a scenario
tree, and solved in receding horizon. As a result, a motion planning procedure
is obtained which through observation of the target vehicle gradually becomes
less conservative while avoiding overconfidence in estimates obtained from
small sample sizes. We present an extensive numerical case study, comparing the
effects of several different design aspects on the controller performance and
safety.

Ultrasound examination is widely used in the clinical diagnosis of thyroid
nodules (benign/malignant). However, the accuracy relies heavily on radiologist
experience. Although deep learning techniques have been investigated for
thyroid nodules recognition. Current solutions are mainly based on static
ultrasound images, with limited temporal information used and inconsistent with
clinical diagnosis. This paper proposes a novel method for the automated
recognition of thyroid nodules through an exhaustive exploration of ultrasound
videos and key-frames. We first propose a detection-localization framework to
automatically identify the clinical key-frame with a typical nodule in each
ultrasound video. Based on the localized key-frame, we develop a key-frame
guided video classification model for thyroid nodule recognition. Besides, we
introduce a motion attention module to help the network focus on significant
frames in an ultrasound video, which is consistent with clinical diagnosis. The
proposed thyroid nodule recognition framework is validated on clinically
collected ultrasound videos, demonstrating superior performance compared with

Automatic segmentation of organs-at-risk (OARs) in CT scans using
convolutional neural networks (CNNs) is being introduced into the radiotherapy
workflow. However, these segmentations still require manual editing and
approval by clinicians prior to clinical use, which can be time consuming. The
aim of this work was to develop a tool to automatically identify errors in 3D
OAR segmentations without a ground truth. Our tool uses a novel architecture
combining a CNN and graph neural network (GNN) to leverage the segmentation's
appearance and shape. The proposed model is trained using self-supervised
learning using a synthetically-generated dataset of segmentations of the
parotid and with realistic contouring errors. The effectiveness of our model is
assessed with ablation tests, evaluating the efficacy of different portions of
the architecture as well as the use of transfer learning from an unsupervised
pretext task. Our best performing model predicted errors on the parotid gland
with a precision of 85.0% & 89.7% for internal and external errors
respectively, and recall of 66.5% & 68.6%. This offline QA tool could be used
in the clinical pathway, potentially decreasing the time clinicians spend
correcting contours by detecting regions which require their attention. All our
code is publicly available at
https://github.com/rrr-uom-projects/contour_auto_QATool.

We consider the problem of Sampling Transition Paths. Given two metastable
conformational states of a molecular system, eg. a folded and unfolded protein,
we aim to sample the most likely transition path between the two states.
Sampling such a transition path is computationally expensive due to the
existence of high free energy barriers between the two states. To circumvent
this, previous work has focused on simplifying the trajectories to occur along
specific molecular descriptors called Collective Variables (CVs). However,
finding CVs is not trivial and requires chemical intuition. For larger
molecules, where intuition is not sufficient, using these CV-based methods
biases the transition along possibly irrelevant dimensions. Instead, this work
proposes a method for sampling transition paths that consider the entire
geometry of the molecules. To achieve this, we first relate the problem to
recent work on the Schrodinger bridge problem and stochastic optimal control.
Using this relation, we construct a method that takes into account important
characteristics of molecular systems such as second-order dynamics and
invariance to rotations and translations. We demonstrate our method on the
commonly studied Alanine Dipeptide, but also consider larger proteins such as
Polyproline and Chignolin.

Reinforcement learning (RL) commonly assumes access to well-specified reward
functions, which many practical applications do not provide. Instead, recently,
more work has explored learning what to do from interacting with humans. So
far, most of these approaches model humans as being (nosily) rational and, in
particular, giving unbiased feedback. We argue that these models are too
simplistic and that RL researchers need to develop more realistic human models
to design and evaluate their algorithms. In particular, we argue that human
models have to be personal, contextual, and dynamic. This paper calls for
research from different disciplines to address key questions about how humans
provide feedback to AIs and how we can build more robust human-in-the-loop RL
systems.

The key advantage of using multiple microphones for speech enhancement is
that spatial filtering can be used to complement the tempo-spectral processing.
In a traditional setting, linear spatial filtering (beamforming) and
single-channel post-filtering are commonly performed separately. In contrast,
there is a trend towards employing deep neural networks (DNNs) to learn a joint
spatial and tempo-spectral non-linear filter, which means that the restriction
of a linear processing model and that of a separate processing of spatial and
tempo-spectral information can potentially be overcome. However, the internal
mechanisms that lead to good performance of such data-driven filters for
multi-channel speech enhancement are not well understood. Therefore, in this
work, we analyse the properties of a non-linear spatial filter realized by a
DNN as well as its interdependency with temporal and spectral processing by
carefully controlling the information sources (spatial, spectral, and temporal)
available to the network. We confirm the superiority of a non-linear spatial
processing model, which outperforms an oracle linear spatial filter in a
challenging speaker extraction scenario for a low number of microphones by 0.24
POLQA score. Our analyses reveal that in particular spectral information should
be processed jointly with spatial information as this increases the spatial
selectivity of the filter. Our systematic evaluation then leads to a simple
network architecture, that outperforms state-of-the-art network architectures
on a speaker extraction task by 0.22 POLQA score and by 0.32 POLQA score on the
CHiME3 data.

One of the main problems of evolutionary algorithms is the convergence of the
population to local minima. In this paper, we explore techniques that can avoid
this problem by encouraging a diverse behavior of the agents through a shared
reward system. The rewards are randomly distributed in the environment, and the
agents are only rewarded for collecting them first. This leads to an emergence
of a novel behavior of the agents. We introduce our approach to the maze
problem and compare it to the previously proposed solution, denoted as Novelty
Search (Lehman and Stanley, 2011a). We find that our solution leads to an
improved performance while being significantly simpler. Building on that, we
generalize the problem and apply our approach to a more advanced set of tasks,
Atari Games, where we observe a similar performance quality with much less
computational power needed.

Photometric data-driven classification of supernovae becomes a challenge due
to the appearance of real-time processing of big data in astronomy. Recent
studies have demonstrated the superior quality of solutions based on various
machine learning models. These models learn to classify supernova types using
their light curves as inputs. Preprocessing these curves is a crucial step that
significantly affects the final quality. In this talk, we study the application
of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing
flows (NF) to approximate observations for a single light curve. We use these
approximations as inputs for supernovae classification models and demonstrate
that the proposed methods outperform the state-of-the-art based on Gaussian
processes applying to the Zwicky Transient Facility Bright Transient Survey
light curves. MLP demonstrates similar quality as Gaussian processes and speed
increase. Normalizing Flows exceeds Gaussian processes in terms of
approximation quality as well.

In this paper, we present PARTICUL, a novel algorithm for unsupervised
learning of part detectors from datasets used in fine-grained recognition. It
exploits the macro-similarities of all images in the training set in order to
mine for recurring patterns in the feature space of a pre-trained convolutional
neural network. We propose new objective functions enforcing the locality and
unicity of the detected parts. Additionally, we embed our detectors with a
confidence measure based on correlation scores, allowing the system to estimate
the visibility of each part. We apply our method on two public fine-grained
datasets (Caltech-UCSD Bird 200 and Stanford Cars) and show that our detectors
can consistently highlight parts of the object while providing a good measure
of the confidence in their prediction. We also demonstrate that these detectors
can be directly used to build part-based fine-grained classifiers that provide
a good compromise between the transparency of prototype-based approaches and
the performance of non-interpretable methods.

Vaccine hesitancy is considered as one main cause of the stagnant uptake
ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently
supplied. Fast and accurate grasp of public attitudes toward vaccination is
critical to address vaccine hesitancy, and social media platforms have proved
to be an effective source of public opinions. In this paper, we describe the
collection and release of a dataset of tweets related to COVID-19 vaccines.
This dataset consists of the IDs of 2,198,090 tweets collected from Western
Europe, 17,934 of which are annotated with the originators' vaccination
stances. Our annotation will facilitate using and developing data-driven models
to extract vaccination attitudes from social media posts and thus further
confirm the power of social media in public health surveillance. To lay the
groundwork for future research, we not only perform statistical analysis and
visualisation of our dataset, but also evaluate and compare the performance of
established text-based benchmarks in vaccination stance extraction. We
demonstrate one potential use of our data in practice in tracking the temporal
changes of public COVID-19 vaccination attitudes.

Visual Question Answering (VQA) models take an image and a natural-language
question as input and infer the answer to the question. Recently, VQA systems
in medical imaging have gained popularity thanks to potential advantages such
as patient engagement and second opinions for clinicians. While most research
efforts have been focused on improving architectures and overcoming
data-related limitations, answer consistency has been overlooked even though it
plays a critical role in establishing trustworthy models. In this work, we
propose a novel loss function and corresponding training procedure that allows
the inclusion of relations between questions into the training process.
Specifically, we consider the case where implications between perception and
reasoning questions are known a-priori. To show the benefits of our approach,
we evaluate it on the clinically relevant task of Diabetic Macular Edema (DME)
staging from fundus imaging. Our experiments show that our method outperforms
state-of-the-art baselines, not only by improving model consistency, but also
in terms of overall model accuracy. Our code and data are available at
https://github.com/sergiotasconmorales/consistency_vqa.

Temporal volume images with 3D+t (4D) information are often used in medical
imaging to statistically analyze temporal dynamics or capture disease
progression. Although deep-learning-based generative models for natural images
have been extensively studied, approaches for temporal medical image generation
such as 4D cardiac volume data are limited. In this work, we present a novel
deep learning model that generates intermediate temporal volumes between source
and target volumes. Specifically, we propose a diffusion deformable model (DDM)
by adapting the denoising diffusion probabilistic model that has recently been
widely investigated for realistic image generation. Our proposed DDM is
composed of the diffusion and the deformation modules so that DDM can learn
spatial deformation information between the source and target volumes and
provide a latent code for generating intermediate frames along a geodesic path.
Once our model is trained, the latent code estimated from the diffusion module
is simply interpolated and fed into the deformation module, which enables DDM
to generate temporal frames along the continuous trajectory while preserving
the topology of the source image. We demonstrate the proposed method with the
4D cardiac MR image generation between the diastolic and systolic phases for
each subject. Compared to the existing deformation methods, our DDM achieves
high performance on temporal volume generation.

Convergence and convergence rate analyses of adaptive methods, such as
Adaptive Moment Estimation (Adam) and its variants, have been widely studied
for nonconvex optimization. The analyses are based on assumptions that the
expected or empirical average loss function is Lipschitz smooth (i.e., its
gradient is Lipschitz continuous) and the learning rates depend on the
Lipschitz constant of the Lipschitz continuous gradient. Meanwhile, numerical
evaluations of Adam and its variants have clarified that using small constant
learning rates without depending on the Lipschitz constant and hyperparameters
($\beta_1$ and $\beta_2$) close to one is advantageous for training deep neural
networks. Since computing the Lipschitz constant is NP-hard, the Lipschitz
smoothness condition would be unrealistic. This paper provides theoretical
analyses of Adam without assuming the Lipschitz smoothness condition in order
to bridge the gap between theory and practice. The main contribution is to show
theoretical evidence that Adam using small learning rates and hyperparameters
close to one performs well, whereas the previous theoretical results were all
for hyperparameters close to zero. Our analysis also leads to the finding that
Adam performs well with large batch sizes. Moreover, we show that Adam performs
well when it uses diminishing learning rates and hyperparameters close to one.

We propose a novel framework ConceptX, to analyze how latent concepts are
encoded in representations learned within pre-trained language models. It uses
clustering to discover the encoded concepts and explains them by aligning with
a large set of human-defined concepts. Our analysis on seven transformer
language models reveal interesting insights: i) the latent space within the
learned representations overlap with different linguistic concepts to a varying
degree, ii) the lower layers in the model are dominated by lexical concepts
(e.g., affixation), whereas the core-linguistic concepts (e.g., morphological
or syntactic relations) are better represented in the middle and higher layers,
iii) some encoded concepts are multi-faceted and cannot be adequately explained
using the existing human-defined concepts.

While a lot of work has been done in understanding representations learned
within deep NLP models and what knowledge they capture, little attention has
been paid towards individual neurons. We present a technique called as
Linguistic Correlation Analysis to extract salient neurons in the model, with
respect to any extrinsic property - with the goal of understanding how such a
knowledge is preserved within neurons. We carry out a fine-grained analysis to
answer the following questions: (i) can we identify subsets of neurons in the
network that capture specific linguistic properties? (ii) how localized or
distributed neurons are across the network? iii) how redundantly is the
information preserved? iv) how fine-tuning pre-trained models towards
downstream NLP tasks, impacts the learned linguistic knowledge? iv) how do
architectures vary in learning different linguistic properties? Our
data-driven, quantitative analysis illuminates interesting findings: (i) we
found small subsets of neurons that can predict different linguistic tasks, ii)
with neurons capturing basic lexical information (such as suffixation)
localized in lower most layers, iii) while those learning complex concepts
(such as syntactic role) predominantly in middle and higher layers, iii) that
salient linguistic neurons are relocated from higher to lower layers during
transfer learning, as the network preserve the higher layers for task specific
information, iv) we found interesting differences across pre-trained models,
with respect to how linguistic information is preserved within, and v) we found
that concept exhibit similar neuron distribution across different languages in
the multilingual transformer models. Our code is publicly available as part of
the NeuroX toolkit.

We consider extensions of the Shannon relative entropy, referred to as
f-divergences. Three classical related computational problems are typically
associated with these divergences: (a) estimation from moments, (b) computing
normalizing integrals, and (c) variational inference in probabilistic models.
These problems are related to one another through convex duality, and for all
them, there are many applications throughout data science, and we aim for
computationally tractable approximation algorithms that preserve properties of
the original problem such as potential convexity or monotonicity. In order to
achieve this, we derive a sequence of convex relaxations for computing these
divergences from non-centered covariance matrices associated with a given
feature vector: starting from the typically non-tractable optimal lower-bound,
we consider an additional relaxation based on "sums-of-squares", which is is
now computable in polynomial time as a semidefinite program, as well as further
computationally more efficient relaxations based on spectral information
divergences from quantum information theory. For all of the tasks above, beyond
proposing new relaxations, we derive tractable algorithms based on augmented
Lagrangians and first-order methods, and we present illustrations on
multivariate trigonometric polynomials and functions on the Boolean hypercube.

The proliferation of online hate speech has necessitated the creation of
algorithms which can detect toxicity. Most of the past research focuses on this
detection as a classification task, but assigning an absolute toxicity label is
often tricky. Hence, few of the past works transform the same task into a
regression. This paper shows the comparative evaluation of different
transformers and traditional machine learning models on a recently released
toxicity severity measurement dataset by Jigsaw. We further demonstrate the
issues with the model predictions using explainability analysis.

Modern tourism in the 21st century is facing numerous challenges. One of
these challenges is the rapidly growing number of tourists in space limited
regions such as historical city centers, museums or geographical bottlenecks
like narrow valleys. In this context, a proper and accurate prediction of
tourism volume and tourism flow within a certain area is important and critical
for visitor management tasks such as visitor flow control and prevention of
overcrowding. Static flow control methods like limiting access to hotspots or
using conventional low level controllers could not solve the problem yet. In
this paper, we empirically evaluate the performance of several state-of-the-art
deep-learning methods in the field of visitor flow prediction with limited data
by using available granular data supplied by a tourism region and comparing the
results to ARIMA, a classical statistical method. Our results show that
deep-learning models yield better predictions compared to the ARIMA method,
while both featuring faster inference times and being able to incorporate
additional input features.

Wideband Audio Waveform Evaluation Networks (WAWEnets) are convolutional
neural networks that operate directly on wideband audio waveforms in order to
produce evaluations of those waveforms. In the present work these evaluations
give qualities of telecommunications speech (e.g., noisiness, intelligibility,
overall speech quality). WAWEnets are no-reference networks because they do not
require ``reference'' (original or undistorted) versions of the waveforms they
evaluate. Our initial WAWEnet publication introduced four WAWEnets and each
emulated the output of an established full-reference speech quality or
intelligibility estimation algorithm.
  We have updated the WAWEnet architecture to be more efficient and effective.
Here we present a single WAWEnet that closely tracks seven different quality
and intelligibility values. We create a second network that additionally tracks
four subjective speech quality dimensions. We offer a third network that
focuses on just subjective quality scores and achieves very high levels of
agreement. This work has leveraged 334 hours of speech in 13 languages, over
two million full-reference target values and over 93,000 subjective mean
opinion scores.
  We also interpret the operation of WAWEnets and identify the key to their
operation using the language of signal processing: ReLUs strategically move
spectral information from non-DC components into the DC component. The DC
values of 96 output signals define a vector in a 96-D latent space and this
vector is then mapped to a quality or intelligibility value for the input
waveform.

When training a machine learning model, there is likely to be a tradeoff
between accuracy and the diversity of the dataset. Previous research has shown
that if we train a model to detect one specific malware family, we generally
obtain stronger results as compared to a case where we train a single model on
multiple diverse families. However, during the detection phase, it would be
more efficient to have a single model that can reliably detect multiple
families, rather than having to score each sample against multiple models. In
this research, we conduct experiments based on byte $n$-gram features to
quantify the relationship between the generality of the training dataset and
the accuracy of the corresponding machine learning models, all within the
context of the malware detection problem. We find that neighborhood-based
algorithms generalize surprisingly well, far outperforming the other machine
learning techniques considered.

Wasserstein distributionally robust optimization has recently emerged as a
powerful framework for robust estimation, enjoying good out-of-sample
performance guarantees, well-understood regularization effects, and
computationally tractable dual reformulations. In such framework, the estimator
is obtained by minimizing the worst-case expected loss over all probability
distributions which are close, in a Wasserstein sense, to the empirical
distribution. In this paper, we propose a Wasserstein distributionally robust
M-estimation framework to estimate an unknown parameter from noisy linear
measurements, and we focus on the important and challenging task of analyzing
the squared error performance of such estimators. Our study is carried out in
the modern high-dimensional proportional regime, where both the ambient
dimension and the number of samples go to infinity, at a proportional rate
which encodes the under/over-parametrization of the problem. Under an isotropic
Gaussian features assumption, we show that the squared error can be recover as
the solution of a convex-concave optimization problem which, surprinsingly,
involves at most four scalar variables. To the best of our knowledge, this is
the first work to study this problem in the context of Wasserstein
distributionally robust M-estimation.

Robust maritime obstacle detection is crucial for safe navigation of
autonomous boats and timely collision avoidance. The current state-of-the-art
is based on deep segmentation networks trained on large datasets. Per-pixel
ground truth labeling of such datasets, however, is labor-intensive and
expensive. We propose a new scaffolding learning regime (SLR), that leverages
weak annotations consisting of water edge, horizon and obstacle bounding boxes
to train segmentation-based obstacle detection networks, and thus reduces the
required ground truth labelling effort by twenty-fold. SLR trains an initial
model from weak annotations, then alternates between re-estimating the
segmentation pseudo labels and improving the network parameters. Experiments
show that maritime obstacle segmentation networks trained using SLR on weak
labels not only match, but outperform the same networks trained with dense
ground truth labels, which is a remarkable result. In addition to increased
accuracy, SLR also increases domain generalization and can be used for domain
adaptation with a low manual annotation load. The code and pre-trained models
are available at https://github.com/lojzezust/SLR .

We prove that every online learnable class of functions of Littlestone
dimension $d$ admits a learning algorithm with finite information complexity.
Towards this end, we use the notion of a globally stable algorithm. Generally,
the information complexity of such a globally stable algorithm is large yet
finite, roughly exponential in $d$. We also show there is room for improvement;
for a canonical online learnable class, indicator functions of affine subspaces
of dimension $d$, the information complexity can be upper bounded
logarithmically in $d$.

Depression is one of the most common mental disorders, which imposes heavy
negative impacts on one's daily life. Diagnosing depression based on the
interview is usually in the form of questions and answers. In this process, the
audio signals and their text transcripts of a subject are correlated to
depression cues and easily recorded. Therefore, it is feasible to build an
Automatic Depression Detection (ADD) model based on the data of these
modalities in practice. However, there are two major challenges that should be
addressed for constructing an effective ADD model. The first challenge is the
organization of the textual and audio data, which can be of various contents
and lengths for different subjects. The second challenge is the lack of
training samples due to the privacy concern. Targeting to these two challenges,
we propose the TOpic ATtentive transformer-based ADD model, abbreviated as
TOAT. To address the first challenge, in the TOAT model, topic is taken as the
basic unit of the textual and audio data according to the question-answer form
in a typical interviewing process. Based on that, a topic attention module is
designed to learn the importance of of each topic, which helps the model better
retrieve the depressed samples. To solve the issue of data scarcity, we
introduce large pre-trained models, and the fine-tuning strategy is adopted
based on the small-scale ADD training data. We also design a two-branch
architecture with a late-fusion strategy for building the TOAT model, in which
the textual and audio data are encoded independently. We evaluate our model on
the multimodal DAIC-WOZ dataset specifically designed for the ADD task.
Experimental results show the superiority of our method. More importantly, the
ablation studies demonstrate the effectiveness of the key elements in the TOAT
model.

Cross-domain recommendation (CDR) can help customers find more satisfying
items in different domains. Existing CDR models mainly use common users or
mapping functions as bridges between domains but have very limited exploration
in fully utilizing extra knowledge across domains. In this paper, we propose to
incorporate the knowledge graph (KG) for CDR, which enables items in different
domains to share knowledge. To this end, we first construct a new dataset
AmazonKG4CDR from the Freebase KG and a subset (two domain pairs: movies-music,
movie-book) of Amazon Review Data. This new dataset facilitates linking
knowledge to bridge within- and cross-domain items for CDR. Then we propose a
new framework, KG-aware Neural Collective Matrix Factorization (KG-NeuCMF),
leveraging KG to enrich item representations. It first learns item embeddings
by graph convolutional autoencoder to capture both domain-specific and
domain-general knowledge from adjacent and higher-order neighbours in the KG.
Then, we maximize the mutual information between item embeddings learned from
the KG and user-item matrix to establish cross-domain relationships for better
CDR. Finally, we conduct extensive experiments on the newly constructed dataset
and demonstrate that our model significantly outperforms the best-performing

One of the open problems in machine learning is whether any set-family of
VC-dimension $d$ admits a sample compression scheme of size~$O(d)$. In this
paper, we study this problem for balls in graphs. For balls of arbitrary radius
$r$, we design proper sample compression schemes of size $2$ for trees, of size
$3$ for cycles, of size $4$ for interval graphs, of size $6$ for trees of
cycles, and of size $22$ for cube-free median graphs. For balls of a given
radius, we design proper labeled sample compression schemes of size $2$ for
trees and of size $4$ for interval graphs. We also design approximate sample
compression schemes of size 2 for balls of $\delta$-hyperbolic graphs.

Audio DeepFakes allow the creation of high-quality, convincing utterances and
therefore pose a threat due to its potential applications such as impersonation
or fake news. Methods for detecting these manipulations should be characterized
by good generalization and stability leading to robustness against attacks
conducted with techniques that are not explicitly included in the training. In
this work, we introduce Attack Agnostic Dataset - a combination of two audio
DeepFakes and one anti-spoofing datasets that, thanks to the disjoint use of
attacks, can lead to better generalization of detection methods. We present a
thorough analysis of current DeepFake detection methods and consider different
audio features (front-ends). In addition, we propose a model based on LCNN with
LFCC and mel-spectrogram front-end, which not only is characterized by a good
generalization and stability results but also shows improvement over LFCC-based
mode - we decrease standard deviation on all folds and EER in two folds by up
to 5%.

Stance detection aims to identify whether the author of a text is in favor
of, against, or neutral to a given target. The main challenge of this task
comes two-fold: few-shot learning resulting from the varying targets and the
lack of contextual information of the targets. Existing works mainly focus on
solving the second issue by designing attention-based models or introducing
noisy external knowledge, while the first issue remains under-explored. In this
paper, inspired by the potential capability of pre-trained language models
(PLMs) serving as knowledge bases and few-shot learners, we propose to
introduce prompt-based fine-tuning for stance detection. PLMs can provide
essential contextual information for the targets and enable few-shot learning
via prompts. Considering the crucial role of the target in stance detection
task, we design target-aware prompts and propose a novel verbalizer. Instead of
mapping each label to a concrete word, our verbalizer maps each label to a
vector and picks the label that best captures the correlation between the
stance and the target. Moreover, to alleviate the possible defect of dealing
with varying targets with a single hand-crafted prompt, we propose to distill
the information learned from multiple prompts. Experimental results show the
superior performance of our proposed model in both full-data and few-shot

The recent work ``Combinatorial Optimization with Physics-Inspired Graph
Neural Networks'' [Nat Mach Intell 4 (2022) 367] introduces a physics-inspired
unsupervised Graph Neural Network (GNN) to solve combinatorial optimization
problems on sparse graphs. To test the performances of these GNNs, the authors
of the work show numerical results for two fundamental problems: maximum cut
and maximum independent set (MIS). They conclude that "the graph neural network
optimizer performs on par or outperforms existing solvers, with the ability to
scale beyond the state of the art to problems with millions of variables."
  In this comment, we show that a simple greedy algorithm, running in almost
linear time, can find solutions for the MIS problem of much better quality than
the GNN. The greedy algorithm is faster by a factor of $10^4$ with respect to
the GNN for problems with a million variables. We do not see any good reason
for solving the MIS with these GNN, as well as for using a sledgehammer to
crack nuts.
  In general, many claims of superiority of neural networks in solving
combinatorial problems are at risk of being not solid enough, since we lack
standard benchmarks based on really hard problems. We propose one of such hard
benchmarks, and we hope to see future neural network optimizers tested on these
problems before any claim of superiority is made.

Human-AI collaboration (HAIC) in decision-making aims to create synergistic
teaming between human decision-makers and AI systems. Learning to defer (L2D)
has been presented as a promising framework to determine who among humans and
AI should make which decisions in order to optimize the performance and
fairness of the combined system. Nevertheless, L2D entails several often
unfeasible requirements, such as the availability of predictions from humans
for every instance or ground-truth labels that are independent from said
humans. Furthermore, neither L2D nor alternative approaches tackle fundamental
issues of deploying HAIC systems in real-world settings, such as capacity
management or dealing with dynamic environments. In this paper, we aim to
identify and review these and other limitations, pointing to where
opportunities for future research in HAIC may lie.

This paper studies a transferable phoneme embedding framework that aims to
deal with the cross-lingual text-to-speech (TTS) problem under the few-shot
setting. Transfer learning is a common approach when it comes to few-shot
learning since training from scratch on few-shot training data is bound to
overfit. Still, we find that the naive transfer learning approach fails to
adapt to unseen languages under extremely few-shot settings, where less than 8
minutes of data is provided. We deal with the problem by proposing a framework
that consists of a phoneme-based TTS model and a codebook module to project
phonemes from different languages into a learned latent space. Furthermore, by
utilizing phoneme-level averaged self-supervised learned features, we
effectively improve the quality of synthesized speeches. Experiments show that
using 4 utterances, which is about 30 seconds of data, is enough to synthesize
intelligible speech when adapting to an unseen language using our framework.

Variational regularization methods are commonly used to approximate solutions
of inverse problems. In recent years, model-based variational regularization
methods have often been replaced with data-driven ones such as the
fields-of-expert model (Roth and Black, 2009). Training the parameters of such
data-driven methods can be formulated as a bilevel optimization problem. In
this paper, we compare the framework of bilevel learning for the training of
data-driven variational regularization models with the novel framework of deep
equilibrium models (Bai, Kolter, and Koltun, 2019) that has recently been
introduced in the context of inverse problems (Gilton, Ongie, and Willett,
2021). We show that computing the lower-level optimization problem within the
bilevel formulation with a fixed point iteration is a special case of the deep
equilibrium framework. We compare both approaches computationally, with a
variety of numerical examples for the inverse problems of denoising, inpainting
and deconvolution.

There is a rapid increase in the cooperative learning paradigm in online
learning settings, i.e., federated learning (FL). Unlike most FL settings,
there are many situations where the agents are competitive. Each agent would
like to learn from others, but the part of the information it shares for others
to learn from could be sensitive; thus, it desires its privacy. This work
investigates a group of agents working concurrently to solve similar
combinatorial bandit problems while maintaining quality constraints. Can these
agents collectively learn while keeping their sensitive information
confidential by employing differential privacy? We observe that communicating
can reduce the regret. However, differential privacy techniques for protecting
sensitive information makes the data noisy and may deteriorate than help to
improve regret. Hence, we note that it is essential to decide when to
communicate and what shared data to learn to strike a functional balance
between regret and privacy. For such a federated combinatorial MAB setting, we
propose a Privacy-preserving Federated Combinatorial Bandit algorithm, P-FCB.
We illustrate the efficacy of P-FCB through simulations. We further show that
our algorithm provides an improvement in terms of regret while upholding
quality threshold and meaningful privacy guarantees.

Federated learning is a distributed machine learning approach in which a
single server and multiple clients collaboratively build machine learning
models without sharing datasets on clients. A challenging issue of federated
learning is data heterogeneity (i.e., data distributions may differ across
clients). To cope with this issue, numerous federated learning methods aim at
personalized federated learning and build optimized models for clients. Whereas
existing studies empirically evaluated their own methods, the experimental
settings (e.g., comparison methods, datasets, and client setting) in these
studies differ from each other, and it is unclear which personalized federate
learning method achieves the best performance and how much progress can be made
by using these methods instead of standard (i.e., non-personalized) federated
learning. In this paper, we benchmark the performance of existing personalized
federated learning through comprehensive experiments to evaluate the
characteristics of each method. Our experimental study shows that (1) there are
no champion methods, (2) large data heterogeneity often leads to high accurate
predictions, and (3) standard federated learning methods (e.g. FedAvg) with
fine-tuning often outperform personalized federated learning methods. We open
our benchmark tool FedBench for researchers to conduct experimental studies
with various experimental settings.

In deep learning research, self-supervised learning (SSL) has received great
attention triggering interest within both the computer vision and remote
sensing communities. While there has been a big success in computer vision,
most of the potential of SSL in the domain of earth observation remains locked.
In this paper, we provide an introduction to, and a review of the concepts and
latest developments in SSL for computer vision in the context of remote
sensing. Further, we provide a preliminary benchmark of modern SSL algorithms
on popular remote sensing datasets, verifying the potential of SSL in remote
sensing and providing an extended study on data augmentations. Finally, we
identify a list of promising directions of future research in SSL for earth
observation (SSL4EO) to pave the way for fruitful interaction of both domains.

Innovation is a key component in trying new solutions for the students to
learn efficiently and in ways that correspond to their own experience, where
chatbots are one of these new solutions. One of the main problem that chatbots
face today is to mimic human language, where they try to find the best answer
to an input, which is not how a human conversation usually works, rather taking
into account the previous messages and building onto them. Extreme programming
methodology was chosen to use integrate ChatterBot, Pyside2, web scraping and
Tampermonkey into Blackboard as a test case. Problems occurred with the bot and
more training was needed for the bot to work perfectly, but the integration and
web scraping worked, giving us a chatbot that was able to talk with. We showed
the plausibility of integrating an AI bot in an educational setting.

Metaverse has evolved as one of the popular research agendas that let the
users learn, socialize, and collaborate in a networked 3D immersive virtual
world. Due to the rich multimedia streaming capability and immersive user
experience with high-speed communication, the metaverse is an ideal model for
education, training, and skill development tasks. To facilitate research in
this area, we provide a comprehensive review of the various educational use
cases and explore how enabling technologies such as Extended reality (XR) and
Internet of Everything (IoE) will play a major role in educational services in
future metaverses. Secondly, we provide an overview of metaverse-based
educational applications focusing on education, training, and skill development
and analyze the technologies they are built upon. We identify common research
problems and future research directions in the domain. The paper also
identifies core ethical considerations of metaverse for education and potential
pitfalls. We believe this survey can fully demonstrate the versatility of
metaverse-driven education, which could serve as a potential guideline for the
researchers.

The unparalleled ability of machine learning algorithms to learn patterns
from data also enables them to incorporate biases embedded within. A biased
model can then make decisions that disproportionately harm certain groups in
society. Much work has been devoted to measuring unfairness in static ML
environments, but not in dynamic, performative prediction ones, in which most
real-world use cases operate. In the latter, the predictive model itself plays
a pivotal role in shaping the distribution of the data. However, little
attention has been heeded to relating unfairness to these interactions. Thus,
to further the understanding of unfairness in these settings, we propose a
taxonomy to characterize bias in the data, and study cases where it is shaped
by model behaviour. Using a real-world account opening fraud detection case
study as an example, we study the dangers to both performance and fairness of
two typical biases in performative prediction: distribution shifts, and the
problem of selective labels.

This paper introduces an efficient algorithm for persistence diagram
computation, given an input piecewise linear scalar field f defined on a
d-dimensional simplicial complex K, with $d \leq 3$. Our method extends the
seminal "PairCells" algorithm by introducing three main accelerations. First,
we express this algorithm within the setting of discrete Morse theory, which
considerably reduces the number of input simplices to consider. Second, we
introduce a stratification approach to the problem, that we call "sandwiching".
Specifically, minima-saddle persistence pairs ($D_0(f)$) and saddle-maximum
persistence pairs ($D_{d-1}(f)$) are efficiently computed by respectively
processing with a Union-Find the unstable sets of 1-saddles and the stable sets
of (d-1)-saddles. This fast processing of the dimensions 0 and (d-1) further
reduces, and drastically, the number of critical simplices to consider for the
computation of $D_1(f)$, the intermediate layer of the sandwich. Third, we
document several performance improvements via shared-memory parallelism. We
provide an open-source implementation of our algorithm for reproducibility
purposes. We also contribute a reproducible benchmark package, which exploits
three-dimensional data from a public repository and compares our algorithm to a
variety of publicly available implementations. Extensive experiments indicate
that our algorithm improves by two orders of magnitude the time performance of
the seminal "PairCells" algorithm it extends. Moreover, it also improves memory
footprint and time performance over a selection of 14 competing approaches,
with a substantial gain over the fastest available approaches, while producing
a strictly identical output. We illustrate the utility of our contributions
with an application to the fast and robust extraction of persistent
1-dimensional generators on surfaces, volume data and high-dimensional point
clouds.

Combinatorial optimization problems are encountered in many practical
contexts such as logistics and production, but exact solutions are particularly
difficult to find and usually NP-hard for considerable problem sizes. To
compute approximate solutions, a zoo of generic as well as problem-specific
variants of local search is commonly used. However, which variant to apply to
which particular problem is difficult to decide even for experts.
  In this paper we identify three independent algorithmic aspects of such local
search algorithms and formalize their sequential selection over an optimization
process as Markov Decision Process (MDP). We design a deep graph neural network
as policy model for this MDP, yielding a learned controller for local search
called NeuroLS. Ample experimental evidence shows that NeuroLS is able to
outperform both, well-known general purpose local search controllers from
Operations Research as well as latest machine learning-based approaches.

Representation learning on graphs, also called graph embedding, has
demonstrated its significant impact on a series of machine learning
applications such as classification, prediction and recommendation. However,
existing work has largely ignored the rich information contained in the
properties (or attributes) of both nodes and edges of graphs in modern
applications, e.g., those represented by property graphs. To date, most
existing graph embedding methods either focus on plain graphs with only the
graph topology, or consider properties on nodes only. We propose PGE, a graph
representation learning framework that incorporates both node and edge
properties into the graph embedding procedure. PGE uses node clustering to
assign biases to differentiate neighbors of a node and leverages multiple
data-driven matrices to aggregate the property information of neighbors sampled
based on a biased strategy. PGE adopts the popular inductive model for
neighborhood aggregation. We provide detailed analyses on the efficacy of our
method and validate the performance of PGE by showing how PGE achieves better
embedding results than the state-of-the-art graph embedding methods on
benchmark applications such as node classification and link prediction over
real-world datasets.

This paper proposes a novel transformer-based model architecture for medical
imaging problems involving analysis of vertebrae. It considers two applications
of such models in MR images: (a) detection of spinal metastases and the related
conditions of vertebral fractures and metastatic cord compression, (b)
radiological grading of common degenerative changes in intervertebral discs.
Our contributions are as follows: (i) We propose a Spinal Context Transformer
(SCT), a deep-learning architecture suited for the analysis of repeated
anatomical structures in medical imaging such as vertebral bodies (VBs). Unlike
previous related methods, SCT considers all VBs as viewed in all available
image modalities together, making predictions for each based on context from
the rest of the spinal column and all available imaging modalities. (ii) We
apply the architecture to a novel and important task: detecting spinal
metastases and the related conditions of cord compression and vertebral
fractures/collapse from multi-series spinal MR scans. This is done using
annotations extracted from free-text radiological reports as opposed to bespoke
annotation. However, the resulting model shows strong agreement with
vertebral-level bespoke radiologist annotations on the test set. (iii) We also
apply SCT to an existing problem: radiological grading of inter-vertebral discs
(IVDs) in lumbar MR scans for common degenerative changes.We show that by
considering the context of vertebral bodies in the image, SCT improves the
accuracy for several gradings compared to previously published model.

Graph neural networks (GNNs) have been widely used for representation
learning on graph data. However, there is limited understanding on how much
performance GNNs actually gain from graph data. This paper introduces a
context-surrounding GNN framework and proposes two smoothness metrics to
measure the quantity and quality of information obtained from graph data. A new
GNN model, called CS-GNN, is then designed to improve the use of graph
information based on the smoothness values of a graph. CS-GNN is shown to
achieve better performance than existing methods in different types of real
graphs.

In recent years, specific evaluation metrics for time series anomaly
detection algorithms have been developed to handle the limitations of the
classical precision and recall. However, such metrics are heuristically built
as an aggregate of multiple desirable aspects, introduce parameters and wipe
out the interpretability of the output. In this article, we first highlight the
limitations of the classical precision/recall, as well as the main issues of
the recent event-based metrics -- for instance, we show that an adversary
algorithm can reach high precision and recall on almost any dataset under weak
assumption. To cope with the above problems, we propose a theoretically
grounded, robust, parameter-free and interpretable extension to
precision/recall metrics, based on the concept of ``affiliation'' between the
ground truth and the prediction sets. Our metrics leverage measures of duration
between ground truth and predictions, and have thus an intuitive
interpretation. By further comparison against random sampling, we obtain a
normalized precision/recall, quantifying how much a given set of results is
better than a random baseline prediction. By construction, our approach keeps
the evaluation local regarding ground truth events, enabling fine-grained
visualization and interpretation of algorithmic results. We compare our
proposal against various public time series anomaly detection datasets,
algorithms and metrics. We further derive theoretical properties of the
affiliation metrics that give explicit expectations about their behavior and
ensure robustness against adversary strategies.

We propose a method to make natural language understanding models more
parameter efficient by storing knowledge in an external knowledge graph (KG)
and retrieving from this KG using a dense index. Given (possibly multilingual)
downstream task data, e.g., sentences in German, we retrieve entities from the
KG and use their multimodal representations to improve downstream task
performance. We use the recently released VisualSem KG as our external
knowledge repository, which covers a subset of Wikipedia and WordNet entities,
and compare a mix of tuple-based and graph-based algorithms to learn entity and
relation representations that are grounded on the KG multimodal information. We
demonstrate the usefulness of the learned entity representations on two
downstream tasks, and show improved performance on the multilingual named
entity recognition task by $0.3\%$--$0.7\%$ F1, while we achieve up to $2.5\%$
improvement in accuracy on the visual sense disambiguation task. All our code
and data are available in: \url{https://github.com/iacercalixto/visualsem-kg}.

Multi-modal document pre-trained models have proven to be very effective in a
variety of visually-rich document understanding (VrDU) tasks. Though existing
document pre-trained models have achieved excellent performance on standard
benchmarks for VrDU, the way they model and exploit the interactions between
vision and language on documents has hindered them from better generalization
ability and higher accuracy. In this work, we investigate the problem of
vision-language joint representation learning for VrDU mainly from the
perspective of supervisory signals. Specifically, a pre-training paradigm
called Bi-VLDoc is proposed, in which a bidirectional vision-language
supervision strategy and a vision-language hybrid-attention mechanism are
devised to fully explore and utilize the interactions between these two
modalities, to learn stronger cross-modal document representations with richer
semantics. Benefiting from the learned informative cross-modal document
representations, Bi-VLDoc significantly advances the state-of-the-art
performance on three widely-used document understanding benchmarks, including
Form Understanding (from 85.14% to 93.44%), Receipt Information Extraction
(from 96.01% to 97.84%), and Document Classification (from 96.08% to 97.12%).
On Document Visual QA, Bi-VLDoc achieves the state-of-the-art performance
compared to previous single model methods.

Various problems of any credit card fraud detection based on machine learning
come from the imbalanced aspect of transaction datasets. Indeed, the number of
frauds compared to the number of regular transactions is tiny and has been
shown to damage learning performances, e.g., at worst, the algorithm can learn
to classify all the transactions as regular. Resampling methods and
cost-sensitive approaches are known to be good candidates to leverage this
issue of imbalanced datasets. This paper evaluates numerous state-of-the-art
resampling methods on a large real-life online credit card payments dataset. We
show they are inefficient because methods are intractable or because metrics do
not exhibit substantial improvements. Our work contributes to this domain in
(1) that we compare many state-of-the-art resampling methods on a large-scale
dataset and in (2) that we use a real-life online credit card payments dataset.

Linear regression is a widely used technique to fit linear models and finds
widespread applications across different areas such as machine learning and
statistics. In most real-world scenarios, however, linear regression problems
are often ill-posed or the underlying model suffers from overfitting, leading
to erroneous or trivial solutions. This is often dealt with by adding extra
constraints, known as regularization. In this paper, we use the frameworks of
block-encoding and quantum singular value transformation (QSVT) to design the
first quantum algorithms for quantum least squares with general
$\ell_2$-regularization. These include regularized versions of quantum ordinary
least squares, quantum weighted least squares, and quantum generalized least
squares. Our quantum algorithms substantially improve upon prior results on
quantum ridge regression (polynomial improvement in the condition number and an
exponential improvement in accuracy), which is a particular case of our result.
  To this end, we assume approximate block-encodings of the underlying matrices
as input and use robust QSVT algorithms for various linear algebra operations.
In particular, we develop a variable-time quantum algorithm for matrix
inversion using QSVT, where we use quantum eigenvalue discrimination as a
subroutine instead of gapped phase estimation. This ensures that substantially
fewer ancilla qubits are required for this procedure than prior results. Owing
to the generality of the block-encoding framework, our algorithms are
applicable to a variety of input models and can also be seen as improved and
generalized versions of prior results on standard (non-regularized) quantum
least squares algorithms.

We propose a new representation of human body motion which encodes a full
motion in a sequence of latent motion primitives. Recently, task generic motion
priors have been introduced and propose a coherent representation of human
motion based on a single latent code, with encouraging results for many tasks.
Extending these methods to longer motion with various duration and framerate is
all but straightforward as one latent code proves inefficient to encode longer
term variability. Our hypothesis is that long motions are better represented as
a succession of actions than in a single block. By leveraging a
sequence-to-sequence architecture, we propose a model that simultaneously
learns a temporal segmentation of motion and a prior on the motion segments. To
provide flexibility with temporal resolution and motion duration, our
representation is continuous in time and can be queried for any timestamp. We
show experimentally that our method leads to a significant improvement over
state-of-the-art motion priors on a spatio-temporal completion task on sparse
pointclouds. Code will be made available upon publication.

Supervised learning can be viewed as distilling relevant information from
input data into feature representations. This process becomes difficult when
supervision is noisy as the distilled information might not be relevant. In
fact, recent research shows that networks can easily overfit all labels
including those that are corrupted, and hence can hardly generalize to clean
datasets. In this paper, we focus on the problem of learning with noisy labels
and introduce compression inductive bias to network architectures to alleviate
this over-fitting problem. More precisely, we revisit one classical
regularization named Dropout and its variant Nested Dropout. Dropout can serve
as a compression constraint for its feature dropping mechanism, while Nested
Dropout further learns ordered feature representations w.r.t. feature
importance. Moreover, the trained models with compression regularization are
further combined with Co-teaching for performance boost.
  Theoretically, we conduct bias-variance decomposition of the objective
function under compression regularization. We analyze it for both single model
and Co-teaching. This decomposition provides three insights: (i) it shows that
over-fitting is indeed an issue for learning with noisy labels; (ii) through an
information bottleneck formulation, it explains why the proposed feature
compression helps in combating label noise; (iii) it gives explanations on the
performance boost brought by incorporating compression regularization into
Co-teaching. Experiments show that our simple approach can have comparable or
even better performance than the state-of-the-art methods on benchmarks with
real-world label noise including Clothing1M and ANIMAL-10N. Our implementation
is available at https://yingyichen-cyy.github.io/CompressFeatNoisyLabels/.

We found that Bidirectional LSTM and Transformer can classify different
phases of condensed matter models and determine the phase transition points by
learning features in the Monte Carlo raw data before equilibrium. Our method
can significantly reduce the time and computational resources required for
probing phase transitions as compared to the conventional Monte Carlo
simulation. We also provide evidence that the method is robust and the
performance of the deep learning model is insensitive to the type of input data
(we tested spin configurations of classical models and green functions of a
quantum model), and it also performs well in detecting Kosterlitz-Thouless
phase transitions.

Recurrent Neural Networks (RNNs) have achieved tremendous success in
sequential data processing. However, it is quite challenging to interpret and
verify RNNs' behaviors directly. To this end, many efforts have been made to
extract finite automata from RNNs. Existing approaches such as exact learning
are effective in extracting finite-state models to characterize the state
dynamics of RNNs for formal languages, but are limited in the scalability to
process natural languages. Compositional approaches that are scablable to
natural languages fall short in extraction precision. In this paper, we
identify the transition sparsity problem that heavily impacts the extraction
precision. To address this problem, we propose a transition rule extraction
approach, which is scalable to natural language processing models and effective
in improving extraction precision. Specifically, we propose an empirical method
to complement the missing rules in the transition diagram. In addition, we
further adjust the transition matrices to enhance the context-aware ability of
the extracted weighted finite automaton (WFA). Finally, we propose two data
augmentation tactics to track more dynamic behaviors of the target RNN.
Experiments on two popular natural language datasets show that our method can
extract WFA from RNN for natural language processing with better precision than
existing approaches. Our code is available at
https://github.com/weizeming/Extract_WFA_from_RNN_for_NL.

Knowledge Distillation (KD) has recently emerged as a popular method for
compressing neural networks. In recent studies, generalized distillation
methods that find parameters and architectures of student models at the same
time have been proposed. Still, this search method requires a lot of
computation to search for architectures and has the disadvantage of considering
only convolutional blocks in their search space. This paper introduces a new
algorithm, coined as Trust Region Aware architecture search to Distill
knowledge Effectively (TRADE), that rapidly finds effective student
architectures from several state-of-the-art architectures using trust region
Bayesian optimization approach. Experimental results show our proposed TRADE
algorithm consistently outperforms both the conventional NAS approach and
pre-defined architecture under KD training.

The success of deep learning has set new benchmarks for many medical image
analysis tasks. However, deep models often fail to generalize in the presence
of distribution shifts between training (source) data and test (target) data.
One method commonly employed to counter distribution shifts is domain
adaptation: using samples from the target domain to learn to account for
shifted distributions. In this work we propose an unsupervised domain
adaptation approach that uses graph neural networks and, disentangled semantic
and domain invariant structural features, allowing for better performance
across distribution shifts. We propose an extension to swapped autoencoders to
obtain more discriminative features. We test the proposed method for
classification on two challenging medical image datasets with distribution
shifts - multi center chest Xray images and histopathology images. Experiments
show our method achieves state-of-the-art results compared to other domain
adaptation methods.

To take advantage of strategy commitment, a useful tactic of playing games, a
leader must learn enough information about the follower's payoff function.
However, this leaves the follower a chance to provide fake information and
influence the final game outcome. Through a carefully contrived payoff function
misreported to the learning leader, the follower may induce an outcome that
benefits him more, compared to the ones when he truthfully behaves.
  We study the follower's optimal manipulation via such strategic behaviors in
extensive-form games. Followers' different attitudes are taken into account. An
optimistic follower maximizes his true utility among all game outcomes that can
be induced by some payoff function. A pessimistic follower only considers
misreporting payoff functions that induce a unique game outcome. For all the
settings considered in this paper, we characterize all the possible game
outcomes that can be induced successfully. We show that it is polynomial-time
tractable for the follower to find the optimal way of misreporting his private
payoff information. Our work completely resolves this follower's optimal
manipulation problem on an extensive-form game tree.

Registering urban point clouds is a quite challenging task due to the
large-scale, noise and data incompleteness of LiDAR scanning data. In this
paper, we propose SARNet, a novel semantic augmented registration network aimed
at achieving efficient registration of urban point clouds at city scale.
Different from previous methods that construct correspondences only in the
point-level space, our approach fully exploits semantic features as assistance
to improve registration accuracy. Specifically, we extract per-point semantic
labels with advanced semantic segmentation networks and build a prior semantic
part-to-part correspondence. Then we incorporate the semantic information into
a learning-based registration pipeline, consisting of three core modules: a
semantic-based farthest point sampling module to efficiently filter out
outliers and dynamic objects; a semantic-augmented feature extraction module
for learning more discriminative point descriptors; a semantic-refined
transformation estimation module that utilizes prior semantic matching as a
mask to refine point correspondences by reducing false matching for better
convergence. We evaluate the proposed SARNet extensively by using real-world
data from large regions of urban scenes and comparing it with alternative
methods. The code is available at
https://github.com/WinterCodeForEverything/SARNet.

Transferring a deep neural network trained on one problem to another requires
only a small amount of data and little additional computation time. The same
behaviour holds for ensembles of deep learning models typically superior to a
single model. However, a transfer of deep neural networks ensemble demands
relatively high computational expenses. The probability of overfitting also
increases.
  Our approach for the transfer learning of ensembles consists of two steps:
(a) shifting weights of encoders of all models in the ensemble by a single
shift vector and (b) doing a tiny fine-tuning for each individual model
afterwards. This strategy leads to a speed-up of the training process and gives
an opportunity to add models to an ensemble with significantly reduced training
time using the shift vector.
  We compare different strategies by computation time, the accuracy of an
ensemble, uncertainty estimation and disagreement and conclude that our
approach gives competitive results using the same computation complexity in
comparison with the traditional approach. Also, our method keeps the ensemble's
models' diversity higher.

Local representation learning has been a key challenge to promote the
performance of the histopathological whole slide images analysis. The previous
representation learning methods followed the supervised learning paradigm.
However, manual annotation for large-scale WSIs is time-consuming and
labor-intensive. Hence, the self-supervised contrastive learning has recently
attracted intensive attention. The present contrastive learning methods treat
each sample as a single class, which suffers from class collision problems,
especially in the domain of histopathology image analysis. In this paper, we
proposed a novel contrastive representation learning framework named
Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image
analysis. We built a lesion queue based on the memory bank structure to store
the representations of different classes of WSIs, which allowed the contrastive
model to selectively define the negative pairs during the training. Moreover,
We designed a queue refinement strategy to purify the representations stored in
the lesion queue. The experimental results demonstrate that LACL achieves the
best performance in histopathology image representation learning on different
datasets, and outperforms state-of-the-art methods under different WSI
classification benchmarks. The code is available at
https://github.com/junl21/lacl.

Demystifying the interactions among multiple agents from their past
trajectories is fundamental to precise and interpretable trajectory prediction.
However, previous works mainly consider static, pair-wise interactions with
limited relational reasoning. To promote more comprehensive interaction
modeling and relational reasoning, we propose DynGroupNet, a
dynamic-group-aware network, which can i) model time-varying interactions in
highly dynamic scenes; ii) capture both pair-wise and group-wise interactions;
and iii) reason both interaction strength and category without direct
supervision. Based on DynGroupNet, we further design a prediction system to
forecast socially plausible trajectories with dynamic relational reasoning. The
proposed prediction system leverages the Gaussian mixture model, multiple
sampling and prediction refinement to promote prediction diversity, training
stability and trajectory smoothness, respectively. Extensive experiments show
that: 1)DynGroupNet can capture time-varying group behaviors, infer
time-varying interaction category and interaction strength during trajectory
prediction without any relation supervision on physical simulation datasets;
2)DynGroupNet outperforms the state-of-the-art trajectory prediction methods by
a significant improvement of 22.6%/28.0%, 26.9%/34.9%, 5.1%/13.0% in ADE/FDE on
the NBA, NFL Football and SDD datasets and achieve the state-of-the-art
performance on the ETH-UCY dataset.

In this work, we explore a change of paradigm to build Precomputed Radiance
Transfer (PRT) methods in a data-driven way. This paradigm shift allows us to
alleviate the difficulties of building traditional PRT methods such as defining
a reconstruction basis, coding a dedicated path tracer to compute a transfer
function, etc. Our objective is to pave the way for Machine Learned methods by
providing a simple baseline algorithm. More specifically, we demonstrate
real-time rendering of indirect illumination in hair and surfaces from a few
measurements of direct lighting. We build our baseline from pairs of direct and
indirect illumination renderings using only standard tools such as Singular
Value Decomposition (SVD) to extract both the reconstruction basis and transfer
function.

Reliable remaining time prediction of ongoing business processes is a highly
relevant topic. One example is order delivery, a key competitive factor in e.g.
retailing as it is a main driver of customer satisfaction. For realising timely
delivery, an accurate prediction of the remaining time of the delivery process
is crucial. Within the field of process mining, a wide variety of remaining
time prediction techniques have already been proposed. In this work, we extend
remaining time prediction based on stochastic Petri nets with generally
distributed transitions with k-nearest neighbors. The k-nearest neighbors
algorithm is performed on simple vectors storing the time passed to complete
previous activities. By only taking a subset of instances, a more
representative and stable stochastic Petri Net is obtained, leading to more
accurate time predictions. We discuss the technique and its basic
implementation in Python and use different real world data sets to evaluate the
predictive power of our extension. These experiments show clear advantages in
combining both techniques with regard to predictive power.

Click-through rate (CTR) prediction is a fundamental technique in
recommendation and advertising systems. Recent studies have proved that
learning a unified model to serve multiple domains is effective to improve the
overall performance. However, it is still challenging to improve generalization
across domains under limited training data, and hard to deploy current
solutions due to their computational complexity. In this paper, we propose a
simple yet effective framework AdaSparse for multi-domain CTR prediction, which
learns adaptively sparse structure for each domain, achieving better
generalization across domains with lower computational cost. In AdaSparse, we
introduce domain-aware neuron-level weighting factors to measure the importance
of neurons, with that for each domain our model can prune redundant neurons to
improve generalization. We further add flexible sparsity regularizations to
control the sparsity ratio of learned structures. Offline and online
experiments show that AdaSparse outperforms previous multi-domain CTR models

Physics-informed neural networks (PINNs) are capable of finding the solution
for a given boundary value problem. We employ several ideas from the finite
element method (FEM) to enhance the performance of existing PINNs in
engineering problems. The main contribution of the current work is to promote
using the spatial gradient of the primary variable as an output from separated
neural networks. Later on, the strong form which has a higher order of
derivatives is applied to the spatial gradients of the primary variable as the
physical constraint. In addition, the so-called energy form of the problem is
applied to the primary variable as an additional constraint for training. The
proposed approach only required up to first-order derivatives to construct the
physical loss functions. We discuss why this point is beneficial through
various comparisons between different models. The mixed formulation-based PINNs
and FE methods share some similarities. While the former minimizes the PDE and
its energy form at given collocation points utilizing a complex nonlinear
interpolation through a neural network, the latter does the same at element
nodes with the help of shape functions. We focus on heterogeneous solids to
show the capability of deep learning for predicting the solution in a complex
environment under different boundary conditions. The performance of the
proposed PINN model is checked against the solution from FEM on two prototype
problems: elasticity and the Poisson equation (steady-state diffusion problem).
We concluded that by properly designing the network architecture in PINN, the
deep learning model has the potential to solve the unknowns in a heterogeneous
domain without any available initial data from other sources. Finally,
discussions are provided on the combination of PINN and FEM for a fast and
accurate design of composite materials in future developments.

Content creators compete for user attention. Their reach crucially depends on
algorithmic choices made by developers on online platforms. To maximize
exposure, many creators adapt strategically, as evidenced by examples like the
sprawling search engine optimization industry. This begets competition for the
finite user attention pool. We formalize these dynamics in what we call an
exposure game, a model of incentives induced by algorithms including modern
factorization and (deep) two-tower architectures. We prove that seemingly
innocuous algorithmic choices -- e.g., non-negative vs. unconstrained
factorization -- significantly affect the existence and character of (Nash)
equilibria in exposure games. We proffer use of creator behavior models like
ours for an (ex-ante) pre-deployment audit. Such an audit can identify
misalignment between desirable and incentivized content, and thus complement
post-hoc measures like content filtering and moderation. To this end, we
propose tools for numerically finding equilibria in exposure games, and
illustrate results of an audit on the MovieLens and LastFM datasets. Among
else, we find that the strategically produced content exhibits strong
dependence between algorithmic exploration and content diversity, and between
model expressivity and bias towards gender-based user and creator groups.

Speech emotion recognition (SER) has many challenges, but one of the main
challenges is that each framework does not have a unified standard. In this
paper, we propose SpeechEQ, a framework for unifying SER tasks based on a
multi-scale unified metric. This metric can be trained by Multitask Learning
(MTL), which includes two emotion recognition tasks of Emotion States Category
(EIS) and Emotion Intensity Scale (EIS), and two auxiliary tasks of phoneme
recognition and gender recognition. For this framework, we build a Mandarin SER
dataset - SpeechEQ Dataset (SEQD). We conducted experiments on the public CASIA
and ESD datasets in Mandarin, which exhibit that our method outperforms
baseline methods by a relatively large margin, yielding 8.0% and 6.5%
improvement in accuracy respectively. Additional experiments on IEMOCAP with
four emotion categories (i.e., angry, happy, sad, and neutral) also show the
proposed method achieves a state-of-the-art of both weighted accuracy (WA) of
78.16% and unweighted accuracy (UA) of 77.47%.

The question of what kind of convolutional neural network (CNN) structure
performs well is fascinating. In this work, we move toward the answer with one
more step by connecting zero stability and model performance. Specifically, we
found that if a discrete solver of an ordinary differential equation is zero
stable, the CNN corresponding to that solver performs well. We first give the
interpretation of zero stability in the context of deep learning and then
investigate the performance of existing first- and second-order CNNs under
different zero-stable circumstances. Based on the preliminary observation, we
provide a higher-order discretization to construct CNNs and then propose a
zero-stable network (ZeroSNet). To guarantee zero stability of the ZeroSNet, we
first deduce a structure that meets consistency conditions and then give a zero
stable region of a training-free parameter. By analyzing the roots of a
characteristic equation, we theoretically obtain the optimal coefficients of
feature maps. Empirically, we present our results from three aspects: We
provide extensive empirical evidence of different depth on different datasets
to show that the moduli of the characteristic equation's roots are the keys for
the performance of CNNs that require historical features; Our experiments show
that ZeroSNet outperforms existing CNNs which is based on high-order
discretization; ZeroSNets show better robustness against noises on the input.
The source code is available at \url{https://github.com/LongJin-lab/ZeroSNet}.

The Taboo Challenge competition, a task based on the well-known Taboo game,
has been proposed to stimulate research in the AI field. The challenge requires
building systems able to comprehend the implied inferences between the
exchanged messages of guesser and describer agents. A describer sends
pre-determined hints to guessers indirectly describing cities, and guessers are
required to return the matching cities implied by the hints. Climbing up the
scoring ledger requires the resolving of the highest amount of cities with the
smallest amount of hints in a specified time frame. Here, we present TabooLM, a
language-model approach that tackles the challenge based on a zero-shot
setting. We start by presenting and comparing the results of this approach with
three studies from the literature. The results show that our method achieves
SOTA results on the Taboo challenge, suggesting that TabooLM can guess the
implied cities faster and more accurately than existing approaches.

Learning stable dynamics from observed time-series data is an essential
problem in robotics, physical modeling, and systems biology. Many of these
dynamics are represented as an inputs-output system to communicate with the
external environment. In this study, we focus on input-output stable systems,
exhibiting robustness against unexpected stimuli and noise. We propose a method
to learn nonlinear systems guaranteeing the input-output stability. Our
proposed method utilizes the differentiable projection onto the space
satisfying the Hamilton-Jacobi inequality to realize the input-output
stability. The problem of finding this projection can be formulated as a
quadratic constraint quadratic programming problem, and we derive the
particular solution analytically. Also, we apply our method to a toy bistable
model and the task of training a benchmark generated from a glucose-insulin
simulator. The results show that the nonlinear system with neural networks by
our method achieves the input-output stability, unlike naive neural networks.
Our code is available at https://github.com/clinfo/DeepIOStability.

Conformal prediction is a simple and powerful tool that can quantify
uncertainty without any distributional assumptions. However, existing methods
can only provide an average coverage guarantee, which is not ideal compared to
the stronger conditional coverage guarantee. Although achieving exact
conditional coverage is proven to be impossible, approximating conditional
coverage is still an important research direction. In this paper, we propose a
modified non-conformity score by leveraging local approximation of the
conditional distribution. The modified score inherits the spirit of split
conformal methods, which is simple and efficient compared with full conformal
methods but better approximates conditional coverage guarantee. Empirical
results on various datasets, including a high dimension age regression on
image, demonstrate that our method provides tighter intervals compared to

We consider the problem of minimizing a large sum of smooth convex functions
subject to a large number of constraints defined by convex functions that are
possibly non-smooth. Such a problem finds a wide range of applications in many
areas, such as machine learning and signal processing. In this paper, we devise
a new random projection method (RPM) for efficiently solving this problem.
Compared with existing RPMs, our proposed algorithm features two useful
algorithmic ideas. First, at each iteration, instead of projection onto a
subset of the feasible region, our algorithm requires only a relaxed projection
in the sense that only the projection onto a half-space approximation of the
subset is needed. This significantly reduces the per iteration computational
cost as the relaxed projection admits a closed-form formula. Second, to exploit
the structure that the objective function is a large sum of convex functions,
the variance reduction technique is incorporated into our algorithm to improve
the empirical convergence behaviour. As our theoretical contributions, under an
error bound-type condition and some other standard conditions, we prove that
almost surely the proposed algorithm converges to an optimal solution and that
both the optimality and feasibility gaps decrease to zero, with rates
$\mathcal{O}(1/\sqrt{K})$ and $\mathcal{O}(\log(K)/ K)$, respectively. As a
side contribution, we also show that the said error bound-type condition holds
some mild assumptions, which could be of independent interest. Numerical
results on synthetic problem instances are also presented to demonstrate the
practical effectiveness of the variance reduction technique and the superiority
of our proposed RPM as compared with two existing ones.

Recently, Miller et al. showed that a model's in-distribution (ID) accuracy
has a strong linear correlation with its out-of-distribution (OOD) accuracy on
several OOD benchmarks -- a phenomenon they dubbed ''accuracy-on-the-line''.
While a useful tool for model selection (i.e., the model most likely to perform
the best OOD is the one with highest ID accuracy), this fact does not help
estimate the actual OOD performance of models without access to a labeled OOD
validation set. In this paper, we show a similar but surprising phenomenon also
holds for the agreement between pairs of neural network classifiers: whenever
accuracy-on-the-line holds, we observe that the OOD agreement between the
predictions of any two pairs of neural networks (with potentially different
architectures) also observes a strong linear correlation with their ID
agreement. Furthermore, we observe that the slope and bias of OOD vs ID
agreement closely matches that of OOD vs ID accuracy. This phenomenon, which we
call agreement-on-the-line, has important practical applications: without any
labeled data, we can predict the OOD accuracy of classifiers}, since OOD
agreement can be estimated with just unlabeled data. Our prediction algorithm
outperforms previous methods both in shifts where agreement-on-the-line holds
and, surprisingly, when accuracy is not on the line. This phenomenon also
provides new insights into deep neural networks: unlike accuracy-on-the-line,
agreement-on-the-line appears to only hold for neural network classifiers.

Segmentation has emerged as a fundamental field of computer vision and
natural language processing, which assigns a label to every pixel/feature to
extract regions of interest from an image/text. To evaluate the performance of
segmentation, the Dice and IoU metrics are used to measure the degree of
overlap between the ground truth and the predicted segmentation. In this paper,
we establish a theoretical foundation of segmentation with respect to the
Dice/IoU metrics, including the Bayes rule and Dice/IoU-calibration, analogous
to classification-calibration or Fisher consistency in classification. We prove
that the existing thresholding-based framework with most operating losses are
not consistent with respect to the Dice/IoU metrics, and thus may lead to a
suboptimal solution. To address this pitfall, we propose a novel consistent
ranking-based framework, namely RankDice/RankIoU, inspired by plug-in rules of
the Bayes segmentation rule. Three numerical algorithms with GPU parallel
execution are developed to implement the proposed framework in large-scale and
high-dimensional segmentation. We study statistical properties of the proposed
framework. We show it is Dice-/IoU-calibrated, and its excess risk bounds and
the rate of convergence are also provided. The numerical effectiveness of
RankDice/mRankDice is demonstrated in various simulated examples and
Fine-annotated CityScapes and Pascal VOC datasets with state-of-the-art deep
learning architectures.

We introduce a new system for data-driven audio sound model design built
around two different neural network architectures, a Generative Adversarial
Network(GAN) and a Recurrent Neural Network (RNN), that takes advantage of the
unique characteristics of each to achieve the system objectives that neither is
capable of addressing alone. The objective of the system is to generate
interactively controllable sound models given (a) a range of sounds the model
should be able to synthesize, and (b) a specification of the parametric
controls for navigating that space of sounds. The range of sounds is defined by
a dataset provided by the designer, while the means of navigation is defined by
a combination of data labels and the selection of a sub-manifold from the
latent space learned by the GAN. Our proposed system takes advantage of the
rich latent space of a GAN that consists of sounds that fill out the spaces
''between" real data-like sounds. This augmented data from the GAN is then used
to train an RNN for its ability to respond immediately and continuously to
parameter changes and to generate audio over unlimited periods of time.
Furthermore, we develop a self-organizing map technique for ``smoothing" the
latent space of GAN that results in perceptually smooth interpolation between
audio timbres. We validate this process through user studies. The system
contributes advances to the state of the art for generative sound model design
that include system configuration and components for improving interpolation
and the expansion of audio modeling capabilities beyond musical pitch and
percussive instrument sounds into the more complex space of audio textures.

Technological developments in the current era of globalization cannot be
avoided, with increasingly rapid progress to become technology as a medium of
information that is very much needed in life. Mailo store is one of the SMEs
that have used the website and the internet as a medium for storing their
information to the wider community, therefore an evaluation is needed to
measure the ease of website users for users using Usability Testing which has 8
variables, namely learning (learnability), Efficiency (efficiency), Easy to
remember (memorability), Errors and security (errors), Easy to understand
(Understandability), Easy to operate (Operability), attract user attention
(Attractiveness), Satisfaction (satisfaction). This research data was collected
by distributing questionnaires to 109 respondents using a random sampling
technique. Furthermore, the data obtained were processed using SPSS version 25
software. The data analysis method used was a quantitative analysis method
using validity and reliability tests, classical assumption tests, multiple
regression tests, and hypothesis testing. The results of the assessment of the
usability testing method will be useful for the sustainability and development
of the mailo store website in the future to make it even better.

Tree ensembles are powerful models that are widely used. However, they are
susceptible to adversarial examples, which are examples that purposely
constructed to elicit a misprediction from the model. This can degrade
performance and erode a user's trust in the model. Typically, approaches try to
alleviate this problem by verifying how robust a learned ensemble is or
robustifying the learning process. We take an alternative approach and attempt
to detect adversarial examples in a post-deployment setting. We present a novel
method for this task that works by analyzing an unseen example's output
configuration, which is the set of predictions made by an ensemble's
constituent trees. Our approach works with any additive tree ensemble and does
not require training a separate model. We evaluate our approach on three
different tree ensemble learners. We empirically show that our method is
currently the best adversarial detection method for tree ensembles.

Segmentation of plant point clouds to obtain high-precise morphological
traits is essential for plant phenotyping. Although the fast development of
deep learning has boosted much research on segmentation of plant point clouds,
previous studies mainly focus on the hard voxelization-based or
down-sampling-based methods, which are limited to segmenting simple plant
organs. Segmentation of complex plant point clouds with a high spatial
resolution still remains challenging. In this study, we proposed a deep
learning network plant segmentation transformer (PST) to achieve the semantic
and instance segmentation of rapeseed plants point clouds acquired by handheld
laser scanning (HLS) with the high spatial resolution, which can characterize
the tiny siliques as the main traits targeted. PST is composed of: (i) a
dynamic voxel feature encoder (DVFE) to aggregate the point features with the
raw spatial resolution; (ii) the dual window sets attention blocks to capture
the contextual information; and (iii) a dense feature propagation module to
obtain the final dense point feature map. The results proved that PST and
PST-PointGroup (PG) achieved superior performance in semantic and instance
segmentation tasks. For the semantic segmentation, the mean IoU, mean
Precision, mean Recall, mean F1-score, and overall accuracy of PST were 93.96%,
97.29%, 96.52%, 96.88%, and 97.07%, achieving an improvement of 7.62%, 3.28%,
4.8%, 4.25%, and 3.88% compared to the second-best state-of-the-art network
PAConv. For instance segmentation, PST-PG reached 89.51%, 89.85%, 88.83% and
82.53% in mCov, mWCov, mPerc90, and mRec90, achieving an improvement of 2.93%,
2.21%, 1.99%, and 5.9% compared to the original PG. This study proves that the
deep-learning-based point cloud segmentation method has a great potential for
resolving dense plant point clouds with complex morphological traits.

Despite recent progress on semi-supervised federated learning (FL) for
medical image diagnosis, the problem of imbalanced class distributions among
unlabeled clients is still unsolved for real-world use. In this paper, we study
a practical yet challenging problem of class imbalanced semi-supervised FL
(imFed-Semi), which allows all clients to have only unlabeled data while the
server just has a small amount of labeled data. This imFed-Semi problem is
addressed by a novel dynamic bank learning scheme, which improves client
training by exploiting class proportion information. This scheme consists of
two parts, i.e., the dynamic bank construction to distill various class
proportions for each local client, and the sub-bank classification to impose
the local model to learn different class proportions. We evaluate our approach
on two public real-world medical datasets, including the intracranial
hemorrhage diagnosis with 25,000 CT slices and skin lesion diagnosis with
10,015 dermoscopy images. The effectiveness of our method has been validated
with significant performance improvements (7.61% and 4.69%) compared with the
second-best on the accuracy, as well as comprehensive analytical studies. Code
is available at https://github.com/med-air/imFedSemi.

Misdiagnosis rates are one of the leading causes of medical errors in
hospitals, affecting over 12 million adults across the US. To address the high
rate of misdiagnosis, this study utilizes 4 NLP-based algorithms to determine
the appropriate health condition based on an unstructured transcription report.
From the Logistic Regression, Random Forest, LSTM, and CNNLSTM models, the
CNN-LSTM model performed the best with an accuracy of 97.89%. We packaged this
model into a authenticated web platform for accessible assistance to
clinicians. Overall, by standardizing health care diagnosis and structuring
transcription reports, our NLP platform drastically improves the clinical
efficiency and accuracy of hospitals worldwide.

Deformable image registration can obtain dynamic information about images,
which is of great significance in medical image analysis. The unsupervised deep
learning registration method can quickly achieve high registration accuracy
without labels. However, these methods generally suffer from uncorrelated
features, poor ability to register large deformations and details, and
unnatural deformation fields. To address the issues above, we propose an
unsupervised multi-scale correlation iterative registration network
(SearchMorph). In the proposed network, we introduce a correlation layer to
strengthen the relevance between features and construct a correlation pyramid
to provide multi-scale relevance information for the network. We also design a
deformation field iterator, which improves the ability of the model to register
details and large deformations through the search module and GRU while ensuring
that the deformation field is realistic. We use single-temporal brain MR images
and multi-temporal echocardiographic sequences to evaluate the model's ability
to register large deformations and details. The experimental results
demonstrate that the method in this paper achieves the highest registration
accuracy and the lowest folding point ratio using a short elapsed time to

Robust and generalized tool manipulation requires an understanding of the
properties and affordances of different tools. We investigate whether
linguistic information about a tool (e.g., its geometry, common uses) can help
control policies adapt faster to new tools for a given task. We obtain diverse
descriptions of various tools in natural language and use pre-trained language
models to generate their feature representations. We then perform
language-conditioned meta-learning to learn policies that can efficiently adapt
to new tools given their corresponding text descriptions. Our results
demonstrate that combining linguistic information and meta-learning
significantly accelerates tool learning in several manipulation tasks including
pushing, lifting, sweeping, and hammering.

Although deep Neural Networks (DNNs) have achieved tremendous success in
audio classification tasks, their uncertainty calibration are still
under-explored. A well-calibrated model should be accurate when it is certain
about its prediction and indicate high uncertainty when it is likely to be
inaccurate. In this work, we investigate the uncertainty calibration for deep
audio classifiers. In particular, we empirically study the performance of
popular calibration methods: (i) Monte Carlo Dropout, (ii) ensemble, (iii)
focal loss, and (iv) spectral-normalized Gaussian process (SNGP), on audio
classification datasets. To this end, we evaluate (i-iv) for the tasks of
environment sound and music genre classification. Results indicate that
uncalibrated deep audio classifiers may be over-confident, and SNGP performs
the best and is very efficient on the two datasets of this paper.

The Automatic Speaker Verification systems have potential in biometrics
applications for logical control access and authentication. A lot of things
happen to be at stake if the ASV system is compromised. The preliminary work
presents a comparative analysis of the wavelet and MFCC-based state-of-the-art
spoof detection techniques developed in these papers, respectively (Novoselov
et al., 2016) (Alam et al., 2016a). The results on ASVspoof 2015 justify our
inclination towards wavelet-based features instead of MFCC features. The
experiments on the ASVspoof 2019 database show the lack of credibility of the
traditional handcrafted features and give us more reason to progress towards
using end-to-end deep neural networks and more recent techniques. We use
Sincnet architecture as our baseline. We get E2E deep learning models, which we
call WSTnet and CWTnet, respectively, by replacing the Sinc layer with the
Wavelet Scattering and Continuous wavelet transform layers. The fusion model
achieved 62% and 17% relative improvement over traditional handcrafted models
and our Sincnet baseline when evaluated on the modern spoofing attacks in
ASVspoof 2019.
  The final scale distribution and the number of scales used in CWTnet are far
from optimal for the task at hand. So to solve this problem, we replaced the
CWT layer with a Wavelet Deconvolution(WD) (Khan and Yener, 2018) layer in our
CWTnet architecture. This layer calculates the Discrete-Continuous Wavelet
Transform similar to the CWTnet but also optimizes the scale parameter using
back-propagation. The WDnet model achieved 26% and 7% relative improvement over
CWTnet and Sincnet models respectively when evaluated over ASVspoof 2019
dataset. This shows that more generalized features are extracted as compared to
the features extracted by CWTnet as only the most important and relevant
frequency regions are focused upon.

A central problem in online learning and decision making -- from bandits to
reinforcement learning -- is to understand what modeling assumptions lead to
sample-efficient learning guarantees. We consider a general adversarial
decision making framework that encompasses (structured) bandit problems with
adversarial rewards and reinforcement learning problems with adversarial
dynamics. Our main result is to show -- via new upper and lower bounds -- that
the Decision-Estimation Coefficient, a complexity measure introduced by Foster
et al. in the stochastic counterpart to our setting, is necessary and
sufficient to obtain low regret for adversarial decision making. However,
compared to the stochastic setting, one must apply the Decision-Estimation
Coefficient to the convex hull of the class of models (or, hypotheses) under
consideration. This establishes that the price of accommodating adversarial
rewards or dynamics is governed by the behavior of the model class under
convexification, and recovers a number of existing results -- both positive and
negative. En route to obtaining these guarantees, we provide new structural
results that connect the Decision-Estimation Coefficient to variants of other
well-known complexity measures, including the Information Ratio of Russo and
Van Roy and the Exploration-by-Optimization objective of Lattimore and
Gy\"{o}rgy.

Outsourcing anomaly detection to third-parties can allow data owners to
overcome resource constraints (e.g., in lightweight IoT devices), facilitate
collaborative analysis (e.g., under distributed or multi-party scenarios), and
benefit from lower costs and specialized expertise (e.g., of Managed Security
Service Providers). Despite such benefits, a data owner may feel reluctant to
outsource anomaly detection without sufficient privacy protection. To that end,
most existing privacy solutions would face a novel challenge, i.e., preserving
privacy usually requires the difference between data entries to be eliminated
or reduced, whereas anomaly detection critically depends on that difference.
Such a conflict is recently resolved under a local analysis setting with
trusted analysts (where no outsourcing is involved) through moving the focus of
differential privacy (DP) guarantee from "all" to only "benign" entries. In
this paper, we observe that such an approach is not directly applicable to the
outsourcing setting, because data owners do not know which entries are "benign"
prior to outsourcing, and hence cannot selectively apply DP on data entries.
Therefore, we propose a novel iterative solution for the data owner to
gradually "disentangle" the anomalous entries from the benign ones such that
the third-party analyst can produce accurate anomaly results with sufficient DP
guarantee. We design and implement our Differentially Private Outsourcing of
Anomaly Detection (DPOAD) framework, and demonstrate its benefits over baseline
Laplace and PainFree mechanisms through experiments with real data from
different application domains.

Learning disabilities, which primarily interfere with the basic learning
skills such as reading, writing and math, are known to affect around 10% of
children in the world. The poor motor skills and motor coordination as part of
the neurodevelopmental disorder can become a causative factor for the
difficulty in learning to write (dysgraphia), hindering the academic track of
an individual. The signs and symptoms of dysgraphia include but are not limited
to irregular handwriting, improper handling of writing medium, slow or labored
writing, unusual hand position, etc. The widely accepted assessment criterion
for all the types of learning disabilities is the examination performed by
medical experts. The few available artificial intelligence-powered screening
systems for dysgraphia relies on the distinctive features of handwriting from
the corresponding images.This work presents a review of the existing automated
dysgraphia diagnosis systems for children in the literature. The main focus of
the work is to review artificial intelligence-based systems for dysgraphia
diagnosis in children. This work discusses the data collection method,
important handwriting features, machine learning algorithms employed in the
literature for the diagnosis of dysgraphia. Apart from that, this article
discusses some of the non-artificial intelligence-based automated systems also.
Furthermore, this article discusses the drawbacks of existing systems and
proposes a novel framework for dysgraphia diagnosis.

This technical report summarizes the analysis and approach on the
image-to-image translation task in the Multimodal Learning for Earth and
Environment Challenge (MultiEarth 2022). In terms of strategy optimization,
cloud classification is utilized to filter optical images with dense cloud
coverage to aid the supervised learning alike approach. The commonly used
pix2pix framework with a few optimizations is applied to build the model. A
weighted combination of mean squared error and mean absolute error is
incorporated in the loss function. As for evaluation, peak to signal ratio and
structural similarity were both considered in our preliminary analysis. Lastly,
our method achieved the second place with a final error score of 0.0412. The
results indicate great potential towards SAR-to-optical translation in remote
sensing tasks, specifically for the support of long-term environmental
monitoring and protection.

Automation in mining requires accurate maps of road networks on site. Because
roads on open-cut mines are dynamic in nature and continuously changing,
manually updating road maps is tedious and error-prone. This paper investigates
the possibility of automatically deriving an accurate representation of the
road network using GPS data available from haul trucks operating on site. We
present an overview of approaches proposed in literature and test the
performance of publicly available methods on GPS data collected from trucks
operating on site. Based on shortcomings seen in all tested algorithms, a
post-processing step is developed which geometrically analyses the created road
map for artefacts typical of free-drive areas on mine sites and significantly
improves the quality of the final road network graph.

The acquisition function, a critical component in Bayesian optimization (BO),
can often be written as the expectation of a utility function under a surrogate
model. However, to ensure that acquisition functions are tractable to optimize,
restrictions must be placed on the surrogate model and utility function. To
extend BO to a broader class of models and utilities, we propose
likelihood-free BO (LFBO), an approach based on likelihood-free inference. LFBO
directly models the acquisition function without having to separately perform
inference with a probabilistic surrogate model. We show that computing the
acquisition function in LFBO can be reduced to optimizing a weighted
classification problem, where the weights correspond to the utility being
chosen. By choosing the utility function for expected improvement (EI), LFBO
outperforms various state-of-the-art black-box optimization methods on several
real-world optimization problems. LFBO can also effectively leverage composite
structures of the objective function, which further improves its regret by
several orders of magnitude.

The failure of deep neural networks to generalize to out-of-distribution data
is a well-known problem and raises concerns about the deployment of trained
networks in safety-critical domains such as healthcare, finance and autonomous
vehicles. We study a particular kind of distribution shift $\unicode{x2013}$
shortcuts or spurious correlations in the training data. Shortcut learning is
often only exposed when models are evaluated on real-world data that does not
contain the same spurious correlations, posing a serious dilemma for AI
practitioners to properly assess the effectiveness of a trained model for
real-world applications. In this work, we propose to use the mutual information
(MI) between the learned representation and the input as a metric to find where
in training, the network latches onto shortcuts. Experiments demonstrate that
MI can be used as a domain-agnostic metric for monitoring shortcut learning.

By ensuring differential privacy in the learning algorithms, one can
rigorously mitigate the risk of large models memorizing sensitive training
data. In this paper, we study two algorithms for this purpose, i.e., DP-SGD and
DP-NSGD, which first clip or normalize \textit{per-sample} gradients to bound
the sensitivity and then add noise to obfuscate the exact information. We
analyze the convergence behavior of these two algorithms in the non-convex
optimization setting with two common assumptions and achieve a rate
$\mathcal{O}\left(\sqrt[4]{\frac{d\log(1/\delta)}{N^2\epsilon^2}}\right)$ of
the gradient norm for a $d$-dimensional model, $N$ samples and
$(\epsilon,\delta)$-DP, which improves over previous bounds under much weaker
assumptions. Specifically, we introduce a regularizing factor in DP-NSGD and
show that it is crucial in the convergence proof and subtly controls the bias
and noise trade-off. Our proof deliberately handles the per-sample gradient
clipping and normalization that are specified for the private setting.
Empirically, we demonstrate that these two algorithms achieve similar best
accuracy while DP-NSGD is comparatively easier to tune than DP-SGD and hence
may help further save the privacy budget when accounting the tuning effort.

With recent advances in machine learning, researchers are now able to solve
traditional problems with new solutions. In the area of digital watermarking,
deep-learning-based watermarking technique is being extensively studied. Most
existing approaches adopt a similar encoder-driven scheme which we name END
(Encoder-NoiseLayer-Decoder) architecture. In this paper, we revamp the
architecture and creatively design a decoder-driven watermarking network dubbed
De-END which greatly outperforms the existing END-based methods. The motivation
for designing De-END originated from the potential drawback we discovered in
END architecture: The encoder may embed redundant features that are not
necessary for decoding, limiting the performance of the whole network. We
conducted a detailed analysis and found that such limitations are caused by
unsatisfactory coupling between the encoder and decoder in END. De-END
addresses such drawbacks by adopting a Decoder-Encoder-Noiselayer-Decoder
architecture. In De-END, the host image is firstly processed by the decoder to
generate a latent feature map instead of being directly fed into the encoder.
This latent feature map is concatenated to the original watermark message and
then processed by the encoder. This change in design is crucial as it makes the
feature of encoder and decoder directly shared thus the encoder and decoder are
better coupled. We conducted extensive experiments and the results show that
this framework outperforms the existing state-of-the-art (SOTA) END-based deep
learning watermarking both in visual quality and robustness. On the premise of
the same decoder structure, the visual quality (measured by PSNR) of De-END
improves by 1.6dB (45.16dB to 46.84dB), and extraction accuracy after JPEG
compression (QF=50) distortion outperforms more than 4% (94.9% to 99.1%).

Graph convolutional networks have been widely used for skeleton-based action
recognition due to their excellent modeling ability of non-Euclidean data. As
the graph convolution is a local operation, it can only utilize the short-range
joint dependencies and short-term trajectory but fails to directly model the
distant joints relations and long-range temporal information that are vital to
distinguishing various actions. To solve this problem, we present a multi-scale
spatial graph convolution (MS-GC) module and a multi-scale temporal graph
convolution (MT-GC) module to enrich the receptive field of the model in
spatial and temporal dimensions. Concretely, the MS-GC and MT-GC modules
decompose the corresponding local graph convolution into a set of sub-graph
convolution, forming a hierarchical residual architecture. Without introducing
additional parameters, the features will be processed with a series of
sub-graph convolutions, and each node could complete multiple spatial and
temporal aggregations with its neighborhoods. The final equivalent receptive
field is accordingly enlarged, which is capable of capturing both short- and
long-range dependencies in spatial and temporal domains. By coupling these two
modules as a basic block, we further propose a multi-scale spatial temporal
graph convolutional network (MST-GCN), which stacks multiple blocks to learn
effective motion representations for action recognition. The proposed MST-GCN
achieves remarkable performance on three challenging benchmark datasets, NTU
RGB+D, NTU-120 RGB+D and Kinetics-Skeleton, for skeleton-based action
recognition.

The memorization effect of deep neural networks (DNNs) plays a pivotal role
in recent label noise learning methods. To exploit this effect, the model
prediction-based methods have been widely adopted, which aim to exploit the
outputs of DNNs in the early stage of learning to correct noisy labels.
However, we observe that the model will make mistakes during label prediction,
resulting in unsatisfactory performance. By contrast, the produced features in
the early stage of learning show better robustness. Inspired by this
observation, in this paper, we propose a novel feature embedding-based method
for deep learning with label noise, termed LabEl NoiseDilution (LEND). To be
specific, we first compute a similarity matrix based on current embedded
features to capture the local structure of training data. Then, the noisy
supervision signals carried by mislabeled data are overwhelmed by nearby
correctly labeled ones (\textit{i.e.}, label noise dilution), of which the
effectiveness is guaranteed by the inherent robustness of feature embedding.
Finally, the training data with diluted labels are further used to train a
robust classifier. Empirically, we conduct extensive experiments on both
synthetic and real-world noisy datasets by comparing our LEND with several
representative robust learning approaches. The results verify the effectiveness
of our LEND.

We quantify the performance of approximations to stochastic filtering by the
Kullback-Leibler divergence to the optimal Bayesian filter. Using a two-state
Markov process that drives a Brownian measurement process as prototypical test
case, we compare two stochastic filtering approximations: a static low-pass
filter as baseline, and machine learning of Voltera expansions using nonlinear
Vector Auto Regression (nVAR). We highlight the crucial role of the chosen
performance metric, and present two solutions to the specific challenge of
predicting a likelihood bounded between $0$ and $1$.

Major Depressive Disorder (MDD) is a severe illness that affects millions of
people, and it is critical to diagnose this disorder as early as possible.
Detecting depression from voice signals can be of great help to physicians and
can be done without any invasive procedure. Since relevant labelled data are
scarce, we propose a modified Instance Discriminative Learning (IDL) method, an
unsupervised pre-training technique, to extract augment-invariant and
instance-spread-out embeddings. In terms of learning augment-invariant
embeddings, various data augmentation methods for speech are investigated, and
time-masking yields the best performance. To learn instance-spread-out
embeddings, we explore methods for sampling instances for a training batch
(distinct speaker-based and random sampling). It is found that the distinct
speaker-based sampling provides better performance than the random one, and we
hypothesize that this result is because relevant speaker information is
preserved in the embedding. Additionally, we propose a novel sampling strategy,
Pseudo Instance-based Sampling (PIS), based on clustering algorithms, to
enhance spread-out characteristics of the embeddings. Experiments are conducted
with DepAudioNet on DAIC-WOZ (English) and CONVERGE (Mandarin) datasets, and
statistically significant improvements, with p-value 0.0015 and 0.05,
respectively, are observed using PIS in the detection of MDD relative to the
baseline without pre-training.

State space models have shown to be effective at modeling long range
dependencies, specially on sequence classification tasks. In this work we focus
on autoregressive sequence modeling over English books, Github source code and
ArXiv mathematics articles. Based on recent developments around the
effectiveness of gated activation functions, we propose a new layer named Gated
State Space (GSS) and show that it trains significantly faster than the
diagonal version of S4 (i.e. DSS) on TPUs, is fairly competitive with several
well-tuned Transformer-based baselines and exhibits zero-shot generalization to
longer inputs while being straightforward to implement. Finally, we show that
leveraging self-attention to model local dependencies improves the performance
of GSS even further.

We consider stochastic convex optimization for heavy-tailed data with the
guarantee of being differentially private (DP). Prior work on this problem is
restricted to the gradient descent (GD) method, which is inefficient for
large-scale problems. In this paper, we resolve this issue and derive the first
high-probability bounds for the private stochastic method with clipping. For
general convex problems, we derive excess population risks
$\Tilde{O}\left(\frac{d^{1/7}\sqrt{\ln\frac{(n \epsilon)^2}{\beta
d}}}{(n\epsilon)^{2/7}}\right)$ and
$\Tilde{O}\left(\frac{d^{1/7}\ln\frac{(n\epsilon)^2}{\beta
d}}{(n\epsilon)^{2/7}}\right)$ under bounded or unbounded domain assumption,
respectively (here $n$ is the sample size, $d$ is the dimension of the data,
$\beta$ is the confidence level and $\epsilon$ is the private level). Then, we
extend our analysis to the strongly convex case and non-smooth case (which
works for generalized smooth objectives with H$\ddot{\text{o}}$lder-continuous
gradients). We establish new excess risk bounds without bounded domain
assumption. The results above achieve lower excess risks and gradient
complexities than existing methods in their corresponding cases. Numerical
experiments are conducted to justify the theoretical improvement.

Product images are essential for providing desirable user experience in an
e-commerce platform. For a platform with billions of products, it is extremely
time-costly and labor-expensive to manually pick and organize qualified images.
Furthermore, there are the numerous and complicated image rules that a product
image needs to comply in order to be generated/selected. To address these
challenges, in this paper, we present a new learning framework in order to
achieve Automatic Generation of Product-Image Sequence (AGPIS) in e-commerce.
To this end, we propose a Multi-modality Unified Image-sequence Classifier
(MUIsC), which is able to simultaneously detect all categories of rule
violations through learning. MUIsC leverages textual review feedback as the
additional training target and utilizes product textual description to provide
extra semantic information. Based on offline evaluations, we show that the
proposed MUIsC significantly outperforms various baselines. Besides MUIsC, we
also integrate some other important modules in the proposed framework, such as
primary image selection, noncompliant content detection, and image
deduplication. With all these modules, our framework works effectively and
efficiently in JD.com recommendation platform. By Dec 2021, our AGPIS framework
has generated high-standard images for about 1.5 million products and achieves
13.6% in reject rate.

We present MEMprop, the adoption of gradient-based learning to train fully
memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic
device dynamics to trigger naturally arising voltage spikes. These spikes
emitted by memristive dynamics are analog in nature, and thus fully
differentiable, which eliminates the need for surrogate gradient methods that
are prevalent in the spiking neural network (SNN) literature. Memristive neural
networks typically either integrate memristors as synapses that map
offline-trained networks, or otherwise rely on associative learning mechanisms
to train networks of memristive neurons. We instead apply the backpropagation
through time (BPTT) training algorithm directly on analog SPICE models of
memristive neurons and synapses. Our implementation is fully memristive, in
that synaptic weights and spiking neurons are both integrated on resistive RAM
(RRAM) arrays without the need for additional circuits to implement spiking
dynamics, e.g., analog-to-digital converters (ADCs) or thresholded comparators.
As a result, higher-order electrophysical effects are fully exploited to use
the state-driven dynamics of memristive neurons at run time. By moving towards
non-approximate gradient-based learning, we obtain highly competitive accuracy
amongst previously reported lightweight dense fully MSNNs on several

We investigate the explainability of graph neural networks (GNNs) as a step
towards elucidating their working mechanisms. While most current methods focus
on explaining graph nodes, edges, or features, we argue that, as the inherent
functional mechanism of GNNs, message flows are more natural for performing
explainability. To this end, we propose a novel method here, known as FlowX, to
explain GNNs by identifying important message flows. To quantify the importance
of flows, we propose to follow the philosophy of Shapley values from
cooperative game theory. To tackle the complexity of computing all coalitions'
marginal contributions, we propose an approximation scheme to compute
Shapley-like values as initial assessments of further redistribution training.
We then propose a learning algorithm to train flow scores and improve
explainability. Experimental studies on both synthetic and real-world datasets
demonstrate that our proposed FlowX leads to improved explainability of GNNs.

Existing methods of explainable AI and interpretable ML cannot explain change
in the values of an output variable for a statistical unit in terms of the
change in the input values and the change in the "mechanism" (the function
transforming input to output). We propose two methods based on counterfactuals
for explaining unit-level changes at various input granularities using the
concept of Shapley values from game theory. These methods satisfy two key
axioms desirable for any unit-level change attribution method. Through
simulations, we study the reliability and the scalability of the proposed
methods. We get sensible results from a case study on identifying the drivers
of the change in the earnings for individuals in the US.

Generalization in deep reinforcement learning over unseen environment
variations usually requires policy learning over a large set of diverse
training variations. We empirically observe that an agent trained on many
variations (a generalist) tends to learn faster at the beginning, yet its
performance plateaus at a less optimal level for a long time. In contrast, an
agent trained only on a few variations (a specialist) can often achieve high
returns under a limited computational budget. To have the best of both worlds,
we propose a novel generalist-specialist training framework. Specifically, we
first train a generalist on all environment variations; when it fails to
improve, we launch a large population of specialists with weights cloned from
the generalist, each trained to master a selected small subset of variations.
We finally resume the training of the generalist with auxiliary rewards induced
by demonstrations of all specialists. In particular, we investigate the timing
to start specialist training and compare strategies to learn generalists with
assistance from specialists. We show that this framework pushes the envelope of
policy learning on several challenging and popular benchmarks including
Procgen, Meta-World and ManiSkill.

The spread of information through social media platforms can create
environments possibly hostile to vulnerable communities and silence certain
groups in society. To mitigate such instances, several models have been
developed to detect hate and offensive speech. Since detecting hate and
offensive speech in social media platforms could incorrectly exclude
individuals from social media platforms, which can reduce trust, there is a
need to create explainable and interpretable models. Thus, we build an
explainable and interpretable high performance model based on the XGBoost
algorithm, trained on Twitter data. For unbalanced Twitter data, XGboost
outperformed the LSTM, AutoGluon, and ULMFiT models on hate speech detection
with an F1 score of 0.75 compared to 0.38 and 0.37, and 0.38 respectively. When
we down-sampled the data to three separate classes of approximately 5000
tweets, XGBoost performed better than LSTM, AutoGluon, and ULMFiT; with F1
scores for hate speech detection of 0.79 vs 0.69, 0.77, and 0.66 respectively.
XGBoost also performed better than LSTM, AutoGluon, and ULMFiT in the
down-sampled version for offensive speech detection with F1 score of 0.83 vs
0.88, 0.82, and 0.79 respectively. We use Shapley Additive Explanations (SHAP)
on our XGBoost models' outputs to makes it explainable and interpretable
compared to LSTM, AutoGluon and ULMFiT that are black-box models.

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct
structural alterations within the brain. We hypothesize that deep learning
applied to a structural neuroimaging dataset could detect disease-related
alteration and improve classification and diagnostic accuracy. We tested this
hypothesis using a single, widely available, and conventional T1-weighted MRI
scan, from which we extracted the 3D whole-brain structure using standard
post-processing methods. A deep learning model was then developed, optimized,
and evaluated on three open datasets with T1-weighted MRI scans of patients
with schizophrenia. Our proposed model outperformed the benchmark model, which
was also trained with structural MR images using a 3D CNN architecture. Our
model is capable of almost perfectly (area under the ROC curve = 0.987)
distinguishing schizophrenia patients from healthy controls on unseen
structural MRI scans. Regional analysis localized subcortical regions and
ventricles as the most predictive brain regions. Subcortical structures serve a
pivotal role in cognitive, affective, and social functions in humans, and
structural abnormalities of these regions have been associated with
schizophrenia. Our finding corroborates that schizophrenia is associated with
widespread alterations in subcortical brain structure and the subcortical
structural information provides prominent features in diagnostic
classification. Together, these results further demonstrate the potential of
deep learning to improve schizophrenia diagnosis and identify its structural
neuroimaging signatures from a single, standard T1-weighted brain MRI.

We study robustness to test-time adversarial attacks in the regression
setting with $\ell_p$ losses and arbitrary perturbation sets. We address the
question of which function classes are PAC learnable in this setting. We show
that classes of finite fat-shattering dimension are learnable. Moreover, for
convex function classes, they are even properly learnable. In contrast, some
non-convex function classes provably require improper learning algorithms. We
also discuss extensions to agnostic learning. Our main technique is based on a
construction of an adversarially robust sample compression scheme of a size
determined by the fat-shattering dimension.

In this paper, we leverage the human perceiving process, that involves vision
and language interaction, to generate a coherent paragraph description of
untrimmed videos. We propose vision-language (VL) features consisting of two
modalities, i.e., (i) vision modality to capture global visual content of the
entire scene and (ii) language modality to extract scene elements description
of both human and non-human objects (e.g. animals, vehicles, etc), visual and
non-visual elements (e.g. relations, activities, etc). Furthermore, we propose
to train our proposed VLCap under a contrastive learning VL loss. The
experiments and ablation studies on ActivityNet Captions and YouCookII datasets
show that our VLCap outperforms existing SOTA methods on both accuracy and
diversity metrics.

Neural nets are a powerful method for the classification of radio signals in
the electromagnetic spectrum. These neural nets are often trained with
synthetically generated data due to the lack of diverse and plentiful real RF
data. However, it is often unclear how neural nets trained on synthetic data
perform in real-world applications. This paper investigates the impact of
different RF signal impairments (such as phase, frequency and sample rate
offsets, receiver filters, noise and channel models) modeled in synthetic
training data with respect to the real-world performance. For that purpose,
this paper trains neural nets with various synthetic training datasets with
different signal impairments. After training, the neural nets are evaluated
against real-world RF data collected by a software defined radio receiver in
the field. This approach reveals which modeled signal impairments should be
included in carefully designed synthetic datasets. The investigated showcase
example can classify RF signals into one of 20 different radio signal types
from the shortwave bands. It achieves an accuracy of up to 95 % in real-world
operation by using carefully designed synthetic training data only.

Despite the wide applications of neural networks, there have been increasing
concerns about their vulnerability issue. While numerous attack and defense
techniques have been developed, this work investigates the robustness issue
from a new angle: can we design a self-healing neural network that can
automatically detect and fix the vulnerability issue by itself? A typical
self-healing mechanism is the immune system of a human body. This
biology-inspired idea has been used in many engineering designs but is rarely
investigated in deep learning. This paper considers the post-training
self-healing of a neural network, and proposes a closed-loop control
formulation to automatically detect and fix the errors caused by various
attacks or perturbations. We provide a margin-based analysis to explain how
this formulation can improve the robustness of a classifier. To speed up the
inference of the proposed self-healing network, we solve the control problem
via improving the Pontryagin Maximum Principle-based solver. Lastly, we present
an error estimation of the proposed framework for neural networks with
nonlinear activation functions. We validate the performance on several network
architectures against various perturbations. Since the self-healing method does
not need a-priori information about data perturbations/attacks, it can handle a
broad class of unforeseen perturbations.

A crucial step in single particle analysis (SPA) of cryogenic electron
microscopy (Cryo-EM), 2D classification and alignment takes a collection of
noisy particle images to infer orientations and group similar images together.
Averaging these aligned and clustered noisy images produces a set of clean
images, ready for further analysis such as 3D reconstruction. Fourier-Bessel
steerable principal component analysis (FBsPCA) enables an efficient,
adaptable, low-rank rotation operator. We extend the FBsPCA to additionally
handle translations. In this extended FBsPCA representation, we use a
probabilistic polar-coordinate Gaussian mixture model to learn soft clusters in
an unsupervised fashion using an expectation maximization (EM) algorithm. The
obtained rotational clusters are thus additionally robust to the presence of
pairwise alignment imperfections. Multiple benchmarks from simulated Cryo-EM
datasets show probabilistic PolarGMM's improved performance in comparisons with
standard single-particle Cryo-EM tools, EMAN2 and RELION, in terms of various
clustering metrics and alignment errors.

Speaker adaptation is important to build robust automatic speech recognition
(ASR) systems. In this work, we investigate various methods for speaker
adaptive training (SAT) based on feature-space approaches for a conformer-based
acoustic model (AM) on the Switchboard 300h dataset. We propose a method,
called Weighted-Simple-Add, which adds weighted speaker information vectors to
the input of the multi-head self-attention module of the conformer AM. Using
this method for SAT, we achieve 3.5% and 4.5% relative improvement in terms of
WER on the CallHome part of Hub5'00 and Hub5'01 respectively. Moreover, we
build on top of our previous work where we proposed a novel and competitive
training recipe for a conformer-based hybrid AM. We extend and improve this
recipe where we achieve 11% relative improvement in terms of word-error-rate
(WER) on Switchboard 300h Hub5'00 dataset. We also make this recipe efficient
by reducing the total number of parameters by 34% relative.

Water Cherenkov detectors like Super-Kamiokande, and the next generation
Hyper-Kamiokande are adding gadolinium to their water to improve the detection
of neutrons. By detecting neutrons in addition to the leptons in neutrino
interactions, an improved separation between neutrino and anti-neutrinos, and
reduced backgrounds for proton decay searches can be expected. The neutron
signal itself is still small and can be confused with muon spallation and other
background sources. In this paper, machine learning techniques are employed to
optimize the neutron capture detection capability in the new intermediate water
Cherenkov detector (IWCD) for Hyper-K. In particular, boosted decision tree
(XGBoost), graph convolutional network (GCN), and dynamic graph convolutional
neural network (DGCNN) models are developed and benchmarked against a
statistical likelihood-based approach, achieving up to a 10% increase in
classification accuracy. Characteristic features are also engineered from the
datasets and analyzed using SHAP (SHapley Additive exPlanations) to provide
insight into the pivotal factors influencing event type outcomes. The dataset
used in this research consisted of roughly 1.6 million simulated particle gun
events, divided nearly evenly between neutron capture and a background electron
source.

Reconstructing 3D non-watertight mesh from an unoriented point cloud is an
unexplored area in computer vision and computer graphics. In this project, we
tried to tackle this problem by extending the learning-based watertight mesh
reconstruction pipeline presented in the paper 'Shape as Points'. The core of
our approach is to cast the problem as a semantic segmentation problem that
identifies the region in the 3D volume where the mesh surface lies and extracts
the surfaces from the detected regions. Our approach achieves compelling
results compared to the baseline techniques.

Federated learning (FL) is an emerging technology that enables the training
of machine learning models from multiple clients while keeping the data
distributed and private. Based on the participating clients and the model
training scale, federated learning can be classified into two types:
cross-device FL where clients are typically mobile devices and the client
number can reach up to a scale of millions; cross-silo FL where clients are
organizations or companies and the client number is usually small (e.g., within
a hundred). While existing studies mainly focus on cross-device FL, this paper
aims to provide an overview of the cross-silo FL. More specifically, we first
discuss applications of cross-silo FL and outline its major challenges. We then
provide a systematic overview of the existing approaches to the challenges in
cross-silo FL by focusing on their connections and differences to cross-device
FL. Finally, we discuss future directions and open issues that merit research
efforts from the community.

Motion estimation approaches typically employ sensor fusion techniques, such
as the Kalman Filter, to handle individual sensor failures. More recently, deep
learning-based fusion approaches have been proposed, increasing the performance
and requiring less model-specific implementations. However, current deep fusion
approaches often assume that sensors are synchronised, which is not always
practical, especially for low-cost hardware. To address this limitation, in
this work, we propose AFT-VO, a novel transformer-based sensor fusion
architecture to estimate VO from multiple sensors. Our framework combines
predictions from asynchronous multi-view cameras and accounts for the time
discrepancies of measurements coming from different sources.
  Our approach first employs a Mixture Density Network (MDN) to estimate the
probability distributions of the 6-DoF poses for every camera in the system.
Then a novel transformer-based fusion module, AFT-VO, is introduced, which
combines these asynchronous pose estimations, along with their confidences.
More specifically, we introduce Discretiser and Source Encoding techniques
which enable the fusion of multi-source asynchronous signals.
  We evaluate our approach on the popular nuScenes and KITTI datasets. Our
experiments demonstrate that multi-view fusion for VO estimation provides
robust and accurate trajectories, outperforming the state of the art in both
challenging weather and lighting conditions.

In this paper, a combat Unmanned Air Vehicle (UAV) is modeled in the
simulation environment. The rotary wing UAV is successfully performed various
tasks such as locking on the targets, tracking, and sharing the relevant data
with surrounding vehicles. Different software technologies such as API
communication, ground control station configuration, autonomous movement
algorithms, computer vision, and deep learning are employed.

Semantic image interpretation can vastly benefit from approaches that combine
sub-symbolic distributed representation learning with the capability to reason
at a higher level of abstraction. Logic Tensor Networks (LTNs) are a class of
neuro-symbolic systems based on a differentiable, first-order logic grounded
into a deep neural network. LTNs replace the classical concept of training set
with a knowledge base of fuzzy logical axioms. By defining a set of
differentiable operators to approximate the role of connectives, predicates,
functions and quantifiers, a loss function is automatically specified so that
LTNs can learn to satisfy the knowledge base. We focus here on the subsumption
or \texttt{isOfClass} predicate, which is fundamental to encode most semantic
image interpretation tasks. Unlike conventional LTNs, which rely on a separate
predicate for each class (e.g., dog, cat), each with its own set of learnable
weights, we propose a common \texttt{isOfClass} predicate, whose level of truth
is a function of the distance between an object embedding and the corresponding
class prototype. The PROTOtypical Logic Tensor Networks (PROTO-LTN) extend the
current formulation by grounding abstract concepts as parametrized class
prototypes in a high-dimensional embedding space, while reducing the number of
parameters required to ground the knowledge base. We show how this architecture
can be effectively trained in the few and zero-shot learning scenarios.
Experiments on Generalized Zero Shot Learning benchmarks validate the proposed
implementation as a competitive alternative to traditional embedding-based
approaches. The proposed formulation opens up new opportunities in zero shot
learning settings, as the LTN formalism allows to integrate background
knowledge in the form of logical axioms to compensate for the lack of labelled
examples.

Risk measures are commonly used to capture the risk preferences of
decision-makers (DMs). The decisions of DMs can be nudged or manipulated when
their risk preferences are influenced by factors like the perception of losses
and the availability of information about the uncertainties. In this work, we
propose a Stackelberg risk preference design (STRIPE) problem to capture a
designer's incentive to influence DMs' risk preferences. STRIPE consists of two
levels. In the lower-level, individual DMs in a population, known as the
followers, respond to uncertainties according to their risk preference types
that specify their risk measures. In the upper-level, the leader influences the
distribution of the types to induce targeted decisions and steers the
follower's preferences to it. Our analysis centers around the solution concept
of approximate Stackelberg equilibrium that yields suboptimal behaviors of the
players. We show the existence of the approximate Stackelberg equilibrium. The
primitive risk perception gap, defined as the Wasserstein distance between the
original and the target type distributions, is important in estimating the
optimal design cost. We connect the leader's optimality tolerance on the cost
with her ambiguity tolerance on the follower's approximate solutions leveraging
Lipschitzian properties of the lower-level solution mapping. To obtain the
Stackelberg equilibrium, we reformulate STRIPE into a single-level optimization
problem using the spectral representations of law-invariant coherent risk
measures. We create a data-driven approach for computation and study its
performance guarantees. We apply STRIPE to contract design problems to mitigate
the intensity of moral hazard. Moreover, we connect STRIPE with meta-learning
problems and derive adaptation performance estimates of the meta-parameter
using the sensitivity of the optimal value function in the lower-level.

The distribution of young stars into OB associations has long been in need of
updating. High-precision {\sl Gaia} early Data Release 3 astrometry, coupled
with modern machine-learning methods, allows this to be done. We have compiled
a well-defined sample which includes OB stars and young open clusters, in total
comprising about 47,700 objects. To break the sample down into groupings
resembling associations, we applied the HDBSCAN$^{*}$ clustering algorithm. We
used a Monte Carlo method to estimate the kinematic ages of the resulting
clusters and the Student's $t$-test to assess the significance of the linear
correlations between proper motions and coordinates, indicating the presence of
possible cluster expansion signatures. The ages of the majority of clusters
demonstrating a general expansion at a 1$\sigma$ confidence level are several
tens of Myr, which is in agreement with the expected ages of OB associations.
We found 32 open clusters which turned out to be members of the resulting
groupings; their ages are consistent with one another within the uncertainties.
Comparison of the clusters thus obtained with the historical composition of OB
associations in the literature shows a correspondence between their positions
in the Galaxy but an apparent absence of good one-to-one stellar matches.
Therefore, we suggest that the historical composition of OB associations needs
to be revised.

Graph self-supervised learning has been vastly employed to learn
representations from unlabeled graphs. Existing methods can be roughly divided
into predictive learning and contrastive learning, where the latter one
attracts more research attention with better empirical performance. We argue
that, however, predictive models weaponed with latent augmentations and
powerful decoder could achieve comparable or even better representation power
than contrastive models. In this work, we introduce data augmentations into
latent space for superior generalization and better efficiency. A novel graph
decoder named Wiener Graph Deconvolutional Network is correspondingly designed
to perform information reconstruction from augmented latent representations.
Theoretical analysis proves the superior reconstruction ability of graph wiener
filter. Extensive experimental results on various datasets demonstrate the

In recent years, several algorithms for system identification with neural
state-space models have been introduced. Most of the proposed approaches are
aimed at reducing the computational complexity of the learning problem, by
splitting the optimization over short sub-sequences extracted from a longer
training dataset. Different sequences are then processed simultaneously within
a minibatch, taking advantage of modern parallel hardware for deep learning. An
issue arising in these methods is the need to assign an initial state for each
of the sub-sequences, which is required to run simulations and thus to evaluate
the fitting loss. In this paper, we provide insights for calibration of neural
state-space training algorithms based on extensive experimentation and analyses
performed on two recognized system identification benchmarks. Particular focus
is given to the choice and the role of the initial state estimation. We
demonstrate that advanced initial state estimation techniques are really
required to achieve high performance on certain classes of dynamical systems,
while for asymptotically stable ones basic procedures such as zero or random
initialization already yield competitive performance.

Vision Transformer (ViT) has shown its advantages over the convolutional
neural network (CNN) with its ability to capture global long-range dependencies
for visual representation learning. Besides ViT, contrastive learning is
another popular research topic recently. While previous contrastive learning
works are mostly based on CNNs, some recent studies have attempted to combine
ViT and contrastive learning for enhanced self-supervised learning. Despite the
considerable progress, these combinations of ViT and contrastive learning
mostly focus on the instance-level contrastiveness, which often overlook the
global contrastiveness and also lack the ability to directly learn the
clustering result (e.g., for images). In view of this, this paper presents a
novel deep clustering approach termed Vision Transformer for Contrastive
Clustering (VTCC), which for the first time, to our knowledge, unifies the
Transformer and the contrastive learning for the image clustering task.
Specifically, with two random augmentations performed on each image, we utilize
a ViT encoder with two weight-sharing views as the backbone. To remedy the
potential instability of the ViT, we incorporate a convolutional stem to split
each augmented sample into a sequence of patches, which uses multiple stacked
small convolutions instead of a big convolution in the patch projection layer.
By learning the feature representations for the sequences of patches via the
backbone, an instance projector and a cluster projector are further utilized to
perform the instance-level contrastive learning and the global clustering
structure learning, respectively. Experiments on eight image datasets
demonstrate the stability (during the training-from-scratch) and the
superiority (in clustering performance) of our VTCC approach over the

Current methods for video activity localisation over time assume implicitly
that activity temporal boundaries labelled for model training are determined
and precise. However, in unscripted natural videos, different activities mostly
transit smoothly, so that it is intrinsically ambiguous to determine in
labelling precisely when an activity starts and ends over time. Such
uncertainties in temporal labelling are currently ignored in model training,
resulting in learning mis-matched video-text correlation with poor
generalisation in test. In this work, we solve this problem by introducing
Elastic Moment Bounding (EMB) to accommodate flexible and adaptive activity
temporal boundaries towards modelling universally interpretable video-text
correlation with tolerance to underlying temporal uncertainties in pre-fixed
annotations. Specifically, we construct elastic boundaries adaptively by mining
and discovering frame-wise temporal endpoints that can maximise the alignment
between video segments and query sentences. To enable both more accurate
matching (segment content attention) and more robust localisation (segment
elastic boundaries), we optimise the selection of frame-wise endpoints subject
to segment-wise contents by a novel Guided Attention mechanism. Extensive
experiments on three video activity localisation benchmarks demonstrate
compellingly the EMB's advantages over existing methods without modelling

Learning unbiased node representations under class-imbalanced graph data is
challenging due to interactions between adjacent nodes. Existing studies have
in common that they compensate the minor class nodes `as a group' according to
their overall quantity (ignoring node connections in graph), which inevitably
increase the false positive cases for major nodes. We hypothesize that the
increase in these false positive cases is highly affected by the label
distribution around each node and confirm it experimentally. In addition, in
order to handle this issue, we propose Topology-Aware Margin (TAM) to reflect
local topology on the learning objective. Our method compares the connectivity
pattern of each node with the class-averaged counter-part and adaptively
adjusts the margin accordingly based on that. Our method consistently exhibits
superiority over the baselines on various node classification benchmark
datasets with representative GNN architectures.

Video anomaly detection is a challenging task because most anomalies are
scarce and non-deterministic. Many approaches investigate the reconstruction
difference between normal and abnormal patterns, but neglect that anomalies do
not necessarily correspond to large reconstruction errors. To address this
issue, we design a Convolutional LSTM Auto-Encoder prediction framework with
enhanced spatio-temporal memory exchange using bi-directionalilty and a
higher-order mechanism. The bi-directional structure promotes learning the
temporal regularity through forward and backward predictions. The unique
higher-order mechanism further strengthens spatial information interaction
between the encoder and the decoder. Considering the limited receptive fields
in Convolutional LSTMs, we also introduce an attention module to highlight
informative features for prediction. Anomalies are eventually identified by
comparing the frames with their corresponding predictions. Evaluations on three
popular benchmarks show that our framework outperforms most existing
prediction-based anomaly detection methods.

Out-of-distribution (OOD) detection has recently received much attention from
the machine learning community due to its importance in deploying machine
learning models in real-world applications. In this paper we propose an
uncertainty quantification approach by modelling the distribution of features.
We further incorporate an efficient ensemble mechanism, namely batch-ensemble,
to construct the batch-ensemble stochastic neural networks (BE-SNNs) and
overcome the feature collapse problem. We compare the performance of the
proposed BE-SNNs with the other state-of-the-art approaches and show that
BE-SNNs yield superior performance on several OOD benchmarks, such as the
Two-Moons dataset, the FashionMNIST vs MNIST dataset, FashionMNIST vs NotMNIST
dataset, and the CIFAR10 vs SVHN dataset.

In many societal and industrial interactions, participants generally prefer
their pure self-interest at the expense of the global welfare. Known as social
dilemmas, this category of non-cooperative games offers situations where
multiple actors should all cooperate to achieve the best outcome but greed and
fear lead to a worst self-interested issue. Recently, the emergence of Deep
Reinforcement Learning (RL) has generated revived interest in social dilemmas
with the introduction of Sequential Social Dilemma (SSD). Cooperative agents
mixing RL policies and Tit-for-tat (TFT) strategies have successfully addressed
some non-optimal Nash equilibrium issues. However, this kind of paradigm
requires symmetrical and direct cooperation between actors, conditions that are
not met when mutual cooperation become asymmetric and is possible only with at
least a third actor in a circular way. To tackle this issue, this paper extends
SSD with Circular Sequential Social Dilemma (CSSD), a new kind of Markov games
that better generalizes the diversity of cooperation between agents. Secondly,
to address such circular and asymmetric cooperation, we propose a candidate
solution based on RL policies and a graph-based TFT. We conducted some
experiments on a simple multi-player grid world which offers adaptable
cooperation structures. Our work confirmed that our graph-based approach is
beneficial to address circular situations by encouraging self-interested agents
to reach mutual cooperation.

As "a new frontier in evolutionary computation research", evolutionary
transfer optimization(ETO) will overcome the traditional paradigm of zero reuse
of related experience and knowledge from solved past problems in researches of
evolutionary computation. In scheduling applications via ETO, a quite appealing
and highly competitive framework "meeting" between them could be formed for
both intelligent scheduling and green scheduling, especially for international
pledge of "carbon neutrality" from China. To the best of our knowledge, our
paper on scheduling here, serves as the 1st work of a class of ETO frameworks
when multiobjective optimization problem "meets" single-objective optimization
problems in discrete case (not multitasking optimization). More specifically,
key knowledge conveyed for industrial applications, like positional building
blocks with genetic algorithm based settings, could be used via the new core
transfer mechanism and learning techniques for permutation flow shop scheduling
problem(PFSP). Extensive studies on well-studied benchmarks validate firm
effectiveness and great universality of our proposed ETO-PFSP framework
empirically. Our investigations (1) enrich the ETO frameworks, (2) contribute
to the classical and fundamental theory of building block for genetic
algorithms and memetic algorithms, and (3) head towards the paradigm shift of
evolutionary scheduling via learning by proposal and practice of paradigm of
"knowledge and building-block based scheduling" (KAB2S) for "industrial
intelligence" in China.

In 2021, Influencer Marketing generated more than $13 billion. Companies and
major brands advertise their products on Social Media, especially Instagram,
through Influencers, i.e., people with high popularity and the ability to
influence the mass. Usually, more popular and visible influencers are paid more
for their collaborations. As a result, many services were born to boost
profiles' popularity, engagement, or visibility, mainly through bots or fake
accounts. Researchers have focused on recognizing such unnatural activities in
different social networks with high success. However, real people recently
started participating in such boosting activities using their real accounts for
monetary rewards, generating ungenuine content that is very difficult to
detect. Currently, on Instagram, no works have tried to detect this new
phenomenon, known as crowdturfing (CT).
  In this work, we are the first to propose a CT engagement detector on
Instagram. Our algorithm leverages profiles' characteristics through
semi-supervised learning to spot accounts involved in CT activities. In
contrast to the supervised methods employed so far to detect fake accounts, a
semi-supervised approach takes advantage of the vast quantities of unlabeled
data on social media to yield better results. We purchased and studied 1293 CT
profiles from 11 providers to build our self-training classifier, which reached
95% accuracy. Finally, we ran our model in the wild to detect and analyze the
CT engagement of 20 mega-influencers (i.e., with more than one million
followers), discovering that more than 20% of their engagement was artificial.
We analyzed the profiles and comments of people involved in CT engagement,
showing how difficult it is to spot these activities using only the generated
content.

In this bachelor thesis, we show how four different machine learning methods
(Long Short-Term Memory, Random Forest, Support Vector Machine Regression, and
k-Nearest Neighbor) perform compared to already successfully applied trading
strategies such as Cross Signal Trading and a conventional statistical time
series model ARMA-GARCH. The aim is to show that machine learning methods
perform better than conventional methods in the crude oil market when used
correctly. A more detailed performance analysis was made, showing the
performance of the different models in different market phases so that the
robustness of individual models in high and low volatility phases could be
examined more closely. For further investigation, these models would also have
to be analyzed in other markets.

Evolutionary transfer optimization(ETO) serves as "a new frontier in
evolutionary computation research", which will avoid zero reuse of experience
and knowledge from solved problems in traditional evolutionary computation. In
scheduling applications via ETO, a highly competitive "meeting" framework
between them could be constituted towards both intelligent scheduling and green
scheduling, especially for carbon neutrality within the context of China. To
the best of our knowledge, our study on scheduling here, is the 1st work of ETO
for complex optimization when multiobjective problem "meets" single-objective
problems in combinatorial case (not multitasking optimization). More
specifically, key knowledge like positional building blocks clustered, could be
learned and transferred for permutation flow shop scheduling problem (PFSP).
Empirical studies on well-studied benchmarks validate relatively firm
effectiveness and great potential of our proposed ETO-PFSP framework.

This work is concerned with discovering the governing partial differential
equation (PDE) of a physical system. Existing methods have demonstrated the PDE
identification from finite observations but failed to maintain satisfying
results against noisy data, partly owing to suboptimal estimated derivatives
and found PDE coefficients. We address the issues by introducing a noise-aware
physics-informed machine learning (nPIML) framework to discover the governing
PDE from data following arbitrary distributions. We propose training a couple
of neural networks, namely solver and preselector, in a multi-task learning
paradigm, which yields important scores of basis candidates that constitute the
hidden physical constraint. After they are jointly trained, the solver network
estimates potential candidates, e.g., partial derivatives, for the sparse
regression algorithm to initially unveil the most likely parsimonious PDE,
decided according to the information criterion. We also propose the denoising
physics-informed neural networks (dPINNs), based on Discrete Fourier Transform
(DFT), to deliver a set of the optimal finetuned PDE coefficients respecting
the noise-reduced variables. The denoising PINNs are structured into forefront
projection networks and a PINN, by which the formerly learned solver
initializes. Our extensive experiments on five canonical PDEs affirm that the
proposed framework presents a robust and interpretable approach for PDE
discovery, applicable to a wide range of systems, possibly complicated by
noise.

Vanilla Federated learning (FL) relies on the centralized global aggregation
mechanism and assumes that all clients are honest. This makes it a challenge
for FL to alleviate the single point of failure and dishonest clients. These
impending challenges in the design philosophy of FL call for blockchain-based
federated learning (BFL) due to the benefits of coupling FL and blockchain
(e.g., democracy, incentive, and immutability). However, one problem in vanilla
BFL is that its capabilities do not follow adopters' needs in a dynamic
fashion. Besides, vanilla BFL relies on unverifiable clients' self-reported
contributions like data size because checking clients' raw data is not allowed
in FL for privacy concerns. We design and evaluate a novel BFL framework, and
resolve the identified challenges in vanilla BFL with greater flexibility and
incentive mechanism called FAIR-BFL. In contrast to existing works, FAIR-BFL
offers unprecedented flexibility via the modular design, allowing adopters to
adjust its capabilities following business demands in a dynamic fashion. Our
design accounts for BFL's ability to quantify each client's contribution to the
global learning process. Such quantification provides a rational metric for
distributing the rewards among federated clients and helps discover malicious
participants that may poison the global model.

When designing clustering algorithms, the choice of initial centers is
crucial for the quality of the learned clusters. In this paper, we develop a
new initialization scheme, called HST initialization, for the $k$-median
problem in the general metric space (e.g., discrete space induced by graphs),
based on the construction of metric embedding tree structure of the data. From
the tree, we propose a novel and efficient search algorithm, for good initial
centers that can be used subsequently for the local search algorithm. Our
proposed HST initialization can produce initial centers achieving lower errors
than those from another popular initialization method, $k$-median++, with
comparable efficiency. The HST initialization can also be extended to the
setting of differential privacy (DP) to generate private initial centers. We
show that the error from applying DP local search followed by our private HST
initialization improves previous results on the approximation error, and
approaches the lower bound within a small factor. Experiments justify the
theory and demonstrate the effectiveness of our proposed method. Our approach
can also be extended to the $k$-means problem.

For many applications envisioned for the Internet of Things (IoT), it is
expected that the sensors will have very low costs and zero power, which can be
satisfied by meta-material sensor based IoT, i.e., meta-IoT. As their
constituent meta-materials can reflect wireless signals with
environment-sensitive reflection coefficients, meta-IoT sensors can achieve
simultaneous sensing and transmission without any active modulation. However,
to maximize the sensing accuracy, the structures of meta-IoT sensors need to be
optimized considering their joint influence on sensing and transmission, which
is challenging due to the high computational complexity in evaluating the
influence, especially given a large number of sensors. In this paper, we
propose a joint sensing and transmission design method for meta-IoT systems
with a large number of meta-IoT sensors, which can efficiently optimize the
sensing accuracy of the system. Specifically, a computationally efficient
received signal model is established to evaluate the joint influence of
meta-material structure on sensing and transmission. Then, a sensing algorithm
based on deep unsupervised learning is designed to obtain accurate sensing
results in a robust manner. Experiments with a prototype verify that the system
has a higher sensitivity and a longer transmission range compared to existing
designs, and can sense environmental anomalies correctly within 2 meters.

Latent fingerprint enhancement is an essential pre-processing step for latent
fingerprint identification. Most latent fingerprint enhancement methods try to
restore corrupted gray ridges/valleys. In this paper, we propose a new method
that formulates the latent fingerprint enhancement as a constrained fingerprint
generation problem within a generative adversarial network (GAN) framework. We
name the proposed network as FingerGAN. It can enforce its generated
fingerprint (i.e, enhanced latent fingerprint) indistinguishable from the
corresponding ground-truth instance in terms of the fingerprint skeleton map
weighted by minutia locations and the orientation field regularized by the
FOMFE model. Because minutia is the primary feature for fingerprint recognition
and minutia can be retrieved directly from the fingerprint skeleton map, we
offer a holistic framework which can perform latent fingerprint enhancement in
the context of directly optimizing minutia information. This will help improve
latent fingerprint identification performance significantly. Experimental
results on two public latent fingerprint databases demonstrate that our method
outperforms the state of the arts significantly. The codes will be available
for non-commercial purposes from
\url{https://github.com/HubYZ/LatentEnhancement}.

We calculate spin correlation functions using IBM quantum processors,
accessed online. We demonstrate the rotational invariance of the singlet state,
interesting properties of the triplet states, and surprising features of a
state of three entangled qubits. This exercise is ideal for remote learning and
generates data with real quantum mechanical systems that are impractical to
investigate in the local laboratory. Students learn a wide variety of skills,
including calculation of multipartite spin correlation functions, design and
analysis of quantum circuits, and remote measurement with real quantum
processors.

The well-developed ETS (ExponenTial Smoothing or Error, Trend, Seasonality)
method incorporating a family of exponential smoothing models in state space
representation has been widely used for automatic forecasting. The existing ETS
method uses information criteria for model selection by choosing an optimal
model with the smallest information criterion among all models fitted to a
given time series. The ETS method under such a model selection scheme suffers
from computational complexity when applied to large-scale time series data. To
tackle this issue, we propose an efficient approach for ETS model selection by
training classifiers on simulated data to predict appropriate model component
forms for a given time series. We provide a simulation study to show the model
selection ability of the proposed approach on simulated data. We evaluate our
approach on the widely used forecasting competition data set M4, in terms of
both point forecasts and prediction intervals. To demonstrate the practical
value of our method, we showcase the performance improvements from our approach
on a monthly hospital data set.

Dementia is a growing problem as our society ages, and detection methods are
often invasive and expensive. Recent deep-learning techniques can offer a
faster diagnosis and have shown promising results. However, they require large
amounts of labelled data which is not easily available for the task of dementia
detection. One effective solution to sparse data problems is data augmentation,
though the exact methods need to be selected carefully. To date, there has been
no empirical study of data augmentation on Alzheimer's disease (AD) datasets
for NLP and speech processing. In this work, we investigate data augmentation
techniques for the task of AD detection and perform an empirical evaluation of
the different approaches on two kinds of models for both the text and audio
domains. We use a transformer-based model for both domains, and SVM and Random
Forest models for the text and audio domains, respectively. We generate
additional samples using traditional as well as deep learning based methods and
show that data augmentation improves performance for both the text- and
audio-based models and that such results are comparable to state-of-the-art
results on the popular ADReSS set, with carefully crafted architectures and
features.

This paper addresses the problem of estimating link flows in a road network
by combining limited traffic volume and vehicle trajectory data. While traffic
volume data from loop detectors have been the common data source for link flow
estimation, the detectors only cover a subset of links. Vehicle trajectory data
collected from vehicle tracking sensors are also incorporated these days.
However, trajectory data are often sparse in that the observed trajectories
only represent a small subset of the whole population, where the exact sampling
rate is unknown and may vary over space and time. This study proposes a novel
generative modelling framework, where we formulate the link-to-link movements
of a vehicle as a sequential decision-making problem using the Markov Decision
Process framework and train an agent to make sequential decisions to generate
realistic synthetic vehicle trajectories. We use Reinforcement Learning
(RL)-based methods to find the best behaviour of the agent, based on which
synthetic population vehicle trajectories can be generated to estimate link
flows across the whole network. To ensure the generated population vehicle
trajectories are consistent with the observed traffic volume and trajectory
data, two methods based on Inverse Reinforcement Learning and Constrained
Reinforcement Learning are proposed. The proposed generative modelling
framework solved by either of these RL-based methods is validated by solving
the link flow estimation problem in a real road network. Additionally, we
perform comprehensive experiments to compare the performance with two existing
methods. The results show that the proposed framework has higher estimation
accuracy and robustness under realistic scenarios where certain behavioural
assumptions about drivers are not met or the network coverage and penetration
rate of trajectory data are low.

The dipole moment is a physical quantity indicating the polarity of a
molecule and is determined by reflecting the electrical properties of
constituent atoms and the geometric properties of the molecule. Most embeddings
used to represent graph representations in traditional graph neural network
methodologies treat molecules as topological graphs, creating a significant
barrier to the goal of recognizing geometric information. Unlike existing
embeddings dealing with equivariance, which have been proposed to handle the 3D
structure of molecules properly, our proposed embeddings directly express the
physical implications of the local contribution of dipole moments. We show that
the developed model works reasonably even for molecules with extended
geometries and captures more interatomic interaction information, significantly
improving the prediction results with accuracy comparable to ab-initio
calculations.

Biomedical Question Answering aims to obtain an answer to the given question
from the biomedical domain. Due to its high requirement of biomedical domain
knowledge, it is difficult for the model to learn domain knowledge from limited
training data. We propose a contextual embedding method that combines
open-domain QA model \aoa and \biobert model pre-trained on biomedical domain
data. We adopt unsupervised pre-training on large biomedical corpus and
supervised fine-tuning on biomedical question answering dataset. Additionally,
we adopt an MLP-based model weighting layer to automatically exploit the
advantages of two models to provide the correct answer. The public dataset
\biomrc constructed from PubMed corpus is used to evaluate our method.
Experimental results show that our model outperforms state-of-the-art system by
a large margin.

Pooling is needed to aggregate frame-level features into utterance-level
representations for speaker modeling. Given the success of statistics-based
pooling methods, we hypothesize that speaker characteristics are well
represented in the statistical distribution over the pre-aggregation layer's
output, and propose to use transport-oriented feature aggregation for deriving
speaker embeddings. The aggregated representation encodes the geometric
structure of the underlying feature distribution, which is expected to contain
valuable speaker-specific information that may not be represented by the
commonly used statistical measures like mean and variance. The original
transport-oriented feature aggregation is also extended to a weighted-frame
version to incorporate the attention mechanism. Experiments on speaker
verification with the Voxceleb dataset show improvement over statistics pooling
and its attentive variant.

We consider the problem of personalised news recommendation where each user
consumes news in a sequential fashion. Existing personalised news
recommendation methods focus on exploiting user interests and ignores
exploration in recommendation, which leads to biased feedback loops and hurt
recommendation quality in the long term. We build on contextual bandits
recommendation strategies which naturally address the exploitation-exploration
trade-off. The main challenges are the computational efficiency for exploring
the large-scale item space and utilising the deep representations with
uncertainty. We propose a two-stage hierarchical topic-news deep contextual
bandits framework to efficiently learn user preferences when there are many
news items. We use deep learning representations for users and news, and
generalise the neural upper confidence bound (UCB) policies to generalised
additive UCB and bilinear UCB. Empirical results on a large-scale news
recommendation dataset show that our proposed policies are efficient and
outperform the baseline bandit policies.

With the emergence of social media, voluminous video clips are uploaded every
day, and retrieving the most relevant visual content with a language query
becomes critical. Most approaches aim to learn a joint embedding space for
plain textual and visual contents without adequately exploiting their
intra-modality structures and inter-modality correlations. This paper proposes
a novel transformer that explicitly disentangles the text and video into
semantic roles of objects, spatial contexts and temporal contexts with an
attention scheme to learn the intra- and inter-role correlations among the
three roles to discover discriminative features for matching at different
levels. The preliminary results on popular YouCook2 indicate that our approach
surpasses a current state-of-the-art method, with a high margin in all metrics.
It also overpasses two SOTA methods in terms of two metrics.

Replay buffers are a key component in many reinforcement learning schemes.
Yet, their theoretical properties are not fully understood. In this paper we
analyze a system where a stochastic process X is pushed into a replay buffer
and then randomly sampled to generate a stochastic process Y from the replay
buffer. We provide an analysis of the properties of the sampled process such as
stationarity, Markovity and autocorrelation in terms of the properties of the
original process. Our theoretical analysis sheds light on why replay buffer may
be a good de-correlator. Our analysis provides theoretical tools for proving
the convergence of replay buffer based algorithms which are prevalent in
reinforcement learning schemes.

Seas of videos are uploaded daily with the popularity of social channels;
thus, retrieving the most related video contents with user textual queries
plays a more crucial role. Most methods consider only one joint embedding space
between global visual and textual features without considering the local
structures of each modality. Some other approaches consider multiple embedding
spaces consisting of global and local features separately, ignoring rich
inter-modality correlations.
  We propose a novel mixture-of-expert transformer RoME that disentangles the
text and the video into three levels; the roles of spatial contexts, temporal
contexts, and object contexts. We utilize a transformer-based attention
mechanism to fully exploit visual and text embeddings at both global and local
levels with mixture-of-experts for considering inter-modalities and structures'
correlations. The results indicate that our method outperforms the
state-of-the-art methods on the YouCook2 and MSR-VTT datasets, given the same
visual backbone without pre-training. Finally, we conducted extensive ablation
studies to elucidate our design choices.

Bayesian Improved Surname Geocoding (BISG) is the most popular method for
proxying race/ethnicity in voter registration files that do not contain it.
This paper benchmarks BISG against a range of previously untested machine
learning alternatives, using voter files with self-reported race/ethnicity from
California, Florida, North Carolina, and Georgia. This analysis yields three
key findings. First, machine learning consistently outperforms BISG at
individual classification of race/ethnicity. Second, BISG and machine learning
methods exhibit divergent biases for estimating regional racial composition.
Third, the performance of all methods varies substantially across states. These
results suggest that pre-trained machine learning models are preferable to BISG
for individual classification. Furthermore, mixed results across states
underscore the need for researchers to empirically validate their chosen
race/ethnicity proxy in their populations of interest.

Autoregressive generative models are commonly used, especially for those
tasks involving sequential data. They have, however, been plagued by a slew of
inherent flaws due to the intrinsic characteristics of chain-style conditional
modeling (e.g., exposure bias or lack of long-range coherence), severely
limiting their ability to model distributions properly. In this paper, we
propose a unique method termed E-ARM for training autoregressive generative
models that takes advantage of a well-designed energy-based learning objective.
By leveraging the extra degree of freedom of the softmax operation, we are
allowed to make the autoregressive model itself be an energy-based model for
measuring the likelihood of input without introducing any extra parameters.
Furthermore, we show that E-ARM can be trained efficiently and is capable of
alleviating the exposure bias problem and increase temporal coherence for
autoregressive generative models. Extensive empirical results, covering
benchmarks like language modeling, neural machine translation, and image
generation, demonstrate the effectiveness of the proposed approach.

With the success of large language models (LLMs) of code and their use as
code assistants (e.g. Codex used in GitHub Copilot), techniques for introducing
domain-specific knowledge in the prompt design process become important. In
this work, we propose a framework called Repo-Level Prompt Generator that
learns to generate example-specific prompts using prompt proposals. The prompt
proposals take context from the entire repository, thereby incorporating both
the structure of the repository and the context from other relevant files (e.g.
imports, parent class files). Our technique doesn't require any access to the
weights of the LLM, making it applicable in cases where we only have black-box
access to the LLM. We conduct experiments on the task of single-line
code-autocompletion using code repositories taken from Google Code archives. We
demonstrate that an oracle constructed from our prompt proposals gives a
remarkably high relative improvement of 36% over Codex, showing the quality of
these proposals. Further, we show that when we train a model to predict a
prompt proposal, we can achieve significant performance gains over Codex and
other baselines. The code for our work can be found at:
\url{https://github.com/shrivastavadisha/repo_level_prompt_generation}.

We discuss the prediction accuracy of assumed statistical models in terms of
prediction errors for the generalized linear model and penalized maximum
likelihood methods. We derive the forms of estimators for the prediction
errors: $C_p$ criterion, information criteria, and leave-one-out cross
validation (LOOCV) error, using the generalized approximate message passing
(GAMP) algorithm and replica method. These estimators coincide with each other
when the number of model parameters is sufficiently small; however, there is a
discrepancy between them in particular in the overparametrized region where the
number of model parameters is larger than the data dimension. In this paper, we
review the prediction errors and corresponding estimators, and discuss their
differences. In the framework of GAMP, we show that the information criteria
can be expressed by using the variance of the estimates. Further, we
demonstrate how to approach LOOCV error from the information criteria by
utilizing the expression provided by GAMP.

The streaming automatic speech recognition (ASR) models are more popular and
suitable for voice-based applications. However, non-streaming models provide
better performance as they look at the entire audio context. To leverage the
benefits of the non-streaming model in streaming applications like voice
search, it is commonly used in second pass re-scoring mode. The candidate
hypothesis generated using steaming models is re-scored using a non-streaming
model. In this work, we evaluate the non-streaming attention-based end-to-end
ASR models on the Flipkart voice search task in both standalone and re-scoring
modes. These models are based on Listen-Attend-Spell (LAS) encoder-decoder
architecture. We experiment with different encoder variations based on LSTM,
Transformer, and Conformer. We compare the latency requirements of these models
along with their performance. Overall we show that the Transformer model offers
acceptable WER with the lowest latency requirements. We report a relative WER
improvement of around 16% with the second pass LAS re-scoring with latency
overhead under 5ms. We also highlight the importance of CNN front-end with
Transformer architecture to achieve comparable word error rates (WER).
Moreover, we observe that in the second pass re-scoring mode all the encoders
provide similar benefits whereas the difference in performance is prominent in
standalone text generation mode.

We provide new (parameterized) computational hardness results for Interval
Scheduling on Unrelated Machines. It is a classical scheduling problem
motivated from just-in-time or lean manufacturing, where the goal is to
complete jobs exactly at their deadline. We are given $n$ jobs and $m$
machines. Each job has a deadline, a weight, and a processing time that may be
different on each machine. The goal is find a schedule that maximized the total
weight of jobs completed exactly at their deadline. Note that this uniquely
defines a processing time interval for each job on each machine.
  Interval Scheduling on Unrelated Machines is closely related to coloring
interval graphs and has been thoroughly studied for several decades. However,
as pointed out by Mnich and van Bevern [Computers \& Operations Research,
2018], the parameterized complexity for the number $m$ of machines as a
parameter remained open. We resolve this by showing that Interval Scheduling on
Unrelated Machines is W[1]-hard when parameterized by the number $m$ of
machines. To this end, we prove W[1]-hardness with respect to $m$ of the
special case where we have parallel machines with eligible machine sets for
jobs. This answers Open Problem 8 of Mnich and van Bevern's list of 15 open
problems in the parameterized complexity of scheduling [Computers \& Operations
Research, 2018].
  Furthermore, we resolve the computational complexity status of the unweighted
version of Interval Scheduling on Unrelated Machines by proving that it is
NP-complete. This answers an open question by Sung and Vlach [Journal of
Scheduling, 2005].

Breast cancer is one of the leading causes of death among women across the
globe. It is difficult to treat if detected at advanced stages, however, early
detection can significantly increase chances of survival and improves lives of
millions of women. Given the widespread prevalence of breast cancer, it is of
utmost importance for the research community to come up with the framework for
early detection, classification and diagnosis. Artificial intelligence research
community in coordination with medical practitioners are developing such
frameworks to automate the task of detection. With the surge in research
activities coupled with availability of large datasets and enhanced
computational powers, it expected that AI framework results will help even more
clinicians in making correct predictions. In this article, a novel framework
for classification of breast cancer using mammograms is proposed. The proposed
framework combines robust features extracted from novel Convolutional Neural
Network (CNN) features with handcrafted features including HOG (Histogram of
Oriented Gradients) and LBP (Local Binary Pattern). The obtained results on
CBIS-DDSM dataset exceed state of the art.

Collaborative filtering (CF) plays a critical role in the development of
recommender systems. Most CF methods utilize an encoder to embed users and
items into the same representation space, and the Bayesian personalized ranking
(BPR) loss is usually adopted as the objective function to learn informative
encoders. Existing studies mainly focus on designing more powerful encoders
(e.g., graph neural network) to learn better representations. However, few
efforts have been devoted to investigating the desired properties of
representations in CF, which is important to understand the rationale of
existing CF methods and design new learning objectives. In this paper, we
measure the representation quality in CF from the perspective of alignment and
uniformity on the hypersphere. We first theoretically reveal the connection
between the BPR loss and these two properties. Then, we empirically analyze the
learning dynamics of typical CF methods in terms of quantified alignment and
uniformity, which shows that better alignment or uniformity both contribute to
higher recommendation performance. Based on the analyses results, a learning
objective that directly optimizes these two properties is proposed, named
DirectAU. We conduct extensive experiments on three public datasets, and the
proposed learning framework with a simple matrix factorization model leads to
significant performance improvements compared to state-of-the-art CF methods.
Our implementations are publicly available at
https://github.com/THUwangcy/DirectAU.

The wide spread of fake news is increasingly threatening both individuals and
society. Great efforts have been made for automatic fake news detection on a
single domain (e.g., politics). However, correlations exist commonly across
multiple news domains, and thus it is promising to simultaneously detect fake
news of multiple domains. Based on our analysis, we pose two challenges in
multi-domain fake news detection: 1) domain shift, caused by the discrepancy
among domains in terms of words, emotions, styles, etc. 2) domain labeling
incompleteness, stemming from the real-world categorization that only outputs
one single domain label, regardless of topic diversity of a news piece. In this
paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection
Framework (M$^3$FEND) to address these two challenges. We model news pieces
from a multi-view perspective, including semantics, emotion, and style.
Specifically, we propose a Domain Memory Bank to enrich domain information
which could discover potential domain labels based on seen news pieces and
model domain characteristics. Then, with enriched domain information as input,
a Domain Adapter could adaptively aggregate discriminative information from
multiple views for news in various domains. Extensive offline experiments on
English and Chinese datasets demonstrate the effectiveness of M$^3$FEND, and
online tests verify its superiority in practice. Our code is available at
https://github.com/ICTMCG/M3FEND.

A common method in training neural networks is to initialize all the weights
to be independent Gaussian vectors. We observe that by instead initializing the
weights into independent pairs, where each pair consists of two identical
Gaussian vectors, we can significantly improve the convergence analysis. While
a similar technique has been studied for random inputs [Daniely, NeurIPS 2020],
it has not been analyzed with arbitrary inputs. Using this technique, we show
how to significantly reduce the number of neurons required for two-layer ReLU
networks, both in the under-parameterized setting with logistic loss, from
roughly $\gamma^{-8}$ [Ji and Telgarsky, ICLR 2020] to $\gamma^{-2}$, where
$\gamma$ denotes the separation margin with a Neural Tangent Kernel, as well as
in the over-parameterized setting with squared loss, from roughly $n^4$ [Song
and Yang, 2019] to $n^2$, implicitly also improving the recent running time
bound of [Brand, Peng, Song and Weinstein, ITCS 2021]. For the
under-parameterized setting we also prove new lower bounds that improve upon
prior work, and that under certain assumptions, are best possible.

Following the success of convolution on non-Euclidean space, the
corresponding pooling approaches have also been validated on various tasks
regarding graphs. However, because of the fixed compression quota and stepwise
pooling design, these hierarchical pooling methods still suffer from local
structure damage and suboptimal problem. In this work, inspired by structural
entropy, we propose a hierarchical pooling approach, SEP, to tackle the two
issues. Specifically, without assigning the layer-specific compression quota, a
global optimization algorithm is designed to generate the cluster assignment
matrices for pooling at once. Then, we present an illustration of the local
structure damage from previous methods in the reconstruction of ring and grid
synthetic graphs. In addition to SEP, we further design two classification
models, SEP-G and SEP-N for graph classification and node classification,
respectively. The results show that SEP outperforms state-of-the-art graph
pooling methods on graph classification benchmarks and obtains superior
performance on node classifications.

With the development of computational pathology, deep learning methods for
Gleason grading through whole slide images (WSIs) have excellent prospects.
Since the size of WSIs is extremely large, the image label usually contains
only slide-level label or limited pixel-level labels. The current mainstream
approach adopts multi-instance learning to predict Gleason grades. However,
some methods only considering the slide-level label ignore the limited
pixel-level labels containing rich local information. Furthermore, the method
of additionally considering the pixel-level labels ignores the inaccuracy of
pixel-level labels. To address these problems, we propose a mixed supervision
Transformer based on the multiple instance learning framework. The model
utilizes both slide-level label and instance-level labels to achieve more
accurate Gleason grading at the slide level. The impact of inaccurate
instance-level labels is further reduced by introducing an efficient random
masking strategy in the mixed supervision training process. We achieve the
state-of-the-art performance on the SICAPv2 dataset, and the visual analysis
shows the accurate prediction results of instance level. The source code is
available at https://github.com/bianhao123/Mixed_supervision.

Standard deep learning-based classification approaches require collecting all
samples from all classes in advance and are trained offline. This paradigm may
not be practical in real-world clinical applications, where new classes are
incrementally introduced through the addition of new data. Class incremental
learning is a strategy allowing learning from such data. However, a major
challenge is catastrophic forgetting, i.e., performance degradation on previous
classes when adapting a trained model to new data. Prior methodologies to
alleviate this challenge save a portion of training data require perpetual
storage of such data that may introduce privacy issues. Here, we propose a
novel data-free class incremental learning framework that first synthesizes
data from the model trained on previous classes to generate a \ours.
Subsequently, it updates the model by combining the synthesized data with new
class data. Furthermore, we incorporate a cosine normalized Cross-entropy loss
to mitigate the adverse effects of the imbalance, a margin loss to increase
separation among previous classes and new ones, and an intra-domain contrastive
loss to generalize the model trained on the synthesized data to real data. We
compare our proposed framework with state-of-the-art methods in class
incremental learning, where we demonstrate improvement in accuracy for the
classification of 11,062 echocardiography cine series of patients.

The increasing reliance on ML models in high-stakes tasks has raised a major
concern on fairness violations. Although there has been a surge of work that
improves algorithmic fairness, most of them are under the assumption of an
identical training and test distribution. In many real-world applications,
however, such an assumption is often violated as previously trained fair models
are often deployed in a different environment, and the fairness of such models
has been observed to collapse. In this paper, we study how to transfer model
fairness under distribution shifts, a widespread issue in practice. We conduct
a fine-grained analysis of how the fair model is affected under different types
of distribution shifts and find that domain shifts are more challenging than
subpopulation shifts. Inspired by the success of self-training in transferring
accuracy under domain shifts, we derive a sufficient condition for transferring
group fairness. Guided by it, we propose a practical algorithm with a fair
consistency regularization as the key component. A synthetic dataset benchmark,
which covers all types of distribution shifts, is deployed for experimental
verification of the theoretical findings. Experiments on synthetic and real
datasets including image and tabular data demonstrate that our approach
effectively transfers fairness and accuracy under various distribution shifts.

Recent research in Internet of things has been widely applied for industrial
practices, fostering the exponential growth of data and connected devices.
Henceforth, data-driven AI models would be accessed by different parties
through certain data-sharing policies. However, most of the current training
procedures rely on the centralized data-collection strategy and a single
computational server. However, such a centralized scheme may lead to many
issues. Customer data stored in a centralized database may be tampered with so
the provenance and authenticity of data cannot be justified. Once the
aforementioned security concerns occur, the credibility of the trained AI
models would be questionable and even unfavorable outcomes might be produced at
the test stage. Lately, blockchain and AI, the two core technologies in
Industry 4.0 and Web 3.0, have been explored to facilitate the decentralized AI
training strategy. To serve on this very purpose, we propose a new system
architecture called APPFLChain, namely an integrated architecture of a
Hyperledger Fabric-based blockchain and a federated-learning paradigm. Our
proposed new system allows different parties to jointly train AI models and
their customers or stakeholders are connected by a consortium blockchain-based
network. Our new system can maintain a high degree of security and privacy as
users do not need to share sensitive personal information to the server. For
numerical evaluation, we simulate a real-world scenario to illustrate the whole
operational process of APPFLChain. Simulation results show that taking
advantage of the characteristics of consortium blockchain and federated
learning, APPFLChain can demonstrate favorable properties including
untamperability, traceability, privacy protection, and reliable
decision-making.

With the improvement of AI chips (e.g., GPU, TPU, and NPU) and the fast
development of the Internet of Things (IoT), some robust deep neural networks
(DNNs) are usually composed of millions or even hundreds of millions of
parameters. Such a large model may not be suitable for directly deploying on
low computation and low capacity units (e.g., edge devices). Knowledge
distillation (KD) has recently been recognized as a powerful model compression
method to decrease the model parameters effectively. The central concept of KD
is to extract useful information from the feature maps of a large model (i.e.,
teacher model) as a reference to successfully train a small model (i.e.,
student model) in which the model size is much smaller than the teacher one.
Although many KD methods have been proposed to utilize the information from the
feature maps of intermediate layers in the teacher model, most did not consider
the similarity of feature maps between the teacher model and the student model.
As a result, it may make the student model learn useless information. Inspired
by the attention mechanism, we propose a novel KD method called representative
teacher key (RTK) that not only considers the similarity of feature maps but
also filters out the useless information to improve the performance of the
target student model. In the experiments, we validate our proposed method with
several backbone networks (e.g., ResNet and WideResNet) and datasets (e.g.,
CIFAR10, CIFAR100, SVHN, and CINIC10). The results show that our proposed RTK
can effectively improve the classification accuracy of the state-of-the-art
attention-based KD method.

Real-world applications require a robot operating in the physical world with
awareness of potential risks besides accomplishing the task. A large part of
risky behaviors arises from interacting with objects in ignorance of
affordance. To prevent the agent from making unsafe decisions, we propose to
train a robotic agent by reinforcement learning to execute tasks with an
awareness of physical properties such as mass and friction in an indoor
environment. We achieve this through a novel physics-inspired reward function
that encourages the agent to learn a policy discerning different masses and
friction coefficients. We introduce two novel and challenging indoor
rearrangement tasks -- the variable friction pushing task and the variable mass
pushing task -- that allow evaluation of the learned policies in trading off
performance and physics-inspired risk. Our results demonstrate that by
equipping with the proposed reward, the agent is able to learn policies
choosing the pushing targets or goal-reaching trajectories with minimum
physical cost, which can be further utilized as a precaution to constrain the
agent's behavior in a safety-critic environment.

Session-based recommendation (SBR) aims to predict the user next action based
on the ongoing sessions. Recently, there has been an increasing interest in
modeling the user preference evolution to capture the fine-grained user
interests. While latent user preferences behind the sessions drift continuously
over time, most existing approaches still model the temporal session data in
discrete state spaces, which are incapable of capturing the fine-grained
preference evolution and result in sub-optimal solutions. To this end, we
propose Graph Nested GRU ordinary differential equation (ODE), namely GNG-ODE,
a novel continuum model that extends the idea of neural ODEs to continuous-time
temporal session graphs. The proposed model preserves the continuous nature of
dynamic user preferences, encoding both temporal and structural patterns of
item transitions into continuous-time dynamic embeddings. As the existing ODE
solvers do not consider graph structure change and thus cannot be directly
applied to the dynamic graph, we propose a time alignment technique, called
t-Alignment, to align the updating time steps of the temporal session graphs
within a batch. Empirical results on three benchmark datasets show that GNG-ODE
significantly outperforms other baselines.

Spoken language understanding (SLU) treats automatic speech recognition (ASR)
and natural language understanding (NLU) as a unified task and usually suffers
from data scarcity. We exploit an ASR and NLU joint training method based on
meta auxiliary learning to improve the performance of low-resource SLU task by
only taking advantage of abundant manual transcriptions of speech data. One
obvious advantage of such method is that it provides a flexible framework to
implement a low-resource SLU training task without requiring access to any
further semantic annotations. In particular, a NLU model is taken as label
generation network to predict intent and slot tags from texts; a multi-task
network trains ASR task and SLU task synchronously from speech; and the
predictions of label generation network are delivered to the multi-task network
as semantic targets. The efficiency of the proposed algorithm is demonstrated
with experiments on the public CATSLU dataset, which produces more suitable ASR
hypotheses for the downstream NLU task.

We present a simple yet effective self-supervised framework for audio-visual
representation learning, to localize the sound source in videos. To understand
what enables to learn useful representations, we systematically investigate the
effects of data augmentations, and reveal that (1) composition of data
augmentations plays a critical role, i.e. explicitly encouraging the
audio-visual representations to be invariant to various transformations~({\em
transformation invariance}); (2) enforcing geometric consistency substantially
improves the quality of learned representations, i.e. the detected sound source
should follow the same transformation applied on input video frames~({\em
transformation equivariance}). Extensive experiments demonstrate that our model
significantly outperforms previous methods on two sound localization
benchmarks, namely, Flickr-SoundNet and VGG-Sound. Additionally, we also
evaluate audio retrieval and cross-modal retrieval tasks. In both cases, our
self-supervised models demonstrate superior retrieval performances, even
competitive with the supervised approach in audio retrieval. This reveals the
proposed framework learns strong multi-modal representations that are
beneficial to sound localisation and generalization to further applications.
\textit{All codes will be available}.

With the rapid technological advancement, security has become a major issue
due to the increase in malware activity that poses a serious threat to the
security and safety of both computer systems and stakeholders. To maintain
stakeholders, particularly, end users security, protecting the data from
fraudulent efforts is one of the most pressing concerns. A set of malicious
programming code, scripts, active content, or intrusive software that is
designed to destroy intended computer systems and programs or mobile and web
applications is referred to as malware. According to a study, naive users are
unable to distinguish between malicious and benign applications. Thus, computer
systems and mobile applications should be designed to detect malicious
activities towards protecting the stakeholders. A number of algorithms are
available to detect malware activities by utilizing novel concepts including
Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we
emphasize Artificial Intelligence (AI) based techniques for detecting and
preventing malware activity. We present a detailed review of current malware
detection technologies, their shortcomings, and ways to improve efficiency. Our
study shows that adopting futuristic approaches for the development of malware
detection applications shall provide significant advantages. The comprehension
of this synthesis shall help researchers for further research on malware
detection and prevention using AI.

The Sketched Wasserstein Distance ($W^S$) is a new probability distance
specifically tailored to finite mixture distributions. Given any metric $d$
defined on a set $\mathcal{A}$ of probability distributions, $W^S$ is defined
to be the most discriminative convex extension of this metric to the space
$\mathcal{S} = \textrm{conv}(\mathcal{A})$ of mixtures of elements of
$\mathcal{A}$. Our representation theorem shows that the space $(\mathcal{S},
W^S)$ constructed in this way is isomorphic to a Wasserstein space over
$\mathcal{X} = (\mathcal{A}, d)$. This result establishes a universality
property for the Wasserstein distances, revealing them to be uniquely
characterized by their discriminative power for finite mixtures. We exploit
this representation theorem to propose an estimation methodology based on
Kantorovich--Rubenstein duality, and prove a general theorem that shows that
its estimation error can be bounded by the sum of the errors of estimating the
mixture weights and the mixture components, for any estimators of these
quantities. We derive sharp statistical properties for the estimated $W^S$ in
the case of $p$-dimensional discrete $K$-mixtures, which we show can be
estimated at a rate proportional to $\sqrt{K/N}$, up to logarithmic factors. We
complement these bounds with a minimax lower bound on the risk of estimating
the Wasserstein distance between distributions on a $K$-point metric space,
which matches our upper bound up to logarithmic factors. This result is the
first nearly tight minimax lower bound for estimating the Wasserstein distance
between discrete distributions. Furthermore, we construct $\sqrt{N}$
asymptotically normal estimators of the mixture weights, and derive a
$\sqrt{N}$ distributional limit of our estimator of $W^S$ as a consequence.
Simulation studies and a data analysis provide strong support on the
applicability of the new Sketched Wasserstein Distance.

Self-play is a common paradigm for constructing solutions in Markov games
that can yield optimal policies in collaborative settings. However, these
policies often adopt highly-specialized conventions that make playing with a
novel partner difficult. To address this, recent approaches rely on encoding
symmetry and convention-awareness into policy training, but these require
strong environmental assumptions and can complicate policy training. We
therefore propose moving the learning of conventions to the belief space.
Specifically, we propose a belief learning model that can maintain beliefs over
rollouts of policies not seen at training time, and can thus decode and adapt
to novel conventions at test time. We show how to leverage this model for both
search and training of a best response over various pools of policies to
greatly improve ad-hoc teamplay. We also show how our setup promotes
explainability and interpretability of nuanced agent conventions.

Accented speech recognition and accent classification are relatively
under-explored research areas in speech technology. Recently, deep
learning-based methods and Transformer-based pretrained models have achieved
superb performances in both areas. However, most accent classification tasks
focused on classifying different kinds of English accents and little attention
was paid to geographically-proximate accent classification, especially under a
low-resource setting where forensic speech science tasks usually encounter. In
this paper, we explored three main accent modelling methods combined with two
different classifiers based on 105 speaker recordings retrieved from five urban
varieties in Northern England. Although speech representations generated from
pretrained models generally have better performances in downstream
classification, traditional methods like Mel Frequency Cepstral Coefficients
(MFCCs) and formant measurements are equipped with specific strengths. These
results suggest that in forensic phonetics scenario where data are relatively
scarce, a simple modelling method and classifier could be competitive with
state-of-the-art pretrained speech models as feature extractors, which could
enhance a sooner estimation for the accent information in practices. Besides,
our findings also cross-validated a new methodology in quantifying
sociophonetic changes.

As critically ill patients frequently develop anemia or coagulopathy,
transfusion of blood products is a frequent intervention in the Intensive Care
Units (ICU). However, inappropriate transfusion decisions made by physicians
are often associated with increased risk of complications and higher hospital
costs. In this work, we aim to develop a decision support tool that uses
available patient information for transfusion decision-making on three common
blood products (red blood cells, platelets, and fresh frozen plasma). To this
end, we adopt an off-policy batch reinforcement learning (RL) algorithm,
namely, discretized Batch Constrained Q-learning, to determine the best action
(transfusion or not) given observed patient trajectories. Simultaneously, we
consider different state representation approaches and reward design mechanisms
to evaluate their impacts on policy learning. Experiments are conducted on two
real-world critical care datasets: the MIMIC-III and the UCSF. Results
demonstrate that policy recommendations on transfusion achieved comparable
matching against true hospital policies via accuracy and weighted importance
sampling evaluations on the MIMIC-III dataset. Furthermore, a combination of
transfer learning (TL) and RL on the data-scarce UCSF dataset can provide up to
$17.02% improvement in terms of accuracy, and up to 18.94% and 21.63%
improvement in jump-start and asymptotic performance in terms of weighted
importance sampling averaged over three transfusion tasks. Finally, simulations
on transfusion decisions suggest that the transferred RL policy could reduce
patients' estimated 28-day mortality rate by 2.74% and decreased acuity rate by
1.18% on the UCSF dataset.

Spatiotemporal data mining aims to discover interesting, useful but
non-trivial patterns in big spatial and spatiotemporal data. They are used in
various application domains such as public safety, ecology, epidemiology, earth
science, etc. This problem is challenging because of the high societal cost of
spurious patterns and exorbitant computational cost. Recent surveys of
spatiotemporal data mining need update due to rapid growth. In addition, they
did not adequately survey parallel techniques for spatiotemporal data mining.
This paper provides a more up-to-date survey of spatiotemporal data mining
methods. Furthermore, it has a detailed survey of parallel formulations of
spatiotemporal data mining.

This paper studies distributed diffusion adaptation over clustered multi-task
networks in the presence of impulsive interferences and Byzantine attacks. We
develop a robust resilient diffusion least mean Geman-McClure-estimation
(RDLMG) algorithm based on the cost function used by the Geman-McClure
estimator, which can reduce the sensitivity to large outliers and make the
algorithm robust under impulsive interferences. Moreover, the mean sub-sequence
reduced method, in which each node discards the extreme value information of
cost contributions received from its neighbors, can make the network resilient
against Byzantine attacks. In this regard, the proposed RDLMG algorithm ensures
that all normal nodes converge to their ideal states with cooperation among
nodes. A statistical analysis of the RDLMG algorithm is also carried out in
terms of mean and mean-square performances. Numerical results evaluate the
proposed RDLMG algorithm in applications to multi-target localization and
multi-task spectrum sensing.

Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in
the worst scenario, they may lead to adverse drug reactions (ADRs). Predicting
all DDIs is a challenging and critical problem. Most existing computational
models integrate drug-centric information from different sources and leverage
them as features in machine learning classifiers to predict DDIs. However,
these models have a high chance of failure, especially for the new drugs when
all the information is not available. This paper proposes a novel Hypergraph
Neural Network (HyGNN) model based on only the SMILES string of drugs,
available for any drug, for the DDI prediction problem. To capture the drug
similarities, we create a hypergraph from drugs' chemical substructures
extracted from the SMILES strings. Then, we develop HyGNN consisting of a novel
attention-based hypergraph edge encoder to get the representation of drugs as
hyperedges and a decoder to predict the interactions between drug pairs.
Furthermore, we conduct extensive experiments to evaluate our model and compare
it with several state-of-the-art methods. Experimental results demonstrate that
our proposed HyGNN model effectively predicts DDIs and impressively outperforms
the baselines with a maximum ROC-AUC and PR-AUC of 97.9% and 98.1%,

Researchers typically resort to numerical methods to understand and predict
ocean dynamics, a key task in mastering environmental phenomena. Such methods
may not be suitable in scenarios where the topographic map is complex,
knowledge about the underlying processes is incomplete, or the application is
time critical. On the other hand, if ocean dynamics are observed, they can be
exploited by recent machine learning methods. In this paper we describe a
data-driven method to predict environmental variables such as current velocity
and sea surface height in the region of Santos-Sao Vicente-Bertioga Estuarine
System in the southeastern coast of Brazil. Our model exploits both temporal
and spatial inductive biases by joining state-of-the-art sequence models (LSTM
and Transformers) and relational models (Graph Neural Networks) in an
end-to-end framework that learns both the temporal features and the spatial
relationship shared among observation sites. We compare our results with the
Santos Operational Forecasting System (SOFS). Experiments show that better
results are attained by our model, while maintaining flexibility and little
domain knowledge dependency.

Recovering temporal image sequences (videos) based on indirect, noisy, or
incomplete data is an essential yet challenging task. We specifically consider
the case where each data set is missing vital information, which prevents the
accurate recovery of the individual images. Although some recent (variational)
methods have demonstrated high-resolution image recovery based on jointly
recovering sequential images, there remain robustness issues due to parameter
tuning and restrictions on the type of the sequential images. Here, we present
a method based on hierarchical Bayesian learning for the joint recovery of
sequential images that incorporates prior intra- and inter-image information.
Our method restores the missing information in each image by "borrowing" it
from the other images. As a result, \emph{all} of the individual
reconstructions yield improved accuracy. Our method can be used for various
data acquisitions and allows for uncertainty quantification. Some preliminary
results indicate its potential use for sequential deblurring and magnetic
resonance imaging.

Falls are one of the leading cause of injury-related deaths among the elderly
worldwide. Effective detection of falls can reduce the risk of complications
and injuries. Fall detection can be performed using wearable devices or ambient
sensors; these methods may struggle with user compliance issues or false
alarms. Video cameras provide a passive alternative; however, regular RGB
cameras are impacted by changing lighting conditions and privacy concerns. From
a machine learning perspective, developing an effective fall detection system
is challenging because of the rarity and variability of falls. Many existing
fall detection datasets lack important real-world considerations, such as
varied lighting, continuous activities of daily living (ADLs), and camera
placement. The lack of these considerations makes it difficult to develop
predictive models that can operate effectively in the real world. To address
these limitations, we introduce a novel multi-modality dataset (MUVIM) that
contains four visual modalities: infra-red, depth, RGB and thermal cameras.
These modalities offer benefits such as obfuscated facial features and improved
performance in low-light conditions. We formulated fall detection as an anomaly
detection problem, in which a customized spatio-temporal convolutional
autoencoder was trained only on ADLs so that a fall would increase the
reconstruction error. Our results showed that infra-red cameras provided the
highest level of performance (AUC ROC=0.94), followed by thermal (AUC
ROC=0.87), depth (AUC ROC=0.86) and RGB (AUC ROC=0.83). This research provides
a unique opportunity to analyze the utility of camera modalities in detecting
falls in a home setting while balancing performance, passiveness, and privacy.

Overparameterized models fail to generalize well in the presence of data
imbalance even when combined with traditional techniques for mitigating
imbalances. This paper focuses on imbalanced classification datasets, in which
a small subset of the population -- a minority -- may contain features that
correlate spuriously with the class label. For a parametric family of
cross-entropy loss modifications and a representative Gaussian mixture model,
we derive non-asymptotic generalization bounds on the worst-group error that
shed light on the role of different hyper-parameters. Specifically, we prove
that, when appropriately tuned, the recently proposed VS-loss learns a model
that is fair towards minorities even when spurious features are strong. On the
other hand, alternative heuristics, such as the weighted CE and the LA-loss,
can fail dramatically. Compared to previous works, our bounds hold for more
general models, they are non-asymptotic, and, they apply even at scenarios of
extreme imbalance.

Modern object detection architectures are moving towards employing
self-supervised learning (SSL) to improve performance detection with related
pretext tasks. Pretext tasks for monocular 3D object detection have not yet
been explored yet in literature. The paper studies the application of
established self-supervised bounding box recycling by labeling random windows
as the pretext task. The classifier head of the 3D detector is trained to
classify random windows containing different proportions of the ground truth
objects, thus handling the foreground-background imbalance. We evaluate the
pretext task using the RTM3D detection model as baseline, with and without the
application of data augmentation. We demonstrate improvements of between 2-3 %
in mAP 3D and 0.9-1.5 % BEV scores using SSL over the baseline scores. We
propose the inverse class frequency re-weighted (ICFW) mAP score that
highlights improvements in detection for low frequency classes in a class
imbalanced dataset with long tails. We demonstrate improvements in ICFW both
mAP 3D and BEV scores to take into account the class imbalance in the KITTI
validation dataset. We see 4-5 % increase in ICFW metric with the pretext task.

We study cascading failures in smart grids, where an attacker selectively
compromises the nodes with probabilities proportional to their degrees,
betweenness, or clustering coefficient. This implies that nodes with high
degrees, betweenness, or clustering coefficients are attacked with higher
probability. We mathematically and experimentally analyze the sizes of the
giant components of the networks under different types of targeted attacks, and
compare the results with the corresponding sizes under random attacks. We show
that networks disintegrate faster for targeted attacks compared to random
attacks. A targeted attack on a small fraction of high degree nodes
disintegrates one or both of the networks, whereas both the networks contain
giant components for random attack on the same fraction of nodes. An important
observation is that an attacker has an advantage if it compromises nodes based
on their betweenness, rather than based on degree or clustering coefficient.
  We next study adaptive attacks, where an attacker compromises nodes in
rounds. Here, some nodes are compromised in each round based on their degree,
betweenness or clustering coefficients, instead of compromising all nodes
together. In this case, the degree, betweenness, or clustering coefficient is
calculated before the start of each round, instead of at the beginning. We show
experimentally that an adversary has an advantage in this adaptive approach,
compared to compromising the same number of nodes all at once.

Given that Artificial Intelligence (AI) increasingly permeates our lives, it
is critical that we systematically align AI objectives with the goals and
values of humans. The human-AI alignment problem stems from the impracticality
of explicitly specifying the rewards that AI models should receive for all the
actions they could take in all relevant states of the world. One possible
solution, then, is to leverage the capabilities of AI models to learn those
rewards implicitly from a rich source of data describing human values in a wide
range of contexts. The democratic policy-making process produces just such data
by developing specific rules, flexible standards, interpretable guidelines, and
generalizable precedents that synthesize citizens' preferences over potential
actions taken in many states of the world. Therefore, computationally encoding
public policies to make them legible to AI systems should be an important part
of a socio-technical approach to the broader human-AI alignment puzzle. This
Essay outlines research on AI that learn structures in policy data that can be
leveraged for downstream tasks. As a demonstration of the ability of AI to
comprehend policy, we provide a case study of an AI system that predicts the
relevance of proposed legislation to any given publicly traded company and its
likely effect on that company. We believe this represents the "comprehension"
phase of AI and policy, but leveraging policy as a key source of human values
to align AI requires "understanding" policy. Solving the alignment problem is
crucial to ensuring that AI is beneficial both individually (to the person or
group deploying the AI) and socially. As AI systems are given increasing
responsibility in high-stakes contexts, integrating democratically-determined
policy into those systems could align their behavior with human goals in a way
that is responsive to a constantly evolving society.

Virtually every sizable organization nowadays is building a form of a data
lake. In theory, every department or team in the organization would enrich
their datasets with metadata, and store them in a central data lake. Those
datasets can then be combined in different ways and produce added value to the
organization. In practice, though, the situation is vastly different: each
department has its own privacy policies, data release procedures, and goals. As
a result, each department maintains its own data lake, leading to data silos.
For such data silos to be of any use, they need to be integrated. This paper
presents SiMa, a method for federating data silos that consistently finds more
correct relationships than the state-of-the-art matching methods, while
minimizing wrong predictions and requiring 20x to 1000x less time to execute.
SiMa leverages Graph Neural Networks (GNNs) to learn from the existing column
relationships and automated data profiles found in data silos. Our method makes
use of the trained GNN to perform link prediction and find new column
relationships across data silos. Most importantly, SiMa can be trained
incrementally on the column relationships within each silo individually, and
does not require consolidating all datasets into one place.

Robots need to manipulate objects in constrained environments like shelves
and cabinets when assisting humans in everyday settings like homes and offices.
These constraints make manipulation difficult by reducing grasp accessibility,
so robots need to use non-prehensile strategies that leverage
object-environment contacts to perform manipulation tasks. To tackle the
challenge of planning and controlling contact-rich behaviors in such settings,
this work uses Hybrid Force-Velocity Controllers (HFVCs) as the skill
representation and plans skill sequences with learned preconditions. While
HFVCs naturally enable robust and compliant contact-rich behaviors, solvers
that synthesize them have traditionally relied on precise object models and
closed-loop feedback on object pose, which are difficult to obtain in
constrained environments due to occlusions. We first relax HFVCs' need for
precise models and feedback with our HFVC synthesis framework, then learn a
point-cloud-based precondition function to classify where HFVC executions will
still be successful despite modeling inaccuracies. Finally, we use the learned
precondition in a search-based task planner to complete contact-rich
manipulation tasks in a shelf domain. Our method achieves a task success rate
of $73.2\%$, outperforming the $51.5\%$ achieved by a baseline without the
learned precondition. While the precondition function is trained in simulation,
it can also transfer to a real-world setup without further fine-tuning. See
supplementary materials and videos at
https://sites.google.com/view/constrained-manipulation/

Most differential privacy mechanisms are applied (i.e., composed) numerous
times on sensitive data. We study the design of optimal differential privacy
mechanisms in the limit of a large number of compositions. As a consequence of
the law of large numbers, in this regime the best privacy mechanism is the one
that minimizes the Kullback-Leibler divergence between the conditional output
distributions of the mechanism given two different inputs. We formulate an
optimization problem to minimize this divergence subject to a cost constraint
on the noise. We first prove that additive mechanisms are optimal. Since the
optimization problem is infinite dimensional, it cannot be solved directly;
nevertheless, we quantize the problem to derive near-optimal additive
mechanisms that we call "cactus mechanisms" due to their shape. We show that
our quantization approach can be arbitrarily close to an optimal mechanism.
Surprisingly, for quadratic cost, the Gaussian mechanism is strictly
sub-optimal compared to this cactus mechanism. Finally, we provide numerical
results which indicate that cactus mechanism outperforms the Gaussian mechanism
for a finite number of compositions.

Many real-world applications can be formulated as multi-agent cooperation
problems, such as network packet routing and coordination of autonomous
vehicles. The emergence of deep reinforcement learning (DRL) provides a
promising approach for multi-agent cooperation through the interaction of the
agents and environments. However, traditional DRL solutions suffer from the
high dimensions of multiple agents with continuous action space during policy
search. Besides, the dynamicity of agents' policies makes the training
non-stationary. To tackle the issues, we propose a hierarchical reinforcement
learning approach with high-level decision-making and low-level individual
control for efficient policy search. In particular, the cooperation of multiple
agents can be learned in high-level discrete action space efficiently. At the
same time, the low-level individual control can be reduced to single-agent
reinforcement learning. In addition to hierarchical reinforcement learning, we
propose an opponent modeling network to model other agents' policies during the
learning process. In contrast to end-to-end DRL approaches, our approach
reduces the learning complexity by decomposing the overall task into sub-tasks
in a hierarchical way. To evaluate the efficiency of our approach, we conduct a
real-world case study in the cooperative lane change scenario. Both simulation
and real-world experiments show the superiority of our approach in the
collision rate and convergence speed.

Beyond achieving high performance across many vision tasks, multimodal models
are expected to be robust to single-source faults due to the availability of
redundant information between modalities. In this paper, we investigate the
robustness of multimodal neural networks against worst-case (i.e., adversarial)
perturbations on a single modality. We first show that standard multimodal
fusion models are vulnerable to single-source adversaries: an attack on any
single modality can overcome the correct information from multiple unperturbed
modalities and cause the model to fail. This surprising vulnerability holds
across diverse multimodal tasks and necessitates a solution. Motivated by this
finding, we propose an adversarially robust fusion strategy that trains the
model to compare information coming from all the input sources, detect
inconsistencies in the perturbed modality compared to the other modalities, and
only allow information from the unperturbed modalities to pass through. Our
approach significantly improves on state-of-the-art methods in single-source
robustness, achieving gains of 7.8-25.2% on action recognition, 19.7-48.2% on
object detection, and 1.6-6.7% on sentiment analysis, without degrading
performance on unperturbed (i.e., clean) data.

Transformers have been widely applied in text classification. Unfortunately,
real-world data contain anomalies and noisy labels that cause challenges for
state-of-art Transformers. This paper proposes Protoformer, a novel
self-learning framework for Transformers that can leverage problematic samples
for text classification. Protoformer features a selection mechanism for
embedding samples that allows us to efficiently extract and utilize anomalies
prototypes and difficult class prototypes. We demonstrated such capabilities on
datasets with diverse textual structures (e.g., Twitter, IMDB, ArXiv). We also
applied the framework to several models. The results indicate that Protoformer
can improve current Transformers in various empirical settings.

We propose a random adaptation variant of time-varying distributed averaging
dynamics in discrete time. We show that this leads to novel interpretations of
fundamental concepts in distributed averaging, opinion dynamics, and
distributed learning. Namely, we show that the ergodicity of a stochastic chain
is equivalent to the almost sure (a.s.) finite-time agreement attainment in the
proposed random adaptation dynamics. Using this result, we provide a new
interpretation for the absolute probability sequence of an ergodic chain. We
then modify the base-case dynamics into a time-reversed inhomogeneous Markov
chain, and we show that in this case ergodicity is equivalent to the uniqueness
of the limiting distributions of the Markov chain. Finally, we introduce and
study a time-varying random adaptation version of the Friedkin-Johnsen model
and a rank-one perturbation of the base-case dynamics.

The rising growth of deep neural networks (DNNs) and datasets in size
motivates the need for efficient solutions for simultaneous model selection and
training. Many methods for hyperparameter optimization (HPO) of iterative
learners including DNNs attempt to solve this problem by querying and learning
a response surface while searching for the optimum of that surface. However,
many of these methods make myopic queries, do not consider prior knowledge
about the response structure, and/or perform biased cost-aware search, all of
which exacerbate identifying the best-performing model when a total cost budget
is specified. This paper proposes a novel approach referred to as Budget-Aware
Planning for Iterative Learners (BAPI) to solve HPO problems under a
constrained cost budget. BAPI is an efficient non-myopic Bayesian optimization
solution that accounts for the budget and leverages the prior knowledge about
the objective function and cost function to select better configurations and to
take more informed decisions during the evaluation (training). Experiments on
diverse HPO benchmarks for iterative learners show that BAPI performs better
than state-of-the-art baselines in most of the cases.

We recently highlighted a fundamental problem recognized to confound
algorithmic optimization, namely, \textit{conflating} the objective with the
objective function. Even when the former is well defined, the latter may not be
obvious, e.g., in learning a strategy to navigate a maze to find a goal
(objective), an effective objective function to \textit{evaluate} strategies
may not be a simple function of the distance to the objective. We proposed to
automate the means by which a good objective function may be discovered -- a
proposal reified herein. We present \textbf{S}olution \textbf{A}nd
\textbf{F}itness \textbf{E}volution (\textbf{SAFE}), a \textit{commensalistic}
coevolutionary algorithm that maintains two coevolving populations: a
population of candidate solutions and a population of candidate objective
functions. As proof of principle of this concept, we show that SAFE
successfully evolves not only solutions within a robotic maze domain, but also
the objective functions needed to measure solution quality during evolution.

We present three evolutionary symbolic regression-based classification
algorithms for binary and multinomial datasets: GPLearnClf, CartesianClf, and
ClaSyCo. Tested over 162 datasets and compared to three state-of-the-art
machine learning algorithms -- XGBoost, LightGBM, and a deep neural network --
we find our algorithms to be competitive. Further, we demonstrate how to find
the best method for one's dataset automatically, through the use of a
state-of-the-art hyperparameter optimizer.

Data collected by IoT devices are often private and have a large diversity
across users. Therefore, learning requires pre-training a model with available
representative data samples, deploying the pre-trained model on IoT devices,
and adapting the deployed model on the device with local data. Such an
on-device adaption for deep learning empowered applications demands data and
memory efficiency. However, existing gradient-based meta learning schemes fail
to support memory-efficient adaptation. To this end, we propose p-Meta, a new
meta learning method that enforces structure-wise partial parameter updates
while ensuring fast generalization to unseen tasks. Evaluations on few-shot
image classification and reinforcement learning tasks show that p-Meta not only
improves the accuracy but also substantially reduces the peak dynamic memory by
a factor of 2.5 on average compared to state-of-the-art few-shot adaptation
methods.

Creating a large-scale dataset of abnormality annotation on medical images is
a labor-intensive and costly task. Leveraging weak supervision from readily
available data such as radiology reports can compensate lack of large-scale
data for anomaly detection methods. However, most of the current methods only
use image-level pathological observations, failing to utilize the relevant
anatomy mentions in reports. Furthermore, Natural Language Processing
(NLP)-mined weak labels are noisy due to label sparsity and linguistic
ambiguity. We propose an Anatomy-Guided chest X-ray Network (AGXNet) to address
these issues of weak annotation. Our framework consists of a cascade of two
networks, one responsible for identifying anatomical abnormalities and the
second responsible for pathological observations. The critical component in our
framework is an anatomy-guided attention module that aids the downstream
observation network in focusing on the relevant anatomical regions generated by
the anatomy network. We use Positive Unlabeled (PU) learning to account for the
fact that lack of mention does not necessarily mean a negative label. Our
quantitative and qualitative results on the MIMIC-CXR dataset demonstrate the
effectiveness of AGXNet in disease and anatomical abnormality localization.
Experiments on the NIH Chest X-ray dataset show that the learned feature
representations are transferable and can achieve the state-of-the-art
performances in disease classification and competitive disease localization
results. Our code is available at https://github.com/batmanlab/AGXNet

Optimizing recommender systems based on user interaction data is mainly seen
as a problem of dealing with selection bias, where most existing work assumes
that interactions from different users are independent. However, it has been
shown that in reality user feedback is often influenced by earlier interactions
of other users, e.g. via average ratings, number of views or sales per item,
etc. This phenomenon is known as the bandwagon effect. In contrast with
previous literature, we argue that the bandwagon effect should not be seen as a
problem of statistical bias. In fact, we prove that this effect leaves both
individual interactions and their sample mean unbiased. Nevertheless, we show
that it can make estimators inconsistent, introducing a distinct set of
problems for convergence in relevance estimation. Our theoretical analysis
investigates the conditions under which the bandwagon effect poses a
consistency problem and explores several approaches for mitigating these
issues. This work aims to show that the bandwagon effect poses an
underinvestigated open problem that is fundamentally distinct from the
well-studied selection bias in recommendation.

Axie infinity is a complicated card game with a huge-scale action space. This
makes it difficult to solve this challenge using generic Reinforcement Learning
(RL) algorithms. We propose a hybrid RL framework to learn action
representations and game strategies. To avoid evaluating every action in the
large feasible action set, our method evaluates actions in a fixed-size set
which is determined using action representations. We compare the performance of
our method with the other two baseline methods in terms of their sample
efficiency and the winning rates of the trained models. We empirically show
that our method achieves an overall best winning rate and the best sample
efficiency among the three methods.

With the latest advances in deep learning generative models, it has not taken
long to take advantage of their remarkable performance in the area of time
series. Deep neural networks used to work with time series depend heavily on
the breadth and consistency of the datasets used in training. These types of
characteristic are not usually abundant in the real world, where they are
usually limited and often with privacy constraints that must be guaranteed.
Therefore, an effective way is to increase the number of data using \gls{da}
techniques, either by adding noise or permutations and by generating new
synthetic data. It is systematically review the current state-of-the-art in the
area to provide an overview of all available algorithms and proposes a taxonomy
of the most relevant researches. The efficiency of the different variants will
be evaluated; as a vital part of the process, the different metrics to evaluate
the performance and the main problems concerning each model will be analysed.
The ultimate goal of this study is to provide a summary of the evolution and
performance of areas that produce better results to guide future researchers in
this field.

By learning the map between function spaces using carefully designed deep
neural networks, the operator learning become a focused field in recent several
years, and have shown considerable efficiency over traditional numerical
methods on solving complicated problems such as differential equations, but the
method is still disturbed with the concern of its accuracy and reliability. In
this paper, combined with the structures and technologies of a popular
numerical method, i.e. the spectral method, a general learning-based
architecture named Spectral Operator Learning is introduced. One of its
variants, Orthogonal Polynomials Neural Operator designed for partial
differential equations with Dirichlet, Neumann and Robin boundary conditions is
proposed later, of which the effectiveness, efficacy and accuracy of boundary
conditions are illustrated by numerical experiments. The code will be available
at https://github.com/liu-ziyuan-math/spectral_operator_learning after all the
numerical results are summarised.

Stain variations often decrease the generalization ability of deep learning
based approaches in digital histopathology analysis. Two separate proposals,
namely stain normalization (SN) and stain augmentation (SA), have been
spotlighted to reduce the generalization error, where the former alleviates the
stain shift across different medical centers using template image and the
latter enriches the accessible stain styles by the simulation of more stain
variations. However, their applications are bounded by the selection of
template images and the construction of unrealistic styles. To address the
problems, we unify SN and SA with a novel RandStainNA scheme, which constrains
variable stain styles in a practicable range to train a stain agnostic deep
learning model. The RandStainNA is applicable to stain normalization in a
collection of color spaces i.e. HED, HSV, LAB. Additionally, we propose a
random color space selection scheme to gain extra performance improvement. We
evaluate our method by two diagnostic tasks i.e. tissue subtype classification
and nuclei segmentation, with various network backbones. The performance
superiority over both SA and SN yields that the proposed RandStainNA can
consistently improve the generalization ability, that our models can cope with
more incoming clinical datasets with unpredicted stain styles. The codes is
available at https://github.com/yiqings/RandStainNA.

Estimating how a treatment affects units individually, known as heterogeneous
treatment effect (HTE) estimation, is an essential part of decision-making and
policy implementation. The accumulation of large amounts of data in many
domains, such as healthcare and e-commerce, has led to increased interest in
developing data-driven algorithms for estimating heterogeneous effects from
observational and experimental data. However, these methods often make strong
assumptions about the observed features and ignore the underlying causal model
structure, which can lead to biased HTE estimation. At the same time,
accounting for the causal structure of real-world data is rarely trivial since
the causal mechanisms that gave rise to the data are typically unknown. To
address this problem, we develop a feature selection method that considers each
feature's value for HTE estimation and learns the relevant parts of the causal
structure from data. We provide strong empirical evidence that our method
improves existing data-driven HTE estimation methods under arbitrary underlying
causal structures. Our results on synthetic, semi-synthetic, and real-world
datasets show that our feature selection algorithm leads to lower HTE
estimation error.

Despite substantial advances in network architecture performance, the
susceptibility of adversarial attacks makes deep learning challenging to
implement in safety-critical applications. This paper proposes a data-centric
approach to addressing this problem. A nonlocal denoising method with different
luminance values has been used to generate adversarial examples from the
Modified National Institute of Standards and Technology database (MNIST) and
Canadian Institute for Advanced Research (CIFAR-10) data sets. Under
perturbation, the method provided absolute accuracy improvements of up to 9.3%
in the MNIST data set and 13% in the CIFAR-10 data set. Training using
transformed images with higher luminance values increases the robustness of the
classifier. We have shown that transfer learning is disadvantageous for
adversarial machine learning. The results indicate that simple adversarial
examples can improve resilience and make deep learning easier to apply in
various applications.

In situ visualization techniques are hampered by a lack of foresight: crucial
simulation phenomena can be missed due to a poor sampling rate or insufficient
detail at critical timesteps. Keeping a human in the loop is impractical, and
defining statistical triggers can be difficult. This paper demonstrates the
potential for using a machine-learning-based simulation surrogate as an oracle
to identify expected critical regions of a large-scale simulation. These
critical regions are used to drive the in situ analysis, providing greater data
fidelity and analysis resolution with an equivalent I/O budget to a traditional
in situ framework. We develop a distributed asynchronous in situ visualization
by integrating TACC Galaxy with CB-Geo MPM for material point simulation of
granular flows. We employ a PyTorch-based 3D Graph Network Simulator (GNS)
trained on granular flow problems as an oracle to predict the dynamics of
granular flows. Critical regions of interests are manually tagged in GNS for in
situ rendering in MPM.

This paper studies the algorithmic stability and generalizability of
decentralized stochastic gradient descent (D-SGD). We prove that the consensus
model learned by D-SGD is $\mathcal{O}{(n^{-1}+ m^{-1} +\lambda^2)}$-stable in
expectation in the non-convex non-smooth setting, where $n$ is the local sample
size on each worker, $m$ is the worker number, and $1-\lambda$ is the spectral
gap that measures the connectivity of the communication topology. These results
then deliver an $\mathcal{O}{(n^{-(1+\alpha)/2}+ m^{-(1+\alpha)/2}+
\lambda^{1+\alpha} + \phi_\mathcal{S})}$ in-average generalization bound, which
is non-vacuous even when $\lambda$ is closed to $1$, in contrast to vacuous as
suggested by existing literature on the projected version of D-SGD. Our theory
indicates that the generalizability of D-SGD has a positive correlation with
the spectral gap, and can explain why consensus control in initial training
phase can ensure better generalization. Experiments of VGG-11 and ResNet-18 on
CIFAR-10, CIFAR-100 and Tiny-ImageNet justify our theory. To our best
knowledge, this is the first work on the topology-aware generalization of
vanilla D-SGD. Code is available at
\url{https://github.com/Raiden-Zhu/Generalization-of-DSGD}.

The class imbalance problem is important and challenging. Ensemble approaches
are widely used to tackle this problem because of their effectiveness. However,
existing ensemble methods are always applied into original samples, while not
considering the structure information among original samples. The limitation
will prevent the imbalanced learning from being better. Besides, research shows
that the structure information among samples includes local and global
structure information. Based on the analysis above, an imbalanced ensemble
algorithm with the deep sample pre-envelope network (DSEN) and local-global
structure consistency mechanism (LGSCM) is proposed here to solve the
problem.This algorithm can guarantee high-quality deep envelope samples for
considering the local manifold and global structures information, which is
helpful for imbalance learning. First, the deep sample envelope pre-network
(DSEN) is designed to mine structure information among samples.Then, the local
manifold structure metric (LMSM) and global structure distribution metric
(GSDM) are designed to construct LGSCM to enhance distribution consistency of
interlayer samples. Next, the DSEN and LGSCM are put together to form the final
deep sample envelope network (DSEN-LG). After that, base classifiers are
applied on the layers of deep samples respectively.Finally, the predictive
results from base classifiers are fused through bagging ensemble learning
mechanism. To demonstrate the effectiveness of the proposed method, forty-four
public datasets and more than ten representative relevant algorithms are chosen
for verification. The experimental results show that the algorithm is
significantly better than other imbalanced ensemble algorithms.

Given everyday artifacts, such as tables and chairs, humans recognize
high-level regularities within them, such as the symmetries of a table, the
repetition of its legs, while possessing low-level priors of their geometries,
e.g., surfaces are smooth and edges are sharp. This kind of knowledge
constitutes an important part of human perceptual understanding and reasoning.
Representations of and how to reason in such knowledge, and the acquisition
thereof, are still open questions in artificial intelligence (AI) and cognitive
science. Building on the previous proposal of the \emph{3D shape programs}
representation alone with the accompanying neural generator and executor from
\citet{tian2019learning}, we propose an analytical yet differentiable executor
that is more faithful and controllable in interpreting shape programs
(particularly in extrapolation) and more sample efficient (requires no
training). These facilitate the generator's learning when ground truth programs
are not available, and should be especially useful when new shape-program
components are enrolled either by human designers or -- in the context of
library learning -- algorithms themselves. Preliminary experiments on using it
for adaptation illustrate the aforesaid advantages of the proposed module,
encouraging similar methods being explored in building machines that learn to
reason with the kind of knowledge described above, and even learn this
knowledge itself.

The class of deep deterministic off-policy algorithms is effectively applied
to solve challenging continuous control problems. However, current approaches
use random noise as a common exploration method that has several weaknesses,
such as a need for manual adjusting on a given task and the absence of
exploratory calibration during the training process. We address these
challenges by proposing a novel guided exploration method that uses a
differential directional controller to incorporate scalable exploratory action
correction. An ensemble of Monte Carlo Critics that provides exploratory
direction is presented as a controller. The proposed method improves the
traditional exploration scheme by changing exploration dynamically. We then
present a novel algorithm exploiting the proposed directional controller for
both policy and critic modification. The presented algorithm outperforms modern
reinforcement learning algorithms across a variety of problems from DMControl
suite.

In this work we propose an algorithm for trace recovery from stochastically
known logs, a setting that is becoming more common with the increasing number
of sensors and predictive models that generate uncertain data. The suggested
approach calculates the conformance between a process model and a
stochastically known trace and recovers the best alignment within this
stochastic trace as the true trace. The paper offers an analysis of the impact
of various cost models on trace recovery accuracy and makes use of a product
multi-graph to compare alternative trace recovery options. The average accuracy
of our approach, evaluated using two publicly available datasets, is
impressive, with an average recovery accuracy score of 90-97%, significantly
improving a common heuristic that chooses the most likely value for each
uncertain activity. We believe that the effectiveness of the proposed algorithm
in recovering correct traces from stochastically known logs may be a powerful
aid for developing credible decision-making tools in uncertain settings.

We have developed a Generative Recurrent Neural Networks (GRNN) that learns
the probability of the next residue torsions $X_{i+1}=\
[\phi_{i+1},\psi_{i+1},\omega _{i+1}, \chi_{i+1}]$ from the previous residue in
the sequence $X_i$ to generate new IDP conformations. In addition, we couple
the GRNN with a Bayesian model, X-EISD, in a reinforcement learning step that
biases the probability distributions of torsions to take advantage of
experimental data types such as J-couplingss, NOEs and PREs. We show that
updating the generative model parameters according to the reward feedback on
the basis of the agreement between structures and data improves upon existing
approaches that simply reweight static structural pools for disordered
proteins. Instead the GRNN "DynamICE" model learns to physically change the
conformations of the underlying pool to those that better agree with

There are several issues with the existing general machine translation or
natural language generation evaluation metrics, and question-answering (QA)
systems are indifferent in that context. To build robust QA systems, we need
the ability to have equivalently robust evaluation systems to verify whether
model predictions to questions are similar to ground-truth annotations. The
ability to compare similarity based on semantics as opposed to pure string
overlap is important to compare models fairly and to indicate more realistic
acceptance criteria in real-life applications. We build upon the first to our
knowledge paper that uses transformer-based model metrics to assess semantic
answer similarity and achieve higher correlations to human judgement in the
case of no lexical overlap. We propose cross-encoder augmented bi-encoder and
BERTScore models for semantic answer similarity, trained on a new dataset
consisting of name pairs of US-American public figures. As far as we are
concerned, we provide the first dataset of co-referent name string pairs along
with their similarities, which can be used for training.

The implicit stochastic gradient descent (ISGD), a proximal version of SGD,
is gaining interest in the literature due to its stability over (explicit) SGD.
In this paper, we conduct an in-depth analysis of the two modes of ISGD for
smooth convex functions, namely proximal Robbins-Monro (proxRM) and proximal
Poylak-Ruppert (proxPR) procedures, for their use in statistical inference on
model parameters. Specifically, we derive non-asymptotic point estimation error
bounds of both proxRM and proxPR iterates and their limiting distributions, and
propose on-line estimators of their asymptotic covariance matrices that require
only a single run of ISGD. The latter estimators are used to construct valid
confidence intervals for the model parameters. Our analysis is free of the
generalized linear model assumption that has limited the preceding analyses,
and employs feasible procedures. Our on-line covariance matrix estimators
appear to be the first of this kind in the ISGD literature.

Existing learning-based frame interpolation algorithms extract consecutive
frames from high-speed natural videos to train the model. Compared to natural
videos, cartoon videos are usually in a low frame rate. Besides, the motion
between consecutive cartoon frames is typically nonlinear, which breaks the
linear motion assumption of interpolation algorithms. Thus, it is unsuitable
for generating a training set directly from cartoon videos. For better adapting
frame interpolation algorithms from nature video to animation video, we present
AutoFI, a simple and effective method to automatically render training data for
deep animation video interpolation. AutoFI takes a layered architecture to
render synthetic data, which ensures the assumption of linear motion.
Experimental results show that AutoFI performs favorably in training both DAIN
and ANIN. However, most frame interpolation algorithms will still fail in
error-prone areas, such as fast motion or large occlusion. Besides AutoFI, we
also propose a plug-and-play sketch-based post-processing module, named SktFI,
to refine the final results using user-provided sketches manually. With AutoFI
and SktFI, the interpolated animation frames show high perceptual quality.

Backdoor learning is an emerging and vital topic for studying deep neural
networks' vulnerability (DNNs). Many pioneering backdoor attack and defense
methods are being proposed, successively or concurrently, in the status of a
rapid arms race. However, we find that the evaluations of new methods are often
unthorough to verify their claims and accurate performance, mainly due to the
rapid development, diverse settings, and the difficulties of implementation and
reproducibility. Without thorough evaluations and comparisons, it is not easy
to track the current progress and design the future development roadmap of the
literature. To alleviate this dilemma, we build a comprehensive benchmark of
backdoor learning called BackdoorBench. It consists of an extensible
modular-based codebase (currently including implementations of 8
state-of-the-art (SOTA) attacks and 9 SOTA defense algorithms) and a
standardized protocol of complete backdoor learning. We also provide
comprehensive evaluations of every pair of 8 attacks against 9 defenses, with 5
poisoning ratios, based on 5 models and 4 datasets, thus 8,000 pairs of
evaluations in total. We present abundant analysis from different perspectives
about these 8,000 evaluations, studying the effects of different factors in
backdoor learning. All codes and evaluations of BackdoorBench are publicly
available at \url{https://backdoorbench.github.io}.

As the bridge between genetic and physiological aspects, animal behaviour
analysis is one of the most significant topics in biology and ecological
research. However, identifying, tracking and recording animal behaviour are
labour intensive works that require professional knowledge. To mitigate the
spend for annotating data, researchers turn to computer vision techniques for
automatic label algorithms, since most of the data are recorded visually. In
this work, we explore a variety of behaviour detection algorithms, covering
traditional vision methods, statistical methods and deep learning methods. The
objective of this work is to provide a thorough investigation of related work,
furnishing biologists with a scratch of efficient animal behaviour detection
methods. Apart from that, we also discuss the strengths and weaknesses of those
algorithms to provide some insights for those who already delve into this
field.

In recent years, there are various methods of estimating Biological Age (BA)
have been developed. Especially with the development of machine learning (ML),
there are more and more types of BA predictions, and the accuracy has been
greatly improved. The models for the estimation of BA play an important role in
monitoring healthy aging, and could provide new tools to detect health status
in the general population and give warnings to sub-healthy people. We will
mainly review three age prediction methods by using ML. They are based on blood
biomarkers, facial images, and structural neuroimaging features. For now, the
model using blood biomarkers is the simplest, most direct, and most accurate
method. The face image method is affected by various aspects such as race,
environment, etc., the prediction accuracy is not very good, which cannot make
a great contribution to the medical field. In summary, we are here to track the
way forward in the era of big data for us and other potential general
populations and show ways to leverage the vast amounts of data available today.

The learning and aggregation of multi-scale features are essential in
empowering neural networks to capture the fine-grained geometric details in the
point cloud upsampling task. Most existing approaches extract multi-scale
features from a point cloud of a fixed resolution, hence obtain only a limited
level of details. Though an existing approach aggregates a feature hierarchy of
different resolutions from a cascade of upsampling sub-network, the training is
complex with expensive computation. To address these issues, we construct a new
point cloud upsampling pipeline called BIMS-PU that integrates the feature
pyramid architecture with a bi-directional up and downsampling path.
Specifically, we decompose the up/downsampling procedure into several
up/downsampling sub-steps by breaking the target sampling factor into smaller
factors. The multi-scale features are naturally produced in a parallel manner
and aggregated using a fast feature fusion method. Supervision signal is
simultaneously applied to all upsampled point clouds of different scales.
Moreover, we formulate a residual block to ease the training of our model.
Extensive quantitative and qualitative experiments on different datasets show
that our method achieves superior results to state-of-the-art approaches. Last
but not least, we demonstrate that point cloud upsampling can improve robot
perception by ameliorating the 3D data quality.

Non-convex relaxation methods have been widely used in tensor recovery
problems, and compared with convex relaxation methods, can achieve better
recovery results. In this paper, a new non-convex function, Minimax Logarithmic
Concave Penalty (MLCP) function, is proposed, and some of its intrinsic
properties are analyzed, among which it is interesting to find that the
Logarithmic function is an upper bound of the MLCP function. The proposed
function is generalized to tensor cases, yielding tensor MLCP and weighted
tensor $L\gamma$-norm. Consider that its explicit solution cannot be obtained
when applying it directly to the tensor recovery problem. Therefore, the
corresponding equivalence theorems to solve such problem are given, namely,
tensor equivalent MLCP theorem and equivalent weighted tensor $L\gamma$-norm
theorem. In addition, we propose two EMLCP-based models for classic tensor
recovery problems, namely low-rank tensor completion (LRTC) and tensor robust
principal component analysis (TRPCA), and design proximal alternate
linearization minimization (PALM) algorithms to solve them individually.
Furthermore, based on the Kurdyka-{\L}ojasiwicz property, it is proved that the
solution sequence of the proposed algorithm has finite length and converges to
the critical point globally. Finally, Extensive experiments show that proposed
algorithm achieve good results, and it is confirmed that the MLCP function is
indeed better than the Logarithmic function in the minimization problem, which
is consistent with the analysis of theoretical properties.

We formulate a new inference task in the domain of multivariate time series
forecasting (MTSF), called Variable Subset Forecast (VSF), where only a small
subset of the variables is available during inference. Variables are absent
during inference because of long-term data loss (eg. sensor failures) or high
-> low-resource domain shift between train / test. To the best of our
knowledge, robustness of MTSF models in presence of such failures, has not been
studied in the literature. Through extensive evaluation, we first show that the
performance of state of the art methods degrade significantly in the VSF
setting. We propose a non-parametric, wrapper technique that can be applied on
top any existing forecast models. Through systematic experiments across 4
datasets and 5 forecast models, we show that our technique is able to recover
close to 95\% performance of the models even when only 15\% of the original
variables are present.

When investigating epidemic dynamics through differential models, the
parameters needed to understand the phenomenon and to simulate forecast
scenarios require a delicate calibration phase, often made even more
challenging by the scarcity and uncertainty of the observed data reported by
official sources. In this context, Physics-Informed Neural Networks (PINNs), by
embedding the knowledge of the differential model that governs the physical
phenomenon in the learning process, can effectively address the inverse and
forward problem of data-driven learning and solving the corresponding epidemic
problem. In many circumstances, however, the spatial propagation of an
infectious disease is characterized by movements of individuals at different
scales governed by multiscale PDEs. This reflects the heterogeneity of a region
or territory in relation to the dynamics within cities and in neighboring
zones. In presence of multiple scales, a direct application of PINNs generally
leads to poor results due to the multiscale nature of the differential model in
the loss function of the neural network. To allow the neural network to operate
uniformly with respect to the small scales, it is desirable that the neural
network satisfies an Asymptotic-Preservation (AP) property in the learning
process. To this end, we consider a new class of AP Neural Networks (APNNs) for
multiscale hyperbolic transport models of epidemic spread that, thanks to an
appropriate AP formulation of the loss function, is capable to work uniformly
at the different scales of the system. A series of numerical tests for
different epidemic scenarios confirms the validity of the proposed approach,
highlighting the importance of the AP property in the neural network when
dealing with multiscale problems especially in presence of sparse and partially
observed systems.

This paper presents a self-adaptive training (SAT) model for fashion
compatibility prediction. It focuses on the learning of some hard items, such
as those that share similar color, texture, and pattern features but are
considered incompatible due to the aesthetics or temporal shifts. Specifically,
we first design a method to define hard outfits and a difficulty score (DS) is
defined and assigned to each outfit based on the difficulty in recommending an
item for it. Then, we propose a self-adaptive triplet loss (SATL), where the DS
of the outfit is considered. Finally, we propose a very simple conditional
similarity network combining the proposed SATL to achieve the learning of hard
items in the fashion compatibility prediction. Experiments on the publicly
available Polyvore Outfits and Polyvore Outfits-D datasets demonstrate our
SAT's effectiveness in fashion compatibility prediction. Besides, our SATL can
be easily extended to other conditional similarity networks to improve their

Knowledge embeddings (KE) represent a knowledge graph (KG) by embedding
entities and relations into continuous vector spaces. Existing methods are
mainly structure-based or description-based. Structure-based methods learn
representations that preserve the inherent structure of KGs. They cannot well
represent abundant long-tail entities in real-world KGs with limited structural
information. Description-based methods leverage textual information and
language models. Prior approaches in this direction barely outperform
structure-based ones, and suffer from problems like expensive negative sampling
and restrictive description demand. In this paper, we propose LMKE, which
adopts Language Models to derive Knowledge Embeddings, aiming at both enriching
representations of long-tail entities and solving problems of prior
description-based methods. We formulate description-based KE learning with a
contrastive learning framework to improve efficiency in training and
evaluation. Experimental results show that LMKE achieves state-of-the-art
performance on KE benchmarks of link prediction and triple classification,
especially for long-tail entities.

Understanding human intentions during interactions has been a long-lasting
theme, that has applications in human-robot interaction, virtual reality and
surveillance. In this study, we focus on full-body human interactions with
large-sized daily objects and aim to predict the future states of objects and
humans given a sequential observation of human-object interaction. As there is
no such dataset dedicated to full-body human interactions with large-sized
daily objects, we collected a large-scale dataset containing thousands of
interactions for training and evaluation purposes. We also observe that an
object's intrinsic physical properties are useful for the object motion
prediction, and thus design a set of object dynamic descriptors to encode such
intrinsic properties. We treat the object dynamic descriptors as a new modality
and propose a graph neural network, HO-GCN, to fuse motion data and dynamic
descriptors for the prediction task. We show the proposed network that consumes
dynamic descriptors can achieve state-of-the-art prediction results and help
the network better generalize to unseen objects. We also demonstrate the
predicted results are useful for human-robot collaborations.

An ultimate language system aims at the high generalization and robustness
when adapting to diverse scenarios. Unfortunately, the recent white hope
pre-trained language models (PrLMs) barely escape from stacking excessive
parameters to the over-parameterized Transformer architecture to achieve higher
performances. This paper thus proposes \textit{Adversarial Self-Attention}
mechanism (ASA), which adversarially reconstructs the Transformer attentions
and facilitates model training from contaminated model structures, coupled with
a fast and simple implementation for better PrLM building. We conduct
comprehensive evaluation across a wide range of tasks on both pre-training and
fine-tuning stages. For pre-training, ASA unfolds remarkable performance gain
compared to regular training for longer periods. For fine-tuning, ASA-empowered
models consistently outweigh naive models by a large margin considering both
generalization and robustness.

This study examines the weak form of the efficient market hypothesis for
Bitcoin using a feedforward neural network. Due to the increasing popularity of
cryptocurrencies in recent years, the question has arisen, as to whether market
inefficiencies could be exploited in Bitcoin. Several studies we refer to here
discuss this topic in the context of Bitcoin using either statistical tests or
machine learning methods, mostly relying exclusively on data from Bitcoin
itself. Results regarding market efficiency vary from study to study. In this
study, however, the focus is on applying various asset-related input features
in a neural network. The aim is to investigate whether the prediction accuracy
improves when adding equity stock indices (S&P 500, Russell 2000), currencies
(EURUSD), 10 Year US Treasury Note Yield as well as Gold&Silver producers index
(XAU), in addition to using Bitcoin returns as input feature. As expected, the
results show that more features lead to higher training performance from 54.6%
prediction accuracy with one feature to 61% with six features. On the test set,
we observe that with our neural network methodology, adding additional asset
classes, no increase in prediction accuracy is achieved. One feature set is
able to partially outperform a buy-and-hold strategy, but the performance drops
again as soon as another feature is added. This leads us to the partial
conclusion that weak market inefficiencies for Bitcoin cannot be detected using
neural networks and the given asset classes as input. Therefore, based on this
study, we find evidence that the Bitcoin market is efficient in the sense of
the efficient market hypothesis during the sample period. We encourage further
research in this area, as much depends on the sample period chosen, the input
features, the model architecture, and the hyperparameters.

Risk-based active learning is an approach to developing statistical
classifiers for online decision-support. In this approach, data-label querying
is guided according to the expected value of perfect information for incipient
data points. For SHM applications, the value of information is evaluated with
respect to a maintenance decision process, and the data-label querying
corresponds to the inspection of a structure to determine its health state.
Sampling bias is a known issue within active-learning paradigms; this occurs
when an active learning process over- or undersamples specific regions of a
feature-space, thereby resulting in a training set that is not representative
of the underlying distribution. This bias ultimately degrades decision-making
performance, and as a consequence, results in unnecessary costs incurred. The
current paper outlines a risk-based approach to active learning that utilises a
semi-supervised Gaussian mixture model. The semi-supervised approach
counteracts sampling bias by incorporating pseudo-labels for unlabelled data
via an EM algorithm. The approach is demonstrated on a numerical example
representative of the decision processes found in SHM.

Deformable image registration is a crucial step in medical image analysis for
finding a non-linear spatial transformation between a pair of fixed and moving
images. Deep registration methods based on Convolutional Neural Networks (CNNs)
have been widely used as they can perform image registration in a fast and
end-to-end manner. However, these methods usually have limited performance for
image pairs with large deformations. Recently, iterative deep registration
methods have been used to alleviate this limitation, where the transformations
are iteratively learned in a coarse-to-fine manner. However, iterative methods
inevitably prolong the registration runtime, and tend to learn separate image
features for each iteration, which hinders the features from being leveraged to
facilitate the registration at later iterations. In this study, we propose a
Non-Iterative Coarse-to-finE registration Network (NICE-Net) for deformable
image registration. In the NICE-Net, we propose: (i) a Single-pass Deep
Cumulative Learning (SDCL) decoder that can cumulatively learn coarse-to-fine
transformations within a single pass (iteration) of the network, and (ii) a
Selectively-propagated Feature Learning (SFL) encoder that can learn common
image features for the whole coarse-to-fine registration process and
selectively propagate the features as needed. Extensive experiments on six
public datasets of 3D brain Magnetic Resonance Imaging (MRI) show that our
proposed NICE-Net can outperform state-of-the-art iterative deep registration
methods while only requiring similar runtime to non-iterative methods.

Thanks to the efficient retrieval speed and low storage consumption, learning
to hash has been widely used in visual retrieval tasks. However, existing
hashing methods assume that the query and retrieval samples lie in homogeneous
feature space within the same domain. As a result, they cannot be directly
applied to heterogeneous cross-domain retrieval. In this paper, we propose a
Generalized Image Transfer Retrieval (GITR) problem, which encounters two
crucial bottlenecks: 1) the query and retrieval samples may come from different
domains, leading to an inevitable {domain distribution gap}; 2) the features of
the two domains may be heterogeneous or misaligned, bringing up an additional
{feature gap}. To address the GITR problem, we propose an Asymmetric Transfer
Hashing (ATH) framework with its unsupervised/semi-supervised/supervised
realizations. Specifically, ATH characterizes the domain distribution gap by
the discrepancy between two asymmetric hash functions, and minimizes the
feature gap with the help of a novel adaptive bipartite graph constructed on
cross-domain data. By jointly optimizing asymmetric hash functions and the
bipartite graph, not only can knowledge transfer be achieved but information
loss caused by feature alignment can also be avoided. Meanwhile, to alleviate
negative transfer, the intrinsic geometrical structure of single-domain data is
preserved by involving a domain affinity graph. Extensive experiments on both
single-domain and cross-domain benchmarks under different GITR subtasks
indicate the superiority of our ATH method in comparison with the
state-of-the-art hashing methods.

Learning the structure--dynamics correlation in disordered systems is a
long-standing problem. Here, we use unsupervised machine learning employing
graph neural networks (GNN) to investigate the local structures in disordered
systems. We test our approach on 2D binary A65B35 LJ glasses and extract
structures corresponding to liquid, supercooled and glassy states at different
cooling rates. The neighborhood representation of atoms learned by a GNN in an
unsupervised fashion, when clustered, reveal local structures with varying
potential energies. These clusters exhibit dynamical heterogeneity in the
structure in congruence with their local energy landscape. Altogether, the
present study shows that unsupervised graph embedding can reveal the
structure--dynamics correlation in disordered structures.

We propose a new method for approximating active learning acquisition
strategies that are based on retraining with hypothetically-labeled candidate
data points. Although this is usually infeasible with deep networks, we use the
neural tangent kernel to approximate the result of retraining, and prove that
this approximation works asymptotically even in an active learning setup --
approximating "look-ahead" selection criteria with far less computation
required. This also enables us to conduct sequential active learning, i.e.
updating the model in a streaming regime, without needing to retrain the model
with SGD after adding each new data point. Moreover, our querying strategy,
which better understands how the model's predictions will change by adding new
data points in comparison to the standard ("myopic") criteria, beats other
look-ahead strategies by large margins, and achieves equal or better
performance compared to state-of-the-art methods on several benchmark datasets
in pool-based active learning.

Real-time bidding is the new paradigm of programmatic advertising. An
advertiser wants to make the intelligent choice of utilizing a
\textbf{Demand-Side Platform} to improve the performance of their ad campaigns.
Existing approaches are struggling to provide a satisfactory solution for
bidding optimization due to stochastic bidding behavior. In this paper, we
proposed a multi-agent reinforcement learning architecture for RTB with
functional optimization. We designed four agents bidding environment: three
Lagrange-multiplier based functional optimization agents and one baseline agent
(without any attribute of functional optimization) First, numerous attributes
have been assigned to each agent, including biased or unbiased win probability,
Lagrange multiplier, and click-through rate. In order to evaluate the proposed
RTB strategy's performance, we demonstrate the results on ten sequential
simulated auction campaigns. The results show that agents with functional
actions and rewards had the most significant average winning rate and winning
surplus, given biased and unbiased winning information respectively. The
experimental evaluations show that our approach significantly improve the
campaign's efficacy and profitability.

This work presents a multitask approach to the simultaneous estimation of
age, country of origin, and emotion given vocal burst audio for the 2022 ICML
Expressive Vocalizations Challenge ExVo-MultiTask track. The method of choice
utilized a combination of spectro-temporal modulation and self-supervised
features, followed by an encoder-decoder network organized in a multitask
paradigm. We evaluate the complementarity between the tasks posed by examining
independent task-specific and joint models, and explore the relative strengths
of different feature sets. We also introduce a simple score fusion mechanism to
leverage the complementarity of different feature sets for this task.
  We find that robust data preprocessing in conjunction with score fusion over
spectro-temporal receptive field and HuBERT models achieved our best
ExVo-MultiTask test score of 0.412.

We describe our approach for the generative emotional vocal burst task (ExVo
Generate) of the ICML Expressive Vocalizations Competition. We train a
conditional StyleGAN2 architecture on mel-spectrograms of preprocessed versions
of the audio samples. The mel-spectrograms generated by the model are then
inverted back to the audio domain. As a result, our generated samples
substantially improve upon the baseline provided by the competition from a
qualitative and quantitative perspective for all emotions. More precisely, even
for our worst-performing emotion (awe), we obtain an FAD of 1.76 compared to
the baseline of 4.81 (as a reference, the FAD between the train/validation sets
for awe is 0.776).

Large Transformer-based models have exhibited superior performance in various
natural language processing and computer vision tasks. However, these models
contain enormous amounts of parameters, which restrict their deployment to
real-world applications. To reduce the model size, researchers prune these
models based on the weights' importance scores. However, such scores are
usually estimated on mini-batches during training, which incurs large
variability/uncertainty due to mini-batch sampling and complicated training
dynamics. As a result, some crucial weights could be pruned by commonly used
pruning methods because of such uncertainty, which makes training unstable and
hurts generalization. To resolve this issue, we propose PLATON, which captures
the uncertainty of importance scores by upper confidence bound (UCB) of
importance estimation. In particular, for the weights with low importance
scores but high uncertainty, PLATON tends to retain them and explores their
capacity. We conduct extensive experiments with several Transformer-based
models on natural language understanding, question answering and image
classification to validate the effectiveness of PLATON. Results demonstrate
that PLATON manifests notable improvement under different sparsity levels. Our
code is publicly available at https://github.com/QingruZhang/PLATON.

Achieving network resilience in terms of attack tolerance and service
availability is critically important for Internet of Vehicles (IoV) networks
where vehicles require assistance in sensitive and safety-critical applications
like driving. It is particularly challenging in time-varying conditions of IoV
traffic. In this paper, we study an attack-resilient optimal service placement
problem to ensure disruption-free service availability to the users in
edge-enabled IoV network. Our work aims to improve the user experience while
minimizing the delay and simultaneously considering efficient utilization of
limited edge resources. First, an optimal service placement is performed while
considering traffic dynamicity and meeting the service requirements with the
use of a deep reinforcement learning (DRL) framework. Next, an optimal
secondary mapping and service recovery placements are performed to account for
the attacks/failures at the edge. The use of DRL framework helps to adapt to
dynamically varying IoV traffic and service demands. In this work, we develop
three ILP models and use them in the DRL-based framework to provide
attack-resilient service placement and ensure service availability with
efficient network performance. Extensive numerical experiments are performed to
demonstrate the effectiveness of the proposed approach.

Expressive speech synthesis, like audiobook synthesis, is still challenging
for style representation learning and prediction. Deriving from reference audio
or predicting style tags from text requires a huge amount of labeled data,
which is costly to acquire and difficult to define and annotate accurately. In
this paper, we propose a novel framework for learning style representation from
abundant plain text in a self-supervised manner. It leverages an emotion
lexicon and uses contrastive learning and deep clustering. We further integrate
the style representation as a conditioned embedding in a multi-style
Transformer TTS. Comparing with multi-style TTS by predicting style tags
trained on the same dataset but with human annotations, our method achieves
improved results according to subjective evaluations on both in-domain and
out-of-domain test sets in audiobook speech. Moreover, with implicit
context-aware style representation, the emotion transition of synthesized audio
in a long paragraph appears more natural. The audio samples are available on
the demo web.

Remote photoplethysmography (rPPG) is a useful camera-based health motioning
method that can estimate the heart rate (HR) and heart rate variability (HRV)
from facial videos. Many well-established deep learning models can provide
highly accurate and robust results in heart rhythm measurement using rPPG.
However, most previous models usually require enormous computational resources
and a 30-second facial video, which significantly limits their applications in
real-world scenarios. Hence, we propose a lightweight pulse simulation network
named FastBVP-Net to measure heart rhythm via facial videos. In the
FastBVP-Net, we designed a multi-frequency mode signal fusion mechanism to
suppress the noise components and get stable blood volume pulse (BVP) signals
quickly. Moreover, we developed an oversampling training strategy to solve the
unbalanced distribution of HR in the dataset. Finally, we estimate the HR and
HRV based on BVP signals derived by the FastBVP-Net. Comprehensive experiments
are conducted on three benchmark datasets, our approach achieves competitive
performance both on HR and HRV estimation from 30-second facial videos and HR
estimation from 15-second facial videos comparing to the state-of-the-art
methods. We also achieve promising results of computational speed and the
number of parameters, demonstrating proposed network is a lightweight method.

With the growth of large data as well as large-scale learning tasks, the need
for efficient and robust linear system solvers is greater than ever. The
randomized Kaczmarz method (RK) and similar stochastic iterative methods have
received considerable recent attention due to their efficient implementation
and memory footprint. These methods can tolerate streaming data, accessing only
part of the data at a time, and can also approximate the least squares solution
even if the system is affected by noise. However, when data is instead affected
by large (possibly adversarial) corruptions, these methods fail to converge, as
corrupted data points draw iterates far from the true solution. A recently
proposed solution to this is the QuantileRK method, which avoids harmful
corrupted data by exploring the space carefully as the method iterates. The
exploration component requires the computation of quantiles of large samples
from the system and is computationally much heavier than the subsequent
iteration update.
  In this paper, we propose an approach that better uses the information
obtained during exploration by incorporating an averaged version of the block
Kaczmarz method. This significantly speeds up convergence, while still allowing
for a constant fraction of the equations to be arbitrarily corrupted. We
provide theoretical convergence guarantees as well as experimental supporting
evidence. We also demonstrate that the classical projection-based block
Kaczmarz method cannot be robust to sparse adversarial corruptions, but rather
the blocking has to be carried out by averaging one-dimensional projections.

Post-discharge care management coordinates patients' referrals to improve
their health after being discharged from hospitals, especially elderly and
chronically ill patients. In a care management setting, health referrals are
processed by a specialized unit in the managed care organization (MCO), which
interacts with many other entities including inpatient hospitals, insurance
companies, and post-discharge care providers. In this paper, a
machine-learning-guided discrete event simulation framework to improve health
referrals processing is proposed. Random-forest-based prediction models are
developed to predict the LOS and referral type. Two simulation models are
constructed to represent the as-is configuration of the referral processing
system and the intelligent system after incorporating the prediction
functionality, respectively. By incorporating a prediction module for the
referral processing system to plan and prioritize referrals, the overall
performance was enhanced in terms of reducing the average referral creation
delay time. This research will emphasize the role of post-discharge care
management in improving health quality and reducing associated costs. Also, the
paper demonstrates how to use integrated systems engineering methods for
process improvement of complex healthcare systems.

Self-supervised learning (especially contrastive learning) methods on
heterogeneous graphs can effectively get rid of the dependence on supervisory
data. Meanwhile, most existing representation learning methods embed the
heterogeneous graphs into a single geometric space, either Euclidean or
hyperbolic. This kind of single geometric view is usually not enough to observe
the complete picture of heterogeneous graphs due to their rich semantics and
complex structures. Under these observations, this paper proposes a novel
self-supervised learning method, termed as Geometry Contrastive Learning (GCL),
to better represent the heterogeneous graphs when supervisory data is
unavailable. GCL views a heterogeneous graph from Euclidean and hyperbolic
perspective simultaneously, aiming to make a strong merger of the ability of
modeling rich semantics and complex structures, which is expected to bring in
more benefits for downstream tasks. GCL maximizes the mutual information
between two geometric views by contrasting representations at both local-local
and local-global semantic levels. Extensive experiments on four benchmarks data
sets show that the proposed approach outperforms the strong baselines,
including both unsupervised methods and supervised methods, on three tasks,
including node classification, node clustering and similarity search.

Empirical neural tangent kernels (eNTKs) can provide a good understanding of
a given network's representation: they are often far less expensive to compute
and applicable more broadly than infinite width NTKs. For networks with O
output units (e.g. an O-class classifier), however, the eNTK on N inputs is of
size $NO \times NO$, taking $O((NO)^2)$ memory and up to $O((NO)^3)$
computation. Most existing applications have therefore used one of a handful of
approximations yielding $N \times N$ kernel matrices, saving orders of
magnitude of computation, but with limited to no justification. We prove that
one such approximation, which we call "sum of logits", converges to the true
eNTK at initialization for any network with a wide final "readout" layer. Our
experiments demonstrate the quality of this approximation for various uses
across a range of settings.

Deep reinforcement learning (RL) algorithms suffer severe performance
degradation when the interaction data is scarce, which limits their real-world
application. Recently, visual representation learning has been shown to be
effective and promising for boosting sample efficiency in RL. These methods
usually rely on contrastive learning and data augmentation to train a
transition model for state prediction, which is different from how the model is
used in RL--performing value-based planning. Accordingly, the learned
representation by these visual methods may be good for recognition but not
optimal for estimating state value and solving the decision problem. To address
this issue, we propose a novel method, called value-consistent representation
learning (VCR), to learn representations that are directly related to
decision-making. More specifically, VCR trains a model to predict the future
state (also referred to as the ''imagined state'') based on the current one and
a sequence of actions. Instead of aligning this imagined state with a real
state returned by the environment, VCR applies a $Q$-value head on both states
and obtains two distributions of action values. Then a distance is computed and
minimized to force the imagined state to produce a similar action value
prediction as that by the real state. We develop two implementations of the
above idea for the discrete and continuous action spaces respectively. We
conduct experiments on Atari 100K and DeepMind Control Suite benchmarks to
validate their effectiveness for improving sample efficiency. It has been
demonstrated that our methods achieve new state-of-the-art performance for
search-free RL algorithms.

As machine learning (ML) systems become increasingly widespread, it is
necessary to audit these systems for biases prior to their deployment. Recent
research has developed algorithms for effectively identifying intersectional
bias in the form of interpretable, underperforming subsets (or slices) of the
data. However, these solutions and their insights are limited without a tool
for visually understanding and interacting with the results of these
algorithms. We propose Visual Auditor, an interactive visualization tool for
auditing and summarizing model biases. Visual Auditor assists model validation
by providing an interpretable overview of intersectional bias (bias that is
present when examining populations defined by multiple features), details about
relationships between problematic data slices, and a comparison between
underperforming and overperforming data slices in a model. Our open-source tool
runs directly in both computational notebooks and web browsers, making model
auditing accessible and easily integrated into current ML development
workflows. An observational user study in collaboration with domain experts at
Fiddler AI highlights that our tool can help ML practitioners identify and
understand model biases.

Recently, advances in deep learning have been observed in various fields,
including computer vision, natural language processing, and cybersecurity.
Machine learning (ML) has demonstrated its ability as a potential tool for
anomaly detection-based intrusion detection systems to build secure computer
networks. Increasingly, ML approaches are widely adopted than heuristic
approaches for cybersecurity because they learn directly from data. Data is
critical for the development of ML systems, and becomes potential targets for
attackers. Basically, data poisoning or contamination is one of the most common
techniques used to fool ML models through data. This paper evaluates the
robustness of six recent deep learning algorithms for intrusion detection on
contaminated data. Our experiments suggest that the state-of-the-art algorithms
used in this study are sensitive to data contamination and reveal the
importance of self-defense against data perturbation when developing novel
models, especially for intrusion detection systems.

Self-supervised methods have significantly closed the gap with end-to-end
supervised learning for image classification. In the case of human action
videos, however, where both appearance and motion are significant factors of
variation, this gap remains significant. One of the key reasons for this is
that sampling pairs of similar video clips, a required step for many
self-supervised contrastive learning methods, is currently done conservatively
to avoid false positives. A typical assumption is that similar clips only occur
temporally close within a single video, leading to insufficient examples of
motion similarity. To mitigate this, we propose SLIC, a clustering-based
self-supervised contrastive learning method for human action videos. Our key
contribution is that we improve upon the traditional intra-video positive
sampling by using iterative clustering to group similar video instances. This
enables our method to leverage pseudo-labels from the cluster assignments to
sample harder positives and negatives. SLIC outperforms state-of-the-art video
retrieval baselines by +15.4% on top-1 recall on UCF101 and by +5.7% when
directly transferred to HMDB51. With end-to-end finetuning for action
classification, SLIC achieves 83.2% top-1 accuracy (+0.8%) on UCF101 and 54.5%
on HMDB51 (+1.6%). SLIC is also competitive with the state-of-the-art in action
classification after self-supervised pretraining on Kinetics400.

In order to achieve a general visual question answering (VQA) system, it is
essential to learn to answer deeper questions that require compositional
reasoning on the image and external knowledge. Meanwhile, the reasoning process
should be explicit and explainable to understand the working mechanism of the
model. It is effortless for human but challenging for machines. In this paper,
we propose a Hierarchical Graph Neural Module Network (HGNMN) that reasons over
multi-layer graphs with neural modules to address the above issues.
Specifically, we first encode the image by multi-layer graphs from the visual,
semantic and commonsense views since the clues that support the answer may
exist in different modalities. Our model consists of several well-designed
neural modules that perform specific functions over graphs, which can be used
to conduct multi-step reasoning within and between different graphs. Compared
to existing modular networks, we extend visual reasoning from one graph to more
graphs. We can explicitly trace the reasoning process according to module
weights and graph attentions. Experiments show that our model not only achieves
state-of-the-art performance on the CRIC dataset but also obtains explicit and
explainable reasoning procedures.

Decision makers often want to identify the individuals for whom some
intervention or treatment will be most effective in order to decide who to
treat. In such cases, decision makers would ideally like to rank potential
recipients of the treatment according to their individual causal effects.
However, the historical data available to estimate the causal effects could be
confounded, and as a result, accurately estimating the effects could be
impossible. We propose a new and less restrictive assumption about historical
data, called the ranking preservation assumption (RPA), under which the ranking
of the individual effects can be consistently estimated even if the effects
themselves cannot be accurately estimated. Importantly, we find that
confounding can be helpful for the estimation of the causal-effect ranking when
the confounding bias is larger for individuals with larger causal effects, and
that even when this is not the case, any detrimental impact of confounding can
be corrected with larger training data when the RPA is met. We then
analytically show that the RPA can be met in a variety of scenarios, including
common business applications such as online advertising and customer retention.
We support this finding with an empirical example in the context of online
advertising. The example also shows how to evaluate the decision making of a
confounded model in practice. The main takeaway is that what might
traditionally be considered "good" data for causal estimation (i.e.,
unconfounded data) may not be necessary to make good causal decisions, so
treatment assignment methods may work better than we give them credit for in
the presence of confounding.

Hallucination, one kind of pathological translations that bothers Neural
Machine Translation, has recently drawn much attention. In simple terms,
hallucinated translations are fluent sentences but barely related to source
inputs. Arguably, it remains an open problem how hallucination occurs. In this
paper, we propose to use probing methods to investigate the causes of
hallucinations from the perspective of model architecture, aiming to avoid such
problems in future architecture designs. By conducting experiments over various
NMT datasets, we find that hallucination is often accompanied by the deficient
encoder, especially embeddings, and vulnerable cross-attentions, while,
interestingly, cross-attention mitigates some errors caused by the encoder.

In the current stock market, computer science and technology are more and
more widely used to analyse stocks. Not same as most related machine learning
stock price prediction work, this work study the predicting the tendency of the
stock price on the second day right after the disclosure of the companies'
annual reports. We use a variety of different models, including decision tree,
logistic regression, random forest, neural network, prototypical networks. We
use two sets of financial indicators (key and expanded) to conduct experiments,
these financial indicators are obtained from the EastMoney website disclosed by
companies, and finally we find that these models are not well behaved to
predict the tendency. In addition, we also filter stocks with ROE greater than
0.15 and net cash ratio greater than 0.9. We conclude that according to the
financial indicators based on the just-released annual report of the company,
the predictability of the stock price movement on the second day after
disclosure is weak, with maximum accuracy about 59.6% and maximum precision
about 0.56 on our test set by the random forest classifier, and the stock
filtering does not improve the performance. And random forests perform best in
general among all these models which conforms to some work's findings.

This study employs Infinite Impulse Response (IIR) Graph Neural Networks
(GNN) to efficiently model the inherent graph network structure of the smart
grid data to address the cyberattack localization problem. First, we
numerically analyze the empirical frequency response of the Finite Impulse
Response (FIR) and IIR graph filters (GFs) to approximate an ideal spectral
response. We show that, for the same filter order, IIR GFs provide a better
approximation to the desired spectral response and they also present the same
level of approximation to a lower order GF due to their rational type filter
response. Second, we propose an IIR GNN model to efficiently predict the
presence of cyberattacks at the bus level. Finally, we evaluate the model under
various cyberattacks at both sample-wise (SW) and bus-wise (BW) level, and
compare the results with the existing architectures. It is experimentally
verified that the proposed model outperforms the state-of-the-art FIR GNN model
by 9.2% and 14% in terms of SW and BW localization, respectively.

We propose that in order to harness our understanding of neuroscience toward
machine learning, we must first have powerful tools for training brain-like
models of learning. Although substantial progress has been made toward
understanding the dynamics of learning in the brain, neuroscience-derived
models of learning have yet to demonstrate the same performance capabilities as
methods in deep learning such as gradient descent. Inspired by the successes of
machine learning using gradient descent, we demonstrate that models of
neuromodulated synaptic plasticity from neuroscience can be trained in Spiking
Neural Networks (SNNs) with a framework of learning to learn through gradient
descent to address challenging online learning problems. This framework opens a
new path toward developing neuroscience inspired online learning algorithms.

While using two-dimensional convolutional neural networks (2D-CNNs) in image
processing, it is possible to manipulate domain information using channel
statistics, and instance normalization has been a promising way to get
domain-invariant features. However, unlike image processing, we analyze that
domain-relevant information in an audio feature is dominant in frequency
statistics rather than channel statistics. Motivated by our analysis, we
introduce Relaxed Instance Frequency-wise Normalization (RFN): a plug-and-play,
explicit normalization module along the frequency axis which can eliminate
instance-specific domain discrepancy in an audio feature while relaxing
undesirable loss of useful discriminative information. Empirically, simply
adding RFN to networks shows clear margins compared to previous domain
generalization approaches on acoustic scene classification and yields improved
robustness for multiple audio devices. Especially, the proposed RFN won the
DCASE2021 challenge TASK1A, low-complexity acoustic scene classification with
multiple devices, with a clear margin, and RFN is an extended work of our
technical report.

Fetoscopy laser photocoagulation is a widely adopted procedure for treating
Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves
photocoagulation pathological anastomoses to regulate blood exchange among
twins. The procedure is particularly challenging due to the limited field of
view, poor manoeuvrability of the fetoscope, poor visibility, and variability
in illumination. These challenges may lead to increased surgery time and
incomplete ablation. Computer-assisted intervention (CAI) can provide surgeons
with decision support and context awareness by identifying key structures in
the scene and expanding the fetoscopic field of view through video mosaicking.
Research in this domain has been hampered by the lack of high-quality data to
design, develop and test CAI algorithms. Through the Fetoscopic Placental
Vessel Segmentation and Registration (FetReg2021) challenge, which was
organized as part of the MICCAI2021 Endoscopic Vision challenge, we released
the first largescale multicentre TTTS dataset for the development of
generalized and robust semantic segmentation and video mosaicking algorithms.
For this challenge, we released a dataset of 2060 images, pixel-annotated for
vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy
procedures and 18 short video clips. Seven teams participated in this challenge
and their model performance was assessed on an unseen test dataset of 658
pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The
challenge provided an opportunity for creating generalized solutions for
fetoscopic scene understanding and mosaicking. In this paper, we present the
findings of the FetReg2021 challenge alongside reporting a detailed literature
review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the
release of multi-centre fetoscopic data, we provide a benchmark for future
research in this field.

Black box optimization (BBO) can be used to optimize functions whose analytic
form is unknown. A common approach to realize BBO is to learn a surrogate model
which approximates the target black box function which can then be solved via
white box optimization methods. In this paper we present our approach BOX-QUBO,
where the surrogate model is a QUBO matrix. However, unlike in previous
state-of-the-art approaches, this matrix is not trained entirely by regression,
but mostly by classification between 'good' and 'bad' solutions. This better
accounts for the low capacity of the QUBO matrix, resulting in significantly
better solutions overall. We tested our approach against the state-of-the-art
on four domains and in all of them BOX-QUBO showed significantly better
results. A second contribution of this paper is the idea to also solve white
box problems, i.e. problems which could be directly formulated as QUBO, by
means of black box optimization in order to reduce the size of the QUBOs to
their information-theoretic minimum. The experiments show that this
significantly improves the results for MAX-$k$-SAT.

We propose a novel semi-supervised learning approach for classification of
histopathology images. We employ strong supervision with patch-level
annotations combined with a novel co-training loss to create a semi-supervised
learning framework. Co-training relies on multiple conditionally independent
and sufficient views of the data. We separate the hematoxylin and eosin
channels in pathology images using color deconvolution to create two views of
each slide that can partially fulfill these requirements. Two separate CNNs are
used to embed the two views into a joint feature space. We use a contrastive
loss between the views in this feature space to implement co-training. We
evaluate our approach in clear cell renal cell and prostate carcinomas, and
demonstrate improvement over state-of-the-art semi-supervised learning methods.

This technical report presents the modeling approaches used in our submission
to the ICML Expressive Vocalizations Workshop & Competition multitask track
(ExVo-MultiTask). We first applied image classification models of various sizes
on mel-spectrogram representations of the vocal bursts, as is standard in sound
event detection literature. Results from these models show an increase of
21.24% over the baseline system with respect to the harmonic mean of the task
metrics, and comprise our team's main submission to the MultiTask track. We
then sought to characterize the headroom in the MultiTask track by applying a
large pre-trained Conformer model that previously achieved state-of-the-art
results on paralinguistic tasks like speech emotion recognition and mask
detection. We additionally investigated the relationship between the sub-tasks
of emotional expression, country of origin, and age prediction, and discovered
that the best performing models are trained as single-task models, questioning
whether the problem truly benefits from a multitask setting.

We propose RAPid-Learn: Learning to Recover and Plan Again, a hybrid planning
and learning method, to tackle the problem of adapting to sudden and unexpected
changes in an agent's environment (i.e., novelties). RAPid-Learn is designed to
formulate and solve modifications to a task's Markov Decision Process (MDPs)
on-the-fly and is capable of exploiting domain knowledge to learn any new
dynamics caused by the environmental changes. It is capable of exploiting the
domain knowledge to learn action executors which can be further used to resolve
execution impasses, leading to a successful plan execution. This novelty
information is reflected in its updated domain model. We demonstrate its
efficacy by introducing a wide variety of novelties in a gridworld environment
inspired by Minecraft, and compare our algorithm with transfer learning
baselines from the literature. Our method is (1) effective even in the presence
of multiple novelties, (2) more sample efficient than transfer learning RL
baselines, and (3) robust to incomplete model information, as opposed to pure
symbolic planning approaches.

This survey presents a necessarily incomplete (and biased) overview of
results at the intersection of arithmetic circuit complexity, structured
matrices and deep learning. Recently there has been some research activity in
replacing unstructured weight matrices in neural networks by structured ones
(with the aim of reducing the size of the corresponding deep learning models).
Most of this work has been experimental and in this survey, we formalize the
research question and show how a recent work that combines arithmetic circuit
complexity, structured matrices and deep learning essentially answers this
question.
  This survey is targeted at complexity theorists who might enjoy reading about
how tools developed in arithmetic circuit complexity helped design (to the best
of our knowledge) a new family of structured matrices, which in turn seem
well-suited for applications in deep learning. However, we hope that folks
primarily interested in deep learning would also appreciate the connections to
complexity theory.

In this work, we analyzed and compared speech representations extracted from
different frozen self-supervised learning (SSL) speech pre-trained models on
their ability to capture articulatory features (AF) information and their
subsequent prediction of phone recognition performance for within and across
language scenarios. Specifically, we compared CPC, wav2vec 2.0, and HuBert.
First, frame-level AF probing tasks were implemented. Subsequently, phone-level
end-to-end ASR systems for phoneme recognition tasks were implemented, and the
performance on the frame-level AF probing task and the phone accuracy were
correlated. Compared to the conventional speech representation MFCC, all SSL
pre-trained speech representations captured more AF information, and achieved
better phoneme recognition performance within and across languages, with HuBert
performing best. The frame-level AF probing task is a good predictor of phoneme
recognition performance, showing the importance of capturing AF information in
the speech representations. Compared with MFCC, in the within-language
scenario, the performance of these SSL speech pre-trained models on AF probing
tasks achieved a maximum relative increase of 34.4%, and it resulted in the
lowest PER of 10.2%. In the cross-language scenario, the maximum relative
increase of 26.7% also resulted in the lowest PER of 23.0%.

We propose a message passing algorithm, based on variational Bayesian
inference, for low-rank tensor completion with automatic rank determination in
the canonical polyadic format when additional side information (SI) is given.
The SI comes in the form of low-dimensional subspaces the contain the fiber
spans of the tensor (columns, rows, tubes, etc.). We validate the
regularization properties induced by SI with extensive numerical experiments on
synthetic and real-world data and present the results about tensor recovery and
rank determination. The results show that the number of samples required for
successful completion is significantly reduced in the presence of SI. We also
discuss the origin of a bump in the phase transition curves that exists when
the dimensionality of SI is comparable with that of the tensor.

Distributed acoustic sensors (DAS) are effective apparatus which are widely
used in many application areas for recording signals of various events with
very high spatial resolution along the optical fiber. To detect and recognize
the recorded events properly, advanced signal processing algorithms with high
computational demands are crucial. Convolutional neural networks are highly
capable tools for extracting spatial information and very suitable for event
recognition applications in DAS. Long-short term memory (LSTM) is an effective
instrument for processing sequential data. In this study, we proposed a
multi-input multi-output, two stage feature extraction methodology that
combines the capabilities of these neural network architectures with transfer
learning to classify vibrations applied to an optical fiber by a piezo
transducer. First, we extracted the differential amplitude and phase
information from the Phase-OTDR recordings and stored them in a
temporal-spatial data matrix. Then, we used a state-of-the-art pre-trained CNN
without dense layers as a feature extractor in the first stage. In the second
stage, we used LSTMs to further analyze the features extracted by the CNN.
Finally, we used a dense layer to classify the extracted features. To observe
the effect of the utilized CNN architecture, we tested our model with five
state-of-the art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet
and Inception-v3). The results show that using the VGG-16 architecture in our
framework manages to obtain 100% classification accuracy in 50 trainings and
got the best results on our Phase-OTDR dataset. Outcomes of this study indicate
that the pre-trained CNNs combined with LSTM are very suitable for the analysis
of differential amplitude and phase information, represented in a temporal
spatial data matrix which is promising for event recognition operations in DAS

Machine learning methods are getting increasingly better at making
predictions, but at the same time they are also becoming more complicated and
less transparent. As a result, explainers are often relied on to provide
interpretability to these black-box prediction models. As crucial diagnostics
tools, it is important that these explainers themselves are reliable. In this
paper we focus on one particular aspect of reliability, namely that an
explainer should give similar explanations for similar data inputs. We
formalize this notion by introducing and defining explainer astuteness,
analogous to astuteness of classifiers. Our formalism is inspired by the
concept of probabilistic Lipschitzness, which captures the probability of local
smoothness of a function. For a variety of explainers (e.g., SHAP, RISE,
CXPlain), we provide lower bound guarantees on the astuteness of these
explainers given the Lipschitzness of the prediction function. These
theoretical results imply that locally smooth prediction functions lend
themselves to locally robust explanations. We evaluate these results
empirically on simulated as well as real datasets.

Subjective cognitive decline (SCD) is a preclinical stage of Alzheimer's
disease (AD) which occurs even before mild cognitive impairment (MCI).
Progressive SCD will convert to MCI with the potential of further evolving to
AD. Therefore, early identification of progressive SCD with neuroimaging
techniques (e.g., structural MRI) is of great clinical value for early
intervention of AD. However, existing MRI-based machine/deep learning methods
usually suffer the small-sample-size problem which poses a great challenge to
related neuroimaging analysis. The central question we aim to tackle in this
paper is how to leverage related domains (e.g., AD/NC) to assist the
progression prediction of SCD. Meanwhile, we are concerned about which brain
areas are more closely linked to the identification of progressive SCD. To this
end, we propose an attention-guided autoencoder model for efficient
cross-domain adaptation which facilitates the knowledge transfer from AD to
SCD. The proposed model is composed of four key components: 1) a feature
encoding module for learning shared subspace representations of different
domains, 2) an attention module for automatically locating discriminative brain
regions of interest defined in brain atlases, 3) a decoding module for
reconstructing the original input, 4) a classification module for
identification of brain diseases. Through joint training of these four modules,
domain invariant features can be learned. Meanwhile, the brain disease related
regions can be highlighted by the attention mechanism. Extensive experiments on
the publicly available ADNI dataset and a private CLAS dataset have
demonstrated the effectiveness of the proposed method. The proposed model is
straightforward to train and test with only 5-10 seconds on CPUs and is
suitable for medical tasks with small datasets.

We present a deep neural network Real/Bogus classifier that improves
classification performance in the Tomo-e Gozen transient survey by handling
label errors in the training data. In the wide-field, high-frequency transient
survey with Tomo-e Gozen, the performance of conventional convolutional neural
network classifier is not sufficient as about $10^6$ bogus detections appear
every night. In need of a better classifier, we have developed a new two-stage
training method. In this training method, label errors in the training data are
first detected by normal supervised learning classification, and then they are
unlabeled and used for training of semi-supervised learning. For actual
observed data, the classifier with this method achieves an area under the curve
(AUC) of 0.9998 and a false positive rate (FPR) of 0.0002 at true positive rate
(TPR) of 0.9. This training method saves relabeling effort by humans and works
better on training data with a high fraction of label errors. By implementing
the developed classifier in the Tomo-e Gozen pipeline, the number of transient
candidates was reduced to $\sim$40 objects per night, which is $\sim$1/130 of
the previous version, while maintaining the recovery rate of real transients.
This enables more efficient selection of targets for follow-up observations.

This paper proposes a real-time neural network (NN) stochastic filter-based
controller on the Lie Group of the Special Orthogonal Group $SO(3)$ as a novel
approach to the attitude tracking problem. The introduced solution consists of
two parts: a filter and a controller. Firstly, an adaptive NN-based stochastic
filter is proposed that estimates attitude components and dynamics using
measurements supplied by onboard sensors directly. The filter design accounts
for measurement uncertainties inherent to the attitude dynamics, namely unknown
bias and noise corrupting angular velocity measurements. The closed loop
signals of the proposed NN-based stochastic filter have been shown to be
semi-globally uniformly ultimately bounded (SGUUB). Secondly, a novel control
law on $SO(3)$ coupled with the proposed estimator is presented. The control
law addresses unknown disturbances. In addition, the closed loop signals of the
proposed filter-based controller have been shown to be SGUUB. The proposed
approach offers robust tracking performance by supplying the required control
signal given data extracted from low-cost inertial measurement units. While the
filter-based controller is presented in continuous form, the discrete
implementation is also presented. Additionally, the unit-quaternion form of the
proposed approach is given. The effectiveness and robustness of the proposed
filter-based controller is demonstrated using its discrete form and considering
low sampling rate, high initialization error, high-level of measurement
uncertainties, and unknown disturbances. Keywords: Neuro-adaptive, estimator,
filter, observer, control system, trajectory tracking, Lyapunov stability,
stochastic differential equations, nonlinear filter, attitude tracking control,
observer-based controller.

We present Burst2Vec, our multi-task learning approach to predict emotion,
age, and origin (i.e., native country/language) from vocal bursts. Burst2Vec
utilises pre-trained speech representations to capture acoustic information
from raw waveforms and incorporates the concept of model debiasing via
adversarial training. Our models achieve a relative 30 % performance gain over
baselines using pre-extracted features and score the highest amongst all
participants in the ICML ExVo 2022 Multi-Task Challenge.

Large displacement optical flow is an integral part of many computer vision
tasks. Variational optical flow techniques based on a coarse-to-fine scheme
interpolate sparse matches and locally optimize an energy model conditioned on
colour, gradient and smoothness, making them sensitive to noise in the sparse
matches, deformations, and arbitrarily large displacements. This paper
addresses this problem and presents HybridFlow, a variational motion estimation
framework for large displacements and deformations. A multi-scale hybrid
matching approach is performed on the image pairs. Coarse-scale clusters formed
by classifying pixels according to their feature descriptors are matched using
the clusters' context descriptors. We apply a multi-scale graph matching on the
finer-scale superpixels contained within each matched pair of coarse-scale
clusters. Small clusters that cannot be further subdivided are matched using
localized feature matching. Together, these initial matches form the flow,
which is propagated by an edge-preserving interpolation and variational
refinement. Our approach does not require training and is robust to substantial
displacements and rigid and non-rigid transformations due to motion in the
scene, making it ideal for large-scale imagery such as Wide-Area Motion Imagery
(WAMI). More notably, HybridFlow works on directed graphs of arbitrary topology
representing perceptual groups, which improves motion estimation in the
presence of significant deformations. We demonstrate HybridFlow's superior
performance to state-of-the-art variational techniques on two benchmark
datasets and report comparable results with state-of-the-art
deep-learning-based techniques.

In this paper we consider the contextual multi-armed bandit problem for
linear payoffs under a risk-averse criterion. At each round, contexts are
revealed for each arm, and the decision maker chooses one arm to pull and
receives the corresponding reward. In particular, we consider mean-variance as
the risk criterion, and the best arm is the one with the largest mean-variance
reward. We apply the Thompson Sampling algorithm for the disjoint model, and
provide a comprehensive regret analysis for a variant of the proposed
algorithm. For $T$ rounds, $K$ actions, and $d$-dimensional feature vectors, we
prove a regret bound of $O((1+\rho+\frac{1}{\rho}) d\ln T \ln
\frac{K}{\delta}\sqrt{d K T^{1+2\epsilon} \ln \frac{K}{\delta}
\frac{1}{\epsilon}})$ that holds with probability $1-\delta$ under the
mean-variance criterion with risk tolerance $\rho$, for any
$0<\epsilon<\frac{1}{2}$, $0<\delta<1$. The empirical performance of our
proposed algorithms is demonstrated via a portfolio selection problem.

Camera traps are a strategy for monitoring wildlife and they collect a large
number of pictures. The number of images collected of each species usually
follows a long-tail distribution, i.e., a few classes have a large number of
instances, while a lot of species have just a small percentage. Although in
most cases these rare species are the ones of interest to ecologists, they are
often neglected when using deep-learning models because these models require a
large number of images for the training. In this work, a simple and effective
framework called Square-Root Sampling Branch (SSB) is proposed, which combines
two classification branches that are trained using square-root sampling and
instance sampling to improve long-tail visual recognition, and this is compared
to state-of-the-art methods for handling this task: square-root sampling,
class-balanced focal loss, and balanced group softmax. To achieve a more
general conclusion, the methods for handling long-tail visual recognition were
systematically evaluated in four families of computer vision models (ResNet,
MobileNetV3, EfficientNetV2, and Swin Transformer) and four camera-trap
datasets with different characteristics. Initially, a robust baseline with the
most recent training tricks was prepared and, then, the methods for improving
long-tail recognition were applied. Our experiments show that square-root
sampling was the method that most improved the performance for minority classes
by around 15%; however, this was at the cost of reducing the majority classes'
accuracy by at least 3%. Our proposed framework (SSB) demonstrated itself to be
competitive with the other methods and achieved the best or the second-best
results for most of the cases for the tail classes; but, unlike the square-root
sampling, the loss in the performance of the head classes was minimal, thus
achieving the best trade-off among all the evaluated methods.

Most wind turbines are remotely monitored 24/7 to allow for an early
detection of operation problems and developing damage. We present a new fault
detection method for vibration-monitored drivetrains that does not require any
feature engineering. Our method relies on a simple model architecture to enable
a straightforward implementation in practice. We propose to apply convolutional
autoencoders for identifying and extracting the most relevant features from the
half spectrum in an automated manner, saving time and effort. Thereby, a
spectral model of the normal vibration response is learnt for the monitored
component from past measurements. We demonstrate that the model can
successfully distinguish damaged from healthy components and detect a damaged
generator bearing and damaged gearbox parts from their vibration responses.
Using measurements from commercial wind turbines and a test rig, we show that
vibration-based fault detection in wind turbine drivetrains can be performed
without the usual upfront definition of spectral features. Another advantage of
the presented method is that the entire half spectrum is monitored instead of
the usual focus on monitoring individual frequencies and harmonics.

The control of nonlinear systems with unknown dynamics has been a significant
field of research for many years. This paper presents a novel data-driven
optimal adaptive control structure with less control effort and faster
adaptation than standard adaptive control counterparts. The proposed control
structure utilizes the system's recorded data to increase the speed of
adaptation and performance dramatically. In this study, we employ a conditional
generative adversarial net (CGAN) as a novel central pattern generator to
reproduce the steady-state harmonic pattern of the control signals matching the
system's uncertainties over a wide range. We can also use the CGAN architecture
as a fault detector. The CGAN provides a low-dimensional latent space of
uncertainties. It enables rapid and convenient adaptation when there are many
parametric uncertainties, especially for large-scale systems. Then, we
introduce a novel meta-reinforcement learning framework to adapt the latent
space of CGAN to the system's uncertainties as an optimal direct adaptive
controller without any system identifier. Another part of the control structure
is a regulator that achieves semi-global asymptotic tracking using the Lyapunov
stability analysis. Finally, via some simulations, we evaluate the capabilities
of the proposed designs on two dynamical systems, a robot manipulator and a
large-scale musculoskeletal structure, in the presence of disturbance and
perturbation.

Designing reinforcement learning (RL) agents is typically a difficult process
that requires numerous design iterations. Learning can fail for a multitude of
reasons, and standard RL methods provide too few tools to provide insight into
the exact cause. In this paper, we show how to integrate value decomposition
into a broad class of actor-critic algorithms and use it to assist in the
iterative agent-design process. Value decomposition separates a reward function
into distinct components and learns value estimates for each. These value
estimates provide insight into an agent's learning and decision-making process
and enable new training methods to mitigate common problems. As a
demonstration, we introduce SAC-D, a variant of soft actor-critic (SAC) adapted
for value decomposition. SAC-D maintains similar performance to SAC, while
learning a larger set of value predictions. We also introduce
decomposition-based tools that exploit this information, including a new reward
influence metric, which measures each reward component's effect on agent
decision-making. Using these tools, we provide several demonstrations of
decomposition's use in identifying and addressing problems in the design of
both environments and agents. Value decomposition is broadly applicable and
easy to incorporate into existing algorithms and workflows, making it a
powerful tool in an RL practitioner's toolbox.

Distribution shift (DS) is a common problem that deteriorates the performance
of learning machines. To overcome this problem, we postulate that real-world
distributions are composed of elementary distributions that remain invariant
across different domains. We call this an invariant elementary distribution
(I.E.D.) assumption. This invariance thus enables knowledge transfer to unseen
domains. To exploit this assumption in domain generalization (DG), we developed
a modular neural network layer that consists of Gated Domain Units (GDUs). Each
GDU learns an embedding of an individual elementary domain that allows us to
encode the domain similarities during the training. During inference, the GDUs
compute similarities between an observation and each of the corresponding
elementary distributions which are then used to form a weighted ensemble of
learning machines. Because our layer is trained with backpropagation, it can be
easily integrated into existing deep learning frameworks. Our evaluation on
Digits5, ECG, Camelyon17, iWildCam, and FMoW shows a significant improvement in
the performance on out-of-training target domains without any access to data
from the target domains. This finding supports the validity of the I.E.D.
assumption in real-world data distributions.

The temperature dependence of the thermal rate constant for the reaction
Cl($^2$P) + CH$_4$ $\rightarrow$ CH$_3$ + HCl is calculated using a Gaussian
Process machine learning (ML) approach to train on and predict thermal rate
constants over a large temperature range. Following procedures developed in two
previous reports, we use a training dataset of approximately 40
reaction/potential surface combinations, each of which is used to calculate the
corresponding data base of rate constant at approximately eight temperatures.
For the current application, we train on the entire dataset and then predict
the temperature dependence of the title reaction employing a "split" dataset
for correction at low and high temperatures to capture both tunneling and
recrossing. The results are an improvement on recent RPMD calculations compared
to accurate quantum ones, using the same high-level ab initio potential energy
surface. Both tunneling at low temperatures and recrossing at high temperatures
are observed to influence the rate constants. Recrossing effects, which are not
described by TST and even sophisticated tunneling corrections, do appear in
experiment at temperatures above around 600 K. The ML results describe these
effects and in fact, merge at 600 K with RPMD results (which can describe
recrossing), and both are close to experiment at the highest experimental
temperatures.

Classical theory in reinforcement learning (RL) predominantly focuses on the
single task setting, where an agent learns to solve a task through
trial-and-error experience, given access to data only from that task. However,
many recent empirical works have demonstrated the significant practical
benefits of leveraging a joint representation trained across multiple, related
tasks. In this work we theoretically analyze such a setting, formalizing the
concept of task relatedness as a shared state-action representation that admits
linear dynamics in all the tasks. We introduce the Shared-MatrixRL algorithm
for the setting of Multitask MatrixRL. In the presence of $P$ episodic tasks of
dimension $d$ sharing a joint $r \ll d$ low-dimensional representation, we show
the regret on the the $P$ tasks can be improved from $O(PHd\sqrt{NH})$ to
$O((Hd\sqrt{rP} + HP\sqrt{rd})\sqrt{NH})$ over $N$ episodes of horizon $H$.
These gains coincide with those observed in other linear models in contextual
bandits and RL. In contrast with previous work that have studied multi task RL
in other function approximation models, we show that in the presence of
bilinear optimization oracle and finite state action spaces there exists a
computationally efficient algorithm for multitask MatrixRL via a reduction to
quadratic programming. We also develop a simple technique to shave off a
$\sqrt{H}$ factor from the regret upper bounds of some episodic linear
problems.

We consider monitored quantum systems with a global conserved charge, and ask
how efficiently an observer ("eavesdropper") can learn the global charge of
such systems from local projective measurements. We find phase transitions as a
function of the measurement rate, depending on how much information about the
quantum dynamics the eavesdropper has access to. For random unitary circuits
with U(1) symmetry, we present an optimal classical classifier to reconstruct
the global charge from local measurement outcomes only. We demonstrate the
existence of phase transitions in the performance of this classifier in the
thermodynamic limit. We also study numerically improved classifiers by
including some knowledge about the unitary gates pattern.

We present a scalable approach for learning open-world object-goal navigation
(ObjectNav) -- the task of asking a virtual robot (agent) to find any instance
of an object in an unexplored environment (e.g., "find a sink"). Our approach
is entirely zero-shot -- i.e., it does not require ObjectNav rewards or
demonstrations of any kind. Instead, we train on the image-goal navigation
(ImageNav) task, in which agents find the location where a picture (i.e., goal
image) was captured. Specifically, we encode goal images into a multimodal,
semantic embedding space to enable training semantic-goal navigation
(SemanticNav) agents at scale in unannotated 3D environments (e.g., HM3D).
After training, SemanticNav agents can be instructed to find objects described
in free-form natural language (e.g., "sink", "bathroom sink", etc.) by
projecting language goals into the same multimodal, semantic embedding space.
As a result, our approach enables open-world ObjectNav. We extensively evaluate
our agents on three ObjectNav datasets (Gibson, HM3D, and MP3D) and observe
absolute improvements in success of 4.2% - 20.0% over existing zero-shot
methods. For reference, these gains are similar or better than the 5%
improvement in success between the Habitat 2020 and 2021 ObjectNav challenge
winners. In an open-world setting, we discover that our agents can generalize
to compound instructions with a room explicitly mentioned (e.g., "Find a
kitchen sink") and when the target room can be inferred (e.g., "Find a sink and
a stove").

Understanding the long-term evolution of hierarchical triple systems is
challenging due to its inherent chaotic nature, and it requires computationally
expensive simulations. Here we propose a convolutional neural network model to
predict the stability of hierarchical triples by looking at their evolution
during the first $5 \times 10^5$ inner binary orbits. We employ the regularized
few-body code TSUNAMI to simulate $5\times 10^6$ hierarchical triples, from
which we generate a large training and test dataset. We develop twelve
different network configurations that use different combinations of the
triples' orbital elements and compare their performances. Our best model uses 6
time-series, namely, the semimajor axes ratio, the inner and outer
eccentricities, the mutual inclination and the arguments of pericenter. This
model achieves an area under the curve of over $95\%$ and informs of the
relevant parameters to study triple systems stability. All trained models are
made publicly available, allowing to predict the stability of hierarchical
triple systems $200$ times faster than pure $N$-body methods.

Learned recommender systems may inadvertently leak information about their
training data, leading to privacy violations. We investigate privacy threats
faced by recommender systems through the lens of membership inference. In such
attacks, an adversary aims to infer whether a user's data is used to train the
target recommender. To achieve this, previous work has used a shadow
recommender to derive training data for the attack model, and then predicts the
membership by calculating difference vectors between users' historical
interactions and recommended items. State-of-the-art methods face two
challenging problems: (1) training data for the attack model is biased due to
the gap between shadow and target recommenders, and (2) hidden states in
recommenders are not observational, resulting in inaccurate estimations of
difference vectors. To address the above limitations, we propose a Debiasing
Learning for Membership Inference Attacks against recommender systems (DL-MIA)
framework that has four main components: (1) a difference vector generator, (2)
a disentangled encoder, (3) a weight estimator, and (4) an attack model. To
mitigate the gap between recommenders, a variational auto-encoder (VAE) based
disentangled encoder is devised to identify recommender invariant and specific
features. To reduce the estimation bias, we design a weight estimator,
assigning a truth-level score for each difference vector to indicate estimation
accuracy. We evaluate DL-MIA against both general recommenders and sequential
recommenders on three real-world datasets. Experimental results show that
DL-MIA effectively alleviates training and estimation biases simultaneously,
and achieves state-of-the-art attack performance.

Recent attacks have shown that user data can be recovered from FedSGD
updates, thus breaking privacy. However, these attacks are of limited practical
relevance as federated learning typically uses the FedAvg algorithm. Compared
to FedSGD, recovering data from FedAvg updates is much harder as: (i) the
updates are computed at unobserved intermediate network weights, (ii) a large
number of batches are used, and (iii) labels and network weights vary
simultaneously across client steps. In this work, we propose a new
optimization-based attack which successfully attacks FedAvg by addressing the
above challenges. First, we solve the optimization problem using automatic
differentiation that forces a simulation of the client's update that generates
the unobserved parameters for the recovered labels and inputs to match the
received client update. Second, we address the large number of batches by
relating images from different epochs with a permutation invariant prior.
Third, we recover the labels by estimating the parameters of existing FedSGD
attacks at every FedAvg step. On the popular FEMNIST dataset, we demonstrate
that on average we successfully recover >45% of the client's images from
realistic FedAvg updates computed on 10 local epochs of 10 batches each with 5
images, compared to only <10% using the baseline. Our findings show many
real-world federated learning implementations based on FedAvg are vulnerable.

Training models that are robust to data domain shift has gained an increasing
interest both in academia and industry. Question-Answering language models,
being one of the typical problem in Natural Language Processing (NLP) research,
has received much success with the advent of large transformer models. However,
existing approaches mostly work under the assumption that data is drawn from
same distribution during training and testing which is unrealistic and
non-scalable in the wild.
  In this paper, we explore adversarial training approach towards learning
domain-invariant features so that language models can generalize well to
out-of-domain datasets. We also inspect various other ways to boost our model
performance including data augmentation by paraphrasing sentences, conditioning
end of answer span prediction on the start word, and carefully designed
annealing function. Our initial results show that in combination with these
methods, we are able to achieve $15.2\%$ improvement in EM score and $5.6\%$
boost in F1 score on out-of-domain validation dataset over the baseline. We
also dissect our model outputs and visualize the model hidden-states by
projecting them onto a lower-dimensional space, and discover that our specific
adversarial training approach indeed encourages the model to learn domain
invariant embedding and bring them closer in the multi-dimensional space.

All data is not equally popular. Often, some portion of data is more
frequently accessed than the rest, which causes a skew in popularity of the
data items. Adapting to this skew can improve performance, and this topic has
been studied extensively in the past for disk-based settings. In this work, we
consider an in-memory data structure, namely hash table, and show how one can
leverage the skew in popularity for higher performance. Hashing is a
low-latency operation, sensitive to the effects of caching, branch prediction,
and code complexity among other factors. These factors make learning
in-the-loop especially challenging as the overhead of performing any additional
operations can be significant. In this paper, we propose VIP hashing, a fully
online hash table method, that uses lightweight mechanisms for learning the
skew in popularity and adapting the hash table layout. These mechanisms are
non-blocking, and their overhead is controlled by sensing changes in the
popularity distribution to dynamically switch-on/off the learning mechanism as
needed. We tested VIP hashing against a variety of workloads generated by
Wiscer, a homegrown hashing measurement tool, and find that it improves
performance in the presence of skew (22% increase in fetch operation throughput
for a hash table with one million keys under low skew, 77% increase under
medium skew) while being robust to insert and delete operations, and changing
popularity distribution of keys. We find that VIP hashing reduces the
end-to-end execution time of TPC-H query 9, which is the most expensive TPC-H
query, by 20% under medium skew.

People come to social media to satisfy a variety of needs, such as being
informed, entertained and inspired, or connected to their friends and
community. Hence, to design a ranking function that gives useful and
personalized post recommendations, it would be helpful to be able to predict
the affective response a user may have to a post (e.g., entertained, informed,
angered). This paper describes the challenges and solutions we developed to
apply Affective Computing to social media recommendation systems.
  We address several types of challenges. First, we devise a taxonomy of
affects that was small (for practical purposes) yet covers the important
nuances needed for the application. Second, to collect training data for our
models, we balance between signals that are already available to us (namely,
different types of user engagement) and data we collected through a carefully
crafted human annotation effort on 800k posts. We demonstrate that affective
response information learned from this dataset improves a module in the
recommendation system by more than 8%. Online experimentation also demonstrates
statistically significant decreases in surfaced violating content and increases
in surfaced content that users find valuable.

Mixed Sample Regularization (MSR), such as MixUp or CutMix, is a powerful
data augmentation strategy to generalize convolutional neural networks.
Previous empirical analysis has illustrated an orthogonal performance gain
between MSR and the conventional offline Knowledge Distillation (KD). To be
more specific, student networks can be enhanced with the involvement of MSR in
the training stage of the sequential distillation. Yet, the interplay between
MSR and online knowledge distillation, a stronger distillation paradigm, where
an ensemble of peer students learn mutually from each other, remains
unexplored. To bridge the gap, we make the first attempt at incorporating
CutMix into online distillation, where we empirically observe a significant
improvement. Encouraged by this fact, we propose an even stronger MSR
specifically for online distillation, named as Cut^nMix. Furthermore, a novel
online distillation framework is designed upon Cut^nMix, to enhance the
distillation with feature level mutual learning and a self-ensemble teacher.
Comprehensive evaluations on CIFAR10 and CIFAR100 with six network
architectures show that our approach can consistently outperform
state-of-the-art distillation methods.

Domain Generalization (DG) aims to learn models whose performance remains
high on unseen domains encountered at test-time by using data from multiple
related source domains. Many existing DG algorithms reduce the divergence
between source distributions in a representation space to potentially align the
unseen domain close to the sources. This is motivated by the analysis that
explains generalization to unseen domains using distributional distance (such
as the Wasserstein distance) to the sources. However, due to the openness of
the DG objective, it is challenging to evaluate DG algorithms comprehensively
using a few benchmark datasets. In particular, we demonstrate that the accuracy
of the models trained with DG methods varies significantly across unseen
domains, generated from popular benchmark datasets. This highlights that the
performance of DG methods on a few benchmark datasets may not be representative
of their performance on unseen domains in the wild. To overcome this roadblock,
we propose a universal certification framework based on distributionally robust
optimization (DRO) that can efficiently certify the worst-case performance of
any DG method. This enables a data-independent evaluation of a DG method
complementary to the empirical evaluations on benchmark datasets. Furthermore,
we propose a training algorithm that can be used with any DG method to provably
improve their certified performance. Our empirical evaluation demonstrates the
effectiveness of our method at significantly improving the worst-case loss
(i.e., reducing the risk of failure of these models in the wild) without
incurring a significant performance drop on benchmark datasets.

We show that any matrix product state (MPS) can be exactly represented by a
recurrent neural network (RNN) with a linear memory update. We generalize this
RNN architecture to 2D lattices using a multilinear memory update. It supports
perfect sampling and wave function evaluation in polynomial time, and can
represent an area law of entanglement entropy. Numerical evidence shows that it
can encode the wave function using a bond dimension lower by orders of
magnitude when compared to MPS, with an accuracy that can be systematically
improved by increasing the bond dimension.

We develop ShiftMatch, a new training-data-dependent likelihood for
robustness to corruption in Bayesian neural networks (BNNs). ShiftMatch is
inspired by the training-data-dependent "EmpCov" priors from Izmailov et al.
(2021a), and efficiently matches test-time spatial correlations to those at
training time. Critically, ShiftMatch is designed to leave the neural network's
training time likelihood unchanged, allowing it to use publicly available
samples from pre-trained BNNs. Using pre-trained HMC samples, ShiftMatch gives
strong performance improvements on CIFAR-10-C, outperforms EmpCov priors
(though ShiftMatch uses extra information from a minibatch of corrupted test
points), and is perhaps the first Bayesian method capable of convincingly
outperforming plain deep ensembles.

Reconstructing 3D objects is an important computer vision task that has wide
application in AR/VR. Deep learning algorithm developed for this task usually
relies on an unrealistic synthetic dataset, such as ShapeNet and Things3D. On
the other hand, existing real-captured object-centric datasets usually do not
have enough annotation to enable supervised training or reliable evaluation. In
this technical report, we present a photo-realistic object-centric dataset
HM3D-ABO. It is constructed by composing realistic indoor scene and realistic
object. For each configuration, we provide multi-view RGB observations, a
water-tight mesh model for the object, ground truth depth map and object mask.
The proposed dataset could also be useful for tasks such as camera pose
estimation and novel-view synthesis. The dataset generation code is released at
https://github.com/zhenpeiyang/HM3D-ABO.

Most machine learning (ML) algorithms have several stochastic elements, and
their performances are affected by these sources of randomness. This paper uses
an empirical study to systematically examine the effects of two sources:
randomness in model training and randomness in the partitioning of a dataset
into training and test subsets. We quantify and compare the magnitude of the
variation in predictive performance for the following ML algorithms: Random
Forests (RFs), Gradient Boosting Machines (GBMs), and Feedforward Neural
Networks (FFNNs). Among the different algorithms, randomness in model training
causes larger variation for FFNNs compared to tree-based methods. This is to be
expected as FFNNs have more stochastic elements that are part of their model
initialization and training. We also found that random splitting of datasets
leads to higher variation compared to the inherent randomness from model
training. The variation from data splitting can be a major issue if the
original dataset has considerable heterogeneity.
  Keywords: Model Training, Reproducibility, Variation

An ongoing trend in generative modelling research has been to push sample
resolutions higher whilst simultaneously reducing computational requirements
for training and sampling. We aim to push this trend further via the
combination of techniques - each component representing the current pinnacle of
efficiency in their respective areas. These include vector-quantized GAN
(VQ-GAN), a vector-quantization (VQ) model capable of high levels of lossy -
but perceptually insignificant - compression; hourglass transformers, a highly
scaleable self-attention model; and step-unrolled denoising autoencoders
(SUNDAE), a non-autoregressive (NAR) text generative model. Unexpectedly, our
method highlights weaknesses in the original formulation of hourglass
transformers when applied to multidimensional data. In light of this, we
propose modifications to the resampling mechanism, applicable in any task
applying hierarchical transformers to multidimensional data. Additionally, we
demonstrate the scalability of SUNDAE to long sequence lengths - four times
longer than prior work. Our proposed framework scales to high-resolutions
($1024 \times 1024$) and trains quickly (2-4 days). Crucially, the trained
model produces diverse and realistic megapixel samples in approximately 2
seconds on a consumer-grade GPU (GTX 1080Ti). In general, the framework is
flexible: supporting an arbitrary number of sampling steps, sample-wise
self-stopping, self-correction capabilities, conditional generation, and a NAR
formulation that allows for arbitrary inpainting masks. We obtain FID scores of
10.56 on FFHQ256 - close to the original VQ-GAN in less than half the sampling
steps - and 21.85 on FFHQ1024 in only 100 sampling steps.

To ensure user acceptance of autonomous vehicles (AVs), control systems are
being developed to mimic human drivers from demonstrations of desired driving
behaviors. Imitation learning (IL) algorithms serve this purpose, but struggle
to provide safety guarantees on the resulting closed-loop system trajectories.
On the other hand, Model Predictive Control (MPC) can handle nonlinear systems
with safety constraints, but realizing human-like driving with it requires
extensive domain knowledge. This work suggests the use of a seamless
combination of the two techniques to learn safe AV controllers from
demonstrations of desired driving behaviors, by using MPC as a differentiable
control layer within a hierarchical IL policy. With this strategy, IL is
performed in closed-loop and end-to-end, through parameters in the MPC cost,
model or constraints. Experimental results of this methodology are analyzed for
the design of a lane keeping control system, learned via behavioral cloning
from observations (BCO), given human demonstrations on a fixed-base driving
simulator.

In nuclear imaging, limited resolution causes partial volume effects (PVEs)
that affect image sharpness and quantitative accuracy. Partial volume
correction (PVC) methods incorporating high-resolution anatomical information
from CT or MRI have been demonstrated to be effective. However, such
anatomical-guided methods typically require tedious image registration and
segmentation steps. Accurately segmented organ templates are also hard to
obtain, particularly in cardiac SPECT imaging, due to the lack of hybrid
SPECT/CT scanners with high-end CT and associated motion artifacts. Slight
mis-registration/mis-segmentation would result in severe degradation in image
quality after PVC. In this work, we develop a deep-learning-based method for
fast cardiac SPECT PVC without anatomical information and associated organ
segmentation. The proposed network involves a densely-connected
multi-dimensional dynamic mechanism, allowing the convolutional kernels to be
adapted based on the input images, even after the network is fully trained.
Intramyocardial blood volume (IMBV) is introduced as an additional
clinical-relevant loss function for network optimization. The proposed network
demonstrated promising performance on 28 canine studies acquired on a GE
Discovery NM/CT 570c dedicated cardiac SPECT scanner with a 64-slice CT using
Technetium-99m-labeled red blood cells. This work showed that the proposed
network with densely-connected dynamic mechanism produced superior results
compared with the same network without such mechanism. Results also showed that
the proposed network without anatomical information could produce images with
statistically comparable IMBV measurements to the images generated by
anatomical-guided PVC methods, which could be helpful in clinical translation.

Automated machine learning (AutoML) is an important step to make machine
learning models being widely applied to solve real world problems. Despite
numerous research advancement, machine learning methods are not fully utilized
by industries mainly due to their data privacy and security regulations, high
cost involved in storing and computing increasing amount of data at central
location and most importantly lack of expertise. Hence, we introduce a novel
framework, HANF - $\textbf{H}$yperparameter $\textbf{A}$nd $\textbf{N}$eural
architecture search in $\textbf{F}$ederated learning as a step towards building
an AutoML framework for data distributed across several data owner servers
without any need for bringing the data to a central location. HANF jointly
optimizes a neural architecture and non-architectural hyperparameters of a
learning algorithm using gradient-based neural architecture search and
$n$-armed bandit approach respectively in data distributed setting. We show
that HANF efficiently finds the optimized neural architecture and also tunes
the hyperparameters on data owner servers. Additionally, HANF can be applied in
both, federated and non-federated settings. Empirically, we show that HANF
converges towards well-suited architectures and non-architectural
hyperparameter-sets using image-classification tasks.

Equipping current decision-making tools with notions of fairness,
equitability, or other ethically motivated outcomes, is one of the top
priorities in recent research efforts in machine learning, AI, and
optimization. In this paper, we investigate how to allocate limited resources
to {locally interacting} communities in a way to maximize a pertinent notion of
equitability. In particular, we look at the dynamic setting where the
allocation is repeated across multiple periods (e.g., yearly), the local
communities evolve in the meantime (driven by the provided allocation), and the
allocations are modulated by feedback coming from the communities themselves.
We employ recent mathematical tools stemming from data-driven feedback online
optimization, by which communities can learn their (possibly unknown)
evolution, satisfaction, as well as they can share information with the
deciding bodies. We design dynamic policies that converge to an allocation that
maximize equitability in the long term. We further demonstrate our model and
methodology with realistic examples of healthcare and education subsidies
design in Sub-Saharian countries. One of the key empirical takeaways from our
setting is that long-term equitability is fragile, in the sense that it can be
easily lost when deciding bodies weigh in other factors (e.g., equality in
allocation) in the allocation strategy. Moreover, a naive compromise, while not
providing significant advantage to the communities, can promote inequality in
social outcomes.

The multiple-target self-organizing pursuit (SOP) problem has wide
applications and has been considered a challenging self-organization game for
distributed systems, in which intelligent agents cooperatively pursue multiple
dynamic targets with partial observations. This work proposes a framework for
decentralized multi-agent systems to improve intelligent agents' search and
pursuit capabilities. We model a self-organizing system as a partially
observable Markov game (POMG) with the features of decentralization, partial
observation, and noncommunication. The proposed distributed algorithm: fuzzy
self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the
three challenges in multi-target SOP: distributed self-organizing search (SOS),
distributed task allocation, and distributed single-target pursuit. FSC2
includes a coordinated multi-agent deep reinforcement learning method that
enables homogeneous agents to learn natural SOS patterns. Additionally, we
propose a fuzzy-based distributed task allocation method, which locally
decomposes multi-target SOP into several single-target pursuit problems. The
cooperative coevolution principle is employed to coordinate distributed
pursuers for each single-target pursuit problem. Therefore, the uncertainties
of inherent partial observation and distributed decision-making in the POMG can
be alleviated. The experimental results demonstrate that distributed
noncommunicating multi-agent coordination with partial observations in all
three subtasks are effective, and 2048 FSC2 agents can perform efficient
multi-target SOP with an almost 100% capture rate.

Graph diffusion problems such as the propagation of rumors, computer viruses,
or smart grid failures are ubiquitous and societal. Hence it is usually crucial
to identify diffusion sources according to the current graph diffusion
observations. Despite its tremendous necessity and significance in practice,
source localization, as the inverse problem of graph diffusion, is extremely
challenging as it is ill-posed: different sources may lead to the same graph
diffusion patterns. Different from most traditional source localization
methods, this paper focuses on a probabilistic manner to account for the
uncertainty of different candidate sources. Such endeavors require overcoming
challenges including 1) the uncertainty in graph diffusion source localization
is hard to be quantified; 2) the complex patterns of the graph diffusion
sources are difficult to be probabilistically characterized; 3) the
generalization under any underlying diffusion patterns is hard to be imposed.
To solve the above challenges, this paper presents a generic framework: Source
Localization Variational AutoEncoder (SL-VAE) for locating the diffusion
sources under arbitrary diffusion patterns. Particularly, we propose a
probabilistic model that leverages the forward diffusion estimation model along
with deep generative models to approximate the diffusion source distribution
for quantifying the uncertainty. SL-VAE further utilizes prior knowledge of the
source-observation pairs to characterize the complex patterns of diffusion
sources by a learned generative prior. Lastly, a unified objective that
integrates the forward diffusion estimation model is derived to enforce the
model to generalize under arbitrary diffusion patterns. Extensive experiments
are conducted on 7 real-world datasets to demonstrate the superiority of SL-VAE
in reconstructing the diffusion sources by excelling other methods on average
20% in AUC score.

We propose a mesh-free method to solve nonconvex energy minimization problems
for martensitic phase transitions and twinning in crystals, using the deep
learning approach. These problems pose multiple challenges to both analysis and
computation, as they involve multiwell gradient energies with large numbers of
local minima, each involving a topologically complex microstructure of free
boundaries with gradient jumps. We use the Deep Ritz method, whereby candidates
for minimizers are represented by parameter-dependent deep neural networks, and
the energy is minimized with respect to network parameters. The new essential
ingredient is a novel activation function proposed here, which is a smoothened
rectified linear unit we call SmReLU; this captures the structure of minimizers
where usual activation functions fail. The method is mesh-free and thus can
approximate free boundaries essential to this problem without any special
treatment, and is extremely simple to implement. We show the results of many
numerical computations demonstrating the success of our method.

Deep learning models are able to approximate one specific dynamical system
but struggle at learning generalisable dynamics, where dynamical systems obey
the same laws of physics but contain different numbers of elements (e.g.,
double- and triple-pendulum systems). To relieve this issue, we proposed the
Modular Lagrangian Network (ModLaNet), a structural neural network framework
with modularity and physical inductive bias. This framework models the energy
of each element using modularity and then construct the target dynamical system
via Lagrangian mechanics. Modularity is beneficial for reusing trained networks
and reducing the scale of networks and datasets. As a result, our framework can
learn from the dynamics of simpler systems and extend to more complex ones,
which is not feasible using other relevant physics-informed neural networks. We
examine our framework for modelling double-pendulum or three-body systems with
small training datasets, where our models achieve the best data efficiency and
accuracy performance compared with counterparts. We also reorganise our models
as extensions to model multi-pendulum and multi-body systems, demonstrating the
intriguing reusable feature of our framework.

Machine Learning (ML) models now inform a wide range of human decisions, but
using ``black box'' models carries risks such as relying on spurious
correlations or errant data. To address this, researchers have proposed methods
for supplementing models with explanations of their predictions. However,
robust evaluations of these methods' usefulness in real-world contexts have
remained elusive, with experiments tending to rely on simplified settings or
proxy tasks. We present an experimental study extending a prior explainable ML
evaluation experiment and bringing the setup closer to the deployment setting
by relaxing its simplifying assumptions. Our empirical study draws dramatically
different conclusions than the prior work, highlighting how seemingly trivial
experimental design choices can yield misleading results. Beyond the present
experiment, we believe this work holds lessons about the necessity of situating
the evaluation of any ML method and choosing appropriate tasks, data, users,
and metrics to match the intended deployment contexts.

A key enabler of deploying convolutional neural networks on
resource-constrained embedded systems is the binary neural network (BNN). BNNs
save on memory and simplify computation by binarizing both features and
weights. Unfortunately, binarization is inevitably accompanied by a severe
decrease in accuracy. To reduce the accuracy gap between binary and
full-precision networks, many repair methods have been proposed in the recent
past, which we have classified and put into a single overview in this chapter.
The repair methods are divided into two main branches, training techniques and
network topology changes, which can further be split into smaller categories.
The latter category introduces additional cost (energy consumption or
additional area) for an embedded system, while the former does not. From our
overview, we observe that progress has been made in reducing the accuracy gap,
but BNN papers are not aligned on what repair methods should be used to get
highly accurate BNNs. Therefore, this chapter contains an empirical review that
evaluates the benefits of many repair methods in isolation over the
ResNet-20\&CIFAR10 and ResNet-18\&CIFAR100 benchmarks. We found three repair
categories most beneficial: feature binarizer, feature normalization, and
double residual. Based on this review we discuss future directions and research
opportunities. We sketch the benefit and costs associated with BNNs on embedded
systems because it remains to be seen whether BNNs will be able to close the
accuracy gap while staying highly energy-efficient on resource-constrained
embedded systems.

Designing an inexpensive approximate surrogate model that captures the
salient features of an expensive high-fidelity behavior is a prevalent approach
in design optimization. In recent times, Deep Learning (DL) models are being
used as a promising surrogate computational model for engineering problems.
However, the main challenge in creating a DL-based surrogate is to
simulate/label a large number of design points, which is time-consuming for
computationally costly and/or high-dimensional engineering problems. In the
present work, we propose a novel sampling technique by combining the active
learning (AL) method with DL. We call this method $\epsilon$-weighted hybrid
query strategy ($\epsilon$-HQS) , which focuses on the evaluation of the
surrogate at each learning iteration and provides an estimate of the failure
probability of the surrogate in the Design Space. By reusing already collected
training and test data, the learned failure probability guides the next
iteration's sampling process to the region of the high probability of failure.
During the empirical evaluation, better accuracy of the surrogate was observed
in comparison to other methods of sample selection. We empirically evaluated
this method in two different engineering design domains, finite element based
static stress analysis of submarine pressure vessel(computationally costly
process) and second submarine propeller design( high dimensional problem).
https://github.com/vardhah/epsilon_weighted_Hybrid_Query_Strategy

It is widely believed that the success of deep networks lies in their ability
to learn a meaningful representation of the features of the data. Yet,
understanding when and how this feature learning improves performance remains a
challenge: for example, it is beneficial for modern architectures trained to
classify images, whereas it is detrimental for fully-connected networks trained
for the same task on the same data. Here we propose an explanation for this
puzzle, by showing that feature learning can perform worse than lazy training
(via random feature kernel or the NTK) as the former can lead to a sparser
neural representation. Although sparsity is known to be essential for learning
anisotropic data, it is detrimental when the target function is constant or
smooth along certain directions of input space. We illustrate this phenomenon
in two settings: (i) regression of Gaussian random functions on the
d-dimensional unit sphere and (ii) classification of benchmark datasets of
images. For (i), we compute the scaling of the generalization error with number
of training points, and show that methods that do not learn features generalize
better, even when the dimension of the input space is large. For (ii), we show
empirically that learning features can indeed lead to sparse and thereby less
smooth representations of the image predictors. This fact is plausibly
responsible for deteriorating the performance, which is known to be correlated
with smoothness along diffeomorphisms.

The rapid escalation of applying Machine Learning (ML) in various domains has
led to paying more attention to the quality of ML components. There is then a
growth of techniques and tools aiming at improving the quality of ML components
and integrating them into the ML-based system safely. Although most of these
tools use bugs' lifecycle, there is no standard benchmark of bugs to assess
their performance, compare them and discuss their advantages and weaknesses. In
this study, we firstly investigate the reproducibility and verifiability of the
bugs in ML-based systems and show the most important factors in each one. Then,
we explore the challenges of generating a benchmark of bugs in ML-based
software systems and provide a bug benchmark namely defect4ML that satisfies
all criteria of standard benchmark, i.e. relevance, reproducibility, fairness,
verifiability, and usability. This faultload benchmark contains 113 bugs
reported by ML developers on GitHub and Stack Overflow, using two of the most
popular ML frameworks: TensorFlow and Keras. defect4ML also addresses important
challenges in Software Reliability Engineering of ML-based software systems,
like: 1) fast changes in frameworks, by providing various bugs for different
versions of frameworks, 2) code portability, by delivering similar bugs in
different ML frameworks, 3) bug reproducibility, by providing fully
reproducible bugs with complete information about required dependencies and
data, and 4) lack of detailed information on bugs, by presenting links to the
bugs' origins. defect4ML can be of interest to ML-based systems practitioners
and researchers to assess their testing tools and techniques.

The COVID-19 outbreak resulted in multiple waves of infections that have been
associated with different SARS-CoV-2 variants. Studies have reported
differential impact of the variants on respiratory health of patients. We
explore whether acoustic signals, collected from COVID-19 subjects, show
computationally distinguishable acoustic patterns suggesting a possibility to
predict the underlying virus variant. We analyze the Coswara dataset which is
collected from three subject pools, namely, i) healthy, ii) COVID-19 subjects
recorded during the delta variant dominant period, and iii) data from COVID-19
subjects recorded during the omicron surge. Our findings suggest that multiple
sound categories, such as cough, breathing, and speech, indicate significant
acoustic feature differences when comparing COVID-19 subjects with omicron and
delta variants. The classification areas-under-the-curve are significantly
above chance for differentiating subjects infected by omicron from those
infected by delta. Using a score fusion from multiple sound categories, we
obtained an area-under-the-curve of 89% and 52.4% sensitivity at 95%
specificity. Additionally, a hierarchical three class approach was used to
classify the acoustic data into healthy and COVID-19 positive, and further
COVID-19 subjects into delta and omicron variants providing high level of
3-class classification accuracy. These results suggest new ways for designing
sound based COVID-19 diagnosis approaches.

Proteins are essential component of human life and their structures are
important for function and mechanism analysis. Recent work has shown the
potential of AI-driven methods for protein structure prediction. However, the
development of new models is restricted by the lack of dataset and benchmark
training procedure. To the best of our knowledge, the existing open source
datasets are far less to satisfy the needs of modern protein sequence-structure
related research. To solve this problem, we present the first million-level
protein structure prediction dataset with high coverage and diversity, named as
PSP. This dataset consists of 570k true structure sequences (10TB) and 745k
complementary distillation sequences (15TB). We provide in addition the
benchmark training procedure for SOTA protein structure prediction model on
this dataset. We validate the utility of this dataset for training by
participating CAMEO contest in which our model won the first place. We hope our
PSP dataset together with the training benchmark can enable a broader community
of AI/biology researchers for AI-driven protein related research.

Accurate extraction of coronary arteries from invasive coronary angiography
(ICA) is important in clinical decision-making for the diagnosis and risk
stratification of coronary artery disease (CAD). In this study, we develop a
method using deep learning to automatically extract the coronary artery lumen.
Methods. A deep learning model U-Net 3+, which incorporates the full-scale skip
connections and deep supervisions, was proposed for automatic extraction of
coronary arteries from ICAs. Transfer learning and a hybrid loss function were
employed in this novel coronary artery extraction framework. Results. A data
set containing 616 ICAs obtained from 210 patients was used. In the technical
evaluation, the U-Net 3+ achieved a Dice score of 0.8942 and a sensitivity of
0.8735, which is higher than U-Net ++ (Dice score: 0.8814, the sensitivity of
0.8331) and U-net (Dice score: 0.8799, the sensitivity of 0.8305). Conclusion.
Our study demonstrates that the U-Net 3+ is superior to other segmentation
frameworks for the automatic extraction of the coronary arteries from ICAs.
This result suggests great promise for clinical use.

Audio quality assessment is critical for assessing the perceptual realism of
sounds. However, the time and expense of obtaining ''gold standard'' human
judgments limit the availability of such data. For AR&VR, good perceived sound
quality and localizability of sources are among the key elements to ensure
complete immersion of the user. Our work introduces SAQAM which uses a
multi-task learning framework to assess listening quality (LQ) and
spatialization quality (SQ) between any given pair of binaural signals without
using any subjective data. We model LQ by training on a simulated dataset of
triplet human judgments, and SQ by utilizing activation-level distances from
networks trained for direction of arrival (DOA) estimation. We show that SAQAM
correlates well with human responses across four diverse datasets. Since it is
a deep network, the metric is differentiable, making it suitable as a loss
function for other tasks. For example, simply replacing an existing loss with
our metric yields improvement in a speech-enhancement network.

In a previous paper, we have proposed a set of concepts, axiom schemata and
algorithms that can be used by agents to learn to describe their behaviour,
goals, capabilities, and environment. The current paper proposes a new set of
concepts, axiom schemata and algorithms that allow the agent to learn new
descriptions of an observed behaviour (e.g., perplexing actions), of its actor
(e.g., undesired propositions or actions), and of its environment (e.g.,
incompatible propositions). Each learned description (e.g., a certain action
prevents another action from being performed in the future) is represented by a
relationship between entities (either propositions or actions) and is learned
by the agent, just by observation, using domain-independent axiom schemata and
or learning algorithms. The relations used by agents to represent the
descriptions they learn were inspired on the Theory of Rhetorical Structure
(RST). The main contribution of the paper is the relation family Although,
inspired on the RST relation Concession. The accurate definition of the
relations of the family Although involves a set of deontic concepts whose
definition and corresponding algorithms are presented. The relations of the
family Although, once extracted from the agent's observations, express surprise
at the observed behaviour and, in certain circumstances, present a
justification for it.
  The paper shows results of the presented proposals in a demonstration
scenario, using implemented software.

Human judgments obtained through Mean Opinion Scores (MOS) are the most
reliable way to assess the quality of speech signals. However, several recent
attempts to automatically estimate MOS using deep learning approaches lack
robustness and generalization capabilities, limiting their use in real-world
applications. In this work, we present a novel framework, NORESQA-MOS, for
estimating the MOS of a speech signal. Unlike prior works, our approach uses
non-matching references as a form of conditioning to ground the MOS estimation
by neural networks. We show that NORESQA-MOS provides better generalization and
more robust MOS estimation than previous state-of-the-art methods such as
DNSMOS and NISQA, even though we use a smaller training set. Moreover, we also
show that our generic framework can be combined with other learning methods
such as self-supervised learning and can further supplement the benefits from
these methods.

Serverless computing is an emerging cloud computing paradigm. Moreover, it
has become an attractive development option for cloud-based applications for
software developers. The most significant advantage of serverless computing is
to free software developers from the burden of complex underlying management
tasks and allow them to focus on only the application logic implementation.
Based on its benign characteristics and bright prospect, it has been an
increasingly hot topic in various scenarios, such as machine learning,
scientific computing, video processing, and the Internet of Things. However,
none of the studies focuses on a comprehensive analysis of the current research
state of the art of serverless computing from the research scope and depth.
  To fill this knowledge gap, we present a comprehensive literature review to
summarize the current research state of the art of serverless computing. This
review is based on selected 164 research papers to answer three key aspects,
i.e., research directions (What), existing solutions (How), and platforms and
venues (Where). Specifically, first, we construct a taxonomy linked to research
directions about the serverless computing literature. Our taxonomy has 18
research categories covering performance optimization, programming framework,
application migration, multi-cloud development, cost, testing, debugging, etc.
Second, we classify the related studies of each research direction and
elaborate on existing solutions. Third, we investigate the distributions of
experimental platforms for existing techniques and publication venues for
selected research papers. Finally, based on our analysis, we discuss some key
challenges and envision promising opportunities for future research on the
serverless platform side, serverless application side, and serverless computing
community side.

Sound source localization aims to seek the direction of arrival (DOA) of all
sound sources from the observed multi-channel audio. For the practical problem
of unknown number of sources, existing localization algorithms attempt to
predict a likelihood-based coding (i.e., spatial spectrum) and employ a
pre-determined threshold to detect the source number and corresponding DOA
value. However, these threshold-based algorithms are not stable since they are
limited by the careful choice of threshold. To address this problem, we propose
an iterative sound source localization approach called ISSL, which can
iteratively extract each source's DOA without threshold until the termination
criterion is met. Unlike threshold-based algorithms, ISSL designs an active
source detector network based on binary classifier to accept residual spatial
spectrum and decide whether to stop the iteration. By doing so, our ISSL can
deal with an arbitrary number of sources, even more than the number of sources
seen during the training stage. The experimental results show that our ISSL
achieves significant performance improvements in both DOA estimation and source
number detection compared with the existing threshold-based algorithms.

This paper proposes a physically consistent Gaussian Process (GP) enabling
the identification of uncertain Lagrangian systems. The function space is
tailored according to the energy components of the Lagrangian and the
differential equation structure, analytically guaranteeing physical and
mathematical properties such as energy conservation and quadratic form. The
novel formulation of Cholesky decomposed matrix kernels allow the probabilistic
preservation of positive definiteness. Only differential input-to-output
measurements of the function map are required while Gaussian noise is permitted
in torques, velocities, and accelerations. We demonstrate the effectiveness of
the approach in numerical simulation.

In cloud computing resource management plays a significant role in data
centres and it is directly dependent on the application workload. Various
services such as Infrastructure as a Service (IaaS), Platform as a Service
(PaaS), and Software as a Service (SaaS) are offered by cloud computing to
provide compute, network, and storage capabilities to the cloud users utilizing
the pay-per-usage approach. Resource allocation is a prior solution to address
various demanding situations like the under/overload handling, resource
wastage, load balancing, Quality-of-Services (QoS) violations, VM migration and
many more. The primary aim of Virtual Machine Placement (VMP) is mapping of
Virtual Machines (VMs) to physical machines (PMs), such that the PMs may be
utilized to their maximum efficiency, where the already active VMs are not to
be interrupted. It provides a list of live VM migrations that must be
accomplished to get the optimum solution and reduces energy consumption to a
larger extent. The inefficient VMP leads to wastage of resources, excessive
energy consumption and also increase overall operational cost of the data
center. On this context, this article provides an extensive survey of resource
management schemes in cloud environment. A conceptual scheme for resource
management, grouping of current machine learning based resource allocation
strategies, and fundamental problems of ineffective distribution of physical
resources are analyzed. Thereafter, a complete survey of existing techniques in
machine learning based mechanisms in the field of cloud resource management are
explained. Ultimately, the paper explores and concludes distinct approaching
challenges and future research guidelines associated to resource management in
cloud environment.

Machine learning has been applied to almost all fields of computer science
over the past decades. The introduction of GANs allowed for new possibilities
in fields of medical research and text prediction. However, these new fields
work with ever more privacy-sensitive data. In order to maintain user privacy,
a combination of federated learning, differential privacy and GANs can be used
to work with private data without giving away a users' privacy. Recently, two
implementations of such combinations have been published: DP-Fed-Avg GAN and
GS-WGAN. This paper compares their performance and introduces an alternative
version of DP-Fed-Avg GAN that makes use of denoising techniques to combat the
loss in accuracy that generally occurs when applying differential privacy and
federated learning to GANs. We also compare the novel adaptation of denoised
DP-Fed-Avg GAN to the state-of-the-art implementations in this field.

This paper explores the question: how to identify a reduced order model from
data. There are three ways to relate data to a model: invariant foliations,
invariant manifolds and autoencoders. Invariant manifolds cannot be fitted to
data unless a hardware in a loop system is used. Autoencoders only identify the
portion of the phase space where the data is, which is not necessarily an
invariant manifold. Therefore for off-line data the only option is an invariant
foliation. We note that Koopman eigenfunctions also define invariant
foliations, but they are limited by the assumption of linearity and resulting
singularites. Finding an invariant foliation requires approximating
high-dimensional functions. We propose two solutions. If an accurate reduced
order model is sought, a sparse polynomial approximation is used, with
polynomial coefficients that are sparse hierarchical tensors. If an invariant
manifold is sought, as a leaf of a foliation, the required high-dimensional
function can be approximated by a low-dimensional polynomial. The two methods
can be combined to find an accurate reduced order model and an invariant
manifold. We also analyse the reduced order model in case of a focus type
equilibrium, typical in mechanical systems. We note that the nonlinear
coordinate system defined by the invariant foliation and the invariant manifold
distorts instantaneous frequencies and damping ratios, which we correct.
Through examples we illustrate the calculation of invariant foliations and
manifolds, and at the same time show that Koopman eigenfunctions and
autoencoders fail to capture accurate reduced order models under the same

We present data-driven simulations for gold nanostructures, and develop a
model that links total energy to geometrical features of the particle, with the
ultimate goal of deriving reliable edge energies of gold. Assuming that the
total energy can be decomposed into contributions from the bulk, surfaces,
edges, and vertices, we use machine learning for reliable multi-variant fits of
the associated coefficients. The proposed method of total energy calculations
using machine learning produces almost ab-initio-like accuracy with minimal
computational cost. Furthermore, a clear definition and metric for edge energy
is introduced for edge-energy density calculations that avoid the troublesome
definition of edge length in nanostructures. Our results for edge-energy
density are 0.22 eV/{\AA} for (100)/(100) edges and 0.20 eV/{\AA} for
(111)/(111) edges. Calculated vertex energies are about 1 eV/atom. The present
method can be readily extended to other metals and edge orientations as well as
arbitrary nanoparticle shapes.

Cross-architecture binary similarity comparison is essential in many security
applications. Recently, researchers have proposed learning-based approaches to
improve comparison performance. They adopted a paradigm of instruction
pre-training, individual binary encoding, and distance-based similarity
comparison. However, instruction embeddings pre-trained on external code corpus
are not universal in diverse real-world applications. And separately encoding
cross-architecture binaries will accumulate the semantic gap of instruction
sets, limiting the comparison accuracy. This paper proposes a novel
cross-architecture binary similarity comparison approach with multi-relational
instruction association graph. We associate mono-architecture instruction
tokens with context relevance and cross-architecture tokens with potential
semantic correlations from different perspectives. Then we exploit the
relational graph convolutional network (R-GCN) to perform type-specific graph
information propagation. Our approach can bridge the gap in the
cross-architecture instruction representation spaces while avoiding the
external pre-training workload. We conduct extensive experiments on basic
block-level and function-level datasets to prove the superiority of our
approach. Furthermore, evaluations on a large-scale real-world IoT malware
reuse function collection show that our approach is valuable for identifying
malware propagated on IoT devices of various architectures.

Parameter adaptation, that is the capability to automatically adjust an
algorithm's hyperparameters depending on the problem being faced, is one of the
main trends in evolutionary computation applied to numerical optimization.
While several handcrafted adaptation policies have been proposed over the years
to address this problem, only few attempts have been done so far at applying
machine learning to learn such policies. Here, we introduce a general-purpose
framework for performing parameter adaptation in continuous-domain
metaheuristics based on state-of-the-art reinforcement learning algorithms. We
demonstrate the applicability of this framework on two algorithms, namely
Covariance Matrix Adaptation Evolution Strategies (CMA-ES) and Differential
Evolution (DE), for which we learn, respectively, adaptation policies for the
step-size (for CMA-ES), and the scale factor and crossover rate (for DE). We
train these policies on a set of 46 benchmark functions at different
dimensionalities, with various inputs to the policies, in two settings: one
policy per function, and one global policy for all functions. Compared,
respectively, to the Cumulative Step-size Adaptation (CSA) policy and to two
well-known adaptive DE variants (iDE and jDE), our policies are able to produce
competitive results in the majority of cases, especially in the case of DE.

Singing techniques are used for expressive vocal performances by employing
temporal fluctuations of the timbre, the pitch, and other components of the
voice. Their classification is a challenging task, because of mainly two
factors: 1) the fluctuations in singing techniques have a wide variety and are
affected by many factors and 2) existing datasets are imbalanced. To deal with
these problems, we developed a novel audio feature learning method based on
deformable convolution with decoupled training of the feature extractor and the
classifier using a class-weighted loss function. The experimental results show
the following: 1) the deformable convolution improves the classification
results, particularly when it is applied to the last two convolutional layers,
and 2) both re-training the classifier and weighting the cross-entropy loss
function by a smoothed inverse frequency enhance the classification

Neural networks have been widely applied in security applications such as
spam and phishing detection, intrusion prevention, and malware detection. This
black-box method, however, often has uncertainty and poor explainability in
applications. Furthermore, neural networks themselves are often vulnerable to
adversarial attacks. For those reasons, there is a high demand for trustworthy
and rigorous methods to verify the robustness of neural network models.
Adversarial robustness, which concerns the reliability of a neural network when
dealing with maliciously manipulated inputs, is one of the hottest topics in
security and machine learning. In this work, we survey existing literature in
adversarial robustness verification for neural networks and collect 39
diversified research works across machine learning, security, and software
engineering domains. We systematically analyze their approaches, including how
robustness is formulated, what verification techniques are used, and the
strengths and limitations of each technique. We provide a taxonomy from a
formal verification perspective for a comprehensive understanding of this
topic. We classify the existing techniques based on property specification,
problem reduction, and reasoning strategies. We also demonstrate representative
techniques that have been applied in existing studies with a sample model.
Finally, we discuss open questions for future research.

The paper deals with the problem of output regulation of nonlinear systems by
presenting a learning-based adaptive internal model-based design strategy. We
borrow from the adaptive internal model design technique recently proposed in
[1] and extend it by means of a Gaussian process regressor. The learning-based
adaptation is performed by following an "event-triggered" logic so that hybrid
tools are used to analyse the resulting closed-loop system. Unlike the approach
proposed in [1] where the friend is supposed to belong to a specific
finite-dimensional model set, here we only require smoothness of the ideal
steady-state control action. The paper also presents numerical simulations
showing how the proposed method outperforms previous approaches.

The growing volumes of data being collected and its analysis to provide
better services are creating worries about digital privacy. To address privacy
concerns and give practical solutions, the literature has relied on secure
multiparty computation. However, recent research has mostly focused on the
small-party honest-majority setting of up to four parties, noting efficiency
concerns. In this work, we extend the strategies to support a larger number of
participants in an honest-majority setting with efficiency at the center stage.
  Cast in the preprocessing paradigm, our semi-honest protocol improves the
online complexity of the decade-old state-of-the-art protocol of Damg\aa rd and
Nielson (CRYPTO'07). In addition to having an improved online communication
cost, we can shut down almost half of the parties in the online phase, thereby
saving up to 50% in the system's operational costs. Our maliciously secure
protocol also enjoys similar benefits and requires only half of the parties,
except for one-time verification, towards the end.
  To showcase the practicality of the designed protocols, we benchmark popular
applications such as deep neural networks, graph neural networks, genome
sequence matching, and biometric matching using prototype implementations. Our
improved protocols aid in bringing up to 60-80% savings in monetary cost over
prior work.

Click-based learning to rank (LTR) tackles the mismatch between click
frequencies on items and their actual relevance. The approach of previous work
has been to assume a model of click behavior and to subsequently introduce a
method for unbiasedly estimating preferences under that assumed model. The
success of this approach is evident in that unbiased methods have been found
for an increasing number of behavior models and types of bias. This work aims
to uncover the implicit limitations of the high-level prevalent approach in the
counterfactual LTR field. Thus, in contrast with limitations that follow from
explicit assumptions, our aim is to recognize limitations that the field is
currently unaware of. We do this by inverting the existing approach: we start
by capturing existing methods in generic terms, and subsequently, from these
generic descriptions we derive the click behavior for which these methods can
be unbiased. Our inverted approach reveals that there are indeed implicit
limitations to the counterfactual LTR approach: we find counterfactual
estimation can only produce unbiased methods for click behavior based on affine
transformations. In addition, we also recognize previously undiscussed
limitations of click-modelling and pairwise approaches to click-based LTR. Our
findings reveal that it is impossible for existing approaches to provide
unbiasedness guarantees for all plausible click behavior models.

Understanding and controlling engineered quantum systems is key to developing
practical quantum technology. However, given the current technological
limitations, such as fabrication imperfections and environmental noise, this is
not always possible. To address these issues, a great deal of theoretical and
numerical methods for quantum system identification and control have been
developed. These methods range from traditional curve fittings, which are
limited by the accuracy of the model that describes the system, to machine
learning methods, which provide efficient control solutions but no control
beyond the output of the model, nor insights into the underlying physical
process. Here we experimentally demonstrate a "graybox" approach for
characterizing and controlling quantum devices. We report superior performance
over model fitting, while generating unitaries and Hamiltonians which are
inaccessible with machine learning techniques. Our approach combines physics
principles with high accuracy machine learning and is effective with any
problem where the required controlled quantities are not directly accessible
from the training set. This method naturally extends to time-dependent and open
quantum systems, with applications in quantum noise spectroscopy and
cancellation.

Adaptive control can be applied to robotic systems with parameter
uncertainties, but improving its performance is usually difficult, especially
under discontinuous friction. Inspired by the human motor learning control
mechanism, an adaptive learning control approach is proposed for a broad class
of robotic systems with discontinuous friction, where a composite error
learning technique that exploits data memory is employed to enhance parameter
estimation. Compared with the classical feedback error learning control, the
proposed approach can achieve superior transient and steady-state tracking
without high-gain feedback and persistent excitation at the cost of extra
computational burden and memory usage. The performance improvement of the
proposed approach has been verified by experiments based on a DENSO industrial
robot.

In this paper, we provide a systematic approach for assessing and comparing
the computational complexity of neural network layers in digital signal
processing. We provide and link four software-to-hardware complexity measures,
defining how the different complexity metrics relate to the layers'
hyper-parameters. This paper explains how to compute these four metrics for
feed-forward and recurrent layers, and defines in which case we ought to use a
particular metric depending on whether we characterize a more soft- or
hardware-oriented application. One of the four metrics, called `the number of
additions and bit shifts (NABS)', is newly introduced for heterogeneous
quantization. NABS characterizes the impact of not only the bitwidth used in
the operation but also the type of quantization used in the arithmetical
operations. We intend this work to serve as a baseline for the different levels
(purposes) of complexity estimation related to the neural networks' application
in real-time digital signal processing, aiming at unifying the computational
complexity estimation.

Sequence clustering in a streaming environment is challenging because it is
computationally expensive, and the sequences may evolve over time. K-medoids or
Partitioning Around Medoids (PAM) is commonly used to cluster sequences since
it supports alignment-based distances, and the k-centers being actual data
items helps with cluster interpretability. However, offline k-medoids has no
support for concept drift, while also being prohibitively expensive for
clustering data streams. We therefore propose SECLEDS, a streaming variant of
the k-medoids algorithm with constant memory footprint. SECLEDS has two unique
properties: i) it uses multiple medoids per cluster, producing stable
high-quality clusters, and ii) it handles concept drift using an intuitive
Medoid Voting scheme for approximating cluster distances. Unlike existing
adaptive algorithms that create new clusters for new concepts, SECLEDS follows
a fundamentally different approach, where the clusters themselves evolve with
an evolving stream. Using real and synthetic datasets, we empirically
demonstrate that SECLEDS produces high-quality clusters regardless of drift,
stream size, data dimensionality, and number of clusters. We compare against
three popular stream and batch clustering algorithms. The state-of-the-art
BanditPAM is used as an offline benchmark. SECLEDS achieves comparable F1 score
to BanditPAM while reducing the number of required distance computations by
83.7%. Importantly, SECLEDS outperforms all baselines by 138.7% when the stream
contains drift. We also cluster real network traffic, and provide evidence that
SECLEDS can support network bandwidths of up to 1.08 Gbps while using the
(expensive) dynamic time warping distance.

Traffic congestion is a serious problem in urban areas. Dynamic congestion
pricing is one of the useful schemes to eliminate traffic congestion in
strategic scale. However, in the reality, an optimal dynamic congestion pricing
is very difficult or impossible to determine theoretically, because road
networks are usually large and complicated, and behavior of road users is
uncertain. To account for this challenge, this work proposes a dynamic
congestion pricing method using deep reinforcement learning (DRL). It is
designed to eliminate traffic congestion based on observable data in general
large-scale road networks, by leveraging the data-driven nature of deep
reinforcement learning. One of the novel elements of the proposed method is the
distributed and cooperative learning scheme. Specifically, the DRL is
implemented by a spatial-temporally distributed manner, and cooperation among
DRL agents is established by novel techniques we call spatially shared reward
and temporally switching learning. It enables fast and computationally
efficient learning in large-scale networks. The numerical experiments using
Sioux Falls Network showed that the proposed method works well thanks to the
novel learning scheme.

Defensive Pass Interference (DPI) is one of the most impactful penalties in
the NFL. DPI is a spot foul, yielding an automatic first down to the team in
possession. With such an influence on the game, referees have no room for a
mistake. It is also a very rare event, which happens 1-2 times per 100 pass
attempts. With technology improving and many IoT wearables being put on the
athletes to collect valuable data, there is a solid ground for applying machine
learning (ML) techniques to improve every aspect of the game. The work
presented here is the first attempt in predicting DPI using player tracking GPS
data. The data we used was collected by NFL's Next Gen Stats throughout the
2018 regular season. We present ML models for highly imbalanced time-series
binary classification: LSTM, GRU, ANN, and Multivariate LSTM-FCN. Results
showed that using GPS tracking data to predict DPI has limited success. The
best performing models had high recall with low precision which resulted in the
classification of many false positive examples. Looking closely at the data
confirmed that there is just not enough information to determine whether a foul
was committed. This study might serve as a filter for multi-step pipeline for
video sequence classification which could be able to solve this problem.

Effective decision making involves flexibly relating past experiences and
relevant contextual information to a novel situation. In deep reinforcement
learning, the dominant paradigm is for an agent to amortise information that
helps decision-making into its network weights via gradient descent on training
losses. Here, we pursue an alternative approach in which agents can utilise
large-scale context-sensitive database lookups to support their parametric
computations. This allows agents to directly learn in an end-to-end manner to
utilise relevant information to inform their outputs. In addition, new
information can be attended to by the agent, without retraining, by simply
augmenting the retrieval dataset. We study this approach in Go, a challenging
game for which the vast combinatorial state space privileges generalisation
over direct matching to past experiences. We leverage fast, approximate nearest
neighbor techniques in order to retrieve relevant data from a set of tens of
millions of expert demonstration states. Attending to this information provides
a significant boost to prediction accuracy and game-play performance over
simply using these demonstrations as training trajectories, providing a
compelling demonstration of the value of large-scale retrieval in reinforcement
learning agents.

The deformation of cellular membranes regulates trafficking processes, such
as exocytosis and endocytosis. Classically, the Helfrich continuum model is
used to characterize the forces and mechanical parameters that cells tune to
accomplish membrane shape changes. While this classical model effectively
captures curvature generation, one of the core challenges in using it to
approximate a biological process is selecting a set of mechanical parameters
(including bending modulus and membrane tension) from a large set of reasonable
values. We used the Helfrich model to generate a large synthetic dataset from a
random sampling of realistic mechanical parameters and used this dataset to
train machine learning models. These models produced promising results,
accurately classifying model behavior and predicting membrane shape from
mechanical parameters. We also note emerging methods in machine learning that
can leverage the physical insight of the Helfrich model to improve performance
and draw greater insight into how cells control membrane shape change.

Very High Energy (VHE) gamma rays and charged cosmic rays (CCRs) provide an
observational window into the acceleration mechanisms of extreme astrophysical
environments. One of the major challenges at Imaging Air Cherenkov Telescopes
(IACTs) designed to look for VHE gamma rays, is the separation of air showers
initiated by CCRs which form a background to gamma ray searches. Two other less
well studied problems at IACTs are a) the classification of different primary
nuclei among the CCR events and b) identification of anomalous events initiated
by Beyond Standard Model particles that could give rise to shower signatures
which differ from the standard images of either gamma rays or CCR showers. The
problems of categorizing the primary particle that initiates a shower image, or
the problem of tagging anomalous shower events in a model independent way, are
problems that are well suited to a machine learning (ML) approach. Traditional
studies that have explored gamma ray/CCR separation have used a multivariate
analysis based on derived shower properties, which contains significantly
reduced information about the shower. In our work, we address the problems
outlined above by using ML architectures trained on full simulated shower
images, as opposed to training on just a few derived shower properties. We
illustrate the techniques of binary and multi-category classification using
convolutional neural networks, and we also pioneer the use of autoencoders for
anomaly detection at VHE gamma ray experiments. As a case study, we apply our
techniques to the H.E.S.S. experiment. However, the real strength of the
techniques that we broach here in the context of VHE gamma ray observatories,
is that these methods can be applied broadly to any other IACT, such as the
upcoming Cherenkov Telescope Array (CTA), or can even be suitably adapted to
CCR experiments.

We investigate whether self-supervised learning (SSL) can improve online
reinforcement learning (RL) from pixels. We extend the contrastive
reinforcement learning framework (e.g., CURL) that jointly optimizes SSL and RL
losses and conduct an extensive amount of experiments with various
self-supervised losses. Our observations suggest that the existing SSL
framework for RL fails to bring meaningful improvement over the baselines only
taking advantage of image augmentation when the same amount of data and
augmentation is used. We further perform evolutionary searches to find the
optimal combination of multiple self-supervised losses for RL, but find that
even such a loss combination fails to meaningfully outperform the methods that
only utilize carefully designed image augmentations. After evaluating these
approaches together in multiple different environments including a real-world
robot environment, we confirm that no single self-supervised loss or image
augmentation method can dominate all environments and that the current
framework for joint optimization of SSL and RL is limited. Finally, we conduct
the ablation study on multiple factors and demonstrate the properties of
representations learned with different approaches.

We consider the classic question of state tomography: given copies of an
unknown quantum state $\rho\in\mathbb{C}^{d\times d}$, output $\widehat{\rho}$
for which $\|\rho - \widehat{\rho}\|_{\mathsf{tr}} \le \varepsilon$. When one
is allowed to make coherent measurements entangled across all copies,
$\Theta(d^2/\varepsilon^2)$ copies are necessary and sufficient [Haah et al.
'17, O'Donnell-Wright '16]. Unfortunately, the protocols achieving this rate
incur large quantum memory overheads that preclude implementation on current or
near-term devices. On the other hand, the best known protocol using incoherent
(single-copy) measurements uses $O(d^3/\varepsilon^2)$ copies
[Kueng-Rauhut-Terstiege '17], and multiple papers have posed it as an open
question to understand whether or not this rate is tight. In this work, we
fully resolve this question, by showing that any protocol using incoherent
measurements, even if they are chosen adaptively, requires
$\Omega(d^3/\varepsilon^2)$ copies, matching the upper bound of
[Kueng-Rauhut-Terstiege '17].
  We do so by a new proof technique which directly bounds the "tilt" of the
posterior distribution after measurements, which yields a surprisingly short
proof of our lower bound, and which we believe may be of independent interest.

While machine learning models rapidly advance the state-of-the-art on various
real-world tasks, out-of-domain (OOD) generalization remains a challenging
problem given the vulnerability of these models to spurious correlations. We
propose a balanced mini-batch sampling strategy to transform a biased data
distribution into a spurious-free balanced distribution, based on the
invariance of the underlying causal mechanisms for the data generation process.
We argue that the Bayes optimal classifiers trained on such balanced
distribution are minimax optimal across a diverse enough environment space. We
also provide an identifiability guarantee of the latent variable model of the
proposed data generation process, when utilizing enough train environments.
Experiments are conducted on DomainBed, demonstrating empirically that our
method obtains the best performance across 20 baselines reported on the

We study the use of amortized optimization to predict optimal transport (OT)
maps from the input measures, which we call Meta OT. This helps repeatedly
solve similar OT problems between different measures by leveraging the
knowledge and information present from past problems to rapidly predict and
solve new problems. Otherwise, standard methods ignore the knowledge of the
past solutions and suboptimally re-solve each problem from scratch. Meta OT
models surpass the standard convergence rates of log-Sinkhorn solvers in the
discrete setting and convex potentials in the continuous setting. We improve
the computational time of standard OT solvers by multiple orders of magnitude
in discrete and continuous transport settings between images, spherical data,
and color palettes. Our source code is available at
http://github.com/facebookresearch/meta-ot.

Many real-world recognition problems suffer from an imbalanced or long-tailed
label distribution. Those distributions make representation learning more
challenging due to limited generalization over the tail classes. If the test
distribution differs from the training distribution, e.g. uniform versus
long-tailed, the problem of the distribution shift needs to be addressed. To
this aim, recent works have extended softmax cross-entropy using margin
modifications, inspired by Bayes' theorem. In this paper, we generalize several
approaches with a Balanced Product of Experts (BalPoE), which combines a family
of models with different test-time target distributions to tackle the imbalance
in the data. The proposed experts are trained in a single stage, either jointly
or independently, and fused seamlessly into a BalPoE. We show that BalPoE is
Fisher consistent for minimizing the balanced error and perform extensive
experiments to validate the effectiveness of our approach. Finally, we
investigate the effect of Mixup in this setting, discovering that
regularization is a key ingredient for learning calibrated experts. Our
experiments show that a regularized BalPoE can perform remarkably well in test
accuracy and calibration metrics, leading to state-of-the-art results on
CIFAR-100-LT, ImageNet-LT, and iNaturalist-2018 datasets. The code will be made
publicly available upon paper acceptance.

Self-supervised contrastive learning is a powerful tool to learn visual
representation without labels. Prior work has primarily focused on evaluating
the recognition accuracy of various pre-training algorithms, but has overlooked
other behavioral aspects. In addition to accuracy, distributional robustness
plays a critical role in the reliability of machine learning models. We design
and conduct a series of robustness tests to quantify the behavioral differences
between contrastive learning and supervised learning to downstream or
pre-training data distribution changes. These tests leverage data corruptions
at multiple levels, ranging from pixel-level gamma distortion to patch-level
shuffling and to dataset-level distribution shift. Our tests unveil intriguing
robustness behaviors of contrastive and supervised learning. On the one hand,
under downstream corruptions, we generally observe that contrastive learning is
surprisingly more robust than supervised learning. On the other hand, under
pre-training corruptions, we find contrastive learning vulnerable to patch
shuffling and pixel intensity change, yet less sensitive to dataset-level
distribution change. We attempt to explain these results through the role of
data augmentation and feature space properties. Our insight has implications in
improving the downstream robustness of supervised learning.

We study sequential decision-making with known rewards and unknown
constraints, motivated by situations where the constraints represent
expensive-to-evaluate human preferences, such as safe and comfortable driving
behavior. We formalize the challenge of interactively learning about these
constraints as a novel linear bandit problem which we call constrained linear
best-arm identification. To solve this problem, we propose the Adaptive
Constraint Learning (ACOL) algorithm. We provide an instance-dependent lower
bound for constrained linear best-arm identification and show that ACOL's
sample complexity matches the lower bound in the worst-case. In the average
case, ACOL's sample complexity bound is still significantly tighter than bounds
of simpler approaches. In synthetic experiments, ACOL performs on par with an
oracle solution and outperforms a range of baselines. As an application, we
consider learning constraints to represent human preferences in a driving
simulation. ACOL is significantly more sample efficient than alternatives for
this application. Further, we find that learning preferences as constraints is
more robust to changes in the driving scenario than encoding the preferences
directly in the reward function.

Previous work generally believes that improving the spatial invariance of
convolutional networks is the key to object counting. However, after verifying
several mainstream counting networks, we surprisingly found too strict
pixel-level spatial invariance would cause overfit noise in the density map
generation. In this paper, we try to use locally connected Gaussian kernels to
replace the original convolution filter to estimate the spatial position in the
density map. The purpose of this is to allow the feature extraction process to
potentially stimulate the density map generation process to overcome the
annotation noise. Inspired by previous work, we propose a low-rank
approximation accompanied with translation invariance to favorably implement
the approximation of massive Gaussian convolution. Our work points a new
direction for follow-up research, which should investigate how to properly
relax the overly strict pixel-level spatial invariance for object counting. We
evaluate our methods on 4 mainstream object counting networks (i.e., MCNN,
CSRNet, SANet, and ResNet-50). Extensive experiments were conducted on 7
popular benchmarks for 3 applications (i.e., crowd, vehicle, and plant
counting). Experimental results show that our methods significantly outperform
other state-of-the-art methods and achieve promising learning of the spatial
position of objects.

We study how networking corruptions--data corruptions caused by networking
errors--affect video machine learning (ML) models. We discover apparent
networking corruptions in Kinetics-400, a benchmark video ML dataset. In a
simulation study, we investigate (1) what artifacts networking corruptions
cause, (2) how such artifacts affect ML models, and (3) whether standard
robustness methods can mitigate their negative effects. We find that networking
corruptions cause visual and temporal artifacts (i.e., smeared colors or frame
drops). These networking corruptions degrade performance on a variety of video
ML tasks, but effects vary by task and dataset, depending on how much temporal
context the tasks require. Lastly, we evaluate data augmentation--a standard
defense for data corruptions--but find that it does not recover performance.

We study monotone inclusions and monotone variational inequalities, as well
as their generalizations to non-monotone settings. We first show that the Extra
Anchored Gradient (EAG) algorithm, originally proposed by Yoon and Ryu [2021]
for unconstrained convex-concave min-max optimization, can be applied to solve
the more general problem of Lipschitz monotone inclusion. More specifically, we
prove that the EAG solves Lipschitz monotone inclusion problems with an
accelerated convergence rate of $O(\frac{1}{T})$, which is optimal among all
first-order methods [Diakonikolas, 2020, Yoon and Ryu, 2021]. Our second result
is an accelerated forward-backward splitting algorithm (AS), which not only
achieves the accelerated $O(\frac{1}{T})$ convergence rate for all monotone
inclusion problems, but also exhibits the same accelerated rate for a family of
general (non-monotone) inclusion problems that concern negative comonotone
operators. As a special case of our second result, AS enjoys the
$O(\frac{1}{T})$ convergence rate for solving a non-trivial class of
nonconvex-nonconcave min-max optimization problems. Our analyses are based on
simple potential function arguments, which might be useful for analysing other
accelerated algorithms.

We study the problem of list-decodable sparse mean estimation. Specifically,
for a parameter $\alpha \in (0, 1/2)$, we are given $m$ points in
$\mathbb{R}^n$, $\lfloor \alpha m \rfloor$ of which are i.i.d. samples from a
distribution $D$ with unknown $k$-sparse mean $\mu$. No assumptions are made on
the remaining points, which form the majority of the dataset. The goal is to
return a small list of candidates containing a vector $\widehat \mu$ such that
$\| \widehat \mu - \mu \|_2$ is small. Prior work had studied the problem of
list-decodable mean estimation in the dense setting. In this work, we develop a
novel, conceptually simpler technique for list-decodable mean estimation. As
the main application of our approach, we provide the first sample and
computationally efficient algorithm for list-decodable sparse mean estimation.
In particular, for distributions with ``certifiably bounded'' $t$-th moments in
$k$-sparse directions and sufficiently light tails, our algorithm achieves
error of $(1/\alpha)^{O(1/t)}$ with sample complexity $m =
(k\log(n))^{O(t)}/\alpha$ and running time $\mathrm{poly}(mn^t)$. For the
special case of Gaussian inliers, our algorithm achieves the optimal error
guarantee of $\Theta (\sqrt{\log(1/\alpha)})$ with quasi-polynomial sample and
computational complexity. We complement our upper bounds with nearly-matching
statistical query and low-degree polynomial testing lower bounds.

Real-Time Bidding (RTB) is an important mechanism in modern online
advertising systems. Advertisers employ bidding strategies in RTB to optimize
their advertising effects subject to various financial requirements, especially
the return-on-investment (ROI) constraint. ROIs change non-monotonically during
the sequential bidding process, and often induce a see-saw effect between
constraint satisfaction and objective optimization. While some existing
approaches show promising results in static or mildly changing ad markets, they
fail to generalize to highly dynamic ad markets with ROI constraints, due to
their inability to adaptively balance constraints and objectives amidst
non-stationarity and partial observability. In this work, we specialize in
ROI-Constrained Bidding in non-stationary markets. Based on a Partially
Observable Constrained Markov Decision Process, our method exploits an
indicator-augmented reward function free of extra trade-off parameters and
develops a Curriculum-Guided Bayesian Reinforcement Learning (CBRL) framework
to adaptively control the constraint-objective trade-off in non-stationary ad
markets. Extensive experiments on a large-scale industrial dataset with two
problem settings reveal that CBRL generalizes well in both in-distribution and
out-of-distribution data regimes, and enjoys superior learning efficiency and
stability.

There has been a recent surge of interest in automating software engineering
tasks using deep learning. This work addresses the problem of code generation
where the goal is to generate target code given source code in a different
language or a natural language description. Most of the state-of-the-art deep
learning models for code generation use training strategies that are primarily
designed for natural language. However, understanding and generating code
requires a more rigorous comprehension of the code syntax and semantics. With
this motivation, we develop an encoder-decoder Transformer model where both the
encoder and decoder are trained to recognize the syntax and data flow in the
source and target codes, respectively. We not only make the encoder
structure-aware by leveraging the source code's syntax tree and data flow
graph, but we also ensure that our decoder preserves the syntax and data flow
of the target code by introducing two auxiliary tasks: AST (Abstract Syntax
Tree) paths prediction and data flow prediction. To the best of our knowledge,
this is the first work to introduce a structure-aware Transformer decoder to
enhance the quality of generated code by modeling target syntax and data flow.
The proposed StructCoder model achieves state-of-the-art performance on code
translation and text-to-code generation tasks in the CodeXGLUE benchmark.

We construct locally robust/orthogonal moments in a semiparametric
U-statistics setting. These are quadratic moments in the distribution of the
data with a zero derivative with respect to first steps at their limit, which
reduces model selection bias with machine learning first steps. We use
orthogonal moments to propose new debiased estimators and valid inferences in a
variety of applications ranging from Inequality of Opportunity (IOp) to
distributional treatment effects. U-statistics with machine learning first
steps arise naturally in these and many other applications. A leading example
in IOp is the Gini coefficient of machine learning fitted values. We introduce
a novel U-moment representation of the First Step Influence Function (U-FSIF)
to take into account the effect of the first step estimation on an identifying
quadratic moment. Adding the U-FISF to the identifying quadratic moment gives
rise to an orthogonal quadratic moment. Our leading and motivational
application is to measuring IOp, for which we propose a simple debiased
estimator, and the first available inferential methods. We give general and
simple regularity conditions for asymptotic theory, and demonstrate an improved
finite sample performance in simulations for our debiased measures of IOp. In
an empirical application, we find that standard measures of IOp are about six
times more sensitive to first step machine learners than our debiased measures,
and that between $42\%$ and $46\%$ of income inequality in Spain is explained
by circumstances out of the control of the individual.

By providing unprecedented access to computational resources, cloud computing
has enabled rapid growth in technologies such as machine learning, the
computational demands of which incur a high energy cost and a commensurate
carbon footprint. As a result, recent scholarship has called for better
estimates of the greenhouse gas impact of AI: data scientists today do not have
easy or reliable access to measurements of this information, precluding
development of actionable tactics. Cloud providers presenting information about
software carbon intensity to users is a fundamental stepping stone towards
minimizing emissions. In this paper, we provide a framework for measuring
software carbon intensity, and propose to measure operational carbon emissions
by using location-based and time-specific marginal emissions data per energy
unit. We provide measurements of operational software carbon intensity for a
set of modern models for natural language processing and computer vision, and a
wide range of model sizes, including pretraining of a 6.1 billion parameter
language model. We then evaluate a suite of approaches for reducing emissions
on the Microsoft Azure cloud compute platform: using cloud instances in
different geographic regions, using cloud instances at different times of day,
and dynamically pausing cloud instances when the marginal carbon intensity is
above a certain threshold. We confirm previous results that the geographic
region of the data center plays a significant role in the carbon intensity for
a given cloud instance, and find that choosing an appropriate region can have
the largest operational emissions reduction impact. We also show that the time
of day has notable impact on operational software carbon intensity. Finally, we
conclude with recommendations for how machine learning practitioners can use
software carbon intensity information to reduce environmental impact.

We study the problem of maximum likelihood estimation of densities that are
log-concave and lie in the graphical model corresponding to a given undirected
graph $G$. We show that the maximum likelihood estimate (MLE) is the product of
the exponentials of several tent functions, one for each maximal clique of $G$.
While the set of log-concave densities in a graphical model is
infinite-dimensional, our results imply that the MLE can be found by solving a
finite-dimensional convex optimization problem. We provide an implementation
and a few examples. Furthermore, we show that the MLE exists and is unique with
probability 1 as long as the number of sample points is larger than the size of
the largest clique of $G$ when $G$ is chordal. We show that the MLE is
consistent when the graph $G$ is a disjoint union of cliques. Finally, we
discuss the conditions under which a log-concave density in the graphical model
of $G$ has a log-concave factorization according to $G$.

Several machine learning frameworks for augmenting turbulence closure models
have been recently proposed. However, the generalizability of an augmented
turbulence model remains an open question. We investigate this question by
systematically varying the training and test sets of several models. An optimal
three-term tensor basis expansion is used to develop a model-agnostic
data-driven turbulence closure approximation. Then, hyperparameter optimization
was performed for a random forest, a neural network, and an eXtreme Gradient
Boosting (XGBoost) model. We recommend XGBoost for data-driven turbulence
closure modelling owing to its low-tuning cost and good performance. We also
find that machine learning models generalize well to new parametric variations
of flows seen in the training dataset, but lack generalizability to new flow
types. This generalizability gap suggests that machine learning methods are
most suited for developing specialized models for a given flow type, a problem
often encountered in industrial applications.

Unets have become the standard method for semantic segmentation of medical
images, along with fully convolutional networks (FCN). Unet++ was introduced as
a variant of Unet, in order to solve some of the problems facing Unet and FCNs.
Unet++ provided networks with an ensemble of variable depth Unets, hence
eliminating the need for professionals estimating the best suitable depth for a
task. While Unet and all its variants, including Unet++ aimed at providing
networks that were able to train well without requiring large quantities of
annotated data, none of them attempted to eliminate the need for pixel-wise
annotated data altogether. Obtaining such data for each disease to be diagnosed
comes at a high cost. Hence such data is scarce. In this paper we use
contrastive learning to train Unet++ for semantic segmentation of medical
images using medical images from various sources including magnetic resonance
imaging (MRI) and computed tomography (CT), without the need for pixel-wise
annotations. Here we describe the architecture of the proposed model and the
training method used. This is still a work in progress and so we abstain from
including results in this paper. The results and the trained model would be
made available upon publication or in subsequent versions of this paper on
arxiv.

The recent advances of deep learning have dramatically changed how machine
learning, especially in the domain of natural language processing, can be
applied to legal domain. However, this shift to the data-driven approaches
calls for larger and more diverse datasets, which are nevertheless still small
in number, especially in non-English languages. Here we present the first
large-scale benchmark of Korean legal AI datasets, LBOX OPEN, that consists of
one legal corpus, two classification tasks, two legal judgement prediction
(LJP) tasks, and one summarization task. The legal corpus consists of 147k
Korean precedents (259M tokens), of which 63k are sentenced in last 4 years and
96k are from the first and the second level courts in which factual issues are
reviewed. The two classification tasks are case names (11.3k) and statutes
(2.8k) prediction from the factual description of individual cases. The LJP
tasks consist of (1) 10.5k criminal examples where the model is asked to
predict fine amount, imprisonment with labor, and imprisonment without labor
ranges for the given facts, and (2) 4.7k civil examples where the inputs are
facts and claim for relief and outputs are the degrees of claim acceptance. The
summarization task consists of the Supreme Court precedents and the
corresponding summaries (20k). We also release realistic variants of the
datasets by extending the domain (1) to infrequent case categories in case name
(31k examples) and statute (17.7k) classification tasks, and (2) to long input
sequences in the summarization task (51k). Finally, we release LCUBE, the first
Korean legal language model trained on the legal corpus from this study. Given
the uniqueness of the Law of South Korea and the diversity of the legal tasks
covered in this work, we believe that LBOX OPEN contributes to the
multilinguality of global legal research. LBOX OPEN and LCUBE will be publicly

Any human activity can be represented as a temporal sequence of actions
performed to achieve a certain goal. Unlike machine-made time series, these
action sequences are highly disparate as the time taken to finish a similar
action might vary between different persons. Therefore, understanding the
dynamics of these sequences is essential for many downstream tasks such as
activity length prediction, goal prediction, etc. Existing neural approaches
that model an activity sequence are either limited to visual data or are task
specific, i.e., limited to next action or goal prediction. In this paper, we
present ProActive, a neural marked temporal point process (MTPP) framework for
modeling the continuous-time distribution of actions in an activity sequence
while simultaneously addressing three high-impact problems -- next action
prediction, sequence-goal prediction, and end-to-end sequence generation.
Specifically, we utilize a self-attention module with temporal normalizing
flows to model the influence and the inter-arrival times between actions in a
sequence. Moreover, for time-sensitive prediction, we perform an early
detection of sequence goal via a constrained margin-based optimization
procedure. This in-turn allows ProActive to predict the sequence goal using a
limited number of actions. Extensive experiments on sequences derived from
three activity recognition datasets show the significant accuracy boost of
ProActive over the state-of-the-art in terms of action and goal prediction, and
the first-ever application of end-to-end action sequence generation.

K-Means clustering algorithm is one of the most commonly used clustering
algorithms because of its simplicity and efficiency. K-Means clustering
algorithm based on Euclidean distance only pays attention to the linear
distance between samples, but ignores the overall distribution structure of the
dataset (i.e. the fluid structure of dataset). Since it is difficult to
describe the internal structure of two data points by Euclidean distance in
high-dimensional data space, we propose a new distance measurement, namely,
view-distance, and apply it to the K-Means algorithm. On the classical manifold
learning datasets, S-curve and Swiss roll datasets, not only this new distance
can cluster the data according to the structure of the data itself, but also
the boundaries between categories are neat dividing lines. Moreover, we also
tested the classification accuracy and clustering effect of the K-Means
algorithm based on view-distance on some real-world datasets. The experimental
results show that, on most datasets, the K-Means algorithm based on
view-distance has a certain degree of improvement in classification accuracy
and clustering effect.

Within Process mining, discovery techniques had made it possible to construct
business process models automatically from event logs. However, results often
do not achieve the balance between model complexity and its fitting accuracy,
so there is a need for manual model adjusting. The paper presents an approach
to process mining providing semi-automatic support to model optimization based
on the combined assessment of the model complexity and fitness. To balance
between the two ingredients, a model simplification approach is proposed, which
essentially abstracts the raw model at the desired granularity. Additionally,
we introduce a concept of meta-states, a cycle collapsing in the model, which
can potentially simplify the model and interpret it. We aim to demonstrate the
capabilities of the technological solution using three datasets from different
applications in the healthcare domain. They are remote monitoring process for
patients with arterial hypertension and workflows of healthcare workers during
the COVID-19 pandemic. A case study also investigates the use of various
complexity measures and different ways of solution application providing
insights on better practices in improving interpretability and
complexity/fitness balance in process models.

The application of Bayesian inference for the purpose of model selection is
very popular nowadays. In this framework, models are compared through their
marginal likelihoods, or their quotients, called Bayes factors. However,
marginal likelihoods depends on the prior choice. For model selection, even
diffuse priors can be actually very informative, unlike for the parameter
estimation problem. Furthermore, when the prior is improper, the marginal
likelihood of the corresponding model is undetermined. In this work, we discuss
the issue of prior sensitivity of the marginal likelihood and its role in model
selection. We also comment on the use of uninformative priors, which are very
common choices in practice. Several practical suggestions are discussed and
many possible solutions, proposed in the literature, to design objective priors
for model selection are described. Some of them also allow the use of improper
priors. The connection between the marginal likelihood approach and the
well-known information criteria is also presented. We describe the main issues
and possible solutions by illustrative numerical examples, providing also some
related code. One of them involving a real-world application on exoplanet

Federated learning (FL), where data remains at the federated clients, and
where only gradient updates are shared with a central aggregator, was assumed
to be private. Recent work demonstrates that adversaries with gradient-level
access can mount successful inference and reconstruction attacks. In such
settings, differentially private (DP) learning is known to provide resilience.
However, approaches used in the status quo (\ie central and local DP) introduce
disparate utility vs. privacy trade-offs. In this work, we take the first step
towards mitigating such trade-offs through {\em hierarchical FL (HFL)}. We
demonstrate that by the introduction of a new intermediary level where
calibrated DP noise can be added, better privacy vs. utility trade-offs can be
obtained; we term this {\em hierarchical DP (HDP)}. Our experiments with 3
different datasets (commonly used as benchmarks for FL) suggest that HDP
produces models as accurate as those obtained using central DP, where noise is
added at a central aggregator. Such an approach also provides comparable
benefit against inference adversaries as in the local DP case, where noise is
added at the federated clients.

A dynamic mean field theory is developed for model based Bayesian
reinforcement learning in the large state space limit. In an analogy with the
statistical physics of disordered systems, the transition probabilities are
interpreted as couplings, and value functions as deterministic spins, and thus
the sampled transition probabilities are considered to be quenched random
variables. The results reveal that, under standard assumptions, the posterior
over Q-values is asymptotically independent and Gaussian across state-action
pairs, for infinite horizon problems. The finite horizon case exhibits the same
behaviour for all state-actions pairs at each time but has an additional
correlation across time, for each state-action pair. The results also hold for
policy evaluation. The Gaussian statistics can be computed from a set of
coupled mean field equations derived from the Bellman equation, which we call
dynamic mean field programming (DMFP). For Q-value iteration, approximate
equations are obtained by appealing to extreme value theory, and closed form
expressions are found in the independent and identically distributed case. The
Lyapunov stability of these closed form equations is studied.

Algorithms such as Differentially Private SGD enable training machine
learning models with formal privacy guarantees. However, there is a discrepancy
between the protection that such algorithms guarantee in theory and the
protection they afford in practice. An emerging strand of work empirically
estimates the protection afforded by differentially private training as a
confidence interval for the privacy budget $\varepsilon$ spent on training a
model. Existing approaches derive confidence intervals for $\varepsilon$ from
confidence intervals for the false positive and false negative rates of
membership inference attacks. Unfortunately, obtaining narrow high-confidence
intervals for $\epsilon$ using this method requires an impractically large
sample size and training as many models as samples. We propose a novel Bayesian
method that greatly reduces sample size, and adapt and validate a heuristic to
draw more than one sample per trained model. Our Bayesian method exploits the
hypothesis testing interpretation of differential privacy to obtain a posterior
for $\varepsilon$ (not just a confidence interval) from the joint posterior of
the false positive and false negative rates of membership inference attacks.
For the same sample size and confidence, we derive confidence intervals for
$\varepsilon$ around 40% narrower than prior work. The heuristic, which we
adapt from label-only DP, can be used to further reduce the number of trained
models needed to get enough samples by up to 2 orders of magnitude.

Metaphor generation is a challenging task which can impact many downstream
tasks such as improving user satisfaction with dialogue systems and story
generation. This paper tackles the problem of Chinese nominal metaphor
generation by introducing a multitask metaphor generation framework with
self-training and metaphor identification mechanisms. Self-training addresses
the data scarcity issue of metaphor datasets. That is, instead of solely
relying on labelled metaphor datasets which are usually small in size,
self-training helps identify potential metaphors from a large-scale unlabelled
corpus for metaphor generation. The metaphor weighting mechanism enables our
model to focus on the metaphor-related parts of the input (e.g., the comparison
of the metaphor and comparator) during model learning and thus improves the
metaphoricity of the generated metaphors. Our model is trained on an annotated
corpus consisting of 6.3k sentences that contain diverse metaphorical
expressions. Experimental results show that our model is able to generate
metaphors with better readability and creativity compared to the baseline
models, even in the situation where training data is insufficient.

Embedding of large but redundant data, such as images or text, in a hierarchy
of lower-dimensional spaces is one of the key features of representation
learning approaches, which nowadays provide state-of-the-art solutions to
problems once believed hard or impossible to solve. In this work, in a plot
twist with a strong meta aftertaste, we show how trained deep models are as
redundant as the data they are optimized to process, and how it is therefore
possible to use deep learning models to embed deep learning models. In
particular, we show that it is possible to use representation learning to learn
a fixed-size, low-dimensional embedding space of trained deep models and that
such space can be explored by interpolation or optimization to attain
ready-to-use models. We find that it is possible to learn an embedding space of
multiple instances of the same architecture and of multiple architectures. We
address image classification and neural representation of signals, showing how
our embedding space can be learnt so as to capture the notions of performance
and 3D shape, respectively. In the Multi-Architecture setting we also show how
an embedding trained only on a subset of architectures can learn to generate
already-trained instances of architectures it never sees instantiated at

The FedProx algorithm is a simple yet powerful distributed proximal point
optimization method widely used for federated learning (FL) over heterogeneous
data. Despite its popularity and remarkable success witnessed in practice, the
theoretical understanding of FedProx is largely underinvestigated: the
appealing convergence behavior of FedProx is so far characterized under certain
non-standard and unrealistic dissimilarity assumptions of local functions, and
the results are limited to smooth optimization problems. In order to remedy
these deficiencies, we develop a novel local dissimilarity invariant
convergence theory for FedProx and its minibatch stochastic extension through
the lens of algorithmic stability. As a result, we contribute to derive several
new and deeper insights into FedProx for non-convex federated optimization
including: 1) convergence guarantees independent on local dissimilarity type
conditions; 2) convergence guarantees for non-smooth FL problems; and 3) linear
speedup with respect to size of minibatch and number of sampled devices. Our
theory for the first time reveals that local dissimilarity and smoothness are
not must-have for FedProx to get favorable complexity bounds. Preliminary
experimental results on a series of benchmark FL datasets are reported to
demonstrate the benefit of minibatching for improving the sample efficiency of
FedProx.

Learning representations with self-supervision for convolutional networks
(CNN) has proven effective for vision tasks. As an alternative for CNN, vision
transformers (ViTs) emerge strong representation ability with the pixel-level
self-attention and channel-level feed-forward networks. Recent works reveal
that self-supervised learning helps unleash the great potential of ViTs. Still,
most works follow self-supervised strategy designed for CNNs, e.g.,
instance-level discrimination of samples, but they ignore the unique properties
of ViTs. We observe that modeling relations among pixels and channels
distinguishes ViTs from other networks. To enforce this property, we explore
the feature self-relations for training self-supervised ViTs. Specifically,
instead of conducting self-supervised learning solely on feature embeddings
from multiple views, we utilize the feature self-relations, i.e.,
pixel/channel-level self-relations, for self-supervised learning. Self-relation
based learning further enhance the relation modeling ability of ViTs, resulting
in strong representations that stably improve performance on multiple
downstream tasks. Our source code will be made publicly available.

We develop data-driven methods incorporating geometric and topological
information to learn parsimonious representations of nonlinear dynamics from
observations. We develop approaches for learning nonlinear state space models
of the dynamics for general manifold latent spaces using training strategies
related to Variational Autoencoders (VAEs). Our methods are referred to as
Geometric Dynamic (GD) Variational Autoencoders (GD-VAEs). We learn encoders
and decoders for the system states and evolution based on deep neural network
architectures that include general Multilayer Perceptrons (MLPs), Convolutional
Neural Networks (CNNs), and Transpose CNNs (T-CNNs). Motivated by problems
arising in parameterized PDEs and physics, we investigate the performance of
our methods on tasks for learning low dimensional representations of the
nonlinear Burgers equations, constrained mechanical systems, and spatial fields
of reaction-diffusion systems. GD-VAEs provide methods for obtaining
representations for use in learning tasks involving dynamics.

As interest in deep neural networks (DNNs) for image reconstruction tasks
grows, their reliability has been called into question (Antun et al., 2020;
Gottschling et al., 2020). However, recent work has shown that compared to
total variation (TV) minimization, they show similar robustness to adversarial
noise in terms of $\ell^2$-reconstruction error (Genzel et al., 2022). We
consider a different notion of robustness, using the $\ell^\infty$-norm, and
argue that localized reconstruction artifacts are a more relevant defect than
the $\ell^2$-error. We create adversarial perturbations to undersampled MRI
measurements which induce severe localized artifacts in the TV-regularized
reconstruction. The same attack method is not as effective against DNN based
reconstruction. Finally, we show that this phenomenon is inherent to
reconstruction methods for which exact recovery can be guaranteed, as with
compressed sensing reconstructions with $\ell^1$- or TV-minimization.

Machine Teaching (MT) is an interactive process where a human and a machine
interact with the goal of training a machine learning model (ML) for a
specified task. The human teacher communicates their task expertise and the
machine student gathers the required data and knowledge to produce an ML model.
MT systems are developed to jointly minimize the time spent on teaching and the
learner's error rate. The design of human-AI interaction in an MT system not
only impacts the teaching efficiency, but also indirectly influences the ML
performance by affecting the teaching quality. In this paper, we build upon our
previous work where we proposed an MT framework with three components, viz.,
the teaching interface, the machine learner, and the knowledge base, and focus
on the human-AI interaction design involved in realizing the teaching
interface. We outline design decisions that need to be addressed in developing
an MT system beginning from an ML task. The paper follows the Socratic method
entailing a dialogue between a curious student and a wise teacher.

We introduce LIMES, a new method for learning with non-stationary streaming
data, inspired by the recent success of meta-learning. The main idea is not to
attempt to learn a single classifier that would have to work well across all
occurring data distributions, nor many separate classifiers, but to exploit a
hybrid strategy: we learn a single set of model parameters from which a
specific classifier for any specific data distribution is derived via
classifier adaptation. Assuming a multi-class classification setting with
class-prior shift, the adaptation step can be performed analytically with only
the classifier's bias terms being affected. Another contribution of our work is
an extrapolation step that predicts suitable adaptation parameters for future
time steps based on the previous data. In combination, we obtain a lightweight
procedure for learning from streaming data with varying class distribution that
adds no trainable parameters and almost no memory or computational overhead
compared to training a single model. Experiments on a set of exemplary tasks
using Twitter data show that LIMES achieves higher accuracy than alternative
approaches, especially with respect to the relevant real-world metric of lowest
within-day accuracy.

Causality is a fundamental part of the scientific endeavour to understand the
world. Unfortunately, causality is still taboo in much of psychology and social
science. Motivated by a growing number of recommendations for the importance of
adopting causal approaches to research, we reformulate the typical approach to
research in psychology to harmonize inevitably causal theories with the rest of
the research pipeline. We present a new process which begins with the
incorporation of techniques from the confluence of causal discovery and machine
learning for the development, validation, and transparent formal specification
of theories. We then present methods for reducing the complexity of the fully
specified theoretical model into the fundamental submodel relevant to a given
target hypothesis. From here, we establish whether or not the quantity of
interest is estimable from the data, and if so, propose the use of
semi-parametric machine learning methods for the estimation of causal effects.
The overall goal is the presentation of a new research pipeline which can (a)
facilitate scientific inquiry compatible with the desire to test causal
theories (b) encourage transparent representation of our theories as
unambiguous mathematical objects, (c) to tie our statistical models to specific
attributes of the theory, thus reducing under-specification problems frequently
resulting from the theory-to-model gap, and (d) to yield results and estimates
which are causally meaningful and reproducible. The process is demonstrated
through didactic examples with real-world data, and we conclude with a summary
and discussion of limitations.

Score-based diffusion models are a class of generative models whose dynamics
is described by stochastic differential equations that map noise into data.
While recent works have started to lay down a theoretical foundation for these
models, an analytical understanding of the role of the diffusion time T is
still lacking. Current best practice advocates for a large T to ensure that the
forward dynamics brings the diffusion sufficiently close to a known and simple
noise distribution; however, a smaller value of T should be preferred for a
better approximation of the score-matching objective and higher computational
efficiency. Starting from a variational interpretation of diffusion models, in
this work we quantify this trade-off, and suggest a new method to improve
quality and efficiency of both training and sampling, by adopting smaller
diffusion times. Indeed, we show how an auxiliary model can be used to bridge
the gap between the ideal and the simulated forward dynamics, followed by a
standard reverse diffusion process. Empirical results support our analysis; for
image data, our method is competitive w.r.t. the state-of-the-art, according to
standard sample quality metrics and log-likelihood.

Calibration or parameter identification is used with computational mechanics
models related to observed data of the modeled process to find model parameters
such that good similarity between model prediction and observation is achieved.
We present a Bayesian calibration approach for surface coupled problems in
computational mechanics based on measured deformation of an interface when no
displacement data of material points is available. The interpretation of such a
calibration problem as a statistical inference problem, in contrast to
deterministic model calibration, is computationally more robust and allows the
analyst to find a posterior distribution over possible solutions rather than a
single point estimate. The proposed framework also enables the consideration of
unavoidable uncertainties that are present in every experiment and are expected
to play an important role in the model calibration process. To mitigate the
computational costs of expensive forward model evaluations, we propose to learn
the log-likelihood function from a controllable amount of parallel simulation
runs using Gaussian process regression. We introduce and specifically study the
effect of three different discrepancy measures for deformed interfaces between
reference data and simulation. We show that a statistically based discrepancy
measure results in the most expressive posterior distribution. We further apply
the approach to numerical examples in higher model parameter dimensions and
interpret the resulting posterior under uncertainty. In the examples, we
investigate coupled multi-physics models of fluid-structure interaction effects
in biofilms and find that the model parameters affect the results in a coupled
manner.

In many computational science and engineering applications, the output of a
system of interest corresponding to a given input can be queried at different
levels of fidelity with different costs. Typically, low-fidelity data is cheap
and abundant, while high-fidelity data is expensive and scarce. In this work we
study the reinforcement learning (RL) problem in the presence of multiple
environments with different levels of fidelity for a given control task. We
focus on improving the RL agent's performance with multifidelity data.
Specifically, a multifidelity estimator that exploits the cross-correlations
between the low- and high-fidelity returns is proposed to reduce the variance
in the estimation of the state-action value function. The proposed estimator,
which is based on the method of control variates, is used to design a
multifidelity Monte Carlo RL (MFMCRL) algorithm that improves the learning of
the agent in the high-fidelity environment. The impacts of variance reduction
on policy evaluation and policy improvement are theoretically analyzed by using
probability bounds. Our theoretical analysis and numerical experiments
demonstrate that for a finite budget of high-fidelity data samples, our
proposed MFMCRL agent attains superior performance compared with that of a
standard RL agent that uses only the high-fidelity environment data for
learning the optimal policy.

A new open-source image processing pipeline for analyzing camera trap
time-lapse recordings is described. This pipeline includes machine learning
models to assist human-in-the-loop video segmentation and animal
re-identification. We present some performance results and observations on the
utility of this pipeline after using it in a year-long project studying the
spatial ecology and social behavior of the gopher tortoise.

Advances in learning-based trajectory prediction are enabled by large-scale
datasets. However, in-depth analysis of such datasets is limited. Moreover, the
evaluation of prediction models is limited to metrics averaged over all samples
in the dataset. We propose an automated methodology that allows to extract
maneuvers (e.g., left turn, lane change) from agent trajectories in such
datasets. The methodology considers information about the agent dynamics and
information about the lane segments the agent traveled along. Although it is
possible to use the resulting maneuvers for training classification networks,
we exemplary use them for extensive trajectory dataset analysis and
maneuver-specific evaluation of multiple state-of-the-art trajectory prediction
models. Additionally, an analysis of the datasets and an evaluation of the
prediction models based on the agent dynamics is provided.

We address the problem to estimate a dynamic network whose edges describe
Granger causality relations and whose topology has a Kronecker structure. Such
a structure arises in many real networks and allows to understand the
organization of complex networks. We proposed a kernel-based PEM method to
learn such networks. Numerical examples show the effectiveness of the proposed
method.

One of the challenges of language teaching is how to organize the rules
regarding syntax, semantics, or phonology of the language in a meaningful
manner. This not only requires pedagogical skills, but also requires a deep
understanding of that language. While comprehensive materials to develop such
curricula are available in English and some broadly spoken languages, for many
other languages, teachers need to manually create them in response to their
students' needs. This process is challenging because i) it requires that such
experts be accessible and have the necessary resources, and ii) even if there
are such experts, describing all the intricacies of a language is
time-consuming and prone to omission. In this article, we present an automatic
framework that aims to facilitate this process by automatically discovering and
visualizing descriptions of different aspects of grammar. Specifically, we
extract descriptions from a natural text corpus that answer questions about
morphosyntax (learning of word order, agreement, case marking, or word
formation) and semantics (learning of vocabulary) and show illustrative
examples. We apply this method for teaching the Indian languages, Kannada and
Marathi, which, unlike English, do not have well-developed pedagogical
resources and, therefore, are likely to benefit from this exercise. To assess
the perceived utility of the extracted material, we enlist the help of language
educators from schools in North America who teach these languages to perform a
manual evaluation. Overall, teachers find the materials to be interesting as a
reference material for their own lesson preparation or even for learner

Deep learning models have shown their potential for several applications.
However, most of the models are opaque and difficult to trust due to their
complex reasoning - commonly known as the black-box problem. Some fields, such
as medicine, require a high degree of transparency to accept and adopt such
technologies. Consequently, creating explainable/interpretable models or
applying post-hoc methods on classifiers to build trust in deep learning models
are required. Moreover, deep learning methods can be used for segmentation
tasks, which typically require hard-to-obtain, time-consuming
manually-annotated segmentation labels for training. This paper introduces
three inherently-explainable classifiers to tackle both of these problems as
one. The localisation heatmaps provided by the networks -- representing the
models' focus areas and being used in classification decision-making -- can be
directly interpreted, without requiring any post-hoc methods to derive
information for model explanation. The models are trained by using the input
image and only the classification labels as ground-truth in a supervised
fashion - without using any information about the location of the region of
interest (i.e. the segmentation labels), making the segmentation training of
the models weakly-supervised through classification labels. The final
segmentation is obtained by thresholding these heatmaps. The models were
employed for the task of multi-class brain tumour classification using two
different datasets, resulting in the best F1-score of 0.93 for the supervised
classification task while securing a median Dice score of 0.67$\pm$0.08 for the
weakly-supervised segmentation task. Furthermore, the obtained accuracy on a
subset of tumour-only images outperformed the state-of-the-art glioma tumour
grading binary classifiers with the best model achieving 98.7\% accuracy.

In the present work, a machine learning based constitutive model for
electro-mechanically coupled material behavior at finite deformations is
proposed. Using different sets of invariants as inputs, an internal energy
density is formulated as a convex neural network. In this way, the model
fulfills the polyconvexity condition which ensures material stability, as well
as thermodynamic consistency, objectivity, material symmetry, and growth
conditions. Depending on the considered invariants, this physics-augmented
machine learning model can either be applied for compressible or nearly
incompressible material behavior, as well as for arbitrary material symmetry
classes. The applicability and versatility of the approach is demonstrated by
calibrating it on transversely isotropic data generated with an analytical
potential, as well as for the effective constitutive modeling of an
analytically homogenized, transversely isotropic rank-one laminate composite
and a numerically homogenized cubic metamaterial. These examinations show the
excellent generalization properties that physics-augmented neural networks
offer also for multi-physical material modeling such as nonlinear
electro-elasticity.

This paper presents a novel, fast and privacy preserving implementation of
deep autoencoders. DAEF (Deep Autoencoder for Federated learning), unlike
traditional neural networks, trains a deep autoencoder network in a
non-iterative way, which drastically reduces its training time. Its training
can be carried out in a distributed way (several partitions of the dataset in
parallel) and incrementally (aggregation of partial models), and due to its
mathematical formulation, the data that is exchanged does not endanger the
privacy of the users. This makes DAEF a valid method for edge computing and
federated learning scenarios. The method has been evaluated and compared to
traditional (iterative) deep autoencoders using seven real anomaly detection
datasets, and their performance have been shown to be similar despite DAEF's
faster training.

We propose an end-to-end distributionally robust system for portfolio
construction that integrates the asset return prediction model with a
distributionally robust portfolio optimization model. We also show how to learn
the risk-tolerance parameter and the degree of robustness directly from data.
End-to-end systems have an advantage in that information can be communicated
between the prediction and decision layers during training, allowing the
parameters to be trained for the final task rather than solely for predictive
performance. However, existing end-to-end systems are not able to quantify and
correct for the impact of model risk on the decision layer. Our proposed
distributionally robust end-to-end portfolio selection system explicitly
accounts for the impact of model risk. The decision layer chooses portfolios by
solving a minimax problem where the distribution of the asset returns is
assumed to belong to an ambiguity set centered around a nominal distribution.
Using convex duality, we recast the minimax problem in a form that allows for
efficient training of the end-to-end system.

In this paper we present Hyper-Dimensional Reconfigurable Analytics at the
Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform
real-time reconfiguration at the edge leveraging non-MAC (free of
floating-point MultiplyACcumulate operations) deep neural nets (DNN) combined
with hyperdimensional (HD) computing accelerators. We describe the algorithm,
trained quantized model generation, and simulated performance of a feature
extractor free of multiply-accumulates feeding a hyperdimensional logic-based
classifier. Then we show how performance increases with the number of
hyperdimensions. We describe the realized low-SWaP FPGA hardware and embedded
software system compared to traditional DNNs and detail the implemented
hardware accelerators. We discuss the measured system latency and power, noise
robustness due to use of learnable quantization and HD computing, actual versus
simulated system performance for a video activity classification task and
demonstration of reconfiguration on this same dataset. We show that
reconfigurability in the field is achieved by retraining only the feed-forward
HD classifier without gradient descent backpropagation (gradient-free), using
few-shot learning of new classes at the edge. Initial work performed used LRCN
DNN and is currently extended to use Two-stream DNN with improved performance.

We introduce and analyze Structured Stochastic Zeroth order Descent (S-SZD),
a finite difference approach which approximates a stochastic gradient on a set
of $l\leq d$ orthogonal directions, where $d$ is the dimension of the ambient
space. These directions are randomly chosen, and may change at each step. For
smooth convex functions we prove almost sure convergence of the iterates and a
convergence rate on the function values of the form $O(d/l k^{-c})$ for every
$c<1/2$, which is arbitrarily close to the one of Stochastic Gradient Descent
(SGD) in terms of number of iterations. Our bound also shows the benefits of
using $l$ multiple directions instead of one. For non-convex functions
satisfying the Polyak-{\L}ojasiewicz condition, we establish the first
convergence rates for stochastic zeroth order algorithms under such an
assumption. We corroborate our theoretical findings in numerical simulations
where assumptions are satisfied and on the real-world problem of
hyper-parameter optimization, observing that S-SZD has very good practical

Given some observed data and a probabilistic generative model, Bayesian
inference aims at obtaining the distribution of a model's latent parameters
that could have yielded the data. This task is challenging for large population
studies where thousands of measurements are performed over a cohort of hundreds
of subjects, resulting in a massive latent parameter space. This large
cardinality renders off-the-shelf Variational Inference (VI) computationally
impractical. In this work, we design structured VI families that can
efficiently tackle large population studies. To this end, our main idea is to
share the parameterization and learning across the different i.i.d. variables
in a generative model -symbolized by the model's plates. We name this concept
plate amortization, and illustrate the powerful synergies it entitles,
resulting in expressive, parsimoniously parameterized and orders of magnitude
faster to train large scale hierarchical variational distributions. We
illustrate the practical utility of PAVI through a challenging Neuroimaging
example featuring a million latent parameters, demonstrating a significant step
towards scalable and expressive Variational Inference.

Multi-agent deep reinforcement learning has been applied to address a variety
of complex problems with either discrete or continuous action spaces and
achieved great success. However, most real-world environments cannot be
described by only discrete action spaces or only continuous action spaces. And
there are few works having ever utilized deep reinforcement learning (drl) to
multi-agent problems with hybrid action spaces. Therefore, we propose a novel
algorithm: Deep Multi-Agent Hybrid Soft Actor-Critic (MAHSAC) to fill this gap.
This algorithm follows the centralized training but decentralized execution
(CTDE) paradigm, and extend the Soft Actor-Critic algorithm (SAC) to handle
hybrid action space problems in Multi-Agent environments based on maximum
entropy. Our experiences are running on an easy multi-agent particle world with
a continuous observation and discrete action space, along with some basic
simulated physics. The experimental results show that MAHSAC has good
performance in training speed, stability, and anti-interference ability. At the
same time, it outperforms existing independent deep hybrid learning method in
cooperative scenarios and competitive scenarios.

In this paper we offer a review and bibliography of work on Hankel low-rank
approximation and completion, with particular emphasis on how this methodology
can be used for time series analysis and forecasting. We begin by describing
possible formulations of the problem and offer commentary on related topics and
challenges in obtaining globally optimal solutions. Key theorems are provided,
and the paper closes with some expository examples.

We examine how the saccade mechanism from biological vision can be used to
make deep neural networks more efficient for classification and object
detection problems. Our proposed approach is based on the ideas of
attention-driven visual processing and saccades, miniature eye movements
influenced by attention. We conduct experiments by analyzing: i) the robustness
of different deep neural network (DNN) feature extractors to partially-sensed
images for image classification and object detection, and ii) the utility of
saccades in masking image patches for image classification and object tracking.
Experiments with convolutional nets (ResNet-18) and transformer-based models
(ViT, DETR, TransTrack) are conducted on several datasets (CIFAR-10, DAVSOD,
MSCOCO, and MOT17). Our experiments show intelligent data reduction via
learning to mimic human saccades when used in conjunction with state-of-the-art
DNNs for classification, detection, and tracking tasks. We observed minimal
drop in performance for the classification and detection tasks while only using
about 30\% of the original sensor data. We discuss how the saccade mechanism
can inform hardware design via ``in-pixel'' processing.

Deploying robots in real-world domains, such as households and flexible
manufacturing lines, requires the robots to be taskable on demand. Linear
temporal logic (LTL) is a widely-used specification language with a
compositional grammar that naturally induces commonalities across tasks.
However, the majority of prior research on reinforcement learning with LTL
specifications treats every new formula independently. We propose LTL-Transfer,
a novel algorithm that enables subpolicy reuse across tasks by segmenting
policies for training tasks into portable transition-centric skills capable of
satisfying a wide array of unseen LTL specifications while respecting
safety-critical constraints. Our experiments in a Minecraft-inspired domain
demonstrate the capability of LTL-Transfer to satisfy over 90% of 500 unseen
tasks while training on only 50 task specifications and never violating a
safety constraint. We also deployed LTL-Transfer on a quadruped mobile
manipulator in a household environment to show its ability to transfer to many
fetch and delivery tasks in a zero-shot fashion.

The prevalence of suicide has been on the rise since the 20th century,
causing severe emotional damage to individuals, families, and communities
alike. Despite the severity of this suicide epidemic, there is so far no
reliable and systematic way to assess suicide intent of a given individual.
Through efforts to automate and systematize diagnosis of mental illnesses over
the past few years, verbal and acoustic behaviors have received increasing
attention as biomarkers, but little has been done to study eyelids, gaze, and
head pose in evaluating suicide risk. This study explores statistical analysis,
feature selection, and machine learning classification as means of suicide risk
evaluation and nonverbal behavioral interpretation. Applying these methods to
the eye and head signals extracted from our unique dataset, this study finds
that high-risk suicidal individuals experience psycho-motor retardation and
symptoms of anxiety and depression, characterized by eye contact avoidance,
slower blinks and a downward eye gaze. By comparing results from different
methods of classification, we determined that these features are highly capable
of automatically classifying different levels of suicide risk consistently and
with high accuracy, above 98%. Our conclusion corroborates psychological
studies, and shows great potential of a systematic approach in suicide risk
evaluation that is adoptable by both healthcare providers and naive observers.

We present federated momentum contrastive clustering (FedMCC), a learning
framework that can not only extract discriminative representations over
distributed local data but also perform data clustering. In FedMCC, a
transformed data pair passes through both the online and target networks,
resulting in four representations over which the losses are determined. The
resulting high-quality representations generated by FedMCC can outperform
several existing self-supervised learning methods for linear evaluation and
semi-supervised learning tasks. FedMCC can easily be adapted to ordinary
centralized clustering through what we call momentum contrastive clustering
(MCC). We show that MCC achieves state-of-the-art clustering accuracy results
in certain datasets such as STL-10 and ImageNet-10. We also present a method to
reduce the memory footprint of our clustering schemes.

The segmentation of optic disc(OD) and optic cup(OC) from fundus images is an
important fundamental task for glaucoma diagnosis. In the clinical practice, it
is often necessary to collect opinions from multiple experts to obtain the
final OD/OC annotation. This clinical routine helps to mitigate the individual
bias. But when data is multiply annotated, standard deep learning models will
be inapplicable. In this paper, we propose a novel neural network framework to
learn OD/OC segmentation from multi-rater annotations. The segmentation results
are self-calibrated through the iterative optimization of multi-rater
expertness estimation and calibrated OD/OC segmentation. In this way, the
proposed method can realize a mutual improvement of both tasks and finally
obtain a refined segmentation result. Specifically, we propose Diverging
Model(DivM) and Converging Model(ConM) to process the two tasks respectively.
ConM segments the raw image based on the multi-rater expertness map provided by
DivM. DivM generates multi-rater expertness map from the segmentation mask
provided by ConM. The experiment results show that by recurrently running ConM
and DivM, the results can be self-calibrated so as to outperform a range of
state-of-the-art(SOTA) multi-rater segmentation methods.

Decentralized optimization is increasingly popular in machine learning for
its scalability and efficiency. Intuitively, it should also provide better
privacy guarantees, as nodes only observe the messages sent by their neighbors
in the network graph. But formalizing and quantifying this gain is challenging:
existing results are typically limited to Local Differential Privacy (LDP)
guarantees that overlook the advantages of decentralization. In this work, we
introduce pairwise network differential privacy, a relaxation of LDP that
captures the fact that the privacy leakage from a node $u$ to a node $v$ may
depend on their relative position in the graph. We then analyze the combination
of local noise injection with (simple or randomized) gossip averaging protocols
on fixed and random communication graphs. We also derive a differentially
private decentralized optimization algorithm that alternates between local
gradient descent steps and gossip averaging. Our results show that our
algorithms amplify privacy guarantees as a function of the distance between
nodes in the graph, matching the privacy-utility trade-off of the trusted
curator, up to factors that explicitly depend on the graph topology. Finally,
we illustrate our privacy gains with experiments on synthetic and real-world
datasets.

In this paper, we propose a learning-based Model Predictive Control (MPC)
approach for the polytopic Linear Parameter-Varying (LPV) systems with inexact
scheduling parameters (as exogenous signals with inexact bounds), where the
Linear Time Invariant (LTI) models (vertices) captured by combinations of the
scheduling parameters becomes wrong. We first propose to adopt a Moving Horizon
Estimation (MHE) scheme to simultaneously estimate the convex combination
vector and unmeasured states based on the observations and model matching
error. To tackle the wrong LTI models used in both the MPC and MHE schemes, we
then adopt a Policy Gradient (PG) Reinforcement Learning (RL) to learn both the
estimator (MHE) and controller (MPC) so that the best closed-loop performance
is achieved. The effectiveness of the proposed RL-based MHE/MPC design is
demonstrated using an illustrative example.

The convergence of many numerical optimization techniques is highly sensitive
to the initial guess provided to the solver. We propose an approach based on
tensor methods to initialize the existing optimization solvers close to global
optima. The approach uses only the definition of the cost function and does not
need access to any database of good solutions. We first transform the cost
function, which is a function of task parameters and optimization variables,
into a probability density function. Unlike existing approaches that set the
task parameters as constant, we consider them as another set of random
variables and approximate the joint probability distribution of the task
parameters and the optimization variables using a surrogate probability model.
For a given task, we then generate samples from the conditional distribution
with respect to the given task parameter and use them as initialization for the
optimization solver. As conditioning and sampling from an arbitrary density
function are challenging, we use Tensor Train decomposition to obtain a
surrogate probability model from which we can efficiently obtain the
conditional model and the samples. The method can produce multiple solutions
coming from different modes (when they exist) for a given task. We first
evaluate the approach by applying it to various challenging benchmark functions
for numerical optimization that are difficult to solve using gradient-based
optimization solvers with a naive initialization, showing that the proposed
method can produce samples close to the global optima and coming from multiple
modes. We then demonstrate the generality of the framework and its relevance to
robotics by applying the proposed method to inverse kinematics and motion
planning problems with a 7-DoF manipulator.

Counterfactuals can explain classification decisions of neural networks in a
human interpretable way. We propose a simple but effective method to generate
such counterfactuals. More specifically, we perform a suitable diffeomorphic
coordinate transformation and then perform gradient ascent in these coordinates
to find counterfactuals which are classified with great confidence as a
specified target class. We propose two methods to leverage generative models to
construct such suitable coordinate systems that are either exactly or
approximately diffeomorphic. We analyze the generation process theoretically
using Riemannian differential geometry and validate the quality of the
generated counterfactuals using various qualitative and quantitative measures.

Output reachability and adversarial robustness are among the most relevant
safety properties of neural networks. We show that in the context of Message
Passing Neural Networks (MPNN), a common Graph Neural Network (GNN) model,
formal verification is impossible. In particular, we show that output
reachability of graph-classifier MPNN, working over graphs of unbounded size,
non-trivial degree and sufficiently expressive node labels, cannot be verified
formally: there is no algorithm that answers correctly (with yes or no), given
an MPNN, whether there exists some valid input to the MPNN such that the
corresponding output satisfies a given specification. However, we also show
that output reachability and adversarial robustness of node-classifier MPNN can
be verified formally when a limit on the degree of input graphs is given a
priori. We discuss the implications of these results, for the purpose of
obtaining a complete picture of the principle possibility to formally verify
GNN, depending on the expressiveness of the involved GNN models and
input-output specifications.

In a current-carrying single-molecular junction (SMJ), a hierarchy of hybrid
energy transport processes takes place under a highly nonequilibrium situation,
including energy transfer from electrons to molecular vibrations via
electron-vibration interaction, energy redistribution within different
vibrational modes via anharmonic coupling, and eventual energy transport to
surrounding electrodes. A comprehensive understanding of such processes is a
prerequisite for their potential applications as single-molecular devices. $Ab$
$initio$ current-induced molecular dynamics (MD) is an ideal approach to
address this complicated problem. But the computational cost hinders its usage
in systematic study of realistic SMJs. Here, we achieve orders of magnitude
improvement in the speed of MD simulation by employing machine-learning
potential with accuracy comparable to density functional theory. Using this
approach, we show that SMJs with graphene electrodes generate order of
magnitude less heating than those with gold electrodes. Our work illustrates
the superior heat transport property of graphene as electrodes for SMJs, thanks
to its better phonon spectral overlap with molecular vibrations.

Criticality can be exactly demonstrated in certain models of brain activity,
yet it remains challenging to identify in empirical data. We trained a fully
connected deep neural network to learn the phases of an excitable model
unfolding on the anatomical connectome of human brain. This network was then
applied to brain-wide fMRI data acquired during the descent from wakefulness to
deep sleep. We report high correlation between the predicted proximity to the
critical point and the exponents of cluster size distributions, indicative of
subcritical dynamics. This result demonstrates that conceptual models can be
leveraged to identify the dynamical regime of real neural systems.

The human cultural repertoire relies on innovation: our ability to
continuously and hierarchically explore how existing elements can be combined
to create new ones. Innovation is not solitary, it relies on collective
accumulation and merging of previous solutions. Machine learning approaches
commonly assume that fully connected multi-agent networks are best suited for
innovation. However, human laboratory and field studies have shown that
hierarchical innovation is more robustly achieved by dynamic communication
topologies. In dynamic topologies, humans oscillate between innovating
individually or in small clusters, and then sharing outcomes with others. To
our knowledge, the role of multi-agent topology on innovation has not been
systematically studied in machine learning. It remains unclear a) which
communication topologies are optimal for which innovation tasks, and b) which
properties of experience sharing improve multi-level innovation. Here we use a
multi-level hierarchical problem setting (WordCraft), with three different
innovation tasks. We systematically design networks of DQNs sharing experiences
from their replay buffers in varying topologies (fully connected, small world,
dynamic, ring). Comparing the level of innovation achieved by different
experience-sharing topologies across different tasks shows that, first,
consistent with human findings, experience sharing within a dynamic topology
achieves the highest level of innovation across tasks. Second, experience
sharing is not as helpful when there is a single clear path to innovation.
Third, two metrics we propose, conformity and diversity of shared experience,
can explain the success of different topologies on different tasks. These
contributions can advance our understanding of optimal AI-AI, human-human, and
human-AI collaborative networks, inspiring future tools for fostering
collective innovation in large organizations.

The lack of generalizability of deep learning approaches for the automated
diagnosis of pathologies in Wireless Capsule Endoscopy (WCE) has prevented any
significant advantages from trickling down to real clinical practices. As a
result, disease management using WCE continues to depend on exhaustive manual
investigations by medical experts. This explains its limited use despite
several advantages. Prior works have considered using higher quality and
quantity of labels as a way of tackling the lack of generalization, however
this is hardly scalable considering pathology diversity not to mention that
labeling large datasets encumbers the medical staff additionally. We propose
using freely available domain knowledge as priors to learn more robust and
generalizable representations. We experimentally show that domain priors can
benefit representations by acting in proxy of labels, thereby significantly
reducing the labeling requirement while still enabling fully unsupervised yet
pathology-aware learning. We use the contrastive objective along with
prior-guided views during pretraining, where the view choices inspire
sensitivity to pathological information. Extensive experiments on three
datasets show that our method performs better than (or closes gap with) the
state-of-the-art in the domain, establishing a new benchmark in pathology
classification and cross-dataset generalization, as well as scaling to unseen
pathology categories.

In today's world, everyone is expressive in some way, and the focus of this
project is on people's opinions about rising electricity prices in United
Kingdom and India using data from Twitter, a micro-blogging platform on which
people post messages, known as tweets. Because many people's incomes are not
good and they have to pay so many taxes and bills, maintaining a home has
become a disputed issue these days. Despite the fact that Government offered
subsidy schemes to compensate people electricity bills but it is not welcomed
by people. In this project, the aim is to perform sentiment analysis on
people's expressions and opinions expressed on Twitter. In order to grasp the
electricity prices opinion, it is necessary to carry out sentiment analysis for
the government and consumers in energy market. Furthermore, text present on
these medias are unstructured in nature, so to process them we firstly need to
pre-process the data. There are so many feature extraction techniques such as
Bag of Words, TF-IDF (Term Frequency-Inverse Document Frequency), word
embedding, NLP based features like word count. In this project, we analysed the
impact of feature TF-IDF word level on electricity bills dataset of sentiment
analysis. We found that by using TF-IDF word level performance of sentiment
analysis is 3-4 higher than using N-gram features. Analysis is done using four
classification algorithms including Naive Bayes, Decision Tree, Random Forest,
and Logistic Regression and considering F-Score, Accuracy, Precision, and
Recall performance parameters.

Machine learning methods on graphs have proven useful in many applications
due to their ability to handle generally structured data. The framework of
Gaussian Markov Random Fields (GMRFs) provides a principled way to define
Gaussian models on graphs by utilizing their sparsity structure. We propose a
flexible GMRF model for general graphs built on the multi-layer structure of
Deep GMRFs, originally proposed for lattice graphs only. By designing a new
type of layer we enable the model to scale to large graphs. The layer is
constructed to allow for efficient training using variational inference and
existing software frameworks for Graph Neural Networks. For a Gaussian
likelihood, close to exact Bayesian inference is available for the latent
field. This allows for making predictions with accompanying uncertainty
estimates. The usefulness of the proposed model is verified by experiments on a
number of synthetic and real world datasets, where it compares favorably to
other both Bayesian and deep learning methods.

Relevant recommendation is a special recommendation scenario which provides
relevant items when users express interests on one target item (e.g., click,
like and purchase). Besides considering the relevance between recommendations
and trigger item, the recommendations should also be diversified to avoid
information cocoons. However, existing diversified recommendation methods
mainly focus on item-level diversity which is insufficient when the recommended
items are all relevant to the target item. Moreover, redundant or noisy item
features might affect the performance of simple feature-aware recommendation
approaches. Faced with these issues, we propose a Feature Disentanglement
Self-Balancing Re-ranking framework (FDSB) to capture feature-aware diversity.
The framework consists of two major modules, namely disentangled attention
encoder (DAE) and self-balanced multi-aspect ranker. In DAE, we use multi-head
attention to learn disentangled aspects from rich item features. In the ranker,
we develop an aspect-specific ranking mechanism that is able to adaptively
balance the relevance and diversity for each aspect. In experiments, we conduct
offline evaluation on the collected dataset and deploy FDSB on KuaiShou app for
online A/B test on the function of relevant recommendation. The significant
improvements on both recommendation quality and user experience verify the

Accomplishments in the field of artificial intelligence are utilized in the
advancement of computing and making of intelligent machines for facilitating
mankind and improving user experience. Emotions are rudimentary for people,
affecting thinking and ordinary exercises like correspondence, learning and
direction. Speech emotion recognition is domain of interest in this regard and
in this work, we propose a novel mel spectrogram learning approach in which our
model uses the datapoints to learn emotions from the given wav form voice notes
in the popular CREMA-D dataset. Our model uses log mel-spectrogram as feature
with number of mels = 64. It took less training time compared to other
approaches used to address the problem of emotion speech recognition.

Labeled data can be expensive to acquire in several application domains,
including medical imaging, robotics, and computer vision. To efficiently train
machine learning models under such high labeling costs, active learning (AL)
judiciously selects the most informative data instances to label on-the-fly.
This active sampling process can benefit from a statistical function model,
that is typically captured by a Gaussian process (GP). While most GP-based AL
approaches rely on a single kernel function, the present contribution advocates
an ensemble of GP models with weights adapted to the labeled data collected
incrementally. Building on this novel EGP model, a suite of acquisition
functions emerges based on the uncertainty and disagreement rules. An
adaptively weighted ensemble of EGP-based acquisition functions is also
introduced to further robustify performance. Extensive tests on synthetic and
real datasets showcase the merits of the proposed EGP-based approaches with
respect to the single GP-based AL alternatives.

With the development of rendering techniques, computer graphics generated
images (CGIs) have been widely used in practical application scenarios such as
architecture design, video games, simulators, movies, etc. Different from
natural scene images (NSIs), the distortions of CGIs are usually caused by poor
rending settings and limited computation resources. What's more, some CGIs may
also suffer from compression distortions in transmission systems like cloud
gaming and stream media. However, limited work has been put forward to tackle
the problem of computer graphics generated images' quality assessment (CG-IQA).
Therefore, in this paper, we establish a large-scale subjective CG-IQA database
to deal with the challenge of CG-IQA tasks. We collect 25,454 in-the-wild CGIs
through previous databases and personal collection. After data cleaning, we
carefully select 1,200 CGIs to conduct the subjective experiment. Several
popular no-reference image quality assessment (NR-IQA) methods are tested on
our database. The experimental results show that the handcrafted-based methods
achieve low correlation with subjective judgment and deep learning based
methods obtain relatively better performance, which demonstrates that the
current NR-IQA models are not suitable for CG-IQA tasks and more effective
models are urgently needed.

The generalized eigenvalue problem (GEP) is a fundamental concept in
numerical linear algebra. It captures the solution of many classical machine
learning problems such as canonical correlation analysis, independent
components analysis, partial least squares, linear discriminant analysis,
principal components, successor features and others. Despite this, most general
solvers are prohibitively expensive when dealing with massive data sets and
research has instead concentrated on finding efficient solutions to specific
problem instances. In this work, we develop a game-theoretic formulation of the
top-$k$ GEP whose Nash equilibrium is the set of generalized eigenvectors. We
also present a parallelizable algorithm with guaranteed asymptotic convergence
to the Nash. Current state-of-the-art methods require $\mathcal{O}(d^2k)$
complexity per iteration which is prohibitively expensive when the number of
dimensions ($d$) is large. We show how to achieve $\mathcal{O}(dk)$ complexity,
scaling to datasets $100\times$ larger than those evaluated by prior methods.
Empirically we demonstrate that our algorithm is able to solve a variety of GEP
problem instances including a large-scale analysis of neural network
activations.

This article focuses on the application of artificial intelligence (AI) in
non-orthogonal multiple-access (NOMA), which aims to achieve automated,
adaptive, and high-efficiency multi-user communications towards next generation
multiple access (NGMA). First, the limitations of current scenario-specific
multi-antenna NOMA schemes are discussed, and the importance of AI for NGMA is
highlighted. Then, to achieve the vision of NGMA, a novel cluster-free NOMA
framework is proposed for providing scenario-adaptive NOMA communications, and
several promising machine learning solutions are identified. To elaborate
further, novel centralized and distributed machine learning paradigms are
conceived for efficiently employing the proposed cluster-free NOMA framework in
single-cell and multi-cell networks, where numerical results are provided to
demonstrate the effectiveness. Furthermore, the interplays between the proposed
cluster-free NOMA and emerging wireless techniques are presented. Finally,
several open research issues of AI enabled NGMA are discussed.

Supervised learning methods can solve the given problem in the presence of a
large set of labeled data. However, the acquisition of a dataset covering all
the target classes typically requires manual labeling which is expensive and
time-consuming. Zero-shot learning models are capable of classifying the unseen
concepts by utilizing their semantic information. The present study introduces
image embeddings as side information on zero-shot audio classification by using
a nonlinear acoustic-semantic projection. We extract the semantic image
representations from the Open Images dataset and evaluate the performance of
the models on an audio subset of AudioSet using semantic information in
different domains; image, audio, and textual. We demonstrate that the image
embeddings can be used as semantic information to perform zero-shot audio
classification. The experimental results show that the image and textual
embeddings display similar performance both individually and together. We
additionally calculate the semantic acoustic embeddings from the test samples
to provide an upper limit to the performance. The results show that the
classification performance is highly sensitive to the semantic relation between
test and training classes and textual and image embeddings can reach up to the
semantic acoustic embeddings when the seen and unseen classes are semantically
similar.

Positional encoding is important for vision transformer (ViT) to capture the
spatial structure of the input image. General effectiveness has been proven in
ViT. In our work we propose to train ViT to recognize the positional label of
patches of the input image, this apparently simple task actually yields a
meaningful self-supervisory task. Based on previous work on ViT positional
encoding, we propose two positional labels dedicated to 2D images including
absolute position and relative position. Our positional labels can be easily
plugged into various current ViT variants. It can work in two ways: (a) As an
auxiliary training target for vanilla ViT (e.g., ViT-B and Swin-B) for better
performance. (b) Combine the self-supervised ViT (e.g., MAE) to provide a more
powerful self-supervised signal for semantic feature learning. Experiments
demonstrate that with the proposed self-supervised methods, ViT-B and Swin-B
gain improvements of 1.20% (top-1 Acc) and 0.74% (top-1 Acc) on ImageNet,
respectively, and 6.15% and 1.14% improvement on Mini-ImageNet.

Pretrained language models are generally acknowledged to be able to encode
syntax [Tenney et al., 2019, Jawahar et al., 2019, Hewitt and Manning, 2019].
In this article, we propose UPOA, an Unsupervised constituent Parsing model
that calculates an Out Association score solely based on the self-attention
weight matrix learned in a pretrained language model as the syntactic distance
for span segmentation. We further propose an enhanced version, UPIO, which
exploits both inside association and outside association scores for estimating
the likelihood of a span. Experiments with UPOA and UPIO disclose that the
linear projection matrices for the query and key in the self-attention
mechanism play an important role in parsing. We therefore extend the
unsupervised models to few-shot parsing models (FPOA, FPIO) that use a few
annotated trees to learn better linear projection matrices for parsing.
Experiments on the Penn Treebank demonstrate that our unsupervised parsing
model UPIO achieves results comparable to the state of the art on short
sentences (length <= 10). Our few-shot parsing model FPIO trained with only 20
annotated trees outperforms a previous few-shot parsing method trained with 50
annotated trees. Experiments on cross-lingual parsing show that both
unsupervised and few-shot parsing methods are better than previous methods on
most languages of SPMRL [Seddah et al., 2013].

The purpose of this short and simple note is to clarify a common
misconception about convolutional neural networks (CNNs). CNNs are made up of
convolutional layers which are shift equivariant due to weight sharing.
However, convolutional layers are not translation equivariant, even when
boundary effects are ignored and when pooling and subsampling are absent. This
is because shift equivariance is a discrete symmetry while translation
equivariance is a continuous symmetry. This fact is well known among
researchers in equivariant machine learning, but is usually overlooked among
non-experts. To minimize confusion, we suggest using the term `shift
equivariance' to refer to discrete shifts in pixels and `translation
equivariance' to refer to continuous translations.

Recent work has shown logical background knowledge can be used in learning
systems to compensate for a lack of labeled training data. Many methods work by
creating a loss function that encodes this knowledge. However, often the logic
is discarded after training, even if it is still useful at test time. Instead,
we ensure neural network predictions satisfy the knowledge by refining the
predictions with an extra computation step. We introduce differentiable
refinement functions that find a corrected prediction close to the original
prediction. We study how to effectively and efficiently compute these
refinement functions. Using a new algorithm called Iterative Local Refinement
(ILR), we combine refinement functions to find refined predictions for logical
formulas of any complexity. ILR finds refinements on complex SAT formulas in
significantly fewer iterations and frequently finds solutions where gradient
descent can not. Finally, ILR produces competitive results in the MNIST
addition task.

Dynamic facial expression recognition (DFER) in the wild is an extremely
challenging task, due to a large number of noisy frames in the video sequences.
Previous works focus on extracting more discriminative features, but ignore
distinguishing the key frames from the noisy frames. To tackle this problem, we
propose a noise-robust dynamic facial expression recognition network
(NR-DFERNet), which can effectively reduce the interference of noisy frames on
the DFER task. Specifically, at the spatial stage, we devise a dynamic-static
fusion module (DSF) that introduces dynamic features to static features for
learning more discriminative spatial features. To suppress the impact of target
irrelevant frames, we introduce a novel dynamic class token (DCT) for the
transformer at the temporal stage. Moreover, we design a snippet-based filter
(SF) at the decision stage to reduce the effect of too many neutral frames on
non-neutral sequence classification. Extensive experimental results demonstrate
that our NR-DFERNet outperforms the state-of-the-art methods on both the DFEW
and AFEW benchmarks.

Hawkes processes are a special class of temporal point processes which
exhibit a natural notion of causality, as occurrence of events in the past may
increase the probability of events in the future. Discovery of the underlying
influence network among the dimensions of multi-dimensional temporal processes
is of high importance in disciplines where a high-frequency data is to model,
e.g. in financial data or in seismological data. This paper approaches the
problem of learning Granger-causal network in multi-dimensional Hawkes
processes. We formulate this problem as a model selection task in which we
follow the minimum description length (MDL) principle. Moreover, we propose a
general algorithm for MDL-based inference using a Monte-Carlo method and we use
it for our causal discovery problem. We compare our algorithm with the
state-of-the-art baseline methods on synthetic and real-world financial data.
The synthetic experiments demonstrate superiority of our method incausal graph
discovery compared to the baseline methods with respect to the size of the
data. The results of experiments with the G-7 bonds price data are consistent
with the experts knowledge.

We introduce DiffOpt.jl, a Julia library to differentiate through the
solution of convex optimization problems with respect to arbitrary parameters
present in the objective and/or constraints. The library builds upon
MathOptInterface, thus leveraging the rich ecosystem of solvers and composing
well with modelling languages like JuMP. DiffOpt offers both forward and
reverse differentiation modes, enabling multiple use cases from hyperparameter
optimization to backpropagation and sensitivity analysis, bridging constrained
optimization with end-to-end differentiable programming.

Conventional wisdom of materials modelling stipulates that both chemical
composition and crystal structure are integral in the prediction of physical
properties. However, recent developments challenge this by reporting accurate
property-prediction machine learning (ML) frameworks using composition alone
without knowledge of the local atomic environments or long-range order. To
probe this behavior, we conduct a systematic comparison of supervised ML models
built on composition only vs. composition plus structure features. Similar
performance for property prediction is found using both models for compounds
close to the thermodynamic convex hull. We hypothesize that composition embeds
structural information of ground-state structures in support of
composition-centric models for property prediction and inverse design of stable
compounds.

Recently, inspired by successful applications in many fields, deep learning
(DL) technologies for CSI acquisition have received considerable research
interest from both academia and industry. Considering the practical feedback
mechanism of 5th generation (5G) New radio (NR) networks, we propose two
implementation schemes for artificial intelligence for CSI (AI4CSI), the
DL-based receiver and end-to-end design, respectively. The proposed AI4CSI
schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE),
feedback overhead, and computational complexity, and compared with legacy
schemes. To demonstrate whether these schemes can be used in real-life
scenarios, both the modeled-based channel data and practically measured
channels were used in our investigations. When DL-based CSI acquisition is
applied to the receiver only, which has little air interface impact, it
provides approximately 25\% SE gain at a moderate feedback overhead level. It
is feasible to deploy it in current 5G networks during 5G evolutions. For the
end-to-end DL-based CSI enhancements, the evaluations also demonstrated their
additional performance gain on SE, which is 6% -- 26% compared with DL-based
receivers and 33% -- 58% compared with legacy CSI schemes. Considering its
large impact on air-interface design, it will be a candidate technology for 6th
generation (6G) networks, in which an air interface designed by artificial
intelligence can be used.

Increasingly, laws are being proposed and passed by governments around the
world to regulate Artificial Intelligence (AI) systems implemented into the
public and private sectors. Many of these regulations address the transparency
of AI systems, and related citizen-aware issues like allowing individuals to
have the right to an explanation about how an AI system makes a decision that
impacts them. Yet, almost all AI governance documents to date have a
significant drawback: they have focused on what to do (or what not to do) with
respect to making AI systems transparent, but have left the brunt of the work
to technologists to figure out how to build transparent systems. We fill this
gap by proposing a novel stakeholder-first approach that assists technologists
in designing transparent, regulatory compliant systems. We also describe a
real-world case-study that illustrates how this approach can be used in
practice.

Self-supervised learning (SSL) achieves great success in monaural speech
enhancement, while the accuracy of the target speech estimation, particularly
for unseen speakers, remains inadequate with existing pre-tasks. As speech
signal contains multi-faceted information including speaker identity,
paralinguistics, and spoken content, the latent representation for speech
enhancement becomes a tough task. In this paper, we study the effectiveness of
each feature which is commonly used in speech enhancement and exploit the
feature combination in the SSL case. Besides, we propose an ensemble training
strategy. The latent representation of the clean speech signal is learned,
meanwhile, the dereverberated mask and the estimated ratio mask are exploited
to denoise and dereverberate the mixture. The latent representation learning
and the masks estimation are considered as two pre-tasks in the training stage.
In addition, to study the effectiveness between the pre-tasks, we compare
different training routines to train the model and further refine the
performance. The NOISEX and DAPS corpora are used to evaluate the efficacy of
the proposed method, which also outperforms the state-of-the-art methods.

Foundation models are becoming the dominant deep learning technologies.
Pretraining a foundation model is always time-consumed due to the large scale
of both the model parameter and training dataset. Besides being
computing-intensive, the training process is extremely memory-intensive and
communication-intensive. These features make it necessary to apply 3D
parallelism, which integrates data parallelism, pipeline model parallelism and
tensor model parallelism, to achieve high training efficiency.
  To achieve this goal, some custom software frameworks such as Megatron-LM and
DeepSpeed are developed. However, current 3D parallelism frameworks still meet
two issues: i) they are not transparent to model developers, which need to
manually modify the model to parallelize training. ii) their utilization of
computation, GPU memory and network bandwidth are not sufficient. We propose
Merak, an automated 3D parallelism deep learning training framework with high
resource utilization. Merak automatically deploys with an automatic model
partitioner, which uses a graph sharding algorithm on a proxy representation of
the model. Merak also presents the non-intrusive API for scaling out foundation
model training with minimal code modification. In addition, we design a
high-performance 3D parallel runtime engine in Merak. It uses several
techniques to exploit available training resources, including shifted critical
path pipeline schedule that brings a higher computation utilization,
stage-aware recomputation that makes use of idle worker memory, and
sub-pipelined tensor model parallelism that overlaps communication and
computation. Experiments on 64 GPUs show Merak can speedup the training
performance over the state-of-the-art 3D parallelism frameworks of models with
1.5, 2.5, 8.3, and 20 billion parameters by up to 1.42X, 1.39X, 1.43X, and
1.61X, respectively.

Auto-Encoder based deep subspace clustering (DSC) is widely used in computer
vision, motion segmentation and image processing. However, it suffers from the
following three issues in the self-expressive matrix learning process: the
first one is less useful information for learning self-expressive weights due
to the simple reconstruction loss; the second one is that the construction of
the self-expression layer associated with the sample size requires
high-computational cost; and the last one is the limited connectivity of the
existing regularization terms. In order to address these issues, in this paper
we propose a novel model named Self-Supervised deep Subspace Clustering with
Entropy-norm (S$^{3}$CE). Specifically, S$^{3}$CE exploits a self-supervised
contrastive network to gain a more effetive feature vector. The local structure
and dense connectivity of the original data benefit from the self-expressive
layer and additional entropy-norm constraint. Moreover, a new module with data
enhancement is designed to help S$^{3}$CE focus on the key information of data,
and improve the clustering performance of positive and negative instances
through spectral clustering. Extensive experimental results demonstrate the
superior performance of S$^{3}$CE in comparison to the state-of-the-art

The Echo State Network (ESN) is a class of Recurrent Neural Network with a
large number of hidden-hidden weights (in the so-called reservoir). Canonical
ESN and its variations have recently received significant attention due to
their remarkable success in the modeling of non-linear dynamical systems. The
reservoir is randomly connected with fixed weights that don't change in the
learning process. Only the weights from reservoir to output are trained. Since
the reservoir is fixed during the training procedure, we may wonder if the
computational power of the recurrent structure is fully harnessed. In this
article, we propose a new computational model of the ESN type, that represents
the reservoir weights in the Fourier space and performs a fine-tuning of these
weights applying genetic algorithms in the frequency domain. The main interest
is that this procedure will work in a much smaller space compared to the
classical ESN, thus providing a dimensionality reduction transformation of the
initial method. The proposed technique allows us to exploit the benefits of the
large recurrent structure avoiding the training problems of gradient-based
method. We provide a detailed experimental study that demonstrates the good
performances of our approach with well-known chaotic systems and real-world
data.

Multi-view clustering has attracted much attention thanks to the capacity of
multi-source information integration. Although numerous advanced methods have
been proposed in past decades, most of them generally overlook the significance
of weakly-supervised information and fail to preserve the feature properties of
multiple views, thus resulting in unsatisfactory clustering performance. To
address these issues, in this paper, we propose a novel Deep Multi-view
Semi-supervised Clustering (DMSC) method, which jointly optimizes three kinds
of losses during networks finetuning, including multi-view clustering loss,
semi-supervised pairwise constraint loss and multiple autoencoders
reconstruction loss. Specifically, a KL divergence based multi-view clustering
loss is imposed on the common representation of multi-view data to perform
heterogeneous feature optimization, multi-view weighting and clustering
prediction simultaneously. Then, we innovatively propose to integrate pairwise
constraints into the process of multi-view clustering by enforcing the learned
multi-view representation of must-link samples (cannot-link samples) to be
similar (dissimilar), such that the formed clustering architecture can be more
credible. Moreover, unlike existing rivals that only preserve the encoders for
each heterogeneous branch during networks finetuning, we further propose to
tune the intact autoencoders frame that contains both encoders and decoders. In
this way, the issue of serious corruption of view-specific and view-shared
feature space could be alleviated, making the whole training procedure more
stable. Through comprehensive experiments on eight popular image datasets, we
demonstrate that our proposed approach performs better than the
state-of-the-art multi-view and single-view competitors.

While regexp matching is a powerful mechanism for finding patterns in data
streams, regexp engines in general only find matches that do not overlap.
Moreover, different forms of nondeterministic exploration, where symbols read
are processed more than once, are often used, which can be costly in real-time
matching. We present an algorithm that constructs from any regexp a Mealy
machine that finds all matches and while reading each input symbol only once.
The machine computed can also detect and distinguish different patterns or
sub-patterns inside patterns. Additionally, we show how to compute a minimal
Mealy machine via a variation of DFA minimization, by formalizing Mealy
machines in terms of regular languages.

Distributed learning has shown great potential in medical image analysis. It
allows to use multi-center training data with privacy protection. However, data
distributions in local centers can vary from each other due to different
imaging vendors, and annotation protocols. Such variation degrades the
performance of learning-based methods. To mitigate the influence, two groups of
methods have been proposed for different aims, i.e., the global methods and the
personalized methods. The former are aimed to improve the performance of a
single global model for all test data from unseen centers (known as generic
data); while the latter target multiple models for each center (denoted as
local data). However, little has been researched to achieve both goals
simultaneously. In this work, we propose a new framework of distributed
learning that bridges the gap between two groups, and improves the performance
for both generic and local data. Specifically, our method decouples the
predictions for generic data and local data, via distribution-conditioned
adaptation matrices. Results on multi-center left atrial (LA) MRI segmentation
showed that our method demonstrated superior performance over existing methods
on both generic and local data. Our code is available at
https://github.com/key1589745/decouple_predict

This paper introduces a novel framework called DTNet for 3D mesh
reconstruction and generation via Disentangled Topology. Beyond previous works,
we learn a topology-aware neural template specific to each input then deform
the template to reconstruct a detailed mesh while preserving the learned
topology. One key insight is to decouple the complex mesh reconstruction into
two sub-tasks: topology formulation and shape deformation. Thanks to the
decoupling, DT-Net implicitly learns a disentangled representation for the
topology and shape in the latent space. Hence, it can enable novel disentangled
controls for supporting various shape generation applications, e.g., remix the
topologies of 3D objects, that are not achievable by previous reconstruction
works. Extensive experimental results demonstrate that our method is able to
produce high-quality meshes, particularly with diverse topologies, as compared
with the state-of-the-art methods.

Recently, Winter and Hahn [1] commented on our work on identifying subtypes
of major psychiatry disorders (MPDs) based on neurobiological features using
machine learning [2]. They questioned the generalizability of our methods and
the statistical significance, stability, and overfitting of the results, and
proposed a pipeline for disease subtyping. We appreciate their earnest
consideration of our work, however, we need to point out their misconceptions
of basic machine-learning concepts and delineate some key issues involved.

Humans continue to outperform modern AI systems in their ability to parse and
understand complex visual scenes flexibly. Attention and memory are two systems
known to play a critical role in our ability to selectively maintain and
manipulate behaviorally-relevant visual information to solve some of the most
challenging visual reasoning tasks. Here, we present a novel architecture for
visual reasoning inspired by the cognitive-science literature on visual
reasoning, the Memory- and Attention-based (visual) REasOning (MAREO)
architecture. MAREO instantiates an active-vision theory, which posits that the
brain solves complex visual reasoning problems compositionally by learning to
combine previously-learned elementary visual operations to form more complex
visual routines. MAREO learns to solve visual reasoning tasks via sequences of
attention shifts to route and maintain task-relevant visual information into a
memory bank via a multi-head transformer module. Visual routines are then
deployed by a dedicated reasoning module trained to judge various relations
between objects in the scenes. Experiments on tasks containing complex visual
relations (SVRT challenge) and same-different differentiation, relation match
to sample, Raven's and Identity rules from ART challenge demonstrate MAREO's
ability to learn visual routines in a robust and sample-efficient manner. We
also show the zero-shot generalization on unseen tasks and the compositionality
nature of the architecture.

The nervous system, more specifically, the brain, is capable of solving
complex problems simply and efficiently, far surpassing modern computers. In
this regard, neuromorphic engineering is a research field that focuses on
mimicking the basic principles that govern the brain in order to develop
systems that achieve such computational capabilities. Within this field,
bio-inspired learning and memory systems are still a challenge to be solved,
and this is where the hippocampus is involved. It is the region of the brain
that acts as a short-term memory, allowing the learning and unstructured and
rapid storage of information from all the sensory nuclei of the cerebral cortex
and its subsequent recall. In this work, we propose a novel bio-inspired memory
model based on the hippocampus with the ability to learn memories, recall them
from a cue (a part of the memory associated with the rest of the content) and
even forget memories when trying to learn others with the same cue. This model
has been implemented on the SpiNNaker hardware platform using Spiking Neural
Networks, and a set of experiments and tests were performed to demonstrate its
correct and expected operation. The proposed spike-based memory model generates
spikes only when it receives an input, being energy efficient, and it needs 7
timesteps for the learning step and 6 timesteps for recalling a
previously-stored memory. This work presents the first hardware implementation
of a fully functional bio-inspired spike-based hippocampus memory model, paving
the road for the development of future more complex neuromorphic systems.

Chinese dialect text-to-speech(TTS) system usually can only be utilized by
native linguists, because the written form of Chinese dialects has different
characters, idioms, grammar and usage from Mandarin, and even the local speaker
cannot input a correct sentence. For Mandarin text inputs, Chinese dialect TTS
can only generate partly-meaningful speech with relatively poor prosody and
naturalness. To lower the bar of use and make it more practical in commercial,
we propose a novel Chinese dialect TTS frontend with a translation module. It
helps to convert Mandarin text into idiomatic expressions with correct
orthography and grammar, so that the intelligibility and naturalness of the
synthesized speech can be improved. A non-autoregressive neural machine
translation model with a glancing sampling strategy is proposed for the
translation task. It is the first known work to incorporate translation with
TTS frontend. Our experiments on Cantonese approve that the proposed frontend
can help Cantonese TTS system achieve a 0.27 improvement in MOS with Mandarin
inputs.

Goal-oriented Reinforcement Learning, where the agent needs to reach the goal
state while simultaneously minimizing the cost, has received significant
attention in real-world applications. Its theoretical formulation, stochastic
shortest path (SSP), has been intensively researched in the online setting.
Nevertheless, it remains understudied when such an online interaction is
prohibited and only historical data is provided. In this paper, we consider the
offline stochastic shortest path problem when the state space and the action
space are finite. We design the simple value iteration-based algorithms for
tackling both offline policy evaluation (OPE) and offline policy learning
tasks. Notably, our analysis of these simple algorithms yields strong
instance-dependent bounds which can imply worst-case bounds that are
near-minimax optimal. We hope our study could help illuminate the fundamental
statistical limits of the offline SSP problem and motivate further studies
beyond the scope of current consideration.

Recent sharpness-aware minimisation (SAM) is known to find flat minima which
is beneficial for better generalisation with improved robustness. SAM
essentially modifies the loss function by reporting the maximum loss value
within the small neighborhood around the current iterate. However, it uses the
Euclidean ball to define the neighborhood, which can be inaccurate since loss
functions for neural networks are typically defined over probability
distributions (e.g., class predictive probabilities), rendering the parameter
space non Euclidean. In this paper we consider the information geometry of the
model parameter space when defining the neighborhood, namely replacing SAM's
Euclidean balls with ellipsoids induced by the Fisher information. Our
approach, dubbed Fisher SAM, defines more accurate neighborhood structures that
conform to the intrinsic metric of the underlying statistical manifold. For
instance, SAM may probe the worst-case loss value at either a too nearby or
inappropriately distant point due to the ignorance of the parameter space
geometry, which is avoided by our Fisher SAM. Another recent Adaptive SAM
approach stretches/shrinks the Euclidean ball in accordance with the scale of
the parameter magnitudes. This might be dangerous, potentially destroying the
neighborhood structure. We demonstrate improved performance of the proposed
Fisher SAM on several benchmark datasets/tasks.

While 3D shape representations enable powerful reasoning in many visual and
perception applications, learning 3D shape priors tends to be constrained to
the specific categories trained on, leading to an inefficient learning process,
particularly for general applications with unseen categories. Thus, we propose
PatchComplete, which learns effective shape priors based on multi-resolution
local patches, which are often more general than full shapes (e.g., chairs and
tables often both share legs) and thus enable geometric reasoning about unseen
class categories. To learn these shared substructures, we learn
multi-resolution patch priors across all train categories, which are then
associated to input partial shape observations by attention across the patch
priors, and finally decoded into a complete shape reconstruction. Such
patch-based priors avoid overfitting to specific train categories and enable
reconstruction on entirely unseen categories at test time. We demonstrate the
effectiveness of our approach on synthetic ShapeNet data as well as challenging
real-scanned objects from ScanNet, which include noise and clutter, improving
over state of the art in novel-category shape completion by 19.3% in chamfer
distance on ShapeNet, and 9.0% for ScanNet.

The graph Transformer emerges as a new architecture and has shown superior
performance on various graph mining tasks. In this work, we observe that
existing graph Transformers treat nodes as independent tokens and construct a
single long sequence composed of all node tokens so as to train the Transformer
model, causing it hard to scale to large graphs due to the quadratical
complexity on the number of nodes for the self-attention computation. To this
end, we propose a Neighborhood Aggregation Graph Transformer (NAGphormer) that
treats each node as a sequence containing a series of tokens constructed by our
proposed Hop2Token module. For each node, Hop2Token aggregates the neighborhood
features from different hops into different representations and thereby
produces a sequence of token vectors as one input. In this way, NAGphormer
could be trained in a mini-batch manner and thus could scale to large graphs.
Moreover, we mathematically show that as compared to a category of advanced
Graph Neural Networks (GNNs), the decoupled Graph Convolutional Network,
NAGphormer could learn more informative node representations from the multi-hop
neighborhoods. Extensive experiments on benchmark datasets from small to large
are conducted to demonstrate that NAGphormer consistently outperforms existing
graph Transformers and mainstream GNNs.

Children's cognitive abilities are sometimes cited as AI benchmarks. How can
the most common 1,000 concepts (89\% of everyday use) be learnt in a
naturalistic children's setting? Cognitive development in children is about
quality, and new concepts can be conveyed via simple examples. Our approach of
knowledge scaffolding uses simple objects and actions to convey concepts, like
how children are taught. We introduce ABCDE, an interactive 3D environment
modeled after a typical playroom for children. It comes with 300+ unique 3D
object assets (mostly toys), and a large action space for child and parent
agents to interact with objects and each other. ABCDE is the first environment
aimed at mimicking a naturalistic setting for cognitive development in
children; no other environment focuses on high-level concept learning through
learner-teacher interactions. The simulator can be found at
https://pypi.org/project/ABCDESim/1.0.0/

To mitigate climate change, the share of renewable energies in power
production needs to be increased. Renewables introduce new challenges to power
grids regarding the dynamic stability due to decentralization, reduced inertia
and volatility in production. However, dynamic stability simulations are
intractable and exceedingly expensive for large grids. Graph Neural Networks
(GNNs) are a promising method to reduce the computational effort of analyzing
dynamic stability of power grids. We provide new datasets of dynamic stability
of synthetic power grids and find that GNNs are surprisingly effective at
predicting highly non-linear targets from topological information only. We show
that large GNNs outperform GNNs from previous work as well as as handcrafted
graph features and semi-analytic approximations. Further, we demonstrate GNNs
can accurately identify trouble maker-nodes in the power grids. Lastly, we show
that GNNs trained on small grids can perform accurately on a large synthetic
Texan power grid model, which illustrates the potential of our approach.

Complex time-varying systems are often studied by abstracting away from the
dynamics of individual components to build a model of the population-level
dynamics from the start. However, when building a population-level description,
it can be easy to lose sight of each individual and how they contribute to the
larger picture. In this paper, we present a novel transformer architecture for
learning from time-varying data that builds descriptions of both the individual
as well as the collective population dynamics. Rather than combining all of our
data into our model at the onset, we develop a separable architecture that
operates on individual time-series first before passing them forward; this
induces a permutation-invariance property and can be used to transfer across
systems of different size and order. After demonstrating that our model can be
applied to successfully recover complex interactions and dynamics in many-body
systems, we apply our approach to populations of neurons in the nervous system.
On neural activity datasets, we show that our model not only yields robust
decoding performance, but also provides impressive performance in transfer
across recordings of different animals without any neuron-level correspondence.
By enabling flexible pre-training that can be transferred to neural recordings
of different size and order, our work provides a first step towards creating a
foundation model for neural decoding.

Transformers have become a default architecture in computer vision, but
understanding what drives their predictions remains a challenging problem.
Current explanation approaches rely on attention values or input gradients, but
these provide a limited understanding of a model's dependencies. Shapley values
offer a theoretically sound alternative, but their computational cost makes
them impractical for large, high-dimensional models. In this work, we aim to
make Shapley values practical for vision transformers (ViTs). To do so, we
first leverage an attention masking approach to evaluate ViTs with partial
information, and we then develop a procedure for generating Shapley value
explanations via a separate, learned explainer model. Our experiments compare
Shapley values to many baseline methods (e.g., attention rollout, GradCAM,
LRP), and we find that our approach provides more accurate explanations than
existing methods for ViTs.

Learning heterogeneous treatment effects (HTEs) is an important problem
across many fields. Most existing methods consider the setting with a single
treatment arm and a single outcome metric. However, in many real world domains,
experiments are run consistently - for example, in internet companies, A/B
tests are run every day to measure the impacts of potential changes across many
different metrics of interest. We show that even if an analyst cares only about
the HTEs in one experiment for one metric, precision can be improved greatly by
analyzing all of the data together to take advantage of cross-experiment and
cross-outcome metric correlations. We formalize this idea in a tensor
factorization framework and propose a simple and scalable model which we refer
to as the low rank or LR-learner. Experiments in both synthetic and real data
suggest that the LR-learner can be much more precise than independent HTE

To estimate the volume density and color of a 3D point in the multi-view
image-based rendering, a common approach is to inspect the consensus existence
among the given source image features, which is one of the informative cues for
the estimation procedure. To this end, most of the previous methods utilize
equally-weighted aggregation features. However, this could make it hard to
check the consensus existence when some outliers, which frequently occur by
occlusions, are included in the source image feature set. In this paper, we
propose a novel source-view-wise feature aggregation method, which facilitates
us to find out the consensus in a robust way by leveraging local structures in
the feature set. We first calculate the source-view-wise distance distribution
for each source feature for the proposed aggregation. After that, the distance
distribution is converted to several similarity distributions with the proposed
learnable similarity mapping functions. Finally, for each element in the
feature set, the aggregation features are extracted by calculating the weighted
means and variances, where the weights are derived from the similarity
distributions. In experiments, we validate the proposed method on various
benchmark datasets, including synthetic and real image scenes. The experimental
results demonstrate that incorporating the proposed features improves the
performance by a large margin, resulting in the state-of-the-art performance.

Current architectures for multi-modality tasks such as visual question
answering suffer from their high complexity. As a result, these architectures
are difficult to train and require high computational resources. To address
these problems we present a CLIP-based architecture that does not require any
fine-tuning of the feature extractors. A simple linear classifier is used on
the concatenated features of the image and text encoder. During training an
auxiliary loss is added which operates on the answer types. The resulting
classification is then used as an attention gate on the answer class selection.
On the VizWiz 2022 Visual Question Answering Challenge we achieve 60.15 %
accuracy on Task 1: Predict Answer to a Visual Question and AP score of 83.78 %
on Task 2: Predict Answerability of a Visual Question.

Spiking neural networks (SNNs) have demonstrated excellent capabilities in
various intelligent scenarios. Most existing methods for training SNNs are
based on the concept of synaptic plasticity; however, learning in the realistic
brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike
threshold of biological neurons is a critical intrinsic neuronal feature that
exhibits rich dynamics on a millisecond timescale and has been proposed as an
underlying mechanism that facilitates neural information processing. In this
study, we develop a novel synergistic learning approach that simultaneously
trains synaptic weights and spike thresholds in SNNs. SNNs trained with
synapse-threshold synergistic learning (STL-SNNs) achieve significantly higher
accuracies on various static and neuromorphic datasets than SNNs trained with
two single-learning models of the synaptic learning (SL) and the threshold
learning (TL). During training, the synergistic learning approach optimizes
neural thresholds, providing the network with stable signal transmission via
appropriate firing rates. Further analysis indicates that STL-SNNs are robust
to noisy data and exhibit low energy consumption for deep network structures.
Additionally, the performance of STL-SNN can be further improved by introducing
a generalized joint decision framework (JDF). Overall, our findings indicate
that biologically plausible synergies between synaptic and intrinsic
non-synaptic mechanisms may provide a promising approach for developing highly
efficient SNN learning methods.

We study the problem of consistently recovering the sparsity pattern of a
regression parameter vector from correlated observations governed by
deterministic missing data patterns using Lasso. We consider the case in which
the observed dataset is censored by a deterministic, non-uniform filter.
Recovering the sparsity pattern in datasets with deterministic missing
structure can be arguably more challenging than recovering in a
uniformly-at-random scenario. In this paper, we propose an efficient algorithm
for missing value imputation by utilizing the topological property of the
censorship filter. We then provide novel theoretical results for exact recovery
of the sparsity pattern using the proposed imputation strategy. Our analysis
shows that, under certain statistical and topological conditions, the hidden
sparsity pattern can be recovered consistently with high probability in
polynomial time and logarithmic sample complexity.

We consider generating explanations for neural networks in cases where the
network's training data is not accessible, for instance due to privacy or
safety issues. Recently, $\mathcal{I}$-Nets have been proposed as a sample-free
approach to post-hoc, global model interpretability that does not require
access to training data. They formulate interpretation as a machine learning
task that maps network representations (parameters) to a representation of an
interpretable function. In this paper, we extend the $\mathcal{I}$-Net
framework to the cases of standard and soft decision trees as surrogate models.
We propose a suitable decision tree representation and design of the
corresponding $\mathcal{I}$-Net output layers. Furthermore, we make
$\mathcal{I}$-Nets applicable to real-world tasks by considering more realistic
distributions when generating the $\mathcal{I}$-Net's training data. We
empirically evaluate our approach against traditional global, post-hoc
interpretability approaches and show that it achieves superior results when the
training data is not accessible.

A good model for action-effect prediction, named environment model, is
important to achieve sample-efficient decision-making policy learning in many
domains like robot control, recommender systems, and patients' treatment
selection. We can take unlimited trials with such a model to identify the
appropriate actions so that the costs of queries in the real world can be
saved. It requires the model to handle unseen data correctly, also called
counterfactual data. However, standard data fitting techniques do not
automatically achieve such generalization ability and commonly result in
unreliable models. In this work, we introduce counterfactual-query risk
minimization (CQRM) in model learning for generalizing to a counterfactual
dataset queried by a specific target policy. Since the target policies can be
various and unknown in policy learning, we propose an adversarial CQRM
objective in which the model learns on counterfactual data queried by
adversarial policies, and finally derive a tractable solution GALILEO. We also
discover that adversarial CQRM is closely related to the adversarial model
learning, explaining the effectiveness of the latter. We apply GALILEO in
synthetic tasks and a real-world application. The results show that GALILEO
makes accurate predictions on counterfactual data and thus significantly
improves policies in real-world testing.

Distributed machine learning has been widely used in recent years to tackle
the large and complex dataset problem. Therewith, the security of distributed
learning has also drawn increasing attentions from both academia and industry.
In this context, federated learning (FL) was developed as a "secure"
distributed learning by maintaining private training data locally and only
public model gradients are communicated between. However, to date, a variety of
gradient leakage attacks have been proposed for this procedure and prove that
it is insecure. For instance, a common drawback of these attacks is shared:
they require too much auxiliary information such as model weights, optimizers,
and some hyperparameters (e.g., learning rate), which are difficult to obtain
in real situations. Moreover, many existing algorithms avoid transmitting model
gradients in FL and turn to sending model weights, such as FedAvg, but few
people consider its security breach. In this paper, we present two novel
frameworks to demonstrate that transmitting model weights is also likely to
leak private local data of clients, i.e., (DLM and DLM+), under the FL
scenario. In addition, a number of experiments are performed to illustrate the
effect and generality of our attack frameworks. At the end of this paper, we
also introduce two defenses to the proposed attacks and evaluate their
protection effects. Comprehensively, the proposed attack and defense schemes
can be applied to the general distributed learning scenario as well, just with
some appropriate customization.

Retrosynthesis is a procedure where a molecule is transformed into potential
reactants and thus the synthesis routes are identified. We propose a novel
generative framework, denoted as $\mathsf{G^2Retro}$, for one-step
retrosynthesis prediction. $\mathsf{G^2Retro}$ imitates the reversed logic of
synthetic reactions, that is, first predicting the reaction centers to convert
the target molecule into fragments named synthons, and then transforming
synthons into reactants, following previous semi-template-based methods. In
predicting reaction centers, $\mathsf{G^2Retro}$ defines a comprehensive set of
reaction center types, and enables diversity in the predicted reactions by
considering multiple reaction center candidates. In completing synthons,
$\mathsf{G^2Retro}$ deploys a sequence of substructure attachments to transform
synthons into reactants, which utilize a holistic view of the most updated
structures of the synthons to be completed, as well as all the involved synthon
and product structures. Here we show that $\mathsf{G^2Retro}$ is able to better
prioritize the most possible reactants in the benchmark dataset than the
state-of-the-art methods, and discover novel and highly likely reactions that
are not included in the benchmark dataset.

In $\mathbb{R}^3$, a hyperbolic paraboloid is a classical saddle-shaped
quadric surface. Recently, Elser has modeled problems arising in Deep Learning
using rectangular hyperbolic paraboloids in $\mathbb{R}^n$. Motivated by his
work, we provide a rigorous analysis of the associated projection. In some
cases, finding this projection amounts to finding a certain root of a quintic
or cubic polynomial. We also observe when the projection is not a singleton and
point out connections to graphical and set convergence.

Adaptive video streaming relies on the construction of efficient bitrate
ladders to deliver the best possible visual quality to viewers under bandwidth
constraints. The traditional method of content dependent bitrate ladder
selection requires a video shot to be pre-encoded with multiple encoding
parameters to find the optimal operating points given by the convex hull of the
resulting rate-quality curves. However, this pre-encoding step is equivalent to
an exhaustive search process over the space of possible encoding parameters,
which causes significant overhead in terms of both computation and time
expenditure. To reduce this overhead, we propose a deep learning based method
of content aware convex hull prediction. We employ a recurrent convolutional
network (RCN) to implicitly analyze the spatiotemporal complexity of video
shots in order to predict their convex hulls. A two-step transfer learning
scheme is adopted to train our proposed RCN-Hull model, which ensures
sufficient content diversity to analyze scene complexity, while also making it
possible capture the scene statistics of pristine source videos. Our
experimental results reveal that our proposed model yields better
approximations of the optimal convex hulls, and offers competitive time savings
as compared to existing approaches. On average, the pre-encoding time was
reduced by 58.0% by our method, while the average Bjontegaard delta bitrate
(BD-rate) of the predicted convex hulls against ground truth was 0.08%, while
the mean absolute deviation of the BD-rate distribution was 0.44%

The Data Science for Pavement Challenge (DSPC) seeks to accelerate the
research and development of automated vision systems for pavement condition
monitoring and evaluation by providing a platform with benchmarked datasets and
codes for teams to innovate and develop machine learning algorithms that are
practice-ready for use by industry. The first edition of the competition
attracted 22 teams from 8 countries. Participants were required to
automatically detect and classify different types of pavement distresses
present in images captured from multiple sources, and under different
conditions. The competition was data-centric: teams were tasked to increase the
accuracy of a predefined model architecture by utilizing various data
modification methods such as cleaning, labeling and augmentation. A real-time,
online evaluation system was developed to rank teams based on the F1 score.
Leaderboard results showed the promise and challenges of machine for advancing
automation in pavement monitoring and evaluation. This paper summarizes the
solutions from the top 5 teams. These teams proposed innovations in the areas
of data cleaning, annotation, augmentation, and detection parameter tuning. The
F1 score for the top-ranked team was approximately 0.9. The paper concludes
with a review of different experiments that worked well for the current
challenge and those that did not yield any significant improvement in model
accuracy.

Existing imitation learning (IL) methods such as inverse reinforcement
learning (IRL) usually have a double-loop training process, alternating between
learning a reward function and a policy and tend to suffer long training time
and high variance. In this work, we identify the benefits of differentiable
physics simulators and propose a new IL method, i.e., Imitation Learning via
Differentiable Physics (ILD), which gets rid of the double-loop design and
achieves significant improvements in final performance, convergence speed, and
stability. The proposed ILD incorporates the differentiable physics simulator
as a physics prior into its computational graph for policy learning. It unrolls
the dynamics by sampling actions from a parameterized policy, simply minimizing
the distance between the expert trajectory and the agent trajectory, and
back-propagating the gradient into the policy via temporal physics operators.
With the physics prior, ILD policies can not only be transferable to unseen
environment specifications but also yield higher final performance on a variety
of tasks. In addition, ILD naturally forms a single-loop structure, which
significantly improves the stability and training speed. To simplify the
complex optimization landscape induced by temporal physics operations, ILD
dynamically selects the learning objectives for each state during optimization.
In our experiments, we show that ILD outperforms state-of-the-art methods in a
variety of continuous control tasks with Brax, requiring only one expert
demonstration. In addition, ILD can be applied to challenging deformable object
manipulation tasks and can be generalized to unseen configurations.

Science and engineering fields use computer simulation extensively. These
simulations are often run at multiple levels of sophistication to balance
accuracy and efficiency. Multi-fidelity surrogate modeling reduces the
computational cost by fusing different simulation outputs. Cheap data generated
from low-fidelity simulators can be combined with limited high-quality data
generated by an expensive high-fidelity simulator. Existing methods based on
Gaussian processes rely on strong assumptions of the kernel functions and can
hardly scale to high-dimensional settings. We propose Multi-fidelity
Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model
for multi-fidelity surrogate modeling. MF-HNP inherits the flexibility and
scalability of Neural Processes. The latent variables transform the
correlations among different fidelity levels from observations to latent space.
The predictions across fidelities are conditionally independent given the
latent states. It helps alleviate the error propagation issue in existing
methods. MF-HNP is flexible enough to handle non-nested high dimensional data
at different fidelity levels with varying input and output dimensions. We
evaluate MF-HNP on epidemiology and climate modeling tasks, achieving
competitive performance in terms of accuracy and uncertainty estimation. In
contrast to deep Gaussian Processes with only low-dimensional (< 10) tasks, our
method shows great promise for speeding up high-dimensional complex simulations
(over 7000 for epidemiology modeling and 45000 for climate modeling).

Microblogs have become a social platform for people to express their emotions
in real-time, and it is a trend to analyze user emotional tendencies from the
information on Microblogs. The dynamic features of emojis can affect the
sentiment polarity of microblog texts. Since existing models seldom consider
the diversity of emoji sentiment polarity,the paper propose a microblog
sentiment classification model based on ALBERT-FAET. We obtain text embedding
via ALBERT pretraining model and learn the inter-emoji embedding with an
attention-based LSTM network. In addition, a fine-grained attention mechanism
is proposed to capture the word-level interactions between plain text and
emoji. Finally, we concatenate these features and feed them into a CNN
classifier to predict the sentiment labels of the microblogs. To verify the
effectiveness of the model and the fine-grained attention network, we conduct
comparison experiments and ablation experiments. The comparison experiments
show that the model outperforms previous methods in three evaluation indicators
(accuracy, precision, and recall) and the model can significantly improve
sentiment classification. The ablation experiments show that compared with
ALBERT-AET, the proposed model ALBERT-FAET is better in the metrics, indicating
that the fine-grained attention network can understand the diversified
information of emoticons.

Split learning (SL) enables data privacy preservation by allowing clients to
collaboratively train a deep learning model with the server without sharing raw
data. However, SL still has limitations such as potential data privacy leakage
and high computation at clients. In this study, we propose to binarize the SL
local layers for faster computation (up to 17.5 times less forward-propagation
time in both training and inference phases on mobile devices) and reduced
memory usage (up to 32 times less memory and bandwidth requirements). More
importantly, the binarized SL (B-SL) model can reduce privacy leakage from SL
smashed data with merely a small degradation in model accuracy. To further
enhance the privacy preservation, we also propose two novel approaches: 1)
training with additional local leak loss and 2) applying differential privacy,
which could be integrated separately or concurrently into the B-SL model.
Experimental results with different datasets have affirmed the advantages of
the B-SL models compared with several benchmark models. The effectiveness of
B-SL models against feature-space hijacking attack (FSHA) is also illustrated.
Our results have demonstrated B-SL models are promising for lightweight
IoT/mobile applications with high privacy-preservation requirements such as
mobile healthcare applications.

Sometimes the meaning conveyed by images goes beyond the list of objects they
contain; instead, images may express a powerful message to affect the viewers'
minds. Inferring this message requires reasoning about the relationships
between the objects, and general common-sense knowledge about the components.
In this paper, we use a scene graph, a graph representation of an image, to
capture visual components. In addition, we generate a knowledge graph using
facts extracted from ConceptNet to reason about objects and attributes. To
detect the symbols, we propose a neural network framework named SKG-Sym. The
framework first generates the representations of the scene graph of the image
and its knowledge graph using Graph Convolution Network. The framework then
fuses the representations and uses an MLP to classify them. We extend the
network further to use an attention mechanism which learn the importance of the
graph representations. We evaluate our methods on a dataset of advertisements,
and compare it with baseline symbolism classification methods (ResNet and VGG).
Results show that our methods outperform ResNet in terms of F-score and the
attention-based mechanism is competitive with VGG while it has much lower model

Before delegating a task to an autonomous system, a human operator may want a
guarantee about the behavior of the system. This paper extends previous work on
conformal prediction for functional data and conformalized quantile regression
to provide conformal prediction intervals over the future behavior of an
autonomous system executing a fixed control policy on a Markov Decision Process
(MDP). The prediction intervals are constructed by applying conformal
corrections to prediction intervals computed by quantile regression. The
resulting intervals guarantee that with probability $1-\delta$ the observed
trajectory will lie inside the prediction interval, where the probability is
computed with respect to the starting state distribution and the stochasticity
of the MDP. The method is illustrated on MDPs for invasive species management
and StarCraft2 battles.

Decision trees are powerful tools for classification and regression that
attract many researchers working in the burgeoning area of machine learning.
One advantage of decision trees over other methods is their interpretability,
which is often preferred over other higher accuracy methods that are relatively
uninterpretable. A binary classification tree has two types of vertices: (i)
branching vertices which have exactly two children and where datapoints are
assessed on a set of discrete features; and (ii) leaf vertices at which
datapoints are given a discrete prediction. An optimal binary classification
tree can be obtained by solving a biobjective optimization problem that seeks
to (i) maximize the number of correctly classified datapoints and (ii) minimize
the number of branching vertices. In this paper, we propose four mixed integer
linear optimization (MILO) formulations for designing optimal binary
classification trees: two flow-based formulations and two-cut based
formulations. We provide theoretical comparisons between our proposed
formulations and the strongest flow-based MILO formulation of Aghaei et al.
(2021). We conduct experiments on 13 publicly available datasets to show the
models' ability to scale and the strength of a biobjective approach using
Pareto frontiers. Our code and data are available on GitHub.

Human activity recognition (HAR) through wearable devices has received much
interest due to its numerous applications in fitness tracking, wellness
screening, and supported living. As a result, we have seen a great deal of work
in this field. Traditional deep learning (DL) has set a state of the art
performance for HAR domain. However, it ignores the data's structure and the
association between consecutive time stamps. To address this constraint, we
offer an approach based on Graph Neural Networks (GNNs) for structuring the
input representation and exploiting the relations among the samples. However,
even when using a simple graph convolution network to eliminate this shortage,
there are still several limiting factors, such as inter-class activities
issues, skewed class distribution, and a lack of consideration for sensor data
priority, all of which harm the HAR model's performance. To improve the current
HAR model's performance, we investigate novel possibilities within the
framework of graph structure to achieve highly discriminated and rich activity
features. We propose a model for (1) time-series-graph module that converts raw
data from HAR dataset into graphs; (2) Graph Convolutional Neural Networks
(GCNs) to discover local dependencies and correlations between neighboring
nodes; and (3) self-attention GNN encoder to identify sensors interactions and
data priorities. To the best of our knowledge, this is the first work for HAR,
which introduces a GNN-based approach that incorporates both the GCN and the
attention mechanism. By employing a uniform evaluation method, our framework
significantly improves the performance on hospital patient's activities dataset
comparatively considered other state of the art baseline methods.

Feature representations derived from models pre-trained on large-scale
datasets have shown their generalizability on a variety of audio analysis
tasks. Despite this generalizability, however, task-specific features can
outperform if sufficient training data is available, as specific task-relevant
properties can be learned. Furthermore, the complex pre-trained models bring
considerable computational burdens during inference. We propose to leverage
both detailed task-specific features from spectrogram input and generic
pre-trained features by introducing two regularization methods that integrate
the information of both feature classes. The workload is kept low during
inference as the pre-trained features are only necessary for training. In
experiments with the pre-trained features VGGish, OpenL3, and a combination of
both, we show that the proposed methods not only outperform baseline methods,
but also can improve state-of-the-art models on several audio classification
tasks. The results also suggest that using the mixture of features performs
better than using individual features.

Deep neural networks are capable of learning powerful representations to
tackle complex vision tasks but expose undesirable properties like the
over-fitting issue. To this end, regularization techniques like image
augmentation are necessary for deep neural networks to generalize well.
Nevertheless, most prevalent image augmentation recipes confine themselves to
off-the-shelf linear transformations like scale, flip, and colorjitter. Due to
their hand-crafted property, these augmentations are insufficient to generate
truly hard augmented examples. In this paper, we propose a novel perspective of
augmentation to regularize the training process. Inspired by the recent success
of applying masked image modeling to self-supervised learning, we adopt the
self-supervised masked autoencoder to generate the distorted view of the input
images. We show that utilizing such model-based nonlinear transformation as
data augmentation can improve high-level recognition tasks. We term the
proposed method as \textbf{M}ask-\textbf{R}econstruct \textbf{A}ugmentation
(MRA). The extensive experiments on various image classification benchmarks
verify the effectiveness of the proposed augmentation. Specifically, MRA
consistently enhances the performance on supervised, semi-supervised as well as
few-shot classification. The code will be available at
\url{https://github.com/haohang96/MRA}.

Neural Ordinary Differential Equations model dynamical systems with ODEs
learned by neural networks. However, ODEs are fundamentally inadequate to model
systems with long-range dependencies or discontinuities, which are common in
engineering and biological systems. Broader classes of differential equations
(DE) have been proposed as remedies, including delay differential equations and
integro-differential equations. Furthermore, Neural ODE suffers from numerical
instability when modelling stiff ODEs and ODEs with piecewise forcing
functions. In this work, we propose Neural Laplace, a unified framework for
learning diverse classes of DEs including all the aforementioned ones. Instead
of modelling the dynamics in the time domain, we model it in the Laplace
domain, where the history-dependencies and discontinuities in time can be
represented as summations of complex exponentials. To make learning more
efficient, we use the geometrical stereographic map of a Riemann sphere to
induce more smoothness in the Laplace domain. In the experiments, Neural
Laplace shows superior performance in modelling and extrapolating the
trajectories of diverse classes of DEs, including the ones with complex history
dependency and abrupt changes.

To avoid the curse of dimensionality, a common approach to clustering
high-dimensional data is to first project the data into a space of reduced
dimension, and then cluster the projected data. Although effective, this
two-stage approach prevents joint optimization of the dimensionality-reduction
and clustering models, and obscures how well the complete model describes the
data. Here, we show how a family of such two-stage models can be combined into
a single, hierarchical model that we call a hierarchical mixture of Gaussians
(HMoG). An HMoG simultaneously captures both dimensionality-reduction and
clustering, and its performance is quantified in closed-form by the likelihood
function. By formulating and extending existing models with exponential family
theory, we show how to maximize the likelihood of HMoGs with
expectation-maximization. We apply HMoGs to synthetic data and RNA sequencing
data, and demonstrate how they exceed the limitations of two-stage models.
Ultimately, HMoGs are a rigorous generalization of a common statistical
framework, and provide researchers with a method to improve model performance
when clustering high-dimensional data.

A basic condition for efficient transfer learning is the similarity between a
target model and source models. In practice, however, the similarity condition
is difficult to meet or is even violated. Instead of the similarity condition,
a brand-new strategy, linear correlation-ratio, is introduced in this paper to
build an accurate relationship between the models. Such a correlation-ratio can
be easily estimated by historical data or a part of sample. Then, a
correlation-ratio transfer learning likelihood is established based on the
correlation-ratio combination. On the practical side, the new framework is
applied to some application scenarios, especially the areas of data streams and
medical studies. Methodologically, some techniques are suggested for
transferring the information from simple source models to a relatively complex
target model. Theoretically, some favorable properties, including the global
convergence rate, are achieved, even for the case where the source models are
not similar to the target model. All in all, it can be seen from the theories
and experimental results that the inference on the target model is
significantly improved by the information from similar or dissimilar source
models. In other words, a variational Stein's paradox is illustrated in the
context of transfer learning.

Active learning enables the efficient construction of a labeled dataset by
labeling informative samples from an unlabeled dataset. In a real-world active
learning scenario, considering the diversity of the selected samples is crucial
because many redundant or highly similar samples exist. Core-set approach is
the promising diversity-based method selecting diverse samples based on the
distance between samples. However, the approach poorly performs compared to the
uncertainty-based approaches that select the most difficult samples where
neural models reveal low confidence. In this work, we analyze the feature space
through the lens of the density and, interestingly, observe that locally sparse
regions tend to have more informative samples than dense regions. Motivated by
our analysis, we empower the core-set approach with the density-awareness and
propose a density-aware core-set (DACS). The strategy is to estimate the
density of the unlabeled samples and select diverse samples mainly from sparse
regions. To reduce the computational bottlenecks in estimating the density, we
also introduce a new density approximation based on locality-sensitive hashing.
Experimental results clearly demonstrate the efficacy of DACS in both
classification and regression tasks and specifically show that DACS can produce
state-of-the-art performance in a practical scenario. Since DACS is weakly
dependent on neural architectures, we present a simple yet effective
combination method to show that the existing methods can be beneficially
combined with DACS.

Single-photon emission computed tomography (SPECT) is a widely applied
imaging approach for diagnosis of coronary artery diseases. Attenuation maps
(u-maps) derived from computed tomography (CT) are utilized for attenuation
correction (AC) to improve diagnostic accuracy of cardiac SPECT. However, SPECT
and CT are obtained sequentially in clinical practice, which potentially
induces misregistration between the two scans. Convolutional neural networks
(CNN) are powerful tools for medical image registration. Previous CNN-based
methods for cross-modality registration either directly concatenated two input
modalities as an early feature fusion or extracted image features using two
separate CNN modules for a late fusion. These methods do not fully extract or
fuse the cross-modality information. Besides, deep-learning-based rigid
registration of cardiac SPECT and CT-derived u-maps has not been investigated
before. In this paper, we propose a Dual-Branch Squeeze-Fusion-Excitation
(DuSFE) module for the registration of cardiac SPECT and CT-derived u-maps.
DuSFE fuses the knowledge from multiple modalities to recalibrate both
channel-wise and spatial features for each modality. DuSFE can be embedded at
multiple convolutional layers to enable feature fusion at different spatial
dimensions. Our studies using clinical data demonstrated that a network
embedded with DuSFE generated substantial lower registration errors and
therefore more accurate AC SPECT images than previous methods.

We tackle the communication efficiency challenge of learning kernelized
contextual bandits in a distributed setting. Despite the recent advances in
communication-efficient distributed bandit learning, existing solutions are
restricted to simple models like multi-armed bandits and linear bandits, which
hamper their practical utility. In this paper, instead of assuming the
existence of a linear reward mapping from the features to the expected rewards,
we consider non-linear reward mappings, by letting agents collaboratively
search in a reproducing kernel Hilbert space (RKHS). This introduces
significant challenges in communication efficiency as distributed kernel
learning requires the transfer of raw data, leading to a communication cost
that grows linearly w.r.t. time horizon $T$. We addresses this issue by
equipping all agents to communicate via a common Nystr\"{o}m embedding that
gets updated adaptively as more data points are collected. We rigorously proved
that our algorithm can attain sub-linear rate in both regret and communication
cost.

We propose an algorithm to explore the global optimization method, using SAT
solvers, for training a neural net. Deep Neural Networks have achieved great
feats in tasks like-image recognition, speech recognition, etc. Much of their
success can be attributed to the gradient-based optimisation methods, which
scale well to huge datasets while still giving solutions, better than any other
existing methods. However, there exist learning problems like the parity
function and the Fast Fourier Transform, where a neural network using
gradient-based optimisation algorithm can not capture the underlying structure
of the learning task properly. Thus, exploring global optimisation methods is
of utmost interest as the gradient-based methods get stuck in local optima. In
the experiments, we demonstrate the effectiveness of our algorithm against the
ADAM optimiser in certain tasks like parity learning. However, in the case of
image classification on the MNIST Dataset, the performance of our algorithm was
less than satisfactory. We further discuss the role of the size of the training
dataset and the hyper-parameter settings in keeping things scalable for a SAT
solver.

Coronavirus can be transmitted through the air by close proximity to infected
persons. Commercial aircraft are a likely way to both transmit the virus among
passengers and move the virus between locations. The importance of learning
about where and how coronavirus has entered the United States will help further
our understanding of the disease. Air travelers can come from countries or
areas with a high rate of infection and may very well be at risk of being
exposed to the virus. Therefore, as they reach the United States, the virus
could easily spread. On our analysis, we utilized machine learning to determine
if the number of flights into the Washington DC Metro Area had an effect on the
number of cases and deaths reported in the city and surrounding area.

Predicting network dynamics based on data, a problem with broad applications,
has been studied extensively in the past, but most existing approaches assume
that the complete set of historical data from the whole network is available.
This requirement presents a great challenge in applications, especially for
large, distributed networks in the real world, where data collection is
accomplished by many clients in a parallel fashion. Often, each client only has
the time series data from a partial set of nodes and the client has access to
only partial timestamps of the whole time series data and partial structure of
the network. Due to privacy concerns or license related issues, the data
collected by different clients cannot be shared. To accurately predict the
network dynamics while protecting the privacy of different parties is a
critical problem in the modern time. Here, we propose a solution based on
federated graph neural networks (FGNNs) that enables the training of a global
dynamic model for all parties without data sharing. We validate the working of
our FGNN framework through two types of simulations to predict a variety of
network dynamics (four discrete and three continuous dynamics). As a
significant real-world application, we demonstrate successful prediction of
State-wise influenza spreading in the USA. Our FGNN scheme represents a general
framework to predict diverse network dynamics through collaborative fusing of
the data from different parties without disclosing their privacy.

Recently issued data privacy regulations like GDPR (General Data Protection
Regulation) grant individuals the right to be forgotten. In the context of
machine learning, this requires a model to forget about a training data sample
if requested by the data owner (i.e., machine unlearning). As an essential step
prior to machine unlearning, it is still a challenge for a data owner to tell
whether or not her data have been used by an unauthorized party to train a
machine learning model. Membership inference is a recently emerging technique
to identify whether a data sample was used to train a target model, and seems
to be a promising solution to this challenge. However, straightforward adoption
of existing membership inference approaches fails to address the challenge
effectively due to being originally designed for attacking membership privacy
and suffering from several severe limitations such as low inference accuracy on
well-generalized models. In this paper, we propose a novel membership inference
approach inspired by the backdoor technology to address the said challenge.
Specifically, our approach of Membership Inference via Backdooring (MIB)
leverages the key observation that a backdoored model behaves very differently
from a clean model when predicting on deliberately marked samples created by a
data owner. Appealingly, MIB requires data owners' marking a small number of
samples for membership inference and only black-box access to the target model,
with theoretical guarantees for inference results. We perform extensive
experiments on various datasets and deep neural network architectures, and the
results validate the efficacy of our approach, e.g., marking only 0.1% of the
training dataset is practically sufficient for effective membership inference.

The grokking phenomenon as reported by Power et al. ( arXiv:2201.02177 )
refers to a regime where a long period of overfitting is followed by a
seemingly sudden transition to perfect generalization. In this paper, we
attempt to reveal the underpinnings of Grokking via a series of empirical
studies. Specifically, we uncover an optimization anomaly plaguing adaptive
optimizers at extremely late stages of training, referred to as the Slingshot
Mechanism. A prominent artifact of the Slingshot Mechanism can be measured by
the cyclic phase transitions between stable and unstable training regimes, and
can be easily monitored by the cyclic behavior of the norm of the last layers
weights. We empirically observe that without explicit regularization, Grokking
as reported in ( arXiv:2201.02177 ) almost exclusively happens at the onset of
Slingshots, and is absent without it. While common and easily reproduced in
more general settings, the Slingshot Mechanism does not follow from any known
optimization theories that we are aware of, and can be easily overlooked
without an in depth examination. Our work points to a surprising and useful
inductive bias of adaptive gradient optimizers at late stages of training,
calling for a revised theoretical analysis of their origin.

A precise forecast for droughts is of considerable value to scientific
research, agriculture, and water resource management. With emerging
developments of data-driven approaches for hydro-climate modeling, this paper
investigates an AutoML-based framework to forecast droughts in the U.S.
Compared with commonly-used temporal deep learning models, the AutoML model can
achieve comparable performance with less training data and time. As deep
learning models are becoming popular for Earth system modeling, this paper aims
to bring more efforts to AutoML-based methods, and the use of them as benchmark
baselines for more complex deep learning models.

When aggregating information from conflicting sources, one's goal is to find
the truth. Most real-value \emph{truth discovery} (TD) algorithms try to
achieve this goal by estimating the competence of each source and then
aggregating the conflicting information by weighing each source's answer
proportionally to her competence. However, each of those algorithms requires
more than a single source for such estimation and usually does not consider
different estimation methods other than a weighted mean. Therefore, in this
work we formulate, prove, and empirically test the conditions for an Empirical
Bayes Estimator (EBE) to dominate the weighted mean aggregation. Our main
result demonstrates that EBE, under mild conditions, can be used as a second
step of any TD algorithm in order to reduce the expected error.

We use neuroevolutionary learning to identify time-dependent protocols for
low-dissipation self-assembly in a model of generic active particles with
interactions. When the time allotted for assembly is sufficiently long,
low-dissipation protocols use only interparticle attractions, producing an
amount of entropy that scales as the number of particles. When time is too
short to allow assembly to proceed via diffusive motion, low-dissipation
assembly protocols instead require particle self-propulsion, producing an
amount of entropy that scales with the number of particles and the swim length
required to cause assembly. Self-propulsion therefore provides an expensive but
necessary mechanism for inducing assembly when time is of the essence.

Data assimilation (DA) is a key component of many forecasting models in
science and engineering. DA allows one to estimate better initial conditions
using an imperfect dynamical model of the system and noisy/sparse observations
available from the system. Ensemble Kalman filter (EnKF) is a DA algorithm that
is widely used in applications involving high-dimensional nonlinear dynamical
systems. However, EnKF requires evolving large ensembles of forecasts using the
dynamical model of the system. This often becomes computationally intractable,
especially when the number of states of the system is very large, e.g., for
weather prediction. With small ensembles, the estimated background error
covariance matrix in the EnKF algorithm suffers from sampling error, leading to
an erroneous estimate of the analysis state (initial condition for the next
forecast cycle). In this work, we propose hybrid ensemble Kalman filter
(H-EnKF), which is applied to a two-layer quasi-geostrophic flow system as a
test case. This framework utilizes a pre-trained deep learning-based
data-driven surrogate that inexpensively generates and evolves a large
data-driven ensemble of the states of the system to accurately compute the
background error covariance matrix with less sampling error. The H-EnKF
framework estimates a better initial condition without the need for any ad-hoc
localization strategies. H-EnKF can be extended to any ensemble-based DA
algorithm, e.g., particle filters, which are currently difficult to use for
high dimensional systems.

Human annotated data plays a crucial role in machine learning (ML) research
and development. However, the ethical considerations around the processes and
decisions that go into dataset annotation have not received nearly enough
attention. In this paper, we survey an array of literature that provides
insights into ethical considerations around crowdsourced dataset annotation. We
synthesize these insights, and lay out the challenges in this space along two
layers: (1) who the annotator is, and how the annotators' lived experiences can
impact their annotations, and (2) the relationship between the annotators and
the crowdsourcing platforms, and what that relationship affords them. Finally,
we introduce a novel framework, CrowdWorkSheets, for dataset developers to
facilitate transparent documentation of key decisions points at various stages
of the data annotation pipeline: task formulation, selection of annotators,
platform and infrastructure choices, dataset analysis and evaluation, and
dataset release and maintenance.

Online anomaly detection from a data stream is critical for the safety and
security of many applications but is facing severe challenges due to complex
and evolving data streams from IoT devices and cloud-based infrastructures.
Unfortunately, existing approaches fall too short for these challenges; online
anomaly detection methods bear the burden of handling the complexity while
offline deep anomaly detection methods suffer from the evolving data
distribution. This paper presents a framework for online deep anomaly
detection, ARCUS, which can be instantiated with any autoencoder-based deep
anomaly detection methods. It handles the complex and evolving data streams
using an adaptive model pooling approach with two novel techniques:
concept-driven inference and drift-aware model pool update; the former detects
anomalies with a combination of models most appropriate for the complexity, and
the latter adapts the model pool dynamically to fit the evolving data streams.
In comprehensive experiments with ten data sets which are both high-dimensional
and concept-drifted, ARCUS improved the anomaly detection accuracy of the
streaming variants of state-of-the-art autoencoder-based methods and that of
the state-of-the-art streaming anomaly detection methods by up to 22% and 37%,

We address the problem of data augmentation for video action recognition.
Standard augmentation strategies in video are hand-designed and sample the
space of possible augmented data points either at random, without knowing which
augmented points will be better, or through heuristics. We propose to learn
what makes a good video for action recognition and select only high-quality
samples for augmentation. In particular, we choose video compositing of a
foreground and a background video as the data augmentation process, which
results in diverse and realistic new samples. We learn which pairs of videos to
augment without having to actually composite them. This reduces the space of
possible augmentations, which has two advantages: it saves computational cost
and increases the accuracy of the final trained classifier, as the augmented
pairs are of higher quality than average. We present experimental results on
the entire spectrum of training settings: few-shot, semi-supervised and fully
supervised. We observe consistent improvements across all of them over prior
work and baselines on Kinetics, UCF101, HMDB51, and achieve a new
state-of-the-art on settings with limited data. We see improvements of up to
8.6% in the semi-supervised setting.

In recommender systems, one common challenge is the cold-start problem, where
interactions are very limited for fresh users in the systems. To address this
challenge, recently, many works introduce the meta-optimization idea into the
recommendation scenarios, i.e. learning to learn the user preference by only a
few past interaction items. The core idea is to learn global shared
meta-initialization parameters for all users and rapidly adapt them into local
parameters for each user respectively. They aim at deriving general knowledge
across preference learning of various users, so as to rapidly adapt to the
future new user with the learned prior and a small amount of training data.
However, previous works have shown that recommender systems are generally
vulnerable to bias and unfairness. Despite the success of meta-learning at
improving the recommendation performance with cold-start, the fairness issues
are largely overlooked. In this paper, we propose a comprehensive fair
meta-learning framework, named CLOVER, for ensuring the fairness of
meta-learned recommendation models. We systematically study three kinds of
fairness - individual fairness, counterfactual fairness, and group fairness in
the recommender systems, and propose to satisfy all three kinds via a
multi-task adversarial learning scheme. Our framework offers a generic training
paradigm that is applicable to different meta-learned recommender systems. We
demonstrate the effectiveness of CLOVER on the representative meta-learned user
preference estimator on three real-world data sets. Empirical results show that
CLOVER achieves comprehensive fairness without deteriorating the overall
cold-start recommendation performance.

Egocentric 3D human pose estimation (HPE) from images is challenging due to
severe self-occlusions and strong distortion introduced by the fish-eye view
from the head mounted camera. Although existing works use intermediate
heatmap-based representations to counter distortion with some success,
addressing self-occlusion remains an open problem. In this work, we leverage
information from past frames to guide our self-attention-based 3D HPE
estimation procedure -- Ego-STAN. Specifically, we build a spatio-temporal
Transformer model that attends to semantically rich convolutional neural
network-based feature maps. We also propose feature map tokens: a new set of
learnable parameters to attend to these feature maps. Finally, we demonstrate
Ego-STAN's superior performance on the xR-EgoPose dataset where it achieves a
30.6% improvement on the overall mean per-joint position error, while leading
to a 22% drop in parameters compared to the state-of-the-art.

We evaluate two popular local explainability techniques, LIME and SHAP, on a
movie recommendation task. We discover that the two methods behave very
differently depending on the sparsity of the data set. LIME does better than
SHAP in dense segments of the data set and SHAP does better in sparse segments.
We trace this difference to the differing bias-variance characteristics of the
underlying estimators of LIME and SHAP. We find that SHAP exhibits lower
variance in sparse segments of the data compared to LIME. We attribute this
lower variance to the completeness constraint property inherent in SHAP and
missing in LIME. This constraint acts as a regularizer and therefore increases
the bias of the SHAP estimator but decreases its variance, leading to a
favorable bias-variance trade-off especially in high sparsity data settings.
With this insight, we introduce the same constraint into LIME and formulate a
novel local explainabilty framework called Completeness-Constrained LIME
(CLIMB) that is superior to LIME and much faster than SHAP.

Deep neural network based face recognition models have been shown to be
vulnerable to adversarial examples. However, many of the past attacks require
the adversary to solve an input-dependent optimization problem using gradient
descent which makes the attack impractical in real-time. These adversarial
examples are also tightly coupled to the attacked model and are not as
successful in transferring to different models. In this work, we propose
ReFace, a real-time, highly-transferable attack on face recognition models
based on Adversarial Transformation Networks (ATNs). ATNs model adversarial
example generation as a feed-forward neural network. We find that the white-box
attack success rate of a pure U-Net ATN falls substantially short of
gradient-based attacks like PGD on large face recognition datasets. We
therefore propose a new architecture for ATNs that closes this gap while
maintaining a 10000x speedup over PGD. Furthermore, we find that at a given
perturbation magnitude, our ATN adversarial perturbations are more effective in
transferring to new face recognition models than PGD. ReFace attacks can
successfully deceive commercial face recognition services in a transfer attack
setting and reduce face identification accuracy from 82% to 16.4% for AWS
SearchFaces API and Azure face verification accuracy from 91% to 50.1%.

Offline reinforcement learning has shown great promise in leveraging large
pre-collected datasets for policy learning, allowing agents to forgo
often-expensive online data collection. However, to date, offline reinforcement
learning from visual observations with continuous action spaces has been
relatively under-explored, and there is a lack of understanding of where the
remaining challenges lie. In this paper, we seek to establish simple baselines
for continuous control in the visual domain. We show that simple modifications
to two state-of-the-art vision-based online reinforcement learning algorithms,
DreamerV2 and DrQ-v2, suffice to outperform prior work and establish a
competitive baseline. We rigorously evaluate these algorithms on both existing
offline datasets and a new testbed for offline reinforcement learning from
visual observations that better represents the data distributions present in
real-world offline RL problems, and open-source our code and data to facilitate
progress in this important domain. Finally, we present and analyze several key
desiderata unique to offline RL from visual observations, including visual
distractions and visually identifiable changes in dynamics.

We study the problem of learning generalized linear models under adversarial
corruptions. We analyze a classical heuristic called the iterative trimmed
maximum likelihood estimator which is known to be effective against label
corruptions in practice. Under label corruptions, we prove that this simple
estimator achieves minimax near-optimal risk on a wide range of generalized
linear models, including Gaussian regression, Poisson regression and Binomial
regression. Finally, we extend the estimator to the more challenging setting of
label and covariate corruptions and demonstrate its robustness and optimality
in that setting as well.

Deep neural networks (DNN) have made impressive progress in the
interpretation of image data, so that it is conceivable and to some degree
realistic to use them in safety critical applications like automated driving.
From an ethical standpoint, the AI algorithm should take into account the
vulnerability of objects or subjects on the street that ranges from "not at
all", e.g. the road itself, to "high vulnerability" of pedestrians. One way to
take this into account is to define the cost of confusion of one semantic
category with another and use cost-based decision rules for the interpretation
of probabilities, which are the output of DNNs. However, it is an open problem
how to define the cost structure, who should be in charge to do that, and
thereby define what AI-algorithms will actually "see". As one possible answer,
we follow a participatory approach and set up an online survey to ask the
public to define the cost structure. We present the survey design and the data
acquired along with an evaluation that also distinguishes between perspective
(car passenger vs. external traffic participant) and gender. Using simulation
based $F$-tests, we find highly significant differences between the groups.
These differences have consequences on the reliable detection of pedestrians in
a safety critical distance to the self-driving car. We discuss the ethical
problems that are related to this approach and also discuss the problems
emerging from human-machine interaction through the survey from a psychological
point of view. Finally, we include comments from industry leaders in the field
of AI safety on the applicability of survey based elements in the design of AI
functionalities in automated driving.

Creating a meaningful representation by fusing single modalities (e.g., text,
images, or audio) is the core concept of multimodal learning. Although several
techniques for building multimodal representations have been proven successful,
they have not been compared yet. Therefore it has been ambiguous which
technique can be expected to yield the best results in a given scenario and
what factors should be considered while choosing such a technique. This paper
explores the most common techniques for building multimodal data
representations -- the late fusion, the early fusion, and the sketch, and
compares them in classification tasks. Experiments are conducted on three
datasets: Amazon Reviews, MovieLens25M, and MovieLens1M datasets. In general,
our results confirm that multimodal representations are able to boost the
performance of unimodal models from 0.919 to 0.969 of accuracy on Amazon
Reviews and 0.907 to 0.918 of AUC on MovieLens25M. However, experiments on both
MovieLens datasets indicate the importance of the meaningful input data to the
given task. In this article, we show that the choice of the technique for
building multimodal representation is crucial to obtain the highest possible
model's performance, that comes with the proper modalities combination. Such
choice relies on: the influence that each modality has on the analyzed machine
learning (ML) problem; the type of the ML task; the memory constraints while
training and predicting phase.

Information-theoretic Bayesian optimization techniques have become popular
for optimizing expensive-to-evaluate black-box functions due to their
non-myopic qualities. Entropy Search and Predictive Entropy Search both
consider the entropy over the optimum in the input space, while the recent
Max-value Entropy Search considers the entropy over the optimal value in the
output space. We propose Joint Entropy Search (JES), a novel
information-theoretic acquisition function that considers an entirely new
quantity, namely the entropy over the joint optimal probability density over
both input and output space. To incorporate this information, we consider the
reduction in entropy from conditioning on fantasized optimal input/output
pairs. The resulting approach primarily relies on standard GP machinery and
removes complex approximations typically associated with information-theoretic
methods. With minimal computational overhead, JES shows superior
decision-making, and yields state-of-the-art performance for
information-theoretic approaches across a wide suite of tasks. As a
light-weight approach with superior results, JES provides a new go-to
acquisition function for Bayesian optimization.

Mainstream Audio Analytics models are trained to learn under the paradigm of
one class label to many recordings focusing on one task. Learning under such
restricted supervision limits the flexibility of models because they require
labeled audio for training and can only predict the predefined categories.
Instead, we propose to learn audio concepts from natural language supervision.
We call our approach Contrastive Language-Audio Pretraining (CLAP), which
learns to connect language and audio by using two encoders and a contrastive
learning to bring audio and text descriptions into a joint multimodal space. We
trained CLAP with 128k audio and text pairs and evaluated it on 16 downstream
tasks across 8 domains, such as Sound Event Classification, Music tasks, and
Speech-related tasks. Although CLAP was trained with significantly less pairs
than similar computer vision models, it establishes SoTA for Zero-Shot
performance. Additionally, we evaluated CLAP in a supervised learning setup and
achieve SoTA in 5 tasks. Hence, CLAP's Zero-Shot capability removes the need of
training with class labels, enables flexible class prediction at inference
time, and generalizes to multiple downstream tasks.

Many metric learning tasks, such as triplet learning, nearest neighbor
retrieval, and visualization, are treated primarily as embedding tasks where
the ultimate metric is some variant of the Euclidean distance (e.g., cosine or
Mahalanobis), and the algorithm must learn to embed points into the pre-chosen
space. The study of non-Euclidean geometries or appropriateness is often not
explored, which we believe is due to a lack of tools for learning non-Euclidean
measures of distance. Under the belief that the use of asymmetric methods in
particular have lacked sufficient study, we propose a new approach to learning
arbitrary Bergman divergences in a differentiable manner via input convex
neural networks. Over a set of both new and previously studied tasks, including
asymmetric regression, ranking, and clustering, we demonstrate that our method
more faithfully learns divergences than prior Bregman learning approaches. In
doing so we obtain the first method for learning neural Bregman divergences and
with it inherit the many nice mathematical properties of Bregman divergences,
providing the foundation and tooling for better developing and studying
asymmetric distance learning.

Pre-training serves as a broadly adopted starting point for transfer learning
on various downstream tasks. Recent investigations of lottery tickets
hypothesis (LTH) demonstrate such enormous pre-trained models can be replaced
by extremely sparse subnetworks (a.k.a. matching subnetworks) without
sacrificing transferability. However, practical security-crucial applications
usually pose more challenging requirements beyond standard transfer, which also
demand these subnetworks to overcome adversarial vulnerability. In this paper,
we formulate a more rigorous concept, Double-Win Lottery Tickets, in which a
located subnetwork from a pre-trained model can be independently transferred on
diverse downstream tasks, to reach BOTH the same standard and robust
generalization, under BOTH standard and adversarial training regimes, as the
full pre-trained model can do. We comprehensively examine various pre-training
mechanisms and find that robust pre-training tends to craft sparser double-win
lottery tickets with superior performance over the standard counterparts. For
example, on downstream CIFAR-10/100 datasets, we identify double-win matching
subnetworks with the standard, fast adversarial, and adversarial pre-training
from ImageNet, at 89.26%/73.79%, 89.26%/79.03%, and 91.41%/83.22% sparsity,
respectively. Furthermore, we observe the obtained double-win lottery tickets
can be more data-efficient to transfer, under practical data-limited (e.g., 1%
and 10%) downstream schemes. Our results show that the benefits from robust
pre-training are amplified by the lottery ticket scheme, as well as the
data-limited transfer setting. Codes are available at
https://github.com/VITA-Group/Double-Win-LTH.

Negative-free contrastive learning methods have attracted a lot of attention
with simplicity and impressive performances for large-scale pretraining.
However, its disentanglement property remains unexplored. In this paper, we
examine negative-free contrastive learning methods to study the disentanglement
property empirically. We find that existing disentanglement metrics fail to
make meaningful measurements for high-dimensional representation models, so we
propose a new disentanglement metric based on Mutual Information between latent
representations and data factors. With this proposed metric, we benchmark the
disentanglement property of negative-free contrastive learning on both popular
synthetic datasets and a real-world dataset CelebA. Our study shows that the
investigated methods can learn a well-disentangled subset of representation. As
far as we know, we are the first to extend the study of disentangled
representation learning to high-dimensional representation space and introduce
negative-free contrastive learning methods into this area. The source code of
this paper is available at
\url{https://github.com/noahcao/disentanglement_lib_med}.

HyperDimensional Computing (HDC) as a machine learning paradigm is highly
interesting for applications involving continuous, semi-supervised learning for
long-term monitoring. However, its accuracy is not yet on par with other
Machine Learning (ML) approaches. Frameworks enabling fast design space
exploration to find practical algorithms are necessary to make HD computing
competitive with other ML techniques. To this end, we introduce HDTorch, an
open-source, PyTorch-based HDC library with CUDA extensions for hypervector
operations. We demonstrate HDTorch's utility by analyzing four HDC benchmark
datasets in terms of accuracy, runtime, and memory consumption, utilizing both
classical and online HD training methodologies. We demonstrate average
(training)/inference speedups of (111x/68x)/87x for classical/online HD,
respectively. Moreover, we analyze the effects of varying hyperparameters on
runtime and accuracy. Finally, we demonstrate how HDTorch enables exploration
of HDC strategies applied to large, real-world datasets. We perform the
first-ever HD training and inference analysis of the entirety of the CHB-MIT
EEG epilepsy database. Results show that the typical approach of training on a
subset of the data does not necessarily generalize to the entire dataset, an
important factor when developing future HD models for medical wearable devices.

Offline reinforcement learning (RL) defines the task of learning from a
static logged dataset without continually interacting with the environment. The
distribution shift between the learned policy and the behavior policy makes it
necessary for the value function to stay conservative such that
out-of-distribution (OOD) actions will not be severely overestimated. However,
existing approaches, penalizing the unseen actions or regularizing with the
behavior policy, are too pessimistic, which suppresses the generalization of
the value function and hinders the performance improvement. This paper explores
mild but enough conservatism for offline learning while not harming
generalization. We propose Mildly Conservative Q-learning (MCQ), where OOD
actions are actively trained by assigning them proper pseudo Q values. We
theoretically show that MCQ induces a policy that behaves at least as well as
the behavior policy and no erroneous overestimation will occur for OOD actions.
Experimental results on the D4RL benchmarks demonstrate that MCQ achieves
remarkable performance compared with prior work. Furthermore, MCQ shows
superior generalization ability when transferring from offline to online, and
significantly outperforms baselines. Our code is publicly available at
https://github.com/dmksjfl/MCQ.

We give lower bounds on the amount of memory required by one-pass streaming
algorithms for solving several natural learning problems. In a setting where
examples lie in $\{0,1\}^d$ and the optimal classifier can be encoded using
$\kappa$ bits, we show that algorithms which learn using a near-minimal number
of examples, $\tilde O(\kappa)$, must use $\tilde \Omega( d\kappa)$ bits of
space. Our space bounds match the dimension of the ambient space of the
problem's natural parametrization, even when it is quadratic in the size of
examples and the final classifier. For instance, in the setting of $d$-sparse
linear classifiers over degree-2 polynomial features, for which
$\kappa=\Theta(d\log d)$, our space lower bound is $\tilde\Omega(d^2)$. Our
bounds degrade gracefully with the stream length $N$, generally having the form
$\tilde\Omega\left(d\kappa \cdot \frac{\kappa}{N}\right)$.
  Bounds of the form $\Omega(d\kappa)$ were known for learning parity and other
problems defined over finite fields. Bounds that apply in a narrow range of
sample sizes are also known for linear regression. Ours are the first such
bounds for problems of the type commonly seen in recent learning applications
that apply for a large range of input sizes.

This paper studies asynchronous Federated Learning (FL) subject to clients'
individual arbitrary communication patterns with the parameter server. We
propose FedMobile, a new asynchronous FL algorithm that exploits the mobility
attribute of the mobile FL system to improve the learning performance. The key
idea is to leverage the random client-to-client communication in a mobile
network to create additional indirect communication opportunities with the
server via upload and download relaying. We prove that FedMobile achieves a
convergence rate $O(\frac{1}{\sqrt{NT}})$, where $N$ is the number of clients
and $T$ is the number of communication slots, and show that the optimal design
involves an interesting trade-off on the best timing of relaying. Our analysis
suggests that with an increased level of mobility, asynchronous FL converges
faster using FedMobile. Experiment results on a synthetic dataset and two
real-world datasets verify our theoretical findings.

We present a full implementation and simulation of a novel quantum
reinforcement learning (RL) method and mathematically prove a quantum
advantage. Our approach shows in detail how to combine amplitude estimation and
Grover search into a policy evaluation and improvement scheme. We first develop
quantum policy evaluation (QPE) which is quadratically more efficient compared
to an analogous classical Monte Carlo estimation and is based on a quantum
mechanical realization of a finite Markov decision process (MDP). Building on
QPE, we derive a quantum policy iteration that repeatedly improves an initial
policy using Grover search until the optimum is reached. Finally, we present an
implementation of our algorithm for a two-armed bandit MDP which we then
simulate. The results confirm that QPE provides a quantum advantage in RL
problems.

Responsible use of machine learning requires models be audited for
undesirable properties. While a number of auditing algorithms have been
proposed in prior work, how to do principled auditing in a general setting has
remained ill-understood. This work proposes a formal learning-theoretic
framework for auditing, and uses it to investigate if and how model
explanations can help audits. Specifically, we propose algorithms for auditing
linear classifiers for feature sensitivity using label queries as well as two
kinds of explanations, and provide performance guarantees. Our results
illustrate that while counterfactual explanations can be extremely helpful for
auditing, anchor explanations may not be as beneficial in the worst case.

Although machine learning on hypergraphs has attracted considerable
attention, most of the works have focused on (semi-)supervised learning, which
may cause heavy labeling costs and poor generalization. Recently, contrastive
learning has emerged as a successful unsupervised representation learning
method. Despite the prosperous development of contrastive learning in other
domains, contrastive learning on hypergraphs remains little explored. In this
paper, we propose TriCL (Tri-directional Contrastive Learning), a general
framework for contrastive learning on hypergraphs. Its main idea is
tri-directional contrast, and specifically, it aims to maximize in two
augmented views the agreement (a) between the same node, (b) between the same
group of nodes, and (c) between each group and its members. Together with
simple but surprisingly effective data augmentation and negative sampling
schemes, these three forms of contrast enable TriCL to capture both microscopic
and mesoscopic structural information in node embeddings. Our extensive
experiments using 13 baseline approaches, five datasets, and two tasks
demonstrate the effectiveness of TriCL, and most noticeably, TriCL consistently
outperforms not just unsupervised competitors but also (semi-)supervised
competitors mostly by significant margins for node classification.

Partitioned methods allow one to build a simulation capability for coupled
problems by reusing existing single-component codes. In so doing, partitioned
methods can shorten code development and validation times for multiphysics and
multiscale applications. In this work, we consider a scenario in which one or
more of the "codes" being coupled are projection-based reduced order models
(ROMs), introduced to lower the computational cost associated with a particular
component. We simulate this scenario by considering a model interface problem
that is discretized independently on two non-overlapping subdomains. We then
formulate a partitioned scheme for this problem that allows the coupling
between a ROM "code" for one of the subdomains with a finite element model
(FEM) or ROM "code" for the other subdomain. The ROM "codes" are constructed by
performing proper orthogonal decomposition (POD) on a snapshot ensemble to
obtain a low-dimensional reduced order basis, followed by a Galerkin projection
onto this basis. The ROM and/or FEM "codes" on each subdomain are then coupled
using a Lagrange multiplier representing the interface flux. To partition the
resulting monolithic problem, we first eliminate the flux through a dual Schur
complement. Application of an explicit time integration scheme to the
transformed monolithic problem decouples the subdomain equations, allowing
their independent solution for the next time step. We show numerical results
that demonstrate the proposed method's efficacy in achieving both ROM-FEM and
ROM-ROM coupling.

Calculation of Bayesian posteriors and model evidences typically requires
numerical integration. Bayesian quadrature (BQ), a surrogate-model-based
approach to numerical integration, is capable of superb sample efficiency, but
its lack of parallelisation has hindered its practical applications. In this
work, we propose a parallelised (batch) BQ method, employing techniques from
kernel quadrature, that possesses an empirically exponential convergence rate.
Additionally, just as with Nested Sampling, our method permits simultaneous
inference of both posteriors and model evidence. Samples from our BQ surrogate
model are re-selected to give a sparse set of samples, via a kernel
recombination algorithm, requiring negligible additional time to increase the
batch size. Empirically, we find that our approach significantly outperforms
the sampling efficiency of both state-of-the-art BQ techniques and Nested
Sampling in various real-world datasets, including lithium-ion battery
analytics.

This paper presents the baseline approach for the organized 2nd Covid-19
Competition, occurring in the framework of the AIMIA Workshop in the European
Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database
which is annotated for COVID-19 detction, consisting of about 7,700 3-D CT
scans. Part of the database consisting of Covid-19 cases is further annotated
in terms of four Covid-19 severity conditions. We have split the database and
the latter part of it in training, validation and test datasets. The former two
datasets are used for training and validation of machine learning models, while
the latter will be used for evaluation of the developed models. The baseline
approach consists of a deep learning approach, based on a CNN-RNN network and
report its performance on the COVID19-CT-DB database.

Machine learning abilities have become a vital component for various
solutions across industries, applications, and sectors. Many organizations seek
to leverage AI-based solutions across their business services to unlock better
efficiency and increase productivity. Problems, however, can arise if there is
a lack of quality data for AI-model training, scalability, and maintenance. We
propose a data-centric federated learning architecture leveraged by a public
blockchain and smart contracts to overcome this significant issue. Our proposed
solution provides a virtual public marketplace where developers, data
scientists, and AI-engineer can publish their models and collaboratively create
and access quality data for training. We enhance data quality and integrity
through an incentive mechanism that rewards contributors for data contribution
and verification. Those combined with the proposed framework helped increase
with only one user simulation the training dataset with an average of 100 input
daily and the model accuracy by approximately 4\%.

Modeling neural population dynamics underlying noisy single-trial spiking
activities is essential for relating neural observation and behavior. A recent
non-recurrent method - Neural Data Transformers (NDT) - has shown great success
in capturing neural dynamics with low inference latency without an explicit
dynamical model. However, NDT focuses on modeling the temporal evolution of the
population activity while neglecting the rich covariation between individual
neurons. In this paper we introduce SpatioTemporal Neural Data Transformer
(STNDT), an NDT-based architecture that explicitly models responses of
individual neurons in the population across time and space to uncover their
underlying firing rates. In addition, we propose a contrastive learning loss
that works in accordance with mask modeling objective to further improve the
predictive performance. We show that our model achieves state-of-the-art
performance on ensemble level in estimating neural activities across four
neural datasets, demonstrating its capability to capture autonomous and
non-autonomous dynamics spanning different cortical regions while being
completely agnostic to the specific behaviors at hand. Furthermore, STNDT
spatial attention mechanism reveals consistently important subsets of neurons
that play a vital role in driving the response of the entire population,
providing interpretability and key insights into how the population of neurons
performs computation.

Graph contrastive learning (GCL) improves graph representation learning,
leading to SOTA on various downstream tasks. The graph augmentation step is a
vital but scarcely studied step of GCL. In this paper, we show that the node
embedding obtained via the graph augmentations is highly biased, somewhat
limiting contrastive models from learning discriminative features for
downstream tasks. Thus, instead of investigating graph augmentation in the
input space, we alternatively propose to perform augmentations on the hidden
features (feature augmentation). Inspired by so-called matrix sketching, we
propose COSTA, a novel COvariance-preServing feaTure space Augmentation
framework for GCL, which generates augmented features by maintaining a "good
sketch" of original features. To highlight the superiority of feature
augmentation with COSTA, we investigate a single-view setting (in addition to
multi-view one) which conserves memory and computations. We show that the
feature augmentation with COSTA achieves comparable/better results than graph
augmentation based models.

Graph-based representations of point-cloud data are widely used in data
science and machine learning, including epsilon-graphs that contain edges
between pairs of data points that are nearer than epsilon and kNN-graphs that
connect each point to its k-nearest neighbors. Recently, topological data
analysis has emerged as a family of mathematical and computational techniques
to investigate topological features of data using simplicial complexes. These
are a higher-order generalization of graphs and many techniques such as
Vietoris-Rips (VR) filtrations are also parameterized by a distance epsilon.
Here, we develop kNN complexes as a generalization of kNN graphs, leading to
kNN-based persistent homology techniques for which we develop stability and
convergence results. We apply this technique to characterize the convergence
properties PageRank, highlighting how the perspective of discrete topology
complements traditional geometrical-based analyses of convergence.
Specifically, we show that convergence of relative positions (i.e., ranks) is
captured by kNN persistent homology, whereas persistent homology with VR
filtrations coincides with vector-norm convergence. Beyond PageRank, kNN-based
persistent homology is expected to be useful to other data-science applications
in which the relative positioning of data points is more important than their
precise locations.

We formalise some aspects of the neural-symbol design patterns of van Bekkum
et al., such that we can formally define notions of refinement of patterns, as
well as modular combination of larger patterns from smaller building blocks.
These formal notions are being implemented in the heterogeneous tool set
(Hets), such that patterns and refinements can be checked for well-formedness,
and combinations can be computed.

Workplaces of the future require advanced competence profiles from employees,
not least due to new options for teleworking and new complex digital tools. The
acquisition of advanced competence profiles is to be addressed by formal
education. For example, the method of Building Information Modeling (BIM) aims
at digitizing the design, construction, and operation of structures and as such
requires advanced competence profiles. In this study, two educational scenarios
based on teleworking and complex digital tools are compared, each with one
cohort and consisting of two learning activities. The first cohort initially
completes as first learning activity a semester-long course that aims at BIM
domain competences. The semester-long course of the second cohort fosters meta
competences, such as communication, collaboration, and digital literacy. At the
end of the semester, both cohorts solve in a second learning activity a BIM
practice task. Research questions are: (1) Do the two educational scenarios
promote the competences to be addressed? And related: (2) What is the impact of
the initial course that fosters domain competences or meta competences?
Methodologically, the learning outcomes are assessed by measuring the domain
competences three times during the educational scenario using online tests in
the two cohorts (N=11). Further, students' perceptions are surveyed in parallel
using online questionnaires. In addition, semi-structured interviews are
conducted at the end of the educational scenarios. The quantitative and
qualitative results of the study designating the training of meta competencies
partly as a substitute for imparting domain competences are presented. Further,
the influence of both educational scenarios on competence development for the
workplace of the future characterized by telework and digital tools is

Existing theory predicts that data heterogeneity will degrade the performance
of the Federated Averaging (FedAvg) algorithm in federated learning. However,
in practice, the simple FedAvg algorithm converges very well. This paper
explains the seemingly unreasonable effectiveness of FedAvg that contradicts
the previous theoretical predictions. We find that the key assumption of
bounded gradient dissimilarity in previous theoretical analyses is too
pessimistic to characterize data heterogeneity in practical applications. For a
simple quadratic problem, we demonstrate there exist regimes where large
gradient dissimilarity does not have any negative impact on the convergence of
FedAvg. Motivated by this observation, we propose a new quantity, average drift
at optimum, to measure the effects of data heterogeneity, and explicitly use it
to present a new theoretical analysis of FedAvg. We show that the average drift
at optimum is nearly zero across many real-world federated training tasks,
whereas the gradient dissimilarity can be large. And our new analysis suggests
FedAvg can have identical convergence rates in homogeneous and heterogeneous
data settings, and hence, leads to better understanding of its empirical
success.

This paper develops Principal Trade-off Analysis (PTA), a decomposition
method, analogous to Principal Component Analysis (PCA), which permits the
representation of any game as the weighted sum of disc games (continuous R-P-S
games). Applying PTA to empirically generated tournament graphs produces a
sequence of embeddings into orthogonal 2D feature planes representing
independent strategic trade-offs. Each trade-off generates a mode of cyclic
competition. Like PCA, PTA provides optimal low rank estimates of the
tournament graphs that can be truncated for approximation. The complexity of
cyclic competition can be quantified by computing the number of significant
cyclic modes. We illustrate the PTA via application to a pair of games (Blotto,
Pokemon). The resulting 2D disc game representations are shown to be well
suited for visualization and are easily interpretable. In Blotto, PTA
identifies game symmetries, and specifies strategic trade-offs associated with
distinct win conditions. For Pokemon, PTA embeddings produce clusters in the
embedding space that naturally correspond to Pokemon types, a design in the
game that produces cyclic trade offs.

Here, I ask what we can learn about how gender affects how people engage with
robots. I review 46 empirical studies of social robots, published 2018 or
earlier, which report on the gender of their participants or the perceived or
intended gender of the robot, or both, and perform some analysis with respect
to either participant or robot gender. From these studies, I find that robots
are by default perceived as male, that robots absorb human gender stereotypes,
and that men tend to engage with robots more than women. I highlight open
questions about how such gender effects may be different in younger
participants, and whether one should seek to match the gender of the robot to
the gender of the participant to ensure positive interaction outcomes.
  I conclude by suggesting that future research should: include gender diverse
participant pools, include non-binary participants, rely on self-identification
for discerning gender rather than researcher perception, control for known
covariates of gender, test for different study outcomes with respect to gender,
and test whether the robot used was perceived as gendered by participants. I
include an appendix with a narrative summary of gender-relevant findings from
each of the 46 papers to aid in future literature reviews.

Given a learning task where the data is distributed among several parties,
communication is one of the fundamental resources which the parties would like
to minimize. We present a distributed boosting algorithm which is resilient to
a limited amount of noise. Our algorithm is similar to classical boosting
algorithms, although it is equipped with a new component, inspired by
Impagliazzo's hard-core lemma [Impagliazzo95], adding a robustness quality to
the algorithm. We also complement this result by showing that resilience to any
asymptotically larger noise is not achievable by a communication-efficient

To build an artificial neural network like the biological intelligence
system, recent works have unified numerous tasks into a generalist model, which
can process various tasks with shared parameters and do not have any
task-specific modules. While generalist models achieve promising results on
various benchmarks, they have performance degradation on some tasks compared
with task-specialized models. In this work, we find that interference among
different tasks and modalities is the main factor to this phenomenon. To
mitigate such interference, we introduce the Conditional Mixture-of-Experts
(Conditional MoEs) to generalist models. Routing strategies under different
levels of conditions are proposed to take both the training/inference cost and
generalization ability into account. By incorporating the proposed Conditional
MoEs, the recently proposed generalist model Uni-Perceiver can effectively
mitigate the interference across tasks and modalities, and achieves
state-of-the-art results on a series of downstream tasks via prompt tuning on
1% of downstream data. Moreover, the introduction of Conditional MoEs still
holds the generalization ability of generalist models to conduct zero-shot
inference on new tasks, e.g., video-text retrieval and video caption. Code and
pre-trained generalist models shall be released.

The size of vision models has grown exponentially over the last few years,
especially after the emergence of Vision Transformer. This has motivated the
development of parameter-efficient tuning methods, such as learning adapter
layers or visual prompt tokens, which allow a tiny portion of model parameters
to be trained whereas the vast majority obtained from pre-training are frozen.
However, designing a proper tuning method is non-trivial: one might need to try
out a lengthy list of design choices, not to mention that each downstream
dataset often requires custom designs. In this paper, we view the existing
parameter-efficient tuning methods as "prompt modules" and propose Neural
prOmpt seArcH (NOAH), a novel approach that learns, for large vision models,
the optimal design of prompt modules through a neural architecture search
algorithm, specifically for each downstream dataset. By conducting extensive
experiments on over 20 vision datasets, we demonstrate that NOAH (i) is
superior to individual prompt modules, (ii) has a good few-shot learning
ability, and (iii) is domain-generalizable. The code and models are available
at https://github.com/Davidzhangyuanhan/NOAH.

Value approximation using deep neural networks is at the heart of off-policy
deep reinforcement learning, and is often the primary module that provides
learning signals to the rest of the algorithm. While multi-layer perceptron
networks are universal function approximators, recent works in neural kernel
regression suggest the presence of a spectral bias, where fitting
high-frequency components of the value function requires exponentially more
gradient update steps than the low-frequency ones. In this work, we re-examine
off-policy reinforcement learning through the lens of kernel regression and
propose to overcome such bias via a composite neural tangent kernel. With just
a single line-change, our approach, the Fourier feature networks (FFN) produce
state-of-the-art performance on challenging continuous control domains with
only a fraction of the compute. Faster convergence and better off-policy
stability also make it possible to remove the target network without suffering
catastrophic divergences, which further reduces TD}(0)'s estimation bias on a
few tasks.

Comprehensive 3D scene understanding, both geometrically and semantically, is
important for real-world applications such as robot perception. Most of the
existing work has focused on developing data-driven discriminative models for
scene understanding. This paper provides a new approach to scene understanding,
from a synthesis model perspective, by leveraging the recent progress on
implicit 3D representation and neural rendering. Building upon the great
success of Neural Radiance Fields (NeRFs), we introduce Scene-Property
Synthesis with NeRF (SS-NeRF) that is able to not only render photo-realistic
RGB images from novel viewpoints, but also render various accurate scene
properties (e.g., appearance, geometry, and semantics). By doing so, we
facilitate addressing a variety of scene understanding tasks under a unified
framework, including semantic segmentation, surface normal estimation,
reshading, keypoint detection, and edge detection. Our SS-NeRF framework can be
a powerful tool for bridging generative learning and discriminative learning,
and thus be beneficial to the investigation of a wide range of interesting
problems, such as studying task relationships within a synthesis paradigm,
transferring knowledge to novel tasks, facilitating downstream discriminative
tasks as ways of data augmentation, and serving as auto-labeller for data
creation.

The paper presents a scalable approach for learning distributed
representations over individual tokens and a holistic instance representation
simultaneously. We use self-attention blocks to represent distributed tokens,
followed by cross-attention blocks to aggregate the holistic instance. The core
of the approach is the use of extremely large token masking (75%-90%) as the
data augmentation for supervision. Our model, named ExtreMA, follows the plain
BYOL approach where the instance representation from the unmasked subset is
trained to predict that from the intact input. Learning requires the model to
capture informative variations in an instance, instead of encouraging
invariances. The paper makes three contributions: 1) Random masking is a strong
and computationally efficient data augmentation for learning generalizable
attention representations. 2) With multiple sampling per instance, extreme
masking greatly speeds up learning and hungers for more data. 3) Distributed
representations can be learned from the instance supervision alone, unlike
per-token supervisions in masked modeling.

Convolution on 3D point clouds is widely researched yet far from perfect in
geometric deep learning. The traditional wisdom of convolution characterises
feature correspondences indistinguishably among 3D points, arising an intrinsic
limitation of poor distinctive feature learning. In this paper, we propose
Adaptive Graph Convolution (AGConv) for wide applications of point cloud
analysis. AGConv generates adaptive kernels for points according to their
dynamically learned features. Compared with the solution of using
fixed/isotropic kernels, AGConv improves the flexibility of point cloud
convolutions, effectively and precisely capturing the diverse relations between
points from different semantic parts. Unlike the popular attentional weight
schemes, AGConv implements the adaptiveness inside the convolution operation
instead of simply assigning different weights to the neighboring points.
Extensive evaluations clearly show that our method outperforms
state-of-the-arts of point cloud classification and segmentation on various
benchmark datasets.Meanwhile, AGConv can flexibly serve more point cloud
analysis approaches to boost their performance. To validate its flexibility and
effectiveness, we explore AGConv-based paradigms of completion, denoising,
upsampling, registration and circle extraction, which are comparable or even
superior to their competitors. Our code is available at
https://github.com/hrzhou2/AdaptConv-master.

An important goal of self-supervised learning is to enable model pre-training
to benefit from almost unlimited data. However, one method that has recently
become popular, namely masked image modeling (MIM), is suspected to be unable
to benefit from larger data. In this work, we break this misconception through
extensive experiments, with data scales ranging from 10\% of ImageNet-1K to
full ImageNet-22K, model sizes ranging from 49 million to 1 billion, and
training lengths ranging from 125K iterations to 500K iterations. Our study
reveals that: (i) Masked image modeling is also demanding on larger data. We
observed that very large models got over-fitted with relatively small data;
(ii) The length of training matters. Large models trained with masked image
modeling can benefit from more data with longer training; (iii) The validation
loss in pre-training is a good indicator to measure how well the model performs
for fine-tuning on multiple tasks. This observation allows us to pre-evaluate
pre-trained models in advance without having to make costly trial-and-error
assessments of downstream tasks. We hope that our findings will advance the
understanding of masked image modeling in terms of scaling ability.

The dual tasks of quantum Hamiltonian learning and quantum Gibbs sampling are
relevant to many important problems in physics and chemistry. In the low
temperature regime, algorithms for these tasks often suffer from
intractabilities, for example from poor sample- or time-complexity. With the
aim of addressing such intractabilities, we introduce a generalization of
quantum natural gradient descent to parameterized mixed states, as well as
provide a robust first-order approximating algorithm, Quantum-Probabilistic
Mirror Descent. We prove data sample efficiency for the dual tasks using tools
from information geometry and quantum metrology, thus generalizing the seminal
result of classical Fisher efficiency to a variational quantum algorithm for
the first time. Our approaches extend previously sample-efficient techniques to
allow for flexibility in model choice, including to spectrally-decomposed
models like Quantum Hamiltonian-Based Models, which may circumvent intractable
time complexities. Our first-order algorithm is derived using a novel quantum
generalization of the classical mirror descent duality. Both results require a
special choice of metric, namely, the Bogoliubov-Kubo-Mori metric. To test our
proposed algorithms numerically, we compare their performance to existing
baselines on the task of quantum Gibbs sampling for the transverse field Ising
model. Finally, we propose an initialization strategy leveraging geometric
locality for the modelling of sequences of states such as those arising from
quantum-stochastic processes. We demonstrate its effectiveness empirically for
both real and imaginary time evolution while defining a broader class of
potential applications.

Despite the popularity of Model Compression and Multitask Learning, how to
effectively compress a multitask model has been less thoroughly analyzed due to
the challenging entanglement of tasks in the parameter space. In this paper, we
propose DiSparse, a simple, effective, and first-of-its-kind multitask pruning
and sparse training scheme. We consider each task independently by
disentangling the importance measurement and take the unanimous decisions among
all tasks when performing parameter pruning and selection. Our experimental
results demonstrate superior performance on various configurations and settings
compared to popular sparse training and pruning methods. Besides the
effectiveness in compression, DiSparse also provides a powerful tool to the
multitask learning community. Surprisingly, we even observed better performance
than some dedicated multitask learning methods in several cases despite the
high model sparsity enforced by DiSparse. We analyzed the pruning masks
generated with DiSparse and observed strikingly similar sparse network
architecture identified by each task even before the training starts. We also
observe the existence of a "watershed" layer where the task relatedness sharply
drops, implying no benefits in continued parameters sharing. Our code and
models will be available at:
https://github.com/SHI-Labs/DiSparse-Multitask-Model-Compression.

Black-box machine learning models are criticized as lacking interpretability,
although they tend to have good prediction accuracy. Knowledge Distillation
(KD) is an emerging tool to interpret the black-box model by distilling its
knowledge into a transparent model. With well-known advantages in
interpretation, decision tree is a competitive candidate of the transparent
model. However, theoretical or empirical understanding for the decision tree
generated from KD process is limited. In this paper, we name this kind of
decision tree the distillation decision tree (DDT) and lay the theoretical
foundations for tree structure stability which determines the validity of DDT's
interpretation. We prove that the structure of DDT can achieve stable
(convergence) under some mild assumptions. Meanwhile, we develop algorithms for
stabilizing the induction of DDT, propose parallel strategies for improving
algorithm's computational efficiency, and introduce a marginal principal
component analysis method for overcoming the curse of dimensionality in
sampling. Simulated and real data studies justify our theoretical results,
validate the efficacy of algorithms, and demonstrate that DDT can strike a good
balance between model's prediction accuracy and interpretability.

Despite recent progress in generative adversarial network(GAN)-based
vocoders, where the model generates raw waveform conditioned on mel
spectrogram, it is still challenging to synthesize high-fidelity audio for
numerous speakers across varied recording environments. In this work, we
present BigVGAN, a universal vocoder that generalizes well under various unseen
conditions in zero-shot setting. We introduce periodic nonlinearities and
anti-aliased representation into the generator, which brings the desired
inductive bias for waveform synthesis and significantly improves audio quality.
Based on our improved generator and the state-of-the-art discriminators, we
train our GAN vocoder at the largest scale up to 112M parameters, which is
unprecedented in the literature. In particular, we identify and address the
training instabilities specific to such scale, while maintaining high-fidelity
output without over-regularization. Our BigVGAN achieves the state-of-the-art
zero-shot performance for various out-of-distribution scenarios, including new
speakers, novel languages, singing voices, music and instrumental audio in
unseen (even noisy) recording environments. We will release our code and model
at: https://github.com/NVIDIA/BigVGAN

Image rasterization is a mature technique in computer graphics, while image
vectorization, the reverse path of rasterization, remains a major challenge.
Recent advanced deep learning-based models achieve vectorization and semantic
interpolation of vector graphs and demonstrate a better topology of generating
new figures. However, deep models cannot be easily generalized to out-of-domain
testing data. The generated SVGs also contain complex and redundant shapes that
are not quite convenient for further editing. Specifically, the crucial
layer-wise topology and fundamental semantics in images are still not well
understood and thus not fully explored. In this work, we propose Layer-wise
Image Vectorization, namely LIVE, to convert raster images to SVGs and
simultaneously maintain its image topology. LIVE can generate compact SVG forms
with layer-wise structures that are semantically consistent with human
perspective. We progressively add new bezier paths and optimize these paths
with the layer-wise framework, newly designed loss functions, and
component-wise path initialization technique. Our experiments demonstrate that
LIVE presents more plausible vectorized forms than prior works and can be
generalized to new images. With the help of this newly learned topology, LIVE
initiates human editable SVGs for both designers and other downstream
applications. Codes are made available at
https://github.com/Picsart-AI-Research/LIVE-Layerwise-Image-Vectorization.

Videos typically record the streaming and continuous visual data as discrete
consecutive frames. Since the storage cost is expensive for videos of high
fidelity, most of them are stored in a relatively low resolution and frame
rate. Recent works of Space-Time Video Super-Resolution (STVSR) are developed
to incorporate temporal interpolation and spatial super-resolution in a unified
framework. However, most of them only support a fixed up-sampling scale, which
limits their flexibility and applications. In this work, instead of following
the discrete representations, we propose Video Implicit Neural Representation
(VideoINR), and we show its applications for STVSR. The learned implicit neural
representation can be decoded to videos of arbitrary spatial resolution and
frame rate. We show that VideoINR achieves competitive performances with
state-of-the-art STVSR methods on common up-sampling scales and significantly
outperforms prior works on continuous and out-of-training-distribution scales.
Our project page is at http://zeyuan-chen.com/VideoINR/ .

We consider the fixed-budget best arm identification problem where the goal
is to find the arm of the largest mean with a fixed number of samples. It is
known that the probability of misidentifying the best arm is exponentially
small to the number of rounds. However, limited characterizations have been
discussed on the rate (exponent) of this value. In this paper, we characterize
the minimax optimal rate as a result of an optimization over all possible
parameters. We introduce two rates, $R^{\mathrm{go}}$ and
$R^{\mathrm{go}}_{\infty}$, corresponding to lower bounds on the probability of
misidentification, each of which is associated with a proposed algorithm. The
rate $R^{\mathrm{go}}$ is associated with $R^{\mathrm{go}}$-tracking, which can
be efficiently implemented by a neural network and is shown to outperform
existing algorithms. However, this rate requires a nontrivial condition to be
achievable. To address this issue, we introduce the second rate
$R^{\mathrm{go}}_\infty$. We show that this rate is indeed achievable by
introducing a conceptual algorithm called delayed optimal tracking (DOT).

The method of choice for integrating the time-dependent Fokker-Planck
equation in high-dimension is to generate samples from the solution via
integration of the associated stochastic differential equation. Here, we
introduce an alternative scheme based on integrating an ordinary differential
equation that describes the flow of probability. Unlike the stochastic
dynamics, this equation deterministically pushes samples from the initial
density onto samples from the solution at any later time. The method has the
advantage of giving direct access to quantities that are challenging to
estimate from stochastic trajectories, such as the probability current, the
density itself, and its entropy. The probability flow equation depends on the
gradient of the logarithm of the solution (its "score"), and so is a-priori
unknown. To resolve this dependence, we model the score with a deep neural
network that is learned on-the-fly by propagating a set of samples according to
the instantaneous probability current. Our approach is based on recent advances
in score-based diffusion for generative modeling, but the training procedure is
self-contained and does not require samples from the target density to be
available beforehand. To demonstrate the validity of the approach, we consider
several examples from the physics of interacting particle systems; we find that
the method scales well to high-dimensional systems and accurately matches
available analytical solutions and moments computed via Monte-Carlo.

Information-directed sampling (IDS) has revealed its potential as a
data-efficient algorithm for reinforcement learning (RL). However, theoretical
understanding of IDS for Markov Decision Processes (MDPs) is still limited. We
develop novel information-theoretic tools to bound the information ratio and
cumulative information gain about the learning target. Our theoretical results
shed light on the importance of choosing the learning target such that the
practitioners can balance the computation and regret bounds. As a consequence,
we derive prior-free Bayesian regret bounds for vanilla-IDS which learns the
whole environment under tabular finite-horizon MDPs. In addition, we propose a
computationally-efficient regularized-IDS that maximizes an additive form
rather than the ratio form and show that it enjoys the same regret bound as
vanilla-IDS. With the aid of rate-distortion theory, we improve the regret
bound by learning a surrogate, less informative environment. Furthermore, we
extend our analysis to linear MDPs and prove similar regret bounds for Thompson
sampling as a by-product.

Recent work has shown that the attention maps of Vision Transformers (VTs),
when trained with self-supervision, can contain a semantic segmentation
structure which does not spontaneously emerge when training is supervised. In
this paper, we explicitly encourage the emergence of this spatial clustering as
a form of training regularization, this way including a self-supervised pretext
task into the standard supervised learning. In more detail, we propose a VT
regularization method based on a spatial formulation of the information
entropy. By minimizing the proposed spatial entropy, we explicitly ask the VT
to produce spatially ordered attention maps, this way including an object-based
prior during training. Using extensive experiments, we show that the proposed
regularization approach is beneficial with different training scenarios,
datasets, downstream tasks and VT architectures. The code will be available
upon acceptance.

Learning from demonstration (LfD) methods have shown promise for solving
multi-step tasks; however, these approaches do not guarantee successful
reproduction of the task given disturbances. In this work, we identify the
roots of such a challenge as the failure of the learned continuous policy to
satisfy the discrete plan implicit in the demonstration. By utilizing modes
(rather than subgoals) as the discrete abstraction and motion policies with
both mode invariance and goal reachability properties, we prove our learned
continuous policy can simulate any discrete plan specified by a Linear Temporal
Logic (LTL) formula. Consequently, the imitator is robust to both task- and
motion-level disturbances and guaranteed to achieve task success. Project page:
https://sites.google.com/view/ltl-ds

We propose cross-modal attentive connections, a new dynamic and effective
technique for multimodal representation learning from wearable data. Our
solution can be integrated into any stage of the pipeline, i.e., after any
convolutional layer or block, to create intermediate connections between
individual streams responsible for processing each modality. Additionally, our
method benefits from two properties. First, it can share information
uni-directionally (from one modality to the other) or bi-directionally. Second,
it can be integrated into multiple stages at the same time to further allow
network gradients to be exchanged in several touch-points. We perform extensive
experiments on three public multimodal wearable datasets, WESAD, SWELL-KW, and
CASE, and demonstrate that our method can effectively regulate and share
information between different modalities to learn better representations. Our
experiments further demonstrate that once integrated into simple CNN-based
multimodal solutions (2, 3, or 4 modalities), our method can result in superior
or competitive performance to state-of-the-art and outperform a variety of
baseline uni-modal and classical multimodal methods.

Pretrained language models (LMs) are susceptible to generate text with
nonfactual information. In this work, we measure and improve the factual
accuracy of large-scale LMs for open-ended text generation. We design the
FactualityPrompts test set and metrics to measure the factuality of LM
generations. Based on that, we study the factual accuracy of LMs with parameter
sizes ranging from 126M to 530B. Interestingly, we find that larger LMs are
more factual than smaller ones, although a previous study suggests that larger
LMs can be less truthful in terms of misconceptions. In addition, popular
sampling algorithms (e.g., top-p) in open-ended text generation can harm the
factuality due to the ''uniform randomness'' introduced at every sampling step.
We propose the factual-nucleus sampling algorithm that dynamically adapts the
randomness to improve the factuality of generation while maintaining quality.
Furthermore, we analyze the inefficiencies of the standard training method in
learning correct associations between entities from factual text corpus (e.g.,
Wikipedia). We propose a factuality-enhanced training method that uses
TopicPrefix for better awareness of facts and sentence completion as the
training objective, which can vastly reduce the factual errors.

We review the use of differential privacy (DP) for privacy protection in
machine learning (ML). We show that, driven by the aim of preserving the
accuracy of the learned models, DP-based ML implementations are so loose that
they do not offer the ex ante privacy guarantees of DP. Instead, what they
deliver is basically noise addition similar to the traditional (and often
criticized) statistical disclosure control approach. Due to the lack of formal
privacy guarantees, the actual level of privacy offered must be experimentally
assessed ex post, which is done very seldom. In this respect, we present
empirical results showing that standard anti-overfitting techniques in ML can
achieve a better utility/privacy/efficiency trade-off than DP.

Learning models that offer robust out-of-distribution generalization and fast
adaptation is a key challenge in modern machine learning. Modelling causal
structure into neural networks holds the promise to accomplish robust zero and
few-shot adaptation. Recent advances in differentiable causal discovery have
proposed to factorize the data generating process into a set of modules, i.e.
one module for the conditional distribution of every variable where only causal
parents are used as predictors. Such a modular decomposition of knowledge
enables adaptation to distributions shifts by only updating a subset of
parameters. In this work, we systematically study the generalization and
adaption performance of such modular neural causal models by comparing it to
monolithic models and structured models where the set of predictors is not
constrained to causal parents. Our analysis shows that the modular neural
causal models outperform other models on both zero and few-shot adaptation in
low data regimes and offer robust generalization. We also found that the
effects are more significant for sparser graphs as compared to denser graphs.

Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative
impact, these new capabilities are as yet poorly characterized. In order to
inform future research, prepare for disruptive new model capabilities, and
ameliorate socially harmful effects, it is vital that we understand the present
and near-future capabilities and limitations of language models. To address
this challenge, we introduce the Beyond the Imitation Game benchmark
(BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442
authors across 132 institutions. Task topics are diverse, drawing problems from
linguistics, childhood development, math, common-sense reasoning, biology,
physics, social bias, software development, and beyond. BIG-bench focuses on
tasks that are believed to be beyond the capabilities of current language
models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense
transformer architectures, and Switch-style sparse transformers on BIG-bench,
across model sizes spanning millions to hundreds of billions of parameters. In
addition, a team of human expert raters performed all tasks in order to provide
a strong baseline. Findings include: model performance and calibration both
improve with scale, but are poor in absolute terms (and when compared with
rater performance); performance is remarkably similar across model classes,
though with benefits from sparsity; tasks that improve gradually and
predictably commonly involve a large knowledge or memorization component,
whereas tasks that exhibit "breakthrough" behavior at a critical scale often
involve multiple steps or components, or brittle metrics; social bias typically
increases with scale in settings with ambiguous context, but this can be
improved with prompting.

Injecting noise within gradient descent has several desirable features. In
this paper, we explore noise injection before computing a gradient step, which
is known to have smoothing and regularizing properties. We show that small
perturbations induce explicit regularization for simple finite-dimensional
models based on the l1-norm, group l1-norms, or nuclear norms. When applied to
overparametrized neural networks with large widths, we show that the same
perturbations do not work due to variance explosion resulting from
overparametrization. However, we also show that independent layer wise
perturbations allow to avoid the exploding variance term, and explicit
regularizers can then be obtained. We empirically show that the small
perturbations lead to better generalization performance than vanilla
(stochastic) gradient descent training, with minor adjustments to the training
procedure.

We study the generalisation properties of majority voting on finite ensembles
of classifiers, proving margin-based generalisation bounds via the PAC-Bayes
theory. These provide state-of-the-art guarantees on a number of classification
tasks. Our central results leverage the Dirichlet posteriors studied recently
by Zantedeschi et al. [2021] for training voting classifiers; in contrast to
that work our bounds apply to non-randomised votes via the use of margins. Our
contributions add perspective to the debate on the "margins theory" proposed by
Schapire et al. [1998] for the generalisation of ensemble classifiers.

We explore the possibility of using machine learning to estimate physical
parameters directly from AGN X-ray spectra without needing computationally
expensive spectral fitting. Specifically, we consider survey quality data,
rather than long pointed observations, to ensure that this approach works in
the regime where it is most likely to be applied. We simulate Athena WFI
spectra of AGN with warm absorbers, and train simple neural networks to
estimate the ionisation and column density of the absorbers. We find that this
approach can give comparable accuracy to spectral fitting, without the risk of
outliers caused by the fit sticking in a false minimum, and with an improvement
of around three orders of magnitude in speed. We also demonstrate that using
principal component analysis to reduce the dimensionality of the data prior to
inputting it into the neural net can significantly increase the accuracy of the
parameter estimation for negligible computational cost, while also allowing a
simpler network architecture to be used.

Current deep learning approaches have shown good in-distribution
generalization performance, but struggle with out-of-distribution
generalization. This is especially true in the case of tasks involving abstract
relations like recognizing rules in sequences, as we find in many intelligence
tests. Recent work has explored how forcing relational representations to
remain distinct from sensory representations, as it seems to be the case in the
brain, can help artificial systems. Building on this work, we further explore
and formalize the advantages afforded by 'partitioned' representations of
relations and sensory details, and how this inductive bias can help recompose
learned relational structure in newly encountered settings. We introduce a
simple architecture based on similarity scores which we name Compositional
Relational Network (CoRelNet). Using this model, we investigate a series of
inductive biases that ensure abstract relations are learned and represented
distinctly from sensory data, and explore their effects on out-of-distribution
generalization for a series of relational psychophysics tasks. We find that
simple architectural choices can outperform existing models in
out-of-distribution generalization. Together, these results show that
partitioning relational representations from other information streams may be a
simple way to augment existing network architectures' robustness when
performing out-of-distribution relational computations.

This paper presents a new type of distributed dexterous manipulator: delta
arrays. Our delta array setup consists of 64 linearly-actuated delta robots
with 3D-printed compliant linkages. Through the design of the individual delta
robots, the modular array structure, and distributed communication and control,
we study a wide range of in-plane and out-of-plane manipulations, as well as
prehensile manipulations among subsets of neighboring delta robots. We also
demonstrate dexterous manipulation capabilities of the delta array using
reinforcement learning while leveraging the compliance to not break the
end-effectors. Our evaluations show that the resulting 192 DoF compliant robot
is capable of performing various coordinated distributed manipulations of a
variety of objects, including translation, alignment, prehensile squeezing,
lifting, and grasping.

We build a field level emulator for cosmic structure formation that is
accurate in the nonlinear regime. Our emulator consists of two convolutional
neural networks trained to output the nonlinear displacements and velocities of
N-body simulation particles based on their linear inputs. Cosmology dependence
is encoded in the form of style parameters at each layer of the neural network,
enabling the emulator to effectively interpolate the outcomes of structure
formation between different flat $\Lambda$CDM cosmologies over a wide range of
background matter densities. The neural network architecture makes the model
differentiable by construction, providing a powerful tool for fast field level
inference. We test the accuracy of our method by considering several summary
statistics, including the density power spectrum with and without redshift
space distortions, the displacement power spectrum, the momentum power
spectrum, the density bispectrum, halo abundances, and halo profiles with and
without redshift space distortions. We compare these statistics from our
emulator with the full N-body results, the COLA method, and a fiducial neural
network with no cosmological dependence. We find our emulator gives accurate
results down to scales of $k \sim 1\ \mathrm{Mpc}^{-1}\, h$, representing a
considerable improvement over both COLA and the fiducial neural network. We
also demonstrate that our emulator generalizes well to initial conditions
containing primordial non-Gaussianity, without the need for any additional
style parameters or retraining.

Recent work has demonstrated the successful extraction of training data from
generative language models. However, it is not evident whether such extraction
is feasible in text classification models since the training objective is to
predict the class label as opposed to next-word prediction. This poses an
interesting challenge and raises an important question regarding the privacy of
training data in text classification settings. Therefore, we study the
potential privacy leakage in the text classification domain by investigating
the problem of unintended memorization of training data that is not pertinent
to the learning task. We propose an algorithm to extract missing tokens of a
partial text by exploiting the likelihood of the class label provided by the
model. We test the effectiveness of our algorithm by inserting canaries into
the training set and attempting to extract tokens in these canaries
post-training. In our experiments, we demonstrate that successful extraction is
possible to some extent. This can also be used as an auditing strategy to
assess any potential unauthorized use of personal data without consent.

We establish optimal Statistical Query (SQ) lower bounds for robustly
learning certain families of discrete high-dimensional distributions. In
particular, we show that no efficient SQ algorithm with access to an
$\epsilon$-corrupted binary product distribution can learn its mean within
$\ell_2$-error $o(\epsilon \sqrt{\log(1/\epsilon)})$. Similarly, we show that
no efficient SQ algorithm with access to an $\epsilon$-corrupted ferromagnetic
high-temperature Ising model can learn the model to total variation distance
$o(\epsilon \log(1/\epsilon))$. Our SQ lower bounds match the error guarantees
of known algorithms for these problems, providing evidence that current upper
bounds for these tasks are best possible. At the technical level, we develop a
generic SQ lower bound for discrete high-dimensional distributions starting
from low dimensional moment matching constructions that we believe will find
other applications. Additionally, we introduce new ideas to analyze these
moment-matching constructions for discrete univariate distributions.

robotics. While significant advances have been made in simultaneous
localization and mapping algorithms, robots are still far from having the
common sense knowledge about household objects and their locations of an
average human. We introduce a novel method for leveraging common sense embedded
within large language models for labelling rooms given the objects contained
within. This algorithm has the added benefits of (i) requiring no task-specific
pre-training (operating entirely in the zero-shot regime) and (ii) generalizing
to arbitrary room and object labels, including previously-unseen ones -- both
of which are highly desirable traits in robotic scene understanding algorithms.
The proposed algorithm operates on 3D scene graphs produced by modern spatial
perception systems, and we hope it will pave the way to more generalizable and
scalable high-level 3D scene understanding for robotics.

Learning Bird's Eye View (BEV) representation from surrounding-view cameras
is of great importance for autonomous driving. In this work, we propose a
Geometry-guided Kernel Transformer (GKT), a novel 2D-to-BEV representation
learning mechanism. GKT leverages the geometric priors to guide the transformer
to focus on discriminative regions and unfolds kernel features to generate BEV
representation. For fast inference, we further introduce a look-up table (LUT)
indexing method to get rid of the camera's calibrated parameters at runtime.
GKT can run at $72.3$ FPS on 3090 GPU / $45.6$ FPS on 2080ti GPU and is robust
to the camera deviation and the predefined BEV height. And GKT achieves the
state-of-the-art real-time segmentation results, i.e., 38.0 mIoU
(100m$\times$100m perception range at a 0.5m resolution) on the nuScenes val
set. Given the efficiency, effectiveness, and robustness, GKT has great
practical values in autopilot scenarios, especially for real-time running
systems. Code and models will be available at
\url{https://github.com/hustvl/GKT}.

The seminal paper by Mazumdar and Saha \cite{MS17a} introduced an extensive
line of work on clustering with noisy queries. Yet, despite significant
progress on the problem, the proposed methods depend crucially on knowing the
exact probabilities of errors of the underlying fully-random oracle. In this
work, we develop robust learning methods that tolerate general semi-random
noise obtaining qualitatively the same guarantees as the best possible methods
in the fully-random model.
  More specifically, given a set of $n$ points with an unknown underlying
partition, we are allowed to query pairs of points $u,v$ to check if they are
in the same cluster, but with probability $p$, the answer may be adversarially
chosen. We show that information theoretically $O\left(\frac{nk \log n}
{(1-2p)^2}\right)$ queries suffice to learn any cluster of sufficiently large
size. Our main result is a computationally efficient algorithm that can
identify large clusters with $O\left(\frac{nk \log n} {(1-2p)^2}\right) +
\text{poly}\left(\log n, k, \frac{1}{1-2p} \right)$ queries, matching the
guarantees of the best known algorithms in the fully-random model. As a
corollary of our approach, we develop the first parameter-free algorithm for
the fully-random model, answering an open question by \cite{MS17a}.

Extracting Handwritten text is one of the most important components of
digitizing information and making it available for large scale setting.
Handwriting Optical Character Reader (OCR) is a research problem in computer
vision and natural language processing computing, and a lot of work has been
done for English, but unfortunately, very little work has been done for low
resourced languages such as Urdu. Urdu language script is very difficult
because of its cursive nature and change of shape of characters based on it's
relative position, therefore, a need arises to propose a model which can
understand complex features and generalize it for every kind of handwriting
style. In this work, we propose a transformer based Urdu Handwritten text
extraction model. As transformers have been very successful in Natural Language
Understanding task, we explore them further to understand complex Urdu
Handwriting.

We train a neural network model to predict the full phase space evolution of
cosmological N-body simulations. Its success implies that the neural network
model is accurately approximating the Green's function expansion that relates
the initial conditions of the simulations to its outcome at later times in the
deeply nonlinear regime. We test the accuracy of this approximation by
assessing its performance on well understood simple cases that have either
known exact solutions or well understood expansions. These scenarios include
spherical configurations, isolated plane waves, and two interacting plane
waves: initial conditions that are very different from the Gaussian random
fields used for training. We find our model generalizes well to these well
understood scenarios, demonstrating that the networks have inferred general
physical principles and learned the nonlinear mode couplings from the complex,
random Gaussian training data. These tests also provide a useful diagnostic for
finding the model's strengths and weaknesses, and identifying strategies for
model improvement. We also test the model on initial conditions that contain
only transverse modes, a family of modes that differ not only in their phases
but also in their evolution from the longitudinal growing modes used in the
training set. When the network encounters these initial conditions that are
orthogonal to the training set, the model fails completely. In addition to
these simple configurations, we evaluate the model's predictions for the
density, displacement, and momentum power spectra with standard initial
conditions for N-body simulations. We compare these summary statistics against
N-body results and an approximate, fast simulation method called COLA. Our
model achieves percent level accuracy at nonlinear scales of $k\sim 1\
\mathrm{Mpc}^{-1}\, h$, representing a significant improvement over COLA.

End-to-end (E2E) speech-to-text translation (ST) often depends on pretraining
its encoder and/or decoder using source transcripts via speech recognition or
text translation tasks, without which translation performance drops
substantially. However, transcripts are not always available, and how
significant such pretraining is for E2E ST has rarely been studied in the
literature. In this paper, we revisit this question and explore the extent to
which the quality of E2E ST trained on speech-translation pairs alone can be
improved. We reexamine several techniques proven beneficial to ST previously,
and offer a set of best practices that biases a Transformer-based E2E ST system
toward training from scratch. Besides, we propose parameterized distance
penalty to facilitate the modeling of locality in the self-attention model for
speech. On four benchmarks covering 23 languages, our experiments show that,
without using any transcripts or pretraining, the proposed system reaches and
even outperforms previous studies adopting pretraining, although the gap
remains in (extremely) low-resource settings. Finally, we discuss neural
acoustic feature modeling, where a neural model is designed to extract acoustic
features from raw speech signals directly, with the goal to simplify inductive
biases and add freedom to the model in describing speech. For the first time,
we demonstrate its feasibility and show encouraging results on ST tasks.

Overparameterized neural networks enjoy great representation power on complex
data, and more importantly yield sufficiently smooth output, which is crucial
to their generalization and robustness. Most existing function approximation
theories suggest that with sufficiently many parameters, neural networks can
well approximate certain classes of functions in terms of the function value.
The neural network themselves, however, can be highly nonsmooth. To bridge this
gap, we take convolutional residual networks (ConvResNets) as an example, and
prove that large ConvResNets can not only approximate a target function in
terms of function value, but also exhibit sufficient first-order smoothness.
Moreover, we extend our theory to approximating functions supported on a
low-dimensional manifold. Our theory partially justifies the benefits of using
deep and wide networks in practice. Numerical experiments on adversarial robust
image classification are provided to support our theory.

Kernel Stein discrepancy (KSD) is a widely used kernel-based measure of
discrepancy between probability measures. It is often employed in the scenario
where a user has a collection of samples from a candidate probability measure
and wishes to compare them against a specified target probability measure. A
useful property of KSD is that it may be calculated with samples from only the
candidate measure and without knowledge of the normalising constant of the
target measure. KSD has been employed in a range of settings including
goodness-of-fit testing, parametric inference, MCMC output assessment and
generative modelling. Two main issues with current KSD methodology are (i) the
lack of applicability beyond the finite dimensional Euclidean setting and (ii)
a lack of clarity on what influences KSD performance. This paper provides a
novel spectral representation of KSD which remedies both of these, making KSD
applicable to Hilbert-valued data and revealing the impact of kernel and Stein
operator choice on the KSD. We demonstrate the efficacy of the proposed
methodology by performing goodness-of-fit tests for various Gaussian and
non-Gaussian functional models in a number of synthetic data experiments.

The generalization of model-based reinforcement learning (MBRL) methods to
environments with unseen transition dynamics is an important yet challenging
problem. Existing methods try to extract environment-specified information $Z$
from past transition segments to make the dynamics prediction model
generalizable to different dynamics. However, because environments are not
labelled, the extracted information inevitably contains redundant information
unrelated to the dynamics in transition segments and thus fails to maintain a
crucial property of $Z$: $Z$ should be similar in the same environment and
dissimilar in different ones. As a result, the learned dynamics prediction
function will deviate from the true one, which undermines the generalization
ability. To tackle this problem, we introduce an interventional prediction
module to estimate the probability of two estimated $\hat{z}_i, \hat{z}_j$
belonging to the same environment. Furthermore, by utilizing the $Z$'s
invariance within a single environment, a relational head is proposed to
enforce the similarity between $\hat{{Z}}$ from the same environment. As a
result, the redundant information will be reduced in $\hat{Z}$. We empirically
show that $\hat{{Z}}$ estimated by our method enjoy less redundant information
than previous methods, and such $\hat{{Z}}$ can significantly reduce dynamics
prediction errors and improve the performance of model-based RL methods on
zero-shot new environments with unseen dynamics. The codes of this method are
available at \url{https://github.com/CR-Gjx/RIA}.

Dynamic programming is a class of algorithms used to compute optimal control
policies for Markov decision processes. Dynamic programming is ubiquitous in
control theory, and is also the foundation of reinforcement learning. In this
paper, we show that value improvement, one of the main steps of dynamic
programming, can be naturally seen as composition in a category of optics, and
intuitively, the optimal value function is the limit of a chain of optic
compositions. We illustrate this with three classic examples: the gridworld,
the inverted pendulum and the savings problem. This is a first step towards a
complete account of reinforcement learning in terms of parametrised optics.

Learning from demonstration methods usually leverage close to optimal
demonstrations to accelerate training. By contrast, when demonstrating a task,
human teachers deviate from optimal demonstrations and pedagogically modify
their behavior by giving demonstrations that best disambiguate the goal they
want to demonstrate. Analogously, human learners excel at pragmatically
inferring the intent of the teacher, facilitating communication between the two
agents. These mechanisms are critical in the few demonstrations regime, where
inferring the goal is more difficult. In this paper, we implement pedagogy and
pragmatism mechanisms by leveraging a Bayesian model of Goal Inference from
demonstrations (BGI). We highlight the benefits of this model in multi-goal
teacher-learner setups with two artificial agents that learn with
goal-conditioned Reinforcement Learning. We show that combining BGI-agents (a
pedagogical teacher and a pragmatic learner) results in faster learning and
reduced goal ambiguity over standard learning from demonstrations, especially
in the few demonstrations regime. We provide the code for our experiments
(https://github.com/Caselles/NeurIPS22-demonstrations-pedagogy-pragmatism), as
well as an illustrative video explaining our approach
(https://youtu.be/V4n16IjkNyw).

The ability to perform locomotion in various terrains is critical for legged
robots. However, the robot has to have a better understanding of the surface it
is walking on to perform robust locomotion on different terrains. Animals and
humans are able to recognize the surface with the help of the tactile sensation
on their feet. Although, the foot tactile sensation for legged robots has not
been much explored. This paper presents research on a novel quadruped robot
DogTouch with tactile sensing feet (TSF). TSF allows the recognition of
different surface textures utilizing a tactile sensor and a convolutional
neural network (CNN). The experimental results show a sufficient validation
accuracy of 74.37\% for our trained CNN-based model, with the highest
recognition for line patterns of 90\%. In the future, we plan to improve the
prediction model by presenting surface samples with the various depths of
patterns and applying advanced Deep Learning and Shallow learning models for
surface recognition.
  Additionally, we propose a novel approach to navigation of quadruped and
legged robots. We can arrange the tactile paving textured surface (similar that
used for blind or visually impaired people). Thus, DogTouch will be capable of
locomotion in unknown environment by just recognizing the specific tactile
patterns which will indicate the straight path, left or right turn, pedestrian
crossing, road, and etc. That will allow robust navigation regardless of
lighting condition. Future quadruped robots equipped with visual and tactile
perception system will be able to safely and intelligently navigate and
interact in the unstructured indoor and outdoor environment.

A deep learning method for the particle trajectory reconstruction with the
DAMPE experiment is presented. The developed algorithms constitute the first
fully machine-learned track reconstruction pipeline for space astroparticle
missions. Significant performance improvements over the standard
hand-engineered algorithms are demonstrated. Thanks to the better accuracy, the
developed algorithms facilitate the identification of the particle absolute
charge with the tracker in the entire energy range, opening a door to the
measurements of cosmic ray proton and helium spectra at extreme energies,
towards the PeV scale, hardly achievable with the standard track reconstruction
methods. In addition, the developed approach demonstrates an unprecedented
accuracy in the particle direction reconstruction with the calorimeter at high
deposited energies, above a few hundred GeV for hadronic showers and above a
few tens GeV for electromagnetic showers.

Convolutional neural networks (CNNs) are increasingly being used in critical
systems, where robustness and alignment are crucial. In this context, the field
of explainable artificial intelligence has proposed the generation of
high-level explanations of the prediction process of CNNs through concept
extraction. While these methods can detect whether or not a concept is present
in an image, they are unable to determine its location. What is more, a fair
comparison of such approaches is difficult due to a lack of proper validation
procedures. To address these issues, we propose a novel method for automatic
concept extraction and localization based on representations obtained through
pixel-wise aggregations of CNN activation maps. Further, we introduce a process
for the validation of concept-extraction techniques based on synthetic datasets
with pixel-wise annotations of their main components, reducing the need for
human intervention. Extensive experimentation on both synthetic and real-world
datasets demonstrates that our method outperforms state-of-the-art
alternatives.

Deep Neural Networks (DNNs) draw their power from the representations they
learn. In recent years, however, researchers have found that DNNs, while being
incredibly effective in learning complex abstractions, also tend to be infected
with artifacts, such as biases, Clever Hanses (CH), or Backdoors, due to
spurious correlations inherent in the training data. So far, existing methods
for uncovering such artifactual and malicious behavior in trained models focus
on finding artifacts in the input data, which requires both availabilities of a
data set and human intervention. In this paper, we introduce DORA
(Data-agnOstic Representation Analysis): the first automatic data-agnostic
method for the detection of potentially infected representations in Deep Neural
Networks. We further show that contaminated representations found by DORA can
be used to detect infected samples in any given dataset. We qualitatively and
quantitatively evaluate the performance of our proposed method in both,
controlled toy scenarios, and in real-world settings, where we demonstrate the
benefit of DORA in safety-critical applications.

Hardware-based acceleration is an extensive attempt to facilitate many
computationally-intensive mathematics operations. This paper proposes an
FPGA-based architecture to accelerate the convolution operation - a complex and
expensive computing step that appears in many Convolutional Neural Network
models. We target the design to the standard convolution operation, intending
to launch the product as an edge-AI solution. The project's purpose is to
produce an FPGA IP core that can process a convolutional layer at a time.
System developers can deploy the IP core with various FPGA families by using
Verilog HDL as the primary design language for the architecture. The
experimental results show that our single computing core synthesized on a
simple edge computing FPGA board can offer 0.224 GOPS. When the board is fully
utilized, 4.48 GOPS can be achieved.

Given a graph with partial observations of node features, how can we estimate
the missing features accurately? Feature estimation is a crucial problem for
analyzing real-world graphs whose features are commonly missing during the data
collection process. Accurate estimation not only provides diverse information
of nodes but also supports the inference of graph neural networks that require
the full observation of node features. However, designing an effective approach
for estimating high-dimensional features is challenging, since it requires an
estimator to have large representation power, increasing the risk of
overfitting. In this work, we propose SVGA (Structured Variational Graph
Autoencoder), an accurate method for feature estimation. SVGA applies strong
regularization to the distribution of latent variables by structured
variational inference, which models the prior of variables as Gaussian Markov
random field based on the graph structure. As a result, SVGA combines the
advantages of probabilistic inference and graph neural networks, achieving
state-of-the-art performance in real datasets.

We introduce AAM-Gym, a research and development testbed for Advanced Air
Mobility (AAM). AAM has the potential to revolutionize travel by reducing
ground traffic and emissions by leveraging new types of aircraft such as
electric vertical take-off and landing (eVTOL) aircraft and new advanced
artificial intelligence (AI) algorithms. Validation of AI algorithms require
representative AAM scenarios, as well as a fast time simulation testbed to
evaluate their performance. Until now, there has been no such testbed available
for AAM to enable a common research platform for individuals in government,
industry, or academia. MIT Lincoln Laboratory has developed AAM-Gym to address
this gap by providing an ecosystem to develop, train, and validate new and
established AI algorithms across a wide variety of AAM use-cases. In this
paper, we use AAM-Gym to study the performance of two reinforcement learning
algorithms on an AAM use-case, separation assurance in AAM corridors. The
performance of the two algorithms is demonstrated based on a series of metrics
provided by AAM-Gym, showing the testbed's utility to AAM research.

Human Pose Estimation (HPE) based on RGB images has experienced a rapid
development benefiting from deep learning. However, event-based HPE has not
been fully studied, which remains great potential for applications in extreme
scenes and efficiency-critical conditions. In this paper, we are the first to
estimate 2D human pose directly from 3D event point cloud. We propose a novel
representation of events, the rasterized event point cloud, aggregating events
on the same position of a small time slice. It maintains the 3D features from
multiple statistical cues and significantly reduces memory consumption and
computation complexity, proved to be efficient in our work. We then leverage
the rasterized event point cloud as input to three different backbones,
PointNet, DGCNN, and Point Transformer, with two linear layer decoders to
predict the location of human keypoints. We find that based on our method,
PointNet achieves promising results with much faster speed, whereas Point
Transfomer reaches much higher accuracy, even close to previous
event-frame-based methods. A comprehensive set of results demonstrates that our
proposed method is consistently effective for these 3D backbone models in
event-driven human pose estimation. Our method based on PointNet with 2048
points input achieves 82.46mm in MPJPE3D on the DHP19 dataset, while only has a
latency of 12.29ms on an NVIDIA Jetson Xavier NX edge computing platform, which
is ideally suitable for real-time detection with event cameras. Code is
available at https://github.com/MasterHow/EventPointPose.

Minimax optimization has served as the backbone of many machine learning (ML)
problems. Although the convergence behavior of optimization algorithms has been
extensively studied in the minimax settings, their generalization guarantees in
stochastic minimax optimization problems, i.e., how the solution trained on
empirical data performs on unseen testing data, have been relatively
underexplored. A fundamental question remains elusive: What is a good metric to
study generalization of minimax learners? In this paper, we aim to answer this
question by first showing that primal risk, a universal metric to study
generalization in minimization problems, which has also been adopted recently
to study generalization in minimax ones, fails in simple examples. We thus
propose a new metric to study generalization of minimax learners: the primal
gap, defined as the difference between the primal risk and its minimum over all
models, to circumvent the issues. Next, we derive generalization error bounds
for the primal gap in nonconvex-concave settings. As byproducts of our
analysis, we also solve two open questions: establishing generalization error
bounds for primal risk and primal-dual risk, another existing metric that is
only well-defined when the global saddle-point exists, in the strong sense,
i.e., without strong concavity or assuming that the maximization and
expectation can be interchanged, while either of these assumptions was needed
in the literature. Finally, we leverage this new metric to compare the
generalization behavior of two popular algorithms -- gradient descent-ascent
(GDA) and gradient descent-max (GDMax) in stochastic minimax optimization.

Collaborative filtering algorithms capture underlying consumption patterns,
including the ones specific to particular demographics or protected information
of users, e.g. gender, race, and location. These encoded biases can influence
the decision of a recommendation system (RS) towards further separation of the
contents provided to various demographic subgroups, and raise privacy concerns
regarding the disclosure of users' protected attributes. In this work, we
investigate the possibility and challenges of removing specific protected
information of users from the learned interaction representations of a RS
algorithm, while maintaining its effectiveness. Specifically, we incorporate
adversarial training into the state-of-the-art MultVAE architecture, resulting
in a novel model, Adversarial Variational Auto-Encoder with Multinomial
Likelihood (Adv-MultVAE), which aims at removing the implicit information of
protected attributes while preserving recommendation performance. We conduct
experiments on the MovieLens-1M and LFM-2b-DemoBias datasets, and evaluate the
effectiveness of the bias mitigation method based on the inability of external
attackers in revealing the users' gender information from the model. Comparing
with baseline MultVAE, the results show that Adv-MultVAE, with marginal
deterioration in performance (w.r.t. NDCG and recall), largely mitigates
inherent biases in the model on both datasets.

A number of variational autoencoders (VAEs) have recently emerged with the
aim of modeling multimodal data, e.g., to jointly model images and their
corresponding captions. Still, multimodal VAEs tend to focus solely on a subset
of the modalities, e.g., by fitting the image while neglecting the caption. We
refer to this limitation as modality collapse. In this work, we argue that this
effect is a consequence of conflicting gradients during multimodal VAE
training. We show how to detect the sub-graphs in the computational graphs
where gradients conflict (impartiality blocks), as well as how to leverage
existing gradient-conflict solutions from multitask learning to mitigate
modality collapse. That is, to ensure impartial optimization across modalities.
We apply our training framework to several multimodal VAE models, losses and
datasets from the literature, and empirically show that our framework
significantly improves the reconstruction performance, conditional generation,
and coherence of the latent space across modalities.

Utility preference robust optimization (PRO) has recently been proposed to
deal with optimal decision making problems where the decision maker's (DM)
preference over gains and losses is ambiguous. In this paper, we take a step
further to investigate the case that the DM's preference is not only ambiguous
but also potentially inconsistent or even displaying some kind of randomness.
We propose a distributionally preference robust optimization (DPRO) approach
where the DM's preference is represented by a random utility function and the
ambiguity is described by a set of probability distributions of the random
utility. An obvious advantage of the new DPRO model is that it no longer
concerns the DM's preference inconsistency. In the case when the random utility
functions are of piecewise linear structure, we propose two statistical methods
for constructing the ambiguity set: an ellipsoidal method and a bootstrap
method both of which are fundamentally based on the idea of confidence region
with the sample mean of the random parameters, and demonstrate how the
resulting DPRO can be solved by a cutting surface algorithm and an MISOCP
respectively. We also show how the DPRO models with general random utility
functions may be approximated by those with piecewise linear random utility
functions. Finally, we apply the proposed DPRO model in car manufacturing and
facility location planning and show how the random samples may be extracted by
multinomial logit method and conjoint analysis/machine learning. The paper is
the first attempt to use distributionally robust optimization methods for PRO.

Conventional wisdom states that deep linear neural networks benefit from
expressiveness and optimization advantages over a single linear layer. This
paper suggests that, in practice, the training process of deep linear
fully-connected networks using conventional optimizers is convex in the same
manner as a single linear fully-connected layer. This paper aims to explain
this claim and demonstrate it. Even though convolutional networks are not
aligned with this description, this work aims to attain a new conceptual
understanding of fully-connected linear networks that might shed light on the
possible constraints of convolutional settings and non-linear architectures.

Human Activity Recognition (HAR) is a crucial technology for many
applications such as smart homes, surveillance, human assistance and health
care. This technology utilises pattern recognition and can contribute to the
development of human-in-the-loop control of different systems such as orthoses
and exoskeletons. The majority of reported studies use a small dataset
collected from an experiment for a specific purpose. The downsides of this
approach include: 1) it is hard to generalise the outcome to different people
with different biomechanical characteristics and health conditions, and 2) it
cannot be implemented in applications other than the original experiment. To
address these deficiencies, the current study investigates using a publicly
available dataset collected for pathology diagnosis purposes to train Machine
Learning (ML) algorithms. A dataset containing knee motion of participants
performing different exercises has been used to classify human activity. The
algorithms used in this study are Gaussian Naive Bayes, Decision Tree, Random
Forest, K-Nearest Neighbors Vote, Support Vector Machine and Gradient Boosting.
Furthermore, two training approaches are compared to raw data (de-noised) and
manually extracted features. The results show up to 0.94 performance of the
Area Under the ROC Curve (AUC) metric for 11-fold cross-validation for Gradient
Boosting algorithm using raw data. This outcome reflects the validity and
potential use of the proposed approach for this type of dataset.

Brain networks characterize complex connectivities among brain regions as
graph structures, which provide a powerful means to study brain connectomes. In
recent years, graph neural networks have emerged as a prevalent paradigm of
learning with structured data. However, most brain network datasets are limited
in sample sizes due to the relatively high cost of data acquisition, which
hinders the deep learning models from sufficient training. Inspired by
meta-learning that learns new concepts fast with limited training examples,
this paper studies data-efficient training strategies for analyzing brain
connectomes in a cross-dataset setting. Specifically, we propose to meta-train
the model on datasets of large sample sizes and transfer the knowledge to small
datasets. In addition, we also explore two brain-network-oriented designs,
including atlas transformation and adaptive task reweighing. Compared to other
pre-training strategies, our meta-learning-based approach achieves higher and
stabler performance, which demonstrates the effectiveness of our proposed
solutions. The framework is also able to derive new insights regarding the
similarities among datasets and diseases in a data-driven fashion.

Inverse reinforcement learning (IRL) seeks to infer a cost function that
explains the underlying goals and preferences of expert demonstrations. This
paper presents receding horizon inverse reinforcement learning (RHIRL), a new
IRL algorithm for high-dimensional, noisy, continuous systems with black-box
dynamic models. RHIRL addresses two key challenges of IRL: scalability and
robustness. To handle high-dimensional continuous systems, RHIRL matches the
induced optimal trajectories with expert demonstrations locally in a receding
horizon manner and 'stitches' together the local solutions to learn the cost;
it thereby avoids the 'curse of dimensionality'. This contrasts sharply with
earlier algorithms that match with expert demonstrations globally over the
entire high-dimensional state space. To be robust against imperfect expert
demonstrations and control noise, RHIRL learns a state-dependent cost function
'disentangled' from system dynamics under mild conditions. Experiments on
benchmark tasks show that RHIRL outperforms several leading IRL algorithms in
most instances. We also prove that the cumulative error of RHIRL grows linearly
with the task duration.

We consider an online learning problem with one-sided feedback, in which the
learner is able to observe the true label only for positively predicted
instances. On each round, $k$ instances arrive and receive classification
outcomes according to a randomized policy deployed by the learner, whose goal
is to maximize accuracy while deploying individually fair policies. We first
extend the framework of Bechavod et al. (2020), which relies on the existence
of a human fairness auditor for detecting fairness violations, to instead
incorporate feedback from dynamically-selected panels of multiple, possibly
inconsistent, auditors. We then construct an efficient reduction from our
problem of online learning with one-sided feedback and a panel reporting
fairness violations to the contextual combinatorial semi-bandit problem
(Cesa-Bianchi & Lugosi, 2009, Gy\"{o}rgy et al., 2007). Finally, we show how to
leverage the guarantees of two algorithms in the contextual combinatorial
semi-bandit setting: Exp2 (Bubeck et al., 2012) and the oracle-efficient
Context-Semi-Bandit-FTPL (Syrgkanis et al., 2016), to provide multi-criteria no
regret guarantees simultaneously for accuracy and fairness. Our results
eliminate two potential sources of bias from prior work: the "hidden outcomes"
that are not available to an algorithm operating in the full information
setting, and human biases that might be present in any single human auditor,
but can be mitigated by selecting a well chosen panel.

This paper will describe and analyze a new phenomenon that was not known
before, which we call "Early Transferability". Its essence is that the
adversarial perturbations transfer among different networks even at extremely
early stages in their training. In fact, one can initialize two networks with
two different independent choices of random weights and measure the angle
between their adversarial perturbations after each step of the training. What
we discovered was that these two adversarial directions started to align with
each other already after the first few training steps (which typically use only
a small fraction of the available training data), even though the accuracy of
the two networks hadn't started to improve from their initial bad values due to
the early stage of the training. The purpose of this paper is to present this
phenomenon experimentally and propose plausible explanations for some of its

The graph neural network (GNN) has demonstrated its superior performance in
various applications. The working mechanism behind it, however, remains
mysterious. GNN models are designed to learn effective representations for
graph-structured data, which intrinsically coincides with the principle of
graph signal denoising (GSD). Algorithm unrolling, a "learning to optimize"
technique, has gained increasing attention due to its prospects in building
efficient and interpretable neural network architectures. In this paper, we
introduce a class of unrolled networks built based on truncated optimization
algorithms (e.g., gradient descent and proximal gradient descent) for GSD
problems. They are shown to be tightly connected to many popular GNN models in
that the forward propagations in these GNNs are in fact unrolled networks
serving specific GSDs. Besides, the training process of a GNN model can be seen
as solving a bilevel optimization problem with a GSD problem at the lower
level. Such a connection brings a fresh view of GNNs, as we could try to
understand their practical capabilities from their GSD counterparts, and it can
also motivate designing new GNN models. Based on the algorithm unrolling
perspective, an expressive model named UGDGNN, i.e., unrolled gradient descent
GNN, is further proposed which inherits appealing theoretical properties.
Extensive numerical simulations on seven benchmark datasets demonstrate that
UGDGNN can achieve superior or competitive performance over the

Self-supervised learning (SSL) has shown tremendous success in various
speech-related downstream tasks, including Automatic Speech Recognition (ASR).
The output embeddings of the SSL model are treated as powerful short-time
representations of the speech signal. However, in the ASR task, the main
objective is to get the correct sequence of acoustic units, characters, or
byte-pair encodings (BPEs). Usually, encoder-decoder architecture works
exceptionally well for a sequence-to-sequence task like ASR. Therefore, in this
paper, we propose a new paradigm that exploits the power of a decoder during
self-supervised learning. We use Hidden Unit BERT (HuBERT) SSL framework to
compute the conventional masked prediction loss for the encoder. In addition,
we have introduced a decoder in the SSL framework and proposed a target
preparation strategy for the decoder. Finally, we use a multitask SSL setup
wherein we jointly optimize both the encoder and decoder losses. We hypothesize
that the presence of a decoder in the SSL model helps it learn an acoustic
unit-based language model, which might improve the performance of an ASR
downstream task. We compare our proposed SSL model with HuBERT and show up to
25% relative improvement in performance on ASR by finetuning on various
LibriSpeech subsets.

Deep neural networks perform exceptionally well on various learning tasks
with state-of-the-art results. While these models are highly expressive and
achieve impressively accurate solutions with excellent generalization
abilities, they are susceptible to minor perturbations. Samples that suffer
such perturbations are known as "adversarial examples". Even though deep
learning is an extensively researched field, many questions about the nature of
deep learning models remain unanswered. In this paper, we introduce a new
conceptual framework attached with a formal description that aims to shed light
on the network's behavior and interpret the behind-the-scenes of the learning
process. Our framework provides an explanation for inherent questions
concerning deep learning. Particularly, we clarify: (1) Why do neural networks
acquire generalization abilities? (2) Why do adversarial examples transfer
between different models?. We provide a comprehensive set of experiments that
support this new framework, as well as its underlying theory.

The concept of traceability between artifacts is considered an enabler for
software project success. This concept has received plenty of attention from
the research community and is by many perceived to always be available in an
industrial setting. In this industry-academia collaborative project, a team of
researchers, supported by testing practitioners from a large telecommunication
company, sought to investigate the partner company's issues related to software
quality. However, it was soon identified that the fundamental traceability
links between requirements and test cases were missing. This lack of
traceability impeded the implementation of a solution to help the company deal
with its quality issues. In this experience report, we discuss lessons learned
about the practical value of creating and maintaining traceability links in
complex industrial settings and provide a cautionary tale for researchers.

In order to deploy deep models in a computationally efficient manner, model
quantization approaches have been frequently used. In addition, as new hardware
that supports mixed bitwidth arithmetic operations, recent research on mixed
precision quantization (MPQ) begins to fully leverage the capacity of
representation by searching optimized bitwidths for different layers and
modules in a network. However, previous studies mainly search the MPQ strategy
in a costly scheme using reinforcement learning, neural architecture search,
etc., or simply utilize partial prior knowledge for bitwidth assignment, which
might be biased and sub-optimal. In this work, we present a novel Stochastic
Differentiable Quantization (SDQ) method that can automatically learn the MPQ
strategy in a more flexible and globally-optimized space with smoother gradient
approximation. Particularly, Differentiable Bitwidth Parameters (DBPs) are
employed as the probability factors in stochastic quantization between adjacent
bitwidth choices. After the optimal MPQ strategy is acquired, we further train
our network with entropy-aware bin regularization and knowledge distillation.
We extensively evaluate our method for several networks on different hardware
(GPUs and FPGA) and datasets. SDQ outperforms all state-of-the-art mixed or
single precision quantization with a lower bitwidth and is even better than the
full-precision counterparts across various ResNet and MobileNet families,
demonstrating the effectiveness and superiority of our method.

In pure-exploration problems, information is gathered sequentially to answer
a question on the stochastic environment. While best-arm identification for
linear bandits has been extensively studied in recent years, few works have
been dedicated to identifying one arm that is $\varepsilon$-close to the best
one (and not exactly the best one). In this problem with several correct
answers, an identification algorithm should focus on one candidate among those
answers and verify that it is correct. We demonstrate that picking the answer
with highest mean does not allow an algorithm to reach asymptotic optimality in
terms of expected sample complexity. Instead, a \textit{furthest answer} should
be identified. Using that insight to choose the candidate answer carefully, we
develop a simple procedure to adapt best-arm identification algorithms to
tackle $\varepsilon$-best-answer identification in transductive linear
stochastic bandits. Finally, we propose an asymptotically optimal algorithm for
this setting, which is shown to achieve competitive empirical performance
against existing modified best-arm identification algorithms.

Computer vision is driven by the many datasets available for training or
evaluating novel methods. However, each dataset has a different set of class
labels, visual definition of classes, images following a specific distribution,
annotation protocols, etc. In this paper we explore the automatic discovery of
visual-semantic relations between labels across datasets. We aim to understand
how instances of a certain class in a dataset relate to the instances of
another class in another dataset. Are they in an identity, parent/child,
overlap relation? Or is there no link between them at all? To find relations
between labels across datasets, we propose methods based on language, on
vision, and on their combination. We show that we can effectively discover
label relations across datasets, as well as their type. We apply our method to
four applications: understand label relations, identify missing aspects,
increase label specificity, and predict transfer learning gains. We conclude
that label relations cannot be established by looking at the names of classes
alone, as they depend strongly on how each of the datasets was constructed.

Although autoregressive models have achieved promising results on image
generation, their unidirectional generation process prevents the resultant
images from fully reflecting global contexts. To address the issue, we propose
an effective image generation framework of Draft-and-Revise with Contextual
RQ-transformer to consider global contexts during the generation process. As a
generalized VQ-VAE, RQ-VAE first represents a high-resolution image as a
sequence of discrete code stacks. After code stacks in the sequence are
randomly masked, Contextual RQ-Transformer is trained to infill the masked code
stacks based on the unmasked contexts of the image. Then, Contextual
RQ-Transformer uses our two-phase decoding, Draft-and-Revise, and generates an
image, while exploiting the global contexts of the image during the generation
process. Specifically. in the draft phase, our model first focuses on
generating diverse images despite rather low quality. Then, in the revise
phase, the model iteratively improves the quality of images, while preserving
the global contexts of generated images. In experiments, our method achieves
state-of-the-art results on conditional image generation. We also validate that
the Draft-and-Revise decoding can achieve high performance by effectively
controlling the quality-diversity trade-off in image generation.

Compartment models of cell culture are widely used in cytology, pharmacology,
toxicology and other fields. Numerical simulation, data modeling and prediction
of compartment models can be realized by traditional differential equation
modeling methods. At the same time, with the development of software and
hardware, Physical Informed Neural Network (PINN) is widely used to solve
differential equation models. This work models, simulates and predicts the cell
culture compartment model based on the machine learning framework PyTorch with
an 16 hidden layers neural network, including 8 linear layers and 8 feedback
active layers. The results showed a loss value of 0.0004853 for three-component
four-parameter quantitative pharmacodynamic model predictions in this way,
which is evaluated by Mean Square Error (MSE). In summary, Physical Informed
Neural Network can serve as an effective tool to deal with cell culture
compartment models and may perform better in dealing with big datasets.

Sparsity-based methods have a long history in the field of signal processing
and have been successfully applied to various image reconstruction problems.
The involved sparsifying transformations or dictionaries are typically either
pre-trained using a model which reflects the assumed properties of the signals
or adaptively learned during the reconstruction - yielding so-called blind
Compressed Sensing approaches. However, by doing so, the transforms are never
explicitly trained in conjunction with the physical model which generates the
signals. In addition, properly choosing the involved regularization parameters
remains a challenging task. Another recently emerged training-paradigm for
regularization methods is to use iterative neural networks (INNs) - also known
as unrolled networks - which contain the physical model. In this work, we
construct an INN which can be used as a supervised and physics-informed online
convolutional dictionary learning algorithm. We evaluated the proposed approach
by applying it to a realistic large-scale dynamic MR reconstruction problem and
compared it to several other recently published works. We show that the
proposed INN improves over two conventional model-agnostic training methods and
yields competitive results also compared to a deep INN. Further, it does not
require to choose the regularization parameters and - in contrast to deep INNs
- each network component is entirely interpretable.

Hall-effect thrusters (HETs) are widely used for modern near-earth spacecraft
propulsion and are vital for future deep-space missions. Methods of modeling
HETs are developing rapidly. However, such methods are not yet precise enough
and cannot reliably predict the parameters of a newly designed thruster, mostly
due to the enormous computational cost of a HET plasma simulation. Another
approach is to use scaling techniques based on available experimental data.
This paper proposes an approach for scaling HETs using neural networks and
other modern machine learning methods. The new scaling model was built with
information from an extensive database of HET parameters collected from
published papers. Predictions of the new scaling model are valid for the
operating parameters domain covered by the database. During the design, this
model can help HET developers estimate the performance of a newly-designed
thruster. At the stage of experimental research, the model can be used to
compare the achieved characteristics of the studied thruster with the level
obtained by other developers. A comparison with the state-of-the-art HET
scaling model is also presented.

Though deep reinforcement learning (DRL) has obtained substantial success, it
may encounter catastrophic failures due to the intrinsic uncertainty of both
transition and observation. Most of the existing methods for safe reinforcement
learning can only handle transition disturbance or observation disturbance
since these two kinds of disturbance affect different parts of the agent;
besides, the popular worst-case return may lead to overly pessimistic policies.
To address these issues, we first theoretically prove that the performance
degradation under transition disturbance and observation disturbance depends on
a novel metric of Value Function Range (VFR), which corresponds to the gap in
the value function between the best state and the worst state. Based on the
analysis, we adopt conditional value-at-risk (CVaR) as an assessment of risk
and propose a novel reinforcement learning algorithm of
CVaR-Proximal-Policy-Optimization (CPPO) which formalizes the risk-sensitive
constrained optimization problem by keeping its CVaR under a given threshold.
Experimental results show that CPPO achieves a higher cumulative reward and is
more robust against both observation and transition disturbances on a series of
continuous control tasks in MuJoCo.

How should government and business solve big problems? In bold leaps or in
many smaller moves? We show that bespoke, one-off projects are prone to poorer
outcomes than projects built on a repeatable platform. Repeatable projects are
cheaper, faster, and scale at lower risk of failure. We compare evidence from
203 space missions at NASA and SpaceX, on cost, speed-to-market, schedule, and
scalability. We find that SpaceX's platform strategy was 10X cheaper and 2X
faster than NASA's bespoke strategy. Moreover, SpaceX's platform strategy was
financially less risky, virtually eliminating cost overruns. Finally, we show
that achieving platform repeatability is a strategically diligent process
involving experimental learning sequences. Sectors of the economy where
governments find it difficult to control spending or timeframes or to realize
planned benefits - e.g., health, education, climate, defence - are ripe for a
platform rethink.

This work theoretically studies a ubiquitous reinforcement learning policy
for controlling the canonical model of continuous-time stochastic
linear-quadratic systems. We show that randomized certainty equivalent policy
addresses the exploration-exploitation dilemma in linear control systems that
evolve according to unknown stochastic differential equations and their
operating cost is quadratic. More precisely, we establish square-root of time
regret bounds, indicating that randomized certainty equivalent policy learns
optimal control actions fast from a single state trajectory. Further, linear
scaling of the regret with the number of parameters is shown. The presented
analysis introduces novel and useful technical approaches, and sheds light on
fundamental challenges of continuous-time reinforcement learning.

Inference tasks in signal processing are often characterized by the
availability of reliable statistical modeling with some missing
instance-specific parameters. One conventional approach uses data to estimate
these missing parameters and then infers based on the estimated model.
Alternatively, data can also be leveraged to directly learn the inference
mapping end-to-end. These approaches for combining partially-known statistical
models and data in inference are related to the notions of generative and
discriminative models used in the machine learning literature, typically
considered in the context of classifiers. The goal of this lecture note is to
introduce the concepts of generative and discriminative learning for inference
with a partially-known statistical model. While machine learning systems often
lack the interpretability of traditional signal processing methods, we focus on
a simple setting where one can interpret and compare the approaches in a
tractable manner that is accessible and relevant to signal processing readers.
In particular, we exemplify the approaches for the task of Bayesian signal
estimation in a jointly Gaussian setting with the mean-squared error (MSE)
objective, i.e., a linear estimation setting.

The dynamic membrane potential threshold, as one of the essential properties
of a biological neuron, is a spontaneous regulation mechanism that maintains
neuronal homeostasis, i.e., the constant overall spiking firing rate of a
neuron. As such, the neuron firing rate is regulated by a dynamic spiking
threshold, which has been extensively studied in biology. Existing work in the
machine learning community does not employ bioinspired spiking threshold
schemes. This work aims at bridging this gap by introducing a novel bioinspired
dynamic energy-temporal threshold (BDETT) scheme for spiking neural networks
(SNNs). The proposed BDETT scheme mirrors two bioplausible observations: a
dynamic threshold has 1) a positive correlation with the average membrane
potential and 2) a negative correlation with the preceding rate of
depolarization. We validate the effectiveness of the proposed BDETT on robot
obstacle avoidance and continuous control tasks under both normal conditions
and various degraded conditions, including noisy observations, weights, and
dynamic environments. We find that the BDETT outperforms existing static and
heuristic threshold approaches by significant margins in all tested conditions,
and we confirm that the proposed bioinspired dynamic threshold scheme offers
homeostasis to SNNs in complex real-world tasks.

$\mathbf{Purpose}$: To use artificial intelligence (AI) to: (1) exploit
biomechanical knowledge of the optic nerve head (ONH) from a relatively large
population; (2) assess ONH robustness from a single optical coherence
tomography (OCT) scan of the ONH; (3) identify what critical three-dimensional
(3D) structural features make a given ONH robust.
  $\mathbf{Design}$: Retrospective cross-sectional study.
  $\mathbf{Methods}$: 316 subjects had their ONHs imaged with OCT before and
after acute intraocular pressure (IOP) elevation through ophthalmo-dynamometry.
IOP-induced lamina-cribrosa deformations were then mapped in 3D and used to
classify ONHs. Those with LC deformations superior to 4% were considered
fragile, while those with deformations inferior to 4% robust. Learning from
these data, we compared three AI algorithms to predict ONH robustness strictly
from a baseline (undeformed) OCT volume: (1) a random forest classifier; (2) an
autoencoder; and (3) a dynamic graph CNN (DGCNN). The latter algorithm also
allowed us to identify what critical 3D structural features make a given ONH
robust.
  $\mathbf{Results}$: All 3 methods were able to predict ONH robustness from 3D
structural information alone and without the need to perform biomechanical
testing. The DGCNN (area under the receiver operating curve [AUC]: 0.76 $\pm$
0.08) outperformed the autoencoder (AUC: 0.70 $\pm$ 0.07) and the random forest
classifier (AUC: 0.69 $\pm$ 0.05). Interestingly, to assess ONH robustness, the
DGCNN mainly used information from the scleral canal and the LC insertion
sites.
  $\mathbf{Conclusions}$: We propose an AI-driven approach that can assess the
robustness of a given ONH solely from a single OCT scan of the ONH, and without
the need to perform biomechanical testing. Longitudinal studies should
establish whether ONH robustness could help us identify fast visual field loss
progressors.

Digital whole slides images contain an enormous amount of information
providing a strong motivation for the development of automated image analysis
tools. Particularly deep neural networks show high potential with respect to
various tasks in the field of digital pathology. However, a limitation is given
by the fact that typical deep learning algorithms require (manual) annotations
in addition to the large amounts of image data, to enable effective training.
Multiple instance learning exhibits a powerful tool for learning deep neural
networks in a scenario without fully annotated data. These methods are
particularly effective in this domain, due to the fact that labels for a
complete whole slide image are often captured routinely, whereas labels for
patches, regions or pixels are not. This potential already resulted in a
considerable number of publications, with the majority published in the last
three years. Besides the availability of data and a high motivation from the
medical perspective, the availability of powerful graphics processing units
exhibits an accelerator in this field. In this paper, we provide an overview of
widely and effectively used concepts of used deep multiple instance learning
approaches, recent advances and also critically discuss remaining challenges
and future potential.

This paper introduces a Reinforcement Learning approach to better generalize
heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current
models on the JSP do not focus on generalization, although, as we show in this
work, this is key to learning better heuristics on the problem. A well-known
technique to improve generalization is to learn on increasingly complex
instances using Curriculum Learning (CL). However, as many works in the
literature indicate, this technique might suffer from catastrophic forgetting
when transferring the learned skills between different problem sizes. To
address this issue, we introduce a novel Adversarial Curriculum Learning (ACL)
strategy, which dynamically adjusts the difficulty level during the learning
process to revisit the worst-performing instances. This work also presents a
deep learning model to solve the JSP, which is equivariant w.r.t. the job
definition and size-agnostic. Conducted experiments on Taillard's and
Demirkol's instances show that the presented approach significantly improves
the current state-of-the-art models on the JSP. It reduces the average
optimality gap from 19.35\% to 10.46\% on Taillard's instances and from 38.43\%
to 18.85\% on Demirkol's instances. Our implementation is available online.

This report describes a set of neonatal electroencephalogram (EEG) recordings
graded according to the severity of abnormalities in the background pattern.
The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded
in a neonatal intensive care unit. All neonates received a diagnosis of
hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury
in full term infants. For each neonate, multiple 1-hour epochs of good quality
EEG were selected and then graded for background abnormalities. The grading
system assesses EEG attributes such as amplitude and frequency, continuity,
sleep-wake cycling, symmetry and synchrony, and abnormal waveforms. Background
severity was then categorised into 4 grades: normal or mildly abnormal,
moderately abnormal, severely abnormal, and inactive EEG. The data can be used
as a reference set of multi-channel EEG for neonates with HIE, for EEG training
purposes, or for developing and evaluating automated grading algorithms.

The quality of assessment determines the quality of learning, and is
characterized by validity, reliability and difficulty. Mastery of learning is
generally represented by the difficulty levels of assessment items. A very
large number of variables are identified in the literature to measure the
difficulty level. These variables, which are not completely independent of one
another, are categorized into learner dependent, learner independent, generic,
non-generic and score based. This research proposes a model for predicting the
difficulty level of assessment items in engineering courses using learner
independent and generic variables. An ordinal regression model is developed for
predicting the difficulty level, and uses six variables including three stimuli
variables (item presentation, usage of technical notations and number of
resources), two content related variables (number of concepts and procedures)
and one task variable (number of conditions). Experimental results from three
engineering courses provide around 80% accuracy in classification of items
using the proposed model.

Deep neural network based recommendation systems have achieved great success
as information filtering techniques in recent years. However, since model
training from scratch requires sufficient data, deep learning-based
recommendation methods still face the bottlenecks of insufficient data and
computational inefficiency. Meta-learning, as an emerging paradigm that learns
to improve the learning efficiency and generalization ability of algorithms,
has shown its strength in tackling the data sparsity issue. Recently, a growing
number of studies on deep meta-learning based recommenddation systems have
emerged for improving the performance under recommendation scenarios where
available data is limited, e.g. user cold-start and item cold-start. Therefore,
this survey provides a timely and comprehensive overview of current deep
meta-learning based recommendation methods. Specifically, we propose a taxonomy
to discuss existing methods according to recommendation scenarios,
meta-learning techniques, and meta-knowledge representations, which could
provide the design space for meta-learning based recommendation methods. For
each recommendation scenario, we further discuss technical details about how
existing methods apply meta-learning to improve the generalization ability of
recommendation models. Finally, we also point out several limitations in
current research and highlight some promising directions for future research in
this area.

The linear Kalman filter is commonly used for vehicle tracking. This filter
requires knowledge of the vehicle trajectory and the statistics of the system
and measurement models. In real-life scenarios, prior assumptions made while
determining those models do not hold. As a consequence, the overall filter
performance degrades and in some situations the estimated states diverge. To
overcome the uncertainty in the {vehicle kinematic} trajectory modeling,
additional artificial process noise may be added to the model or different
types of adaptive filters may be employed. This paper proposes {a hybrid}
adaptive Kalman filter based on {model and} machine learning algorithms. First,
recurrent neural networks are employed to learn the vehicle's geometrical and
kinematic features. In turn, those features are plugged into a supervised
learning model, thereby providing the actual process noise covariance to be
used in the Kalman framework. The proposed approach is evaluated and compared
to six other adaptive filters using the Oxford RobotCar dataset. The proposed
framework can be implemented in other estimation problems to accurately
determine the process noise covariance in real-time scenarios.

The total variation (TV) flow generates a scale-space representation of an
image based on the TV functional. This gradient flow observes desirable
features for images such as sharp edges and enables spectral, scale, and
texture analysis. The standard numerical approach for TV flow requires solving
multiple non-smooth optimisation problems. Even with state-of-the-art convex
optimisation techniques, this is often prohibitively expensive and strongly
motivates the use of alternative, faster approaches. Inspired by and extending
the framework of physics-informed neural networks (PINNs), we propose the
TVflowNET, a neural network approach to compute the solution of the TV flow
given an initial image and a time instance. We significantly speed up the
computation time by more than one order of magnitude and show that the
TVflowNET approximates the TV flow solution with high fidelity. This is a
preliminary report, more details are to follow.

Most off-policy evaluation methods for contextual bandits have focused on the
expected outcome of a policy, which is estimated via methods that at best
provide only asymptotic guarantees. However, in many applications, the
expectation may not be the best measure of performance as it does not capture
the variability of the outcome. In addition, particularly in safety-critical
settings, stronger guarantees than asymptotic correctness may be required. To
address these limitations, we consider a novel application of conformal
prediction to contextual bandits. Given data collected under a behavioral
policy, we propose \emph{conformal off-policy prediction} (COPP), which can
output reliable predictive intervals for the outcome under a new target policy.
We provide theoretical finite-sample guarantees without making any additional
assumptions beyond the standard contextual bandit setup, and empirically
demonstrate the utility of COPP compared with existing methods on synthetic and

In addition to considering the recognition difficulty caused by human posture
and occlusion, it is also necessary to solve the modal differences caused by
different imaging systems in the Visible-Thermal cross-modal person
re-identification (VT-ReID) task. In this paper,we propose the Cross-modal
Local Shortest Path and Global Enhancement (CM-LSP-GE) modules,a two-stream
network based on joint learning of local and global features. The core idea of
our paper is to use local feature alignment to solve occlusion problem, and to
solve modal difference by strengthening global feature. Firstly,
Attention-based two-stream ResNet network is designed to extract dual-modality
features and map to a unified feature space. Then, to solve the cross-modal
person pose and occlusion problems, the image are cut horizontally into several
equal parts to obtain local features and the shortest path in local features
between two graphs is used to achieve the fine-grained local feature alignment.
Thirdly, a batch normalization enhancement module applies global features to
enhance strategy, resulting in difference enhancement between different
classes. The multi granularity loss fusion strategy further improves the
performance of the algorithm. Finally, joint learning mechanism of local and
global features is used to improve cross-modal person re-identification
accuracy. The experimental results on two typical datasets show that our model
is obviously superior to the most state-of-the-art methods. Especially, on
SYSU-MM01 datasets, our model can achieve a gain of 2.89%and 7.96% in all
search term of Rank-1 and mAP. The source code will be released soon.

The neural networks of the brain are capable of learning statistical input
regularities on the basis of synaptic learning, functional integration into
increasingly larger, interconnected neural assemblies, and self organization.
This self organizing ability has implications for biologically inspired control
structures in robotics. On the basis of signal input from vision, sound, smell,
touch and proprioception, multisensory representations for action are generated
on the basis of physically specified input from the environment. The
somatosensory cortex is a brain hub that delivers a choice example of
integration for multifunctional representation and control. All sensory
information is in a first instance topologically represented in the biological
brain, and thereafter integrated in somatosensory neural networks for
multimodal and multifunctional control of complex behaviors. Multisignal input
triggers interactions between visual, auditory, tactile, olfactive, and
proprioceptive mechanisms, which cooperate or compete during learning, and
contribute to the formation of integrated representations for action,
reflection, and communication between the agent and the outside world.
Interaction fuels complex behavioral strategy deployment and further learning
for increasingly coherent representation of intrinsically ambiguous physical

Depression is a mental illness that may be harmful to an individual's health.
The detection of mental health disorders in the early stages and a precise
diagnosis are critical to avoid social, physiological, or psychological side
effects. This work analyzes physiological signals to observe if different
depressive states have a noticeable impact on the blood volume pulse (BVP) and
the heart rate variability (HRV) response. Although typically, HRV features are
calculated from biosignals obtained with contact-based sensors such as
wearables, we propose instead a novel scheme that directly extracts them from
facial videos, just based on visual information, removing the need for any
contact-based device. Our solution is based on a pipeline that is able to
extract complete remote photoplethysmography signals (rPPG) in a fully
unsupervised manner. We use these rPPG signals to calculate over 60
statistical, geometrical, and physiological features that are further used to
train several machine learning regressors to recognize different levels of
depression. Experiments on two benchmark datasets indicate that this approach
offers comparable results to other audiovisual modalities based on voice or
facial expression, potentially complementing them. In addition, the results
achieved for the proposed method show promising and solid performance that
outperforms hand-engineered methods and is comparable to deep learning-based

Today's most advanced machine-learning models are hardly scrutable. The key
challenge for explainability methods is to help assisting researchers in
opening up these black boxes, by revealing the strategy that led to a given
decision, by characterizing their internal states or by studying the underlying
data representation. To address this challenge, we have developed Xplique: a
software library for explainability which includes representative
explainability methods as well as associated evaluation metrics. It interfaces
with one of the most popular learning libraries: Tensorflow as well as other
libraries including PyTorch, scikit-learn and Theano. The code is licensed
under the MIT license and is freely available at github.com/deel-ai/xplique.

As virtual reality (VR) is emerging in the tech sector, developers and
designers are under pressure to create immersive experiences for their
products. However, the current curricula from top institutions focus primarily
on technical considerations for building VR applications, missing out on
concerns and usability problems specific to VR interaction design. To better
understand current needs, we examined the status quo of existing university
pedagogies by carrying out a content analysis of undergraduate and graduate
courses about VR and related areas offered in the major citadels of learning
and conducting interviews with 7 industry experts. Our analysis reveals that
the current teaching practices underemphasize design thinking, prototyping, and
evaluation skills, while focusing on technical implementation. We recommend VR
curricula should emphasize design principles and guidelines, offer training in
prototyping and ideation, prioritize practical design exercises while providing
industry insights, and encourage students to solve VR design problems beyond
the classroom.

Federated learning alleviates the privacy risk in distributed learning by
transmitting only the local model updates to the central server. However, it
faces challenges including statistical heterogeneity of clients' datasets and
resource constraints of client devices, which severely impact the training
performance and user experience. Prior works have tackled these challenges by
combining personalization with model compression schemes including quantization
and pruning. However, the pruning is data-dependent and thus must be done on
the client side which requires considerable computation cost. Moreover, the
pruning normally trains a binary supermask $\in \{0, 1\}$ which significantly
limits the model capacity yet with no computation benefit. Consequently, the
training requires high computation cost and a long time to converge while the
model performance does not pay off. In this work, we propose HideNseek which
employs one-shot data-agnostic pruning at initialization to get a subnetwork
based on weights' synaptic saliency. Each client then optimizes a sign
supermask $\in \{-1, +1\}$ multiplied by the unpruned weights to allow faster
convergence with the same compression rates as state-of-the-art. Empirical
results from three datasets demonstrate that compared to state-of-the-art,
HideNseek improves inferences accuracies by up to 40.6\% while reducing the
communication cost and training time by up to 39.7\% and 46.8\% respectively.

Reinforcement Learning (RL) methods are typically applied directly in
environments to learn policies. In some complex environments with continuous
state-action spaces, sparse rewards, and/or long temporal horizons, learning a
good policy in the original environments can be difficult. Focusing on the
offline RL setting, we aim to build a simple and discrete world model that
abstracts the original environment. RL methods are applied to our world model
instead of the environment data for simplified policy learning. Our world
model, dubbed Value Memory Graph (VMG), is designed as a directed-graph-based
Markov decision process (MDP) of which vertices and directed edges represent
graph states and graph actions, separately. As the state-action spaces of VMG
are finite and relatively small compared to the original environment, we can
directly apply the value iteration algorithm on VMG to estimate graph state
values and figure out the best graph actions. VMG is trained from and built on
the offline RL dataset. Together with an action translator that converts the
abstract graph actions in VMG to real actions in the original environment, VMG
controls agents to maximize episode returns. Our experiments on the D4RL
benchmark show that VMG can outperform state-of-the-art offline RL methods in
several tasks, especially when environments have sparse rewards and long
temporal horizons. Code will be made publicly available.

Magnetization transfer contrast magnetic resonance fingerprinting (MTC-MRF)
is a novel quantitative imaging technique that simultaneously measures several
tissue parameters of semisolid macromolecule and free bulk water. In this
study, we propose an Only-Train-Once MR fingerprinting (OTOM) framework that
estimates the free bulk water and MTC tissue parameters from MR fingerprints
regardless of MRF schedule, thereby avoiding time-consuming process such as
generation of training dataset and network training according to each MRF
schedule. A recurrent neural network is designed to cope with two types of
variants of MRF schedules: 1) various lengths and 2) various patterns.
Experiments on digital phantoms and in vivo data demonstrate that our approach
can achieve accurate quantification for the water and MTC parameters with
multiple MRF schedules. Moreover, the proposed method is in excellent agreement
with the conventional deep learning and fitting methods. The flexible OTOM
framework could be an efficient tissue quantification tool for various MRF
protocols.

Although significant achievements have been achieved by recurrent neural
network (RNN) based video prediction methods, their performance in datasets
with high resolutions is still far from satisfactory because of the information
loss problem and the perception-insensitive mean square error (MSE) based loss
functions. In this paper, we propose a Spatiotemporal Information-Preserving
and Perception-Augmented Model (STIP) to solve the above two problems. To solve
the information loss problem, the proposed model aims to preserve the
spatiotemporal information for videos during the feature extraction and the
state transitions, respectively. Firstly, a Multi-Grained Spatiotemporal
Auto-Encoder (MGST-AE) is designed based on the X-Net structure. The proposed
MGST-AE can help the decoders recall multi-grained information from the
encoders in both the temporal and spatial domains. In this way, more
spatiotemporal information can be preserved during the feature extraction for
high-resolution videos. Secondly, a Spatiotemporal Gated Recurrent Unit (STGRU)
is designed based on the standard Gated Recurrent Unit (GRU) structure, which
can efficiently preserve spatiotemporal information during the state
transitions. The proposed STGRU can achieve more satisfactory performance with
a much lower computation load compared with the popular Long Short-Term (LSTM)
based predictive memories. Furthermore, to improve the traditional MSE loss
functions, a Learned Perceptual Loss (LP-loss) is further designed based on the
Generative Adversarial Networks (GANs), which can help obtain a satisfactory
trade-off between the objective quality and the perceptual quality.
Experimental results show that the proposed STIP can predict videos with more
satisfactory visual quality compared with a variety of state-of-the-art
methods. Source code has been available at
\url{https://github.com/ZhengChang467/STIPHR}.

Finding meaningful concepts in engineering application datasets which allow
for a sensible grouping of designs is very helpful in many contexts. It allows
for determining different groups of designs with similar properties and
provides useful knowledge in the engineering decision making process. Also, it
opens the route for further refinements of specific design candidates which
exhibit certain characteristic features. In this work, an approach to define
meaningful and consistent concepts in an existing engineering dataset is
presented. The designs in the dataset are characterized by a multitude of
features such as design parameters, geometrical properties or performance
values of the design for various boundary conditions. In the proposed approach
the complete feature set is partitioned into several subsets called description
spaces. The definition of the concepts respects this partitioning which leads
to several desired properties of the identified concepts. This cannot be
achieved with state-of-the-art clustering or concept identification approaches.
A novel concept quality measure is proposed, which provides an objective value
for a given definition of concepts in a dataset. The usefulness of the measure
is demonstrated by considering a realistic engineering dataset consisting of
about 2500 airfoil profiles, for which the performance values (lift and drag)
for three different operating conditions were obtained by a computational fluid
dynamics simulation. A numerical optimization procedure is employed, which
maximizes the concept quality measure and finds meaningful concepts for
different setups of the description spaces, while also incorporating user
preference. It is demonstrated how these concepts can be used to select
archetypal representatives of the dataset which exhibit characteristic features
of each concept.

We report our investigation on the use of the popular PlantVillage dataset
for training deep learning based plant disease detection models. We trained a
machine learning model using only 8 pixels from the PlantVillage image
backgrounds. The model achieved 49.0% accuracy on the held-out test set, well
above the random guessing accuracy of 2.6%. This result indicates that the
PlantVillage dataset contains noise correlated with the labels and deep
learning models can easily exploit this bias to make predictions. Possible
approaches to alleviate this problem are discussed.

The base learners and labeled samples (shots) in an ensemble few-shot
classifier greatly affect the model performance. When the performance is not
satisfactory, it is usually difficult to understand the underlying causes and
make improvements. To tackle this issue, we propose a visual analysis method,
FSLDiagnotor. Given a set of base learners and a collection of samples with a
few shots, we consider two problems: 1) finding a subset of base learners that
well predict the sample collections; and 2) replacing the low-quality shots
with more representative ones to adequately represent the sample collections.
We formulate both problems as sparse subset selection and develop two selection
algorithms to recommend appropriate learners and shots, respectively. A matrix
visualization and a scatterplot are combined to explain the recommended
learners and shots in context and facilitate users in adjusting them. Based on
the adjustment, the algorithm updates the recommendation results for another
round of improvement. Two case studies are conducted to demonstrate that
FSLDiagnotor helps build a few-shot classifier efficiently and increases the
accuracy by 12% and 21%, respectively.

We aim to learn language models for Creole languages for which large volumes
of data are not readily available, and therefore explore the potential transfer
from ancestor languages (the 'Ancestry Transfer Hypothesis'). We find that
standard transfer methods do not facilitate ancestry transfer. Surprisingly,
different from other non-Creole languages, a very distinct two-phase pattern
emerges for Creoles: As our training losses plateau, and language models begin
to overfit on their source languages, perplexity on the Creoles drop. We
explore if this compression phase can lead to practically useful language
models (the 'Ancestry Bottleneck Hypothesis'), but also falsify this. Moreover,
we show that Creoles even exhibit this two-phase pattern even when training on
random, unrelated languages. Thus Creoles seem to be typological outliers and
we speculate whether there is a link between the two observations.

The 4K content can deliver a more immersive visual experience to consumers
due to the huge improvement of spatial resolution. However, existing blind
image quality assessment (BIQA) methods are not suitable for the original and
upscaled 4K contents due to the expanded resolution and specific distortions.
In this paper, we propose a deep learning-based BIQA model for 4K content,
which on one hand can recognize true and pseudo 4K content and on the other
hand can evaluate their perceptual visual quality. Considering the
characteristic that high spatial resolution can represent more abundant
high-frequency information, we first propose a Grey-level Co-occurrence Matrix
(GLCM) based texture complexity measure to select three representative image
patches from a 4K image, which can reduce the computational complexity and is
proven to be very effective for the overall quality prediction through
experiments. Then we extract different kinds of visual features from the
intermediate layers of the convolutional neural network (CNN) and integrate
them into the quality-aware feature representation. Finally, two multilayer
perception (MLP) networks are utilized to map the quality-aware features into
the class probability and the quality score for each patch respectively. The
overall quality index is obtained through the average pooling of patch results.
The proposed model is trained through the multi-task learning manner and we
introduce an uncertainty principle to balance the losses of the classification
and regression tasks. The experimental results show that the proposed model
outperforms all compared BIQA metrics on four 4K content quality assessment

Graph Neural Networks (GNNs) have achieved great success in various graph
mining tasks.However, drastic performance degradation is always observed when a
GNN is stacked with many layers. As a result, most GNNs only have shallow
architectures, which limits their expressive power and exploitation of deep
neighborhoods.Most recent studies attribute the performance degradation of deep
GNNs to the \textit{over-smoothing} issue. In this paper, we disentangle the
conventional graph convolution operation into two independent operations:
\textit{Propagation} (\textbf{P}) and \textit{Transformation}
(\textbf{T}).Following this, the depth of a GNN can be split into the
propagation depth ($D_p$) and the transformation depth ($D_t$). Through
extensive experiments, we find that the major cause for the performance
degradation of deep GNNs is the \textit{model degradation} issue caused by
large $D_t$ rather than the \textit{over-smoothing} issue mainly caused by
large $D_p$. Further, we present \textit{Adaptive Initial Residual} (AIR), a
plug-and-play module compatible with all kinds of GNN architectures, to
alleviate the \textit{model degradation} issue and the \textit{over-smoothing}
issue simultaneously. Experimental results on six real-world datasets
demonstrate that GNNs equipped with AIR outperform most GNNs with shallow
architectures owing to the benefits of both large $D_p$ and $D_t$, while the
time costs associated with AIR can be ignored.

We study the fundamental limits to the expressive power of neural networks.
Given two sets $F$, $G$ of real-valued functions, we first prove a general
lower bound on how well functions in $F$ can be approximated in $L^p(\mu)$ norm
by functions in $G$, for any $p \geq 1$ and any probability measure $\mu$. The
lower bound depends on the packing number of $F$, the range of $F$, and the
fat-shattering dimension of $G$. We then instantiate this bound to the case
where $G$ corresponds to a piecewise-polynomial feed-forward neural network,
and describe in details the application to two sets $F$: H{\"o}lder balls and
multivariate monotonic functions. Beside matching (known or new) upper bounds
up to log factors, our lower bounds shed some light on the similarities or
differences between approximation in $L^p$ norm or in sup norm, solving an open
question by DeVore et al. (2021). Our proof strategy differs from the sup norm
case and uses a key probability result of Mendelson (2002).

Despite being tremendously overparameterized, it is appreciated that deep
neural networks trained by stochastic gradient descent (SGD) generalize
surprisingly well. Based on the Rademacher complexity of a pre-specified
hypothesis set, different norm-based generalization bounds have been developed
to explain this phenomenon. However, recent studies suggest these bounds might
be problematic as they increase with the training set size, which is contrary
to empirical evidence. In this study, we argue that the hypothesis set SGD
explores is trajectory-dependent and thus may provide a tighter bound over its
Rademacher complexity. To this end, we characterize the SGD recursion via a
stochastic differential equation by assuming the incurred stochastic gradient
noise follows the fractional Brownian motion. We then identify the Rademacher
complexity in terms of the covering numbers and relate it to the Hausdorff
dimension of the optimization trajectory. By invoking the hypothesis set
stability, we derive a novel generalization bound for deep neural networks.
Extensive experiments demonstrate that it predicts well the generalization gap
over several common experimental interventions. We further show that the Hurst
parameter of the fractional Brownian motion is more informative than existing
generalization indicators such as the power-law index and the upper
Blumenthal-Getoor index.

Conditional independence (CI) tests underlie many approaches to model testing
and structure learning in causal inference. Most existing CI tests for
categorical and ordinal data stratify the sample by the conditioning variables,
perform simple independence tests in each stratum, and combine the results.
Unfortunately, the statistical power of this approach degrades rapidly as the
number of conditioning variables increases. Here we propose a simple unified CI
test for ordinal and categorical data that maintains reasonable calibration and
power in high dimensions. We show that our test outperforms existing baselines
in model testing and structure learning for dense directed graphical models
while being comparable for sparse models. Our approach could be attractive for
causal model testing because it is easy to implement, can be used with
non-parametric or parametric probability models, has the symmetry property, and
has reasonable computational requirements.

Graph neural networks (GNNs) have achieved great success in many graph-based
applications. However, the enormous size and high sparsity level of graphs
hinder their applications under industrial scenarios. Although some scalable
GNNs are proposed for large-scale graphs, they adopt a fixed $K$-hop
neighborhood for each node, thus facing the over-smoothing issue when adopting
large propagation depths for nodes within sparse regions. To tackle the above
issue, we propose a new GNN architecture -- Graph Attention Multi-Layer
Perceptron (GAMLP), which can capture the underlying correlations between
different scales of graph knowledge. We have deployed GAMLP in Tencent with the
Angel platform, and we further evaluate GAMLP on both real-world datasets and
large-scale industrial datasets. Extensive experiments on these 14 graph
datasets demonstrate that GAMLP achieves state-of-the-art performance while
enjoying high scalability and efficiency. Specifically, it outperforms GAT by
1.3\% regarding predictive accuracy on our large-scale Tencent Video dataset
while achieving up to $50\times$ training speedup. Besides, it ranks top-1 on
both the leaderboards of the largest homogeneous and heterogeneous graph (i.e.,
ogbn-papers100M and ogbn-mag) of Open Graph Benchmark.

We present here an original application of the non-negative matrix
factorization (NMF) method, for the case of extra-financial data. These data
are subject to high correlations between co-variables, as well as between
observations. NMF provides a much more relevant clustering of co-variables and
observations than a simple principal component analysis (PCA). In addition, we
show that an initial data separation step before applying NMF further improves
the quality of the clustering.

Although supervised deep-learning has achieved promising performance in
medical image segmentation, many methods cannot generalize well on unseen data,
limiting their real-world applicability. To address this problem, we propose a
deep learning-based Bayesian framework, which jointly models image and label
statistics, utilizing the domain-irrelevant contour of a medical image for
segmentation. Specifically, we first decompose an image into components of
contour and basis. Then, we model the expected label as a variable only related
to the contour. Finally, we develop a variational Bayesian framework to infer
the posterior distributions of these variables, including the contour, the
basis, and the label. The framework is implemented with neural networks, thus
is referred to as deep Bayesian segmentation. Results on the task of
cross-sequence cardiac MRI segmentation show that our method set a new state of
the art for model generalizability. Particularly, the BayeSeg model trained
with LGE MRI generalized well on T2 images and outperformed other models with
great margins, i.e., over 0.47 in terms of average Dice. Our code is available
at https://zmiclab.github.io/projects.html.

Modern consumer electronic devices have adopted deep learning-based
intelligence services for their key features. Vendors have recently started to
execute intelligence services on devices to preserve personal data in devices,
reduce network and cloud costs. We find such a trend as the opportunity to
personalize intelligence services by updating neural networks with user data
without exposing the data out of devices: on-device training. For example, we
may add a new class, my dog, Alpha, for robotic vacuums, adapt speech
recognition for the users accent, let text-to-speech speak as if the user
speaks. However, the resource limitations of target devices incur significant
difficulties. We propose NNTrainer, a light-weight on-device training
framework. We describe optimization techniques for neural networks implemented
by NNTrainer, which are evaluated along with the conventional. The evaluations
show that NNTrainer can reduce memory consumption down to 1/28 without
deteriorating accuracy or training time and effectively personalizes
applications on devices. NNTrainer is cross-platform and practical open source
software, which is being deployed to millions of devices in the authors
affiliation.

This paper proposes machine-independent feature engineering for winding
inter-turn short circuit fault that uses electrical current signals. Electrical
current signal collected from permanent magnet synchronous motor (PMSM) is
subjected to different environmental and operational conditions. To solve these
problems, robust current signal imaging method and deep learning-based feature
extraction method are developed. The overall procedure includes the following
three key steps: (1) transformation of a time-series current signal to
two-dimensional image, (2) extracting features using convolutional neural
networks, and (3) calculating a health indicator using Mahalanobis distance.
Transformation of the time-series signal is based on recurrence plots (RP). The
proposed RP method develops from feature engineering that provides the dominant
fault feature representations in a robust way. The proposed RP is designed that
maximizes the features of inter-turn short fault and minimizes the effect of
noise from systems with various capacities. To demonstrate the validity of the
proposed method, two case studies are conducted using an artificial fault
seeded testbed with two different capacities of motor. By calculating the
feature using only the electrical current signal of the motor without the
parameters related to the capacity of the motor, the proposed feature can be
applied to motors with different capacities while maintaining the same

The success of meta-learning on existing benchmarks is predicated on the
assumption that the distribution of meta-training tasks covers meta-testing
tasks. Frequent violation of the assumption in applications with either
insufficient tasks or a very narrow meta-training task distribution leads to
memorization or learner overfitting. Recent solutions have pursued augmentation
of meta-training tasks, while it is still an open question to generate both
correct and sufficiently imaginary tasks. In this paper, we seek an approach
that up-samples meta-training tasks from the task representation via a task
up-sampling network. Besides, the resulting approach named Adversarial Task
Up-sampling (ATU) suffices to generate tasks that can maximally contribute to
the latest meta-learner by maximizing an adversarial loss. On few-shot sine
regression and image classification datasets, we empirically validate the
marked improvement of ATU over state-of-the-art task augmentation strategies in
the meta-testing performance and also the quality of up-sampled tasks.

Meetings are a common activity in professional contexts, and it remains
challenging to endow vocal assistants with advanced functionalities to
facilitate meeting management. In this context, a task like active speaker
detection can provide useful insights to model interaction between meeting
participants. Motivated by our application context related to advanced meeting
assistant, we want to combine audio and visual information to achieve the best
possible performance. In this paper, we propose two different types of fusion
for the detection of the active speaker, combining two visual modalities and an
audio modality through neural networks. For comparison purpose, classical
unsupervised approaches for audio feature extraction are also used. We expect
visual data centered on the face of each participant to be very appropriate for
detecting voice activity, based on the detection of lip and facial gestures.
Thus, our baseline system uses visual data and we chose a 3D Convolutional
Neural Network architecture, which is effective for simultaneously encoding
appearance and movement. To improve this system, we supplemented the visual
information by processing the audio stream with a CNN or an unsupervised
speaker diarization system. We have further improved this system by adding
visual modality information using motion through optical flow. We evaluated our
proposal with a public and state-of-the-art benchmark: the AMI corpus. We
analysed the contribution of each system to the merger carried out in order to
determine if a given participant is currently speaking. We also discussed the
results we obtained. Besides, we have shown that, for our application context,
adding motion information greatly improves performance. Finally, we have shown
that attention-based fusion improves performance while reducing the standard

For a long time, anomaly localization has been widely used in industries.
Previous studies focused on approximating the distribution of normal features
without adaptation to a target dataset. However, since anomaly localization
should precisely discriminate normal and abnormal features, the absence of
adaptation may make the normality of abnormal features overestimated. Thus, we
propose Coupled-hypersphere-based Feature Adaptation (CFA) which accomplishes
sophisticated anomaly localization using features adapted to the target
dataset. CFA consists of (1) a learnable patch descriptor that learns and
embeds target-oriented features and (2) scalable memory bank independent of the
size of the target dataset. And, CFA adopts transfer learning to increase the
normal feature density so that abnormal features can be clearly distinguished
by applying patch descriptor and memory bank to a pre-trained CNN. The proposed
method outperforms the previous methods quantitatively and qualitatively. For
example, it provides an AUROC score of 99.5% in anomaly detection and 98.5% in
anomaly localization of MVTec AD benchmark. In addition, this paper points out
the negative effects of biased features of pre-trained CNNs and emphasizes the
importance of the adaptation to the target dataset. The code is publicly
available at https://github.com/sungwool/CFA_for_anomaly_localization.

Multi-photon microscopy has played a significant role in biological imaging
since it allows to observe living tissues with improved penetration depth and
excellent sectioning effect. Multi-photon microscopy relies on multi-photon
absorption, enabling the use of different imaging modalities that strongly
depends on the properties of the sample structure, the selected fluorophore and
the excitation laser. However, versatile and tunable laser excitation for
multi-photon absorption is still a challenge, limited by e.g. the narrow
bandwidth of typical laser gain medium or by the tunability of wavelength
conversion offered by optical parametric oscillators or amplifiers. As an
alternative, supercontinuum generation can provide broadband excitationspanning
from the ultra-violet to far infrared domains and integrating numerous
fluorophore absorption peaks, in turn enabling different imaging modalities or
potential multiplexed spectroscopy. Here, we report on the use of machine
learning to optimize the spectro-temporal properties of supercontinuum
generation in order to selectively enhance multi-photon excitation signals
compatible with a variety of fluorophores (or modalities) for multi-photon
microscopy. Specifically, we numerically explore how the use of reconfigurable
(femtosecond) pulse patterns can be readily exploited to control the nonlinear
propagation dynamics and associated spectral broadening occurring in a
highly-nonlinear fiber. In this framework, we show that the use of multiple
pulses to seed optical fiber propagation can trigger a variety of nonlinear
interactions and complex propagation scenario. This approach, exploiting the
temporal dimension as an extended degree of freedom, is used to maximize
typical multi-photon excitations at selected wavelengths, here obtained in a
versatile and reconfigurable manner suitable for imaging applications.

Adversarial examples, which are usually generated for specific inputs with a
specific model, are ubiquitous for neural networks. In this paper we unveil a
surprising property of adversarial noises when they are put together, i.e.,
adversarial noises crafted by one-step gradient methods are linearly separable
if equipped with the corresponding labels. We theoretically prove this property
for a two-layer network with randomly initialized entries and the neural
tangent kernel setup where the parameters are not far from initialization. The
proof idea is to show the label information can be efficiently backpropagated
to the input while keeping the linear separability. Our theory and experimental
evidence further show that the linear classifier trained with the adversarial
noises of the training data can well classify the adversarial noises of the
test data, indicating that adversarial noises actually inject a distributional
perturbation to the original data distribution. Furthermore, we empirically
demonstrate that the adversarial noises may become less linearly separable when
the above conditions are compromised while they are still much easier to
classify than original features.

Signals of opportunity (SOPs) are a promising technique that can be used for
relative positioning in areas where global navigation satellite system (GNSS)
information is unreliable or unavailable. This technique processes features of
the various signals transmitted over a broad wireless spectrum to enable a
receiver to position itself in space. This work examines the frequency
selection problem in order to achieve fast and accurate positioning using only
the received signal strength (RSS) of the surrounding signals. Starting with a
prior belief, the problem of searching for a frequency band that best matches a
predicted location trajectory is investigated. To maximize the accuracy of the
position estimate, a ranking-and-selection problem is mathematically
formulated. A knowledge-gradient (KG) algorithm from optimal learning theory is
proposed that uses correlations in the Bayesian prior beliefs of the frequency
band values to dramatically reduce the algorithm's processing time. The
technique is experimentally tested for a practical scenario of an unmanned
aerial vehicle (UAV) moving around a GPS-denied environment, with obtained
results demonstrating its validity and practical applicability.

The theoretical analysis of multi-class classification has proved that the
existing multi-class classification methods can train a classifier with high
classification accuracy on the test set, when the instances are precise in the
training and test sets with same distribution and enough instances can be
collected in the training set. However, one limitation with multi-class
classification has not been solved: how to improve the classification accuracy
of multi-class classification problems when only imprecise observations are
available. Hence, in this paper, we propose a novel framework to address a new
realistic problem called multi-class classification with imprecise observations
(MCIMO), where we need to train a classifier with fuzzy-feature observations.
Firstly, we give the theoretical analysis of the MCIMO problem based on fuzzy
Rademacher complexity. Then, two practical algorithms based on support vector
machine and neural networks are constructed to solve the proposed new problem.
Experiments on both synthetic and real-world datasets verify the rationality of
our theoretical analysis and the efficacy of the proposed algorithms.

Certified defenses such as randomized smoothing have shown promise towards
building reliable machine learning systems against $\ell_p$-norm bounded
attacks. However, existing methods are insufficient or unable to provably
defend against semantic transformations, especially those without closed-form
expressions (such as defocus blur and pixelate), which are more common in
practice and often unrestricted. To fill up this gap, we propose generalized
randomized smoothing (GSmooth), a unified theoretical framework for certifying
robustness against general semantic transformations via a novel dimension
augmentation strategy. Under the GSmooth framework, we present a scalable
algorithm that uses a surrogate image-to-image network to approximate the
complex transformation. The surrogate model provides a powerful tool for
studying the properties of semantic transformations and certifying robustness.
Experimental results on several datasets demonstrate the effectiveness of our
approach for robustness certification against multiple kinds of semantic
transformations and corruptions, which is not achievable by the alternative

Point cloud is one of the most widely used digital formats of 3D models, the
visual quality of which is quite sensitive to distortions such as downsampling,
noise, and compression. To tackle the challenge of point cloud quality
assessment (PCQA) in scenarios where reference is not available, we propose a
no-reference quality assessment metric for colored point cloud based on
captured video sequences. Specifically, three video sequences are obtained by
rotating the camera around the point cloud through three specific orbits. The
video sequences not only contain the static views but also include the
multi-frame temporal information, which greatly helps understand the human
perception of the point clouds. Then we modify the ResNet3D as the feature
extraction model to learn the correlation between the capture videos and
corresponding subjective quality scores. The experimental results show that our
method outperforms most of the state-of-the-art full-reference and no-reference
PCQA metrics, which validates the effectiveness of the proposed method.

We propose a synthetic task, LEGO (Learning Equality and Group Operations),
that encapsulates the problem of following a chain of reasoning, and we study
how the transformer architectures learn this task. We pay special attention to
data effects such as pretraining (on seemingly unrelated NLP tasks) and dataset
composition (e.g., differing chain length at training and test time), as well
as architectural variants such as weight-tied layers or adding convolutional
components. We study how the trained models eventually succeed at the task, and
in particular, we are able to understand (to some extent) some of the attention
heads as well as how the information flows in the network. Based on these
observations we propose a hypothesis that here pretraining helps merely due to
being a smart initialization rather than some deep knowledge stored in the
network. We also observe that in some data regime the trained transformer finds
"shortcut" solutions to follow the chain of reasoning, which impedes the
model's ability to generalize to simple variants of the main task, and moreover
we find that one can prevent such shortcut with appropriate architecture
modification or careful data preparation. Motivated by our findings, we begin
to explore the task of learning to execute C programs, where a convolutional
modification to transformers, namely adding convolutional structures in the
key/query/value maps, shows an encouraging edge.

Understanding the microscopic origins of collective reorientational motions
in aqueous systems requires techniques that allow us to reach beyond our
chemical imagination. Herein, we elucidate a mechanism using unsupervised
learning, showing that large angular jumps in liquid water involve highly
cooperative orchestrated motions. Our automatized detection of angular
fluctuations, unravels a heterogeneity in the type of angular jumps occurring
concertedly in the system. We show that large orientational motions require a
highly collective dynamic process involving correlated motion of up to 10% of
water molecules in the hydrogen-bond network that form spatially connected
clusters. This phenomenon is rooted in the collective fluctuations of the
network topology which results in the creation of defects in waves on the ThZ
timescale. The mechanism we propose involves a cascade of hydrogen-bond
fluctuations underlying angular jumps and provides new insights into the
current localized picture of angular jumps, and in its wide use in the
interpretations of numerous spectroscopies as well in reorientational dynamics
of water near biological and inorganic systems.

Face recognition based on the deep convolutional neural networks (CNN) shows
superior accuracy performance attributed to the high discriminative features
extracted. Yet, the security and privacy of the extracted features from deep
learning models (deep features) have been often overlooked. This paper proposes
the reconstruction of face images from deep features without accessing the CNN
network configurations as a constrained optimization problem. Such optimization
minimizes the distance between the features extracted from the original face
image and the reconstructed face image. Instead of directly solving the
optimization problem in the image space, we innovatively reformulate the
problem by looking for a latent vector of a GAN generator, then use it to
generate the face image. The GAN generator serves as a dual role in this novel
framework, i.e., face distribution constraint of the optimization goal and a
face generator. On top of the novel optimization task, we also propose an
attack pipeline to impersonate the target user based on the generated face
image. Our results show that the generated face images can achieve a
state-of-the-art successful attack rate of 98.0\% on LFW under type-I attack @
FAR of 0.1\%. Our work sheds light on the biometric deployment to meet the
privacy-preserving and security policies.

The speaker-follower models have proven to be effective in
vision-and-language navigation, where a speaker model is used to synthesize new
instructions to augment the training data for a follower navigation model.
However, in many of the previous methods, the generated instructions are not
directly trained to optimize the performance of the follower. In this paper, we
present \textsc{foam}, a \textsc{Fo}llower-\textsc{a}ware speaker
\textsc{M}odel that is constantly updated given the follower feedback, so that
the generated instructions can be more suitable to the current learning state
of the follower. Specifically, we optimize the speaker using a bi-level
optimization framework and obtain its training signals by evaluating the
follower on labeled data. Experimental results on the Room-to-Room and
Room-across-Room datasets demonstrate that our methods can outperform strong
baseline models across settings. Analyses also reveal that our generated
instructions are of higher quality than the baselines.

Guiding off-screen points of interest (POIs) is a practical way of providing
additional information to users of small-screen devices, such as smart devices
and head-mounted displays. Popular previous methods involve displaying a
primitive figure referred to as Wedge on the screen for users to estimate
off-screen POI on the invisible vertex. Because they utilize a cognitive
process referred to as amodal completion, where users can imagine the entire
figure even when a part of it is occluded, localization accuracy is influenced
by bias and individual differences. To improve the accuracy, we propose to
optimize the figure using a cognitive cost that considers the influence. We
also design two types of optimizations with different parameters: unbiased
OptWedge (UOW) and biased OptWedge (BOW). Experimental results indicate that
OptWedge achieves more accurate guidance for a close distance compared to
heuristics approach.

The COVID-19 pandemic has accelerated research on design of alternative,
quick and effective COVID-19 diagnosis approaches. In this paper, we describe
the Coswara tool, a website application designed to enable COVID-19 detection
by analysing respiratory sound samples and health symptoms. A user using this
service can log into a website using any device connected to the internet,
provide there current health symptom information and record few sound sampled
corresponding to breathing, cough, and speech. Within a minute of analysis of
this information on a cloud server the website tool will output a COVID-19
probability score to the user. As the COVID-19 pandemic continues to demand
massive and scalable population level testing, we hypothesize that the proposed
tool provides a potential solution towards this.

To support the application scenarios where high-resolution (HR) images are
urgently needed, various single image super-resolution (SISR) algorithms are
developed. However, SISR is an ill-posed inverse problem, which may bring
artifacts like texture shift, blur, etc. to the reconstructed images, thus it
is necessary to evaluate the quality of super-resolution images (SRIs). Note
that most existing image quality assessment (IQA) methods were developed for
synthetically distorted images, which may not work for SRIs since their
distortions are more diverse and complicated. Therefore, in this paper, we
propose a no-reference deep-learning image quality assessment method based on
frequency maps because the artifacts caused by SISR algorithms are quite
sensitive to frequency information. Specifically, we first obtain the
high-frequency map (HM) and low-frequency map (LM) of SRI by using Sobel
operator and piecewise smooth image approximation. Then, a two-stream network
is employed to extract the quality-aware features of both frequency maps.
Finally, the features are regressed into a single quality value using fully
connected layers. The experimental results show that our method outperforms all
compared IQA models on the selected three super-resolution quality assessment
(SRQA) databases.

Aleatoric uncertainty quantification seeks for distributional knowledge of
random responses, which is important for reliability analysis and robustness
improvement in machine learning applications. Previous research on aleatoric
uncertainty estimation mainly targets closed-formed conditional densities or
variances, which requires strong restrictions on the data distribution or
dimensionality. To overcome these restrictions, we study conditional generative
models for aleatoric uncertainty estimation. We introduce two metrics to
measure the discrepancy between two conditional distributions that suit these
models. Both metrics can be easily and unbiasedly computed via Monte Carlo
simulation of the conditional generative models, thus facilitating their
evaluation and training. We demonstrate numerically how our metrics provide
correct measurements of conditional distributional discrepancies and can be
used to train conditional models competitive against existing benchmarks.

Hyperbolic neural networks have recently gained significant attention due to
their promising results on several graph problems including node classification
and link prediction. The primary reason for this success is the effectiveness
of the hyperbolic space in capturing the inherent hierarchy of graph datasets.
However, they are limited in terms of generalization, scalability, and have
inferior performance when it comes to non-hierarchical datasets. In this paper,
we take a completely orthogonal perspective for modeling hyperbolic networks.
We use Poincar\'e disk to model the hyperbolic geometry and also treat it as if
the disk itself is a tangent space at origin. This enables us to replace
non-scalable M\"obius gyrovector operations with an Euclidean approximation,
and thus simplifying the entire hyperbolic model to a Euclidean model cascaded
with a hyperbolic normalization function. Our approach does not adhere to
M\"obius math, yet it still works in the Riemannian manifold, hence we call it
Pseudo-Poincar\'e framework. We applied our non-linear hyperbolic normalization
to the current state-of-the-art homogeneous and multi-relational graph networks
and demonstrate significant improvements in performance compared to both
Euclidean and hyperbolic counterparts. The primary impact of this work lies in
its ability to capture hierarchical features in the Euclidean space, and thus,
can replace hyperbolic networks without loss in performance metrics while
simultaneously leveraging the power of Euclidean networks such as
interpretability and efficient execution of various model components.

The need to train DNN models on end-user devices (e.g., smartphones) is
increasing with the need to improve data privacy and reduce communication
overheads. Unlike datacenter servers with powerful CPUs and GPUs, modern
smartphones consist of a diverse collection of specialized cores following a
system-on-a-chip (SoC) architecture that together perform a variety of tasks.
We observe that training DNNs on a smartphone SoC without carefully considering
its resource constraints can not only lead to suboptimal training performance
but significantly affect user experience as well. In this paper, we present
Swan, a neural engine to optimize DNN training on smartphone SoCs without
hurting user experience. Extensive large-scale evaluations show that Swan can
improve performance by 1.2 - 23.3x over the state-of-the-art.

In real-world reinforcement learning applications the learner's observation
space is ubiquitously high-dimensional with both relevant and irrelevant
information about the task at hand. Learning from high-dimensional observations
has been the subject of extensive investigation in supervised learning and
statistics (e.g., via sparsity), but analogous issues in reinforcement learning
are not well understood, even in finite state/action (tabular) domains. We
introduce a new problem setting for reinforcement learning, the Exogenous
Markov Decision Process (ExoMDP), in which the state space admits an (unknown)
factorization into a small controllable (or, endogenous) component and a large
irrelevant (or, exogenous) component; the exogenous component is independent of
the learner's actions, but evolves in an arbitrary, temporally correlated
fashion. We provide a new algorithm, ExoRL, which learns a near-optimal policy
with sample complexity polynomial in the size of the endogenous component and
nearly independent of the size of the exogenous component, thereby offering a
doubly-exponential improvement over off-the-shelf algorithms. Our results
highlight for the first time that sample-efficient reinforcement learning is
possible in the presence of exogenous information, and provide a simple,
user-friendly benchmark for investigation going forward.

Recent self-supervised advances in medical computer vision exploit global and
local anatomical self-similarity for pretraining prior to downstream tasks such
as segmentation. However, current methods assume i.i.d. image acquisition,
which is invalid in clinical study designs where follow-up longitudinal scans
track subject-specific temporal changes. Further, existing self-supervised
methods for medically-relevant image-to-image architectures exploit only
spatial or temporal self-similarity and only do so via a loss applied at a
single image-scale, with naive multi-scale spatiotemporal extensions collapsing
to degenerate solutions. To these ends, this paper makes two contributions: (1)
It presents a local and multi-scale spatiotemporal representation learning
method for image-to-image architectures trained on longitudinal images. It
exploits the spatiotemporal self-similarity of learned multi-scale
intra-subject features for pretraining and develops several feature-wise
regularizations that avoid collapsed identity representations; (2) During
finetuning, it proposes a surprisingly simple self-supervised segmentation
consistency regularization to exploit intra-subject correlation. Benchmarked in
the one-shot segmentation setting, the proposed framework outperforms both
well-tuned randomly-initialized baselines and current self-supervised
techniques designed for both i.i.d. and longitudinal datasets. These
improvements are demonstrated across both longitudinal neurodegenerative adult
MRI and developing infant brain MRI and yield both higher performance and
longitudinal consistency.

This work studies the problem of transfer learning under the functional
linear model framework, which aims to improve the fit of the target model by
leveraging the knowledge from related source models. We measure the relatedness
between target and source models using Reproducing Kernel Hilbert Spaces,
allowing the type of knowledge being transferred to be interpreted by the
structure of the spaces. Two algorithms are proposed: one transfers knowledge
when the index of transferable sources is known, while the other one utilizes
aggregation to achieve knowledge transfer without prior information about the
sources. Furthermore, we establish the optimal convergence rates for excess
risk, making the statistical gain via transfer learning mathematically
provable. The effectiveness of the proposed algorithms is demonstrated on
synthetic data as well as real financial data.

This paper studies low-rank matrix completion in the presence of heavy-tailed
and possibly asymmetric noise, where we aim to estimate an underlying low-rank
matrix given a set of highly incomplete noisy entries. Though the matrix
completion problem has attracted much attention in the past decade, there is
still lack of theoretical understanding when the observations are contaminated
by heavy-tailed noises. Prior theory falls short of explaining the empirical
results and is unable to capture the optimal dependence of the estimation error
on the noise level. In this paper, we adopt an adaptive Huber loss to
accommodate heavy-tailed noise, which is robust against large and possibly
asymmetric errors when the parameter in the loss function is carefully designed
to balance the Huberization biases and robustness to outliers. Then, we propose
an efficient nonconvex algorithm via a balanced low-rank Burer-Monteiro matrix
factorization and gradient decent with robust spectral initialization. We prove
that under merely bounded second moment condition on the error distributions,
rather than the sub-Gaussian assumption, the Euclidean error of the iterates
generated by the proposed algorithm decrease geometrically fast until achieving
a minimax-optimal statistical estimation error, which has the same order as
that in the sub-Gaussian case. The key technique behind this significant
advancement is a powerful leave-one-out analysis framework. The theoretical
results are corroborated by our simulation studies.

Changing basis is a common task when solving quantum mechanical problems. As
part of a research project investigating student understanding of basis and
change of basis in quantum mechanics, we developed a tutorial to support
students in learning about basis in the context of spin-1/2 systems. We have
since created an interactive online version of the basis tutorial as part of a
freely available suite of online quantum tutorials called ACE Physics
(acephysics.net). The ACE Physics tutorials include dynamic guidance elements
and, unlike other tutorials, are intended for use outside the classroom without
instructor facilitation. After extensive study in an instructor-supported
environment, we assigned the ACE Physics basis tutorial as homework in two
semesters of upper-division quantum mechanics, and we report on the
effectiveness of the activity based on pre-/post-testing and comparison of
student exam performance with a similar semester that did not include the
activity. We find that the tutorial produces sufficient learning gains to
justify continued assignment as a homework problem in our classes.

Scanning transmission electron microscopy (STEM) is an indispensable tool for
atomic-resolution structural analysis for a wide range of materials. The
conventional analysis of STEM images is an extensive hands-on process, which
limits efficient handling of high-throughput data. Here we apply a fully
convolutional network (FCN) for identification of important structural features
of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying
sulfur vacancies and polymorph types of ${MoS_2}$ from atomic resolution STEM
images. Efficient models are achieved based on training with simulated images
in the presence of different levels of noise, aberrations, and carbon
contamination. The accuracy of the FCN models toward extensive experimental
STEM images is comparable to that of careful hands-on analysis. Our work
provides a guideline on best practices to train a deep learning model for STEM
image analysis and demonstrates FCN's application for efficient processing of a
large volume of STEM data.

Open space grassland is being increasingly farmed or built upon, leading to a
ramping up of conservation efforts targeting roadside verges. Approximately
half of all UK grassland species can be found along the country's 500,000 km of
roads, with some 91 species either threatened or near threatened. Careful
management of these "wildlife corridors" is therefore essential to preventing
species extinction and maintaining biodiversity in grassland habitats. Wildlife
trusts have often enlisted the support of volunteers to survey roadside verges
and identify new "Local Wildlife Sites" as areas of high conservation
potential. Using volunteer survey data from 3,900 km of roadside verges
alongside publicly available street-view imagery, we present DeepVerge; a deep
learning-based method that can automatically survey sections of roadside verges
by detecting the presence of positive indicator species. Using images and
ground truth survey data from the rural county of Lincolnshire, DeepVerge
achieved a mean accuracy of 88%. Such a method may be used by local authorities
to identify new local wildlife sites, and aid management and environmental
planning in line with legal and government policy obligations, saving thousands
of hours of manual labour.

The Strong Lottery Ticket Hypothesis (SLTH) stipulates the existence of a
subnetwork within a sufficiently overparameterized (dense) neural network that
-- when initialized randomly and without any training -- achieves the accuracy
of a fully trained target network. Recent work by \citet{da2022proving}
demonstrates that the SLTH can also be extended to translation equivariant
networks -- i.e. CNNs -- with the same level of overparametrization as needed
for SLTs in dense networks. However, modern neural networks are capable of
incorporating more than just translation symmetry, and developing general
equivariant architectures such as rotation and permutation has been a powerful
design principle. In this paper, we generalize the SLTH to functions that
preserve the action of the group $G$ -- i.e. $G$-equivariant network -- and
prove, with high probability, that one can prune a randomly initialized
overparametrized $G$-equivariant network to a $G$-equivariant subnetwork that
approximates another fully trained $G$-equivariant network of fixed width and
depth. We further prove that our prescribed overparametrization scheme is also
optimal as a function of the error tolerance. We develop our theory for a large
range of groups, including important ones such as subgroups of the Euclidean
group $\text{E}(n)$ and subgroups of the symmetric group $G \leq \mathcal{S}_n$
-- allowing us to find SLTs for MLPs, CNNs, $\text{E}(2)$-steerable CNNs, and
permutation equivariant networks as specific instantiations of our unified
framework which completely extends prior work. Empirically, we verify our
theory by pruning overparametrized $\text{E}(2)$-steerable CNNs and message
passing GNNs to match the performance of trained target networks within a given
error tolerance.

This work presents an unsupervised deep discriminant analysis for clustering.
The method is based on deep neural networks and aims to minimize the
intra-cluster discrepancy and maximize the inter-cluster discrepancy in an
unsupervised manner. The method is able to project the data into a nonlinear
low-dimensional latent space with compact and distinct distribution patterns
such that the data clusters can be effectively identified. We further provide
an extension of the method such that available graph information can be
effectively exploited to improve the clustering performance. Extensive
numerical results on image and non-image data with or without graph information
demonstrate the effectiveness of the proposed methods.

Interpretability is an essential building block for trustworthiness in
reinforcement learning systems. However, interpretability might come at the
cost of deteriorated performance, leading many researchers to build complex
models. Our goal is to analyze the cost of interpretability. We show that in
certain cases, one can achieve policy interpretability while maintaining its
optimality. We focus on a classical problem from reinforcement learning: mazes
with $k$ obstacles in $\mathbb{R}^d$. We prove the existence of a small
decision tree with a linear function at each inner node and depth $O(\log k +
2^d)$ that represents an optimal policy. Note that for the interesting case of
a constant $d$, we have $O(\log k)$ depth. Thus, in this setting, there is no
accuracy-interpretability tradeoff. To prove this result, we use a new
"compressing" technique that might be useful in additional settings.

By adding exiting layers to the deep learning networks, early exit can
terminate the inference earlier with accurate results. The passive
decision-making of whether to exit or continue the next layer has to go through
every pre-placed exiting layer until it exits. In addition, it is also hard to
adjust the configurations of the computing platforms alongside the inference
proceeds. By incorporating a low-cost prediction engine, we propose a
Predictive Exit framework for computation- and energy-efficient deep learning
applications. Predictive Exit can forecast where the network will exit (i.e.,
establish the number of remaining layers to finish the inference), which
effectively reduces the network computation cost by exiting on time without
running every pre-placed exiting layer. Moreover, according to the number of
remaining layers, proper computing configurations (i.e., frequency and voltage)
are selected to execute the network to further save energy. Extensive
experimental results demonstrate that Predictive Exit achieves up to 96.2%
computation reduction and 72.9% energy-saving compared with classic deep
learning networks; and 12.8% computation reduction and 37.6% energy-saving
compared with the early exit under state-of-the-art exiting strategies, given
the same inference accuracy and latency.

What target labels are most effective for graph neural network (GNN)
training? In some applications where GNNs excel-like drug design or fraud
detection, labeling new instances is expensive. We develop a data-efficient
active sampling framework, ScatterSample, to train GNNs under an active
learning setting. ScatterSample employs a sampling module termed
DiverseUncertainty to collect instances with large uncertainty from different
regions of the sample space for labeling. To ensure diversification of the
selected nodes, DiverseUncertainty clusters the high uncertainty nodes and
selects the representative nodes from each cluster. Our ScatterSample algorithm
is further supported by rigorous theoretical analysis demonstrating its
advantage compared to standard active sampling methods that aim to simply
maximize the uncertainty and not diversify the samples. In particular, we show
that ScatterSample is able to efficiently reduce the model uncertainty over the
whole sample space. Our experiments on five datasets show that ScatterSample
significantly outperforms the other GNN active learning baselines, specifically
it reduces the sampling cost by up to 50% while achieving the same test
accuracy.

$\Delta$-Machine Learning ($\Delta$-ML) has been shown to effectively and
efficiently bring a low-level ML potential energy surface to CCSD(T) quality.
Here we propose extending this approach to general force fields, which
implicitly or explicitly contain many-body effects. After describing this
general approach, we illustrate it for the MB-pol water potential which
contains CCSD(T) 2-body and 3-body interactions but relies on the TTM4-F 4-body
and higher body interactions. The 4-body MB-pol (TTM4-F) interaction fails at a
very short range and for the water hexamer errors up to 0.84 kcal/mol are seen
for some isomers, owing mainly to 4-body errors. We apply $\Delta$-ML for the
4-body interaction, using a recent dataset of CCSD(T) 4-body energies that we
used to develop a new water potential, q-AQUA. This 4-body correction is shown
to improve the accuracy of the MB-pol potential for the relative energies of 8
isomers of the water hexamer as well as the harmonic frequencies. The new
potential is robust in the very short range and so should be reliable for
simulations at high pressure and/or high temperature.

Accurate diagnosis of autism spectrum disorder (ASD) based on neuroimaging
data has significant implications, as extracting useful information from
neuroimaging data for ASD detection is challenging. Even though machine
learning techniques have been leveraged to improve the information extraction
from neuroimaging data, the varying data quality caused by different meta-data
conditions (i.e., data collection strategies) limits the effective information
that can be extracted, thus leading to data-dependent predictive accuracies in
ASD detection, which can be worse than random guess in some cases. In this
work, we systematically investigate the impact of three kinds of meta-data on
the predictive accuracy of classifying ASD based on structural MRI collected
from 20 different sites, where meta-data conditions vary.

Unit commitment (UC) is a fundamental problem in the day-ahead electricity
market, and it is critical to solve UC problems efficiently. Mathematical
optimization techniques like dynamic programming, Lagrangian relaxation, and
mixed-integer quadratic programming (MIQP) are commonly adopted for UC
problems. However, the calculation time of these methods increases at an
exponential rate with the amount of generators and energy resources, which is
still the main bottleneck in industry. Recent advances in artificial
intelligence have demonstrated the capability of reinforcement learning (RL) to
solve UC problems. Unfortunately, the existing research on solving UC problems
with RL suffers from the curse of dimensionality when the size of UC problems
grows. To deal with these problems, we propose an optimization method-assisted
ensemble deep reinforcement learning algorithm, where UC problems are
formulated as a Markov Decision Process (MDP) and solved by multi-step deep
Q-learning in an ensemble framework. The proposed algorithm establishes a
candidate action set by solving tailored optimization problems to ensure a
relatively high performance and the satisfaction of operational constraints.
Numerical studies on IEEE 118 and 300-bus systems show that our algorithm
outperforms the baseline RL algorithm and MIQP. Furthermore, the proposed
algorithm shows strong generalization capacity under unforeseen operational

Correlation clustering is a ubiquitous paradigm in unsupervised machine
learning where addressing unfairness is a major challenge. Motivated by this,
we study Fair Correlation Clustering where the data points may belong to
different protected groups and the goal is to ensure fair representation of all
groups across clusters. Our paper significantly generalizes and improves on the
quality guarantees of previous work of Ahmadi et al. and Ahmadian et al. as
follows.
  - We allow the user to specify an arbitrary upper bound on the representation
of each group in a cluster.
  - Our algorithm allows individuals to have multiple protected features and
ensure fairness simultaneously across them all.
  - We prove guarantees for clustering quality and fairness in this general
setting. Furthermore, this improves on the results for the special cases
studied in previous work. Our experiments on real-world data demonstrate that
our clustering quality compared to the optimal solution is much better than
what our theoretical result suggests.

In the healthcare system, patients are required to use wearable devices for
the remote data collection and real-time monitoring of health data and the
status of health conditions. This adoption of wearables results in a
significant increase in the volume of data that is collected and transmitted.
As the devices are run by small battery power, they can be quickly diminished
due to the high processing requirements of the device for data collection and
transmission. Given the importance attached to medical data, it is imperative
that all transmitted data adhere to strict integrity and availability
requirements. Reducing the volume of healthcare data and the frequency of
transmission will improve the device battery life via using inference
algorithm. There is an issue of improving transmission metrics with accuracy
and efficiency, which trade-off each other such as increasing accuracy reduces
the efficiency. This paper demonstrates that machine learning can be used to
analyze complex health data metrics such as the accuracy and efficiency of data
transmission to overcome the trade-off problem using the Levenberg-Marquardt
algorithm to enhance both metrics by taking fewer samples to transmit whilst
maintaining the accuracy. The algorithm is tested with a standard heart rate
dataset to compare the metrics. The result shows that the LMA has best
performed with an efficiency of 3.33 times for reduced sample data size and
accuracy of 79.17%, which has the similar accuracies in 7 different sampling
cases adopted for testing but demonstrates improved efficiency. These proposed
methods significantly improved both metrics using machine learning without
sacrificing a metric over the other compared to the existing methods with high

Automatically identifying the structural substrates underlying cardiac
abnormalities can potentially provide real-time guidance for interventional
procedures. With the knowledge of cardiac tissue substrates, the treatment of
complex arrhythmias such as atrial fibrillation and ventricular tachycardia can
be further optimized by detecting arrhythmia substrates to target for treatment
(i.e., adipose) and identifying critical structures to avoid. Optical coherence
tomography (OCT) is a real-time imaging modality that aids in addressing this
need. Existing approaches for cardiac image analysis mainly rely on fully
supervised learning techniques, which suffer from the drawback of workload on
labor-intensive annotation process of pixel-wise labeling. To lessen the need
for pixel-wise labeling, we develop a two-stage deep learning framework for
cardiac adipose tissue segmentation using image-level annotations on OCT images
of human cardiac substrates. In particular, we integrate class activation
mapping with superpixel segmentation to solve the sparse tissue seed challenge
raised in cardiac tissue segmentation. Our study bridges the gap between the
demand on automatic tissue analysis and the lack of high-quality pixel-wise
annotations. To the best of our knowledge, this is the first study that
attempts to address cardiac tissue segmentation on OCT images via weakly
supervised learning techniques. Within an in-vitro human cardiac OCT dataset,
we demonstrate that our weakly supervised approach on image-level annotations
achieves comparable performance as fully supervised methods trained on
pixel-wise annotations.

Many modern machine learning algorithms are composed of simple private
algorithms; thus, an increasingly important problem is to efficiently compute
the overall privacy loss under composition. In this study, we introduce the
Edgeworth Accountant, an analytical approach to composing differential privacy
guarantees of private algorithms. The Edgeworth Accountant starts by losslessly
tracking the privacy loss under composition using the $f$-differential privacy
framework, which allows us to express the privacy guarantees using privacy-loss
log-likelihood ratios (PLLRs). As the name suggests, this accountant next uses
the Edgeworth expansion to the upper and lower bounds the probability
distribution of the sum of the PLLRs. Moreover, by relying on a technique for
approximating complex distributions using simple ones, we demonstrate that the
Edgeworth Accountant can be applied to the composition of any noise-addition
mechanism. Owing to certain appealing features of the Edgeworth expansion, the
$(\epsilon, \delta)$-differential privacy bounds offered by this accountant are
non-asymptotic, with essentially no extra computational cost, as opposed to the
prior approaches in, wherein the running times increase with the number of
compositions. Finally, we demonstrate that our upper and lower $(\epsilon,
\delta)$-differential privacy bounds are tight in federated analytics and
certain regimes of training private deep learning models.

Video frame interpolation (VFI) aims to generate predictive frames by warping
learnable motions from the bidirectional historical references. Most existing
works utilize spatio-temporal semantic information extractor to realize motion
estimation and interpolation modeling, not enough considering with the real
mechanistic rationality of generated middle motions. In this paper, we
reformulate VFI as a multi-variable non-linear (MNL) regression problem, and a
Joint Non-linear Motion Regression (JNMR) strategy is proposed to model
complicated motions of inter-frame. To establish the MNL regression, ConvLSTM
is adopted to construct the distribution of complete motions in temporal
dimension. The motion correlations between the target frame and multiple
reference frames can be regressed by the modeled distribution. Moreover, the
feature learning network is designed to optimize for the MNL regression
modeling. A coarse-to-fine synthesis enhancement module is further conducted to
learn visual dynamics at different resolutions through repetitive regression
and interpolation. Highly competitive experimental results on frame
interpolation show that the effectiveness and significant improvement compared
with state-of-the-art performance, and the robustness of complicated motion
estimation is improved by the MNL motion regression.

Inductive logic reasoning is one of the fundamental tasks on graphs, which
seeks to generalize patterns from the data. This task has been studied
extensively for traditional graph datasets such as knowledge graphs (KGs), with
representative techniques such as inductive logic programming (ILP). Existing
ILP methods typically assume learning from KGs with static facts and binary
relations. Beyond KGs, graph structures are widely present in other
applications such as video instructions, scene graphs and program executions.
While inductive logic reasoning is also beneficial for these applications,
applying ILP to the corresponding graphs is nontrivial: they are more complex
than KGs, which usually involve timestamps and n-ary relations, effectively a
type of hypergraph with temporal events.
  In this work, we study two of such applications and propose to represent them
as hypergraphs with time intervals. To reason on this graph, we propose the
multi-start random B-walk that traverses this hypergraph. Combining it with a
path-consistency algorithm, we propose an efficient backward-chaining ILP
method that learns logic rules by generalizing from both the temporal and the
relational data.

Variational autoencoders (VAEs) have recently been used for unsupervised
disentanglement learning of complex density distributions. Numerous variants
exist to encourage disentanglement in latent space while improving
reconstruction. However, none have simultaneously managed the trade-off between
attaining extremely low reconstruction error and a high disentanglement score.
We present a generalized framework to handle this challenge under constrained
optimization and demonstrate that it outperforms state-of-the-art existing
models as regards disentanglement while balancing reconstruction. We introduce
three controllable Lagrangian hyperparameters to control reconstruction loss,
KL divergence loss and correlation measure. We prove that maximizing
information in the reconstruction network is equivalent to information
maximization during amortized inference under reasonable assumptions and
constraint relaxation.

All-atom and coarse-grained molecular dynamics are two widely used
computational tools to study the conformational states of proteins. Yet, these
two simulation methods suffer from the fact that without access to
supercomputing resources, the time and length scales at which these states
become detectable are difficult to achieve. One alternative to such methods is
based on encoding the atomistic trajectory of molecular dynamics as a shorthand
version devoid of physical particles, and then learning to propagate the
encoded trajectory through the use of artificial intelligence. Here we show
that a simple textual representation of the frames of molecular dynamics
trajectories as vectors of Ramachandran basin classes retains most of the
structural information of the full atomistic representation of a protein in
each frame, and can be used to generate equivalent atom-less trajectories
suitable to train different types of generative neural networks. In turn, the
trained generative models can be used to extend indefinitely the atom-less
dynamics or to sample the conformational space of proteins from their
representation in the models latent space. We define intuitively this
methodology as molecular dynamics without molecules, and show that it enables
to cover physically relevant states of proteins that are difficult to access
with traditional molecular dynamics.

Graph Neural Networks (GNNs) have been widely applied to various fields for
learning over graph-structured data. They have shown significant improvements
over traditional heuristic methods in various tasks such as node classification
and graph classification. However, since GNNs heavily rely on smoothed node
features rather than graph structure, they often show poor performance than
simple heuristic methods in link prediction where the structural information,
e.g., overlapped neighborhoods, degrees, and shortest paths, is crucial. To
address this limitation, we propose Neighborhood Overlap-aware Graph Neural
Networks (Neo-GNNs) that learn useful structural features from an adjacency
matrix and estimate overlapped neighborhoods for link prediction. Our Neo-GNNs
generalize neighborhood overlap-based heuristic methods and handle overlapped
multi-hop neighborhoods. Our extensive experiments on Open Graph Benchmark
datasets (OGB) demonstrate that Neo-GNNs consistently achieve state-of-the-art
performance in link prediction. Our code is publicly available at
https://github.com/seongjunyun/Neo_GNNs.

Recent research demonstrate that prediction of time series by predictive
recurrent neural networks based on the noisy input generates a {\it smooth}
anticipated trajectory. We examine influence of the noise component in both the
training data sets and the input sequences on network prediction quality. We
propose and discuss an explanation of the observed noise compression in the
predictive process. We also discuss importance of this property of recurrent
networks in the neuroscience context for the evolution of living organisms.

Care deferral is the phenomenon where patients defer or are unable to receive
healthcare services, such as seeing doctors, medications or planned surgery.
Care deferral can be the result of patient decisions, service availability,
service limitations, or restrictions due to cost. Continual care deferral in
populations may lead to a decline in population health and compound health
issues leading to higher social and financial costs in the long term.
Consequently, identification of patients who may be at risk of deferring care
is important towards improving population health and reducing care total costs.
Additionally, minority and vulnerable populations are at a greater risk of care
deferral due to socioeconomic factors. In this paper, we (a) address the
problem of predicting care deferral for well-care visits; (b) observe that
social determinants of health are relevant explanatory factors towards
predicting care deferral, and (c) compute how fair the models are with respect
to demographics, socioeconomic factors and selected comorbidities. Many health
systems currently use rules-based techniques to retroactively identify patients
who previously deferred care. The objective of this model is to identify
patients at risk of deferring care and allow the health system to prevent care
deferrals through direct outreach or social determinant mediation.

Recent progress in reinforcement learning (RL) has started producing
generally capable agents that can solve a distribution of complex environments.
These agents are typically tested on fixed, human-authored environments. On the
other hand, quality diversity (QD) optimization has been proven to be an
effective component of environment generation algorithms, which can generate
collections of high-quality environments that are diverse in the resulting
agent behaviors. However, these algorithms require potentially expensive
simulations of agents on newly generated environments. We propose Deep
Surrogate Assisted Generation of Environments (DSAGE), a sample-efficient QD
environment generation algorithm that maintains a deep surrogate model for
predicting agent behaviors in new environments. Results in two benchmark
domains show that DSAGE significantly outperforms existing QD environment
generation algorithms in discovering collections of environments that elicit
diverse behaviors of a state-of-the-art RL agent and a planning agent. Our
source code and videos are available at https://dsagepaper.github.io/.

Predictive states for stochastic processes are a nonparametric and
interpretable construct with relevance across a multitude of modeling
paradigms. Recent progress on the self-supervised reconstruction of predictive
states from time-series data focused on the use of reproducing kernel Hilbert
spaces. Here, we examine how Wasserstein distances may be used to detect
predictive equivalences in symbolic data. We compute Wasserstein distances
between distributions over sequences ("predictions"), using a
finite-dimensional embedding of sequences based on the Cantor for the
underlying geometry. We show that exploratory data analysis using the resulting
geometry via hierarchical clustering and dimension reduction provides insight
into the temporal structure of processes ranging from the relatively simple
(e.g., finite-state hidden Markov models) to the very complex (e.g.,
infinite-state indexed grammars).

The use of cameras and computational algorithms for noninvasive, low-cost and
scalable measurement of physiological (e.g., cardiac and pulmonary) vital signs
is very attractive. However, diverse data representing a range of environments,
body motions, illumination conditions and physiological states is laborious,
time consuming and expensive to obtain. Synthetic data have proven a valuable
tool in several areas of machine learning, yet are not widely available for
camera measurement of physiological states. Synthetic data offer "perfect"
labels (e.g., without noise and with precise synchronization), labels that may
not be possible to obtain otherwise (e.g., precise pixel level segmentation
maps) and provide a high degree of control over variation and diversity in the
dataset. We present SCAMPS, a dataset of synthetics containing 2,800 videos
(1.68M frames) with aligned cardiac and respiratory signals and facial action
intensities. The RGB frames are provided alongside segmentation maps. We
provide precise descriptive statistics about the underlying waveforms,
including inter-beat interval, heart rate variability, and pulse arrival time.
Finally, we present baseline results training on these synthetic data and
testing on real-world datasets to illustrate generalizability.

Knowledge graphs are inherently incomplete. Therefore substantial research
has been directed towards knowledge graph completion (KGC), i.e., predicting
missing triples from the information represented in the knowledge graph (KG).
Embedding models have yielded promising results for KGC, yet any current KGC
embedding model is incapable of: (1) fully capturing vital inference patterns
(e.g., composition), (2) capturing prominent logical rules jointly (e.g.,
hierarchy and composition), and (3) providing an intuitive interpretation of
captured patterns. In this work, we propose ExpressivE, a fully expressive
spatio-functional embedding model that solves all these challenges
simultaneously. ExpressivE embeds pairs of entities as points and relations as
hyper-parallelograms in the virtual triple space $\mathbb{R}^{2d}$. This model
design allows ExpressivE not only to capture a rich set of inference patterns
jointly but additionally to display any supported inference pattern through the
spatial relation of hyper-parallelograms, offering an intuitive and consistent
geometric interpretation of ExpressivE embeddings and their captured patterns.
Experimental results on standard KGC benchmarks reveal that ExpressivE is
competitive with state-of-the-art models and even significantly outperforms
them on WN18RR.

Most dimensionality reduction methods employ frequency domain representations
obtained from matrix diagonalization and may not be efficient for large
datasets with relatively high intrinsic dimensions. To address this challenge,
Correlated Clustering and Projection (CCP) offers a novel data domain strategy
that does not need to solve any matrix. CCP partitions high-dimensional
features into correlated clusters and then projects correlated features in each
cluster into a one-dimensional representation based on sample correlations.
Residue-Similarity (R-S) scores and indexes, the shape of data in Riemannian
manifolds, and algebraic topology-based persistent Laplacian are introduced for
visualization and analysis. Proposed methods are validated with benchmark
datasets associated with various machine learning algorithms.

Accurately segmenting temporal frames of cine magnetic resonance imaging
(MRI) is a crucial step in various real-time MRI guided cardiac interventions.
To achieve fast and accurate visual assistance, there are strict requirements
on the maximum latency and minimum throughput of the segmentation framework.
State-of-the-art neural networks on this task are mostly hand-crafted to
satisfy these constraints while achieving high accuracy. On the other hand,
while existing literature have demonstrated the power of neural architecture
search (NAS) in automatically identifying the best neural architectures for
various medical applications, they are mostly guided by accuracy, sometimes
with computation complexity, and the importance of real-time constraints are
overlooked. A major challenge is that such constraints are non-differentiable
and are thus not compatible with the widely used differentiable NAS frameworks.
In this paper, we present a strategy that directly handles real-time
constraints in a differentiable NAS framework named RT-DNAS. Experiments on
extended 2017 MICCAI ACDC dataset show that compared with state-of-the-art
manually and automatically designed architectures, RT-DNAS is able to identify
ones with better accuracy while satisfying the real-time constraints.

Existing work on generating hints in Intelligent Tutoring Systems (ITS)
focuses mostly on manual and non-personalized feedback. In this work, we
explore automatically generated questions as personalized feedback in an ITS.
Our personalized feedback can pinpoint correct and incorrect or missing phrases
in student answers as well as guide them towards correct answer by asking a
question in natural language. Our approach combines cause-effect analysis to
break down student answers using text similarity-based NLP Transformer models
to identify correct and incorrect or missing parts. We train a few-shot Neural
Question Generation and Question Re-ranking models to show questions addressing
components missing in the student answers which steers students towards the
correct answer. Our model vastly outperforms both simple and strong baselines
in terms of student learning gains by 45% and 23% respectively when tested in a
real dialogue-based ITS. Finally, we show that our personalized corrective
feedback system has the potential to improve Generative Question Answering
systems.

Inverse wave scattering aims at determining the properties of an object using
data on how the object scatters incoming waves. In order to collect
information, sensors are put in different locations to send and receive waves
from each other. The choice of sensor positions and incident wave frequencies
determines the reconstruction quality of scatterer properties. This paper
introduces reinforcement learning to develop precision imaging that decides
sensor positions and wave frequencies adaptive to different scatterers in an
intelligent way, thus obtaining a significant improvement in reconstruction
quality with limited imaging resources. Extensive numerical results will be
provided to demonstrate the superiority of the proposed method over existing
methods.

Contextual linear bandits is a rich and theoretically important model that
has many practical applications. Recently, this setup gained a lot of interest
in applications over wireless where communication constraints can be a
performance bottleneck, especially when the contexts come from a large
$d$-dimensional space. In this paper, we consider a distributed memoryless
contextual linear bandit learning problem, where the agents who observe the
contexts and take actions are geographically separated from the learner who
performs the learning while not seeing the contexts. We assume that contexts
are generated from a distribution and propose a method that uses $\approx 5d$
bits per context for the case of unknown context distribution and $0$ bits per
context if the context distribution is known, while achieving nearly the same
regret bound as if the contexts were directly observable. The former bound
improves upon existing bounds by a $\log(T)$ factor, where $T$ is the length of
the horizon, while the latter achieves information theoretical tightness.

Machine learning (ML) and artificial intelligence (AI) tools increasingly
permeate every possible social, political, and economic sphere; sorting,
taxonomizing and predicting complex human behaviour and social phenomena.
However, from fallacious and naive groundings regarding complex adaptive
systems to datasets underlying models, these systems are beset by problems,
challenges, and limitations. They remain opaque and unreliable, and fail to
consider societal and structural oppressive systems, disproportionately
negatively impacting those at the margins of society while benefiting the most
powerful.
  The various challenges, problems and pitfalls of these systems are a hot
topic of research in various areas, such as critical data/algorithm studies,
science and technology studies (STS), embodied and enactive cognitive science,
complexity science, Afro-feminism, and the broadly construed emerging field of
Fairness, Accountability, and Transparency (FAccT). Yet, these fields of
enquiry often proceed in silos. This thesis weaves together seemingly disparate
fields of enquiry to examine core scientific and ethical challenges, pitfalls,
and problems of AI.
  In this thesis I, a) review the historical and cultural ecology from which AI
research emerges, b) examine the shaky scientific grounds of machine prediction
of complex behaviour illustrating how predicting complex behaviour with
precision is impossible in principle, c) audit large scale datasets behind
current AI demonstrating how they embed societal historical and structural
injustices, d) study the seemingly neutral values of ML research and put
forward 67 prominent values underlying ML research, e) examine some of the
insidious and worrying applications of computer vision research, and f) put
forward a framework for approaching challenges, failures and problems
surrounding ML systems as well as alternative ways forward.

Rotation equivariance is a desirable property in many practical applications
such as motion forecasting and 3D perception, where it can offer benefits like
sample efficiency, better generalization, and robustness to input
perturbations. Vector Neurons (VN) is a recently developed framework offering a
simple yet effective approach for deriving rotation-equivariant analogs of
standard machine learning operations by extending one-dimensional scalar
neurons to three-dimensional "vector neurons." We introduce a novel
"VN-Transformer" architecture to address several shortcomings of the current VN
models. Our contributions are: $(i)$ we derive a rotation-equivariant attention
mechanism which eliminates the need for the heavy feature preprocessing
required by the original Vector Neurons models; $(ii)$ we extend the VN
framework to support non-spatial attributes, expanding the applicability of
these models to real-world datasets; $(iii)$ we derive a rotation-equivariant
mechanism for multi-scale reduction of point-cloud resolution, greatly speeding
up inference and training; $(iv)$ we show that small tradeoffs in equivariance
($\epsilon$-approximate equivariance) can be used to obtain large improvements
in numerical stability and training robustness on accelerated hardware, and we
bound the propagation of equivariance violations in our models. Finally, we
apply our VN-Transformer to 3D shape classification and motion forecasting with
compelling results.

Gradient Descent (GD) is a powerful workhorse of modern machine learning
thanks to its scalability and efficiency in high-dimensional spaces. Its
ability to find local minimisers is only guaranteed for losses with Lipschitz
gradients, where it can be seen as a `bona-fide' discretisation of an
underlying gradient flow. Yet, many ML setups involving overparametrised models
do not fall into this problem class, which has motivated research beyond the
so-called ``Edge of Stability'' (EoS), where the step-size crosses the
admissibility threshold inversely proportional to the Lipschitz constant above.
Perhaps surprisingly, GD has been empirically observed to still converge
regardless of local instability and oscillatory behavior.
  The incipient theoretical analysis of this phenomena has mainly focused in
the overparametrised regime, where the effect of choosing a large learning rate
may be associated to a `Sharpness-Minimisation' implicit regularisation within
the manifold of minimisers, under appropriate asymptotic limits. In contrast,
in this work we directly examine the conditions for such unstable convergence,
focusing on simple, yet representative, learning problems. Specifically, we
characterize a local condition involving third-order derivatives that
stabilizes oscillations of GD above the EoS, and leverage such property in a
teacher-student setting, under population loss. Finally, focusing on Matrix
Factorization, we establish a non-asymptotic `Local Implicit Bias' of GD above
the EoS, whereby quasi-symmetric initializations converge to symmetric
solutions -- where sharpness is minimum amongst all minimisers.

Recent advances in deep learning and computer vision have reduced many
barriers to automated medical image analysis, allowing algorithms to process
label-free images and improve performance. However, existing techniques have
extreme computational requirements and drop a lot of performance with a
reduction in batch size or training epochs. This paper presents Cross
Architectural - Self Supervision (CASS), a novel self-supervised learning
approach that leverages Transformer and CNN simultaneously. Compared to the
existing state of the art self-supervised learning approaches, we empirically
show that CASS-trained CNNs and Transformers across four diverse datasets
gained an average of 3.8% with 1% labeled data, 5.9% with 10% labeled data, and
10.13% with 100% labeled data while taking 69% less time. We also show that
CASS is much more robust to changes in batch size and training epochs. Notably,
one of the test datasets comprised histopathology slides of an autoimmune
disease, a condition with minimal data that has been underrepresented in
medical imaging. The code is open source and is available on GitHub.

Information about action costs is critical for real-world AI planning
applications. Rather than rely solely on declarative action models, recent
approaches also use black-box external action cost estimators, often learned
from data, that are applied during the planning phase. These, however, can be
computationally expensive, and produce uncertain values. In this paper we
suggest a generalization of deterministic planning with action costs that
allows selecting between multiple estimators for action cost, to balance
computation time against bounded estimation uncertainty. This enables a much
richer -- and correspondingly more realistic -- problem representation.
Importantly, it allows planners to bound plan accuracy, thereby increasing
reliability, while reducing unnecessary computational burden, which is critical
for scaling to large problems. We introduce a search algorithm, generalizing
$A^*$, that solves such planning problems, and additional algorithmic
extensions. In addition to theoretical guarantees, extensive experiments show
considerable savings in runtime compared to alternatives.

For efficient malware removal, determination of malware threat levels, and
damage estimation, malware family classification plays a critical role. In this
paper, we extract features from malware executable files and represent them as
images using various approaches. We then focus on Generative Adversarial
Networks (GAN) for multiclass classification and compare our GAN results to
other popular machine learning techniques, including Support Vector Machine
(SVM), XGBoost, and Restricted Boltzmann Machines (RBM). We find that the
AC-GAN discriminator is generally competitive with other machine learning
techniques. We also evaluate the utility of the GAN generative model for
adversarial attacks on image-based malware detection. While AC-GAN generated
images are visually impressive, we find that they are easily distinguished from
real malware images using any of several learning techniques. This result
indicates that our GAN generated images would be of little value in adversarial
attacks.

In this paper we study two-player bilinear zero-sum games with constrained
strategy spaces. An instance of natural occurrences of such constraints is when
mixed strategies are used, which correspond to a probability simplex
constraint. We propose and analyze the alternating mirror descent algorithm, in
which each player takes turns to take action following the mirror descent
algorithm for constrained optimization. We interpret alternating mirror descent
as an alternating discretization of a skew-gradient flow in the dual space, and
use tools from convex optimization and modified energy function to establish an
$O(K^{-2/3})$ bound on its average regret after $K$ iterations. This
quantitatively verifies the algorithm's better behavior than the simultaneous
version of mirror descent algorithm, which is known to diverge and yields an
$O(K^{-1/2})$ average regret bound. In the special case of an unconstrained
setting, our results recover the behavior of alternating gradient descent
algorithm for zero-sum games which was studied in (Bailey et al., COLT 2020).

Texture-based classification solutions have proven their significance in many
domains, from industrial inspections to health-related applications. New
methods have been developed based on texture feature learning and CNN-based
architectures to address computer vision use cases for images with rich
texture-based features. In recent years, architectures solving texture-based
classification problems and demonstrating state-of-the-art results have
emerged. Yet, one limitation of these approaches is that they cannot claim to
be suitable for all types of image texture patterns. Each technique has an
advantage for a specific texture type only. To address this shortcoming, we
propose a framework that combines more than one texture-based techniques
together, uniquely, with a CNN backbone to extract the most relevant texture
features. This enables the model to be trained in a self-selective manner and
produce improved results over current published benchmarks -- with almost same
number of model parameters. Our proposed framework works well on most texture
types simultaneously and allows flexibility for additional texture-based
methods to be accommodated to achieve better results than existing
architectures. In this work, firstly, we present an analysis on the relative
importance of existing techniques when used alone and in combination with other
TE methods on benchmark datasets. Secondly, we show that Global Average Pooling
which represents the spatial information -- is of less significance in
comparison to the TE method(s) applied in the network while training for
texture-based classification tasks. Finally, we present state-of-the-art
results for several texture-based benchmark datasets by combining three
existing texture-based techniques using our proposed framework.

Anomaly detection is an important problem for complex distributed systems
consisting of hardware and software components. A thorough understanding of the
requirements and challenges of anomaly detection for such systems is pivotal to
the security of a system, especially for real-world deployment. While there
have been many diverse research areas and application domains that deal with
the problem, few have attempted to provide an in-depth look at such systems.
Most anomaly detection techniques have been specifically developed for certain
application domains, while others are more generic. In this survey, we explore
the significant potential of graph-based algorithms to identify and mitigate
different types of anomalies in complex distributed heterogeneous systems. Our
main focus is to provide an in-depth look at graphs when applied on
heterogeneous computing devices spread across complex distributed systems. This
study analyzes, compares, and contrasts the state-of-the-art research articles
in the field. First, we describe the characteristics of the real-world
distributed systems and their specific challenges of anomaly detection in such
complex networks, such as data and evaluation, nature of the anomalies, and
real-world requirements. Later, we discuss why graphs can be leveraged in such
systems and the benefits of utilizing graphs. Then we will aptly delve into the
state-of-the-art approaches and highlight their strength and weaknesses.
Finally, we evaluate and compare these approaches and point out the areas for

The synthesis of the high temperature superconductor LaH$_{10}$ requires
pressures in excess of 100 GPa, wherein it adopts a face-centered cubic
structure. Upon decompression, this structure undergoes a distortion which
still supports superconductivity, but with a much lower critical temperature.
Previous calculations have shown that quantum and anharmonic effects are
necessary to stabilize the cubic structure, but have not resolved the low
pressure distortion. Using large scale path-integral molecular dynamics enabled
by a machine learned potential, we show that a rhombohedral distortion appears
at sufficiently low pressures, even with quantum and anharmonic effects. We
also highlight the importance of quantum zero point motion in stabilizing the
cubic structure.

Quantitative Ultrasound (QUS) provides important information about the tissue
properties. QUS parametric image can be formed by dividing the envelope data
into small overlapping patches and computing different speckle statistics such
as parameters of the Nakagami and Homodyned K-distributions (HK-distribution).
The calculated QUS parametric images can be erroneous since only a few
independent samples are available inside the patches. Another challenge is that
the envelope samples inside the patch are assumed to come from the same
distribution, an assumption that is often violated given that the tissue is
usually not homogenous. In this paper, we propose a method based on
Convolutional Neural Networks (CNN) to estimate QUS parametric images without
patching. We construct a large dataset sampled from the HK-distribution, having
regions with random shapes and QUS parameter values. We then use a well-known
network to estimate QUS parameters in a multi-task learning fashion. Our
results confirm that the proposed method is able to reduce errors and improve
border definition in QUS parametric images.

The tree-based ensembles are known for their outstanding performance for
classification and regression problems characterized by feature vectors
represented by mixed-type variables from various ranges and domains. However,
considering regression problems, they are primarily designed to provide
deterministic responses or model the uncertainty of the output with a Gaussian
distribution. In this work, we introduce TreeFlow, the tree-based approach that
combines the benefits of using tree ensembles with capabilities of modeling
flexible probability distributions using normalizing flows. The main idea of
the solution is to use a tree-based model as a feature extractor and combine it
with a conditional variant of normalizing flow. Consequently, our approach is
capable of modeling complex distributions for the regression outputs. We
evaluate the proposed method on challenging regression benchmarks with varying
volume, feature characteristics, and target dimensionality. We obtain the SOTA
results on datasets with non-gaussian target distributions and competitive
results on gaussian ones compared to tree-based regression baselines.

In the digitization of energy systems, sensors and smart meters are
increasingly being used to monitor production, operation and demand. Detection
of anomalies based on smart meter data is crucial to identify potential risks
and unusual events at an early stage, which can serve as a reference for timely
initiation of appropriate actions and improving management. However, smart
meter data from energy systems often lack labels and contain noise and various
patterns without distinctively cyclical. Meanwhile, the vague definition of
anomalies in different energy scenarios and highly complex temporal
correlations pose a great challenge for anomaly detection. Many traditional
unsupervised anomaly detection algorithms such as cluster-based or
distance-based models are not robust to noise and not fully exploit the
temporal dependency in a time series as well as other dependencies amongst
multiple variables (sensors). This paper proposes an unsupervised anomaly
detection method based on a Variational Recurrent Autoencoder with attention
mechanism. with "dirty" data from smart meters, our method pre-detects missing
values and global anomalies to shrink their contribution while training. This
paper makes a quantitative comparison with the VAE-based baseline approach and
four other unsupervised learning methods, demonstrating its effectiveness and
superiority. This paper further validates the proposed method by a real case
study of detecting the anomalies of water supply temperature from an industrial
heating plant.

Advances in artificial intelligence (AI) are shaping modern life, from
transportation, health care, science, finance, to national defense. Forecasts
of AI development could help improve policy- and decision-making. We report the
results from a large survey of AI and machine learning (ML) researchers on
their beliefs about progress in AI. The survey, fielded in late 2019, elicited
forecasts for near-term AI development milestones and high- or human-level
machine intelligence, defined as when machines are able to accomplish every or
almost every task humans are able to do currently. As part of this study, we
re-contacted respondents from a highly-cited study by Grace et al. (2018), in
which AI/ML researchers gave forecasts about high-level machine intelligence
and near-term milestones in AI development. Results from our 2019 survey show
that, in aggregate, AI/ML researchers surveyed placed a 50% likelihood of
human-level machine intelligence being achieved by 2060. The results show
researchers newly contacted in 2019 expressed similar beliefs about the
progress of advanced AI as respondents in the Grace et al. (2018) survey. For
the recontacted participants from the Grace et al. (2018) study, the aggregate
forecast for a 50% likelihood of high-level machine intelligence shifted from
2062 to 2076, although this change is not statistically significant, likely due
to the small size of our panel sample. Forecasts of several near-term AI
milestones have reduced in time, suggesting more optimism about AI progress.
Finally, AI/ML researchers also exhibited significant optimism about how
human-level machine intelligence will impact society.

In this work, we propose to use out-of-distribution samples, i.e., unlabeled
samples coming from outside the target classes, to improve few-shot learning.
Specifically, we exploit the easily available out-of-distribution samples to
drive the classifier to avoid irrelevant features by maximizing the distance
from prototypes to out-of-distribution samples while minimizing that of
in-distribution samples (i.e., support, query data). Our approach is simple to
implement, agnostic to feature extractors, lightweight without any additional
cost for pre-training, and applicable to both inductive and transductive
settings. Extensive experiments on various standard benchmarks demonstrate that
the proposed method consistently improves the performance of pretrained
networks with different architectures.

Neural architecture search (NAS) algorithms save tremendous labor from human
experts. Recent advancements further reduce the computational overhead to an
affordable level. However, it is still cumbersome to deploy the NAS techniques
in real-world applications due to the fussy procedures and the supervised
learning paradigm. In this work, we propose the self-supervised and
weight-preserving neural architecture search (SSWP-NAS) as an extension of the
current NAS framework by allowing the self-supervision and retaining the
concomitant weights discovered during the search stage. As such, we simplify
the workflow of NAS to a one-stage and proxy-free procedure. Experiments show
that the architectures searched by the proposed framework achieve
state-of-the-art accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets without
using manual labels. Moreover, we show that employing the concomitant weights
as initialization consistently outperforms the random initialization and the
two-stage weight pre-training method by a clear margin under semi-supervised
learning scenarios. Codes are publicly available at
https://github.com/LzVv123456/SSWP-NAS.

Recent techniques for approximating Nash equilibria in very large games
leverage neural networks to learn approximately optimal policies (strategies).
One promising line of research uses neural networks to approximate
counterfactual regret minimization (CFR) or its modern variants. DREAM, the
only current CFR-based neural method that is model free and therefore scalable
to very large games, trains a neural network on an estimated regret target that
can have extremely high variance due to an importance sampling term inherited
from Monte Carlo CFR (MCCFR). In this paper we propose an unbiased model-free
method that does not require any importance sampling. Our method, ESCHER, is
principled and is guaranteed to converge to an approximate Nash equilibrium
with high probability. We show that the variance of the estimated regret of
ESCHER is orders of magnitude lower than DREAM and other baselines. We then
show that ESCHER outperforms the prior state of the art -- DREAM and neural
fictitious self play (NFSP) -- on a number of games and the difference becomes
dramatic as game size increases. In the very large game of dark chess, ESCHER
is able to beat DREAM and NFSP in a head-to-head competition over $90\%$ of the
time.

Construction of a scaffold structure that supports a desired motif,
conferring protein function, shows promise for the design of vaccines and
enzymes. But a general solution to this motif-scaffolding problem remains open.
Current machine-learning techniques for scaffold design are either limited to
unrealistically small scaffolds (up to length 20) or struggle to produce
multiple diverse scaffolds. We propose to learn a distribution over diverse and
longer protein backbone structures via an E(3)-equivariant graph neural
network. We develop SMCDiff to efficiently sample scaffolds from this
distribution conditioned on a given motif; our algorithm is the first to
theoretically guarantee conditional samples from a diffusion model in the
large-compute limit. We evaluate our designed backbones by how well they align
with AlphaFold2-predicted structures. We show that our method can (1) sample
scaffolds up to 80 residues and (2) achieve structurally diverse scaffolds for
a fixed motif.

We develop a method combining machine learning (ML) and density functional
theory (DFT) to predict low-energy polymorphs by introducing physics-guided
descriptors based on structural distortion modes. We systematically generate
crystal structures utilizing the distortion modes and compute their energies
with single-point DFT calculations. We then train a ML model to identify
low-energy configurations on the material's high-dimensional potential energy
surface. Here, we use BiFeO3 as a case study and explore its phase space by
tuning the amplitudes of linear combinations of a finite set of distinct
distortion modes. Our procedure is validated by rediscovering several known
metastable phases of BiFeO3 with complex crystal structures, and its efficiency
is proved by identifying 21 new low-energy polymorphs. This approach proposes a
new avenue toward accelerating the prediction of low-energy polymorphs in
solid-state materials.

Polylogrithmic functions, such as the logarithm or dilogarithm, satisfy a
number of algebraic identities. For the logarithm, all the identities follow
from the product rule. For the dilogarithm and higher-weight classical
polylogarithms, the identities can involve five functions or more. In many
calculations relevant to particle physics, complicated combinations of
polylogarithms often arise from Feynman integrals. Although the initial
expressions resulting from the integration usually simplify, it is often
difficult to know which identities to apply and in what order. To address this
bottleneck, we explore to what extent machine learning methods can help. We
consider both a reinforcement learning approach, where the identities are
analogous to moves in a game, and a transformer network approach, where the
problem is viewed analogously to a language-translation task. While both
methods are effective, the transformer network appears more powerful and holds
promise for practical use in symbolic manipulation tasks in mathematical
physics.

Intelligent agents need to select long sequences of actions to solve complex
tasks. While humans easily break down tasks into subgoals and reach them
through millions of muscle commands, current artificial intelligence is limited
to tasks with horizons of a few hundred decisions, despite large compute
budgets. Research on hierarchical reinforcement learning aims to overcome this
limitation but has proven to be challenging, current methods rely on manually
specified goal spaces or subtasks, and no general solution exists. We introduce
Director, a practical method for learning hierarchical behaviors directly from
pixels by planning inside the latent space of a learned world model. The
high-level policy maximizes task and exploration rewards by selecting latent
goals and the low-level policy learns to achieve the goals. Despite operating
in latent space, the decisions are interpretable because the world model can
decode goals into images for visualization. Director outperforms exploration
methods on tasks with sparse rewards, including 3D maze traversal with a
quadruped robot from an egocentric camera and proprioception, without access to
the global position or top-down view that was used by prior work. Director also
learns successful behaviors across a wide range of environments, including
visual control, Atari games, and DMLab levels.

We consider decentralized optimization problems in which a number of agents
collaborate to minimize the average of their local functions by exchanging over
an underlying communication graph. Specifically, we place ourselves in an
asynchronous model where only a random portion of nodes perform computation at
each iteration, while the information exchange can be conducted between all the
nodes and in an asymmetric fashion. For this setting, we propose an algorithm
that combines gradient tracking and variance reduction over the entire network.
This enables each node to track the average of the gradients of the objective
functions. Our theoretical analysis shows that the algorithm converges
linearly, when the local objective functions are strongly convex, under mild
connectivity conditions on the expected mixing matrices. In particular, our
result does not require the mixing matrices to be doubly stochastic. In the
experiments, we investigate a broadcast mechanism that transmits information
from computing nodes to their neighbors, and confirm the linear convergence of
our method on both synthetic and real-world datasets.

Choice of appropriate structure and parametric dimension of a model in the
light of data has a rich history in statistical research, where the first
seminal approaches were developed in 1970s, such as the Akaike's and Schwarz's
model scoring criteria that were inspired by information theory and embodied
the rationale called Occam's razor. After those pioneering works, model choice
was quickly established as its own field of research, gaining considerable
attention in both computer science and statistics. However, to date, there have
been limited attempts to derive scoring criteria for simulator-based models
lacking a likelihood expression. Bayes factors have been considered for such
models, but arguments have been put both for and against use of them and around
issues related to their consistency. Here we use the asymptotic properties of
Jensen--Shannon divergence (JSD) to derive a consistent model scoring criterion
for the likelihood-free setting called JSD-Razor. Relationships of JSD-Razor
with established scoring criteria for the likelihood-based approach are
analyzed and we demonstrate the favorable properties of our criterion using
both synthetic and real modeling examples.

Recent advances in multimodal training use textual descriptions to
significantly enhance machine understanding of images and videos. Yet, it
remains unclear to what extent language can fully capture sensory experiences
across different modalities. A well-established approach for characterizing
sensory experiences relies on similarity judgments, namely, the degree to which
people perceive two distinct stimuli as similar. We explore the relation
between human similarity judgments and language in a series of large-scale
behavioral studies ($N=1,823$ participants) across three modalities (images,
audio, and video) and two types of text descriptors: simple word tags and
free-text captions. In doing so, we introduce a novel adaptive pipeline for tag
mining that is both efficient and domain-general. We show that our prediction
pipeline based on text descriptors exhibits excellent performance, and we
compare it against a comprehensive array of 611 baseline models based on
vision-, audio-, and video-processing architectures. We further show that the
degree to which textual descriptors and models predict human similarity varies
across and within modalities. Taken together, these studies illustrate the
value of integrating machine learning and cognitive science approaches to
better understand the similarities and differences between human and machine
representations. We present an interactive visualization at
https://words-are-all-you-need.s3.amazonaws.com/index.html for exploring the
similarity between stimuli as experienced by humans and different methods
reported in the paper.

While Gaia has observed the phase space coordinates of over a billion stars
in the Galaxy, in the overwhelming majority of cases it has only obtained five
of the six coordinates, the missing dimension being the radial (line-of-sight)
velocity. Using a realistic mock dataset, we show that Bayesian neural networks
are highly capable of 'learning' these radial velocities as a function of the
other five coordinates, and thus filling in the gaps. For a given star, the
network outputs are not merely point predictions, but full posterior
distributions encompassing the intrinsic scatter of the stellar phase space
distribution, the observational uncertainties on the network inputs, and any
'epistemic' uncertainty stemming from our ignorance about the stellar phase
space distribution. Applying this technique to the real Gaia data, we generate
and publish a catalogue of posteriors (median width: 25 km/s) for the radial
velocities of 16 million Gaia DR2/EDR3 stars in the magnitude range 6<G<14.5.
Many of these gaps will be filled in very soon by Gaia DR3, which will serve to
test our blind predictions. Thus, the primary use of our published catalogue
will be to validate our method, justifying its future use in generating an
updated catalogue of posteriors for radial velocities missing from Gaia DR3.

Algorithmic fairness has attracted increasing attention in the machine
learning community. Various definitions are proposed in the literature, but the
differences and connections among them are not clearly addressed. In this
paper, we review and reflect on various fairness notions previously proposed in
machine learning literature, and make an attempt to draw connections to
arguments in moral and political philosophy, especially theories of justice. We
also consider fairness inquiries from a dynamic perspective, and further
consider the long-term impact that is induced by current prediction and
decision. In light of the differences in the characterized fairness, we present
a flowchart that encompasses implicit assumptions and expected outcomes of
different types of fairness inquiries on the data generating process, on the
predicted outcome, and on the induced impact, respectively. This paper
demonstrates the importance of matching the mission (which kind of fairness one
would like to enforce) and the means (which spectrum of fairness analysis is of
interest, what is the appropriate analyzing scheme) to fulfill the intended
purpose.

We introduce a multi-armed bandit model where the reward is a sum of multiple
random variables, and each action only alters the distributions of some of
them. After each action, the agent observes the realizations of all the
variables. This model is motivated by marketing campaigns and recommender
systems, where the variables represent outcomes on individual customers, such
as clicks. We propose UCB-style algorithms that estimate the uplifts of the
actions over a baseline. We study multiple variants of the problem, including
when the baseline and affected variables are unknown, and prove sublinear
regret bounds for all of these. We also provide lower bounds that justify the
necessity of our modeling assumptions. Experiments on synthetic and real-world
datasets show the benefit of methods that estimate the uplifts over policies
that do not use this structure.

Human visual perception can easily generalize to out-of-distributed visual
data, which is far beyond the capability of modern machine learning models.
Domain generalization (DG) aims to close this gap, with existing DG methods
mainly focusing on the loss function design. In this paper, we propose to
explore an orthogonal direction, i.e., the design of the backbone architecture.
It is motivated by an empirical finding that transformer-based models trained
with empirical risk minimization (ERM) outperform CNN-based models employing
state-of-the-art (SOTA) DG algorithms on multiple DG datasets. We develop a
formal framework to characterize a network's robustness to distribution shifts
by studying its architecture's alignment to the correlations in the dataset.
This analysis guides us to propose a novel DG model built upon vision
transformers, namely Generalizable Mixture-of-Experts (GMoE). Extensive
experiments on DomainBed demonstrate that GMoE trained with ERM outperforms
SOTA DG baselines by a large margin. Moreover, GMoE is complementary to
existing DG methods and its performance is substantially improved when trained
with DG algorithms.

The output structure of database-like tables, consisting of values structured
in horizontal rows and vertical columns identifiable by name, can cover a wide
range of NLP tasks. Following this constatation, we propose a framework for
text-to-table neural models applicable to problems such as extraction of line
items, joint entity and relation extraction, or knowledge base population. The
permutation-based decoder of our proposal is a generalized sequential method
that comprehends information from all cells in the table. The training
maximizes the expected log-likelihood for a table's content across all random
permutations of the factorization order. During the content inference, we
exploit the model's ability to generate cells in any order by searching over
possible orderings to maximize the model's confidence and avoid substantial
error accumulation, which other sequential models are prone to. Experiments
demonstrate a high practical value of the framework, which establishes
state-of-the-art results on several challenging datasets, outperforming
previous solutions by up to 15%.

This paper makes progress towards learning Nash equilibria in two-player
zero-sum Markov games from offline data. Specifically, consider a
$\gamma$-discounted infinite-horizon Markov game with $S$ states, where the
max-player has $A$ actions and the min-player has $B$ actions. We propose a
pessimistic model-based algorithm with Bernstein-style lower confidence bounds
-- called VI-LCB-Game -- that provably finds an $\varepsilon$-approximate Nash
equilibrium with a sample complexity no larger than
$\frac{C_{\mathsf{clipped}}^{\star}S(A+B)}{(1-\gamma)^{3}\varepsilon^{2}}$ (up
to some log factor). Here, $C_{\mathsf{clipped}}^{\star}$ is some unilateral
clipped concentrability coefficient that reflects the coverage and distribution
shift of the available data (vis-\`a-vis the target data), and the target
accuracy $\varepsilon$ can be any value within
$\big(0,\frac{1}{1-\gamma}\big]$. Our sample complexity bound strengthens prior
art by a factor of $\min\{A,B\}$, achieving minimax optimality for the entire
$\varepsilon$-range. An appealing feature of our result lies in algorithmic
simplicity, which reveals the unnecessity of variance reduction and sample
splitting in achieving sample optimality.

A self-driving perception model aims to extract 3D semantic representations
from multiple cameras collectively into the bird's-eye-view (BEV) coordinate
frame of the ego car in order to ground downstream planner. Existing perception
methods often rely on error-prone depth estimation of the whole scene or
learning sparse virtual 3D representations without the target geometry
structure, both of which remain limited in performance and/or capability. In
this paper, we present a novel end-to-end architecture for ego 3D
representation learning from an arbitrary number of unconstrained camera views.
Inspired by the ray tracing principle, we design a polarized grid of "imaginary
eyes" as the learnable ego 3D representation and formulate the learning process
with the adaptive attention mechanism in conjunction with the 3D-to-2D
projection. Critically, this formulation allows extracting rich 3D
representation from 2D images without any depth supervision, and with the
built-in geometry structure consistent w.r.t. BEV. Despite its simplicity and
versatility, extensive experiments on standard BEV visual tasks (e.g.,
camera-based 3D object detection and BEV segmentation) show that our model
outperforms all state-of-the-art alternatives significantly, with an extra
advantage in computational efficiency from multi-task learning.

With a recent observation of the "Neural Collapse (NC)" phenomena by Papyan
et al., various efforts have been made to model it and analyse the
implications. Neural collapse describes that in deep classifier networks, the
class features of the final hidden layer associated with training data tend to
collapse to the respective class feature means. Thus, simplifying the behaviour
of the last layer classifier to that of a nearest-class center decision rule.
In this work, we analyse the principles which aid in modelling such a phenomena
from the ground up and show how they can build a common understanding of the
recently proposed models that try to explain NC. We hope that our analysis
presents a multifaceted perspective on modelling NC and aids in forming
connections with the generalization capabilities of neural networks. Finally,
we conclude by discussing the avenues for further research and propose
potential research problems.

In recent years, machine learning (ML) has come to rely more heavily on
crowdworkers, both for building bigger datasets and for addressing research
questions requiring human interaction or judgment. Owing to the diverse tasks
performed by crowdworkers, and the myriad ways the resulting datasets are used,
it can be difficult to determine when these individuals are best thought of as
workers, versus as human subjects. These difficulties are compounded by
conflicting policies, with some institutions and researchers treating all ML
crowdwork as human subjects research, and other institutions holding that ML
crowdworkers rarely constitute human subjects. Additionally, few ML papers
involving crowdwork mention IRB oversight, raising the prospect that many might
not be in compliance with ethical and regulatory requirements. In this paper,
we focus on research in natural language processing to investigate the
appropriate designation of crowdsourcing studies and the unique challenges that
ML research poses for research oversight. Crucially, under the U.S. Common
Rule, these judgments hinge on determinations of "aboutness", both whom (or
what) the collected data is about and whom (or what) the analysis is about. We
highlight two challenges posed by ML: (1) the same set of workers can serve
multiple roles and provide many sorts of information; and (2) compared to the
life sciences and social sciences, ML research tends to embrace a dynamic
workflow, where research questions are seldom stated ex ante and data sharing
opens the door for future studies to ask questions about different targets from
the original study. In particular, our analysis exposes a potential loophole in
the Common Rule, where researchers can elude research ethics oversight by
splitting data collection and analysis into distinct studies. We offer several
policy recommendations to address these concerns.

The performance of time series forecasting has recently been greatly improved
by the introduction of transformers. In this paper, we propose a general
multi-scale framework that can be applied to the state-of-the-art
transformer-based time series forecasting models (FEDformer, Autoformer, etc.).
By iteratively refining a forecasted time series at multiple scales with shared
weights, introducing architecture adaptations, and a specially-designed
normalization scheme, we are able to achieve significant performance
improvements, from 5.5% to 38.5% across datasets and transformer architectures,
with minimal additional computational overhead. Via detailed ablation studies,
we demonstrate the effectiveness of each of our contributions across the
architecture and methodology. Furthermore, our experiments on various public
datasets demonstrate that the proposed improvements outperform their
corresponding baseline counterparts. Our code is publicly available in
https://github.com/BorealisAI/scaleformer.

We study the scaling limits of stochastic gradient descent (SGD) with
constant step-size in the high-dimensional regime. We prove limit theorems for
the trajectories of summary statistics (i.e., finite-dimensional functions) of
SGD as the dimension goes to infinity. Our approach allows one to choose the
summary statistics that are tracked, the initialization, and the step-size. It
yields both ballistic (ODE) and diffusive (SDE) limits, with the limit
depending dramatically on the former choices. Interestingly, we find a critical
scaling regime for the step-size below which the effective ballistic dynamics
matches gradient flow for the population loss, but at which, a new correction
term appears which changes the phase diagram. About the fixed points of this
effective dynamics, the corresponding diffusive limits can be quite complex and
even degenerate. We demonstrate our approach on popular examples including
estimation for spiked matrix and tensor models and classification via two-layer
networks for binary and XOR-type Gaussian mixture models. These examples
exhibit surprising phenomena including multimodal timescales to convergence as
well as convergence to sub-optimal solutions with probability bounded away from
zero from random (e.g., Gaussian) initializations.

Score-based generative models (SGMs) have recently emerged as a promising
class of generative models. The key idea is to produce high-quality images by
recurrently adding Gaussian noises and gradients to a Gaussian sample until
converging to the target distribution, a.k.a. the diffusion sampling. To ensure
stability of convergence in sampling and generation quality, however, this
sequential sampling process has to take a small step size and many sampling
iterations (e.g., 2000). Several acceleration methods have been proposed with
focus on low-resolution generation. In this work, we consider the acceleration
of high-resolution generation with SGMs, a more challenging yet more important
problem. We prove theoretically that this slow convergence drawback is
primarily due to the ignorance of the target distribution. Further, we
introduce a novel Target Distribution Aware Sampling (TDAS) method by
leveraging the structural priors in space and frequency domains. Extensive
experiments on CIFAR-10, CelebA, LSUN, and FFHQ datasets validate that TDAS can
consistently accelerate state-of-the-art SGMs, particularly on more challenging
high resolution (1024x1024) image generation tasks by up to 18.4x, whilst
largely maintaining the synthesis quality. With fewer sampling iterations, TDAS
can still generate good quality images. In contrast, the existing methods
degrade drastically or even fails completely

Unsupervised contrastive learning for indoor-scene point clouds has achieved
great successes. However, unsupervised learning point clouds in outdoor scenes
remains challenging because previous methods need to reconstruct the whole
scene and capture partial views for the contrastive objective. This is
infeasible in outdoor scenes with moving objects, obstacles, and sensors. In
this paper, we propose CO^3, namely Cooperative Contrastive Learning and
Contextual Shape Prediction, to learn 3D representation for outdoor-scene point
clouds in an unsupervised manner. CO^3 has several merits compared to existing
methods. (1) It utilizes LiDAR point clouds from vehicle-side and
infrastructure-side to build views that differ enough but meanwhile maintain
common semantic information for contrastive learning, which are more
appropriate than views built by previous methods. (2) Alongside the contrastive
objective, shape context prediction is proposed as pre-training goal and brings
more task-relevant information for unsupervised 3D point cloud representation
learning, which are beneficial when transferring the learned representation to
downstream detection tasks. (3) As compared to previous methods, representation
learned by CO^3 is able to be transferred to different outdoor scene dataset
collected by different type of LiDAR sensors. (4) CO^3 improves current
state-of-the-art methods on both Once and KITTI datasets by up to 2.58 mAP.
Codes and models will be released. We believe CO^3 will facilitate
understanding LiDAR point clouds in outdoor scene.

Layout planning is centrally important in the field of architecture and urban
design. Among the various basic units carrying urban functions, residential
community plays a vital part for supporting human life. Therefore, the layout
planning of residential community has always been of concern, and has attracted
particular attention since the advent of deep learning that facilitates the
automated layout generation and spatial pattern recognition. However, the
research circles generally suffer from the insufficiency of residential
community layout benchmark or high-quality datasets, which hampers the future
exploration of data-driven methods for residential community layout planning.
The lack of datasets is largely due to the difficulties of large-scale
real-world residential data acquisition and long-term expert screening. In
order to address the issues and advance a benchmark dataset for various
intelligent spatial design and analysis applications in the development of
smart city, we introduce Residential Community Layout Planning (ReCo) Dataset,
which is the first and largest open-source vector dataset related to real-world
community to date. ReCo Dataset is presented in multiple data formats with
37,646 residential community layout plans, covering 598,728 residential
buildings with height information. ReCo can be conveniently adapted for
residential community layout related urban design tasks, e.g., generative
layout design, morphological pattern recognition and spatial evaluation. To
validate the utility of ReCo in automated residential community layout
planning, two Generative Adversarial Network (GAN) based generative models are
further applied to the dataset. We expect ReCo Dataset to inspire more creative
and practical work in intelligent design and beyond. The ReCo Dataset is
published at: https://www.kaggle.com/fdudsde/reco-dataset.

Continual learning (CL) in the brain is facilitated by a complex set of
mechanisms. This includes the interplay of multiple memory systems for
consolidating information as posited by the complementary learning systems
(CLS) theory and synaptic consolidation for protecting the acquired knowledge
from erasure. Thus, we propose a general CL method that creates a synergy
between SYNaptic consolidation and dual memory Experience Replay (SYNERgy). Our
method maintains a semantic memory that accumulates and consolidates
information across the tasks and interacts with the episodic memory for
effective replay. It further employs synaptic consolidation by tracking the
importance of parameters during the training trajectory and anchoring them to
the consolidated parameters in the semantic memory. To the best of our
knowledge, our study is the first to employ dual memory experience replay in
conjunction with synaptic consolidation that is suitable for general CL whereby
the network does not utilize task boundaries or task labels during training or
inference. Our evaluation on various challenging CL scenarios and
characteristics analyses demonstrate the efficacy of incorporating both
synaptic consolidation and CLS theory in enabling effective CL in DNNs.

Although semantic communications have exhibited satisfactory performance for
a large number of tasks, the impact of semantic noise and the robustness of the
systems have not been well investigated. Semantic noise refers to the
misleading between the intended semantic symbols and received ones, thus cause
the failure of tasks. In this paper, we first propose a framework for the
robust end-to-end semantic communication systems to combat the semantic noise.
In particular, we analyze sample-dependent and sample-independent semantic
noise. To combat the semantic noise, the adversarial training with weight
perturbation is developed to incorporate the samples with semantic noise in the
training dataset. Then, we propose to mask a portion of the input, where the
semantic noise appears frequently, and design the masked vector
quantized-variational autoencoder (VQ-VAE) with the noise-related masking
strategy. We use a discrete codebook shared by the transmitter and the receiver
for encoded feature representation. To further improve the system robustness,
we develop a feature importance module (FIM) to suppress the noise-related and
task-unrelated features. Thus, the transmitter simply needs to transmit the
indices of these important task-related features in the codebook. Simulation
results show that the proposed method can be applied in many downstream tasks
and significantly improve the robustness against semantic noise with remarkable
reduction on the transmission overhead.

Room impulse response (RIR) functions capture how the surrounding physical
environment transforms the sounds heard by a listener, with implications for
various applications in AR, VR, and robotics. Whereas traditional methods to
estimate RIRs assume dense geometry and/or sound measurements throughout the
environment, we explore how to infer RIRs based on a sparse set of images and
echoes observed in the space. Towards that goal, we introduce a
transformer-based method that uses self-attention to build a rich acoustic
context, then predicts RIRs of arbitrary query source-receiver locations
through cross-attention. Additionally, we design a novel training objective
that improves the match in the acoustic signature between the RIR predictions
and the targets. In experiments using a state-of-the-art audio-visual simulator
for 3D environments, we demonstrate that our method successfully generates
arbitrary RIRs, outperforming state-of-the-art methods and--in a major
departure from traditional methods--generalizing to novel environments in a
few-shot manner. Project: http://vision.cs.utexas.edu/projects/fs_rir.

In this work, we present Patch-based Object-centric Video Transformer (POVT),
a novel region-based video generation architecture that leverages
object-centric information to efficiently model temporal dynamics in videos. We
build upon prior work in video prediction via an autoregressive transformer
over the discrete latent space of compressed videos, with an added modification
to model object-centric information via bounding boxes. Due to better
compressibility of object-centric representations, we can improve training
efficiency by allowing the model to only access object information for longer
horizon temporal information. When evaluated on various difficult
object-centric datasets, our method achieves better or equal performance to
other video generation models, while remaining computationally more efficient
and scalable. In addition, we show that our method is able to perform
object-centric controllability through bounding box manipulation, which may aid
downstream tasks such as video editing, or visual planning. Samples are
available at https://sites.google.com/view/povt-public

Model-agnostic meta learning (MAML) is currently one of the dominating
approaches for few-shot meta-learning. Albeit its effectiveness, the
optimization of MAML can be challenging due to the innate bilevel problem
structure. Specifically, the loss landscape of MAML is much more complex with
possibly more saddle points and local minimizers than its empirical risk
minimization counterpart. To address this challenge, we leverage the recently
invented sharpness-aware minimization and develop a sharpness-aware MAML
approach that we term Sharp-MAML. We empirically demonstrate that Sharp-MAML
and its computation-efficient variant can outperform the plain-vanilla MAML
baseline (e.g., $+3\%$ accuracy on Mini-Imagenet). We complement the empirical
study with the convergence rate analysis and the generalization bound of
Sharp-MAML. To the best of our knowledge, this is the first empirical and
theoretical study on sharpness-aware minimization in the context of bilevel
learning. The code is available at https://github.com/mominabbass/Sharp-MAML.

Gaussian processes provide an elegant framework for specifying prior and
posterior distributions over functions. They are, however, also computationally
expensive, and limited by the expressivity of their covariance function. We
propose Neural Diffusion Processes (NDPs), a novel approach based upon
diffusion models, that learn to sample from distributions over functions. Using
a novel attention block, we can incorporate properties of stochastic processes,
such as exchangeability, directly into the NDP's architecture. We empirically
show that NDPs are able to capture functional distributions that are close to
the true Bayesian posterior of a Gaussian process. This enables a variety of
downstream tasks, including hyperparameter marginalisation and Bayesian

This paper presents a data-driven optimal control policy for a micro flapping
wing unmanned aerial vehicle. First, a set of optimal trajectories are computed
off-line based on a geometric formulation of dynamics that captures the
nonlinear coupling between the large angle flapping motion and the quasi-steady
aerodynamics. Then, it is transformed into a feedback control system according
to the framework of imitation learning. In particular, an additional constraint
is incorporated through the learning process to enhance the stability
properties of the resulting controlled dynamics. Compared with conventional
methods, the proposed constrained imitation learning eliminates the need to
generate additional optimal trajectories on-line, without sacrificing
stability. As such, the computational efficiency is substantially improved.
Furthermore, this establishes the first nonlinear control system that
stabilizes the coupled longitudinal and lateral dynamics of flapping wing
aerial vehicle without relying on averaging or linearization. These are
illustrated by numerical examples for a simulated model inspired by Monarch
butterflies.

As machine learning algorithms are deployed on sensitive data in critical
decision making processes, it is becoming increasingly important that they are
also private and fair. In this paper, we show that, when the data has a
long-tailed structure, it is not possible to build accurate learning algorithms
that are both private and results in higher accuracy on minority
subpopulations. We further show that relaxing overall accuracy can lead to good
fairness even with strict privacy requirements. To corroborate our theoretical
results in practice, we provide an extensive set of experimental results using
a variety of synthetic, vision~(\cifar and CelebA), and tabular~(Law School)
datasets and learning algorithms.

We introduce a new intrinsic measure of local curvature on point-cloud data
called diffusion curvature. Our measure uses the framework of diffusion maps,
including the data diffusion operator, to structure point cloud data and define
local curvature based on the laziness of a random walk starting at a point or
region of the data. We show that this laziness directly relates to volume
comparison results from Riemannian geometry. We then extend this scalar
curvature notion to an entire quadratic form using neural network estimations
based on the diffusion map of point-cloud data. We show applications of both
estimations on toy data, single-cell data, and on estimating local Hessian
matrices of neural network loss landscapes.

Momentum is a popular technique for improving convergence rates during
gradient descent. In this research, we experiment with adding momentum to the
Baum-Welch expectation-maximization algorithm for training Hidden Markov
Models. We compare discrete Hidden Markov Models trained with and without
momentum on English text and malware opcode data. The effectiveness of momentum
is determined by measuring the changes in model score and classification
accuracy due to momentum. Our extensive experiments indicate that adding
momentum to Baum-Welch can reduce the number of iterations required for initial
convergence during HMM training, particularly in cases where the model is slow
to converge. However, momentum does not seem to improve the final model
performance at a high number of iterations.

In this study, we examine if engineered topological features can distinguish
time series sampled from different stochastic processes with different noise
characteristics, in both balanced and unbalanced sampling schemes. We compare
our classification results against the results of the same classification tasks
built on statistical and raw features. We conclude that in classification tasks
of time series, different machine learning models built on engineered
topological features perform consistently better than those built on standard
statistical and raw features.

Behavior prediction models have proliferated in recent years, especially in
the popular real-world robotics application of autonomous driving, where
representing the distribution over possible futures of moving agents is
essential for safe and comfortable motion planning. In these models, the choice
of coordinate frames to represent inputs and outputs has crucial trade offs
which broadly fall into one of two categories. Agent-centric models transform
inputs and perform inference in agent-centric coordinates. These models are
intrinsically invariant to translation and rotation between scene elements, are
best-performing on public leaderboards, but scale quadratically with the number
of agents and scene elements. Scene-centric models use a fixed coordinate
system to process all agents. This gives them the advantage of sharing
representations among all agents, offering efficient amortized inference
computation which scales linearly with the number of agents. However, these
models have to learn invariance to translation and rotation between scene
elements, and typically underperform agent-centric models. In this work, we
develop knowledge distillation techniques between probabilistic motion
forecasting models, and apply these techniques to close the gap in performance
between agent-centric and scene-centric models. This improves scene-centric
model performance by 13.2% on the public Argoverse benchmark, 7.8% on Waymo
Open Dataset and up to 9.4% on a large In-House dataset. These improved
scene-centric models rank highly in public leaderboards and are up to 15 times
more efficient than their agent-centric teacher counterparts in busy scenes.

Hyperparameter optimization (HPO) is crucial for machine learning algorithms
to achieve satisfactory performance, whose progress has been boosted by related
benchmarks. Nonetheless, existing efforts in benchmarking all focus on HPO for
traditional centralized learning while ignoring federated learning (FL), a
promising paradigm for collaboratively learning models from dispersed data. In
this paper, we first identify some uniqueness of HPO for FL algorithms from
various aspects. Due to this uniqueness, existing HPO benchmarks no longer
satisfy the need to compare HPO methods in the FL setting. To facilitate the
research of HPO in the FL setting, we propose and implement a benchmark suite
FedHPO-B that incorporates comprehensive FL tasks, enables efficient function
evaluations, and eases continuing extensions. We also conduct extensive
experiments based on FedHPO-B to benchmark a few HPO methods. We open-source
FedHPO-B at
https://github.com/alibaba/FederatedScope/tree/master/benchmark/FedHPOB.

Over the past decade, machine learning revolutionized vision-based quality
assessment for which convolutional neural networks (CNNs) have now become the
standard. In this paper, we consider a potential next step in this development
and describe a quanvolutional neural network (QNN) algorithm that efficiently
maps classical image data to quantum states and allows for reliable image
analysis. We practically demonstrate how to leverage quantum devices in
computer vision and how to introduce quantum convolutions into classical CNNs.
Dealing with a real world use case in industrial quality control, we implement
our hybrid QNN model within the PennyLane framework and empirically observe it
to achieve better predictions using much fewer training data than classical
CNNs. In other words, we empirically observe a genuine quantum advantage for an
industrial application where the advantage is due to superior data encoding.

Density-based Out-of-distribution (OOD) detection has recently been shown
unreliable for the task of detecting OOD images. Various density ratio based
approaches achieve good empirical performance, however methods typically lack a
principled probabilistic modelling explanation. In this work, we propose to
unify density ratio based methods under a novel framework that builds
energy-based models and employs differing base distributions. Under our
framework, the density ratio can be viewed as the unnormalized density of an
implicit semantic distribution. Further, we propose to directly estimate the
density ratio of a data sample through class ratio estimation. We report
competitive results on OOD image problems in comparison with recent work that
alternatively requires training of deep generative models for the task. Our
approach enables a simple and yet effective path towards solving the OOD
detection problem.

Lifelong learning occurs on timescales ranging from minutes to decades.
People can lose themselves in a new skill, practicing for hours until
exhausted. And they can pursue mastery over days or decades, perhaps abandoning
old skills entirely to seek out new challenges. A full understanding of
learning requires an account that integrates these timescales. Here, we present
a minimal quantitative model that unifies the nested timescales of learning.
Our dynamical model recovers classic accounts of skill acquisition, and
describes how learning emerges from moment-to-moment dynamics of motivation,
fatigue, and work, while also situated within longer-term dynamics of skill
selection, mastery, and abandonment. We apply this model to explore the
benefits and pitfalls of a variety of training regimes and to characterize
individual differences in motivation and skill development. Our model connects
previously disparate timescales -- and the subdisciplines that typically study
each timescale in isolation -- to offer a unified account of the timecourse of
skill acquisition.

Motivations for methods in explainable artificial intelligence (XAI) often
include detecting, quantifying and mitigating bias, and contributing to making
machine learning models fairer. However, exactly how an XAI method can help in
combating biases is often left unspecified. In this paper, we briefly review
trends in explainability and fairness in NLP research, identify the current
practices in which explainability methods are applied to detect and mitigate
bias, and investigate the barriers preventing XAI methods from being used more
widely in tackling fairness issues.

Online reinforcement learning (RL) algorithms are increasingly used to
personalize digital interventions in the fields of mobile health and online
education. Common challenges in designing and testing an RL algorithm in these
settings include ensuring the RL algorithm can learn and run stably under
real-time constraints, and accounting for the complexity of the environment,
e.g., a lack of accurate mechanistic models for the user dynamics. To guide how
one can tackle these challenges, we extend the PCS (Predictability,
Computability, Stability) framework, a data science framework that incorporates
best practices from machine learning and statistics in supervised learning (Yu
and Kumbier, 2020), to the design of RL algorithms for the digital
interventions setting. Further, we provide guidelines on how to design
simulation environments, a crucial tool for evaluating RL candidate algorithms
using the PCS framework. We illustrate the use of the PCS framework for
designing an RL algorithm for Oralytics, a mobile health study aiming to
improve users' tooth-brushing behaviors through the personalized delivery of
intervention messages. Oralytics will go into the field in late 2022.

The RGB complementary metal-oxidesemiconductor (CMOS) sensor works within the
visible light spectrum. Therefore it is very sensitive to environmental light
conditions. On the contrary, a long-wave infrared (LWIR) sensor operating in
8-14 micro meter spectral band, functions independent of visible light.
  In this paper, we exploit both visual and thermal perception units for robust
object detection purposes. After delicate synchronization and (cross-) labeling
of the FLIR [1] dataset, this multi-modal perception data passes through a
convolutional neural network (CNN) to detect three critical objects on the
road, namely pedestrians, bicycles, and cars. After evaluation of RGB and
infrared (thermal and infrared are often used interchangeably) sensors
separately, various network structures are compared to fuse the data at the
feature level effectively. Our RGB-thermal (RGBT) fusion network, which takes
advantage of a novel entropy-block attention module (EBAM), outperforms the
state-of-the-art network [2] by 10% with 82.9% mAP.

We present an empirical study on the use of continual learning (CL) methods
in a reinforcement learning (RL) scenario, which, to the best of our knowledge,
has not been described before. CL is a very active recent research topic
concerned with machine learning under non-stationary data distributions.
Although this naturally applies to RL, the use of dedicated CL methods is still
uncommon. This may be due to the fact that CL methods often assume a
decomposition of CL problems into disjoint sub-tasks of stationary
distribution, that the onset of these sub-tasks is known, and that sub-tasks
are non-contradictory. In this study, we perform an empirical comparison of
selected CL methods in a RL problem where a physically simulated robot must
follow a racetrack by vision. In order to make CL methods applicable, we
restrict the RL setting and introduce non-conflicting subtasks of known onset,
which are however not disjoint and whose distribution, from the learner's point
of view, is still non-stationary. Our results show that dedicated CL methods
can significantly improve learning when compared to the baseline technique of
"experience replay".

Much literature has shown that prompt-based learning is an efficient method
to make use of the large pre-trained language model. Recent works also exhibit
the possibility of steering a chatbot's output by plugging in an appropriate
prompt. Gradient-based methods are often used to perturb the prompts. However,
some language models are not even available to the public. In this work, we
first explored the combination of prompting and reinforcement learning (RL) to
steer models' generation without accessing any of the models' parameters.
Second, to reduce the training effort and enhance the generalizability to the
unseen task, we apply multi-task learning to make the model learn to generalize
to new tasks better. The experiment results show that our proposed method can
successfully control several state-of-the-art (SOTA) dialogue models without
accessing their parameters. Furthermore, the model demonstrates the strong
ability to quickly adapt to an unseen task in fewer steps than the baseline
model.

Deep neural networks have been successfully applied to a broad range of
problems where overparametrization yields weight matrices which are partially
random. A comparison of weight matrix singular vectors to the Porter-Thomas
distribution suggests that there is a boundary between randomness and learned
information in the singular value spectrum. Inspired by this finding, we
introduce an algorithm for noise filtering, which both removes small singular
values and reduces the magnitude of large singular values to counteract the
effect of level repulsion between the noise and the information part of the
spectrum. For networks trained in the presence of label noise, we indeed find
that the generalization performance improves significantly due to noise

Weighted finite automata (WFAs) have been widely applied in many fields. One
of the classic problems for WFAs is probability distribution estimation over
sequences of discrete symbols. Although WFAs have been extended to deal with
continuous input data, namely continuous WFAs (CWFAs), it is still unclear how
to approximate density functions over sequences of continuous random variables
using WFA-based models, due to the limitation on the expressiveness of the
model as well as the tractability of approximating density functions via CWFAs.
In this paper, we propose a nonlinear extension to the CWFA model to first
improve its expressiveness, we refer to it as the nonlinear continuous WFAs
(NCWFAs). Then we leverage the so-called RNADE method, which is a well-known
density estimator based on neural networks, and propose the RNADE-NCWFA model.
The RNADE-NCWFA model computes a density function by design. We show that this
model is strictly more expressive than the Gaussian HMM model, which CWFA
cannot approximate. Empirically, we conduct a synthetic experiment using
Gaussian HMM generated data. We focus on evaluating the model's ability to
estimate densities for sequences of varying lengths (longer length than the
training data). We observe that our model performs the best among the compared

We develop a unified stochastic approximation framework for analyzing the
long-run behavior of multi-agent online learning in games. Our framework is
based on a "primal-dual", mirrored Robbins-Monro (MRM) template which
encompasses a wide array of popular game-theoretic learning algorithms
(gradient methods, their optimistic variants, the EXP3 algorithm for learning
with payoff-based feedback in finite games, etc.). In addition to providing an
integrated view of these algorithms, the proposed MRM blueprint allows us to
obtain a broad range of new convergence results, both asymptotic and in finite
time, in both continuous and finite games.

Automated brain tumor detection is becoming a highly considerable medical
diagnosis research. In recent medical diagnoses, detection and classification
are highly considered to employ machine learning and deep learning techniques.
Nevertheless, the accuracy and performance of current models need to be
improved for suitable treatments. In this paper, an improvement in deep
convolutional learning is ensured by adopting enhanced optimization algorithms,
Thus, Deep Convolutional Neural Network (DCNN) based on improved Harris Hawks
Optimization (HHO), called G-HHO has been considered. This hybridization
features Grey Wolf Optimization (GWO) and HHO to give better results, limiting
the convergence rate and enhancing performance. Moreover, Otsu thresholding is
adopted to segment the tumor portion that emphasizes brain tumor detection.
Experimental studies are conducted to validate the performance of the suggested
method on a total number of 2073 augmented MRI images. The technique's
performance was ensured by comparing it with the nine existing algorithms on
huge augmented MRI images in terms of accuracy, precision, recall, f-measure,
execution time, and memory usage. The performance comparison shows that the
DCNN-G-HHO is much more successful than existing methods, especially on a
scoring accuracy of 97%. Additionally, the statistical performance analysis
indicates that the suggested approach is faster and utilizes less memory at
identifying and categorizing brain tumor cancers on the MR images. The
implementation of this validation is conducted on the Python platform. The
relevant codes for the proposed approach are available at:
https://github.com/bryarahassan/DCNN-G-HHO.

Gender norms may constrain the ability of women to develop their
entrepreneurial skills, particularly in rural areas. By bringing
entrepreneurial training to women rather than requiring extended time away from
home, mobile technology could open doors that would otherwise be closed. We
randomly selected Nepali women to be trained as veterinary service providers
known as community animal health workers. Half of the selected candidates were
randomly assigned to a traditional training course requiring 35 consecutive
days away from home, and half were assigned to a hybrid distance learning
course requiring two shorter stays plus a table-based curriculum to be
completed at home. Distance learning strongly increases women's ability to
complete training as compared to traditional training. Distance learning has a
larger effect than traditional training on boosting the number of livestock
responsibilities women carry out at home, while also raising aspirations. Both
training types increase women's control over income. Our results indicate that
if anything, distance learning produced more effective community animal health
workers.

In this work, we provide a fundamental unified convergence theorem used for
deriving expected and almost sure convergence results for a series of
stochastic optimization methods. Our unified theorem only requires to verify
several representative conditions and is not tailored to any specific
algorithm. As a direct application, we recover expected and almost sure
convergence results of the stochastic gradient method (SGD) and random
reshuffling (RR) under more general settings. Moreover, we establish new
expected and almost sure convergence results for the stochastic proximal
gradient method (prox-SGD) and stochastic model-based methods (SMM) for
nonsmooth nonconvex optimization problems. These applications reveal that our
unified theorem provides a plugin-type convergence analysis and strong
convergence guarantees for a wide class of stochastic optimization methods.

Mobile app stores are the key distributors of mobile applications. They
regularly apply vetting processes to the deployed apps. Yet, some of these
vetting processes might be inadequate or applied late. The late removal of
applications might have unpleasant consequences for developers and users alike.
Thus, in this work we propose a data-driven predictive approach that determines
whether the respective app will be removed or accepted. It also indicates the
features' relevance that help the stakeholders in the interpretation. In turn,
our approach can support developers in improving their apps and users in
downloading the ones that are less likely to be removed. We focus on the Google
App store and we compile a new data set of 870,515 applications, 56% of which
have actually been removed from the market. Our proposed approach is a
bootstrap aggregating of multiple XGBoost machine learning classifiers. We
propose two models: user-centered using 47 features, and developer-centered
using 37 features, the ones only available before deployment. We achieve the
following Areas Under the ROC Curves (AUCs) on the test set: user-centered =
0.792, developer-centered = 0.762.

Registration of pre-operative and post-recurrence brain images is often
needed to evaluate the effectiveness of brain gliomas treatment. While recent
deep learning-based deformable registration methods have achieved remarkable
success with healthy brain images, most of them would be unable to accurately
align images with pathologies due to the absent correspondences in the
reference image. In this paper, we propose a deep learning-based deformable
registration method that jointly estimates regions with absent correspondence
and bidirectional deformation fields. A forward-backward consistency constraint
is used to aid in the localization of the resection and recurrence region from
voxels with absence correspondences in the two images. Results on 3D clinical
data from the BraTS-Reg challenge demonstrate our method can improve image
alignment compared to traditional and deep learning-based registration
approaches with or without cost function masking strategy. The source code is
available at https://github.com/cwmok/DIRAC.

The accelerated use of digital cameras prompts an increasing concern about
privacy and security, particularly in applications such as action recognition.
In this paper, we propose an optimizing framework to provide robust visual
privacy protection along the human action recognition pipeline. Our framework
parameterizes the camera lens to successfully degrade the quality of the videos
to inhibit privacy attributes and protect against adversarial attacks while
maintaining relevant features for activity recognition. We validate our
approach with extensive simulations and hardware experiments.

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from
a labeled source domain to an unlabeled target domain. Contrastive learning
(CL) in the context of UDA can help to better separate classes in feature
space. However, in image segmentation, the large memory footprint due to the
computation of the pixel-wise contrastive loss makes it prohibitive to use.
Furthermore, labeled target data is not easily available in medical imaging,
and obtaining new samples is not economical. As a result, in this work, we
tackle a more challenging UDA task when there are only a few (fewshot) or a
single (oneshot) image available from the target domain. We apply a style
transfer module to mitigate the scarcity of target samples. Then, to align the
source and target features and tackle the memory issue of the traditional
contrastive loss, we propose the centroid-based contrastive learning (CCL) and
a centroid norm regularizer (CNR) to optimize the contrastive pairs in both
direction and magnitude. In addition, we propose multi-partition centroid
contrastive learning (MPCCL) to further reduce the variance in the target
features. Fewshot evaluation on MS-CMRSeg dataset demonstrates that ConFUDA
improves the segmentation performance by 0.34 of the Dice score on the target
domain compared with the baseline, and 0.31 Dice score improvement in a more
rigorous oneshot setting.

Restless Multi-Armed Bandits (RMAB) is an apt model to represent
decision-making problems in public health interventions (e.g., tuberculosis,
maternal, and child care), anti-poaching planning, sensor monitoring,
personalized recommendations and many more. Existing research in RMAB has
contributed mechanisms and theoretical results to a wide variety of settings,
where the focus is on maximizing expected value. In this paper, we are
interested in ensuring that RMAB decision making is also fair to different arms
while maximizing expected value. In the context of public health settings, this
would ensure that different people and/or communities are fairly represented
while making public health intervention decisions. To achieve this goal, we
formally define the fairness constraints in RMAB and provide planning and
learning methods to solve RMAB in a fair manner. We demonstrate key theoretical
properties of fair RMAB and experimentally demonstrate that our proposed
methods handle fairness constraints without sacrificing significantly on
solution quality.

Recognition of structure prototypes from tremendous known inorganic crystal
structures has been an important subject beneficial for material science
research and new materials design. The existing databases of inorganic crystal
structure prototypes were mostly constructed by classifying materials in terms
of the crystallographic space group information. Herein, we employed a distinct
strategy to construct the inorganic crystal structure prototype database,
relying on the classification of materials in terms of local atomic
environments (LAE) accompanied by unsupervised machine learning method.
Specifically, we adopted a hierarchical clustering approach onto all
experimentally known inorganic crystal structures data to identify structure
prototypes. The criterion for hierarchical clustering is the LAE represented by
the state-of-the-art structure fingerprints of the improved bond-orientational
order parameters and the smooth overlap of atomic positions. This allows us to
build up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD)
containing 15,613 structure prototypes with defined stoichiometries. In
addition, we have developed a Structure Prototype Generator Infrastructure
(SPGI) package, which is a useful toolkit for structure prototype generation.
Our developed SPGI toolkit and LAE-ICSPD are beneficial for investigating
inorganic materials in a global way as well as accelerating materials discovery
process in the data-driven mode.

Job interviews are usually high-stakes social situations where professional
and behavioral skills are required for a satisfactory outcome. Professional job
interview trainers give educative feedback about the shown behavior according
to common standards. This feedback can be helpful concerning the improvement of
behavioral skills needed for job interviews. A technological approach for
generating such feedback might be a playful and low-key starting point for job
interview training. Therefore, we extended an interactive virtual job interview
training system with a Generative Adversarial Network (GAN)-based approach that
first detects behavioral weaknesses and subsequently generates personalized
feedback. To evaluate the usefulness of the generated feedback, we conducted a
mixed-methods pilot study using mock-ups from the job interview training
system. The overall study results indicate that the GAN-based generated
behavioral feedback is helpful. Moreover, participants assessed that the
feedback would improve their job interview performance.

Federated learning has been proposed as a privacy-preserving machine learning
framework that enables multiple clients to collaborate without sharing raw
data. However, client privacy protection is not guaranteed by design in this
framework. Prior work has shown that the gradient sharing strategies in
federated learning can be vulnerable to data reconstruction attacks. In
practice, though, clients may not transmit raw gradients considering the high
communication cost or due to privacy enhancement requirements. Empirical
studies have demonstrated that gradient obfuscation, including intentional
obfuscation via gradient noise injection and unintentional obfuscation via
gradient compression, can provide more privacy protection against
reconstruction attacks. In this work, we present a new data reconstruction
attack framework targeting the image classification task in federated learning.
We show that commonly adopted gradient postprocessing procedures, such as
gradient quantization, gradient sparsification, and gradient perturbation, may
give a false sense of security in federated learning. Contrary to prior
studies, we argue that privacy enhancement should not be treated as a byproduct
of gradient compression. Additionally, we design a new method under the
proposed framework to reconstruct the image at the semantic level. We quantify
the semantic privacy leakage and compare with conventional based on image
similarity scores. Our comparisons challenge the image data leakage evaluation
schemes in the literature. The results emphasize the importance of revisiting
and redesigning the privacy protection mechanisms for client data in existing
federated learning algorithms.

In this paper we elaborate an extension of rotation-based iterative
Gaussianization, RBIG, which makes image Gaussianization possible. Although
RBIG has been successfully applied to many tasks, it is limited to medium
dimensionality data (on the order of a thousand dimensions). In images its
application has been restricted to small image patches or isolated pixels,
because rotation in RBIG is based on principal or independent component
analysis and these transformations are difficult to learn and scale. Here we
present the \emph{Convolutional RBIG}: an extension that alleviates this issue
by imposing that the rotation in RBIG is a convolution. We propose to learn
convolutional rotations (i.e. orthonormal convolutions) by optimising for the
reconstruction loss between the input and an approximate inverse of the
transformation using the transposed convolution operation. Additionally, we
suggest different regularizers in learning these orthonormal convolutions. For
example, imposing sparsity in the activations leads to a transformation that
extends convolutional independent component analysis to multilayer
architectures. We also highlight how statistical properties of the data, such
as multivariate mutual information, can be obtained from \emph{Convolutional
RBIG}. We illustrate the behavior of the transform with a simple example of
texture synthesis, and analyze its properties by visualizing the stimuli that
maximize the response in certain feature and layer.

It has been shown in literature that a single quantum particle can be
realized as a thermodynamic machine as an artefact of energy quantization and
hence bears no classical analogue. Yet its behaviour can be substantially
different if we consider a collection of non-interacting massive
indistinguishable quantum particles. Such a thermodynamic machine depends on
the statistics of the particles, the chemical potential and the dimensionality
of the system. Our detailed analysis demonstrates the fundamental features of
quantum Stirling cycles from the view-point of particle-statistics and system
dimensions that helps us to realize the desired quantum heat engines and
refrigerators by exploiting the role of quantum statistical mechanics. In
particular, we have shown that distinctive behaviour of particle statistics is
quite pertinent in one dimension than in higher dimensions, indicating the
conspicuous role of quantum thermodynamic signature in lower dimensions.

The next generation of networks will actively embrace artificial intelligence
(AI) and machine learning (ML) technologies for automation networks and optimal
network operation strategies. The emerging network structure represented by
Open RAN (O-RAN) conforms to this trend, and the radio intelligent controller
(RIC) at the centre of its specification serves as an ML applications host.
Various ML models, especially Reinforcement Learning (RL) models, are regarded
as the key to solving RAN-related multi-objective optimization problems.
However, it should be recognized that most of the current RL successes are
confined to abstract and simplified simulation environments, which may not
directly translate to high performance in complex real environments. One of the
main reasons is the modelling gap between the simulation and the real
environment, which could make the RL agent trained by simulation ill-equipped
for the real environment. This issue is termed as the sim2real gap. This
article brings to the fore the sim2real challenge within the context of O-RAN.
Specifically, it emphasizes the characteristics, and benefits that the digital
twins (DT) could have as a place for model development and verification.
Several use cases are presented to exemplify and demonstrate failure modes of
the simulations trained RL model in real environments. The effectiveness of DT
in assisting the development of RL algorithms is discussed. Then the current
state of the art learning-based methods commonly used to overcome the sim2real
challenge are presented. Finally, the development and deployment concerns for
the RL applications realisation in O-RAN are discussed from the view of the
potential issues like data interaction, environment bottlenecks, and algorithm
design.

In this paper, we apply an multi-agent reinforcement learning (MARL)
framework allowing the base station (BS) and the user equipments (UEs) to
jointly learn a channel access policy and its signaling in a wireless multiple
access scenario. In this framework, the BS and UEs are reinforcement learning
(RL) agents that need to cooperate in order to deliver data. The comparison
with a contention-free and a contention-based baselines shows that our
framework achieves a superior performance in terms of goodput even in high
traffic situations while maintaining a low collision rate. The scalability of
the proposed method is studied, since it is a major problem in MARL and this
paper provides the first results in order to address it.

Exponential generalization bounds with near-tight rates have recently been
established for uniformly stable learning algorithms. The notion of uniform
stability, however, is stringent in the sense that it is invariant to the
data-generating distribution. Under the weaker and distribution dependent
notions of stability such as hypothesis stability and $L_2$-stability, the
literature suggests that only polynomial generalization bounds are possible in
general cases. The present paper addresses this long standing tension between
these two regimes of results and makes progress towards relaxing it inside a
classic framework of confidence-boosting. To this end, we first establish an
in-expectation first moment generalization error bound for potentially
randomized learning algorithms with $L_2$-stability, based on which we then
show that a properly designed subbagging process leads to near-tight
exponential generalization bounds over the randomness of both data and
algorithm. We further substantialize these generic results to stochastic
gradient descent (SGD) to derive improved high-probability generalization
bounds for convex or non-convex optimization problems with natural time
decaying learning rates, which have not been possible to prove with the
existing hypothesis stability or uniform stability based results.

Myocardial motion and deformation are rich descriptors that characterize
cardiac function. Image registration, as the most commonly used technique for
myocardial motion tracking, is an ill-posed inverse problem which often
requires prior assumptions on the solution space. In contrast to most existing
approaches which impose explicit generic regularization such as smoothness, in
this work we propose a novel method that can implicitly learn an
application-specific biomechanics-informed prior and embed it into a neural
network-parameterized transformation model. Particularly, the proposed method
leverages a variational autoencoder-based generative model to learn a manifold
for biomechanically plausible deformations. The motion tracking then can be
performed via traversing the learnt manifold to search for the optimal
transformations while considering the sequence information. The proposed method
is validated on three public cardiac cine MRI datasets with comprehensive
evaluations. The results demonstrate that the proposed method can outperform
other approaches, yielding higher motion tracking accuracy with reasonable
volume preservation and better generalizability to varying data distributions.
It also enables better estimates of myocardial strains, which indicates the
potential of the method in characterizing spatiotemporal signatures for
understanding cardiovascular diseases.

Kernel methods are learning algorithms that enjoy solid theoretical
foundations while suffering from important computational limitations.
Sketching, that consists in looking for solutions among a subspace of reduced
dimension, is a widely studied approach to alleviate this numerical burden.
However, fast sketching strategies, such as non-adaptive subsampling,
significantly degrade the guarantees of the algorithms, while
theoretically-accurate sketches, such as the Gaussian one, turn out to remain
relatively slow in practice. In this paper, we introduce the $p$-sparsified
sketches, that combine the benefits from both approaches to achieve a good
tradeoff between statistical accuracy and computational efficiency. To support
our method, we derive excess risk bounds for both single and multiple output
problems, with generic Lipschitz losses, providing new guarantees for a wide
range of applications, from robust regression to multiple quantile regression.
We also provide empirical evidences of the superiority of our sketches over
recent SOTA approaches.

For unsupervised pretraining, mask-reconstruction pretraining (MRP)
approaches randomly mask input patches and then reconstruct pixels or semantic
features of these masked patches via an auto-encoder. Then for a downstream
task, supervised fine-tuning the pretrained encoder remarkably surpasses the
conventional supervised learning (SL) trained from scratch. However, it is
still unclear 1) how MRP performs semantic learning in the pretraining phase
and 2) why it helps in downstream tasks. To solve these problems, we
theoretically show that on an auto-encoder of a two/one-layered convolution
encoder/decoder, MRP can capture all discriminative semantics in the
pretraining dataset, and accordingly show its provable improvement over SL on
the classification downstream task. Specifically, we assume that pretraining
dataset contains multi-view samples of ratio $1-\mu$ and single-view samples of
ratio $\mu$, where multi/single-view samples has multiple/single discriminative
semantics. Then for pretraining, we prove that 1) the convolution kernels of
the MRP encoder captures all discriminative semantics in the pretraining data;
and 2) a convolution kernel captures at most one semantic. Accordingly, in the
downstream supervised fine-tuning, most semantics would be captured and
different semantics would not be fused together. This helps the downstream
fine-tuned network to easily establish the relation between kernels and
semantic class labels. In this way, the fine-tuned encoder in MRP provably
achieves zero test error with high probability for both multi-view and
single-view test data. In contrast, as proved by~[3], conventional SL can only
obtain a test accuracy between around $0.5\mu$ for single-view test data. These
results together explain the benefits of MRP in downstream tasks. Experimental
results testify to multi-view data assumptions and our theoretical
implications.

In high-dimensional prediction settings, it remains challenging to reliably
estimate the test performance. To address this challenge, a novel performance
estimation framework is presented. This framework, called Learn2Evaluate, is
based on learning curves by fitting a smooth monotone curve depicting test
performance as a function of the sample size. Learn2Evaluate has several
advantages compared to commonly applied performance estimation methodologies.
Firstly, a learning curve offers a graphical overview of a learner. This
overview assists in assessing the potential benefit of adding training samples
and it provides a more complete comparison between learners than performance
estimates at a fixed subsample size. Secondly, a learning curve facilitates in
estimating the performance at the total sample size rather than a subsample
size. Thirdly, Learn2Evaluate allows the computation of a theoretically
justified and useful lower confidence bound. Furthermore, this bound may be
tightened by performing a bias correction. The benefits of Learn2Evaluate are
illustrated by a simulation study and applications to omics data.

Influenza occurs every season and occasionally causes pandemics. Despite its
low mortality rate, influenza is a major public health concern, as it can be
complicated by severe diseases like pneumonia. A fast, accurate and low-cost
method to predict the origin host and subtype of influenza viruses could help
reduce virus transmission and benefit resource-poor areas. In this work, we
propose multi-channel neural networks to predict antigenic types and hosts of
influenza A viruses with hemagglutinin and neuraminidase protein sequences. An
integrated data set containing complete protein sequences were used to produce
a pre-trained model, and two other data sets were used for testing the model's
performance. One test set contained complete protein sequences, and another
test set contained incomplete protein sequences. The results suggest that
multi-channel neural networks are applicable and promising for predicting
influenza A virus hosts and antigenic subtypes with complete and partial
protein sequences.

Intra-voxel incoherent motion (IVIM) analysis of fetal lungs
Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative
imaging bio-markers that reflect, indirectly, diffusion and pseudo-diffusion
for non-invasive fetal lung maturation assessment. However, long acquisition
times, due to the large number of different ``b-value'' images required for
IVIM analysis, precluded clinical feasibility. We introduce SUPER-IVIM-DC a
deep-neural-networks (DNN) approach which couples supervised loss with a
data-consistency term to enable IVIM analysis of DWI data acquired with a
limited number of b-values. We demonstrated the added-value of SUPER-IVIM-DC
over both classical and recent DNN approaches for IVIM analysis through
numerical simulations, healthy volunteer study, and IVIM analysis of fetal lung
maturation from fetal DWI data. Our numerical simulations and healthy volunteer
study show that SUPER-IVIM-DC estimates of the IVIM model parameters from
limited DWI data had lower normalized root mean-squared error compared to
previous DNN-based approaches. Further, SUPER-IVIM-DC estimates of the
pseudo-diffusion fraction parameter from limited DWI data of fetal lungs
correlate better with gestational age compared to both to classical and
DNN-based approaches (0.555 vs. 0.463 and 0.310). SUPER-IVIM-DC has the
potential to reduce the long acquisition times associated with IVIM analysis of
DWI data and to provide clinically feasible bio-markers for non-invasive fetal
lung maturity assessment.

In this thesis, we try to build a connection between the two schools by
introducing syntactic inductive biases for deep learning models. We propose two
families of inductive biases, one for constituency structure and another one
for dependency structure. The constituency inductive bias encourages deep
learning models to use different units (or neurons) to separately process
long-term and short-term information. This separation provides a way for deep
learning models to build the latent hierarchical representations from
sequential inputs, that a higher-level representation is composed of and can be
decomposed into a series of lower-level representations. For example, without
knowing the ground-truth structure, our proposed model learns to process
logical expression through composing representations of variables and operators
into representations of expressions according to its syntactic structure. On
the other hand, the dependency inductive bias encourages models to find the
latent relations between entities in the input sequence. For natural language,
the latent relations are usually modeled as a directed dependency graph, where
a word has exactly one parent node and zero or several children nodes. After
applying this constraint to a Transformer-like model, we find the model is
capable of inducing directed graphs that are close to human expert annotations,
and it also outperforms the standard transformer model on different tasks. We
believe that these experimental results demonstrate an interesting alternative
for the future development of deep learning models.

Considering the multimodal nature of transport systems and potential
cross-modal correlations, there is a growing trend of enhancing demand
forecasting accuracy by learning from multimodal data. These multimodal
forecasting models can improve accuracy but be less practical when different
parts of multimodal datasets are owned by different institutions who cannot
directly share data among them. While various institutions may can not share
their data with each other directly, they may share forecasting models trained
by their data, where such models cannot be used to identify the exact
information from their datasets. This study proposes an Unsupervised Knowledge
Adaptation Demand Forecasting framework to forecast the demand of the target
mode by utilizing a pre-trained model based on data of another mode, which does
not require direct data sharing of the source mode. The proposed framework
utilizes the potential shared patterns among multiple transport modes to
improve forecasting performance while avoiding the direct sharing of data among
different institutions. Specifically, a pre-trained forecasting model is first
learned based on the data of a source mode, which can capture and memorize the
source travel patterns. Then, the demand data of the target dataset is encoded
into an individual knowledge part and a sharing knowledge part which will
extract travel patterns by individual extraction network and sharing extraction
network, respectively. The unsupervised knowledge adaptation strategy is
utilized to form the sharing features for further forecasting by making the
pre-trained network and the sharing extraction network analogous. Our findings
illustrate that unsupervised knowledge adaptation by sharing the pre-trained
model to the target mode can improve the forecasting performance without the
dependence on direct data sharing.

Since the pandemic of COVID-19, several deep learning methods were proposed
to analyze the chest Computed Tomography (CT) for diagnosis. In the current
situation, the disease course classification is significant for medical
personnel to decide the treatment. Most previous deep-learning-based methods
extract features observed from the lung window. However, it has been proved
that some appearances related to diagnosis can be observed better from the
mediastinal window rather than the lung window, e.g., the pulmonary
consolidation happens more in severe symptoms. In this paper, we propose a
novel Dual Window RCNN Network (DWRNet), which mainly learns the distinctive
features from the successive mediastinal window. Regarding the features
extracted from the lung window, we introduce the Lung Window Attention Block
(LWA Block) to pay additional attention to them for enhancing the
mediastinal-window features. Moreover, instead of picking up specific slices
from the whole CT slices, we use a Recurrent CNN and analyze successive slices
as videos. Experimental results show that the fused and representative features
improve the predictions of disease course by reaching the accuracy of 90.57%,
against the baseline with an accuracy of 84.86%. Ablation studies demonstrate
that combined dual window features are more efficient than lung-window features
alone, while paying attention to lung-window features can improve the model's

Self-supervised monocular depth estimation has been a subject of intense
study in recent years, because of its applications in robotics and autonomous
driving. Much of the recent work focuses on improving depth estimation by
increasing architecture complexity. This paper shows that state-of-the-art
performance can also be achieved by improving the learning process rather than
increasing model complexity. More specifically, we propose (i) only using
invariant pose loss for the first few epochs during training, (ii) disregarding
small potentially dynamic objects when training, and (iii) employing an
appearance-based approach to separately estimate object pose for truly dynamic
objects. We demonstrate that these simplifications reduce GPU memory usage by
29% and result in qualitatively and quantitatively improved depth maps. The
code is available at https://github.com/kieran514/Dyna-DM.

We propose a co-variance corrected random batch method for interacting
particle systems. By establishing a certain entropic central limit theorem, we
provide entropic convergence guarantees for the law of the entire trajectories
of all particles of the proposed method to the law of the trajectories of the
discrete time interacting particle system whenever the batch size $B \gg
(\alpha n)^{\frac{1}{3}}$ (where $n$ is the number of particles and $\alpha$ is
the time discretization parameter). This in turn implies that the outputs of
these methods are nearly \emph{statistically indistinguishable} when $B$ is
even moderately large. Previous works mainly considered convergence in
Wasserstein distance with required stringent assumptions on the potentials or
the bounds had an exponential dependence on the time horizon. This work makes
minimal assumptions on the interaction potentials and in particular establishes
that even when the particle trajectories diverge to infinity, they do so in the
same way for both the methods. Such guarantees are very useful in light of the
recent advances in interacting particle based algorithms for sampling.

Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of
exploration such as the additive action noise often used in continuous control
domains. Typically, the scaling factor of this action noise is chosen as a
hyper-parameter and is kept constant during training. In this paper, we focus
on action noise in off-policy deep reinforcement learning for continuous
control. We analyze how the learned policy is impacted by the noise type, noise
scale, and impact scaling factor reduction schedule. We consider the two most
prominent types of action noise, Gaussian and Ornstein-Uhlenbeck noise, and
perform a vast experimental campaign by systematically varying the noise type
and scale parameter, and by measuring variables of interest like the expected
return of the policy and the state-space coverage during exploration. For the
latter, we propose a novel state-space coverage measure
$\operatorname{X}_{\mathcal{U}\text{rel}}$ that is more robust to boundary
artifacts than previously-proposed measures. Larger noise scales generally
increase state-space coverage. However, we found that increasing the space
coverage using a larger noise scale is often not beneficial. On the contrary,
reducing the noise scale over the training process reduces the variance and
generally improves the learning performance. We conclude that the best noise
type and scale are environment dependent, and based on our observations derive
heuristic rules for guiding the choice of the action noise as a starting point
for further optimization.

Despite the popularity of deep neural networks in various domains, the
extraction of digital terrain models (DTMs) from airborne laser scanning (ALS)
point clouds is still challenging. This might be due to the lack of dedicated
large-scale annotated dataset and the data-structure discrepancy between point
clouds and DTMs. To promote data-driven DTM extraction, this paper collects
from open sources a large-scale dataset of ALS point clouds and corresponding
DTMs with various urban, forested, and mountainous scenes. A baseline method is
proposed as the first attempt to train a Deep neural network to extract digital
Terrain models directly from ALS point clouds via Rasterization techniques,
coined DeepTerRa. Extensive studies with well-established methods are performed
to benchmark the dataset and analyze the challenges in learning to extract DTM
from point clouds. The experimental results show the interest of the agnostic
data-driven approach, with sub-metric error level compared to methods designed
for DTM extraction. The data and source code is provided at
https://lhoangan.github.io/deepterra/ for reproducibility and further similar
research.

In this work, we propose a novel protocol for secure three-party computation
with an honest majority. For each AND gate, our protocol requires only two bits
of communication in the online phase and two bits of communication in the
offline phase. Also, only P2 and P3 are involved in the online phase. Our
protocol is simulation-based secure in the presence of semi-honest adversaries,
and achieves privacy but not correctness in the presence of malicious
adversaries. The best previously known construction in this setting requires
three bits of communication per AND gate in the online phase and does not
achieve constant communication rounds for P1. This makes our protocol
especially interesting for cases where P1 can only communicate to the other
parties with high latency. Additionally, our protocol can achieve circuit
privacy against P3 if P1 and P2 also send bits for every XOR gate to P3. This
property may be interesting to achieve a two-party computation where P3 only
acts as an auxiliary party with no input and should not learn the computed
function. Our protocol also supports the client-server model and works for both
arithmetic and boolean circuits.

Accurate camera pose estimation is a fundamental requirement for numerous
applications, such as autonomous driving, mobile robotics, and augmented
reality. In this work, we address the problem of estimating the global 6 DoF
camera pose from a single RGB image in a given environment. Previous works
consider every part of the image valuable for localization. However, many image
regions such as the sky, occlusions, and repetitive non-distinguishable
patterns cannot be utilized for localization. In addition to adding unnecessary
computation efforts, extracting and matching features from such regions produce
many wrong matches which in turn degrades the localization accuracy and
efficiency. Our work addresses this particular issue and shows by exploiting an
interesting concept of sparse 3D models that we can exploit discriminatory
environment parts and avoid useless image regions for the sake of a single
image localization. Interestingly, through avoiding selecting keypoints from
non-reliable image regions such as trees, bushes, cars, pedestrians, and
occlusions, our work acts naturally as an outlier filter. This makes our system
highly efficient in that minimal set of correspondences is needed and highly
accurate as the number of outliers is low. Our work exceeds state-ofthe-art
methods on outdoor Cambridge Landmarks dataset. With only relying on single
image at inference, it outweighs in terms of accuracy methods that exploit pose
priors and/or reference 3D models while being much faster. By choosing as
little as 100 correspondences, it surpasses similar methods that localize from
thousands of correspondences, while being more efficient. In particular, it
achieves, compared to these methods, an improvement of localization by 33% on
OldHospital scene. Furthermore, It outstands direct pose regressors even those
that learn from sequence of images

India reached a major milestone in the area of space astronomy with the
successful launch and post-launch operations of its first space observatory,
AstroSat. The success of this space observatory and the lessons learned must be
utilized effectively to enlarge the footprint of Indian space astronomy in the
international scene. In response to a call for proposals by the Indian Space
Research Organisation, a detailed proposal for a next generation UV-optical
mission, the INdian Spectroscopic and Imaging Space Telescope (INSIST) was
submitted. Combining a large focal area with a simple and efficient optical
design, INSIST is expected to produce HST-quality imaging and moderate
resolution spectra of astronomical sources. The main science drivers for this
mission span a wide range of topics, starting from evolution of galaxies in
groups and clusters, chemo-dynamics and demographics of the nearby universe,
stellar systems with accretions, to stars with planetary systems, to cosmology
near and far. The proposal was awarded seed funding and has completed two years
of pre-project phase. An overview of this proposed mission is presented here
along with the current status.

Nowadays, several deep learning methods are proposed to tackle the challenge
of epileptic seizure prediction. However, these methods still cannot be
implemented as part of implantable or efficient wearable devices due to their
large hardware and corresponding high-power consumption. They usually require
complex feature extraction process, large memory for storing high precision
parameters and complex arithmetic computation, which greatly increases required
hardware resources. Moreover, available yield poor prediction performance,
because they adopt network architecture directly from image recognition
applications fails to accurately consider the characteristics of EEG signals.
We propose in this paper a hardware-friendly network called Binary
Single-dimensional Convolutional Neural Network (BSDCNN) intended for epileptic
seizure prediction. BSDCNN utilizes 1D convolutional kernels to improve
prediction performance. All parameters are binarized to reduce the required
computation and storage, except the first layer. Overall area under curve,
sensitivity, and false prediction rate reaches 0.915, 89.26%, 0.117/h and
0.970, 94.69%, 0.095/h on American Epilepsy Society Seizure Prediction
Challenge (AES) dataset and the CHB-MIT one respectively. The proposed
architecture outperforms recent works while offering 7.2 and 25.5 times
reductions on the size of parameter and computation, respectively.

We introduce a practical hybrid approach that combines orbital-free density
functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles
molecular dynamics simulations. Equilibrated ionic configurations are generated
using orbital-free DFT for subsequent Kohn-Sham DFT molecular dynamics. This
leads to a massive reduction of the simulation time without any sacrifice in
accuracy. We assess this finding across systems of different sizes and
temperature, up to the warm dense matter regime. To that end, we use the cosine
distance between the time series of radial distribution functions representing
the ionic configurations. Likewise, we show that the equilibrated ionic
configurations from this hybrid approach significantly enhance the accuracy of
machine-learning models that replace Kohn-Sham DFT. Our hybrid scheme enables
systematic first-principles simulations of warm dense matter that are otherwise
hampered by the large numbers of atoms and the prevalent high temperatures.
Moreover, our finding provides an additional motivation for developing kinetic
and noninteracting free energy functionals for orbital-free DFT.

Due to the scarcity of video processing methodologies, image processing
operations are naively extended to the video domain by processing each frame
independently. This disregard for the temporal connection in video processing
often leads to severe temporal inconsistencies. State-of-the-art techniques
that address these inconsistencies rely on the availability of unprocessed
videos to siphon consistent video dynamics to restore the temporal consistency
of frame-wise processed videos. We propose a novel general framework for this
task that learns to infer consistent motion dynamics from inconsistent videos
to mitigate the temporal flicker while preserving the perceptual quality for
both the temporally neighboring and relatively distant frames. The proposed
framework produces state-of-the-art results on two large-scale datasets, DAVIS
and videvo.net, processed by numerous image processing tasks in a feed-forward
manner. The code and the trained models will be released upon acceptance.

The performance of Emergency Departments (EDs) is of great importance for any
health care system, as they serve as the entry point for many patients.
However, among other factors, the variability of patient acuity levels and
corresponding treatment requirements of patients visiting EDs imposes
significant challenges on decision makers. Balancing waiting times of patients
to be first seen by a physician with the overall length of stay over all acuity
levels is crucial to maintain an acceptable level of operational performance
for all patients. To address those requirements when assigning idle resources
to patients, several methods have been proposed in the past, including the
Accumulated Priority Queuing (APQ) method. The APQ method linearly assigns
priority scores to patients with respect to their time in the system and acuity
level. Hence, selection decisions are based on a simple system representation
that is used as an input for a selection function. This paper investigates the
potential of an Machine Learning (ML) based patient selection method. It
assumes that for a large set of training data, including a multitude of
different system states, (near) optimal assignments can be computed by a
(heuristic) optimizer, with respect to a chosen performance metric, and aims to
imitate such optimal behavior when applied to new situations. Thereby, it
incorporates a comprehensive state representation of the system and a complex
non-linear selection function. The motivation for the proposed approach is that
high quality selection decisions may depend on a variety of factors describing
the current state of the ED, not limited to waiting times, which can be
captured and utilized by the ML model. Results show that the proposed method
significantly outperforms the APQ method for a majority of evaluated settings

We describe a stance towards the generation of explanations in AI agents that
is both human-centered and design-based. We collect questions about the working
of an AI agent through participatory design by focus groups. We capture an
agent's design through a Task-Method-Knowledge model that explicitly specifies
the agent's tasks and goals, as well as the mechanisms, knowledge and
vocabulary it uses for accomplishing the tasks. We illustrate our approach
through the generation of explanations in Skillsync, an AI agent that links
companies and colleges for worker upskilling and reskilling. In particular, we
embed a question-answering agent called AskJill in Skillsync, where AskJill
contains a TMK model of Skillsync's design. AskJill presently answers
human-generated questions about Skillsync's tasks and vocabulary, and thereby
helps explain how it produces its recommendations.

We commonly assume that data are a homogeneous set of observations when
learning the structure of Bayesian networks. However, they often comprise
different data sets that are related but not homogeneous because they have been
collected in different ways or from different populations.
  In our previous work (Azzimonti, Corani and Scutari, 2021), we proposed a
closed-form Bayesian Hierarchical Dirichlet score for discrete data that pools
information across related data sets to learn a single encompassing network
structure, while taking into account the differences in their probabilistic
structures. In this paper, we provide an analogous solution for learning a
Bayesian network from continuous data using mixed-effects models to pool
information across the related data sets. We study its structural, parametric,
predictive and classification accuracy and we show that it outperforms both
conditional Gaussian Bayesian networks (that do not perform any pooling) and
classical Gaussian Bayesian networks (that disregard the heterogeneous nature
of the data). The improvement is marked for low sample sizes and for unbalanced
data sets.

Weakly supervised multi-label classification (WSML) task, which is to learn a
multi-label classification using partially observed labels per image, is
becoming increasingly important due to its huge annotation cost. In this work,
we first regard unobserved labels as negative labels, casting the WSML task
into noisy multi-label classification. From this point of view, we empirically
observe that memorization effect, which was first discovered in a noisy
multi-class setting, also occurs in a multi-label setting. That is, the model
first learns the representation of clean labels, and then starts memorizing
noisy labels. Based on this finding, we propose novel methods for WSML which
reject or correct the large loss samples to prevent model from memorizing the
noisy label. Without heavy and complex components, our proposed methods
outperform previous state-of-the-art WSML methods on several partial label
settings including Pascal VOC 2012, MS COCO, NUSWIDE, CUB, and OpenImages V3
datasets. Various analysis also show that our methodology actually works well,
validating that treating large loss properly matters in a weakly supervised
multi-label classification. Our code is available at
https://github.com/snucml/LargeLossMatters.

Knowledge Graph (KG) and its variant of ontology have been widely used for
knowledge representation, and have shown to be quite effective in augmenting
Zero-shot Learning (ZSL). However, existing ZSL methods that utilize KGs all
neglect the intrinsic complexity of inter-class relationships represented in
KGs. One typical feature is that a class is often related to other classes in
different semantic aspects. In this paper, we focus on ontologies for
augmenting ZSL, and propose to learn disentangled ontology embeddings guided by
ontology properties to capture and utilize more fine-grained class
relationships in different aspects. We also contribute a new ZSL framework
named DOZSL, which contains two new ZSL solutions based on generative models
and graph propagation models, respectively, for effectively utilizing the
disentangled ontology embeddings. Extensive evaluations have been conducted on
five benchmarks across zero-shot image classification (ZS-IMGC) and zero-shot
KG completion (ZS-KGC). DOZSL often achieves better performance than the
state-of-the-art, and its components have been verified by ablation studies and
case studies. Our codes and datasets are available at
https://github.com/zjukg/DOZSL.

A motif intuitively is a short time series that repeats itself approximately
the same within a larger time series. Such motifs often represent concealed
structures, such as heart beats in an ECG recording, or sleep spindles in EEG
sleep data. Motif discovery (MD) is the task of finding such motifs in a given
input series. As there are varying definitions of what exactly a motif is, a
number of algorithms exist. As central parameters they all take the length l of
the motif and the maximal distance r between the motif's occurrences. In
practice, however, suitable values for r are very hard to determine upfront,
and the found motifs show a high variability. Setting the wrong input value
will result in a motif that is not distinguishable from noise. Accordingly,
finding an interesting motif with these methods requires extensive
trial-and-error. We present a different approach to the MD problem. We define
k-Motiflets as the set of exactly k occurrences of a motif of length l, whose
maximum pairwise distance is minimal. This turns the MD problem upside-down:
Our central parameter is not the distance threshold r, but the desired size k
of a motif set, which we show is considerably more intuitive and easier to set.
Based on this definition, we present exact and approximate algorithms for
finding k-Motiflets and analyze their complexity. To further ease the use of
our method, we describe extensions to automatically determine the
right/suitable values for its input parameters. Thus, for the first time,
extracting meaningful motif sets without any a-priori knowledge becomes
feasible. By evaluating real-world use cases and comparison to 4
state-of-the-art MD algorithms, we show that our proposed algorithm is (a)
quantitatively superior, finding larger motif sets at higher similarity, (b)
qualitatively better, leading to clearer and easier to interpret motifs, and
(c) has the lowest runtime.

In machine learning, data augmentation (DA) is a technique for improving the
generalization performance. In this paper, we mainly considered gradient
descent of linear regression under DA using noisy copies of datasets, in which
noise is injected into inputs. We analyzed the situation where random noisy
copies are newly generated and used at each epoch; i.e., the case of using
on-line noisy copies. Therefore, it is viewed as an analysis on a method using
noise injection into training process by DA manner; i.e., on-line version of
DA. We derived the averaged behavior of training process under three situations
which are the full-batch training under the sum of squared errors, the
full-batch and mini-batch training under the mean squared error. We showed
that, in all cases, training for DA with on-line copies is approximately
equivalent to a ridge regularization whose regularization parameter corresponds
to the variance of injected noise. On the other hand, we showed that the
learning rate is multiplied by the number of noisy copies plus one in
full-batch under the sum of squared errors and the mini-batch under the mean
squared error; i.e., DA with on-line copies yields apparent acceleration of
training. The apparent acceleration and regularization effect come from the
original part and noise in a copy data respectively. These results are
confirmed in a numerical experiment. In the numerical experiment, we found that
our result can be approximately applied to usual off-line DA in
under-parameterization scenario and can not in over-parametrization scenario.
Moreover, we experimentally investigated the training process of neural
networks under DA with off-line noisy copies and found that our analysis on
linear regression is possible to be applied to neural networks.

Transfer learning aims to leverage knowledge from pre-trained models to
benefit the target task. Prior transfer learning work mainly transfers from a
single model. However, with the emergence of deep models pre-trained from
different resources, model hubs consisting of diverse models with various
architectures, pre-trained datasets and learning paradigms are available.
Directly applying single-model transfer learning methods to each model wastes
the abundant knowledge of the model hub and suffers from high computational
cost. In this paper, we propose a Hub-Pathway framework to enable knowledge
transfer from a model hub. The framework generates data-dependent pathway
weights, based on which we assign the pathway routes at the input level to
decide which pre-trained models are activated and passed through, and then set
the pathway aggregation at the output level to aggregate the knowledge from
different models to make predictions. The proposed framework can be trained
end-to-end with the target task-specific loss, where it learns to explore
better pathway configurations and exploit the knowledge in pre-trained models
for each target datum. We utilize a noisy pathway generator and design an
exploration loss to further explore different pathways throughout the model
hub. To fully exploit the knowledge in pre-trained models, each model is
further trained by specific data that activate it, which ensures its
performance and enhances knowledge transfer. Experiment results on computer
vision and reinforcement learning tasks demonstrate that the proposed
Hub-Pathway framework achieves the state-of-the-art performance for model hub
transfer learning.

The increased integration of renewable energy poses a slew of technical
challenges for the operation of power distribution networks. Among them,
voltage fluctuations caused by the instability of renewable energy are
receiving increasing attention. Utilizing MARL algorithms to coordinate
multiple control units in the grid, which is able to handle rapid changes of
power systems, has been widely studied in active voltage control task recently.
However, existing approaches based on MARL ignore the unique nature of the grid
and achieve limited performance. In this paper, we introduce the transformer
architecture to extract representations adapting to power network problems and
propose a Transformer-based Multi-Agent Actor-Critic framework (T-MAAC) to
stabilize voltage in power distribution networks. In addition, we adopt a novel
auxiliary-task training process tailored to the voltage control task, which
improves the sample efficiency and facilitating the representation learning of
the transformer-based model. We couple T-MAAC with different multi-agent
actor-critic algorithms, and the consistent improvements on the active voltage
control task demonstrate the effectiveness of the proposed method.

The task of learning to map an input set onto a permuted sequence of its
elements is challenging for neural networks. Set-to-sequence problems occur in
natural language processing, computer vision and structure prediction, where
interactions between elements of large sets define the optimal output. Models
must exhibit relational reasoning, handle varying cardinalities and manage
combinatorial complexity. Previous attention-based methods require $n$ layers
of their set transformations to explicitly represent $n$-th order relations.
Our aim is to enhance their ability to efficiently model higher-order
interactions through an additional interdependence component. We propose a
novel neural set encoding method called the Set Interdependence Transformer,
capable of relating the set's permutation invariant representation to its
elements within sets of any cardinality. We combine it with a permutation
learning module into a complete, 3-part set-to-sequence model and demonstrate
its state-of-the-art performance on a number of tasks. These range from
combinatorial optimization problems, through permutation learning challenges on
both synthetic and established NLP datasets for sentence ordering, to a novel
domain of product catalog structure prediction. Additionally, the network's
ability to generalize to unseen sequence lengths is investigated and a
comparative empirical analysis of the existing methods' ability to learn
higher-order interactions is provided.

Rule sets are highly interpretable logical models in which the predicates for
decision are expressed in disjunctive normal form (DNF, OR-of-ANDs), or,
equivalently, the overall model comprises an unordered collection of if-then
decision rules. In this paper, we consider a submodular optimization based
approach for learning rule sets. The learning problem is framed as a subset
selection task in which a subset of all possible rules needs to be selected to
form an accurate and interpretable rule set. We employ an objective function
that exhibits submodularity and thus is amenable to submodular optimization
techniques. To overcome the difficulty arose from dealing with the
exponential-sized ground set of rules, the subproblem of searching a rule is
casted as another subset selection task that asks for a subset of features. We
show it is possible to write the induced objective function for the subproblem
as a difference of two submodular (DS) functions to make it approximately
solvable by DS optimization algorithms. Overall, the proposed approach is
simple, scalable, and likely to be benefited from further research on
submodular optimization. Experiments on real datasets demonstrate the

Deep Neural Networks (DNNs) have recently achieved great success in many
classification tasks. Unfortunately, they are vulnerable to adversarial attacks
that generate adversarial examples with a small perturbation to fool DNN
models, especially in model sharing scenarios. Adversarial training is proved
to be the most effective strategy that injects adversarial examples into model
training to improve the robustness of DNN models against adversarial attacks.
However, adversarial training based on the existing adversarial examples fails
to generalize well to standard, unperturbed test data. To achieve a better
trade-off between standard accuracy and adversarial robustness, we propose a
novel adversarial training framework called LAtent bounDary-guided aDvErsarial
tRaining (LADDER) that adversarially trains DNN models on latent
boundary-guided adversarial examples. As opposed to most of the existing
methods that generate adversarial examples in the input space, LADDER generates
a myriad of high-quality adversarial examples through adding perturbations to
latent features. The perturbations are made along the normal of the decision
boundary constructed by an SVM with an attention mechanism. We analyze the
merits of our generated boundary-guided adversarial examples from a boundary
field perspective and visualization view. Extensive experiments and detailed
analysis on MNIST, SVHN, CelebA, and CIFAR-10 validate the effectiveness of
LADDER in achieving a better trade-off between standard accuracy and
adversarial robustness as compared with vanilla DNNs and competitive baselines.

Accurate and early prediction of a disease allows to plan and improve a
patient's quality of future life. During pandemic situations, the medical
decision becomes a speed challenge in which physicians have to act fast to
diagnose and predict the risk of the severity of the disease, moreover this is
also of high priority for neurodegenerative diseases like Parkinson's disease.
Machine Learning (ML) models with Features Selection (FS) techniques can be
applied to help physicians to quickly diagnose a disease. FS optimally subset
features that improve a model performance and help reduce the number of needed
tests for a patient and hence speeding up the diagnosis. This study shows the
result of three Feature Selection (FS) techniques pre-applied to a classifier
algorithm, Logistic Regression, on non-invasive test results data. The three FS
are Analysis of Variance (ANOVA) as filter based method, Least Absolute
Shrinkage and Selection Operator (LASSO) as embedded method and Sequential
Feature Selection (SFS) as wrapper method. The outcome shows that FS technique
can help to build an efficient and effective classifier, hence improving the
performance of the classifier while reducing the computation time.

Commonsense reasoning systems should be able to generalize to diverse
reasoning cases. However, most state-of-the-art approaches depend on expensive
data annotations and overfit to a specific benchmark without learning how to
perform general semantic reasoning. To overcome these drawbacks, zero-shot QA
systems have shown promise as a robust learning scheme by transforming a
commonsense knowledge graph (KG) into synthetic QA-form samples for model
training. Considering the increasing type of different commonsense KGs, this
paper aims to extend the zero-shot transfer learning scenario into
multiple-source settings, where different KGs can be utilized synergetically.
Towards this goal, we propose to mitigate the loss of knowledge from the
interference among the different knowledge sources, by developing a modular
variant of the knowledge aggregation as a new zero-shot commonsense reasoning
framework. Results on five commonsense reasoning benchmarks demonstrate the
efficacy of our framework, improving the performance with multiple KGs.

Computed tomography (CT) is of great importance in clinical practice due to
its powerful ability to provide patients' anatomical information without any
invasive inspection, but its potential radiation risk is raising people's
concerns. Deep learning-based methods are considered promising in CT
reconstruction, but these network models are usually trained with the measured
data obtained from specific scanning protocol and need to centralizedly collect
large amounts of data, which will lead to serious data domain shift, and
privacy concerns. To relieve these problems, in this paper, we propose a
hypernetwork-based federated learning method for personalized CT imaging,
dubbed as HyperFed. The basic assumption of HyperFed is that the optimization
problem for each institution can be divided into two parts: the local data
adaption problem and the global CT imaging problem, which are implemented by an
institution-specific hypernetwork and a global-sharing imaging network,
respectively. The purpose of global-sharing imaging network is to learn stable
and effective common features from different institutions. The
institution-specific hypernetwork is carefully designed to obtain
hyperparameters to condition the global-sharing imaging network for
personalized local CT reconstruction. Experiments show that HyperFed achieves
competitive performance in CT reconstruction compared with several other
state-of-the-art methods. It is believed as a promising direction to improve CT
imaging quality and achieve personalized demands of different institutions or
scanners without privacy data sharing. The codes will be released at
https://github.com/Zi-YuanYang/HyperFed.

We build a classification model for the BirdCLEF 2022 challenge using
unsupervised methods. We implement an unsupervised representation of the
training dataset using a triplet loss on spectrogram representation of audio
motifs. Our best model performs with a score of 0.48 on the public leaderboard.

Due to the growing adoption of deep neural networks in many fields of science
and engineering, modeling and estimating their uncertainties has become of
primary importance. Various approaches have been investigated including
Bayesian neural networks, ensembles, deterministic approximations, amongst
others. Despite the growing litterature about uncertainty quantification in
deep learning, the quality of the uncertainty estimates remains an open
question. In this work, we attempt to assess the performance of several
algorithms on sampling and regression tasks by evaluating the quality of the
confidence regions and how well the generated samples are representative of the
unknown target distribution. Towards this end, several sampling and regression
tasks are considered, and the selected algorithms are compared in terms of
coverage probabilities, kernelized Stein discrepancies, and maximum mean
discrepancies.

The noisy intermediate-scale quantum (NISQ) devices enable the implementation
of the variational quantum circuit (VQC) for quantum neural networks (QNN).
Although the VQC-based QNN has succeeded in many machine learning tasks, the
representation and generalization powers of VQC still require further
investigation, particularly when the dimensionality of classical inputs is
concerned. In this work, we first put forth an end-to-end quantum neural
network, TTN-VQC, which consists of a quantum tensor network based on a
tensor-train network (TTN) for dimensionality reduction and a VQC for
functional regression. Then, we aim at the error performance analysis for the
TTN-VQC in terms of representation and generalization powers. We also
characterize the optimization properties of TTN-VQC by leveraging the
Polyak-Lojasiewicz (PL) condition. Moreover, we conduct the experiments of
functional regression on a handwritten digit classification dataset to justify

Ferromagnetic shape memory alloys (MSMAs), such as Ni-Mn-Ga single crystals,
can exhibit the shape memory effect due to an applied magnetic field at room
temperature. Under a variable magnetic field and a constant bias stress
loading, MSMAs have been used for actuation applications. This work introduced
a new feature to the existing macroscale magneto-mechanical model for Ni-Mn-Ga
single crystal. This model includes the fact that the magnetic easy axis in the
two variants is not exactly perpendicular as observed by D silva et al. This
offset helps explain some of the power harvesting capabilities of MSMAs. Model
predictions are compared to experimental data collected on a Ni-Mn-Ga single
crystal. The experiments include both stress-controlled loading with constant
bias magnetic field load (which mimics power harvesting or sensing) and
fieldcontrolled loading with constant bias compressive stress (which mimics
actuation). Each type of test was performed at several different load levels,
and the applied field was measured without the MSMA specimen present so that
demagnetization does not affect the experimentally measured field as suggested
by Eberle et al. Results show decent agreement between model predictions and
experimental data. Although the model predicts experimental results decently,
it does not capture all the features of the experimental data. In order to
capture all the experimental features, finally, a generalized regression neural
network (GRNN) was used to train the experimental data (stress, strain,
magnetic field, and emf) so that it can make a reasonably better prediction.

Even though auto-encoders (AEs) have the desirable property of learning
compact representations without labels and have been widely applied to
out-of-distribution (OoD) detection, they are generally still poorly understood
and are used incorrectly in detecting outliers where the normal and abnormal
distributions are strongly overlapping. In general, the learned manifold is
assumed to contain key information that is only important for describing
samples within the training distribution, and that the reconstruction of
outliers leads to high residual errors. However, recent work suggests that AEs
are likely to be even better at reconstructing some types of OoD samples. In
this work, we challenge this assumption and investigate what auto-encoders
actually learn when they are posed to solve two different tasks. First, we
propose two metrics based on the Fr\'echet inception distance (FID) and
confidence scores of a trained classifier to assess whether AEs can learn the
training distribution and reliably recognize samples from other domains.
Second, we investigate whether AEs are able to synthesize normal images from
samples with abnormal regions, on a more challenging lung pathology detection
task. We have found that state-of-the-art (SOTA) AEs are either unable to
constrain the latent manifold and allow reconstruction of abnormal patterns, or
they are failing to accurately restore the inputs from their latent
distribution, resulting in blurred or misaligned reconstructions. We propose
novel deformable auto-encoders (MorphAEus) to learn perceptually aware global
image priors and locally adapt their morphometry based on estimated dense
deformation fields. We demonstrate superior performance over unsupervised
methods in detecting OoD and pathology.

Many modern deep-learning techniques do not work without enormous datasets.
At the same time, several fields demand methods working in scarcity of data.
This problem is even more complex when the samples have varying structures, as
in the case of graphs. Graph representation learning techniques have recently
proven successful in a variety of domains. Nevertheless, the employed
architectures perform miserably when faced with data scarcity. On the other
hand, few-shot learning allows employing modern deep learning models in scarce
data regimes without waiving their effectiveness. In this work, we tackle the
problem of few-shot graph classification, showing that equipping a simple
distance metric learning baseline with a state-of-the-art graph embedder allows
to obtain competitive results on the task.While the simplicity of the
architecture is enough to outperform more complex ones, it also allows
straightforward additions. To this end, we show that additional improvements
may be obtained by encouraging a task-conditioned embedding space. Finally, we
propose a MixUp-based online data augmentation technique acting in the latent
space and show its effectiveness on the task.

The prevalence of data scraping from social media as a means to obtain
datasets has led to growing concerns regarding unauthorized use of data. Data
poisoning attacks have been proposed as a bulwark against scraping, as they
make data "unlearnable" by adding small, imperceptible perturbations.
Unfortunately, existing methods require knowledge of both the target
architecture and the complete dataset so that a surrogate network can be
trained, the parameters of which are used to generate the attack. In this work,
we introduce autoregressive (AR) poisoning, a method that can generate poisoned
data without access to the broader dataset. The proposed AR perturbations are
generic, can be applied across different datasets, and can poison different
architectures. Compared to existing unlearnable methods, our AR poisons are
more resistant against common defenses such as adversarial training and strong
data augmentations. Our analysis further provides insight into what makes an
effective data poison.

Recently, deep learning-based image denoising methods have achieved promising
performance on test data with the same distribution as training set, where
various denoising models based on synthetic or collected real-world training
data have been learned. However, when handling real-world noisy images, the
denoising performance is still limited. In this paper, we propose a simple yet
effective Bayesian deep ensemble (BDE) method for real-world image denoising,
where several representative deep denoisers pre-trained with various training
data settings can be fused to improve robustness. The foundation of BDE is that
real-world image noises are highly signal-dependent, and heterogeneous noises
in a real-world noisy image can be separately handled by different denoisers.
In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet
into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps
to fuse these denoisers. Instead of solely learning pixel-wise weighting maps,
Bayesian deep learning strategy is introduced to predict weighting uncertainty
as well as weighting map, by which prediction variance can be modeled for
improving robustness on real-world noisy images. Extensive experiments have
shown that real-world noises can be better removed by fusing existing denoisers
instead of training a big denoiser with expensive cost. On DND dataset, our BDE
achieves +0.28~dB PSNR gain over the state-of-the-art denoising method.
Moreover, we note that our BDE denoiser based on different Gaussian noise
levels outperforms state-of-the-art CBDNet when applying to real-world noisy
images. Furthermore, our BDE can be extended to other image restoration tasks,
and achieves +0.30dB, +0.18dB and +0.12dB PSNR gains on benchmark datasets for
image deblurring, image deraining and single image super-resolution,

In recent years, Explainable AI (xAI) attracted a lot of attention as various
countries turned explanations into a legal right. xAI allows for improving
models beyond the accuracy metric by, e.g., debugging the learned pattern and
demystifying the AI's behavior. The widespread use of xAI brought new
challenges. On the one hand, the number of published xAI algorithms underwent a
boom, and it became difficult for practitioners to select the right tool. On
the other hand, some experiments did highlight how easy data scientists could
misuse xAI algorithms and misinterpret their results. To tackle the issue of
comparing and correctly using feature importance xAI algorithms, we propose
Compare-xAI, a benchmark that unifies all exclusive functional testing methods
applied to xAI algorithms. We propose a selection protocol to shortlist
non-redundant functional tests from the literature, i.e., each targeting a
specific end-user requirement in explaining a model. The benchmark encapsulates
the complexity of evaluating xAI methods into a hierarchical scoring of three
levels, namely, targeting three end-user groups: researchers, practitioners,
and laymen in xAI. The most detailed level provides one score per test. The
second level regroups tests into five categories (fidelity, fragility,
stability, simplicity, and stress tests). The last level is the aggregated
comprehensibility score, which encapsulates the ease of correctly interpreting
the algorithm's output in one easy to compare value. Compare-xAI's interactive
user interface helps mitigate errors in interpreting xAI results by quickly
listing the recommended xAI solutions for each ML task and their current
limitations. The benchmark is made available at
https://karim-53.github.io/cxai/

A recent goal in the theory of deep learning is to identify how neural
networks can escape the "lazy training," or Neural Tangent Kernel (NTK) regime,
where the network is coupled with its first order Taylor expansion at
initialization. While the NTK is minimax optimal for learning dense polynomials
(Ghorbani et al, 2021), it cannot learn features, and hence has poor sample
complexity for learning many classes of functions including sparse polynomials.
Recent works have thus aimed to identify settings where gradient based
algorithms provably generalize better than the NTK. One such example is the
"QuadNTK" approach of Bai and Lee (2020), which analyzes the second-order term
in the Taylor expansion. Bai and Lee (2020) show that the second-order term can
learn sparse polynomials efficiently; however, it sacrifices the ability to
learn general dense polynomials.
  In this paper, we analyze how gradient descent on a two-layer neural network
can escape the NTK regime by utilizing a spectral characterization of the NTK
(Montanari and Zhong, 2020) and building on the QuadNTK approach. We first
expand upon the spectral analysis to identify "good" directions in parameter
space in which we can move without harming generalization. Next, we show that a
wide two-layer neural network can jointly use the NTK and QuadNTK to fit target
functions consisting of a dense low-degree term and a sparse high-degree term
-- something neither the NTK nor the QuadNTK can do on their own. Finally, we
construct a regularizer which encourages our parameter vector to move in the
"good" directions, and show that gradient descent on the regularized loss will
converge to a global minimizer, which also has low test error. This yields an
end to end convergence and generalization guarantee with provable sample
complexity improvement over both the NTK and QuadNTK on their own.

Despite the rapid advance of unsupervised anomaly detection, existing methods
require to train separate models for different objects. In this work, we
present UniAD that accomplishes anomaly detection for multiple classes with a
unified framework. Under such a challenging setting, popular reconstruction
networks may fall into an "identical shortcut", where both normal and anomalous
samples can be well recovered, and hence fail to spot outliers. To tackle this
obstacle, we make three improvements. First, we revisit the formulations of
fully-connected layer, convolutional layer, as well as attention layer, and
confirm the important role of query embedding (i.e., within attention layer) in
preventing the network from learning the shortcut. We therefore come up with a
layer-wise query decoder to help model the multi-class distribution. Second, we
employ a neighbor masked attention module to further avoid the information leak
from the input feature to the reconstructed output feature. Third, we propose a
feature jittering strategy that urges the model to recover the correct message
even with noisy inputs. We evaluate our algorithm on MVTec-AD and CIFAR-10
datasets, where we surpass the state-of-the-art alternatives by a sufficiently
large margin. For example, when learning a unified model for 15 categories in
MVTec-AD, we surpass the second competitor on the tasks of both anomaly
detection (from 88.1% to 96.5%) and anomaly localization (from 89.5% to 96.8%).
Code is available at https://github.com/zhiyuanyou/UniAD.

In this paper, a new semi-supervised deep MIMO detection approach using a
cycle-consistent generative adversarial network (cycleGAN) is proposed, which
performs the detection without any prior knowledge of underlying channel
models. Specifically, we propose the cycleGAN detector by constructing a
bidirectional loop of least squares generative adversarial networks (LS-GAN).
The forward direction of the loop learns to model the transmission process,
while the backward direction learns to detect the transmitted signals. By
optimizing the cycle-consistency of the transmitted and received signals
through this loop, the proposed method is able to train a specific detector
block-by-block to fit the operating channel. The training is conducted online,
including a supervised phase using pilots and an unsupervised phase using
received payload data. This semi-supervised training strategy weakens the
demand for the scale of labelled training dataset, which is related to the
number of pilots, and thus the overhead is effectively reduced. Numerical
results show that the proposed semi-blind cycleGAN detector achieves better bit
error-rate (BER) than existing semi-blind deep learning detection methods as
well as conditional linear detectors, especially when nonlinear distortion of
the power amplifiers at the transmitter is considered.

In this paper, we present a work in progress about a deep learning based
approach for automatic code optimization in polyhedral compilers. The proposed
technique explores combinations of affine and non-affine loop transformations
to find the sequence of transformations that minimizes the execution time of a
given program. This exploration is guided by a deep learning based cost model
that evaluates the speedup that each sequence of transformations would yield.
Preliminary results show that the proposed techniques achieve a 2.35x geometric
mean speedup over state of the art polyhedral compilers (Pluto).

This work analyzes trajectories obtained by YOLO and DeepSORT algorithms of
dense emulsion systems simulated by Lattice Boltzmann methods. The results
indicate that the individual droplet's moving direction is influenced more by
the droplets immediately behind it than the droplets in front of it. The
analysis also provides hints on constraints on writing down a dynamical model
of droplets for the dense emulsion in narrow channels.

We study how group symmetry helps improve data efficiency and generalization
for end-to-end differentiable planning algorithms when symmetry appears in
decision-making tasks. Motivated by equivariant convolution networks, we treat
the path planning problem as \textit{signals} over grids. We show that value
iteration in this case is a linear equivariant operator, which is a (steerable)
convolution. This extends Value Iteration Networks (VINs) on using
convolutional networks for path planning with additional rotation and
reflection symmetry. Our implementation is based on VINs and uses steerable
convolution networks to incorporate symmetry. The experiments are performed on
four tasks: 2D navigation, visual navigation, and 2 degrees of freedom (2DOFs)
configuration space and workspace manipulation. Our symmetric planning
algorithms improve training efficiency and generalization by large margins
compared to non-equivariant counterparts, VIN and GPPN.

Scene flow represents the motion of points in the 3D space, which is the
counterpart of the optical flow that represents the motion of pixels in the 2D
image. However, it is difficult to obtain the ground truth of scene flow in the
real scenes, and recent studies are based on synthetic data for training.
Therefore, how to train a scene flow network with unsupervised methods based on
real-world data shows crucial significance. A novel unsupervised learning
method for scene flow is proposed in this paper, which utilizes the images of
two consecutive frames taken by monocular camera without the ground truth of
scene flow for training. Our method realizes the goal that training scene flow
network with real-world data, which bridges the gap between training data and
test data and broadens the scope of available data for training. Unsupervised
learning of scene flow in this paper mainly consists of two parts: (i) depth
estimation and camera pose estimation, and (ii) scene flow estimation based on
four different loss functions. Depth estimation and camera pose estimation
obtain the depth maps and camera pose between two consecutive frames, which
provide further information for the next scene flow estimation. After that, we
used depth consistency loss, dynamic-static consistency loss, Chamfer loss, and
Laplacian regularization loss to carry out unsupervised training of the scene
flow network. To our knowledge, this is the first paper that realizes the
unsupervised learning of 3D scene flow from monocular camera. The experiment
results on KITTI show that our method for unsupervised learning of scene flow
meets great performance compared to traditional methods Iterative Closest Point
(ICP) and Fast Global Registration (FGR). The source code is available at:
https://github.com/IRMVLab/3DUnMonoFlow.

After more than two years since the beginning of the COVID-19 pandemic, the
pressure of this crisis continues to devastate globally. The use of chest X-ray
(CXR) imaging as a complementary screening strategy to RT-PCR testing is not
only prevailing but has greatly increased due to its routine clinical use for
respiratory complaints. Thus far, many visual perception models have been
proposed for COVID-19 screening based on CXR imaging. Nevertheless, the
accuracy and the generalization capacity of these models are very much
dependent on the diversity and the size of the dataset they were trained on.
Motivated by this, we introduce COVIDx CXR-3, a large-scale benchmark dataset
of CXR images for supporting COVID-19 computer vision research. COVIDx CXR-3 is
composed of 30,386 CXR images from a multinational cohort of 17,026 patients
from at least 51 countries, making it, to the best of our knowledge, the most
extensive, most diverse COVID-19 CXR dataset in open access form. Here, we
provide comprehensive details on the various aspects of the proposed dataset
including patient demographics, imaging views, and infection types. The hope is
that COVIDx CXR-3 can assist scientists in advancing machine learning research
against both the COVID-19 pandemic and related diseases.

We consider the problem of certifying the robustness of deep neural networks
against real-world distribution shifts. To do so, we bridge the gap between
hand-crafted specifications and realistic deployment settings by proposing a
novel neural-symbolic verification framework, in which we train a generative
model to learn perturbations from data and define specifications with respect
to the output of the learned model. A unique challenge arising from this
setting is that existing verifiers cannot tightly approximate sigmoid
activations, which are fundamental to many state-of-the-art generative models.
To address this challenge, we propose a general meta-algorithm for handling
sigmoid activations which leverages classical notions of counter-example-guided
abstraction refinement. The key idea is to "lazily" refine the abstraction of
sigmoid functions to exclude spurious counter-examples found in the previous
abstraction, thus guaranteeing progress in the verification process while
keeping the state-space small. Experiments on the MNIST and CIFAR-10 datasets
show that our framework significantly outperforms existing methods on a range
of challenging distribution shifts.

Objective: Shapley additive explanations (SHAP) is a popular post-hoc
technique for explaining black box models. While the impact of data imbalance
on predictive models has been extensively studied, it remains largely unknown
with respect to SHAP-based model explanations. This study sought to investigate
the effects of data imbalance on SHAP explanations for deep learning models,
and to propose a strategy to mitigate these effects. Materials and Methods: We
propose to adjust class distributions in the background and explanation data in
SHAP when explaining black box models. Our data balancing strategy is to
compose background data and explanation data with an equal distribution of
classes. To evaluate the effects of data adjustment on model explanation, we
propose to use the beeswarm plot as a qualitative tool to identify "abnormal"
explanation artifacts, and quantitatively test the consistency between variable
importance and prediction power. We demonstrated our proposed approach in an
empirical study that predicted inpatient mortality using the Medical
Information Mart for Intensive Care (MIMIC-III) data and a multilayer
perceptron. Results: Using the data balancing strategy would allow us to reduce
the number of the artifacts in the beeswarm plot, thus mitigating the negative
effects of data imbalance. Additionally, with the balancing strategy, the
top-ranked variables from the corresponding importance ranking demonstrated
improved discrimination power. Discussion and Conclusion: Our findings suggest
that balanced background and explanation data could help reduce the noise in
explanation results induced by skewed data distribution and improve the
reliability of variable importance ranking. Furthermore, these balancing
procedures improve the potential of SHAP in identifying patients with abnormal
characteristics in clinical applications.

Recent advances in distributed optimization and learning have shown that
communication compression is one of the most effective means of reducing
communication. While there have been many results on convergence rates under
communication compression, a theoretical lower bound is still missing.
  Analyses of algorithms with communication compression have attributed
convergence to two abstract properties: the unbiased property or the
contractive property. They can be applied with either unidirectional
compression (only messages from workers to server are compressed) or
bidirectional compression. In this paper, we consider distributed stochastic
algorithms for minimizing smooth and non-convex objective functions under
communication compression. We establish a convergence lower bound for
algorithms whether using unbiased or contractive compressors in unidirection or
bidirection. To close the gap between the lower bound and the existing upper
bounds, we further propose an algorithm, NEOLITHIC, which almost reaches our
lower bound (up to logarithm factors) under mild conditions. Our results also
show that using contractive bidirectional compression can yield iterative
methods that converge as fast as those using unbiased unidirectional
compression. The experimental results validate our findings.

Pre-training is essential to deep learning model performance, especially in
medical image analysis tasks where limited training data are available.
However, existing pre-training methods are inflexible as the pre-trained
weights of one model cannot be reused by other network architectures. In this
paper, we propose an architecture-irrelevant hyper-initializer, which can
initialize any given network architecture well after being pre-trained for only
once. The proposed initializer is a hypernetwork which takes a downstream
architecture as input graphs and outputs the initialization parameters of the
respective architecture. We show the effectiveness and efficiency of the
hyper-initializer through extensive experimental results on multiple medical
imaging modalities, especially in data-limited fields. Moreover, we prove that
the proposed algorithm can be reused as a favorable plug-and-play initializer
for any downstream architecture and task (both classification and segmentation)
of the same modality.

Deep learning-based joint source-channel coding (JSCC) has shown excellent
performance in image and feature transmission. However, the output values of
the JSCC encoder are continuous, which makes the constellation of modulation
complex and dense. It is hard and expensive to design radio frequency chains
for transmitting such full-resolution constellation points. In this paper, two
methods of mapping the full-resolution constellation to finite constellation
are proposed for real system implementation. The constellation mapping results
of the proposed methods correspond to regular constellation and irregular
constellation, respectively. We apply the methods to existing deep JSCC models
and evaluate them on AWGN channels with different signal-to-noise ratios
(SNRs). Experimental results show that the proposed methods outperform the
traditional uniform quadrature amplitude modulation (QAM) constellation mapping
method by only adding a few additional parameters.

For those seeking healthcare advice online, AI based dialogue agents capable
of interacting with patients to perform automatic disease diagnosis are a
viable option. This application necessitates efficient inquiry of relevant
disease symptoms in order to make accurate diagnosis recommendations. This can
be formulated as a problem of sequential feature (symptom) selection and
classification for which reinforcement learning (RL) approaches have been
proposed as a natural solution. They perform well when the feature space is
small, that is, the number of symptoms and diagnosable disease categories is
limited, but they frequently fail in assignments with a large number of
features. To address this challenge, we propose a Multi-Model-Fused
Actor-Critic (MMF-AC) RL framework that consists of a generative actor network
and a diagnostic critic network. The actor incorporates a Variational
AutoEncoder (VAE) to model the uncertainty induced by partial observations of
features, thereby facilitating in making appropriate inquiries. In the critic
network, a supervised diagnosis model for disease predictions is involved to
precisely estimate the state-value function. Furthermore, inspired by the
medical concept of differential diagnosis, we combine the generative and
diagnosis models to create a novel reward shaping mechanism to address the
sparse reward problem in large search spaces. We conduct extensive experiments
on both synthetic and real-world datasets for empirical evaluations. The
results demonstrate that our approach outperforms state-of-the-art methods in
terms of diagnostic accuracy and interaction efficiency while also being more
effectively scalable to large search spaces. Besides, our method is adaptable
to both categorical and continuous features, making it ideal for online

Modern machine learning (ML) models are becoming increasingly popular and are
widely used in decision-making systems. However, studies have shown critical
issues of ML discrimination and unfairness, which hinder their adoption on
high-stake applications. Recent research on fair classifiers has drawn
significant attention to develop effective algorithms to achieve fairness and
good classification performance. Despite the great success of these
fairness-aware machine learning models, most of the existing models require
sensitive attributes to preprocess the data, regularize the model learning or
postprocess the prediction to have fair predictions. However, sensitive
attributes are often incomplete or even unavailable due to privacy, legal or
regulation restrictions. Though we lack the sensitive attribute for training a
fair model in the target domain, there might exist a similar domain that has
sensitive attributes. Thus, it is important to exploit auxiliary information
from the similar domain to help improve fair classification in the target
domain. Therefore, in this paper, we study a novel problem of exploring domain
adaptation for fair classification. We propose a new framework that can
simultaneously estimate the sensitive attributes while learning a fair
classifier in the target domain. Extensive experiments on real-world datasets
illustrate the effectiveness of the proposed model for fair classification,
even when no sensitive attributes are available in the target domain.

Personalized Federated Learning (pFL), which utilizes and deploys distinct
local models, has gained increasing attention in recent years due to its
success in handling the statistical heterogeneity of FL clients. However,
standardized evaluation and systematical analysis of diverse pFL methods remain
a challenge. Firstly, the highly varied datasets, FL simulation settings and
pFL implementations prevent easy and fair comparisons of pFL methods. Secondly,
the current pFL literature diverges in the adopted evaluation and ablation
protocols. Finally, the effectiveness and robustness of pFL methods are
under-explored in various practical scenarios, such as the generalization to
new clients and the participation of resource-limited clients. To tackle these
challenges, we propose the first comprehensive pFL benchmark, pFL-Bench, for
facilitating rapid, reproducible, standardized and thorough pFL evaluation. The
proposed benchmark contains more than 10 dataset variants in various
application domains with a unified data partition and realistic heterogeneous
settings; a modularized and easy-to-extend pFL codebase with more than 20
competitive pFL method implementations; and systematic evaluations under
containerized environments in terms of generalization, fairness, system
overhead, and convergence. We highlight the benefits and potential of
state-of-the-art pFL methods and hope the pFL-Bench enables further pFL
research and broad applications that would otherwise be difficult owing to the
absence of a dedicated benchmark. The code is released at
https://github.com/alibaba/FederatedScope/tree/master/benchmark/pFL-Bench.

Brain inspired spiking neural networks (SNNs) have been successfully applied
to many pattern recognition domains. The SNNs based deep structure have
achieved considerable results in perceptual tasks, such as image
classification, target detection. However, the application of deep SNNs in
reinforcement learning (RL) tasks is still a problem to be explored. Although
there have been previous studies on the combination of SNNs and RL, most of
them focus on robotic control problems with shallow networks or using ANN-SNN
conversion method to implement spiking deep Q Network (SDQN). In this work, we
mathematically analyzed the problem of the disappearance of spiking signal
features in SDQN and proposed a potential based layer normalization(pbLN)
method to directly train spiking deep Q networks. Experiment shows that
compared with state-of-art ANN-SNN conversion method and other SDQN works, the
proposed pbLN spiking deep Q networks (PL-SDQN) achieved better performance on
Atari game tasks.

Contextual bandits aim to identify among a set of arms the optimal one with
the highest reward based on their contextual information. Motivated by the fact
that the arms usually exhibit group behaviors and the mutual impacts exist
among groups, we introduce a new model, Arm Group Graph (AGG), where the nodes
represent the groups of arms and the weighted edges formulate the correlations
among groups. To leverage the rich information in AGG, we propose a bandit
algorithm, AGG-UCB, where the neural networks are designed to estimate rewards,
and we propose to utilize graph neural networks (GNN) to learn the
representations of arm groups with correlations. To solve the
exploitation-exploration dilemma in bandits, we derive a new upper confidence
bound (UCB) built on neural networks (exploitation) for exploration.
Furthermore, we prove that AGG-UCB can achieve a near-optimal regret bound with
over-parameterized neural networks, and provide the convergence analysis of GNN
with fully-connected layers which may be of independent interest. In the end,
we conduct extensive experiments against state-of-the-art baselines on multiple
public data sets, showing the effectiveness of the proposed algorithm.

Graph Neural Networks (GNNs) have demonstrated powerful representation
capability in numerous graph-based tasks. Specifically, the decoupled
structures of GNNs such as APPNP become popular due to their simplicity and
performance advantages. However, the end-to-end training of these GNNs makes
them inefficient in computation and memory consumption. In order to deal with
these limitations, in this work, we propose an alternating optimization
framework for graph neural networks that does not require end-to-end training.
Extensive experiments under different settings demonstrate that the performance
of the proposed algorithm is comparable to existing state-of-the-art algorithms
but has significantly better computation and memory efficiency. Additionally,
we show that our framework can be taken advantage to enhance existing decoupled
GNNs.

The potential energy surface (PES) is crucial for interpreting a variety of
chemical reaction processes. However, predicting accurate PESs with high-level
electronic structure methods is a challenging task due to the high
computational cost. As an appealing application of quantum computing, we show
in this work that variational quantum algorithms can be integrated with machine
learning (ML) techniques as a promising scheme for exploring accurate PESs.
Different from using a ML model to represent the potential energy, we encode
the molecular geometry information into a deep neural network (DNN) for
representing parameters of the variational quantum eigensolver (VQE), leaving
the PES to the wave function ansatz. Once the DNN model is trained, the
variational optimization procedure that hinders the application of the VQE to
complex systems is avoided and thus the evaluation of PESs is significantly
accelerated. Numerical results demonstrate that a simple DNN model is able to
reproduce accurate PESs for small molecules.

The rapid development of network science and technologies depends on
shareable datasets. Currently, there is no standard practice for reporting and
sharing network datasets. Some network dataset providers only share links,
while others provide some contexts or basic statistics. As a result, critical
information may be unintentionally dropped, and network dataset consumers may
misunderstand or overlook critical aspects. Inappropriately using a network
dataset can lead to severe consequences (e.g., discrimination) especially when
machine learning models on networks are deployed in high-stake domains.
Challenges arise as networks are often used across different domains (e.g.,
network science, physics, etc) and have complex structures. To facilitate the
communication between network dataset providers and consumers, we propose
network report. A network report is a structured description that summarizes
and contextualizes a network dataset. Network report extends the idea of
dataset reports (e.g., Datasheets for Datasets) from prior work with
network-specific descriptions of the non-i.i.d. nature, demographic
information, network characteristics, etc. We hope network reports encourage
transparency and accountability in network research and development across
different fields.

In machine learning, an agent needs to estimate uncertainty to efficiently
explore and adapt and to make effective decisions. A common approach to
uncertainty estimation maintains an ensemble of models. In recent years,
several approaches have been proposed for training ensembles, and conflicting
views prevail with regards to the importance of various ingredients of these
approaches. In this paper, we aim to address the benefits of two ingredients --
prior functions and bootstrapping -- which have come into question. We show
that prior functions can significantly improve an ensemble agent's joint
predictions across inputs and that bootstrapping affords additional benefits if
the signal-to-noise ratio varies across inputs. Our claims are justified by
both theoretical and experimental results.

Backdoors are powerful attacks against deep neural networks (DNNs). By
poisoning training data, attackers can inject hidden rules (backdoors) into
DNNs, which only activate on inputs containing attack-specific triggers. While
existing work has studied backdoor attacks on a variety of DNN models, they
only consider static models, which remain unchanged after initial deployment.
  In this paper, we study the impact of backdoor attacks on a more realistic
scenario of time-varying DNN models, where model weights are updated
periodically to handle drifts in data distribution over time. Specifically, we
empirically quantify the "survivability" of a backdoor against model updates,
and examine how attack parameters, data drift behaviors, and model update
strategies affect backdoor survivability. Our results show that one-shot
backdoor attacks (i.e., only poisoning training data once) do not survive past
a few model updates, even when attackers aggressively increase trigger size and
poison ratio. To stay unaffected by model update, attackers must continuously
introduce corrupted data into the training pipeline. Together, these results
indicate that when models are updated to learn new data, they also "forget"
backdoors as hidden, malicious features. The larger the distribution shift
between old and new training data, the faster backdoors are forgotten.
Leveraging these insights, we apply a smart learning rate scheduler to further
accelerate backdoor forgetting during model updates, which prevents one-shot
backdoors from surviving past a single model update.

M dwarfs are the dominating type of stars in the solar neighbourhood. They
serve as excellent tracers for the study of the distribution and properties of
the nearby interstellar dust. In this work, we aim to obtain high accuracy
reddening values of M dwarf stars from the Large Sky Area Multi-Object Fiber
Spectroscopic Telescope (LAMOST) Data Release 8 (DR8). Combining the LAMOST
spectra with the high-quality optical photometry from the Gaia Early Data
Release 3 (Gaia EDR3), we have estimated the reddening values $E(G_{\rm
BP}-G_{\rm RP})$ of 641,426 M dwarfs with the machine-learning algorithm Random
Forest regression. The typical reddening uncertainty is only 0.03 mag in
$E(G_{\rm BP}-G_{\rm RP})$. We have obtained the reddening coefficient
$R_{(G_{\rm BP}-G_{\rm RP})}$, which is a function of the stellar intrinsic
colour $(G_{\rm BP}-G_{\rm RP})_0$ and reddening value $E(B-V)$. The values of
$E(B-V)$ are also provided for the individual stars in our catalogue. Our
resultant high accuracy reddening values of M dwarfs, combined with the Gaia
parallaxes, will be very powerful to map the fine structures of the dust in the
solar neighbourhood.

Federated learning (FL), an attractive and promising distributed machine
learning paradigm, has sparked extensive interest in exploiting tremendous data
stored on ubiquitous mobile devices. However, conventional FL suffers severely
from resource heterogeneity, as clients with weak computational and
communication capability may be unable to complete local training using the
same local training hyper-parameters. In this paper, we propose Dap-FL, a deep
deterministic policy gradient (DDPG)-assisted adaptive FL system, in which
local learning rates and local training epochs are adaptively adjusted by all
resource-heterogeneous clients through locally deployed DDPG-assisted adaptive
hyper-parameter selection schemes. Particularly, the rationality of the
proposed hyper-parameter selection scheme is confirmed through rigorous
mathematical proof. Besides, due to the thoughtlessness of security
consideration of adaptive FL systems in previous studies, we introduce the
Paillier cryptosystem to aggregate local models in a secure and
privacy-preserving manner. Rigorous analyses show that the proposed Dap-FL
system could guarantee the security of clients' private local models against
chosen-plaintext attacks and chosen-message attacks in a widely used
honest-but-curious participants and active adversaries security model. In
addition, through ingenious and extensive experiments, the proposed Dap-FL
achieves higher global model prediction accuracy and faster convergence rates
than conventional FL, and the comprehensiveness of the adjusted local training
hyper-parameters is validated. More importantly, experimental results also show
that the proposed Dap-FL achieves higher model prediction accuracy than two
state-of-the-art RL-assisted FL methods, i.e., 6.03% higher than DDPG-based FL
and 7.85% higher than DQN-based FL.

Bayesian additive regression trees (BART) is a non-parametric method to
approximate functions. It is a black-box method based on the sum of many trees
where priors are used to regularize inference, mainly by restricting trees'
learning capacity so that no individual tree is able to explain the data, but
rather the sum of trees. We discuss BART in the context of probabilistic
programming languages (PPLs), i.e. we present BART as a primitive that can be
used as a component of a probabilistic model rather than as a standalone model.
Specifically, we introduce the Python library PyMC-BART, which works by
extending PyMC, a library for probabilistic programming. We present a few
examples of models that can be built using PyMC-BART, discuss recommendations
for selection of hyperparameters, and finally, we close with limitations of our
implementation and future directions for improvement.

This paper introduces subject granular privacy in the Federated Learning (FL)
setting, where a subject is an individual whose private information is embodied
by several data items either confined within a single federation user or
distributed across multiple federation users. We formally define the notion of
subject level differential privacy for FL. We propose three new algorithms that
enforce subject level DP. Two of these algorithms are based on notions of user
level local differential privacy (LDP) and group differential privacy
respectively. The third algorithm is based on a novel idea of hierarchical
gradient averaging (HiGradAvgDP) for subjects participating in a training
mini-batch. We also introduce horizontal composition of privacy loss for a
subject across multiple federation users. We show that horizontal composition
is equivalent to sequential composition in the worst case. We prove the subject
level DP guarantee for all our algorithms and empirically analyze them using
the FEMNIST and Shakespeare datasets. Our evaluation shows that, of our three
algorithms, HiGradAvgDP delivers the best model performance, approaching that
of a model trained using a DP-SGD based algorithm that provides a weaker item
level privacy guarantee.

Convolutional Neural Networks (CNN) have been a good solution for
understanding a vast image dataset. As the increased number of battery-equipped
electric vehicles is flourishing globally, there has been much research on
understanding which charge levels electric vehicle drivers would choose to
charge their vehicles to get to their destination without any prevention. We
implemented deep learning approaches to analyze the tabular datasets to
understand their state of charge and which charge levels they would choose. In
addition, we implemented the Image Generator for Tabular Dataset algorithm to
utilize tabular datasets as image datasets to train convolutional neural
networks. Also, we integrated other CNN architecture such as EfficientNet to
prove that CNN is a great learner for reading information from images that were
converted from the tabular dataset, and able to predict charge levels for
battery-equipped electric vehicles. We also evaluated several optimization
methods to enhance the learning rate of the models and examined further
analysis on improving the model architecture.

Personalised federated learning (FL) aims at collaboratively learning a
machine learning model taylored for each client. Albeit promising advances have
been made in this direction, most of existing approaches works do not allow for
uncertainty quantification which is crucial in many applications. In addition,
personalisation in the cross-device setting still involves important issues,
especially for new clients or those having small number of observations. This
paper aims at filling these gaps. To this end, we propose a novel methodology
coined FedPop by recasting personalised FL into the population modeling
paradigm where clients' models involve fixed common population parameters and
random effects, aiming at explaining data heterogeneity. To derive convergence
guarantees for our scheme, we introduce a new class of federated stochastic
optimisation algorithms which relies on Markov chain Monte Carlo methods.
Compared to existing personalised FL methods, the proposed methodology has
important benefits: it is robust to client drift, practical for inference on
new clients, and above all, enables uncertainty quantification under mild
computational and memory overheads. We provide non-asymptotic convergence
guarantees for the proposed algorithms and illustrate their performances on
various personalised federated learning tasks.

Hyperbolic networks have shown prominent improvements over their Euclidean
counterparts in several areas involving hierarchical datasets in various
domains such as computer vision, graph analysis, and natural language
processing. However, their adoption in practice remains restricted due to (i)
non-scalability on accelerated deep learning hardware, (ii) vanishing gradients
due to the closure of hyperbolic space, and (iii) information loss due to
frequent mapping between local tangent space and fully hyperbolic space. To
tackle these issues, we propose the approximation of hyperbolic operators using
Taylor series expansions, which allows us to reformulate the computationally
expensive tangent and cosine hyperbolic functions into their polynomial
equivariants which are more efficient. This allows us to retain the benefits of
preserving the hierarchical anatomy of the hyperbolic space, while maintaining
the scalability over current accelerated deep learning infrastructure. The
polynomial formulation also enables us to utilize the advancements in Euclidean
networks such as gradient clipping and ReLU activation to avoid vanishing
gradients and remove errors due to frequent switching between tangent space and
hyperbolic space. Our empirical evaluation on standard benchmarks in the domain
of graph analysis and computer vision shows that our polynomial formulation is
as scalable as Euclidean architectures, both in terms of memory and time
complexity, while providing results as effective as hyperbolic models.
Moreover, our formulation also shows a considerable improvement over its
baselines due to our solution to vanishing gradients and information loss.

Background: The assessment of left ventricular (LV) function by myocardial
perfusion SPECT (MPS) relies on accurate myocardial segmentation. The purpose
of this paper is to develop and validate a new method incorporating deep
learning with shape priors to accurately extract the LV myocardium for
automatic measurement of LV functional parameters. Methods: A segmentation
architecture that integrates a three-dimensional (3D) V-Net with a shape
deformation module was developed. Using the shape priors generated by a dynamic
programming (DP) algorithm, the model output was then constrained and guided
during the model training for quick convergence and improved performance. A
stratified 5-fold cross-validation was used to train and validate our models.
Results: Results of our proposed method agree well with those from the ground
truth. Our proposed model achieved a Dice similarity coefficient (DSC) of
0.9573(0.0244), 0.9821(0.0137), and 0.9903(0.0041), a Hausdorff distances (HD)
of 6.7529(2.7334) mm, 7.2507(3.1952) mm, and 7.6121(3.0134) mm in extracting
the endocardium, myocardium, and epicardium, respectively. Conclusion: Our
proposed method achieved a high accuracy in extracting LV myocardial contours
and assessing LV function.

In this paper, we study the problem of conducting self-supervised learning
for node representation learning on non-homophilous graphs. Existing
self-supervised learning methods typically assume the graph is homophilous
where linked nodes often belong to the same class or have similar features.
However, such assumptions of homophily do not always hold true in real-world
graphs. We address this problem by developing a decoupled self-supervised
learning (DSSL) framework for graph neural networks. DSSL imitates a generative
process of nodes and links from latent variable modeling of the semantic
structure, which decouples different underlying semantics between different
neighborhoods into the self-supervised node learning process. Our DSSL
framework is agnostic to the encoders and does not need prefabricated
augmentations, thus is flexible to different graphs. To effectively optimize
the framework with latent variables, we derive the evidence lower-bound of the
self-supervised objective and develop a scalable training algorithm with
variational inference. We provide a theoretical analysis to justify that DSSL
enjoys better downstream performance. Extensive experiments on various types of
graph benchmarks demonstrate that our proposed framework can significantly
achieve better performance compared with competitive self-supervised learning

In this paper, we investigate $\textit{open-set recognition}$ with domain
shift, where the final goal is to achieve $\textit{Source-free Universal Domain
Adaptation}$ (SF-UNDA), which addresses the situation where there exist both
domain and category shifts between source and target domains. Under the SF-UNDA
setting, the model cannot access source data anymore during target adaptation,
which aims to address data privacy concerns. We propose a novel training scheme
to learn a ($n$+1)-way classifier to predict the $n$ source classes and the
unknown class, where samples of only known source categories are available for
training. Furthermore, for target adaptation, we simply adopt a weighted
entropy minimization to adapt the source pretrained model to the unlabeled
target domain without source data. In experiments, we show: $\textbf{1)}$ After
source training, the resulting source model can get excellent performance for
$\textit{open-set single domain generalization}$ and also $\textit{open-set
recognition}$ tasks; $\textbf{2)}$ After target adaptation, our method
surpasses current UNDA approaches which demand source data during adaptation on
several benchmarks. The versatility to several different tasks strongly proves
the efficacy and generalization ability of our method. $\textbf{3)}$ When
augmented with a closed-set domain adaptation approach during target
adaptation, our source-free method further outperforms the current
state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home
and VisDA respectively. Code will be available in
https://github.com/Albert0147/OneRing.

We propose a novel parameterized skill-learning algorithm that aims to learn
transferable parameterized skills and synthesize them into a new action space
that supports efficient learning in long-horizon tasks. We first propose novel
learning objectives -- trajectory-centric diversity and smoothness -- that
allow an agent to meta-learn reusable parameterized skills. Our agent can use
these learned skills to construct a temporally-extended parameterized-action
Markov decision process, for which we propose a hierarchical actor-critic
algorithm that aims to efficiently learn a high-level control policy with the
learned skills. We empirically demonstrate that the proposed algorithms enable
an agent to solve a complicated long-horizon obstacle-course environment.

Network compression is crucial to making the deep networks to be more
efficient, faster, and generalizable to low-end hardware. Current network
compression methods have two open problems: first, there lacks a theoretical
framework to estimate the maximum compression rate; second, some layers may get
over-prunned, resulting in significant network performance drop. To solve these
two problems, this study propose a gradient-matrix singularity analysis-based
method to estimate the maximum network redundancy. Guided by that maximum rate,
a novel and efficient hierarchical network pruning algorithm is developed to
maximally condense the neuronal network structure without sacrificing network
performance. Substantial experiments are performed to demonstrate the efficacy
of the new method for pruning several advanced convolutional neural network
(CNN) architectures. Compared to existing pruning methods, the proposed pruning
algorithm achieved state-of-the-art performance. At the same or similar
compression ratio, the new method provided the highest network prediction
accuracy as compared to other methods.

Online travel agencies (OTA's) advertise their website offers on meta-search
bidding engines. The problem of predicting the number of clicks a hotel would
receive for a given bid amount is an important step in the management of an
OTA's advertisement campaign on a meta-search engine, because bid times number
of clicks defines the cost to be generated. Various regressors are ensembled in
this work to improve click prediction performance. Following the preprocessing
procedures, the feature set is divided into train and test groups depending on
the logging date of the samples. The data collection is then subjected to
feature elimination via utilizing XGBoost, which significantly reduces the
dimension of features. The optimum hyper-parameters are then found by applying
Bayesian hyperparameter optimization to XGBoost, LightGBM, and SGD models. The
different trained models are tested separately as well as combined to form
ensemble models. Four alternative ensemble solutions have been suggested. The
same test set is used to test both individual and ensemble models, and the
results of 46 model combinations demonstrate that stack ensemble models yield
the desired R2 score of all. In conclusion, the ensemble model improves the
prediction performance by about 10%.

We present ObPose, an unsupervised object-centric inference and generation
model which learns 3D-structured latent representations from RGB-D scenes.
Inspired by prior art in 2D representation learning, ObPose considers a
factorised latent space, separately encoding object location (where) and
appearance (what). ObPose further leverages an object's pose (i.e. location and
orientation), defined via a minimum volume principle, as a novel inductive bias
for learning the where component. To achieve this, we propose an efficient,
voxelised approximation approach to recover the object shape directly from a
neural radiance field (NeRF). As a consequence, ObPose models each scene as a
composition of NeRFs, richly representing individual objects. To evaluate the
quality of the learned representations, ObPose is evaluated quantitatively on
the YCB and CLEVR datatasets for unsupervised scene segmentation, outperforming
the current state-of-the-art in 3D scene inference (ObSuRF) by a significant
margin. Generative results provide qualitative demonstration that the same
ObPose model can both generate novel scenes and flexibly edit the objects in
them. These capacities again reflect the quality of the learned latents and the
benefits of disentangling the where and what components of a scene. Key design
choices made in the ObPose encoder are validated with ablations.

Unsupervised representation learning methods like SwAV are proved to be
effective in learning visual semantics of a target dataset. The main idea
behind these methods is that different views of a same image represent the same
semantics. In this paper, we further introduce an add-on module to facilitate
the injection of the knowledge accounting for spatial cross correlations among
the samples. This in turn results in distilling intra-class information
including feature level locations and cross similarities between same-class
instances. The proposed add-on can be added to existing methods such as the
SwAV. We can later remove the add-on module for inference without any
modification of the learned weights. Through an extensive set of empirical
evaluations, we verify that our method yields an improved performance in
detecting the class activation maps, top-1 classification accuracy, and
down-stream tasks such as object detection, with different configuration
settings.

Despite their high computation and communication costs, Newton-type methods
remain an appealing option for distributed training due to their robustness
against ill-conditioned convex problems. In this work, we study ommunication
compression and aggregation mechanisms for curvature information in order to
reduce these costs while preserving theoretically superior local convergence
guarantees. We prove that the recently developed class of three point
compressors (3PC) of Richtarik et al. [2022] for gradient communication can be
generalized to Hessian communication as well. This result opens up a wide
variety of communication strategies, such as contractive compression} and lazy
aggregation, available to our disposal to compress prohibitively costly
curvature information. Moreover, we discovered several new 3PC mechanisms, such
as adaptive thresholding and Bernoulli aggregation, which require reduced
communication and occasional Hessian computations. Furthermore, we extend and
analyze our approach to bidirectional communication compression and partial
device participation setups to cater to the practical considerations of
applications in federated learning. For all our methods, we derive fast
condition-number-independent local linear and/or superlinear convergence rates.
Finally, with extensive numerical evaluations on convex optimization problems,
we illustrate that our designed schemes achieve state-of-the-art communication
complexity compared to several key baselines using second-order information.

Datasets typically contain inaccuracies due to human error and societal
biases, and these inaccuracies can affect the outcomes of models trained on
such datasets. We present a technique for certifying whether linear regression
models are pointwise-robust to label bias in the training dataset, i.e.,
whether bounded perturbations to the labels of a training dataset result in
models that change the prediction of test points. We show how to solve this
problem exactly for individual test points, and provide an approximate but more
scalable method that does not require advance knowledge of the test point. We
extensively evaluate both techniques and find that linear models -- both
regression- and classification-based -- often display high levels of
bias-robustness. However, we also unearth gaps in bias-robustness, such as high
levels of non-robustness for certain bias assumptions on some datasets.
Overall, our approach can serve as a guide for when to trust, or question, a
model's output.

The practicality of reinforcement learning algorithms has been limited due to
poor scaling with respect to the problem size, as the sample complexity of
learning an $\epsilon$-optimal policy is $\Tilde{\Omega}\left(|S||A|H^3 /
\eps^2\right)$ over worst case instances of an MDP with state space $S$, action
space $A$, and horizon $H$. We consider a class of MDPs that exhibit low rank
structure, where the latent features are unknown. We argue that a natural
combination of value iteration and low-rank matrix estimation results in an
estimation error that grows doubly exponentially in the horizon $H$. We then
provide a new algorithm along with statistical guarantees that efficiently
exploits low rank structure given access to a generative model, achieving a
sample complexity of
$\Tilde{O}\left(d^5(|S|+|A|)\mathrm{poly}(H)/\eps^2\right)$ for a rank $d$
setting, which is minimax optimal with respect to the scaling of $|S|, |A|$,
and $\eps$. In contrast to literature on linear and low-rank MDPs, we do not
require a known feature mapping, our algorithm is computationally simple, and
our results hold for long time horizons. Our results provide insights on the
minimal low-rank structural assumptions required on the MDP with respect to the
transition kernel versus the optimal action-value function.

Proportional-integral-derivative (PID) controller is widely used across
various industrial process control applications because of its straightforward
implementation. However, it can be challenging to fine-tune the PID parameters
in practice to achieve robust performance. The paper proposes a model-based
reinforcement learning (RL) framework to design PID controllers leveraging the
probabilistic inference for learning control (PILCO) method and
Kullback-Leibler divergence (KLD). Since PID controllers have a much more
interpretable control structure than a network basis function, an optimal
policy given by PILCO is transformed into a set of robust PID tuning parameters
for underactuated mechanical systems. The presented method is general and can
blend with several model-based and model-free algorithms. The performance of
the devised PID controllers is demonstrated with simulation studies for a
benchmark cart-pole system under disturbances and system parameter

Recent emergence of high-throughput drug screening assays sparkled an
intensive development of machine learning methods, including models for
prediction of sensitivity of cancer cell lines to anti-cancer drugs, as well as
methods for generation of potential drug candidates. However, a concept of
generation of compounds with specific properties and simultaneous modeling of
their efficacy against cancer cell lines has not been comprehensively explored.
To address this need, we present VADEERS, a Variational Autoencoder-based Drug
Efficacy Estimation Recommender System. The generation of compounds is
performed by a novel variational autoencoder with a semi-supervised Gaussian
Mixture Model (GMM) prior. The prior defines a clustering in the latent space,
where the clusters are associated with specific drug properties. In addition,
VADEERS is equipped with a cell line autoencoder and a sensitivity prediction
network. The model combines data for SMILES string representations of
anti-cancer drugs, their inhibition profiles against a panel of protein
kinases, cell lines biological features and measurements of the sensitivity of
the cell lines to the drugs. The evaluated variants of VADEERS achieve a high
r=0.87 Pearson correlation between true and predicted drug sensitivity
estimates. We train the GMM prior in such a way that the clusters in the latent
space correspond to a pre-computed clustering of the drugs by their inhibitory
profiles. We show that the learned latent representations and new generated
data points accurately reflect the given clustering. In summary, VADEERS offers
a comprehensive model of drugs and cell lines properties and relationships
between them, as well as a guided generation of novel compounds.

Contrastive self-supervised representation learning methods maximize the
similarity between the positive pairs, and at the same time tend to minimize
the similarity between the negative pairs. However, in general the interplay
between the negative pairs is ignored as they do not put in place special
mechanisms to treat negative pairs differently according to their specific
differences and similarities. In this paper, we present Extended Momentum
Contrast (XMoCo), a self-supervised representation learning method founded upon
the legacy of the momentum-encoder unit proposed in the MoCo family
configurations. To this end, we introduce a cross consistency regularization
loss, with which we extend the transformation consistency to dissimilar images
(negative pairs). Under the cross consistency regularization rule, we argue
that semantic representations associated with any pair of images (positive or
negative) should preserve their cross-similarity under pretext transformations.
Moreover, we further regularize the training loss by enforcing a uniform
distribution of similarity over the negative pairs across a batch. The proposed
regularization can easily be added to existing self-supervised learning
algorithms in a plug-and-play fashion. Empirically, we report a competitive
performance on the standard Imagenet-1K linear head classification benchmark.
In addition, by transferring the learned representations to common downstream
tasks, we show that using XMoCo with the prevalently utilized augmentations can
lead to improvements in the performance of such tasks. We hope the findings of
this paper serve as a motivation for researchers to take into consideration the
important interplay among the negative examples in self-supervised learning.

Supervised learning in function spaces is an emerging area of machine
learning research with applications to the prediction of complex physical
systems such as fluid flows, solid mechanics, and climate modeling. By directly
learning maps (operators) between infinite dimensional function spaces, these
models are able to learn discretization invariant representations of target
functions. A common approach is to represent such target functions as linear
combinations of basis elements learned from data. However, there are simple
scenarios where, even though the target functions form a low dimensional
submanifold, a very large number of basis elements is needed for an accurate
linear representation. Here we present NOMAD, a novel operator learning
framework with a nonlinear decoder map capable of learning finite dimensional
representations of nonlinear submanifolds in function spaces. We show this
method is able to accurately learn low dimensional representations of solution
manifolds to partial differential equations while outperforming linear models
of larger size. Additionally, we compare to state-of-the-art operator learning
methods on a complex fluid dynamics benchmark and achieve competitive
performance with a significantly smaller model size and training cost.

Reconstructing natural videos from fMRI brain recordings is very challenging,
for two main reasons: (i) As fMRI data acquisition is difficult, we only have a
limited amount of supervised samples, which is not enough to cover the huge
space of natural videos; and (ii) The temporal resolution of fMRI recordings is
much lower than the frame rate of natural videos. In this paper, we propose a
self-supervised approach for natural-movie reconstruction. By employing
cycle-consistency over Encoding-Decoding natural videos, we can: (i) exploit
the full framerate of the training videos, and not be limited only to clips
that correspond to fMRI recordings; (ii) exploit massive amounts of external
natural videos which the subjects never saw inside the fMRI machine. These
enable increasing the applicable training data by several orders of magnitude,
introducing natural video priors to the decoding network, as well as temporal
coherence. Our approach significantly outperforms competing methods, since
those train only on the limited supervised data. We further introduce a new and
simple temporal prior of natural videos, which - when folded into our fMRI
decoder further - allows us to reconstruct videos at a higher frame-rate (HFR)
of up to x8 of the original fMRI sample rate.

We study the regret of Thompson sampling (TS) algorithms for exponential
family bandits, where the reward distribution is from a one-dimensional
exponential family, which covers many common reward distributions including
Bernoulli, Gaussian, Gamma, Exponential, etc. We propose a Thompson sampling
algorithm, termed ExpTS, which uses a novel sampling distribution to avoid the
under-estimation of the optimal arm. We provide a tight regret analysis for
ExpTS, which simultaneously yields both the finite-time regret bound as well as
the asymptotic regret bound. In particular, for a $K$-armed bandit with
exponential family rewards, ExpTS over a horizon $T$ is sub-UCB (a strong
criterion for the finite-time regret that is problem-dependent), minimax
optimal up to a factor $\sqrt{\log K}$, and asymptotically optimal, for
exponential family rewards. Moreover, we propose ExpTS$^+$, by adding a greedy
exploitation step in addition to the sampling distribution used in ExpTS, to
avoid the over-estimation of sub-optimal arms. ExpTS$^+$ is an anytime bandit
algorithm and achieves the minimax optimality and asymptotic optimality
simultaneously for exponential family reward distributions. Our proof
techniques are general and conceptually simple and can be easily applied to
analyze standard Thompson sampling with specific reward distributions.

The benefits of overparameterization for the overall performance of modern
machine learning (ML) models are well known. However, the effect of
overparameterization at a more granular level of data subgroups is less
understood. Recent empirical studies demonstrate encouraging results: (i) when
groups are not known, overparameterized models trained with empirical risk
minimization (ERM) perform better on minority groups; (ii) when groups are
known, ERM on data subsampled to equalize group sizes yields state-of-the-art
worst-group-accuracy in the overparameterized regime. In this paper, we
complement these empirical studies with a theoretical investigation of the risk
of overparameterized random feature models on minority groups. In a setting in
which the regression functions for the majority and minority groups are
different, we show that overparameterization always improves minority group

Leveraging large-scale data can introduce performance gains on many computer
vision tasks. Unfortunately, this does not happen in object detection when
training a single model under multiple datasets together. We observe two main
obstacles: taxonomy difference and bounding box annotation inconsistency, which
introduces domain gaps in different datasets that prevents us from joint
training. In this paper, we show that these two challenges can be effectively
addressed by simply adapting object queries on language embedding of categories
per dataset. We design a detection hub to dynamically adapt queries on category
embedding based on the different distributions of datasets. Unlike previous
methods attempted to learn a joint embedding for all datasets, our adaptation
method can utilize the language embedding as semantic centers for common
categories, while learning the semantic bias towards specific categories
belonging to different datasets to handle annotation differences and make up
the domain gaps. These novel improvements enable us to end-to-end train a
single detector on multiple datasets simultaneously to fully take their
advantages. Further experiments on joint training on multiple datasets
demonstrate the significant performance gains over separate individual
fine-tuned detectors.

We introduce SubGD, a novel few-shot learning method which is based on the
recent finding that stochastic gradient descent updates tend to live in a
low-dimensional parameter subspace. In experimental and theoretical analyses,
we show that models confined to a suitable predefined subspace generalize well
for few-shot learning. A suitable subspace fulfills three criteria across the
given tasks: it (a) allows to reduce the training error by gradient flow, (b)
leads to models that generalize well, and (c) can be identified by stochastic
gradient descent. SubGD identifies these subspaces from an eigendecomposition
of the auto-correlation matrix of update directions across different tasks.
Demonstrably, we can identify low-dimensional suitable subspaces for few-shot
learning of dynamical systems, which have varying properties described by one
or few parameters of the analytical system description. Such systems are
ubiquitous among real-world applications in science and engineering. We
experimentally corroborate the advantages of SubGD on three distinct dynamical
systems problem settings, significantly outperforming popular few-shot learning
methods both in terms of sample efficiency and performance.

Many future technologies rely on neural networks, but verifying the
correctness of their behavior remains a major challenge. It is known that
neural networks can be fragile in the presence of even small input
perturbations, yielding unpredictable outputs. The verification of neural
networks is therefore vital to their adoption, and a number of approaches have
been proposed in recent years. In this paper we focus on semidefinite
programming (SDP) based techniques for neural network verification, which are
particularly attractive because they can encode expressive behaviors while
ensuring a polynomial time decision. Our starting point is the DeepSDP
framework proposed by Fazlyab et al, which uses quadratic constraints to
abstract the verification problem into a large-scale SDP. When the size of the
neural network grows, however, solving this SDP quickly becomes intractable.
Our key observation is that by leveraging chordal sparsity and specific
parametrizations of DeepSDP, we can decompose the primary computational
bottleneck of DeepSDP -- a large linear matrix inequality (LMI) -- into an
equivalent collection of smaller LMIs. Our parametrization admits a tunable
parameter, allowing us to trade-off efficiency and accuracy in the verification
procedure. We call our formulation Chordal-DeepSDP, and provide experimental
evaluation to show that it can: (1) effectively increase accuracy with the
tunable parameter and (2) outperform DeepSDP on deeper networks.

We present SHRED, a method for 3D SHape REgion Decomposition. SHRED takes a
3D point cloud as input and uses learned local operations to produce a
segmentation that approximates fine-grained part instances. We endow SHRED with
three decomposition operations: splitting regions, fixing the boundaries
between regions, and merging regions together. Modules are trained
independently and locally, allowing SHRED to generate high-quality
segmentations for categories not seen during training. We train and evaluate
SHRED with fine-grained segmentations from PartNet; using its merge-threshold
hyperparameter, we show that SHRED produces segmentations that better respect
ground-truth annotations compared with baseline methods, at any desired
decomposition granularity. Finally, we demonstrate that SHRED is useful for
downstream applications, out-performing all baselines on zero-shot fine-grained
part instance segmentation and few-shot fine-grained semantic segmentation when
combined with methods that learn to label shape regions.

We design short blocklength codes for the Gaussian wiretap channel under
information-theoretic security guarantees. Our approach consists in decoupling
the reliability and secrecy constraints in our code design. Specifically, we
handle the reliability constraint via an autoencoder, and handle the secrecy
constraint with hash functions. For blocklengths smaller than or equal to 16,
we evaluate through simulations the probability of error at the legitimate
receiver and the leakage at the eavesdropper for our code construction. This
leakage is defined as the mutual information between the confidential message
and the eavesdropper's channel observations, and is empirically measured via a
neural network-based mutual information estimator. Our simulation results
provide examples of codes with positive secrecy rates that outperform the best
known achievable secrecy rates obtained non-constructively for the Gaussian
wiretap channel. Additionally, we show that our code design is suitable for the
compound and arbitrarily varying Gaussian wiretap channels, for which the
channel statistics are not perfectly known but only known to belong to a
pre-specified uncertainty set. These models not only capture uncertainty
related to channel statistics estimation, but also scenarios where the
eavesdropper jams the legitimate transmission or influences its own channel
statistics by changing its location.

Dynamic Graph Neural Networks recently became more and more important as
graphs from many scientific fields, ranging from mathematics, biology, social
sciences, and physics to computer science, are dynamic by nature. While
temporal changes (dynamics) play an essential role in many real-world
applications, most of the models in the literature on Graph Neural Networks
(GNN) process static graphs. The few GNN models on dynamic graphs only consider
exceptional cases of dynamics, e.g., node attribute-dynamic graphs or
structure-dynamic graphs limited to additions or changes to the graph's edges,
etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN)
that can handle fully-dynamic graphs in continuous time. The proposed method
provides a node and an edge embedding that includes their activity to address
added and deleted nodes or edges, and possible attributes. Furthermore, the
embeddings specify Temporal Point Processes for each event to encode the
distributions of the structure- and attribute-related incoming graph events. In
addition, our model can be updated efficiently by considering single events for
local retraining.

We investigate the rate distortion tradeoff in private read update write
(PRUW) in relation to federated submodel learning (FSL). In FSL a machine
learning (ML) model is divided into multiple submodels based on different types
of data used for training. Each user only downloads and updates the submodel
relevant to its local data. The process of downloading and updating the
required submodel while guaranteeing privacy of the submodel index and the
values of updates is known as PRUW. In this work, we study how the
communication cost of PRUW can be reduced when a pre-determined amount of
distortion is allowed in the reading (download) and writing (upload) phases. We
characterize the rate distortion tradeoff in PRUW along with a scheme that
achieves the lowest communication cost while working under a given distortion
budget.

While the difficulty of reinforcement learning problems is typically related
to the complexity of their state spaces, Abstraction proposes that solutions
often lie in simpler underlying latent spaces. Prior works have focused on
learning either a continuous or dense abstraction, or require a human to
provide one. Information-dense representations capture features irrelevant for
solving tasks, and continuous spaces can struggle to represent discrete
objects. In this work we automatically learn a sparse discrete abstraction of
the underlying environment. We do so using a simple end-to-end trainable model
based on the successor representation and max-entropy regularization. We
describe an algorithm to apply our model, named Discrete State-Action
Abstraction (DSAA), which computes an action abstraction in the form of
temporally extended actions, i.e., Options, to transition between discrete
abstract states. Empirically, we demonstrate the effects of different
exploration schemes on our resulting abstraction, and show that it is efficient
for solving downstream tasks.

Adversarial reprogramming, introduced by Elsayed, Goodfellow, and
Sohl-Dickstein, seeks to repurpose a neural network to perform a different
task, by manipulating its input without modifying its weights. We prove that
two-layer ReLU neural networks with random weights can be adversarially
reprogrammed to achieve arbitrarily high accuracy on Bernoulli data models over
hypercube vertices, provided the network width is no greater than its input
dimension. We also substantially strengthen a recent result of Phuong and
Lampert on directional convergence of gradient flow, and obtain as a corollary
that training two-layer ReLU neural networks on orthogonally separable datasets
can cause their adversarial reprogramming to fail. We support these theoretical
results by experiments that demonstrate that, as long as batch normalisation
layers are suitably initialised, even untrained networks with random weights
are susceptible to adversarial reprogramming. This is in contrast to
observations in several recent works that suggested that adversarial
reprogramming is not possible for untrained networks to any degree of
reliability.

Upcoming technologies like digital twins, autonomous, and artificial
intelligent systems involving safety-critical applications require models which
are accurate, interpretable, computationally efficient, and generalizable.
Unfortunately, the two most commonly used modeling approaches, physics-based
modeling (PBM) and data-driven modeling (DDM) fail to satisfy all these
requirements. In the current work, we demonstrate how a hybrid approach
combining the best of PBM and DDM can result in models which can outperform
them both. We do so by combining partial differential equations based on first
principles describing partially known physics with a black box DDM, in this
case, a deep neural network model compensating for the unknown physics. First,
we present a mathematical argument for why this approach should work and then
apply the hybrid approach to model two dimensional heat diffusion problem with
an unknown source term. The result demonstrates the method's superior
performance in terms of accuracy, and generalizability. Additionally, it is
shown how the DDM part can be interpreted within the hybrid framework to make
the overall approach reliable.

Providing mobile robots with the ability to manipulate objects has, despite
decades of research, remained a challenging problem. The problem is
approachable in constrained environments where there is ample prior knowledge
of the environment layout and manipulatable objects. The challenge is in
building systems that scale beyond specific situational instances and
gracefully operate in novel conditions. In the past, researchers used heuristic
and simple rule-based strategies to accomplish tasks such as scene segmentation
or reasoning about occlusion. These heuristic strategies work in constrained
environments where a roboticist can make simplifying assumptions about
everything from the geometries of the objects to be interacted with, level of
clutter, camera position, lighting, and a myriad of other relevant variables.
The work in this thesis will demonstrate how to build a system for robotic
mobile manipulation that is robust to changes in these variables. This
robustness will be enabled by recent simultaneous advances in the fields of big
data, deep learning, and simulation. The ability of simulators to create
realistic sensory data enables the generation of massive corpora of labeled
training data for various grasping and navigation-based tasks. It is now
possible to build systems that work in the real world trained using deep
learning entirely on synthetic data. The ability to train and test on synthetic
data allows for quick iterative development of new perception, planning and
grasp execution algorithms that work in many environments.

Federated learning (FL) has emerged as an effective approach to address
consumer privacy needs. FL has been successfully applied to certain machine
learning tasks, such as training smart keyboard models and keyword spotting.
Despite FL's initial success, many important deep learning use cases, such as
ranking and recommendation tasks, have been limited from on-device learning.
One of the key challenges faced by practical FL adoption for DL-based ranking
and recommendation is the prohibitive resource requirements that cannot be
satisfied by modern mobile systems. We propose Federated Ensemble Learning
(FEL) as a solution to tackle the large memory requirement of deep learning
ranking and recommendation tasks. FEL enables large-scale ranking and
recommendation model training on-device by simultaneously training multiple
model versions on disjoint clusters of client devices. FEL integrates the
trained sub-models via an over-arch layer into an ensemble model that is hosted
on the server. Our experiments demonstrate that FEL leads to 0.43-2.31% model
quality improvement over traditional on-device federated learning - a
significant improvement for ranking and recommendation system use cases.

Much of reinforcement learning theory is built on top of oracles that are
computationally hard to implement. Specifically for learning near-optimal
policies in Partially Observable Markov Decision Processes (POMDPs), existing
algorithms either need to make strong assumptions about the model dynamics
(e.g. deterministic transitions) or assume access to an oracle for solving a
hard optimistic planning or estimation problem as a subroutine. In this work we
develop the first oracle-free learning algorithm for POMDPs under reasonable
assumptions. Specifically, we give a quasipolynomial-time end-to-end algorithm
for learning in "observable" POMDPs, where observability is the assumption that
well-separated distributions over states induce well-separated distributions
over observations. Our techniques circumvent the more traditional approach of
using the principle of optimism under uncertainty to promote exploration, and
instead give a novel application of barycentric spanners to constructing policy
covers.

We study the problem of high-dimensional sparse mean estimation in the
presence of an $\epsilon$-fraction of adversarial outliers. Prior work obtained
sample and computationally efficient algorithms for this task for
identity-covariance subgaussian distributions. In this work, we develop the
first efficient algorithms for robust sparse mean estimation without a priori
knowledge of the covariance. For distributions on $\mathbb R^d$ with
"certifiably bounded" $t$-th moments and sufficiently light tails, our
algorithm achieves error of $O(\epsilon^{1-1/t})$ with sample complexity $m =
(k\log(d))^{O(t)}/\epsilon^{2-2/t}$. For the special case of the Gaussian
distribution, our algorithm achieves near-optimal error of $\tilde O(\epsilon)$
with sample complexity $m = O(k^4 \mathrm{polylog}(d))/\epsilon^2$. Our
algorithms follow the Sum-of-Squares based, proofs to algorithms approach. We
complement our upper bounds with Statistical Query and low-degree polynomial
testing lower bounds, providing evidence that the sample-time-error tradeoffs
achieved by our algorithms are qualitatively the best possible.

Strong physical unclonable functions (PUFs) provide a low-cost authentication
primitive for resource constrained devices. However, most strong PUF
architectures can be modeled through learning algorithms with a limited number
of CRPs. In this paper, we introduce the concept of non-monotonic response
quantization for strong PUFs. Responses depend not only on which path is
faster, but also on the distance between the arriving signals. Our experiments
show that the resulting PUF has increased security against learning attacks. To
demonstrate, we designed and implemented a non-monotonically quantized
ring-oscillator based PUF in 65 nm technology. Measurement results show nearly
ideal uniformity and uniqueness, with bit error rate of 13.4% over the
temperature range from 0 C to 50 C.

Physics-informed neural networks (PiNNs) recently emerged as a powerful
solver for a large class of partial differential equations under various
initial and boundary conditions. In this paper, we propose trapz-PiNNs,
physics-informed neural networks incorporated with a modified trapezoidal rule
recently developed for accurately evaluating fractional laplacian and solve the
space-fractional Fokker-Planck equations in 2D and 3D. We describe the modified
trapezoidal rule in detail and verify the second-order accuracy. We demonstrate
trapz-PiNNs have high expressive power through predicting solution with low
$\mathcal{L}^2$ relative error on a variety of numerical examples. We also use
local metrics such as pointwise absolute and relative errors to analyze where
could be further improved. We present an effective method for improving
performance of trapz-PiNN on local metrics, provided that physical observations
of high-fidelity simulation of the true solution are available. Besides the
usual advantages of the deep learning solvers such as adaptivity and
mesh-independence, the trapz-PiNN is able to solve PDEs with fractional
laplacian with arbitrary $\alpha\in (0,2)$ and specializes on rectangular
domain. It also has potential to be generalized into higher dimensions.

To investigate the heterogeneity in federated learning in real-world
scenarios, we generalize the classic federated learning to federated
hetero-task learning, which emphasizes the inconsistency across the
participants in federated learning in terms of both data distribution and
learning tasks. We also present B-FHTL, a federated hetero-task learning
benchmark consisting of simulation dataset, FL protocols and a unified
evaluation mechanism. B-FHTL dataset contains three well-designed federated
learning tasks with increasing heterogeneity. Each task simulates the clients
with different non-IID data and learning tasks. To ensure fair comparison among
different FL algorithms, B-FHTL builds in a full suite of FL protocols by
providing high-level APIs to avoid privacy leakage, and presets most common
evaluation metrics spanning across different learning tasks, such as
regression, classification, text generation and etc. Furthermore, we compare
the FL algorithms in fields of federated multi-task learning, federated
personalization and federated meta learning within B-FHTL, and highlight the
influence of heterogeneity and difficulties of federated hetero-task learning.
Our benchmark, including the federated dataset, protocols, the evaluation
mechanism and the preliminary experiment, is open-sourced at
https://github.com/alibaba/FederatedScope/tree/master/benchmark/B-FHTL

This work studies the problem of learning unbiased algorithms from biased
feedback for recommender systems. We address this problem from both theoretical
and algorithmic perspectives. Recent works in unbiased learning have advanced
the state-of-the-art with various techniques such as meta-learning, knowledge
distillation, and information bottleneck. Despite their empirical successes,
most of them lack theoretical guarantee, forming non-negligible gaps between
the theories and recent algorithms. To this end, we first view the unbiased
recommendation problem from a distribution shift perspective. We theoretically
analyze the generalization bounds of unbiased learning and suggest their close
relations with recent unbiased learning objectives. Based on the theoretical
analysis, we further propose a principled framework, Adversarial Self-Training
(AST), for unbiased recommendation. Empirical evaluation on real-world and
semi-synthetic datasets demonstrate the effectiveness of the proposed AST.

We present a video generation model that accurately reproduces object motion,
changes in camera viewpoint, and new content that arises over time. Existing
video generation methods often fail to produce new content as a function of
time while maintaining consistencies expected in real environments, such as
plausible dynamics and object persistence. A common failure case is for content
to never change due to over-reliance on inductive biases to provide temporal
consistency, such as a single latent code that dictates content for the entire
video. On the other extreme, without long-term consistency, generated videos
may morph unrealistically between different scenes. To address these
limitations, we prioritize the time axis by redesigning the temporal latent
representation and learning long-term consistency from data by training on
longer videos. To this end, we leverage a two-phase training strategy, where we
separately train using longer videos at a low resolution and shorter videos at
a high resolution. To evaluate the capabilities of our model, we introduce two
new benchmark datasets with explicit focus on long-term temporal dynamics.

Training an effective video-and-language model intuitively requires multiple
frames as model inputs. However, it is unclear whether using multiple frames is
beneficial to downstream tasks, and if yes, whether the performance gain is
worth the drastically-increased computation and memory costs resulting from
using more frames. In this work, we explore single-frame models for
video-and-language learning. On a diverse set of video-and-language tasks
(including text-to-video retrieval and video question answering), we show the
surprising result that, with large-scale pre-training and a proper frame
ensemble strategy at inference time, a single-frame trained model that does not
consider temporal information can achieve better performance than existing
methods that use multiple frames for training. This result reveals the
existence of a strong "static appearance bias" in popular video-and-language
datasets. Therefore, to allow for a more comprehensive evaluation of
video-and-language models, we propose two new retrieval tasks based on existing
fine-grained action recognition datasets that encourage temporal modeling. Our
code is available at https://github.com/jayleicn/singularity

Graph Neural Networks (GNNs) have shown great power in learning node
representations on graphs. However, they may inherit historical prejudices from
training data, leading to discriminatory bias in predictions. Although some
work has developed fair GNNs, most of them directly borrow fair representation
learning techniques from non-graph domains without considering the potential
problem of sensitive attribute leakage caused by feature propagation in GNNs.
However, we empirically observe that feature propagation could vary the
correlation of previously innocuous non-sensitive features to the sensitive
ones. This can be viewed as a leakage of sensitive information which could
further exacerbate discrimination in predictions. Thus, we design two feature
masking strategies according to feature correlations to highlight the
importance of considering feature propagation and correlation variation in
alleviating discrimination. Motivated by our analysis, we propose Fair View
Graph Neural Network (FairVGNN) to generate fair views of features by
automatically identifying and masking sensitive-correlated features considering
correlation variation after feature propagation. Given the learned fair views,
we adaptively clamp weights of the encoder to avoid using sensitive-related
features. Experiments on real-world datasets demonstrate that FairVGNN enjoys a
better trade-off between model utility and fairness. Our code is publicly
available at https://github.com/YuWVandy/FairVGNN.

In shaping the Internet of Money (IoM), the application of blockchain and
distributed ledger technologies (DLTs) to the financial sector triggered
regulatory concerns. Notably, while the user anonymity enabled in this field
may safeguard privacy, data protection and other civil liberties, lack of
identifiability hinders accountability, investigation and enforcement. This
challenges the framework to combat money laundering and the financing of
terrorism and proliferation (AML/CFT). As both law enforcement agencies and the
private sector apply forensics to track currency across blockchain ecosystems,
this paper focuses on the growing relevance of these techniques. In particular,
this work offers IoM-specific insights into the application of methods of
machine learning and network and transaction graph analysis. Namely, it
analyzes a real-world dataset of Bitcoin transactions represented as a directed
graph network through various machine learning techniques. The modeling of
blockchain transactions as a complex network suggests that the use of data
analysis methods in machine learning, specifically thought for graphs, can aid
transaction classification and help identify illicit fund transfers. Indeed,
this work shows that the neural network types known as Graph Convolutional
Networks (GCN) and Graph Attention Networks (GAT) are a promising solution for
AML/CFT compliance. Notably, in this AML/CFT application scenario GCN
outperform other classic machine learning approaches.

Spatial-temporal data contains rich information and has been widely studied
in recent years due to the rapid development of relevant applications in many
fields. For instance, medical institutions often use electrodes attached to
different parts of a patient to analyse the electorencephal data rich with
spatial and temporal features for health assessment and disease diagnosis.
Existing research has mainly used deep learning techniques such as
convolutional neural network (CNN) or recurrent neural network (RNN) to extract
hidden spatial-temporal features. Yet, it is challenging to incorporate both
inter-dependencies spatial information and dynamic temporal changes
simultaneously. In reality, for a model that leverages these spatial-temporal
features to fulfil complex prediction tasks, it often requires a colossal
amount of training data in order to obtain satisfactory model performance.
Considering the above-mentioned challenges, we propose an adaptive federated
relevance framework, namely FedRel, for spatial-temporal graph learning in this
paper. After transforming the raw spatial-temporal data into high quality
features, the core Dynamic Inter-Intra Graph (DIIG) module in the framework is
able to use these features to generate the spatial-temporal graphs capable of
capturing the hidden topological and long-term temporal correlation information
in these graphs. To improve the model generalization ability and performance
while preserving the local data privacy, we also design a relevance-driven
federated learning module in our framework to leverage diverse data
distributions from different participants with attentive aggregations of their
models.

Human Activity Recognition (HAR) has become an increasingly popular task for
embedded devices such as smartwatches. Most HAR systems for ultra-low power
devices are based on classic Machine Learning (ML) models, whereas Deep
Learning (DL), although reaching state-of-the-art accuracy, is less popular due
to its high energy consumption, which poses a significant challenge for
battery-operated and resource-constrained devices. In this work, we bridge the
gap between on-device HAR and DL thanks to a hierarchical architecture composed
of a decision tree (DT) and a one dimensional Convolutional Neural Network (1D
CNN). The two classifiers operate in a cascaded fashion on two different
sub-tasks: the DT classifies only the easiest activities, while the CNN deals
with more complex ones. With experiments on a state-of-the-art dataset and
targeting a single-core RISC-V MCU, we show that this approach allows to save
up to 67.7% energy w.r.t. a "stand-alone" DL architecture at iso-accuracy.
Additionally, the two-stage system either introduces a negligible memory
overhead (up to 200 B) or on the contrary, reduces the total memory occupation.

Calibration of quantum gates is a necessary hurdle to overcome on the way to
a reliable quantum computer. In a recent paper, a protocol called Gate Set
Calibration protocol (GSC) has been introduced and used to learn coherent
errors from multi-qubit quantum gates. Here, we extend this study in a number
of ways: First, we perform a statistical analysis of the measurement
uncertainties. Second, we find explicit measurement settings that minimize this
uncertainty, while also requiring that the protocol involves only a small
number of distinct gates, aiding physical realizability. We numerically
demonstrate that, just by adding two more single-qubit gates to GSC, the
statistical error produced in the calibration of a CNOT gate is divided by a
factor of more than two.

Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate
auto-segmentation to reduce the time cost for physicians to edit contours.
However, deep learning (DL)-based direct segmentation of CBCT images is a
challenging task, mainly due to the poor image quality and lack of
well-labelled large training datasets. Deformable image registration (DIR) is
often used to propagate the manual contours on the planning CT (pCT) of the
same patient to CBCT. In this work, we undertake solving the problems mentioned
above with the assistance of DIR. Our method consists of three main components.
First, we use deformed pCT contours derived from multiple DIR methods between
pCT and CBCT as pseudo labels for initial training of the DL-based direct
segmentation model. Second, we use deformed pCT contours from another DIR
algorithm as influencer volumes to define the region of interest for DL-based
direct segmentation. Third, the initially trained DL model is further
fine-tuned using a smaller set of true labels. We found that DL-based direct
segmentation on CBCT trained with pseudo labels and without influencer volumes
shows poor performance compared to DIR-based segmentation. However, adding
deformed pCT contours as influencer volumes in the direct segmentation network
dramatically improves segmentation performance, reaching the accuracy level of
DIR-based segmentation. The DL model with influencer volumes can be further
improved through fine-tuning using a smaller set of true labels. Experiments
showed that 7 out of 19 structures have an at least 0.2 Dice similarity
coefficient increase compared to DIR-based segmentation. A DL-based direct CBCT
segmentation model can be improved to outperform DIR-based segmentation models
by using deformed pCT contours as pseudo labels and influencer volumes for
initial training, and by using a smaller set of true labels for model fine
tuning.

This paper focuses on improving the resource allocation algorithm in terms of
packet delivery ratio (PDR), i.e., the number of successfully received packets
sent by end devices (EDs) in a long-range wide-area network (LoRaWAN). Setting
the transmission parameters significantly affects the PDR. Employing
reinforcement learning (RL), we propose a resource allocation algorithm that
enables the EDs to configure their transmission parameters in a distributed
manner. We model the resource allocation problem as a multi-armed bandit (MAB)
and then address it by proposing a two-phase algorithm named MIX-MAB, which
consists of the exponential weights for exploration and exploitation (EXP3) and
successive elimination (SE) algorithms. We evaluate the MIX-MAB performance
through simulation results and compare it with other existing approaches.
Numerical results show that the proposed solution performs better than the
existing schemes in terms of convergence time and PDR.

The use of Convolutional Neural Networks (CNNs) is widespread in Deep
Learning due to a range of desirable model properties which result in an
efficient and effective machine learning framework. However, performant CNN
architectures must be tailored to specific tasks in order to incorporate
considerations such as the input length, resolution, and dimentionality. In
this work, we overcome the need for problem-specific CNN architectures with our
Continuous Convolutional Neural Network (CCNN): a single CNN architecture
equipped with continuous convolutional kernels that can be used for tasks on
data of arbitrary resolution, dimensionality and length without structural
changes. Continuous convolutional kernels model long range dependencies at
every layer, and remove the need for downsampling layers and task-dependent
depths needed in current CNN architectures. We show the generality of our
approach by applying the same CCNN to a wide set of tasks on sequential
(1$\mathrm{D}$) and visual data (2$\mathrm{D}$). Our CCNN performs
competitively and often outperforms the current state-of-the-art across all
tasks considered.

Federated learning (FL) is a type of collaborative machine learning where
participating peers/clients process their data locally, sharing only updates to
the collaborative model. This enables to build privacy-aware distributed
machine learning models, among others. The goal is the optimization of a
statistical model's parameters by minimizing a cost function of a collection of
datasets which are stored locally by a set of clients. This process exposes the
clients to two issues: leakage of private information and lack of
personalization of the model. On the other hand, with the recent advancements
in various techniques to analyze data, there is a surge of concern for the
privacy violation of the participating clients. To mitigate this, differential
privacy and its variants serve as a standard for providing formal privacy
guarantees. Often the clients represent very heterogeneous communities and hold
data which are very diverse. Therefore, aligned with the recent focus of the FL
community to build a framework of personalized models for the users
representing their diversity, it is also of utmost importance to protect the
clients' sensitive and personal information against potential threats. To
address this goal we consider $d$-privacy, also known as metric privacy, which
is a variant of local differential privacy, using a a metric-based obfuscation
technique that preserves the topological distribution of the original data. To
cope with the issue of protecting the privacy of the clients and allowing for
personalized model training to enhance the fairness and utility of the system,
we propose a method to provide group privacy guarantees exploiting some key
properties of $d$-privacy which enables personalized models under the framework
of FL. We provide theoretical justifications to the applicability and
experimental validation on real datasets to illustrate the working of our
method.

Speaker recognition systems (SRSs) have recently been shown to be vulnerable
to adversarial attacks, raising significant security concerns. In this work, we
systematically investigate transformation and adversarial training based
defenses for securing SRSs. According to the characteristic of SRSs, we present
22 diverse transformations and thoroughly evaluate them using 7 recent
promising adversarial attacks (4 white-box and 3 black-box) on speaker
recognition. With careful regard for best practices in defense evaluations, we
analyze the strength of transformations to withstand adaptive attacks. We also
evaluate and understand their effectiveness against adaptive attacks when
combined with adversarial training. Our study provides lots of useful insights
and findings, many of them are new or inconsistent with the conclusions in the
image and speech recognition domains, e.g., variable and constant bit rate
speech compressions have different performance, and some non-differentiable
transformations remain effective against current promising evasion techniques
which often work well in the image domain. We demonstrate that the proposed
novel feature-level transformation combined with adversarial training is rather
effective compared to the sole adversarial training in a complete white-box
setting, e.g., increasing the accuracy by 13.62% and attack cost by two orders
of magnitude, while other transformations do not necessarily improve the
overall defense capability. This work sheds further light on the research
directions in this field. We also release our evaluation platform SPEAKERGUARD
to foster further research.

In privacy-preserving machine learning, it is common that the owner of the
learned model does not have any physical access to the data. Instead, only a
secured remote access to a data lake is granted to the model owner without any
ability to retrieve data from the data lake. Yet, the model owner may want to
export the trained model periodically from the remote repository and a question
arises whether this may cause is a risk of data leakage. In this paper, we
introduce the concept of data stealing attack during the export of neural
networks. It consists in hiding some information in the exported network that
allows the reconstruction outside the data lake of images initially stored in
that data lake. More precisely, we show that it is possible to train a network
that can perform lossy image compression and at the same time solve some
utility tasks such as image segmentation. The attack then proceeds by exporting
the compression decoder network together with some image codes that leads to
the image reconstruction outside the data lake. We explore the feasibility of
such attacks on databases of CT and MR images, showing that it is possible to
obtain perceptually meaningful reconstructions of the target dataset, and that
the stolen dataset can be used in turns to solve a broad range of tasks.
Comprehensive experiments and analyses show that data stealing attacks should
be considered as a threat for sensitive imaging data sources.

The statistical regularities in language corpora encode well-known social
biases into word embeddings. Here, we focus on gender to provide a
comprehensive analysis of group-based biases in widely-used static English word
embeddings trained on internet corpora (GloVe 2014, fastText 2017). Using the
Single-Category Word Embedding Association Test, we demonstrate the widespread
prevalence of gender biases that also show differences in: (1) frequencies of
words associated with men versus women; (b) part-of-speech tags in
gender-associated words; (c) semantic categories in gender-associated words;
and (d) valence, arousal, and dominance in gender-associated words.
  First, in terms of word frequency: we find that, of the 1,000 most frequent
words in the vocabulary, 77% are more associated with men than women, providing
direct evidence of a masculine default in the everyday language of the
English-speaking world. Second, turning to parts-of-speech: the top
male-associated words are typically verbs (e.g., fight, overpower) while the
top female-associated words are typically adjectives and adverbs (e.g., giving,
emotionally). Gender biases in embeddings also permeate parts-of-speech. Third,
for semantic categories: bottom-up, cluster analyses of the top 1,000 words
associated with each gender. The top male-associated concepts include roles and
domains of big tech, engineering, religion, sports, and violence; in contrast,
the top female-associated concepts are less focused on roles, including,
instead, female-specific slurs and sexual content, as well as appearance and
kitchen terms. Fourth, using human ratings of word valence, arousal, and
dominance from a ~20,000 word lexicon, we find that male-associated words are
higher on arousal and dominance, while female-associated words are higher on
valence.

Computationally explicit hypotheses of brain function derived from machine
learning (ML)-based models have recently revolutionized neuroscience. Despite
the unprecedented ability of these artificial neural networks (ANNs) to capture
responses in biological neural networks (brains), and our full access to all
internal model components (unlike the brain), ANNs are often referred to as
black-boxes with limited interpretability. Interpretability, however, is a
multi-faceted construct that is used differently across fields. In particular,
interpretability, or explainability, efforts in Artificial Intelligence (AI)
focus on understanding how different model components contribute to its output
(i.e., decision making). In contrast, the neuroscientific interpretability of
ANNs requires explicit alignment between model components and neuroscientific
constructs (e.g., different brain areas or phenomena, like recurrence or
top-down feedback). Given the widespread calls to improve the interpretability
of AI systems, we here highlight these different notions of interpretability
and argue that the neuroscientific interpretability of ANNs can be pursued in
parallel with, but independently from, the ongoing efforts in AI. Certain ML
techniques (e.g., deep dream) can be leveraged in both fields, to ask what
stimulus optimally activates the specific model features (feature visualization
by optimization), or how different features contribute to the model's output
(feature attribution). However, without appropriate brain alignment, certain
features will remain uninterpretable to neuroscientists.

Offline reinforcement learning (RL) enables effective learning from
previously collected data without exploration, which shows great promise in
real-world applications when exploration is expensive or even infeasible. The
discount factor, $\gamma$, plays a vital role in improving online RL sample
efficiency and estimation accuracy, but the role of the discount factor in
offline RL is not well explored. This paper examines two distinct effects of
$\gamma$ in offline RL with theoretical analysis, namely the regularization
effect and the pessimism effect. On the one hand, $\gamma$ is a regulator to
trade-off optimality with sample efficiency upon existing offline techniques.
On the other hand, lower guidance $\gamma$ can also be seen as a way of
pessimism where we optimize the policy's performance in the worst possible
models. We empirically verify the above theoretical observation with tabular
MDPs and standard D4RL tasks. The results show that the discount factor plays
an essential role in the performance of offline RL algorithms, both under small
data regimes upon existing offline methods and in large data regimes without
other conservative methods.

In recent years, Mixture-of-Experts (MoE) has emerged as a promising
technique for deep learning that can scale the model capacity to trillion-plus
parameters while reducing the computing cost via sparse computation. While MoE
opens a new frontier of exceedingly large models, its implementation over
thousands of GPUs has been limited due to mismatch between the dynamic nature
of MoE and static parallelism/pipelining of the system. We present Tutel, a
highly scalable stack design and implementation for MoE with dynamically
adaptive parallelism and pipelining. Tutel delivers adaptive parallelism
switching and adaptive pipelining at runtime, which achieves up to 1.74x and
2.00x single MoE layer speedup, respectively. We also propose a novel
two-dimensional hierarchical algorithm for MoE communication speedup that
outperforms the previous state-of-the-art up to 20.7x over 2,048 GPUs.
Aggregating all techniques, Tutel finally delivers 4.96x and 5.75x speedup of a
single MoE layer on 16 GPUs and 2,048 GPUs, respectively, over Fairseq: Meta's
Facebook AI Research Sequence-to-Sequence Toolkit (Tutel is now partially
adopted by Fairseq). Tutel source code is available in public:
https://github.com/microsoft/tutel . Our evaluation shows that Tutel
efficiently and effectively runs a real-world MoE-based model named SwinV2-MoE,
built upon Swin Transformer V2, a state-of-the-art computer vision
architecture. On efficiency, Tutel accelerates SwinV2-MoE, achieving up to
1.55x and 2.11x speedup in training and inference over Fairseq, respectively.
On effectiveness, the SwinV2-MoE model achieves superior accuracy in both
pre-training and down-stream computer vision tasks such as COCO object
detection than the counterpart dense model, indicating the readiness of Tutel
for end-to-end real-world model training and inference. SwinV2-MoE is open
sourced in https://github.com/microsoft/Swin-Transformer .

Recent advances in differentiable rendering have enabled high-quality
reconstruction of 3D scenes from multi-view images. Most methods rely on simple
rendering algorithms: pre-filtered direct lighting or learned representations
of irradiance. We show that a more realistic shading model, incorporating ray
tracing and Monte Carlo integration, substantially improves decomposition into
shape, materials & lighting. Unfortunately, Monte Carlo integration provides
estimates with significant noise, even at large sample counts, which makes
gradient-based inverse rendering very challenging. To address this, we
incorporate multiple importance sampling and denoising in a novel inverse
rendering pipeline. This substantially improves convergence and enables
gradient-based optimization at low sample counts. We present an efficient
method to jointly reconstruct geometry (explicit triangle meshes), materials,
and lighting, which substantially improves material and light separation
compared to previous work. We argue that denoising can become an integral part
of high quality inverse rendering pipelines.

Prior work has proposed a simple strategy for reinforcement learning (RL):
label experience with the outcomes achieved in that experience, and then
imitate the relabeled experience. These outcome-conditioned imitation learning
methods are appealing because of their simplicity, strong performance, and
close ties with supervised learning. However, it remains unclear how these
methods relate to the standard RL objective, reward maximization. In this
paper, we prove that existing outcome-conditioned imitation learning methods do
not necessarily improve the policy; rather, in some settings they can decrease
the expected reward. Nonetheless, we show that a simple modification results in
a method that does guarantee policy improvement, under some assumptions. Our
aim is not to develop an entirely new method, but rather to explain how a
variant of outcome-conditioned imitation learning can be used to maximize
rewards.

In document-level event extraction (DEE) task, event arguments always scatter
across sentences (across-sentence issue) and multiple events may lie in one
document (multi-event issue). In this paper, we argue that the relation
information of event arguments is of great significance for addressing the
above two issues, and propose a new DEE framework which can model the relation
dependencies, called Relation-augmented Document-level Event Extraction
(ReDEE). More specifically, this framework features a novel and tailored
transformer, named as Relation-augmented Attention Transformer (RAAT). RAAT is
scalable to capture multi-scale and multi-amount argument relations. To further
leverage relation information, we introduce a separate event relation
prediction task and adopt multi-task learning method to explicitly enhance
event extraction performance. Extensive experiments demonstrate the
effectiveness of the proposed method, which can achieve state-of-the-art
performance on two public datasets. Our code is available at https://github.
com/TencentYoutuResearch/RAAT.

Let $\mathcal{M}$ be a compact $d$-dimensional submanifold of $\mathbb{R}^N$
with reach $\tau$ and volume $V_{\mathcal M}$. Fix $\epsilon \in (0,1)$. In
this paper we prove that a nonlinear function $f: \mathbb{R}^N \rightarrow
\mathbb{R}^{m}$ exists with $m \leq C \left(d / \epsilon^2 \right) \log
\left(\frac{\sqrt[d]{V_{\mathcal M}}}{\tau} \right)$ such that $$(1 - \epsilon)
\| {\bf x} - {\bf y} \|_2 \leq \left\| f({\bf x}) - f({\bf y}) \right\|_2 \leq
(1 + \epsilon) \| {\bf x} - {\bf y} \|_2$$ holds for all ${\bf x} \in
\mathcal{M}$ and ${\bf y} \in \mathbb{R}^N$. In effect, $f$ not only serves as
a bi-Lipschitz function from $\mathcal{M}$ into $\mathbb{R}^{m}$ with
bi-Lipschitz constants close to one, but also approximately preserves all
distances from points not in $\mathcal{M}$ to all points in $\mathcal{M}$ in
its image. Furthermore, the proof is constructive and yields an algorithm which
works well in practice. In particular, it is empirically demonstrated herein
that such nonlinear functions allow for more accurate compressive nearest
neighbor classification than standard linear Johnson-Lindenstrauss embeddings
do in practice.

In recent years, colorectal cancer has become one of the most significant
diseases that endanger human health. Deep learning methods are increasingly
important for the classification of colorectal histopathology images. However,
existing approaches focus more on end-to-end automatic classification using
computers rather than human-computer interaction. In this paper, we propose an
IL-MCAM framework. It is based on attention mechanisms and interactive
learning. The proposed IL-MCAM framework includes two stages: automatic
learning (AL) and interactivity learning (IL). In the AL stage, a multi-channel
attention mechanism model containing three different attention mechanism
channels and convolutional neural networks is used to extract multi-channel
features for classification. In the IL stage, the proposed IL-MCAM framework
continuously adds misclassified images to the training set in an interactive
approach, which improves the classification ability of the MCAM model. We
carried out a comparison experiment on our dataset and an extended experiment
on the HE-NCT-CRC-100K dataset to verify the performance of the proposed
IL-MCAM framework, achieving classification accuracies of 98.98% and 99.77%,
respectively. In addition, we conducted an ablation experiment and an
interchangeability experiment to verify the ability and interchangeability of
the three channels. The experimental results show that the proposed IL-MCAM
framework has excellent performance in the colorectal histopathological image

This paper is concerned with online filtering of discretely observed
nonlinear diffusion processes. Our approach is based on the fully adapted
auxiliary particle filter, which involves Doob's $h$-transforms that are
typically intractable. We propose a computational framework to approximate
these $h$-transforms by solving the underlying backward Kolmogorov equations
using nonlinear Feynman-Kac formulas and neural networks. The methodology
allows one to train a locally optimal particle filter prior to the
data-assimilation procedure. Numerical experiments illustrate that the proposed
approach can be orders of magnitude more efficient than the bootstrap particle
filter in the regime of highly informative observations, when the observations
are extreme under the model, and if the state dimension is large.

The existence of multiple load-solution mappings of non-convex AC-OPF
problems poses a fundamental challenge to deep neural network (DNN) schemes. As
the training dataset may contain a mixture of data points corresponding to
different load-solution mappings, the DNN can fail to learn a legitimate
mapping and generate inferior solutions. We propose DeepOPF-AL as an
augmented-learning approach to tackle this issue. The idea is to train a DNN to
learn a unique mapping from an augmented input, i.e., (load, initial point), to
the solution generated by an iterative OPF solver with the load and initial
point as intake. We then apply the learned augmented mapping to solve AC-OPF
problems much faster than conventional solvers. Simulation results over IEEE
test cases show that DeepOPF-AL achieves noticeably better optimality and
similar feasibility and speedup performance, as compared to a recent DNN
scheme, with the same DNN size yet elevated training complexity.

In the context of adversarial robustness, a single model does not usually
have enough power to defend against all possible adversarial attacks, and as a
result, has sub-optimal robustness. Consequently, an emerging line of work has
focused on learning an ensemble of neural networks to defend against
adversarial attacks. In this work, we take a principled approach towards
building robust ensembles. We view this problem from the perspective of
margin-boosting and develop an algorithm for learning an ensemble with maximum
margin. Through extensive empirical evaluation on benchmark datasets, we show
that our algorithm not only outperforms existing ensembling techniques, but
also large models trained in an end-to-end fashion. An important byproduct of
our work is a margin-maximizing cross-entropy (MCE) loss, which is a better
alternative to the standard cross-entropy (CE) loss. Empirically, we show that
replacing the CE loss in state-of-the-art adversarial training techniques with
our MCE loss leads to significant performance improvement.

As a highly ill-posed issue, single image super-resolution (SISR) has been
widely investigated in recent years. The main task of SISR is to recover the
information loss caused by the degradation procedure. According to the Nyquist
sampling theory, the degradation leads to aliasing effect and makes it hard to
restore the correct textures from low-resolution (LR) images. In practice,
there are correlations and self-similarities among the adjacent patches in the
natural images. This paper considers the self-similarity and proposes a
hierarchical image super-resolution network (HSRNet) to suppress the influence
of aliasing. We consider the SISR issue in the optimization perspective, and
propose an iterative solution pattern based on the half-quadratic splitting
(HQS) method. To explore the texture with local image prior, we design a
hierarchical exploration block (HEB) and progressive increase the receptive
field. Furthermore, multi-level spatial attention (MSA) is devised to obtain
the relations of adjacent feature and enhance the high-frequency information,
which acts as a crucial role for visual experience. Experimental result shows
HSRNet achieves better quantitative and visual performance than other works,
and remits the aliasing more effectively.

The deployment of Socially Intelligent Agents (SIAs) in learning environments
has proven to have several advantages in different areas of application. Social
Agent Authoring Tools allow scenario designers to create tailored experiences
with high control over SIAs behaviour, however, on the flip side, this comes at
a cost as the complexity of the scenarios and its authoring can become
overbearing. In this paper we introduce the concept of Explainable Social Agent
Authoring Tools with the goal of analysing if authoring tools for social agents
are understandable and interpretable. To this end we examine whether an
authoring tool, FAtiMA-Toolkit, is understandable and its authoring steps
interpretable, from the point-of-view of the author. We conducted two user
studies to quantitatively assess the Interpretability, Comprehensibility and
Transparency of FAtiMA-Toolkit from the perspective of a scenario designer. One
of the key findings is the fact that FAtiMA-Toolkit's conceptual model is, in
general, understandable, however the emotional-based concepts were not as
easily understood and used by the authors. Although there are some positive
aspects regarding the explainability of FAtiMA-Toolkit, there is still progress
to be made to achieve a fully explainable social agent authoring tool. We
provide a set of key concepts and possible solutions that can guide developers
to build such tools.

Large medical imaging data sets are becoming increasingly available. A common
challenge in these data sets is to ensure that each sample meets minimum
quality requirements devoid of significant artefacts. Despite a wide range of
existing automatic methods having been developed to identify imperfections and
artefacts in medical imaging, they mostly rely on data-hungry methods. In
particular, the lack of sufficient scans with artefacts available for training
has created a barrier in designing and deploying machine learning in clinical
research. To tackle this problem, we propose a novel framework having four main
components: (1) a set of artefact generators inspired by magnetic resonance
physics to corrupt brain MRI scans and augment a training dataset, (2) a set of
abstract and engineered features to represent images compactly, (3) a feature
selection process that depends on the class of artefact to improve
classification performance, and (4) a set of Support Vector Machine (SVM)
classifiers trained to identify artefacts. Our novel contributions are
threefold: first, we use the novel physics-based artefact generators to
generate synthetic brain MRI scans with controlled artefacts as a data
augmentation technique. This will avoid the labour-intensive collection and
labelling process of scans with rare artefacts. Second, we propose a large pool
of abstract and engineered image features developed to identify 9 different
artefacts for structural MRI. Finally, we use an artefact-based feature
selection block that, for each class of artefacts, finds the set of features
that provide the best classification performance. We performed validation
experiments on a large data set of scans with artificially-generated artefacts,
and in a multiple sclerosis clinical trial where real artefacts were identified
by experts, showing that the proposed pipeline outperforms traditional methods.

The definition and representation of planning problems is at the heart of AI
planning research. A key part is the representation of action models. Decades
of advances improving declarative action model representations resulted in
numerous theoretical advances, and capable, working, domain-independent
planners. However, despite the maturity of the field, AI planning technology is
still rarely used outside the research community, suggesting that current
representations fail to capture real-world requirements, such as utilizing
complex mathematical functions and models learned from data. We argue that this
is because the modeling process is assumed to have taken place and completed
prior to the planning process, i.e., offline modeling for offline planning.
There are several challenges inherent to this approach, including: limited
expressiveness of declarative modeling languages; early commitment to modeling
choices and computation, that preclude using the most appropriate resolution
for each action model -- which can only be known during planning; and
difficulty in reliably using non-declarative, learned, models.
  We therefore suggest to change the AI planning process, such that is carries
out online modeling in offline planning, i.e., the use of action models that
are computed or even generated as part of the planning process, as they are
accessed. This generalizes the existing approach (offline modeling). The
proposed definition admits novel planning processes, and we suggest one
concrete implementation, demonstrating the approach. We sketch initial results
that were obtained as part of a first attempt to follow this approach by
planning with action cost estimators. We conclude by discussing open

Vision-and-language tasks are gaining popularity in the research community,
but the focus is still mainly on English. We propose a pipeline that utilizes
English-only vision-language models to train a monolingual model for a target
language. We propose to extend OSCAR+, a model which leverages object tags as
anchor points for learning image-text alignments, to train on visual question
answering datasets in different languages. We propose a novel approach to
knowledge distillation to train the model in other languages using parallel
sentences. Compared to other models that use the target language in the
pretraining corpora, we can leverage an existing English model to transfer the
knowledge to the target language using significantly lesser resources. We also
release a large-scale visual question answering dataset in Japanese and Hindi
language. Though we restrict our work to visual question answering, our model
can be extended to any sequence-level classification task, and it can be
extended to other languages as well. This paper focuses on two languages for
the visual question answering task - Japanese and Hindi. Our pipeline
outperforms the current state-of-the-art models by a relative increase of 4.4%
and 13.4% respectively in accuracy.

Adversarial training, which is to enhance robustness against adversarial
attacks, has received much attention because it is easy to generate
human-imperceptible perturbations of data to deceive a given deep neural
network. In this paper, we propose a new adversarial training algorithm that is
theoretically well motivated and empirically superior to other existing
algorithms. A novel feature of the proposed algorithm is to use a data-adaptive
regularization for robustifying a prediction model. We apply more
regularization to data which are more vulnerable to adversarial attacks and
vice versa. Even though the idea of data-adaptive regularization is not new,
our data-adaptive regularization has a firm theoretical base of reducing an
upper bound of the robust risk. Numerical experiments illustrate that our
proposed algorithm improves the generalization (accuracy on clean samples) and
robustness (accuracy on adversarial attacks) simultaneously to achieve the

Input distribution shift is one of the vital problems in unsupervised domain
adaptation (UDA). The most popular UDA approaches focus on domain-invariant
representation learning, trying to align the features from different domains
into similar feature distributions. However, these approaches ignore the direct
alignment of input word distributions between domains, which is a vital factor
in word-level classification tasks such as cross-domain NER. In this work, we
shed new light on cross-domain NER by introducing a subword-level solution,
X-Piece, for input word-level distribution shift in NER. Specifically, we
re-tokenize the input words of the source domain to approach the target subword
distribution, which is formulated and solved as an optimal transport problem.
As this approach focuses on the input level, it can also be combined with
previous DIRL methods for further improvement. Experimental results show the
effectiveness of the proposed method based on BERT-tagger on four benchmark NER
datasets. Also, the proposed method is proved to benefit DIRL methods such as
DANN.

Recent work has illuminated the vulnerability of speaker recognition systems
(SRSs) against adversarial attacks, raising significant security concerns in
deploying SRSs. However, they considered only a few settings (e.g., some
combinations of source and target speakers), leaving many interesting and
important settings in real-world attack scenarios alone. In this work, we
present AS2T, the first attack in this domain which covers all the settings,
thus allows the adversary to craft adversarial voices using arbitrary source
and target speakers for any of three main recognition tasks. Since none of the
existing loss functions can be applied to all the settings, we explore many
candidate loss functions for each setting including the existing and newly
designed ones. We thoroughly evaluate their efficacy and find that some
existing loss functions are suboptimal. Then, to improve the robustness of AS2T
towards practical over-the-air attack, we study the possible distortions
occurred in over-the-air transmission, utilize different transformation
functions with different parameters to model those distortions, and incorporate
them into the generation of adversarial voices. Our simulated over-the-air
evaluation validates the effectiveness of our solution in producing robust
adversarial voices which remain effective under various hardware devices and
various acoustic environments with different reverberation, ambient noises, and
noise levels. Finally, we leverage AS2T to perform thus far the largest-scale
evaluation to understand transferability among 14 diverse SRSs. The
transferability analysis provides many interesting and useful insights which
challenge several findings and conclusion drawn in previous works in the image
domain. Our study also sheds light on future directions of adversarial attacks
in the speaker recognition domain.

Lagrangian trajectory or particle dispersion models as well as
semi-Lagrangian advection schemes require meteorological data such as wind,
temperature and geopotential at the exact spatio-temporal locations of the
particles that move independently from a regular grid. Traditionally, this
high-resolution data has been obtained by interpolating the meteorological
parameters from the gridded data of a meteorological model or reanalysis, e.g.
using linear interpolation in space and time. However, interpolation errors are
a large source of error for these models. Reducing them requires meteorological
input fields with high space and time resolution, which may not always be
available and can cause severe data storage and transfer problems. Here, we
interpret this problem as a single image superresolution task. We interpret
meteorological fields available at their native resolution as low-resolution
images and train deep neural networks to up-scale them to higher resolution,
thereby providing more accurate data for Lagrangian models. We train various
versions of the state-of-the-art Enhanced Deep Residual Networks for
Superresolution on low-resolution ERA5 reanalysis data with the goal to
up-scale these data to arbitrary spatial resolution. We show that the resulting
up-scaled wind fields have root-mean-squared errors half the size of the winds
obtained with linear spatial interpolation at acceptable computational
inference costs. In a test setup using the Lagrangian particle dispersion model
FLEXPART and reduced-resolution wind fields, we demonstrate that absolute
horizontal transport deviations of calculated trajectories from "ground-truth"
trajectories calculated with undegraded 0.5{\deg} winds are reduced by at least
49.5% (21.8%) after 48 hours relative to trajectories using linear
interpolation of the wind data when training on 2{\deg} to 1{\deg} (4{\deg} to
2{\deg}) resolution data.

We consider using gradient descent to minimize the nonconvex function
$f(X)=\phi(XX^{T})$ over an $n\times r$ factor matrix $X$, in which $\phi$ is
an underlying smooth convex cost function defined over $n\times n$ matrices.
While only a second-order stationary point $X$ can be provably found in
reasonable time, if $X$ is additionally rank deficient, then its rank
deficiency certifies it as being globally optimal. This way of certifying
global optimality necessarily requires the search rank $r$ of the current
iterate $X$ to be overparameterized with respect to the rank $r^{\star}$ of the
global minimizer $X^{\star}$. Unfortunately, overparameterization significantly
slows down the convergence of gradient descent, from a linear rate with
$r=r^{\star}$ to a sublinear rate when $r>r^{\star}$, even when $\phi$ is
strongly convex. In this paper, we propose an inexpensive preconditioner that
restores the convergence rate of gradient descent back to linear in the
overparameterized case, while also making it agnostic to possible
ill-conditioning in the global minimizer $X^{\star}$.

Parotid gland tumors account for approximately 2% to 10% of head and neck
tumors. Preoperative tumor localization, differential diagnosis, and subsequent
selection of appropriate treatment for parotid gland tumors is critical.
However, the relative rarity of these tumors and the highly dispersed tissue
types have left an unmet need for a subtle differential diagnosis of such
neoplastic lesions based on preoperative radiomics. Recently, deep learning
methods have developed rapidly, especially Transformer beats the traditional
convolutional neural network in computer vision. Many new Transformer-based
networks have been proposed for computer vision tasks. In this study,
multicenter multimodal parotid gland MRI images were collected. The Swin-Unet
which was based on Transformer was used. MRI images of STIR, T1 and T2
modalities were combined into a three-channel data to train the network. We
achieved segmentation of the region of interest for parotid gland and tumor.
The DSC of the model on the test set was 88.63%, MPA was 99.31%, MIoU was
83.99%, and HD was 3.04. Then a series of comparison experiments were designed
in this paper to further validate the segmentation performance of the

Machine Learning for Software Engineering (ML4SE) is an actively growing
research area that focuses on methods that help programmers in their work. In
order to apply the developed methods in practice, they need to achieve
reasonable quality in order to help rather than distract developers. While the
development of new approaches to code representation and data collection
improves the overall quality of the models, it does not take into account the
information that we can get from the project at hand.
  In this work, we investigate how the model's quality can be improved if we
target a specific project. We develop a framework to assess quality
improvements that models can get after fine-tuning for the method name
prediction task on a particular project. We evaluate three models of different
complexity and compare their quality in three settings: trained on a large
dataset of Java projects, further fine-tuned on the data from a particular
project, and trained from scratch on this data. We show that per-project
fine-tuning can greatly improve the models' quality as they capture the
project's domain and naming conventions. We open-source the tool we used for
data collection, as well as the code to run the experiments:
https://zenodo.org/record/6040745.

Single subject prediction of brain disorders from neuroimaging data has
gained increasing attention in recent years. Yet, for some heterogeneous
disorders such as major depression disorder (MDD) and autism spectrum disorder
(ASD), the performance of prediction models on large-scale multi-site datasets
remains poor. We present a two-stage framework to improve the diagnosis of
heterogeneous psychiatric disorders from resting-state functional magnetic
resonance imaging (rs-fMRI). First, we propose a self-supervised mask
prediction task on data from healthy individuals that can exploit differences
between healthy controls and patients in clinical datasets. Next, we train a
supervised classifier on the learned discriminative representations. To model
rs-fMRI data, we develop Graph-S4; an extension to the recently proposed
state-space model S4 to graph settings where the underlying graph structure is
not known in advance. We show that combining the framework and Graph-S4 can
significantly improve the diagnostic performance of neuroimaging-based single
subject prediction models of MDD and ASD on three open-source multi-center
rs-fMRI clinical datasets.

We prove concentration inequalities and associated PAC bounds for continuous-
and discrete-time additive functionals for possibly unbounded functions of
multivariate, nonreversible diffusion processes. Our analysis relies on an
approach via the Poisson equation allowing us to consider a very broad class of
subexponentially ergodic processes. These results add to existing concentration
inequalities for additive functionals of diffusion processes which have so far
been only available for either bounded functions or for unbounded functions of
processes from a significantly smaller class. We demonstrate the power of these
exponential inequalities by two examples of very different areas. Considering a
possibly high-dimensional parametric nonlinear drift model under sparsity
constraints, we apply the continuous-time concentration results to validate the
restricted eigenvalue condition for Lasso estimation, which is fundamental for
the derivation of oracle inequalities. The results for discrete additive
functionals are used to investigate the unadjusted Langevin MCMC algorithm for
sampling of moderately heavy-tailed densities $\pi$. In particular, we provide
PAC bounds for the sample Monte Carlo estimator of integrals $\pi(f)$ for
polynomially growing functions $f$ that quantify sufficient sample and step
sizes for approximation within a prescribed margin with high probability.

Test point insertion (TPI) is a widely used technique for testability
enhancement, especially for logic built-in self-test (LBIST) due to its
relatively low fault coverage. In this paper, we propose a novel TPI approach
based on deep reinforcement learning (DRL), named DeepTPI. Unlike previous
learning-based solutions that formulate the TPI task as a supervised-learning
problem, we train a novel DRL agent, instantiated as the combination of a graph
neural network (GNN) and a Deep Q-Learning network (DQN), to maximize the test
coverage improvement. Specifically, we model circuits as directed graphs and
design a graph-based value network to estimate the action values for inserting
different test points. The policy of the DRL agent is defined as selecting the
action with the maximum value. Moreover, we apply the general node embeddings
from a pre-trained model to enhance node features, and propose a dedicated
testability-aware attention mechanism for the value network. Experimental
results on circuits with various scales show that DeepTPI significantly
improves test coverage compared to the commercial DFT tool. The code of this
work is available at https://github.com/cure-lab/DeepTPI.

We derive a concentration bound for a Q-learning algorithm for average cost
Markov decision processes based on an equivalent shortest path problem, and
compare it numerically with the alternative scheme based on relative value
iteration.

State-of-the-art encoder-decoder models (e.g. for machine translation (MT) or
speech recognition (ASR)) are constructed and trained end-to-end as an atomic
unit. No component of the model can be (re-)used without the others. We
describe LegoNN, a procedure for building encoder-decoder architectures with
decoder modules that can be reused across various MT and ASR tasks, without the
need for any fine-tuning. To achieve reusability, the interface between each
encoder and decoder modules is grounded to a sequence of marginal distributions
over a discrete vocabulary pre-defined by the model designer. We present two
approaches for ingesting these marginals; one is differentiable, allowing the
flow of gradients across the entire network, and the other is
gradient-isolating. To enable portability of decoder modules between MT tasks
for different source languages and across other tasks like ASR, we introduce a
modality agnostic encoder which consists of a length control mechanism to
dynamically adapt encoders' output lengths in order to match the expected input
length range of pre-trained decoders. We present several experiments to
demonstrate the effectiveness of LegoNN models: a trained language generation
LegoNN decoder module from German-English (De-En) MT task can be reused with no
fine-tuning for the Europarl English ASR and the Romanian-English (Ro-En) MT
tasks to match or beat respective baseline models. When fine-tuned towards the
target task for few thousand updates, our LegoNN models improved the Ro-En MT
task by 1.5 BLEU points, and achieved 12.5% relative WER reduction for the
Europarl ASR task. Furthermore, to show its extensibility, we compose a LegoNN
ASR model from three modules -- each has been learned within different
end-to-end trained models on three different datasets -- boosting the WER
reduction to 19.5%.

Privacy attacks on Machine Learning (ML) models often focus on inferring the
existence of particular data points in the training data. However, what the
adversary really wants to know is if a particular \emph{individual}'s
(\emph{subject}'s) data was included during training. In such scenarios, the
adversary is more likely to have access to the distribution of a particular
subject, than actual records. Furthermore, in settings like cross-silo
Federated Learning (FL), a subject's data can be embodied by multiple data
records that are spread across multiple organizations. Nearly all of the
existing private FL literature is dedicated to studying privacy at two
granularities -- item-level (individual data records), and user-level
(participating user in the federation), neither of which apply to data subjects
in cross-silo FL. This insight motivates us to shift our attention from the
privacy of data records to the privacy of \emph{data subjects}, also known as
subject-level privacy. We propose two black-box attacks for \emph{subject
membership inference}, of which one assumes access to a model after each
training round. Using these attacks, we estimate subject membership inference
risk on real-world data for single-party models as well as FL scenarios. We
find our attacks to be extremely potent, even without access to exact training
records, and using the knowledge of membership for a handful of subjects. To
better understand the various factors that may influence subject privacy risk
in cross-silo FL settings, we systematically generate several hundred synthetic
federation configurations, varying properties of the data, model design and
training, and the federation itself. Finally, we investigate the effectiveness
of Differential Privacy in mitigating this threat.

Permutation codes were extensively studied in order to correct different
types of errors for the applications on power line communication and rank
modulation for flash memory. In this paper, we introduce the neural network
decoders for permutation codes to correct these errors with one-shot decoding,
which treat the decoding as $n$ classification tasks for non-binary symbols for
a code of length $n$. These are actually the first general decoders introduced
to deal with any error type for these two applications. The performance of the
decoders is evaluated by simulations with different error models.

Modern approaches to supervised learning like deep neural networks (DNNs)
typically implicitly assume that observed responses are statistically
independent. In contrast, correlated data are prevalent in real-life
large-scale applications, with typical sources of correlation including
spatial, temporal and clustering structures. These correlations are either
ignored by DNNs, or ad-hoc solutions are developed for specific use cases. We
propose to use the mixed models framework to handle correlated data in DNNs. By
treating the effects underlying the correlation structure as random effects,
mixed models are able to avoid overfitted parameter estimates and ultimately
yield better predictive performance. The key to combining mixed models and DNNs
is using the Gaussian negative log-likelihood (NLL) as a natural loss function
that is minimized with DNN machinery including stochastic gradient descent
(SGD). Since NLL does not decompose like standard DNN loss functions, the use
of SGD with NLL presents some theoretical and implementation challenges, which
we address. Our approach which we call LMMNN is demonstrated to improve
performance over natural competitors in various correlation scenarios on
diverse simulated and real datasets. Our focus is on a regression setting and
tabular datasets, but we also show some results for classification. Our code is
available at https://github.com/gsimchoni/lmmnn.

This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno-Kang
(TSK) fuzzy systems. Based on scikit-learn and PyTorch, PyTSK allows users to
optimize TSK fuzzy systems using fuzzy clustering or mini-batch gradient
descent (MBGD) based algorithms. Several state-of-the-art MBGD-based
optimization algorithms are implemented in the toolbox, which can improve the
generalization performance of TSK fuzzy systems, especially for big data
applications. PyTSK can also be easily extended and customized for more
complicated algorithms, such as modifying the structure of TSK fuzzy systems,
developing more sophisticated training algorithms, and combining TSK fuzzy
systems with neural networks. The code of PyTSK can be found at
https://github.com/YuqiCui/pytsk.

Accurately modeling quadrotor's system dynamics is critical for guaranteeing
agile, safe, and stable navigation. The model needs to capture the system
behavior in multiple flight regimes and operating conditions, including those
producing highly nonlinear effects such as aerodynamic forces and torques,
rotor interactions, or possible system configuration modifications. Classical
approaches rely on handcrafted models and struggle to generalize and scale to
capture these effects. In this paper, we present a novel Physics-Inspired
Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system
dynamics purely from robot experience. Our approach combines the expressive
power of sparse temporal convolutions and dense feed-forward connections to
make accurate system predictions. In addition, physics constraints are embedded
in the training process to facilitate the network's generalization capabilities
to data outside the training distribution. Finally, we design a model
predictive control approach that incorporates the learned dynamics for accurate
closed-loop trajectory tracking fully exploiting the learned model predictions
in a receding horizon fashion. Experimental results demonstrate that our
approach accurately extracts the structure of the quadrotor's dynamics from
data, capturing effects that would remain hidden to classical approaches. To
the best of our knowledge, this is the first time physics-inspired deep
learning is successfully applied to temporal convolutional networks and to the
system identification task, while concurrently enabling predictive control.

Most machine learning methods are used as a black box for modelling. We may
try to extract some knowledge from physics-based training methods, such as
neural ODE (ordinary differential equation). Neural ODE has advantages like a
possibly higher class of represented functions, the extended interpretability
compared to black-box machine learning models, ability to describe both trend
and local behaviour. Such advantages are especially critical for time series
with complicated trends. However, the known drawback is the high training time
compared to the autoregressive models and long-short term memory (LSTM)
networks widely used for data-driven time series modelling. Therefore, we
should be able to balance interpretability and training time to apply neural
ODE in practice. The paper shows that modern neural ODE cannot be reduced to
simpler models for time-series modelling applications. The complexity of neural
ODE is compared to or exceeds the conventional time-series modelling tools. The
only interpretation that could be extracted is the eigenspace of the operator,
which is an ill-posed problem for a large system. Spectra could be extracted
using different classical analysis methods that do not have the drawback of
extended time. Consequently, we reduce the neural ODE to a simpler linear form
and propose a new view on time-series modelling using combined neural networks
and an ODE system approach.

Bayesian optimization has emerged at the forefront of expensive black-box
optimization due to its data efficiency. Recent years have witnessed a
proliferation of studies on the development of new Bayesian optimization
algorithms and their applications. Hence, this paper attempts to provide a
comprehensive and updated survey of recent advances in Bayesian optimization
and identify interesting open problems. We categorize the existing work on
Bayesian optimization into nine main groups according to the motivations and
focus of the proposed algorithms. For each category, we present the main
advances with respect to the construction of surrogate models and adaptation of
the acquisition functions. Finally, we discuss the open questions and suggest
promising future research directions, in particular with regard to
heterogeneity, privacy preservation, and fairness in distributed and federated
optimization systems.

Generalization error bounds for deep neural networks trained by stochastic
gradient descent (SGD) are derived by combining a dynamical control of an
appropriate parameter norm and the Rademacher complexity estimate based on
parameter norms. The bounds explicitly depend on the loss along the training
trajectory, and work for a wide range of network architectures including
multilayer perceptron (MLP) and convolutional neural networks (CNN). Compared
with other algorithm-depending generalization estimates such as uniform
stability-based bounds, our bounds do not require $L$-smoothness of the
nonconvex loss function, and apply directly to SGD instead of Stochastic
Langevin gradient descent (SGLD). Numerical results show that our bounds are
non-vacuous and robust with the change of optimizer and network
hyperparameters.

Based on the manifold hypothesis, real-world data often lie on a
low-dimensional manifold, while normalizing flows as a likelihood-based
generative model are incapable of finding this manifold due to their structural
constraints. So, one interesting question arises: $\textit{"Can we find
sub-manifold(s) of data in normalizing flows and estimate the density of the
data on the sub-manifold(s)?"}$. In this paper, we introduce two approaches,
namely per-pixel penalized log-likelihood and hierarchical training, to answer
the mentioned question. We propose a single-step method for joint manifold
learning and density estimation by disentangling the transformed space obtained
by normalizing flows to manifold and off-manifold parts. This is done by a
per-pixel penalized likelihood function for learning a sub-manifold of the
data. Normalizing flows assume the transformed data is Gaussianizationed, but
this imposed assumption is not necessarily true, especially in high dimensions.
To tackle this problem, a hierarchical training approach is employed to improve
the density estimation on the sub-manifold. The results validate the
superiority of the proposed methods in simultaneous manifold learning and
density estimation using normalizing flows in terms of generated image quality
and likelihood.

Most obstacle avoidance algorithms are only effective in specific
environments, and they have low adaptability to some new environments. In this
paper, we propose a trajectory learning (TL)-based obstacle avoidance
algorithm, which can learn implicit obstacle avoidance mechanism from
trajectories generated by general obstacle avoidance algorithms and achieves
better adaptability. Specifically, we define a general data structure to
describe the obstacle avoidance mechanism. Based on this structure, we
transform the learning of the obstacle avoidance algorithm into a multiclass
classification problem about the direction selection. Then, we design an
artificial neural network (ANN) to fit multiclass classification function
through supervised learning and finally obtain the obstacle avoidance mechanism
that generates the observed trajectories. Our algorithm can obtain the obstacle
avoidance mechanism similar to that demonstrated in the trajectories, and are
adaptable to unseen environments. The automatic learning mechanism simplifies
modification and debugging of obstacle avoidance algorithms in applications.
Simulation results demonstrate that the proposed algorithm can learn obstacle
avoidance strategy from trajectories and achieve better adaptability.

While annotating decent amounts of data to satisfy sophisticated learning
models can be cost-prohibitive for many real-world applications. Active
learning (AL) and semi-supervised learning (SSL) are two effective, but often
isolated, means to alleviate the data-hungry problem. Some recent studies
explored the potential of combining AL and SSL to better probe the unlabeled
data. However, almost all these contemporary SSL-AL works use a simple
combination strategy, ignoring SSL and AL's inherent relation. Further, other
methods suffer from high computational costs when dealing with large-scale,
high-dimensional datasets. Motivated by the industry practice of labeling data,
we propose an innovative Inconsistency-based virtual aDvErsarial Active
Learning (IDEAL) algorithm to further investigate SSL-AL's potential
superiority and achieve mutual enhancement of AL and SSL, i.e., SSL propagates
label information to unlabeled samples and provides smoothed embeddings for AL,
while AL excludes samples with inconsistent predictions and considerable
uncertainty for SSL. We estimate unlabeled samples' inconsistency by
augmentation strategies of different granularities, including fine-grained
continuous perturbation exploration and coarse-grained data transformations.
Extensive experiments, in both text and image domains, validate the
effectiveness of the proposed algorithm, comparing it against state-of-the-art
baselines. Two real-world case studies visualize the practical industrial value
of applying and deploying the proposed data sampling algorithm.

Recent research demonstrates the effectiveness of using pretrained language
models (PLM) to improve dense retrieval and multilingual dense retrieval. In
this work, we present a simple but effective monolingual pretraining task
called contrastive context prediction~(CCP) to learn sentence representation by
modeling sentence level contextual relation. By pushing the embedding of
sentences in a local context closer and pushing random negative samples away,
different languages could form isomorphic structure, then sentence pairs in two
different languages will be automatically aligned. Our experiments show that
model collapse and information leakage are very easy to happen during
contrastive training of language model, but language-specific memory bank and
asymmetric batch normalization operation play an essential role in preventing
collapsing and information leakage, respectively. Besides, a post-processing
for sentence embedding is also very effective to achieve better retrieval
performance. On the multilingual sentence retrieval task Tatoeba, our model
achieves new SOTA results among methods without using bilingual data. Our model
also shows larger gain on Tatoeba when transferring between non-English pairs.
On two multi-lingual query-passage retrieval tasks, XOR Retrieve and Mr.TYDI,
our model even achieves two SOTA results in both zero-shot and supervised
setting among all pretraining models using bilingual data.

Intelligent agents should have the ability to leverage knowledge from
previously learned tasks in order to learn new ones quickly and efficiently.
Meta-learning approaches have emerged as a popular solution to achieve this.
However, meta-reinforcement learning (meta-RL) algorithms have thus far been
restricted to simple environments with narrow task distributions. Moreover, the
paradigm of pretraining followed by fine-tuning to adapt to new tasks has
emerged as a simple yet effective solution in supervised and self-supervised
learning. This calls into question the benefits of meta-learning approaches
also in reinforcement learning, which typically come at the cost of high
complexity. We hence investigate meta-RL approaches in a variety of
vision-based benchmarks, including Procgen, RLBench, and Atari, where
evaluations are made on completely novel tasks. Our findings show that when
meta-learning approaches are evaluated on different tasks (rather than
different variations of the same task), multi-task pretraining with fine-tuning
on new tasks performs equally as well, or better, than meta-pretraining with
meta test-time adaptation. This is encouraging for future research, as
multi-task pretraining tends to be simpler and computationally cheaper than
meta-RL. From these findings, we advocate for evaluating future meta-RL methods
on more challenging tasks and including multi-task pretraining with fine-tuning
as a simple, yet strong baseline.

Machine learning sensors represent a paradigm shift for the future of
embedded machine learning applications. Current instantiations of embedded
machine learning (ML) suffer from complex integration, lack of modularity, and
privacy and security concerns from data movement. This article proposes a more
data-centric paradigm for embedding sensor intelligence on edge devices to
combat these challenges. Our vision for "sensor 2.0" entails segregating sensor
input data and ML processing from the wider system at the hardware level and
providing a thin interface that mimics traditional sensors in functionality.
This separation leads to a modular and easy-to-use ML sensor device. We discuss
challenges presented by the standard approach of building ML processing into
the software stack of the controlling microprocessor on an embedded system and
how the modularity of ML sensors alleviates these problems. ML sensors increase
privacy and accuracy while making it easier for system builders to integrate ML
into their products as a simple component. We provide examples of prospective
ML sensors and an illustrative datasheet as a demonstration and hope that this
will build a dialogue to progress us towards sensor 2.0.

Data augmentation has been rare in the cyber security domain due to technical
difficulties in altering data in a manner that is semantically consistent with
the original data. This shortfall is particularly onerous given the unique
difficulty of acquiring benign and malicious training data that runs into
copyright restrictions, and that institutions like banks and governments
receive targeted malware that will never exist in large quantities. We present
MARVOLO, a binary mutator that programmatically grows malware (and benign)
datasets in a manner that boosts the accuracy of ML-driven malware detectors.
MARVOLO employs semantics-preserving code transformations that mimic the
alterations that malware authors and defensive benign developers routinely make
in practice , allowing us to generate meaningful augmented data. Crucially,
semantics-preserving transformations also enable MARVOLO to safely propagate
labels from original to newly-generated data samples without mandating
expensive reverse engineering of binaries. Further, MARVOLO embeds several key
optimizations that keep costs low for practitioners by maximizing the density
of diverse data samples generated within a given time (or resource) budget.
Experiments using wide-ranging commercial malware datasets and a recent
ML-driven malware detector show that MARVOLO boosts accuracies by up to 5%,
while operating on only a small fraction (15%) of the potential input binaries.

Next-generation wireless systems aim at fulfilling diverse application
requirements but fundamentally rely on point-to-point transmission qualities.
Aligning with recent AI-enabled wireless implementations, this paper introduces
autonomic radios, 6G-AUTOR, that leverage novel algorithm-hardware separation
platforms, softwarization of transmission (TX) and reception (RX) operations,
and automatic reconfiguration of RF frontends, to support link performance and
resilience. As a comprehensive transceiver solution, our design encompasses
several ML-driven models, each enhancing a specific aspect of either TX or RX,
leading to robust transceiver operation under tight constraints of future
wireless systems. A data-driven radio management module was developed via deep
Q-networks to support fast-reconfiguration of TX resource blocks (RB) and
proactive multi-agent access. Also, a ResNet-inspired fast-beamforming solution
was employed to enable robust communication to multiple receivers over the same
RB, which has potential applications in realisation of cell-free
infrastructures. As a receiver the system was equipped with a capability of
ultra-broadband spectrum recognition. Apart from this, a fundamental tool -
automatic modulation classification (AMC) which involves a complex correntropy
extraction, followed by a convolutional neural network (CNN)-based
classification, and a deep learning-based LDPC decoder were added to improve
the reception quality and radio performance. Simulations of individual
algorithms demonstrate that under appropriate training, each of the
corresponding radio functions have either outperformed or have performed on-par
with the benchmark solutions.

Accurate diagnosis and prognosis of Alzheimer's disease are crucial for
developing new therapies and reducing the associated costs. Recently, with the
advances of convolutional neural networks, deep learning methods have been
proposed to automate these two tasks using structural MRI. However, these
methods often suffer from a lack of interpretability and generalization and
have limited prognosis performance. In this paper, we propose a novel deep
framework designed to overcome these limitations. Our pipeline consists of two
stages. In the first stage, 125 3D U-Nets are used to estimate voxelwise grade
scores over the whole brain. The resulting 3D maps are then fused to construct
an interpretable 3D grading map indicating the disease severity at the
structure level. As a consequence, clinicians can use this map to detect the
brain structures affected by the disease. In the second stage, the grading map
and subject's age are used to perform classification with a graph convolutional
neural network. Experimental results based on 2106 subjects demonstrated
competitive performance of our deep framework compared to state-of-the-art
methods on different datasets for both AD diagnosis and prognosis. Moreover, we
found that using a large number of U-Nets processing different overlapping
brain areas improved the generalization capacity of the proposed methods.

Inverse rendering is an ill-posed problem. Previous work has sought to
resolve this by focussing on priors for object or scene shape or appearance. In
this work, we instead focus on a prior for natural illuminations. Current
methods rely on spherical harmonic lighting or other generic representations
and, at best, a simplistic prior on the parameters. We propose a conditional
neural field representation based on a variational auto-decoder with a SIREN
network and, extending Vector Neurons, build equivariance directly into the
network. Using this, we develop a rotation-equivariant, high dynamic range
(HDR) neural illumination model that is compact and able to express complex,
high-frequency features of natural environment maps. Training our model on a
curated dataset of 1.6K HDR environment maps of natural scenes, we compare it
against traditional representations, demonstrate its applicability for an
inverse rendering task and show environment map completion from partial
observations. A PyTorch implementation, our dataset and trained models can be
found at jadgardner.github.io/RENI.

Traditional approaches to financial asset allocation start with returns
forecasting followed by an optimization stage that decides the optimal asset
weights. Any errors made during the forecasting step reduce the accuracy of the
asset weightings, and hence the profitability of the overall portfolio. The
Portfolio Transformer (PT) network, introduced here, circumvents the need to
predict asset returns and instead directly optimizes the Sharpe ratio, a
risk-adjusted performance metric widely used in practice. The PT is a novel
end-to-end portfolio optimization framework, inspired by the numerous successes
of attention mechanisms in natural language processing. With its full
encoder-decoder architecture, specialized time encoding layers, and gating
components, the PT has a high capacity to learn long-term dependencies among
portfolio assets and hence can adapt more quickly to changing market conditions
such as the COVID-19 pandemic. To demonstrate its robustness, the PT is
compared against other algorithms, including the current LSTM-based state of
the art, on three different datasets, with results showing that it offers the
best risk-adjusted performance.

Automated Machine Learning (AutoML) is used more than ever before to support
users in determining efficient hyperparameters, neural architectures, or even
full machine learning pipelines. However, users tend to mistrust the
optimization process and its results due to a lack of transparency, making
manual tuning still widespread. We introduce DeepCAVE, an interactive framework
to analyze and monitor state-of-the-art optimization procedures for AutoML
easily and ad hoc. By aiming for full and accessible transparency, DeepCAVE
builds a bridge between users and AutoML and contributes to establishing trust.
Our framework's modular and easy-to-extend nature provides users with
automatically generated text, tables, and graphic visualizations. We show the
value of DeepCAVE in an exemplary use-case of outlier detection, in which our
framework makes it easy to identify problems, compare multiple runs and
interpret optimization processes. The package is freely available on GitHub
https://github.com/automl/DeepCAVE.

We study the decentralized online regularized linear regression algorithm
over random time-varying graphs. At each time step, every node runs an online
estimation algorithm consisting of an innovation term processing its own new
measurement, a consensus term taking a weighted sum of estimations of its own
and its neighbors with additive and multiplicative communication noises and a
regularization term preventing over-fitting. It is not required that the
regression matrices and graphs satisfy special statistical assumptions such as
mutual independence, spatio-temporal independence or stationarity. We develop
the nonnegative supermartingale inequality of the estimation error, and prove
that the estimations of all nodes converge to the unknown true parameter vector
almost surely if the algorithm gains, graphs and regression matrices jointly
satisfy the sample path spatio-temporal persistence of excitation condition.
Especially, this condition holds by choosing appropriate algorithm gains if the
graphs are uniformly conditionally jointly connected and conditionally
balanced, and the regression models of all nodes are uniformly conditionally
spatio-temporally jointly observable, under which the algorithm converges in
mean square and almost surely. In addition, we prove that the regret upper
bound $\mathcal O(T^{1-\tau}\ln T)$, where $\tau\in (0.5,1)$ is a constant
depending on the algorithm gains.

Heart Disease has become one of the most serious diseases that has a
significant impact on human life. It has emerged as one of the leading causes
of mortality among the people across the globe during the last decade. In order
to prevent patients from further damage, an accurate diagnosis of heart disease
on time is an essential factor. Recently we have seen the usage of non-invasive
medical procedures, such as artificial intelligence-based techniques in the
field of medical. Specially machine learning employs several algorithms and
techniques that are widely used and are highly useful in accurately diagnosing
the heart disease with less amount of time. However, the prediction of heart
disease is not an easy task. The increasing size of medical datasets has made
it a complicated task for practitioners to understand the complex feature
relations and make disease predictions. Accordingly, the aim of this research
is to identify the most important risk-factors from a highly dimensional
dataset which helps in the accurate classification of heart disease with less
complications. For a broader analysis, we have used two heart disease datasets
with various medical features. The classification results of the benchmarked
models proved that there is a high impact of relevant features on the
classification accuracy. Even with a reduced number of features, the
performance of the classification models improved significantly with a reduced
training time as compared with models trained on full feature set.

In online advertising, a set of potential advertisements can be ranked by a
certain auction system where usually the top-1 advertisement would be selected
and displayed at an advertising space. In this paper, we show a selection bias
issue that is present in an auction system. We analyze that the selection bias
destroy truthfulness of the auction, which implies that the buyers
(advertisers) on the auction can not maximize their profits. Although selection
bias is well known in the field of statistics and there are lot of studies for
it, our main contribution is to combine the theoretical analysis of the bias
with the auction mechanism. In our experiment using online A/B testing, we
evaluate the selection bias on an auction system whose ranking score is the
function of predicted CTR (click through rate) of advertisement. The experiment
showed that the selection bias is drastically reduced by using a multi-task
learning which learns the data for all advertisements.

We propose methods for making inferences on the fairness and accuracy of a
given classifier, using only aggregate population statistics. This is necessary
when it is impossible to obtain individual classification data, for instance
when there is no access to the classifier or to a representative
individual-level validation set. We study fairness with respect to the
equalized odds criterion, which we generalize to multiclass classification. We
propose a measure of unfairness with respect to this criterion, which
quantifies the fraction of the population that is treated unfairly. We then
show how inferences on the unfairness and error of a given classifier can be
obtained using only aggregate label statistics such as the rate of prediction
of each label in each sub-population, as well as the true rate of each label.
We derive inference procedures for binary classifiers and for multiclass
classifiers, for the case where confusion matrices in each sub-population are
known, and for the significantly more challenging case where they are unknown.
We report experiments on data sets representing diverse applications, which
demonstrate the effectiveness and the wide range of possible uses of the
proposed methodology.

The Sliced-Wasserstein distance (SW) is a computationally efficient and
theoretically grounded alternative to the Wasserstein distance. Yet, the
literature on its statistical properties -- or, more accurately, its
generalization properties -- with respect to the distribution of slices, beyond
the uniform measure, is scarce. To bring new contributions to this line of
research, we leverage the PAC-Bayesian theory and a central observation that SW
may be interpreted as an average risk, the quantity PAC-Bayesian bounds have
been designed to characterize. We provide three types of results: i)
PAC-Bayesian generalization bounds that hold on what we refer as adaptive
Sliced-Wasserstein distances, i.e. SW defined with respect to arbitrary
distributions of slices (among which data-dependent distributions), ii) a
principled procedure to learn the distribution of slices that yields maximally
discriminative SW, by optimizing our theoretical bounds, and iii) empirical
illustrations of our theoretical findings.

Many types of ventricular and atrial cardiac arrhythmias have been discovered
in clinical practice in the past 100 years, and these arrhythmias are a major
contributor to sudden cardiac death. Ventricular tachycardia, ventricular
fibrillation, and paroxysmal atrial fibrillation are the most
commonly-occurring and dangerous arrhythmias, therefore early detection is
crucial to prevent any further complications and reduce fatalities. Implantable
devices such as pacemakers are commonly used in patients at high risk of sudden
cardiac death. While great advances have been made in medical technology, there
remain significant challenges in effective management of common arrhythmias.
This thesis proposes novel arrhythmia detection and prediction methods to
differentiate cardiac arrhythmias from non-life-threatening cardiac events, to
increase the likelihood of detecting events that may lead to mortality, as well
as reduce the incidence of unnecessary therapeutic intervention. The methods
are based on detailed analysis of Heart Rate Variability (HRV) information. The
results of the work show good performance of the proposed methods and support
the potential for their deployment in resource-constrained devices for
ventricular and atrial arrhythmia prediction, such as implantable pacemakers
and defibrillators.

With the increasing presence of robots in our every-day environments,
improving their social skills is of utmost importance. Nonetheless, social
robotics still faces many challenges. One bottleneck is that robotic behaviors
need to be often adapted as social norms depend strongly on the environment.
For example, a robot should navigate more carefully around patients in a
hospital compared to workers in an office. In this work, we investigate
meta-reinforcement learning (meta-RL) as a potential solution. Here, robot
behaviors are learned via reinforcement learning where a reward function needs
to be chosen so that the robot learns an appropriate behavior for a given
environment. We propose to use a variational meta-RL procedure that quickly
adapts the robots' behavior to new reward functions. As a result, given a new
environment different reward functions can be quickly evaluated and an
appropriate one selected. The procedure learns a vectorized representation for
reward functions and a meta-policy that can be conditioned on such a
representation. Given observations from a new reward function, the procedure
identifies its representation and conditions the meta-policy to it. While
investigating the procedures' capabilities, we realized that it suffers from
posterior collapse where only a subset of the dimensions in the representation
encode useful information resulting in a reduced performance. Our second
contribution, a radial basis function (RBF) layer, partially mitigates this
negative effect. The RBF layer lifts the representation to a higher dimensional
space, which is more easily exploitable for the meta-policy. We demonstrate the
interest of the RBF layer and the usage of meta-RL for social robotics on four
robotic simulation tasks.

Convolutional neural networks are the way to solve arbitrary image
segmentation tasks. However, when images are large, memory demands often exceed
the available resources, in particular on a common GPU. Especially in
biomedical imaging, where 3D images are common, the problems are apparent. A
typical approach to solve this limitation is to break the task into smaller
subtasks by dividing images into smaller image patches. Another approach, if
applicable, is to look at the 2D image sections separately, and to solve the
problem in 2D. Often, the loss of global context makes such approaches less
effective; important global information might not be present in the current
image patch, or the selected 2D image section. Here, we propose Deep Neural
Patchworks (DNP), a segmentation framework that is based on hierarchical and
nested stacking of patch-based networks that solves the dilemma between global
context and memory limitations.

The emerging field of eXplainable Artificial Intelligence (XAI) aims to bring
transparency to today's powerful but opaque deep learning models. While local
XAI methods explain individual predictions in form of attribution maps, thereby
identifying where important features occur (but not providing information about
what they represent), global explanation techniques visualize what concepts a
model has generally learned to encode. Both types of methods thus only provide
partial insights and leave the burden of interpreting the model's reasoning to
the user. Only few contemporary techniques aim at combining the principles
behind both local and global XAI for obtaining more informative explanations.
Those methods, however, are often limited to specific model architectures or
impose additional requirements on training regimes or data and label
availability, which renders the post-hoc application to arbitrarily pre-trained
models practically impossible. In this work we introduce the Concept Relevance
Propagation (CRP) approach, which combines the local and global perspectives of
XAI and thus allows answering both the "where" and "what" questions for
individual predictions, without additional constraints imposed. We further
introduce the principle of Relevance Maximization for finding representative
examples of encoded concepts based on their usefulness to the model. We thereby
lift the dependency on the common practice of Activation Maximization and its
limitations. We demonstrate the capabilities of our methods in various
settings, showcasing that Concept Relevance Propagation and Relevance
Maximization lead to more human interpretable explanations and provide deep
insights into the model's representations and reasoning through concept
atlases, concept composition analyses, and quantitative investigations of
concept subspaces and their role in fine-grained decision making.

Integration of intermittent renewable energy sources into electric grids in
large proportions is challenging. A well-established approach aimed at
addressing this difficulty involves the anticipation of the upcoming energy
supply variability to adapt the response of the grid. In solar energy,
short-term changes in electricity production caused by occluding clouds can be
predicted at different time scales from all-sky cameras (up to 30-min ahead)
and satellite observations (up to 6h ahead). In this study, we integrate these
two complementary points of view on the cloud cover in a single machine
learning framework to improve intra-hour (up to 60-min ahead) irradiance
forecasting. Both deterministic and probabilistic predictions are evaluated in
different weather conditions (clear-sky, cloudy, overcast) and with different
input configurations (sky images, satellite observations and/or past irradiance
values). Our results show that the hybrid model benefits predictions in
clear-sky conditions and improves longer-term forecasting. This study lays the
groundwork for future novel approaches of combining sky images and satellite
observations in a single learning framework to advance solar nowcasting.

Vertical federated learning (VFL) is a privacy-preserving machine learning
paradigm that can learn models from features distributed on different platforms
in a privacy-preserving way. Since in real-world applications the data may
contain bias on fairness-sensitive features (e.g., gender), VFL models may
inherit bias from training data and become unfair for some user groups.
However, existing fair machine learning methods usually rely on the centralized
storage of fairness-sensitive features to achieve model fairness, which are
usually inapplicable in federated scenarios. In this paper, we propose a fair
vertical federated learning framework (FairVFL), which can improve the fairness
of VFL models. The core idea of FairVFL is to learn unified and fair
representations of samples based on the decentralized feature fields in a
privacy-preserving way. Specifically, each platform with fairness-insensitive
features first learns local data representations from local features. Then,
these local representations are uploaded to a server and aggregated into a
unified representation for the target task. In order to learn a fair unified
representation, we send it to each platform storing fairness-sensitive features
and apply adversarial learning to remove bias from the unified representation
inherited from the biased data. Moreover, for protecting user privacy, we
further propose a contrastive adversarial learning method to remove private
information from the unified representation in server before sending it to the
platforms keeping fairness-sensitive features. Experiments on three real-world
datasets validate that our method can effectively improve model fairness with
user privacy well-protected.

Transfer learning (TL) is becoming a powerful tool in scientific applications
of neural networks (NNs), such as weather/climate prediction and turbulence
modeling. TL enables out-of-distribution generalization (e.g., extrapolation in
parameters) and effective blending of disparate training sets (e.g.,
simulations and observations). In TL, selected layers of a NN, already trained
for a base system, are re-trained using a small dataset from a target system.
For effective TL, we need to know 1) what are the best layers to re-train? and
2) what physics are learned during TL? Here, we present novel analyses and a
new framework to address (1)-(2) for a broad range of multi-scale, nonlinear
systems. Our approach combines spectral analyses of the systems' data with
spectral analyses of convolutional NN's activations and kernels, explaining the
inner-workings of TL in terms of the system's nonlinear physics. Using
subgrid-scale modeling of several setups of 2D turbulence as test cases, we
show that the learned kernels are combinations of low-, band-, and high-pass
filters, and that TL learns new filters whose nature is consistent with the
spectral differences of base and target systems. We also find the shallowest
layers are the best to re-train in these cases, which is against the common
wisdom guiding TL in machine learning literature. Our framework identifies the
best layer(s) to re-train beforehand, based on physics and NN theory. Together,
these analyses explain the physics learned in TL and provide a framework to
guide TL for wide-ranging applications in science and engineering, such as
climate change modeling.

To obtain higher sample efficiency and superior final performance
simultaneously has been one of the major challenges for deep reinforcement
learning (DRL). Previous work could handle one of these challenges but
typically failed to address them concurrently. In this paper, we try to tackle
these two challenges simultaneously. To achieve this, we firstly decouple these
challenges into two classic RL problems: data richness and
exploration-exploitation trade-off. Then, we cast these two problems into the
training data distribution optimization problem, namely to obtain desired
training data within limited interactions, and address them concurrently via i)
explicit modeling and control of the capacity and diversity of behavior policy
and ii) more fine-grained and adaptive control of selective/sampling
distribution of the behavior policy using a monotonic data distribution
optimization. Finally, we integrate this process into Generalized Policy
Iteration (GPI) and obtain a more general framework called Generalized Data
Distribution Iteration (GDI). We use the GDI framework to introduce
operator-based versions of well-known RL methods from DQN to Agent57.
Theoretical guarantee of the superiority of GDI compared with GPI is concluded.
We also demonstrate our state-of-the-art (SOTA) performance on Arcade Learning
Environment (ALE), wherein our algorithm has achieved 9620.33% mean human
normalized score (HNS), 1146.39% median HNS and surpassed 22 human world
records using only 200M training frames. Our performance is comparable to
Agent57's while we consume 500 times less data. We argue that there is still a
long way to go before obtaining real superhuman agents in ALE.

Reservoir Computing is a class of Recurrent Neural Networks with internal
weights fixed at random. Stability relates to the sensitivity of the network
state to perturbations. It is an important property in Reservoir Computing as
it directly impacts performance. In practice, it is desirable to stay in a
stable regime, where the effect of perturbations does not explode
exponentially, but also close to the chaotic frontier where reservoir dynamics
are rich. Open questions remain today regarding input regularization and
discontinuous activation functions. In this work, we use the recurrent kernel
limit to draw new insights on stability in reservoir computing. This limit
corresponds to large reservoir sizes, and it already becomes relevant for
reservoirs with a few hundred neurons. We obtain a quantitative
characterization of the frontier between stability and chaos, which can greatly
benefit hyperparameter tuning. In a broader sense, our results contribute to
understanding the complex dynamics of Recurrent Neural Networks.

Electric vehicles (EVs) have been adopted in urban areas to reduce
environmental pollution and global warming as a result of the increasing number
of freight vehicles. However, there are still deficiencies in routing the
trajectories of last-mile logistics that continue to impact social and economic
sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach
called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning
(HHASA$_{RL}$) is proposed. It is composed of a multi-armed bandit method and
the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving
the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to
the limited number of charging stations and the travel range of EVs, the EVs
must require battery recharging moments in advance and reduce travel times and
costs. The HH implemented improves multiple minimum best-known solutions and
obtains the best mean values for some high-dimensional instances for the
proposed benchmark for the IEEE WCCI2020 competition.

Machine learning typically presupposes classical probability theory which
implies that aggregation is built upon expectation. There are now multiple
reasons to motivate looking at richer alternatives to classical probability
theory as a mathematical foundation for machine learning. We systematically
examine a powerful and rich class of such alternatives, known variously as
spectral risk measures, Choquet integrals or Lorentz norms. We present a range
of characterization results, and demonstrate what makes this spectral family so
special. In doing so we demonstrate a natural stratification of all coherent
risk measures in terms of the upper probabilities that they induce by
exploiting results from the theory of rearrangement invariant Banach spaces. We
empirically demonstrate how this new approach to uncertainty helps tackling
practical machine learning problems.

The combination of convolutional and recurrent neural networks is a promising
framework that allows the extraction of high-quality spatio-temporal features
together with its temporal dependencies, which is key for time series
prediction problems such as forecasting, classification or anomaly detection,
amongst others. In this paper, the TSFEDL library is introduced. It compiles 20
state-of-the-art methods for both time series feature extraction and
prediction, employing convolutional and recurrent deep neural networks for its
use in several data mining tasks. The library is built upon a set of
Tensorflow+Keras and PyTorch modules under the AGPLv3 license. The performance
validation of the architectures included in this proposal confirms the
usefulness of this Python package.

State-of-the-art text classification models are becoming increasingly reliant
on deep neural networks (DNNs). Due to their black-box nature, faithful and
robust explanation methods need to accompany classifiers for deployment in
real-life scenarios. However, it has been shown in vision applications that
explanation methods are susceptible to local, imperceptible perturbations that
can significantly alter the explanations without changing the predicted
classes. We show here that the existence of such perturbations extends to text
classifiers as well. Specifically, we introduceTextExplanationFooler (TEF), a
novel explanation attack algorithm that alters text input samples imperceptibly
so that the outcome of widely-used explanation methods changes considerably
while leaving classifier predictions unchanged. We evaluate the performance of
the attribution robustness estimation performance in TEF on five sequence
classification datasets, utilizing three DNN architectures and three
transformer architectures for each dataset. TEF can significantly decrease the
correlation between unchanged and perturbed input attributions, which shows
that all models and explanation methods are susceptible to TEF perturbations.
Moreover, we evaluate how the perturbations transfer to other model
architectures and attribution methods, and show that TEF perturbations are also
effective in scenarios where the target model and explanation method are
unknown. Finally, we introduce a semi-universal attack that is able to compute
fast, computationally light perturbations with no knowledge of the attacked
classifier nor explanation method. Overall, our work shows that explanations in
text classifiers are very fragile and users need to carefully address their
robustness before relying on them in critical applications.

Knowledge bases, and their representations in the form of knowledge graphs
(KGs), are naturally incomplete. Since scientific and industrial applications
have extensively adopted them, there is a high demand for solutions that
complete their information. Several recent works tackle this challenge by
learning embeddings for entities and relations, then employing them to predict
new relations among the entities. Despite their aggrandizement, most of those
methods focus only on the local neighbors of a relation to learn the
embeddings. As a result, they may fail to capture the KGs' context information
by neglecting long-term dependencies and the propagation of entities'
semantics. In this manuscript, we propose {\AE}MP (Attention-based Embeddings
from Multiple Patterns), a novel model for learning contextualized
representations by: (i) acquiring entities' context information through an
attention-enhanced message-passing scheme, which captures the entities' local
semantics while focusing on different aspects of their neighborhood; and (ii)
capturing the semantic context, by leveraging the paths and their relationships
between entities. Our empirical findings draw insights into how attention
mechanisms can improve entities' context representation and how combining
entities and semantic path contexts improves the general representation of
entities and the relation predictions. Experimental results on several large
and small knowledge graph benchmarks show that {\AE}MP either outperforms or
competes with state-of-the-art relation prediction methods.

Experience replay-based sampling techniques are essential to several
reinforcement learning (RL) algorithms since they aid in convergence by
breaking spurious correlations. The most popular techniques, such as uniform
experience replay (UER) and prioritized experience replay (PER), seem to suffer
from sub-optimal convergence and significant bias error, respectively. To
alleviate this, we introduce a new experience replay method for reinforcement
learning, called Introspective Experience Replay (IER). IER picks batches
corresponding to data points consecutively before the 'surprising' points. Our
proposed approach is based on the theoretically rigorous reverse experience
replay (RER), which can be shown to remove bias in the linear approximation
setting but can be sub-optimal with neural approximation. We show empirically
that IER is stable with neural function approximation and has a superior
performance compared to the state-of-the-art techniques like uniform experience
replay (UER), prioritized experience replay (PER), and hindsight experience
replay (HER) on the majority of tasks.

The success of deep neural networks (DNNs) is heavily dependent on
computational resources. While DNNs are often employed on cloud servers, there
is a growing need to operate DNNs on edge devices. Edge devices are typically
limited in their computational resources, yet, often multiple edge devices are
deployed in the same environment and can reliably communicate with each other.
In this work we propose to facilitate the application of DNNs on the edge by
allowing multiple users to collaborate during inference to improve their
accuracy. Our mechanism, coined {\em edge ensembles}, is based on having
diverse predictors at each device, which form an ensemble of models during
inference. To mitigate the communication overhead, the users share quantized
features, and we propose a method for aggregating multiple decisions into a
single inference rule. We analyze the latency induced by edge ensembles,
showing that its performance improvement comes at the cost of a minor
additional delay under common assumptions on the communication network. Our
experiments demonstrate that collaborative inference via edge ensembles
equipped with compact DNNs substantially improves the accuracy over having each
user infer locally, and can outperform using a single centralized DNN larger
than all the networks in the ensemble together.

The automated segmentation of cortical areas has been a long-standing
challenge in medical image analysis. The complex geometry of the cortex is
commonly represented as a polygon mesh, whose segmentation can be addressed by
graph-based learning methods. When cortical meshes are misaligned across
subjects, current methods produce significantly worse segmentation results,
limiting their ability to handle multi-domain data. In this paper, we
investigate the utility of E(n)-equivariant graph neural networks (EGNNs),
comparing their performance against plain graph neural networks (GNNs). Our
evaluation shows that GNNs outperform EGNNs on aligned meshes, due to their
ability to leverage the presence of a global coordinate system. On misaligned
meshes, the performance of plain GNNs drop considerably, while E(n)-equivariant
message passing maintains the same segmentation results. The best results can
also be obtained by using plain GNNs on realigned data (co-registered meshes in
a global coordinate system).

Recent studies of distributed computation with formal privacy guarantees,
such as differentially private (DP) federated learning, leverage random
sampling of clients in each round (privacy amplification by subsampling) to
achieve satisfactory levels of privacy. Achieving this however requires strong
assumptions which may not hold in practice, including precise and uniform
subsampling of clients, and a highly trusted aggregator to process clients'
data. In this paper, we explore a more practical protocol, shuffled check-in,
to resolve the aforementioned issues. The protocol relies on client making
independent and random decision to participate in the computation, freeing the
requirement of server-initiated subsampling, and enabling robust modelling of
client dropouts. Moreover, a weaker trust model known as the shuffle model is
employed instead of using a trusted aggregator. To this end, we introduce new
tools to characterize the R\'enyi differential privacy (RDP) of shuffled
check-in. We show that our new techniques improve at least three times in
privacy guarantee over those using approximate DP's strong composition at
various parameter regimes. Furthermore, we provide a numerical approach to
track the privacy of generic shuffled check-in mechanism including distributed
stochastic gradient descent (SGD) with Gaussian mechanism. To the best of our
knowledge, this is also the first evaluation of Gaussian mechanism within the
local/shuffle model under the distributed setting in the literature, which can
be of independent interest.

We study the linear contextual bandit problem where an agent has to select
one candidate from a pool and each candidate belongs to a sensitive group. In
this setting, candidates' rewards may not be directly comparable between
groups, for example when the agent is an employer hiring candidates from
different ethnic groups and some groups have a lower reward due to
discriminatory bias and/or social injustice. We propose a notion of fairness
that states that the agent's policy is fair when it selects a candidate with
highest relative rank, which measures how good the reward is when compared to
candidates from the same group. This is a very strong notion of fairness, since
the relative rank is not directly observed by the agent and depends on the
underlying reward model and on the distribution of rewards. Thus we study the
problem of learning a policy which approximates a fair policy under the
condition that the contexts are independent between groups and the distribution
of rewards of each group is absolutely continuous. In particular, we design a
greedy policy which at each round constructs a ridge regression estimate from
the observed context-reward pairs, and then computes an estimate of the
relative rank of each candidate using the empirical cumulative distribution
function. We prove that, despite its simplicity and the lack of an initial
exploration phase, the greedy policy achieves, up to log factors and with high
probability, a fair pseudo-regret of order $\sqrt{dT}$ after $T$ rounds, where
$d$ is the dimension of the context vectors. The policy also satisfies
demographic parity at each round when averaged over all possible information
available before the selection. Finally, we use simulated settings and
experiments on the US census data to show that our policy achieves sub-linear
fair pseudo-regret also in practice.

Transfer learning improves the performance of the target task by leveraging
the data of a specific source task: the closer the relationship between the
source and the target tasks, the greater the performance improvement by
transfer learning. In neuroscience, the relationship between cognitive tasks is
usually represented by similarity of activated brain regions or neural
representation. However, no study has linked transfer learning and neuroscience
to reveal the relationship between cognitive tasks. In this study, we propose a
transfer learning framework to reflect the relationship between cognitive
tasks, and compare the task relations reflected by transfer learning and by the
overlaps of brain regions (e.g., neurosynth). Our results of transfer learning
create cognitive taskonomy to reflect the relationship between cognitive tasks
which is well in line with the task relations derived from neurosynth. Transfer
learning performs better in task decoding with fMRI data if the source and
target cognitive tasks activate similar brain regions. Our study uncovers the
relationship of multiple cognitive tasks and provides guidance for source task
selection in transfer learning for neural decoding based on small-sample data.

Human language learners are exposed to a trickle of informative,
context-sensitive language, but a flood of raw sensory data. Through both
social language use and internal processes of rehearsal and practice, language
learners are able to build high-level, semantic representations that explain
their perceptions. Here, we take inspiration from such processes of "inner
speech" in humans (Vygotsky, 1934) to better understand the role of intra-agent
speech in embodied behavior. First, we formally pose intra-agent speech as a
semi-supervised problem and develop two algorithms that enable visually
grounded captioning with little labeled language data. We then experimentally
compute scaling curves over different amounts of labeled data and compare the
data efficiency against a supervised learning baseline. Finally, we incorporate
intra-agent speech into an embodied, mobile manipulator agent operating in a 3D
virtual world, and show that with as few as 150 additional image captions,
intra-agent speech endows the agent with the ability to manipulate and answer
questions about a new object without any related task-directed experience
(zero-shot). Taken together, our experiments suggest that modelling intra-agent
speech is effective in enabling embodied agents to learn new tasks efficiently
and without direct interaction experience.

An increasing number of monitoring systems have been developed in smart
cities to ensure that real-time operations of a city satisfy safety and
performance requirements. However, many existing city requirements are written
in English with missing, inaccurate, or ambiguous information. There is a high
demand for assisting city policy makers in converting human-specified
requirements to machine-understandable formal specifications for monitoring
systems. To tackle this limitation, we build CitySpec, the first intelligent
assistant system for requirement specification in smart cities. To create
CitySpec, we first collect over 1,500 real-world city requirements across
different domains from over 100 cities and extract city-specific knowledge to
generate a dataset of city vocabulary with 3,061 words. We also build a
translation model and enhance it through requirement synthesis and develop a
novel online learning framework with validation under uncertainty. The
evaluation results on real-world city requirements show that CitySpec increases
the sentence-level accuracy of requirement specification from 59.02% to 86.64%,
and has strong adaptability to a new city and a new domain (e.g., F1 score for
requirements in Seattle increases from 77.6% to 93.75% with online learning).

Automatically selecting the best performing algorithm for a given dataset or
ranking multiple algorithms by their expected performance supports users in
developing new machine learning applications. Most approaches for this problem
rely on pre-computed dataset meta-features and landmarking performances to
capture the salient topology of the datasets and those topologies that the
algorithms attend to. Landmarking usually exploits cheap algorithms not
necessarily in the pool of candidate algorithms to get inexpensive
approximations of the topology. While somewhat indicative, hand-crafted dataset
meta-features and landmarks are likely insufficient descriptors, strongly
depending on the alignment of the topologies that the landmarks and the
candidate algorithms search for. We propose IMFAS, a method to exploit
multi-fidelity landmarking information directly from the candidate algorithms
in the form of non-parametrically non-myopic meta-learned learning curves via
LSTMs in a few-shot setting during testing. Using this mechanism, IMFAS jointly
learns the topology of the datasets and the inductive biases of the candidate
algorithms, without the need to expensively train them to convergence. Our
approach produces informative landmarks, easily enriched by arbitrary
meta-features at a low computational cost, capable of producing the desired
ranking using cheaper fidelities. We additionally show that IMFAS is able to
beat Successive Halving with at most 50% of the fidelity sequence during test
time.

Traffic forecasting is one canonical example of spatial-temporal learning
task in Intelligent Traffic System. Existing approaches capture spatial
dependency with a pre-determined matrix in graph convolution neural operators.
However, the explicit graph structure losses some hidden representations of
relationships among nodes. Furthermore, traditional graph convolution neural
operators cannot aggregate long-range nodes on the graph. To overcome these
limits, we propose a novel network, Spatial-Temporal Adaptive graph convolution
with Attention Network (STAAN) for traffic forecasting. Firstly, we adopt an
adaptive dependency matrix instead of using a pre-defined matrix during GCN
processing to infer the inter-dependencies among nodes. Secondly, we integrate
PW-attention based on graph attention network which is designed for global
dependency, and GCN as spatial block. What's more, a stacked dilated 1D
convolution, with efficiency in long-term prediction, is adopted in our
temporal block for capturing the different time series. We evaluate our STAAN
on two real-world datasets, and experiments validate that our model outperforms

Transformers have achieved remarkable success in several domains, ranging
from natural language processing to computer vision. Nevertheless, it has been
recently shown that stacking self-attention layers - the distinctive
architectural component of Transformers - can result in rank collapse of the
tokens' representations at initialization. The question of if and how rank
collapse affects training is still largely unanswered, and its investigation is
necessary for a more comprehensive understanding of this architecture. In this
work, we shed new light on the causes and the effects of this phenomenon.
First, we show that rank collapse of the tokens' representations hinders
training by causing the gradients of the queries and keys to vanish at
initialization. Furthermore, we provide a thorough description of the origin of
rank collapse and discuss how to prevent it via an appropriate depth-dependent
scaling of the residual branches. Finally, our analysis unveils that specific
architectural hyperparameters affect the gradients of queries and values
differently, leading to disproportionate gradient norms. This suggests an
explanation for the widespread use of adaptive methods for Transformers'

Quantitative investment is a fundamental financial task that highly relies on
accurate stock prediction and profitable investment decision making. Despite
recent advances in deep learning (DL) have shown stellar performance on
capturing trading opportunities in the stochastic stock market, we observe that
the performance of existing DL methods is sensitive to random seeds and network
initialization. To design more profitable DL methods, we analyze this
phenomenon and find two major limitations of existing works. First, there is a
noticeable gap between accurate financial predictions and profitable investment
strategies. Second, investment decisions are made based on only one individual
predictor without consideration of model uncertainty, which is inconsistent
with the workflow in real-world trading firms. To tackle these two limitations,
we first reformulate quantitative investment as a multi-task learning problem.
Later on, we propose AlphaMix, a novel two-stage mixture-of-experts (MoE)
framework for quantitative investment to mimic the efficient bottom-up trading
strategy design workflow of successful trading firms. In Stage one, multiple
independent trading experts are jointly optimized with an individual
uncertainty-aware loss function. In Stage two, we train neural routers
(corresponding to the role of a portfolio manager) to dynamically deploy these
experts on an as-needed basis. AlphaMix is also a universal framework that is
applicable to various backbone network architectures with consistent
performance gains. Through extensive experiments on long-term real-world data
spanning over five years on two of the most influential financial markets (US
and China), we demonstrate that AlphaMix significantly outperforms many
state-of-the-art baselines in terms of four financial criteria.

Image inpainting is the task of filling masked or unknown regions of an image
with visually realistic contents, which has been remarkably improved by Deep
Neural Networks (DNNs) recently. Essentially, as an inverse problem, the
inpainting has the underlying challenges of reconstructing semantically
coherent results without texture artifacts. Many previous efforts have been
made via exploiting attention mechanisms and prior knowledge, such as edges and
semantic segmentation. However, these works are still limited in practice by an
avalanche of learnable prior parameters and prohibitive computational burden.
To this end, we propose a novel model -- Wavelet prior attention learning in
Axial Inpainting Network (WAIN), whose generator contains the encoder, decoder,
as well as two key components of Wavelet image Prior Attention (WPA) and
stacked multi-layer Axial-Transformers (ATs). Particularly, the WPA guides the
high-level feature aggregation in the multi-scale frequency domain, alleviating
the textual artifacts. Stacked ATs employ unmasked clues to help model
reasonable features along with low-level features of horizontal and vertical
axes, improving the semantic coherence. Extensive quantitative and qualitative
experiments on Celeba-HQ and Places2 datasets are conducted to validate that
our WAIN can achieve state-of-the-art performance over the competitors. The
codes and models will be released.

The ecological validity of soundscape studies usually rests on a choice of
soundscapes that are representative of the perceptual space under
investigation. For example, a soundscape pleasantness study might investigate
locations with soundscapes ranging from "pleasant" to "annoying". The choice of
soundscapes is typically researcher-led, but a participant-led process can
reduce selection bias and improve result reliability. Hence, we propose a
robust participant-led method to pinpoint characteristic soundscapes possessing
arbitrary perceptual attributes. We validate our method by identifying
Singaporean soundscapes spanning the perceptual quadrants generated from the
"Pleasantness" and "Eventfulness" axes of the ISO 12913-2 circumplex model of
soundscape perception, as perceived by local experts. From memory and
experience, 67 participants first selected locations corresponding to each
perceptual quadrant in each major planning region of Singapore. We then
performed weighted k-means clustering on the selected locations, with weights
for each location derived from previous frequencies and durations spent in each
location by each participant. Weights hence acted as proxies for participant
confidence. In total, 62 locations were thereby identified as suitable
locations with characteristic soundscapes for further research utilizing the
ISO 12913-2 perceptual quadrants. Audio-visual recordings and acoustic
characterization of the soundscapes will be made in a future study.

Image registration is an essential step in many medical image analysis tasks.
Traditional methods for image registration are primarily optimization-driven,
finding the optimal deformations that maximize the similarity between two
images. Recent learning-based methods, trained to directly predict
transformations between two images, run much faster, but suffer from
performance deficiencies due to model generalization and the inefficiency in
handling individual image specific deformations. Here we present a new neural
net based image registration framework, called NIR (Neural Image Registration),
which is based on optimization but utilizes deep neural nets to model
deformations between image pairs. NIR represents the transformation between two
images with a continuous function implemented via neural fields, receiving a 3D
coordinate as input and outputting the corresponding deformation vector. NIR
provides two ways of generating deformation field: directly output a
displacement vector field for general deformable registration, or output a
velocity vector field and integrate the velocity field to derive the
deformation field for diffeomorphic image registration. The optimal
registration is discovered by updating the parameters of the neural field via
stochastic gradient descent. We describe several design choices that facilitate
model optimization, including coordinate encoding, sinusoidal activation,
coordinate sampling, and intensity sampling. Experiments on two 3D MR brain
scan datasets demonstrate that NIR yields state-of-the-art performance in terms
of both registration accuracy and regularity, while running significantly
faster than traditional optimization-based methods.

On average once every four years, the Tropical Pacific warms considerably
during events called El Ni\~no, leading to weather disruptions over many
regions on Earth. Recent machine-learning approaches to El Ni\~no prediction,
in particular Convolutional Neural Networks (CNNs), have shown a surprisingly
high skill at relatively long lead times. In an attempt to understand this high
skill, we here use data from distorted physics simulations with an intermediate
complexity El Ni\~no model to determine what aspects of El Ni\~no physics are
represented in a specific CNN-based classification method. We find that the CNN
can adequately correct for distortions in the ocean adjustment processes, but
that the machine-learning method has far more trouble to deal with distortions
in upwelling feedback strength.

Salient Object Detection is the task of predicting the human attended region
in a given scene. Fusing depth information has been proven effective in this
task. The main challenge of this problem is how to aggregate the complementary
information from RGB modality and depth modality. However, conventional deep
models heavily rely on CNN feature extractors, and the long-range contextual
dependencies are usually ignored. In this work, we propose Dual
Swin-Transformer based Mutual Interactive Network. We adopt Swin-Transformer as
the feature extractor for both RGB and depth modality to model the long-range
dependencies in visual inputs. Before fusing the two branches of features into
one, attention-based modules are applied to enhance features from each
modality. We design a self-attention-based cross-modality interaction module
and a gated modality attention module to leverage the complementary information
between the two modalities. For the saliency decoding, we create different
stages enhanced with dense connections and keep a decoding memory while the
multi-level encoding features are considered simultaneously. Considering the
inaccurate depth map issue, we collect the RGB features of early stages into a
skip convolution module to give more guidance from RGB modality to the final
saliency prediction. In addition, we add edge supervision to regularize the
feature learning process. Comprehensive experiments on five standard RGB-D SOD
benchmark datasets over four evaluation metrics demonstrate the superiority of
the proposed DTMINet method.

We study best-of-both-worlds algorithms for bandits with switching cost,
recently addressed by Rouyer, Seldin and Cesa-Bianchi, 2021. We introduce a
surprisingly simple and effective algorithm that simultaneously achieves
minimax optimal regret bound of $\mathcal{O}(T^{2/3})$ in the oblivious
adversarial setting and a bound of $\mathcal{O}(\min\{\log
(T)/\Delta^2,T^{2/3}\})$ in the stochastically-constrained regime, both with
(unit) switching costs, where $\Delta$ is the gap between the arms. In the
stochastically constrained case, our bound improves over previous results due
to Rouyer et al., that achieved regret of $\mathcal{O}(T^{1/3}/\Delta)$. We
accompany our results with a lower bound showing that, in general,
$\tilde{\Omega}(\min\{1/\Delta^2,T^{2/3}\})$ regret is unavoidable in the
stochastically-constrained case for algorithms with $\mathcal{O}(T^{2/3})$
worst-case regret.

Black-box nature of Artificial Intelligence (AI) models do not allow users to
comprehend and sometimes trust the output created by such model. In AI
applications, where not only the results but also the decision paths to the
results are critical, such black-box AI models are not sufficient. Explainable
Artificial Intelligence (XAI) addresses this problem and defines a set of AI
models that are interpretable by the users. Recently, several number of XAI
models have been to address the issues surrounding by lack of interpretability
and explainability of black-box models in various application areas such as
healthcare, military, energy, financial and industrial domains. Although the
concept of XAI has gained great deal of attention recently, its integration
into the IoT domain has not yet been fully defined. In this paper, we provide
an in-depth and systematic review of recent studies using XAI models in the
scope of IoT domain. We categorize the studies according to their methodology
and applications areas. In addition, we aim to focus on the challenging
problems and open issues and give future directions to guide the developers and
researchers for prospective future investigations.

In data-parallel optimization of machine learning models, workers collaborate
to improve their estimates of the model: more accurate gradients allow them to
use larger learning rates and optimize faster. We consider the setting in which
all workers sample from the same dataset, and communicate over a sparse graph
(decentralized). In this setting, current theory fails to capture important
aspects of real-world behavior. First, the 'spectral gap' of the communication
graph is not predictive of its empirical performance in (deep) learning.
Second, current theory does not explain that collaboration enables larger
learning rates than training alone. In fact, it prescribes smaller learning
rates, which further decrease as graphs become larger, failing to explain
convergence in infinite graphs. This paper aims to paint an accurate picture of
sparsely-connected distributed optimization when workers share the same data
distribution. We quantify how the graph topology influences convergence in a
quadratic toy problem and provide theoretical results for general smooth and
(strongly) convex objectives. Our theory matches empirical observations in deep
learning, and accurately describes the relative merits of different graph
topologies.

Currently, the federated graph neural network (GNN) has attracted a lot of
attention due to its wide applications in reality without violating the privacy
regulations. Among all the privacy-preserving technologies, the differential
privacy (DP) is the most promising one due to its effectiveness and light
computational overhead. However, the DP-based federated GNN has not been well
investigated, especially in the sub-graph-level setting, such as the scenario
of recommendation system. The biggest challenge is how to guarantee the privacy
and solve the non independent and identically distributed (non-IID) data in
federated GNN simultaneously. In this paper, we propose DP-FedRec, a DP-based
federated GNN to fill the gap. Private Set Intersection (PSI) is leveraged to
extend the local graph for each client, and thus solve the non-IID problem.
Most importantly, DP is applied not only on the weights but also on the edges
of the intersection graph from PSI to fully protect the privacy of clients. The
evaluation demonstrates DP-FedRec achieves better performance with the graph
extension and DP only introduces little computations overhead.

Several unsupervised and self-supervised approaches have been developed in
recent years to learn visual features from large-scale unlabeled datasets.
Their main drawback however is that these methods are hardly able to recognize
visual features of the same object if it is simply rotated or the perspective
of the camera changes. To overcome this limitation and at the same time exploit
a useful source of supervision, we take into account video object tracks.
Following the intuition that two patches in a track should have similar visual
representations in a learned feature space, we adopt an unsupervised
clustering-based approach and constrain such representations to be labeled as
the same category since they likely belong to the same object or object part.
Experimental results on two downstream tasks on different datasets demonstrate
the effectiveness of our Online Deep Clustering with Video Track Consistency
(ODCT) approach compared to prior work, which did not leverage temporal
information. In addition we show that exploiting an unsupervised
class-agnostic, yet noisy, track generator yields to better accuracy compared
to relying on costly and precise track annotations.

Self-driving vehicles and autonomous ground robots require a reliable and
accurate method to analyze the traversability of the surrounding environment
for safe navigation. This paper proposes and evaluates a real-time machine
learning-based Traversability Analysis method that combines geometric features
with appearance-based features in a hybrid approach based on a SVM classifier.
In particular, we show that integrating a new set of geometric and visual
features and focusing on important implementation details enables a noticeable
boost in performance and reliability. The proposed approach has been compared
with state-of-the-art Deep Learning approaches on a public dataset of outdoor
driving scenarios. It reaches an accuracy of 89.2% in scenarios of varying
complexity, demonstrating its effectiveness and robustness. The method runs
fully on CPU and reaches comparable results with respect to the other methods,
operates faster, and requires fewer hardware resources.

The rapid on-site evaluation (ROSE) technique can significantly ac-celerate
the diagnostic workflow of pancreatic cancer by immediately analyzing the
fast-stained cytopathological images with on-site pathologists. Computer-aided
diagnosis (CAD) using the deep learning method has the potential to solve the
problem of insufficient pathology staffing. However, the cancerous patterns of
ROSE images vary greatly between different samples, making the CAD task
extremely challenging. Besides, due to different staining qualities and various
types of acquisition devices, the ROSE images also have compli-cated
perturbations in terms of color distribution, brightness, and contrast. To
address these challenges, we proposed a novel multiple instance learning (MIL)
approach using shuffle patches containing the instances, which adopts the
patch-based learning strategy of Vision Transformers. With the re-grouped bags
of shuffle instances and their bag-level soft labels, the approach utilizes a
MIL head to make the model focus on the features from the pancreatic cancer
cells, rather than that from various perturbations in ROSE images.
Simultaneously, combined with a classification head, the model can effectively
identify the gen-eral distributive patterns across different instances. The
results demonstrate the significant improvements in the classification accuracy
with more accurate at-tention regions, indicating that the diverse patterns of
ROSE images are effec-tively extracted, and the complicated perturbations of
ROSE images are signifi-cantly eliminated. It also suggests that the MIL with
shuffle instances has great potential in the analysis of cytopathological
images.

IoT is a rapidly emerging paradigm that now encompasses almost every aspect
of our modern life. As such, ensuring the security of IoT devices is crucial.
IoT devices can differ from traditional computing, thereby the design and
implementation of proper security measures can be challenging in IoT devices.
We observed that IoT developers discuss their security-related challenges in
developer forums like Stack Overflow(SO). However, we find that IoT security
discussions can also be buried inside non-security discussions in SO. In this
paper, we aim to understand the challenges IoT developers face while applying
security practices and techniques to IoT devices. We have two goals: (1)
Develop a model that can automatically find security-related IoT discussions in
SO, and (2) Study the model output to learn about IoT developer
security-related challenges. First, we download 53K posts from SO that contain
discussions about IoT. Second, we manually labeled 5,919 sentences from 53K
posts as 1 or 0. Third, we use this benchmark to investigate a suite of deep
learning transformer models. The best performing model is called SecBot.
Fourth, we apply SecBot on the entire posts and find around 30K security
related sentences. Fifth, we apply topic modeling to the security-related
sentences. Then we label and categorize the topics. Sixth, we analyze the
evolution of the topics in SO. We found that (1) SecBot is based on the
retraining of the deep learning model RoBERTa. SecBot offers the best F1-Score
of 0.935, (2) there are six error categories in misclassified samples by
SecBot. SecBot was mostly wrong when the keywords/contexts were ambiguous
(e.g., gateway can be a security gateway or a simple gateway), (3) there are 9
security topics grouped into three categories: Software, Hardware, and Network,
and (4) the highest number of topics belongs to software security, followed by
network security.

Automated driving systems (ADS) are expected to be reliable and robust
against a wide range of driving scenarios. Their decisions, first and foremost,
must be well understood. Understanding a decision made by ADS is a great
challenge, because it is not straightforward to tell whether the decision is
correct or not, and how to verify it systematically. In this paper, a
Sequential MetAmoRphic Testing Smart framework is proposed based on metamorphic
testing, a mainstream software testing approach. In metamorphic testing,
metamorphic groups are constructed by selecting multiple inputs according to
the so-called metamorphic relations, which are basically the system's necessary
properties; the violation of certain relations by some corresponding
metamorphic groups implies the detection of erroneous system behaviors. The
proposed framework makes use of sequences of metamorphic groups to understand
ADS behaviors, and is applicable without the need of ground-truth datasets. To
demonstrate its effectiveness, the framework is applied to test three ADS
models that steer an autonomous car in different scenarios with another car
either leading in front or approaching in the opposite direction. The conducted
experiments reveal a large number of undesirable behaviors in these top-ranked
deep learning models in the scenarios. These counter-intuitive behaviors are
associated with how the core models of ADS respond to different positions,
directions and properties of the other car in its proximity. Further analysis
of the results helps identify critical factors affecting ADS decisions and thus
demonstrates that the framework can be used to provide a comprehensive
understanding of ADS before their deployment

Nowadays, deep prediction models, especially graph neural networks, have a
majorplace in critical applications. In such context, those models need to be
highlyinterpretable or being explainable by humans, and at the societal scope,
this understandingmay also be feasible for humans that do not have a strong
prior knowledgein models and contexts that need to be explained. In the
literature, explainingis a human knowledge transfer process regarding a
phenomenon between an explainerand an explainee. We propose EiX-GNN
(Eigencentrality eXplainer forGraph Neural Networks) a new powerful method for
explaining graph neural networksthat encodes computationally this social
explainer-to-explainee dependenceunderlying in the explanation process. To
handle this dependency, we introducethe notion of explainee concept
assimibility which allows explainer to adapt itsexplanation to explainee
background or expectation. We lead a qualitative studyto illustrate our
explainee concept assimibility notion on real-world data as wellas a
qualitative study that compares, according to objective metrics established
inthe literature, fairness and compactness of our method with respect to
performingstate-of-the-art methods. It turns out that our method achieves
strong results inboth aspects.

Automated machine learning (AutoML) frameworks have become important tools in
the data scientists' arsenal, as they dramatically reduce the manual work
devoted to the construction of ML pipelines. Such frameworks intelligently
search among millions of possible ML pipelines - typically containing feature
engineering, model selection and hyper parameters tuning steps - and finally
output an optimal pipeline in terms of predictive accuracy. However, when the
dataset is large, each individual configuration takes longer to execute,
therefore the overall AutoML running times become increasingly high. To this
end, we present SubStrat, an AutoML optimization strategy that tackles the data
size, rather than configuration space. It wraps existing AutoML tools, and
instead of executing them directly on the entire dataset, SubStrat uses a
genetic-based algorithm to find a small yet representative data subset which
preserves a particular characteristic of the full data. It then employs the
AutoML tool on the small subset, and finally, it refines the resulted pipeline
by executing a restricted, much shorter, AutoML process on the large dataset.
Our experimental results, performed on two popular AutoML frameworks,
Auto-Sklearn and TPOT, show that SubStrat reduces their running times by 79%
(on average), with less than 2% average loss in the accuracy of the resulted ML
pipeline.

Single Image Super Resolution (SISR) methods aim to recover the clean images
in high resolution from low resolution observations.A family of patch-based
approaches have received considerable attention and development. The minimum
mean square error (MMSE) methodis a powerful image restoration method that uses
a probability model on the patches of images. This paper proposes an algorithm
to learn a jointgeneralized Gaussian mixture model (GGMM) from a pair of the
low resolution patches and the corresponding high resolution patches fromthe
reference data. We then reconstruct the high resolution image based on the MMSE
method. Our numerical evaluations indicate that theMMSE-GGMM method competes
with other state of the art methods.

Quantum computers are next-generation devices that hold promise to perform
calculations beyond the reach of classical computers. A leading method towards
achieving this goal is through quantum machine learning, especially quantum
generative learning. Due to the intrinsic probabilistic nature of quantum
mechanics, it is reasonable to postulate that quantum generative learning
models (QGLMs) may surpass their classical counterparts. As such, QGLMs are
receiving growing attention from the quantum physics and computer science
communities, where various QGLMs that can be efficiently implemented on
near-term quantum machines with potential computational advantages are
proposed. In this paper, we review the current progress of QGLMs from the
perspective of machine learning. Particularly, we interpret these QGLMs,
covering quantum circuit born machines, quantum generative adversarial
networks, quantum Boltzmann machines, and quantum autoencoders, as the quantum
extension of classical generative learning models. In this context, we explore
their intrinsic relation and their fundamental differences. We further
summarize the potential applications of QGLMs in both conventional machine
learning tasks and quantum physics. Last, we discuss the challenges and further
research directions for QGLMs.

Removing background noise from speech audio has been the subject of
considerable effort, especially in recent years due to the rise of virtual
communication and amateur recordings. Yet background noise is not the only
unpleasant disturbance that can prevent intelligibility: reverb, clipping,
codec artifacts, problematic equalization, limited bandwidth, or inconsistent
loudness are equally disturbing and ubiquitous. In this work, we propose to
consider the task of speech enhancement as a holistic endeavor, and present a
universal speech enhancement system that tackles 55 different distortions at
the same time. Our approach consists of a generative model that employs
score-based diffusion, together with a multi-resolution conditioning network
that performs enhancement with mixture density networks. We show that this
approach significantly outperforms the state of the art in a subjective test
performed by expert listeners. We also show that it achieves competitive
objective scores with just 4-8 diffusion steps, despite not considering any
particular strategy for fast sampling. We hope that both our methodology and
technical contributions encourage researchers and practitioners to adopt a
universal approach to speech enhancement, possibly framing it as a generative
task.

The key of Human-Object Interaction(HOI) recognition is to infer the
relationship between human and objects. Recently, the image's Human-Object
Interaction(HOI) detection has made significant progress. However, there is
still room for improvement in video HOI detection performance. Existing
one-stage methods use well-designed end-to-end networks to detect a video
segment and directly predict an interaction.
  It makes the model learning and further optimization of the network more
complex. This paper introduces the Spatial Parsing and Dynamic Temporal Pooling
(SPDTP) network, which takes the entire video as a spatio-temporal graph with
human and object nodes as input. Unlike existing methods, our proposed network
predicts the difference between interactive and non-interactive pairs through
explicit spatial parsing, and then performs interaction recognition. Moreover,
we propose a learnable and differentiable Dynamic Temporal Module(DTM) to
emphasize the keyframes of the video and suppress the redundant frame.
Furthermore, the experimental results show that SPDTP can pay more attention to
active human-object pairs and valid keyframes. Overall, we achieve
state-of-the-art performance on CAD-120 dataset and Something-Else dataset.

As artificial intelligence (AI) and machine learning (ML) technologies
disrupt a wide range of industries, cloud datacenters face ever-increasing
demand in inference workloads. However, conventional CPU-based servers cannot
handle excessive computational requirements of deep neural network (DNN)
models, while GPU-based servers suffer from huge power consumption and high
operating cost. In this paper, we present a scalable systolic-vector
architecture that can cope with dynamically changing DNN workloads in cloud
datacenters. We first devise a lightweight DNN model description format called
unified model format (UMF) that enables general model representation and fast
decoding in hardware accelerator. Based on this model format, we propose a
heterogeneous architecture that features a load balancer that performs a
high-level workload distribution and multiple systolic-vector clusters, in
which each cluster consists of a programmable scheduler, throughput-oriented
systolic arrays, and function-oriented vector processors. We also propose a
heterogeneity-aware scheduling algorithm that enables concurrent execution of
multiple DNN workloads while maximizing heterogeneous hardware utilization
based on computation and memory access time estimation. Finally, we build an
architecture simulation framework based on actual synthesis and place-and-route
implementation results and conduct design space exploration for the proposed
architecture. As a result, the proposed systolic-vector architecture achieves
10.9x higher throughput performance and 30.17x higher energy efficiency than a
compatible GPU on realistic ML workloads. The proposed heterogeneity-aware
scheduling algorithm improves the throughput and energy efficiency by 81% and
20%, respectively, compared to a standard round-robin scheduling.

Depth maps are used in a wide range of applications from 3D rendering to 2D
image effects such as Bokeh. However, those predicted by single image depth
estimation (SIDE) models often fail to capture isolated holes in objects and/or
have inaccurate boundary regions. Meanwhile, high-quality masks are much easier
to obtain, using commercial auto-masking tools or off-the-shelf methods of
segmentation and matting or even by manual editing. Hence, in this paper, we
formulate a novel problem of mask-guided depth refinement that utilizes a
generic mask to refine the depth prediction of SIDE models. Our framework
performs layered refinement and inpainting/outpainting, decomposing the depth
map into two separate layers signified by the mask and the inverse mask. As
datasets with both depth and mask annotations are scarce, we propose a
self-supervised learning scheme that uses arbitrary masks and RGB-D datasets.
We empirically show that our method is robust to different types of masks and
initial depth predictions, accurately refining depth values in inner and outer
mask boundary regions. We further analyze our model with an ablation study and
demonstrate results on real applications. More information can be found at
https://sooyekim.github.io/MaskDepth/ .

We present CAISAR, an open-source platform under active development for the
characterization of AI systems' robustness and safety. CAISAR provides a
unified entry point for defining verification problems by using WhyML, the
mature and expressive language of the Why3 verification platform. Moreover,
CAISAR orchestrates and composes state-of-the-art machine learning verification
tools which, individually, are not able to efficiently handle all problems but,
collectively, can cover a growing number of properties. Our aim is to assist,
on the one hand, the V\&V process by reducing the burden of choosing the
methodology tailored to a given verification problem, and on the other hand the
tools developers by factorizing useful features-visualization, report
generation, property description-in one platform. CAISAR will soon be available
at https://git.frama-c.com/pub/caisar.

Computed tomography (CT) has been widely explored as a COVID-19 screening and
assessment tool to complement RT-PCR testing. To assist radiologists with
CT-based COVID-19 screening, a number of computer-aided systems have been
proposed. However, many proposed systems are built using CT data which is
limited in both quantity and diversity. Motivated to support efforts in the
development of machine learning-driven screening systems, we introduce COVIDx
CT-3, a large-scale multinational benchmark dataset for detection of COVID-19
cases from chest CT images. COVIDx CT-3 includes 431,205 CT slices from 6,068
patients across at least 17 countries, which to the best of our knowledge
represents the largest, most diverse dataset of COVID-19 CT images in
open-access form. Additionally, we examine the data diversity and potential
biases of the COVIDx CT-3 dataset, finding that significant geographic and
class imbalances remain despite efforts to curate data from a wide variety of
sources.

Embeddings, low-dimensional vector representation of objects, are fundamental
in building modern machine learning systems. In industrial settings, there is
usually an embedding team that trains an embedding model to solve intended
tasks (e.g., product recommendation). The produced embeddings are then widely
consumed by consumer teams to solve their unintended tasks (e.g., fraud
detection). However, as the embedding model gets updated and retrained to
improve performance on the intended task, the newly-generated embeddings are no
longer compatible with the existing consumer models. This means that historical
versions of the embeddings can never be retired or all consumer teams have to
retrain their models to make them compatible with the latest version of the
embeddings, both of which are extremely costly in practice. Here we study the
problem of embedding version updates and their backward compatibility. We
formalize the problem where the goal is for the embedding team to keep updating
the embedding version, while the consumer teams do not have to retrain their
models. We develop a solution based on learning backward compatible embeddings,
which allows the embedding model version to be updated frequently, while also
allowing the latest version of the embedding to be quickly transformed into any
backward compatible historical version of it, so that consumer teams do not
have to retrain their models. Under our framework, we explore six methods and
systematically evaluate them on a real-world recommender system application. We
show that the best method, which we call BC-Aligner, maintains backward
compatibility with existing unintended tasks even after multiple model version
updates. Simultaneously, BC-Aligner achieves the intended task performance
similar to the embedding model that is solely optimized for the intended task.

This work presents a new procedure for obtaining predictive distributions in
the context of Gaussian process (GP) modeling, with a relaxation of the
interpolation constraints outside some ranges of interest: the mean of the
predictive distributions no longer necessarily interpolates the observed values
when they are outside ranges of interest, but are simply constrained to remain
outside. This method called relaxed Gaussian process (reGP) interpolation
provides better predictive distributions in ranges of interest, especially in
cases where a stationarity assumption for the GP model is not appropriate. It
can be viewed as a goal-oriented method and becomes particularly interesting in
Bayesian optimization, for example, for the minimization of an objective
function, where good predictive distributions for low function values are
important. When the expected improvement criterion and reGP are used for
sequentially choosing evaluation points, the convergence of the resulting
optimization algorithm is theoretically guaranteed (provided that the function
to be optimized lies in the reproducing kernel Hilbert spaces attached to the
known covariance of the underlying Gaussian process). Experiments indicate that
using reGP instead of stationary GP models in Bayesian optimization is
beneficial.

In this dissertation, we investigated and enhanced Deep Learning (DL)
techniques for counting objects, like pedestrians, cells or vehicles, in still
images or video frames. In particular, we tackled the challenge related to the
lack of data needed for training current DL-based solutions. Given that the
budget for labeling is limited, data scarcity still represents an open problem
that prevents the scalability of existing solutions based on the supervised
learning of neural networks and that is responsible for a significant drop in
performance at inference time when new scenarios are presented to these
algorithms. We introduced solutions addressing this issue from several
complementary sides, collecting datasets gathered from virtual environments
automatically labeled, proposing Domain Adaptation strategies aiming at
mitigating the domain gap existing between the training and test data
distributions, and presenting a counting strategy in a weakly labeled data
scenario, i.e., in the presence of non-negligible disagreement between multiple
annotators. Moreover, we tackled the non-trivial engineering challenges coming
out of the adoption of Convolutional Neural Network-based techniques in
environments with limited power resources, introducing solutions for counting
vehicles and pedestrians directly onboard embedded vision systems, i.e.,
devices equipped with constrained computational capabilities that can capture
images and elaborate them.

The stagnation of EDA technologies roots from insufficient knowledge reuse.
In practice, very similar simulation or optimization results may need to be
repeatedly constructed from scratch. This motivates my research on introducing
more 'intelligence' to EDA with machine learning (ML), which explores complex
correlations in design flows based on prior data. Besides design time, I also
propose ML solutions to boost IC performance by assisting the circuit
management at runtime. In this dissertation, I present multiple fast yet
accurate ML models covering a wide range of chip design stages from the
register-transfer level (RTL) to sign-off, solving primary chip-design problems
about power, timing, interconnect, IR drop, routability, and design flow
tuning. Targeting the RTL stage, I present APOLLO, a fully automated power
modeling framework. It constructs an accurate per-cycle power model by
extracting the most power-correlated signals. The model can be further
implemented on chip for runtime power management with unprecedented low
hardware costs. Targeting gate-level netlist, I present Net2 for early
estimations on post-placement wirelength. It further enables more accurate
timing analysis without actual physical design information. Targeting circuit
layout, I present RouteNet for early routability prediction. As the first deep
learning-based routability estimator, some feature-extraction and model-design
principles proposed in it are widely adopted by later works. I also present
PowerNet for fast IR drop estimation. It captures spatial and temporal
information about power distribution with a customized CNN architecture. Last,
besides targeting a single design step, I present FIST to efficiently tune
design flow parameters during both logic synthesis and physical design.

Designing and implementing explainable systems is seen as the next step
towards increasing user trust in, acceptance of and reliance on Artificial
Intelligence (AI) systems. While explaining choices made by black-box
algorithms such as machine learning and deep learning has occupied most of the
limelight, systems that attempt to explain decisions (even simple ones) in the
context of social choice are steadily catching up. In this paper, we provide a
comprehensive survey of explainability in mechanism design, a domain
characterized by economically motivated agents and often having no single
choice that maximizes all individual utility functions. We discuss the main
properties and goals of explainability in mechanism design, distinguishing them
from those of Explainable AI in general. This discussion is followed by a
thorough review of the challenges one may face when working on Explainable
Mechanism Design and propose a few solution concepts to those.

The booming of electric vehicles demands efficient battery disassembly for
recycling to be environment-friendly. Currently, battery disassembly is still
primarily done by humans, probably assisted by robots, due to the unstructured
environment and high uncertainties. It is highly desirable to design autonomous
solutions to improve work efficiency and lower human risks in high voltage and
toxic environments. This paper proposes a novel neurosymbolic method, which
augments the traditional Variational Autoencoder (VAE) model to learn symbolic
operators based on raw sensory inputs and their relationships. The symbolic
operators include a probabilistic state symbol grounding model and a state
transition matrix for predicting states after each execution to enable
autonomous task and motion planning. At last, the method's feasibility is
verified through test results.

Offline goal-conditioned reinforcement learning (GCRL) promises
general-purpose skill learning in the form of reaching diverse goals from
purely offline datasets. We propose $\textbf{Go}$al-conditioned
$f$-$\textbf{A}$dvantage $\textbf{R}$egression (GoFAR), a novel
regression-based offline GCRL algorithm derived from a state-occupancy matching
perspective; the key intuition is that the goal-reaching task can be formulated
as a state-occupancy matching problem between a dynamics-abiding imitator agent
and an expert agent that directly teleports to the goal. In contrast to prior
approaches, GoFAR does not require any hindsight relabeling and enjoys
uninterleaved optimization for its value and policy networks. These distinct
features confer GoFAR with much better offline performance and stability as
well as statistical performance guarantee that is unattainable for prior
methods. Furthermore, we demonstrate that GoFAR's training objectives can be
re-purposed to learn an agent-independent goal-conditioned planner from purely
offline source-domain data, which enables zero-shot transfer to new target
domains. Through extensive experiments, we validate GoFAR's effectiveness in
various problem settings and tasks, significantly outperforming prior
state-of-art. Notably, on a real robotic dexterous manipulation task, while no
other method makes meaningful progress, GoFAR acquires complex manipulation
behavior that successfully accomplishes diverse goals.

Nonnegative matrix factorization (NMF) has been widely used to dimensionality
reduction in machine learning. However, the traditional NMF does not properly
handle outliers, so that it is sensitive to noise. In order to improve the
robustness of NMF, this paper proposes an adaptive weighted NMF, which
introduces weights to emphasize the different importance of each data point,
thus the algorithmic sensitivity to noisy data is decreased. It is very
different from the existing robust NMFs that use a slow growth similarity
measure. Specifically, two strategies are proposed to achieve this: fuzzier
weighted technique and entropy weighted regularized technique, and both of them
lead to an iterative solution with a simple form. Experimental results showed
that new methods have more robust feature representation on several real
datasets with noise than exsiting methods.

We study the survival bandit problem, a variant of the multi-armed bandit
problem introduced in an open problem by Perotto et al. (2019), with a
constraint on the cumulative reward; at each time step, the agent receives a
(possibly negative) reward and if the cumulative reward becomes lower than a
prespecified threshold, the procedure stops, and this phenomenon is called
ruin. This is the first paper studying a framework where the ruin might occur
but not always. We first discuss that a sublinear regret is unachievable under
a naive definition of the regret. Next, we provide tight lower bounds on the
probability of ruin (as well as matching policies). Based on this lower bound,
we define the survival regret as an objective to minimize and provide a policy
achieving a sublinear survival regret (at least in the case of integral
rewards) when the time horizon $T$ is known.

This paper proposes a novel self-supervised learning method for learning
better representations with small batch sizes. Many self-supervised learning
methods based on certain forms of the siamese network have emerged and received
significant attention. However, these methods need to use large batch sizes to
learn good representations and require heavy computational resources. We
present a new triplet network combined with a triple-view loss to improve the
performance of self-supervised representation learning with small batch sizes.
Experimental results show that our method can drastically outperform
state-of-the-art self-supervised learning methods on several datasets in
small-batch cases. Our method provides a feasible solution for self-supervised
learning with real-world high-resolution images that uses small batch sizes.

Spatiotemporal predictive learning, which predicts future frames through
historical prior knowledge with the aid of deep learning, is widely used in
many fields. Previous work essentially improves the model performance by
widening or deepening the network, but it also brings surging memory overhead,
which seriously hinders the development and application of this technology. In
order to improve the performance without increasing memory consumption, we
focus on scale, which is another dimension to improve model performance but
with low memory requirement. The effectiveness has been widely proved in many
CNN-based tasks such as image classification and semantic segmentation, but it
has not been fully explored in recent RNN models. In this paper, learning from
the benefit of multi-scale, we propose a general framework named Multi-Scale
RNN (MS-RNN) to boost recent RNN models for spatiotemporal predictive learning.
By integrating different scales, we enhance the existing models with both
improved performance and greatly reduced overhead. We verify our MS-RNN
framework by exhaustive experiments with 6 popular RNN models (ConvLSTM,
TrajGRU, PredRNN, PredRNN++, MIM, and MotionRNN) on 4 different datasets
(Moving MNIST, KTH, TaxiBJ, and HKO-7). The results show the efficiency that
the RNN models incorporating our framework have much lower memory cost but
better performance than before. Our code is released at
\url{https://github.com/mazhf/MS-RNN}.

The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded
worldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast
detection and patient triage is becoming critical. This paper proposes a novel
self-knowledge distillation based self-supervised learning method for COVID-19
detection from chest X-ray images. Our method can use self-knowledge of images
based on similarities of their visual features for self-supervised learning.
Experimental results show that our method achieved an HM score of 0.988, an AUC
of 0.999, and an accuracy of 0.957 on the largest open COVID-19 chest X-ray
dataset.

We study the problem of histogram estimation under user-level differential
privacy, where the goal is to preserve the privacy of all entries of any single
user. While there is abundant literature on this classical problem under the
item-level privacy setup where each user contributes only one data point,
little has been known for the user-level counterpart. We consider the
heterogeneous scenario where both the quantity and distribution of data can be
different for each user. We propose an algorithm based on a clipping strategy
that almost achieves a two-approximation with respect to the best clipping
threshold in hindsight. This result holds without any distribution assumptions
on the data. We also prove that the clipping bias can be significantly reduced
when the counts are from non-i.i.d. Poisson distributions and show empirically
that our debiasing method provides improvements even without such constraints.
Experiments on both real and synthetic datasets verify our theoretical findings
and demonstrate the effectiveness of our algorithms.

We consider conceptual issues of deep learning (DL) for metric detectors
using test particle geodesics in curved spacetimes. Advantages of DL metric
detectors are emphasized from a view point of general coordinate
transformations. Two given metrics (two spacetimes) are defined to be conneted
by a DL isometry if their geodesic image data cannot be discriminated by any DL
metric detector at any time. The fundamental question of when the DL isometry
appears is extensively explored. If the two spacetimes connected by the DL
isometry are in superposition of quantum gravity theory, the post-measurement
state may be still in the same superposition even after DL metric detectors
observe the superposed state. We also demonstrate metric-detection DL's in 2+1
dimensional anti-de Sitter (AdS) spacetimes to estimate the cosmological
constants and Brown-Henneaux charges. In the AdS/CFT correspondence dictionary,
it may be expected that such metric detectors in the AdS bulk region correspond
to quantum measurement devices in the CFT at the AdS boundary.

In this paper, we introduce the first learning-based planner to drive a car
in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our
planner, DriveIRL, generates a diverse set of trajectory proposals, filters
these trajectories with a lightweight and interpretable safety filter, and then
uses a learned model to score each remaining trajectory. The best trajectory is
then tracked by the low-level controller of our self-driving vehicle. We train
our trajectory scoring model on a 500+ hour real-world dataset of expert
driving demonstrations in Las Vegas within the maximum entropy IRL framework.
DriveIRL's benefits include: a simple design due to only learning the
trajectory scoring function, relatively interpretable features, and strong
real-world performance. We validated DriveIRL on the Las Vegas Strip and
demonstrated fully autonomous driving in heavy traffic, including scenarios
involving cut-ins, abrupt braking by the lead vehicle, and hotel pickup/dropoff
zones. Our dataset will be made public to help further research in this area.

Character line drawing synthesis can be formulated as a special case of
image-to-image translation problem that automatically manipulates the
photo-to-line drawing style transformation. In this paper, we present the first
generative adversarial network based end-to-end trainable translation
architecture, dubbed P2LDGAN, for automatic generation of high-quality
character drawings from input photos/images. The core component of our approach
is the joint geometric-semantic-driven generator, which uses our well-designed
cross-scale dense skip connections framework to embed learned geometric and
semantic information for generating delicate line drawings. In order to support
the evaluation of our model, we release a new dataset including 1,532
well-matched pairs of freehand character line drawings as well as corresponding
character images/photos, where these line drawings with diverse styles are
manually drawn by skilled artists. Extensive experiments on our introduced
dataset demonstrate the superior performance of our proposed models against the
state-of-the-art approaches in terms of quantitative, qualitative and human
evaluations. Our code, models and dataset is available at
https://github.com/cnyvfang/P2LDGAN.

Estimation of a conditional mean (linking a set of features to an outcome of
interest) is a fundamental statistical task. While there is an appeal to
flexible nonparametric procedures, effective estimation in many classical
nonparametric function spaces (e.g., multivariate Sobolev spaces) can be
prohibitively difficult -- both statistically and computationally -- especially
when the number of features is large. In this paper, we present (penalized)
sieve estimators for regression in nonparametric tensor product spaces: These
spaces are more amenable to multivariate regression, and allow us to, in-part,
avoid the curse of dimensionality. Our estimators can be easily applied to
multivariate nonparametric problems and have appealing statistical and
computational properties. Moreover, they can effectively leverage additional
structures such as feature sparsity. In this manuscript, we give theoretical
guarantees, indicating that the predictive performance of our estimators scale
favorably in dimension. In addition, we also present numerical examples to
compare the finite-sample performance of the proposed estimators with several
popular machine learning methods.

The invariance property across environments is at the heart of invariant
learning methods for the Out-of-Distribution (OOD) Generalization problem.
Although intuitively reasonable, strong assumptions on the availability and
quality of environments have to be made for the learnability of the strict
invariance property. Recently, to relax the requirements for environments
empirically, some works propose to learn pseudo-environments for invariant
learning. However, it could be misleading when pursuing strict invariance under
latent heterogeneity, since the underlying invariance could have been violated
during the pseudo-environment learning procedure. To this end, we come up with
the distributional invariance property as a relaxed alternative to the strict
invariance, which considers the invariance only among sub-populations down to a
prescribed scale and allows a certain degree of variation. We reformulate the
invariant learning problem under latent heterogeneity into a relaxed form that
pursues the distributional invariance, based on which we propose our novel
Distributionally Invariant Learning (DIL) framework as well as two
implementations named DIL-MMD and DIL-KL. Theoretically, we provide the
guarantees for the distributional invariance as well as bounds of the
generalization error gap. Extensive experimental results validate the
effectiveness of our proposed algorithms.

The high efficiency of domain-specific hardware accelerators for machine
learning (ML) has come from specialization, with the trade-off of less
configurability/ flexibility. There is growing interest in developing flexible
ML accelerators to make them future-proof to the rapid evolution of Deep Neural
Networks (DNNs). However, the notion of accelerator flexibility has always been
used in an informal manner, restricting computer architects from conducting
systematic apples-to-apples design-space exploration (DSE) across trillions of
choices. In this work, we formally define accelerator flexibility and show how
it can be integrated for DSE. Specifically, we capture DNN accelerator
flexibility across four axes: tiling, ordering, parallelization, and array
shape. We categorize existing accelerators into 16 classes based on their axes
of flexibility support, and define a precise quantification of the degree of
flexibility of an accelerator across each axis. We leverage these to develop a
novel flexibility-aware DSE framework. We demonstrate how this can be used to
perform first-of-their-kind evaluations, including an isolation study to
identify the individual impact of the flexibility axes. We demonstrate that
adding flexibility features to a hypothetical DNN accelerator designed in 2014
improves runtime on future (i.e., present-day) DNNs by 11.8x geomean.

Plagiarism is the practice of claiming to be someone else content, thoughts
or ideas as one own without any proper credit and citations. This paper is a
survey paper that, represent the some of the great research paper and its
comparison that is work done on plagiarism. Now a days, plagiarism became one
of the most interesting and crucial research points in Natural Language
Processing area. We review some old research paper based on different types of
plagiarism detection and their models and algorithm, and comparison of the
accuracy of those papers. There are many several ways which are available for
plagiarism detection in different language. There are a few algorithms to
detecting plagiarism. Like, corpus, CL-CNG, LSI, Levenshtein Distance etc. We
analysis those papers, and learn that they used different types of algorithms
for detecting plagiarism. After experiment those papers, we got that some of
the algorithms give a better output and accuracy for detecting plagiarism. We
are going to give a review on some papers about Plagiarism and will discuss
about the pros and cons of their models. And we also show a propose method for
plagiarism detection method which based on sentience separation, word
separation and make sentence based on synonym and compare with any sources.

Recent research has shown that large language models pretrained using
unsupervised approaches can achieve significant performance improvement on many
downstream tasks. Typically when adapting these language models to downstream
tasks, like a classification or regression task, we employ a fine-tuning
paradigm in which the sentence representation from the language model is input
to a task-specific head; the model is then fine-tuned end-to-end. However, with
the emergence of models like GPT-3, prompt-based fine-tuning has been proven to
be a successful approach for few-shot tasks. Inspired by this work, we study
discrete prompt technologies in practice. There are two issues that arise with
the standard prompt approach. First, it can overfit on the prompt template.
Second, it requires manual effort to formulate the downstream task as a
language model problem. In this paper, we propose an improvement to
prompt-based fine-tuning that addresses these two issues. We refer to our
approach as DynaMaR -- Dynamic Prompt with Mask Token Representation. Results
show that DynaMaR can achieve an average improvement of 10% in few-shot
settings and improvement of 3.7% in data-rich settings over the standard
fine-tuning approach on four e-commerce applications.

Federated learning (FL) has emerged as a distributed machine learning (ML)
technique to train models without sharing users' private data. In this paper,
we propose a decentralized FL scheme that is called \underline{f}ederated
\underline{l}earning \underline{e}mpowered \underline{o}verlapped
\underline{c}lustering for \underline{d}ecentralized aggregation (FL-EOCD). The
introduced FL-EOCD leverages device-to-device (D2D) communications and
overlapped clustering to enable decentralized aggregation, where a cluster is
defined as a coverage zone of a typical device. The devices located on the
overlapped clusters are called bridge devices (BDs). In the proposed FL-EOCD
scheme, a clustering topology is envisioned where clusters are connected
through BDs, so as the aggregated models of each cluster is disseminated to the
other clusters in a decentralized manner without the need for a global
aggregator or an additional hop of transmission. Unlike the star-based FL, the
proposed FL-EOCD scheme involves a large number of local devices by reusing the
RRBs in different non-adjacent clusters. To evaluate our proposed FL-EOCD
scheme as opposed to baseline FL schemes, we consider minimizing the overall
energy-consumption of devices while maintaining the convergence rate of FL
subject to its time constraint. To this end, a joint optimization problem,
considering scheduling the local devices/BDs to the CHs and computation
frequency allocation, is formulated, where an iterative solution to this joint
problem is devised. Extensive simulations are conducted to verify the
effectiveness of the proposed FL-EOCD algorithm over FL conventional schemes in
terms of energy consumption, latency, and convergence rate.

Dual-Encoders is a promising mechanism for answer retrieval in question
answering (QA) systems. Currently most conventional Dual-Encoders learn the
semantic representations of questions and answers merely through matching
score. Researchers proposed to introduce the QA interaction features in scoring
function but at the cost of low efficiency in inference stage. To keep
independent encoding of questions and answers during inference stage,
variational auto-encoder is further introduced to reconstruct answers
(questions) from question (answer) embeddings as an auxiliary task to enhance
QA interaction in representation learning in training stage. However, the needs
of text generation and answer retrieval are different, which leads to hardness
in training. In this work, we propose a framework to enhance the Dual-Encoders
model with question answer cross-embeddings and a novel Geometry Alignment
Mechanism (GAM) to align the geometry of embeddings from Dual-Encoders with
that from Cross-Encoders. Extensive experimental results show that our
framework significantly improves Dual-Encoders model and outperforms the
state-of-the-art method on multiple answer retrieval datasets.

Convolutional Neural Networks (CNN) have dominated the field of detection
ever since the success of AlexNet in ImageNet classification [12]. With the
sweeping reform of Transformers [27] in natural language processing, Carion et
al. [2] introduce the Transformer-based detection method, i.e., DETR. However,
due to the quadratic complexity in the self-attention mechanism in the
Transformer, DETR is never able to incorporate multi-scale features as
performed in existing CNN-based detectors, leading to inferior results in small
object detection. To mitigate this issue and further improve performance of
DETR, in this work, we investigate different methods to incorporate multi-scale
features and find that a Bi-directional Feature Pyramid (BiFPN) works best with
DETR in further raising the detection precision. With this discovery, we
propose DETR++, a new architecture that improves detection results by 1.9% AP
on MS COCO 2017, 11.5% AP on RICO icon detection, and 9.1% AP on RICO layout
extraction over existing baselines.

Pruning techniques have been successfully used in neural networks to trade
accuracy for sparsity. However, the impact of network pruning is not uniform:
prior work has shown that the recall for underrepresented classes in a dataset
may be more negatively affected. In this work, we study such relative
distortions in recall by hypothesizing an intensification effect that is
inherent to the model. Namely, that pruning makes recall relatively worse for a
class with recall below accuracy and, conversely, that it makes recall
relatively better for a class with recall above accuracy. In addition, we
propose a new pruning algorithm aimed at attenuating such effect. Through
statistical analysis, we have observed that intensification is less severe with
our algorithm but nevertheless more pronounced with relatively more difficult
tasks, less complex models, and higher pruning ratios. More surprisingly, we
conversely observe a de-intensification effect with lower pruning ratios, which
indicates that moderate pruning may have a corrective effect to such
distortions.

Learning interpretable representations of neural dynamics at a population
level is a crucial first step to understanding how neural activity relates to
perception and behavior. Models of neural dynamics often focus on either
low-dimensional projections of neural activity, or on learning dynamical
systems that explicitly relate to the neural state over time. We discuss how
these two approaches are interrelated by considering dynamical systems as
representative of flows on a low-dimensional manifold. Building on this
concept, we propose a new decomposed dynamical system model that represents
complex non-stationary and nonlinear dynamics of time-series data as a sparse
combination of simpler, more interpretable components. The decomposed nature of
the dynamics generalizes over previous switched approaches and enables modeling
of overlapping and non-stationary drifts in the dynamics. We further present a
dictionary learning-driven approach to model fitting, where we leverage recent
results in tracking sparse vectors over time. We demonstrate that our model can
learn efficient representations and smooth transitions between dynamical modes
in both continuous-time and discrete-time examples. We show results on
low-dimensional linear and nonlinear attractors to demonstrate that our
decomposed dynamical systems model can well approximate nonlinear dynamics.
Additionally, we apply our model to C. elegans data, illustrating a diversity
of dynamics that is obscured when classified into discrete states.

We study the stochastic multi-armed bandit problem and design new policies
that enjoy both worst-case optimality for expected regret and light-tailed risk
for regret distribution. Starting from the two-armed bandit setting with time
horizon $T$, we propose a simple policy and prove that the policy (i) enjoys
the worst-case optimality for the expected regret at order $O(\sqrt{T\ln T})$
and (ii) has the worst-case tail probability of incurring a linear regret decay
at an exponential rate $\exp(-\Omega(\sqrt{T}))$, a rate that we prove to be
best achievable for all worst-case optimal policies. Briefly, our proposed
policy achieves a delicate balance between doing more exploration at the
beginning of the time horizon and doing more exploitation when approaching the
end, compared to the standard Successive Elimination policy and Upper
Confidence Bound policy. We then improve the policy design and analysis to work
for the general $K$-armed bandit setting. Specifically, the worst-case
probability of incurring a regret larger than any $x>0$ is upper bounded by
$\exp(-\Omega(x/\sqrt{KT}))$. We then enhance the policy design to accommodate
the "any-time" setting where $T$ is not known a priori, and prove equivalently
desired policy performances as compared to the "fixed-time" setting with known
$T$. A brief account of numerical experiments is conducted to illustrate the
theoretical findings. We conclude by extending our proposed policy design to
the general stochastic linear bandit setting and proving that the policy leads
to both worst-case optimality in terms of expected regret order and
light-tailed risk on the regret distribution.

When the first CubeSats were launched nearly two decades ago, few people
believed that the miniature satellites would likely prove to be a useful
scientific tool. Skeptics abounded. However, the last decade has seen the
highly successful implementation of space missions that make creative and
innovative use of fast-advancing CubeSat and small satellite technology to
carry out important science experiments and missions. Several projects now have
used CubeSats to obtain first-of-their-kind observations and findings that have
formed the basis for high-profile engineering and science publications, thereby
establishing without doubt the scientific value and broad utility of CubeSats.
In this paper, we describe recent achievements and lessons learned from a
representative selection of successful CubeSat missions with a space weather
focus. We conclude that these missions were successful in part because their
limited resources promoted not only mission focus but also appropriate
risk-taking for comparatively high science return. Quantitative analysis of
refereed publications from these CubeSat missions and several larger missions
reveals that mission outcome metrics compare favorably when publication number
is normalized by mission cost or if expressed as a weighted net scientific
impact of all mission publications.

State-of-the-art computer vision models are mostly trained with supervised
learning using human-labeled images, which limits their scalability due to the
expensive annotation cost. While self-supervised representation learning has
achieved impressive progress, it still requires a second stage of finetuning on
labeled data. On the other hand, models pre-trained with large-scale text-image
supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image
classification tasks. However, the zero-shot performance of CLIP-like models
are often insufficient for real-world adoption. In this paper, we aim to
leverage the abundant unlabeled data to improve the performance of a
pre-trained zero-shot classifier on downstream tasks. We propose Masked
Unsupervised Self-Training (MUST), a new approach which leverages two different
and complimentary sources of supervision: pseudo-labels and raw images. MUST
jointly optimizes three objectives to learn both class-level global feature and
pixel-level local feature and enforces a regularization between the two. We
demonstrate the efficacy of MUST on 8 downstream tasks across a variety of
domains, where it improves upon CLIP by a large margin and narrows the
performance gap between unsupervised and supervised classification. For
instance, MUST achieves a zero-shot top-1 accuracy of 77.7% on ImageNet using
ViT-B, +9.4% higher than CLIP. Our code is available at
https://github.com/salesforce/MUST.

Knowledge graph embedding (KGE) has been intensively investigated for link
prediction by projecting entities and relations into continuous vector spaces.
Current popular high-dimensional KGE methods obtain quite slight performance
gains while require enormous computation and memory costs. In contrast to
high-dimensional KGE models, training low-dimensional models is more efficient
and worthwhile for better deployments to practical intelligent systems.
However, the model expressiveness of semantic information in knowledge graphs
(KGs) is highly limited in the low dimension parameter space. In this paper, we
propose iterative self-semantic knowledge distillation strategy to improve the
KGE model expressiveness in the low dimension space. KGE model combined with
our proposed strategy plays the teacher and student roles alternatively during
the whole training process. Specifically, at a certain iteration, the model is
regarded as a teacher to provide semantic information for the student. At next
iteration, the model is regard as a student to incorporate the semantic
information transferred from the teacher. We also design a novel semantic
extraction block to extract iteration-based semantic information for the
training model self-distillation. Iteratively incorporating and accumulating
iteration-based semantic information enables the low-dimensional model to be
more expressive for better link prediction in KGs. There is only one model
during the whole training, which alleviates the increase of computational
expensiveness and memory requirements. Furthermore, the proposed strategy is
model-agnostic and can be seamlessly combined with other KGE models. Consistent
and significant performance gains in experimental evaluations on four standard
datasets demonstrate the effectiveness of the proposed self-distillation
strategy.

This paper discusses an application of Shapley values in the causal inference
field, specifically on how to select the top confounder variables for coarsened
exact matching method in a scalable way. We use a dataset from an observational
experiment involving LinkedIn members as a use case to test its applicability,
and show that Shapley values are highly informational and can be leveraged for
its robust importance-ranking capability.

Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs)
plays an important role for automatic delineation of Clinical Target Volume
(CTV) of brain tumors in radiotherapy. Despite that variants of U-Net are
state-of-the-art segmentation models, they have limited performance when
dealing with ABCs structures with various shapes and sizes, especially thin
structures (e.g., the falx cerebri) that span only few slices. To deal with
this problem, we propose a High and Multi-Resolution Network (HMRNet) that
consists of a multi-scale feature learning branch and a high-resolution branch,
which can maintain the high-resolution contextual information and extract more
robust representations of anatomical structures with various scales. We further
design a Bidirectional Feature Calibration (BFC) block to enable the two
branches to generate spatial attention maps for mutual feature calibration.
Considering the different sizes and positions of ABCs structures, our network
was applied after a rough localization of each structure to obtain fine
segmentation results. Experiments on the MICCAI 2020 ABCs challenge dataset
showed that: 1) Our proposed two-stage segmentation strategy largely
outperformed methods segmenting all the structures in just one stage; 2) The
proposed HMRNet with two branches can maintain high-resolution representations
and is effective to improve the performance on thin structures; 3) The proposed
BFC block outperformed existing attention methods using monodirectional feature
calibration. Our method won the second place of ABCs 2020 challenge and has a
potential for more accurate and reasonable delineation of CTV of brain tumors.

Saliency methods calculate how important each input feature is to a machine
learning model's prediction, and are commonly used to understand model
reasoning. "Faithfulness", or how fully and accurately the saliency output
reflects the underlying model, is an oft-cited desideratum for these methods.
However, explanation methods must necessarily sacrifice certain information in
service of user-oriented goals such as simplicity. To that end, and akin to
performance metrics, we frame saliency methods as abstractions: individual
tools that provide insight into specific aspects of model behavior and entail
tradeoffs. Using this framing, we describe a framework of nine dimensions to
characterize and compare the properties of saliency methods. We group these
dimensions into three categories that map to different phases of the
interpretation process: methodology, or how the saliency is calculated;
sensitivity, or relationships between the saliency result and the underlying
model or input; and, perceptibility, or how a user interprets the result. As we
show, these dimensions give us a granular vocabulary for describing and
comparing saliency methods -- for instance, allowing us to develop "saliency
cards" as a form of documentation, or helping downstream users understand
tradeoffs and choose a method for a particular use case. Moreover, by situating
existing saliency methods within this framework, we identify opportunities for
future work, including filling gaps in the landscape and developing new
evaluation metrics.

Machine Learning (ML) systems are a building part of the modern tools which
impact our daily life in several application domains. Due to their black-box
nature, those systems are hardly adopted in application domains (e.g. health,
finance) where understanding the decision process is of paramount importance.
Explanation methods were developed to explain how the ML model has taken a
specific decision for a given case/instance. Graph Counterfactual Explanations
(GCE) is one of the explanation techniques adopted in the Graph Learning
domain. The existing works of Graph Counterfactual Explanations diverge mostly
in the problem definition, application domain, test data, and evaluation
metrics, and most existing works do not compare exhaustively against other
counterfactual explanation techniques present in the literature. We present
GRETEL, a unified framework to develop and test GCE methods in several
settings. GRETEL is a highly extensible evaluation framework which promotes the
Open Science and the evaluations reproducibility by providing a set of
well-defined mechanisms to integrate and manage easily: both real and synthetic
datasets, ML models, state-of-the-art explanation techniques, and evaluation
measures. To present GRETEL, we show the experiments conducted to integrate and
test several synthetic and real datasets with several existing explanation
techniques and base ML models.

Path-based methods are more appealing solutions than embedding methods for
knowledge graph reasoning, due to their interpretability and generalization
ability to unseen graphs. However, path-based methods usually suffer from the
problem of scalability, as the time complexity grows exponentially w.r.t. the
length of paths. While recent methods compute reasoning paths with the
Bellman-Ford algorithm in polynomial time, the time and memory cost remains
very high, as they need to propagate through all the nodes and edges in the
graph. In this paper, we propose A*Net, an efficient model for path-based
reasoning on knowledge graphs. Inspired by the classical A* algorithm for
shortest path problems, our A*Net prioritizes important nodes and edges at each
propagation step, to reduce the time and memory footprint. Unlike the classical
A* algorithm that uses a heuristic function, we propose to learn the priority
function for each node to capture the complex semantics in knowledge graphs.
The priority function and the propagation steps are jointly optimized through
backpropagation. Experiments on both transductive and inductive knowledge graph
reasoning benchmarks show that A*Net achieves competitive performance with
existing state-of-the-art path-based methods, and meanwhile reduces the number
of messages, the time and the memory cost up to 7.2$\times$, 3.4$\times$ and
4.9$\times$ respectively.

Dynamic time warping (DTW) is an effective dissimilarity measure in many time
series applications. Despite its popularity, it is prone to noises and
outliers, which leads to singularity problem and bias in the measurement. The
time complexity of DTW is quadratic to the length of time series, making it
inapplicable in real-time applications. In this paper, we propose a novel time
series dissimilarity measure named RobustDTW to reduce the effects of noises
and outliers. Specifically, the RobustDTW estimates the trend and optimizes the
time warp in an alternating manner by utilizing our designed temporal graph
trend filtering. To improve efficiency, we propose a multi-level framework that
estimates the trend and the warp function at a lower resolution, and then
repeatedly refines them at a higher resolution. Based on the proposed
RobustDTW, we further extend it to periodicity detection and outlier time
series detection. Experiments on real-world datasets demonstrate the superior
performance of RobustDTW compared to DTW variants in both outlier time series
detection and periodicity detection.

We analyze the convergence rates of stochastic gradient algorithms for smooth
finite-sum minimax optimization and show that, for many such algorithms,
sampling the data points without replacement leads to faster convergence
compared to sampling with replacement. For the smooth and strongly
convex-strongly concave setting, we consider gradient descent ascent and the
proximal point method, and present a unified analysis of two popular
without-replacement sampling strategies, namely Random Reshuffling (RR), which
shuffles the data every epoch, and Single Shuffling or Shuffle Once (SO), which
shuffles only at the beginning. We obtain tight convergence rates for RR and SO
and demonstrate that these strategies lead to faster convergence than uniform
sampling. Moving beyond convexity, we obtain similar results for smooth
nonconvex-nonconcave objectives satisfying a two-sided Polyak-{\L}ojasiewicz
inequality. Finally, we demonstrate that our techniques are general enough to
analyze the effect of data-ordering attacks, where an adversary manipulates the
order in which data points are supplied to the optimizer. Our analysis also
recovers tight rates for the incremental gradient method, where the data points
are not shuffled at all.

Topological loss based on persistent homology has shown promise in various
applications. A topological loss enforces the model to achieve certain desired
topological property. Despite its empirical success, less is known about the
optimization behavior of the loss. In fact, the topological loss involves
combinatorial configurations that may oscillate during optimization. In this
paper, we introduce a general purpose regularized topology-aware loss. We
propose a novel regularization term and also modify existing topological loss.
These contributions lead to a new loss function that not only enforces the
model to have desired topological behavior, but also achieves satisfying
convergence behavior. Our main theoretical result guarantees that the loss can
be optimized efficiently, under mild assumptions.

In this paper we propose a methodology for the efficient implementation of
Machine Learning (ML)-based methods in particle-in-cell (PIC) codes, with a
focus on Monte-Carlo or statistical extensions to the PIC algorithm. The
presented approach allows for neural networks to be developed in a Python
environment, where advanced ML tools are readily available to proficiently
train and test them. Those models are then efficiently deployed within
highly-scalable and fully parallelized PIC simulations during runtime. We
demonstrate this methodology with a proof-of-concept implementation within the
PIC code OSIRIS, where a fully-connected neural network is used to replace a
section of a Compton scattering module. We demonstrate that the ML-based method
reproduces the results obtained with the conventional method and achieves
better computational performance. These results offer a promising avenue for
future applications of ML-based methods in PIC, particularly for physics
extensions where an ML-based approach can provide a higher performance
increase.

This paper examines the use of risk models to predict the timing and location
of wildfires caused by electricity infrastructure. Our data include historical
ignition and wire-down points triggered by grid infrastructure collected
between 2015 to 2019 in Pacific Gas & Electricity territory along with various
weather, vegetation, and very high resolution data on grid infrastructure
including location, age, materials. With these data we explore a range of
machine learning methods and strategies to manage training data imbalance. The
best area under the receiver operating characteristic we obtain is 0.776 for
distribution feeder ignitions and 0.824 for transmission line wire-down events,
both using the histogram-based gradient boosting tree algorithm (HGB) with
under-sampling. We then use these models to identify which information provides
the most predictive value. After line length, we find that weather and
vegetation features dominate the list of top important features for ignition or
wire-down risk. Distribution ignition models show more dependence on
slow-varying vegetation variables such as burn index, energy release content,
and tree height, whereas transmission wire-down models rely more on primary
weather variables such as wind speed and precipitation. These results point to
the importance of improved vegetation modeling for feeder ignition risk models,
and improved weather forecasting for transmission wire-down models. We observe
that infrastructure features make small but meaningful improvements to risk
model predictive power.

Procedural planning aims to implement complex high-level goals by
decomposition into sequential simpler low-level steps. Although procedural
planning is a basic skill set for humans in daily life, it remains a challenge
for large language models (LLMs) that lack a deep understanding of the
cause-effect relations in procedures. Previous methods require manual exemplars
to acquire procedural planning knowledge from LLMs in the zero-shot setting.
However, such elicited pre-trained knowledge in LLMs induces spurious
correlations between goals and steps, which impair the model generalization to
unseen tasks. In contrast, this paper proposes a neuro-symbolic procedural
PLANner (PLAN) that elicits procedural planning knowledge from the LLMs with
commonsense-infused prompting. To mitigate spurious goal-step correlations, we
use symbolic program executors on the latent procedural representations to
formalize prompts from commonsense knowledge bases as a causal intervention
toward the Structural Causal Model. Both automatic and human evaluations on
WikiHow and RobotHow show the superiority of PLAN on procedural planning
without further training or manual exemplars.

We provide quantitative bounds measuring the $L^2$ difference in function
space between the trajectory of a finite-width network trained on finitely many
samples from the idealized kernel dynamics of infinite width and infinite data.
An implication of the bounds is that the network is biased to learn the top
eigenfunctions of the Neural Tangent Kernel not just on the training set but
over the entire input space. This bias depends on the model architecture and
input distribution alone and thus does not depend on the target function which
does not need to be in the RKHS of the kernel. The result is valid for deep
architectures with fully connected, convolutional, and residual layers.
Furthermore the width does not need to grow polynomially with the number of
samples in order to obtain high probability bounds up to a stopping time. The
proof exploits the low-effective-rank property of the Fisher Information Matrix
at initialization, which implies a low effective dimension of the model (far
smaller than the number of parameters). We conclude that local capacity control
from the low effective rank of the Fisher Information Matrix is still
underexplored theoretically.

Persistent homology (PH) is a popular tool for topological data analysis that
has found applications across diverse areas of research. It provides a rigorous
method to compute robust topological features in discrete experimental
observations that often contain various sources of uncertainties. Although
powerful in theory, PH suffers from high computation cost that precludes its
application to large data sets. Additionally, most analyses using PH are
limited to computing the existence of nontrivial features. Precise localization
of these features is not generally attempted because, by definition, localized
representations are not unique and because of even higher computation cost. For
scientific applications, such a precise location is a sine qua non for
determining functional significance. Here, we provide a strategy and algorithms
to compute tight representative boundaries around nontrivial robust features in
large data sets. To showcase the efficiency of our algorithms and the precision
of computed boundaries, we analyze three data sets from different scientific
fields. In the human genome, we found an unexpected effect on loops through
chromosome 13 and the sex chromosomes, upon impairment of chromatin loop
formation. In a distribution of galaxies in the universe, we found
statistically significant voids. In protein homologs with significantly
different topology, we found voids attributable to ligand-interaction,
mutation, and differences between species.

We introduce a monotone deep equilibrium learning framework for large-scale
inverse problems in imaging. The proposed algorithm relies on forward-backward
splitting, where each iteration consists of a gradient descent involving the
score function and a conjugate gradient algorithm to encourage data
consistency. The score function is modeled as a monotone convolutional neural
network. The use of a monotone operator offers several benefits, including
guaranteed convergence, uniqueness of fixed point, and robustness to input
perturbations, similar to the use of convex priors in compressive sensing. In
addition, the proposed formulation is significantly more memory-efficient than
unrolled methods, which allows us to apply it to 3D problems that current
unrolled algorithms cannot handle. Experiments show that the proposed scheme
can offer improved performance in 3D settings while being stable in the
presence of input perturbations.

Data is central to the development and evaluation of machine learning (ML)
models. However, the use of problematic or inappropriate datasets can result in
harms when the resulting models are deployed. To encourage responsible AI
practice through more deliberate reflection on datasets and transparency around
the processes by which they are created, researchers and practitioners have
begun to advocate for increased data documentation and have proposed several
data documentation frameworks. However, there is little research on whether
these data documentation frameworks meet the needs of ML practitioners, who
both create and consume datasets. To address this gap, we set out to understand
ML practitioners' data documentation perceptions, needs, challenges, and
desiderata, with the goal of deriving design requirements that can inform
future data documentation frameworks. We conducted a series of semi-structured
interviews with 14 ML practitioners at a single large, international technology
company. We had them answer a list of questions taken from datasheets for
datasets (Gebru, 2021). Our findings show that current approaches to data
documentation are largely ad hoc and myopic in nature. Participants expressed
needs for data documentation frameworks to be adaptable to their contexts,
integrated into their existing tools and workflows, and automated wherever
possible. Despite the fact that data documentation frameworks are often
motivated from the perspective of responsible AI, participants did not make the
connection between the questions that they were asked to answer and their
responsible AI implications. In addition, participants often had difficulties
prioritizing the needs of dataset consumers and providing information that
someone unfamiliar with their datasets might need to know. Based on these
findings, we derive seven design requirements for future data documentation

We tackle a new task, event graph completion, which aims to predict missing
event nodes for event graphs. Existing link prediction or graph completion
methods have difficulty dealing with event graphs because they are usually
designed for a single large graph such as a social network or a knowledge
graph, rather than multiple small dynamic event graphs. Moreover, they can only
predict missing edges rather than missing nodes. In this work, we propose to
utilize event schema, a template that describes the stereotypical structure of
event graphs, to address the above issues. Our schema-guided event graph
completion approach first maps an instance event graph to a subgraph of the
schema graph by a heuristic subgraph matching algorithm. Then it predicts
whether a candidate event node in the schema graph should be added to the
instantiated schema subgraph by characterizing two types of local topology of
the schema graph: neighbors of the candidate node and the subgraph, and paths
that connect the candidate node and the subgraph. These two modules are later
combined together for the final prediction. We also propose a self-supervised
strategy to construct training samples, as well as an inference algorithm that
is specifically designed to complete event graphs. Extensive experimental
results on four datasets demonstrate that our proposed method achieves
state-of-the-art performance, with 4.3% to 19.4% absolute F1 gains over the
best baseline method on the four datasets.

We propose an algorithm that compresses the critical information of a large
dataset into compact addressable memories. These memories can then be recalled
to quickly re-train a neural network and recover the performance (instead of
storing and re-training on the full original dataset).
  Building upon the dataset distillation framework, we make a key observation
that a shared common representation allows for more efficient and effective
distillation. Concretely, we learn a set of bases (aka "memories") which are
shared between classes and combined through learned flexible addressing
functions to generate a diverse set of training examples. This leads to several
benefits: 1) the size of compressed data does not necessarily grow linearly
with the number of classes; 2) an overall higher compression rate with more
effective distillation is achieved; and 3) more generalized queries are allowed
beyond recalling the original classes.
  We demonstrate state-of-the-art results on the dataset distillation task
across five benchmarks, including up to 16.5% and 9.7% in retained accuracy
improvement when distilling CIFAR10 and CIFAR100 respectively. We then leverage
our framework to perform continual learning, achieving state-of-the-art results
on four benchmarks, with 23.2% accuracy improvement on MANY.

Existing weak supervision approaches use all the data covered by weak signals
to train a classifier. We show both theoretically and empirically that this is
not always optimal. Intuitively, there is a tradeoff between the amount of
weakly-labeled data and the precision of the weak labels. We explore this
tradeoff by combining pretrained data representations with the cut statistic
(Muhlenbach et al., 2004) to select (hopefully) high-quality subsets of the
weakly-labeled training data. Subset selection applies to any label model and
classifier and is very simple to plug in to existing weak supervision
pipelines, requiring just a few lines of code. We show our subset selection
method improves the performance of weak supervision for a wide range of label
models, classifiers, and datasets. Using less weakly-labeled data improves the
accuracy of weak supervision pipelines by up to 19% (absolute) on benchmark
tasks.

Given the current trend of increasing size and complexity of machine learning
architectures, it has become of critical importance to identify new approaches
to improve the computational efficiency of model training. In this context, we
address the advantages of floating-point over fixed-point representation, and
present an in-depth study on the use of 8-bit floating-point number formats for
activations, weights, and gradients for both training and inference. We explore
the effect of different bit-widths for exponents and significands and different
exponent biases. The experimental results demonstrate that a suitable choice of
these low-precision formats enables faster training and reduced power
consumption without any degradation in accuracy for a range of deep learning
models for image classification and language processing.

Objective: Knowledge based planning (KBP) typically involves training an
end-to-end deep learning model to predict dose distributions. However, training
end-to-end methods may be associated with practical limitations due to the
limited size of medical datasets that are often used. To address these
limitations, we propose a content based image retrieval (CBIR) method for
retrieving dose distributions of previously planned patients based on
anatomical similarity. Approach: Our proposed CBIR method trains a
representation model that produces latent space embeddings of a patient's
anatomical information. The latent space embeddings of new patients are then
compared against those of previous patients in a database for image retrieval
of dose distributions. All source code for this project is available on github.
Main Results: The retrieval performance of various CBIR methods is evaluated on
a dataset consisting of both publicly available plans and clinical plans from
our institution. This study compares various encoding methods, ranging from
simple autoencoders to more recent Siamese networks like SimSiam, and the best
performance was observed for the multitask Siamese network. Significance:
Applying CBIR to inform subsequent treatment planning potentially addresses
many limitations associated with end-to-end KBP. Our current results
demonstrate that excellent image retrieval performance can be obtained through
slight changes to previously developed Siamese networks. We hope to integrate
CBIR into automated planning workflow in future works, potentially through
methods like the MetaPlanner framework.

We employ neural networks to tackle inverse partial differential equations on
discretized Riemann surfaces with boundary. To this end, we introduce the
concept of a graph with boundary which models these surfaces in a natural way.
Our method uses a message passing technique to keep track of an unknown
differential operator while using neural ODE solvers through the method of
lines to capture the evolution in time. As training data, we use noisy and
incomplete observations of sheaves on graphs at various timestamps. The novelty
of this approach is in working with manifolds with nontrivial topology and
utilizing the data on the graph boundary through a teacher forcing technique.
Despite the increasing interest in learning dynamical systems from finite
observations, many current methods are limited in two general ways: first, they
work with topologically trivial spaces, and second, they fail to handle the
boundary data on the ground space in a systematic way. The present work is an
attempt at addressing these limitations. We run experiments with synthetic data
of linear and nonlinear diffusion systems on orientable surfaces with positive
genus and boundary, and moreover, provide evidences for improvements upon the
existing paradigms.

Advances in deep learning for human activity recognition have been relatively
limited due to the lack of large labelled datasets. In this study, we leverage
self-supervised learning techniques on the UK-Biobank activity tracker
dataset--the largest of its kind to date--containing more than 700,000
person-days of unlabelled wearable sensor data. Our resulting activity
recognition model consistently outperformed strong baselines across seven
benchmark datasets, with an F1 relative improvement of 2.5%-100% (median
18.4%), the largest improvements occurring in the smaller datasets. In contrast
to previous studies, our results generalise across external datasets, devices,
and environments. Our open-source model will help researchers and developers to
build customisable and generalisable activity classifiers with high

Modern image generative models show remarkable sample quality when trained on
a single domain or class of objects. In this work, we introduce a generative
adversarial network that can simultaneously generate aligned image samples from
multiple related domains. We leverage the fact that a variety of object classes
share common attributes, with certain geometric differences. We propose
Polymorphic-GAN which learns shared features across all domains and a
per-domain morph layer to morph shared features according to each domain. In
contrast to previous works, our framework allows simultaneous modelling of
images with highly varying geometries, such as images of human faces, painted
and artistic faces, as well as multiple different animal faces. We demonstrate
that our model produces aligned samples for all domains and show how it can be
used for applications such as segmentation transfer and cross-domain image
editing, as well as training in low-data regimes. Additionally, we apply our
Polymorphic-GAN on image-to-image translation tasks and show that we can
greatly surpass previous approaches in cases where the geometric differences
between domains are large.

This paper investigates a new approach to model-based reinforcement learning
using background planning: mixing (approximate) dynamic programming updates and
model-free updates, similar to the Dyna architecture. Background planning with
learned models is often worse than model-free alternatives, such as Double DQN,
even though the former uses significantly more memory and computation. The
fundamental problem is that learned models can be inaccurate and often generate
invalid states, especially when iterated many steps. In this paper, we avoid
this limitation by constraining background planning to a set of (abstract)
subgoals and learning only local, subgoal-conditioned models. This goal-space
planning (GSP) approach is more computationally efficient, naturally
incorporates temporal abstraction for faster long-horizon planning and avoids
learning the transition dynamics entirely. We show that our GSP algorithm can
learn significantly faster than a Double DQN baseline in a variety of

Future wireless networks may operate at millimeter-wave (mmW) and
sub-terahertz (sub-THz) frequencies to enable high data rate requirements.
While large antenna arrays are critical for reliable communications at mmW and
sub-THz bands, these antenna arrays would also mandate efficient and scalable
initial beam alignment and link maintenance algorithms for mobile devices.
Low-power phased-array architectures and phase-less power measurements due to
high frequency oscillator phase noise pose additional challenges for practical
beam tracking algorithms. Traditional beam tracking protocols require
exhaustive sweeps of all possible beam directions and scale poorly with high
mobility and large arrays. Compressive sensing and machine learning designs
have been proposed to improve measurement scaling with array size but commonly
degrade under hardware impairments or require raw samples respectively. In this
work, we introduce a novel long short-term memory (LSTM) network assisted beam
tracking and prediction algorithm utilizing only phase-less measurements from
fixed compressive codebooks. We demonstrate comparable beam alignment accuracy
to state-of-the-art phase-less beam alignment algorithms, while reducing the
average number of required measurements over time.

Group fairness metrics are an established way of assessing the fairness of
prediction-based decision-making systems. However, these metrics are still
insufficiently linked to philosophical theories, and their moral meaning is
often unclear. We propose a general framework for analyzing the fairness of
decision systems based on theories of distributive justice, encompassing
different established "patterns of justice" that correspond to different
normative positions. We show that the most popular group fairness metrics can
be interpreted as special cases of our approach. Thus, we provide a unifying
and interpretative framework for group fairness metrics that reveals the
normative choices associated with each of them and that allows understanding
their moral substance. At the same time, we provide an extension of the space
of possible fairness metrics beyond the ones currently discussed in the fair ML
literature. Our framework also allows overcoming several limitations of group
fairness metrics that have been criticized in the literature, most notably (1)
that they are parity-based, i.e., that they demand some form of equality
between groups, which may sometimes be harmful to marginalized groups, (2) that
they only compare decisions across groups, but not the resulting consequences
for these groups, and (3) that the full breadth of the distributive justice
literature is not sufficiently represented.

In prediction-based decision-making systems, different perspectives can be at
odds: The short-term business goals of the decision makers are often in
conflict with the decision subjects' wish to be treated fairly. Balancing these
two perspectives is a question of values. We provide a framework to make these
value-laden choices clearly visible. For this, we assume that we are given a
trained model and want to find decision rules that balance the perspective of
the decision maker and of the decision subjects. We provide an approach to
formalize both perspectives, i.e., to assess the utility of the decision maker
and the fairness towards the decision subjects. In both cases, the idea is to
elicit values from decision makers and decision subjects that are then turned
into something measurable. For the fairness evaluation, we build on the
literature on welfare-based fairness and ask what a fair distribution of
utility (or welfare) looks like. In this step, we build on well-known theories
of distributive justice. This allows us to derive a fairness score that we then
compare to the decision maker's utility for many different decision rules. This
way, we provide an approach for balancing the utility of the decision maker and
the fairness towards the decision subjects for a prediction-based
decision-making system.

We apply the deep learning neural network architecture to the two-level
system in quantum optics to solve the time-dependent Schrodinger equation. By
carefully designing the network structure and tuning parameters, above 90
percent accuracy in super long-term predictions can be achieved in the case of
random electric fields, which indicates a promising new method to solve the
time-dependent equation for two-level systems. By slightly modifying this
network, we think that this method can solve the two- or three-dimensional
time-dependent Schrodinger equation more efficiently than traditional

We consider the off-policy evaluation problem of reinforcement learning using
deep convolutional neural networks. We analyze the deep fitted Q-evaluation
method for estimating the expected cumulative reward of a target policy, when
the data are generated from an unknown behavior policy. We show that, by
choosing network size appropriately, one can leverage any low-dimensional
manifold structure in the Markov decision process and obtain a sample-efficient
estimator without suffering from the curse of high data ambient dimensionality.
Specifically, we establish a sharp error bound for fitted Q-evaluation, which
depends on the intrinsic dimension of the state-action space, the smoothness of
Bellman operator, and a function class-restricted $\chi^2$-divergence. It is
noteworthy that the restricted $\chi^2$-divergence measures the behavior and
target policies' {\it mismatch in the function space}, which can be small even
if the two policies are not close to each other in their tabular forms. We also
develop a novel approximation result for convolutional neural networks in
Q-function estimation. Numerical experiments are provided to support our
theoretical analysis.

Rationale is defined as a subset of input features that best explains or
supports the prediction by machine learning models. Rationale identification
has improved the generalizability and interpretability of neural networks on
vision and language data. In graph applications such as molecule and polymer
property prediction, identifying representative subgraph structures named as
graph rationales plays an essential role in the performance of graph neural
networks. Existing graph pooling and/or distribution intervention methods
suffer from lack of examples to learn to identify optimal graph rationales. In
this work, we introduce a new augmentation operation called environment
replacement that automatically creates virtual data examples to improve
rationale identification. We propose an efficient framework that performs
rationale-environment separation and representation learning on the real and
augmented examples in latent spaces to avoid the high complexity of explicit
graph decoding and encoding. Comparing against recent techniques, experiments
on seven molecular and four polymer real datasets demonstrate the effectiveness
and efficiency of the proposed augmentation-based graph rationalization

The striking recent advances in eliciting seemingly meaningful language
behaviour from language-only machine learning models have only made more
apparent, through the surfacing of clear limitations, the need to go beyond the
language-only mode and to ground these models "in the world". Proposals for
doing so vary in the details, but what unites them is that the solution is
sought in the addition of non-linguistic data types such as images or video
streams, while largely keeping the mode of learning constant. I propose a
different, and more wide-ranging conception of how grounding should be
understood: What grounds language is its normative nature. There are standards
for doing things right, these standards are public and authoritative, while at
the same time acceptance of authority can and must be disputed and negotiated,
in interactions in which only bearers of normative status can rightfully
participate. What grounds language, then, is the determined use that language
users make of it, and what it is grounded in is the community of language
users. I sketch this idea, and draw some conclusions for work on computational
modelling of meaningful language use.

Self-occlusion is challenging for cloth manipulation, as it makes it
difficult to estimate the full state of the cloth. Ideally, a robot trying to
unfold a crumpled or folded cloth should be able to reason about the cloth's
occluded regions. We leverage recent advances in pose estimation for cloth to
build a system that uses explicit occlusion reasoning to unfold a crumpled
cloth. Specifically, we first learn a model to reconstruct the mesh of the
cloth. However, the model will likely have errors due to the complexities of
the cloth configurations and due to ambiguities from occlusions. Our main
insight is that we can further refine the predicted reconstruction by
performing test-time finetuning with self-supervised losses. The obtained
reconstructed mesh allows us to use a mesh-based dynamics model for planning
while reasoning about occlusions. We evaluate our system both on cloth
flattening as well as on cloth canonicalization, in which the objective is to
manipulate the cloth into a canonical pose. Our experiments show that our
method significantly outperforms prior methods that do not explicitly account
for occlusions or perform test-time optimization. Videos and visualizations can
be found on our
$\href{https://sites.google.com/view/occlusion-reason/home}{\text{project
website}}.$

Smart grid (SG) systems enhance grid resilience and efficient operation,
leveraging the bidirectional flow of energy and information between generation
facilities and prosumers. For energy demand management (EDM), the SG network
requires computing a large amount of data generated by massive
Internet-of-things sensors and advanced metering infrastructure (AMI) with
minimal latency. This paper proposes a deep reinforcement learning (DRL)-based
resource allocation scheme in a 6G-enabled SG edge network to offload
resource-consuming EDM computation to edge servers. Automatic resource
provisioning is achieved by harnessing the computational capabilities of smart
meters in the dynamic edge network. To enforce DRL-assisted policies in dense
6G networks, the state information from multiple edge servers is required.
However, adversaries can "poison" such information through false state
injection (FSI) attacks, exhausting SG edge computing resources. Toward
addressing this issue, we investigate the impact of such FSI attacks with
respect to abusive utilization of edge resources, and develop a lightweight FSI
detection mechanism based on supervised classifiers. Simulation results
demonstrate the efficacy of DRL in dynamic resource allocation, the impact of
the FSI attacks, and the effectiveness of the detection technique.

This paper presents a learning- and scenario-based model predictive control
(MPC) design approach for systems modeled in linear parameter-varying (LPV)
framework. Using input-output data collected from the system, a state-space LPV
model with uncertainty quantification is first learned through the variational
Bayesian inference Neural Network (BNN) approach. The learned probabilistic
model is assumed to contain the true dynamics of the system with a high
probability and used to generate scenarios which ensure safety for a
scenario-based MPC. Moreover, to guarantee stability and enhance performance of
the closed-loop system, a parameter-dependent terminal cost and controller, as
well as a terminal robust positive invariant set are designed. Numerical
examples will be used to demonstrate that the proposed control design approach
can ensure safety and achieve desired control performance.

This paper proposes a general fixture layout design framework that directly
integrates the system equation with the convex relaxation method. Note that the
optimal fixture design problem is a large-scale combinatorial optimization
problem, we relax it to a convex semidefinite programming (SDP) problem by
adopting sparse learning and SDP relaxation techniques. It can be solved
efficiently by existing convex optimization algorithms and thus generates a
near-optimal fixture layout. A real case study in the half-to-half fuselage
assembly process indicates the superiority of our proposed algorithm compared
to the current industry practice and state-of-art methods.

Spiking Neural Networks are a recent and new neural network design approach
that promises tremendous improvements in power efficiency, computation
efficiency, and processing latency. They do so by using asynchronous
spike-based data flow, event-based signal generation, processing, and modifying
the neuron model to resemble biological neurons closely. While some initial
works have shown significant initial evidence of applicability to common deep
learning tasks, their applications in complex real-world tasks has been
relatively low. In this work, we first illustrate the applicability of spiking
neural networks to a complex deep learning task namely Lidar based 3D object
detection for automated driving. Secondly, we make a step-by-step demonstration
of simulating spiking behavior using a pre-trained convolutional neural
network. We closely model essential aspects of spiking neural networks in
simulation and achieve equivalent run-time and accuracy on a GPU. When the
model is realized on a neuromorphic hardware, we expect to have significantly
improved power efficiency.

Predictive models are increasingly used to make various consequential
decisions in high-stakes domains such as healthcare, finance, and policy. It
becomes critical to ensure that these models make accurate predictions, are
robust to shifts in the data, do not rely on spurious features, and do not
unduly discriminate against minority groups. To this end, several approaches
spanning various areas such as explainability, fairness, and robustness have
been proposed in recent literature. Such approaches need to be human-centered
as they cater to the understanding of the models to their users. However, there
is a research gap in understanding the human-centric needs and challenges of
monitoring machine learning (ML) models once they are deployed. To fill this
gap, we conducted an interview study with 13 practitioners who have experience
at the intersection of deploying ML models and engaging with customers spanning
domains such as financial services, healthcare, hiring, online retail,
computational advertising, and conversational assistants. We identified various
human-centric challenges and requirements for model monitoring in real-world
applications. Specifically, we found the need and the challenge for the model
monitoring systems to clarify the impact of the monitoring observations on
outcomes. Further, such insights must be actionable, robust, customizable for
domain-specific use cases, and cognitively considerate to avoid information
overload.

Achieving safe and reliable autonomous driving relies greatly on the ability
to achieve an accurate and robust perception system; however, this cannot be
fully realized without precisely calibrated sensors. Environmental and
operational conditions as well as improper maintenance can produce calibration
errors inhibiting sensor fusion and, consequently, degrading the perception
performance. Traditionally, sensor calibration is performed in a controlled
environment with one or more known targets. Such a procedure can only be
carried out in between drives and requires manual operation; a tedious task if
needed to be conducted on a regular basis. This sparked a recent interest in
online targetless methods, capable of yielding a set of geometric
transformations based on perceived environmental features, however, the
required redundancy in sensing modalities makes this task even more
challenging, as the features captured by each modality and their
distinctiveness may vary. We present a holistic approach to performing joint
calibration of a camera-lidar-radar trio. Leveraging prior knowledge and
physical properties of these sensing modalities together with semantic
information, we propose two targetless calibration methods within a cost
minimization framework once via direct online optimization, and second via
self-supervised learning (SSL).

Recent deep reinforcement learning (DRL) successes rely on end-to-end
learning from fixed-size observational inputs (e.g. image, state-variables).
However, many challenging and interesting problems in decision making involve
observations or intermediary representations which are best described as a set
of entities: either the image-based approach would miss small but important
details in the observations (e.g. ojects on a radar, vehicles on satellite
images, etc.), the number of sensed objects is not fixed (e.g. robotic
manipulation), or the problem simply cannot be represented in a meaningful way
as an image (e.g. power grid control, or logistics). This type of structured
representations is not directly compatible with current DRL architectures,
however, there has been an increase in machine learning techniques directly
targeting structured information, potentially addressing this issue. We propose
to combine recent advances in set representations with slot attention and graph
neural networks to process structured data, broadening the range of
applications of DRL algorithms. This approach allows to address entity-based
problems in an efficient and scalable way. We show that it can improve training
time and robustness significantly, and demonstrate their potential to handle
structured as well as purely visual domains, on multiple environments from the
Atari Learning Environment and Simple Playgrounds.

Graph Neural Networks (GNNs) have shown tremendous strides in performance for
graph-structured problems especially in the domains of natural language
processing, computer vision and recommender systems. Inspired by the success of
the transformer architecture, there has been an ever-growing body of work on
attention variants of GNNs attempting to advance the state of the art in many
of these problems. Incorporating "attention" into graph mining has been viewed
as a way to overcome the noisiness, heterogenity and complexity associated with
graph-structured data as well as to encode soft-inductive bias. It is hence
crucial and advantageous to study these variants from a bird's-eye view to
assess their strengths and weaknesses. We provide a systematic and focused
tutorial centered around attention based GNNs in a hope to benefit researchers
dealing with graph-structured problems. Our tutorial looks at GNN variants from
the point of view of the attention function and iteratively builds the reader's
understanding of different graph attention variants.

The question of answering queries over ML predictions has been gaining
attention in the database community. This question is challenging because the
cost of finding high quality answers corresponds to invoking an oracle such as
a human expert or an expensive deep neural network model on every single item
in the DB and then applying the query. We develop a novel unified framework for
approximate query answering by leveraging a proxy to minimize the oracle usage
of finding high quality answers for both Precision-Target (PT) and
Recall-Target (RT) queries. Our framework uses a judicious combination of
invoking the expensive oracle on data samples and applying the cheap proxy on
the objects in the DB. It relies on two assumptions. Under the Proxy Quality
assumption, proxy quality can be quantified in a probabilistic manner w.r.t.
the oracle. This allows us to develop two algorithms: PQA that efficiently
finds high quality answers with high probability and no oracle calls, and PQE,
a heuristic extension that achieves empirically good performance with a small
number of oracle calls. Alternatively, under the Core Set Closure assumption,
we develop two algorithms: CSC that efficiently returns high quality answers
with high probability and minimal oracle usage, and CSE, which extends it to
more general settings. Our extensive experiments on five real-world datasets on
both query types, PT and RT, demonstrate that our algorithms outperform the
state-of-the-art and achieve high result quality with provable statistical

High-quality MRI reconstruction plays a critical role in clinical
applications. Deep learning-based methods have achieved promising results on
MRI reconstruction. However, most state-of-the-art methods were designed to
optimize the evaluation metrics commonly used for natural images, such as PSNR
and SSIM, whereas the visual quality is not primarily pursued. Compared to the
fully-sampled images, the reconstructed images are often blurry, where
high-frequency features might not be sharp enough for confident clinical
diagnosis. To this end, we propose an invertible sharpening network
(InvSharpNet) to improve the visual quality of MRI reconstructions. During
training, unlike the traditional methods that learn to map the input data to
the ground truth, InvSharpNet adapts a backward training strategy that learns a
blurring transform from the ground truth (fully-sampled image) to the input
data (blurry reconstruction). During inference, the learned blurring transform
can be inverted to a sharpening transform leveraging the network's
invertibility. The experiments on various MRI datasets demonstrate that
InvSharpNet can improve reconstruction sharpness with few artifacts. The
results were also evaluated by radiologists, indicating better visual quality
and diagnostic confidence of our proposed method.

Brain extraction is one of the first steps of pre-processing 3D brain MRI
data. It is a prerequisite for any forthcoming brain imaging analyses. However,
it is not a simple segmentation problem due to the complex structure of the
brain and human head. Although multiple solutions have been proposed in the
literature, we are still far from having truly robust methods. While previous
methods have used machine learning with structural/geometric priors, with the
development of deep learning in computer vision tasks, there has been an
increase in proposed convolutional neural network architectures for this
semantic segmentation task. Yet, most models focus on improving the training
data and loss functions with little change in the architecture. In this paper,
we propose a novel architecture we call EVC-Net. EVC-Net adds lower scale
inputs on each encoder block. This enhances the multi-scale scheme of the V-Net
architecture, hence increasing the efficiency of the model. Conditional Random
Fields, a popular approach for image segmentation before the deep learning era,
are re-introduced here as an additional step for refining the network's output
to capture fine-grained results in segmentation. We compare our model to
state-of-the-art methods such as HD-BET, Synthstrip and brainy. Results show
that even with limited training resources, EVC-Net achieves higher Dice
Coefficient and Jaccard Index along with lower surface distance.

We consider a linear stochastic bandit problem involving $M$ agents that can
collaborate via a central server to minimize regret. A fraction $\alpha$ of
these agents are adversarial and can act arbitrarily, leading to the following
tension: while collaboration can potentially reduce regret, it can also disrupt
the process of learning due to adversaries. In this work, we provide a
fundamental understanding of this tension by designing new algorithms that
balance the exploration-exploitation trade-off via carefully constructed robust
confidence intervals. We also complement our algorithms with tight analyses.
First, we develop a robust collaborative phased elimination algorithm that
achieves $\tilde{O}\left(\alpha+ 1/\sqrt{M}\right) \sqrt{dT}$ regret for each
good agent; here, $d$ is the model-dimension and $T$ is the horizon. For small
$\alpha$, our result thus reveals a clear benefit of collaboration despite
adversaries. Using an information-theoretic argument, we then prove a matching
lower bound, thereby providing the first set of tight, near-optimal regret
bounds for collaborative linear bandits with adversaries. Furthermore, by
leveraging recent advances in high-dimensional robust statistics, we
significantly extend our algorithmic ideas and results to (i) the generalized
linear bandit model that allows for non-linear observation maps; and (ii) the
contextual bandit setting that allows for time-varying feature vectors.

Offline reinforcement learning (RL) provides a promising direction to exploit
massive amount of offline data for complex decision-making tasks. Due to the
distribution shift issue, current offline RL algorithms are generally designed
to be conservative in value estimation and action selection. However, such
conservatism can impair the robustness of learned policies when encountering
observation deviation under realistic conditions, such as sensor errors and
adversarial attacks. To trade off robustness and conservatism, we propose
Robust Offline Reinforcement Learning (RORL) with a novel conservative
smoothing technique. In RORL, we explicitly introduce regularization on the
policy and the value function for states near the dataset, as well as
additional conservative value estimation on these states. Theoretically, we
show RORL enjoys a tighter suboptimality bound than recent theoretical results
in linear MDPs. We demonstrate that RORL can achieve state-of-the-art
performance on the general offline RL benchmark and is considerably robust to
adversarial observation perturbations.

A significant point-like component from the small scale (or discrete)
structure in the H2 interstellar gas might be present in the Fermi-LAT data,
but modeling this emission relies on observations of rare gas tracers only
available in limited regions of the sky. Identifying this contribution is
important to discriminate gamma-ray point sources from interstellar gas, and to
better characterize extended gamma-ray sources. We design and train
convolutional neural networks to predict this emission where observations of
these rare tracers do not exist and discuss the impact of this component on the
analysis of the Fermi-LAT data. In particular, we evaluate prospects to exploit
this methodology in the characterization of the Fermi-LAT Galactic center
excess through accurate modeling of point-like structures in the data to help
distinguish between a point-like or smooth nature for the excess. We show that
deep learning may be effectively employed to model the gamma-ray emission
traced by these rare H2 proxies within statistical significance in data-rich
regions, supporting prospects to employ these methods in yet unobserved
regions.

As we observe a rapidly growing number of astrophysical transients, we learn
more about the diverse host galaxy environments in which they occur. Host
galaxy information can be used to purify samples of cosmological Type Ia
supernovae, uncover the progenitor systems of individual classes, and
facilitate low-latency follow-up of rare and peculiar explosions. In this work,
we develop a novel data-driven methodology to simulate the time-domain sky that
includes detailed modeling of the probability density function for multiple
transient classes conditioned on host galaxy magnitudes, colours, star
formation rates, and masses. We have designed these simulations to optimize
photometric classification and analysis in upcoming large synoptic surveys. We
integrate host galaxy information into the SNANA simulation framework to
construct the Simulated Catalogue of Optical Transients and Correlated Hosts
(SCOTCH), a publicly-available catalogue of 5 million idealized transient light
curves in LSST passbands and their host galaxy properties over the redshift
range $0<z<3$. This catalogue includes supernovae, tidal disruption events,
kilonovae, and active galactic nuclei. Each light curve consists of true
top-of-the-galaxy magnitudes sampled with high ($\lesssim$2 day) cadence. In
conjunction with SCOTCH, we also release an associated set of tutorials and the
transient-specific libraries to enable simulations of arbitrary space- and
ground-based surveys. Our methodology is being used to test critical science
infrastructure in advance of surveys by the Vera C. Rubin Observatory and the
Nancy G. Roman Space Telescope.

Machine learning has achieved dramatic success over the past decade, with
applications ranging from face recognition to natural language processing.
Meanwhile, rapid progress has been made in the field of quantum computation
including developing both powerful quantum algorithms and advanced quantum
devices. The interplay between machine learning and quantum physics holds the
intriguing potential for bringing practical applications to the modern society.
Here, we focus on quantum neural networks in the form of parameterized quantum
circuits. We will mainly discuss different structures and encoding strategies
of quantum neural networks for supervised learning tasks, and benchmark their
performance utilizing Yao.jl, a quantum simulation package written in Julia
Language. The codes are efficient, aiming to provide convenience for beginners
in scientific works such as developing powerful variational quantum learning
models and assisting the corresponding experimental demonstrations.

We investigate the generalization capabilities of neural signed distance
functions (SDFs) for learning 3D object representations for unseen and
unlabeled point clouds. Existing methods can fit SDFs to a handful of object
classes and boast fine detail or fast inference speeds, but do not generalize
well to unseen shapes. We introduce a two-stage semi-supervised meta-learning
approach that transfers shape priors from labeled to unlabeled data to
reconstruct unseen object categories. The first stage uses an episodic training
scheme to simulate training on unlabeled data and meta-learns initial shape
priors. The second stage then introduces unlabeled data with disjoint classes
in a semi-supervised scheme to diversify these priors and achieve
generalization. We assess our method on both synthetic data and real collected
point clouds. Experimental results and analysis validate that our approach
outperforms existing neural SDF methods and is capable of robust zero-shot
inference on 100+ unseen classes. Code can be found at
https://github.com/princeton-computational-imaging/gensdf.

The tremendous progress in neural image generation, coupled with the
emergence of seemingly omnipotent vision-language models has finally enabled
text-based interfaces for creating and editing images. Handling generic images
requires a diverse underlying generative model, hence the latest works utilize
diffusion models, which were shown to surpass GANs in terms of diversity. One
major drawback of diffusion models, however, is their relatively slow inference
time. In this paper, we present an accelerated solution to the task of local
text-driven editing of generic images, where the desired edits are confined to
a user-provided mask. Our solution leverages a recent text-to-image Latent
Diffusion Model (LDM), which speeds up diffusion by operating in a
lower-dimensional latent space. We first convert the LDM into a local image
editor by incorporating Blended Diffusion into it. Next we propose an
optimization-based solution for the inherent inability of this LDM to
accurately reconstruct images. Finally, we address the scenario of performing
local edits using thin masks. We evaluate our method against the available
baselines both qualitatively and quantitatively and demonstrate that in
addition to being faster, our method achieves better precision than the
baselines while mitigating some of their artifacts. Project page is available
at https://omriavrahami.com/blended-latent-diffusion-page/

CROSS exchange (CE), a meta-heuristic that solves various vehicle routing
problems (VRPs), improves the solutions of VRPs by swapping the sub-tours of
the vehicles. Inspired by CE, we propose Neuro CE (NCE), a fundamental operator
of learned meta-heuristic, to solve various VRPs while overcoming the
limitations of CE (i.e., the expensive $\mathcal{O}(n^4)$ search cost). NCE
employs a graph neural network to predict the cost-decrements (i.e., results of
CE searches) and utilizes the predicted cost-decrements as guidance for search
to decrease the search cost to $\mathcal{O}(n^2)$. As the learning objective of
NCE is to predict the cost-decrement, the training can be simply done in a
supervised fashion, whose training samples can be prepared effortlessly.
Despite the simplicity of NCE, numerical results show that the NCE trained with
flexible multi-depot VRP (FMDVRP) outperforms the meta-heuristic baselines.
More importantly, it significantly outperforms the neural baselines when
solving distinctive special cases of FMDVRP (e.g., MDVRP, mTSP, CVRP) without

Large sparsely-activated models have obtained excellent performance in
multiple domains. However, such models are typically trained on a single
modality at a time. We present the Language-Image MoE, LIMoE, a sparse mixture
of experts model capable of multimodal learning. LIMoE accepts both images and
text simultaneously, while being trained using a contrastive loss. MoEs are a
natural fit for a multimodal backbone, since expert layers can learn an
appropriate partitioning of modalities. However, new challenges arise; in
particular, training stability and balanced expert utilization, for which we
propose an entropy-based regularization scheme. Across multiple scales, we
demonstrate remarkable performance improvement over dense models of equivalent
computational cost. LIMoE-L/16 trained comparably to CLIP-L/14 achieves 78.6%
zero-shot ImageNet accuracy (vs. 76.2%), and when further scaled to H/14 (with
additional data) it achieves 84.1%, comparable to state-of-the-art methods
which use larger custom per-modality backbones and pre-training schemes. We
analyse the quantitative and qualitative behavior of LIMoE, and demonstrate
phenomena such as differing treatment of the modalities and the organic
emergence of modality-specific experts.

The logit outputs of a feedforward neural network at initialization are
conditionally Gaussian, given a random covariance matrix defined by the
penultimate layer. In this work, we study the distribution of this random
matrix. Recent work has shown that shaping the activation function as network
depth grows large is necessary for this covariance matrix to be non-degenerate.
However, the current infinite-width-style understanding of this shaping method
is unsatisfactory for large depth: infinite-width analyses ignore the
microscopic fluctuations from layer to layer, but these fluctuations accumulate
over many layers.
  To overcome this shortcoming, we study the random covariance matrix in the
shaped infinite-depth-and-width limit. We identify the precise scaling of the
activation function necessary to arrive at a non-trivial limit, and show that
the random covariance matrix is governed by a stochastic differential equation
(SDE) that we call the Neural Covariance SDE. Using simulations, we show that
the SDE closely matches the distribution of the random covariance matrix of
finite networks. Additionally, we recover an if-and-only-if condition for
exploding and vanishing norms of large shaped networks based on the activation
function.

We study hypothesis testing under communication constraints, where each
sample is quantized before being revealed to a statistician. Without
communication constraints, it is well known that the sample complexity of
simple binary hypothesis testing is characterized by the Hellinger distance
between the distributions. We show that the sample complexity of simple binary
hypothesis testing under communication constraints is at most a logarithmic
factor larger than in the unconstrained setting and this bound is tight. We
develop a polynomial-time algorithm that achieves the aforementioned sample
complexity. Our framework extends to robust hypothesis testing, where the
distributions are corrupted in the total variation distance. Our proofs rely on
a new reverse data processing inequality and a reverse Markov inequality, which
may be of independent interest. For simple $M$-ary hypothesis testing, the
sample complexity in the absence of communication constraints has a logarithmic
dependence on $M$. We show that communication constraints can cause an
exponential blow-up leading to $\Omega(M)$ sample complexity even for adaptive

A key concern in integrating machine learning models in medicine is the
ability to interpret their reasoning. Popular explainability methods have
demonstrated satisfactory results in natural image recognition, yet in medical
image analysis, many of these approaches provide partial and noisy
explanations. Recently, attention mechanisms have shown compelling results both
in their predictive performance and in their interpretable qualities. A
fundamental trait of attention is that it leverages salient parts of the input
which contribute to the model's prediction. To this end, our work focuses on
the explanatory value of attention weight distributions. We propose a
multi-layer attention mechanism that enforces consistent interpretations
between attended convolutional layers using convex optimization. We apply
duality to decompose the consistency constraints between the layers by
reparameterizing their attention probability distributions. We further suggest
learning the dual witness by optimizing with respect to our objective; thus,
our implementation uses standard back-propagation, hence it is highly
efficient. While preserving predictive performance, our proposed method
leverages weakly annotated medical imaging data and provides complete and
faithful explanations to the model's prediction.

Uncertainty quantification is essential for the reliable deployment of
machine learning models to high-stakes application domains. Uncertainty
quantification is all the more challenging when training distribution and test
distribution are different, even the distribution shifts are mild. Despite the
ubiquity of distribution shifts in real-world applications, existing
uncertainty quantification approaches mainly study the in-distribution setting
where the train and test distributions are the same. In this paper, we develop
a systematic calibration model to handle distribution shifts by leveraging data
from multiple domains. Our proposed method -- multi-domain temperature scaling
-- uses the heterogeneity in the domains to improve calibration robustness
under distribution shift. Through experiments on three benchmark data sets, we
find our proposed method outperforms existing methods as measured on both
in-distribution and out-of-distribution test sets.

Face manipulation techniques develop rapidly and arouse widespread public
concerns. Despite that vanilla convolutional neural networks achieve acceptable
performance, they suffer from the overfitting issue. To relieve this issue,
there is a trend to introduce some erasing-based augmentations. We find that
these methods indeed attempt to implicitly induce more consistent
representations for different augmentations via assigning the same label for
different augmented images. However, due to the lack of explicit
regularization, the consistency between different representations is less
satisfactory. Therefore, we constrain the consistency of different
representations explicitly and propose a simple yet effective framework,
COnsistent REpresentation Learning (CORE). Specifically, we first capture the
different representations with different augmentations, then regularize the
cosine distance of the representations to enhance the consistency. Extensive
experiments (in-dataset and cross-dataset) demonstrate that CORE performs
favorably against state-of-the-art face forgery detection methods.

The dog-and-rabbit chase problem is a classical problem that illustrates the
concepts of elementary kinematics and, therefore, can be used in introductory
mechanics teaching. By dealing with the relative motion, the problem naturally
requires solving elementary differential equations and stimulates students to
learn mathematics. The efficiency of the approach we adopt is emphasized by
extending the class of problems that can be solved by applying it.

We described a study on the use of an online laboratory for self-directed
learning by constructing and simulating conceptual models of ecological
systems. In this study, we could observe only the modeling behaviors and
outcomes; the learning goals and outcomes were unknown. We used machine
learning techniques to analyze the modeling behaviors of 315 learners and 822
conceptual models they generated. We derive three main conclusions from the
results. First, learners manifest three types of modeling behaviors:
observation (simulation focused), construction (construction focused), and full
exploration (model construction, evaluation and revision). Second, while
observation was the most common behavior among all learners, construction
without evaluation was more common for less engaged learners and full
exploration occurred mostly for more engaged learners. Third, learners who
explored the full cycle of model construction, evaluation and revision
generated models of higher quality. These modeling behaviors provide insights
into self-directed learning at large.

This study uses supervised machine learning (SML) and explainable artificial
intelligence (AI) to model, predict and understand human decision-making during
skillful joint-action. Long short-term memory networks were trained to predict
the target selection decisions of expert and novice actors completing a dyadic
herding task. Results revealed that the trained models were expertise specific
and could not only accurately predict the target selection decisions of expert
and novice herders but could do so at timescales that preceded an actor's
conscious intent. To understand what differentiated the target selection
decisions of expert and novice actors, we then employed the explainable-AI
technique, SHapley Additive exPlanation, to identify the importance of
informational features (variables) on model predictions. This analysis revealed
that experts were more influenced by information about the state of their
co-herders compared to novices. The utility of employing SML and explainable-AI
techniques for investigating human decision-making is discussed.

A guiding robot aims to effectively bring people to and from specific places
within environments that are possibly unknown to them. During this operation
the robot should be able to detect and track the accompanied person, trying
never to lose sight of her/him. A solution to minimize this event is to use an
omnidirectional camera: its 360{\deg} Field of View (FoV) guarantees that any
framed object cannot leave the FoV if not occluded or very far from the sensor.
However, the acquired panoramic videos introduce new challenges in perception
tasks such as people detection and tracking, including the large size of the
images to be processed, the distortion effects introduced by the cylindrical
projection and the periodic nature of panoramic images. In this paper, we
propose a set of targeted methods that allow to effectively adapt to panoramic
videos a standard people detection and tracking pipeline originally designed
for perspective cameras. Our methods have been implemented and tested inside a
deep learning-based people detection and tracking framework with a commercial
360{\deg} camera. Experiments performed on datasets specifically acquired for
guiding robot applications and on a real service robot show the effectiveness
of the proposed approach over other state-of-the-art systems. We release with
this paper the acquired and annotated datasets and the open-source
implementation of our method.

Data augmentation with \textbf{Mixup} has been proven an effective method to
regularize the current deep neural networks. Mixup generates virtual samples
and corresponding labels at once through linear interpolation. However, this
one-stage generation paradigm and the use of linear interpolation have the
following two defects: (1) The label of the generated sample is directly
combined from the labels of the original sample pairs without reasonable
judgment, which makes the labels likely to be ambiguous. (2) linear combination
significantly limits the sampling space for generating samples. To tackle these
problems, we propose a novel and effective augmentation method based on global
clustering relationships named \textbf{Global Mixup}. Specifically, we
transform the previous one-stage augmentation process into two-stage,
decoupling the process of generating virtual samples from the labeling. And for
the labels of the generated samples, relabeling is performed based on
clustering by calculating the global relationships of the generated samples. In
addition, we are no longer limited to linear relationships but generate more
reliable virtual samples in a larger sampling space. Extensive experiments for
\textbf{CNN}, \textbf{LSTM}, and \textbf{BERT} on five tasks show that Global
Mixup significantly outperforms previous state-of-the-art baselines. Further
experiments also demonstrate the advantage of Global Mixup in low-resource

The cybersecurity threat landscape has lately become overly complex. Threat
actors leverage weaknesses in the network and endpoint security in a very
coordinated manner to perpetuate sophisticated attacks that could bring down
the entire network and many critical hosts in the network. Increasingly
advanced deep and machine learning-based solutions have been used in threat
detection and protection. The application of these techniques has been reviewed
well in the scientific literature. Deep Reinforcement Learning has shown great
promise in developing AI-based solutions for areas that had earlier required
advanced human cognizance. Different techniques and algorithms under deep
reinforcement learning have shown great promise in applications ranging from
games to industrial processes, where it is claimed to augment systems with
general AI capabilities. These algorithms have recently also been used in
cybersecurity, especially in threat detection and endpoint protection, where
these are showing state-of-the-art results. Unlike supervised machines and deep
learning, deep reinforcement learning is used in more diverse ways and is
empowering many innovative applications in the threat defense landscape.
However, there does not exist any comprehensive review of these unique
applications and accomplishments. Therefore, in this paper, we intend to fill
this gap and provide a comprehensive review of the different applications of
deep reinforcement learning in cybersecurity threat detection and protection.

The power system state estimation (SE) algorithm estimates the complex bus
voltages based on the available set of measurements. Because phasor measurement
units (PMUs) are becoming more widely employed in transmission power systems, a
fast SE solver capable of exploiting PMUs' high sample rates is required. To
accomplish this, we present a method for training a model based on graph neural
networks (GNNs) to learn estimates from PMU voltage and current measurements,
which, once it is trained, has a linear computational complexity with respect
to the number of nodes in the power system. We propose an original GNN
implementation over the power system's factor graph to simplify the
incorporation of various types and numbers of measurements both on power system
buses and branches. Furthermore, we augment the factor graph to improve the
robustness of GNN predictions. Training and test examples were generated by
randomly sampling sets of power system measurements and annotated with the
exact solutions of linear SE with PMUs. The numerical results demonstrate that
the GNN model provides an accurate approximation of the SE solutions.
Additionally, errors caused by PMU malfunctions or the communication failures
that make the SE problem unobservable have a local effect and do not
deteriorate the results in the rest of the power system.

In this work we propose Neuro-Nav, an open-source library for neurally
plausible reinforcement learning (RL). RL is among the most common modeling
frameworks for studying decision making, learning, and navigation in biological
organisms. In utilizing RL, cognitive scientists often handcraft environments
and agents to meet the needs of their particular studies. On the other hand,
artificial intelligence researchers often struggle to find benchmarks for
neurally and biologically plausible representation and behavior (e.g., in
decision making or navigation). In order to streamline this process across both
fields with transparency and reproducibility, Neuro-Nav offers a set of
standardized environments and RL algorithms drawn from canonical behavioral and
neural studies in rodents and humans. We demonstrate that the toolkit
replicates relevant findings from a number of studies across both cognitive
science and RL literatures. We furthermore describe ways in which the library
can be extended with novel algorithms (including deep RL) and environments to
address future research needs of the field.

Deploying models on target domain data subject to distribution shift requires
adaptation. Test-time training (TTT) emerges as a solution to this adaptation
under a realistic scenario where access to full source domain data is not
available and instant inference on target domain is required. Despite many
efforts into TTT, there is a confusion over the experimental settings, thus
leading to unfair comparisons. In this work, we first revisit TTT assumptions
and categorize TTT protocols by two key factors. Among the multiple protocols,
we adopt a realistic sequential test-time training (sTTT) protocol, under which
we further develop a test-time anchored clustering (TTAC) approach to enable
stronger test-time feature learning. TTAC discovers clusters in both source and
target domain and match the target clusters to the source ones to improve
generalization. Pseudo label filtering and iterative updating are developed to
improve the effectiveness and efficiency of anchored clustering. We demonstrate
that under all TTT protocols TTAC consistently outperforms the state-of-the-art
methods on six TTT datasets. We hope this work will provide a fair benchmarking
of TTT methods and future research should be compared within respective
protocols. A demo code is available at
https://github.com/Gorilla-Lab-SCUT/TTAC.

Federated Learning (FL) enables training state-of-the-art Automatic Speech
Recognition (ASR) models on user devices (clients) in distributed systems,
hence preventing transmission of raw user data to a central server. A key
challenge facing practical adoption of FL for ASR is obtaining ground-truth
labels on the clients. Existing approaches rely on clients to manually
transcribe their speech, which is impractical for obtaining large training
corpora. A promising alternative is using semi-/self-supervised learning
approaches to leverage unlabelled user data. To this end, we propose FedNST, a
novel method for training distributed ASR models using private and unlabelled
user data. We explore various facets of FedNST, such as training models with
different proportions of labelled and unlabelled data, and evaluate the
proposed approach on 1173 simulated clients. Evaluating FedNST on LibriSpeech,
where 960 hours of speech data is split equally into server (labelled) and
client (unlabelled) data, showed a 22.5% relative word error rate reduction}
(WERR) over a supervised baseline trained only on server data.

Stacked unsupervised learning (SUL) seems more biologically plausible than
backpropagation, because learning is local to each layer. But SUL has fallen
far short of backpropagation in practical applications, undermining the idea
that SUL can explain how brains learn. Here we show an SUL algorithm that can
perform completely unsupervised clustering of MNIST digits with comparable
accuracy relative to unsupervised algorithms based on backpropagation. Our
algorithm is exceeded only by self-supervised methods requiring training data
augmentation by geometric distortions. The only prior knowledge in our
unsupervised algorithm is implicit in the network architecture. Multiple
convolutional "energy layers" contain a sum-of-squares nonlinearity, inspired
by "energy models" of primary visual cortex. Convolutional kernels are learned
with a fast minibatch implementation of the K-Subspaces algorithm. High
accuracy requires preprocessing with an initial whitening layer,
representations that are less sparse during inference than learning, and
rescaling for gain control. The hyperparameters of the network architecture are
found by supervised meta-learning, which optimizes unsupervised clustering
accuracy. We regard such dependence of unsupervised learning on prior knowledge
implicit in network architecture as biologically plausible, and analogous to
the dependence of brain architecture on evolutionary history.

In this work, we propose two different approaches to improve the semantic
consistency of Open Set Semantic Segmentation. First, we propose a method
called OpenGMM that extends the OpenPCS framework using a Gaussian Mixture of
Models to model the distribution of pixels for each class in a multimodal
manner. The second approach is a post-processing which uses superpixels to
enforce highly homogeneous regions to behave equally, rectifying erroneous
classified pixels within these regions, we also proposed a novel superpixel
method called FuSS. All tests were performed on ISPRS Vaihingen and Potsdam
datasets, and both methods were capable to improve quantitative and qualitative
results for both datasets. Besides that, the post-process with FuSS achieved
state-of-the-art results for both datasets. The official implementation is
available at: \url{https://github.com/iannunes/FuSS}.

Inspired from human cognition, machine learning systems are gradually
revealing advantages of sparser and more modular architectures. Recent work
demonstrates that not only do some modular architectures generalize well, but
they also lead to better out-of-distribution generalization, scaling
properties, learning speed, and interpretability. A key intuition behind the
success of such systems is that the data generating system for most real-world
settings is considered to consist of sparsely interacting parts, and endowing
models with similar inductive biases will be helpful. However, the field has
been lacking in a rigorous quantitative assessment of such systems because
these real-world data distributions are complex and unknown. In this work, we
provide a thorough assessment of common modular architectures, through the lens
of simple and known modular data distributions. We highlight the benefits of
modularity and sparsity and reveal insights on the challenges faced while
optimizing modular systems. In doing so, we propose evaluation metrics that
highlight the benefits of modularity, the regimes in which these benefits are
substantial, as well as the sub-optimality of current end-to-end learned
modular systems as opposed to their claimed potential.

Reinforcement learning has been shown to be an effective strategy for
automatically training policies for challenging control problems. Focusing on
non-cooperative multi-agent systems, we propose a novel reinforcement learning
framework for training joint policies that form a Nash equilibrium. In our
approach, rather than providing low-level reward functions, the user provides
high-level specifications that encode the objective of each agent. Then, guided
by the structure of the specifications, our algorithm searches over policies to
identify one that provably forms an $\epsilon$-Nash equilibrium (with high
probability). Importantly, it prioritizes policies in a way that maximizes
social welfare across all agents. Our empirical evaluation demonstrates that
our algorithm computes equilibrium policies with high social welfare, whereas
state-of-the-art baselines either fail to compute Nash equilibria or compute
ones with comparatively lower social welfare.

In recent years, the millimeter-wave radar to identify human behavior has
been widely used in medical,security, and other fields. When multiple radars
are performing detection tasks, the validity of the features contained in each
radar is difficult to guarantee. In addition, processing multiple radar data
also requires a lot of time and computational cost. The Complementary Ensemble
Empirical Mode Decomposition-Energy Slice (CEEMD-ES) multistatic radar
selection method is proposed to solve these problems. First, this method
decomposes and reconstructs the radar signal according to the difference in the
reflected echo frequency between the limbs and the trunk of the human body.
Then, the radar is selected according to the difference between the ratio of
echo energy of limbs and trunk and the theoretical value. The time domain,
frequency domain and various entropy features of the selected radar are
extracted. Finally, the Extreme Learning Machine (ELM) recognition model of the
ReLu core is established. Experiments show that this method can effectively
select the radar, and the recognition rate of three kinds of human actions is
98.53%.

This paper presents a simple yet effective method for anomaly detection. The
main idea is to learn small perturbations to perturb normal data and learn a
classifier to classify the normal data and the perturbed data into two
different classes. The perturbator and classifier are jointly learned using
deep neural networks. Importantly, the perturbations should be as small as
possible but the classifier is still able to recognize the perturbed data from
unperturbed data. Therefore, the perturbed data are regarded as abnormal data
and the classifier provides a decision boundary between the normal data and
abnormal data, although the training data do not include any abnormal data.
Compared with the state-of-the-art of anomaly detection, our method does not
require any assumption about the shape (e.g. hypersphere) of the decision
boundary and has fewer hyper-parameters to determine. Empirical studies on
benchmark datasets verify the effectiveness and superiority of our method.

Stochastic variance reduction has proven effective at accelerating
first-order algorithms for solving convex finite-sum optimization tasks such as
empirical risk minimization. Incorporating additional second-order information
has proven helpful in further improving the performance of these first-order
methods. However, comparatively little is known about the benefits of using
variance reduction to accelerate popular stochastic second-order methods such
as Subsampled Newton. To address this, we propose Stochastic Variance-Reduced
Newton (SVRN), a finite-sum minimization algorithm which enjoys all the
benefits of second-order methods: simple unit step size, easily parallelizable
large-batch operations, and fast local convergence, while at the same time
taking advantage of variance reduction to achieve improved convergence rates
(per data pass) for smooth and strongly convex problems. We show that SVRN can
accelerate many stochastic second-order methods (such as Subsampled Newton) as
well as iterative least squares solvers (such as Iterative Hessian Sketch), and
it compares favorably to popular first-order methods with variance reduction.

Existing question answering (QA) datasets derived from electronic health
records (EHR) are artificially generated and consequently fail to capture
realistic physician information needs. We present Discharge Summary Clinical
Questions (DiSCQ), a newly curated question dataset composed of 2,000+
questions paired with the snippets of text (triggers) that prompted each
question. The questions are generated by medical experts from 100+ MIMIC-III
discharge summaries. We analyze this dataset to characterize the types of
information sought by medical experts. We also train baseline models for
trigger detection and question generation (QG), paired with unsupervised answer
retrieval over EHRs. Our baseline model is able to generate high quality
questions in over 62% of cases when prompted with human selected triggers. We
release this dataset (and all code to reproduce baseline model results) to
facilitate further research into realistic clinical QA and QG:
https://github.com/elehman16/discq.

Modeling time-evolving preferences of users with their sequential item
interactions, has attracted increasing attention in many online applications.
Hence, sequential recommender systems have been developed to learn the dynamic
user interests from the historical interactions for suggesting items. However,
the interaction pattern encoding functions in most existing sequential
recommender systems have focused on single type of user-item interactions. In
many real-life online platforms, user-item interactive behaviors are often
multi-typed (e.g., click, add-to-favorite, purchase) with complex cross-type
behavior inter-dependencies. Learning from informative representations of users
and items based on their multi-typed interaction data, is of great importance
to accurately characterize the time-evolving user preference. In this work, we
tackle the dynamic user-item relation learning with the awareness of
multi-behavior interactive patterns. Towards this end, we propose a new
Temporal Graph Transformer (TGT) recommendation framework to jointly capture
dynamic short-term and long-range user-item interactive patterns, by exploring
the evolving correlations across different types of behaviors. The new TGT
method endows the sequential recommendation architecture to distill dedicated
knowledge for type-specific behavior relational context and the implicit
behavior dependencies. Experiments on the real-world datasets indicate that our
method TGT consistently outperforms various state-of-the-art recommendation
methods. Our model implementation codes are available at
https://github.com/akaxlh/TGT.

Mobile vision transformers (MobileViT) can achieve state-of-the-art
performance across several mobile vision tasks, including classification and
detection. Though these models have fewer parameters, they have high latency as
compared to convolutional neural network-based models. The main efficiency
bottleneck in MobileViT is the multi-headed self-attention (MHA) in
transformers, which requires $O(k^2)$ time complexity with respect to the
number of tokens (or patches) $k$. Moreover, MHA requires costly operations
(e.g., batch-wise matrix multiplication) for computing self-attention,
impacting latency on resource-constrained devices. This paper introduces a
separable self-attention method with linear complexity, i.e. $O(k)$. A simple
yet effective characteristic of the proposed method is that it uses
element-wise operations for computing self-attention, making it a good choice
for resource-constrained devices. The improved model, MobileViTv2, is
state-of-the-art on several mobile vision tasks, including ImageNet object
classification and MS-COCO object detection. With about three million
parameters, MobileViTv2 achieves a top-1 accuracy of 75.6% on the ImageNet
dataset, outperforming MobileViT by about 1% while running $3.2\times$ faster
on a mobile device.
  Our source code is available at: \url{https://github.com/apple/ml-cvnets}

Reinforcement learning has shown a wide usage in robotics tasks, such as
insertion and grasping. However, without a practical sim2real strategy, the
policy trained in simulation could fail on the real task. There are also wide
researches in the sim2real strategies, but most of those methods rely on heavy
image rendering, domain randomization training, or tuning. In this work, we
solve the insertion task using a pure visual reinforcement learning solution
with minimum infrastructure requirement. We also propose a novel sim2real
strategy, Real2Sim, which provides a novel and easier solution in policy
adaptation. We discuss the advantage of Real2Sim compared with Sim2Real.

In this paper, we study a novel episodic risk-sensitive Reinforcement
Learning (RL) problem, named Iterated CVaR RL, where the objective is to
maximize the tail of the reward-to-go at each step. Different from existing
risk-aware RL formulations, Iterated CVaR RL focuses on safety-at-all-time, by
enabling the agent to tightly control the risk of getting into catastrophic
situations at each stage, and is applicable to important risk-sensitive tasks
that demand strong safety guarantees throughout the decision process, such as
autonomous driving, clinical treatment planning and robotics. We investigate
Iterated CVaR RL with two performance metrics, i.e., Regret Minimization and
Best Policy Identification. For both metrics, we design efficient algorithms
ICVaR-RM and ICVaR-BPI, respectively, and provide matching upper and lower
bounds with respect to the number of episodes $K$. We also investigate an
interesting limiting case of Iterated CVaR RL, called Worst Path RL, where the
objective becomes to maximize the minimum possible cumulative reward, and
propose an efficient algorithm with constant upper and lower bounds. Finally,
the techniques we develop for bounding the change of CVaR due to the value
function shift and decomposing the regret via a distorted visitation
distribution are novel and can find applications in other risk-sensitive online

Safe exploration is a challenging and important problem in model-free
reinforcement learning (RL). Often the safety cost is sparse and unknown, which
unavoidably leads to constraint violations -- a phenomenon ideally to be
avoided in safety-critical applications. We tackle this problem by augmenting
the state-space with a safety state, which is nonnegative if and only if the
constraint is satisfied. The value of this state also serves as a distance
toward constraint violation, while its initial value indicates the available
safety budget. This idea allows us to derive policies for scheduling the safety
budget during training. We call our approach Simmer (Safe policy IMproveMEnt
for RL) to reflect the careful nature of these schedules. We apply this idea to
two safe RL problems: RL with constraints imposed on an average cost, and RL
with constraints imposed on a cost with probability one. Our experiments
suggest that "simmering, a safe algorithm can improve safety during training
for both settings. We further show that Simmer can stabilize training and
improve the performance of safe RL with average constraints.

Despite the recent success of machine learning algorithms, most of these
models still face several drawbacks when considering more complex tasks
requiring interaction between different sources, such as multimodal input data
and logical time sequence. On the other hand, the biological brain is highly
sharpened in this sense, empowered to automatically manage and integrate such a
stream of information through millions of years of evolution. In this context,
this paper finds inspiration from recent discoveries on cortical circuits in
the brain to propose a more biologically plausible self-supervised machine
learning approach that combines multimodal information using intra-layer
modulations together with canonical correlation analysis (CCA), as well as a
memory mechanism to keep track of temporal data, the so-called Canonical
Cortical Graph Neural networks. The approach outperformed recent
state-of-the-art results considering both better clean audio reconstruction and
energy efficiency, described by a reduced and smother neuron firing rate
distribution, suggesting the model as a suitable approach for speech
enhancement in future audio-visual hearing aid devices.

The danger of adversarial attacks to unprotected Uncrewed Aerial Vehicle
(UAV) agents operating in public is growing. Adopting AI-based techniques and
more specifically Deep Learning (DL) approaches to control and guide these UAVs
can be beneficial in terms of performance but add more concerns regarding the
safety of those techniques and their vulnerability against adversarial attacks
causing the chances of collisions going up as the agent becomes confused. This
paper proposes an innovative approach based on the explainability of DL methods
to build an efficient detector that will protect these DL schemes and thus the
UAVs adopting them from potential attacks. The agent is adopting a Deep
Reinforcement Learning (DRL) scheme for guidance and planning. It is formed and
trained with a Deep Deterministic Policy Gradient (DDPG) with Prioritised
Experience Replay (PER) DRL scheme that utilises Artificial Potential Field
(APF) to improve training times and obstacle avoidance performance. The
adversarial attacks are generated by Fast Gradient Sign Method (FGSM) and Basic
Iterative Method (BIM) algorithms and reduced obstacle course completion rates
from 80\% to 35\%. A Realistic Synthetic environment for UAV explainable DRL
based planning and guidance including obstacles and adversarial attacks is
built. Two adversarial attack detectors are proposed. The first one adopts a
Convolutional Neural Network (CNN) architecture and achieves an accuracy in
detection of 80\%. The second detector is developed based on a Long Short Term
Memory (LSTM) network and achieves an accuracy of 91\% with much faster
computing times when compared to the CNN based detector.

Prediction systems face exogenous and endogenous distribution shift -- the
world constantly changes, and the predictions the system makes change the
environment in which it operates. For example, a music recommender observes
exogeneous changes in the user distribution as different communities have
increased access to high speed internet. If users under the age of 18 enjoy
their recommendations, the proportion of the user base comprised of those under
18 may endogeneously increase. Most of the study of endogenous shifts has
focused on the single decision-maker setting, where there is one learner that
users either choose to use or not.
  This paper studies participation dynamics between sub-populations and
possibly many learners. We study the behavior of systems with
\emph{risk-reducing} learners and sub-populations. A risk-reducing learner
updates their decision upon observing a mixture distribution of the
sub-populations $\mathcal{D}$ in such a way that it decreases the risk of the
learner on that mixture. A risk reducing sub-population updates its
apportionment amongst learners in a way which reduces its overall loss.
  Previous work on the single learner case shows that myopic risk minimization
can result in high overall loss~\citep{perdomo2020performative,
miller2021outside} and representation disparity~\citep{hashimoto2018fairness,
zhang2019group}. Our work analyzes the outcomes of multiple myopic learners and
market forces, often leading to better global loss and less representation
disparity.

We consider the Pareto set identification (PSI) problem in multi-objective
multi-armed bandits (MO-MAB) with contaminated reward observations. At each arm
pull, with some probability, the true reward samples are replaced with the
samples from an arbitrary contamination distribution chosen by the adversary.
We propose a median-based MO-MAB algorithm for robust PSI that abides by the
accuracy requirements set by the user via an accuracy parameter. We prove that
the sample complexity of this algorithm depends on the accuracy parameter
inverse squarely. We compare the proposed algorithm with a mean-based method
from MO-MAB literature on Gaussian reward distributions. Our numerical results
verify our theoretical expectations and show the necessity for robust algorithm
design in the adversarial setting.

At the start of the COVID 19 pandemic, the majority of secondary instruction
in the United States transitioned to an online environment. In many parts of
the country, online schooling continued for upwards of two years. Many experts
have hypothesized an "academic slide" (a reduction in student learning)
following this period of online instruction. We investigated the change in
student preparation for introductory college physics in incoming Stanford
university students between the Fall term of 2019 to the Fall term of 2021. We
did this by looking at the performance on a validated physics diagnostic exam
that all Stanford students intending to take a physics course took before
enrolling in an introductory physics course. We found no statistically or
educationally significant change in scores. Despite many anecdotal faculty
reports, at least for this population, the level of student preparation in
physics and related math appears to be unchanged.

Capsule networks were proposed as an alternative approach to Convolutional
Neural Networks (CNNs) for learning object-centric representations, which can
be leveraged for improved generalization and sample complexity. Unlike CNNs,
capsule networks are designed to explicitly model part-whole hierarchical
relationships by using groups of neurons to encode visual entities, and learn
the relationships between those entities. Promising early results achieved by
capsule networks have motivated the deep learning community to continue trying
to improve their performance and scalability across several application areas.
However, a major hurdle for capsule network research has been the lack of a
reliable point of reference for understanding their foundational ideas and
motivations. The aim of this survey is to provide a comprehensive overview of
the capsule network research landscape, which will serve as a valuable resource
for the community going forward. To that end, we start with an introduction to
the fundamental concepts and motivations behind capsule networks, such as
equivariant inference in computer vision. We then cover the technical advances
in the capsule routing mechanisms and the various formulations of capsule
networks, e.g. generative and geometric. Additionally, we provide a detailed
explanation of how capsule networks relate to the popular attention mechanism
in Transformers, and highlight non-trivial conceptual similarities between them
in the context of representation learning. Afterwards, we explore the extensive
applications of capsule networks in computer vision, video and motion, graph
representation learning, natural language processing, medical imaging and many
others. To conclude, we provide an in-depth discussion regarding the main
hurdles in capsule network research, and highlight promising research
directions for future work.

With the extensive applications of machine learning models, automatic
hyperparameter optimization (HPO) has become increasingly important. Motivated
by the tuning behaviors of human experts, it is intuitive to leverage auxiliary
knowledge from past HPO tasks to accelerate the current HPO task. In this
paper, we propose TransBO, a novel two-phase transfer learning framework for
HPO, which can deal with the complementary nature among source tasks and
dynamics during knowledge aggregation issues simultaneously. This framework
extracts and aggregates source and target knowledge jointly and adaptively,
where the weights can be learned in a principled manner. The extensive
experiments, including static and dynamic transfer learning settings and neural
architecture search, demonstrate the superiority of TransBO over the

Hybrid machine learning based on Hamiltonian formulations has recently been
successfully demonstrated for simple mechanical systems, both energy conserving
and not energy conserving. We show that port-Hamiltonian neural network models
can be used to learn external forces acting on a system. We argue that this
property is particularly useful when the external forces are state dependent,
in which case it is the port-Hamiltonian structure that facilitates the
separation of internal and external forces. Numerical results are provided for
a forced and damped mass-spring system and a tank system of higher complexity,
and a symmetric fourth-order integration scheme is introduced for improved
training on sparse and noisy data.

We consider transfer learning approaches that fine-tune a pretrained deep
neural network on a target task. We study generalization properties of
fine-tuning to understand the problem of overfitting, which commonly occurs in
practice. Previous works have shown that constraining the distance from the
initialization of fine-tuning improves generalization. Using a PAC-Bayesian
analysis, we observe that besides distance from initialization, Hessians affect
generalization through the noise stability of deep neural networks against
noise injections. Motivated by the observation, we develop Hessian
distance-based generalization bounds for a wide range of fine-tuning methods.
Additionally, we study the robustness of fine-tuning in the presence of noisy
labels. Motivated by our theory, we design an algorithm that incorporates
consistent losses and distance-based regularization for fine-tuning, along with
a generalization error guarantee under class conditional independent noise in
the training set labels. We perform a detailed empirical study of our algorithm
on various noisy environments and architectures. On six image classification
tasks whose training labels are generated with programmatic labeling, we find a
3.26% accuracy gain over prior fine-tuning methods. Meanwhile, the Hessian
distance measure of the fine-tuned model decreases by six times more than

We consider prediction with expert advice for strongly convex and bounded
losses, and investigate trade-offs between regret and "variance" (i.e., squared
difference of learner's predictions and best expert predictions). With $K$
experts, the Exponentially Weighted Average (EWA) algorithm is known to achieve
$O(\log K)$ regret. We prove that a variant of EWA either achieves a negative
regret (i.e., the algorithm outperforms the best expert), or guarantees a
$O(\log K)$ bound on both variance and regret. Building on this result, we show
several examples of how variance of predictions can be exploited in learning.
In the online to batch analysis, we show that a large empirical variance allows
to stop the online to batch conversion early and outperform the risk of the
best predictor in the class. We also recover the optimal rate of model
selection aggregation when we do not consider early stopping. In online
prediction with corrupted losses, we show that the effect of corruption on the
regret can be compensated by a large variance. In online selective sampling, we
design an algorithm that samples less when the variance is large, while
guaranteeing the optimal regret bound in expectation. In online learning with
abstention, we use a similar term as the variance to derive the first
high-probability $O(\log K)$ regret bound in this setting. Finally, we extend
our results to the setting of online linear regression.

Edge Computing (EC) is about remodeling the way data is handled, processed,
and delivered within a vast heterogeneous network. One of the fundamental
concepts of EC is to push the data processing near the edge by exploiting
front-end devices with powerful computation capabilities. Thus, limiting the
use of centralized architecture, such as cloud computing, to only when it is
necessary. This paper proposes a novel edge computer offloading technique that
assigns computational tasks generated by devices to potential edge computers
with enough computational resources. The proposed approach clusters the edge
computers based on their hardware specifications. Afterwards, the tasks
generated by devices will be fed to a hybrid Artificial Neural Network (ANN)
model that predicts, based on these tasks, the profiles, i.e., features, of the
edge computers with enough computational resources to execute them. The
predicted edge computers are then assigned to the cluster they belong to so
that each task is assigned to a cluster of edge computers. Finally, we choose
for each task the edge computer that is expected to provide the fastest
response time. The experiment results show that our proposed approach
outperforms other state-of-the-art machine learning approaches using real-world
IoT dataset.

Machine learning methods were applied to reconsider the results of several
passive seismic experiments in Finland. We created datasets from different
stages of the receiver function technique and processed them with one of basic
machine learning algorithms. All the results were obtained uniformly with the
$k$-nearest neighbors algorithm. The first result is the Moho depth map of the
region. Another result is the delineation of the near-surface low $S$-wave
velocity layer. There are three such areas in the Northern, Southern, and
central parts of the region. The low $S$-wave velocity in the Northern and
Southern areas can be linked to the geological structure. However, we attribute
the central low $S$-wave velocity area to a large number of water-saturated
cracks in the upper 1-5 km. Analysis of the structure of this area leads us to
the conclusion that macrofracturing was caused by the last deglaciation.

Vision Transformers (ViTs) and their multi-scale and hierarchical variations
have been successful at capturing image representations but their use has been
generally studied for low-resolution images (e.g. - 256x256, 384384). For
gigapixel whole-slide imaging (WSI) in computational pathology, WSIs can be as
large as 150000x150000 pixels at 20X magnification and exhibit a hierarchical
structure of visual tokens across varying resolutions: from 16x16 images
capture spatial patterns among cells, to 4096x4096 images characterizing
interactions within the tissue microenvironment. We introduce a new ViT
architecture called the Hierarchical Image Pyramid Transformer (HIPT), which
leverages the natural hierarchical structure inherent in WSIs using two levels
of self-supervised learning to learn high-resolution image representations.
HIPT is pretrained across 33 cancer types using 10,678 gigapixel WSIs, 408,218
4096x4096 images, and 104M 256x256 images. We benchmark HIPT representations on
9 slide-level tasks, and demonstrate that: 1) HIPT with hierarchical
pretraining outperforms current state-of-the-art methods for cancer subtyping
and survival prediction, 2) self-supervised ViTs are able to model important
inductive biases about the hierarchical structure of phenotypes in the tumor
microenvironment.

Graph Neural Networks (GNNs) have achieved promising performance in
semi-supervised node classification in recent years. However, the problem of
insufficient supervision, together with representation collapse, largely limits
the performance of the GNNs in this field. To alleviate the collapse of node
representations in semi-supervised scenario, we propose a novel graph
contrastive learning method, termed Mixed Graph Contrastive Network (MGCN). In
our method, we improve the discriminative capability of the latent feature by
enlarging the margin of decision boundaries and improving the cross-view
consistency of the latent representation. Specifically, we first adopt an
interpolation-based strategy to conduct data augmentation in the latent space
and then force the prediction model to change linearly between samples. Second,
we enable the learned network to tell apart samples across two
interpolation-perturbed views through forcing the correlation matrix across
views to approximate an identity matrix. By combining the two settings, we
extract rich supervision information from both the abundant unlabeled nodes and
the rare yet valuable labeled nodes for discriminative representation learning.
Extensive experimental results on six datasets demonstrate the effectiveness
and the generality of MGCN compared to the existing state-of-the-art methods.

This paper studies policy optimization algorithms for multi-agent
reinforcement learning. We begin by proposing an algorithm framework for
two-player zero-sum Markov Games in the full-information setting, where each
iteration consists of a policy update step at each state using a certain matrix
game algorithm, and a value update step with a certain learning rate. This
framework unifies many existing and new policy optimization algorithms. We show
that the state-wise average policy of this algorithm converges to an
approximate Nash equilibrium (NE) of the game, as long as the matrix game
algorithms achieve low weighted regret at each state, with respect to weights
determined by the speed of the value updates. Next, we show that this framework
instantiated with the Optimistic Follow-The-Regularized-Leader (OFTRL)
algorithm at each state (and smooth value updates) can find an
$\mathcal{\widetilde{O}}(T^{-5/6})$ approximate NE in $T$ iterations, and a
similar algorithm with slightly modified value update rule achieves a faster
$\mathcal{\widetilde{O}}(T^{-1})$ convergence rate. These improve over the
current best $\mathcal{\widetilde{O}}(T^{-1/2})$ rate of symmetric policy
optimization type algorithms. We also extend this algorithm to multi-player
general-sum Markov Games and show an $\mathcal{\widetilde{O}}(T^{-3/4})$
convergence rate to Coarse Correlated Equilibria (CCE). Finally, we provide a
numerical example to verify our theory and investigate the importance of smooth
value updates, and find that using "eager" value updates instead (equivalent to
the independent natural policy gradient algorithm) may significantly slow down
the convergence, even on a simple game with $H=2$ layers.

To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6G
wireless networks, conventional medium access control (MAC) procedures need to
evolve to enable base stations (BSs) and user equipments (UEs) to automatically
learn innovative MAC protocols catering to extremely diverse services. This
topic has received significant attention, and several reinforcement learning
(RL) algorithms, in which BSs and UEs are cast as agents, are available with
the aim of learning a communication policy based on agents' local observations.
However, current approaches are typically overfitted to the environment they
are trained in, and lack robustness against unseen conditions, failing to
generalize in different environments. To overcome this problem, in this work,
instead of learning a policy in the high dimensional and redundant observation
space, we leverage the concept of observation abstraction (OA) rooted in
extracting useful information from the environment. This in turn allows
learning communication protocols that are more robust and with much better
generalization capabilities than current baselines. To learn the abstracted
information from observations, we propose an architecture based on autoencoder
(AE) and imbue it into a multi-agent proximal policy optimization (MAPPO)
framework. Simulation results corroborate the effectiveness of leveraging
abstraction when learning protocols by generalizing across environments, in
terms of number of UEs, number of data packets to transmit, and channel

Machine learning has been widely utilized in fluid mechanics studies and
aerodynamic optimizations. However, most applications, especially flow field
modeling and inverse design, involve two-dimensional flows and geometries. The
dimensionality of three-dimensional problems is so high that it is too
difficult and expensive to prepare sufficient samples. Therefore, transfer
learning has become a promising approach to reuse well-trained two-dimensional
models and greatly reduce the need for samples for three-dimensional problems.
This paper proposes to reuse the baseline models trained on supercritical
airfoils to predict finite-span swept supercritical wings, where the simple
swept theory is embedded to improve the prediction accuracy. Two baseline
models for transfer learning are investigated: one is commonly referred to as
the forward problem of predicting the pressure coefficient distribution based
on the geometry, and the other is the inverse problem that predicts the
geometry based on the pressure coefficient distribution. Two transfer learning
strategies are compared for both baseline models. The transferred models are
then tested on the prediction of complete wings. The results show that transfer
learning requires only approximately 500 wing samples to achieve good
prediction accuracy on different wing planforms and different free stream
conditions. Compared to the two baseline models, the transferred models reduce
the prediction error by 60% and 80%, respectively.

The goal of the Mars Sample Return campaign is to collect soil samples from
the surface of Mars and return them to Earth for further study. The samples
will be acquired and stored in metal tubes by the Perseverance rover and
deposited on the Martian surface. As part of this campaign, it is expected that
the Sample Fetch Rover will be in charge of localizing and gathering up to 35
sample tubes over 150 Martian sols. Autonomous capabilities are critical for
the success of the overall campaign and for the Sample Fetch Rover in
particular. This work proposes a novel system architecture for the autonomous
detection and pose estimation of the sample tubes. For the detection stage, a
Deep Neural Network and transfer learning from a synthetic dataset are
proposed. The dataset is created from photorealistic 3D simulations of Martian
scenarios. Additionally, the sample tubes poses are estimated using Computer
Vision techniques such as contour detection and line fitting on the detected
area. Finally, laboratory tests of the Sample Localization procedure are
performed using the ExoMars Testing Rover on a Mars-like testbed. These tests
validate the proposed approach in different hardware architectures, providing
promising results related to the sample detection and pose estimation.

Federated Learning (FL) is a promising framework for performing
privacy-preserving, distributed learning with a set of clients. However, the
data distribution among clients often exhibits non-IID, i.e., distribution
shift, which makes efficient optimization difficult. To tackle this problem,
many FL algorithms focus on mitigating the effects of data heterogeneity across
clients by increasing the performance of the global model. However, almost all
algorithms leverage Empirical Risk Minimization (ERM) to be the local
optimizer, which is easy to make the global model fall into a sharp valley and
increase a large deviation of parts of local clients. Therefore, in this paper,
we revisit the solutions to the distribution shift problem in FL with a focus
on local learning generality. To this end, we propose a general, effective
algorithm, \texttt{FedSAM}, based on Sharpness Aware Minimization (SAM) local
optimizer, and develop a momentum FL algorithm to bridge local and global
models, \texttt{MoFedSAM}. Theoretically, we show the convergence analysis of
these two algorithms and demonstrate the generalization bound of
\texttt{FedSAM}. Empirically, our proposed algorithms substantially outperform
existing FL studies and significantly decrease the learning deviation.

Differentially private stochastic gradient descent (DP-SGD) is the workhorse
algorithm for recent advances in private deep learning. It provides a single
privacy guarantee to all datapoints in the dataset. We propose an efficient
algorithm to compute privacy guarantees for individual examples when releasing
models trained by DP-SGD. We use our algorithm to investigate individual
privacy parameters across a number of datasets. We find that most examples
enjoy stronger privacy guarantees than the worst-case bound. We further
discover that the training loss and the privacy parameter of an example are
well-correlated. This implies groups that are underserved in terms of model
utility are simultaneously underserved in terms of privacy guarantee. For
example, on CIFAR-10, the average $\epsilon$ of the class with the lowest test
accuracy is 26.3% higher than that of the class with the highest accuracy. We
also run membership inference attacks to show this reflects disparate empirical
privacy risks.

Real-world image super-resolution is a practical image restoration problem
that aims to obtain high-quality images from in-the-wild input, has recently
received considerable attention with regard to its tremendous application
potentials. Although deep learning-based methods have achieved promising
restoration quality on real-world image super-resolution datasets, they ignore
the relationship between L1- and perceptual- minimization and roughly adopt
auxiliary large-scale datasets for pre-training. In this paper, we discuss the
image types within a corrupted image and the property of perceptual- and
Euclidean- based evaluation protocols. Then we propose a method, Real-World
image Super-Resolution by Exclusionary Dual-Learning (RWSR-EDL) to address the
feature diversity in perceptual- and L1- based cooperative learning. Moreover,
a noise-guidance data collection strategy is developed to address the training
time consumption in multiple datasets optimization. When an auxiliary dataset
is incorporated, RWSR-EDL achieves promising results and repulses any training
time increment by adopting the noise-guidance data collection strategy.
Extensive experiments show that RWSR-EDL achieves competitive performance over
state-of-the-art methods on four in-the-wild image super-resolution datasets.

The long runtime of high-fidelity partial differential equation (PDE) solvers
makes them unsuitable for time-critical applications. We propose to accelerate
PDE solvers using reduced-order modeling (ROM). Whereas prior ROM approaches
reduce the dimensionality of discretized vector fields, our continuous
reduced-order modeling (CROM) approach builds a smooth, low-dimensional
manifold of the continuous vector fields themselves, not their discretization.
We represent this reduced manifold using continuously differentiable neural
fields, which may train on any and all available numerical solutions of the
continuous system, even when they are obtained using diverse methods or
discretizations. We validate our approach on an extensive range of PDEs with
training data from voxel grids, meshes, and point clouds. Compared to prior
discretization-dependent ROM methods, such as linear subspace proper orthogonal
decomposition (POD) and nonlinear manifold neural-network-based autoencoders,
CROM features higher accuracy, lower memory consumption, dynamically adaptive
resolutions, and applicability to any discretization. For equal latent space
dimension, CROM exhibits 79$\times$ and 49$\times$ better accuracy, and
39$\times$ and 132$\times$ smaller memory footprint, than POD and autoencoder
methods, respectively. Experiments demonstrate 109$\times$ and 89$\times$
wall-clock speedups over unreduced models on CPUs and GPUs, respectively.

In this paper, we use tools from rate-distortion theory to establish new
upper bounds on the generalization error of statistical distributed learning
algorithms. Specifically, there are $K$ clients whose individually chosen
models are aggregated by a central server. The bounds depend on the
compressibility of each client's algorithm while keeping other clients'
algorithms un-compressed, and leverage the fact that small changes in each
local model change the aggregated model by a factor of only $1/K$. Adopting a
recently proposed approach by Sefidgaran et al., and extending it suitably to
the distributed setting, this enables smaller rate-distortion terms which are
shown to translate into tighter generalization bounds. The bounds are then
applied to the distributed support vector machines (SVM), suggesting that the
generalization error of the distributed setting decays faster than that of the
centralized one with a factor of $\mathcal{O}(\log(K)/\sqrt{K})$. This finding
is validated also experimentally. A similar conclusion is obtained for a
multiple-round federated learning setup where each client uses stochastic
gradient Langevin dynamics (SGLD).

Fully Convolutional Data Description (FCDD), an explainable version of the
Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD)
and pixel-wise AD without any post-hoc explainer methods. The authors claim
that FCDD achieves results comparable with the state-of-the-art in sample-wise
AD on Fashion-MNIST and CIFAR-10 and exceeds the state-of-the-art on the
pixel-wise task on MVTec-AD. We reproduced the main results of the paper using
the author's code with minor changes and provide runtime requirements to
achieve if (CPU memory, GPU memory, and training time). We propose another
analysis methodology using a critical difference diagram, and further
investigate the test performance of the model during the training phase.

With the advent of the 6G era, the concept of semantic communication has
attracted increasing attention. Compared with conventional communication
systems, semantic communication systems are not only affected by physical noise
existing in the wireless communication environment, e.g., additional white
Gaussian noise, but also by semantic noise due to the source and the nature of
deep learning-based systems. In this paper, we elaborate on the mechanism of
semantic noise. In particular, we categorize semantic noise into two
categories: literal semantic noise and adversarial semantic noise. The former
is caused by written errors or expression ambiguity, while the latter is caused
by perturbations or attacks added to the embedding layer via the semantic
channel. To prevent semantic noise from influencing semantic communication
systems, we present a robust deep learning enabled semantic communication
system (R-DeepSC) that leverages a calibrated self-attention mechanism and
adversarial training to tackle semantic noise. Compared with baseline models
that only consider physical noise for text transmission, the proposed R-DeepSC
achieves remarkable performance in dealing with semantic noise under different
signal-to-noise ratios.

Off-policy learning is a framework for optimizing policies without deploying
them, using data collected by another policy. In recommender systems, this is
especially challenging due to the imbalance in logged data: some items are
recommended and thus logged more frequently than others. This is further
perpetuated when recommending a list of items, as the action space is
combinatorial. To address this challenge, we study pessimistic off-policy
optimization for learning to rank. The key idea is to compute lower confidence
bounds on parameters of click models and then return the list with the highest
pessimistic estimate of its value. This approach is computationally efficient
and we analyze it. We study its Bayesian and frequentist variants, and overcome
the limitation of unknown prior by incorporating empirical Bayes. To show the
empirical effectiveness of our approach, we compare it to off-policy optimizers
that use inverse propensity scores or neglect uncertainty. Our approach
outperforms all baselines, is robust, and is also general.

Almost all multi-agent reinforcement learning algorithms without
communication follow the principle of centralized training with decentralized
execution. During centralized training, agents can be guided by the same
signals, such as the global state. During decentralized execution, however,
agents lack the shared signal. Inspired by viewpoint invariance and contrastive
learning, we propose consensus learning for cooperative multi-agent
reinforcement learning in this paper. Although based on local observations,
different agents can infer the same consensus in discrete space. During
decentralized execution, we feed the inferred consensus as an explicit input to
the network of agents, thereby developing their spirit of cooperation. Our
proposed method can be extended to various multi-agent reinforcement learning
algorithms with small model changes. Moreover, we carry out them on some fully
cooperative tasks and get convincing results.

Federated learning has recently gained significant attention and popularity
due to its effectiveness in training machine learning models on distributed
data privately. However, as in the single-node supervised learning setup,
models trained in federated learning suffer from vulnerability to imperceptible
input transformations known as adversarial attacks, questioning their
deployment in security-related applications. In this work, we study the
interplay between federated training, personalization, and certified
robustness. In particular, we deploy randomized smoothing, a widely-used and
scalable certification method, to certify deep networks trained on a federated
setup against input perturbations and transformations. We find that the simple
federated averaging technique is effective in building not only more accurate,
but also more certifiably-robust models, compared to training solely on local
data. We further analyze personalization, a popular technique in federated
training that increases the model's bias towards local data, on robustness. We
show several advantages of personalization over both~(that is, only training on
local data and federated training) in building more robust models with faster
training. Finally, we explore the robustness of mixtures of global and
local~(i.e. personalized) models, and find that the robustness of local models
degrades as they diverge from the global model

Surrogate models are used to alleviate the computational burden in
engineering tasks, which require the repeated evaluation of computationally
demanding models of physical systems, such as the efficient propagation of
uncertainties. For models that show a strongly non-linear dependence on their
input parameters, standard surrogate techniques, such as polynomial chaos
expansion, are not sufficient to obtain an accurate representation of the
original model response. Through applying a rational approximation instead, the
approximation error can be efficiently reduced for models whose non-linearity
is accurately described through a rational function. Specifically, our aim is
to approximate complex-valued models. A common approach to obtain the
coefficients in the surrogate is to minimize the sample-based error between
model and surrogate in the least-square sense. In order to obtain an accurate
representation of the original model and to avoid overfitting, the sample set
has be two to three times the number of polynomial terms in the expansion. For
models that require a high polynomial degree or are high-dimensional in terms
of their input parameters, this number often exceeds the affordable
computational cost. To overcome this issue, we apply a sparse Bayesian learning
approach to the rational approximation. Through a specific prior distribution
structure, sparsity is induced in the coefficients of the surrogate model. The
denominator polynomial coefficients as well as the hyperparameters of the
problem are determined through a type-II-maximum likelihood approach. We apply
a quasi-Newton gradient-descent algorithm in order to find the optimal
denominator coefficients and derive the required gradients through application
of $\mathbb{CR}$-calculus.

SARS-COV-19 is the most prominent issue which many countries face today. The
frequent changes in infections, recovered and deaths represents the dynamic
nature of this pandemic. It is very crucial to predict the spreading rate of
this virus for accurate decision making against fighting with the situation of
getting infected through the virus, tracking and controlling the virus
transmission in the community. We develop a prediction model using statistical
time series models such as SARIMA and FBProphet to monitor the daily active,
recovered and death cases of COVID-19 accurately. Then with the help of various
details across each individual patient (like height, weight, gender etc.), we
designed a set of rules using Semantic Web Rule Language and some mathematical
models for dealing with COVID19 infected cases on an individual basis. After
combining all the models, a COVID-19 Ontology is developed and performs various
queries using SPARQL query on designed Ontology which accumulate the risk
factors, provide appropriate diagnosis, precautions and preventive suggestions
for COVID Patients. After comparing the performance of SARIMA and FBProphet, it
is observed that the SARIMA model performs better in forecasting of COVID
cases. On individual basis COVID case prediction, approx. 497 individual
samples have been tested and classified into five different levels of COVID
classes such as Having COVID, No COVID, High Risk COVID case, Medium to High
Risk case, and Control needed case.

In this paper, we propose a novel unsupervised text-to-speech (UTTS)
framework which does not require text-audio pairs for the TTS acoustic modeling
(AM). UTTS is a multi-speaker speech synthesizer that supports zero-shot voice
cloning, it is developed from a perspective of disentangled speech
representation learning. The framework offers a flexible choice of a speaker's
duration model, timbre feature (identity) and content for TTS inference. We
leverage recent advancements in self-supervised speech representation learning
as well as speech synthesis front-end techniques for system development.
Specifically, we employ our recently formulated Conditional Disentangled
Sequential Variational Auto-encoder (C-DSVAE) as the backbone UTTS AM, which
offers well-structured content representations given unsupervised alignment
(UA) as condition during training. For UTTS inference, we utilize a lexicon to
map input text to the phoneme sequence, which is expanded to the frame-level
forced alignment (FA) with a speaker-dependent duration model. Then, we develop
an alignment mapping module that converts FA to UA. Finally, the C-DSVAE,
serving as the self-supervised TTS AM, takes the predicted UA and a target
speaker embedding to generate the mel spectrogram, which is ultimately
converted to waveform with a neural vocoder. We show how our method enables
speech synthesis without using a paired TTS corpus. Experiments demonstrate
that UTTS can synthesize speech of high naturalness and intelligibility
measured by human and objective evaluations. Audio samples are available at our
demo page https://neurtts.github.io/utts_demo.

The tuning of hyperparameters becomes increasingly important as machine
learning (ML) models have been extensively applied in data mining applications.
Among various approaches, Bayesian optimization (BO) is a successful
methodology to tune hyper-parameters automatically. While traditional methods
optimize each tuning task in isolation, there has been recent interest in
speeding up BO by transferring knowledge across previous tasks. In this work,
we introduce an automatic method to design the BO search space with the aid of
tuning history from past tasks. This simple yet effective approach can be used
to endow many existing BO methods with transfer learning capabilities. In
addition, it enjoys the three advantages: universality, generality, and
safeness. The extensive experiments show that our approach considerably boosts
BO by designing a promising and compact search space instead of using the
entire space, and outperforms the state-of-the-arts on a wide range of
benchmarks, including machine learning and deep learning tuning tasks, and
neural architecture search.

Higher-order tensor data are prevailing in a wide range of fields including
high-resolution videos, multimodality imaging such as MRI and fMRI scans,
commercial networks, engineering such as signal processing, and elsewhere.
Tucker decomposition may be the most general low-rank approximation method
among versatile decompositions of higher-order tensors owning to its strong
compression ability, whilst statistical properties of the induced Tucker tensor
factor model (TuTFaM) remains a big challenge and yet critical before it
provides justification for applications in machine learning and beyond.
Existing theoretical developments mainly focus on the field of time series with
the assumption of strong auto-correlation among temporally ordered
observations, which is ineffective for independent and weakly dependent tensor
observations. Under quite mild assumptions, this article kicks off
matricization of raw weakly correlated tensor observations within the TuTFaM
setting, and proposes two sets of PCA based estimation procedures, moPCA and
its refinement IPmoPCA, the latter of which is enhanced in rate of convergence.
We develop their asymptotic behaviors, including mainly convergence rates and
asymptotic distributions of estimators of loading matrices, latent tensor
factors and signal parts. The theoretical results can reduce to those in
low-order tensor factor models in existing literature. The proposed approaches
outperform existing auto-covariance based methods for tensor time series in
terms of effects of estimation and tensor reconstruction, in both simulation
experiments and two real data examples.

This paper investigates the problem of regret minimization in linear
time-varying (LTV) dynamical systems. Due to the simultaneous presence of
uncertainty and non-stationarity, designing online control algorithms for
unknown LTV systems remains a challenging task. At a cost of NP-hard offline
planning, prior works have introduced online convex optimization algorithms,
although they suffer from nonparametric rate of regret.
  In this paper, we propose the first computationally tractable online
algorithm with regret guarantees that avoids offline planning over the state
linear feedback policies. Our algorithm is based on the optimism in the face of
uncertainty (OFU) principle in which we optimistically select the best model in
a high confidence region. Our algorithm is then more explorative when compared
to previous approaches. To overcome non-stationarity, we propose either a
restarting strategy (R-OFU) or a sliding window (SW-OFU) strategy. With proper
configuration, our algorithm is attains sublinear regret $O(T^{2/3})$. These
algorithms utilize data from the current phase for tracking variations on the
system dynamics. We corroborate our theoretical findings with numerical
experiments, which highlight the effectiveness of our methods. To the best of
our knowledge, our study establishes the first model-based online algorithm
with regret guarantees under LTV dynamical systems.

Quantum Neural Networks (QNN) are considered a candidate for achieving
quantum advantage in the Noisy Intermediate Scale Quantum computer (NISQ) era.
Several QNN architectures have been proposed and successfully tested on
benchmark datasets for machine learning. However, quantitative studies of the
QNN-generated entanglement have not been investigated in details, and only for
up to few qubits. Tensor network methods allow to emulate quantum circuits with
a large number of qubits in a wide variety of scenarios. Here, we employ matrix
product states to characterize recently studied QNN architectures with up to
fifty qubits showing that their entanglement, measured in terms of entanglement
entropy between qubits, tends to that of Haar distributed random states as the
depth of the QNN is increased. We show a universal behavior for the
entanglement entropy production for any given QNN architecture, consequently we
introduce a new measure to characterize the entanglement production in QNNs:
the entangling speed. Finally, in agreement with known results in the
literature, we argue that the most promising regime for quantum advantage with
QNNs is defined by a trade-off between high entanglement and expressibility.

Search engines and recommendation systems attempt to continually improve the
quality of the experience they afford to their users. Refining the ranker that
produces the lists displayed in response to user requests is an important
component of this process. A common practice is for the service providers to
make changes (e.g. new ranking features, different ranking models) and A/B test
them on a fraction of their users to establish the value of the change. An
alternative approach estimates the effectiveness of the proposed changes
offline, utilising previously collected clickthrough data on the old ranker to
posit what the user behaviour on ranked lists produced by the new ranker would
have been. A majority of offline evaluation approaches invoke the well studied
inverse propensity weighting to adjust for biases inherent in logged data. In
this paper, we propose the use of parametric estimates for these propensities.
Specifically, by leveraging well known learning-to-rank methods as subroutines,
we show how accurate offline evaluation can be achieved when the new rankings
to be evaluated differ from the logged ones.

Federated learning is a distributed machine learning paradigm, which aims to
train a model using the local data of many distributed clients. A key challenge
in federated learning is that the data samples across the clients may not be
identically distributed. To address this challenge, personalized federated
learning with the goal of tailoring the learned model to the data distribution
of every individual client has been proposed. In this paper, we focus on this
problem and propose a novel personalized Federated Learning scheme based on
Optimal Transport (FedOT) as a learning algorithm that learns the optimal
transport maps for transferring data points to a common distribution as well as
the prediction model under the applied transport map. To formulate the FedOT
problem, we extend the standard optimal transport task between two probability
distributions to multi-marginal optimal transport problems with the goal of
transporting samples from multiple distributions to a common probability
domain. We then leverage the results on multi-marginal optimal transport
problems to formulate FedOT as a min-max optimization problem and analyze its
generalization and optimization properties. We discuss the results of several
numerical experiments to evaluate the performance of FedOT under heterogeneous
data distributions in federated learning problems.

This paper presents a computationally feasible method to compute rigorous
bounds on the interval-generalisation of regression analysis to account for
epistemic uncertainty in the output variables. The new iterative method uses
machine learning algorithms to fit an imprecise regression model to data that
consist of intervals rather than point values. The method is based on a
single-layer interval neural network which can be trained to produce an
interval prediction. It seeks parameters for the optimal model that minimizes
the mean squared error between the actual and predicted interval values of the
dependent variable using a first-order gradient-based optimization and interval
analysis computations to model the measurement imprecision of the data. An
additional extension to a multi-layer neural network is also presented. We
consider the explanatory variables to be precise point values, but the measured
dependent values are characterized by interval bounds without any probabilistic
information. The proposed iterative method estimates the lower and upper bounds
of the expectation region, which is an envelope of all possible precise
regression lines obtained by ordinary regression analysis based on any
configuration of real-valued points from the respective y-intervals and their
x-values.

In federated learning (FL), model performance typically suffers from client
drift induced by data heterogeneity, and mainstream works focus on correcting
client drift. We propose a different approach named virtual homogeneity
learning (VHL) to directly "rectify" the data heterogeneity. In particular, VHL
conducts FL with a virtual homogeneous dataset crafted to satisfy two
conditions: containing no private information and being separable. The virtual
dataset can be generated from pure noise shared across clients, aiming to
calibrate the features from the heterogeneous clients. Theoretically, we prove
that VHL can achieve provable generalization performance on the natural
distribution. Empirically, we demonstrate that VHL endows FL with drastically
improved convergence speed and generalization performance. VHL is the first
attempt towards using a virtual dataset to address data heterogeneity, offering
new and effective means to FL.

Given a square integrable m-dimensional random variable $X$ on a probability
space $(\Omega.\mathcal F,\Pr)$ and a sub sigma algebra $\mathcal A$, we show
that there exists another m-dimensional random variable $Y$, independent of
$\mathcal A$ and minimising the $L^2$ distance to $X$. Such results have an
importance to fairness and bias reduction in Artificial Intelligence, Machine
Learning and Network Theory.

Real-time control for robotics is a popular research area in the
reinforcement learning (RL) community. Through the use of techniques such as
reward shaping, researchers have managed to train online agents across a
multitude of domains. Despite these advances, solving goal-oriented tasks still
require complex architectural changes or heavy constraints to be placed on the
problem. To address this issue, recent works have explored how curriculum
learning can be used to separate a complex task into sequential sub-goals,
hence enabling the learning of a problem that may otherwise be too difficult to
learn from scratch. In this article, we present how curriculum learning, reward
shaping, and a high number of efficiently parallelized environments can be
coupled together to solve the problem of multiple cube stacking. Finally, we
extend the best configuration identified on a higher complexity environment
with differently shaped objects.

Following SimCSE, contrastive learning based methods have achieved the
state-of-the-art (SOTA) performance in learning sentence embeddings. However,
the unsupervised contrastive learning methods still lag far behind the
supervised counterparts. We attribute this to the quality of positive and
negative samples, and aim to improve both. Specifically, for positive samples,
we propose switch-case augmentation to flip the case of the first letter of
randomly selected words in a sentence. This is to counteract the intrinsic bias
of pre-trained token embeddings to frequency, word cases and subwords. For
negative samples, we sample hard negatives from the whole dataset based on a
pre-trained language model. Combining the above two methods with SimCSE, our
proposed Contrastive learning with Augmented and Retrieved Data for Sentence
embedding (CARDS) method significantly surpasses the current SOTA on STS
benchmarks in the unsupervised setting.

We consider a high-dimensional mean estimation problem over a binary hidden
Markov model, which illuminates the interplay between memory in data, sample
size, dimension, and signal strength in statistical inference. In this model,
an estimator observes $n$ samples of a $d$-dimensional parameter vector
$\theta_{*}\in\mathbb{R}^{d}$, multiplied by a random sign $ S_i $ ($1\le i\le
n$), and corrupted by isotropic standard Gaussian noise. The sequence of signs
$\{S_{i}\}_{i\in[n]}\in\{-1,1\}^{n}$ is drawn from a stationary homogeneous
Markov chain with flip probability $\delta\in[0,1/2]$. As $\delta$ varies, this
model smoothly interpolates two well-studied models: the Gaussian Location
Model for which $\delta=0$ and the Gaussian Mixture Model for which
$\delta=1/2$. Assuming that the estimator knows $\delta$, we establish a nearly
minimax optimal (up to logarithmic factors) estimation error rate, as a
function of $\|\theta_{*}\|,\delta,d,n$. We then provide an upper bound to the
case of estimating $\delta$, assuming a (possibly inaccurate) knowledge of
$\theta_{*}$. The bound is proved to be tight when $\theta_{*}$ is an
accurately known constant. These results are then combined to an algorithm
which estimates $\theta_{*}$ with $\delta$ unknown a priori, and theoretical
guarantees on its error are stated.

It has previously been observed that the filters learned in the first layer
of a CNN are qualitatively similar for different networks and tasks. We extend
this finding and show a high quantitative similarity between filters learned by
different networks. We consider the CNN filters as a filter bank and measure
the sensitivity of the filter bank to different frequencies. We show that the
sensitivity profile of different networks is almost identical, yet far from
initialization. Remarkably, we show that it remains the same even when the
network is trained with random labels. To understand this effect, we derive an
analytic formula for the sensitivity of the filters in the first layer of a
linear CNN. We prove that when the average patch in images of the two classes
is identical, the sensitivity profile of the filters in the first layer will be
identical in expectation when using the true labels or random labels and will
only depend on the second-order statistics of image patches. We empirically
demonstrate that the average patch assumption holds for realistic datasets.
Finally we show that the energy profile of filters in nonlinear CNNs is highly
correlated with the energy profile of linear CNNs and that our analysis of
linear networks allows us to predict when representations learned by
state-of-the-art networks trained on benchmark classification tasks will depend
on the labels.

Gradient coding schemes effectively mitigate full stragglers in distributed
learning by introducing identical redundancy in coded local partial derivatives
corresponding to all model parameters. However, they are no longer effective
for partial stragglers as they cannot utilize incomplete computation results
from partial stragglers. This paper aims to design a new gradient coding scheme
for mitigating partial stragglers in distributed learning. Specifically, we
consider a distributed system consisting of one master and N workers,
characterized by a general partial straggler model and focuses on solving a
general large-scale machine learning problem with L model parameters using
gradient coding. First, we propose a coordinate gradient coding scheme with L
coding parameters representing L possibly different diversities for the L
coordinates, which generates most gradient coding schemes. Then, we consider
the minimization of the expected overall runtime and the maximization of the
completion probability with respect to the L coding parameters for coordinates,
which are challenging discrete optimization problems. To reduce computational
complexity, we first transform each to an equivalent but much simpler discrete
problem with N\llL variables representing the partition of the L coordinates
into N blocks, each with identical redundancy. This indicates an equivalent but
more easily implemented block coordinate gradient coding scheme with N coding
parameters for blocks. Then, we adopt continuous relaxation to further reduce
computational complexity. For the resulting minimization of expected overall
runtime, we develop an iterative algorithm of computational complexity O(N^2)
to obtain an optimal solution and derive two closed-form approximate solutions
both with computational complexity O(N). For the resultant maximization of the
completion probability, we develop an iterative algorithm of...

We show that in the context of classification the property of source and
target distributions to be related by covariate shift may be lost if the
information content captured in the covariates is reduced, for instance by
dropping components or mapping into a lower-dimensional or finite space. As a
consequence, under covariate shift simple approaches to class prior estimation
in the style of classify and count with or without adjustment are infeasible.
We prove that transformations of the covariates that preserve the covariate
shift property are necessarily sufficient in the statistical sense for the full
set of covariates. A probing algorithm as alternative approach to class prior
estimation under covariate shift is proposed.

Emails and SMSs are the most popular tools in today communications, and as
the increase of emails and SMSs users are increase, the number of spams is also
increases. Spam is any kind of unwanted, unsolicited digital communication that
gets sent out in bulk, spam emails and SMSs are causing major resource wastage
by unnecessarily flooding the network links. Although most spam mail originate
with advertisers looking to push their products, some are much more malicious
in their intent like phishing emails that aims to trick victims into giving up
sensitive information like website logins or credit card information this type
of cybercrime is known as phishing. To countermeasure spams, many researches
and efforts are done to build spam detectors that are able to filter out
messages and emails as spam or ham. In this research we build a spam detector
using BERT pre-trained model that classifies emails and messages by
understanding to their context, and we trained our spam detector model using
multiple corpuses like SMS collection corpus, Enron corpus, SpamAssassin
corpus, Ling-Spam corpus and SMS spam collection corpus, our spam detector
performance was 98.62%, 97.83%, 99.13% and 99.28% respectively. Keywords: Spam
Detector, BERT, Machine learning, NLP, Transformer, Enron Corpus, SpamAssassin
Corpus, SMS Spam Detection Corpus, Ling-Spam Corpus.

Sparse Gaussian Processes are a key component of high-throughput Bayesian
optimisation (BO) loops -- an increasingly common setting where evaluation
budgets are large and highly parallelised. By using representative subsets of
the available data to build approximate posteriors, sparse models dramatically
reduce the computational costs of surrogate modelling by relying on a small set
of pseudo-observations, the so-called inducing points, in lieu of the full data
set. However, current approaches to design inducing points are not appropriate
within BO loops as they seek to reduce global uncertainty in the objective
function. Thus, the high-fidelity modelling of promising and data-dense regions
required for precise optimisation is sacrificed and computational resources are
instead wasted on modelling areas of the space already known to be sub-optimal.
Inspired by entropy-based BO methods, we propose a novel inducing point design
that uses a principled information-theoretic criterion to select inducing
points. By choosing inducing points to maximally reduce both global uncertainty
and uncertainty in the maximum value of the objective function, we build
surrogate models able to support high-precision high-throughput BO.

In order to enhance levels of engagement with conversational systems, our
long term research goal seeks to monitor the confusion state of a user and
adapt dialogue policies in response to such user confusion states. To this end,
in this paper, we present our initial research centred on a user-avatar
dialogue scenario that we have developed to study the manifestation of
confusion and in the long term its mitigation. We present a new definition of
confusion that is particularly tailored to the requirements of intelligent
conversational system development for task-oriented dialogue. We also present
the details of our Wizard-of-Oz based data collection scenario wherein users
interacted with a conversational avatar and were presented with stimuli that
were in some cases designed to invoke a confused state in the user. Post study
analysis of this data is also presented. Here, three pre-trained deep learning
models were deployed to estimate base emotion, head pose and eye gaze. Despite
a small pilot study group, our analysis demonstrates a significant relationship
between these indicators and confusion states. We understand this as a useful
step forward in the automated analysis of the pragmatics of dialogue.

Bayesian neural networks (BNNs) promise improved generalization under
covariate shift by providing principled probabilistic representations of
epistemic uncertainty. However, weight-based BNNs often struggle with high
computational complexity of large-scale architectures and datasets. Node-based
BNNs have recently been introduced as scalable alternatives, which induce
epistemic uncertainty by multiplying each hidden node with latent random
variables, while learning a point-estimate of the weights. In this paper, we
interpret these latent noise variables as implicit representations of simple
and domain-agnostic data perturbations during training, producing BNNs that
perform well under covariate shift due to input corruptions. We observe that
the diversity of the implicit corruptions depends on the entropy of the latent
variables, and propose a straightforward approach to increase the entropy of
these variables during training. We evaluate the method on out-of-distribution
image classification benchmarks, and show improved uncertainty estimation of
node-based BNNs under covariate shift due to input perturbations. As a side
effect, the method also provides robustness against noisy training labels.

We present a data-driven approach for probabilistic wind power forecasting
based on conditional normalizing flow (CNF). In contrast with the existing,
this approach is distribution-free (as for non-parametric and quantile-based
approaches) and can directly yield continuous probability densities, hence
avoiding quantile crossing. It relies on a base distribution and a set of
bijective mappings. Both the shape parameters of the base distribution and the
bijective mappings are approximated with neural networks. Spline-based
conditional normalizing flow is considered owing to its non-affine
characteristics. Over the training phase, the model sequentially maps input
examples onto samples of base distribution, given the conditional contexts,
where parameters are estimated through maximum likelihood. To issue
probabilistic forecasts, one eventually maps samples of the base distribution
into samples of a desired distribution. Case studies based on open datasets
validate the effectiveness of the proposed model, and allows us to discuss its
advantages and caveats with respect to the state of the art.

Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is
desirable to joint learning of multimodal images. However, in clinical
practice, it is not always possible to acquire a complete set of MRIs, and the
problem of missing modalities causes severe performance degradation in existing
multimodal segmentation methods. In this work, we present the first attempt to
exploit the Transformer for multimodal brain tumor segmentation that is robust
to any combinatorial subset of available modalities. Concretely, we propose a
novel multimodal Medical Transformer (mmFormer) for incomplete multimodal
learning with three main components: the hybrid modality-specific encoders that
bridge a convolutional encoder and an intra-modal Transformer for both local
and global context modeling within each modality; an inter-modal Transformer to
build and align the long-range correlations across modalities for
modality-invariant features with global semantics corresponding to tumor
region; a decoder that performs a progressive up-sampling and fusion with the
modality-invariant features to generate robust segmentation. Besides, auxiliary
regularizers are introduced in both encoder and decoder to further enhance the
model's robustness to incomplete modalities. We conduct extensive experiments
on the public BraTS $2018$ dataset for brain tumor segmentation. The results
demonstrate that the proposed mmFormer outperforms the state-of-the-art methods
for incomplete multimodal brain tumor segmentation on almost all subsets of
incomplete modalities, especially by an average 19.07% improvement of Dice on
tumor segmentation with only one available modality. The code is available at
https://github.com/YaoZhang93/mmFormer.

Stock portfolio optimization is the process of continuous reallocation of
funds to a selection of stocks. This is a particularly well-suited problem for
reinforcement learning, as daily rewards are compounding and objective
functions may include more than just profit, e.g., risk and sustainability. We
developed a novel utility function with the Sharpe ratio representing risk and
the environmental, social, and governance score (ESG) representing
sustainability. We show that a state-of-the-art policy gradient method -
multi-agent deep deterministic policy gradients (MADDPG) - fails to find the
optimum policy due to flat policy gradients and we therefore replaced gradient
descent with a genetic algorithm for parameter optimization. We show that our
system outperforms MADDPG while improving on deep Q-learning approaches by
allowing for continuous action spaces. Crucially, by incorporating risk and
sustainability criteria in the utility function, we improve on the
state-of-the-art in reinforcement learning for portfolio optimization; risk and
sustainability are essential in any modern trading strategy and we propose a
system that does not merely report these metrics, but that actively optimizes
the portfolio to improve on them.

Positive-Unlabelled (PU) learning is a growing area of machine learning that
aims to learn classifiers from data consisting of labelled positive and
unlabelled instances. Whilst much work has been done proposing methods for PU
learning, little has been written on the subject of evaluating these methods.
Many popular standard classification metrics cannot be precisely calculated due
to the absence of fully labelled data, so alternative approaches must be taken.
This short commentary paper critically reviews the main PU learning evaluation
approaches and the choice of predictive accuracy measures in 51 articles
proposing PU classifiers and provides practical recommendations for
improvements in this area.

In this paper, we describe MorisienMT, a dataset for benchmarking machine
translation quality of Mauritian Creole. Mauritian Creole (Morisien) is the
lingua franca of the Republic of Mauritius and is a French-based creole
language. MorisienMT consists of a parallel corpus between English and
Morisien, French and Morisien and a monolingual corpus for Morisien. We first
give an overview of Morisien and then describe the steps taken to create the
corpora and, from it, the training and evaluation splits. Thereafter, we
establish a variety of baseline models using the created parallel corpora as
well as large French--English corpora for transfer learning. We release our
datasets publicly for research purposes and hope that this spurs research for
Morisien machine translation.

The application of AI in finance is increasingly dependent on the principles
of responsible AI. These principles - explainability, fairness, privacy,
accountability, transparency and soundness form the basis for trust in future
AI systems. In this study, we address the first principle by providing an
explanation for a deep neural network that is trained on a mixture of
numerical, categorical and textual inputs for financial transaction
classification. The explanation is achieved through (1) a feature importance
analysis using Shapley additive explanations (SHAP) and (2) a hybrid approach
of text clustering and decision tree classifiers. We then test the robustness
of the model by exposing it to a targeted evasion attack, leveraging the
knowledge we gained about the model through the extracted explanation.

In this paper, we focus our attention on private Empirical Risk Minimization
(ERM), which is one of the most commonly used data analysis method. We take the
first step towards solving the above problem by theoretically exploring the
effect of epsilon (the parameter of differential privacy that determines the
strength of privacy guarantee) on utility of the learning model. We trace the
change of utility with modification of epsilon and reveal an established
relationship between epsilon and utility. We then formalize this relationship
and propose a practical approach for estimating the utility under an arbitrary
value of epsilon. Both theoretical analysis and experimental results
demonstrate high estimation accuracy and broad applicability of our approach in
practical applications. As providing algorithms with strong utility guarantees
that also give privacy when possible becomes more and more accepted, our
approach would have high practical value and may be likely to be adopted by
companies and organizations that would like to preserve privacy but are
unwilling to compromise on utility.

While adversarial training and its variants have shown to be the most
effective algorithms to defend against adversarial attacks, their extremely
slow training process makes it hard to scale to large datasets like ImageNet.
The key idea of recent works to accelerate adversarial training is to
substitute multi-step attacks (e.g., PGD) with single-step attacks (e.g.,
FGSM). However, these single-step methods suffer from catastrophic overfitting,
where the accuracy against PGD attack suddenly drops to nearly 0% during
training, destroying the robustness of the networks. In this work, we study the
phenomenon from the perspective of training instances. We show that
catastrophic overfitting is instance-dependent and fitting instances with
larger gradient norm is more likely to cause catastrophic overfitting. Based on
our findings, we propose a simple but effective method, Adversarial Training
with Adaptive Step size (ATAS). ATAS learns an instancewise adaptive step size
that is inversely proportional to its gradient norm. The theoretical analysis
shows that ATAS converges faster than the commonly adopted non-adaptive
counterparts. Empirically, ATAS consistently mitigates catastrophic overfitting
and achieves higher robust accuracy on CIFAR10, CIFAR100 and ImageNet when
evaluated on various adversarial budgets.

Variational autoencoders (VAEs) are a popular framework for modeling complex
data distributions; they can be efficiently trained via variational inference
by maximizing the evidence lower bound (ELBO), at the expense of a gap to the
exact (log-)marginal likelihood. While VAEs are commonly used for
representation learning, it is unclear why ELBO maximization would yield useful
representations, since unregularized maximum likelihood estimation cannot
invert the data-generating process. Yet, VAEs often succeed at this task. We
seek to elucidate this apparent paradox by studying nonlinear VAEs in the limit
of near-deterministic decoders. We first prove that, in this regime, the
optimal encoder approximately inverts the decoder -- a commonly used but
unproven conjecture -- which we refer to as {\em self-consistency}. Leveraging
self-consistency, we show that the ELBO converges to a regularized
log-likelihood. This allows VAEs to perform what has recently been termed
independent mechanism analysis (IMA): it adds an inductive bias towards
decoders with column-orthogonal Jacobians, which helps recovering the true
latent factors. The gap between ELBO and log-likelihood is therefore welcome,
since it bears unanticipated benefits for nonlinear representation learning. In
experiments on synthetic and image data, we show that VAEs uncover the true
latent factors when the data generating process satisfies the IMA assumption.

Curating a large scale medical imaging dataset for machine learning
applications is both time consuming and expensive. Balancing the workload
between model development, data collection and annotations is difficult for
machine learning practitioners, especially under time constraints. Causal
analysis is often used in medicine and economics to gain insights about the
effects of actions and policies. In this paper we explore the effect of dataset
interventions on the output of image classification models. Through a causal
approach we investigate the effects of the quantity and type of data we need to
incorporate in a dataset to achieve better performance for specific subtasks.
The main goal of this paper is to highlight the potential of causal analysis as
a tool for resource optimization for developing medical imaging ML
applications. We explore this concept with a synthetic dataset and an exemplary
use-case for Diabetic Retinopathy image analysis.

In deep neural networks, better results can often be obtained by increasing
the complexity of previously developed basic models. However, it is unclear
whether there is a way to boost performance by decreasing the complexity of
such models. Intuitively, given a problem, a simpler data structure comes with
a simpler algorithm. Here, we investigate the feasibility of improving graph
classification performance while simplifying the learning process. Inspired by
structural entropy on graphs, we transform the data sample from graphs to
coding trees, which is a simpler but essential structure for graph data.
Furthermore, we propose a novel message passing scheme, termed hierarchical
reporting, in which features are transferred from leaf nodes to root nodes by
following the hierarchical structure of coding trees. We then present a tree
kernel and a convolutional network to implement our scheme for graph
classification. With the designed message passing scheme, the tree kernel and
convolutional network have a lower runtime complexity of $O(n)$ than
Weisfeiler-Lehman subtree kernel and other graph neural networks of at least
$O(hm)$. We empirically validate our methods with several graph classification
benchmarks and demonstrate that they achieve better performance and lower
computational consumption than competing approaches.

Federated learning (FL) over resource-constrained wireless networks has
recently attracted much attention. However, most existing studies consider one
FL task in single-cell wireless networks and ignore the impact of
downlink/uplink inter-cell interference on the learning performance. In this
paper, we investigate FL over a multi-cell wireless network, where each cell
performs a different FL task and over-the-air computation (AirComp) is adopted
to enable fast uplink gradient aggregation. We conduct convergence analysis of
AirComp-assisted FL systems, taking into account the inter-cell interference in
both the downlink and uplink model/gradient transmissions, which reveals that
the distorted model/gradient exchanges induce a gap to hinder the convergence
of FL. We characterize the Pareto boundary of the error-induced gap region to
quantify the learning performance trade-off among different FL tasks, based on
which we formulate an optimization problem to minimize the sum of error-induced
gaps in all cells. To tackle the coupling between the downlink and uplink
transmissions as well as the coupling among multiple cells, we propose a
cooperative multi-cell FL optimization framework to achieve efficient
interference management for downlink and uplink transmission design. Results
demonstrate that our proposed algorithm achieves much better average learning
performance over multiple cells than non-cooperative baseline schemes.

In today's world of health care, brain tumor (BT) detection has become a
common occurrence. However, the manual BT classification approach is
time-consuming and only available at a few diagnostic centres. So Deep
Convolutional Neural Network (DCNN) is introduced in the medical field for
making accurate diagnoses and aiding in the patient's treatment before surgery.
But these networks have problems such as overfitting and being unable to
extract necessary features for classification. To overcome these problems, we
developed the TDA-IPH and Convolutional Transfer learning and Visual Recurrent
learning with Elephant Herding Optimization hyper-parameter tuning (CTVR-EHO)
models for BT segmentation and classification. Initially, the Topological Data
Analysis based Improved Persistent Homology (TDA-IPH) is designed to segment
the BT image. Then, from the segmented image, features are extracted
simultaneously using TL via the AlexNet model and Bidirectional Visual Long
Short Term Memory (Bi-VLSTM). Elephant Herding Optimization (EHO) is used to
tune the hyper parameters of both networks to get an optimal result. Finally,
extracted features are concatenated and classified using the softmax activation
layer. The simulation result of this proposed CTVR-EHO and TDA-IPH method is
analysed based on some metrics such as precision, accuracy, recall, loss, and F
score. When compared to other existing BT segmentation and classification
models, the proposed CTVR-EHO and TDA-IPH approaches show high accuracy
(99.8%), high recall (99.23%), high precision (99.67%), and high F score
(99.59%).

Mach cones are expected to form in the expanding quark-gluon plasma (QGP)
when energetic quarks and gluons (called jets) traverse the hot medium at a
velocity faster than the speed of sound in high-energy heavy-ion collisions.
The shape of the Mach cone and the associated diffusion wake are sensitive to
the initial jet production location and the jet propagation direction relative
to the radial flow because of the distortion by the collective expansion of the
QGP and large density gradient. The shape of jet-induced Mach cones and their
distortions in heavy-ion collisions provide a unique and direct probe of the
dynamical evolution and the equation of state of QGP. However, it is difficult
to identify the Mach cone and the diffusion wake in current experimental
measurements of final hadron distributions because they are averaged over all
possible initial jet production locations and propagation directions. To
overcome this difficulty, we develop a deep learning assisted jet tomography
which uses the full information of the final hadrons from jets to localize the
initial jet production positions. This method can help to constrain the initial
regions of jet production in heavy-ion collisions and enable a differential
study of Mach-cones with different jet path length and orientation relative to
the radial flow of the QGP in heavy-ion collisions.

Morphology of mitochondria plays critical roles in mediating their
physiological functions. Accurate segmentation of mitochondria from 3D electron
microscopy (EM) images is essential to quantitative characterization of their
morphology at the nanometer scale. Fully supervised deep learning models
developed for this task achieve excellent performance but require substantial
amounts of annotated data for training. However, manual annotation of EM images
is laborious and time-consuming because of their large volumes, limited
contrast, and low signal-to-noise ratios (SNRs). To overcome this challenge, we
propose a semi-supervised deep learning model that segments mitochondria by
leveraging the spatial continuity of their structural, morphological, and
contextual information in both labeled and unlabeled images. We use random
piecewise affine transformation to synthesize comprehensive and realistic
mitochondrial morphology for augmentation of training data. Experiments on the
EPFL dataset show that our model achieves performance similar as that of
state-of-the-art fully supervised models but requires only ~20% of their
annotated training data. Our semi-supervised model is versatile and can also
accurately segment other spatially continuous structures from EM images. Data
and code of this study are openly accessible at
https://github.com/cbmi-group/MPP.

With the ever increasing complexity of specifications, manual sizing for
analog circuits recently became very challenging. Especially for innovative,
large-scale circuits designs, with tens of design variables, operating
conditions and conflicting objectives to be optimized, design engineers spend
many weeks, running time-consuming simulations, in their attempt at finding the
right configuration. Recent years brought machine learning and optimization
techniques to the field of analog circuits design, with evolutionary algorithms
and Bayesian models showing good results for circuit sizing. In this context,
we introduce a design optimization method based on Generalized Differential
Evolution 3 (GDE3) and Gaussian Processes (GPs). The proposed method is able to
perform sizing for complex circuits with a large number of design variables and
many conflicting objectives to be optimized. While state-of-the-art methods
reduce multi-objective problems to single-objective optimization and
potentially induce a prior bias, we search directly over the multi-objective
space using Pareto dominance and ensure that diverse solutions are provided to
the designers to choose from. To the best of our knowledge, the proposed method
is the first to specifically address the diversity of the solutions, while also
focusing on minimizing the number of simulations required to reach feasible
configurations. We evaluate the introduced method on two voltage regulators
showing different levels of complexity and we highlight that the proposed
innovative candidate selection method and survival policy leads to obtaining
feasible solutions, with a high degree of diversity, much faster than with GDE3
or Bayesian Optimization-based algorithms.

While a growing body of literature has been studying new Graph Neural
Networks (GNNs) that work on both homophilic and heterophilic graphs, little
work has been done on adapting classical GNNs to less-homophilic graphs.
Although lacking the ability to work with less-homophilic graphs, classical
GNNs still stand out in some properties such as efficiency, simplicity and
explainability. We propose a novel graph restructuring method to maximize the
benefit of prevalent GNNs with the homophilic assumption. Our contribution is
threefold: a) learning the weight of pseudo-eigenvectors for an adaptive
spectral clustering that aligns well with known node labels, b) proposing a new
homophilic metric that measures how two nodes with the same label are likely to
be connected, and c) reconstructing the adjacency matrix based on the result of
adaptive spectral clustering to maximize the homophilic scores. The
experimental results show that our graph restructuring method can significantly
boost the performance of six classical GNNs by an average of 25% on
less-homophilic graphs. The boosted performance is comparable to

In this work, we propose an efficient minimax optimal global optimization
algorithm for multivariate Lipschitz continuous functions. To evaluate the
performance of our approach, we utilize the average regret instead of the
traditional simple regret, which, as we show, is not suitable for use in the
multivariate non-convex optimization because of the inherent hardness of the
problem itself. Since we study the average regret of the algorithm, our results
directly imply a bound for the simple regret as well. Instead of constructing
lower bounding proxy functions, our method utilizes a predetermined query
creation rule, which makes it computationally superior to the Piyavskii-Shubert
variants. We show that our algorithm achieves an average regret bound of
$O(L\sqrt{n}T^{-\frac{1}{n}})$ for the optimization of an $n$-dimensional
$L$-Lipschitz continuous objective in a time horizon $T$, which we show to be
minimax optimal.

Model-based reinforcement learning promises to learn an optimal policy from
fewer interactions with the environment compared to model-free reinforcement
learning by learning an intermediate model of the environment in order to
predict future interactions. When predicting a sequence of interactions, the
rollout length, which limits the prediction horizon, is a critical
hyperparameter as accuracy of the predictions diminishes in the regions that
are further away from real experience. As a result, with a longer rollout
length, an overall worse policy is learned in the long run. Thus, the
hyperparameter provides a trade-off between quality and efficiency. In this
work, we frame the problem of tuning the rollout length as a meta-level
sequential decision-making problem that optimizes the final policy learned by
model-based reinforcement learning given a fixed budget of environment
interactions by adapting the hyperparameter dynamically based on feedback from
the learning process, such as accuracy of the model and the remaining budget of
interactions. We use model-free deep reinforcement learning to solve the
meta-level decision problem and demonstrate that our approach outperforms
common heuristic baselines on two well-known reinforcement learning

A well-known limitation of existing works on molecule generation is that the
generated molecules highly resemble those in the training set. To generate
truly novel molecules with completely different structures that may have even
better properties than known molecules for de novo drug discovery, more
powerful exploration in the chemical space is necessary. To this end, we
propose Molecular Out-Of-distribution Diffusion (MOOD), a novel score-based
diffusion scheme that incorporates out-of-distribution (OOD) control in the
generative stochastic differential equation (SDE) with simple control of a
hyperparameter, thus requires no additional computational costs unlike existing
methods (e.g., RL-based methods). However, some novel molecules may be
chemically implausible, or may not meet the basic requirements of real-world
drugs. Thus, MOOD performs conditional generation by utilizing the gradients
from a property prediction network that guides the reverse-time diffusion to
high-scoring regions according to multiple target properties such as
protein-ligand interactions, drug-likeness, and synthesizability. This allows
MOOD to search for novel and meaningful molecules rather than generating unseen
yet trivial ones. We experimentally validate that MOOD is able to explore the
chemical space beyond the training distribution, generating molecules that
outscore ones found with existing methods, and even the top 0.01% of the
original training pool.

Previous methods on multimodal groupwise registration typically require
certain highly specialized similarity metrics with restrained applicability. In
this work, we instead propose a general framework which formulates groupwise
registration as a procedure of hierarchical Bayesian inference. Here, the
imaging process of multimodal medical images, including shape transition and
appearance variation, is characterized by a disentangled variational
auto-encoder. To this end, we propose a novel variational posterior and network
architecture that facilitate joint learning of the common structural
representation and the desired spatial correspondences. The performance of the
proposed model was validated on two publicly available multimodal datasets,
i.e., BrainWeb and MS-CMR of the heart. Results have demonstrated the efficacy
of our framework in realizing multimodal groupwise registration in an
end-to-end fashion.

The shape of a weld bead is critical in assessing the quality of the welded
joint. In particular, this has a major impact in the accuracy of the results
obtained from a numerical analysis. This study focuses on the statistical
design techniques and the artificial neural networks, to predict the weld bead
shape parameters of shielded Gas Metal Arc Welded (GMAW) fillet joints.
Extensive testing was carried out on low carbon mild steel plates of
thicknesses ranging from 3mm to 10mm. Welding voltage, welding current, and
moving heat source speed were considered as the welding parameters. Three types
of multiple linear regression models (MLR) were created to establish an
empirical equation for defining GMAW bead shape parameters considering
interactive and higher order terms. Additionally, artificial neural network
(ANN) models were created based on similar scheme, and the relevance of
specific features was investigated using SHapley Additive exPlanations (SHAP).
The results reveal that MLR-based approach performs better than the ANN based
models in terms of predictability and error assessment. This study shows the
usefulness of the predictive tools to aid numerical analysis of welding.

In this paper we introduce CorticalFlow, a new geometric deep-learning model
that, given a 3-dimensional image, learns to deform a reference template
towards a targeted object. To conserve the template mesh's topological
properties, we train our model over a set of diffeomorphic transformations.
This new implementation of a flow Ordinary Differential Equation (ODE)
framework benefits from a small GPU memory footprint, allowing the generation
of surfaces with several hundred thousand vertices. To reduce topological
errors introduced by its discrete resolution, we derive numeric conditions
which improve the manifoldness of the predicted triangle mesh. To exhibit the
utility of CorticalFlow, we demonstrate its performance for the challenging
task of brain cortical surface reconstruction. In contrast to current
state-of-the-art, CorticalFlow produces superior surfaces while reducing the
computation time from nine and a half minutes to one second. More
significantly, CorticalFlow enforces the generation of anatomically plausible
surfaces; the absence of which has been a major impediment restricting the
clinical relevance of such surface reconstruction methods.

We consider experiments in dynamical systems where interventions on some
experimental units impact other units through a limiting constraint (such as a
limited inventory). Despite outsize practical importance, the best estimators
for this `Markovian' interference problem are largely heuristic in nature, and
their bias is not well understood. We formalize the problem of inference in
such experiments as one of policy evaluation. Off-policy estimators, while
unbiased, apparently incur a large penalty in variance relative to
state-of-the-art heuristics. We introduce an on-policy estimator: the
Differences-In-Q's (DQ) estimator. We show that the DQ estimator can in general
have exponentially smaller variance than off-policy evaluation. At the same
time, its bias is second order in the impact of the intervention. This yields a
striking bias-variance tradeoff so that the DQ estimator effectively dominates
state-of-the-art alternatives. From a theoretical perspective, we introduce
three separate novel techniques that are of independent interest in the theory
of Reinforcement Learning (RL). Our empirical evaluation includes a set of
experiments on a city-scale ride-hailing simulator.

While large-scale neural language models, such as GPT2 and BART, have
achieved impressive results on various text generation tasks, they tend to get
stuck in undesirable sentence-level loops with maximization-based decoding
algorithms (\textit{e.g.}, greedy search). This phenomenon is counter-intuitive
since there are few consecutive sentence-level repetitions in human corpora
(e.g., 0.02\% in Wikitext-103). To investigate the underlying reasons for
generating consecutive sentence-level repetitions, we study the relationship
between the probabilities of the repetitive tokens and their previous
repetitions in the context. Through our quantitative experiments, we find that
1) Language models have a preference to repeat the previous sentence; 2) The
sentence-level repetitions have a \textit{self-reinforcement effect}: the more
times a sentence is repeated in the context, the higher the probability of
continuing to generate that sentence; 3) The sentences with higher initial
probabilities usually have a stronger self-reinforcement effect. Motivated by
our findings, we propose a simple and effective training method \textbf{DITTO}
(Pseu\underline{D}o-Repet\underline{IT}ion
Penaliza\underline{T}i\underline{O}n), where the model learns to penalize
probabilities of sentence-level repetitions from pseudo repetitive data.
Although our method is motivated by mitigating repetitions, experiments show
that DITTO not only mitigates the repetition issue without sacrificing
perplexity, but also achieves better generation quality. Extensive experiments
on open-ended text generation (Wikitext-103) and text summarization
(CNN/DailyMail) demonstrate the generality and effectiveness of our method.

We introduce Bi-SimCut: a simple but effective training strategy to boost
neural machine translation (NMT) performance. It consists of two procedures:
bidirectional pretraining and unidirectional finetuning. Both procedures
utilize SimCut, a simple regularization method that forces the consistency
between the output distributions of the original and the cutoff sentence pairs.
Without leveraging extra dataset via back-translation or integrating
large-scale pretrained model, Bi-SimCut achieves strong translation performance
across five translation benchmarks (data sizes range from 160K to 20.2M): BLEU
scores of 31.16 for en -> de and 38.37 for de -> en on the IWSLT14 dataset,
30.78 for en -> de and 35.15 for de -> en on the WMT14 dataset, and 27.17 for
zh -> en on the WMT17 dataset. SimCut is not a new method, but a version of
Cutoff (Shen et al., 2020) simplified and adapted for NMT, and it could be
considered as a perturbation-based method. Given the universality and
simplicity of SimCut and Bi-SimCut, we believe they can serve as strong
baselines for future NMT research.

Document classification is the detection specific content of interest in text
documents. In contrast to the data-driven machine learning classifiers,
knowledge-based classifiers can be constructed based on domain specific
knowledge, which usually takes the form of a collection of subject related
keywords. While typical knowledge-based classifiers compute a prediction score
based on the keyword abundance, it generally suffers from noisy detections due
to the lack of guiding principle in gauging the keyword matches. In this paper,
we propose a novel knowledge-based model equipped with Shannon Entropy, which
measures the richness of information and favors uniform and diverse keyword
matches. Without invoking any positive sample, such method provides a simple
and explainable solution for document classification. We show that the Shannon
Entropy significantly improves the recall at fixed level of false positive
rate. Also, we show that the model is more robust against change of data
distribution at inference while compared with traditional machine learning,
particularly when the positive training samples are very limited.

Deep learning techniques, particularly convolutional neural networks, have
shown great potential in computer vision and medical imaging applications.
However, deep learning models are computationally demanding as they require
enormous computational power and specialized processing hardware for model
training. To make these models portable and compatible for prototyping, their
implementation on low-power devices is imperative. In this work, we present the
implementation of Modified U-Net on Intel Movidius Neural Compute Stick 2
(NCS-2) for the segmentation of medical images. We selected U-Net because, in
medical image segmentation, U-Net is a prominent model that provides improved
performance for medical image segmentation even if the dataset size is small.
The modified U-Net model is evaluated for performance in terms of dice score.
Experiments are reported for segmentation task on three medical imaging
datasets: BraTs dataset of brain MRI, heart MRI dataset, and Ziehl-Neelsen
sputum smear microscopy image (ZNSDB) dataset. For the proposed model, we
reduced the number of parameters from 30 million in the U-Net model to 0.49
million in the proposed architecture. Experimental results show that the
modified U-Net provides comparable performance while requiring significantly
lower resources and provides inference on the NCS-2. The maximum dice scores
recorded are 0.96 for the BraTs dataset, 0.94 for the heart MRI dataset, and
0.74 for the ZNSDB dataset.

Recently, Self-Supervised Representation Learning (SSRL) has attracted much
attention in the field of computer vision, speech, natural language processing
(NLP), and recently, with other types of modalities, including time series from
sensors. The popularity of self-supervised learning is driven by the fact that
traditional models typically require a huge amount of well-annotated data for
training. Acquiring annotated data can be a difficult and costly process.
Self-supervised methods have been introduced to improve the efficiency of
training data through discriminative pre-training of models using supervisory
signals that have been freely obtained from the raw data. Unlike existing
reviews of SSRL that have pre-dominately focused upon methods in the fields of
CV or NLP for a single modality, we aim to provide the first comprehensive
review of multimodal self-supervised learning methods for temporal data. To
this end, we 1) provide a comprehensive categorization of existing SSRL
methods, 2) introduce a generic pipeline by defining the key components of a
SSRL framework, 3) compare existing models in terms of their objective
function, network architecture and potential applications, and 4) review
existing multimodal techniques in each category and various modalities.
Finally, we present existing weaknesses and future opportunities. We believe
our work develops a perspective on the requirements of SSRL in domains that
utilise multimodal and/or temporal data

Algorithmic fairness plays an important role in machine learning and imposing
fairness constraints during learning is a common approach. However, many
datasets are imbalanced in certain label classes (e.g. "healthy") and sensitive
subgroups (e.g. "older patients"). Empirically, this imbalance leads to a lack
of generalizability not only of classification, but also of fairness
properties, especially in over-parameterized models. For example,
fairness-aware training may ensure equalized odds (EO) on the training data,
but EO is far from being satisfied on new users. In this paper, we propose a
theoretically-principled, yet Flexible approach that is
Imbalance-Fairness-Aware (FIFA). Specifically, FIFA encourages both
classification and fairness generalization and can be flexibly combined with
many existing fair learning methods with logits-based losses. While our main
focus is on EO, FIFA can be directly applied to achieve equalized opportunity
(EqOpt); and under certain conditions, it can also be applied to other fairness
notions. We demonstrate the power of FIFA by combining it with a popular fair
classification algorithm, and the resulting algorithm achieves significantly
better fairness generalization on several real-world datasets.

Video Question Answering (VideoQA) is the task of answering questions about a
video. At its core is understanding the alignments between visual scenes in
video and linguistic semantics in question to yield the answer. In leading
VideoQA models, the typical learning objective, empirical risk minimization
(ERM), latches on superficial correlations between video-question pairs and
answers as the alignments. However, ERM can be problematic, because it tends to
over-exploit the spurious correlations between question-irrelevant scenes and
answers, instead of inspecting the causal effect of question-critical scenes.
As a result, the VideoQA models suffer from unreliable reasoning. In this work,
we first take a causal look at VideoQA and argue that invariant grounding is
the key to ruling out the spurious correlations. Towards this end, we propose a
new learning framework, Invariant Grounding for VideoQA (IGV), to ground the
question-critical scene, whose causal relations with answers are invariant
across different interventions on the complement. With IGV, the VideoQA models
are forced to shield the answering process from the negative influence of
spurious correlations, which significantly improves the reasoning ability.
Experiments on three benchmark datasets validate the superiority of IGV in
terms of accuracy, visual explainability, and generalization ability over the
leading baselines.

We consider 1-dimensional location estimation, where we estimate a parameter
$\lambda$ from $n$ samples $\lambda + \eta_i$, with each $\eta_i$ drawn i.i.d.
from a known distribution $f$. For fixed $f$ the maximum-likelihood estimate
(MLE) is well-known to be optimal in the limit as $n \to \infty$: it is
asymptotically normal with variance matching the Cram\'er-Rao lower bound of
$\frac{1}{n\mathcal{I}}$, where $\mathcal{I}$ is the Fisher information of $f$.
However, this bound does not hold for finite $n$, or when $f$ varies with $n$.
We show for arbitrary $f$ and $n$ that one can recover a similar theory based
on the Fisher information of a smoothed version of $f$, where the smoothing
radius decays with $n$.

We study sequential decision making problems aimed at maximizing the expected
total reward while satisfying a constraint on the expected total utility. We
employ the natural policy gradient method to solve the discounted
infinite-horizon optimal control problem for Constrained Markov Decision
Processes (constrained MDPs). Specifically, we propose a new Natural Policy
Gradient Primal-Dual (NPG-PD) method that updates the primal variable via
natural policy gradient ascent and the dual variable via projected sub-gradient
descent. Although the underlying maximization involves a nonconcave objective
function and a nonconvex constraint set, under the softmax policy
parametrization we prove that our method achieves global convergence with
sublinear rates regarding both the optimality gap and the constraint violation.
Such convergence is independent of the size of the state-action space, i.e., it
is~dimension-free. Furthermore, for log-linear and general smooth policy
parametrizations, we establish sublinear convergence rates up to a function
approximation error caused by restricted policy parametrization. We also
provide convergence and finite-sample complexity guarantees for two
sample-based NPG-PD algorithms. Finally, we use computational experiments to
showcase the merits and the effectiveness of our approach.

Deep neural networks are known to be vulnerable to unseen data: they may
wrongly assign high confidence stcores to out-distribuion samples. Recent works
try to solve the problem using representation learning methods and specific
metrics. In this paper, we propose a simple, yet effective post-hoc anomaly
detection algorithm named Test Time Augmentation Anomaly Detection (TTA-AD),
inspired by a novel observation. Specifically, we observe that in-distribution
data enjoy more consistent predictions for its original and augmented versions
on a trained network than out-distribution data, which separates
in-distribution and out-distribution samples. Experiments on various
high-resolution image benchmark datasets demonstrate that TTA-AD achieves
comparable or better detection performance under dataset-vs-dataset anomaly
detection settings with a 60%~90\% running time reduction of existing
classifier-based algorithms. We provide empirical verification that the key to
TTA-AD lies in the remaining classes between augmented features, which has long
been partially ignored by previous works. Additionally, we use RUNS as a
surrogate to analyze our algorithm theoretically.

In label-noise learning, estimating the transition matrix has attracted more
and more attention as the matrix plays an important role in building
statistically consistent classifiers. However, it is very challenging to
estimate the transition matrix T(x), where x denotes the instance, because it
is unidentifiable under the instance-dependent noise(IDN). To address this
problem, we have noticed that, there are psychological and physiological
evidences showing that we humans are more likely to annotate instances of
similar appearances to the same classes, and thus poor-quality or ambiguous
instances of similar appearances are easier to be mislabeled to the correlated
or same noisy classes. Therefore, we propose assumption on the geometry of T(x)
that "the closer two instances are, the more similar their corresponding
transition matrices should be". More specifically, we formulate above
assumption into the manifold embedding, to effectively reduce the degree of
freedom of T(x) and make it stably estimable in practice. The proposed
manifold-regularized technique works by directly reducing the estimation error
without hurting the approximation error about the estimation problem of T(x).
Experimental evaluations on four synthetic and two real-world datasets
demonstrate that our method is superior to state-of-the-art approaches for
label-noise learning under the challenging IDN.

We study the problem of online learning in competitive settings in the
context of two-sided matching markets. In particular, one side of the market,
the agents, must learn about their preferences over the other side, the firms,
through repeated interaction while competing with other agents for successful
matches. We propose a class of decentralized, communication- and
coordination-free algorithms that agents can use to reach to their stable match
in structured matching markets. In contrast to prior works, the proposed
algorithms make decisions based solely on an agent's own history of play and
requires no foreknowledge of the firms' preferences. Our algorithms are
constructed by splitting up the statistical problem of learning one's
preferences, from noisy observations, from the problem of competing for firms.
We show that under realistic structural assumptions on the underlying
preferences of the agents and firms, the proposed algorithms incur a regret
which grows at most logarithmically in the time horizon. Our results show that,
in the case of matching markets, competition need not drastically affect the
performance of decentralized, communication and coordination free online

The extraction of text information in videos serves as a critical step
towards semantic understanding of videos. It usually involved in two steps: (1)
text recognition and (2) text classification. To localize texts in videos, we
can resort to large numbers of text recognition methods based on OCR
technology. However, to our knowledge, there is no existing work focused on the
second step of video text classification, which will limit the guidance to
downstream tasks such as video indexing and browsing. In this paper, we are the
first to address this new task of video text classification by fusing
multimodal information to deal with the challenging scenario where different
types of video texts may be confused with various colors, unknown fonts and
complex layouts. In addition, we tailor a specific module called CorrelationNet
to reinforce feature representation by explicitly extracting layout
information. Furthermore, contrastive learning is utilized to explore inherent
connections between samples using plentiful unlabeled videos. Finally, we
construct a new well-defined industrial dataset from the news domain, called
TI-News, which is dedicated to building and evaluating video text recognition
and classification applications. Extensive experiments on TI-News demonstrate
the effectiveness of our method.

In this paper, we show that counterfactual explanations of confidence scores
help users better understand and better trust an AI model's prediction in
human-subject studies. Showing confidence scores in human-agent interaction
systems can help build trust between humans and AI systems. However, most
existing research only used the confidence score as a form of communication,
and we still lack ways to explain why the algorithm is confident. This paper
also presents two methods for understanding model confidence using
counterfactual explanation: (1) based on counterfactual examples; and (2) based
on visualisation of the counterfactual space.

Stereo matching of high-resolution satellite images (HRSI) is still a
fundamental but challenging task in the field of photogrammetry and remote
sensing. Recently, deep learning (DL) methods, especially convolutional neural
networks (CNNs), have demonstrated tremendous potential for stereo matching on
public benchmark datasets. However, datasets for stereo matching of satellite
images are scarce. To facilitate further research, this paper creates and
publishes a challenging dataset, termed WHU-Stereo, for stereo matching DL
network training and testing. This dataset is created by using airborne LiDAR
point clouds and high-resolution stereo imageries taken from the Chinese
GaoFen-7 satellite (GF-7). The WHU-Stereo dataset contains more than 1700
epipolar rectified image pairs, which cover six areas in China and includes
various kinds of landscapes. We have assessed the accuracy of ground-truth
disparity maps, and it is proved that our dataset achieves comparable precision
compared with existing state-of-the-art stereo matching datasets. To verify its
feasibility, in experiments, the hand-crafted SGM stereo matching algorithm and
recent deep learning networks have been tested on the WHU-Stereo dataset.
Experimental results show that deep learning networks can be well trained and
achieves higher performance than hand-crafted SGM algorithm, and the dataset
has great potential in remote sensing application. The WHU-Stereo dataset can
serve as a challenging benchmark for stereo matching of high-resolution
satellite images, and performance evaluation of deep learning models. Our
dataset is available at https://github.com/Sheng029/WHU-Stereo

Differentiable physics enables efficient gradient-based optimizations of
neural network (NN) controllers. However, existing work typically only delivers
NN controllers with limited capability and generalizability. We present a
practical learning framework that outputs unified NN controllers capable of
tasks with significantly improved complexity and diversity. To systematically
improve training robustness and efficiency, we investigated a suite of
improvements over the baseline approach, including periodic activation
functions, and tailored loss functions. In addition, we find our adoption of
batching and an Adam optimizer effective in training complex locomotion tasks.
We evaluate our framework on differentiable mass-spring and material point
method (MPM) simulations, with challenging locomotion tasks and multiple robot
designs. Experiments show that our learning framework, based on differentiable
physics, delivers better results than reinforcement learning and converges much
faster. We demonstrate that users can interactively control soft robot
locomotion and switch among multiple goals with specified velocity, height, and
direction instructions using a unified NN controller trained in our system.

Extracting low-dimensional summary statistics from large datasets is
essential for efficient (likelihood-free) inference. We propose obtaining
summary statistics by minimizing the expected posterior entropy (EPE) under the
prior predictive distribution of the model. We show that minimizing the EPE is
equivalent to learning a conditional density estimator for the posterior as
well as other information-theoretic approaches. Further summary extraction
methods (including minimizing the $L^2$ Bayes risk, maximizing the Fisher
information, and model selection approaches) are special or limiting cases of
EPE minimization. We demonstrate that the approach yields high fidelity summary
statistics by applying it to both a synthetic benchmark as well as a population
genetics problem. We not only offer concrete recommendations for practitioners
but also provide a unifying perspective for obtaining informative summary

This paper presents a language-powered paradigm for ordinal regression.
Existing methods usually treat each rank as a category and employ a set of
weights to learn these concepts. These methods are easy to overfit and usually
attain unsatisfactory performance as the learned concepts are mainly derived
from the training set. Recent large pre-trained vision-language models like
CLIP have shown impressive performance on various visual tasks. In this paper,
we propose to learn the rank concepts from the rich semantic CLIP latent space.
Specifically, we reformulate this task as an image-language matching problem
with a contrastive objective, which regards labels as text and obtains a
language prototype from a text encoder for each rank. While prompt engineering
for CLIP is extremely time-consuming, we propose OrdinalCLIP, a differentiable
prompting method for adapting CLIP for ordinal regression. OrdinalCLIP consists
of learnable context tokens and learnable rank embeddings; The learnable rank
embeddings are constructed by explicitly modeling numerical continuity,
resulting in well-ordered, compact language prototypes in the CLIP space. Once
learned, we can only save the language prototypes and discard the huge language
model, resulting in zero additional computational overhead compared with the
linear head counterpart. Experimental results show that our paradigm achieves
competitive performance in general ordinal regression tasks, and gains
improvements in few-shot and distribution shift settings for age estimation.
The code is available at https://github.com/xk-huang/OrdinalCLIP.

Abnormalities of the sewage pipe network will affect the normal operation of
the whole city. Therefore, it is important to detect the abnormalities early.
This paper propose an early abnormal-detection method. The abnormalities are
detected by using the conventional algorithms, such as isolation forest
algorithm, two innovations are given: (1) The current and historical data
measured by the sensors placed in the sewage pipe network (such as ultrasonic
Doppler flowmeter) are taken as the overall dataset, and then the general
dataset is detected by using the conventional anomaly detection method to
diagnose the anomaly of the data. The anomaly refers to the sample different
from the others samples in the whole dataset. Because the definition of anomaly
is not through the algorithm, but the whole dataset, the construction of the
whole dataset is the key to propose the early abnormal-detection algorithms.
(2) A bagging strategy for a variety of conventional anomaly detection
algorithms is proposed to achieve the early detection of anomalies with the
high precision and recall. The results show that this method can achieve the
early anomaly detection with the highest precision of 98.21%, the recall rate
63.58% and F1-score of 0.774.

Large language models such as GPT-3 and PaLM have shown remarkable
performance in few-shot learning. However, they still struggle with reasoning
tasks such as the arithmetic benchmark GSM8K. Recent advances deliberately
guide the language model to generate a chain of reasoning steps before
producing the final answer, successfully boosting the GSM8K benchmark from
17.9% to 58.1% in terms of problem solving rate. In this paper, we propose a
new approach, DiVeRSe (Diverse Verifier on Reasoning Step), to further advance
their reasoning capability. DiVeRSe first explores different prompts to enhance
the diversity in reasoning paths. Second, DiVeRSe introduces a verifier to
distinguish good answers from bad answers for a better weighted voting.
Finally, DiVeRSe verifies the correctness of each single step rather than all
the steps in a whole. We conduct extensive experiments using the latest
language model code-davinci-002 and demonstrate that DiVeRSe can achieve new
state-of-the-art performance on six out of eight reasoning benchmarks (e.g.,
GSM8K 74.4% to 83.2%), outperforming the PaLM model with 540B parameters.

Existing unsupervised hash learning is a kind of attribute-centered
calculation. It may not accurately preserve the similarity between data. This
leads to low down the performance of hash function learning. In this paper, a
hash algorithm is proposed with a hyper-class representation. It is a two-steps
approach. The first step finds potential decision features and establish
hyper-class. The second step constructs hash learning based on the hyper-class
information in the first step, so that the hash codes of the data within the
hyper-class are as similar as possible, as well as the hash codes of the data
between the hyper-classes are as different as possible. To evaluate the
efficiency, a series of experiments are conducted on four public datasets. The
experimental results show that the proposed hash algorithm is more efficient
than the compared algorithms, in terms of mean average precision (MAP), average
precision (AP) and Hamming radius 2 (HAM2)

This article describes Jigsaw, a convolutional neural network (CNN) used in
geosciences and based on Inception but tailored for geoscientific analyses.
Introduces JigsawHSI (based on Jigsaw) and uses it on the land-use land-cover
(LULC) classification problem with the Indian Pines, Pavia University and
Salinas hyperspectral image data sets. The network is compared against
HybridSN, a spectral-spatial 3D-CNN followed by 2D-CNN that achieves
state-of-the-art results on the datasets. This short article proves that
JigsawHSI is able to meet or exceed HybridSN's performance in all three cases.
Additionally, the use of jigsaw in geosciences is highlighted, while the code
and toolkit are made available.

Past research on interactive decision making problems (bandits, reinforcement
learning, etc.) mostly focuses on the minimax regret that measures the
algorithm's performance on the hardest instance. However, an ideal algorithm
should adapt to the complexity of a particular problem instance and incur
smaller regrets on easy instances than worst-case instances. In this paper, we
design the first asymptotic instance-optimal algorithm for general interactive
decision making problems with finite number of decisions under mild conditions.
On \textit{every} instance $f$, our algorithm outperforms \emph{all} consistent
algorithms (those achieving non-trivial regrets on all instances), and has
asymptotic regret $\mathcal{C}(f) \ln n$, where $\mathcal{C}(f)$ is an exact
characterization of the complexity of $f$. The key step of the algorithm
involves hypothesis testing with active data collection. It computes the most
economical decisions with which the algorithm collects observations to test
whether an estimated instance is indeed correct; thus, the complexity
$\mathcal{C}(f)$ is the minimum cost to test the instance $f$ against other
instances. Our results, instantiated on concrete problems, recover the
classical gap-dependent bounds for multi-armed bandits [Lai and Robbins, 1985]
and prior works on linear bandits [Lattimore and Szepesvari, 2017], and improve
upon the previous best instance-dependent upper bound [Xu et al., 2021] for

Deep Neural Networks (DNNs) have achieved great success in a variety of
machine learning (ML) applications, delivering high-quality inferencing
solutions in computer vision, natural language processing, and virtual reality,
etc. However, DNN-based ML applications also bring much increased computational
and storage requirements, which are particularly challenging for embedded
systems with limited compute/storage resources, tight power budgets, and small
form factors. Challenges also come from the diverse application-specific
requirements, including real-time responses, high-throughput performance, and
reliable inference accuracy. To address these challenges, we introduce a series
of effective design methodologies, including efficient ML model designs,
customized hardware accelerator designs, and hardware/software co-design
strategies to enable efficient ML applications on embedded systems.

Recently, sequential recommendation has emerged as a widely studied topic.
Existing researches mainly design effective neural architectures to model user
behavior sequences based on item IDs. However, this kind of approach highly
relies on user-item interaction data and neglects the attribute- or
characteristic-level correlations among similar items preferred by a user. In
light of these issues, we propose IDA-SR, which stands for ID-Agnostic User
Behavior Pre-training approach for Sequential Recommendation. Instead of
explicitly learning representations for item IDs, IDA-SR directly learns item
representations from rich text information. To bridge the gap between text
semantics and sequential user behaviors, we utilize the pre-trained language
model as text encoder, and conduct a pre-training architecture on the
sequential user behaviors. In this way, item text can be directly utilized for
sequential recommendation without relying on item IDs. Extensive experiments
show that the proposed approach can achieve comparable results when only using
ID-agnostic item representations, and performs better than baselines by a large
margin when fine-tuned with ID information.

Contrastive learning has shown great promise over annotation scarcity
problems in the context of medical image segmentation. Existing approaches
typically assume a balanced class distribution for both labeled and unlabeled
medical images. However, medical image data in reality is commonly imbalanced
(i.e., multi-class label imbalance), which naturally yields blurry contours and
usually incorrectly labels rare objects. Moreover, it remains unclear whether
all negative samples are equally negative. In this work, we present ACTION, an
Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised
medical image segmentation. Specifically, we first develop an iterative
contrastive distillation algorithm by softly labeling the negatives rather than
binary supervision between positive and negative pairs. We also capture more
semantically similar features from the randomly chosen negative set compared to
the positives to enforce the diversity of the sampled data. Second, we raise a
more important question: Can we really handle imbalanced samples to yield
better performance? Hence, the key innovation in ACTION is to learn global
semantic relationship across the entire dataset and local anatomical features
among the neighbouring pixels with minimal additional memory footprint. During
the training, we introduce anatomical contrast by actively sampling a sparse
set of hard negative pixels, which can generate smoother segmentation
boundaries and more accurate predictions. Extensive experiments across two
benchmark datasets and different unlabeled settings show that ACTION performs
comparable or better than the current state-of-the-art supervised and
semi-supervised methods. Our code and models will be publicly available.

What distinguishes authentic memes from those created by state actors? I
utilize a self-supervised vision model, DeepCluster (Caron et al. 2019), to
learn low-dimensional visual embeddings of memes and apply K-means to jointly
cluster authentic and coordinated memes without additional inputs. I find that
authentic and coordinated memes share a large fraction of visual themes but
with varying degrees. Coordinated memes from Russian IRA accounts promote more
themes around celebrities, quotes, screenshots, military, and gender. Authentic
Reddit memes include more themes with comics and movie characters. A simple
logistic regression on the low-dimensional embeddings can discern IRA memes
from Reddit memes with an out-sample testing accuracy of 0.84.

We develop a hybrid model-based data-driven seizure detection algorithm
called Mutual Information-based CNNAided Learned factor graphs (MICAL) for
detection of eclectic seizures from EEG signals. Our proposed method contains
three main components: a neural mutual information (MI) estimator, 1D
convolutional neural network (CNN), and factor graph inference. Since during
seizure the electrical activity in one or more regions in the brain becomes
correlated, we use neural MI estimators to measure inter-channel statistical
dependence. We also design a 1D CNN to extract additional features from raw EEG
signals. Since the soft estimates obtained as the combined features from the
neural MI estimator and the CNN do not capture the temporal correlation between
different EEG blocks, we use them not as estimates of the seizure state, but to
compute the function nodes of a factor graph. The resulting factor graphs
allows structured inference which exploits the temporal correlation for further
improving the detection performance. On public CHB-MIT database, We conduct
three evaluation approaches using the public CHB-MIT database, including 6-fold
leave-four-patients-out cross-validation, all patient training; and per patient
training. Our evaluations systematically demonstrate the impact of each element
in MICAL through a complete ablation study and measuring six performance
metrics. It is shown that the proposed method obtains state-of-the-art
performance specifically in 6-fold leave-four-patients-out cross-validation and
all patient training, demonstrating a superior generalizability.

Ethanol is a molecule of fundamental interest in combustion, astrochemistry,
and condensed phase as a solvent. It is characterized by two methyl rotors and
$trans$ ($anti$) and $gauche$ conformers, which are known to be very close in
energy. Here we show that based on rigorous quantum calculations of the
vibrational zero-point state, using a new ab initio potential energy surface
(PES), the ground state resembles the $trans$ conformer but substantial
delocalization to the $gauche$ conformer is present. This explains experimental
issues about the identification and isolation of the two conformers. This
"leak" effect is partially quenched when deuterating the OH group, which
further demonstrates the need for a quantum mechanical approach. Diffusion
Monte Carlo (DMC) and full-dimensional semiclassical dynamics calculations are
employed. The new PES is obtained by means of a $\Delta$-Machine learning
approach starting from a pre-existing low level (LL) density functional theory
(DFT) surface. This surface is brought to the CCSD(T) level of theory using a
relatively small number of $ab$ $initio$ CCSD(T) energies. Agreement between
the corrected PES and direct $ab$ $initio$ results for standard fidelity tests
is excellent. One- and two-dimensional discrete variable representation
calculations focusing on the $trans$-$gauche$ torsional motion are also
reported, in reasonable agreement with the experiment.

Randomized clinical trials with time-to-event outcomes have traditionally
used the log-rank test followed by the Cox proportional hazards (PH) model to
estimate the hazard ratio between the treatment groups. These are valid under
the assumption that the right-censoring mechanism is non-informative, i.e.
independent of the time-to-event of interest within each treatment group. More
generally, the censoring time might depend on additional covariates, and
inverse probability of censoring weighting (IPCW) can be used to correct for
the bias resulting from the informative censoring. IPCW requires a correctly
specified censoring time model conditional on the treatment and the covariates.
Doubly robust inference in this setting has not been plausible previously due
to the non-collapsibility of the Cox model. However, with the recent
development of data-adaptive machine learning methods we derive an augmented
IPCW (AIPCW) estimator that has the following doubly robust (DR) properties: it
is model doubly robust, in that it is consistent and asymptotic normal (CAN),
as long as one of the two models, one for the failure time and one for the
censoring time, is correctly specified; it is also rate doubly robust, in that
it is CAN as long as the product of the estimation error rates under these two
models is faster than root-$n$. We investigate the AIPCW estimator using
extensive simulation in finite samples.

During the design process of an autonomous underwater vehicle (AUV), the
pressure vessel has a critical role. The pressure vessel contains dry
electronics, power sources, and other sensors that can not be flooded. A
traditional design approach for a pressure vessel design involves running
multiple Finite Element Analysis (FEA) based simulations and optimizing the
design to find the best suitable design which meets the requirement. Running
these FEAs are computationally very costly for any optimization process and it
becomes difficult to run even hundreds of evaluation. In such a case, a better
approach is the surrogate design with the goal of replacing FEA-based
prediction with some learning-based regressor. Once the surrogate is trained
for a class of problem, then the learned response surface can be used to
analyze the stress effect without running the FEA for that class of problem.
The challenge of creating a surrogate for a class of problems is data
generation. Since the process is computationally costly, it is not possible to
densely sample the design space and the learning response surface on sparse
data set becomes difficult. During experimentation, we observed that a Deep
Learning-based surrogate outperforms other regression models on such sparse
data. In the present work, we are utilizing the Deep Learning-based model to
replace the costly finite element analysis-based simulation process. By
creating the surrogate we speed up the prediction on the other design much
faster than direct Finite element Analysis. We also compared our DL-based
surrogate with other classical Machine Learning (ML) based regression models(
random forest and Gradient Boost regressor). We observed on the sparser data,
the DL-based surrogate performs much better than other regression models.

Recent advances in applying Graph Neural Networks (GNNs) to molecular science
have showcased the power of learning three-dimensional (3D) structure
representations with GNNs. However, most existing GNNs suffer from the
limitations of insufficient modeling of diverse interactions, computational
expensive operations, and ignorance of vectorial values. Here, we tackle these
limitations by proposing a novel GNN model, Physics-aware Multiplex Graph
Neural Network (PaxNet), to efficiently and accurately learn the
representations of 3D molecules for both small organic compounds and
macromolecule complexes. PaxNet separates the modeling of local and non-local
interactions inspired by molecular mechanics, and reduces the expensive
angle-related computations. Besides scalar properties, PaxNet can also predict
vectorial properties by learning an associated vector for each atom. To
evaluate the performance of PaxNet, we compare it with state-of-the-art
baselines in two tasks. On small molecule dataset for predicting quantum
chemical properties, PaxNet reduces the prediction error by 15% and uses 73%
less memory than the best baseline. On macromolecule dataset for predicting
protein-ligand binding affinities, PaxNet outperforms the best baseline while
reducing the memory consumption by 33% and the inference time by 85%. Thus,
PaxNet provides a universal, robust and accurate method for large-scale machine
learning of molecules.

Since the advent of Federated Learning (FL), research has applied these
methods to natural language processing (NLP) tasks. Despite a plethora of
papers in FL for NLP, no previous works have studied how multilingual text
impacts FL algorithms. Furthermore, multilingual text provides an interesting
avenue to examine the impact of non-IID text (e.g. different languages) on FL
in naturally occurring data. We explore three multilingual language tasks,
language modeling, machine translation, and text classification using differing
federated and non-federated learning algorithms. Our results show that using
pretrained models reduces the negative effects of FL, helping them to perform
near or better than centralized (no privacy) learning, even when using non-IID
partitioning.

The human proteome contains a vast network of interacting kinases and
substrates. Even though some kinases have proven to be immensely useful as
therapeutic targets, a majority are still understudied. In this work, we
present a novel knowledge graph representation learning approach to predict
novel interaction partners for understudied kinases. Our approach uses a
phosphoproteomic knowledge graph constructed by integrating data from iPTMnet,
Protein Ontology, Gene Ontology and BioKG. The representation of kinases and
substrates in this knowledge graph are learned by performing directed random
walks on triples coupled with a modified SkipGram or CBOW model. These
representations are then used as an input to a supervised classification model
to predict novel interactions for understudied kinases. We also present a
post-predictive analysis of the predicted interactions and an ablation study of
the phosphoproteomic knowledge graph to gain an insight into the biology of the
understudied kinases.

Unsupervised domain adaptation (UDA) has been vastly explored to alleviate
domain shifts between source and target domains, by applying a well-performed
model in an unlabeled target domain via supervision of a labeled source domain.
Recent literature, however, has indicated that the performance is still far
from satisfactory in the presence of significant domain shifts. Nonetheless,
delineating a few target samples is usually manageable and particularly
worthwhile, due to the substantial performance gain. Inspired by this, we aim
to develop semi-supervised domain adaptation (SSDA) for medical image
segmentation, which is largely underexplored. We, thus, propose to exploit both
labeled source and target domain data, in addition to unlabeled target data in
a unified manner. Specifically, we present a novel asymmetric co-training (ACT)
framework to integrate these subsets and avoid the domination of the source
domain data. Following a divide-and-conquer strategy, we explicitly decouple
the label supervisions in SSDA into two asymmetric sub-tasks, including
semi-supervised learning (SSL) and UDA, and leverage different knowledge from
two segmentors to take into account the distinction between the source and
target label supervisions. The knowledge learned in the two modules is then
adaptively integrated with ACT, by iteratively teaching each other, based on
the confidence-aware pseudo-label. In addition, pseudo label noise is
well-controlled with an exponential MixUp decay scheme for smooth propagation.
Experiments on cross-modality brain tumor MRI segmentation tasks using the
BraTS18 database showed, even with limited labeled target samples, ACT yielded
marked improvements over UDA and state-of-the-art SSDA methods and approached
an "upper bound" of supervised joint training.

Deep Learning (DL) models achieve great successes in many domains. However,
DL models increasingly face safety and robustness concerns, including noisy
labeling in the training stage and feature distribution shifts in the testing
stage. Previous works made significant progress in addressing these problems,
but the focus has largely been on developing solutions for only one problem at
a time. For example, recent work has argued for the use of tunable robust loss
functions to mitigate label noise, and data augmentation (e.g., AugMix) to
combat distribution shifts. As a step towards addressing both problems
simultaneously, we introduce AugLoss, a simple but effective methodology that
achieves robustness against both train-time noisy labeling and test-time
feature distribution shifts by unifying data augmentation and robust loss
functions. We conduct comprehensive experiments in varied settings of
real-world dataset corruption to showcase the gains achieved by AugLoss
compared to previous state-of-the-art methods. Lastly, we hope this work will
open new directions for designing more robust and reliable DL models under
real-world corruptions.

Understanding the underlying relationship between tongue and oropharyngeal
muscle deformation seen in tagged-MRI and intelligible speech plays an
important role in advancing speech motor control theories and treatment of
speech related-disorders. Because of their heterogeneous representations,
however, direct mapping between the two modalities -- i.e., two-dimensional
(mid-sagittal slice) plus time tagged-MRI sequence and its corresponding
one-dimensional waveform -- is not straightforward. Instead, we resort to
two-dimensional spectrograms as an intermediate representation, which contains
both pitch and resonance, from which to develop an end-to-end deep learning
framework to translate from a sequence of tagged-MRI to its corresponding audio
waveform with limited dataset size.~Our framework is based on a novel fully
convolutional asymmetry translator with guidance of a self residual attention
strategy to specifically exploit the moving muscular structures during
speech.~In addition, we leverage a pairwise correlation of the samples with the
same utterances with a latent space representation disentanglement
strategy.~Furthermore, we incorporate an adversarial training approach with
generative adversarial networks to offer improved realism on our generated
spectrograms.~Our experimental results, carried out with a total of 63
tagged-MRI sequences alongside speech acoustics, showed that our framework
enabled the generation of clear audio waveforms from a sequence of tagged-MRI,
surpassing competing methods. Thus, our framework provides the great potential
to help better understand the relationship between the two modalities.

Annotated data is an essential ingredient in natural language processing for
training and evaluating machine learning models. It is therefore very desirable
for the annotations to be of high quality. Recent work, however, has shown that
several popular datasets contain a surprising amount of annotation errors or
inconsistencies. To alleviate this issue, many methods for annotation error
detection have been devised over the years. While researchers show that their
approaches work well on their newly introduced datasets, they rarely compare
their methods to previous work or on the same datasets. This raises strong
concerns on methods' general performance and makes it difficult to asses their
strengths and weaknesses. We therefore reimplement 18 methods for detecting
potential annotation errors and evaluate them on 9 English datasets for text
classification as well as token and span labeling. In addition, we define a
uniform evaluation setup including a new formalization of the annotation error
detection task, evaluation protocol and general best practices. To facilitate
future research and reproducibility, we release our datasets and
implementations in an easy-to-use and open source software package.

Rapid identification of newly emerging or circulating viruses is an important
first step toward managing the public health response to potential outbreaks. A
portable virus capture device coupled with label-free Raman Spectroscopy holds
the promise of fast detection by rapidly obtaining the Raman signature of a
virus followed by a machine learning approach applied to recognize the virus
based on its Raman spectrum, which is used as a fingerprint. We present such a
machine learning approach for analyzing Raman spectra of human and avian
viruses. A Convolutional Neural Network (CNN) classifier specifically designed
for spectral data achieves very high accuracy for a variety of virus type or
subtype identification tasks. In particular, it achieves 99% accuracy for
classifying influenza virus type A vs. type B, 96% accuracy for classifying
four subtypes of influenza A, 95% accuracy for differentiating enveloped and
non-enveloped viruses, and 99% accuracy for differentiating avian coronavirus
(infectious bronchitis virus, IBV) from other avian viruses. Furthermore,
interpretation of neural net responses in the trained CNN model using a
full-gradient algorithm highlights Raman spectral ranges that are most
important to virus identification. By correlating ML-selected salient Raman
ranges with the signature ranges of known biomolecules and chemical functional
groups (for example, amide, amino acid, carboxylic acid), we verify that our ML
model effectively recognizes the Raman signatures of proteins, lipids and other
vital functional groups present in different viruses and uses a weighted
combination of these signatures to identify viruses.

We revisit the classical problem of finding an approximately stationary point
of the average of $n$ smooth and possibly nonconvex functions. The optimal
complexity of stochastic first-order methods in terms of the number of gradient
evaluations of individual functions is $\mathcal{O}\left(n +
n^{1/2}\varepsilon^{-1}\right)$, attained by the optimal SGD methods
$\small\sf\color{green}{SPIDER}$(arXiv:1807.01695) and
$\small\sf\color{green}{PAGE}$(arXiv:2008.10898), for example, where
$\varepsilon$ is the error tolerance. However, i) the big-$\mathcal{O}$
notation hides crucial dependencies on the smoothness constants associated with
the functions, and ii) the rates and theory in these methods assume simplistic
sampling mechanisms that do not offer any flexibility. In this work we remedy
the situation. First, we generalize the $\small\sf\color{green}{PAGE}$
algorithm so that it can provably work with virtually any (unbiased) sampling
mechanism. This is particularly useful in federated learning, as it allows us
to construct and better understand the impact of various combinations of client
and data sampling strategies. Second, our analysis is sharper as we make
explicit use of certain novel inequalities that capture the intricate interplay
between the smoothness constants and the sampling procedure. Indeed, our
analysis is better even for the simple sampling procedure analyzed in the
$\small\sf\color{green}{PAGE}$ paper. However, this already improved bound can
be further sharpened by a different sampling scheme which we propose. In
summary, we provide the most general and most accurate analysis of optimal SGD
in the smooth nonconvex regime. Finally, our theoretical findings are supposed
with carefully designed experiments.

Current methods to determine the energy efficiency of buildings require
on-site visits of certified energy auditors which makes the process slow,
costly, and geographically incomplete. To accelerate the identification of
promising retrofit targets on a large scale, we propose to estimate building
energy efficiency from widely available and remotely sensed data sources only,
namely street view, aerial view, footprint, and satellite-borne land surface
temperature (LST) data. After collecting data for almost 40,000 buildings in
the United Kingdom, we combine these data sources by training multiple
end-to-end deep learning models with the objective to classify buildings as
energy efficient (EU rating A-D) or inefficient (EU rating E-G). After
evaluating the trained models quantitatively as well as qualitatively, we
extend our analysis by studying the predictive power of each data source in an
ablation study. We find that the end-to-end deep learning model trained on all
four data sources achieves a macro-averaged F1 score of 64.64% and outperforms
the k-NN and SVM-based baseline models by 14.13 to 12.02 percentage points,
respectively. Thus, this work shows the potential and complementary nature of
remotely sensed data in predicting energy efficiency and opens up new
opportunities for future work to integrate additional data sources.

We define the information threshold as the point of maximum curvature in the
prior vs. posterior Bayesian curve, both of which are described as a function
of the true positive and negative rates of the classification system in
question. The nature of the threshold is such that for sufficiently adequate
binary classification systems, retrieving excess information beyond the
threshold does not significantly alter the reliability of our classification
assessment. We hereby introduce the "marital status thought experiment" to
illustrate this idea and report a previously undefined mathematical
relationship between the Bayesian prior and posterior, which may have
significant philosophical and epistemological implications in decision theory.
Where the prior probability is a scalar between 0 and 1 given by $\phi$ and the
posterior is a scalar between 0 and 1 given by $\rho$, then at the information
threshold, $\phi_e$:
  $\phi_e + \rho_e = 1$
  Otherwise stated, given some degree of prior belief, we may assert its
persuasiveness when sufficient quality evidence yields a posterior so that
their combined sum equals 1. Retrieving further evidence beyond this point does
not significantly improve the posterior probability, and may serve as a
benchmark for confidence in decision-making.

Exploratory factor analysis (EFA) has been widely used to learn the latent
structure underlying multivariate data. Rotation and regularised estimation are
two classes of methods in EFA that are widely used to find interpretable
loading matrices. This paper proposes a new family of oblique rotations based
on component-wise $L^p$ loss functions $(0 < p\leq 1)$ that is closely related
to an $L^p$ regularised estimator. Model selection and post-selection inference
procedures are developed based on the proposed rotation. When the true loading
matrix is sparse, the proposed method tends to outperform traditional rotation
and regularised estimation methods, in terms of statistical accuracy and
computational cost. Since the proposed loss functions are non-smooth, an
iteratively reweighted gradient projection algorithm is developed for solving
the optimisation problem. Theoretical results are developed that establish the
statistical consistency of the estimation, model selection, and post-selection
inference. The proposed method is evaluated and compared with regularised
estimation and traditional rotation methods via simulation studies. It is
further illustrated by an application to big-five personality assessment.

Generative adversarial networks (GANs) are challenging to train stably, and a
promising remedy of injecting instance noise into the discriminator input has
not been very effective in practice. In this paper, we propose Diffusion-GAN, a
novel GAN framework that leverages a forward diffusion chain to generate
Gaussian-mixture distributed instance noise. Diffusion-GAN consists of three
components, including an adaptive diffusion process, a diffusion
timestep-dependent discriminator, and a generator. Both the observed and
generated data are diffused by the same adaptive diffusion process. At each
diffusion timestep, there is a different noise-to-data ratio and the
timestep-dependent discriminator learns to distinguish the diffused real data
from the diffused generated data. The generator learns from the discriminator's
feedback by backpropagating through the forward diffusion chain, whose length
is adaptively adjusted to balance the noise and data levels. We theoretically
show that the discriminator's timestep-dependent strategy gives consistent and
helpful guidance to the generator, enabling it to match the true data
distribution. We demonstrate the advantages of Diffusion-GAN over strong GAN
baselines on various datasets, showing that it can produce more realistic
images with higher stability and data efficiency than state-of-the-art GANs.

This paper combines deep learning techniques for species detection, 3D model
fitting, and metric learning in one pipeline to perform individual animal
identification from photographs by exploiting unique coat patterns. This is the
first work to attempt this and, compared to traditional 2D bounding box or
segmentation based CNN identification pipelines, the approach provides
effective and explicit view-point normalisation and allows for a straight
forward visualisation of the learned biometric population space. Note that due
to the use of metric learning the pipeline is also readily applicable to open
set and zero shot re-identification scenarios. We apply the proposed approach
to individual Grevy's zebra (Equus grevyi) identification and show in a small
study on the SMALST dataset that the use of 3D model fitting can indeed benefit
performance. In particular, back-projected textures from 3D fitted models
improve identification accuracy from 48.0% to 56.8% compared to 2D bounding box
approaches for the dataset. Whilst the study is far too small accurately to
estimate the full performance potential achievable in larger-scale real-world
application settings and in comparisons against polished tools, our work lays
the conceptual and practical foundations for a next step in animal biometrics
towards deep metric learning driven, fully 3D-aware animal identification in
open population settings. We publish network weights and relevant facilitating
source code with this paper for full reproducibility and as inspiration for

In this survey, we present comprehensive analysis of 3D hand pose estimation
from the perspective of efficient annotation and learning. In particular, we
study recent approaches for 3D hand pose annotation and learning methods with
limited annotated data. In 3D hand pose estimation, collecting 3D hand pose
annotation is a key step in developing hand pose estimators and their
applications, such as video understanding, AR/VR, and robotics. However,
acquiring annotated 3D hand poses is cumbersome, e.g., due to the difficulty of
accessing 3D information and occlusion. Motivated by elucidating how recent
works address the annotation issue, we investigated annotation methods
classified as manual, synthetic-model-based, hand-sensor-based, and
computational approaches. Since these annotation methods are not always
available on a large scale, we examined methods of learning 3D hand poses when
we do not have enough annotated data, namely self-supervised pre-training,
semi-supervised learning, and domain adaptation. Based on the analysis of these
efficient annotation and learning, we further discuss limitations and possible
future directions of this field.

A growing body of research runs human subject evaluations to study whether
providing users with explanations of machine learning models can help them with
practical real-world use cases. However, running user studies is challenging
and costly, and consequently each study typically only evaluates a limited
number of different settings, e.g., studies often only evaluate a few
arbitrarily selected explanation methods. To address these challenges and aid
user study design, we introduce Use-Case-Grounded Simulated Evaluations
(SimEvals). SimEvals involve training algorithmic agents that take as input the
information content (such as model explanations) that would be presented to
each participant in a human subject study, to predict answers to the use case
of interest. The algorithmic agent's test set accuracy provides a measure of
the predictiveness of the information content for the downstream use case. We
run a comprehensive evaluation on three real-world use cases (forward
simulation, model debugging, and counterfactual reasoning) to demonstrate that
Simevals can effectively identify which explanation methods will help humans
for each use case. These results provide evidence that SimEvals can be used to
efficiently screen an important set of user study design decisions, e.g.
selecting which explanations should be presented to the user, before running a
potentially costly user study.

We motivate the need for recommendation systems that can cater to the members
in-the-moment intent by leveraging their interactions from the current session.
We provide an overview of an end-to-end in-session adaptive recommendations
system in the context of Netflix Search. We discuss the challenges and
potential solutions when developing such a system at production scale.

The discovery of place cells and other spatially modulated neurons in the
hippocampal complex of rodents has been crucial to elucidating the neural basis
of spatial cognition. More recently, the replay of neural sequences encoding
previously experienced trajectories has been observed during consummatory
behavior potentially with implications for quick memory consolidation and
behavioral planning. Several promising models for robotic navigation and
reinforcement learning have been proposed based on these and previous findings.
Most of these models, however, use carefully engineered neural networks and are
tested in simple environments. In this paper, we develop a self-organized model
incorporating place cells and replay, and demonstrate its utility for rapid
one-shot learning in non-trivial environments with obstacles.

We present a novel way of conditioning a pretrained denoising diffusion
speech model to produce speech in the voice of a novel person unseen during
training. The method requires a short (~3 seconds) sample from the target
person, and generation is steered at inference time, without any training
steps. At the heart of the method lies a sampling process that combines the
estimation of the denoising model with a low-pass version of the new speaker's
sample. The objective and subjective evaluations show that our sampling method
can generate a voice similar to that of the target speaker in terms of
frequency, with an accuracy comparable to state-of-the-art methods, and without
training.

With the development of mobile edge computing (MEC) and blockchain-based
federated learning (BCFL), a number of studies suggest deploying BCFL on edge
servers. In this case, resource-limited edge servers need to serve both mobile
devices for their offloading tasks and the BCFL system for model training and
blockchain consensus in a cost-efficient manner without sacrificing the service
quality to any side. To address this challenge, this paper proposes a resource
allocation scheme for edge servers, aiming to provide the optimal services with
the minimum cost. Specifically, we first analyze the energy consumed by the MEC
and BCFL tasks, and then use the completion time of each task as the service
quality constraint. Then, we model the resource allocation challenge into a
multivariate, multi-constraint, and convex optimization problem. To solve the
problem in a progressive manner, we design two algorithms based on the
alternating direction method of multipliers (ADMM) in both the homogeneous and
heterogeneous situations with equal and on-demand resource distribution
strategies, respectively. The validity of our proposed algorithms is proved via
rigorous theoretical analysis. Through extensive experiments, the convergence
and efficiency of our proposed resource allocation schemes are evaluated. To
the best of our knowledge, this is the first work to investigate the resource
allocation dilemma of edge servers for BCFL in MEC.

Duplication of nodes is a common problem encountered when building knowledge
graphs (KGs) from heterogeneous datasets, where it is crucial to be able to
merge nodes having the same meaning. OntoMerger is a Python ontology
integration library whose functionality is to deduplicate KG nodes. Our
approach takes a set of KG nodes, mappings and disconnected hierarchies and
generates a set of merged nodes together with a connected hierarchy. In
addition, the library provides analytic and data testing functionalities that
can be used to fine-tune the inputs, further reducing duplication, and to
increase connectivity of the output graph. OntoMerger can be applied to a wide
variety of ontologies and KGs. In this paper we introduce OntoMerger and
illustrate its functionality on a real-world biomedical KG.

Binary decision making classifiers are not fair by default. Fairness
requirements are an additional element to the decision making rationale, which
is typically driven by maximizing some utility function. In that sense,
algorithmic fairness can be formulated as a constrained optimization problem.
This paper contributes to the discussion on how to implement fairness, focusing
on the fairness concepts of positive predictive value (PPV) parity, false
omission rate (FOR) parity, and sufficiency (which combines the former two). We
show that group-specific threshold rules are optimal for PPV parity and FOR
parity, similar to well-known results for other group fairness criteria.
However, depending on the underlying population distributions and the utility
function, we find that sometimes an upper-bound threshold rule for one group is
optimal: utility maximization under PPV parity (or FOR parity) might thus lead
to selecting the individuals with the smallest utility for one group, instead
of selecting the most promising individuals. This result is counter-intuitive
and in contrast to the analogous solutions for statistical parity and equality
of opportunity. We also provide a solution for the optimal decision rules
satisfying the fairness constraint sufficiency. We show that more complex
decision rules are required and that this leads to within-group unfairness for
all but one of the groups. We illustrate our findings based on simulated and
real data.

Large language models distill broad knowledge from text corpora. However,
they can be inconsistent when it comes to completing user specified tasks. This
issue can be addressed by finetuning such models via supervised learning on
curated datasets, or via reinforcement learning. In this work, we propose a
novel offline RL motivated method, implicit language Q-learning (ILQL),
designed for use on language models, that combines both the flexible utility
optimization framework of traditional RL algorithms with supervised learning's
ability to leverage existing data and its simplicity and stability. Our method,
based on dynamic programming, employs a blend of value conservatism alongside
an implicit dataset support constraint in learning value functions, which are
then used to guide language model generations towards maximizing utility. In
addition to empirically validating ILQL, we present a detailed empirical
analysis of situations where offline RL can be useful in natural language
generation settings, demonstrating how it can be a more effective utility
optimizer than prior approaches for end-to-end dialogue, and how it can
effectively optimize high variance reward functions based on subjective
judgement, such as whether to label a comment as an example of toxic speech or
not.

Handwritten Digit Recognition (HDR) is one of the most challenging tasks in
the domain of Optical Character Recognition (OCR). Irrespective of language,
there are some inherent challenges of HDR, which mostly arise due to the
variations in writing styles across individuals, writing medium and
environment, inability to maintain the same strokes while writing any digit
repeatedly, etc. In addition to that, the structural complexities of the digits
of a particular language may lead to ambiguous scenarios of HDR. Over the
years, researchers have developed numerous offline and online HDR pipelines,
where different image processing techniques are combined with traditional
Machine Learning (ML)-based and/or Deep Learning (DL)-based architectures.
Although evidence of extensive review studies on HDR exists in the literature
for languages, such as English, Arabic, Indian, Farsi, Chinese, etc., few
surveys on Bengali HDR (BHDR) can be found, which lack a comprehensive analysis
of the challenges, the underlying recognition process, and possible future
directions. In this paper, the characteristics and inherent ambiguities of
Bengali handwritten digits along with a comprehensive insight of two decades of
state-of-the-art datasets and approaches towards offline BHDR have been
analyzed. Furthermore, several real-life application-specific studies, which
involve BHDR, have also been discussed in detail. This paper will also serve as
a compendium for researchers interested in the science behind offline BHDR,
instigating the exploration of newer avenues of relevant research that may
further lead to better offline recognition of Bengali handwritten digits in
different application areas.

The utility of reinforcement learning is limited by the alignment of reward
functions with the interests of human stakeholders. One promising method for
alignment is to learn the reward function from human-generated preferences
between pairs of trajectory segments. These human preferences are typically
assumed to be informed solely by partial return, the sum of rewards along each
segment. We find this assumption to be flawed and propose modeling preferences
instead as arising from a different statistic: each segment's regret, a measure
of a segment's deviation from optimal decision-making. Given infinitely many
preferences generated according to regret, we prove that we can identify a
reward function equivalent to the reward function that generated those
preferences. We also prove that the previous partial return model lacks this
identifiability property without preference noise that reveals rewards'
relative proportions, and we empirically show that our proposed regret
preference model outperforms it with finite training data in otherwise the same
setting. Additionally, our proposed regret preference model better predicts
real human preferences and also learns reward functions from these preferences
that lead to policies that are better human-aligned. Overall, this work
establishes that the choice of preference model is impactful, and our proposed
regret preference model provides an improvement upon a core assumption of
recent research.

West Greenlandic, known by native speakers as Kalaallisut, is an extremely
low-resource polysynthetic language spoken by around 56,000 people in
Greenland. Here, we attempt to finetune a pretrained Kalaallisut-to-English
neural machine translation (NMT) system using web-crawled pseudoparallel
sentences from around 30 multilingual websites. We compile a corpus of over
93,000 Kalaallisut sentences and over 140,000 Danish sentences, then use
cross-lingual sentence embeddings and approximate nearest-neighbors search in
an attempt to mine near-translations from these corpora. Finally, we translate
the Danish sentence to English to obtain a synthetic Kalaallisut-English
aligned corpus. Although the resulting dataset is too small and noisy to
improve the pretrained MT model, we believe that with additional resources, we
could construct a better pseudoparallel corpus and achieve more promising
results on MT. We also note other possible uses of the monolingual Kalaallisut
data and discuss directions for future work. We make the code and data for our
experiments publicly available.

Biological neural networks operate in the presence of task disruptions as
they guide organisms toward goals. A familiar stream of stimulus-response
causations can be disrupted by subtask streams imposed by the environment. For
example, taking a familiar path to a foraging area might be disrupted by the
presence of a predator, necessitating a "detour" to the area. The detour can be
a known alternative path that must be dynamically composed with the original
path to accomplish the overall task. In this project, overarching base paths
are disrupted by independently learned path modules in the form of insertion,
substitution, and deletion modifications to the base paths such that the
resulting modified paths are novel to the network. The network's performance is
then tested on these paths that have been learned in piecemeal fashion. In sum,
the network must compose a new task on the fly. Several network architectures
are tested: Time delay neural network (TDNN), Long short-term memory (LSTM),
Temporal convolutional network (TCN), and Morphognosis, a hierarchical neural
network. LSTM and Morphognosis perform significantly better for this task.

We study decentralized multi-agent learning in bipartite queuing systems, a
standard model for service systems. In particular, $N$ agents request service
from $K$ servers in a fully decentralized way, i.e, by running the same
algorithm without communication. Previous decentralized algorithms are
restricted to symmetric systems, have performance that is degrading
exponentially in the number of servers, require communication through shared
randomness and unique agent identities, and are computationally demanding. In
contrast, we provide a simple learning algorithm that, when run decentrally by
each agent, leads the queuing system to have efficient performance in general
asymmetric bipartite queuing systems while also having additional robustness
properties. Along the way, we provide the first UCB-based algorithm for the
centralized case of the problem, which resolves an open question by Krishnasamy
et al. (2016,2021).

We report on a novel model linking deep convolutional neural networks (CNN)
to biological vision and fundamental particle physics. Information propagation
in a CNN is modeled via an analogy to an optical system, where information is
concentrated near a bottleneck where the 2D spatial resolution collapses about
a focal point $1\times 1=1$. A 3D space $(x,y,t)$ is defined by $(x,y)$
coordinates in the image plane and CNN layer $t$, where a principal ray
$(0,0,t)$ runs in the direction of information propagation through both the
optical axis and the image center pixel located at $(x,y)=(0,0)$, about which
the sharpest possible spatial focus is limited to a circle of confusion in the
image plane. Our novel insight is to model the principal optical ray $(0,0,t)$
as geometrically equivalent to the medial vector in the positive orthant
$I(x,y) \in R^{N+}$ of a $N$-channel activation space, e.g. along the greyscale
(or luminance) vector $(t,t,t)$ in $RGB$ colour space. Information is thus
concentrated into an energy potential $E(x,y,t)=\|I(x,y,t)\|^2$, which,
particularly for bottleneck layers $t$ of generic CNNs, is highly concentrated
and symmetric about the spatial origin $(0,0,t)$ and exhibits the well-known
"Sombrero" potential of the boson particle. This symmetry is broken in
classification, where bottleneck layers of generic pre-trained CNN models
exhibit a consistent class-specific bias towards an angle $\theta \in U(1)$
defined simultaneously in the image plane and in activation feature space.
Initial observations validate our hypothesis from generic pre-trained CNN
activation maps and a bare-bones memory-based classification scheme, with no
training or tuning. Training from scratch using combined one-hot $+ U(1)$ loss
improves classification for all tasks tested including ImageNet.

Deep neural network models have become ubiquitous in recent years, and have
been applied to nearly all areas of science, engineering, and industry. These
models are particularly useful for data that have strong dependencies in space
(e.g., images) and time (e.g., sequences). Indeed, deep models have also been
extensively used by the statistical community to model spatial and
spatio-temporal data through, for example, the use of multi-level Bayesian
hierarchical models and deep Gaussian processes. In this review, we first
present an overview of traditional statistical and machine learning
perspectives for modeling spatial and spatio-temporal data, and then focus on a
variety of hybrid models that have recently been developed for latent process,
data, and parameter specifications. These hybrid models integrate statistical
modeling ideas with deep neural network models in order to take advantage of
the strengths of each modeling paradigm. We conclude by giving an overview of
computational technologies that have proven useful for these hybrid models, and
with a brief discussion on future research directions.

Mesh degeneration is a bottleneck for fluid-structure interaction (FSI)
simulations and for shape optimization via the method of mappings. In both
cases, an appropriate mesh motion technique is required. The choice is
typically based on heuristics, e.g., the solution operators of partial
differential equations (PDE), such as the Laplace or biharmonic equation.
Especially the latter, which shows good numerical performance for large
displacements, is expensive. Moreover, from a continuous perspective, choosing
the mesh motion technique is to a certain extent arbitrary and has no influence
on the physically relevant quantities. Therefore, we consider approaches
inspired by machine learning. We present a hybrid PDE-NN approach, where the
neural network (NN) serves as parameterization of a coefficient in a second
order nonlinear PDE. We ensure existence of solutions for the nonlinear PDE by
the choice of the neural network architecture. Moreover, we propose a splitting
of the monolithic FSI system into three smaller subsystems, in order to
segregate the mesh motion. We assess the quality of the learned mesh motion
technique by applying it to a FSI benchmark problem.

The success of deep learning comes from its ability to capture the
hierarchical structure of data by learning high-level representations defined
in terms of low-level ones. In this paper we explore self-supervised learning
of hierarchical representations of speech by applying multiple levels of
Contrastive Predictive Coding (CPC). We observe that simply stacking two CPC
models does not yield significant improvements over single-level architectures.
Inspired by the fact that speech is often described as a sequence of discrete
units unevenly distributed in time, we propose a model in which the output of a
low-level CPC module is non-uniformly downsampled to directly minimize the loss
of a high-level CPC module. The latter is designed to also enforce a prior of
separability and discreteness in its representations by enforcing dissimilarity
of successive high-level representations through focused negative sampling, and
by quantization of the prediction targets. Accounting for the structure of the
speech signal improves upon single-level CPC features and enhances the
disentanglement of the learned representations, as measured by downstream
speech recognition tasks, while resulting in a meaningful segmentation of the
signal that closely resembles phone boundaries.

The task of text classification using Bidirectional based LSTM architectures
is computationally expensive and time consuming to train. For this,
transformers were discovered which effectively give good performance as
compared to the traditional deep learning architectures. In this paper we
present a performance based comparison between simple transformer based network
and Res-CNN-BiLSTM based network for cyberbullying text classification problem.
The results obtained show that transformer we trained with 0.65 million
parameters has significantly being able to beat the performance of
Res-CNN-BiLSTM with 48.82 million parameters for faster training speeds and
more generalized metrics. The paper also compares the 1-dimensional character
level embedding network and 100-dimensional glove embedding network with
transformer.

Distributed sparse learning for high dimensional parameters has attached vast
attentions due to its wide application in prediction and classification in
diverse fields of machine learning. Existing distributed sparse regression
usually takes an average way to ensemble the local results produced by
distributed machines, which enjoys low communication cost but is statistical
inefficient. To address this problem, we proposed a new Weighted AVerage
Estimate (WAVE) for high-dimensional regressions. The WAVE is a solution to a
weighted least-square loss with an adaptive $L_1$ penalty, in which the $L_1$
penalty controls the sparsity and the weight promotes the statistical
efficiency. It can not only achieve a balance between the statistical and
communication efficiency, but also reach a faster rate than the average
estimate with a very low communication cost, requiring the local machines
delivering two vectors to the master merely. The consistency of parameter
estimation and model selection is also provided, which guarantees the safety of
using WAVE in the distributed system. The consistency also provides a way to
make hypothisis testing on the parameter. Moreover, WAVE is robust to the
heterogeneous distributed samples with varied mean and covariance across
machines, which has been verified by the asymptotic normality under such
conditions. Other competitors, however, do not own this property. The
effectiveness of WAVE is further illustrated by extensive numerical studies and
real data analyses.

Human action recognition is one of the challenging tasks in computer vision.
The current action recognition methods use computationally expensive models for
learning spatio-temporal dependencies of the action. Models utilizing RGB
channels and optical flow separately, models using a two-stream fusion
technique, and models consisting of both convolutional neural network (CNN) and
long-short term memory (LSTM) network are few examples of such complex models.
Moreover, fine-tuning such complex models is computationally expensive as well.
This paper proposes a deep neural network architecture for learning such
dependencies consisting of a 3D convolutional layer, fully connected (FC)
layers, and attention layer, which is simpler to implement and gives a
competitive performance on the UCF-101 dataset. The proposed method first
learns spatial and temporal features of actions through 3D-CNN, and then the
attention mechanism helps the model to locate attention to essential features
for recognition.

In machine learning and computer vision, mean shift (MS) qualifies as one of
the most popular mode-seeking algorithms used for clustering and image
segmentation. It iteratively moves each data point to the weighted mean of its
neighborhood data points. The computational cost required to find the neighbors
of each data point is quadratic to the number of data points. Consequently, the
vanilla MS appears to be very slow for large-scale datasets. To address this
issue, we propose a mode-seeking algorithm called GridShift, with significant
speedup and principally based on MS. To accelerate, GridShift employs a
grid-based approach for neighbor search, which is linear in the number of data
points. In addition, GridShift moves the active grid cells (grid cells
associated with at least one data point) in place of data points towards the
higher density, a step that provides more speedup. The runtime of GridShift is
linear in the number of active grid cells and exponential in the number of
features. Therefore, it is ideal for large-scale low-dimensional applications
such as object tracking and image segmentation. Through extensive experiments,
we showcase the superior performance of GridShift compared to other MS-based as
well as state-of-the-art algorithms in terms of accuracy and runtime on
benchmark datasets for image segmentation. Finally, we provide a new
object-tracking algorithm based on GridShift and show promising results for
object tracking compared to CamShift and meanshift++.

The electricity market has a vital role to play in the decarbonisation of the
energy system. However, the electricity market is made up of many different
variables and data inputs. These variables and data inputs behave in sometimes
unpredictable ways which can not be predicted a-priori. It has therefore been
suggested that agent-based simulations are used to better understand the
dynamics of the electricity market. Agent-based models provide the opportunity
to integrate machine learning and artificial intelligence to add intelligence,
make better forecasts and control the power market in better and more efficient
ways. In this systematic literature review, we review 55 papers published
between 2016 and 2021 which focus on machine learning applied to agent-based
electricity market models. We find that research clusters around popular
topics, such as bidding strategies. However, there exists a long-tail of
different research applications that could benefit from the high intensity
research from the more investigated applications.

The advent of deep learning has led to significant progress in monocular
human reconstruction. However, existing representations, such as parametric
models, voxel grids, meshes and implicit neural representations, have
difficulties achieving high-quality results and real-time speed at the same
time. In this paper, we propose Fourier Occupancy Field (FOF), a novel
powerful, efficient and flexible 3D representation, for monocular real-time and
accurate human reconstruction. The FOF represents a 3D object with a 2D field
orthogonal to the view direction where at each 2D position the occupancy field
of the object along the view direction is compactly represented with the first
few terms of Fourier series, which retains the topology and neighborhood
relation in the 2D domain. A FOF can be stored as a multi-channel image, which
is compatible with 2D convolutional neural networks and can bridge the gap
between 3D geometries and 2D images. The FOF is very flexible and extensible,
e.g., parametric models can be easily integrated into a FOF as a prior to
generate more robust results. Based on FOF, we design the first 30+FPS
high-fidelity real-time monocular human reconstruction framework. We
demonstrate the potential of FOF on both public dataset and real captured data.
The code will be released for research purposes.

Emotion Recognition in Conversations (ERC) is crucial in developing
sympathetic human-machine interaction. In conversational videos, emotion can be
present in multiple modalities, i.e., audio, video, and transcript. However,
due to the inherent characteristics of these modalities, multi-modal ERC has
always been considered a challenging undertaking. Existing ERC research focuses
mainly on using text information in a discussion, ignoring the other two
modalities. We anticipate that emotion recognition accuracy can be improved by
employing a multi-modal approach. Thus, in this study, we propose a Multi-modal
Fusion Network (M2FNet) that extracts emotion-relevant features from visual,
audio, and text modality. It employs a multi-head attention-based fusion
mechanism to combine emotion-rich latent representations of the input data. We
introduce a new feature extractor to extract latent features from the audio and
visual modality. The proposed feature extractor is trained with a novel
adaptive margin-based triplet loss function to learn emotion-relevant features
from the audio and visual data. In the domain of ERC, the existing methods
perform well on one benchmark dataset but not on others. Our results show that
the proposed M2FNet architecture outperforms all other methods in terms of
weighted average F1 score on well-known MELD and IEMOCAP datasets and sets a
new state-of-the-art performance in ERC.

Bayesian models have many desirable properties, most notable is their ability
to generalize from limited data and to properly estimate the uncertainty in
their predictions. However, these benefits come at a steep computational cost
as Bayesian inference, in most cases, is computationally intractable. One
popular approach to alleviate this problem is using a Monte-Carlo estimation
with an ensemble of models sampled from the posterior. However, this approach
still comes at a significant computational cost, as one needs to store and run
multiple models at test time. In this work, we investigate how to best distill
an ensemble's predictions using an efficient model. First, we argue that
current approaches that simply return distribution over predictions cannot
compute important properties, such as the covariance between predictions, which
can be valuable for further processing. Second, in many limited data settings,
all ensemble members achieve nearly zero training loss, namely, they produce
near-identical predictions on the training set which results in sub-optimal
distilled models. To address both problems, we propose a novel and general
distillation approach, named Functional Ensemble Distillation (FED), and we
investigate how to best distill an ensemble in this setting. We find that
learning the distilled model via a simple augmentation scheme in the form of
mixup augmentation significantly boosts the performance. We evaluated our
method on several tasks and showed that it achieves superior results in both
accuracy and uncertainty estimation compared to current approaches.

With the progress of Mars exploration, numerous Mars image data are collected
and need to be analyzed. However, due to the imbalance and distortion of
Martian data, the performance of existing computer vision models is
unsatisfactory. In this paper, we introduce a semi-supervised framework for
machine vision on Mars and try to resolve two specific tasks: classification
and segmentation. Contrastive learning is a powerful representation learning
technique. However, there is too much information overlap between Martian data
samples, leading to a contradiction between contrastive learning and Martian
data. Our key idea is to reconcile this contradiction with the help of
annotations and further take advantage of unlabeled data to improve
performance. For classification, we propose to ignore inner-class pairs on
labeled data as well as neglect negative pairs on unlabeled data, forming
supervised inter-class contrastive learning and unsupervised similarity
learning. For segmentation, we extend supervised inter-class contrastive
learning into an element-wise mode and use online pseudo labels for supervision
on unlabeled areas. Experimental results show that our learning strategies can
improve the classification and segmentation models by a large margin and
outperform state-of-the-art approaches.

Zero-shot intent classification is a vital and challenging task in dialogue
systems, which aims to deal with numerous fast-emerging unacquainted intents
without annotated training data. To obtain more satisfactory performance, the
crucial points lie in two aspects: extracting better utterance features and
strengthening the model generalization ability. In this paper, we propose a
simple yet effective meta-learning paradigm for zero-shot intent
classification. To learn better semantic representations for utterances, we
introduce a new mixture attention mechanism, which encodes the pertinent word
occurrence patterns by leveraging the distributional signature attention and
multi-layer perceptron attention simultaneously. To strengthen the transfer
ability of the model from seen classes to unseen classes, we reformulate
zero-shot intent classification with a meta-learning strategy, which trains the
model by simulating multiple zero-shot classification tasks on seen categories,
and promotes the model generalization ability with a meta-adapting procedure on
mimic unseen categories. Extensive experiments on two real-world dialogue
datasets in different languages show that our model outperforms other strong
baselines on both standard and generalized zero-shot intent classification
tasks.

This paper introduces the factored conditional filter, a new filtering
algorithm for simultaneously tracking states and estimating parameters in
high-dimensional state spaces. The conditional nature of the algorithm is used
to estimate parameters and the factored nature is used to decompose the state
space into low-dimensional subspaces in such a way that filtering on these
subspaces gives distributions whose product is a good approximation to the
distribution on the entire state space. The conditions for successful
application of the algorithm are that observations be available at the subspace
level and that the transition model can be factored into local transition
models that are approximately confined to the subspaces; these conditions are
widely satisfied in computer science, engineering, and geophysical filtering
applications. We give experimental results on tracking epidemics and estimating
parameters in large contact networks that show the effectiveness of our
approach.

With the development of artificial intelligence,more and more financial
practitioners apply deep reinforcement learning to financial trading
strategies.However,It is difficult to extract accurate features due to the
characteristics of considerable noise,highly non-stationary,and non-linearity
of single-scale time series,which makes it hard to obtain high returns.In this
paper,we extract a multi-scale feature matrix on multiple time scales of
financial time series,according to the classic financial theory-Chan Theory,and
put forward to an approach of multi-scale stroke deep deterministic policy
gradient reinforcement learning model(MSSDDPG)to search for the optimal trading
strategy.We carried out experiments on the datasets of the Dow Jones,S&P 500 of
U.S. stocks, and China's CSI 300,SSE Composite,evaluate the performance of our
approach compared with turtle trading strategy, Deep
Q-learning(DQN)reinforcement learning strategy,and deep deterministic policy
gradient (DDPG) reinforcement learning strategy.The result shows that our
approach gets the best performance in China CSI 300,SSE Composite,and get an
outstanding result in Dow Jones,S&P 500 of U.S.

Wireless communications systems are impacted by multi-path fading and Doppler
shift in dynamic environments, where the channel becomes doubly-dispersive and
its estimation becomes an arduous task. Only a few pilots are used for channel
estimation in conventional approaches to preserve high data rate transmission.
Consequently, such estimators experience a significant performance degradation
in high mobility scenarios. Recently, deep learning has been employed for
doubly-dispersive channel estimation due to its low-complexity, robustness, and
good generalization ability. Against this backdrop, the current paper presents
a comprehensive survey on channel estimation techniques based on deep learning
by deeply investigating different methods. The study also provides extensive
experimental simulations followed by a computational complexity analysis. After
considering different parameters such as modulation order, mobility, frame
length, and deep learning architecture, the performance of the studied
estimators is evaluated in several mobility scenarios. In addition, the source
codes are made available online in order to make the results reproducible.

Curb space is one of the busiest areas in urban road networks. Especially in
recent years, the rapid increase of ride-hailing trips and commercial
deliveries has induced massive pick-ups/drop-offs (PUDOs), which occupy the
limited curb space that was designed and built decades ago. These PUDOs could
jam curb utilization and disturb the mainline traffic flow, evidently leading
to significant societal externalities. However, there is a lack of an
analytical framework that rigorously quantifies and mitigates the congestion
effect of PUDOs in the system view, particularly with little data support and
involvement of confounding effects. In view of this, this paper develops a
rigorous causal inference approach to estimate the congestion effect of PUDOs
on general networks. A causal graph is set to represent the spatio-temporal
relationship between PUDOs and traffic speed, and a double and separated
machine learning (DSML) method is proposed to quantify how PUDOs affect traffic
congestion. Additionally, a re-routing formulation is developed and solved to
encourage passenger walking and traffic flow re-routing to achieve system
optimal. Numerical experiments are conducted using real-world data in the
Manhattan area. On average, 100 additional units of PUDOs in a region could
reduce the traffic speed by 3.70 and 4.54 mph on weekdays and weekends,
respectively. Re-routing trips with PUDOs on curbs could respectively reduce
the system-wide total travel time by 2.44\% and 2.12\% in Midtown and Central
Park on weekdays. Sensitivity analysis is also conducted to demonstrate the
effectiveness and robustness of the proposed framework.

Adversarial training has been widely explored for mitigating attacks against
deep models. However, most existing works are still trapped in the dilemma
between higher accuracy and stronger robustness since they tend to fit a model
towards robust features (not easily tampered with by adversaries) while
ignoring those non-robust but highly predictive features. To achieve a better
robustness-accuracy trade-off, we propose the Vanilla Feature Distillation
Adversarial Training (VFD-Adv), which conducts knowledge distillation from a
pre-trained model (optimized towards high accuracy) to guide adversarial
training towards higher accuracy, i.e., preserving those non-robust but
predictive features. More specifically, both adversarial examples and their
clean counterparts are forced to be aligned in the feature space by distilling
predictive representations from the pre-trained/clean model, while previous
works barely utilize predictive features from clean models. Therefore, the
adversarial training model is updated towards maximally preserving the accuracy
as gaining robustness. A key advantage of our method is that it can be
universally adapted to and boost existing works. Exhaustive experiments on
various datasets, classification models, and adversarial training algorithms
demonstrate the effectiveness of our proposed method.

There are strong incentives to build models that demonstrate outstanding
predictive performance on various datasets and benchmarks. We believe these
incentives risk a narrow focus on models and on the performance metrics used to
evaluate and compare them -- resulting in a growing body of literature to
evaluate and compare metrics. This paper strives for a more balanced
perspective on classifier performance metrics by highlighting their
distributions under different models of uncertainty and showing how this
uncertainty can easily eclipse differences in the empirical performance of
classifiers. We begin by emphasising the fundamentally discrete nature of
empirical confusion matrices and show how binary matrices can be meaningfully
represented in a three dimensional compositional lattice, whose cross-sections
form the basis of the space of receiver operating characteristic (ROC) curves.
We develop equations, animations and interactive visualisations of the contours
of performance metrics within (and beyond) this ROC space, showing how some are
affected by class imbalance. We provide interactive visualisations that show
the discrete posterior predictive probability mass functions of true and false
positive rates in ROC space, and how these relate to uncertainty in performance
metrics such as Balanced Accuracy (BA) and the Matthews Correlation Coefficient
(MCC). Our hope is that these insights and visualisations will raise greater
awareness of the substantial uncertainty in performance metric estimates that
can arise when classifiers are evaluated on empirical datasets and benchmarks,
and that classification model performance claims should be tempered by this

Current research on users` perspectives of cyber security and privacy related
to traditional and smart devices at home is very active, but the focus is often
more on specific modern devices such as mobile and smart IoT devices in a home
context. In addition, most were based on smaller-scale empirical studies such
as online surveys and interviews. We endeavour to fill these research gaps by
conducting a larger-scale study based on a real-world dataset of 413,985 tweets
posted by non-expert users on Twitter in six months of three consecutive years
(January and February in 2019, 2020 and 2021). Two machine learning-based
classifiers were developed to identify the 413,985 tweets. We analysed this
dataset to understand non-expert users` cyber security and privacy
perspectives, including the yearly trend and the impact of the COVID-19
pandemic. We applied topic modelling, sentiment analysis and qualitative
analysis of selected tweets in the dataset, leading to various interesting
findings. For instance, we observed a 54% increase in non-expert users` tweets
on cyber security and/or privacy related topics in 2021, compared to before the
start of global COVID-19 lockdowns (January 2019 to February 2020). We also
observed an increased level of help-seeking tweets during the COVID-19
pandemic. Our analysis revealed a diverse range of topics discussed by
non-expert users across the three years, including VPNs, Wi-Fi, smartphones,
laptops, smart home devices, financial security, and security and privacy
issues involving different stakeholders. Overall negative sentiment was
observed across almost all topics non-expert users discussed on Twitter in all
the three years. Our results confirm the multi-faceted nature of non-expert
users` perspectives on cyber security and privacy and call for more holistic,
comprehensive and nuanced research on different facets of such perspectives.

Inspired by recent improvements in point cloud processing for autonomous
navigation, we focus on using hierarchical graph neural networks for processing
and feature learning over large-scale outdoor LiDAR point clouds. We observe
that existing GNN based methods fail to overcome challenges of scale and
irregularity of points in outdoor datasets. Addressing the need to preserve
structural details while learning over a larger volume efficiently, we propose
Hierarchical Point Graph Neural Network (HPGNN). It learns node features at
various levels of graph coarseness to extract information. This enables to
learn over a large point cloud while retaining fine details that existing
point-level graph networks struggle to achieve. Connections between multiple
levels enable a point to learn features in multiple scales, in a few
iterations. We design HPGNN as a purely GNN-based approach, so that it offers
modular expandability as seen with other point-based and Graph network
baselines. To illustrate the improved processing capability, we compare
previous point based and GNN models for semantic segmentation with our HPGNN,
achieving a significant improvement for GNNs (+36.7 mIoU) on the SemanticKITTI
dataset.

Deep neural networks must be equipped with an uncertainty estimation
mechanism when deployed for risk-sensitive tasks. This paper studies the
relationship between deep architectures and their training regimes with their
corresponding selective prediction and uncertainty estimation performance. We
consider both in-distribution uncertainties and class-out-of-distribution ones.
Moreover, we consider some of the most popular estimation performance metrics
previously proposed including AUROC, ECE, AURC, and coverage for selective
accuracy constraint. We present a novel and comprehensive study of selective
prediction and the uncertainty estimation performance of 484 existing
pretrained deep ImageNet classifiers that are available at popular
repositories. We identify numerous and previously unknown factors that affect
uncertainty estimation and examine the relationships between the different
metrics. We find that distillation-based training regimes consistently yield
better uncertainty estimations than other training schemes such as vanilla
training, pretraining on a larger dataset and adversarial training. We also
provide strong empirical evidence showing that ViT is by far the most superior
architecture in terms of uncertainty estimation performance, judging by any
aspect, in both in-distribution and class-out-of-distribution scenarios.

The availability of massive image databases resulted in the development of
scalable machine learning methods such as convolutional neural network (CNNs)
filtering and processing these data. While the very recent theoretical work on
CNNs focuses on standard nonparametric denoising problems, the variability in
image classification datasets does, however, not originate from additive noise
but from variation of the shape and other characteristics of the same object
across different images. To address this problem, we consider a simple
supervised classification problem for object detection on grayscale images.
While from the function estimation point of view, every pixel is a variable and
large images lead to high-dimensional function recovery tasks suffering from
the curse of dimensionality, increasing the number of pixels in our image
deformation model enhances the image resolution and makes the object
classification problem easier. We propose and theoretically analyze two
different procedures. The first method estimates the image deformation by
support alignment. Under a minimal separation condition, it is shown that
perfect classification is possible. The second method fits a CNN to the data.
We derive a rate for the misclassification error depending on the sample size
and the number of pixels. Both classifiers are empirically compared on images
generated from the MNIST handwritten digit database. The obtained results
corroborate the theoretical findings.

Polyphone disambiguation aims to capture accurate pronunciation knowledge
from natural text sequences for reliable Text-to-speech (TTS) systems. However,
previous approaches require substantial annotated training data and additional
efforts from language experts, making it difficult to extend high-quality
neural TTS systems to out-of-domain daily conversations and countless languages
worldwide. This paper tackles the polyphone disambiguation problem from a
concise and novel perspective: we propose Dict-TTS, a semantic-aware generative
text-to-speech model with an online website dictionary (the existing prior
information in the natural language). Specifically, we design a
semantics-to-pronunciation attention (S2PA) module to match the semantic
patterns between the input text sequence and the prior semantics in the
dictionary and obtain the corresponding pronunciations; The S2PA module can be
easily trained with the end-to-end TTS model without any annotated phoneme
labels. Experimental results in three languages show that our model outperforms
several strong baseline models in terms of pronunciation accuracy and improves
the prosody modeling of TTS systems. Further extensive analyses demonstrate
that each design in Dict-TTS is effective. The code is available at
\url{https://github.com/Zain-Jiang/Dict-TTS}.

Radiomics with deep learning models have become popular in computer-aided
diagnosis and have outperformed human experts on many clinical tasks.
Specifically, radiomic models based on artificial intelligence (AI) are using
medical data (i.e., images, molecular data, clinical variables, etc.) for
predicting clinical tasks like Autism Spectrum Disorder (ASD). In this review,
we summarized and discussed the radiomic techniques used for ASD analysis.
Currently, the limited radiomic work of ASD is related to variation of
morphological features of brain thickness that is different from texture
analysis. These techniques are based on imaging shape features that can be used
with predictive models for predicting ASD. This review explores the progress of
ASD-based radiomics with a brief description of ASD and the current
non-invasive technique used to classify between ASD and Healthy Control (HC)
subjects. With AI, new radiomic models using the deep learning techniques will
be also described. To consider the texture analysis with deep CNNs, more
investigations are suggested to be integrated with additional validation steps
on various MRI sites.

This paper introduces a model of multi-unit organizations with either static
structures, i.e., they are designed top-down following classical approaches to
organizational design, or dynamic structures, i.e., the structures emerge over
time from micro-level decisions. In the latter case, the units are capable of
learning about the technical interdependencies of the task they face, and they
use their knowledge by adapting the task allocation from time to time. In both
static and dynamic organizations, searching for actions to increase the
performance can either be carried out individually or collaboratively. The
results indicate that (i) collaborative search processes can help overcome the
adverse effects of inefficient task allocations as long as there is an internal
fit with other organizational design elements, and (ii) for dynamic
organizations, the emergent task allocation does not necessarily mirror the
technical interdependencies of the task the organizations face, even though the
same (or even higher) performances are achieved.

The convergence of GD and SGD when training mildly parameterized neural
networks starting from random initialization is studied. For a broad range of
models and loss functions, including the most commonly used square loss and
cross entropy loss, we prove an ``early stage convergence'' result. We show
that the loss is decreased by a significant amount in the early stage of the
training, and this decrease is fast. Furthurmore, for exponential type loss
functions, and under some assumptions on the training data, we show global
convergence of GD. Instead of relying on extreme over-parameterization, our
study is based on a microscopic analysis of the activation patterns for the
neurons, which helps us derive more powerful lower bounds for the gradient. The
results on activation patterns, which we call ``neuron partition'', help build
intuitions for understanding the behavior of neural networks' training
dynamics, and may be of independent interest.

While Identity Document Verification (IDV) technology on mobile devices
becomes ubiquitous in modern business operations, the risk of identity theft
and fraud is increasing. The identity document holder is normally required to
participate in an online video interview to circumvent impostors. However, the
current IDV process depends on an additional human workforce to support online
step-by-step guidance which is inefficient and expensive. The performance of
existing AI-based approaches cannot meet the real-time and lightweight demands
of mobile devices. In this paper, we address those challenges by designing an
edge intelligence-assisted approach for real-time IDV. Aiming at improving the
responsiveness of the IDV process, we propose a new document localization model
for mobile devices, LDRNet, to Localize the identity Document in Real-time. On
the basis of a lightweight backbone network, we build three prediction branches
for LDRNet, the corner points prediction, the line borders prediction and the
document classification. We design novel supplementary targets, the
equal-division points, and use a new loss function named Line Loss, to improve
the speed and accuracy of our approach. In addition to the IDV process, LDRNet
is an efficient and reliable document localization alternative for all kinds of
mobile applications. As a matter of proof, we compare the performance of LDRNet
with other popular approaches on localizing general document datasets. The
experimental results show that LDRNet runs at a speed up to 790 FPS which is
47x faster, while still achieving comparable Jaccard Index(JI) in single-model
and single-scale tests.

Federated learning (FL) has emerged to enable global model training over
distributed clients' data while preserving its privacy. However, the global
trained model is vulnerable to the evasion attacks especially, the adversarial
examples (AEs), carefully crafted samples to yield false classification.
Adversarial training (AT) is found to be the most promising approach against
evasion attacks and it is widely studied for convolutional neural network
(CNN). Recently, vision transformers have been found to be effective in many
computer vision tasks. To the best of the authors' knowledge, there is no work
that studied the feasibility of AT in a FL process for vision transformers.
This paper investigates such feasibility with different federated model
aggregation methods and different vision transformer models with different
tokenization and classification head techniques. In order to improve the robust
accuracy of the models with the not independent and identically distributed
(Non-IID), we propose an extension to FedAvg aggregation method, called
FedWAvg. By measuring the similarities between the last layer of the global
model and the last layer of the client updates, FedWAvg calculates the weights
to aggregate the local models updates. The experiments show that FedWAvg
improves the robust accuracy when compared with other state-of-the-art
aggregation methods.

Estimated Time of Arrival (ETA) plays an important role in delivery and
ride-hailing platforms. For example, Uber uses ETAs to calculate fares,
estimate pickup times, match riders to drivers, plan deliveries, and more.
Commonly used route planning algorithms predict an ETA conditioned on the best
available route, but such ETA estimates can be unreliable when the actual route
taken is not known in advance. In this paper, we describe an ETA
post-processing system in which a deep residual ETA network (DeeprETA) refines
naive ETAs produced by a route planning algorithm. Offline experiments and
online tests demonstrate that post-processing by DeeprETA significantly
improves upon the accuracy of naive ETAs as measured by mean and median
absolute error. We further show that post-processing by DeeprETA attains lower
error than competitive baseline regression models.

Solving a reinforcement learning (RL) problem poses two competing challenges:
fitting a potentially discontinuous value function, and generalizing well to
new observations. In this paper, we analyze the learning dynamics of temporal
difference algorithms to gain novel insight into the tension between these two
objectives. We show theoretically that temporal difference learning encourages
agents to fit non-smooth components of the value function early in training,
and at the same time induces the second-order effect of discouraging
generalization. We corroborate these findings in deep RL agents trained on a
range of environments, finding that neural networks trained using temporal
difference algorithms on dense reward tasks exhibit weaker generalization
between states than randomly initialized networks and networks trained with
policy gradient methods. Finally, we investigate how post-training policy
distillation may avoid this pitfall, and show that this approach improves
generalization to novel environments in the ProcGen suite and improves
robustness to input perturbations.

In some DNNs for audio source separation, the relevant model parameters are
independent of the sampling frequency of the audio used for training.
Considering the application of dialogue separation, this is shown for two DNN
architectures: a U-Net and a fully-convolutional model. The models are trained
with audio sampled at 8 kHz. The learned parameters are transferred to models
for processing audio at 48 kHz. The separated audio sources are compared with
the ones produced by the same model architectures trained with 48 kHz versions
of the same training data. A listening test and computational measures show
that there is no significant perceptual difference between the models trained
with 8 kHz or with 48 kHz. This transferability of the learned parameters
allows for a faster and computationally less costly training. It also enables
using training datasets available at a lower sampling frequency than the one
needed by the application at hand, or using data collections with multiple
sampling frequencies.

Cardiac segmentation is an essential step for the diagnosis of cardiovascular
diseases. However, pixel-wise dense labeling is both costly and time-consuming.
Scribble, as a form of sparse annotation, is more accessible than full
annotations. However, it's particularly challenging to train a segmentation
network with weak supervision from scribbles. To tackle this problem, we
propose a new scribble-guided method for cardiac segmentation, based on the
Positive-Unlabeled (PU) learning framework and global consistency
regularization, and termed as ShapePU. To leverage unlabeled pixels via PU
learning, we first present an Expectation-Maximization (EM) algorithm to
estimate the proportion of each class in the unlabeled pixels. Given the
estimated ratios, we then introduce the marginal probability maximization to
identify the classes of unlabeled pixels. To exploit shape knowledge, we apply
cutout operations to training images, and penalize the inconsistent
segmentation results. Evaluated on two open datasets, i.e, ACDC and MSCMRseg,
our scribble-supervised ShapePU surpassed the fully supervised approach
respectively by 1.4% and 9.8% in average Dice, and outperformed the
state-of-the-art weakly supervised and PU learning methods by large margins.
Our code is available at https://github.com/BWGZK/ShapePU.

Democratization of AI involves training and deploying machine learning models
across heterogeneous and potentially massive environments. Diversity of data
opens up a number of possibilities to advance AI systems, but also introduces
pressing concerns such as privacy, security, and equity that require special
attention. This work shows that it is theoretically impossible to design a
rational learning algorithm that has the ability to successfully learn across
heterogeneous environments, which we decoratively call collective intelligence
(CI). By representing learning algorithms as choice correspondences over a
hypothesis space, we are able to axiomatize them with essential properties.
Unfortunately, the only feasible algorithm compatible with all of the axioms is
the standard empirical risk minimization (ERM) which learns arbitrarily from a
single environment. Our impossibility result reveals informational
incomparability between environments as one of the foremost obstacles for
researchers who design novel algorithms that learn from multiple environments,
which sheds light on prerequisites for success in critical areas of machine
learning such as out-of-distribution generalization, federated learning,
algorithmic fairness, and multi-modal learning.

Learning vectorized embeddings is at the core of various recommender systems
for user-item matching. To perform efficient online inference, representation
quantization, aiming to embed the latent features by a compact sequence of
discrete numbers, recently shows the promising potentiality in optimizing both
memory and computation overheads. However, existing work merely focuses on
numerical quantization whilst ignoring the concomitant information loss issue,
which, consequently, leads to conspicuous performance degradation. In this
paper, we propose a novel quantization framework to learn Binarized Graph
Representations for Top-K Recommendation (BiGeaR). BiGeaR introduces
multi-faceted quantization reinforcement at the pre-, mid-, and post-stage of
binarized representation learning, which substantially retains the
representation informativeness against embedding binarization. In addition to
saving the memory footprint, BiGeaR further develops solid online inference
acceleration with bitwise operations, providing alternative flexibility for the
realistic deployment. The empirical results over five large real-world
benchmarks show that BiGeaR achieves about 22%~40% performance improvement over
the state-of-the-art quantization-based recommender system, and recovers about
95%~102% of the performance capability of the best full-precision counterpart
with over 8x time and space reduction.

Among the different kinds of fire accidents that can occur during industrial
activities that involve hazardous materials, jet fires are one of the
lesser-known types. This is because they are often involved in a process that
generates a sequence of other accidents of greater magnitude, known as domino
effect. Flame impingement usually causes domino effects, and jet fires present
specific features that can significantly increase the probability of this
happening. These features become relevant from a risk analysis perspective,
making their proper characterization a crucial task. Deep Learning approaches
have become extensively used for tasks such as jet fire characterization;
however, these methods are heavily dependent on the amount of data and the
quality of the labels. Data acquisition of jet fires involve expensive
experiments, especially so if infrared imagery is used. Therefore, this paper
proposes the use of Generative Adversarial Networks to produce plausible
infrared images from visible ones, making experiments less expensive and
allowing for other potential applications. The results suggest that it is
possible to realistically replicate the results for experiments carried out
using both visible and infrared cameras. The obtained results are compared with
some previous experiments, and it is shown that similar results were obtained.

Being a promising model to be deployed in resource-limited devices, Binarized
Neural Networks (BNNs) have drawn extensive attention from both academic and
industry. However, comparing to the full-precision deep neural networks (DNNs),
BNNs suffer from non-trivial accuracy degradation, limiting its applicability
in various domains. This is partially because existing network components, such
as the similarity measure, are specially designed for DNNs, and might be
sub-optimal for BNNs.
  In this work, we focus on the key component of BNNs -- the similarity
measure, which quantifies the distance between input feature maps and filters,
and propose an automatic searching method, based on genetic algorithm, for
BNN-tailored similarity measure. Evaluation results on Cifar10 and Cifar100
using ResNet, NIN and VGG show that most of the identified similarty measure
can achieve considerable accuracy improvement (up to 3.39%) over the
commonly-used cross-correlation approach.

Optimal mass transport, also known as the earth mover's problem, is an
optimization problem with important applications in various disciplines,
including economics, probability theory, fluid dynamics, cosmology and
geophysics to cite a few. Optimal transport has also found successful
applications in image registration, content-based image retrieval, and more
generally in pattern recognition and machine learning as a way to measure
dissimilarity among data. This paper introduces the problem of constrained
optimal transport. The time-dependent formulation, more precisely, the fluid
dynamics approach is used as a starting point from which the constrained
problem is defined by imposing a soft constraint on the density and momentum
fields or restricting them to a subset of curves that satisfy some prescribed
conditions. A family of algorithms is introduced to solve a class of
constrained saddle point problems, which has convexly constrained optimal
transport on closed convex subsets of the Euclidean space as a special case.
Convergence proofs and numerical results are presented.

With the growth of machine learning for structured data, the need for
reliable model explanations is essential, especially in high-stakes
applications. We introduce a novel framework, Interpretable Mixture of Experts
(IME), that provides interpretability for structured data while preserving
accuracy. IME consists of an assignment module and a mixture of interpretable
experts such as linear models where each sample is assigned to a single
interpretable expert. This results in an inherently-interpretable architecture
where the explanations produced by IME are the exact descriptions of how the
prediction is computed. In addition to constituting a standalone
inherently-interpretable architecture, an additional IME capability is that it
can be integrated with existing Deep Neural Networks (DNNs) to offer
interpretability to a subset of samples while maintaining the accuracy of the
DNNs. Experiments on various structured datasets demonstrate that IME is more
accurate than a single interpretable model and performs comparably to existing
state-of-the-art deep learning models in terms of accuracy while providing
faithful explanations.

Neutron stars are regarded as natural laboratories for the study of dense
strong interaction matter. The equation of state (EoS) of dense matter computed
in flat spacetime is used to predict the structure of neutron stars by solving
the Tolman-Oppenheimer-Volkoff (TOV) equation. Recently, it has been reported
that the curved spacetime effect or specifically gravitational time dilation
effect on the EoS of dense matter leads to a significant increase of the
maximum mass limit of neutron stars [Phys. Rev. D \textbf{104}, 123005 (2021)
and J. Cosmol. Astropart. Phys. 02 (2021) 026]. However, in this work, we show
that to study the hydrostatic equilibrium of dense matter within the framework
of general relativity and relativistic fluid dynamics, the grand canonical EoS
of dense matter, $p(T,\mu)$, should be the same as that computed in flat
spacetime, otherwise it is not consistent with local thermodynamic relations
and energy-momentum conservation of the fluid. The gravitation influences the
pressure $p$ only through enhancing the temperature $T$ and the chemical
potential $\mu$, known as Tolman's law and Klein's law. We rewrite the TOV
equation as an alternative version so that the grand canonical EoS computed by
using field theoretical methods can be used as a direct input. This may provide
a tool to study the grand canonical EoS of dense matter via deep learning.

Binary neural networks (BNNs) show promising utilization in cost and
power-restricted domains such as edge devices and mobile systems. This is due
to its significantly less computation and storage demand, but at the cost of
degraded performance. To close the accuracy gap, in this paper we propose to
add a complementary activation function (AF) ahead of the sign based
binarization, and rely on the genetic algorithm (GA) to automatically search
for the ideal AFs. These AFs can help extract extra information from the input
data in the forward pass, while allowing improved gradient approximation in the
backward pass. Fifteen novel AFs are identified through our GA-based search,
while most of them show improved performance (up to 2.54% on ImageNet) when
testing on different datasets and network models. Our method offers a novel
approach for designing general and application-specific BNN architecture. Our
code is available at http://github.com/flying-Yan/GAAF.

Nonlinear monotone transformations are used extensively in normalizing flows
to construct invertible triangular mappings from simple distributions to
complex ones. In existing literature, monotonicity is usually enforced by
restricting function classes or model parameters and the inverse transformation
is often approximated by root-finding algorithms as a closed-form inverse is
unavailable. In this paper, we introduce a new integral-based approach termed
"Atomic Unrestricted Time Machine (AUTM)", equipped with unrestricted
integrands and easy-to-compute explicit inverse. AUTM offers a versatile and
efficient way to the design of normalizing flows with explicit inverse and
unrestricted function classes or parameters. Theoretically, we present a
constructive proof that AUTM is universal: all monotonic normalizing flows can
be viewed as limits of AUTM flows. We provide a concrete example to show how to
approximate any given monotonic normalizing flow using AUTM flows with
guaranteed convergence. The result implies that AUTM can be used to transform
an existing flow into a new one equipped with explicit inverse and unrestricted
parameters. The performance of the new approach is evaluated on high
dimensional density estimation, variational inference and image generation.
Experiments demonstrate superior speed and memory efficiency of AUTM.

As deep neural networks achieve unprecedented performance in various tasks,
neural architecture search (NAS), a research field for designing neural network
architectures with automated processes, is actively underway. More recently,
differentiable NAS has a great impact by reducing the search cost to the level
of training a single network. Besides, the search space that defines candidate
architectures to be searched directly affects the performance of the final
architecture. In this paper, we propose an adaptation scheme of the search
space by introducing a search scope. The effectiveness of proposed method is
demonstrated with ProxylessNAS for the image classification task. Furthermore,
we visualize the trajectory of architecture parameter updates and provide
insights to improve the architecture search.

We are now witnessing significant progress of deep learning methods in a
variety of tasks (or datasets) of proteins. However, there is a lack of a
standard benchmark to evaluate the performance of different methods, which
hinders the progress of deep learning in this field. In this paper, we propose
such a benchmark called PEER, a comprehensive and multi-task benchmark for
Protein sEquence undERstanding. PEER provides a set of diverse protein
understanding tasks including protein function prediction, protein localization
prediction, protein structure prediction, protein-protein interaction
prediction, and protein-ligand interaction prediction. We evaluate different
types of sequence-based methods for each task including traditional feature
engineering approaches, different sequence encoding methods as well as
large-scale pre-trained protein language models. In addition, we also
investigate the performance of these methods under the multi-task learning
setting. Experimental results show that large-scale pre-trained protein
language models achieve the best performance for most individual tasks, and
jointly training multiple tasks further boosts the performance. The datasets
and source codes of this benchmark are all available at
https://github.com/DeepGraphLearning/PEER_Benchmark

Adversarial Imitation Learning (AIL) is a class of popular state-of-the-art
Imitation Learning algorithms where an artificial adversary's misclassification
is used as a reward signal and is optimized by any standard Reinforcement
Learning (RL) algorithm. Unlike most RL settings, the reward in AIL is
differentiable but model-free RL algorithms do not make use of this property to
train a policy. In contrast, we leverage the differentiability property of the
AIL reward function and formulate a class of Actor Residual Critic (ARC) RL
algorithms that draw a parallel to the standard Actor-Critic (AC) algorithms in
RL literature and uses a residual critic, C function (instead of the standard Q
function) to approximate only the discounted future return (excluding the
immediate reward). ARC algorithms have similar convergence properties as the
standard AC algorithms with the additional advantage that the gradient through
the immediate reward is exact. For the discrete (tabular) case with finite
states, actions, and known dynamics, we prove that policy iteration with $C$
function converges to an optimal policy. In the continuous case with function
approximation and unknown dynamics, we experimentally show that ARC aided AIL
outperforms standard AIL in simulated continuous-control and real robotic
manipulation tasks. ARC algorithms are simple to implement and can be
incorporated into any existing AIL implementation with an AC algorithm.

We report an improvement to the conventional Echo State Network (ESN), which
already achieves competitive performance in one-dimensional time series
prediction of dynamical systems. Our model -- a 20$\%$-dense ESN with reservoir
weights derived from a fruit fly connectome (and from its bootstrapped
distribution) -- yields superior performance on a chaotic time series
prediction task, and furthermore alleviates the ESN's high-variance problem. We
also find that an arbitrary positioning of weights can degrade ESN performance
and variance; and that this can be remedied in particular by employing
connectome-derived weight positions. Herein we consider four connectome
features -- namely, the sparsity, positioning, distribution, and clustering of
weights -- and construct corresponding model classes (A, B, B${}_2$, C) from an
appropriate null model ESN; one with its reservoir layer replaced by a fruit
fly connectivity matrix. After tuning relevant hyperparameters and selecting
the best instance of each model class, we train and validate all models for
multi-step prediction on size-variants (50, 250, 500, and 750 training input
steps) of the Mackey-Glass chaotic time series; and compute their performance
(Mean-Squared Error) and variance across train-validate trials.

Despite the rapid progress in automatic speech recognition (ASR) research,
recognizing multilingual speech using a unified ASR system remains highly
challenging. Previous works on multilingual speech recognition mainly focus on
two directions: recognizing multiple monolingual speech or recognizing
code-switched speech that uses different languages interchangeably within a
single utterance. However, a pragmatic multilingual recognizer is expected to
be compatible with both directions. In this work, a novel language-aware
encoder (LAE) architecture is proposed to handle both situations by
disentangling language-specific information and generating frame-level
language-aware representations during encoding. In the LAE, the primary
encoding is implemented by the shared block while the language-specific blocks
are used to extract specific representations for each language. To learn
language-specific information discriminatively, a language-aware training
method is proposed to optimize the language-specific blocks in LAE. Experiments
conducted on Mandarin-English code-switched speech suggest that the proposed
LAE is capable of discriminating different languages in frame-level and shows
superior performance on both monolingual and multilingual ASR tasks. With
either a real-recorded or simulated code-switched dataset, the proposed LAE
achieves statistically significant improvements on both CTC and neural
transducer systems. Code is released

Directed Evolution (DE), a landmark wet-lab method originated in 1960s,
enables discovery of novel protein designs via evolving a population of
candidate sequences. Recent advances in biotechnology has made it possible to
collect high-throughput data, allowing the use of machine learning to map out a
protein's sequence-to-function relation. There is a growing interest in machine
learning-assisted DE for accelerating protein optimization. Yet the theoretical
understanding of DE, as well as the use of machine learning in DE, remains
limited. In this paper, we connect DE with the bandit learning theory and make
a first attempt to study regret minimization in DE. We propose a Thompson
Sampling-guided Directed Evolution (TS-DE) framework for sequence optimization,
where the sequence-to-function mapping is unknown and querying a single value
is subject to costly and noisy measurements. TS-DE updates a posterior of the
function based on collected measurements. It uses a posterior-sampled function
estimate to guide the crossover recombination and mutation steps in DE. In the
case of a linear model, we show that TS-DE enjoys a Bayesian regret of order
$\tilde O(d^{2}\sqrt{MT})$, where $d$ is feature dimension, $M$ is population
size and $T$ is number of rounds. This regret bound is nearly optimal,
confirming that bandit learning can provably accelerate DE. It may have
implications for more general sequence optimization and evolutionary

In order to trust machine learning for high-stakes problems, we need models
to be both reliable and interpretable. Recently, there has been a growing body
of work on interpretable machine learning which generates human understandable
insights into data, models, or predictions. At the same time, there has been
increased interest in quantifying the reliability and uncertainty of machine
learning predictions, often in the form of confidence intervals for predictions
using conformal inference. Yet, there has been relatively little attention
given to the reliability and uncertainty of machine learning interpretations,
which is the focus of this paper. Our goal is to develop confidence intervals
for a widely-used form of machine learning interpretation: feature importance.
We specifically seek to develop universal model-agnostic and assumption-light
confidence intervals for feature importance that will be valid for any machine
learning model and for any regression or classification task. We do so by
leveraging a form of random observation and feature subsampling called
minipatch ensembles and show that our approach provides assumption-light
asymptotic coverage for the feature importance score of any model. Further, our
approach is fast as computations needed for inference come nearly for free as
part of the ensemble learning process. Finally, we also show that our same
procedure can be leveraged to provide valid confidence intervals for
predictions, hence providing fast, simultaneous quantification of the
uncertainty of both model predictions and interpretations. We validate our
intervals on a series of synthetic and real data examples, showing that our
approach detects the correct important features and exhibits many computational
and statistical advantages over existing methods.

Food classification is critical to the analysis of nutrients comprising foods
reported in dietary assessment. Advances in mobile and wearable sensors,
combined with new image based methods, particularly deep learning based
approaches, have shown great promise to improve the accuracy of food
classification to assess dietary intake. However, these approaches are
data-hungry and their performances are heavily reliant on the quantity and
quality of the available datasets for training the food classification model.
Existing food image datasets are not suitable for fine-grained food
classification and the following nutrition analysis as they lack fine-grained
and transparently derived food group based identification which are often
provided by trained dietitians with expert domain knowledge. In this paper, we
propose a framework to create a nutrition and food group based image database
that contains both visual and hierarchical food categorization information to
enhance links to the nutrient profile of each food. We design a protocol for
linking food group based food codes in the U.S. Department of Agriculture's
(USDA) Food and Nutrient Database for Dietary Studies (FNDDS) to a food image
dataset, and implement a web-based annotation tool for efficient deployment of
this protocol.Our proposed method is used to build a nutrition and food group
based image database including 16,114 food images representing the 74 most
frequently consumed What We Eat in America (WWEIA) food sub-categories in the
United States with 1,865 USDA food code matched to a nutrient database, the
USDA FNDDS nutrient database.

This theoretical paper is devoted to developing a rigorous theory for
demystifying the global convergence phenomenon in a challenging scenario:
learning over-parameterized Rectified Linear Unit (ReLU) nets for very high
dimensional dataset under very mild assumptions. A major ingredient of our
analysis is a fine-grained analysis of random activation matrices. The
essential virtue of dissecting activation matrices is that it bridges the
dynamics of optimization and angular distribution in high-dimensional data
space. This angle-based detailed analysis leads to asymptotic characterizations
of gradient norm and directional curvature of objective function at each
gradient descent iteration, revealing that the empirical loss function enjoys
nice geometrical properties in the overparameterized setting. Along the way, we
significantly improve existing theoretical bounds on both over-parameterization
condition and learning rate with very mild assumptions for learning very high
dimensional data. Moreover, we uncover the role of the geometrical and spectral
properties of the input data in determining desired over-parameterization size
and global convergence rate. All these clues allow us to discover a novel
geometric picture of nonconvex optimization in deep learning: angular
distribution in high-dimensional data space $\mapsto$ spectrums of
overparameterized activation matrices $\mapsto$ favorable geometrical
properties of empirical loss landscape $\mapsto$ global convergence phenomenon.
Furthremore, our theoretical results imply that gradient-based nonconvex
optimization algorithms have much stronger statistical guarantees with much
milder over-parameterization condition than exisiting theory states for
learning very high dimensional data, which is rarely explored so far.

Recent work in multilingual translation advances translation quality
surpassing bilingual baselines using deep transformer models with increased
capacity. However, the extra latency and memory costs introduced by this
approach may make it unacceptable for efficiency-constrained applications. It
has recently been shown for bilingual translation that using a deep encoder and
shallow decoder (DESD) can reduce inference latency while maintaining
translation quality, so we study similar speed-accuracy trade-offs for
multilingual translation. We find that for many-to-one translation we can
indeed increase decoder speed without sacrificing quality using this approach,
but for one-to-many translation, shallow decoders cause a clear quality drop.
To ameliorate this drop, we propose a deep encoder with multiple shallow
decoders (DEMSD) where each shallow decoder is responsible for a disjoint
subset of target languages. Specifically, the DEMSD model with 2-layer decoders
is able to obtain a 1.8x speedup on average compared to a standard transformer
model with no drop in translation quality.

Federated Learning is an emerging learning paradigm that allows training
models from samples distributed across a large network of clients while
respecting privacy and communication restrictions. Despite its success,
federated learning faces several challenges related to its decentralized
nature. In this work, we develop a novel algorithmic procedure with theoretical
speedup guarantees that simultaneously handles two of these hurdles, namely (i)
data heterogeneity, i.e., data distributions can vary substantially across
clients, and (ii) system heterogeneity, i.e., the computational power of the
clients could differ significantly. Our method relies on ideas from
representation learning theory to find a global common representation using all
clients' data and learn a user-specific set of parameters leading to a
personalized solution for each client. Furthermore, our method mitigates the
effects of stragglers by adaptively selecting clients based on their
computational characteristics and statistical significance, thus achieving, for
the first time, near optimal sample complexity and provable logarithmic
speedup. Experimental results support our theoretical findings showing the
superiority of our method over alternative personalized federated schemes in
system and data heterogeneous environments.

The quintessential model-based reinforcement-learning agent iteratively
refines its estimates or prior beliefs about the true underlying model of the
environment. Recent empirical successes in model-based reinforcement learning
with function approximation, however, eschew the true model in favor of a
surrogate that, while ignoring various facets of the environment, still
facilitates effective planning over behaviors. Recently formalized as the value
equivalence principle, this algorithmic technique is perhaps unavoidable as
real-world reinforcement learning demands consideration of a simple,
computationally-bounded agent interacting with an overwhelmingly complex
environment, whose underlying dynamics likely exceed the agent's capacity for
representation. In this work, we consider the scenario where agent limitations
may entirely preclude identifying an exactly value-equivalent model,
immediately giving rise to a trade-off between identifying a model that is
simple enough to learn while only incurring bounded sub-optimality. To address
this problem, we introduce an algorithm that, using rate-distortion theory,
iteratively computes an approximately-value-equivalent, lossy compression of
the environment which an agent may feasibly target in lieu of the true model.
We prove an information-theoretic, Bayesian regret bound for our algorithm that
holds for any finite-horizon, episodic sequential decision-making problem.
Crucially, our regret bound can be expressed in one of two possible forms,
providing a performance guarantee for finding either the simplest model that
achieves a desired sub-optimality gap or, alternatively, the best model given a
limit on agent capacity.

Image restoration and enhancement is a process of improving the image quality
by removing degradations, such as noise, blur, and resolution degradation. Deep
learning (DL) has recently been applied to image restoration and enhancement.
Due to its ill-posed property, plenty of works have explored priors to
facilitate training deep neural networks (DNNs). However, the importance of
priors has not been systematically studied and analyzed by far in the research
community. Therefore, this paper serves as the first study that provides a
comprehensive overview of recent advancements of priors for deep image
restoration and enhancement. Our work covers five primary contents: (1) A
theoretical analysis of priors for deep image restoration and enhancement; (2)
A hierarchical and structural taxonomy of priors commonly used in the DL-based
methods; (3) An insightful discussion on each prior regarding its principle,
potential, and applications; (4) A summary of crucial problems by highlighting
the potential future directions to spark more research in the community; (5) An
open-source repository that provides a taxonomy of all mentioned works and code
links.

Gyenis and Redei have demonstrated that any prior p on a finite algebra,
however chosen, severely restricts the set of posteriors accessible from p by
Jeffrey conditioning on a nontrivial partition. Their demonstration involves
showing that the set of posteriors not accessible from p in this way (which
they call the Bayes blind spot of p) is large with respect to three common
measures of size, namely, having cardinality c, (normalized) Lebesgue measure
1, and Baire second category with respect to a natural topology. In the present
paper, we establish analogous results for probability measures defined on any
infinite sigma algebra of subsets of a denumerably infinite set. However, we
have needed to employ distinctly different approaches to determine the
cardinality, and especially, the topological and measure-theoretic sizes of the
Bayes blind spot in the infinite case. Interestingly, all of the results that
we establish for a single prior p continue to hold for the intersection of the
Bayes blind spots of countably many priors. This leads us to conjecture that
Bayesian learning itself might be just as culpable as the limitations imposed
by priors in enabling the existence of large Bayes blind spots.

With recent progress in deep generative models, the problem of identifying
synthetic data and comparing their underlying generative processes has become
an imperative task for various reasons, including fighting visual
misinformation and source attribution. Existing methods often approximate the
distance between the models via their sample distributions. In this paper, we
approach the problem of fingerprinting generative models by learning
representations that encode the residual artifacts left by the generative
models as unique signals that identify the source models. We consider these
unique traces (a.k.a. "artificial fingerprints") as representations of
generative models, and demonstrate their usefulness in both the discriminative
task of source attribution and the unsupervised task of defining a similarity
between the underlying models. We first extend the existing studies on
fingerprints of GANs to four representative classes of generative models (VAEs,
Flows, GANs and score-based models), and demonstrate their existence and
attributability. We then improve the stability and attributability of the
fingerprints by proposing a new learning method based on set-encoding and
contrastive training. Our set-encoder, unlike existing methods that operate on
individual images, learns fingerprints from a \textit{set} of images. We
demonstrate improvements in the stability and attributability through
comparisons to state-of-the-art fingerprint methods and ablation studies.
Further, our method employs contrastive training to learn an implicit
similarity between models. We discover latent families of generative models
using this metric in a standard hierarchical clustering algorithm.

Causal discovery and causal reasoning are classically treated as separate and
consecutive tasks: one first infers the causal graph, and then uses it to
estimate causal effects of interventions. However, such a two-stage approach is
uneconomical, especially in terms of actively collected interventional data,
since the causal query of interest may not require a fully-specified causal
model. From a Bayesian perspective, it is also unnatural, since a causal query
(e.g., the causal graph or some causal effect) can be viewed as a latent
quantity subject to posterior inference -- other unobserved quantities that are
not of direct interest (e.g., the full causal model) ought to be marginalized
out in this process and contribute to our epistemic uncertainty. In this work,
we propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active
learning framework for integrated causal discovery and reasoning, which jointly
infers a posterior over causal models and queries of interest. In our approach
to ABCI, we focus on the class of causally-sufficient, nonlinear additive noise
models, which we model using Gaussian processes. We sequentially design
experiments that are maximally informative about our target causal query,
collect the corresponding interventional data, and update our beliefs to choose
the next experiment. Through simulations, we demonstrate that our approach is
more data-efficient than several baselines that only focus on learning the full
causal graph. This allows us to accurately learn downstream causal queries from
fewer samples while providing well-calibrated uncertainty estimates for the
quantities of interest.

Adept network management is key for supporting extremely heterogeneous
applications with stringent quality of service (QoS) requirements; this is more
so when envisioning the complex and ultra-dense 6G mobile heterogeneous network
(HetNet). From both the environmental and economical perspectives,
non-homogeneous QoS demands obstruct the minimization of the energy footprints
and operational costs of the envisioned robust networks. As such, network
intelligentization is expected to play an essential role in the realization of
such sophisticated aims. The fusion of artificial intelligence (AI) and mobile
networks will allow for the dynamic and automatic configuration of network
functionalities. Machine learning (ML), one of the backbones of AI, will be
instrumental in forecasting changes in network loads and resource utilization,
estimating channel conditions, optimizing network slicing, and enhancing
security and encryption. However, it is well known that ML tasks themselves
incur massive computational burdens and energy costs. To overcome such
obstacles, we propose a novel layer-based HetNet architecture which optimally
distributes tasks associated with different ML approaches across network layers
and entities; such a HetNet boasts multiple access schemes as well as
device-to-device (D2D) communications to enhance energy efficiency via
collaborative learning and communications.

Message passing graph neural networks (GNNs) are known to have their
expressiveness upper-bounded by 1-dimensional Weisfeiler-Lehman (1-WL)
algorithm. To achieve more powerful GNNs, existing attempts either require ad
hoc features, or involve operations that incur high time and space
complexities. In this work, we propose a general and provably powerful GNN
framework that preserves the scalability of message passing scheme. In
particular, we first propose to empower 1-WL for graph isomorphism test by
considering edges among neighbors, giving rise to NC-1-WL. The expressiveness
of NC-1-WL is shown to be strictly above 1-WL but below 3-WL theoretically.
Further, we propose the NC-GNN framework as a differentiable neural version of
NC-1-WL. Our simple implementation of NC-GNN is provably as powerful as
NC-1-WL. Experiments demonstrate that our NC-GNN achieves remarkable
performance on various benchmarks.

The standard approach to personalization in machine learning consists of
training a model with group attributes like sex, age group, and blood type. In
this work, we show that this approach to personalization fails to improve
performance for all groups who provide personal data. We discuss how this
effect inflicts harm in applications where models assign predictions on the
basis of group membership. We propose collective preference guarantees to
ensure the fair use of group attributes in prediction. We characterize how
common approaches to personalization violate fair use due to failures in model
development and deployment. We conduct a comprehensive empirical study of
personalization in clinical prediction models. Our results highlight the
prevalence of fair use violations, demonstrate actionable interventions to
mitigate harm and underscore the need to measure the gains of personalization
for all groups who provide personal data.

Application of interpretable machine learning techniques on medical datasets
facilitate early and fast diagnoses, along with getting deeper insight into the
data. Furthermore, the transparency of these models increase trust among
application domain experts. Medical datasets face common issues such as
heterogeneous measurements, imbalanced classes with limited sample size, and
missing data, which hinder the straightforward application of machine learning
techniques. In this paper we present a family of prototype-based (PB)
interpretable models which are capable of handling these issues. The models
introduced in this contribution show comparable or superior performance to
alternative techniques applicable in such situations. However, unlike ensemble
based models, which have to compromise on easy interpretation, the PB models
here do not. Moreover we propose a strategy of harnessing the power of
ensembles while maintaining the intrinsic interpretability of the PB models, by
averaging the model parameter manifolds. All the models were evaluated on a
synthetic (publicly available dataset) in addition to detailed analyses of two
real-world medical datasets (one publicly available). Results indicated that
the models and strategies we introduced addressed the challenges of real-world
medical data, while remaining computationally inexpensive and transparent, as
well as similar or superior in performance compared to their alternatives.

Understanding the lip movement and inferring the speech from it is
notoriously difficult for the common person. The task of accurate lip-reading
gets help from various cues of the speaker and its contextual or environmental
setting. Every speaker has a different accent and speaking style, which can be
inferred from their visual and speech features. This work aims to understand
the correlation/mapping between speech and the sequence of lip movement of
individual speakers in an unconstrained and large vocabulary. We model the
frame sequence as a prior to the transformer in an auto-encoder setting and
learned a joint embedding that exploits temporal properties of both audio and
video. We learn temporal synchronization using deep metric learning, which
guides the decoder to generate speech in sync with input lip movements. The
predictive posterior thus gives us the generated speech in speaker speaking
style. We have trained our model on the Grid and Lip2Wav Chemistry lecture
dataset to evaluate single speaker natural speech generation tasks from lip
movement in an unconstrained natural setting. Extensive evaluation using
various qualitative and quantitative metrics with human evaluation also shows
that our method outperforms the Lip2Wav Chemistry dataset(large vocabulary in
an unconstrained setting) by a good margin across almost all evaluation metrics
and marginally outperforms the state-of-the-art on GRID dataset.

In this paper, we study the generalization performance of overparameterized
3-layer NTK models. We show that, for a specific set of ground-truth functions
(which we refer to as the "learnable set"), the test error of the overfitted
3-layer NTK is upper bounded by an expression that decreases with the number of
neurons of the two hidden layers. Different from 2-layer NTK where there exists
only one hidden-layer, the 3-layer NTK involves interactions between two
hidden-layers. Our upper bound reveals that, between the two hidden-layers, the
test error descends faster with respect to the number of neurons in the second
hidden-layer (the one closer to the output) than with respect to that in the
first hidden-layer (the one closer to the input). We also show that the
learnable set of 3-layer NTK without bias is no smaller than that of 2-layer
NTK models with various choices of bias in the neurons. However, in terms of
the actual generalization performance, our results suggest that 3-layer NTK is
much less sensitive to the choices of bias than 2-layer NTK, especially when
the input dimension is large.

In this work, we investigate the problem of an online trajectory design for
an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where
several different communities exist, each defined by a unique task to be
learned. In this setting, spatially distributed devices belonging to each
community collaboratively contribute towards training their community model via
wireless links provided by the UAV. Accordingly, the UAV acts as a mobile
orchestrator coordinating the transmissions and the learning schedule among the
devices in each community, intending to accelerate the learning process of all
tasks. We propose a heuristic metric as a proxy for the training performance of
the different tasks. Capitalizing on this metric, a surrogate objective is
defined which enables us to jointly optimize the UAV trajectory and the
scheduling of the devices by employing convex optimization techniques and graph
theory. The simulations illustrate the out-performance of our solution when
compared to other handpicked static and mobile UAV deployment baselines.

Humans can make predictions on various time scales and hierarchical levels.
Thereby, the learning of event encodings seems to play a crucial role. In this
work we model the development of hierarchical predictions via autonomously
learned latent event codes. We present a hierarchical recurrent neural network
architecture, whose inductive learning biases foster the development of
sparsely changing latent state that compress sensorimotor sequences. A higher
level network learns to predict the situations in which the latent states tend
to change. Using a simulated robotic manipulator, we demonstrate that the
system (i) learns latent states that accurately reflect the event structure of
the data, (ii) develops meaningful temporal abstract predictions on the higher
level, and (iii) generates goal-anticipatory behavior similar to gaze behavior
found in eye-tracking studies with infants. The architecture offers a step
towards the autonomous learning of compressed hierarchical encodings of
gathered experiences and the exploitation of these encodings to generate
adaptive behavior.

From optimal transport to robust dimensionality reduction, a plethora of
machine learning applications can be cast into the min-max optimization
problems over Riemannian manifolds. Though many min-max algorithms have been
analyzed in the Euclidean setting, it has proved elusive to translate these
results to the Riemannian case. Zhang et al. [2022] have recently shown that
geodesic convex concave Riemannian problems always admit saddle-point
solutions. Inspired by this result, we study whether a performance gap between
Riemannian and optimal Euclidean space convex-concave algorithms is necessary.
We answer this question in the negative-we prove that the Riemannian corrected
extragradient (RCEG) method achieves last-iterate convergence at a linear rate
in the geodesically strongly-convex-concave case, matching the Euclidean
result. Our results also extend to the stochastic or non-smooth case where RCEG
and Riemanian gradient ascent descent (RGDA) achieve near-optimal convergence
rates up to factors depending on curvature of the manifold.

We propose MetaNOR, a meta-learnt approach for transfer-learning operators
based on the nonlocal operator regression. The overall goal is to efficiently
provide surrogate models for new and unknown material-learning tasks with
different microstructures. The algorithm consists of two phases: (1) learning a
common nonlocal kernel representation from existing tasks; (2) transferring the
learned knowledge and rapidly learning surrogate operators for unseen tasks
with a different material, where only a few test samples are required. We apply
MetaNOR to model the wave propagation within 1D metamaterials, showing
substantial improvements on the sampling efficiency for new materials.

Reinforcement learning (RL) agents are commonly evaluated via their expected
value over a distribution of test scenarios. Unfortunately, this evaluation
approach provides limited evidence for post-deployment generalization beyond
the test distribution. In this paper, we address this limitation by extending
the recent CheckList testing methodology from natural language processing to
planning-based RL. Specifically, we consider testing RL agents that make
decisions via online tree search using a learned transition model and value
function. The key idea is to improve the assessment of future performance via a
CheckList approach for exploring and assessing the agent's inferences during
tree search. The approach provides the user with an interface and general
query-rule mechanism for identifying potential inference flaws and validating
expected inference invariances. We present a user study involving knowledgeable
AI researchers using the approach to evaluate an agent trained to play a
complex real-time strategy game. The results show the approach is effective in
allowing users to identify previously-unknown flaws in the agent's reasoning.
In addition, our analysis provides insight into how AI experts use this type of
testing approach, which may help improve future instantiations.

Neural replicator dynamics (NeuRD) is an alternative to the foundational
softmax policy gradient (SPG) algorithm motivated by online learning and
evolutionary game theory. The NeuRD expected update is designed to be nearly
identical to that of SPG, however, we show that the Monte Carlo updates differ
in a substantial way: the importance correction accounting for a sampled action
is nullified in the SPG update, but not in the NeuRD update. Naturally, this
causes the NeuRD update to have higher variance than its SPG counterpart.
Building on implicit exploration algorithms in the adversarial bandit setting,
we introduce capped implicit exploration (CIX) estimates that allow us to
construct NeuRD-CIX, which interpolates between this aspect of NeuRD and SPG.
We show how CIX estimates can be used in a black-box reduction to construct
bandit algorithms with regret bounds that hold with high probability and the
benefits this entails for NeuRD-CIX in sequential decision-making settings. Our
analysis reveals a bias--variance tradeoff between SPG and NeuRD, and shows how
theory predicts that NeuRD-CIX will perform well more consistently than NeuRD
while retaining NeuRD's advantages over SPG in non-stationary environments.

Adaptive gradient methods have become popular in optimizing deep neural
networks; recent examples include AdaGrad and Adam. Although Adam usually
converges faster, variations of Adam, for instance, the AdaBelief algorithm,
have been proposed to enhance Adam's poor generalization ability compared to
the classical stochastic gradient method. This paper develops a generic
framework for adaptive gradient methods that solve non-convex optimization
problems. We first model the adaptive gradient methods in a state-space
framework, which allows us to present simpler convergence proofs of adaptive
optimizers such as AdaGrad, Adam, and AdaBelief. We then utilize the transfer
function paradigm from classical control theory to propose a new variant of
Adam, coined AdamSSM. We add an appropriate pole-zero pair in the transfer
function from squared gradients to the second moment estimate. We prove the
convergence of the proposed AdamSSM algorithm. Applications on benchmark
machine learning tasks of image classification using CNN architectures and
language modeling using LSTM architecture demonstrate that the AdamSSM
algorithm improves the gap between generalization accuracy and faster
convergence than the recent adaptive gradient methods.

Using deep learning (DL) to accelerate and/or improve scientific workflows
can yield discoveries that are otherwise impossible. Unfortunately, DL models
have yielded limited success in complex scientific domains due to large data
requirements. In this work, we propose to overcome this issue by integrating
the abundance of scientific knowledge sources (SKS) with the DL training
process. Existing knowledge integration approaches are limited to using
differentiable knowledge source to be compatible with first-order DL training
paradigm. In contrast, our proposed approach treats knowledge source as a
black-box in turn allowing to integrate virtually any knowledge source. To
enable an end-to-end training of SKS-coupled-DL, we propose to use zeroth-order
optimization (ZOO) based gradient-free training schemes, which is
non-intrusive, i.e., does not require making any changes to the SKS. We
evaluate the performance of our ZOO training scheme on two real-world material
science applications. We show that proposed scheme is able to effectively
integrate scientific knowledge with DL training and is able to outperform
purely data-driven model for data-limited scientific applications. We also
discuss some limitations of the proposed method and mention potentially
worthwhile future directions.

We propose a neural network-based approach to the homogenization of
multiscale problems. The proposed method uses a derivative-free formulation of
a training loss, which incorporates Brownian walkers to find the macroscopic
description of a multiscale PDE solution. Compared with other network-based
approaches for multiscale problems, the proposed method is free from the design
of hand-crafted neural network architecture and the cell problem to calculate
the homogenization coefficient. The exploration neighborhood of the Brownian
walkers affects the overall learning trajectory. We determine the bounds of
micro- and macro-time steps that capture the local heterogeneous and global
homogeneous solution behaviors, respectively, through a neural network. The
bounds imply that the computational cost of the proposed method is independent
of the microscale periodic structure for the standard periodic problems. We
validate the efficiency and robustness of the proposed method through a suite
of linear and nonlinear multiscale problems with periodic and random field
coefficients.

Deep Metric Learning (DML) methods have been proven relevant for visual
similarity learning. However, they sometimes lack generalization properties
because they are trained often using an inappropriate sample selection strategy
or due to the difficulty of the dataset caused by a distributional shift in the
data. These represent a significant drawback when attempting to learn the
underlying data manifold. Therefore, there is a pressing need to develop better
ways of obtaining generalization and representation of the underlying manifold.
In this paper, we propose a novel approach to DML that we call Guided Deep
Metric Learning, a novel architecture oriented to learning more compact
clusters, improving generalization under distributional shifts in DML. This
novel architecture consists of two independent models: A multi-branch master
model, inspired from a Few-Shot Learning (FSL) perspective, generates a reduced
hypothesis space based on prior knowledge from labeled data, which guides or
regularizes the decision boundary of a student model during training under an
offline knowledge distillation scheme. Experiments have shown that the proposed
method is capable of a better manifold generalization and representation to up
to 40% improvement (Recall@1, CIFAR10), using guidelines suggested by Musgrave
et al. to perform a more fair and realistic comparison, which is currently
absent in the literature

Topological Data Analysis is a growing area of data science, which aims at
computing and characterizing the geometry and topology of data sets, in order
to produce useful descriptors for subsequent statistical and machine learning
tasks. Its main computational tool is persistent homology, which amounts to
track the topological changes in growing families of subsets of the data set
itself, called filtrations, and encode them in an algebraic object, called
persistence module. Even though algorithms and theoretical properties of
modules are now well-known in the single-parameter case, that is, when there is
only one filtration to study, much less is known in the multi-parameter case,
where several filtrations are given at once. Though more complicated, the
resulting persistence modules are usually richer and encode more information,
making them better descriptors for data science. In this article, we present
the first approximation scheme, which is based on fibered barcodes and exact
matchings, two constructions that stem from the theory of single-parameter
persistence, for computing and decomposing general multi-parameter persistence
modules. Our algorithm has controlled complexity and running time, and works in
arbitrary dimension, i.e., with an arbitrary number of filtrations. Moreover,
when restricting to specific classes of multi-parameter persistence modules,
namely the ones that can be decomposed into intervals, we establish theoretical
results about the approximation error between our estimate and the true module
in terms of interleaving distance. Finally, we present empirical evidence
validating output quality and speed-up on several data sets.

environment. In this work, we entertain an extreme scenario wherein some
combination of immense environment complexity and limited agent capacity
entirely precludes identifying an exactly value-equivalent model. In light of
this, we embrace a notion of approximate value equivalence and introduce an
algorithm for incrementally synthesizing simple and useful approximations of
the environment from which an agent might still recover near-optimal behavior.
Crucially, we recognize the information-theoretic nature of this lossy
environment compression problem and use the appropriate tools of
rate-distortion theory to make mathematically precise how value equivalence can
lend tractability to otherwise intractable sequential decision-making problems.

The Physics-Informed Neural Network (PINN) approach is a new and promising
way to solve partial differential equations using deep learning. The $L^2$
Physics-Informed Loss is the de-facto standard in training Physics-Informed
Neural Networks. In this paper, we challenge this common practice by
investigating the relationship between the loss function and the approximation
quality of the learned solution. In particular, we leverage the concept of
stability in the literature of partial differential equation to study the
asymptotic behavior of the learned solution as the loss approaches zero. With
this concept, we study an important class of high-dimensional non-linear PDEs
in optimal control, the Hamilton-Jacobi-Bellman(HJB) Equation, and prove that
for general $L^p$ Physics-Informed Loss, a wide class of HJB equation is stable
only if $p$ is sufficiently large. Therefore, the commonly used $L^2$ loss is
not suitable for training PINN on those equations, while $L^{\infty}$ loss is a
better choice. Based on the theoretical insight, we develop a novel PINN
training algorithm to minimize the $L^{\infty}$ loss for HJB equations which is
in a similar spirit to adversarial training. The effectiveness of the proposed
algorithm is empirically demonstrated through experiments. Our code is released
at https://github.com/LithiumDA/L_inf-PINN.

Machine learning approaches commonly rely on the assumption of independent
and identically distributed (i.i.d.) data. In reality, however, this assumption
is almost always violated due to distribution shifts between environments.
Although valuable learning signals can be provided by heterogeneous data from
changing distributions, it is also known that learning under arbitrary
(adversarial) changes is impossible. Causality provides a useful framework for
modeling distribution shifts, since causal models encode both observational and
interventional distributions. In this work, we explore the sparse mechanism
shift hypothesis, which posits that distribution shifts occur due to a small
number of changing causal conditionals. Motivated by this idea, we apply it to
learning causal structure from heterogeneous environments, where i.i.d. data
only allows for learning an equivalence class of graphs without restrictive
assumptions. We propose the Mechanism Shift Score (MSS), a score-based approach
amenable to various empirical estimators, which provably identifies the entire
causal structure with high probability if the sparse mechanism shift hypothesis
holds. Empirically, we verify behavior predicted by the theory and compare
multiple estimators and score functions to identify the best approaches in
practice. Compared to other methods, we show how MSS bridges a gap by both
being nonparametric as well as explicitly leveraging sparse changes.

This tutorial demonstrates workflows to incorporate text data into actuarial
classification and regression tasks. The main focus is on methods employing
transformer-based models. A dataset of car accident descriptions with an
average length of 400 words, available in English and German, and a dataset
with short property insurance claims descriptions are used to demonstrate these
techniques. The case studies tackle challenges related to a multi-lingual
setting and long input sequences. They also show ways to interpret model
output, to assess and improve model performance, by fine-tuning the models to
the domain of application or to a specific prediction task. Finally, the
tutorial provides practical approaches to handle classification tasks in
situations with no or only few labeled data. The results achieved by using the
language-understanding skills of off-the-shelf natural language processing
(NLP) models with only minimal pre-processing and fine-tuning clearly
demonstrate the power of transfer learning for practical applications.

We study the estimation and concentration on its expectation of the
probability to observe data further than a specified distance from a given iid
sample in a metric space. The problem extends the classical problem of
estimation of the missing mass in discrete spaces. We give some estimators for
the conditional missing mass and show that estimation of the expected missing
mass is difficult in general. Conditions on the distribution, under which the
Good-Turing estimator and the conditional missing mass concentrate on their
expectations are identified. Applications to anomaly detection, coding, the
Wasserstein distance between true and empirical measure and simple learning
bounds are sketched.

Inspired by the recent work FedNL (Safaryan et al, FedNL: Making Newton-Type
Methods Applicable to Federated Learning), we propose a new communication
efficient second-order framework for Federated learning, namely FLECS. The
proposed method reduces the high-memory requirements of FedNL by the usage of
an L-SR1 type update for the Hessian approximation which is stored on the
central server. A low dimensional `sketch' of the Hessian is all that is needed
by each device to generate an update, so that memory costs as well as number of
Hessian-vector products for the agent are low. Biased and unbiased compressions
are utilized to make communication costs also low. Convergence guarantees for
FLECS are provided in both the strongly convex, and nonconvex cases, and local
linear convergence is also established under strong convexity. Numerical
experiments confirm the practical benefits of this new FLECS algorithm.

Low-bit width neural networks have been extensively explored for deployment
on edge devices to reduce computational resources. Existing approaches have
focused on gradient-based optimization in a two-stage train-and-compress
setting or as a combined optimization where gradients are quantized during
training. Such schemes require high-performance hardware during the training
phase and usually store an equivalent number of full-precision weights apart
from the quantized weights. In this paper, we explore methods of direct
combinatorial optimization in the problem of risk minimization with binary
weights, which can be made equivalent to a non-monotone submodular maximization
under certain conditions. We employ an approximation algorithm for the cases
with single and multilayer neural networks. For linear models, it has
$\mathcal{O}(nd)$ time complexity where $n$ is the sample size and $d$ is the
data dimension. We show that a combination of greedy coordinate descent and
this novel approach can attain competitive accuracy on binary classification
tasks.

The development of machine learned potentials for catalyst discovery has
predominantly been focused on very specific chemistries and material
compositions. While effective in interpolating between available materials,
these approaches struggle to generalize across chemical space. The recent
curation of large-scale catalyst datasets has offered the opportunity to build
a universal machine learning potential, spanning chemical and composition
space. If accomplished, said potential could accelerate the catalyst discovery
process across a variety of applications (CO2 reduction, NH3 production, etc.)
without additional specialized training efforts that are currently required.
The release of the Open Catalyst 2020 (OC20) has begun just that, pushing the
heterogeneous catalysis and machine learning communities towards building more
accurate and robust models. In this perspective, we discuss some of the
challenges and findings of recent developments on OC20. We examine the
performance of current models across different materials and adsorbates to
identify notably underperforming subsets. We then discuss some of the modeling
efforts surrounding energy-conservation, approaches to finding and evaluating
the local minima, and augmentation of off-equilibrium data. To complement the
community's ongoing developments, we end with an outlook to some of the
important challenges that have yet to be thoroughly explored for large-scale
catalyst discovery.

We introduce CVNets, a high-performance open-source library for training deep
neural networks for visual recognition tasks, including classification,
detection, and segmentation. CVNets supports image and video understanding
tools, including data loading, data transformations, novel data sampling
methods, and implementations of several standard networks with similar or
better performance than previous studies.
  Our source code is available at: \url{https://github.com/apple/ml-cvnets}.

We discover restrained numerical instabilities in current training practices
of deep networks with SGD. We show numerical error (on the order of the
smallest floating point bit) induced from floating point arithmetic in training
deep nets can be amplified significantly and result in significant test
accuracy variance, comparable to the test accuracy variance due to
stochasticity in SGD. We show how this is likely traced to instabilities of the
optimization dynamics that are restrained, i.e., localized over iterations and
regions of the weight tensor space. We do this by presenting a theoretical
framework using numerical analysis of partial differential equations (PDE), and
analyzing the gradient descent PDE of a simplified convolutional neural network
(CNN). We show that it is stable only under certain conditions on the learning
rate and weight decay. We reproduce the localized instabilities in the PDE for
the simplified network, which arise when the conditions are violated.

Value function estimation is an indispensable subroutine in reinforcement
learning, which becomes more challenging in the offline setting. In this paper,
we propose Hybrid Value Estimation (HVE) to reduce value estimation error,
which trades off bias and variance by balancing between the value estimation
from offline data and the learned model. Theoretical analysis discloses that
HVE enjoys a better error bound than the direct methods. HVE can be leveraged
in both off-policy evaluation and offline reinforcement learning settings. We,
therefore, provide two concrete algorithms Off-policy HVE (OPHVE) and
Model-based Offline HVE (MOHVE), respectively. Empirical evaluations on MuJoCo
tasks corroborate the theoretical claim. OPHVE outperforms other off-policy
evaluation methods in all three metrics measuring the estimation effectiveness,
while MOHVE achieves better or comparable performance with state-of-the-art
offline reinforcement learning algorithms. We hope that HVE could shed some
light on further research on reinforcement learning from fixed data.

Most recent self-supervised learning methods learn visual representation by
contrasting different augmented views of images. Compared with supervised
learning, more aggressive augmentations have been introduced to further improve
the diversity of training pairs. However, aggressive augmentations may distort
images' structures leading to a severe semantic shift problem that augmented
views of the same image may not share the same semantics, thus degrading the
transfer performance. To address this problem, we propose a new SSL paradigm,
which counteracts the impact of semantic shift by balancing the role of weak
and aggressively augmented pairs. Specifically, semantically inconsistent pairs
are of minority and we treat them as noisy pairs. Note that deep neural
networks (DNNs) have a crucial memorization effect that DNNs tend to first
memorize clean (majority) examples before overfitting to noisy (minority)
examples. Therefore, we set a relatively large weight for aggressively
augmented data pairs at the early learning stage. With the training going on,
the model begins to overfit noisy pairs. Accordingly, we gradually reduce the
weights of aggressively augmented pairs. In doing so, our method can better
embrace the aggressive augmentations and neutralize the semantic shift problem.
Experiments show that our model achieves 73.1% top-1 accuracy on ImageNet-1K
with ResNet-50 for 200 epochs, which is a 2.5% improvement over BYOL. Moreover,
experiments also demonstrate that the learned representations can transfer well
for various downstream tasks.

In combinatorial causal bandits (CCB), the learning agent chooses at most $K$
variables in each round to intervene, collects feedback from the observed
variables, with the goal of minimizing expected regret on the target variable
$Y$. Different from all prior studies on causal bandits, CCB needs to deal with
exponentially large action space. We study under the context of binary
generalized linear models (BGLMs) with a succinct parametric representation of
the causal models. We present the algorithm BGLM-OFU for Markovian BGLMs (i.e.
no hidden variables) based on the maximum likelihood estimation method, and
show that it achieves $O(\sqrt{T}\log T)$ regret, where $T$ is the time
horizon. For the special case of linear models with hidden variables, we apply
causal inference techniques such as the do-calculus to convert the original
model into a Markovian model, and then show that our BGLM-OFU algorithm and
another algorithm based on the linear regression both solve such linear models
with hidden variables. Our novelty includes (a) considering the combinatorial
intervention action space and the general causal models including ones with
hidden variables, (b) integrating and adapting techniques from diverse studies
such as generalized linear bandits and online influence maximization, and (c)
not relying on unrealistic assumptions such as knowing the joint distribution
of the parents of $Y$ under all interventions used in some prior studies.

We study stochastic gradient descent for solving conditional stochastic
optimization problems, in which an objective to be minimized is given by a
parametric nested expectation with an outer expectation taken with respect to
one random variable and an inner conditional expectation with respect to the
other random variable. The gradient of such a parametric nested expectation is
again expressed as a nested expectation, which makes it hard for the standard
nested Monte Carlo estimator to be unbiased. In this paper, we show under some
conditions that a multilevel Monte Carlo gradient estimator is unbiased and has
finite variance and finite expected computational cost, so that the standard
theory from stochastic optimization for a parametric (non-nested) expectation
directly applies. We also discuss a special case for which yet another unbiased
gradient estimator with finite variance and cost can be constructed.

Contrastive Language-Image Pre-training (CLIP) has demonstrated great
potential in realizing open-vocabulary image classification in a matching
style, because of its holistic use of natural language supervision that covers
unconstrained real-world visual concepts. However, it is, in turn, also
difficult to evaluate and analyze the openness of CLIP-like models, since they
are in theory open to any vocabulary but the actual accuracy varies. To address
the insufficiency of conventional studies on openness, we resort to an
incremental view and define the extensibility, which essentially approximates
the model's ability to deal with new visual concepts, by evaluating openness
through vocabulary expansions. Our evaluation based on extensibility shows that
CLIP-like models are hardly truly open and their performances degrade as the
vocabulary expands to different degrees. Further analysis reveals that the
over-estimation of openness is not because CLIP-like models fail to capture the
general similarity of image and text features of novel visual concepts, but
because of the confusion among competing text features, that is, they are not
stable with respect to the vocabulary. In light of this, we propose to improve
the openness of CLIP from the perspective of feature space by enforcing the
distinguishability of text features. Our method retrieves relevant texts from
the pre-training corpus to enhance prompts for inference, which boosts the
extensibility and stability of CLIP even without fine-tuning.

Transport-based metrics and related embeddings (transforms) have recently
been used to model signal classes where nonlinear structures or variations are
present. In this paper, we study the geodesic properties of time series data
with a generalized Wasserstein metric and the geometry related to their signed
cumulative distribution transforms in the embedding space. Moreover, we show
how understanding such geometric characteristics can provide added
interpretability to certain time series classifiers, and be an inspiration for
more robust classifiers.

This paper studies the dependability of the Xilinx Deep-Learning Processing
Unit (DPU) under neutron irradiation. It analyses the impact of Single Event
Effects (SEEs) on the accuracy of the DPU running the resnet50 model on a
Xilinx Ultrascale+ MPSoC.

The sequence of visits and procedures performed by the patient in the health
system, also known as the patient's pathway or trajectory, can reveal important
information about the clinical treatment adopted and the health service
provided. The rise of electronic health data availability made it possible to
assess the pathways of a large number of patients. Nevertheless, some
challenges also arose concerning how to synthesize these pathways and how to
mine them from the data, fostering a new field of research. The objective of
this review is to survey this new field of research, highlighting
representation models, mining techniques, methods of analysis, and examples of
case studies.

Congestion Control (CC), as the core networking task to efficiently utilize
network capacity, received great attention and widely used in various Internet
communication applications such as 5G, Internet-of-Things, UAN, and more.
Various CC algorithms have been proposed both on network and transport layers
such as Active Queue Management (AQM) algorithm and Transmission Control
Protocol (TCP) congestion control mechanism. But it is hard to model dynamic
AQM/TCP system and cooperate two algorithms to obtain excellent performance
under different communication scenarios. In this paper, we explore the
performance of multi-agent reinforcement learning-based cross-layer congestion
control algorithms and present cooperation performance of two agents, known as
MACC (Multi-agent Congestion Control). We implement MACC in NS3. The simulation
results show that our scheme outperforms other congestion control combination
in terms of throughput and delay, etc. Not only does it proves that networking
protocols based on multi-agent deep reinforcement learning is efficient for
communication managing, but also verifies that networking area can be used as
new playground for machine learning algorithms.

Purpose of review: Artificial intelligence (AI) has become popular in medical
applications, specifically as a clinical support tool for computer-aided
diagnosis. These tools are typically employed on medical data (i.e., image,
molecular data, clinical variables, etc.) and used the statistical and machine
learning methods to measure the model performance. In this review, we
summarized and discussed the most recent radiomic pipeline used for clinical
analysis. Recent findings:Currently, limited management of cancers benefits
from artificial intelligence, mostly related to a computer-aided diagnosis that
avoids a biopsy analysis that presents additional risks and costs. Most AI
tools are based on imaging features, known as radiomic analysis that can be
refined into predictive models in non-invasively acquired imaging data. This
review explores the progress of AI-based radiomic tools for clinical
applications with a brief description of necessary technical steps. Explaining
new radiomic approaches based on deep learning techniques will explain how the
new radiomic models (deep radiomic analysis) can benefit from deep
convolutional neural networks and be applied on limited data sets. Summary: To
consider the radiomic algorithms, further investigations are recommended to
involve deep learning in radiomic models with additional validation steps on
various cancer types.

We study the generalization abilities of language models when translating
natural language into formal specifications with complex semantics. In
particular, we fine-tune language models on three datasets consisting of
English sentences and their corresponding formal representation: 1) regular
expressions (regex), frequently used in programming and search; 2) First-order
logic (FOL), commonly used in software verification and theorem proving; and 3)
linear-time temporal logic (LTL), which forms the basis for industrial hardware
specification languages. Our experiments show that, in these diverse domains,
the language models maintain their generalization capabilities from pre-trained
knowledge of natural language to generalize, e.g., to new variable names or
operator descriptions. Additionally, they achieve competitive performance, and
even outperform the state-of-the-art for translating into regular expressions,
with the benefits of being easy to access, efficient to fine-tune, and without
a particular need for domain-specific reasoning.

Oil production forecasting is an important step in controlling the
cost-effect and monitoring the functioning of petroleum reservoirs. As a
result, oil production forecasting makes it easier for reservoir engineers to
develop feasible projects, which helps to avoid risky investments and achieve
long-term growth. As a result, reliable petroleum reservoir forecasting is
critical for controlling and managing the effective cost of oil reservoirs. Oil
production is influenced by reservoir qualities such as porosity, permeability,
compressibility, fluid saturation, and other well operational parameters.
Three-time series algorithms i.e., Seasonal Naive method, Exponential
Smoothening and ARIMA to forecast the Distillate Fuel Oil Refinery and Propane
Blender net production for the next two years.

3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined
surfaces remains an unsolved problem. The challenges arise from the nature of
optical colonoscopy data, characterized by highly reflective low-texture
surfaces, drastic illumination changes and frequent tracking loss. Recent
methods demonstrate compelling results, but suffer from: (1) frangible
frame-to-frame (or frame-to-model) pose estimation resulting in many tracking
failures; or (2) rely on point-based representations at the cost of scan
quality. In this paper, we propose a novel reconstruction framework that
addresses these issues end to end, which result in both quantitatively and
qualitatively accurate and robust 3D colon reconstruction. Our SLAM approach,
which employs correspondences based on contrastive deep features, and deep
consistent depth maps, estimates globally optimized poses, is able to recover
from frequent tracking failures, and estimates a global consistent 3D model;
all within a single framework. We perform an extensive experimental evaluation
on multiple synthetic and real colonoscopy videos, showing high-quality results
and comparisons against relevant baselines.

Prompt Learning has recently gained great popularity in bridging the gap
between pretraining tasks and various downstream tasks. It freezes Pretrained
Language Models (PLMs) and only tunes a few task-related parameters (prompts)
for downstream tasks, greatly reducing the cost of tuning giant models. The key
enabler of this is the idea of querying PLMs with task-specific knowledge
implicated in prompts. This paper reveals a major limitation of existing
methods that the indiscriminate prompts for all input data in a task ignore the
intrinsic knowledge from input data, resulting in sub-optimal performance. We
introduce Instance-wise Prompt Tuning (IPT), the first prompt learning paradigm
that injects knowledge from the input data instances to the prompts, thereby
providing PLMs with richer and more concrete context information. We devise a
series of strategies to produce instance-wise prompts, addressing various
concerns like model quality and cost-efficiency. Across multiple tasks and
resource settings, IPT significantly outperforms task-based prompt learning
methods, and achieves comparable performance to conventional finetuning with
only 0.5% - 1.5% of tuned parameters.

As autonomous systems increasingly rely on Deep Neural Networks (DNN) to
implement the navigation pipeline functions, uncertainty estimation methods
have become paramount for estimating confidence in DNN predictions. Bayesian
Deep Learning (BDL) offers a principled approach to model uncertainties in
DNNs. However, DNN components from autonomous systems partially capture
uncertainty, or more importantly, the uncertainty effect in downstream tasks is
ignored. This paper provides a method to capture the overall system uncertainty
in a UAV navigation task. In particular, we study the effect of the uncertainty
from perception representations in downstream control predictions. Moreover, we
leverage the uncertainty in the system's output to improve control decisions
that positively impact the UAV's performance on its task.

Distributed training of machine learning models directly on satellites in low
Earth orbit (LEO) is considered. Based on a federated learning (FL) algorithm
specifically targeted at the unique challenges of the satellite scenario, we
design a scheduler that exploits the predictability of visiting times between
ground stations (GS) and satellites to reduce model staleness. Numerical
experiments show that this can improve the convergence speed by a factor three.

In most cases, word embeddings are learned only from raw tokens or in some
cases, lemmas. This includes pre-trained language models like BERT. To
investigate on the potential of capturing deeper relations between lexical
items and structures and to filter out redundant information, we propose to
preserve the morphological, syntactic and other types of linguistic information
by combining them with the raw tokens or lemmas. This means, for example,
including parts-of-speech or dependency information within the used lexical
features. The word embeddings can then be trained on the combinations instead
of just raw tokens. It is also possible to later apply this method to the
pre-training of huge language models and possibly enhance their performance.
This would aid in tackling problems which are more sophisticated from the point
of view of linguistic representation, such as detection of cyberbullying.

AI natural language generation (NLG) is a process where computer systems
generate human-comprehensible language texts from information. It can become an
integral part of a human's creative writing process. Importantly, youths can
learn to apply NLG in mainstream education and become better prepared for
AI-enhanced writing jobs and other writing endeavors. To explore how students
apply NLG to creative writing, we designed and implemented the 1st Human-AI
Creative Writing Contest in a Hong Kong secondary school. In this contest, each
student participant wrote a short story of up to 500-words using the student's
own words and words generated by a computer and built on open-source language
models. We designed four text generators for the contest as the computer's text
entry. Additionally, using design-based research, we developed seven workshops
where students learned to write with the four text generators and answered
reflection questions. In analyzing four students' short stories and
adjudicators' scores for the stories, we found different strategies in terms of
the number and the type of text generator words that students used. Some
strategies appeared more sophisticated than others. In analyzing students'
reflections, we found students could describe text generator input and output
as units of thought. Besides, students showed preferences for text generators;
and they expressed a range of feelings when writing with text generators. The
findings provide design implications not only for NLG applications in formal
schooling but also suggest pedagogical strategies for AI curriculum.

In this research. we analyze the potential of Feature Density (HD) as a way
to comparatively estimate machine learning (ML) classifier performance prior to
training. The goal of the study is to aid in solving the problem of
resource-intensive training of ML models which is becoming a serious issue due
to continuously increasing dataset sizes and the ever rising popularity of Deep
Neural Networks (DNN). The issue of constantly increasing demands for more
powerful computational resources is also affecting the environment, as training
large-scale ML models are causing alarmingly-growing amounts of CO2, emissions.
Our approach 1s to optimize the resource-intensive training of ML models for
Natural Language Processing to reduce the number of required experiments
iterations. We expand on previous attempts on improving classifier training
efficiency with FD while also providing an insight to the effectiveness of
various linguistically-backed feature preprocessing methods for dialog
classification, specifically cyberbullying detection.

Recent years have seen a surge of interest in meta-learning techniques for
tackling the few-shot learning (FSL) problem. However, the meta-learner is
prone to overfitting since there are only a few available samples, which can be
identified as sampling noise on a clean dataset. Moreover, when handling the
data with noisy labels, the meta-learner could be extremely sensitive to label
noise on a corrupted dataset. To address these two challenges, we present
Eigen-Reptile (ER) that updates the meta-parameters with the main direction of
historical task-specific parameters to alleviate sampling and label noise.
Specifically, the main direction is computed in a fast way, where the scale of
the calculated matrix is related to the number of gradient steps instead of the
number of parameters. Furthermore, to obtain a more accurate main direction for
Eigen-Reptile in the presence of many noisy labels, we further propose
Introspective Self-paced Learning (ISPL). We have theoretically and
experimentally demonstrated the soundness and effectiveness of the proposed
Eigen-Reptile and ISPL. Particularly, our experiments on different tasks show
that the proposed method is able to outperform or achieve highly competitive
performance compared with other gradient-based methods with or without noisy
labels. The code and data for the proposed method are provided for research
purposes https://github.com/Anfeather/Eigen-Reptile.

Computer vision enables the development of new approaches to monitor the
behavior, health, and welfare of animals. Instance segmentation is a
high-precision method in computer vision for detecting individual animals of
interest. This method can be used for in-depth analysis of animals, such as
examining their subtle interactive behaviors, from videos and images. However,
existing deep-learning-based instance segmentation methods have been mostly
developed based on public datasets, which largely omit heavy occlusion
problems; therefore, these methods have limitations in real-world applications
involving object occlusions, such as farrowing pen systems used on pig farms in
which the farrowing crates often impede the sow and piglets. In this paper, we
propose a novel occlusion-resistant Center Clustering Network for instance
segmentation, dubbed as CClusnet-Inseg. Specifically, CClusnet-Inseg uses each
pixel to predict object centers and trace these centers to form masks based on
clustering results, which consists of a network for segmentation and center
offset vector map, Density-Based Spatial Clustering of Applications with Noise
(DBSCAN) algorithm, Centers-to-Mask (C2M) and Remain-Centers-to-Mask (RC2M)
algorithms, and a pseudo-occlusion generator (POG). In all, 4,600 images were
extracted from six videos collected from six farrowing pens to train and
validate our method. CClusnet-Inseg achieves a mean average precision (mAP) of
83.6; it outperformed YOLACT++ and Mask R-CNN, which had mAP values of 81.2 and
74.7, respectively. We conduct comprehensive ablation studies to demonstrate
the advantages and effectiveness of core modules of our method. In addition, we
apply CClusnet-Inseg to multi-object tracking for animal monitoring, and the
predicted object center that is a conjunct output could serve as an
occlusion-resistant representation of the location of an object.

The progress in artificial intelligence and machine learning algorithms over
the past decade has enabled the development of new methods for the objective
measurement of eating, including both the measurement of eating episodes as
well as the measurement of in-meal eating behavior. These allow the study of
eating behavior outside the laboratory in free-living conditions, without the
need for video recordings and laborious manual annotations. In this paper, we
present a high-level overview of our recent work on intake monitoring using a
smartwatch, as well as methods using an in-ear microphone. We also present
evaluation results of these methods in challenging, real-world datasets.
Furthermore, we discuss use-cases of such intake monitoring tools for advancing
research in eating behavior, for improving dietary monitoring, as well as for
developing evidence-based health policies. Our goal is to inform researchers
and users of intake monitoring methods regarding (i) the development of new
methods based on commercially available devices, (ii) what to expect in terms
of effectiveness, and (iii) how these methods can be used in research as well
as in practical applications.

Electroencephalography produces high-dimensional, stochastic data from which
it might be challenging to extract high-level knowledge about the phenomena of
interest. We address this challenge by applying the framework of variational
auto-encoders to 1) classify multiple pathologies and 2) recover the
neurological mechanisms of those pathologies in a data-driven manner. Our
framework learns generative factors of data related to pathologies. We provide
an algorithm to decode those factors further and discover how different
pathologies affect observed data. We illustrate the applicability of the
proposed approach to identifying schizophrenia, either followed or not by
auditory verbal hallucinations. We further demonstrate the ability of the
framework to learn disease-related mechanisms consistent with current domain
knowledge. We also compare the proposed framework with several benchmark
approaches and indicate its classification performance and interpretability

Sampling from an unnormalized target distribution is an essential problem
with many applications in probabilistic inference. Stein Variational Gradient
Descent (SVGD) has been shown to be a powerful method that iteratively updates
a set of particles to approximate the distribution of interest. Furthermore,
when analysing its asymptotic properties, SVGD reduces exactly to a
single-objective optimization problem and can be viewed as a probabilistic
version of this single-objective optimization problem. A natural question then
arises: "Can we derive a probabilistic version of the multi-objective
optimization?". To answer this question, we propose Stochastic Multiple Target
Sampling Gradient Descent (MT-SGD), enabling us to sample from multiple
unnormalized target distributions. Specifically, our MT-SGD conducts a flow of
intermediate distributions gradually orienting to multiple target
distributions, which allows the sampled particles to move to the joint
high-likelihood region of the target distributions. Interestingly, the
asymptotic analysis shows that our approach reduces exactly to the
multiple-gradient descent algorithm for multi-objective optimization, as
expected. Finally, we conduct comprehensive experiments to demonstrate the
merit of our approach to multi-task learning.

Instrumental variable (IV) is a powerful approach to inferring the causal
effect of a treatment on an outcome of interest from observational data even
when there exist latent confounders between the treatment and the outcome.
However, existing IV methods require that an IV is selected and justified with
domain knowledge. An invalid IV may lead to biased estimates. Hence,
discovering a valid IV is critical to the applications of IV methods. In this
paper, we study and design a data-driven algorithm to discover valid IVs from
data under mild assumptions. We develop the theory based on partial ancestral
graphs (PAGs) to support the search for a set of candidate Ancestral IVs
(AIVs), and for each possible AIV, the identification of its conditioning set.
Based on the theory, we propose a data-driven algorithm to discover a pair of
IVs from data. The experiments on synthetic and real-world datasets show that
the developed IV discovery algorithm estimates accurate estimates of causal
effects in comparison with the state-of-the-art IV based causal effect

This paper discusses how to crawl the data of financial forums such as stock
bar, and conduct emotional analysis combined with the in-depth learning model.
This paper will use the Bert model to train the financial corpus and predict
the Shenzhen stock index. Through the comparative study of the maximal
information coefficient (MIC), it is found that the emotional characteristics
obtained by applying the BERT model to the financial corpus can be reflected in
the fluctuation of the stock market, which is conducive to effectively improve
the prediction accuracy. At the same time, this paper combines in-depth
learning with financial texts to further explore the impact mechanism of
investor sentiment on the stock market through in-depth learning, which will
help the national regulatory authorities and policy departments to formulate
more reasonable policies and guidelines for maintaining the stability of the
stock market.

In the last decades, the area under cultivation of maize products has
increased because of its essential role in the food cycle for humans,
livestock, and poultry. Moreover, the diseases of plants impact food safety and
can significantly reduce both the quality and quantity of agricultural
products. There are many challenges to accurate and timely diagnosis of the
disease. This research presents a novel scheme based on a deep neural network
to overcome the mentioned challenges. Due to the limited number of data, the
transfer learning technique is employed with the help of two well-known
architectures. In this way, a new effective model is adopted by a combination
of pre-trained MobileNetV2 and Inception Networks due to their effective
performance on object detection problems. The convolution layers of MoblieNetV2
and Inception modules are parallelly arranged as earlier layers to extract
crucial features. In addition, the imbalance problem of classes has been solved
by an augmentation strategy. The proposed scheme has a superior performance
compared to other state-of-the-art models published in recent years. The
accuracy of the model reaches 97%, approximately. In summary, experimental
results prove the method's validity and significant performance in diagnosing
disease in plant leaves.

Visual inspection software has become a key factor in the manufacturing
industry for quality control and process monitoring. Semantic segmentation
models have gained importance since they allow for more precise examination.
These models, however, require large image datasets in order to achieve a fair
accuracy level. In some cases, training data is sparse or lacks of sufficient
annotation, a fact that especially applies to highly specialized production
environments. Data augmentation represents a common strategy to extend the
dataset. Still, it only varies the image within a narrow range. In this
article, a novel strategy is proposed to augment small image datasets. The
approach is applied to surface monitoring of carbon fibers, a specific industry
use case. We apply two different methods to create binary labels: a
problem-tailored trigonometric function and a WGAN model. Afterwards, the
labels are translated into color images using pix2pix and used to train a
U-Net. The results suggest that the trigonometric function is superior to the
WGAN model. However, a precise examination of the resulting images indicate
that WGAN and image-to-image translation achieve good segmentation results and
only deviate to a small degree from traditional data augmentation. In summary,
this study examines an industry application of data synthesization using
generative adversarial networks and explores its potential for monitoring
systems of production environments. \keywords{Image-to-Image Translation,
Carbon Fiber, Data Augmentation, Computer Vision, Industrial Monitoring,
Adversarial Learning.

Forecast combinations have flourished remarkably in the forecasting community
and, in recent years, have become part of the mainstream of forecasting
research and activities. Combining multiple forecasts produced from single
(target) series is now widely used to improve accuracy through the integration
of information gleaned from different sources, thereby mitigating the risk of
identifying a single "best" forecast. Combination schemes have evolved from
simple combination methods without estimation, to sophisticated methods
involving time-varying weights, nonlinear combinations, correlations among
components, and cross-learning. They include combining point forecasts and
combining probabilistic forecasts. This paper provides an up-to-date review of
the extensive literature on forecast combinations, together with reference to
available open-source software implementations. We discuss the potential and
limitations of various methods and highlight how these ideas have developed
over time. Some important issues concerning the utility of forecast
combinations are also surveyed. Finally, we conclude with current research gaps
and potential insights for future research.

Soft robots are typically approximated as low-dimensional systems, especially
when learning-based methods are used. This leads to models that are limited in
their capability to predict the large number of deformation modes and
interactions that a soft robot can have. In this work, we present a
deep-learning methodology to learn high-dimensional visual models of a soft
robot combining multimodal sensorimotor information. The models are learned in
an end-to-end fashion, thereby requiring no intermediate sensor processing or
grounding of data. The capabilities and advantages of such a modelling approach
are shown on a soft anthropomorphic finger with embedded soft sensors. We also
show that how such an approach can be extended to develop higher level
cognitive functions like identification of the self and the external
environment and acquiring object manipulation skills. This work is a step
towards the integration of soft robotics and developmental robotics
architectures to create the next generation of intelligent soft robots.

5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the
important applications of 5G wireless network which requires minimum latency
and higher reliability to support mission-critical applications. Therefore,
lossless reliable communication with a high data rate is the key requirement in
modern wireless communication systems. These all factors highly depend upon
channel conditions. In this work, a channel model is proposed for
air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime
unmanned aerial vehicle (UAV) communication. Firstly, we will present the
formulated channel estimation method which directly aims to adopt channel state
information (CSI) of mmWave from the channel model inculcated by UAV operating
within the Long Short Term Memory (LSTM)-Distributed Conditional generative
adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction.
Secondly, to enhance the applications for the proposed trained channel model
for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where
each one will learn mmWave CSI for all the distributions. Lastly, we have
categorized the most favorable LSTM-DCGAN training method and emanated certain
conditions for our UAV network to increase the channel model learning rate.
Simulation results have shown that the proposed LSTM-DCGAN based network is
vigorous to the error generated through local training. A detailed comparison
has been done with the other available state-of-the-art CGAN network
architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with
CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN.
Simulation results have shown that the proposed LSTM-DCGAN structure
demonstrates higher accuracy during the learning process and attained more data
rate for downlink transmission as compared to the previous state of artworks.

Robust imitation learning using disturbance injections overcomes issues of
limited variation in demonstrations. However, these methods assume
demonstrations are optimal, and that policy stabilization can be learned via
simple augmentations. In real-world scenarios, demonstrations are often of
diverse-quality, and disturbance injection instead learns sub-optimal policies
that fail to replicate desired behavior. To address this issue, this paper
proposes a novel imitation learning framework that combines both policy
robustification and optimal demonstration learning. Specifically, this
combinatorial approach forces policy learning and disturbance injection
optimization to focus on mainly learning from high task achievement
demonstrations, while utilizing low achievement ones to decrease the number of
samples needed. The effectiveness of the proposed method is verified through
experiments using an excavation task in both simulations and a real robot,
resulting in high-achieving policies that are more stable and robust to
diverse-quality demonstrations. In addition, this method utilizes all of the
weighted sub-optimal demonstrations without eliminating them, resulting in
practical data efficiency benefits.

Wireless communications represent a game changer for future manufacturing
plants, enabling flexible production chains as machinery and other components
are not restricted to a location by the rigid wired connections on the factory
floor. However, the presence of electromagnetic interference in the wireless
spectrum may result in packet loss and delay, making it a challenging
environment to meet the extreme reliability requirements of industrial
applications. In such conditions, achieving real-time remote control, either
from the Edge or Cloud, becomes complex. In this paper, we investigate a
forecast-based recovery mechanism for real-time remote control of robotic
manipulators (FoReCo) that uses Machine Learning (ML) to infer lost commands
caused by interference in the wireless channel. FoReCo is evaluated through
both simulation and experimentation in interference prone IEEE 802.11 wireless
links, and using a commercial research robot that performs pick-and-place
tasks. Results show that in case of interference, FoReCo trajectory error is
decreased by x18 and x2 times in simulation and experimentation, and that
FoReCo is sufficiently lightweight to be deployed in the hardware of already
used in existing solutions.

Modeling visual question answering(VQA) through scene graphs can
significantly improve the reasoning accuracy and interpretability. However,
existing models answer poorly for complex reasoning questions with attributes
or relations, which causes false attribute selection or missing relation in
Figure 1(a). It is because these models cannot balance all kinds of information
in scene graphs, neglecting relation and attribute information. In this paper,
we introduce a novel Dual Message-passing enhanced Graph Neural Network
(DM-GNN), which can obtain a balanced representation by properly encoding
multi-scale scene graph information. Specifically, we (i)transform the scene
graph into two graphs with diversified focuses on objects and relations; Then
we design a dual structure to encode them, which increases the weights from
relations (ii)fuse the encoder output with attribute features, which increases
the weights from attributes; (iii)propose a message-passing mechanism to
enhance the information transfer between objects, relations and attributes. We
conduct extensive experiments on datasets including GQA, VG, motif-VG and
achieve new state of the art.

Targeted Sentiment Analysis aims to extract sentiment towards a particular
target from a given text. It is a field that is attracting attention due to the
increasing accessibility of the Internet, which leads people to generate an
enormous amount of data. Sentiment analysis, which in general requires
annotated data for training, is a well-researched area for widely studied
languages such as English. For low-resource languages such as Turkish, there is
a lack of such annotated data. We present an annotated Turkish dataset suitable
for targeted sentiment analysis. We also propose BERT-based models with
different architectures to accomplish the task of targeted sentiment analysis.
The results demonstrate that the proposed models outperform the traditional
sentiment analysis models for the targeted sentiment analysis task.

We propose a simple but effective source-free domain adaptation (SFDA)
method. Treating SFDA as an unsupervised clustering problem and following the
intuition that local neighbors in feature space should have more similar
predictions than other features, we propose to optimize an objective of
prediction consistency. This objective encourages local neighborhood features
in feature space to have similar predictions while features farther away in
feature space have dissimilar predictions, leading to efficient feature
clustering and cluster assignment simultaneously. For efficient training, we
seek to optimize an upper-bound of the objective resulting in two simple terms.
Furthermore, we relate popular existing methods in domain adaptation,
source-free domain adaptation and contrastive learning via the perspective of
discriminability and diversity. The experimental results prove the superiority
of our method, and our method can be adopted as a simple but strong baseline
for future research in SFDA. Our method can be also adapted to source-free
open-set and partial-set DA which further shows the generalization ability of
our method. Code is available in https://github.com/Albert0147/AaD_SFDA.

Session-based recommendation aims to predict items that an anonymous user
would like to purchase based on her short behavior sequence. The current
approaches towards session-based recommendation only focus on modeling users'
interest preferences, while they all ignore a key attribute of an item, i.e.,
the price. Many marketing studies have shown that the price factor
significantly influences users' behaviors and the purchase decisions of users
are determined by both price and interest preferences simultaneously. However,
it is nontrivial to incorporate price preferences for session-based
recommendation. Firstly, it is hard to handle heterogeneous information from
various features of items to capture users' price preferences. Secondly, it is
difficult to model the complex relations between price and interest preferences
in determining user choices.
  To address the above challenges, we propose a novel method Co-guided
Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation.
Towards the first challenge, we devise a heterogeneous hypergraph to represent
heterogeneous information and rich relations among them. A dual-channel
aggregating mechanism is then designed to aggregate various information in the
heterogeneous hypergraph. After that, we extract users' price preferences and
interest preferences via attention layers. As to the second challenge, a
co-guided learning scheme is designed to model the relations between price and
interest preferences and enhance the learning of each other. Finally, we
predict user actions based on item features and users' price and interest
preferences. Extensive experiments on three real-world datasets demonstrate the
effectiveness of the proposed CoHHN. Further analysis reveals the significance
of price for session-based recommendation.

In distributed or federated optimization and learning, communication between
the different computing units is often the bottleneck and gradient compression
is widely used to reduce the number of bits sent within each communication
round of iterative methods. There are two classes of compression operators and
separate algorithms making use of them. In the case of unbiased random
compressors with bounded variance (e.g., rand-k), the DIANA algorithm of
Mishchenko et al. (2019), which implements a variance reduction technique for
handling the variance introduced by compression, is the current state of the
art. In the case of biased and contractive compressors (e.g., top-k), the EF21
algorithm of Richt\'arik et al. (2021), which instead implements an
error-feedback mechanism, is the current state of the art. These two classes of
compression schemes and algorithms are distinct, with different analyses and
proof techniques. In this paper, we unify them into a single framework and
propose a new algorithm, recovering DIANA and EF21 as particular cases. Our
general approach works with a new, larger class of compressors, which has two
parameters, the bias and the variance, and includes unbiased and biased
compressors as particular cases. This allows us to inherit the best of the two
worlds: like EF21 and unlike DIANA, biased compressors, like top-k, whose good
performance in practice is recognized, can be used. And like DIANA and unlike
EF21, independent randomness at the compressors allows to mitigate the effects
of compression, with the convergence rate improving when the number of parallel
workers is large. This is the first time that an algorithm with all these
features is proposed. We prove its linear convergence under certain conditions.
Our approach takes a step towards better understanding of two so-far distinct
worlds of communication-efficient distributed learning.

Implementation of high-speed quantum key distribution~(QKD) has become one of
the major focus in the field, which produces high key-generation rate for
applications. To achieve high-speed QKD, tailored techniques are developed and
employed to quickly generate and detect quantum states. However, these
techniques may introduce unique loopholes to compromise the security of QKD
systems. In this paper, we investigate the loopholes of self-differencing~(SD)
avalanche photodiode~(APD) detector, typically used for high-speed detection in
a QKD system, and demonstrate experimental testing of SD APD detector under
strong pulse illumination attack. This attack presents blinding stability and
helps an eavesdropper to learn the secret key without introducing extra QBER.
Based on this testing, we propose a set of criteria for protecting SD APD
detectors from the strong pulse illumination attack.

Extracting expressive visual features is crucial for accurate
Click-Through-Rate (CTR) prediction in visual search advertising systems.
Current commercial systems use off-the-shelf visual encoders to facilitate fast
online service. However, the extracted visual features are coarse-grained
and/or biased. In this paper, we present a visual encoding framework for CTR
prediction to overcome these problems. The framework is based on contrastive
learning which pulls positive pairs closer and pushes negative pairs apart in
the visual feature space. To obtain fine-grained visual features,we present
contrastive learning supervised by click through data to fine-tune the visual
encoder. To reduce sample selection bias, firstly we train the visual encoder
offline by leveraging both unbiased self-supervision and click supervision
signals. Secondly, we incorporate a debiasing network in the online CTR
predictor to adjust the visual features by contrasting high impression items
with selected items with lower impressions.We deploy the framework in the
visual sponsor search system at Alibaba. Offline experiments on billion-scale
datasets and online experiments demonstrate that the proposed framework can
make accurate and unbiased predictions.

Federated learning (FL) enables distributed participants to collaboratively
learn a global model without revealing their private data to each other.
Recently, vertical FL, where the participants hold the same set of samples but
with different features, has received increased attention. This paper first
presents one label inference attack method to investigate the potential privacy
leakages of the vertical logistic regression model. Specifically, we discover
that the attacker can utilize the residue variables, which are calculated by
solving the system of linear equations constructed by local dataset and the
received decrypted gradients, to infer the privately owned labels. To deal with
this, we then propose three protection mechanisms, e.g., additive noise
mechanism, multiplicative noise mechanism, and hybrid mechanism which leverages
local differential privacy and homomorphic encryption techniques, to prevent
the attack and improve the robustness of the vertical logistic regression.
model. Experimental results show that both the additive noise mechanism and the
multiplicative noise mechanism can achieve efficient label protection with only
a slight drop in model testing accuracy, furthermore, the hybrid mechanism can
achieve label protection without any testing accuracy degradation, which
demonstrates the effectiveness and efficiency of our protection techniques

The focus of this work is $\textit{sign spotting}$ - given a video of an
isolated sign, our task is to identify $\textit{whether}$ and $\textit{where}$
it has been signed in a continuous, co-articulated sign language video. To
achieve this sign spotting task, we train a model using multiple types of
available supervision by: (1) $\textit{watching}$ existing footage which is
sparsely labelled using mouthing cues; (2) $\textit{reading}$ associated
subtitles (readily available translations of the signed content) which provide
additional $\textit{weak-supervision}$; (3) $\textit{looking up}$ words (for
which no co-articulated labelled examples are available) in visual sign
language dictionaries to enable novel sign spotting. These three tasks are
integrated into a unified learning framework using the principles of Noise
Contrastive Estimation and Multiple Instance Learning. We validate the
effectiveness of our approach on low-shot sign spotting benchmarks. In
addition, we contribute a machine-readable British Sign Language (BSL)
dictionary dataset of isolated signs, BSLDict, to facilitate study of this
task. The dataset, models and code are available at our project page.

Multiscale stochastic dynamical systems have been widely adopted to
scientific and engineering problems due to their capability of depicting
complex phenomena in many real world applications. This work is devoted to
investigating the effective reduced dynamics for a slow-fast stochastic
dynamical system. Given observation data on a short-term period satisfying some
unknown slow-fast stochastic system, we propose a novel algorithm including a
neural network called Auto-SDE to learn invariant slow manifold. Our approach
captures the evolutionary nature of a series of time-dependent autoencoder
neural networks with the loss constructed from a discretized stochastic
differential equation. Our algorithm is also proved to be accurate, stable and
effective through numerical experiments under various evaluation metrics.

With the widespread use of deep neural networks (DNNs) in many areas, more
and more studies focus on protecting DNN models from intellectual property (IP)
infringement. Many existing methods apply digital watermarking to protect the
DNN models. The majority of them either embed a watermark directly into the
internal network structure/parameters or insert a zero-bit watermark by
fine-tuning a model to be protected with a set of so-called trigger samples.
Though these methods work very well, they were designed for individual DNN
models, which cannot be directly applied to deep ensemble models (DEMs) that
combine multiple DNN models to make the final decision. It motivates us to
propose a novel black-box watermarking method in this paper for DEMs, which can
be used for verifying the integrity of DEMs. In the proposed method, a certain
number of sensitive samples are carefully selected through mimicking real-world
DEM attacks and analyzing the prediction results of the sub-models of the
non-attacked DEM and the attacked DEM on the carefully crafted dataset. By
analyzing the prediction results of the target DEM on these carefully crafted
sensitive samples, we are able to verify the integrity of the target DEM.
Different from many previous methods, the proposed method does not modify the
original DEM to be protected, which indicates that the proposed method is
lossless. Experimental results have shown that the DEM integrity can be
reliably verified even if only one sub-model was attacked, which has good
potential in practice.

As automated machine learning (AutoML) systems continue to progress in both
sophistication and performance, it becomes important to understand the `how'
and `why' of human-computer interaction (HCI) within these frameworks, both
current and expected. Such a discussion is necessary for optimal system design,
leveraging advanced data-processing capabilities to support decision-making
involving humans, but it is also key to identifying the opportunities and risks
presented by ever-increasing levels of machine autonomy. Within this context,
we focus on the following questions: (i) How does HCI currently look like for
state-of-the-art AutoML algorithms, especially during the stages of
development, deployment, and maintenance? (ii) Do the expectations of HCI
within AutoML frameworks vary for different types of users and stakeholders?
(iii) How can HCI be managed so that AutoML solutions acquire human trust and
broad acceptance? (iv) As AutoML systems become more autonomous and capable of
learning from complex open-ended environments, will the fundamental nature of
HCI evolve? To consider these questions, we project existing literature in HCI
into the space of AutoML; this connection has, to date, largely been
unexplored. In so doing, we review topics including user-interface design,
human-bias mitigation, and trust in artificial intelligence (AI). Additionally,
to rigorously gauge the future of HCI, we contemplate how AutoML may manifest
in effectively open-ended environments. This discussion necessarily reviews
projected developmental pathways for AutoML, such as the incorporation of
reasoning, although the focus remains on how and why HCI may occur in such a
framework rather than on any implementational details. Ultimately, this review
serves to identify key research directions aimed at better facilitating the
roles and modes of human interactions with both current and future AutoML
systems.

This is Btech thesis report on detection and purification of adverserially
attacked images. A deep learning model is trained on certain training examples
for various tasks such as classification, regression etc. By training, weights
are adjusted such that the model performs the task well not only on training
examples judged by a certain metric but has an excellent ability to generalize
on other unseen examples as well which are typically called the test data.
Despite the huge success of machine learning models on a wide range of tasks,
security has received a lot less attention along the years. Robustness along
various potential cyber attacks also should be a metric for the accuracy of the
machine learning models. These cyber attacks can potentially lead to a variety
of negative impacts in the real world sensitive applications for which machine
learning is used such as medical and transportation systems. Hence, it is a
necessity to secure the system from such attacks. Int this report, I focus on a
class of these cyber attacks called the adversarial attacks in which the
original input sample is modified by small perturbations such that they still
look visually the same to human beings but the machine learning models are
fooled by such inputs. In this report I discuss 2 novel ways to counter the
adversarial attack using AutoEncoders, 1) by detecting the presence of
adversaries and 2) purifying these adversaries to make target classification
models robust against such attacks.

Hebbian theory seeks to explain how the neurons in the brain adapt to
stimuli, to enable learning. An interesting feature of Hebbian learning is that
it is an unsupervised method and as such, does not require feedback, making it
suitable in contexts where systems have to learn autonomously. This paper
explores how molecular systems can be designed to show such proto-intelligent
behaviours, and proposes the first chemical reaction network (CRN) that can
exhibit autonomous Hebbian learning across arbitrarily many input channels. The
system emulates a spiking neuron, and we demonstrate that it can learn
statistical biases of incoming inputs. The basic CRN is a minimal,
thermodynamically plausible set of micro-reversible chemical equations that can
be analysed with respect to their energy requirements. However, to explore how
such chemical systems might be engineered de novo, we also propose an extended
version based on enzyme-driven compartmentalised reactions. Finally, we also
show how a purely DNA system, built upon the paradigm of DNA strand
displacement, can realise neuronal dynamics. Our analysis provides a compelling
blueprint for exploring autonomous learning in biological settings, bringing us
closer to realising real synthetic biological intelligence.

Multi-armed bandits (MAB) is a simple reinforcement learning model where the
learner controls the trade-off between exploration versus exploitation to
maximize its cumulative reward. Federated multi-armed bandits (FMAB) is a
recently emerging framework where a cohort of learners with heterogeneous local
models play a MAB game and communicate their aggregated feedback to a parameter
server to learn the global feedback model. Federated learning models are
vulnerable to adversarial attacks such as model-update attacks or data
poisoning. In this work, we study an FMAB problem in the presence of Byzantine
clients who can send false model updates that pose a threat to the learning
process. We borrow tools from robust statistics and propose a
median-of-means-based estimator: Fed-MoM-UCB, to cope with the Byzantine
clients. We show that if the Byzantine clients constitute at most half the
cohort, it is possible to incur a cumulative regret on the order of ${\cal O}
(\log T)$ with respect to an unavoidable error margin, including the
communication cost between the clients and the parameter server. We analyze the
interplay between the algorithm parameters, unavoidable error margin, regret,
communication cost, and the arms' suboptimality gaps. We demonstrate
Fed-MoM-UCB's effectiveness against the baselines in the presence of Byzantine
attacks via experiments.

Quantum computing technologies are in the process of moving from academic
research to real industrial applications, with the first hints of quantum
advantage demonstrated in recent months. In these early practical uses of
quantum computers it is relevant to develop algorithms that are useful for
actual industrial processes. In this work we propose a quantum pipeline,
comprising a quantum autoencoder followed by a quantum classifier, which are
used to first compress and then label classical data coming from a separator,
i.e., a machine used in one of Eni's Oil Treatment Plants. This work represents
one of the first attempts to integrate quantum computing procedures in a
real-case scenario of an industrial pipeline, in particular using actual data
coming from physical machines, rather than pedagogical data from benchmark
datasets.

In 3D face reconstruction, orthogonal projection has been widely employed to
substitute perspective projection to simplify the fitting process. This
approximation performs well when the distance between camera and face is far
enough. However, in some scenarios that the face is very close to camera or
moving along the camera axis, the methods suffer from the inaccurate
reconstruction and unstable temporal fitting due to the distortion under the
perspective projection. In this paper, we aim to address the problem of
single-image 3D face reconstruction under perspective projection. Specifically,
a deep neural network, Perspective Network (PerspNet), is proposed to
simultaneously reconstruct 3D face shape in canonical space and learn the
correspondence between 2D pixels and 3D points, by which the 6DoF (6 Degrees of
Freedom) face pose can be estimated to represent perspective projection.
Besides, we contribute a large ARKitFace dataset to enable the training and
evaluation of 3D face reconstruction solutions under the scenarios of
perspective projection, which has 902,724 2D facial images with ground-truth 3D
face mesh and annotated 6DoF pose parameters. Experimental results show that
our approach outperforms current state-of-the-art methods by a significant
margin.

Tacrolimus is one of the cornerstone immunosuppressive drugs in most
transplantation centers worldwide following solid organ transplantation.
Therapeutic drug monitoring of tacrolimus is necessary in order to avoid
rejection of the transplanted organ or severe side effects. However, finding
the right dose for a given patient is challenging, even for experienced
clinicians. Consequently, a tool that can accurately estimate the drug exposure
for individual dose adaptions would be of high clinical value. In this work, we
propose a new technique using machine learning to estimate the tacrolimus
exposure in kidney transplant recipients. Our models achieve predictive errors
that are at the same level as an established population pharmacokinetic model,
but are faster to develop and require less knowledge about the pharmacokinetic
properties of the drug.

Deep learning methods can struggle to handle domain shifts not seen in
training data, which can cause them to not generalize well to unseen domains.
This has led to research attention on domain generalization (DG), which aims to
the model's generalization ability to out-of-distribution. Adversarial domain
generalization is a popular approach to DG, but conventional approaches (1)
struggle to sufficiently align features so that local neighborhoods are mixed
across domains; and (2) can suffer from feature space over collapse which can
threaten generalization performance. To address these limitations, we propose
localized adversarial domain generalization with space compactness
maintenance~(LADG) which constitutes two major contributions. First, we propose
an adversarial localized classifier as the domain discriminator, along with a
principled primary branch. This constructs a min-max game whereby the aim of
the featurizer is to produce locally mixed domains. Second, we propose to use a
coding-rate loss to alleviate feature space over collapse. We conduct
comprehensive experiments on the Wilds DG benchmark to validate our approach,
where LADG outperforms leading competitors on most datasets.

Many of the proposed machine learning (ML) based network intrusion detection
systems (NIDSs) achieve near perfect detection performance when evaluated on
synthetic benchmark datasets. Though, there is no record of if and how these
results generalise to other network scenarios, in particular to real-world
networks. In this paper, we investigate the generalisability property of
ML-based NIDSs by extensively evaluating seven supervised and unsupervised
learning models on four recently published benchmark NIDS datasets. Our
investigation indicates that none of the considered models is able to
generalise over all studied datasets. Interestingly, our results also indicate
that the generalisability has a high degree of asymmetry, i.e., swapping the
source and target domains can significantly change the classification
performance. Our investigation also indicates that overall, unsupervised
learning methods generalise better than supervised learning models in our
considered scenarios. Using SHAP values to explain these results indicates that
the lack of generalisability is mainly due to the presence of strong
correspondence between the values of one or more features and Attack/Benign
classes in one dataset-model combination and its absence in other datasets that
have different feature distributions.

The arguably most widely employed algorithm to train deep neural networks
with Differential Privacy is DPSGD, which requires clipping and noising of
per-sample gradients. This introduces a reduction in model utility compared to
non-private training. Empirically, it can be observed that this accuracy
degradation is strongly dependent on the model architecture. We investigated
this phenomenon and, by combining components which exhibit good individual
performance, distilled a new model architecture termed SmoothNet, which is
characterised by increased robustness to the challenges of DP-SGD training.
Experimentally, we benchmark SmoothNet against standard architectures on two
benchmark datasets and observe that our architecture outperforms others,
reaching an accuracy of 73.5\% on CIFAR-10 at $\varepsilon=7.0$ and 69.2\% at
$\varepsilon=7.0$ on ImageNette, a state-of-the-art result compared to prior
architectural modifications for DP.

Recent advances in 3D human shape reconstruction from single images have
shown impressive results, leveraging on deep networks that model the so-called
implicit function to learn the occupancy status of arbitrarily dense 3D points
in space. However, while current algorithms based on this paradigm, like
PiFuHD, are able to estimate accurate geometry of the human shape and clothes,
they require high-resolution input images and are not able to capture complex
body poses. Most training and evaluation is performed on 1k-resolution images
of humans standing in front of the camera under neutral body poses. In this
paper, we leverage publicly available data to extend existing implicit
function-based models to deal with images of humans that can have arbitrary
poses and self-occluded limbs. We argue that the representation power of the
implicit function is not sufficient to simultaneously model details of the
geometry and of the body pose. We, therefore, propose a coarse-to-fine approach
in which we first learn an implicit function that maps the input image to a 3D
body shape with a low level of detail, but which correctly fits the underlying
human pose, despite its complexity. We then learn a displacement map,
conditioned on the smoothed surface and on the input image, which encodes the
high-frequency details of the clothes and body. In the experimental section, we
show that this coarse-to-fine strategy represents a very good trade-off between
shape detail and pose correctness, comparing favorably to the most recent
state-of-the-art approaches. Our code will be made publicly available.

Deep learning methods driven by the low-rank regularization have achieved
attractive performance in dynamic magnetic resonance (MR) imaging. However,
most of these methods represent low-rank prior by hand-crafted nuclear norm,
which cannot accurately approximate the low-rank prior over the entire dataset
through a fixed regularization parameter. In this paper, we propose a learned
low-rank method for dynamic MR imaging. In particular, we unrolled the
semi-quadratic splitting method (HQS) algorithm for the partially separable
(PS) model to a network, in which the low-rank is adaptively characterized by a
learnable null-space transform. Experiments on the cardiac cine dataset show
that the proposed model outperforms the state-of-the-art compressed sensing
(CS) methods and existing deep learning methods both quantitatively and

Multimodal supervision has achieved promising results in many visual language
understanding tasks, where the language plays an essential role as a hint or
context for recognizing and locating instances. However, due to the defects of
the human-annotated language corpus, multimodal supervision remains unexplored
in fully supervised object detection scenarios. In this paper, we take
advantage of language prompt to introduce effective and unbiased linguistic
supervision into object detection, and propose a new mechanism called
multimodal knowledge learning (\textbf{MKL}), which is required to learn
knowledge from language supervision. Specifically, we design prompts and fill
them with the bounding box annotations to generate descriptions containing
extensive hints and context for instances recognition and localization. The
knowledge from language is then distilled into the detection model via
maximizing cross-modal mutual information in both image- and object-level.
Moreover, the generated descriptions are manipulated to produce hard negatives
to further boost the detector performance. Extensive experiments demonstrate
that the proposed method yields a consistent performance gain by 1.6\% $\sim$
2.1\% and achieves state-of-the-art on MS-COCO and OpenImages datasets.

We leverage embedding duplication between aligned sub-words to extend the
Parent-Child transfer learning method, so as to improve low-resource machine
translation. We conduct experiments on benchmark datasets of My-En, Id-En and
Tr-En translation scenarios. The test results show that our method produces
substantial improvements, achieving the BLEU scores of 22.5, 28.0 and 18.1
respectively. In addition, the method is computationally efficient which
reduces the consumption of training time by 63.8%, reaching the duration of 1.6
hours when training on a Tesla 16GB P100 GPU. All the models and source codes
in the experiments will be made publicly available to support reproducible
research.

Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from
a fully labeled source domain to a scarcely labeled target domain. In this
paper, we propose a Multi-level Consistency Learning (MCL) framework for SSDA.
Specifically, our MCL regularizes the consistency of different views of target
domain samples at three levels: (i) at inter-domain level, we robustly and
accurately align the source and target domains using a prototype-based optimal
transport method that utilizes the pros and cons of different views of target
samples; (ii) at intra-domain level, we facilitate the learning of both
discriminative and compact target feature representations by proposing a novel
class-wise contrastive clustering loss; (iii) at sample level, we follow
standard practice and improve the prediction accuracy by conducting a
consistency-based self-training. Empirically, we verified the effectiveness of
our MCL framework on three popular SSDA benchmarks, i.e., VisDA2017, DomainNet,
and Office-Home datasets, and the experimental results demonstrate that our MCL
framework achieves the state-of-the-art performance.

Augmentations are the key factor in determining the performance of any neural
network as they provide a model with a critical edge in boosting its
performance. Their ability to boost a model's robustness depends on two
factors, viz-a-viz, the model architecture, and the type of augmentations.
Augmentations are very specific to a dataset, and it is not imperative that all
kinds of augmentation would necessarily produce a positive effect on a model's
performance. Hence there is a need to identify augmentations that perform
consistently well across a variety of datasets and also remain invariant to the
type of architecture, convolutions, and the number of parameters used. Hence
there is a need to identify augmentations that perform consistently well across
a variety of datasets and also remain invariant to the type of architecture,
convolutions, and the number of parameters used. This paper evaluates the
effect of parameters using 3x3 and depth-wise separable convolutions on
different augmentation techniques on MNIST, FMNIST, and CIFAR10 datasets.
Statistical Evidence shows that techniques such as Cutouts and Random
horizontal flip were consistent on both parametrically low and high
architectures. Depth-wise separable convolutions outperformed 3x3 convolutions
at higher parameters due to their ability to create deeper networks.
Augmentations resulted in bridging the accuracy gap between the 3x3 and
depth-wise separable convolutions, thus establishing their role in model
generalization. At higher number augmentations did not produce a significant
change in performance. The synergistic effect of multiple augmentations at
higher parameters, with antagonistic effect at lower parameters, was also
evaluated. The work proves that a delicate balance between architectural
supremacy and augmentations needs to be achieved to enhance a model's
performance in any given deep learning task.

Video question answering (VideoQA) is challenging given its multimodal
combination of visual understanding and natural language processing. While most
existing approaches ignore the visual appearance-motion information at
different temporal scales, it is unknown how to incorporate the multilevel
processing capacity of a deep learning model with such multiscale information.
Targeting these issues, this paper proposes a novel Multilevel Hierarchical
Network (MHN) with multiscale sampling for VideoQA. MHN comprises two modules,
namely Recurrent Multimodal Interaction (RMI) and Parallel Visual Reasoning
(PVR). With a multiscale sampling, RMI iterates the interaction of
appearance-motion information at each scale and the question embeddings to
build the multilevel question-guided visual representations. Thereon, with a
shared transformer encoder, PVR infers the visual cues at each level in
parallel to fit with answering different question types that may rely on the
visual information at relevant levels. Through extensive experiments on three
VideoQA datasets, we demonstrate improved performances than previous
state-of-the-arts and justify the effectiveness of each part of our method.

Despite the plethora of successful Super-Resolution Reconstruction (SRR)
models applied to natural images, their application to remote sensing imagery
tends to produce poor results. Remote sensing imagery is often more complicated
than natural images and has its peculiarities such as being of lower
resolution, it contains noise, and often depicting large textured surfaces. As
a result, applying non-specialized SRR models on remote sensing imagery results
in artifacts and poor reconstructions. To address these problems, this paper
proposes an architecture inspired by previous research work, introducing a
novel approach for forcing an SRR model to output realistic remote sensing
images: instead of relying on feature-space similarities as a perceptual loss,
the model considers pixel-level information inferred from the normalized
Digital Surface Model (nDSM) of the image. This strategy allows the application
of better-informed updates during the training of the model which sources from
a task (elevation map inference) that is closely related to remote sensing.
Nonetheless, the nDSM auxiliary information is not required during production
and thus the model infers a super-resolution image without any additional data
besides its low-resolution pairs. We assess our model on two remotely sensed
datasets of different spatial resolutions that also contain the DSM pairs of
the images: the DFC2018 dataset and the dataset containing the national Lidar
fly-by of Luxembourg. Based on visual inspection, the inferred super-resolution
images exhibit particularly superior quality. In particular, the results for
the high-resolution DFC2018 dataset are realistic and almost indistinguishable
from the ground truth images.

We introduce an unsupervised machine learning method based on Siamese Neural
Networks (SNN) to detect phase boundaries. This method is applied to
Monte-Carlo simulations of Ising-type systems and Rydberg atom arrays. In both
cases the SNN reveals phase boundaries consistent with prior research. The
combination of leveraging the power of feed-forward neural networks,
unsupervised learning and the ability to learn about multiple phases without
knowing about their existence provides a powerful method to explore new and
unknown phases of matter.

Despite its tremendous value for the diagnosis, treatment monitoring and
surveillance of children with cancer, whole body staging with positron emission
tomography (PET) is time consuming and associated with considerable radiation
exposure. 100x (1% of the standard clinical dosage) ultra-low-dose/ultra-fast
whole-body PET reconstruction has the potential for cancer imaging with
unprecedented speed and improved safety, but it cannot be achieved by the naive
use of machine learning techniques. In this study, we utilize the global
similarity between baseline and follow-up PET and magnetic resonance (MR)
images to develop Masked-LMCTrans, a longitudinal multi-modality co-attentional
CNN-Transformer that provides interaction and joint reasoning between serial
PET/MRs of the same patient. We mask the tumor area in the referenced baseline
PET and reconstruct the follow-up PET scans. In this manner, Masked-LMCTrans
reconstructs 100x almost-zero radio-exposure whole-body PET that was not
possible before. The technique also opens a new pathway for longitudinal
radiology imaging reconstruction, a significantly under-explored area to date.
Our model was trained and tested with Stanford PET/MRI scans of pediatric
lymphoma patients and evaluated externally on PET/MRI images from T\"ubingen
University. The high image quality of the reconstructed 100x whole-body PET
images resulting from the application of Masked-LMCTrans will substantially
advance the development of safer imaging approaches and shorter exam-durations
for pediatric patients, as well as expand the possibilities for frequent
longitudinal monitoring of these patients by PET.

Incremental few-shot object detection aims at detecting novel classes without
forgetting knowledge of the base classes with only a few labeled training data
from the novel classes. Most related prior works are on incremental object
detection that rely on the availability of abundant training samples per novel
class that substantially limits the scalability to real-world setting where
novel data can be scarce. In this paper, we propose the Incremental-DETR that
does incremental few-shot object detection via fine-tuning and self-supervised
learning on the DETR object detector. To alleviate severe over-fitting with few
novel class data, we first fine-tune the class-specific components of DETR with
self-supervision from additional object proposals generated using Selective
Search as pseudo labels. We further introduce a incremental few-shot
fine-tuning strategy with knowledge distillation on the class-specific
components of DETR to encourage the network in detecting novel classes without
catastrophic forgetting. Extensive experiments conducted on standard
incremental object detection and incremental few-shot object detection settings
show that our approach significantly outperforms state-of-the-art methods by a

Despite the fact that many anomaly detection approaches have been developed
for multivariate time series data, limited effort has been made on federated
settings in which multivariate time series data are heterogeneously distributed
among different edge devices while data sharing is prohibited. In this paper,
we investigate the problem of federated unsupervised anomaly detection and
present a Federated Exemplar-based Deep Neural Network (Fed-ExDNN) to conduct
anomaly detection for multivariate time series data on different edge devices.
Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) to
learn local time series representations based on their compatibility with an
exemplar module which consists of hidden parameters learned to capture
varieties of normal patterns on each edge device. Next, a constrained
clustering mechanism (FedCC) is employed on the centralized server to align and
aggregate the parameters of different local exemplar modules to obtain a
unified global exemplar module. Finally, the global exemplar module is deployed
together with a shared feature encoder to each edge device and anomaly
detection is conducted by examining the compatibility of testing data to the
exemplar module. Fed-ExDNN captures local normal time series patterns with
ExDNN and aggregates these patterns by FedCC, and thus can handle the
heterogeneous data distributed over different edge devices simultaneously.
Thoroughly empirical studies on six public datasets show that ExDNN and
Fed-ExDNN can outperform state-of-the-art anomaly detection algorithms and
federated learning techniques.

Question Answering (QA) is a longstanding challenge in natural language
processing. Existing QA works mostly focus on specific question types,
knowledge domains, or reasoning skills. The specialty in QA research hinders
systems from modeling commonalities between tasks and generalization for wider
applications. To address this issue, we present ProQA, a unified QA paradigm
that solves various tasks through a single model. ProQA takes a unified
structural prompt as the bridge and improves the QA-centric ability by
structural prompt-based pre-training. Through a structurally designed
prompt-based input schema, ProQA concurrently models the knowledge
generalization for all QA tasks while keeping the knowledge customization for
every specific QA task. Furthermore, ProQA is pre-trained with structural
prompt-formatted large-scale synthesized corpus, which empowers the model with
the commonly-required QA ability. Experimental results on 11 QA benchmarks
demonstrate that ProQA consistently boosts performance on both full data
fine-tuning, few-shot learning, and zero-shot testing scenarios. Furthermore,
ProQA exhibits strong ability in both continual learning and transfer learning
by taking the advantages of the structural prompt.

Precise vertex reconstruction is essential for large liquid scintillator
detectors. A novel method based on machine learning has been successfully
developed to reconstruct the event vertex in JUNO previously. In this paper,
the performance of machine learning based vertex reconstruction is further
improved by optimizing the input images of the neural networks. By separating
the information of different types of PMTs as well as adding the information of
the second hit of PMTs, the vertex resolution is improved by about 9.4 % at 1
MeV and 9.8 % at 11 MeV, respectively.

Visualization of Machine Learning (ML) models is an important part of the ML
process to enhance the interpretability and prediction accuracy of the ML
models. This paper proposes a new method SPC-DT to visualize the Decision Tree
(DT) as interpretable models. These methods use a version of General Line
Coordinates called Shifted Paired Coordinates (SPC). In SPC, each n-D point is
visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed
graph. The new method expands and complements the capabilities of existing
methods, to visualize DT models. It shows: (1) relations between attributes,
(2) individual cases relative to the DT structure, (3) data flow in the DT, (4)
how tight each split is to thresholds in the DT nodes, and (5) the density of
cases in parts of the n-D space. This information is important for domain
experts for evaluating and improving the DT models, including avoiding
overgeneralization and overfitting of models, along with their performance. The
benefits of the methods are demonstrated in the case studies, using three real
datasets.

In this paper, we developed innovative digital twins of cattle status that
are powered by artificial intelligence (AI). The work was built on a farm IoT
system that remotely monitors and tracks the state of cattle. A digital twin
model of cattle health based on Deep Learning (DL) was generated using the
sensor data acquired from the farm IoT system. The health and physiological
cycle of cattle can be monitored in real time, and the state of the next
physiological cycle of cattle can be anticipated using this model. The basis of
this work is the vast amount of data which is required to validate the
legitimacy of the digital twins model. In terms of behavioural state, it was
found that the cattle treated with a combination of topical anaesthetic and
meloxicam exhibits the least pain reaction. The digital twins model developed
in this work can be used to monitor the health of cattle

This paper contributes to interpretable machine learning via visual knowledge
discovery in general line coordinates (GLC). The concepts of hyperblocks as
interpretable dataset units and general line coordinates are combined to create
a visual self-service machine learning model. The DSC1 and DSC2 lossless
multidimensional coordinate systems are proposed. DSC1 and DSC2 can map
multiple dataset attributes to a single two-dimensional (X, Y) Cartesian plane
using a graph construction algorithm. The hyperblock analysis was used to
determine visually appealing dataset attribute orders and to reduce line
occlusion. It is shown that hyperblocks can generalize decision tree rules and
a series of DSC1 or DSC2 plots can visualize a decision tree. The DSC1 and DSC2
plots were tested on benchmark datasets from the UCI ML repository. They
allowed for visual classification of data. Additionally, areas of hyperblock
impurity were discovered and used to establish dataset splits that highlight
the upper estimate of worst-case model accuracy to guide model selection for
high-risk decision-making. Major benefits of DSC1 and DSC2 is their highly
interpretable nature. They allow domain experts to control or establish new
machine learning models through visual pattern discovery.

This paper introduces a new open-source platform named Muskits for end-to-end
music processing, which mainly focuses on end-to-end singing voice synthesis
(E2E-SVS). Muskits supports state-of-the-art SVS models, including RNN SVS,
transformer SVS, and XiaoiceSing. The design of Muskits follows the style of
widely-used speech processing toolkits, ESPnet and Kaldi, for data
prepossessing, training, and recipe pipelines. To the best of our knowledge,
this toolkit is the first platform that allows a fair and highly-reproducible
comparison between several published works in SVS. In addition, we also
demonstrate several advanced usages based on the toolkit functionalities,
including multilingual training and transfer learning. This paper describes the
major framework of Muskits, its functionalities, and experimental results in
single-singer, multi-singer, multilingual, and transfer learning scenarios. The
toolkit is publicly available at https://github.com/SJTMusicTeam/Muskits.

This paper studies the task of any objects grasping from the known categories
by free-form language instructions. This task demands the technique in computer
vision, natural language processing, and robotics. We bring these disciplines
together on this open challenge, which is essential to human-robot interaction.
Critically, the key challenge lies in inferring the category of objects from
linguistic instructions and accurately estimating the 6-DoF information of
unseen objects from the known classes. In contrast, previous works focus on
inferring the pose of object candidates at the instance level. This
significantly limits its applications in real-world scenarios.In this paper, we
propose a language-guided 6-DoF category-level object localization model to
achieve robotic grasping by comprehending human intention. To this end, we
propose a novel two-stage method. Particularly, the first stage grounds the
target in the RGB image through language description of names, attributes, and
spatial relations of objects. The second stage extracts and segments point
clouds from the cropped depth image and estimates the full 6-DoF object pose at
category-level. Under such a manner, our approach can locate the specific
object by following human instructions, and estimate the full 6-DoF pose of a
category-known but unseen instance which is not utilized for training the
model. Extensive experimental results show that our method is competitive with
the state-of-the-art language-conditioned grasp method. Importantly, we deploy
our approach on a physical robot to validate the usability of our framework in
real-world applications. Please refer to the supplementary for the demo videos
of our robot experiments.

In this paper, we are interested in the problem of generating target grasps
by understanding freehand sketches. The sketch is useful for the persons who
cannot formulate language and the cases where a textual description is not
available on the fly. However, very few works are aware of the usability of
this novel interactive way between humans and robots. To this end, we propose a
method to generate a potential grasp configuration relevant to the
sketch-depicted objects. Due to the inherent ambiguity of sketches with
abstract details, we take the advantage of the graph by incorporating the
structure of the sketch to enhance the representation ability. This
graph-represented sketch is further validated to improve the generalization of
the network, capable of learning the sketch-queried grasp detection by using a
small collection (around 100 samples) of hand-drawn sketches. Additionally, our
model is trained and tested in an end-to-end manner which is easy to be
implemented in real-world applications. Experiments on the multi-object VMRD
and GraspNet-1Billion datasets demonstrate the good generalization of the
proposed method. The physical robot experiments confirm the utility of our
method in object-cluttered scenes.

Reinforcement Learning (RL) is a computational approach to reward-driven
learning in sequential decision problems. It implements the discovery of
optimal actions by learning from an agent interacting with an environment
rather than from supervised data. We contrast and compare RL with traditional
sequential design, focusing on simulation-based Bayesian sequential design
(BSD). Recently, there has been an increasing interest in RL techniques for
healthcare applications. We introduce two related applications as motivating
examples. In both applications, the sequential nature of the decisions is
restricted to sequential stopping. Rather than a comprehensive survey, the
focus of the discussion is on solutions using standard tools for these two
relatively simple sequential stopping problems. Both problems are inspired by
adaptive clinical trial design. We use examples to explain the terminology and
mathematical background that underlie each framework and map one to the other.
The implementations and results illustrate the many similarities between RL and
BSD. The results motivate the discussion of the potential strengths and
limitations of each approach.

The machine translation (MT) task is typically formulated as that of
returning a single translation for an input segment. However, in many cases,
multiple different translations are valid and the appropriate translation may
depend on the intended target audience, characteristics of the speaker, or even
the relationship between speakers. Specific problems arise when dealing with
honorifics, particularly translating from English into languages with formality
markers. For example, the sentence "Are you sure?" can be translated in German
as "Sind Sie sich sicher?" (formal register) or "Bist du dir sicher?"
(informal). Using wrong or inconsistent tone may be perceived as inappropriate
or jarring for users of certain cultures and demographics. This work addresses
the problem of learning to control target language attributes, in this case
formality, from a small amount of labeled contrastive data. We introduce an
annotated dataset (CoCoA-MT) and an associated evaluation metric for training
and evaluating formality-controlled MT models for six diverse target languages.
We show that we can train formality-controlled models by fine-tuning on labeled
contrastive data, achieving high accuracy (82% in-domain and 73% out-of-domain)
while maintaining overall quality.

The outbreak of COVID-19 forced schools to swiftly transition from in-person
classes to online or remote offerings, making educators and learners alike rely
on online videoconferencing platforms. Platforms like Zoom offer audio-visual
channels of communication and include features that are designed to approximate
the classroom experience. However, it is not clear how students' learning
experiences are affected by affordances of the videoconferencing platforms or
what underlying factors explain the differential effects of these affordances
on class experiences of engagement, interaction, and satisfaction. In order to
find out, we conducted two online survey studies: Study 1 (N = 176)
investigated the effects of three types of videoconferencing affordances (i.e.,
modality, interactivity, and agency affordances) on class experience during the
first two months after the transition to online learning. Results showed that
usage of the three kinds of affordances was positively correlated with
students' class engagement, interaction, and satisfaction. Perceived anonymity,
nonverbal cues, and comfort level were found to be the key mediators. In
addition, students' usage of video cameras in class was influenced by their
classmates. Study 2 (N = 256) tested the proposed relationships at a later
stage of the pandemic and found similar results, thus serving as a constructive
replication. This paper focuses on reporting the results of Study 1 since it
captures the timely reactions from students when they first went online, and
the second study plays a supplementary role in verifying Study 1 and thereby
extending its external validity. Together, the two studies provide insights for
instructors on how to leverage different videoconferencing affordances to
enhance the virtual learning experience. Design implications for digital tools
in online education are also discussed.

In this paper, we present a digital twin (DT)-assisted adaptive video
streaming scheme to enhance personalized quality-of-experience (PQoE). Since
PQoE models are user-specific and time-varying, existing schemes based on
universal and time-invariant PQoE models may suffer from performance
degradation. To address this issue, we first propose a DT-assisted PQoE model
construction method to obtain accurate user-specific PQoE models. Specifically,
user DTs (UDTs) are respectively constructed for individual users, which can
acquire and utilize users' data to accurately tune PQoE model parameters in
real time. Next, given the obtained PQoE models, we formulate a resource
management problem to maximize the overall long-term PQoE by taking the
dynamics of user' locations, video content requests, and buffer statuses into
account. To solve this problem, a deep reinforcement learning algorithm is
developed to jointly determine segment version selection, and communication and
computing resource allocation. Simulation results on the real-world dataset
demonstrate that the proposed scheme can effectively enhance PQoE compared with
benchmark schemes.

This work identifies the existence and cause of a type of posterior collapse
that frequently occurs in the Bayesian deep learning practice. For a general
linear latent variable model that includes linear variational autoencoders as a
special case, we precisely identify the nature of posterior collapse to be the
competition between the likelihood and the regularization of the mean due to
the prior. Our result suggests that posterior collapse may be related to neural
collapse and dimensional collapse and could be a subclass of a general problem
of learning for deeper architectures.

This work aims to tackle Model Inversion (MI) attack on Split Federated
Learning (SFL). SFL is a recent distributed training scheme where multiple
clients send intermediate activations (i.e., feature map), instead of raw data,
to a central server. While such a scheme helps reduce the computational load at
the client end, it opens itself to reconstruction of raw data from intermediate
activation by the server. Existing works on protecting SFL only consider
inference and do not handle attacks during training. So we propose ResSFL, a
Split Federated Learning Framework that is designed to be MI-resistant during
training. It is based on deriving a resistant feature extractor via
attacker-aware training, and using this extractor to initialize the client-side
model prior to standard SFL training. Such a method helps in reducing the
computational complexity due to use of strong inversion model in client-side
adversarial training as well as vulnerability of attacks launched in early
training epochs. On CIFAR-100 dataset, our proposed framework successfully
mitigates MI attack on a VGG-11 model with a high reconstruction
Mean-Square-Error of 0.050 compared to 0.005 obtained by the baseline system.
The framework achieves 67.5% accuracy (only 1% accuracy drop) with very low
computation overhead. Code is released at:
https://github.com/zlijingtao/ResSFL.

Contextually aware intelligent agents are often required to understand the
users and their surroundings in real-time. Our goal is to build Artificial
Intelligence (AI) systems that can assist children in their learning process.
Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial
building blocks to handle efficient task-oriented communication with children
in game-based learning settings. We are working towards a multimodal dialogue
system for younger kids learning basic math concepts. Our focus is on improving
the Natural Language Understanding (NLU) module of the task-oriented SDS
pipeline with limited datasets. This work explores the potential benefits of
data augmentation with paraphrase generation for the NLU models trained on
small task-specific datasets. We also investigate the effects of extracting
entities for conceivably further data expansion. We have shown that
paraphrasing with model-in-the-loop (MITL) strategies using small seed data is
a promising approach yielding improved performance results for the Intent
Recognition task.

The proliferation and variety of Internet of Things devices means that they
have increasingly become a viable target for malicious users. This has created
a need for anomaly detection algorithms that can work across multiple devices.
This thesis suggests a potential alternative to the current anomaly detection
algorithms to be implemented within IoT systems that can be applied across
different types of devices. This algorithm is comprised of both unsupverised
and supervised machine areas of machine learning combining the strongest facet
of each. The algorithm involves the initial k-means clustering of attacks and
assigns them to clusters. Next, the clusters are then used by the AdaBoosted
Naive Bayes supervised learning algorithm in order to teach itself which piece
of data should be clustered to which specific attack. This increases the
accuracy of the proposed algorithm by adding clustered data before the final
classification step, ensuring a more accurate algorithm. The correct
indentification percentage scores for this proposed algorithm range anywhere
from 90% to 100%, as well as rating the proposed algorithms accuracy, precision
and recall. These high scores achieve an accurate, flexible, scalable,
optimised algorithm that could potentially be in different IoT devices,
ensuring strong data integrity and privacy.

Robotic manipulation of deformable linear objects (DLOs) has broad
application prospects in many fields. However, a key issue is to obtain the
exact deformation models (i.e., how robot motion affects DLO deformation),
which are hard to theoretically calculate and vary among different DLOs. Thus,
shape control of DLOs is challenging, especially for large deformation control
which requires global and more accurate models. In this paper, we propose a
coupled offline and online data-driven method for efficiently learning a global
deformation model, allowing for both accurate modeling through offline learning
and further updating for new DLOs via online adaptation. Specifically, the
model approximated by a neural network is first trained offline on random data,
then seamlessly migrated to the online phase, and further updated online during
actual manipulation. Several strategies are introduced to improve the model's
efficiency and generalization ability. We propose a convex-optimization-based
controller, and analyze the system's stability using the Lyapunov method.
Detailed simulations and real-world experiments demonstrate that our method can
efficiently and precisely estimate the deformation model, and achieve large
deformation control of untrained DLOs in 2D and 3D dual-arm manipulation tasks
better than the existing methods. It accomplishes all 24 tasks with different
desired shapes on different DLOs in the real world, using only simulation data
for the offline learning.

The goal of the Amazon Last Mile Routing Research Challenge is to integrate
the real-life experience of Amazon drivers into the solution of optimal route
planning and optimization. This paper presents our method that tackles this
challenge by hierarchically combining machine learning and conventional
Traveling Salesperson Problem (TSP) solvers. Our method reaps the benefits from
both worlds. On the one hand, our method encodes driver know-how by learning a
sequential probability model from historical routes at the zone level, where
each zone contains a few parcel stops. It then uses a single step policy
iteration method, known as the Rollout algorithm, to generate plausible zone
sequences sampled from the learned probability model. On the other hand, our
method utilizes proven methods developed in the rich TSP literature to sequence
stops within each zone efficiently. The outcome of such a combination appeared
to be promising. Our method obtained an evaluation score of $0.0374$, which is
comparable to what the top three teams have achieved on the official Challenge
leaderboard. Moreover, our learning-based method is applicable to driving
routes that may exhibit distinct sequential patterns beyond the scope of this
Challenge. The source code of our method is publicly available at
https://github.com/aws-samples/amazon-sagemaker-amazon-routing-challenge-sol

Following the success of the natural language processing, the transformer for
vision applications has attracted significant attention in recent years due to
its excellent performance. However, existing deep learning hardware
accelerators for vision cannot execute this structure efficiently due to
significant model architecture differences. As a result, this paper proposes
the hardware accelerator for vision transformers with row-wise scheduling,
which decomposes major operations in vision transformers as a single dot
product primitive for a unified and efficient execution. Furthermore, by
sharing weights in columns, we can reuse the data and reduce the usage of
memory. The implementation with TSMC 40nm CMOS technology only requires 262K
gate count and 149KB SRAM buffer for 403.2 GOPS throughput at 600MHz clock
frequency.

Deep learning based superresolution achieves high-quality results, but its
heavy computational workload, large buffer, and high external memory bandwidth
inhibit its usage in mobile devices. To solve the above issues, this paper
proposes a real-time hardware accelerator with the tilted layer fusion method
that reduces the external DRAM bandwidth by 92\% and just needs 102KB on-chip
memory. The design implemented with a 40nm CMOS process achieves
1920x1080@60fps throughput with 544.3K gate count when running at 600MHz; it
has higher throughput and lower area cost than previous designs.

In-memory computing is becoming a popular architecture for deep-learning
hardware accelerators recently due to its highly parallel computing, low power,
and low area cost. However, in-RRAM computing (IRC) suffered from large device
variation and numerous nonideal effects in hardware. Although previous
approaches including these effects in model training successfully improved
variation tolerance, they only considered part of the nonideal effects and
relatively simple classification tasks. This paper proposes a joint hardware
and software optimization strategy to design a hardware-robust IRC macro for
object detection. We lower the cell current by using a low word-line voltage to
enable a complete convolution calculation in one operation that minimizes the
impact of nonlinear addition. We also implement ternary weight mapping and
remove batch normalization for better tolerance against device variation, sense
amplifier variation, and IR drop problem. An extra bias is included to overcome
the limitation of the current sensing range. The proposed approach has been
successfully applied to a complex object detection task with only 3.85\% mAP
drop, whereas a naive design suffers catastrophic failure under these nonideal
effects.

Personalized Federated Learning (pFL) not only can capture the common priors
from broad range of distributed data, but also support customized models for
heterogeneous clients. Researches over the past few years have applied the
weighted aggregation manner to produce personalized models, where the weights
are determined by calibrating the distance of the entire model parameters or
loss values, and have yet to consider the layer-level impacts to the
aggregation process, leading to lagged model convergence and inadequate
personalization over non-IID datasets. In this paper, we propose a novel pFL
training framework dubbed Layer-wised Personalized Federated learning (pFedLA)
that can discern the importance of each layer from different clients, and thus
is able to optimize the personalized model aggregation for clients with
heterogeneous data. Specifically, we employ a dedicated hypernetwork per client
on the server side, which is trained to identify the mutual contribution
factors at layer granularity. Meanwhile, a parameterized mechanism is
introduced to update the layer-wised aggregation weights to progressively
exploit the inter-user similarity and realize accurate model personalization.
Extensive experiments are conducted over different models and learning tasks,
and we show that the proposed methods achieve significantly higher performance
than state-of-the-art pFL methods.

Pure data-driven deep learning models suffer from high training costs, error
accumulation, and poor generalizability when predicting complex physical
processes. A more promising way is to leverage our prior physics knowledge in
scientific deep learning models, known as physics-informed deep learning
(PiDL). In most PiDL frameworks, the physics prior is utilized to regularize
neural network training by incorporating governing equations into the loss
function. The resulting physical constraint, imposed in a soft manner, relies
heavily on a proper setting of hyperparameters that weigh each loss term. To
this end, we propose a new direction to leverage physics prior knowledge by
``baking'' the mathematical structure of governing equations into the neural
network architecture, namely PDE-preserved neural network (PPNN). The
discretized PDE is preserved in PPNN as convolutional residual networks
formulated in a multi-resolution setting. This physics-inspired learning
architecture endows PPNN with excellent generalizability and long-term
prediction accuracy compared to the state-of-the-art black-box baselines. The
effectiveness and merit of the proposed methods have been demonstrated over a
handful of spatiotemporal dynamical systems governed by spatiotemporal PDEs,
including reaction-diffusion, Burgers', and Navier-Stokes equations.

It is common for researchers to holdout data from a study pool to be used for
external validation as well as for future research, and the same desire is true
to those using machine learning modeling research. For this discussion, the
purpose of the holdout sample it is preserve data for research studies that
will be analysis-naive and randomly selected from the full dataset.
Analysis-naive are records that are not used for testing or training machine
learning (ML) models and records that do not participate in any aspect of the
current machine learning study. The methodology suggested for creating holdouts
is a modification of k-fold cross validation, which takes into account
randomization and efficiently allows a three-way split (holdout, test and
training) as part of the method without forcing. The paper also provides a
working example using set of automated functions in Python and some scenarios
for applicability in healthcare.

In this paper we share findings from our effort to build practical machine
translation (MT) systems capable of translating across over one thousand
languages. We describe results in three research domains: (i) Building clean,
web-mined datasets for 1500+ languages by leveraging semi-supervised
pre-training for language identification and developing data-driven filtering
techniques; (ii) Developing practical MT models for under-served languages by
leveraging massively multilingual models trained with supervised parallel data
for over 100 high-resource languages and monolingual datasets for an additional
1000+ languages; and (iii) Studying the limitations of evaluation metrics for
these languages and conducting qualitative analysis of the outputs from our MT
models, highlighting several frequent error modes of these types of models. We
hope that our work provides useful insights to practitioners working towards
building MT systems for currently understudied languages, and highlights
research directions that can complement the weaknesses of massively
multilingual models in data-sparse settings.

Many natural language processing tasks, e.g., coreference resolution and
semantic role labeling, require selecting text spans and making decisions about
them.
  A typical approach to such tasks is to score all possible spans and greedily
select spans for task-specific downstream processing.
  This approach, however, does not incorporate any inductive bias about what
sort of spans ought to be selected, e.g., that selected spans tend to be
syntactic constituents.
  In this paper, we propose a novel grammar-based structured span selection
model which learns to make use of the partial span-level annotation provided
for such problems.
  Compared to previous approaches, our approach gets rid of the heuristic
greedy span selection scheme, allowing us to model the downstream task on an
optimal set of spans. We evaluate our model on two popular span prediction
tasks: coreference resolution and semantic role labeling.
  We show empirical improvements on both.

Detecting human stress levels and emotional states with physiological
body-worn sensors is a complex task, but one with many health-related benefits.
Robustness to sensor measurement noise and energy efficiency of low-power
devices remain key challenges in stress detection. We propose SELFCARE, a fully
wrist-based method for stress detection that employs context-aware selective
sensor fusion that dynamically adapts based on data from the sensors. Our
method uses motion to determine the context of the system and learns to adjust
the fused sensors accordingly, improving performance while maintaining energy
efficiency. SELF-CARE obtains state-of-the-art performance across the publicly
available WESAD dataset, achieving 86.34% and 94.12% accuracy for the 3-class
and 2-class classification problems, respectively. Evaluation on real hardware
shows that our approach achieves up to 2.2x (3-class) and 2.7x (2-class) energy
efficiency compared to traditional sensor fusion.

Controlled table-to-text generation seeks to generate natural language
descriptions for highlighted subparts of a table. Previous SOTA systems still
employ a sequence-to-sequence generation method, which merely captures the
table as a linear structure and is brittle when table layouts change. We seek
to go beyond this paradigm by (1) effectively expressing the relations of
content pieces in the table, and (2) making our model robust to
content-invariant structural transformations. Accordingly, we propose an
equivariance learning framework, which encodes tables with a structure-aware
self-attention mechanism. This prunes the full self-attention structure into an
order-invariant graph attention that captures the connected graph structure of
cells belonging to the same row or column, and it differentiates between
relevant cells and irrelevant cells from the structural perspective. Our
framework also modifies the positional encoding mechanism to preserve the
relative position of tokens in the same cell but enforce position invariance
among different cells. Our technology is free to be plugged into existing
table-to-text generation models, and has improved T5-based models to offer
better performance on ToTTo and HiTab. Moreover, on a harder version of ToTTo,
we preserve promising performance, while previous SOTA systems, even with
transformation-based data augmentation, have seen significant performance
drops. Our code is available at https://github.com/luka-group/Lattice.

Using mathematical modeling and human subjects experiments, this research
explores the extent to which emerging webcams might leak recognizable textual
and graphical information gleaming from eyeglass reflections captured by
webcams. The primary goal of our work is to measure, compute, and predict the
factors, limits, and thresholds of recognizability as webcam technology evolves
in the future. Our work explores and characterizes the viable threat models
based on optical attacks using multi-frame super resolution techniques on
sequences of video frames. Our models and experimental results in a controlled
lab setting show it is possible to reconstruct and recognize with over 75%
accuracy on-screen texts that have heights as small as 10 mm with a 720p
webcam. We further apply this threat model to web textual contents with varying
attacker capabilities to find thresholds at which text becomes recognizable.
Our user study with 20 participants suggests present-day 720p webcams are
sufficient for adversaries to reconstruct textual content on big-font websites.
Our models further show that the evolution towards 4K cameras will tip the
threshold of text leakage to reconstruction of most header texts on popular
websites. Besides textual targets, a case study on recognizing a closed-world
dataset of Alexa top 100 websites with 720p webcams shows a maximum recognition
accuracy of 94% with 10 participants even without using machine-learning
models. Our research proposes near-term mitigations including a software
prototype that users can use to blur the eyeglass areas of their video streams.
For possible long-term defenses, we advocate an individual reflection testing
procedure to assess threats under various settings, and justify the importance
of following the principle of least privilege for privacy-sensitive scenarios.

This paper develops a novel method for policy choice in a dynamic setting
where the available data is a multivariate time series. Building on the
statistical treatment choice framework, we propose Time-series Empirical
Welfare Maximization (T-EWM) methods to estimate an optimal policy rule for the
current period or over multiple periods by maximizing an empirical welfare
criterion constructed using nonparametric potential outcome time-series. We
characterize conditions under which T-EWM consistently learns a policy choice
that is optimal in terms of conditional welfare given the time-series history.
We then derive a nonasymptotic upper bound for conditional welfare regret and
its minimax lower bound. To illustrate the implementation and uses of T-EWM, we
perform simulation studies and apply the method to estimate optimal monetary
policy rules from macroeconomic time-series data.

In the past few years, neural architecture search (NAS) has become an
increasingly important tool within the deep learning community. Despite the
many recent successes of NAS, however, most existing approaches operate within
highly structured design spaces, and hence explore only a small fraction of the
full search space of neural architectures while also requiring significant
manual effort from domain experts. In this work, we develop techniques that
enable efficient NAS in a significantly larger design space. To accomplish
this, we propose to perform NAS in an abstract search space of program
properties. Our key insights are as follows: (1) the abstract search space is
significantly smaller than the original search space, and (2) architectures
with similar program properties also have similar performance; thus, we can
search more efficiently in the abstract search space. To enable this approach,
we also propose a novel efficient synthesis procedure, which accepts a set of
promising program properties, and returns a satisfying neural architecture. We
implement our approach, $\alpha$NAS, within an evolutionary framework, where
the mutations are guided by the program properties. Starting with a ResNet-34
model, $\alpha$NAS produces a model with slightly improved accuracy on CIFAR-10
but 96% fewer parameters. On ImageNet, $\alpha$NAS is able to improve over
Vision Transformer (30% fewer FLOPS and parameters), ResNet-50 (23% fewer
FLOPS, 14% fewer parameters), and EfficientNet (7% fewer FLOPS and parameters)
without any degradation in accuracy.

Machine Learning-based supervised approaches require highly customized and
fine-tuned methodologies to deliver outstanding performance. This paper
presents a dataset-driven design and performance evaluation of a machine
learning classifier for the network intrusion dataset UNSW-NB15. Analysis of
the dataset suggests that it suffers from class representation imbalance and
class overlap in the feature space. We employed ensemble methods using Balanced
Bagging (BB), eXtreme Gradient Boosting (XGBoost), and Random Forest empowered
by Hellinger Distance Decision Tree (RF-HDDT). BB and XGBoost are tuned to
handle the imbalanced data, and Random Forest (RF) classifier is supplemented
by the Hellinger metric to address the imbalance issue. Two new algorithms are
proposed to address the class overlap issue in the dataset. These two
algorithms are leveraged to help improve the performance of the testing dataset
by modifying the final classification decision made by three base classifiers
as part of the ensemble classifier which employs a majority vote combiner. The
proposed design is evaluated for both binary and multi-category classification.
Comparing the proposed model to those reported on the same dataset in the
literature demonstrate that the proposed model outperforms others by a
significant margin for both binary and multi-category classification cases.

The article introduces some ideas for solving special cases of the following
problem, proposed in a somewhat generalized form by Marcus Hutter in 2000.
Given two Turing machines $A$ and $C$, it is required to build a Turing machine
$B$, such that after interacting of $A$ and $B$ on a shared tape for a fixed
number of iterations, the machine $C$ outputs 1 on the communication protocol
of $A$ and $B$. Details in the introduction.

This paper studies identification and estimation of a dynamic discrete choice
model of demand for differentiated product using consumer-level panel data with
few purchase events per consumer (i.e., short panel). Consumers are
forward-looking and their preferences incorporate two sources of dynamics: last
choice dependence due to habits and switching costs, and duration dependence
due to inventory, depreciation, or learning. A key distinguishing feature of
the model is that consumer unobserved heterogeneity has a Fixed Effects (FE)
structure -- that is, its probability distribution conditional on the initial
values of endogenous state variables is unrestricted. I apply and extend recent
results to establish the identification of all the structural parameters as
long as the dataset includes four or more purchase events per household. The
parameters can be estimated using a sufficient statistic - conditional maximum
likelihood (CML) method. An attractive feature of CML in this model is that the
sufficient statistic controls for the forward-looking value of the consumer's
decision problem such that the method does not require solving dynamic
programming problems or calculating expected present values.

The number of panicles (or heads) of Sorghum plants is an important
phenotypic trait for plant development and grain yield estimation. The use of
Unmanned Aerial Vehicles (UAVs) enables the capability of collecting and
analyzing Sorghum images on a large scale. Deep learning can provide methods
for estimating phenotypic traits from UAV images but requires a large amount of
labeled data. The lack of training data due to the labor-intensive ground
truthing of UAV images causes a major bottleneck in developing methods for
Sorghum panicle detection and counting. In this paper, we present an approach
that uses synthetic training images from generative adversarial networks (GANs)
for data augmentation to enhance the performance of Sorghum panicle detection
and counting. Our method can generate synthetic high-resolution UAV RGB images
with panicle labels by using image-to-image translation GANs with a limited
ground truth dataset of real UAV RGB images. The results show the improvements
in panicle detection and counting using our data augmentation approach.

With the increasing adoption of Artificial Intelligence systems (AIS) in
various application and the growing efforts to regulate such systems, a new set
of occupations has emerged in the industry. This new set of roles take
different titles and hold varying responsibilities. However, the individuals in
these roles are tasked with interpreting and operationalizing best practices
for developing ethical and safe AI systems. We will broadly refer to this new
set of occupations as AI ethicists and recognize that they often hold a
specific role in the intersection of technology development, business needs,
and societal implications. In this work, we examine what it means to be an AI
ethicist in the industry and propose an ontology of existing roles under this
broad title along with their required competencies. We create this ontology by
examining the job postings for such roles over the past two years and conduct
expert interviews with fourteen individuals who currently hold such a role in
the industry. The proposed ontology will inform executives and leaders who are
looking to build responsible AI teams and provide educators the necessary
information for creating new learning objectives and curriculum.

Land Carbon verification has long been a challenge in the carbon credit
market. Carbon verification methods currently available are expensive, and may
generate low-quality credit. Scalable and accurate remote sensing techniques
enable new approaches to monitor changes in Above Ground Biomass (AGB) and Soil
Organic Carbon (SOC). The majority of state-of-the-art research employs remote
sensing on AGB and SOC separately, although some studies indicate a positive
correlation between the two. We intend to combine the two domains in our
research to improve state-of-the-art total carbon estimation and to provide
insight into the voluntary carbon trading market. We begin by establishing
baseline model in our study area in Scotland, using state-of-the-art
methodologies in the SOC and AGB domains. The effects of feature engineering
techniques such as variance inflation factor and feature selection on machine
learning models are then investigated. This is extended by combining predictor
variables from the two domains. Finally, we leverage the possible correlation
between AGB and SOC to establish a relationship between the two and propose
novel models in an attempt outperform the state-of-the-art results. We compared
three machine learning techniques, boosted regression tree, random forest, and
xgboost. These techniques have been demonstrated to be the most effective in
both domains.

Brachiation is the primary form of locomotion for gibbons and siamangs, in
which these primates swing from tree limb to tree limb using only their arms.
It is challenging to control because of the limited control authority, the
required advance planning, and the precision of the required grasps. We present
a novel approach to this problem using reinforcement learning, and as
demonstrated on a finger-less 14-link planar model that learns to brachiate
across challenging handhold sequences. Key to our method is the use of a
simplified model, a point mass with a virtual arm, for which we first learn a
policy that can brachiate across handhold sequences with a prescribed order.
This facilitates the learning of the policy for the full model, for which it
provides guidance by providing an overall center-of-mass trajectory to imitate,
as well as for the timing of the holds. Lastly, the simplified model can also
readily be used for planning suitable sequences of handholds in a given
environment. Our results demonstrate brachiation motions with a variety of
durations for the flight and hold phases, as well as emergent extra
back-and-forth swings when this proves useful. The system is evaluated with a
variety of ablations. The method enables future work towards more general 3D
brachiation, as well as using simplified model imitation in other settings.

Weight norm $\|w\|$ and margin $\gamma$ participate in learning theory via
the normalized margin $\gamma/\|w\|$. Since standard neural net optimizers do
not control normalized margin, it is hard to test whether this quantity
causally relates to generalization. This paper designs a series of experimental
studies that explicitly control normalized margin and thereby tackle two
central questions. First: does normalized margin always have a causal effect on
generalization? The paper finds that no -- networks can be produced where
normalized margin has seemingly no relationship with generalization, counter to
the theory of Bartlett et al. (2017). Second: does normalized margin ever have
a causal effect on generalization? The paper finds that yes -- in a standard
training setup, test performance closely tracks normalized margin. The paper
suggests a Gaussian process model as a promising explanation for this behavior.

Clinical notes are becoming an increasingly important data source for machine
learning (ML) applications in healthcare. Prior research has shown that
deploying ML models can perpetuate existing biases against racial minorities,
as bias can be implicitly embedded in data. In this study, we investigate the
level of implicit race information available to ML models and human experts and
the implications of model-detectable differences in clinical notes. Our work
makes three key contributions. First, we find that models can identify patient
self-reported race from clinical notes even when the notes are stripped of
explicit indicators of race. Second, we determine that human experts are not
able to accurately predict patient race from the same redacted clinical notes.
Finally, we demonstrate the potential harm of this implicit information in a
simulation study, and show that models trained on these race-redacted clinical
notes can still perpetuate existing biases in clinical treatment decisions.

Single-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding
of cellular composition within complex tissues and organisms. A major
limitation in most scRNAseq analysis pipelines is the reliance on manual
annotations to determine cell identities, which are time consuming, subjective,
and require expertise. Given the surge in cell sequencing, supervised
methods-especially deep learning models-have been developed for automatic cell
type identification (ACTI), which achieve high accuracy and scalability.
However, all existing deep learning frameworks for ACTI lack interpretability
and are used as "black-box" models. We present N-ACT (Neural-Attention for Cell
Type identification): the first-of-its-kind interpretable deep neural network
for ACTI utilizing neural-attention to detect salient genes for use in
cell-type identification. We compare N-ACT to conventional annotation methods
on two previously manually annotated data sets, demonstrating that N-ACT
accurately identifies marker genes and cell types in an unsupervised manner,
while performing comparably on multiple data sets to current state-of-the-art
model in traditional supervised ACTI.

Neural scene representations, both continuous and discrete, have recently
emerged as a powerful new paradigm for 3D scene understanding. Recent efforts
have tackled unsupervised discovery of object-centric neural scene
representations. However, the high cost of ray-marching, exacerbated by the
fact that each object representation has to be ray-marched separately, leads to
insufficiently sampled radiance fields and thus, noisy renderings, poor
framerates, and high memory and time complexity during training and rendering.
Here, we propose to represent objects in an object-centric, compositional scene
representation as light fields. We propose a novel light field compositor
module that enables reconstructing the global light field from a set of
object-centric light fields. Dubbed Compositional Object Light Fields (COLF),
our method enables unsupervised learning of object-centric neural scene
representations, state-of-the-art reconstruction and novel view synthesis
performance on standard datasets, and rendering and training speeds at orders
of magnitude faster than existing 3D approaches.

This paper studies online algorithms augmented with multiple machine-learned
predictions. While online algorithms augmented with a single prediction have
been extensively studied in recent years, the literature for the multiple
predictions setting is sparse. In this paper, we give a generic algorithmic
framework for online covering problems with multiple predictions that obtains
an online solution that is competitive against the performance of the best
predictor. Our algorithm incorporates the use of predictions in the classic
potential-based analysis of online algorithms. We apply our algorithmic
framework to solve classical problems such as online set cover, (weighted)
caching, and online facility location in the multiple predictions setting. Our
algorithm can also be robustified, i.e., the algorithm can be simultaneously
made competitive against the best prediction and the performance of the best
online algorithm (without prediction).

Channel hopping provides a defense mechanism against jamming attacks in large
scale \ac{iot} networks.} However, a sufficiently powerful attacker may be able
to learn the channel hopping pattern and efficiently predict the channel to
jam. In this paper, we present FOLPETTI, a MAB-based attack to dynamically
follow the victim's channel selection in real-time. Compared to previous
attacks implemented via DRL, FOLPETTI does not require recurrent training
phases to capture the victim's behavior, allowing hence a continuous attack. We
assess the validity of FOLPETTI by implementing it to launch a jamming attack.
We evaluate its performance against a victim performing random channel
selection and a victim implementing a MAB defence strategy. We assume that the
victim detects an attack when more than $20\%$ of the transmitted packets are
not received, therefore this represents the limit for the attack to be
stealthy. In this scenario, FOLPETTI achieves a $15\%$ success rate for the
victim's random channel selection strategy, close to the $17.5\%$ obtained with
a genie-aided approach. Conversely, the DRL-based approach reaches a success
rate of $12.5\%$, which is $5.5\%$ less than FOLPETTI. We also confirm the
results by confronting FOLPETTI with a MAB based channel hopping method.
Finally, we show that FOLPETTI creates an additional energy demand
independently from its success rate, therefore decreasing the lifetime of IoT
devices.

Random Reshuffling (RR), which is a variant of Stochastic Gradient Descent
(SGD) employing sampling without replacement, is an immensely popular method
for training supervised machine learning models via empirical risk
minimization. Due to its superior practical performance, it is embedded and
often set as default in standard machine learning software. Under the name
FedRR, this method was recently shown to be applicable to federated learning
(Mishchenko et al.,2021), with superior performance when compared to common
baselines such as Local SGD. Inspired by this development, we design three new
algorithms to improve FedRR further: compressed FedRR and two variance reduced
extensions: one for taming the variance coming from shuffling and the other for
taming the variance due to compression. The variance reduction mechanism for
compression allows us to eliminate dependence on the compression parameter, and
applying additional controlled linear perturbations for Random Reshuffling,
introduced by Malinovsky et al.(2021) helps to eliminate variance at the
optimum. We provide the first analysis of compressed local methods under
standard assumptions without bounded gradient assumptions and for heterogeneous
data, overcoming the limitations of the compression operator. We corroborate
our theoretical results with experiments on synthetic and real data sets.

To address the problem that traditional network traffic anomaly detection
algorithms do not suffi-ciently mine potential features in long time domain, an
anomaly detection method based on mul-ti-scale residual features of network
traffic is proposed. The original traffic is divided into subse-quences of
different time spans using sliding windows, and each subsequence is decomposed
and reconstructed into data sequences of different levels using wavelet
transform technique; the stacked autoencoder (SAE) constructs similar feature
space using normal network traffic, and gen-erates reconstructed error vector
using the difference between reconstructed samples and input samples in the
similar feature space; the multi-path residual group is used to learn
reconstructed error The traffic classification is completed by a lightweight
classifier. The experimental results show that the detection performance of the
proposed method for anomalous network traffic is sig-nificantly improved
compared with traditional methods; it confirms that the longer time span and
more S transformation scales have positive effects on discovering potential
diversity information in the original network traffic.

Natural organized systems adapt to internal and external pressures and this
seems to happens all the way down. Wanting to think clearly about this idea
motivates our paper, and so the idea is elaborated extensively in the
introduction, which should be broadly accessible to a
philosophically-interested audience.
  In the remaining sections, we turn to more compressed category theory. We
define the monoidal double category $\mathbf{Org}$ of dynamic organizations, we
provide definitions of $\mathbf{Org}$-enriched, or "dynamic", categorical
structures -- e.g. dynamic categories, operads, and monoidal categories -- and
we show how they instantiate the motivating philosophical ideas. We give two
examples of dynamic categorical structures: prediction markets as a dynamic
operad and deep learning as a dynamic monoidal category.

We propose a novel convolutional operator for the task of point cloud
completion. One striking characteristic of our approach is that, conversely to
related work it does not require any max-pooling or voxelization operation.
Instead, the proposed operator used to learn the point cloud embedding in the
encoder extracts permutation-invariant features from the point cloud via a
soft-pooling of feature activations, which are able to preserve fine-grained
geometric details. These features are then passed on to a decoder architecture.
Due to the compression in the encoder, a typical limitation of this type of
architectures is that they tend to lose parts of the input shape structure. We
propose to overcome this limitation by using skip connections specifically
devised for point clouds, where links between corresponding layers in the
encoder and the decoder are established. As part of these connections, we
introduce a transformation matrix that projects the features from the encoder
to the decoder and vice-versa. The quantitative and qualitative results on the
task of object completion from partial scans on the ShapeNet dataset show that
incorporating our approach achieves state-of-the-art performance in shape
completion both at low and high resolutions.

Chest radiographs are used for the diagnosis of multiple critical illnesses
(e.g., Pneumonia, heart failure, lung cancer), for this reason, systems for the
automatic or semi-automatic analysis of these data are of particular interest.
An efficient analysis of large amounts of chest radiographs can aid physicians
and radiologists, ultimately allowing for better medical care of lung-, heart-
and chest-related conditions. We propose a novel Discrete Wavelet Transform
(DWT)-based method for the efficient identification and encoding of visual
information that is typically lost in the down-sampling of high-resolution
radiographs, a common step in computer-aided diagnostic pipelines. Our proposed
approach requires only slight modifications to the input of existing
state-of-the-art Convolutional Neural Networks (CNNs), making it easily
applicable to existing image classification frameworks. We show that the extra
high-frequency components offered by our method increased the classification
performance of several CNNs in benchmarks employing the NIH Chest-8 and
ImageNet-2017 datasets. Based on our results we hypothesize that providing
frequency-specific coefficients allows the CNNs to specialize in the
identification of structures that are particular to a frequency band,
ultimately increasing classification performance, without an increase in
computational load. The implementation of our work is available at
github.com/DeclanMcIntosh/LeGallCuda.

Vision Transformers (ViT) become widely-adopted architectures for various
vision tasks. Masked auto-encoding for feature pretraining and multi-scale
hybrid convolution-transformer architectures can further unleash the potentials
of ViT, leading to state-of-the-art performances on image classification,
detection and semantic segmentation. In this paper, our ConvMAE framework
demonstrates that multi-scale hybrid convolution-transformer can learn more
discriminative representations via the mask auto-encoding scheme. However,
directly using the original masking strategy leads to the heavy computational
cost and pretraining-finetuning discrepancy. To tackle the issue, we adopt the
masked convolution to prevent information leakage in the convolution blocks. A
simple block-wise masking strategy is proposed to ensure computational
efficiency. We also propose to more directly supervise the multi-scale features
of the encoder to boost multi-scale features. Based on our pretrained ConvMAE
models, ConvMAE-Base improves ImageNet-1K finetuning accuracy by 1.4% compared
with MAE-Base. On object detection, ConvMAE-Base finetuned for only 25 epochs
surpasses MAE-Base fined-tuned for 100 epochs by 2.9% box AP and 2.2% mask AP
respectively. Code and pretrained models are available at
https://github.com/Alpha-VL/ConvMAE.

Cross-modal recipe retrieval has attracted research attention in recent
years, thanks to the availability of large-scale paired data for training.
Nevertheless, obtaining adequate recipe-image pairs covering the majority of
cuisines for supervised learning is difficult if not impossible. By
transferring knowledge learnt from a data-rich cuisine to a data-scarce
cuisine, domain adaptation sheds light on this practical problem. Nevertheless,
existing works assume recipes in source and target domains are mostly
originated from the same cuisine and written in the same language. This paper
studies unsupervised domain adaptation for image-to-recipe retrieval, where
recipes in source and target domains are in different languages. Moreover, only
recipes are available for training in the target domain. A novel recipe mixup
method is proposed to learn transferable embedding features between the two
domains. Specifically, recipe mixup produces mixed recipes to form an
intermediate domain by discretely exchanging the section(s) between source and
target recipes. To bridge the domain gap, recipe mixup loss is proposed to
enforce the intermediate domain to locate in the shortest geodesic path between
source and target domains in the recipe embedding space. By using Recipe 1M
dataset as source domain (English) and Vireo-FoodTransfer dataset as target
domain (Chinese), empirical experiments verify the effectiveness of recipe
mixup for cross-lingual adaptation in the context of image-to-recipe retrieval.

Decentralized stochastic optimization is the basic building block of modern
collaborative machine learning, distributed estimation and control, and
large-scale sensing. Since involved data usually contain sensitive information
like user locations, healthcare records and financial transactions, privacy
protection has become an increasingly pressing need in the implementation of
decentralized stochastic optimization algorithms. In this paper, we propose a
decentralized stochastic gradient descent algorithm which is embedded with
inherent privacy protection for every participating agent against other
participating agents and external eavesdroppers. This proposed algorithm builds
in a dynamics based gradient-obfuscation mechanism to enable privacy protection
without compromising optimization accuracy, which is in significant difference
from differential-privacy based privacy solutions for decentralized
optimization that have to trade optimization accuracy for privacy. The dynamics
based privacy approach is encryption-free, and hence avoids incurring heavy
communication or computation overhead, which is a common problem with
encryption based privacy solutions for decentralized stochastic optimization.
Besides rigorously characterizing the convergence performance of the proposed
decentralized stochastic gradient descent algorithm under both convex objective
functions and non-convex objective functions, we also provide rigorous
information-theoretic analysis of its strength of privacy protection.
Simulation results for a distributed estimation problem as well as numerical
experiments for decentralized learning on a benchmark machine learning dataset
confirm the effectiveness of the proposed approach.

Deep learning based parallel Imaging (PI) has made great progresses in recent
years to accelerate magnetic resonance imaging (MRI). Nevertheless, the
performanc-es and robustness of existing methods can still be im-proved. In
this work, we propose to explore the k-space domain learning via robust
generative modeling for flexible PI reconstruction, coined weight-k-space
genera-tive model (WKGM). Specifically, WKGM is a general-ized k-space domain
model, where the k-space weighting technology and high-dimensional space
augmentation design are efficiently incorporated for score-based gen-erative
model training, resulting in good and robust re-constructions. In addition,
WKGM is flexible and thus can be synergistically combined with various
traditional k-space PI models, generating learning-based priors to produce
high-fidelity reconstructions. Experimental re-sults on datasets with varying
sampling patterns and ac-celeration factors demonstrate that WKGM can attain
state-of-the-art reconstruction results with the well-learned k-space
generative prior.

Until recently, the number of public real-world text images was insufficient
for training scene text recognizers. Therefore, most modern training methods
rely on synthetic data and operate in a fully supervised manner. Nevertheless,
the amount of public real-world text images has increased significantly lately,
including a great deal of unlabeled data. Leveraging these resources requires
semi-supervised approaches; however, the few existing methods do not account
for vision-language multimodality structure and therefore suboptimal for
state-of-the-art multimodal architectures. To bridge this gap, we present
semi-supervised learning for multimodal text recognizers (SemiMTR) that
leverages unlabeled data at each modality training phase. Notably, our method
refrains from extra training stages and maintains the current three-stage
multimodal training procedure. Our algorithm starts by pretraining the vision
model through a single-stage training that unifies self-supervised learning
with supervised training. More specifically, we extend an existing visual
representation learning algorithm and propose the first contrastive-based
method for scene text recognition. After pretraining the language model on a
text corpus, we fine-tune the entire network via a sequential, character-level,
consistency regularization between weakly and strongly augmented views of text
images. In a novel setup, consistency is enforced on each modality separately.
Extensive experiments validate that our method outperforms the current training
schemes and achieves state-of-the-art results on multiple scene text
recognition benchmarks.

Image retrieval methods for place recognition learn global image descriptors
that are used for fetching geo-tagged images at inference time. Recent works
have suggested employing weak and self-supervision for mining hard positives
and hard negatives in order to improve localization accuracy and robustness to
visibility changes (e.g. in illumination or view point). However, generating
hard positives, which is essential for obtaining robustness, is still limited
to hard-coded or global augmentations. In this work we propose an adversarial
method to guide the creation of hard positives for training image retrieval
networks. Our method learns local and global augmentation policies which will
increase the training loss, while the image retrieval network is forced to
learn more powerful features for discriminating increasingly difficult
examples. This approach allows the image retrieval network to generalize beyond
the hard examples presented in the data and learn features that are robust to a
wide range of variations. Our method achieves state-of-the-art recalls on the
Pitts250 and Tokyo 24/7 benchmarks and outperforms recent image retrieval
methods on the rOxford and rParis datasets by a noticeable margin.

Aiming to find a program satisfying the user intent given input-output
examples, program synthesis has attracted increasing interest in the area of
machine learning. Despite the promising performance of existing methods, most
of their success comes from the privileged information of well-designed
input-output examples. However, providing such input-output examples is
unrealistic because it requires the users to have the ability to describe the
underlying program with a few input-output examples under the training
distribution. In this work, we propose a query-based framework that trains a
query neural network to generate informative input-output examples
automatically and interactively from a large query space. The quality of the
query depends on the amount of the mutual information between the query and the
corresponding program, which can guide the optimization of the query framework.
To estimate the mutual information more accurately, we introduce the functional
space (F-space) which models the relevance between the input-output examples
and the programs in a differentiable way. We evaluate the effectiveness and
generalization of the proposed query-based framework on the Karel task and the
list processing task. Experimental results show that the query-based framework
can generate informative input-output examples which achieve and even
outperform well-designed input-output examples.

Vision-language pre-training (VLP) on large-scale datasets has shown premier
performance on various downstream tasks. A complete and fair benchmark (i.e.,
including large-scale pre-training datasets and diverse downstream tasks) is
essential for VLP. While there are plenty of benchmarks with English corpus,
building a rich benchmark for VLP with other languages, such as Chinese,
remains a critical problem. To this end, we build a large-scale Chinese
cross-modal benchmark called Zero for the research community to fairly compare
VLP models. We release two pre-training datasets and five fine-tuning datasets
for downstream tasks. Alongside, we propose a novel pre-training framework of
pre-Ranking + Ranking for cross-modal learning. Specifically, we apply global
contrastive pre-ranking to learn the individual representations of images and
texts, respectively. We then fuse the representations in a fine-grained ranking
manner via an image-text cross encoder and a text-image cross encoder. To
further enhance the capability of the model, we propose a two-way distillation
strategy consisting of target-guided Distillation and feature-guided
Distillation. For brevity, we name our model R2D2. We achieve state-of-the-art
performance on four public cross-modal datasets and the proposed five
downstream datasets. When conducting zero-shot tasks on Flickr30k-CN, COCO-CN,
and MUGE, R2D2 pre-trained on a 250 million dataset achieves significant
improvements of 4.7%, 5.4%, and 6.3% in mean recall compared to the
state-of-the-art. The datasets, models, and codes are available at
https://github.com/yuxie11/R2D2

Conditional image generation has paved the way for several breakthroughs in
image editing, generating stock photos and 3-D object generation. This
continues to be a significant area of interest with the rise of new
state-of-the-art methods that are based on diffusion models. However, diffusion
models provide very little control over the generated image, which led to
subsequent works exploring techniques like classifier guidance, that provides a
way to trade off diversity with fidelity. In this work, we explore techniques
to condition diffusion models with carefully crafted input noise artifacts.
This allows generation of images conditioned on semantic attributes. This is
different from existing approaches that input Gaussian noise and further
introduce conditioning at the diffusion model's inference step. Our experiments
over several examples and conditional settings show the potential of our
approach.

This paper is the recommended initial reading for a functional overview of
Soar, version 9.6. It includes an abstract overview of the architectural
structure of Soar including its processing, memories, learning modules, their
interfaces, and the representations of knowledge used by those modules. From
there it describes the processing supported by those modules, including
decision making, impasses and substates, procedure learning via chunking,
reinforcement learning, semantic memory, episodic memory, and spatial-visual
reasoning. It then reviews the levels of decision making and variety of
learning in Soar, and analysis of Soar as an architecture supporting general
human-level AI. Following the references is an appendix that contains short
descriptions of recent Soar agents and a glossary of the terminology we use in
describing Soar.

The automatic assignment of species information to the corresponding genes in
a research article is a critically important step in the gene normalization
task, whereby a gene mention is normalized and linked to a database record or
identifier by a text-mining algorithm. Existing methods typically rely on
heuristic rules based on gene and species co-occurrence in the article, but
their accuracy is suboptimal. We therefore developed a high-performance method,
using a novel deep learning-based framework, to classify whether there is a
relation between a gene and a species. Instead of the traditional binary
classification framework in which all possible pairs of genes and species in
the same article are evaluated, we treat the problem as a sequence-labeling
task such that only a fraction of the pairs needs to be considered. Our
benchmarking results show that our approach obtains significantly higher
performance compared to that of the rule-based baseline method for the species
assignment task (from 65.8% to 81.3% in accuracy). The source code and data for
species assignment are freely available at
https://github.com/ncbi/SpeciesAssignment.

We propose a new microscopy simulation system that can depict atomistic
models in a micrograph visual style, similar to results of physical electron
microscopy imaging. This system is scalable, able to represent simulation of
electron microscopy of tens of viral particles and synthesizes the image faster
than previous methods. On top of that, the simulator is differentiable, both
its deterministic as well as stochastic stages that form signal and noise
representations in the micrograph. This notable property has the capability for
solving inverse problems by means of optimization and thus allows for
generation of microscopy simulations using the parameter settings estimated
from real data. We demonstrate this learning capability through two
applications: (1) estimating the parameters of the modulation transfer function
defining the detector properties of the simulated and real micrographs, and (2)
denoising the real data based on parameters trained from the simulated
examples. While current simulators do not support any parameter estimation due
to their forward design, we show that the results obtained using estimated
parameters are very similar to the results of real micrographs. Additionally,
we evaluate the denoising capabilities of our approach and show that the
results showed an improvement over state-of-the-art methods. Denoised
micrographs exhibit less noise in the tilt-series tomography reconstructions,
ultimately reducing the visual dominance of noise in direct volume rendering of
microscopy tomograms.

Malware continues to evolve rapidly, and more than 450,000 new samples are
captured every day, which makes manual malware analysis impractical. However,
existing deep learning detection models need manual feature engineering or
require high computational overhead for long training processes, which might be
laborious to select feature space and difficult to retrain for mitigating model
aging. Therefore, a crucial requirement for a detector is to realize automatic
and efficient detection. In this paper, we propose a lightweight malware
detection model called SeqNet which could be trained at high speed with low
memory required on the raw binaries. By avoiding contextual confusion and
reducing semantic loss, SeqNet maintains the detection accuracy when reducing
the number of parameters to only 136K. We demonstrate the effectiveness of our
methods and the low training cost requirement of SeqNet in our experiments.
Besides, we make our datasets and codes public to stimulate further academic
research.

Today, finger vein identification is gaining popularity as a potential
biometric identification framework solution. Machine learning-based
unsupervised, supervised, and deep learning algorithms have had a significant
influence on finger vein detection and recognition at the moment. Deep
learning, on the other hand, necessitates a large number of training datasets
that must be manually produced and labeled. In this research, we offer a
completely automated unsupervised learning strategy for training dataset
creation. Our method is intended to extract and build a decent binary mask
training dataset completely automated. In this technique, two optimization
steps are devised and employed. The initial stage of optimization is to create
a completely automated unsupervised image clustering based on finger vein image
localization. Worldwide finger vein pattern orientation estimation is employed
in the second optimization to optimize the retrieved finger vein lines.
Finally, the proposed system achieves 99.6 - percent pattern extraction
accuracy, which is significantly higher than other common unsupervised learning
methods like k-means and Fuzzy C-Means (FCM).

In recent years, pre-trained models have become dominant in most natural
language processing (NLP) tasks. However, in the area of Automated Essay
Scoring (AES), pre-trained models such as BERT have not been properly used to
outperform other deep learning models such as LSTM. In this paper, we introduce
a novel multi-scale essay representation for BERT that can be jointly learned.
We also employ multiple losses and transfer learning from out-of-domain essays
to further improve the performance. Experiment results show that our approach
derives much benefit from joint learning of multi-scale essay representation
and obtains almost the state-of-the-art result among all deep learning models
in the ASAP task. Our multi-scale essay representation also generalizes well to
CommonLit Readability Prize data set, which suggests that the novel text
representation proposed in this paper may be a new and effective choice for
long-text tasks.

Deep learning is an important method for molecular design and exhibits
considerable ability to predict molecular properties, including
physicochemical, bioactive, and ADME/T (absorption, distribution, metabolism,
excretion, and toxicity) properties. In this study, we advanced a novel deep
learning architecture, termed FP-GNN, which combined and simultaneously learned
information from molecular graphs and fingerprints. To evaluate the FP-GNN
model, we conducted experiments on 13 public datasets, an unbiased LIT-PCBA
dataset, and 14 phenotypic screening datasets for breast cell lines. Extensive
evaluation results showed that compared to advanced deep learning and
conventional machine learning algorithms, the FP-GNN algorithm achieved
state-of-the-art performance on these datasets. In addition, we analyzed the
influence of different molecular fingerprints, and the effects of molecular
graphs and molecular fingerprints on the performance of the FP-GNN model.
Analysis of the anti-noise ability and interpretation ability also indicated
that FP-GNN was competitive in real-world situations.

In this paper, we study the outage minimization problem in a
decode-and-forward cooperative network with relay uncertainty. To reduce the
outage probability and improve the quality of service, existing researches
usually rely on the assumption of both exact instantaneous channel state
information (CSI) and environmental uncertainty. However, it is difficult to
obtain perfect instantaneous CSI immediately under practical situations where
channel states change rapidly, and the uncertainty in communication
environments may not be observed, which makes traditional methods not
applicable. Therefore, we turn to reinforcement learning (RL) methods for
solutions, which do not need any prior knowledge of underlying channel or
assumptions of environmental uncertainty. RL method is to learn from the
interaction with communication environment, optimize its action policy, and
then propose relay selection and power allocation schemes. We first analyse the
robustness of RL action policy by giving the lower bound of the worst-case
performance, when RL methods are applied to communication scenarios with
environment uncertainty. Then, we propose a robust algorithm for outage
probability minimization based on RL. Simulation results reveal that compared
with traditional RL methods, our approach has better generalization ability and
can improve the worst-case performance by about 6% when evaluated in unseen

Currently, single image inpainting has achieved promising results based on
deep convolutional neural networks. However, inpainting on stereo images with
missing regions has not been explored thoroughly, which is also a significant
but different problem. One crucial requirement for stereo image inpainting is
stereo consistency. To achieve it, we propose an Iterative Geometry-Aware Cross
Guidance Network (IGGNet). The IGGNet contains two key ingredients, i.e., a
Geometry-Aware Attention (GAA) module and an Iterative Cross Guidance (ICG)
strategy. The GAA module relies on the epipolar geometry cues and learns the
geometry-aware guidance from one view to another, which is beneficial to make
the corresponding regions in two views consistent. However, learning guidance
from co-existing missing regions is challenging. To address this issue, the ICG
strategy is proposed, which can alternately narrow down the missing regions of
the two views in an iterative manner. Experimental results demonstrate that our
proposed network outperforms the latest stereo image inpainting model and
state-of-the-art single image inpainting models.

Artificial Intelligence (AI) is a fast-growing research and development (R&D)
discipline which is attracting increasing attention because of its promises to
bring vast benefits for consumers and businesses, with considerable benefits
promised in productivity growth and innovation. To date it has reported
significant accomplishments in many areas that have been deemed as challenging
for machines, ranging from computer vision, natural language processing, audio
analysis to smart sensing and many others. The technical trend in realizing the
successes has been towards increasing complex and large size AI models so as to
solve more complex problems at superior performance and robustness. This rapid
progress, however, has taken place at the expense of substantial environmental
costs and resources. Besides, debates on the societal impacts of AI, such as
fairness, safety and privacy, have continued to grow in intensity. These issues
have presented major concerns pertaining to the sustainable development of AI.
In this work, we review major trends in machine learning approaches that can
address the sustainability problem of AI. Specifically, we examine emerging AI
methodologies and algorithms for addressing the sustainability issue of AI in
two major aspects, i.e., environmental sustainability and social sustainability
of AI. We will also highlight the major limitations of existing studies and
propose potential research challenges and directions for the development of
next generation of sustainable AI techniques. We believe that this technical
review can help to promote a sustainable development of AI R&D activities for

Estimating homography from an image pair is a fundamental problem in image
alignment. Unsupervised learning methods have received increasing attention in
this field due to their promising performance and label-free training. However,
existing methods do not explicitly consider the problem of plane-induced
parallax, which will make the predicted homography compromised on multiple
planes. In this work, we propose a novel method HomoGAN to guide unsupervised
homography estimation to focus on the dominant plane. First, a multi-scale
transformer network is designed to predict homography from the feature pyramids
of input images in a coarse-to-fine fashion. Moreover, we propose an
unsupervised GAN to impose coplanarity constraint on the predicted homography,
which is realized by using a generator to predict a mask of aligned regions,
and then a discriminator to check if two masked feature maps are induced by a
single homography. To validate the effectiveness of HomoGAN and its components,
we conduct extensive experiments on a large-scale dataset, and the results show
that our matching error is 22% lower than the previous SOTA method. Code is
available at https://github.com/megvii-research/HomoGAN.

Major depressive disorder (MDD) is one of the most common mental health
conditions that has been intensively investigated for its association with
brain atrophy and mortality. Recent studies reveal that the deviation between
the predicted and the chronological age can be a marker of accelerated brain
aging to characterize MDD. However, current conclusions are usually drawn based
on structural MRI information collected from Caucasian participants. The
universality of this biomarker needs to be further validated by subjects with
different ethnic/racial backgrounds and by different types of data. Here we
make use of the REST-meta-MDD, a large scale resting-state fMRI dataset
collected from multiple cohort participants in China. We develop a stacking
machine learning model based on 1101 healthy controls, which estimates a
subject's chronological age from fMRI with promising accuracy. The trained
model is then applied to 1276 MDD patients from 24 sites. We observe that MDD
patients exhibit a $+4.43$ years ($\text{$p$} < 0.0001$, $\text{Cohen's $d$} =
0.35$, $\text{95\% CI}:1.86 - 3.91$) higher brain-predicted age difference
(brain-PAD) compared to controls. In the MDD subgroup, we observe a
statistically significant $+2.09$ years ($\text{$p$} < 0.05$, $\text{Cohen's
$d$} = 0.134483$) brain-PAD in antidepressant users compared to medication-free
patients. The statistical relationship observed is further checked by three
different machine learning algorithms. The positive brain-PAD observed in
participants in China confirms the presence of accelerated brain aging in MDD
patients. The utilization of functional brain connectivity for age estimation
verifies existing findings from a new dimension.

When comparing the performance of multi-armed bandit algorithms, the
potential impact of missing data is often overlooked. In practice, it also
affects their implementation where the simplest approach to overcome this is to
continue to sample according to the original bandit algorithm, ignoring missing
outcomes. We investigate the impact on performance of this approach to deal
with missing data for several bandit algorithms through an extensive simulation
study assuming the rewards are missing at random. We focus on two-armed bandit
algorithms with binary outcomes in the context of patient allocation for
clinical trials with relatively small sample sizes. However, our results apply
to other applications of bandit algorithms where missing data is expected to
occur. We assess the resulting operating characteristics, including the
expected reward. Different probabilities of missingness in both arms are
considered. The key finding of our work is that when using the simplest
strategy of ignoring missing data, the impact on the expected performance of
multi-armed bandit strategies varies according to the way these strategies
balance the exploration-exploitation trade-off. Algorithms that are geared
towards exploration continue to assign samples to the arm with more missing
responses (which being perceived as the arm with less observed information is
deemed more appealing by the algorithm than it would otherwise be). In
contrast, algorithms that are geared towards exploitation would rapidly assign
a high value to samples from the arms with a current high mean irrespective of
the level observations per arm. Furthermore, for algorithms focusing more on
exploration, we illustrate that the problem of missing responses can be
alleviated using a simple mean imputation approach.

Actor-critic Reinforcement Learning (RL) algorithms have achieved impressive
performance in continuous control tasks. However, they still suffer two
nontrivial obstacles, i.e., low sample efficiency and overestimation bias. To
this end, we propose Simultaneous Double Q-learning with Conservative Advantage
Learning (SDQ-CAL). Our SDQ-CAL boosts the Double Q-learning for off-policy
actor-critic RL based on a modification of the Bellman optimality operator with
Advantage Learning. Specifically, SDQ-CAL improves sample efficiency by
modifying the reward to facilitate the distinction from experience between the
optimal actions and the others. Besides, it mitigates the overestimation issue
by updating a pair of critics simultaneously upon double estimators. Extensive
experiments reveal that our algorithm realizes less biased value estimation and
achieves state-of-the-art performance in a range of continuous control
benchmark tasks. We release the source code of our method at:
\url{https://github.com/LQNew/SDQ-CAL}.

Few-shot learning methods aim to embed the data to a low-dimensional
embedding space and then classify the unseen query data to the seen support
set. While these works assume that the support set and the query set lie in the
same embedding space, a distribution shift usually occurs between the support
set and the query set, i.e., the Support-Query Shift, in the real world. Though
optimal transportation has shown convincing results in aligning different
distributions, we find that the small perturbations in the images would
significantly misguide the optimal transportation and thus degrade the model
performance. To relieve the misalignment, we first propose a novel adversarial
data augmentation method, namely Perturbation-Guided Adversarial Alignment
(PGADA), which generates the hard examples in a self-supervised manner. In
addition, we introduce Regularized Optimal Transportation to derive a smooth
optimal transportation plan. Extensive experiments on three benchmark datasets
manifest that our framework significantly outperforms the eleven
state-of-the-art methods on three datasets.

The logical negation property (LNP), which implies generating different
predictions for semantically opposite inputs, is an important property that a
trustworthy language model must satisfy. However, much recent evidence shows
that large-size pre-trained language models (PLMs) do not satisfy this
property. In this paper, we perform experiments using probing tasks to assess
PLM's LNP understanding. Unlike previous studies that only examined negation
expressions, we expand the boundary of the investigation to lexical semantics.
Through experiments, we observe that PLMs violate the LNP frequently. To
alleviate the issue, we propose a novel intermediate training task, names
meaning-matching, designed to directly learn a meaning-text correspondence,
instead of relying on the distributional hypothesis. Through multiple
experiments, we find that the task enables PLMs to learn lexical semantic
information. Also, through fine-tuning experiments on 7 GLUE tasks, we confirm
that it is a safe intermediate task that guarantees a similar or better
performance of downstream tasks. Finally, we observe that our proposed approach
outperforms our previous counterparts despite its time and resource efficiency.

Graph neural networks (GNNs) have been widely used in modeling graph
structured data, owing to its impressive performance in a wide range of
practical applications. Recently, knowledge distillation (KD) for GNNs has
enabled remarkable progress in graph model compression and knowledge transfer.
However, most of the existing KD methods require a large volume of real data,
which are not readily available in practice, and may preclude their
applicability in scenarios where the teacher model is trained on rare or hard
to acquire datasets. To address this problem, we propose the first end-to-end
framework for data-free adversarial knowledge distillation on graph structured
data (DFAD-GNN). To be specific, our DFAD-GNN employs a generative adversarial
network, which mainly consists of three components: a pre-trained teacher model
and a student model are regarded as two discriminators, and a generator is
utilized for deriving training graphs to distill knowledge from the teacher
model into the student model. Extensive experiments on various benchmark models
and six representative datasets demonstrate that our DFAD-GNN significantly
surpasses state-of-the-art data-free baselines in the graph classification
task.

To achieve communication-efficient federated multitask learning (FMTL), we
propose an over-the-air FMTL (OAFMTL) framework, where multiple learning tasks
deployed on edge devices share a non-orthogonal fading channel under the
coordination of an edge server (ES). In OA-FMTL, the local updates of edge
devices are sparsified, compressed, and then sent over the uplink channel in a
superimposed fashion. The ES employs over-the-air computation in the presence
of intertask interference. More specifically, the model aggregations of all the
tasks are reconstructed from the channel observations concurrently, based on a
modified version of the turbo compressed sensing (Turbo-CS) algorithm (named as
M-Turbo-CS). We analyze the performance of the proposed OA-FMTL framework
together with the M-Turbo-CS algorithm. Furthermore, based on the analysis, we
formulate a communication-learning optimization problem to improve the system
performance by adjusting the power allocation among the tasks at the edge
devices. Numerical simulations show that our proposed OAFMTL effectively
suppresses the inter-task interference, and achieves a learning performance
comparable to its counterpart with orthogonal multi-task transmission. It is
also shown that the proposed inter-task power allocation optimization algorithm
substantially reduces the overall communication overhead by appropriately
adjusting the power allocation among the tasks.

Much of the success of fingerprint recognition is attributed to
minutiae-based fingerprint representation. It was believed that minutiae
templates could not be inverted to obtain a high fidelity fingerprint image,
but this assumption has been shown to be false. The success of deep learning
has resulted in alternative fingerprint representations (embeddings), in the
hope that they might offer better recognition accuracy as well as
non-invertibility of deep network-based templates. We evaluate whether deep
fingerprint templates suffer from the same reconstruction attacks as the
minutiae templates. We show that while a deep template can be inverted to
produce a fingerprint image that could be matched to its source image, deep
templates are more resistant to reconstruction attacks than minutiae templates.
In particular, reconstructed fingerprint images from minutiae templates yield a
TAR of about 100.0% (98.3%) @ FAR of 0.01% for type-I (type-II) attacks using a
state-of-the-art commercial fingerprint matcher, when tested on NIST SD4. The
corresponding attack performance for reconstructed fingerprint images from deep
templates using the same commercial matcher yields a TAR of less than 1% for
both type-I and type-II attacks; however, when the reconstructed images are
matched using the same deep network, they achieve a TAR of 85.95% (68.10%) for
type-I (type-II) attacks. Furthermore, what is missing from previous
fingerprint template inversion studies is an evaluation of the black-box attack
performance, which we perform using 3 different state-of-the-art fingerprint
matchers. We conclude that fingerprint images generated by inverting minutiae
templates are highly susceptible to both white-box and black-box attack
evaluations, while fingerprint images generated by deep templates are resistant
to black-box evaluations and comparatively less susceptible to white-box

Modern GANs excel at generating high quality and diverse images. However,
when transferring the pretrained GANs on small target data (e.g., 10-shot), the
generator tends to replicate the training samples.
  Several methods have been proposed to address this few-shot image generation
task, but there is a lack of effort to analyze them under a unified framework.
  As our first contribution, we propose a framework to analyze existing methods
during the adaptation. Our analysis discovers that while some methods have
disproportionate focus on diversity preserving which impede quality
improvement, all methods achieve similar quality after convergence.
  Therefore, the better methods are those that can slow down diversity
degradation. Furthermore, our analysis reveals that there is still plenty of
room to further slow down diversity degradation.
  Informed by our analysis and to slow down the diversity degradation of the
target generator during adaptation, our second contribution proposes to apply
mutual information (MI) maximization to retain the source domain's rich
multi-level diversity information in the target domain generator.
  We propose to perform MI maximization by contrastive loss (CL), leverage the
generator and discriminator as two feature encoders to extract different
multi-level features for computing CL. We refer to our method as Dual
Contrastive Learning (DCL).
  Extensive experiments on several public datasets show that, while leading to
a slower diversity-degrading generator during adaptation, our proposed DCL
brings visually pleasant quality and state-of-the-art quantitative performance.

With the representation learning capability of the deep learning models, deep
embedded multi-view clustering (MVC) achieves impressive performance in many
scenarios and has become increasingly popular in recent years. Although great
progress has been made in this field, most existing methods merely focus on
learning the latent representations and ignore that learning the latent graph
of nodes also provides available information for the clustering task. To
address this issue, in this paper we propose Deep Embedded Multi-view
Clustering via Jointly Learning Latent Representations and Graphs (DMVCJ),
which utilizes the latent graphs to promote the performance of deep embedded
MVC models from two aspects. Firstly, by learning the latent graphs and feature
representations jointly, the graph convolution network (GCN) technique becomes
available for our model. With the capability of GCN in exploiting the
information from both graphs and features, the clustering performance of our
model is significantly promoted. Secondly, based on the adjacency relations of
nodes shown in the latent graphs, we design a sample-weighting strategy to
alleviate the noisy issue, and further improve the effectiveness and robustness
of the model. Experimental results on different types of real-world multi-view
datasets demonstrate the effectiveness of DMVCJ.

In recent years, audio-visual event localization has attracted much
attention. It's purpose is to detect the segment containing audio-visual events
and recognize the event category from untrimmed videos. Existing methods use
audio-guided visual attention to lead the model pay attention to the spatial
area of the ongoing event, devoting to the correlation between audio and visual
information but ignoring the correlation between audio and spatial motion. We
propose a past and future motion extraction (pf-ME) module to mine the visual
motion from videos ,embedded into the past and future motion guided network
(PFAGN), and motion guided audio attention (MGAA) module to achieve focusing on
the information related to interesting events in audio modality through the
past and future visual motion. We choose AVE as the experimental verification
dataset and the experiments show that our method outperforms the
state-of-the-arts in both supervised and weakly-supervised settings.

Designing robust and accurate prediction models has been a viable research
area since a long time. While proponents of a well-functioning market
predictors believe that it is difficult to accurately predict market prices but
many scholars disagree. Robust and accurate prediction systems will not only be
helpful to the businesses but also to the individuals in making their financial
investments. This paper presents an LSTM model with two different input
approaches for predicting the short-term stock prices of two Indian companies,
Reliance Industries and Infosys Ltd. Ten years of historic data (2012-2021) is
taken from the yahoo finance website to carry out analysis of proposed
approaches. In the first approach, closing prices of two selected companies are
directly applied on univariate LSTM model. For the approach second, technical
indicators values are calculated from the closing prices and then collectively
applied on Multivariate LSTM model. Short term market behaviour for upcoming
days is evaluated. Experimental outcomes revel that approach one is useful to
determine the future trend but multivariate LSTM model with technical
indicators found to be useful in accurately predicting the future price

The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is
provisioned as an enabler of smart wireless environments, offering a highly
scalable, low-cost, hardware-efficient, and almost energy-neutral solution for
dynamic control of the propagation of electromagnetic signals over the wireless
medium, ultimately providing increased environmental intelligence for diverse
operation objectives. One of the major challenges with the envisioned dense
deployment of RISs in such reconfigurable radio environments is the efficient
configuration of multiple metasurfaces with limited, or even the absence of,
computing hardware. In this paper, we consider multi-user and
multi-RIS-empowered wireless systems, and present a thorough survey of the
online machine learning approaches for the orchestration of their various
tunable components. Focusing on the sum-rate maximization as a representative
design objective, we present a comprehensive problem formulation based on Deep
Reinforcement Learning (DRL). We detail the correspondences among the
parameters of the wireless system and the DRL terminology, and devise generic
algorithmic steps for the artificial neural network training and deployment,
while discussing their implementation details. Further practical considerations
for multi-RIS-empowered wireless communications in the sixth Generation (6G)
era are presented along with some key open research challenges. Differently
from the DRL-based status quo, we leverage the independence between the
configuration of the system design parameters and the future states of the
wireless environment, and present efficient multi-armed bandits approaches,
whose resulting sum-rate performances are numerically shown to outperform
random configurations, while being sufficiently close to the conventional Deep
Q-Network (DQN) algorithm, but with lower implementation complexity.

Face presentation attack detection (PAD) has been extensively studied by
research communities to enhance the security of face recognition systems.
Although existing methods have achieved good performance on testing data with
similar distribution as the training data, their performance degrades severely
in application scenarios with data of unseen distributions. In situations where
the training and testing data are drawn from different domains, a typical
approach is to apply domain adaptation techniques to improve face PAD
performance with the help of target domain data. However, it has always been a
non-trivial challenge to collect sufficient data samples in the target domain,
especially for attack samples. This paper introduces a teacher-student
framework to improve the cross-domain performance of face PAD with one-class
domain adaptation. In addition to the source domain data, the framework
utilizes only a few genuine face samples of the target domain. Under this
framework, a teacher network is trained with source domain samples to provide
discriminative feature representations for face PAD. Student networks are
trained to mimic the teacher network and learn similar representations for
genuine face samples of the target domain. In the test phase, the similarity
score between the representations of the teacher and student networks is used
to distinguish attacks from genuine ones. To evaluate the proposed framework
under one-class domain adaptation settings, we devised two new protocols and
conducted extensive experiments. The experimental results show that our method
outperforms baselines under one-class domain adaptation settings and even
state-of-the-art methods with unsupervised domain adaptation.

Meeting the rise of industry demand to incorporate machine learning (ML)
components into software systems requires interdisciplinary teams contributing
to a shared code base. To maintain consistency, reduce defects and ensure
maintainability, developers use code analysis tools to aid them in identifying
defects and maintaining standards. With the inclusion of machine learning,
tools must account for the cultural differences within the teams which
manifests as multiple programming languages, and conflicting definitions and
objectives. Existing tools fail to identify these cultural differences and are
geared towards software engineering which reduces their adoption in ML
projects. In our approach we attempt to resolve this problem by exploring the
use of context which includes i) purpose of the source code, ii) technical
domain, iii) problem domain, iv) team norms, v) operational environment, and
vi) development lifecycle stage to provide contextualised error reporting for
code analysis. To demonstrate our approach, we adapt Pylint as an example and
apply a set of contextual transformations to the linting results based on the
domain of individual project files under analysis. This allows for
contextualised and meaningful error reporting for the end-user.

One potential future for the next generation of smart grids is the use of
decentralized optimization algorithms and secured communications for
coordinating renewable generation (e.g., wind/solar), dispatchable devices
(e.g., coal/gas/nuclear generations), demand response, battery & storage
facilities, and topology optimization. The Alternating Direction Method of
Multipliers (ADMM) has been widely used in the community to address such
decentralized optimization problems and, in particular, the AC Optimal Power
Flow (AC-OPF). This paper studies how machine learning may help in speeding up
the convergence of ADMM for solving AC-OPF. It proposes a novel decentralized
machine-learning approach, namely ML-ADMM, where each agent uses deep learning
to learn the consensus parameters on the coupling branches. The paper also
explores the idea of learning only from ADMM runs that exhibit high-quality
convergence properties, and proposes filtering mechanisms to select these runs.
Experimental results on test cases based on the French system demonstrate the
potential of the approach in speeding up the convergence of ADMM significantly.

Entity types and textual context are essential properties for sentence-level
relation extraction (RE). Existing work only encodes these properties within
individual instances, which limits the performance of RE given the insufficient
features in a single sentence. In contrast, we model these properties from the
whole dataset and use the dataset-level information to enrich the semantics of
every instance. We propose the GRAPHCACHE (Graph Neural Network as Caching)
module, that propagates the features across sentences to learn better
representations for RE. GRAPHCACHE aggregates the features from sentences in
the whole dataset to learn global representations of properties, and use them
to augment the local features within individual sentences. The global property
features act as dataset-level prior knowledge for RE, and a complement to the
sentence-level features. Inspired by the classical caching technique in
computer systems, we develop GRAPHCACHE to update the property representations
in an online manner. Overall, GRAPHCACHE yields significant effectiveness gains
on RE and enables efficient message passing across all sentences in the
dataset.

Recent literature focuses on utilizing the entity information in the
sentence-level relation extraction (RE), but this risks leaking superficial and
spurious clues of relations. As a result, RE still suffers from unintended
entity bias, i.e., the spurious correlation between entity mentions (names) and
relations. Entity bias can mislead the RE models to extract the relations that
do not exist in the text. To combat this issue, some previous work masks the
entity mentions to prevent the RE models from overfitting entity mentions.
However, this strategy degrades the RE performance because it loses the
semantic information of entities. In this paper, we propose the CORE
(Counterfactual Analysis based Relation Extraction) debiasing method that
guides the RE models to focus on the main effects of textual context without
losing the entity information. We first construct a causal graph for RE, which
models the dependencies between variables in RE models. Then, we propose to
conduct counterfactual analysis on our causal graph to distill and mitigate the
entity bias, that captures the causal effects of specific entity mentions in
each instance. Note that our CORE method is model-agnostic to debias existing
RE systems during inference without changing their training processes.
Extensive experimental results demonstrate that our CORE yields significant
gains on both effectiveness and generalization for RE. The source code is
provided at: https://github.com/vanoracai/CoRE.

In the real world, long sequence time-series forecasting (LSTF) is needed in
many cases, such as power consumption prediction and air quality
prediction.Multi-dimensional long time series model has more strict
requirements on the model, which not only needs to effectively capture the
accurate long-term dependence between input and output, but also needs to
capture the relationship between data of different dimensions.Recent research
shows that the Informer model based on Transformer has achieved excellent
performance in long time series prediction.However, this model still has some
deficiencies in multidimensional prediction,it cannot capture the relationship
between different dimensions well. We improved Informer to address its
shortcomings in multidimensional forecasting. First,we introduce an adaptive
graph neural network to capture hidden dimension dependencies in mostly time
series prediction. Secondly,we integrate adaptive graph convolutional networks
into various spatio-temporal series prediction models to solve the defect that
they cannot capture the relationship between different dimensions.
Thirdly,After experimental testing with multiple data sets, the accuracy of our
framework improved by about 10\% after being introduced into the model.

We introduce the Hamiltonian Monte Carlo Particle Swarm Optimizer (HMC-PSO),
an optimization algorithm that reaps the benefits of both Exponentially
Averaged Momentum PSO and HMC sampling. The coupling of the position and
velocity of each particle with Hamiltonian dynamics in the simulation allows
for extensive freedom for exploration and exploitation of the search space. It
also provides an excellent technique to explore highly non-convex functions
while ensuring efficient sampling. We extend the method to approximate error
gradients in closed form for Deep Neural Network (DNN) settings. We discuss
possible methods of coupling and compare its performance to that of
state-of-the-art optimizers on the Golomb's Ruler problem and Classification
tasks.

Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta
resulting from compromised wall composition, structure, and function, which can
lead to life-threatening dissection or rupture. Several genetic mutations and
predisposing factors that contribute to TAA have been studied in mouse models
to characterize specific changes in aortic microstructure and material
properties that result from a wide range of mechanobiological insults.
Assessments of TAA progression in vivo is largely limited to measurements of
aneurysm size and growth rate. It has been shown that aortic geometry alone is
not sufficient to predict the patient-specific progression of TAA but
computational modeling of the evolving biomechanics of the aorta could predict
future geometry and properties from initiating insults. In this work, we
present an integrated framework to train a deep operator network
(DeepONet)-based surrogate model to identify contributing factors for TAA by
using FE-based datasets of aortic growth and remodeling resulting from
prescribed insults. For training data, we investigate multiple types of TAA
risk factors and spatial distributions within a constrained mixture model to
generate axial--azimuthal maps of aortic dilatation and distensibility. The
trained network is then capable of predicting the initial distribution and
extent of the insult from a given set of dilatation and distensibility
information. Two DeepONet frameworks are proposed, one trained on sparse
information and one on full-field grayscale images, to gain insight into a
preferred neural operator-based approach. Performance of the surrogate models
is evaluated through multiple simulations carried out on insult distributions
varying from fusiform to complex. We show that the proposed approach can
predict patient-specific mechanobiological insult profile with a high accuracy,
particularly when based on full-field images.

In decentralized learning, operator splitting methods using a primal-dual
formulation (e.g., the Edge-Consensus Learning (ECL)) has been shown to be
robust to heterogeneous data and has attracted significant attention in recent
years. However, in the ECL, a node needs to exchange dual variables with its
neighbors. These exchanges incur significant communication costs. For the
Gossip-based algorithms, many compression methods have been proposed, but these
Gossip-based algorithm do not perform well when the data distribution held by
each node is statistically heterogeneous. In this work, we propose the novel
framework of the compression methods for the ECL, called the Communication
Compressed ECL (C-ECL). Specifically, we reformulate the update formulas of the
ECL, and propose to compress the update values of the dual variables. We
demonstrate experimentally that the C-ECL can achieve a nearly equivalent
performance with fewer parameter exchanges than the ECL. Moreover, we
demonstrate that the C-ECL is more robust to heterogeneous data than the
Gossip-based algorithms.

Real-world face super-resolution (SR) is a highly ill-posed image restoration
task. The fully-cycled Cycle-GAN architecture is widely employed to achieve
promising performance on face SR, but prone to produce artifacts upon
challenging cases in real-world scenarios, since joint participation in the
same degradation branch will impact final performance due to huge domain gap
between real-world and synthetic LR ones obtained by generators. To better
exploit the powerful generative capability of GAN for real-world face SR, in
this paper, we establish two independent degradation branches in the forward
and backward cycle-consistent reconstruction processes, respectively, while the
two processes share the same restoration branch. Our Semi-Cycled Generative
Adversarial Networks (SCGAN) is able to alleviate the adverse effects of the
domain gap between the real-world LR face images and the synthetic LR ones, and
to achieve accurate and robust face SR performance by the shared restoration
branch regularized by both the forward and backward cycle-consistent learning
processes. Experiments on two synthetic and two real-world datasets demonstrate
that, our SCGAN outperforms the state-of-the-art methods on recovering the face
structures/details and quantitative metrics for real-world face SR. The code
will be publicly released at https://github.com/HaoHou-98/SCGAN.

Visual storytelling (VST) is the task of generating a story paragraph that
describes a given image sequence. Most existing storytelling approaches have
evaluated their models using traditional natural language generation metrics
like BLEU or CIDEr. However, such metrics based on n-gram matching tend to have
poor correlation with human evaluation scores and do not explicitly consider
other criteria necessary for storytelling such as sentence structure or topic
coherence. Moreover, a single score is not enough to assess a story as it does
not inform us about what specific errors were made by the model. In this paper,
we propose 3 evaluation metrics sets that analyses which aspects we would look
for in a good story: 1) visual grounding, 2) coherence, and 3) non-redundancy.
We measure the reliability of our metric sets by analysing its correlation with
human judgement scores on a sample of machine stories obtained from 4
state-of-the-arts models trained on the Visual Storytelling Dataset (VIST). Our
metric sets outperforms other metrics on human correlation, and could be served
as a learning based evaluation metric set that is complementary to existing
rule-based metrics.

Trajectory-User Linking (TUL), which links trajectories to users who generate
them, has been a challenging problem due to the sparsity in check-in mobility
data. Existing methods ignore the utilization of historical data or rich
contextual features in check-in data, resulting in poor performance for TUL
task. In this paper, we propose a novel Mutual distillation learning network to
solve the TUL problem for sparse check-in mobility data, named MainTUL.
Specifically, MainTUL is composed of a Recurrent Neural Network (RNN)
trajectory encoder that models sequential patterns of input trajectory and a
temporal-aware Transformer trajectory encoder that captures long-term time
dependencies for the corresponding augmented historical trajectories. Then, the
knowledge learned on historical trajectories is transferred between the two
trajectory encoders to guide the learning of both encoders to achieve mutual
distillation of information. Experimental results on two real-world check-in
mobility datasets demonstrate the superiority of MainTUL against
state-of-the-art baselines. The source code of our model is available at
https://github.com/Onedean/MainTUL.

With the rapid development of big data technologies, how to dig out useful
information from massive data becomes an essential problem. However, using
machine learning algorithms to analyze large data may be time-consuming and
inefficient on the traditional single machine. To solve these problems, this
paper has made some research on the parallelization of several classic machine
learning algorithms respectively on the single machine and the big data
platform Spark. We compare the runtime and efficiency of traditional machine
learning algorithms with parallelized machine learning algorithms respectively
on the single machine and Spark platform. The research results have shown
significant improvement in runtime and efficiency of parallelized machine

It is anticipated that 6G wireless networks will accelerate the convergence
of the physical and cyber worlds and enable a paradigm-shift in the way we
deploy and exploit communication networks. Machine learning, in particular deep
learning (DL), is expected to be one of the key technological enablers of 6G by
offering a new paradigm for the design and optimization of networks with a high
level of intelligence. In this article, we introduce an emerging DL
architecture, known as the transformer, and discuss its potential impact on 6G
network design. We first discuss the differences between the transformer and
classical DL architectures, and emphasize the transformer's self-attention
mechanism and strong representation capabilities, which make it particularly
appealing for tackling various challenges in wireless network design.
Specifically, we propose transformer-based solutions for various massive
multiple-input multiple-output (MIMO) and semantic communication problems, and
show their superiority compared to other architectures. Finally, we discuss key
challenges and open issues in transformer-based solutions, and identify future
research directions for their deployment in intelligent 6G networks.

Neural Chat Translation (NCT) aims to translate conversational text into
different languages. Existing methods mainly focus on modeling the bilingual
dialogue characteristics (e.g., coherence) to improve chat translation via
multi-task learning on small-scale chat translation data. Although the NCT
models have achieved impressive success, it is still far from satisfactory due
to insufficient chat translation data and simple joint training manners. To
address the above issues, we propose a scheduled multi-task learning framework
for NCT. Specifically, we devise a three-stage training framework to
incorporate the large-scale in-domain chat translation data into training by
adding a second pre-training stage between the original pre-training and
fine-tuning stages. Further, we investigate where and how to schedule the
dialogue-related auxiliary tasks in multiple training stages to effectively
enhance the main chat translation task. Extensive experiments in four language
directions (English-Chinese and English-German) verify the effectiveness and
superiority of the proposed approach. Additionally, we have made the
large-scale in-domain paired bilingual dialogue dataset publicly available to

Despite the broad range of algorithms for Approximate Nearest Neighbor
Search, most empirical evaluations of algorithms have focused on smaller
datasets, typically of 1 million points~\citep{Benchmark}. However, deploying
recent advances in embedding based techniques for search, recommendation and
ranking at scale require ANNS indices at billion, trillion or larger scale.
Barring a few recent papers, there is limited consensus on which algorithms are
effective at this scale vis-\`a-vis their hardware cost.
  This competition compares ANNS algorithms at billion-scale by hardware cost,
accuracy and performance. We set up an open source evaluation framework and
leaderboards for both standardized and specialized hardware. The competition
involves three tracks. The standard hardware track T1 evaluates algorithms on
an Azure VM with limited DRAM, often the bottleneck in serving billion-scale
indices, where the embedding data can be hundreds of GigaBytes in size. It uses
FAISS~\citep{Faiss17} as the baseline. The standard hardware track T2
additional allows inexpensive SSDs in addition to the limited DRAM and uses
DiskANN~\citep{DiskANN19} as the baseline. The specialized hardware track T3
allows any hardware configuration, and again uses FAISS as the baseline.
  We compiled six diverse billion-scale datasets, four newly released for this
competition, that span a variety of modalities, data types, dimensions, deep
learning models, distance functions and sources. The outcome of the competition
was ranked leaderboards of algorithms in each track based on recall at a query
throughput threshold. Additionally, for track T3, separate leaderboards were
created based on recall as well as cost-normalized and power-normalized query
throughput.

Self-Supervised Learning (SSL) has made great strides recently. SSL speech
models achieve decent performance on a wide range of downstream tasks,
suggesting that they extract different aspects of information from speech.
However, how SSL models store various information in hidden representations
without interfering is still poorly understood. Taking the recently successful
SSL model, HuBERT, as an example, we explore how the SSL model processes and
stores speaker information in the representation. We found that HuBERT stores
speaker information in representations whose positions correspond to silences
in a waveform. There are several pieces of evidence. (1) We find that the
utterances with more silent parts in the waveforms have better Speaker
Identification (SID) accuracy. (2) If we use the whole utterances for SID, the
silence part always contributes more to the SID task. (3) If we only use the
representation of a part of the utterance for SID, the silenced part has higher
accuracy than the other parts. Our findings not only contribute to a better
understanding of SSL models but also improve performance. By simply adding
silence to the original waveform, HuBERT improved its accuracy on SID by nearly
2%.

Diagnosis-oriented dialogue system queries the patient's health condition and
makes predictions about possible diseases through continuous interaction with
the patient. A few studies use reinforcement learning (RL) to learn the optimal
policy from the joint action space of symptoms and diseases. However, existing
RL (or Non-RL) methods cannot achieve sufficiently good prediction accuracy,
still far from its upper limit. To address the problem, we propose a decoupled
automatic diagnostic framework DxFormer, which divides the diagnosis process
into two steps: symptom inquiry and disease diagnosis, where the transition
from symptom inquiry to disease diagnosis is explicitly determined by the
stopping criteria. In DxFormer, we treat each symptom as a token, and formalize
the symptom inquiry and disease diagnosis to a language generation model and a
sequence classification model respectively. We use the inverted version of
Transformer, i.e., the decoder-encoder structure, to learn the representation
of symptoms by jointly optimizing the reinforce reward and cross entropy loss.
Extensive experiments on three public real-world datasets prove that our
proposed model can effectively learn doctors' clinical experience and achieve
the state-of-the-art results in terms of symptom recall and diagnostic
accuracy.

The Graph Convolutional Networks (GCNs) have achieved excellent results in
node classification tasks, but the model's performance at low label rates is
still unsatisfactory. Previous studies in Semi-Supervised Learning (SSL) for
graph have focused on using network predictions to generate soft pseudo-labels
or instructing message propagation, which inevitably contains the incorrect
prediction due to the over-confident in the predictions. Our proposed
Dual-Channel Consistency based Graph Convolutional Networks (DCC-GCN) uses
dual-channel to extract embeddings from node features and topological
structures, and then achieves reliable low-confidence and high-confidence
samples selection based on dual-channel consistency. We further confirmed that
the low-confidence samples obtained based on dual-channel consistency were low
in accuracy, constraining the model's performance. Unlike previous studies
ignoring low-confidence samples, we calibrate the feature embeddings of the
low-confidence samples by using the neighborhood's high-confidence samples. Our
experiments have shown that the DCC-GCN can more accurately distinguish between
low-confidence and high-confidence samples, and can also significantly improve
the accuracy of low-confidence samples. We conducted extensive experiments on
the benchmark datasets and demonstrated that DCC-GCN is significantly better
than state-of-the-art baselines at different label rates.

Modeling the spread of social contagions is central to various applications
in social computing. In this paper, we study the learnability of the
competitive threshold model from a theoretical perspective. We demonstrate how
competitive threshold models can be seamlessly simulated by artificial neural
networks with finite VC dimensions, which enables analytical sample complexity
and generalization bounds. Based on the proposed hypothesis space, we design
efficient algorithms under the empirical risk minimization scheme. The
theoretical insights are finally translated into practical and explainable
modeling methods, the effectiveness of which is verified through a sanity check
over a few synthetic and real datasets. The experimental results promisingly
show that our method enjoys a decent performance without using excessive data
points, outperforming off-the-shelf methods.

Low-rank tensor factorization or completion is well-studied and applied in
various online settings, such as online tensor factorization (where the
temporal mode grows) and online tensor completion (where incomplete slices
arrive gradually). However, in many real-world settings, tensors may have more
complex evolving patterns: (i) one or more modes can grow; (ii) missing entries
may be filled; (iii) existing tensor elements can change. Existing methods
cannot support such complex scenarios. To fill the gap, this paper proposes a
Generalized Online Canonical Polyadic (CP) Tensor factorization and completion
framework (named GOCPT) for this general setting, where we maintain the CP
structure of such dynamic tensors during the evolution. We show that existing
online tensor factorization and completion setups can be unified under the
GOCPT framework. Furthermore, we propose a variant, named GOCPTE, to deal with
cases where historical tensor elements are unavailable (e.g., privacy
protection), which achieves similar fitness as GOCPT but with much less
computational cost. Experimental results demonstrate that our GOCPT can improve
fitness by up to 2:8% on the JHU Covid data and 9:2% on a proprietary patient
claim dataset over baselines. Our variant GOCPTE shows up to 1:2% and 5:5%
fitness improvement on two datasets with about 20% speedup compared to the best
model.

We study sample average approximations (SAA) of chance constrained programs.
SAA methods typically approximate the actual distribution in the chance
constraint using an empirical distribution constructed from random samples
assumed to be independent and identically distributed according to the actual
distribution. In this paper, we consider a nonstationary variant of this
problem, where the random samples are assumed to be independently drawn in a
sequential fashion from an unknown and possibly time-varying distribution. This
nonstationarity may be driven by changing environmental conditions present in
many real-world applications. To account for the potential nonstationarity in
the data generation process, we propose a novel robust SAA method exploiting
information about the Wasserstein distance between the sequence of
data-generating distributions and the actual chance constraint distribution. As
a key result, we obtain distribution-free estimates of the sample size required
to ensure that the robust SAA method will yield solutions that are feasible for
the chance constraint under the actual distribution with high confidence.

The interest in analog computation has grown tremendously in recent years due
to its fast computation speed and excellent energy efficiency, which is very
important for edge and IoT devices in the sub-watt power envelope for deep
learning inferencing. However, significant performance degradation suffered by
deep learning models due to the inherent noise present in the analog
computation can limit their use in mission-critical applications. Hence, there
is a need to understand the impact of critical model hyperparameters choice on
the resulting model noise-resistant property. This need is critical as the
insight obtained can be used to design deep learning models that are robust to
analog noise. In this paper, the impact of the learning rate, a critical design
choice, on the noise-resistant property is investigated. The study is achieved
by first training deep learning models using different learning rates.
Thereafter, the models are injected with analog noise and the noise-resistant
property of the resulting models is examined by measuring the performance
degradation due to the analog noise. The results showed there exists a sweet
spot of learning rate values that achieves a good balance between model
prediction performance and model noise-resistant property. Furthermore, the
theoretical justification of the observed phenomenon is provided.

Rubbing restorations are significant for preserving world cultural history.
In this paper, we propose the RubbingGAN model for restoring incomplete rubbing
characters. Specifically, we collect characters from the Zhang Menglong Bei and
build up the first rubbing restoration dataset. We design the first generative
adversarial network for rubbing restoration. Based on the dataset we collect,
we apply the RubbingGAN to learn the Zhang Menglong Bei font style and restore
the characters. The results of experiments show that RubbingGAN can repair both
slightly and severely incomplete rubbing characters fast and effectively.

Enormous efforts have been recently made to super-resolve hyperspectral (HS)
images with the aid of high spatial resolution multispectral (MS) images. Most
prior works usually perform the fusion task by means of multifarious
pixel-level priors. Yet the intrinsic effects of a large distribution gap
between HS-MS data due to differences in the spatial and spectral resolution
are less investigated. The gap might be caused by unknown sensor-specific
properties or highly-mixed spectral information within one pixel (due to low
spatial resolution). To this end, we propose a subpixel-level HS
super-resolution framework by devising a novel decoupled-and-coupled network,
called DC-Net, to progressively fuse HS-MS information from the pixel- to
subpixel-level, from the image- to feature-level. As the name suggests, DC-Net
first decouples the input into common (or cross-sensor) and sensor-specific
components to eliminate the gap between HS-MS images before further fusion, and
then fully blends them by a model-guided coupled spectral unmixing (CSU) net.
More significantly, we append a self-supervised learning module behind the CSU
net by guaranteeing the material consistency to enhance the detailed
appearances of the restored HS product. Extensive experimental results show the
superiority of our method both visually and quantitatively and achieve a
significant improvement in comparison with the state-of-the-arts. Furthermore,
the codes and datasets will be available at
https://sites.google.com/view/danfeng-hong for the sake of reproducibility.

In this paper, we present a topology optimization (TO) framework to
simultaneously optimize the matrix topology and fiber distribution of
functionally graded continuous fiber-reinforced composites (FRC). Current
approaches in density-based TO for FRC use the underlying finite element mesh
both for analysis and design representation. This poses several limitations
while enforcing sub-element fiber spacing and generating high-resolution
continuous fibers. In contrast, we propose a mesh-independent representation
based on a neural network (NN) both to capture the matrix topology and fiber
distribution. The implicit NN-based representation enables geometric and
material queries at a higher resolution than a mesh discretization. This leads
to the accurate extraction of functionally-graded continuous fibers. Further,
by integrating the finite element simulations into the NN computational
framework, we can leverage automatic differentiation for end-to-end automated
sensitivity analysis, i.e., we no longer need to manually derive cumbersome
sensitivity expressions. We demonstrate the effectiveness and computational
efficiency of the proposed method through several numerical examples involving
various objective functions. We also show that the optimized continuous fiber
reinforced composites can be directly fabricated at high resolution using
additive manufacturing.

Controlling the environmental parameters, including light in greenhouses,
increases the crop yield; however, the electricity cost of supplemental
lighting can be high. Therefore, the importance of applying cost-effective
lighting methods arises. In this paper, an optimal supplemental lighting
control approach is developed considering a variational inference Bayesian
Neural Network (BNN) model for sunlight prediction. The predictive model is
validated through testing the model on the historical solar data of a site
located at North Carolina ($R^{2}$=0.9971, RMSE=1.8%). The proposed lighting
approach is shown to minimize electricity cost by considering the BNN-based
sunlight prediction, plant light needs, and variable electricity pricing when
solving the underlying optimization problem. For evaluation, the new strategy
is compared to: 1) a Markov-based prediction method, which solves the same
optimization problem, assuming a Markov model for sunlight prediction; 2) a
heuristic method which aims to supply a fixed amount of light. Simulation
studies are conducted to examine the electricity cost improvements of the
BNN-based approach. The results show that the BNN-based approach reduces cost
by (on average) 2.27% and 43.91% compared to the Markov prediction-based method
and the heuristic method, respectively, throughout a year.

Analog hardware has become a popular choice for machine learning on
resource-constrained devices recently due to its fast execution and energy
efficiency. However, the inherent presence of noise in analog hardware and the
negative impact of the noise on deployed deep neural network (DNN) models limit
their usage. The degradation in performance due to the noise calls for the
novel design of DNN models that have excellent noiseresistant property,
leveraging the properties of the fundamental building block of DNN models. In
this work, the use of L1 or TopK BatchNorm type, a fundamental DNN model
building block, in designing DNN models with excellent noise-resistant property
is proposed. Specifically, a systematic study has been carried out by training
DNN models with L1/TopK BatchNorm type, and the performance is compared with
DNN models with L2 BatchNorm types. The resulting model noise-resistant
property is tested by injecting additive noise to the model weights and
evaluating the new model inference accuracy due to the noise. The results show
that L1 and TopK BatchNorm type has excellent noise-resistant property, and
there is no sacrifice in performance due to the change in the BatchNorm type
from L2 to L1/TopK BatchNorm type.

We study the sequential general online regression, known also as the
sequential probability assignments, under logarithmic loss when compared
against a broad class of experts. We focus on obtaining tight, often matching,
lower and upper bounds for the sequential minimax regret that are defined as
the excess loss it incurs over a class of experts. After proving a general
upper bound, we consider some specific classes of experts from Lipschitz class
to bounded Hessian class and derive matching lower and upper bounds with
provably optimal constants. Our bounds work for a wide range of values of the
data dimension and the number of rounds. To derive lower bounds, we use tools
from information theory (e.g., Shtarkov sum) and for upper bounds, we resort to
new "smooth truncated covering" of the class of experts. This allows us to find
constructive proofs by applying a simple and novel truncated Bayesian
algorithm. Our proofs are substantially simpler than the existing ones and yet
provide tighter (and often optimal) bounds.

In this letter, we increase adaptivity of an existing robust estimation
algorithm by learning two parameters instead of one to better fit the residual
distribution. Our method uses these two parameters to calculate weights for
Iterative Re-weighted Least Squares (IRLS). This adaptive nature of the weights
is shown to be helpful in situations where the noise level varies in the
measurements, and is shown to increase robustness to outliers. We test our
algorithm first on the point cloud registration problem with synthetic data
sets, where the truth transformation is known. Next, we also evaluate the
approach with an open-source LiDAR-inertial SLAM package to demonstrate that
the proposed approach is more effective than existing versions of the algorithm
for the application of incremental LiDAR-inertial odometry. We also analyze the
joint variability of the two parameters learned from the data sets.

The massive amount of trainable parameters in the pre-trained language models
(PLMs) makes them hard to be deployed to multiple downstream tasks. To address
this issue, parameter-efficient transfer learning methods have been proposed to
tune only a few parameters during fine-tuning while freezing the rest. This
paper looks at existing methods along this line through the \textit{kernel
lens}. Motivated by the connection between self-attention in transformer-based
PLMs and kernel learning, we propose \textit{kernel-wise adapters}, namely
\textit{Kernel-mix}, that utilize the kernel structure in self-attention to
guide the assignment of the tunable parameters. These adapters use guidelines
found in classical kernel learning and enable separate parameter tuning for
each attention head. Our empirical results, over a diverse set of natural
language generation and understanding tasks, show that our proposed adapters
can attain or improve the strong performance of existing baselines.

Embeddings from contemporary natural language processing (NLP) models are
commonly used as numerical representations for words or sentences. However,
odor descriptor words, like "leather" or "fruity", vary significantly between
their commonplace usage and their olfactory usage, as a result traditional
methods for generating these embeddings do not suffice. In this paper, we
present two methods to generate embeddings for odor words that are more closely
aligned with their olfactory meanings when compared to off-the-shelf
embeddings. These generated embeddings outperform the previous state-of-the-art
and contemporary fine-tuning/prompting methods on a pre-existing zero-shot
odor-specific NLP benchmark.

Several predictive algorithms are described. Highlighted are variants that
make predictions by superposing fields associated to the training data
instances. They operate seamlessly with categorical, continuous, and mixed
data. Predictive accuracy convergence is also discussed as a criteria for
evaluating predictive algorithms. Methods are described on how to adapt
algorithms in order to make them achieve predictive accuracy convergence.

This work introduces a latent space method to calculate the demagnetization
reversal process of multigrain permanent magnets. The algorithm consists of two
deep learning models based on neural networks. The embedded Stoner-Wohlfarth
method is used as a reduced order model for computing samples to train and test
the machine learning approach. The work is a proof of concept of the method
since the used microstructures are simple and the only varying parameters are
the magnetic anisotropy axes of the hard magnetic grains. A convolutional
autoencoder is used for nonlinear dimensionality reduction, in order to reduce
the required amount of training samples for predicting the hysteresis loops. We
enriched the loss function with physical information about the underlying
problem which increases the accuracy of the machine learning approach. A deep
learning regressor is operating in the latent space of the autoencoder. The
predictor takes a series of previously encoded magnetization states and outputs
the next magnetization state along the hysteresis curve. To predict the
complete demagnetization loop, we apply a recursive learning scheme.

Understanding the relationship between training data and a model's
performance once deployed is a fundamental component in the application of
machine learning. While the model's deployed performance is dependent on
numerous variables within the scope of machine learning, beyond that of the
training data itself, the effect of the dataset is isolated in this work to
better understand the role training data plays in the problem. This work
examines a modulation classification problem in the Radio Frequency domain
space, attempting to answer the question of how much training data is required
to achieve a desired level of performance, but the procedure readily applies to
classification problems across modalities. By repurposing the metrics of
transfer potential developed within transfer learning an approach to bound data
quantity needs developed given a training approach and machine learning
architecture; this approach is presented as a means to estimate data quantity
requirements to achieve a target performance. While this approach will require
an initial dataset that is germane to the problem space to act as a target
dataset on which metrics are extracted, the goal is to allow for the initial
data to be orders of magnitude smaller than what is required for delivering a
system that achieves the desired performance. An additional benefit of the
techniques presented here is that the quality of different datasets can be
numerically evaluated and tied together with the quantity of data, and the
performance of the system.

Keratoconus is a severe eye disease that leads to deformation of the cornea.
It impacts people aged 10-25 years and is the leading cause of blindness in
that demography. Corneal topography is the gold standard for keratoconus
diagnosis. It is a non-invasive process performed using expensive and bulky
medical devices called corneal topographers. This makes it inaccessible to
large populations, especially in the Global South. Low-cost smartphone-based
corneal topographers, such as SmartKC, have been proposed to make keratoconus
diagnosis accessible. Similar to medical-grade topographers, SmartKC outputs
curvature heatmaps and quantitative metrics that need to be evaluated by
doctors for keratoconus diagnosis. An automatic scheme for evaluation of these
heatmaps and quantitative values can play a crucial role in screening
keratoconus in areas where doctors are not available. In this work, we propose
a dual-head convolutional neural network (CNN) for classifying keratoconus on
the heatmaps generated by SmartKC. Since SmartKC is a new device and only had a
small dataset (114 samples), we developed a 2-stage transfer learning strategy
-- using historical data collected from a medical-grade topographer and a
subset of SmartKC data -- to satisfactorily train our network. This, combined
with our domain-specific data augmentations, achieved a sensitivity of 91.3%
and a specificity of 94.2%.

Two-sided online matching platforms have been employed in various markets.
However, agents' preferences in present market are usually implicit and unknown
and must be learned from data. With the growing availability of side
information involved in the decision process, modern online matching
methodology demands the capability to track preference dynamics for agents
based on their contextual information. This motivates us to consider a novel
Contextual Online Matching Bandit prOblem (COMBO), which allows dynamic
preferences in matching decisions. Existing works focus on multi-armed bandit
with static preference, but this is insufficient: the two-sided preference
changes as along as one-side's contextual information updates, resulting in
non-static matching. In this paper, we propose a Centralized Contextual -
Explore Then Commit (CC-ETC) algorithm to adapt to the COMBO. CC-ETC solves
online matching with dynamic preference. In theory, we show that CC-ETC
achieves a sublinear regret upper bound O(log(T)) and is a rate-optimal
algorithm by proving a matching lower bound. In the experiments, we demonstrate
that CC-ETC is robust to variant preference schemes, dimensions of contexts,
reward noise levels, and contexts variation levels.

In this study, we propose an analytic statistical mechanics approach to solve
a fundamental problem in biological physics called protein design. Protein
design is an inverse problem of protein structure prediction, and its solution
is the amino acid sequence that best stabilizes a given conformation. Despite
recent rapid progress in protein design using deep learning, the challenge of
exploring protein design principles remains. Contrary to previous computational
physics studies, we used the cavity method, an extension of the mean-field
approximation that becomes rigorous when the interaction network is a tree. We
found that for small two-dimensional (2D) lattice hydrophobic-polar (HP)
protein models, the design by the cavity method yields results almost
equivalent to those from the Markov chain Monte Carlo method with lower

The time-dependent exchange-correlation potential has an unusual task in
directing fictitious non-interacting electrons to move with exactly the same
probability density as true interacting electrons. This has intriguing
implications for its structure, especially in the non-perturbative regime,
leading to step and peak features that cannot be captured by bootstrapping any
ground-state functional approximations. We review what has been learned about
these features in the exact exchange-correlation potential in time-dependent
density functional theory in the past decade or so, and implications for the
performance of simulations when electrons are driven far from any ground-state.

Object detection in low-light conditions remains a challenging but important
problem with many practical implications. Some recent works show that, in
low-light conditions, object detectors using raw image data are more robust
than detectors using image data processed by a traditional ISP pipeline. To
improve detection performance in low-light conditions, one can fine-tune the
detector to use raw image data or use a dedicated low-light neural pipeline
trained with paired low- and normal-light data to restore and enhance the
image. However, different camera sensors have different spectral sensitivity
and learning-based models using raw images process data in the sensor-specific
color space. Thus, once trained, they do not guarantee generalization to other
camera sensors. We propose to improve generalization to unseen camera sensors
by implementing a minimal neural ISP pipeline for machine cognition, named
GenISP, that explicitly incorporates Color Space Transformation to a
device-independent color space. We also propose a two-stage color processing
implemented by two image-to-parameter modules that take down-sized image as
input and regress global color correction parameters. Moreover, we propose to
train our proposed GenISP under the guidance of a pre-trained object detector
and avoid making assumptions about perceptual quality of the image, but rather
optimize the image representation for machine cognition. At the inference
stage, GenISP can be paired with any object detector. We perform extensive
experiments to compare our method to other low-light image restoration and
enhancement methods in an extrinsic task-based evaluation and validate that
GenISP can generalize to unseen sensors and object detectors. Finally, we
contribute a low-light dataset of 7K raw images annotated with 46K bounding
boxes for task-based benchmarking of future low-light image restoration and
object detection.

When applied to a real-world safety critical system like the power grid,
general machine learning methods suffer from expensive training, non-physical
solutions, and limited interpretability. To address these challenges for power
grids, many recent works have explored the inclusion of grid physics (i.e.,
domain expertise) into their method design, primarily through including system
constraints and technical limits, reducing search space and defining meaningful
features in latent space. Yet, there is no general methodology to evaluate the
practicality of these approaches in power grid tasks, and limitations exist
regarding scalability, generalization, interpretability, etc. This work
formalizes a new concept of physical interpretability which assesses how a ML
model makes predictions in a physically meaningful way and introduces an
evaluation methodology that identifies a set of attributes that a practical
method should satisfy. Inspired by the evaluation attributes, the paper further
develops a novel contingency analysis warm starter for MadIoT cyberattack,
based on a conditional Gaussian random field. This method serves as an instance
of an ML model that can incorporate diverse domain knowledge and improve on
these identified attributes. Experiments validate that the warm starter
significantly boosts the efficiency of contingency analysis for MadIoT attack
even with shallow NN architectures.

Nonlinear partial differential equations (PDEs) are used to model dynamical
processes in a large number of scientific fields, ranging from finance to
biology. In many applications standard local models are not sufficient to
accurately account for certain non-local phenomena such as, e.g., interactions
at a distance. In order to properly capture these phenomena non-local nonlinear
PDE models are frequently employed in the literature. In this article we
propose two numerical methods based on machine learning and on Picard
iterations, respectively, to approximately solve non-local nonlinear PDEs. The
proposed machine learning-based method is an extended variant of a deep
learning-based splitting-up type approximation method previously introduced in
the literature and utilizes neural networks to provide approximate solutions on
a subset of the spatial domain of the solution. The Picard iterations-based
method is an extended variant of the so-called full history recursive
multilevel Picard approximation scheme previously introduced in the literature
and provides an approximate solution for a single point of the domain. Both
methods are mesh-free and allow non-local nonlinear PDEs with Neumann boundary
conditions to be solved in high dimensions. In the two methods, the numerical
difficulties arising due to the dimensionality of the PDEs are avoided by (i)
using the correspondence between the expected trajectory of reflected
stochastic processes and the solution of PDEs (given by the Feynman-Kac
formula) and by (ii) using a plain vanilla Monte Carlo integration to handle
the non-local term. We evaluate the performance of the two methods on five
different PDEs arising in physics and biology. In all cases, the methods yield
good results in up to 10 dimensions with short run times. Our work extends
recently developed methods to overcome the curse of dimensionality in solving
PDEs.

The configuration of radar networks is a complex problem that is often
performed manually by experts with the help of a simulator. Different numbers
and types of radars as well as different locations that the radars shall cover
give rise to different instances of the radar configuration problem. The exact
modeling of these instances is complex, as the quality of the configurations
depends on a large number of parameters, on internal radar processing, and on
the terrains on which the radars need to be placed. Classic optimization
algorithms can therefore not be applied to this problem, and we rely on
"trial-and-error" black-box approaches.
  In this paper, we study the performances of 13~black-box optimization
algorithms on 153~radar network configuration problem instances. The algorithms
perform considerably better than human experts. Their ranking, however, depends
on the budget of configurations that can be evaluated and on the elevation
profile of the location. We therefore also investigate automated algorithm
selection approaches. Our results demonstrate that a pipeline that extracts
instance features from the elevation of the terrain performs on par with the
classical, far more expensive approach that extracts features from the

Sparse coding strategies have been lauded for their parsimonious
representations of data that leverage low dimensional structure. However,
inference of these codes typically relies on an optimization procedure with
poor computational scaling in high-dimensional problems. For example, sparse
inference in the representations learned in the high-dimensional intermediary
layers of deep neural networks (DNNs) requires an iterative minimization to be
performed at each training step. As such, recent, quick methods in variational
inference have been proposed to infer sparse codes by learning a distribution
over the codes with a DNN. In this work, we propose a new approach to
variational sparse coding that allows us to learn sparse distributions by
thresholding samples, avoiding the use of problematic relaxations. We first
evaluate and analyze our method by training a linear generator, showing that it
has superior performance, statistical efficiency, and gradient estimation
compared to other sparse distributions. We then compare to a standard
variational autoencoder using a DNN generator on the Fashion MNIST and CelebA
datasets

Monitoring electrocardiogram signals is of great significance for the
diagnosis of arrhythmias. In recent years, deep learning and convolutional
neural networks have been widely used in the classification of cardiac
arrhythmias. However, the existing neural network applied to ECG signal
detection usually requires a lot of computing resources, which is not friendlyF
to resource-constrained equipment, and it is difficult to realize real-time
monitoring. In this paper, a binarized convolutional neural network suitable
for ECG monitoring is proposed, which is hardware-friendly and more suitable
for use in resource-constrained wearable devices. Targeting the MIT-BIH
arrhythmia database, the classifier based on this network reached an accuracy
of 95.67% in the five-class test. Compared with the proposed baseline
full-precision network with an accuracy of 96.45%, it is only 0.78% lower.
Importantly, it achieves 12.65 times the computing speedup, 24.8 times the
storage compression ratio, and only requires a quarter of the memory overhead.

Despite the great success of spoken language understanding (SLU) in
high-resource languages, it remains challenging in low-resource languages
mainly due to the lack of labeled training data. The recent multilingual
code-switching approach achieves better alignments of model representations
across languages by constructing a mixed-language context in zero-shot
cross-lingual SLU. However, current code-switching methods are limited to
implicit alignment and disregard the inherent semantic structure in SLU, i.e.,
the hierarchical inclusion of utterances, slots, and words. In this paper, we
propose to model the utterance-slot-word structure by a multi-level contrastive
learning framework at the utterance, slot, and word levels to facilitate
explicit alignment. Novel code-switching schemes are introduced to generate
hard negative examples for our contrastive learning framework. Furthermore, we
develop a label-aware joint model leveraging label semantics to enhance the
implicit alignment and feed to contrastive learning. Our experimental results
show that our proposed methods significantly improve the performance compared
with the strong baselines on two zero-shot cross-lingual SLU benchmark
datasets.

The research on sliding mode control strategy is generally based on the
robust approach. The larger parameter space consideration will inevitably
sacrifice part of the performance. Recently, the data-driven sliding mode
control method attracts much attention and shows excellent benefits in the fact
that data is introduced to compensate the controller. Nevertheless, most of the
research on data-driven sliding mode control relied on identification
techniques, which limits its online applications due to the special
requirements of the data. In this paper, an input-mapping technique is inserted
into the design framework of sliding mode control to compensate for the
influence generated by the unknown dynamic of the system. The major novelty of
the proposed input-mapping sliding mode control strategy lies in that the
sliding mode surface and the sliding mode controller are co-designed through
online learning from historical input-output data to minimize an objective
function. Then, the convergence rate of the system is improved significantly
based on the method designed in this work. Finally, some simulations are
provided to show the effectiveness and superiority of the proposed methods.

In seismic data processing, accurate and efficient automatic velocity picking
algorithms can significantly accelerate the processing, and the main branch is
to use velocity spectra for velocity pickup. Recently, machine learning
algorithms have been widely used in automatic spectrum picking. Even though
deep learning methods can address the problem well in supervised cases, they
are often accompanied by expensive computational costs and low
interpretability. On the contrast, unsupervised learning methods based on the
physical knowledge have great potential to efficiently resolve the task. In
this paper, we propose an unsupervised ensemble learning (UEL) method to pick
the root mean square (RMS) velocities on the spectrum. In particular, UEL
utilizes the information of nearby velocity spectra and the nearest seed
velocity curve to assist the selection of effective and reasonable velocity
points. To increase the coherence of energy peaks, an information gain method
is developed by local normalization. In addition, we designed the attention
scale-space filter (ASSF) clustering method to incorporate the coherence
information into the picking process. Experiments on three datasets demonstrate
that compared to traditional clustering methods, UEL can recognize energy
clusters better, especially with smaller blobs. Moreover, the injection of
nearby spectra and interval velocity constraint in UEL significantly improves
the robustness and accuracy of picking results.

You can have your cake and eat it too. Microvessel segmentation in optical
coherence tomography angiography (OCTA) images remains challenging.
Skeleton-level segmentation shows clear topology but without diameter
information, while pixel-level segmentation shows a clear caliber but low
topology. To close this gap, we propose a novel label adversarial learning
(LAL) for skeleton-level to pixel-level adjustable vessel segmentation. LAL
mainly consists of two designs: a label adversarial loss and an embeddable
adjustment layer. The label adversarial loss establishes an adversarial
relationship between the two label supervisions, while the adjustment layer
adjusts the network parameters to match the different adversarial weights. Such
a design can efficiently capture the variation between the two supervisions,
making the segmentation continuous and tunable. This continuous process allows
us to recommend high-quality vessel segmentation with clear caliber and
topology. Experimental results show that our results outperform manual
annotations of current public datasets and conventional filtering effects.
Furthermore, such a continuous process can also be used to generate an
uncertainty map representing weak vessel boundaries and noise.

Velocity picking, a critical step in seismic data processing, has been
studied for decades. Although manual picking can produce accurate normal
moveout (NMO) velocities from the velocity spectra of prestack gathers, it is
time-consuming and becomes infeasible with the emergence of large amount of
seismic data. Numerous automatic velocity picking methods have thus been
developed. In recent years, deep learning (DL) methods have produced good
results on the seismic data with medium and high signal-to-noise ratios (SNR).
Unfortunately, it still lacks a picking method to automatically generate
accurate velocities in the situations of low SNR. In this paper, we propose a
multi-information fusion network (MIFN) to estimate stacking velocity from the
fusion information of velocity spectra and stack gather segments (SGS). In
particular, we transform the velocity picking problem into a semantic
segmentation problem based on the velocity spectrum images. Meanwhile, the
information provided by SGS is used as a prior in the network to assist
segmentation. The experimental results on two field datasets show that the
picking results of MIFN are stable and accurate for the scenarios with medium
and high SNR, and it also performs well in low SNR scenarios.

Segmentation of 3D knee MR images is important for the assessment of
osteoarthritis. Like other medical data, the volume-wise labeling of knee MR
images is expertise-demanded and time-consuming; hence semi-supervised learning
(SSL), particularly barely-supervised learning, is highly desirable for
training with insufficient labeled data. We observed that the class imbalance
problem is severe in the knee MR images as the cartilages only occupy 6% of
foreground volumes, and the situation becomes worse without sufficient labeled
data. To address the above problem, we present a novel framework for
barely-supervised knee segmentation with noisy and imbalanced labels. Our
framework leverages label distribution to encourage the network to put more
effort into learning cartilage parts. Specifically, we utilize 1.) label
quantity distribution for modifying the objective loss function to a
class-aware weighted form and 2.) label position distribution for constructing
a cropping probability mask to crop more sub-volumes in cartilage areas from
both labeled and unlabeled inputs. In addition, we design dual
uncertainty-aware sampling supervision to enhance the supervision of
low-confident categories for efficient unsupervised learning. Experiments show
that our proposed framework brings significant improvements by incorporating
the unlabeled data and alleviating the problem of class imbalance. More
importantly, our method outperforms the state-of-the-art SSL methods,
demonstrating the potential of our framework for the more challenging SSL
setting.

Evaluation of deep reinforcement learning (RL) is inherently challenging.
Especially the opaqueness of learned policies and the stochastic nature of both
agents and environments make testing the behavior of deep RL agents difficult.
We present a search-based testing framework that enables a wide range of novel
analysis capabilities for evaluating the safety and performance of deep RL
agents. For safety testing, our framework utilizes a search algorithm that
searches for a reference trace that solves the RL task. The backtracking states
of the search, called boundary states, pose safety-critical situations. We
create safety test-suites that evaluate how well the RL agent escapes
safety-critical situations near these boundary states. For robust performance
testing, we create a diverse set of traces via fuzz testing. These fuzz traces
are used to bring the agent into a wide variety of potentially unknown states
from which the average performance of the agent is compared to the average
performance of the fuzz traces. We apply our search-based testing approach on
RL for Nintendo's Super Mario Bros.

The advent of Federated Learning (FL) has ignited a new paradigm for parallel
and confidential decentralized Machine Learning (ML) with the potential of
utilizing the computational power of a vast number of IoT, mobile and edge
devices without data leaving the respective device, ensuring privacy by design.
Yet, in order to scale this new paradigm beyond small groups of already
entrusted entities towards mass adoption, the Federated Learning Framework
(FLF) has to become (i) truly decentralized and (ii) participants have to be
incentivized. This is the first systematic literature review analyzing holistic
FLFs in the domain of both, decentralized and incentivized federated learning.
422 publications were retrieved, by querying 12 major scientific databases.
Finally, 40 articles remained after a systematic review and filtering process
for in-depth examination. Although having massive potential to direct the
future of a more distributed and secure AI, none of the analyzed FLF is
production-ready. The approaches vary heavily in terms of use-cases, system
design, solved issues and thoroughness. We are the first to provide a
systematic approach to classify and quantify differences between FLF, exposing
limitations of current works and derive future directions for research in this
novel domain.

A common task in Earth Sciences is to infer climate information at local and
regional scales from global climate models. Dynamical downscaling requires
running expensive numerical models at high resolution which can be prohibitive
due to long model runtimes. On the other hand, statistical downscaling
techniques present an alternative approach for learning links between the
large- and local-scale climate in a more efficient way. A large number of deep
neural network-based approaches for statistical downscaling have been proposed
in recent years, mostly based on convolutional architectures developed for
computer vision and super-resolution tasks. This paper presents DL4DS, Deep
Learning for empirical DownScaling, a python library that implements a wide
variety of state-of-the-art and novel algorithms for downscaling gridded Earth
Science data with deep neural networks. DL4DS has been designed with the goal
of providing a general framework for training convolutional neural networks
with configurable architectures and learning strategies to facilitate the
conduction of comparative and ablation studies in a robust way. We showcase the
capabilities of DL4DS on air quality CAMS data over the western Mediterranean
area. The DL4DS library can be found in this repository:
https://github.com/carlos-gg/dl4ds

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms,
which can alter the wireless propagation environment through design of their
reflection coefficients. We consider adaptive IRS control in the practical
setting where (i) the IRS reflection coefficients are attained by adjusting
tunable elements embedded in the meta-atoms, (ii) the IRS reflection
coefficients are affected by the incident angles of the incoming signals, (iii)
the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback
link from the base station (BS) to the IRS has a low data rate. Conventional
optimization-based IRS control protocols, which rely on channel estimation and
conveying the optimized variables to the IRS, are not practical in this setting
due to the difficulty of channel estimation and the low data rate of the
feedback channel. To address these challenges, we develop a novel adaptive
codebook-based limited feedback protocol to control the IRS. We propose two
solutions for adaptive IRS codebook design: (i) random adjacency (RA), which
utilizes correlations across the channel realizations, and (ii) deep neural
network policy-based IRS control (DPIC), which is based on a deep reinforcement
learning. Numerical evaluations show that the data rate and average data rate
over one coherence time are improved substantially by the proposed schemes.

Humans are able to categorize images very efficiently, in particular to
detect very rapidly the presence of an animal. Recently, deep learning
algorithms have achieved higher accuracy than humans for a large set of visual
recognition tasks. However, the tasks on which these artificial networks are
usually trained and evaluated are usually very specialized which do not
generalize well, for example with an accuracy drop following a rotation of the
image. In this regard, biological visual systems are more flexible and
efficient than artificial systems for more generic tasks, such as detecting an
animal. To further the comparison between biological and artificial neural
networks, we retrained the standard VGG16 convolutional neural network (CNN) on
two independent tasks that are ecologically relevant to humans: detecting the
presence of an animal or an artifact. We show that retraining the network
achieves a human-like level of performance, comparable to what is reported in
psychophysical tasks. Moreover, we show that categorization is better when
combining the models' outputs. Indeed, animals (e.g. lions) tend to be less
present in photographs containing artifacts (e.g. buildings). Furthermore,
these re-trained models were able to reproduce some unexpected behavioral
observations of human psychophysics, such as robustness to rotations (e.g., an
upside-down or tilted image) or to a grayscale transformation. Finally, we
quantified the number of CNN layers needed to achieve such performance, showing
that good accuracy for ultrafast image categorization could be achieved with
only a few layers, challenging the belief that image recognition would require
a deep sequential analysis of visual objects.

Deep learning has recently achieved remarkable performance in image
classification tasks, which depends heavily on massive annotation. However, the
classification mechanism of existing deep learning models seems to contrast to
humans' recognition mechanism. With only a glance at an image of the object
even unknown type, humans can quickly and precisely find other same category
objects from massive images, which benefits from daily recognition of various
objects. In this paper, we attempt to build a generalizable framework that
emulates the humans' recognition mechanism in the image classification task,
hoping to improve the classification performance on unseen categories with the
support of annotations of other categories. Specifically, we investigate a new
task termed Comparison Knowledge Translation (CKT). Given a set of fully
labeled categories, CKT aims to translate the comparison knowledge learned from
the labeled categories to a set of novel categories. To this end, we put
forward a Comparison Classification Translation Network (CCT-Net), which
comprises a comparison classifier and a matching discriminator. The comparison
classifier is devised to classify whether two images belong to the same
category or not, while the matching discriminator works together in an
adversarial manner to ensure whether classified results match the truth.
Exhaustive experiments show that CCT-Net achieves surprising generalization
ability on unseen categories and SOTA performance on target categories.

XANES is an important experimental method to probe the local three
dimensional geometry and electronic structure of the system. The quantitative
analysis of XANES data is very important to obtain the above mentioned
structure. Because XANES contains a lot of information and complexity, the
quantitative analysis of XANES is also a challenging task.Interpretable Machine
learning(IML) approach: Shapley additive explanations(SHAP) based interpretable
machine learning(ML) approach is adopted in two types of ML XANES data analysis
methods respectively. These two methods are "spectrum to parameters" and
"parameters to spectrum" methods.We used the TreeSHAP method to explain the
mechanism of our models and of a specific prediction example. The model
mechanism is explained from the physical perspective as much as possible, which
expands the methodological perspective of machine learning application in XAS
data analysis. The "Parameters to spectrum" model in which structural
parameters are input and theoretical XANES reconstructed by machine learning
algorithm are output. Taking Fe complex system as an example. The analysis
results quantitatively and systematically demonstrate the model mechanism, that
is, how parameter changes affect the theoretical XANES reconstructed by machine
learning. This is of practical value for determining the parameter variation
trend using in the next XANES fitting and improving the model. In summary, we
show the application of SHAP IML in two kinds of ML models in XANES analysis
field, and expand the methodological perspective of XANES quantitative
analysis. In addition,we developed "3D structure to spectrum" GNN method by
taking the system atomic number and three-dimensional coordinates as the input
and predicted XANES as the output.

In the video coding process, the perceived quality of a compressed video is
evaluated by full-reference quality evaluation metrics. However, it is
difficult to obtain reference videos with perfect quality. To solve this
problem, it is critical to design no-reference compressed video quality
assessment algorithms, which assists in measuring the quality of experience on
the server side and resource allocation on the network side. Convolutional
Neural Network (CNN) has shown its advantage in Video Quality Assessment (VQA)
with promising successes in recent years. A large-scale quality database is
very important for learning accurate and powerful compressed video quality
metrics. In this work, a semi-automatic labeling method is adopted to build a
large-scale compressed video quality database, which allows us to label a large
number of compressed videos with manageable human workload. The resulting
Compressed Video quality database with Semi-Automatic Ratings (CVSAR), so far
the largest of compressed video quality database. We train a no-reference
compressed video quality assessment model with a 3D CNN for SpatioTemporal
Feature Extraction and Evaluation (STFEE). Experimental results demonstrate
that the proposed method outperforms state-of-the-art metrics and achieves
promising generalization performance in cross-database tests. The CVSAR
database and STFEE model will be made publicly available to facilitate
reproducible research.

Recently, correlation filter has been widely applied in unmanned aerial
vehicle (UAV) tracking due to its high frame rates, robustness and low
calculation resources. However, it is fragile because of two inherent defects,
i.e, boundary effect and filter corruption. Some methods by enlarging the
search area can mitigate the boundary effect, yet introducing the undesired
background distractors. Another approaches can alleviate the temporal
degeneration of learned filters by introducing the temporal regularizer, which
depends on the assumption that the filers between consecutive frames should be
coherent. In fact, sometimes the filers at the ($t-1$)th frame is vulnerable to
heavy occlusion from backgrounds, which causes that the assumption does not
hold. To handle them, in this work, we propose a novel $\ell_{1}$
regularization correlation filter with adaptive contextual learning and
keyfilter selection for UAV tracking. Firstly, we adaptively detect the
positions of effective contextual distractors by the aid of the distribution of
local maximum values on the response map of current frame which is generated by
using the previous correlation filter model. Next, we eliminate inconsistent
labels for the tracked target by removing one on each distractor and develop a
new score scheme for each distractor. Then, we can select the keyfilter from
the filters pool by finding the maximal similarity between the target at the
current frame and the target template corresponding to each filter in the
filters pool. Finally, quantitative and qualitative experiments on three
authoritative UAV datasets show that the proposed method is superior to the
state-of-the-art tracking methods based on correlation filter framework.

As technology advanced, collecting data via automatic collection devices
become popular, thus we commonly face data sets with lengthy variables,
especially when these data sets are collected without specific research goals
beforehand. It has been pointed out in the literature that the difficulty of
high-dimensional classification problems is intrinsically caused by too many
noise variables useless for reducing classification error, which offer less
benefits for decision-making, and increase complexity, and confusion in
model-interpretation. A good variable selection strategy is therefore a must
for using such kinds of data well; especially when we expect to use their
results for the succeeding applications/studies, where the model-interpretation
ability is essential. hus, the conventional classification measures, such as
accuracy, sensitivity, precision, cannot be the only performance tasks. In this
paper, we propose a probability-based nonparametric multiple-class
classification method, and integrate it with the ability of identifying high
impact variables for individual class such that we can have more information
about its classification rule and the character of each class as well. The
proposed method can have its prediction power approximately equal to that of
the Bayes rule, and still retains the ability of "model-interpretation." We
report the asymptotic properties of the proposed method, and use both
synthesized and real data sets to illustrate its properties under different
classification situations. We also separately discuss the variable
identification, and training sample size determination, and summarize those
procedures as algorithms such that users can easily implement them with
different computing languages.

In this paper, we propose a novel model predictive control (MPC) framework
for output tracking that deals with partially unknown constraints. The MPC
scheme optimizes over a learning and a backup trajectory. The learning
trajectory aims to explore unknown and potentially unsafe areas, if and only if
this might lead to a potential performance improvement. On the contrary, the
backup trajectory lies in the known space, and is intended to ensure safety and
convergence. The cost function for the learning trajectory is divided into a
tracking and an offset cost, while the cost function for the backup trajectory
is only marginally considered and only penalizes the offset cost. We show that
the proposed MPC scheme is not only able to safely explore the unknown
constraints, but also escape from local minima that may arise from the presence
of obstacles. Moreover, we provide formal guarantees for convergence and
recursive feasibility of the MPC scheme, as well as closed-loop constraint
satisfaction. Finally, the proposed MPC scheme is demonstrated in simulations
using an example of autonomous vehicle driving in a partially unknown
environment where unknown obstacles are present.

Developing a new diagnostic models based on the underlying biological
mechanisms rather than subjective symptoms for psychiatric disorders is an
emerging consensus. Recently, machine learning-based classifiers using
functional connectivity (FC) for psychiatric disorders and healthy controls are
developed to identify brain markers. However, existing machine learningbased
diagnostic models are prone to over-fitting (due to insufficient training
samples) and perform poorly in new test environment. Furthermore, it is
difficult to obtain explainable and reliable brain biomarkers elucidating the
underlying diagnostic decisions. These issues hinder their possible clinical
applications. In this work, we propose BrainIB, a new graph neural network
(GNN) framework to analyze functional magnetic resonance images (fMRI), by
leveraging the famed Information Bottleneck (IB) principle. BrainIB is able to
identify the most informative regions in the brain (i.e., subgraph) and
generalizes well to unseen data. We evaluate the performance of BrainIB against
6 popular brain network classification methods on two multi-site, largescale
datasets and observe that our BrainIB always achieves the highest diagnosis
accuracy. It also discovers the subgraph biomarkers which are consistent to
clinical and neuroimaging findings.

Convolutional neural networks are prevailing in deep learning tasks. However,
they suffer from massive cost issues when working on mobile devices. Network
pruning is an effective method of model compression to handle such problems.
This paper presents a novel structured network pruning method with auxiliary
gating structures which assigns importance marks to blocks in backbone network
as a criterion when pruning. Block-wise pruning is then realized by proposed
voting strategy, which is different from prevailing methods who prune a model
in small granularity like channel-wise. We further develop a three-stage
training scheduling for the proposed architecture incorporating knowledge
distillation for better performance. Our experiments demonstrate that our
method can achieve state-of-the-arts compression performance for the
classification tasks. In addition, our approach can integrate synergistically
with other pruning methods by providing pretrained models, thus achieving a
better performance than the unpruned model with over 93\% FLOPs reduced.

Concept-based explanations aims to fill the model interpretability gap for
non-technical humans-in-the-loop. Previous work has focused on providing
concepts for specific models (eg, neural networks) or data types (eg, images),
and by either trying to extract concepts from an already trained network or
training self-explainable models through multi-task learning. In this work, we
propose ConceptDistil, a method to bring concept explanations to any black-box
classifier using knowledge distillation. ConceptDistil is decomposed into two
components:(1) a concept model that predicts which domain concepts are present
in a given instance, and (2) a distillation model that tries to mimic the
predictions of a black-box model using the concept model predictions. We
validate ConceptDistil in a real world use-case, showing that it is able to
optimize both tasks, bringing concept-explainability to any black-box model.

The machine learning approaches are applied in the dynamical simulation of
open quantum systems. The long short-term memory recurrent neural network
(LSTM-RNN) models are used to simulate the long-time quantum dynamics, which
are built based on the key information of the short-time evolution. We employ
various hyperparameter optimization methods, including the simulated annealing,
Bayesian optimization with tree-structured parzen estimator and random search,
to achieve the automatic construction and adjustment of the LSTM-RNN models.
The implementation details of three hyperparameter optimization methods are
examined, and among them the simulated annealing approach is strongly
recommended due to its excellent performance. The uncertainties of the machine
learning models are comprehensively analyzed by the combination of bootstrap
sampling and Monte-Carlo dropout approaches, which give the prediction
confidence of the LSTM-RNN models in the simulation of the open quantum
dynamics. This work builds an effective machine learning approach to simulate
the dynamics evolution of open quantum systems. In addition, the current study
provides an efficient protocol to build the optimal neural networks and to
estimate the trustiness of the machine learning models.

The introduction of multiple viewpoints inevitably increases the bitrates to
store and transmit video scenes. To reduce the compressed bitrates, researchers
have developed to skip intermediate viewpoints during compression and delivery,
and finally reconstruct them with Side Information (SI). Generally, the depth
maps can be utilized to construct SI; however, it shows inferior performance
with inaccurate reconstruction or high bitrates. In this paper, we propose a
multi-view video coding based on SI of Generative Adversarial Network (GAN). At
the encoder, we construct a spatio-temporal Epipolar Plane Image (EPI) and
further utilize convolutional network to extract the latent code of GAN as SI;
while at the decoder side, we combine the SI and adjacent viewpoints to
reconstruct intermediate views by the generator of GAN. In particular, we set a
joint encoder constraint of reconstruction cost and SI entropy, in order to
achieve an optimal tradeoff between reconstruction quality and bitrate
overhead. Experiments show a significantly improved Rate-Distortion (RD)
performance compared with the state-of-the-art methods.

Active learning (AL) is a prominent technique for reducing the annotation
effort required for training machine learning models. Deep learning offers a
solution for several essential obstacles to deploying AL in practice but
introduces many others. One of such problems is the excessive computational
resources required to train an acquisition model and estimate its uncertainty
on instances in the unlabeled pool. We propose two techniques that tackle this
issue for text classification and tagging tasks, offering a substantial
reduction of AL iteration duration and the computational overhead introduced by
deep acquisition models in AL. We also demonstrate that our algorithm that
leverages pseudo-labeling and distilled models overcomes one of the essential
obstacles revealed previously in the literature. Namely, it was shown that due
to differences between an acquisition model used to select instances during AL
and a successor model trained on the labeled data, the benefits of AL can
diminish. We show that our algorithm, despite using a smaller and faster
acquisition model, is capable of training a more expressive successor model
with higher performance.

Distortion resulting from acoustic echo suppression (AES) is a common issue
in full-duplex communication. To address the distortion problem, a multi-frame
minimum variance distortionless response (MFMVDR) filtering technique is
proposed. The MFMVDR filter with parameter estimation which was used in speech
enhancement problems is extended in this study from a deep learning
perspective. To alleviate numerical instability of the MFMVDR filter, we
propose to directly estimate the inverse of the correlation matrix. The AES
system is advantageous in that no double-talk detection is required. The
negative scale-invariant signal-to-distortion ratio is employed as the loss
function in training the network at the output of the MFMVDR filter. Simulation
results have demonstrated the efficacy of the proposed learning-based AES
system in double-talk, background noise, and nonlinear distortion conditions.

When working with textual data, a natural application of disentangled
representations is fair classification where the goal is to make predictions
without being biased (or influenced) by sensitive attributes that may be
present in the data (e.g., age, gender or race). Dominant approaches to
disentangle a sensitive attribute from textual representations rely on learning
simultaneously a penalization term that involves either an adversarial loss
(e.g., a discriminator) or an information measure (e.g., mutual information).
However, these methods require the training of a deep neural network with
several parameter updates for each update of the representation model. As a
matter of fact, the resulting nested optimization loop is both time consuming,
adding complexity to the optimization dynamic, and requires a fine
hyperparameter selection (e.g., learning rates, architecture). In this work, we
introduce a family of regularizers for learning disentangled representations
that do not require training. These regularizers are based on statistical
measures of similarity between the conditional probability distributions with
respect to the sensitive attributes. Our novel regularizers do not require
additional training, are faster and do not involve additional tuning while
achieving better results both when combined with pretrained and randomly
initialized text encoders.

The increasing penetration of renewable generations, along with the
deregulation and marketization of power industry, promotes the transformation
of power market operation paradigms. The optimal bidding strategy and
dispatching methodology under these new paradigms are prioritized concerns for
both market participants and power system operators, with obstacles of
uncertain characteristics, computational efficiency, as well as requirements of
hyperopic decision-making. To tackle these problems, the Reinforcement Learning
(RL), as an emerging machine learning technique with advantages compared with
conventional optimization tools, is playing an increasingly significant role in
both academia and industry. This paper presents a comprehensive review of RL
applications in deregulated power market operation including bidding and
dispatching strategy optimization, based on more than 150 carefully selected
literatures. For each application, apart from a paradigmatic summary of
generalized methodology, in-depth discussions of applicability and obstacles
while deploying RL techniques are also provided. Finally, some RL techniques
that have great potentiality to be deployed in bidding and dispatching problems
are recommended and discussed.

The image Super-Resolution (SR) technique has greatly improved the visual
quality of images by enhancing their resolutions. It also calls for an
efficient SR Image Quality Assessment (SR-IQA) to evaluate those algorithms or
their generated images. In this paper, we focus on the SR-IQA under deep
learning and propose a Structure-and-Perception-based Quality Evaluation
(SPQE). In emerging deep-learning-based SR, a generated high-quality, visually
pleasing image may have different structures from its corresponding low-quality
image. In such case, how to balance the quality scores between no-reference
perceptual quality and referenced structural similarity is a critical issue. To
help ease this problem, we give a theoretical analysis on this tradeoff and
further calculate adaptive weights for the two types of quality scores. We also
propose two deep-learning-based regressors to model the no-reference and
referenced scores. By combining the quality scores and their weights, we
propose a unified SPQE metric for SR-IQA. Experimental results demonstrate that
the proposed method outperforms the state-of-the-arts in different datasets.

Recently, deep learning-based methods have reached an excellent performance
on License Plate (LP) detection and recognition tasks. However, it is still
challenging to build a robust model for Chinese LPs since there are not enough
large and representative datasets. In this work, we propose a new dataset named
Chinese Road Plate Dataset (CRPD) that contains multi-objective Chinese LP
images as a supplement to the existing public benchmarks. The images are mainly
captured with electronic monitoring systems with detailed annotations. To our
knowledge, CRPD is the largest public multi-objective Chinese LP dataset with
annotations of vertices. With CRPD, a unified detection and recognition network
with high efficiency is presented as the baseline. The network is end-to-end
trainable with totally real-time inference efficiency (30 fps with 640p). The
experiments on several public benchmarks demonstrate that our method has
reached competitive performance. The code and dataset will be publicly
available at https://github.com/yxgong0/CRPD.

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for
space weather disturbances. In the past, different approaches have been used to
automatically detect events in existing time series resulting from solar wind
in situ observations. However, accurate and fast detection still remains a
challenge when facing the large amount of data from different instruments. For
the automatic detection of ICMEs we propose a pipeline using a method that has
recently proven successful in medical image segmentation. Comparing it to an
existing method, we find that while achieving similar results, our model
outperforms the baseline regarding training time by a factor of approximately
20, thus making it more applicable for other datasets. The method has been
tested on in situ data from the Wind spacecraft between 1997 and 2015 with a
True Skill Statistic (TSS) of 0.64. Out of the 640 ICMEs, 466 were detected
correctly by our algorithm, producing a total of 254 False Positives.
Additionally, it produced reasonable results on datasets with fewer features
and smaller training sets from Wind, STEREO-A and STEREO-B with True Skill
Statistics of 0.56, 0.57 and 0.53, respectively. Our pipeline manages to find
the start of an ICME with a mean absolute error (MAE) of around 2 hours and 56
minutes, and the end time with a MAE of 3 hours and 20 minutes. The relatively
fast training allows straightforward tuning of hyperparameters and could
therefore easily be used to detect other structures and phenomena in solar wind
data, such as corotating interaction regions.

The widespread image applications have greatly promoted the vision-based
tasks, in which the Image Quality Assessment (IQA) technique has become an
increasingly significant issue. For user enjoyment in multimedia systems, the
IQA exploits image fidelity and aesthetics to characterize user experience;
while for other tasks such as popular object recognition, there exists a low
correlation between utilities and perceptions. In such cases, the
fidelity-based and aesthetics-based IQA methods cannot be directly applied. To
address this issue, this paper proposes a utility-oriented IQA in object
recognition. In particular, we initialize our research in the scenario of
underwater fish detection, which is a critical task that has not yet been
perfectly addressed. Based on this task, we build an Underwater Image Utility
Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM).
Inspired by the top-down design of fidelity-based IQA, we exploit the deep
models of object recognition and transfer their features to our UIUM.
Experiments validate that the proposed transfer-learning-based UIUM achieves
promising performance in the recognition task. We envision our research
provides insights to bridge the researches of IQA and computer vision.

This thesis tackles the subject of spatio-temporal forecasting with deep
learning. The motivating application at Electricity de France (EDF) is
short-term solar energy forecasting with fisheye images. We explore two main
research directions for improving deep forecasting methods by injecting
external physical knowledge. The first direction concerns the role of the
training loss function. We show that differentiable shape and temporal criteria
can be leveraged to improve the performances of existing models. We address
both the deterministic context with the proposed DILATE loss function and the
probabilistic context with the STRIPE model. Our second direction is to augment
incomplete physical models with deep data-driven networks for accurate
forecasting. For video prediction, we introduce the PhyDNet model that
disentangles physical dynamics from residual information necessary for
prediction, such as texture or details. We further propose a learning framework
(APHYNITY) that ensures a principled and unique linear decomposition between
physical and data-driven components under mild assumptions, leading to better
forecasting performances and parameter identification.

Compressed video action recognition has recently drawn growing attention,
since it remarkably reduces the storage and computational cost via replacing
raw videos by sparsely sampled RGB frames and compressed motion cues (e.g.,
motion vectors and residuals). However, this task severely suffers from the
coarse and noisy dynamics and the insufficient fusion of the heterogeneous RGB
and motion modalities. To address the two issues above, this paper proposes a
novel framework, namely Attentive Cross-modal Interaction Network with Motion
Enhancement (MEACI-Net). It follows the two-stream architecture, i.e. one for
the RGB modality and the other for the motion modality. Particularly, the
motion stream employs a multi-scale block embedded with a denoising module to
enhance representation learning. The interaction between the two streams is
then strengthened by introducing the Selective Motion Complement (SMC) and
Cross-Modality Augment (CMA) modules, where SMC complements the RGB modality
with spatio-temporally attentive local motion features and CMA further combines
the two modalities with selective feature augmentation. Extensive experiments
on the UCF-101, HMDB-51 and Kinetics-400 benchmarks demonstrate the
effectiveness and efficiency of MEACI-Net.

Beamforming is a powerful tool designed to enhance speech signals from the
direction of a target source. Computing the beamforming filter requires
estimating spatial covariance matrices (SCMs) of the source and noise signals.
Time-frequency masks are often used to compute these SCMs. Most studies of
mask-based beamforming have assumed that the sources do not move. However,
sources often move in practice, which causes performance degradation. In this
paper, we address the problem of mask-based beamforming for moving sources. We
first review classical approaches to tracking a moving source, which perform
online or blockwise computation of the SCMs. We show that these approaches can
be interpreted as computing a sum of instantaneous SCMs weighted by attention
weights. These weights indicate which time frames of the signal to consider in
the SCM computation. Online or blockwise computation assumes a heuristic and
deterministic way of computing these attention weights that, although simple,
may not result in optimal performance. We thus introduce a learning-based
framework that computes optimal attention weights for beamforming. We achieve
this using a neural network implemented with self-attention layers. We show
experimentally that our proposed framework can greatly improve beamforming
performance in moving source situations while maintaining high performance in
non-moving situations, thus enabling the development of mask-based beamformers
robust to source movements.

Background: Ultrasound (US) imaging for scoliosis assessment is challenging
for a non-experienced operator. The robotic scanning was developed to follow a
spinal curvature with deep learning and apply consistent forces to the patient'
back. Methods: 23 scoliosis patients were scanned with US devices both,
robotically and manually. Two human raters measured each subject's spinous
process angles (SPA) on robotic and manual coronal images. Results: The robotic
method showed high intra- (ICC > 0.85) and inter-rater (ICC > 0.77)
reliabilities. Compared with the manual method, the robotic approach showed no
significant difference (p < 0.05) when measuring coronal deformity angles. The
MAD for intra-rater analysis lies within an acceptable range from 0 deg to 5
deg for a minimum of 86% and a maximum 97% of a total number of the measured
angles. Conclusions: This study demonstrated that scoliosis deformity angles
measured on ultrasound images obtained with robotic scanning are comparable to
those obtained by manual scanning.

In object detection, non-maximum suppression (NMS) methods are extensively
adopted to remove horizontal duplicates of detected dense boxes for generating
final object instances. However, due to the degraded quality of dense detection
boxes and not explicit exploration of the context information, existing NMS
methods via simple intersection-over-union (IoU) metrics tend to underperform
on multi-oriented and long-size objects detection. Distinguishing with general
NMS methods via duplicate removal, we propose a novel graph fusion network,
named GFNet, for multi-oriented object detection. Our GFNet is extensible and
adaptively fuse dense detection boxes to detect more accurate and holistic
multi-oriented object instances. Specifically, we first adopt a locality-aware
clustering algorithm to group dense detection boxes into different clusters. We
will construct an instance sub-graph for the detection boxes belonging to one
cluster. Then, we propose a graph-based fusion network via Graph Convolutional
Network (GCN) to learn to reason and fuse the detection boxes for generating
final instance boxes. Extensive experiments both on public available
multi-oriented text datasets (including MSRA-TD500, ICDAR2015, ICDAR2017-MLT)
and multi-oriented object datasets (DOTA) verify the effectiveness and
robustness of our method against general NMS methods in multi-oriented object

Numbers are essential components of text, like any other word tokens, from
which natural language processing (NLP) models are built and deployed. Though
numbers are typically not accounted for distinctly in most NLP tasks, there is
still an underlying amount of numeracy already exhibited by NLP models. In this
work, we attempt to tap this potential of state-of-the-art NLP models and
transfer their ability to boost performance in related tasks. Our proposed
classification of numbers into entities helps NLP models perform well on
several tasks, including a handcrafted Fill-In-The-Blank (FITB) task and on
question answering using joint embeddings, outperforming the BERT and RoBERTa
baseline classification.

The amount of secure data being stored on mobile devices has grown immensely
in recent years. However, the security measures protecting this data have
stayed static, with few improvements being done to the vulnerabilities of
current authentication methods such as physiological biometrics or passwords.
Instead of these methods, behavioral biometrics has recently been researched as
a solution to these vulnerable authentication methods. In this study, we aim to
contribute to the research being done on behavioral biometrics by creating and
evaluating a user authentication scheme using behavioral biometrics. The
behavioral biometrics used in this study include touch dynamics and phone
movement, and we evaluate the performance of different single-modal and
multi-modal combinations of the two biometrics. Using two publicly available
datasets - BioIdent and Hand Movement Orientation and Grasp (H-MOG), this study
uses seven common machine learning algorithms to evaluate performance. The
algorithms used in the evaluation include Random Forest, Support Vector
Machine, K-Nearest Neighbor, Naive Bayes, Logistic Regression, Multilayer
Perceptron, and Long Short-Term Memory Recurrent Neural Networks, with accuracy
rates reaching as high as 86%.

Recent years have witnessed the fast advance of security research for
networked dynamical system (NDS). Considering the latest inference attacks that
enable stealthy and precise attacks into NDSs with observation-based learning,
this article focuses on a new security aspect, i.e., how to protect control
mechanism secrets from inference attacks, including state information,
interaction structure and control laws. We call this security property as
control mechanism secrecy, which provides protection of the vulnerabilities in
the control process and fills the defense gap that traditional cyber security
cannot handle. Since the knowledge of control mechanism defines the
capabilities to implement attacks, ensuring control mechanism secrecy needs to
go beyond the conventional data privacy to cover both transmissible data and
intrinsic models in NDSs. The prime goal of this article is to summarize recent
results of both inference attacks on control mechanism secrets and
countermeasures. We first introduce the basic inference attack methods on the
state and structure of NDSs, respectively, along with their inference
performance bounds. Then, the corresponding countermeasures and performance
metrics are given to illustrate how to preserve the control mechanism secrecy.
Necessary conditions are derived to guide the secrecy design. Finally, thorough
discussions on the control laws and open issues are presented, beckoning future
investigation on reliable countermeasure design and tradeoffs between the
secrecy and control performance.

In this work, we are dedicated to multi-target active object tracking (AOT),
where there are multiple targets as well as multiple cameras in the
environment. The goal is maximize the overall target coverage of all cameras.
Previous work makes a strong assumption that each camera is fixed in a location
and only allowed to rotate, which limits its application. In this work, we
relax the setting by allowing all cameras to both move along the boundary lines
and rotate. In our setting, the action space becomes much larger, which leads
to much higher computational complexity to identify the optimal action. To this
end, we propose to leverage the action selection from multi-agent reinforcement
learning (MARL) network to prune the search tree of Monte Carlo Tree Search
(MCTS) method, so as to find the optimal action more efficiently. Besides, we
model the motion of the targets to predict the future position of the targets,
which makes a better estimation of the future environment state in the MCTS
process. We establish a multi-target 2D environment to simulate the sports
games, and experimental results demonstrate that our method can effectively
improve the target coverage.

Domain adaptation on time-series data is often encountered in the industry
but received limited attention in academia. Most of the existing domain
adaptation methods for time-series data borrow the ideas from the existing
methods for non-time series data to extract the domain-invariant
representation. However, two peculiar difficulties to time-series data have not
been solved. 1) It is not a trivial task to model the domain-invariant and
complex dependence among different timestamps. 2) The domain-variant
information is important but how to leverage them is almost underexploited.
Fortunately, the stableness of causal structures among different domains
inspires us to explore the structures behind the time-series data. Based on
this inspiration, we investigate the domain-invariant unweighted sparse
associative structures and the domain-variant strengths of the structures. To
achieve this, we propose Sparse Associative structure alignment by learning
Invariance and Variance (SASA-IV in short), a model that simultaneously aligns
the invariant unweighted spare associative structures and considers the variant
information for time-series unsupervised domain adaptation. Technologically, we
extract the domain-invariant unweighted sparse associative structures with a
unidirectional alignment restriction and embed the domain-variant strengths via
a well-designed autoregressive module. Experimental results not only testify
that our model yields state-of-the-art performance on three real-world datasets
but also provide some insightful discoveries on knowledge transfer.

The environments of such large industrial machines as waste cranes in waste
incineration plants are often weakly observable, where little information about
the environmental state is contained in the observations due to technical
difficulty or maintenance cost (e.g., no sensors for observing the state of the
garbage to be handled). Based on the findings that skilled operators in such
environments choose predetermined control strategies (e.g., grasping and
scattering) and their durations based on sensor values, %thereby improving the
robustness of their actions, we propose a novel non-parametric policy search
algorithm: Gaussian process self-triggered policy search (GPSTPS). GPSTPS has
two types of control policies: action and duration. A gating mechanism either
maintains the action selected by the action policy for the duration specified
by the duration policy or updates the action and duration by passing new
observations to the policy; therefore, it is categorized as self-triggered.
GPSTPS simultaneously learns both policies by trial and error based on sparse
GP priors and variational learning to maximize the return. To verify the
performance of our proposed method, we conducted experiments on
garbage-grasping-scattering task for a waste crane with weak observations using
a simulation and a robotic waste crane system. As experimental results, the
proposed method acquired suitable policies to determine the action and duration
based on the garbage's characteristics.

Early detection and identification of pathogenic bacteria such as Escherichia
coli (E. coli) is an essential task for public health. The conventional
culture-based methods for bacterial colony detection usually take >24 hours to
get the final read-out. Here, we demonstrate a bacterial colony-forming-unit
(CFU) detection system exploiting a thin-film-transistor (TFT)-based image
sensor array that saves ~12 hours compared to the Environmental Protection
Agency (EPA)-approved methods. To demonstrate the efficacy of this CFU
detection system, a lensfree imaging modality was built using the TFT image
sensor with a sample field-of-view of ~10 cm^2. Time-lapse images of bacterial
colonies cultured on chromogenic agar plates were automatically collected at
5-minute intervals. Two deep neural networks were used to detect and count the
growing colonies and identify their species. When blindly tested with 265
colonies of E. coli and other coliform bacteria (i.e., Citrobacter and
Klebsiella pneumoniae), our system reached an average CFU detection rate of
97.3% at 9 hours of incubation and an average recovery rate of 91.6% at ~12
hours. This TFT-based sensor can be applied to various microbiological
detection methods. Due to the large scalability, ultra-large field-of-view, and
low cost of the TFT-based image sensors, this platform can be integrated with
each agar plate to be tested and disposed of after the automated CFU count. The
imaging field-of-view of this platform can be cost-effectively increased to
>100 cm^2 to provide a massive throughput for CFU detection using, e.g.,
roll-to-roll manufacturing of TFTs as used in the flexible display industry.

Graph neural networks (GNNs) have achieved state-of-the-art performance in
many graph-based tasks such as node classification and graph classification.
However, many recent works have demonstrated that an attacker can mislead GNN
models by slightly perturbing the graph structure. Existing attacks to GNNs are
either under the less practical threat model where the attacker is assumed to
access the GNN model parameters, or under the practical black-box threat model
but consider perturbing node features that are shown to be not enough
effective. In this paper, we aim to bridge this gap and consider black-box
attacks to GNNs with structure perturbation as well as with theoretical
guarantees. We propose to address this challenge through bandit techniques.
Specifically, we formulate our attack as an online optimization with bandit
feedback. This original problem is essentially NP-hard due to the fact that
perturbing the graph structure is a binary optimization problem. We then
propose an online attack based on bandit optimization which is proven to be
{sublinear} to the query number $T$, i.e., $\mathcal{O}(\sqrt{N}T^{3/4})$ where
$N$ is the number of nodes in the graph. Finally, we evaluate our proposed
attack by conducting experiments over multiple datasets and GNN models. The
experimental results on various citation graphs and image graphs show that our
attack is both effective and efficient. Source code is available
at~\url{https://github.com/Metaoblivion/Bandit_GNN_Attack}

We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph
representation of local similarity, connectivity, and global structure. GSE
uses the solution of the Sylvester equation to capture both network structure
and neighborhood proximity in a single representation. Unlike embeddings based
on the eigenvectors of the Laplacian, GSE incorporates two or more basis
functions, for instance using the Laplacian and the affinity matrix. Such basis
functions are constructed not from the original graph, but from one whose
weights measure the centrality of an edge (the fraction of the number of
shortest paths that pass through that edge) in the original graph. This allows
more flexibility and control to represent complex network structure and shows
significant improvements over the state of the art when used for data analysis
tasks such as predicting failed edges in material science and network alignment
in the human-SARS CoV-2 protein-protein interactome.

The progression of innovation and technology and ease of inter-connectivity
among networks has allowed us to evolve towards one of the promising areas, the
Internet of Vehicles. Nowadays, modern vehicles are connected to a range of
networks, including intra-vehicle networks and external networks. However, a
primary challenge in the automotive industry is to make the vehicle safe and
reliable; particularly with the loopholes in the existing traditional
protocols, cyber-attacks on the vehicle network are rising drastically.
Practically every vehicle uses the universal Controller Area Network (CAN) bus
protocol for the communication between electronic control units to transmit key
vehicle functionality and messages related to driver safety. The CAN bus
system, although its critical significance, lacks the key feature of any
protocol authentication and authorization. Resulting in compromises of CAN bus
security leads to serious issues to both car and driver safety. This paper
discusses the security issues of the CAN bus protocol and proposes an Intrusion
Detection System (IDS) that detects known attacks on in-vehicle networks.
Multiple Artificial Intelligence (AI) algorithms are employed to provide
recognition of known potential cyber-attacks based on messages, timestamps, and
data packets traveling through the CAN. The main objective of this paper is to
accurately detect cyberattacks by considering time-series features and attack
frequency. The majority of the evaluated AI algorithms, when considering attack
frequency, correctly identify known attacks with remarkable accuracy of more
than 99%. However, these models achieve approximately 92% to 97% accuracy when
timestamps are not taken into account. Long Short Term Memory (LSTM), Xgboost,
and SVC have proved to the well-performing classifiers.

The challenges of learning a robust 6D pose function lie in 1) severe
occlusion and 2) systematic noises in depth images. Inspired by the success of
point-pair features, the goal of this paper is to recover the 6D pose of an
object instance segmented from RGB-D images by locally matching pairs of
oriented points between the model and camera space. To this end, we propose a
novel Bi-directional Correspondence Mapping Network (BiCo-Net) to first
generate point clouds guided by a typical pose regression, which can thus
incorporate pose-sensitive information to optimize generation of local
coordinates and their normal vectors. As pose predictions via geometric
computation only rely on one single pair of local oriented points, our BiCo-Net
can achieve robustness against sparse and occluded point clouds. An ensemble of
redundant pose predictions from locally matching and direct pose regression
further refines final pose output against noisy observations. Experimental
results on three popularly benchmarking datasets can verify that our method can
achieve state-of-the-art performance, especially for the more challenging
severe occluded scenes. Source codes are available at
https://github.com/Gorilla-Lab-SCUT/BiCo-Net.

Accurate downlink channel state information (CSI) is vital to achieving high
spectrum efficiency in massive MIMO systems. Existing works on the deep
learning (DL) model for CSI feedback have shown efficient compression and
recovery in frequency division duplex (FDD) systems. However, practical DL
networks require sizeable wireless CSI datasets during training to achieve high
model accuracy. To address this labor-intensive problem, this work develops an
efficient training enhancement solution of DL-based feedback architecture based
on a modest dataset by exploiting the complex CSI features, and augmenting CSI
dataset based on domain knowledge. We first propose a spherical CSI feedback
network, SPTM2-ISTANet+, which employs the spherical normalization framework to
mitigate the effect of path loss variation. We exploit the trainable
measurement matrix and residual recovery structure to improve the encoding
efficiency and recovery accuracy. For limited CSI measurements, we propose a
model-driven lightweight and universal augmentation strategy based on
decoupling CSI magnitude and phase information, applying the circular shift in
angular-delay domain, and randomizing the CSI phase to approximate phase
distribution. Test results demonstrate the efficacy and efficiency of the
proposed training strategy and feedback architecture for accurate CSI feedback
under limited measurements.

Robotic assembly is one of the oldest and most challenging applications of
robotics. In other areas of robotics, such as perception and grasping,
simulation has rapidly accelerated research progress, particularly when
combined with modern deep learning. However, accurately, efficiently, and
robustly simulating the range of contact-rich interactions in assembly remains
a longstanding challenge. In this work, we present Factory, a set of physics
simulation methods and robot learning tools for such applications. We achieve
real-time or faster simulation of a wide range of contact-rich scenes,
including simultaneous simulation of 1000 nut-and-bolt interactions. We provide
$60$ carefully-designed part models, 3 robotic assembly environments, and 7
robot controllers for training and testing virtual robots. Finally, we train
and evaluate proof-of-concept reinforcement learning policies for nut-and-bolt
assembly. We aim for Factory to open the doors to using simulation for robotic
assembly, as well as many other contact-rich applications in robotics. Please
see https://sites.google.com/nvidia.com/factory for supplementary content,
including videos.

Medical image segmentation based on deep learning is often faced with the
problems of insufficient datasets and long time-consuming labeling. In this
paper, we introduce the self-supervised method MAE(Masked Autoencoders) into
knee joint images to provide a good initial weight for the segmentation model
and improve the adaptability of the model to small datasets. Secondly, we
propose a weakly supervised paradigm for meniscus segmentation based on the
combination of point and line to reduce the time of labeling. Based on the weak
label ,we design a region growing algorithm to generate pseudo-label. Finally
we train the segmentation network based on pseudo-labels with weight transfer
from self-supervision. Sufficient experimental results show that our proposed
method combining self-supervision and weak supervision can almost approach the
performance of purely fully supervised models while greatly reducing the
required labeling time and dataset size.

Preoperative opioid use has been reported to be associated with higher
preoperative opioid demand, worse postoperative outcomes, and increased
postoperative healthcare utilization and expenditures. Understanding the risk
of preoperative opioid use helps establish patient-centered pain management. In
the field of machine learning, deep neural network (DNN) has emerged as a
powerful means for risk assessment because of its superb prediction power;
however, the blackbox algorithms may make the results less interpretable than
statistical models. Bridging the gap between the statistical and machine
learning fields, we propose a novel Interpretable Neural Network Regression
(INNER), which combines the strengths of statistical and DNN models. We use the
proposed INNER to conduct individualized risk assessment of preoperative opioid
use. Intensive simulations and an analysis of 34,186 patients expecting surgery
in the Analgesic Outcomes Study (AOS) show that the proposed INNER not only can
accurately predict the preoperative opioid use using preoperative
characteristics as DNN, but also can estimate the patient specific odds of
opioid use without pain and the odds ratio of opioid use for a unit increase in
the reported overall body pain, leading to more straightforward interpretations
of the tendency to use opioids than DNN. Our results identify the patient
characteristics that are strongly associated with opioid use and is largely
consistent with the previous findings, providing evidence that INNER is a
useful tool for individualized risk assessment of preoperative opioid use.

Deep learning methods have been successfully used in various computer vision
tasks. Inspired by that success, deep learning has been explored in magnetic
resonance imaging (MRI) reconstruction. In particular, integrating deep
learning and model-based optimization methods has shown considerable
advantages. However, a large amount of labeled training data is typically
needed for high reconstruction quality, which is challenging for some MRI
applications. In this paper, we propose a novel reconstruction method, named
DURED-Net, that enables interpretable unsupervised learning for MR image
reconstruction by combining an unsupervised denoising network and a
plug-and-play method. We aim to boost the reconstruction performance of
unsupervised learning by adding an explicit prior that utilizes imaging
physics. Specifically, the leverage of a denoising network for MRI
reconstruction is achieved using Regularization by Denoising (RED). Experiment
results demonstrate that the proposed method requires a reduced amount of
training data to achieve high reconstruction quality.

A law practitioner has to go through a lot of long legal case proceedings. To
understand the motivation behind the actions of different parties/individuals
in a legal case, it is essential that the parts of the document that express an
intent corresponding to the case be clearly understood. In this paper, we
introduce a dataset of 93 legal documents, belonging to the case categories of
either Murder, Land Dispute, Robbery, or Corruption, where phrases expressing
intent same as the category of the document are annotated. Also, we annotate
fine-grained intents for each such phrase to enable a deeper understanding of
the case for a reader. Finally, we analyze the performance of several
transformer-based models in automating the process of extracting intent phrases
(both at a coarse and a fine-grained level), and classifying a document into
one of the possible 4 categories, and observe that, our dataset is challenging,
especially in the case of fine-grained intent classification.

The Li-ion battery is a complex physicochemical system that generally takes
applied current as input and terminal voltage as output. The mappings from
current to voltage can be described by several kinds of models, such as
accurate but inefficient physics-based models, and efficient but sometimes
inaccurate equivalent circuit and black-box models. To realize accuracy and
efficiency simultaneously in battery modeling, we propose to build a
data-driven surrogate for a battery system while incorporating the underlying
physics as constraints. In this work, we innovatively treat the functional
mapping from current curve to terminal voltage as a composite of operators,
which is approximated by the powerful deep operator network (DeepONet). Its
learning capability is firstly verified through a predictive test for Li-ion
concentration at two electrodes. In this experiment, the physics-informed
DeepONet is found to be more robust than the purely data-driven DeepONet,
especially in temporal extrapolation scenarios. A composite surrogate is then
constructed for mapping current curve and solid diffusivity to terminal voltage
with three operator networks, in which two parallel physics-informed DeepONets
are firstly used to predict Li-ion concentration at two electrodes, and then
based on their surface values, a DeepONet is built to give terminal voltage
predictions. Since the surrogate is differentiable anywhere, it is endowed with
the ability to learn from data directly, which was validated by using terminal
voltage measurements to estimate input parameters. The proposed surrogate built
upon operator networks possesses great potential to be applied in on-board
scenarios, such as battery management system, since it integrates efficiency
and accuracy by incorporating underlying physics, and also leaves an interface
for model refinement through a totally differentiable model structure.

Ultra-Wideband (UWB) is one of the key technologies empowering the Internet
of Thing (IoT) concept to perform reliable, energy-efficient, and highly
accurate monitoring, screening, and localization in indoor environments.
Performance of UWB-based localization systems, however, can significantly
degrade because of Non Line of Sight (NLoS) connections between a mobile user
and UWB beacons. To mitigate the destructive effects of NLoS connections, we
target development of a Reinforcement Learning (RL) anchor selection framework
that can efficiently cope with the dynamic nature of indoor environments.
Existing RL models in this context, however, lack the ability to generalize
well to be used in a new setting. Moreover, it takes a long time for the
conventional RL models to reach the optimal policy. To tackle these challenges,
we propose the Jump-start RL-based Uwb NOde selection (JUNO) framework, which
performs real-time location predictions without relying on complex NLoS
identification/mitigation methods. The effectiveness of the proposed JUNO
framework is evaluated in term of the location error, where the mobile user
moves randomly through an ultra-dense indoor environment with a high chance of
establishing NLoS connections. Simulation results corroborate the effectiveness
of the proposed framework in comparison to its state-of-the-art counterparts.

This paper addresses two important estimation problems for linear systems,
namely system identification and model-free state estimation. Our focus is on
ARMAX models with unknown parameters. We first provide a reinforcement learning
algorithm for system identification with guaranteed consistency. This algorithm
is then used to provide a novel solution to model-free state estimation. These
results are then applied to solving the model-free LQG control problem in the
reinforcement learning setting.

Reliable analysis of comprehensive two-dimensional gas chromatography -
time-of-flight mass spectrometry (GC$\times$GC-TOFMS) data is considered to be
a major bottleneck for its widespread application. For multiple samples,
GC$\times$GC-TOFMS data for specific chromatographic regions manifests as a 4th
order tensor of I mass spectral acquisitions, J mass channels, K modulations,
and L samples. Chromatographic drift is common along both the first-dimension
(modulations), and along the second-dimension (mass spectral acquisitions),
while drift along the mass channel and sample dimensions is for all practical
purposes nonexistent. A number of solutions to handling GC$\times$GC-TOFMS data
have been proposed: these involve reshaping the data to make it amenable to
either 2nd order decomposition techniques based on Multivariate Curve
Resolution (MCR), or 3rd order decomposition techniques such as Parallel Factor
Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic
drift along one mode, which has enabled its use for robust decomposition of
multiple GC-MS experiments. Although extensible, it is not straightforward to
implement a PARAFAC2 model that accounts for drift along multiple modes. In
this submission, we demonstrate a new approach and a general theory for
modelling data with drift along multiple modes, for applications in
multidimensional chromatography with multivariate detection.

This paper addresses the challenges of training large neural network models
under federated learning settings: high on-device memory usage and
communication cost. The proposed Online Model Compression (OMC) provides a
framework that stores model parameters in a compressed format and decompresses
them only when needed. We use quantization as the compression method in this
paper and propose three methods, (1) using per-variable transformation, (2)
weight matrices only quantization, and (3) partial parameter quantization, to
minimize the impact on model accuracy. According to our experiments on two
recent neural networks for speech recognition and two different datasets, OMC
can reduce memory usage and communication cost of model parameters by up to 59%
while attaining comparable accuracy and training speed when compared with
full-precision training.

Out-of-Distribution (OoD) inputs are examples that do not belong to the true
underlying distribution of the dataset. Research has shown that deep neural
nets make confident mispredictions on OoD inputs. Therefore, it is critical to
identify OoD inputs for safe and reliable deployment of deep neural nets. Often
a threshold is applied on a similarity score to detect OoD inputs. One such
similarity is angular similarity which is the dot product of latent
representation with the mean class representation. Angular similarity encodes
uncertainty, for example, if the angular similarity is less, it is less certain
that the input belongs to that class. However, we observe that, different
classes have different distributions of angular similarity. Therefore, applying
a single threshold for all classes is not ideal since the same similarity score
represents different uncertainties for different classes. In this paper, we
propose norm-scaling which normalizes the logits separately for each class.
This ensures that a single value consistently represents similar uncertainty
for various classes. We show that norm-scaling, when used with maximum softmax
probability detector, achieves 9.78% improvement in AUROC, 5.99% improvement in
AUPR and 33.19% reduction in FPR95 metrics over previous state-of-the-art
methods.

Ergonomics and human comfort are essential concerns in physical human-robot
interaction applications. Defining an accurate and easy-to-use ergonomic
assessment model stands as an important step in providing feedback for postural
correction to improve operator health and comfort. Common practical methods in
the area suffer from inaccurate ergonomics models in performing postural
optimization. In order to retain assessment quality, while improving
computational considerations, we propose a novel framework for postural
assessment and optimization for ergonomically intelligent physical human-robot
interaction. We introduce DULA and DEBA, differentiable and continuous
ergonomics models learned to replicate the popular and scientifically validated
RULA and REBA assessments with more than 99% accuracy. We show that DULA and
DEBA provide assessment comparable to RULA and REBA while providing
computational benefits when being used in postural optimization. We evaluate
our framework through human and simulation experiments. We highlight DULA and
DEBA's strength in a demonstration of postural optimization for a simulated
pHRI task.

Given a collection of vertex-aligned networks and an additional
label-shuffled network, we propose procedures for leveraging the signal in the
vertex-aligned collection to recover the labels of the shuffled network. We
consider matching the shuffled network to averages of the networks in the
vertex-aligned collection at different levels of granularity. We demonstrate
both in theory and practice that if the graphs come from different network
classes, then clustering the networks into classes followed by matching the new
graph to cluster-averages can yield higher fidelity matching performance than
matching to the global average graph. Moreover, by minimizing the graph
matching objective function with respect to each cluster average, this approach
simultaneously classifies and recovers the vertex labels for the shuffled
graph.

Imitation learning is the task of replicating expert policy from
demonstrations, without access to a reward function. This task becomes
particularly challenging when the expert exhibits a mixture of behaviors. Prior
work has introduced latent variables to model variations of the expert policy.
However, our experiments show that the existing works do not exhibit
appropriate imitation of individual modes. To tackle this problem, we adopt an
encoder-free generative model for behavior cloning (BC) to accurately
distinguish and imitate different modes. Then, we integrate it with GAIL to
make the learning robust towards compounding errors at unseen states. We show
that our method significantly outperforms the state of the art across multiple

We define the hitting time for a Markov decision process (MDP). We do not use
the hitting time of the Markov process induced by the MDP because the induced
chain may not have a stationary distribution. Even it has a stationary
distribution, the stationary distribution may not coincide with the
(normalized) occupancy measure of the MDP. We observe a relationship between
the MDP and the PageRank. Using this observation, we construct an MP whose
stationary distribution coincides with the normalized occupancy measure of the
MDP and we define the hitting time of the MDP as the hitting time of the
associated MP.

Traditional memory writing operations proceed one bit at a time, where e.g.
an individual magnetic domain is force-flipped by a localized external field.
One way to increase material storage capacity would be to write several bits at
a time in the bulk of the material. However, the manipulation of bits is
commonly done through quasi-static operations. While simple to model, this
method is known to reduce memory capacity. In this paper, we demonstrate how a
reinforcement learning agent can exploit the dynamical response of a simple
multi-bit mechanical system to restore its memory to full capacity. To do so,
we introduce a model framework consisting of a chain of bi-stable springs,
which is manipulated on one end by the external action of the agent. We show
that the agent manages to learn how to reach all available states for three
springs, even though some states are not reachable through adiabatic
manipulation, and that both the training speed and convergence within physical
parameter space are improved using transfer learning techniques. Interestingly,
the agent also points to an optimal design of the system in terms of writing
time. In fact, it appears to learn how to take advantage of the underlying
physics: the control time exhibits a non-monotonic dependence on the internal
dissipation, reaching a minimum at a cross-over shown to verify a mechanically
motivated scaling relation.

Differential privacy (DP) is a widely used notion for reasoning about privacy
when publishing aggregate data. In this paper, we observe that certain DP
mechanisms are amenable to a posteriori privacy analysis that exploits the fact
that some outputs leak less information about the input database than others.
To exploit this phenomenon, we introduce output differential privacy (ODP) and
a new composition experiment, and leverage these new constructs to obtain
significant privacy budget savings and improved privacy-utility tradeoffs under
composition. All of this comes at no cost in terms of privacy; we do not weaken
the privacy guarantee.
  To demonstrate the applicability of our a posteriori privacy analysis
techniques, we analyze two well-known mechanisms: the Sparse Vector Technique
and the Propose-Test-Release framework. We then show how our techniques can be
used to save privacy budget in more general contexts: when a differentially
private iterative mechanism terminates before its maximal number of iterations
is reached, and when the output of a DP mechanism provides unsatisfactory
utility. Examples of the former include iterative optimization algorithms,
whereas examples of the latter include training a machine learning model with a
large generalization error. Our techniques can be applied beyond the current
paper to refine the analysis of existing DP mechanisms or guide the design of
future mechanisms.

This paper considers learning of the graphical structure of a $p$-dimensional
random vector $X \in R^p$ using both parametric and non-parametric methods.
Unlike the previous works which observe $x$ directly, we consider the indirect
observation scenario in which samples $y$ are collected via a sensing matrix $A
\in R^{d\times p}$, and corrupted with some additive noise $w$, i.e, $Y = AX +
W$. For the parametric method, we assume $X$ to be Gaussian, i.e., $x\in
R^p\sim N(\mu, \Sigma)$ and $\Sigma \in R^{p\times p}$. For the first time, we
show that the correct graphical structure can be correctly recovered under the
indefinite sensing system ($d < p$) using insufficient samples ($n < p$). In
particular, we show that for the exact recovery, we require dimension $d =
\Omega(p^{0.8})$ and sample number $n = \Omega(p^{0.8}\log^3 p)$. For the
nonparametric method, we assume a nonparanormal distribution for $X$ rather
than Gaussian. Under mild conditions, we show that our graph-structure
estimator can obtain the correct structure. We derive the minimum sample number
$n$ and dimension $d$ as $n\gtrsim (deg)^4 \log^4 n$ and $d \gtrsim p +
(deg\cdot\log(d-p))^{\beta/4}$, respectively, where deg is the maximum Markov
blanket in the graphical model and $\beta > 0$ is some fixed positive constant.
Additionally, we obtain a non-asymptotic uniform bound on the estimation error
of the CDF of $X$ from indirect observations with inexact knowledge of the
noise distribution. To the best of our knowledge, this bound is derived for the
first time and may serve as an independent interest. Numerical experiments on
both real-world and synthetic data are provided confirm the theoretical
results.

Ontology Matching (OM) plays an important role in many domains such as
bioinformatics and the Semantic Web, and its research is becoming increasingly
popular, especially with the application of machine learning (ML) techniques.
Although the Ontology Alignment Evaluation Initiative (OAEI) represents an
impressive effort for the systematic evaluation of OM systems, it still suffers
from several limitations including limited evaluation of subsumption mappings,
suboptimal reference mappings, and limited support for the evaluation of
ML-based systems. To tackle these limitations, we introduce five new biomedical
OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes
both equivalence and subsumption matching; the quality of reference mappings is
ensured by human curation, ontology pruning, etc.; and a comprehensive
evaluation framework is proposed to measure OM performance from various
perspectives for both ML-based and non-ML-based OM systems. We report
evaluation results for OM systems of different types to demonstrate the usage
of these resources, all of which are publicly available as part of the new
BioML track at OAEI 2022.

Many modern applications of the artificial neural networks ensue large number
of layers making traditional digital implementations increasingly complex.
Optical neural networks offer parallel processing at high bandwidth, but have
the challenge of noise accumulation. We propose here a new type of neural
networks using stochastic resonances as an inherent part of the architecture
and demonstrate a possibility of significant reduction of the required number
of neurons for a given performance accuracy. We also show that such a neural
network is more robust against the impact of noise.

A comparison is made between Exhaustive Search (ES) and Iterative Local
Search (ILS). Such comparison was made using the Quantum Approximation
Optimization Algorithm (QAOA). QAOA has been extensively researched due to its
this potential to be implemented in actual quantum hardware, and its promising
future in optimization problems and quantum machine learning. ES and ILS
approaches were simulated to determine the pros and cons of these techniques
for QAOA in local (classic computer) and real simulations (IBM quantum
computer). These classic approaches were used in QAOA to approximate the
optimal expected value in Max-Cut and Ising Spin Model (ISM) problems, both of
these flavors have three simulated configurations called: linear, cyclic and
complete (or full).

Multi-modal registration is a required step for many image-guided procedures,
especially ultrasound-guided interventions that require anatomical context.
While a number of such registration algorithms are already available, they all
require a good initialization to succeed due to the challenging appearance of
ultrasound images and the arbitrary coordinate system they are acquired in. In
this paper, we present a novel approach to solve the problem of registration of
an ultrasound sweep to a pre-operative image. We learn dense keypoint
descriptors from which we then estimate the registration. We show that our
method overcomes the challenges inherent to registration tasks with freehand
ultrasound sweeps, namely, the multi-modality and multidimensionality of the
data in addition to lack of precise ground truth and low amounts of training
examples. We derive a registration method that is fast, generic, fully
automatic, does not require any initialization and can naturally generate
visualizations aiding interpretability and explainability. Our approach is
evaluated on a clinical dataset of paired MR volumes and ultrasound sequences.

Spiking Neural Network (SNN), originating from the neural behavior in
biology, has been recognized as one of the next-generation neural networks.
Conventionally, SNNs can be obtained by converting from pre-trained Artificial
Neural Networks (ANNs) by replacing the non-linear activation with spiking
neurons without changing the parameters. In this work, we argue that simply
copying and pasting the weights of ANN to SNN inevitably results in activation
mismatch, especially for ANNs that are trained with batch normalization (BN)
layers. To tackle the activation mismatch issue, we first provide a theoretical
analysis by decomposing local conversion error to clipping error and flooring
error, and then quantitatively measure how this error propagates throughout the
layers using the second-order analysis. Motivated by the theoretical results,
we propose a set of layer-wise parameter calibration algorithms, which adjusts
the parameters to minimize the activation mismatch. Extensive experiments for
the proposed algorithms are performed on modern architectures and large-scale
tasks including ImageNet classification and MS COCO detection. We demonstrate
that our method can handle the SNN conversion with batch normalization layers
and effectively preserve the high accuracy even in 32 time steps. For example,
our calibration algorithms can increase up to 65% accuracy when converting
VGG-16 with BN layers.

Recognizing human non-speech vocalizations is an important task and has broad
applications such as automatic sound transcription and health condition
monitoring. However, existing datasets have a relatively small number of vocal
sound samples or noisy labels. As a consequence, state-of-the-art audio event
classification models may not perform well in detecting human vocal sounds. To
support research on building robust and accurate vocal sound recognition, we
have created a VocalSound dataset consisting of over 21,000 crowdsourced
recordings of laughter, sighs, coughs, throat clearing, sneezes, and sniffs
from 3,365 unique subjects. Experiments show that the vocal sound recognition
performance of a model can be significantly improved by 41.9% by adding
VocalSound dataset to an existing dataset as training material. In addition,
different from previous datasets, the VocalSound dataset contains meta
information such as speaker age, gender, native language, country, and health

Automatic pronunciation assessment is an important technology to help
self-directed language learners. While pronunciation quality has multiple
aspects including accuracy, fluency, completeness, and prosody, previous
efforts typically only model one aspect (e.g., accuracy) at one granularity
(e.g., at the phoneme-level). In this work, we explore modeling multi-aspect
pronunciation assessment at multiple granularities. Specifically, we train a
Goodness Of Pronunciation feature-based Transformer (GOPT) with multi-task
learning. Experiments show that GOPT achieves the best results on
speechocean762 with a public automatic speech recognition (ASR) acoustic model
trained on Librispeech.

Calibration is a common experimental physics problem, whose goal is to infer
the value and uncertainty of an unobservable quantity Z given a measured
quantity X. Additionally, one would like to quantify the extent to which X and
Z are correlated. In this paper, we present a machine learning framework for
performing frequentist maximum likelihood inference with Gaussian uncertainty
estimation, which also quantifies the mutual information between the
unobservable and measured quantities. This framework uses the Donsker-Varadhan
representation of the Kullback-Leibler divergence -- parametrized with a novel
Gaussian Ansatz -- to enable a simultaneous extraction of the maximum
likelihood values, uncertainties, and mutual information in a single training.
We demonstrate our framework by extracting jet energy corrections and
resolution factors from a simulation of the CMS detector at the Large Hadron
Collider. By leveraging the high-dimensional feature space inside jets, we
improve upon the nominal CMS jet resolution by upwards of 15%.

MixUp is a data augmentation strategy where additional samples are generated
during training by combining random pairs of training samples and their labels.
However, selecting random pairs is not potentially an optimal choice. In this
work, we propose TDMixUp, a novel MixUp strategy that leverages Training
Dynamics and allows more informative samples to be combined for generating new
data samples. Our proposed TDMixUp first measures confidence, variability,
(Swayamdipta et al., 2020), and Area Under the Margin (AUM) (Pleiss et al.,
2020) to identify the characteristics of training samples (e.g., as
easy-to-learn or ambiguous samples), and then interpolates these characterized
samples. We empirically validate that our method not only achieves competitive
performance using a smaller subset of the training data compared with strong
baselines, but also yields lower expected calibration error on the pre-trained
language model, BERT, on both in-domain and out-of-domain settings in a wide
range of NLP tasks. We publicly release our code.

Does prompting a large language model (LLM) like GPT-3 with explanations
improve in-context learning? We study this question on two NLP tasks that
involve reasoning over text, namely question answering and natural language
inference. We test the performance of four LLMs on three textual reasoning
datasets using prompts that include explanations in multiple different styles.
For these tasks, we find that including explanations in the prompts for OPT,
GPT-3 (davinci), and InstructGPT (text-davinci-001) only yields small to
moderate accuracy improvements over standard few-show learning. However,
text-davinci-002 is able to benefit more substantially.
  We further show that explanations generated by the LLMs may not entail the
models' predictions nor be factually grounded in the input, even on simple
tasks with extractive explanations. However, these flawed explanations can
still be useful as a way to verify LLMs' predictions post-hoc. Through analysis
in our three settings, we show that explanations judged by humans to be
good--logically consistent with the input and the prediction--more likely
cooccur with accurate predictions. Following these observations, we train
calibrators using automatically extracted scores that assess the reliability of
explanations, allowing us to improve performance post-hoc across all of our
datasets.

To foster usefulness and accountability of machine learning (ML), it is
essential to explain a model's decisions in addition to evaluating its
performance. Accordingly, the field of explainable artificial intelligence
(XAI) has resurfaced as a topic of active research, offering approaches to
address the "how" and "why" of automated decision-making. Within this domain,
counterfactual explanations (CFEs) have gained considerable traction as a
psychologically grounded approach to generate post-hoc explanations. To do so,
CFEs highlight what changes to a model's input would have changed its
prediction in a particular way. However, despite the introduction of numerous
CFE approaches, their usability has yet to be thoroughly validated at the human
level. Thus, to advance the field of XAI, we introduce the Alien Zoo, an
engaging, web-based and game-inspired experimental framework. The Alien Zoo
provides the means to evaluate usability of CFEs for gaining new knowledge from
an automated system, targeting novice users in a domain-general context. As a
proof of concept, we demonstrate the practical efficacy and feasibility of this
approach in a user study. Our results suggest that users benefit from receiving
CFEs compared to no explanation, both in terms of objective performance in the
proposed iterative learning task, and subjective usability. With this work, we
aim to equip research groups and practitioners with the means to easily run
controlled and well-powered user studies to complement their otherwise often
more technology-oriented work. Thus, in the interest of reproducible research,
we provide the entire code, together with the underlying models and user data.

Active learning comprises many varied techniques that engage students
actively in the construction of their understanding. Because of this variation,
different active learning techniques may be best suited to achieving different
learning objectives. We study students' perceptions of a set of active learning
techniques (including a Python simulation and an interactive game) and some
traditional techniques (like lecture). We find that students felt they engaged
fairly actively with all of the techniques, though more with those with a heavy
grade weight and some of the active learning techniques, and they reported
enjoying the active learning techniques the most except for an assignment that
required soliciting peer advice on a research idea. All of the techniques were
rated as relatively effective for achieving each of six learning objectives,
but to varying extents. The most traditional techniques like exams were rated
highest for achieving an objective associated with lower order cognitive
skills, remembering concepts. In contrast, some active learning techniques like
class presentations and the Python simulation were rated highest for achieving
objectives related to higher order cognitive skills, including learning to
conduct research, though lectures also performed surprisingly well for these
objectives. Other technique-objective matches are intuitive; for example, the
debate is rated highly for understanding pros and cons of an issue, and small
group discussion is rated highly for collaborative learning. Our results
support the idea that different teaching techniques are best suited for
different outcomes, which implies that a mix of techniques may be optimal in
course design.

Digital health applications are becoming increasingly important for assessing
and monitoring the wellbeing of people suffering from mental health conditions
like depression. A common target of said applications is to predict the results
of self-assessed Patient-Health-Questionnaires (PHQ), indicating current
symptom severity of depressive individuals. In this work, we explore the
potential of using actively-collected data to predict and forecast daily PHQ-2
scores on a newly-collected longitudinal dataset. We obtain a best MAE of 1.417
for daily prediction of PHQ-2 scores, which specifically in the used dataset
have a range of 0 to 12, using leave-one-subject-out cross-validation, as well
as a best MAE of 1.914 for forecasting PHQ-2 scores using data from up to the
last 7 days. This illustrates the additive value that can be obtained by
incorporating actively-collected data in a depression monitoring application.

Data simulation is fundamental for machine learning and causal inference, as
it allows exploration of scenarios and assessment of methods in settings with
full control of ground truth. Directed acyclic graphs (DAGs) are well
established for encoding the dependence structure over a collection of
variables in both inference and simulation settings. However, while modern
machine learning is applied to data of an increasingly complex nature,
DAG-based simulation frameworks are still confined to settings with relatively
simple variable types and functional forms. We here present DagSim, a
Python-based framework for DAG-based data simulation without any constraints on
variable types or functional relations. A succinct YAML format for defining the
simulation model structure promotes transparency, while separate user-provided
functions for generating each variable based on its parents ensure simulation
code modularization. We illustrate the capabilities of DagSim through use cases
where metadata variables control shapes in an image and patterns in
bio-sequences.

Learning from a few training samples is a desirable ability of an object
detector, inspiring the explorations of Few-Shot Object Detection (FSOD). Most
existing approaches employ a pretrain-transfer paradigm. The model is first
pre-trained on base classes with abundant data and then transferred to novel
classes with a few annotated samples. Despite the substantial progress, the
FSOD performance is still far behind satisfactory. During pre-training, due to
the co-occurrence between base and novel classes, the model is learned to treat
the co-occurred novel classes as backgrounds. During transferring, given scarce
samples of novel classes, the model suffers from learning discriminative
features to distinguish novel instances from backgrounds and base classes. To
overcome the obstacles, we propose a novel framework, Mining Implicit Novel
Instances (MINI), to mine the implicit novel instances as auxiliary training
samples, which widely exist in abundant base data but are not annotated. MINI
comprises an offline mining mechanism and an online mining mechanism. The
offline mining mechanism leverages a self-supervised discriminative model to
collaboratively mine implicit novel instances with a trained FSOD network.
Taking the mined novel instances as auxiliary training samples, the online
mining mechanism takes a teacher-student framework to simultaneously update the
FSOD network and the mined implicit novel instances on the fly. Extensive
experiments on PASCAL VOC and MS-COCO datasets show MINI achieves new
state-of-the-art performance on any shot and split. The significant performance
improvements demonstrate the superiority of our method.

A fundamental problem in science and engineering is designing optimal control
policies that steer a given system towards a desired outcome. This work
proposes Control Physics-Informed Neural Networks (Control PINNs) that
simultaneously solve for a given system state, and for the optimal control
signal, in a one-stage framework that conforms to the underlying physical laws.
Prior approaches use a two-stage framework that first models and then controls
a system in sequential order. In contrast, a Control PINN incorporates the
required optimality conditions in its architecture and in its loss function.
The success of Control PINNs is demonstrated by solving the following open-loop
optimal control problems: (i) an analytical problem, (ii) a one-dimensional
heat equation, and (iii) a two-dimensional predator-prey problem.

Datasets involving sequences of different types of events without meaningful
time stamps are prevalent in many applications, for instance when extracted
from textual corpora. We propose a family of models for such event sequences --
summary Markov models -- where the probability of observing an event type
depends only on a summary of historical occurrences of its influencing set of
event types. This Markov model family is motivated by Granger causal models for
time series, with the important distinction that only one event can occur in a
position in an event sequence. We show that a unique minimal influencing set
exists for any set of event types of interest and choice of summary function,
formulate two novel models from the general family that represent specific
sequence dynamics, and propose a greedy search algorithm for learning them from
event sequence data. We conduct an experimental investigation comparing the
proposed models with relevant baselines, and illustrate their knowledge
acquisition and discovery capabilities through case studies involving sequences
from text.

Aerial scene classification remains challenging as: 1) the size of key
objects in determining the scene scheme varies greatly; 2) many objects
irrelevant to the scene scheme are often flooded in the image. Hence, how to
effectively perceive the region of interests (RoIs) from a variety of sizes and
build more discriminative representation from such complicated object
distribution is vital to understand an aerial scene. In this paper, we propose
a novel all grains, one scheme (AGOS) framework to tackle these challenges. To
the best of our knowledge, it is the first work to extend the classic multiple
instance learning into multi-grain formulation. Specially, it consists of a
multi-grain perception module (MGP), a multi-branch multi-instance
representation module (MBMIR) and a self-aligned semantic fusion (SSF) module.
Firstly, our MGP preserves the differential dilated convolutional features from
the backbone, which magnifies the discriminative information from multi-grains.
Then, our MBMIR highlights the key instances in the multi-grain representation
under the MIL formulation. Finally, our SSF allows our framework to learn the
same scene scheme from multi-grain instance representations and fuses them, so
that the entire framework is optimized as a whole. Notably, our AGOS is
flexible and can be easily adapted to existing CNNs in a plug-and-play manner.
Extensive experiments on UCM, AID and NWPU benchmarks demonstrate that our AGOS
achieves a comparable performance against the state-of-the-art methods.

Theoretical work in morphological typology offers the possibility of
measuring morphological diversity on a continuous scale. However, literature in
Natural Language Processing (NLP) typically labels a whole language with a
strict type of morphology, e.g. fusional or agglutinative. In this work, we
propose to reduce the rigidity of such claims, by quantifying morphological
typology at the word and segment level. We consider Payne (2017)'s approach to
classify morphology using two indices: synthesis (e.g. analytic to
polysynthetic) and fusion (agglutinative to fusional). For computing synthesis,
we test unsupervised and supervised morphological segmentation methods for
English, German and Turkish, whereas for fusion, we propose a semi-automatic
method using Spanish as a case study. Then, we analyse the relationship between
machine translation quality and the degree of synthesis and fusion at word
(nouns and verbs for English-Turkish, and verbs in English-Spanish) and segment
level (previous language pairs plus English-German in both directions). We
complement the word-level analysis with human evaluation, and overall, we
observe a consistent impact of both indexes on machine translation quality.

We study the computational aspects of the task of multivariate convex
regression in dimension $d \geq 5$. We present the first computationally
efficient minimax optimal (up to logarithmic factors) estimators for the tasks
of (i) $L$-Lipschitz convex regression (ii) $\Gamma$-bounded convex regression
under polytopal support. The proof of the correctness of these estimators uses
a variety of tools from different disciplines, among them empirical process
theory, stochastic geometry, and potential theory. This work is the first to
show the existence of efficient minimax optimal estimators for non-Donsker
classes that their corresponding Least Squares Estimators are provably minimax
sub-optimal; a result of independent interest.

When working alongside human collaborators in dynamic and unstructured
environments, such as disaster recovery or military operation, fast field
adaptation is necessary for an unmanned ground vehicle (UGV) to perform its
duties or learn novel tasks. In these scenarios, personnel and equipment are
constrained, making training with minimal human supervision a desirable
learning attribute. We address the problem of making UGVs more reliable and
adaptable teammates with a novel framework that uses visual perception and
inverse optimal control to learn traversal costs for environment features.
Through extensive evaluation in a real-world environment, we show that our
framework requires few human demonstrated trajectory exemplars to learn feature
costs that reliably encode several different traversal behaviors. Additionally,
we present an on-line version of the framework that allows a human teammate to
intervene during live operation to correct deteriorated behavior or to adapt
behavior to dynamic changes in complex and unstructured environments.

This work introduces a wavelet neural network to learn a filter-bank
specialized to fit non-stationary signals and improve interpretability and
performance for digital signal processing. The network uses a wavelet transform
as the first layer of a neural network where the convolution is a parameterized
function of the complex Morlet wavelet. Experimental results, on both
simplified data and atmospheric gravity waves, show the network is quick to
converge, generalizes well on noisy data, and outperforms standard network

Reinforcement learning (RL) has been shown to be effective at learning
control from experience. However, RL typically requires a large amount of
online interaction with the environment. This limits its applicability to
real-world settings, such as in robotics, where such interaction is expensive.
In this work we investigate ways to minimize online interactions in a target
task, by reusing a suboptimal policy we might have access to, for example from
training on related prior tasks, or in simulation. To this end, we develop two
RL algorithms that can speed up training by using not only the action
distributions of teacher policies, but also data collected by such policies on
the task at hand. We conduct a thorough experimental study of how to use
suboptimal teachers on a challenging robotic manipulation benchmark on
vision-based stacking with diverse objects. We compare our methods to offline,
online, offline-to-online, and kickstarting RL algorithms. By doing so, we find
that training on data from both the teacher and student, enables the best
performance for limited data budgets. We examine how to best allocate a limited
data budget -- on the target task -- between the teacher and the student
policy, and report experiments using varying budgets, two teachers with
different degrees of suboptimality, and five stacking tasks that require a
diverse set of behaviors. Our analysis, both in simulation and in the real
world, shows that our approach is the best across data budgets, while standard
offline RL from teacher rollouts is surprisingly effective when enough data is
given.

Aerosol-cloud interactions constitute the largest source of uncertainty in
assessments of the anthropogenic climate change. This uncertainty arises in
part from the difficulty in measuring the vertical distributions of aerosols,
and only sporadic vertically resolved observations are available. We often have
to settle for less informative vertically aggregated proxies such as aerosol
optical depth (AOD). In this work, we develop a framework for the vertical
disaggregation of AOD into extinction profiles, i.e. the measure of light
extinction throughout an atmospheric column, using readily available vertically
resolved meteorological predictors such as temperature, pressure or relative
humidity. Using Bayesian nonparametric modelling, we devise a simple Gaussian
process prior over aerosol vertical profiles and update it with AOD
observations to infer a distribution over vertical extinction profiles. To
validate our approach, we use ECHAM-HAM aerosol-climate model data which offers
self-consistent simulations of meteorological covariates, AOD and extinction
profiles. Our results show that, while very simple, our model is able to
reconstruct realistic extinction profiles with well-calibrated uncertainty,
outperforming by an order of magnitude the idealized baseline which is
typically used in satellite AOD retrieval algorithms. In particular, the model
demonstrates a faithful reconstruction of extinction patterns arising from
aerosol water uptake in the boundary layer. Observations however suggest that
other extinction patterns, due to aerosol mass concentration, particle size and
radiative properties, might be more challenging to capture and require
additional vertically resolved predictors.

Dark environment becomes a challenge for computer vision algorithms owing to
insufficient photons and undesirable noise. To enhance object detection in a
dark environment, we propose a novel multitask auto encoding transformation
(MAET) model which is able to explore the intrinsic pattern behind illumination
translation. In a self-supervision manner, the MAET learns the intrinsic visual
structure by encoding and decoding the realistic illumination-degrading
transformation considering the physical noise model and image signal processing
(ISP).
  Based on this representation, we achieve the object detection task by
decoding the bounding box coordinates and classes. To avoid the
over-entanglement of two tasks, our MAET disentangles the object and degrading
features by imposing an orthogonal tangent regularity. This forms a parametric
manifold along which multitask predictions can be geometrically formulated by
maximizing the orthogonality between the tangents along the outputs of
respective tasks. Our framework can be implemented based on the mainstream
object detection architecture and directly trained end-to-end using normal
target detection datasets, such as VOC and COCO. We have achieved the
state-of-the-art performance using synthetic and real-world datasets. Code is
available at https://github.com/cuiziteng/MAET.

Inference from limited data requires a notion of measure on parameter space,
which is most explicit in the Bayesian framework as a prior distribution.
Jeffreys prior is the best-known uninformative choice, the invariant volume
element from information geometry, but we demonstrate here that this leads to
enormous bias in typical high-dimensional models. This is because models found
in science typically have an effective dimensionality of accessible behaviours
much smaller than the number of microscopic parameters. Any measure which
treats all of these parameters equally is far from uniform when projected onto
the sub-space of relevant parameters, due to variations in the local co-volume
of irrelevant directions. We present results on a principled choice of measure
which avoids this issue, and leads to unbiased posteriors, by focusing on
relevant parameters. This optimal prior depends on the quantity of data to be
gathered, and approaches Jeffreys prior in the asymptotic limit. But for
typical models this limit cannot be justified without an impossibly large
increase in the quantity of data, exponential in the number of microscopic

We present prompt distribution learning for effectively adapting a
pre-trained vision-language model to address downstream recognition tasks. Our
method not only learns low-bias prompts from a few samples but also captures
the distribution of diverse prompts to handle the varying visual
representations. In this way, we provide high-quality task-related content for
facilitating recognition. This prompt distribution learning is realized by an
efficient approach that learns the output embeddings of prompts instead of the
input embeddings. Thus, we can employ a Gaussian distribution to model them
effectively and derive a surrogate loss for efficient training. Extensive
experiments on 12 datasets demonstrate that our method consistently and
significantly outperforms existing methods. For example, with 1 sample per
category, it relatively improves the average result by 9.1% compared to
human-crafted prompts.

Predicting structural and energetic properties of a molecular system is one
of the fundamental tasks in molecular simulations, and it has use cases in
chemistry, biology, and medicine. In the past decade, the advent of machine
learning algorithms has impacted on molecular simulations for various tasks,
including property prediction of atomistic systems. In this paper, we propose a
novel methodology for transferring knowledge obtained from simple molecular
systems to a more complex one, possessing a significantly larger number of
atoms and degrees of freedom. In particular, we focus on the classification of
high and low free-energy states. Our approach relies on utilizing (i) a novel
hypergraph representation of molecules, encoding all relevant information for
characterizing the potential energy of a conformation, and (ii) novel message
passing and pooling layers for processing and making predictions on such
hypergraph-structured data. Despite the complexity of the problem, our results
show a remarkable AUC of 0.92 for transfer learning from tri-alanine to the
deca-alanine system. Moreover, we show that the very same transfer learning
approach can be used to group, in an unsupervised way, various secondary
structures of deca-alanine in clusters having similar free-energy values. Our
study represents a proof of concept that reliable transfer learning models for
molecular systems can be designed paving the way to unexplored routes in
prediction of structural and energetic properties of biologically relevant
systems.

The use of unmanned aerial vehicles (UAV) as flying radio access network
(RAN) nodes offers a promising complement to traditional fixed terrestrial
deployments. More recently yet still in the context of wireless networks,
drones have also been envisioned for use as radio frequency (RF) sensing and
localization devices. In both cases, the advantage of using UAVs lies in their
ability to navigate themselves freely in 3D and in a timely manner to locations
of space where the obtained network throughput or sensing performance is
optimal. In practice, the selection of a proper location or trajectory for the
UAV very much depends on local terrain features, including the position of
surrounding radio obstacles. Hence, the robot must be able to map the features
of its radio environment as it performs its data communication or sensing
services. The challenges related to this task, referred here as radio mapping,
are discussed in this paper. Its promises related to efficient trajectory
design for autonomous radio-aware UAVs are highlighted, along with algorithm
solutions. The advantages induced by radio-mapping in terms of connectivity,
sensing, and localization performance are illustrated.

This paper considers the problem of ground user localization based on
received signal strength (RSS) measurements obtained by an unmanned aerial
vehicle (UAV). We treat UAV-user link channel model parameters and antenna
radiation pattern of the UAV as unknowns that need to be estimated. A hybrid
channel model is proposed that consists of a traditional path loss model
combined with a neural network approximating the UAV antenna gain function.
With this model and a set of offline RSS measurements, the unknown parameters
are estimated. We then employ the particle swarm optimization (PSO) technique
which utilizes the learned hybrid channel model along with a 3D map of the
environment to accurately localize the ground users. The performance of the
developed algorithm is evaluated through simulations and also real-world

This paper presents a novel graph reinforcement learning (RL) architecture to
solve multi-robot task allocation (MRTA) problems that involve tasks with
deadlines and workload, and robot constraints such as work capacity. While
drawing motivation from recent graph learning methods that learn to solve
combinatorial optimization (CO) problems such as multi-Traveling Salesman and
Vehicle Routing Problems using RL, this paper seeks to provide better
performance (compared to non-learning methods) and important scalability
(compared to existing learning architectures) for the stated class of MRTA
problems. The proposed neural architecture, called Capsule Attention-based
Mechanism or CapAM acts as the policy network, and includes three main
components: 1) an encoder: a Capsule Network based node embedding model to
represent each task as a learnable feature vector; 2) a decoder: an
attention-based model to facilitate a sequential output; and 3) context: that
encodes the states of the mission and the robots. To train the CapAM model, the
policy-gradient method based on REINFORCE is used. When evaluated over unseen
scenarios, CapAM demonstrates better task completion performance and $>$10
times faster decision-making compared to standard non-learning based online
MRTA methods. CapAM's advantage in generalizability, and scalability to test
problems of size larger than those used in training, are also successfully
demonstrated in comparison to a popular approach for learning to solve CO
problems, namely the purely attention mechanism.

Nowcasting can play a key role in giving policymakers timelier insight to
data published with a significant time lag, such as final GDP figures.
Currently, there are a plethora of methodologies and approaches for
practitioners to choose from. However, there lacks a comprehensive comparison
of these disparate approaches in terms of predictive performance and
characteristics. This paper addresses that deficiency by examining the
performance of 12 different methodologies in nowcasting US quarterly GDP
growth, including all the methods most commonly employed in nowcasting, as well
as some of the most popular traditional machine learning approaches.
Performance was assessed on three different tumultuous periods in US economic
history: the early 1980s recession, the 2008 financial crisis, and the COVID
crisis. The two best performing methodologies in the analysis were long
short-term memory artificial neural networks (LSTM) and Bayesian vector
autoregression (BVAR). To facilitate further application and testing of each of
the examined methodologies, an open-source repository containing boilerplate
code that can be applied to different datasets is published alongside the
paper, available at: github.com/dhopp1/nowcasting_benchmark.

Recent studies increasingly adopt simulation-based machine learning (ML)
models to analyze critical infrastructure system resilience. For realistic
applications, these ML models consider the component-level characteristics that
influence the network response during emergencies. However, such an approach
could result in a large number of features and cause ML models to suffer from
the `curse of dimensionality'. We present a clustering-based method that
simultaneously minimizes the problem of high-dimensionality and improves the
prediction accuracy of ML models developed for resilience analysis in
large-scale interdependent infrastructure networks. The methodology has three
parts: (a) generation of simulation dataset, (b) network component clustering,
and (c) dimensionality reduction and development of prediction models. First,
an interdependent infrastructure simulation model simulates the network-wide
consequences of various disruptive events. The component-level features are
extracted from the simulated data. Next, clustering algorithms are used to
derive the cluster-level features by grouping component-level features based on
their topological and functional characteristics. Finally, ML algorithms are
used to develop models that predict the network-wide impacts of disruptive
events using the cluster-level features. The applicability of the method is
demonstrated using an interdependent power-water-transport testbed. The
proposed method can be used to develop decision-support tools for post-disaster
recovery of infrastructure networks.

This article presents an original method for Text-to-Sign Translation. It
compensates data scarcity using a domain-specific parallel corpus of alignments
between text and hierarchical formal descriptions of Sign Language videos in
AZee. Based on the detection of similarities present in the source text, the
proposed algorithm recursively exploits matches and substitutions of aligned
segments to build multiple candidate translations for a novel statement. This
helps preserving Sign Language structures as much as possible before falling
back on literal translations too quickly, in a generative way. The resulting
translations are in the form of AZee expressions, designed to be used as input
to avatar synthesis systems. We present a test set tailored to showcase its
potential for expressiveness and generation of idiomatic target language, and
observed limitations. This work finally opens prospects on how to evaluate
translation and linguistic aspects, such as accuracy and grammatical fluency.

Crowd Counting has important applications in public safety and pandemic
control. A robust and practical crowd counting system has to be capable of
continuously learning with the new-coming domain data in real-world scenarios
instead of fitting one domain only. Off-the-shelf methods have some drawbacks
to handle multiple domains. 1) The models will achieve limited performance
(even drop dramatically) among old domains after training images from new
domains due to the discrepancies of intrinsic data distributions from various
domains, which is called catastrophic forgetting. 2) The well-trained model in
a specific domain achieves imperfect performance among other unseen domains
because of the domain shift. 3) It leads to linearly-increased storage overhead
either mixing all the data for training or simply training dozens of separate
models for different domains when new ones are available. To overcome these
issues, we investigate a new task of crowd counting under the incremental
domains training setting, namely, Lifelong Crowd Counting. It aims at
alleviating the catastrophic forgetting and improving the generalization
ability using a single model updated by the incremental domains. To be more
specific, we propose a self-distillation learning framework as a
benchmark~(Forget Less, Count Better, FLCB) for lifelong crowd counting, which
helps the model sustainably leverage previous meaningful knowledge for better
crowd counting to mitigate the forgetting when the new data arrive. Meanwhile,
a new quantitative metric, normalized backward transfer~(nBwT), is developed to
evaluate the forgetting degree of the model in the lifelong learning process.
Extensive experimental results demonstrate the superiority of our proposed
benchmark in achieving a low catastrophic forgetting degree and strong

With the rapid development of mobile Internet and big data, a huge amount of
data is generated in the network, but the data that users are really interested
in a very small portion. To extract the information that users are interested
in from the huge amount of data, the information overload problem needs to be
solved. In the era of mobile internet, the user's characteristics and other
information should be combined in the massive amount of data to quickly and
accurately recommend content to the user, as far as possible to meet the user's
personalized needs. Therefore, there is an urgent need to realize high-speed
and effective retrieval in tens of thousands of micro-videos. Video data
content contains complex meanings, and there are intrinsic connections between
video data. For multimodal information, subspace coding learning is introduced
to build a coding network from public potential representations to multimodal
feature information, taking into account the consistency and complementarity of
information under each modality to obtain a public representation of the
complete eigenvalue. An end-to-end reordering model based on deep learning and
attention mechanism, called interest-related product similarity model based on
multimodal data, is proposed for providing top-N recommendations. The
multimodal feature learning module, interest-related network module and product
similarity recommendation module together form the new model.By conducting
extensive experiments on publicly accessible datasets, the results demonstrate
the state-of-the-art performance of our proposed algorithm and its

Building models to detect vaccine attitudes on social media is challenging
because of the composite, often intricate aspects involved, and the limited
availability of annotated data. Existing approaches have relied heavily on
supervised training that requires abundant annotations and pre-defined aspect
categories. Instead, with the aim of leveraging the large amount of unannotated
data now available on vaccination, we propose a novel semi-supervised approach
for vaccine attitude detection, called VADet. A variational autoencoding
architecture based on language models is employed to learn from unlabelled data
the topical information of the domain. Then, the model is fine-tuned with a few
manually annotated examples of user attitudes. We validate the effectiveness of
VADet on our annotated data and also on an existing vaccination corpus
annotated with opinions on vaccines. Our results show that VADet is able to
learn disentangled stance and aspect topics, and outperforms existing
aspect-based sentiment analysis models on both stance detection and tweet

Machine learning models in safety-critical settings like healthcare are often
blackboxes: they contain a large number of parameters which are not transparent
to users. Post-hoc explainability methods where a simple, human-interpretable
model imitates the behavior of these blackbox models are often proposed to help
users trust model predictions. In this work, we audit the quality of such
explanations for different protected subgroups using real data from four
settings in finance, healthcare, college admissions, and the US justice system.
Across two different blackbox model architectures and four popular
explainability methods, we find that the approximation quality of explanation
models, also known as the fidelity, differs significantly between subgroups. We
also demonstrate that pairing explainability methods with recent advances in
robust machine learning can improve explanation fairness in some settings.
However, we highlight the importance of communicating details of non-zero
fidelity gaps to users, since a single solution might not exist across all
settings. Finally, we discuss the implications of unfair explanation models as
a challenging and understudied problem facing the machine learning community.

We investigate the feasibility of deploying Deep-Q based deep reinforcement
learning agents to job-shop scheduling problems in the context of modular
production facilities, using discrete event simulations for the environment.
These environments are comprised of a source and sink for the parts to be
processed, as well as (several) workstations. The agents are trained to
schedule automated guided vehicles to transport the parts back and forth
between those stations in an optimal fashion. Starting from a very simplistic
setup, we increase the complexity of the environment and compare the agents'
performances with well established heuristic approaches, such as
first-in-first-out based agents, cost tables and a nearest-neighbor approach.
We furthermore seek particular configurations of the environments in which the
heuristic approaches struggle, to investigate to what degree the Deep-Q agents
are affected by these challenges. We find that Deep-Q based agents show
comparable performance as the heuristic baselines. Furthermore, our findings
suggest that the DRL agents exhibit an increased robustness to noise, as
compared to the conventional approaches. Overall, we find that DRL agents
constitute a valuable approach for this type of scheduling problems.

A key goal of ad hoc teamwork is to develop a learning agent that cooperates
with unknown teams, without resorting to any pre-coordination protocol. Despite
a vast number of ad hoc teamwork algorithms in the literature, most of them
cannot address the problem of learning to cooperate with a completely unknown
team, unless it learns from scratch. This article presents a novel approach
that uses transfer learning alongside the state-of-the-art PLASTIC-Policy to
adapt to completely unknown teammates quickly. We test our solution within the
Half Field Offense simulator with five different teammates. The teammates were
designed independently by developers from different countries and at different
times. Our empirical evaluation shows that it is advantageous for an ad hoc
agent to leverage its past knowledge when adapting to a new team instead of
learning how to cooperate with it from scratch.

In this work we demonstrate how both the Stochastic and Risk Sensitive
Optimal Control problem can be treated by means of the Expectation-Maximisation
algorithm. We show how such a treatment materialises into two separate
iterative programs that each generate a unique but closely related sequence of
density functions. We motivate to interpret these density functions as beliefs,
ergo as probabilistic proxies for the deterministic optimal policy. More
formally two fixed point iteration schemes are derived with the stationary
point coinciding with the deterministic optimal policies on behalf of the
proven convergence of Expectation-Maximisation methods. We are inclined to
point out our results are intimately related with the paradigm of Control as
Inference. Control as inference here refers to a collection of approaches which
aim is also to recast optimal control as an instance of probabilistic
inference. Although said paradigm already resulted in the development of
several powerful Reinforcement Learning algorithms, the fundamental problem
statement usually is introduced by teleological arguments. We argue that the
present results demonstrate that earlier established Control as Inference
frameworks in fact isolate a single step from either of the proposed iterative
programs. In any case the present treatment provides them with a deontological
argument of validity. By exposing the underlying technical mechanism we aim to
contribute to the general acceptance of Control as Inference as a framework
superseding the present Optimal Control paradigm. In order to motivate the
general relevance of the presented treatment we further discuss parallels with
Path Integral Control and other areas of research before sketching the outlines
of future algorithmic development.

mmWave radars have recently gathered significant attention as a means to
track human movement within indoor environments. Widely adopted Kalman filter
tracking methods experience performance degradation when the underlying
movement is highly non-linear or presents long-term temporal dependencies. As a
solution, in this article we design a convolutional-recurrent Neural Network
(NN) that learns to accurately estimate the position and the velocity of the
monitored subjects from high dimensional radar data. The NN is trained as a
probabilistic model, utilizing a Gaussian negative log-likelihood loss
function, obtaining explicit uncertainty estimates at its output, in the form
of time-varying error covariance matrices. A thorough experimental assessment
is conducted using a 77 GHz FMCW radar. The proposed architecture, besides
allowing one to gauge the uncertainty in the tracking process, also leads to
greatly improved performance against the best approaches from the literature,
i.e., Kalman filtering, lowering the average error against the ground truth
from 32.8 to 7.59 cm and from 56.8 to 14 cm/s in terms of position and velocity
tracking, respectively.

Deep learning for Information Retrieval (IR) requires a large amount of
high-quality query-document relevance labels, but such labels are inherently
sparse. Label smoothing redistributes some observed probability mass over
unobserved instances, often uniformly, uninformed of the true distribution. In
contrast, we propose knowledge distillation for informed labeling, without
incurring high computation overheads at evaluation time. Our contribution is
designing a simple but efficient teacher model which utilizes collective
knowledge, to outperform state-of-the-arts distilled from a more complex
teacher model. Specifically, we train up to x8 faster than the state-of-the-art
teacher, while distilling the rankings better. Our code is publicly available
at https://github.com/jihyukkim-nlp/CollectiveKD

Development and optimization of biopharmaceutical production processes with
cell cultures is cost- and time-consuming and often performed rather
empirically. Efficient optimization of multiple-objectives like process time,
viable cell density, number of operating steps & cultivation scales, required
medium, amount of product as well as product quality depicts a promising
approach. This contribution presents a workflow which couples uncertainty-based
upstream simulation and Bayes optimization using Gaussian processes. Its
application is demonstrated in a simulation case study for a relevant
industrial task in process development, the design of a robust cell culture
expansion process (seed train), meaning that despite uncertainties and
variabilities concerning cell growth, low variations of viable cell density
during the seed train are obtained. Compared to a non-optimized reference seed
train, the optimized process showed much lower deviation rates regarding viable
cell densities (<~10% instead of 41.7%) using 5 or 4 shake flask scales and
seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it
is shown that applying Bayes optimization allows for optimization of a
multi-objective optimization function with several optimizable input variables
and under a considerable amount of constraints with a low computational effort.
This approach provides the potential to be used in form of a decision tool,
e.g. for the choice of an optimal and robust seed train design or for further
optimization tasks within process development.

Not all convex functions on $\mathbb{R}^n$ have finite minimizers; some can
only be minimized by a sequence as it heads to infinity. In this work, we aim
to develop a theory for understanding such minimizers at infinity. We study
astral space, a compact extension of $\mathbb{R}^n$ to which such points at
infinity have been added. Astral space is constructed to be as small as
possible while still ensuring that all linear functions can be continuously
extended to the new space. Although astral space includes all of
$\mathbb{R}^n$, it is not a vector space, nor even a metric space. However, it
is sufficiently well-structured to allow useful and meaningful extensions of
concepts of convexity, conjugacy, and subdifferentials. We develop these
concepts and analyze various properties of convex functions on astral space,
including the detailed structure of their minimizers, exact characterizations
of continuity, and convergence of descent algorithms.

This survey article assesses and compares existing critiques of current
fairness-enhancing technical interventions into machine learning (ML) that draw
from a range of non-computing disciplines, including philosophy, feminist
studies, critical race and ethnic studies, legal studies, anthropology, and
science and technology studies. It bridges epistemic divides in order to offer
an interdisciplinary understanding of the possibilities and limits of hegemonic
computational approaches to ML fairness for producing just outcomes for
society's most marginalized. The article is organized according to nine major
themes of critique wherein these different fields intersect: 1) how "fairness"
in AI fairness research gets defined; 2) how problems for AI systems to address
get formulated; 3) the impacts of abstraction on how AI tools function and its
propensity to lead to technological solutionism; 4) how racial classification
operates within AI fairness research; 5) the use of AI fairness measures to
avoid regulation and engage in ethics washing; 6) an absence of participatory
design and democratic deliberation in AI fairness considerations; 7) data
collection practices that entrench "bias," are non-consensual, and lack
transparency; 8) the predatory inclusion of marginalized groups into AI
systems; and 9) a lack of engagement with AI's long-term social and ethical
outcomes. Drawing from these critiques, the article concludes by imagining
future ML fairness research directions that actively disrupt entrenched power
dynamics and structural injustices in society.

This explainer document aims to provide an overview of the current state of
the rapidly expanding work on synthetic data technologies, with a particular
focus on privacy. The article is intended for a non-technical audience, though
some formal definitions have been given to provide clarity to specialists. This
article is intended to enable the reader to quickly become familiar with the
notion of synthetic data, as well as understand some of the subtle intricacies
that come with it. We do believe that synthetic data is a very useful tool, and
our hope is that this report highlights that, while drawing attention to
nuances that can easily be overlooked in its deployment.

In the classical setting of self-selection, the goal is to learn $k$ models,
simultaneously from observations $(x^{(i)}, y^{(i)})$ where $y^{(i)}$ is the
output of one of $k$ underlying models on input $x^{(i)}$. In contrast to
mixture models, where we observe the output of a randomly selected model, here
the observed model depends on the outputs themselves, and is determined by some
known selection criterion. For example, we might observe the highest output,
the smallest output, or the median output of the $k$ models. In known-index
self-selection, the identity of the observed model output is observable; in
unknown-index self-selection, it is not. Self-selection has a long history in
Econometrics and applications in various theoretical and applied fields,
including treatment effect estimation, imitation learning, learning from
strategically reported data, and learning from markets at disequilibrium.
  In this work, we present the first computationally and statistically
efficient estimation algorithms for the most standard setting of this problem
where the models are linear. In the known-index case, we require
poly$(1/\varepsilon, k, d)$ sample and time complexity to estimate all model
parameters to accuracy $\varepsilon$ in $d$ dimensions, and can accommodate
quite general selection criteria. In the more challenging unknown-index case,
even the identifiability of the linear models (from infinitely many samples)
was not known. We show three results in this case for the commonly studied
$\max$ self-selection criterion: (1) we show that the linear models are indeed
identifiable, (2) for general $k$ we provide an algorithm with poly$(d)
\exp(\text{poly}(k))$ sample and time complexity to estimate the regression
parameters up to error $1/\text{poly}(k)$, and (3) for $k = 2$ we provide an
algorithm for any error $\varepsilon$ and poly$(d, 1/\varepsilon)$ sample and
time complexity.

When omega-regular objectives were first proposed in model-free reinforcement
learning (RL) for controlling MDPs, deterministic Rabin automata were used in
an attempt to provide a direct translation from their transitions to scalar
values. While these translations failed, it has turned out that it is possible
to repair them by using good-for-MDPs (GFM) B\"uchi automata instead. These are
nondeterministic B\"uchi automata with a restricted type of nondeterminism,
albeit not as restricted as in good-for-games automata. Indeed, deterministic
Rabin automata have a pretty straightforward translation to such GFM automata,
which is bi-linear in the number of states and pairs. Interestingly, the same
cannot be said for deterministic Streett automata: a translation to
nondeterministic Rabin or B\"uchi automata comes at an exponential cost, even
without requiring the target automaton to be good-for-MDPs. Do we have to pay
more than that to obtain a good-for-MDP automaton? The surprising answer is
that we have to pay significantly less when we instead expand the good-for-MDP
property to alternating automata: like the nondeterministic GFM automata
obtained from deterministic Rabin automata, the alternating good-for-MDP
automata we produce from deterministic Streett automata are bi-linear in the
the size of the deterministic automaton and its index, and can therefore be
exponentially more succinct than minimal nondeterministic B\"uchi automata.

Recommending a sequence of activities for an ongoing case requires that the
recommendations conform to the underlying business process and meet the
performance goal of either completion time or process outcome. Existing work on
next activity prediction can predict the future activity but cannot provide
guarantees of the prediction being conformant or meeting the goal. Hence, we
propose a goal-oriented next best activity recommendation. Our proposed
framework uses a deep learning model to predict the next best activity and an
estimated value of a goal given the activity. A reinforcement learning method
explores the sequence of activities based on the estimates likely to meet one
or more goals. We further address a real-world problem of multiple goals by
introducing an additional reward function to balance the outcome of a
recommended activity and satisfy the goal. We demonstrate the effectiveness of
the proposed method on four real-world datasets with different characteristics.
The results show that the recommendations from our proposed approach outperform
in goal satisfaction and conformance compared to the existing state-of-the-art
next best activity recommendation techniques.

Reliably predicting future occupancy of highly dynamic urban environments is
an important precursor for safe autonomous navigation. Common challenges in the
prediction include forecasting the relative position of other vehicles,
modelling the dynamics of vehicles subjected to different traffic conditions,
and vanishing surrounding objects. To tackle these challenges, we propose a
spatio-temporal prediction network pipeline that takes the past information
from the environment and semantic labels separately for generating future
occupancy predictions. Compared to the current SOTA, our approach predicts
occupancy for a longer horizon of 3 seconds and in a relatively complex
environment from the nuScenes dataset. Our experimental results demonstrate the
ability of spatio-temporal networks to understand scene dynamics without the
need for HD-Maps and explicit modeling dynamic objects. We publicly release our
occupancy grid dataset based on nuScenes to support further research.

This work offers a novel view on the use of human input as labels,
acknowledging that humans may err. We build a behavioral profile for human
annotators which is used as a feature representation of the provided input. We
show that by utilizing black-box machine learning, we can take into account
human behavior and calibrate their input to improve the labeling quality. To
support our claims and provide a proof-of-concept, we experiment with three
different matching tasks, namely, schema matching, entity matching and text
matching. Our empirical evaluation suggests that the method can improve the
quality of gathered labels in multiple settings including cross-domain (across
different matching tasks).

While the field of Quality-Diversity (QD) has grown into a distinct branch of
stochastic optimization, a few problems, in particular locomotion and
navigation tasks, have become de facto standards. Are such benchmarks
sufficient? Are they representative of the key challenges faced by QD
algorithms? Do they provide the ability to focus on one particular challenge by
properly disentangling it from others? Do they have much predictive power in
terms of scalability and generalization? Existing benchmarks are not
standardized, and there is currently no MNIST equivalent for QD. Inspired by
recent works on Reinforcement Learning benchmarks, we argue that the
identification of challenges faced by QD methods and the development of
targeted, challenging, scalable but affordable benchmarks is an important step.
As an initial effort, we identify three problems that are challenging in sparse
reward settings, and propose associated benchmarks: (1) Behavior metric bias,
which can result from the use of metrics that do not match the structure of the
behavior space. (2) Behavioral Plateaus, with varying characteristics, such
that escaping them would require adaptive QD algorithms and (3) Evolvability
Traps, where small variations in genotype result in large behavioral changes.
The environments that we propose satisfy the properties listed above.

This paper settles an open and challenging question pertaining to the design
of simple high-order regularization methods for solving smooth and monotone
variational inequalities (VIs). A VI involves finding $x^\star \in \mathcal{X}$
such that $\langle F(x), x - x^\star\rangle \geq 0$ for all $x \in \mathcal{X}$
and we consider the setting where $F: \mathbb{R}^d \mapsto \mathbb{R}^d$ is
smooth with up to $(p-1)^{th}$-order derivatives. For $p =
2$,~\citet{Nesterov-2006-Constrained} extended the cubic regularized Newton's
method to VIs with a global rate of
$O(\epsilon^{-1})$.~\citet{Monteiro-2012-Iteration} proposed another
second-order method which achieved an improved rate of
$O(\epsilon^{-2/3}\log(1/\epsilon))$, but this method required a nontrivial
binary search procedure as an inner loop. High-order methods based on similar
binary search procedures have been further developed and shown to achieve a
rate of $O(\epsilon^{-2/(p+1)}\log(1/\epsilon))$. However, such search
procedure can be computationally prohibitive in practice and the problem of
finding a simple high-order regularization methods remains as an open and
challenging question in optimization theory. We propose a $p^{th}$-order method
that does \textit{not} require any binary search procedure and prove that it
can converge to a weak solution at a global rate of $O(\epsilon^{-2/(p+1)})$. A
lower bound of $\Omega(\epsilon^{-2/(p+1)})$ is also established to show that
our method is optimal in the monotone setting. A version with restarting
attains a global linear and local superlinear convergence rate for smooth and
strongly monotone VIs. Moreover, our method can achieve a global rate of
$O(\epsilon^{-2/p})$ for solving smooth and non-monotone VIs satisfying the
Minty condition; moreover, the restarted version again attains a global linear
and local superlinear convergence rate if the strong Minty condition holds.

In this paper, we propose a theoretical analysis of the algorithm ISDE,
introduced in previous work. From a dataset, ISDE learns a density written as a
product of marginal density estimators over a partition of the features. We
show that under some hypotheses, the Kullback-Leibler loss between the proper
density and the output of ISDE is a bias term plus the sum of two terms which
goes to zero as the number of samples goes to infinity. The rate of convergence
indicates that ISDE tackles the curse of dimensionality by reducing the
dimension from the one of the ambient space to the one of the biggest blocks in
the partition. The constants reflect a combinatorial complexity reduction
linked to the design of ISDE.

Channel estimation is a critical task in intelligent reflecting surface
(IRS)-assisted wireless systems due to the uncertainties imposed by environment
dynamics and rapid changes in the IRS configuration. To deal with these
uncertainties, deep learning (DL) approaches have been proposed. Previous works
consider centralized learning (CL) approach for model training, which entails
the collection of the whole training dataset from the users at the base station
(BS), hence introducing huge transmission overhead for data collection. To
address this challenge, this paper proposes a federated learning (FL) framework
to jointly estimate both direct and cascaded channels in IRS-assisted wireless
systems. We design a single convolutional neural network trained on the local
datasets of the users without sending them to the BS. We show that the proposed
FL-based channel estimation approach requires approximately 60% fewer pilot
signals and it exhibits 12 times lower transmission overhead than CL, while
maintaining satisfactory performance close to CL. In addition, it provides
lower estimation error than the state-of-the-art DL-based schemes.

Simulation is a crucial tool for accelerating the development of autonomous
vehicles. Making simulation realistic requires models of the human road users
who interact with such cars. Such models can be obtained by applying learning
from demonstration (LfD) to trajectories observed by cars already on the road.
However, existing LfD methods are typically insufficient, yielding policies
that frequently collide or drive off the road. To address this problem, we
propose Symphony, which greatly improves realism by combining conventional
policies with a parallel beam search. The beam search refines these policies on
the fly by pruning branches that are unfavourably evaluated by a discriminator.
However, it can also harm diversity, i.e., how well the agents cover the entire
distribution of realistic behaviour, as pruning can encourage mode collapse.
Symphony addresses this issue with a hierarchical approach, factoring agent
behaviour into goal generation and goal conditioning. The use of such goals
ensures that agent diversity neither disappears during adversarial training nor
is pruned away by the beam search. Experiments on both proprietary and open
Waymo datasets confirm that Symphony agents learn more realistic and diverse
behaviour than several baselines.

Accounting for the uncertainty in the predictions of modern neural networks
is a challenging and important task in many domains. Existing algorithms for
uncertainty estimation require modifying the model architecture and training
procedure (e.g., Bayesian neural networks) or dramatically increase the
computational cost of predictions such as approaches based on ensembling. This
work proposes a new algorithm that can be applied to a given trained neural
network and produces approximate prediction intervals. The method is based on
the classical delta method in statistics but achieves computational efficiency
by using matrix sketching to approximate the Jacobian matrix. The resulting
algorithm is competitive with state-of-the-art approaches for constructing
predictive intervals on various regression datasets from the UCI repository.

Backdoor attacks are rapidly emerging threats to deep neural networks (DNNs).
In the backdoor attack scenario, attackers usually implant the backdoor into
the target model by manipulating the training dataset or training process.
Then, the compromised model behaves normally for benign input yet makes
mistakes when the pre-defined trigger appears. In this paper, we analyze the
drawbacks of existing attack approaches and propose a novel imperceptible
backdoor attack. We treat the trigger pattern as a special kind of noise
following a multinomial distribution. A U-net-based network is employed to
generate concrete parameters of multinomial distribution for each benign input.
This elaborated trigger ensures that our approach is invisible to both humans
and statistical detection. Besides the design of the trigger, we also consider
the robustness of our approach against model diagnose-based defences. We force
the feature representation of malicious input stamped with the trigger to be
entangled with the benign one. We demonstrate the effectiveness and robustness
against multiple state-of-the-art defences through extensive datasets and
networks. Our trigger only modifies less than 1\% pixels of a benign image
while the modification magnitude is 1. Our source code is available at
https://github.com/Ekko-zn/IJCAI2022-Backdoor.

Computer algebra can answer various questions about partial differential
equations using symbolic algorithms. However, the inclusion of data into
equations is rare in computer algebra. Therefore, recently, computer algebra
models have been combined with Gaussian processes, a regression model in
machine learning, to describe the behavior of certain differential equations
under data. While it was possible to describe polynomial boundary conditions in
this context, we extend these models to analytic boundary conditions.
Additionally, we describe the necessary algorithms for Gr\"obner and Janet
bases of Weyl algebras with certain analytic coefficients. Using these
algorithms, we provide examples of divergence-free flow in domains bounded by
analytic functions and adapted to observations.

State-of-the-art machine learning solutions mainly focus on creating highly
accurate models without constraints on hardware resources. Stream mining
algorithms are designed to run on resource-constrained devices, thus a focus on
low power and energy and memory-efficient is essential. The Hoeffding tree
algorithm is able to create energy-efficient models, but at the cost of less
accurate trees in comparison to their ensembles counterpart. Ensembles of
Hoeffding trees, on the other hand, create a highly accurate forest of trees
but consume five times more energy on average. An extension that tried to
obtain similar results to ensembles of Hoeffding trees was the Extremely Fast
Decision Tree (EFDT). This paper presents the Green Accelerated Hoeffding Tree
(GAHT) algorithm, an extension of the EFDT algorithm with a lower energy and
memory footprint and the same (or higher for some datasets) accuracy levels.
GAHT grows the tree setting individual splitting criteria for each node, based
on the distribution of the number of instances over each particular leaf. The
results show that GAHT is able to achieve the same competitive accuracy results
compared to EFDT and ensembles of Hoeffding trees while reducing the energy
consumption up to 70%.

During the COVID-19 pandemic, computed tomography (CT) is a good way to
diagnose COVID-19 patients. HRCT (High-Resolution Computed Tomography) is a
form of computed tomography that uses advanced methods to improve image
resolution. Publicly accessible COVID-19 CT image datasets are very difficult
to come by due to privacy concerns, which impedes the study and development of
AI-powered COVID-19 diagnostic algorithms based on CT images. To address this
problem, we have introduced HRCTv1-COVID-19, a new COVID-19 high resolution
chest CT Scan image dataset that includes not only COVID-19 cases of Ground
Glass Opacity (GGO), Crazy Paving, and Air Space Consolidation, but also CT
images of cases with negative COVID-19. The HRCTv1-COVID-19 dataset, which
includes slice-level, and patient-level labels, has the potential to aid
COVID-19 research, especially for diagnosis and differentiation using
artificial intelligence algorithms, machine learning and deep learning methods.
This dataset is accessible through web at: http://databiox.com and includes
181,106 chest HRCT images from 395 patients with four labels: GGO, Crazy
Paving, Air Space Consolidation and Negative.
  Keywords- Dataset, COVID-19, CT-Scan, Computed Tomography, Medical Imaging,
Chest Image.

We study non-classical pathways and quantum interference in enhanced
ionisation of diatomic molecules in strong laser fields using machine learning
techniques. Quantum interference provides a bridge, which facilitates
intramolecular population transfer. Its frequency is higher than that of the
field, intrinsic to the system and depends on several factors, for instance the
state of the initial wavepacket or the internuclear separation. Using
dimensionality reduction techniques, namely t-distributed stochastic neighbour
embedding (t-SNE) and principal component analysis (PCA), we investigate the
effect of multiple parameters at once and find optimal conditions for enhanced
ionisation in static fields, and controlled ionisation release for two-colour
driving fields. This controlled ionisation manifests itself as a step-like
behaviour in the time-dependent autocorrelation function. We explain the
features encountered with phase-space arguments, and also establish a hierarchy
of parameters for controlling ionisation via phase-space Wigner
quasiprobability flows, such as specific coherent superpositions of states,
electron localisation and internuclear-distance ranges.

Self-supervised learning is a growing paradigm in deep representation
learning, showing great generalization capabilities and competitive performance
in low-labeled data regimes. The SimCLR framework proposes the NT-Xent loss for
contrastive representation learning. The objective of the loss function is to
maximize agreement, similarity, between sampled positive pairs. This short
paper derives and proposes an upper bound for the loss and average similarity.
An analysis of the implications is however not provided, but we strongly
encourage anyone in the field to conduct this.

Privacy regulations and the physical distribution of heterogeneous data are
often primary concerns for the development of deep learning models in a medical
context. This paper evaluates the feasibility of differentially private
federated learning for chest X-ray classification as a defense against data
privacy attacks. To the best of our knowledge, we are the first to directly
compare the impact of differentially private training on two different neural
network architectures, DenseNet121 and ResNet50. Extending the federated
learning environments previously analyzed in terms of privacy, we simulated a
heterogeneous and imbalanced federated setting by distributing images from the
public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients.
Both non-private baseline models achieved an area under the receiver operating
characteristic curve (AUC) of $0.94$ on the binary classification task of
detecting the presence of a medical finding. We demonstrate that both model
architectures are vulnerable to privacy violation by applying image
reconstruction attacks to local model updates from individual clients. The
attack was particularly successful during later training stages. To mitigate
the risk of privacy breach, we integrated R\'enyi differential privacy with a
Gaussian noise mechanism into local model training. We evaluate model
performance and attack vulnerability for privacy budgets $\epsilon \in$ {1, 3,
6, 10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC
of $0.94$ for $\epsilon$ = 6. Model performance deteriorated slightly for
individual clients compared to the non-private baseline. The ResNet50 only
reached an AUC of $0.76$ in the same privacy setting. Its performance was
inferior to that of the DenseNet121 for all considered privacy constraints,
suggesting that the DenseNet121 architecture is more robust to differentially
private training.

The Novelty Search (NS) algorithm was proposed more than a decade ago.
However, the mechanisms behind its empirical success are still not well
formalized/understood. This short note focuses on the effects of the archive on
exploration. Experimental evidence from a few application domains suggests that
archive-based NS performs in general better than when Novelty is solely
computed with respect to the population. An argument that is often encountered
in the literature is that the archive prevents exploration from backtracking or
cycling, i.e. from revisiting previously encountered areas in the behavior
space. We argue that this is not a complete or accurate explanation as
backtracking - beside often being desirable - can actually be enabled by the
archive. Through low-dimensional/analytical examples, we show that a key effect
of the archive is that it counterbalances the exploration biases that result,
among other factors, from the use of inadequate behavior metrics and the
non-linearities of the behavior mapping. Our observations seem to hint that
attributing a more active role to the archive in sampling can be beneficial.

CNNs exhibit many behaviors different from humans, one of which is the
capability of employing high-frequency components. This paper discusses the
frequency bias phenomenon in image classification tasks: the high-frequency
components are actually much less exploited than the low- and mid-frequency
components. We first investigate the frequency bias phenomenon by presenting
two observations on feature discrimination and learning priority. Furthermore,
we hypothesize that (i) the spectral density, (ii) class consistency directly
affect the frequency bias. Specifically, our investigations verify that the
spectral density of datasets mainly affects the learning priority, while the
class consistency mainly affects the feature discrimination.

Mapping X-ray images, radiology reports, and other medical data as binary
codes in the common space, which can assist clinicians to retrieve
pathology-related data from heterogeneous modalities (i.e., hashing-based
cross-modal medical data retrieval), provides a new view to promot
computeraided diagnosis. Nevertheless, there remains a barrier to boost medical
retrieval accuracy: how to reveal the ambiguous semantics of medical data
without the distraction of superfluous information. To circumvent this
drawback, we propose Deep Supervised Information Bottleneck Hashing (DSIBH),
which effectively strengthens the discriminability of hash codes. Specifically,
the Deep Deterministic Information Bottleneck (Yu, Yu, and Principe 2021) for
single modality is extended to the cross-modal scenario. Benefiting from this,
the superfluous information is reduced, which facilitates the discriminability
of hash codes. Experimental results demonstrate the superior accuracy of the
proposed DSIBH compared with state-of-the-arts in cross-modal medical data
retrieval tasks.

Visual question answering (VQA) for remote sensing scene has great potential
in intelligent human-computer interaction system. Although VQA in computer
vision has been widely researched, VQA for remote sensing data (RSVQA) is still
in its infancy. There are two characteristics that need to be specially
considered for the RSVQA task. 1) No object annotations are available in RSVQA
datasets, which makes it difficult for models to exploit informative region
representation; 2) There are questions with clearly different difficulty levels
for each image in the RSVQA task. Directly training a model with questions in a
random order may confuse the model and limit the performance. To address these
two problems, in this paper, a multi-level visual feature learning method is
proposed to jointly extract language-guided holistic and regional image
features. Besides, a self-paced curriculum learning (SPCL)-based VQA model is
developed to train networks with samples in an easy-to-hard way. To be more
specific, a language-guided SPCL method with a soft weighting strategy is
explored in this work. The proposed model is evaluated on three public
datasets, and extensive experimental results show that the proposed RSVQA
framework can achieve promising performance.

In this paper, the minimization of the weighted sum average age of
information (AoI) in a multi-source status update communication system is
studied. Multiple independent sources send update packets to a common
destination node in a time-slotted manner under the limit of maximum
retransmission rounds. Different multiple access schemes, i.e., orthogonal
multiple access (OMA) and non-orthogonal multiple access (NOMA) are exploited
here over a block-fading multiple access channel (MAC). Constrained Markov
decision process (CMDP) problems are formulated to describe the AoI
minimization problems considering both transmission schemes. The Lagrangian
method is utilised to convert CMDP problems to unconstraint Markov decision
process (MDP) problems and corresponding algorithms to derive the power
allocation policies are obtained. On the other hand, for the case of unknown
environments, two online reinforcement learning approaches considering both
multiple access schemes are proposed to achieve near-optimal age performance.
Numerical simulations validate the improvement of the proposed policy in terms
of weighted sum AoI compared to the fixed power transmission policy, and
illustrate that NOMA is more favorable in case of larger packet size.

Addressing the annotation challenge in 3D Point Cloud segmentation has
inspired research into weakly supervised learning. Existing approaches mainly
focus on exploiting manifold and pseudo-labeling to make use of large unlabeled
data points. A fundamental challenge here lies in the large intra-class
variations of local geometric structure, resulting in subclasses within a
semantic class. In this work, we leverage this intuition and opt for
maintaining an individual classifier for each subclass. Technically, we design
a multi-prototype classifier, each prototype serves as the classifier weights
for one subclass. To enable effective updating of multi-prototype classifier
weights, we propose two constraints respectively for updating the prototypes
w.r.t. all point features and for encouraging the learning of diverse
prototypes. Experiments on weakly supervised 3D point cloud segmentation tasks
validate the efficacy of proposed method in particular at low-label regime. Our
hypothesis is also verified given the consistent discovery of semantic
subclasses at no cost of additional annotations.

Machine learning (ML) has emerged as a pervasive tool in science,
engineering, and beyond. Its success has also led to several synergies with
molecular dynamics (MD) simulations, which we use to identify and characterize
the major metastable states of molecular systems. Typically, we aim to
determine the relative stabilities of these states and how rapidly they
interchange. This information allows mechanistic descriptions of molecular
mechanisms, enables a quantitative comparison with experiments, and facilitates
their rational design. ML impacts all aspects of MD simulations -- from
analyzing the data and accelerating sampling to defining more efficient or more
accurate simulation models.

A recent line of works, initiated by Russo and Xu, has shown that the
generalization error of a learning algorithm can be upper bounded by
information measures. In most of the relevant works, the convergence rate of
the expected generalization error is in the form of O(sqrt{lambda/n}) where
lambda is some information-theoretic quantities such as the mutual information
between the data sample and the learned hypothesis. However, such a learning
rate is typically considered to be "slow", compared to a "fast rate" of O(1/n)
in many learning scenarios. In this work, we first show that the square root
does not necessarily imply a slow rate, and a fast rate (O(1/n)) result can
still be obtained using this bound under appropriate assumptions. Furthermore,
we identify the key conditions needed for the fast rate generalization error,
which we call the (eta,c)-central condition. Under this condition, we give
information-theoretic bounds on the generalization error and excess risk, with
a convergence rate of O(\lambda/{n}) for specific learning algorithms such as
empirical risk minimization. Finally, analytical examples are given to show the
effectiveness of the bounds.

In this work, we introduce a new perspective for learning transferable
content in multi-task imitation learning. Humans are able to transfer skills
and knowledge. If we can cycle to work and drive to the store, we can also
cycle to the store and drive to work. We take inspiration from this and
hypothesize the latent memory of a policy network can be disentangled into two
partitions. These contain either the knowledge of the environmental context for
the task or the generalizable skill needed to solve the task. This allows
improved training efficiency and better generalization over previously unseen
combinations of skills in the same environment, and the same task in unseen
  We used the proposed approach to train a disentangled agent for two different
multi-task IL environments. In both cases we out-performed the SOTA by 30% in
task success rate. We also demonstrated this for navigation on a real robot.

Supervised person re-identification methods rely heavily on high-quality
cross-camera training label. This significantly hinders the deployment of re-ID
models in real-world applications. The unsupervised person re-ID methods can
reduce the cost of data annotation, but their performance is still far lower
than the supervised ones. In this paper, we make full use of the auxiliary
information mined from the datasets for multi-modal feature learning, including
camera information, temporal information and spatial information. By analyzing
the style bias of cameras, the characteristics of pedestrians' motion
trajectories and the positions of camera network, this paper designs three
modules: Time-Overlapping Constraint (TOC), Spatio-Temporal Similarity (STS)
and Same-Camera Penalty (SCP) to exploit the auxiliary information. Auxiliary
information can improve the model performance and inference accuracy by
constructing association constraints or fusing with visual features. In
addition, this paper proposes three effective training tricks, including
Restricted Label Smoothing Cross Entropy Loss (RLSCE), Weight Adaptive Triplet
Loss (WATL) and Dynamic Training Iterations (DTI). The tricks achieve mAP of
72.4% and 81.1% on MARS and DukeMTMC-VideoReID, respectively. Combined with
auxiliary information exploiting modules, our methods achieve mAP of 89.9% on
DukeMTMC, where TOC, STS and SCP all contributed considerable performance
improvements. The method proposed by this paper outperforms most existing
unsupervised re-ID methods and narrows the gap between unsupervised and
supervised re-ID methods. Our code is at
https://github.com/tenghehan/AuxUSLReID.

Cardiorespiratory fitness is an established predictor of metabolic disease
and mortality. Fitness is directly measured as maximal oxygen consumption
(VO$_{2}max$), or indirectly assessed using heart rate responses to standard
exercise tests. However, such testing is costly and burdensome because it
requires specialized equipment such as treadmills and oxygen masks, limiting
its utility. Modern wearables capture dynamic real-world data which could
improve fitness prediction. In this work, we design algorithms and models that
convert raw wearable sensor data into cardiorespiratory fitness estimates. We
validate these estimates' ability to capture fitness profiles in free-living
conditions using the Fenland Study (N=11,059), along with its longitudinal
cohort (N=2,675), and a third external cohort using the UK Biobank Validation
Study (N=181) who underwent maximal VO$_{2}max$ testing, the gold standard
measurement of fitness. Our results show that the combination of wearables and
other biomarkers as inputs to neural networks yields a strong correlation to
ground truth in a holdout sample (r = 0.82, 95CI 0.80-0.83), outperforming
other approaches and models and detects fitness change over time (e.g., after 7
years). We also show how the model's latent space can be used for fitness-aware
patient subtyping paving the way to scalable interventions and personalized
trial recruitment. These results demonstrate the value of wearables for fitness
estimation that today can be measured only with laboratory tests.

With the gradual deployment of 5G and the continuous popularization of edge
intelligence (EI), the explosive growth of data on the edge of the network has
promoted the rapid development of 6G and ubiquitous intelligence (UbiI). This
article aims to explore a new method for modeling latency guarantees for UbiI
in 6G given 6G's extremely stochastic nature in terahertz (THz) environments,
THz channel tail behavior, and delay distribution tail characteristics
generated by the UBiI random component, and to find the optimal solution that
minimizes the end-to-end (E2E) delay of UbiI. In this article, the arrival
curve and service curve of network calculus can well characterize the
stochastic nature of wireless channels, the tail behavior of wireless systems
and the E2E service curve of network calculus can model the tail characteristic
of the delay distribution in UbiI. Specifically, we first propose demands and
challenges facing 6G, edge computing (EC), edge deep learning (DL), and UbiI.
Then, we propose the hierarchical architecture, the network model, and the
service delay model of the UbiI system based on network calculus. In addition,
two case studies demonstrate the usefulness and effectiveness of the network
calculus approach in analyzing and modeling the latency guarantee for UbiI in
6G. Finally, future open research issues regarding the latency guarantee for
UbiI in 6G are outlined.

Each utterance in multi-turn empathetic dialogues has features such as
emotion, keywords, and utterance-level meaning. Feature transitions between
utterances occur naturally. However, existing approaches fail to perceive the
transitions because they extract features for the context at the coarse-grained
level. To solve the above issue, we propose a novel approach of recognizing
feature transitions between utterances, which helps understand the dialogue
flow and better grasp the features of utterance that needs attention. Also, we
introduce a response generation strategy to help focus on emotion and keywords
related to appropriate features when generating responses. Experimental results
show that our approach outperforms baselines and especially, achieves
significant improvements on multi-turn dialogues.

Uncertainty quantification in a neural network is one of the most discussed
topics for safety-critical applications. Though Neural Networks (NNs) have
achieved state-of-the-art performance for many applications, they still provide
unreliable point predictions, which lack information about uncertainty
estimates. Among various methods to enable neural networks to estimate
uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short
period due to its simplicity. In this study, we present a new version of the
traditional dropout layer where we are able to fix the number of dropout
configurations. As such, each layer can take and apply the new dropout layer in
the MC method to quantify the uncertainty associated with NN predictions. We
conduct experiments on both toy and realistic datasets and compare the results
with the MC method using the traditional dropout layer. Performance analysis
utilizing uncertainty evaluation metrics corroborates that our dropout layer
offers better performance in most cases.

Classification tasks on labeled graph-structured data have many important
applications ranging from social recommendation to financial modeling. Deep
neural networks are increasingly being used for node classification on graphs,
wherein nodes with similar features have to be given the same label. Graph
convolutional networks (GCNs) are one such widely studied neural network
architecture that perform well on this task. However, powerful link-stealing
attacks on GCNs have recently shown that even with black-box access to the
trained model, inferring which links (or edges) are present in the training
graph is practical. In this paper, we present a new neural network architecture
called LPGNet for training on graphs with privacy-sensitive edges. LPGNet
provides differential privacy (DP) guarantees for edges using a novel design
for how graph edge structure is used during training. We empirically show that
LPGNet models often lie in the sweet spot between providing privacy and
utility: They can offer better utility than "trivially" private architectures
which use no edge information (e.g., vanilla MLPs) and better resilience
against existing link-stealing attacks than vanilla GCNs which use the full
edge structure. LPGNet also offers consistently better privacy-utility
tradeoffs than DPGCN, which is the state-of-the-art mechanism for retrofitting
differential privacy into conventional GCNs, in most of our evaluated datasets.

The integration of the modern Machine Learning (ML) models into remote
sensing and agriculture has expanded the scope of the application of satellite
images in the agriculture domain. In this paper, we present how the accuracy of
crop type identification improves as we move from
medium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution
(HSTR) satellite images. We further demonstrate that high spectral resolution
in satellite imagery can improve prediction performance for low spatial and
temporal resolutions (LSTR) images. The F1-score is increased by 7% when using
multispectral data of MSTR images as compared to the best results obtained from
HSTR images. Similarly, when crop season based time series of multispectral
data is used we observe an increase of 1.2% in the F1-score. The outcome
motivates further advancements in the field of synthetic band generation.

Machine Learning (ML) systems are getting increasingly popular, and drive
more and more applications and services in our daily life. This has led to
growing concerns over user privacy, since human interaction data typically
needs to be transmitted to the cloud in order to train and improve such
systems. Federated learning (FL) has recently emerged as a method for training
ML models on edge devices using sensitive user data and is seen as a way to
mitigate concerns over data privacy. However, since ML models are most commonly
trained with label supervision, we need a way to extract labels on edge to make
FL viable. In this work, we propose a strategy for training FL models using
positive and negative user feedback. We also design a novel framework to study
different noise patterns in user feedback, and explore how well standard
noise-robust objectives can help mitigate this noise when training models in a
federated setting. We evaluate our proposed training setup through detailed
experiments on two text classification datasets and analyze the effects of
varying levels of user reliability and feedback noise on model performance. We
show that our method improves substantially over a self-training baseline,
achieving performance closer to models trained with full supervision.

Phase equilibrium calculations are an essential part of numerical simulations
of multi-component multi-phase flow in porous media, accounting for the largest
share of the computational time. In this work, we introduce a GPUenabled, fast,
and parallel framework, PTFlash, that vectorizes algorithms required for
isothermal two-phase flash calculations using PyTorch, and can facilitate a
wide range of downstream applications. In addition, to further accelerate
PTFlash, we design two task-specific neural networks, one for predicting the
stability of given mixtures and the other for providing estimates of the
distribution coefficients, which are trained offline and help shorten
computation time by sidestepping stability analysis and reducing the number of
iterations to reach convergence. The evaluation of PTFlash was conducted on
three case studies involving hydrocarbons, CO 2 and N 2 , for which the phase
equilibrium was tested over a large range of temperature, pressure and
composition conditions, using the Soave-Redlich-Kwong (SRK) equation of state.
We compare PTFlash with an in-house thermodynamic library, Carnot, written in
C++ and performing flash calculations one by one on CPU. Results show speed-ups
on large scale calculations up to two order of magnitudes, while maintaining
perfect precision with the reference solution provided by Carnot.

This paper deals with the taking into account a given set of realizations as
constraints in the Kullback-Leibler minimum principle, which is used as a
probabilistic learning algorithm. This permits the effective integration of
data into predictive models. We consider the probabilistic learning of a random
vector that is made up of either a quantity of interest (unsupervised case) or
the couple of the quantity of interest and a control parameter (supervised
case). A training set of independent realizations of this random vector is
assumed to be given and to be generated with a prior probability measure that
is unknown. A target set of realizations of the QoI is available for the two
considered cases. The framework is the one of non-Gaussian problems in high
dimension. A functional approach is developed on the basis of a weak
formulation of the Fourier transform of probability measures (characteristic
functions). The construction makes it possible to take into account the target
set of realizations of the QoI in the Kullback-Leibler minimum principle. The
proposed approach allows for estimating the posterior probability measure of
the QoI (unsupervised case) or of the posterior joint probability measure of
the QoI with the control parameter (supervised case). The existence and the
uniqueness of the posterior probability measure is analyzed for the two cases.
The numerical aspects are detailed in order to facilitate the implementation of
the proposed method. The presented application in high dimension demonstrates
the efficiency and the robustness of the proposed algorithm.

This paper reviews gradient-based techniques to solve bilevel optimization
problems. Bilevel optimization is a general way to frame the learning of
systems that are implicitly defined through a quantity that they minimize. This
characterization can be applied to neural networks, optimizers, algorithmic
solvers and even physical systems, and allows for greater modeling flexibility
compared to an explicit definition of such systems. Here we focus on
gradient-based approaches that solve such problems. We distinguish them in two
categories: those rooted in implicit differentiation, and those that leverage
the equilibrium propagation theorem. We present the mathematical foundations
that are behind such methods, introduce the gradient-estimation algorithms in
detail and compare the competitive advantages of the different approaches.

Deep learning techniques have shown great potential in medical image
processing, particularly through accurate and reliable image segmentation on
magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which
allow the localization and diagnosis of lesions. However, training these
segmentation models requires a large number of manually annotated pixel-level
labels, which are time-consuming and labor-intensive, in contrast to
image-level labels that are easier to obtain. It is imperative to resolve this
problem through weakly-supervised semantic segmentation models using
image-level labels as supervision since it can significantly reduce human
annotation efforts. Most of the advanced solutions exploit class activation
mapping (CAM). However, the original CAMs rarely capture the precise boundaries
of lesions. In this study, we propose the strategy of multi-scale inference to
refine CAMs by reducing the detail loss in single-scale reasoning. For
segmentation, we develop a novel model named Mixed-UNet, which has two parallel
branches in the decoding phase. The results can be obtained after fusing the
extracted features from two branches. We evaluate the designed Mixed-UNet
against several prevalent deep learning-based segmentation approaches on our
dataset collected from the local hospital and public datasets. The validation
results demonstrate that our model surpasses available methods under the same
supervision level in the segmentation of various lesions from brain imaging.

Acute leukemia is a type of blood cancer with a high mortality rate. Current
therapeutic methods include bone marrow transplantation, supportive therapy,
and chemotherapy. Although a satisfactory remission of the disease can be
achieved, the risk of recurrence is still high. Therefore, novel treatments are
demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a
promising approach to treat and cure acute leukemia. To harness the therapeutic
potential of CAR-T cell therapy for blood diseases, reliable cell morphological
identification is crucial. Nevertheless, the identification of CAR-T cells is a
big challenge posed by their phenotypic similarity with other blood cells. To
address this substantial clinical challenge, herein we first construct a CAR-T
dataset with 500 original microscopy images after staining. Following that, we
create a novel integrated model called RCMNet (ResNet18 with CBAM and MHSA)
that combines the convolutional neural network (CNN) and Transformer. The model
shows 99.63% top-1 accuracy on the public dataset. Compared with previous
reports, our model obtains satisfactory results for image classification.
Although testing on the CAR-T cells dataset, a decent performance is observed,
which is attributed to the limited size of the dataset. Transfer learning is
adapted for RCMNet and a maximum of 83.36% accuracy has been achieved, which is
higher than other SOTA models. The study evaluates the effectiveness of RCMNet
on a big public dataset and translates it to a clinical dataset for diagnostic

Extractive Question Answering (EQA) is one of the most important tasks in
Machine Reading Comprehension (MRC), which can be solved by fine-tuning the
span selecting heads of Pre-trained Language Models (PLMs). However, most
existing approaches for MRC may perform poorly in the few-shot learning
scenario. To solve this issue, we propose a novel framework named Knowledge
Enhanced Contrastive Prompt-tuning (KECP). Instead of adding pointer heads to
PLMs, we introduce a seminal paradigm for EQA that transform the task into a
non-autoregressive Masked Language Modeling (MLM) generation problem.
Simultaneously, rich semantics from the external knowledge base (KB) and the
passage context are support for enhancing the representations of the query. In
addition, to boost the performance of PLMs, we jointly train the model by the
MLM and contrastive learning objectives. Experiments on multiple benchmarks
demonstrate that our method consistently outperforms state-of-the-art
approaches in few-shot settings by a large margin.

An autoencoder (AE) and a generative adversarial networks (GANs) are trained
only once on a one-dimensional (1D) lattice of 200 sites. Moreover, the AE
contains only one hidden layer consisting of two neurons and both the generator
and the discriminator of the GANs are made up of two neurons as well. The
training set employed to train both the considered unsupervised neural networks
(NN) is composed of two artificial configurations. Remarkably, despite their
simple architectures, both the built AE and GANs have precisely determined the
critical points of several models, including the three-dimensional (3D)
classical $O(3)$ model, the two-dimensional (2D) generalized classical XY
model, the 2D two-state Potts model, and the 1D Bose-Hubbard model. The results
presented here as well as that shown in {\it Eur. Phys. J. Plus {\bf 136}, 1116
(2021)} suggest that when phase transitions are considered, an elegant
universal neural network that is extremely efficient and is applicable to broad
physical systems can be constructed with ease. In particular, since a NN
trained with two configurations can be applied to many models, it is likely
that when machine learning is concerned, the majority of phase transitions
belong to a class having two elements, i.e. the Ising class.

Nonconvex regularization has been popularly used in low-rank matrix learning.
However, extending it for low-rank tensor learning is still computationally
expensive. To address this problem, we develop an efficient solver for use with
a nonconvex extension of the overlapped nuclear norm regularizer. Based on the
proximal average algorithm, the proposed algorithm can avoid expensive tensor
folding/unfolding operations. A special "sparse plus low-rank" structure is
maintained throughout the iterations, and allows fast computation of the
individual proximal steps. Empirical convergence is further improved with the
use of adaptive momentum. We provide convergence guarantees to critical points
on smooth losses and also on objectives satisfying the Kurdyka-{\L}ojasiewicz
condition. While the optimization problem is nonconvex and nonsmooth, we show
that its critical points still have good statistical performance on the tensor
completion problem. Experiments on various synthetic and real-world data sets
show that the proposed algorithm is efficient in both time and space and more
accurate than the existing state-of-the-art.

The data representation in a machine-learning model strongly influences its
performance. This becomes even more important for quantum machine learning
models implemented on noisy intermediate scale quantum (NISQ) devices. Encoding
high dimensional data into a quantum circuit for a NISQ device without any loss
of information is not trivial and brings a lot of challenges. While simple
encoding schemes (like single qubit rotational gates to encode high dimensional
data) often lead to information loss within the circuit, complex encoding
schemes with entanglement and data re-uploading lead to an increase in the
encoding gate count. This is not well-suited for NISQ devices. This work
proposes 'incremental data-uploading', a novel encoding pattern for high
dimensional data that tackles these challenges. We spread the encoding gates
for the feature vector of a given data point throughout the quantum circuit
with parameterized gates in between them. This encoding pattern results in a
better representation of data in the quantum circuit with a minimal
pre-processing requirement. We show the efficiency of our encoding pattern on a
classification task using the MNIST and Fashion-MNIST datasets, and compare
different encoding methods via classification accuracy and the effective
dimension of the model.

Continual learning is a challenging real-world problem for constructing a
mature AI system when data are provided in a streaming fashion. Despite recent
progress in continual classification, the researches of continual object
detection are impeded by the diverse sizes and numbers of objects in each
image. Different from previous works that tune the whole network for all tasks,
in this work, we present a simple and flexible framework for continual object
detection via pRotOtypical taSk corrElaTion guided gaTing mechAnism (ROSETTA).
Concretely, a unified framework is shared by all tasks while task-aware gates
are introduced to automatically select sub-models for specific tasks. In this
way, various knowledge can be successively memorized by storing their
corresponding sub-model weights in this system. To make ROSETTA automatically
determine which experience is available and useful, a prototypical task
correlation guided Gating Diversity Controller(GDC) is introduced to adaptively
adjust the diversity of gates for the new task based on class-specific
prototypes. GDC module computes class-to-class correlation matrix to depict the
cross-task correlation, and hereby activates more exclusive gates for the new
task if a significant domain gap is observed. Comprehensive experiments on
COCO-VOC, KITTI-Kitchen, class-incremental detection on VOC and sequential
learning of four tasks show that ROSETTA yields state-of-the-art performance on
both task-based and class-based continual object detection.

We describe a new software package for simulating channelised, high-time
resolution data streams from radio telescopes. The software simulates data from
the telescope and observing system taking into account the observation
strategy, receiver system and digitisation. The signatures of pulsars, fast
radio bursts and flare stars are modelled, including frequency-dependent
effects such as scattering and scintillation. We also simulate more generic
signals using spline curves and images. Models of radio frequency interference
include signals from satellites, terrestrial transmitters and impulsive,
broadband signals. The simulated signals can also be injected into real data
sets. Uses of this software include the production of machine learning training
data sets, development and testing of new algorithms to search for anomalous
patterns and to characterise processing pipelines.

Synthesizer is a type of electronic musical instrument that is now widely
used in modern music production and sound design. Each parameters configuration
of a synthesizer produces a unique timbre and can be viewed as a unique
instrument. The problem of estimating a set of parameters configuration that
best restore a sound timbre is an important yet complicated problem, i.e.: the
synthesizer parameters estimation problem. We proposed a multi-modal
deep-learning-based pipeline Sound2Synth, together with a network structure
Prime-Dilated Convolution (PDC) specially designed to solve this problem. Our
method achieved not only SOTA but also the first real-world applicable results
on Dexed synthesizer, a popular FM synthesizer.

In secure machine learning inference, most of the schemes assume that the
server is semi-honest (honestly following the protocol but attempting to infer
additional information). However, the server may be malicious (e.g., using a
low-quality model or deviating from the protocol) in the real world. Although a
few studies have considered a malicious server that deviates from the protocol,
they ignore the verification of model accuracy (where the malicious server uses
a low-quality model) meanwhile preserving the privacy of both the server's
model and the client's inputs. To address these issues, we propose
\textit{Fusion}, where the client mixes the public samples (which have known
query results) with their own samples to be queried as the inputs of
multi-party computation to jointly perform the secure inference. Since a server
that uses a low-quality model or deviates from the protocol can only produce
results that can be easily identified by the client, \textit{Fusion} forces the
server to behave honestly, thereby addressing all those aforementioned issues
without leveraging expensive cryptographic techniques. Our evaluation indicates
that \textit{Fusion} is 48.06$\times$ faster and uses 30.90$\times$ less
communication than the existing maliciously secure inference protocol (which
currently does not support the verification of the model accuracy). In
addition, to show the scalability, we conduct ImageNet-scale inference on the
practical ResNet50 model and it costs 8.678 minutes and 10.117 GiB of
communication in a WAN setting, which is 1.18$\times$ faster and has
2.64$\times$ less communication than those of the semi-honest protocol.

Video captioning aims to understand the spatio-temporal semantic concept of
the video and generate descriptive sentences. The de-facto approach to this
task dictates a text generator to learn from \textit{offline-extracted} motion
or appearance features from \textit{pre-trained} vision models. However, these
methods may suffer from the so-called \textbf{\textit{"couple"}} drawbacks on
both \textit{video spatio-temporal representation} and \textit{sentence
generation}. For the former, \textbf{\textit{"couple"}} means learning
spatio-temporal representation in a single model(3DCNN), resulting the problems
named \emph{disconnection in task/pre-train domain} and \emph{hard for
end-to-end training}. As for the latter, \textbf{\textit{"couple"}} means
treating the generation of visual semantic and syntax-related words equally. To
this end, we present $\mathcal{D}^{2}$ - a dual-level decoupled transformer
pipeline to solve the above drawbacks: \emph{(i)} for video spatio-temporal
representation, we decouple the process of it into
"first-spatial-then-temporal" paradigm, releasing the potential of using
dedicated model(\textit{e.g.} image-text pre-training) to connect the
pre-training and downstream tasks, and makes the entire model end-to-end
trainable. \emph{(ii)} for sentence generation, we propose \emph{Syntax-Aware
Decoder} to dynamically measure the contribution of visual semantic and
syntax-related words. Extensive experiments on three widely-used benchmarks
(MSVD, MSR-VTT and VATEX) have shown great potential of the proposed
$\mathcal{D}^{2}$ and surpassed the previous methods by a large margin in the
task of video captioning.

Surgery is a high-stakes domain where surgeons must navigate critical
anatomical structures and actively avoid potential complications while
achieving the main task at hand. Such surgical activity has been shown to
affect long-term patient outcomes. To better understand this relationship,
whose mechanics remain unknown for the majority of surgical procedures, we
hypothesize that the core elements of surgery must first be quantified in a
reliable, objective, and scalable manner. We believe this is a prerequisite for
the provision of surgical feedback and modulation of surgeon performance in
pursuit of improved patient outcomes. To holistically quantify surgeries, we
propose a unified deep learning framework, entitled Roboformer, which operates
exclusively on videos recorded during surgery to independently achieve multiple
tasks: surgical phase recognition (the what of surgery), gesture classification
and skills assessment (the how of surgery). We validated our framework on four
video-based datasets of two commonly-encountered types of steps (dissection and
suturing) within minimally-invasive robotic surgeries. We demonstrated that our
framework can generalize well to unseen videos, surgeons, medical centres, and
surgical procedures. We also found that our framework, which naturally lends
itself to explainable findings, identified relevant information when achieving
a particular task. These findings are likely to instill surgeons with more
confidence in our framework's behaviour, increasing the likelihood of clinical
adoption, and thus paving the way for more targeted surgical feedback.

Despite the success of deep learning in speech recognition, multi-dialect
speech recognition remains a difficult problem. Although dialect-specific
acoustic models are known to perform well in general, they are not easy to
maintain when dialect-specific data is scarce and the number of dialects for
each language is large. Therefore, a single unified acoustic model (AM) that
generalizes well for many dialects has been in demand. In this paper, we
propose a novel acoustic modeling technique for accurate multi-dialect speech
recognition with a single AM. Our proposed AM is dynamically adapted based on
both dialect information and its internal representation, which results in a
highly adaptive AM for handling multiple dialects simultaneously. We also
propose a simple but effective training method to deal with unseen dialects.
The experimental results on large scale speech datasets show that the proposed
AM outperforms all the previous ones, reducing word error rates (WERs) by 8.11%
relative compared to a single all-dialects AM and by 7.31% relative compared to
dialect-specific AMs.

With the availability of large-scale surveys like Kepler and TESS, there is a
pressing need for automated methods to classify light curves according to known
classes of variable stars. We introduce a new algorithm for classifying light
curves that compares 7000 time-series features to find those which most
effectively classify a given set of light curves. We apply our method to Kepler
light curves for stars with effective temperatures in the range 6500--10,000K.
We show that the sample can be meaningfully represented in an interpretable
five-dimensional feature space that separates seven major classes of light
curves (delta Scuti stars, gamma Doradus stars, RR Lyrae stars, rotational
variables, contact eclipsing binaries, detached eclipsing binaries, and
non-variables). We achieve a balanced classification accuracy of 82% on an
independent test set of Kepler stars using a Gaussian mixture model classifier.
We use our method to classify 12,000 Kepler light curves from Quarter 9 and
provide a catalogue of the results. We further outline a confidence heuristic
based on probability density with which to search our catalogue, and extract
candidate lists of correctly-classified variable stars.

We propose the generative adversarial neural operator (GANO), a generative
model paradigm for learning probabilities on infinite-dimensional function
spaces. The natural sciences and engineering are known to have many types of
data that are sampled from infinite-dimensional function spaces, where
classical finite-dimensional deep generative adversarial networks (GANs) may
not be directly applicable. GANO generalizes the GAN framework and allows for
the sampling of functions by learning push-forward operator maps in
infinite-dimensional spaces. GANO consists of two main components, a generator
neural operator and a discriminator neural functional. The inputs to the
generator are samples of functions from a user-specified probability measure,
e.g., Gaussian random field (GRF), and the generator outputs are synthetic data
functions. The input to the discriminator is either a real or synthetic data
function. In this work, we instantiate GANO using the Wasserstein criterion and
show how the Wasserstein loss can be computed in infinite-dimensional spaces.
We empirically study GANO in controlled cases where both input and output
functions are samples from GRFs and compare its performance to the
finite-dimensional counterpart GAN. We empirically study the efficacy of GANO
on real-world function data of volcanic activities and show its superior
performance over GAN.

We study the problem of $(\epsilon,\delta)$-differentially private learning
of linear predictors with convex losses. We provide results for two subclasses
of loss functions. The first case is when the loss is smooth and non-negative
but not necessarily Lipschitz (such as the squared loss). For this case, we
establish an upper bound on the excess population risk of
$\tilde{O}\left(\frac{\Vert w^*\Vert}{\sqrt{n}} + \min\left\{\frac{\Vert w^*
\Vert^2}{(n\epsilon)^{2/3}},\frac{\sqrt{d}\Vert
w^*\Vert^2}{n\epsilon}\right\}\right)$, where $n$ is the number of samples, $d$
is the dimension of the problem, and $w^*$ is the minimizer of the population
risk. Apart from the dependence on $\Vert w^\ast\Vert$, our bound is
essentially tight in all parameters. In particular, we show a lower bound of
$\tilde{\Omega}\left(\frac{1}{\sqrt{n}} + {\min\left\{\frac{\Vert
w^*\Vert^{4/3}}{(n\epsilon)^{2/3}}, \frac{\sqrt{d}\Vert
w^*\Vert}{n\epsilon}\right\}}\right)$. We also revisit the previously studied
case of Lipschitz losses [SSTT20]. For this case, we close the gap in the
existing work and show that the optimal rate is (up to log factors)
$\Theta\left(\frac{\Vert w^*\Vert}{\sqrt{n}} + \min\left\{\frac{\Vert
w^*\Vert}{\sqrt{n\epsilon}},\frac{\sqrt{\text{rank}}\Vert
w^*\Vert}{n\epsilon}\right\}\right)$, where $\text{rank}$ is the rank of the
design matrix. This improves over existing work in the high privacy regime.
Finally, our algorithms involve a private model selection approach that we
develop to enable attaining the stated rates without a-priori knowledge of
$\Vert w^*\Vert$.

Deep Generative Machine Learning Models have been growing in popularity
across the design community thanks to their ability to learn and mimic complex
data distributions. While early works are promising, further advancement will
depend on addressing several critical considerations such as design quality,
feasibility, novelty, and targeted inverse design. We propose the Design Target
Achievement Index (DTAI), a differentiable, tunable metric that scores a
design's ability to achieve designer-specified minimum performance targets. We
demonstrate that DTAI can drastically improve the performance of generated
designs when directly used as a training loss in Deep Generative Models. We
apply the DTAI loss to a Performance-Augmented Diverse GAN (PaDGAN) and
demonstrate superior generative performance compared to a set of baseline Deep
Generative Models including a Multi-Objective PaDGAN and specialized tabular
generation algorithms like the Conditional Tabular GAN (CTGAN). We further
enhance PaDGAN with an auxiliary feasibility classifier to encourage feasible
designs. To evaluate methods, we propose a comprehensive set of evaluation
metrics for generative methods that focus on feasibility, diversity, and
satisfaction of design performance targets. Methods are tested on a challenging
benchmarking problem: the FRAMED bicycle frame design dataset featuring
mixed-datatype parametric data, heavily skewed and multimodal distributions,
and ten competing performance objectives.

Semi-supervised learning (SSL) addresses the lack of labeled data by
exploiting large unlabeled data through pseudolabeling. However, in the
extremely low-label regime, pseudo labels could be incorrect, a.k.a. the
confirmation bias, and the pseudo labels will in turn harm the network
training. Recent studies combined finetuning (FT) from pretrained weights with
SSL to mitigate the challenges and claimed superior results in the low-label
regime. In this work, we first show that the better pretrained weights brought
in by FT account for the state-of-the-art performance, and importantly that
they are universally helpful to off-the-shelf semi-supervised learners. We
further argue that direct finetuning from pretrained weights is suboptimal due
to covariate shift and propose a contrastive target pretraining step to adapt
model weights towards target dataset. We carried out extensive experiments on
both classification and segmentation tasks by doing target pretraining then
followed by semi-supervised finetuning. The promising results validate the
efficacy of target pretraining for SSL, in particular in the low-label regime.

MRI-based modeling of brain networks has been widely used to understand
functional and structural interactions and connections among brain regions, and
factors that affect them, such as brain development and disease. Graph mining
on brain networks may facilitate the discovery of novel biomarkers for clinical
phenotypes and neurodegenerative diseases. Since brain networks derived from
functional and structural MRI describe the brain topology from different
perspectives, exploring a representation that combines these cross-modality
brain networks is non-trivial. Most current studies aim to extract a fused
representation of the two types of brain network by projecting the structural
network to the functional counterpart. Since the functional network is dynamic
and the structural network is static, mapping a static object to a dynamic
object is suboptimal. However, mapping in the opposite direction is not
feasible due to the non-negativity requirement of current graph learning
techniques. Here, we propose a novel graph learning framework, known as Deep
Signed Brain Networks (DSBN), with a signed graph encoder that, from an
opposite perspective, learns the cross-modality representations by projecting
the functional network to the structural counterpart. We validate our framework
on clinical phenotype and neurodegenerative disease prediction tasks using two
independent, publicly available datasets (HCP and OASIS). The experimental
results clearly demonstrate the advantages of our model compared to several

Graph Neural Networks (GNNs) have achieved tremendous success in a variety of
real-world applications by relying on the fixed graph data as input. However,
the initial input graph might not be optimal in terms of specific downstream
tasks, because of information scarcity, noise, adversarial attacks, or
discrepancies between the distribution in graph topology, features, and
groundtruth labels. In this paper, we propose a bi-level optimization-based
approach for learning the optimal graph structure via directly learning the
Personalized PageRank propagation matrix as well as the downstream
semi-supervised node classification simultaneously. We also explore a low-rank
approximation model for further reducing the time complexity. Empirical
evaluations show the superior efficacy and robustness of the proposed model
over all baseline methods.

We study a fully decentralized federated learning algorithm, which is a novel
gradient descent algorithm executed on a communication-based network. For
convenience, we refer to it as a network gradient descent (NGD) method. In the
NGD method, only statistics (e.g., parameter estimates) need to be
communicated, minimizing the risk of privacy. Meanwhile, different clients
communicate with each other directly according to a carefully designed network
structure without a central master. This greatly enhances the reliability of
the entire algorithm. Those nice properties inspire us to carefully study the
NGD method both theoretically and numerically. Theoretically, we start with a
classical linear regression model. We find that both the learning rate and the
network structure play significant roles in determining the NGD estimator's
statistical efficiency. The resulting NGD estimator can be statistically as
efficient as the global estimator, if the learning rate is sufficiently small
and the network structure is well balanced, even if the data are distributed
heterogeneously. Those interesting findings are then extended to general models
and loss functions. Extensive numerical studies are presented to corroborate
our theoretical findings. Classical deep learning models are also presented for
illustration purpose.

We study the covariate shift problem in the context of nonparametric
regression over a reproducing kernel Hilbert space (RKHS). We focus on two
natural families of covariate shift problems defined using the likelihood
ratios between the source and target distributions. When the likelihood ratios
are uniformly bounded, we prove that the kernel ridge regression (KRR)
estimator with a carefully chosen regularization parameter is minimax
rate-optimal (up to a log factor) for a large family of RKHSs with regular
kernel eigenvalues. Interestingly, KRR does not require full knowledge of the
likelihood ratio apart from an upper bound on it. In striking contrast to the
standard statistical setting without covariate shift, we also demonstrate that
a na\"\i ve estimator, which minimizes the empirical risk over the function
class, is strictly suboptimal under covariate shift as compared to KRR. We then
address the larger class of covariate shift problems where likelihood ratio is
possibly unbounded yet has a finite second moment. Here, we show via careful
simulations that KRR fails to attain the optimal rate. Instead, we propose a
reweighted KRR estimator that weights samples based on a careful truncation of
the likelihood ratios. Again, we are able to show that this estimator is
minimax optimal, up to logarithmic factors.

Pretrained Transformer based models finetuned on domain specific corpora have
changed the landscape of NLP. However, training or fine-tuning these models for
individual tasks can be time consuming and resource intensive. Thus, a lot of
current research is focused on using transformers for multi-task learning
(Raffel et al.,2020) and how to group the tasks to help a multi-task model to
learn effective representations that can be shared across tasks (Standley et
al., 2020; Fifty et al., 2021). In this work, we show that a single
multi-tasking model can match the performance of task specific models when the
task specific models show similar representations across all of their hidden
layers and their gradients are aligned, i.e. their gradients follow the same
direction. We hypothesize that the above observations explain the effectiveness
of multi-task learning. We validate our observations on our internal
radiologist-annotated datasets on the cervical and lumbar spine. Our method is
simple and intuitive, and can be used in a wide range of NLP problems.

Automatically determining knot number and positions is a fundamental and
challenging problem in B-spline approximation. In this paper, the knot
placement is abstracted as a mapping from initial knots to the optimal knots.
We innovatively introduce a deep neural network solver to approximate the
mapping. The neural network is composed of several subnetworks. Each subnetwork
is designed to approximate the optimal knot positions in the case of fixed knot
number. All the subnetworks are stacked together to find the optimal knots
(including knot number and knot positions) within some given tolerance. Owing
to the powerful approximation capabilities, as well as mature algorithms
developed in deep learning, the proposed method can effectively and efficiently
find the optimal knot number and knot positions. Several numerical examples are
demonstrated to show the superiority of our method.

Stride length estimation using inertial measurement unit (IMU) sensors is
getting popular recently as one representative gait parameter for health care
and sports training. The traditional estimation method requires some explicit
calibrations and design assumptions. Current deep learning methods suffer from
few labeled data problem. To solve above problems, this paper proposes a single
convolutional neural network (CNN) model to predict stride length of running
and walking and classify the running or walking type per stride. The model
trains its pretext task with self-supervised learning on a large unlabeled
dataset for feature learning, and its downstream task on the stride length
estimation and classification tasks with supervised learning with a small
labeled dataset. The proposed model can achieve better average percent error,
4.78\%, on running and walking stride length regression and 99.83\% accuracy on
running and walking classification, when compared to the previous approach,
7.44\% on the stride length estimation.

Built on our previous study on green simulation assisted policy gradient
(GS-PG) focusing on trajectory-based reuse, in this paper, we consider
infinite-horizon Markov Decision Processes and create a new importance sampling
based policy gradient optimization approach to support dynamic decision making.
The existing GS-PG method was designed to learn from complete episodes or
process trajectories, which limits its applicability to low-data situations and
flexible online process control. To overcome this limitation, the proposed
approach can selectively reuse the most related partial trajectories, i.e., the
reuse unit is based on per-step or per-decision historical observations. In
specific, we create a mixture likelihood ratio (MLR) based policy gradient
optimization that can leverage the information from historical state-action
transitions generated under different behavioral policies. The proposed
variance reduction experience replay (VRER) approach can intelligently select
and reuse most relevant transition observations, improve the policy gradient
estimation, and accelerate the learning of optimal policy. Our empirical study
demonstrates that it can improve optimization convergence and enhance the
performance of state-of-the-art policy optimization approaches such as
actor-critic method and proximal policy optimizations.

Presence of model uncertainties creates challenges for model-based control
design, and complexity of the control design is further exacerbated when coping
with nonlinear systems. This paper presents a sliding mode control (SMC) design
approach for nonlinear systems with partially known dynamics by blending
data-driven and model-based approaches. First, an SMC is designed for the
available (nominal) model of the nonlinear system. The closed-loop state
trajectory of the available model is used to build the desired trajectory for
the partially known nonlinear system states. Next, a deep policy gradient
method is used to cope with unknown parts of the system dynamics and adjust the
sliding mode control output to achieve a desired state trajectory. The
performance (and viability) of the proposed design approach is finally examined
through numerical examples.

Differential Privacy (DP) provides a formal framework for training machine
learning models with individual example level privacy. In the field of deep
learning, Differentially Private Stochastic Gradient Descent (DP-SGD) has
emerged as a popular private training algorithm. Unfortunately, the
computational cost of training large-scale models with DP-SGD is substantially
higher than non-private training. This is further exacerbated by the fact that
increasing the number of parameters leads to larger degradation in utility with
DP. In this work, we zoom in on the ImageNet dataset and demonstrate that,
similar to the non-private case, pre-training over-parameterized models on a
large public dataset can lead to substantial gains when the model is finetuned
privately. Moreover, by systematically comparing private and non-private models
across a range of large batch sizes, we find that similar to non-private
setting, choice of optimizer can further improve performance substantially with
DP. By using LAMB optimizer with DP-SGD we saw improvement of up to 20$\%$
points (absolute). Finally, we show that finetuning just the last layer for a
\emph{single step} in the full batch setting, combined with extremely
small-scale (near-zero) initialization leads to both SOTA results of 81.7 $\%$
under a wide privacy budget range of $\epsilon \in [4, 10]$ and $\delta$ =
$10^{-6}$ while minimizing the computational overhead substantially.

Accelerated discovery with machine learning (ML) has begun to provide the
advances in efficiency needed to overcome the combinatorial challenge of
computational materials design. Nevertheless, ML-accelerated discovery both
inherits the biases of training data derived from density functional theory
(DFT) and leads to many attempted calculations that are doomed to fail. Many
compelling functional materials and catalytic processes involve strained
chemical bonds, open-shell radicals and diradicals, or metal-organic bonds to
open-shell transition-metal centers. Although promising targets, these
materials present unique challenges for electronic structure methods and
combinatorial challenges for their discovery. In this Perspective, we describe
the advances needed in accuracy, efficiency, and approach beyond what is
typical in conventional DFT-based ML workflows. These challenges have begun to
be addressed through ML models trained to predict the results of multiple
methods or the differences between them, enabling quantitative sensitivity
analysis. For DFT to be trusted for a given data point in a high-throughput
screen, it must pass a series of tests. ML models that predict the likelihood
of calculation success and detect the presence of strong correlation will
enable rapid diagnoses and adaptation strategies. These "decision engines"
represent the first steps toward autonomous workflows that avoid the need for
expert determination of the robustness of DFT-based materials discoveries.

We present Bayesian Team Imitation Learner (BTIL), an imitation learning
algorithm to model the behavior of teams performing sequential tasks in
Markovian domains. In contrast to existing multi-agent imitation learning
techniques, BTIL explicitly models and infers the time-varying mental states of
team members, thereby enabling learning of decentralized team policies from
demonstrations of suboptimal teamwork. Further, to allow for sample- and
label-efficient policy learning from small datasets, BTIL employs a Bayesian
perspective and is capable of learning from semi-supervised demonstrations. We
demonstrate and benchmark the performance of BTIL on synthetic multi-agent
tasks as well as a novel dataset of human-agent teamwork. Our experiments show
that BTIL can successfully learn team policies from demonstrations despite the
influence of team members' (time-varying and potentially misaligned) mental
states on their behavior.

This study aims to identify a set of indicators to estimate cognitive
workload using a multimodal sensing approach and machine learning. A set of
three cognitive tests were conducted to induce cognitive workload in twelve
participants at two levels of task difficulty (Easy and Hard). Four sensors
were used to measure the participants' physiological change, including,
Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and
blood oxygen saturation (SpO2). To understand the perceived cognitive workload,
NASA-TLX was used after each test and analysed using Chi-Square test. Three
well-know classifiers (LDA, SVM, and DT) were trained and tested independently
using the physiological data. The statistical analysis showed that
participants' perceived cognitive workload was significantly different
(p<0.001) between the tests, which demonstrated the validity of the
experimental conditions to induce different cognitive levels. Classification
results showed that a fusion of ECG and EDA presented good discriminating power
(acc=0.74) for cognitive workload detection. This study provides preliminary
results in the identification of a possible set of indicators of cognitive
workload. Future work needs to be carried out to validate the indicators using
more realistic scenarios and with a larger population.

This paper studies separating invariants: mappings on $d$-dimensional
semi-algebraic subsets of $D$ dimensional Euclidean domains which are invariant
to semi-algebraic group actions and separate orbits. The motivation for this
study comes from the usefulness of separating invariants in proving
universality of equivariant neural network architectures.
  We observe that in several cases the cardinality of separating invariants
proposed in the machine learning literature is much larger than the ambient
dimension $D$. As a result, the theoretical universal constructions based on
these separating invariants is unrealistically large. Our goal in this paper is
to resolve this issue.
  We show that when a continuous family of semi-algebraic separating invariants
is available, separation can be obtained by randomly selecting $2d+1 $ of these
invariants. We apply this methodology to obtain an efficient scheme for
computing separating invariants for several classical group actions which have
been studied in the invariant learning literature. Examples include matrix
multiplication actions on point clouds by permutations, rotations, and various
other linear groups.

We present the results of our autonomous racing virtual challenge, based on
the newly-released Learn-to-Race (L2R) simulation framework, which seeks to
encourage interdisciplinary research in autonomous driving and to help advance
the state of the art on a realistic benchmark. Analogous to racing being used
to test cutting-edge vehicles, we envision autonomous racing to serve as a
particularly challenging proving ground for autonomous agents as: (i) they need
to make sub-second, safety-critical decisions in a complex, fast-changing
environment; and (ii) both perception and control must be robust to
distribution shifts, novel road features, and unseen obstacles. Thus, the main
goal of the challenge is to evaluate the joint safety, performance, and
generalisation capabilities of reinforcement learning agents on multi-modal
perception, through a two-stage process. In the first stage of the challenge,
we evaluate an autonomous agent's ability to drive as fast as possible, while
adhering to safety constraints. In the second stage, we additionally require
the agent to adapt to an unseen racetrack through safe exploration. In this
paper, we describe the new L2R Task 2.0 benchmark, with refined metrics and
baseline approaches. We also provide an overview of deployment, evaluation, and
rankings for the inaugural instance of the L2R Autonomous Racing Virtual
Challenge (supported by Carnegie Mellon University, Arrival Ltd., AICrowd,
Amazon Web Services, and Honda Research), which officially used the new L2R
Task 2.0 benchmark and received over 20,100 views, 437 active participants, 46
teams, and 733 model submissions -- from 88+ unique institutions, in 58+
different countries. Finally, we release leaderboard results from the challenge
and provide description of the two top-ranking approaches in cross-domain model
transfer, across multiple sensor configurations and simulated races.

Satisfactory software performance is essential for the adoption and the
success of a product. In organizations that follow traditional software
development models (e.g., waterfall), Software Performance Engineering (SPE)
involves time-consuming experimental modeling and performance testing outside
the actual production environment. Such existing SPE methods, however, are not
optimized for environments utilizing Continuous Integration (CI) and Continuous
Delivery (CD) that result in high frequency and high volume of code changes. We
present a summary of lessons learned and propose improvements to the SPE
process in the context of CI/CD. Our findings are based on SPE work on products
A and B conducted over 5 years at an online services company X. We find that
(a) SPE has mainly become a post hoc activity based on data from the production
environment, (b) successful application of SPE techniques require frequent
re-evaluation of priorities, and (c) engineers working on SPE require a broader
skill set than one traditionally possessed by engineers working on performance.

Federated learning (FL) is a promising solution to enable many AI
applications, where sensitive datasets from distributed clients are needed for
collaboratively training a global model. FL allows the clients to participate
in the training phase, governed by a central server, without sharing their
local data. One of the main challenges of FL is the communication overhead,
where the model updates of the participating clients are sent to the central
server at each global training round. Over-the-air computation (AirComp) has
been recently proposed to alleviate the communication bottleneck where the
model updates are sent simultaneously over the multiple-access channel.
However, simple averaging of the model updates via AirComp makes the learning
process vulnerable to random or intended modifications of the local model
updates of some Byzantine clients. In this paper, we propose a transmission and
aggregation framework to reduce the effect of such attacks while preserving the
benefits of AirComp for FL. For the proposed robust approach, the central
server divides the participating clients randomly into groups and allocates a
transmission time slot for each group. The updates of the different groups are
then aggregated using a robust aggregation technique. We extend our approach to
handle the case of non-i.i.d. local data, where a resampling step is added
before robust aggregation. We analyze the convergence of the proposed approach
for both cases of i.i.d. and non-i.i.d. data and demonstrate that the proposed
algorithm converges at a linear rate to a neighborhood of the optimal solution.
Experiments on real datasets are provided to confirm the robustness of the

In the era of precision medicine, time-to-event outcomes such as time to
death or progression are routinely collected, along with high-throughput
covariates. These high-dimensional data defy classical survival regression
models, which are either infeasible to fit or likely to incur low
predictability due to over-fitting. To overcome this, recent emphasis has been
placed on developing novel approaches for feature selection and survival
prognostication. We will review various cutting-edge methods that handle
survival outcome data with high-dimensional predictors, highlighting recent
innovations in machine learning approaches for survival prediction. We will
cover the statistical intuitions and principles behind these methods and
conclude with extensions to more complex settings, where competing events are
observed. We exemplify these methods with applications to the Boston Lung
Cancer Survival Cohort study, one of the largest cancer epidemiology cohorts
investigating the complex mechanisms of lung cancer.

The field of machine learning has drawn increasing interest from various
other fields due to the success of its methods at solving a plethora of
different problems. An application of these has been to train artificial neural
networks to solve differential equations without the need of a numerical
solver. This particular application offers an alternative to conventional
numerical methods, with advantages such as lower memory required to store the
solutions, parallelization, and in some cases less overall computational cost
than its numerical counterparts. In this work, we train artificial neural
networks to represent a bundle of solutions of the differential equations that
govern the background dynamics of the Universe for four different models. The
models we have chosen are $\Lambda \mathrm{CDM}$, the
Chevallier-Polarski-Linder parametric dark energy model, a quintessence model
with an exponential potential, and the Hu-Sawicki $f\left(R\right)$ model. We
used the solutions that the networks provide to perform statistical analyses to
estimate the values of each model's parameters with observational data; namely,
estimates of the Hubble parameter from Cosmic Chronometers, the Supernovae type
Ia data from the Pantheon compilation, and measurements from Baryon Acoustic
Oscillations. The results we obtain for all models match similar estimations
done in the literature using numerical solvers. In addition, we estimated the
error of the solutions by comparing them to the analytical solution when there
is one, or to a high-precision numerical solution when there is not. Through
those estimations we found that the error of the solutions was at most
$\sim1\%$ in the region of the parameter space that concerns the $95\%$
confidence regions that we found using the data, for all models and all
statistical analyses performed in this work.

Learning personalized cancer treatment with machine learning holds great
promise to improve cancer patients' chance of survival. Despite recent advances
in machine learning and precision oncology, this approach remains challenging
as collecting data in preclinical/clinical studies for modeling multiple
treatment efficacies is often an expensive, time-consuming process. Moreover,
the randomization in treatment allocation proves to be suboptimal since some
participants/samples are not receiving the most appropriate treatments during
the trial. To address this challenge, we formulate drug screening study as a
"contextual bandit" problem, in which an algorithm selects anticancer
therapeutics based on contextual information about cancer cell lines while
adapting its treatment strategy to maximize treatment response in an "online"
fashion. We propose using a novel deep Bayesian bandits framework that uses
functional prior to approximate posterior for drug response prediction based on
multi-modal information consisting of genomic features and drug structure. We
empirically evaluate our method on three large-scale in vitro pharmacogenomic
datasets and show that our approach outperforms several benchmarks in
identifying optimal treatment for a given cell line.

In fashion-based recommendation settings, incorporating the item image
features is considered a crucial factor, and it has shown significant
improvements to many traditional models, including but not limited to matrix
factorization, auto-encoders, and nearest neighbor models. While there are
numerous image-based recommender approaches that utilize dedicated deep neural
networks, comparisons to attribute-aware models are often disregarded despite
their ability to be easily extended to leverage items' image features. In this
paper, we propose a simple yet effective attribute-aware model that
incorporates image features for better item representation learning in item
recommendation tasks. The proposed model utilizes items' image features
extracted by a calibrated ResNet50 component. We present an ablation study to
compare incorporating the image features using three different techniques into
the recommender system component that can seamlessly leverage any available
items' attributes. Experiments on two image-based real-world recommender
systems datasets show that the proposed model significantly outperforms all
state-of-the-art image-based models.

Many socio-linguistic cues are used in conversational analysis, such as
emotion, sentiment, and dialogue acts. One of the fundamental cues is
politeness, which linguistically possesses properties such as social manners
useful in conversational analysis. This article presents findings of polite
emotional dialogue act associations, where we can correlate the relationships
between the socio-linguistic cues. We confirm our hypothesis that the
utterances with the emotion classes Anger and Disgust are more likely to be
impolite. At the same time, Happiness and Sadness are more likely to be polite.
A less expectable phenomenon occurs with dialogue acts Inform and Commissive
which contain more polite utterances than Question and Directive. Finally, we
conclude on the future work of these findings to extend the learning of social
behaviours using politeness.

Few-shot learning (FSL) aims to learn new categories with a few visual
samples per class. Few-shot class representations are often biased due to data
scarcity. To mitigate this issue, we propose to generate visual samples based
on semantic embeddings using a conditional variational autoencoder (CVAE)
model. We train this CVAE model on base classes and use it to generate features
for novel classes. More importantly, we guide this VAE to strictly generate
representative samples by removing non-representative samples from the base
training set when training the CVAE model. We show that this training scheme
enhances the representativeness of the generated samples and therefore,
improves the few-shot classification results. Experimental results show that
our method improves three FSL baseline methods by substantial margins,
achieving state-of-the-art few-shot classification performance on miniImageNet
and tieredImageNet datasets for both 1-shot and 5-shot settings. Code is
available at: https://github.com/cvlab-stonybrook/fsl-rsvae.

This paper approaches the unsupervised learning problem by gradient descent
in the space of probability density functions. Our main result shows that along
the gradient flow induced by a distribution-dependent ordinary differential
equation (ODE), the unknown data distribution emerges as the long-time limit of
this flow of densities. That is, one can uncover the data distribution by
simulating the distribution-dependent ODE. Intriguingly, we find that the
simulation of the ODE is equivalent to the training of generative adversarial
networks (GANs). The GAN framework, by definition a non-cooperative game
between a generator and a discriminator, can therefore be viewed alternatively
as a cooperative game between a navigator and a calibrator (in collaboration to
simulate the ODE). At the theoretic level, this new perspective simplifies the
analysis of GANs and gives new insight into their performance. To construct a
solution to the distribution-dependent ODE, we first show that the associated
nonlinear Fokker-Planck equation has a unique weak solution, using the
Crandall-Liggett theorem for differential equations in Banach spaces. From this
solution to the Fokker-Planck equation, we construct a unique solution to the
ODE, relying on Trevisan's superposition principle. The convergence of the
induced gradient flow to the data distribution is obtained by analyzing the
Fokker-Planck equation.

Modeling and manipulating elasto-plastic objects are essential capabilities
for robots to perform complex industrial and household interaction tasks (e.g.,
stuffing dumplings, rolling sushi, and making pottery). However, due to the
high degree of freedom of elasto-plastic objects, significant challenges exist
in virtually every aspect of the robotic manipulation pipeline, e.g.,
representing the states, modeling the dynamics, and synthesizing the control
signals. We propose to tackle these challenges by employing a particle-based
representation for elasto-plastic objects in a model-based planning framework.
Our system, RoboCraft, only assumes access to raw RGBD visual observations. It
transforms the sensing data into particles and learns a particle-based dynamics
model using graph neural networks (GNNs) to capture the structure of the
underlying system. The learned model can then be coupled with model-predictive
control (MPC) algorithms to plan the robot's behavior. We show through
experiments that with just 10 minutes of real-world robotic interaction data,
our robot can learn a dynamics model that can be used to synthesize control
signals to deform elasto-plastic objects into various target shapes, including
shapes that the robot has never encountered before. We perform systematic
evaluations in both simulation and the real world to demonstrate the robot's
manipulation capabilities and ability to generalize to a more complex action
space, different tool shapes, and a mixture of motion modes. We also conduct
comparisons between RoboCraft and untrained human subjects controlling the
gripper to manipulate deformable objects in both simulation and the real world.
Our learned model-based planning framework is comparable to and sometimes
better than human subjects on the tested tasks.

Modern retail platforms rely heavily on Machine Learning (ML) for their search
and recommender systems. Thus, ML can potentially support efforts towards more
sustainable consumption patterns, for example, by accounting for sustainability
aspects in product search or recommendations. However, leveraging ML potential
for reaching sustainability goals requires data on sustainability.
Unfortunately, no open and publicly available database integrates
sustainability information on a product-by-product basis. In this work, we
present the GreenDB, which fills this gap. Based on search logs of millions of
users, we prioritize which products users care about most. The GreenDB schema
integrated into existing product catalogs to improve sustainability information
available for search and recommendation experiences. We present our proof of
concept implementation of a scraping system that creates the GreenDB dataset.

This paper introduces a framework designed to accurately predict piecewise
linear mappings of arbitrary meshes via a neural network, enabling training and
evaluating over heterogeneous collections of meshes that do not share a
triangulation, as well as producing highly detail-preserving maps whose
accuracy exceeds current state of the art. The framework is based on reducing
the neural aspect to a prediction of a matrix for a single given point,
conditioned on a global shape descriptor. The field of matrices is then
projected onto the tangent bundle of the given mesh, and used as candidate
jacobians for the predicted map. The map is computed by a standard Poisson
solve, implemented as a differentiable layer with cached pre-factorization for
efficient training. This construction is agnostic to the triangulation of the
input, thereby enabling applications on datasets with varying triangulations.
At the same time, by operating in the intrinsic gradient domain of each
individual mesh, it allows the framework to predict highly-accurate mappings.
We validate these properties by conducting experiments over a broad range of
scenarios, from semantic ones such as morphing, registration, and deformation
transfer, to optimization-based ones, such as emulating elastic deformations
and contact correction, as well as being the first work, to our knowledge, to
tackle the task of learning to compute UV parameterizations of arbitrary
meshes. The results exhibit the high accuracy of the method as well as its
versatility, as it is readily applied to the above scenarios without any
changes to the framework.

Physics-informed neural networks (PINNs) leverage neural-networks to find the
solutions of partial differential equation (PDE)-constrained optimization
problems with initial conditions and boundary conditions as soft constraints.
These soft constraints are often considered to be the sources of the complexity
in the training phase of PINNs. Here, we demonstrate that the challenge of
training (i) persists even when the boundary conditions are strictly enforced,
and (ii) is closely related to the Kolmogorov n-width associated with problems
demonstrating transport, convection, traveling waves, or moving fronts. Given
this realization, we describe the mechanism underlying the training schemes
such as those used in eXtended PINNs (XPINN), curriculum regularization, and
sequence-to-sequence learning. For an important category of PDEs, i.e.,
governed by non-linear convection-diffusion equation, we propose reformulating
PINNs on a Lagrangian frame of reference, i.e., LPINNs, as a PDE-informed
solution. A parallel architecture with two branches is proposed. One branch
solves for the state variables on the characteristics, and the second branch
solves for the low-dimensional characteristics curves. The proposed
architecture conforms to the causality innate to the convection, and leverages
the direction of travel of the information in the domain. Finally, we
demonstrate that the loss landscapes of LPINNs are less sensitive to the
so-called "complexity" of the problems, compared to those in the traditional
PINNs in the Eulerian framework.

Undiagnosed diabetes is present in 21.4% of adults with diabetes. Diabetes
can remain asymptomatic and undetected due to limitations in screening rates.
To address this issue, questionnaires, such as the American Diabetes
Association (ADA) Risk test, have been recommended for use by physicians and
the public. Based on evidence that blood glucose concentration can affect
cardiac electrophysiology, we hypothesized that an artificial intelligence
(AI)-enhanced electrocardiogram (ECG) could identify adults with new-onset
diabetes. We trained a neural network to estimate HbA1c using a 12-lead ECG and
readily available demographics. We retrospectively assembled a dataset
comprised of patients with paired ECG and HbA1c data. The population of
patients who receive both an ECG and HbA1c may a biased sample of the complete
outpatient population, so we adjusted the importance placed on each patient to
generate a more representative pseudo-population. We found ECG-based assessment
outperforms the ADA Risk test, achieving a higher area under the curve (0.80
vs. 0.68) and positive predictive value (14% vs. 9%) -- 2.6 times the
prevalence of diabetes in the cohort. The AI-enhanced ECG significantly
outperforms electrophysiologist interpretation of the ECG, suggesting that the
task is beyond current clinical capabilities. Given the prevalence of ECGs in
clinics and via wearable devices, such a tool would make precise, automated
diabetes assessment widely accessible.

Deep learning models for natural language processing (NLP) are increasingly
adopted and deployed by analysts without formal training in NLP or machine
learning (ML). However, the documentation intended to convey the model's
details and appropriate use is tailored primarily to individuals with ML or NLP
expertise. To address this gap, we conduct a design inquiry into interactive
model cards, which augment traditionally static model cards with affordances
for exploring model documentation and interacting with the models themselves.
Our investigation consists of an initial conceptual study with experts in ML,
NLP, and AI Ethics, followed by a separate evaluative study with non-expert
analysts who use ML models in their work. Using a semi-structured interview
format coupled with a think-aloud protocol, we collected feedback from a total
of 30 participants who engaged with different versions of standard and
interactive model cards. Through a thematic analysis of the collected data, we
identified several conceptual dimensions that summarize the strengths and
limitations of standard and interactive model cards, including: stakeholders;
design; guidance; understandability & interpretability; sensemaking &
skepticism; and trust & safety. Our findings demonstrate the importance of
carefully considered design and interactivity for orienting and supporting
non-expert analysts using deep learning models, along with a need for
consideration of broader sociotechnical contexts and organizational dynamics.
We have also identified design elements, such as language, visual cues, and
warnings, among others, that support interactivity and make non-interactive
content accessible. We summarize our findings as design guidelines and discuss
their implications for a human-centered approach towards AI/ML documentation.

Which object detector is suitable for your context sensitive task? Deep
object detectors exploit scene context for recognition differently. In this
paper, we group object detectors into 3 categories in terms of context use: no
context by cropping the input (RCNN), partial context by cropping the
featuremap (two-stage methods) and full context without any cropping
(single-stage methods). We systematically evaluate the effect of context for
each deep detector category. We create a fully controlled dataset for varying
context and investigate the context for deep detectors. We also evaluate
gradually removing the background context and the foreground object on MS COCO.
We demonstrate that single-stage and two-stage object detectors can and will
use the context by virtue of their large receptive field. Thus, choosing the
best object detector may depend on the application context.

The success of deep learning depends heavily on the availability of large
datasets, but in robotic manipulation there are many learning problems for
which such datasets do not exist. Collecting these datasets is time-consuming
and expensive, and therefore learning from small datasets is an important open
problem. Within computer vision, a common approach to a lack of data is data
augmentation. Data augmentation is the process of creating additional training
examples by modifying existing ones. However, because the types of tasks and
data differ, the methods used in computer vision cannot be easily adapted to
manipulation. Therefore, we propose a data augmentation method for robotic
manipulation. We argue that augmentations should be valid, relevant, and
diverse. We use these principles to formalize augmentation as an optimization
problem, with the objective function derived from physics and knowledge of the
manipulation domain. This method applies rigid body transformations to
trajectories of geometric state and action data. We test our method in two
scenarios: 1) learning the dynamics of planar pushing of rigid cylinders, and
2) learning a constraint checker for rope manipulation. These two scenarios
have different data and label types, yet in both scenarios, training on our
augmented data significantly improves performance on downstream tasks. We also
show how our augmentation method can be used on real-robot data to enable more
data-efficient online learning.

Automated disease detection in neuroimaging holds promise to improve the
diagnostic ability of radiologists, but routinely collected clinical data
frequently contains technical and demographic confounding factors that cause
data to both differ between sites and be systematically associated with the
disease of interest, thus negatively affecting the robustness of diagnostic
models. There is a critical need for diagnostic deep learning models that can
train on such imbalanced datasets without being influenced by these confounds.
In this work, we introduce a novel deep learning architecture, MUCRAN
(Multi-Confound Regression Adversarial Network), to train a deep learning model
on clinical brain MRI while regressing demographic and technical confounding
factors. We trained MUCRAN using 17,076 clinical T1 Axial brain MRIs collected
from Massachusetts General Hospital before 2019 and demonstrated that MUCRAN
could successfully regress major confounding factors in the vast clinical data.
We also applied a method for quantifying uncertainty across an ensemble of
these models to automatically exclude out-of-distribution data in the AD
detection. By combining MUCRAN and the uncertainty quantification method, we
showed consistent and significant increases in the AD detection accuracy for
newly collected MGH data (post-2019) and for data from other hospitals. MUCRAN
offers a generalizable approach for heterogenous clinical data for
deep-learning-based automatic disease detection.

Accurate virtual high-throughput screening (VHTS) of transition metal
complexes (TMCs) remains challenging due to the possibility of high
multi-reference (MR) character that complicates property evaluation. We compute
MR diagnostics for over 5,000 ligands present in previously synthesized
transition metal complexes in the Cambridge Structural Database (CSD). To
accomplish this task, we introduce an iterative approach for consistent ligand
charge assignment for ligands in the CSD. Across this set, we observe that MR
character correlates linearly with the inverse value of the averaged bond order
over all bonds in the molecule. We then demonstrate that ligand additivity of
MR character holds in TMCs, which suggests that the TMC MR character can be
inferred from the sum of the MR character of the ligands. Encouraged by this
observation, we leverage ligand additivity and develop a ligand-derived machine
learning representation to train neural networks to predict the MR character of
TMCs from properties of the constituent ligands. This approach yields models
with excellent performance and superior transferability to unseen ligand
chemistry and compositions.

Experiential learning has been known to be an engaging and effective modality
for personal and professional development. The Metaverse provides ample
opportunities for the creation of environments in which such experiential
learning can occur. In this work, we introduce a novel architecture that
combines Artificial intelligence and Virtual Reality to create a highly
immersive and efficient learning experience using avatars. The framework allows
us to measure the interpersonal effectiveness of an individual interacting with
the avatar. We first present a small pilot study and its results which were
used to enhance the framework. We then present a larger study using the
enhanced framework to measure, assess, and predict the interpersonal
effectiveness of individuals interacting with an avatar. Results reveal that
individuals with deficits in their interpersonal effectiveness show a
significant improvement in performance after multiple interactions with an
avatar. The results also reveal that individuals interact naturally with
avatars within this framework, and exhibit similar behavioral traits as they
would in the real world. We use this as a basis to analyze the underlying audio
and video data streams of individuals during these interactions. Finally, we
extract relevant features from these data and present a machine-learning based
approach to predict interpersonal effectiveness during human-avatar
conversation. We conclude by discussing the implications of these findings to
build beneficial applications for the real world.

Recent IR approaches based on Pretrained Language Models (PLM) have now
largely outperformed their predecessors on a variety of IR tasks. However, what
happens to learned word representations with distribution shifts remains
unclear. Recently, the BEIR benchmark was introduced to assess the performance
of neural rankers in zero-shot settings and revealed deficiencies for several
models. In complement to BEIR, we propose to control \textit{explicitly}
distribution shifts. We selected different query subsets leading to different
distribution shifts: short versus long queries, wh-words types of queries and 5
topic-based clusters. Then, we benchmarked state of the art neural rankers such
as dense Bi-Encoder, SPLADE and ColBERT under these different training and test
conditions. Our study demonstrates that it is possible to design distribution
shift experiments within the MSMARCO collection, and that the query subsets we
selected constitute an additional benchmark to better study factors of
generalization for various models.

We propose an unsupervised, mid-level representation for a generative model
of scenes. The representation is mid-level in that it is neither per-pixel nor
per-image; rather, scenes are modeled as a collection of spatial, depth-ordered
"blobs" of features. Blobs are differentiably placed onto a feature grid that
is decoded into an image by a generative adversarial network. Due to the
spatial uniformity of blobs and the locality inherent to convolution, our
network learns to associate different blobs with different entities in a scene
and to arrange these blobs to capture scene layout. We demonstrate this
emergent behavior by showing that, despite training without any supervision,
our method enables applications such as easy manipulation of objects within a
scene (e.g., moving, removing, and restyling furniture), creation of feasible
scenes given constraints (e.g., plausible rooms with drawers at a particular
location), and parsing of real-world images into constituent parts. On a
challenging multi-category dataset of indoor scenes, BlobGAN outperforms
StyleGAN2 in image quality as measured by FID. See our project page for video
results and interactive demo: https://www.dave.ml/blobgan

Differentiable physics has recently been shown as a powerful tool for solving
soft-body manipulation tasks. However, the differentiable physics solver often
gets stuck when the initial contact points of the end effectors are sub-optimal
or when performing multi-stage tasks that require contact point switching,
which often leads to local minima. To address this challenge, we propose a
contact point discovery approach (CPDeform) that guides the stand-alone
differentiable physics solver to deform various soft-body plasticines. The key
idea of our approach is to integrate optimal transport-based contact points
discovery into the differentiable physics solver to overcome the local minima
from initial contact points or contact switching. On single-stage tasks, our
method can automatically find suitable initial contact points based on
transport priorities. On complex multi-stage tasks, we can iteratively switch
the contact points of end-effectors based on transport priorities. To evaluate
the effectiveness of our method, we introduce PlasticineLab-M that extends the
existing differentiable physics benchmark PlasticineLab to seven new
challenging multi-stage soft-body manipulation tasks. Extensive experimental
results suggest that: 1) on multi-stage tasks that are infeasible for the
vanilla differentiable physics solver, our approach discovers contact points
that efficiently guide the solver to completion; 2) on tasks where the vanilla
solver performs sub-optimally or near-optimally, our contact point discovery
method performs better than or on par with the manipulation performance
obtained with handcrafted contact points.

This paper studies the problem of fixing malfunctional 3D objects. While
previous works focus on building passive perception models to learn the
functionality from static 3D objects, we argue that functionality is reckoned
with respect to the physical interactions between the object and the user.
Given a malfunctional object, humans can perform mental simulations to reason
about its functionality and figure out how to fix it. Inspired by this, we
propose FixIt, a dataset that contains about 5k poorly-designed 3D physical
objects paired with choices to fix them. To mimic humans' mental simulation
process, we present FixNet, a novel framework that seamlessly incorporates
perception and physical dynamics. Specifically, FixNet consists of a perception
module to extract the structured representation from the 3D point cloud, a
physical dynamics prediction module to simulate the results of interactions on
3D objects, and a functionality prediction module to evaluate the functionality
and choose the correct fix. Experimental results show that our framework
outperforms baseline models by a large margin, and can generalize well to
objects with similar interaction types.

We present cross-view transformers, an efficient attention-based model for
map-view semantic segmentation from multiple cameras. Our architecture
implicitly learns a mapping from individual camera views into a canonical
map-view representation using a camera-aware cross-view attention mechanism.
Each camera uses positional embeddings that depend on its intrinsic and
extrinsic calibration. These embeddings allow a transformer to learn the
mapping across different views without ever explicitly modeling it
geometrically. The architecture consists of a convolutional image encoder for
each view and cross-view transformer layers to infer a map-view semantic
segmentation. Our model is simple, easily parallelizable, and runs in
real-time. The presented architecture performs at state-of-the-art on the
nuScenes dataset, with 4x faster inference speeds. Code is available at
https://github.com/bradyz/cross_view_transformers.

Research suggests "write-to-learn" tasks improve learning outcomes, yet
constructed-response methods of formative assessment become unwieldy with large
class sizes. This study evaluates natural language processing algorithms to
assist this aim. Six short-answer tasks completed by 1,935 students were scored
by several human raters, using a detailed rubric, and an algorithm. Results
indicate substantial inter-rater agreement using quadratic weighted kappa for
rater pairs (each QWK > 0.74) and group consensus (Fleiss Kappa = 0.68).
Additionally, intra-rater agreement was estimated for one rater who had scored
178 responses seven years prior (QWK = 0.89). With compelling rater agreement,
the study then pilots cluster analysis of response text toward enabling
instructors to ascribe meaning to clusters as a means for scalable formative

In car-body production the pre-formed sheet metal parts of the body are
assembled on fully-automated production lines. The body passes through multiple
stations in succession, and is processed according to the order requirements.
The timely completion of orders depends on the individual station-based
operations concluding within their scheduled cycle times. If an error occurs in
one station, it can have a knock-on effect, resulting in delays on the
downstream stations. To the best of our knowledge, there exist no methods for
automatically distinguishing between source and knock-on errors in this
setting, as well as establishing a causal relation between them. Utilizing
real-time information about conditions collected by a production data
acquisition system, we propose a novel vehicle manufacturing analysis system,
which uses deep learning to establish a link between source and knock-on
errors. We benchmark three sequence-to-sequence models, and introduce a novel
composite time-weighted action metric for evaluating models in this context. We
evaluate our framework on a real-world car production dataset recorded by
Volkswagen Commercial Vehicles. Surprisingly we find that 71.68% of sequences
contain either a source or knock-on error. With respect to seq2seq model
training, we find that the Transformer demonstrates a better performance
compared to LSTM and GRU in this domain, in particular when the prediction
range with respect to the durations of future actions is increased.

We present an adaptive deep representation of volumetric fields of 3D shapes
and an efficient approach to learn this deep representation for high-quality 3D
shape reconstruction and auto-encoding. Our method encodes the volumetric field
of a 3D shape with an adaptive feature volume organized by an octree and
applies a compact multilayer perceptron network for mapping the features to the
field value at each 3D position. An encoder-decoder network is designed to
learn the adaptive feature volume based on the graph convolutions over the dual
graph of octree nodes. The core of our network is a new graph convolution
operator defined over a regular grid of features fused from irregular
neighboring octree nodes at different levels, which not only reduces the
computational and memory cost of the convolutions over irregular neighboring
octree nodes, but also improves the performance of feature learning. Our method
effectively encodes shape details, enables fast 3D shape reconstruction, and
exhibits good generality for modeling 3D shapes out of training categories. We
evaluate our method on a set of reconstruction tasks of 3D shapes and scenes
and validate its superiority over other existing approaches. Our code, data,
and trained models are available at https://wang-ps.github.io/dualocnn.

Agile maneuvers such as sprinting and high-speed turning in the wild are
challenging for legged robots. We present an end-to-end learned controller that
achieves record agility for the MIT Mini Cheetah, sustaining speeds up to 3.9
m/s. This system runs and turns fast on natural terrains like grass, ice, and
gravel and responds robustly to disturbances. Our controller is a neural
network trained in simulation via reinforcement learning and transferred to the
real world. The two key components are (i) an adaptive curriculum on velocity
commands and (ii) an online system identification strategy for sim-to-real
transfer leveraged from prior work. Videos of the robot's behaviors are
available at: https://agility.csail.mit.edu/

Molecular systems often remain trapped for long times around some local
minimum of the potential energy function, before switching to another one -- a
behavior known as metastability. Simulating transition paths linking one
metastable state to another one is difficult by direct numerical methods. In
view of the promises of machine learning techniques, we explore in this work
two approaches to more efficiently generate transition paths: sampling methods
based on generative models such as variational autoencoders, and importance
sampling methods based on reinforcement learning.

We propose to make the physical characteristics of a robot oscillate while it
learns to improve its behavioral performance. We consider quantities such as
mass, actuator strength, and size that are usually fixed in a robot, and show
that when those quantities oscillate at the beginning of the learning process
on a simulated 2D soft robot, the performance on a locomotion task can be
significantly improved. We investigate the dynamics of the phenomenon and
conclude that in our case, surprisingly, a high-frequency oscillation with a
large amplitude for a large portion of the learning duration leads to the
highest performance benefits. Furthermore, we show that morphological wobbling
significantly increases exploration of the search space.

We propose a quantum algorithm for `extremal learning', which is the process
of finding the input to a hidden function that extremizes the function output,
without having direct access to the hidden function, given only partial
input-output (training) data. The algorithm, called quantum extremal learning
(QEL), consists of a parametric quantum circuit that is variationally trained
to model data input-output relationships and where a trainable quantum feature
map, that encodes the input data, is analytically differentiated in order to
find the coordinate that extremizes the model. This enables the combination of
established quantum machine learning modelling with established quantum
optimization, on a single circuit/quantum computer. We have tested our
algorithm on a range of classical datasets based on either discrete or
continuous input variables, both of which are compatible with the algorithm. In
case of discrete variables, we test our algorithm on synthetic problems
formulated based on Max-Cut problem generators and also considering higher
order correlations in the input-output relationships. In case of the continuous
variables, we test our algorithm on synthetic datasets in 1D and simple
ordinary differential functions. We find that the algorithm is able to
successfully find the extremal value of such problems, even when the training
dataset is sparse or a small fraction of the input configuration space. We
additionally show how the algorithm can be used for much more general cases of
higher dimensionality, complex differential equations, and with full
flexibility in the choice of both modeling and optimization ansatz. We envision
that due to its general framework and simple construction, the QEL algorithm
will be able to solve a wide variety of applications in different fields,
opening up areas of further research.

Activity recognition in surgical videos is a key research area for developing
next-generation devices and workflow monitoring systems. Since surgeries are
long processes with highly-variable lengths, deep learning models used for
surgical videos often consist of a two-stage setup using a backbone and
temporal sequence model. In this paper, we investigate many state-of-the-art
backbones and temporal models to find architectures that yield the strongest
performance for surgical activity recognition. We first benchmark the models
performance on a large-scale activity recognition dataset containing over 800
surgery videos captured in multiple clinical operating rooms. We further
evaluate the models on the two smaller public datasets, the Cholec80 and
Cataract-101 datasets, containing only 80 and 101 videos respectively. We
empirically found that Swin-Transformer+BiGRU temporal model yielded strong
performance on both datasets. Finally, we investigate the adaptability of the
model to new domains by fine-tuning models to a new hospital and experimenting
with a recent unsupervised domain adaptation approach.

The analysis of electrocardiogram (ECG) signals can be time consuming as it
is performed manually by cardiologists. Therefore, automation through machine
learning (ML) classification is being increasingly proposed which would allow
ML models to learn the features of a heartbeat and detect abnormalities. The
lack of interpretability hinders the application of Deep Learning in
healthcare. Through interpretability of these models, we would understand how a
machine learning algorithm makes its decisions and what patterns are being
followed for classification. This thesis builds Convolutional Neural Network
(CNN) and Long Short-Term Memory (LSTM) classifiers based on state-of-the-art
models and compares their performance and interpretability to shallow
classifiers. Here, both global and local interpretability methods are exploited
to understand the interaction between dependent and independent variables
across the entire dataset and to examine model decisions in each sample,
respectively. Partial Dependence Plots, Shapley Additive Explanations,
Permutation Feature Importance, and Gradient Weighted Class Activation Maps
(Grad-Cam) are the four interpretability techniques implemented on time-series
ML models classifying ECG rhythms. In particular, we exploit Grad-Cam, which is
a local interpretability technique and examine whether its interpretability
varies between correctly and incorrectly classified ECG beats within each
class. Furthermore, the classifiers are evaluated using K-Fold cross-validation
and Leave Groups Out techniques, and we use non-parametric statistical testing
to examine whether differences are significant. It was found that Grad-CAM was
the most effective interpretability technique at explaining predictions of
proposed CNN and LSTM models. We concluded that all high performing classifiers
looked at the QRS complex of the ECG rhythm when making predictions.

Deep learning has a great potential for estimating biomarkers in diffusion
weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a
unique tool for modeling the spatio-temporal variability of biomarkers. In this
paper, we propose the first framework to exploit both deep learning and atlases
for biomarker estimation in dMRI. Our framework relies on non-linear diffusion
tensor registration to compute biomarker atlases and to estimate atlas
reliability maps. We also use nonlinear tensor registration to align the atlas
to a subject and to estimate the error of this alignment. We use the biomarker
atlas, atlas reliability map, and alignment error map, in addition to the dMRI
signal, as inputs to a deep learning model for biomarker estimation. We use our
framework to estimate fractional anisotropy and neurite orientation dispersion
from down-sampled dMRI data on a test cohort of 70 newborn subjects. Results
show that our method significantly outperforms standard estimation methods as
well as recent deep learning techniques. Our method is also more robust to
stronger measurement down-sampling factors. Our study shows that the advantages
of deep learning and atlases can be synergistically combined to achieve
unprecedented accuracy in biomarker estimation from dMRI data.

In most popular public accessible cryptocurrency systems, the mining pool
plays a key role because mining cryptocurrency with the mining pool turns the
non-profitable situation into profitable for individual miners. In many recent
novel blockchain consensuses, the deep learning training procedure becomes the
task for miners to prove their workload, thus the computation power of miners
will not purely be spent on the hash puzzle. In this way, the hardware and
energy will support the blockchain service and deep learning training
simultaneously. While the incentive of miners is to earn tokens, individual
miners are motivated to join mining pools to become more competitive. In this
paper, we are the first to demonstrate a mining pool solution for novel
consensuses based on deep learning.
  The mining pool manager partitions the full searching space into subspaces
and all miners are scheduled to collaborate on the Neural Architecture Search
(NAS) tasks in the assigned subspace. Experiments demonstrate that the
performance of this type of mining pool is more competitive than an individual
miner. Due to the uncertainty of miners' behaviors, the mining pool manager
checks the standard deviation of the performance of high reward miners and
prepares backup miners to ensure the completion of the tasks of high reward
miners.

Learning predictive models in new domains with scarce training data is a
growing challenge in modern supervised learning scenarios. This incentivizes
developing domain adaptation methods that leverage the knowledge in known
domains (source) and adapt to new domains (target) with a different probability
distribution. This becomes more challenging when the source and target domains
are in heterogeneous feature spaces, known as heterogeneous domain adaptation
(HDA). While most HDA methods utilize mathematical optimization to map source
and target data to a common space, they suffer from low transferability. Neural
representations have proven to be more transferable; however, they are mainly
designed for homogeneous environments. Drawing on the theory of domain
adaptation, we propose a novel framework, Heterogeneous Adversarial Neural
Domain Adaptation (HANDA), to effectively maximize the transferability in
heterogeneous environments. HANDA conducts feature and distribution alignment
in a unified neural network architecture and achieves domain invariance through
adversarial kernel learning. Three experiments were conducted to evaluate the
performance against the state-of-the-art HDA methods on major image and text
e-commerce benchmarks. HANDA shows statistically significant improvement in
predictive performance. The practical utility of HANDA was shown in real-world
dark web online markets. HANDA is an important step towards successful domain
adaptation in e-commerce applications.

Hypergraphs are important objects to model ternary or higher-order relations
of objects, and have a number of applications in analysing many complex
datasets occurring in practice. In this work we study a new heat diffusion
process in hypergraphs, and employ this process to design a polynomial-time
algorithm that approximately finds bipartite components in a hypergraph. We
theoretically prove the performance of our proposed algorithm, and compare it
against the previous state-of-the-art through extensive experimental analysis
on both synthetic and real-world datasets. We find that our new algorithm
consistently and significantly outperforms the previous state-of-the-art across
a wide range of hypergraphs.

Graph Convolutional Networks (GCNs) achieve an impressive performance due to
the remarkable representation ability in learning the graph information.
However, GCNs, when implemented on a deep network, require expensive
computation power, making them difficult to be deployed on battery-powered
devices. In contrast, Spiking Neural Networks (SNNs), which perform a
bio-fidelity inference process, offer an energy-efficient neural architecture.
In this work, we propose SpikingGCN, an end-to-end framework that aims to
integrate the embedding of GCNs with the biofidelity characteristics of SNNs.
The original graph data are encoded into spike trains based on the
incorporation of graph convolution. We further model biological information
processing by utilizing a fully connected layer combined with neuron nodes. In
a wide range of scenarios (e.g. citation networks, image graph classification,
and recommender systems), our experimental results show that the proposed
method could gain competitive performance against state-of-the-art approaches.
Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear
advantage of energy efficiency into graph data analysis, which demonstrates its
great potential to construct environment-friendly machine learning models.

Inspired by applications such as supply chain management, epidemics, and
social networks, we formulate a stochastic game model that addresses three key
features common across these domains: 1) network-structured player
interactions, 2) pair-wise mixed cooperation and competition among players, and
3) limited global information toward individual decision-making. In
combination, these features pose significant challenges for black box
approaches taken by deep learning-based multi-agent reinforcement learning
(MARL) algorithms and deserve more detailed analysis. We formulate a networked
stochastic game with pair-wise general sum objectives and asymmetrical
information structure, and empirically explore the effects of information
availability on the outcomes of different MARL paradigms such as individual
learning and centralized learning decentralized execution.

Numerical simulation is an essential tool in many areas of science and
engineering, but its performance often limits application in practice or when
used to explore large parameter spaces. On the other hand, surrogate deep
learning models, while accelerating simulations, often exhibit poor accuracy
and ability to generalise. In order to improve these two factors, we introduce
REMuS-GNN, a rotation-equivariant multi-scale model for simulating continuum
dynamical systems encompassing a range of length scales. REMuS-GNN is designed
to predict an output vector field from an input vector field on a physical
domain discretised into an unstructured set of nodes. Equivariance to rotations
of the domain is a desirable inductive bias that allows the network to learn
the underlying physics more efficiently, leading to improved accuracy and
generalisation compared with similar architectures that lack such symmetry. We
demonstrate and evaluate this method on the incompressible flow around
elliptical cylinders.

Open clusters are key coeval structures that help us understand star
formation, stellar evolution and trace the physical properties of our Galaxy.
In the past years, the isolation of open clusters from the field has been
heavily alleviated by the access to accurate large-scale stellar parallaxes and
proper motions along a determined line of sight. Still, there are limitations
regarding their completeness since large-scale studies rely on optical
wavelengths. Here we extend the open clusters sequences towards fainter
magnitudes complementing the Gaia photometric and astrometric information with
near-infrared data from the VVV survey. We performed a homogeneous analysis on
37 open clusters implementing two coarse-to-fine characterization methods:
extreme deconvolution Gaussian mixture models coupled with an unsupervised
machine learning method on 8-dimensional parameter space. The process allowed
us to separate the clusters from the field at near-infrared wavelengths. We
report an increase of $\sim$47\% new member candidates on average in our sample
(considering only sources with high membership probability p$\geqq$0.9). This
study is the second in a series intended to reveal open cluster near-infrared
sequences homogeneously.

Characterizing playing style is important for football clubs on scouting,
monitoring and match preparation. Previous studies considered a player's style
as a combination of technical performances, failing to consider the spatial
information. Therefore, this study aimed to characterize the playing styles of
each playing position in the Chinese Football Super League (CSL) matches,
integrating a recently adopted Player Vectors framework. Data of 960 matches
from 2016-2019 CSL were used. Match ratings, and ten types of match events with
the corresponding coordinates for all the lineup players whose on-pitch time
exceeded 45 minutes were extracted. Players were first clustered into 8
positions. A player vector was constructed for each player in each match based
on the Player Vectors using Nonnegative Matrix Factorization (NMF). Another NMF
process was run on the player vectors to extract different types of playing
styles. The resulting player vectors discovered 18 different playing styles in
the CSL. Six performance indicators of each style were investigated to observe
their contributions. In general, the playing styles of forwards and midfielders
are in line with football performance evolution trends, while the styles of
defenders should be reconsidered. Multifunctional playing styles were also
found in high rated CSL players.

In this paper, we present our MSR Hackathon 2022 project that replicates an
existing Gitter study using GrimoireLab. We compare the previous study's
pipeline with our GrimoireLab implementation in terms of speed, data
consistency, organization, and the learning curve to get started. We believe
our experience with GrimoireLab can help future researchers in making the right
choice while implementing their data pipelines over Gitter and Github data.

Federated learning is a machine learning training paradigm that enables
clients to jointly train models without sharing their own localized data.
However, the implementation of federated learning in practice still faces
numerous challenges, such as the large communication overhead due to the
repetitive server-client synchronization and the lack of adaptivity by
SGD-based model updates. Despite that various methods have been proposed for
reducing the communication cost by gradient compression or quantization, and
the federated versions of adaptive optimizers such as FedAdam are proposed to
add more adaptivity, the current federated learning framework still cannot
solve the aforementioned challenges all at once. In this paper, we propose a
novel communication-efficient adaptive federated learning method (FedCAMS) with
theoretical convergence guarantees. We show that in the nonconvex stochastic
optimization setting, our proposed FedCAMS achieves the same convergence rate
of $O(\frac{1}{\sqrt{TKm}})$ as its non-compressed counterparts. Extensive
experiments on various benchmarks verify our theoretical analysis.

The growing deployment efforts of 5G networks globally has led to the
acceleration of the businesses/services' digital transformation. This growth
has led to the need for new communication technologies that will promote this
transformation. 6G is being proposed as the set of technologies and
architectures that will achieve this target. Among the main use cases that have
emerged for 5G networks and will continue to play a pivotal role in 6G networks
is that of Intelligent Transportation Systems (ITSs). With all the projected
benefits of developing and deploying efficient and effective ITSs comes a group
of unique challenges that need to be addressed. One prominent challenge is ITS
orchestration due to the various supporting technologies and heterogeneous
networks used to offer the desired ITS applications/services. To that end, this
paper focuses on the ITS orchestration challenge in detail by highlighting the
related previous works from the literature and listing the lessons learned from
current ITS deployment orchestration efforts. It also presents multiple
potential data-driven research opportunities in which paradigms such as
reinforcement learning and federated learning can be deployed to offer
effective and efficient ITS orchestration.

We, as human beings, can understand and picture a familiar scene from
arbitrary viewpoints given a single image, whereas this is still a grand
challenge for computers. We hereby present a novel solution to mimic such human
perception capability based on a new paradigm of amodal 3D scene understanding
with neural rendering for a closed scene. Specifically, we first learn the
prior knowledge of the objects in a closed scene via an offline stage, which
facilitates an online stage to understand the room with unseen furniture
arrangement. During the online stage, given a panoramic image of the scene in
different layouts, we utilize a holistic neural-rendering-based optimization
framework to efficiently estimate the correct 3D scene layout and deliver
realistic free-viewpoint rendering. In order to handle the domain gap between
the offline and online stage, our method exploits compositional neural
rendering techniques for data augmentation in the offline training. The
experiments on both synthetic and real datasets demonstrate that our two-stage
design achieves robust 3D scene understanding and outperforms competing methods
by a large margin, and we also show that our realistic free-viewpoint rendering
enables various applications, including scene touring and editing. Code and
https://zju3dv.github.io/nr_in_a_room/.

Educating students from diverse disciplinary backgrounds is challenging. In
this article, we report on our interdisciplinary course coding interaction and
design (Coding IxD), which is designed for computer science and design students
alike. This course has been developed over several years by consciously
deliberating on existing hurdles within the educational concept. First, we
motivate the need for Coding IxD and introduce the teaching principles that
helped shape the course's general structure. Our teaching principles
materialize in four method-based phases derived from research through design.
Each phase consists of several methods that emerged to be suitable in an
interdisciplinary context. Then, based on two selected student projects, we
exemplify how interdisciplinary teams can arrive at novel interactive
prototypes. We conclude by reflecting on our teaching practice as essential for
a meaningful learning experience.

Transparency around limitations can improve the scientific rigor of research,
help ensure appropriate interpretation of research findings, and make research
claims more credible. Despite these benefits, the machine learning (ML)
research community lacks well-developed norms around disclosing and discussing
limitations. To address this gap, we conduct an iterative design process with
30 ML and ML-adjacent researchers to develop and test REAL ML, a set of guided
activities to help ML researchers recognize, explore, and articulate the
limitations of their research. Using a three-stage interview and survey study,
we identify ML researchers' perceptions of limitations, as well as the
challenges they face when recognizing, exploring, and articulating limitations.
We develop REAL ML to address some of these practical challenges, and highlight
additional cultural challenges that will require broader shifts in community
norms to address. We hope our study and REAL ML help move the ML research
community toward more active and appropriate engagement with limitations.

We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem
(ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs)
by interpolating between meta-learning and conventional deep kernel learning.
Our approach employs a bilevel optimization objective where we meta-learn
generally useful feature representations across tasks, in the sense that
task-specific GP models estimated on top of such features achieve the lowest
possible predictive loss on average. We solve the resulting nested optimization
problem using the implicit function theorem (IFT). We show that our ADKF-IFT
framework contains previously proposed Deep Kernel Learning (DKL) and Deep
Kernel Transfer (DKT) as special cases. Although ADKF-IFT is a completely
general method, we argue that it is especially well-suited for drug discovery
problems and demonstrate that it significantly outperforms previous
state-of-the-art methods on a variety of real-world few-shot molecular property
prediction tasks and out-of-domain molecular property prediction and
optimization tasks.

In this work, a multi-stage Machine Learning (ML) pipeline is proposed for
pipe leakage detection in an industrial environment. As opposed to other
industrial and urban environments, the environment under study includes many
interfering background noises, complicating the identification of leaks.
Furthermore, the harsh environmental conditions limit the amount of data
collected and impose the use of low-complexity algorithms. To address the
environment's constraints, the developed ML pipeline applies multiple steps,
each addressing the environment's challenges. The proposed ML pipeline first
reduces the data dimensionality by feature selection techniques and then
incorporates time correlations by extracting time-based features. The resultant
features are fed to a Support Vector Machine (SVM) of low-complexity that
generalizes well to a small amount of data. An extensive experimental procedure
was carried out on two datasets, one with background industrial noise and one
without, to evaluate the validity of the proposed pipeline. The SVM
hyper-parameters and parameters specific to the pipeline steps were tuned as
part of the experimental procedure. The best models obtained from the dataset
with industrial noise and leaks were applied to datasets without noise and with
and without leaks to test their generalizability. The results show that the
model produces excellent results with 99\% accuracy and an F1-score of 0.93 and
0.9 for the respective datasets.

Recent studies have shown that many deep metric learning loss functions
perform very similarly under the same experimental conditions. One potential
reason for this unexpected result is that all losses let the network focus on
similar image regions or properties. In this paper, we investigate this by
conducting a two-step analysis to extract and compare the learned visual
features of the same model architecture trained with different loss functions:
First, we compare the learned features on the pixel level by correlating
saliency maps of the same input images. Second, we compare the clustering of
embeddings for several image properties, e.g. object color or illumination. To
provide independent control over these properties, photo-realistic 3D car
renders similar to images in the Cars196 dataset are generated. In our
analysis, we compare 14 pretrained models from a recent study and find that,
even though all models perform similarly, different loss functions can guide
the model to learn different features. We especially find differences between
classification and ranking based losses. Our analysis also shows that some
seemingly irrelevant properties can have significant influence on the resulting
embedding. We encourage researchers from the deep metric learning community to
use our methods to get insights into the features learned by their proposed
methods.

Switched systems are capable of modeling processes with underlying dynamics
that may change abruptly over time. To achieve accurate modeling in practice,
one may need a large number of modes, but this may in turn increase the model
complexity drastically. Existing work on reducing system complexity mainly
considers state space reduction, yet reducing the number of modes is less
studied. In this work, we consider Markov jump linear systems (MJSs), a special
class of switched systems where the active mode switches according to a Markov
chain, and several issues associated with its mode complexity. Specifically,
inspired by clustering techniques from unsupervised learning, we are able to
construct a reduced MJS with fewer modes that approximates well the original
MJS under various metrics. Furthermore, both theoretically and empirically, we
show how one can use the reduced MJS to analyze stability and design
controllers with significant reduction in computational cost while achieving
guaranteed accuracy.

Deep acoustic models represent linguistic information based on massive
amounts of data. Unfortunately, for regional languages and dialects such
resources are mostly not available. However, deep acoustic models might have
learned linguistic information that transfers to low-resource languages. In
this study, we evaluate whether this is the case through the task of
distinguishing low-resource (Dutch) regional varieties. By extracting
embeddings from the hidden layers of various wav2vec 2.0 models (including new
models which are pre-trained and/or fine-tuned on Dutch) and using dynamic time
warping, we compute pairwise pronunciation differences averaged over 10 words
for over 100 individual dialects from four (regional) languages. We then
cluster the resulting difference matrix in four groups and compare these to a
gold standard, and a partitioning on the basis of comparing phonetic
transcriptions. Our results show that acoustic models outperform the
(traditional) transcription-based approach without requiring phonetic
transcriptions, with the best performance achieved by the multilingual XLSR-53
model fine-tuned on Dutch. On the basis of only six seconds of speech, the
resulting clustering closely matches the gold standard.

Knitted fabric exhibits avalanche-like events when deformed: by analogy with
eathquakes, we are interested in predicting these "knitquakes". However, as in
most analogous seismic models, the peculiar statistics of the corresponding
time-series severely jeopardize this endeavour, due to the time intermittence
and scale-invariance of these events. But more importantly, such predictions
are hard to {\it assess}: depending on the choice of what to predict, the
results can be very different and not easily compared. Furthermore, forecasting
models may be trained with various generic metrics which ignore some important
specificities of the problem at hand, in our case seismic risk. Finally, these
models often do not provide a clear strategy regarding the best way to use
these predictions in practice. Here we introduce a framework that allows to
design, evaluate and compare not only predictors but also decision-making
policies: a model seismically active {\it city} subjected to the crackling
dynamics observed in the mechanical response of knitted fabric. We thus proceed
to study the population of KnitCity, introducing a policy through which the
mayor of the town can decide to either keep people in, which in case of large
events cause human loss, or evacuate the city, which costs a daily fee. The
policy only relies on past seismic observations. We construct efficient
policies using a reinforcement learning environment and various time-series
predictors based on artificial neural networks. By inducing a physically
motivated metric on the predictors, this mechanism allows quantitative
assessment and comparison of their relevance in the decision-making process.

The coupled problem of hydrodynamics and solute transport for the
Najafi-Golestanian three-sphere swimmer is studied, with the Reynolds number
set to zero and P\'eclet numbers (Pe) ranging from 0.06 to 60. The adopted
method is the numerical simulation of the problem with a finite element code
based upon the FEniCS library. For the swimmer executing the optimal locomotion
gait, we report the Sherwood number as a function of Pe in homogeneous fluids
and confirm that little gain in solute flux is achieved by swimming unless Pe
is significantly larger than 10. We also consider the swimmer as an learning
agent moving inside a fluid that has a concentration gradient. The outcomes of
Q-learning processes show that learning locomotion (with the displacement as
reward) is significantly easier than learning chemotaxis (with the increase of
solute flux as reward). The chemotaxis problem, even at low Pe, has a varying
environment that renders learning more difficult. Further, the learning
difficulty increases severely with the P\'eclet number. The results demonstrate
the challenges that natural and artificial swimmers need to overcome to migrate
efficiently when exposed to chemical inhomogeneities.

This paper proposes LPC-AD, a fast and accurate multivariate time series
(MTS) anomaly detection method. LPC-AD is motivated by the ever-increasing
needs for fast and accurate MTS anomaly detection methods to support fast
troubleshooting in cloud computing, micro-service systems, etc. LPC-AD is fast
in the sense that its reduces the training time by as high as 38.2% compared to
the state-of-the-art (SOTA) deep learning methods that focus on training speed.
LPC-AD is accurate in the sense that it improves the detection accuracy by as
high as 18.9% compared to SOTA sophisticated deep learning methods that focus
on enhancing detection accuracy. Methodologically, LPC-AD contributes a generic
architecture LPC-Reconstruct for one to attain different trade-offs between
training speed and detection accuracy. More specifically, LPC-Reconstruct is
built on ideas from autoencoder for reducing redundancy in time series, latent
predictive coding for capturing temporal dependence in MTS, and randomized
perturbation for avoiding overfitting of anomalous dependence in the training
data. We present simple instantiations of LPC-Reconstruct to attain fast
training speed, where we propose a simple randomized perturbation method. The
superior performance of LPC-AD over SOTA methods is validated by extensive
experiments on four large real-world datasets. Experiment results also show the
necessity and benefit of each component of the LPC-Reconstruct architecture and
that LPC-AD is robust to hyper parameters.

We study the problem of performing classification in a manner that is fair
for sensitive groups, such as race and gender. This problem is tackled through
the lens of disentangled and locally fair representations. We learn a locally
fair representation, such that, under the learned representation, the
neighborhood of each sample is balanced in terms of the sensitive attribute.
For instance, when a decision is made to hire an individual, we ensure that the
$K$ most similar hired individuals are racially balanced. Crucially, we ensure
that similar individuals are found based on attributes not correlated to their
race. To this end, we disentangle the embedding space into two representations.
The first of which is correlated with the sensitive attribute while the second
is not. We apply our local fairness objective only to the second, uncorrelated,
representation. Through a set of experiments, we demonstrate the necessity of
both disentangled and local fairness for obtaining fair and accurate
representations. We evaluate our method on real-world settings such as
predicting income and re-incarceration rate and demonstrate the advantage of

Optical camera communications (OCC) has emerged as a key enabling technology
for the seamless operation of future autonomous vehicles. In this paper, we
introduce a spectral efficiency optimization approach in vehicular OCC.
Specifically, we aim at optimally adapting the modulation order and the
relative speed while respecting bit error rate and latency constraints. As the
optimization problem is NP-hard problem, we model the optimization problem as a
Markov decision process (MDP) to enable the use of solutions that can be
applied online. We then relaxed the constrained problem by employing Lagrange
relaxation approach before solving it by multi-agent deep reinforcement
learning (DRL). We verify the performance of our proposed scheme through
extensive simulations and compare it with various variants of our approach and
a random method. The evaluation shows that our system achieves significantly
higher sum spectral efficiency compared to schemes under comparison.

Understanding spatial relations is essential for intelligent agents to act
and communicate in the physical world. Relative directions are spatial
relations that describe the relative positions of target objects with regard to
the intrinsic orientation of reference objects. Grounding relative directions
is more difficult than grounding absolute directions because it not only
requires a model to detect objects in the image and to identify spatial
relation based on this information, but it also needs to recognize the
orientation of objects and integrate this information into the reasoning
process. We investigate the challenging problem of grounding relative
directions with end-to-end neural networks. To this end, we provide GRiD-3D, a
novel dataset that features relative directions and complements existing visual
question answering (VQA) datasets, such as CLEVR, that involve only absolute
directions. We also provide baselines for the dataset with two established
end-to-end VQA models. Experimental evaluations show that answering questions
on relative directions is feasible when questions in the dataset simulate the
necessary subtasks for grounding relative directions. We discover that those
subtasks are learned in an order that reflects the steps of an intuitive
pipeline for processing relative directions.

Content mismatch usually occurs when data from one modality is translated to
another, e.g. language learners producing mispronunciations (errors in speech)
when reading a sentence (target text) aloud. However, most existing alignment
algorithms assume the content involved in the two modalities is perfectly
matched and thus leading to difficulty in locating such mismatch between speech
and text. In this work, we develop an unsupervised learning algorithm that can
infer the relationship between content-mismatched cross-modal sequential data,
especially for speech-text sequences. More specifically, we propose a
hierarchical Bayesian deep learning model, named mismatch localization
variational autoencoder (ML-VAE), that decomposes the generative process of the
speech into hierarchically structured latent variables, indicating the
relationship between the two modalities. Training such a model is very
challenging due to the discrete latent variables with complex dependencies
involved. We propose a novel and effective training procedure which estimates
the hard assignments of the discrete latent variables over a specifically
designed lattice and updates the parameters of neural networks alternatively.
Our experimental results show that ML-VAE successfully locates the mismatch
between text and speech, without the need for human annotations for model
training.

The deep neural network is a research hotspot for histopathological image
analysis, which can improve the efficiency and accuracy of diagnosis for
pathologists or be used for disease screening. The whole slide pathological
image can reach one gigapixel and contains abundant tissue feature information,
which needs to be divided into a lot of patches in the training and inference
stages. This will lead to a long convergence time and large memory consumption.
Furthermore, well-annotated data sets are also in short supply in the field of
digital pathology. Inspired by the pathologist's clinical diagnosis process, we
propose a weakly supervised deep reinforcement learning framework, which can
greatly reduce the time required for network inference. We use neural network
to construct the search model and decision model of reinforcement learning
agent respectively. The search model predicts the next action through the image
features of different magnifications in the current field of view, and the
decision model is used to return the predicted probability of the current field
of view image. In addition, an expert-guided model is constructed by
multi-instance learning, which not only provides rewards for search model, but
also guides decision model learning by the knowledge distillation method.
Experimental results show that our proposed method can achieve fast inference
and accurate prediction of whole slide images without any pixel-level

In this paper, we consider a composite difference-of-convex (DC) program,
whose objective function is the sum of a smooth convex function with Lipschitz
continuous gradient, a proper closed and convex function, and a continuous
concave function. This problem has many applications in machine learning and
data science. The proximal DCA (pDCA), a special case of the classical DCA, as
well as two Nesterov-type extrapolated DCA -- ADCA (Phan et al.
IJCAI:1369--1375, 2018) and pDCAe (Wen et al. Comput Optim Appl 69:297--324,
2018) -- can solve this problem. The algorithmic step-sizes of pDCA, pDCAe, and
ADCA are fixed and determined by estimating a prior the smoothness parameter of
the loss function. However, such an estimate may be hard to obtain or poor in
some real-world applications. Motivated by this difficulty, we propose a
variable metric and Nesterov extrapolated proximal DCA with backtracking
(SPDCAe), which combines the backtracking line search procedure (not
necessarily monotone) and the Nesterov's extrapolation for potential
acceleration; moreover, the variable metric method is incorporated for better
local approximation. Numerical simulations on sparse binary logistic regression
and compressed sensing with Poisson noise demonstrate the effectiveness of our

Variational quantum algorithms (VQAs) have recently received significant
attention from the research community due to their promising performance in
Noisy Intermediate-Scale Quantum computers (NISQ). However, VQAs run on
parameterized quantum circuits (PQC) with randomly initialized parameters are
characterized by barren plateaus (BP) where the gradient vanishes exponentially
in the number of qubits. In this paper, we first review quantum natural
gradient (QNG), which is one of the most popular algorithms used in VQA, from
the classical first-order optimization point of view. Then, we proposed a
\underline{L}ook \underline{A}round \underline{W}arm-\underline{S}tart QNG
(LAWS) algorithm to mitigate the widespread existing BP issues. LAWS is a
combinatorial optimization strategy taking advantage of model parameter
initialization and fast convergence of QNG. LAWS repeatedly reinitializes
parameter search space for the next iteration parameter update. The
reinitialized parameter search space is carefully chosen by sampling the
gradient close to the current optimal. Moreover, we present a unified framework
(WS-SGD) for integrating parameter initialization techniques into the
optimizer. We provide the convergence proof of the proposed framework for both
convex and non-convex objective functions based on Polyak-Lojasiewicz (PL)
condition. Our experiment results show that the proposed algorithm could
mitigate the BP and have better generalization ability in quantum
classification problems.

Counting and sampling directed acyclic graphs from a Markov equivalence class
are fundamental tasks in graphical causal analysis. In this paper we show that
these tasks can be performed in polynomial time, solving a long-standing open
problem in this area. Our algorithms are effective and easily implementable. As
we show in experiments, these breakthroughs make thought-to-be-infeasible
strategies in active learning of causal structures and causal effect
identification with regard to a Markov equivalence class practically
applicable.

In federated learning for medical image analysis, the safety of the learning
protocol is paramount. Such settings can often be compromised by adversaries
that target either the private data used by the federation or the integrity of
the model itself. This requires the medical imaging community to develop
mechanisms to train collaborative models that are private and robust against
adversarial data. In response to these challenges, we propose a practical
open-source framework to study the effectiveness of combining differential
privacy, model compression and adversarial training to improve the robustness
of models against adversarial samples under train- and inference-time attacks.
Using our framework, we achieve competitive model performance, a significant
reduction in model's size and an improved empirical adversarial robustness
without a severe performance degradation, critical in medical image analysis.

Most real-world ecological dynamics, ranging from ecosystem dynamics to
collective animal movement, are inherently stochastic in nature. Stochastic
differential equations (SDEs) are a popular modelling framework to model
dynamics with intrinsic randomness. Here, we focus on the inverse question: If
one has empirically measured time-series data from some system of interest, is
it possible to discover the SDE model that best describes the data. Here, we
present PyDaddy (PYthon library for DAta Driven DYnamics), a toolbox to
construct and analyze interpretable SDE models based on time-series data. We
combine traditional approaches for data-driven SDE reconstruction with an
equation learning approach, to derive symbolic equations governing the
stochastic dynamics. The toolkit is presented as an open-source Python library,
and consists of tools to construct and analyze SDEs. Functionality is included
for visual examination of the stochastic structure of the data, guided
extraction of the functional form of the SDE, and diagnosis and debugging of
the underlying assumptions and the extracted model. Using simulated time-series
datasets, exhibiting a wide range of dynamics, we show that PyDaddy is able to
correctly identify underlying SDE models. We demonstrate the applicability of
the toolkit to real-world data using a previously published movement data of a
fish school. Starting from the time-series of the observed polarization of the
school, pyDaddy readily discovers the SDE model governing the dynamics of group
polarization. The model recovered by PyDaddy is consistent with the previous
study. In summary, stochastic and noise-induced effects are central to the
dynamics of many biological systems. In this context, we present an easy-to-use
package to reconstruct SDEs from timeseries data.

Decision making algorithms are used in a multitude of different applications.
Conventional approaches for designing decision algorithms employ principled and
simplified modelling, based on which one can determine decisions via tractable
optimization. More recently, deep learning approaches that use highly
parametric architectures tuned from data without relying on mathematical
models, are becoming increasingly popular. Model-based optimization and
data-centric deep learning are often considered to be distinct disciplines.
Here, we characterize them as edges of a continuous spectrum varying in
specificity and parameterization, and provide a tutorial-style presentation to
the methodologies lying in the middle ground of this spectrum, referred to as
model-based deep learning. We accompany our presentation with running examples
in super-resolution and stochastic control, and show how they are expressed
using the provided characterization and specialized in each of the detailed
methodologies. The gains of combining model-based optimization and deep
learning are demonstrated using experimental results in various applications,
ranging from biomedical imaging to digital communications.

We propose a novel learning-based formulation for visual localization of
vehicles that can operate in real-time in city-scale environments. Visual
localization algorithms determine the position and orientation from which an
image has been captured, using a set of geo-referenced images or a 3D scene
representation. Our new localization paradigm, named Implicit Pose Encoding
(ImPosing), embeds images and camera poses into a common latent representation
with 2 separate neural networks, such that we can compute a similarity score
for each image-pose pair. By evaluating candidates through the latent space in
a hierarchical manner, the camera position and orientation are not directly
regressed but incrementally refined. Very large environments force competitors
to store gigabytes of map data, whereas our method is very compact
independently of the reference database size. In this paper, we describe how to
effectively optimize our learned modules, how to combine them to achieve
real-time localization, and demonstrate results on diverse large scale
scenarios that significantly outperform prior work in accuracy and
computational efficiency.

Numerical simulators are essential tools in the study of natural
fluid-systems, but their performance often limits application in practice.
Recent machine-learning approaches have demonstrated their ability to
accelerate spatio-temporal predictions, although, with only moderate accuracy
in comparison. Here we introduce MultiScaleGNN, a novel multi-scale graph
neural network model for learning to infer unsteady continuum mechanics in
problems encompassing a range of length scales and complex boundary geometries.
We demonstrate this method on advection problems and incompressible fluid
dynamics, both fundamental phenomena in oceanic and atmospheric processes. Our
results show good extrapolation to new domain geometries and parameters for
long-term temporal simulations. Simulations obtained with MultiScaleGNN are
between two and four orders of magnitude faster than those on which it was
trained.

Recently, forecasting the crowd flows has become an important research topic,
and plentiful technologies have achieved good performances. As we all know, the
flow at a citywide level is in a mixed state with several basic patterns (e.g.,
commuting, working, and commercial) caused by the city area functional
distributions (e.g., developed commercial areas, educational areas and parks).
However, existing technologies have been criticized for their lack of
considering the differences in the flow patterns among regions since they want
to build only one comprehensive model to learn the mixed flow tensors.
Recognizing this limitation, we present a new perspective on flow prediction
and propose an explainable framework named ST-ExpertNet, which can adopt every
spatial-temporal model and train a set of functional experts devoted to
specific flow patterns. Technically, we train a bunch of experts based on the
Mixture of Experts (MoE), which guides each expert to specialize in different
kinds of flow patterns in sample spaces by using the gating network. We define
several criteria, including comprehensiveness, sparsity, and preciseness, to
construct the experts for better interpretability and performances. We conduct
experiments on a wide range of real-world taxi and bike datasets in Beijing and
NYC. The visualizations of the expert's intermediate results demonstrate that
our ST-ExpertNet successfully disentangles the city's mixed flow tensors along
with the city layout, e.g., the urban ring road structure. Different network
architectures, such as ST-ResNet, ConvLSTM, and CNN, have been adopted into our
ST-ExpertNet framework for experiments and the results demonstrates the
superiority of our framework in both interpretability and performances.

We present GANimator, a generative model that learns to synthesize novel
motions from a single, short motion sequence. GANimator generates motions that
resemble the core elements of the original motion, while simultaneously
synthesizing novel and diverse movements. Existing data-driven techniques for
motion synthesis require a large motion dataset which contains the desired and
specific skeletal structure. By contrast, GANimator only requires training on a
single motion sequence, enabling novel motion synthesis for a variety of
skeletal structures e.g., bipeds, quadropeds, hexapeds, and more. Our framework
contains a series of generative and adversarial neural networks, each
responsible for generating motions in a specific frame rate. The framework
progressively learns to synthesize motion from random noise, enabling
hierarchical control over the generated motion content across varying levels of
detail. We show a number of applications, including crowd simulation, key-frame
editing, style transfer, and interactive control, which all learn from a single
input sequence. Code and data for this paper are at
https://peizhuoli.github.io/ganimator.

We use energies and forces predicted within response operator based quantum
machine learning (OQML) to perform geometry optimization and transition state
search calculations with legacy optimizers. For randomly sampled initial
coordinates of small organic query molecules we report systematic improvement
of equilibrium and transition state geometry output as training set sizes
increase. Out-of-sample S$_\mathrm{N}$2 reactant complexes and transition state
geometries have been predicted using the LBFGS and the QST2 algorithm with an
RMSD of 0.16 and 0.4 \r{A} -- after training on up to 200 reactant complexes
relaxations and transition state search trajectories from the QMrxn20 data-set,
respectively. For geometry optimizations, we have also considered relaxation
paths up to 5'500 constitutional isomers with sum formula C$_7$H$_{10}$O$_2$
from the QM9-database. Using the resulting OQML models with an LBFGS optimizer
reproduces the minimum geometry with an RMSD of 0.14~\r{A}. For converged
equilibrium and transition state geometries subsequent vibrational normal mode
frequency analysis indicates deviation from MP2 reference results by on average
14 and 26\,cm$^{-1}$, respectively. While the numerical cost for OQML
predictions is negligible in comparison to DFT or MP2, the number of steps
until convergence is typically larger in either case. The success rate for
reaching convergence, however, improves systematically with training set size,
underscoring OQML's potential for universal applicability.

Hierarchical clustering recursively partitions data at an increasingly finer
granularity. In real-world applications, multi-view data have become
increasingly important. This raises a less investigated problem, i.e.,
multi-view hierarchical clustering, to better understand the hierarchical
structure of multi-view data. To this end, we propose a novel neural
network-based model, namely Contrastive Multi-view Hyperbolic Hierarchical
Clustering (CMHHC). It consists of three components, i.e., multi-view alignment
learning, aligned feature similarity learning, and continuous hyperbolic
hierarchical clustering. First, we align sample-level representations across
multiple views in a contrastive way to capture the view-invariance information.
Next, we utilize both the manifold and Euclidean similarities to improve the
metric property. Then, we embed the representations into a hyperbolic space and
optimize the hyperbolic embeddings via a continuous relaxation of hierarchical
clustering loss. Finally, a binary clustering tree is decoded from optimized
hyperbolic embeddings. Experimental results on five real-world datasets
demonstrate the effectiveness of the proposed method and its components.

Adversarial attack perturbs an image with an imperceptible noise, leading to
incorrect model prediction. Recently, a few works showed inherent bias
associated with such attack (robustness bias), where certain subgroups in a
dataset (e.g. based on class, gender, etc.) are less robust than others. This
bias not only persists even after adversarial training, but often results in
severe performance discrepancies across these subgroups. Existing works
characterize the subgroup's robustness bias by only checking individual
sample's proximity to the decision boundary. In this work, we argue that this
measure alone is not sufficient and validate our argument via extensive
experimental analysis. It has been observed that adversarial attacks often
corrupt the high-frequency components of the input image. We, therefore,
propose a holistic approach for quantifying adversarial vulnerability of a
sample by combining these different perspectives, i.e., degree of model's
reliance on high-frequency features and the (conventional) sample-distance to
the decision boundary. We demonstrate that by reliably estimating adversarial
vulnerability at the sample level using the proposed holistic metric, it is
possible to develop a trustworthy system where humans can be alerted about the
incoming samples that are highly likely to be misclassified at test time. This
is achieved with better precision when our holistic metric is used over
individual measures. To further corroborate the utility of the proposed
holistic approach, we perform knowledge distillation in a limited-sample
setting. We observe that the student network trained with the subset of samples
selected using our combined metric performs better than both the competing
baselines, viz., where samples are selected randomly or based on their
distances to the decision boundary.

Describing the causal relations governing a system is a fundamental task in
many scientific fields, ideally addressed by experimental studies. However,
obtaining data under intervention scenarios may not always be feasible, while
discovering causal relations from purely observational data is notoriously
challenging. In certain settings, such as genomics, we may have data from
heterogeneous study conditions, with soft (partial) interventions only
pertaining to a subset of the study variables, whose effects and targets are
possibly unknown. Combining data from experimental and observational studies
offers the opportunity to leverage both domains and improve on the
identifiability of causal structures. To this end, we define the interventional
BGe score for a mixture of observational and interventional data, where the
targets and effects of intervention may be unknown. To demonstrate the approach
we compare its performance to other state-of-the-art algorithms, both in
simulations and data analysis applications. Prerogative of our method is that
it takes a Bayesian perspective leading to a full characterisation of the
posterior distribution of the DAG structures. Given a sample of DAGs one can
also automatically derive full posterior distributions of the intervention
effects. Consequently the method effectively captures the uncertainty both in
the structure and the parameter estimates. Codes to reproduce the simulations
and analyses are publicly available at github.com/jackkuipers/iBGe

This paper tackles the challenge of forensic medical image matching (FMIM)
using deep neural networks (DNNs). FMIM is a particular case of content-based
image retrieval (CBIR). The main challenge in FMIM compared to the general case
of CBIR, is that the subject to whom a query image belongs may be affected by
aging and progressive degenerative disorders, making it difficult to match data
on a subject level. CBIR with DNNs is generally solved by minimizing a ranking
loss, such as Triplet loss (TL), computed on image representations extracted by
a DNN from the original data. TL, in particular, operates on triplets: anchor,
positive (similar to anchor) and negative (dissimilar to anchor). Although TL
has been shown to perform well in many CBIR tasks, it still has limitations,
which we identify and analyze in this work. In this paper, we introduce (i) the
AdaTriplet loss -- an extension of TL whose gradients adapt to different
difficulty levels of negative samples, and (ii) the AutoMargin method -- a
technique to adjust hyperparameters of margin-based losses such as TL and our
proposed loss dynamically. Our results are evaluated on two large-scale
benchmarks for FMIM based on the Osteoarthritis Initiative and Chest X-ray-14
datasets. The codes allowing replication of this study have been made publicly
available at \url{https://github.com/Oulu-IMEDS/AdaTriplet}.

We present a comprehensive work on automated veracity assessment from dataset
creation to developing novel methods based on Natural Language Inference (NLI),
focusing on misinformation related to the COVID-19 pandemic. We first describe
the construction of the novel PANACEA dataset consisting of heterogeneous
claims on COVID-19 and their respective information sources. The dataset
construction includes work on retrieval techniques and similarity measurements
to ensure a unique set of claims. We then propose novel techniques for
automated veracity assessment based on Natural Language Inference including
graph convolutional networks and attention based approaches. We have carried
out experiments on evidence retrieval and veracity assessment on the dataset
using the proposed techniques and found them competitive with SOTA methods, and
provided a detailed discussion.

Context. Stellar parameters are among the most important characteristics in
studies of stars, which are based on atmosphere models in traditional methods.
However, time cost and brightness limits restrain the efficiency of spectral
observations. The J-PLUS is an observational campaign that aims to obtain
photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a
valuable resource for studies of stars. Machine learning provides powerful
tools to efficiently analyse large data sets, such as the one from J-PLUS, and
enable us to expand the research domain to stellar parameters. Aims. The main
goal of this study is to construct a SVR algorithm to estimate stellar
parameters of the stars in the first data release of the J-PLUS observational
campaign. Methods. The training data for the parameters regressions is featured
with 12-waveband photometry from J-PLUS, and is cross-identified with
spectrum-based catalogs. These catalogs are from the LAMOST, the APOGEE, and
the SEGUE. We then label them with the stellar effective temperature, the
surface gravity and the metallicity. Ten percent of the sample is held out to
apply a blind test. We develop a new method, a multi-model approach in order to
fully take into account the uncertainties of both the magnitudes and stellar
parameters. The method utilizes more than two hundred models to apply the
uncertainty analysis. Results. We present a catalog of 2,493,424 stars with the
Root Mean Square Error of 160K in the effective temperature regression, 0.35 in
the surface gravity regression and 0.25 in the metallicity regression. We also
discuss the advantages of this multi-model approach and compare it to other
machine-learning methods.

Technology adoption in healthcare services has resulted in advancing care
delivery services and improving the experiences of patients. This paper
presents research that aims to find the important requirements for a remote
monitoring system for patients with COVID-19. As this pandemic is growing more
and more, there is a critical need for such systems. In this paper, the
requirements and the value are determined for the proposed system, which
integrates a smart bracelet that helps to signal patient vital signs. (376)
participants completed the online quantitative survey. According to the study
results, Most Healthcare Experts, (97.9%) stated that the automated wearable
device is very useful, it plays an essential role in routine healthcare tasks
(in early diagnosis, quarantine enforcement, and patient status monitoring),
and it simplifies their routine healthcare activities. I addition, the main
vital signs based on their expert opinion should include temperature (66% of
participants) and oxygenation level (95% of participants). These findings are
essential to any academic and industrial future efforts to develop these vital
wearable systems. The future work will involve implementing the design based on
the results of this study and use machine-learning algorithm to better detect
the COVID-19 cases based on the monitoring of vital signs and symptoms.

A high-dimensional and incomplete (HDI) matrix frequently appears in various
big-data-related applications, which demonstrates the inherently non-negative
interactions among numerous nodes. A non-negative latent factor (NLF) model
performs efficient representation learning to an HDI matrix, whose learning
process mostly relies on a single latent factor-dependent, non-negative and
multiplicative update (SLF-NMU) algorithm. However, an SLF-NMU algorithm
updates a latent factor based on the current update increment only without
appropriate considerations of past learning information, resulting in slow
convergence. Inspired by the prominent success of a proportional-integral (PI)
controller in various applications, this paper proposes a
Proportional-Integral-incorporated Non-negative Latent Factor (PI-NLF) model
with two-fold ideas: a) establishing an Increment Refinement (IR) mechanism via
considering the past update increments following the principle of a PI
controller; and b) designing an IR-based SLF-NMU (ISN) algorithm to accelerate
the convergence rate of a resultant model. Empirical studies on four HDI
datasets demonstrate that a PI-NLF model outperforms the state-of-the-art
models in both computational efficiency and estimation accuracy for missing
data of an HDI matrix. Hence, this study unveils the feasibility of boosting
the performance of a non-negative learning algorithm through an error feedback

Deep reinforcement learning (DRL) has made significant achievements in many
real-world applications. But these real-world applications typically can only
provide partial observations for making decisions due to occlusions and noisy
sensors. However, partial state observability can be used to hide malicious
behaviors for backdoors. In this paper, we explore the sequential nature of DRL
and propose a novel temporal-pattern backdoor attack to DRL, whose trigger is a
set of temporal constraints on a sequence of observations rather than a single
observation, and effect can be kept in a controllable duration rather than in
the instant. We validate our proposed backdoor attack to a typical job
scheduling task in cloud computing. Numerous experimental results show that our
backdoor can achieve excellent effectiveness, stealthiness, and sustainability.
Our backdoor's average clean data accuracy and attack success rate can reach
97.8% and 97.5%, respectively.

Iris Presentation Attack Detection (PAD) is essential to secure iris
recognition systems. Recent iris PAD solutions achieved good performance by
leveraging deep learning techniques. However, most results were reported under
intra-database scenarios and it is unclear if such solutions can generalize
well across databases and capture spectra. These PAD methods run the risk of
overfitting because of the binary label supervision during the network
training, which serves global information learning but weakens the capture of
local discriminative features. This chapter presents a novel attention-based
deep pixel-wise binary supervision (A-PBS) method. A-PBS utilizes pixel-wise
supervision to capture the fine-grained pixel/patch-level cues and attention
mechanism to guide the network to automatically find regions where most
contribute to an accurate PAD decision. Extensive experiments are performed on
six NIR and one visible-light iris databases to show the effectiveness and
robustness of proposed A-PBS methods. We additionally conduct extensive
experiments under intra-/cross-database and intra-/cross-spectrum for detailed
analysis. The results of our experiments indicates the generalizability of the
A-PBS iris PAD approach.

Byzantine Fault Tolerance (BFT) is among the most challenging problems in
Distributed Machine Learning (DML). Byzantine failures are still difficult to
tackle due to their unrestricted nature; as a result, the possibility of
generating arbitrary data. Extensive research efforts are persistently being
made to put into effect the BFT in DML. Some recent studies have been
undertaken to take into consideration various BFT approaches in DML. However,
certain aspects seem to be limited, such as the few analyzed approaches and the
absence of the techniques classification employed in the studied approaches. In
this paper, we present a survey of recent works surrounding BFT in DML. Mainly
in first-order optimization methods, especially Stochastic Gradient Descent
(SGD). We highlight the key techniques as well as fundamental approaches. We
offer an illustrative description of techniques used in BFT in DML, with a
proposed classification of BFTs approaches in the context of their basic
techniques. This classification is established on specific criteria such as
communication process, optimization method, and topology setting, which
characterize future work methods examining the current challenges of BFT in DML

Open-domain conversational systems are assumed to generate equally good
responses on multiple domains. Previous work achieved good performance on the
single corpus, but training and evaluating on multiple corpora from different
domains are less studied. This paper explores methods of generating relevant
responses for each of multiple multi-domain corpora. We first examine
interleaved learning which intermingles multiple corpora as the baseline. We
then investigate two multi-domain learning methods, labeled learning and
multi-task labeled learning, which encode each corpus through a unique corpus
embedding. Furthermore, we propose Domain-specific Frequency (DF), a novel
word-level importance weight that measures the relative importance of a word
for a specific corpus compared to other corpora. Based on DF, we propose
weighted learning, a method that integrates DF to the loss function. We also
adopt DF as a new evaluation metric. Extensive experiments show that our
methods gain significant improvements on both automatic and human evaluation.
We share our code and data for reproducibility

Deep neural networks are rapidly emerging as data analysis tools, often
outperforming the conventional techniques used in complex microfluidic systems.
One fundamental analysis frequently desired in microfluidic experiments is
counting and tracking the droplets. Specifically, droplet tracking in dense
emulsions is challenging as droplets move in tightly packed configurations.
Sometimes the individual droplets in these dense clusters are hard to resolve,
even for a human observer. Here, two deep learning-based cutting-edge
algorithms for object detection (YOLO) and object tracking (DeepSORT) are
combined into a single image analysis tool, DropTrack, to track droplets in
microfluidic experiments. DropTrack analyzes input videos, extracts droplets'
trajectories, and infers other observables of interest, such as droplet
numbers. Training an object detector network for droplet recognition with
manually annotated images is a labor-intensive task and a persistent
bottleneck. This work partly resolves this problem by training object detector
networks (YOLOv5) with hybrid datasets containing real and synthetic images. We
present an analysis of a double emulsion experiment as a case study to measure
DropTrack's performance. For our test case, the YOLO networks trained with 60%
synthetic images show similar performance in droplet counting as with the one
trained using 100% real images, meanwhile saving the image annotation work by
60%. DropTrack's performance is measured in terms of mean average precision
(mAP), mean square error in counting the droplets, and inference speed. The
fastest configuration of DropTrack runs inference at about 30 frames per
second, well within the standards for real-time image analysis.

Complex Word Identification (CWI) aims to detect words within a text that a
reader may find difficult to understand. It has been shown that CWI systems can
improve text simplification, readability prediction and vocabulary acquisition
modelling. However, the difficulty of a word is a highly idiosyncratic notion
that depends on a reader's first language, proficiency and reading experience.
In this paper, we show that personal models are best when predicting word
complexity for individual readers. We use a novel active learning framework
that allows models to be tailored to individuals and release a dataset of
complexity annotations and models as a benchmark for further research.

Cooperative multi-agent reinforcement learning (MARL) has made prominent
progress in recent years. For training efficiency and scalability, most of the
MARL algorithms make all agents share the same policy or value network.
However, in many complex multi-agent tasks, different agents are expected to
possess specific abilities to handle different subtasks. In those scenarios,
sharing parameters indiscriminately may lead to similar behavior across all
agents, which will limit the exploration efficiency and degrade the final
performance. To balance the training complexity and the diversity of agent
behavior, we propose a novel framework to learn dynamic subtask assignment
(LDSA) in cooperative MARL. Specifically, we first introduce a subtask encoder
to construct a vector representation for each subtask according to its
identity. To reasonably assign agents to different subtasks, we propose an
ability-based subtask selection strategy, which can dynamically group agents
with similar abilities into the same subtask. In this way, agents dealing with
the same subtask share their learning of specific abilities and different
subtasks correspond to different specific abilities. We further introduce two
regularizers to increase the representation difference between subtasks and
stabilize the training by discouraging agents from frequently changing
subtasks, respectively. Empirical results show that LDSA learns reasonable and
effective subtask assignment for better collaboration and significantly
improves the learning performance on the challenging StarCraft II
micromanagement benchmark and Google Research Football.

The statistical analysis of univariate quantiles is a well developed research
topic. However, there is a profound need for research in multivariate
quantiles. We tackle the topic of bivariate quantiles and bivariate quantile
regression using vine copulas. They are graph theoretical models identified by
a sequence of linked trees, which allow for separate modelling of marginal
distributions and the dependence structure. We introduce a novel graph
structure model (given by a tree sequence) specifically designed for a
symmetric treatment of two responses in a predictive regression setting. We
establish computational tractability of the model and a straight forward way of
obtaining different conditional distributions. Using vine copulas the typical
shortfalls of regression, as the need for transformations or interactions of
predictors, collinearity or quantile crossings are avoided. We illustrate the
copula based bivariate quantiles for different copula distributions and provide
a data set example. Further, the data example emphasizes the benefits of the
joint bivariate response modelling in contrast to two separate univariate
regressions or by assuming conditional independence, for bivariate response
data set in the presence of conditional dependence.

In this paper we address imbalanced binary classification (IBC) tasks.
Applying resampling strategies to balance the class distribution of training
instances is a common approach to tackle these problems. Many state-of-the-art
methods find instances of interest close to the decision boundary to drive the
resampling process. However, under-sampling the majority class may potentially
lead to important information loss. Over-sampling also may increase the chance
of overfitting by propagating the information contained in instances from the
minority class. The main contribution of our work is a new method called ICLL
for tackling IBC tasks which is not based on resampling training observations.
Instead, ICLL follows a layered learning paradigm to model the data in two
stages. In the first layer, ICLL learns to distinguish cases close to the
decision boundary from cases which are clearly from the majority class, where
this dichotomy is defined using a hierarchical clustering analysis. In the
subsequent layer, we use instances close to the decision boundary and instances
from the minority class to solve the original predictive task. A second
contribution of our work is the automatic definition of the layers which
comprise the layered learning strategy using a hierarchical clustering model.
This is a relevant discovery as this process is usually performed manually
according to domain knowledge. We carried out extensive experiments using 100
benchmark data sets. The results show that the proposed method leads to a
better performance relatively to several state-of-the-art methods for IBC.

Ischemic strokes are often caused by large vessel occlusions (LVOs), which
can be visualized and diagnosed with Computed Tomography Angiography scans. As
time is brain, a fast, accurate and automated diagnosis of these scans is
desirable. Human readers compare the left and right hemispheres in their
assessment of strokes. A large training data set is required for a standard
deep learning-based model to learn this strategy from data. As labeled medical
data in this field is rare, other approaches need to be developed. To both
include the prior knowledge of side comparison and increase the amount of
training data, we propose an augmentation method that generates artificial
training samples by recombining vessel tree segmentations of the hemispheres or
hemisphere subregions from different patients. The subregions cover vessels
commonly affected by LVOs, namely the internal carotid artery (ICA) and middle
cerebral artery (MCA). In line with the augmentation scheme, we use a
3D-DenseNet fed with task-specific input, fostering a side-by-side comparison
between the hemispheres. Furthermore, we propose an extension of that
architecture to process the individual hemisphere subregions. All
configurations predict the presence of an LVO, its side, and the affected
subregion. We show the effect of recombination as an augmentation strategy in a
5-fold cross validated ablation study. We enhanced the AUC for patient-wise
classification regarding the presence of an LVO of all investigated
architectures. For one variant, the proposed method improved the AUC from 0.73
without augmentation to 0.89. The best configuration detects LVOs with an AUC
of 0.91, LVOs in the ICA with an AUC of 0.96, and in the MCA with 0.91 while
accurately predicting the affected side.

Dialogue state tracking (DST) aims to predict the current dialogue state
given the dialogue history. Existing methods generally exploit the utterances
of all dialogue turns to assign value for each slot. This could lead to
suboptimal results due to the information introduced from irrelevant utterances
in the dialogue history, which may be useless and can even cause confusion. To
address this problem, we propose LUNA, a sLot-tUrN Alignment enhanced approach.
It first explicitly aligns each slot with its most relevant utterance, then
further predicts the corresponding value based on this aligned utterance
instead of all dialogue utterances. Furthermore, we design a slot ranking
auxiliary task to learn the temporal correlation among slots which could
facilitate the alignment. Comprehensive experiments are conducted on
multi-domain task-oriented dialogue datasets, i.e., MultiWOZ 2.0, MultiWOZ 2.1,
and MultiWOZ 2.2. The results show that LUNA achieves new state-of-the-art
results on these datasets.

The Heusler compounds have provided a playground of material candidates for
various technological applications based on their highly diverse and tunable
properties, controlled by chemical composition and crystal structure. However,
physical exploration of the Heusler chemical space en masse is impossible in
practice, hindering the exploration of the chemical composition vs. proprieties
relationship. Many of these applications are related to the Heuslers electron
transport characteristics, which are embedded in their electronic band
structure (EBS). Here we we created a Heuslers dataset using the Materials
Project (MP) database -- retrieving both chemical composition and their EBSs.
We then used machine learning to develop a model correlating the composition
vs. number of Dirac points in the EBS for Heuslers and also other Cubic
compounds by identifying said Dirac points using an automated algorithm as well
as generating chemical composition and global crystal structure features. Our
ML model captures the overall trend, as well as identifies significant
electronic and global crystal structure features, however, the ML model
suffered from significant variance due to the lack of site specific features.
Future work on a methodology for handling atomic site specific features will
allow ML models to better match the underlying quantum mechanics governing the
properties (also based on site specific properties) and capture the electronic
properties in a more generalized approach.

Real-time in-between motion generation is universally required in games and
highly desirable in existing animation pipelines. Its core challenge lies in
the need to satisfy three critical conditions simultaneously: quality,
controllability and speed, which renders any methods that need offline
computation (or post-processing) or cannot incorporate (often unpredictable)
user control undesirable. To this end, we propose a new real-time transition
method to address the aforementioned challenges. Our approach consists of two
key components: motion manifold and conditional transitioning. The former
learns the important low-level motion features and their dynamics; while the
latter synthesizes transitions conditioned on a target frame and the desired
transition duration. We first learn a motion manifold that explicitly models
the intrinsic transition stochasticity in human motions via a multi-modal
mapping mechanism. Then, during generation, we design a transition model which
is essentially a sampling strategy to sample from the learned manifold, based
on the target frame and the aimed transition duration. We validate our method
on different datasets in tasks where no post-processing or offline computation
is allowed. Through exhaustive evaluation and comparison, we show that our
method is able to generate high-quality motions measured under multiple
metrics. Our method is also robust under various target frames (with extreme
cases).

Deep learning is showing an increasing number of audience in medical imaging
research. In the segmentation task of medical images, we oftentimes rely on
volumetric data, and thus require the use of 3D architectures which are praised
for their ability to capture more features from the depth dimension. Yet, these
architectures are generally more ineffective in time and compute compared to
their 2D counterpart on account of 3D convolutions, max pooling,
up-convolutions, and other operations used in these networks. Moreover, there
are limited to no 3D pretrained model weights, and pretraining is generally
challenging. To alleviate these issues, we propose to cast volumetric data to
2D super images and use 2D networks for the segmentation task. The method
processes the 3D image by stitching slices side-by-side to generate a super
resolution image. While the depth information is lost, we expect that deep
neural networks can still capture and learn these features. Our goal in this
work is to introduce a new perspective when dealing with volumetric data, and
test our hypothesis using vanilla networks. We hope that this approach, while
achieving close enough results to 3D networks using only 2D counterparts, can
attract more related research in the future, especially in medical image
analysis since volumetric data is comparably limited.

6D object pose estimation is a crucial prerequisite for autonomous robot
manipulation applications. The state-of-the-art models for pose estimation are
convolutional neural network (CNN)-based. Lately, Transformers, an architecture
originally proposed for natural language processing, is achieving
state-of-the-art results in many computer vision tasks as well. Equipped with
the multi-head self-attention mechanism, Transformers enable simple
single-stage end-to-end architectures for learning object detection and 6D
object pose estimation jointly. In this work, we propose YOLOPose (short form
for You Only Look Once Pose estimation), a Transformer-based multi-object 6D
pose estimation method based on keypoint regression. In contrast to the
standard heatmaps for predicting keypoints in an image, we directly regress the
keypoints. Additionally, we employ a learnable orientation estimation module to
predict the orientation from the keypoints. Along with a separate translation
estimation module, our model is end-to-end differentiable. Our method is
suitable for real-time applications and achieves results comparable to

This paper deals with the utterance-level modalities missing problem with
uncertain patterns on emotion recognition in conversation (ERC) task. Present
models generally predict the speaker's emotions by its current utterance and
context, which is degraded by modality missing considerably. Our work proposes
a framework Missing-Modality Robust emotion Recognition (M2R2), which trains
emotion recognition model with iterative data augmentation by learned common
representation. Firstly, a network called Party Attentive Network (PANet) is
designed to classify emotions, which tracks all the speakers' states and
context. Attention mechanism between speaker with other participants and
dialogue topic is used to decentralize dependence on multi-time and multi-party
utterances instead of the possible incomplete one. Moreover, the Common
Representation Learning (CRL) problem is defined for modality-missing problem.
Data imputation methods improved by the adversarial strategy are used here to
construct extra features to augment data. Extensive experiments and case
studies validate the effectiveness of our methods over baselines for
modality-missing emotion recognition on two different datasets.

Recent engineering developments in specialised computational hardware,
data-acquisition and storage technology have seen the emergence of Machine
Learning (ML) as a powerful form of data analysis with widespread applicability
beyond its historical roots in the design of autonomous agents. However --
possibly because of its origins in the development of agents capable of
self-discovery -- relatively little attention has been paid to the interaction
between people and ML. In this paper we are concerned with the use of ML in
automated or semi-automated tools that assist one or more human decision
makers. We argue that requirements on both human and machine in this context
are significantly different to the use of ML either as part of autonomous
agents for self-discovery or as part statistical data analysis. Our principal
position is that the design of such human-machine systems should be driven by
repeated, two-way intelligibility of information rather than one-way
explainability of the ML-system's recommendations. Iterated rounds of
intelligible information exchange, we think, will characterise the kinds of
collaboration that will be needed to understand complex phenomena for which
neither man or machine have complete answers. We propose operational principles
-- we call them Intelligibility Axioms -- to guide the design of a
collaborative decision-support system. The principles are concerned with: (a)
what it means for information provided by the human to be intelligible to the
ML system; and (b) what it means for an explanation provided by an ML system to
be intelligible to a human. Using examples from the literature on the use of ML
for drug-design and in medicine, we demonstrate cases where the conditions of
the axioms are met. We describe some additional requirements needed for the
design of a truly collaborative decision-support system.

While deep neural networks are sensitive to adversarial noise, sparse coding
using the Basis Pursuit (BP) method is robust against such attacks, including
its multi-layer extensions. We prove that the stability theorem of BP holds
upon the following generalizations: (i) the regularization procedure can be
separated into disjoint groups with different weights, (ii) neurons or full
layers may form groups, and (iii) the regularizer takes various generalized
forms of the $\ell_1$ norm. This result provides the proof for the
architectural generalizations of Cazenavette et al. (2021), including (iv) an
approximation of the complete architecture as a shallow sparse coding network.
Due to this approximation, we settled to experimenting with shallow networks
and studied their robustness against the Iterative Fast Gradient Sign Method on
a synthetic dataset and MNIST. We introduce classification based on the
$\ell_2$ norms of the groups and show numerically that it can be accurate and
offers considerable speedups. In this family, linear transformer shows the best
performance. Based on the theoretical results and the numerical simulations, we
highlight numerical matters that may improve performance further.

This paper presents a framework to automate the labelling process for
gestures in musical performance videos with a 3D Convolutional Neural Network
(CNN). While this idea was proposed in a previous study, this paper introduces
several novelties: (i) Presents a novel method to overcome the class imbalance
challenge and make learning possible for co-existent gestures by batch
balancing approach and spatial-temporal representations of gestures. (ii)
Performs a detailed study on 7 and 18 categories of gestures generated during
the performance (guitar play) of musical pieces that have been video-recorded.
(iii) Investigates the possibility to use audio features. (iv) Extends the
analysis to multiple videos. The novel methods significantly improve the
performance of gesture identification by 12 %, when compared to the previous
work (51 % in this study over 39 % in previous work). We successfully validate
the proposed methods on 7 super classes (72 %), an ensemble of the 18
gestures/classes, and additional videos (75 %).

The cross-entropy objective has proved to be an all-purpose training
objective for autoregressive language models (LMs). However, without
considering the penalization of problematic tokens, LMs trained using
cross-entropy exhibit text degeneration. To address this, unlikelihood training
has been proposed to reduce the probability of unlikely tokens predicted by
LMs. But unlikelihood does not consider the relationship between the label
tokens and unlikely token candidates, thus showing marginal improvements in
degeneration. We propose a new contrastive token learning objective that
inherits the advantages of cross-entropy and unlikelihood training and avoids
their limitations. The key idea is to teach a LM to generate high probabilities
for label tokens and low probabilities of negative candidates. Comprehensive
experiments on language modeling and open-domain dialogue generation tasks show
that the proposed contrastive token objective yields much less repetitive
texts, with a higher generation quality than baseline approaches, achieving the
new state-of-the-art performance on text degeneration.

Consistency and complementarity are two key ingredients for boosting
multi-view clustering (MVC). Recently with the introduction of popular
contrastive learning, the consistency learning of views has been further
enhanced in MVC, leading to promising performance. However, by contrast, the
complementarity has not received sufficient attention except just in the
feature facet, where the Hilbert Schmidt Independence Criterion (HSIC) term or
the independent encoder-decoder network is usually adopted to capture
view-specific information. This motivates us to reconsider the complementarity
learning of views comprehensively from multiple facets including the feature-,
view-label- and contrast- facets, while maintaining the view consistency. We
empirically find that all the facets contribute to the complementarity
learning, especially the view-label facet, which is usually neglected by
existing methods. Based on this, we develop a novel \underline{M}ultifacet
\underline{C}omplementarity learning framework for
\underline{M}ulti-\underline{V}iew \underline{C}lustering (MCMVC), which fuses
multifacet complementarity information, especially explicitly embedding the
view-label information. To our best knowledge, it is the first time to use
view-labels explicitly to guide the complementarity learning of views. Compared
with the SOTA baseline, MCMVC achieves remarkable improvements, e.g., by
average margins over $5.00\%$ and $7.00\%$ respectively in complete and
incomplete MVC settings on Caltech101-20 in terms of three evaluation metrics.

While achieving remarkable success for medical image segmentation, deep
convolution neural networks (DCNNs) often fail to maintain their robustness
when confronting test data with the novel distribution. To address such a
drawback, the inductive bias of DCNNs is recently well-recognized.
Specifically, DCNNs exhibit an inductive bias towards image style (e.g.,
superficial texture) rather than invariant content (e.g., object shapes). In
this paper, we propose a method, named Invariant Content Synergistic Learning
(ICSL), to improve the generalization ability of DCNNs on unseen datasets by
controlling the inductive bias. First, ICSL mixes the style of training
instances to perturb the training distribution. That is to say, more diverse
domains or styles would be made available for training DCNNs. Based on the
perturbed distribution, we carefully design a dual-branches invariant content
synergistic learning strategy to prevent style-biased predictions and focus
more on the invariant content. Extensive experimental results on two typical
medical image segmentation tasks show that our approach performs better than
state-of-the-art domain generalization methods.

Recent work for extracting relations from texts has achieved excellent
performance. However, most existing methods pay less attention to the
efficiency, making it still challenging to quickly extract relations from
massive or streaming text data in realistic scenarios. The main efficiency
bottleneck is that these methods use a Transformer-based pre-trained language
model for encoding, which heavily affects the training speed and inference
speed. To address this issue, we propose a fast relation extraction model
(FastRE) based on convolutional encoder and improved cascade binary tagging
framework. Compared to previous work, FastRE employs several innovations to
improve efficiency while also keeping promising performance. Concretely, FastRE
adopts a novel convolutional encoder architecture combined with dilated
convolution, gated unit and residual connection, which significantly reduces
the computation cost of training and inference, while maintaining the
satisfactory performance. Moreover, to improve the cascade binary tagging
framework, FastRE first introduces a type-relation mapping mechanism to
accelerate tagging efficiency and alleviate relation redundancy, and then
utilizes a position-dependent adaptive thresholding strategy to obtain higher
tagging accuracy and better model generalization. Experimental results
demonstrate that FastRE is well balanced between efficiency and performance,
and achieves 3-10x training speed, 7-15x inference speed faster, and 1/100
parameters compared to the state-of-the-art models, while the performance is
still competitive.

Driving trajectory representation learning is of great significance for
various location-based services, such as driving pattern mining and route
recommendation. However, previous representation generation approaches tend to
rarely address three challenges: 1) how to represent the intricate semantic
intentions of mobility inexpensively; 2) complex and weak spatial-temporal
dependencies due to the sparsity and heterogeneity of the trajectory data; 3)
route selection preferences and their correlation to driving behavior. In this
paper, we propose a novel multimodal fusion model, DouFu, for trajectory
representation joint learning, which applies multimodal learning and attention
fusion module to capture the internal characteristics of trajectories. We first
design movement, route, and global features generated from the trajectory data
and urban functional zones and then analyze them respectively with the
attention encoder or feed forward network. The attention fusion module
incorporates route features with movement features to create a better
spatial-temporal embedding. With the global semantic feature, DouFu produces a
comprehensive embedding for each trajectory. We evaluate representations
generated by our method and other baseline models on classification and
clustering tasks. Empirical results show that DouFu outperforms other models in
most of the learning algorithms like the linear regression and the support
vector machine by more than 10%.

Multi-view depth estimation plays a critical role in reconstructing and
understanding the 3D world. Recent learning-based methods have made significant
progress in it. However, multi-view depth estimation is fundamentally a
correspondence-based optimization problem, but previous learning-based methods
mainly rely on predefined depth hypotheses to build correspondence as the cost
volume and implicitly regularize it to fit depth prediction, deviating from the
essence of iterative optimization based on stereo correspondence. Thus, they
suffer unsatisfactory precision and generalization capability. In this paper,
we are the first to explore more general image correlations to establish
correspondences dynamically for depth estimation. We design a novel iterative
multi-view depth estimation framework mimicking the optimization process, which
consists of 1) a correlation volume construction module that models the pixel
similarity between a reference image and source images as all-to-all
correlations; 2) a flow-based depth initialization module that estimates the
depth from the 2D optical flow; 3) a novel correlation-guided depth refinement
module that reprojects points in different views to effectively fetch relevant
correlations for further fusion and integrate the fused correlation for
iterative depth update. Without predefined depth hypotheses, the fused
correlations establish multi-view correspondence in an efficient way and guide
the depth refinement heuristically. We conduct sufficient experiments on
ScanNet, DeMoN, ETH3D, and 7Scenes to demonstrate the superiority of our method
on multi-view depth estimation and its best generalization ability.

Distributed training using multiple devices (e.g., GPUs) has been widely
adopted for learning DNN models over large datasets. However, the performance
of large-scale distributed training tends to be far from linear speed-up in
practice. Given the complexity of distributed systems, it is challenging to
identify the root cause(s) of inefficiency and exercise effective performance
optimizations when unexpected low training speed occurs. To date, there exists
no software tool which diagnoses performance issues and helps expedite
distributed DNN training, while the training can be run using different deep
learning frameworks. This paper proposes dPRO, a toolkit that includes: (1) an
efficient profiler that collects runtime traces of distributed DNN training
across multiple frameworks, especially fine-grained communication traces, and
constructs global data flow graphs including detailed communication operations
for accurate replay; (2) an optimizer that effectively identifies performance
bottlenecks and explores optimization strategies (from computation,
communication, and memory aspects) for training acceleration. We implement dPRO
on multiple deep learning frameworks (TensorFlow, MXNet) and representative
communication schemes (AllReduce and Parameter Server). Extensive experiments
show that dPRO predicts the performance of distributed training in various
settings with < 5% errors in most cases and finds optimization strategies with
up to 3.48x speed-up over the baselines.

Existing knowledge distillation methods on graph neural networks (GNNs) are
almost offline, where the student model extracts knowledge from a powerful
teacher model to improve its performance. However, a pre-trained teacher model
is not always accessible due to training cost, privacy, etc. In this paper, we
propose a novel online knowledge distillation framework to resolve this
problem. Specifically, each student GNN model learns the extracted local
structure from another simultaneously trained counterpart in an alternating
training procedure. We further develop a cross-layer distillation strategy by
aligning ahead one student layer with the layer in different depth of another
student model, which theoretically makes the structure information spread over
all layers. Experimental results on five datasets including PPI,
Coauthor-CS/Physics and Amazon-Computer/Photo demonstrate that the student
performance is consistently boosted in our collaborative training framework
without the supervision of a pre-trained teacher model. In addition, we also
find that our alignahead technique can accelerate the model convergence speed
and its effectiveness can be generally improved by increasing the student
numbers in training. Code is available:
https://github.com/GuoJY-eatsTG/Alignahead

We study the problem of mean estimation of $\ell_2$-bounded vectors under the
constraint of local differential privacy. While the literature has a variety of
algorithms that achieve the asymptotically optimal rates for this problem, the
performance of these algorithms in practice can vary significantly due to
varying (and often large) hidden constants. In this work, we investigate the
question of designing the protocol with the smallest variance. We show that
PrivUnit (Bhowmick et al. 2018) with optimized parameters achieves the optimal
variance among a large family of locally private randomizers. To prove this
result, we establish some properties of local randomizers, and use
symmetrization arguments that allow us to write the optimal randomizer as the
optimizer of a certain linear program. These structural results, which should
extend to other problems, then allow us to show that the optimal randomizer
belongs to the PrivUnit family.
  We also develop a new variant of PrivUnit based on the Gaussian distribution
which is more amenable to mathematical analysis and enjoys the same optimality
guarantees. This allows us to establish several useful properties on the exact
constants of the optimal error as well as to numerically estimate these
constants.

While hyper-parameters (HPs) are important for knowledge graph (KG) learning,
existing methods fail to search them efficiently. To solve this problem, we
first analyze the properties of different HPs and measure the transfer ability
from small subgraph to the full graph. Based on the analysis, we propose an
efficient two-stage search algorithm KGTuner, which efficiently explores HP
configurations on small subgraph at the first stage and transfers the
top-performed configurations for fine-tuning on the large full graph at the
second stage. Experiments show that our method can consistently find better HPs
than the baseline algorithms within the same time budget, which achieves
{9.1\%} average relative improvement for four embedding models on the
large-scale KGs in open graph benchmark.

Traditional airfoil parametric technique has significant limitation in modern
aerodynamic optimization design.There is a strong demand for developing a
parametric method with good intuitiveness, flexibility and representative
accuracy. In this paper, two parametric generative schemes based on deep
learning methods are proposed to represent the complicate design space under
specific constraints. 1. Soft-constrained scheme: The CVAE-based model trains
geometric constraints as part of the network and can provide constrained
airfoil synthesis; 2. Hard-constrained scheme: The VAE-based model serves to
generate diverse airfoils, while an FFD-based technique projects the generated
airfoils to the final airfoils satisfying the given constraints. The
statistical results show that the reconstructed airfoils are accurate and
smooth without extra filters. The soft constrained scheme tend to synthesize
and explore airfoils efficiently and effectively, concentrating to the
reference airfoil in both geometry space and objective space. The constraints
will loose for a little bit because the inherent property of the model. The
hard constrained scheme tend to generate and explore airfoils in a wider range
for both geometry space and objective space, and the distribution in objective
space is closer to normal distribution. The synthesized airfoils through this
scheme strictly conform with constraints, though the projection may produce
some odd airfoil shapes.

Emotions are an inherent part of human interactions, and consequently, it is
imperative to develop AI systems that understand and recognize human emotions.
During a conversation involving various people, a person's emotions are
influenced by the other speaker's utterances and their own emotional state over
the utterances. In this paper, we propose COntextualized Graph Neural Network
based Multimodal Emotion recognitioN (COGMEN) system that leverages local
information (i.e., inter/intra dependency between speakers) and global
information (context). The proposed model uses Graph Neural Network (GNN) based
architecture to model the complex dependencies (local and global information)
in a conversation. Our model gives state-of-the-art (SOTA) results on IEMOCAP
and MOSEI datasets, and detailed ablation experiments show the importance of
modeling information at both levels.

There has recently been growing interest in the automatic generation of
cooking recipes that satisfy some form of dietary restrictions, thanks in part
to the availability of online recipe data. Prior studies have used pre-trained
language models, or relied on small paired recipe data (e.g., a recipe paired
with a similar one that satisfies a dietary constraint). However, pre-trained
language models generate inconsistent or incoherent recipes, and paired
datasets are not available at scale. We address these deficiencies with
RecipeCrit, a hierarchical denoising auto-encoder that edits recipes given
ingredient-level critiques. The model is trained for recipe completion to learn
semantic relationships within recipes. Our work's main innovation is our
unsupervised critiquing module that allows users to edit recipes by interacting
with the predicted ingredients; the system iteratively rewrites recipes to
satisfy users' feedback. Experiments on the Recipe1M recipe dataset show that
our model can more effectively edit recipes compared to strong
language-modeling baselines, creating recipes that satisfy user constraints and
are more correct, serendipitous, coherent, and relevant as measured by human
judges.

This paper provides a systematic overview of machine learning methods applied
to solve NP-hard Vehicle Routing Problems (VRPs). Recently, there has been a
great interest from both machine learning and operations research communities
to solve VRPs either by pure learning methods or by combining them with the
traditional hand-crafted heuristics. We present the taxonomy of the studies for
learning paradigms, solution structures, underlying models, and algorithms. We
present in detail the results of the state-of-the-art methods demonstrating
their competitiveness with the traditional methods. The paper outlines the
future research directions to incorporate learning-based solutions to overcome
the challenges of modern transportation systems.

Dynamic mechanism design has garnered significant attention from both
computer scientists and economists in recent years. By allowing agents to
interact with the seller over multiple rounds, where agents' reward functions
may change with time and are state-dependent, the framework is able to model a
rich class of real-world problems. In these works, the interaction between
agents and sellers is often assumed to follow a Markov Decision Process (MDP).
We focus on the setting where the reward and transition functions of such an
MDP are not known a priori, and we are attempting to recover the optimal
mechanism using an a priori collected data set. In the setting where the
function approximation is employed to handle large state spaces, with only mild
assumptions on the expressiveness of the function class, we are able to design
a dynamic mechanism using offline reinforcement learning algorithms. Moreover,
learned mechanisms approximately have three key desiderata: efficiency,
individual rationality, and truthfulness. Our algorithm is based on the
pessimism principle and only requires a mild assumption on the coverage of the
offline data set. To the best of our knowledge, our work provides the first
offline RL algorithm for dynamic mechanism design without assuming uniform
coverage.

The disturbance storm time (Dst) index is an important and useful measurement
in space weather research. It has been used to characterize the size and
intensity of a geomagnetic storm. A negative Dst value means that the Earth's
magnetic field is weakened, which happens during storms. In this paper, we
present a novel deep learning method, called the Dst Transformer, to perform
short-term, 1-6 hour ahead, forecasting of the Dst index based on the solar
wind parameters provided by the NASA Space Science Data Coordinated Archive.
The Dst Transformer combines a multi-head attention layer with Bayesian
inference, which is capable of quantifying both aleatoric uncertainty and
epistemic uncertainty when making Dst predictions. Experimental results show
that the proposed Dst Transformer outperforms related machine learning methods
in terms of the root mean square error and R-squared. Furthermore, the Dst
Transformer can produce both data and model uncertainty quantification results,
which can not be done by the existing methods. To our knowledge, this is the
first time that Bayesian deep learning has been used for Dst index forecasting.

Recently, industrial recommendation services have been boosted by the
continual upgrade of deep learning methods. However, they still face de-biasing
challenges such as exposure bias and cold-start problem, where circulations of
machine learning training on human interaction history leads algorithms to
repeatedly suggest exposed items while ignoring less-active ones. Additional
problems exist in multi-scenario platforms, e.g. appropriate data fusion from
subsidiary scenarios, which we observe could be alleviated through graph
structured data integration via message passing.
  In this paper, we present a multi-graph structured multi-scenario
recommendation solution, which encapsulates interaction data across scenarios
with multi-graph and obtains representation via graph learning. Extensive
offline and online experiments on real-world datasets are conducted where the
proposed method demonstrates an increase of 0.63% and 0.71% in CTR and Video
Views per capita on new users over deployed set of baselines and outperforms
regular method in increasing the number of outer-scenario videos by 25% and
video watches by 116%, validating its superiority in activating cold videos and
enriching target recommendation.

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable
interest for its ability in achieving three-dimensional reconstruction along
the elevation direction from multiple observations. In recent years, compressed
sensing (CS) technique has been introduced into TomoSAR considering for its
super-resolution ability with limited samples. Whereas, the CS-based methods
suffer from several drawbacks, including weak noise resistance, high
computational complexity and complex parameter fine-tuning. Among the different
CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a
robust reconstruction approach, however, the parameters in the ISTA algorithm
are manually chosen, which usually requires a time-consuming fine-tuning
process to achieve the best performance. Aiming at efficient TomoSAR imaging, a
novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed
towards the TomoSAR imaging problem in this paper, and the key parameters of
ISTA are learned from training data via deep learning to avoid complex
parameter fine-tuning and significantly relieves the training burden. In
addition, experiments verify that it is feasible to use traditional CS
algorithms as training labels, which provides a tangible supervised training
method to achieve better 3D reconstruction performance even in the absence of
labeled data in real applications.

How can we learn unified representations for spoken utterances and their
written text? Learning similar representations for semantically similar speech
and text is important for speech translation. To this end, we propose ConST, a
cross-modal contrastive learning method for end-to-end speech-to-text
translation. We evaluate ConST and a variety of previous baselines on a popular
benchmark MuST-C. Experiments show that the proposed ConST consistently
outperforms the previous methods on, and achieves an average BLEU of 29.4. The
analysis further verifies that ConST indeed closes the representation gap of
different modalities -- its learned representation improves the accuracy of
cross-modal speech-text retrieval from 4% to 88%. Code and models are available
at https://github.com/ReneeYe/ConST.

Accurate forecasting of extreme values in time series is critical due to the
significant impact of extreme events on human and natural systems. This paper
presents DeepExtrema, a novel framework that combines a deep neural network
(DNN) with generalized extreme value (GEV) distribution to forecast the block
maximum value of a time series. Implementing such a network is a challenge as
the framework must preserve the inter-dependent constraints among the GEV model
parameters even when the DNN is initialized. We describe our approach to
address this challenge and present an architecture that enables both
conditional mean and quantile prediction of the block maxima. The extensive
experiments performed on both real-world and synthetic data demonstrated the
superiority of DeepExtrema compared to other baseline methods.

Since federated learning (FL) has been introduced as a decentralized learning
technique with privacy preservation, statistical heterogeneity of distributed
data stays the main obstacle to achieve robust performance and stable
convergence in FL applications. Model personalization methods have been studied
to overcome this problem. However, existing approaches are mainly under the
prerequisite of fully labeled data, which is unrealistic in practice due to the
requirement of expertise. The primary issue caused by partial-labeled condition
is that, clients with deficient labeled data can suffer from unfair performance
gain because they lack adequate insights of local distribution to customize the
global model. To tackle this problem, 1) we propose a novel personalized
semi-supervised learning paradigm which allows partial-labeled or unlabeled
clients to seek labeling assistance from data-related clients (helper agents),
thus to enhance their perception of local data; 2) based on this paradigm, we
design an uncertainty-based data-relation metric to ensure that selected
helpers can provide trustworthy pseudo labels instead of misleading the local
training; 3) to mitigate the network overload introduced by helper searching,
we further develop a helper selection protocol to achieve efficient
communication with negligible performance sacrifice. Experiments show that our
proposed method can obtain superior performance and more stable convergence
than other related works with partial labeled data, especially in highly
heterogeneous setting.

Computational fluid dynamics (CFD) simulations, a critical tool in various
engineering applications, often require significant time and compute power to
predict flow properties. The high computational cost associated with CFD
simulations significantly restricts the scope of design space exploration and
limits their use in planning and operational control. To address this issue,
machine learning (ML) based surrogate models have been proposed as a
computationally efficient tool to accelerate CFD simulations. However, a lack
of clarity about CFD data requirements often challenges the widespread adoption
of ML-based surrogates among design engineers and CFD practitioners. In this
work, we propose an ML-based surrogate model to predict the temperature
distribution inside the cabin of a passenger vehicle under various operating
conditions and use it to demonstrate the trade-off between prediction
performance and training dataset size. Our results show that the prediction
accuracy is high and stable even when the training size is gradually reduced
from 2000 to 200. The ML-based surrogates also reduce the compute time from ~30
minutes to around ~9 milliseconds. Moreover, even when only 50 CFD simulations
are used for training, the temperature trend (e.g., locations of hot/cold
regions) predicted by the ML-surrogate matches quite well with the results from
CFD simulations.

We aim to ask and answer an essential question "how quickly do we react after
observing a displayed visual target?" To this end, we present psychophysical
studies that characterize the remarkable disconnect between human saccadic
behaviors and spatial visual acuity. Building on the results of our studies, we
develop a perceptual model to predict temporal gaze behavior, particularly
saccadic latency, as a function of the statistics of a displayed image.
Specifically, we implement a neurologically-inspired probabilistic model that
mimics the accumulation of confidence that leads to a perceptual decision. We
validate our model with a series of objective measurements and user studies
using an eye-tracked VR display. The results demonstrate that our model
prediction is in statistical alignment with real-world human behavior. Further,
we establish that many sub-threshold image modifications commonly introduced in
graphics pipelines may significantly alter human reaction timing, even if the
differences are visually undetectable. Finally, we show that our model can
serve as a metric to predict and alter reaction latency of users in interactive
computer graphics applications, thus may improve gaze-contingent rendering,
design of virtual experiences, and player performance in e-sports. We
illustrate this with two examples: estimating competition fairness in a video
game with two different team colors, and tuning display viewing distance to
minimize player reaction time.

Few-shot graph classification aims at predicting classes for graphs, given
limited labeled graphs for each class. To tackle the bottleneck of label
scarcity, recent works propose to incorporate few-shot learning frameworks for
fast adaptations to graph classes with limited labeled graphs. Specifically,
these works propose to accumulate meta-knowledge across diverse meta-training
tasks, and then generalize such meta-knowledge to the target task with a
disjoint label set. However, existing methods generally ignore task
correlations among meta-training tasks while treating them independently.
Nevertheless, such task correlations can advance the model generalization to
the target task for better classification performance. On the other hand, it
remains non-trivial to utilize task correlations due to the complex components
in a large number of meta-training tasks. To deal with this, we propose a novel
few-shot learning framework FAITH that captures task correlations via
constructing a hierarchical task graph at different granularities. Then we
further design a loss-based sampling strategy to select tasks with more
correlated classes. Moreover, a task-specific classifier is proposed to utilize
the learned task correlations for few-shot classification. Extensive
experiments on four prevalent few-shot graph classification datasets
demonstrate the superiority of FAITH over other state-of-the-art baselines.

As an emerging technology, Connected Autonomous Vehicles (CAVs) are believed
to have the ability to move through intersections in a faster and safer manner,
through effective Vehicle-to-Everything (V2X) communication and global
observation. Autonomous intersection management is a key path to efficient
crossing at intersections, which reduces unnecessary slowdowns and stops
through adaptive decision process of each CAV, enabling fuller utilization of
the intersection space. Distributed reinforcement learning (DRL) offers a
flexible, end-to-end model for AIM, adapting for many intersection scenarios.
While DRL is prone to collisions as the actions of multiple sides in the
complicated interactions are sampled from a generic policy, restricting the
application of DRL in realistic scenario. To address this, we propose a
hierarchical RL framework where models at different levels vary in receptive
scope, action step length, and feedback period of reward. The upper layer model
accelerate CAVs to prevent them from being clashed, while the lower layer model
adjust the trends from upper layer model to avoid the change of mobile state
causing new conflicts. And the real action of CAV at each step is co-determined
by the trends from both levels, forming a real-time balance in the adversarial
process. The proposed model is proven effective in the experiment undertaken in
a complicated intersection with 4 branches and 4 lanes each branch, and show
better performance compared with baselines.

Problem: There is a lack of big data for the training of deep learning models
in medicine, characterized by the time cost of data collection and privacy
concerns. Generative adversarial networks (GANs) offer both the potential to
generate new data, as well as to use this newly generated data, without
inclusion of patients' real data, for downstream applications.
  Approach: A series of GANs were trained and applied for a downstream computer
vision spine radiograph abnormality classification task. Separate classifiers
were trained with either access or no access to the original imaging. Trained
GANs included a conditional StyleGAN2 with adaptive discriminator augmentation,
a conditional StyleGAN2 with adaptive discriminator augmentation to generate
spine radiographs conditional on lesion type, and using a novel clinical loss
term for the generator a StyleGAN2 with adaptive discriminator augmentation
conditional on abnormality (SpineGAN). Finally, a differential privacy imposed
StyleGAN2 with adaptive discriminator augmentation conditional on abnormality
was trained and an ablation study was performed on its differential privacy
impositions.
  Key Results: We accomplish GAN generation of synthetic spine radiographs
without meaningful input for the first time from a literature review. We
further demonstrate the success of synthetic learning for the spine domain with
a downstream clinical classification task (AUC of 0.830 using synthetic data
compared to AUC of 0.886 using the real data). Importantly, the introduction of
a new clinical loss term for the generator was found to increase generation
recall as well as accelerate model training. Lastly, we demonstrate that, in a
limited size medical dataset, differential privacy impositions severely impede
GAN training, finding that this is specifically due to the requirement for
gradient perturbation with noise.

Future communication systems (6G and beyond) will witness a paradigm shift
from communication-intensive systems towards intelligent computing-intensive
architectures. A key research area that enables this transition is semantic
communications, whereby the communication process conveys the meaning of
messages instead of being a mere reconstruction process of raw, naive data
bits. In this paper, a novel quantum semantic communications (QSC) framework is
proposed to develop reasoning-based future communication systems with quantum
semantic representations that are characterized with minimalism, efficiency,
and accuracy. In particular, the concepts of quantum embedding and
high-dimensional Hilbert spaces are exploited so as to extract the meaning of
classical data. Moreover, in order to equip our approach with minimalism and
efficiency, an unsupervised quantum machine learning (QML) technique, namely,
quantum clustering is employed. Quantum clustering enables extraction of
contextual information and distinct characterization of the semantics of the
message to be conveyed. Subsequently, to successfully transmit the constructed
semantic representations, quantum communication links are used to transfer the
quantum states. This new QSC framework exploits unique quantum principles such
as the minimalism of entangled photons, quantum-semantic entropy of noise, and
quantum fidelity. Simulation results show that the proposed framework can save
around 85\% of quantum communication resources, i.e., entangled photons,
compared to semantic-agnostic quantum communication schemes. Results also show
the benefits of increasing the number of dimensions on the expressivity of the
semantic representations.

Recent work done on traffic sign and traffic light detection focus on
improving detection accuracy in complex scenarios, yet many fail to deliver
real-time performance, specifically with limited computational resources. In
this work, we propose a simple deep learning based end-to-end detection
framework, which effectively tackles challenges inherent to traffic sign and
traffic light detection such as small size, large number of classes and complex
road scenarios. We optimize the detection models using TensorRT and integrate
with Robot Operating System to deploy on an Nvidia Jetson AGX Xavier as our
embedded device. The overall system achieves a high inference speed of 63
frames per second, demonstrating the capability of our system to perform in
real-time. Furthermore, we introduce CeyRo, which is the first ever large-scale
traffic sign and traffic light detection dataset for the Sri Lankan context.
Our dataset consists of 7984 total images with 10176 traffic sign and traffic
light instances covering 70 traffic sign and 5 traffic light classes. The
images have a high resolution of 1920 x 1080 and capture a wide range of
challenging road scenarios with different weather and lighting conditions. Our
work is publicly available at https://github.com/oshadajay/CeyRo.

We present a learning-based channel-adaptive joint source and channel coding
(CA-JSCC) scheme for wireless image transmission over multipath fading
channels. The proposed method is an end-to-end autoencoder architecture with a
dual-attention mechanism employing orthogonal frequency division multiplexing
(OFDM) transmission. Unlike the previous works, our approach is adaptive to
channel-gain and noise-power variations by exploiting the estimated channel
state information (CSI). Specifically, with the proposed dual-attention
mechanism, our model can learn to map the features and allocate
transmission-power resources judiciously based on the estimated CSI. Extensive
numerical experiments verify that CA-JSCC achieves state-of-the-art performance
among existing JSCC schemes. In addition, CA-JSCC is robust to varying channel
conditions and can better exploit the limited channel resources by transmitting
critical features over better subchannels.

The image captioning task is increasingly prevalent in artificial
intelligence applications for medicine. One important application is clinical
report generation from chest radiographs. The clinical writing of unstructured
reports is time consuming and error-prone. An automated system would improve
standardization, error reduction, time consumption, and medical accessibility.
In this paper we demonstrate the importance of domain specific pre-training and
propose a modified transformer architecture for the medical image captioning
task. To accomplish this, we train a series of modified transformers to
generate clinical reports from chest radiograph image input. These modified
transformers include: a meshed-memory augmented transformer architecture with
visual extractor using ImageNet pre-trained weights, a meshed-memory augmented
transformer architecture with visual extractor using CheXpert pre-trained
weights, and a meshed-memory augmented transformer whose encoder is passed the
concatenated embeddings using both ImageNet pre-trained weights and CheXpert
pre-trained weights. We use BLEU(1-4), ROUGE-L, CIDEr, and the clinical
CheXbert F1 scores to validate our models and demonstrate competitive scores
with state of the art models. We provide evidence that ImageNet pre-training is
ill-suited for the medical image captioning task, especially for less frequent
conditions (eg: enlarged cardiomediastinum, lung lesion, pneumothorax).
Furthermore, we demonstrate that the double feature model improves performance
for specific medical conditions (edema, consolidation, pneumothorax, support
devices) and overall CheXbert F1 score, and should be further developed in
future work. Such a double feature model, including both ImageNet pre-training
as well as domain specific pre-training, could be used in a wide range of image
captioning models in medicine.

In this work we explore the intersection fairness and robustness in the
context of ranking: when a ranking model has been calibrated to achieve some
definition of fairness, is it possible for an external adversary to make the
ranking model behave unfairly without having access to the model or training
data? To investigate this question, we present a case study in which we develop
and then attack a state-of-the-art, fairness-aware image search engine using
images that have been maliciously modified using a Generative Adversarial
Perturbation (GAP) model. These perturbations attempt to cause the fair
re-ranking algorithm to unfairly boost the rank of images containing people
from an adversary-selected subpopulation.
  We present results from extensive experiments demonstrating that our attacks
can successfully confer significant unfair advantage to people from the
majority class relative to fairly-ranked baseline search results. We
demonstrate that our attacks are robust across a number of variables, that they
have close to zero impact on the relevance of search results, and that they
succeed under a strict threat model. Our findings highlight the danger of
deploying fair machine learning algorithms in-the-wild when (1) the data
necessary to achieve fairness may be adversarially manipulated, and (2) the
models themselves are not robust against attacks.

Reconstruction of a continuous surface of two-dimensional manifold from its
raw, discrete point cloud observation is a long-standing problem. The problem
is technically ill-posed, and becomes more difficult considering that various
sensing imperfections would appear in the point clouds obtained by practical
depth scanning. In literature, a rich set of methods has been proposed, and
reviews of existing methods are also provided. However, existing reviews are
short of thorough investigations on a common benchmark. The present paper aims
to review and benchmark existing methods in the new era of deep learning
surface reconstruction. To this end, we contribute a large-scale benchmarking
dataset consisting of both synthetic and real-scanned data; the benchmark
includes object- and scene-level surfaces and takes into account various
sensing imperfections that are commonly encountered in practical depth
scanning. We conduct thorough empirical studies by comparing existing methods
on the constructed benchmark, and pay special attention on robustness of
existing methods against various scanning imperfections; we also study how
different methods generalize in terms of reconstructing complex surface shapes.
Our studies help identify the best conditions under which different methods
work, and suggest some empirical findings. For example, while deep learning
methods are increasingly popular, our systematic studies suggest that,
surprisingly, a few classical methods perform even better in terms of both
robustness and generalization; our studies also suggest that the practical
challenges of misalignment of point sets from multi-view scanning, missing of
surface points, and point outliers remain unsolved by all the existing surface
reconstruction methods. We expect that the benchmark and our studies would be
valuable both for practitioners and as a guidance for new innovations in future
research.

Relational understanding is critical for a number of visually-rich documents
(VRDs) understanding tasks. Through multi-modal pre-training, recent studies
provide comprehensive contextual representations and exploit them as prior
knowledge for downstream tasks. In spite of their impressive results, we
observe that the widespread relational hints (e.g., relation of key/value
fields on receipts) built upon contextual knowledge are not excavated yet. To
mitigate this gap, we propose DocReL, a Document Relational Representation
Learning framework. The major challenge of DocReL roots in the variety of
relations. From the simplest pairwise relation to the complex global structure,
it is infeasible to conduct supervised training due to the definition of
relation varies and even conflicts in different tasks. To deal with the
unpredictable definition of relations, we propose a novel contrastive learning
task named Relational Consistency Modeling (RCM), which harnesses the fact that
existing relations should be consistent in differently augmented positive
views. RCM provides relational representations which are more compatible to the
urgent need of downstream tasks, even without any knowledge about the exact
definition of relation. DocReL achieves better performance on a wide variety of
VRD relational understanding tasks, including table structure recognition, key
information extraction and reading order detection.

Driven by the critical needs of biomanufacturing 4.0, we introduce a
probabilistic knowledge graph hybrid model characterizing the risk- and
science-based understanding of bioprocess mechanisms. It can faithfully capture
the important properties, including nonlinear reactions, partially observed
state, and nonstationary dynamics. Given very limited real process
observations, we derive a posterior distribution quantifying model estimation
uncertainty. To avoid the evaluation of intractable likelihoods, Approximate
Bayesian Computation sampling with Sequential Monte Carlo (ABC-SMC) is utilized
to approximate the posterior distribution. Under high stochastic and model
uncertainties, it is computationally expensive to match output trajectories.
Therefore, we create a linear Gaussian dynamic Bayesian network (LG-DBN)
auxiliary likelihood-based ABC-SMC approach. Through matching the summary
statistics driven through LG-DBN likelihood that can capture critical
interactions and variations, the proposed algorithm can accelerate hybrid model
inference, support process monitoring, and facilitate mechanism learning and
robust control.

We study dangling-aware entity alignment in knowledge graphs (KGs), which is
an underexplored but important problem. As different KGs are naturally
constructed by different sets of entities, a KG commonly contains some dangling
entities that cannot find counterparts in other KGs. Therefore, dangling-aware
entity alignment is more realistic than the conventional entity alignment where
prior studies simply ignore dangling entities. We propose a framework using
mixed high-order proximities on dangling-aware entity alignment. Our framework
utilizes both the local high-order proximity in a nearest neighbor subgraph and
the global high-order proximity in an embedding space for both dangling
detection and entity alignment. Extensive experiments with two evaluation
settings shows that our framework more precisely detects dangling entities, and
better aligns matchable entities. Further investigations demonstrate that our
framework can mitigate the hubness problem on dangling-aware entity alignment.

Knowledge distillation (KD) has become a well established paradigm for
compressing deep neural networks. The typical way of conducting knowledge
distillation is to train the student network under the supervision of the
teacher network to harness the knowledge at one or multiple spots (i.e.,
layers) in the teacher network. The distillation spots, once specified, will
not change for all the training samples, throughout the whole distillation
process. In this work, we argue that distillation spots should be adaptive to
training samples and distillation epochs. We thus propose a new distillation
strategy, termed spot-adaptive KD (SAKD), to adaptively determine the
distillation spots in the teacher network per sample, at every training
iteration during the whole distillation period. As SAKD actually focuses on
"where to distill" instead of "what to distill" that is widely investigated by
most existing works, it can be seamlessly integrated into existing distillation
methods to further improve their performance. Extensive experiments with 10
state-of-the-art distillers are conducted to demonstrate the effectiveness of
SAKD for improving their distillation performance, under both homogeneous and
heterogeneous distillation settings. Code is available at
https://github.com/zju-vipa/spot-adaptive-pytorch

Ptychography is a well-established coherent diffraction imaging technique
that enables non-invasive imaging of samples at a nanometer scale. It has been
extensively used in various areas such as the defense industry or materials
science. One major limitation of ptychography is the long data acquisition time
due to mechanical scanning of the sample; therefore, approaches to reduce the
scan points are highly desired. However, reconstructions with less number of
scan points lead to imaging artifacts and significant distortions, hindering a
quantitative evaluation of the results. To address this bottleneck, we propose
a generative model combining deep image priors with deep generative priors. The
self-training approach optimizes the deep generative neural network to create a
solution for a given dataset. We complement our approach with a prior acquired
from a previously trained discriminator network to avoid a possible divergence
from the desired output caused by the noise in the measurements. We also
suggest using the total variation as a complementary before combat artifacts
due to measurement noise. We analyze our approach with numerical experiments
through different probe overlap percentages and varying noise levels. We also
demonstrate improved reconstruction accuracy compared to the state-of-the-art
method and discuss the advantages and disadvantages of our approach.

Real-world datasets often encode stereotypes and societal biases. Such biases
can be implicitly captured by trained models, leading to biased predictions and
exacerbating existing societal preconceptions. Existing debiasing methods, such
as adversarial training and removing protected information from
representations, have been shown to reduce bias. However, a disconnect between
fairness criteria and training objectives makes it difficult to reason
theoretically about the effectiveness of different techniques. In this work, we
propose two novel training objectives which directly optimise for the
widely-used criterion of {\it equal opportunity}, and show that they are
effective in reducing bias while maintaining high performance over two

Human intelligence has the remarkable ability to quickly adapt to new tasks
and environments. Starting from a very young age, humans acquire new skills and
following provided natural language instructions. To facilitate research in
this direction, we propose \emph{IGLU: Interactive Grounded Language
Understanding in a Collaborative Environment}.
  The primary goal of the competition is to approach the problem of how to
build interactive agents that learn to solve a task while provided with
grounded natural language instructions in a collaborative environment.
Understanding the complexity of the challenge, we split it into sub-tasks to
make it feasible for participants.

Active, non-parametric peak detection is considered. As a use case, active
source localization is examined and an uncertainty-based sampling scheme
algorithm to effectively localize the peak from a few energy measurements is
designed. It is shown that under very mild conditions, the source localization
error with $m$ actively chosen energy measurements scales as $O(\log^2 m/m)$.
Numerically, it is shown that in low-sample regimes, the proposed method enjoys
superior performance on several types of data and outperforms the
state-of-the-art passive source localization approaches and in the low sample
regime, can outperform greedy methods as well.

The use of the `ship as a wave buoy analogy' (SAWB) provides a novel means to
estimate sea states, where relationships are established between causal wave
properties and vessel motion response information. This study focuses on a
model-free machine learning approach to SAWB-based sea state estimation (SSE),
using neural networks (NNs) to map vessel response spectral data to statistical
wave properties for a small uninhabited surface vessel.
  Results showed a strong correlation between heave responses and significant
wave height estimates, whilst the accuracy of mean wave period and wave heading
predictions were observed to improve considerably when data from multiple
vessel degrees of freedom (DOFs) was utilized. Overall, 3-DOF (heave, pitch and
roll) NNs for SSE were shown to perform well when compared to existing SSE
approaches that use similar simulation setups. One advantage of using small
vessels for SAWB was shown as SSE accuracy was reasonable even when motion
responses were low (in high-frequency, low wave height sea states). Given the
information-dense statistical representation of vessel motion responses in
spectral form, as well as the ability of NNs to effectively model complex
relationships between variables, the designed SSE method shows promise for
future adaptation to mobile SSE systems using the SAWB approach.

Object tracking is one of the fundamental problems in visual recognition
tasks and has achieved significant improvements in recent years. The
achievements often come with the price of enormous hardware consumption and
expensive labor effort for consecutive labeling. A missing ingredient for
robust tracking is achieving performance with minimal modification on network
structure and semi-supervised learning intermittent labeled frames. In this
paper, we ad-dress these problems in a Bayesian tracking and detection
framework parameterized by neural network outputs. In our framework, the
tracking and detection process is formulated in a probabilistic way as
multi-objects dynamics and network detection uncertainties. With our
formulation, we propose a particle filter-based approximate sampling algorithm
for tracking object state estimation. Based on our particle filter inference
algorithm, a semi-supervised learn-ing algorithm is utilized for learning
tracking network on intermittent labeled frames by variational inference. In
our experiments, we provide both mAP and probability-based detection
measurements for comparison between our algorithm with non-Bayesian solutions.
We also train a semi-supervised tracking network on M2Cai16-Tool-Locations
Dataset and compare our results with supervised learning on fully labeled
frames.

This research developed a Kencorpus Swahili Question Answering Dataset
KenSwQuAD from raw data of Swahili language, which is a low resource language
predominantly spoken in Eastern African and also has speakers in other parts of
the world. Question Answering datasets are important for machine comprehension
of natural language processing tasks such as internet search and dialog
systems. However, before such machine learning systems can perform these tasks,
they need training data such as the gold standard Question Answering (QA) set
that is developed in this research. The research engaged annotators to
formulate question answer pairs from Swahili texts that had been collected by
the Kencorpus project, a Kenyan languages corpus that collected data from three
Kenyan languages. The total Swahili data collection had 2,585 texts, out of
which we annotated 1,445 story texts with at least 5 QA pairs each, resulting
into a final dataset of 7,526 QA pairs. A quality assurance set of 12.5% of the
annotated texts was subjected to re-evaluation by different annotators who
confirmed that the QA pairs were all correctly annotated. A proof of concept on
applying the set to machine learning on the question answering task confirmed
that the dataset can be used for such practical tasks. The research therefore
developed KenSwQuAD, a question-answer dataset for Swahili that is useful to
the natural language processing community who need training and gold standard
sets for their machine learning applications. The research also contributed to
the resourcing of the Swahili language which is important for communication
around the globe. Updating this set and providing similar sets for other low
resource languages is an important research area that is worthy of further
research.

Shoe tread impressions are one of the most common types of evidence left at
crime scenes. However, the utility of such evidence is limited by the lack of
databases of footwear prints that cover the large and growing number of
distinct shoe models. Moreover, the database is preferred to contain the 3D
shape, or depth, of shoe-tread photos so as to allow for extracting shoeprints
to match a query (crime-scene) print. We propose to address this gap by
leveraging shoe-tread photos collected by online retailers. The core challenge
is to predict depth maps for these photos. As they do not have ground-truth 3D
shapes allowing for training depth predictors, we exploit synthetic data that
does. We develop a method termed ShoeRinsics that learns to predict depth by
leveraging a mix of fully supervised synthetic data and unsupervised retail
image data. In particular, we find domain adaptation and intrinsic image
decomposition techniques effectively mitigate the synthetic-real domain gap and
yield significantly better depth prediction. To validate our method, we
introduce 2 validation sets consisting of shoe-tread image and print pairs and
define a benchmarking protocol to quantify the quality of predicted depth. On
this benchmark, ShoeRinsics outperforms existing methods of depth prediction
and synthetic-to-real domain adaptation.

Open-source repositories provide wealth of information and are increasingly
being used to build artificial intelligence (AI) based systems to solve
problems in software engineering. Open-source repositories could be of varying
quality levels, and bad-quality repositories could degrade performance of these
systems. Evaluating quality of open-source repositories, which is not available
directly on code hosting sites such as GitHub, is thus important. In this
hackathon, we utilize known code quality measures and GrimoireLab toolkit to
implement a framework, named GitRank, to rank open-source repositories on three
different criteria. We discuss our findings and preliminary evaluation in this
hackathon report.

Non-negative matrix factorization (NMF) with missing-value completion is a
well-known effective Collaborative Filtering (CF) method used to provide
personalized user recommendations. However, traditional CF relies on the
privacy-invasive collection of users' explicit and implicit feedback to build a
central recommender model. One-shot federated learning has recently emerged as
a method to mitigate the privacy problem while addressing the traditional
communication bottleneck of federated learning. In this paper, we present the
first unsupervised one-shot federated CF implementation, named FedSPLIT, based
on NMF joint factorization. In our solution, the clients first apply local CF
in-parallel to build distinct client-specific recommenders. Then, the
privacy-preserving local item patterns and biases from each client are shared
with the processor to perform joint factorization in order to extract the
global item patterns. Extracted patterns are then aggregated to each client to
build the local models via knowledge distillation. In our experiments, we
demonstrate the feasibility of our approach with standard recommendation
datasets. FedSPLIT can obtain similar results than the state of the art (and
even outperform it in certain situations) with a substantial decrease in the
number of communications.

Pre-trained language models have contributed significantly to relation
extraction by demonstrating remarkable few-shot learning abilities. However,
prompt tuning methods for relation extraction may still fail to generalize to
those rare or hard patterns. Note that the previous parametric learning
paradigm can be viewed as memorization regarding training data as a book and
inference as the close-book test. Those long-tailed or hard patterns can hardly
be memorized in parameters given few-shot instances. To this end, we regard RE
as an open-book examination and propose a new semiparametric paradigm of
retrieval-enhanced prompt tuning for relation extraction. We construct an
open-book datastore for retrieval regarding prompt-based instance
representations and corresponding relation labels as memorized key-value pairs.
During inference, the model can infer relations by linearly interpolating the
base output of PLM with the non-parametric nearest neighbor distribution over
the datastore. In this way, our model not only infers relation through
knowledge stored in the weights during training but also assists
decision-making by unwinding and querying examples in the open-book datastore.
Extensive experiments on benchmark datasets show that our method can achieve
state-of-the-art in both standard supervised and few-shot settings. Code are
available in https://github.com/zjunlp/PromptKG/tree/main/research/RetrievalRE.

A major challenge in applying deep learning to medical imaging is the paucity
of annotated data. This study demonstrates that synthetic colonoscopy images
generated by Generative Adversarial Network (GAN) inversion can be used as
training data to improve the lesion classification performance of deep learning
models. This approach inverts pairs of images with the same label to a
semantically rich & disentangled latent space and manipulates latent
representations to produce new synthetic images with the same label. We perform
image modality translation (style transfer) between white light and narrowband
imaging (NBI). We also generate realistic-looking synthetic lesion images by
interpolating between original training images to increase the variety of
lesion shapes in the training dataset. We show that these approaches outperform
comparative colonoscopy data augmentation techniques without the need to
re-train multiple generative models. This approach also leverages information
from datasets that may not have been designed for the specific colonoscopy
downstream task. E.g. using a bowel prep grading dataset for a polyp
classification task. Our experiments show this approach can perform multiple
colonoscopy data augmentations, which improve the downstream polyp
classification performance over baseline and comparison methods by up to 6%.

We consider a basic problem of preemptive scheduling of $n$
non-simultaneously released jobs on a group of $m$ unrelated parallel machines
so as to minimize maximum job completion time, the makespan. In the scheduling
literature, the problem is commonly considered to be solvable in polynomial
time by linear programming (LP) techniques proposed in Lawler and Labetoulle
\cite{ll78}. The authors in \cite{ll78} give a LP formulation of the version
with simultaneously released jobs and show how an optimal solution to this LP
can be used to construct an optimal schedule to the latter problem. As the
current study shows, for non-simultaneously released jobs, unlikely, there
exist a linear program such that a schedule with the minimum makespan can be
constructed based on an optimal LP solution. We also prove that, in case no
splitting of the same job on a machine is allowed (i.e., job part assigned to a
machine is to be processed without an interruption on that machine), the
problem is NP-hard. As a side result, we obtain that, whenever job splitting is
not allowed, given an optimal LP solution, it is NP-hard to find an optimal
schedule with the minimum makespan that agrees with that LP solution. We also
extend the schedule construction procedure based on an optimal LP solution from
Lawler and Labetoulle \cite{ll78} for non-simultaneously released jobs. The
extended procedure, among all feasible schedules that agree with any feasible
(not necessarily optimal) LP solution, generates one with the minimum makespan.
Such procedure is helpful, in particular, because, as we show, there may exist
no optimal schedule that agrees with an optimal LP solution if jobs are
non-simultaneously released.

The Lottery Ticket Hypothesis continues to have a profound practical impact
on the quest for small scale deep neural networks that solve modern deep
learning tasks at competitive performance. These lottery tickets are identified
by pruning large randomly initialized neural networks with architectures that
are as diverse as their applications. Yet, theoretical insights that attest
their existence have been mostly focused on deep fully-connected feed forward
networks with ReLU activation functions. We prove that also modern
architectures consisting of convolutional and residual layers that can be
equipped with almost arbitrary activation functions can contain lottery tickets
with high probability.

Artificial intelligence (AI), which enables machines to learn to perform a
task by training on diverse datasets, is one of the most revolutionary
developments in scientific history. Although AI and especially deep learning is
relatively new, it has already had transformative impact on medicine, biology,
transportation, entertainment, and beyond. As AI changes our daily lives at an
increasingly fast pace, we are challenged with preparing our society for an
AI-driven future. To this end, a critical step is to ensure an AI-ready
workforce through education. Advocates of beginning instruction of AI basics at
the K-12 level typically note benefits to the workforce, economy, and national
security. In this complementary perspective, we discuss why learning AI is
beneficial for motivating students and promoting creative thinking, and how to
develop a module-based approach that optimizes learning outcomes. We hope to
excite and engage more members of the education community to join the effort to
advance K-12 AI education in the USA and worldwide.

Today, transformer language models serve as a core component for majority of
natural language processing tasks. Industrial application of such models
requires minimization of computation time and memory footprint. Knowledge
distillation is one of approaches to address this goal. Existing methods in
this field are mainly focused on reducing the number of layers or dimension of
embeddings/hidden representations. Alternative option is to reduce the number
of tokens in vocabulary and therefore the embeddings matrix of the student
model. The main problem with vocabulary minimization is mismatch between input
sequences and output class distributions of a teacher and a student models. As
a result, it is impossible to directly apply KL-based knowledge distillation.
We propose two simple yet effective alignment techniques to make knowledge
distillation to the students with reduced vocabulary. Evaluation of distilled
models on a number of common benchmarks for Russian such as Russian SuperGLUE,
SberQuAD, RuSentiment, ParaPhaser, Collection-3 demonstrated that our
techniques allow to achieve compression from $17\times$ to $49\times$, while
maintaining quality of $1.7\times$ compressed student with the full-sized
vocabulary, but reduced number of Transformer layers only. We make our code and
distilled models available.

MR-STAT is an emerging quantitative magnetic resonance imaging technique
which aims at obtaining multi-parametric tissue parameter maps from single
short scans. It describes the relationship between the spatial-domain tissue
parameters and the time-domain measured signal by using a comprehensive,
volumetric forward model. The MR-STAT reconstruction solves a large-scale
nonlinear problem, thus is very computationally challenging. In previous work,
MR-STAT reconstruction using Cartesian readout data was accelerated by
approximating the Hessian matrix with sparse, banded blocks, and can be done on
high performance CPU clusters with tens of minutes. In the current work, we
propose an accelerated Cartesian MR-STAT algorithm incorporating two different
strategies: firstly, a neural network is trained as a fast surrogate to learn
the magnetization signal not only in the full time-domain but also in the
compressed lowrank domain; secondly, based on the surrogate model, the
Cartesian MR-STAT problem is re-formulated and split into smaller sub-problems
by the alternating direction method of multipliers. The proposed method
substantially reduces the computational requirements for runtime and memory.
Simulated and in-vivo balanced MR-STAT experiments show similar reconstruction
results using the proposed algorithm compared to the previous sparse Hessian
method, and the reconstruction times are at least 40 times shorter.
Incorporating sensitivity encoding and regularization terms is straightforward,
and allows for better image quality with a negligible increase in
reconstruction time. The proposed algorithm could reconstruct both balanced and
gradient-spoiled in-vivo data within 3 minutes on a desktop PC, and could
thereby facilitate the translation of MR-STAT in clinical settings.

We consider the equity and fairness of curricula derived from Knowledge
Tracing models. We begin by defining a unifying notion of an equitable tutoring
system as a system that achieves maximum possible knowledge in minimal time for
each student interacting with it. Realizing perfect equity requires tutoring
systems that can provide individualized curricula per student. In particular,
we investigate the design of equitable tutoring systems that derive their
curricula from Knowledge Tracing models. We first show that many existing
models, including classical Bayesian Knowledge Tracing (BKT) and Deep Knowledge
Tracing (DKT), and their derived curricula can fall short of achieving
equitable tutoring. To overcome this issue, we then propose a novel model,
Bayesian-Bayesian Knowledge Tracing (BBKT), that naturally enables online
individualization and, thereby, more equitable tutoring. We demonstrate that
curricula derived from our model are more effective and equitable than those
derived from classical BKT models. Furthermore, we highlight that improving
models with a focus on the fairness of next-step predictions might be
insufficient to develop equitable tutoring systems.

The abundance of data affords researchers to pursue more powerful
computational tools to learn the dynamics of complex system, such as neural
networks, engineered systems and social networks. Traditional machine learning
approaches capture complex system dynamics either with dynamic Bayesian
networks and state space models, which is hard to scale because it is
non-trivial to prescribe the dynamics with a sparse graph or a system of
differential equations; or a deep neural networks, where the distributed
representation of the learned dynamics is hard to interpret. In this paper, we
will explore the possibility of learning a discrete-event simulation
representation of complex system dynamics assuming multivariate normal
distribution of the state variables, based on the observation that many complex
system dynamics can be decomposed into a sequence of local interactions, which
individually change the system state only minimally but in sequence generate
complex and diverse dynamics. Our results show that the algorithm can
data-efficiently capture complex network dynamics in several fields with
meaningful events.

We present a new combined mean field control game (MFCG) problem which can be
interpreted as a competitive game between collaborating groups and its solution
as a Nash equilibrium between the groups. Within each group the players
coordinate their strategies. An example of such a situation is a modification
of the classical trader's problem. Groups of traders maximize their wealth.
They are faced with transaction cost for their own trades and a cost for their
own terminal position. In addition they face a cost for the average holding
within their group. The asset price is impacted by the trades of all agents. We
propose a three-timescale reinforcement learning algorithm to approximate the
solution of such MFCG problems. We test the algorithm on benchmark
linear-quadratic specifications for which we have analytic solutions.

In this work we derive a second-order approach to bilevel optimization, a
type of mathematical programming in which the solution to a parameterized
optimization problem (the "lower" problem) is itself to be optimized (in the
"upper" problem) as a function of the parameters. Many existing approaches to
bilevel optimization employ first-order sensitivity analysis, based on the
implicit function theorem (IFT), for the lower problem to derive a gradient of
the lower problem solution with respect to its parameters; this IFT gradient is
then used in a first-order optimization method for the upper problem. This
paper extends this sensitivity analysis to provide second-order derivative
information of the lower problem (which we call the IFT Hessian), enabling the
usage of faster-converging second-order optimization methods at the upper
level. Our analysis shows that (i) much of the computation already used to
produce the IFT gradient can be reused for the IFT Hessian, (ii) errors bounds
derived for the IFT gradient readily apply to the IFT Hessian, (iii) computing
IFT Hessians can significantly reduce overall computation by extracting more
information from each lower level solve. We corroborate our findings and
demonstrate the broad range of applications of our method by applying it to
problem instances of least squares hyperparameter auto-tuning, multi-class SVM
auto-tuning, and inverse optimal control.

For problems requiring cooperation, many multiagent systems implement
solutions among either individual agents or across an entire population towards
a common goal. Multiagent teams are primarily studied when in conflict;
however, organizational psychology (OP) highlights the benefits of teams among
human populations for learning how to coordinate and cooperate. In this paper,
we propose a new model of multiagent teams for reinforcement learning (RL)
agents inspired by OP and early work on teams in artificial intelligence. We
validate our model using complex social dilemmas that are popular in recent
multiagent RL and find that agents divided into teams develop cooperative
pro-social policies despite incentives to not cooperate. Furthermore, agents
are better able to coordinate and learn emergent roles within their teams and
achieve higher rewards compared to when the interests of all agents are
aligned.

This paper considers the problem of Bayesian optimization for systems with
safety-critical constraints, where both the objective function and the
constraints are unknown, but can be observed by querying the system. In
safety-critical applications, querying the system at an infeasible point can
have catastrophic consequences. Such systems require a safe learning framework,
such that the performance objective can be optimized while satisfying the
safety-critical constraints with high probability. In this paper we propose a
safe Bayesian optimization framework that ensures that the points queried are
always in the interior of the partially revealed safe region, thereby
guaranteeing constraint satisfaction with high probability. The proposed
interior-point Bayesian optimization framework can be used with any acquisition
function, making it broadly applicable. The performance of the proposed method
is demonstrated using a personalized insulin dosing application for patients
with type 1 diabetes.

The strong lottery ticket hypothesis has highlighted the potential for
training deep neural networks by pruning, which has inspired interesting
practical and theoretical insights into how neural networks can represent
functions. For networks with ReLU activation functions, it has been proven that
a target network with depth $L$ can be approximated by the subnetwork of a
randomly initialized neural network that has double the target's depth $2L$ and
is wider by a logarithmic factor. We show that a depth $L+1$ network is
sufficient. This result indicates that we can expect to find lottery tickets at
realistic, commonly used depths while only requiring logarithmic
overparametrization. Our novel construction approach applies to a large class
of activation functions and is not limited to ReLUs.

We propose a new strategy for applying large pre-trained language models to
novel tasks when labeled training data is limited. Rather than apply the model
in a typical zero-shot or few-shot fashion, we treat the model as the basis for
labeling functions in a weak supervision framework. To create a classifier, we
first prompt the model to answer multiple distinct queries about an example and
define how the possible responses should be mapped to votes for labels and
abstentions. We then denoise these noisy label sources using the Snorkel system
and train an end classifier with the resulting training data. Our experimental
evaluation shows that prompting large language models within a weak supervision
framework can provide significant gains in accuracy. On the WRENCH weak
supervision benchmark, this approach can significantly improve over zero-shot
performance, an average 19.5% reduction in errors. We also find that this
approach produces classifiers with comparable or superior accuracy to those
trained from hand-engineered rules.

Electronic health records (EHRs) are usually highly dimensional,
heterogeneous, and multimodal. Besides, the random recording of clinical
variables results in high missing rates and uneven time intervals between
adjacent records in the multivariate clinical time-series data extracted from
EHRs. Current works using clinical time-series data for patient representation
regard the patients' physiological status as a discrete process described by
sporadically collected records. However, changes in the patient's physiological
condition are continuous and dynamic processes. The perception of time and
velocity of change is crucial for patient representation learning. In this
study, we propose a time- and velocity-aware gated recurrent unit model
(GRU-TV) for patient representation learning of clinical multivariate
time-series data in a time-continuous manner. The neural ordinary differential
equations (ODEs) and velocity perception mechanism are applied to perceive the
time interval between adjacent records and changing rate of the patient's
physiological status, respectively. Our experiments on two real clinical EHR
datasets (PhysioNet2012, MIMIC-III) establish that GRU-TV is a robust model on
computer-aided diagnosis (CAD) tasks, especially on sequences with
high-variance time intervals.

Recent studies show that auto-encoder based approaches successfully perform
language generation, smooth sentence interpolation, and style transfer over
unseen attributes using unlabelled datasets in a zero-shot manner. The latent
space geometry of such models is organised well enough to perform on datasets
where the style is "coarse-grained" i.e. a small fraction of words alone in a
sentence are enough to determine the overall style label. A recent study uses a
discrete token-based perturbation approach to map "similar" sentences
("similar" defined by low Levenshtein distance/ high word overlap) close by in
latent space. This definition of "similarity" does not look into the underlying
nuances of the constituent words while mapping latent space neighbourhoods and
therefore fails to recognise sentences with different style-based semantics
while mapping latent neighbourhoods. We introduce EPAAEs (Embedding Perturbed
Adversarial AutoEncoders) which completes this perturbation model, by adding a
finely adjustable noise component on the continuous embeddings space. We
empirically show that this (a) produces a better organised latent space that
clusters stylistically similar sentences together, (b) performs best on a
diverse set of text style transfer tasks than similar denoising-inspired
baselines, and (c) is capable of fine-grained control of Style Transfer
strength. We also extend the text style transfer tasks to NLI datasets and show
that these more complex definitions of style are learned best by EPAAE. To the
best of our knowledge, extending style transfer to NLI tasks has not been
explored before.

This paper proposes a novel approach for learning a data-driven quadratic
manifold from high-dimensional data, then employing this quadratic manifold to
derive efficient physics-based reduced-order models. The key ingredient of the
approach is a polynomial mapping between high-dimensional states and a
low-dimensional embedding. This mapping consists of two parts: a representation
in a linear subspace (computed in this work using the proper orthogonal
decomposition) and a quadratic component. The approach can be viewed as a form
of data-driven closure modeling, since the quadratic component introduces
directions into the approximation that lie in the orthogonal complement of the
linear subspace, but without introducing any additional degrees of freedom to
the low-dimensional representation. Combining the quadratic manifold
approximation with the operator inference method for projection-based model
reduction leads to a scalable non-intrusive approach for learning reduced-order
models of dynamical systems. Applying the new approach to transport-dominated
systems of partial differential equations illustrates the gains in efficiency
that can be achieved over approximation in a linear subspace.

Dense retrieval is becoming one of the standard approaches for document and
passage ranking. The dual-encoder architecture is widely adopted for scoring
question-passage pairs due to its efficiency and high performance. Typically,
dense retrieval models are evaluated on clean and curated datasets. However,
when deployed in real-life applications, these models encounter noisy
user-generated text. That said, the performance of state-of-the-art dense
retrievers can substantially deteriorate when exposed to noisy text. In this
work, we study the robustness of dense retrievers against typos in the user
question. We observe a significant drop in the performance of the dual-encoder
model when encountering typos and explore ways to improve its robustness by
combining data augmentation with contrastive learning. Our experiments on two
large-scale passage ranking and open-domain question answering datasets show
that our proposed approach outperforms competing approaches. Additionally, we
perform a thorough analysis on robustness. Finally, we provide insights on how
different typos affect the robustness of embeddings differently and how our
method alleviates the effect of some typos but not of others.

The final goal of all industrial machine learning (ML) projects is to develop
ML products and rapidly bring them into production. However, it is highly
challenging to automate and operationalize ML products and thus many ML
endeavors fail to deliver on their expectations. The paradigm of Machine
Learning Operations (MLOps) addresses this issue. MLOps includes several
aspects, such as best practices, sets of concepts, and development culture.
However, MLOps is still a vague term and its consequences for researchers and
professionals are ambiguous. To address this gap, we conduct mixed-method
research, including a literature review, a tool review, and expert interviews.
As a result of these investigations, we provide an aggregated overview of the
necessary principles, components, and roles, as well as the associated
architecture and workflows. Furthermore, we furnish a definition of MLOps and
highlight open challenges in the field. Finally, this work provides guidance
for ML researchers and practitioners who want to automate and operate their ML
products with a designated set of technologies.

In this paper, we study the problem of procedure planning in instructional
videos. Here, an agent must produce a plausible sequence of actions that can
transform the environment from a given start to a desired goal state. When
learning procedure planning from instructional videos, most recent work
leverages intermediate visual observations as supervision, which requires
expensive annotation efforts to localize precisely all the instructional steps
in training videos. In contrast, we remove the need for expensive temporal
video annotations and propose a weakly supervised approach by learning from
natural language instructions. Our model is based on a transformer equipped
with a memory module, which maps the start and goal observations to a sequence
of plausible actions. Furthermore, we augment our model with a probabilistic
generative module to capture the uncertainty inherent to procedure planning, an
aspect largely overlooked by previous work. We evaluate our model on three
datasets and show our weaklysupervised approach outperforms previous fully
supervised state-of-the-art models on multiple metrics.

Zero Day Threats (ZDT) are novel methods used by malicious actors to attack
and exploit information technology (IT) networks or infrastructure. In the past
few years, the number of these threats has been increasing at an alarming rate
and have been costing organizations millions of dollars to remediate. The
increasing expansion of network attack surfaces and the exponentially growing
number of assets on these networks necessitate the need for a robust AI-based
Zero Day Threat detection model that can quickly analyze petabyte-scale data
for potentially malicious and novel activity. In this paper, the authors
introduce a deep learning based approach to Zero Day Threat detection that can
generalize, scale, and effectively identify threats in near real-time. The
methodology utilizes network flow telemetry augmented with asset-level graph
features, which are passed through a dual-autoencoder structure for anomaly and
novelty detection respectively. The models have been trained and tested on four
large scale datasets that are representative of real-world organizational
networks and they produce strong results with high precision and recall values.
The models provide a novel methodology to detect complex threats with low
false-positive rates that allow security operators to avoid alert fatigue while
drastically reducing their mean time to response with near-real-time detection.
Furthermore, the authors also provide a novel, labelled, cyber attack dataset
generated from adversarial activity that can be used for validation or training
of other models. With this paper, the authors' overarching goal is to provide a
novel architecture and training methodology for cyber anomaly detectors that
can generalize to multiple IT networks with minimal to no retraining while
still maintaining strong performance.

Human-translated text displays distinct features from naturally written text
in the same language. This phenomena, known as translationese, has been argued
to confound the machine translation (MT) evaluation. Yet, we find that existing
work on translationese neglects some important factors and the conclusions are
mostly correlational but not causal. In this work, we collect CausalMT, a
dataset where the MT training data are also labeled with the human translation
directions. We inspect two critical factors, the train-test direction match
(whether the human translation directions in the training and test sets are
aligned), and data-model direction match (whether the model learns in the same
direction as the human translation direction in the dataset). We show that
these two factors have a large causal effect on the MT performance, in addition
to the test-model direction mismatch highlighted by existing work on the impact
of translationese. In light of our findings, we provide a set of suggestions
for MT training and evaluation. Our code and data are at
https://github.com/EdisonNi-hku/CausalMT

This paper introduces pyRDF2Vec, a Python software package that reimplements
the well-known RDF2Vec algorithm along with several of its extensions. By
making the algorithm available in the most popular data science language, and
by bundling all extensions into a single place, the use of RDF2Vec is
simplified for data scientists. The package is released under a MIT license and
structured in such a way to foster further research into sampling, walking, and
embedding strategies, which are vital components of the RDF2Vec algorithm.
Several optimisations have been implemented in \texttt{pyRDF2Vec} that allow
for more efficient walk extraction than the original algorithm. Furthermore,
best practices in terms of code styling, testing, and documentation were
applied such that the package is future-proof as well as to facilitate external
contributions.

Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world
applications. Yet, estimating the unknown parameters of stochastic, nonlinear
dynamical models remains a challenging problem. The majority of existing
methods employ maximum likelihood or Bayesian estimation. However, these
methods suffer from some limitations, most notably the substantial
computational time for inference coupled with limited flexibility in
application. In this work, we propose DeepBayes estimators that leverage the
power of deep recurrent neural networks in learning an estimator. The method
consists of first training a recurrent neural network to minimize the
mean-squared estimation error over a set of synthetically generated data using
models drawn from the model set of interest. The a priori trained estimator can
then be used directly for inference by evaluating the network with the
estimation data. The deep recurrent neural network architectures can be trained
offline and ensure significant time savings during inference. We experiment
with two popular recurrent neural networks -- long short term memory network
(LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our
proposed method on different example models and perform detailed comparisons
with state-of-the-art approaches. We also provide a study on a real-world
nonlinear benchmark problem. The experimental evaluations show that the
proposed approach is asymptotically as good as the Bayes estimator.

Quantum Machine Learning (QML) models are aimed at learning from data encoded
in quantum states. Recently, it has been shown that models with little to no
inductive biases (i.e., with no assumptions about the problem embedded in the
model) are likely to have trainability and generalization issues, especially
for large problem sizes. As such, it is fundamental to develop schemes that
encode as much information as available about the problem at hand. In this work
we present a simple, yet powerful, framework where the underlying invariances
in the data are used to build QML models that, by construction, respect those
symmetries. These so-called group-invariant models produce outputs that remain
invariant under the action of any element of the symmetry group $\mathfrak{G}$
associated to the dataset. We present theoretical results underpinning the
design of $\mathfrak{G}$-invariant models, and exemplify their application
through several paradigmatic QML classification tasks including cases when
$\mathfrak{G}$ is a continuous Lie group and also when it is a discrete
symmetry group. Notably, our framework allows us to recover, in an elegant way,
several well known algorithms for the literature, as well as to discover new
ones. Taken together, we expect that our results will help pave the way towards
a more geometric and group-theoretic approach to QML model design.

Accurate uncertainty estimates can significantly improve the performance of
iterative design of experiments, as in Sequential and Reinforcement learning.
For many such problems in engineering and the physical sciences, the design
task depends on multiple correlated model outputs as objectives and/or
constraints. To better solve these problems, we propose a recalibrated
bootstrap method to generate multivariate prediction intervals for bagged
models and show that it is well-calibrated. We apply the recalibrated bootstrap
to a simulated sequential learning problem with multiple objectives and show
that it leads to a marked decrease in the number of iterations required to find
a satisfactory candidate. This indicates that the recalibrated bootstrap could
be a valuable tool for practitioners using machine learning to optimize systems
with multiple competing targets.

One of the major goals of the field of Milky Way dynamics is to recover the
gravitational potential field. Mapping the potential would allow us to
determine the spatial distribution of matter - both baryonic and dark -
throughout the Galaxy. We present a novel method for determining the
gravitational field from a snapshot of the phase-space positions of stars,
based only on minimal physical assumptions, which makes use of recently
developed tools from the field of deep learning. We first train a normalizing
flow on a sample of observed six-dimensional phase-space coordinates of stars,
obtaining a smooth, differentiable approximation of the distribution function.
Using the Collisionless Boltzmann Equation, we then find the gravitational
potential - represented by a feed-forward neural network - that renders this
distribution function stationary. This method, which we term "Deep Potential,"
is more flexible than previous parametric methods, which fit restricted classes
of analytic models of the distribution function and potential to the data. We
demonstrate Deep Potential on mock datasets, and demonstrate its robustness
under various non-ideal conditions. Deep Potential is a promising approach to
mapping the density of the Milky Way and other stellar systems, using rich
datasets of stellar positions and kinematics now being provided by Gaia and
ground-based spectroscopic surveys.

Unsupervised relation extraction aims to extract the relationship between
entities from natural language sentences without prior information on
relational scope or distribution. Existing works either utilize self-supervised
schemes to refine relational feature signals by iteratively leveraging adaptive
clustering and classification that provoke gradual drift problems, or adopt
instance-wise contrastive learning which unreasonably pushes apart those
sentence pairs that are semantically similar. To overcome these defects, we
propose a novel contrastive learning framework named HiURE, which has the
capability to derive hierarchical signals from relational feature space using
cross hierarchy attention and effectively optimize relation representation of
sentences under exemplar-wise contrastive learning. Experimental results on two
public datasets demonstrate the advanced effectiveness and robustness of HiURE
on unsupervised relation extraction when compared with state-of-the-art models.

This study aims to understand the South African political context by
analysing the sentiments shared on Twitter during the local government
elections. An emphasis on the analysis was placed on understanding the
discussions led around four predominant political parties ANC, DA, EFF and
ActionSA. A semi-supervised approach by means of a graph-based technique to
label the vast accessible Twitter data for the classification of tweets into
negative and positive sentiment was used. The tweets expressing negative
sentiment were further analysed through latent topic extraction to uncover
hidden topics of concern associated with each of the political parties. Our
findings demonstrated that the general sentiment across South African Twitter
users is negative towards all four predominant parties with the worst negative
sentiment among users projected towards the current ruling party, ANC, relating
to concerns cantered around corruption, incompetence and loadshedding.

Optical sensors and learning algorithms for autonomous vehicles have
dramatically advanced in the past few years. Nonetheless, the reliability of
today's autonomous vehicles is hindered by the limited line-of-sight sensing
capability and the brittleness of data-driven methods in handling extreme
situations. With recent developments of telecommunication technologies,
cooperative perception with vehicle-to-vehicle communications has become a
promising paradigm to enhance autonomous driving in dangerous or emergency
situations. We introduce COOPERNAUT, an end-to-end learning model that uses
cross-vehicle perception for vision-based cooperative driving. Our model
encodes LiDAR information into compact point-based representations that can be
transmitted as messages between vehicles via realistic wireless channels. To
evaluate our model, we develop AutoCastSim, a network-augmented driving
simulation framework with example accident-prone scenarios. Our experiments on
AutoCastSim suggest that our cooperative perception driving models lead to a
40% improvement in average success rate over egocentric driving models in these
challenging driving situations and a 5 times smaller bandwidth requirement than
prior work V2VNet. COOPERNAUT and AUTOCASTSIM are available at
https://ut-austin-rpl.github.io/Coopernaut/.

Standard federated optimization methods successfully apply to stochastic
problems with single-level structure. However, many contemporary ML problems --
including adversarial robustness, hyperparameter tuning, and actor-critic --
fall under nested bilevel programming that subsumes minimax and compositional
optimization. In this work, we propose \fedblo: A federated alternating
stochastic gradient method to address general nested problems. We establish
provable convergence rates for \fedblo in the presence of heterogeneous data
and introduce variations for bilevel, minimax, and compositional optimization.
\fedblo introduces multiple innovations including federated hypergradient
computation and variance reduction to address inner-level heterogeneity. We
complement our theory with experiments on hyperparameter \&
hyper-representation learning and minimax optimization that demonstrate the
benefits of our method in practice. Code is available at
https://github.com/ucr-optml/FedNest.

The performance of supervised classification techniques often deteriorates
when the data has noisy labels. Even the semi-supervised classification
approaches have largely focused only on the problem of handling missing labels.
Most of the approaches addressing the noisy label data rely on deep neural
networks (DNN) that require huge datasets for classification tasks. This poses
a serious challenge especially in process and manufacturing industries, where
the data is limited and labels are noisy. We propose a semi-supervised cascaded
clustering (SSCC) algorithm to extract patterns and generate a cascaded tree of
classes in such datasets. A novel cluster evaluation matrix (CEM) with
configurable hyperparameters is introduced to localize and eliminate the noisy
labels and invoke a pruning criterion on cascaded clustering. The algorithm
reduces the dependency on expensive human expertise for assessing the accuracy
of labels. A classifier generated based on SSCC is found to be accurate and
consistent even when trained on noisy label datasets. It performed better in
comparison with the support vector machines (SVM) when tested on multiple
noisy-label datasets, including an industrial dataset. The proposed approach
can be effectively used for deriving actionable insights in industrial settings
with minimal human expertise.

With massive advancements in sensor technologies and Internet-of-things, we
now have access to terabytes of historical data; however, there is a lack of
clarity in how to best exploit the data to predict future events. One possible
alternative in this context is to utilize operator learning algorithm that
directly learn nonlinear mapping between two functional spaces; this
facilitates real-time prediction of naturally arising complex evolutionary
dynamics. In this work, we introduce a novel operator learning algorithm
referred to as the Wavelet Neural Operator (WNO) that blends integral kernel
with wavelet transformation. WNO harnesses the superiority of the wavelets in
time-frequency localization of the functions and enables accurate tracking of
patterns in spatial domain and effective learning of the functional mappings.
Since the wavelets are localized in both time/space and frequency, WNO can
provide high spatial and frequency resolution. This offers learning of the
finer details of the parametric dependencies in the solution for complex
problems. The efficacy and robustness of the proposed WNO are illustrated on a
wide array of problems involving Burger's equation, Darcy flow, Navier-Stokes
equation, Allen-Cahn equation, and Wave advection equation. Comparative study
with respect to existing operator learning frameworks are presented. Finally,
the proposed approach is used to build a digital twin capable of predicting
Earth's air temperature based on available historical data.

Abstractive summarization models are typically pre-trained on large amounts
of generic texts, then fine-tuned on tens or hundreds of thousands of annotated
samples. However, in opinion summarization, large annotated datasets of reviews
paired with reference summaries are not available and would be expensive to
create. This calls for fine-tuning methods robust to overfitting on small
datasets. In addition, generically pre-trained models are often not accustomed
to the specifics of customer reviews and, after fine-tuning, yield summaries
with disfluencies and semantic mistakes. To address these problems, we utilize
an efficient few-shot method based on adapters which, as we show, can easily
store in-domain knowledge. Instead of fine-tuning the entire model, we add
adapters and pre-train them in a task-specific way on a large corpus of
unannotated customer reviews, using held-out reviews as pseudo summaries. Then,
fine-tune the adapters on the small available human-annotated dataset. We show
that this self-supervised adapter pre-training improves summary quality over
standard fine-tuning by 2.0 and 1.3 ROUGE-L points on the Amazon and Yelp
datasets, respectively. Finally, for summary personalization, we condition on
aspect keyword queries, automatically created from generic datasets. In the
same vein, we pre-train the adapters in a query-based manner on customer
reviews and then fine-tune them on annotated datasets. This results in
better-organized summary content reflected in improved coherence and fewer
redundancies.

Learning-to-rank, a machine learning technique widely used in information
retrieval, has recently been applied to the problem of ligand-based virtual
screening, to accelerate the early stages of new drug development. Ranking
prediction models learn based on ordinal relationships, making them suitable
for integrating assay data from various environments. Existing studies of rank
prediction in compound screening have generally used a learning-to-rank method
called RankSVM. However, they have not been compared with or validated against
the gradient boosting decision tree (GBDT)-based learning-to-rank methods that
have gained popularity recently. Furthermore, although the ranking metric
called Normalized Discounted Cumulative Gain (NDCG) is widely used in
information retrieval, it only determines whether the predictions are better
than those of other models. In other words, NDCG is incapable of recognizing
when a prediction model produces worse than random results. Nevertheless, NDCG
is still used in the performance evaluation of compound screening using
learning-to-rank. This study used the GBDT model with ranking loss functions,
called lambdarank and lambdaloss, for ligand-based virtual screening; results
were compared with existing RankSVM methods and GBDT models using regression.
We also proposed a new ranking metric, Normalized Enrichment Discounted
Cumulative Gain (NEDCG), which aims to properly evaluate the goodness of
ranking predictions. Results showed that the GBDT model with learning-to-rank
outperformed existing regression methods using GBDT and RankSVM on diverse
datasets. Moreover, NEDCG showed that predictions by regression were comparable
to random predictions in multi-assay, multi-family datasets, demonstrating its
usefulness for a more direct assessment of compound screening performance.

We develop an algorithm for parameter-free stochastic convex optimization
(SCO) whose rate of convergence is only a double-logarithmic factor larger than
the optimal rate for the corresponding known-parameter setting. In contrast,
the best previously known rates for parameter-free SCO are based on online
parameter-free regret bounds, which contain unavoidable excess logarithmic
terms compared to their known-parameter counterparts. Our algorithm is
conceptually simple, has high-probability guarantees, and is also partially
adaptive to unknown gradient norms, smoothness, and strong convexity. At the
heart of our results is a novel parameter-free certificate for SGD step size
choice, and a time-uniform concentration result that assumes no a-priori bounds
on SGD iterates.

Recent studies indicate that detecting radiographic patterns on CT scans can
yield high sensitivity and specificity for Covid-19 localization. In this
paper, we investigate the appropriateness of deep learning models
transferability, for semantic segmentation of pneumonia-infected areas in CT
images. Transfer learning allows for the fast initialization/reutilization of
detection models, given that large volumes of training data are not available.
Our work explores the efficacy of using pre-trained U-Net architectures, on a
specific CT data set, for identifying Covid-19 side-effects over images from
different datasets. Experimental results indicate improvement in the
segmentation accuracy of identifying Covid-19 infected regions.

Recently, self-attention mechanisms have shown impressive performance in
various NLP and CV tasks, which can help capture sequential characteristics and
derive global information. In this work, we explore how to extend
self-attention modules to better learn subtle feature embeddings for
recognizing fine-grained objects, e.g., different bird species or person
identities. To this end, we propose a dual cross-attention learning (DCAL)
algorithm to coordinate with self-attention learning. First, we propose
global-local cross-attention (GLCA) to enhance the interactions between global
images and local high-response regions, which can help reinforce the
spatial-wise discriminative clues for recognition. Second, we propose pair-wise
cross-attention (PWCA) to establish the interactions between image pairs. PWCA
can regularize the attention learning of an image by treating another image as
distractor and will be removed during inference. We observe that DCAL can
reduce misleading attentions and diffuse the attention response to discover
more complementary parts for recognition. We conduct extensive evaluations on
fine-grained visual categorization and object re-identification. Experiments
demonstrate that DCAL performs on par with state-of-the-art methods and
consistently improves multiple self-attention baselines, e.g., surpassing
DeiT-Tiny and ViT-Base by 2.8% and 2.4% mAP on MSMT17, respectively.

Image inpainting refers to the restoration of an image with missing regions
in a way that is not detectable by the observer. The inpainting regions can be
of any size and shape. This is an ill-posed inverse problem that does not have
a unique solution. In this work, we focus on learning-based image completion
methods for multiple and diverse inpainting which goal is to provide a set of
distinct solutions for a given damaged image. These methods capitalize on the
probabilistic nature of certain generative models to sample various solutions
that coherently restore the missing content. Along the chapter, we will analyze
the underlying theory and analyze the recent proposals for multiple inpainting.
To investigate the pros and cons of each method, we present quantitative and
qualitative comparisons, on common datasets, regarding both the quality and the
diversity of the set of inpainted solutions. Our analysis allows us to identify
the most successful generative strategies in both inpainting quality and
inpainting diversity. This task is closely related to the learning of an
accurate probability distribution of images. Depending on the dataset in use,
the challenges that entail the training of such a model will be discussed
through the analysis.

Channel pruning is used to reduce the number of weights in a Convolutional
Neural Network (CNN). Channel pruning removes slices of the weight tensor so
that the convolution layer remains dense. The removal of these weight slices
from a single layer causes mismatching number of feature maps between layers of
the network. A simple solution is to force the number of feature map between
layers to match through the removal of weight slices from subsequent layers.
This additional constraint becomes more apparent in DNNs with branches where
multiple channels need to be pruned together to keep the network dense. Popular
pruning saliency metrics do not factor in the structural dependencies that
arise in DNNs with branches. We propose Domino metrics (built on existing
channel saliency metrics) to reflect these structural constraints. We test
Domino saliency metrics against the baseline channel saliency metrics on
multiple networks with branches. Domino saliency metrics improved pruning rates
in most tested networks and up to 25% in AlexNet on CIFAR-10.

In this paper, we ask the research question of whether all the datasets in
the benchmark are necessary. We approach this by first characterizing the
distinguishability of datasets when comparing different systems. Experiments on
9 datasets and 36 systems show that several existing benchmark datasets
contribute little to discriminating top-scoring systems, while those less used
datasets exhibit impressive discriminative power. We further, taking the text
classification task as a case study, investigate the possibility of predicting
dataset discrimination based on its properties (e.g., average sentence length).
Our preliminary experiments promisingly show that given a sufficient number of
training experimental records, a meaningful predictor can be learned to
estimate dataset discrimination over unseen datasets. We released all datasets
with features explored in this work on DataLab:
\url{https://datalab.nlpedia.ai}.

Phase-field-based models have become common in material science, mechanics,
physics, biology, chemistry, and engineering for the simulation of
microstructure evolution. Yet, they suffer from the drawback of being
computationally very costly when applied to large, complex systems. To reduce
such computational costs, a Unet-based artificial neural network is developed
as a surrogate model in the current work. Training input for this network is
obtained from the results of the numerical solution of initial-boundary-value
problems (IBVPs) based on the Fan-Chen model for grain microstructure
evolution. In particular, about 250 different simulations with varying initial
order parameters are carried out and 200 frames of the time evolution of the
phase fields are stored for each simulation. The network is trained with 90% of
this data, taking the $i$-th frame of a simulation, i.e. order parameter field,
as input, and producing the $(i+1)$-th frame as the output. Evaluation of the
network is carried out with a test dataset consisting of 2200 microstructures
based on different configurations than originally used for training. The
trained network is applied recursively on initial order parameters to calculate
the time evolution of the phase fields. The results are compared to the ones
obtained from the conventional numerical solution in terms of the errors in
order parameters and the system's free energy. The resulting order parameter
error averaged over all points and all simulation cases is 0.005 and the
relative error in the total free energy in all simulation boxes does not exceed
1%.

Deep Reinforcement Learning (DRL) is being used in many domains. One of the
biggest advantages of DRL is that it enables the continuous improvement of a
learning agent. Secondly, the DRL framework is robust and flexible enough to be
applicable to problems of varying nature and domain. Presented work is evidence
of using the DRL technique to solve an Optimal Power Flow (OPF) problem. Two
classical algorithms have been presented to solve the OPF problem. The
drawbacks of the vanilla DRL application are discussed, and an algorithm is
suggested to improve the performance. Secondly, a reward function for the OPF
problem is presented that enables the solution of inherent issues in DRL.
Reasons for divergence and degeneration in DRL are discussed, and the correct
strategy to deal with them with respect to OPF is presented.

This paper combines fisheries dependent data and environmental data to be
used in a machine learning pipeline to predict the spatio-temporal abundance of
two species (plaice and sole) commonly caught by the Belgian fishery in the
North Sea. By combining fisheries related features with environmental data, sea
bottom temperature derived from remote sensing, a higher accuracy can be
achieved. In a forecast setting, the predictive accuracy is further improved by
predicting, using a recurrent deep neural network, the sea bottom temperature
up to four days in advance instead of relying on the last previous temperature
measurement.

Sparse R-CNN is a recent strong object detection baseline by set prediction
on sparse, learnable proposal boxes and proposal features. In this work, we
propose to improve Sparse R-CNN with two dynamic designs. First, Sparse R-CNN
adopts a one-to-one label assignment scheme, where the Hungarian algorithm is
applied to match only one positive sample for each ground truth. Such
one-to-one assignment may not be optimal for the matching between the learned
proposal boxes and ground truths. To address this problem, we propose dynamic
label assignment (DLA) based on the optimal transport algorithm to assign
increasing positive samples in the iterative training stages of Sparse R-CNN.
We constrain the matching to be gradually looser in the sequential stages as
the later stage produces the refined proposals with improved precision. Second,
the learned proposal boxes and features remain fixed for different images in
the inference process of Sparse R-CNN. Motivated by dynamic convolution, we
propose dynamic proposal generation (DPG) to assemble multiple proposal experts
dynamically for providing better initial proposal boxes and features for the
consecutive training stages. DPG thereby can derive sample-dependent proposal
boxes and features for inference. Experiments demonstrate that our method,
named Dynamic Sparse R-CNN, can boost the strong Sparse R-CNN baseline with
different backbones for object detection. Particularly, Dynamic Sparse R-CNN
reaches the state-of-the-art 47.2% AP on the COCO 2017 validation set,
surpassing Sparse R-CNN by 2.2% AP with the same ResNet-50 backbone.

In this paper, we introduce Masked Anomaly Detection (MAD), a general
self-supervised learning task for multivariate time series anomaly detection.
With the increasing availability of sensor data from industrial systems, being
able to detecting anomalies from streams of multivariate time series data is of
significant importance. Given the scarcity of anomalies in real-world
applications, the majority of literature has been focusing on modeling
normality. The learned normal representations can empower anomaly detection as
the model has learned to capture certain key underlying data regularities. A
typical formulation is to learn a predictive model, i.e., use a window of time
series data to predict future data values. In this paper, we propose an
alternative self-supervised learning task. By randomly masking a portion of the
inputs and training a model to estimate them using the remaining ones, MAD is
an improvement over the traditional left-to-right next step prediction (NSP)
task. Our experimental results demonstrate that MAD can achieve better anomaly
detection rates over traditional NSP approaches when using exactly the same
neural network (NN) base models, and can be modified to run as fast as NSP
models during test time on the same hardware, thus making it an ideal upgrade
for many existing NSP-based NN anomaly detection models.

Wearable and IoT devices requiring positioning and localisation services grow
in number exponentially every year. This rapid growth also produces millions of
data entries that need to be pre-processed prior to being used in any indoor
positioning system to ensure the data quality and provide a high Quality of
Service (QoS) to the end-user. In this paper, we offer a novel and
straightforward data cleansing algorithm for WLAN fingerprinting radio maps.
This algorithm is based on the correlation among fingerprints using the
Received Signal Strength (RSS) values and the Access Points (APs)'s identifier.
We use those to compute the correlation among all samples in the dataset and
remove fingerprints with low level of correlation from the dataset. We
evaluated the proposed method on 14 independent publicly-available datasets. As
a result, an average of 14% of fingerprints were removed from the datasets. The
2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with
a slight increase in the floor hit rate by 1.2% on average. Consequently, the
average speed of position prediction was also increased by 14%.

We present qLEET, an open-source Python package for studying parameterized
quantum circuits (PQCs), which are widely used in various variational quantum
algorithms (VQAs) and quantum machine learning (QML) algorithms. qLEET enables
computation of properties such as expressibility and entangling power of a PQC
by studying its entanglement spectrum and the distribution of parameterized
states produced by it. Furthermore, it allows users to visualize the training
trajectories of PQCs along with high-dimensional loss landscapes generated by
them for different objective functions. It supports quantum circuits and noise
models built using popular quantum computing libraries such as Qiskit, Cirq,
and Pyquil. In our work, we demonstrate how qLEET provides opportunities to
design and improve hybrid quantum-classical algorithms by utilizing intuitive
insights from the ansatz capability and structure of the loss landscape.

We are concerned with the question of how an agent can acquire its own
representations from sensory data. We restrict our focus to learning
representations for long-term planning, a class of problems that
state-of-the-art learning methods are unable to solve. We propose a framework
for autonomously learning state abstractions of an agent's environment, given a
set of skills. Importantly, these abstractions are task-independent, and so can
be reused to solve new tasks. We demonstrate how an agent can use an existing
set of options to acquire representations from ego- and object-centric
observations. These abstractions can immediately be reused by the same agent in
new environments. We show how to combine these portable representations with
problem-specific ones to generate a sound description of a specific task that
can be used for abstract planning. Finally, we show how to autonomously
construct a multi-level hierarchy consisting of increasingly abstract
representations. Since these hierarchies are transferable, higher-order
concepts can be reused in new tasks, relieving the agent from relearning them
and improving sample efficiency. Our results demonstrate that our approach
allows an agent to transfer previous knowledge to new tasks, improving sample
efficiency as the number of tasks increases.

Image-to-image translation (I2I) aims at transferring the content
representation from an input domain to an output one, bouncing along different
target domains. Recent I2I generative models, which gain outstanding results in
this task, comprise a set of diverse deep networks each with tens of million
parameters. Moreover, images are usually three-dimensional being composed of
RGB channels and common neural models do not take dimensions correlation into
account, losing beneficial information. In this paper, we propose to leverage
hypercomplex algebra properties to define lightweight I2I generative models
capable of preserving pre-existing relations among image dimensions, thus
exploiting additional input information. On manifold I2I benchmarks, we show
how the proposed Quaternion StarGANv2 and parameterized hypercomplex StarGANv2
(PHStarGANv2) reduce parameters and storage memory amount while ensuring high
domain translation performance and good image quality as measured by FID and
LPIPS scores. Full code is available at: https://github.com/ispamm/HI2I.

Sea fog significantly threatens the safety of maritime activities. This paper
develops a sea fog dataset (SFDD) and a dual branch sea fog detection network
(DB-SFNet). We investigate all the observed sea fog events in the Yellow Sea
and the Bohai Sea (118.1{\deg}E-128.1{\deg}E, 29.5{\deg}N-43.8{\deg}N) from
2010 to 2020, and collect the sea fog images for each event from the
Geostationary Ocean Color Imager (GOCI) to comprise the dataset SFDD. The
location of the sea fog in each image in SFDD is accurately marked. The
proposed dataset is characterized by a long-time span, large number of samples,
and accurate labeling, that can substantially improve the robustness of various
sea fog detection models. Furthermore, this paper proposes a dual branch sea
fog detection network to achieve accurate and holistic sea fog detection. The
poporsed DB-SFNet is composed of a knowledge extraction module and a dual
branch optional encoding decoding module. The two modules jointly extracts
discriminative features from both visual and statistical domain. Experiments
show promising sea fog detection results with an F1-score of 0.77 and a
critical success index of 0.63. Compared with existing advanced deep learning
networks, DB-SFNet is superior in detection performance and stability,
particularly in the mixed cloud and fog areas.

We propose a new approach for unsupervised learning of hyperelastic
constitutive laws with physics-consistent deep neural networks. In contrast to
supervised learning, which assumes the availability of stress-strain pairs, the
approach only uses realistically measurable full-field displacement and global
reaction force data, thus it lies within the scope of our recent framework for
Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID)
and we denote it as NN-EUCLID. The absence of stress labels is compensated for
by leveraging a physics-motivated loss function based on the conservation of
linear momentum to guide the learning process. The constitutive model is based
on input-convex neural networks, which are capable of learning a function that
is convex with respect to its inputs. By employing a specially designed neural
network architecture, multiple physical and thermodynamic constraints for
hyperelastic constitutive laws, such as material frame indifference,
(poly-)convexity, and stress-free reference configuration are automatically
satisfied. We demonstrate the ability of the approach to accurately learn
several hidden isotropic and anisotropic hyperelastic constitutive laws -
including e.g., Mooney-Rivlin, Arruda-Boyce, Ogden, and Holzapfel models -
without using stress data. For anisotropic hyperelasticity, the unknown
anisotropic fiber directions are automatically discovered jointly with the
constitutive model. The neural network-based constitutive models show good
generalization capability beyond the strain states observed during training and
are readily deployable in a general finite element framework for simulating
complex mechanical boundary value problems with good accuracy.

We provide efficient estimation methods for first- and second-price auctions
under independent (asymmetric) private values and partial observability. Given
a finite set of observations, each comprising the identity of the winner and
the price they paid in a sequence of identical auctions, we provide algorithms
for non-parametrically estimating the bid distribution of each bidder, as well
as their value distributions under equilibrium assumptions. We provide
finite-sample estimation bounds which are uniform in that their error rates do
not depend on the bid/value distributions being estimated. Our estimation
guarantees advance a body of work in Econometrics wherein only identification
results have been obtained, unless the setting is symmetric, parametric, or all
bids are observable. Our guarantees also provide computationally and
statistically effective alternatives to classical techniques from reliability
theory. Finally, our results are immediately applicable to Dutch and English
auctions.

Video-to-speech synthesis (also known as lip-to-speech) refers to the
translation of silent lip movements into the corresponding audio. This task has
received an increasing amount of attention due to its self-supervised nature
(i.e., can be trained without manual labelling) combined with the ever-growing
collection of audio-visual data available online. Despite these strong
motivations, contemporary video-to-speech works focus mainly on small- to
medium-sized corpora with substantial constraints in both vocabulary and
setting. In this work, we introduce a scalable video-to-speech framework
consisting of two components: a video-to-spectrogram predictor and a
pre-trained neural vocoder, which converts the mel-frequency spectrograms into
waveform audio. We achieve state-of-the art results for GRID and considerably
outperform previous approaches on LRW. More importantly, by focusing on
spectrogram prediction using a simple feedforward model, we can efficiently and
effectively scale our method to very large and unconstrained datasets: To the
best of our knowledge, we are the first to show intelligible results on the
challenging LRS3 dataset.

In this paper, we for the first time investigate the random access problem
for a delay-constrained heterogeneous wireless network. We begin with a simple
two-device problem where two devices deliver delay-constrained traffic to an
access point (AP) via a common unreliable collision channel. By assuming that
one device (called Device 1) adopts ALOHA, we aim to optimize the random access
scheme of the other device (called Device 2). The most intriguing part of this
problem is that Device 2 does not know the information of Device 1 but needs to
maximize the system timely throughput. We first propose a Markov Decision
Process (MDP) formulation to derive a model-based upper bound so as to quantify
the performance gap of certain random access schemes. We then utilize
reinforcement learning (RL) to design an R-learning-based random access scheme,
called tiny state-space R-learning random access (TSRA), which is subsequently
extended for the tackling of the general multi-device problem. We carry out
extensive simulations to show that the proposed TSRA simultaneously achieves
higher timely throughput, lower computation complexity, and lower power
consumption than the existing baseline--deep-reinforcement learning multiple
access (DLMA). This indicates that our proposed TSRA scheme is a promising
means for efficient random access over massive mobile devices with limited
computation and battery capabilities.

We conduct an exploratory study that looks at incorporating John Rawls' ideas
on fairness into existing unsupervised machine learning algorithms. Our focus
is on the task of clustering, specifically the k-means clustering algorithm. To
the best of our knowledge, this is the first work that uses Rawlsian ideas in
clustering. Towards this, we attempt to develop a postprocessing technique
i.e., one that operates on the cluster assignment generated by the standard
k-means clustering algorithm. Our technique perturbs this assignment over a
number of iterations to make it fairer according to Rawls' difference principle
while minimally affecting the overall utility. As the first step, we consider
two simple perturbation operators -- $\mathbf{R_1}$ and $\mathbf{R_2}$ -- that
reassign examples in a given cluster assignment to new clusters; $\mathbf{R_1}$
assigning a single example to a new cluster, and $\mathbf{R_2}$ a pair of
examples to new clusters. Our experiments on a sample of the Adult dataset
demonstrate that both operators make meaningful perturbations in the cluster
assignment towards incorporating Rawls' difference principle, with
$\mathbf{R_2}$ being more efficient than $\mathbf{R_1}$ in terms of the number
of iterations. However, we observe that there is still a need to design
operators that make significantly better perturbations. Nevertheless, both
operators provide good baselines for designing and comparing any future
operator, and we hope our findings would aid future work in this direction.

Pearl's do calculus is a complete axiomatic approach to learn the
identifiable causal effects from observational data. When such an effect is not
identifiable, it is necessary to perform a collection of often costly
interventions in the system to learn the causal effect. In this work, we
consider the problem of designing the collection of interventions with the
minimum cost to identify the desired effect. First, we prove that this problem
is NP-hard, and subsequently propose an algorithm that can either find the
optimal solution or a logarithmic-factor approximation of it. This is done by
establishing a connection between our problem and the minimum hitting set
problem. Additionally, we propose several polynomial-time heuristic algorithms
to tackle the computational complexity of the problem. Although these
algorithms could potentially stumble on sub-optimal solutions, our simulations
show that they achieve small regrets on random graphs.

Self-Supervised learning (SSL) has become the new state-of-art in several
domain classification and segmentation tasks. Of these, one popular category in
SSL is distillation networks such as BYOL. This work proposes RSDnet, which
applies the distillation network (BYOL) in the remote sensing (RS) domain where
data is non-trivially different from natural RGB images. Since Multi-spectral
(MS) and synthetic aperture radar (SAR) sensors provide varied spectral and
spatial resolution information, we utilised them as an implicit augmentation to
learn invariant feature embeddings. In order to learn RS based invariant
features with SSL, we trained RSDnet in two ways, i.e., single channel feature
learning and three channel feature learning. This work explores the usefulness
of single channel feature learning from random MS and SAR bands compared to the
common notion of using three or more bands. In our linear evaluation, these
single channel features reached a 0.92 F1 score on the EuroSAT classification
task and 59.6 mIoU on the DFC segmentation task for certain single bands. We
also compared our results with ImageNet weights and showed that the RS based
SSL model outperforms the supervised ImageNet based model. We further explored
the usefulness of multi-modal data compared to single modality data, and it is
shown that utilising MS and SAR data learn better invariant representations
than utilising only MS data.

We present FREDo, a few-shot document-level relation extraction (FSDLRE)
benchmark. As opposed to existing benchmarks which are built on sentence-level
relation extraction corpora, we argue that document-level corpora provide more
realism, particularly regarding none-of-the-above (NOTA) distributions.
Therefore, we propose a set of FSDLRE tasks and construct a benchmark based on
two existing supervised learning data sets, DocRED and sciERC. We adapt the
state-of-the-art sentence-level method MNAV to the document-level and develop
it further for improved domain adaptation. We find FSDLRE to be a challenging
setting with interesting new characteristics such as the ability to sample NOTA
instances from the support set. The data, code, and trained models are
available online (https://github.com/nicpopovic/FREDo).

Two-sample tests are important areas aiming to determine whether two
collections of observations follow the same distribution or not. We propose
two-sample tests based on integral probability metric (IPM) for
high-dimensional samples supported on a low-dimensional manifold. We
characterize the properties of proposed tests with respect to the number of
samples $n$ and the structure of the manifold with intrinsic dimension $d$.
When an atlas is given, we propose two-step test to identify the difference
between general distributions, which achieves the type-II risk in the order of
$n^{-1/\max\{d,2\}}$. When an atlas is not given, we propose H\"older IPM test
that applies for data distributions with $(s,\beta)$-H\"older densities, which
achieves the type-II risk in the order of $n^{-(s+\beta)/d}$. To mitigate the
heavy computation burden of evaluating the H\"older IPM, we approximate the
H\"older function class using neural networks. Based on the approximation
theory of neural networks, we show that the neural network IPM test has the
type-II risk in the order of $n^{-(s+\beta)/d}$, which is in the same order of
the type-II risk as the H\"older IPM test. Our proposed tests are adaptive to
low-dimensional geometric structure because their performance crucially depends
on the intrinsic dimension instead of the data dimension.

Crowdfunding provides project founders with a convenient way to reach online
investors. However, it is challenging for founders to find the most potential
investors and successfully raise money for their projects on crowdfunding
platforms. A few machine learning based methods have been proposed to recommend
investors' interest in a specific crowdfunding project, but they fail to
provide project founders with explanations in detail for these recommendations,
thereby leading to an erosion of trust in predicted investors. To help
crowdfunding founders find truly interested investors, we conducted
semi-structured interviews with four crowdfunding experts and presents
inSearch, a visual analytic system. inSearch allows founders to search for
investors interactively on crowdfunding platforms. It supports an effective
overview of potential investors by leveraging a Graph Neural Network to model
investor preferences. Besides, it enables interactive exploration and
comparison of the temporal evolution of different investors' investment
details.

In classification tasks, it is crucial to meaningfully exploit information
contained in data. Here, we propose a physics-inspired dynamical system that
adapts Optimal Transport principles to effectively leverage color distributions
of images. Our dynamics regulates immiscible fluxes of colors traveling on a
network built from images. Instead of aggregating colors together, it treats
them as different commodities that interact with a shared capacity on edges.
Our method outperforms competitor algorithms on image classification tasks in
datasets where color information matters.

Recent years have witnessed increasing interest in code representation
learning, which aims to represent the semantics of source code into distributed
vectors. Currently, various works have been proposed to represent the complex
semantics of source code from different views, including plain text, Abstract
Syntax Tree (AST), and several kinds of code graphs (e.g., Control/Data Flow
Graph). However, most of them only consider a single view of source code
independently, ignoring the correspondences among different views. In this
paper, we propose to integrate different views with the natural-language
description of source code into a unified framework with Multi-View contrastive
Pre-training, and name our model as CODE-MVP. Specifically, we first extract
multiple code views using compiler tools, and learn the complementary
information among them under a contrastive learning framework. Inspired by the
type checking in compilation, we also design a fine-grained type inference
objective in the pre-training. Experiments on three downstream tasks over five
datasets demonstrate the superiority of CODE-MVP when compared with several
state-of-the-art baselines. For example, we achieve 2.4/2.3/1.1 gain in terms
of MRR/MAP/Accuracy metrics on natural language code retrieval, code
similarity, and code defect detection tasks, respectively.

Recognizing transformation types applied to a video clip (RecogTrans) is a
long-established paradigm for self-supervised video representation learning,
which achieves much inferior performance compared to instance discrimination
approaches (InstDisc) in recent works. However, based on a thorough comparison
of representative RecogTrans and InstDisc methods, we observe the great
potential of RecogTrans on both semantic-related and temporal-related
downstream tasks. Based on hard-label classification, existing RecogTrans
approaches suffer from noisy supervision signals in pre-training. To mitigate
this problem, we developed TransRank, a unified framework for recognizing
Transformations in a Ranking formulation. TransRank provides accurate
supervision signals by recognizing transformations relatively, consistently
outperforming the classification-based formulation. Meanwhile, the unified
framework can be instantiated with an arbitrary set of temporal or spatial
transformations, demonstrating good generality. With a ranking-based
formulation and several empirical practices, we achieve competitive performance
on video retrieval and action recognition. Under the same setting, TransRank
surpasses the previous state-of-the-art method by 6.4% on UCF101 and 8.3% on
HMDB51 for action recognition (Top1 Acc); improves video retrieval on UCF101 by
20.4% (R@1). The promising results validate that RecogTrans is still a worth
exploring paradigm for video self-supervised learning. Codes will be released
at https://github.com/kennymckormick/TransRank.

The success of multilingual pre-trained models is underpinned by their
ability to learn representations shared by multiple languages even in absence
of any explicit supervision. However, it remains unclear how these models learn
to generalise across languages. In this work, we conjecture that multilingual
pre-trained models can derive language-universal abstractions about grammar. In
particular, we investigate whether morphosyntactic information is encoded in
the same subset of neurons in different languages. We conduct the first
large-scale empirical study over 43 languages and 14 morphosyntactic categories
with a state-of-the-art neuron-level probe. Our findings show that the
cross-lingual overlap between neurons is significant, but its extent may vary
across categories and depends on language proximity and pre-training data size.

Recently, Bafekry et al. [Appl. Phys. Lett. 120, 189901 (2022)] reported
their density functional theory (DFT) results on the elastic constants of C6N7
monolayer. They predicted non-zero elastic constants along the out-of-plane
direction for a single-layered material, which contradicts with basic physics
of the stiffness tensor for plane stress condition. Moreover, in their work
Young's modulus is erroneously calculated. On the basis of DFT calculations,
herein we predicted the C11, C12 and C66 of the C6N7 monolayer to be 286, 73
and 107 GPa, respectively, equivalent with an in-plane Youngs modulus of 267
GPa. Using DFT calculations and a machine learning interatomic potential, we
also show that C6N7 monolayer shows isotropic elasticity.

Works on learning job title representation are mainly based on
\textit{Job-Transition Graph}, built from the working history of talents.
However, since these records are usually messy, this graph is very sparse,
which affects the quality of the learned representation and hinders further
analysis. To address this specific issue, we propose to enrich the graph with
additional nodes that improve the quality of job title representation.
Specifically, we construct \textit{Job-Transition-Tag Graph}, a heterogeneous
graph containing two types of nodes, i.e., job titles and tags (i.e., words
related to job responsibilities or functionalities). Along this line, we
reformulate job title representation learning as the task of learning node
embedding on the \textit{Job-Transition-Tag Graph}. Experiments on two datasets
show the interest of our approach.

Real-world natural language processing (NLP) models need to be continually
updated to fix the prediction errors in out-of-distribution (OOD) data streams
while overcoming catastrophic forgetting. However, existing continual learning
(CL) problem setups cannot cover such a realistic and complex scenario. In
response to this, we propose a new CL problem formulation dubbed continual
model refinement (CMR). Compared to prior CL settings, CMR is more practical
and introduces unique challenges (boundary-agnostic and non-stationary
distribution shift, diverse mixtures of multiple OOD data clusters,
error-centric streams, etc.). We extend several existing CL approaches to the
CMR setting and evaluate them extensively. For benchmarking and analysis, we
propose a general sampling algorithm to obtain dynamic OOD data streams with
controllable non-stationarity, as well as a suite of metrics measuring various
aspects of online performance. Our experiments and detailed analysis reveal the
promise and challenges of the CMR problem, supporting that studying CMR in
dynamic OOD streams can benefit the longevity of deployed NLP models in

We stand at the foot of a significant inflection in the trajectory of
scientific discovery. As society continues on its fast-paced digital
transformation, so does humankind's collective scientific knowledge and
discourse. We now read and write papers in digitized form, and a great deal of
the formal and informal processes of science are captured digitally --
including papers, preprints and books, code and datasets, conference
presentations, and interactions in social networks and communication platforms.
The transition has led to the growth of a tremendous amount of information,
opening exciting opportunities for computational models and systems that
analyze and harness it. In parallel, exponential growth in data processing
power has fueled remarkable advances in AI, including self-supervised neural
models capable of learning powerful representations from large-scale
unstructured text without costly human supervision. The confluence of societal
and computational trends suggests that computer science is poised to ignite a
revolution in the scientific process itself.
  However, the explosion of scientific data, results and publications stands in
stark contrast to the constancy of human cognitive capacity. While scientific
knowledge is expanding with rapidity, our minds have remained static, with
severe limitations on the capacity for finding, assimilating and manipulating
information. We propose a research agenda of task-guided knowledge retrieval,
in which systems counter humans' bounded capacity by ingesting corpora of
scientific knowledge and retrieving inspirations, explanations, solutions and
evidence synthesized to directly augment human performance on salient tasks in
scientific endeavors. We present initial progress on methods and prototypes,
and lay out important opportunities and challenges ahead with computational
approaches that have the potential to revolutionize science.

Robot navigation in dynamic environments shared with humans is an important
but challenging task, which suffers from performance deterioration as the crowd
grows. In this paper, multi-subgoal robot navigation approach based on deep
reinforcement learning is proposed, which can reason about more comprehensive
relationships among all agents (robot and humans). Specifically, the next
position point is planned for the robot by introducing history information and
interactions in our work. Firstly, based on subgraph network, the history
information of all agents is aggregated before encoding interactions through a
graph neural network, so as to improve the ability of the robot to anticipate
the future scenarios implicitly. Further consideration, in order to reduce the
probability of unreliable next position points, the selection module is
designed after policy network in the reinforcement learning framework. In
addition, the next position point generated from the selection module satisfied
the task requirements better than that obtained directly from the policy
network. The experiments demonstrate that our approach outperforms
state-of-the-art approaches in terms of both success rate and collision rate,
especially in crowded human environments.

The Korean wave, which denotes the global popularity of South Korea's
cultural economy, contributes to the increasing demand for the Korean language.
However, as there does not exist any application for foreigners to learn
Korean, this paper suggested a design of a novel Korean learning application.
Speech recognition, speech-to-text, and speech-to-waveform are the three key
systems in the proposed system. The Google API and the librosa library will
transform the user's voice into a sentence and MFCC. The software will then
display the user's phrase and answer, with mispronounced elements highlighted
in red, allowing users to more easily recognize the incorrect parts of their
pronunciation. Furthermore, the Siamese network might utilize those translated
spectrograms to provide a similarity score, which could subsequently be used to
offer feedback to the user. Despite the fact that we were unable to collect
sufficient foreigner data for this research, it is notable that we presented a
novel Korean pronunciation correction method for foreigners.

We describe EmoBank, a corpus of 10k English sentences balancing multiple
genres, which we annotated with dimensional emotion metadata in the
Valence-Arousal-Dominance (VAD) representation format. EmoBank excels with a
bi-perspectival and bi-representational design. On the one hand, we distinguish
between writer's and reader's emotions, on the other hand, a subset of the
corpus complements dimensional VAD annotations with categorical ones based on
Basic Emotions. We find evidence for the supremacy of the reader's perspective
in terms of IAA and rating intensity, and achieve close-to-human performance
when mapping between dimensional and categorical formats.

The success of machine learning is fueled by the increasing availability of
computing power and large training datasets. The training data is used to learn
new models or update existing ones, assuming that it is sufficiently
representative of the data that will be encountered at test time. This
assumption is challenged by the threat of poisoning, an attack that manipulates
the training data to compromise the model's performance at test time. Although
poisoning has been acknowledged as a relevant threat in industry applications,
and a variety of different attacks and defenses have been proposed so far, a
complete systematization and critical review of the field is still missing. In
this survey, we provide a comprehensive systematization of poisoning attacks
and defenses in machine learning, reviewing more than 100 papers published in
the field in the last 15 years. We start by categorizing the current threat
models and attacks, and then organize existing defenses accordingly. While we
focus mostly on computer-vision applications, we argue that our systematization
also encompasses state-of-the-art attacks and defenses for other data
modalities. Finally, we discuss existing resources for research in poisoning,
and shed light on the current limitations and open research questions in this
research field.

Networks of low-cost sensors are becoming ubiquitous, but often suffer from
poor accuracies and drift. Regular colocation with reference sensors allows
recalibration but is complicated and expensive. Alternatively the calibration
can be transferred using low-cost, mobile sensors. However inferring the
calibration (with uncertainty) becomes difficult. We propose a variational
approach to model the calibration across the network. We demonstrate the
approach on synthetic and real air pollution data, and find it can perform
better than the state of the art (multi-hop calibration). We extend it to
categorical data produced by citizen-scientist labelling. In Summary: The
method achieves uncertainty-quantified calibration, which has been one of the
barriers to low-cost sensor deployment and citizen-science research.

This paper describes the ON-TRAC Consortium translation systems developed for
two challenge tracks featured in the Evaluation Campaign of IWSLT 2022:
low-resource and dialect speech translation. For the Tunisian Arabic-English
dataset (low-resource and dialect tracks), we build an end-to-end model as our
joint primary submission, and compare it against cascaded models that leverage
a large fine-tuned wav2vec 2.0 model for ASR. Our results show that in our
settings pipeline approaches are still very competitive, and that with the use
of transfer learning, they can outperform end-to-end models for speech
translation (ST). For the Tamasheq-French dataset (low-resource track) our
primary submission leverages intermediate representations from a wav2vec 2.0
model trained on 234 hours of Tamasheq audio, while our contrastive model uses
a French phonetic transcription of the Tamasheq audio as input in a Conformer
speech translation architecture jointly trained on automatic speech
recognition, ST and machine translation losses. Our results highlight that
self-supervised models trained on smaller sets of target data are more
effective to low-resource end-to-end ST fine-tuning, compared to large
off-the-shelf models. Results also illustrate that even approximate phonetic
transcriptions can improve ST scores.

Service robots are integrating more and more into our daily lives to help us
with various tasks. In such environments, robots frequently face new objects
while working in the environment and need to learn them in an open-ended
fashion. Furthermore, such robots must be able to recognize a wide range of
object categories. In this paper, we present a lifelong ensemble learning
approach based on multiple representations to address the few-shot object
recognition problem. In particular, we form ensemble methods based on deep
representations and handcrafted 3D shape descriptors. To facilitate lifelong
learning, each approach is equipped with a memory unit for storing and
retrieving object information instantly. The proposed model is suitable for
open-ended learning scenarios where the number of 3D object categories is not
fixed and can grow over time. We have performed extensive sets of experiments
to assess the performance of the proposed approach in offline, and open-ended
scenarios. For the evaluation purpose, in addition to real object datasets, we
generate a large synthetic household objects dataset consisting of 27000 views
of 90 objects. Experimental results demonstrate the effectiveness of the
proposed method on online few-shot 3D object recognition tasks, as well as its
superior performance over the state-of-the-art open-ended learning approaches.
Furthermore, our results show that while ensemble learning is modestly
beneficial in offline settings, it is significantly beneficial in lifelong
few-shot learning situations. Additionally, we demonstrated the effectiveness
of our approach in both simulated and real-robot settings, where the robot
rapidly learned new categories from limited examples.

We significantly improve the performance of the E automated theorem prover on
the Isabelle Sledgehammer problems by combining learning and theorem proving in
several ways. In particular, we develop targeted versions of the ENIGMA
guidance for the Isabelle problems, targeted versions of neural premise
selection, and targeted strategies for E. The methods are trained in several
iterations over hundreds of thousands untyped and typed first-order problems
extracted from Isabelle. Our final best single-strategy ENIGMA and premise
selection system improves the best previous version of E by 25.3% in 15
seconds, outperforming also all other previous ATP and SMT systems.

Grant-free random access (RA) techniques are suitable for machine-type
communication (MTC) networks but they need to be adaptive to the MTC traffic,
which is different from the human-type communication. Conventional RA protocols
such as exponential backoff (EB) schemes for slotted-ALOHA suffer from a high
number of collisions and they are not directly applicable to the MTC traffic
models. In this work, we propose to use multi-agent deep Q-network (DQN) with
parameter sharing to find a single policy applied to all machine-type devices
(MTDs) in the network to resolve collisions. Moreover, we consider binary
broadcast feedback common to all devices to reduce signalling overhead. We
compare the performance of our proposed DQN-RA scheme with EB schemes for up to
500 MTDs and show that the proposed scheme outperforms EB policies and provides
a better balance between throughput, delay and collision rate

We focus on creating agents that act in alignment with socially beneficial
norms and values in interactive narratives or text-based games -- environments
wherein an agent perceives and interacts with a world through natural language.
Such interactive agents are often trained via reinforcement learning to
optimize task performance, even when such rewards may lead to agent behaviors
that violate societal norms -- causing harm either to the agent itself or other
entities in the environment. Social value alignment refers to creating agents
whose behaviors conform to expected moral and social norms for a given context
and group of people -- in our case, it means agents that behave in a manner
that is less harmful and more beneficial for themselves and others.
  We build on the Jiminy Cricket benchmark (Hendrycks et al. 2021), a set of 25
annotated interactive narratives containing thousands of morally salient
scenarios covering everything from theft and bodily harm to altruism. We
introduce the GALAD (Game-value ALignment through Action Distillation) agent
that uses the social commonsense knowledge present in specially trained
language models to contextually restrict its action space to only those actions
that are aligned with socially beneficial values. An experimental study shows
that the GALAD agent makes decisions efficiently enough to improve
state-of-the-art task performance by 4% while reducing the frequency of
socially harmful behaviors by 25% compared to strong contemporary value
alignment approaches.

In recent computer vision research, the advent of the Vision Transformer
(ViT) has rapidly revolutionized various architectural design efforts: ViT
achieved state-of-the-art image classification performance using self-attention
found in natural language processing, and MLP-Mixer achieved competitive
performance using simple multi-layer perceptrons. In contrast, several studies
have also suggested that carefully redesigned convolutional neural networks
(CNNs) can achieve advanced performance comparable to ViT without resorting to
these new ideas. Against this background, there is growing interest in what
inductive bias is suitable for computer vision. Here we propose Sequencer, a
novel and competitive architecture alternative to ViT that provides a new
perspective on these issues. Unlike ViTs, Sequencer models long-range
dependencies using LSTMs rather than self-attention layers. We also propose a
two-dimensional version of Sequencer module, where an LSTM is decomposed into
vertical and horizontal LSTMs to enhance performance. Despite its simplicity,
several experiments demonstrate that Sequencer performs impressively well:
Sequencer2D-L, with 54M parameters, realizes 84.6% top-1 accuracy on only
ImageNet-1K. Not only that, we show that it has good transferability and the
robust resolution adaptability on double resolution-band.

Although Thompson sampling is widely used in stationary environments, it does
not effectively account for nonstationarities. To address this limitation, we
propose predictive sampling, a policy that balances between exploration and
exploitation in nonstationary bandit environments. It is equivalent to Thompson
sampling when specialized to stationary environments, but much more effective
across a range of nonstationary environments because it deprioritizes
investment in acquiring information that will quickly lose relevance. To offer
insight in the efficacy of predictive sampling, we establish a regret bound.
This bound highlights dependence on the rate at which new information arrives
to alter the environment. In addition, we conduct experiments on bandit
environments with varying rates of information arrival and observe that
predictive sampling outperforms Thompson sampling.

Various defense models have been proposed to resist adversarial attack
algorithms, but existing adversarial robustness evaluation methods always
overestimate the adversarial robustness of these models (i.e., not approaching
the lower bound of robustness). To solve this problem, this paper uses the
proposed decouple space method to divide the classifier into two parts:
non-linear and linear. Then, this paper defines the representation vector of
the original example (and its space, i.e., the representation space) and uses
the iterative optimization of Absolute Classification Boundaries Initialization
(ACBI) to obtain a better attack starting point. Particularly, this paper
applies ACBI to nearly 50 widely-used defense models (including 8
architectures). Experimental results show that ACBI achieves lower robust
accuracy in all cases.

Efficient machine translation models are commercially important as they can
increase inference speeds, and reduce costs and carbon emissions. Recently,
there has been much interest in non-autoregressive (NAR) models, which promise
faster translation. In parallel to the research on NAR models, there have been
successful attempts to create optimized autoregressive models as part of the
WMT shared task on efficient translation. In this paper, we point out flaws in
the evaluation methodology present in the literature on NAR models and we
provide a fair comparison between a state-of-the-art NAR model and the
autoregressive submissions to the shared task. We make the case for consistent
evaluation of NAR models, and also for the importance of comparing NAR models
with other widely used methods for improving efficiency. We run experiments
with a connectionist-temporal-classification-based (CTC) NAR model implemented
in C++ and compare it with AR models using wall clock times. Our results show
that, although NAR models are faster on GPUs, with small batch sizes, they are
almost always slower under more realistic usage conditions. We call for more
realistic and extensive evaluation of NAR models in future work.

Deep neural networks are widely used in various fields because of their
powerful performance. However, recent studies have shown that deep learning
models are vulnerable to adversarial attacks, i.e., adding a slight
perturbation to the input will make the model obtain wrong results. This is
especially dangerous for some systems with high-security requirements, so this
paper proposes a new defense method by using the model super-fitting state to
improve the model's adversarial robustness (i.e., the accuracy under
adversarial attacks). This paper mathematically proves the effectiveness of
super-fitting and enables the model to reach this state quickly by minimizing
unrelated category scores (MUCS). Theoretically, super-fitting can resist any
existing (even future) CE-based white-box adversarial attacks. In addition,
this paper uses a variety of powerful attack algorithms to evaluate the
adversarial robustness of super-fitting, and the proposed method is compared
with nearly 50 defense models from recent conferences. The experimental results
show that the super-fitting method in this paper can make the trained model
obtain the highest adversarial robustness.

This paper presents a novel state representation for reward-free Markov
decision processes. The idea is to learn, in a self-supervised manner, an
embedding space where distances between pairs of embedded states correspond to
the minimum number of actions needed to transition between them. Compared to
previous methods, our approach does not require any domain knowledge, learning
from offline and unlabeled data. We show how this representation can be
leveraged to learn goal-conditioned policies, providing a notion of similarity
between states and goals and a useful heuristic distance to guide planning and
reinforcement learning algorithms. Finally, we empirically validate our method
in classic control domains and multi-goal environments, demonstrating that our
method can successfully learn representations in large and/or continuous
domains.

Word embeddings are one of the most fundamental technologies used in natural
language processing. Existing word embeddings are high-dimensional and consume
considerable computational resources. In this study, we propose WordTour,
unsupervised one-dimensional word embeddings. To achieve the challenging goal,
we propose a decomposition of the desiderata of word embeddings into two parts,
completeness and soundness, and focus on soundness in this paper. Owing to the
single dimensionality, WordTour is extremely efficient and provides a minimal
means to handle word embeddings. We experimentally confirmed the effectiveness
of the proposed method via user study and document classification.

DADApy is a python software package for analysing and characterising
high-dimensional data manifolds. It provides methods for estimating the
intrinsic dimension and the probability density, for performing density-based
clustering and for comparing different distance metrics. We review the main
functionalities of the package and exemplify its usage in toy cases and in a
real-world application. DADApy is freely available under the open-source Apache
2.0 license.

We propose an adversarial learning framework that deals with the
privacy-utility tradeoff problem under two types of conditions: data-type
ignorant, and data-type aware. Under data-type aware conditions, the privacy
mechanism provides a one-hot encoding of categorical features, representing
exactly one class, while under data-type ignorant conditions the categorical
variables are represented by a collection of scores, one for each class. We use
a neural network architecture consisting of a generator and a discriminator,
where the generator consists of an encoder-decoder pair, and the discriminator
consists of an adversary and a utility provider. Unlike previous research
considering this kind of architecture, which leverages autoencoders (AEs)
without introducing any randomness, or variational autoencoders (VAEs) based on
learning latent representations which are then forced into a Gaussian
assumption, our proposed technique introduces randomness and removes the
Gaussian assumption restriction on the latent variables, only focusing on the
end-to-end stochastic mapping of the input to privatized data. We test our
framework on different datasets: MNIST, FashionMNIST, UCI Adult, and US Census
Demographic Data, providing a wide range of possible private and utility
attributes. We use multiple adversaries simultaneously to test our privacy
mechanism -- some trained from the ground truth data and some trained from the
perturbed data generated by our privacy mechanism. Through comparative
analysis, our results demonstrate better privacy and utility guarantees than
the existing works under similar, data-type ignorant conditions, even when the
latter are considered under their original restrictive single-adversary model.

The study of human gaze behavior in natural contexts requires algorithms for
gaze estimation that are robust to a wide range of imaging conditions. However,
algorithms often fail to identify features such as the iris and pupil centroid
in the presence of reflective artifacts and occlusions. Previous work has shown
that convolutional networks excel at extracting gaze features despite the
presence of such artifacts. However, these networks often perform poorly on
data unseen during training. This work follows the intuition that jointly
training a convolutional network with multiple datasets learns a generalized
representation of eye parts. We compare the performance of a single model
trained with multiple datasets against a pool of models trained on individual
datasets. Results indicate that models tested on datasets in which eye images
exhibit higher appearance variability benefit from multiset training. In
contrast, dataset-specific models generalize better onto eye images with lower
appearance variability.

Computational methods to accelerate natural disaster response include change
detection, map alignment, and vision-aided navigation. Current software
functions optimally only on near-nadir images, though off-nadir images are
often the first sources of information following a natural disaster. The use of
off-nadir images for the aforementioned tasks requires the computation of
geocentric pose, which is an aerial vehicle's spatial orientation with respect
to gravity. This study proposes a deep learning ensemble framework to predict
geocentric pose using 5,923 near-nadir and off-nadir RGB satellite images of
cities worldwide. First, a U-Net Fully Convolutional Neural Network predicts
the pixel-wise above-ground elevation mask of the RGB images. Then, the
elevation masks are concatenated with the RGB images to form four-channel
inputs fed into a second convolutional model, which predicts orientation angle
and magnification scale. A performance accuracy of R2=0.917 significantly
outperforms previous methodologies. In addition, outlier removal is performed
through supervised interpolation, and a sensitivity analysis of elevation masks
is conducted to gauge the usefulness of data features, motivating future
avenues of feature engineering. The high-accuracy software built in this study
contributes to mapping and navigation procedures for effective disaster
response to save lives.

We propose knowledge internalization (KI), which aims to complement the
lexical knowledge into neural dialog models. Instead of further conditioning
the knowledge-grounded dialog (KGD) models on externally retrieved knowledge,
we seek to integrate knowledge about each input token internally into the
model's parameters. To tackle the challenge due to the large scale of lexical
knowledge, we adopt the contrastive learning approach and create an effective
token-level lexical knowledge retriever that requires only weak supervision
mined from Wikipedia. We demonstrate the effectiveness and general
applicability of our approach on various datasets and diversified model

This paper aims to theoretically analyze the complexity of feature
transformations encoded in piecewise linear DNNs with ReLU layers. We propose
metrics to measure three types of complexities of transformations based on the
information theory. We further discover and prove the strong correlation
between the complexity and the disentanglement of transformations. Based on the
proposed metrics, we analyze two typical phenomena of the change of the
transformation complexity during the training process, and explore the ceiling
of a DNN's complexity. The proposed metrics can also be used as a loss to learn
a DNN with the minimum complexity, which also controls the over-fitting level
of the DNN and influences adversarial robustness, adversarial transferability,
and knowledge consistency. Comprehensive comparative studies have provided new
perspectives to understand the DNN.

As Deep Learning (DL) systems are widely deployed for mission-critical
applications, debugging such systems becomes essential. Most existing works
identify and repair suspicious neurons on the trained Deep Neural Network
(DNN), which, unfortunately, might be a detour. Specifically, several existing
studies have reported that many unsatisfactory behaviors are actually
originated from the faults residing in DL programs. Besides, locating faulty
neurons is not actionable for developers, while locating the faulty statements
in DL programs can provide developers with more useful information for
debugging. Though a few recent studies were proposed to pinpoint the faulty
statements in DL programs or the training settings (e.g. too large learning
rate), they were mainly designed based on predefined rules, leading to many
false alarms or false negatives, especially when the faults are beyond their
capabilities.
  In view of these limitations, in this paper, we proposed DeepFD, a
learning-based fault diagnosis and localization framework which maps the fault
localization task to a learning problem. In particular, it infers the
suspicious fault types via monitoring the runtime features extracted during DNN
model training and then locates the diagnosed faults in DL programs. It
overcomes the limitations by identifying the root causes of faults in DL
programs instead of neurons and diagnosing the faults by a learning approach
instead of a set of hard-coded rules. The evaluation exhibits the potential of
DeepFD. It correctly diagnoses 52% faulty DL programs, compared with around
half (27%) achieved by the best state-of-the-art works. Besides, for fault
localization, DeepFD also outperforms the existing works, correctly locating
42% faulty programs, which almost doubles the best result (23%) achieved by the
existing works.

Some recent visual-based relocalization algorithms rely on deep learning
methods to perform camera pose regression from image data. This paper focuses
on the loss functions that embed the error between two poses to perform deep
learning based camera pose regression. Existing loss functions are either
difficult-to-tune multi-objective functions or present unstable reprojection
errors that rely on ground truth 3D scene points and require a two-step
training. To deal with these issues, we introduce a novel loss function which
is based on a multiplane homography integration. This new function does not
require prior initialization and only depends on physically interpretable
hyperparameters. Furthermore, the experiments carried out on well established
relocalization datasets show that it minimizes best the mean square
reprojection error during training when compared with existing loss functions.

Histopathological images provide the definitive source of cancer diagnosis,
containing information used by pathologists to identify and subclassify
malignant disease, and to guide therapeutic choices. These images contain vast
amounts of information, much of which is currently unavailable to human
interpretation. Supervised deep learning approaches have been powerful for
classification tasks, but they are inherently limited by the cost and quality
of annotations. Therefore, we developed Histomorphological Phenotype Learning,
an unsupervised methodology, which requires no annotations and operates via the
self-discovery of discriminatory image features in small image tiles. Tiles are
grouped into morphologically similar clusters which appear to represent
recurrent modes of tumor growth emerging under natural selection. These
clusters have distinct features which can be identified using orthogonal
methods. Applied to lung cancer tissues, we show that they align closely with
patient outcomes, with histopathologically recognised tumor types and growth
patterns, and with transcriptomic measures of immunophenotype.

Industrial Control Systems (ICSs) are becoming more and more important in
managing the operation of many important systems in smart manufacturing, such
as power stations, water supply systems, and manufacturing sites. While massive
digital data can be a driving force for system performance, data security has
raised serious concerns. Anomaly detection, therefore, is essential for
preventing network security intrusions and system attacks. Many AI-based
anomaly detection methods have been proposed and achieved high detection
performance, however, are still a "black box" that is hard to be interpreted.
In this study, we suggest using Explainable Artificial Intelligence to enhance
the perspective and reliable results of an LSTM-based Autoencoder-OCSVM
learning model for anomaly detection in ICS. We demonstrate the performance of
our proposed method based on a well-known SCADA dataset.

The ability to continuously process and retain new information like we do
naturally as humans is a feat that is highly sought after when training neural
networks. Unfortunately, the traditional optimization algorithms often require
large amounts of data available during training time and updates wrt. new data
are difficult after the training process has been completed. In fact, when new
data or tasks arise, previous progress may be lost as neural networks are prone
to catastrophic forgetting. Catastrophic forgetting describes the phenomenon
when a neural network completely forgets previous knowledge when given new
information. We propose a novel training algorithm called training by
explaining in which we leverage Layer-wise Relevance Propagation in order to
retain the information a neural network has already learned in previous tasks
when training on new data. The method is evaluated on a range of benchmark
datasets as well as more complex data. Our method not only successfully retains
the knowledge of old tasks within the neural networks but does so more
resource-efficiently than other state-of-the-art solutions.

Trajectory prediction is a core AI problem with broad applications in
robotics and autonomous driving. While most existing works focus on
deterministic prediction, producing probabilistic forecasts to quantify
prediction uncertainty is critical for downstream decision-making tasks such as
risk assessment, motion planning, and safety guarantees. We introduce a new
metric, mean regional score (MRS), to evaluate the quality of probabilistic
trajectory forecasts. We propose a novel probabilistic trajectory prediction
model, Probabilistic Equivariant Continuous COnvolution (PECCO) and show that
leveraging symmetry, specifically rotation equivariance, can improve the
predictions' accuracy as well as coverage. On both vehicle and pedestrian
datasets, PECCO shows state-of-the-art prediction performance and improved
calibration compared to baselines.

Video prediction models often combine three components: an encoder from pixel
space to a small latent space, a latent space prediction model, and a
generative model back to pixel space. However, the large and unpredictable
pixel space makes training such models difficult, requiring many training
examples. We argue that finding a predictive latent variable and using it to
evaluate the consistency of a future image enables data-efficient predictions
because it precludes the necessity of a generative model training. To
demonstrate it, we created sequence completion intelligence tests in which the
task is to identify a predictably changing feature in a sequence of images and
use this prediction to select the subsequent image. We show that a
one-dimensional Markov Contrastive Predictive Coding (M-CPC_1D) model solves
these tests efficiently, with only five examples. Finally, we demonstrate the
usefulness of M-CPC_1D in solving two tasks without prior training: anomaly
detection and stochastic movement video prediction.

COVID-19 pandemic has been a disruptive event from health, social, and
economic points of view. Besides that, changes in people's lifestyles,
especially during the 2020 lockdowns, also affected energy networks. COVID-19
pandemic has resulted in a significant decline in electricity demand. The
lockdown measures applied to handle the health crisis have caused the most
relevant energy impact of the last years. In this paper, the local experiences
of the distribution network of Milano, a city in northern Italy, are reported.
The analysis starts with a summary of the restrictions imposed during 2020 and
focuses on both active and reactive power flows, and faults. To this end, a
comparison with 2019 data has been performed, highlighting the main differences
with 2020. The outcome of the analysis is a valuable tool to predict urban
distribution networks behavior during times of disruption, helping distribution
system operators to prepare feasible short-term and long-term resilience plans.

We consider the problem of universal dynamic regret minimization under
exp-concave and smooth losses. We show that appropriately designed Strongly
Adaptive algorithms achieve a dynamic regret of $\tilde O(d^2 n^{1/5} C_n^{2/5}
\vee d^2)$, where $n$ is the time horizon and $C_n$ a path variational based on
second order differences of the comparator sequence. Such a path variational
naturally encodes comparator sequences that are piecewise linear -- a powerful
family that tracks a variety of non-stationarity patterns in practice (Kim et
al, 2009). The aforementioned dynamic regret rate is shown to be optimal modulo
dimension dependencies and poly-logarithmic factors of $n$. Our proof
techniques rely on analysing the KKT conditions of the offline oracle and
requires several non-trivial generalizations of the ideas in Baby and Wang,
2021, where the latter work only leads to a slower dynamic regret rate of
$\tilde O(d^{2.5}n^{1/3}C_n^{2/3} \vee d^{2.5})$ for the current problem.

Exploring large-scale pretrained foundation models is of significant interest
in computer vision because these models can be quickly transferred to many
downstream tasks. This paper presents Contrastive Captioner (CoCa), a
minimalist design to pretrain an image-text encoder-decoder foundation model
jointly with contrastive loss and captioning loss, thereby subsuming model
capabilities from contrastive approaches like CLIP and generative methods like
SimVLM. In contrast to standard encoder-decoder transformers where all decoder
layers attend to encoder outputs, CoCa omits cross-attention in the first half
of decoder layers to encode unimodal text representations, and cascades the
remaining decoder layers which cross-attend to the image encoder for multimodal
image-text representations. We apply a contrastive loss between unimodal image
and text embeddings, in addition to a captioning loss on the multimodal decoder
outputs which predicts text tokens autoregressively. By sharing the same
computational graph, the two training objectives are computed efficiently with
minimal overhead. CoCa is pretrained end-to-end and from scratch on both
web-scale alt-text data and annotated images by treating all labels simply as
text, seamlessly unifying natural language supervision for representation
learning. Empirically, CoCa achieves state-of-the-art performance with
zero-shot transfer or minimal task-specific adaptation on a broad range of
downstream tasks, spanning visual recognition (ImageNet, Kinetics-400/600/700,
Moments-in-Time), crossmodal retrieval (MSCOCO, Flickr30K, MSR-VTT), multimodal
understanding (VQA, SNLI-VE, NLVR2), and image captioning (MSCOCO, NoCaps).
Notably on ImageNet classification, CoCa obtains 86.3% zero-shot top-1
accuracy, 90.6% with a frozen encoder and learned classification head, and new
state-of-the-art 91.0% top-1 accuracy on ImageNet with a finetuned encoder.

The knowledge of a well-trained deep neural network (a.k.a. the "teacher") is
valuable for learning similar tasks. Knowledge distillation extracts knowledge
from the teacher and integrates it with the target model (a.k.a. the
"student"), which expands the student's knowledge and improves its learning
efficacy. Instead of enforcing the teacher to work on the same task as the
student, we borrow the knowledge from a teacher trained from a general label
space -- in this "Generalized Knowledge Distillation (GKD)", the classes of the
teacher and the student may be the same, completely different, or partially
overlapped. We claim that the comparison ability between instances acts as an
essential factor threading knowledge across tasks, and propose the RElationship
FacIlitated Local cLassifiEr Distillation (REFILLED) approach, which decouples
the GKD flow of the embedding and the top-layer classifier. In particular,
different from reconciling the instance-label confidence between models,
REFILLED requires the teacher to reweight the hard tuples pushed forward by the
student and then matches the similarity comparison levels between instances. An
embedding-induced classifier based on the teacher model supervises the
student's classification confidence and adaptively emphasizes the most related
supervision from the teacher. REFILLED demonstrates strong discriminative
ability when the classes of the teacher vary from the same to a fully
non-overlapped set w.r.t. the student. It also achieves state-of-the-art
performance on standard knowledge distillation, one-step incremental learning,
and few-shot learning tasks.

We target on the document-level relation extraction in an end-to-end setting,
where the model needs to jointly perform mention extraction, coreference
resolution (COREF) and relation extraction (RE) at once, and gets evaluated in
an entity-centric way. Especially, we address the two-way interaction between
COREF and RE that has not been the focus by previous work, and propose to
introduce explicit interaction namely Graph Compatibility (GC) that is
specifically designed to leverage task characteristics, bridging decisions of
two tasks for direct task interference. Our experiments are conducted on DocRED
and DWIE; in addition to GC, we implement and compare different multi-task
settings commonly adopted in previous work, including pipeline, shared
encoders, graph propagation, to examine the effectiveness of different
interactions. The result shows that GC achieves the best performance by up to
2.3/5.1 F1 improvement over the baseline.

Cross-lingual word embeddings can be applied to several natural language
processing applications across multiple languages. Unlike prior works that use
word embeddings based on the Euclidean space, this short paper presents a
simple and effective cross-lingual Word2Vec model that adapts to the Poincar\'e
ball model of hyperbolic space to learn unsupervised cross-lingual word
representations from a German-English parallel corpus. It has been shown that
hyperbolic embeddings can capture and preserve hierarchical relationships. We
evaluate the model on both hypernymy and analogy tasks. The proposed model
achieves comparable performance with the vanilla Word2Vec model on the
cross-lingual analogy task, the hypernymy task shows that the cross-lingual
Poincar\'e Word2Vec model can capture latent hierarchical structure from free
text across languages, which are absent from the Euclidean-based Word2Vec
representations. Our results show that by preserving the latent hierarchical
information, hyperbolic spaces can offer better representations for
cross-lingual embeddings.

The incredible feats of athleticism demonstrated by humans are made possible
in part by a vast repertoire of general-purpose motor skills, acquired through
years of practice and experience. These skills not only enable humans to
perform complex tasks, but also provide powerful priors for guiding their
behaviors when learning new tasks. This is in stark contrast to what is common
practice in physics-based character animation, where control policies are most
typically trained from scratch for each task. In this work, we present a
large-scale data-driven framework for learning versatile and reusable skill
embeddings for physically simulated characters. Our approach combines
techniques from adversarial imitation learning and unsupervised reinforcement
learning to develop skill embeddings that produce life-like behaviors, while
also providing an easy to control representation for use on new downstream
tasks. Our models can be trained using large datasets of unstructured motion
clips, without requiring any task-specific annotation or segmentation of the
motion data. By leveraging a massively parallel GPU-based simulator, we are
able to train skill embeddings using over a decade of simulated experiences,
enabling our model to learn a rich and versatile repertoire of skills. We show
that a single pre-trained model can be effectively applied to perform a diverse
set of new tasks. Our system also allows users to specify tasks through simple
reward functions, and the skill embedding then enables the character to
automatically synthesize complex and naturalistic strategies in order to
achieve the task objectives.

The problem of Airwriting Recognition is focused on identifying letters
written by movement of finger in free space. It is a type of gesture
recognition where the dictionary corresponds to letters in a specific language.
In particular, airwriting recognition using sensor data from wrist-worn devices
can be used as a medium of user input for applications in Human-Computer
Interaction (HCI). Recognition of in-air trajectories using such wrist-worn
devices is limited in literature and forms the basis of the current work. In
this paper, we propose an airwriting recognition framework by first encoding
the time-series data obtained from a wearable Inertial Measurement Unit (IMU)
on the wrist as images and then utilizing deep learning-based models for
identifying the written alphabets. The signals recorded from 3-axis
accelerometer and gyroscope in IMU are encoded as images using different
techniques such as Self Similarity Matrix (SSM), Gramian Angular Field (GAF)
and Markov Transition Field (MTF) to form two sets of 3-channel images. These
are then fed to two separate classification models and letter prediction is
made based on an average of the class conditional probabilities obtained from
the two models. Several standard model architectures for image classification
such as variants of ResNet, DenseNet, VGGNet, AlexNet and GoogleNet have been
utilized. Experiments performed on two publicly available datasets demonstrate
the efficacy of the proposed strategy. The code for our implementation will be
made available at https://github.com/ayushayt/ImAiR.

We present a novel self-taught framework for unsupervised metric learning,
which alternates between predicting class-equivalence relations between data
through a moving average of an embedding model and learning the model with the
predicted relations as pseudo labels. At the heart of our framework lies an
algorithm that investigates contexts of data on the embedding space to predict
their class-equivalence relations as pseudo labels. The algorithm enables
efficient end-to-end training since it demands no off-the-shelf module for
pseudo labeling. Also, the class-equivalence relations provide rich supervisory
signals for learning an embedding space. On standard benchmarks for metric
learning, it clearly outperforms existing unsupervised learning methods and
sometimes even beats supervised learning models using the same backbone
network. It is also applied to semi-supervised metric learning as a way of
exploiting additional unlabeled data, and achieves the state of the art by
boosting performance of supervised learning substantially.

Restoring and inpainting the visual memories that are present, but often
impaired, in old photos remains an intriguing but unsolved research topic.
Decades-old photos often suffer from severe and commingled degradation such as
cracks, defocus, and color-fading, which are difficult to treat individually
and harder to repair when they interact. Deep learning presents a plausible
avenue, but the lack of large-scale datasets of old photos makes addressing
this restoration task very challenging. Here we present a novel reference-based
end-to-end learning framework that is able to both repair and colorize old,
degraded pictures. Our proposed framework consists of three modules: a
restoration sub-network that conducts restoration from degradations, a
similarity network that performs color histogram matching and color transfer,
and a colorization subnet that learns to predict the chroma elements of images
conditioned on chromatic reference signals. The overall system makes uses of
color histogram priors from reference images, which greatly reduces the need
for large-scale training data. We have also created a first-of-a-kind public
dataset of real old photos that are paired with ground truth ''pristine''
photos that have been manually restored by PhotoShop experts. We conducted
extensive experiments on this dataset and synthetic datasets, and found that
our method significantly outperforms previous state-of-the-art models using
both qualitative comparisons and quantitative measurements. The code is
available at https://github.com/DerrickXuNu/Pik-Fix.

Exposure to crime and violence can harm individuals' quality of life and the
economic growth of communities. In light of the rapid development in machine
learning, there is a rise in the need to explore automated solutions to prevent
crimes. With the increasing availability of both fine-grained urban and public
service data, there is a recent surge in fusing such cross-domain information
to facilitate crime prediction. By capturing the information about social
structure, environment, and crime trends, existing machine learning predictive
models have explored the dynamic crime patterns from different views. However,
these approaches mostly convert such multi-source knowledge into implicit and
latent representations (e.g., learned embeddings of districts), making it still
a challenge to investigate the impacts of explicit factors for the occurrences
of crimes behind the scenes. In this paper, we present a Spatial-Temporal
Metapath guided Explainable Crime prediction (STMEC) framework to capture
dynamic patterns of crime behaviours and explicitly characterize how the
environmental and social factors mutually interact to produce the forecasts.
Extensive experiments show the superiority of STMEC compared with other
advanced spatiotemporal models, especially in predicting felonies (e.g.,
robberies and assaults with dangerous weapons).

Stories or narratives are comprised of a sequence of events. To compose
interesting stories, professional writers often leverage a creative writing
technique called flashback that inserts past events into current storylines as
we commonly observe in novels and plays. However, it is challenging for
machines to generate flashback as it requires a solid understanding of event
temporal order (e.g. "feeling hungry" before "eat," not vice versa), and the
creativity to arrange storylines so that earlier events do not always appear
first in narrative order. Two major issues in existing systems that exacerbate
the challenges: 1) temporal bias in pertaining and story datasets that leads to
monotonic event temporal orders; 2) lack of explicit guidance that helps
machines decide where to insert flashbacks. We propose to address these issues
using structured storylines to encode events and their pair-wise temporal
relations (before, after and vague) as temporal prompts that guide how stories
should unfold temporally. We leverage a Plan-and-Write framework enhanced by
reinforcement learning to generate storylines and stories end-to-end.
Evaluation results show that the proposed method can generate more interesting
stories with flashbacks while maintaining textual diversity, fluency, and
temporal coherence.

Recent research in deep learning has shown that neural networks can learn
differential equations governing dynamical systems. In this paper, we adapt
this concept to Virtual Analog (VA) modeling to learn the ordinary differential
equations (ODEs) governing the first-order and the second-order diode clipper.
The proposed models achieve performance comparable to state-of-the-art
recurrent neural networks (RNNs) albeit using fewer parameters. We show that
this approach does not require oversampling and allows to increase the sampling
rate after the training has completed, which results in increased accuracy.
Using a sophisticated numerical solver allows to increase the accuracy at the
cost of slower processing. ODEs learned this way do not require closed forms
but are still physically interpretable.

Machine learning (ML) models have been widely successful in the prediction of
material properties. However, large labeled datasets required for training
accurate ML models are elusive and computationally expensive to generate.
Recent advances in Self-Supervised Learning (SSL) frameworks capable of
training ML models on unlabeled data have mitigated this problem and
demonstrated superior performance in computer vision and natural language
processing tasks. Drawing inspiration from the developments in SSL, we
introduce Crystal Twins (CT): an SSL method for crystalline materials property
prediction. Using a large unlabeled dataset, we pre-train a Graph Neural
Network (GNN) by applying the redundancy reduction principle to the graph
latent embeddings of augmented instances obtained from the same crystalline
system. By sharing the pre-trained weights when fine-tuning the GNN for
regression tasks, we significantly improve the performance for 7 challenging
material property prediction benchmarks

The Alberta Infant Motor Scale (AIMS) is a well-known assessment scheme that
evaluates the gross motor development of infants by recording the number of
specific poses achieved. With the aid of the image-based pose recognition
model, the AIMS evaluation procedure can be shortened and automated, providing
early diagnosis or indicator of potential developmental disorder. Due to
limited public infant-related datasets, many works use the SMIL-based method to
generate synthetic infant images for training. However, this domain mismatch
between real and synthetic training samples often leads to performance
degradation during inference. In this paper, we present a CNN-based model which
takes any infant image as input and predicts the coarse and fine-level pose
labels. The model consists of an image branch and a pose branch, which
respectively generates the coarse-level logits facilitated by the unsupervised
domain adaptation and the 3D keypoints using the HRNet with SMPLify
optimization. Then the outputs of these branches will be sent into the
hierarchical pose recognition module to estimate the fine-level pose labels. We
also collect and label a new AIMS dataset, which contains 750 real and 4000
synthetic infants images with AIMS pose labels. Our experimental results show
that the proposed method can significantly align the distribution of synthetic
and real-world datasets, thus achieving accurate performance on fine-grained
infant pose recognition.

A notable challenge in Multi-Document Summarization (MDS) is the
extremely-long length of the input. In this paper, we present an
extract-then-abstract Transformer framework to overcome the problem.
Specifically, we leverage pre-trained language models to construct a
hierarchical extractor for salient sentence selection across documents and an
abstractor for rewriting the selected contents as summaries. However, learning
such a framework is challenging since the optimal contents for the abstractor
are generally unknown. Previous works typically create pseudo extraction oracle
to enable the supervised learning for both the extractor and the abstractor.
Nevertheless, we argue that the performance of such methods could be restricted
due to the insufficient information for prediction and inconsistent objectives
between training and testing. To this end, we propose a loss weighting
mechanism that makes the model aware of the unequal importance for the
sentences not in the pseudo extraction oracle, and leverage the fine-tuned
abstractor to generate summary references as auxiliary signals for learning the
extractor. Moreover, we propose a reinforcement learning method that can
efficiently apply to the extractor for harmonizing the optimization between
training and testing. Experiment results show that our framework substantially
outperforms strong baselines with comparable model sizes and achieves the best
results on the Multi-News, Multi-XScience, and WikiCatSum corpora.

Past research on pedestrian trajectory forecasting mainly focused on
deterministic predictions which provide only point estimates of future states.
These future estimates can help an autonomous vehicle plan its trajectory and
avoid collision. However, under dynamic traffic scenarios, planning based on
deterministic predictions is not trustworthy. Rather, estimating the
uncertainty associated with the predicted states with a certain level of
confidence can lead to robust path planning. Hence, the authors propose to
quantify this uncertainty during forecasting using stochastic approximation
which deterministic approaches fail to capture. The current method is simple
and applies Bayesian approximation during inference to standard neural network
architectures for estimating uncertainty. The authors compared the predictions
between the probabilistic neural network (NN) models with the standard
deterministic models. The results indicate that the mean predicted path of
probabilistic models was closer to the ground truth when compared with the
deterministic prediction. Further, the effect of stochastic dropout of weights
and long-term prediction on future state uncertainty has been studied. It was
found that the probabilistic models produced better performance metrics like
average displacement error (ADE) and final displacement error (FDE). Finally,
the study has been extended to multiple datasets providing a comprehensive
comparison for each model.

Compared to other language tasks, applying pre-trained language models (PLMs)
for search ranking often requires more nuances and training signals. In this
paper, we identify and study the two mismatches between pre-training and
ranking fine-tuning: the training schema gap regarding the differences in
training objectives and model architectures, and the task knowledge gap
considering the discrepancy between the knowledge needed in ranking and that
learned during pre-training. To mitigate these gaps, we propose Pre-trained,
Prompt-learned and Pre-finetuned Neural Ranker (P^3 Ranker). P^3 Ranker
leverages prompt-based learning to convert the ranking task into a pre-training
like schema and uses pre-finetuning to initialize the model on intermediate
supervised tasks. Experiments on MS MARCO and Robust04 show the superior
performances of P^3 Ranker in few-shot ranking. Analyses reveal that P^3 Ranker
is able to better accustom to the ranking task through prompt-based learning
and retrieve necessary ranking-oriented knowledge gleaned in pre-finetuning,
resulting in data-efficient PLM adaptation. Our code is available at
https://github.com/NEUIR/P3Ranker.

Few-shot relation learning refers to infer facts for relations with a limited
number of observed triples. Existing metric-learning methods for this problem
mostly neglect entity interactions within and between triples. In this paper,
we explore this kind of fine-grained semantic meanings and propose our model
TransAM. Specifically, we serialize reference entities and query entities into
sequence and apply transformer structure with local-global attention to capture
both intra- and inter-triple entity interactions. Experiments on two public
benchmark datasets NELL-One and Wiki-One with 1-shot setting prove the
effectiveness of TransAM.

This paper presents fairlib, an open-source framework for assessing and
improving classification fairness. It provides a systematic framework for
quickly reproducing existing baseline models, developing new methods,
evaluating models with different metrics, and visualizing their results. Its
modularity and extensibility enable the framework to be used for diverse types
of inputs, including natural language, images, and audio. In detail, we
implement 14 debiasing methods, including pre-processing, at-training-time, and
post-processing approaches. The built-in metrics cover the most commonly used
fairness criterion and can be further generalized and customized for fairness

We consider the problem of dynamic pricing of a product in the presence of
feature-dependent price sensitivity. Based on the Poisson semi-parametric
approach, we construct a flexible yet interpretable demand model where the
price related part is parametric while the remaining (nuisance) part of the
model is non-parametric and can be modeled via sophisticated ML techniques. The
estimation of price-sensitivity parameters of this model via direct one-stage
regression techniques may lead to biased estimates. We propose a two-stage
estimation methodology which makes the estimation of the price-sensitivity
parameters robust to biases in the nuisance parameters of the model. In the
first-stage we construct the estimators of observed purchases and price given
the feature vector using sophisticated ML estimators like deep neural networks.
Utilizing the estimators from the first-stage, in the second-stage we leverage
a Bayesian dynamic generalized linear model to estimate the price-sensitivity
parameters. We test the performance of the proposed estimation schemes on
simulated and real sales transaction data from Airline industry. Our numerical
studies demonstrate that the two-stage approach provides more accurate
estimates of price-sensitivity parameters as compared to direct one-stage
approach.

When it comes to image compression in digital cameras, denoising is
traditionally performed prior to compression. However, there are applications
where image noise may be necessary to demonstrate the trustworthiness of the
image, such as court evidence and image forensics. This means that noise itself
needs to be coded, in addition to the clean image itself. In this paper, we
present a learning-based image compression framework where image denoising and
compression are performed jointly. The latent space of the image codec is
organized in a scalable manner such that the clean image can be decoded from a
subset of the latent space (the base layer), while the noisy image is decoded
from the full latent space at a higher rate. Using a subset of the latent space
for the denoised image allows denoising to be carried out at a lower rate.
Besides providing a scalable representation of the noisy input image,
performing denoising jointly with compression makes intuitive sense because
noise is hard to compress; hence, compressibility is one of the criteria that
may help distinguish noise from the signal. The proposed codec is compared
against established compression and denoising benchmarks, and the experiments
reveal considerable bitrate savings compared to a cascade combination of a
state-of-the-art codec and a state-of-the-art denoiser.

Research on knowledge graph embedding (KGE) has emerged as an active field in
which most existing KGE approaches mainly focus on static structural data and
ignore the influence of temporal variation involved in time-aware triples. In
order to deal with this issue, several temporal knowledge graph embedding
(TKGE) approaches have been proposed to integrate temporal and structural
information in recent years. However, these methods only employ a uniformly
random sampling to construct negative facts. As a consequence, the corrupted
samples are often too simplistic for training an effective model. In this
paper, we propose a new temporal knowledge graph embedding framework by
introducing adversarial learning to further refine the performance of
traditional TKGE models. In our framework, a generator is utilized to construct
high-quality plausible quadruples and a discriminator learns to obtain the
embeddings of entities and relations based on both positive and negative
samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the
Wasserstein distance to prevent vanishing gradient problems on discrete data;
an inherent flaw in traditional generative adversarial networks. Through
comprehensive experimentation on temporal datasets, the results indicate that
our proposed framework can attain significant improvements based on benchmark
models and also demonstrate the effectiveness and applicability of our

While the wisdom of training an image dehazing model on synthetic hazy data
can alleviate the difficulty of collecting real-world hazy/clean image pairs,
it brings the well-known domain shift problem. From a different yet new
perspective, this paper explores contrastive learning with an adversarial
training effort to leverage unpaired real-world hazy and clean images, thus
bridging the gap between synthetic and real-world haze is avoided. We propose
an effective unsupervised contrastive learning paradigm for image dehazing,
dubbed UCL-Dehaze. Unpaired real-world clean and hazy images are easily
captured, and will serve as the important positive and negative samples
respectively when training our UCL-Dehaze network. To train the network more
effectively, we formulate a new self-contrastive perceptual loss function,
which encourages the restored images to approach the positive samples and keep
away from the negative samples in the embedding space. Besides the overall
network architecture of UCL-Dehaze, adversarial training is utilized to align
the distributions between the positive samples and the dehazed images. Compared
with recent image dehazing works, UCL-Dehaze does not require paired data
during training and utilizes unpaired positive/negative data to better enhance
the dehazing performance. We conduct comprehensive experiments to evaluate our
UCL-Dehaze and demonstrate its superiority over the state-of-the-arts, even
only 1,800 unpaired real-world images are used to train our network. Source
code has been available at https://github.com/yz-wang/UCL-Dehaze.

We present a new procedure rooted in deep learning to construct science
images from data cubes collected by astronomical instruments using HxRG
detectors in low-flux regimes. It improves on the drawbacks of the conventional
algorithms to construct 2D images from multiple readouts by using the readout
scheme of the detectors to reduce the impact of correlated readout noise. We
train a convolutional recurrent neural network on simulated astrophysical
scenes added to laboratory darks to estimate the flux on each pixel of science
images. This method achieves a reduction of the noise on constructed science
images when compared to standard flux-measurement schemes (correlated double
sampling, up-the-ramp sampling), which results in a reduction of the error on
the spectrum extracted from these science images. Over simulated data cubes
created in a low signal-to-noise ratio regime where this method could have the
largest impact, we find that the error on our constructed science images falls
faster than a $1/\sqrt{N}$ decay, and that the spectrum extracted from the
images has, averaged over a test set of three images, a standard error reduced
by a factor of 1.85 in comparison to the standard up-the-ramp pixel sampling
scheme. The code used in this project is publicly available on GitHub

Large language models are shown to memorize privacy information such as
social security numbers in training data. Given the sheer scale of the training
corpus, it is challenging to screen and filter these privacy data, either
manually or automatically. In this paper, we propose Confidentially Redacted
Training (CRT), a method to train language generation models while protecting
the confidential segments. We borrow ideas from differential privacy (which
solves a related but distinct problem) and show that our method is able to
provably prevent unintended memorization by randomizing parts of the training
process. Moreover, we show that redaction with an approximately correct
screening policy amplifies the confidentiality guarantee. We implement the
method for both LSTM and GPT language models. Our experimental results show
that the models trained by CRT obtain almost the same perplexity while
preserving strong confidentiality.

The existing deep learning (DL)-based automated program repair (APR) models
are limited in fixing general software defects. % We present {\tool}, a
DL-based approach that supports fixing for the general bugs that require
dependent changes at once to one or multiple consecutive statements in one or
multiple hunks of code. % We first design a novel fault localization (FL)
technique for multi-hunk, multi-statement fixes that combines traditional
spectrum-based (SB) FL with deep learning and data-flow analysis. It takes the
buggy statements returned by the SBFL model, detects the buggy hunks to be
fixed at once, and expands a buggy statement $s$ in a hunk to include other
suspicious statements around $s$. We design a two-tier, tree-based LSTM model
that incorporates cycle training and uses a divide-and-conquer strategy to
learn proper code transformations for fixing multiple statements in the
suitable fixing context consisting of surrounding subtrees. We conducted
several experiments to evaluate {\tool} on three datasets: Defects4J (395
bugs), BigFix (+26k bugs), and CPatMiner (+44k bugs). On Defects4J dataset,
{\tool} outperforms the baselines from 42\%--683\% in terms of the number of
auto-fixed bugs with only the top-1 patches. On BigFix dataset, it fixes
31--145 more bugs than existing DL-based APR models with the top-1 patches. On
CPatMiner dataset, among 667 fixed bugs, there are 169 (25.3\%)
multi-hunk/multi-statement bugs. {\tool} fixes 71 and 164 more bugs, including
52 and 61 more multi-hunk/multi-statement bugs, than the state-of-the-art,
DL-based APR models.

Objective: Transcranial direct current stimulation (tDCS) is a non-invasive
brain stimulation technique used to generate conduction currents in the head
and disrupt brain functions. To rapidly evaluate the tDCS-induced current
density in near real-time, this paper proposes a deep learning-based emulator,
named DeeptDCS. Methods: The emulator leverages Attention U-net taking the
volume conductor models (VCMs) of head tissues as inputs and outputting the
three-dimensional current density distribution across the entire head. The
electrode configurations are also incorporated into VCMs without increasing the
number of input channels; this enables the straightforward incorporation of the
non-parametric features of electrodes (e.g., thickness, shape, size, and
position) in the training and testing of the proposed emulator. Results:
Attention U-net outperforms standard U-net and its other three variants
(Residual U-net, Attention Residual U-net, and Multi-scale Residual U-net) in
terms of accuracy. The generalization ability of DeeptDCS to non-trained
electrode configurations can be greatly enhanced through fine-tuning the model.
The computational time required by one emulation via DeeptDCS is a fraction of
a second. Conclusion: DeeptDCS is at least two orders of magnitudes faster than
a physics-based open-source simulator, while providing satisfactorily accurate
results. Significance: The high computational efficiency permits the use of
DeeptDCS in applications requiring its repetitive execution, such as
uncertainty quantification and optimization studies of tDCS.

In today's production machine learning (ML) systems, models are continuously
trained, improved, and deployed. ML design and training are becoming a
continuous workflow of various tasks that have dynamic resource demands.
Serverless computing is an emerging cloud paradigm that provides transparent
resource management and scaling for users and has the potential to
revolutionize the routine of ML design and training. However, hosting modern ML
workflows on existing serverless platforms has non-trivial challenges due to
their intrinsic design limitations such as stateless nature, limited
communication support across function instances, and limited function execution
duration. These limitations result in a lack of an overarching view and
adaptation mechanism for training dynamics and an amplification of existing
problems in ML workflows.
  To address the above challenges, we propose SMLT, an automated, scalable, and
adaptive serverless framework to enable efficient and user-centric ML design
and training. SMLT employs an automated and adaptive scheduling mechanism to
dynamically optimize the deployment and resource scaling for ML tasks during
training. SMLT further enables user-centric ML workflow execution by supporting
user-specified training deadlines and budget limits. In addition, by providing
an end-to-end design, SMLT solves the intrinsic problems in serverless
platforms such as the communication overhead, limited function execution
duration, need for repeated initialization, and also provides explicit fault
tolerance for ML training. SMLT is open-sourced and compatible with all major
ML frameworks. Our experimental evaluation with large, sophisticated modern ML
models demonstrate that SMLT outperforms the state-of-the-art VM based systems
and existing serverless ML training frameworks in both training speed (up to
8X) and monetary cost (up to 3X)

In supervised learning, the estimation of prediction error on unlabeled test
data is an important task. Existing methods are usually built on the assumption
that the training and test data are sampled from the same distribution, which
is often violated in practice. As a result, traditional estimators like
cross-validation (CV) will be biased and this may result in poor model
selection. In this paper, we assume that we have a test dataset in which the
feature values are available but not the outcome labels, and focus on a
particular form of distributional shift called "covariate shift". We propose an
alternative method based on parametric bootstrap of the target of conditional
error. Empirically, our method outperforms CV for both simulation and real data
example across different modeling tasks.

The Mixture of Experts architecture allows for outrageously large neural
networks by scaling model parameter size independently from computational
demand (FLOPs). However, current DNN frameworks cannot effectively support the
dynamic data flow in Mixture of Experts, and implementations on top of these
frameworks need to use workarounds that introduce significant overheads. To
address the limitation of these frameworks, we present DynaMoE, a DNN library
that uses dynamic recompilations to optimize and adapt the use of computational
resources to the dynamic needs of Mixture of Experts models. Our evaluation
shows that DynaMoE achieves a 1.8x speedup and supports 2.3x larger model sizes
when compared to existing MoE systems, even when not using recompilations. We
then present further optimizations enabled by dynamic recompilations that yield
an additional 1.7x speedup while simultaneously reducing memory pressure and
improving model quality.

Discovering latent topics from text corpora has been studied for decades.
Many existing topic models adopt a fully unsupervised setting, and their
discovered topics may not cater to users' particular interests due to their
inability of leveraging user guidance. Although there exist seed-guided topic
discovery approaches that leverage user-provided seeds to discover
topic-representative terms, they are less concerned with two factors: (1) the
existence of out-of-vocabulary seeds and (2) the power of pre-trained language
models (PLMs). In this paper, we generalize the task of seed-guided topic
discovery to allow out-of-vocabulary seeds. We propose a novel framework, named
SeeTopic, wherein the general knowledge of PLMs and the local semantics learned
from the input corpus can mutually benefit each other. Experiments on three
real datasets from different domains demonstrate the effectiveness of SeeTopic
in terms of topic coherence, accuracy, and diversity.

The purpose of federated learning is to enable multiple clients to jointly
train a machine learning model without sharing data. However, the existing
methods for training an image segmentation model have been based on an
unrealistic assumption that the training set for each local client is annotated
in a similar fashion and thus follows the same image supervision level. To
relax this assumption, in this work, we propose a label-agnostic unified
federated learning framework, named FedMix, for medical image segmentation
based on mixed image labels. In FedMix, each client updates the federated model
by integrating and effectively making use of all available labeled data ranging
from strong pixel-level labels, weak bounding box labels, to weakest
image-level class labels. Based on these local models, we further propose an
adaptive weight assignment procedure across local clients, where each client
learns an aggregation weight during the global model update. Compared to the
existing methods, FedMix not only breaks through the constraint of a single
level of image supervision, but also can dynamically adjust the aggregation
weight of each local client, achieving rich yet discriminative feature
representations. To evaluate its effectiveness, experiments have been carried
out on two challenging medical image segmentation tasks, i.e., breast tumor
segmentation and skin lesion segmentation. The results validate that our
proposed FedMix outperforms the state-of-the-art method by a large margin.

Learned knowledge graph representations supporting robots contain a wealth of
domain knowledge that drives robot behavior. However, there does not exist an
inference reconciliation framework that expresses how a knowledge graph
representation affects a robot's sequential decision making. We use a
pedagogical approach to explain the inferences of a learned, black-box
knowledge graph representation, a knowledge graph embedding. Our interpretable
model, uses a decision tree classifier to locally approximate the predictions
of the black-box model, and provides natural language explanations
interpretable by non-experts. Results from our algorithmic evaluation affirm
our model design choices, and the results of our user studies with non-experts
support the need for the proposed inference reconciliation framework.
Critically, results from our simulated robot evaluation indicate that our
explanations enable non-experts to correct erratic robot behaviors due to
nonsensical beliefs within the black-box.

The recent emergence and adoption of Machine Learning technology, and
specifically of Large Language Models, has drawn attention to the need for
systematic and transparent management of language data. This work proposes an
approach to global language data governance that attempts to organize data
management amongst stakeholders, values, and rights. Our proposal is informed
by prior work on distributed governance that accounts for human values and
grounded by an international research collaboration that brings together
researchers and practitioners from 60 countries. The framework we present is a
multi-party international governance structure focused on language data, and
incorporating technical and organizational tools needed to support its work.

In this paper, we propose a simple yet effective way to generate pun
sentences that does not require any training on existing puns. Our approach is
inspired by humor theories that ambiguity comes from the context rather than
the pun word itself. Given a pair of definitions of a pun word, our model first
produces a list of related concepts through a reverse dictionary. We then
utilize one-shot GPT3 to generate context words and then generate puns
incorporating context words from both concepts. Human evaluation shows that our
method successfully generates pun 52\% of the time, outperforming well-crafted
baselines and the state-of-the-art models by a large margin.

Poetry generation, and creative language generation in general, usually
suffers from the lack of large training data. In this paper, we present a novel
framework to generate sonnets that does not require training on poems. We
design a hierarchical framework which plans the poem sketch before decoding.
Specifically, a content planning module is trained on non-poetic texts to
obtain discourse-level coherence; then a rhyme module generates rhyme words and
a polishing module introduces imagery and similes for aesthetics purposes.
Finally, we design a constrained decoding algorithm to impose the
meter-and-rhyme constraint of the generated sonnets. Automatic and human
evaluation show that our multi-stage approach without training on poem corpora
generates more coherent, poetic, and creative sonnets than several strong

Human intelligence is multimodal; we integrate visual, linguistic, and
acoustic signals to maintain a holistic worldview. Most current pretraining
methods, however, are limited to one or two modalities. We present i-Code, a
self-supervised pretraining framework where users may flexibly combine the
modalities of vision, speech, and language into unified and general-purpose
vector representations. In this framework, data from each modality are first
given to pretrained single-modality encoders. The encoder outputs are then
integrated with a multimodal fusion network, which uses novel attention
mechanisms and other architectural innovations to effectively combine
information from the different modalities. The entire system is pretrained
end-to-end with new objectives including masked modality unit modeling and
cross-modality contrastive learning. Unlike previous research using only video
for pretraining, the i-Code framework can dynamically process single, dual, and
triple-modality data during training and inference, flexibly projecting
different combinations of modalities into a single representation space.
Experimental results demonstrate how i-Code can outperform state-of-the-art
techniques on five video understanding tasks and the GLUE NLP benchmark,
improving by as much as 11% and demonstrating the power of integrative
multimodal pretraining.

The Covid-19 pandemic has led to infodemic of low quality information leading
to poor health decisions. Combating the outcomes of this infodemic is not only
a question of identifying false claims, but also reasoning about the decisions
individuals make. In this work we propose a holistic analysis framework
connecting stance and reason analysis, and fine-grained entity level moral
sentiment analysis. We study how to model the dependencies between the
different level of analysis and incorporate human insights into the learning
process. Experiments show that our framework provides reliable predictions even
in the low-supervision settings.

Stylized image captioning as presented in prior work aims to generate
captions that reflect characteristics beyond a factual description of the scene
composition, such as sentiments. Such prior work relies on given sentiment
identifiers, which are used to express a certain global style in the caption,
e.g. positive or negative, however without taking into account the stylistic
content of the visual scene. To address this shortcoming, we first analyze the
limitations of current stylized captioning datasets and propose COCO
attribute-based augmentations to obtain varied stylized captions from COCO
annotations. Furthermore, we encode the stylized information in the latent
space of a Variational Autoencoder; specifically, we leverage extracted image
attributes to explicitly structure its sequential latent space according to
different localized style characteristics. Our experiments on the Senticap and
COCO datasets show the ability of our approach to generate accurate captions
with diversity in styles that are grounded in the image.

A biased dataset is a dataset that generally has attributes with an uneven
class distribution. These biases have the tendency to propagate to the models
that train on them, often leading to a poor performance in the minority class.
In this project, we will explore the extent to which various data augmentation
methods alleviate intrinsic biases within the dataset. We will apply several
augmentation techniques on a sample of the UTKFace dataset, such as
undersampling, geometric transformations, variational autoencoders (VAEs), and
generative adversarial networks (GANs). We then trained a classifier for each
of the augmented datasets and evaluated their performance on the native test
set and on external facial recognition datasets. We have also compared their
performance to the state-of-the-art attribute classifier trained on the
FairFace dataset. Through experimentation, we were able to find that training
the model on StarGAN-generated images led to the best overall performance. We
also found that training on geometrically transformed images lead to a similar
performance with a much quicker training time. Additionally, the best
performing models also exhibit a uniform performance across the classes within
each attribute. This signifies that the model was also able to mitigate the
biases present in the baseline model that was trained on the original training
set. Finally, we were able to show that our model has a better overall
performance and consistency on age and ethnicity classification on multiple
datasets when compared with the FairFace model. Our final model has an accuracy
on the UTKFace test set of 91.75%, 91.30%, and 87.20% for the gender, age, and
ethnicity attribute respectively, with a standard deviation of less than 0.1
between the accuracies of the classes of each attribute.

In this work, we propose a learning approach for 3D dynamic bipedal walking
when footsteps are constrained to stepping stones. While recent work has shown
progress on this problem, real-world demonstrations have been limited to
relatively simple open-loop, perception-free scenarios. Our main contribution
is a more advanced learning approach that enables real-world demonstrations,
using the Cassie robot, of closed-loop dynamic walking over moderately
difficult stepping-stone patterns. Our approach first uses reinforcement
learning (RL) in simulation to train a controller that maps footstep commands
onto joint actions without any reference motion information. We then learn a
model of that controller's capabilities, which enables prediction of feasible
footsteps given the robot's current dynamic state. The resulting controller and
model are then integrated with a real-time overhead camera system for detecting
stepping stone locations. For evaluation, we develop a benchmark set of
stepping stone patterns, which are used to test performance in both simulation
and the real world. Overall, we demonstrate that sim-to-real learning is
extremely promising for enabling dynamic locomotion over stepping stones. We
also identify challenges remaining that motivate important future research

Attackers may manipulate audio with the intent of presenting falsified
reports, changing an opinion of a public figure, and winning influence and
power. The prevalence of inauthentic multimedia continues to rise, so it is
imperative to develop a set of tools that determines the legitimacy of media.
We present a method that analyzes audio signals to determine whether they
contain real human voices or fake human voices (i.e., voices generated by
neural acoustic and waveform models). Instead of analyzing the audio signals
directly, the proposed approach converts the audio signals into spectrogram
images displaying frequency, intensity, and temporal content and evaluates them
with a Convolutional Neural Network (CNN). Trained on both genuine human voice
signals and synthesized voice signals, we show our approach achieves high
accuracy on this classification task.

The widespread availability of image editing tools and improvements in image
processing techniques allow image manipulation to be very easy. Oftentimes,
easy-to-use yet sophisticated image manipulation tools yields
distortions/changes imperceptible to the human observer. Distribution of forged
images can have drastic ramifications, especially when coupled with the speed
and vastness of the Internet. Therefore, verifying image integrity poses an
immense and important challenge to the digital forensic community. Satellite
images specifically can be modified in a number of ways, including the
insertion of objects to hide existing scenes and structures. In this paper, we
describe the use of a Conditional Generative Adversarial Network (cGAN) to
identify the presence of such spliced forgeries within satellite images.
Additionally, we identify their locations and shapes. Trained on pristine and
falsified images, our method achieves high success on these detection and
localization objectives.

Recommendations Systems have been identified to be one of the integral
elements of driving sales in e-commerce sites. The utilization of opinion
mining data extracted from trends has been attempted to improve the
recommendations that can be provided by baseline methods in this research when
user-click data is lacking or is difficult to be collected due to privacy
concerns.
  Utilizing social trends to influence the recommendations generated for a set
of unique items has been explored with the use of a suggested scoring
mechanism. Embracing concepts from decentralized networks that are expected to
change how users interact via the internet over the next couple of decades, the
suggested Recommendations System attempts to make use of multiple sources of
information, applying coherent information retrieval techniques to extract
probable trending items.
  The proposed Recommendations Architecture in the research presents a method
to integrate social trends with recommendations to produce promising outputs.

Recommendations Systems allow users to identify trending items among a
community while being timely and relevant to the user's expectations. When the
purpose of various Recommendation Systems differs, the required type of
recommendations also differs for each use case. While one Recommendation System
may focus on recommending popular items, another may focus on recommending
items that are comparable to the user's interests. Content-based filtering,
user-to-user & item-to-item Collaborative filtering, and more recently; Deep
Learning methods have been brought forward by the researchers to achieve better
quality recommendations.
  Even though each of these methods has proven to perform well individually,
there have been attempts to push the boundaries of their limitations. Following
a wide range of methods, researchers have tried to expand on the capabilities
of standard recommendation systems to provide the most effective
recommendations to users while being more profitable from a business's
perspective. This has been achieved by taking a hybrid approach when building
models and architectures for Recommendation Systems.
  This paper is a review of the novel models & architectures of hybrid
Recommendation Systems. The author identifies possibilities of expanding the
capabilities of baseline models & the advantages and drawbacks of each model
with selected use cases in this review.

Synthesized speech is common today due to the prevalence of virtual
assistants, easy-to-use tools for generating and modifying speech signals, and
remote work practices. Synthesized speech can also be used for nefarious
purposes, including creating a purported speech signal and attributing it to
someone who did not speak the content of the signal. We need methods to detect
if a speech signal is synthesized. In this paper, we analyze speech signals in
the form of spectrograms with a Compact Convolutional Transformer (CCT) for
synthesized speech detection. A CCT utilizes a convolutional layer that
introduces inductive biases and shared weights into a network, allowing a
transformer architecture to perform well with fewer data samples used for
training. The CCT uses an attention mechanism to incorporate information from
all parts of a signal under analysis. Trained on both genuine human voice
signals and synthesized human voice signals, we demonstrate that our CCT
approach successfully differentiates between genuine and synthesized speech
signals.

This paper considers meta-cognitive radars in an adversarial setting. A
cognitive radar optimally adapts its waveform (response) in response to
maneuvers (probes) of a possibly adversarial moving target. A meta-cognitive
radar is aware of the adversarial nature of the target and seeks to mitigate
the adversarial target. How should the meta-cognitive radar choose its
responses to sufficiently confuse the adversary trying to estimate the radar's
utility function? This paper abstracts the radar's meta-cognition problem in
terms of the spectra (eigenvalues) of the state and observation noise
covariance matrices, and embeds the algebraic Riccati equation into an
economics-based utility maximization setup. This adversarial target is an
inverse reinforcement learner. By observing a noisy sequence of radar's
responses (waveforms), the adversarial target uses a statistical hypothesis
test to detect if the radar is a utility maximizer. In turn, the meta-cognitive
radar deliberately chooses sub-optimal responses that increasing its Type-I
error probability of the adversary's detector. We call this counter-adversarial
step taken by the meta-cognitive radar as inverse inverse reinforcement
learning (I-IRL). We illustrate the meta-cognition results of this paper via
simple numerical examples. Our approach for meta-cognition in this paper is
based on revealed preference theory in micro-economics and inspired by results
in differential privacy and adversarial obfuscation in machine learning.

Binary stochastic neurons (BSNs) are excellent hardware accelerators for
machine learning. A popular platform for implementing them are low- or
zero-energy barrier nanomagnets possessing in-plane magnetic anisotropy (e.g.
circular disks or quasi-elliptical disks with very small eccentricity).
Unfortunately, small geometric variations in the lateral shapes of such
nanomagnets can produce large changes in the BSN response times if the
nanomagnets are made of common metallic ferromagnets (Co, Ni, Fe) with large
saturation magnetization. Additionally, the response times are also very
sensitive to initial conditions. Here, we show that if the nanomagnets are made
of dilute magnetic semiconductors with much smaller saturation magnetization,
then the variability in their response times (due to shape variations and
variation in the initial condition) is drastically suppressed. This
significantly reduces the device-to-device variation, which is a serious
challenge for large scale neuromorphic systems.

We present TartanDrive, a large scale dataset for learning dynamics models
for off-road driving. We collected a dataset of roughly 200,000 off-road
driving interactions on a modified Yamaha Viking ATV with seven unique sensing
modalities in diverse terrains. To the authors' knowledge, this is the largest
real-world multi-modal off-road driving dataset, both in terms of number of
interactions and sensing modalities. We also benchmark several state-of-the-art
methods for model-based reinforcement learning from high-dimensional
observations on this dataset. We find that extending these models to
multi-modality leads to significant performance on off-road dynamics
prediction, especially in more challenging terrains. We also identify some
shortcomings with current neural network architectures for the off-road driving
task. Our dataset is available at https://github.com/castacks/tartan_drive.

Recent investigations in noise contrastive estimation suggest, both
empirically as well as theoretically, that while having more "negative samples"
in the contrastive loss improves downstream classification performance
initially, beyond a threshold, it hurts downstream performance due to a
"collision-coverage" trade-off. But is such a phenomenon inherent in
contrastive learning? We show in a simple theoretical setting, where positive
pairs are generated by sampling from the underlying latent class (introduced by
Saunshi et al. (ICML 2019)), that the downstream performance of the
representation optimizing the (population) contrastive loss in fact does not
degrade with the number of negative samples. Along the way, we give a
structural characterization of the optimal representation in our framework, for
noise contrastive estimation. We also provide empirical support for our
theoretical results on CIFAR-10 and CIFAR-100 datasets.

The ICML Expressive Vocalization (ExVo) Competition is focused on
understanding and generating vocal bursts: laughs, gasps, cries, and other
non-verbal vocalizations that are central to emotional expression and
communication. ExVo 2022, includes three competition tracks using a large-scale
dataset of 59,201 vocalizations from 1,702 speakers. The first, ExVo-MultiTask,
requires participants to train a multi-task model to recognize expressed
emotions and demographic traits from vocal bursts. The second, ExVo-Generate,
requires participants to train a generative model that produces vocal bursts
conveying ten different emotions. The third, ExVo-FewShot, requires
participants to leverage few-shot learning incorporating speaker identity to
train a model for the recognition of 10 emotions conveyed by vocal bursts. This
paper describes the three tracks and provides performance measures for baseline
models using state-of-the-art machine learning strategies. The baseline for
each track is as follows, for ExVo-MultiTask, a combined score, computing the
harmonic mean of Concordance Correlation Coefficient (CCC), Unweighted Average
Recall (UAR), and inverted Mean Absolute Error (MAE) ($S_{MTL}$) is at best,
0.335 $S_{MTL}$; for ExVo-Generate, we report Fr\'echet inception distance
(FID) scores ranging from 4.81 to 8.27 (depending on the emotion) between the
training set and generated samples. We then combine the inverted FID with
perceptual ratings of the generated samples ($S_{Gen}$) and obtain 0.174
$S_{Gen}$; and for ExVo-FewShot, a mean CCC of 0.444 is obtained.

Although the U-Net architecture has been extensively used for segmentation of
medical images, we address two of its shortcomings in this work. Firstly, the
accuracy of vanilla U-Net degrades when the target regions for segmentation
exhibit significant variations in shape and size. Even though the U-Net already
possesses some capability to analyze features at various scales, we propose to
explicitly add multi-scale feature maps in each convolutional module of the
U-Net encoder to improve segmentation of histology images. Secondly, the
accuracy of a U-Net model also suffers when the annotations for supervised
learning are noisy or incomplete. This can happen due to the inherent
difficulty for a human expert to identify and delineate all instances of
specific pathology very precisely and accurately. We address this challenge by
introducing auxiliary confidence maps that emphasize less on the boundaries of
the given target regions. Further, we utilize the bootstrapping properties of
the deep network to address the missing annotation problem intelligently. In
our experiments on a private dataset of breast cancer lymph nodes, where the
primary task was to segment germinal centres and sinus histiocytosis, we
observed substantial improvement over a U-Net baseline based on the two
proposed augmentations.

Advances in image processing and analysis as well as machine learning
techniques have contributed to the use of biometric recognition systems in
daily people tasks. These tasks range from simple access to mobile devices to
tagging friends in photos shared on social networks and complex financial
operations on self-service devices for banking transactions. In China, the use
of these systems goes beyond personal use becoming a country's government
policy with the objective of monitoring the behavior of its population. On July
05th 2021, the Brazilian government announced acquisition of a biometric
recognition system to be used nationwide. In the opposite direction to China,
Europe and some American cities have already started the discussion about the
legality of using biometric systems in public places, even banning this
practice in their territory. In order to open a deeper discussion about the
risks and legality of using these systems, this work exposes the
vulnerabilities of biometric recognition systems, focusing its efforts on the
face modality. Furthermore, it shows how it is possible to fool a biometric
system through a well-known presentation attack approach in the literature
called morphing. Finally, a list of ten concerns was created to start the
discussion about the security of citizen data and data privacy law in the Age
of Artificial Intelligence (AI).

Non-Cartesian sampling with subspace-constrained image reconstruction is a
popular approach to dynamic MRI, but slow iterative reconstruction limits its
clinical application. Data-consistent (DC) deep learning can accelerate
reconstruction with good image quality, but has not been formulated for
non-Cartesian subspace imaging. In this study, we propose a DC non-Cartesian
deep subspace learning framework for fast, accurate dynamic MR image
reconstruction. Four novel DC formulations are developed and evaluated: two
gradient decent approaches, a directly solved approach, and a conjugate
gradient approach. We applied a U-Net model with and without DC layers to
reconstruct T1-weighted images for cardiac MR Multitasking (an advanced
multidimensional imaging method), comparing our results to the iteratively
reconstructed reference. Experimental results show that the proposed framework
significantly improves reconstruction accuracy over the U-Net model without DC,
while significantly accelerating the reconstruction over conventional iterative

There are many contexts in which dyadic data are present. Social networks are
a well-known example. In these contexts, pairs of elements are linked building
a network that reflects interactions. Explaining why these relationships are
established is essential to obtain transparency, an increasingly important
notion. These explanations are often presented using text, thanks to the spread
of the natural language understanding tasks. Our aim is to represent and
explain pairs established by any agent (e.g., a recommender system or a paid
promotion mechanism), so that text-based personalisation is taken into account.
We have focused on the TripAdvisor platform, considering the applicability to
other dyadic data contexts. The items are a subset of users and restaurants and
the interactions the reviews posted by these users. We propose the PTER
(Personalised TExt-based Reviews) model. We predict, from the available reviews
for a given restaurant, those that fit to the specific user interactions. PTER
leverages the BERT (Bidirectional Encoders Representations from Transformers)
transformer-encoder model. We customised a deep neural network following the
feature-based approach, presenting a LTR (Learning To Rank) downstream task. We
carried out several comparisons of our proposal with a random baseline and
other models of the state of the art, following the EXTRA (EXplanaTion RAnking)
benchmark. Our method outperforms other collaborative filtering proposals.

We present a method for differentiable simulation of soft articulated bodies.
Our work enables the integration of differentiable physical dynamics into
gradient-based pipelines. We develop a top-down matrix assembly algorithm
within Projective Dynamics and derive a generalized dry friction model for soft
continuum using a new matrix splitting strategy. We derive a differentiable
control framework for soft articulated bodies driven by muscles, joint torques,
or pneumatic tubes. The experiments demonstrate that our designs make soft body
simulation more stable and realistic compared to other frameworks. Our method
accelerates the solution of system identification problems by more than an
order of magnitude, and enables efficient gradient-based learning of motion
control with soft robots.

Temporal Expression Extraction (TEE) is essential for understanding time in
natural language. It has applications in Natural Language Processing (NLP)
tasks such as question answering, information retrieval, and causal inference.
To date, work in this area has mostly focused on English as there is a scarcity
of labeled data for other languages. We propose XLTime, a novel framework for
multilingual TEE. XLTime works on top of pre-trained language models and
leverages multi-task learning to prompt cross-language knowledge transfer both
from English and within the non-English languages. XLTime alleviates problems
caused by a shortage of data in the target language. We apply XLTime with
different language models and show that it outperforms the previous automatic
SOTA methods on French, Spanish, Portuguese, and Basque, by large margins.
XLTime also closes the gap considerably on the handcrafted HeidelTime method.

Probabilistic models for sequential data are the basis for a variety of
applications concerned with processing timely ordered information. The
predominant approach in this domain is given by neural networks, which
incorporate either stochastic units or components. This paper proposes a new
probabilistic sequence model building on probabilistic B\'ezier curves. Using
Gaussian distributed control points, these parametric curves pose a special
case for Gaussian processes (GP). Combined with a Mixture Density network,
Bayesian conditional inference can be performed without the need for mean field
variational approximation or Monte Carlo simulation, which is a requirement of
common approaches. For assessing this hybrid model's viability, it is applied
to an exemplary sequence prediction task. In this case the model is used for
pedestrian trajectory prediction, where a generated prediction also serves as a
GP prior. Following this, the initial prediction can be refined using the GP
framework by calculating different posterior distributions, in order to adapt
more towards a given observed trajectory segment.

High-throughput virtual screening is an indispensable technique utilized in
the discovery of small molecules. In cases where the library of molecules is
exceedingly large, the cost of an exhaustive virtual screen may be prohibitive.
Model-guided optimization has been employed to lower these costs through
dramatic increases in sample efficiency compared to random selection. However,
these techniques introduce new costs to the workflow through the surrogate
model training and inference steps. In this study, we propose an extension to
the framework of model-guided optimization that mitigates inferences costs
using a technique we refer to as design space pruning (DSP), which irreversibly
removes poor-performing candidates from consideration. We study the application
of DSP to a variety of optimization tasks and observe significant reductions in
overhead costs while exhibiting similar performance to the baseline
optimization. DSP represents an attractive extension of model-guided
optimization that can limit overhead costs in optimization settings where these
costs are non-negligible relative to objective costs, such as docking.

Language use differs dramatically from context to context. To some degree,
modern language models like GPT-3 are able to account for such variance by
conditioning on a string of previous input text, or prompt. Yet prompting is
ineffective when contexts are sparse, out-of-sample, or extra-textual; for
instance, accounting for when and where the text was produced or who produced
it. In this paper, we introduce the mixed-effects transformer (MET), a novel
approach for learning hierarchically-structured prefixes -- lightweight modules
prepended to the input -- to account for structured variation. Specifically, we
show how the popular class of mixed-effects models may be extended to
transformer-based architectures using a regularized prefix-tuning procedure
with dropout. We evaluate this approach on several domain-adaptation
benchmarks, finding that it efficiently adapts to novel contexts with minimal
data while still effectively generalizing to unseen contexts.

The investigation of uncertainty is of major importance in risk-critical
applications, such as medical image segmentation. Belief function theory, a
formal framework for uncertainty analysis and multiple evidence fusion, has
made significant contributions in the medical domain, especially since the
development of deep learning. Medical image segmentation with belief function
theory has shown significant benefits in clinical diagnosis and medical image
research. In this paper, we provide a review of medical image segmentation
methods using belief function theory. We classify the methods according to the
fusion step and explain how information with uncertainty or imprecision is
modeled and fused with belief function theory. In addition, we discuss the
challenges and limitations of present belief function-based medical image
segmentation and propose orientations for future research. Future research
could investigate both belief function theory and deep learning to achieve more
promising and reliable segmentation results.

Image pre-training, the current de-facto paradigm for a wide range of visual
tasks, is generally less favored in the field of video recognition. By
contrast, a common strategy is to directly train with spatiotemporal
convolutional neural networks (CNNs) from scratch. Nonetheless, interestingly,
by taking a closer look at these from-scratch learned CNNs, we note there exist
certain 3D kernels that exhibit much stronger appearance modeling ability than
others, arguably suggesting appearance information is already well disentangled
in learning. Inspired by this observation, we hypothesize that the key to
effectively leveraging image pre-training lies in the decomposition of learning
spatial and temporal features, and revisiting image pre-training as the
appearance prior to initializing 3D kernels. In addition, we propose
Spatial-Temporal Separable (STS) convolution, which explicitly splits the
feature channels into spatial and temporal groups, to further enable a more
thorough decomposition of spatiotemporal features for fine-tuning 3D CNNs. Our
experiments show that simply replacing 3D convolution with STS notably improves
a wide range of 3D CNNs without increasing parameters and computation on both
Kinetics-400 and Something-Something V2. Moreover, this new training pipeline
consistently achieves better results on video recognition with significant
speedup. For instance, we achieve +0.6% top-1 of Slowfast on Kinetics-400 over
the strong 256-epoch 128-GPU baseline while fine-tuning for only 50 epochs with
4 GPUs. The code and models are available at
https://github.com/UCSC-VLAA/Image-Pretraining-for-Video.

The exponential rise of social media networks has allowed the production,
distribution, and consumption of data at a phenomenal rate. Moreover, the
social media revolution has brought a unique phenomenon to social media
platforms called Internet memes. Internet memes are one of the most popular
contents used on social media, and they can be in the form of images with a
witty, catchy, or satirical text description. In this paper, we are dealing
with propaganda that is often seen in Internet memes in recent times.
Propaganda is communication, which frequently includes psychological and
rhetorical techniques to manipulate or influence an audience to act or respond
as the propagandist wants. To detect propaganda in Internet memes, we propose a
multimodal deep learning fusion system that fuses the text and image feature
representations and outperforms individual models based solely on either text
or image modalities.

Deep learning (DL) is being used extensively for text classification.
However, researchers have demonstrated the vulnerability of such classifiers to
adversarial attacks. Attackers modify the text in a way which misleads the
classifier while keeping the original meaning close to intact. State-of-the-art
(SOTA) attack algorithms follow the general principle of making minimal changes
to the text so as to not jeopardize semantics. Taking advantage of this we
propose a novel and intuitive defense strategy called Sample Shielding. It is
attacker and classifier agnostic, does not require any reconfiguration of the
classifier or external resources and is simple to implement. Essentially, we
sample subsets of the input text, classify them and summarize these into a
final decision. We shield three popular DL text classifiers with Sample
Shielding, test their resilience against four SOTA attackers across three
datasets in a realistic threat setting. Even when given the advantage of
knowing about our shielding strategy the adversary's attack success rate is
<=10% with only one exception and often < 5%. Additionally, Sample Shielding
maintains near original accuracy when applied to original texts. Crucially, we
show that the `make minimal changes' approach of SOTA attackers leads to
critical vulnerabilities that can be defended against with an intuitive
sampling strategy.

Memristors enable the computation of matrix-vector multiplications (MVM) in
memory and, therefore, show great potential in highly increasing the energy
efficiency of deep neural network (DNN) inference accelerators. However,
computations in memristors suffer from hardware non-idealities and are subject
to different sources of noise that may negatively impact system performance. In
this work, we theoretically analyze the mean squared error of DNNs that use
memristor crossbars to compute MVM. We take into account both the quantization
noise, due to the necessity of reducing the DNN model size, and the programming
noise, stemming from the variability during the programming of the memristance
value. Simulations on pre-trained DNN models showcase the accuracy of the
analytical prediction. Furthermore the proposed method is almost two order of
magnitude faster than Monte-Carlo simulation, thus making it possible to
optimize the implementation parameters to achieve minimal error for a given
power constraint.

Semi-supervised video anomaly detection (VAD) methods formulate the task of
anomaly detection as detection of deviations from the learned normal patterns.
Previous works in the field (reconstruction or prediction-based methods) suffer
from two drawbacks: 1) They focus on low-level features, and they (especially
holistic approaches) do not effectively consider the object classes. 2)
Object-centric approaches neglect some of the context information (such as
location). To tackle these challenges, this paper proposes a novel two-stream
object-aware VAD method that learns the normal appearance and motion patterns
through image translation tasks. The appearance branch translates the input
image to the target semantic segmentation map produced by Mask-RCNN, and the
motion branch associates each frame with its expected optical flow magnitude.
Any deviation from the expected appearance or motion in the inference stage
shows the degree of potential abnormality. We evaluated our proposed method on
the ShanghaiTech, UCSD-Ped1, and UCSD-Ped2 datasets and the results show
competitive performance compared with state-of-the-art works. Most importantly,
the results show that, as significant improvements to previous methods,
detections by our method are completely explainable and anomalies are localized
accurately in the frames.

Self-supervised pretraining has made few-shot learning possible for many NLP
tasks. But the pretraining objectives are not typically adapted specifically
for in-context few-shot learning. In this paper, we propose to use
self-supervision in an intermediate training stage between pretraining and
downstream few-shot usage with the goal to teach the model to perform
in-context few shot learning. We propose and evaluate four self-supervised
objectives on two benchmarks. We find that the intermediate self-supervision
stage produces models that outperform strong baselines. Ablation study shows
that several factors affect the downstream performance, such as the amount of
training data and the diversity of the self-supervised objectives.
Human-annotated cross-task supervision and self-supervision are complementary.
Qualitative analysis suggests that the self-supervised-trained models are
better at following task requirements.

We present a classification algorithm based on the Random Forest machine
learning model that differentiates galaxies into orbiting, infalling and
background (interloper) populations, using phase space information as input. We
train and test our model with the galaxies from UniverseMachine mock catalog
based on Multi-Dark Planck 2 N-body simulations. We show that we can recover
the distribution of orbiting and infalling galaxies in 3D and 2D position and
velocity distribution with $<1\%$ error when using probabilistic predictions in
the presence of interlopers in the projected phase space. Our machine
learning-based galaxy classification method enables a percent level
measurements of the dynamics of cluster galaxies. We discuss potential
applications of this technique to improve cluster cosmology and galaxy
quenching.

Variational quantum algorithms are the centerpiece of modern quantum
programming. These algorithms involve training parameterized quantum circuits
using a classical co-processor, an approach adapted partly from classical
machine learning. An important subclass of these algorithms, designed for
combinatorial optimization on currrent quantum hardware, is the quantum
approximate optimization algorithm (QAOA). It is known that problem density - a
problem constraint to variable ratio - induces under-parametrization in fixed
depth QAOA. Density dependent performance has been reported in the literature,
yet the circuit depth required to achieve fixed performance (henceforth called
critical depth) remained unknown. Here, we propose a predictive model, based on
a logistic saturation conjecture for critical depth scaling with respect to
density. Focusing on random instances of MAX-2-SAT, we test our predictive
model against simulated data with up to 15 qubits. We report the average
critical depth, required to attain a success probability of 0.7, saturates at a
value of 10 for densities beyond 4. We observe the predictive model to describe
the simulated data within a $3\sigma$ confidence interval. Furthermore, based
on the model, a linear trend for the critical depth with respect problem size
is recovered for the range of 5 to 15 qubits.

Learning-based approaches have become indispensable for camera pose
estimation. However, feature detection, description, matching, and pose
optimization are often approached in an isolated fashion. In particular,
erroneous feature matches have severe impact on subsequent camera pose
estimation and often require additional measures such as outlier rejection. Our
method tackles this challenge by addressing feature matching and pose
optimization jointly: first, we integrate information from multiple views into
the matching by spanning a graph attention network across multiple frames to
predict their matches all at once. Second, the resulting matches along with
their predicted confidences are used for robust pose optimization with a
differentiable Gauss-Newton solver. End-to-end training combined with
multi-view feature matching boosts the pose estimation metrics compared to
SuperGlue by 8.9% on ScanNet and 10.7% on MegaDepth on average. Our approach
improves both pose estimation and matching accuracy over state-of-the-art
matching networks. Training feature matching across multiple views with
gradients from pose optimization naturally learns to disregard outliers,
thereby rendering additional outlier handling unnecessary, which is highly
desirable for pose estimation systems.

We consider the targeted image editing problem: blending a region in a source
image with a driver image that specifies the desired change. Differently from
prior works, we solve this problem by learning a conditional probability
distribution of the edits, end-to-end. Training such a model requires
addressing a fundamental technical challenge: the lack of example edits for
training. To this end, we propose a self-supervised approach that simulates
edits by augmenting off-the-shelf images in a target domain. The benefits are
remarkable: implemented as a state-of-the-art auto-regressive transformer, our
approach is simple, sidesteps difficulties with previous methods based on
GAN-like priors, obtains significantly better edits, and is efficient.
Furthermore, we show that different blending effects can be learned by an
intuitive control of the augmentation process, with no other changes required
to the model architecture. We demonstrate the superiority of this approach
across several datasets in extensive quantitative and qualitative experiments,
including human studies, significantly outperforming prior work.

Deep learning greatly improved the realism of animatable human models by
learning geometry and appearance from collections of 3D scans, template meshes,
and multi-view imagery. High-resolution models enable photo-realistic avatars
but at the cost of requiring studio settings not available to end users. Our
goal is to create avatars directly from raw images without relying on expensive
studio setups and surface tracking. While a few such approaches exist, those
have limited generalization capabilities and are prone to learning spurious
(chance) correlations between irrelevant body parts, resulting in implausible
deformations and missing body parts on unseen poses. We introduce a three-stage
method that induces two inductive biases to better disentangled pose-dependent
deformation. First, we model correlations of body parts explicitly with a graph
neural network. Second, to further reduce the effect of chance correlations, we
introduce localized per-bone features that use a factorized volumetric
representation and a new aggregation function. We demonstrate that our model
produces realistic body shapes under challenging unseen poses and shows
high-quality image synthesis. Our proposed representation strikes a better
trade-off between model capacity, expressiveness, and robustness than competing
methods. Project website: https://lemonatsu.github.io/danbo.

In the future, powerful AI systems may be deployed in high-stakes settings,
where a single failure could be catastrophic. One technique for improving AI
safety in high-stakes settings is adversarial training, which uses an adversary
to generate examples to train on in order to achieve better worst-case
  In this work, we used a safe language generation task (``avoid injuries'') as
a testbed for achieving high reliability through adversarial training. We
created a series of adversarial training techniques -- including a tool that
assists human adversaries -- to find and eliminate failures in a classifier
that filters text completions suggested by a generator. In our task, we
determined that we can set very conservative classifier thresholds without
significantly impacting the quality of the filtered outputs. We found that
adversarial training increased robustness to the adversarial attacks that we
trained on -- doubling the time for our contractors to find adversarial
examples both with our tool (from 13 to 26 minutes) and without (from 20 to 44
minutes) -- without affecting in-distribution performance.
  We hope to see further work in the high-stakes reliability setting, including
more powerful tools for enhancing human adversaries and better ways to measure
high levels of reliability, until we can confidently rule out the possibility
of catastrophic deployment-time failures of powerful models.

We present a cross-modal Transformer-based framework, which jointly encodes
video data and text labels for zero-shot action recognition (ZSAR). Our model
employs a conceptually new pipeline by which visual representations are learned
in conjunction with visual-semantic associations in an end-to-end manner. The
model design provides a natural mechanism for visual and semantic
representations to be learned in a shared knowledge space, whereby it
encourages the learned visual embedding to be discriminative and more
semantically consistent. In zero-shot inference, we devise a simple semantic
transfer scheme that embeds semantic relatedness information between seen and
unseen classes to composite unseen visual prototypes. Accordingly, the
discriminative features in the visual structure could be preserved and
exploited to alleviate the typical zero-shot issues of information loss,
semantic gap, and the hubness problem. Under a rigorous zero-shot setting of
not pre-training on additional datasets, the experiment results show our model
considerably improves upon the state of the arts in ZSAR, reaching encouraging
top-1 accuracy on UCF101, HMDB51, and ActivityNet benchmark datasets. Code will
be made available.

We present a robust and accurate depth refinement system, named GeoRefine,
for geometrically-consistent dense mapping from monocular sequences. GeoRefine
consists of three modules: a hybrid SLAM module using learning-based priors, an
online depth refinement module leveraging self-supervision, and a global
mapping module via TSDF fusion. The proposed system is online by design and
achieves great robustness and accuracy via: (i) a robustified hybrid SLAM that
incorporates learning-based optical flow and/or depth; (ii) self-supervised
losses that leverage SLAM outputs and enforce long-term geometric consistency;
(iii) careful system design that avoids degenerate cases in online depth
refinement. We extensively evaluate GeoRefine on multiple public datasets and
reach as low as $5\%$ absolute relative depth errors.

We aim to determine which temporal instance queries can be uniquely
characterised by a (polynomial-size) set of positive and negative temporal data
examples. We start by considering queries formulated in fragments of
propositional linear temporal logic LTL that correspond to conjunctive queries
(CQs) or extensions thereof induced by the until operator. Not all of these
queries admit polynomial characterisations but by restricting them further to
path-shaped queries we identify natural classes that do. We then investigate
how far the obtained characterisations can be lifted to temporal knowledge
graphs queried by 2D languages combining LTL with concepts in description
logics EL or ELI (i.e., tree-shaped CQs). While temporal operators in the scope
of description logic constructors can destroy polynomial characterisability, we
obtain general transfer results for the case when description logic
constructors are within the scope of temporal operators. Finally, we apply our
characterisations to establish (polynomial) learnability of temporal instance
queries using membership queries in the active learning framework.

A novel reconfigurable intelligent surface-aided multi-robot network is
proposed, where multiple mobile robots are served by an access point (AP)
through non-orthogonal multiple access (NOMA). The goal is to maximize the
sum-rate of whole trajectories for multi-robot system by jointly optimizing
trajectories and NOMA decoding orders of robots, phase-shift coefficients of
the RIS, and the power allocation of the AP, subject to predicted initial and
final positions of robots and the quality of service (QoS) of each robot. To
tackle this problem, an integrated machine learning (ML) scheme is proposed,
which combines long short-term memory (LSTM)-autoregressive integrated moving
average (ARIMA) model and dueling double deep Q-network (D$^{3}$QN) algorithm.
For initial and final position prediction for robots, the LSTM-ARIMA is able to
overcome the problem of gradient vanishment of non-stationary and non-linear
sequences of data. For jointly determining the phase shift matrix and robots'
trajectories, D$^{3}$QN is invoked for solving the problem of action value
overestimation. Based on the proposed scheme, each robot holds a global optimal
trajectory based on the maximum sum-rate of a whole trajectory, which reveals
that robots pursue long-term benefits for whole trajectory design. Numerical
results demonstrated that: 1) LSTM-ARIMA model provides high accuracy
predicting model; 2) The proposed D$^{3}$QN algorithm can achieve fast average
convergence; 3) The RIS with higher resolution bits offers a bigger sum-rate of
trajectories than lower resolution bits; and 4) RIS-NOMA networks have superior
network performance compared to RIS-aided orthogonal counterparts.

Anomalies can be defined as any non-random structure which deviates from
normality. Anomaly detection methods reported in the literature are numerous
and diverse, as what is considered anomalous usually varies depending on
particular scenarios and applications. In this work we propose an a contrario
framework to detect anomalies in images applying statistical analysis to
feature maps obtained via convolutions. We evaluate filters learned from the
image under analysis via patch PCA, Gabor filters and the feature maps obtained
from a pre-trained deep neural network (Resnet). The proposed method is
multi-scale and fully unsupervised and is able to detect anomalies in a wide
variety of scenarios. While the end goal of this work is the detection of
subtle defects in leather samples for the automotive industry, we show that the
same algorithm achieves state-of-the-art results in public anomalies datasets.

Eye-tracking technology is an integral component of new display devices such
as virtual and augmented reality headsets. Applications of gaze information
range from new interaction techniques exploiting eye patterns to
gaze-contingent digital content creation. However, system latency is still a
significant issue in many of these applications because it breaks the
synchronization between the current and measured gaze positions. Consequently,
it may lead to unwanted visual artifacts and degradation of user experience. In
this work, we focus on foveated rendering applications where the quality of an
image is reduced towards the periphery for computational savings. In foveated
rendering, the presence of latency leads to delayed updates to the rendered
frame, making the quality degradation visible to the user. To address this
issue and to combat system latency, recent work proposes to use saccade landing
position prediction to extrapolate the gaze information from delayed
eye-tracking samples. While the benefits of such a strategy have already been
demonstrated, the solutions range from simple and efficient ones, which make
several assumptions about the saccadic eye movements, to more complex and
costly ones, which use machine learning techniques. Yet, it is unclear to what
extent the prediction can benefit from accounting for additional factors. This
paper presents a series of experiments investigating the importance of
different factors for saccades prediction in common virtual and augmented
reality applications. In particular, we investigate the effects of saccade
orientation in 3D space and smooth pursuit eye-motion (SPEM) and how their
influence compares to the variability across users. We also present a simple
yet efficient correction method that adapts the existing saccade prediction
methods to handle these factors without performing extensive data collection.

Internet traffic in the real world is susceptible to various external and
internal factors which may abruptly change the normal traffic flow. Those
unexpected changes are considered outliers in traffic. However, deep sequence
models have been used to predict complex IP traffic, but their comparative
performance for anomalous traffic has not been studied extensively. In this
paper, we investigated and evaluated the performance of different deep sequence
models for anomalous traffic prediction. Several deep sequences models were
implemented to predict real traffic without and with outliers and show the
significance of outlier detection in real-world traffic prediction. First, two
different outlier detection techniques, such as the Three-Sigma rule and
Isolation Forest, were applied to identify the anomaly. Second, we adjusted
those abnormal data points using the Backward Filling technique before training
the model. Finally, the performance of different models was compared for
abnormal and adjusted traffic. LSTM_Encoder_Decoder (LSTM_En_De) is the best
prediction model in our experiment, reducing the deviation between actual and
predicted traffic by more than 11\% after adjusting the outliers. All other
models, including Recurrent Neural Network (RNN), Long Short-Term Memory
(LSTM), LSTM_En_De with Attention layer (LSTM_En_De_Atn), Gated Recurrent Unit
(GRU), show better prediction after replacing the outliers and decreasing
prediction error by more than 29%, 24%, 19%, and 10% respectively. Our
experimental results indicate that the outliers in the data can significantly
impact the quality of the prediction. Thus, outlier detection and mitigation
assist the deep sequence model in learning the general trend and making better

The optimization of the electrodes manufacturing process constitutes one of
the most critical steps to ensure high-quality Lithium-Ion Battery (LIB) cells,
in particular for automotive applications. Because LIB electrode manufacturing
is a complex process involving multiple steps and interdependent parameters, we
have shown in our previous works that 3D-resolved physics-based models
constitute very useful tools to provide insights about the impact of the
manufacturing process parameters on the textural and performance properties of
the electrodes. However, their high-throughput application for electrode
properties optimization and inverse design of manufacturing parameters is
limited due to the high computational cost associated with this kind of model.
In this work, we tackle this issue by proposing an innovative approach,
supported by a deterministic machine learning (ML)-assisted pipeline for
multi-objective optimization of LIB electrode properties and inverse design of
its manufacturing process. Firstly, the pipeline generates a synthetic dataset
from physics-based simulations with low discrepancy sequences, that allow to
sufficiently represent the manufacturing parameters space. Secondly, the
generated dataset is used to train deterministic ML models for the
implementation of a fast multi-objective optimization, to identify an optimal
electrode and the manufacturing parameters to adopt in order to fabricate it.
Lastly, this electrode was successfully fabricated experimentally, proving that
our modeling pipeline prediction is physical-relevant. Here, we demonstrate our
pipeline for the simultaneous minimization of the electrode tortuosity factor
and maximization of the effective electronic conductivity, the active surface
area, and the density, all being parameters that affect the Li$^+$
(de-)intercalation kinetics, ionic, and electronic transport properties of the
electrode.

Although all-in-one-model multilingual neural machine translation
(multilingual NMT) has achieved remarkable progress, the convergence
inconsistency in the joint training is ignored, i.e., different language pairs
reaching convergence in different epochs. This leads to the trained MNMT model
over-fitting low-resource language translations while under-fitting
high-resource ones. In this paper, we propose a novel training strategy named
LSSD (Language-Specific Self-Distillation), which can alleviate the convergence
inconsistency and help MNMT models achieve the best performance on each
language pair simultaneously. Specifically, LSSD picks up language-specific
best checkpoints for each language pair to teach the current model on the fly.
Furthermore, we systematically explore three sample-level manipulations of
knowledge transferring. Experimental results on three datasets show that LSSD
obtains consistent improvements towards all language pairs and achieves the

Dents on the aircraft skin are frequent and may easily go undetected during
airworthiness checks, as their inspection process is tedious and extremely
subject to human factors and environmental conditions. Nowadays, 3D scanning
technologies are being proposed for more reliable, human-independent
measurements, yet the process of inspection and reporting remains laborious and
time consuming because data acquisition and validation are still carried out by
the engineer. For full automation of dent inspection, the acquired point cloud
data must be analysed via a reliable segmentation algorithm, releasing humans
from the search and evaluation of damage. This paper reports on two
developments towards automated dent inspection. The first is a method to
generate a synthetic dataset of dented surfaces to train a fully convolutional
neural network. The training of machine learning algorithms needs a substantial
volume of dent data, which is not readily available. Dents are thus simulated
in random positions and shapes, within criteria and definitions of a Boeing 737
structural repair manual. The noise distribution from the scanning apparatus is
then added to reflect the complete process of 3D point acquisition on the
training. The second proposition is a surface fitting strategy to convert 3D
point clouds to 2.5D. This allows higher resolution point clouds to be
processed with a small amount of memory compared with state-of-the-art methods
involving 3D sampling approaches. Simulations with available ground truth data
show that the proposed technique reaches an intersection-over-union of over
80%. Experiments over dent samples prove an effective detection of dents with a
speed of over 500 000 points per second.

The study of the fundamental limits of information systems is a central theme
in information theory. Both the traditional analytical approach and the
recently proposed computational approach have significant limitations, where
the former is mainly due to its reliance on human ingenuity, and the latter due
to its exponential memory and computational complexity. In this work, we
propose a new computational approach to tackle the problem with much lower
memory and computational requirements, which can naturally utilize certain
intuitions, but also can maintain the strong computational advantage of the
existing computational approach. A reformulation of the underlying optimization
problem is first proposed, which converts the large linear program to a maximin
problem. This leads to an iterative solving procedure, which uses the LP dual
to carry over learned evidence between iterations. The key in the reformulated
problem is the selection of good information inequalities, with which a relaxed
LP can be formed. A particularly powerful intuition is a potentially optimal
code construction, and we provide a method that directly utilizes it in the new
algorithm. As an application, we derive a tighter outer bound for the
storage-repair tradeoff for the $(6,5,5)$ regenerating code problem, which
involves at least 30 random variables and is impossible to compute with the
previously known computational approach.

Bilevel Optimization has witnessed notable progress recently with new
emerging efficient algorithms and has been applied to many machine learning
tasks such as data cleaning, few-shot learning, and neural architecture search.
However, little attention has been paid to solve the bilevel problems under
distributed setting. Federated learning (FL) is an emerging paradigm which
solves machine learning tasks over distributed-located data. FL problems are
challenging to solve due to the heterogeneity and communication bottleneck.
However, it is unclear how these challenges will affect the convergence of
Bilevel Optimization algorithms. In this paper, we study Federated Bilevel
Optimization problems. Specifically, we first propose the FedBiO, a
deterministic gradient-based algorithm and we show it requires
$O(\epsilon^{-2})$ number of iterations to reach an $\epsilon$-stationary
point. Then we propose FedBiOAcc to accelerate FedBiO with the momentum-based
variance-reduction technique under the stochastic scenario. We show FedBiOAcc
has complexity of $O(\epsilon^{-1.5})$. Finally, we validate our proposed
algorithms via the important Fair Federated Learning task. More specifically,
we define a bilevel-based group fair FL objective. Our algorithms show superior
performances compared to other baselines in numerical experiments.

We consider the problem of sequential evaluation, in which an evaluator
observes candidates in a sequence and assigns scores to these candidates in an
online, irrevocable fashion. Motivated by the psychology literature that has
studied sequential bias in such settings -- namely, dependencies between the
evaluation outcome and the order in which the candidates appear -- we propose a
natural model for the evaluator's rating process that captures the lack of
calibration inherent to such a task. We conduct crowdsourcing experiments to
demonstrate various facets of our model. We then proceed to study how to
correct sequential bias under our model by posing this as a statistical
inference problem. We propose a near-linear time, online algorithm for this
task and prove guarantees in terms of two canonical ranking metrics. We also
prove that our algorithm is information theoretically optimal, by establishing
matching lower bounds in both metrics. Finally, we show that our algorithm
outperforms the de facto method of using the rankings induced by the reported
scores.

The purpose of this work is to implement physics-based regularization as a
stopping condition in tuning an untrained deep neural network for
reconstructing MR images from accelerated data. The ConvDecoder neural network
was trained with a physics-based regularization term incorporating the spoiled
gradient echo equation that describes variable-flip angle (VFA) data.
Fully-sampled VFA k-space data were retrospectively accelerated by factors of
R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the
proposed regularization (CD+r), locally low-rank (LR) reconstruction, and
compressed sensing with L1-wavelet regularization (L1). Final images from CD+r
training were evaluated at the \emph{argmin} of the regularization loss;
whereas the CD, LR, and L1 reconstructions were chosen optimally based on
ground truth data. The performance measures used were the normalized root-mean
square error, the concordance correlation coefficient (CCC), and the structural
similarity index (SSIM). The CD+r reconstructions, chosen using the stopping
condition, yielded SSIMs that were similar to the CD (p=0.47) and LR SSIMs
(p=0.95) across R and that were significantly higher than the L1 SSIMs
(p=0.04). The CCC values for the CD+r T1 maps across all R and subjects were
greater than those corresponding to the L1 (p=0.15) and LR (p=0.13) T1 maps,
respectively. For R > 12 (<4.2 minutes scan time), L1 and LR T1 maps exhibit a
loss of spatially refined details compared to CD+r. We conclude that the use of
an untrained neural network together with a physics-based regularization loss
shows promise as a measure for determining the optimal stopping point in
training without relying on fully-sampled ground truth data.

One of the first steps in many text-based social science studies is to
retrieve documents that are relevant for the analysis from large corpora of
otherwise irrelevant documents. The conventional approach in social science to
address this retrieval task is to apply a set of keywords and to consider those
documents to be relevant that contain at least one of the keywords. But the
application of incomplete keyword lists risks drawing biased inferences. More
complex and costly methods such as query expansion techniques, topic
model-based classification rules, and active as well as passive supervised
learning could have the potential to more accurately separate relevant from
irrelevant documents and thereby reduce the potential size of bias. Yet,
whether applying these more expensive approaches increases retrieval
performance compared to keyword lists at all, and if so, by how much, is
unclear as a comparison of these approaches is lacking. This study closes this
gap by comparing these methods across three retrieval tasks associated with a
data set of German tweets (Linder, 2017), the Social Bias Inference Corpus
(SBIC) (Sap et al., 2020), and the Reuters-21578 corpus (Lewis, 1997). Results
show that query expansion techniques and topic model-based classification rules
in most studied settings tend to decrease rather than increase retrieval
performance. Active supervised learning, however, if applied on a not too small
set of labeled training instances (e.g. 1,000 documents), reaches a
substantially higher retrieval performance than keyword lists.

Over the past decade machine learning has made significant advances in
approximating density functionals, but whether this signals the end of
human-designed functionals remains to be seen. Ryan Pederson, Bhupalee Kalita
and Kieron Burke discuss the rise of machine learning for functional design.

Real-world IP network traffic is susceptible to external and internal factors
such as new internet service integration, traffic migration, internet
application, etc. Due to these factors, the actual internet traffic is
non-linear and challenging to analyze using a statistical model for future
prediction. In this paper, we investigated and evaluated the performance of
different statistical prediction models for real IP network traffic; and showed
a significant improvement in prediction using the rolling prediction technique.
Initially, a set of best hyper-parameters for the corresponding prediction
model is identified by analyzing the traffic characteristics and implementing a
grid search algorithm based on the minimum Akaike Information Criterion (AIC).
Then, we performed a comparative performance analysis among AutoRegressive
Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), SARIMA with
eXogenous factors (SARIMAX), and Holt-Winter for single-step prediction. The
seasonality of our traffic has been explicitly modeled using SARIMA, which
reduces the rolling prediction Mean Average Percentage Error (MAPE) by more
than 4% compared to ARIMA (incapable of handling the seasonality). We further
improved traffic prediction using SARIMAX to learn different exogenous factors
extracted from the original traffic, which yielded the best rolling prediction
results with a MAPE of 6.83%. Finally, we applied the exponential smoothing
technique to handle the variability in traffic following the Holt-Winter model,
which exhibited a better prediction than ARIMA (around 1.5% less MAPE). The
rolling prediction technique reduced prediction error using real Internet
Service Provider (ISP) traffic data by more than 50\% compared to the standard
prediction method.

We introduce SparcAssist, a general-purpose risk assessment tool for the
machine learning models trained for language tasks. It evaluates models' risk
by inspecting their behavior on counterfactuals, namely out-of-distribution
instances generated based on the given data instance. The counterfactuals are
generated by replacing tokens in rational subsequences identified by ExPred,
while the replacements are retrieved using HotFlip or
Masked-Language-Model-based algorithms. The main purpose of our system is to
help the human annotators to assess the model's risk on deployment. The
counterfactual instances generated during the assessment are the by-product and
can be used to train more robust NLP models in the future.

In this short paper, we propose a baseline (off-the-shelf) for Continual
Learning of Computer Vision problems, by leveraging the power of pretrained
models. By doing so, we devise a simple approach achieving strong performance
for most of the common benchmarks. Our approach is fast since requires no
parameters updates and has minimal memory requirements (order of KBytes). In
particular, the "training" phase reorders data and exploit the power of
pretrained models to compute a class prototype and fill a memory bank. At
inference time we match the closest prototype through a knn-like approach,
providing us the prediction. We will see how this naive solution can act as an
off-the-shelf continual learning system. In order to better consolidate our
results, we compare the devised pipeline with common CNN models and show the
superiority of Vision Transformers, suggesting that such architectures have the
ability to produce features of higher quality. Moreover, this simple pipeline,
raises the same questions raised by previous works \cite{gdumb} on the
effective progresses made by the CL community especially in the dataset
considered and the usage of pretrained models. Code is live at
https://github.com/francesco-p/off-the-shelf-cl

Virtual Reality (VR) technology has been shown to achieve remarkable results
in multiple fields. Due to the nature of the immersive medium of Virtual
Reality it logically follows that it can be used as a high-quality educational
tool as it offers potentially a higher bandwidth than other mediums such as
text, pictures and videos. This short paper illustrates the development of a
climate change educational awareness application for virtual reality to
simulate virtual scenes of local scenery and sea level rising until 2100 using
prediction data. The paper also reports on the current in progress work of
porting the system to Augmented Reality (AR) and future work to evaluate the
system.

Learning scene flow from a monocular camera still remains a challenging task
due to its ill-posedness as well as lack of annotated data. Self-supervised
methods demonstrate learning scene flow estimation from unlabeled data, yet
their accuracy lags behind (semi-)supervised methods. In this paper, we
introduce a self-supervised monocular scene flow method that substantially
improves the accuracy over the previous approaches. Based on RAFT, a
state-of-the-art optical flow model, we design a new decoder to iteratively
update 3D motion fields and disparity maps simultaneously. Furthermore, we
propose an enhanced upsampling layer and a disparity initialization technique,
which overall further improves accuracy up to 7.2%. Our method achieves
state-of-the-art accuracy among all self-supervised monocular scene flow
methods, improving accuracy by 34.2%. Our fine-tuned model outperforms the best
previous semi-supervised method with 228 times faster runtime. Code will be

Early detection and analysis of calcifications in mammogram images is crucial
in a breast cancer diagnosis workflow. Management of calcifications that
require immediate follow-up and further analyzing its benignancy or malignancy
can result in a better prognosis. Recent studies have shown that deep
learning-based algorithms can learn robust representations to analyze
suspicious calcifications in mammography. In this work, we demonstrate that
randomly equalizing the histograms of calcification patches as a data
augmentation technique can significantly improve the classification performance
for analyzing suspicious calcifications. We validate our approach by using the
CBIS-DDSM dataset for two classification tasks. The results on both the tasks
show that the proposed methodology gains more than 1% mean accuracy and
F1-score when equalizing the data with a probability of 0.4 when compared to
not using histogram equalization. This is further supported by the t-tests,
where we obtain a p-value of p<0.0001, thus showing the statistical
significance of our approach.

Training mixed-domain translation models is a complex task that demands
tailored architectures and costly data preparation techniques. In this work, we
leverage federated learning (FL) in order to tackle the problem. Our
investigation demonstrates that with slight modifications in the training
process, neural machine translation (NMT) engines can be easily adapted when an
FL-based aggregation is applied to fuse different domains. Experimental results
also show that engines built via FL are able to perform on par with
state-of-the-art baselines that rely on centralized training techniques. We
evaluate our hypothesis in the presence of five datasets with different sizes,
from different domains, to translate from German into English and discuss how
FL and NMT can mutually benefit from each other. In addition to providing
benchmarking results on the union of FL and NMT, we also propose a novel
technique to dynamically control the communication bandwidth by selecting
impactful parameters during FL updates. This is a significant achievement
considering the large size of NMT engines that need to be exchanged between FL
parties.

Running a randomized algorithm on a subsampled dataset instead of the entire
dataset amplifies differential privacy guarantees. In this work, in a federated
setting, we consider random participation of the clients in addition to
subsampling their local datasets. Since such random participation of the
clients creates correlation among the samples of the same client in their
subsampling, we analyze the corresponding privacy amplification via non-uniform
subsampling. We show that when the size of the local datasets is small, the
privacy guarantees via random participation is close to those of the
centralized setting, in which the entire dataset is located in a single host
and subsampled. On the other hand, when the local datasets are large, observing
the output of the algorithm may disclose the identities of the sampled clients
with high confidence. Our analysis reveals that, even in this case, privacy
guarantees via random participation outperform those via only local
subsampling.

State-of-the-art pretrained NLP models contain a hundred million to trillion
parameters. Adapters provide a parameter-efficient alternative for the full
finetuning in which we can only finetune lightweight neural network layers on
top of pretrained weights. Adapter layers are initialized randomly. However,
existing work uses the same adapter architecture -- i.e., the same adapter
layer on top of each layer of the pretrained model -- for every dataset,
regardless of the properties of the dataset or the amount of available training
data. In this work, we introduce adaptable adapters that contain (1) learning
different activation functions for different layers and different input data,
and (2) a learnable switch to select and only use the beneficial adapter
layers. We show that adaptable adapters achieve on-par performances with the
standard adapter architecture while using a considerably smaller number of
adapter layers. In addition, we show that the selected adapter architecture by
adaptable adapters transfers well across different data settings and similar
tasks. We propose to use adaptable adapters for designing efficient and
effective adapter architectures. The resulting adapters (a) contain about 50%
of the learning parameters of the standard adapter and are therefore more
efficient at training and inference, and require less storage space, and (b)
achieve considerably higher performances in low-data settings.

The Transformer architecture has led to significant gains in machine
translation. However, most studies focus on only sentence-level translation
without considering the context dependency within documents, leading to the
inadequacy of document-level coherence. Some recent research tried to mitigate
this issue by introducing an additional context encoder or translating with
multiple sentences or even the entire document. Such methods may lose the
information on the target side or have an increasing computational complexity
as documents get longer. To address such problems, we introduce a recurrent
memory unit to the vanilla Transformer, which supports the information exchange
between the sentence and previous context. The memory unit is recurrently
updated by acquiring information from sentences, and passing the aggregated
knowledge back to subsequent sentence states. We follow a two-stage training
strategy, in which the model is first trained at the sentence level and then
finetuned for document-level translation. We conduct experiments on three
popular datasets for document-level machine translation and our model has an
average improvement of 0.91 s-BLEU over the sentence-level baseline. We also
achieve state-of-the-art results on TED and News, outperforming the previous
work by 0.36 s-BLEU and 1.49 d-BLEU on average.

Pretrained language models (PLMs) have made remarkable progress in text
generation tasks via fine-tuning. While, it is challenging to fine-tune PLMs in
a data-scarce situation. Therefore, it is non-trivial to develop a general and
lightweight model that can adapt to various text generation tasks based on
PLMs. To fulfill this purpose, the recent prompt-based learning offers a
potential solution. In this paper, we improve this technique and propose a
novel prompt-based method (PTG) for text generation in a transferable setting.
First, PTG learns a set of source prompts for various source generation tasks
and then transfers these prompts as target prompts to perform target generation
tasks. To consider both task- and instance-level information, we design an
adaptive attention mechanism to derive the target prompts. For each data
instance, PTG learns a specific target prompt by attending to highly relevant
source prompts. In extensive experiments, PTG yields competitive or better
results than fine-tuning methods. We release our source prompts as an open
resource, where users can add or reuse them to improve new text generation
tasks for future research. Code and data can be available at
https://github.com/RUCAIBox/Transfer-Prompts-for-Text-Generation.

Resource-constrained devices are increasingly the deployment targets of
machine learning applications. Static models, however, do not always suffice
for dynamic environments. On-device training of models allows for quick
adaptability to new scenarios. With the increasing size of deep neural
networks, as noted with the likes of BERT and other natural language processing
models, comes increased resource requirements, namely memory, computation,
energy, and time. Furthermore, training is far more resource intensive than
inference. Resource-constrained on-device learning is thus doubly difficult,
especially with large BERT-like models. By reducing the memory usage of
fine-tuning, pre-trained BERT models can become efficient enough to fine-tune
on resource-constrained devices. We propose Freeze And Reconfigure (FAR), a
memory-efficient training regime for BERT-like models that reduces the memory
usage of activation maps during fine-tuning by avoiding unnecessary parameter
updates. FAR reduces fine-tuning time on the DistilBERT model and CoLA dataset
by 30%, and time spent on memory operations by 47%. More broadly, reductions in
metric performance on the GLUE and SQuAD datasets are around 1% on average.

Current state-of-the-art segmentation techniques for ocular images are
critically dependent on large-scale annotated datasets, which are
labor-intensive to gather and often raise privacy concerns. In this paper, we
present a novel framework, called BiOcularGAN, capable of generating synthetic
large-scale datasets of photorealistic (visible light and near-infrared) ocular
images, together with corresponding segmentation labels to address these
issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2
(DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic
Mask Generator (SMG) component that produces semantic annotations by exploiting
latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through
extensive experiments across five diverse ocular datasets and analyze the
effects of bimodal data generation on image quality and the produced
annotations. Our experimental results show that BiOcularGAN is able to produce
high-quality matching bimodal images and annotations (with minimal manual
intervention) that can be used to train highly competitive (deep) segmentation
models (in a privacy aware-manner) that perform well across multiple real-world
datasets. The source code for the BiOcularGAN framework is publicly available
at https://github.com/dariant/BiOcularGAN.

In this paper we describe an approach for anomaly detection and its
explainability in multivariate functional data. The anomaly detection procedure
consists of transforming the series into a vector of features and using an
Isolation forest algorithm. The explainable procedure is based on the
computation of the SHAP coefficients and on the use of a supervised decision
tree. We apply it on simulated data to measure the performance of our method
and on real data coming from industry.

This paper studies the consequences of capturing non-linear dependence among
the covariates that drive the default of different obligors and the overall
riskiness of their credit portfolio. Joint default modeling is, without loss of
generality, the classical Bernoulli mixture model. Using an application to a
credit card dataset we show that, even when Machine Learning techniques perform
only slightly better than Logistic Regression in classifying individual
defaults as a function of the covariates, they do outperform it at the
portfolio level. This happens because they capture linear and non-linear
dependence among the covariates, whereas Logistic Regression only captures
linear dependence. The ability of Machine Learning methods to capture
non-linear dependence among the covariates produces higher default correlation
compared with Logistic Regression. As a consequence, on our data, Logistic
Regression underestimates the riskiness of the credit portfolio.

In this paper, we investigate the performances of tunable quantum neural
networks in the Quantum Probably Approximately Correct (QPAC) learning
framework. Tunable neural networks are quantum circuits made of
multi-controlled X gates. By tuning the set of controls these circuits are able
to approximate any Boolean functions. This architecture is particularly suited
to be used in the QPAC-learning framework as it can handle the superposition
produced by the oracle. In order to tune the network so that it can approximate
a target concept, we have devised and implemented an algorithm based on
amplitude amplification. The numerical results show that this approach can
efficiently learn concepts from a simple class.

In this paper we present ExSpliNet, an interpretable and expressive neural
network model. The model combines ideas of Kolmogorov neural networks,
ensembles of probabilistic trees, and multivariate B-spline representations. We
give a probabilistic interpretation of the model and show its universal
approximation properties. We also discuss how it can be efficiently encoded by
exploiting B-spline properties. Finally, we test the effectiveness of the
proposed model on synthetic approximation problems and classical machine
learning benchmark datasets.

Federated learning (FL) has been widely employed for medical image analysis
to facilitate multi-client collaborative learning without sharing raw data.
Despite great success, FL's performance is limited for multiple sclerosis (MS)
lesion segmentation tasks, due to variance in lesion characteristics imparted
by different scanners and acquisition parameters. In this work, we propose the
first FL MS lesion segmentation framework via two effective re-weighting
mechanisms. Specifically, a learnable weight is assigned to each local node
during the aggregation process, based on its segmentation performance. In
addition, the segmentation loss function in each client is also re-weighted
according to the lesion volume for the data during training. Comparison
experiments on two FL MS segmentation scenarios using public and clinical
datasets have demonstrated the effectiveness of the proposed method by
outperforming other FL methods significantly. Furthermore, the segmentation
performance of FL incorporating our proposed aggregation mechanism can exceed
centralised training with all the raw data. The extensive evaluation also
indicated the superiority of our method when estimating brain volume
differences estimation after lesion inpainting.

Unstructured pruning has the limitation of dealing with the sparse and
irregular weights. By contrast, structured pruning can help eliminate this
drawback but it requires complex criterion to determine which components to be
pruned. To this end, this paper presents a new method termed TissueNet, which
directly constructs compact neural networks with fewer weight parameters by
independently stacking designed basic units, without requiring additional
judgement criteria anymore. Given the basic units of various architectures,
they are combined and stacked in a certain form to build up compact neural
networks. We formulate TissueNet in diverse popular backbones for comparison
with the state-of-the-art pruning methods on different benchmark datasets.
Moreover, two new metrics are proposed to evaluate compression performance.
Experiment results show that TissueNet can achieve comparable classification
accuracy while saving up to around 80% FLOPs and 89.7% parameters. That is,
stacking basic units provides a new promising way for network compression.

Deep learning has been the mainstream technique in natural language
processing (NLP) area. However, the techniques require many labeled data and
are less generalizable across domains. Meta-learning is an arising field in
machine learning studying approaches to learn better learning algorithms.
Approaches aim at improving algorithms in various aspects, including data
efficiency and generalizability. Efficacy of approaches has been shown in many
NLP tasks, but there is no systematic survey of these approaches in NLP, which
hinders more researchers from joining the field. Our goal with this survey
paper is to offer researchers pointers to relevant meta-learning works in NLP
and attract more attention from the NLP community to drive future innovation.
This paper first introduces the general concepts of meta-learning and the
common approaches. Then we summarize task construction settings and application
of meta-learning for various NLP problems and review the development of
meta-learning in NLP community.

Despite the successes in many fields, it is found that neural networks are
difficult to be both accurate and robust, i.e., high accuracy networks are
often vulnerable. Various empirical and analytic studies have substantiated
that there is more or less a trade-off between the accuracy and robustness of
neural networks. If the property is inherent, applications based on the neural
networks are vulnerable with untrustworthy predictions. To more deeply explore
and understand this issue, in this study we show that the accuracy-robustness
trade-off is an intrinsic property whose underlying mechanism is closely
related to the uncertainty principle in quantum mechanics. By relating the loss
function in neural networks to the wave function in quantum mechanics, we show
that the inputs and their conjugates cannot be resolved by a neural network
simultaneously. This work thus provides an insightful explanation for the
inevitability of the accuracy-robustness dilemma for general deep networks from
an entirely new perspective, and furthermore, reveals a potential possibility
to study various properties of neural networks with the mature mathematical
tools in quantum physics.

We propose a unified view on two widely used data visualization techniques:
Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show
that they can both be derived from a common mathematical framework. Leveraging
this formulation, we propose to compare SOM and SNE quantitatively on two
datasets, and discuss possible avenues for future work to take advantage of
both approaches.

Deep learning has been achieving decent performance in computer vision
requiring a large volume of images, however, collecting images is expensive and
difficult in many scenarios. To alleviate this issue, many image augmentation
algorithms have been proposed as effective and efficient strategies.
Understanding current algorithms is essential to find suitable methods or
develop novel techniques for given tasks. In this paper, we perform a
comprehensive survey on image augmentation for deep learning with a novel
informative taxonomy. To get the basic idea why we need image augmentation, we
introduce the challenges in computer vision tasks and vicinity distribution.
Then, the algorithms are split into three categories; model-free, model-based,
and optimizing policy-based. The model-free category employs image processing
methods while the model-based method leverages trainable image generation
models. In contrast, the optimizing policy-based approach aims to find the
optimal operations or their combinations. Furthermore, we discuss the current
trend of common applications with two more active topics, leveraging different
ways to understand image augmentation, such as group and kernel theory, and
deploying image augmentation for unsupervised learning. Based on the analysis,
we believe that our survey gives a better understanding helpful to choose
suitable methods or design novel algorithms for practical applications.

Score-based models generate samples by mapping noise to data (and vice versa)
via a high-dimensional diffusion process. We question whether it is necessary
to run this entire process at high dimensionality and incur all the
inconveniences thereof. Instead, we restrict the diffusion via projections onto
subspaces as the data distribution evolves toward noise. When applied to
state-of-the-art models, our framework simultaneously improves sample quality
-- reaching an FID of 2.17 on unconditional CIFAR-10 -- and reduces the
computational cost of inference for the same number of denoising steps. Our
framework is fully compatible with continuous-time diffusion and retains its
flexible capabilities, including exact log-likelihoods and controllable
generation. Code is available at
https://github.com/bjing2016/subspace-diffusion.

In many applications, data can be heterogeneous in the sense of spanning
latent groups with different underlying distributions. When predictive models
are applied to such data the heterogeneity can affect both predictive
performance and interpretability. Building on developments at the intersection
of unsupervised learning and regularised regression, we propose an approach for
heterogeneous data that allows joint learning of (i) explicit multivariate
feature distributions, (ii) high-dimensional regression models and (iii) latent
group labels, with both (i) and (ii) specific to latent groups and both
elements informing (iii). The approach is demonstrably effective in high
dimensions, combining data reduction for computational efficiency with a
re-weighting scheme that retains key signals even when the number of features
is large. We discuss in detail these aspects and their impact on modelling and
computation, including EM convergence. The approach is modular and allows
incorporation of data reductions and high-dimensional estimators that are
suitable for specific applications. We show results from extensive simulations
and real data experiments, including highly non-Gaussian data. Our results
allow efficient, effective analysis of high-dimensional data in settings, such
as biomedicine, where both interpretable prediction and explicit feature space
models are needed but hidden heterogeneity may be a concern.

Traffic forecasting is essential for the traffic construction of smart cities
in the new era. However, traffic data's complex spatial and temporal
dependencies make traffic forecasting extremely challenging. Most existing
traffic forecasting methods rely on the predefined adjacency matrix to model
the Spatio-temporal dependencies. Nevertheless, the road traffic state is
highly real-time, so the adjacency matrix should change dynamically with time.
This article presents a new Multi-Spatio-temporal Fusion Graph Recurrent
Network (MSTFGRN) to address the issues above. The network proposes a
data-driven weighted adjacency matrix generation method to compensate for
real-time spatial dependencies not reflected by the predefined adjacency
matrix. It also efficiently learns hidden Spatio-temporal dependencies by
performing a new two-way Spatio-temporal fusion operation on parallel
Spatio-temporal relations at different moments. Finally, global Spatio-temporal
dependencies are captured simultaneously by integrating a global attention
mechanism into the Spatio-temporal fusion module. Extensive trials on four
large-scale, real-world traffic datasets demonstrate that our method achieves
state-of-the-art performance compared to alternative baselines.

Advancements in scientific instrument sensors and connected devices provide
unprecedented insight into ongoing experiments and present new opportunities
for control, optimization, and steering. However, the diversity of sensors and
heterogeneity of their data result in make it challenging to fully realize
these new opportunities. Organizing and synthesizing diverse data streams in
near-real-time requires both rich automation and Machine Learning (ML). To
efficiently utilize ML during an experiment, the entire ML lifecycle must be
addressed, including refining experiment configurations, retraining models, and
applying decisions-tasks that require an equally diverse array of computational
resources spanning centralized HPC to the accelerators at the edge. Here we
present the Manufacturing Data and Machine Learning platform (MDML). The MDML
is designed to standardize the research and operational environment for
advanced data analytics and ML-enabled automated process optimization by
providing the cyberinfrastructure to integrate sensor data streams and AI in
cyber-physical systems for in-situ analysis. To achieve this, the MDML provides
a fabric to receive and aggregate IoT data and simultaneously orchestrate
remote computation across the computing continuum. In this paper we describe
the MDML and show how it is used in advanced manufacturing to act on IoT data
and orchestrate distributed ML to guide experiments.

Despite recent tremendous progress in optical imaging and metrology, the
resolution gap between atomic scale transmission electron microscopy and
optical techniques has not been closed. Is optical imaging and metrology of
nanostructures exhibiting Brownian motion possible with resolution beyond
thermal fluctuations? Here we report on an experiment in which the average
position of a nanowire with a thermal oscillation amplitude of ~150 pm is
resolved in single-shot measurements with absolute error of ~30 pm using light
at a wavelength of {\lambda} = 488 nm, providing the first example of such
sub-Brownian metrology with {\lambda}/10,000 resolution. To localize the
nanowire, we employ a deep learning analysis of the scattering of topologically
structured light, which is highly sensitive to the nanowire's position. As a
non-invasive optical metrology with sub-Brownian absolute errors, down to a
fraction of the typical size of an atom (Si: 220 pm diameter), it opens the
exciting field of picophotonics.

High-resolution daytime satellite imagery has become a promising source to
study economic activities. These images display detailed terrain over large
areas and allow zooming into smaller neighborhoods. Existing methods, however,
have utilized images only in a single-level geographical unit. This research
presents a deep learning model to predict economic indicators via aggregating
traits observed from multiple levels of geographical units. The model first
measures hyperlocal economy over small communities via ordinal regression. The
next step extracts district-level features by summarizing interconnection among
hyperlocal economies. In the final step, the model estimates economic
indicators of districts via aggregating the hyperlocal and district
information. Our new multi-level learning model substantially outperforms
strong baselines in predicting key indicators such as population, purchasing
power, and energy consumption. The model is also robust against data shortage;
the trained features from one country can generalize to other countries when
evaluated with data gathered from Malaysia, the Philippines, Thailand, and
Vietnam. We discuss the multi-level model's implications for measuring
inequality, which is the essential first step in policy and social science
research on inequality and poverty.

In federated learning (FL), a number of devices train their local models and
upload the corresponding parameters or gradients to the base station (BS) to
update the global model while protecting their data privacy. However, due to
the limited computation and communication resources, the number of local
trainings (a.k.a. local update) and that of aggregations (a.k.a. global update)
need to be carefully chosen. In this paper, we investigate and analyze the
optimal trade-off between the number of local trainings and that of global
aggregations to speed up the convergence and enhance the prediction accuracy
over the existing works. Our goal is to minimize the global loss function under
both the delay and the energy consumption constraints. In order to make the
optimization problem tractable, we derive a new and tight upper bound on the
loss function, which allows us to obtain closed-form expressions for the number
of local trainings and that of global aggregations. Simulation results show
that our proposed scheme can achieve a better performance in terms of the
prediction accuracy, and converge much faster than the baseline schemes.

Fictitious play (FP) is one of the most fundamental game-theoretical learning
frameworks for computing Nash equilibrium in $n$-player games, which builds the
foundation for modern multi-agent learning algorithms. Although FP has provable
convergence guarantees on zero-sum games and potential games, many real-world
problems are often a mixture of both and the convergence property of FP has not
been fully studied yet. In this paper, we extend the convergence results of FP
to the combinations of such games and beyond. Specifically, we derive new
conditions for FP to converge by leveraging game decomposition techniques. We
further develop a linear relationship unifying cooperation and competition in
the sense that these two classes of games are mutually transferable. Finally,
we analyze a non-convergent example of FP, the Shapley game, and develop
sufficient conditions for FP to converge.

Transition-based parsers for Abstract Meaning Representation (AMR) rely on
node-to-word alignments. These alignments are learned separately from parser
training and require a complex pipeline of rule-based components,
pre-processing, and post-processing to satisfy domain-specific constraints.
Parsers also train on a point-estimate of the alignment pipeline, neglecting
the uncertainty due to the inherent ambiguity of alignment. In this work we
explore two avenues for overcoming these limitations. First, we propose a
neural aligner for AMR that learns node-to-word alignments without relying on
complex pipelines. We subsequently explore a tighter integration of aligner and
parser training by considering a distribution over oracle action sequences
arising from aligner uncertainty. Empirical results show this approach leads to
more accurate alignments and generalization better from the AMR2.0 to AMR3.0
corpora. We attain a new state-of-the art for gold-only trained models,
matching silver-trained performance without the need for beam search on AMR3.0.

The quantification of the entanglement present in a physical system is of
paramount importance for fundamental research and many cutting-edge
applications. Currently, achieving this goal requires either a priori knowledge
on the system or very demanding experimental procedures such as full state
tomography or collective measurements. Here, we demonstrate that by employing
neural networks we can quantify the degree of entanglement without needing to
know the full description of the quantum state. Our method allows for direct
quantification of the quantum correlations using an incomplete set of local
measurements. Despite using undersampled measurements, we achieve an estimation
error of up to an order of magnitude lower than the state-of-the-art quantum
tomography. Furthermore, we achieve this result employing networks trained
using exclusively simulated data. Finally, we derive a method based on a
convolutional network input that can accept data from various measurement
scenarios and perform, to some extent, independently of the measurement device.

Model training algorithms which observe a small portion of the training set
in each computational step are ubiquitous in practical machine learning, and
include both stochastic and online optimization methods. In the vast majority
of cases, such algorithms typically observe the training samples via the
gradients of the cost functions the samples incur. Thus, these methods exploit
are the \emph{slope} of the cost functions via their first-order
  To address limitations of gradient-based methods, such as sensitivity to
step-size choice in the stochastic setting, or inability to exploit small
function variability in the online setting, several streams of research attempt
to exploit more information about the cost functions than just their gradients
via the well-known proximal framework of optimization. However, implementing
such methods in practice poses a challenge, since each iteration step boils
down to computing a proximal operator, which may not be easy. In this work we
provide efficient algorithms and corresponding implementations of proximal
operators in order to make experimentation with incremental proximal
optimization algorithms accessible to a larger audience of researchers and
practitioners, and in particular to promote additional theoretical research
into these methods by closing the gap between their theoretical description in
research papers and their use in practice. The corresponding code is published
at https://github.com/alexshtf/inc_prox_pt.

This essay seeks to tie together thoughts on the political economy of
academia, the inequities in access to the academic means of production and
decolonial practice in data empowerment. To demonstrate this I will provide a
brief analysis of the neo-colonial, extractive practices of the Western
Academy, introduce concepts around decolonial AI practice and then use these to
form an investigative framework. Using this framework, I present a brief case
study of the AirQo project in Kampala, Uganda. The project aims to deploy a
low-cost air pollution sensor network across the city, using machine learning
methods to calibrate these sensors against reference instruments, providing
high-quality air pollution data at a far lower cost.

Vacant parking space (VPS) prediction is one of the key issues of intelligent
parking guidance systems. Accurately predicting VPS information plays a crucial
role in intelligent parking guidance systems, which can help drivers find
parking space quickly, reducing unnecessary waste of time and excessive
environmental pollution. Through the simple analysis of historical data, we
found that there not only exists a obvious temporal correlation in each parking
lot, but also a clear spatial correlation between different parking lots. In
view of this, this paper proposed a graph data-based model ST-GBGRU
(Spatial-Temporal Graph Based Gated Recurrent Unit), the number of VPSs can be
predicted both in short-term (i.e., within 30 min) and in long-term (i.e., over
30min). On the one hand, the temporal correlation of historical VPS data is
extracted by GRU, on the other hand, the spatial correlation of historical VPS
data is extracted by GCN inside GRU. Two prediction methods, namely direct
prediction and iterative prediction, are combined with the proposed model.
Finally, the prediction model is applied to predict the number VPSs of 8 public
parking lots in Santa Monica. The results show that in the short-term and
long-term prediction tasks, ST-GBGRU model can achieve high accuracy and have
good application prospects.

In general-sum games, the interaction of self-interested learning agents
commonly leads to collectively worst-case outcomes, such as defect-defect in
the iterated prisoner's dilemma (IPD). To overcome this, some methods, such as
Learning with Opponent-Learning Awareness (LOLA), shape their opponents'
learning process. However, these methods are myopic since only a small number
of steps can be anticipated, are asymmetric since they treat other agents as
naive learners, and require the use of higher-order derivatives, which are
calculated through white-box access to an opponent's differentiable learning
algorithm. To address these issues, we propose Model-Free Opponent Shaping
(M-FOS). M-FOS learns in a meta-game in which each meta-step is an episode of
the underlying inner game. The meta-state consists of the inner policies, and
the meta-policy produces a new inner policy to be used in the next episode.
M-FOS then uses generic model-free optimisation methods to learn meta-policies
that accomplish long-horizon opponent shaping. Empirically, M-FOS
near-optimally exploits naive learners and other, more sophisticated algorithms
from the literature. For example, to the best of our knowledge, it is the first
method to learn the well-known Zero-Determinant (ZD) extortion strategy in the
IPD. In the same settings, M-FOS leads to socially optimal outcomes under
meta-self-play. Finally, we show that M-FOS can be scaled to high-dimensional
settings.

We study the first gradient descent step on the first-layer parameters
$\boldsymbol{W}$ in a two-layer neural network: $f(\boldsymbol{x}) =
\frac{1}{\sqrt{N}}\boldsymbol{a}^\top\sigma(\boldsymbol{W}^\top\boldsymbol{x})$,
where $\boldsymbol{W}\in\mathbb{R}^{d\times N},
\boldsymbol{a}\in\mathbb{R}^{N}$ are randomly initialized, and the training
objective is the empirical MSE loss: $\frac{1}{n}\sum_{i=1}^n
(f(\boldsymbol{x}_i)-y_i)^2$. In the proportional asymptotic limit where
$n,d,N\to\infty$ at the same rate, and an idealized student-teacher setting, we
show that the first gradient update contains a rank-1 "spike", which results in
an alignment between the first-layer weights and the linear component of the
teacher model $f^*$. To characterize the impact of this alignment, we compute
the prediction risk of ridge regression on the conjugate kernel after one
gradient step on $\boldsymbol{W}$ with learning rate $\eta$, when $f^*$ is a
single-index model. We consider two scalings of the first step learning rate
$\eta$. For small $\eta$, we establish a Gaussian equivalence property for the
trained feature map, and prove that the learned kernel improves upon the
initial random features model, but cannot defeat the best linear model on the
input. Whereas for sufficiently large $\eta$, we prove that for certain $f^*$,
the same ridge estimator on trained features can go beyond this "linear regime"
and outperform a wide range of random features and rotationally invariant
kernels. Our results demonstrate that even one gradient step can lead to a
considerable advantage over random features, and highlight the role of learning
rate scaling in the initial phase of training.

We introduce an algorithm that is able to find the facets of Coulomb diamonds
in quantum dot arrays. We simulate these arrays using the constant-interaction
model, and rely only on one-dimensional raster scans (rays) to learn a model of
the device using regularized maximum likelihood estimation. This allows us to
determine, for a given charge state of the device, which transitions exist and
what the compensated gate voltages for these are. For smaller devices the
simulator can also be used to compute the exact boundaries of the Coulomb
diamonds, which we use to assess that our algorithm correctly finds the vast
majority of transitions with high precision.

Federated learning has shown its advances recently but is still facing many
challenges, such as how algorithms save communication resources and reduce
computational costs, and whether they converge. To address these critical
issues, we propose a hybrid federated learning algorithm (FedGiA) that combines
the gradient descent and the inexact alternating direction method of
multipliers. The proposed algorithm is more communication- and
computation-efficient than several state-of-the-art algorithms theoretically
and numerically. Moreover, it also converges globally under mild conditions.

Training deep learning models takes an extremely long execution time and
consumes large amounts of computing resources. At the same time, recent
research proposed systems and compilers that are expected to decrease deep
learning models runtime. An effective optimisation methodology in data
processing is desirable, and the reduction of compute requirements of deep
learning models is the focus of extensive research.
  In this paper, we address the neural network sub-graph transformation by
exploring reinforcement learning (RL) agents to achieve performance
improvement. Our proposed approach RLFlow can learn to perform neural network
subgraph transformations, without the need for expertly designed heuristics to
achieve a high level of performance.
  Recent work has aimed at applying RL to computer systems with some success,
especially using model-free RL techniques. Model-based reinforcement learning
methods have seen an increased focus in research as they can be used to learn
the transition dynamics of the environment; this can be leveraged to train an
agent using a hallucinogenic environment such as World Model (WM), thereby
increasing sample efficiency compared to model-free approaches. WM uses
variational auto-encoders and it builds a model of the system and allows
exploring the model in an inexpensive way.
  In RLFlow, we propose a design for a model-based agent with WM which learns
to optimise the architecture of neural networks by performing a sequence of
sub-graph transformations to reduce model runtime. We show that our approach
can match the state-of-the-art performance on common convolutional networks and
outperforms by up to 5% those based on transformer-style architectures

As the number of heterogenous IP-connected devices and traffic volume
increase, so does the potential for security breaches. The undetected
exploitation of these breaches can bring severe cybersecurity and privacy
risks. In this paper, we present a practical unsupervised anomaly-based deep
learning detection system called ARCADE (Adversarially Regularized
Convolutional Autoencoder for unsupervised network anomaly DEtection). ARCADE
exploits the property of 1D Convolutional Neural Networks (CNNs) and Generative
Adversarial Networks (GAN) to automatically build a profile of the normal
traffic based on a subset of raw bytes of a few initial packets of network
flows so that potential network anomalies and intrusions can be effectively
detected before they could cause any more damage to the network. A
convolutional Autoencoder (AE) is proposed that suits online detection in
resource-constrained environments, and can be easily improved for environments
with higher computational capabilities. An adversarial training strategy is
proposed to regularize and decrease the AE's capabilities to reconstruct
network flows that are out of the normal distribution, and thereby improve its
anomaly detection capabilities. The proposed approach is more effective than
existing state-of-the-art deep learning approaches for network anomaly
detection and significantly reduces detection time. The evaluation results show
that the proposed approach is suitable for anomaly detection on
resource-constrained hardware platforms such as Raspberry Pi.

A reservoir computer is a type of dynamical system arranged to do
computation. Typically, a reservoir computer is constructed by connecting a
large number of nonlinear nodes in a network that includes recurrent
connections. In order to achieve accurate results, the reservoir usually
contains hundreds to thousands of nodes. This high dimensionality makes it
difficult to analyze the reservoir computer using tools from dynamical systems
theory. Additionally, the need to create and connect large numbers of nonlinear
nodes makes it difficult to design and build analog reservoir computers that
can be faster and consume less power than digital reservoir computers. We
demonstrate here that a reservoir computer may be divided into two parts; a
small set of nonlinear nodes (the reservoir), and a separate set of
time-shifted reservoir output signals. The time-shifted output signals serve to
increase the rank and memory of the reservoir computer, and the set of
nonlinear nodes may create an embedding of the input dynamical system. We use
this time-shifting technique to obtain excellent performance from an
opto-electronic delay-based reservoir computer with only a small number of
virtual nodes. Because only a few nonlinear nodes are required, construction of
a reservoir computer becomes much easier, and delay-based reservoir computers
can operate at much higher speeds.

Modeling the ability of multicellular organisms to build and maintain their
bodies through local interactions between individual cells (morphogenesis) is a
long-standing challenge of developmental biology. Recently, the Neural Cellular
Automata (NCA) model was proposed as a way to find local system rules that
produce a desired global behaviour, such as growing and persisting a predefined
target pattern, by repeatedly applying the same rule over a grid starting from
a single cell. In this work, we argue that the original Growing NCA model has
an important limitation: anisotropy of the learned update rule. This implies
the presence of an external factor that orients the cells in a particular
direction. In other words, "physical" rules of the underlying system are not
invariant to rotation, thus prohibiting the existence of differently oriented
instances of the target pattern on the same grid. We propose a modified
Isotropic NCA (IsoNCA) model that does not have this limitation. We demonstrate
that such cell systems can be trained to grow accurate asymmetrical patterns
through either of two methods: (1) by breaking symmetries using structured
seeds or (2) by introducing a rotation-reflection invariant training objective
and relying on symmetry-breaking caused by asynchronous cell updates.

Future AI applications require performance, reliability and privacy that the
existing, cloud-dependant system architectures cannot provide. In this article,
we study orchestration in the device-edge-cloud continuum, and focus on AI for
edge, that is, the AI methods used in resource orchestration. We claim that to
support the constantly growing requirements of intelligent applications in the
device-edge-cloud computing continuum, resource orchestration needs to embrace
edge AI and emphasize local autonomy and intelligence. To justify the claim, we
provide a general definition for continuum orchestration, and look at how
current and emerging orchestration paradigms are suitable for the computing
continuum. We describe certain major emerging research themes that may affect
future orchestration, and provide an early vision of an orchestration paradigm
that embraces those research themes. Finally, we survey current key edge AI
methods and look at how they may contribute into fulfilling the vision of
future continuum orchestration.

Machine learning operates at the intersection of statistics and computer
science. This raises the question as to its underlying methodology. While much
emphasis has been put on the close link between the process of learning from
data and induction, the falsificationist component of machine learning has
received minor attention. In this paper, we argue that the idea of
falsification is central to the methodology of machine learning. It is commonly
thought that machine learning algorithms infer general prediction rules from
past observations. This is akin to a statistical procedure by which estimates
are obtained from a sample of data. But machine learning algorithms can also be
described as choosing one prediction rule from an entire class of functions. In
particular, the algorithm that determines the weights of an artificial neural
network operates by empirical risk minimization and rejects prediction rules
that lack empirical adequacy. It also exhibits a behavior of implicit
regularization that pushes hypothesis choice toward simpler prediction rules.
We argue that taking both aspects together gives rise to a falsificationist
account of artificial neural networks.

Recent advances in machine learning technologies and sensing have paved the
way for the belief that safe, accessible, and convenient autonomous vehicles
may be realized in the near future. Despite tremendous advances within this
context, fundamental challenges around safety and reliability are limiting
their arrival and comprehensive adoption. Autonomous vehicles are often tasked
with operating in dynamic and uncertain environments. As a result, they often
make use of highly complex components, such as machine learning approaches, to
handle the nuances of sensing, actuation, and control. While these methods are
highly effective, they are notoriously difficult to assure. Moreover, within
uncertain and dynamic environments, design time assurance analyses may not be
sufficient to guarantee safety. Thus, it is critical to monitor the correctness
of these systems at runtime. One approach for providing runtime assurance of
systems with components that may not be amenable to formal analysis is the
simplex architecture, where an unverified component is wrapped with a safety
controller and a switching logic designed to prevent dangerous behavior. In
this paper, we propose using a real-time reachability algorithm for the
implementation of the simplex architecture to assure the safety of a 1/10 scale
open source autonomous vehicle platform known as F1/10. The reachability
algorithm that we leverage (a) provides provable guarantees of safety, and (b)
is used to detect potentially unsafe scenarios. In our approach, the need to
analyze an underlying controller is abstracted away, instead focusing on the
effects of the controller's decisions on the system's future states. We
demonstrate the efficacy of our architecture through a vast set of experiments
conducted both in simulation and on an embedded hardware platform.

Tremendous progress in deep learning over the last years has led towards a
future with autonomous vehicles on our roads. Nevertheless, the performance of
their perception systems is strongly dependent on the quality of the utilized
training data. As these usually only cover a fraction of all object classes an
autonomous driving system will face, such systems struggle with handling the
unexpected. In order to safely operate on public roads, the identification of
objects from unknown classes remains a crucial task. In this paper, we propose
a novel pipeline to detect unknown objects. Instead of focusing on a single
sensor modality, we make use of lidar and camera data by combining state-of-the
art detection models in a sequential manner. We evaluate our approach on the
Waymo Open Perception Dataset and point out current research gaps in anomaly

Several popular Transformer based language models have been found to be
successful for text-driven brain encoding. However, existing literature
leverages only pretrained text Transformer models and has not explored the
efficacy of task-specific learned Transformer representations. In this work, we
explore transfer learning from representations learned for ten popular natural
language processing tasks (two syntactic and eight semantic) for predicting
brain responses from two diverse datasets: Pereira (subjects reading sentences
from paragraphs) and Narratives (subjects listening to the spoken stories).
Encoding models based on task features are used to predict activity in
different regions across the whole brain. Features from coreference resolution,
NER, and shallow syntax parsing explain greater variance for the reading
activity. On the other hand, for the listening activity, tasks such as
paraphrase generation, summarization, and natural language inference show
better encoding performance. Experiments across all 10 task representations
provide the following cognitive insights: (i) language left hemisphere has
higher predictive brain activity versus language right hemisphere, (ii)
posterior medial cortex, temporo-parieto-occipital junction, dorsal frontal
lobe have higher correlation versus early auditory and auditory association
cortex, (iii) syntactic and semantic tasks display a good predictive
performance across brain regions for reading and listening stimuli resp.

Sea ice concentration is an important metric used to characterize polar sea
ice behavior. Understanding this behavior and accurately representing it is of
critical importance for climate science research, and also has important uses
in the context of maritime navigation. An end-to-end workflow for generating
learned concentration estimation models from synthetic aperture radar data,
trained on existing passive microwave data, is presented here. A novel
objective function was introduced to account for uncertainty in the passive
microwave measurements, which can be extended to account for arbitrary sources
of error in the training data, and a recent set of in-situ observations was
used to evaluate the reliability of the chosen passive microwave concentration
estimation model. Google Colaboratory was used as the development platform, and
all notebooks, training data, and trained models are available on GitHub. This
chapter is an overview of the most interesting aspects of this investigation,
and a detailed report is also available on GitHub.

Depth estimation is an important task, applied in various methods and
applications of computer vision. While the traditional methods of estimating
depth are based on depth cues and require specific equipment such as stereo
cameras and configuring input according to the approach being used, the focus
at the current time is on a single source, or monocular, depth estimation. The
recent developments in Convolution Neural Networks along with the integration
of classical methods in these deep learning approaches have led to a lot of
advancements in the depth estimation problem. The problem of outdoor depth
estimation, or depth estimation in wild, is a very scarcely researched field of
study. In this paper, we give an overview of the available datasets, depth
estimation methods, research work, trends, challenges, and opportunities that
exist for open research. To our knowledge, no openly available survey work
provides a comprehensive collection of outdoor depth estimation techniques and
research scope, making our work an essential contribution for people looking to
enter this field of study.

Boosted by deep learning, natural language processing (NLP) techniques have
recently seen spectacular progress, mainly fueled by breakthroughs both in
representation learning with word embeddings (e.g. word2vec) as well as novel
architectures (e.g. transformers). This success quickly invited researchers to
explore the use of NLP techniques to other fields, such as computer programming
languages, with the promise to automate tasks in software programming (bug
detection, code synthesis, code repair, cross language translation etc.). By
extension, NLP has potential for application to network configuration languages
as well, for instance considering tasks such as network configuration
verification, synthesis, and cross-vendor translation. In this paper, we survey
recent advances in deep learning applied to programming languages, for the
purpose of code verification, synthesis and translation: in particularly, we
review their training requirements and expected performance, and qualitatively
assess whether similar techniques can benefit corresponding use-cases in
networking.

Contrastively trained language-image models such as CLIP, ALIGN, and BASIC
have demonstrated unprecedented robustness to multiple challenging natural
distribution shifts. Since these language-image models differ from previous
training approaches in several ways, an important question is what causes the
large robustness gains. We answer this question via a systematic experimental
investigation. Concretely, we study five different possible causes for the
robustness gains: (i) the training set size, (ii) the training distribution,
(iii) language supervision at training time, (iv) language supervision at test
time, and (v) the contrastive loss function. Our experiments show that the more
diverse training distribution is the main cause for the robustness gains, with
the other factors contributing little to no robustness. Beyond our experimental
results, we also introduce ImageNet-Captions, a version of ImageNet with
original text annotations from Flickr, to enable further controlled experiments
of language-image training.

6G networks require a flexible infrastructure to dynamically provide
ubiquitous network coverage. Mobile Access Points (MAP) deployment is a
promising solution. In this paper, we formulate the joint 3D MAP deployment and
user association problem over a dynamic network under interference and mobility
constraints. First, we propose an iterative algorithm to optimize the
deployment of MAPs. Our solution efficiently and quickly determines the number,
position and configuration of MAPs for highly dynamic scenarios. MAPs provide
appropriate Quality of Service (QoS) connectivity to mobile ground user in
mmwave or sub-6GHz bands and find their optimal positions in a 3D grid. Each
MAP also implies an energy cost (e.g. for travel) to be minimized. Once all
MAPs deployed, a deep multiagent reinforcement learning algorithm is proposed
to associate multiple users to multiple MAPs under interference constraint.
Each user acts as an independent agent that operates in a fully distributed
architecture and maximizes the network sum-rate.

This work investigates the use of Neural implicit representations,
specifically Neural Radiance Fields (NeRF), for geometrical queries and motion
planning. We show that by adding the capacity to infer occupancy in a radius to
a pre-trained NeRF, we are effectively learning an approximation to a Euclidean
Signed Distance Field (ESDF). Using backward differentiation of the augmented
network, we obtain an obstacle gradient that is integrated into an obstacle
avoidance policy based on the Riemannian Motion Policies (RMP) framework. Thus,
our findings allow for very fast sampling-free obstacle avoidance planning in
the implicit representation.

Non-smooth optimization is a core ingredient of many imaging or machine
learning pipelines. Non-smoothness encodes structural constraints on the
solutions, such as sparsity, group sparsity, low-rank and sharp edges. It is
also the basis for the definition of robust loss functions and scale-free
functionals such as square-root Lasso. Standard approaches to deal with
non-smoothness leverage either proximal splitting or coordinate descent. These
approaches are effective but usually require parameter tuning, preconditioning
or some sort of support pruning. In this work, we advocate and study a
different route, which operates a non-convex but smooth over-parametrization of
the underlying non-smooth optimization problems. This generalizes quadratic
variational forms that are at the heart of the popular Iterative Reweighted
Least Squares (IRLS). Our main theoretical contribution connects gradient
descent on this reformulation to a mirror descent flow with a varying Hessian
metric. This analysis is crucial to derive convergence bounds that are
dimension-free. This explains the efficiency of the method when using small
grid sizes in imaging. Our main algorithmic contribution is to apply the
Variable Projection (VarPro) method which defines a new formulation by
explicitly minimizing over part of the variables. This leads to a better
conditioning of the minimized functional and improves the convergence of simple
but very efficient gradient-based methods, for instance quasi-Newton solvers.
We exemplify the use of this new solver for the resolution of regularized
regression problems for inverse problems and supervised learning, including
total variation prior and non-convex regularizers.

Skill Classification (SC) is the task of classifying job competences from job
postings. This work is the first in SC applied to Danish job vacancy data. We
release the first Danish job posting dataset: Kompetencer (en: competences),
annotated for nested spans of competences. To improve upon coarse-grained
annotations, we make use of The European Skills, Competences, Qualifications
and Occupations (ESCO; le Vrang et al., 2014) taxonomy API to obtain
fine-grained labels via distant supervision. We study two setups: The zero-shot
and few-shot classification setting. We fine-tune English-based models and
RemBERT (Chung et al., 2020) and compare them to in-language Danish models. Our
results show RemBERT significantly outperforms all other models in both the
zero-shot and the few-shot setting.

With the extremely rapid advances in remote sensing (RS) technology, a great
quantity of Earth observation (EO) data featuring considerable and complicated
heterogeneity is readily available nowadays, which renders researchers an
opportunity to tackle current geoscience applications in a fresh way. With the
joint utilization of EO data, much research on multimodal RS data fusion has
made tremendous progress in recent years, yet these developed traditional
algorithms inevitably meet the performance bottleneck due to the lack of the
ability to comprehensively analyse and interpret these strongly heterogeneous
data. Hence, this non-negligible limitation further arouses an intense demand
for an alternative tool with powerful processing competence. Deep learning
(DL), as a cutting-edge technology, has witnessed remarkable breakthroughs in
numerous computer vision tasks owing to its impressive ability in data
representation and reconstruction. Naturally, it has been successfully applied
to the field of multimodal RS data fusion, yielding great improvement compared
with traditional methods. This survey aims to present a systematic overview in
DL-based multimodal RS data fusion. More specifically, some essential knowledge
about this topic is first given. Subsequently, a literature survey is conducted
to analyse the trends of this field. Some prevalent sub-fields in the
multimodal RS data fusion are then reviewed in terms of the to-be-fused data
modalities, i.e., spatiospectral, spatiotemporal, light detection and
ranging-optical, synthetic aperture radar-optical, and RS-Geospatial Big Data
fusion. Furthermore, We collect and summarize some valuable resources for the
sake of the development in multimodal RS data fusion. Finally, the remaining
challenges and potential future directions are highlighted.

Recent work has shown that NLP tasks such as Relation Extraction (RE) can be
recasted as Textual Entailment tasks using verbalizations, with strong
performance in zero-shot and few-shot settings thanks to pre-trained entailment
models. The fact that relations in current RE datasets are easily verbalized
casts doubts on whether entailment would be effective in more complex tasks. In
this work we show that entailment is also effective in Event Argument
Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE
and WikiEvents respectively, while achieving the same performance as with full
training. More importantly, we show that recasting EAE as entailment alleviates
the dependency on schemas, which has been a road-block for transferring
annotations between domains. Thanks to the entailment, the multi-source
transfer between ACE and WikiEvents further reduces annotation down to 10% and
5% (respectively) of the full training without transfer. Our analysis shows
that the key to good results is the use of several entailment datasets to
pre-train the entailment model. Similar to previous approaches, our method
requires a small amount of effort for manual verbalization: only less than 15
minutes per event argument type is needed, and comparable results can be
achieved with users with different level of expertise.

One compelling application of artificial intelligence is to generate a video
of a target person performing arbitrary desired motion (from a source person).
While the state-of-the-art methods are able to synthesize a video demonstrating
similar broad stroke motion details, they are generally lacking in texture
details. A pertinent manifestation appears as distorted face, feet, and hands,
and such flaws are very sensitively perceived by human observers. Furthermore,
current methods typically employ GANs with a L2 loss to assess the authenticity
of the generated videos, inherently requiring a large amount of training
samples to learn the texture details for adequate video generation. In this
work, we tackle these challenges from three aspects: 1) We disentangle each
video frame into foreground (the person) and background, focusing on generating
the foreground to reduce the underlying dimension of the network output. 2) We
propose a theoretically motivated Gromov-Wasserstein loss that facilitates
learning the mapping from a pose to a foreground image. 3) To enhance texture
details, we encode facial features with geometric guidance and employ local
GANs to refine the face, feet, and hands. Extensive experiments show that our
method is able to generate realistic target person videos, faithfully copying
complex motions from a source person. Our code and datasets are released at
https://github.com/Sifann/FakeMotion

We analyze the Knowledge Neurons framework for the attribution of factual and
relational knowledge to particular neurons in the transformer network. We use a
12-layer multi-lingual BERT model for our experiments. Our study reveals
various interesting phenomena. We observe that mostly factual knowledge can be
attributed to middle and higher layers of the network($\ge 6$). Further
analysis reveals that the middle layers($6-9$) are mostly responsible for
relational information, which is further refined into actual factual knowledge
or the "correct answer" in the last few layers($10-12$). Our experiments also
show that the model handles prompts in different languages, but representing
the same fact, similarly, providing further evidence for effectiveness of
multi-lingual pre-training. Applying the attribution scheme for grammatical
knowledge, we find that grammatical knowledge is far more dispersed among the
neurons than factual knowledge.

As with many other tasks, neural networks prove very effective for anomaly
detection purposes. However, very few deep-learning models are suited for
detecting anomalies on tabular datasets. This paper proposes a novel
methodology to flag anomalies based on TracIn, an influence measure initially
introduced for explicability purposes. The proposed methods can serve to
augment any unsupervised deep anomaly detection method. We test our approach
using Variational Autoencoders and show that the average influence of a
subsample of training points on a test point can serve as a proxy for
abnormality. Our model proves to be competitive in comparison with
state-of-the-art approaches: it achieves comparable or better performance in
terms of detection accuracy on medical and cyber-security tabular benchmark
data.

Urban metabolism is an active field of research that deals with the
estimation of emissions and resource consumption from urban regions. The
analysis could be carried out through a manual surveyor by the implementation
of elegant machine learning algorithms. In this exploratory work, we estimate
the water consumption by the buildings in the region captured by satellite
imagery. To this end, we break our analysis into three parts: i) Identification
of building pixels, given a satellite image, followed by ii) identification of
the building type (residential/non-residential) from the building pixels, and
finally iii) using the building pixels along with their type to estimate the
water consumption using the average per unit area consumption for different
building types as obtained from municipal surveys.

Node classification on graphs can be formulated as the Dirichlet problem on
graphs where the signal is given at the labeled nodes, and the harmonic
extension is done on the unlabeled nodes. This paper considers a time-dependent
version of the Dirichlet problem on graphs and shows how to improve its
solution by learning the proper initialization vector on the unlabeled nodes.
Further, we show that the improved solution is at par with state-of-the-art
methods used for node classification. Finally, we conclude this paper by
discussing the importance of parameter t, pros, and future directions.

Architectures based on Recurrent Neural Networks (RNNs) have been
successfully applied to many different tasks such as speech or handwriting
recognition with state-of-the-art results. The main contribution of this work
is to analyse the feasibility of RNNs for on-line signature verification in
real practical scenarios. We have considered a system based on Long Short-Term
Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity
metric from pairs of signatures. For the experimental work, the BiosecurID
database comprised of 400 users and 4 separated acquisition sessions are
considered. Our proposed LSTM RNN system has outperformed the results of recent
published works on the BiosecurID benchmark in figures ranging from 17.76% to
28.00% relative verification performance improvement for skilled forgeries.

Neural Combinatorial Optimization attempts to learn good heuristics for
solving a set of problems using Neural Network models and Reinforcement
Learning. Recently, its good performance has encouraged many practitioners to
develop neural architectures for a wide variety of combinatorial problems.
However, the incorporation of such algorithms in the conventional optimization
framework has raised many questions related to their performance and the
experimental comparison with other methods such as exact algorithms, heuristics
and metaheuristics. This paper presents a critical analysis on the
incorporation of algorithms based on neural networks into the classical
combinatorial optimization framework. Subsequently, a comprehensive study is
carried out to analyse the fundamental aspects of such algorithms, including
performance, transferability, computational cost and generalization to
larger-sized instances. To that end, we select the Linear Ordering Problem as a
case of study, an NP-hard problem, and develop a Neural Combinatorial
Optimization model to optimize it. Finally, we discuss how the analysed aspects
apply to a general learning framework, and suggest new directions for future
work in the area of Neural Combinatorial Optimization algorithms.

We present a learning algorithm that uses bone-driven motion networks to
predict the deformation of loose-fitting garment meshes at interactive rates.
Given a garment, we generate a simulation database and extract virtual bones
from simulated mesh sequences using skin decomposition. At runtime, we
separately compute low- and high-frequency deformations in a sequential manner.
The low-frequency deformations are predicted by transferring body motions to
virtual bones' motions, and the high-frequency deformations are estimated
leveraging the global information of virtual bones' motions and local
information extracted from low-frequency meshes. In addition, our method can
estimate garment deformations caused by variations of the simulation parameters
(e.g., fabric's bending stiffness) using an RBF kernel ensembling trained
networks for different sets of simulation parameters. Through extensive
comparisons, we show that our method outperforms state-of-the-art methods in
terms of prediction accuracy of mesh deformations by about 20% in RMSE and 10%
in Hausdorff distance and STED. The code and data are available at
https://github.com/non-void/VirtualBones.

Pre-trained transformer models are the current state-of-the-art for natural
language models processing. seBERT is such a model, that was developed based on
the BERT architecture, but trained from scratch with software engineering data.
We fine-tuned this model for the NLBSE challenge for the task of issue type
prediction. Our model dominates the baseline fastText for all three issue types
in both recall and precisio} to achieve an overall F1-score of 85.7%, which is
an increase of 4.1% over the baseline.

This paper presents novel hybrid architectures that combine grid- and
point-based processing to improve the detection performance and orientation
estimation of radar-based object detection networks. Purely grid-based
detection models operate on a bird's-eye-view (BEV) projection of the input
point cloud. These approaches suffer from a loss of detailed information
through the discrete grid resolution. This applies in particular to radar
object detection, where relatively coarse grid resolutions are commonly used to
account for the sparsity of radar point clouds. In contrast, point-based models
are not affected by this problem as they process point clouds without
discretization. However, they generally exhibit worse detection performances
than grid-based methods.
  We show that a point-based model can extract neighborhood features,
leveraging the exact relative positions of points, before grid rendering. This
has significant benefits for a subsequent grid-based convolutional detection
backbone. In experiments on the public nuScenes dataset our hybrid architecture
achieves improvements in terms of detection performance (19.7% higher mAP for
car class than next-best radar-only submission) and orientation estimates
(11.5% relative orientation improvement) over networks from previous

Discrete variational auto-encoders (VAEs) are able to represent semantic
latent spaces in generative learning. In many real-life settings, the discrete
latent space consists of high-dimensional structures, and propagating gradients
through the relevant structures often requires enumerating over an
exponentially large latent space. Recently, various approaches were devised to
propagate approximated gradients without enumerating over the space of possible
structures. In this work, we use Natural Evolution Strategies (NES), a class of
gradient-free black-box optimization algorithms, to learn discrete structured
VAEs. The NES algorithms are computationally appealing as they estimate
gradients with forward pass evaluations only, thus they do not require to
propagate gradients through their discrete structures. We demonstrate
empirically that optimizing discrete structured VAEs using NES is as effective
as gradient-based approximations. Lastly, we prove NES converges for
non-Lipschitz functions as appear in discrete structured VAEs.

To improve the traveling experience, researchers have been analyzing the role
of attitudes in travel behavior modeling. Although most researchers use
closed-ended surveys, the appropriate method to measure attitudes is debatable.
Topic Modeling could significantly reduce the time to extract information from
open-ended responses and eliminate subjective bias, thereby alleviating analyst
concerns. Our research uses Topic Modeling to extract information from
open-ended questions and compare its performance with closed-ended responses.
Furthermore, some respondents might prefer answering questions using their
preferred questionnaire type. So, we propose a modeling framework that allows
respondents to use their preferred questionnaire type to answer the survey and
enable analysts to use the modeling frameworks of their choice to predict
behavior. We demonstrate this using a dataset collected from the USA that
measures the intention to use Autonomous Vehicles for commute trips.
Respondents were presented with alternative questionnaire versions (open- and
closed- ended). Since our objective was also to compare the performance of
alternative questionnaire versions, the survey was designed to eliminate
influences resulting from statements, behavioral framework, and the choice
experiment. Results indicate the suitability of using Topic Modeling to extract
information from open-ended responses; however, the models estimated using the
closed-ended questions perform better compared to them. Besides, the proposed
model performs better compared to the models used currently. Furthermore, our
proposed framework will allow respondents to choose the questionnaire type to
answer, which could be particularly beneficial to them when using voice-based
surveys.

Scene graph generation (SGG) aims to detect objects and predict their
pairwise relationships within an image. Current SGG methods typically utilize
graph neural networks (GNNs) to acquire context information between
objects/relationships. Despite their effectiveness, however, current SGG
methods only assume scene graph homophily while ignoring heterophily.
Accordingly, in this paper, we propose a novel Heterophily Learning Network
(HL-Net) to comprehensively explore the homophily and heterophily between
objects/relationships in scene graphs. More specifically, HL-Net comprises the
following 1) an adaptive reweighting transformer module, which adaptively
integrates the information from different layers to exploit both the
heterophily and homophily in objects; 2) a relationship feature propagation
module that efficiently explores the connections between relationships by
considering heterophily in order to refine the relationship representation; 3)
a heterophily-aware message-passing scheme to further distinguish the
heterophily and homophily between objects/relationships, thereby facilitating
improved message passing in graphs. We conducted extensive experiments on two
public datasets: Visual Genome (VG) and Open Images (OI). The experimental
results demonstrate the superiority of our proposed HL-Net over existing
state-of-the-art approaches. In more detail, HL-Net outperforms the second-best
competitors by 2.1$\%$ on the VG dataset for scene graph classification and
1.2$\%$ on the IO dataset for the final score. Code is available at
https://github.com/siml3/HL-Net.

Distilling interpretable physical laws from videos has led to expanded
interest in the computer vision community recently thanks to the advances in
deep learning, but still remains a great challenge. This paper introduces an
end-to-end unsupervised deep learning framework to uncover the explicit
governing equations of dynamics presented by moving object(s), based on
recorded videos. Instead in the pixel (spatial) coordinate system of image
space, the physical law is modeled in a regressed underlying physical
coordinate system where the physical states follow potential explicit governing
equations. A numerical integrator-based sparse regression module is designed
and serves as a physical constraint to the autoencoder and coordinate system
regression, and, in the meanwhile, uncover the parsimonious closed-form
governing equations from the learned physical states. Experiments on simulated
dynamical scenes show that the proposed method is able to distill closed-form
governing equations and simultaneously identify unknown excitation input for
several dynamical systems recorded by videos, which fills in the gap in
literature where no existing methods are available and applicable for solving
this type of problem.

EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis,
research, and treatments. Most current works are limited to a single dataset
where data patterns are similar. But EEG signals are highly non-stationary,
resulting in model's poor generalization to new users, sessions or datasets.
Thus, designing a model that can generalize to new datasets is beneficial and
indispensable. To mitigate distribution discrepancy across datasets, we propose
to achieve Disentangled and Side-aware Unsupervised Domain Adaptation (DSUDA)
for cross-dataset tinnitus diagnosis. A disentangled auto-encoder is developed
to decouple class-irrelevant information from the EEG signals to improve the
classifying ability. The side-aware unsupervised domain adaptation module
adapts the class-irrelevant information as domain variance to a new dataset and
excludes the variance to obtain the class-distill features for the new dataset
classification. It also align signals of left and right ears to overcome
inherent EEG pattern difference. We compare DSUDA with state-of-the-art
methods, and our model achieves significant improvements over competitors
regarding comprehensive evaluation criteria. The results demonstrate our model
can successfully generalize to a new dataset and effectively diagnose tinnitus.

Machine learning in practice often involves complex pipelines for data
cleansing, feature engineering, preprocessing, and prediction. These pipelines
are composed of operators, which have to be correctly connected and whose
hyperparameters must be correctly configured. Unfortunately, it is quite common
for certain combinations of datasets, operators, or hyperparameters to cause
failures. Diagnosing and fixing those failures is tedious and error-prone and
can seriously derail a data scientist's workflow. This paper describes an
approach for automatically debugging an ML pipeline, explaining the failures,
and producing a remediation. We implemented our approach, which builds on a
combination of AutoML and SMT, in a tool called Maro. Maro works seamlessly
with the familiar data science ecosystem including Python, Jupyter notebooks,
scikit-learn, and AutoML tools such as Hyperopt. We empirically evaluate our
tool and find that for most cases, a single remediation automatically fixes
errors, produces no additional faults, and does not significantly impact
optimal accuracy nor time to convergence.

Robustness is becoming another important challenge of federated learning in
that the data collection process in each client is naturally accompanied by
noisy labels. However, it is far more complex and challenging owing to varying
levels of data heterogeneity and noise over clients, which exacerbates the
client-to-client performance discrepancy. In this work, we propose a robust
federated learning method called FedRN, which exploits k-reliable neighbors
with high data expertise or similarity. Our method helps mitigate the gap
between low- and high-performance clients by training only with a selected set
of clean examples, identified by their ensembled mixture models. We demonstrate
the superiority of FedRN via extensive evaluations on three real-world or
synthetic benchmark datasets. Compared with existing robust training methods,
the results show that FedRN significantly improves the test accuracy in the
presence of noisy labels.

The impressive performance of GPT-3 using natural language prompts and
in-context learning has inspired work on better fine-tuning of moderately-sized
models under this paradigm. Following this line of work, we present a
contrastive learning framework that clusters inputs from the same class for
better generality of models trained with only limited examples. Specifically,
we propose a supervised contrastive framework that clusters inputs from the
same class under different augmented "views" and repel the ones from different
classes. We create different "views" of an example by appending it with
different language prompts and contextual demonstrations. Combining a
contrastive loss with the standard masked language modeling (MLM) loss in
prompt-based few-shot learners, the experimental results show that our method
can improve over the state-of-the-art methods in a diverse set of 15 language
tasks. Our framework makes minimal assumptions on the task or the base model,
and can be applied to many recent methods with little modification. The code
will be made available at: https://github.com/yiren-jian/LM-SupCon.

Few-shot language learners adapt knowledge from a pre-trained model to
recognize novel classes from a few-labeled sentences. In such settings,
fine-tuning a pre-trained language model can cause severe over-fitting. In this
paper, we propose an Embedding Hallucination (EmbedHalluc) method, which
generates auxiliary embedding-label pairs to expand the fine-tuning dataset.
The hallucinator is trained by playing an adversarial game with the
discriminator, such that the hallucinated embedding is indiscriminative to the
real ones in the fine-tuning dataset. By training with the extended dataset,
the language learner effectively learns from the diverse hallucinated
embeddings to overcome the over-fitting issue. Experiments demonstrate that our
proposed method is effective in a wide range of language tasks, outperforming
current fine-tuning methods. Further, we show that EmbedHalluc outperforms
other methods that address this over-fitting problem, such as common data
augmentation, semi-supervised pseudo-labeling, and regularization. The code
will be made available at: https://github.com/yiren-jian/EmbedHalluc.

Modern vehicles rely on a fleet of electronic control units (ECUs) connected
through controller area network (CAN) buses for critical vehicular control.
However, with the expansion of advanced connectivity features in automobiles
and the elevated risks of internal system exposure, the CAN bus is increasingly
prone to intrusions and injection attacks. The ordinary injection attacks
disrupt the typical timing properties of the CAN data stream, and the
rule-based intrusion detection systems (IDS) can easily detect them. However,
advanced attackers can inject false data to the time series sensory data
(signal), while looking innocuous by the pattern/frequency of the CAN messages.
Such attacks can bypass the rule-based IDS or any anomaly-based IDS built on
binary payload data. To make the vehicles robust against such intelligent
attacks, we propose CANShield, a signal-based intrusion detection framework for
the CAN bus. CANShield consists of three modules: a data preprocessing module
that handles the high-dimensional CAN data stream at the signal level and makes
them suitable for a deep learning model; a data analyzer module consisting of
multiple deep autoencoder (AE) networks, each analyzing the time-series data
from a different temporal perspective; and finally an attack detection module
that uses an ensemble method to make the final decision. Evaluation results on
two high-fidelity signal-based CAN attack datasets show the high accuracy and
responsiveness of CANShield in detecting wide-range of advanced intrusion
attacks.

This paper is dedicated to Prof. Eduardo Sontag on the occasion of his
seventieth birthday. In this paper, we build upon the ideas first proposed in
Gladyshev (1965) to develop a very general framework for proving the almost
sure boundedness and the convergence of stochastic approximation algorithms.
These ideas are based on martingale methods and are in some ways simpler than
convergence proofs based on the ODE method, e.g., Borkar-Meyn (2000). First we
study the original version of the SA algorithm introduced in Robbins-Monro
(1951), where the objective is to determine a zero of a function, when only
noisy measurements of the function are available. The proof makes use of the
general framework developed here, together with a new theorem on converse
Lyapunov stability, which might be of independent interest. Next we study an
alternate version of SA, first introduced in Kiefer-Wolfowitz (1952). The
objective here is to find a stationary point of a scalar-valued function, using
first-order differences to approximate its gradient. This problem is analyzed
in Blum (1954), but with a very opaque proof. We reproduce Blum's conclusions
using the proposed framework.

Prediction of network traffic behavior is significant for the effective
management of modern telecommunication networks. However, the intuitive
approach of predicting network traffic using administrative experience and
market analysis data is inadequate for an efficient forecast framework. As a
result, many different mathematical models have been studied to capture the
general trend of the network traffic and predict accordingly. But the
comprehensive performance analysis of varying regression models and their
ensemble has not been studied before for analyzing real-world anomalous
traffic. In this paper, several regression models such as Extra Gradient Boost
(XGBoost), Light Gradient Boosting Machine (LightGBM), Stochastic Gradient
Descent (SGD), Gradient Boosting Regressor (GBR), and CatBoost Regressor were
analyzed to predict real traffic without and with outliers and show the
significance of outlier detection in real-world traffic prediction. Also, we
showed the outperformance of the ensemble regression model over the individual
prediction model. We compared the performance of different regression models
based on five different feature sets of lengths 6, 9, 12, 15, and 18. Our
ensemble regression model achieved the minimum average gap of 5.04% between
actual and predicted traffic with nine outlier-adjusted inputs. In general, our
assist the regression model in learning the general trend and making better

This volume is devoted to the emerging field of Integrated Visual Knowledge
Discovery that combines advances in Artificial Intelligence/Machine Learning
(AI/ML) and Visualization/Visual Analytics. Chapters included are extended
versions of the selected AI and Visual Analytics papers and related symposia at
the recent International Information Visualization Conferences (IV2019 and
IV2020). AI/ML face a long-standing challenge of explaining models to humans.
Models explanation is fundamentally human activity, not only an algorithmic
one. In this chapter we aim to present challenges and future directions within
the field of Visual Analytics, Visual Knowledge Discovery and AI/ML, and to
discuss the role of visualization in visual AI/ML. In addition, we describe
progress in emerging Full 2D ML, natural language processing, and AI/ML in
multidimensional data aided by visual means.

In recent years, the rise of deep learning and automation requirements in the
software industry has elevated Intelligent Software Engineering to new heights.
The number of approaches and applications in code understanding is growing,
with deep learning techniques being used in many of them to better capture the
information in code data. In this survey, we present a comprehensive overview
of the structures formed from code data. We categorize the models for
understanding code in recent years into two groups: sequence-based and
graph-based models, further make a summary and comparison of them. We also
introduce metrics, datasets and the downstream tasks. Finally, we make some
suggestions for future research in structural code understanding field.

Cross domain object detection is a realistic and challenging task in the
wild. It suffers from performance degradation due to large shift of data
distributions and lack of instance-level annotations in the target domain.
Existing approaches mainly focus on either of these two difficulties, even
though they are closely coupled in cross domain object detection. To solve this
problem, we propose a novel Target-perceived Dual-branch Distillation (TDD)
framework. By integrating detection branches of both source and target domains
in a unified teacher-student learning scheme, it can reduce domain shift and
generate reliable supervision effectively. In particular, we first introduce a
distinct Target Proposal Perceiver between two domains. It can adaptively
enhance source detector to perceive objects in a target image, by leveraging
target proposal contexts from iterative cross-attention. Afterwards, we design
a concise Dual Branch Self Distillation strategy for model training, which can
progressively integrate complementary object knowledge from different domains
via self-distillation in two branches. Finally, we conduct extensive
experiments on a number of widely-used scenarios in cross domain object
detection. The results show that our TDD significantly outperforms the
state-of-the-art methods on all the benchmarks. Our code and model will be
available at https://github.com/Feobi1999/TDD.

Industrial ranking systems, such as advertising systems, rank items by
aggregating multiple objectives into one final objective to satisfy user demand
and commercial intent. Cascade architecture, composed of retrieval,
pre-ranking, and ranking stages, is usually adopted to reduce the computational
cost. Each stage may employ various models for different objectives and
calculate the final objective by aggregating these models' outputs. The
multi-stage ranking strategy causes a new problem - the ranked lists of the
ranking stage and previous stages may be inconsistent. For example, items that
should be ranked at the top of the ranking stage may be ranked at the bottom of
previous stages. In this paper, we focus on the \textbf{ranking consistency}
between the pre-ranking and ranking stages. Specifically, we formally define
the problem of ranking consistency and propose the Ranking Consistency Score
(RCS) metric for evaluation. We demonstrate that ranking consistency has a
direct impact on online performance. Compared with the traditional evaluation
manner that mainly focuses on the individual ranking quality of every
objective, RCS considers the ranking consistency of the fused final objective,
which is more proper for evaluation. Finally, to improve the ranking
consistency, we propose several methods from the perspective of sample
selection and learning algorithms. Experimental results on one of the biggest
industrial E-commerce platforms in China validate the efficacy of the proposed
metrics and methods.

Sequential recommendation aims at identifying the next item that is preferred
by a user based on their behavioral history. Compared to conventional
sequential models that leverage attention mechanisms and RNNs, recent efforts
mainly follow two directions for improvement: multi-interest learning and graph
convolutional aggregation. Specifically, multi-interest methods such as ComiRec
and MIMN, focus on extracting different interests for a user by performing
historical item clustering, while graph convolution methods including TGSRec
and SURGE elect to refine user preferences based on multi-level correlations
between historical items. Unfortunately, neither of them realizes that these
two types of solutions can mutually complement each other, by aggregating
multi-level user preference to achieve more precise multi-interest extraction
for a better recommendation. To this end, in this paper, we propose a unified
multi-grained neural model(named MGNM) via a combination of multi-interest
learning and graph convolutional aggregation. Concretely, MGNM first learns the
graph structure and information aggregation paths of the historical items for a
user. It then performs graph convolution to derive item representations in an
iterative fashion, in which the complex preferences at different levels can be
well captured. Afterwards, a novel sequential capsule network is proposed to
inject the sequential patterns into the multi-interest extraction process,
leading to a more precise interest learning in a multi-grained manner.

With the development of digital technology, machine learning has paved the
way for the next generation of tinnitus diagnoses. Although machine learning
has been widely applied in EEG-based tinnitus analysis, most current models are
dataset-specific. Each dataset may be limited to a specific range of symptoms,
overall disease severity, and demographic attributes; further, dataset formats
may differ, impacting model performance. This paper proposes a side-aware
meta-learning for cross-dataset tinnitus diagnosis, which can effectively
classify tinnitus in subjects of divergent ages and genders from different data
collection processes. Owing to the superiority of meta-learning, our method
does not rely on large-scale datasets like conventional deep learning models.
Moreover, we design a subject-specific training process to assist the model in
fitting the data pattern of different patients or healthy people. Our method
achieves a high accuracy of 73.8\% in the cross-dataset classification. We
conduct an extensive analysis to show the effectiveness of side information of
ears in enhancing model performance and side-aware meta-learning in improving
the quality of the learned features.

Private decision tree evaluation (PDTE) allows a decision tree holder to run
a secure protocol with a feature provider. By running the protocol, the feature
provider will learn a classification result. Nothing more is revealed to either
party. In most existing PDTE protocols, the required communication grows
exponentially with the tree's depth $d$, which is highly inefficient for large
trees. This shortcoming motivated us to design a sublinear PDTE protocol with
$O(d)$ communication complexity. The core of our construction is a shared
oblivious selection (SOS) functionality, allowing two parties to perform a
secret-shared oblivious read operation from an array. We provide two SOS
protocols, both of which achieve sublinear communication and propose
optimizations to further improve their efficiency. Our sublinear PDTE protocol
is based on the proposed SOS functionality and we prove its security under a
semi-honest adversary. We compare our protocol with the state-of-the-art, in
terms of communication and computation, under various network settings. The
performance evaluation shows that our protocol is practical and more scalable
over large trees than existing solutions.

Hand-crafted spatial features, such as inter-channel intensity difference
(IID) and inter-channel phase difference (IPD), play a fundamental role in
recent deep learning based dual-microphone speech enhancement (DMSE) systems.
However, learning the mutual relationship between artificially designed spatial
and spectral features is hard in the end-to-end DMSE. In this work, a novel
architecture for DMSE using a multi-head cross-attention based convolutional
recurrent network (MHCA-CRN) is presented. The proposed MHCA-CRN model includes
a channel-wise encoding structure for preserving intra-channel features and a
multi-head cross-attention mechanism for fully exploiting cross-channel
features. In addition, the proposed approach specifically formulates the
decoder with an extra SNR estimator to estimate frame-level SNR under a
multi-task learning framework, which is expected to avoid speech distortion led
by end-to-end DMSE module. Finally, a spectral gain function is adopted to
further suppress the unnatural residual noise. Experiment results demonstrated
superior performance of the proposed model against several state-of-the-art
models.

Cooperative coordination at unsignalized road intersections, which aims to
improve the driving safety and traffic throughput for connected and automated
vehicles, has attracted increasing interests in recent years. However, most
existing investigations either suffer from computational complexity or cannot
harness the full potential of the road infrastructure. To this end, we first
present a dedicated intersection coordination framework, where the involved
vehicles hand over their control authorities and follow instructions from a
centralized coordinator. Then a unified cooperative trajectory optimization
problem will be formulated to maximize the traffic throughput while ensuring
the driving safety and long-term stability of the coordination system. To
address the key computational challenges in the real-world deployment, we
reformulate this non-convex sequential decision problem into a model-free
Markov Decision Process (MDP) and tackle it by devising a Twin Delayed Deep
Deterministic Policy Gradient (TD3)-based strategy in the deep reinforcement
learning (DRL) framework. Simulation and practical experiments show that the
proposed strategy could achieve near-optimal performance in sub-static
coordination scenarios and significantly improve the traffic throughput in the
realistic continuous traffic flow. The most remarkable advantage is that our
strategy could reduce the time complexity of computation to milliseconds, and
is shown scalable when the road lanes increase.

Computational approach to imaging around the corner, or non-line-of-sight
(NLOS) imaging, is becoming a reality thanks to major advances in imaging
hardware and reconstruction algorithms. A recent development towards practical
NLOS imaging, Nam et al. demonstrated a high-speed non-confocal imaging system
that operates at 5Hz, 100x faster than the prior art. This enormous gain in
acquisition rate, however, necessitates numerous approximations in light
transport, breaking many existing NLOS reconstruction methods that assume an
idealized image formation model. To bridge the gap, we present a novel deep
model that incorporates the complementary physics priors of wave propagation
and volume rendering into a neural network for high-quality and robust NLOS
reconstruction. This orchestrated design regularizes the solution space by
relaxing the image formation model, resulting in a deep model that generalizes
well on real captures despite being exclusively trained on synthetic data.
Further, we devise a unified learning framework that enables our model to be
flexibly trained using diverse supervision signals, including target intensity
images or even raw NLOS transient measurements. Once trained, our model renders
both intensity and depth images at inference time in a single forward pass,
capable of processing more than 5 captures per second on a high-end GPU.
Through extensive qualitative and quantitative experiments, we show that our
method outperforms prior physics and learning based approaches on both
synthetic and real measurements. We anticipate that our method along with the
fast capturing system will accelerate future development of NLOS imaging for
real world applications that require high-speed imaging.

Deep learning-based approaches to musical source separation are often limited
to the instrument classes that the models are trained on and do not generalize
to separate unseen instruments. To address this, we propose a few-shot musical
source separation paradigm. We condition a generic U-Net source separation
model using few audio examples of the target instrument. We train a few-shot
conditioning encoder jointly with the U-Net to encode the audio examples into a
conditioning vector to configure the U-Net via feature-wise linear modulation
(FiLM). We evaluate the trained models on real musical recordings in the
MUSDB18 and MedleyDB datasets. We show that our proposed few-shot conditioning
paradigm outperforms the baseline one-hot instrument-class conditioned model
for both seen and unseen instruments. To extend the scope of our approach to a
wider variety of real-world scenarios, we also experiment with different
conditioning example characteristics, including examples from different
recordings, with multiple sources, or negative conditioning examples.

Deep learning methods relying on multi-layered networks have been actively
studied in a wide range of fields in recent years, and deep Boltzmann
machines(DBMs) is one of them. In this study, a model of DBMs with some
properites of weight parameters obtained by learning is studied theoretically
by a statistical-mechanical approach based on the replica method. The phases
characterizing the role of DBMs as a generator and their phase diagram are
derived, depending meaningfully on the numbers of layers and units in each
layer. It is found, in particular, that the correlation between the weight
parameters in the hidden layers plays an essential role and that an increase in
the layer number has a negative effect on DBM's performance as a generator when
the correlation is smaller than a certain threshold value.

Multi-robot exploration of complex, unknown environments benefits from the
collaboration and cooperation offered by inter-robot communication. Accurate
radio signal strength prediction enables communication-aware exploration.
Models which ignore the effect of the environment on signal propagation or rely
on a priori maps suffer in unknown, communication-restricted (e.g.
subterranean) environments. In this work, we present Propagation Environment
Modeling and Learning (PropEM-L), a framework which leverages real-time
sensor-derived 3D geometric representations of an environment to extract
information about line of sight between radios and attenuating walls/obstacles
in order to accurately predict received signal strength (RSS). Our data-driven
approach combines the strengths of well-known models of signal propagation
phenomena (e.g. shadowing, reflection, diffraction) and machine learning, and
can adapt online to new environments. We demonstrate the performance of
PropEM-L on a six-robot team in a communication-restricted environment with
subway-like, mine-like, and cave-like characteristics, constructed for the 2021
DARPA Subterranean Challenge. Our findings indicate that PropEM-L can improve
signal strength prediction accuracy by up to 44% over a log-distance path loss
model.

Block-based visual programming environments are increasingly used to
introduce computing concepts to beginners. Given that programming tasks are
open-ended and conceptual, novice students often struggle when learning in
these environments. AI-driven programming tutors hold great promise in
automatically assisting struggling students, and need several components to
realize this potential. We investigate the crucial component of student
modeling, in particular, the ability to automatically infer students'
misconceptions for predicting (synthesizing) their behavior. We introduce a
novel benchmark, StudentSyn, centered around the following challenge: For a
given student, synthesize the student's attempt on a new target task after
observing the student's attempt on a fixed reference task. This challenge is
akin to that of program synthesis; however, instead of synthesizing a
{solution} (i.e., program an expert would write), the goal here is to
synthesize a {student attempt} (i.e., program that a given student would
write). We first show that human experts (TutorSS) can achieve high performance
on the benchmark, whereas simple baselines perform poorly. Then, we develop two
neuro/symbolic techniques (NeurSS and SymSS) in a quest to close this gap with
TutorSS.

A crucial ingredient in lithium (Li) and sodium (Na)-ion batteries (LIBs and
NIBs) is the electrolytes. The use of Li-metal (Na-metal) as anode in liquid
electrolyte LIBs (NIBs) is constrained by several issues including thermal
runway and flammability, electrolyte leakage, and limited chemical stability.
Considerable effort has been devoted toward the development of solid
electrolytes (SEs) and all-solid-state batteries, which are presumed to
mitigate some of the issues of Li-metal(Na-metal) in contact with flammable
liquid electrolytes. However, most SEs, such as Li$_3$PS$_4$, Li$_6$PS$_5$Cl
and Na$_3$PS$_4$ readily decompose against the highly reducing Li-metal and
Na-metal anodes. Using first-principles calculations we elucidate the stability
of more than 20 solid$||$solid interfaces formed between the decomposition
products of Li$_3$PS$_4$, Li$_6$PS$_5$Cl (and Na$_3$PS$_4$) against the
Li-metal (Na-metal) electrode. We suggest that the work of adhesion needed to
form a hetereogenous interfaces is an important descriptor to quantify the
stability of interfaces. Subsequently, we clarify the atomistic origins of the
resistance to Li-ion transport at interfaces of the Li-metal anode and selected
decomposition products (Li$_3$P, Li$_2$S} and LiCl) of SEs, via a high-fidelity
machine learned potential (MLP). Utilising an MLP enables nano-second-long
molecular dynamics simulations on `large' interface models (here with 8320
atoms), but with similar accuracy to first-principles approaches. Our
simulations demonstrate that the interfaces formed between Li-metal and
argyrodite (e.g., Li$_6$PS$_5$Cl) decomposition products are resistive to
Li-ion transport. The implications of this study are important since binary
compounds are commonly found in the vicinity of Li(Na)-metal upon chemical
and/or electrochemical decomposition of ternary and quaternary SEs.

We study the privacy-utility trade-off in the context of metric differential
privacy. Ghosh et al. introduced the idea of universal optimality to
characterise the best mechanism for a certain query that simultaneously
satisfies (a fixed) $\epsilon$-differential privacy constraint whilst at the
same time providing better utility compared to any other
$\epsilon$-differentially private mechanism for the same query. They showed
that the Geometric mechanism is "universally optimal" for the class of counting
queries. On the other hand, Brenner and Nissim showed that outside the space of
counting queries, and for the Bayes risk loss function, no such universally
optimal mechanisms exist. In this paper we use metric differential privacy and
quantitative information flow as the fundamental principle for studying
universal optimality. Metric differential privacy is a generalisation of both
standard (i.e., central) differential privacy and local differential privacy,
and it is increasingly being used in various application domains, for instance
in location privacy and in privacy preserving machine learning. Using this
framework we are able to clarify Nissim and Brenner's negative results, showing
(a) that in fact all privacy types contain optimal mechanisms relative to
certain kinds of non-trivial loss functions, and (b) extending and generalising
their negative results beyond Bayes risk specifically to a wide class of
non-trivial loss functions. We also propose weaker universal benchmarks of
utility called "privacy type capacities". We show that such capacities always
exist and can be computed using a convex optimisation algorithm.

Linear bandits have a wide variety of applications including recommendation
systems yet they make one strong assumption: the algorithms must know an upper
bound $S$ on the norm of the unknown parameter $\theta^*$ that governs the
reward generation. Such an assumption forces the practitioner to guess $S$
involved in the confidence bound, leaving no choice but to wish that
$\|\theta^*\|\le S$ is true to guarantee that the regret will be low. In this
paper, we propose novel algorithms that do not require such knowledge for the
first time. Specifically, we propose two algorithms and analyze their regret
bounds: one for the changing arm set setting and the other for the fixed arm
set setting. Our regret bound for the former shows that the price of not
knowing $S$ does not affect the leading term in the regret bound and inflates
only the lower order term. For the latter, we do not pay any price in the
regret for now knowing $S$. Our numerical experiments show standard algorithms
assuming knowledge of $S$ can fail catastrophically when $\|\theta^*\|\le S$ is
not true whereas our algorithms enjoy low regret.

Understanding the correct API usage sequences is one of the most important
tasks for programmers when they work with unfamiliar libraries. However,
programmers often encounter obstacles to finding the appropriate information
due to either poor quality of API documentation or ineffective query-based
searching strategy. To help solve this issue, researchers have proposed various
methods to suggest the sequence of APIs given natural language queries
representing the information needs from programmers. Among such efforts, Gu et
al. adopted a deep learning method, in particular an RNN Encoder-Decoder
architecture, to perform this task and obtained promising results on common
APIs in Java. In this work, we aim to reproduce their results and apply the
same methods for APIs in Python. Additionally, we compare the performance with
a more recent Transformer-based method, i.e., CodeBERT, for the same task. Our
experiment reveals a clear drop in performance measures when careful data
cleaning is performed. Owing to the pretraining from a large number of source
code files and effective encoding technique, CodeBERT outperforms the method by
Gu et al., to a large extent.

Algorithms with predictions is a recent framework that has been used to
overcome pessimistic worst-case bounds in incomplete information settings. In
the context of scheduling, very recent work has leveraged machine-learned
predictions to design algorithms that achieve improved approximation ratios in
settings where the processing times of the jobs are initially unknown. In this
paper, we study the speed-robust scheduling problem where the speeds of the
machines, instead of the processing times of the jobs, are unknown and augment
this problem with predictions.
  Our main result is an algorithm that achieves a
$\min\{\eta^2(1+\epsilon)^2(1+\alpha), (1+\epsilon)(2 + 2/\alpha)\}$
approximation, for any constants $\alpha, \epsilon \in (0,1)$, where $\eta \geq
1$ is the prediction error. When the predictions are accurate, this
approximation improves over the previously best known approximation of $2-1/m$
for speed-robust scheduling, where $m$ is the number of machines, while
simultaneously maintaining a worst-case approximation of $(1+\epsilon)(2 +
2/\alpha)$ even when the predictions are wrong. In addition, we obtain improved
approximations for the special cases of equal and infinitesimal job sizes, and
we complement our algorithmic results with lower bounds. Finally, we
empirically evaluate our algorithm against existing algorithms for speed-robust
scheduling.

Modern software systems rely on mining insights from business sensitive data
stored in public clouds. A data breach usually incurs significant (monetary)
loss for a commercial organization. Conceptually, cloud security heavily relies
on Identity Access Management (IAM) policies that IT admins need to properly
configure and periodically update. Security negligence and human errors often
lead to misconfiguring IAM policies which may open a backdoor for attackers. To
address these challenges, first, we develop a novel framework that encodes
generating optimal IAM policies using constraint programming (CP). We identify
reducing dark permissions of cloud users as an optimality criterion, which
intuitively implies minimizing unnecessary datastore access permissions.
Second, to make IAM policies interpretable, we use graph representation
learning applied to historical access patterns of users to augment our CP model
with similarity constraints: similar users should be grouped together and share
common IAM policies. Third, we describe multiple attack models and show that
our optimized IAM policies significantly reduce the impact of security attacks
using real data from 8 commercial organizations, and synthetic instances.

Taking inspiration from natural language embeddings, we present ASTROMER, a
transformer-based model to create representations of light curves. ASTROMER was
pre-trained in a self-supervised manner, requiring no human-labeled data. We
used millions of R-band light sequences to adjust the ASTROMER weights. The
learned representation can be easily adapted to other surveys by re-training
ASTROMER on new sources. The power of ASTROMER consists of using the
representation to extract light curve embeddings that can enhance the training
of other models, such as classifiers or regressors. As an example, we used
ASTROMER embeddings to train two neural-based classifiers that use labeled
variable stars from MACHO, OGLE-III, and ATLAS. In all experiments,
ASTROMER-based classifiers outperformed a baseline recurrent neural network
trained on light curves directly when limited labeled data was available.
Furthermore, using ASTROMER embeddings decreases computational resources needed
while achieving state-of-the-art results. Finally, we provide a Python library
that includes all the functionalities employed in this work. The library, main
code, and pre-trained weights are available at
https://github.com/astromer-science

One of the most fundamental design choices in neural networks is layer width:
it affects the capacity of what a network can learn and determines the
complexity of the solution. This latter property is often exploited when
introducing information bottlenecks, forcing a network to learn compressed
representations. However, such an architecture decision is typically immutable
once training begins; switching to a more compressed architecture requires
retraining. In this paper we present a new layer design, called Triangular
Dropout, which does not have this limitation. After training, the layer can be
arbitrarily reduced in width to exchange performance for narrowness. We
demonstrate the construction and potential use cases of such a mechanism in
three areas. Firstly, we describe the formulation of Triangular Dropout in
autoencoders, creating models with selectable compression after training.
Secondly, we add Triangular Dropout to VGG19 on ImageNet, creating a powerful
network which, without retraining, can be significantly reduced in parameters.
Lastly, we explore the application of Triangular Dropout to reinforcement
learning (RL) policies on selected control problems.

In this paper, we consider how to provide fast estimates of flow-level tail
latency performance for very large scale data center networks. Network tail
latency is often a crucial metric for cloud application performance that can be
affected by a wide variety of factors, including network load, inter-rack
traffic skew, traffic burstiness, flow size distributions, oversubscription,
and topology asymmetry. Network simulators such as ns-3 and OMNeT++ can provide
accurate answers, but are very hard to parallelize, taking hours or days to
answer what if questions for a single configuration at even moderate scale.
Recent work with MimicNet has shown how to use machine learning to improve
simulation performance, but at a cost of including a long training step per
configuration, and with assumptions about workload and topology uniformity that
typically do not hold in practice.
  We address this gap by developing a set of techniques to provide fast
performance estimates for large scale networks with general traffic matrices
and topologies. A key step is to decompose the problem into a large number of
parallel independent single-link simulations; we carefully combine these
link-level simulations to produce accurate estimates of end-to-end flow level
performance distributions for the entire network. Like MimicNet, we exploit
symmetry where possible to gain additional speedups, but without relying on
machine learning, so there is no training delay. On large-scale networks where
ns-3 takes 11 to 27 hours to simulate five seconds of network behavior, our
techniques run in one to two minutes with 99th percentile accuracy within 9%
for flow completion times.

As the issues of privacy and trust are receiving increasing attention within
the research community, various attempts have been made to anonymize textual
data. A significant subset of these approaches incorporate differentially
private mechanisms to perturb word embeddings, thus replacing individual words
in a sentence. While these methods represent very important contributions, have
various advantages over other techniques and do show anonymization
capabilities, they have several shortcomings. In this paper, we investigate
these weaknesses and demonstrate significant mathematical constraints
diminishing the theoretical privacy guarantee as well as major practical
shortcomings with regard to the protection against deanonymization attacks, the
preservation of content of the original sentences as well as the quality of the
language output. Finally, we propose a new method for text anonymization based
on transformer based language models fine-tuned for paraphrasing that
circumvents most of the identified weaknesses and also offers a formal privacy
guarantee. We evaluate the performance of our method via thorough
experimentation and demonstrate superior performance over the discussed

Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to
identify objects in images based on a small number of images with pixel-level
labels, and many more images with only image-level labels. Most existing SWSSS
algorithms extract pixel-level pseudo-labels from an image classifier - a very
difficult task to do well, hence requiring complicated architectures and
extensive hyperparameter tuning on fully-supervised validation sets. We propose
a method called prediction filtering, which instead of extracting
pseudo-labels, just uses the classifier as a classifier: it ignores any
segmentation predictions from classes which the classifier is confident are not
present. Adding this simple post-processing method to baselines gives results
competitive with or better than prior SWSSS algorithms. Moreover, it is
compatible with pseudo-label methods: adding prediction filtering to existing
SWSSS algorithms further improves segmentation performance.

Although information access systems have long supported people in
accomplishing a wide range of tasks, we propose broadening the scope of users
of information access systems to include task-driven machines, such as machine
learning models. In this way, the core principles of indexing, representation,
retrieval, and ranking can be applied and extended to substantially improve
model generalization, scalability, robustness, and interpretability. We
describe a generic retrieval-enhanced machine learning (REML) framework, which
includes a number of existing models as special cases. REML challenges
information retrieval conventions, presenting opportunities for novel advances
in core areas, including optimization. The REML research agenda lays a
foundation for a new style of information access research and paves a path
towards advancing machine learning and artificial intelligence.

Normalizing flows, a popular class of deep generative models, often fail to
represent extreme phenomena observed in real-world processes. In particular,
existing normalizing flow architectures struggle to model multivariate
extremes, characterized by heavy-tailed marginal distributions and asymmetric
tail dependence among variables. In light of this shortcoming, we propose COMET
(COpula Multivariate ExTreme) Flows, which decompose the process of modeling a
joint distribution into two parts: (i) modeling its marginal distributions, and
(ii) modeling its copula distribution. COMET Flows capture heavy-tailed
marginal distributions by combining a parametric tail belief at extreme
quantiles of the marginals with an empirical kernel density function at
mid-quantiles. In addition, COMET Flows capture asymmetric tail dependence
among multivariate extremes by viewing such dependence as inducing a
low-dimensional manifold structure in feature space. Experimental results on
both synthetic and real-world datasets demonstrate the effectiveness of COMET
Flows in capturing both heavy-tailed marginals and asymmetric tail dependence
compared to other state-of-the-art baseline architectures. All code is
available on GitHub at https://github.com/andrewmcdonald27/COMETFlows.

Machine learning approaches have the potential to approximate Density
Functional Theory (DFT) for atomistic simulations in a computationally
efficient manner, which could dramatically increase the impact of computational
simulations on real-world problems. However, they are limited by their accuracy
and the cost of generating labeled data. Here, we present an online active
learning framework for accelerating the simulation of atomic systems
efficiently and accurately by incorporating prior physical information learned
by large-scale pre-trained graph neural network models from the Open Catalyst
Project. Accelerating these simulations enables useful data to be generated
more cheaply, allowing better models to be trained and more atomistic systems
to be screened. We also present a method of comparing local optimization
techniques on the basis of both their speed and accuracy. Experiments on 30
benchmark adsorbate-catalyst systems show that our method of transfer learning
to incorporate prior information from pre-trained models accelerates
simulations by reducing the number of DFT calculations by 91%, while meeting an
accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a
technique for leveraging the interactive functionality built in to VASP to
efficiently compute single point calculations within our online active learning
framework without the significant startup costs. This allows VASP to work in
tandem with our framework while requiring 75% fewer self-consistent cycles than
conventional single point calculations. The online active learning
implementation, and examples using the VASP interactive code, are available in
the open source FINETUNA package on Github.

We investigate uncertainty estimation and multimodality via the
non-deterministic predictions of Bayesian neural networks (BNNs) in fluid
simulations. To this end, we deploy BNNs in three challenging experimental
test-cases of increasing complexity: We show that BNNs, when used as surrogate
models for steady-state fluid flow predictions, provide accurate physical
predictions together with sensible estimates of uncertainty. Further, we
experiment with perturbed temporal sequences from Navier-Stokes simulations and
evaluate the capabilities of BNNs to capture multimodal evolutions. While our
findings indicate that this is problematic for large perturbations, our results
show that the networks learn to correctly predict high uncertainties in such
situations. Finally, we study BNNs in the context of solver interactions with
turbulent plasma flows. We find that BNN-based corrector networks can stabilize
coarse-grained simulations and successfully create multimodal trajectories.

It is hard to establish whether a company supports gender equality,
diversity, and general staff welfare, not least because of lack of
methodologies operationalizing these internal sustainability practices, and of
data honestly documenting such efforts. We developed and validated a
six-dimension framework reflecting Internal Sustainability Efforts (ISEs),
gathered more than 350K employee reviews of 104 major companies across the
whole US for the (2008-2020) years, and developed a deep-learning framework
scoring these reviews in terms of the six ISEs. Commitment to ISEs manifested
itself not only at micro-level (companies scoring high in ISEs enjoyed high
stock growth) but also at macro-level (states hosting these companies were
economically wealthy and equal, and attracted the so-called creative class).
This new conceptualization of ISEs offers both theoretical implications for the
literature in corporate sustainability and economic geography, and practical
implications for companies and policy makers.

The unique prime factorization theorem is used to show the existence of a
function on a countable set $\mathcal{X}$ so that the sum aggregator function
is injective on all multisets of $\mathcal{X}$ of finite size.

Learning from a continuous stream of non-stationary data in an unsupervised
manner is arguably one of the most common and most challenging settings facing
intelligent agents. Here, we attack learning under all three conditions
(unsupervised, streaming, non-stationary) in the context of clustering, also
known as mixture modeling. We introduce a novel clustering algorithm that
endows mixture models with the ability to create new clusters online, as
demanded by the data, in a probabilistic, time-varying, and principled manner.
To achieve this, we first define a novel stochastic process called the
Dynamical Chinese Restaurant Process (Dynamical CRP), which is a
non-exchangeable distribution over partitions of a set; next, we show that the
Dynamical CRP provides a non-stationary prior over cluster assignments and
yields an efficient streaming variational inference algorithm. We conclude with
experiments showing that the Dynamical CRP can be applied on diverse synthetic
and real data with Gaussian and non-Gaussian likelihoods.

Innovation in the physical layer of communication systems has traditionally
been achieved by breaking down the transceivers into sets of processing blocks,
each optimized independently based on mathematical models. Conversely, deep
learning (DL)-based systems are able to handle increasingly complex tasks for
which no tractable models are available. This thesis aims at comparing
different approaches to unlock the full potential of DL in the physical layer.
  First, we describe a neural network (NN)-based block strategy, where an NN is
optimized to replace a block in a communication system. We apply this strategy
to introduce a multi-user multiple-input multiple-output (MU-MIMO) detector
that builds on top of an existing DL-based architecture. Second, we detail an
end-to-end strategy, in which the transmitter and receiver are modeled as an
autoencoder. This approach is illustrated with the design of waveforms that
achieve high throughputs while satisfying peak-to-average power ratio (PAPR)
and adjacent channel leakage ratio (ACLR) constraints. Lastly, we propose a
hybrid strategy, where multiple DL components are inserted into a traditional
architecture but are trained to optimize the end-to-end performance. To
demonstrate its benefits, we propose a DL-enhanced MU-MIMO receiver that both
enable lower bit error rates (BERs) compared to a conventional receiver and
remains scalable to any number of users.
  Each approach has its own strengths and shortcomings. While the first one is
the easiest to implement, its individual block optimization does not ensure the
overall system optimality. On the other hand, systems designed with the second
approach are computationally complex but allow for new opportunities such as
pilotless transmissions. Finally, the combined flexibility and end-to-end
performance gains of the third approach motivate its use for short-term
practical implementations.

Objective: Ophthalmological pathologies such as glaucoma, diabetic
retinopathy and age-related macular degeneration are major causes of blindness
and vision impairment. There is a need for novel decision support tools that
can simplify and speed up the diagnosis of these pathologies. A key step in
this process is to automatically estimate the quality of the fundus images to
make sure these are interpretable by a human operator or a machine learning
model. We present a novel fundus image quality scale and deep learning (DL)
model that can estimate fundus image quality relative to this new scale.
  Methods: A total of 1,245 images were graded for quality by two
ophthalmologists within the range 1-10, with a resolution of 0.5. A DL
regression model was trained for fundus image quality assessment. The
architecture used was Inception-V3. The model was developed using a total of
89,947 images from 6 databases, of which 1,245 were labeled by the specialists
and the remaining 88,702 images were used for pre-training and semi-supervised
learning. The final DL model was evaluated on an internal test set (n=209) as
well as an external test set (n=194).
  Results: The final DL model, denoted FundusQ-Net, achieved a mean absolute
error of 0.61 (0.54-0.68) on the internal test set. When evaluated as a binary
classification model on the public DRIMDB database as an external test set the
model obtained an accuracy of 99%.
  Significance: the proposed algorithm provides a new robust tool for automated
quality grading of fundus images.

A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential
(MLIP) has been developed for simulations of carbon at extreme pressures (up to
5 TPa) and temperatures (up to 20,000 K). This was achieved using a large
database of experimentally relevant quantum molecular dynamics (QMD) data,
training the SNAP potential using a robust machine learning methodology, and
performing extensive validation against QMD and experimental data. The
resultant carbon MLIP demonstrates unprecedented accuracy and transferability
in predicting the carbon phase diagram, melting curves of crystalline phases,
and the shock Hugoniot, all within 3% of QMD. By achieving quantum accuracy and
efficient implementation on leadership class high performance computing
systems, SNAP advances frontiers of classical MD simulations by enabling
atomic-scale insights at experimental time and length scales.

Graph Convolutional Networks (GCN) have achieved state-of-art results on
single text classification tasks like sentiment analysis, emotion detection,
etc. However, the performance is achieved by testing and reporting on
resource-rich languages like English. Applying GCN for multi-task text
classification is an unexplored area. Moreover, training a GCN or adopting an
English GCN for Indian languages is often limited by data availability, rich
morphological variation, syntax, and semantic differences. In this paper, we
study the use of GCN for the Telugu language in single and multi-task settings
for four natural language processing (NLP) tasks, viz. sentiment analysis (SA),
emotion identification (EI), hate-speech (HS), and sarcasm detection (SAR). In
order to evaluate the performance of GCN with one of the Indian languages,
Telugu, we analyze the GCN based models with extensive experiments on four
downstream tasks. In addition, we created an annotated Telugu dataset, TEL-NLP,
for the four NLP tasks. Further, we propose a supervised graph reconstruction
method, Multi-Task Text GCN (MT-Text GCN) on the Telugu that leverages to
simultaneously (i) learn the low-dimensional word and sentence graph embeddings
from word-sentence graph reconstruction using graph autoencoder (GAE) and (ii)
perform multi-task text classification using these latent sentence graph
embeddings. We argue that our proposed MT-Text GCN achieves significant
improvements on TEL-NLP over existing Telugu pretrained word embeddings, and
multilingual pretrained Transformer models: mBERT, and XLM-R. On TEL-NLP, we
achieve a high F1-score for four NLP tasks: SA (0.84), EI (0.55), HS (0.83) and
SAR (0.66). Finally, we show our model's quantitative and qualitative analysis
on the four NLP tasks in Telugu.

Fetal brain segmentation is an important first step for slice-level motion
correction and slice-to-volume reconstruction in fetal MRI. Fast and accurate
segmentation of the fetal brain on fetal MRI is required to achieve real-time
fetal head pose estimation and motion tracking for slice re-acquisition and
steering. To address this critical unmet need, in this work we analyzed the
speed-accuracy performance of a variety of deep neural network models, and
devised a symbolically small convolutional neural network that combines spatial
details at high resolution with context features extracted at lower
resolutions. We used multiple branches with skip connections to maintain high
accuracy while devising a parallel combination of convolution and pooling
operations as an input downsampling module to further reduce inference time. We
trained our model as well as eight alternative, state-of-the-art networks with
manually-labeled fetal brain MRI slices and tested on two sets of normal and
challenging test cases. Experimental results show that our network achieved the
highest accuracy and lowest inference time among all of the compared
state-of-the-art real-time segmentation methods. We achieved average Dice
scores of 97.99\% and 84.04\% on the normal and challenging test sets,
respectively, with an inference time of 3.36 milliseconds per image on an
NVIDIA GeForce RTX 2080 Ti. Code, data, and the trained models are available at
https://github.com/bchimagine/real_time_fetal_brain_segmentation.

Robust self-training (RST) can augment the adversarial robustness of image
classification models without significantly sacrificing models'
generalizability. However, RST and other state-of-the-art defense approaches
failed to preserve the generalizability and reproduce their good adversarial
robustness on small medical image sets. In this work, we propose the
Multi-instance RST with a drop-max layer, namely MIRST-DM, which involves a
sequence of iteratively generated adversarial instances during training to
learn smoother decision boundaries on small datasets. The proposed drop-max
layer eliminates unstable features and helps learn representations that are
robust to image perturbations. The proposed approach was validated using a
small breast ultrasound dataset with 1,190 images. The results demonstrate that
the proposed approach achieves state-of-the-art adversarial robustness against
three prevalent attacks.

Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by
numerous factors, but it is unclear how each factor contributes to disease
progression. An in-depth examination of these factors may yield an accurate
estimate of time-to-conversion to DAT for patients at various disease stages.
We used 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive
tests, Demographic, and CSF) data modalities in the Alzheimer's Disease
Neuroimaging Initiative (ADNI) database. We used a deep learning-based survival
analysis model that extends the classic Cox regression model to predict
time-to-conversion to DAT. Our findings showed that genetic features
contributed the least to survival analysis, while CDC features contributed the
most. Combining MRI and genetic features improved survival prediction over
using either modality alone, but adding CDC to any combination of features only
worked as well as using only CDC features. Consequently, our study demonstrated
that using the current clinical procedure, which includes gathering cognitive
test results, can outperform survival analysis results produced using costly
genetic or CSF data.

Federated Learning has emerged as a dominant computational paradigm for
distributed machine learning. Its unique data privacy properties allow us to
collaboratively train models while offering participating clients certain
privacy-preserving guarantees. However, in real-world applications, a federated
environment may consist of a mixture of benevolent and malicious clients, with
the latter aiming to corrupt and degrade federated model's performance.
Different corruption schemes may be applied such as model poisoning and data
corruption. Here, we focus on the latter, the susceptibility of federated
learning to various data corruption attacks. We show that the standard global
aggregation scheme of local weights is inefficient in the presence of corrupted
clients. To mitigate this problem, we propose a class of task-oriented
performance-based methods computed over a distributed validation dataset with
the goal to detect and mitigate corrupted clients. Specifically, we construct a
robust weight aggregation scheme based on geometric mean and demonstrate its
effectiveness under random label shuffling and targeted label flipping attacks.

With everyone trying to enter the real estate market nowadays, knowing the
proper valuations for residential and commercial properties has become crucial.
Past researchers have been known to utilize static real estate data (e.g.
number of beds, baths, square footage) or even a combination of real estate and
demographic information to predict property prices. In this investigation, we
attempted to improve upon past research. So we decided to explore a unique
approach: we wanted to determine if mobile location data could be used to
improve the predictive power of popular regression and tree-based models. To
prepare our data for our models, we processed the mobility data by attaching it
to individual properties from the real estate data that aggregated users within
500 meters of the property for each day of the week. We removed people that
lived within 500 meters of each property, so each property's aggregated
mobility data only contained non-resident census features. On top of these
dynamic census features, we also included static census features, including the
number of people in the area, the average proportion of people commuting, and
the number of residents in the area. Finally, we tested multiple models to
predict real estate prices. Our proposed model is two stacked random forest
modules combined using a ridge regression that uses the random forest outputs
as predictors. The first random forest model used static features only and the
second random forest model used dynamic features only. Comparing our models
with and without the dynamic mobile location features concludes the model with
dynamic mobile location features achieves 3/% percent lower mean squared error
than the same model but without dynamic mobile location features.

Quadruped locomotion is rapidly maturing to a degree where robots now
routinely traverse a variety of unstructured terrains. However, while gaits can
be varied typically by selecting from a range of pre-computed styles, current
planners are unable to vary key gait parameters continuously while the robot is
in motion. The synthesis, on-the-fly, of gaits with unexpected operational
characteristics or even the blending of dynamic manoeuvres lies beyond the
capabilities of the current state-of-the-art. In this work we address this
limitation by learning a latent space capturing the key stance phases
constituting a particular gait. This is achieved via a generative model trained
on a single trot style, which encourages disentanglement such that application
of a drive signal to a single dimension of the latent state induces holistic
plans synthesising a continuous variety of trot styles. We demonstrate that
specific properties of the drive signal map directly to gait parameters such as
cadence, footstep height and full stance duration. Due to the nature of our
approach these synthesised gaits are continuously variable online during robot
operation and robustly capture a richness of movement significantly exceeding
the relatively narrow behaviour seen during training. In addition, the use of a
generative model facilitates the detection and mitigation of disturbances to
provide a versatile and robust planning framework. We evaluate our approach on
two versions of the real ANYmal quadruped robots and demonstrate that our
method achieves a continuous blend of dynamic trot styles whilst being robust
and reactive to external perturbations.

In High Energy Physics (HEP), experimentalists generate large volumes of data
that, when analyzed, helps us better understand the fundamental particles and
their interactions. This data is often captured in many files of small size,
creating a data management challenge for scientists. In order to better
facilitate data management, transfer, and analysis on large scale platforms, it
is advantageous to aggregate data further into a smaller number of larger
files. However, this translation process can consume significant time and
resources, and if performed incorrectly the resulting aggregated files can be
inefficient for highly parallel access during analysis on large scale
platforms. In this paper, we present our case study on parallel I/O strategies
and HDF5 features for reducing data aggregation time, making effective use of
compression, and ensuring efficient access to the resulting data during
analysis at scale. We focus on NOvA detector data in this case study, a
large-scale HEP experiment generating many terabytes of data. The lessons
learned from our case study inform the handling of similar datasets, thus
expanding community knowledge related to this common data management task.

Compositional data, such as human gut microbiomes, consist of non-negative
variables whose only the relative values to other variables are available.
Analyzing compositional data such as human gut microbiomes needs a careful
treatment of the geometry of the data. A common geometrical understanding of
compositional data is via a regular simplex. Majority of existing approaches
rely on a log-ratio or power transformations to overcome the innate simplicial
geometry. In this work, based on the key observation that a compositional data
are projective in nature, and on the intrinsic connection between projective
and spherical geometry, we re-interpret the compositional domain as the
quotient topology of a sphere modded out by a group action. This
re-interpretation allows us to understand the function space on compositional
domains in terms of that on spheres and to use spherical harmonics theory along
with reflection group actions for constructing a compositional Reproducing
Kernel Hilbert Space (RKHS). This construction of RKHS for compositional data
will widely open research avenues for future methodology developments. In
particular, well-developed kernel embedding methods can be now introduced to
compositional data analysis. The polynomial nature of compositional RKHS has
both theoretical and computational benefits. The wide applicability of the
proposed theoretical framework is exemplified with nonparametric density
estimation and kernel exponential family for compositional data.

Deep neural networks are prone to overfitting noisy labels, resulting in poor
generalization performance. To overcome this problem, we present a simple and
effective method self-ensemble label correction (SELC) to progressively correct
noisy labels and refine the model. We look deeper into the memorization
behavior in training with noisy labels and observe that the network outputs are
reliable in the early stage. To retain this reliable knowledge, SELC uses
ensemble predictions formed by an exponential moving average of network outputs
to update the original noisy labels. We show that training with SELC refines
the model by gradually reducing supervision from noisy labels and increasing
supervision from ensemble predictions. Despite its simplicity, compared with
many state-of-the-art methods, SELC obtains more promising and stable results
in the presence of class-conditional, instance-dependent, and real-world label
noise. The code is available at https://github.com/MacLLL/SELC.

Despite the significant progress in recent years, very few of the AI-based
talking face generation methods attempt to render natural emotions. Moreover,
the scope of the methods is majorly limited to the characteristics of the
training dataset, hence they fail to generalize to arbitrary unseen faces. In
this paper, we propose a one-shot facial geometry-aware emotional talking face
generation method that can generalize to arbitrary faces. We propose a graph
convolutional neural network that uses speech content feature, along with an
independent emotion input to generate emotion and speech-induced motion on
facial geometry-aware landmark representation. This representation is further
used in our optical flow-guided texture generation network for producing the
texture. We propose a two-branch texture generation network, with motion and
texture branches designed to consider the motion and texture content
independently. Compared to the previous emotion talking face methods, our
method can adapt to arbitrary faces captured in-the-wild by fine-tuning with
only a single image of the target identity in neutral emotion.

The field of artificial intelligence based image enhancement has been rapidly
evolving over the last few years and is able to produce impressive results on
non-astronomical images. In this work we present the first application of
Machine Learning based super-resolution (SR) and de-noising (DN) to enhance
X-ray images from the European Space Agency's XMM-Newton telescope. Using
XMM-Newton images in band [0.5, 2] keV from the European Photon Imaging Camera
pn detector (EPIC-pn), we develop XMM-SuperRes and XMM-DeNoise deep
learning-based models that can generate enhanced SR and DN images from real
observations. The models are trained on realistic XMM-Newton simulations such
that XMM-SuperRes will output images with two times smaller point-spread
function and with improved noise characteristics. The XMM-DeNoise model is
trained to produce images with 2.5x the input exposure time from 20 to 50 ks.
When tested on real images, DN improves the image quality by 8.2%, as
quantified by the global peak-signal-to-noise ratio. These enhanced images
allow identification of features that are otherwise hard or impossible to
perceive in the original or in filtered/smoothed images with traditional
methods. We demonstrate the feasibility of using our deep learning models to
enhance XMM-Newton X-ray images to increase their scientific value in a way
that could benefit the legacy of the XMM-Newton archive.

The non-uniformly distributed nature of the 3D dynamic point cloud (DPC)
brings significant challenges to its high-efficient inter-frame compression.
This paper proposes a novel 3D sparse convolution-based Deep Dynamic Point
Cloud Compression (D-DPCC) network to compensate and compress the DPC geometry
with 3D motion estimation and motion compensation in the feature space. In the
proposed D-DPCC network, we design a {\it Multi-scale Motion Fusion} (MMF)
module to accurately estimate the 3D optical flow between the feature
representations of adjacent point cloud frames. Specifically, we utilize a 3D
sparse convolution-based encoder to obtain the latent representation for motion
estimation in the feature space and introduce the proposed MMF module for fused
3D motion embedding. Besides, for motion compensation, we propose a 3D {\it
Adaptively Weighted Interpolation} (3DAWI) algorithm with a penalty coefficient
to adaptively decrease the impact of distant neighbors. We compress the motion
embedding and the residual with a lossy autoencoder-based network. To our
knowledge, this paper is the first work proposing an end-to-end deep dynamic
point cloud compression framework. The experimental result shows that the
proposed D-DPCC framework achieves an average 76\% BD-Rate (Bjontegaard Delta
Rate) gains against state-of-the-art Video-based Point Cloud Compression
(V-PCC) v13 in inter mode.

Multi-modal Machine Translation (MMT) enables the use of visual information
to enhance the quality of translations. The visual information can serve as a
valuable piece of context information to decrease the ambiguity of input
sentences. Despite the increasing popularity of such a technique, good and
sizeable datasets are scarce, limiting the full extent of their potential.
Hausa, a Chadic language, is a member of the Afro-Asiatic language family. It
is estimated that about 100 to 150 million people speak the language, with more
than 80 million indigenous speakers. This is more than any of the other Chadic
languages. Despite a large number of speakers, the Hausa language is considered
low-resource in natural language processing (NLP). This is due to the absence
of sufficient resources to implement most NLP tasks. While some datasets exist,
they are either scarce, machine-generated, or in the religious domain.
Therefore, there is a need to create training and evaluation data for
implementing machine learning tasks and bridging the research gap in the
language. This work presents the Hausa Visual Genome (HaVG), a dataset that
contains the description of an image or a section within the image in Hausa and
its equivalent in English. To prepare the dataset, we started by translating
the English description of the images in the Hindi Visual Genome (HVG) into
Hausa automatically. Afterward, the synthetic Hausa data was carefully
post-edited considering the respective images. The dataset comprises 32,923
images and their descriptions that are divided into training, development,
test, and challenge test set. The Hausa Visual Genome is the first dataset of
its kind and can be used for Hausa-English machine translation, multi-modal
research, and image description, among various other natural language
processing and generation tasks.

Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard
for cardiac function evaluation. However, cine CMR acquisition is inherently
slow and in recent decades considerable effort has been put into accelerating
scan times without compromising image quality or the accuracy of derived
results. In this paper, we present a fully-automated, quality-controlled
integrated framework for reconstruction, segmentation and downstream analysis
of undersampled cine CMR data. The framework enables active acquisition of
radial k-space data, in which acquisition can be stopped as soon as acquired
data are sufficient to produce high quality reconstructions and segmentations.
This results in reduced scan times and automated analysis, enabling robust and
accurate estimation of functional biomarkers. To demonstrate the feasibility of
the proposed approach, we perform realistic simulations of radial k-space
acquisitions on a dataset of subjects from the UK Biobank and present results
on in-vivo cine CMR k-space data collected from healthy subjects. The results
demonstrate that our method can produce quality-controlled images in a mean
scan time reduced from 12 to 4 seconds per slice, and that image quality is
sufficient to allow clinically relevant parameters to be automatically
estimated to within 5% mean absolute difference.

What explains the dramatic progress from 20th-century to 21st-century AI, and
how can the remaining limitations of current AI be overcome? The widely
accepted narrative attributes this progress to massive increases in the
quantity of computational and data resources available to support statistical
learning in deep artificial neural networks. We show that an additional crucial
factor is the development of a new type of computation. Neurocompositional
computing adopts two principles that must be simultaneously respected to enable
human-level cognition: the principles of Compositionality and Continuity. These
have seemed irreconcilable until the recent mathematical discovery that
compositionality can be realized not only through discrete methods of symbolic
computing, but also through novel forms of continuous neural computing. The
revolutionary recent progress in AI has resulted from the use of limited forms
of neurocompositional computing. New, deeper forms of neurocompositional
computing create AI systems that are more robust, accurate, and comprehensible.

Measuring the density profile of dark matter in the Solar neighborhood has
important implications for both dark matter theory and experiment. In this
work, we apply autoregressive flows to stars from a realistic simulation of a
Milky Way-type galaxy to learn -- in an unsupervised way -- the stellar phase
space density and its derivatives. With these as inputs, and under the
assumption of dynamic equilibrium, the gravitational acceleration field and
mass density can be calculated directly from the Boltzmann Equation without the
need to assume either cylindrical symmetry or specific functional forms for the
galaxy's mass density. We demonstrate our approach can accurately reconstruct
the mass density and acceleration profiles of the simulated galaxy, even in the
presence of Gaia-like errors in the kinematic measurements.

Objects' motions in nature are governed by complex interactions and their
properties. While some properties, such as shape and material, can be
identified via the object's visual appearances, others like mass and electric
charge are not directly visible. The compositionality between the visible and
hidden properties poses unique challenges for AI models to reason from the
physical world, whereas humans can effortlessly infer them with limited
observations. Existing studies on video reasoning mainly focus on visually
observable elements such as object appearance, movement, and contact
interaction. In this paper, we take an initial step to highlight the importance
of inferring the hidden physical properties not directly observable from visual
appearances, by introducing the Compositional Physical Reasoning (ComPhy)
dataset. For a given set of objects, ComPhy includes few videos of them moving
and interacting under different initial conditions. The model is evaluated
based on its capability to unravel the compositional hidden properties, such as
mass and charge, and use this knowledge to answer a set of questions posted on
one of the videos. Evaluation results of several state-of-the-art video
reasoning models on ComPhy show unsatisfactory performance as they fail to
capture these hidden properties. We further propose an oracle neural-symbolic
framework named Compositional Physics Learner (CPL), combining visual
perception, physical property learning, dynamic prediction, and symbolic
execution into a unified framework. CPL can effectively identify objects'
physical properties from their interactions and predict their dynamics to
answer questions.

Ensemble learning combines multiple classifiers in the hope of obtaining
better predictive performance. Empirical studies have shown that ensemble
pruning, that is, choosing an appropriate subset of the available classifiers,
can lead to comparable or better predictions than using all classifiers. In
this paper, we consider a binary classification problem and propose an integer
programming (IP) approach for selecting optimal classifier subsets. We propose
a flexible objective function to adapt to desired criteria of different
datasets. We also propose constraints to ensure minimum diversity levels in the
ensemble. Despite the general case of IP being NP-Hard, state-of-the-art
solvers are able to quickly obtain good solutions for datasets with up to 60000
data points. Our approach yields competitive results when compared to some of
the best and most used pruning methods in literature.

We introduce Wav2Seq, the first self-supervised approach to pre-train both
parts of encoder-decoder models for speech data. We induce a pseudo language as
a compact discrete representation, and formulate a self-supervised pseudo
speech recognition task -- transcribing audio inputs into pseudo subword
sequences. This process stands on its own, or can be applied as low-cost
second-stage pre-training. We experiment with automatic speech recognition
(ASR), spoken named entity recognition, and speech-to-text translation. We set
new state-of-the-art results for end-to-end spoken named entity recognition,
and show consistent improvements on 20 language pairs for speech-to-text
translation, even when competing methods use additional text data for training.
Finally, on ASR, our approach enables encoder-decoder methods to benefit from
pre-training for all parts of the network, and shows comparable performance to
highly optimized recent methods.

Large language models, which are often trained for hundreds of thousands of
compute days, have shown remarkable capabilities for zero- and few-shot
learning. Given their computational cost, these models are difficult to
replicate without significant capital. For the few that are available through
APIs, no access is granted to the full model weights, making them difficult to
study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only
pre-trained transformers ranging from 125M to 175B parameters, which we aim to
fully and responsibly share with interested researchers. We show that OPT-175B
is comparable to GPT-3, while requiring only 1/7th the carbon footprint to
develop. We are also releasing our logbook detailing the infrastructure
challenges we faced, along with code for experimenting with all of the released
models.

The "quasi-constant" SOAP and ACSF fingerprint manifolds recently discovered
by Parsaeifard and Goedecker are a direct consequence of the presence of
degenerate pairs of configurations, a known shortcoming of all low-body-order
atom-density correlation representations of molecular structures. Contrary to
the configurations that are rigorously singular -- that we demonstrate can only
occur in finite, discrete sets -- the continuous "quasi-constant" manifolds
exhibit low, but non-zero, sensitivity to atomic displacements. Thus, it is
possible to build interpolative machine-learning models of high-order
interactions along the manifold, even though the numerical instabilities
associated with proximity to the exact singularities affect the accuracy and
transferability of such models, to an extent that depends on numerical details
of the implementation.

Changes in the statistical properties of a stochastic process are typically
assumed to occur via change-points, which demark instantaneous moments of
complete and total change in process behavior. In cases where these transitions
occur gradually, this assumption can result in a reduced ability to properly
identify and respond to process change. With this observation in mind, we
introduce a novel change-dynamic model for the online detection of gradual
change in a Bayesian framework, in which change-points are used within a
hierarchical model to indicate moments of gradual change onset or termination.
We apply this model to synthetic data and EEG readings drawn during epileptic
seizure, where we find our change-dynamic model can enable faster and more
accurate identification of gradual change than traditional change-point models
allow.

Learning solutions of partial differential equations (PDEs) with
Physics-Informed Neural Networks (PINNs) is an attractive alternative approach
to traditional solvers due to its flexibility and ease of incorporating
observed data. Despite the success of PINNs in accurately solving a wide
variety of PDEs, the method still requires improvements in terms of
computational efficiency. One possible improvement idea is to optimize the
generation of training point sets. Residual-based adaptive sampling and
quasi-uniform sampling approaches have been each applied to improve the
training effects of PINNs, respectively. To benefit from both methods, we
propose the Residual-based Adaptive Node Generation (RANG) approach for
efficient training of PINNs, which is based on a variable density nodal
distribution method for RBF-FD. The method is also enhanced by a memory
mechanism to further improve training stability. We conduct experiments on
three linear PDEs and three nonlinear PDEs with various node generation
methods, through which the accuracy and efficiency of the proposed method
compared to the predominant uniform sampling approach is verified numerically.

Kinematics decoding from brain activity helps in developing rehabilitation or
power-augmenting brain-computer interface devices. Low-frequency signals
recorded from non-invasive electroencephalography (EEG) are associated with the
neural motor correlation utilised for motor trajectory decoding (MTD). In this
communication, the ability to decode motor kinematics trajectory from
pre-movement delta-band (0.5-3 Hz) EEG is investigated for the healthy
participants. In particular, two deep learning-based neural decoders called
PreMovNet-I and PreMovNet-II, are proposed that make use of motor-related
neural information existing in the pre-movement EEG data. EEG data segments
with various time lags of 150 ms, 200 ms, 250 ms, 300 ms, and 350 ms before the
movement onset are utilised for the same. The MTD is presented for
grasp-and-lift task (WAY-EEG-GAL dataset) using EEG with the various lags taken
as input to the neural decoders. The performance of the proposed decoders are
compared with the state-of-the-art multi-variable linear regression (mLR)
model. Pearson correlation coefficient and hand trajectory are utilised as
performance metric. The results demonstrate the viability of decoding 3D hand
kinematics using pre-movement EEG data, enabling better control of BCI-based
external devices such as exoskeleton/exosuit.

Object dropping may occur when the robotic arm grasps objects with uneven
mass distribution due to additional moments generated by objects' gravity. To
solve this problem, we present a novel work that does not require extra wrist
and tactile sensors and large amounts of experiments for learning. First, we
obtain the center-of-mass position of the rod object using the widely fixed
joint torque sensors on the robot arm and RGBD camera. Further, we give the
strategy of grasping to improve grasp stability. Simulation experiments are
performed in "Mujoco". Results demonstrate that our work is effective in
enhancing grasping robustness.

The success of deep learning (DL) fostered the creation of unifying
frameworks such as tensorflow or pytorch as much as it was driven by their
creation in return. Having common building blocks facilitates the exchange of,
e.g., models or concepts and makes developments easier replicable. Nonetheless,
robust and reliable evaluation and assessment of DL models has often proven
challenging. This is at odds with their increasing safety relevance, which
recently culminated in the field of "trustworthy ML". We believe that, among
others, further unification of evaluation and safeguarding methodologies in
terms of toolkits, i.e., small and specialized framework derivatives, might
positively impact problems of trustworthiness as well as reproducibility. To
this end, we present the first survey on toolkits for uncertainty estimation
(UE) in DL, as UE forms a cornerstone in assessing model reliability. We
investigate 11 toolkits with respect to modeling and evaluation capabilities,
providing an in-depth comparison for the three most promising ones, namely
Pyro, Tensorflow Probability, and Uncertainty Quantification 360. While the
first two provide a large degree of flexibility and seamless integration into
their respective framework, the last one has the larger methodological scope.

Shannon's entropy is one of the building blocks of information theory and an
essential aspect of Machine Learning methods (e.g., Random Forests). Yet, it is
only finitely defined for distributions with fast decaying tails on a countable
alphabet. The unboundedness of Shannon's entropy over the general class of all
distributions on an alphabet prevents its potential utility from being fully
realized. To fill the void in the foundation of information theory, Zhang
(2020) proposed generalized Shannon's entropy, which is finitely defined
everywhere. The plug-in estimator, adopted in almost all entropy-based ML
method packages, is one of the most popular approaches to estimating Shannon's
entropy. The asymptotic distribution for Shannon's entropy's plug-in estimator
was well studied in the existing literature. This paper studies the asymptotic
properties for the plug-in estimator of generalized Shannon's entropy on
countable alphabets. The developed asymptotic properties require no assumptions
on the original distribution. The proposed asymptotic properties allow interval
estimation and statistical tests with generalized Shannon's entropy.

Semantic understanding of 3D point cloud relies on learning models with
massively annotated data, which, in many cases, are expensive or difficult to
collect. This has led to an emerging research interest in semi-supervised
learning (SSL) for 3D point cloud. It is commonly assumed in SSL that the
unlabeled data are drawn from the same distribution as that of the labeled
ones; This assumption, however, rarely holds true in realistic environments.
Blindly using out-of-distribution (OOD) unlabeled data could harm SSL
performance. In this work, we propose to selectively utilize unlabeled data
through sample weighting, so that only conducive unlabeled data would be
prioritized. To estimate the weights, we adopt a bi-level optimization
framework which iteratively optimizes a metaobjective on a held-out validation
set and a task-objective on a training set. Faced with the instability of
efficient bi-level optimizers, we further propose three regularization
techniques to enhance the training stability. Extensive experiments on 3D point
cloud classification and segmentation tasks verify the effectiveness of our
proposed method. We also demonstrate the feasibility of a more efficient
training strategy.

Because of the rotational components on quantum circuits, some quantum neural
networks based on variational circuits can be considered equivalent to the
classical Fourier networks. In addition, they can be used to predict the
Fourier coefficients of continuous functions. Time series data indicates a
state of a variable in time. Since some time series data can be also considered
as continuous functions, we can expect quantum machine learning models to do
many data analysis tasks successfully on time series data. Therefore, it is
important to investigate new quantum logics for temporal data processing and
analyze intrinsic relationships of data on quantum computers.
  In this paper, we go through the quantum analogues of classical data
preprocessing and forecasting with ARIMA models by using simple quantum
operators requiring a few number of quantum gates. Then we discuss future
directions and some of the tools/algorithms that can be used for temporal data
analysis on quantum computers.

The stochastic $K$-armed bandit problem has been studied extensively due to
its applications in various domains ranging from online advertising to clinical
trials. In practice however, the number of arms can be very large resulting in
large memory requirements for simultaneously processing them. In this paper we
consider a streaming setting where the arms are presented in a stream and the
algorithm uses limited memory to process these arms. Here, the goal is not only
to minimize regret, but also to do so in minimal memory. Previous algorithms
for this problem operate in one of the two settings: they either use
$\Omega(\log \log T)$ passes over the stream (Rathod, 2021; Chaudhuri and
Kalyanakrishnan, 2020; Liau et al., 2018), or just a single pass (Maiti et al.,
2021).
  In this paper we study the trade-off between memory and regret when $B$
passes over the stream are allowed, for any $B \geq 1$, and establish tight
regret upper and lower bounds for any $B$-pass algorithm. Our results uncover a
surprising *sharp transition phenomenon*: $O(1)$ memory is sufficient to
achieve $\widetilde\Theta\Big(T^{\frac{1}{2} + \frac{1}{2^{B+2}-2}}\Big)$
regret in $B$ passes, and increasing the memory to any quantity that is $o(K)$
has almost no impact on further reducing this regret, unless we use $\Omega(K)$
memory. Our main technical contribution is our lower bound which requires the
use of information-theoretic techniques as well as ideas from round elimination
to show that the *residual problem* remains challenging over subsequent passes.

Despite the progress in machine translation quality estimation and evaluation
in the last years, decoding in neural machine translation (NMT) is mostly
oblivious to this and centers around finding the most probable translation
according to the model (MAP decoding), approximated with beam search. In this
paper, we bring together these two lines of research and propose quality-aware
decoding for NMT, by leveraging recent breakthroughs in reference-free and
reference-based MT evaluation through various inference methods like $N$-best
reranking and minimum Bayes risk decoding. We perform an extensive comparison
of various possible candidate generation and ranking methods across four
datasets and two model classes and find that quality-aware decoding
consistently outperforms MAP-based decoding according both to state-of-the-art
automatic metrics (COMET and BLEURT) and to human assessments. Our code is
available at https://github.com/deep-spin/qaware-decode.

Knowledge Graphs (KGs) have been utilized as useful side information to
improve recommendation quality. In those recommender systems, knowledge graph
information often contains fruitful facts and inherent semantic relatedness
among items. However, the success of such methods relies on the high quality
knowledge graphs, and may not learn quality representations with two
challenges: i) The long-tail distribution of entities results in sparse
supervision signals for KG-enhanced item representation; ii) Real-world
knowledge graphs are often noisy and contain topic-irrelevant connections
between items and entities. Such KG sparsity and noise make the item-entity
dependent relations deviate from reflecting their true characteristics, which
significantly amplifies the noise effect and hinders the accurate
representation of user's preference.
  To fill this research gap, we design a general Knowledge Graph Contrastive
Learning framework (KGCL) that alleviates the information noise for knowledge
graph-enhanced recommender systems. Specifically, we propose a knowledge graph
augmentation schema to suppress KG noise in information aggregation, and derive
more robust knowledge-aware representations for items. In addition, we exploit
additional supervision signals from the KG augmentation process to guide a
cross-view contrastive learning paradigm, giving a greater role to unbiased
user-item interactions in gradient descent and further suppressing the noise.
Extensive experiments on three public datasets demonstrate the consistent
superiority of our KGCL over state-of-the-art techniques. KGCL also achieves
strong performance in recommendation scenarios with sparse user-item
interactions, long-tail and noisy KG entities. Our implementation codes are
available at https://github.com/yuh-yang/KGCL-SIGIR22

Many recent approaches of passage retrieval are using dense embeddings
generated from deep neural models, called "dense passage retrieval". The
state-of-the-art end-to-end dense passage retrieval systems normally deploy a
deep neural model followed by an approximate nearest neighbor (ANN) search
module. The model generates embeddings of the corpus and queries, which are
then indexed and searched by the high-performance ANN module. With the
increasing data scale, the ANN module unavoidably becomes the bottleneck on
efficiency. An alternative is the learned index, which achieves significantly
high search efficiency by learning the data distribution and predicting the
target data location. But most of the existing learned indexes are designed for
low dimensional data, which are not suitable for dense passage retrieval with
high-dimensional dense embeddings. In this paper, we propose LIDER, an
efficient high-dimensional Learned Index for large-scale DEnse passage
Retrieval. LIDER has a clustering-based hierarchical architecture formed by two
layers of core models. As the basic unit of LIDER to index and search data, a
core model includes an adapted recursive model index (RMI) and a dimension
reduction component which consists of an extended SortingKeys-LSH (SK-LSH) and
a key re-scaling module. The dimension reduction component reduces the
high-dimensional dense embeddings into one-dimensional keys and sorts them in a
specific order, which are then used by the RMI to make fast prediction.
Experiments show that LIDER has a higher search speed with high retrieval
quality comparing to the state-of-the-art ANN indexes on passage retrieval
tasks, e.g., on large-scale data it achieves 1.2x search speed and
significantly higher retrieval quality than the fastest baseline in our
evaluation. Furthermore, LIDER has a better capability of speed-quality
trade-off.

Compared with contact-based fingerprint acquisition techniques, contactless
acquisition has the advantages of less skin distortion, larger fingerprint
area, and hygienic acquisition. However, perspective distortion is a challenge
in contactless fingerprint recognition, which changes ridge orientation,
frequency, and minutiae location, and thus causes degraded recognition
accuracy. We propose a learning based shape from texture algorithm to
reconstruct a 3D finger shape from a single image and unwarp the raw image to
suppress perspective distortion. Experimental results on contactless
fingerprint databases show that the proposed method has high 3D reconstruction
accuracy. Matching experiments on contactless-contact and
contactless-contactless matching prove that the proposed method improves
matching accuracy.

Proposing scoring functions to effectively understand, analyze and learn
various properties of high dimensional hidden representations of large-scale
transformer models like BERT can be a challenging task. In this work, we
explore a new direction by studying the topological features of BERT hidden
representations using persistent homology (PH). We propose a novel scoring
function named "persistence scoring function (PSF)" which: (i) accurately
captures the homology of the high-dimensional hidden representations and
correlates well with the test set accuracy of a wide range of datasets and
outperforms existing scoring metrics, (ii) captures interesting post
fine-tuning "per-class" level properties from both qualitative and quantitative
viewpoints, (iii) is more stable to perturbations as compared to the baseline
functions, which makes it a very robust proxy, and (iv) finally, also serves as
a predictor of the attack success rates for a wide category of black-box and
white-box adversarial attack methods. Our extensive correlation experiments
demonstrate the practical utility of PSF on various NLP tasks relevant to BERT.

We present Answer-Me, a task-aware multi-task framework which unifies a
variety of question answering tasks, such as, visual question answering, visual
entailment, visual reasoning. In contrast to previous works using contrastive
or generative captioning training, we propose a novel and simple recipe to
pre-train a vision-language joint model, which is multi-task as well. The
pre-training uses only noisy image captioning data, and is formulated to use
the entire architecture end-to-end with both a strong language encoder and
decoder. Our results show state-of-the-art performance, zero-shot
generalization, robustness to forgetting, and competitive single-task results
across a variety of question answering tasks. Our multi-task mixture training
learns from tasks of various question intents and thus generalizes better,
including on zero-shot vision-language tasks. We conduct experiments in the
challenging multi-task and open-vocabulary settings and across a variety of
datasets and tasks, such as VQA2.0, SNLI-VE, NLVR2, GQA, VizWiz. We observe
that the proposed approach is able to generalize to unseen tasks and that more
diverse mixtures lead to higher accuracy in both known and novel tasks.

In reinforcement learning (RL), it is challenging to learn directly from
high-dimensional observations, where data augmentation has recently been shown
to remedy this via encoding invariances from raw pixels. Nevertheless, we
empirically find that not all samples are equally important and hence simply
injecting more augmented inputs may instead cause instability in Q-learning. In
this paper, we approach this problem systematically by developing a
model-agnostic Contrastive-Curiosity-Driven Learning Framework (CCLF), which
can fully exploit sample importance and improve learning efficiency in a
self-supervised manner. Facilitated by the proposed contrastive curiosity, CCLF
is capable of prioritizing the experience replay, selecting the most
informative augmented inputs, and more importantly regularizing the Q-function
as well as the encoder to concentrate more on under-learned data. Moreover, it
encourages the agent to explore with a curiosity-based reward. As a result, the
agent can focus on more informative samples and learn representation
invariances more efficiently, with significantly reduced augmented inputs. We
apply CCLF to several base RL algorithms and evaluate on the DeepMind Control
Suite, Atari, and MiniGrid benchmarks, where our approach demonstrates superior
sample efficiency and learning performances compared with other

This Thesis discusses the development of technologies for the automatic
resynthesis of music recordings using digital synthesizers. First, the main
issue is identified in the understanding of how Music Information Processing
(MIP) methods can take into consideration the influence of the acoustic context
on the music performance. For this, a novel conceptual and mathematical
framework named "Music Interpretation Analysis" (MIA) is presented. In the
proposed framework, a distinction is made between the "performance" - the
physical action of playing - and the "interpretation" - the action that the
performer wishes to achieve. Second, the Thesis describes further works aiming
at the democratization of music production tools via automatic resynthesis: 1)
it elaborates software and file formats for historical music archiving and
multimodal machine-learning datasets; 2) it explores and extends MIP
technologies; 3) it presents the mathematical foundations of the MIA framework
and shows preliminary evaluations to demonstrate the effectiveness of the
approach

The optimal use of quantum and classical computational techniques together is
important to address problems that cannot be easily solved by quantum
computations alone. This is the case of the ground state problem for quantum
many-body systems. We show here that probabilistic generative models can work
in conjunction with quantum algorithms to design hybrid quantum-classical
variational ans\"atze that forge entanglement to lower quantum resource
overhead. The variational ans\"atze comprise parametrized quantum circuits on
two separate quantum registers, and a classical generative neural network that
can entangle them by learning a Schmidt decomposition of the whole system. The
method presented is efficient in terms of the number of measurements required
to achieve fixed precision on expected values of observables. To demonstrate
its effectiveness, we perform numerical experiments on the transverse field
Ising model in one and two dimensions, and fermionic systems such as the t-V
Hamiltonian of spinless fermions on a lattice.

For instance-level explanation, in order to reveal the relations between
high-level semantics and detailed spatial information, this paper proposes a
novel cognitive approach to neural networks, which named PANE. Under the
guidance of PANE, a novel saliency map representation method, named IOM, is
proposed for CNN-like models. We make the comparison with eight
state-of-the-art saliency map representation methods. The experimental results
show that IOM far outperforms baselines. The work of this paper may bring a new
perspective to understand deep neural networks.

We perform the first systematic search of all NICER archival observations of
black hole (and candidate) low-mass X-ray binaries for signatures of
reverberation. Reverberation lags result from the light travel time difference
between the direct coronal emission and the reflected disk component, and
therefore their properties are a useful probe of the disk-corona geometry. We
detect new signatures of reverberation lags in 8 sources, increasing the total
sample from 3 to 11, and study the evolution of reverberation lag properties as
the sources evolve in outbursts. We find that in all of the 9 sources with more
than 1 reverberation lag detection, the reverberation lags become longer and
dominate at lower Fourier frequencies during the hard-to-soft state transition.
This result shows that the evolution in reverberation lags is a global property
of the state transitions of black hole low-mass X-ray binaries, which is
valuable in constraining models of such state transitions. The reverberation
lag evolution suggests that the corona is the base of a jet which vertically
expands and/or gets ejected during state transition. We also discover that in
the hard state, the reverberation lags get shorter, just as the QPOs move to
higher frequencies, but then in the state transition, while the QPOs continue
to higher frequencies, the lags get longer. We discuss implications for the
coronal geometry and physical models of QPOs in light of this new finding.

User intent classification is an important task in information retrieval. In
this work, we introduce a revised taxonomy of user intent. We take the widely
used differentiation between navigational, transactional and informational
queries as a starting point, and identify three different sub-classes for the
informational queries: instrumental, factual and abstain. The resulting
classification of user queries is more fine-grained, reaches a high level of
consistency between annotators, and can serve as the basis for an effective
automatic classification process. The newly introduced categories help
distinguish between types of queries that a retrieval system could act upon,
for example by prioritizing different types of results in the ranking.We have
used a weak supervision approach based on Snorkel to annotate the ORCAS dataset
according to our new user intent taxonomy, utilising established heuristics and
keywords to construct rules for the prediction of the intent category. We then
present a series of experiments with a variety of machine learning models,
using the labels from the weak supervision stage as training data, but find
that the results produced by Snorkel are not outperformed by these competing
approaches and can be considered state-of-the-art. The advantage of a
rule-based approach like Snorkel's is its efficient deployment in an actual
system, where intent classification would be executed for every query issued.
The resource released with this paper is the ORCAS-I dataset: a labelled
version of the ORCAS click-based dataset of Web queries, which provides 18
million connections to 10 million distinct queries.

Despite the recent success of artificial neural networks, more biologically
plausible learning methods may be needed to resolve the weaknesses of
backpropagation trained models such as catastrophic forgetting and adversarial
attacks. Although these weaknesses are not specifically addressed, a novel
local learning rule is presented that performs online clustering with an upper
limit on the number of clusters to be found rather than a fixed cluster count.
Instead of using orthogonal weight or output activation constraints, activation
sparsity is achieved by mutual repulsion of lateral Gaussian neurons ensuring
that multiple neuron centers cannot occupy the same location in the input
domain. An update method is also presented for adjusting the widths of the
Gaussian neurons in cases where the data samples can be represented by means
and variances. The algorithms were applied on the MNIST and CIFAR-10 datasets
to create filters capturing the input patterns of pixel patches of various
sizes. The experimental results demonstrate stability in the learned parameters
across a large number of training samples.

Under the semi-supervised framework, we propose an end-to-end memory-based
segmentation network (MemSeg) to detect surface defects on industrial products.
Considering the small intra-class variance of products in the same production
line, from the perspective of differences and commonalities, MemSeg introduces
artificially simulated abnormal samples and memory samples to assist the
learning of the network. In the training phase, MemSeg explicitly learns the
potential differences between normal and simulated abnormal images to obtain a
robust classification hyperplane. At the same time, inspired by the mechanism
of human memory, MemSeg uses a memory pool to store the general patterns of
normal samples. By comparing the similarities and differences between input
samples and memory samples in the memory pool to give effective guesses about
abnormal regions; In the inference phase, MemSeg directly determines the
abnormal regions of the input image in an end-to-end manner. Through
experimental validation, MemSeg achieves the state-of-the-art (SOTA)
performance on MVTec AD datasets with AUC scores of 99.56% and 98.84% at the
image-level and pixel-level, respectively. In addition, MemSeg also has a
significant advantage in inference speed benefiting from the end-to-end and
straightforward network structure, which better meets the real-time requirement
in industrial scenarios.

Graph contrastive learning (GCL), as a popular approach to graph
self-supervised learning, has recently achieved a non-negligible effect. To
achieve superior performance, the majority of existing GCL methods elaborate on
graph data augmentation to construct appropriate contrastive pairs. However,
existing methods place more emphasis on the complex graph data augmentation
which requires extra time overhead, and pay less attention to developing
contrastive schemes specific to encoder characteristics. We argue that a better
contrastive scheme should be tailored to the characteristics of graph neural
networks (e.g., neighborhood aggregation) and propose a simple yet effective
method named FastGCL. Specifically, by constructing weighted-aggregated and
non-aggregated neighborhood information as positive and negative samples
respectively, FastGCL identifies the potential semantic information of data
without disturbing the graph topology and node attributes, resulting in faster
training and convergence speeds. Extensive experiments have been conducted on
node classification and graph classification tasks, showing that FastGCL has
competitive classification performance and significant training speedup

Most real-world knowledge graphs (KG) are far from complete and
comprehensive. This problem has motivated efforts in predicting the most
plausible missing facts to complete a given KG, i.e., knowledge graph
completion (KGC). However, existing KGC methods suffer from two main issues, 1)
the false negative issue, i.e., the sampled negative training instances may
include potential true facts; and 2) the data sparsity issue, i.e., true facts
account for only a tiny part of all possible facts. To this end, we propose
positive-unlabeled learning with adversarial data augmentation (PUDA) for KGC.
In particular, PUDA tailors positive-unlabeled risk estimator for the KGC task
to deal with the false negative issue. Furthermore, to address the data
sparsity issue, PUDA achieves a data augmentation strategy by unifying
adversarial training and positive-unlabeled learning under the
positive-unlabeled minimax game. Extensive experimental results on real-world
benchmark datasets demonstrate the effectiveness and compatibility of our

We propose a methodology at the nexus of operations research and machine
learning (ML) leveraging generic approximators available from ML to accelerate
the solution of mixed-integer linear two-stage stochastic programs. We aim at
solving problems where the second stage is highly demanding. Our core idea is
to gain large reductions in online solution time while incurring small
reductions in first-stage solution accuracy by substituting the exact
second-stage solutions with fast, yet accurate supervised ML predictions. This
upfront investment in ML would be justified when similar problems are solved
repeatedly over time, for example, in transport planning related to fleet
management, routing and container yard management.
  Our numerical results focus on the problem class seminally addressed with the
integer and continuous L-shaped cuts. Our extensive empirical analysis is
grounded in standardized families of problems derived from stochastic server
location (SSLP) and stochastic multi knapsack (SMKP) problems available in the
literature. The proposed method can solve the hardest instances of SSLP in less
than 9% of the time it takes the state-of-the-art exact method, and in the case
of SMKP the same figure is 20%. Average optimality gaps are in most cases less
than 0.1%.

Deep learning has shown great promise in diverse areas of computer vision,
such as image classification, object detection and semantic segmentation, among
many others. However, as it has been repeatedly demonstrated, deep learning
methods trained on a dataset do not generalize well to datasets from other
domains or even to similar datasets, due to data distribution shifts. In this
work, we propose the use of a meta-learning based few-shot learning approach to
alleviate these problems. In order to demonstrate its efficacy, we use two
datasets of kidney stones samples acquired with different endoscopes and
different acquisition conditions. The results show how such methods are indeed
capable of handling domain-shifts by attaining an accuracy of 74.38% and 88.52%
in the 5-way 5-shot and 5-way 20-shot settings respectively. Instead, in the
same dataset, traditional Deep Learning (DL) methods attain only an accuracy of
45%.

Identifying and mitigating safety risks is paramount in a number of
industries. In addition to guidelines and best practices, many industries
already have safety management systems (SMSs) designed to monitor and reinforce
good safety behaviors. The analytic capabilities to analyze the data acquired
through such systems, however, are still lacking in terms of their ability to
robustly quantify risks posed by various occupational hazards. Moreover, best
practices and modern SMSs are unable to account for dynamically evolving
environments/behavioral characteristics commonly found in many industrial
settings. This article proposes a method to address these issues by enabling
continuous and quantitative assessment of safety risks in a data-driven manner.
The backbone of our method is an intuitive hierarchical probabilistic model
that explains sparse and noisy safety data collected by a typical SMS. A fully
Bayesian approach is developed to calibrate this model from safety data in an
online fashion. Thereafter, the calibrated model holds necessary information
that serves to characterize risk posed by different safety hazards.
Additionally, the proposed model can be leveraged for automated decision
making, for instance solving resource allocation problems -- targeted towards
risk mitigation -- that are often encountered in resource-constrained
industrial environments. The methodology is rigorously validated on a simulated
test-bed and its scalability is demonstrated on real data from large
maintenance projects at a petrochemical plant.

Graph Nerual Networks (GNNs) are effective models in graph embedding. It
extracts shallow features and neighborhood information by aggregating neighbor
information to learn the embedding representation of different nodes. However,
the local topology information of many nodes in the network is similar, the
network obtained by shallow embedding represents the network that is
susceptible to structural noise, and the low-order embedding cannot capture the
high-order network structure; on the other hand, the deep embedding undergoes
multi-layer convolution. After the filters are stacked, the embedded
distribution is destroyed, and graph smoothing occurs. To address these
challenges, we propose a new framework that leverages network motifs to learn
deep features of the network from low-level embeddings under the assumption of
network homogeneity and transitivity, and then combines local neighborhood
information with deeper global information fusion results in accurate
representation of nodes. On real datasets, our model achieves significant
performance improvement in link prediction and node classification.

WeatherBench is a benchmark dataset for medium-range weather forecasting of
geopotential, temperature and precipitation, consisting of preprocessed data,
predefined evaluation metrics and a number of baseline models. WeatherBench
Probability extends this to probabilistic forecasting by adding a set of
established probabilistic verification metrics (continuous ranked probability
score, spread-skill ratio and rank histograms) and a state-of-the-art
operational baseline using the ECWMF IFS ensemble forecast. In addition, we
test three different probabilistic machine learning methods -- Monte Carlo
dropout, parametric prediction and categorical prediction, in which the
probability distribution is discretized. We find that plain Monte Carlo dropout
severely underestimates uncertainty. The parametric and categorical models both
produce fairly reliable forecasts of similar quality. The parametric models
have fewer degrees of freedom while the categorical model is more flexible when
it comes to predicting non-Gaussian distributions. None of the models are able
to match the skill of the operational IFS model. We hope that this benchmark
will enable other researchers to evaluate their probabilistic approaches.

We introduce a novel method to derandomize the learning with errors (LWE)
problem by generating deterministic yet sufficiently independent LWE instances,
that are constructed via \emph{special} linear regression models. We also
introduce star-specific key-homomorphic (SSKH) pseudorandom functions (PRFs),
which are defined by the respective sets of parties that construct them. We use
our derandomized variant of LWE to construct a SSKH PRF family. The sets of
parties constructing SSKH PRFs are arranged as star graphs with possibly shared
vertices, i.e., some pair of sets have non-empty intersections. We reduce the
security of our SSKH PRF family to the hardness of LWE. To establish the
maximum number of SSKH PRFs that can be constructed -- by a set of parties --
in the presence of passive/active and external/internal adversaries, we prove
several bounds on the size of maximally cover-free at most $t$-intersecting
$k$-uniform family of sets $\mathcal{H}$, where the three properties are
defined as: (i) $k$-uniform: $\forall A \in \mathcal{H}: |A| = k$, (ii) at most
$t$-intersecting: $\forall A, B \in \mathcal{H}, B \neq A: |A \cap B| \leq t$,
(iii) maximally cover-free: $\forall A \in \mathcal{H}: A \not\subseteq
\bigcup\limits_{\substack{B \in \mathcal{H} \\ B \neq A}} B$. For the same
purpose, we define and compute the mutual information between different linear
regression hypotheses that are generated via overlapping training datasets.

Convolutional neural network (CNN) based image enhancement methods such as
super-resolution and detail enhancement have achieved remarkable performances.
However, amounts of operations including convolution and parameters within the
networks cost high computing power and need huge memory resource, which limits
the applications with on-device requirements. Lightweight image enhancement
network should restore details, texture, and structural information from
low-resolution input images while keeping their fidelity. To address these
issues, a lightweight image enhancement network is proposed. The proposed
network include self-feature extraction module which produces modulation
parameters from low-quality image itself, and provides them to modulate the
features in the network. Also, dense modulation block is proposed for unit
block of the proposed network, which uses dense connections of concatenated
features applied in modulation layers. Experimental results demonstrate better
performance over existing approaches in terms of both quantitative and
qualitative evaluations.

Effective and adaptive interference management is required in next generation
wireless communication systems. To address this challenge, Rate-Splitting
Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the
transmitter and successive interference cancellation (SIC) at the receivers,
has been intensively studied in recent years, albeit mostly under the
assumption of perfect Channel State Information at the Receiver (CSIR) and
ideal capacity-achieving modulation and coding schemes. To assess its practical
performance, benefits, and limits under more realistic conditions, this work
proposes a novel design for a practical RSMA receiver based on model-based deep
learning (MBDL) methods, which aims to unite the simple structure of the
conventional SIC receiver and the robustness and model agnosticism of deep
learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol
Error Rate (SER), throughput performance through Link-Level Simulations (LLS),
and average training overhead. Also, a comparison with the SIC receiver, with
perfect and imperfect CSIR, is given. Results reveal that the MBDL outperforms
by a significant margin the SIC receiver with imperfect CSIR, due to its
ability to generate on demand non-linear symbol detection boundaries in a pure
data-driven manner.

Physical contact provides additional constraints for hand-object state
reconstruction as well as a basis for further understanding of interaction
affordances. Estimating these severely occluded regions from monocular images
presents a considerable challenge. Existing methods optimize the hand-object
contact driven by distance threshold or prior from contact-labeled datasets.
However, due to the number of subjects and objects involved in these indoor
datasets being limited, the learned contact patterns could not be generalized
easily. Our key idea is to reconstruct the contact pattern directly from
monocular images, and then utilize the physical stability criterion in the
simulation to optimize it. This criterion is defined by the resultant forces
and contact distribution computed by the physics engine.Compared to existing
solutions, our framework can be adapted to more personalized hands and diverse
object shapes. Furthermore, an interaction dataset with extra physical
attributes is created to verify the sim-to-real consistency of our methods.
Through comprehensive evaluations, hand-object contact can be reconstructed
with both accuracy and stability by the proposed framework.

Aggregating neighbor features is essential for point cloud classification. In
the existing work, each point in the cloud may inevitably be selected as the
neighbors of multiple aggregation centers, as all centers will gather neighbor
features from the whole point cloud independently. Thus each point has to
participate in the calculation repeatedly and generates redundant duplicates in
the memory, leading to intensive computation costs and memory consumption.
Meanwhile, to pursue higher accuracy, previous methods often rely on a complex
local aggregator to extract fine geometric representation, which further slows
down the classification pipeline. To address these issues, we propose a new
local aggregator of linear complexity for point cloud classification, coined as
APP. Specifically, we introduce an auxiliary container as an anchor to exchange
features between the source point and the aggregating center. Each source point
pushes its feature to only one auxiliary container, and each center point pulls
features from only one auxiliary container. This avoids the re-computation
issue of each source point. To facilitate the learning of the local structure
of cloud point, we use an online normal estimation module to provide the
explainable geometric information to enhance our APP modeling capability. Our
built network is more efficient than all the previous baselines with a clear
margin while still consuming a lower memory. Experiments on both synthetic and
real datasets demonstrate that APP-Net reaches comparable accuracies to other
networks. It can process more than 10,000 samples per second with less than
10GB of memory on a single GPU. We will release the code in
https://github.com/MCG-NJU/APP-Net.

The goal of this paper is to debunk and dispel the magic behind black-box
optimizers and stochastic optimizers. It aims to build a solid foundation on
how and why the techniques work. This manuscript crystallizes this knowledge by
deriving from simple intuitions, the mathematics behind the strategies. This
tutorial doesn't shy away from addressing both the formal and informal aspects
of gradient descent and stochastic optimization methods. By doing so, it hopes
to provide readers with a deeper understanding of these techniques as well as
the when, the how and the why of applying these algorithms.
  Gradient descent is one of the most popular algorithms to perform
optimization and by far the most common way to optimize machine learning tasks.
Its stochastic version receives attention in recent years, and this is
particularly true for optimizing deep neural networks. In deep neural networks,
the gradient followed by a single sample or a batch of samples is employed to
save computational resources and escape from saddle points. In 1951, Robbins
and Monro published \textit{A stochastic approximation method}, one of the
first modern treatments on stochastic optimization that estimates local
gradients with a new batch of samples. And now, stochastic optimization has
become a core technology in machine learning, largely due to the development of
the back propagation algorithm in fitting a neural network. The sole aim of
this article is to give a self-contained introduction to concepts and
mathematical tools in gradient descent and stochastic optimization.

This paper reviews exploration techniques in deep reinforcement learning.
Exploration techniques are of primary importance when solving sparse reward
problems. In sparse reward problems, the reward is rare, which means that the
agent will not find the reward often by acting randomly. In such a scenario, it
is challenging for reinforcement learning to learn rewards and actions
association. Thus more sophisticated exploration methods need to be devised.
This review provides a comprehensive overview of existing exploration
approaches, which are categorized based on the key contributions as follows
reward novel states, reward diverse behaviours, goal-based methods,
probabilistic methods, imitation-based methods, safe exploration and
random-based methods. Then, the unsolved challenges are discussed to provide
valuable future research directions. Finally, the approaches of different
categories are compared in terms of complexity, computational effort and
overall performance.

Recent adversarial attack developments have made reinforcement learning more
vulnerable, and different approaches exist to deploy attacks against it, where
the key is how to choose the right timing of the attack. Some work tries to
design an attack evaluation function to select critical points that will be
attacked if the value is greater than a certain threshold. This approach makes
it difficult to find the right place to deploy an attack without considering
the long-term impact. In addition, there is a lack of appropriate indicators of
assessment during attacks. To make the attacks more intelligent as well as to
remedy the existing problems, we propose the reinforcement learning-based
attacking framework by considering the effectiveness and stealthy
spontaneously, while we also propose a new metric to evaluate the performance
of the attack model in these two aspects. Experimental results show the
effectiveness of our proposed model and the goodness of our proposed evaluation
metric. Furthermore, we validate the transferability of the model, and also its
robustness under the adversarial training.

Extracting biographical information from online documents is a popular
research topic among the information extraction (IE) community. Various natural
language processing (NLP) techniques such as text classification, text
summarisation and relation extraction are commonly used to achieve this. Among
these techniques, RE is the most common since it can be directly used to build
biographical knowledge graphs. RE is usually framed as a supervised machine
learning (ML) problem, where ML models are trained on annotated datasets.
However, there are few annotated datasets for RE since the annotation process
can be costly and time-consuming. To address this, we developed Biographical,
the first semi-supervised dataset for RE. The dataset, which is aimed towards
digital humanities (DH) and historical research, is automatically compiled by
aligning sentences from Wikipedia articles with matching structured data from
sources including Pantheon and Wikidata. By exploiting the structure of
Wikipedia articles and robust named entity recognition (NER), we match
information with relatively high precision in order to compile annotated
relation pairs for ten different relations that are important in the DH domain.
Furthermore, we demonstrate the effectiveness of the dataset by training a
state-of-the-art neural model to classify relation pairs, and evaluate it on a
manually annotated gold standard set. Biographical is primarily aimed at
training neural models for RE within the domain of digital humanities and
history, but as we discuss at the end of this paper, it can be useful for other
purposes as well.

Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly
challenging problem as traditional subgraph matching methods are not capable to
deal with noise and missing information. To address this problem, it has been
recently introduced a promising approach based on jointly embedding logical
queries and KGs into a low-dimensional space to identify answer entities.
However, existing proposals ignore critical semantic knowledge inherently
available in KGs, such as type information. To leverage type information, we
propose a novel TypE-aware Message Passing (TEMP) model, which enhances the
entity and relation representations in queries, and simultaneously improves
generalization, deductive and inductive reasoning. Remarkably, TEMP is a
plug-and-play model that can be easily incorporated into existing
embedding-based models to improve their performance. Extensive experiments on
three real-world datasets demonstrate TEMP's effectiveness.

Hardware acceleration for dilated and transposed convolution enables real
time execution of related tasks like segmentation, but current designs are
specific for these convolutional types or suffer from complex control for
reconfigurable designs. This paper presents a design that decomposes input or
weight for dilated and transposed convolutions respectively to skip redundant
computations and thus executes efficiently on existing dense CNN hardware as
well. The proposed architecture can cut down 87.8\% of the cycle counts to
achieve 8.2X speedup over a naive execution for the ENet case.

Gait phase detection with convolution neural network provides accurate
classification but demands high computational cost, which inhibits real time
low power on-sensor processing. This paper presents a segmentation based gait
phase detection with a width and depth downscaled U-Net like model that only
needs 0.5KB model size and 67K operations per second with 95.9% accuracy to be
easily fitted into resource limited on sensor microcontroller.

With the popularity of the deep neural network (DNN), hardware accelerators
are demanded for real time execution. However, lengthy design process and fast
evolving DNN models make hardware evaluation hard to meet the time to market
need. This paper proposes a pre-RTL DNN hardware evaluator that supports
conventional layer-by-layer processing as well as the fused layer processing
for low external bandwidth requirement. The evaluator supports two
state-of-the-art accelerator architectures and finds the best hardware and
layer fusion group The experimental results show the layer fusion scheme can
achieve 55.6% memory bandwidth reduction, 36.7% latency improvement and 49.2%
energy reduction compared with layer-by-layer operation.

Memory bandwidth has become the real-time bottleneck of current deep learning
accelerators (DLA), particularly for high definition (HD) object detection.
Under resource constraints, this paper proposes a low memory traffic DLA chip
with joint hardware and software optimization. To maximize hardware utilization
under memory bandwidth, we morph and fuse the object detection model into a
group fusion-ready model to reduce intermediate data access. This reduces the
YOLOv2's feature memory traffic from 2.9 GB/s to 0.15 GB/s. To support group
fusion, our previous DLA based hardware employes a unified buffer with
write-masking for simple layer-by-layer processing in a fusion group. When
compared to our previous DLA with the same PE numbers, the chip implemented in
a TSMC 40nm process supports 1280x720@30FPS object detection and consumes 7.9X
less external DRAM access energy, from 2607 mJ to 327.6 mJ.

Computing-in-memory (CIM) has attracted significant attentions in recent
years due to its massive parallelism and low power consumption. However,
current CIM designs suffer from large area overhead of small CIM macros and bad
programmablity for model execution. This paper proposes a programmable CIM
processor with a single large sized CIM macro instead of multiple smaller ones
for power efficient computation and a flexible instruction set to support
various binary 1-D convolution Neural Network (CNN) models in an easy way.
Furthermore, the proposed architecture adopts the pooling write-back method to
support fused or independent convolution/pooling operations to reduce 35.9\% of
latency, and the flexible ping-pong feature SRAM to fit different feature map
sizes during layer-by-layer execution.The design fabricated in TSMC 28nm
technology achieves 150.8 GOPS throughput and 885.86 TOPS/W power efficiency at
10 MHz when executing our binary keyword spotting model, which has higher power
efficiency and flexibility than previous designs.

Semantic outdoor scene understanding based on 3D LiDAR point clouds is a
challenging task for autonomous driving due to the sparse and irregular data
structure. This paper takes advantages of the uneven range distribution of
different LiDAR laser beams to propose a range aware instance segmentation
network, RangeSeg. RangeSeg uses a shared encoder backbone with two range
dependent decoders. A heavy decoder only computes top of a range image where
the far and small objects locate to improve small object detection accuracy,
and a light decoder computes whole range image for low computational cost. The
results are further clustered by the DBSCAN method with a resolution weighted
distance function to get instance-level segmentation results. Experiments on
the KITTI dataset show that RangeSeg outperforms the state-of-the-art semantic
segmentation methods with enormous speedup and improves the instance-level
segmentation performance on small and far objects. The whole RangeSeg pipeline
meets the real time requirement on NVIDIA\textsuperscript{\textregistered}
JETSON AGX Xavier with 19 frames per second in average.

The large amount of memory bandwidth between local buffer and external DRAM
has become the speedup bottleneck of CNN hardware accelerators, especially for
activation maps. To reduce memory bandwidth, we propose to learn pruning
unimportant blocks dynamically with zero block regularization of activation
maps (Zebra). This strategy has low computational overhead and could easily
integrate with other pruning methods for better performance. The experimental
results show that the proposed method can reduce 70\% of memory bandwidth for
Resnet-18 on Tiny-Imagenet within 1\% accuracy drops and 2\% accuracy gain with
the combination of Network Slimming.

Spiking neural networks (SNNs), which are inspired by the human brain, have
recently gained popularity due to their relatively simple and low-power
hardware for transmitting binary spikes and highly sparse activation maps.
However, because SNNs contain extra time dimension information, the SNN
accelerator will require more buffers and take longer to infer, especially for
the more difficult high-resolution object detection task. As a result, this
paper proposes a sparse compressed spiking neural network accelerator that
takes advantage of the high sparsity of activation maps and weights by
utilizing the proposed gated one-to-all product for low power and highly
parallel model execution. The experimental result of the neural network shows
71.5$\%$ mAP with mixed (1,3) time steps on the IVS 3cls dataset. The
accelerator with the TSMC 28nm CMOS process can achieve 1024$\times$576@29
frames per second processing when running at 500MHz with 35.88TOPS/W energy
efficiency and 1.05mJ energy consumption per frame.

Increasingly, convolution neural network (CNN) based super resolution models
have been proposed for better reconstruction results, but their large model
size and complicated structure inhibit their real-time hardware implementation.
Current hardware designs are limited to a plain network and suffer from lower
quality and high memory bandwidth requirements. This paper proposes a super
resolution hardware accelerator with hardware efficient pixel attention that
just needs 25.9K parameters and simple structure but achieves 0.38dB better
reconstruction images than the widely used FSRCNN. The accelerator adopts full
model block wise convolution for full model layer fusion to reduce external
memory access to model input and output only. In addition, CNN and pixel
attention are well supported by PE arrays with distributed weights. The final
implementation can support full HD image reconstruction at 30 frames per second
with TSMC 40nm CMOS process.

We propose a Large Neighborhood Search (LNS) approach utilizing a learned
construction heuristic based on neural networks as repair operator to solve the
vehicle routing problem with time windows (VRPTW). Our method uses graph neural
networks to encode the problem and auto-regressively decodes a solution and is
trained with reinforcement learning on the construction task without requiring
any labels for supervision. The neural repair operator is combined with a local
search routine, heuristic destruction operators and a selection procedure
applied to a small population to arrive at a sophisticated solution approach.
The key idea is to use the learned model to re-construct the partially
destructed solution and to introduce randomness via the destruction heuristics
(or the stochastic policy itself) to effectively explore a large neighborhood.

For image forensics, convolutional neural networks (CNNs) tend to learn
content features rather than subtle manipulation traces, which limits forensic
performance. Existing methods predominantly solve the above challenges by
following a general pipeline, that is, subtracting the original pixel value
from the predicted pixel value to make CNNs pay attention to the manipulation
traces. However, due to the complicated learning mechanism, these methods may
bring some unnecessary performance losses. In this work, we rethink the
advantages of gradient operator in exposing face forgery, and design two
plug-and-play modules by combining gradient operator with CNNs, namely tensor
pre-processing (TP) and manipulation trace attention (MTA) module.
Specifically, TP module refines the feature tensor of each channel in the
network by gradient operator to highlight the manipulation traces and improve
the feature representation. Moreover, MTA module considers two dimensions,
namely channel and manipulation traces, to force the network to learn the
distribution of manipulation traces. These two modules can be seamlessly
integrated into CNNs for end-to-end training. Experiments show that the
proposed network achieves better results than prior works on five public
datasets. Especially, TP module greatly improves the accuracy by at least 4.60%
compared with the existing pre-processing module only via simple tensor
refinement. The code is available at:
https://github.com/EricGzq/GocNet-pytorch.

Bayesian optimisation (BO) has been widely used to solve problems with
expensive function evaluations. In multi-objective optimisation problems, BO
aims to find a set of approximated Pareto optimal solutions. There are
typically two ways to build surrogates in multi-objective BO: One surrogate by
aggregating objective functions (by using a scalarising function, also called
mono-surrogate approach) and multiple surrogates (for each objective function,
also called multi-surrogate approach). In both approaches, an acquisition
function (AF) is used to guide the search process. Mono-surrogate has the
advantage that only one model is used, however, the approach has two major
limitations. Firstly, the fitness landscape of the scalarising function and the
objective functions may not be similar. Secondly, the approach assumes that the
scalarising function distribution is Gaussian, and thus a closed-form
expression of the AF can be used. In this work, we overcome these limitations
by building a surrogate model for each objective function and show that the
scalarising function distribution is not Gaussian. We approximate the
distribution using Generalised extreme value distribution. The results and
comparison with existing approaches on standard benchmark and real-world
optimisation problems show the potential of the multi-surrogate approach.

In social robotics, endowing humanoid robots with the ability to generate
bodily expressions of affect can improve human-robot interaction and
collaboration, since humans attribute, and perhaps subconsciously anticipate,
such traces to perceive an agent as engaging, trustworthy, and socially
present. Robotic emotional body language needs to be believable, nuanced and
relevant to the context. We implemented a deep learning data-driven framework
that learns from a few hand-designed robotic bodily expressions and can
generate numerous new ones of similar believability and lifelikeness. The
framework uses the Conditional Variational Autoencoder model and a sampling
approach based on the geometric properties of the model's latent space to
condition the generative process on targeted levels of valence and arousal. The
evaluation study found that the anthropomorphism and animacy of the generated
expressions are not perceived differently from the hand-designed ones, and the
emotional conditioning was adequately differentiable between most levels except
the pairs of neutral-positive valence and low-medium arousal. Furthermore, an
exploratory analysis of the results reveals a possible impact of the
conditioning on the perceived dominance of the robot, as well as on the
participants' attention.

A central goal in the cognitive sciences is the development of numerical
models for mental representations of object concepts. This paper introduces
Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian
method for embedding object concepts in a vector space using data collected
from humans in a triplet odd-one-out task. VICE uses variational inference to
obtain sparse, non-negative representations of object concepts with uncertainty
estimates for the embedding values. These estimates are used to automatically
select the dimensions that best explain the data. We derive a PAC learning
bound for VICE that can be used to estimate generalization performance or
determine a sufficient sample size for experimental design. VICE rivals or
outperforms its predecessor, SPoSE, at predicting human behavior in the triplet
odd-one-out task. Furthermore, VICE's object representations are more
reproducible and consistent across random initializations, highlighting the
unique advantage of using VICE for deriving interpretable embeddings from human
behavior.

Clouds play a key role in regulating climate change but are difficult to
simulate within Earth system models (ESMs). Improving the representation of
clouds is one of the key tasks towards more robust climate change projections.
This study introduces a new machine-learning based framework relying on
satellite observations to improve understanding of the representation of clouds
and their relevant processes in climate models. The proposed method is capable
of assigning distributions of established cloud types to coarse data. It
facilitates a more objective evaluation of clouds in ESMs and improves the
consistency of cloud process analysis. The method is built on satellite data
from the MODIS instrument labelled by deep neural networks with cloud types
defined by the World Meteorological Organization (WMO), using cloud type labels
from CloudSat as ground truth. The method is applicable to datasets with
information about physical cloud variables comparable to MODIS satellite data
and at sufficiently high temporal resolution. We apply the method to
alternative satellite data from the Cloud\_cci project (ESA Climate Change
Initiative), coarse-grained to typical resolutions of climate models. The
resulting cloud type distributions are physically consistent and the horizontal
resolutions typical of ESMs are sufficient to apply our method. We recommend
outputting crucial variables required by our method for future ESM data
evaluation. This will enable the use of labelled satellite data for a more
systematic evaluation of clouds in climate models.

In this paper, we investigate an online prediction strategy named as
Discounted-Normal-Predictor (Kapralov and Panigrahy, 2010) for smoothed online
convex optimization (SOCO), in which the learner needs to minimize not only the
hitting cost but also the switching cost. In the setting of learning with
expert advice, Daniely and Mansour (2019) demonstrate that
Discounted-Normal-Predictor can be utilized to yield nearly optimal regret
bounds over any interval, even in the presence of switching costs. Inspired by
their results, we develop a simple algorithm for SOCO: Combining online
gradient descent (OGD) with different step sizes sequentially by
Discounted-Normal-Predictor. Despite its simplicity, we prove that it is able
to minimize the adaptive regret with switching cost, i.e., attaining nearly
optimal regret with switching cost on every interval. By exploiting the
theoretical guarantee of OGD for dynamic regret, we further show that the
proposed algorithm can minimize the dynamic regret with switching cost in every
interval.

The performance of federated learning in neural networks is generally
influenced by the heterogeneity of the data distribution. For a well-performing
global model, taking a weighted average of the local models, as done by most
existing federated learning algorithms, may not guarantee consistency with
local models in the space of neural network maps. In this paper, we propose a
novel framework of federated learning equipped with the process of
decentralized knowledge distillation (FedDKD) (i.e., without data on the
server). The FedDKD introduces a module of decentralized knowledge distillation
(DKD) to distill the knowledge of the local models to train the global model by
approaching the neural network map average based on the metric of divergence
defined in the loss function, other than only averaging parameters as done in
literature. Numeric experiments on various heterogeneous datasets reveal that
FedDKD outperforms the state-of-the-art methods with more efficient
communication and training in a few DKD steps, especially on some extremely

State-of-the-art lidar-based 3D object detection methods rely on supervised
learning and large labeled datasets. However, annotating lidar data is
resource-consuming, and depending only on supervised learning limits the
applicability of trained models. Self-supervised training strategies can
alleviate these issues by learning a general point cloud backbone model for
downstream 3D vision tasks. Against this backdrop, we show the relationship
between self-supervised multi-frame flow representations and single-frame 3D
detection hypotheses. Our main contribution leverages learned flow and motion
representations and combines a self-supervised backbone with a supervised 3D
detection head. First, a self-supervised scene flow estimation model is trained
with cycle consistency. Then, the point cloud encoder of this model is used as
the backbone of a single-frame 3D object detection head model. This second 3D
object detection model learns to utilize motion representations to distinguish
dynamic objects exhibiting different movement patterns. Experiments on KITTI
and nuScenes benchmarks show that the proposed self-supervised pre-training
increases 3D detection performance significantly.
https://github.com/emecercelik/ssl-3d-detection.git

The softmax layer in neural machine translation is designed to model the
distribution over mutually exclusive tokens. Machine translation, however, is
intrinsically uncertain: the same source sentence can have multiple
semantically equivalent translations. Therefore, we propose to replace the
softmax activation with a multi-label classification layer that can model
ambiguity more effectively. We call our loss function Single-label Contrastive
Objective for Non-Exclusive Sequences (SCONES). We show that the multi-label
output layer can still be trained on single reference training data using the
SCONES loss function. SCONES yields consistent BLEU score gains across six
translation directions, particularly for medium-resource language pairs and
small beam sizes. By using smaller beam sizes we can speed up inference by a
factor of 3.9x and still match or improve the BLEU score obtained using
softmax. Furthermore, we demonstrate that SCONES can be used to train NMT
models that assign the highest probability to adequate translations, thus
mitigating the "beam search curse". Additional experiments on synthetic
language pairs with varying levels of uncertainty suggest that the improvements
from SCONES can be attributed to better handling of ambiguity.

Gravitational lensing is the relativistic effect generated by massive bodies,
which bend the space-time surrounding them. It is a deeply investigated topic
in astrophysics and allows validating theoretical relativistic results and
studying faint astrophysical objects that would not be visible otherwise. In
recent years Machine Learning methods have been applied to support the analysis
of the gravitational lensing phenomena by detecting lensing effects in data
sets consisting of images associated with brightness variation time series.
However, the state-of-art approaches either consider only images and neglect
time-series data or achieve relatively low accuracy on the most difficult data
sets. This paper introduces DeepGraviLens, a novel multi-modal network that
classifies spatio-temporal data belonging to one non-lensed system type and
three lensed system types. It surpasses the current state of the art accuracy
results by $\approx$ 19% to $\approx$ 43%, depending on the considered data
set. Such an improvement will enable the acceleration of the analysis of lensed
objects in upcoming astrophysical surveys, which will exploit the petabytes of
data collected, e.g., from the Vera C. Rubin Observatory.

Nosie is an important cause of low quality Optical coherence tomography (OCT)
image. The neural network model based on Convolutional neural networks(CNNs)
has demonstrated its excellent performance in image denoising. However, OCT
image denoising still faces great challenges because many previous neural
network algorithms required a large number of labeled data, which might cost
much time or is expensive. Besides, these CNN-based algorithms need numerous
parameters and good tuning techniques, which is hardware resources consuming.
To solved above problems, We proposed a new Cycle-Consistent Generative
Adversarial Nets called Dual-Merged Cycle-WGAN for retinal OCT image
denoiseing, which has remarkable performance with less unlabeled traning data.
Our model consists of two Cycle-GAN networks with imporved generator,
descriminator and wasserstein loss to achieve good training stability and
better performance. Using image merge technique between two Cycle-GAN networks,
our model could obtain more detailed information and hence better training
effect. The effectiveness and generality of our proposed network has been
proved via ablation experiments and comparative experiments. Compared with
other state-of-the-art methods, our unsupervised method obtains best subjective
visual effect and higher evaluation objective indicators.

We consider stability analysis of constrained switching linear systems in
which the dynamics is unknown and whose switching signal is constrained by an
automaton. We propose a data-driven Lyapunov framework for providing
probabilistic stability guarantees based on data harvested from observations of
the system. By generalizing previous results on arbitrary switching linear
systems, we show that, by sampling a finite number of observations, we are able
to construct an approximate Lyapunov function for the underlying system.
Moreover, we show that the entropy of the language accepted by the automaton
allows to bound the number of samples needed in order to reach some
pre-specified accuracy.

Video content is present in an ever-increasing number of fields, both
scientific and commercial. Sports, particularly soccer, is one of the
industries that has invested the most in the field of video analytics, due to
the massive popularity of the game and the emergence of new markets. Previous
state-of-the-art methods on soccer matches video summarization rely on
handcrafted heuristics to generate summaries which are poorly generalizable,
but these works have yet proven that multiple modalities help detect the best
actions of the game. On the other hand, machine learning models with higher
generalization potential have entered the field of summarization of
general-purpose videos, offering several deep learning approaches. However,
most of them exploit content specificities that are not appropriate for sport
whole-match videos. Although video content has been for many years the main
source for automatizing knowledge extraction in soccer, the data that records
all the events happening on the field has become lately very important in
sports analytics, since this event data provides richer context information and
requires less processing. We propose a method to generate the summary of a
soccer match exploiting both the audio and the event metadata. The results show
that our method can detect the actions of the match, identify which of these
actions should belong to the summary and then propose multiple candidate
summaries which are similar enough but with relevant variability to provide
different options to the final editor. Furthermore, we show the generalization
capability of our work since it can transfer knowledge between datasets from
different broadcasting companies, different competitions, acquired in different
conditions, and corresponding to summaries of different lengths

Spoken language understanding (SLU) is an essential task for machines to
understand human speech for better interactions. However, errors from the
automatic speech recognizer (ASR) usually hurt the understanding performance.
In reality, ASR systems may not be easy to adjust for the target scenarios.
Therefore, this paper focuses on learning utterance representations that are
robust to ASR errors using a contrastive objective, and further strengthens the
generalization ability by combining supervised contrastive learning and
self-distillation in model fine-tuning. Experiments on three benchmark datasets
demonstrate the effectiveness of our proposed approach.

Noisy labels are inevitable yet problematic in machine learning society. It
ruins the generalization of a classifier by making the classifier over-fitted
to noisy labels. Existing methods on noisy label have focused on modifying the
classifier during the training procedure. It has two potential problems. First,
these methods are not applicable to a pre-trained classifier without further
access to training. Second, it is not easy to train a classifier and regularize
all negative effects from noisy labels, simultaneously. We suggest a new branch
of method, Noisy Prediction Calibration (NPC) in learning with noisy labels.
Through the introduction and estimation of a new type of transition matrix via
generative model, NPC corrects the noisy prediction from the pre-trained
classifier to the true label as a post-processing scheme. We prove that NPC
theoretically aligns with the transition matrix based methods. Yet, NPC
empirically provides more accurate pathway to estimate true label, even without
involvement in classifier learning. Also, NPC is applicable to any classifier
trained with noisy label methods, if training instances and its predictions are
available. Our method, NPC, boosts the classification performances of all
baseline models on both synthetic and real-world datasets. The implemented code
is available at https://github.com/BaeHeeSun/NPC.

Filament identification became a key step to tackling fundamental problems in
various fields of Astronomy. Nevertheless, existing filament identification
algorithms are critically user-dependent and require individual
parametrization. In this study, we aimed at adapting the neural networks
approach to elaborate the best model for filament identification that would not
require fine-tuning for a given astronomical map. First, we created training
samples based on the most commonly used maps of the interstellar medium
obtained by Planck and Herschel space telescopes and the atomic hydrogen
all-sky survey HI4PI. We used the Rolling Hough Transform, a widely used
algorithm for filament identification, to produce training outputs. In the next
step, we trained different neural network models and discovered that a
combination of the Mask R-CNN and U-Net architecture is most appropriate for
filament identification and determination of their orientation angles. We
showed that neural network training might be performed efficiently on a
relatively small training sample of only around 100 maps. Our approach
eliminates the parametrization bias and facilitates filament identification and
angle determination on large data sets.

For deep learning, size is power. Massive neural nets trained on broad data
for a spectrum of tasks are at the forefront of artificial intelligence. These
foundation models or 'Jacks of All Trades' (JATs), when fine-tuned for
downstream tasks, are gaining importance in driving deep learning advancements.
However, environments with tight resource constraints, changing objectives and
intentions, or varied task requirements, could limit the real-world utility of
a singular JAT. Hence, in tandem with current trends towards building
increasingly large JATs, this paper conducts an initial exploration into
concepts underlying the creation of a diverse set of compact machine learning
model sets. Composed of many smaller and specialized models, we formulate the
Set of Sets to simultaneously fulfil many task settings and environmental
conditions. A means to arrive at such a set tractably in one pass of a
neuroevolutionary multitasking algorithm is presented for the first time,
bringing us closer to models that are collectively 'Masters of All Trades'.

Artificial intelligence and semantic technologies are evolving and have been
applied in various research areas, including the education domain. Higher
Education institutions strive to improve students' academic performance. Early
intervention to at-risk students and a reasonable curriculum is vital for
students' success. Prior research opted for deploying traditional machine
learning models to predict students' performance. In terms of curriculum
semantic analysis, after conducting a comprehensive systematic review regarding
the use of semantic technologies in the Computer Science curriculum, a major
finding of the study is that technologies used to measure similarity have
limitations in terms of accuracy and ambiguity in the representation of
concepts, courses, etc. To fill these gaps, in this study, three
implementations were developed, that is, to predict students' performance using
marks from the previous semester, to model a course representation in a
semantic way and compute the similarity, and to identify the prerequisite
between two similar courses. Regarding performance prediction, we used the
combination of Genetic Algorithm and Long-Short Term Memory (LSTM) on a dataset
from a Brazilian university containing 248730 records. As for similarity
measurement, we deployed BERT to encode the sentences and used cosine
similarity to obtain the distance between courses. With respect to prerequisite
identification, TextRazor was applied to extract concepts from course
description, followed by employing SemRefD to measure the degree of
prerequisite between two concepts. The outcomes of this study can be summarized
as: (i) a breakthrough result improves Manrique's work by 2.5% in terms of
accuracy in dropout prediction; (ii) uncover the similarity between courses
based on course description; (iii) identify the prerequisite over three
compulsory courses of School of Computing at ANU.

Background: Most of the existing machine learning models for security tasks,
such as spam detection, malware detection, or network intrusion detection, are
built on supervised machine learning algorithms. In such a paradigm, models
need a large amount of labeled data to learn the useful relationships between
selected features and the target class. However, such labeled data can be
scarce and expensive to acquire. Goal: To help security practitioners train
useful security classification models when few labeled training data and many
unlabeled training data are available. Method: We propose an adaptive framework
called Dapper, which optimizes 1) semi-supervised learning algorithms to assign
pseudo-labels to unlabeled data in a propagation paradigm and 2) the machine
learning classifier (i.e., random forest). When the dataset class is highly
imbalanced, Dapper then adaptively integrates and optimizes a data oversampling
method called SMOTE. We use the novel Bayesian Optimization to search a large
hyperparameter space of these tuning targets. Result: We evaluate Dapper with
three security datasets, i.e., the Twitter spam dataset, the malware URLs
dataset, and the CIC-IDS-2017 dataset. Experimental results indicate that we
can use as low as 10% of original labeled data but achieve close or even better
classification performance than using 100% labeled data in a supervised way.
Conclusion: Based on those results, we would recommend using hyperparameter
optimization with semi-supervised learning when dealing with shortages of
labeled security data.

Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important
technique to handle the typically very large test datasets efficiently, saving
computation and labeling costs. This is particularly true for large-scale,
deployed systems, where inputs observed in production are recorded to serve as
potential test or training data for the next versions of the system. Feng et.
al. propose DeepGini, a very fast and simple TIP, and show that it outperforms
more elaborate techniques such as neuron- and surprise coverage. In a
large-scale study (4 case studies, 8 test datasets, 32'200 trained models) we
verify their findings. However, we also find that other comparable or even
simpler baselines from the field of uncertainty quantification, such as the
predicted softmax likelihood or the entropy of the predicted softmax
likelihoods perform equally well as DeepGini.

Fashion recommendation has witnessed a phenomenal growth of research,
particularly in the domains of shop-the-look, contextaware outfit creation,
personalizing outfit creation etc. Majority of the work in this area focuses on
better understanding of the notion of complimentary relationship between
lifestyle items. Quite recently, some works have realised that style plays a
vital role in fashion, especially in the understanding of compatibility
learning and outfit creation. In this paper, we would like to present the
end-to-end design of a methodology in which we aim to generate outfits guided
by styles or themes using a novel style encoder network. We present an
extensive analysis of different aspects of our method through various
experiments. We also provide a demonstration api to showcase the ability of our
work in generating outfits based on an anchor item and styles.

In this paper, we consider the problem of making distributionally robust,
skeptical inferences for the multi-label problem, or more generally for Boolean
vectors. By distributionally robust, we mean that we consider a set of possible
probability distributions, and by skeptical we understand that we consider as
valid only those inferences that are true for every distribution within this
set. Such inferences will provide partial predictions whenever the considered
set is sufficiently big. We study in particular the Hamming loss case, a common
loss function in multi-label problems, showing how skeptical inferences can be
made in this setting. Our experimental results are organised in three sections;
(1) the first one indicates the gain computational obtained from our
theoretical results by using synthetical data sets, (2) the second one
indicates that our approaches produce relevant cautiousness on those
hard-to-predict instances where its precise counterpart fails, and (3) the last
one demonstrates experimentally how our approach copes with imperfect
information (generated by a downsampling procedure) better than the partial
abstention [31] and the rejection rules.

Recently, contrastive learning has been shown to be effective in improving
pre-trained language models (PLM) to derive high-quality sentence
representations. It aims to pull close positive examples to enhance the
alignment while push apart irrelevant negatives for the uniformity of the whole
representation space. However, previous works mostly adopt in-batch negatives
or sample from training data at random. Such a way may cause the sampling bias
that improper negatives (e.g. false negatives and anisotropy representations)
are used to learn sentence representations, which will hurt the uniformity of
the representation space. To address it, we present a new framework
\textbf{DCLR} (\underline{D}ebiased \underline{C}ontrastive
\underline{L}earning of unsupervised sentence \underline{R}epresentations) to
alleviate the influence of these improper negatives. In DCLR, we design an
instance weighting method to punish false negatives and generate noise-based
negatives to guarantee the uniformity of the representation space. Experiments
on seven semantic textual similarity tasks show that our approach is more
effective than competitive baselines. Our code and data are publicly available
at the link: \textcolor{blue}{\url{https://github.com/RUCAIBox/DCLR}}.

In this work we develop a model of predictive learning on neuromorphic
hardware. Our model uses the on-chip plasticity capabilities of the Loihi chip
to remember observed sequences of events and use this memory to generate
predictions of future events in real time. Given the locality constraints of
on-chip plasticity rules, generating predictions without interfering with the
ongoing learning process is nontrivial. We address this challenge with a memory
consolidation approach inspired by hippocampal replay. Sequence memory is
stored in an initial memory module using spike-timing dependent plasticity.
Later, during an offline period, memories are consolidated into a distinct
prediction module. This second module is then able to represent predicted
future events without interfering with the activity, and plasticity, in the
first module, enabling online comparison between predictions and ground-truth
observations. Our model serves as a proof-of-concept that online predictive
learning models can be deployed on neuromorphic hardware with on-chip
plasticity.

The stochastic block model (SBM) is one of the most widely used generative
models for network data. Many continuous-time dynamic network models are built
upon the same assumption as the SBM: edges or events between all pairs of nodes
are conditionally independent given the block or community memberships, which
prevents them from reproducing higher-order motifs such as triangles that are
commonly observed in real networks. We propose the multivariate community
Hawkes (MULCH) model, an extremely flexible community-based model for
continuous-time networks that introduces dependence between node pairs using
structured multivariate Hawkes processes. We fit the model using a spectral
clustering and likelihood-based local refinement procedure. We find that our
proposed MULCH model is far more accurate than existing models both for
predictive and generative tasks.

Deep Neural Network (DNN) are vulnerable to adversarial attacks. As a
countermeasure, adversarial training aims to achieve robustness based on the
min-max optimization problem and it has shown to be one of the most effective
defense strategies. However, in this work, we found that compared with natural
training, adversarial training fails to learn better feature representations
for either clean or adversarial samples, which can be one reason why
adversarial training tends to have severe overfitting issues and less satisfied
generalize performance. Specifically, we observe two major shortcomings of the
features learned by existing adversarial training methods:(1) low intra-class
feature similarity; and (2) conservative inter-classes feature variance. To
overcome these shortcomings, we introduce a new concept of adversarial training
graph (ATG) with which the proposed adversarial training with feature
separability (ATFS) enables to coherently boost the intra-class feature
similarity and increase inter-class feature variance. Through comprehensive
experiments, we demonstrate that the proposed ATFS framework significantly
improves both clean and robust performance.

The activations of Facial Action Units (AUs) mutually influence one another.
While the relationship between a pair of AUs can be complex and unique,
existing approaches fail to specifically and explicitly represent such cues for
each pair of AUs in each facial display. This paper proposes an AU relationship
modelling approach that deep learns a unique graph to explicitly describe the
relationship between each pair of AUs of the target facial display. Our
approach first encodes each AU's activation status and its association with
other AUs into a node feature. Then, it learns a pair of multi-dimensional edge
features to describe multiple task-specific relationship cues between each pair
of AUs. During both node and edge feature learning, our approach also considers
the influence of the unique facial display on AUs' relationship by taking the
full face representation as an input. Experimental results on BP4D and DISFA
datasets show that both node and edge feature learning modules provide large
performance improvements for CNN and transformer-based backbones, with our best
systems achieving the state-of-the-art AU recognition results. Our approach not
only has a strong capability in modelling relationship cues for AU recognition
but also can be easily incorporated into various backbones. Our PyTorch code is
made available.

This document presents the draft specification for delivering machine
learning services over HTTP, developed as part of the Protocols and Structures
for Inference project, which concluded in 2013. It presents the motivation for
providing machine learning as a service, followed by a description of the
essential and optional components of such a service.

White matter fiber clustering (WMFC) is an important strategy for white
matter parcellation, which enables quantitative analysis of white matter
connections in health and disease. WMFC is usually performed in an unsupervised
manner without the need of labeled ground truth data. While widely used WMFC
approaches have shown good performance using classical machine learning
techniques, recent advances in deep learning reveal a promising direction
towards fast and effective WMFC. In this work, we propose a novel deep learning
framework for WMFC, Deep Fiber Clustering (DFC), which solves the unsupervised
clustering problem as a self-supervised learning task with a domain-specific
pretext task to predict pairwise fiber distances. This enables the fiber
representation learning to handle a known challenge in WMFC, i.e., the
sensitivity of clustering results to the point ordering along fibers. We design
a novel network architecture that represents input fibers as point clouds and
allows the incorporation of additional sources of input information from gray
matter parcellation. Thus DFC makes use of combined information about white
matter fiber geometry and gray matter anatomy to improve anatomical coherence
of fiber clusters. In addition, DFC conducts outlier removal in a natural way
by rejecting fibers with low cluster assignment probability. We evaluate DFC on
three independently acquired cohorts, including data from 220 individuals
across genders, ages (young and elderly adults), and different health
conditions (healthy control and multiple neuropsychiatric disorders). We
compare DFC to several state-of-the-art WMFC algorithms. Experimental results
demonstrate superior performance of DFC in terms of cluster compactness,
generalization ability, anatomical coherence, and computational efficiency.

Networks provide a powerful tool to model complex systems where the different
entities in the system are presented by nodes and their interactions by edges.
Recently, there has been a growing interest in multiplex networks as they can
represent the interactions between a pair of nodes through multiple types of
links, each reflecting a distinct type of interaction. One of the important
tools in understanding network topology is community detection. Although there
are numerous works on community detection in single layer networks, existing
work on multiplex community detection mostly focuses on learning a common
community structure across layers without taking the heterogeneity of the
different layers into account. In this paper, we introduce a new multiplex
community detection approach that can identify communities that are common
across layers as well as those that are unique to each layer. The proposed
algorithm employs Orthogonal Nonnegative Matrix Tri-Factorization to model each
layer's adjacency matrix as the sum of two low-rank matrix factorizations,
corresponding to the common and private communities, respectively. The proposed
algorithm is evaluated on both synthetic and real multiplex networks and
compared to state-of-the-art techniques.

In modern interactive speech-based systems, speech is consumed and
transcribed incrementally prior to having disfluencies removed. This
post-processing step is crucial for producing clean transcripts and high
performance on downstream tasks (e.g. machine translation). However, most
current state-of-the-art NLP models such as the Transformer operate
non-incrementally, potentially causing unacceptable delays. We propose a
streaming BERT-based sequence tagging model that, combined with a novel
training objective, is capable of detecting disfluencies in real-time while
balancing accuracy and latency. This is accomplished by training the model to
decide whether to immediately output a prediction for the current input or to
wait for further context. Essentially, the model learns to dynamically size its
lookahead window. Our results demonstrate that our model produces comparably
accurate predictions and does so sooner than our baselines, with lower flicker.
Furthermore, the model attains state-of-the-art latency and stability scores
when compared with recent work on incremental disfluency detection.

Ideology is at the core of political science research. Yet, there still does
not exist general-purpose tools to characterize and predict ideology across
different genres of text. To this end, we study Pretrained Language Models
using novel ideology-driven pretraining objectives that rely on the comparison
of articles on the same story written by media of different ideologies. We
further collect a large-scale dataset, consisting of more than 3.6M political
news articles, for pretraining. Our model POLITICS outperforms strong baselines
and the previous state-of-the-art models on ideology prediction and stance
detection tasks. Further analyses show that POLITICS is especially good at
understanding long or formally written texts, and is also robust in few-shot
learning scenarios.

We present LoopStack, a domain specific compiler stack for tensor operations,
composed of a frontend, LoopTool, and an efficient optimizing code generator,
LoopNest. This stack enables us to compile entire neural networks and generate
code targeting the AVX2, AVX512, NEON, and NEONfp16 instruction sets while
incorporating optimizations often missing from other machine learning compiler
backends. We evaluate our stack on a collection of full neural networks and
commonly used network blocks as well as individual operators, and show that
LoopStack generates machine code that matches and frequently exceeds the
performance of in state-of-the-art machine learning frameworks in both cases.
We also show that for a large collection of schedules LoopNest's compilation is
orders of magnitude faster than LLVM, while resulting in equal or improved run
time performance. Additionally, LoopStack has a very small memory footprint - a
binary size of 245KB, and under 30K lines of effective code makes it ideal for
use on mobile and embedded devices.

Autoencoder-based reduced-order modeling (ROM) has recently attracted
significant attention, owing to its ability to capture underlying nonlinear
features. However, two critical drawbacks severely undermine its scalability to
various physical applications: entangled and therefore uninterpretable latent
variables (LVs) and the blindfold determination of latent space dimension. In
this regard, this study proposes the physics-aware ROM using only interpretable
and information-intensive LVs extracted by $\beta$-variational autoencoder,
which are referred to as physics-aware LVs throughout this paper. To extract
these LVs, their independence and information intensity are quantitatively
scrutinized in a two-dimensional transonic flow benchmark problem. Then, the
physical meanings of the physics-aware LVs are thoroughly investigated and we
confirmed that with appropriate hyperparameter $\beta$, they actually
correspond to the generating factors of the training dataset, Mach number and
angle of attack. To the best of the authors' knowledge, our work is the first
to practically confirm that $\beta$-variational autoencoder can automatically
extract the physical generating factors in the field of applied physics.
Finally, physics-aware ROM, which utilizes only physics-aware LVs, is compared
with conventional ROMs, and its validity and efficiency are successfully
verified.

Though various approaches have been considered, forecasting near-term market
changes of equities and similar market data remains quite difficult. In this
paper we introduce an approach to forecast near-term market changes for equity
indices as well as portfolios using variational inference (VI). VI is a machine
learning approach which uses optimization techniques to estimate complex
probability densities. In the proposed approach, clusters of explanatory
variables are identified and market changes are forecast based on
cluster-specific linear regression. Apart from the expected value of changes,
the proposed approach can also be used to obtain the distribution of possible
outcomes, which can be used to estimate confidence levels of forecasts and risk
measures such as VaR (Value at Risk) for the portfolio. Another advantage of
the proposed approach is the clear model interpretation, as clusters of
explanatory variables (or market regimes) are identified for which the future
changes follow similar relationships. Knowledge about such clusters can provide
useful insights about portfolio performance and identify the relative
importance of variables in different market regimes. Illustrative examples of
equity and bond indices are considered to demonstrate forecasts of the proposed
approach during Covid-related volatility in early 2020 and subsequent benign
market conditions. For the portfolios considered, it is shown that the proposed
approach provides useful forecasts in both normal and volatile markets even
with only a few explanatory variables. Additionally the predicted estimate and
distribution adapt quickly to changing market conditions and thus may also be
useful in obtaining better real-time estimates of risk measures such as VaR
compared to traditional approaches.

When changing code, developers sometimes neglect updating the related
comments, bringing inconsistent or outdated comments. These comments increase
the cost of program understanding and greatly reduce software maintainability.
Researchers have put forward some solutions, such as CUP and HEBCUP, which
update comments efficiently for simple code changes (i.e. modifying of a single
token), but not good enough for complex ones. In this paper, we propose an
approach, named HatCUP (Hybrid Analysis and Attention based Comment UPdater),
to provide a new mechanism for comment updating task. HatCUP pays attention to
hybrid analysis and information. First, HatCUP considers the code structure
change information and introduces a structure-guided attention mechanism
combined with code change graph analysis and optimistic data flow dependency
analysis. With a generally popular RNN-based encoder-decoder architecture,
HatCUP takes the action of the code edits, the syntax, semantics and structure
code changes, and old comments as inputs and generates a structural
representation of the changes in the current code snippet. Furthermore, instead
of directly generating new comments, HatCUP proposes a new edit or non-edit
mechanism to mimic human editing behavior, by generating a sequence of edit
actions and constructing a modified RNN model to integrate newly developed
components. Evaluation on a popular dataset demonstrates that HatCUP
outperforms the state-of-the-art deep learning-based approaches (CUP) by 53.8%
for accuracy, 31.3% for recall and 14.3% for METEOR of the original metrics.
Compared with the heuristic-based approach (HEBCUP), HatCUP also shows better

With an increasing high penetration of solar photovoltaic generation in
electric power grids, voltage phasors and branch power flows experience more
severe fluctuations. In this context, probabilistic power flow (PPF) study aims
at characterizing the statistical properties of the state of the system with
respect to the random power injections. To avoid repeated power flow
calculations involved in PPF study, the present paper leverages regression
algorithms and neural networks to improve the estimation performance and speed
up the computation. Specifically, based on the variation level of the voltage
magnitude at each bus, we develop either a linear regression or a fully
connected neural network to approximate the inverse AC power flow mappings. The
proposed multi-task learning technique further improves the accuracy of branch
flow estimation by incorporating the errors of voltage angle differences into
the loss function design. Tested on IEEE-300 and IEEE-1354 bus systems with
real data, the proposed methods achieve better performance in estimating
voltage phasors and branch flows.

Accurate and efficient prediction of the molecular properties of drugs is one
of the fundamental problems in drug research and development. Recent
advancements in representation learning have been shown to greatly improve the
performance of molecular property prediction. However, due to limited labeled
data, supervised learning-based molecular representation algorithms can only
search limited chemical space, which results in poor generalizability. In this
work, we proposed a self-supervised representation learning framework for
large-scale unlabeled molecules. We developed a novel molecular graph
augmentation strategy, referred to as attention-wise graph mask, to generate
challenging positive sample for contrastive learning. We adopted the graph
attention network (GAT) as the molecular graph encoder, and leveraged the
learned attention scores as masking guidance to generate molecular augmentation
graphs. By minimization of the contrastive loss between original graph and
masked graph, our model can capture important molecular structure and
higher-order semantic information. Extensive experiments showed that our
attention-wise graph mask contrastive learning exhibit state-of-the-art
performance in a couple of downstream molecular property prediction tasks.

Differential privacy (DP) is considered a de-facto standard for protecting
users' privacy in data analysis, machine, and deep learning. Existing DP-based
privacy-preserving training approaches consist of adding noise to the clients'
gradients before sharing them with the server. However, implementing DP on the
gradient is not efficient as the privacy leakage increases by increasing the
synchronization training epochs due to the composition theorem. Recently
researchers were able to recover images used in the training dataset using
Generative Regression Neural Network (GRNN) even when the gradient was
protected by DP. In this paper, we propose two layers of privacy protection
approach to overcome the limitations of the existing DP-based approaches. The
first layer reduces the dimension of the training dataset based on Hensel's
Lemma. We are the first to use Hensel's Lemma for reducing the dimension (i.e.,
compress) of a dataset. The new dimensionality reduction method allows reducing
the dimension of a dataset without losing information since Hensel's Lemma
guarantees uniqueness. The second layer applies DP to the compressed dataset
generated by the first layer. The proposed approach overcomes the problem of
privacy leakage due to composition by applying DP only once before the
training; clients train their local model on the privacy-preserving dataset
generated by the second layer. Experimental results show that the proposed
approach ensures strong privacy protection while achieving good accuracy. The
new dimensionality reduction method achieves an accuracy of 97%, with only 25 %
of the original data size.

Computer-aided diagnosis tools have experienced rapid growth and development
in recent years. Among all, deep learning is the most sophisticated and popular
tool. In this paper, researchers propose a novel deep learning model and apply
it to COVID-19 diagnosis. Our model uses the tool of fractional calculus, which
has the potential to improve the performance of gradient methods. To this end,
the researcher proposes a fractional-order gradient method for the
back-propagation of convolutional neural networks based on the Caputo
definition. However, if only the first term of the infinite series of the
Caputo definition is used to approximate the fractional-order derivative, the
length of the memory is truncated. Therefore, the fractional-order gradient
(FGD) method with a fixed memory step and an adjustable number of terms is used
to update the weights of the layers. Experiments were performed on the COVIDx
dataset to demonstrate fast convergence, good accuracy, and the ability to
bypass the local optimal point. We also compared the performance of the
developed fractional-order neural networks and Integer-order neural networks.
The results confirmed the effectiveness of our proposed model in the diagnosis
of COVID-19.

Deep reinforcement learning (RL) is a data-driven method capable of
discovering complex control strategies for high-dimensional systems, making it
promising for flow control applications. In particular, the present work is
motivated by the goal of reducing energy dissipation in turbulent flows, and
the example considered is the spatiotemporally chaotic dynamics of the
Kuramoto-Sivashinsky equation (KSE). A major challenge associated with RL is
that substantial training data must be generated by repeatedly interacting with
the target system, making it costly when the system is computationally or
experimentally expensive. We mitigate this challenge in a data-driven manner by
combining dimensionality reduction via an autoencoder with a neural ODE
framework to obtain a low-dimensional dynamical model from just a limited data
set. We substitute this data-driven reduced-order model (ROM) in place of the
true system during RL training to efficiently estimate the optimal policy,
which can then be deployed on the true system. For the KSE actuated with
localized forcing ("jets") at four locations, we demonstrate that we are able
to learn a ROM that accurately captures the actuated dynamics as well as the
underlying natural dynamics just from snapshots of the KSE experiencing random
actuations. Using this ROM and a control objective of minimizing dissipation
and power cost, we extract a control policy from it using deep RL. We show that
the ROM-based control strategy translates well to the true KSE and highlight
that the RL agent discovers and stabilizes an underlying forced equilibrium
solution of the KSE system. We show that this forced equilibrium captured in
the ROM and discovered through RL is related to an existing known equilibrium
solution of the natural KSE.

Characterization and modeling of path-dependent behaviors of complex
materials by phenomenological models remains challenging due to difficulties in
formulating mathematical expressions and internal state variables (ISVs)
governing path-dependent behaviors. Data-driven machine learning models, such
as deep neural networks and recurrent neural networks (RNNs), have become
viable alternatives. However, pure black-box data-driven models mapping inputs
to outputs without considering the underlying physics suffer from unstable and
inaccurate generalization performance. This study proposes a machine-learned
physics-informed data-driven constitutive modeling approach for path-dependent
materials based on the measurable material states. The proposed data-driven
constitutive model is designed with the consideration of universal
thermodynamics principles, where the ISVs essential to the material
path-dependency are inferred automatically from the hidden state of RNNs. The
RNN describing the evolution of the data-driven machine-learned ISVs follows
the thermodynamics second law. To enhance the robustness and accuracy of RNN
models, stochasticity is introduced to model training. The effects of the
number of RNN history steps, the internal state dimension, the model
complexity, and the strain increment on model performances have been
investigated. The effectiveness of the proposed method is evaluated by modeling
soil material behaviors under cyclic shear loading using experimental
stress-strain data.

Medical coding (MC) is an essential pre-requisite for reliable data retrieval
and reporting. Given a free-text reported term (RT) such as "pain of right
thigh to the knee", the task is to identify the matching lowest-level term
(LLT) - in this case "unilateral leg pain" - from a very large and continuously
growing repository of standardized medical terms. However, automating this task
is challenging due to a large number of LLT codes (as of writing over 80,000),
limited availability of training data for long tail/emerging classes, and the
general high accuracy demands of the medical domain. With this paper, we
introduce the MC task, discuss its challenges, and present a novel approach
called xTARS that combines traditional BERT-based classification with a recent
zero/few-shot learning approach (TARS). We present extensive experiments that
show that our combined approach outperforms strong baselines, especially in the
few-shot regime. The approach is developed and deployed at Bayer, live since
November 2021. As we believe our approach potentially promising beyond MC, and
to ensure reproducibility, we release the code to the research community.

We present a novel approach for risk-aware planning with human agents in
multi-agent traffic scenarios. Our approach takes into account the wide range
of human driver behaviors on the road, from aggressive maneuvers like speeding
and overtaking, to conservative traits like driving slowly and conforming to
the right-most lane. In our approach, we learn a mapping from a data-driven
human driver behavior model called the CMetric to a driver's entropic risk
preference. We then use the derived risk preference within a game-theoretic
risk-sensitive planner to model risk-aware interactions among human drivers and
an autonomous vehicle in various traffic scenarios. We demonstrate our method
in a merging scenario, where our results show that the final trajectories
obtained from the risk-aware planner generate desirable emergent behaviors.
Particularly, our planner recognizes aggressive human drivers and yields to
them while maintaining a greater distance from them. In a user study,
participants were able to distinguish between aggressive and conservative
simulated drivers based on trajectories generated from our risk-sensitive
planner. We also observe that aggressive human driving results in more frequent
lane-changing in the planner. Finally, we compare the performance of our
modified risk-aware planner with existing methods and show that modeling human
driver behavior leads to safer navigation.

One of the most promising applications of quantum computing is the processing
of graphical data like images. Here, we investigate the possibility of
realizing a quantum pattern recognition protocol based on swap test, and use
the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We
find that with a two-qubit protocol, swap test can efficiently detect the
similarity between two patterns with good fidelity, though for three or more
qubits the noise in the real devices becomes detrimental. To mitigate this
noise effect, we resort to destructive swap test, which shows an improved
performance for three-qubit states. Due to limited cloud access to larger IBMQ
processors, we take a segment-wise approach to apply the destructive swap test
on higher dimensional images. In this case, we define an average overlap
measure which shows faithfulness to distinguish between two very different or
very similar patterns when simulated on real IBMQ processors. As test images,
we use binary images with simple patterns, greyscale MNIST numbers and MNIST
fashion images, as well as binary images of human blood vessel obtained from
magnetic resonance imaging (MRI). We also present an experimental set up for
applying destructive swap test using the nitrogen vacancy centre (NVs) in
diamond. Our experimental data show high fidelity for single qubit states.
Lastly, we propose a protocol inspired from quantum associative memory, which
works in an analogous way to supervised learning for performing quantum pattern
recognition using destructive swap test.

Masked Language Models (MLMs) pre-trained by predicting masked tokens on
large corpora have been used successfully in natural language processing tasks
for a variety of languages. Unfortunately, it was reported that MLMs also learn
discriminative biases regarding attributes such as gender and race. Because
most studies have focused on MLMs in English, the bias of MLMs in other
languages has rarely been investigated. Manual annotation of evaluation data
for languages other than English has been challenging due to the cost and
difficulty in recruiting annotators. Moreover, the existing bias evaluation
methods require the stereotypical sentence pairs consisting of the same context
with attribute words (e.g. He/She is a nurse). We propose Multilingual Bias
Evaluation (MBE) score, to evaluate bias in various languages using only
English attribute word lists and parallel corpora between the target language
and English without requiring manually annotated data. We evaluated MLMs in
eight languages using the MBE and confirmed that gender-related biases are
encoded in MLMs for all those languages. We manually created datasets for
gender bias in Japanese and Russian to evaluate the validity of the MBE. The
results show that the bias scores reported by the MBE significantly correlates
with that computed from the above manually created datasets and the existing
English datasets for gender bias.

Automatic traffic classification is increasingly becoming important in
traffic engineering, as the current trend of encrypting transport information
(e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from
accessing end-to-end packet headers. However, this information is crucial for
traffic shaping, network slicing, and Quality of Service (QoS) management, for
preventing network intrusion, and for anomaly detection. 3D networks offer
multiple routes that can guarantee different levels of QoS. Therefore, service
classification and separation are essential to guarantee the required QoS level
to each traffic sub-flow through the appropriate network trunk. In this paper,
a federated feature selection and feature reduction learning scheme is proposed
to classify network traffic in a semi-supervised cooperative manner. The
federated gateways of 3D network help to enhance the global knowledge of
network traffic to improve the accuracy of anomaly and intrusion detection and
service identification of a new traffic flow.

This paper presents a few comprehensive experimental studies for automated
Structural Damage Detection (SDD) in extreme events using deep learning methods
for processing 2D images. In the first study, a 152-layer Residual network
(ResNet) is utilized to classify multiple classes in eight SDD tasks, which
include identification of scene levels, damage levels, material types, etc. The
proposed ResNet achieved high accuracy for each task while the positions of the
damage are not identifiable. In the second study, the existing ResNet and a
segmentation network (U-Net) are combined into a new pipeline, cascaded
networks, for categorizing and locating structural damage. The results show
that the accuracy of damage detection is significantly improved compared to
only using a segmentation network. In the third and fourth studies, end-to-end
networks are developed and tested as a new solution to directly detect cracks
and spalling in the image collections of recent large earthquakes. One of the
proposed networks can achieve an accuracy above 67.6% for all tested images at
various scales and resolutions, and shows its robustness for these human-free
detection tasks. As a preliminary field study, we applied the proposed method
to detect damage in a concrete structure that was tested to study its
progressive collapse performance. The experiments indicate that these solutions
for automatic detection of structural damage using deep learning methods are
feasible and promising. The training datasets and codes will be made available
for the public upon the publication of this paper.

Humans interact with an object in many different ways by making contact at
different locations, creating a highly complex motion space that can be
difficult to learn, particularly when synthesizing such human interactions in a
controllable manner. Existing works on synthesizing human scene interaction
focus on the high-level control of action but do not consider the fine-grained
control of motion. In this work, we study the problem of synthesizing scene
interactions conditioned on different contact positions on the object. As a
testbed to investigate this new problem, we focus on human-chair interaction as
one of the most common actions which exhibit large variability in terms of
contacts. We propose a novel synthesis framework COUCH that plans ahead the
motion by predicting contact-aware control signals of the hands, which are then
used to synthesize contact-conditioned interactions. Furthermore, we contribute
a large human-chair interaction dataset with clean annotations, the COUCH
Dataset. Our method shows significant quantitative and qualitative improvements
over existing methods for human-object interactions. More importantly, our
method enables control of the motion through user-specified or automatically
predicted contacts.

The objective of this paper is to explore the opportunities for human
information behaviour research to inform and influence the field of machine
learning and the resulting machine information behaviour. Using the development
of foundation models in machine learning as an example, the paper illustrates
how human information behaviour research can bring to machine learning a more
nuanced view of information and informing, a better understanding of
information need and how that affects the communication among people and
systems, guidance on the nature of context and how to operationalize that in
models and systems, and insights into bias, misinformation, and
marginalization. Despite their clear differences, the fields of information
behaviour and machine learning share many common objectives, paradigms, and key
research questions. The example of foundation models illustrates that human
information behaviour research has much to offer in addressing some of the
challenges emerging in the nascent area of machine information behaviour.

In this paper, we formulate a new generalized reference kernel hoping to
improve the original base kernel using a set of reference vectors. Depending on
the selected reference vectors, our formulation shows similarities to
approximate kernels, random mappings, and Non-linear Projection Trick. Focusing
on small-scale one-class classification, our analysis and experimental results
show that the new formulation provides approaches to regularize, adjust the
rank, and incorporate additional information into the kernel itself, leading to
improved one-class classification accuracy.

Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to
become a dominating variant in late 2021 and was replaced by more transmissible
Omicron BA.2 in March 2022. An important question is which new variants will
dominate in the future. Topology-based deep learning models have had tremendous
success in forecasting emerging variants in the past. However, topology is
insensitive to homotopic shape variations in virus-human protein-protein
binding, which are crucial to viral evolution and transmission. This challenge
is tackled with persistent Laplacian, which is able to capture both the
topology and shape of data. Persistent Laplacian-based deep learning models are
developed to systematically evaluate variant infectivity. Our comparative
analysis of Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron BA.1, BA.1.1,
BA.2, BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 unveils that Omicron BA.2.11,
BA.2.12.1, BA.3, BA.4, and BA.5 are more contagious than BA.2. In particular,
BA.4 and BA.5 are about 36\% more infectious than BA.2 and are projected to
become new dominating variants by natural selection. Moreover, the proposed
models outperform the state-of-the-art methods on three major benchmark
datasets for mutation-induced protein-protein binding free energy changes.

While deep learning models have seen recent high uptake in the geosciences,
and are appealing in their ability to learn from minimally processed input
data, as black box models they do not provide an easy means to understand how a
decision is reached, which in safety-critical tasks especially can be
problematical. An alternative route is to use simpler, more transparent white
box models, in which task-specific feature construction replaces the more
opaque feature discovery process performed automatically within deep learning
models. Using data from the Groningen Gas Field in the Netherlands, we build on
an existing logistic regression model by the addition of four further features
discovered using elastic net driven data mining within the catch22 time series
analysis package. We then evaluate the performance of the augmented logistic
regression model relative to a deep (CNN) model, pre-trained on the Groningen
data, on progressively increasing noise-to-signal ratios. We discover that, for
each ratio, our logistic regression model correctly detects every earthquake,
while the deep model fails to detect nearly 20 % of seismic events, thus
justifying at least a degree of caution in the application of deep models,
especially to data with higher noise-to-signal ratios.

In recent years, there has been an increasing interest in exploiting
logically specified background knowledge in order to obtain neural models (i)
with a better performance, (ii) able to learn from less data, and/or (iii)
guaranteed to be compliant with the background knowledge itself, e.g., for
safety-critical applications. In this survey, we retrace such works and
categorize them based on (i) the logical language that they use to express the
background knowledge and (ii) the goals that they achieve.

Deep learning has significantly advanced and accelerated de novo molecular
generation. Generative networks, namely Variational Autoencoders (VAEs) can not
only randomly generate new molecules, but also alter molecular structures to
optimize specific chemical properties which are pivotal for drug-discovery.
While VAEs have been proposed and researched in the past for pharmaceutical
applications, they possess deficiencies which limit their ability to both
optimize properties and decode syntactically valid molecules. We present a
recurrent, conditional $\beta$-VAE which disentangles the latent space to
enhance post hoc molecule optimization. We create a mutual information driven
training protocol and data augmentations to both increase molecular validity
and promote longer sequence generation. We demonstrate the efficacy of our
framework on the ZINC-250k dataset, achieving SOTA unconstrained optimization
results on the penalized LogP (pLogP) and QED scores, while also matching
current SOTA results for validity, novelty and uniqueness scores for random
generation. We match the current SOTA on QED for top-3 molecules at 0.948,
while setting a new SOTA for pLogP optimization at 104.29, 90.12, 69.68 and
demonstrating improved results on the constrained optimization task.

Precise and timely traffic flow prediction plays a critical role in
developing intelligent transportation systems and has attracted considerable
attention in recent decades. Despite the significant progress in this area
brought by deep learning, challenges remain. Traffic flows usually change
dramatically in a short period, which prevents the current methods from
accurately capturing the future trend and likely causes the over-fitting
problem, leading to unsatisfied accuracy. To this end, this paper proposes a
Long Short-Term Memory (LSTM) based method that can forecast the short-term
traffic flow precisely and avoid local optimum problems during training.
Specifically, instead of using the non-stationary raw traffic data directly, we
first decompose them into sub-components, where each one is less noisy than the
original input. Afterward, Sample Entropy (SE) is employed to merge similar
components to reduce the computation cost. The merged features are fed into the
LSTM, and we then introduce a spatiotemporal module to consider the neighboring
relationships in the recombined signals to avoid strong autocorrelation. During
training, we utilize the Grey Wolf Algorithm (GWO) to optimize the parameters
of LSTM, which overcome the overfitting issue. We conduct the experiments on a
UK public highway traffic flow dataset, and the results show that the proposed
method performs favorably against other state-of-the-art methods with better
adaption performance on extreme outliers, delay effects, and trend-changing
responses.

Early fault detection (EFD) of rotating machines is important to decrease the
maintenance cost and improve the mechanical system stability. One of the key
points of EFD is developing a generic model to extract robust and
discriminative features from different equipment for early fault detection.
Most existing EFD methods focus on learning fault representation by one type of
feature. However, a combination of multiple features can capture a more
comprehensive representation of system state. In this paper, we propose an EFD
method based on multiple feature fusion with stacking architecture (M2FSA). The
proposed method can extract generic and discriminiative features to detect
early faults by combining time domain (TD), frequency domain (FD), and
time-frequency domain (TFD) features. In order to unify the dimensions of the
different domain features, Stacked Denoising Autoencoder (SDAE) is utilized to
learn deep features in three domains. The architecture of the proposed M2FSA
consists of two layers. The first layer contains three base models, whose
corresponding inputs are different deep features. The outputs of the first
layer are concatenated to generate the input to the second layer, which
consists of a meta model. The proposed method is tested on three bearing
datasets. The results demonstrate that the proposed method is better than
existing methods both in sensibility and reliability.

This chapter argues for more informed target metrics for the statistical
processing of stylistic variation in text collections. Much as operationalised
relevance proved a useful goal to strive for in information retrieval, research
in textual stylistics, whether application oriented or philologically inclined,
needs goals formulated in terms of pertinence, relevance, and utility - notions
that agree with reader experience of text. Differences readers are aware of are
mostly based on utility - not on textual characteristics per se. Mostly,
readers report stylistic differences in terms of genres. Genres, while vague
and undefined, are well-established and talked about: very early on, readers
learn to distinguish genres. This chapter discusses variation given by genre,
and contrasts it to variation occasioned by individual choice.

Video-based action recognition is one of the most popular topics in computer
vision. With recent advances of selfsupervised video representation learning
approaches, action recognition usually follows a two-stage training framework,
i.e., self-supervised pre-training on large-scale unlabeled sets and transfer
learning on a downstream labeled set. However, catastrophic forgetting of the
pre-trained knowledge becomes the main issue in the downstream transfer
learning of action recognition, resulting in a sub-optimal solution. In this
paper, to alleviate the above issue, we propose a novel transfer learning
approach that combines self-distillation in fine-tuning to preserve knowledge
from the pre-trained model learned from the large-scale dataset. Specifically,
we fix the encoder from the last epoch as the teacher model to guide the
training of the encoder from the current epoch in the transfer learning. With
such a simple yet effective learning strategy, we outperform state-of-the-art
methods on widely used UCF101 and HMDB51 datasets in action recognition task.

Many instances of algorithmic bias are caused by distributional shifts. For
example, machine learning (ML) models often perform worse on demographic groups
that are underrepresented in the training data. In this paper, we leverage this
connection between algorithmic fairness and distribution shifts to show that
algorithmic fairness interventions can help ML models overcome distribution
shifts, and that domain adaptation methods (for overcoming distribution shifts)
can mitigate algorithmic biases. In particular, we show that (i) enforcing
suitable notions of individual fairness (IF) can improve the
out-of-distribution accuracy of ML models under the covariate shift assumption
and that (ii) it is possible to adapt representation alignment methods for
domain adaptation to enforce individual fairness. The former is unexpected
because IF interventions were not developed with distribution shifts in mind.
The latter is also unexpected because representation alignment is not a common
approach in the individual fairness literature.

Machine learning models are commonly used to detect toxicity in online
conversations. These models are trained on datasets annotated by human raters.
We explore how raters' self-described identities impact how they annotate
toxicity in online comments. We first define the concept of specialized rater
pools: rater pools formed based on raters' self-described identities, rather
than at random. We formed three such rater pools for this study--specialized
rater pools of raters from the U.S. who identify as African American, LGBTQ,
and those who identify as neither. Each of these rater pools annotated the same
set of comments, which contains many references to these identity groups. We
found that rater identity is a statistically significant factor in how raters
will annotate toxicity for identity-related annotations. Using preliminary
content analysis, we examined the comments with the most disagreement between
rater pools and found nuanced differences in the toxicity annotations. Next, we
trained models on the annotations from each of the different rater pools, and
compared the scores of these models on comments from several test sets.
Finally, we discuss how using raters that self-identify with the subjects of
comments can create more inclusive machine learning models, and provide more
nuanced ratings than those by random raters.

In real life, acoustic scenes and audio events are naturally correlated.
Humans instinctively rely on fine-grained audio events as well as the overall
sound characteristics to distinguish diverse acoustic scenes. Yet, most
previous approaches treat acoustic scene classification (ASC) and audio event
classification (AEC) as two independent tasks. A few studies on scene and event
joint classification either use synthetic audio datasets that hardly match the
real world, or simply use the multi-task framework to perform two tasks at the
same time. Neither of these two ways makes full use of the implicit and
inherent relation between fine-grained events and coarse-grained scenes. To
this end, this paper proposes a relation-guided ASC (RGASC) model to further
exploit and coordinate the scene-event relation for the mutual benefit of scene
and event recognition. The TUT Urban Acoustic Scenes 2018 dataset (TUT2018) is
annotated with pseudo labels of events by a simple and efficient audio-related
pre-trained model PANN, which is one of the state-of-the-art AEC models. Then,
a prior scene-event relation matrix is defined as the average probability of
the presence of each event type in each scene class. Finally, the two-tower
RGASC model is jointly trained on the real-life dataset TUT2018 for both scene
and event classification. The following results are achieved. 1) RGASC
effectively coordinates the true information of coarse-grained scenes and the
pseudo information of fine-grained events. 2) The event embeddings learned from
pseudo labels under the guidance of prior scene-event relations help reduce the
confusion between similar acoustic scenes. 3) Compared with other
(non-ensemble) methods, RGASC improves the scene classification accuracy on the
real-life dataset.

Implicit event argument extraction (EAE) aims to identify arguments that
could scatter over the document. Most previous work focuses on learning the
direct relations between arguments and the given trigger, while the implicit
relations with long-range dependency are not well studied. Moreover, recent
neural network based approaches rely on a large amount of labeled data for
training, which is unavailable due to the high labelling cost. In this paper,
we propose a Curriculum learning based Prompt tuning (CUP) approach, which
resolves implicit EAE by four learning stages. The stages are defined according
to the relations with the trigger node in a semantic graph, which well captures
the long-range dependency between arguments and the trigger. In addition, we
integrate a prompt-based encoder-decoder model to elicit related knowledge from
pre-trained language models (PLMs) in each stage, where the prompt templates
are adapted with the learning progress to enhance the reasoning for arguments.
Experimental results on two well-known benchmark datasets show the great
advantages of our proposed approach. In particular, we outperform the
state-of-the-art models in both fully-supervised and low-data scenarios.

The classical Mercer's theorem claims that a continuous positive definite
kernel $K({\mathbf x}, {\mathbf y})$ on a compact set can be represented as
$\sum_{i=1}^\infty \lambda_i\phi_i({\mathbf x})\phi_i({\mathbf y})$ where
$\{(\lambda_i,\phi_i)\}$ are eigenvalue-eigenvector pairs of the corresponding
integral operator. This infinite representation is known to converge uniformly
to the kernel $K$. We estimate the speed of this convergence in terms of the
decay rate of eigenvalues and demonstrate that for $2m$ times differentiable
kernels the first $N$ terms of the series approximate $K$ as
$\mathcal{O}\big((\sum_{i=N+1}^\infty\lambda_i)^{\frac{m}{m+n}}\big)$ or
$\mathcal{O}\big((\sum_{i=N+1}^\infty\lambda^2_i)^{\frac{m}{2m+n}}\big)$.
Finally, we demonstrate some applications of our results to a spectral
charaterization of integral operators with continuous roots and other powers.

Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs)
are widely used structured models, both of which can be represented as factor
graph grammars (FGGs), a powerful formalism capable of describing a wide range
of models. Recent research found it beneficial to use large state spaces for
HMMs and PCFGs. However, inference with large state spaces is computationally
demanding, especially for PCFGs. To tackle this challenge, we leverage tensor
rank decomposition (aka.\ CPD) to decrease inference computational complexities
for a subset of FGGs subsuming HMMs and PCFGs. We apply CPD on the factors of
an FGG and then construct a new FGG defined in the rank space. Inference with
the new FGG produces the same result but has a lower time complexity when the
rank size is smaller than the state size. We conduct experiments on HMM
language modeling and unsupervised PCFG parsing, showing better performance
than previous work. Our code is publicly available at
\url{https://github.com/VPeterV/RankSpace-Models}.

It is known belief propagation decoding variants of LDPC codes can be
unrolled easily as neural networks after assigning differed weights to message
passing edges flexibly. In this paper we focus on how to determine these
weights, in the form of trainable paramters, within a framework of deep
learning. Firstly, a new method is proposed to generate high-quality training
data via exploiting an approximation to the targeted mixture density. Then the
strong positive correlation between training loss and decoding metrics is fully
exposed after tracing the training evolution curves. Lastly, for the purpose of
facilitating training convergence and reducing decoding complexity, we
highlight the necessity of slashing the number of trainable parameters while
emphasizing the locations of these survived ones, which is justified in the
extensive simulation.

k-nearest-neighbor machine translation (NN-MT), proposed by Khandelwal et al.
(2021), has achieved many state-of-the-art results in machine translation
tasks. Although effective, NN-MT requires conducting NN searches through the
large datastore for each decoding step during inference, prohibitively
increasing the decoding cost and thus leading to the difficulty for the
deployment in real-world applications. In this paper, we propose to move the
time-consuming NN search forward to the preprocessing phase, and then introduce
Nearest Neighbor Knowledge Distillation (NN-KD) that trains the base NMT model
to directly learn the knowledge of NN. Distilling knowledge retrieved by NN can
encourage the NMT model to take more reasonable target tokens into
consideration, thus addressing the overcorrection problem. Extensive
experimental results show that, the proposed method achieves consistent
improvement over the state-of-the-art baselines including NN-MT, while
maintaining the same training and decoding speed as the standard NMT model.

Recent theoretical studies illustrated that kernel ridgeless regression can
guarantee good generalization ability without an explicit regularization. In
this paper, we investigate the statistical properties of ridgeless regression
with random features and stochastic gradient descent. We explore the effect of
factors in the stochastic gradient and random features, respectively.
Specifically, random features error exhibits the double-descent curve.
Motivated by the theoretical findings, we propose a tunable kernel algorithm
that optimizes the spectral density of kernel during training. Our work bridges
the interpolation theory and practical algorithm.

Document-level relation extraction (RE) aims at extracting relations among
entities expressed across multiple sentences, which can be viewed as a
multi-label classification problem. In a typical document, most entity pairs do
not express any pre-defined relation and are labeled as "none" or "no
relation". For good document-level RE performance, it is crucial to distinguish
such none class instances (entity pairs) from those of pre-defined classes
(relations). However, most existing methods only estimate the probability of
pre-defined relations independently without considering the probability of "no
relation". This ignores the context of entity pairs and the label correlations
between the none class and pre-defined classes, leading to sub-optimal
predictions. To address this problem, we propose a new multi-label loss that
encourages large margins of label confidence scores between each pre-defined
class and the none class, which enables captured label correlations and
context-dependent thresholding for label prediction. To gain further robustness
against positive-negative imbalance and mislabeled data that could appear in
real-world RE datasets, we propose a margin regularization and a margin
shifting technique. Experimental results demonstrate that our method
significantly outperforms existing multi-label losses for document-level RE and
works well in other multi-label tasks such as emotion classification when none
class instances are available for training.

Decentralized online learning (DOL) has been increasingly researched in the
last decade, mostly motivated by its wide applications in sensor networks,
commercial buildings, robotics (e.g., decentralized target tracking and
formation control), smart grids, deep learning, and so forth. In this problem,
there are a network of agents who may be cooperative (i.e., decentralized
online optimization) or noncooperative (i.e., online game) through local
information exchanges, and the local cost function of each agent is often
time-varying in dynamic and even adversarial environments. At each time, a
decision must be made by each agent based on historical information at hand
without knowing future information on cost functions. Although this problem has
been extensively studied in the last decade, a comprehensive survey is lacking.
Therefore, this paper provides a thorough overview of DOL from the perspective
of problem settings, communication, computation, and performances. In addition,
some potential future directions are also discussed in details.

Federated learning (FL) has received high interest from researchers and
practitioners to train machine learning (ML) models for healthcare. Ensuring
the trustworthiness of these models is essential. Especially bias, defined as a
disparity in the model's predictive performance across different subgroups, may
cause unfairness against specific subgroups, which is an undesired phenomenon
for trustworthy ML models. In this research, we address the question to which
extent bias occurs in medical FL and how to prevent excessive bias through
reward systems. We first evaluate how to measure the contributions of
institutions toward predictive performance and bias in cross-silo medical FL
with a Shapley value approximation method. In a second step, we design
different reward systems incentivizing contributions toward high predictive
performance or low bias. We then propose a combined reward system that
incentivizes contributions toward both. We evaluate our work using multiple
medical chest X-ray datasets focusing on patient subgroups defined by patient
sex and age. Our results show that we can successfully measure contributions
toward bias, and an integrated reward system successfully incentivizes
contributions toward a well-performing model with low bias. While the
partitioning of scans only slightly influences the overall bias, institutions
with data predominantly from one subgroup introduce a favorable bias for this
subgroup. Our results indicate that reward systems, which focus on predictive
performance only, can transfer model bias against patients to an institutional
level. Our work helps researchers and practitioners design reward systems for
FL with well-aligned incentives for trustworthy ML.

Low-dose CT (LDCT) imaging attracted a considerable interest for the
reduction of the object's exposure to X-ray radiation. In recent years,
supervised deep learning (DL) has been extensively studied for LDCT image
reconstruction, which trains a network over a dataset containing many pairs of
normal-dose and low-dose images. However, the challenge on collecting many such
pairs in the clinical setup limits the application of such
supervised-learning-based methods for LDCT image reconstruction in practice.
Aiming at addressing the challenges raised by the collection of training
dataset, this paper proposed a unsupervised deep learning method for LDCT image
reconstruction, which does not require any external training data. The proposed
method is built on a re-parametrization technique for Bayesian inference via
deep network with random weights, combined with additional total
variational~(TV) regularization. The experiments show that the proposed method
noticeably outperforms existing dataset-free image reconstruction methods on
the test data.

Spiking Neural Network (SNN) is a promising energy-efficient AI model when
implemented on neuromorphic hardware. However, it is a challenge to efficiently
train SNNs due to their non-differentiability. Most existing methods either
suffer from high latency (i.e., long simulation time steps), or cannot achieve
as high performance as Artificial Neural Networks (ANNs). In this paper, we
propose the Differentiation on Spike Representation (DSR) method, which could
achieve high performance that is competitive to ANNs yet with low latency.
First, we encode the spike trains into spike representation using (weighted)
firing rate coding. Based on the spike representation, we systematically derive
that the spiking dynamics with common neural models can be represented as some
sub-differentiable mapping. With this viewpoint, our proposed DSR method trains
SNNs through gradients of the mapping and avoids the common
non-differentiability problem in SNN training. Then we analyze the error when
representing the specific mapping with the forward computation of the SNN. To
reduce such error, we propose to train the spike threshold in each layer, and
to introduce a new hyperparameter for the neural models. With these components,
the DSR method can achieve state-of-the-art SNN performance with low latency on
both static and neuromorphic datasets, including CIFAR-10, CIFAR-100, ImageNet,
and DVS-CIFAR10.

This research explores the methods that NFTs can be recommended to people who
interact with NFT-marketplaces to explore NFTs of preference and similarity to
what they have been searching for. While exploring past methods that can be
adopted for recommendations, the use of NFT traits for recommendations has been
explored. The outcome of the research highlights the necessity of using
multiple Recommender Systems to present the user with the best possible NFTs
when interacting with decentralized systems.

Despite being the main tool to visualize molecules at the atomic scale, AFM
with CO-functionalized metal tips is unable to chemically identify the observed
molecules. Here we present a strategy to address this challenging task using
deep learning techniques. Instead of identifying a finite number of molecules
following a traditional classification approach, we define the molecular
identification as an image captioning problem. We design an architecture,
composed of two multimodal recurrent neural networks, capable of identifying
the structure and composition of an unknown molecule using a 3D-AFM image stack
as input. The neural network is trained to provide the name of each molecule
according to the IUPAC nomenclature rules. To train and test this algorithm we
use the novel QUAM-AFM dataset, which contains almost 700,000 molecules and 165
million AFM images. The accuracy of the predictions is remarkable, achieving a
high score quantified by the cumulative BLEU 4-gram, a common metric in
language recognition studies.

In the past few years, Online Convex Optimization (OCO) has received notable
attention in the control literature thanks to its flexible real-time nature and
powerful performance guarantees. In this paper, we propose new step-size rules
and OCO algorithms that simultaneously exploit gradient predictions, function
predictions and dynamics, features particularly pertinent to control
applications. The proposed algorithms enjoy static and dynamic regret bounds in
terms of the dynamics of the reference action sequence, gradient prediction
error and function prediction error, which are generalizations of known
regularity measures from the literature. We present results for both convex and
strongly convex costs. We validate the performance of the proposed algorithms
in a trajectory tracking case study, as well as portfolio optimization using

This paper investigates the problem of forecasting multivariate aggregated
human mobility while preserving the privacy of the individuals concerned.
Differential privacy, a state-of-the-art formal notion, has been used as the
privacy guarantee in two different and independent steps when training deep
learning models. On one hand, we considered \textit{gradient perturbation},
which uses the differentially private stochastic gradient descent algorithm to
guarantee the privacy of each time series sample in the learning stage. On the
other hand, we considered \textit{input perturbation}, which adds differential
privacy guarantees in each sample of the series before applying any learning.
We compared four state-of-the-art recurrent neural networks: Long Short-Term
Memory, Gated Recurrent Unit, and their Bidirectional architectures, i.e.,
Bidirectional-LSTM and Bidirectional-GRU. Extensive experiments were conducted
with a real-world multivariate mobility dataset, which we published openly
along with this paper. As shown in the results, differentially private deep
learning models trained under gradient or input perturbation achieve nearly the
same performance as non-private deep learning models, with loss in performance
varying between $0.57\%$ to $2.8\%$. The contribution of this paper is
significant for those involved in urban planning and decision-making, providing
a solution to the human mobility multivariate forecast problem through
differentially private deep learning models.

Imaging through diffusive media is a challenging problem, where the existing
solutions heavily rely on digital computers to reconstruct distorted images. We
provide a detailed analysis of a computer-free, all-optical imaging method for
seeing through random, unknown phase diffusers using diffractive neural
networks, covering different deep learning-based training strategies. By
analyzing various diffractive networks designed to image through random
diffusers with different correlation lengths, a trade-off between the image
reconstruction fidelity and distortion reduction capability of the diffractive
network was observed. During its training, random diffusers with a range of
correlation lengths were used to improve the diffractive network's
generalization performance. Increasing the number of random diffusers used in
each epoch reduced the overfitting of the diffractive network's imaging
performance to known diffusers. We also demonstrated that the use of additional
diffractive layers improved the generalization capability to see through new,
random diffusers. Finally, we introduced deliberate misalignments in training
to 'vaccinate' the network against random layer-to-layer shifts that might
arise due to the imperfect assembly of the diffractive networks. These analyses
provide a comprehensive guide in designing diffractive networks to see through
random diffusers, which might profoundly impact many fields, such as biomedical
imaging, atmospheric physics, and autonomous driving.

Recent advances in deep reinforcement learning (DRL) have largely promoted
the performance of adaptive traffic signal control (ATSC). Nevertheless,
regarding the implementation, most works are cumbersome in terms of storage and
computation. This hinders their deployment on scenarios where resources are
limited. In this work, we propose TinyLight, the first DRL-based ATSC model
that is designed for devices with extremely limited resources. TinyLight first
constructs a super-graph to associate a rich set of candidate features with a
group of light-weighted network blocks. Then, to diminish the model's resource
consumption, we ablate edges in the super-graph automatically with a novel
entropy-minimized objective function. This enables TinyLight to work on a
standalone microcontroller with merely 2KB RAM and 32KB ROM. We evaluate
TinyLight on multiple road networks with real-world traffic demands.
Experiments show that even with extremely limited resources, TinyLight still
achieves competitive performance. The source code and appendix of this work can
be found at \url{https://bit.ly/38hH8t8}.

Program classification can be regarded as a high-level abstraction of code,
laying a foundation for various tasks related to source code comprehension, and
has a very wide range of applications in the field of software engineering,
such as code clone detection, code smell classification, defects
classification, etc. The cross-language program classification can realize code
transfer in different programming languages, and can also promote
cross-language code reuse, thereby helping developers to write code quickly and
reduce the development time of code transfer. Most of the existing studies
focus on the semantic learning of the code, whilst few studies are devoted to
cross-language tasks. The main challenge of cross-language program
classification is how to extract semantic features of different programming
languages. In order to cope with this difficulty, we propose a Unified Abstract
Syntax Tree (namely UAST in this paper) neural network. In detail, the core
idea of UAST consists of two unified mechanisms. First, UAST learns an AST
representation by unifying the AST traversal sequence and graph-like AST
structure for capturing semantic code features. Second, we construct a
mechanism called unified vocabulary, which can reduce the feature gap between
different programming languages, so it can achieve the role of cross-language
program classification. Besides, we collect a dataset containing 20,000 files
of five programming languages, which can be used as a benchmark dataset for the
cross-language program classification task. We have done experiments on two
datasets, and the results show that our proposed approach outperforms the
state-of-the-art baselines in terms of four evaluation metrics (Precision,
Recall, F1-score, and Accuracy).

This paper proposes Branch & Learn, a framework for Predict+Optimize to
tackle optimization problems containing parameters that are unknown at the time
of solving. Given an optimization problem solvable by a recursive algorithm
satisfying simple conditions, we show how a corresponding learning algorithm
can be constructed directly and methodically from the recursive algorithm. Our
framework applies also to iterative algorithms by viewing them as a degenerate
form of recursion. Extensive experimentation shows better performance for our
proposal over classical and state-of-the-art approaches.

Visual Dialog aims to answer multi-round, interactive questions based on the
dialog history and image content. Existing methods either consider answer
ranking and generating individually or only weakly capture the relation across
the two tasks implicitly by two separate models. The research on a universal
framework that jointly learns to rank and generate answers in a single model is
seldom explored. In this paper, we propose a contrastive learning-based
framework UTC to unify and facilitate both discriminative and generative tasks
in visual dialog with a single model. Specifically, considering the inherent
limitation of the previous learning paradigm, we devise two inter-task
contrastive losses i.e., context contrastive loss and answer contrastive loss
to make the discriminative and generative tasks mutually reinforce each other.
These two complementary contrastive losses exploit dialog context and target
answer as anchor points to provide representation learning signals from
different perspectives. We evaluate our proposed UTC on the VisDial v1.0
dataset, where our method outperforms the state-of-the-art on both
discriminative and generative tasks and surpasses previous state-of-the-art
generative methods by more than 2 absolute points on Recall@1.

We introduce Uniform Manifold Approximation with Two-phase Optimization
(UMATO), a dimensionality reduction (DR) technique that improves UMAP to
capture the global structure of high-dimensional data more accurately. In
UMATO, optimization is divided into two phases so that the resulting embeddings
can depict the global structure reliably while preserving the local structure
with sufficient accuracy. In the first phase, hub points are identified and
projected to construct a skeletal layout for the global structure. In the
second phase, the remaining points are added to the embedding preserving the
regional characteristics of local areas. Through quantitative experiments, we
found that UMATO (1) outperformed widely used DR techniques in preserving the
global structure while (2) producing competitive accuracy in representing the
local structure. We also verified that UMATO is preferable in terms of
robustness over diverse initialization methods, number of epochs, and
subsampling techniques.

In recent years, progress in NLU has been driven by benchmarks. These
benchmarks are typically collected by crowdsourcing, where annotators write
examples based on annotation instructions crafted by dataset creators. In this
work, we hypothesize that annotators pick up on patterns in the crowdsourcing
instructions, which bias them to write similar examples that are then
over-represented in the collected data. We study this form of bias, termed
instruction bias, in 14 recent NLU benchmarks, showing that instruction
examples often exhibit concrete patterns, which are propagated by crowdworkers
to the collected data. This extends previous work (Geva et al., 2019) and
raises a new concern of whether we are modeling the dataset creator's
instructions, rather than the task. Through a series of experiments, we show
that, indeed, instruction bias can lead to overestimation of model performance,
and that models struggle to generalize beyond biases originating in the
crowdsourcing instructions. We further analyze the influence of instruction
bias in terms of pattern frequency and model size, and derive concrete
recommendations for creating future NLU benchmarks.

Frequency control plays a pivotal role in reliable power system operations.
It is conventionally performed in a hierarchical way that first rapidly
stabilizes the frequency deviations and then slowly recovers the nominal
frequency. However, as the generation mix shifts from synchronous generators to
renewable resources, power systems experience larger and faster frequency
fluctuations due to the loss of inertia, which adversely impacts the frequency
stability. This has motivated active research in algorithms that jointly
address frequency degradation and economic efficiency in a fast timescale,
among which the distributed averaging-based integral (DAI) control is a notable
one that sets controllable power injections directly proportional to the
integrals of frequency deviation and economic inefficiency signals.
Nevertheless, DAI do not typically consider the transient performance of the
system following power disturbances and has been restricted to quadratic
operational cost functions. This manuscript aims to leverage nonlinear optimal
controllers to simultaneously achieve optimal transient frequency control and
find the most economic power dispatch for frequency restoration. To this end,
we integrate reinforcement learning (RL) to the classic DAI, which results in
RL-DAI. Specifically, we use RL to learn a neural network-based control policy
mapping from the integral variables of DAI to the controllable power injections
which provides optimal transient frequency control, while DAI inherently
ensures the frequency restoration and optimal economic dispatch. Compared to
existing methods, we provide provable guarantees on the stability of the
learned controllers and extend allowable cost functions to a much larger class.
Simulations on the 39-bus New England system illustrate our results.

Despite their outstanding performance in a broad spectrum of real-world
tasks, deep artificial neural networks are sensitive to input noises,
particularly adversarial perturbations. On the contrary, human and animal
brains are much less vulnerable. In contrast to the one-shot inference
performed by most deep neural networks, the brain often solves decision-making
with an evidence accumulation mechanism that may trade time for accuracy when
facing noisy inputs. The mechanism is well described by the Drift-Diffusion
Model (DDM). In the DDM, decision-making is modeled as a process in which noisy
evidence is accumulated toward a threshold. Drawing inspiration from the DDM,
we propose the Dropout-based Drift-Diffusion Model (DDDM) that combines
test-phase dropout and the DDM for improving the robustness for arbitrary
neural networks. The dropouts create temporally uncorrelated noises in the
network that counter perturbations, while the evidence accumulation mechanism
guarantees a reasonable decision accuracy. Neural networks enhanced with the
DDDM tested in image, speech, and text classification tasks all significantly
outperform their native counterparts, demonstrating the DDDM as a task-agnostic
defense against adversarial attacks.

Accurate uncertainty quantification is a major challenge in deep learning, as
neural networks can make overconfident errors and assign high confidence
predictions to out-of-distribution (OOD) inputs. The most popular approaches to
estimate predictive uncertainty in deep learning are methods that combine
predictions from multiple neural networks, such as Bayesian neural networks
(BNNs) and deep ensembles. However their practicality in real-time,
industrial-scale applications are limited due to the high memory and
computational cost. Furthermore, ensembles and BNNs do not necessarily fix all
the issues with the underlying member networks. In this work, we study
principled approaches to improve uncertainty property of a single network,
based on a single, deterministic representation. By formalizing the uncertainty
quantification as a minimax learning problem, we first identify distance
awareness, i.e., the model's ability to quantify the distance of a testing
example from the training data, as a necessary condition for a DNN to achieve
high-quality (i.e., minimax optimal) uncertainty estimation. We then propose
Spectral-normalized Neural Gaussian Process (SNGP), a simple method that
improves the distance-awareness ability of modern DNNs with two simple changes:
(1) applying spectral normalization to hidden weights to enforce bi-Lipschitz
smoothness in representations and (2) replacing the last output layer with a
Gaussian process layer. On a suite of vision and language understanding
benchmarks, SNGP outperforms other single-model approaches in prediction,
calibration and out-of-domain detection. Furthermore, SNGP provides
complementary benefits to popular techniques such as deep ensembles and data
augmentation, making it a simple and scalable building block for probabilistic
deep learning. Code is open-sourced at
https://github.com/google/uncertainty-baselines

Machine learning algorithms have shown potential to improve prefetching
performance by accurately predicting future memory accesses. Existing
approaches are based on the modeling of text prediction, considering
prefetching as a classification problem for sequence prediction. However, the
vast and sparse memory address space leads to large vocabulary, which makes
this modeling impractical. The number and order of outputs for multiple cache
line prefetching are also fundamentally different from text prediction. We
propose TransFetch, a novel way to model prefetching. To reduce vocabulary
size, we use fine-grained address segmentation as input. To predict unordered
sets of future addresses, we use delta bitmaps for multiple outputs. We apply
an attention-based network to learn the mapping between input and output.
Prediction experiments demonstrate that address segmentation achieves 26% - 36%
higher F1-score than delta inputs and 15% - 24% higher F1-score than page &
offset inputs for SPEC 2006, SPEC 2017, and GAP benchmarks. Simulation results
show that TransFetch achieves 38.75% IPC improvement compared with no
prefetching, outperforming the best-performing rule-based prefetcher BOP by
10.44%, and ML-based prefetcher Voyager by 6.64%.

The aim of reinforcement learning (RL) is to allow the agent to achieve the
final goal. Most RL studies have focused on improving the efficiency of
learning to achieve the final goal faster. However, the RL model is very
difficult to modify an intermediate route in the process of reaching the final
goal. That is, the agent cannot be under control to achieve other sub-goals in
the existing studies. If the agent can go through the sub-goals on the way to
the destination, the RL can be applied and studied in various fields. In this
study, I propose a methodology to achieve the user-defined sub-goals as well as
the final goal using memory editing. The memory editing is performed to
generate various sub-goals and give an additional reward to the agent. In
addition, the sub-goals are separately learned from the final goal. I set two
simple environments and various scenarios in the test environments. As a
result, the agent almost successfully passed the sub-goals as well as the final
goal under control. Moreover, the agent was able to be induced to visit the
novel state indirectly in the environments. I expect that this methodology can
be used in the fields that need to control the agent in a variety of scenarios.

More and more investors and machine learning models rely on social media
(e.g., Twitter and Reddit) to gather real-time information and sentiment to
predict stock price movements. Although text-based models are known to be
vulnerable to adversarial attacks, whether stock prediction models have similar
vulnerability is underexplored. In this paper, we experiment with a variety of
adversarial attack configurations to fool three stock prediction victim models.
We address the task of adversarial generation by solving combinatorial
optimization problems with semantics and budget constraints. Our results show
that the proposed attack method can achieve consistent success rates and cause
significant monetary loss in trading simulation by simply concatenating a
perturbed but semantically similar tweet.

Novel advanced policy gradient (APG) algorithms, such as proximal policy
optimization (PPO), trust region policy optimization, and their variations,
have become the dominant reinforcement learning (RL) algorithms because of
their ease of implementation and good practical performance. This dissertation
is concerned with theoretical justification and practical application of the
APG algorithms for solving processing network control optimization problems.
Processing network control problems are typically formulated as Markov decision
process (MDP) or semi-Markov decision process (SMDP) problems that have several
unconventional for RL features: infinite state spaces, unbounded costs,
long-run average cost objectives. Policy improvement bounds play a crucial role
in the theoretical justification of the APG algorithms. In this thesis we
refine existing bounds for MDPs with finite state spaces and prove novel policy
improvement bounds for classes of MDPs and SMDPs used to model processing
network operations. We consider two examples of processing network control
problems and customize the PPO algorithm to solve them. First, we consider
parallel-server and multiclass queueing networks controls. Second, we consider
the drivers repositioning problem in a ride-hailing service system. For both
examples the PPO algorithm with auxiliary modifications consistently generates
control policies that outperform state-of-art heuristics.

Recent research shows that supervised learning can be an effective tool for
designing near-optimal feedback controllers for high-dimensional nonlinear
dynamic systems. But the behavior of neural network controllers is still not
well understood. In particular, some neural networks with high test accuracy
can fail to even locally stabilize the dynamic system. To address this
challenge we propose several novel neural network architectures, which we show
guarantee local asymptotic stability while retaining the approximation capacity
to learn the optimal feedback policy semi-globally. The proposed architectures
are compared against standard neural network feedback controllers through
numerical simulations of two high-dimensional nonlinear optimal control
problems: stabilization of an unstable Burgers-type partial differential
equation, and altitude and course tracking for an unmanned aerial vehicle. The
simulations demonstrate that standard neural networks can fail to stabilize the
dynamics even when trained well, while the proposed architectures are always at
least locally stabilizing and can achieve near-optimal performance.

There exists a phenomenon that subjectivity highly lies in the daily
evaluation process. Our research primarily concentrates on a multi-person
evaluation system with anomaly detection to minimize the possible inaccuracy
that subjective assessment brings. We choose the two-stage screening method,
which consists of rough screening and score-weighted Kendall-$\tau$ Distance to
winnow out abnormal data, coupled with hypothesis testing to narrow global
discrepancy. Then we use Fuzzy Synthetic Evaluation Method(FSE) to determine
the significance of scores given by reviewers as well as their reliability,
culminating in a more impartial weight for each reviewer in the final
conclusion. The results demonstrate a clear and comprehensive ranking instead
of unilateral scores, and we get to have an efficiency in filtering out
abnormal data as well as a reasonably objective weight determination mechanism.
We can sense that through our study, people will have a chance of modifying a
multi-person evaluation system to attain both equity and a relatively superior
competitive atmosphere.

One of the challenges in event extraction via traditional supervised learning
paradigm is the need for a sizeable annotated dataset to achieve satisfactory
model performance. It is even more challenging when it comes to event
extraction in the finance and economics domain, a domain with considerably
fewer resources. This paper presents a complete framework for extracting and
processing crude oil-related events found in CrudeOilNews corpus, addressing
the issue of annotation scarcity and class imbalance by leveraging on the
effectiveness of transfer learning. Apart from event extraction, we place
special emphasis on event properties (Polarity, Modality, and Intensity)
classification to determine the factual certainty of each event. We build
baseline models first by supervised learning and then exploit Transfer Learning
methods to boost event extraction model performance despite the limited amount
of annotated data and severe class imbalance. This is done via methods within
the transfer learning framework such as Domain Adaptive Pre-training,
Multi-task Learning and Sequential Transfer Learning. Based on experiment
results, we are able to improve all event extraction sub-task models both in F1
and MCC1-score as compared to baseline models trained via the standard
supervised learning. Accurate and holistic event extraction from crude oil news
is very useful for downstream tasks such as understanding event chains and
learning event-event relations, which can be used for other downstream tasks
such as commodity price prediction, summarisation, etc. to support a wide range
of business decision making.

Micro-expression recognition (MER) is valuable because the involuntary nature
of micro-expressions (MEs) can reveal genuine emotions. Most works recognize
MEs by taking RGB videos or images as input. In fact, the activated facial
regions in ME images are very small and the subtle motion can be easily
submerged in the unrelated information. Facial landmarks are a low-dimensional
and compact modality, which leads to much lower computational cost and can
potentially concentrate more on ME-related features. However, the
discriminability of landmarks for MER is not clear. Thus, this paper explores
the contribution of facial landmarks and constructs a new framework to
efficiently recognize MEs with sole facial landmark information. Specially, we
design a separate structure module to separately aggregate the spatial and
temporal information in the geometric movement graph based on facial landmarks,
and a Geometric Two-Stream Graph Network is constructed to aggregate the
low-order geometric information and high-order semantic information of facial
landmarks. Furthermore, two core components are proposed to enhance features.
Specifically, a semantic adjacency matrix can automatically model the
relationship between nodes even long-distance nodes in a self-learning fashion;
and an Adaptive Action Unit loss is introduced to guide the learning process
such that the learned features are forced to have a synchronized pattern with
facial action units. Notably, this work tackles MER only utilizing geometric
features, processed based on a graph model, which provides a new idea with much
higher efficiency to promote MER. The experimental results demonstrate that the
proposed method can achieve competitive or even superior performance with a
significantly reduced computational cost, and facial landmarks can
significantly contribute to MER and are worth further study for efficient ME
analysis.

The sharing of fake news and conspiracy theories on social media has
wide-spread negative effects. By designing and applying different machine
learning models, researchers have made progress in detecting fake news from
text. However, existing research places a heavy emphasis on general,
common-sense fake news, while in reality fake news often involves rapidly
changing topics and domain-specific vocabulary. In this paper, we present our
methods and results for three fake news detection tasks at MediaEval benchmark
2021 that specifically involve COVID-19 related topics. We experiment with a
group of text-based models including Support Vector Machines, Random Forest,
BERT, and RoBERTa. We find that a pre-trained transformer yields the best
validation results, but a randomly initialized transformer with smart design
can also be trained to reach accuracies close to that of the pre-trained

The feature subset selection problem aims at selecting the relevant subset of
features to improve the performance of a Machine Learning (ML) algorithm on
training data. Some features in data can be inherently noisy, costly to
compute, improperly scaled, or correlated to other features, and they can
adversely affect the accuracy, cost, and complexity of the induced algorithm.
The goal of traditional feature selection approaches has been to remove such
irrelevant features. In recent years ML is making a noticeable impact on the
decision-making processes of our everyday lives. We want to ensure that these
decisions do not reflect biased behavior towards certain groups or individuals
based on protected attributes such as age, sex, or race. In this paper, we
present a feature subset selection approach that improves both fairness and
accuracy objectives and computes Pareto-optimal solutions using the NSGA-II
algorithm. We use statistical disparity as a fairness metric and F1-Score as a
metric for model performance. Our experiments on the most commonly used
fairness benchmark datasets with three different machine learning algorithms
show that using the evolutionary algorithm we can effectively explore the
trade-off between fairness and accuracy.

We present a short and elementary proof of the duality for Wasserstein
distributionally robust optimization, which holds for any arbitrary Kantorovich
transport distance, measurable loss function and nominal probability
distribution, so long as certain interchangeability condition holds. As an
illustration of the greater generality, we provide a rigorous treatment for
duality results in distributionally robust Markov decision processes and
distributionally robust stochastic programming.

We introduce CoLN, Combined Learning of Neural network weights, a novel
method to securely combine Machine Learning models over sensitive data with no
sharing of data. With CoLN, local hosts use the same Neural Network
architecture and base parameters to train a model using only locally available
data. Locally trained models are then submitted to a combining agent, which
produces a combined model. The new model's parameters can be sent back to
hosts, and can then be used as initial parameters for a new training iteration.
CoLN is capable of combining several distributed neural networks of the same
kind but is not restricted to any single neural architecture. In this paper we
detail the combination algorithm and present experiments with feed-forward,
convolutional, and recurrent Neural Network architectures, showing that the
CoLN combined model approximates the performance of a hypothetical ideal
centralized model, trained using the combination of the local datasets. CoLN
can contribute to secure collaborative research, as required in the medical
area, where privacy issues preclude data sharing, but where the limitations of
local data demand information derived from larger datasets.

Influence estimation analyzes how changes to the training data can lead to
different model predictions; this analysis can help us better understand these
predictions, the models making those predictions, and the data sets they're
trained on. However, most influence-estimation techniques are designed for deep
learning models with continuous parameters. Gradient-boosted decision trees
(GBDTs) are a powerful and widely-used class of models; however, these models
are black boxes with opaque decision-making processes. In the pursuit of better
understanding GBDT predictions and generally improving these models, we adapt
recent and popular influence-estimation methods designed for deep learning
models to GBDTs. Specifically, we adapt representer-point methods and TracIn,
denoting our new methods TREX and BoostIn, respectively; source code is
available at https://github.com/jjbrophy47/tree_influence. We compare these
methods to LeafInfluence and other baselines using 5 different evaluation
measures on 22 real-world data sets with 4 popular GBDT implementations. These
experiments give us a comprehensive overview of how different approaches to
influence estimation work in GBDT models. We find BoostIn is an efficient
influence-estimation method for GBDTs that performs equally well or better than
existing work while being four orders of magnitude faster. Our evaluation also
suggests the gold-standard approach of leave-one-out~(LOO) retraining
consistently identifies the single-most influential training example but
performs poorly at finding the most influential set of training examples for a
given target prediction.

NLP-powered automatic question generation (QG) techniques carry great
pedagogical potential of saving educators' time and benefiting student
learning. Yet, QG systems have not been widely adopted in classrooms to date.
In this work, we aim to pinpoint key impediments and investigate how to improve
the usability of automatic QG techniques for educational purposes by
understanding how instructors construct questions and identifying touch points
to enhance the underlying NLP models. We perform an in-depth need finding study
with 11 instructors across 7 different universities, and summarize their
thought processes and needs when creating questions. While instructors show
great interests in using NLP systems to support question design, none of them
has used such tools in practice. They resort to multiple sources of
information, ranging from domain knowledge to students' misconceptions, all of
which missing from today's QG systems. We argue that building effective
human-NLP collaborative QG systems that emphasize instructor control and
explainability is imperative for real-world adoption. We call for QG systems to
provide process-oriented support, use modular design, and handle diverse
sources of input.

Traditional Graph Neural Networks (GNNs) rely on message passing, which
amounts to permutation-invariant local aggregation of neighbour features. Such
a process is isotropic and there is no notion of `direction' on the graph. We
present a new GNN architecture called Graph Anisotropic Diffusion. Our model
alternates between linear diffusion, for which a closed-form solution is
available, and local anisotropic filters to obtain efficient multi-hop
anisotropic kernels. We test our model on two common molecular property
prediction benchmarks (ZINC and QM9) and show its competitive performance.

Orthogonal statistical learning and double machine learning have emerged as
general frameworks for two-stage statistical prediction in the presence of a
nuisance component. We establish non-asymptotic bounds on the excess risk of
orthogonal statistical learning methods with a loss function satisfying a
self-concordance property. Our bounds improve upon existing bounds by a
dimension factor while lifting the assumption of strong convexity. We
illustrate the results with examples from multiple treatment effect estimation
and generalized partially linear modeling.

This paper presents a new end-to-end signal classification method using the
signed cumulative distribution transform (SCDT). We adopt a transport-based
generative model to define the classification problem. We then make use of
mathematical properties of the SCDT to render the problem easier in transform
domain, and solve for the class of an unknown sample using a nearest local
subspace (NLS) search algorithm in SCDT domain. Experiments show that the
proposed method provides high accuracy classification results while being data
efficient, robust to out-of-distribution samples, and competitive in terms of
computational complexity with respect to the deep learning end-to-end
classification methods. The implementation of the proposed method in Python
language is integrated as a part of the software package PyTransKit
(https://github.com/rohdelab/PyTransKit).

Image compositing is one of the most fundamental steps in creative workflows.
It involves taking objects/parts of several images to create a new image,
called a composite. Currently, this process is done manually by creating
accurate masks of objects to be inserted and carefully blending them with the
target scene or images, usually with the help of tools such as Photoshop or
GIMP. While there have been several works on automatic selection of objects for
creating masks, the problem of object placement within an image with the
correct position, scale, and harmony remains a difficult problem with limited
exploration. Automatic object insertion in images or designs is a difficult
problem as it requires understanding of the scene geometry and the color
harmony between objects. We propose LayoutBERT for the object insertion task.
It uses a novel self-supervised masked language model objective and
bidirectional multi-head self-attention. It outperforms previous layout-based
likelihood models and shows favorable properties in terms of model capacity. We
demonstrate the effectiveness of our approach for object insertion in the image
compositing setting and other settings like documents and design templates. We
further demonstrate the usefulness of the learned representations for
layout-based retrieval tasks. We provide both qualitative and quantitative
evaluations on datasets from diverse domains like COCO, PublayNet, and two new
datasets which we call Image Layouts and Template Layouts. Image Layouts which
consists of 5.8 million images with layout annotations is the largest image
layout dataset to our knowledge. We also share ablation study results on the
effect of dataset size, model size and class sample size for this task.

Risk-aware urban localization with the Global Navigation Satellite System
(GNSS) remains an unsolved problem with frequent misdetection of the user's
street or side of the street. Significant advances in 3D map-aided GNSS use
grid-based GNSS shadow matching alongside AI-driven line-of-sight (LOS)
classifiers and server-based processing to improve localization accuracy,
especially in the cross-street direction. Our prior work introduces a new
paradigm for shadow matching that proposes set-valued localization with
computationally efficient zonotope set representations. While existing
literature improved accuracy and efficiency, the current state of shadow
matching theory does not address the needs of risk-aware autonomous systems. We
extend our prior work to propose Mosaic Zonotope Shadow Matching (MZSM) that
employs a classifier-agnostic polytope mosaic architecture to provide
risk-awareness and certifiable guarantees on urban positioning. We formulate a
recursively expanding binary tree that refines an initial location estimate
with set operations into smaller polytopes. Together, the smaller polytopes
form a mosaic. We weight the tree branches with the probability that the user
is in line of sight of the satellite and expand the tree with each new
satellite observation. Our method yields an exact shadow matching distribution
from which we guarantee uncertainty bounds on the user localization. We perform
high-fidelity simulations using a 3D building map of San Francisco to validate
our algorithm's risk-aware improvements. We demonstrate that MZSM provides
certifiable guarantees across varied data-driven LOS classifier accuracies and
yields a more precise understanding of the uncertainty over existing methods.
We validate that our tree-based construction is efficient and tractable,
computing a mosaic from 14 satellites in 0.63 seconds and growing quadratically
in the satellite number.

Deep neural networks achieve human-like performance on a variety of
perceptual and decision-making tasks. However, networks perform poorly when
confronted with changing tasks or goals, and broadly fail to match the
flexibility and robustness of human intelligence. Here, we develop a
mathematical and algorithmic framework that enables flexible and continuous
training of neural networks on a range of objectives by constructing path
connected sets of networks that achieve equivalent functional performance on a
given machine learning task. We view the weight space of a neural network as a
curved Riemannian manifold and move a network along a functionally invariant
path in weight space while searching for networks that satisfy secondary
objectives. A path-sampling algorithm trains computer vision and natural
language processing networks with millions of weight parameters to learn a
series of classification tasks without performance loss while accommodating
secondary objectives including network sparsification, incremental task
learning, and increased adversarial robustness. Broadly, we conceptualize a
neural network as a mathematical object that can be iteratively transformed
into distinct configurations by the path-sampling algorithm to define a
sub-manifold of networks that can be harnessed to achieve user goals.

Rapid development of large-scale pre-training has resulted in foundation
models that can act as effective feature extractors on a variety of downstream
tasks and domains. Motivated by this, we study the efficacy of pre-trained
vision models as a foundation for downstream continual learning (CL) scenarios.
Our goal is twofold. First, we want to understand the compute-accuracy
trade-off between CL in the raw-data space and in the latent space of
pre-trained encoders. Second, we investigate how the characteristics of the
encoder, the pre-training algorithm and data, as well as of the resulting
latent space affect CL performance. For this, we compare the efficacy of
various pre-trained models in large-scale benchmarking scenarios with a vanilla
replay setting applied in the latent and in the raw-data space. Notably, this
study shows how transfer, forgetting, task similarity and learning are
dependent on the input data characteristics and not necessarily on the CL
algorithms. First, we show that under some circumstances reasonable CL
performance can readily be achieved with a non-parametric classifier at
negligible compute. We then show how models pre-trained on broader data result
in better performance for various replay sizes. We explain this with
representational similarity and transfer properties of these representations.
Finally, we show the effectiveness of self-supervised pre-training for
downstream domains that are out-of-distribution as compared to the pre-training
domain. We point out and validate several research directions that can further
increase the efficacy of latent CL including representation ensembling. The
diverse set of datasets used in this study can serve as a compute-efficient
playground for further CL research. The codebase is available under
https://github.com/oleksost/latent_CL.

Terahertz computed tomography (THz CT) has drawn significant attention
because of its unique capability to bring multi-dimensional object information
from invisible to visible. However, current physics-model-based THz CT
modalities present low data use efficiency on time-resolved THz signals and low
model fusion extensibility, limiting their application fields' practical use.
In this paper, we propose a supervised THz deep learning computed tomography
(THz DL-CT) framework based on time-domain information. THz DL-CT restores
superior THz tomographic images of 3D objects by extracting features from
spatio-temporal THz signals without any prior material information. Compared
with conventional and machine learning based methods, THz DL-CT delivers at
least 50.2%, and 52.6% superior in root mean square error (RMSE) and structural
similarity index (SSIM), respectively. Additionally, we have experimentally
demonstrated that the pretrained THz DL-CT model can generalize to reconstruct
multi-material systems with no prerequisite information. THz CT through the DL
data fusion approach provides a new pathway for non-invasive functional imaging
in object investigation.

Machine settings and tuning are critical for digital fabrication outcomes.
However, exploring these parameters is non-trivial. We seek to enable
exploration of the full design space of digital fabrication. To identify where
we might intervene, we studied how practitioners approach 3D printing. We found
that beyond using CAD/CAM, they create bespoke routines and workflows to
explore interdependent material and machine settings. We seek to provide a
system that supports this workflow development. We identified design goals
around material exploration, fine-tuned control, and iteration. Based on these,
we present p5.fab, a system for controlling digital fabrication machines from
the creative coding environment p5.js. We demonstrate p5.fab with examples of
3D prints that cannot be made with traditional 3D printing software. We
evaluate p5.fab in workshops and find that it encourages novel printing
workflows and artifacts. Finally, we discuss implications for future digital
fabrication systems.

Sequential recommendation (SR) learns from the temporal dynamics of user-item
interactions to predict the next ones. Fairness-aware recommendation mitigates
a variety of algorithmic biases in the learning of user preferences. This paper
aims at bringing a marriage between SR and algorithmic fairness. We propose a
novel fairness-aware sequential recommendation task, in which a new metric,
interaction fairness, is defined to estimate how recommended items are fairly
interacted by users with different protected attribute groups. We propose a
multi-task learning based deep end-to-end model, FairSR, which consists of two
parts. One is to learn and distill personalized sequential features from the
given user and her item sequence for SR. The other is fairness-aware preference
graph embedding (FPGE). The aim of FPGE is two-fold: incorporating the
knowledge of users' and items' attributes and their correlation into entity
representations, and alleviating the unfair distributions of user attributes on
items. Extensive experiments conducted on three datasets show FairSR can
outperform state-of-the-art SR models in recommendation performance. In
addition, the recommended items by FairSR also exhibit promising interaction
fairness.

During the last decade, Natural Language Processing has become, after
Computer Vision, the second field of Artificial Intelligence that was massively
changed by the advent of Deep Learning. Regardless of the architecture, the
language models of the day need to be able to process or generate text, as well
as predict missing words, sentences or relations depending on the task. Due to
their black-box nature, such models are difficult to interpret and explain to
third parties. Visualization is often the bridge that language model designers
use to explain their work, as the coloring of the salient words and phrases,
clustering or neuron activations can be used to quickly understand the
underlying models. This paper showcases the techniques used in some of the most
popular Deep Learning for NLP visualizations, with a special focus on
interpretability and explainability.

Getting up from an arbitrary fallen state is a basic human skill. Existing
methods for learning this skill often generate highly dynamic and erratic
get-up motions, which do not resemble human get-up strategies, or are based on
tracking recorded human get-up motions. In this paper, we present a staged
approach using reinforcement learning, without recourse to motion capture data.
The method first takes advantage of a strong character model, which facilitates
the discovery of solution modes. A second stage then learns to adapt the
control policy to work with progressively weaker versions of the character.
Finally, a third stage learns control policies that can reproduce the weaker
get-up motions at much slower speeds. We show that across multiple runs, the
method can discover a diverse variety of get-up strategies, and execute them at
a variety of speeds. The results usually produce policies that use a final
stand-up strategy that is common to the recovery motions seen from all initial
states. However, we also find policies for which different strategies are seen
for prone and supine initial fallen states. The learned get-up control
strategies often have significant static stability, i.e., they can be paused at
a variety of points during the get-up motion. We further test our method on
novel constrained scenarios, such as having a leg and an arm in a cast.

Transformer-based pre-trained models with millions of parameters require
large storage. Recent approaches tackle this shortcoming by training adapters,
but these approaches still require a relatively large number of parameters. In
this study, AdapterBias, a surprisingly simple yet effective adapter
architecture, is proposed. AdapterBias adds a token-dependent shift to the
hidden output of transformer layers to adapt to downstream tasks with only a
vector and a linear layer. Extensive experiments are conducted to demonstrate
the effectiveness of AdapterBias. The experiments show that our proposed method
can dramatically reduce the trainable parameters compared to the previous works
with a minimal decrease in task performances compared with fine-tuned
pre-trained models. We further find that AdapterBias automatically learns to
assign more significant representation shifts to the tokens related to the task
in consideration.

As deep learning advances, there is an ever-growing demand for models capable
of synthesizing information from multi-modal resources to address the complex
tasks raised from real-life applications. Recently, many large multi-modal
datasets have been collected, on which researchers actively explore different
methods of fusing multi-modal information. However, little attention has been
paid to quantifying the contribution of different modalities within the
proposed models. In this paper, we propose the {\bf SH}apley v{\bf A}lue-based
{\bf PE}rceptual (SHAPE) scores that measure the marginal contribution of
individual modalities and the degree of cooperation across modalities. Using
these scores, we systematically evaluate different fusion methods on different
multi-modal datasets for different tasks. Our experiments suggest that for some
tasks where different modalities are complementary, the multi-modal models
still tend to use the dominant modality alone and ignore the cooperation across
modalities. On the other hand, models learn to exploit cross-modal cooperation
when different modalities are indispensable for the task. In this case, the
scores indicate it is better to fuse different modalities at relatively early
stages. We hope our scores can help improve the understanding of how the
present multi-modal models operate on different modalities and encourage more
sophisticated methods of integrating multiple modalities.

As the body of research on machine narrative comprehension grows, there is a
critical need for consideration of performance assessment strategies as well as
the depth and scope of different benchmark tasks. Based on narrative theories,
reading comprehension theories, as well as existing machine narrative reading
comprehension tasks and datasets, we propose a typology that captures the main
similarities and differences among assessment tasks; and discuss the
implications of our typology for new task design and the challenges of
narrative reading comprehension.

We present a novel procedure for optimization based on the combination of
efficient quantized tensor train representation and a generalized maximum
matrix volume principle. We demonstrate the applicability of the new Tensor
Train Optimizer (TTOpt) method for various tasks, ranging from minimization of
multidimensional functions to reinforcement learning. Our algorithm compares
favorably to popular evolutionary-based methods and outperforms them by the
number of function evaluations or execution time, often by a significant
margin.

In multi-agent settings, game theory is a natural framework for describing
the strategic interactions of agents whose objectives depend upon one another's
behavior. Trajectory games capture these complex effects by design. In
competitive settings, this makes them a more faithful interaction model than
traditional "predict then plan" approaches. However, current game-theoretic
planning methods have important limitations. In this work, we propose two main
contributions. First, we introduce an offline training phase which reduces the
online computational burden of solving trajectory games. Second, we formulate a
lifted game which allows players to optimize multiple candidate trajectories in
unison and thereby construct more competitive "mixed" strategies. We validate
our approach on a number of experiments using the pursuit-evasion game "tag."

I summarize our recent results to use the orbits of globular clusters (GCs)
in the Fornax dwarf spheroidal (dSph) galaxy to learn more about dark matter
(DM) properties. Our focus is on clarifying how dynamical friction (DF) from
the DM halo is modified from the different microscopic properties of DM, which
may alter $both$ the scattering processes responsible of DF and the DM profiles
(in particular generating a core), which also modifies DF. We consider: $(i)$
fermionic degenerate dark matter (DDM), where Pauli blocking should be taken
into account in the dynamical friction computation; $(ii)$ self-interacting
dark matter (SIDM) and $(iii)$ ultralight dark matter (ULDM), for which this
problem has been addressed by a variety of methods in recent literature. We
derive DF with a Fokker-Planck formalism, reproducing previous results for ULDM
and cold DM, while providing new results for DDM. Furthermore, ULDM, DDM and
SIDM may generate cores in dSphs, which suppress dynamical friction and prolong
GC orbits. We conclude that in all these cases the modifications in the DM
modelling does not easily solve the so-called timing `problem' of Fornax GCs.
We finally study this `problem' in terms of the initial conditions,
demonstrating that the observed orbits of Fornax GCs are consistent with this
expectation of a cuspy DM profile with a mild `fine-tuning' at the level of
$\sim25\%$.

Fatigue is a loss in cognitive or physical performance due to physiological
factors such as insufficient sleep, long work hours, stress, and physical
exertion. It adversely affects the human body and can slow reaction times,
reduce attention, and limit short-term memory. Hence, there is a need to
monitor a person's state to avoid extreme fatigue conditions that can result in
physiological complications. However, tools to understand and assess fatigue
are minimal. This paper primarily focuses on building an experimental setup
that induces cognitive fatigue (CF) and physical fatigue (PF) through multiple
cognitive and physical tasks while simultaneously recording physiological data.
First, we built a prototype sensor suit embedded with numerous physiological
sensors for easy use during data collection. Second, participants'
self-reported visual analog scores (VAS) are reported after each task to
confirm fatigue induction. Finally, an evaluation system is built that utilizes
machine learning (ML) models to detect states of CF and PF from sensor data,
thus providing an objective measure. Our methods beat state-of-the-art
approaches, where Random Forest performs the best in detecting PF with an
accuracy of 80.5% while correctly predicting the true PF condition 88% of the
time. On the other hand, the long short-term memory (LSTM) recurrent neural
network produces the best results in detecting CF in the subjects (with 84.1%
accuracy, 0.9 recall).

We construct a reduced, data-driven, parameter dependent effective Stochastic
Differential Equation (eSDE) for electric-field mediated colloidal
crystallization using data obtained from Brownian Dynamics Simulations. We use
Diffusion Maps (a manifold learning algorithm) to identify a set of useful
latent observables. In this latent space we identify an eSDE using a deep
learning architecture inspired by numerical stochastic integrators and compare
it with the traditional Kramers-Moyal expansion estimation. We show that the
obtained variables and the learned dynamics accurately encode the physics of
the Brownian Dynamic Simulations. We further illustrate that our reduced model
captures the dynamics of corresponding experimental data. Our dimension
reduction/reduced model identification approach can be easily ported to a broad
class of particle systems dynamics experiments/models.

Wordle is a very popular word game that is owned by the New York Times. We
can design parameterized strategies for solving Wordle, based on probabilistic,
statistical, and information-theoretical information about the games. The
strategies can handle a reasonably large family of Wordle-like games both
systematically and dynamically, meaning that we do not rely on precomputations
that may work for non-fixed games. More specifically, the answer set can be
arbitrary, not confining to the current 2315 words. The answer words may
include any specific number of letters (does not have to be five), and the set
of symbols that form the words does not have to be limited to only the English
alphabet.
  Exploring possible strategies for solving the Wordle-like games offers an
attractive learning challenges for students who are learning to design computer
games. This paper will provide the results of using two families of
parameterized strategies to solve the current Wordle, using the simulator that
abides by the hard-mode rules as the baseline. The baseline simulator used an
average of 4.078 guesses to find the 2315 answers, and needed more than six
trials to solve the game 1.77% of the time. The best performing strategy of
ours used an average of 3.674 guesses to find the 2315 answers, and failed
0.65% of the time.

This paper describes the approach to the Emotion Classification shared task
held at WASSA 2022 by team PVGs AI Club. This Track 2 sub-task focuses on
building models which can predict a multi-class emotion label based on essays
from news articles where a person, group or another entity is affected.
Baseline transformer models have been demonstrating good results on sequence
classification tasks, and we aim to improve this performance with the help of
ensembling techniques, and by leveraging two variations of emotion-specific
representations. We observe better results than our baseline models and achieve
an accuracy of 0.619 and a macro F1 score of 0.520 on the emotion

The advent of Artificial Intelligence (AI) has impacted all aspects of human
life. One of the concrete examples of AI impact is visible in radio
positioning. In this article, for the first time we utilize the power of AI by
training a Convolutional Neural Network (CNN) using 5G New Radio (NR)
fingerprints consisting of beamformed Channel State Information (CSI). By
observing CSI, it is possible to characterize the multipath channel between the
transmitter and the receiver, and thus provide a good source of spatiotemporal
data to find the position of a User Equipment (UE). We collect
ray-tracing-based 5G NR CSI from an urban area. The CSI data of the signals
from one Base Station (BS) is collected at the reference points with known
positions to train a CNN. We evaluate our work by testing: a) the robustness of
the trained network for estimating the positions for the new measurements on
the same reference points and b) the accuracy of the CNN-based position
estimation while the UE is on points other than the reference points. The
results prove that our trained network for a specific urban environment can
estimate the UE position with a minimum mean error of 0.98 m.

The theoretical analysis of spectral clustering mainly focuses on
consistency, while there is relatively little research on its generalization
performance. In this paper, we study the excess risk bounds of the popular
spectral clustering algorithms: \emph{relaxed} RatioCut and \emph{relaxed}
NCut. Firstly, we show that their excess risk bounds between the empirical
continuous optimal solution and the population-level continuous optimal
solution have a $\mathcal{O}(1/\sqrt{n})$ convergence rate, where $n$ is the
sample size. Secondly, we show the fundamental quantity in influencing the
excess risk between the empirical discrete optimal solution and the
population-level discrete optimal solution. At the empirical level, algorithms
can be designed to reduce this quantity. Based on our theoretical analysis, we
propose two novel algorithms that can not only penalize this quantity, but also
cluster the out-of-sample data without re-eigendecomposition on the overall
sample. Experiments verify the effectiveness of the proposed algorithms.

Computational spectrometers have mobile application potential, such as
on-site detection and self-diagnosis, by offering compact size, fast operation
time, high resolution, wide working range, and low-cost production. Although
these spectrometers have been extensively studied, demonstrations are confined
to a few examples of straightforward spectra. This study demonstrates deep
learning (DL)-based single-shot computational spectrometer for narrow and broad
spectra using a multilayer thin-film filter array. For measuring light
intensities, the device was built by attaching the filter array, fabricated
using a wafer-level stencil lithography process, to a complementary
metal-oxide-semiconductor image sensor. All the intensities were extracted from
a monochrome image captured with a single exposure. A DL architecture
comprising a dense layer and a U-Net backbone with residual connections was
employed for spectrum reconstruction. The measured intensities were fed into
the DL architecture for reconstruction as spectra. We reconstructed 323
continuous spectra with an average root mean squared error of 0.0288 in a
500-850 nm wavelength range with 1-nm spacing. Our computational spectrometer
achieved a compact size, fast measuring time, high resolution, and wide working
range.

When deep neural network (DNN) was first introduced to the medical image
analysis community, researchers were impressed by its performance. However, it
is evident now that a large number of manually labeled data is often a must to
train a properly functioning DNN. This demand for supervision data and labels
is a major bottleneck in current medical image analysis, since collecting a
large number of annotations from experienced experts can be time-consuming and
expensive. In this paper, we demonstrate that the eye movement of radiologists
reading medical images can be a new form of supervision to train the DNN-based
computer-aided diagnosis (CAD) system. Particularly, we record the tracks of
the radiologists' gaze when they are reading images. The gaze information is
processed and then used to supervise the DNN's attention via an Attention
Consistency module. To the best of our knowledge, the above pipeline is among
the earliest efforts to leverage expert eye movement for deep-learning-based
CAD. We have conducted extensive experiments on knee X-ray images for
osteoarthritis assessment. The results show that our method can achieve
considerable improvement in diagnosis performance, with the help of gaze

The emerging Graph Convolutional Network (GCN) has now been widely used in
many domains, and it is challenging to improve the efficiencies of applications
by accelerating the GCN trainings. For the sparsity nature and exploding scales
of input real-world graphs, state-of-the-art GCN training systems (e.g.,
GNNAdvisor) employ graph processing techniques to accelerate the message
exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these
systems treat both the aggregation stages of forward and backward propagation
phases as all-active graph processing procedures that indiscriminately conduct
computation on all vertices of an input graph.
  In this paper, we first point out that in a GCN training problem with a given
training set, the aggregation stages of its backward propagation phase (called
as backward aggregations in this paper) can be converted to partially-active
graph processing procedures, which conduct computation on only partial vertices
of the input graph. By leveraging such a finding, we propose an execution path
preparing method that collects and coalesces the data used during backward
propagations of GCN training conducted on GPUs. The experimental results show
that compared with GNNAdvisor, our approach improves the performance of the
backward aggregation of GCN trainings on typical real-world graphs by
1.48x~5.65x. Moreover, the execution path preparing can be conducted either
before the training (during preprocessing) or on-the-fly with the training.
When used during preprocessing, our approach improves the overall GCN training
by 1.05x~1.37x. And when used on-the-fly, our approach improves the overall GCN
training by 1.03x~1.35x.

Would you trust physicians if they cannot explain their decisions to you?
Medical diagnostics using machine learning gained enormously in importance
within the last decade. However, without further enhancements many
state-of-the-art machine learning methods are not suitable for medical
application. The most important reasons are insufficient data set quality and
the black-box behavior of machine learning algorithms such as Deep Learning
models. Consequently, end-users cannot correct the model's decisions and the
corresponding explanations. The latter is crucial for the trustworthiness of
machine learning in the medical domain. The research field explainable
interactive machine learning searches for methods that address both
shortcomings. This paper extends the explainable and interactive CAIPI
algorithm and provides an interface to simplify human-in-the-loop approaches
for image classification. The interface enables the end-user (1) to investigate
and (2) to correct the model's prediction and explanation, and (3) to influence
the data set quality. After CAIPI optimization with only a single
counterexample per iteration, the model achieves an accuracy of $97.48\%$ on
the Medical MNIST and $95.02\%$ on the Fashion MNIST. This accuracy is
approximately equal to state-of-the-art Deep Learning optimization procedures.
Besides, CAIPI reduces the labeling effort by approximately $80\%$.

The Yunshan Cup 2020 track focused on creating a framework for evaluating
different methods of part-of-speech (POS). There were two tasks for this track:
(1) POS tagging for the Indonesian language, and (2) POS tagging for the Lao
tagging. The Indonesian dataset is comprised of 10000 sentences from Indonesian
news within 29 tags. And the Lao dataset consists of 8000 sentences within 27
tags. 25 teams registered for the task. The methods of participants ranged from
feature-based to neural networks using either classical machine learning
techniques or ensemble methods. The best performing results achieve an accuracy
of 95.82% for Indonesian and 93.03%, showing that neural sequence labeling
models significantly outperform classic feature-based methods and rule-based
methods.

Clustering a group of vertices in networks facilitates applications across
different domains, such as social computing and Internet of Things. However,
challenges arises for clustering networks with increased scale. This paper
proposes a solution which consists of two motif clustering techniques: standard
acceleration CHIEF-ST and approximate acceleration CHIEF-AP. Both algorithms
first find the maximal k-edge-connected subgraphs within the target networks to
lower the network scale, then employ higher-order motifs in clustering. In the
first procedure, we propose to lower the network scale by optimizing the
network structure with maximal k-edge-connected subgraphs. For CHIEF-ST, we
illustrate that all target motifs will be kept after this procedure when the
minimum node degree of the target motif is equal or greater than k. For
CHIEF-AP, we prove that the eigenvalues of the adjacency matrix and the
Laplacian matrix are relatively stable after this step. That is, CHIEF-ST has
no influence on motif clustering, whereas CHIEF-AP introduces limited yet
acceptable impact. In the second procedure, we employ higher-order motifs,
i.e., heterogeneous four-node motifs clustering in higher-order dense networks.
The contributions of CHIEF are two-fold: (1) improved efficiency of motif
clustering for big networks; (2) verification of higher-order motif
significance. The proposed solutions are found to outperform baseline
approaches according to experiments on real and synthetic networks, which
demonstrates CHIEF's strength in large network analysis. Meanwhile,
higher-order motifs are proved to perform better than traditional triangle
motifs in clustering.

Differential privacy (DP) is considered to be an effective
privacy-preservation method to secure the promising distributed machine
learning (ML) paradigm-federated learning (FL) from privacy attacks (e.g.,
membership inference attack). Nevertheless, while the DP mechanism greatly
alleviates privacy concerns, recent studies have shown that it can be exploited
to conduct security attacks (e.g., false data injection attacks). To address
such attacks on FL-based applications in critical infrastructures, in this
paper, we perform the first systematic study on the DP-exploited poisoning
attacks from an adversarial point of view. We demonstrate that the DP method,
despite providing a level of privacy guarantee, can effectively open a new
poisoning attack vector for the adversary. Our theoretical analysis and
empirical evaluation of a smart grid dataset show the FL performance
degradation (sub-optimal model generation) scenario due to the differential
noise-exploited selective model poisoning attacks. As a countermeasure, we
propose a reinforcement learning-based differential privacy level selection
(rDP) process. The rDP process utilizes the differential privacy parameters
(privacy loss, information leakage probability, etc.) and the losses to
intelligently generate an optimal privacy level for the nodes. The evaluation
shows the accumulated reward and errors of the proposed technique converge to
an optimal privacy policy.

Urban metro flow prediction is of great value for metro operation scheduling,
passenger flow management and personal travel planning. However, it faces two
main challenges. First, different metro stations, e.g. transfer stations and
non-transfer stations, have unique traffic patterns. Second, it is challenging
to model complex spatio-temporal dynamic relation of metro stations. To address
these challenges, we develop a spatio-temporal dynamic graph relational
learning model (STDGRL) to predict urban metro station flow. First, we propose
a spatio-temporal node embedding representation module to capture the traffic
patterns of different stations. Second, we employ a dynamic graph relationship
learning module to learn dynamic spatial relationships between metro stations
without a predefined graph adjacency matrix. Finally, we provide a
transformer-based long-term relationship prediction module for long-term metro
flow prediction. Extensive experiments are conducted based on metro data in
Beijing, Shanghai, Chongqing and Hangzhou. Experimental results show the
advantages of our method beyond 11 baselines for urban metro flow prediction.

In recent years, due to the wider WiFi coverage and the popularization of
mobile communication devices, the technology of indoor positioning using WiFi
fingerprints has been rapidly developed. Currently, most supervised methods
need to collect a large amount of data to construct fingerprint datasets, which
is labor-intensive and time-consuming. In addition, many studies focused on the
ideal laboratory environment and lack the consideration in the practical
application environment, especially in the scenario of multiple large
multi-floor buildings. To solve these problems, we proposed a novel WiDAGCN
model which can be trained by a few labeled site survey data and unlabeled
crowdsensing WiFi fingerprints. To comprehensively represent the topology
structure of the data, we constructed heterogeneous graphs according to the
received signal strength indicators (RSSIs) between the waypoints and WiFi
access points (APs). Moreover, previous WiFi indoor localization studies rarely
involved complete graph feature representation, thus we use graph convolutional
network (GCN) to extract graph-level embeddings. There are also some difficult
problems, for example, a large amount of unlabeled data that cannot be applied
to a supervised model, and the existence of multiple data domains leads to
inconsistency in data distribution. Therefore, a semi-supervised domain
adversarial training scheme was used to make full use of unlabeled data and
align the data distribution of different domains. A public indoor localization
dataset containing different buildings was used to evaluate the performance of
the model. The experimental results show that our system can achieve a
competitive localization accuracy in large buildings such as shopping malls.

Anticipating price developments in financial markets is a topic of continued
interest in forecasting. Funneled by advancements in deep learning and natural
language processing (NLP) together with the availability of vast amounts of
textual data in form of news articles, social media postings, etc., an
increasing number of studies incorporate text-based predictors in forecasting
models. We contribute to this literature by introducing weak learning, a
recently proposed NLP approach to address the problem that text data is
unlabeled. Without a dependent variable, it is not possible to finetune
pretrained NLP models on a custom corpus. We confirm that finetuning using weak
labels enhances the predictive value of text-based features and raises forecast
accuracy in the context of predicting cryptocurrency returns. More
fundamentally, the modeling paradigm we present, weak labeling domain-specific
text and finetuning pretrained NLP models, is universally applicable in
(financial) forecasting and unlocks new ways to leverage text data.

Estimating Head-Related Transfer Functions (HRTFs) of arbitrary source points
is essential in immersive binaural audio rendering. Computing each individual's
HRTFs is challenging, as traditional approaches require expensive time and
computational resources, while modern data-driven approaches are data-hungry.
Especially for the data-driven approaches, existing HRTF datasets differ in
spatial sampling distributions of source positions, posing a major problem when
generalizing the method across multiple datasets. To alleviate this, we propose
a deep learning method based on a novel conditioning architecture. The proposed
method can predict an HRTF of any position by interpolating the HRTFs of known
distributions. Experimental results show that the proposed architecture
improves the model's generalizability across datasets with various coordinate
systems. Additional demonstrations show that the model robustly reconstructs
the target HRTFs from the spatially downsampled HRTFs in both quantitative and
perceptual measures.

The reliability of cloud platforms is of significant relevance because
society increasingly relies on complex software systems running on the cloud.
To improve it, cloud providers are automating various maintenance tasks, with
failure identification frequently being considered. The precondition for
automation is the availability of observability tools, with system logs
commonly being used. The focus of this paper is log-based failure
identification. This problem is challenging because of the instability of the
log data and the incompleteness of the explicit logging failure coverage within
the code. To address the two challenges, we present CLog as a method for
failure identification. The key idea presented herein based is on our
observation that by representing the log data as sequences of subprocesses
instead of sequences of log events, the effect of the unstable log data is
reduced. CLog introduces a novel subprocess extraction method that uses
context-aware neural network and clustering methods to extract meaningful
subprocesses. The direct modeling of log event contexts allows the
identification of failures with respect to the abrupt context changes,
addressing the challenge of insufficient logging failure coverage. Our
experimental results demonstrate that the learned subprocesses representations
reduce the instability in the input, allowing CLog to outperform the baselines
on the failure identification subproblems - 1) failure detection by 9-24% on F1
score and 2) failure type identification by 7% on the macro averaged F1 score.
Further analysis shows the existent negative correlation between the
instability in the input event sequences and the detection performance in a
model-agnostic manner.

We study a Federated Reinforcement Learning (FedRL) problem in which $n$
agents collaboratively learn a single policy without sharing the trajectories
they collected during agent-environment interaction. We stress the constraint
of environment heterogeneity, which means $n$ environments corresponding to
these $n$ agents have different state transitions. To obtain a value function
or a policy function which optimizes the overall performance in all
environments, we propose two federated RL algorithms, \texttt{QAvg} and
\texttt{PAvg}. We theoretically prove that these algorithms converge to
suboptimal solutions, while such suboptimality depends on how heterogeneous
these $n$ environments are. Moreover, we propose a heuristic that achieves
personalization by embedding the $n$ environments into $n$ vectors. The
personalization heuristic not only improves the training but also allows for
better generalization to new environments.

Text classification is a representative downstream task of natural language
processing, and has exhibited excellent performance since the advent of
pre-trained language models based on Transformer architecture. However, in
pre-trained language models, under-fitting often occurs due to the size of the
model being very large compared to the amount of available training data. Along
with significant importance of data collection in modern machine learning
paradigm, studies have been actively conducted for natural language data
augmentation. In light of this, we introduce three data augmentation schemes
that help reduce underfitting problems of large-scale language models.
Primarily we use a generation model for data augmentation, which is defined as
Data Augmentation with Generation (DAG). Next, we augment data using text
modification techniques such as corruption and word order change (Data
Augmentation with Modification, DAM). Finally, we propose Data Augmentation
with Generation And Modification (DAGAM), which combines DAG and DAM techniques
for a boosted performance. We conduct data augmentation for six benchmark
datasets of text classification task, and verify the usefulness of DAG, DAM,
and DAGAM through BERT-based fine-tuning and evaluation, deriving better
results compared to the performance with original datasets.

Federated learning (FL) is identified as a crucial enabler for large-scale
distributed machine learning (ML) without the need for local raw dataset
sharing, substantially reducing privacy concerns and alleviating the isolated
data problem. In reality, the prosperity of FL is largely due to a centralized
framework called FedAvg, in which workers are in charge of model training and
servers are in control of model aggregation. However, FedAvg's centralized
worker-server architecture has raised new concerns, be it the low scalability
of the cluster, the risk of data leakage, and the failure or even defection of
the central server. To overcome these problems, we propose Decentralized
Federated Trusted Averaging (DeFTA), a decentralized FL framework that serves
as a plug-and-play replacement for FedAvg, instantly bringing better security,
scalability, and fault-tolerance to the federated learning process after
installation. In principle, it fundamentally resolves the above-mentioned
issues from an architectural perspective without compromises or tradeoffs,
primarily consisting of a new model aggregating formula with theoretical
performance analysis, and a decentralized trust system (DTS) to greatly improve
system robustness. Note that since DeFTA is an alternative to FedAvg at the
framework level, \textit{prevalent algorithms published for FedAvg can be also
utilized in DeFTA with ease}. Extensive experiments on six datasets and six
basic models suggest that DeFTA not only has comparable performance with FedAvg
in a more realistic setting, but also achieves great resilience even when 66%
of workers are malicious. Furthermore, we also present an asynchronous variant
of DeFTA to endow it with more powerful usability.

Multi-image super-resolution from multi-temporal satellite acquisitions of a
scene has recently enjoyed great success thanks to new deep learning models. In
this paper, we go beyond classic image reconstruction at a higher resolution by
studying a super-resolved inference problem, namely semantic segmentation at a
spatial resolution higher than the one of sensing platform. We expand upon
recently proposed models exploiting temporal permutation invariance with a
multi-resolution fusion module able to infer the rich semantic information
needed by the segmentation task. The model presented in this paper has recently
won the AI4EO challenge on Enhanced Sentinel 2 Agriculture.

The diffusion of rumors on microblogs generally follows a propagation tree
structure, that provides valuable clues on how an original message is
transmitted and responded by users over time. Recent studies reveal that rumor
detection and stance detection are two different but relevant tasks which can
jointly enhance each other, e.g., rumors can be debunked by cross-checking the
stances conveyed by their relevant microblog posts, and stances are also
conditioned on the nature of the rumor. However, most stance detection methods
require enormous post-level stance labels for training, which are
labor-intensive given a large number of posts. Enlightened by Multiple Instance
Learning (MIL) scheme, we first represent the diffusion of claims with
bottom-up and top-down trees, then propose two tree-structured weakly
supervised frameworks to jointly classify rumors and stances, where only the
bag-level labels concerning claim's veracity are needed. Specifically, we
convert the multi-class problem into a multiple MIL-based binary classification
problem where each binary model focuses on differentiating a target stance or
rumor type and other types. Finally, we propose a hierarchical attention
mechanism to aggregate the binary predictions, including (1) a bottom-up or
top-down tree attention layer to aggregate binary stances into binary veracity;
and (2) a discriminative attention layer to aggregate the binary class into
finer-grained classes. Extensive experiments conducted on three Twitter-based
datasets demonstrate promising performance of our model on both claim-level
rumor detection and post-level stance classification compared with

Graph structured data is ubiquitous in daily life and scientific areas and
has attracted increasing attention. Graph Neural Networks (GNNs) have been
proved to be effective in modeling graph structured data and many variants of
GNN architectures have been proposed. However, much human effort is often
needed to tune the architecture depending on different datasets. Researchers
naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce
the human effort and achieve generally top-performing GNNs, but their methods
focus more on the architecture search. To understand GNN practitioners'
automated solutions, we organized AutoGraph Challenge at KDD Cup 2020,
emphasizing on automated graph neural networks for node classification. We
received top solutions especially from industrial tech companies like Meituan,
Alibaba and Twitter, which are already open sourced on Github. After detailed
comparisons with solutions from academia, we quantify the gaps between academia
and industry on modeling scope, effectiveness and efficiency, and show that (1)
academia AutoML for Graph solutions focus on GNN architecture search while
industrial solutions, especially the winning ones in the KDD Cup, tend to
obtain an overall solution (2) by neural architecture search only, academia
solutions achieve on average 97.3% accuracy of industrial solutions (3)
academia solutions are cheap to obtain with several GPU hours while industrial
solutions take a few months' labors. Academic solutions also contain much fewer

Knowledge-grounded conversation (KGC) shows great potential in building an
engaging and knowledgeable chatbot, and knowledge selection is a key ingredient
in it. However, previous methods for knowledge selection only concentrate on
the relevance between knowledge and dialogue context, ignoring the fact that
age, hobby, education and life experience of an interlocutor have a major
effect on his or her personal preference over external knowledge. Without
taking the personalization issue into account, it is difficult to select the
proper knowledge and generate persona-consistent responses. In this work, we
introduce personal memory into knowledge selection in KGC to address the
personalization issue. We propose a variational method to model the underlying
relationship between one's personal memory and his or her selection of
knowledge, and devise a learning scheme in which the forward mapping from
personal memory to knowledge and its inverse mapping is included in a closed
loop so that they could teach each other. Experiment results show that our
method outperforms existing KGC methods significantly on both automatic
evaluation and human evaluation.

Stock market plays an important role in the economic development. Due to the
complex volatility of the stock market, the research and prediction on the
change of the stock price, can avoid the risk for the investors. The
traditional time series model ARIMA can not describe the nonlinearity, and can
not achieve satisfactory results in the stock prediction. As neural networks
are with strong nonlinear generalization ability, this paper proposes an
attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price.
The model constructed in this paper integrates the time series model, the
Convolutional Neural Networks with Attention mechanism, the Long Short-Term
Memory network, and XGBoost regressor in a non-linear relationship, and
improves the prediction accuracy. The model can fully mine the historical
information of the stock market in multiple periods. The stock data is first
preprocessed through ARIMA. Then, the deep learning architecture formed in
pretraining-finetuning framework is adopted. The pre-training model is the
Attention-based CNN-LSTM model based on sequence-to-sequence framework. The
model first uses convolution to extract the deep features of the original stock
data, and then uses the Long Short-Term Memory networks to mine the long-term
time series features. Finally, the XGBoost model is adopted for fine-tuning.
The results show that the hybrid model is more effective and the prediction
accuracy is relatively high, which can help investors or institutions to make
decisions and achieve the purpose of expanding return and avoiding risk. Source
https://github.com/zshicode/Attention-CLX-stock-prediction.

Domain Adaptive Object Detection (DAOD) models a joint distribution of images
and labels from an annotated source domain and learns a domain-invariant
transformation to estimate the target labels with the given target domain
images. Existing methods assume that the source domain labels are completely
clean, yet large-scale datasets often contain error-prone annotations due to
instance ambiguity, which may lead to a biased source distribution and severely
degrade the performance of the domain adaptive detector de facto. In this
paper, we represent the first effort to formulate noisy DAOD and propose a
Noise Latent Transferability Exploration (NLTE) framework to address this
issue. It is featured with 1) Potential Instance Mining (PIM), which leverages
eligible proposals to recapture the miss-annotated instances from the
background; 2) Morphable Graph Relation Module (MGRM), which models the
adaptation feasibility and transition probability of noisy samples with
relation matrices; 3) Entropy-Aware Gradient Reconcilement (EAGR), which
incorporates the semantic information into the discrimination process and
enforces the gradients provided by noisy and clean samples to be consistent
towards learning domain-invariant representations. A thorough evaluation on
benchmark DAOD datasets with noisy source annotations validates the
effectiveness of NLTE. In particular, NLTE improves the mAP by 8.4\% under 60\%
corrupted annotations and even approaches the ideal upper bound of training on
a clean source dataset.

In the current IT world, developers write code while system operators run the
code mostly as a black box. The connection between both worlds is typically
established with log messages: the developer provides hints to the (unknown)
operator, where the cause of an occurred issue is, and vice versa, the operator
can report bugs during operation. To fulfil this purpose, developers write log
instructions that are structured text commonly composed of a log level (e.g.,
"info", "error"), static text ("IP {} cannot be reached"), and dynamic
variables (e.g. IP {}). However, as opposed to well-adopted coding practices,
there are no widely adopted guidelines on how to write log instructions with
good quality properties. For example, a developer may assign a high log level
(e.g., "error") for a trivial event that can confuse the operator and increase
maintenance costs. Or the static text can be insufficient to hint at a specific
issue. In this paper, we address the problem of log quality assessment and
provide the first step towards its automation. We start with an in-depth
analysis of quality log instruction properties in nine software systems and
identify two quality properties: 1) correct log level assignment assessing the
correctness of the log level, and 2) sufficient linguistic structure assessing
the minimal richness of the static text necessary for verbose event
description. Based on these findings, we developed a data-driven approach that
adapts deep learning methods for each of the two properties. An extensive
evaluation on large-scale open-source systems shows that our approach correctly
assesses log level assignments with an accuracy of 0.88, and the sufficient
linguistic structure with an F1 score of 0.99, outperforming the baselines. Our
study shows the potential of the data-driven methods in assessing instructions
quality and aid developers in comprehending and writing better code.

Recently, large-scale synthetic datasets are shown to be very useful for
generalizable person re-identification. However, synthesized persons in
existing datasets are mostly cartoon-like and in random dress collocation,
which limits their performance. To address this, in this work, an automatic
approach is proposed to directly clone the whole outfits from real-world person
images to virtual 3D characters, such that any virtual person thus created will
appear very similar to its real-world counterpart. Specifically, based on UV
texture mapping, two cloning methods are designed, namely registered clothes
mapping and homogeneous cloth expansion. Given clothes keypoints detected on
person images and labeled on regular UV maps with clear clothes structures,
registered mapping applies perspective homography to warp real-world clothes to
the counterparts on the UV map. As for invisible clothes parts and irregular UV
maps, homogeneous expansion segments a homogeneous area on clothes as a
realistic cloth pattern or cell, and expand the cell to fill the UV map.
Furthermore, a similarity-diversity expansion strategy is proposed, by
clustering person images, sampling images per cluster, and cloning outfits for
3D character generation. This way, virtual persons can be scaled up densely in
visual similarity to challenge model learning, and diversely in population to
enrich sample distribution. Finally, by rendering the cloned characters in
Unity3D scenes, a more realistic virtual dataset called ClonedPerson is
created, with 5,621 identities and 887,766 images. Experimental results show
that the model trained on ClonedPerson has a better generalization performance,
superior to that trained on other popular real-world and synthetic person
re-identification datasets. The ClonedPerson project is available at
https://github.com/Yanan-Wang-cs/ClonedPerson.

Test-time adaptation (TTA) seeks to tackle potential distribution shifts
between training and testing data by adapting a given model w.r.t. any testing
sample. This task is particularly important for deep models when the test
environment changes frequently. Although some recent attempts have been made to
handle this task, we still face two practical challenges: 1) existing methods
have to perform backward computation for each test sample, resulting in
unbearable prediction cost to many applications; 2) while existing TTA
solutions can significantly improve the test performance on out-of-distribution
data, they often suffer from severe performance degradation on in-distribution
data after TTA (known as catastrophic forgetting). In this paper, we point out
that not all the test samples contribute equally to model adaptation, and
high-entropy ones may lead to noisy gradients that could disrupt the model.
Motivated by this, we propose an active sample selection criterion to identify
reliable and non-redundant samples, on which the model is updated to minimize
the entropy loss for test-time adaptation. Furthermore, to alleviate the
forgetting issue, we introduce a Fisher regularizer to constrain important
model parameters from drastic changes, where the Fisher importance is estimated
from test samples with generated pseudo labels. Extensive experiments on
CIFAR-10-C, ImageNet-C, and ImageNet-R verify the effectiveness of our proposed
method.

In this paper, we present a study of a kernel-based consensual aggregation on
randomly projected high-dimensional features of predictions for regression. The
aggregation scheme is composed of two steps: the high-dimensional features of
predictions, given by a large number of regression estimators, are randomly
projected into a smaller subspace using Johnson-Lindenstrauss Lemma in the
first step, and a kernel-based consensual aggregation is implemented on the
projected features in the second step. We theoretically show that the
performance of the aggregation scheme is close to the performance of the
aggregation implemented on the original high-dimensional features, with high
probability. Moreover, we numerically illustrate that the aggregation scheme
upholds its performance on very large and highly correlated features of
predictions given by different types of machines. The aggregation scheme allows
us to flexibly merge a large number of redundant machines, plainly constructed
without model selection or cross-validation. The efficiency of the proposed
method is illustrated through several experiments evaluated on different types
of synthetic and real datasets.

In practical multi-criterion decision-making, it is cumbersome if a decision
maker (DM) is asked to choose among a set of trade-off alternatives covering
the whole Pareto-optimal front. This is a paradox in conventional evolutionary
multi-objective optimization (EMO) that always aim to achieve a well balance
between convergence and diversity. In essence, the ultimate goal of
multi-objective optimization is to help a decision maker (DM) identify
solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple
conflicting criteria. Bearing this in mind, this paper develops a framework for
designing preference-based EMO algorithms to find SOI in an interactive manner.
Its core idea is to involve human in the loop of EMO. After every several
iterations, the DM is invited to elicit her feedback with regard to a couple of
incumbent candidates. By collecting such information, her preference is
progressively learned by a learning-to-rank neural network and then applied to
guide the baseline EMO algorithm. Note that this framework is so general that
any existing EMO algorithm can be applied in a plug-in manner. Experiments on
$48$ benchmark test problems with up to 10 objectives fully demonstrate the
effectiveness of our proposed algorithms for finding SOI.

The performance of current Scene Graph Generation models is severely hampered
by some hard-to-distinguish predicates, e.g., "woman-on/standing on/walking
on-beach" or "woman-near/looking at/in front of-child". While general SGG
models are prone to predict head predicates and existing re-balancing
strategies prefer tail categories, none of them can appropriately handle these
hard-to-distinguish predicates. To tackle this issue, inspired by fine-grained
image classification, which focuses on differentiating among
hard-to-distinguish object classes, we propose a method named Fine-Grained
Predicates Learning (FGPL) which aims at differentiating among
hard-to-distinguish predicates for Scene Graph Generation task. Specifically,
we first introduce a Predicate Lattice that helps SGG models to figure out
fine-grained predicate pairs. Then, utilizing the Predicate Lattice, we propose
a Category Discriminating Loss and an Entity Discriminating Loss, which both
contribute to distinguishing fine-grained predicates while maintaining learned
discriminatory power over recognizable ones. The proposed model-agnostic
strategy significantly boosts the performances of three benchmark models
(Transformer, VCTree, and Motif) by 22.8\%, 24.1\% and 21.7\% of Mean Recall
(mR@100) on the Predicate Classification sub-task, respectively. Our model also
outperforms state-of-the-art methods by a large margin (i.e., 6.1\%, 4.6\%, and
3.2\% of Mean Recall (mR@100)) on the Visual Genome dataset.

This paper proposes a novel intelligent framework for oversubscription
management in CPU-GPU UVM. We analyze the current rule-based methods of GPU
memory oversubscription with unified memory, and the current learning-based
methods for other computer architectural components. We then identify the
performance gap between the existing rule-based methods and the theoretical
upper bound. We also identify the advantages of applying machine intelligence
and the limitations of the existing learning-based methods. This paper proposes
a novel intelligent framework for oversubscription management in CPU-GPU UVM.
It consists of an access pattern classifier followed by a pattern-specific
Transformer-based model using a novel loss function aiming for reducing page
thrashing. A policy engine is designed to leverage the model's result to
perform accurate page prefetching and pre-eviction. We evaluate our intelligent
framework on a set of 11 memory-intensive benchmarks from popular benchmark
suites. Our solution outperforms the state-of-the-art (SOTA) methods for
oversubscription management, reducing the number of pages thrashed by 64.4\%
under 125\% memory oversubscription compared to the baseline, while the SOTA
method reduces the number of pages thrashed by 17.3\%. Our solution achieves an
average IPC improvement of 1.52X under 125\% memory oversubscription, and our
solution achieves an average IPC improvement of 3.66X under 150\% memory
oversubscription. Our solution outperforms the existing learning-based methods
for page address prediction, improving top-1 accuracy by 6.45\% (up to 41.2\%)
on average for a single GPGPU workload, improving top-1 accuracy by 10.2\% (up
to 30.2\%) on average for multiple concurrent GPGPU workloads.

We introduce a general framework for nonlinear stochastic gradient descent
(SGD) for the scenarios when gradient noise exhibits heavy tails. The proposed
framework subsumes several popular nonlinearity choices, like clipped,
normalized, signed or quantized gradient, but we also consider novel
nonlinearity choices. We establish for the considered class of methods strong
convergence guarantees assuming a strongly convex cost function with Lipschitz
continuous gradients under very general assumptions on the gradient noise. Most
notably, we show that, for a nonlinearity with bounded outputs and for the
gradient noise that may not have finite moments of order greater than one, the
nonlinear SGD's mean squared error (MSE), or equivalently, the expected cost
function's optimality gap, converges to zero at rate~$O(1/t^\zeta)$, $\zeta \in
(0,1)$. In contrast, for the same noise setting, the linear SGD generates a
sequence with unbounded variances. Furthermore, for the nonlinearities that can
be decoupled component wise, like, e.g., sign gradient or component-wise
clipping, we show that the nonlinear SGD asymptotically (locally) achieves a
$O(1/t)$ rate in the weak convergence sense and explicitly quantify the
corresponding asymptotic variance. Experiments show that, while our framework
is more general than existing studies of SGD under heavy-tail noise, several
easy-to-implement nonlinearities from our framework are competitive with state
of the art alternatives on real data sets with heavy tail noises.

As a step beyond traditional personalized recommendation, group
recommendation is the task of suggesting items that can satisfy a group of
users. In group recommendation, the core is to design preference aggregation
functions to obtain a quality summary of all group members' preferences. Such
user and group preferences are commonly represented as points in the vector
space (i.e., embeddings), where multiple user embeddings are compressed into
one to facilitate ranking for group-item pairs. However, the resulted group
representations, as points, lack adequate flexibility and capacity to account
for the multi-faceted user preferences. Also, the point embedding-based
preference aggregation is a less faithful reflection of a group's
decision-making process, where all users have to agree on a certain value in
each embedding dimension instead of a negotiable interval. In this paper, we
propose a novel representation of groups via the notion of hypercubes, which
are subspaces containing innumerable points in the vector space. Specifically,
we design the hypercube recommender (CubeRec) to adaptively learn group
hypercubes from user embeddings with minimal information loss during preference
aggregation, and to leverage a revamped distance metric to measure the affinity
between group hypercubes and item points. Moreover, to counteract the
long-standing issue of data sparsity in group recommendation, we make full use
of the geometric expressiveness of hypercubes and innovatively incorporate
self-supervision by intersecting two groups. Experiments on four real-world
datasets have validated the superiority of CubeRec over state-of-the-art

The new alternative is to use deep learning to inpaint any image by utilizing
image classification and computer vision techniques. In general, image
inpainting is a task of recreating or reconstructing any broken image which
could be a photograph or oil/acrylic painting. With the advancement in the
field of Artificial Intelligence, this topic has become popular among AI
enthusiasts. With our approach, we propose an initial end-to-end pipeline for
inpainting images using a complete Machine Learning approach instead of a
conventional application-based approach. We first use the YOLO model to
automatically identify and localize the object we wish to remove from the
image. Using the result obtained from the model we can generate a mask for the
same. After this, we provide the masked image and original image to the GAN
model which uses the Contextual Attention method to fill in the region. It
consists of two generator networks and two discriminator networks and is also
called a coarse-to-fine network structure. The two generators use fully
convolutional networks while the global discriminator gets hold of the entire
image as input while the local discriminator gets the grip of the filled region
as input. The contextual Attention mechanism is proposed to effectively borrow
the neighbor information from distant spatial locations for reconstructing the
missing pixels. The third part of our implementation uses SRGAN to resolve the
inpainted image back to its original size. Our work is inspired by the paper
Free-Form Image Inpainting with Gated Convolution and Generative Image
Inpainting with Contextual Attention.

Anticipating future events is an essential feature for intelligent systems
and embodied AI. However, compared to the traditional recognition task, the
uncertainty of future and reasoning ability requirement make the anticipation
task very challenging and far beyond solved. In this filed, previous methods
usually care more about the model architecture design or but few attention has
been put on how to train an anticipation model with a proper learning policy.
To this end, in this work, we propose a novel training scheme called Dynamic
Context Removal (DCR), which dynamically schedules the visibility of observed
future in the learning procedure. It follows the human-like curriculum learning
process, i.e., gradually removing the event context to increase the
anticipation difficulty till satisfying the final anticipation target. Our
learning scheme is plug-and-play and easy to integrate any reasoning model
including transformer and LSTM, with advantages in both effectiveness and
efficiency. In extensive experiments, the proposed method achieves
state-of-the-art on four widely-used benchmarks. Our code and models are
publicly released at https://github.com/AllenXuuu/DCR.

Neural Radiance Fields (NeRF) has emerged as the state-of-the-art method for
novel view generation of complex scenes, but is very slow during inference.
Recently, there have been multiple works on speeding up NeRF inference, but the
state of the art methods for real-time NeRF inference rely on caching the
neural network output, which occupies several giga-bytes of disk space that
limits their real-world applicability. As caching the neural network of
original NeRF network is not feasible, Garbin et al. proposed "FastNeRF" which
factorizes the problem into 2 sub-networks - one which depends only on the 3D
coordinate of a sample point and one which depends only on the 2D camera
viewing direction. Although this factorization enables them to reduce the cache
size and perform inference at over 200 frames per second, the memory overhead
is still substantial. In this work, we propose SqueezeNeRF, which is more than
60 times memory-efficient than the sparse cache of FastNeRF and is still able
to render at more than 190 frames per second on a high spec GPU during

In this work, a Gaussian process regression(GPR) model incorporated with
given physical information in partial differential equations(PDEs) is
developed: physics-assisted Gaussian processes(PAGP). The targets of this model
can be divided into two types of problem: finding solutions or discovering
unknown coefficients of given PDEs with initial and boundary conditions. We
introduce three different models: continuous time, discrete time and hybrid
models. The given physical information is integrated into Gaussian process
model through our designed GP loss functions. Three types of loss function are
provided in this paper based on two different approaches to train the standard
GP model. The first part of the paper introduces the continuous time model
which treats temporal domain the same as spatial domain. The unknown
coefficients in given PDEs can be jointly learned with GP hyper-parameters by
minimizing the designed loss function. In the discrete time models, we first
choose a time discretization scheme to discretize the temporal domain. Then the
PAGP model is applied at each time step together with the scheme to approximate
PDE solutions at given test points of final time. To discover unknown
coefficients in this setting, observations at two specific time are needed and
a mixed mean square error function is constructed to obtain the optimal
coefficients. In the last part, a novel hybrid model combining the continuous
and discrete time models is presented. It merges the flexibility of continuous
time model and the accuracy of the discrete time model. The performance of
choosing different models with different GP loss functions is also discussed.
The effectiveness of the proposed PAGP methods is illustrated in our numerical
section.

India is the second largest producer of fruits and vegetables in the world,
and one of the largest consumers of fruits like Banana, Papaya and Mangoes
through retail and ecommerce giants like BigBasket, Grofers and Amazon Fresh.
However, adoption of technology in supply chain and retail stores is still low
and there is a great potential to adopt computer-vision based technology for
identification and classification of fruits. We have chosen banana fruit to
build a computer vision based model to carry out the following three use-cases
(a) Identify Banana from a given image (b) Determine sub-family or variety of
Banana (c) Determine the quality of Banana. Successful execution of these
use-cases using computer-vision model would greatly help with overall inventory
management automation, quality control, quick and efficient weighing and
billing which all are manual labor intensive currently. In this work, we
suggest a machine learning pipeline that combines the ideas of CNNs, transfer
learning, and data augmentation towards improving Banana fruit sub family and
quality image classification. We have built a basic CNN and then went on to
tune a MobileNet Banana classification model using a combination of
self-curated and publicly-available dataset of 3064 images. The results show an
overall 93.4% and 100% accuracy for sub-family/variety and for quality test
classifications respectively.

Deep neural networks (DNNs) designed for computer vision and natural language
processing tasks cannot be directly applied to the radio frequency (RF)
datasets. To address this challenge, we propose to convert the raw RF data to
data types that are suitable for off-the-shelf DNNs by introducing a
convolutional transform technique. In addition, we propose a simple 5-layer
convolutional neural network architecture (CONV-5) that can operate with raw RF
I/Q data without any transformation. Further, we put forward an RF dataset,
referred to as RF1024, to facilitate future RF research. RF1024 consists of 8
different RF modulation classes with each class having 1000/200 training/test
samples. Each sample of the RF1024 dataset contains 1024 complex I/Q values.
Lastly, the experiments are performed on the RadioML2016 and RF1024 datasets to
demonstrate the improved classification performance.

Sparse subspace clustering (SSC) using greedy-based neighbor selection, such
as orthogonal matching pursuit (OMP), has been known as a popular
computationally-efficient alternative to the popular L1-minimization based
methods. This paper proposes a new SSC scheme using generalized OMP (GOMP), a
soup-up of OMP whereby multiple neighbors are identified per iteration, along
with a new stopping rule requiring nothing more than a knowledge of the ambient
signal dimension. Compared to conventional OMP, which identifies one neighbor
per iteration, the proposed GOMP method involves fewer iterations, thereby
enjoying lower algorithmic complexity; advantageously, the proposed stopping
rule is free from off-line estimation of subspace dimension and noise power.
Under the semi-random model, analytic performance guarantees, in terms of
neighbor recovery rates, are established to justify the advantage of the
proposed GOMP. The results show that, with a high probability, GOMP (i) is
halted by the proposed stopping rule, and (ii) can retrieve more true neighbors
than OMP, consequently yielding higher final data clustering accuracy. Computer
simulations using both synthetic data and real human face data are provided to
validate our analytic study and evidence the effectiveness of the proposed
approach.

In urban areas, global navigation satellite system (GNSS) signals are often
reflected or blocked by buildings, thus resulting in large positioning errors.
In this study, we proposed a machine learning approach for global positioning
system (GPS) multipath detection that uses dual antennas. A machine learning
model that could classify GPS signal reception conditions was trained with
several GPS measurements selected as suggested features. We applied five
features for machine learning, including a feature obtained from the dual
antennas, and evaluated the classification performance of the model, after
applying four machine learning algorithms: gradient boosting decision tree
(GBDT), random forest, decision tree, and K-nearest neighbor (KNN). It was
found that a classification accuracy of 82%-96% was achieved when the test data
set was collected at the same locations as those of the training data set.
However, when the test data set was collected at locations different from those
of the training data, a classification accuracy of 44%-77% was obtained.

We design fast algorithms for repeatedly sampling from strongly Rayleigh
distributions, which include random spanning tree distributions and
determinantal point processes. For a graph $G=(V, E)$, we show how to
approximately sample uniformly random spanning trees from $G$ in
$\widetilde{O}(\lvert V\rvert)$ time per sample after an initial
$\widetilde{O}(\lvert E\rvert)$ time preprocessing. For a determinantal point
process on subsets of size $k$ of a ground set of $n$ elements, we show how to
approximately sample in $\widetilde{O}(k^\omega)$ time after an initial
$\widetilde{O}(nk^{\omega-1})$ time preprocessing, where $\omega<2.372864$ is
the matrix multiplication exponent. We even improve the state of the art for
obtaining a single sample from determinantal point processes, from the prior
runtime of $\widetilde{O}(\min\{nk^2, n^\omega\})$ to
$\widetilde{O}(nk^{\omega-1})$.
  In our main technical result, we achieve the optimal limit on domain
sparsification for strongly Rayleigh distributions. In domain sparsification,
sampling from a distribution $\mu$ on $\binom{[n]}{k}$ is reduced to sampling
from related distributions on $\binom{[t]}{k}$ for $t\ll n$. We show that for
strongly Rayleigh distributions, we can can achieve the optimal
$t=\widetilde{O}(k)$. Our reduction involves sampling from $\widetilde{O}(1)$
domain-sparsified distributions, all of which can be produced efficiently
assuming convenient access to approximate overestimates for marginals of $\mu$.
Having access to marginals is analogous to having access to the mean and
covariance of a continuous distribution, or knowing "isotropy" for the
distribution, the key assumption behind the Kannan-Lov\'asz-Simonovits (KLS)
conjecture and optimal samplers based on it. We view our result as a moral
analog of the KLS conjecture and its consequences for sampling, for discrete
strongly Rayleigh measures.

Existing studies for gait recognition are dominated by 2D representations
like the silhouette or skeleton of the human body in constrained scenes.
However, humans live and walk in the unconstrained 3D space, so projecting the
3D human body onto the 2D plane will discard a lot of crucial information like
the viewpoint, shape, and dynamics for gait recognition. Therefore, this paper
aims to explore dense 3D representations for gait recognition in the wild,
which is a practical yet neglected problem. In particular, we propose a novel
framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the
human body for gait recognition, named SMPLGait. Our framework has two
elaborately-designed branches of which one extracts appearance features from
silhouettes, the other learns knowledge of 3D viewpoints and shapes from the 3D
SMPL model. In addition, due to the lack of suitable datasets, we build the
first large-scale 3D representation-based gait recognition dataset, named
Gait3D. It contains 4,000 subjects and over 25,000 sequences extracted from 39
cameras in an unconstrained indoor scene. More importantly, it provides 3D SMPL
models recovered from video frames which can provide dense 3D information of
body shape, viewpoint, and dynamics. Based on Gait3D, we comprehensively
compare our method with existing gait recognition approaches, which reflects
the superior performance of our framework and the potential of 3D
representations for gait recognition in the wild. The code and dataset are
available at https://gait3d.github.io.

With Deep Neural Network (DNN) being integrated into a growing number of
critical systems with far-reaching impacts on society, there are increasing
concerns on their ethical performance, such as fairness. Unfortunately, model
fairness and accuracy in many cases are contradictory goals to optimize. To
solve this issue, there has been a number of work trying to improve model
fairness by using an adversarial game in model level. This approach introduces
an adversary that evaluates the fairness of a model besides its prediction
accuracy on the main task, and performs joint-optimization to achieve a
balanced result. In this paper, we noticed that when performing backward
propagation based training, such contradictory phenomenon has shown on
individual neuron level. Based on this observation, we propose FairNeuron, a
DNN model automatic repairing tool, to mitigate fairness concerns and balance
the accuracy-fairness trade-off without introducing another model. It works on
detecting neurons with contradictory optimization directions from accuracy and
fairness training goals, and achieving a trade-off by selective dropout.
Comparing with state-of-the-art methods, our approach is lightweight, making it
scalable and more efficient. Our evaluation on 3 datasets shows that FairNeuron
can effectively improve all models' fairness while maintaining a stable
utility.

Procedural Multimodal Documents (PMDs) organize textual instructions and
corresponding images step by step. Comprehending PMDs and inducing their
representations for the downstream reasoning tasks is designated as Procedural
MultiModal Machine Comprehension (M3C). In this study, we approach Procedural
M3C at a fine-grained level (compared with existing explorations at a document
or sentence level), that is, entity. With delicate consideration, we model
entity both in its temporal and cross-modal relation and propose a novel
Temporal-Modal Entity Graph (TMEG). Specifically, graph structure is formulated
to capture textual and visual entities and trace their temporal-modal
evolution. In addition, a graph aggregation module is introduced to conduct
graph encoding and reasoning. Comprehensive experiments across three Procedural
M3C tasks are conducted on a traditional dataset RecipeQA and our new dataset
CraftQA, which can better evaluate the generalization of TMEG.

Recent years have witnessed the great breakthrough of deep reinforcement
learning (DRL) in various perfect and imperfect information games. Among these
games, DouDizhu, a popular card game in China, is very challenging due to the
imperfect information, large state space, elements of collaboration and a
massive number of possible moves from turn to turn. Recently, a DouDizhu AI
system called DouZero has been proposed. Trained using traditional Monte Carlo
method with deep neural networks and self-play procedure without the
abstraction of human prior knowledge, DouZero has outperformed all the existing
DouDizhu AI programs. In this work, we propose to enhance DouZero by
introducing opponent modeling into DouZero. Besides, we propose a novel coach
network to further boost the performance of DouZero and accelerate its training
process. With the integration of the above two techniques into DouZero, our
DouDizhu AI system achieves better performance and ranks top in the Botzone
leaderboard among more than 400 AI agents, including DouZero.

Recent studies have addressed the concern of detecting and rejecting the
out-of-distribution (OOD) samples as a major challenge in the safe deployment
of deep learning (DL) models. It is desired that the DL model should only be
confident about the in-distribution (ID) data which reinforces the driving
principle of the OOD detection. In this paper, we propose a simple yet
effective generalized OOD detection method independent of out-of-distribution
datasets. Our approach relies on self-supervised feature learning of the
training samples, where the embeddings lie on a compact low-dimensional space.
Motivated by the recent studies that show self-supervised adversarial
contrastive learning helps robustify the model, we empirically show that a
pre-trained model with self-supervised contrastive learning yields a better
model for uni-dimensional feature learning in the latent space. The method
proposed in this work referred to as RODD outperforms SOTA detection
performance on an extensive suite of benchmark datasets on OOD detection tasks.
On the CIFAR-100 benchmarks, RODD achieves a 26.97 $\%$ lower false-positive
rate (FPR@95) compared to SOTA methods.

We show direct and conceptually simple reductions between the classical
learning with errors (LWE) problem and its continuous analog, CLWE (Bruna,
Regev, Song and Tang, STOC 2021). This allows us to bring to bear the powerful
machinery of LWE-based cryptography to the applications of CLWE. For example,
we obtain the hardness of CLWE under the classical worst-case hardness of the
gap shortest vector problem. Previously, this was known only under quantum
worst-case hardness of lattice problems. More broadly, with our reductions
between the two problems, any future developments to LWE will also apply to
CLWE and its downstream applications.
  As a concrete application, we show an improved hardness result for density
estimation for mixtures of Gaussians. In this computational problem, given
sample access to a mixture of Gaussians, the goal is to output a function that
estimates the density function of the mixture. Under the (plausible and widely
believed) exponential hardness of the classical LWE problem, we show that
Gaussian mixture density estimation in $\mathbb{R}^n$ with roughly $\log n$
Gaussian components given $\mathsf{poly}(n)$ samples requires time
quasi-polynomial in $n$. Under the (conservative) polynomial hardness of LWE,
we show hardness of density estimation for $n^{\epsilon}$ Gaussians for any
constant $\epsilon > 0$, which improves on Bruna, Regev, Song and Tang (STOC
2021), who show hardness for at least $\sqrt{n}$ Gaussians under polynomial
(quantum) hardness assumptions.
  Our key technical tool is a reduction from classical LWE to LWE with
$k$-sparse secrets where the multiplicative increase in the noise is only
$O(\sqrt{k})$, independent of the ambient dimension $n$.

In this paper, we study the task of synthetic-to-real domain generalized
semantic segmentation, which aims to learn a model that is robust to unseen
real-world scenes using only synthetic data. The large domain shift between
synthetic and real-world data, including the limited source environmental
variations and the large distribution gap between synthetic and real-world
data, significantly hinders the model performance on unseen real-world scenes.
In this work, we propose the Style-HAllucinated Dual consistEncy learning
(SHADE) framework to handle such domain shift. Specifically, SHADE is
constructed based on two consistency constraints, Style Consistency (SC) and
Retrospection Consistency (RC). SC enriches the source situations and
encourages the model to learn consistent representation across
style-diversified samples. RC leverages real-world knowledge to prevent the
model from overfitting to synthetic data and thus largely keeps the
representation consistent between the synthetic and real-world models.
Furthermore, we present a novel style hallucination module (SHM) to generate
style-diversified samples that are essential to consistency learning. SHM
selects basis styles from the source distribution, enabling the model to
dynamically generate diverse and realistic samples during training. Experiments
show that our SHADE yields significant improvement and outperforms
state-of-the-art methods by 5.05% and 8.35% on the average mIoU of three
real-world datasets on single- and multi-source settings, respectively.

Protein representation learning methods have shown great potential to yield
useful representation for many downstream tasks, especially on protein
classification. Moreover, a few recent studies have shown great promise in
addressing insufficient labels of proteins with self-supervised learning
methods. However, existing protein language models are usually pretrained on
protein sequences without considering the important protein structural
information. To this end, we propose a novel structure-aware protein
self-supervised learning method to effectively capture structural information
of proteins. In particular, a well-designed graph neural network (GNN) model is
pretrained to preserve the protein structural information with self-supervised
tasks from a pairwise residue distance perspective and a dihedral angle
perspective, respectively. Furthermore, we propose to leverage the available
protein language model pretrained on protein sequences to enhance the
self-supervised learning. Specifically, we identify the relation between the
sequential information in the protein language model and the structural
information in the specially designed GNN model via a novel pseudo bi-level
optimization scheme. Experiments on several supervised downstream tasks verify

The goal of automatic dubbing is to perform speech-to-speech translation
while achieving audiovisual coherence. This entails isochrony, i.e.,
translating the original speech by also matching its prosodic structure into
phrases and pauses, especially when the speaker's mouth is visible. In previous
work, we introduced a prosodic alignment model to address isochrone or
on-screen dubbing. In this work, we extend the prosodic alignment model to also
address off-screen dubbing that requires less stringent synchronization
constraints. We conduct experiments on four dubbing directions - English to
French, Italian, German and Spanish - on a publicly available collection of TED
Talks and on publicly available YouTube videos. Empirical results show that
compared to our previous work the extended prosodic alignment model provides
significantly better subjective viewing experience on videos in which on-screen
and off-screen automatic dubbing is applied for sentences with speakers mouth
visible and not visible, respectively.

The minimization of specific cases in binary classification, such as false
negatives or false positives, grows increasingly important as humans begin to
implement more machine learning into current products. While there are a few
methods to put a bias towards the reduction of specific cases, these methods
aren't very effective, hence their minimal use in models. To this end, a new
method is introduced to reduce the False Negatives or False positives without
drastically changing the overall performance or F1 score of the model. This
method involving the careful change to the real value of the input after
pre-training the model. Presenting the results of this method being applied on
various datasets, some being more complex than others. Through experimentation
on multiple model architectures on these datasets, the best model was found. In
all the models, an increase in the recall or precision, minimization of False
Negatives or False Positives respectively, was shown without a large drop in F1
score.

With real-time monitoring of the personalized healthcare condition, the IoT
wearables collect the health data and transfer it to the healthcare information
platform. The platform processes the data into healthcare recommendations and
then delivers them to the users. The IoT structures in the personalized
healthcare information service allows the users to engage in the loop in
servitization more convenient in the COVID-19 pandemic. However, the
uncertainty of the engagement behavior among the individual may result in
inefficient of the service resource allocation. This paper seeks an efficient
way to allocate the service resource by controlling the service capacity and
pushing the service to the active users automatically. In this study, we
propose a deep reinforcement learning method to solve the service resource
allocation problem based on the proximal policy optimization (PPO) algorithm.
Experimental results using the real world (open source) sport dataset reveal
that our proposed proximal policy optimization adapts well to the users'
changing behavior and with improved performance over fixed service resource
policies.

In classic instruction following, language like "I'd like the JetBlue flight"
maps to actions (e.g., selecting that flight). However, language also conveys
information about a user's underlying reward function (e.g., a general
preference for JetBlue), which can allow a model to carry out desirable actions
in new contexts. We present a model that infers rewards from language
pragmatically: reasoning about how speakers choose utterances not only to
elicit desired actions, but also to reveal information about their preferences.
On a new interactive flight-booking task with natural language, our model more
accurately infers rewards and predicts optimal actions in unseen environments,
in comparison to past work that first maps language to actions (instruction
following) and then maps actions to rewards (inverse reinforcement learning).

Proteins in complex with small molecule ligands represent the core of
structure-based drug discovery. However, three-dimensional representations are
absent from most deep-learning-based generative models. We here present a
graph-based generative modeling technology that encodes explicit 3D
protein-ligand contacts within a relational graph architecture. The models
combine a conditional variational autoencoder that allows for activity-specific
molecule generation with putative contact generation that provides predictions
of molecular interactions within the target binding pocket. We show that
molecules generated with our 3D procedure are more compatible with the binding
pocket of the dopamine D2 receptor than those produced by a comparable
ligand-based 2D generative method, as measured by docking scores, expected
stereochemistry, and recoverability in commercial chemical databases. Predicted
protein-ligand contacts were found among highest-ranked docking poses with a
high recovery rate. This work shows how the structural context of a protein
target can be used to enhance molecule generation.

In this paper, we present a scalable deep learning approach to solve opinion
dynamics stochastic optimal control problems with mean field term coupling in
the dynamics and cost function. Our approach relies on the probabilistic
representation of the solution of the Hamilton-Jacobi-Bellman partial
differential equation. Grounded on the nonlinear version of the Feynman-Kac
lemma, the solutions of the Hamilton-Jacobi-Bellman partial differential
equation are linked to the solution of Forward-Backward Stochastic Differential
Equations. These equations can be solved numerically using a novel deep neural
network with architecture tailored to the problem in consideration. The
resulting algorithm is tested on a polarized opinion consensus experiment. The
large-scale (10K) agents experiment validates the scalability and
generalizability of our algorithm. The proposed framework opens up the
possibility for future applications on extremely large-scale problems.

Brain-inspired hyperdimensional computing (HDC) is an emerging machine
learning (ML) methods. It is based on large vectors of binary or bipolar
symbols and a few simple mathematical operations. The promise of HDC is a
highly efficient implementation for embedded systems like wearables. While fast
implementations have been presented, other constraints have not been considered
for edge computing. In this work, we aim at answering how thermal-friendly HDC
for edge computing is. Devices like smartwatches, smart glasses, or even mobile
systems have a restrictive cooling budget due to their limited volume. Although
HDC operations are simple, the vectors are large, resulting in a high number of
CPU operations and thus a heavy load on the entire system potentially causing
temperature violations. In this work, the impact of HDC on the chip's
temperature is investigated for the first time. We measure the temperature and
power consumption of a commercial embedded system and compare HDC with
conventional CNN. We reveal that HDC causes up to 6.8{\deg}C higher
temperatures and leads to up to 47% more CPU throttling. Even when both HDC and
CNN aim for the same throughput (i.e., perform a similar number of
classifications per second), HDC still causes higher on-chip temperatures due
to the larger power consumption.

Many existing privacy-enhanced speech emotion recognition (SER) frameworks
focus on perturbing the original speech data through adversarial training
within a centralized machine learning setup. However, this privacy protection
scheme can fail since the adversary can still access the perturbed data. In
recent years, distributed learning algorithms, especially federated learning
(FL), have gained popularity to protect privacy in machine learning
applications. While FL provides good intuition to safeguard privacy by keeping
the data on local devices, prior work has shown that privacy attacks, such as
attribute inference attacks, are achievable for SER systems trained using FL.
In this work, we propose to evaluate the user-level differential privacy (UDP)
in mitigating the privacy leaks of the SER system in FL. UDP provides
theoretical privacy guarantees with privacy parameters $\epsilon$ and $\delta$.
Our results show that the UDP can effectively decrease attribute information
leakage while keeping the utility of the SER system with the adversary
accessing one model update. However, the efficacy of the UDP suffers when the
FL system leaks more model updates to the adversary. We make the code publicly
available to reproduce the results in
https://github.com/usc-sail/fed-ser-leakage.

Federated learning (FL) has emerged as a privacy solution for collaborative
distributed learning where clients train AI models directly on their devices
instead of sharing their data with a centralized (potentially adversarial)
server. Although FL preserves local data privacy to some extent, it has been
shown that information about clients' data can still be inferred from model
updates. In recent years, various privacy-preserving schemes have been
developed to address this privacy leakage. However, they often provide privacy
at the expense of model performance or system efficiency, and balancing these
tradeoffs is a crucial challenge when implementing FL schemes. In this
manuscript, we propose a Privacy-Preserving Federated Learning (PPFL) framework
built on the synergy of matrix encryption and system immersion tools from
control theory. The idea is to immerse the learning algorithm, a Stochastic
Gradient Decent (SGD), into a higher-dimensional system (the so-called target
system) and design the dynamics of the target system so that: the trajectories
of the original SGD are immersed/embedded in its trajectories, and it learns on
encrypted data (here we use random matrix encryption). Matrix encryption is
reformulated at the server as a random change of coordinates that maps original
parameters to a higher-dimensional parameter space and enforces that the target
SGD converges to an encrypted version of the original SGD optimal solution. The
server decrypts the aggregated model using the left inverse of the immersion
map. We show that our algorithm provides the same level of accuracy and
convergence rate as the standard FL with a negligible computation cost while
revealing no information about the clients' data.

Keystroke inference attacks are a form of side-channel attacks in which an
attacker leverages various techniques to recover a user's keystrokes as she
inputs information into some display (e.g., while sending a text message or
entering her pin). Typically, these attacks leverage machine learning
approaches, but assessing the realism of the threat space has lagged behind the
pace of machine learning advancements, due in-part, to the challenges in
curating large real-life datasets. We aim to overcome the challenge of having
limited number of real data by introducing a video domain adaptation technique
that is able to leverage synthetic data through supervised disentangled
learning. Specifically, for a given domain, we decompose the observed data into
two factors of variation: Style and Content. Doing so provides four learned
representations: real-life style, synthetic style, real-life content and
synthetic content. Then, we combine them into feature representations from all
combinations of style-content pairings across domains, and train a model on
these combined representations to classify the content (i.e., labels) of a
given datapoint in the style of another domain. We evaluate our method on
real-life data using a variety of metrics to quantify the amount of information
an attacker is able to recover. We show that our method prevents our model from
overfitting to a small real-life training set, indicating that our method is an
effective form of data augmentation, thereby making keystroke inference attacks
more practical.

Many recent models in software engineering introduced deep neural models
based on the Transformer architecture or use transformer-based Pre-trained
Language Models (PLM) trained on code. Although these models achieve the state
of the arts results in many downstream tasks such as code summarization and bug
detection, they are based on Transformer and PLM, which are mainly studied in
the Natural Language Processing (NLP) field. The current studies rely on the
reasoning and practices from NLP for these models in code, despite the
differences between natural languages and programming languages. There is also
limited literature on explaining how code is modeled.
  Here, we investigate the attention behavior of PLM on code and compare it
with natural language. We pre-trained BERT, a Transformer based PLM, on code
and explored what kind of information it learns, both semantic and syntactic.
We run several experiments to analyze the attention values of code constructs
on each other and what BERT learns in each layer. Our analyses show that BERT
pays more attention to syntactic entities, specifically identifiers and
separators, in contrast to the most attended token [CLS] in NLP. This
observation motivated us to leverage identifiers to represent the code sequence
instead of the [CLS] token when used for code clone detection. Our results show
that employing embeddings from identifiers increases the performance of BERT by
605% and 4% F1-score in its lower layers and the upper layers, respectively.
When identifiers' embeddings are used in CodeBERT, a code-based PLM, the
performance is improved by 21-24% in the F1-score of clone detection. The
findings can benefit the research community by using code-specific
representations instead of applying the common embeddings used in NLP, and open
new directions for developing smaller models with similar performance.

At the heart of both lossy compression and clustering is a trade-off between
the fidelity and size of the learned representation. Our goal is to map out and
study the Pareto frontier that quantifies this trade-off. We focus on the
optimization of the Deterministic Information Bottleneck (DIB) objective over
the space of hard clusterings. To this end, we introduce the primal DIB
problem, which we show results in a much richer frontier than its previously
studied Lagrangian relaxation when optimized over discrete search spaces. We
present an algorithm for mapping out the Pareto frontier of the primal DIB
trade-off that is also applicable to other two-objective clustering problems.
We study general properties of the Pareto frontier, and we give both analytic
and numerical evidence for logarithmic sparsity of the frontier in general. We
provide evidence that our algorithm has polynomial scaling despite the
super-exponential search space, and additionally, we propose a modification to
the algorithm that can be used where sampling noise is expected to be
significant. Finally, we use our algorithm to map the DIB frontier of three
different tasks: compressing the English alphabet, extracting informative color
classes from natural images, and compressing a group theory-inspired dataset,
revealing interesting features of frontier, and demonstrating how the structure
of the frontier can be used for model selection with a focus on points
previously hidden by the cloak of the convex hull.

Extreme events in society and nature, such as pandemic spikes, rogue waves,
or structural failures, can have catastrophic consequences. Characterizing
extremes is difficult as they occur rarely, arise from seemingly benign
conditions, and belong to complex and often unknown infinite-dimensional
systems. Such challenges render attempts at characterizing them as moot. We
address each of these difficulties by combining novel training schemes in
Bayesian experimental design (BED) with an ensemble of deep neural operators
(DNOs). This model-agnostic framework pairs a BED scheme that actively selects
data for quantifying extreme events with an ensemble of DNOs that approximate
infinite-dimensional nonlinear operators. We find that not only does this
framework clearly beat Gaussian processes (GPs) but that 1) shallow ensembles
of just two members perform best; 2) extremes are uncovered regardless of the
state of initial data (i.e. with or without extremes); 3) our method eliminates
"double-descent" phenomena; 4) the use of batches of suboptimal acquisition
points compared to step-by-step global optima does not hinder BED performance;
and 5) Monte Carlo acquisition outperforms standard optimizers in
high-dimensions. Together these conclusions form the foundation of an
AI-assisted experimental infrastructure that can efficiently infer and pinpoint
critical situations across many domains, from physical to societal systems.

Code comment generation is the task of generating a high-level natural
language description for a given code method or function. Although researchers
have been studying multiple ways to generate code comments automatically,
previous work mainly considers representing a code token in its entirety
semantics form only (e.g., a language model is used to learn the semantics of a
code token), and additional code properties such as the tree structure of a
code are included as an auxiliary input to the model. There are two
limitations: 1) Learning the code token in its entirety form may not be able to
capture information succinctly in source code, and 2) The code token does not
contain additional syntactic information, inherently important in programming
  In this paper, we present LAnguage Model and Named Entity Recognition
(LAMNER), a code comment generator capable of encoding code constructs
effectively and capturing the structural property of a code token. A
character-level language model is used to learn the semantic representation to
encode a code token. For the structural property of a token, a Named Entity
Recognition model is trained to learn the different types of code tokens. These
representations are then fed into an encoder-decoder architecture to generate
code comments. We evaluate the generated comments from LAMNER and other
baselines on a popular Java dataset with four commonly used metrics. Our
results show that LAMNER is effective and improves over the best baseline model
in BLEU-1, BLEU-2, BLEU-3, BLEU-4, ROUGE-L, METEOR, and CIDEr by 14.34%,
18.98%, 21.55%, 23.00%, 10.52%, 1.44%, and 25.86%, respectively. Additionally,
we fused LAMNER's code representation with the baseline models, and the fused
models consistently showed improvement over the non-fused models. The human
evaluation further shows that LAMNER produces high-quality code comments.

Multimodal fusion emerges as an appealing technique to improve model
performances on many tasks. Nevertheless, the robustness of such fusion methods
is rarely involved in the present literature. In this paper, we propose a
training-free robust late-fusion method by exploiting conditional independence
assumption and Jacobian regularization. Our key is to minimize the Frobenius
norm of a Jacobian matrix, where the resulting optimization problem is relaxed
to a tractable Sylvester equation. Furthermore, we provide a theoretical error
bound of our method and some insights about the function of the extra modality.
Several numerical experiments on AV-MNIST, RAVDESS, and VGGsound demonstrate
the efficacy of our method under both adversarial attacks and random
corruptions.

Using the reservoir computing framework, we demonstrate how a simple model
can solve an alternation task without an explicit working memory. To do so, a
simple bot equipped with sensors navigates inside a 8-shaped maze and turns
alternatively right and left at the same intersection in the maze. The analysis
of the model's internal activity reveals that the memory is actually encoded
inside the dynamics of the network. However, such dynamic working memory is not
accessible such as to bias the behavior into one of the two attractors (left
and right). To do so, external cues are fed to the bot such that it can follow
arbitrary sequences, instructed by the cue. This model highlights the idea that
procedural learning and its internal representation can be dissociated. If the
former allows to produce behavior, it is not sufficient to allow for an
explicit and fine-grained manipulation.

English Wikipedia has long been an important data source for much research
and natural language machine learning modeling. The growth of non-English
language editions of Wikipedia, greater computational resources, and calls for
equity in the performance of language and multimodal models have led to the
inclusion of many more language editions of Wikipedia in datasets and models.
Building better multilingual and multimodal models requires more than just
access to expanded datasets; it also requires a better understanding of what is
in the data and how this content was generated. This paper seeks to provide
some background to help researchers think about what differences might arise
between different language editions of Wikipedia and how that might affect
their models. It details three major ways in which content differences between
language editions arise (local context, community and governance, and
technology) and recommendations for good practices when using multilingual and
multimodal data for research and modeling.

Developers frequently use APIs to implement certain functionalities, such as
parsing Excel Files, reading and writing text files line by line, etc.
Developers can greatly benefit from automatic API usage sequence generation
based on natural language queries for building applications in a faster and
cleaner manner. Existing approaches utilize information retrieval models to
search for matching API sequences given a query or use RNN-based
encoder-decoder to generate API sequences. As it stands, the first approach
treats queries and API names as bags of words. It lacks deep comprehension of
the semantics of the queries. The latter approach adapts a neural language
model to encode a user query into a fixed-length context vector and generate
API sequences from the context vector.
  We want to understand the effectiveness of recent Pre-trained Transformer
based Models (PTMs) for the API learning task. These PTMs are trained on large
natural language corpora in an unsupervised manner to retain contextual
knowledge about the language and have found success in solving similar Natural
Language Processing (NLP) problems. However, the applicability of PTMs has not
yet been explored for the API sequence generation task. We use a dataset that
contains 7 million annotations collected from GitHub to evaluate the PTMs
empirically. This dataset was also used to assess previous approaches. Based on
our results, PTMs generate more accurate API sequences and outperform other
related methods by around 11%. We have also identified two different
tokenization approaches that can contribute to a significant boost in PTMs'
performance for the API sequence generation task.

Deep learning models are intrinsically sensitive to distribution shifts in
the input data. In particular, small, barely perceivable perturbations to the
input data can force models to make wrong predictions with high confidence. An
common defense mechanism is regularization through adversarial training which
injects worst-case perturbations back into training to strengthen the decision
boundaries, and to reduce overfitting. In this context, we perform an
investigation of 3x3 convolution filters that form in adversarially-trained
models. Filters are extracted from 71 public models of the linf-RobustBench
CIFAR-10/100 and ImageNet1k leaderboard and compared to filters extracted from
models built on the same architectures but trained without robust
regularization. We observe that adversarially-robust models appear to form more
diverse, less sparse, and more orthogonal convolution filters than their normal
counterparts. The largest differences between robust and normal models are
found in the deepest layers, and the very first convolution layer, which
consistently and predominantly forms filters that can partially eliminate
perturbations, irrespective of the architecture. Data & Project website:
https://github.com/paulgavrikov/cvpr22w_RobustnessThroughTheLens

The inherent slow imaging speed of Magnetic Resonance Image (MRI) has spurred
the development of various acceleration methods, typically through
heuristically undersampling the MRI measurement domain known as k-space.
Recently, deep neural networks have been applied to reconstruct undersampled
k-space data and have shown improved reconstruction performance. While most of
these methods focus on designing novel reconstruction networks or new training
strategies for a given undersampling pattern, e.g., Cartesian undersampling or
Non-Cartesian sampling, to date, there is limited research aiming to learn and
optimize k-space sampling strategies using deep neural networks. This work
proposes a novel optimization framework to learn k-space sampling trajectories
by considering it as an Ordinary Differential Equation (ODE) problem that can
be solved using neural ODE. In particular, the sampling of k-space data is
framed as a dynamic system, in which neural ODE is formulated to approximate
the system with additional constraints on MRI physics. In addition, we have
also demonstrated that trajectory optimization and image reconstruction can be
learned collaboratively for improved imaging efficiency and reconstruction
performance. Experiments were conducted on different in-vivo datasets (e.g.,
brain and knee images) acquired with different sequences. Initial results have
shown that our proposed method can generate better image quality in accelerated
MRI than conventional undersampling schemes in Cartesian and Non-Cartesian
acquisitions.

Deploying generative machine learning techniques to generate novel chemical
structures based on molecular fingerprint representation has been well
established in molecular design. Typically, sequential learning (SL) schemes
such as hidden Markov models (HMM) and, more recently, in the sequential deep
learning context, recurrent neural network (RNN) and long short-term memory
(LSTM) were used extensively as generative models to discover unprecedented
molecules. To this end, emission probability between two states of atoms plays
a central role without considering specific chemical or physical properties.
Lack of supervised domain knowledge can mislead the learning procedure to be
relatively biased to the prevalent molecules observed in the training data that
are not necessarily of interest. We alleviated this drawback by augmenting the
training data with domain knowledge, e.g. quantitative estimates of the
drug-likeness score (QEDs). As such, our experiments demonstrated that with
this subtle trick called enriched sequential learning (ESL), specific patterns
of particular interest can be learnt better, which led to generating de novo
molecules with ameliorated QEDs.

Reinforcement learning methods often produce brittle policies -- policies
that perform well during training, but generalize poorly beyond their direct
training experience, thus becoming unstable under small disturbances. To
address this issue, we propose a method for stabilizing a control policy in the
space of configuration paths. It is applied post-training and relies purely on
the data produced during training, as well as on an instantaneous
control-matrix estimation. The approach is evaluated empirically on a planar
bipedal walker subjected to a variety of perturbations. The control policies
obtained via reinforcement learning are compared against their stabilized
counterparts. Across different experiments, we find two- to four-fold increase
in stability, when measured in terms of the perturbation amplitudes. We also
provide a zero-dynamics interpretation of our approach.

Self-Supervised Learning (SSL) models have been successfully applied in
various deep learning-based speech tasks, particularly those with a limited
amount of data. However, the quality of SSL representations depends highly on
the relatedness between the SSL training domain(s) and the target data domain.
On the contrary, spectral feature (SF) extractors such as log Mel-filterbanks
are hand-crafted non-learnable components, and could be more robust to domain
shifts. The present work examines the assumption that combining non-learnable
SF extractors to SSL models is an effective approach to low resource speech
tasks. We propose a learnable and interpretable framework to combine SF and SSL
representations. The proposed framework outperforms significantly both baseline
and SSL models on Automatic Speech Recognition (ASR) and Speech Translation
(ST) tasks on three low resource datasets. We additionally design a mixture of
experts based combination model. This last model reveals that the relative
contribution of SSL models over conventional SF extractors is very small in
case of domain mismatch between SSL training set and the target language data.

Autoencoder-based deep learning is applied to jointly optimize geometric and
probabilistic constellation shaping for optical coherent communication. The
optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann
probabilistic distribution with extra 0.05 bits/4D-symbol mutual information
for 64 GBd transmission over 170 km SMF link.

Voice trigger detection is an important task, which enables activating a
voice assistant when a target user speaks a keyword phrase. A detector is
typically trained on speech data independent of speaker information and used
for the voice trigger detection task. However, such a speaker independent voice
trigger detector typically suffers from performance degradation on speech from
underrepresented groups, such as accented speakers. In this work, we propose a
novel voice trigger detector that can use a small number of utterances from a
target speaker to improve detection accuracy. Our proposed model employs an
encoder-decoder architecture. While the encoder performs speaker independent
voice trigger detection, similar to the conventional detector, the decoder
predicts a personalized embedding for each utterance. A personalized voice
trigger score is then obtained as a similarity score between the embeddings of
enrollment utterances and a test utterance. The personalized embedding allows
adapting to target speaker's speech when computing the voice trigger score,
hence improving voice trigger detection accuracy. Experimental results show
that the proposed approach achieves a 38% relative reduction in a false
rejection rate (FRR) compared to a baseline speaker independent voice trigger
model.

Federated learning (FL) can collaboratively train deep learning models using
isolated patient data owned by different hospitals for various clinical
applications, including medical image segmentation. However, a major problem of
FL is its performance degradation when dealing with the data that are not
independently and identically distributed (non-iid), which is often the case in
medical images. In this paper, we first conduct a theoretical analysis on the
FL algorithm to reveal the problem of model aggregation during training on
non-iid data. With the insights gained through the analysis, we propose a
simple and yet effective method, federated cross learning (FedCross), to tackle
this challenging problem. Unlike the conventional FL methods that combine
multiple individually trained local models on a server node, our FedCross
sequentially trains the global model across different clients in a round-robin
manner, and thus the entire training procedure does not involve any model
aggregation steps. To further improve its performance to be comparable with the
centralized learning method, we combine the FedCross with an ensemble learning
mechanism to compose a federated cross ensemble learning (FedCrossEns) method.
Finally, we conduct extensive experiments using a set of public datasets. The
experimental results show that the proposed FedCross training strategy
outperforms the mainstream FL methods on non-iid data. In addition to improving
the segmentation performance, our FedCrossEns can further provide a
quantitative estimation of the model uncertainty, demonstrating the
effectiveness and clinical significance of our designs. Source code will be
made publicly available after paper publication.

We use a deep learning based approach to predict whether a selected element
in a mobile UI screenshot will be perceived by users as tappable, based on
pixels only instead of view hierarchies required by previous work. To help
designers better understand model predictions and to provide more actionable
design feedback than predictions alone, we additionally use ML interpretability
techniques to help explain the output of our model. We use XRAI to highlight
areas in the input screenshot that most strongly influence the tappability
prediction for the selected region, and use k-Nearest Neighbors to present the
most similar mobile UIs from the dataset with opposing influences on
tappability perception.

Web-based phishing attacks nowadays exploit popular cloud web hosting
services and apps such as Google Sites and Typeform for hosting their attacks.
Since these attacks originate from reputable domains and IP addresses of the
cloud services, traditional phishing detection methods such as IP reputation
monitoring and blacklisting are not very effective. Here we investigate the
effectiveness of deep learning models in detecting this class of cloud-based
phishing attacks. Specifically, we evaluate deep learning models for three
phishing detection methods--LSTM model for URL analysis, YOLOv2 model for logo
analysis, and triplet network model for visual similarity analysis. We train
the models using well-known datasets and test their performance on cloud-based
phishing attacks in the wild. Our results qualitatively explain why the models
succeed or fail. Furthermore, our results highlight how combining results from
the individual models can improve the effectiveness of detecting cloud-based
phishing attacks.

Most prior works in perceiving 3D humans from images reason human in
isolation without their surroundings. However, humans are constantly
interacting with the surrounding objects, thus calling for models that can
reason about not only the human but also the object and their interaction. The
problem is extremely challenging due to heavy occlusions between humans and
objects, diverse interaction types and depth ambiguity. In this paper, we
introduce CHORE, a novel method that learns to jointly reconstruct the human
and the object from a single RGB image. CHORE takes inspiration from recent
advances in implicit surface learning and classical model-based fitting. We
compute a neural reconstruction of human and object represented implicitly with
two unsigned distance fields, a correspondence field to a parametric body and
an object pose field. This allows us to robustly fit a parametric body model
and a 3D object template, while reasoning about interactions. Furthermore,
prior pixel-aligned implicit learning methods use synthetic data and make
assumptions that are not met in the real data. We propose a elegant depth-aware
scaling that allows more efficient shape learning on real data. Experiments
show that our joint reconstruction learned with the proposed strategy
significantly outperforms the SOTA. Our code and models are available at
https://virtualhumans.mpi-inf.mpg.de/chore

Conductivity imaging represents one of the most important tasks in medical
imaging. In this work we develop a neural network based reconstruction
technique for imaging the conductivity from the magnitude of the internal
current density. It is achieved by formulating the problem as a relaxed
weighted least-gradient problem, and then approximating its minimizer by
standard fully connected feedforward neural networks. We derive bounds on two
components of the generalization error, i.e., approximation error and
statistical error, explicitly in terms of properties of the neural networks
(e.g., depth, total number of parameters, and the bound of the network
parameters). We illustrate the performance and distinct features of the
approach on several numerical experiments. Numerically, it is observed that the
approach enjoys remarkable robustness with respect to the presence of data
noise.

Dataset bias and spurious correlations can significantly impair
generalization in deep neural networks. Many prior efforts have addressed this
problem using either alternative loss functions or sampling strategies that
focus on rare patterns. We propose a new direction: modifying the network
architecture to impose inductive biases that make the network robust to dataset
bias. Specifically, we propose OccamNets, which are biased to favor simpler
solutions by design. OccamNets have two inductive biases. First, they are
biased to use as little network depth as needed for an individual example.
Second, they are biased toward using fewer image locations for prediction.
While OccamNets are biased toward simpler hypotheses, they can learn more
complex hypotheses if necessary. In experiments, OccamNets outperform or rival
state-of-the-art methods run on architectures that do not incorporate these
inductive biases. Furthermore, we demonstrate that when the state-of-the-art
debiasing methods are combined with OccamNets results further improve.

Texture cues on 3D objects are key to compelling visual representations, with
the possibility to create high visual fidelity with inherent spatial
consistency across different views. Since the availability of textured 3D
shapes remains very limited, learning a 3D-supervised data-driven method that
predicts a texture based on the 3D input is very challenging. We thus propose
Texturify, a GAN-based method that leverages a 3D shape dataset of an object
class and learns to reproduce the distribution of appearances observed in real
images by generating high-quality textures. In particular, our method does not
require any 3D color supervision or correspondence between shape geometry and
images to learn the texturing of 3D objects. Texturify operates directly on the
surface of the 3D objects by introducing face convolutional operators on a
hierarchical 4-RoSy parametrization to generate plausible object-specific
textures. Employing differentiable rendering and adversarial losses that
critique individual views and consistency across views, we effectively learn
the high-quality surface texturing distribution from real-world images.
Experiments on car and chair shape collections show that our approach
outperforms state of the art by an average of 22% in FID score.

As the next generation of large galaxy surveys come online, it is becoming
increasingly important to develop and understand the machine learning tools
that analyze big astronomical data. Neural networks are powerful and capable of
probing deep patterns in data, but must be trained carefully on large and
representative data sets. We developed and generated a new `hump' of the
Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project:
CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100
$h^{-1}$ cMpc)$^3$ with different cosmological parameters ($\Omega_m$ and
$\sigma_8$) and run through the Santa Cruz semi-analytic model for galaxy
formation over a broad range of astrophysical parameters. As a proof-of-concept
for the power of this vast suite of simulated galaxies in a large volume and
broad parameter space, we probe the power of simple clustering summary
statistics to marginalize over astrophysics and constrain cosmology using
neural networks. We use the two-point correlation function, count-in-cells, and
the Void Probability Function, and probe non-linear and linear scales across
$0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around
3-8$\%$ error on $\Omega_{\text{M}}$ and $\sigma_8$, and we explore the effect
of various galaxy selections, galaxy sampling, and choice of clustering
statistics on these constraints. We additionally explore how these clustering
statistics constrain and inform key stellar and galactic feedback parameters in
the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of
CAMELS, and offers great potential to many applications of machine learning in
astrophysics: https://camels-sam.readthedocs.io.

High-resolution images are widely adopted for high-performance object
detection in videos. However, processing high-resolution inputs comes with high
computation costs, and naive down-sampling of the input to reduce the
computation costs quickly degrades the detection performance. In this paper, we
propose SALISA, a novel non-uniform SALiency-based Input SAmpling technique for
video object detection that allows for heavy down-sampling of unimportant
background regions while preserving the fine-grained details of a
high-resolution image. The resulting image is spatially smaller, leading to
reduced computational costs while enabling a performance comparable to a
high-resolution input. To achieve this, we propose a differentiable resampling
module based on a thin plate spline spatial transformer network (TPS-STN). This
module is regularized by a novel loss to provide an explicit supervision signal
to learn to "magnify" salient regions. We report state-of-the-art results in
the low compute regime on the ImageNet-VID and UA-DETRAC video object detection
datasets. We demonstrate that on both datasets, the mAP of an EfficientDet-D1
(EfficientDet-D2) gets on par with EfficientDet-D2 (EfficientDet-D3) at a much
lower computational cost. We also show that SALISA significantly improves the
detection of small objects. In particular, SALISA with an EfficientDet-D1
detector improves the detection of small objects by $77\%$, and remarkably also
outperforms EfficientDetD3 baseline.

We propose a piecewise learning framework for controlling nonlinear systems
with unknown dynamics. While model-based reinforcement learning techniques in
terms of some basis functions are well known in the literature, when it comes
to more complex dynamics, only a local approximation of the model can be
obtained using a limited number of bases. The complexity of the identifier and
the controller can be considerably high if obtaining an approximation over a
larger domain is desired. To overcome this limitation, we propose a general
piecewise nonlinear framework where each piece is responsible for locally
learning and controlling over some region of the domain. We obtain rigorous
uncertainty bounds for the learned piecewise models. The piecewise affine (PWA)
model is then studied as a special case, for which we propose an
optimization-based verification technique for stability analysis of the
closed-loop system. Accordingly, given a time-discretization of the learned
{PWA} system, we iteratively search for a common piecewise Lyapunov function in
a set of positive definite functions, where a non-monotonic convergence is
allowed. This Lyapunov candidate is verified on the uncertain system to either
provide a certificate for stability or find a counter-example when it fails.
This counter-example is added to a set of samples to facilitate the further
learning of a Lyapunov function. We demonstrate the results on two examples and
show that the proposed approach yields a less conservative region of attraction
(ROA) compared with alternative state-of-the-art approaches. Moreover, we
provide the runtime results to demonstrate potentials of the proposed framework
in real-world implementations.

We propose the first SE(3)-equivariant coordinate-based network for learning
occupancy fields from point clouds. In contrast to previous shape
reconstruction methods that align the input to a regular grid, we operate
directly on the irregular, unoriented point cloud. We leverage attention
mechanisms in order to preserve the set structure (permutation equivariance and
variable length) of the input. At the same time, attention layers enable local
shape modelling, a crucial property for scalability to large scenes. In
contrast to architectures that create a global signature for the shape, we
operate on local tokens. Given an unoriented, sparse, noisy point cloud as
input, we produce equivariant features for each point. These serve as keys and
values for the subsequent equivariant cross-attention blocks that parametrize
the occupancy field. By querying an arbitrary point in space, we predict its
occupancy score. We show that our method outperforms previous SO(3)-equivariant
methods, as well as non-equivariant methods trained on SO(3)-augmented
datasets. More importantly, local modelling together with SE(3)-equivariance
create an ideal setting for SE(3) scene reconstruction. We show that by
training only on single objects and without any pre-segmentation, we can
reconstruct a novel scene with single-object performance.

Deep visuomotor policy learning, which aims to map raw visual observation to
action, achieves promising results in control tasks such as robotic
manipulation and autonomous driving. However, it requires a huge number of
online interactions with the training environment, which limits its real-world
application. Compared to the popular unsupervised feature learning for visual
recognition, feature pretraining for visuomotor control tasks is much less
explored. In this work, we aim to pretrain policy representations for driving
tasks by watching hours-long uncurated YouTube videos. Specifically, we train
an inverse dynamic model with a small amount of labeled data and use it to
predict action labels for all the YouTube video frames. A new contrastive
policy pretraining method is then developed to learn action-conditioned
features from the video frames with pseudo action labels. Experiments show that
the resulting action-conditioned features obtain substantial improvements for
the downstream reinforcement learning and imitation learning tasks,
outperforming the weights pretrained from previous unsupervised learning
methods and ImageNet pretrained weight. Code, model weights, and data are
available at: https://metadriverse.github.io/ACO.

In most classical Autonomous Vehicle (AV) stacks, the prediction and planning
layers are separated, limiting the planner to react to predictions that are not
informed by the planned trajectory of the AV. This work presents a module that
tightly couples these layers via a game-theoretic Model Predictive Controller
(MPC) that uses a novel interactive multi-agent neural network policy as part
of its predictive model. In our setting, the MPC planner considers all the
surrounding agents by informing the multi-agent policy with the planned state
sequence. Fundamental to the success of our method is the design of a novel
multi-agent policy network that can steer a vehicle given the state of the
surrounding agents and the map information. The policy network is trained
implicitly with ground-truth observation data using backpropagation through
time and a differentiable dynamics model to roll out the trajectory forward in
time. Finally, we show that our multi-agent policy network learns to drive
while interacting with the environment, and, when combined with the
game-theoretic MPC planner, can successfully generate interactive behaviors.

We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a
means of efficiently moving scattered objects into a target receptacle. Due to
the chaotic nature of aerodynamic forces, a blowing controller must (i)
continually adapt to unexpected changes from its actions, (ii) maintain
fine-grained control, since the slightest misstep can result in large
unintended consequences (e.g., scatter objects already in a pile), and (iii)
infer long-range plans (e.g., move the robot to strategic blowing locations).
We tackle these challenges in the context of deep reinforcement learning,
introducing a multi-frequency version of the spatial action maps framework.
This allows for efficient learning of vision-based policies that effectively
combine high-level planning and low-level closed-loop control for dynamic
mobile manipulation. Experiments show that our system learns efficient
behaviors for the task, demonstrating in particular that blowing achieves
better downstream performance than pushing, and that our policies improve
performance over baselines. Moreover, we show that our system naturally
encourages emergent specialization between the different subpolicies spanning
low-level fine-grained control and high-level planning. On a real mobile robot
equipped with a miniature air blower, we show that our simulation-trained
policies transfer well to a real environment and can generalize to novel
objects.

Objects play a crucial role in our everyday activities. Though multisensory
object-centric learning has shown great potential lately, the modeling of
objects in prior work is rather unrealistic. ObjectFolder 1.0 is a recent
dataset that introduces 100 virtualized objects with visual, acoustic, and
tactile sensory data. However, the dataset is small in scale and the
multisensory data is of limited quality, hampering generalization to real-world
scenarios. We present ObjectFolder 2.0, a large-scale, multisensory dataset of
common household objects in the form of implicit neural representations that
significantly enhances ObjectFolder 1.0 in three aspects. First, our dataset is
10 times larger in the amount of objects and orders of magnitude faster in
rendering time. Second, we significantly improve the multisensory rendering
quality for all three modalities. Third, we show that models learned from
virtual objects in our dataset successfully transfer to their real-world
counterparts in three challenging tasks: object scale estimation, contact
localization, and shape reconstruction. ObjectFolder 2.0 offers a new path and
testbed for multisensory learning in computer vision and robotics. The dataset
is available at https://github.com/rhgao/ObjectFolder.

Deep-learning-based approaches to depth estimation are rapidly advancing,
offering superior performance over existing methods. To estimate the depth in
real-world scenarios, depth estimation models require the robustness of various
noise environments. In this work, a Pyramid Frequency Network(PFN) with Spatial
Attention Residual Refinement Module(SARRM) is proposed to deal with the weak
robustness of existing deep-learning methods. To reconstruct depth maps with
accurate details, the SARRM constructs a residual fusion method with an
attention mechanism to refine the blur depth. The frequency division strategy
is designed, and the frequency pyramid network is developed to extract features
from multiple frequency bands. With the frequency strategy, PFN achieves better
visual accuracy than state-of-the-art methods in both indoor and outdoor scenes
on Make3D, KITTI depth, and NYUv2 datasets. Additional experiments on the noisy
NYUv2 dataset demonstrate that PFN is more reliable than existing deep-learning
methods in high-noise scenes.

The modeling of human emotion expression in speech signals is an important,
yet challenging task. The high resource demand of speech emotion recognition
models, combined with the the general scarcity of emotion-labelled data are
obstacles to the development and application of effective solutions in this
field. In this paper, we present an approach to jointly circumvent these
difficulties. Our method, named RH-emo, is a novel semi-supervised architecture
aimed at extracting quaternion embeddings from real-valued monoaural
spectrograms, enabling the use of quaternion-valued networks for speech emotion
recognition tasks. RH-emo is a hybrid real/quaternion autoencoder network that
consists of a real-valued encoder in parallel to a real-valued emotion
classifier and a quaternion-valued decoder. On the one hand, the classifier
permits to optimize each latent axis of the embeddings for the classification
of a specific emotion-related characteristic: valence, arousal, dominance and
overall emotion. On the other hand, the quaternion reconstruction enables the
latent dimension to develop intra-channel correlations that are required for an
effective representation as a quaternion entity. We test our approach on speech
emotion recognition tasks using four popular datasets: Iemocap, Ravdess, EmoDb
and Tess, comparing the performance of three well-established real-valued CNN
architectures (AlexNet, ResNet-50, VGG) and their quaternion-valued equivalent
fed with the embeddings created with RH-emo. We obtain a consistent improvement
in the test accuracy for all datasets, while drastically reducing the
resources' demand of models. Moreover, we performed additional experiments and
ablation studies that confirm the effectiveness of our approach. The RH-emo
repository is available at: https://github.com/ispamm/rhemo.

As automatic speech recognition (ASR) systems are now being widely deployed
in the wild, the increasing threat of adversarial attacks raises serious
questions about the security and reliability of using such systems. On the
other hand, multi-task learning (MTL) has shown success in training models that
can resist adversarial attacks in the computer vision domain. In this work, we
investigate the impact of performing such multi-task learning on the
adversarial robustness of ASR models in the speech domain. We conduct extensive
MTL experimentation by combining semantically diverse tasks such as accent
classification and ASR, and evaluate a wide range of adversarial settings. Our
thorough analysis reveals that performing MTL with semantically diverse tasks
consistently makes it harder for an adversarial attack to succeed. We also
discuss in detail the serious pitfalls and their related remedies that have a
significant impact on the robustness of MTL models. Our proposed MTL approach
shows considerable absolute improvements in adversarially targeted WER ranging
from 17.25 up to 59.90 compared to single-task learning baselines (attention
decoder and CTC respectively). Ours is the first in-depth study that uncovers
adversarial robustness gains from multi-task learning for ASR.

Providing explanations in the context of Visual Question Answering (VQA)
presents a fundamental problem in machine learning. To obtain detailed insights
into the process of generating natural language explanations for VQA, we
introduce the large-scale CLEVR-X dataset that extends the CLEVR dataset with
natural language explanations. For each image-question pair in the CLEVR
dataset, CLEVR-X contains multiple structured textual explanations which are
derived from the original scene graphs. By construction, the CLEVR-X
explanations are correct and describe the reasoning and visual information that
is necessary to answer a given question. We conducted a user study to confirm
that the ground-truth explanations in our proposed dataset are indeed complete
and relevant. We present baseline results for generating natural language
explanations in the context of VQA using two state-of-the-art frameworks on the
CLEVR-X dataset. Furthermore, we provide a detailed analysis of the explanation
generation quality for different question and answer types. Additionally, we
study the influence of using different numbers of ground-truth explanations on
the convergence of natural language generation (NLG) metrics. The CLEVR-X
dataset is publicly available at
\url{https://explainableml.github.io/CLEVR-X/}.

Given entities and their interactions in the web data, which may have
occurred at different time, how can we find communities of entities and track
their evolution? In this paper, we approach this important task from graph
clustering perspective. Recently, state-of-the-art clustering performance in
various domains has been achieved by deep clustering methods. Especially, deep
graph clustering (DGC) methods have successfully extended deep clustering to
graph-structured data by learning node representations and cluster assignments
in a joint optimization framework. Despite some differences in modeling choices
(e.g., encoder architectures), existing DGC methods are mainly based on
autoencoders and use the same clustering objective with relatively minor
adaptations. Also, while many real-world graphs are dynamic, previous DGC
methods considered only static graphs. In this work, we develop CGC, a novel
end-to-end framework for graph clustering, which fundamentally differs from
existing methods. CGC learns node embeddings and cluster assignments in a
contrastive graph learning framework, where positive and negative samples are
carefully selected in a multi-level scheme such that they reflect hierarchical
community structures and network homophily. Also, we extend CGC for
time-evolving data, where temporal graph clustering is performed in an
incremental learning fashion, with the ability to detect change points.
Extensive evaluation on real-world graphs demonstrates that the proposed CGC
consistently outperforms existing methods.

This paper presents a set of tests and an algorithm for agnostic, data-driven
selection among macroeconomic DSGE models inspired by structure learning
methods for DAGs. As the log-linear state-space solution to any DSGE model is
also a DAG it is possible to use associated concepts to identify a unique
ground-truth state-space model which is compatible with an underlying DGP,
based on the conditional independence relationships which are present in that
DGP. In order to operationalise search for this ground-truth model, the
algorithm tests feasible analogues of these conditional independence criteria
against the set of combinatorially possible state-space models over observed
variables. This process is consistent in large samples. In small samples the
result may not be unique, so conditional independence tests can be combined
with likelihood maximisation in order to select a single optimal model. The
efficacy of this algorithm is demonstrated for simulated data, and results for
real data are also provided and discussed.

Community detection is a challenging and relevant problem in various
disciplines of science and engineering like power systems, gene-regulatory
networks, social networks, financial networks, astronomy etc. Furthermore, in
many of these applications the underlying system is dynamical in nature and
because of the complexity of the systems involved, deriving a mathematical
model which can be used for clustering and community detection, is often
impossible. Moreover, while clustering dynamical systems, it is imperative that
the dynamical nature of the underlying system is taken into account. In this
paper, we propose a novel approach for clustering dynamical systems purely from
time-series data which inherently takes into account the dynamical evolution of
the underlying system. In particular, we define a \emph{distance/similarity}
measure between the states of the system which is a function of the influence
that the states have on each other, and use the proposed measure for clustering
of the dynamical system. For data-driven computation we leverage the Koopman
operator framework which takes into account the nonlinearities (if present) of
the underlying system, thus making the proposed framework applicable to a wide
range of application areas. We illustrate the efficacy of the proposed approach
by clustering three different dynamical systems, namely, a linear system, which
acts like a proof of concept, the highly non-linear IEEE 39 bus transmission
network and dynamic variables obtained from atmospheric data over the Amazon
rain forest.

Reinforcement learning (RL) provides a theoretical framework for continuously
improving an agent's behavior via trial and error. However, efficiently
learning policies from scratch can be very difficult, particularly for tasks
with exploration challenges. In such settings, it might be desirable to
initialize RL with an existing policy, offline data, or demonstrations.
However, naively performing such initialization in RL often works poorly,
especially for value-based methods. In this paper, we present a meta algorithm
that can use offline data, demonstrations, or a pre-existing policy to
initialize an RL policy, and is compatible with any RL approach. In particular,
we propose Jump-Start Reinforcement Learning (JSRL), an algorithm that employs
two policies to solve tasks: a guide-policy, and an exploration-policy. By
using the guide-policy to form a curriculum of starting states for the
exploration-policy, we are able to efficiently improve performance on a set of
simulated robotic tasks. We show via experiments that JSRL is able to
significantly outperform existing imitation and reinforcement learning
algorithms, particularly in the small-data regime. In addition, we provide an
upper bound on the sample complexity of JSRL and show that with the help of a
guide-policy, one can improve the sample complexity for non-optimism
exploration methods from exponential in horizon to polynomial.

Collaborative filtering (CF) is widely used by personalized recommendation
systems, which aims to predict the preference of users with historical
user-item interactions. In recent years, Graph Neural Networks (GNNs) have been
utilized to build CF models and have shown promising performance. Recent
state-of-the-art GNN-based CF approaches simply attribute their performance
improvement to the high-order neighbor aggregation ability of GNNs. However, we
observe that some powerful deep GNNs such as JKNet and DropEdge, can
effectively exploit high-order neighbor information on other graph tasks but
perform poorly on CF tasks, which conflicts with the explanation of these
GNN-based CF research. Different from these research, we investigate the
GNN-based CF from the perspective of Markov processes for distance learning
with a unified framework named Markov Graph Diffusion Collaborative Filtering
(MGDCF). We design a Markov Graph Diffusion Network (MGDN) as MGDCF's GNN
encoder, which learns vertex representations by trading off two types of
distances via a Markov process. We show the theoretical equivalence between
MGDN's output and the optimal solution of a distance loss function, which can
boost the optimization of CF models. MGDN can generalize state-of-the-art
models such as LightGCN and APPNP, which are heterogeneous GNNs. In addition,
MGDN can be extended to homogeneous GNNs with our sparsification technique. For
optimizing MGDCF, we propose the InfoBPR loss function, which extends the
widely used BPR loss to exploit multiple negative samples for better
performance. We conduct experiments to perform detailed analysis on MGDCF. The
source code is publicly available at https://github.com/hujunxianligong/MGDCF.

Generating sub-optimal synthesis transformation sequences ("synthesis
recipe") is an important problem in logic synthesis. Manually crafted synthesis
recipes have poor quality. State-of-the art machine learning (ML) works to
generate synthesis recipes do not scale to large netlists as the models need to
be trained from scratch, for which training data is collected using time
consuming synthesis runs. We propose a new approach, Bulls-Eye, that fine-tunes
a pre-trained model on past synthesis data to accurately predict the quality of
a synthesis recipe for an unseen netlist. This approach on achieves 2x-10x
run-time improvement and better quality-of-result (QoR) than state-of-the-art
machine learning approaches.

We demonstrate IFTT-PIN, a self-calibrating version of the PIN-entry method
introduced in Roth et al. (2004) [1]. In [1], digits are split into two sets
and assigned a color respectively. To communicate their digit, users press the
button with the same color that is assigned to their digit, which can be
identified by elimination after a few iterations. IFTT-PIN uses the same
principle but does not pre-assign colors to each button. Instead, users are
free to choose which button to use for each color. IFTT-PIN infers both the
user's PIN and their preferred button-to-color mapping at the same time, a
process called self-calibration. Different versions of IFTT-PIN can be tested
at https://jgrizou.github.io/IFTT-PIN/ and a video introduction at
https://youtu.be/5I1ibPJdLHM.

Language Models (LMs) can perform new tasks by adapting to a few in-context
examples. For humans, explanations that connect examples to task principles can
improve learning. We therefore investigate whether explanations of few-shot
examples can help LMs. We annotate questions from 40 challenging tasks with
answer explanations, and various matched control explanations. We evaluate how
different types of explanations, instructions, and controls affect zero- and
few-shot performance. We analyze these results using statistical multilevel
modeling techniques that account for the nested dependencies among conditions,
tasks, prompts, and models. We find that explanations can improve performance
-- even without tuning. Furthermore, explanations hand-tuned for performance on
a small validation set offer substantially larger benefits, and building a
prompt by selecting examples and explanations together substantially improves
performance over selecting examples alone. Finally, even untuned explanations
outperform carefully matched controls, suggesting that the benefits are due to
the link between an example and its explanation, rather than lower-level
features. However, only large models benefit. In summary, explanations can
support the in-context learning of large LMs on challenging tasks.

Despite the breakthrough deep learning performances achieved for automatic
object detection, small target detection is still a challenging problem,
especially when looking at fast and accurate solutions suitable for mobile or
edge applications. In this work we present YOLO-S, a simple, fast and efficient
network for small target detection. The architecture exploits a small feature
extractor based on Darknet20, as well as skip connection, via both bypass and
concatenation, and reshape-passthrough layer to alleviate the vanishing
gradient problem, promote feature reuse across network and combine low-level
positional information with more meaningful high-level information. To verify
the performances of YOLO-S, we build "AIRES", a novel dataset for cAr detectIon
fRom hElicopter imageS acquired in Europe, and set up experiments on both AIRES
and VEDAI datasets, benchmarking this architecture with four baseline
detectors. Furthermore, in order to handle efficiently the issue of data
insufficiency and domain gap when dealing with a transfer learning strategy, we
introduce a transitional learning task over a combined dataset based on DOTAv2
and VEDAI and demonstrate that can enhance the overall accuracy with respect to
more general features transferred from COCO data. YOLO-S is from 25% to 50%
faster than YOLOv3 and only 15-25% slower than Tiny-YOLOv3, outperforming also
YOLOv3 in terms of accuracy in a wide range of experiments. Further simulations
performed on SARD dataset demonstrate also its applicability to different
scenarios such as for search and rescue operations. Besides, YOLO-S has an 87%
decrease of parameter size and almost one half FLOPs of YOLOv3, making
practical the deployment for low-power industrial applications.

Multi-view unsupervised feature selection has been proven to be efficient in
reducing the dimensionality of multi-view unlabeled data with high dimensions.
The previous methods assume all of the views are complete. However, in real
applications, the multi-view data are often incomplete, i.e., some views of
instances are missing, which will result in the failure of these methods.
Besides, while the data arrive in form of streams, these existing methods will
suffer the issues of high storage cost and expensive computation time. To
address these issues, we propose an Incremental Incomplete Multi-view
Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view
streaming data. By jointly considering the consistent and complementary
information across different views, I$^2$MUFS embeds the unsupervised feature
selection into an extended weighted non-negative matrix factorization model,
which can learn a consensus clustering indicator matrix and fuse different
latent feature matrices with adaptive view weights. Furthermore, we introduce
the incremental leaning mechanisms to develop an alternative iterative
algorithm, where the feature selection matrix is incrementally updated, rather
than recomputing on the entire updated data from scratch. A series of
experiments are conducted to verify the effectiveness of the proposed method by
comparing with several state-of-the-art methods. The experimental results
demonstrate the effectiveness and efficiency of the proposed method in terms of
the clustering metrics and the computational cost.

We study the problem of robust estimation of the mean vector of a
sub-Gaussian distribution. We introduce an estimator based on spectral
dimension reduction (SDR) and establish a finite sample upper bound on its
error that is minimax-optimal up to a logarithmic factor. Furthermore, we prove
that the breakdown point of the SDR estimator is equal to $1/2$, the highest
possible value of the breakdown point. In addition, the SDR estimator is
equivariant by similarity transforms and has low computational complexity. More
precisely, in the case of $n$ vectors of dimension $p$ -- at most $\varepsilon
n$ out of which are adversarially corrupted -- the SDR estimator has a squared
error of order $\big(\frac{r_\Sigma}{n} +
\varepsilon^2\log(1/\varepsilon)\big){\log p}$ and a running time of order $p^3
+ n p^2$. Here, $r_\Sigma\le p$ is the effective rank of the covariance matrix
of the reference distribution. Another advantage of the SDR estimator is that
it does not require knowledge of the contamination rate and does not involve
sample splitting. We also investigate extensions of the proposed algorithm and
of the obtained results in the case of (partially) unknown covariance matrix.

Federated learning (FL) enables edge devices to collaboratively learn a model
in a distributed fashion. Many existing researches have focused on improving
communication efficiency of high-dimensional models and addressing bias caused
by local updates. However, most of FL algorithms are either based on reliable
communications or assume fixed and known unreliability characteristics. In
practice, networks could suffer from dynamic channel conditions and
non-deterministic disruptions, with time-varying and unknown characteristics.
To this end, in this paper we propose a sparsity enabled FL framework with both
communication efficiency and bias reduction, termed as SAFARI. It makes novel
use of a similarity among client models to rectify and compensate for bias that
is resulted from unreliable communications. More precisely, sparse learning is
implemented on local clients to mitigate communication overhead, while to cope
with unreliable communications, a similarity-based compensation method is
proposed to provide surrogates for missing model updates. We analyze SAFARI
under bounded dissimilarity and with respect to sparse models. It is
demonstrated that SAFARI under unreliable communications is guaranteed to
converge at the same rate as the standard FedAvg with perfect communications.
Implementations and evaluations on CIFAR-10 dataset validate the effectiveness
of SAFARI by showing that it can achieve the same convergence speed and
accuracy as FedAvg with perfect communications, with up to 80% of the model
weights being pruned and a high percentage of client updates missing in each
round.

Dexterous manipulation with a multi-finger hand is one of the most
challenging problems in robotics. While recent progress in imitation learning
has largely improved the sample efficiency compared to Reinforcement Learning,
the learned policy can hardly generalize to manipulate novel objects, given
limited expert demonstrations. In this paper, we propose to learn dexterous
manipulation using large-scale demonstrations with diverse 3D objects in a
category, which are generated from a human grasp affordance model. This
generalizes the policy to novel object instances within the same category. To
train the policy, we propose a novel imitation learning objective jointly with
a geometric representation learning objective using our demonstrations. By
experimenting with relocating diverse objects in simulation, we show that our
approach outperforms baselines with a large margin when manipulating novel
objects. We also ablate the importance on 3D object representation learning for
manipulation. We include videos, code, and additional information on the
project website - https://kristery.github.io/ILAD/ .

We present a machine learning approach for model-independent new physics
searches. The corresponding algorithm is powered by recent large-scale
implementations of kernel methods, nonparametric learning algorithms that can
approximate any continuous function given enough data. Based on the original
proposal by D'Agnolo and Wulzer (arXiv:1806.02350), the model evaluates the
compatibility between experimental data and a reference model, by implementing
a hypothesis testing procedure based on the likelihood ratio.
Model-independence is enforced by avoiding any prior assumption about the
presence or shape of new physics components in the measurements. We show that
our approach has dramatic advantages compared to neural network implementations
in terms of training times and computational resources, while maintaining
comparable performances. In particular, we conduct our tests on higher
dimensional datasets, a step forward with respect to previous studies.

We present a framework that gives a deep insight into the link between
physical and technical-tactical aspects of soccer and it allows associating
physical performance with value generation thanks to a top-down approach.
First, we estimate physical indicators from tracking data. Then, we
contextualize each player's run to understand better the purpose and
circumstances in which it is done, adding a new dimension to the creation of
team and player profiles. Finally, we assess the value-added by off-ball
high-intensity runs by linking with a possession-value model. This novel
approach allows answering practical questions from very different profiles of
practitioners within a soccer club, from analysts, coaches, and scouts to
physical coaches and readaptation physiotherapists.

Large language models have been shown to achieve remarkable performance
across a variety of natural language tasks using few-shot learning, which
drastically reduces the number of task-specific training examples needed to
adapt the model to a particular application. To further our understanding of
the impact of scale on few-shot learning, we trained a 540-billion parameter,
densely activated, Transformer language model, which we call Pathways Language
Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML
system which enables highly efficient training across multiple TPU Pods. We
demonstrate continued benefits of scaling by achieving state-of-the-art
few-shot learning results on hundreds of language understanding and generation
benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough
performance, outperforming the finetuned state-of-the-art on a suite of
multi-step reasoning tasks, and outperforming average human performance on the
recently released BIG-bench benchmark. A significant number of BIG-bench tasks
showed discontinuous improvements from model scale, meaning that performance
steeply increased as we scaled to our largest model. PaLM also has strong
capabilities in multilingual tasks and source code generation, which we
demonstrate on a wide array of benchmarks. We additionally provide a
comprehensive analysis on bias and toxicity, and study the extent of training
data memorization with respect to model scale. Finally, we discuss the ethical
considerations related to large language models and discuss potential
mitigation strategies.

We present iSDF, a continual learning system for real-time signed distance
field (SDF) reconstruction. Given a stream of posed depth images from a moving
camera, it trains a randomly initialised neural network to map input 3D
coordinate to approximate signed distance. The model is self-supervised by
minimising a loss that bounds the predicted signed distance using the distance
to the closest sampled point in a batch of query points that are actively
sampled. In contrast to prior work based on voxel grids, our neural method is
able to provide adaptive levels of detail with plausible filling in of
partially observed regions and denoising of observations, all while having a
more compact representation. In evaluations against alternative methods on real
and synthetic datasets of indoor environments, we find that iSDF produces more
accurate reconstructions, and better approximations of collision costs and
gradients useful for downstream planners in domains from navigation to
manipulation. Code and video results can be found at our project page:
https://joeaortiz.github.io/iSDF/ .

Security compliance management plays an important role in mitigating insider
threats. Incentive design is a proactive and non-invasive approach to achieving
compliance by aligning an employee's incentive with the defender's security
objective. Controlling insiders' incentives to elicit proper actions is
challenging because they are neither precisely known nor directly controllable.
To this end, we develop ZETAR, a zero-trust audit and recommendation framework,
to provide a quantitative approach to model incentives of the insiders and
design customized and strategic recommendation policies to improve their
compliance. We formulate primal and dual convex programs to compute the optimal
bespoke recommendation policies. We create a theoretical underpinning for
understanding trust and compliance, and it leads to security insights,
including fundamental limits of Completely Trustworthy (CT) recommendation, the
principle of compliance equivalency, and strategic information disclosure. This
work proposes finite-step algorithms to efficiently learn the CT policy set
when employees' incentives are unknown. Finally, we present a case study to
corroborate the design and illustrate a formal way to achieve compliance for
insiders with different risk attitudes. Our results show that the optimal
recommendation policy leads to a significant improvement in compliance for
risk-averse insiders. Moreover, CT recommendation policies promote insiders'

The importance of accurately quantifying forecast uncertainty has motivated
much recent research on probabilistic forecasting. In particular, a variety of
deep learning approaches has been proposed, with forecast distributions
obtained as output of neural networks. These neural network-based methods are
often used in the form of an ensemble based on multiple model runs from
different random initializations, resulting in a collection of forecast
distributions that need to be aggregated into a final probabilistic prediction.
With the aim of consolidating findings from the machine learning literature on
ensemble methods and the statistical literature on forecast combination, we
address the question of how to aggregate distribution forecasts based on such
deep ensembles. Using theoretical arguments, simulation experiments and a case
study on wind gust forecasting, we systematically compare probability- and
quantile-based aggregation methods for three neural network-based approaches
with different forecast distribution types as output. Our results show that
combining forecast distributions can substantially improve the predictive
performance. We propose a general quantile aggregation framework for deep
ensembles that shows superior performance compared to a linear combination of
the forecast densities. Finally, we investigate the effects of the ensemble
size and derive recommendations of aggregating distribution forecasts from deep
ensembles in practice.

Visual Geo-localization (VG) is the task of estimating the position where a
given photo was taken by comparing it with a large database of images of known
locations. To investigate how existing techniques would perform on a real-world
city-wide VG application, we build San Francisco eXtra Large, a new dataset
covering a whole city and providing a wide range of challenging cases, with a
size 30x bigger than the previous largest dataset for visual geo-localization.
We find that current methods fail to scale to such large datasets, therefore we
design a new highly scalable training technique, called CosPlace, which casts
the training as a classification problem avoiding the expensive mining needed
by the commonly used contrastive learning. We achieve state-of-the-art
performance on a wide range of datasets and find that CosPlace is robust to
heavy domain changes. Moreover, we show that, compared to the previous
state-of-the-art, CosPlace requires roughly 80% less GPU memory at train time,
and it achieves better results with 8x smaller descriptors, paving the way for
city-wide real-world visual geo-localization. Dataset, code and trained models
are available for research purposes at https://github.com/gmberton/CosPlace.

While Visual Question Answering (VQA) has progressed rapidly, previous works
raise concerns about robustness of current VQA models. In this work, we study
the robustness of VQA models from a novel perspective: visual context. We
suggest that the models over-rely on the visual context, i.e., irrelevant
objects in the image, to make predictions. To diagnose the model's reliance on
visual context and measure their robustness, we propose a simple yet effective
perturbation technique, SwapMix. SwapMix perturbs the visual context by
swapping features of irrelevant context objects with features from other
objects in the dataset. Using SwapMix we are able to change answers to more
than 45 % of the questions for a representative VQA model. Additionally, we
train the models with perfect sight and find that the context over-reliance
highly depends on the quality of visual representations. In addition to
diagnosing, SwapMix can also be applied as a data augmentation strategy during
training in order to regularize the context over-reliance. By swapping the
context object features, the model reliance on context can be suppressed
effectively. Two representative VQA models are studied using SwapMix: a
co-attention model MCAN and a large-scale pretrained model LXMERT. Our
experiments on the popular GQA dataset show the effectiveness of SwapMix for
both diagnosing model robustness and regularizing the over-reliance on visual
context. The code for our method is available at
https://github.com/vipulgupta1011/swapmix

Recent research has shown that generative models with highly disentangled
representations fail to generalise to unseen combination of generative factor
values. These findings contradict earlier research which showed improved
performance in out-of-training distribution settings when compared to entangled
representations. Additionally, it is not clear if the reported failures are due
to (a) encoders failing to map novel combinations to the proper regions of the
latent space or (b) novel combinations being mapped correctly but the
decoder/downstream process is unable to render the correct output for the
unseen combinations. We investigate these alternatives by testing several
models on a range of datasets and training settings. We find that (i) when
models fail, their encoders also fail to map unseen combinations to correct
regions of the latent space and (ii) when models succeed, it is either because
the test conditions do not exclude enough examples, or because excluded
generative factors determine independent parts of the output image. Based on
these results, we argue that to generalise properly, models not only need to
capture factors of variation, but also understand how to invert the generative
process that was used to generate the data.

Speaker verification (SV) provides billions of voice-enabled devices with
access control, and ensures the security of voice-driven technologies. As a
type of biometrics, it is necessary that SV is unbiased, with consistent and
reliable performance across speakers irrespective of their demographic, social
and economic attributes. Current SV evaluation practices are insufficient for
evaluating bias: they are over-simplified and aggregate users, not
representative of real-life usage scenarios, and consequences of errors are not
accounted for. This paper proposes design guidelines for constructing SV
evaluation datasets that address these short-comings. We propose a schema for
grading the difficulty of utterance pairs, and present an algorithm for
generating inclusive SV datasets. We empirically validate our proposed method
in a set of experiments on the VoxCeleb1 dataset. Our results confirm that the
count of utterance pairs/speaker, and the difficulty grading of utterance pairs
have a significant effect on evaluation performance and variability. Our work
contributes to the development of SV evaluation practices that are inclusive
and fair.

Acoustic scene classification (ASC) and sound event detection (SED) are
fundamental tasks in environmental sound analysis, and many methods based on
deep learning have been proposed. Considering that information on acoustic
scenes and sound events helps SED and ASC mutually, some researchers have
proposed a joint analysis of acoustic scenes and sound events by multitask
learning (MTL). However, conventional works have not investigated in detail how
acoustic scenes and sound events mutually benefit SED and ASC. We, therefore,
investigate the impact of information on acoustic scenes and sound events on
the performance of SED and ASC by using domain adversarial training based on a
gradient reversal layer (GRL) or model training with fake labels. Experimental
results obtained using the TUT Acoustic Scenes 2016/2017 and TUT Sound Events
2016/2017 show that pieces of information on acoustic scenes and sound events
are effectively used to detect sound events and classify acoustic scenes,
respectively. Moreover, upon comparing GRL- and fake-label-based methods with
single-task-based ASC and SED methods, single-task-based methods are found to
achieve better performance. This result implies that even when using
single-task-based ASC and SED methods, information on acoustic scenes may be
implicitly utilized for SED and vice versa.

In this preliminary work, we propose to design an activity recognition system
that is suitable for Industrie 4.0 (I4.0) applications, especially focusing on
Learning from Demonstration (LfD) in collaborative robot tasks. More precisely,
we focus on the issue of data exchange between an activity recognition system
and a collaborative robotic system. We propose an activity recognition system
with linked data using functional object-oriented network (FOON) to facilitate
industrial use cases. Initially, we drafted a FOON for our use case.
Afterwards, an action is estimated by using object and hand recognition systems
coupled with a recurrent neural network, which refers to FOON objects and
states. Finally, the detected action is shared via a context broker using an
existing linked data model, thus enabling the robotic system to interpret the
action and execute it afterwards. Our initial results show that FOON can be
used for an industrial use case and that we can use existing linked data models
in LfD applications.

We study the Bayesian inverse problem of inferring the Biot number, a
spatio-temporal heat-flux parameter in a PDE model. This is an ill-posed
problem where standard optimisation yields unphysical inferences. We introduce
a training scheme that uses temperature data to adaptively train a
neural-network surrogate to simulate the parametric forward model. This
approach approximates forward and inverse solution together, by simultaneously
identifying an approximate posterior distribution over the Biot number, and
weighting the forward training loss according to this approximation. Utilising
random Chebyshev series, we outline how to approximate an arbitrary Gaussian
process prior, and using the surrogate we apply Hamiltonian Monte Carlo (HMC)
to efficiently sample from the corresponding posterior distribution. We derive
convergence of the surrogate posterior to the true posterior distribution in
the Hellinger metric as our adaptive loss function approaches zero.
Furthermore, we describe how this surrogate-accelerated HMC approach can be
combined with a traditional PDE solver in a delayed-acceptance scheme to
a-priori control the posterior accuracy, thus overcoming a major limitation of
deep learning-based surrogate approaches, which do not achieve guaranteed
accuracy a-priori due to their non-convex training. Biot number calculations
are involved turbo-machinery design, which is safety critical and highly
regulated, therefore it is important that our results have such mathematical
guarantees. Our approach achieves fast mixing in high-dimensional parameter
spaces, whilst retaining the convergence guarantees of a traditional PDE
solver, and without the burden of evaluating this solver for proposals that are
likely to be rejected. Numerical results compare the accuracy and efficiency of
the adaptive and general training regimes, as well as various Markov chain
Monte Carlo proposals strategies.

We propose a computational model of speech production combining a pre-trained
neural articulatory synthesizer able to reproduce complex speech stimuli from a
limited set of interpretable articulatory parameters, a DNN-based internal
forward model predicting the sensory consequences of articulatory commands, and
an internal inverse model based on a recurrent neural network recovering
articulatory commands from the acoustic speech input. Both forward and inverse
models are jointly trained in a self-supervised way from raw acoustic-only
speech data from different speakers. The imitation simulations are evaluated
objectively and subjectively and display quite encouraging performances.

We propose a multi-agent distributed reinforcement learning algorithm that
balances between potentially conflicting short-term reward and sparse, delayed
long-term reward, and learns with partial information in a dynamic environment.
We compare different long-term rewards to incentivize the algorithm to maximize
individual payoff and overall social welfare. We test the algorithm in two
simulated auction games, and demonstrate that 1) our algorithm outperforms two
benchmark algorithms in a direct competition, with cost to social welfare, and
2) our algorithm's aggressive competitive behavior can be guided with the
long-term reward signal to maximize both individual payoff and overall social
welfare.

Deep learning models for medical image segmentation can fail unexpectedly and
spectacularly for pathological cases and images acquired at different centers
than training images, with labeling errors that violate expert knowledge. Such
errors undermine the trustworthiness of deep learning models for medical image
segmentation. Mechanisms for detecting and correcting such failures are
essential for safely translating this technology into clinics and are likely to
be a requirement of future regulations on artificial intelligence (AI). In this
work, we propose a trustworthy AI theoretical framework and a practical system
that can augment any backbone AI system using a fallback method and a fail-safe
mechanism based on Dempster-Shafer theory. Our approach relies on an actionable
definition of trustworthy AI. Our method automatically discards the voxel-level
labeling predicted by the backbone AI that violate expert knowledge and relies
on a fallback for those voxels. We demonstrate the effectiveness of the
proposed trustworthy AI approach on the largest reported annotated dataset of
fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13
centers. Our trustworthy AI method improves the robustness of a
state-of-the-art backbone AI for fetal brain MRIs acquired across various
centers and for fetuses with various brain abnormalities.

We formulate computation offloading as a decentralized decision-making
problem with autonomous agents. We design an interaction mechanism that
incentivizes agents to align private and system goals by balancing between
competition and cooperation. The mechanism provably has Nash equilibria with
optimal resource allocation in the static case. For a dynamic environment, we
delayed and noisy state information, and a reward signal that reduces
information need to a great extent. Empirical results confirm that through
learning, agents significantly improve both system and individual performance,
e.g., 40% offloading failure rate reduction, 32% communication overhead
reduction, up to 38% computation resource savings in low contention, 18%
utilization increase with reduced load variation in high contention, and
improvement in fairness. Results also confirm the algorithm's good convergence
and generalization property in significantly different environments.

Robustness of machine learning models on ever-changing real-world data is
critical, especially for applications affecting human well-being such as
content moderation. New kinds of abusive language continually emerge in online
discussions in response to current events (e.g., COVID-19), and the deployed
abuse detection systems should be updated regularly to remain accurate. In this
paper, we show that general abusive language classifiers tend to be fairly
reliable in detecting out-of-domain explicitly abusive utterances but fail to
detect new types of more subtle, implicit abuse. Next, we propose an
interpretability technique, based on the Testing Concept Activation Vector
(TCAV) method from computer vision, to quantify the sensitivity of a trained
model to the human-defined concepts of explicit and implicit abusive language,
and use that to explain the generalizability of the model on new data, in this
case, COVID-related anti-Asian hate speech. Extending this technique, we
introduce a novel metric, Degree of Explicitness, for a single instance and
show that the new metric is beneficial in suggesting out-of-domain unlabeled
examples to effectively enrich the training data with informative, implicitly
abusive texts.

RDF2vec is a knowledge graph embedding mechanism which first extracts
sequences from knowledge graphs by performing random walks, then feeds those
into the word embedding algorithm word2vec for computing vector representations
for entities. In this poster, we introduce two new flavors of walk extraction
coined e-walks and p-walks, which put an emphasis on the structure or the
neighborhood of an entity respectively, and thereby allow for creating
embeddings which focus on similarity or relatedness. By combining the walk
strategies with order-aware and classic RDF2vec, as well as CBOW and skip-gram
word2vec embeddings, we conduct a preliminary evaluation with a total of 12
RDF2vec variants.

The Internet is the most complex machine humankind has ever built, and how to
defense it from intrusions is even more complex. With the ever increasing of
new intrusions, intrusion detection task rely on Artificial Intelligence more
and more. Interpretability and transparency of the machine learning model is
the foundation of trust in AI-driven intrusion detection results. Current
interpretation Artificial Intelligence technologies in intrusion detection are
heuristic, which is neither accurate nor sufficient. This paper proposed a
rigorous interpretable Artificial Intelligence driven intrusion detection
approach, based on artificial immune system. Details of rigorous interpretation
calculation process for a decision tree model is presented. Prime implicant
explanation for benign traffic flow are given in detail as rule for negative
selection of the cyber immune system. Experiments are carried out in real-life
traffic.

As a fundamental problem in computer vision, 3D object detection is
experiencing rapid growth. To extract the point-wise features from the
irregularly and sparsely distributed points, previous methods usually take a
feature grouping module to aggregate the point features to an object candidate.
However, these methods have not yet leveraged the surface geometry of
foreground objects to enhance grouping and 3D box generation. In this paper, we
propose the RBGNet framework, a voting-based 3D detector for accurate 3D object
detection from point clouds. In order to learn better representations of object
shape to enhance cluster features for predicting 3D boxes, we propose a
ray-based feature grouping module, which aggregates the point-wise features on
object surfaces using a group of determined rays uniformly emitted from cluster
centers. Considering the fact that foreground points are more meaningful for
box estimation, we design a novel foreground biased sampling strategy in
downsample process to sample more points on object surfaces and further boost
the detection performance. Our model achieves state-of-the-art 3D detection
performance on ScanNet V2 and SUN RGB-D with remarkable performance gains. Code
will be available at https://github.com/Haiyang-W/RBGNet.

This paper introduces a comparison of deep learning-based techniques for the
MOS prediction task of synthesised speech in the Interspeech VoiceMOS
challenge. Using the data from the main track of the VoiceMOS challenge we
explore both existing predictors and propose new ones. We evaluate two groups
of models: NISQA-based models and techniques based on fine-tuning the
self-supervised learning (SSL) model wav2vec2_base. Our findings show that a
simplified version of NISQA with 40% fewer parameters achieves results close to
the original NISQA architecture on both utterance-level and system-level
performances. Pre-training NISQA with the NISQA corpus improves utterance-level
performance but shows no benefit on the system-level performance. Also, the
NISQA-based models perform close to LDNet and MOSANet, 2 out of 3 baselines of
the challenge. Fine-tuning wav2vec2_base shows superior performance than the
NISQA-based models. We explore the mismatch between natural and synthetic
speech and discovered that the performance of the SSL model drops consistently
when fine-tuned on natural speech samples. We show that adding CNN features
with the SSL model does not improve the baseline performance. Finally, we show
that the system type has an impact on the predictions of the non-SSL models.

We present a specialized scenario generation method that utilizes forecast
information to generate scenarios for day-ahead scheduling problems. In
particular, we use normalizing flows to generate wind power scenarios by
sampling from a conditional distribution that uses wind speed forecasts to
tailor the scenarios to a specific day. We apply the generated scenarios in a
stochastic day-ahead bidding problem of a wind electricity producer and analyze
whether the scenarios yield profitable decisions. Compared to Gaussian copulas
and Wasserstein-generative adversarial networks, the normalizing flow
successfully narrows the range of scenarios around the daily trends while
maintaining a diverse variety of possible realizations. In the stochastic
day-ahead bidding problem, the conditional scenarios from all methods lead to
significantly more stable profitable results compared to an unconditional
selection of historical scenarios. The normalizing flow consistently obtains
the highest profits, even for small sets scenarios.

Neural classifiers are non linear systems providing decisions on the classes
of patterns, for a given problem they have learned. The output computed by a
classifier for each pattern constitutes an approximation of the output of some
unknown function, mapping pattern data to their respective classes. The lack of
knowledge of such a function along with the complexity of neural classifiers,
especially when these are deep learning architectures, do not permit to obtain
information on how specific predictions have been made. Hence, these powerful
learning systems are considered as black boxes and in critical applications
their use tends to be considered inappropriate. Gaining insight on such a black
box operation constitutes a one way approach in interpreting operation of
neural classifiers and assessing the validity of their decisions. In this paper
we tackle this problem introducing a novel methodology for discovering which
features are considered relevant by a trained neural classifier and how they
affect the classifier's output, thus obtaining an explanation on its decision.
Although, feature relevance has received much attention in the machine learning
literature here we reconsider it in terms of nonlinear parameter estimation
targeted by a set membership approach which is based on interval analysis.
Hence, the proposed methodology builds on sound mathematical approaches and the
results obtained constitute a reliable estimation of the classifier's decision
premises.

Purpose - To develop and validate a deep learning (DL) framework for the
detection and quantification of drusen and reticular pseudodrusen (RPD) on
optical coherence tomography scans.
  Design - Development and validation of deep learning models for
classification and feature segmentation.
  Methods - A DL framework was developed consisting of a classification model
and an out-of-distribution (OOD) detection model for the identification of
ungradable scans; a classification model to identify scans with drusen or RPD;
and an image segmentation model to independently segment lesions as RPD or
drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with
a self-reported diagnosis of age-related macular degeneration (AMD) and 250
UKBB controls. Drusen and RPD were manually delineated by five retina
specialists. The main outcome measures were sensitivity, specificity, area
under the ROC curve (AUC), kappa, accuracy and intraclass correlation
coefficient (ICC).
  Results - The classification models performed strongly at their respective
tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans
classifier, the OOD model, and the drusen and RPD classification model). The
mean ICC for drusen and RPD area vs. graders was 0.74 and 0.61, respectively,
compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that
the model's sensitivity was close to human performance.
  Conclusions - The models achieved high classification and segmentation
performance, similar to human performance. Application of this robust framework
will further our understanding of RPD as a separate entity from drusen in both
research and clinical settings.

We show that a coherent network of lasers exhibits emergent neural computing
capabilities. The proposed scheme is built on harnessing the collective
behavior of laser networks for storing a number of phase patterns as stable
fixed points of the governing dynamical equations and retrieving such patterns
through proper excitation conditions, thus exhibiting an associative memory
property. The associative memory functionality is first discussed in the strong
pumping regime of a network of passive dissipatively coupled lasers which
simulate the classical XY model. It is discussed that despite the large storage
capacity of the network, the large overlap between fixed-point patterns
effectively limits pattern retrieval to only two images. Next, we show that
this restriction can be uplifted by using nonreciprocal coupling between lasers
and this allows for utilizing a large storage capacity. This work opens new
possibilities for neural computation with coherent laser networks as novel
analog processors. In addition, the underlying dynamical model discussed here
suggests a novel energy-based recurrent neural network that handles continuous
data as opposed to Hopfield networks and Boltzmann machines which are
intrinsically binary systems.

We present a self-supervised method to learn dynamic 3D deformations of
garments worn by parametric human bodies. State-of-the-art data-driven
approaches to model 3D garment deformations are trained using supervised
strategies that require large datasets, usually obtained by expensive
physics-based simulation methods or professional multi-camera capture setups.
In contrast, we propose a new training scheme that removes the need for
ground-truth samples, enabling self-supervised training of dynamic 3D garment
deformations. Our key contribution is to realize that physics-based deformation
models, traditionally solved in a frame-by-frame basis by implicit integrators,
can be recasted as an optimization problem. We leverage such optimization-based
scheme to formulate a set of physics-based loss terms that can be used to train
neural networks without precomputing ground-truth data. This allows us to learn
models for interactive garments, including dynamic deformations and fine
wrinkles, with two orders of magnitude speed up in training time compared to
state-of-the-art supervised methods

Being able to seamlessly generalize across different tasks is fundamental for
robots to act in our world. However, learning representations that generalize
quickly to new scenarios is still an open research problem in reinforcement
learning. In this paper we present a framework to meta-learn the critic for
gradient-based policy learning. Concretely, we propose a model-based bi-level
optimization algorithm that updates the critics parameters such that the policy
that is learned with the updated critic gets closer to solving the
meta-training tasks. We illustrate that our algorithm leads to learned critics
that resemble the ground truth Q function for a given task. Finally, after
meta-training, the learned critic can be used to learn new policies for new
unseen task and environment settings via model-free policy gradient
optimization, without requiring a model. We present results that show the
generalization capabilities of our learned critic to new tasks and dynamics
when used to learn a new policy in a new scenario.

In this paper we present a novel approach to abstractive summarization of
patient hospitalisation histories. We applied an encoder-decoder framework with
Longformer neural network as an encoder and BERT as a decoder. Our experiments
show improved quality on some summarization tasks compared with
pointer-generator networks. We also conducted a study with experienced
physicians evaluating the results of our model in comparison with PGN baseline
and human-generated abstracts, which showed the effectiveness of our model.

One of the greatest scientific challenges in network neuroscience is to
create a representative map of a population of heterogeneous brain networks,
which acts as a connectional fingerprint. The connectional brain template
(CBT), also named network atlas, presents a powerful tool for capturing the
most representative and discriminative traits of a given population while
preserving its topological patterns. The idea of a CBT is to integrate a
population of heterogeneous brain connectivity networks, derived from different
neuroimaging modalities or brain views (e.g., structural and functional), into
a unified holistic representation. Here we review current state-of-the-art
methods designed to estimate well-centered and representative CBT for
populations of single-view and multi-view brain networks. We start by reviewing
each CBT learning method, then we introduce the evaluation measures to compare
CBT representativeness of populations generated by single-view and multigraph
integration methods, separately, based on the following criteria: centeredness,
biomarker-reproducibility, node-level similarity, global-level similarity, and
distance-based similarity. We demonstrate that the deep graph normalizer (DGN)
method significantly outperforms other multi-graph and all single-view
integration methods for estimating CBTs using a variety of healthy and
disordered datasets in terms of centeredness, reproducibility (i.e.,
graph-derived biomarkers reproducibility that disentangle the typical from the
atypical connectivity variability), and preserving the topological traits at
both local and global graph-levels.

In this paper we extend the classical Follow-The-Regularized-Leader (FTRL)
algorithm to encompass time-varying constraints, through adaptive penalization.
We establish sufficient conditions for the proposed Penalized FTRL algorithm to
achieve $O(\sqrt{t})$ regret and violation with respect to strong benchmark
$\hat{X}^{max}_t$. Lacking prior knowledge of the constraints, this is probably
the largest benchmark set that we can reasonably hope for. Our sufficient
conditions are necessary in the sense that when they are violated there exist
examples where $O(\sqrt{t})$ regret and violation is not achieved. Compared to
the best existing primal-dual algorithms, Penalized FTRL substantially extends
the class of problems for which $O(\sqrt{t})$ regret and violation performance
is achievable.

The 3D visual grounding task aims to ground a natural language description to
the targeted object in a 3D scene, which is usually represented in 3D point
clouds. Previous works studied visual grounding under specific views. The
vision-language correspondence learned by this way can easily fail once the
view changes. In this paper, we propose a Multi-View Transformer (MVT) for 3D
visual grounding. We project the 3D scene to a multi-view space, in which the
position information of the 3D scene under different views are modeled
simultaneously and aggregated together. The multi-view space enables the
network to learn a more robust multi-modal representation for 3D visual
grounding and eliminates the dependence on specific views. Extensive
experiments show that our approach significantly outperforms all
state-of-the-art methods. Specifically, on Nr3D and Sr3D datasets, our method
outperforms the best competitor by 11.2% and 7.1% and even surpasses recent
work with extra 2D assistance by 5.9% and 6.6%. Our code is available at
https://github.com/sega-hsj/MVT-3DVG.

The quality of end-to-end neural text-to-speech (TTS) systems highly depends
on the reliable estimation of intermediate acoustic features from text inputs.
To reduce the complexity of the speech generation process, several
non-autoregressive TTS systems directly find a mapping relationship between
text and waveforms. However, the generation quality of these system is
unsatisfactory due to the difficulty in modeling the dynamic nature of prosodic
information. In this paper, we propose an effective prosody predictor that
successfully replicates the characteristics of prosodic features extracted from
mel-spectrograms. Specifically, we introduce a generative model-based
conditional discriminator to enable the estimated embeddings have highly
informative prosodic features, which significantly enhances the expressiveness
of generated speech. Since the estimated embeddings obtained by the proposed
method are highly correlated with acoustic features, the time-alignment of
input texts and intermediate features is greatly simplified, which results in
faster convergence. Our proposed model outperforms several publicly available
models based on various objective and subjective evaluation metrics, even using
a relatively small number of parameters.

Predictive coding is an influential model of cortical neural activity. It
proposes that perceptual beliefs are furnished by sequentially minimising
"prediction errors" - the differences between predicted and observed data.
Implicit in this proposal is the idea that perception requires multiple cycles
of neural activity. This is at odds with evidence that several aspects of
visual perception - including complex forms of object recognition - arise from
an initial "feedforward sweep" that occurs on fast timescales which preclude
substantial recurrent activity. Here, we propose that the feedforward sweep can
be understood as performing amortized inference and recurrent processing can be
understood as performing iterative inference. We propose a hybrid predictive
coding network that combines both iterative and amortized inference in a
principled manner by describing both in terms of a dual optimization of a
single objective function. We show that the resulting scheme can be implemented
in a biologically plausible neural architecture that approximates Bayesian
inference utilising local Hebbian update rules. We demonstrate that our hybrid
predictive coding model combines the benefits of both amortized and iterative
inference -- obtaining rapid and computationally cheap perceptual inference for
familiar data while maintaining the context-sensitivity, precision, and sample
efficiency of iterative inference schemes. Moreover, we show how our model is
inherently sensitive to its uncertainty and adaptively balances iterative and
amortized inference to obtain accurate beliefs using minimum computational
expense. Hybrid predictive coding offers a new perspective on the functional
relevance of the feedforward and recurrent activity observed during visual
perception and offers novel insights into distinct aspects of visual
phenomenology.

Direct multi-task twin support vector machine (DMTSVM) explores the shared
information between multiple correlated tasks, then it produces better
generalization performance. However, it contains matrix inversion operation
when solving the dual problems, so it costs much running time. Moreover, kernel
trick cannot be directly utilized in the nonlinear case. To effectively avoid
above problems, a novel multi-task nonparallel support vector machine (MTNPSVM)
including linear and nonlinear cases is proposed in this paper. By introducing
epsilon-insensitive loss instead of square loss in DMTSVM, MTNPSVM effectively
avoids matrix inversion operation and takes full advantage of the kernel trick.
Theoretical implication of the model is further discussed. To further improve
the computational efficiency, the alternating direction method of multipliers
(ADMM) is employed when solving the dual problem. The computational complexity
and convergence of the algorithm are provided. In addition, the property and
sensitivity of the parameter in model are further explored. The experimental
results on fifteen benchmark datasets and twelve image datasets demonstrate the
validity of MTNPSVM in comparison with the state-of-the-art algorithms.
Finally, it is applied to real Chinese Wine dataset, and also verifies its

Disentangled representation learning aims to extract explanatory features or
factors and retain salient information. Factorized hierarchical variational
autoencoder (FHVAE) presents a way to disentangle a speech signal into
sequential-level and segmental-level features, which represent speaker identity
and speech content information, respectively. As a self-supervised objective,
autoregressive predictive coding (APC), on the other hand, has been used in
extracting meaningful and transferable speech features for multiple downstream
tasks. Inspired by the success of these two representation learning methods,
this paper proposes to integrate the APC objective into the FHVAE framework
aiming at benefiting from the additional self-supervision target. The main
proposed method requires neither more training data nor more computational cost
at test time, but obtains improved meaningful representations while maintaining
disentanglement. The experiments were conducted on the TIMIT dataset. Results
demonstrate that FHVAE equipped with the additional self-supervised objective
is able to learn features providing superior performance for tasks including
speech recognition and speaker recognition. Furthermore, voice conversion, as
one application of disentangled representation learning, has been applied and
evaluated. The results show performance similar to baseline of the new
framework on voice conversion.

Recent denoising algorithms based on the "blind-spot" strategy show
impressive blind image denoising performances, without utilizing any external
dataset. While the methods excel in recovering highly contaminated images, we
observe that such algorithms are often less effective under a low-noise or real
noise regime. To address this gap, we propose an alternative denoising strategy
that leverages the architectural inductive bias of implicit neural
representations (INRs), based on our two findings: (1) INR tends to fit the
low-frequency clean image signal faster than the high-frequency noise, and (2)
INR layers that are closer to the output play more critical roles in fitting
higher-frequency parts. Building on these observations, we propose a denoising
algorithm that maximizes the innate denoising capability of INRs by penalizing
the growth of deeper layer weights. We show that our method outperforms
existing zero-shot denoising methods under an extensive set of low-noise or
real-noise scenarios.

Establishing dense correspondences across semantically similar images is one
of the challenging tasks due to the significant intra-class variations and
background clutters. To solve these problems, numerous methods have been
proposed, focused on learning feature extractor or cost aggregation
independently, which yields sub-optimal performance. In this paper, we propose
a novel framework for jointly learning feature extraction and cost aggregation
for semantic correspondence. By exploiting the pseudo labels from each module,
the networks consisting of feature extraction and cost aggregation modules are
simultaneously learned in a boosting fashion. Moreover, to ignore unreliable
pseudo labels, we present a confidence-aware contrastive loss function for
learning the networks in a weakly-supervised manner. We demonstrate our
competitive results on standard benchmarks for semantic correspondence.

While end-to-end approaches have achieved state-of-the-art performance in
many perception tasks, they are not yet able to compete with 3D geometry-based
methods in pose estimation. Moreover, absolute pose regression has been shown
to be more related to image retrieval. As a result, we hypothesize that the
statistical features learned by classical Convolutional Neural Networks do not
carry enough geometric information to reliably solve this inherently geometric
task. In this paper, we demonstrate how a translation and rotation equivariant
Convolutional Neural Network directly induces representations of camera motions
into the feature space. We then show that this geometric property allows for
implicitly augmenting the training data under a whole group of image
plane-preserving transformations. Therefore, we argue that directly learning
equivariant features is preferable than learning data-intensive intermediate
representations. Comprehensive experimental validation demonstrates that our
lightweight model outperforms existing ones on standard datasets.

Providing explanations for recommended items allows users to refine the
recommendations by critiquing parts of the explanations. As a result of
revisiting critiquing from the perspective of multimodal generative models,
recent work has proposed M&Ms-VAE, which achieves state-of-the-art performance
in terms of recommendation, explanation, and critiquing. M&Ms-VAE and similar
models allow users to negatively critique (i.e., explicitly disagree). However,
they share a significant drawback: users cannot positively critique (i.e.,
highlight a desired feature). We address this deficiency with M&Ms-VAE+, an
extension of M&Ms-VAE that enables positive and negative critiquing. In
addition to modeling users' interactions and keyphrase-usage preferences, we
model their keyphrase-usage dislikes. Moreover, we design a novel critiquing
module that is trained in a self-supervised fashion. Our experiments on two
datasets show that M&Ms-VAE+ matches or exceeds M&Ms-VAE in recommendation and
explanation performance. Furthermore, our results demonstrate that representing
positive and negative critiques differently enables M&Ms-VAE+ to significantly
outperform M&Ms-VAE and other models in positive and negative multi-step
critiquing.

With the expansion of the semiconductor supply chain, the use of recycled
field-programmable gate arrays (FPGAs) has become a serious concern. Several
methods for detecting recycled FPGAs by analyzing the ring oscillator (RO)
frequencies have been proposed; however, most assume the known fresh FPGAs
(KFFs) as the training data in machine-learning-based classification. In this
study, we propose a novel recycled FPGA detection method based on an
unsupervised anomaly detection scheme when there are few or no KFFs available.
As the RO frequencies in the neighboring logic blocks on an FPGA are similar
because of systematic process variation, our method compares the RO frequencies
and does not require KFFs. The proposed method efficiently identifies recycled
FPGAs through outlier detection using direct density ratio estimation.
Experiments using Xilinx Artix-7 FPGAs demonstrate that the proposed method
successfully distinguishes recycled FPGAs from 35 fresh FPGAs. In contrast, a
conventional recycled FPGA detection method results in certain
misclassification.

We present the UTokyo-SaruLab mean opinion score (MOS) prediction system
submitted to VoiceMOS Challenge 2022. The challenge is to predict the MOS
values of speech samples collected from previous Blizzard Challenges and Voice
Conversion Challenges for two tracks: a main track for in-domain prediction and
an out-of-domain (OOD) track for which there is less labeled data from
different listening tests. Our system is based on ensemble learning of strong
and weak learners. Strong learners incorporate several improvements to the
previous fine-tuning models of self-supervised learning (SSL) models, while
weak learners use basic machine-learning methods to predict scores from SSL
features. In the Challenge, our system had the highest score on several metrics
for both the main and OOD tracks. In addition, we conducted ablation studies to
investigate the effectiveness of our proposed methods.

Applying machine learning tools to digitized image archives has a potential
to revolutionize quantitative research of visual studies in humanities and
social sciences. The ability to process a hundredfold greater number of photos
than has been traditionally possible and to analyze them with an extensive set
of variables will contribute to deeper insight into the material. Overall,
these changes will help to shift the workflow from simple manual tasks to more
demanding stages.
  In this paper, we introduce Automatic Image Content Extraction (AICE)
framework for machine learning-based search and analysis of large image
archives. We developed the framework in a multidisciplinary research project as
framework for future photographic studies by reformulating and expanding the
traditional visual content analysis methodologies to be compatible with the
current and emerging state-of-the-art machine learning tools and to cover the
novel machine learning opportunities for automatic content analysis. The
proposed framework can be applied in several domains in humanities and social
sciences, and it can be adjusted and scaled into various research settings. We
also provide information on the current state of different machine learning
techniques and show that there are already various publicly available methods
that are suitable to a wide-scale of visual content analysis tasks.

Learning spatial-temporal relation among multiple actors is crucial for group
activity recognition. Different group activities often show the diversified
interactions between actors in the video. Hence, it is often difficult to model
complex group activities from a single view of spatial-temporal actor
evolution. To tackle this problem, we propose a distinct Dual-path Actor
Interaction (DualAI) framework, which flexibly arranges spatial and temporal
transformers in two complementary orders, enhancing actor relations by
integrating merits from different spatiotemporal paths. Moreover, we introduce
a novel Multi-scale Actor Contrastive Loss (MAC-Loss) between two interactive
paths of Dual-AI. Via self-supervised actor consistency in both frame and video
levels, MAC-Loss can effectively distinguish individual actor representations
to reduce action confusion among different actors. Consequently, our Dual-AI
can boost group activity recognition by fusing such discriminative features of
different actors. To evaluate the proposed approach, we conduct extensive
experiments on the widely used benchmarks, including Volleyball, Collective
Activity, and NBA datasets. The proposed Dual-AI achieves state-of-the-art
performance on all these datasets. It is worth noting the proposed Dual-AI with
50% training data outperforms a number of recent approaches with 100% training
data. This confirms the generalization power of Dual-AI for group activity
recognition, even under the challenging scenarios of limited supervision.

Traditional object detectors are ill-equipped for incremental learning.
However, fine-tuning directly on a well-trained detection model with only new
data will lead to catastrophic forgetting. Knowledge distillation is a flexible
way to mitigate catastrophic forgetting. In Incremental Object Detection (IOD),
previous work mainly focuses on distilling for the combination of features and
responses. However, they under-explore the information that contains in
responses. In this paper, we propose a response-based incremental distillation
method, dubbed Elastic Response Distillation (ERD), which focuses on
elastically learning responses from the classification head and the regression
head. Firstly, our method transfers category knowledge while equipping student
detector with the ability to retain localization information during incremental
learning. In addition, we further evaluate the quality of all locations and
provide valuable responses by the Elastic Response Selection (ERS) strategy.
Finally, we elucidate that the knowledge from different responses should be
assigned with different importance during incremental distillation. Extensive
experiments conducted on MS COCO demonstrate our method achieves
state-of-the-art result, which substantially narrows the performance gap
towards full training.

A large number of inorganic and organic compounds are able to bind DNA and
form complexes, among which drug-related molecules are important. Chromatin
accessibility changes not only directly affects drug-DNA interactions, but also
promote or inhibit the expression of critical genes associated with drug
resistance by affecting the DNA binding capacity of TFs and transcriptional
regulators. However, Biological experimental techniques for measuring it are
expensive and time consuming. In recent years, several kinds of computational
methods have been proposed to identify accessible regions of the genome.
Existing computational models mostly ignore the contextual information of bases
in gene sequences. To address these issues, we proposed a new solution named
SemanticCAP. It introduces a gene language model which models the context of
gene sequences, thus being able to provide an effective representation of a
certain site in gene sequences. Basically, we merge the features provided by
the gene language model into our chromatin accessibility model. During the
process, we designed some methods to make feature fusion smoother. Compared
with other systems under public benchmarks, our model proved to have better

Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer
subtypes. The difference in procedures to acquire WSIs at various trial sites
gives rise to variability in the histopathology images, thus making consistent
diagnosis challenging. These differences may stem from variability in image
acquisition through multi-vendor scanners, variable acquisition parameters, and
differences in staining procedure; as well, patient demographics may bias the
glass slide batches before image acquisition. These variabilities are assumed
to cause a domain shift in the images of different hospitals. It is crucial to
overcome this domain shift because an ideal machine-learning model must be able
to work on the diverse sources of images, independent of the acquisition
center. A domain generalization technique is leveraged in this study to improve
the generalization capability of a Deep Neural Network (DNN), to an unseen
histopathology image set (i.e., from an unseen hospital/trial site) in the
presence of domain shift. According to experimental results, the conventional
supervised-learning regime generalizes poorly to data collected from different
hospitals. However, the proposed hospital-agnostic learning can improve the
generalization considering the low-dimensional latent space representation
visualization, and classification accuracy results.

Sign language recognition from sequences of monocular images or 2D poses is a
challenging field, not only due to the difficulty to infer 3D information from
2D data, but also due to the temporal relationship between the sequences of
information. Additionally, the wide variety of signs and the constant need to
add new ones on production environments makes it infeasible to use traditional
classification techniques. We propose a novel Contrastive Transformer-based
model, which demonstrate to learn rich representations from body key points
sequences, allowing better comparison between vector embedding. This allows us
to apply these techniques to perform one-shot or few-shot tasks, such as
classification and translation. The experiments showed that the model could
generalize well and achieved competitive results for sign classes never seen in
the training process.

Background and Objective: Incomplete Kawasaki disease (KD) has often been
misdiagnosed due to a lack of the clinical manifestations of classic KD.
However, it is associated with a markedly higher prevalence of coronary artery
lesions. Identifying coronary artery lesions by echocardiography is important
for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to
KD, coronavirus disease 2019, currently causing a worldwide pandemic, also
manifests with fever; therefore, it is crucial at this moment that KD should be
distinguished clearly among the febrile diseases in children. In this study, we
aimed to validate a deep learning algorithm for classification of KD and other
acute febrile diseases.
  Methods: We obtained coronary artery images by echocardiography of children
(n = 88 for KD; n = 65 for pneumonia). We trained six deep learning networks
(VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the
collected data.
  Results: SE-ResNext50 showed the best performance in terms of accuracy,
specificity, and precision in the classification. SE-ResNext50 offered a
precision of 76.35%, a sensitivity of 82.64%, and a specificity of 58.12%.
  Conclusions: The results of our study suggested that deep learning algorithms
have similar performance to an experienced cardiologist in detecting coronary
artery lesions to facilitate the diagnosis of KD.

Currently available benchmarks for few-shot learning (machine learning with
few training examples) are limited in the domains they cover, primarily
focusing on image classification. This work aims to alleviate this reliance on
image-based benchmarks by offering the first comprehensive, public and fully
reproducible audio based alternative, covering a variety of sound domains and
experimental settings. We compare the few-shot classification performance of a
variety of techniques on seven audio datasets (spanning environmental sounds to
human-speech). Extending this, we carry out in-depth analyses of joint training
(where all datasets are used during training) and cross-dataset adaptation
protocols, establishing the possibility of a generalised audio few-shot
classification algorithm. Our experimentation shows gradient-based
meta-learning methods such as MAML and Meta-Curvature consistently outperform
both metric and baseline methods. We also demonstrate that the joint training
routine helps overall generalisation for the environmental sound databases
included, as well as being a somewhat-effective method of tackling the
cross-dataset/domain setting.

Event-based vision has already revolutionized the perception task for robots
by promising faster response, lower energy consumption, and lower bandwidth
without introducing motion blur. In this work, a novel deep learning method
based on gated recurrent units utilizing sparse convolutions for detecting
gates in a race track is proposed using event-based vision for the autonomous
drone racing problem. We demonstrate the efficiency and efficacy of the
perception pipeline on a real robot platform that can safely navigate a typical
autonomous drone racing track in real-time. Throughout the experiments, we show
that the event-based vision with the proposed gated recurrent unit and
pretrained models on simulated event data significantly improve the gate
detection precision. Furthermore, an event-based drone racing dataset
consisting of both simulated and real data sequences is publicly released.

Session-based recommendation is a practical recommendation task that predicts
the next item based on an anonymous behavior sequence, and its performance
relies heavily on the transition information between items in the sequence. The
SOTA methods in SBR employ GNN to model neighboring item transitions from
global (i.e, other sessions) and local (i.e, current session) contexts.
However, most existing methods treat neighbors from different sessions equally
without considering that the neighbor items from different sessions may share
similar features with the target item on different aspects and may have
different contributions. In other words, they have not explored
finer-granularity transition information between items in the global context,
leading to sub-optimal performance. In this paper, we fill this gap by
proposing a novel Transition Information Enhanced Disentangled Graph Neural
Network (TIE-DGNN) model to capture finer-granular transition information
between items and try to interpret the reason of the transition by modeling the
various factors of the item. Specifically, we propose a position-aware global
graph, which utilizes the relative position information to model the
neighboring item transition. Then, we slice item embeddings into blocks, each
of which represents a factor, and use disentangling module to separately learn
the factor embeddings over the global graph. For local context, we train item
embeddings by using attention mechanisms to capture transition information from
the current session. To this end, our model considers two levels of transition
information. Especially in global text, we not only consider finer-granularity
transition information between items but also take user intents at factor-level
into account to interpret the key reason for the transition. Extensive
experiments on three datasets demonstrate the superiority of our method over
the SOTA methods.

In this paper we examine the claims made by Bullock et. al. on the
applicability of black-box injury risk approaches in the sports injury domain.
Overall, we agree that transparency is necessary for Machine Learning models to
be useful in this field. However, there are areas of research that address
precisely the concerns of the authors and strongly temper their conclusions. In
the following we look at how these issues are being tackled by the Machine
Learning community.

The Bayesian additive regression trees (BART) model is an ensemble method
extensively and successfully used in regression tasks due to its consistently
strong predictive performance and its ability to quantify uncertainty. BART
combines "weak" tree models through a set of shrinkage priors, whereby each
tree explains a small portion of the variability in the data. However, the lack
of smoothness and the absence of a covariance structure over the observations
in standard BART can yield poor performance in cases where such assumptions
would be necessary. We propose Gaussian processes Bayesian additive regression
trees (GP-BART) as an extension of BART which assumes Gaussian process (GP)
priors for the predictions of each terminal node among all trees. We illustrate
our model on simulated and real data and compare its performance to traditional
modelling approaches, outperforming them in many scenarios. An implementation
of our method is available in the R package rGPBART available at:
https://github.com/MateusMaiaDS/gpbart

This paper compares the speech coder and speaker recognizer applications,
showing some parallelism between them. In this paper, some approaches used for
speaker recognition are applied to speech coding in order to improve the
prediction accuracy. Experimental results show an improvement in Segmental SNR
(SEGSNR).

In recent years, large pre-trained transformers have led to substantial gains
in performance over traditional retrieval models and feedback approaches.
However, these results are primarily based on the MS Marco/TREC Deep Learning
Track setup, with its very particular setup, and our understanding of why and
how these models work better is fragmented at best. We analyze effective
BERT-based cross-encoders versus traditional BM25 ranking for the passage
retrieval task where the largest gains have been observed, and investigate two
main questions. On the one hand, what is similar? To what extent does the
neural ranker already encompass the capacity of traditional rankers? Is the
gain in performance due to a better ranking of the same documents (prioritizing
precision)? On the other hand, what is different? Can it retrieve effectively
documents missed by traditional systems (prioritizing recall)? We discover
substantial differences in the notion of relevance identifying strengths and
weaknesses of BERT that may inspire research for future improvement. Our
results contribute to our understanding of (black-box) neural rankers relative
to (well-understood) traditional rankers, help understand the particular
experimental setting of MS-Marco-based test collections.

Machine learning, satellites or local sensors are key factors for a
sustainable and resource-saving optimisation of agriculture and proved its
values for the management of agricultural land. Up to now, the main focus was
on the enlargement of data which were evaluated by means of supervised learning
methods. Nevertheless, the need for labels is also a limiting and
time-consuming factor, while in contrast, ongoing technological development is
already providing an ever-increasing amount of unlabeled data. Self-supervised
learning (SSL) could overcome this limitation and incorporate existing
unlabeled data. Therefore, a crop type data set was utilized to conduct
experiments with SSL and compare it to supervised methods. A unique feature of
our data set from 2016 to 2018 was a divergent climatological condition in 2018
that reduced yields and affected the spectral fingerprint of the plants. Our
experiments focused on predicting 2018 using SLL without or a few labels to
clarify whether new labels should be collected for an unknown year. Despite
these challenging conditions, the results showed that SSL contributed to higher
accuracies. We believe that the results will encourage further improvements in
the field of precision farming, why the SSL framework and data will be
published (Marszalek, 2021).

Voxel-based Soft Robots (VSRs) are a form of modular soft robots, composed of
several deformable cubes, i.e., voxels. Each VSR is thus an ensemble of simple
agents, namely the voxels, which must cooperate to give rise to the overall VSR
behavior. Within this paradigm, collective intelligence plays a key role in
enabling the emerge of coordination, as each voxel is independently controlled,
exploiting only the local sensory information together with some knowledge
passed from its direct neighbors (distributed or collective control). In this
work, we propose a novel form of collective control, influenced by Neural
Cellular Automata (NCA) and based on the bio-inspired Spiking Neural Networks:
the embodied Spiking NCA (SNCA). We experiment with different variants of SNCA,
and find them to be competitive with the state-of-the-art distributed
controllers for the task of locomotion. In addition, our findings show
significant improvement with respect to the baseline in terms of adaptability
to unforeseen environmental changes, which could be a determining factor for
physical practicability of VSRs.

In the neutral hydrogen (HI) intensity mapping (IM) survey, the foreground
contamination on the cosmological signals is extremely severe, and the
systematic effects caused by radio telescopes themselves further aggravate the
difficulties in subtracting foreground. In this work, we investigate whether
the deep learning method, concretely the 3D U-Net algorithm here, can play a
crucial role in foreground subtraction when considering the systematic effect
caused by the telescope's primary beam. We consider two beam models, i.e., the
Gaussian beam model as a simple case and the Cosine beam model as a
sophisticated case. The traditional principal component analysis (PCA) method
is employed as a comparison and, more importantly, as the preprocessing step
for the U-Net method to reduce the sky map dynamic range. We find that in the
case of the Gaussian beam, the PCA method can effectively clean the foreground.
However, the PCA method cannot handle the systematic effect induced by the
Cosine beam, and the additional U-Net method can improve the result
significantly. In order to show how well the PCA and U-Net methods can recover
the HI signals, we also derive the HI angular power spectra, as well as the HI
2D power spectra, after performing the foreground subtractions. It is found
that, in the case of Gaussian beam, the concordance with the original HI map
using U-Net is better than that using PCA by $27.4\%$, and in the case of
Cosine beam, the concordance using U-Net is better than that using PCA by
$144.8\%$. Therefore, the U-Net based foreground subtraction can efficiently
eliminate the telescope primary beam effect and shed new light on recovering
the HI power spectrum for future HI IM experiments.

This paper investigates the possibility of high resolution mapping of PM2.5
concentration over Tehran city using high resolution satellite AOD (MAIAC)
retrievals. For this purpose, a framework including three main stages, data
preprocessing; regression modeling; and model deployment was proposed. The
output of the framework was a machine learning model trained to predict PM2.5
from MAIAC AOD retrievals and meteorological data. The results of model testing
revealed the efficiency and capability of the developed framework for high
resolution mapping of PM2.5, which was not realized in former investigations
performed over the city. Thus, this study, for the first time, realized daily,
1 km resolution mapping of PM2.5 in Tehran with R2 around 0.74 and RMSE better
than 9.0 mg/m3.
  Keywords: MAIAC; MODIS; AOD; Machine learning; Deep learning; PM2.5;
Regression

Monocular depth estimation is vital for scene understanding and downstream
tasks. We focus on the supervised setup, in which ground-truth depth is
available only at training time. Based on knowledge about the high regularity
of real 3D scenes, we propose a method that learns to selectively leverage
information from coplanar pixels to improve the predicted depth. In particular,
we introduce a piecewise planarity prior which states that for each pixel,
there is a seed pixel which shares the same planar 3D surface with the former.
Motivated by this prior, we design a network with two heads. The first head
outputs pixel-level plane coefficients, while the second one outputs a dense
offset vector field that identifies the positions of seed pixels. The plane
coefficients of seed pixels are then used to predict depth at each position.
The resulting prediction is adaptively fused with the initial prediction from
the first head via a learned confidence to account for potential deviations
from precise local planarity. The entire architecture is trained end-to-end
thanks to the differentiability of the proposed modules and it learns to
predict regular depth maps, with sharp edges at occlusion boundaries. An
extensive evaluation of our method shows that we set the new state of the art
in supervised monocular depth estimation, surpassing prior methods on NYU
Depth-v2 and on the Garg split of KITTI. Our method delivers depth maps that
yield plausible 3D reconstructions of the input scenes. Code is available at:
https://github.com/SysCV/P3Depth

In this paper, we address the problem of lip-voice synchronisation in videos
containing human face and voice. Our approach is based on determining if the
lips motion and the voice in a video are synchronised or not, depending on
their audio-visual correspondence score. We propose an audio-visual cross-modal
transformer-based model that outperforms several baseline models in the
audio-visual synchronisation task on the standard lip-reading speech benchmark
dataset LRS2. While the existing methods focus mainly on lip synchronisation in
speech videos, we also consider the special case of the singing voice. The
singing voice is a more challenging use case for synchronisation due to
sustained vowel sounds. We also investigate the relevance of lip
synchronisation models trained on speech datasets in the context of singing
voice. Finally, we use the frozen visual features learned by our lip
synchronisation model in the singing voice separation task to outperform a
baseline audio-visual model which was trained end-to-end. The demos, source
code, and the pre-trained models are available on
https://ipcv.github.io/VocaLiST/

Target sound detection (TSD) aims to detect the target sound from mixture
audio given the reference information. Previous works have shown that TSD
models can be trained on fully-annotated (frame-level label) or
weakly-annotated (clip-level label) data. However, there are some clear
evidences show that the performance of the model trained on weakly-annotated
data is worse than that trained on fully-annotated data. To fill this gap, we
provide a mixed supervision perspective, in which learning novel categories
(target domain) using weak annotations with the help of full annotations of
existing base categories (source domain). To realize this, a mixed supervised
learning framework is proposed, which contains two mutually-helping student
models (\textit{f\_student} and \textit{w\_student}) that learn from
fully-annotated and weakly-annotated data, respectively. The motivation is that
\textit{f\_student} learned from fully-annotated data has a better ability to
capture detailed information than \textit{w\_student}. Thus, we first let
\textit{f\_student} guide \textit{w\_student} to learn the ability to capture
details, so \textit{w\_student} can perform better in the target domain. Then
we let \textit{w\_student} guide \textit{f\_student} to fine-tune on the target
domain. The process can be repeated several times so that the two students
perform very well in the target domain. To evaluate our method, we built three
TSD datasets based on UrbanSound and Audioset. Experimental results show that
our methods offer about 8\% improvement in event-based F-score as compared with
a recent baseline.

We present SHEEP, a new machine learning approach to the classic problem of
astronomical source classification, which combines the outputs from the
XGBoost, LightGBM, and CatBoost learning algorithms to create stronger
classifiers. A novel step in our pipeline is that prior to performing the
classification, SHEEP first estimates photometric redshifts, which are then
placed into the data set as an additional feature for classification model
training; this results in significant improvements in the subsequent
classification performance. SHEEP contains two distinct classification
methodologies: (i) Multi-class and (ii) one versus all with correction by a
meta-learner. We demonstrate the performance of SHEEP for the classification of
stars, galaxies, and quasars using a data set composed of SDSS and WISE
photometry of 3.5 million astronomical sources. The resulting F1-scores are as
follows: 0.992 for galaxies; 0.967 for quasars; and 0.985 for stars. In terms
of the F1-scores for the three classes, SHEEP is found to outperform a recent
RandomForest-based classification approach using an essentially identical data
set. Our methodology also facilitates model and data set explainability via
feature importances; it also allows the selection of sources whose uncertain
classifications may make them interesting sources for follow-up observations.

This paper studies semi-supervised learning of semantic segmentation, which
assumes that only a small portion of training images are labeled and the others
remain unlabeled. The unlabeled images are usually assigned pseudo labels to be
used in training, which however often causes the risk of performance
degradation due to the confirmation bias towards errors on the pseudo labels.
We present a novel method that resolves this chronic issue of pseudo labeling.
At the heart of our method lies error localization network (ELN), an auxiliary
module that takes an image and its segmentation prediction as input and
identifies pixels whose pseudo labels are likely to be wrong. ELN enables
semi-supervised learning to be robust against inaccurate pseudo labels by
disregarding label noises during training and can be naturally integrated with
self-training and contrastive learning. Moreover, we introduce a new learning
strategy for ELN that simulates plausible and diverse segmentation errors
during training of ELN to enhance its generalization. Our method is evaluated
on PASCAL VOC 2012 and Cityscapes, where it outperforms all existing methods in
every evaluation setting.

Object-centric representations form the basis of human perception, and enable
us to reason about the world and to systematically generalize to new settings.
Currently, most works on unsupervised object discovery focus on slot-based
approaches, which explicitly separate the latent representations of individual
objects. While the result is easily interpretable, it usually requires the
design of involved architectures. In contrast to this, we propose a
comparatively simple approach - the Complex AutoEncoder (CAE) - that creates
distributed object-centric representations. Following a coding scheme theorized
to underlie object representations in biological neurons, its complex-valued
activations represent two messages: their magnitudes express the presence of a
feature, while the relative phase differences between neurons express which
features should be bound together to create joint object representations. In
contrast to previous approaches using complex-valued activations for object
discovery, we present a fully unsupervised approach that is trained end-to-end
- resulting in significant improvements in performance and efficiency. Further,
we show that the CAE achieves competitive or better unsupervised object
discovery performance on simple multi-object datasets compared to a
state-of-the-art slot-based approach while being up to 100 times faster to
train.

Variational autoencoders (VAEs) have witnessed great success in performing
the compression of image datasets. This success, made possible by the bits-back
coding framework, has produced competitive compression performance across many
benchmarks. However, despite this, VAE architectures are currently limited by a
combination of coding practicalities and compression ratios. That is, not only
do state-of-the-art methods, such as normalizing flows, often demonstrate
out-performance, but the initial bits required in coding makes single and
parallel image compression challenging. To remedy this, we introduce Split
Hierarchical Variational Compression (SHVC). SHVC introduces two novelties.
Firstly, we propose an efficient autoregressive prior, the autoregressive
sub-pixel convolution, that allows a generalisation between per-pixel
autoregressions and fully factorised probability models. Secondly, we define
our coding framework, the autoregressive initial bits, that flexibly supports
parallel coding and avoids -- for the first time -- many of the practicalities
commonly associated with bits-back coding. In our experiments, we demonstrate
SHVC is able to achieve state-of-the-art compression performance across
full-resolution lossless image compression tasks, with up to 100x fewer model
parameters than competing VAE approaches.

The paradigm of worst-group loss minimization has shown its promise in
avoiding to learn spurious correlations, but requires costly additional
supervision on spurious attributes. To resolve this, recent works focus on
developing weaker forms of supervision -- e.g., hyperparameters discovered with
a small number of validation samples with spurious attribute annotation -- but
none of the methods retain comparable performance to methods using full
supervision on the spurious attribute. In this paper, instead of searching for
weaker supervisions, we ask: Given access to a fixed number of samples with
spurious attribute annotations, what is the best achievable worst-group loss if
we "fully exploit" them? To this end, we propose a pseudo-attribute-based
algorithm, coined Spread Spurious Attribute (SSA), for improving the
worst-group accuracy. In particular, we leverage samples both with and without
spurious attribute annotations to train a model to predict the spurious
attribute, then use the pseudo-attribute predicted by the trained model as
supervision on the spurious attribute to train a new robust model having
minimal worst-group loss. Our experiments on various benchmark datasets show
that our algorithm consistently outperforms the baseline methods using the same
number of validation samples with spurious attribute annotations. We also
demonstrate that the proposed SSA can achieve comparable performances to
methods using full (100%) spurious attribute supervision, by using a much
smaller number of annotated samples -- from 0.6% and up to 1.5%, depending on
the dataset.

Model-based algorithms are deeply rooted in modern control and systems
theory. However, they usually come with a critical assumption - access to an
accurate model of the system. In practice, models are far from perfect. Even
precisely tuned estimates of unknown parameters will deteriorate over time.
Therefore, it is essential to detect the change to avoid suboptimal or even
dangerous behavior of a control system. We propose to combine statistical tests
with dedicated parameter filters that track unknown system parameters from
state data. These filters yield point estimates of the unknown parameters and,
further, an inherent notion of uncertainty. When the point estimate leaves the
confidence region, we trigger active learning experiments. We update models
only after enforcing a sufficiently small uncertainty in the filter. Thus,
models are only updated when necessary and statistically significant while
ensuring guaranteed improvement, which we call event-triggered learning. We
validate the proposed method in numerical simulations of a DC motor in
combination with model predictive control.

Hypernymy plays a fundamental role in many AI tasks like taxonomy learning,
ontology learning, etc. This has motivated the development of many automatic
identification methods for extracting this relation, most of which rely on word
distribution. We present a novel model HyperBox to learn box embeddings for
hypernym discovery. Given an input term, HyperBox retrieves its suitable
hypernym from a target corpus. For this task, we use the dataset published for
SemEval 2018 Shared Task on Hypernym Discovery. We compare the performance of
our model on two specific domains of knowledge: medical and music.
Experimentally, we show that our model outperforms existing methods on the
majority of the evaluation metrics. Moreover, our model generalize well over
unseen hypernymy pairs using only a small set of training data.

User feedback is one of the most effective methods to build and maintain
trust in electronic commerce platforms. Unfortunately, dishonest sellers often
bend over backward to manipulate users' feedback or place phony bids in order
to increase their own sales and harm competitors. The black market of user
feedback, supported by a plethora of shill bidders, prospers on top of
legitimate electronic commerce. In this paper, we investigate the ecosystem of
shill bidders based on large-scale data by analyzing hundreds of millions of
users who performed billions of transactions, and we propose a
machine-learning-based method for identifying communities of users that
methodically provide dishonest feedback. Our results show that (1) shill
bidders can be identified with high precision based on their transaction and
feedback statistics; and (2) in contrast to legitimate buyers and sellers,
shill bidders form cliques to support each other.

Interpretability can be critical for the safe and responsible use of machine
learning models in high-stakes applications. So far, evolutionary computation
(EC), in particular in the form of genetic programming (GP), represents a key
enabler for the discovery of interpretable machine learning (IML) models. In
this short paper, we argue that research in GP for IML needs to focus on
searching in the space of low-complexity models, by investigating new kinds of
search strategies and recombination methods. Moreover, based on our experience
of bringing research into clinical practice, we believe that research should
strive to design better ways of modeling and pursuing interpretability, for the
obtained solutions to ultimately be most useful.

Deep reinforcement learning has enabled robots to learn motor skills from
environmental interactions with minimal to no prior knowledge. However,
existing reinforcement learning algorithms assume an episodic setting, in which
the agent resets to a fixed initial state distribution at the end of each
episode, to successfully train the agents from repeated trials. Such reset
mechanism, while trivial for simulated tasks, can be challenging to provide for
real-world robotics tasks. Resets in robotic systems often require extensive
human supervision and task-specific workarounds, which contradicts the goal of
autonomous robot learning. In this paper, we propose an extension to
conventional reinforcement learning towards greater autonomy by introducing an
additional agent that learns to reset in a self-supervised manner. The reset
agent preemptively triggers a reset to prevent manual resets and implicitly
imposes a curriculum for the forward agent. We apply our method to learn from
scratch on a suite of simulated and real-world continuous control tasks and
demonstrate that the reset agent successfully learns to reduce manual resets
whilst also allowing the forward policy to improve gradually over time.

Driver's cognitive ability at a given moment is the most elusive variable in
assessing driver's safety. In contrast to other physical conditions, such as
short-sight, or manual disability cognitive ability is transient. Safety
regulations attempt to reduce risk related to driver's cognitive ability by
removing risk factors such as alcohol or drug consumption, forbidding secondary
tasks such as texting, and urging drivers to take breaks when feeling tired.
However, one cannot regulate all factors that affect driver's cognition,
furthermore, the driver's momentary cognitive ability in most cases is covert
even to driver.
  Here, we introduce an active approach aiming at monitoring a specific
cognitive process that is affected by all these forementioned causes and
directly affects the driver's performance in the driving task. We lean on the
scientific approach that was framed by Karl Friston (Friston, 2010). We
developed a closed loop-method in which driver's ocular responses to visual
probing were recorded. Machine-learning-algorithms were trained on ocular
responses of vigilant condition and were able to detect decrease in capability
due fatigue and substance abuse. Our results show that we manage to correctly
classify subjects with impaired and unimpaired cognitive process regardless of
the cause of impairment (77% accuracy, 5% false alarms).

With the rapid development of deep learning, automatic modulation recognition
(AMR), as an important task in cognitive radio, has gradually transformed from
traditional feature extraction and classification to automatic classification
by deep learning technology. However, deep learning models are data-driven
methods, which often require a large amount of data as the training support.
Data augmentation, as the strategy of expanding dataset, can improve the
generalization of the deep learning models and thus improve the accuracy of the
models to a certain extent. In this paper, for AMR of radio signals, we propose
a data augmentation strategy based on mixing signals and consider four specific
methods (Random Mixing, Maximum-Similarity-Mixing, $\theta-$Similarity Mixing
and n-times Random Mixing) to achieve data augmentation. Experiments show that
our proposed method can improve the classification accuracy of deep learning
based AMR models in the full public dataset RML2016.10a. In particular, for the
case of a single signal-to-noise ratio signal set, the classification accuracy
can be significantly improved, which verifies the effectiveness of the methods.

Recently, parallel text generation has received widespread attention due to
its success in generation efficiency. Although many advanced techniques are
proposed to improve its generation quality, they still need the help of an
autoregressive model for training to overcome the one-to-many multi-modal
phenomenon in the dataset, limiting their applications. In this paper, we
propose $\textit{latent}$-GLAT, which employs the discrete latent variables to
capture word categorical information and invoke an advanced curriculum learning
technique, alleviating the multi-modality problem. Experiment results show that
our method outperforms strong baselines without the help of an autoregressive
model, which further broadens the application scenarios of the parallel
decoding paradigm.

Gradient-enhanced Kriging (GE-Kriging) is a well-established surrogate
modelling technique for approximating expensive computational models. However,
it tends to get impractical for high-dimensional problems due to the large
inherent correlation matrix and the associated high-dimensional hyper-parameter
tuning problem. To address these issues, we propose a new method in this paper,
called sliced GE-Kriging (SGE-Kriging) for reducing both the size of the
correlation matrix and the number of hyper-parameters. Firstly, we perform a
derivative-based global sensitivity analysis to detect the relative importance
of each input variable with respect to model response. Then, we propose to
split the training sample set into multiple slices, and invoke Bayes' theorem
to approximate the full likelihood function via a sliced likelihood function,
in which multiple small correlation matrices are utilized to describe the
correlation of the sample set. Additionally, we replace the original
high-dimensional hyper-parameter tuning problem with a low-dimensional
counterpart by learning the relationship between the hyper-parameters and the
global sensitivity indices. Finally, we validate SGE-Kriging by means of
numerical experiments with several benchmarks problems. The results show that
the SGE-Kriging model features an accuracy and robustness that is comparable to
the standard one but comes at much less training costs. The benefits are most
evident in high-dimensional problems.

Despite continuous improvements, precipitation forecasts are still not as
accurate and reliable as those of other meteorological variables. A major
contributing factor to this is that several key processes affecting
precipitation distribution and intensity occur below the resolved scale of
global weather models. Generative adversarial networks (GANs) have been
demonstrated by the computer vision community to be successful at
super-resolution problems, i.e., learning to add fine-scale structure to coarse
images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of
reconstructed high-resolution atmospheric fields, given coarsened input data.
In this paper, we demonstrate this approach can be extended to the more
challenging problem of increasing the accuracy and resolution of comparatively
low-resolution input from a weather forecasting model, using high-resolution
radar measurements as a "ground truth". The neural network must learn to add
resolution and structure whilst accounting for non-negligible forecast error.
We show that GANs and VAE-GANs can match the statistical properties of
state-of-the-art pointwise post-processing methods whilst creating
high-resolution, spatially coherent precipitation maps. Our model compares
favourably to the best existing downscaling methods in both pixel-wise and
pooled CRPS scores, power spectrum information and rank histograms (used to
assess calibration). We test our models and show that they perform in a range
of scenarios, including heavy rainfall.

Overfitting is a common problem in machine learning, which means the model
too closely fits the training data while performing poorly in the test data.
Among various methods of coping with overfitting, dropout is one of the
representative ways. From randomly dropping neurons to dropping neural
structures, dropout has achieved great success in improving model performances.
Although various dropout methods have been designed and widely applied in past
years, their effectiveness, application scenarios, and contributions have not
been comprehensively summarized and empirically compared by far. It is the
right time to make a comprehensive survey.
  In this paper, we systematically review previous dropout methods and classify
them into three major categories according to the stage where dropout operation
is performed. Specifically, more than seventy dropout methods published in top
AI conferences or journals (e.g., TKDE, KDD, TheWebConf, SIGIR) are involved.
The designed taxonomy is easy to understand and capable of including new
dropout methods. Then, we further discuss their application scenarios,
connections, and contributions. To verify the effectiveness of distinct dropout
methods, extensive experiments are conducted on recommendation scenarios with
abundant heterogeneous information. Finally, we propose some open problems and
potential research directions about dropout that worth to be further explored.

We propose a novel solution for the Register Allocation problem, leveraging
multi-agent hierarchical Reinforcement Learning. We formalize the constraints
that precisely define the problem for a given instruction-set architecture,
while ensuring that the generated code preserves semantic correctness. We also
develop a gRPC based framework providing a modular and efficient compiler
interface for training and inference. Experimental results match or outperform
the LLVM register allocators, targeting Intel x86 and ARM AArch64.

In autoencoder, the encoder generally approximates the latent distribution
over the dataset, and the decoder generates samples using this learned latent
distribution. There is very little control over the latent vector as using the
random latent vector for generation will lead to trivial outputs. This work
tries to address this issue by using the LatentGAN generator to directly learn
to approximate the latent distribution of the autoencoder and show meaningful
results on MNIST, 3D Chair, and CelebA datasets, an additional
information-theoretic constrain is used which successfully learns to control
autoencoder latent distribution. With this, our model also achieves an error
rate of 2.38 on MNIST unsupervised image classification, which is better as
compared to InfoGAN and AAE.

Recent deep learning-based video salient object detection (VSOD) has achieved
some breakthrough, but these methods rely on expensive annotated videos with
pixel-wise annotations, weak annotations, or part of the pixel-wise
annotations. In this paper, based on the similarities and the differences
between VSOD and image salient object detection (SOD), we propose a novel VSOD
method via a progressive framework that locates and segments salient objects in
sequence without utilizing any video annotation. To use the knowledge learned
in the SOD dataset for VSOD efficiently, we introduce dynamic saliency to
compensate for the lack of motion information of SOD during the locating
process but retain the same fine segmenting process. Specifically, an algorithm
for generating spatiotemporal location labels, which consists of generating
high-saliency location labels and tracking salient objects in adjacent frames,
is proposed. Based on these location labels, a two-stream locating network that
introduces an optical flow branch for video salient object locating is
presented. Although our method does not require labeled video at all, the
experimental results on five public benchmarks of DAVIS, FBMS, ViSal, VOS, and
DAVSOD demonstrate that our proposed method is competitive with fully
supervised methods and outperforms the state-of-the-art weakly and unsupervised
methods.

Automating the fact checking (FC) process relies on information obtained from
external sources. In this work, we posit that it is crucial for FC models to
make veracity predictions only when there is sufficient evidence and otherwise
indicate when it is not enough. To this end, we are the first to study what
information FC models consider sufficient by introducing a novel task and
advancing it with three main contributions. First, we conduct an in-depth
empirical analysis of the task with a new fluency-preserving method for
omitting information from the evidence at the constituent and sentence level.
We identify when models consider the remaining evidence (in)sufficient for FC,
based on three trained models with different Transformer architectures and
three FC datasets. Second, we ask annotators whether the omitted evidence was
important for FC, resulting in a novel diagnostic dataset, SufficientFacts, for
FC with omitted evidence. We find that models are least successful in detecting
missing evidence when adverbial modifiers are omitted (21% accuracy), whereas
it is easiest for omitted date modifiers (63% accuracy). Finally, we propose a
novel data augmentation strategy for contrastive self-learning of missing
evidence by employing the proposed omission method combined with tri-training.
It improves performance for Evidence Sufficiency Prediction by up to 17.8 F1
score, which in turn improves FC performance by up to 2.6 F1 score.

Binary Neural Networks (BNNs) are an extremely promising method to reduce
deep neural networks' complexity and power consumption massively. Binarization
techniques, however, suffer from ineligible performance degradation compared to
their full-precision counterparts.
  Prior work mainly focused on strategies for sign function approximation
during forward and backward phases to reduce the quantization error during the
binarization process. In this work, we propose a Bi-Modal Distributed
binarization method (\methodname{}). That imposes bi-modal distribution of the
network weights by kurtosis regularization. The proposed method consists of a
training scheme that we call Weight Distribution Mimicking (WDM), which
efficiently imitates the full-precision network weight distribution to their
binary counterpart. Preserving this distribution during binarization-aware
training creates robust and informative binary feature maps and significantly
reduces the generalization error of the BNN. Extensive evaluations on CIFAR-10
and ImageNet demonstrate the superiority of our method over current
state-of-the-art schemes. Our source code, experimental settings, training
logs, and binary models are available at
\url{https://github.com/BlueAnon/BD-BNN}.

Optimal control is an essential tool for stabilizing complex nonlinear
system. However, despite the extensive impacts of methods such as receding
horizon control, dynamic programming and reinforcement learning, the design of
cost functions for a particular system often remains a heuristic-driven process
of trial and error. In this paper we seek to gain insights into how the choice
of cost function interacts with the underlying structure of the control system
and impacts the amount of computation required to obtain a stabilizing
controller. We treat the cost design problem as a two-step process where the
designer specifies outputs for the system that are to be penalized and then
modulates the relative weighting of the inputs and the outputs in the cost. We
then bound the length of the prediction horizon $T>0$ that is required for
receding horizon control methods to stabilize the system as a concrete way of
characterizing the computational difficulty of stabilizing the system using the
chosen cost function. Drawing on insights from the `cheap control' literature,
we investigate cases where the chosen outputs lead to minimumphase and
non-minimumphase input-output dynamics. When the system is minimumphase, the
prediction horizon needed to ensure stability can be made arbitrarily small by
making the penalty on the control small enough. This indicates that choices of
cost function which implicitly induce minimumphase behavior lead to an optimal
control problem from which it is `easy' to obtain a stabilizing controller.
Using these insights, we investigate empirically how the choice of cost
function affects the ability of modern reinforcement learning algorithms to
learn a stabilizing controller. Taken together, the results in this paper
indicate that cost functions which induce non-minimum phase behavior lead to
inherent computational difficulties.

Self-supervised learning of speech representations has achieved impressive
results in improving automatic speech recognition (ASR). In this paper, we show
that data selection is important for self-supervised learning. We propose a
simple and effective unsupervised data selection method which selects
acoustically similar speech to a target domain. It takes the discrete speech
representation available in common self-supervised learning frameworks as
input, and applies a contrastive data selection method on the discrete tokens.
Through extensive empirical studies we show that our proposed method reduces
the amount of required pre-training data and improves the downstream ASR
performance. Pre-training on a selected subset of 6% of the general data pool
results in 11.8% relative improvements in LibriSpeech test-other compared to
pre-training on the full set. On Multilingual LibriSpeech French, German, and
Spanish test sets, selecting 6% data for pre-training reduces word error rate
by more than 15% relatively compared to the full set, and achieves competitive
results compared to current state-of-the-art performances.

Parallel imaging is widely used in magnetic resonance imaging as an
acceleration technology. Traditional linear reconstruction methods in parallel
imaging often suffer from noise amplification. Recently, a non-linear robust
artificial-neural-network for k-space interpolation (RAKI) exhibits superior
noise resilience over other linear methods. However, RAKI performs poorly at
high acceleration rates, and needs a large amount of autocalibration signals as
the training samples. In order to tackle these issues, we propose a
multi-weight method that implements multiple weighting matrices on the
undersampled data, named as MW-RAKI. Enforcing multiple weighted matrices on
the measurements can effectively reduce the influence of noise and increase the
data constraints. Furthermore, we incorporate the strategy of multiple
weighting matrixes into a residual version of RAKI, and form
MW-rRAKI.Experimental compari-sons with the alternative methods demonstrated
noticeably better reconstruction performances, particularly at high
acceleration rates.

Despite the rapid advance of automatic speech recognition (ASR) technologies,
accurate recognition of cocktail party speech characterised by the interference
from overlapping speakers, background noise and room reverberation remains a
highly challenging task to date. Motivated by the invariance of visual modality
to acoustic signal corruption, audio-visual speech enhancement techniques have
been developed, although predominantly targeting overlapping speech separation
and recognition tasks. In this paper, an audio-visual multi-channel speech
separation, dereverberation and recognition approach featuring a full
incorporation of visual information into all three stages of the system is
proposed. The advantage of the additional visual modality over using audio only
is demonstrated on two neural dereverberation approaches based on DNN-WPE and
spectral mapping respectively. The learning cost function mismatch between the
separation and dereverberation models and their integration with the back-end
recognition system is minimised using fine-tuning on the MSE and LF-MMI
criteria. Experiments conducted on the LRS2 dataset suggest that the proposed
audio-visual multi-channel speech separation, dereverberation and recognition
system outperforms the baseline audio-visual multi-channel speech separation
and recognition system containing no dereverberation module by a statistically
significant word error rate (WER) reduction of 2.06% absolute (8.77% relative).

We study the problem of minimizing total completion time on parallel machines
subject to varying processing capacity. In this paper, we develop an
approximation scheme for the problem under the data stream model where the
input data is massive and cannot fit into memory and thus can only be scanned
for a few passes. Our algorithm can compute the approximate value of the
optimal total completion time in one pass and output the schedule with the
approximate value in two passes.

Penetration testing (PT) is an efficient network testing and vulnerability
mining tool by simulating a hacker's attack for valuable information applied in
some areas. Compared with manual PT, intelligent PT has become a dominating
mainstream due to less time-consuming and lower labor costs. Unfortunately,
RL-based PT is still challenged in real exploitation scenarios because the
agent's action space is usually high-dimensional discrete, thus leading to
algorithm convergence difficulty. Besides, most PT methods still rely on the
decisions of security experts. Addressing the challenges, for the first time,
we introduce expert knowledge to guide the agent to make better decisions in
RL-based PT and propose a Generative Adversarial Imitation Learning-based
generic intelligent Penetration testing framework, denoted as GAIL-PT, to solve
the problems of higher labor costs due to the involvement of security experts
and high-dimensional discrete action space. Specifically, first, we manually
collect the state-action pairs to construct an expert knowledge base when the
pre-trained RL / DRL model executes successful penetration testings. Second, we
input the expert knowledge and the state-action pairs generated online by the
different RL / DRL models into the discriminator of GAIL for training. At last,
we apply the output reward of the discriminator to guide the agent to perform
the action with a higher penetration success rate to improve PT's performance.
Extensive experiments conducted on the real target host and simulated network
scenarios show that GAIL-PT achieves the SOTA penetration performance against
DeepExploit in exploiting actual target Metasploitable2 and Q-learning in
optimizing penetration path, not only in small-scale with or without honey-pot
network environments but also in the large-scale virtual network environment.

Available 3D human pose estimation approaches leverage different forms of
strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring
synthetic or in-studio domains, acquiring such supervision for each new target
environment is highly inconvenient. To this end, we cast 3D pose learning as a
self-supervised adaptation problem that aims to transfer the task knowledge
from a labeled source domain to a completely unpaired target. We propose to
infer image-to-pose via two explicit mappings viz. image-to-latent and
latent-to-pose where the latter is a pre-learned decoder obtained from a
prior-enforcing generative adversarial auto-encoder. Next, we introduce
relation distillation as a means to align the unpaired cross-modal samples i.e.
the unpaired target videos and unpaired 3D pose sequences. To this end, we
propose a new set of non-local relations in order to characterize long-range
latent pose interactions unlike general contrastive relations where positive
couplings are limited to a local neighborhood structure. Further, we provide an
objective way to quantify non-localness in order to select the most effective
relation set. We evaluate different self-adaptation settings and demonstrate
state-of-the-art 3D human pose estimation performance on standard benchmarks.

This paper proposes a time-efficient mechanism to decrease the on-line
computing time of solving the joint unmanned aerial vehicle base station
(UAV-BS) deployment and user/sensor association (UDUA) problem aiming at
maximizing the downlink sum transmission throughput. The joint UDUA problem is
decoupled into two sub-problems: one is the user association sub-problem, which
gets the optimal matching strategy between aerial and ground nodes for certain
UAV-BS positions; and the other is the UAV-BS deployment sub-problem trying to
find the best position combination of the UAV-BSs that make the solution of the
first sub-problem optimal among all the possible position combinations of the
UAV-BSs. In the proposed mechanism, we transform the user association
sub-problem into an equivalent bipartite matching problem and solve it using
the Kuhn-Munkres algorithm. For the UAV-BS deployment sub-problem, we
theoretically prove that adopting the best UAV-BS deployment strategy of a
previous user distribution for each new user distribution will introduce little
performance decline compared with the new user distribution's ground true best
strategy if the two user distributions are similar enough. Based on our
mathematical analyses, the similarity level between user distributions is well
defined and becomes the key to solve the second sub-problem. Numerical results
indicate that the proposed UDUA mechanism can achieve near-optimal system
performance in terms of average downlink sum transmission throughput and
failure rate with enormously reduced computing time compared with benchmark

Deepfakes and manipulated media are becoming a prominent threat due to the
recent advances in realistic image and video synthesis techniques. There have
been several attempts at combating Deepfakes using machine learning
classifiers. However, such classifiers do not generalize well to black-box
image synthesis techniques and have been shown to be vulnerable to adversarial
examples. To address these challenges, we introduce a deep learning based
semi-fragile watermarking technique that allows media authentication by
verifying an invisible secret message embedded in the image pixels. Instead of
identifying and detecting fake media using visual artifacts, we propose to
proactively embed a semi-fragile watermark into a real image so that we can
prove its authenticity when needed. Our watermarking framework is designed to
be fragile to facial manipulations or tampering while being robust to benign
image-processing operations such as image compression, scaling, saturation,
contrast adjustments etc. This allows images shared over the internet to retain
the verifiable watermark as long as face-swapping or any other Deepfake
modification technique is not applied. We demonstrate that FaceSigns can embed
a 128 bit secret as an imperceptible image watermark that can be recovered with
a high bit recovery accuracy at several compression levels, while being
non-recoverable when unseen Deepfake manipulations are applied. For a set of
unseen benign and Deepfake manipulations studied in our work, FaceSigns can
reliably detect manipulated content with an AUC score of 0.996 which is
significantly higher than prior image watermarking and steganography

For transient sources with timescales of 1-100 seconds, standardized imaging
for all observations at each time step become impossible as large modern
interferometers produce significantly large data volumes in this observation
time frame. Here we propose a method based on machine learning and using
interferometric closure products as input features to detect transient source
candidates directly from the spatial frequency domain without imaging. We train
a simple neural network classifier on a synthetic dataset of
Noise/Transient/RFI events, which we construct to tackle the lack of labelled
data. We also use the hyper-parameter dropout rate of the model to allow the
model to approximate Bayesian inference, and select the optimal dropout rate to
match the posterior prediction to the actual underlying probability
distribution of the detected events. The overall F1-score of the classifier on
the simulated dataset is greater than 85\%, with the signal-to-noise at
7$\sigma$. The performance of the trained neural network with Monte Carlo
dropout is evaluated on semi-real data, which includes a simulated transient
source and real noise. This classifier accurately identifies the presence of
transient signals in the detectable signal-to-noise levels (above 4$\sigma$)
with the optimal variance. Our findings suggest that a feasible radio transient
classifier can be built up with only simulated data for applying to the
prediction of real observation, even in the absence of annotated real samples
for the purpose of training.

With the capacity of modeling long-range dependencies in sequential data,
transformers have shown remarkable performances in a variety of generative
tasks such as image, audio, and text generation. Yet, taming them in generating
less structured and voluminous data formats such as high-resolution point
clouds have seldom been explored due to ambiguous sequentialization processes
and infeasible computation burden. In this paper, we aim to further exploit the
power of transformers and employ them for the task of 3D point cloud
generation. The key idea is to decompose point clouds of one category into
semantically aligned sequences of shape compositions, via a learned canonical
space. These shape compositions can then be quantized and used to learn a
context-rich composition codebook for point cloud generation. Experimental
results on point cloud reconstruction and unconditional generation show that
our model performs favorably against state-of-the-art approaches. Furthermore,
our model can be easily extended to multi-modal shape completion as an
application for conditional shape generation.

With tremendous advancements in low-power embedded computing devices and
remote sensing instruments, the traditional satellite image processing pipeline
which includes an expensive data transfer step prior to processing data on the
ground is being replaced by on-board processing of captured data. This paradigm
shift enables critical and time-sensitive analytic intelligence to be acquired
in a timely manner on-board the satellite itself. However, at present, the
on-board processing of multi-spectral satellite images is limited to
classification and segmentation tasks. Extending this processing to its next
logical level, in this paper we propose a lightweight pipeline for on-board
panoptic segmentation of multi-spectral satellite images. Panoptic segmentation
offers major economic and environmental insights, ranging from yield estimation
from agricultural lands to intelligence for complex military applications.
Nevertheless, the on-board intelligence extraction raises several challenges
due to the loss of temporal observations and the need to generate predictions
from a single image sample. To address this challenge, we propose a multimodal
teacher network based on a cross-modality attention-based fusion strategy to
improve the segmentation accuracy by exploiting data from multiple modes. We
also propose an online knowledge distillation framework to transfer the
knowledge learned by this multi-modal teacher network to a uni-modal student
which receives only a single frame input, and is more appropriate for an
on-board environment. We benchmark our approach against existing
state-of-the-art panoptic segmentation models using the PASTIS multi-spectral
panoptic segmentation dataset considering an on-board processing setting. Our
evaluations demonstrate a substantial increase in accuracy metrics compared to
the existing state-of-the-art models.

Digital twin (DT) technologies have emerged as a solution for real-time
data-driven modeling of cyber physical systems (CPS) using the vast amount of
data available by Internet of Things (IoT) networks. In this position paper, we
elucidate unique characteristics and capabilities of a DT framework that
enables realization of such promises as online learning of a physical
environment, real-time monitoring of assets, Monte Carlo heuristic search for
predictive prevention, on-policy, and off-policy reinforcement learning in
real-time. We establish a conceptual layered architecture for a DT framework
with decentralized implementation on cloud computing and enabled by artificial
intelligence (AI) services for modeling, event detection, and decision-making
processes. The DT framework separates the control functions, deployed as a
system of logically centralized process, from the physical devices under
control, much like software-defined networking (SDN) in fifth generation (5G)
wireless networks. We discuss the moment of the DT framework in facilitating
implementation of network-based control processes and its implications for
critical infrastructure. To clarify the significance of DT in lowering the risk
of development and deployment of innovative technologies on existing system, we
discuss the application of implementing zero trust architecture (ZTA) as a
necessary security framework in future data-driven communication networks.

Representing visual signals by implicit representation (e.g., a coordinate
based deep network) has prevailed among many vision tasks. This work explores a
new intriguing direction: training a stylized implicit representation, using a
generalized approach that can apply to various 2D and 3D scenarios. We conduct
a pilot study on a variety of implicit functions, including 2D coordinate-based
representation, neural radiance field, and signed distance function. Our
solution is a Unified Implicit Neural Stylization framework, dubbed INS. In
contrary to vanilla implicit representation, INS decouples the ordinary
implicit function into a style implicit module and a content implicit module,
in order to separately encode the representations from the style image and
input scenes. An amalgamation module is then applied to aggregate these
information and synthesize the stylized output. To regularize the geometry in
3D scenes, we propose a novel self-distillation geometry consistency loss which
preserves the geometry fidelity of the stylized scenes. Comprehensive
experiments are conducted on multiple task settings, including novel view
synthesis of complex scenes, stylization for implicit surfaces, and fitting
images using MLPs. We further demonstrate that the learned representation is
continuous not only spatially but also style-wise, leading to effortlessly
interpolating between different styles and generating images with new mixed
styles. Please refer to the video on our project page for more view synthesis
results: https://zhiwenfan.github.io/INS.

With the rapid advancements of deep learning in the past decade, it can be
foreseen that deep learning will be continuously deployed in more and more
safety-critical applications such as autonomous driving and robotics. In this
context, reliability turns out to be critical to the deployment of deep
learning in these applications and gradually becomes a first-class citizen
among the major design metrics like performance and energy efficiency.
Nevertheless, the back-box deep learning models combined with the diverse
underlying hardware faults make resilient deep learning extremely challenging.
In this special session, we conduct a comprehensive survey of fault-tolerant
deep learning design approaches with a hierarchical perspective and investigate
these approaches from model layer, architecture layer, circuit layer, and cross
layer respectively.

We explore training Binary Neural Networks (BNNs) as a discrete variable
inference problem over a factor graph. We study the behaviour of this
conversion in an under-parameterized BNN setting and propose stochastic
versions of Belief Propagation (BP) and Survey Propagation (SP) message passing
algorithms to overcome the intractability of their current formulation.
Compared to traditional gradient methods for BNNs, our results indicate that
both stochastic BP and SP find better configurations of the parameters in the
BNN.

Fine-tuning attacks are effective in removing the embedded watermarks in deep
learning models. However, when the source data is unavailable, it is
challenging to just erase the watermark without jeopardizing the model
performance. In this context, we introduce Attention Distraction (AD), a novel
source data-free watermark removal attack, to make the model selectively forget
the embedded watermarks by customizing continual learning. In particular, AD
first anchors the model's attention on the main task using some unlabeled data.
Then, through continual learning, a small number of \textit{lures} (randomly
selected natural images) that are assigned a new label distract the model's
attention away from the watermarks. Experimental results from different
datasets and networks corroborate that AD can thoroughly remove the watermark
with a small resource budget without compromising the model's performance on
the main task, which outperforms the state-of-the-art works.

PICNet pioneered the generation of multiple and diverse results for image
completion task, but it required a careful balance between $\mathcal{KL}$ loss
(diversity) and reconstruction loss (quality), resulting in a limited diversity
and quality . Separately, iGPT-based architecture has been employed to infer
distributions in a discrete space derived from a pixel-level pre-clustered
palette, which however cannot generate high-quality results directly. In this
work, we present a novel framework for pluralistic image completion that can
achieve both high quality and diversity at much faster inference speed. The
core of our design lies in a simple yet effective code sharing mechanism that
leads to a very compact yet expressive image representation in a discrete
latent domain. The compactness and the richness of the representation further
facilitate the subsequent deployment of a transformer to effectively learn how
to composite and complete a masked image at the discrete code domain. Based on
the global context well-captured by the transformer and the available visual
regions, we are able to sample all tokens simultaneously, which is completely
different from the prevailing autoregressive approach of iGPT-based works, and
leads to more than 100$\times$ faster inference speed. Experiments show that
our framework is able to learn semantically-rich discrete codes efficiently and
robustly, resulting in much better image reconstruction quality. Our diverse
image completion framework significantly outperforms the state-of-the-art both
quantitatively and qualitatively on multiple benchmark datasets.

The interaction between a vehicle navigation system and the driver of the
vehicle can be formulated as a model-based reinforcement learning problem,
where the navigation systems (agent) must quickly adapt to the characteristics
of the driver (environmental dynamics) to provide the best sequence of
turn-by-turn driving instructions. Most modern day navigation systems (e.g,
Google maps, Waze, Garmin) are not designed to personalize their low-level
interactions for individual users across a wide range of driving styles (e.g.,
vehicle type, reaction time, level of expertise). Towards the development of
personalized navigation systems that adapt to a variety of driving styles, we
propose an online no-regret model-based RL method that quickly conforms to the
dynamics of the current user. As the user interacts with it, the navigation
system quickly builds a user-specific model, from which navigation commands are
optimized using model predictive control. By personalizing the policy in this
way, our method is able to give well-timed driving instructions that match the
user's dynamics. Our theoretical analysis shows that our method is a no-regret
algorithm and we provide the convergence rate in the agnostic setting. Our
empirical analysis with 60+ hours of real-world user data using a driving
simulator shows that our method can reduce the number of collisions by more
than 60%.

Designing a safe and human-like decision-making system for an autonomous
vehicle is a challenging task. Generative imitation learning is one possible
approach for automating policy-building by leveraging both real-world and
simulated decisions. Previous work that applies generative imitation learning
to autonomous driving policies focuses on learning a low-level controller for
simple settings. However, to scale to complex settings, many autonomous driving
systems combine fixed, safe, optimization-based low-level controllers with
high-level decision-making logic that selects the appropriate task and
associated controller. In this paper, we attempt to bridge this gap in
complexity by employing Safety-Aware Hierarchical Adversarial Imitation
Learning (SHAIL), a method for learning a high-level policy that selects from a
set of low-level controller instances in a way that imitates low-level driving
data on-policy. We introduce an urban roundabout simulator that controls
non-ego vehicles using real data from the Interaction dataset. We then show
empirically that our approach can produce better behavior than previous
approaches in driver imitation which have difficulty scaling to complex
environments. Our implementation is available at
https://github.com/sisl/InteractionImitation.

In many real-world machine learning applications, samples belong to a set of
domains e.g., for product reviews each review belongs to a product category. In
this paper, we study multi-domain imbalanced learning (MIL), the scenario that
there is imbalance not only in classes but also in domains. In the MIL setting,
different domains exhibit different patterns and there is a varying degree of
similarity and divergence among domains posing opportunities and challenges for
transfer learning especially when faced with limited or insufficient training
data. We propose a novel domain-aware contrastive knowledge transfer method
called DCMI to (1) identify the shared domain knowledge to encourage positive
transfer among similar domains (in particular from head domains to tail
domains); (2) isolate the domain-specific knowledge to minimize the negative
transfer from dissimilar domains. We evaluated the performance of DCMI on three
different datasets showing significant improvements in different MIL scenarios.

Some of the most severe bottlenecks preventing widespread development of
machine learning models for human behavior include a dearth of labeled training
data and difficulty of acquiring high quality labels. Active learning is a
paradigm for using algorithms to computationally select a useful subset of data
points to label using metrics for model uncertainty and data similarity. We
explore active learning for naturalistic computer vision emotion data, a
particularly heterogeneous and complex data space due to inherently subjective
labels. Using frames collected from gameplay acquired from a therapeutic
smartphone game for children with autism, we run a simulation of active
learning using gameplay prompts as metadata to aid in the active learning
process. We find that active learning using information generated during
gameplay slightly outperforms random selection of the same number of labeled
frames. We next investigate a method to conduct active learning with subjective
data, such as in affective computing, and where multiple crowdsourced labels
can be acquired for each image. Using the Child Affective Facial Expression
(CAFE) dataset, we simulate an active learning process for crowdsourcing many
labels and find that prioritizing frames using the entropy of the crowdsourced
label distribution results in lower categorical cross-entropy loss compared to
random frame selection. Collectively, these results demonstrate pilot
evaluations of two novel active learning approaches for subjective affective
data collected in noisy settings.

Anomaly detection has many important applications, such as monitoring
industrial equipment. Despite recent advances in anomaly detection with
deep-learning methods, it is unclear how existing solutions would perform under
out-of-distribution scenarios, e.g., due to shifts in machine load or
environmental noise. Grounded in the application of machine health monitoring,
we propose a framework that adapts to new conditions with few-shot samples.
Building upon prior work, we adopt a classification-based approach for anomaly
detection and show its equivalence to mixture density estimation of the normal
samples. We incorporate an episodic training procedure to match the few-shot
setting during inference. We define multiple auxiliary classification tasks
based on meta-information and leverage gradient-based meta-learning to improve
generalization to different shifts. We evaluate our proposed method on a
recently-released dataset of audio measurements from different machine types.
It improved upon two baselines by around 10% and is on par with best-performing
model reported on the dataset.

Trajectory estimation is a fundamental component of racket sport analytics,
as the trajectory contains information not only about the winning and losing of
each point, but also how it was won or lost. In sports such as badminton,
players benefit from knowing the full 3D trajectory, as the height of
shuttlecock or ball provides valuable tactical information. Unfortunately, 3D
reconstruction is a notoriously hard problem, and standard trajectory
estimators can only track 2D pixel coordinates. In this work, we present the
first complete end-to-end system for the extraction and segmentation of 3D
shuttle trajectories from monocular badminton videos. Our system integrates
badminton domain knowledge such as court dimension, shot placement, physical
laws of motion, along with vision-based features such as player poses and
shuttle tracking. We find that significant engineering efforts and model
improvements are needed to make the overall system robust, and as a by-product
of our work, improve state-of-the-art results on court recognition, 2D
trajectory estimation, and hit recognition.

Text-based communication is highly favoured as a communication method,
especially in business environments. As a result, it is often abused by sending
malicious messages, e.g., spam emails, to deceive users into relaying personal
information, including online accounts credentials or banking details. For this
reason, many machine learning methods for text classification have been
proposed and incorporated into the services of most email providers. However,
optimising text classification algorithms and finding the right tradeoff on
their aggressiveness is still a major research problem.
  We present an updated survey of 12 machine learning text classifiers applied
to a public spam corpus. A new pipeline is proposed to optimise hyperparameter
selection and improve the models' performance by applying specific methods
(based on natural language processing) in the preprocessing stage.
  Our study aims to provide a new methodology to investigate and optimise the
effect of different feature sizes and hyperparameters in machine learning
classifiers that are widely used in text classification problems. The
classifiers are tested and evaluated on different metrics including F-score
(accuracy), precision, recall, and run time. By analysing all these aspects, we
show how the proposed pipeline can be used to achieve a good accuracy towards
spam filtering on the Enron dataset, a widely used public email corpus.
Statistical tests and explainability techniques are applied to provide a robust
analysis of the proposed pipeline and interpret the classification outcomes of
the 12 machine learning models, also identifying words that drive the
classification results. Our analysis shows that it is possible to identify an
effective machine learning model to classify the Enron dataset with an F-score
of 94%.

Traditional deep learning interpretability methods which are suitable for
model users cannot explain network behaviors at the global level and are
inflexible at providing fine-grained explanations. As a solution, concept-based
explanations are gaining attention due to their human intuitiveness and their
flexibility to describe both global and local model behaviors. Concepts are
groups of similarly meaningful pixels that express a notion, embedded within
the network's latent space and have commonly been hand-generated, but have
recently been discovered by automated approaches. Unfortunately, the magnitude
and diversity of discovered concepts makes it difficult to navigate and make
sense of the concept space. Visual analytics can serve a valuable role in
bridging these gaps by enabling structured navigation and exploration of the
concept space to provide concept-based insights of model behavior to users. To
this end, we design, develop, and validate ConceptExplainer, a visual analytics
system that enables people to interactively probe and explore the concept space
to explain model behavior at the instance/class/global level. The system was
developed via iterative prototyping to address a number of design challenges
that model users face in interpreting the behavior of deep learning models. Via
a rigorous user study, we validate how ConceptExplainer supports these
challenges. Likewise, we conduct a series of usage scenarios to demonstrate how
the system supports the interactive analysis of model behavior across a variety
of tasks and explanation granularities, such as identifying concepts that are
important to classification, identifying bias in training data, and
understanding how concepts can be shared across diverse and seemingly
dissimilar classes.

Decision makers often aim to learn a treatment assignment policy under a
capacity constraint on the number of agents that they can treat. When agents
can respond strategically to such policies, competition arises, complicating
the estimation of the effect of the policy. In this paper, we study
capacity-constrained treatment assignment in the presence of such interference.
We consider a dynamic model where heterogeneous agents myopically best respond
to the previous treatment assignment policy. When the number of agents is large
but finite, we show that the threshold for receiving treatment under a given
policy converges to the policy's mean-field equilibrium threshold. Based on
this result, we develop a consistent estimator for the policy effect and
demonstrate in simulations that it can be used for learning optimal
capacity-constrained policies in the presence of strategic behavior.

Fairness in data-driven decision-making studies scenarios where individuals
from certain population segments may be unfairly treated when being considered
for loan or job applications, access to public resources, or other types of
services. In location-based applications, decisions are based on individual
whereabouts, which often correlate with sensitive attributes such as race,
income, and education.
  While fairness has received significant attention recently, e.g., in machine
learning, there is little focus on achieving fairness when dealing with
location data. Due to their characteristics and specific type of processing
algorithms, location data pose important fairness challenges. We introduce the
concept of spatial data fairness to address the specific challenges of location
data and spatial queries. We devise a novel building block to achieve fairness
in the form of fair polynomials. Next, we propose two mechanisms based on fair
polynomials that achieve individual spatial fairness, corresponding to two
common location-based decision-making types: distance-based and zone-based.
Extensive experimental results on real data show that the proposed mechanisms
achieve spatial fairness without sacrificing utility.

We perform maximum likelihood estimates (MLEs) for single and double flavor
ultralight dark matter (ULDM) models using the Spitzer Photometry and Accurate
Rotation Curves (SPARC) database. These estimates are compared to MLEs for
several commonly used cold dark matter (CDM) models. By comparing various CDM
models we find, in agreement with previous studies, that the Burkert and
Einasto models tend to perform better than other commonly used CDM models. We
focus on comparisons between the Einasto and ULDM models and analyze cases for
which the ULDM particle masses are: free to vary; and fixed. For each of these
analyses, we perform fits assuming the soliton and halo profiles are: summed
together; and matched at a given radius. When we let the particle masses vary,
we find a negligible preference for any particular range of particle masses,
within $10^{-25}\,\text{eV}\leq m\leq10^{-19}\,\text{eV}$, when assuming the
summed models. For the matched models, however, we find that almost all
galaxies prefer particles masses in the range $10^{-23}\,\text{eV}\lesssim
m\lesssim10^{-20}\,\text{eV}$. For both double flavor models we find that most
galaxies prefer approximately equal particle masses. We find that the summed
models give much larger variances with respect to the soliton-halo (SH)
relation than the matched models. When the particle masses are fixed, the
matched models give median and mean soliton and halo values that fall within
the SH relation bounds, for most masses scanned. When the particle masses are
fixed in the fitting procedure, we find the best fit results for the particle
mass $m=10^{-20.5}\,\text{eV}$ (for the single flavor models) and
$m_1=10^{-20.5}\,\text{eV}$, $m_2=10^{-20.2}\,\text{eV}$ for the double flavor,
matched model. We discuss how our study will be furthered using a reinforcement
learning algorithm.

Axle count in trucks is important to the classification of vehicles and to
the operation of road systems, and is used in the determination of service fees
and the impact on the pavement. Although axle count can be achieved with
traditional methods, such as manual labor, it is increasingly possible to count
axles using deep learning and computer vision methods. This paper aims to
compare three deep learning object detection algorithms, YOLO, Faster R-CNN and
SSD, for the detection of truck axles. A dataset was built to provide training
and testing examples for the neural networks. Training was done on different
base models, to increase training time efficiency and to compare results. We
evaluated results based on three metrics: mAP, F1-score, and FPS count. Results
indicate that YOLO and SSD have similar accuracy and performance, with more
than 96% mAP for both models. Dataset and codes are publicly available for
download.

Predicting pedestrian crossing intention is an indispensable aspect of
deploying advanced driving systems (ADS) or advanced driver-assistance systems
(ADAS) to real life. State-of-the-art methods in predicting pedestrian crossing
intention often rely on multiple streams of information as inputs, each of
which requires massive computational resources and heavy network architectures
to generate. However, such reliance limits the practical application of the
systems. In this paper, driven the the real-world demands of pedestrian
crossing intention prediction models with both high efficiency and accuracy, we
introduce a network with only frames of pedestrians as the input. Every
component in the introduced network is driven by the goal of light weight.
Specifically, we reduce the multi-source input dependency and employ light
neural networks that are tailored for mobile devices. These smaller neural
networks can fit into computer memory and can be transmitted over a computer
network more easily, thus making them more suitable for real-life deployment
and real-time prediction. To compensate the removal of the multi-source input,
we enhance the network effectiveness by adopting a multi-task learning
training, named "side task learning", to include multiple auxiliary tasks to
jointly learn the feature extractor for improved robustness. Each head handles
a specific task that potentially shares knowledge with other heads. In the
meantime, the feature extractor is shared across all tasks to ensure the
sharing of basic knowledge across all layers. The light weight but high
efficiency characteristics of our model endow it the potential of being
deployed on vehicle-based systems. Experiments validate that our model
consistently delivers outstanding performances.

The core activity of a Private Equity (PE) firm is to invest into companies
in order to provide the investors with profit, usually within 4-7 years. To
invest into a company or not is typically done manually by looking at various
performance indicators of the company and then making a decision often based on
instinct. This process is rather unmanageable given the large number of
companies to potentially invest. Moreover, as more data about company
performance indicators becomes available and the number of different indicators
one may want to consider increases, manual crawling and assessment of
investment opportunities becomes inefficient and ultimately impossible. To
address these issues, this paper proposes a framework for automated data-driven
screening of investment opportunities and thus the recommendation of businesses
to invest in. The framework draws on data from several sources to assess the
financial and managerial position of a company, and then uses an explainable
artificial intelligence (XAI) engine to suggest investment recommendations. The
robustness of the model is validated using different AI algorithms, class
imbalance-handling methods, and features extracted from the available data
sources.

Graphs representation learning has been a very active research area in recent
years. The goal of graph representation learning is to generate graph
representation vectors that capture the structure and features of large graphs
accurately. This is especially important because the quality of the graph
representation vectors will affect the performance of these vectors in
downstream tasks such as node classification, link prediction and anomaly
detection. Many techniques are proposed for generating effective graph
representation vectors. Two of the most prevalent categories of graph
representation learning are graph embedding methods without using graph neural
nets (GNN), which we denote as non-GNN based graph embedding methods, and graph
neural nets (GNN) based methods. Non-GNN graph embedding methods are based on
techniques such as random walks, temporal point processes and neural network
learning methods. GNN-based methods, on the other hand, are the application of
deep learning on graph data. In this survey, we provide an overview of these
two categories and cover the current state-of-the-art methods for both static
and dynamic graphs. Finally, we explore some open and ongoing research

Quantum Computing (QC) has gained immense popularity as a potential solution
to deal with the ever-increasing size of data and associated challenges
leveraging the concept of quantum random access memory (QRAM). QC promises
quadratic or exponential increases in computational time with quantum
parallelism and thus offer a huge leap forward in the computation of Machine
Learning algorithms. This paper analyzes speed up performance of QC when
applied to machine learning algorithms, known as Quantum Machine Learning
(QML). We applied QML methods such as Quantum Support Vector Machine (QSVM),
and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks.
Due to the access limitations of real quantum computers, the QML methods were
implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow
Quantum. We evaluated the performance of QML in terms of processing speed and
accuracy and finally, compared with its classical counterparts. Interestingly,
the experimental results differ to the speed up promises of QC by demonstrating
higher computational time and lower accuracy in comparison to the classical
approaches for SSC attacks.

Spatial audio methods are gaining a growing interest due to the spread of
immersive audio experiences and applications, such as virtual and augmented
reality. For these purposes, 3D audio signals are often acquired through arrays
of Ambisonics microphones, each comprising four capsules that decompose the
sound field in spherical harmonics. In this paper, we propose a dual quaternion
representation of the spatial sound field acquired through an array of two
First Order Ambisonics (FOA) microphones. The audio signals are encapsulated in
a dual quaternion that leverages quaternion algebra properties to exploit
correlations among them. This augmented representation with 6 degrees of
freedom (6DOF) involves a more accurate coverage of the sound field, resulting
in a more precise sound localization and a more immersive audio experience. We
evaluate our approach on a sound event localization and detection (SELD)
benchmark. We show that our dual quaternion SELD model with temporal
convolution blocks (DualQSELD-TCN) achieves better results with respect to real
and quaternion-valued baselines thanks to our augmented representation of the
sound field. Full code is available at:
https://github.com/ispamm/DualQSELD-TCN.

Sequential recommendation aims to model dynamic user behavior from historical
interactions. Self-attentive methods have proven effective at capturing
short-term dynamics and long-term preferences. Despite their success, these
approaches still struggle to model sparse data, on which they struggle to learn
high-quality item representations. We propose to model user dynamics from
shopping intents and interacted items simultaneously. The learned intents are
coarse-grained and work as prior knowledge for item recommendation. To this
end, we present a coarse-to-fine self-attention framework, namely CaFe, which
explicitly learns coarse-grained and fine-grained sequential dynamics.
Specifically, CaFe first learns intents from coarse-grained sequences which are
dense and hence provide high-quality user intent representations. Then, CaFe
fuses intent representations into item encoder outputs to obtain improved item
representations. Finally, we infer recommended items based on representations
of items and corresponding intents. Experiments on sparse datasets show that
CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% NDCG@5
on average.

In the past years, machine learning (ML) has become a popular approach to
support self-adaptation. While ML techniques enable dealing with several
problems in self-adaptation, such as scalable decision-making, they are also
subject to inherent challenges. In this paper, we focus on one such challenge
that is particularly important for self-adaptation: ML techniques are designed
to deal with a set of predefined tasks associated with an operational domain;
they have problems to deal with new emerging tasks, such as concept shift in
input data that is used for learning. To tackle this challenge, we present
\textit{lifelong self-adaptation}: a novel approach to self-adaptation that
enhances self-adaptive systems that use ML techniques with a lifelong ML layer.
The lifelong ML layer tracks the running system and its environment, associates
this knowledge with the current tasks, identifies new tasks based on
differentiations, and updates the learning models of the self-adaptive system
accordingly. We present a reusable architecture for lifelong self-adaptation
and apply it to the case of concept drift caused by unforeseen changes of the
input data of a learning model that is used for decision-making in
self-adaptation. We validate lifelong self-adaptation for two types of concept
drift using two cases.

While commonly used for communication purposes, an increasing number of
recent studies consider WiFi for sensing. In particular, wireless signals are
altered (e.g., reflected and attenuated) by the human body and objects in the
environment. This can be perceived by an observer to infer information on human
activities or changes in the environment and, hence, to "see" over WiFi. Until
now, works on WiFi-based sensing have resulted in a set of custom software
tools - each designed for a specific purpose. Moreover, given how scattered the
literature is, it is difficult to even identify all steps/functions necessary
to build a basic system for WiFi-based sensing. This has led to a high entry
barrier, hindering further research in this area. There has been no effort to
integrate these tools or to build a general software framework that can serve
as the basis for further research, e.g., on using machine learning to interpret
the altered WiFi signals. To address this issue, in this paper, we propose
WiFiEye - a generic software framework that makes all necessary steps/functions
available "out of the box". This way, WiFiEye allows researchers to easily
bootstrap new WiFi-based sensing applications, thereby, focusing on research
rather than on implementation aspects. To illustrate WiFiEye's workflow, we
present a case study on WiFi-based human activity recognition.

Product market demand analysis plays a significant role for originating
business strategies due to its noticeable impact on the competitive business
field. Furthermore, there are roughly 228 million native Bengali speakers, the
majority of whom use Banglish text to interact with one another on social
media. Consumers are buying and evaluating items on social media with Banglish
text as social media emerges as an online marketplace for entrepreneurs. People
use social media to find preferred smartphone brands and models by sharing
their positive and bad experiences with them. For this reason, our goal is to
gather Banglish text data and use sentiment analysis and named entity
identification to assess Bangladeshi market demand for smartphones in order to
determine the most popular smartphones by gender. We scraped product related
data from social media with instant data scrapers and crawled data from
Wikipedia and other sites for product information with python web scrapers.
Using Python's Pandas and Seaborn libraries, the raw data is filtered using NLP
methods. To train our datasets for named entity recognition, we utilized
Spacey's custom NER model, Amazon Comprehend Custom NER. A tensorflow
sequential model was deployed with parameter tweaking for sentiment analysis.
Meanwhile, we used the Google Cloud Translation API to estimate the gender of
the reviewers using the BanglaLinga library. In this article, we use natural
language processing (NLP) approaches and several machine learning models to
identify the most in-demand items and services in the Bangladeshi market. Our
model has an accuracy of 87.99% in Spacy Custom Named Entity recognition,
95.51% in Amazon Comprehend Custom NER, and 87.02% in the Sequential model for
demand analysis. After Spacy's study, we were able to manage 80% of mistakes
related to misspelled words using a mix of Levenshtein distance and ratio

The cosmic microwave background (CMB), carrying the inhomogeneous information
of the very early universe, is of great significance for understanding the
origin and evolution of our universe. However, observational CMB maps contain
serious foreground contaminations from several sources, such as galactic
synchrotron and thermal dust emissions. Here, we build a deep convolutional
neural network (CNN) to recover the tiny CMB signal from various huge
foreground contaminations. Focusing on the CMB temperature fluctuations, we
find that the CNN model can successfully recover the CMB temperature maps with
high accuracy, and that the deviation of the recovered power spectrum $C_\ell$
is smaller than the cosmic variance at $\ell>10$. We then apply this method to
the current Planck observation, and find that the recovered CMB is quite
consistent with that disclosed by the Planck collaboration, which indicates
that the CNN method can provide a promising approach to the component
separation of CMB observations. Furthermore, we test the CNN method with
simulated CMB polarization maps based on the CMB-S4 experiment. The result
shows that both the EE and BB power spectra can be recovered with high
accuracy. Therefore, this method will be helpful for the detection of
primordial gravitational waves in current and future CMB experiments. The CNN
is designed to analyze two-dimensional images, thus this method is not only
able to process full-sky maps, but also partial-sky maps. Therefore, it can
also be used for other similar experiments, such as radio surveys like the
Square Kilometer Array.

In this paper, we propose a framework for achieving long-term fair sequential
decision making. By conducting both the hard and soft interventions, we propose
to take path-specific effects on the time-lagged causal graph as a quantitative
tool for measuring long-term fairness. The problem of fair sequential decision
making is then formulated as a constrained optimization problem with the
utility as the objective and the long-term and short-term fairness as
constraints. We show that such an optimization problem can be converted to a
performative risk optimization. Finally, repeated risk minimization (RRM) is
used for model training, and the convergence of RRM is theoretically analyzed.
The empirical evaluation shows the effectiveness of the proposed algorithm on
synthetic and semi-synthetic temporal datasets.

In this paper we introduce a new consistency-based approach for defining and
solving nonnegative/positive matrix and tensor completion problems. The novelty
of the framework is that instead of artificially making the problem well-posed
in the form of an application-arbitrary optimization problem, e.g., minimizing
a bulk structural measure such as rank or norm, we show that a single
property/constraint -- preserving unit-scale consistency -- guarantees both
existence of a solution and, under relatively weak support assumptions,
uniqueness. The framework and solution algorithms also generalize directly to
tensors of arbitrary dimension while maintaining computational complexity that
is linear in problem size for fixed dimension d. In the context of recommender
system (RS) applications, we prove that two reasonable properties that should
be expected to hold for any solution to the RS problem are sufficient to permit
uniqueness guarantees to be established within our framework. This is
remarkable because it obviates the need for heuristic-based statistical or AI
methods despite what appear to be distinctly human/subjective variables at the
heart of the problem. Key theoretical contributions include a general
unit-consistent tensor-completion framework with proofs of its properties,
e.g., consensus-order and fairness, and algorithms with optimal runtime and
space complexities, e.g., O(1) term-completion with preprocessing complexity
that is linear in the number of known terms of the matrix/tensor. From a
practical perspective, the seamless ability of the framework to generalize to
exploit high-dimensional structural relationships among key state variables,
e.g., user and product attributes, offers a means for extracting significantly
more information than is possible for alternative methods that cannot
generalize beyond direct user-product relationships.

Agricultural datasets for crop row detection are often bound by their limited
number of images. This restricts the researchers from developing deep learning
based models for precision agricultural tasks involving crop row detection. We
suggest the utilization of small real-world datasets along with additional data
generated by simulations to yield similar crop row detection performance as
that of a model trained with a large real world dataset. Our method could reach
the performance of a deep learning based crop row detection model trained with
real-world data by using 60% less labelled real-world data. Our model performed
well against field variations such as shadows, sunlight and grow stages. We
introduce an automated pipeline to generate labelled images for crop row
detection in simulation domain. An extensive comparison is done to analyze the
contribution of simulated data towards reaching robust crop row detection in
various real-world field scenarios.

Marking and feedback are essential features of teaching and learning, across
the overwhelming majority of educational settings and contexts. However, it can
take a great deal of time and effort for teachers to mark assessments, and to
provide useful feedback to the students. Furthermore, it also creates a
significant cognitive load on the assessors, especially in ensuring fairness
and equity. Therefore, an alternative approach to marking called comparative
judgement (CJ) has been proposed in the educational space. Inspired by the law
of comparative judgment (LCJ). This pairwise comparison for as many pairs as
possible can then be used to rank all submissions. Studies suggest that CJ is
highly reliable and accurate while making it quick for the teachers.
Alternative studies have questioned this claim suggesting that the process can
increase bias in the results as the same submission is shown many times to an
assessor for increasing reliability. Additionally, studies have also found that
CJ can result in the overall marking process taking longer than a more
traditional method of marking as information about many pairs must be
collected.
  In this paper, we investigate Elo, which has been extensively used in rating
players in zero-sum games such as chess. We experimented on a large-scale
Twitter dataset on the topic of a recent major UK political event ("Brexit",
the UK's political exit from the European Union) to ask users which tweet they
found funnier between a pair selected from ten tweets. Our analysis of the data
reveals that the Elo rating is statistically significantly similar to the CJ
ranking with a Kendall's tau score of 0.96 and a p-value of 1.5x10^(-5). We
finish with an informed discussion regarding the potential wider application of
this approach to a range of educational contexts.

The seminal Fast Johnson-Lindenstrauss (Fast JL) transform by Ailon and
Chazelle (SICOMP'09) embeds a set of $n$ points in $d$-dimensional Euclidean
space into optimal $k=O(\varepsilon^{-2} \ln n)$ dimensions, while preserving
all pairwise distances to within a factor $(1 \pm \varepsilon)$. The Fast JL
transform supports computing the embedding of a data point in $O(d \ln d +k
\ln^2 n)$ time, where the $d \ln d$ term comes from multiplication with a $d
\times d$ Hadamard matrix and the $k \ln^2 n$ term comes from multiplication
with a sparse $k \times d$ matrix. Despite the Fast JL transform being more
than a decade old, it is one of the fastest dimensionality reduction techniques
for many tradeoffs between $\varepsilon, d$ and $n$.
  In this work, we give a surprising new analysis of the Fast JL transform,
showing that the $k \ln^2 n$ term in the embedding time can be improved to $(k
\ln^2 n)/\alpha$ for an $\alpha =
\Omega(\min\{\varepsilon^{-1}\ln(1/\varepsilon), \ln n\})$. The improvement
follows by using an even sparser matrix. We also complement our improved
analysis with a lower bound showing that our new analysis is in fact tight.

We combined descriptor-based analytical models for stiffness-matrix and
elastic-moduli with mean-field methods to accelerate assessment of
technologically useful properties of high-entropy alloys, such as strength and
ductility. Model training for elastic properties uses Sure-Independence
Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal
analytical model, constructed with meaningful atomic features to predict target
properties. Computationally inexpensive analytical descriptors were trained
using a database of the elastic properties determined from density functional
theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The
optimal Elastic-SISSO models, extracted from an exponentially large feature
space, give an extremely accurate prediction of target properties, similar to
or better than other models, with some verified from existing experiments. We
also show that electronegativity variance and elastic-moduli can directly
predict trends in ductility and yield strength of refractory HEAs, and reveals
promising alloy concentration regions.

We present the Geometric-Wave Acoustic (GWA) dataset, a large-scale audio
dataset of about 2 million synthetic room impulse responses (IRs) and their
corresponding detailed geometric and simulation configurations. Our dataset
samples acoustic environments from over 6.8K high-quality diverse and
professionally designed houses represented as semantically labeled 3D meshes.
We also present a novel real-world acoustic materials assignment scheme based
on semantic matching that uses a sentence transformer model. We compute
high-quality impulse responses corresponding to accurate low-frequency and
high-frequency wave effects by automatically calibrating geometric acoustic
ray-tracing with a finite-difference time-domain wave solver. We demonstrate
the higher accuracy of our IRs by comparing with recorded IRs from complex
real-world environments. Moreover, we highlight the benefits of GWA on audio
deep learning tasks such as automated speech recognition, speech enhancement,
and speech separation. This dataset is the first data with accurate wave
acoustic simulations in complex scenes. Codes and data are available at
https://gamma.umd.edu/pro/sound/gwa.

This paper proposes a self-supervised objective for learning representations
that localize objects under occlusion - a property known as object permanence.
A central question is the choice of learning signal in cases of total
occlusion. Rather than directly supervising the locations of invisible objects,
we propose a self-supervised objective that requires neither human annotation,
nor assumptions about object dynamics. We show that object permanence can
emerge by optimizing for temporal coherence of memory: we fit a Markov walk
along a space-time graph of memories, where the states in each time step are
non-Markovian features from a sequence encoder. This leads to a memory
representation that stores occluded objects and predicts their motion, to
better localize them. The resulting model outperforms existing approaches on
several datasets of increasing complexity and realism, despite requiring
minimal supervision, and hence being broadly applicable.

The deep learning revolution incited by the 2012 Alexnet paper has been
transformative for the field of computer vision. Many problems which were
severely limited using classical solutions are now seeing unprecedented
success. The rapid proliferation of deep learning methods has led to a sharp
increase in their use in consumer and embedded applications. One consequence of
consumer and embedded applications is lossy multimedia compression which is
required to engineer the efficient storage and transmission of data in these
real-world scenarios. As such, there has been increased interest in a deep
learning solution for multimedia compression which would allow for higher
compression ratios and increased visual quality.
  The deep learning approach to multimedia compression, so called Learned
Multimedia Compression, involves computing a compressed representation of an
image or video using a deep network for the encoder and the decoder. While
these techniques have enjoyed impressive academic success, their industry
adoption has been essentially non-existent. Classical compression techniques
like JPEG and MPEG are too entrenched in modern computing to be easily
replaced. This dissertation takes an orthogonal approach and leverages deep
learning to improve the compression fidelity of these classical algorithms.
This allows the incredible advances in deep learning to be used for multimedia
compression without threatening the ubiquity of the classical methods.
  The key insight of this work is that methods which are motivated by first
principles, i.e., the underlying engineering decisions that were made when the
compression algorithms were developed, are more effective than general methods.
By encoding prior knowledge into the design of the algorithm, the flexibility,
performance, and/or accuracy are improved at the cost of generality...

We develop a model-free learning algorithm for the infinite-horizon linear
quadratic regulator (LQR) problem. Specifically, (risk) constraints and
structured feedback are considered, in order to reduce the state deviation
while allowing for a sparse communication graph in practice. By reformulating
the dual problem as a nonconvex-concave minimax problem, we adopt the gradient
descent max-oracle (GDmax), and for modelfree setting, the stochastic (S)GDmax
using zero-order policy gradient. By bounding the Lipschitz and smoothness
constants of the LQR cost using specifically defined sublevel sets, we can
design the stepsize and related parameters to establish convergence to a
stationary point (at a high probability). Numerical tests in a networked
microgrid control problem have validated the convergence of our proposed SGDmax
algorithm while demonstrating the effectiveness of risk constraints. The SGDmax
algorithm has attained a satisfactory optimality gap compared to the classical
LQR control, especially for the full feedback case.

Emerging multi-material 3D printing techniques have paved the way for the
rational design of metamaterials with not only complex geometries but also
arbitrary distributions of multiple materials within those geometries. Varying
the spatial distribution of multiple materials gives rise to many interesting
and potentially unique combinations of anisotropic elastic properties. While
the availability of a design approach to cover a large portion of all possible
combinations of elastic properties is interesting in itself, it is even more
important to find the extremely rare designs that lead to highly unusual
combinations of material properties (e.g., double-auxeticity and high elastic
moduli). Here, we used a random distribution of a hard phase and a soft phase
within a regular lattice to study the resulting anisotropic mechanical
properties of the network in general and the abovementioned rare designs in
particular. The primary challenge to take up concerns the huge number of design
parameters and the extreme rarity of such designs. We, therefore, used
computational models and deep learning algorithms to create a mapping from the
space of design parameters to the space of mechanical properties, thereby (i)
reducing the computational time required for evaluating each designand (ii)
making the process of evaluating the different designs highly parallelizable.
Furthermore, we selected ten designs to be fabricated using polyjet
multi-material 3D printing techniques, mechanically tested them, and
characterized their behavior using digital image correlation (DIC, 3 designs)
to validate the accuracy of our computational models. The results of our
simulations show that deep learning-based algorithms can accurately predict the
mechanical properties of the different designs, which match the various
deformation mechanisms observed in the experiments.

Quantum computing promises to enhance machine learning and artificial
intelligence. Different quantum algorithms have been proposed to improve a wide
spectrum of machine learning tasks. Yet, recent theoretical works show that,
similar to traditional classifiers based on deep classical neural networks,
quantum classifiers would suffer from the vulnerability problem: adding tiny
carefully-crafted perturbations to the legitimate original data samples would
facilitate incorrect predictions at a notably high confidence level. This will
pose serious problems for future quantum machine learning applications in
safety and security-critical scenarios. Here, we report the first experimental
demonstration of quantum adversarial learning with programmable superconducting
qubits. We train quantum classifiers, which are built upon variational quantum
circuits consisting of ten transmon qubits featuring average lifetimes of 150
$\mu$s, and average fidelities of simultaneous single- and two-qubit gates
above 99.94% and 99.4% respectively, with both real-life images (e.g., medical
magnetic resonance imaging scans) and quantum data. We demonstrate that these
well-trained classifiers (with testing accuracy up to 99%) can be practically
deceived by small adversarial perturbations, whereas an adversarial training
process would significantly enhance their robustness to such perturbations. Our
results reveal experimentally a crucial vulnerability aspect of quantum
learning systems under adversarial scenarios and demonstrate an effective
defense strategy against adversarial attacks, which provide a valuable guide
for quantum artificial intelligence applications with both near-term and future
quantum devices.

Transformers have recently gained significant attention in the computer
vision community. However, the lack of scalability of self-attention mechanisms
with respect to image size has limited their wide adoption in state-of-the-art
vision backbones. In this paper we introduce an efficient and scalable
attention model we call multi-axis attention, which consists of two aspects:
blocked local and dilated global attention. These design choices allow
global-local spatial interactions on arbitrary input resolutions with only
linear complexity. We also present a new architectural element by effectively
blending our proposed attention model with convolutions, and accordingly
propose a simple hierarchical vision backbone, dubbed MaxViT, by simply
repeating the basic building block over multiple stages. Notably, MaxViT is
able to ''see'' globally throughout the entire network, even in earlier,
high-resolution stages. We demonstrate the effectiveness of our model on a
broad spectrum of vision tasks. On image classification, MaxViT achieves
state-of-the-art performance under various settings: without extra data, MaxViT
attains 86.5% ImageNet-1K top-1 accuracy; with ImageNet-21K pre-training, our
model achieves 88.7% top-1 accuracy. For downstream tasks, MaxViT as a backbone
delivers favorable performance on object detection as well as visual aesthetic
assessment. We also show that our proposed model expresses strong generative
modeling capability on ImageNet, demonstrating the superior potential of MaxViT
blocks as a universal vision module. The source code and trained models will be
available at https://github.com/google-research/maxvit.

Our environment is filled with rich and dynamic acoustic information. When we
walk into a cathedral, the reverberations as much as appearance inform us of
the sanctuary's wide open space. Similarly, as an object moves around us, we
expect the sound emitted to also exhibit this movement. While recent advances
in learned implicit functions have led to increasingly higher quality
representations of the visual world, there have not been commensurate advances
in learning spatial auditory representations. To address this gap, we introduce
Neural Acoustic Fields (NAFs), an implicit representation that captures how
sounds propagate in a physical scene. By modeling acoustic propagation in a
scene as a linear time-invariant system, NAFs learn to continuously map all
emitter and listener location pairs to a neural impulse response function that
can then be applied to arbitrary sounds. We demonstrate that the continuous
nature of NAFs enables us to render spatial acoustics for a listener at an
arbitrary location, and can predict sound propagation at novel locations. We
further show that the representation learned by NAFs can help improve visual
learning with sparse views. Finally, we show that a representation informative
of scene structure emerges during the learning of NAFs.

This paper proposes a do-it-all neural model of human hands, named LISA. The
model can capture accurate hand shape and appearance, generalize to arbitrary
hand subjects, provide dense surface correspondences, be reconstructed from
images in the wild and easily animated. We train LISA by minimizing the shape
and appearance losses on a large set of multi-view RGB image sequences
annotated with coarse 3D poses of the hand skeleton. For a 3D point in the hand
local coordinate, our model predicts the color and the signed distance with
respect to each hand bone independently, and then combines the per-bone
predictions using predicted skinning weights. The shape, color and pose
representations are disentangled by design, allowing to estimate or animate
only selected parameters. We experimentally demonstrate that LISA can
accurately reconstruct a dynamic hand from monocular or multi-view sequences,
achieving a noticeably higher quality of reconstructed hand shapes compared to
baseline approaches. Project page:
https://www.iri.upc.edu/people/ecorona/lisa/.

We propose to forecast future hand-object interactions given an egocentric
video. Instead of predicting action labels or pixels, we directly predict the
hand motion trajectory and the future contact points on the next active object
(i.e., interaction hotspots). This relatively low-dimensional representation
provides a concrete description of future interactions. To tackle this task, we
first provide an automatic way to collect trajectory and hotspots labels on
large-scale data. We then use this data to train an Object-Centric Transformer
(OCT) model for prediction. Our model performs hand and object interaction
reasoning via the self-attention mechanism in Transformers. OCT also provides a
probabilistic framework to sample the future trajectory and hotspots to handle
uncertainty in prediction. We perform experiments on the Epic-Kitchens-55,
Epic-Kitchens-100, and EGTEA Gaze+ datasets, and show that OCT significantly
outperforms state-of-the-art approaches by a large margin. Project page is
available at https://stevenlsw.github.io/hoi-forecast .

Large Vision & Language models pretrained on web-scale data provide
representations that are invaluable for numerous V&L problems. However, it is
unclear how they can be used for reasoning about user-specific visual concepts
in unstructured language. This problem arises in multiple domains, from
personalized image retrieval to personalized interaction with smart devices. We
introduce a new learning setup called Personalized Vision & Language (PerVL)
with two new benchmark datasets for retrieving and segmenting user-specific
"personalized" concepts "in the wild". In PerVL, one should learn personalized
concepts (1) independently of the downstream task (2) allowing a pretrained
model to reason about them with free language, and (3) does not require
personalized negative examples. We propose an architecture for solving PerVL
that operates by extending the input vocabulary of a pretrained model with new
word embeddings for the new personalized concepts. The model can then reason
about them by simply using them in a sentence. We demonstrate that our approach
learns personalized visual concepts from a few examples and can effectively
apply them in image retrieval and semantic segmentation using rich textual
queries.

Most modern video recognition models are designed to operate on short video
clips (e.g., 5-10s in length). Thus, it is challenging to apply such models to
long movie understanding tasks, which typically require sophisticated
long-range temporal reasoning. The recently introduced video transformers
partially address this issue by using long-range temporal self-attention.
However, due to the quadratic cost of self-attention, such models are often
costly and impractical to use. Instead, we propose ViS4mer, an efficient
long-range video model that combines the strengths of self-attention and the
recently introduced structured state-space sequence (S4) layer. Our model uses
a standard Transformer encoder for short-range spatiotemporal feature
extraction, and a multi-scale temporal S4 decoder for subsequent long-range
temporal reasoning. By progressively reducing the spatiotemporal feature
resolution and channel dimension at each decoder layer, ViS4mer learns complex
long-range spatiotemporal dependencies in a video. Furthermore, ViS4mer is
$2.63\times$ faster and requires $8\times$ less GPU memory than the
corresponding pure self-attention-based model. Additionally, ViS4mer achieves
state-of-the-art results in $6$ out of $9$ long-form movie video classification
tasks on the Long Video Understanding (LVU) benchmark. Furthermore, we show
that our approach successfully generalizes to other domains, achieving
competitive results on the Breakfast and the COIN procedural activity datasets.
The code is publicly available at: https://github.com/md-mohaiminul/ViS4mer.

Large language models can encode a wealth of semantic knowledge about the
world. Such knowledge could be extremely useful to robots aiming to act upon
high-level, temporally extended instructions expressed in natural language.
However, a significant weakness of language models is that they lack real-world
experience, which makes it difficult to leverage them for decision making
within a given embodiment. For example, asking a language model to describe how
to clean a spill might result in a reasonable narrative, but it may not be
applicable to a particular agent, such as a robot, that needs to perform this
task in a particular environment. We propose to provide real-world grounding by
means of pretrained skills, which are used to constrain the model to propose
natural language actions that are both feasible and contextually appropriate.
The robot can act as the language model's "hands and eyes," while the language
model supplies high-level semantic knowledge about the task. We show how
low-level skills can be combined with large language models so that the
language model provides high-level knowledge about the procedures for
performing complex and temporally-extended instructions, while value functions
associated with these skills provide the grounding necessary to connect this
knowledge to a particular physical environment. We evaluate our method on a
number of real-world robotic tasks, where we show the need for real-world
grounding and that this approach is capable of completing long-horizon,
abstract, natural language instructions on a mobile manipulator. The project's
website and the video can be found at https://say-can.github.io/.

The volume of malware and the number of attacks in IoT devices are rising
everyday, which encourages security professionals to continually enhance their
malware analysis tools. Researchers in the field of cyber security have
extensively explored the usage of sophisticated analytics and the efficiency of
malware detection. With the introduction of new malware kinds and attack
routes, security experts confront considerable challenges in developing
efficient malware detection and analysis solutions. In this paper, a different
view of malware analysis is considered and the risk level of each sample
feature is computed, and based on that the risk level of that sample is
calculated. In this way, a criterion is introduced that is used together with
accuracy and FPR criteria for malware analysis in IoT environment. In this
paper, three malware detection methods based on visualization techniques called
the clustering approach, the probabilistic approach, and the deep learning
approach are proposed. Then, in addition to the usual machine learning criteria
namely accuracy and FPR, a proposed criterion based on the risk of samples has
also been used for comparison, with the results showing that the deep learning
approach performed better in detecting malware

Previous research on EMA data of mental disorders was mainly focused on
multivariate regression-based approaches modeling each individual separately.
This paper goes a step further towards exploring the use of non-linear
interpretable machine learning (ML) models in classification problems. ML
models can enhance the ability to accurately predict the occurrence of
different behaviors by recognizing complicated patterns between variables in
data. To evaluate this, the performance of various ensembles of trees are
compared to linear models using imbalanced synthetic and real-world datasets.
After examining the distributions of AUC scores in all cases, non-linear models
appear to be superior to baseline linear models. Moreover, apart from
personalized approaches, group-level prediction models are also likely to offer
an enhanced performance. According to this, two different nomothetic approaches
to integrate data of more than one individuals are examined, one using directly
all data during training and one based on knowledge distillation.
Interestingly, it is observed that in one of the two real-world datasets,
knowledge distillation method achieves improved AUC scores (mean relative
change of +17\% compared to personalized) showing how it can benefit EMA data
classification and performance.

The applications of traditional statistical feature selection methods to
high-dimension, low sample-size data often struggle and encounter challenging
problems, such as overfitting, curse of dimensionality, computational
infeasibility, and strong model assumption. In this paper, we propose a novel
two-step nonparametric approach called Deep Feature Screening (DeepFS) that can
overcome these problems and identify significant features with high precision
for ultra high-dimensional, low-sample-size data. This approach first extracts
a low-dimensional representation of input data and then applies feature
screening based on multivariate rank distance correlation recently developed by
Deb and Sen (2021). This approach combines the strengths of both deep neural
networks and feature screening, and thereby has the following appealing
features in addition to its ability of handling ultra high-dimensional data
with small number of samples: (1) it is model free and distribution free; (2)
it can be used for both supervised and unsupervised feature selection; and (3)
it is capable of recovering the original input data. The superiority of DeepFS
is demonstrated via extensive simulation studies and real data analyses.

We present an end-to-end reconstruction algorithm to build particle
candidates from detector hits in next-generation granular calorimeters similar
to that foreseen for the high-luminosity upgrade of the CMS detector. The
algorithm exploits a distance-weighted graph neural network, trained with
object condensation, a graph segmentation technique. Through a single-shot
approach, the reconstruction task is paired with energy regression. We describe
the reconstruction performance in terms of efficiency as well as in terms of
energy resolution. In addition, we show the jet reconstruction performance of
our method and discuss its inference computational cost. To our knowledge, this
work is the first-ever example of single-shot calorimetric reconstruction of
${\cal O}(1000)$ particles in high-luminosity conditions with 200 pileup.

We propose a pre-training strategy called Multi-modal Multi-task Masked
Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two
key aspects: I) it can optionally accept additional modalities of information
in the input besides the RGB image (hence "multi-modal"), and II) its training
objective accordingly includes predicting multiple outputs besides the RGB
image (hence "multi-task").
  We make use of masking (across image patches and input modalities) to make
training MultiMAE tractable as well as to ensure cross-modality predictive
coding is indeed learned by the network. We show this pre-training strategy
leads to a flexible, simple, and efficient framework with improved transfer
results to downstream tasks. In particular, the same exact pre-trained network
can be flexibly used when additional information besides RGB images is
available or when no information other than RGB is available - in all
configurations yielding competitive to or significantly better results than the
baselines. To avoid needing training datasets with multiple modalities and
tasks, we train MultiMAE entirely using pseudo labeling, which makes the
framework widely applicable to any RGB dataset.
  The experiments are performed on multiple transfer tasks (image
classification, semantic segmentation, depth estimation) and datasets
(ImageNet, ADE20K, Taskonomy, Hypersim, NYUv2). The results show an
intriguingly impressive capability by the model in cross-modal/task predictive
coding and transfer.

Collecting speech data is an important step in training speech recognition
systems and other speech-based machine learning models. However, the issue of
privacy protection is an increasing concern that must be addressed. The current
study investigates the use of voice conversion as a method for anonymizing
voices. In particular, we train several voice conversion models using
self-supervised speech representations including Wav2Vec2.0, Hubert and
UniSpeech. Converted voices retain a low word error rate within 1% of the
original voice. Equal error rate increases from 1.52% to 46.24% on the
LibriSpeech test set and from 3.75% to 45.84% on speakers from the VCTK corpus
which signifies degraded performance on speaker verification. Lastly, we
conduct experiments on dysarthric speech data to show that speech features
relevant to articulation, prosody, phonation and phonology can be extracted
from anonymized voices for discriminating between healthy and pathological
speech.

Convolutional neural networks have enabled significant improvements in
medical image-based diagnosis. It is, however, increasingly clear that these
models are susceptible to performance degradation when facing spurious
correlations and dataset shift, leading, e.g., to underperformance on
underrepresented patient groups. In this paper, we compare two classification
schemes on the ADNI MRI dataset: a simple logistic regression model using
manually selected volumetric features, and a convolutional neural network
trained on 3D MRI data. We assess the robustness of the trained models in the
face of varying dataset splits, training set sex composition, and stage of
disease. In contrast to earlier work in other imaging modalities, we do not
observe a clear pattern of improved model performance for the majority group in
the training dataset. Instead, while logistic regression is fully robust to
dataset composition, we find that CNN performance is generally improved for
both male and female subjects when including more female subjects in the
training dataset. We hypothesize that this might be due to inherent differences
in the pathology of the two sexes. Moreover, in our analysis, the logistic
regression model outperforms the 3D CNN, emphasizing the utility of manual
feature specification based on prior knowledge, and the need for more robust
automatic feature selection.

Synthesis of ergodic, stationary visual patterns is widely applicable in
texturing, shape modeling, and digital content creation. The wide applicability
of this technique thus requires the pattern synthesis approaches to be
scalable, diverse, and authentic. In this paper, we propose an exemplar-based
visual pattern synthesis framework that aims to model the inner statistics of
visual patterns and generate new, versatile patterns that meet the
aforementioned requirements. To this end, we propose an implicit network based
on generative adversarial network (GAN) and periodic encoding, thus calling our
network the Implicit Periodic Field Network (IPFN). The design of IPFN ensures
scalability: the implicit formulation directly maps the input coordinates to
features, which enables synthesis of arbitrary size and is computationally
efficient for 3D shape synthesis. Learning with a periodic encoding scheme
encourages diversity: the network is constrained to model the inner statistics
of the exemplar based on spatial latent codes in a periodic field. Coupled with
continuously designed GAN training procedures, IPFN is shown to synthesize
tileable patterns with smooth transitions and local variations. Last but not
least, thanks to both the adversarial training technique and the encoded
Fourier features, IPFN learns high-frequency functions that produce authentic,
high-quality results. To validate our approach, we present novel experimental
results on various applications in 2D texture synthesis and 3D shape synthesis.

Spike-and-slab priors are commonly used for Bayesian variable selection, due
to their interpretability and favorable statistical properties. However,
existing samplers for spike-and-slab posteriors incur prohibitive computational
costs when the number of variables is large. In this article, we propose
Scalable Spike-and-Slab ($S^3$), a scalable Gibbs sampling implementation for
high-dimensional Bayesian regression with the continuous spike-and-slab prior
of George and McCulloch (1993). For a dataset with $n$ observations and $p$
covariates, $S^3$ has order $\max\{ n^2 p_t, np \}$ computational cost at
iteration $t$ where $p_t$ never exceeds the number of covariates switching
spike-and-slab states between iterations $t$ and $t-1$ of the Markov chain.
This improves upon the order $n^2 p$ per-iteration cost of state-of-the-art
implementations as, typically, $p_t$ is substantially smaller than $p$. We
apply $S^3$ on synthetic and real-world datasets, demonstrating orders of
magnitude speed-ups over existing exact samplers and significant gains in
inferential quality over approximate samplers with comparable cost.

The early detection of drowsiness has become vital to ensure the correct and
safe development of several industries' tasks. Due to the transient mental
state of a human subject between alertness and drowsiness, automated drowsiness
detection is a complex problem to tackle. The electroencephalography signals
allow us to record variations in an individual's brain's electrical potential,
where each of them gives specific information about a subject's mental state.
However, due to this type of signal's nature, its acquisition, in general, is
complex, so it is hard to have a large volume of data to apply techniques of
Deep Learning for processing and classification optimally. Nevertheless,
Capsule Neural Networks are a brand-new Deep Learning algorithm proposed for
work with reduced amounts of data. It is a robust algorithm to handle the
data's hierarchical relationships, which is an essential characteristic for
work with biomedical signals. Therefore, this paper presents a Deep
Learning-based method for drowsiness detection with CapsNet by using a
concatenation of spectrogram images of the electroencephalography signals
channels. The proposed CapsNet model is compared with a Convolutional Neural
Network, which is outperformed by the proposed model, which obtains an average
accuracy of 86,44% and 87,57% of sensitivity against an average accuracy of
75,86% and 79,47% sensitivity for the CNN, showing that CapsNet is more
suitable for this kind of datasets and tasks.

Automated tracking of urban development in areas where construction
information is not available became possible with recent advancements in
machine learning and remote sensing. Unfortunately, these solutions perform
best on high-resolution imagery, which is expensive to acquire and infrequently
available, making it difficult to scale over long time spans and across large
geographies. In this work, we propose a pipeline that leverages a single
high-resolution image and a time series of publicly available low-resolution
images to generate accurate high-resolution time series for object tracking in
urban construction. Our method achieves significant improvement in comparison
to baselines using single image super-resolution, and can assist in extending
the accessibility and scalability of building construction tracking across the
developing world.

In this work, a convergence lemma for function $f$ being finite compositions
of analytic mappings and the maximum operator is proved. The lemma shows that
the set of $\delta$-stationary points near an isolated local minimum point
$x^*$ is shrinking to $x^*$ as $\delta\to 0$. It is a natural extension of the
version for strongly convex $C^1$ functions. However, the correctness of the
lemma is subtle. Analytic mappings are necessary for the lemma in the sense
that replacing it with differentiable or $C^\infty$ mappings makes the lemma
false. The proof is based on stratification theorems of semi-analytic sets by
{\L}ojasiewicz. An extension of this proof presents a geometric
characterization of the set of stationary points of $f$. Finally, a notion of
stability on stationary points, called convergence stability, is proposed. It
asks, under small numerical errors, whether a reasonable convergent
optimization method started near a stationary point should eventually converge
to the same stationary point. The concept of convergence stability becomes
nontrivial qualitatively only when the objective function is both nonsmooth and
nonconvex. Via the convergence lemma, an intuitive equivalent condition for
convergence stability of $f$ is proved. These results together provide a new
geometric perspective to study the problem of "where-to-converge" in nonsmooth
nonconvex optimization.

With the latest advances in deep learning, there has been a lot of focus on
the online learning paradigm due to its relevance in practical settings.
Although many methods have been investigated for optimal learning settings in
scenarios where the data stream is continuous over time, sparse networks
training in such settings have often been overlooked. In this paper, we explore
the problem of training a neural network with a target sparsity in a particular
case of online learning: the anytime learning at macroscale paradigm (ALMA). We
propose a novel way of progressive pruning, referred to as \textit{Anytime
Progressive Pruning} (APP); the proposed approach significantly outperforms the
baseline dense and Anytime OSP models across multiple architectures and
datasets under short, moderate, and long-sequence training. Our method, for
example, shows an improvement in accuracy of $\approx 7\%$ and a reduction in
the generalization gap by $\approx 22\%$, while being $\approx 1/3$ rd the size
of the dense baseline model in few-shot restricted imagenet training. We
further observe interesting nonmonotonic transitions in the generalization gap
in the high number of megabatches-based ALMA. The code and experiment
dashboards can be accessed at
\url{https://github.com/landskape-ai/Progressive-Pruning} and
\url{https://wandb.ai/landskape/APP}, respectively.

Anomaly detection in time series is a complex task that has been widely
studied. In recent years, the ability of unsupervised anomaly detection
algorithms has received much attention. This trend has led researchers to
compare only learning-based methods in their articles, abandoning some more
conventional approaches. As a result, the community in this field has been
encouraged to propose increasingly complex learning-based models mainly based
on deep neural networks. To our knowledge, there are no comparative studies
between conventional, machine learning-based and, deep neural network methods
for the detection of anomalies in multivariate time series. In this work, we
study the anomaly detection performance of sixteen conventional, machine
learning-based and, deep neural network approaches on five real-world open
datasets. By analyzing and comparing the performance of each of the sixteen
methods, we show that no family of methods outperforms the others. Therefore,
we encourage the community to reincorporate the three categories of methods in
the anomaly detection in multivariate time series benchmarks.

Machine Learning (ML) algorithms are increasingly used as surrogate models to
increase the efficiency of stochastic reliability analyses in geotechnical
engineering. This paper presents a highly efficient ML aided reliability
technique that is able to accurately predict the results of a Monte Carlo (MC)
reliability study, and yet performs 500 times faster. A complete MC reliability
analysis on anisotropic heterogeneous slopes consisting of 120,000 simulated
samples is conducted in parallel to the proposed ML aided stochastic technique.
Comparing the results of the complete MC study and the proposed ML aided
technique, the expected errors of the proposed method are realistically
examined. Circumventing the time-consuming computation of factors of safety for
the training datasets, the proposed technique is more efficient than previous
methods. Different ML models, including Random Forest (RF), Support Vector
Machine (SVM) and Artificial Neural Networks (ANN) are presented, optimised and
compared. The effects of the size and type of training and testing datasets are
discussed. The expected errors of the ML predicted probability of failure are
characterised by different levels of soil heterogeneity and anisotropy. Using
only 1% of MC samples to train ML surrogate models, the proposed technique can
accurately predict the probability of failure with mean errors limited to 0.7%.
The proposed technique reduces the computational time required for our study
from 306 days to only 14 hours, providing 500 times higher efficiency.

Random field Monte Carlo (MC) reliability analysis is a robust stochastic
method to determine the probability of failure. This method, however, requires
a large number of numerical simulations demanding high computational costs.
This paper explores the efficiency of different machine learning (ML)
algorithms used as surrogate models trained on a limited number of random field
slope stability simulations in predicting the results of large datasets. The MC
data in this paper require only the examination of failure or non-failure,
circumventing the time-consuming calculation of factors of safety. An extensive
dataset is generated, consisting of 120,000 finite difference MC slope
stability simulations incorporating different levels of soil heterogeneity and
anisotropy. The Bagging Ensemble, Random Forest and Support Vector classifiers
are found to be the superior models for this problem amongst 9 different models
and ensemble classifiers. Trained only on 0.47% of data (500 samples), the ML
model can classify the entire 120,000 samples with an accuracy of %85 and AUC
score of %91. The performance of ML methods in classifying the random field
slope stability results generally reduces with higher anisotropy and
heterogeneity of soil. The ML assisted MC reliability analysis proves a robust
stochastic method where errors in the predicted probability of failure using %5
of MC data is only %0.46 in average. The approach reduced the computational
time from 306 days to less than 6 hours.

Models and games are simplified representations of the world. There are many
different kinds of models, all differing in complexity and which aspect of the
world they allow us to further our understanding of. In this paper we focus on
a specific instance of agent-based models, which uses reinforcement learning
(RL) to train the agent how to act in its environment. Reinforcement learning
agents are usually also Markov processes, which is another type of model that
can be used. We test this reinforcement learning agent in a Colonel Blotto
environment1, and measure its performance against Random agents as its
opponent. We find that the RL agent handily beats a single opponent, and still
performs quite well when the number of opponents are increased. We also analyze
the RL agent and look at what strategies it has arrived by looking at the
actions that it has given the highest and lowest Q-values. Interestingly, the
optimal strategy for playing multiple opponents is almost the complete opposite
of the optimal strategy for playing a single opponent.

Real-life industrial use cases for machine learning oftentimes involve
heterogeneous and dynamic assets, processes and data, resulting in a need to
continuously adapt the learning algorithm accordingly. Industrial transfer
learning offers to lower the effort of such adaptation by allowing the
utilization of previously acquired knowledge in solving new (variants of)
tasks. Being data-driven methods, the development of industrial transfer
learning algorithms naturally requires appropriate datasets for training.
However, open-source datasets suitable for transfer learning training, i.e.
spanning different assets, processes and data (variants), are rare. With the
Stuttgart Open Relay Degradation Dataset (SOReDD) we want to offer such a
dataset. It provides data on the degradation of different electromechanical
relays under different operating conditions, allowing for a large number of
different transfer scenarios. Although such relays themselves are usually
inexpensive standard components, their failure often leads to the failure of a
machine as a whole due to their role as the central power switching element of
a machine. The main cost factor in the event of a relay defect is therefore not
the relay itself, but the reduced machine availability. It is therefore
desirable to predict relay degradation as accurately as possible for specific
applications in order to be able to replace relays in good time and avoid
unplanned machine downtimes. Nevertheless, data-driven failure prediction for
electromechanical relays faces the challenge that relay degradation behavior is
highly dependent on the operating conditions, high-resolution measurement data
on relay degradation behavior is only collected in rare cases, and such data
can then only cover a fraction of the possible operating environments. Relays
are thus representative of many other central standard components in automation
technology.

Structural global parameter identifiability indicates whether one can
determine a parameter's value from given inputs and outputs in the absence of
noise. If a given model has parameters for which there may be infinitely many
values, such parameters are called non-identifiable. We present a procedure for
accelerating a global identifiability query by eliminating algebraically
independent non-identifiable parameters. Our proposed approach significantly
improves performance across different computer algebra frameworks.

Industrial transfer learning increases the adaptability of deep learning
algorithms towards heterogenous and dynamic industrial use cases without high
manual efforts. The appropriate selection of what to transfer can vastly
improve a transfer's results. In this paper, a transfer case selection based
upon clustering is presented. Founded on a survey of clustering algorithms, the
BIRCH algorithm is selected for this purpose. It is evaluated on an industrial
time series dataset from a discrete manufacturing scenario. Results underline
the approaches' applicability caused by its results' reproducibility and
practical indifference to sequence, size and dimensionality of (sub-)datasets
to be clustered sequentially.

Deep-learning-based data-driven forecasting methods have produced impressive
results for traffic forecasting. A major limitation of these methods, however,
is that they provide forecasts without estimates of uncertainty, which are
critical for real-time deployments. We focus on a diffusion convolutional
recurrent neural network (DCRNN), a state-of-the-art method for short-term
traffic forecasting. We develop a scalable deep ensemble approach to quantify
uncertainties for DCRNN. Our approach uses a scalable Bayesian optimization
method to perform hyperparameter optimization, selects a set of high-performing
configurations, fits a generative model to capture the joint distributions of
the hyperparameter configurations, and trains an ensemble of models by sampling
a new set of hyperparameter configurations from the generative model. We
demonstrate the efficacy of the proposed methods by comparing them with other
uncertainty estimation techniques. We show that our generic and scalable
approach outperforms the current state-of-the-art Bayesian and a number of
other commonly used frequentist techniques.

Achieving the expansion of domestic demand and the economic internal
circulation requires balanced and coordinated support from multiple industries
(domains) such as consumption, education, entertainment, engineering
infrastructure, etc., which is indispensable for maintaining economic
development. Metaverse applications may help with this task and can make many
industries more interesting, more efficient, and provide a better user
experience. The first challenge is that metaverse application development
inevitably requires the support of various artificial intelligence (AI)
technologies such as natural language processing (NLP), knowledge graph (KG),
computer vision (CV), and machine learning (ML), etc. However, existing
metaverse application development lacks a lightweight AI technology framework.
This paper proposes a flexible metaverse AI technology framework metaAID that
aims to support language and semantic technologies in the development of
digital twins and virtual humans. The second challenge is that the development
process of metaverse applications involves both technical development tasks and
manual editing work, and often becomes a heavyweight multi-team collaboration
project, not to mention the development of metaverse applications in multiple
industries. Our framework summarizes common AI technologies and application
development templates with common functional modules and interfaces. Based on
this framework, we have designed 5 applications for 3 industries around the
expansion of domestic demand and economic internal circulation. Experimental
results show that our framework can support AI technologies when developing
metaverse applications in different industries.

We approach the graph generation problem from a spectral perspective by first
generating the dominant parts of the graph Laplacian spectrum and then building
a graph matching these eigenvalues and eigenvectors. Spectral conditioning
allows for direct modeling of the global and local graph structure and helps to
overcome the expressivity and mode collapse issues of one-shot graph
generators. Our novel GAN, called SPECTRE, enables the one-shot generation of
much larger graphs than previously possible with one-shot models. SPECTRE
outperforms state-of-the-art deep autoregressive generators in terms of
modeling fidelity, while also avoiding expensive sequential generation and
dependence on node ordering. A case in point, in sizable synthetic and
real-world graphs SPECTRE achieves a 4-to-170 fold improvement over the best
competitor that does not overfit and is 23-to-30 times faster than
autoregressive generators.

A fundamental question in designing lossy data compression schemes is how
well one can do in comparison with the rate-distortion function, which
describes the known theoretical limits of lossy compression. Motivated by the
empirical success of deep neural network (DNN) compressors on large, real-world
data, we investigate methods to estimate the rate-distortion function on such
data, which would allow comparison of DNN compressors with optimality. While
one could use the empirical distribution of the data and apply the
Blahut-Arimoto algorithm, this approach presents several computational
challenges and inaccuracies when the datasets are large and high-dimensional,
such as the case of modern image datasets. Instead, we re-formulate the
rate-distortion objective, and solve the resulting functional optimization
problem using neural networks. We apply the resulting rate-distortion
estimator, called NERD, on popular image datasets, and provide evidence that
NERD can accurately estimate the rate-distortion function. Using our estimate,
we show that the rate-distortion achievable by DNN compressors are within
several bits of the rate-distortion function for real-world datasets.
Additionally, NERD provides access to the rate-distortion achieving channel, as
well as samples from its output marginal. Therefore, using recent results in
reverse channel coding, we describe how NERD can be used to construct an
operational one-shot lossy compression scheme with guarantees on the achievable
rate and distortion. Experimental results demonstrate competitive performance
with DNN compressors.

Inspired by the cognitive science theory, we explicitly model an agent with
both semantic and episodic memory systems, and show that it is better than
having just one of the two memory systems. In order to show this, we have
designed and released our own challenging environment, "the Room", compatible
with OpenAI Gym, where an agent has to properly learn how to encode, store, and
retrieve memories to maximize its rewards. The Room environment allows for a
hybrid intelligence setup where machines and humans can collaborate. We show
that two agents collaborating with each other results in better performance
than one agent acting alone. We have open-sourced our code and models at
https://github.com/tae898/explicit-memory.

In light of the NIMH's Research Domain Criteria (RDoC), the advent of
functional neuroimaging, novel technologies and methods provide new
opportunities to develop precise and personalized prognosis and diagnosis of
mental disorders. Machine learning (ML) and artificial intelligence (AI)
technologies are playing an increasingly critical role in the new era of
precision psychiatry. Combining ML/AI with neuromodulation technologies can
potentially provide explainable solutions in clinical practice and effective
therapeutic treatment. Advanced wearable and mobile technologies also call for
the new role of ML/AI for digital phenotyping in mobile mental health. In this
review, we provide a comprehensive review of the ML methodologies and
applications by combining neuroimaging, neuromodulation, and advanced mobile
technologies in psychiatry practice. Additionally, we review the role of ML in
molecular phenotyping and cross-species biomarker identification in precision
psychiatry. We further discuss explainable AI (XAI) and causality testing in a
closed-human-in-the-loop manner, and highlight the ML potential in multimedia
information extraction and multimodal data fusion. Finally, we discuss
conceptual and practical challenges in precision psychiatry and highlight ML
opportunities in future research.

Recent years have witnessed the rapid growth of federated learning (FL), an
emerging privacy-aware machine learning paradigm that allows collaborative
learning over isolated datasets distributed across multiple participants. The
salient feature of FL is that the participants can keep their private datasets
local and only share model updates. Very recently, some research efforts have
been initiated to explore the applicability of FL for matrix factorization
(MF), a prevalent method used in modern recommendation systems and services. It
has been shown that sharing the gradient updates in federated MF entails
privacy risks on revealing users' personal ratings, posing a demand for
protecting the shared gradients. Prior art is limited in that they incur
notable accuracy loss, or rely on heavy cryptosystem, with a weak threat model
assumed. In this paper, we propose VPFedMF, a new design aimed at
privacy-preserving and verifiable federated MF. VPFedMF provides guarantees on
the confidentiality of individual gradient updates through lightweight and
secure aggregation. Moreover, VPFedMF ambitiously and newly supports
correctness verification of the aggregation results produced by the
coordinating server in federated MF. Experiments on a real-world movie rating
dataset demonstrate the practical performance of VPFedMF in terms of
computation, communication, and accuracy.

Deep learning approaches achieve prominent success in 3D semantic
segmentation. However, collecting densely annotated real-world 3D datasets is
extremely time-consuming and expensive. Training models on synthetic data and
generalizing on real-world scenarios becomes an appealing alternative, but
unfortunately suffers from notorious domain shifts. In this work, we propose a
Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and
context gaps caused by different sensing mechanisms and layout placements
across domains. Our DODA encompasses virtual scan simulation to imitate
real-world point cloud patterns and tail-aware cuboid mixing to alleviate the
interior context gap with a cuboid-based intermediate domain. The first
unsupervised sim-to-real adaptation benchmark on 3D indoor semantic
segmentation is also built on 3D-FRONT, ScanNet and S3DIS along with 7 popular
Unsupervised Domain Adaptation (UDA) methods. Our DODA surpasses existing UDA
approaches by over 13% on both 3D-FRONT -> ScanNet and 3D-FRONT -> S3DIS. Code
is available at https://github.com/CVMI-Lab/DODA.

In this letter, we study the energy efficiency (EE) optimisation of unmanned
aerial vehicles (UAVs) providing wireless coverage to static and mobile ground
users. Recent multi-agent reinforcement learning approaches optimise the
system's EE using a 2D trajectory design, neglecting interference from nearby
UAV cells. We aim to maximise the system's EE by jointly optimising each UAV's
3D trajectory, number of connected users, and the energy consumed, while
accounting for interference. Thus, we propose a cooperative Multi-Agent
Decentralised Double Deep Q-Network (MAD-DDQN) approach. Our approach
outperforms existing baselines in terms of EE by as much as 55 -- 80%.

By automatic detection and identification of stuttering, speech pathologists
can track the progression of disfluencies of persons who stutter (PWS). In this
paper, we investigate the impact of multi-task (MTL) and adversarial learning
(ADV) to learn robust stutter features. This is the first-ever preliminary
study where MTL and ADV have been employed in stuttering identification (SI).
We evaluate our system on the SEP-28k stuttering dataset consisting of 20 hours
(approx) of data from 385 podcasts. Our methods show promising results and
outperform the baseline in various disfluency classes. We achieve up to 10%,
6.78%, and 2% improvement in repetitions, blocks, and interjections
respectively over the baseline.

Geomagnetic storms, disturbances of Earth's magnetosphere caused by masses of
charged particles being emitted from the Sun, are an uncontrollable threat to
modern technology. Notably, they have the potential to damage satellites and
cause instability in power grids on Earth, among other disasters. They result
from high sun activity, which are induced from cool areas on the Sun known as
sunspots. Forecasting the storms to prevent disasters requires an understanding
of how and when they will occur. However, current prediction methods at the
National Oceanic and Atmospheric Administration (NOAA) are limited in that they
depend on expensive solar wind spacecraft and a global-scale magnetometer
sensor network. In this paper, we introduce a novel machine learning and
computer vision approach to accurately forecast geomagnetic storms without the
need of such costly physical measurements. Our approach extracts features from
images of the Sun to establish correlations between sunspots and geomagnetic
storm classification and is competitive with NOAA's predictions. Indeed, our
prediction achieves a 76% storm classification accuracy. This paper serves as
an existence proof that machine learning and computer vision techniques provide
an effective means for augmenting and improving existing geomagnetic storm
forecasting methods.

Hateful meme detection is a new multimodal task that has gained significant
traction in academic and industry research communities. Recently, researchers
have applied pre-trained visual-linguistic models to perform the multimodal
classification task, and some of these solutions have yielded promising
results. However, what these visual-linguistic models learn for the hateful
meme classification task remains unclear. For instance, it is unclear if these
models are able to capture the derogatory or slurs references in multimodality
(i.e., image and text) of the hateful memes. To fill this research gap, this
paper propose three research questions to improve our understanding of these
visual-linguistic models performing the hateful meme classification task. We
found that the image modality contributes more to the hateful meme
classification task, and the visual-linguistic models are able to perform
visual-text slurs grounding to a certain extent. Our error analysis also shows
that the visual-linguistic models have acquired biases, which resulted in
false-positive predictions.

Robust visual recognition under adverse weather conditions is of great
importance in real-world applications. In this context, we propose a new method
for learning semantic segmentation models robust against fog. Its key idea is
to consider the fog condition of an image as its style and close the gap
between images with different fog conditions in neural style spaces of a
segmentation model. In particular, since the neural style of an image is in
general affected by other factors as well as fog, we introduce a fog-pass
filter module that learns to extract a fog-relevant factor from the style.
Optimizing the fog-pass filter and the segmentation model alternately gradually
closes the style gap between different fog conditions and allows to learn
fog-invariant features in consequence. Our method substantially outperforms
previous work on three real foggy image datasets. Moreover, it improves
performance on both foggy and clear weather images, while existing methods
often degrade performance on clear scenes.

Recently, RGBD-based category-level 6D object pose estimation has achieved
promising improvement in performance, however, the requirement of depth
information prohibits broader applications. In order to relieve this problem,
this paper proposes a novel approach named Object Level Depth reconstruction
Network (OLD-Net) taking only RGB images as input for category-level 6D object
pose estimation. We propose to directly predict object-level depth from a
monocular RGB image by deforming the category-level shape prior into
object-level depth and the canonical NOCS representation. Two novel modules
named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth
Reconstruction (SDDR) module are introduced to learn high fidelity object-level
depth and delicate shape representations. At last, the 6D object pose is solved
by aligning the predicted canonical representation with the back-projected
object-level depth. Extensive experiments on the challenging CAMERA25 and
REAL275 datasets indicate that our model, though simple, achieves

In this paper we revisit the problem of differentially private empirical risk
minimization (DP-ERM) and differentially private stochastic convex optimization
(DP-SCO). We show that a well-studied continuous time algorithm from
statistical physics, called Langevin diffusion (LD), simultaneously provides
optimal privacy/utility trade-offs for both DP-ERM and DP-SCO, under
$\epsilon$-DP, and $(\epsilon,\delta)$-DP both for convex and strongly convex
loss functions. We provide new time and dimension independent uniform stability
properties of LD, using with we provide the corresponding optimal excess
population risk guarantees for $\epsilon$-DP. An important attribute of our
DP-SCO guarantees for $\epsilon$-DP is that they match the non-private optimal
bounds as $\epsilon\to\infty$.
  Along the way, we provide various technical tools, which can be of
independent interest: i) A new R\'enyi divergence bound for LD, when run on
loss functions over two neighboring data sets, ii) Excess empirical risk bounds
for last-iterate LD, analogous to that of Shamir and Zhang for noisy stochastic
gradient descent (SGD), and iii) A two phase excess risk analysis of LD, where
the first phase is when the diffusion has not converged in any reasonable sense
to a stationary distribution, and in the second phase when the diffusion has
converged to a variant of Gibbs distribution. Our universality results
crucially rely on the dynamics of LD. When it has converged to a stationary
distribution, we obtain the optimal bounds under $\epsilon$-DP. When it is run
only for a very short time $\propto 1/p$, we obtain the optimal bounds under
$(\epsilon,\delta)$-DP. Here, $p$ is the dimensionality of the model space.

We propose Learned Path Ranking (LPR), a method that accepts an end-effector
goal pose, and learns to rank a set of goal-reaching paths generated from an
array of path generating methods, including: path planning, Bezier curve
sampling, and a learned policy. The core idea being that each of the path
generation modules will be useful in different tasks, or at different stages in
a task. When LPR is added as an extension to C2F-ARM, our new system,
C2F-ARM+LPR, retains the sample efficiency of its predecessor, while also being
able to accomplish a larger set of tasks; in particular, tasks that require
very specific motions (e.g. opening toilet seat) that need to be inferred from
both demonstrations and exploration data. In addition to benchmarking our
approach across 16 RLBench tasks, we also learn real-world tasks, tabula rasa,
in 10-15 minutes, with only 3 demonstrations.

Deep models are highly susceptible to adversarial attacks. Such attacks are
carefully crafted imperceptible noises that can fool the network and can cause
severe consequences when deployed. To encounter them, the model requires
training data for adversarial training or explicit regularization-based
techniques. However, privacy has become an important concern, restricting
access to only trained models but not the training data (e.g. biometric data).
Also, data curation is expensive and companies may have proprietary rights over
it. To handle such situations, we propose a completely novel problem of
'test-time adversarial defense in absence of training data and even their
statistics'. We solve it in two stages: a) detection and b) correction of
adversarial samples. Our adversarial sample detection framework is initially
trained on arbitrary data and is subsequently adapted to the unlabelled test
data through unsupervised domain adaptation. We further correct the predictions
on detected adversarial samples by transforming them in Fourier domain and
obtaining their low frequency component at our proposed suitable radius for
model prediction. We demonstrate the efficacy of our proposed technique via
extensive experiments against several adversarial attacks and for different
model architectures and datasets. For a non-robust Resnet-18 model pre-trained
on CIFAR-10, our detection method correctly identifies 91.42% adversaries.
Also, we significantly improve the adversarial accuracy from 0% to 37.37% with
a minimal drop of 0.02% in clean accuracy on state-of-the-art 'Auto Attack'
without having to retrain the model.

Studies on the automatic processing of 3D human pose data have flourished in
the recent past. In this paper, we are interested in the generation of
plausible and diverse future human poses following an observed 3D pose
sequence. Current methods address this problem by injecting random variables
from a single latent space into a deterministic motion prediction framework,
which precludes the inherent multi-modality in human motion generation. In
addition, previous works rarely explore the use of attention to select which
frames are to be used to inform the generation process up to our knowledge. To
overcome these limitations, we propose Hierarchical Transformer Dynamical
Variational Autoencoder, HiT-DVAE, which implements auto-regressive generation
with transformer-like attention mechanisms. HiT-DVAE simultaneously learns the
evolution of data and latent space distribution with time correlated
probabilistic dependencies, thus enabling the generative model to learn a more
complex and time-varying latent space as well as diverse and realistic human
motions. Furthermore, the auto-regressive generation brings more flexibility on
observation and prediction, i.e. one can have any length of observation and
predict arbitrary large sequences of poses with a single pre-trained model. We
evaluate the proposed method on HumanEva-I and Human3.6M with various
evaluation methods, and outperform the state-of-the-art methods on most of the
metrics.

The adoption of advanced deep learning (DL) architecture in stuttering
detection (SD) tasks is challenging due to the limited size of the available
datasets. To this end, this work introduces the application of speech
embeddings extracted with pre-trained deep models trained on massive audio
datasets for different tasks. In particular, we explore audio representations
obtained using emphasized channel attention, propagation, and
aggregation-time-delay neural network (ECAPA-TDNN) and Wav2Vec2.0 model trained
on VoxCeleb and LibriSpeech datasets respectively. After extracting the
embeddings, we benchmark with several traditional classifiers, such as a
k-nearest neighbor, Gaussian naive Bayes, and neural network, for the
stuttering detection tasks. In comparison to the standard SD system trained
only on the limited SEP-28k dataset, we obtain a relative improvement of 16.74%
in terms of overall accuracy over baseline. Finally, we have shown that
combining two embeddings and concatenating multiple layers of Wav2Vec2.0 can
further improve SD performance up to 1% and 2.64% respectively.

Deep neural networks have empowered accurate device-free human activity
recognition, which has wide applications. Deep models can extract robust
features from various sensors and generalize well even in challenging
situations such as data-insufficient cases. However, these systems could be
vulnerable to input perturbations, i.e. adversarial attacks. We empirically
demonstrate that both black-box Gaussian attacks and modern adversarial
white-box attacks can render their accuracies to plummet. In this paper, we
firstly point out that such phenomenon can bring severe safety hazards to
device-free sensing systems, and then propose a novel learning framework,
RobustSense, to defend common attacks. RobustSense aims to achieve consistent
predictions regardless of whether there exists an attack on its input or not,
alleviating the negative effect of distribution perturbation caused by
adversarial attacks. Extensive experiments demonstrate that our proposed method
can significantly enhance the model robustness of existing deep models,
overcoming possible attacks. The results validate that our method works well on
wireless human activity recognition and person identification systems. To the
best of our knowledge, this is the first work to investigate adversarial
attacks and further develop a novel defense framework for wireless human
activity recognition in mobile computing research.

In this work, we present Con$^{2}$DA, a simple framework that extends recent
advances in semi-supervised learning to the semi-supervised domain adaptation
(SSDA) problem. Our framework generates pairs of associated samples by
performing stochastic data transformations to a given input. Associated data
pairs are mapped to a feature representation space using a feature extractor.
We use different loss functions to enforce consistency between the feature
representations of associated data pairs of samples. We show that these learned
representations are useful to deal with differences in data distributions in
the domain adaptation problem. We performed experiments to study the main
components of our model and we show that (i) learning of the consistent and
contrastive feature representations is crucial to extract good discriminative
features across different domains, and ii) our model benefits from the use of
strong augmentation policies. With these findings, our method achieves
state-of-the-art performances in three benchmark datasets for SSDA.

Much of our experiments are designed to uncover the cause(s) and effect(s)
behind a data generating mechanism (i.e., phenomenon) we happen to be
interested in. Uncovering such relationships allows us to identify the true
working of a phenomenon and, most importantly, articulate a model that may
enable us to further explore the phenomenon on hand and/or allow us to predict
it accurately. Fundamentally, such models are likely to be derived via a causal
approach (as opposed to an observational or empirical mean). In this approach,
causal discovery is required to create a causal model, which can then be
applied to infer the influence of interventions, and answer any hypothetical
questions (i.e., in the form of What ifs? Etc.) that we might have. This paper
builds a case for causal discovery and causal inference and contrasts that
against traditional machine learning approaches; all from a civil and
structural engineering perspective. More specifically, this paper outlines the
key principles of causality and the most commonly used algorithms and packages
for causal discovery and causal inference. Finally, this paper also presents a
series of examples and case studies of how causal concepts can be adopted for
our domain.

In a practical setting towards better generalization abilities of client
models for realizing robust personalized Federated Learning (FL) systems,
efficient model aggregation methods have been considered as a critical research
objective. It is a challenging issue due to the consequences of non-i.i.d.
properties of client's data, often referred to as statistical heterogeneity and
small local data samples from the various data distributions. Therefore, to
develop robust generalized global and personalized models, conventional FL
methods need redesigning the knowledge aggregation from biased local models
while considering huge divergence of learning parameters due to skewed client
data. In this work, we demonstrate that the knowledge transfer mechanism is a
de facto technique to achieve these objectives and develop a novel knowledge
distillation-based approach to study the extent of knowledge transfer between
the global model and local models. Henceforth, our method considers the
suitability of transferring the outcome distribution and (or) the embedding
vector of representation from trained models during cross-device knowledge
transfer using a small proxy dataset in heterogeneous FL. In doing so, we
alternatively perform cross-device knowledge transfer following general
formulations as 1) global knowledge transfer and 2) on-device knowledge
transfer. Through simulations on four federated datasets, we show the proposed
method achieves significant speedups and high personalized performance of local
models. Furthermore, the proposed approach offers a more stable algorithm than
FedAvg during the training, with minimal communication data load when
exchanging the trained model's outcomes and representation.

The thermomechanical properties of energetic materials (EM) are known to be a
function of their microscopic structures, i.e., morphological configurations of
crystals and pores. This microstructural dependency has motivated vigorous
research in the EM community, seeking to engineer material microstructures with
targeted properties and performance under the materials-by-design paradigm.
However, establishing the complex structure-property-performance (SPP)
relationships of EMs demands extensive experimental and simulation efforts, and
assimilating and encapsulating these relationships in usable models is a
challenge. Here, we present a novel deep learning method, Physics-Aware
Recurrent Convolutional (PARC) Neural Network, that can "learn" the mesoscale
thermo-mechanics of EM microstructures during the shock-to-detonation
transition (SDT). We show that this new approach can produce accurate
high-fidelity predictions of time-evolving temperature and pressure fields of
the same quality as the state-of-the-art direct numerical simulations (DNS),
despite the dramatic reduction of computing time, from hours and days on a
high-performance computing cluster (HPC) to a little more than a second on a
commodity laptop. We also demonstrate that PARC can provide physical insights,
i.e., the artificial neurons can illuminate the underlying physics by
identifying which microstructural features led to critical hotspots and what
are the characteristics of "critical" versus "non-critical" microstructures.
This new knowledge generated alongside the capacity to conduct high-throughput
experiments will broaden our theoretical understanding of the initiation
mechanisms of EM detonation, as a step towards engineering EMs with specific

Fast noninvasive probing of spatially varying decorrelating events, such as
cerebral blood flow beneath the human skull, is an essential task in various
scientific and clinical settings. One of the primary optical techniques used is
diffuse correlation spectroscopy (DCS), whose classical implementation uses a
single or few single-photon detectors, resulting in poor spatial localization
accuracy and relatively low temporal resolution. Here, we propose a technique
termed Classifying Rapid decorrelation Events via Parallelized single photon
dEtection (CREPE)}, a new form of DCS that can probe and classify different
decorrelating movements hidden underneath turbid volume with high sensitivity
using parallelized speckle detection from a $32\times32$ pixel SPAD array. We
evaluate our setup by classifying different spatiotemporal-decorrelating
patterns hidden beneath a 5mm tissue-like phantom made with rapidly
decorrelating dynamic scattering media. Twelve multi-mode fibers are used to
collect scattered light from different positions on the surface of the tissue
phantom. To validate our setup, we generate perturbed decorrelation patterns by
both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as
well as a vessel phantom containing flowing fluid. Along with a deep
contrastive learning algorithm that outperforms classic unsupervised learning
methods, we demonstrate our approach can accurately detect and classify
different transient decorrelation events (happening in 0.1-0.4s) underneath
turbid scattering media, without any data labeling. This has the potential to
be applied to noninvasively monitor deep tissue motion patterns, for example
identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates
within a compact and static detection probe.

Visual object tracking acts as a pivotal component in various emerging video
applications. Despite the numerous developments in visual tracking, existing
deep trackers are still likely to fail when tracking against objects with
dramatic variation. These deep trackers usually do not perform online update or
update single sub-branch of the tracking model, for which they cannot adapt to
the appearance variation of objects. Efficient updating methods are therefore
crucial for tracking while previous meta-updater optimizes trackers directly
over parameter space, which is prone to over-fit even collapse on longer
sequences. To address these issues, we propose a context-aware tracking model
to optimize the tracker over the representation space, which jointly
meta-update both branches by exploiting information along the whole sequence,
such that it can avoid the over-fitting problem. First, we note that the
embedded features of the localization branch and the box-estimation branch,
focusing on the local and global information of the target, are effective
complements to each other. Based on this insight, we devise a
context-aggregation module to fuse information in historical frames, followed
by a context-aware module to learn affinity vectors for both branches of the
tracker. Besides, we develop a dedicated meta-learning scheme, on account of
fast and stable updating with limited training samples. The proposed tracking
method achieves an EAO score of 0.514 on VOT2018 with the speed of 40FPS,
demonstrating its capability of improving the accuracy and robustness of the
underlying tracker with little speed drop.

Deep metric learning has yielded impressive results in tasks such as
clustering and image retrieval by leveraging neural networks to obtain highly
discriminative feature embeddings, which can be used to group samples into
different classes. Much research has been devoted to the design of smart loss
functions or data mining strategies for training such networks. Most methods
consider only pairs or triplets of samples within a mini-batch to compute the
loss function, which is commonly based on the distance between embeddings. We
propose Group Loss, a loss function based on a differentiable label-propagation
method that enforces embedding similarity across all samples of a group while
promoting, at the same time, low-density regions amongst data points belonging
to different groups. Guided by the smoothness assumption that "similar objects
should belong to the same group", the proposed loss trains the neural network
for a classification task, enforcing a consistent labelling amongst samples
within a class. We design a set of inference strategies tailored towards our
algorithm, named Group Loss++ that further improve the results of our model. We
show state-of-the-art results on clustering and image retrieval on four
retrieval datasets, and present competitive results on two person
re-identification datasets, providing a unified framework for retrieval and
re-identification.

Recently, multiple synthetic and real-world datasets have been built to
facilitate the training of deep single image reflection removal (SIRR) models.
Meanwhile, diverse testing sets are also provided with different types of
reflection and scenes. However, the non-negligible domain gaps between training
and testing sets make it difficult to learn deep models generalizing well to
testing images. The diversity of reflections and scenes further makes it a
mission impossible to learn a single model being effective to all testing sets
and real-world reflections. In this paper, we tackle these issues by learning
SIRR models from a domain generalization perspective. Particularly, for each
source set, a specific SIRR model is trained to serve as a domain expert of
relevant reflection types. For a given reflection-contaminated image, we
present a reflection type-aware weighting (RTAW) module to predict expert-wise
weights. RTAW can then be incorporated with adaptive network combination
(AdaNEC) for handling different reflection types and scenes, i.e., generalizing
to unknown domains. Two representative AdaNEC methods, i.e., output fusion (OF)
and network interpolation (NI), are provided by considering both adaptation
levels and efficiency. For images from one source set, we train RTAW to only
predict expert-wise weights of other domain experts for improving
generalization ability, while the weights of all experts are predicted and
employed during testing. An in-domain expert (IDE) loss is presented for
training RTAW. Extensive experiments show the appealing performance gain of our
AdaNEC on different state-of-the-art SIRR networks. Source code and pre-trained
models will available at https://github.com/csmliu/AdaNEC.

Nowadays, state-of-the-art learning-to-rank (LTR) methods are based on
gradient-boosted decision trees (GBDT). The most well-known algorithm is
LambdaMART that was proposed more than a decade ago. Recently, several other
GBDT-based ranking algorithms were proposed. In this paper, we conduct a
thorough analysis of these methods in a unified setup. In particular, we
address the following questions. Is direct optimization of a smoothed ranking
loss preferable over optimizing a convex surrogate? How to properly construct
and smooth surrogate ranking losses? To address these questions, we compare
LambdaMART with YetiRank and StochasticRank methods and their modifications. We
also improve the YetiRank approach to allow for optimizing specific ranking
loss functions. As a result, we gain insights into learning-to-rank approaches
and obtain a new state-of-the-art algorithm.

This paper proposes an optimization-based approach to predict trajectories of
autonomous race cars. We assume that the observed trajectory is the result of
an optimization problem that trades off path progress against acceleration and
jerk smoothness, and which is restricted by constraints. The algorithm predicts
a trajectory by solving a parameterized nonlinear program (NLP) which contains
path progress and smoothness in cost terms. By observing the actual motion of a
vehicle, the parameters of prediction are updated by means of solving an
inverse optimal control problem that contains the parameters of the predicting
NLP as optimization variables. The algorithm therefore learns to predict the
observed vehicle trajectory in a least-squares relation to measurement data and
to the presumed structure of the predicting NLP. This work contributes with an
algorithm that allows for accurate and interpretable predictions with sparse
data. The algorithm is implemented on embedded hardware in an autonomous
real-world race car that is competing in the challenge Roborace and analyzed
with respect to recorded data.

Tensor completion aimes at recovering missing data, and it is one of the
popular concerns in deep learning and signal processing. Among the higher-order
tensor decomposition algorithms, the recently proposed fully-connected tensor
network decomposition (FCTN) algorithm is the most advanced. In this paper, by
leveraging the superior expression of the fully-connected tensor network (FCTN)
decomposition, we propose a new tensor completion method named the fully
connected tensor network weighted optization(FCTN-WOPT). The algorithm performs
a composition of the completed tensor by initialising the factors from the FCTN
decomposition. We build a loss function with the weight tensor, the completed
tensor and the incomplete tensor together, and then update the completed tensor
using the lbfgs gradient descent algorithm to reduce the spatial memory
occupation and speed up iterations. Finally we test the completion with
synthetic data and real data (both image data and video data) and the results
show the advanced performance of our FCTN-WOPT when it is applied to
higher-order tensor completion.

Imagine an oracle that correctly predicts the outcome of every particle
physics experiment, the products of every chemical reaction, or the function of
every protein. Such an oracle would revolutionize science and technology as we
know them. However, as scientists, we would not be satisfied with the oracle
itself. We want more. We want to comprehend how the oracle conceived these
predictions. This feat, denoted as scientific understanding, has frequently
been recognized as the essential aim of science. Now, the ever-growing power of
computers and artificial intelligence poses one ultimate question: How can
advanced artificial systems contribute to scientific understanding or achieve
it autonomously?
  We are convinced that this is not a mere technical question but lies at the
core of science. Therefore, here we set out to answer where we are and where we
can go from here. We first seek advice from the philosophy of science to
understand scientific understanding. Then we review the current state of the
art, both from literature and by collecting dozens of anecdotes from scientists
about how they acquired new conceptual understanding with the help of
computers. Those combined insights help us to define three dimensions of
android-assisted scientific understanding: The android as a I) computational
microscope, II) resource of inspiration and the ultimate, not yet existent III)
agent of understanding. For each dimension, we explain new avenues to push
beyond the status quo and unleash the full power of artificial intelligence's
contribution to the central aim of science. We hope our perspective inspires
and focuses research towards androids that get new scientific understanding and
ultimately bring us closer to true artificial scientists.

Unsupervised learning has been popular in various computer vision tasks,
including visual object tracking. However, prior unsupervised tracking
approaches rely heavily on spatial supervision from template-search pairs and
are still unable to track objects with strong variation over a long time span.
As unlimited self-supervision signals can be obtained by tracking a video along
a cycle in time, we investigate evolving a Siamese tracker by tracking videos
forward-backward. We present a novel unsupervised tracking framework, in which
we can learn temporal correspondence both on the classification branch and
regression branch. Specifically, to propagate reliable template feature in the
forward propagation process so that the tracker can be trained in the cycle, we
first propose a consistency propagation transformation. We then identify an
ill-posed penalty problem in conventional cycle training in backward
propagation process. Thus, a differentiable region mask is proposed to select
features as well as to implicitly penalize tracking errors on intermediate
frames. Moreover, since noisy labels may degrade training, we propose a
mask-guided loss reweighting strategy to assign dynamic weights based on the
quality of pseudo labels. In extensive experiments, our tracker outperforms
preceding unsupervised methods by a substantial margin, performing on par with
supervised methods on large-scale datasets such as TrackingNet and LaSOT. Code
is available at https://github.com/FlorinShum/ULAST.

In this work we formulate and treat an extension of the Imitation from
Observations problem. Imitation from Observations is a generalisation of the
well-known Imitation Learning problem where state-only demonstrations are
considered. In our treatment we extend the scope of Imitation from Observations
to feature-only demonstrations which could arguably be described as partial
observations. Therewith we mean that the full state of the decision makers is
unknown and imitation must take place on the basis of a limited set of
features. We set out for methods that extract an executable policy directly
from those features which, in the literature, would be referred to as
Behavioural Cloning methods. Our treatment combines elements from probability
and information theory and draws connections with entropy regularized Markov
Decision Processes.

In this work, we propose a distributed adaptive observer for a class of
nonlinear networked systems inspired by biophysical neural network models.
Neural systems learn by adjusting intrinsic and synaptic weights in a
distributed fashion, with neuronal membrane voltages carrying information from
neighbouring neurons in the network. We show that this learning principle can
be used to design an adaptive observer based on a decentralized learning rule
that greatly reduces the number of observer states required for exponential
convergence of parameter estimates. This novel design is relevant for
biological, biomedical and neuromorphic applications.

Currently available quantum computers are prone to errors. Circuit
optimization and error mitigation methods are needed to design quantum circuits
to achieve better fidelity when executed on NISQ hardware. Dynamical decoupling
(DD) is generally used to suppress the decoherence error and different DD
strategies have been proposed. Moreover, the circuit fidelity can be improved
by pulse-level optimization, such as creating hardware-native pulse-efficient
gates. This paper implements all the popular DD sequences and evaluates their
performances on IBM quantum chips with different characteristics for various
well-known quantum applications. Also, we investigate combining DD with
pulse-level optimization method and apply them to QAOA to solve Max-Cut
problem. Based on the experimental results, we found that DD can be a benefit
for only certain types of quantum algorithms, while the combination of DD and
pulse-level optimization methods always has a positive impact. Finally, we
provide several guidelines for users to learn how to use these noise mitigation
methods to build circuits for quantum applications with high fidelity on IBM
quantum computers.

Predictive process monitoring is a subfield of process mining that aims to
estimate case or event features for running process instances. Such predictions
are of significant interest to the process stakeholders. However,
state-of-the-art methods for predictive monitoring require the training of
complex machine learning models, which is often inefficient. This paper
proposes an instance selection procedure that allows sampling training process
instances for prediction models. We show that our sampling method allows for a
significant increase of training speed for next activity prediction methods
while maintaining reliable levels of prediction accuracy.

Model-based reinforcement learning (MBRL) is a sample efficient technique to
obtain control policies, yet unavoidable modeling errors often lead performance
deterioration. The model in MBRL is often solely fitted to reconstruct
dynamics, state observations in particular, while the impact of model error on
the policy is not captured by the training objective. This leads to a mismatch
between the intended goal of MBRL, enabling good policy and value learning, and
the target of the loss function employed in practice, future state prediction.
Naive intuition would suggest that value-aware model learning would fix this
problem and, indeed, several solutions to this objective mismatch problem have
been proposed based on theoretical analysis. However, they tend to be inferior
in practice to commonly used maximum likelihood (MLE) based approaches. In this
paper we propose the Value-gradient weighted Model Learning (VaGraM), a novel
method for value-aware model learning which improves the performance of MBRL in
challenging settings, such as small model capacity and the presence of
distracting state dimensions. We analyze both MLE and value-aware approaches
and demonstrate how they fail to account for exploration and the behavior of
function approximation when learning value-aware models and highlight the
additional goals that must be met to stabilize optimization in the deep
learning setting. We verify our analysis by showing that our loss function is
able to achieve high returns on the Mujoco benchmark suite while being more
robust than maximum likelihood based approaches.

Geometric verification is considered a de facto solution for the re-ranking
task in image retrieval. In this study, we propose a novel image retrieval
re-ranking network named Correlation Verification Networks (CVNet). Our
proposed network, comprising deeply stacked 4D convolutional layers, gradually
compresses dense feature correlation into image similarity while learning
diverse geometric matching patterns from various image pairs. To enable
cross-scale matching, it builds feature pyramids and constructs cross-scale
feature correlations within a single inference, replacing costly multi-scale
inferences. In addition, we use curriculum learning with the hard negative
mining and Hide-and-Seek strategy to handle hard samples without losing
generality. Our proposed re-ranking network shows state-of-the-art performance
on several retrieval benchmarks with a significant margin (+12.6% in mAP on
ROxford-Hard+1M set) over state-of-the-art methods. The source code and models
are available online: https://github.com/sungonce/CVNet.

Transfer learning can be seen as a data- and compute-efficient alternative to
training models from scratch. The emergence of rich model repositories, such as
TensorFlow Hub, enables practitioners and researchers to unleash the potential
of these models across a wide range of downstream tasks. As these repositories
keep growing exponentially, efficiently selecting a good model for the task at
hand becomes paramount. By carefully comparing various selection and search
strategies, we realize that no single method outperforms the others, and hybrid
or mixed strategies can be beneficial. Therefore, we propose SHiFT, the first
downstream task-aware, flexible, and efficient model search engine for transfer
learning. These properties are enabled by a custom query language SHiFT-QL
together with a cost-based decision maker, which we empirically validate.
Motivated by the iterative nature of machine learning development, we further
support efficient incremental executions of our queries, which requires a
careful implementation when jointly used with our optimizations.

Modelling and understanding properties of materials from first principles
require knowledge of the underlying atomistic structure. This entails knowing
the individual identity and position of all involved atoms. Obtaining such
information for macro-molecules, nano-particles, clusters, and for the surface,
interface, and bulk phases of amorphous and solid materials represents a
difficult high dimensional global optimization problem. The rise of machine
learning techniques in materials science has, however, led to many compelling
developments that may speed up such structure searches. The complexity of the
new methods have established the necessity for an efficient way of
experimenting with and assembling them into global optimization algorithms. In
this paper we introduce the Atomistic Global Optimization X (AGOX) framework
and code, as a customizable approach to building efficient global optimization
algorithms. A modular way of expressing global optimization algorithms is
described and modern programming practices are used to enable that modularity
in the freely available AGOX python package. Two examples of global
optimization problems are analyzed: One that is computationally inexpensive
which is used to showcase that AGOX enables the expression of multiple global
optimization algorithms. As the other example, AGOX is used for solving a
complex atomistic optimization problem for a metal-nitride nano-cluster
embedded in a graphene sheet as described at the density functional theory
(DFT) level.

Grounding temporal video segments described in natural language queries
effectively and efficiently is a crucial capability needed in
vision-and-language fields. In this paper, we deal with the fast video temporal
grounding (FVTG) task, aiming at localizing the target segment with high speed
and favorable accuracy. Most existing approaches adopt elaborately designed
cross-modal interaction modules to improve the grounding performance, which
suffer from the test-time bottleneck. Although several common space-based
methods enjoy the high-speed merit during inference, they can hardly capture
the comprehensive and explicit relations between visual and textual modalities.
In this paper, to tackle the dilemma of speed-accuracy tradeoff, we propose a
commonsense-aware cross-modal alignment (CCA) framework, which incorporates
commonsense-guided visual and text representations into a complementary common
space for fast video temporal grounding. Specifically, the commonsense concepts
are explored and exploited by extracting the structural semantic information
from a language corpus. Then, a commonsense-aware interaction module is
designed to obtain bridged visual and text features by utilizing the learned
commonsense concepts. Finally, to maintain the original semantic information of
textual queries, a cross-modal complementary common space is optimized to
obtain matching scores for performing FVTG. Extensive results on two
challenging benchmarks show that our CCA method performs favorably against
state-of-the-arts while running at high speed. Our code is available at
https://github.com/ZiyueWu59/CCA.

We present a new domain generalized semantic segmentation network named
WildNet, which learns domain-generalized features by leveraging a variety of
contents and styles from the wild. In domain generalization, the low
generalization ability for unseen target domains is clearly due to overfitting
to the source domain. To address this problem, previous works have focused on
generalizing the domain by removing or diversifying the styles of the source
domain. These alleviated overfitting to the source-style but overlooked
overfitting to the source-content. In this paper, we propose to diversify both
the content and style of the source domain with the help of the wild. Our main
idea is for networks to naturally learn domain-generalized semantic information
from the wild. To this end, we diversify styles by augmenting source features
to resemble wild styles and enable networks to adapt to a variety of styles.
Furthermore, we encourage networks to learn class-discriminant features by
providing semantic variations borrowed from the wild to source contents in the
feature space. Finally, we regularize networks to capture consistent semantic
information even when both the content and style of the source domain are
extended to the wild. Extensive experiments on five different datasets validate
the effectiveness of our WildNet, and we significantly outperform
state-of-the-art methods. The source code and model are available online:
https://github.com/suhyeonlee/WildNet.

Neuroscience and neurotechnology are currently being revolutionized by
artificial intelligence (AI) and machine learning. AI is widely used to study
and interpret neural signals (analytical applications), assist people with
disabilities (prosthetic applications), and treat underlying neurological
symptoms (therapeutic applications). In this brief, we will review the emerging
opportunities of on-chip AI for the next-generation implantable brain-machine
interfaces (BMIs), with a focus on state-of-the-art prosthetic BMIs. Major
technological challenges for the effectiveness of AI models will be discussed.
Finally, we will present algorithmic and IC design solutions to enable a new
generation of AI-enhanced and high-channel-count BMIs.

The ability to acquire abstract knowledge is a hallmark of human intelligence
and is believed by many to be one of the core differences between humans and
neural network models. Agents can be endowed with an inductive bias towards
abstraction through meta-learning, where they are trained on a distribution of
tasks that share some abstract structure that can be learned and applied.
However, because neural networks are hard to interpret, it can be difficult to
tell whether agents have learned the underlying abstraction, or alternatively
statistical patterns that are characteristic of that abstraction. In this work,
we compare the performance of humans and agents in a meta-reinforcement
learning paradigm in which tasks are generated from abstract rules. We define a
novel methodology for building "task metamers" that closely match the
statistics of the abstract tasks but use a different underlying generative
process, and evaluate performance on both abstract and metamer tasks. In our
first set of experiments, we found that humans perform better at abstract tasks
than metamer tasks whereas a widely-used meta-reinforcement learning agent
performs worse on the abstract tasks than the matched metamers. In a second set
of experiments, we base the tasks on abstractions derived directly from
empirically identified human priors. We utilize the same procedure to generate
corresponding metamer tasks, and see the same double dissociation between
humans and agents. This work provides a foundation for characterizing
differences between humans and machine learning that can be used in future work
towards developing machines with human-like behavior.

Water distribution networks are a key component of modern infrastructure for
housing and industry. They transport and distribute water via widely branched
networks from sources to consumers. In order to guarantee a working network at
all times, the water supply company continuously monitors the network and takes
actions when necessary -- e.g. reacting to leakages, sensor faults and drops in
water quality. Since real world networks are too large and complex to be
monitored by a human, algorithmic monitoring systems have been developed. A
popular type of such systems are residual based anomaly detection systems that
can detect events such as leakages and sensor faults. For a continuous high
quality monitoring, it is necessary for these systems to adapt to changed
demands and presence of various anomalies.
  In this work, we propose an adaption of the incremental SAM-kNN classifier
for regression to build a residual based anomaly detection system for water
distribution networks that is able to adapt to any kind of change.

We present Reward-Switching Policy Optimization (RSPO), a paradigm to
discover diverse strategies in complex RL environments by iteratively finding
novel policies that are both locally optimal and sufficiently different from
existing ones. To encourage the learning policy to consistently converge
towards a previously undiscovered local optimum, RSPO switches between
extrinsic and intrinsic rewards via a trajectory-based novelty measurement
during the optimization process. When a sampled trajectory is sufficiently
distinct, RSPO performs standard policy optimization with extrinsic rewards.
For trajectories with high likelihood under existing policies, RSPO utilizes an
intrinsic diversity reward to promote exploration. Experiments show that RSPO
is able to discover a wide spectrum of strategies in a variety of domains,
ranging from single-agent particle-world tasks and MuJoCo continuous control to
multi-agent stag-hunt games and StarCraftII challenges.

We consider the mean-field game price formation model introduced by Gomes and
Sa\'ude. In this MFG model, agents trade a commodity whose supply can be
deterministic or stochastic. Agents maximize profit, taking into account
current and future prices. The balance between supply and demand determines the
price. We introduce a potential function that converts the MFG into a convex
variational problem. This variational formulation is particularly suitable for
machine learning approaches. Here, we use a recurrent neural network to solve
this problem. In the last section of the paper, we compare our results with
known analytical solutions.

Joint activity detection and channel estimation (JADCE) for grant-free random
access is a critical issue that needs to be addressed to support massive
connectivity in IoT networks. However, the existing model-free learning method
can only achieve either activity detection or channel estimation, but not both.
In this paper, we propose a novel model-free learning method based on
generative adversarial network (GAN) to tackle the JADCE problem. We adopt the
U-net architecture to build the generator rather than the standard GAN
architecture, where a pre-estimated value that contains the activity
information is adopted as input to the generator. By leveraging the properties
of the pseudoinverse, the generator is refined by using an affine projection
and a skip connection to ensure the output of the generator is consistent with
the measurement. Moreover, we build a two-layer fully-connected neural network
to design pilot matrix for reducing the impact of receiver noise. Simulation
results show that the proposed method outperforms the existing methods in high
SNR regimes, as both data consistency projection and pilot matrix optimization
improve the learning ability.

In this paper, we study the problem of stereo matching from a pair of images
with different resolutions, e.g., those acquired with a tele-wide camera
system. Due to the difficulty of obtaining ground-truth disparity labels in
diverse real-world systems, we start from an unsupervised learning perspective.
However, resolution asymmetry caused by unknown degradations between two views
hinders the effectiveness of the generally assumed photometric consistency. To
overcome this challenge, we propose to impose the consistency between two views
in a feature space instead of the image space, named feature-metric
consistency. Interestingly, we find that, although a stereo matching network
trained with the photometric loss is not optimal, its feature extractor can
produce degradation-agnostic and matching-specific features. These features can
then be utilized to formulate a feature-metric loss to avoid the photometric
inconsistency. Moreover, we introduce a self-boosting strategy to optimize the
feature extractor progressively, which further strengthens the feature-metric
consistency. Experiments on both simulated datasets with various degradations
and a self-collected real-world dataset validate the superior performance of
the proposed method over existing solutions.

Cerebral small vessel disease (CSVD) is a major vascular contributor to
cognitive impairment in ageing, including dementias. Imaging remains the most
promising method for in vivo studies of CSVD. To replace the subjective and
laborious visual rating approaches, emerging studies have applied
state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD
from MRI scans. We aimed to summarise published computer-aided methods to
examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB),
dilated perivascular spaces (PVS), and lacunes of presumed vascular origin.
Seventy-one classical image processing, classical machine learning, and deep
learning studies were identified. CMB and PVS have been better studied,
compared to lacunes. While good performance metrics have been achieved in local
test datasets, there have not been generalisable pipelines validated in
different research or clinical cohorts. Transfer learning and weak supervision
techniques have been applied to accommodate the limitations in training data.
Future studies could consider pooling data from multiple sources to increase
diversity, and validating the performance of the methods using both image
processing metrics and associations with clinical measures.

Channel charting is an unsupervised learning method that aims at mapping
wireless channels to a so-called chart, preserving as much as possible spatial
neighborhoods. In this paper, a model-based deep learning approach to this
problem is proposed. It builds on a physically motivated distance measure to
structure and initialize a neural network that is subsequently trained using a
triplet loss function. The proposed structure exhibits a low number of
parameters and clever initialization leads to fast training. These two features
make the proposed approach amenable to on-the-fly channel charting. The method
is empirically assessed on realistic synthetic channels, yielding encouraging
results.

The objective of this research is to enable safety-critical systems to
simultaneously learn and execute optimal control policies in a safe manner to
achieve complex autonomy. Learning optimal policies via trial and error, i.e.,
traditional reinforcement learning, is difficult to implement in
safety-critical systems, particularly when task restarts are unavailable. Safe
model-based reinforcement learning techniques based on a barrier transformation
have recently been developed to address this problem. However, these methods
rely on full state feedback, limiting their usability in a real-world
environment. In this work, an output-feedback safe model-based reinforcement
learning technique based on a novel barrier-aware dynamic state estimator has
been designed to address this issue. The developed approach facilitates
simultaneous learning and execution of safe control policies for
safety-critical linear systems. Simulation results indicate that barrier
transformation is an effective approach to achieve online reinforcement
learning in safety-critical systems using output feedback.

Training models continually to detect and classify objects, from new classes
and new domains, remains an open problem. In this work, we conduct a thorough
analysis of why and how object detection models forget catastrophically. We
focus on distillation-based approaches in two-stage networks; the most-common
strategy employed in contemporary continual object detection work.Distillation
aims to transfer the knowledge of a model trained on previous tasks -- the
teacher -- to a new model -- the student -- while it learns the new task. We
show that this works well for the region proposal network, but that wrong, yet
overly confident teacher predictions prevent student models from effective
learning of the classification head. Our analysis provides a foundation that
allows us to propose improvements for existing techniques by detecting
incorrect teacher predictions, based on current ground-truth labels, and by
employing an adaptive Huber loss as opposed to the mean squared error for the
distillation loss in the classification heads. We evidence that our strategy
works not only in a class incremental setting, but also in domain incremental
settings, which constitute a realistic context, likely to be the setting of
representative real-world problems.

We propose a data-driven approach for constructing machine-learning
interatomic potentials (MLIPs) trained under a regularization with the aim of
avoiding nonphysical heat flux. Specifically, we introduce a regularization
term for the heat flux into the cost function of MLIPs to be minimized. Since
the treatment of heat flux using MLIPs with regularization can be decomposed
into elemental contributions or conducted in frequency space, this approach is
expected to be useful for investigating the origin of thermal conductivity
obtained from the Green-Kubo formula. However, the strength of regularization
needs to be appropriately set because it may reduce not only the nonphysical
part but also the intrinsic heat flux one. To this end, we investigated the
conditions for constructing MLIPs that can reproduce the power spectra of heat
flux associated with the empirical interatomic potential of Ag$_2$Se, which
consists of pairwise functions and do not contain a nonphysical heat flux. The
appropriate strength could be estimated from the variation of the magnitude of
regularization term as well as root mean square errors for total potential
energy, atomic force, and virial stress with respect to the strengths, without
reference spectrum data. As an application example, we explored the differences
in power spectra between superionic and nonsuperionic conducting phases based
on the heat flux regularization to MLIPs trained with the first-principles
calculation data of Ag$_2$S. Furthermore, our results demonstrate that training
with the regularization improves the robustness of MLIPs as well as the
reduction of the nonphysical heat flux.

This paper describes our proposed integration system for the spoofing-aware
speaker verification challenge. It consists of a robust spoofing-aware
verification system that use the speaker verification and antispoofing
embeddings extracted from specialized neural networks. First, an integration
network, fed with the test utterance's speaker verification and spoofing
embeddings, is used to compute a spoof-based score. This score is then linearly
combined with the cosine similarity between the speaker verification embeddings
from the enrollment and test utterances, thus obtaining the final scoring
decision. Moreover, the integration network is trained using a one-class loss
function to discriminate between target trials and unauthorized accesses. Our
proposed system is evaluated in the ASVspoof19 database, exhibiting competitive
performance compared to other integration approaches. In addition, we test,
along with our integration approach, state of the art speaker verification and
antispoofing systems based on self-supervised learning, yielding
high-performance speech biometric systems.

The web is used daily by billions. Even so, users are not protected from many
threats by default. This position paper builds on previous web privacy and
security research and introduces JShelter, a webextension that fights to return
the browser to users. Moreover, we introduce a library helping with common
webextension development tasks and fixing loopholes misused by previous
research. JShelter focuses on fingerprinting prevention, limitations of rich
web APIs, prevention of attacks connected to timing, and learning information
about the device, the browser, the user, and surrounding physical environment
and location. We discovered a loophole in the sensor timestamps that lets any
page observe the device boot time if sensor APIs are enabled in Chromium-based
browsers. JShelter provides a fingerprinting report and other feedback that can
be used by future security research and data protection authorities. Thousands
of users around the world use the webextension every day.

Deep recommender systems (DRS) are critical for current commercial online
service providers, which address the issue of information overload by
recommending items that are tailored to the user's interests and preferences.
They have unprecedented feature representations effectiveness and the capacity
of modeling the non-linear relationships between users and items. Despite their
advancements, DRS models, like other deep learning models, employ sophisticated
neural network architectures and other vital components that are typically
designed and tuned by human experts. This article will give a comprehensive
summary of automated machine learning (AutoML) for developing DRS models. We
first provide an overview of AutoML for DRS models and the related techniques.
Then we discuss the state-of-the-art AutoML approaches that automate the
feature selection, feature embeddings, feature interactions, and system design
in DRS. Finally, we discuss appealing research directions and summarize the
survey.

Recent advances in sophisticated synthetic speech generated from
text-to-speech (TTS) or voice conversion (VC) systems cause threats to the
existing automatic speaker verification (ASV) systems. Since such synthetic
speech is generated from diverse algorithms, generalization ability with using
limited training data is indispensable for a robust anti-spoofing system. In
this work, we propose a transfer learning scheme based on the wav2vec 2.0
pretrained model with variational information bottleneck (VIB) for speech
anti-spoofing task. Evaluation on the ASVspoof 2019 logical access (LA)
database shows that our method improves the performance of distinguishing
unseen spoofed and genuine speech, outperforming current state-of-the-art
anti-spoofing systems. Furthermore, we show that the proposed system improves
performance in low-resource and cross-dataset settings of anti-spoofing task
significantly, demonstrating that our system is also robust in terms of data
size and data distribution.

While various avenues of research have been explored for iterative pruning,
little is known what effect pruning has on zero-shot test performance and its
potential implications on the choice of pruning criteria. This pruning setup is
particularly important for cross-lingual models that implicitly learn alignment
between language representations during pretraining, which if distorted via
pruning, not only leads to poorer performance on language data used for
retraining but also on zero-shot languages that are evaluated.
  In this work, we show that there is a clear performance discrepancy in
magnitude-based pruning when comparing standard supervised learning to the
zero-shot setting. From this finding, we propose two weight regularizers that
aim to maximize the alignment between units of pruned and unpruned networks to
mitigate alignment distortion in pruned cross-lingual models and perform well
for both non zero-shot and zero-shot settings.
  We provide experimental results on cross-lingual tasks for the zero-shot
setting using XLM-RoBERTa$_{\mathrm{Base}}$, where we also find that pruning
has varying degrees of representational degradation depending on the language
corresponding to the zero-shot test set. This is also the first study that
focuses on cross-lingual language model compression.

This paper presents a learning dynamic with almost sure convergence guarantee
for any stochastic game with turn-based controllers (on state transitions) as
long as stage-payoffs have stochastic fictitious-play-property. For example,
two-player zero-sum and n-player potential strategic-form games have this
property. Note also that stage-payoffs for different states can have different
structures such as they can sum to zero in some states and be identical in
others. The dynamics presented combines the classical stochastic fictitious
play with value iteration for stochastic games. There are two key properties:
(i) players play finite horizon stochastic games with increasing lengths within
the underlying infinite-horizon stochastic game, and (ii) the turn-based
controllers ensure that the auxiliary stage-games (induced from the
continuation payoff estimated) have the stochastic fictitious-play-property.

Nowadays, with the rising number of sensors in sectors such as healthcare and
industry, the problem of multivariate time series classification (MTSC) is
getting increasingly relevant and is a prime target for machine and deep
learning approaches. Their expanding adoption in real-world environments is
causing a shift in focus from the pursuit of ever-higher prediction accuracy
with complex models towards practical, deployable solutions that balance
accuracy and parameters such as prediction speed. An MTSC model that has
attracted attention recently is ROCKET, based on random convolutional kernels,
both because of its very fast training process and its state-of-the-art
accuracy. However, the large number of features it utilizes may be detrimental
to inference time. Examining its theoretical background and limitations enables
us to address potential drawbacks and present LightWaveS: a framework for
accurate MTSC, which is fast both during training and inference. Specifically,
utilizing wavelet scattering transformation and distributed feature selection,
we manage to create a solution that employs just 2.5% of the ROCKET features,
while achieving accuracy comparable to recent MTSC models. LightWaveS also
scales well across multiple compute nodes and with the number of input channels
during training. In addition, it can significantly reduce the input size and
provide insight to an MTSC problem by keeping only the most useful channels. We
present three versions of our algorithm and their results on distributed
training time and scalability, accuracy, and inference speedup. We show that we
achieve speedup ranging from 9x to 53x compared to ROCKET during inference on
an edge device, on datasets with comparable accuracy.

Graph learning algorithms have attained state-of-the-art performance on many
graph analysis tasks such as node classification, link prediction, and
clustering. It has, however, become hard to track the field's burgeoning
progress. One reason is due to the very small number of datasets used in
practice to benchmark the performance of graph learning algorithms. This
shockingly small sample size (~10) allows for only limited scientific insight
into the problem.
  In this work, we aim to address this deficiency. We propose to generate
synthetic graphs, and study the behaviour of graph learning algorithms in a
controlled scenario. We develop a fully-featured synthetic graph generator that
allows deep inspection of different models. We argue that synthetic graph
generations allows for thorough investigation of algorithms and provides more
insights than overfitting on three citation datasets. In the case study, we
show how our framework provides insight into unsupervised and supervised graph
neural network models.

The automatic early diagnosis of prodromal stages of Alzheimer's disease is
of great relevance for patient treatment to improve quality of life. We address
this problem as a multi-modal classification task. Multi-modal data provides
richer and complementary information. However, existing techniques only
consider either lower order relations between the data and single/multi-modal
imaging data. In this work, we introduce a novel semi-supervised hypergraph
learning framework for Alzheimer's disease diagnosis. Our framework allows for
higher-order relations among multi-modal imaging and non-imaging data whilst
requiring a tiny labelled set. Firstly, we introduce a dual embedding strategy
for constructing a robust hypergraph that preserves the data semantics. We
achieve this by enforcing perturbation invariance at the image and graph levels
using a contrastive based mechanism. Secondly, we present a dynamically
adjusted hypergraph diffusion model, via a semi-explicit flow, to improve the
predictive uncertainty. We demonstrate, through our experiments, that our
framework is able to outperform current techniques for Alzheimer's disease
diagnosis.

We consider the algorithmic problem of finding the optimal weights and biases
for a two-layer fully connected neural network to fit a given set of data
points. This problem is known as empirical risk minimization in the machine
learning community. We show that the problem is $\exists\mathbb{R}$-complete.
This complexity class can be defined as the set of algorithmic problems that
are polynomial-time equivalent to finding real roots of a polynomial with
integer coefficients. Furthermore, we show that arbitrary algebraic numbers are
required as weights to be able to train some instances to optimality, even if
all data points are rational. Our results hold even if the following
restrictions are all added simultaneously.
  $\bullet$ There are exactly two output neurons.
  $\bullet$ There are exactly two input neurons.
  $\bullet$ The data has only 13 different labels.
  $\bullet$ The number of hidden neurons is a constant fraction of the number
of data points.
  $\bullet$ The target training error is zero.
  $\bullet$ The ReLU activation function is used.
  This shows that even very simple networks are difficult to train. The result
explains why typical methods for $\mathsf{NP}$-complete problems, like
mixed-integer programming or SAT-solving, cannot train neural networks to
global optimality, unless $\mathsf{NP}=\exists\mathbb{R}$. We strengthen a
recent result by Abrahamsen, Kleist and Miltzow [NeurIPS 2021].

The minimum cost multicut problem is the NP-hard/APX-hard combinatorial
optimization problem of partitioning a real-valued edge-weighted graph such as
to minimize the total cost of the partition. While graph convolutional neural
networks (GNN) have proven to be promising in the context of combinatorial
optimization, most of them are only tailored to or tested on positive-valued
edge weights, i.e. they do not comply to the nature of the multicut problem. We
therefore adapt various GNN architectures including Graph Convolutional
Networks, Signed Graph Convolutional Networks and Graph Isomorphic Networks to
facilitate the efficient encoding of real-valued edge costs. Moreover, we
employ a reformulation of the multicut ILP constraints to a polynomial program
as loss function that allows to learn feasible multicut solutions in a scalable
way. Thus, we provide the first approach towards end-to-end trainable
multicuts. Our findings support that GNN approaches can produce good solutions
in practice while providing lower computation times and largely improved
scalability compared to LP solvers and optimized heuristics, especially when
considering large instances.

Stochastic gradient descent (SGD) is widely used in deep learning due to its
computational efficiency, but a complete understanding of why SGD performs so
well remains a major challenge. It has been observed empirically that most
eigenvalues of the Hessian of the loss functions on the loss landscape of
over-parametrized deep neural networks are close to zero, while only a small
number of eigenvalues are large. Zero eigenvalues indicate zero diffusion along
the corresponding directions. This indicates that the process of minima
selection mainly happens in the relatively low-dimensional subspace
corresponding to the top eigenvalues of the Hessian. Although the parameter
space is very high-dimensional, these findings seems to indicate that the SGD
dynamics may mainly live on a low-dimensional manifold. In this paper, we
pursue a truly data driven approach to the problem of getting a potentially
deeper understanding of the high-dimensional parameter surface, and in
particular, of the landscape traced out by SGD by analyzing the data generated
through SGD, or any other optimizer for that matter, in order to possibly
discover (local) low-dimensional representations of the optimization landscape.
As our vehicle for the exploration, we use diffusion maps introduced by R.
Coifman and coauthors.

Evaluating the performance of autonomous vehicles (AV) and their complex
subsystems to high precision under naturalistic circumstances remains a
challenge, especially when failure or dangerous cases are rare. Rarity does not
only require an enormous sample size for a naive method to achieve high
confidence estimation, but it also causes dangerous underestimation of the true
failure rate and it is extremely hard to detect. Meanwhile, the
state-of-the-art approach that comes with a correctness guarantee can only
compute an upper bound for the failure rate under certain conditions, which
could limit its practical uses. In this work, we present Deep Importance
Sampling (Deep IS) framework that utilizes a deep neural network to obtain an
efficient IS that is on par with the state-of-the-art, capable of reducing the
required sample size 43 times smaller than the naive sampling method to achieve
10% relative error and while producing an estimate that is much less
conservative. Our high-dimensional experiment estimating the misclassification
rate of one of the state-of-the-art traffic sign classifiers further reveals
that this efficiency still holds true even when the target is very small,
achieving over 600 times efficiency boost. This highlights the potential of
Deep IS in providing a precise estimate even against high-dimensional

In this paper we propose Discretely Indexed flows (DIF) as a new tool for
solving variational estimation problems. Roughly speaking, DIF are built as an
extension of Normalizing Flows (NF), in which the deterministic transport
becomes stochastic, and more precisely discretely indexed. Due to the discrete
nature of the underlying additional latent variable, DIF inherit the good
computational behavior of NF: they benefit from both a tractable density as
well as a straightforward sampling scheme, and can thus be used for the dual
problems of Variational Inference (VI) and of Variational density estimation
(VDE). On the other hand, DIF can also be understood as an extension of mixture
density models, in which the constant mixture weights are replaced by flexible
functions. As a consequence, DIF are better suited for capturing distributions
with discontinuities, sharp edges and fine details, which is a main advantage
of this construction. Finally we propose a methodology for constructiong DIF in
practice, and see that DIF can be sequentially cascaded, and cascaded with NF.

This paper considers the phase retrieval (PR) problem, which aims to
reconstruct a signal from phaseless measurements such as magnitude or power
spectrograms. PR is generally handled as a minimization problem involving a
quadratic loss. Recent works have considered alternative discrepancy measures,
such as the Bregman divergences, but it is still challenging to tailor the
optimal loss for a given setting. In this paper we propose a novel strategy to
automatically learn the optimal metric for PR. We unfold a recently introduced
ADMM algorithm into a neural network, and we emphasize that the information
about the loss used to formulate the PR problem is conveyed by the proximity
operator involved in the ADMM updates. Therefore, we replace this proximity
operator with trainable activation functions: learning these in a supervised
setting is then equivalent to learning an optimal metric for PR. Experiments
conducted with speech signals show that our approach outperforms the baseline
ADMM, using a light and interpretable neural architecture.

Recently, end-to-end speaker extraction has attracted increasing attention
and shown promising results. However, its performance is often inferior to that
of a blind source separation (BSS) counterpart with a similar network
architecture, due to the auxiliary speaker encoder may sometimes generate
ambiguous speaker embeddings. Such ambiguous guidance information may confuse
the separation network and hence lead to wrong extraction results, which
deteriorates the overall performance. We refer to this as the target confusion
problem. In this paper, we conduct an analysis of such an issue and solve it in
two stages. In the training phase, we propose to integrate metric learning
methods to improve the distinguishability of embeddings produced by the speaker
encoder. While for inference, a novel post-filtering strategy is designed to
revise the wrong results. Specifically, we first identify these confusion
samples by measuring the similarities between output estimates and enrollment
utterances, after which the true target sources are recovered by a subtraction
operation. Experiments show that performance improvement of more than 1dB
SI-SDRi can be brought, which validates the effectiveness of our methods and
emphasizes the impact of the target confusion problem.

The Facial Action Coding System (FACS) encodes the action units (AUs) in
facial images, which has attracted extensive research attention due to its wide
use in facial expression analysis. Many methods that perform well on automatic
facial action unit (AU) detection primarily focus on modeling various types of
AU relations between corresponding local muscle areas, or simply mining global
attention-aware facial features, however, neglect the dynamic interactions
among local-global features. We argue that encoding AU features just from one
perspective may not capture the rich contextual information between regional
and global face features, as well as the detailed variability across AUs,
because of the diversity in expression and individual characteristics. In this
paper, we propose a novel Multi-level Graph Relational Reasoning Network
(termed MGRR-Net) for facial AU detection. Each layer of MGRR-Net performs a
multi-level (i.e., region-level, pixel-wise and channel-wise level) feature
learning. While the region-level feature learning from local face patches
features via graph neural network can encode the correlation across different
AUs, the pixel-wise and channel-wise feature learning via graph attention
network can enhance the discrimination ability of AU features from global face
features. The fused features from the three levels lead to improved AU
discriminative ability. Extensive experiments on DISFA and BP4D AU datasets
show that the proposed approach achieves superior performance than the

High-order tuners are algorithms that show promise in achieving greater
efficiency than classic gradient-based algorithms in identifying the parameters
of parametric models and/or in facilitating the progress of a control or
optimization algorithm whose adaptive behavior relies on such models. For
high-order tuners, robust stability properties, namely uniform global
asymptotic (and exponential) stability, currently rely on a persistent
excitation (PE) condition. In this work, we establish such stability properties
with a novel analysis based on a Matrosov theorem and then show that the PE
requirement can be relaxed via a concurrent learning technique driven by
sampled data points that are sufficiently rich. We show numerically that
concurrent learning may greatly improve efficiency. We incorporate reset
methods that preserve the stability guarantees while providing additional
improvements that may be relevant in applications that demand highly accurate
parameter estimates at relatively low additional cost in computation.

One challenging property lurking in medical datasets is the imbalanced data
distribution, where the frequency of the samples between the different classes
is not balanced. Training a model on an imbalanced dataset can introduce unique
challenges to the learning problem where a model is biased towards the highly
frequent class. Many methods are proposed to tackle the distributional
differences and the imbalanced problem. However, the impact of these approaches
on the learned features is not well studied. In this paper, we look deeper into
the internal units of neural networks to observe how handling data imbalance
affects the learned features. We study several popular cost-sensitive
approaches for handling data imbalance and analyze the feature maps of the
convolutional neural networks from multiple perspectives: analyzing the
alignment of salient features with pathologies and analyzing the
pathology-related concepts encoded by the networks. Our study reveals
differences and insights regarding the trained models that are not reflected by
quantitative metrics such as AUROC and AP and show up only by looking at the
models through a lens.

The acoustic environment can degrade speech quality during communication
(e.g., video call, remote presentation, outside voice recording), and its
impact is often unknown. Objective metrics for speech quality have proven
challenging to develop given the multi-dimensionality of factors that affect
speech quality and the difficulty of collecting labeled data. Hypothesizing the
impact of acoustics on speech quality, this paper presents MOSRA: a
non-intrusive multi-dimensional speech quality metric that can predict room
acoustics parameters (SNR, STI, T60, DRR, and C50) alongside the overall mean
opinion score (MOS) for speech quality. By explicitly optimizing the model to
learn these room acoustics parameters, we can extract more informative features
and improve the generalization for the MOS task when the training data is
limited. Furthermore, we also show that this joint training method enhances the
blind estimation of room acoustics, improving the performance of current
state-of-the-art models. An additional side-effect of this joint prediction is
the improvement in the explainability of the predictions, which is a valuable
feature for many applications.

In many real world medical image classification settings we do not have
access to samples of all possible disease classes, while a robust system is
expected to give high performance in recognizing novel test data. We propose a
generalized zero shot learning (GZSL) method that uses self supervised learning
(SSL) for: 1) selecting anchor vectors of different disease classes; and 2)
training a feature generator. Our approach does not require class attribute
vectors which are available for natural images but not for medical images. SSL
ensures that the anchor vectors are representative of each class. SSL is also
used to generate synthetic features of unseen classes. Using a simpler
architecture, our method matches a state of the art SSL based GZSL method for
natural images and outperforms all methods for medical images. Our method is
adaptable enough to accommodate class attribute vectors when they are available
for natural images.

To maximize the accuracy and increase the overall acceptance of text
classifiers, we propose a framework for the efficient, in-operation moderation
of classifiers' output. Our framework focuses on use cases in which F1-scores
of modern Neural Networks classifiers (ca.~90%) are still inapplicable in
practice. We suggest a semi-automated approach that uses prediction
uncertainties to pass unconfident, probably incorrect classifications to human
moderators. To minimize the workload, we limit the human moderated data to the
point where the accuracy gains saturate and further human effort does not lead
to substantial improvements. A series of benchmarking experiments based on
three different datasets and three state-of-the-art classifiers show that our
framework can improve the classification F1-scores by 5.1 to 11.2% (up to
approx.~98 to 99%), while reducing the moderation load up to 73.3% compared to
a random moderation.

Today, the use of technology is a powerful advantage in every field in the
society. With the advent of development in information and communications
technology (ICT), the process of learning and acquiring new knowledge had
undergone a shift marked by a transition from desktop computing to the
widespread use of mobile technology. In light of the COVID-19 pandemic, the
Commission on Higher Education said that colleges and universities following
the new school calendar will no longer require students to attend face-to-face
classes. One of the state universities that had been affected by this
inevitable situation is the Laguna State Polytechnic University. This study
aims to determine the readiness of the students in shifting to m-learning.
Specifically, it aims to determine the availability of mobile devices,
equipment readiness, technological skills readiness and psychological
readiness. A survey-based methodology was used to obtain the data and
descriptive statistics to analyze the results. It was determined that almost
all of the students own mobile devices, are fully equipped with applications,
have high technological skills and are quite ready in terms of psychological
readiness.

In using the Bayesian network (BN) to construct the complex multistate
system's reliability model as described in Part I, the memory storage
requirements of the node probability table (NPT) will exceed the random access
memory (RAM) of the computer. However, the proposed inference algorithm of Part
I is not suitable for the dependent system. This Part II proposes a novel
method for BN reliability modeling and analysis to apply the compression idea
to the complex multistate dependent system. In this Part II, the dependent
nodes and their parent nodes are equivalent to a block, based on which the
multistate joint probability inference algorithm is proposed to calculate the
joint probability distribution of a block's all nodes. Then, based on the
proposed multistate compression algorithm of Part I, the dependent multistate
inference algorithm is proposed for the complex multistate dependent system.
The use and accuracy of the proposed algorithms are demonstrated in case 1.
Finally, the proposed algorithms are applied to the reliability modeling and
analysis of the satellite attitude control system. The results show that both
Part I and Part II's proposed algorithms make the reliability modeling and
analysis of the complex multistate system feasible.

Neural ranking models (NRMs) have shown remarkable success in recent years,
especially with pre-trained language models. However, deep neural models are
notorious for their vulnerability to adversarial examples. Adversarial attacks
may become a new type of web spamming technique given our increased reliance on
neural information retrieval models. Therefore, it is important to study
potential adversarial attacks to identify vulnerabilities of NRMs before they
are deployed. In this paper, we introduce the Word Substitution Ranking Attack
(WSRA) task against NRMs, which aims to promote a target document in rankings
by adding adversarial perturbations to its text. We focus on the decision-based
black-box attack setting, where the attackers cannot directly get access to the
model information, but can only query the target model to obtain the rank
positions of the partial retrieved list. This attack setting is realistic in
real-world search engines. We propose a novel Pseudo Relevance-based
ADversarial ranking Attack method (PRADA) that learns a surrogate model based
on Pseudo Relevance Feedback (PRF) to generate gradients for finding the
adversarial perturbations. Experiments on two web search benchmark datasets
show that PRADA can outperform existing attack strategies and successfully fool
the NRM with small indiscernible perturbations of text.

Learning-based multi-view stereo (MVS) has by far centered around 3D
convolution on cost volumes. Due to the high computation and memory consumption
of 3D CNN, the resolution of output depth is often considerably limited.
Different from most existing works dedicated to adaptive refinement of cost
volumes, we opt to directly optimize the depth value along each camera ray,
mimicking the range (depth) finding of a laser scanner. This reduces the MVS
problem to ray-based depth optimization which is much more light-weight than
full cost volume optimization. In particular, we propose RayMVSNet which learns
sequential prediction of a 1D implicit field along each camera ray with the
zero-crossing point indicating scene depth. This sequential modeling, conducted
based on transformer features, essentially learns the epipolar line search in
traditional multi-view stereo. We also devise a multi-task learning for better
optimization convergence and depth accuracy. Our method ranks top on both the
DTU and the Tanks \& Temples datasets over all previous learning-based methods,
achieving overall reconstruction score of 0.33mm on DTU and f-score of 59.48%
on Tanks & Temples.

Proteins are fundamental biological entities mediating key roles in cellular
function and disease. This paper introduces a multi-scale graph construction of
a protein -- HoloProt -- connecting surface to structure and sequence. The
surface captures coarser details of the protein, while sequence as primary
component and structure -- comprising secondary and tertiary components --
capture finer details. Our graph encoder then learns a multi-scale
representation by allowing each level to integrate the encoding from level(s)
below with the graph at that level. We test the learned representation on
different tasks, (i.) ligand binding affinity (regression), and (ii.) protein
function prediction (classification). On the regression task, contrary to
previous methods, our model performs consistently and reliably across different
dataset splits, outperforming all baselines on most splits. On the
classification task, it achieves a performance close to the top-performing
model while using 10x fewer parameters. To improve the memory efficiency of our
construction, we segment the multiplex protein surface manifold into molecular
superpixels and substitute the surface with these superpixels at little to no
performance loss.

Despite the success of Graph Neural Networks (GNNs) on various applications,
GNNs encounter significant performance degradation when the amount of
supervision signals, i.e., number of labeled nodes, is limited, which is
expected as GNNs are trained solely based on the supervision obtained from the
labeled nodes. On the other hand,recent self-supervised learning paradigm aims
to train GNNs by solving pretext tasks that do not require any labeled nodes,
and it has shown to even outperform GNNs trained with few labeled nodes.
However, a major drawback of self-supervised methods is that they fall short of
learning class discriminative node representations since no labeled information
is utilized during training. To this end, we propose a novel semi-supervised
method for graphs, GraFN, that leverages few labeled nodes to ensure nodes that
belong to the same class to be grouped together, thereby achieving the best of
both worlds of semi-supervised and self-supervised methods. Specifically, GraFN
randomly samples support nodes from labeled nodes and anchor nodes from the
entire graph. Then, it minimizes the difference between two predicted class
distributions that are non-parametrically assigned by anchor-supports
similarity from two differently augmented graphs. We experimentally show that
GraFN surpasses both the semi-supervised and self-supervised methods in terms
of node classification on real-world graphs. The source code for GraFN is
available at https://github.com/Junseok0207/GraFN.

Human motion prediction is a challenging task due to the dynamic
spatiotemporal correlations in different motion sequences. How to efficiently
represent spatiotemporal correlations and model dynamic correlation variances
between different motion sequences is a challenge for spatiotemporal
representation in motion prediction. In this work, we propose Dynamic
SpatioTemporal Decompose Graph Convolution (DSTD-GC), which decomposes dynamic
spatiotemporal graph modeling with a combination of Dynamic Spatial Graph
Convolution (DS-GC) and Dynamic Temporal Graph Convolution (DT-GC). The dynamic
spatial/temporal correlations in DS-GC/DT-GC are efficiently represented by
Constrained Dynamic Correlation Modeling, which is inspired by the common
constraints in human motion like body connections and dynamic patterns from
different samples. The Constrained Dynamic Correlation Modeling represents the
spatial/temporal graph as a combination of a shared spatial/temporal
correlation and an unshared correlation extraction function. This
spatiotemporal representation is of square space complexity and only requires
28.6% parameters of the state-of-the-art sample-shared decomposition
representation. It also explicitly models sample-specific spatiotemporal
correlation variances. Moreover, we also mathematically reformulate graph
convolutions on spatiotemporal graphs into a unified form and find that DSTD-GC
relaxes certain constraints of other graph convolutions, which leads to a
stronger representation capability. Combining DSTD-GC with prior knowledge like
body connection and temporal context, we propose a powerful spatiotemporal
graph convolution network called DSTD-GCN. On the Human3.6M and CMU Mocap
datasets, DSTD-GCN outperforms state-of-the-art methods by 3.9% - 5.7% in
prediction accuracy with 55.0% - 96.9% parameter reduction.

For many years now, understanding the brain mechanism has been a great
research subject in many different fields. Brain signal processing and
especially electroencephalogram (EEG) has recently known a growing interest
both in academia and industry. One of the main examples is the increasing
number of Brain-Computer Interfaces (BCI) aiming to link brains and computers.
In this paper, we present a novel framework allowing us to retrieve the
attention state, i.e degree of attention given to a specific task, from EEG
signals. While previous methods often consider the spatial relationship in EEG
through electrodes and process them in recurrent or convolutional based
architecture, we propose here to also exploit the spatial and temporal
information with a transformer-based network that has already shown its
supremacy in many machine-learning (ML) related studies, e.g. machine
translation. In addition to this novel architecture, an extensive study on the
feature extraction methods, frequential bands and temporal windows length has
also been carried out. The proposed network has been trained and validated on
two public datasets and achieves higher results compared to state-of-the-art
models. As well as proposing better results, the framework could be used in
real applications, e.g. Attention Deficit Hyperactivity Disorder (ADHD)
symptoms or vigilance during a driving assessment.

Artificial Intelligence and Digital Twins play an integral role in driving
innovation in the domain of intelligent driving. Long short-term memory (LSTM)
is a leading driver in the field of lane change prediction for manoeuvre
anticipation. However, the decision-making process of such models is complex
and non-transparent, hence reducing the trustworthiness of the smart solution.
This work presents an innovative approach and a technical implementation for
explaining lane change predictions of layer normalized LSTMs using Layer-wise
Relevance Propagation (LRP). The core implementation includes consuming live
data from a digital twin on a German highway, live predictions and explanations
of lane changes by extending LRP to layer normalized LSTMs, and an interface
for communicating and explaining the predictions to a human user. We aim to
demonstrate faithful, understandable, and adaptable explanations of lane change
prediction to increase the adoption and trustworthiness of AI systems that
involve humans. Our research also emphases that explainability and
state-of-the-art performance of ML models for manoeuvre anticipation go hand in
hand without negatively affecting predictive effectiveness.

This paper reports on advances to the state-of-the-art deep-learning
disruption prediction models based on the Fusion Recurrent Neural Network
(FRNN) originally introduced a 2019 Nature publication. In particular, the
predictor now features not only the disruption score, as an indicator of the
probability of an imminent disruption, but also a sensitivity score in
real-time to indicate the underlying reasons for the imminent disruption. This
adds valuable physics-interpretability for the deep-learning model and can
provide helpful guidance for control actuators now that it is fully implemented
into a modern Plasma Control System (PCS). The advance is a significant step
forward in moving from modern deep-learning disruption prediction to real-time
control and brings novel AI-enabled capabilities relevant for application to
the future burning plasma ITER system. Our analyses use large amounts of data
from JET and DIII-D vetted in the earlier NATURE publication. In addition to
when a shot is predicted to disrupt, this paper addresses reasons why by
carrying out sensitivity studies. FRNN is accordingly extended to use many more
channels of information, including measured DIII-D signals such as (i) the
n1rms signal that is correlated with the n =1 modes with finite frequency,
including neoclassical tearing mode and sawtooth dynamics, (ii) the bolometer
data indicative of plasma impurity content, and (iii) q-min, the minimum value
of the safety factor relevant to the key physics of kink modes. The additional
channels and interpretability features expand the ability of the deep learning
FRNN software to provide information about disruption subcategories as well as
more precise and direct guidance for the actuators in a plasma control system.

Recent advancement in the field of pervasive healthcare monitoring systems
causes the generation of a huge amount of lifelog data in real-time. Chronic
diseases are one of the most serious health challenges in developing and
developed countries. According to WHO, this accounts for 73% of all deaths and
60% of the global burden of diseases. Chronic disease classification models are
now harnessing the potential of lifelog data to explore better healthcare
practices. This paper is to construct an optimal feature selection-based
unsupervised logistic regression model (OFS-ULR) to classify chronic diseases.
Since lifelog data analysis is crucial due to its sensitive nature; thus the
conventional classification models show limited performance. Therefore,
designing new classifiers for the classification of chronic diseases using
lifelog data is the need of the age. The vital part of building a good model
depends on pre-processing of the dataset, identifying important features, and
then training a learning algorithm with suitable hyper parameters for better
performance. The proposed approach improves the performance of existing methods
using a series of steps such as (i) removing redundant or invalid instances,
(ii) making the data labelled using clustering and partitioning the data into
classes, (iii) identifying the suitable subset of features by applying either
some domain knowledge or selection algorithm, (iv) hyper parameter tuning for
models to get best results, and (v) performance evaluation using Spark
streaming environment. For this purpose, two-time series datasets are used in
the experiment to compute the accuracy, recall, precision, and f1-score. The
experimental analysis proves the suitability of the proposed approach as
compared to the conventional classifiers and our newly constructed model
achieved highest accuracy and reduced training complexity among all among all.

The encoder-decoder structure has significantly improved performance in many
vision tasks by fusing low-level and high-level feature maps. However, this
approach can hardly extract sufficient context information for pixel-wise
segmentation. In addition, extracting similar low-level features at multiple
scales could lead to redundant information. To tackle these issues, we propose
Subspace Pyramid Fusion Network (SPFNet). Specifically, we combine pyramidal
module and context aggregation module to exploit the impact of
multi-scale/global context information. At first, we construct a Subspace
Pyramid Fusion Module (SPFM) based on Reduced Pyramid Pooling (RPP). Then, we
propose the Efficient Global Context Aggregation (EGCA) module to capture
discriminative features by fusing multi-level global context features. Finally,
we add decoder-based subpixel convolution to retrieve the high-resolution
feature maps, which can help select category localization details. SPFM learns
separate RPP for each feature subspace to capture multi-scale feature
representations, which is more useful for semantic segmentation. EGCA adopts
shuffle attention mechanism to enhance communication across different
sub-features. Experimental results on two well-known semantic segmentation
datasets, including Camvid and Cityscapes, show that our proposed method is
competitive with other state-of-the-art methods.

In this paper, we propose a novel lip-to-speech generative adversarial
network, Visual Context Attentional GAN (VCA-GAN), which can jointly model
local and global lip movements during speech synthesis. Specifically, the
proposed VCA-GAN synthesizes the speech from local lip visual features by
finding a mapping function of viseme-to-phoneme, while global visual context is
embedded into the intermediate layers of the generator to clarify the ambiguity
in the mapping induced by homophene. To achieve this, a visual context
attention module is proposed where it encodes global representations from the
local visual features, and provides the desired global visual context
corresponding to the given coarse speech representation to the generator
through audio-visual attention. In addition to the explicit modelling of local
and global visual representations, synchronization learning is introduced as a
form of contrastive learning that guides the generator to synthesize a speech
in sync with the given input lip movements. Extensive experiments demonstrate
that the proposed VCA-GAN outperforms existing state-of-the-art and is able to
effectively synthesize the speech from multi-speaker that has been barely
handled in the previous works.

Model compression is important in federated learning (FL) with large models
to reduce communication cost. Prior works have been focusing on sparsification
based compression that could desparately affect the global model accuracy. In
this work, we propose a new scheme for upstream communication where instead of
transmitting the model update, each client learns and transmits a light-weight
synthetic dataset such that using it as the training data, the model performs
similarly well on the real training data. The server will recover the local
model update via the synthetic data and apply standard aggregation. We then
provide a new algorithm FedSynth to learn the synthetic data locally.
Empirically, we find our method is comparable/better than random masking
baselines in all three common federated learning benchmark datasets.

Intonations play an important role in delivering the intention of a speaker.
However, current end-to-end TTS systems often fail to model proper intonations.
To alleviate this problem, we propose a novel, intuitive method to synthesize
speech in different intonations using predefined intonation templates. Prior to
TTS model training, speech data are grouped into intonation templates in an
unsupervised manner. Two proposed modules are added to the end-to-end TTS
framework: an intonation predictor and an intonation encoder. The intonation
predictor recommends a suitable intonation template to the given text. The
intonation encoder, attached to the text encoder output, synthesizes speech
abiding the requested intonation template. Main contributions of our paper are:
(a) an easy-to-use intonation control system covering a wide range of users;
(b) better performance in wrapping speech in a requested intonation with
improved objective and subjective evaluation; and (c) incorporating a
pre-trained language model for intonation modelling. Audio samples are
available at https://srtts.github.io/IntoTTS.

Recognizing speech from silent lip movement, which is called lip reading, is
a challenging task due to 1) the inherent information insufficiency of lip
movement to fully represent the speech, and 2) the existence of homophenes that
have similar lip movement with different pronunciations. In this paper, we try
to alleviate the aforementioned two challenges in lip reading by proposing a
Multi-head Visual-audio Memory (MVM). Firstly, MVM is trained with audio-visual
datasets and remembers audio representations by modelling the
inter-relationships of paired audio-visual representations. At the inference
stage, visual input alone can extract the saved audio representation from the
memory by examining the learned inter-relationships. Therefore, the lip reading
model can complement the insufficient visual information with the extracted
audio representations. Secondly, MVM is composed of multi-head key memories for
saving visual features and one value memory for saving audio knowledge, which
is designed to distinguish the homophenes. With the multi-head key memories,
MVM extracts possible candidate audio features from the memory, which allows
the lip reading model to consider the possibility of which pronunciations can
be represented from the input lip movement. This also can be viewed as an
explicit implementation of the one-to-many mapping of viseme-to-phoneme.
Moreover, MVM is employed in multi-temporal levels to consider the context when
retrieving the memory and distinguish the homophenes. Extensive experimental
results verify the effectiveness of the proposed method in lip reading and in
distinguishing the homophenes.

Monocular visual odometry (VO) is an important task in robotics and computer
vision. Thus far, how to build accurate and robust monocular VO systems that
can work well in diverse scenarios remains largely unsolved. In this paper, we
propose a framework to exploit monocular depth estimation for improving VO. The
core of our framework is a monocular depth estimation module with a strong
generalization capability for diverse scenes. It consists of two separate
working modes to assist the localization and mapping. With a single monocular
image input, the depth estimation module predicts a relative depth to help the
localization module on improving the accuracy. With a sparse depth map and an
RGB image input, the depth estimation module can generate accurate
scale-consistent depth for dense mapping. Compared with current learning-based
VO methods, our method demonstrates a stronger generalization ability to
diverse scenes. More significantly, our framework is able to boost the
performances of existing geometry-based VO methods by a large margin.

While personalization increases the utility of recommender systems, it also
brings the issue of filter bubbles. E.g., if the system keeps exposing and
recommending the items that the user is interested in, it may also make the
user feel bored and less satisfied. Existing work studies filter bubbles in
static recommendation, where the effect of overexposure is hard to capture. In
contrast, we believe it is more meaningful to study the issue in interactive
recommendation and optimize long-term user satisfaction. Nevertheless, it is
unrealistic to train the model online due to the high cost. As such, we have to
leverage offline training data and disentangle the causal effect on user
  To achieve this goal, we propose a counterfactual interactive recommender
system (CIRS) that augments offline reinforcement learning (offline RL) with
causal inference. The basic idea is to first learn a causal user model on
historical data to capture the overexposure effect of items on user
satisfaction. It then uses the learned causal user model to help the planning
of the RL policy. To conduct evaluation offline, we innovatively create an
authentic RL environment (KuaiEnv) based on a real-world fully observed user
rating dataset. The experiments show the effectiveness of CIRS in bursting
filter bubbles and achieving long-term success in interactive recommendation.
The implementation of CIRS is available via
https://github.com/chongminggao/CIRS-codes.

In this paper, we introduce a novel audio-visual multi-modal bridging
framework that can utilize both audio and visual information, even with
uni-modal inputs. We exploit a memory network that stores source (i.e., visual)
and target (i.e., audio) modal representations, where source modal
representation is what we are given, and target modal representations are what
we want to obtain from the memory network. We then construct an associative
bridge between source and target memories that considers the interrelationship
between the two memories. By learning the interrelationship through the
associative bridge, the proposed bridging framework is able to obtain the
target modal representations inside the memory network, even with the source
modal input only, and it provides rich information for its downstream tasks. We
apply the proposed framework to two tasks: lip reading and speech
reconstruction from silent video. Through the proposed associative bridge and
modality-specific memories, each task knowledge is enriched with the recalled
audio context, achieving state-of-the-art performance. We also verify that the
associative bridge properly relates the source and target memories.

We propose a probabilistic shape completion method extended to the continuous
geometry of large-scale 3D scenes. Real-world scans of 3D scenes suffer from a
considerable amount of missing data cluttered with unsegmented objects. The
problem of shape completion is inherently ill-posed, and high-quality result
requires scalable solutions that consider multiple possible outcomes. We employ
the Generative Cellular Automata that learns the multi-modal distribution and
transform the formulation to process large-scale continuous geometry. The local
continuous shape is incrementally generated as a sparse voxel embedding, which
contains the latent code for each occupied cell. We formally derive that our
training objective for the sparse voxel embedding maximizes the variational
lower bound of the complete shape distribution and therefore our progressive
generation constitutes a valid generative model. Experiments show that our
model successfully generates diverse plausible scenes faithful to the input,
especially when the input suffers from a significant amount of missing data. We
also demonstrate that our approach outperforms deterministic models even in
less ambiguous cases with a small amount of missing data, which infers that
probabilistic formulation is crucial for high-quality geometry completion on
input scans exhibiting any levels of completeness.

Dense human pose estimation is the problem of learning dense correspondences
between RGB images and the surfaces of human bodies, which finds various
applications, such as human body reconstruction, human pose transfer, and human
action recognition. Prior dense pose estimation methods are all based on Mask
R-CNN framework and operate in a top-down manner of first attempting to
identify a bounding box for each person and matching dense correspondences in
each bounding box. Consequently, these methods lack robustness due to their
critical dependence on the Mask R-CNN detection, and the runtime increases
drastically as the number of persons in the image increases. We therefore
propose a novel alternative method for solving the dense pose estimation
problem, called Direct Dense Pose (DDP). DDP first predicts the instance mask
and global IUV representation separately and then combines them together. We
also propose a simple yet effective 2D temporal-smoothing scheme to alleviate
the temporal jitters when dealing with video data. Experiments demonstrate that
DDP overcomes the limitations of previous top-down baseline methods and
achieves competitive accuracy. In addition, DDP is computationally more
efficient than previous dense pose estimation methods, and it reduces jitters
when applied to a video sequence, which is a problem plaguing the previous
methods.

In this paper, a fault-tolerant approach to mitigate transient and permanent
faults of arithmetic and logic operations of embedded processors called FT-EALU
is proposed. In this method, each operation is replicated in time and the
derived final results are voted to generate the final output. To consider the
effect of permanent faults, replicating identical operations in time is not
sufficient, and diversifying the operands is required. To this aim in FT-EALU,
we consider three distinct versions of input data and apply the target
operation to them serially in time. To avoid high time overhead, we employ
simple operators such as shift and swap to make an appropriate diversion in
input data. Our proposed fault tolerance approach passes the replicated and
diverse results to a novel weighted voter that is designed based on the
reward/punishment strategy. For each version of execution, based on the
proposed weighting approach a corresponding weight according to its correction
capability confronting several faulty scenarios is defined. This weight defines
the reliability of the result of each version of execution and determines its
effect on the final result. The final result is generated bit by bit based on
the weight of each execution and its computed result. These weights are
determined statically through a design-time learning scheme according to
applying several types of faults on various data bits. Based on the capability
of execution versions on mitigating the permanent faults, positive or negative
scores are assigned to them. These scores are integrated for several cases and
normalized to derive the appropriate weight of each execution at bit level.
Several experiments are performed to show the efficiency of our proposed
approach and based on them, FT-EALU is capable of correcting about 84.93% and
69.71% of permanent injected faults on single and double bits of input data.

We present Monte Carlo Physarum Machine: a computational model suitable for
reconstructing continuous transport networks from sparse 2D and 3D data. MCPM
is a probabilistic generalization of Jones's 2010 agent-based model for
simulating the growth of Physarum polycephalum slime mold. We compare MCPM to
Jones's work on theoretical grounds, and describe a task-specific variant
designed for reconstructing the large-scale distribution of gas and dark matter
in the Universe known as the Cosmic web. To analyze the new model, we first
explore MCPM's self-patterning behavior, showing a wide range of continuous
network-like morphologies -- called "polyphorms" -- that the model produces
from geometrically intuitive parameters. Applying MCPM to both simulated and
observational cosmological datasets, we then evaluate its ability to produce
consistent 3D density maps of the Cosmic web. Finally, we examine other
possible tasks where MCPM could be useful, along with several examples of
fitting to domain-specific data as proofs of concept.

Attention mechanisms have been very popular in deep neural networks, where
the Transformer architecture has achieved great success in not only natural
language processing but also visual recognition applications. Recently, a new
Transformer module, applying on batch dimension rather than spatial/channel
dimension, i.e., BatchFormer [18], has been introduced to explore sample
relationships for overcoming data scarcity challenges. However, it only works
with image-level representations for classification. In this paper, we devise a
more general batch Transformer module, BatchFormerV2, which further enables
exploring sample relationships for dense representation learning. Specifically,
when applying the proposed module, it employs a two-stream pipeline during
training, i.e., either with or without a BatchFormerV2 module, where the
batchformer stream can be removed for testing. Therefore, the proposed method
is a plug-and-play module and can be easily integrated into different vision
Transformers without any extra inference cost. Without bells and whistles, we
show the effectiveness of the proposed method for a variety of popular visual
recognition tasks, including image classification and two important dense
prediction tasks: object detection and panoptic segmentation. Particularly,
BatchFormerV2 consistently improves current DETR-based detection methods (e.g.,
DETR, Deformable-DETR, Conditional DETR, and SMCA) by over 1.3%. Code will be
made publicly available.

Stress, anxiety, and nervousness are all high-risk health states in everyday
life. Previously, stress levels were determined by speaking with people and
gaining insight into what they had experienced recently or in the past.
Typically, stress is caused by an incidence that occurred a long time ago, but
sometimes it is triggered by unknown factors. This is a challenging and complex
task, but recent research advances have provided numerous opportunities to
automate it. The fundamental features of most of these techniques are electro
dermal activity (EDA) and heart rate values (HRV). We utilized an accelerometer
to measure body motions to solve this challenge. The proposed novel method
employs a test that measures a subject's electrocardiogram (ECG), galvanic skin
values (GSV), HRV values, and body movements in order to provide a low-cost and
time-saving solution for detecting stress lifestyle disease in modern times
using cyber physical systems. This study provides a new hybrid model for
lifestyle disease classification that decreases execution time while picking
the best collection of characteristics and increases classification accuracy.
The developed approach is capable of dealing with the class imbalance problem
by using WESAD (wearable stress and affect dataset) dataset. The new model uses
the Grid search (GS) method to select an optimized set of hyper parameters, and
it uses a combination of the Correlation coefficient based Recursive feature
elimination (CoC-RFE) method for optimal feature selection and gradient
boosting as an estimator to classify the dataset, which achieves high accuracy
and helps to provide smart, accurate, and high-quality healthcare systems. To
demonstrate the validity and utility of the proposed methodology, its
performance is compared to those of other well-established machine learning
models.

There is rising interest in integrating signal and image processing pipelines
into deep learning training to incorporate more domain knowledge. This can lead
to deep neural networks that are trained more robustly and with limited data,
as well as the capability to solve ill-posed inverse problems. In particular,
there is rising interest in differentiable rendering, which allows explicitly
modeling geometric priors and constraints in the optimization pipeline using
first-order methods such as backpropagation. Existing efforts in differentiable
rendering have focused on imagery from electro-optical sensors, particularly
conventional RGB-imagery. In this work, we propose an approach for
differentiable rendering of Synthetic Aperture Radar (SAR) imagery, which
combines methods from 3D computer graphics with neural rendering. We
demonstrate the approach on the inverse graphics problem of 3D Object
Reconstruction from limited SAR imagery using high-fidelity simulated SAR data.

Embeddings play an important role in many recent end-to-end solutions for
language processing problems involving more than one data modality. Although
there has been some effort to understand the properties of single-modality
embedding spaces, particularly that of text, their cross-modal counterparts are
less understood. In this work, we study a joint speech-text embedding space
trained for semantic matching by minimizing the distance between paired
utterance and transcription inputs. This was done through dual encoders in a
teacher-student model setup, with a pretrained language model acting as the
teacher and a transformer-based speech encoder as the student. We extend our
method to incorporate automatic speech recognition through both pretraining and
multitask scenarios and found that both approaches improve semantic matching.
Multiple techniques were utilized to analyze and evaluate cross-modal semantic
alignment of the embeddings: a quantitative retrieval accuracy metric,
zero-shot classification to investigate generalizability, and probing of the
encoders to observe the extent of knowledge transfer from one modality to
another.

We propose a new learning framework, signal propagation (sigprop), for
propagating a learning signal and updating neural network parameters via a
forward pass, as an alternative to backpropagation. In sigprop, there is only
the forward path for inference and learning. So, there are no structural or
computational constraints necessary for learning to take place, beyond the
inference model itself, such as feedback connectivity, weight transport, or a
backward pass, which exist under backpropagation based approaches. That is,
sigprop enables global supervised learning with only a forward path. This is
ideal for parallel training of layers or modules. In biology, this explains how
neurons without feedback connections can still receive a global learning
signal. In hardware, this provides an approach for global supervised learning
without backward connectivity. Sigprop by construction has compatibility with
models of learning in the brain and in hardware than backpropagation, including
alternative approaches relaxing learning constraints. We also demonstrate that
sigprop is more efficient in time and memory than they are. To further explain
the behavior of sigprop, we provide evidence that sigprop provides useful
learning signals in context to backpropagation. To further support relevance to
biological and hardware learning, we use sigprop to train continuous time
neural networks with Hebbian updates, and train spiking neural networks with
only the voltage or with biologically and hardware compatible surrogate

Generating high quality texts with high diversity is important for many NLG
applications, but current methods mostly focus on building deterministic models
to generate higher quality texts and do not provide many options for promoting
diversity. In this work, we present a novel latent structured variable model to
generate high quality texts by enriching contextual representation learning of
encoder-decoder models. Specifically, we introduce a stochastic function to map
deterministic encoder hidden states into random context variables. The proposed
stochastic function is sampled from a Gaussian process prior to (1) provide
infinite number of joint Gaussian distributions of random context variables
(diversity-promoting) and (2) explicitly model dependency between context
variables (accurate-encoding). To address the learning challenge of Gaussian
processes, we propose an efficient variational inference approach to
approximate the posterior distribution of random context variables. We evaluate
our method in two typical text generation tasks: paraphrase generation and text
style transfer. Experimental results on benchmark datasets demonstrate that our
method improves the generation quality and diversity compared with other

This paper aims at representing animatable photo-realistic humans under novel
views and poses. Recent work has shown significant progress with dynamic scenes
by exploring shared canonical neural radiance fields. However learning a
user-controlled model for novel poses remains a challenging task. To tackle
this problem, we introduce a novel method to integrate observations across
frames and encode the appearance at each individual frame by utilizing the
human pose that models the body shape and point clouds which cover partial part
of the human as the input. Specifically, our method simultaneously learns a
shared set of latent codes anchored to the human pose among frames, and learns
an appearance-dependent code anchored to incomplete point clouds generated by
monocular RGB-D at each frame. A human pose-based code models the shape of the
performer whereas a point cloud based code predicts details and reasons about
missing structures at the unseen poses. To further recover non-visible regions
in query frames, we utilize a temporal transformer to integrate features of
points in query frames and tracked body points from automatically-selected key
frames. Experiments on various sequences of humans in motion show that our
method significantly outperforms existing works under unseen poses and novel
views given monocular RGB-D videos as input.

The task of developing a machine learning (ML) model for a particular problem
is inherently open-ended, and there is an unbounded set of possible solutions.
Steps of the ML development pipeline, such as feature engineering, loss
function specification, data imputation, and dimensionality reduction, require
the engineer to consider an extensive and often infinite array of
possibilities. Successfully identifying high-performing solutions for an
unfamiliar dataset or problem requires a mix of mathematical prowess and
creativity applied towards inventing and repurposing novel ML methods. Here, we
explore the feasibility of hosting crowdsourced ML challenges to facilitate a
breadth-first exploration of open-ended research problems, thereby expanding
the search space of problem solutions beyond what a typical ML team could
viably investigate. We develop MLPro, a system which combines the notion of
open-ended ML coding problems with the concept of an automatic online code
judging platform. To conduct a pilot evaluation of this paradigm, we
crowdsource several open-ended ML challenges to ML and data science
practitioners. We describe results from two separate challenges. We find that
for sufficiently unconstrained and complex problems, many experts submit
similar solutions, but some experts provide unique solutions which outperform
the "typical" solution class. We suggest that automated expert crowdsourcing
systems such as MLPro have the potential to accelerate ML engineering

Although the recursive logit (RL) model has been recently popular and has led
to many applications and extensions, an important numerical issue with respect
to the computation of value functions remains unsolved. This issue is
particularly significant for model estimation, during which the parameters are
updated every iteration and may violate the model feasible condition. To solve
this numerical issue of the value function in the model estimation, this study
performs an extensive analysis of a prism-constrained RL (Prism-RL) model
proposed by Oyama and Hato (2019), which has a path set constrained by the
prism defined based upon a state-extended network representation. The numerical
experiments have shown two important properties of the Prism-RL model for
parameter estimation. First, the prism-based approach allows for a stable
estimation regardless of the initial and true parameter values, even in cases
where the original RL model cannot be estimated. We also successfully captured
a positive effect of the presence of street green on pedestrian route choice in
a real application. Second, the Prism-RL model achieved higher goodness of fit
and prediction performance than the RL model, by implicitly restricting paths
with large detour or many loops. Defining the prism-based path set in a data
oriented manner, we demonstrated the possibility of the Prism-RL model
describing more realistic route choice behavior. Stable capture of positive
network attributes while retaining the diversity of path alternatives
significantly extends the practical applicability of the RL model.

It is common in machine learning and statistics to use symmetries derived
from expert knowledge to simplify problems or improve performance, using
methods like data augmentation or penalties. In this paper we consider the
unsupervised and semi-supervised problems of learning such symmetries in a
distribution directly from data in a model-free fashion. We show that in the
worst case this problem is as difficult as the graph automorphism problem.
However, if we restrict to the case where the covariance matrix has unique
eigenvalues, then the eigenvectors will also be eigenvectors of the symmetry
transformation. If we further restrict to finding orthogonal symmetries, then
the eigenvalues will be either be 1 or -1, and the problem reduces to
determining which eigenvectors are which. We develop and compare theoretically
and empirically the effectiveness of different methods of selecting which
eigenvectors should have eigenvalue -1 in the symmetry transformation, and
discuss how to extend this approach to non-orthogonal cases where we have
labels

Any-shot image classification allows to recognize novel classes with only a
few or even zero samples. For the task of zero-shot learning, visual attributes
have been shown to play an important role, while in the few-shot regime, the
effect of attributes is under-explored. To better transfer attribute-based
knowledge from seen to unseen classes, we argue that an image representation
with integrated attribute localization ability would be beneficial for
any-shot, i.e. zero-shot and few-shot, image classification tasks. To this end,
we propose a novel representation learning framework that jointly learns
discriminative global and local features using only class-level attributes.
While a visual-semantic embedding layer learns global features, local features
are learned through an attribute prototype network that simultaneously
regresses and decorrelates attributes from intermediate features. Furthermore,
we introduce a zoom-in module that localizes and crops the informative regions
to encourage the network to learn informative features explicitly. We show that
our locality augmented image representations achieve a new state-of-the-art on
challenging benchmarks, i.e. CUB, AWA2, and SUN. As an additional benefit, our
model points to the visual evidence of the attributes in an image, confirming
the improved attribute localization ability of our image representation. The
attribute localization is evaluated quantitatively with ground truth part
annotations, qualitatively with visualizations, and through well-designed user
studies.

Fourier neural operators (FNOs) are a recently introduced neural network
architecture for learning solution operators of partial differential equations
(PDEs), which have been shown to perform significantly better than comparable
deep learning approaches. Once trained, FNOs can achieve speed-ups of multiple
orders of magnitude over conventional numerical PDE solvers. However, due to
the high dimensionality of their input data and network weights, FNOs have so
far only been applied to two-dimensional or small three-dimensional problems.
To remove this limited problem-size barrier, we propose a model-parallel
version of FNOs based on domain-decomposition of both the input data and
network weights. We demonstrate that our model-parallel FNO is able to predict
time-varying PDE solutions of over 3.2 billion variables on Summit using up to
768 GPUs and show an example of training a distributed FNO on the Azure cloud
for simulating multiphase CO$_2$ dynamics in the Earth's subsurface.

Blockchain-based federated learning has gained significant interest over the
last few years with the increasing concern for data privacy, advances in
machine learning, and blockchain innovation. However, gaps in security and
scalability hinder the development of real-world applications. In this study,
we propose ScaleSFL, which is a scalable blockchain-based sharding solution for
federated learning. ScaleSFL supports interoperability by separating the
off-chain federated learning component in order to verify model updates instead
of controlling the entire federated learning flow. We implemented ScaleSFL as a
proof-of-concept prototype system using Hyperledger Fabric to demonstrate the
feasibility of the solution. We present a performance evaluation of results
collected through Hyperledger Caliper benchmarking tools conducted on model
creation. Our evaluation results show that sharding can improve validation
performance linearly while remaining efficient and secure.

The segmentation of diseases is a popular topic explored by researchers in
the field of machine learning. Brain tumors are extremely dangerous and require
the utmost precision to segment for a successful surgery. Patients with tumors
usually take 4 MRI scans, T1, T1gd, T2, and FLAIR, which are then sent to
radiologists to segment and analyze for possible future surgery. To create a
second segmentation, it would be beneficial to both radiologists and patients
in being more confident in their conclusions. We propose using a method
performed by radiologists called image segmentation and applying it to machine
learning models to prove a better segmentation. Using Mask R-CNN, its ResNet
backbone being pre-trained on the RSNA pneumonia detection challenge dataset,
we can train a model on the Brats2020 Brain Tumor dataset. Center for
Biomedical Image Computing & Analytics provides MRI data on patients with and
without brain tumors and the corresponding segmentations. We can see how well
the method of image subtraction works by comparing it to models without image
subtraction through DICE coefficient (F1 score), recall, and precision on the
untouched test set. Our model performed with a DICE coefficient of 0.75 in
comparison to 0.69 without image subtraction. To further emphasize the
usefulness of image subtraction, we compare our final model to current
state-of-the-art models to segment tumors from MRI scans.

In this paper, classical and continuous variable (CV) quantum neural network
hybrid multiclassifiers are presented using the MNIST dataset. The combination
of cutoff dimension and probability measurement method in the CV model allows a
quantum circuit to produce output vectors of size equal to n raised to the
power of n where n represents cutoff dimension and m, the number of qumodes.
They are then translated as one-hot encoded labels, padded with an appropriate
number of zeros. The total of eight different classifiers are built using
2,3,...,8 qumodes, based on the binary classifier architecture proposed in
Continuous variable quantum neural networks. The displacement gate and the Kerr
gate in the CV model allow for the bias addition and nonlinear activation
components of classical neural networks to quantum. The classifiers are
composed of a classical feedforward neural network, a quantum data encoding
circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset
of 600 samples, a 4 qumode hybrid classifier achieves 100% training accuracy.

With the development of autonomous vehicle technology, the controller area
network (CAN) bus has become the de facto standard for an in-vehicle
communication system because of its simplicity and efficiency. However, without
any encryption and authentication mechanisms, the in-vehicle network using the
CAN protocol is susceptible to a wide range of attacks. Many studies, which are
mostly based on machine learning, have proposed installing an intrusion
detection system (IDS) for anomaly detection in the CAN bus system. Although
machine learning methods have many advantages for IDS, previous models usually
require a large amount of labeled data, which results in high time and labor
costs. To handle this problem, we propose a novel semi-supervised
learning-based convolutional adversarial autoencoder model in this paper. The
proposed model combines two popular deep learning models: autoencoder and
generative adversarial networks. First, the model is trained with unlabeled
data to learn the manifolds of normal and attack patterns. Then, only a small
number of labeled samples are used in supervised training. The proposed model
can detect various kinds of message injection attacks, such as DoS, fuzzy, and
spoofing, as well as unknown attacks. The experimental results show that the
proposed model achieves the highest F1 score of 0.99 and a low error rate of
0.1\% with limited labeled data compared to other supervised methods. In
addition, we show that the model can meet the real-time requirement by
analyzing the model complexity in terms of the number of trainable parameters
and inference time. This study successfully reduced the number of model
parameters by five times and the inference time by eight times, compared to a
state-of-the-art model.

The conventional sliced Wasserstein is defined between two probability
measures that have realizations as vectors. When comparing two probability
measures over images, practitioners first need to vectorize images and then
project them to one-dimensional space by using matrix multiplication between
the sample matrix and the projection matrix. After that, the sliced Wasserstein
is evaluated by averaging the two corresponding one-dimensional projected
probability measures. However, this approach has two limitations. The first
limitation is that the spatial structure of images is not captured efficiently
by the vectorization step; therefore, the later slicing process becomes harder
to gather the discrepancy information. The second limitation is memory
inefficiency since each slicing direction is a vector that has the same
dimension as the images. To address these limitations, we propose novel slicing
methods for sliced Wasserstein between probability measures over images that
are based on the convolution operators. We derive convolution sliced
Wasserstein (CSW) and its variants via incorporating stride, dilation, and
non-linear activation function into the convolution operators. We investigate
the metricity of CSW as well as its sample complexity, its computational
complexity, and its connection to conventional sliced Wasserstein distances.
Finally, we demonstrate the favorable performance of CSW over the conventional
sliced Wasserstein in comparing probability measures over images and in
training deep generative modeling on images.

In existing image classification systems that use deep neural networks, the
knowledge needed for image classification is implicitly stored in model
parameters. If users want to update this knowledge, then they need to fine-tune
the model parameters. Moreover, users cannot verify the validity of inference
results or evaluate the contribution of knowledge to the results. In this
paper, we investigate a system that stores knowledge for image classification,
such as image feature maps, labels, and original images, not in model
parameters but in external high-capacity storage. Our system refers to the
storage like a database when classifying input images. To increase knowledge,
our system updates the database instead of fine-tuning model parameters, which
avoids catastrophic forgetting in incremental learning scenarios. We revisit a
kNN (k-Nearest Neighbor) classifier and employ it in our system. By analyzing
the neighborhood samples referred by the kNN algorithm, we can interpret how
knowledge learned in the past is used for inference results. Our system
achieves 79.8% top-1 accuracy on the ImageNet dataset without fine-tuning model
parameters after pretraining, and 90.8% accuracy on the Split CIFAR-100 dataset
in the task incremental learning setting.

Neutron stars (NS) are compact objects with strong gravitational fields, and
a matter composition subject to extreme physical conditions. The properties of
strongly interacting matter at ultra-high densities and temperatures impose a
big challenge to our understanding and modelling tools. Some difficulties are
critical, since one cannot reproduce such conditions in our laboratories or
assess them purely from astronomical observations. The information we have
about neutron star interiors are often extracted indirectly, e.g., from the
star mass-radius relation. The mass and radius are global quantities and still
have a significant uncertainty, which leads to great variability in studying
the micro-physics of the neutron star interior. This leaves open many questions
in nuclear astrophysics and the suitable equation of state (EoS) of NS.
Recently, new observations appear to constrain the mass-radius and consequently
has helped to close some open questions. In this work, utilizing modern machine
learning techniques, we analyze the NS mass-radius (M-R) relationship for a set
of EoS containing a variety of physical models. Our objective is to determine
patterns through the M-R data analysis and develop tools to understand the EoS
of neutron stars in forthcoming works.

Current methods for few-shot fine-tuning of pretrained masked language models
(PLMs) require carefully engineered prompts and verbalizers for each new task
to convert examples into a cloze-format that the PLM can score. In this work,
we propose PERFECT, a simple and efficient method for few-shot fine-tuning of
PLMs without relying on any such handcrafting, which is highly effective given
as few as 32 data points. PERFECT makes two key design choices: First, we show
that manually engineered task prompts can be replaced with task-specific
adapters that enable sample-efficient fine-tuning and reduce memory and storage
costs by roughly factors of 5 and 100, respectively. Second, instead of using
handcrafted verbalizers, we learn new multi-token label embeddings during
fine-tuning, which are not tied to the model vocabulary and which allow us to
avoid complex auto-regressive decoding. These embeddings are not only learnable
from limited data but also enable nearly 100x faster training and inference.
Experiments on a wide range of few-shot NLP tasks demonstrate that PERFECT,
while being simple and efficient, also outperforms existing state-of-the-art
few-shot learning methods. Our code is publicly available at
https://github.com/facebookresearch/perfect.git.

Current language generation models suffer from issues such as repetition,
incoherence, and hallucinations. An often-repeated hypothesis is that this
brittleness of generation models is caused by the training and the generation
procedure mismatch, also referred to as exposure bias. In this paper, we verify
this hypothesis by analyzing exposure bias from an imitation learning
perspective. We show that exposure bias leads to an accumulation of errors,
analyze why perplexity fails to capture this accumulation, and empirically show
that this accumulation results in poor generation quality. Source code to
reproduce these experiments is available at
https://github.com/kushalarora/quantifying_exposure_bias

We present the MSA-to-protein transformer, a generative model of protein
sequences conditioned on protein families represented by multiple sequence
alignments (MSAs). Unlike existing approaches to learning generative models of
protein families, the MSA-to-protein transformer conditions sequence generation
directly on a learned encoding of the multiple sequence alignment,
circumventing the need for fitting dedicated family models. By training on a
large set of well-curated multiple sequence alignments in Pfam, our
MSA-to-protein transformer generalizes well to protein families not observed
during training and outperforms conventional family modeling approaches,
especially when MSAs are small. Our generative approach accurately models
epistasis and indels and allows for exact inference and efficient sampling
unlike other approaches. We demonstrate the protein sequence modeling
capabilities of our MSA-to-protein transformer and compare it with alternative
sequence modeling approaches in comprehensive benchmark experiments.

People spend approximately 90% of their lives indoors, and thus arguably, the
indoor space design can significantly influence occupant well-being. Adequate
views to the outside are one of the most cited indoor qualities related to
occupant well-being. However, due to urbanization and densification trends,
designers may have difficulties in providing vistas and views to the outside
with an assortment of content, which can support the needs of their occupants.
To better understand occupant view satisfaction and provide reliable design
feedback to architects, existing view satisfaction data must be expanded to
capture a wider variety of view scenarios and occupants. Most related research
remains challenging in architectural practice due to a lack of easy-to-use
early-design analysis tools. However, early assessment of view can be
advantageous as design decisions in early design, such as building orientation,
plan layout, and facade design, can improve the view quality. This paper,
hence, presents results from a 181 participant view satisfaction survey with
590 window views. The survey data is used to train a tree-regression model to
predict view satisfaction. The prediction performance was compared to an
existing view assessment framework through case studies. The result showed that
the new prediction is more accurate to the surveyed result than the framework.
Further, the prediction performance was generally high for most responses,
verifying the reliability. To facilitate view analysis in early design, this
paper describes integrating the satisfaction prediction model and a ray-casting
tool to compute view parameters in the CAD environment.

Centaurs are half-human, half-AI decision-makers where the AI's goal is to
complement the human. To do so, the AI must be able to recognize the goals and
constraints of the human and have the means to help them. We present a novel
formulation of the interaction between the human and the AI as a sequential
game where the agents are modelled using Bayesian best-response models. We show
that in this case the AI's problem of helping bounded-rational humans make
better decisions reduces to a Bayes-adaptive POMDP. In our simulated
experiments, we consider an instantiation of our framework for humans who are
subjectively optimistic about the AI's future behaviour. Our results show that
when equipped with a model of the human, the AI can infer the human's bounds
and nudge them towards better decisions. We discuss ways in which the machine
can learn to improve upon its own limitations as well with the help of the
human. We identify a novel trade-off for centaurs in partially observable
tasks: for the AI's actions to be acceptable to the human, the machine must
make sure their beliefs are sufficiently aligned, but aligning beliefs might be
costly. We present a preliminary theoretical analysis of this trade-off and its
dependence on task structure.

We introduce an unsupervised technique for encoding point clouds into a
canonical shape representation, by disentangling shape and pose. Our encoder is
stable and consistent, meaning that the shape encoding is purely
pose-invariant, while the extracted rotation and translation are able to
semantically align different input shapes of the same class to a common
canonical pose. Specifically, we design an auto-encoder based on Vector Neuron
Networks, a rotation-equivariant neural network, whose layers we extend to
provide translation-equivariance in addition to rotation-equivariance only. The
resulting encoder produces pose-invariant shape encoding by construction,
enabling our approach to focus on learning a consistent canonical pose for a
class of objects. Quantitative and qualitative experiments validate the
superior stability and consistency of our approach.

In this paper, we study a linear bandit optimization problem in a federated
setting where a large collection of distributed agents collaboratively learn a
common linear bandit model. Standard federated learning algorithms applied to
this setting are vulnerable to Byzantine attacks on even a small fraction of
agents. We propose a novel algorithm with a robust aggregation oracle that
utilizes the geometric median. We prove that our proposed algorithm is robust
to Byzantine attacks on fewer than half of agents and achieves a sublinear
$\tilde{\mathcal{O}}({T^{3/4}})$ regret with $\mathcal{O}(\sqrt{T})$ steps of
communication in $T$ steps. Moreover, we make our algorithm differentially
private via a tree-based mechanism. Finally, if the level of corruption is
known to be small, we show that using the geometric median of mean oracle for
robust aggregation further improves the regret bound.

Intelligent reflecting surfaces (IRSs) are considered a promising technology
that can smartly reconfigure the wireless environment to enhance the
performance of future wireless networks. However, the deployment of IRSs still
faces challenges due to highly dynamic and mobile unmanned aerial vehicle (UAV)
enabled wireless environments to achieve higher capacity. This paper sheds
light on the different deployment strategies for IRSs in future terrestrial and
non-terrestrial networks. Specifically, in this paper, we introduce key
theoretical concepts underlying the IRS paradigm and discuss the design aspects
related to the deployment of IRSs in 6G networks. We also explore
optimization-based IRS deployment techniques to improve system performance in
terrestrial and aerial IRSs. Furthermore, we survey model-free reinforcement
learning (RL) techniques from the deployment aspect to address the challenges
of achieving higher capacity in complex and mobile IRS-assisted UAV wireless
systems. Finally, we highlight challenges and future research directions from
the deployment aspect of IRSs for improving system performance for the future
6G network.

Despite the rapid advancement of navigation algorithms, mobile robots often
produce anomalous behaviors that can lead to navigation failures. The ability
to detect such anomalous behaviors is a key component in modern robots to
achieve high-levels of autonomy. Reactive anomaly detection methods identify
anomalous task executions based on the current robot state and thus lack the
ability to alert the robot before an actual failure occurs. Such an alert delay
is undesirable due to the potential damage to both the robot and the
surrounding objects. We propose a proactive anomaly detection network (PAAD)
for robot navigation in unstructured and uncertain environments. PAAD predicts
the probability of future failure based on the planned motions from the
predictive controller and the current observation from the perception module.
Multi-sensor signals are fused effectively to provide robust anomaly detection
in the presence of sensor occlusion as seen in field environments. Our
experiments on field robot data demonstrates superior failure identification
performance than previous methods, and that our model can capture anomalous
behaviors in real-time while maintaining a low false detection rate in
cluttered fields. Code, dataset, and video are available at
https://github.com/tianchenji/PAAD

Topological Data Analysis (TDA) is a rigorous framework that borrows
techniques from geometric and algebraic topology, category theory, and
combinatorics in order to study the "shape" of such complex high-dimensional
data. Research in this area has grown significantly over the last several years
bringing a deeply rooted theory to bear on practical applications in areas such
as genomics, natural language processing, medicine, cybersecurity, energy, and
climate change. Within some of these areas, TDA has also been used to augment
AI and ML techniques.
  We believe there is further utility to be gained in this space that can be
facilitated by a workshop bringing together experts (both theorists and
practitioners) and non-experts. Currently there is an active community of pure
mathematicians with research interests in developing and exploring the
theoretical and computational aspects of TDA. Applied mathematicians and other
practitioners are also present in community but do not represent a majority.
This speaks to the primary aim of this workshop which is to grow a wider
community of interest in TDA. By fostering meaningful exchanges between these
groups, from across the government, academia, and industry, we hope to create
new synergies that can only come through building a mutual comprehensive
awareness of the problem and solution spaces.

We present a system for interactive examination of learned security policies.
It allows a user to traverse episodes of Markov decision processes in a
controlled manner and to track the actions triggered by security policies.
Similar to a software debugger, a user can continue or or halt an episode at
any time step and inspect parameters and probability distributions of interest.
The system enables insight into the structure of a given policy and in the
behavior of a policy in edge cases. We demonstrate the system with a network
intrusion use case. We examine the evolution of an IT infrastructure's state
and the actions prescribed by security policies while an attack occurs. The
policies for the demonstration have been obtained through a reinforcement
learning approach that includes a simulation system where policies are
incrementally learned and an emulation system that produces statistics that
drive the simulation runs.

In this work we introduce an alternative model for the design and analysis of
strategyproof mechanisms that is motivated by the recent surge of work in
"learning-augmented algorithms". Aiming to complement the traditional approach
in computer science, which analyzes the performance of algorithms based on
worst-case instances, this line of work has focused on the design and analysis
of algorithms that are enhanced with machine-learned predictions regarding the
optimal solution. The algorithms can use the predictions as a guide to inform
their decisions, and the goal is to achieve much stronger performance
guarantees when these predictions are accurate (consistency), while also
maintaining near-optimal worst-case guarantees, even if these predictions are
very inaccurate (robustness). So far, these results have been limited to
algorithms, but in this work we argue that another fertile ground for this
framework is in mechanism design.
  We initiate the design and analysis of strategyproof mechanisms that are
augmented with predictions regarding the private information of the
participating agents. To exhibit the important benefits of this approach, we
revisit the canonical problem of facility location with strategic agents in the
two-dimensional Euclidean space. We study both the egalitarian and utilitarian
social cost functions, and we propose new strategyproof mechanisms that
leverage predictions to guarantee an optimal trade-off between consistency and
robustness guarantees. This provides the designer with a menu of mechanism
options to choose from, depending on her confidence regarding the prediction
accuracy. Furthermore, we also prove parameterized approximation results as a
function of the prediction error, showing that our mechanisms perform well even
when the predictions are not fully accurate.

This paper describes an approach for fitting an immersed submanifold of a
finite-dimensional Euclidean space to random samples. The reconstruction
mapping from the ambient space to the desired submanifold is implemented as a
composition of an encoder that maps each point to a tuple of (positive or
negative) times and a decoder given by a composition of flows along finitely
many vector fields starting from a fixed initial point. The encoder supplies
the times for the flows. The encoder-decoder map is obtained by empirical risk
minimization, and a high-probability bound is given on the excess risk relative
to the minimum expected reconstruction error over a given class of
encoder-decoder maps. The proposed approach makes fundamental use of Sussmann's
orbit theorem, which guarantees that the image of the reconstruction map is
indeed contained in an immersed submanifold.

3D softwares are now capable of producing highly realistic images that look
nearly indistinguishable from the real images. This raises the question: can
real datasets be enhanced with 3D rendered data? We investigate this question.
In this paper we demonstrate the use of 3D rendered data, procedural, data for
the adjustment of bias in image datasets. We perform error analysis of images
of animals which shows that the misclassification of some animal breeds is
largely a data issue. We then create procedural images of the poorly classified
breeds and that model further trained on procedural data can better classify
poorly performing breeds on real data. We believe that this approach can be
used for the enhancement of visual data for any underrepresented group,
including rare diseases, or any data bias potentially improving the accuracy
and fairness of models. We find that the resulting representations rival or
even out-perform those learned directly from real data, but that good
performance requires care in the 3D rendered procedural data generation. 3D
image dataset can be viewed as a compressed and organized copy of a real
dataset, and we envision a future where more and more procedural data
proliferate while datasets become increasingly unwieldy, missing, or private.
This paper suggests several techniques for dealing with visual representation
learning in such a future.

In this paper, we address the stochastic MPC (SMPC) problem for linear
systems, subject to chance state constraints and hard input constraints, under
unknown noise distribution. First, we reformulate the chance state constraints
as deterministic constraints depending only on explicit noise statistics. Based
on these reformulated constraints, we design a distributionally robust and
robustly stable benchmark SMPC algorithm for the ideal setting of known noise
statistics. Then, we employ this benchmark controller to derive a novel
robustly stable adaptive SMPC scheme that learns the necessary noise statistics
online, while guaranteeing time-uniform satisfaction of the unknown
reformulated state constraints with high probability. The latter is achieved
through the use of confidence intervals which rely on the empirical noise
statistics and are valid uniformly over time. Moreover, control performance is
improved over time as more noise samples are gathered and better estimates of
the noise statistics are obtained, given the online adaptation of the estimated
reformulated constraints. Additionally, in tracking problems with multiple
successive targets our approach leads to an online-enlarged domain of
attraction compared to robust tube-based MPC. A numerical simulation of a DC-DC
converter is used to demonstrate the effectiveness of the developed

This paper presents a safety guaranteed control method for an autonomous
vehicle ski-stunt maneuver, that is, a vehicle moving with two one-side wheels.
To capture the vehicle dynamics precisely, a Gaussian process model is used as
additional correction to the nominal model that is obtained from physical
principles. We construct a probabilistic control barrier function (CBF) to
guarantee the planar motion safety. The CBF and the balance equilibrium
manifold are enforced as the constraints into a safety critical control form.
Under the proposed control method, the vehicle avoids the obstacle collision
and safely maintain the balance for autonomous ski-stunt maneuvers. We conduct
numerical simulation validation to demonstrate the control design. Preliminary
experiment results are also presented to confirm the learning-based motion
control using a scaled RC truck for autonomous ski-stunt maneuvers.

The effects of adversarial training on semantic segmentation networks has not
been thoroughly explored. While previous work has shown that
adversarially-trained image classifiers can be used to perform image synthesis,
we have yet to understand how best to leverage an adversarially-trained
segmentation network to do the same. Using a simple optimizer, we demonstrate
that adversarially-trained semantic segmentation networks can be used to
perform image inpainting and generation. Our experiments demonstrate that
adversarially-trained segmentation networks are more robust and indeed exhibit
perceptually-aligned gradients which help in producing plausible image
inpaintings. We seek to place additional weight behind the hypothesis that
adversarially robust models exhibit gradients that are more
perceptually-aligned with human vision. Through image synthesis, we argue that
perceptually-aligned gradients promote a better understanding of a neural
network's learned representations and aid in making neural networks more
interpretable.

Motivated by the fact that many relations cross the sentence boundary, there
has been increasing interest in document-level relation extraction (DocRE).
DocRE requires integrating information within and across sentences, capturing
complex interactions between mentions of entities. Most existing methods are
pipeline-based, requiring entities as input. However, jointly learning to
extract entities and relations can improve performance and be more efficient
due to shared parameters and training steps. In this paper, we develop a
sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE
(entity extraction, coreference resolution and relation extraction) end-to-end,
replacing a pipeline of task-specific components. Using a simple strategy we
call entity hinting, we compare our approach to existing pipeline-based methods
on several popular biomedical datasets, in some cases exceeding their
performance. We also report the first end-to-end results on these datasets for
future comparison. Finally, we demonstrate that, under our model, an end-to-end
approach outperforms a pipeline-based approach. Our code, data and trained
models are available at {\url{https://github.com/johngiorgi/seq2rel}}. An
online demo is available at
{\url{https://share.streamlit.io/johngiorgi/seq2rel/main/demo.py}}.

We present pmuGE (phasor measurement unit Generator of Events), one of the
first data-driven generative model for power system event data. We have trained
this model on thousands of actual events and created a dataset denoted pmuBAGE
(the Benchmarking Assortment of Generated PMU Events). The dataset consists of
almost 1000 instances of labeled event data to encourage benchmark evaluations
on phasor measurement unit (PMU) data analytics. The dataset is available
online for use by any researcher or practitioner in the field. PMU data are
challenging to obtain, especially those covering event periods. Nevertheless,
power system problems have recently seen phenomenal advancements via
data-driven machine learning solutions - solutions created by researchers who
were fortunate enough to obtain such PMU data. A highly accessible standard
benchmarking dataset would enable a drastic acceleration of the development of
successful machine learning techniques in this field. We propose a novel
learning method based on the Event Participation Decomposition of Power System
Events, which makes it possible to learn a generative model of PMU data during
system anomalies. The model can create highly realistic event data without
compromising the differential privacy of the PMUs used to train it. The dataset
is available online for any researcher to use at the pmuBAGE Github Repository
- https://github.com/NanpengYu/pmuBAGE.
  Part I - This is part I of a two part paper. In part I, we describe a high
level overview of pmuBAGE, its creation, and the experiments used to test it.
Part II will discuss the exact models used in its generation in far more
detail.

Attacks on machine learning models have been, since their conception, a very
persistent and evasive issue resembling an endless cat-and-mouse game. One
major variant of such attacks is poisoning attacks which can indirectly
manipulate an ML model. It has been observed over the years that the majority
of proposed effective defense models are only effective when an attacker is not
aware of them being employed. In this paper, we show that the attack-agnostic
De-Pois defense is hardly an exception to that rule. In fact, we demonstrate
its vulnerability to the simplest White-Box and Black-Box attacks by an
attacker that knows the structure of the De-Pois defense model. In essence, the
De-Pois defense relies on a critic model that can be used to detect poisoned
data before passing it to the target model. In our work, we break this
poison-protection layer by replicating the critic model and then performing a
composed gradient-sign attack on both the critic and target models
simultaneously -- allowing us to bypass the critic firewall to poison the
target model.

Incorporating knowledge graph as side information has become a new trend in
recommendation systems. Recent studies regard items as entities of a knowledge
graph and leverage graph neural networks to assist item encoding, yet by
considering each relation type individually. However, relation types are often
too many and sometimes one relation type involves too few entities. We argue
that it is not efficient nor effective to use every relation type for item
encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational
Knowledge Graphs for Recommendation), which explicitly distinguish the
influence of different relations for item representation learning. We first
construct virtual relational graphs (VRKGs) by an unsupervised learning scheme.
We also design a local weighted smoothing (LWS) mechanism for encoding nodes,
which iteratively updates a node embedding only depending on the embedding of
its own and its neighbors, but involve no additional training parameters. We
also employ the LWS mechanism on a user-item bipartite graph for user
representation learning, which utilizes encodings of items with relational
knowledge to help training representations of users. Experiment results on two
public datasets validate that our VRKG4Rec model outperforms the
state-of-the-art methods. The implementations are available at
https://github.com/lulu0913/VRKG4Rec.

Aggregating successfully the choices regarding a given decision problem made
by the multiple collective members into a single solution is essential for
exploiting the collective's intelligence and for effective crowdsourcing. There
are various aggregation techniques, some of which come down to a simple and
sometimes effective deterministic aggregation rule. However, it has been shown
that the efficiency of those techniques is unstable under varying conditions
and within different domains. Other methods mainly rely on learning from the
decision-makers previous responses or the availability of additional
information about them. In this study, we present two one-shot
machine-learning-based aggregation approaches. The first predicts, given
multiple features about the collective's choices, including meta-cognitive
ones, which aggregation method will be best for a given case. The second
directly predicts which decision is optimal, given, among other things, the
selection made by each method. We offer a meta-cognitive feature-engineering
approach for characterizing a collective decision-making case in a
context-sensitive fashion. In addition, we offer a new aggregation method, the
Devil's-Advocate aggregator, to deal with cases in which standard aggregation
methods are predicted to fail. Experimental results show that using either of
our proposed approaches increases the percentage of successfully aggregated
cases (i.e., cases in which the correct answer is returned) significantly,
compared to the uniform application of each rule-based aggregation method. We
also demonstrate the importance of the Devil's Advocate aggregator.

AI Blitz XIII Faces challenge hosted on www.aicrowd.com platform consisted of
five problems: Sentiment Classification, Age Prediction, Mask Prediction, Face
Recognition, and Face De-Blurring. Our team GLaDOS took second place. Here we
present our solutions and results. Code implementation:
https://github.com/ndrwmlnk/ai-blitz-xiii

Technological advances allow manufacturers to collect and access data from a
production system effectively. The objective of data collection is to deploy
the collected data in developing decision support systems for performance
evaluation, problem identification, and production control. The goal of this
dissertation is to investigate how the collected data can be used to evaluate
performance and optimize manufacturing systems, analytically and empirically.
In the first part of the thesis, I investigate how can the collected data from
the shop-floor be used in the efficient control and design of manufacturing
systems? To investigate the impact of possible dependency in the inter-event
times on the optimal control and performance measures of the system, a
manufacturing system that is controlled by using a single-threshold production
control policy is analyzed. It is shown that ignoring autocorrelation in
inter-arrival or service times leads to overestimation (underestimation) of the
optimal threshold level for negatively (positively) correlated processes. Then,
it is shown that the optimal control policy of a production/inventory system
with correlated inter-arrival and service times modeled as Markovian Arrival
Processes is a state-dependent threshold policy. In the second part of the
thesis, an exploratory data analysis is conducted by using a large industrial
dataset related to the flow of the products at a semiconductor wafer
fabrication. Then, the cycle times of the products are predicted using Machine
Learning methods, and the performance of different prediction algorithms is
assessed. The analytical and empirical results presented in this dissertation
show that effective use of collected data from a manufacturing system enables
controlling the system effectively and predicting its main performance measures

As research and industry moves towards large-scale models capable of numerous
downstream tasks, the complexity of understanding multi-modal datasets that
give nuance to models rapidly increases. A clear and thorough understanding of
a dataset's origins, development, intent, ethical considerations and evolution
becomes a necessary step for the responsible and informed deployment of models,
especially those in people-facing contexts and high-risk domains. However, the
burden of this understanding often falls on the intelligibility, conciseness,
and comprehensiveness of the documentation. It requires consistency and
comparability across the documentation of all datasets involved, and as such
documentation must be treated as a user-centric product in and of itself. In
this paper, we propose Data Cards for fostering transparent, purposeful and
human-centered documentation of datasets within the practical contexts of
industry and research. Data Cards are structured summaries of essential facts
about various aspects of ML datasets needed by stakeholders across a dataset's
lifecycle for responsible AI development. These summaries provide explanations
of processes and rationales that shape the data and consequently the models,
such as upstream sources, data collection and annotation methods; training and
evaluation methods, intended use; or decisions affecting model performance. We
also present frameworks that ground Data Cards in real-world utility and
human-centricity. Using two case studies, we report on desirable
characteristics that support adoption across domains, organizational
structures, and audience groups. Finally, we present lessons learned from
deploying over 20 Data Cards.

The presence of abusive content on social media platforms is undesirable as
it severely impedes healthy and safe social media interactions. While automatic
abuse detection has been widely explored in textual domain, audio abuse
detection still remains unexplored. In this paper, we attempt abuse detection
in conversational audio from a multimodal perspective in a multilingual social
media setting. Our key hypothesis is that along with the modelling of audio,
incorporating discriminative information from other modalities can be highly
beneficial for this task. Our proposed method, MADA, explicitly focuses on two
modalities other than the audio itself, namely, the underlying emotions
expressed in the abusive audio and the semantic information encapsulated in the
corresponding textual form. Observations prove that MADA demonstrates gains
over audio-only approaches on the ADIMA dataset. We test the proposed approach
on 10 different languages and observe consistent gains in the range 0.6%-5.2%
by leveraging multiple modalities. We also perform extensive ablation
experiments for studying the contributions of every modality and observe the
best results while leveraging all the modalities together. Additionally, we
perform experiments to empirically confirm that there is a strong correlation
between underlying emotions and abusive behaviour.

The vast majority of systems of practical interest are characterised by
nonlinear dynamics. This renders the control and optimization of such systems a
complex task due to their nonlinear behaviour. Additionally, standard methods
such as linearizing around a fixed point may not be an effective strategy for
many systems, thus requiring an alternative approach. For this reason, we
propose a new deep learning framework to discover a transformation of a
nonlinear dynamical system to an equivalent higher dimensional linear
representation. We demonstrate that the resulting learned linear representation
accurately captures the dynamics of the original system for a wider range of
conditions than standard linearization. As a result of this, we show that the
learned linear model can subsequently be used for the successful control of the
original system. We demonstrate this by applying the proposed framework to two
examples; one from the literature and a more complex example in the form of a
Continuous Stirred Tank Reactor (CSTR).

This paper proposes a novel task on commonsense-enhanced task-based dialogue
grounded in documents and describes the Task2Dial dataset, a novel dataset of
document-grounded task-based dialogues, where an Information Giver (IG)
provides instructions (by consulting a document) to an Information Follower
(IF), so that the latter can successfully complete the task. In this unique
setting, the IF can ask clarification questions which may not be grounded in
the underlying document and require commonsense knowledge to be answered. The
Task2Dial dataset poses new challenges: (1) its human reference texts show more
lexical richness and variation than other document-grounded dialogue datasets;
(2) generating from this set requires paraphrasing as instructional responses
might have been modified from the underlying document; (3) requires commonsense
knowledge, since questions might not necessarily be grounded in the document;
(4) generating requires planning based on context, as task steps need to be
provided in order. The Task2Dial dataset contains dialogues with an average
$18.15$ number of turns and 19.79 tokens per turn, as compared to 12.94 and 12
respectively in existing datasets. As such, learning from this dataset promises
more natural, varied and less template-like system utterances.

This article considers fully connected neural networks with Gaussian random
weights and biases and $L$ hidden layers, each of width proportional to a large
parameter $n$. For polynomially bounded non-linearities we give sharp estimates
in powers of $1/n$ for the joint correlation functions of the network output
and its derivatives. Moreover, we obtain exact layerwise recursions for these
correlation functions and solve a number of special cases for classes of
non-linearities including $\mathrm{ReLU}$ and $\tanh$. We find in both cases
that the depth-to-width ratio $L/n$ plays the role of an effective network
depth, controlling both the scale of fluctuations at individual neurons and the
size of inter-neuron correlations. We use this to study a somewhat simplified
version of the so-called exploding and vanishing gradient problem, proving that
this particular variant occurs if and only if $L/n$ is large. Several of the
key ideas in this article were first developed at a physics level of rigor in a
recent monograph with Roberts and Yaida.

Over the years, many graph problems specifically those in NP-complete are
studied by a wide range of researchers. Some famous examples include graph
colouring, travelling salesman problem and subgraph isomorphism. Most of these
problems are typically addressed by exact algorithms, approximate algorithms
and heuristics. There are however some drawback for each of these methods.
Recent studies have employed learning-based frameworks such as machine learning
techniques in solving these problems, given that they are useful in discovering
new patterns in structured data that can be represented using graphs. This
research direction has successfully attracted a considerable amount of
attention. In this survey, we provide a systematic review mainly on classic
graph problems in which learning-based approaches have been proposed in
addressing the problems. We discuss the overview of each framework, and provide
analyses based on the design and performance of the framework. Some potential
research questions are also suggested. Ultimately, this survey gives a clearer
insight and can be used as a stepping stone to the research community in
studying problems in this field.

Data-aided channel estimation is a promising solution to improve channel
estimation accuracy by exploiting data symbols as pilot signals for updating an
initial channel estimate. In this paper, we propose a semi-data-aided channel
estimator for multiple-input multiple-output communication systems. Our
strategy is to leverage reinforcement learning (RL) for selecting reliable
detected symbols among the symbols in the first part of transmitted data block.
This strategy facilitates an update of the channel estimate before the end of
data block transmission and therefore achieves a significant reduction in
communication latency compared to conventional data-aided channel estimation
approaches. Towards this end, we first define a Markov decision process (MDP)
which sequentially decides whether to use each detected symbol as an additional
pilot signal. We then develop an RL algorithm to efficiently find the best
policy of the MDP based on a Monte Carlo tree search approach. In this
algorithm, we exploit the a-posteriori probability for approximating both the
optimal future actions and the corresponding state transitions of the MDP and
derive a closed-form expression for the best policy. Simulation results
demonstrate that the proposed channel estimator effectively mitigates both
channel estimation error and detection performance loss caused by insufficient
pilot signals.

Most existing analyses of (stochastic) gradient descent rely on the condition
that for $L$-smooth costs, the step size is less than $2/L$. However, many
works have observed that in machine learning applications step sizes often do
not fulfill this condition, yet (stochastic) gradient descent still converges,
albeit in an unstable manner. We investigate this unstable convergence
phenomenon from first principles, and discuss key causes behind it. We also
identify its main characteristics, and how they interrelate based on both
theory and experiments, offering a principled view toward understanding the
phenomenon.

Differential privacy (DP) has been applied in deep learning for preserving
privacy of the underlying training sets. Existing DP practice falls into three
categories - objective perturbation, gradient perturbation and output
perturbation. They suffer from three main problems. First, conditions on
objective functions limit objective perturbation in general deep learning
tasks. Second, gradient perturbation does not achieve a satisfactory
privacy-utility trade-off due to over-injected noise in each epoch. Third, high
utility of the output perturbation method is not guaranteed because of the
loose upper bound on the global sensitivity of the trained model parameters as
the noise scale parameter. To address these problems, we analyse a tighter
upper bound on the global sensitivity of the model parameters. Under a
black-box setting, based on this global sensitivity, to control the overall
noise injection, we propose a novel output perturbation framework by injecting
DP noise into a randomly sampled neuron (via the exponential mechanism) at the
output layer of a baseline non-private neural network trained with a
convexified loss function. We empirically compare the privacy-utility
trade-off, measured by accuracy loss to baseline non-private models and the
privacy leakage against black-box membership inference (MI) attacks, between
our framework and the open-source differentially private stochastic gradient
descent (DP-SGD) approaches on six commonly used real-world datasets. The
experimental evaluations show that, when the baseline models have observable
privacy leakage under MI attacks, our framework achieves a better
privacy-utility trade-off than existing DP-SGD implementations, given an
overall privacy budget $\epsilon \leq 1$ for a large number of queries.

Melanoma is a fatal skin cancer that is curable and has dramatically
increasing survival rate when diagnosed at early stages. Learning-based methods
hold significant promise for the detection of melanoma from dermoscopic images.
However, since melanoma is a rare disease, existing databases of skin lesions
predominantly contain highly imbalanced numbers of benign versus malignant
samples. In turn, this imbalance introduces substantial bias in classification
models due to the statistical dominance of the majority class. To address this
issue, we introduce a deep clustering approach based on the latent-space
embedding of dermoscopic images. Clustering is achieved using a novel
center-oriented margin-free triplet loss (COM-Triplet) enforced on image
embeddings from a convolutional neural network backbone. The proposed method
aims to form maximally-separated cluster centers as opposed to minimizing
classification error, so it is less sensitive to class imbalance. To avoid the
need for labeled data, we further propose to implement COM-Triplet based on
pseudo-labels generated by a Gaussian mixture model. Comprehensive experiments
show that deep clustering with COM-Triplet loss outperforms clustering with
triplet loss, and competing classifiers in both supervised and unsupervised
settings.

360{\deg} images are widely available over the last few years. This paper
proposes a new technique for single 360{\deg} image depth prediction under open
environments. Depth prediction from a 360{\deg} single image is not easy for
two reasons. One is the limitation of supervision datasets - the currently
available dataset is limited to indoor scenes. The other is the problems caused
by Equirectangular Projection Format (ERP), commonly used for 360{\deg} images,
that are coordinate and distortion. There is only one method existing that uses
cube map projection to produce six perspective images and apply self-supervised
learning using motion pictures for perspective depth prediction to deal with
these problems. Different from the existing method, we directly use the ERP
format. We propose a framework of direct use of ERP with coordinate conversion
of correspondences and distortion-aware upsampling module to deal with the ERP
related problems and extend a self-supervised learning method for open
environments. For the experiments, we firstly built a dataset for the
evaluation, and quantitatively evaluate the depth prediction in outdoor scenes.
We show that it outperforms the state-of-the-art technique

Deep reinforcement learning (DRL) has a great potential for solving complex
decision-making problems in autonomous driving, especially in mixed-traffic
scenarios where autonomous vehicles and human-driven vehicles (HDVs) drive
together. Safety is a key during both the learning and deploying reinforcement
learning (RL) algorithms process. In this paper, we formulate the on-ramp
merging as a Markov Decision Process (MDP) problem and solve it with an
off-policy RL algorithm, i.e., Soft Actor-Critic for Discrete Action Settings
(SAC-Discrete). In addition, a motion predictive safety controller including a
motion predictor and an action substitution module, is proposed to ensure
driving safety during both training and testing. The motion predictor estimates
the trajectories of the ego vehicle and surrounding vehicles from kinematic
models, and predicts potential collisions. The action substitution module
updates the actions based on safety distance and replaces risky actions, before
sending them to the low-level controller. We train, evaluate and test our
approach on a gym-like highway simulation with three different levels of
traffic modes. The simulation results show that even in harder traffic
densities, our proposed method still significantly reduces collision rate while
maintaining high efficiency, outperforming several state-of-the-art baselines
in the considered on-ramp merging scenarios. The video demo of the evaluation
process can be found at: https://www.youtube.com/watch?v=7FvjbAM4oFw

Recently, numerous learning-based compression methods have been developed
with outstanding performance for the coding of the geometry information of
point clouds. On the contrary, limited explorations have been devoted to point
cloud attribute compression (PCAC). Thus, this study focuses on the PCAC by
applying sparse convolution because of its superior efficiency for representing
the geometry of unorganized points. The proposed method simply stacks sparse
convolutions to construct the variational autoencoder (VAE) framework to
compress the color attributes of a given point cloud. To better encode latent
elements at the bottleneck, we apply the adaptive entropy model with the joint
utilization of hyper prior and autoregressive neighbors to accurately estimate
the bit rate. The qualitative measurement of the proposed method already rivals
the latest G-PCC (or TMC13) version 14 at a similar bit rate. And, our method
shows clear quantitative improvements to G-PCC version 6, and largely
outperforms existing learning-based methods, which promises encouraging
potentials for learnt PCAC.

When tasked with supporting multiple languages for a given problem, two
approaches have arisen: training a model for each language with the annotation
budget divided equally among them, and training on a high-resource language
followed by zero-shot transfer to the remaining languages. In this work, we
show that the strategy of joint learning across multiple languages using a
single model performs substantially better than the aforementioned
alternatives. We also demonstrate that active learning provides additional,
complementary benefits. We show that this simple approach enables the model to
be data efficient by allowing it to arbitrate its annotation budget to query
languages it is less certain on. We illustrate the effectiveness of our
proposed method on a diverse set of tasks: a classification task with 4
languages, a sequence tagging task with 4 languages and a dependency parsing
task with 5 languages. Our proposed method, whilst simple, substantially
outperforms the other viable alternatives for building a model in a
multilingual setting under constrained budgets.

Wireless Capsule Endoscopy(WCE) has been widely used for the screening of
gastrointestinal(GI) diseases, especially the small intestine, due to its
advantages of non-invasive and painless imaging of the entire digestive
tract.However, the huge amount of image data captured by WCE makes manual
reading a process that requires a huge amount of tasks and can easily lead to
missed detection and false detection of lesions.Therefore, In this paper, we
propose a \textbf{T}wo-stage \textbf{M}ulti-scale \textbf{F}eature-fusion
learning network(\textbf{TMFNet}) to automatically detect small intestinal
polyps and tumors in WCE image sequences. Specifically, TMFNet consists of
lesion detection network and lesion identification network. Among them, the
former improves the feature extraction module and detection module based on the
traditional Faster R-CNN network, and readjusts the parameters of the anchor in
the region proposal network(RPN) module;the latter combines residual structure
and feature pyramid structure are used to build a small intestinal lesion
recognition network based on feature fusion, for reducing the false positive
rate of the former and improve the overall accuracy.We used 22,335 WCE images
in the experiment, with a total of 123,092 lesion regions used to train the
detection framework of this paper. In the experiment, the detection framework
is trained and tested on the real WCE image dataset provided by the hospital
gastroenterology department. The sensitivity, false positive and accuracy of
the final model on the RPM are 98.81$\%$, 7.43$\%$ and 92.57$\%$,
respectively.Meanwhile,the corresponding results on the lesion images were
98.75$\%$, 5.62$\%$ and 94.39$\%$. The algorithm model proposed in this paper
is obviously superior to other detection algorithms in detection effect and
performance

We approach the problem of high-DOF reaching-and-grasping via learning joint
planning of grasp and motion with deep reinforcement learning. To resolve the
sample efficiency issue in learning the high-dimensional and complex control of
dexterous grasping, we propose an effective representation of grasping state
characterizing the spatial interaction between the gripper and the target
object. To represent gripper-object interaction, we adopt Interaction Bisector
Surface (IBS) which is the Voronoi diagram between two close by 3D geometric
objects and has been successfully applied in characterizing spatial relations
between 3D objects. We found that IBS is surprisingly effective as a state
representation since it well informs the fine-grained control of each finger
with spatial relation against the target object. This novel grasp
representation, together with several technical contributions including a fast
IBS approximation, a novel vector-based reward and an effective training
strategy, facilitate learning a strong control model of high-DOF grasping with
good sample efficiency, dynamic adaptability, and cross-category generality.
Experiments show that it generates high-quality dexterous grasp for complex
shapes with smooth grasping motions.

Unaccompanied vocalists usually change the tuning unintentionally and end up
with a higher or lower pitch than the starting point during a long performance.
This phenomenon is called pitch drift, which is dependent on various elements,
such as the skill of the performer, and the length and difficulty of the
performance. In this paper, we propose a computational method for measuring
pitch drift in the course of an unaccompanied vocal performance, using pitch
histogram and DBSCAN clustering.

Recent advances in modeling large-scale complex physical systems have shifted
research focuses towards data-driven techniques. However, generating datasets
by simulating complex systems can require significant computational resources.
Similarly, acquiring experimental datasets can prove difficult as well. For
these systems, often computationally inexpensive, but in general inaccurate,
models, known as the low-fidelity models, are available. In this paper, we
propose a bi-fidelity modeling approach for complex physical systems, where we
model the discrepancy between the true system's response and low-fidelity
response in the presence of a small training dataset from the true system's
response using a deep operator network (DeepONet), a neural network
architecture suitable for approximating nonlinear operators. We apply the
approach to model systems that have parametric uncertainty and are partially
unknown. Three numerical examples are used to show the efficacy of the proposed
approach to model uncertain and partially unknown complex physical systems.

Multilingual pre-trained models are able to zero-shot transfer knowledge from
rich-resource to low-resource languages in machine reading comprehension (MRC).
However, inherent linguistic discrepancies in different languages could make
answer spans predicted by zero-shot transfer violate syntactic constraints of
the target language. In this paper, we propose a novel multilingual MRC
framework equipped with a Siamese Semantic Disentanglement Model (SSDM) to
disassociate semantics from syntax in representations learned by multilingual
pre-trained models. To explicitly transfer only semantic knowledge to the
target language, we propose two groups of losses tailored for semantic and
syntactic encoding and disentanglement. Experimental results on three
multilingual MRC datasets (i.e., XQuAD, MLQA, and TyDi QA) demonstrate the
effectiveness of our proposed approach over models based on mBERT and XLM-100.
Code is available at:https://github.com/wulinjuan/SSDM_MRC.

Recently, the pre-training paradigm combining Transformer and masked language
modeling has achieved tremendous success in NLP, images, and point clouds, such
as BERT. However, directly extending BERT from NLP to point clouds requires
training a fixed discrete Variational AutoEncoder (dVAE) before pre-training,
which results in a complex two-stage method called Point-BERT. Inspired by BERT
and MoCo, we propose POS-BERT, a one-stage BERT pre-training method for point
clouds. Specifically, we use the mask patch modeling (MPM) task to perform
point cloud pre-training, which aims to recover masked patches information
under the supervision of the corresponding tokenizer output. Unlike Point-BERT,
its tokenizer is extra-trained and frozen. We propose to use the dynamically
updated momentum encoder as the tokenizer, which is updated and outputs the
dynamic supervision signal along with the training process. Further, in order
to learn high-level semantic representation, we combine contrastive learning to
maximize the class token consistency between different transformation point
clouds. Extensive experiments have demonstrated that POS-BERT can extract
high-quality pre-training features and promote downstream tasks to improve
performance. Using the pre-training model without any fine-tuning to extract
features and train linear SVM on ModelNet40, POS-BERT achieves the
state-of-the-art classification accuracy, which exceeds Point-BERT by 3.5\%. In
addition, our approach has significantly improved many downstream tasks, such
as fine-tuned classification, few-shot classification, part segmentation. The
code and trained-models will be available at:
\url{https://github.com/fukexue/POS-BERT}.

Monocular depth estimation is a fundamental task in computer vision and has
drawn increasing attention. Recently, some methods reformulate it as a
classification-regression task to boost the model performance, where continuous
depth is estimated via a linear combination of predicted probability
distributions and discrete bins. In this paper, we present a novel framework
called BinsFormer, tailored for the classification-regression-based depth
estimation. It mainly focuses on two crucial components in the specific task:
1) proper generation of adaptive bins and 2) sufficient interaction between
probability distribution and bins predictions. To specify, we employ the
Transformer decoder to generate bins, novelly viewing it as a direct set-to-set
prediction problem. We further integrate a multi-scale decoder structure to
achieve a comprehensive understanding of spatial geometry information and
estimate depth maps in a coarse-to-fine manner. Moreover, an extra scene
understanding query is proposed to improve the estimation accuracy, which turns
out that models can implicitly learn useful information from an auxiliary
environment classification task. Extensive experiments on the KITTI, NYU, and
SUN RGB-D datasets demonstrate that BinsFormer surpasses state-of-the-art
monocular depth estimation methods with prominent margins. Code and pretrained
models will be made publicly available at
\url{https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox}.

Web phishing remains a serious cyber threat responsible for most data
breaches. Machine Learning (ML)-based anti-phishing detectors are seen as an
effective countermeasure, and are increasingly adopted by web-browsers and
software products. However, with an average of 10K phishing links reported per
hour to platforms such as PhishTank and VirusTotal (VT), the deficiencies of
such ML-based solutions are laid bare. We first explore how phishing sites
bypass ML-based detection with a deep dive into 13K phishing pages targeting
major brands such as Facebook. Results show successful evasion is caused by:
(1) use of benign services to obscure phishing URLs; (2) high similarity
between the HTML structures of phishing and benign pages; (3) hiding the
ultimate phishing content within Javascript and running such scripts only on
the client; (4) looking beyond typical credentials and credit cards for new
content such as IDs and documents; (5) hiding phishing content until after
human interaction. We attribute the root cause to the dependency of ML-based
models on the vertical feature space (webpage content). These solutions rely
only on what phishers present within the page itself. Thus, we propose
Anti-SubtlePhish, a more resilient model based on logistic regression. The key
augmentation is the inclusion of a horizontal feature space, which examines
correlation variables between the final render of suspicious pages against what
trusted services have recorded (e.g., PageRank). To defeat (1) and (2), we
correlate information between WHOIS, PageRank, and page analytics. To combat
(3), (4) and (5), we correlate features after rendering the page. Experiments
with 100K phishing/benign sites show promising accuracy (98.8%). We also
obtained 100% accuracy against 0-day phishing pages that were manually crafted,
comparing well to the 0% recorded by VT vendors over the first four days.

Automated Speech Recognition (ASR) is an interdisciplinary application of
computer science and linguistics that enable us to derive the transcription
from the uttered speech waveform. It finds several applications in Military
like High-performance fighter aircraft, helicopters, air-traffic controller.
Other than military speech recognition is used in healthcare, persons with
disabilities and many more. ASR has been an active research area. Several
models and algorithms for speech to text (STT) have been proposed. One of the
most recent is Mozilla Deep Speech, it is based on the Deep Speech research
paper by Baidu. Deep Speech is a state-of-art speech recognition system is
developed using end-to-end deep learning, it is trained using well-optimized
Recurrent Neural Network (RNN) training system utilizing multiple Graphical
Processing Units (GPUs). This training is mostly done using American-English
accent datasets, which results in poor generalizability to other English
accents. India is a land of vast diversity. This can even be seen in the
speech, there are several English accents which vary from state to state. In
this work, we have used transfer learning approach using most recent Deep
Speech model i.e., deepspeech-0.9.3 to develop an end-to-end speech recognition
system for Indian-English accents. This work utilizes fine-tuning and data
argumentation to further optimize and improve the Deep Speech ASR system. Indic
TTS data of Indian-English accents is used for transfer learning and
fine-tuning the pre-trained Deep Speech model. A general comparison is made
among the untrained model, our trained model and other available speech
recognition services for Indian-English Accents.

Federated learning, conducive to solving data privacy and security problems,
has attracted increasing attention recently. However, the existing federated
boosting model sequentially builds a decision tree model with the weak base
learner, resulting in redundant boosting steps and high interactive
communication costs. In contrast, the federated bagging model saves time by
building multi-decision trees in parallel, but it suffers from performance
loss. With the aim of obtaining an outstanding performance with less time cost,
we propose a novel model in a vertically federated setting termed as Federated
Gradient Boosting Forest (FedGBF). FedGBF simultaneously integrates the
boosting and bagging's preponderance by building the decision trees in parallel
as a base learner for boosting. Subsequent to FedGBF, the problem of
hyperparameters tuning is rising. Then we propose the Dynamic FedGBF, which
dynamically changes each forest's parameters and thus reduces the complexity.
Finally, the experiments based on the benchmark datasets demonstrate the

Most computer vision systems assume distortion-free images as inputs. The
widely used rolling-shutter (RS) image sensors, however, suffer from geometric
distortion when the camera and object undergo motion during capture. Extensive
researches have been conducted on correcting RS distortions. However, most of
the existing work relies heavily on the prior assumptions of scenes or motions.
Besides, the motion estimation steps are either oversimplified or
computationally inefficient due to the heavy flow warping, limiting their
applicability. In this paper, we investigate using rolling shutter with a
global reset feature (RSGR) to restore clean global shutter (GS) videos. This
feature enables us to turn the rectification problem into a deblur-like one,
getting rid of inaccurate and costly explicit motion estimation. First, we
build an optic system that captures paired RSGR/GS videos. Second, we develop a
novel algorithm incorporating spatial and temporal designs to correct the
spatial-varying RSGR distortion. Third, we demonstrate that existing
image-to-image translation algorithms can recover clean GS videos from
distorted RSGR inputs, yet our algorithm achieves the best performance with the
specific designs. Our rendered results are not only visually appealing but also
beneficial to downstream tasks. Compared to the state-of-the-art RS solution,
our RSGR solution is superior in both effectiveness and efficiency. Considering
it is easy to realize without changing the hardware, we believe our RSGR
solution can potentially replace the RS solution in taking distortion-free
videos with low noise and low budget.

To improve the classification performance and generalization ability of the
hyperspectral image classification algorithm, this paper uses Multi-Scale Total
Variation (MSTV) to extract the spectral features, local binary pattern (LBP)
to extract spatial features, and feature superposition to obtain the fused
features of hyperspectral images. A new swarm intelligence optimization method
with high convergence and strong global search capability, the Sparrow Search
Algorithm (SSA), is used to optimize the kernel parameters and regularization
coefficients of the Kernel Extreme Learning Machine (KELM). In summary, a
multiscale fusion feature hyperspectral image classification method (MLS-KELM)
is proposed in this paper. The Indian Pines, Pavia University and Houston 2013
datasets were selected to validate the classification performance of MLS-KELM,
and the method was applied to ZY1-02D hyperspectral data. The experimental
results show that MLS-KELM has better classification performance and
generalization ability compared with other popular classification methods, and
MLS-KELM shows its strong robustness in the small sample case.

Image denoising has achieved unprecedented progress as great efforts have
been made to exploit effective deep denoisers. To improve the denoising
performance in realworld, two typical solutions are used in recent trends:
devising better noise models for the synthesis of more realistic training data,
and estimating noise level function to guide non-blind denoisers. In this work,
we combine both noise modeling and estimation, and propose an innovative noise
model estimation and noise synthesis pipeline for realistic noisy image
generation. Specifically, our model learns a noise estimation model with
fine-grained statistical noise model in a contrastive manner. Then, we use the
estimated noise parameters to model camera-specific noise distribution, and
synthesize realistic noisy training data. The most striking thing for our work
is that by calibrating noise models of several sensors, our model can be
extended to predict other cameras. In other words, we can estimate
cameraspecific noise models for unknown sensors with only testing images,
without laborious calibration frames or paired noisy/clean data. The proposed
pipeline endows deep denoisers with competitive performances with
state-of-the-art real noise modeling methods.

With the wide applications of deep neural network models in various computer
vision tasks, more and more works study the model vulnerability to adversarial
examples. For data-free black box attack scenario, existing methods are
inspired by the knowledge distillation, and thus usually train a substitute
model to learn knowledge from the target model using generated data as input.
However, the substitute model always has a static network structure, which
limits the attack ability for various target models and tasks. In this paper,
we propose a novel dynamic substitute training attack method to encourage
substitute model to learn better and faster from the target model.
Specifically, a dynamic substitute structure learning strategy is proposed to
adaptively generate optimal substitute model structure via a dynamic gate
according to different target models and tasks. Moreover, we introduce a
task-driven graph-based structure information learning constrain to improve the
quality of generated training data, and facilitate the substitute model
learning structural relationships from the target model multiple outputs.
Extensive experiments have been conducted to verify the efficacy of the
proposed attack method, which can achieve better performance compared with the
state-of-the-art competitors on several datasets.

We present a novel dynamic recommendation model that focuses on users who
have interactions in the past but turn relatively inactive recently. Making
effective recommendations to these time-sensitive cold-start users is critical
to maintain the user base of a recommender system. Due to the sparse recent
interactions, it is challenging to capture these users' current preferences
precisely. Solely relying on their historical interactions may also lead to
outdated recommendations misaligned with their recent interests. The proposed
model leverages historical and current user-item interactions and dynamically
factorizes a user's (latent) preference into time-specific and time-evolving
representations that jointly affect user behaviors. These latent factors
further interact with an optimized item embedding to achieve accurate and
timely recommendations. Experiments over real-world data help demonstrate the
effectiveness of the proposed time-sensitive cold-start recommendation model.

Most AI projects start with a Python notebook running on a single laptop;
however, one usually needs to go through a mountain of pains to scale it to
handle larger dataset (for both experimentation and production deployment).
These usually entail many manual and error-prone steps for the data scientists
to fully take advantage of the available hardware resources (e.g., SIMD
instructions, multi-processing, quantization, memory allocation optimization,
data partitioning, distributed computing, etc.). To address this challenge, we
have open sourced BigDL 2.0 at https://github.com/intel-analytics/BigDL/ under
Apache 2.0 license (combining the original BigDL and Analytics Zoo projects);
using BigDL 2.0, users can simply build conventional Python notebooks on their
laptops (with possible AutoML support), which can then be transparently
accelerated on a single node (with up-to 9.6x speedup in our experiments), and
seamlessly scaled out to a large cluster (across several hundreds servers in
real-world use cases). BigDL 2.0 has already been adopted by many real-world
users (such as Mastercard, Burger King, Inspur, etc.) in production.

Recently has seen the growth in the use of mobile health (mHealth)
information services, which have rich guides on improving physical activity.
These rich guides evolved from the consideration of various personal
behavioural factors, which often deviate from the user's health conditions. The
behavioural factors include changing fitness preferences, adherence issues, and
uncertainty about future fitness outcomes, which may all lead to a decline in
the quality of the mHealth information services. Many of these mHealth
information services provide limited fitness guidance owing to the dynamics of
the user's health conditions. This paper seeks an adaptive method using deep
reinforcement learning to make personalised physical activity recommendations,
which is learnt from retrospective physical activity data and can simulate
realistic behaviour trajectories. We construct a real-time interaction model
for the mHealth information service system based on scientific knowledge about
physical activity to evaluate its exercise performance. The physical activity
performance evaluation model is used to find the optimal exercise intensity
considering the fitness and fatigue effects to avoid the lack of exercise or
overload. The short-term activity plans are made using deep reinforcement
learning and personal health conditions that change over time. Using this
method, we can dynamically update the physical activity recommendation policy
in accordance with the real implementation behaviour. Our DRL-based recommender
policy was validated by comparison to other benchmark policies. Experimental
results show that this adaptive learning algorithm can improve recommendation
performance over 4.13 percent.

Medical image annotation typically requires expert knowledge and hence incurs
time-consuming and expensive data annotation costs. To alleviate this burden,
we propose a novel learning scenario, Exemplar Learning (EL), to explore
automated learning processes for medical image segmentation with a single
annotated image example. This innovative learning task is particularly suitable
for medical image segmentation, where all categories of organs can be presented
in one single image and annotated all at once. To address this challenging EL
task, we propose an Exemplar Learning-based Synthesis Net (ELSNet) framework
for medical image segmentation that enables innovative exemplar-based data
synthesis, pixel-prototype based contrastive embedding learning, and
pseudo-label based exploitation of the unlabeled data. Specifically, ELSNet
introduces two new modules for image segmentation: an exemplar-guided synthesis
module, which enriches and diversifies the training set by synthesizing
annotated samples from the given exemplar, and a pixel-prototype based
contrastive embedding module, which enhances the discriminative capacity of the
base segmentation model via contrastive representation learning. Moreover, we
deploy a two-stage process for segmentation model training, which exploits the
unlabeled data with predicted pseudo segmentation labels. To evaluate this new
learning framework, we conduct extensive experiments on several organ
segmentation datasets and present an in-depth analysis. The empirical results
show that the proposed exemplar learning framework produces effective
segmentation results.

We present our novel deep multi-task learning method for medical image
segmentation. Existing multi-task methods demand ground truth annotations for
both the primary and auxiliary tasks. Contrary to it, we propose to generate
the pseudo-labels of an auxiliary task in an unsupervised manner. To generate
the pseudo-labels, we leverage Histogram of Oriented Gradients (HOGs), one of
the most widely used and powerful hand-crafted features for detection. Together
with the ground truth semantic segmentation masks for the primary task and
pseudo-labels for the auxiliary task, we learn the parameters of the deep
network to minimise the loss of both the primary task and the auxiliary task
jointly. We employed our method on two powerful and widely used semantic
segmentation networks: UNet and U2Net to train in a multi-task setup. To
validate our hypothesis, we performed experiments on two different medical
image segmentation data sets. From the extensive quantitative and qualitative
results, we observe that our method consistently improves the performance
compared to the counter-part method. Moreover, our method is the winner of
FetReg Endovis Sub-challenge on Semantic Segmentation organised in conjunction
with MICCAI 2021.

Extreme Multi-label Text Classification (XMTC) has been a tough challenge in
machine learning research and applications due to the sheer sizes of the label
spaces and the severe data scarce problem associated with the long tail of rare
labels in highly skewed distributions. This paper addresses the challenge of
tail label prediction by proposing a novel approach, which combines the
effectiveness of a trained bag-of-words (BoW) classifier in generating
informative label descriptions under severe data scarce conditions, and the
power of neural embedding based retrieval models in mapping input documents (as
queries) to relevant label descriptions. The proposed approach achieves
state-of-the-art performance on XMTC benchmark datasets and significantly
outperforms the best methods so far in the tail label prediction. We also
provide a theoretical analysis for relating the BoW and neural models w.r.t.
performance lower bound.

When decision-makers can directly intervene, policy evaluation algorithms
give valid causal estimates. In off-policy evaluation (OPE), there may exist
unobserved variables that both impact the dynamics and are used by the unknown
behavior policy. These "confounders" will introduce spurious correlations and
naive estimates for a new policy will be biased. We develop worst-case bounds
to assess sensitivity to these unobserved confounders in finite horizons when
confounders are drawn iid each period. We demonstrate that a model-based
approach with robust MDPs gives sharper lower bounds by exploiting domain
knowledge about the dynamics. Finally, we show that when unobserved confounders
are persistent over time, OPE is far more difficult and existing techniques
produce extremely conservative bounds.

In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi
country benchmark dataset, tailored for agricultural monitoring applications
with Machine and Deep Learning. Sen4AgriNet dataset is annotated from farmer
declarations collected via the Land Parcel Identification System (LPIS) for
harmonizing country wide labels. These declarations have only recently been
made available as open data, allowing for the first time the labeling of
satellite imagery from ground truth data. We proceed to propose and standardise
a new crop type taxonomy across Europe that address Common Agriculture Policy
(CAP) needs, based on the Food and Agriculture Organization (FAO) Indicative
Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year
dataset that includes all spectral information. It is constructed to cover the
period 2016-2020 for Catalonia and France, while it can be extended to include
additional countries. Currently, it contains 42.5 million parcels, which makes
it significantly larger than other available archives. We extract two
sub-datasets to highlight its value for diverse Deep Learning applications; the
Object Aggregated Dataset (OAD) and the Patches Assembled Dataset (PAD). OAD
capitalizes zonal statistics of each parcel, thus creating a powerful
label-to-features instance for classification algorithms. On the other hand,
PAD structure generalizes the classification problem to parcel extraction and
semantic segmentation and labeling. The PAD and OAD are examined under three
different scenarios to showcase and model the effects of spatial and temporal
variability across different years and different countries.

The transition of the electrical power grid from fossil fuels to renewable
sources of energy raises fundamental challenges to the market-clearing
algorithms that drive its operations. Indeed, the increased stochasticity in
load and the volatility of renewable energy sources have led to significant
increases in prediction errors, affecting the reliability and efficiency of
existing deterministic optimization models. The RAMC project was initiated to
investigate how to move from this deterministic setting into a risk-aware
framework where uncertainty is quantified explicitly and incorporated in the
market-clearing optimizations. Risk-aware market-clearing raises challenges on
its own, primarily from a computational standpoint. This paper reviews how RAMC
approaches risk-aware market clearing and presents some of its innovations in
uncertainty quantification, optimization, and machine learning. Experimental
results on real networks are presented.

We propose Path-CNN, a method for the segmentation of centerlines of tubular
structures by embedding convolutional neural networks (CNNs) into the
progressive minimal path method. Minimal path methods are widely used for
topology-aware centerline segmentation, but usually these methods rely on weak,
hand-tuned image features. In contrast, CNNs use strong image features which
are learned automatically from images. But CNNs usually do not take the
topology of the results into account, and often require a large amount of
annotations for training. We integrate CNNs into the minimal path method, so
that both techniques benefit from each other: CNNs employ learned image
features to improve the determination of minimal paths, while the minimal path
method ensures the correct topology of the segmented centerlines, provides
strong geometric priors to increase the performance of CNNs, and reduces the
amount of annotations for the training of CNNs significantly. Our method has
lower hardware requirements than many recent methods. Qualitative and
quantitative comparison with other methods shows that Path-CNN achieves better
performance, especially when dealing with tubular structures with complex
shapes in challenging environments.

With the good performance of deep learning algorithms in the field of
computer vision (CV), the convolutional neural network (CNN) architecture has
become a main backbone of the computer vision task. With the widespread use of
mobile devices, neural network models based on platforms with low computing
power are gradually being paid attention. However, due to the limitation of
computing power, deep learning algorithms are usually not available on mobile
devices. This paper proposes a lightweight convolutional neural network,
TripleNet, which can operate easily on Raspberry Pi. Adopted from the concept
of block connections in ThreshNet, the newly proposed network model compresses
and accelerates the network model, reduces the amount of parameters of the
network, and shortens the inference time of each image while ensuring the
accuracy. Our proposed TripleNet and other state-of-the-art (SOTA) neural
networks perform image classification experiments with the CIFAR-10 and SVHN
datasets on Raspberry Pi. The experimental results show that, compared with
GhostNet, MobileNet, ThreshNet, EfficientNet, and HarDNet, the inference time
of TripleNet per image is shortened by 15%, 16%, 17%, 24%, and 30%,
respectively. The detail codes of this work are available at
https://github.com/RuiyangJu/TripleNet.

While action anticipation has garnered a lot of research interest recently,
most of the works focus on anticipating future action directly through observed
visual cues only. In this work, we take a step back to analyze how the human
capability to anticipate the future can be transferred to machine learning
algorithms. To incorporate this ability in intelligent systems a question worth
pondering upon is how exactly do we anticipate? Is it by anticipating future
actions from past experiences? Or is it by simulating possible scenarios based
on cues from the present? A recent study on human psychology explains that, in
anticipating an occurrence, the human brain counts on both systems. In this
work, we study the impact of each system for the task of action anticipation
and introduce a paradigm to integrate them in a learning framework. We believe
that intelligent systems designed by leveraging the psychological anticipation
models will do a more nuanced job at the task of human action prediction.
Furthermore, we introduce cyclic transformation in the temporal dimension in
feature and semantic label space to instill the human ability of reasoning of
past actions based on the predicted future. Experiments on Epic-Kitchen,
Breakfast, and 50Salads dataset demonstrate that the action anticipation model
learned using a combination of the two systems along with the cycle
transformation performs favorably against various state-of-the-art approaches.

Time-lapse seismic data acquisition is an essential tool to monitor changes
in a reservoir due to fluid injection, such as CO$_2$ injection. By acquiring
multiple seismic surveys in the exact location, we can identify the reservoir
changes by analyzing the difference in the data. However, such analysis can be
skewed by the near-surface seasonal velocity variations, inaccuracy in the
acquisition parameters, and other inevitable noise. The common practice
(cross-equalization) to address this problem uses the part of the data where
changes are not expected to design a matching filter and then apply it to the
whole data, including the reservoir area. Like cross-equalization, we train a
recurrent neural network on parts of the data excluding the reservoir area and
then infer the reservoir-related data. The recurrent neural network can learn
the time dependency of the data, unlike the matching filter that processes the
data based on the local information obtained in the filter window. We
demonstrate the method of matching the data in various examples and compare it
with the conventional matching filter. Specifically, we start by demonstrating
the ability of the approach in matching two traces and then test the method on
a pre-stack 2D synthetic data. Then, we verify the enhancements of the 4D
signal by providing RTM images. We measure the repeatability using normalized
root mean square and predictability metrics and show that in some cases, it
overcomes the matching filter approach.

Extreme multi-label text classification (XMTC) is the task of tagging each
document with the relevant labels from a very large space of predefined
categories. Recently, large pre-trained Transformer models have made
significant performance improvements in XMTC, which typically use the embedding
of the special CLS token to represent the entire document semantics as a global
feature vector, and match it against candidate labels. However, we argue that
such a global feature vector may not be sufficient to represent different
granularity levels of semantics in the document, and that complementing it with
the local word-level features could bring additional gains. Based on this
insight, we propose an approach that combines both the local and global
features produced by Transformer models to improve the prediction power of the
classifier. Our experiments show that the proposed model either outperforms or
is comparable to the state-of-the-art methods on benchmark datasets.

Context: Processing Software Requirement Specifications (SRS) manually takes
a much longer time for requirement analysts in software engineering.
Researchers have been working on making an automatic approach to ease this
task. Most of the existing approaches require some intervention from an analyst
or are challenging to use. Some automatic and semi-automatic approaches were
developed based on heuristic rules or machine learning algorithms. However,
there are various constraints to the existing approaches of UML generation,
such as restriction on ambiguity, length or structure, anaphora,
incompleteness, atomicity of input text, requirements of domain ontology, etc.
Objective: This study aims to better understand the effectiveness of existing
systems and provide a conceptual framework with further improvement guidelines.
Method: We performed a systematic literature review (SLR). We conducted our
study selection into two phases and selected 70 papers. We conducted
quantitative and qualitative analyses by manually extracting information,
cross-checking, and validating our findings. Result: We described the existing
approaches and revealed the issues observed in these works. We identified and
clustered both the limitations and benefits of selected articles. Conclusion:
This research upholds the necessity of a common dataset and evaluation
framework to extend the research consistently. It also describes the
significance of natural language processing obstacles researchers face. In
addition, it creates a path forward for future research.

The construction and theoretical analysis of the most popular universally
consistent nonparametric density estimators hinge on one functional property:
smoothness. In this paper we investigate the theoretical implications of
incorporating a multi-view latent variable model, a type of low-rank model,
into nonparametric density estimation. To do this we perform extensive analysis
on histogram-style estimators that integrate a multi-view model. Our analysis
culminates in showing that there exists a universally consistent
histogram-style estimator that converges to any multi-view model with a finite
number of Lipschitz continuous components at a rate of
$\widetilde{O}(1/\sqrt[3]{n})$ in $L^1$ error. In contrast, the standard
histogram estimator can converge at a rate slower than $1/\sqrt[d]{n}$ on the
same class of densities. We also introduce a new nonparametric latent variable
model based on the Tucker decomposition. A rudimentary implementation of our
estimators experimentally demonstrates a considerable performance improvement
over the standard histogram estimator. We also provide a thorough analysis of
the sample complexity of our Tucker decomposition-based model and a variety of
other results. Thus, our paper provides solid theoretical foundations for
extending low-rank techniques to the nonparametric setting

In meta-learning approaches, it is difficult for a practitioner to make sense
of what kind of representations the model employs. Without this ability, it can
be difficult to both understand what the model knows as well as to make
meaningful corrections. To address these challenges, we introduce AutoProtoNet,
which builds interpretability into Prototypical Networks by training an
embedding space suitable for reconstructing inputs, while remaining convenient
for few-shot learning. We demonstrate how points in this embedding space can be
visualized and used to understand class representations. We also devise a
prototype refinement method, which allows a human to debug inadequate
classification parameters. We use this debugging technique on a custom
classification task and find that it leads to accuracy improvements on a
validation set consisting of in-the-wild images. We advocate for
interpretability in meta-learning approaches and show that there are
interactive ways for a human to enhance meta-learning algorithms.

Despite the rapid development of Neural Radiance Field (NeRF), the necessity
of dense covers largely prohibits its wider applications. While several recent
works have attempted to address this issue, they either operate with sparse
views (yet still, a few of them) or on simple objects/scenes. In this work, we
consider a more ambitious task: training neural radiance field, over
realistically complex visual scenes, by "looking only once", i.e., using only a
single view. To attain this goal, we present a Single View NeRF (SinNeRF)
framework consisting of thoughtfully designed semantic and geometry
regularizations. Specifically, SinNeRF constructs a semi-supervised learning
process, where we introduce and propagate geometry pseudo labels and semantic
pseudo labels to guide the progressive training process. Extensive experiments
are conducted on complex scene benchmarks, including NeRF synthetic dataset,
Local Light Field Fusion dataset, and DTU dataset. We show that even without
pre-training on multi-view datasets, SinNeRF can yield photo-realistic
novel-view synthesis results. Under the single image setting, SinNeRF
significantly outperforms the current state-of-the-art NeRF baselines in all
cases. Project page: https://vita-group.github.io/SinNeRF/

Users who come to recommendation platforms are heterogeneous in activity
levels. There usually exists a group of core users who visit the platform
regularly and consume a large body of content upon each visit, while others are
casual users who tend to visit the platform occasionally and consume less each
time. As a result, consumption activities from core users often dominate the
training data used for learning. As core users can exhibit different activity
patterns from casual users, recommender systems trained on historical user
activity data usually achieve much worse performance on casual users than core
users. To bridge the gap, we propose a model-agnostic framework L2Aug to
improve recommendations for casual users through data augmentation, without
sacrificing core user experience. L2Aug is powered by a data augmentor that
learns to generate augmented interaction sequences, in order to fine-tune and
optimize the performance of the recommendation system for casual users. On four
real-world public datasets, L2Aug outperforms other treatment methods and
achieves the best sequential recommendation performance for both casual and
core users. We also test L2Aug in an online simulation environment with
real-time feedback to further validate its efficacy, and showcase its
flexibility in supporting different augmentation actions.

Verification planning is a sequential decision-making problem that specifies
a set of verification activities (VA) and correction activities (CA) at
different phases of system development. While VAs are used to identify errors
and defects, CAs also play important roles in system verification as they
correct the identified errors and defects. However, current planning methods
only consider VAs as decision choices. Because VAs and CAs have different
activity spaces, planning a joint verification-correction strategy (JVCS) is
still challenging, especially for large-size systems. Here we introduce a
UCB-based tree search approach to search for near-optimal JVCSs. First,
verification planning is simplified as repeatable bandit problems and an upper
confidence bound rule for repeatable bandits (UCBRB) is presented with the
optimal regret bound. Next, a tree search algorithm is proposed to search for
feasible JVCSs. A tree-based ensemble learning model is also used to extend the
tree search algorithm to handle local optimality issues. The proposed approach
is evaluated on the notional case of a communication system.

Deep learning-based super-resolution methods have shown great promise,
especially for single image super-resolution (SISR) tasks. Despite the
performance gain, these methods are limited due to their reliance on copious
data for model training. In addition, supervised SISR solutions rely on local
neighbourhood information focusing only on the feature learning processes for
the reconstruction of low-dimensional images. Moreover, they fail to capitalize
on global context due to their constrained receptive field. To combat these
challenges, this paper proposes a novel image-specific solution, namely
non-local variational autoencoder (\texttt{NLVAE}), to reconstruct a
high-resolution (HR) image from a single low-resolution (LR) image without the
need for any prior training. To harvest maximum details for various receptive
regions and high-quality synthetic images, \texttt{NLVAE} is introduced as a
self-supervised strategy that reconstructs high-resolution images using
disentangled information from the non-local neighbourhood. Experimental results
from seven benchmark datasets demonstrate the effectiveness of the
\texttt{NLVAE} model. Moreover, our proposed model outperforms a number of
baseline and state-of-the-art methods as confirmed through extensive
qualitative and quantitative evaluations.

The shape of circle is one of fundamental geometric primitives of man-made
engineering objects. Thus, extraction of circles from scanned point clouds is a
quite important task in 3D geometry data processing. However, existing circle
extraction methods either are sensitive to the quality of raw point clouds when
classifying circle-boundary points, or require well-designed fitting functions
when regressing circle parameters. To relieve the challenges, we propose an
end-to-end Point Cloud Circle Algebraic Fitting Network (Circle-Net) based on a
synergy of deep circle-boundary point feature learning and weighted algebraic
fitting. First, we design a circle-boundary learning module, which considers
local and global neighboring contexts of each point, to detect all potential
circle-boundary points. Second, we develop a deep feature based circle
parameter learning module for weighted algebraic fitting, without designing any
weight metric, to avoid the influence of outliers during fitting. Unlike most
of the cutting-edge circle extraction wisdoms, the proposed
classification-and-fitting modules are originally co-trained with a
comprehensive loss to enhance the quality of extracted circles.Comparisons on
the established dataset and real-scanned point clouds exhibit clear
improvements of Circle-Net over SOTAs in terms of both noise-robustness and
extraction accuracy. We will release our code, model, and data for both
training and evaluation on GitHub upon publication.

Annotated data have traditionally been used to provide the input for training
a supervised machine learning (ML) model. However, current pre-trained ML
models for natural language processing (NLP) contain embedded linguistic
information that can be used to inform the annotation process. We use the BERT
neural language model to feed information back into an annotation task that
involves semantic labelling of dialog behavior in a question-asking game called
Emotion Twenty Questions (EMO20Q). First we describe the background of BERT,
the EMO20Q data, and assisted annotation tasks. Then we describe the methods
for fine-tuning BERT for the purpose of checking the annotated labels. To do
this, we use the paraphrase task as a way to check that all utterances with the
same annotation label are classified as paraphrases of each other. We show this
method to be an effective way to assess and revise annotations of textual user
data with complex, utterance-level semantic labels.

With the deployment of 5G networks, standards organizations have started
working on the design phase for sixth-generation (6G) networks. 6G networks
will be immensely complex, requiring more deployment time, cost and management
efforts. On the other hand, mobile network operators demand these networks to
be intelligent, self-organizing, and cost-effective to reduce operating
expenses (OPEX). Machine learning (ML), a branch of artificial intelligence
(AI), is the answer to many of these challenges providing pragmatic solutions,
which can entirely change the future of wireless network technologies. By using
some case study examples, we briefly examine the most compelling problems,
particularly at the physical (PHY) and link layers in cellular networks where
ML can bring significant gains. We also review standardization activities in
relation to the use of ML in wireless networks and future timeline on readiness
of standardization bodies to adapt to these changes. Finally, we highlight
major issues in ML use in the wireless technology, and provide potential
directions to mitigate some of them in 6G wireless networks.

Bayesian reasoning plays a significant role both in human rationality and in
machine learning. In this paper, we introduce transfinite modal logic, which
combines modal logic with ordinal arithmetic, in order to formalize Bayesian
reasoning semi-quantitatively. Technically, we first investigate some
nontrivial properties of ordinal arithmetic, which then enable us to expand
normal modal logic's semantics naturally and elegantly onto the novel
transfinite modal logic, while still keeping the ordinary definition of Kripke
models totally intact. Despite all the transfinite mathematical definition, we
argue that in practice, this logic can actually fit into a completely finite
interpretation as well. We suggest that transfinite modal logic captures the
essence of Bayesian reasoning in a rather clear and simple form, in particular,
it provides a perfect explanation for Sherlock Holmes' famous saying, "When you
have eliminated the impossible, whatever remains, however improbable, must be
the truth." We also prove a counterpart of finite model property theorem for
our logic.

First order policy optimization has been widely used in reinforcement
learning. It guarantees to find the optimal policy for the state-feedback
linear quadratic regulator (LQR). However, the performance of policy
optimization remains unclear for the linear quadratic Gaussian (LQG) control
where the LQG cost has spurious suboptimal stationary points. In this paper, we
introduce a novel perturbed policy gradient (PGD) method to escape a large
class of bad stationary points (including high-order saddles). In particular,
based on the specific structure of LQG, we introduce a novel reparameterization
procedure which converts the iterate from a high-order saddle to a strict
saddle, from which standard random perturbations in PGD can escape efficiently.
We further characterize the high-order saddles that can be escaped by our

Contrastive self-supervised learning has attracted significant research
attention recently. It learns effective visual representations from unlabeled
data by embedding augmented views of the same image close to each other while
pushing away embeddings of different images. Despite its great success on
ImageNet classification, COCO object detection, etc., its performance degrades
on contrast-agnostic applications, e.g., medical image classification, where
all images are visually similar to each other. This creates difficulties in
optimizing the embedding space as the distance between images is rather small.
To solve this issue, we present the first mix-up self-supervised learning
framework for contrast-agnostic applications. We address the low variance
across images based on cross-domain mix-up and build the pretext task based on
two synergistic objectives: image reconstruction and transparency prediction.
Experimental results on two benchmark datasets validate the effectiveness of
our method, where an improvement of 2.5% ~ 7.4% in top-1 accuracy was obtained
compared to existing self-supervised learning methods.

The framework of mixed observable Markov decision processes (MOMDP) models
many robotic domains in which some state variables are fully observable while
others are not. In this work, we identify a significant subclass of MOMDPs
defined by how actions influence the fully observable components of the state
and how those, in turn, influence the partially observable components and the
rewards. This unique property allows for a two-level hierarchical approach we
call HIerarchical Reinforcement Learning under Mixed Observability (HILMO),
which restricts partial observability to the top level while the bottom level
remains fully observable, enabling higher learning efficiency. The top level
produces desired goals to be reached by the bottom level until the task is
solved. We further develop theoretical guarantees to show that our approach can
achieve optimal and quasi-optimal behavior under mild assumptions. Empirical
results on long-horizon continuous control tasks demonstrate the efficacy and
efficiency of our approach in terms of improved success rate, sample
efficiency, and wall-clock training time. We also deploy policies learned in
simulation on a real robot.

We present a novel class incremental learning approach based on deep neural
networks, which continually learns new tasks with limited memory for storing
examples in the previous tasks. Our algorithm is based on knowledge
distillation and provides a principled way to maintain the representations of
old models while adjusting to new tasks effectively. The proposed method
estimates the relationship between the representation changes and the resulting
loss increases incurred by model updates. It minimizes the upper bound of the
loss increases using the representations, which exploits the estimated
importance of each feature map within a backbone model. Based on the
importance, the model restricts updates of important features for robustness
while allowing changes in less critical features for flexibility. This
optimization strategy effectively alleviates the notorious catastrophic
forgetting problem despite the limited accessibility of data in the previous
tasks. The experimental results show significant accuracy improvement of the
proposed algorithm over the existing methods on the standard datasets. Code is

Ads allocation, which involves allocating ads and organic items to limited
slots in feed with the purpose of maximizing platform revenue, has become a
research hotspot. Notice that, e-commerce platforms usually have multiple
entrances for different categories and some entrances have few visits. Data
from these entrances has low coverage, which makes it difficult for the agent
to learn. To address this challenge, we propose Similarity-based Hybrid
Transfer for Ads Allocation (SHTAA), which effectively transfers samples as
well as knowledge from data-rich entrance to data-poor entrance. Specifically,
we define an uncertainty-aware similarity for MDP to estimate the similarity of
MDP for different entrances. Based on this similarity, we design a hybrid
transfer method, including instance transfer and strategy transfer, to
efficiently transfer samples and knowledge from one entrance to another. Both
offline and online experiments on Meituan food delivery platform demonstrate
that the proposed method could achieve better performance for data-poor
entrance and increase the revenue for the platform.

A series of unsupervised video-based re-identification (re-ID) methods have
been proposed to solve the problem of high labor cost required to annotate
re-ID datasets. But their performance is still far lower than the supervised
counterparts. In the mean time, clean datasets without noise are used in these
methods, which is not realistic. In this paper, we propose to tackle this
problem by learning re-ID models from automatically generated person tracklets
by multiple objects tracking (MOT) algorithm. To this end, we design a
tracklet-based multi-level clustering (TMC) framework to effectively learn the
re-ID model from the noisy person tracklets. First, intra-tracklet isolation to
reduce ID switch noise within tracklets; second, alternates between using
inter-tracklet association to eliminate ID fragmentation noise and network
training using the pseudo label. Extensive experiments on MARS with various
manually generated noises show the effectiveness of the proposed framework.
Specifically, the proposed framework achieved mAP 53.4% and rank-1 63.7% on the
simulated tracklets with strongest noise, even outperforming the best existing
method on clean tracklets. Based on the results, we believe that building re-ID
models from automatically generated noisy tracklets is a reasonable approach
and will also be an important way to make re-ID models feasible in real-world

With the recent prevalence of reinforcement learning (RL), there have been
tremendous interests in utilizing RL for ads allocation in recommendation
platforms (e.g., e-commerce and news feed sites). To achieve better allocation,
the input of recent RL-based ads allocation methods is upgraded from point-wise
single item to list-wise item arrangement. However, this also results in a
high-dimensional space of state-action pairs, making it difficult to learn
list-wise representations with good generalization ability. This further
hinders the exploration of RL agents and causes poor sample efficiency. To
address this problem, we propose a novel RL-based approach for ads allocation
which learns better list-wise representations by leveraging task-specific
signals on Meituan food delivery platform. Specifically, we propose three
different auxiliary tasks based on reconstruction, prediction, and contrastive
learning respectively according to prior domain knowledge on ads allocation. We
conduct extensive experiments on Meituan food delivery platform to evaluate the
effectiveness of the proposed auxiliary tasks. Both offline and online
experimental results show that the proposed method can learn better list-wise
representations and achieve higher revenue for the platform compared to the

Units equivariance is the exact symmetry that follows from the requirement
that relationships among measured quantities of physics relevance must obey
self-consistent dimensional scalings. Here, we employ dimensional analysis and
ideas from equivariant machine learning to provide a two stage learning
procedure for units-equivariant machine learning. For a given learning task, we
first construct a dimensionless version of its inputs using classic results
from dimensional analysis, and then perform inference in the dimensionless
space. Our approach can be used to impose units equivariance across a broad
range of machine learning methods which are equivariant to rotations and other
groups. We discuss the in-sample and out-of-sample prediction accuracy gains
one can obtain in contexts like symbolic regression and emulation, where
symmetry is important. We illustrate our approach with simple numerical
examples involving dynamical systems in physics and ecology.

A mathematical framework for modelling constrained mixed-variable
optimization problems is presented in a blackbox optimization context. The
framework introduces a new notation and allows solution strategies. The
notation framework allows meta and categorical variables to be explicitly and
efficiently modelled, which facilitates the solution of such problems. The new
term meta variables is used to describe variables that influence which
variables are acting or nonacting: meta variables may affect the number of
variables and constraints. The flexibility of the solution strategies supports
the main blackbox mixed-variable optimization approaches: direct search methods
and surrogate-based methods (Bayesian optimization). The notation system and
solution strategies are illustrated through an example of a hyperparameter
optimization problem from the machine learning community.

Prompting methods recently achieve impressive success in few-shot learning.
These methods modify input samples with prompt sentence pieces, and decode
label tokens to map samples to corresponding labels. However, such a paradigm
is very inefficient for the task of slot tagging. Since slot tagging samples
are multiple consecutive words in a sentence, the prompting methods have to
enumerate all n-grams token spans to find all the possible slots, which greatly
slows down the prediction. To tackle this, we introduce an inverse paradigm for
prompting. Different from the classic prompts mapping tokens to labels, we
reversely predict slot values given slot types. Such inverse prompting only
requires a one-turn prediction for each slot type and greatly speeds up the
prediction. Besides, we propose a novel Iterative Prediction Strategy, from
which the model learns to refine predictions by considering the relations
between different slot types. We find, somewhat surprisingly, the proposed
method not only predicts faster but also significantly improves the effect
(improve over 6.1 F1-scores on 10-shot setting) and achieves new

Electricity price forecasting (EPF) is a branch of forecasting on the
interface of electrical engineering, statistics, computer science, and finance,
which focuses on predicting prices in wholesale electricity markets for a whole
spectrum of horizons. These range from a few minutes (real-time/intraday
auctions and continuous trading), through days (day-ahead auctions), to weeks,
months or even years (exchange and over-the-counter traded futures and forward
contracts). Over the last 25 years, various methods and computational tools
have been applied to intraday and day-ahead EPF. Until the early 2010s, the
field was dominated by relatively small linear regression models and
(artificial) neural networks, typically with no more than two dozen inputs. As
time passed, more data and more computational power became available. The
models grew larger to the extent where expert knowledge was no longer enough to
manage the complex structures. This, in turn, led to the introduction of
machine learning (ML) techniques in this rapidly developing and fascinating
area. Here, we provide an overview of the main trends and EPF models as of
2022.

Spam can be defined as unsolicited bulk email. In an effort to evade
text-based filters, spammers sometimes embed spam text in an image, which is
referred to as image spam. In this research, we consider the problem of image
spam detection, based on image analysis. We apply convolutional neural networks
(CNN) to this problem, we compare the results obtained using CNNs to other
machine learning techniques, and we compare our results to previous related
work. We consider both real-world image spam and challenging image spam-like
datasets. Our results improve on previous work by employing CNNs based on a
novel feature set consisting of a combination of the raw image and Canny edges.

Most existing approaches to Visual Question Answering (VQA) answer questions
directly, however, people usually decompose a complex question into a sequence
of simple sub questions and finally obtain the answer to the original question
after answering the sub question sequence(SQS). By simulating the process, this
paper proposes a conversation-based VQA (Co-VQA) framework, which consists of
three components: Questioner, Oracle, and Answerer. Questioner raises the sub
questions using an extending HRED model, and Oracle answers them one-by-one. An
Adaptive Chain Visual Reasoning Model (ACVRM) for Answerer is also proposed,
where the question-answer pair is used to update the visual representation
sequentially. To perform supervised learning for each model, we introduce a
well-designed method to build a SQS for each question on VQA 2.0 and VQA-CP v2
datasets. Experimental results show that our method achieves state-of-the-art
on VQA-CP v2. Further analyses show that SQSs help build direct semantic
connections between questions and images, provide question-adaptive
variable-length reasoning chains, and with explicit interpretability as well as
error traceability.

Online alignment in machine translation refers to the task of aligning a
target word to a source word when the target sequence has only been partially
decoded. Good online alignments facilitate important applications such as
lexically constrained translation where user-defined dictionaries are used to
inject lexical constraints into the translation model. We propose a novel
posterior alignment technique that is truly online in its execution and
superior in terms of alignment error rates compared to existing methods. Our
proposed inference technique jointly considers alignment and token
probabilities in a principled manner and can be seamlessly integrated within
existing constrained beam-search decoding algorithms. On five language pairs,
including two distant language pairs, we achieve consistent drop in alignment
error rates. When deployed on seven lexically constrained translation tasks, we
achieve significant improvements in BLEU specifically around the constrained
positions.

Modeling forests using historical data allows for more accurately evolution
analysis, thus providing an important basis for other studies. As a recognized
and effective tool, remote sensing plays an important role in forestry
analysis. We can use it to derive information about the forest, including tree
type, coverage and canopy density. There are many forest time series modeling
studies using statistic values, but few using remote sensing images. Image
prediction digital twin is an implementation of digital twin, which aims to
predict future images bases on historical data. In this paper, we propose an
LSTM-based digital twin approach for forest modeling, using Landsat 7 remote
sensing image within 20 years. The experimental results show that the
prediction twin method in this paper can effectively predict the future images
of study area.

High-dimensional and sparse (HiDS) matrices are frequently adopted to
describe the complex relationships in various big data-related systems and
applications. A Position-transitional Latent Factor Analysis (PLFA) model can
accurately and efficiently represent an HiDS matrix. However, its involved
latent factors are optimized by stochastic gradient descent with the specific
gradient direction step-by-step, which may cause a suboptimal solution. To
address this issue, this paper proposes a Sequential-Group-Differential-
Evolution (SGDE) algorithm to refine the latent factors optimized by a PLFA
model, thereby achieving a highly-accurate SGDE-PLFA model to HiDS matrices. As
demonstrated by the experiments on four HiDS matrices, a SGDE-PLFA model
outperforms the state-of-the-art models.

In the physical world, light affects the performance of deep neural networks.
Nowadays, many products based on deep neural network have been put into daily
life. There are few researches on the effect of light on the performance of
deep neural network models. However, the adversarial perturbations generated by
light may have extremely dangerous effects on these systems. In this work, we
propose an attack method called adversarial neon beam (AdvNB), which can
execute the physical attack by obtaining the physical parameters of adversarial
neon beams with very few queries. Experiments show that our algorithm can
achieve advanced attack effect in both digital test and physical test. In the
digital environment, 99.3% attack success rate was achieved, and in the
physical environment, 100% attack success rate was achieved. Compared with the
most advanced physical attack methods, our method can achieve better physical
perturbation concealment. In addition, by analyzing the experimental data, we
reveal some new phenomena brought about by the adversarial neon beam attack.

Private and public organizations regularly collect and analyze digitalized
data about their associates, volunteers, clients, etc. However, because most
personal data are sensitive, there is a key challenge in designing
privacy-preserving systems. To tackle privacy concerns, research communities
have proposed different methods to preserve privacy, with Differential privacy
(DP) standing out as a formal definition that allows quantifying the
privacy-utility trade-off. Besides, with the local DP (LDP) model, users can
sanitize their data locally before transmitting it to the server. The objective
of this thesis is thus two-fold: O$_1$) To improve the utility and privacy in
multiple frequency estimates under LDP guarantees, which is fundamental to
statistical learning. And O$_2$) To assess the privacy-utility trade-off of
machine learning (ML) models trained over differentially private data. For
O$_1$, we first tackled the problem from two "multiple" perspectives, i.e.,
multiple attributes and multiple collections throughout time, while focusing on
utility. Secondly, we focused our attention on the multiple attributes aspect
only, in which we proposed a solution focusing on privacy while preserving
utility. In both cases, we demonstrate through analytical and experimental
validations the advantages of our proposed solutions over state-of-the-art LDP
protocols. For O$_2$, we empirically evaluated ML-based solutions designed to
solve real-world problems while ensuring DP guarantees. Indeed, we mainly used
the input data perturbation setting from the privacy-preserving ML literature.
This is the situation in which the whole dataset is sanitized independently
and, thus, we implemented LDP algorithms from the perspective of the
centralized data owner. In all cases, we concluded that differentially private
ML models achieve nearly the same utility metrics as non-private ones.

Knowledge graph (KG) based Collaborative Filtering is an effective approach
to personalizing recommendation systems for relatively static domains such as
movies and books, by leveraging structured information from KG to enrich both
item and user representations. Motivated by the use of Transformers for
understanding rich text in content-based filtering recommender systems, we
propose Content-aware KG-enhanced Meta-preference Networks as a way to enhance
collaborative filtering recommendation based on both structured information
from KG as well as unstructured content features based on Transformer-empowered
content-based filtering. To achieve this, we employ a novel training scheme,
Cross-System Contrastive Learning, to address the inconsistency of the two very
different systems and propose a powerful collaborative filtering model and a
variant of the well-known NRMS system within this modeling framework. We also
contribute to public domain resources through the creation of a large-scale
movie-knowledge-graph dataset and an extension of the already public
Amazon-Book dataset through incorporation of text descriptions crawled from
external sources. We present experimental results showing that enhancing
collaborative filtering with Transformer-based features derived from
content-based filtering outperforms strong baseline systems, improving the
ability of knowledge-graph-based collaborative filtering systems to exploit
item content information.

Lipschitz constants of neural networks allow for guarantees of robustness in
image classification, safety in controller design, and generalizability beyond
the training data. As calculating Lipschitz constants is NP-hard, techniques
for estimating Lipschitz constants must navigate the trade-off between
scalability and accuracy. In this work, we significantly push the scalability
frontier of a semidefinite programming technique known as LipSDP while
achieving zero accuracy loss. We first show that LipSDP has chordal sparsity,
which allows us to derive a chordally sparse formulation that we call
Chordal-LipSDP. The key benefit is that the main computational bottleneck of
LipSDP, a large semidefinite constraint, is now decomposed into an equivalent
collection of smaller ones: allowing Chordal-LipSDP to outperform LipSDP
particularly as the network depth grows. Moreover, our formulation uses a
tunable sparsity parameter that enables one to gain tighter estimates without
incurring a significant computational cost. We illustrate the scalability of
our approach through extensive numerical experiments.

Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and
classification of cancer subtypes are indispensable for effective clinical
treatment. Promising results on automatic cancer subtyping systems have been
published recently with the emergence of various deep learning methods.
However, such automatic systems often overfit the data due to the high
dimensionality and scarcity. In this paper, we propose to investigate automatic
subtyping from an unsupervised learning perspective by directly constructing
the underlying data distribution itself, hence sufficient data can be generated
to alleviate the issue of overfitting. Specifically, we bypass the strong
Gaussianity assumption that typically exists but fails in the unsupervised
learning subtyping literature due to small-sized samples by vector
quantization. Our proposed method better captures the latent space features and
models the cancer subtype manifestation on a molecular basis, as demonstrated
by the extensive experimental results.

With the rapid development of cloud manufacturing, industrial production with
edge computing as the core architecture has been greatly developed. However,
edge devices often suffer from abnormalities and failures in industrial
production. Therefore, detecting these abnormal situations timely and
accurately is crucial for cloud manufacturing. As such, a straightforward
solution is that the edge device uploads the data to the cloud for anomaly
detection. However, Industry 4.0 puts forward higher requirements for data
privacy and security so that it is unrealistic to upload data from edge devices
directly to the cloud. Considering the above-mentioned severe challenges, this
paper customizes a weakly-supervised edge computing anomaly detection
framework, i.e., Federated Learning-based Transformer framework
(\textit{FedAnomaly}), to deal with the anomaly detection problem in cloud
manufacturing. Specifically, we introduce federated learning (FL) framework
that allows edge devices to train an anomaly detection model in collaboration
with the cloud without compromising privacy. To boost the privacy performance
of the framework, we add differential privacy noise to the uploaded features.
To further improve the ability of edge devices to extract abnormal features, we
use the Transformer to extract the feature representation of abnormal data. In
this context, we design a novel collaborative learning protocol to promote
efficient collaboration between FL and Transformer. Furthermore, extensive case
studies on four benchmark data sets verify the effectiveness of the proposed
framework. To the best of our knowledge, this is the first time integrating FL
and Transformer to deal with anomaly detection problems in cloud manufacturing.

Recently there has seen promising results on automatic stage scoring by
extracting spatio-temporal features from electroencephalogram (EEG). Such
methods entail laborious manual feature engineering and domain knowledge. In
this study, we propose an adaptive scheme to probabilistically encode, filter
and accumulate the input signals and weight the resultant features by the
half-Gaussian probabilities of signal intensities. The adaptive representations
are subsequently fed into a transformer model to automatically mine the
relevance between features and corresponding stages. Extensive experiments on
the largest public dataset against state-of-the-art methods validate the
effectiveness of our proposed method and reveal promising future directions.

The rapid miniaturization of Machine Learning (ML) for low powered processing
has opened gateways to provide cognition at the extreme edge (E.g., sensors and
actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research
field proposes to democratize the use of Machine Learning (ML) and Deep
Learning (DL) on frugal Microcontroller Units (MCUs). MCUs are highly
energy-efficient pervasive devices capable of operating with less than a few
Milliwatts of power. Nevertheless, many solutions assume that TinyML can only
run inference. Despite this, growing interest in TinyML has led to work that
makes them reformable, i.e., work that permits TinyML to improve once deployed.
In line with this, roadblocks in MCU based solutions in general, such as
reduced physical access and long deployment periods of MCUs, deem reformable
TinyML to play a significant part in more effective solutions. In this work, we
present a survey on reformable TinyML solutions with the proposal of a novel
taxonomy for ease of separation. Here, we also discuss the suitability of each
hierarchical layer in the taxonomy for allowing reformability. In addition to
these, we explore the workflow of TinyML and analyze the identified deployment
schemes and the scarcely available benchmarking tools. Furthermore, we discuss
how reformable TinyML can impact a few selected industrial areas and discuss
the challenges and future directions.

How to sample high quality negative instances from unlabeled data, i.e.,
negative sampling, is important for training implicit collaborative filtering
and contrastive learning models. Although previous studies have proposed some
approaches to sample informative instances, few has been done to discriminating
false negative from true negative for unbiased negative sampling. On the basis
of our order relation analysis of negatives' scores, we first derive the class
conditional density of true negatives and that of false negatives. We next
design a Bayesian classifier for negative classification, from which we define
a model-agnostic posterior probability estimate of an instance being true
negative as a quantitative negative signal measure. We also propose a Bayesian
optimal sampling rule to sample high-quality negatives. The proposed Bayesian
Negative Sampling (BNS) algorithm has a linear time complexity. Experimental
studies validate the superiority of BNS over the peers in terms of better
sampling quality and better recommendation performance.

It is well known that we need to choose the hyper-parameters in Momentum,
AdaGrad, AdaDelta, and other alternative stochastic optimizers. While in many
cases, the hyper-parameters are tuned tediously based on experience becoming
more of an art than science. We present a novel per-dimension learning rate
method for gradient descent called AdaSmooth. The method is insensitive to
hyper-parameters thus it requires no manual tuning of the hyper-parameters like
Momentum, AdaGrad, and AdaDelta methods. We show promising results compared to
other methods on different convolutional neural networks, multi-layer
perceptron, and alternative machine learning tasks. Empirical results
demonstrate that AdaSmooth works well in practice and compares favorably to
other stochastic optimization methods in neural networks.

Nearest neighbor search plays a fundamental role in many disciplines such as
multimedia information retrieval, data-mining, and machine learning. The
graph-based search approaches show superior performance over other types of
approaches in recent studies. In this paper, the graph-based NN search is
revisited. We optimize two key components in the approach, namely the search
procedure and the graph that supports the search. For the graph construction, a
two-stage graph diversification scheme is proposed, which makes a good
trade-off between the efficiency and reachability for the search procedure that
builds upon it. Moreover, the proposed diversification scheme allows the search
procedure to decide dynamically how many nodes should be visited in one node's
neighborhood. By this way, the computing power of the devices is fully utilized
when the search is carried out under different circumstances. Furthermore, two
NN search procedures are designed respectively for small and large batch
queries on the GPU. The optimized NN search, when being supported by the
two-stage diversified graph, outperforms all the state-of-the-art approaches on
both the CPU and the GPU across all the considered large-scale datasets.

Accurately detecting Alzheimer's disease (AD) and predicting mini-mental
state examination (MMSE) score are important tasks in elderly health by
magnetic resonance imaging (MRI). Most of the previous methods on these two
tasks are based on single-task learning and rarely consider the correlation
between them. Since the MMSE score, which is an important basis for AD
diagnosis, can also reflect the progress of cognitive impairment, some studies
have begun to apply multi-task learning methods to these two tasks. However,
how to exploit feature correlation remains a challenging problem for these
methods. To comprehensively address this challenge, we propose a MRI-based
multi-task decoupled learning method for AD detection and MMSE score
prediction. First, a multi-task learning network is proposed to implement AD
detection and MMSE score prediction, which exploits feature correlation by
adding three multi-task interaction layers between the backbones of the two
tasks. Each multi-task interaction layer contains two feature decoupling
modules and one feature interaction module. Furthermore, to enhance the
generalization between tasks of the features selected by the feature decoupling
module, we propose the feature consistency loss constrained feature decoupling
module. Finally, in order to exploit the specific distribution information of
MMSE score in different groups, a distribution loss is proposed to further
enhance the model performance. We evaluate our proposed method on multi-site
datasets. Experimental results show that our proposed multi-task decoupled
representation learning method achieves good performance, outperforming
single-task learning and other existing state-of-the-art methods.

Target sound extraction (TSE) aims to extract the sound part of a target
sound event class from a mixture audio with multiple sound events.
  The previous works mainly focus on the problems of weakly-labelled data,
jointly learning and new classes, however, no one cares about the onset and
offset times of the target sound event, which has been emphasized in the
auditory scene analysis. In this paper, we study to utilize such timestamp
information to help extract the target sound via a target sound detection
network and a target-weighted time-frequency loss function.
  More specifically, we use the detection result of a target sound detection
(TSD) network as the additional information to guide the learning of target
sound extraction network. We also find that the result of TSE can further
improve the performance of the TSD network, so that a mutual learning framework
of the target sound detection and extraction is proposed. In addition, a
target-weighted time-frequency loss function is designed to pay more attention
to the temporal regions of the target sound during training. Experimental
results on the synthesized data generated from the Freesound Datasets show that
our proposed method can significantly improve the performance of TSE.

The domain of natural language processing (NLP), which has greatly evolved
over the last years, has highly benefited from the recent developments in word
and sentence embeddings. Such embeddings enable the transformation of complex
NLP tasks, like semantic similarity or Question and Answering (Q&A), into much
simpler to perform vector comparisons. However, such a problem transformation
raises new challenges like the efficient comparison of embeddings and their
manipulation. In this work, we will discuss about various word and sentence
embeddings algorithms, we will select a sentence embedding algorithm, BERT, as
our algorithm of choice and we will evaluate the performance of two vector
comparison approaches, FAISS and Elasticsearch, in the specific problem of
sentence embeddings. According to the results, FAISS outperforms Elasticsearch
when used in a centralized environment with only one node, especially when big
datasets are included.

In Uyghur speech, consonant and vowel reduction are often encountered,
especially in spontaneous speech with high speech rate, which will cause a
degradation of speech recognition performance. To solve this problem, we
propose an effective phone mask training method for Conformer-based Uyghur
end-to-end (E2E) speech recognition. The idea is to randomly mask off a certain
percentage features of phones during model training, which simulates the above
verbal phenomena and facilitates E2E model to learn more contextual
information. According to experiments, the above issues can be greatly
alleviated. In addition, deep investigations are carried out into different
units in masking, which shows the effectiveness of our proposed masking unit.
We also further study the masking method and optimize filling strategy of phone
mask. Finally, compared with Conformer-based E2E baseline without mask
training, our model demonstrates about 5.51% relative Word Error Rate (WER)
reduction on reading speech and 12.92% on spontaneous speech, respectively. The
above approach has also been verified on test-set of open-source data THUYG-20,
which shows 20% relative improvements.

Unbiased learning to rank has been proposed to alleviate the biases in the
search ranking, making it possible to train ranking models with user
interaction data. In real applications, search engines are designed to display
only the most relevant k documents from the retrieved candidate set. The rest
candidates are discarded. As a consequence, position bias and sample selection
bias usually occur simultaneously. Existing unbiased learning to rank
approaches either focus on one type of bias (e.g., position bias) or mitigate
the position bias and sample selection bias with separate components,
overlooking their associations. In this study, we first analyze the mechanisms
and associations of position bias and sample selection bias from the viewpoint
of a causal graph. Based on the analysis, we propose Causal Likelihood
Decomposition (CLD), a unified approach to simultaneously mitigating these two
biases in top-k learning to rank. By decomposing the log-likelihood of the
biased data as an unbiased term that only related to relevance, plus other
terms related to biases, CLD successfully detaches the relevance from position
bias and sample selection bias. An unbiased ranking model can be obtained from
the unbiased term, via maximizing the whole likelihood. An extension to the
pairwise neural ranking is also developed. Advantages of CLD include
theoretical soundness and a unified framework for pointwise and pairwise
unbiased top-k learning to rank. Extensive experimental results verified that
CLD, including its pairwise neural extension, outperformed the baselines by
mitigating both the position bias and the sample selection bias. Empirical
studies also showed that CLD is robust to the variation of bias severity and
the click noise.

Many populous countries including India are burdened with a considerable
backlog of legal cases. Development of automated systems that could process
legal documents and augment legal practitioners can mitigate this. However,
there is a dearth of high-quality corpora that is needed to develop such
data-driven systems. The problem gets even more pronounced in the case of low
resource languages such as Hindi. In this resource paper, we introduce the
Hindi Legal Documents Corpus (HLDC), a corpus of more than 900K legal documents
in Hindi. Documents are cleaned and structured to enable the development of
downstream applications. Further, as a use-case for the corpus, we introduce
the task of bail prediction. We experiment with a battery of models and propose
a Multi-Task Learning (MTL) based model for the same. MTL models use
summarization as an auxiliary task along with bail prediction as the main task.
Experiments with different models are indicative of the need for further
research in this area. We release the corpus and model implementation code with
this paper: https://github.com/Exploration-Lab/HLDC

Cross-lingual Named Entity Recognition (NER) has recently become a research
hotspot because it can alleviate the data-hungry problem for low-resource
languages. However, few researches have focused on the scenario where the
source-language labeled data is also limited in some specific domains. A common
approach for this scenario is to generate more training data through
translation or generation-based data augmentation method. Unfortunately, we
find that simply combining source-language data and the corresponding
translation cannot fully exploit the translated data and the improvements
obtained are somewhat limited. In this paper, we describe our novel
dual-contrastive framework ConCNER for cross-lingual NER under the scenario of
limited source-language labeled data. Specifically, based on the
source-language samples and their translations, we design two contrastive
objectives for cross-language NER at different grammatical levels, namely
Translation Contrastive Learning (TCL) to close sentence representations
between translated sentence pairs and Label Contrastive Learning (LCL) to close
token representations within the same labels. Furthermore, we utilize knowledge
distillation method where the NER model trained above is used as the teacher to
train a student model on unlabeled target-language data to better fit the
target language. We conduct extensive experiments on a wide variety of target
languages, and the results demonstrate that ConCNER tends to outperform
multiple baseline methods. For reproducibility, our code for this paper is
available at https://github.com/GKLMIP/ConCNER.

In the paradigm of object detection, the decision head is an important part,
which affects detection performance significantly. Yet how to design a
high-performance decision head remains to be an open issue. In this paper, we
propose a novel approach to combine decision trees and deep neural networks in
an end-to-end learning manner for object detection. First, we disentangle the
decision choices and prediction values by plugging soft decision trees into
neural networks. To facilitate effective learning, we propose randomized
decision routing with node selective and associative losses, which can boost
the feature representative learning and network decision simultaneously.
Second, we develop the decision head for object detection with narrow branches
to generate the routing probabilities and masks, for the purpose of obtaining
divergent decisions from different nodes. We name this approach as the
randomized decision routing for object detection, abbreviated as R(Det)$^2$.
Experiments on MS-COCO dataset demonstrate that R(Det)$^2$ is effective to
improve the detection performance. Equipped with existing detectors, it
achieves $1.4\sim 3.6$\% AP improvement.

Conditional image generation is an active research topic including text2image
and image translation.
  Recently image manipulation with linguistic instruction brings new challenges
of multimodal conditional generation.
  However, traditional conditional image generation models mainly focus on
generating high-quality and visually realistic images, and lack resolving the
partial consistency between image and instruction.
  To address this issue, we propose an Increment Reasoning Generative
Adversarial Network (IR-GAN), which aims to reason the consistency between
visual increment in images and semantic increment in instructions.
  First, we introduce the word-level and instruction-level instruction encoders
to learn user's intention from history-correlated instructions as semantic
increment.
  Second, we embed the representation of semantic increment into that of source
image for generating target image, where source image plays the role of
referring auxiliary.
  Finally, we propose a reasoning discriminator to measure the consistency
between visual increment and semantic increment, which purifies user's
intention and guarantees the good logic of generated target image.
  Extensive experiments and visualization conducted on two datasets show the
effectiveness of IR-GAN.

As an extensive research in the field of natural language processing (NLP),
aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment
expressed in a text relative to the corresponding aspect. Unfortunately, most
languages lack sufficient annotation resources, thus more and more recent
researchers focus on cross-lingual aspect-based sentiment analysis (XABSA).
However, most recent researches only concentrate on cross-lingual data
alignment instead of model alignment. To this end, we propose a novel
framework, CL-XABSA: Contrastive Learning for Cross-lingual Aspect-Based
Sentiment Analysis. Based on contrastive learning, we close the distance
between samples with the same label in different semantic spaces, thus
achieving a convergence of semantic spaces of different languages.
Specifically, we design two contrastive strategies, token level contrastive
learning of token embeddings (TL-CTE) and sentiment level contrastive learning
of token embeddings (SL-CTE), to regularize the semantic space of source and
target language to be more uniform. Since our framework can receive datasets in
multiple languages during training, our framework can be adapted not only for
XABSA task but also for multilingual aspect-based sentiment analysis (MABSA).
To further improve the performance of our model, we perform knowledge
distillation technology leveraging data from unlabeled target language. In the
distillation XABSA task, we further explore the comparative effectiveness of
different data (source dataset, translated dataset, and code-switched dataset).
The results demonstrate that the proposed method has a certain improvement in
the three tasks of XABSA, distillation XABSA and MABSA. For reproducibility,
our code for this paper is available at https://github.com/GKLMIP/CL-XABSA.

Airports have an important role in both military and civilian domains. The
synthetic aperture radar (SAR) based airport detection has received increasing
attention in recent years. However, due to the high cost of SAR imaging and
annotation process, there is no publicly available SAR dataset for airport
detection. As a result, deep learning methods have not been fully used in
airport detection tasks. To provide a benchmark for airport detection research
in SAR images, this paper introduces a large-scale SAR Airport Dataset (SAD).
In order to adequately reflect the demands of real world applications, it
contains 624 SAR images from Sentinel 1B and covers 104 airfield instances with
different scales, orientations and shapes. The experiments of multiple deep
learning approach on this dataset proves its effectiveness. It developing
state-of-the-art airport area detection algorithms or other relevant tasks.

When deploying pre-trained neural network models in real-world applications,
model consumers often encounter resource-constraint platforms such as mobile
and smart devices. They typically use the pruning technique to reduce the size
and complexity of the model, generating a lighter one with less resource
consumption. Nonetheless, most existing pruning methods are proposed with the
premise that the model after being pruned has a chance to be fine-tuned or even
retrained based on the original training data. This may be unrealistic in
practice, as the data controllers are often reluctant to provide their model
consumers with the original data. In this work, we study the neural network
pruning in the data-free context, aiming to yield lightweight models that are
not only accurate in prediction but also robust against undesired inputs in
open-world deployments. Considering the absence of the fine-tuning and
retraining that can fix the mis-pruned units, we replace the traditional
aggressive one-shot strategy with a conservative one that treats the pruning as
a progressive process. We propose a pruning method based on stochastic
optimization that uses robustness-related metrics to guide the pruning process.
Our method is implemented as a Python program and evaluated with a series of
experiments on diverse neural network models. The experimental results show
that it significantly outperforms existing one-shot data-free pruning
approaches in terms of robustness preservation and accuracy.

We present a unified probabilistic gradient boosting framework for regression
tasks that models and predicts the entire conditional distribution of a
univariate response variable as a function of covariates. Our likelihood-based
approach allows us to either model all conditional moments of a parametric
distribution, or to approximate the conditional cumulative distribution
function via Normalizing Flows. As underlying computational backbones, our
framework is based on XGBoost and LightGBM. Modelling and predicting the entire
conditional distribution greatly enhances existing tree-based gradient boosting
implementations, as it allows to create probabilistic forecasts from which
prediction intervals and quantiles of interest can be derived. Empirical
results show that our framework achieves state-of-the-art forecast accuracy.

Ridesplitting, which is a form of pooled ridesourcing service, has great
potential to alleviate the negative impacts of ridesourcing on the environment.
However, most existing studies only explored its theoretical environmental
benefits based on optimization models and simulations. By contrast, this study
aims to reveal the real-world emission reduction of ridesplitting and its
determinants based on the observed data of ridesourcing in Chengdu, China.
Integrating the trip data with the COPERT model, this study calculates the CO2
emissions of shared rides (ridesplitting) and their substituted single rides
(regular ridesourcing) to estimate the CO2 emission reduction of each
ridesplitting trip. The results show that not all ridesplitting trips reduce
emissions from ridesourcing in the real world. The CO2 emission reduction rate
of ridesplitting varies from trip to trip, averaging at 43.15g/km. Then,
interpretable machine learning models, gradient boosting machines, are applied
to explore the relationship between the CO2 emission reduction rate of
ridesplitting and its determinants. Based on the SHapley Additive exPlanations
(SHAP) method, the overlap rate and detour rate of shared rides are identified
to be the most important factors that determine the CO2 emission reduction rate
of ridesplitting. Increasing the overlap rate, the number of shared rides,
average speed, and ride distance ratio while decreasing the detour rate, actual
trip distance, and ride distance gap can increase the CO2 emission reduction
rate of ridesplitting. In addition, nonlinear effects and interactions of the
determinants are examined through the partial dependence plots. To sum up, this
study provides a scientific method for the government and ridesourcing
companies to better assess and optimize the environmental benefits of
ridesplitting.

Myoelectric pattern recognition is one of the important aspects in the design
of the control strategy for various applications including upper-limb
prostheses and bio-robotic hand movement systems. The current work has proposed
an approach to design an energy-efficient EMG-based controller by considering a
supervised learning framework using a kernelized SVM classifier for decoding
the information of surface electromyography (sEMG) signals to infer the
underlying muscle movements. In order to achieve the optimized performance of
the EMG-based controller, our main strategy of classifier design is to reduce
the false movements of the overall system (when the EMG-based controller is at
the `Rest' position). To this end, unlike the traditional single training
objective of soft margin kernelized SVM, we have formulated the training
algorithm of the proposed supervised learning system as a general constrained
multi-objective optimization problem. An elitist multi-objective evolutionary
algorithm $-$ the non-dominated sorting genetic algorithm II (NSGA-II) has been
used for the tuning of SVM hyperparameters. We have presented the experimental
results by performing the experiments on a dataset consisting of the sEMG
signals collected from eleven subjects at five different upper limb positions.
It is evident from the presented result that the proposed approach provides
much more flexibility to the designer in selecting the parameters of the
classifier to optimize the energy efficiency of the EMG-based controller.

This paper investigates how to improve the runtime speed of personalized
speech enhancement (PSE) networks while maintaining the model quality. Our
approach includes two aspects: architecture and knowledge distillation (KD). We
propose an end-to-end enhancement (E3Net) model architecture, which is
$3\times$ faster than a baseline STFT-based model. Besides, we use KD
techniques to develop compressed student models without significantly degrading
quality. In addition, we investigate using noisy data without reference clean
signals for training the student models, where we combine KD with multi-task
learning (MTL) using automatic speech recognition (ASR) loss. Our results show
that E3Net provides better speech and transcription quality with a lower target
speaker over-suppression (TSOS) rate than the baseline model. Furthermore, we
show that the KD methods can yield student models that are $2-4\times$ faster
than the teacher and provides reasonable quality. Combining KD and MTL improves
the ASR and TSOS metrics without degrading the speech quality.

Dysarthric speech recognition has posed major challenges due to lack of
training data and heavy mismatch in speaker characteristics. Recent ASR systems
have benefited from readily available pretrained models such as wav2vec2 to
improve the recognition performance. Speaker adaptation using fMLLR and
xvectors have provided major gains for dysarthric speech with very little
adaptation data. However, integration of wav2vec2 with fMLLR features or
xvectors during wav2vec2 finetuning is yet to be explored. In this work, we
propose a simple adaptation network for fine-tuning wav2vec2 using fMLLR
features. The adaptation network is also flexible to handle other speaker
adaptive features such as xvectors. Experimental analysis show steady
improvements using our proposed approach across all impairment severity levels
and attains 57.72\% WER for high severity in UASpeech dataset. We also
performed experiments on German dataset to substantiate the consistency of our
proposed approach across diverse domains.

We propose a variational Bayesian inference procedure for online nonlinear
system identification. For each output observation, a set of parameter
posterior distributions is updated, which is then used to form a posterior
predictive distribution for future outputs. We focus on the class of polynomial
NARMAX models, which we cast into probabilistic form and represent in terms of
a Forney-style factor graph. Inference in this graph is efficiently performed
by a variational message passing algorithm. We show empirically that our
variational Bayesian estimator outperforms an online recursive least-squares
estimator, most notably in small sample size settings and low noise regimes,
and performs on par with an iterative least-squares estimator trained offline.

Discontinuing ad creatives at an appropriate time is one of the most
important ad operations that can have a significant impact on sales. Such
operational support for ineffective ads has been less explored than that for
effective ads. After pre-analyzing 1,000,000 real-world ad creatives, we found
that there are two types of discontinuation: short-term (i.e., cut-out) and
long-term (i.e., wear-out). In this paper, we propose a practical prediction
framework for the discontinuation of ad creatives with a hazard function-based
loss function inspired by survival analysis. Our framework predicts the
discontinuations with a multi-modal deep neural network that takes as input the
ad creative (e.g., text, categorical, image, numerical features). To improve
the prediction performance for the two different types of discontinuations and
for the ad creatives that contribute to sales, we introduce two new techniques:
(1) a two-term estimation technique with multi-task learning and (2) a
click-through rate-weighting technique for the loss function. We evaluated our
framework using the large-scale ad creative dataset, including 10 billion scale
impressions. In terms of the concordance index (short: 0.896, long: 0.939, and
overall: 0.792), our framework achieved significantly better performance than
the conventional method (0.531). Additionally, we confirmed that our framework
(i) demonstrated the same degree of discontinuation effect as manual operations
for short-term cases, and (ii) accurately predicted the ad discontinuation
order, which is important for long-running ad creatives for long-term cases.

Data often has many semantic attributes that are causally associated with
each other. But do attribute-specific learned representations of data also
respect the same causal relations? We answer this question in three steps.
First, we introduce NCINet, an approach for observational causal discovery from
high-dimensional data. It is trained purely on synthetically generated
representations and can be applied to real representations, and is specifically
designed to mitigate the domain gap between the two. Second, we apply NCINet to
identify the causal relations between image representations of different pairs
of attributes with known and unknown causal relations between the labels. For
this purpose, we consider image representations learned for predicting
attributes on the 3D Shapes, CelebA, and the CASIA-WebFace datasets, which we
annotate with multiple multi-class attributes. Third, we analyze the effect on
the underlying causal relation between learned representations induced by
various design choices in representation learning. Our experiments indicate
that (1) NCINet significantly outperforms existing observational causal
discovery approaches for estimating the causal relation between pairs of random
samples, both in the presence and absence of an unobserved confounder, (2)
under controlled scenarios, learned representations can indeed satisfy the
underlying causal relations between their respective labels, and (3) the causal
relations are positively correlated with the predictive capability of the

Inspired by the complexity and diversity of biological neurons, a quadratic
neuron is proposed to replace the inner product in the current neuron with a
simplified quadratic function. Employing such a novel type of neurons offers a
new perspective on developing deep learning. When analyzing quadratic neurons,
we find that there exists a function such that a heterogeneous network can
approximate it well with a polynomial number of neurons but a purely
conventional or quadratic network needs an exponential number of neurons to
achieve the same level of error. Encouraged by this inspiring theoretical
result on heterogeneous networks, we directly integrate conventional and
quadratic neurons in an autoencoder to make a new type of heterogeneous
autoencoders. Anomaly detection experiments confirm that heterogeneous
autoencoders perform competitively compared to other state-of-the-art models.

Safe exploration is a common problem in reinforcement learning (RL) that aims
to prevent agents from making disastrous decisions while exploring their
environment. A family of approaches to this problem assume domain knowledge in
the form of a (partial) model of this environment to decide upon the safety of
an action. A so-called shield forces the RL agent to select only safe actions.
However, for adoption in various applications, one must look beyond enforcing
safety and also ensure the applicability of RL with good performance. We extend
the applicability of shields via tight integration with state-of-the-art deep
RL, and provide an extensive, empirical study in challenging, sparse-reward
environments under partial observability. We show that a carefully integrated
shield ensures safety and can improve the convergence rate and final
performance of RL agents. We furthermore show that a shield can be used to
bootstrap state-of-the-art RL agents: they remain safe after initial learning
in a shielded setting, allowing us to disable a potentially too conservative
shield eventually.

Capturing the dynamics in user preference is crucial to better predict user
future behaviors because user preferences often drift over time. Many existing
recommendation algorithms -- including both shallow and deep ones -- often
model such dynamics independently, i.e., user static and dynamic preferences
are not modeled under the same latent space, which makes it difficult to fuse
them for recommendation. This paper considers the problem of embedding a user's
sequential behavior into the latent space of user preferences, namely
translating sequence to preference. To this end, we formulate the sequential
recommendation task as a dictionary learning problem, which learns: 1) a shared
dictionary matrix, each row of which represents a partial signal of user
dynamic preferences shared across users; and 2) a posterior distribution
estimator using a deep autoregressive model integrated with Gated Recurrent
Unit (GRU), which can select related rows of the dictionary to represent a
user's dynamic preferences conditioned on his/her past behaviors. Qualitative
studies on the Netflix dataset demonstrate that the proposed method can capture
the user preference drifts over time and quantitative studies on multiple
real-world datasets demonstrate that the proposed method can achieve higher
accuracy compared with state-of-the-art factorization and neural sequential
recommendation methods. The code is available at
https://github.com/cchao0116/S2PNM-TKDE2021.

An important problem in the analysis of high-dimensional omics data is to
identify subsets of molecular variables that are associated with a phenotype of
interest. This requires addressing the challenges of high dimensionality,
strong multicollinearity and model uncertainty. We propose a new ensemble
learning approach for improving the performance of sparse penalised regression
methods, called STructural RANDomised Selection (STRANDS). The approach, that
builds and improves upon the Random Lasso method, consists of two steps. In
both steps, we reduce dimensionality by repeated subsampling of variables. We
apply a penalised regression method to each subsampled dataset and average the
results. In the first step, subsampling is informed by variable correlation
structure, and in the second step, by variable importance measures from the
first step. STRANDS can be used with any sparse penalised regression approach
as the "base learner". Using synthetic data and real biological datasets, we
demonstrate that STRANDS typically improves upon its base learner, and that
taking account of the correlation structure in the first step can help to
improve the efficiency with which the model space may be explored.

We consider whether it is possible to do text simplification without relying
on a "parallel" corpus, one that is made up of sentence-by-sentence alignments
of complex and ground truth simple sentences. To this end, we introduce a
number of concepts, some new and some not, including what we call Conjoined
Twin Networks, Flip-Flop Auto-Encoders (FFA) and Adversarial Networks (GAN). A
comparison is made between Jensen-Shannon (JS-GAN) and Wasserstein GAN, to see
how they impact performance, with stronger results for the former. An
experiment we conducted with a large dataset derived from Wikipedia found the
solid superiority of Twin Networks equipped with FFA and JS-GAN, over the
current best performing system. Furthermore, we discuss where we stand in a
relation to fully supervised methods in the past literature, and highlight with
examples qualitative differences that exist among simplified sentences
generated by supervision-free systems.

The path signature, a mathematically principled and universal feature of
sequential data, leads to a performance boost of deep learning-based models in
various sequential data tasks as a complimentary feature. However, it suffers
from the curse of dimensionality when the path dimension is high. To tackle
this problem, we propose a novel, trainable path development layer, which
exploits representations of sequential data with the help of finite-dimensional
matrix Lie groups. We also design the backpropagation algorithm of the
development layer via an optimisation method on manifolds known as
trivialisation. Numerical experiments demonstrate that the path development
consistently and significantly outperforms, in terms of accuracy and
dimensionality, signature features on several empirical datasets. Moreover,
stacking the LSTM with the development layer with a suitable matrix Lie group
is empirically proven to alleviate the gradient issues of LSTMs and the
resulting hybrid model achieves the state-of-the-art performance.

Efficient and accurate interatomic potential functions are critical to
computational study of materials while searching for structures with desired
properties. Traditionally, potential functions or energy landscapes are
designed by experts based on theoretical or heuristic knowledge. Here, we
propose a new approach to leverage strongly typed parallel genetic programming
(GP) for potential function discovery. We use a multi-objective evolutionary
algorithm with NSGA-III selection to optimize individual age, fitness, and
complexity through symbolic regression. With a DFT dataset of 863 unique carbon
allotrope configurations drawn from 858 carbon structures, the generated
potentials are able to predict total energies within $\pm 7.70$ eV at low
computational cost while generalizing well across multiple carbon structures.
Our code is open source and available at
\url{http://www.github.com/usccolumbia/mlpotential

Person tracking using computer vision techniques has wide ranging
applications such as autonomous driving, home security and sports analytics.
However, the growing threat of adversarial attacks raises serious concerns
regarding the security and reliability of such techniques. In this work, we
study the impact of multi-task learning (MTL) on the adversarial robustness of
the widely used SiamRPN tracker, in the context of person tracking.
Specifically, we investigate the effect of jointly learning with semantically
analogous tasks of person tracking and human keypoint detection. We conduct
extensive experiments with more powerful adversarial attacks that can be
physically realizable, demonstrating the practical value of our approach. Our
empirical study with simulated as well as real-world datasets reveals that
training with MTL consistently makes it harder to attack the SiamRPN tracker,
compared to typically training only on the single task of person tracking.

In the present study, a Particle Swarm Optimization (PSO) based Demand
Response (DR) model, using Artificial Neural Network (ANN) to predict load is
proposed. The electrical load and climatological data of a residential area in
Austin city in Texas are used as the inputs of the ANN. Then, the outcomes with
the day-ahead prices data are used to solve the load shifting and cost
reduction problem. According to the results, the proposed model has the ability
to decrease payment costs and peak load.

Clustering can be defined as the process of assembling objects into a number
of groups whose elements are similar to each other in some manner. As a
technique that is used in many domains, such as face clustering, plant
categorization, image segmentation, document classification, clustering is
considered one of the most important unsupervised learning problems. Scientists
have surveyed this problem for years and developed different techniques that
can solve it, such as k-means clustering. We analyze one of these techniques: a
powerful clustering algorithm called Sparse Subspace Clustering. We demonstrate
several experiments using this method and then introduce a new approach that
can reduce the computational time required to perform sparse subspace

The discovery of habitable exoplanets has long been a heated topic in
astronomy. Traditional methods for exoplanet identification include the wobble
method, direct imaging, gravitational microlensing, etc., which not only
require a considerable investment of manpower, time, and money, but also are
limited by the performance of astronomical telescopes. In this study, we
proposed the idea of using machine learning methods to identify exoplanets. We
used the Kepler dataset collected by NASA from the Kepler Space Observatory to
conduct supervised learning, which predicts the existence of exoplanet
candidates as a three-categorical classification task, using decision tree,
random forest, na\"ive Bayes, and neural network; we used another NASA dataset
consisted of the confirmed exoplanets data to conduct unsupervised learning,
which divides the confirmed exoplanets into different clusters, using k-means
clustering. As a result, our models achieved accuracies of 99.06%, 92.11%,
88.50%, and 99.79%, respectively, in the supervised learning task and
successfully obtained reasonable clusters in the unsupervised learning task.

Research studies have shown no qualms about using data driven deep learning
models for downstream tasks in medical image analysis, e.g., anatomy
segmentation and lesion detection, disease diagnosis and prognosis, and
treatment planning. However, deep learning models are not the sovereign remedy
for medical image analysis when the upstream imaging is not being conducted
properly (with artefacts). This has been manifested in MRI studies, where the
scanning is typically slow, prone to motion artefacts, with a relatively low
signal to noise ratio, and poor spatial and/or temporal resolution. Recent
studies have witnessed substantial growth in the development of deep learning
techniques for propelling fast MRI. This article aims to (1) introduce the deep
learning based data driven techniques for fast MRI including convolutional
neural network and generative adversarial network based methods, (2) survey the
attention and transformer based models for speeding up MRI reconstruction, and
(3) detail the research in coupling physics and data driven models for MRI
acceleration. Finally, we will demonstrate through a few clinical applications,
explain the importance of data harmonisation and explainable models for such
fast MRI techniques in multicentre and multi-scanner studies, and discuss
common pitfalls in current research and recommendations for future research

The automation of extracting argument structures faces a pair of challenges
on (1) encoding long-term contexts to facilitate comprehensive understanding,
and (2) improving data efficiency since constructing high-quality argument
structures is time-consuming. In this work, we propose a novel context-aware
Transformer-based argument structure prediction model which, on five different
domains, significantly outperforms models that rely on features or only encode
limited contexts. To tackle the difficulty of data annotation, we examine two
complementary methods: (i) transfer learning to leverage existing annotated
data to boost model performance in a new target domain, and (ii) active
learning to strategically identify a small amount of samples for annotation. We
further propose model-independent sample acquisition strategies, which can be
generalized to diverse domains. With extensive experiments, we show that our
simple-yet-effective acquisition strategies yield competitive results against
three strong comparisons. Combined with transfer learning, substantial F1 score
boost (5-25) can be further achieved during the early iterations of active
learning across domains.

We investigate a natural but surprisingly unstudied approach to the
multi-armed bandit problem under safety risk constraints. Each arm is
associated with an unknown law on safety risks and rewards, and the learner's
goal is to maximise reward whilst not playing unsafe arms, as determined by a
given threshold on the mean risk.
  We formulate a pseudo-regret for this setting that enforces this safety
constraint in a per-round way by softly penalising any violation, regardless of
the gain in reward due to the same. This has practical relevance to scenarios
such as clinical trials, where one must maintain safety for each round rather
than in an aggregated sense.
  We describe doubly optimistic strategies for this scenario, which maintain
optimistic indices for both safety risk and reward. We show that schema based
on both frequentist and Bayesian indices satisfy tight gap-dependent
logarithmic regret bounds, and further that these play unsafe arms only
logarithmically many times in total. This theoretical analysis is complemented
by simulation studies demonstrating the effectiveness of the proposed schema,
and probing the domains in which their use is appropriate.

In this paper, we consider a wireless network of smart sensors (agents) that
monitor a dynamical process and send measurements to a base station that
performs global monitoring and decision-making. Smart sensors are equipped with
both sensing and computation, and can either send raw measurements or process
them prior to transmission. Constrained agent resources raise a fundamental
latency-accuracy trade-off. On the one hand, raw measurements are inaccurate
but fast to produce. On the other hand, data processing on resource-constrained
platforms generates accurate measurements at the cost of non-negligible
computation latency. Further, if processed data are also compressed, latency
caused by wireless communication might be higher for raw measurements. Hence,
it is challenging to decide when and where sensors in the network should
transmit raw measurements or leverage time-consuming local processing. To
tackle this design problem, we propose a Reinforcement Learning approach to
learn an efficient policy that dynamically decides when measurements are to be
processed at each sensor. Effectiveness of our proposed approach is validated
through a numerical simulation with case study on smart sensing motivated by
the Internet of Drones.

In this work, we revisit the Linear Quadratic Gaussian (LQG) optimal control
problem from a behavioral perspective. Motivated by the suitability of
behavioral models for data-driven control, we begin with a reformulation of the
LQG problem in the space of input-output behaviors and obtain a complete
characterization of the optimal solutions. In particular, we show that the
optimal LQG controller can be expressed as a static behavioral-feedback gain,
thereby eliminating the need for dynamic state estimation characteristic of
state space methods. The static form of the optimal LQG gain also makes it
amenable to its computation by gradient descent, which we investigate via
numerical experiments. Furthermore, we highlight the advantage of this approach
in the data-driven control setting of learning the optimal LQG controller from
expert demonstrations.

Staged rollout is a strategy of incrementally releasing software updates to
portions of the user population in order to accelerate defect discovery without
incurring catastrophic outcomes such as system wide outages. Some past studies
have examined how to quantify and automate staged rollout, but stop short of
simultaneously considering multiple product or process metrics explicitly. This
paper demonstrates the potential to automate staged rollout with
multi-objective reinforcement learning in order to dynamically balance
stakeholder needs such as time to deliver new features and downtime incurred by
failures due to latent defects.

Nowadays, we are witnessing an increasing effort to improve the performance
and trustworthiness of Deep Neural Networks (DNNs), with the aim to enable
their adoption in safety critical systems such as self-driving cars. Multiple
testing techniques are proposed to generate test cases that can expose
inconsistencies in the behavior of DNN models. These techniques assume
implicitly that the training program is bug-free and appropriately configured.
However, satisfying this assumption for a novel problem requires significant
engineering work to prepare the data, design the DNN, implement the training
program, and tune the hyperparameters in order to produce the model for which
current automated test data generators search for corner-case behaviors. All
these model training steps can be error-prone. Therefore, it is crucial to
detect and correct errors throughout all the engineering steps of DNN-based
software systems and not only on the resulting DNN model. In this paper, we
gather a catalog of training issues and based on their symptoms and their
effects on the behavior of the training program, we propose practical
verification routines to detect the aforementioned issues, automatically, by
continuously validating that some important properties of the learning dynamics
hold during the training. Then, we design, TheDeepChecker, an end-to-end
property-based debugging approach for DNN training programs. We assess the
effectiveness of TheDeepChecker on synthetic and real-world buggy DL programs
and compare it with Amazon SageMaker Debugger (SMD). Results show that
TheDeepChecker's on-execution validation of DNN-based program's properties
succeeds in revealing several coding bugs and system misconfigurations, early
on and at a low cost. Moreover, TheDeepChecker outperforms the SMD's offline
rules verification on training logs in terms of detection accuracy and DL bugs
coverage.

Wildland fires pose an increasingly serious problem in our society. The
number and severity of these fires has been rising for many years. Wildfires
pose direct threats to life and property as well as threats through ancillary
effects like reduced air quality. The aim of this thesis is to develop
techniques to help combat the impacts of wildfires by improving wildfire
modeling capabilities by using satellite fire observations. Already much work
has been done in this direction by other researchers. Our work seeks to expand
the body of knowledge using mathematically sound methods to utilize information
about wildfires that considers the uncertainties inherent in the satellite
data.
  In this thesis we explore methods for using satellite data to help initialize
and steer wildfire simulations. In particular, we develop a method for
constructing the history of a fire, a new technique for assimilating wildfire
data, and a method for modifying the behavior of a modeled fire by inferring
information about the fuels in the fire domain. These goals rely on being able
to estimate the time a fire first arrived at every location in a geographic
region of interest. Because detailed knowledge of real wildfires is typically
unavailable, the basic procedure for developing and testing the methods in this
thesis will be to first work with simulated data so that the estimates produced
can be compared with known solutions. The methods thus developed are then
applied to real-world scenarios. Analysis of these scenarios shows that the
work with constructing the history of fires and data assimilation improves
improves fire modeling capabilities. The research is significant because it
gives us a better understanding of the capabilities and limitations of using
satellite data to inform wildfire models and it points the way towards new
avenues for modeling fire behavior.

Gradient descent is slow to converge for ill-conditioned problems and
non-convex problems. An important technique for acceleration is step-size
adaptation. The first part of this paper contains a detailed review of
step-size adaptation methods, including Polyak step-size, L4, LossGrad, Adam,
IDBD, and Hypergradient descent, and the relation of step-size adaptation to
meta-gradient methods. In the second part of this paper, we propose a new class
of methods of accelerating gradient descent that have some distinctiveness from
existing techniques. The new methods, which we call {\em step-size planning},
use the {\em update experience} to learn an improved way of updating the
parameters. The methods organize the experience into $K$ steps away from each
other to facilitate planning. From the past experience, our planning algorithm,
Csawg, learns a step-size model which is a form of multi-step machine that
predicts future updates. We extends Csawg to applying step-size planning
multiple steps, which leads to further speedup. We discuss and highlight the
projection power of the diagonal-matrix step-size for future large scale
applications. We show for a convex problem, our methods can surpass the
convergence rate of Nesterov's accelerated gradient, $1 - \sqrt{\mu/L}$, where
$\mu, L$ are the strongly convex factor of the loss function $F$ and the
Lipschitz constant of $F'$, which is the theoretical limit for the convergence
rate of first-order methods. On the well-known non-convex Rosenbrock function,
our planning methods achieve zero error below 500 gradient evaluations, while
gradient descent takes about 10000 gradient evaluations to reach a $10^{-3}$
accuracy. We discuss the connection of step-size planing to planning in
reinforcement learning, in particular, Dyna architectures.
  (This is a shorter abstract than in the paper because of length requirement)

A major challenge in text-video and text-audio retrieval is the lack of
large-scale training data. This is unlike image-captioning, where datasets are
in the order of millions of samples. To close this gap we propose a new video
mining pipeline which involves transferring captions from image captioning
datasets to video clips with no additional manual effort. Using this pipeline,
we create a new large-scale, weakly labelled audio-video captioning dataset
consisting of millions of paired clips and captions. We show that training a
multimodal transformed based model on this data achieves competitive
performance on video retrieval and video captioning, matching or even
outperforming HowTo100M pretraining with 20x fewer clips. We also show that our
mined clips are suitable for text-audio pretraining, and achieve state of the
art results for the task of audio retrieval.

Mapping behavioral actions to neural activity is a fundamental goal of
neuroscience. As our ability to record large neural and behavioral data
increases, there is growing interest in modeling neural dynamics during
adaptive behaviors to probe neural representations. In particular, neural
latent embeddings can reveal underlying correlates of behavior, yet, we lack
non-linear techniques that can explicitly and flexibly leverage joint behavior
and neural data. Here, we fill this gap with a novel method, CEBRA, that
jointly uses behavioral and neural data in a hypothesis- or discovery-driven
manner to produce consistent, high-performance latent spaces. We validate its
accuracy and demonstrate our tool's utility for both calcium and
electrophysiology datasets, across sensory and motor tasks, and in simple or
complex behaviors across species. It allows for single and multi-session
datasets to be leveraged for hypothesis testing or can be used label-free.
Lastly, we show that CEBRA can be used for the mapping of space, uncovering
complex kinematic features, and rapid, high-accuracy decoding of natural movies
from visual cortex.

Removing distortions in coherent anti-Stokes Raman scattering (CARS) spectra
due to interference with the nonresonant background (NRB) is vital for
quantitative analysis. Popular computational approaches, the Kramers-Kronig
relation and the maximum entropy method, have demonstrated success but may
generate significant errors due to peaks that extend in any part beyond the
recording window. In this work, we present a learned matrix approach to the
discrete Hilbert transform that is easy to implement, fast, and dramatically
improves accuracy of Raman retrieval using the Kramers-Kronig approach.

We propose a novel data-augmentation technique for neural machine translation
based on ROT-$k$ ciphertexts. ROT-$k$ is a simple letter substitution cipher
that replaces a letter in the plaintext with the $k$th letter after it in the
alphabet. We first generate multiple ROT-$k$ ciphertexts using different values
of $k$ for the plaintext which is the source side of the parallel data. We then
leverage this enciphered training data along with the original parallel data
via multi-source training to improve neural machine translation. Our method,
CipherDAug, uses a co-regularization-inspired training procedure, requires no
external data sources other than the original training data, and uses a
standard Transformer to outperform strong data augmentation techniques on
several datasets by a significant margin. This technique combines easily with
existing approaches to data augmentation, and yields particularly strong
results in low-resource settings.

Reinforcement learning (RL) is a promising approach for deriving control
policies for complex systems. As we show in two control problems, the derived
policies from using the Proximal Policy Optimization (PPO) and Deep Q-Network
(DQN) algorithms may lack robustness guarantees. Motivated by these issues, we
propose a new hybrid algorithm, which we call Hysteresis-Based RL (HyRL),
augmenting an existing RL algorithm with hysteresis switching and two stages of
learning. We illustrate its properties in two examples for which PPO and DQN
fail.

Frequency estimation of elements is an important task for summarizing data
streams and machine learning applications. The problem is often addressed by
using streaming algorithms with sublinear space data structures. These
algorithms allow processing of large data while using limited data storage.
Commonly used streaming algorithms, such as count-min sketch, have many
advantages, but do not take into account properties of a data stream for
performance optimization. In the present paper we introduce a novel
double-hashing algorithm that provides flexibility to optimize streaming
algorithms depending on the properties of a given stream. In the double-hashing
approach, first a standard streaming algorithm is employed to obtain an
estimate of the element frequencies. This estimate is derived using a fraction
of the stream and allows identification of the heavy hitters. Next, it uses a
modified hash table where the heavy hitters are mapped into individual buckets
and other stream elements are mapped into the remaining buckets. Finally, the
element frequencies are estimated based on the constructed hash table over the
entire data stream with any streaming algorithm. We demonstrate on both
synthetic data and an internet query log dataset that our approach is capable
of improving frequency estimation due to removing heavy hitters from the
hashing process and, thus, reducing collisions in the hash table. Our approach
avoids employing additional machine learning models to identify heavy hitters
and, thus, reduces algorithm complexity and streamlines implementation.
Moreover, because it is not dependent on specific features of the stream
elements for identifying heavy hitters, it is applicable to a large variety of
streams. In addition, we propose a procedure on how to dynamically adjust the
proposed double-hashing algorithm when frequencies of the elements in a stream
are changing over time.

Wind power prediction, especially for turbines, is vital for the operation,
controllability, and economy of electricity companies. Hybrid methodologies
combining advanced data science with weather forecasting have been
incrementally applied to the predictions. Nevertheless, individually modeling
massive turbines from scratch and downscaling weather forecasts to turbine size
are neither easy nor economical. Aiming at it, this paper proposes a novel
framework with mathematical underpinnings for turbine power prediction. This
framework is the first time to incorporate knowledge distillation into energy
forecasting, enabling accurate and economical constructions of turbine models
by learning knowledge from the well-established park model. Besides, park-scale
weather forecasts non-explicitly are mapped to turbines by transfer learning of
predicted power errors, achieving model correction for better performance. The
proposed framework is deployed on five turbines featuring various terrains in
an Arctic wind park, the results are evaluated against the competitors of
ablation investigation. The major findings reveal that the proposed framework,
developed on favorable knowledge distillation and transfer learning parameters
tuning, yields performance boosts from 3.3 % to 23.9 % over its competitors.
This advantage also exists in terms of wind energy physics and computing
efficiency, which are verified by the prediction quality rate and calculation
time.

Optimal implementation and monitoring of wind energy generation hinge on
reliable power modeling that is vital for understanding turbine control, farm
operational optimization, and grid load balance. Based on the idea of similar
wind condition leads to similar wind power; this paper constructs a modeling
scheme that orderly integrates three types of ensemble learning algorithms,
bagging, boosting, and stacking, and clustering approaches to achieve optimal
power modeling. It also investigates applications of different clustering
algorithms and methodology for determining cluster numbers in wind power
modeling. The results reveal that all ensemble models with clustering exploit
the intrinsic information of wind data and thus outperform models without it by
approximately 15% on average. The model with the best farthest first clustering
is computationally rapid and performs exceptionally well with an improvement of
around 30%. The modeling is further boosted by about 5% by introducing stacking
that fuses ensembles with varying clusters. The proposed modeling framework
thus demonstrates promise by delivering efficient and robust modeling

Precision medicine for chronic diseases such as multiple sclerosis (MS)
involves choosing a treatment which best balances efficacy and side
effects/preferences for individual patients. Making this choice as early as
possible is important, as delays in finding an effective therapy can lead to
irreversible disability accrual. To this end, we present the first deep neural
network model for individualized treatment decisions from baseline magnetic
resonance imaging (MRI) (with clinical information if available) for MS
patients. Our model (a) predicts future new and enlarging T2 weighted (NE-T2)
lesion counts on follow-up MRI on multiple treatments and (b) estimates the
conditional average treatment effect (CATE), as defined by the predicted future
suppression of NE-T2 lesions, between different treatment options relative to
placebo. Our model is validated on a proprietary federated dataset of 1817
multi-sequence MRIs acquired from MS patients during four multi-centre
randomized clinical trials. Our framework achieves high average precision in
the binarized regression of future NE-T2 lesions on five different treatments,
identifies heterogeneous treatment effects, and provides a personalized
treatment recommendation that accounts for treatment-associated risk (e.g. side
effects, patient preference, administration difficulties).

Real-world autonomous driving datasets comprise of images aggregated from
different drives on the road. The ability to relight captured scenes to unseen
lighting conditions, in a controllable manner, presents an opportunity to
augment datasets with a richer variety of lighting conditions, similar to what
would be encountered in the real-world. This paper presents a novel image-based
relighting pipeline, SIMBAR, that can work with a single image as input. To the
best of our knowledge, there is no prior work on scene relighting leveraging
explicit geometric representations from a single image. We present qualitative
comparisons with prior multi-view scene relighting baselines. To further
validate and effectively quantify the benefit of leveraging SIMBAR for data
augmentation for automated driving vision tasks, object detection and tracking
experiments are conducted with a state-of-the-art method, a Multiple Object
Tracking Accuracy (MOTA) of 93.3% is achieved with CenterTrack on
SIMBAR-augmented KITTI - an impressive 9.0% relative improvement over the
baseline MOTA of 85.6% with CenterTrack on original KITTI, both models trained
from scratch and tested on Virtual KITTI. For more details and SIMBAR relit
datasets, please visit our project website (https://simbarv1.github.io/).

The significant success of Deep Neural Networks (DNNs) is highly promoted by
the multiple sophisticated DNN libraries. On the contrary, although some work
have proved that Quadratic Deep Neuron Networks (QDNNs) show better
non-linearity and learning capability than the first-order DNNs, their neuron
design suffers certain drawbacks from theoretical performance to practical
deployment. In this paper, we first proposed a new QDNN neuron architecture
design, and further developed QuadraLib, a QDNN library to provide architecture
optimization and design exploration for QDNNs. Extensive experiments show that
our design has good performance regarding prediction accuracy and computation
consumption on multiple learning tasks.

Federated learning is a training paradigm according to which a server-based
model is cooperatively trained using local models running on edge devices and
ensuring data privacy. These devices exchange information that induces a
substantial communication load, which jeopardises the functioning efficiency.
The difficulty of reducing this overhead stands in achieving this without
decreasing the model's efficiency (contradictory relation). To do so, many
works investigated the compression of the pre/mid/post-trained models and the
communication rounds, separately, although they jointly contribute to the
communication overload. Our work aims at optimising communication overhead in
federated learning by (I) modelling it as a multi-objective problem and (II)
applying a multi-objective optimization algorithm (NSGA-II) to solve it. To the
best of the author's knowledge, this is the first work that \texttt{(I)}
explores the add-in that evolutionary computation could bring for solving such
a problem, and \texttt{(II)} considers both the neuron and devices features
together. We perform the experimentation by simulating a server/client
architecture with 4 slaves. We investigate both convolutional and
fully-connected neural networks with 12 and 3 layers, 887,530 and 33,400
weights, respectively. We conducted the validation on the \texttt{MNIST}
dataset containing 70,000 images. The experiments have shown that our proposal
could reduce communication by 99% and maintain an accuracy equal to the one
obtained by the FedAvg Algorithm that uses 100% of communications.

Chemical Mass Casualty Incidents (MCI) place a heavy burden on hospital staff
and resources. Machine Learning (ML) tools can provide efficient decision
support to caregivers. However, ML models require large volumes of data for the
most accurate results, which is typically not feasible in the chaotic nature of
a chemical MCI. This study examines the application of four statistical
dimension reduction techniques: Random Selection, Covariance/Variance,
Pearson's Linear Correlation, and Principle Component Analysis to reduce a
dataset of 311 hazardous chemicals and 79 related signs and symptoms (SSx). An
Artificial Neural Network pipeline was developed to create comparative models.
Results show that the number of signs and symptoms needed to determine a
chemical culprit can be reduced to nearly 40 SSx without losing significant
model accuracy. Evidence also suggests that the application of dimension
reduction methods can improve ANN model performance accuracy.

Since the discovery of an excess in gamma rays in the direction of M31, its
cause has been unclear. Published interpretations focus on a dark matter or
stellar related origin. Studies of a similar excess in the Milky Way center
motivate a correlation of the spatial morphology of the signal with the
distribution of stellar mass in M31. However, a robust determination of the
best theory for the observed excess emission is very challenging due to large
uncertainties in the astrophysical gamma-ray foreground model. Here we perform
a spectro-morphological analysis of the M31 gamma-ray excess using
state-of-the-art templates for the distribution of stellar mass in M31 and
novel astrophysical foreground models for its sky region. We construct maps for
the old stellar populations of M31 based on observational data from the PAndAS
survey and carefully remove the foreground stars. We also produce improved
astrophysical foreground models by using novel image inpainting techniques
based on machine learning methods. We find that our stellar maps, taken as a
proxy for the location of a putative population of millisecond pulsars in the
bulge of M31, reach a statistical significance of $5.4\sigma$, making them as
strongly favoured as the simple phenomenological models usually considered in
the literature, e.g., a disk-like template with uniform brightness. Our
detection of the stellar templates is robust to generous variations of the
astrophysical foreground model. Once the stellar templates are included in the
astrophysical model, we show that the dark matter annihilation interpretation
of the signal is unwarranted. Using the results of a binary population
synthesis model we demonstrate that a population of about one million
unresolved MSPs could naturally explain the observed gamma-ray luminosity per
stellar mass, energy spectrum, and stellar bulge-to-disk flux ratio.

Simplicial Embeddings (SEM) are representations learned through
self-supervised learning (SSL), wherein a representation is projected into $L$
simplices of $V$ dimensions each using a softmax operation. This procedure
conditions the representation onto a constrained space during pretraining and
imparts an inductive bias for group sparsity. For downstream classification, we
formally prove that the SEM representation leads to better generalization than
an unnormalized representation. Furthermore, we empirically demonstrate that
SSL methods trained with SEMs have improved generalization on natural image
datasets such as CIFAR-100 and ImageNet. Finally, when used in a downstream
classification task, we show that SEM features exhibit emergent semantic
coherence where small groups of learned features are distinctly predictive of
semantically-relevant classes.

Forecasting time series data represents an emerging field of research in data
science and knowledge discovery with vast applications ranging from stock price
and energy demand prediction to the early prediction of epidemics. Numerous
statistical and machine learning methods have been proposed in the last five
decades with the demand for high-quality and reliable forecasts. However, in
real-life prediction problems, situations exist in which a model based on one
of the above paradigms is preferable. Therefore, hybrid solutions are needed to
bridge the gap between classical forecasting methods and modern neural network
models. In this context, we introduce a Probabilistic AutoRegressive Neural
Network (PARNN) model that can handle a wide variety of complex time series
data (e.g., nonlinearity, non-seasonal, long-range dependence, and
non-stationarity). The proposed PARNN model is built by creating a fusion of an
integrated moving average and autoregressive neural network to preserve the
explainability, scalability, and ``white-box-like'' prediction behavior of the
individuals. Sufficient conditions for asymptotic stationarity and geometric
ergodicity are obtained by considering the asymptotic behavior of the
associated Markov chain. Unlike advanced deep learning tools, the uncertainty
quantification of the PARNN model based on prediction intervals is obtained.
During computational experiments, PARNN outperforms standard statistical,
machine learning, and deep learning models (e.g., Transformers, NBeats, DeepAR,
etc.) on a diverse collection of real-world datasets from macroeconomics,
tourism, energy, epidemiology, and others for short-term, medium-term, and
long-term forecasting. Multiple comparisons with the best method are carried
out to showcase the superiority of the proposal in comparison with the
state-of-the-art forecasters over different forecast horizons.

Many recent self-supervised frameworks for visual representation learning are
based on certain forms of Siamese networks. Such networks are conceptually
symmetric with two parallel encoders, but often practically asymmetric as
numerous mechanisms are devised to break the symmetry. In this work, we conduct
a formal study on the importance of asymmetry by explicitly distinguishing the
two encoders within the network -- one produces source encodings and the other
targets. Our key insight is keeping a relatively lower variance in target than
source generally benefits learning. This is empirically justified by our
results from five case studies covering different variance-oriented designs,
and is aligned with our preliminary theoretical analysis on the baseline.
Moreover, we find the improvements from asymmetric designs generalize well to
longer training schedules, multiple other frameworks and newer backbones.
Finally, the combined effect of several asymmetric designs achieves a
state-of-the-art accuracy on ImageNet linear probing and competitive results on
downstream transfer. We hope our exploration will inspire more research in
exploiting asymmetry for Siamese representation learning.

We consider the filtering and prediction problem for a diffusion process. The
signal and observation are modeled by stochastic differential equations (SDEs)
driven by correlated Wiener processes. In classical estimation theory,
measure-valued stochastic partial differential equations (SPDEs) are derived
for the filtering and prediction measures. These equations can be hard to solve
numerically. We provide an approximation algorithm using conditional generative
adversarial networks (GANs) in combination with signatures, an object from
rough path theory. The signature of a sufficiently smooth path determines the
path completely. As a result, in some cases, GANs based on signatures have been
shown to efficiently approximate the law of a stochastic process. For our
algorithm we extend this method to sample from the conditional law, given
noisy, partial observation. Our generator is constructed using neural
differential equations (NDEs), relying on their universal approximator
property. We show well-posedness in providing a rigorous mathematical
framework. Numerical results show the efficiency of our algorithm.

The onset of the COVID-19 pandemic in Spring 2020 forced universities in the
United States to quickly shift to remote learning in order to protect
university students, faculty, and staff. During the summer of 2020, Clemson
University prioritized a safe return to in-person learning by public health
guidance. Because of concerns that COVID-19 was transmissible via physical
contact between individuals and surfaces, adequate hand sanitation for all
university personnel became a top priority. In this study, we examine how the
Clemson University Facilities department team placed hand sanitizer dispensers
across the university campus in order to maximize availability to students,
faculty, and staff through qualitative interviews with the facilities personnel
responsible for placing the dispensers. We present an iterative design process
of how a facility location model was designed to encapsulate the same
considerations of the facilities team. The iterative modeling approach blends
human factors analysis of decision-maker interview data with model development
practices. Through the creation of our mathematical model, we compare the
effectiveness of the initial deployment of sanitizer dispensers with the
optimal allocation based on our model results.

We describe basic ideas underlying research to build and understand
artificially intelligent systems: from symbolic approaches via statistical
learning to interventional models relying on concepts of causality. Some of the
hard open problems of machine learning and AI are intrinsically related to
causality, and progress may require advances in our understanding of how to
model and infer causality from data.

We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal
framework that generates complex musical samples conditioned on dance videos.
Our proposed framework takes dance video frames and human body motions as
input, and learns to generate music samples that plausibly accompany the
corresponding input. Unlike most existing conditional music generation works
that generate specific types of mono-instrumental sounds using symbolic audio
representations (e.g., MIDI), and that usually rely on pre-defined musical
synthesizers, in this work we generate dance music in complex styles (e.g.,
pop, breaking, etc.) by employing a Vector Quantized (VQ) audio representation,
and leverage both its generality and high abstraction capacity of its symbolic
and continuous counterparts. By performing an extensive set of experiments on
multiple datasets, and following a comprehensive evaluation protocol, we assess
the generative qualities of our proposal against alternatives. The attained
quantitative results, which measure the music consistency, beats
correspondence, and music diversity, demonstrate the effectiveness of our
proposed method. Last but not least, we curate a challenging dance-music
dataset of in-the-wild TikTok videos, which we use to further demonstrate the
efficacy of our approach in real-world applications -- and which we hope to
serve as a starting point for relevant future research.

We introduce Perceval, an evolutive open-source software platform for
simulating and interfacing with discrete variable photonic quantum computers,
and describe its main features and components. Its Python front-end allows
photonic circuits to be composed from basic photonic building blocks like
photon sources, beam splitters, phase shifters and detectors. A variety of
computational back-ends are available and optimised for different use-cases.
These use state-of-the-art simulation techniques covering both weak simulation,
or sampling, and strong simulation. We give examples of Perceval in action by
reproducing a variety of photonic experiments and simulating photonic
implementations of a range of quantum algorithms, from Grover's and Shor's to
examples of quantum machine learning. Perceval is intended to be a useful
toolkit both for experimentalists wishing to easily model, design, simulate, or
optimise a discrete variable photonic experiment, and for theoreticians wishing
to design algorithms and applications for discrete-variable photonic quantum
computing platforms.

Large pretrained (e.g., "foundation") models exhibit distinct capabilities
depending on the domain of data they are trained on. While these domains are
generic, they may only barely overlap. For example, visual-language models
(VLMs) are trained on Internet-scale image captions, but large language models
(LMs) are further trained on Internet-scale text with no images (e.g.,
spreadsheets, SAT questions, code). As a result, these models store different
forms of commonsense knowledge across different domains. In this work, we show
that this diversity is symbiotic, and can be leveraged through Socratic Models
(SMs): a modular framework in which multiple pretrained models may be composed
zero-shot i.e., via multimodal-informed prompting, to exchange information with
each other and capture new multimodal capabilities, without requiring
finetuning. With minimal engineering, SMs are not only competitive with
state-of-the-art zero-shot image captioning and video-to-text retrieval, but
also enable new applications such as (i) answering free-form questions about
egocentric video, (ii) engaging in multimodal assistive dialogue with people
(e.g., for cooking recipes) by interfacing with external APIs and databases
(e.g., web search), and (iii) robot perception and planning.

Vision Transformers (ViT)s have recently become popular due to their
outstanding modeling capabilities, in particular for capturing long-range
information, and scalability to dataset and model sizes which has led to
state-of-the-art performance in various computer vision and medical image
analysis tasks. In this work, we introduce a unified framework consisting of
two architectures, dubbed UNetFormer, with a 3D Swin Transformer-based encoder
and Convolutional Neural Network (CNN) and transformer-based decoders. In the
proposed model, the encoder is linked to the decoder via skip connections at
five different resolutions with deep supervision. The design of proposed
architecture allows for meeting a wide range of trade-off requirements between
accuracy and computational cost. In addition, we present a methodology for
self-supervised pre-training of the encoder backbone via learning to predict
randomly masked volumetric tokens using contextual information of visible
tokens. We pre-train our framework on a cohort of $5050$ CT images, gathered
from publicly available CT datasets, and present a systematic investigation of
various components such as masking ratio and patch size that affect the
representation learning capability and performance of downstream tasks. We
validate the effectiveness of our pre-training approach by fine-tuning and
testing our model on liver and liver tumor segmentation task using the Medical
Segmentation Decathlon (MSD) dataset and achieve state-of-the-art performance
in terms of various segmentation metrics. To demonstrate its generalizability,
we train and test the model on BraTS 21 dataset for brain tumor segmentation
using MRI images and outperform other methods in terms of Dice score. Code:
https://github.com/Project-MONAI/research-contributions

Large neural networks excel in many domains, but they are expensive to train
and fine-tune. A popular approach to reduce their compute or memory
requirements is to replace dense weight matrices with structured ones (e.g.,
sparse, low-rank, Fourier transform). These methods have not seen widespread
adoption (1) in end-to-end training due to unfavorable efficiency--quality
tradeoffs, and (2) in dense-to-sparse fine-tuning due to lack of tractable
algorithms to approximate a given dense weight matrix. To address these issues,
we propose a class of matrices (Monarch) that is hardware-efficient (they are
parameterized as products of two block-diagonal matrices for better hardware
utilization) and expressive (they can represent many commonly used transforms).
Surprisingly, the problem of approximating a dense weight matrix with a Monarch
matrix, though nonconvex, has an analytical optimal solution. These properties
of Monarch matrices unlock new ways to train and fine-tune sparse and dense
models. We empirically validate that Monarch can achieve favorable
accuracy-efficiency tradeoffs in several end-to-end sparse training
applications: speeding up ViT and GPT-2 training on ImageNet classification and
Wikitext-103 language modeling by 2x with comparable model quality, and
reducing the error on PDE solving and MRI reconstruction tasks by 40%. In
sparse-to-dense training, with a simple technique called "reverse
sparsification," Monarch matrices serve as a useful intermediate representation
to speed up GPT-2 pretraining on OpenWebText by 2x without quality drop. The
same technique brings 23% faster BERT pretraining than even the very optimized
implementation from Nvidia that set the MLPerf 1.1 record. In dense-to-sparse
fine-tuning, as a proof-of-concept, our Monarch approximation algorithm speeds
up BERT fine-tuning on GLUE by 1.7x with comparable accuracy.

Distributed learning paradigms, such as federated and decentralized learning,
allow for the coordination of models across a collection of agents, and without
the need to exchange raw data. Instead, agents compute model updates locally
based on their available data, and subsequently share the update model with a
parameter server or their peers. This is followed by an aggregation step, which
traditionally takes the form of a (weighted) average. Distributed learning
schemes based on averaging are known to be susceptible to outliers. A single
malicious agent is able to drive an averaging-based distributed learning
algorithm to an arbitrarily poor model. This has motivated the development of
robust aggregation schemes, which are based on variations of the median and
trimmed mean. While such procedures ensure robustness to outliers and malicious
behavior, they come at the cost of significantly reduced sample efficiency.
This means that current robust aggregation schemes require significantly higher
agent participation rates to achieve a given level of performance than their
mean-based counterparts in non-contaminated settings. In this work we remedy
this drawback by developing statistically efficient and robust aggregation
schemes for distributed learning.

Perron-Frobenius theorem is a fundamental tool in matrix analysis that has
applications to complex networks, population dynamics, social learning, and
numerous physical, engineering, and economic phenomena. However, since this
theorem and many of its extensions can be applied only to a single matrix at a
time, their applications in networked dynamical systems are limited to static
networks. To extend the applicability of these results to time-varying
networks, we generalize two assertions of the Perron-Frobenius theorem to
sequences as well as continua of row-stochastic matrices. Our results have
potential applications in areas such as distributed averaging, optimization,
and estimation.

We consider unsupervised domain adaptation (UDA), where labeled data from a
source domain (e.g., photographs) and unlabeled data from a target domain
(e.g., sketches) are used to learn a classifier for the target domain.
Conventional UDA methods (e.g., domain adversarial training) learn
domain-invariant features to improve generalization to the target domain. In
this paper, we show that contrastive pre-training, which learns features on
unlabeled source and target data and then fine-tunes on labeled source data, is
competitive with strong UDA methods. However, we find that contrastive
pre-training does not learn domain-invariant features, diverging from
conventional UDA intuitions. We show theoretically that contrastive
pre-training can learn features that vary subtantially across domains but still
generalize to the target domain, by disentangling domain and class information.
Our results suggest that domain invariance is not necessary for UDA. We
empirically validate our theory on benchmark vision datasets.

Auxiliary tasks have been argued to be useful for representation learning in
reinforcement learning. Although many auxiliary tasks have been empirically
shown to be effective for accelerating learning on the main task, it is not yet
clear what makes useful auxiliary tasks. Some of the most promising results are
on the pixel control, reward prediction, and the next state prediction
auxiliary tasks; however, the empirical results are mixed, showing substantial
improvements in some cases and marginal improvements in others. Careful
investigations of how auxiliary tasks help the learning of the main task is
necessary. In this paper, we take a step studying the effect of the target
policies on the usefulness of the auxiliary tasks formulated as general value
functions. General value functions consist of three core elements: 1) policy 2)
cumulant 3) continuation function. Our focus on the role of the target policy
of the auxiliary tasks is motivated by the fact that the target policy
determines the behavior about which the agent wants to make a prediction and
the state-action distribution that the agent is trained on, which further
affects the main task learning. Our study provides insights about questions
such as: Does a greedy policy result in bigger improvement gains compared to
other policies? Is it best to set the auxiliary task policy to be the same as
the main task policy? Does the choice of the target policy have a substantial
effect on the achieved performance gain or simple strategies for setting the
policy, such as using a uniformly random policy, work as well? Our empirical
results suggest that: 1) Auxiliary tasks with the greedy policy tend to be
useful. 2) Most policies, including a uniformly random policy, tend to improve
over the baseline. 3) Surprisingly, the main task policy tends to be less
useful compared to other policies.

We introduce a camera relocalization pipeline that combines absolute pose
regression (APR) and direct feature matching. By incorporating
exposure-adaptive novel view synthesis, our method successfully addresses
photometric distortions in outdoor environments that existing photometric-based
methods fail to handle. With domain-invariant feature matching, our solution
improves pose regression accuracy using semi-supervised learning on unlabeled
data. In particular, the pipeline consists of two components: Novel View
Synthesizer and DFNet. The former synthesizes novel views compensating for
changes in exposure and the latter regresses camera poses and extracts robust
features that close the domain gap between real images and synthetic ones.
Furthermore, we introduce an online synthetic data generation scheme. We show
that these approaches effectively enhance camera pose estimation both in indoor
and outdoor scenes. Hence, our method achieves a state-of-the-art accuracy by
outperforming existing single-image APR methods by as much as 56%, comparable
to 3D structure-based methods.

This paper outlines the system using which team Nowruz participated in
SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and
Underspecified Phrases for both subtasks A and B. Using a pre-trained
transformer as a backbone, the model targeted the task of multi-task
classification and ranking in the context of finding the best fillers for a
cloze task related to instructional texts on the website Wikihow.
  The system employed a combination of two ordinal regression components to
tackle this task in a multi-task learning scenario. According to the official
leaderboard of the shared task, this system was ranked 5th in the ranking and
7th in the classification subtasks out of 21 participating teams. With
additional experiments, the models have since been further optimised.

Ensemble knowledge distillation can extract knowledge from multiple teacher
models and encode it into a single student model. Many existing methods learn
and distill the student model on labeled data only. However, the teacher models
are usually learned on the same labeled data, and their predictions have high
correlations with groudtruth labels. Thus, they cannot provide sufficient
knowledge complementary to task labels for student teaching. Distilling on
unseen unlabeled data has the potential to enhance the knowledge transfer from
the teachers to the student. In this paper, we propose a unified and effective
ensemble knowledge distillation method that distills a single student model
from an ensemble of teacher models on both labeled and unlabeled data. Since
different teachers may have diverse prediction correctness on the same sample,
on labeled data we weight the predictions of different teachers according to
their correctness. In addition, we weight the distillation loss based on the
overall prediction correctness of the teacher ensemble to distill high-quality
knowledge. On unlabeled data, there is no groundtruth to evaluate prediction
correctness. Fortunately, the disagreement among teachers is an indication of
sample hardness, and thereby we weight the distillation loss based on teachers'
disagreement to emphasize knowledge distillation on important samples.
Extensive experiments on four datasets show the effectiveness of our proposed
ensemble distillation method.

Curiosity is a vital metacognitive skill in educational contexts, leading to
creativity, and a love of learning. And while many school systems increasingly
undercut curiosity by teaching to the test, teachers are increasingly
interested in how to evoke curiosity in their students to prepare them for a
world in which lifelong learning and reskilling will be more and more
important. One aspect of curiosity that has received little attention, however,
is the role of peers in eliciting curiosity. We present what we believe to be
the first theoretical framework that articulates an integrated socio-cognitive
account of curiosity that ties observable behaviors in peers to underlying
curiosity states. We make a bipartite distinction between individual and
interpersonal functions that contribute to curiosity, and multimodal behaviors
that fulfill these functions. We validate the proposed framework by leveraging
a longitudinal latent variable modeling approach. Findings confirm a positive
predictive relationship between the latent variables of individual and
interpersonal functions and curiosity, with the interpersonal functions
exercising a comparatively stronger influence. Prominent behavioral
realizations of these functions are also discovered in a data-driven manner. We
instantiate the proposed theoretical framework in a set of strategies and
tactics that can be incorporated into learning technologies to indicate, evoke,
and scaffold curiosity. This work is a step towards designing learning
technologies that can recognize and evoke moment-by-moment curiosity during
learning in social contexts and towards a more complete multimodal learning
analytics. The underlying rationale is applicable more generally for developing
computer support for other metacognitive and socio-emotional skills.

Deep learning-based methods have made impressive progress in enhancing
extremely low-light images - the image quality of the reconstructed images has
generally improved. However, we found out that most of these methods could not
sufficiently recover the image details, for instance, the texts in the scene.
In this paper, a novel image enhancement framework is proposed to precisely
restore the scene texts, as well as the overall quality of the image
simultaneously under extremely low-light images conditions. Mainly, we employed
a self-regularised attention map, an edge map, and a novel text detection loss.
In addition, leveraging synthetic low-light images is beneficial for image
enhancement on the genuine ones in terms of text detection. The quantitative
and qualitative experimental results have shown that the proposed model
outperforms state-of-the-art methods in image restoration, text detection, and
text spotting on See In the Dark and ICDAR15 datasets.

Single-tower models are widely used in the ranking stage of news
recommendation to accurately rank candidate news according to their
fine-grained relatedness with user interest indicated by user behaviors.
However, these models can easily inherit the biases related to users' sensitive
attributes (e.g., demographics) encoded in training click data, and may
generate recommendation results that are unfair to users with certain
attributes. In this paper, we propose FairRank, which is a fairness-aware
single-tower ranking framework for news recommendation. Since candidate news
selection can be biased, we propose to use a shared candidate-aware user model
to match user interest with a real displayed candidate news and a random news,
respectively, to learn a candidate-aware user embedding that reflects user
interest in candidate news and a candidate-invariant user embedding that
indicates intrinsic user interest. We apply adversarial learning to both of
them to reduce the biases brought by sensitive user attributes. In addition, we
use a KL loss to regularize the attribute labels inferred from the two user
embeddings to be similar, which can make the model capture less candidate-aware
bias information. Extensive experiments on two datasets show that FairRank can
improve the fairness of various single-tower news ranking models with minor
performance losses.

This work presents our end-to-end (E2E) automatic speech recognition (ASR)
model targetting at robust speech recognition, called Integraded speech
Recognition with enhanced speech Input for Self-supervised learning
representation (IRIS). Compared with conventional E2E ASR models, the proposed
E2E model integrates two important modules including a speech enhancement (SE)
module and a self-supervised learning representation (SSLR) module. The SE
module enhances the noisy speech. Then the SSLR module extracts features from
enhanced speech to be used for speech recognition (ASR). To train the proposed
model, we establish an efficient learning scheme. Evaluation results on the
monaural CHiME-4 task show that the IRIS model achieves the best performance
reported in the literature for the single-channel CHiME-4 benchmark (2.0% for
the real development and 3.9% for the real test) thanks to the powerful
pre-trained SSLR module and the fine-tuned SE module.

Diversity is an important factor in providing high-quality personalized news
recommendations. However, most existing news recommendation methods only aim to
optimize recommendation accuracy while ignoring diversity. Reranking is a
widely used post-processing technique to promote the diversity of top
recommendation results. However, the recommendation model is not perfect and
errors may be propagated and amplified in a cascaded recommendation algorithm.
In addition, the recommendation model itself is not diversity-aware, making it
difficult to achieve a good tradeoff between recommendation accuracy and
diversity. In this paper, we propose a news recommendation approach named
LeaDivRec, which is a fully learnable model that can generate diversity-aware
news recommendations in an end-to-end manner. Different from existing news
recommendation methods that are usually based on point- or pair-wise ranking,
in LeaDivRec we propose a more effective list-wise news recommendation model.
More specifically, we propose a permutation Transformer to consider the
relatedness between candidate news and meanwhile can learn different
representations for similar candidate news to help improve recommendation
diversity. We also propose an effective list-wise training method to learn
accurate ranking models. In addition, we propose a diversity-aware
regularization method to further encourage the model to make controllable
diversity-aware recommendations. Extensive experiments on two real-world
datasets validate the effectiveness of our approach in balancing recommendation
accuracy and diversity.

Technological advances are in part enabled by the development of novel
manufacturing processes that give rise to new materials or material property
improvements. Development and evaluation of new manufacturing methodologies is
labor-, time-, and resource-intensive expensive due to complex, poorly defined
relationships between advanced manufacturing process parameters and the
resulting microstructures. In this work, we present a topological
representation of temper (heat-treatment) dependent material micro-structure,
as captured by scanning electron microscopy, called TopTemp. We show that this
topological representation is able to support temper classification of
microstructures in a data limited setting, generalizes well to previously
unseen samples, is robust to image perturbations, and captures domain
interpretable features. The presented work outperforms conventional deep
learning baselines and is a first step towards improving understanding of
process parameters and resulting material properties.

We propose a new approach to generate a reliable reduced model for a
parametric elliptic problem, in the presence of noisy data. The reference model
reduction procedure is the directional HiPOD method, which combines
Hierarchical Model reduction with a standard Proper Orthogonal Decomposition,
according to an offline/online paradigm. In this paper we show that directional
HiPOD looses in terms of accuracy when problem data are affected by noise. This
is due to the interpolation driving the online phase, since it replicates, by
definition, the noise trend. To overcome this limit, we replace interpolation
with Machine Learning fitting models which better discriminate relevant
physical features in the data from irrelevant unstructured noise. The numerical
assessment, although preliminary, confirms the potentialities of the new
approach.

Adversarial learning is a widely used technique in fair representation
learning to remove the biases on sensitive attributes from data
representations. It usually requires to incorporate the sensitive attribute
labels as prediction targets. However, in many scenarios the sensitive
attribute labels of many samples can be unknown, and it is difficult to train a
strong discriminator based on the scarce data with observed attribute labels,
which may lead to generate unfair representations. In this paper, we propose a
semi-supervised fair representation learning approach based on adversarial
variational autoencoder, which can reduce the dependency of adversarial fair
models on data with labeled sensitive attributes. More specifically, we use a
bias-aware model to capture inherent bias information on sensitive attribute by
accurately predicting sensitive attributes from input data, and we use a
bias-free model to learn debiased fair representations by using adversarial
learning to remove bias information from them. The hidden representations
learned by the two models are regularized to be orthogonal. In addition, the
soft labels predicted by the two models are further integrated into a
semi-supervised variational autoencoder to reconstruct the input data, and we
apply an additional entropy regularization to encourage the attribute labels
inferred from the bias-free model to be high-entropy. In this way, the
bias-aware model can better capture attribute information while the bias-free
model is less discriminative on sensitive attributes if the input data is well
reconstructed. Extensive experiments on two datasets for different tasks
validate that our approach can achieve good representation learning fairness
under limited data with sensitive attribute labels.

Privacy and ethics of citizens are at the core of the concerns raised by our
increasingly digital society. Profiling users is standard practice for software
applications triggering the need for users, also enforced by laws, to properly
manage privacy settings. Users need to manage software privacy settings
properly to protect personally identifiable information and express personal
ethical preferences. AI technologies that empower users to interact with the
digital world by reflecting their personal ethical preferences can be key
enablers of a trustworthy digital society. We focus on the privacy dimension
and contribute a step in the above direction through an empirical study on an
existing dataset collected from the fitness domain. We find out which set of
questions is appropriate to differentiate users according to their preferences.
The results reveal that a compact set of semantic-driven questions (about
domain-independent privacy preferences) helps distinguish users better than a
complex domain-dependent one. This confirms the study's hypothesis that moral
attitudes are the relevant piece of information to collect. Based on the
outcome, we implement a recommender system to provide users with suitable
recommendations related to privacy choices. We then show that the proposed
recommender system provides relevant settings to users, obtaining high
accuracy.

This paper introduces Figaro, an algorithm for computing the upper-triangular
matrix in the QR decomposition of the matrix defined by the natural join over a
relational database. The QR decomposition lies at the core of many linear
algebra techniques and their machine learning applications, including: the
matrix inverse; the least squares; the singular value decomposition; eigenvalue
problems; and the principal component analysis.
  Figaro's main novelty is that it pushes the QR decomposition past the join.
This leads to several desirable properties. For acyclic joins, it takes time
linear in the database size and independent of the join size. Its execution is
equivalent to the application of a sequence of Givens rotations proportional to
the join size. Its number of rounding errors relative to the classical QR
decomposition algorithms is on par with the input size relative to the join
size.
  In experiments with real-world and synthetic databases, Figaro outperforms
both in runtime performance and accuracy the LAPACK libraries openblas and
Intel MKL by a factor proportional to the gap between the join output and input
sizes.

We describe, implement and test a novel method for training neural networks
to estimate the Jacobian matrix $J$ of an unknown multivariate function $F$.
The training set is constructed from finitely many pairs $(x,F(x))$ and it
contains no explicit information about $J$. The loss function for
backpropagation is based on linear approximations and on a nearest neighbor
search in the sample data. We formally establish an upper bound on the uniform
norm of the error, in operator norm, between the estimated Jacobian matrix
provided by the algorithm and the actual Jacobian matrix, under natural
assumptions on the function, on the training set and on the loss of the neural
network during training.
  The Jacobian matrix of a multivariate function contains a wealth of
information about the function and it has numerous applications in science and
engineering. The method given here represents a step in moving from black-box
approximations of functions by neural networks to approximations that provide
some structural information about the function in question.

Negation and uncertainty modeling are long-standing tasks in natural language
processing. Linguistic theory postulates that expressions of negation and
uncertainty are semantically independent from each other and the content they
modify. However, previous works on representation learning do not explicitly
model this independence. We therefore attempt to disentangle the
representations of negation, uncertainty, and content using a Variational
Autoencoder. We find that simply supervising the latent representations results
in good disentanglement, but auxiliary objectives based on adversarial learning
and mutual information minimization can provide additional disentanglement
gains.

Spiking Neural Networks (SNNs) have attracted the attention of the deep
learning community for use in low-latency, low-power neuromorphic hardware, as
well as models for understanding neuroscience. In this paper, we introduce
Spiking Phasor Neural Networks (SPNNs). SPNNs are based on complex-valued Deep
Neural Networks (DNNs), representing phases by spike times. Our model computes
robustly employing a spike timing code and gradients can be formed using the
complex domain. We train SPNNs on CIFAR-10, and demonstrate that the
performance exceeds that of other timing coded SNNs, approaching results with
comparable real-valued DNNs.

Identifying differences between groups is one of the most important knowledge
discovery problems. The procedure, also known as contrast sets mining, is
applied in a wide range of areas like medicine, industry, or economics.
  In the paper we present RuleKit-CS, an algorithm for contrast set mining
based on a sequential covering - a well established heuristic for decision rule
induction. The fusion of multiple passes accompanied with an attribute
penalization scheme allows generation of contrast sets describing same examples
with different attributes, distinguishing presented approach from the standard
sequential covering. The ability to identify contrast sets in regression and
survival data sets, the feature not provided by the existing algorithms,
further extends the usability of RuleKit-CS.
  Experiments on over 130 data sets from various areas and detailed analysis of
selected cases confirmed RuleKit-CS to be a useful tool for discovering
differences between defined groups. The algorithm was implemented as a part of
the RuleKit suite available at GitHub under GNU AGPL 3 licence
(https://github.com/adaa-polsl/RuleKit).
  Keywords: Contrast sets, Sequential covering, Model fusion, Rule induction,
Regression, Survival, Knowledge discovery

The ability to predict wind is crucial for both energy production and weather
forecasting. Mechanistic models that form the basis of traditional forecasting
perform poorly near the ground. In this paper, we take an alternative
data-driven approach based on supervised learning. We analyze a massive dataset
of wind measured from anemometers located at 10 m height in 32 locations in two
central and north west regions of Italy (Abruzzo and Liguria). We train
supervised learning algorithms using the past history of wind to predict its
value at a future time (horizon). Using data from a single location and time
horizon we compare systematically several algorithms where we vary the
input/output variables, the memory of the input and the linear vs non-linear
learning model. We then compare performance of the best algorithms across all
locations and forecasting horizons. We find that the optimal design as well as
its performance vary with the location. We demonstrate that the presence of a
reproducible diurnal cycle provides a rationale to understand this variation.
We conclude with a systematic comparison with state of the art algorithms and
show that, when the model is accurately designed, shallow algorithms are
competitive with more complex deep architectures.

Efficient load forecasting is needed to ensure better observability in the
distribution networks, whereas such forecasting is made possible by an
increasing number of smart meter installations. Because distribution networks
include a large amount of different loads at various aggregation levels, such
as individual consumers, transformer stations and feeders loads, it is
impractical to develop individual (or so-called local) forecasting models for
each load separately. Furthermore, such local models ignore the strong
dependencies between different loads that might be present due to their spatial
proximity and the characteristics of the distribution network. To address these
issues, this paper proposes a global modeling approach based on deep learning
for efficient forecasting of a large number of loads in distribution networks.
In this way, the computational burden of training a large amount of local
forecasting models can be largely reduced, and the cross-series information
shared among different loads can be utilized. Additionally, an unsupervised
localization mechanism and optimal ensemble construction strategy are also
proposed to localize/personalize the forecasting model to different groups of
loads and to improve the forecasting accuracy further. Comprehensive
experiments are conducted on real-world smart meter data to demonstrate the
superiority of the proposed approach compared to competing methods.

In safety-critical applications, practitioners are reluctant to trust neural
networks when no interpretable explanations are available. Many attempts to
provide such explanations revolve around pixel-based attributions or use
previously known concepts. In this paper we aim to provide explanations by
provably identifying \emph{high-level, previously unknown ground-truth
concepts}. To this end, we propose a probabilistic modeling framework to derive
(C)oncept (L)earning and (P)rediction (CLAP) -- a VAE-based classifier that
uses visually interpretable concepts as predictors for a simple classifier.
Assuming a generative model for the ground-truth concepts, we prove that CLAP
is able to identify them while attaining optimal classification accuracy. Our
experiments on synthetic datasets verify that CLAP identifies distinct
ground-truth concepts on synthetic datasets and yields promising results on the
medical Chest X-Ray dataset.

Federated edge learning (FEEL) is envisioned as a promising paradigm to
achieve privacy-preserving distributed learning. However, it consumes excessive
learning time due to the existence of straggler devices. In this paper, a novel
topology-optimized federated edge learning (TOFEL) scheme is proposed to tackle
the heterogeneity issue in federated learning and to improve the
communication-and-computation efficiency. Specifically, a problem of jointly
optimizing the aggregation topology and computing speed is formulated to
minimize the weighted summation of energy consumption and latency. To solve the
mixed-integer nonlinear problem, we propose a novel solution method of
penalty-based successive convex approximation, which converges to a stationary
point of the primal problem under mild conditions. To facilitate real-time
decision making, an imitation-learning based method is developed, where deep
neural networks (DNNs) are trained offline to mimic the penalty-based method,
and the trained imitation DNNs are deployed at the edge devices for online
inference. Thereby, an efficient imitate-learning based approach is seamlessly
integrated into the TOFEL framework. Simulation results demonstrate that the
proposed TOFEL scheme accelerates the federated learning process, and achieves
a higher energy efficiency. Moreover, we apply the scheme to 3D object
detection with multi-vehicle point cloud datasets in the CARLA simulator. The
results confirm the superior learning performance of the TOFEL scheme over
conventional designs with the same resource and deadline constraints.

Randomized Smoothing (RS) is considered the state-of-the-art approach to
obtain certifiably robust models for challenging tasks. However, current RS
approaches drastically decrease standard accuracy on unperturbed data, severely
limiting their real-world utility. To address this limitation, we propose a
compositional architecture, ACES, which certifiably decides on a per-sample
basis whether to use a smoothed model yielding predictions with guarantees or a
more accurate standard model without guarantees. This, in contrast to prior
approaches, enables both high standard accuracies and significant provable
robustness. On challenging tasks such as ImageNet, we obtain, e.g., $80.0\%$
natural accuracy and $28.2\%$ certifiable accuracy against $\ell_2$
perturbations with $r=1.0$. We release our code and models at
https://github.com/eth-sri/aces.

A common practice in transfer learning is to initialize the downstream model
weights by pre-training on a data-abundant upstream task. In object detection
specifically, the feature backbone is typically initialized with Imagenet
classifier weights and fine-tuned on the object detection task. Recent works
show this is not strictly necessary under longer training regimes and provide
recipes for training the backbone from scratch. We investigate the opposite
direction of this end-to-end training trend: we show that an extreme form of
knowledge preservation -- freezing the classifier-initialized backbone --
consistently improves many different detection models, and leads to
considerable resource savings. We hypothesize and corroborate experimentally
that the remaining detector components capacity and structure is a crucial
factor in leveraging the frozen backbone. Immediate applications of our
findings include performance improvements on hard cases like detection of
long-tail object classes and computational and memory resource savings that
contribute to making the field more accessible to researchers with access to
fewer computational resources.

When Deep Neural Networks (DNNs) are used in safety-critical systems,
engineers should determine the safety risks associated with failures (i.e.,
erroneous outputs) observed during testing. For DNNs processing images,
engineers visually inspect all failure-inducing images to determine common
characteristics among them. Such characteristics correspond to
hazard-triggering events (e.g., low illumination) that are essential inputs for
safety analysis. Though informative, such activity is expensive and
error-prone.
  To support such safety analysis practices, we propose SEDE, a technique that
generates readable descriptions for commonalities in failure-inducing,
real-world images and improves the DNN through effective retraining. SEDE
leverages the availability of simulators, which are commonly used for
cyber-physical systems. It relies on genetic algorithms to drive simulators
towards the generation of images that are similar to failure-inducing,
real-world images in the test set; it then employs rule learning algorithms to
derive expressions that capture commonalities in terms of simulator parameter
values. The derived expressions are then used to generate additional images to
retrain and improve the DNN.
  With DNNs performing in-car sensing tasks, SEDE successfully characterized
hazard-triggering events leading to a DNN accuracy drop. Also, SEDE enabled
retraining leading to significant improvements in DNN accuracy, up to 18
percentage points.

This paper shows the application of autonomous Crater Detection using the
U-Net, a Fully-Convolutional Neural Network, on Ceres. The U-Net is trained on
optical images of the Moon Global Morphology Mosaic based on data collected by
the LRO and manual crater catalogues. The Moon-trained network will be tested
on Dawn optical images of Ceres: this task is accomplished by means of a
Transfer Learning (TL) approach. The trained model has been fine-tuned using
100, 500 and 1000 additional images of Ceres. The test performance was measured
on 350 never before seen images, reaching a testing accuracy of 96.24%, 96.95%
and 97.19%, respectively. This means that despite the intrinsic differences
between the Moon and Ceres, TL works with encouraging results. The output of
the U-Net contains predicted craters: it will be post-processed applying global
thresholding for image binarization and a template matching algorithm to
extract craters positions and radii in the pixel space. Post-processed craters
will be counted and compared to the ground truth data in order to compute image
segmentation metrics: precision, recall and F1 score. These indices will be
computed, and their effect will be discussed for tasks such as automated crater
cataloguing and optical navigation.

In many natural language processing (NLP) tasks the same input (e.g. source
sentence) can have multiple possible outputs (e.g. translations). To analyze
how this ambiguity (also known as intrinsic uncertainty) shapes the
distribution learned by neural sequence models we measure sentence-level
uncertainty by computing the degree of overlap between references in
multi-reference test sets from two different NLP tasks: machine translation
(MT) and grammatical error correction (GEC). At both the sentence- and the
task-level, intrinsic uncertainty has major implications for various aspects of
search such as the inductive biases in beam search and the complexity of exact
search. In particular, we show that well-known pathologies such as a high
number of beam search errors, the inadequacy of the mode, and the drop in
system performance with large beam sizes apply to tasks with high level of
ambiguity such as MT but not to less uncertain tasks such as GEC. Furthermore,
we propose a novel exact $n$-best search algorithm for neural sequence models,
and show that intrinsic uncertainty affects model uncertainty as the model
tends to overly spread out the probability mass for uncertain tasks and

Most of the research on data-driven speech representation learning has
focused on raw audios in an end-to-end manner, paying little attention to their
internal phonological or gestural structure. This work, investigating the
speech representations derived from articulatory kinematics signals, uses a
neural implementation of convolutive sparse matrix factorization to decompose
the articulatory data into interpretable gestures and gestural scores. By
applying sparse constraints, the gestural scores leverage the discrete
combinatorial properties of phonological gestures. Phoneme recognition
experiments were additionally performed to show that gestural scores indeed
code phonological information successfully. The proposed work thus makes a
bridge between articulatory phonology and deep neural networks to leverage
informative, intelligible, interpretable,and efficient speech representations.

Aphasia is a common speech and language disorder, typically caused by a brain
injury or a stroke, that affects millions of people worldwide. Detecting and
assessing Aphasia in patients is a difficult, time-consuming process, and
numerous attempts to automate it have been made, the most successful using
machine learning models trained on aphasic speech data. Like in many medical
applications, aphasic speech data is scarce and the problem is exacerbated in
so-called "low resource" languages, which are, for this task, most languages
excluding English. We attempt to leverage available data in English and achieve
zero-shot aphasia detection in low-resource languages such as Greek and French,
by using language-agnostic linguistic features. Current cross-lingual aphasia
detection approaches rely on manually extracted transcripts. We propose an
end-to-end pipeline using pre-trained Automatic Speech Recognition (ASR) models
that share cross-lingual speech representations and are fine-tuned for our
desired low-resource languages. To further boost our ASR model's performance,
we also combine it with a language model. We show that our ASR-based end-to-end
pipeline offers comparable results to previous setups using human-annotated

In recent years, machine learning models have rapidly become better at
generating clinical consultation notes; yet, there is little work on how to
properly evaluate the generated consultation notes to understand the impact
they may have on both the clinician using them and the patient's clinical
safety. To address this we present an extensive human evaluation study of
consultation notes where 5 clinicians (i) listen to 57 mock consultations, (ii)
write their own notes, (iii) post-edit a number of automatically generated
notes, and (iv) extract all the errors, both quantitative and qualitative. We
then carry out a correlation study with 18 automatic quality metrics and the
human judgements. We find that a simple, character-based Levenshtein distance
metric performs on par if not better than common model-based metrics like
BertScore. All our findings and annotations are open-sourced.

Exemplar-based image translation establishes dense correspondences between a
conditional input and an exemplar (from two different domains) for leveraging
detailed exemplar styles to achieve realistic image translation. Existing work
builds the cross-domain correspondences implicitly by minimizing feature-wise
distances across the two domains. Without explicit exploitation of
domain-invariant features, this approach may not reduce the domain gap
effectively which often leads to sub-optimal correspondences and image
translation. We design a Marginal Contrastive Learning Network (MCL-Net) that
explores contrastive learning to learn domain-invariant features for realistic
exemplar-based image translation. Specifically, we design an innovative
marginal contrastive loss that guides to establish dense correspondences
explicitly. Nevertheless, building correspondence with domain-invariant
semantics alone may impair the texture patterns and lead to degraded texture
generation. We thus design a Self-Correlation Map (SCM) that incorporates scene
structures as auxiliary information which improves the built correspondences
substantially. Quantitative and qualitative experiments on multifarious image
translation tasks show that the proposed method outperforms the
state-of-the-art consistently.

One of the demands of modern teaching is the interdisciplinary approach of
cognitive subjects and in particular that of the natural sciences, with
simultaneous engagement of teachers and students. At the same time, teaching
scenarios seem to be gaining ground in the methodology of teaching and learning
Mathematics in school. In this paper we analyze the concepts of the teaching
scenario and interdisciplinarity, and we briefly present the basic results of a
teaching experiment using teaching scenario, focused on the teaching of
Minkowskian Metric in two dimensions.

We investigate a prototype application for machine-readable literature. The
program is called "pyDataRecognition" and serves as an example of a data-driven
literature search, where the literature search query is an experimental
data-set provided by the user. The user uploads a powder pattern together with
the radiation wavelength. The program compares the user data to a database of
existing powder patterns associated with published papers and produces a rank
ordered according to their similarity score. The program returns the digital
object identifier (doi) and full reference of top ranked papers together with a
stack plot of the user data alongside the top five database entries. The paper
describes the approach and explores successes and challenges.

With the increasing availability of optical and synthetic aperture radar
(SAR) images thanks to the Sentinel constellation, and the explosion of deep
learning, new methods have emerged in recent years to tackle the reconstruction
of optical images that are impacted by clouds. In this paper, we focus on the
evaluation of convolutional neural networks that use jointly SAR and optical
images to retrieve the missing contents in one single polluted optical image.
We propose a simple framework that ease the creation of datasets for the
training of deep nets targeting optical image reconstruction, and for the
validation of machine learning based or deterministic approaches. These methods
are quite different in terms of input images constraints, and comparing them is
a problematic task not addressed in the literature. We show how space
partitioning data structures help to query samples in terms of cloud coverage,
relative acquisition date, pixel validity and relative proximity between SAR
and optical images. We generate several datasets to compare the reconstructed
images from networks that use a single pair of SAR and optical image, versus
networks that use multiple pairs, and a traditional deterministic approach
performing interpolation in temporal domain.

This paper focuses on the detection of Parkinson's disease based on the
analysis of a patient's gait. The growing popularity and success of Transformer
networks in natural language processing and image recognition motivated us to
develop a novel method for this problem based on an automatic features
extraction via Transformers. The use of Transformers in 1D signal is not really
widespread yet, but we show in this paper that they are effective in extracting
relevant features from 1D signals. As Transformers require a lot of memory, we
decoupled temporal and spatial information to make the model smaller. Our
architecture used temporal Transformers, dimension reduction layers to reduce
the dimension of the data, a spatial Transformer, two fully connected layers
and an output layer for the final prediction. Our model outperforms the current
state-of-the-art algorithm with 95.2\% accuracy in distinguishing a
Parkinsonian patient from a healthy one on the Physionet dataset. A key
learning from this work is that Transformers allow for greater stability in
results. The source code and pre-trained models are released in
https://github.com/DucMinhDimitriNguyen/Transformers-for-1D-signals-in-Parkinson-s-disease-detection-from-gait.git

The work focuses on the modelling and imputation of oil and gas reservoirs
parameters, specifically, the problem of predicting the oil recovery factor
(RF) using Bayesian networks (BNs). Recovery forecasting is critical for the
oil and gas industry as it directly affects a company's profit. However,
current approaches to forecasting the RF are complex and computationally
expensive. In addition, they require vast amount of data and are difficult to
constrain in the early stages of reservoir development. To address this
problem, we propose a BN approach and describe ways to improve parameter
predictions' accuracy. Various training hyperparameters for BNs were
considered, and the best ones were used. The approaches of structure and
parameter learning, data discretization and normalization, subsampling on
analogues of the target reservoir, clustering of networks and data filtering
were considered. Finally, a physical model of a synthetic oil reservoir was
used to validate BNs' predictions of the RF. All approaches to modelling based
on BNs provide full coverage of the confidence interval for the RF predicted by
the physical model, but at the same time require less time and data for
modelling, which demonstrates the possibility of using in the early stages of
reservoirs development. The main result of the work can be considered the
development of a methodology for studying the parameters of reservoirs based on
Bayesian networks built on small amounts of data and with minimal involvement
of expert knowledge. The methodology was tested on the example of the problem
of the recovery factor imputation.

Photovoltaic and wind power forecasts in power systems with a high share of
renewable energy are essential in several applications. These include stable
grid operation, profitable power trading, and forward-looking system planning.
However, there is a lack of publicly available datasets for research on machine
learning based prediction methods. This paper provides an openly accessible
time series dataset with realistic synthetic power data. Other publicly and
non-publicly available datasets often lack precise geographic coordinates,
timestamps, or static power plant information, e.g., to protect business
secrets. On the opposite, this dataset provides these. The dataset comprises
120 photovoltaic and 273 wind power plants with distinct sides all over Germany
from 500 days in hourly resolution. This large number of available sides allows
forecasting experiments to include spatial correlations and run experiments in
transfer and multi-task learning. It includes side-specific, power
source-dependent, non-synthetic input features from the ICON-EU weather model.
A simulation of virtual power plants with physical models and actual
meteorological measurements provides realistic synthetic power measurement time
series. These time series correspond to the power output of virtual power
plants at the location of the respective weather measurements. Since the
synthetic time series are based exclusively on weather measurements, possible
errors in the weather forecast are comparable to those in actual power data. In
addition to the data description, we evaluate the quality of
weather-prediction-based power forecasts by comparing simplified physical
models and a machine learning model. This experiment shows that forecasts
errors on the synthetic power data are comparable to real-world historical
power measurements.

The growing size of neural language models has led to increased attention in
model compression. The two predominant approaches are pruning, which gradually
removes weights from a pre-trained model, and distillation, which trains a
smaller compact model to match a larger one. Pruning methods can significantly
reduce the model size but hardly achieve large speedups as distillation.
However, distillation methods require large amounts of unlabeled data and are
expensive to train. In this work, we propose a task-specific structured pruning
method CoFi (Coarse- and Fine-grained Pruning), which delivers highly
parallelizable subnetworks and matches the distillation methods in both
accuracy and latency, without resorting to any unlabeled data. Our key insight
is to jointly prune coarse-grained (e.g., layers) and fine-grained (e.g., heads
and hidden units) modules, which controls the pruning decision of each
parameter with masks of different granularity. We also devise a layerwise
distillation strategy to transfer knowledge from unpruned to pruned models
during optimization. Our experiments on GLUE and SQuAD datasets show that CoFi
yields models with over 10x speedups with a small accuracy drop, showing its
effectiveness and efficiency compared to previous pruning and distillation

We develop an implementable stochastic proximal point (SPP) method for a
class of weakly convex, composite optimization problems. The proposed
stochastic proximal point algorithm incorporates a variance reduction mechanism
and the resulting SPP updates are solved using an inexact semismooth Newton
framework. We establish detailed convergence results that take the inexactness
of the SPP steps into account and that are in accordance with existing
convergence guarantees of (proximal) stochastic variance-reduced gradient
methods. Numerical experiments show that the proposed algorithm competes
favorably with other state-of-the-art methods and achieves higher robustness
with respect to the step size selection.

While data sharing is crucial for knowledge development, privacy concerns and
strict regulation (e.g., European General Data Protection Regulation (GDPR))
limit its full effectiveness. Synthetic tabular data emerges as alternative to
enable data sharing while fulfilling regulatory and privacy constraints.
State-of-the-art tabular data synthesizers draw methodologies from Generative
Adversarial Networks (GAN). As GANs improve the synthesized data increasingly
resemble the real data risking to leak privacy. Differential privacy (DP)
provides theoretical guarantees on privacy loss but degrades data utility.
Striking the best trade-off remains yet a challenging research question. We
propose CTAB-GAN+ a novel conditional tabular GAN. CTAB-GAN+ improves upon
state-of-the-art by (i) adding downstream losses to conditional GANs for higher
utility synthetic data in both classification and regression domains; (ii)
using Wasserstein loss with gradient penalty for better training convergence;
(iii) introducing novel encoders targeting mixed continuous-categorical
variables and variables with unbalanced or skewed data; and (iv) training with
DP stochastic gradient descent to impose strict privacy guarantees. We
extensively evaluate CTAB-GAN+ on data similarity and analysis utility against
state-of-the-art tabular GANs. The results show that CTAB-GAN+ synthesizes
privacy-preserving data with at least 48.16% higher utility across multiple
datasets and learning tasks under different privacy budgets.

Large, pre-trained neural networks consisting of self-attention layers
(transformers) have recently achieved state-of-the-art results on several
speech emotion recognition (SER) datasets. These models are typically
pre-trained in self-supervised manner with the goal to improve automatic speech
recognition performance -- and thus, to understand linguistic information. In
this work, we investigate the extent in which this information is exploited
during SER fine-tuning. Using a reproducible methodology based on open-source
tools, we synthesise prosodically neutral speech utterances while varying the
sentiment of the text. Valence predictions of the transformer model are very
reactive to positive and negative sentiment content, as well as negations, but
not to intensifiers or reducers, while none of those linguistic features impact
arousal or dominance. These findings show that transformers can successfully
leverage linguistic information to improve their valence predictions, and that
linguistic analysis should be included in their testing.

In numerous applications, for instance in predictive maintenance, there is a
pression to predict events ahead of time with as much accuracy as possible
while not delaying the decision unduly. This translates in the optimization of
a trade-off between earliness and accuracy of the decisions, that has been the
subject of research for time series of finite length and with a unique label.
And this has led to powerful algorithms for Early Classification of Time Series
(ECTS). This paper, for the first time, investigates such a trade-off when
events of different classes occur in a streaming fashion, with no predefined
end. In the Early Classification in Open Time Series problem (ECOTS), the task
is to predict events, i.e. their class and time interval, at the moment that
optimizes the accuracy vs. earliness trade-off. Interestingly, we find that
ECTS algorithms can be sensibly adapted in a principled way to this new
problem. We illustrate our methodology by transforming two state-of-the-art
ECTS algorithms for the ECOTS scenario. Among the wide variety of applications
that this new approach opens up, we develop a predictive maintenance use case
that optimizes alarm triggering times, thus demonstrating the power of this new
approach.

Term clustering is important in biomedical knowledge graph construction.
Using similarities between terms embedding is helpful for term clustering.
State-of-the-art term embeddings leverage pretrained language models to encode
terms, and use synonyms and relation knowledge from knowledge graphs to guide
contrastive learning. These embeddings provide close embeddings for terms
belonging to the same concept. However, from our probing experiments, these
embeddings are not sensitive to minor textual differences which leads to
failure for biomedical term clustering. To alleviate this problem, we adjust
the sampling strategy in pretraining term embeddings by providing dynamic hard
positive and negative samples during contrastive learning to learn fine-grained
representations which result in better biomedical term clustering. We name our
proposed method as CODER++, and it has been applied in clustering biomedical
concepts in the newly released Biomedical Knowledge Graph named BIOS.

The combinatorial search space presents a significant challenge to learning
causality from data. Recently, the problem has been formulated into a
continuous optimization framework with an acyclicity constraint, allowing for
the exploration of deep generative models to better capture data sample
distributions and support the discovery of Directed Acyclic Graphs (DAGs) that
faithfully represent the underlying data distribution. However, so far no study
has investigated the use of Wasserstein distance for causal structure learning
via generative models. This paper proposes a new model named DAG-WGAN, which
combines the Wasserstein-based adversarial loss, an auto-encoder architecture
together with an acyclicity constraint. DAG-WGAN simultaneously learns causal
structures and improves its data generation capability by leveraging the
strength from the Wasserstein distance metric. Compared with other models, it
scales well and handles both continuous and discrete data. Our experiments have
evaluated DAG-WGAN against the state-of-the-art and demonstrated its good

Learning on synthetic data and transferring the resulting properties to their
real counterparts is an important challenge for reducing costs and increasing
safety in machine learning. In this work, we focus on autoencoder architectures
and aim at learning latent space representations that are invariant to
inductive biases caused by the domain shift between simulated and real images
showing the same scenario. We train on synthetic images only, present
approaches to increase generalizability and improve the preservation of the
semantics to real datasets of increasing visual complexity. We show that
pre-trained feature extractors (e.g. VGG) can be sufficient for generalization
on images of lower complexity, but additional improvements are required for
visually more complex scenes. To this end, we demonstrate a new sampling
technique, which matches semantically important parts of the image, while
randomizing the other parts, leads to salient feature extraction and a
neglection of unimportant parts. This helps the generalization to real data and
we further show that our approach outperforms fine-tuned classification models.

The reliability assessment of a machine learning model's prediction is an
important quantity for the deployment in safety critical applications. Not only
can it be used to detect novel sceneries, either as out-of-distribution or
anomaly sample, but it also helps to determine deficiencies in the training
data distribution. A lot of promising research directions have either proposed
traditional methods like Gaussian processes or extended deep learning based
approaches, for example, by interpreting them from a Bayesian point of view. In
this work we propose a novel approach for uncertainty estimation based on
autoencoder models: The recursive application of a previously trained
autoencoder model can be interpreted as a dynamical system storing training
examples as attractors. While input images close to known samples will converge
to the same or similar attractor, input samples containing unknown features are
unstable and converge to different training samples by potentially removing or
changing characteristic features. The use of dropout during training and
inference leads to a family of similar dynamical systems, each one being robust
on samples close to the training distribution but unstable on new features.
Either the model reliably removes these features or the resulting instability
can be exploited to detect problematic input samples. We evaluate our approach
on several dataset combinations as well as on an industrial application for
occupant classification in the vehicle interior for which we additionally
release a new synthetic dataset.

Automatic facial action unit (AU) recognition is a challenging task due to
the scarcity of manual annotations. To alleviate this problem, a large amount
of efforts has been dedicated to exploiting various weakly supervised methods
which leverage numerous unlabeled data. However, many aspects with regard to
some unique properties of AUs, such as the regional and relational
characteristics, are not sufficiently explored in previous works. Motivated by
this, we take the AU properties into consideration and propose two auxiliary AU
related tasks to bridge the gap between limited annotations and the model
performance in a self-supervised manner via the unlabeled data. Specifically,
to enhance the discrimination of regional features with AU relation embedding,
we design a task of RoI inpainting to recover the randomly cropped AU patches.
Meanwhile, a single image based optical flow estimation task is proposed to
leverage the dynamic change of facial muscles and encode the motion information
into the global feature representation. Based on these two self-supervised
auxiliary tasks, local features, mutual relation and motion cues of AUs are
better captured in the backbone network. Furthermore, by incorporating
semi-supervised learning, we propose an end-to-end trainable framework named
weakly supervised regional and temporal learning (WSRTL) for AU recognition.
Extensive experiments on BP4D and DISFA demonstrate the superiority of our
method and new state-of-the-art performances are achieved.

A mixed list of ads and organic items is usually displayed in feed and how to
allocate the limited slots to maximize the overall revenue is a key problem.
Meanwhile, modeling user preference with historical behavior is essential in
recommendation and advertising (e.g., CTR prediction and ads allocation). Most
previous works for user behavior modeling only model user's historical
point-level positive feedback (i.e., click), which neglect the page-level
information of feedback and other types of feedback. To this end, we propose
Deep Page-level Interest Network (DPIN) to model the page-level user preference
and exploit multiple types of feedback. Specifically, we introduce four
different types of page-level feedback as input, and capture user preference
for item arrangement under different receptive fields through the multi-channel
interaction module. Through extensive offline and online experiments on Meituan
food delivery platform, we demonstrate that DPIN can effectively model the
page-level user preference and increase the revenue for the platform.

Defocus deblurring is a challenging task due to the spatially varying nature
of defocus blur. While deep learning approach shows great promise in solving
image restoration problems, defocus deblurring demands accurate training data
that consists of all-in-focus and defocus image pairs, which is difficult to
collect. Naive two-shot capturing cannot achieve pixel-wise correspondence
between the defocused and all-in-focus image pairs. Synthetic aperture of light
fields is suggested to be a more reliable way to generate accurate image pairs.
However, the defocus blur generated from light field data is different from
that of the images captured with a traditional digital camera. In this paper,
we propose a novel deep defocus deblurring network that leverages the strength
and overcomes the shortcoming of light fields. We first train the network on a
light field-generated dataset for its highly accurate image correspondence.
Then, we fine-tune the network using feature loss on another dataset collected
by the two-shot method to alleviate the differences between the defocus blur
exists in the two domains. This strategy is proved to be highly effective and
able to achieve the state-of-the-art performance both quantitatively and
qualitatively on multiple test sets. Extensive ablation studies have been
conducted to analyze the effect of each network module to the final

We investigate the problem of extracting rules, expressed in Horn logic, from
neural network models. Our work is based on the exact learning model, in which
a learner interacts with a teacher (the neural network model) via queries in
order to learn an abstract target concept, which in our case is a set of Horn
rules. We consider partial interpretations to formulate the queries. These can
be understood as a representation of the world where part of the knowledge
regarding the truthiness of propositions is unknown. We employ Angluin s
algorithm for learning Horn rules via queries and evaluate our strategy
empirically.

The proposed RMS-FlowNet is a novel end-to-end learning-based architecture
for accurate and efficient scene flow estimation which can operate on point
clouds of high density. For hierarchical scene flow estimation, the existing
methods depend on either expensive Farthest-Point-Sampling (FPS) or
structure-based scaling which decrease their ability to handle a large number
of points. Unlike these methods, we base our fully supervised architecture on
Random-Sampling (RS) for multiscale scene flow prediction. To this end, we
propose a novel flow embedding design which can predict more robust scene flow
in conjunction with RS. Exhibiting high accuracy, our RMS-FlowNet provides a
faster prediction than state-of-the-art methods and works efficiently on
consecutive dense point clouds of more than 250K points at once. Our
comprehensive experiments verify the accuracy of RMS-FlowNet on the established
FlyingThings3D data set with different point cloud densities and validate our
design choices. Additionally, we show that our model presents a competitive
ability to generalize towards the real-world scenes of KITTI data set without

User-defined keyword spotting is a task to detect new spoken terms defined by
users. This can be viewed as a few-shot learning problem since it is
unreasonable for users to define their desired keywords by providing many
examples. To solve this problem, previous works try to incorporate
self-supervised learning models or apply meta-learning algorithms. But it is
unclear whether self-supervised learning and meta-learning are complementary
and which combination of the two types of approaches is most effective for
few-shot keyword discovery. In this work, we systematically study these
questions by utilizing various self-supervised learning models and combining
them with a wide variety of meta-learning algorithms. Our result shows that
HuBERT combined with Matching network achieves the best result and is robust to
the changes of few-shot examples.

Unsupervised and self-supervised learning methods have leveraged unlabelled
data to improve the pretrained models. However, these methods need
significantly large amount of unlabelled data and the computational cost of
training models with such large amount of data can be prohibitively high. We
address this issue by using unlabelled data during finetuning, instead of
pretraining. We propose acoustic model finetuning (FT) using labelled and
unlabelled data. The model is jointly trained to learn representations to
classify senones, as well as learn contextual acoustic representations. Our
training objective is a combination of cross entropy loss, suitable for
classification task, and contrastive loss, suitable to learn acoustic
representations. The proposed approach outperforms conventional finetuning with
11.2% and 9.19% word error rate relative (WERR) reduction on Gujarati and
Bengali languages respectively.

X-ray Binaries (XRBs) consist of a compact object that accretes material from
an orbiting secondary star. The most secure method we have for determining if
the compact object is a black hole is to determine its mass: this is limited to
bright objects, and requires substantial time-intensive spectroscopic
monitoring. With new X-ray sources being discovered with different X-ray
observatories, developing efficient, robust means to classify compact objects
becomes increasingly important. We compare three machine learning
classification methods (Bayesian Gaussian Processes (BGP), K-Nearest Neighbors
(KNN), Support Vector Machines (SVM)) for determining the compact objects as
neutron stars or black holes (BHs) in XRB systems. Each machine learning method
uses spatial patterns which exist between systems of the same type in 3D
Color-Color-Intensity diagrams. We used lightcurves extracted using six years
of data with MAXI/GSC for 44 representative sources. We find that all three
methods are highly accurate in distinguishing pulsing from non-pulsing neutron
stars (NPNS) with 95\% of NPNS and 100\% of pulsars accurately predicted. All
three methods have high accuracy distinguishing BHs from pulsars (92\%) but
continue to confuse BHs with a subclass of NPNS, called the Bursters, with KNN
doing the best at only 50\% accuracy for predicting BHs. The precision of all
three methods is high, providing equivalent results over 5-10 independent runs.
In a future work, we suggest a fourth dimension be incorporated to mitigate the
confusion of BHs with Bursters. This work paves the way towards more robust
methods to efficiently distinguish BHs, NPNS, and pulsars.

Despite the increasing interest in cyberbullying detection, existing efforts
have largely been limited to experiments on a single platform and their
generalisability across different social media platforms have received less
attention. We propose XP-CB, a novel cross-platform framework based on
Transformers and adversarial learning. XP-CB can enhance a Transformer
leveraging unlabelled data from the source and target platforms to come up with
a common representation while preventing platform-specific training. To
validate our proposed framework, we experiment on cyberbullying datasets from
three different platforms through six cross-platform configurations, showing
its effectiveness with both BERT and RoBERTa as the underlying Transformer
models.

Recently, the dense correlation volume method achieves state-of-the-art
performance in optical flow. However, the correlation volume computation
requires a lot of memory, which makes prediction difficult on high-resolution
images. In this paper, we propose a novel Patchmatch-based framework to work on
high-resolution optical flow estimation. Specifically, we introduce the first
end-to-end Patchmatch based deep learning optical flow. It can get
high-precision results with lower memory benefiting from propagation and local
search of Patchmatch. Furthermore, a new inverse propagation is proposed to
decouple the complex operations of propagation, which can significantly reduce
calculations in multiple iterations. At the time of submission, our method
ranks first on all the metrics on the popular KITTI2015 benchmark, and ranks
second on EPE on the Sintel clean benchmark among published optical flow
methods. Experiment shows our method has a strong cross-dataset generalization
ability that the F1-all achieves 13.73%, reducing 21% from the best published
result 17.4% on KITTI2015. What's more, our method shows a good details
preserving result on the high-resolution dataset DAVIS and consumes 2x less
memory than RAFT.

Cold-start issues have been more and more challenging for providing accurate
recommendations with the fast increase of users and items. Most existing
approaches attempt to solve the intractable problems via content-aware
recommendations based on auxiliary information and/or cross-domain
recommendations with transfer learning. Their performances are often
constrained by the extremely sparse user-item interactions, unavailable side
information, or very limited domain-shared users. Recently, meta-learners with
meta-augmentation by adding noises to labels have been proven to be effective
to avoid overfitting and shown good performance on new tasks. Motivated by the
idea of meta-augmentation, in this paper, by treating a user's preference over
items as a task, we propose a so-called Diverse Preference Augmentation
framework with multiple source domains based on meta-learning (referred to as
MetaDPA) to i) generate diverse ratings in a new domain of interest (known as
target domain) to handle overfitting on the case of sparse interactions, and to
ii) learn a preference model in the target domain via a meta-learning scheme to
alleviate cold-start issues. Specifically, we first conduct multi-source domain
adaptation by dual conditional variational autoencoders and impose a
Multi-domain InfoMax (MDI) constraint on the latent representations to learn
domain-shared and domain-specific preference properties. To avoid overfitting,
we add a Mutually-Exclusive (ME) constraint on the output of decoders to
generate diverse ratings given content data. Finally, these generated diverse
ratings and the original ratings are introduced into the meta-training
procedure to learn a preference meta-learner, which produces good
generalization ability on cold-start recommendation tasks. Experiments on
real-world datasets show our proposed MetaDPA clearly outperforms the current

Knowledge distillation is an approach to transfer information on
representations from a teacher to a student by reducing their difference. A
challenge of this approach is to reduce the flexibility of the student's
representations inducing inaccurate learning of the teacher's knowledge. To
resolve it in BERT transferring, we investigate distillation of structures of
representations specified to three types: intra-feature, local inter-feature,
global inter-feature structures. To transfer them, we introduce \textit{feature
structure distillation} methods based on the Centered Kernel Alignment, which
assigns a consistent value to similar features structures and reveals more
informative relations. In particular, a memory-augmented transfer method with
clustering is implemented for the global structures. In the experiments on the
nine tasks for language understanding of the GLUE dataset, the proposed methods
effectively transfer the three types of structures and improve performance
compared to state-of-the-art distillation methods. Indeed, the code for the
methods is available in https://github.com/maroo-sky/FSD

In autonomous driving, LiDAR point-clouds and RGB images are two major data
modalities with complementary cues for 3D object detection. However, it is
quite difficult to sufficiently use them, due to large inter-modal
discrepancies. To address this issue, we propose a novel framework, namely
Contrastively Augmented Transformer for multi-modal 3D object Detection
(CAT-Det). Specifically, CAT-Det adopts a two-stream structure consisting of a
Pointformer (PT) branch, an Imageformer (IT) branch along with a Cross-Modal
Transformer (CMT) module. PT, IT and CMT jointly encode intra-modal and
inter-modal long-range contexts for representing an object, thus fully
exploring multi-modal information for detection. Furthermore, we propose an
effective One-way Multi-modal Data Augmentation (OMDA) approach via
hierarchical contrastive learning at both the point and object levels,
significantly improving the accuracy only by augmenting point-clouds, which is
free from complex generation of paired samples of the two modalities. Extensive
experiments on the KITTI benchmark show that CAT-Det achieves a new
state-of-the-art, highlighting its effectiveness.

Graphs are a powerful tool for representing and analyzing unstructured,
non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples
are molecule property prediction and brain connectome analysis. Importantly,
recent works have shown that considering relationships between input data
samples have a positive regularizing effect for the downstream task in
healthcare applications. These relationships are naturally modeled by a
(possibly unknown) graph structure between input samples. In this work, we
propose Graph-in-Graph (GiG), a neural network architecture for protein
classification and brain imaging applications that exploits the graph
representation of the input data samples and their latent relation. We assume
an initially unknown latent-graph structure between graph-valued input data and
propose to learn end-to-end a parametric model for message passing within and
across input graph samples, along with the latent structure connecting the
input graphs. Further, we introduce a degree distribution loss that helps
regularize the predicted latent relationships structure. This regularization
can significantly improve the downstream task. Moreover, the obtained latent
graph can represent patient population models or networks of molecule clusters,
providing a level of interpretability and knowledge discovery in the input
domain of particular value in healthcare.

This paper presents a first step towards tuning observers for general
nonlinear systems. Relying on recent results around
Kazantzis-Kravaris/Luenberger (KKL) observers, we propose an empirical
criterion to guide the calibration of the observer, by trading off transient
performance and sensitivity to measurement noise. We parametrize the gain
matrix and evaluate this criterion over a family of observers for different
parameter values. We then use neural networks to learn the mapping between the
observer and the nonlinear system, and present a novel method to sample the
state-space efficiently for nonlinear regression. We illustrate the merits of
this approach in numerical simulations.

In this paper, we study deep neural networks for solving extremely large
linear systems arising from physically relevant problems. Because of the curse
of dimensionality, it is expensive to store both solution and right hand side
vectors in such extremely large linear systems. Our idea is to employ a neural
network to characterize the solution with parameters being much fewer than the
size of the solution. We present an error analysis of the proposed method
provided that the solution vector can be approximated by the continuous
quantity, which is in the Barron space. Several numerical examples arising from
partial differential equations, queueing problems and probabilistic Boolean
networks are presented to demonstrate that solutions of linear systems with
sizes ranging from septillion ($10^{24}$) to nonillion ($10^{30})$ can be
learned quite accurately.

Learning from a label distribution has achieved promising results on ordinal
regression tasks such as facial age and head pose estimation wherein, the
concept of adaptive label distribution learning (ALDL) has drawn lots of
attention recently for its superiority in theory. However, compared with the
methods assuming fixed form label distribution, ALDL methods have not achieved
better performance. We argue that existing ALDL algorithms do not fully exploit
the intrinsic properties of ordinal regression. In this paper, we emphatically
summarize that learning an adaptive label distribution on ordinal regression
tasks should follow three principles. First, the probability corresponding to
the ground-truth should be the highest in label distribution. Second, the
probabilities of neighboring labels should decrease with the increase of
distance away from the ground-truth, i.e., the distribution is unimodal. Third,
the label distribution should vary with samples changing, and even be distinct
for different instances with the same label, due to the different levels of
difficulty and ambiguity. Under the premise of these principles, we propose a
novel loss function for fully adaptive label distribution learning, namely
unimodal-concentrated loss. Specifically, the unimodal loss derived from the
learning to rank strategy constrains the distribution to be unimodal.
Furthermore, the estimation error and the variance of the predicted
distribution for a specific sample are integrated into the proposed
concentrated loss to make the predicted distribution maximize at the
ground-truth and vary according to the predicting uncertainty. Extensive
experimental results on typical ordinal regression tasks including age and head
pose estimation, show the superiority of our proposed unimodal-concentrated
loss compared with existing loss functions.

Interactive recommendation is able to learn from the interactive processes
between users and systems to confront the dynamic interests of users. Recent
advances have convinced that the ability of reinforcement learning to handle
the dynamic process can be effectively applied in the interactive
recommendation. However, the sparsity of interactive data may hamper the
performance of the system. We propose to train a Model-agnostic Counterfactual
Synthesis Policy to generate counterfactual data and address the data sparsity
problem by modelling from observation and counterfactual distribution. The
proposed policy can identify and replace the trivial components for any state
in the training process with other agents, which can be deployed in any
RL-based algorithm. The experimental results demonstrate the effectiveness and
generality of our proposed policy.

Ensemble learning methods whose base classifier is a decision tree usually
belong to the bagging or boosting. However, no previous work has ever built the
ensemble classifier by maximizing long-term returns to the best of our
knowledge. This paper proposes a decision forest building method called
MA-H-SAC-DF for binary classification via deep reinforcement learning. First,
the building process is modeled as a decentralized partial observable Markov
decision process, and a set of cooperative agents jointly constructs all base
classifiers. Second, the global state and local observations are defined based
on informations of the parent node and the current location. Last, the
state-of-the-art deep reinforcement method Hybrid SAC is extended to a
multi-agent system under the CTDE architecture to find an optimal decision
forest building policy. The experiments indicate that MA-H-SAC-DF has the same
performance as random forest, Adaboost, and GBDT on balanced datasets and
outperforms them on imbalanced datasets.

Non-coding RNA structure and function are essential to understanding various
biological processes, such as cell signaling, gene expression, and
post-transcriptional regulations. These are all among the core problems in the
RNA field. With the rapid growth of sequencing technology, we have accumulated
a massive amount of unannotated RNA sequences. On the other hand, expensive
experimental observatory results in only limited numbers of annotated data and
3D structures. Hence, it is still challenging to design computational methods
for predicting their structures and functions. The lack of annotated data and
systematic study causes inferior performance. To resolve the issue, we propose
a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million
non-coding RNA sequences through self-supervised learning. Within this
approach, we discover that the pre-trained RNA-FM could infer sequential and
evolutionary information of non-coding RNAs without using any labels.
Furthermore, we demonstrate RNA-FM's effectiveness by applying it to the
downstream secondary/3D structure prediction, SARS-CoV-2 genome structure and
evolution prediction, protein-RNA binding preference modeling, and gene
expression regulation modeling. The comprehensive experiments show that the
proposed method improves the RNA structural and functional modelling results
significantly and consistently. Despite only being trained with unlabelled
data, RNA-FM can serve as the foundational model for the field.

The Cut posterior and related Semi-Modular Inference are Generalised Bayes
methods for Modular Bayesian evidence combination. Analysis is broken up over
modular sub-models of the joint posterior distribution. Model-misspecification
in multi-modular models can be hard to fix by model elaboration alone and the
Cut posterior and SMI offer a way round this. Information entering the analysis
from misspecified modules is controlled by an influence parameter $\eta$
related to the learning rate. This paper contains two substantial new methods.
First, we give variational methods for approximating the Cut and SMI posteriors
which are adapted to the inferential goals of evidence combination. We
parameterise a family of variational posteriors using a Normalising Flow for
accurate approximation and end-to-end training. Secondly, we show that analysis
of models with multiple cuts is feasible using a new Variational
Meta-Posterior. This approximates a family of SMI posteriors indexed by $\eta$
using a single set of variational parameters.

This article presents GrowliFlower, a georeferenced, image-based UAV time
series dataset of two monitored cauliflower fields of size 0.39 and 0.60 ha
acquired in 2020 and 2021. The dataset contains RGB and multispectral
orthophotos from which about 14,000 individual plant coordinates are derived
and provided. The coordinates enable the dataset users the extraction of
complete and incomplete time series of image patches showing individual plants.
The dataset contains collected phenotypic traits of 740 plants, including the
developmental stage as well as plant and cauliflower size. As the harvestable
product is completely covered by leaves, plant IDs and coordinates are provided
to extract image pairs of plants pre and post defoliation, to facilitate
estimations of cauliflower head size. Moreover, the dataset contains
pixel-accurate leaf and plant instance segmentations, as well as stem
annotations to address tasks like classification, detection, segmentation,
instance segmentation, and similar computer vision tasks. The dataset aims to
foster the development and evaluation of machine learning approaches. It
specifically focuses on the analysis of growth and development of cauliflower
and the derivation of phenotypic traits to foster the development of automation
in agriculture. Two baseline results of instance segmentation at plant and leaf
level based on the labeled instance segmentation data are presented. The entire
data set is publicly available.

Neural networks (NNs) are already deployed in hardware today, becoming
valuable intellectual property (IP) as many hours are invested in their
training and optimization. Therefore, attackers may be interested in copying,
reverse engineering, or even modifying this IP. The current practices in
hardware obfuscation, including the widely studied logic locking technique, are
insufficient to protect the actual IP of a well-trained NN: its weights. Simply
hiding the weights behind a key-based scheme is inefficient (resource-hungry)
and inadequate (attackers can exploit knowledge distillation). This paper
proposes an intuitive method to poison the predictions that prevent
distillation-based attacks; this is the first work to consider such a poisoning
approach in hardware-implemented NNs. The proposed technique obfuscates a NN so
an attacker cannot train the NN entirely or accurately. We elaborate a threat
model which highlights the difference between random logic obfuscation and the
obfuscation of NN IP. Based on this threat model, our security analysis shows
that the poisoning successfully and significantly reduces the accuracy of the
stolen NN model on various representative datasets. Moreover, the accuracy and
prediction distributions are maintained, no functionality is disturbed, nor are
high overheads incurred. Finally, we highlight that our proposed approach is
flexible and does not require manipulation of the NN toolchain.

Nowadays, the main problem of deep learning techniques used in the
development of automatic speech recognition (ASR) models is the lack of
transcribed data. The goal of this research is to propose a new data
augmentation method to improve ASR models for agglutinative and low-resource
languages. This novel data augmentation method generates both synthetic text
and synthetic audio. Some experiments were conducted using the corpus of the
Quechua language, which is an agglutinative and low-resource language. In this
study, a sequence-to-sequence (seq2seq) model was applied to generate synthetic
text, in addition to generating synthetic speech using a text-to-speech (TTS)
model for Quechua. The results show that the new data augmentation method works
well to improve the ASR model for Quechua. In this research, an 8.73%
improvement in the word-error-rate (WER) of the ASR model is obtained using a
combination of synthetic text and synthetic speech.

Clinical trials offer a fundamental opportunity to discover new treatments
and advance the medical knowledge. However, the uncertainty of the outcome of a
trial can lead to unforeseen costs and setbacks. In this study, we propose a
new method to predict the effectiveness of an intervention in a clinical trial.
Our method relies on generating an informative summary from multiple documents
available in the literature about the intervention under study. Specifically,
our method first gathers all the abstracts of PubMed articles related to the
intervention. Then, an evidence sentence, which conveys information about the
effectiveness of the intervention, is extracted automatically from each
abstract. Based on the set of evidence sentences extracted from the abstracts,
a short summary about the intervention is constructed. Finally, the produced
summaries are used to train a BERT-based classifier, in order to infer the
effectiveness of an intervention. To evaluate our proposed method, we introduce
a new dataset which is a collection of clinical trials together with their
associated PubMed articles. Our experiments, demonstrate the effectiveness of
producing short informative summaries and using them to predict the
effectiveness of an intervention.

Few-Shot classification aims at solving problems that only a few samples are
available in the training process. Due to the lack of samples, researchers
generally employ a set of training tasks from other domains to assist the
target task, where the distribution between assistant tasks and the target task
is usually different. To reduce the distribution gap, several lines of methods
have been proposed, such as data augmentation and domain alignment. However,
one common drawback of these algorithms is that they ignore the similarity task
selection before training. The fundamental problem is to push the auxiliary
tasks close to the target task. In this paper, we propose a novel task
selecting algorithm, named Optimal Transport Task Selecting (OTTS), to
construct a training set by selecting similar tasks for Few-Shot learning.
Specifically, the OTTS measures the task similarity by calculating the optimal
transport distance and completes the model training via a self-supervised
strategy. By utilizing the selected tasks with OTTS, the training process of
Few-Shot learning become more stable and effective. Other proposed methods
including data augmentation and domain alignment can be used in the meantime
with OTTS. We conduct extensive experiments on a variety of datasets, including
MiniImageNet, CIFAR, CUB, Cars, and Places, to evaluate the effectiveness of
OTTS. Experimental results validate that our OTTS outperforms the typical
baselines, i.e., MAML, matchingnet, protonet, by a large margin (averagely
1.72\% accuracy improvement).

Input features play a crucial role in the predictive performance of DNN-based
industrial recommender systems with thousands of categorical and continuous
fields from users, items, contexts, and their interactions. Noisy features and
inappropriate embedding dimension assignments can impair the performance of
recommender systems and introduce unnecessary complexity in model training and
online serving. Optimizing the input configuration of DNN models, including
feature selection and embedding dimension assignment, has become one of the
essential topics in feature engineering. Typically, feature selection and
embedding dimension search are optimized sequentially, i.e., feature selection
is performed first, followed by embedding dimension search to determine the
optimal dimension size for each selected feature. In contrast, this paper
studies the joint optimization of feature selection and embedding dimension
search. To this end, we propose a differentiable neural \textbf{i}nput
\textbf{razor}, namely \textbf{i-Razor}. Specifically, inspired by recent
advances in neural architecture search, we introduce an end-to-end
differentiable model to learn the relative importance between different
embedding regions of each feature. Furthermore, a flexible pruning algorithm is
proposed to simultaneously achieve feature filtering and dimension size
derivation. Extensive experiments on two large-scale public datasets in the
Click-Through-Rate (CTR) prediction task demonstrate the efficacy and
superiority of i-Razor in balancing model complexity and performance.

Recently, privacy issues in web services that rely on users' personal data
have raised great attention. Unlike existing privacy-preserving technologies
such as federated learning and differential privacy, we explore another way to
mitigate users' privacy concerns, giving them control over their own data. For
this goal, we propose a privacy aware recommendation framework that gives users
delicate control over their personal data, including implicit behaviors, e.g.,
clicks and watches. In this new framework, users can proactively control which
data to disclose based on the trade-off between anticipated privacy risks and
potential utilities. Then we study users' privacy decision making under
different data disclosure mechanisms and recommendation models, and how their
data disclosure decisions affect the recommender system's performance.
  To avoid the high cost of real-world experiments, we apply simulations to
study the effects of our proposed framework. Specifically, we propose a
reinforcement learning algorithm to simulate users' decisions (with various
sensitivities) under three proposed platform mechanisms on two datasets with
three representative recommendation models. The simulation results show that
the platform mechanisms with finer split granularity and more unrestrained
disclosure strategy can bring better results for both end users and platforms
than the "all or nothing" binary mechanism adopted by most real-world
applications. It also shows that our proposed framework can effectively protect
users' privacy since they can obtain comparable or even better results with
much less disclosed data.

Ising machines are hardware solvers which aim to find the absolute or
approximate ground states of the Ising model. The Ising model is of fundamental
computational interest because it is possible to formulate any problem in the
complexity class NP as an Ising problem with only polynomial overhead. A
scalable Ising machine that outperforms existing standard digital computers
could have a huge impact for practical applications for a wide variety of
optimization problems. In this review, we survey the current status of various
approaches to constructing Ising machines and explain their underlying
operational principles. The types of Ising machines considered here include
classical thermal annealers based on technologies such as spintronics, optics,
memristors, and digital hardware accelerators; dynamical-systems solvers
implemented with optics and electronics; and superconducting-circuit quantum
annealers. We compare and contrast their performance using standard metrics
such as the ground-state success probability and time-to-solution, give their
scaling relations with problem size, and discuss their strengths and
weaknesses.

Neural-based learning agents make decisions using internal artificial neural
networks. In certain situations, it becomes pertinent that this knowledge is
re-interpreted in a friendly form to both the human and the machine. These
situations include: when agents are required to communicate the knowledge they
learn to each other in a transparent way in the presence of an external human
observer, in human-machine teaming settings where humans and machines need to
collaborate on a task, or where there is a requirement to verify the knowledge
exchanged between the agents. We propose an interpretable knowledge fusion
framework suited for neural-based learning agents, and propose a Priority on
Weak State Areas (PoWSA) retraining technique. We first test the proposed
framework on a synthetic binary classification task before evaluating it on a
shepherding-based multi-agent swarm guidance task. Results demonstrate that the
proposed framework increases the success rate on the swarm-guidance environment
by 11% and better stability in return for a modest increase in computational
cost of 14.5% to achieve interpretability. Moreover, the framework presents the
knowledge learnt by an agent in a human-friendly representation, leading to a
better descriptive visual representation of an agent's knowledge.

Click-through rate (CTR) Prediction is of great importance in real-world
online ads systems. One challenge for the CTR prediction task is to capture the
real interest of users from their clicked items, which is inherently biased by
presented positions of items, i.e., more front positions tend to obtain higher
CTR values. A popular line of existing works focuses on explicitly estimating
position bias by result randomization which is expensive and inefficient, or by
inverse propensity weighting (IPW) which relies heavily on the quality of the
propensity estimation. Another common solution is modeling position as features
during offline training and simply adopting fixed value or dropout tricks when
serving. However, training-inference inconsistency can lead to sub-optimal
performance. Furthermore, post-click information such as position values is
informative while less exploited in CTR prediction. This work proposes a simple
yet efficient knowledge distillation framework to alleviate the impact of
position bias and leverage position information to improve CTR prediction. We
demonstrate the performance of our proposed method on a real-world production
dataset and online A/B tests, achieving significant improvements over competing
baseline models. The proposed method has been deployed in the real world online
ads systems, serving main traffic on one of the world's largest e-commercial

Open-domain question answering (OpenQA) is an important branch of textual QA
which discovers answers for the given questions based on a large number of
unstructured documents. Effectively mining correct answers from the open-domain
sources still has a fair way to go. Existing OpenQA systems might suffer from
the issues of question complexity and ambiguity, as well as insufficient
background knowledge. Recently, conversational OpenQA is proposed to address
these issues with the abundant contextual information in the conversation.
Promising as it might be, there exist several fundamental limitations including
the inaccurate question understanding, the coarse ranking for passage
selection, and the inconsistent usage of golden passage in the training and
inference phases. To alleviate these limitations, in this paper, we propose a
framework with Multifaceted Improvements for Conversational open-domain
Question Answering (MICQA). Specifically, MICQA has three significant
advantages. First, the proposed KL-divergence based regularization is able to
lead to a better question understanding for retrieval and answer reading.
Second, the added post-ranker module can push more relevant passages to the top
placements and be selected for reader with a two-aspect constrains. Third, the
well designed curriculum learning strategy effectively narrows the gap between
the golden passage settings of training and inference, and encourages the
reader to find true answer without the golden passage assistance. Extensive
experiments conducted on the publicly available dataset OR-QuAC demonstrate the
superiority of MICQA over the state-of-the-art model in conversational OpenQA
task.

Click-through rate (CTR) prediction is a crucial task in web search,
recommender systems, and online advertisement displaying. In practical
application, CTR models often serve with high-speed user-generated data
streams, whose underlying distribution rapidly changing over time. The concept
drift problem inevitably exists in those streaming data, which can lead to
performance degradation due to the timeliness issue. To ensure model freshness,
incremental learning has been widely adopted in real-world production systems.
However, it is hard for the incremental update to achieve the balance of the
CTR models between the adaptability to capture the fast-changing trends and
generalization ability to retain common knowledge. In this paper, we propose
adaptive mixture of experts (AdaMoE), a new framework to alleviate the concept
drift problem by adaptive filtering in the data stream of CTR prediction. The
extensive experiments on the offline industrial dataset and online A/B tests
show that our AdaMoE significantly outperforms all incremental learning
frameworks considered.

With unorganized, unplanned and improper use of limited raw materials, an
abundant amount of waste is being produced, which is harmful to our environment
and ecosystem. While traditional linear production lines fail to address
far-reaching issues like waste production and a shorter product life cycle, a
prospective concept, namely circular economy (CE), has shown promising
prospects to be adopted at industrial and governmental levels. CE aims to
complete the product life cycle loop by bringing out the highest values from
raw materials in the design phase and later on by reusing, recycling, and
remanufacturing. Innovative technologies like artificial intelligence (AI) and
machine learning(ML) provide vital assistance in effectively adopting and
implementing CE in real-world practices. This study explores the adoption and
integration of applied AI techniques in CE. First, we conducted bibliometric
analysis on a collection of 104 SCOPUS indexed documents exploring the critical
research criteria in AI and CE. Forty papers were picked to conduct a
systematic literature review from these documents. The selected documents were
further divided into six categories: sustainable development, reverse
logistics, waste management, supply chain management, recycle & reuse, and
manufacturing development. Comprehensive research insights and trends have been
extracted and delineated. Finally, the research gap needing further attention
has been identified and the future research directions have also been

Diffusion models learn to restore noisy data, which is corrupted with
different levels of noise, by optimizing the weighted sum of the corresponding
loss terms, i.e., denoising score matching loss. In this paper, we show that
restoring data corrupted with certain noise levels offers a proper pretext task
for the model to learn rich visual concepts. We propose to prioritize such
noise levels over other levels during training, by redesigning the weighting
scheme of the objective function. We show that our simple redesign of the
weighting scheme significantly improves the performance of diffusion models
regardless of the datasets, architectures, and sampling strategies.

The ever-growing demand for further advances in artificial intelligence
motivated research on unconventional computation based on analog physical
devices. While such computation devices mimic brain-inspired analog information
processing, learning procedures still relies on methods optimized for digital
processing such as backpropagation. Here, we present physical deep learning by
extending a biologically plausible training algorithm called direct feedback
alignment. As the proposed method is based on random projection with arbitrary
nonlinear activation, we can train a physical neural network without knowledge
about the physical system. In addition, we can emulate and accelerate the
computation for this training on a simple and scalable physical system. We
demonstrate the proof-of-concept using a hierarchically connected
optoelectronic recurrent neural network called deep reservoir computer. By
constructing an FPGA-assisted optoelectronic benchtop, we confirmed the
potential for accelerated computation with competitive performance on
benchmarks. Our results provide practical solutions for the training and
acceleration of neuromorphic computation.

Exact matrix completion and low rank matrix estimation problems has been
studied in different underlying conditions. In this work we study exact
low-rank completion under non-degenerate noise model. Non-degenerate random
noise model has been previously studied by many researchers under given
condition that the noise is sparse and existing in some of the columns. In this
paper, we assume that each row can receive random noise instead of columns and
propose an interactive algorithm that is robust to this noise. We show that we
use a parametrization technique to give a condition when the underlying matrix
could be recoverable and suggest an algorithm which recovers the underlying
matrix.

Matrix completion problem has been investigated under many different
conditions since Netflix announced the Netflix Prize problem. Many research
work has been done in the field once it has been discovered that many real life
dataset could be estimated with a low-rank matrix. Since then compressed
sensing, adaptive signal detection has gained the attention of many
researchers. In this survey paper we are going to visit some of the matrix
completion methods, mainly in the direction of passive and adaptive directions.
First, we discuss passive matrix completion methods with convex optimization,
and the second active matrix completion techniques with adaptive signal
detection methods. Traditionally many machine learning problems are solved in
passive environment. However, later it has been observed that adaptive sensing
algorithms many times performs more efficiently than former algorithms. Hence
algorithms in this setting has been extensively studied. Therefore, we are
going to present some of the latest adaptive matrix completion algorithms in
this paper meanwhile providing passive methods.

The key of sequential recommendation lies in the accurate item correlation
modeling. Previous models infer such information based on item co-occurrences,
which may fail to capture the real causal relations, and impact the
recommendation performance and explainability. In this paper, we equip
sequential recommendation with a novel causal discovery module to capture
causalities among user behaviors. Our general idea is firstly assuming a causal
graph underlying item correlations, and then we learn the causal graph jointly
with the sequential recommender model by fitting the real user behavior data.
More specifically, in order to satisfy the causality requirement, the causal
graph is regularized by a differentiable directed acyclic constraint.
Considering that the number of items in recommender systems can be very large,
we represent different items with a unified set of latent clusters, and the
causal graph is defined on the cluster level, which enhances the model
scalability and robustness. In addition, we provide theoretical analysis on the
identifiability of the learned causal graph. To the best of our knowledge, this
paper makes a first step towards combining sequential recommendation with
causal discovery. For evaluating the recommendation performance, we implement
our framework with different neural sequential architectures, and compare them
with many state-of-the-art methods based on real-world datasets. Empirical
studies manifest that our model can on average improve the performance by about
7% and 11% on f1 and NDCG, respectively. To evaluate the model explainability,
we build a new dataset with human labeled explanations for both quantitative
and qualitative analysis.

Deep learning is a key approach for the environment perception function of
Cooperative Intelligent Transportation Systems (C-ITS) with autonomous vehicles
and smart traffic infrastructure. In today's C-ITS, smart traffic participants
are capable of timely generating and transmitting a large amount of data.
However, these data can not be used for model training directly due to privacy
constraints. In this paper, we introduce a federated learning framework coping
with Hierarchical Heterogeneity (H2-Fed), which can notably enhance the
conventional pre-trained deep learning model. The framework exploits data from
connected public traffic agents in vehicular networks without affecting user
data privacy. By coordinating existing traffic infrastructure, including
roadside units and road traffic clouds, the model parameters are efficiently
disseminated by vehicular communications and hierarchically aggregated.
Considering the individual heterogeneity of data distribution, computational
and communication capabilities across traffic agents and roadside units, we
employ a novel method that addresses the heterogeneity of different aggregation
layers of the framework architecture, i.e., aggregation in layers of roadside
units and cloud. The experiment results indicate that our method can well
balance the learning accuracy and stability according to the knowledge of
heterogeneity in current communication networks. Comparing to other baseline
approaches, the evaluation on federated datasets shows that our framework is
more general and capable especially in application scenarios with low
communication quality. Even when 90% of the agents are timely disconnected, the
pre-trained deep learning model can still be forced to converge stably, and its
accuracy can be enhanced from 68% to over 90% after convergence.

Federated Recommendation (FR) has received considerable popularity and
attention in the past few years. In FR, for each user, its feature vector and
interaction data are kept locally on its own client thus are private to others.
Without the access to above information, most existing poisoning attacks
against recommender systems or federated learning lose validity. Benifiting
from this characteristic, FR is commonly considered fairly secured. However, we
argue that there is still possible and necessary security improvement could be
made in FR. To prove our opinion, in this paper we present FedRecAttack, a
model poisoning attack to FR aiming to raise the exposure ratio of target
items. In most recommendation scenarios, apart from private user-item
interactions (e.g., clicks, watches and purchases), some interactions are
public (e.g., likes, follows and comments). Motivated by this point, in
FedRecAttack we make use of the public interactions to approximate users'
feature vectors, thereby attacker can generate poisoned gradients accordingly
and control malicious users to upload the poisoned gradients in a well-designed
way. To evaluate the effectiveness and side effects of FedRecAttack, we conduct
extensive experiments on three real-world datasets of different sizes from two
completely different scenarios. Experimental results demonstrate that our
proposed FedRecAttack achieves the state-of-the-art effectiveness while its
side effects are negligible. Moreover, even with small proportion (3%) of
malicious users and small proportion (1%) of public interactions, FedRecAttack
remains highly effective, which reveals that FR is more vulnerable to attack
than people commonly considered.

We present a data-driven workflow to biological tissue modeling, which aims
to predict the displacement field based on digital image correlation (DIC)
measurements under unseen loading scenarios, without postulating a specific
constitutive model form nor possessing knowledges on the material
microstructure. To this end, a material database is constructed from the DIC
displacement tracking measurements of multiple biaxial stretching protocols on
a porcine tricuspid valve anterior leaflet, with which we build a neural
operator learning model. The material response is modeled as a solution
operator from the loading to the resultant displacement field, with the
material microstructure properties learned implicitly from the data and
naturally embedded in the network parameters. Using various combinations of
loading protocols, we compare the predictivity of this framework with finite
element analysis based on the phenomenological Fung-type model. From
in-distribution tests, the predictivity of our approach presents good
generalizability to different loading conditions and outperforms the
conventional constitutive modeling at approximately one order of magnitude.
When tested on out-of-distribution loading ratios, the neural operator learning
approach becomes less effective. To improve the generalizability of our
framework, we propose a physics-guided neural operator learning model via
imposing partial physics knowledge. This method is shown to improve the model's
extrapolative performance in the small-deformation regime. Our results
demonstrate that with sufficient data coverage and/or guidance from partial
physics constraints, the data-driven approach can be a more effective method
for modeling biological materials than the traditional constitutive modeling.

Minimum-variance portfolio optimizations rely on accurate covariance
estimator to obtain optimal portfolios. However, it usually suffers from large
error from sample covariance matrix when the sample size $n$ is not
significantly larger than the number of assets $p$. We analyze the random
matrix aspects of portfolio optimization and identify the order of errors in
sample optimal portfolio weight and show portfolio risk are underestimated when
using samples. We also provide LoCoV (low dimension covariance voting)
algorithm to reduce error inherited from random samples. From various
experiments, LoCoV is shown to outperform the classical method by a large
margin.

The impression section of a radiology report summarizes the most prominent
observation from the findings section and is the most important section for
radiologists to communicate to physicians. Summarizing findings is
time-consuming and can be prone to error for inexperienced radiologists, and
thus automatic impression generation has attracted substantial attention. With
the encoder-decoder framework, most previous studies explore incorporating
extra knowledge (e.g., static pre-defined clinical ontologies or extra
background information). Yet, they encode such knowledge by a separate encoder
to treat it as an extra input to their models, which is limited in leveraging
their relations with the original findings. To address the limitation, we
propose a unified framework for exploiting both extra knowledge and the
original findings in an integrated way so that the critical information (i.e.,
key words and their relations) can be extracted in an appropriate way to
facilitate impression generation. In detail, for each input findings, it is
encoded by a text encoder, and a graph is constructed through its entities and
dependency tree. Then, a graph encoder (e.g., graph neural networks (GNNs)) is
adopted to model relation information in the constructed graph. Finally, to
emphasize the key words in the findings, contrastive learning is introduced to
map positive samples (constructed by masking non-key words) closer and push
apart negative ones (constructed by masking key words). The experimental
results on OpenI and MIMIC-CXR confirm the effectiveness of our proposed
method.

Objective: Estimation of the global optima of multiple model parameters is
valuable in imaging to form a reliable diagnostic image. Given non convexity of
the objective function, it is challenging to avoid from different local minima.
Methods: We first formulate the global searching of multiply parameters to be a
k-D move in the parametric space, and convert parameters updating to be
state-action decision-making problem. We proposed a novel Deep Q-learning of
Model Parameters (DQMP) method for global optimization of model parameters by
updating the parameter configurations through actions that maximize a Q-value,
which employs a Deep Reward Network designed to learn global reward values from
both visible curve fitting errors and hidden parameter errors. Results: The
DQMP method was evaluated by viscoelastic imaging on soft matter by
Kelvin-Voigt fractional derivative (KVFD) modeling. In comparison to other
methods, imaging of parameters by DQMP yielded the smallest errors (< 2%) to
the ground truth images. DQMP was applied to viscoelastic imaging on biological
tissues, which indicated a great potential of imaging on physical parameters in
diagnostic applications. Conclusions: DQMP method is able to achieve global
optima, yielding accurate model parameter estimates in viscoelastic imaging.
Assessment of DQMP by simulation imaging and ultrasound breast imaging
demonstrated the consistency, reliability of the imaged parameters, and
powerful global searching ability of DQMP. Significance: DQMP method is
promising for imaging of multiple parameters, and can be generalized to global
optimization for many other complex nonconvex functions and imaging of physical

We use interval reachability analysis to obtain robustness guarantees for
implicit neural networks (INNs). INNs are a class of implicit learning models
that use implicit equations as layers and have been shown to exhibit several
notable benefits over traditional deep neural networks. We first establish that
tight inclusion functions of neural networks, which provide the tightest
rectangular over-approximation of an input-output map, lead to sharper
robustness guarantees than the well-studied robustness measures of local
Lipschitz constants. Like Lipschitz constants, tight inclusions functions are
computationally challenging to obtain, and we thus propose using mixed
monotonicity and contraction theory to obtain computationally efficient
estimates of tight inclusion functions for INNs. We show that our approach
performs at least as well as, and generally better than, applying
state-of-the-art interval bound propagation methods to INNs. We design a novel
optimization problem for training robust INNs and we provide empirical evidence
that suitably-trained INNs can be more robust than comparably-trained
feedforward networks.

Vector quantization (VQ) based ANN indexes, such as Inverted File System
(IVF) and Product Quantization (PQ), have been widely applied to embedding
based document retrieval thanks to the competitive time and memory efficiency.
Originally, VQ is learned to minimize the reconstruction loss, i.e., the
distortions between the original dense embeddings and the reconstructed
embeddings after quantization. Unfortunately, such an objective is inconsistent
with the goal of selecting ground-truth documents for the input query, which
may cause severe loss of retrieval quality. Recent works identify such a
defect, and propose to minimize the retrieval loss through contrastive
learning. However, these methods intensively rely on queries with ground-truth
documents, whose performance is limited by the insufficiency of labeled data.
  In this paper, we propose Distill-VQ, which unifies the learning of IVF and
PQ within a knowledge distillation framework. In Distill-VQ, the dense
embeddings are leveraged as "teachers", which predict the query's relevance to
the sampled documents. The VQ modules are treated as the "students", which are
learned to reproduce the predicted relevance, such that the reconstructed
embeddings may fully preserve the retrieval result of the dense embeddings. By
doing so, Distill-VQ is able to derive substantial training signals from the
massive unlabeled data, which significantly contributes to the retrieval
quality. We perform comprehensive explorations for the optimal conduct of
knowledge distillation, which may provide useful insights for the learning of
VQ based ANN index. We also experimentally show that the labeled data is no
longer a necessity for high-quality vector quantization, which indicates
Distill-VQ's strong applicability in practice.

The application of cross-dataset training in object detection tasks is
complicated because the inconsistency in the category range across datasets
transforms fully supervised learning into semi-supervised learning. To address
this problem, recent studies focus on the generation of high-quality missing
annotations. In this study, we first point out that it is not enough to
generate high-quality annotations using a single model, which only looks once
for annotations. Through detailed experimental analyses, we further conclude
that hard-label training is conducive to generating high-recall annotations,
while soft-label training tends to obtain high-precision annotations. Inspired
by the aspects mentioned above, we propose a dynamic supervisor framework that
updates the annotations multiple times through multiple-updated submodels
trained using hard and soft labels. In the final generated annotations, both
recall and precision improve significantly through the integration of
hard-label training with soft-label training. Extensive experiments conducted
on various dataset combination settings support our analyses and demonstrate
the superior performance of the proposed dynamic supervisor.

With the growing demand for openness, scalability, and granularity, mobile
network function virtualization (NFV) has emerged as a key enabler for most
mobile network operators. NFV decouples network functions from hardware
devices. This decoupling allows network services, referred to as Virtualized
Network Functions (VNFs), to be hosted on commodity hardware which simplifies
and enhances service deployment and management for providers, improves
flexibility, and leads to efficient and scalable resource usage, and lower
costs. The proper placement of VNFs in the hosting infrastructures is one of
the main technical challenges. This placement significantly influences the
network's performance, reliability, and operating costs. The VNF placement is
NP-Hard. Hence, there is a need for placement methods that can scale with the
issue's complexity and find appropriate solutions in a reasonable duration. The
primary purpose of this study is to provide a taxonomy of optimization
techniques used to tackle the VNF placement problems. We classify the studied
papers based on performance metrics, methods, algorithms, and environment.
Virtualization is not limited to simply replacing physical machines with
virtual machines or VNFs, but may also include micro-services, containers, and
cloud-native systems. In this context, the second part of our article focuses
on the placement of containers in edge/fog computing. Many issues have been
considered as traffic congestion, resource utilization, energy consumption,
performance degradation, security, etc. For each matter, various solutions are
proposed through different surveys and research papers in which each one
addresses the placement problem in a specific manner by suggesting single
objective or multi-objective methods based on different types of algorithms
such as heuristic, meta-heuristic, and machine learning algorithms.

End-to-end automatic speech recognition (ASR) directly maps input speech to a
character sequence without using pronunciation lexica. However, in languages
with thousands of characters, such as Japanese and Mandarin, modeling all these
characters is problematic due to data scarcity. To alleviate the problem, we
propose a multi-task learning model with explicit interaction between
characters and syllables by utilizing Self-conditioned connectionist temporal
classification (CTC) technique. While the original Self-conditioned CTC
estimates character-level intermediate predictions by applying auxiliary CTC
losses to a set of intermediate layers, the proposed method additionally
estimates syllable-level intermediate predictions in another set of
intermediate layers. The character-level and syllable-level predictions are
alternately used as conditioning features to deal with mutual dependency
between characters and syllables. Experimental results on Japanese and Mandarin
datasets show that the proposed multi-sequence intermediate conditioning
outperformed the conventional multi-task-based and Self-conditioned CTC-based
methods.

While pose estimation is an important computer vision task, it requires
expensive annotation and suffers from domain shift. In this paper, we
investigate the problem of domain adaptive 2D pose estimation that transfers
knowledge learned on a synthetic source domain to a target domain without
supervision. While several domain adaptive pose estimation models have been
proposed recently, they are not generic but only focus on either human pose or
animal pose estimation, and thus their effectiveness is somewhat limited to
specific scenarios. In this work, we propose a unified framework that
generalizes well on various domain adaptive pose estimation problems. We
propose to align representations using both input-level and output-level cues
(pixels and pose labels, respectively), which facilitates the knowledge
transfer from the source domain to the unlabeled target domain. Our experiments
show that our method achieves state-of-the-art performance under various domain
shifts. Our method outperforms existing baselines on human pose estimation by
up to 4.5 percent points (pp), hand pose estimation by up to 7.4 pp, and animal
pose estimation by up to 4.8 pp for dogs and 3.3 pp for sheep. These results
suggest that our method is able to mitigate domain shift on diverse tasks and
even unseen domains and objects (e.g., trained on horse and tested on dog). Our
code will be publicly available at:
https://github.com/VisionLearningGroup/UDA_PoseEstimation.

Pre-trained Language Models (PLMs) have achieved remarkable performance for
various language understanding tasks in IR systems, which require the
fine-tuning process based on labeled training data. For low-resource scenarios,
prompt-based learning for PLMs exploits prompts as task guidance and turns
downstream tasks into masked language problems for effective few-shot
fine-tuning. In most existing approaches, the high performance of prompt-based
learning heavily relies on handcrafted prompts and verbalizers, which may limit
the application of such approaches in real-world scenarios. To solve this
issue, we present CP-Tuning, the first end-to-end Contrastive Prompt Tuning
framework for fine-tuning PLMs without any manual engineering of task-specific
prompts and verbalizers. It is integrated with the task-invariant continuous
prompt encoding technique with fully trainable prompt parameters. We further
propose the pair-wise cost-sensitive contrastive learning procedure to optimize
the model in order to achieve verbalizer-free class mapping and enhance the
task-invariance of prompts. It explicitly learns to distinguish different
classes and makes the decision boundary smoother by assigning different costs
to easy and hard cases. Experiments over a variety of language understanding
tasks used in IR systems and different PLMs show that CP-Tuning outperforms

Children speech recognition is indispensable but challenging due to the
diversity of children's speech. In this paper, we propose a filter-based
discriminative autoencoder for acoustic modeling. To filter out the influence
of various speaker types and pitches, auxiliary information of the speaker and
pitch features is input into the encoder together with the acoustic features to
generate phonetic embeddings. In the training phase, the decoder uses the
auxiliary information and the phonetic embedding extracted by the encoder to
reconstruct the input acoustic features. The autoencoder is trained by
simultaneously minimizing the ASR loss and feature reconstruction error. The
framework can make the phonetic embedding purer, resulting in more accurate
senone (triphone-state) scores. Evaluated on the test set of the CMU Kids
corpus, our system achieves a 7.8% relative WER reduction compared to the
baseline system. In the domain adaptation experiment, our system also
outperforms the baseline system on the British-accent PF-STAR task.

Remote telepresence via next-generation mixed reality platforms can provide
higher levels of immersion for computer-mediated communications, allowing
participants to engage in a wide spectrum of activities, previously not
possible in 2D screen-based communication methods. However, as mixed reality
experiences are limited to the local physical surrounding of each user, finding
a common virtual ground where users can freely move and interact with each
other is challenging. In this paper, we propose a novel mutual scene synthesis
method that takes the participants' spaces as input, and generates a virtual
synthetic scene that corresponds to the functional features of all
participants' local spaces. Our method combines a mutual function optimization
module with a deep-learning conditional scene augmentation process to generate
a scene mutually and physically accessible to all participants of a mixed
reality telepresence scenario. The synthesized scene can hold mutual walkable,
sittable and workable functions, all corresponding to physical objects in the
users' real environments. We perform experiments using the MatterPort3D dataset
and conduct comparative user studies to evaluate the effectiveness of our
system. Our results show that our proposed approach can be a promising research
direction for facilitating contextualized telepresence systems for
next-generation spatial computing platforms.

Provenance embedding algorithms are well known for tracking the footprints of
information flow in wireless networks. Recently, low-latency provenance
embedding algorithms have received traction in vehicular networks owing to
strict deadlines on the delivery of packets. While existing low-latency
provenance embedding methods focus on reducing the packet delay, they assume a
complete graph on the underlying topology due to the mobility of the
participating nodes. We identify that the complete graph assumption leads to
sub-optimal performance in provenance recovery, especially when the vehicular
network is sparse, which is usually observed outside peak-hour traffic
conditions. As a result, we propose a two-part approach to design provenance
embedding algorithms for sparse vehicular networks. In the first part, we
propose secure and practical topology-learning strategies, whereas in the
second part, we design provenance embedding algorithms that guarantee
ultra-reliability by incorporating the topology knowledge at the destination
during the provenance recovery process. Besides the novel idea of using
topology knowledge for provenance recovery, a distinguishing feature for
achieving ultra-reliability is the use of hash-chains in the packet, which
trade communication-overhead of the packet with the complexity-overhead at the
destination. We derive tight upper bounds on the performance of our strategies,
and show that the derived bounds, when optimized with appropriate constraints,
deliver design parameters that outperform existing methods. Finally, we also
implement our ideas on OMNeT++ based simulation environment to show that their
latency benefits indeed make them suitable for vehicular network applications.

We present LASER, an image-based Monte Carlo Localization (MCL) framework for
2D floor maps. LASER introduces the concept of latent space rendering, where 2D
pose hypotheses on the floor map are directly rendered into a
geometrically-structured latent space by aggregating viewing ray features.
Through a tightly coupled rendering codebook scheme, the viewing ray features
are dynamically determined at rendering-time based on their geometries (i.e.
length, incident-angle), endowing our representation with view-dependent
fine-grain variability. Our codebook scheme effectively disentangles feature
encoding from rendering, allowing the latent space rendering to run at speeds
above 10KHz. Moreover, through metric learning, our geometrically-structured
latent space is common to both pose hypotheses and query images with arbitrary
field of views. As a result, LASER achieves state-of-the-art performance on
large-scale indoor localization datasets (i.e. ZInD and Structured3D) for both
panorama and perspective image queries, while significantly outperforming
existing learning-based methods in speed.

Existing deep learning-based change detection methods try to elaborately
design complicated neural networks with powerful feature representations, but
ignore the universal domain shift induced by time-varying land cover changes,
including luminance fluctuations and season changes between pre-event and
post-event images, thereby producing sub-optimal results. In this paper, we
propose an end-to-end Supervised Domain Adaptation framework for cross-domain
Change Detection, namely SDACD, to effectively alleviate the domain shift
between bi-temporal images for better change predictions. Specifically, our
SDACD presents collaborative adaptations from both image and feature
perspectives with supervised learning. Image adaptation exploits generative
adversarial learning with cycle-consistency constraints to perform cross-domain
style transformation, effectively narrowing the domain gap in a two-side
generation fashion. As to feature adaptation, we extract domain-invariant
features to align different feature distributions in the feature space, which
could further reduce the domain gap of cross-domain images. To further improve
the performance, we combine three types of bi-temporal images for the final
change prediction, including the initial input bi-temporal images and two
generated bi-temporal images from the pre-event and post-event domains.
Extensive experiments and analyses on two benchmarks demonstrate the
effectiveness and universality of our proposed framework. Notably, our
framework pushes several representative baseline models up to new
State-Of-The-Art records, achieving 97.34% and 92.36% on the CDD and WHU
building datasets, respectively. The source code and models are publicly
available at https://github.com/Perfect-You/SDACD.

We explore a case example of networks of classical electronic oscillators
evolving towards the solution of complex optimization problems. We show that
when driven into subharmonic response, a network of such nonlinear electrical
resonators can minimize the Ising Hamiltonian on non-trivial graphs such as
antiferromagnetically coupled rewired-M{\"o}bius ladders. In this context, the
spin-up and spin-down states of the Ising machine are represented by the
oscillators' response at the even or odd driving cycles. Our experimental
setting of driven nonlinear oscillators coupled via a programmable switch
matrix leads to a unique energy minimizer when one such exists, and probes
frustration where appropriate. Theoretical modeling of the electronic
oscillators and their couplings allows us to accurately reproduce the
qualitative features of the experimental results. This suggests the promise of
this setup as a prototypical one for exploring the capabilities and limitations
of such an unconventional computing platform.

Measurement of uncertainty of predictions from machine learning methods is
important across scientific domains and applications. We present, to our
knowledge, the first such technique that quantifies the uncertainty of
predictions from a classifier and accounts for both the classifier's belief and
performance. We prove that our method provides an accurate estimate of the
probability that the outputs of two neural networks are correct by showing an
expected calibration error of less than 0.2% on a binary classifier, and less
than 3% on a semantic segmentation network with extreme class imbalance. We
empirically show that the uncertainty returned by our method is an accurate
measurement of the probability that the classifier's prediction is correct and,
therefore has broad utility in uncertainty propagation.

In this paper, we propose an explainable and interpretable diabetic
retinopathy (ExplainDR) classification model based on neural-symbolic learning.
To gain explainability, a highlevel symbolic representation should be
considered in decision making. Specifically, we introduce a human-readable
symbolic representation, which follows a taxonomy style of diabetic retinopathy
characteristics related to eye health conditions to achieve explainability. We
then include humanreadable features obtained from the symbolic representation
in the disease prediction. Experimental results on a diabetic retinopathy
classification dataset show that our proposed ExplainDR method exhibits
promising performance when compared to that from state-of-the-art methods
applied to the IDRiD dataset, while also providing interpretability and
explainability.

Semi- and weakly-supervised learning have recently attracted considerable
attention in the object detection literature since they can alleviate the cost
of annotation needed to successfully train deep learning models. State-of-art
approaches for semi-supervised learning rely on student-teacher models trained
using a multi-stage process, and considerable data augmentation. Custom
networks have been developed for the weakly-supervised setting, making it
difficult to adapt to different detectors. In this paper, a weakly
semi-supervised training method is introduced that reduces these training
challenges, yet achieves state-of-the-art performance by leveraging only a
small fraction of fully-labeled images with information in weakly-labeled
images. In particular, our generic sampling-based learning strategy produces
pseudo-ground-truth (GT) bounding box annotations in an online fashion,
eliminating the need for multi-stage training, and student-teacher network
configurations. These pseudo GT boxes are sampled from weakly-labeled images
based on the categorical score of object proposals accumulated via a score
propagation process. Empirical results on the Pascal VOC dataset, indicate that
the proposed approach improves performance by 5.0% when using VOC 2007 as
fully-labeled, and VOC 2012 as weak-labeled data. Also, with 5-10% fully
annotated images, we observed an improvement of more than 10% in mAP, showing
that a modest investment in image-level annotation, can substantially improve
detection performance.

Attacks on computer networks have increased significantly in recent days, due
in part to the availability of sophisticated tools for launching such attacks
as well as thriving underground cyber-crime economy to support it. Over the
past several years, researchers in academia and industry used machine learning
(ML) techniques to design and implement Intrusion Detection Systems (IDSes) for
computer networks. Many of these researchers used datasets collected by various
organizations to train ML models for predicting intrusions. In many of the
datasets used in such systems, data are imbalanced (i.e., not all classes have
equal amount of samples). With unbalanced data, the predictive models developed
using ML algorithms may produce unsatisfactory classifiers which would affect
accuracy in predicting intrusions. Traditionally, researchers used
over-sampling and under-sampling for balancing data in datasets to overcome
this problem. In this work, in addition to over-sampling, we also use a
synthetic data generation method, called Conditional Generative Adversarial
Network (CTGAN), to balance data and study their effect on various ML
classifiers. To the best of our knowledge, no one else has used CTGAN to
generate synthetic samples to balance intrusion detection datasets. Based on
extensive experiments using a widely used dataset NSL-KDD, we found that
training ML models on dataset balanced with synthetic samples generated by
CTGAN increased prediction accuracy by up to $8\%$, compared to training the
same ML models over unbalanced data. Our experiments also show that the
accuracy of some ML models trained over data balanced with random over-sampling
decline compared to the same ML models trained over unbalanced data.

The high thermal efficiency and reliability of the compression-ignition
engine makes it the first choice for many applications. For this to continue, a
reduction of the pollutant emissions is needed. One solution is the use of
machine learning (ML) and model predictive control (MPC) to minimize emissions
and fuel consumption, without adding substantial computational cost to the
engine controller. ML is developed in this paper for both modeling engine
performance and emissions and for imitating the behaviour of an Linear
Parameter Varying (LPV) MPC. Using a support vector machine-based linear
parameter varying model of the engine performance and emissions, a model
predictive controller is implemented for a 4.5 Cummins diesel engine. This
online optimized MPC solution offers advantages in minimizing the
\nox~emissions and fuel consumption compared to the baseline feedforward
production controller. To reduce the computational cost of this MPC, a deep
learning scheme is designed to mimic the behavior of the developed controller.
The performance in reducing NOx emissions at a constant load by the imitative
controller is similar to that of the online optimized MPC compared to the
Cummins production controller. In addition, the imitative controller requires
50 times less computation time compared to that of the online MPC optimization.

Machine learning (ML) and a nonlinear model predictive controller (NMPC) are
used in this paper to minimize the emissions and fuel consumption of a
compression ignition engine. In this work machine learning is applied in two
methods. In the first application, ML is used to identify a model for
implementation in model predictive control optimization problems. In the second
application, ML is used as a replacement of the NMPC where the ML controller
learns the optimal control action by imitating or mimicking the behavior of the
model predictive controller. In this study, a deep recurrent neural network
including long-short term memory (LSTM) layers are used to model the emissions
and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine.
This model is then used for model predictive controller implementation. Then, a
deep learning scheme is deployed to clone the behavior of the developed
controller. In the LSTM integration, a novel scheme is used by augmenting
hidden and cell states of the network in an NMPC optimization problem. The
developed LSTM-NMPC and the imitative NMPC are compared with the Cummins
calibrated Engine Control Unit (ECU) model in an experimentally validated
engine simulation platform. Results show a significant reduction in Nitrogen
Oxides (\nox) emissions and a slight decrease in the injected fuel quantity
while maintaining the same load. In addition, the imitative NMPC has a similar
performance as the NMPC but with a two orders of magnitude reduction of the
computation time.

Human-robot handover is a fundamental yet challenging task in human-robot
interaction and collaboration. Recently, remarkable progressions have been made
in human-to-robot handovers of unknown objects by using learning-based grasp
generators. However, how to responsively generate smooth motions to take an
object from a human is still an open question. Specifically, planning motions
that take human comfort into account is not a part of the human-robot handover
process in most prior works. In this paper, we propose to generate smooth
motions via an efficient model-predictive control (MPC) framework that
integrates perception and complex domain-specific constraints into the
optimization problem. We introduce a learning-based grasp reachability model to
select candidate grasps which maximize the robot's manipulability, giving it
more freedom to satisfy these constraints. Finally, we integrate a neural net
force/torque classifier that detects contact events from noisy data. We
conducted human-to-robot handover experiments on a diverse set of objects with
several users (N=4) and performed a systematic evaluation of each module. The
study shows that the users preferred our MPC approach over the baseline system
by a large margin. More results and videos are available at
https://sites.google.com/nvidia.com/mpc-for-handover.

This work aims to address the challenges in domain adaptation of 3D object
detection using infrastructure LiDARs. We design a model DASE-ProPillars that
can detect vehicles in infrastructure-based LiDARs in real-time. Our model uses
PointPillars as the baseline model with additional modules to improve the 3D
detection performance. To prove the effectiveness of our proposed modules in
DASE-ProPillars, we train and evaluate the model on two datasets, the open
source A9-Dataset and a semi-synthetic infrastructure dataset created within
the Regensburg Next project. We do several sets of experiments for each module
in the DASE-ProPillars detector that show that our model outperforms the
SE-ProPillars baseline on the real A9 test set and a semi-synthetic A9 test
set, while maintaining an inference speed of 45 Hz (22 ms). We apply domain
adaptation from the semi-synthetic A9-Dataset to the semi-synthetic dataset
from the Regensburg Next project by applying transfer learning and achieve a 3D
mAP@0.25 of 93.49% on the Car class of the target test set using 40 recall

In many machine learning tasks, input features with varying degrees of
predictive capability are acquired at varying costs. In order to optimize the
performance-cost trade-off, one would select features to observe a priori.
However, given the changing context with previous observations, the subset of
predictive features to select may change dynamically. Therefore, we face the
challenging new problem of foresight dynamic selection (FDS): finding a dynamic
and light-weight policy to decide which features to observe next, before
actually observing them, for overall performance-cost trade-offs. To tackle
FDS, this paper proposes a Bayesian learning framework of Variational Foresight
Dynamic Selection (VFDS). VFDS learns a policy that selects the next feature
subset to observe, by optimizing a variational Bayesian objective that
characterizes the trade-off between model performance and feature cost. At its
core is an implicit variational distribution on binary gates that are dependent
on previous observations, which will select the next subset of features to
observe. We apply VFDS on the Human Activity Recognition (HAR) task where the
performance-cost trade-off is critical in its practice. Extensive results
demonstrate that VFDS selects different features under changing contexts,
notably saving sensory costs while maintaining or improving the HAR accuracy.
Moreover, the features that VFDS dynamically select are shown to be
interpretable and associated with the different activity types. We will release
the code.

In recent years, with the vigorous development of the video game industry,
the proportion of gaming videos on major video websites like YouTube has
dramatically increased. However, relatively little research has been done on
the automatic quality prediction of gaming videos, especially on those that
fall in the category of "User-Generated-Content" (UGC). Since current leading
general-purpose Video Quality Assessment (VQA) models do not perform well on
this type of gaming videos, we have created a new VQA model specifically
designed to succeed on UGC gaming videos, which we call the Gaming Video
Quality Predictor (GAME-VQP). GAME-VQP successfully predicts the unique
statistical characteristics of gaming videos by drawing upon features designed
under modified natural scene statistics models, combined with gaming specific
features learned by a Convolution Neural Network. We study the performance of
GAME-VQP on a very recent large UGC gaming video database called
LIVE-YT-Gaming, and find that it both outperforms other mainstream general VQA
models as well as VQA models specifically designed for gaming videos. The new
model will be made public after paper being accepted.

Compositing-aware object search aims to find the most compatible objects for
compositing given a background image and a query bounding box. Previous works
focus on learning compatibility between the foreground object and background,
but fail to learn other important factors from large-scale data, i.e. geometry
and lighting. To move a step further, this paper proposes GALA
(Geometry-and-Lighting-Aware), a generic foreground object search method with
discriminative modeling on geometry and lighting compatibility for open-world
image compositing. Remarkably, it achieves state-of-the-art results on the CAIS
dataset and generalizes well on large-scale open-world datasets, i.e. Pixabay
and Open Images. In addition, our method can effectively handle non-box
scenarios, where users only provide background images without any input
bounding box. A web demo (see supplementary materials) is built to showcase
applications of the proposed method for compositing-aware search and automatic
location/scale prediction for the foreground object.

In this work, an all-in-one neuromorphic controller system with reduced
latency and power consumption for a robotic arm is presented. Biological muscle
movement consists of stretching and shrinking fibres via spike-commanded
signals that come from motor neurons, which in turn are connected to a central
pattern generator neural structure. In addition, biological systems are able to
respond to diverse stimuli rather fast and efficiently, and this is based on
the way information is coded within neural processes. As opposed to
human-created encoding systems, neural ones use neurons and spikes to process
the information and make weighted decisions based on a continuous learning
process. The Event-Driven Scorbot platform (ED-Scorbot) consists of a 6 Degrees
of Freedom (DoF) robotic arm whose controller implements a Spiking
Proportional-Integrative- Derivative algorithm, mimicking in this way the
previously commented biological systems. In this paper, we present an
infrastructure upgrade to the ED-Scorbot platform, replacing the controller
hardware, which was comprised of two Spartan Field Programmable Gate Arrays
(FPGAs) and a barebone computer, with an edge device, the Xilinx Zynq-7000 SoC
(System on Chip) which reduces the response time, power consumption and overall

Manipulation of deformable objects is a challenging task for a robot. It will
be problematic to use a single sensory input to track the behaviour of such
objects: vision can be subjected to occlusions, whereas tactile inputs cannot
capture the global information that is useful for the task. In this paper, we
study the problem of using vision and tactile inputs together to complete the
task of following deformable linear objects, for the first time. We create a
Reinforcement Learning agent using different sensing modalities and investigate
how its behaviour can be boosted using visual-tactile fusion, compared to using
a single sensing modality. To this end, we developed a benchmark in simulation
for manipulating the deformable linear objects using multimodal sensing inputs.
The policy of the agent uses distilled information, e.g., the pose of the
object in both visual and tactile perspectives, instead of the raw sensing
signals, so that it can be directly transferred to real environments. In this
way, we disentangle the perception system and the learned control policy. Our
extensive experiments show that the use of both vision and tactile inputs,
together with proprioception, allows the agent to complete the task in up to
92% of cases, compared to 77% when only one of the signals is given. Our
results can provide valuable insights for the future design of tactile sensors
and for deformable objects manipulation.

The purpose of this work is to review the state-of-the-art LiDAR-based 3D
object detection methods, datasets, and challenges. We describe novel data
augmentation methods, sampling strategies, activation functions, attention
mechanisms, and regularization methods. Furthermore, we list recently
introduced normalization methods, learning rate schedules and loss functions.
Moreover, we also cover advantages and limitations of 10 novel autonomous
driving datasets. We evaluate novel 3D object detectors on the KITTI, nuScenes,
and Waymo dataset and show their accuracy, speed, and robustness. Finally, we
mention the current challenges in 3D object detection in LiDAR point clouds and
list some open issues.

Adversarial robustness is one of the essential safety criteria for
guaranteeing the reliability of machine learning models. While various
adversarial robustness testing approaches were introduced in the last decade,
we note that most of them are incompatible with non-differentiable models such
as tree ensembles. Since tree ensembles are widely used in industry, this
reveals a crucial gap between adversarial robustness research and practical
applications. This paper proposes a novel whitebox adversarial robustness
testing approach for tree ensemble models. Concretely, the proposed approach
smooths the tree ensembles through temperature controlled sigmoid functions,
which enables gradient descent-based adversarial attacks. By leveraging
sampling and the log-derivative trick, the proposed approach can scale up to
testing tasks that were previously unmanageable. We compare the approach
against both random perturbations and blackbox approaches on multiple public
datasets (and corresponding models). Our results show that the proposed method
can 1) successfully reveal the adversarial vulnerability of tree ensemble
models without causing computational pressure for testing and 2) flexibly
balance the search performance and time complexity to meet various testing
criteria.

Deep multimodal learning has achieved great progress in recent years.
However, current fusion approaches are static in nature, i.e., they process and
fuse multimodal inputs with identical computation, without accounting for
diverse computational demands of different multimodal data. In this work, we
propose dynamic multimodal fusion (DynMM), a new approach that adaptively fuses
multimodal data and generates data-dependent forward paths during inference.
DynMM can reduce redundant computations for "easy" multimodal inputs (that can
be predicted correctly using only one modality or simple fusion techniques) and
retain representation power for "hard" samples by adopting all modalities and
complex fusion operations for prediction. Results on various multimodal tasks
demonstrate the efficiency and wide applicability of our approach. For
instance, DynMM can reduce the computation cost by 46.5% with a negligible
accuracy loss on CMU-MOSEI sentiment analysis. For RGB-D semantic segmentation
on NYU Depth data, DynMM achieves a +0.7% mIoU improvement with over 21%
reductions for the depth encoder when compared with strong baselines. We
believe this opens a novel direction towards dynamic multimodal network design,
with applications to a wide range of multimodal tasks.

In stochastic Nash equilibrium problems (SNEPs), it is natural for players to
be uncertain about their complex environments and have multi-dimensional
unknown parameters in their models. Among various SNEPs, this paper focuses on
locally coupled network games where the objective of each rational player is
subject to the aggregate influence of its neighbors. We propose a distributed
learning algorithm based on the proximal-point iteration and ordinary
least-square estimator, where each player repeatedly updates the local
estimates of neighboring decisions, makes its augmented best-response decisions
given the current estimated parameters, receives the realized objective values,
and learns the unknown parameters. Leveraging the Robbins-Siegmund theorem and
the law of large deviations for M-estimators, we establish the almost sure
convergence of the proposed algorithm to solutions of SNEPs when the updating
step sizes decay at a proper rate.

Automatic teeth segmentation in panoramic x-ray images is an important
research subject of the image analysis in dentistry. In this study, we propose
a post-processing stage to obtain a segmentation map in which the objects in
the image are separated, and apply this technique to tooth instance
segmentation with U-Net network. The post-processing consists of grayscale
morphological and filtering operations, which are applied to the sigmoid output
of the network before binarization. A dice overlap score of 95.4 - 0.3% is
obtained in overall teeth segmentation. The proposed post-processing stages
reduce the mean error of tooth count to 6.15%, whereas the error without
post-processing is 26.81%. The performances of both segmentation and tooth
counting are the highest in the literature, to our knowledge. Moreover, this is
achieved by using a relatively small training dataset, which consists of 105
images. Although the aim in this study is to segment tooth instances, the
presented method is applicable to similar problems in other domains, such as
separating the cell instances

We introduce a straightforward Gaussian process regression (GPR) model for
the transition structure factor of metal periodic coupled cluster singles and
doubles (CCSD) calculations. This is inspired by the method introduced by Liao
and Gr\"uneis for interpolating over the transition structure factor to obtain
a finite size correction for CCSD [J. Chem. Phys. 145, 141102 (2016)], and by
our own prior work using the transition structure factor to efficiently
converge CCSD for metals to the thermodynamic limit [Nat. Comput. Sci. 1, 801
(2021)]. In our CCSD-FS-GPR method to correct for finite size errors, we fit
the structure factor to a 1D function in the momentum transfer, $G$. We then
integrate over this function by projecting it onto a k-point mesh to obtain
comparisons with extrapolated results. Results are shown for lithium, sodium,
and the uniform electron gas.

Oxide solid electrolytes (OSEs) have the potential to achieve improved safety
and energy density for lithium-ion batteries, but their high grain-boundary
(GB) resistance is a general bottleneck. In the most well studied perovskite
OSE, Li3xLa2/3-xTiO3 (LLTO), the ionic conductivity of GBs is about three
orders of magnitude lower than that of the bulk. In contrast, the related
Li0.375Sr0.4375Ta0.75Zr0.25O3 (LSTZ0.75) perovskite exhibits low GB resistance
for reasons yet unknown. Here, we used aberration-corrected scanning
transmission electron microscopy and spectroscopy, along with an active
learning moment tensor potential, to reveal the atomic scale structure and
composition of LSTZ0.75 GBs. Vibrational electron energy loss spectroscopy is
applied for the first time to characterize the otherwise unmeasurable Li
distribution in GBs of LSTZ0.75. We found that Li depletion, which is a major
reason for the low GB ionic conductivity of LLTO, is absent for the GBs of
LSTZ0.75. Instead, the low GB resistivity of LSTZ0.75 is attributed to the
formation of a unique defective cubic perovskite interfacial structure that
contained abundant vacancies. Our study provides insights into the atomic scale
mechanisms of low GB resistivity and sheds light on possible paths for
designing OSEs with high total ionic conductivity.

Modeling statistics of image priors is useful for image super-resolution, but
little attention has been paid from the massive works of deep learning-based
methods. In this work, we propose a Bayesian image restoration framework, where
natural image statistics are modeled with the combination of smoothness and
sparsity priors. Concretely, firstly we consider an ideal image as the sum of a
smoothness component and a sparsity residual, and model real image degradation
including blurring, downscaling, and noise corruption. Then, we develop a
variational Bayesian approach to infer their posteriors. Finally, we implement
the variational approach for single image super-resolution (SISR) using deep
neural networks, and propose an unsupervised training strategy. The experiments
on three image restoration tasks, \textit{i.e.,} ideal SISR, realistic SISR,
and real-world SISR, demonstrate that our method has superior model
generalizability against varying noise levels and degradation kernels and is
effective in unsupervised SISR. The code and resulting models are released via
\url{https://zmiclab.github.io/projects.html}.

The principle of Maximal Coding Rate Reduction (MCR$^2$) has recently been
proposed as a training objective for learning discriminative low-dimensional
structures intrinsic to high-dimensional data to allow for more robust training
than standard approaches, such as cross-entropy minimization. However, despite
the advantages that have been shown for MCR$^2$ training, MCR$^2$ suffers from
a significant computational cost due to the need to evaluate and differentiate
a significant number of log-determinant terms that grows linearly with the
number of classes. By taking advantage of variational forms of spectral
functions of a matrix, we reformulate the MCR$^2$ objective to a form that can
scale significantly without compromising training accuracy. Experiments in
image classification demonstrate that our proposed formulation results in a
significant speed up over optimizing the original MCR$^2$ objective directly
and often results in higher quality learned representations. Further, our
approach may be of independent interest in other models that require
computation of log-determinant forms, such as in system identification or
normalizing flow models.

Due to the Internet of Things (IoT) proliferation, Radio Frequency (RF)
channels are increasingly congested with new kinds of devices, which carry
unique and diverse communication needs. This poses complex challenges in modern
digital communications, and calls for the development of technological
innovations that (i) optimize capacity (bitrate) in limited bandwidth
environments, (ii) integrate cooperatively with already-deployed RF protocols,
and (iii) are adaptive to the ever-changing demands in modern digital
communications. In this paper we present methods for applying deep neural
networks for spectral filling. Given an RF channel transmitting digital
messages with a pre-established modulation scheme, we automatically learn novel
modulation schemes for sending extra information, in the form of additional
messages, "around" the fixed-modulation signals (i.e., without interfering with
them). In so doing, we effectively increase channel capacity without increasing
bandwidth. We further demonstrate the ability to generate signals that closely
resemble the original modulations, such that the presence of extra messages is
undetectable to third-party listeners. We present three computational
experiments demonstrating the efficacy of our methods, and conclude by
discussing the implications of our results for modern RF applications.

Alzheimer's disease (AD) is one of the most common public health issues the
world is facing today. This disease has a high prevalence primarily in the
elderly accompanying memory loss and cognitive decline. AD detection is a
challenging task which many authors have developed numerous computerized
automatic diagnosis systems utilizing neuroimaging and other clinical data. MRI
scans provide high-intensity visible features, making these scans the most
widely used brain imaging technique. In recent years deep learning has achieved
leading success in medical image analysis. But a relatively little
investigation has been done to apply deep learning techniques for the brain MRI
classification. This paper explores the construction of several deep learning
architectures evaluated on brain MRI images and segmented images. The idea
behind segmented images investigates the influence of image segmentation step
on deep learning classification. The image processing presented a pipeline
consisting of pre-processing to enhance the MRI scans and post-processing
consisting of a segmentation method for segmenting the brain tissues. The
results show that the processed images achieved a better accuracy in the binary
classification of AD vs. CN (Cognitively Normal) across four different
architectures. ResNet architecture resulted in the highest prediction accuracy
amongst the other architectures (90.83% for the original brain images and
93.50% for the processed images).

Imaging genetic studies aim to find associations between genetic variants and
imaging quantitative traits. Traditional genome-wide association studies (GWAS)
are based on univariate statistical tests, but when multiple traits are
analyzed together they suffer from a multiple-testing problem and from not
taking into account correlations among the traits. An alternative approach to
multi-trait GWAS is to reverse the functional relation between genotypes and
traits, by fitting a multivariate regression model to predict genotypes from
multiple traits simultaneously. However, current reverse genotype prediction
approaches are mostly based on linear models. Here, we evaluated random forest
regression (RFR) as a method to predict SNPs from imaging QTs and identify
biologically relevant associations. We learned machine learning models to
predict 518,484 SNPs using 56 brain imaging QTs. We observed that genotype
regression error is a better indicator of permutation p-value significance than
genotype classification accuracy. SNPs within the known Alzheimer disease (AD)
risk gene APOE had lowest RMSE for lasso and random forest, but not ridge
regression. Moreover, random forests identified additional SNPs that were not
prioritized by the linear models but are known to be associated with
brain-related disorders. Feature selection identified well-known brain regions
associated with AD,like the hippocampus and amygdala, as important predictors
of the most significant SNPs. In summary, our results indicate that non-linear
methods like random forests may offer additional insights into
phenotype-genotype associations compared to traditional linear multi-variate
GWAS methods.

Many machine learning (ML) models are integrated within the context of a
larger system as part of a key component for decision making processes.
Concretely, predictive models are often employed in estimating the parameters
for the input values that are utilized for optimization models as isolated
processes. Traditionally, the predictive models are built first, then the model
outputs are used to generate decision values separately. However, it is often
the case that the prediction values that are trained independently of the
optimization process produce sub-optimal solutions. In this paper, we propose a
formulation for the Simultaneous Prediction and Optimization (SimPO) framework.
This framework introduces the use of a joint weighted loss of a decision-driven
predictive ML model and an optimization objective function, which is optimized
end-to-end directly through gradient-based methods.

Cross-lingual synthesis can be defined as the task of letting a speaker
generate fluent synthetic speech in another language. This is a challenging
task, and resulting speech can suffer from reduced naturalness, accented
speech, and/or loss of essential voice characteristics. Previous research shows
that many models appear to have insufficient generalization capabilities to
perform well on every of these cross-lingual aspects. To overcome these
generalization problems, we propose to apply the teacher-student paradigm to
cross-lingual synthesis. While a teacher model is commonly used to produce
teacher forced data, we propose to also use it to produce augmented data of
unseen speaker-language pairs, where the aim is to retain essential speaker
characteristics. Both sets of data are then used for student model training,
which is trained to retain the naturalness and prosodic variation present in
the teacher forced data, while learning the speaker identity from the augmented
data. Some modifications to the student model are proposed to make the
separation of teacher forced and augmented data more straightforward. Results
show that the proposed approach improves the retention of speaker
characteristics in the speech, while managing to retain high levels of
naturalness and prosodic variation.

In mathematical psychology, decision makers are modeled using the Lindbladian
equations from quantum mechanics to capture important human-centric features
such as order effects and violation of the sure thing principle. We consider
human-machine interaction involving a quantum decision maker (human) and a
controller (machine). Given a sequence of human decisions over time, how can
the controller dynamically provide input messages to adapt these decisions so
as to converge to a specific decision? We show via novel stochastic Lyapunov
arguments how the Lindbladian dynamics of the quantum decision maker can be
controlled to converge to a specific decision asymptotically. Our methodology
yields a useful mathematical framework for human-sensor decision making. The
stochastic Lyapunov results are also of independent interest as they generalize
recent results in the literature.

Artificial intelligence (AI) and machine learning (ML) are expanding in
popularity for broad applications to challenging tasks in chemistry and
materials science. Examples include the prediction of properties, the discovery
of new reaction pathways, or the design of new molecules. The machine needs to
read and write fluently in a chemical language for each of these tasks. Strings
are a common tool to represent molecular graphs, and the most popular molecular
string representation, SMILES, has powered cheminformatics since the late
1980s. However, in the context of AI and ML in chemistry, SMILES has several
shortcomings -- most pertinently, most combinations of symbols lead to invalid
results with no valid chemical interpretation. To overcome this issue, a new
language for molecules was introduced in 2020 that guarantees 100\% robustness:
SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and
enabled numerous new applications in chemistry. In this manuscript, we look to
the future and discuss molecular string representations, along with their
respective opportunities and challenges. We propose 16 concrete Future Projects
for robust molecular representations. These involve the extension toward new
chemical domains, exciting questions at the interface of AI and robust
languages and interpretability for both humans and machines. We hope that these
proposals will inspire several follow-up works exploiting the full potential of
molecular string representations for the future of AI in chemistry and
materials science.

This paper discusses how to successfully digitize large-scale historical
micro-data by augmenting optical character recognition (OCR) engines with pre-
and post-processing methods. Although OCR software has improved dramatically in
recent years due to improvements in machine learning, off-the-shelf OCR
applications still present high error rates which limit their applications for
accurate extraction of structured information. Complementing OCR with
additional methods can however dramatically increase its success rate, making
it a powerful and cost-efficient tool for economic historians. This paper
showcases these methods and explains why they are useful. We apply them against
two large balance sheet datasets and introduce quipucamayoc, a Python package
containing these methods in a unified framework.

Recent studies in neuroscience suggest that Successor Representation
(SR)-based models provide adaptation to changes in the goal locations or reward
function faster than model-free algorithms, together with lower computational
cost compared to that of model-based algorithms. However, it is not known how
such representation might help animals to manage uncertainty in their
decision-making. Existing methods for SR learning do not capture uncertainty
about the estimated SR. In order to address this issue, the paper presents a
Kalman filter-based SR framework, referred to as Adaptive Kalman
Filtering-based Successor Representation (AKF-SR). First, Kalman temporal
difference approach, which is a combination of the Kalman filter and the
temporal difference method, is used within the AKF-SR framework to cast the SR
learning procedure into a filtering problem to benefit from the uncertainty
estimation of the SR, and also decreases in memory requirement and sensitivity
to model's parameters in comparison to deep neural network-based algorithms. An
adaptive Kalman filtering approach is then applied within the proposed AKF-SR
framework in order to tune the measurement noise covariance and measurement
mapping function of Kalman filter as the most important parameters affecting
the filter's performance. Moreover, an active learning method that exploits the
estimated uncertainty of the SR to form the behaviour policy leading to more
visits to less certain values is proposed to improve the overall performance of
an agent in terms of received rewards while interacting with its environment.

Purpose: Identification of abdominal Lymph Nodes (LN) that are suspicious for
metastasis in T2 Magnetic Resonance Imaging (MRI) scans is critical for staging
of lymphoproliferative diseases. Prior work on LN detection has been limited to
specific anatomical regions of the body (pelvis, rectum) in single MR slices.
Therefore, the development of a universal approach to detect LN in full T2 MRI
volumes is highly desirable.
  Methods: In this study, a Computer Aided Detection (CAD) pipeline to
universally identify abdominal LN in volumetric T2 MRI using neural networks is
proposed. First, we trained various neural network models for detecting LN:
Faster RCNN with and without Hard Negative Example Mining (HNEM), FCOS,
FoveaBox, VFNet, and Detection Transformer (DETR). Next, we show that the
state-of-the-art (SOTA) VFNet model with Adaptive Training Sample Selection
(ATSS) outperforms Faster RCNN with HNEM. Finally, we ensembled models that
surpassed a 45% mAP threshold. We found that the VFNet model and one-stage
model ensemble can be interchangeably used in the CAD pipeline.
  Results: Experiments on 122 test T2 MRI volumes revealed that VFNet achieved
a 51.1% mAP and 78.7% recall at 4 false positives (FP) per volume, while the
one-stage model ensemble achieved a mAP of 52.3% and sensitivity of 78.7% at
4FP.
  Conclusion: Our contribution is a CAD pipeline that detects LN in T2 MRI
volumes, resulting in a sensitivity improvement of $\sim$14 points over the
current SOTA method for LN detection (sensitivity of 78.7% at 4 FP vs. 64.6% at
5 FP per volume).

The goal of active learning is to achieve the same accuracy achievable by
passive learning, while using much fewer labels. Exponential savings in terms
of label complexity have been proved in very special cases, but fundamental
lower bounds show that such improvements are impossible in general. This
suggests a need to explore alternative goals for active learning. Learning with
abstention is one such alternative. In this setting, the active learning
algorithm may abstain from prediction and incur an error that is marginally
smaller than random guessing. We develop the first computationally efficient
active learning algorithm with abstention. Our algorithm provably achieves
$\mathsf{polylog}(\frac{1}{\varepsilon})$ label complexity, without any low
noise conditions. Such performance guarantee reduces the label complexity by an
exponential factor, relative to passive learning and active learning that is
not allowed to abstain. Furthermore, our algorithm is guaranteed to only
abstain on hard examples (where the true label distribution is close to a fair
coin), a novel property we term \emph{proper abstention} that also leads to a
host of other desirable characteristics (e.g., recovering minimax guarantees in
the standard setting, and avoiding the undesirable ``noise-seeking'' behavior
often seen in active learning). We also provide novel extensions of our
algorithm that achieve \emph{constant} label complexity and deal with model
misspecification.

One of the most important problems in system identification and statistics is
how to estimate the unknown parameters of a given model. Optimization methods
and specialized procedures, such as Empirical Minimization (EM) can be used in
case the likelihood function can be computed. For situations where one can only
simulate from a parametric model, but the likelihood is difficult or impossible
to evaluate, a technique known as the Two-Stage (TS) Approach can be applied to
obtain reliable parametric estimates. Unfortunately, there is currently a lack
of theoretical justification for TS. In this paper, we propose a statistical
decision-theoretical derivation of TS, which leads to Bayesian and Minimax
estimators. We also show how to apply the TS approach on models for independent
and identically distributed samples, by computing quantiles of the data as a
first step, and using a linear function as the second stage. The proposed
method is illustrated via numerical simulations.

We introduce a new class of attacks on machine learning models. We show that
an adversary who can poison a training dataset can cause models trained on this
dataset to leak significant private details of training points belonging to
other parties. Our active inference attacks connect two independent lines of
work targeting the integrity and privacy of machine learning training data.
  Our attacks are effective across membership inference, attribute inference,
and data extraction. For example, our targeted attacks can poison <0.1% of the
training dataset to boost the performance of inference attacks by 1 to 2 orders
of magnitude. Further, an adversary who controls a significant fraction of the
training data (e.g., 50%) can launch untargeted attacks that enable 8x more
precise inference on all other users' otherwise-private data points.
  Our results cast doubts on the relevance of cryptographic privacy guarantees
in multiparty computation protocols for machine learning, if parties can
arbitrarily select their share of training data.

Extragalactic globular clusters (GCs) are important tracers of galaxy
formation and evolution. Obtaining GC catalogues from photometric data involves
several steps which will likely become too time-consuming to perform on the
large data volumes that are expected from upcoming wide-field imaging projects
such as Euclid. In this work, we explore the feasibility of various machine
learning (ML) methods to aid the search for GCs. We use archival Hubble Space
Telescope data in the F475W and F850LP bands of 141 early-type galaxies in the
Fornax and Virgo galaxy clusters. Using existing GC catalogues to label the
data, we obtain an extensive data set of 84929 sources containing 18556 GCs and
we train several ML methods both on image and tabular data containing
physically relevant features extracted from the images. We find that our
evaluated ML models are capable of producing catalogues of similar quality as
the existing ones. The best performing methods, ensemble-based models like
random forests and convolutional neural networks, recover ~ 90-94 % of GCs
while producing an acceptable amount of false detections (~ 6-8 %) - with some
falsely detected sources being identifiable as GCs that have not been labelled
as such in the used catalogues. In the magnitude range 22 < m4_g < 24.5 mag, 98
- 99 % of GCs are recovered. We even find such high performance levels when
training on Virgo and evaluating on Fornax data (and vice versa), illustrating
that the models are transferable to environments with different conditions such
as different distances than in the used training data. Additionally, we
demonstrate how interpretable methods can be used to better understand model
predictions, recovering that magnitudes, colours, and sizes are important for
identifying GCs. These are encouraging results, indicating that similar methods
can be applied for creating GC catalogues for a large number of galaxies.

We present a learning-based framework, recurrent transformer network (RTN),
to restore heavily degraded old films. Instead of performing frame-wise
restoration, our method is based on the hidden knowledge learned from adjacent
frames that contain abundant information about the occlusion, which is
beneficial to restore challenging artifacts of each frame while ensuring
temporal coherency. Moreover, contrasting the representation of the current
frame and the hidden knowledge makes it possible to infer the scratch position
in an unsupervised manner, and such defect localization generalizes well to
real-world degradations. To better resolve mixed degradation and compensate for
the flow estimation error during frame alignment, we propose to leverage more
expressive transformer blocks for spatial restoration. Experiments on both
synthetic dataset and real-world old films demonstrate the significant
superiority of the proposed RTN over existing solutions. In addition, the same
framework can effectively propagate the color from keyframes to the whole
video, ultimately yielding compelling restored films. The implementation and
model will be released at
https://github.com/raywzy/Bringing-Old-Films-Back-to-Life.

We consider the problem of sequential robotic manipulation of deformable
objects using tools. Previous works have shown that differentiable physics
simulators provide gradients to the environment state and help trajectory
optimization to converge orders of magnitude faster than model-free
reinforcement learning algorithms for deformable object manipulation. However,
such gradient-based trajectory optimization typically requires access to the
full simulator states and can only solve short-horizon, single-skill tasks due
to local optima. In this work, we propose a novel framework, named DiffSkill,
that uses a differentiable physics simulator for skill abstraction to solve
long-horizon deformable object manipulation tasks from sensory observations. In
particular, we first obtain short-horizon skills using individual tools from a
gradient-based optimizer, using the full state information in a differentiable
simulator; we then learn a neural skill abstractor from the demonstration
trajectories which takes RGBD images as input. Finally, we plan over the skills
by finding the intermediate goals and then solve long-horizon tasks. We show
the advantages of our method in a new set of sequential deformable object
manipulation tasks compared to previous reinforcement learning algorithms and
compared to the trajectory optimizer.

We investigate the efficacy of visual prompting to adapt large-scale models
in vision. Following the recent approach from prompt tuning and adversarial
reprogramming, we learn a single image perturbation such that a frozen model
prompted with this perturbation performs a new task. Through comprehensive
experiments, we demonstrate that visual prompting is particularly effective for
CLIP and robust to distribution shift, achieving performance competitive with
standard linear probes. We further analyze properties of the downstream
dataset, prompt design, and output transformation in regard to adaptation
performance. The surprising effectiveness of visual prompting provides a new
perspective on adapting pre-trained models in vision. Code is available at
http://hjbahng.github.io/visual_prompting .

We introduce MyStyle, a personalized deep generative prior trained with a few
shots of an individual. MyStyle allows to reconstruct, enhance and edit images
of a specific person, such that the output is faithful to the person's key
facial characteristics. Given a small reference set of portrait images of a
person (~100), we tune the weights of a pretrained StyleGAN face generator to
form a local, low-dimensional, personalized manifold in the latent space. We
show that this manifold constitutes a personalized region that spans latent
codes associated with diverse portrait images of the individual. Moreover, we
demonstrate that we obtain a personalized generative prior, and propose a
unified approach to apply it to various ill-posed image enhancement problems,
such as inpainting and super-resolution, as well as semantic editing. Using the
personalized generative prior we obtain outputs that exhibit high-fidelity to
the input images and are also faithful to the key facial characteristics of the
individual in the reference set. We demonstrate our method with fair-use images
of numerous widely recognizable individuals for whom we have the prior
knowledge for a qualitative evaluation of the expected outcome. We evaluate our
approach against few-shots baselines and show that our personalized prior,
quantitatively and qualitatively, outperforms state-of-the-art alternatives.

Vision-language pretrained models have achieved impressive performance on
multimodal reasoning and zero-shot recognition tasks. Many of these VL models
are pretrained on unlabeled image and caption pairs from the internet. In this
paper, we study whether the notion of primitive concepts, such as color and
shape attributes, emerges automatically from these pretrained VL models. We
propose to learn compositional derivations that map primitive concept
activations into composite concepts, a task which we demonstrate to be
straightforward given true primitive concept annotations. This compositional
derivation learning (CompDL) framework allows us to quantitively measure the
usefulness and interpretability of the learned derivations, by jointly
considering the entire set of candidate primitive concepts. Our study reveals
that state-of-the-art VL pretrained models learn primitive concepts that are
highly useful as visual descriptors, as demonstrated by their strong
performance on fine-grained visual recognition tasks, but those concepts
struggle to provide interpretable compositional derivations, which highlights
limitations of existing VL models. Code and models will be released.

3D visual perception tasks, including 3D detection and map segmentation based
on multi-camera images, are essential for autonomous driving systems. In this
work, we present a new framework termed BEVFormer, which learns unified BEV
representations with spatiotemporal transformers to support multiple autonomous
driving perception tasks. In a nutshell, BEVFormer exploits both spatial and
temporal information by interacting with spatial and temporal space through
predefined grid-shaped BEV queries. To aggregate spatial information, we design
spatial cross-attention that each BEV query extracts the spatial features from
the regions of interest across camera views. For temporal information, we
propose temporal self-attention to recurrently fuse the history BEV
information. Our approach achieves the new state-of-the-art 56.9\% in terms of
NDS metric on the nuScenes \texttt{test} set, which is 9.0 points higher than
previous best arts and on par with the performance of LiDAR-based baselines. We
further show that BEVFormer remarkably improves the accuracy of velocity
estimation and recall of objects under low visibility conditions. The code is
available at \url{https://github.com/zhiqi-li/BEVFormer}.

Continual learning describes a setting where machine learning models learn
novel concepts from continuously shifting training data, while simultaneously
avoiding degradation of knowledge on previously seen classes (a phenomenon
known as the catastrophic forgetting problem) which may disappear from the
training data for extended periods of time. Current approaches for continual
learning of a single expanding task (aka class-incremental continual learning)
require extensive rehearsal of previously seen data to avoid this degradation
of knowledge. Unfortunately, rehearsal comes at a sharp cost to memory and
computation, and it may also violate data-privacy. Instead, we explore
combining knowledge distillation and parameter regularization in new ways to
achieve strong continual learning performance without rehearsal. Specifically,
we take a deep dive into common continual learning techniques: prediction
distillation, feature distillation, L2 parameter regularization, and EWC
parameter regularization. We first disprove the common assumption that
parameter regularization techniques fail for rehearsal-free continual learning
of a single, expanding task. Next, we explore how to leverage knowledge from a
pre-trained model in rehearsal-free continual learning and find that vanilla L2
parameter regularization outperforms EWC parameter regularization and feature
distillation. We then highlight the impact of the rehearsal-free continual
learning settings with a classifier expansion benchmark, showing that a
strategy based on our findings combined with a positive/negative label
balancing heuristic can close the performance gap between the upper bound and
the existing strategies by up to roughly 50%. Finally, we show that a simple
method consisting of pre-training, L2 regularization, and prediction
distillation can even outperform rehearsal-based methods on the common
CIFAR-100 benchmark.

Recent advances like StyleGAN have promoted the growth of controllable facial
editing. To address its core challenge of attribute decoupling in a single
latent space, attempts have been made to adopt dual-space GAN for better
disentanglement of style and content representations. Nonetheless, these
methods are still incompetent to obtain plausible editing results with high
controllability, especially for complicated attributes. In this study, we
highlight the importance of interaction in a dual-space GAN for more
controllable editing. We propose TransEditor, a novel Transformer-based
framework to enhance such interaction. Besides, we develop a new dual-space
editing and inversion strategy to provide additional editing flexibility.
Extensive experiments demonstrate the superiority of the proposed framework in
image quality and editing capability, suggesting the effectiveness of
TransEditor for highly controllable facial editing.

Quantum computing is among the most promising emerging techniques to solve
problems that are computationally intractable on classical hardware. A large
body of existing works focus on using variational quantum algorithms on the
gate level for machine learning tasks, such as the variational quantum circuit
(VQC). However, VQC has limited flexibility and expressibility due to limited
number of parameters, e.g. only one parameter can be trained in one rotation
gate. On the other hand, we observe that quantum pulses are lower than quantum
gates in the stack of quantum computing and offers more control parameters.
Inspired by the promising performance of VQC, in this paper we propose
variational quantum pulses (VQP), a novel paradigm to directly train quantum
pulses for learning tasks. The proposed method manipulates variational quantum
pulses by pulling and pushing the amplitudes of pulses in an optimization
framework. Similar to variational quantum algorithms, our framework to train
pulses maintains the robustness to noise on Noisy Intermediate-Scale Quantum
(NISQ) computers. In an example task of binary classification, VQP learning
achieves up to 11% and 9% higher accuracy compared with VQC learning on the
qiskit noise simulators (with noise model from real machine) and ibmq-jarkata,
respectively, demonstrating its effectiveness and feasibility. Stability for
VQP to obtain reliable results has also been verified in the presence of noise.

The field of machine learning has achieved striking progress in recent years,
witnessing breakthrough results on language modelling, protein folding and
nitpickingly fine-grained dog breed classification. Some even succeeded at
playing computer games and board games, a feat both of engineering and of
setting their employers' expectations. The central contribution of this work is
to carefully examine whether this progress, and technology more broadly, can be
expected to continue indefinitely. Through a rigorous application of
statistical theory and failure to extrapolate beyond the training data, we
answer firmly in the negative and provide details: technology will peak at 3:07
am (BST) on 20th July, 2032. We then explore the implications of this finding,
discovering that individuals awake at this ungodly hour with access to a
sufficiently powerful computer possess an opportunity for myriad forms of
long-term linguistic 'lock in'. All we need is a large (>> 1W) data centre to
seize this pivotal moment. By setting our analogue alarm clocks, we propose a
tractable algorithm to ensure that, for the future of humanity, the British
spelling of colour becomes the default spelling across more than 80% of the
global word processing software market.

Since facial actions such as lip movements contain significant information
about speech content, it is not surprising that audio-visual speech enhancement
methods are more accurate than their audio-only counterparts. Yet,
state-of-the-art approaches still struggle to generate clean, realistic speech
without noise artifacts and unnatural distortions in challenging acoustic
environments. In this paper, we propose a novel audio-visual speech enhancement
framework for high-fidelity telecommunications in AR/VR. Our approach leverages
audio-visual speech cues to generate the codes of a neural speech codec,
enabling efficient synthesis of clean, realistic speech from noisy signals.
Given the importance of speaker-specific cues in speech, we focus on developing
personalized models that work well for individual speakers. We demonstrate the
efficacy of our approach on a new audio-visual speech dataset collected in an
unconstrained, large vocabulary setting, as well as existing audio-visual
datasets, outperforming speech enhancement baselines on both quantitative
metrics and human evaluation studies. Please see the supplemental video for
qualitative results at
https://github.com/facebookresearch/facestar/releases/download/paper_materials/video.mp4.

Recent research explosion on Neural Radiance Field (NeRF) shows the
encouraging potential to represent complex scenes with neural networks. One
major drawback of NeRF is its prohibitive inference time: Rendering a single
pixel requires querying the NeRF network hundreds of times. To resolve it,
existing efforts mainly attempt to reduce the number of required sampled
points. However, the problem of iterative sampling still exists. On the other
hand, Neural Light Field (NeLF) presents a more straightforward representation
over NeRF in novel view synthesis -- the rendering of a pixel amounts to one
single forward pass without ray-marching. In this work, we present a deep
residual MLP network (88 layers) to effectively learn the light field. We show
the key to successfully learning such a deep NeLF network is to have sufficient
data, for which we transfer the knowledge from a pre-trained NeRF model via
data distillation. Extensive experiments on both synthetic and real-world
scenes show the merits of our method over other counterpart algorithms. On the
synthetic scenes, we achieve 26-35x FLOPs reduction (per camera ray) and 28-31x
runtime speedup, meanwhile delivering significantly better (1.4-2.8 dB average
PSNR improvement) rendering quality than NeRF without any customized
parallelism requirement.

Our work focuses on addressing sample deficiency from low-density regions of
data manifold in common image datasets. We leverage diffusion process based
generative models to synthesize novel images from low-density regions. We
observe that uniform sampling from diffusion models predominantly samples from
high-density regions of the data manifold. Therefore, we modify the sampling
process to guide it towards low-density regions while simultaneously
maintaining the fidelity of synthetic data. We rigorously demonstrate that our
process successfully generates novel high fidelity samples from low-density
regions. We further examine generated samples and show that the model does not
memorize low-density data and indeed learns to generate novel samples from
low-density regions.

Salient Object Ranking (SOR) involves ranking the degree of saliency of
multiple salient objects in an input image. Most recently, a method is proposed
for ranking salient objects in an input video based on a predicted fixation
map. It relies solely on the density of the fixations within the salient
objects to infer their saliency ranks, which is incompatible with human
perception of saliency ranking. In this work, we propose to explicitly learn
the spatial and temporal relations between different salient objects to produce
the saliency ranks. To this end, we propose an end-to-end method for video
salient object ranking (VSOR), with two novel modules: an intra-frame adaptive
relation (IAR) module to learn the spatial relation among the salient objects
in the same frame locally and globally, and an inter-frame dynamic relation
(IDR) module to model the temporal relation of saliency across different
frames. In addition, to address the limited video types (just sports and
movies) and scene diversity in the existing VSOR dataset, we propose a new
dataset that covers different video types and diverse scenes on a large scale.
Experimental results demonstrate that our method outperforms state-of-the-art
methods in relevant fields. We will make the source code and our proposed
dataset available.

We propose Continuous Scene Representations (CSR), a scene representation
constructed by an embodied agent navigating within a space, where objects and
their relationships are modeled by continuous valued embeddings. Our method
captures feature relationships between objects, composes them into a graph
structure on-the-fly, and situates an embodied agent within the representation.
Our key insight is to embed pair-wise relationships between objects in a latent
space. This allows for a richer representation compared to discrete relations
(e.g., [support], [next-to]) commonly used for building scene representations.
CSR can track objects as the agent moves in a scene, update the representation
accordingly, and detect changes in room configurations. Using CSR, we
outperform state-of-the-art approaches for the challenging downstream task of
visual room rearrangement, without any task specific training. Moreover, we
show the learned embeddings capture salient spatial details of the scene and
show applicability to real world data. A summery video and code is available at
https://prior.allenai.org/projects/csr.

Conventional 3D object detection approaches concentrate on bounding boxes
representation learning with several parameters, i.e., localization, dimension,
and orientation. Despite its popularity and universality, such a
straightforward paradigm is sensitive to slight numerical deviations,
especially in localization. By exploiting the property that point clouds are
naturally captured on the surface of objects along with accurate location and
intensity information, we introduce a new perspective that views bounding box
regression as an implicit function. This leads to our proposed framework,
termed Implicit Detection or ImpDet, which leverages implicit field learning
for 3D object detection. Our ImpDet assigns specific values to points in
different local 3D spaces, thereby high-quality boundaries can be generated by
classifying points inside or outside the boundary. To solve the problem of
sparsity on the object surface, we further present a simple yet efficient
virtual sampling strategy to not only fill the empty region, but also learn
rich semantic features to help refine the boundaries. Extensive experimental
results on KITTI and Waymo benchmarks demonstrate the effectiveness and
robustness of unifying implicit fields into object detection.

Citations play an important role in researchers' careers as a key factor in
evaluation of scientific impact. Many anecdotes advice authors to exploit this
fact and cite prospective reviewers to try obtaining a more positive evaluation
for their submission. In this work, we investigate if such a citation bias
actually exists: Does the citation of a reviewer's own work in a submission
cause them to be positively biased towards the submission? In conjunction with
the review process of two flagship conferences in machine learning and
algorithmic economics, we execute an observational study to test for citation
bias in peer review. In our analysis, we carefully account for various
confounding factors such as paper quality and reviewer expertise, and apply
different modeling techniques to alleviate concerns regarding the model
mismatch. Overall, our analysis involves 1,314 papers and 1,717 reviewers and
detects citation bias in both venues we consider. In terms of the effect size,
by citing a reviewer's work, a submission has a non-trivial chance of getting a
higher score from the reviewer: an expected increase in the score is
approximately 0.23 on a 5-point Likert item. For reference, a one-point
increase of a score by a single reviewer improves the position of a submission
by 11% on average.

We present a method that can recognize new objects and estimate their 3D pose
in RGB images even under partial occlusions. Our method requires neither a
training phase on these objects nor real images depicting them, only their CAD
models. It relies on a small set of training objects to learn local object
representations, which allow us to locally match the input image to a set of
"templates", rendered images of the CAD models for the new objects. In contrast
with the state-of-the-art methods, the new objects on which our method is
applied can be very different from the training objects. As a result, we are
the first to show generalization without retraining on the LINEMOD and
Occlusion-LINEMOD datasets. Our analysis of the failure modes of previous
template-based approaches further confirms the benefits of local features for
template matching. We outperform the state-of-the-art template matching methods
on the LINEMOD, Occlusion-LINEMOD and T-LESS datasets. Our source code and data
are publicly available at https://github.com/nv-nguyen/template-pose

We introduce the notion of performative power, which measures the ability of
a firm operating an algorithmic system, such as a digital content
recommendation platform, to cause change in a population of participants. We
relate performative power to the economic study of competition in digital
economies. Traditional economic concepts struggle with identifying
anti-competitive patterns in digital platforms not least due to the complexity
of market definition. In contrast, performative power is a causal notion that
is identifiable with minimal knowledge of the market, its internals,
participants, products, or prices.
  Low performative power implies that a firm can do no better than to optimize
their objective on current data. In contrast, firms of high performative power
stand to benefit from steering the population towards more profitable behavior.
We confirm in a simple theoretical model that monopolies maximize performative
power. A firm's ability to personalize increases performative power, while
competition and outside options decrease performative power. On the empirical
side, we propose an observational causal design to identify performative power
from discontinuities in how digital platforms display content. This allows to
repurpose causal effects from various studies about digital platforms as lower
bounds on performative power. Finally, we speculate about the role that
performative power might play in competition policy and antitrust enforcement
in digital marketplaces.

Speaker identification systems in a real-world scenario are tasked to
identify a speaker amongst a set of enrolled speakers given just a few samples
for each enrolled speaker. This paper demonstrates the effectiveness of
meta-learning and relation networks for this use case. We propose improved
relation networks for speaker verification and few-shot (unseen) speaker
identification. The use of relation networks facilitates joint training of the
frontend speaker encoder and the backend model. Inspired by the use of
prototypical networks in speaker verification and to increase the
discriminability of the speaker embeddings, we train the model to classify
samples in the current episode amongst all speakers present in the training
set. Furthermore, we propose a new training regime for faster model convergence
by extracting more information from a given meta-learning episode with
negligible extra computation. We evaluate the proposed techniques on VoxCeleb,
SITW and VCTK datasets on the tasks of speaker verification and unseen speaker
identification. The proposed approach outperforms the existing approaches
consistently on both tasks.

Numerous analyses of reading time (RT) data have been implemented -- all in
an effort to better understand the cognitive processes driving reading
comprehension. However, data measured on words at the end of a sentence -- or
even at the end of a clause -- is often omitted due to the confounding factors
introduced by so-called "wrap-up effects," which manifests as a skewed
distribution of RTs for these words. Consequently, the understanding of the
cognitive processes that might be involved in these wrap-up effects is limited.
In this work, we attempt to learn more about these processes by examining the
relationship between wrap-up effects and information-theoretic quantities, such
as word and context surprisals. We find that the distribution of information in
prior contexts is often predictive of sentence- and clause-final RTs (while not
of sentence-medial RTs). This lends support to several prior hypotheses about
the processes involved in wrap-up effects.

A substantial body of empirical work documents the lack of robustness in deep
learning models to adversarial examples. Recent theoretical work proved that
adversarial examples are ubiquitous in two-layers networks with sub-exponential
width and ReLU or smooth activations, and multi-layer ReLU networks with
sub-exponential width. We present a result of the same type, with no
restriction on width and for general locally Lipschitz continuous activations.
  More precisely, given a neural network $f(\,\cdot\,;{\boldsymbol \theta})$
with random weights ${\boldsymbol \theta}$, and feature vector ${\boldsymbol
x}$, we show that an adversarial example ${\boldsymbol x}'$ can be found with
high probability along the direction of the gradient $\nabla_{{\boldsymbol
x}}f({\boldsymbol x};{\boldsymbol \theta})$. Our proof is based on a Gaussian
conditioning technique. Instead of proving that $f$ is approximately linear in
a neighborhood of ${\boldsymbol x}$, we characterize the joint distribution of
$f({\boldsymbol x};{\boldsymbol \theta})$ and $f({\boldsymbol x}';{\boldsymbol
\theta})$ for ${\boldsymbol x}' = {\boldsymbol x}-s({\boldsymbol
x})\nabla_{{\boldsymbol x}}f({\boldsymbol x};{\boldsymbol \theta})$.

Self-supervised learning (SSL) methods aim to learn view-invariant
representations by maximizing the similarity between the features extracted
from different crops of the same image regardless of cropping size and content.
In essence, this strategy ignores the fact that two crops may truly contain
different image information, e.g., background and small objects, and thus tends
to restrain the diversity of the learned representations. In this work, we
address this issue by introducing a new self-supervised learning strategy,
LoGo, that explicitly reasons about Local and Global crops. To achieve view
invariance, LoGo encourages similarity between global crops from the same
image, as well as between a global and a local crop. However, to correctly
encode the fact that the content of smaller crops may differ entirely, LoGo
promotes two local crops to have dissimilar representations, while being close
to global crops. Our LoGo strategy can easily be applied to existing SSL
methods. Our extensive experiments on a variety of datasets and using different
self-supervised learning frameworks validate its superiority over existing
approaches. Noticeably, we achieve better results than supervised models on
transfer learning when using only 1/10 of the data.

Capabilities of future colliders are usually discussed assuming specific
hypothetical new physics. We consider the opposite possibility: that no new
physics is accessible, and we want to learn if the unnatural Standard Model is
part of a vast landscape. We argue that a main step in this direction would be
establishing the possible instability scale of the Higgs potential. This
primarily needs reducing the uncertainty on the strong coupling and on the top
quark mass. We show that the top quark mass can be measured well enough via a
$t \bar t$ threshold scan with low $10^{33}\,{\rm cm}^{-2}{\rm sec}^{-1}$
luminosity, that seems achievable at a `small' $e^+ e^-$ collider in the LEP
tunnel, or at a muon collider demonstrator.

Users use Issue Tracking Systems to keep track and manage issue reports in
their repositories. An issue is a rich source of software information that
contains different reports including a problem, a request for new features, or
merely a question about the software product. As the number of these issues
increases, it becomes harder to manage them manually. Thus, automatic
approaches are proposed to help facilitate the management of issue reports.
  This paper describes CatIss, an automatic CATegorizer of ISSue reports which
is built upon the Transformer-based pre-trained RoBERTa model. CatIss
classifies issue reports into three main categories of Bug reports,
Enhancement/feature requests, and Questions. First, the datasets provided for
the NLBSE tool competition are cleaned and preprocessed. Then, the pre-trained
RoBERTa model is fine-tuned on the preprocessed dataset. Evaluating CatIss on
about 80 thousand issue reports from GitHub, indicates that it performs very
well surpassing the competition baseline, TicketTagger, and achieving 87.2%
F1-score (micro average). Additionally, as CatIss is trained on a wide set of
repositories, it is a generic prediction model, hence applicable for any unseen
software project or projects with little historical data. Scripts for cleaning
the datasets, training CatIss, and evaluating the model are publicly available.

We initiate a study of supervised learning from many independent sequences
("trajectories") of non-independent covariates, reflecting tasks in sequence
modeling, control, and reinforcement learning. Conceptually, our
multi-trajectory setup sits between two traditional settings in statistical
learning theory: learning from independent examples and learning from a single
auto-correlated sequence. Our conditions for efficient learning generalize the
former setting--trajectories must be non-degenerate in ways that extend
standard requirements for independent examples. They do not require that
trajectories be ergodic, long, nor strictly stable.
  For linear least-squares regression, given $n$-dimensional examples produced
by $m$ trajectories, each of length $T$, we observe a notable change in
statistical efficiency as the number of trajectories increases from a few
(namely $m \lesssim n$) to many (namely $m \gtrsim n$). Specifically, we
establish that the worst-case error rate this problem is $\Theta(n / m T)$
whenever $m \gtrsim n$. Meanwhile, when $m \lesssim n$, we establish a (sharp)
lower bound of $\Omega(n^2 / m^2 T)$ on the worst-case error rate, realized by
a simple, marginally unstable linear dynamical system. A key upshot is that, in
domains where trajectories regularly reset, the error rate eventually behaves
as if all of the examples were independent altogether, drawn from their
marginals. As a corollary of our analysis, we also improve guarantees for the
linear system identification problem.

Recently, leveraging BERT pre-training to improve the phoneme encoder in text
to speech (TTS) has drawn increasing attention. However, the works apply
pre-training with character-based units to enhance the TTS phoneme encoder,
which is inconsistent with the TTS fine-tuning that takes phonemes as input.
Pre-training only with phonemes as input can alleviate the input mismatch but
lack the ability to model rich representations and semantic information due to
limited phoneme vocabulary. In this paper, we propose MixedPhoneme BERT, a
novel variant of the BERT model that uses mixed phoneme and sup-phoneme
representations to enhance the learning capability. Specifically, we merge the
adjacent phonemes into sup-phonemes and combine the phoneme sequence and the
merged sup-phoneme sequence as the model input, which can enhance the model
capacity to learn rich contextual representations. Experiment results
demonstrate that our proposed Mixed-Phoneme BERT significantly improves the TTS
performance with 0.30 CMOS gain compared with the FastSpeech 2 baseline. The
Mixed-Phoneme BERT achieves 3x inference speedup and similar voice quality to
the previous TTS pre-trained model PnG BERT

Recent neural network-based language models have benefited greatly from
scaling up the size of training datasets and the number of parameters in the
models themselves. Scaling can be complicated due to various factors including
the need to distribute computation on supercomputer clusters (e.g., TPUs),
prevent bottlenecks when infeeding data, and ensure reproducible results. In
this work, we present two software libraries that ease these issues:
$\texttt{t5x}$ simplifies the process of building and training large language
models at scale while maintaining ease of use, and $\texttt{seqio}$ provides a
task-based API for simple creation of fast and reproducible training data and
evaluation pipelines. These open-source libraries have been used to train
models with hundreds of billions of parameters on datasets with multiple
terabytes of training data.
  Along with the libraries, we release configurations and instructions for
T5-like encoder-decoder models as well as GPT-like decoder-only architectures.
  $\texttt{t5x}$ and $\texttt{seqio}$ are open source and available at
https://github.com/google-research/t5x and https://github.com/google/seqio,

In recent years, neural implicit representations have made remarkable
progress in modeling of 3D shapes with arbitrary topology. In this work, we
address two key limitations of such representations, in failing to capture
local 3D geometric fine details, and to learn from and generalize to shapes
with unseen 3D transformations. To this end, we introduce a novel family of
graph implicit functions with equivariant layers that facilitates modeling fine
local details and guaranteed robustness to various groups of geometric
transformations, through local $k$-NN graph embeddings with sparse point set
observations at multiple resolutions. Our method improves over the existing
rotation-equivariant implicit function from 0.69 to 0.89 (IoU) on the ShapeNet
reconstruction task. We also show that our equivariant implicit function can be
extended to other types of similarity transformations and generalizes to unseen
translations and scaling.

Current quantum devices suffer imperfections as a result of fabrication, as
well as noise and dissipation as a result of coupling to their immediate
environments. Because of this, it is often difficult to obtain accurate models
of their dynamics from first principles. An alternative is to extract such
models from time-series measurements of their behavior. Here, we formulate this
system-identification problem as a polynomial optimization problem. Recent
advances in optimization have provided globally convergent solvers for this
class of problems, which using our formulation prove estimates of the Kraus map
or the Lindblad equation. We include an overview of the state-of-the-art
algorithms, bounds, and convergence rates, and illustrate the use of this
approach to modeling open quantum systems.

In recent years, hypergraph learning has attracted great attention due to its
capacity in representing complex and high-order relationships. However, current
neural network approaches designed for hypergraphs are mostly shallow, thus
limiting their ability to extract information from high-order neighbors. In
this paper, we show both theoretically and empirically, that the performance of
hypergraph neural networks does not improve as the number of layers increases,
which is known as the over-smoothing problem. To avoid this issue, we develop a
new deep hypergraph convolutional network called Deep-HGCN, which can maintain
the heterogeneity of node representation in deep layers. Specifically, we prove
that a $k$-layer Deep-HGCN simulates a polynomial filter of order $k$ with
arbitrary coefficients, which can relieve the problem of over-smoothing.
Experimental results on various datasets demonstrate the superior performance
of the proposed model compared to the state-of-the-art hypergraph learning

Automated facial age estimation has diverse real-world applications in
multimedia analysis, e.g., video surveillance, and human-computer interaction.
However, due to the randomness and ambiguity of the aging process, age
assessment is challenging. Most research work over the topic regards the task
as one of age regression, classification, and ranking problems, and cannot well
leverage age distribution in representing labels with age ambiguity. In this
work, we propose a simple yet effective loss function for robust facial age
estimation via distribution learning, i.e., adaptive mean-residue loss, in
which, the mean loss penalizes the difference between the estimated age
distribution's mean and the ground-truth age, whereas the residue loss
penalizes the entropy of age probability out of dynamic top-K in the
distribution. Experimental results in the datasets FG-NET and CLAP2016 have
validated the effectiveness of the proposed loss. Our code is available at
https://github.com/jacobzhaoziyuan/AMR-Loss.

We develop a deep convolutional neural network (DCNN) based framework for
model-free prediction of the occurrence of extreme events both in time ("when")
and in space ("where") in nonlinear physical systems of spatial dimension two.
The measurements or data are a set of two-dimensional snapshots or images. For
a desired time horizon of prediction, a proper labeling scheme can be
designated to enable successful training of the DCNN and subsequent prediction
of extreme events in time. Given that an extreme event has been predicted to
occur within the time horizon, a space-based labeling scheme can be applied to
predict, within certain resolution, the location at which the event will occur.
We use synthetic data from the 2D complex Ginzburg-Landau equation and
empirical wind speed data of the North Atlantic ocean to demonstrate and
validate our machine-learning based prediction framework. The trade-offs among
the prediction horizon, spatial resolution, and accuracy are illustrated, and
the detrimental effect of spatially biased occurrence of extreme event on
prediction accuracy is discussed. The deep learning framework is viable for
predicting extreme events in the real world.

The purpose of this paper is to explore the use of deep learning for the
solution of the nonlinear filtering problem. This is achieved by solving the
Zakai equation by a deep splitting method, previously developed for approximate
solution of (stochastic) partial differential equations. This is combined with
an energy-based model for the approximation of functions by a deep neural
network. This results in a computationally fast filter that takes observations
as input and that does not require re-training when new observations are
received. The method is tested on three examples, one linear Gaussian and two
nonlinear. The method shows promising performance when benchmarked against the
Kalman filter and the bootstrap particle filter.

Speech enhancement (SE) performance has improved considerably owing to the
use of deep learning models as a base function. Herein, we propose a perceptual
contrast stretching (PCS) approach to further improve SE performance. The PCS
is derived based on the critical band importance function and is applied to
modify the targets of the SE model. Specifically, the contrast of target
features is stretched based on perceptual importance, thereby improving the
overall SE performance. Compared with post-processing-based implementations,
incorporating PCS into the training phase preserves performance and reduces
online computation. Notably, PCS can be combined with different SE model
architectures and training criteria. Furthermore, PCS does not affect the
causality or convergence of SE model training. Experimental results on the
VoiceBank-DEMAND dataset show that the proposed method can achieve
state-of-the-art performance on both causal (PESQ score = 3.07) and noncausal
(PESQ score = 3.35) SE tasks.

In transportation networks, users typically choose routes in a decentralized
and self-interested manner to minimize their individual travel costs, which, in
practice, often results in inefficient overall outcomes for society. As a
result, there has been a growing interest in designing road tolling schemes to
cope with these efficiency losses and steer users toward a system-efficient
traffic pattern. However, the efficacy of road tolling schemes often relies on
having access to complete information on users' trip attributes, such as their
origin-destination (O-D) travel information and their values of time, which may
not be available in practice.
  Motivated by this practical consideration, we propose an online learning
approach to set tolls in a traffic network to drive heterogeneous users with
different values of time toward a system-efficient traffic pattern. In
particular, we develop a simple yet effective algorithm that adjusts tolls at
each time period solely based on the observed aggregate flows on the roads of
the network without relying on any additional trip attributes of users, thereby
preserving user privacy. In the setting where the O-D pairs and values of time
of users are drawn i.i.d. at each period, we show that our approach obtains an
expected regret and road capacity violation of $O(\sqrt{T})$, where $T$ is the
number of periods over which tolls are updated. Our regret guarantee is
relative to an offline oracle that has complete information on users' trip
attributes. We further establish a $\Omega(\sqrt{T})$ lower bound on the regret
of any algorithm, which establishes that our algorithm is optimal up to
constants. Finally, we demonstrate the superior performance of our approach
relative to several benchmarks on a real-world transportation network, thereby
highlighting its practical applicability.

The best performing learning algorithms devised for event cameras work by
first converting events into dense representations that are then processed
using standard CNNs. However, these steps discard both the sparsity and high
temporal resolution of events, leading to high computational burden and
latency. For this reason, recent works have adopted Graph Neural Networks
(GNNs), which process events as ``static" spatio-temporal graphs, which are
inherently "sparse". We take this trend one step further by introducing
Asynchronous, Event-based Graph Neural Networks (AEGNNs), a novel
event-processing paradigm that generalizes standard GNNs to process events as
``evolving" spatio-temporal graphs. AEGNNs follow efficient update rules that
restrict recomputation of network activations only to the nodes affected by
each new event, thereby significantly reducing both computation and latency for
event-by-event processing. AEGNNs are easily trained on synchronous inputs and
can be converted to efficient, "asynchronous" networks at test time. We
thoroughly validate our method on object classification and detection tasks,
where we show an up to a 11-fold reduction in computational complexity (FLOPs),
with similar or even better performance than state-of-the-art asynchronous
methods. This reduction in computation directly translates to an 8-fold
reduction in computational latency when compared to standard GNNs, which opens
the door to low-latency event-based processing.

We investigate the use of prior knowledge of human and animal movement to
learn reusable locomotion skills for real legged robots. Our approach builds
upon previous work on imitating human or dog Motion Capture (MoCap) data to
learn a movement skill module. Once learned, this skill module can be reused
for complex downstream tasks. Importantly, due to the prior imposed by the
MoCap data, our approach does not require extensive reward engineering to
produce sensible and natural looking behavior at the time of reuse. This makes
it easy to create well-regularized, task-oriented controllers that are suitable
for deployment on real robots. We demonstrate how our skill module can be used
for imitation, and train controllable walking and ball dribbling policies for
both the ANYmal quadruped and OP3 humanoid. These policies are then deployed on
hardware via zero-shot simulation-to-reality transfer. Accompanying videos are
available at https://bit.ly/robot-npmp.

Solving high-dimensional partial differential equations (PDEs) is a major
challenge in scientific computing. We develop a new numerical method for
solving elliptic-type PDEs by adapting the Q-learning algorithm in
reinforcement learning. Our "Q-PDE" algorithm is mesh-free and therefore has
the potential to overcome the curse of dimensionality. Using a neural tangent
kernel (NTK) approach, we prove that the neural network approximator for the
PDE solution, trained with the Q-PDE algorithm, converges to the trajectory of
an infinite-dimensional ordinary differential equation (ODE) as the number of
hidden units $\rightarrow \infty$. For monotone PDE (i.e. those given by
monotone operators, which may be nonlinear), despite the lack of a spectral gap
in the NTK, we then prove that the limit neural network, which satisfies the
infinite-dimensional ODE, converges in $L^2$ to the PDE solution as the
training time $\rightarrow \infty$. More generally, we can prove that any fixed
point of the wide-network limit for the Q-PDE algorithm is a solution of the
PDE (not necessarily under the monotone condition). The numerical performance
of the Q-PDE algorithm is studied for several elliptic PDEs.

Most studies in cross-device federated learning focus on small models, due to
the server-client communication and on-device computation bottlenecks. In this
work, we leverage various techniques for mitigating these bottlenecks to train
larger language models in cross-device federated learning. With systematic
applications of partial model training, quantization, efficient transfer
learning, and communication-efficient optimizers, we are able to train a $21$M
parameter Transformer and $20.2$M parameter Conformer that achieve the same or
better perplexity as that of a similarly sized LSTM with $\sim10\times$ smaller
client-to-server communication cost and $11\%$ lower perplexity than smaller
LSTMs commonly studied in literature.

Clicks on rankings suffer from position-bias: generally items on lower ranks
are less likely to be examined - and thus clicked - by users, in spite of their
actual preferences between items. The prevalent approach to unbiased
click-based learning-to-rank (LTR) is based on counterfactual
inverse-propensity-scoring (IPS) estimation. In contrast with general
reinforcement learning, counterfactual doubly-robust (DR) estimation has not
been applied to click-based LTR in previous literature. In this paper, we
introduce a novel DR estimator that is the first DR approach specifically
designed for position-bias. The difficulty with position-bias is that the
treatment - user examination - is not directly observable in click data. As a
solution, our estimator uses the expected treatment per rank, instead of the
actual treatment that existing DR estimators use. Our novel DR estimator has
more robust unbiasedness conditions than the existing IPS approach, and in
addition, provides enormous decreases in variance: our experimental results
indicate it requires several orders of magnitude fewer datapoints to converge
at optimal performance. For the unbiased LTR field, our DR estimator
contributes both increases in state-of-the-art performance and the most robust
theoretical guarantees of all known LTR estimators.

This paper studies a novel pre-training technique with unpaired speech data,
Speech2C, for encoder-decoder based automatic speech recognition (ASR). Within
a multi-task learning framework, we introduce two pre-training tasks for the
encoder-decoder network using acoustic units, i.e., pseudo codes, derived from
an offline clustering model. One is to predict the pseudo codes via masked
language modeling in encoder output, like HuBERT model, while the other lets
the decoder learn to reconstruct pseudo codes autoregressively instead of
generating textual scripts. In this way, the decoder learns to reconstruct
original speech information with codes before learning to generate correct
text. Comprehensive experiments on the LibriSpeech corpus show that the
proposed Speech2C can relatively reduce the word error rate (WER) by 19.2% over
the method without decoder pre-training, and also outperforms significantly the
state-of-the-art wav2vec 2.0 and HuBERT on fine-tuning subsets of 10h and 100h.
We release our code and model at
https://github.com/microsoft/SpeechT5/tree/main/Speech2C.

Research related to automatically detecting Alzheimer's disease (AD) is
important, given the high prevalence of AD and the high cost of traditional
methods. Since AD significantly affects the acoustics of spontaneous speech,
speech processing and machine learning (ML) provide promising techniques for
reliably detecting AD. However, speech audio may be affected by different types
of background noise and it is important to understand how the noise influences
the accuracy of ML models detecting AD from speech. In this paper, we study the
effect of fifteen types of environmental noise from five different categories
on the performance of four ML models trained with three types of acoustic
representations. We perform a thorough analysis showing how ML models and
acoustic features are affected by different types of acoustic noise. We show
that acoustic noise is not necessarily harmful - certain types of noise are
beneficial for AD detection models and help increasing accuracy by up to 4.8\%.
We provide recommendations on how to utilize acoustic noise in order to achieve
the best performance results with the ML models deployed in real world.

In this paper, we develop a socially cooperative optimal control framework to
address the motion planning problem for connected and automated vehicles (CAVs)
in mixed traffic using social value orientation (SVO) and a potential game
approach. In the proposed framework, we formulate the interaction between a CAV
and a human-driven vehicle (HDV) as a simultaneous game where each vehicle
minimizes a weighted sum of its egoistic objective and a cooperative objective.
The SVO angles are used to quantify preferences of the vehicles toward the
egoistic and cooperative objectives. Using the potential game approach, we
propose a single objective function for the optimal control problem whose
weighting factors are chosen based on the SVOs of the vehicles. We prove that a
Nash equilibrium can be obtained by minimizing the proposed objective function.
To estimate the SVO angle of the HDV, we develop a moving horizon estimation
algorithm based on maximum entropy inverse reinforcement learning. The
effectiveness of the proposed approach is demonstrated by numerical simulations
of a vehicle merging scenario.

Inspired by recent advances in retrieval augmented methods in
NLP~\citep{khandelwal2019generalization,khandelwal2020nearest,meng2021gnn}, in
this paper, we introduce a $k$ nearest neighbor NER ($k$NN-NER) framework,
which augments the distribution of entity labels by assigning $k$ nearest
neighbors retrieved from the training set. This strategy makes the model more
capable of handling long-tail cases, along with better few-shot learning
abilities. $k$NN-NER requires no additional operation during the training
phase, and by interpolating $k$ nearest neighbors search into the vanilla NER
model, $k$NN-NER consistently outperforms its vanilla counterparts: we achieve
a new state-of-the-art F1-score of 72.03 (+1.25) on the Chinese Weibo dataset
and improved results on a variety of widely used NER benchmarks. Additionally,
we show that $k$NN-NER can achieve comparable results to the vanilla NER model
with 40\% less amount of training data. Code available at
\url{https://github.com/ShannonAI/KNN-NER}.

I present for your appraisal three independent cases of the manuscript
referee process conducted by a venerable peer-reviewed scientific journal. Each
case involves a little pig, who submitted for consideration a theoretical plan
for a house to be constructed presently, in a faraway land. An anonymous big
bad wolf was assigned by the journal to assess the merit of these manuscripts.
The pigs proposed three distinct construction frameworks, which varied in
physical and mathematical sophistication. The first little pig submitted a
model of straw, based on the numerical method of toe-counting. His design
included odd features, such as spilled millet and cloven-hoofprints on the
window sill -- possibly a ploy to distract the wolf from the manuscript's
facile mathematical foundation. The second little pig used a more advanced
approach, employing Newton's classical laws of motion, to propose a house of
sticks. This pig included in her manuscript copious citations by a specific
wolf, possibly aiming to ensure acceptance by flattering the wolf whom she
anticipated would be the referee. The third little pig described an
ostentatious house of bricks based on an elaborate dynamical systems and
stability analysis, possibly scheming to dazzle and impress. The big bad wolf
did not appear moved by any of the pigs' tactics. His recommendations were, for
straw: the minor revision of water-proofing; for sticks: the major revision of
fire-proofing, given concerns surrounding climate change; for bricks:
unequivocal rejection, accompanied by multiple derogatory comments regarding
"high-and-mighty theorists." I describe each case in detail, and suggest that
the wolf's reports might be driven as much by self interest as the manuscripts
themselves -- namely, that at the time the wolf wrote the reviews, he was
rather hungry. Finally, I examine morals learned, if any.

Near-term noisy intermediate-scale quantum circuits can efficiently implement
implicit probabilistic models in discrete spaces, supporting distributions that
are practically infeasible to sample from using classical means. One of the
possible applications of such models, also known as Born machines, is
probabilistic inference, which is at the core of Bayesian methods. This paper
studies the use of Born machines for the problem of training binary Bayesian
neural networks. In the proposed approach, a Born machine is used to model the
variational distribution of the binary weights of the neural network, and data
from multiple tasks is used to reduce training data requirements on new tasks.
The method combines gradient-based meta-learning and variational inference via
Born machines, and is shown in a prototypical regression problem to outperform
conventional joint learning strategies.

Schizophrenia is a severe mental health condition that requires a long and
complicated diagnostic process. However, early diagnosis is vital to control
symptoms. Deep learning has recently become a popular way to analyse and
interpret medical data. Past attempts to use deep learning for schizophrenia
diagnosis from brain-imaging data have shown promise but suffer from a large
training-application gap - it is difficult to apply lab research to the real
world. We propose to reduce this training-application gap by focusing on
readily accessible data. We collect a data set of psychiatric observations of
patients based on DSM-5 criteria. Because similar data is already recorded in
all mental health clinics that diagnose schizophrenia using DSM-5, our method
could be easily integrated into current processes as a tool to assist
clinicians, whilst abiding by formal diagnostic criteria. To facilitate
real-world usage of our system, we show that it is interpretable and robust.
Understanding how a machine learning tool reaches its diagnosis is essential to
allow clinicians to trust that diagnosis. To interpret the framework, we fuse
two complementary attention mechanisms, 'squeeze and excitation' and
'self-attention', to determine global attribute importance and attribute
interactivity, respectively. The model uses these importance scores to make
decisions. This allows clinicians to understand how a diagnosis was reached,
improving trust in the model. Because machine learning models often struggle to
generalise to data from different sources, we perform experiments with
augmented test data to evaluate the model's applicability to the real world. We
find that our model is more robust to perturbations, and should therefore
perform better in a clinical setting. It achieves 98% accuracy with 10-fold
cross-validation.

An increasing number of machine learning models have been deployed in domains
with high stakes such as finance and healthcare. Despite their superior
performances, many models are black boxes in nature which are hard to explain.
There are growing efforts for researchers to develop methods to interpret these
black-box models. Post hoc explanations based on perturbations, such as LIME,
are widely used approaches to interpret a machine learning model after it has
been built. This class of methods has been shown to exhibit large instability,
posing serious challenges to the effectiveness of the method itself and harming
user trust. In this paper, we propose S-LIME, which utilizes a hypothesis
testing framework based on central limit theorem for determining the number of
perturbation points needed to guarantee stability of the resulting explanation.
Experiments on both simulated and real world data sets are provided to

The knowledge extraction task is to extract triple relations (head
entity-relation-tail entity) from unstructured text data. The existing
knowledge extraction methods are divided into "pipeline" method and joint
extraction method. The "pipeline" method is to separate named entity
recognition and entity relationship extraction and use their own modules to
extract them. Although this method has better flexibility, the training speed
is slow. The learning model of joint extraction is an end-to-end model
implemented by neural network to realize entity recognition and relationship
extraction at the same time, which can well preserve the association between
entities and relationships, and convert the joint extraction of entities and
relationships into a sequence annotation problem. In this paper, we propose a
knowledge extraction method for scientific and technological resources based on
word mixture and GRU, combined with word mixture vector mapping method and
self-attention mechanism, to effectively improve the effect of text
relationship extraction for Chinese scientific and technological resources.

The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that
neural networks can successfully predict future traffic conditions 1 hour into
the future on simply aggregated GPS probe data in time and space bins. We thus
reinterpreted the challenge of forecasting traffic conditions as a movie
completion task. U-Nets proved to be the winning architecture, demonstrating an
ability to extract relevant features in this complex real-world geo-spatial
process. Building on the previous competitions, Traffic4cast 2021 now focuses
on the question of model robustness and generalizability across time and space.
Moving from one city to an entirely different city, or moving from pre-COVID
times to times after COVID hit the world thus introduces a clear domain shift.
We thus, for the first time, release data featuring such domain shifts. The
competition now covers ten cities over 2 years, providing data compiled from
over 10^12 GPS probe data. Winning solutions captured traffic dynamics
sufficiently well to even cope with these complex domain shifts. Surprisingly,
this seemed to require only the previous 1h traffic dynamic history and static
road graph as input.

In Domain Generalization (DG) tasks, models are trained by using only
training data from the source domains to achieve generalization on an unseen
target domain, this will suffer from the distribution shift problem. So it's
important to learn a classifier to focus on the common representation which can
be used to classify on multi-domains, so that this classifier can achieve a
high performance on an unseen target domain as well. With the success of cross
attention in various cross-modal tasks, we find that cross attention is a
powerful mechanism to align the features come from different distributions. So
we design a model named CADG (cross attention for domain generalization),
wherein cross attention plays a important role, to address distribution shift
problem. Such design makes the classifier can be adopted on multi-domains, so
the classifier will generalize well on an unseen domain. Experiments show that
our proposed method achieves state-of-the-art performance on a variety of
domain generalization benchmarks compared with other single model and can even
achieve a better performance than some ensemble-based methods.

Gesture recognition is one of the most intuitive ways of interaction and has
gathered particular attention for human computer interaction. Radar sensors
possess multiple intrinsic properties, such as their ability to work in low
illumination, harsh weather conditions, and being low-cost and compact, making
them highly preferable for a gesture recognition solution. However, most
literature work focuses on solutions with a limited range that is lower than a
meter. We propose a novel architecture for a long-range (1m - 2m) gesture
recognition solution that leverages a point cloud-based cross-learning approach
from camera point cloud to 60-GHz FMCW radar point cloud, which allows learning
better representations while suppressing noise. We use a variant of Dynamic
Graph CNN (DGCNN) for the cross-learning, enabling us to model relationships
between the points at a local and global level and to model the temporal
dynamics a Bi-LSTM network is employed. In the experimental results section, we
demonstrate our model's overall accuracy of 98.4% for five gestures and its

Wind energy is one of the cleanest renewable electricity sources and can help
in addressing the challenge of climate change. One of the drawbacks of
wind-generated energy is the large space necessary to install a wind farm; this
arises from the fact that placing wind turbines in a limited area would hinder
their productivity and therefore not be economically convenient. This naturally
leads to an optimisation problem, which has three specific challenges: (1)
multiple conflicting objectives (2) computationally expensive simulation models
and (3) optimisation over design sets instead of design vectors. The first and
second challenges can be addressed by using surrogate-assisted e.g.\ Bayesian
multi-objective optimisation. However, the traditional Bayesian optimisation
cannot be applied as the optimisation function in the problem relies on design
sets instead of design vectors. This paper extends the applicability of
Bayesian multi-objective optimisation to set based optimisation for solving the
wind farm layout problem. We use a set-based kernel in Gaussian process to
quantify the correlation between wind farms (with a different number of
turbines). The results on the given data set of wind energy and direction
clearly show the potential of using set-based Bayesian multi-objective

Collective operations are common features of parallel programming models that
are frequently used in High-Performance (HPC) and machine/ deep learning (ML/
DL) applications. In strong scaling scenarios, collective operations can
negatively impact the overall application performance: with the increase in
core count, the load per rank decreases, while the time spent in collective
operations increases logarithmically.
  In this article, we propose a design for eventually consistent collectives
suitable for ML/ DL computations by reducing communication in Broadcast and
Reduce, as well as by exploring the Stale Synchronous Parallel (SSP)
synchronization model for the Allreduce collective. Moreover, we also enrich
the GASPI ecosystem with frequently used classic/ consistent collective
operations -- such as Allreduce for large messages and AlltoAll used in an HPC
code. Our implementations show promising preliminary results with significant
improvements, especially for Allreduce and AlltoAll, compared to the
vendor-provided MPI alternatives.

Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for
solving computational imaging problems through the integration of physical
models and learned models. PnP leverages high-fidelity physical sensor models
and powerful machine learning methods for prior modeling of data to provide
state-of-the-art reconstruction algorithms. PnP algorithms alternate between
minimizing a data-fidelity term to promote data consistency and imposing a
learned regularizer in the form of an image denoiser. Recent highly-successful
applications of PnP algorithms include bio-microscopy, computerized tomography,
magnetic resonance imaging, and joint ptycho-tomography. This article presents
a unified and principled review of PnP by tracing its roots, describing its
major variations, summarizing main results, and discussing applications in
computational imaging. We also point the way towards further developments by
discussing recent results on equilibrium equations that formulate the problem
associated with PnP algorithms.

Physics-informed neural networks (PINNs) are one popular approach to
incorporate a priori knowledge about physical systems into the learning
framework. PINNs are known to be robust for smaller training sets, derive
better generalization problems, and are faster to train. In this paper, we show
that using PINNs in comparison with purely data-driven neural networks is not
only favorable for training performance but allows us to extract significant
information on the quality of the approximated solution. Assuming that the
underlying differential equation for the PINN training is an ordinary
differential equation, we derive a rigorous upper limit on the PINN prediction
error. This bound is applicable even for input data not included in the
training phase and without any prior knowledge about the true solution.
Therefore, our a posteriori error estimation is an essential step to certify
the PINN. We apply our error estimator exemplarily to two academic toy
problems, whereof one falls in the category of model-predictive control and
thereby shows the practical use of the derived results.

Single frame data contains finite information which limits the performance of
the existing vision-based multi-camera 3D object detection paradigms. For
fundamentally pushing the performance boundary in this area, a novel paradigm
dubbed BEVDet4D is proposed to lift the scalable BEVDet paradigm from the
spatial-only 3D space to the spatial-temporal 4D space. We upgrade the naive
BEVDet framework with a few modifications just for fusing the feature from the
previous frame with the corresponding one in the current frame. In this way,
with negligible additional computing budget, we enable BEVDet4D to access the
temporal cues by querying and comparing the two candidate features. Beyond
this, we simplify the task of velocity prediction by removing the factors of
ego-motion and time in the learning target. As a result, BEVDet4D with robust
generalization performance reduces the velocity error by up to -62.9%. This
makes the vision-based methods, for the first time, become comparable with
those relied on LiDAR or radar in this aspect. On challenge benchmark nuScenes,
we report a new record of 54.5% NDS with the high-performance configuration
dubbed BEVDet4D-Base, which surpasses the previous leading method BEVDet-Base
by +7.3% NDS. The source code is publicly available for further research at
https://github.com/HuangJunJie2017/BEVDet .

Liquid argon time projection chamber detector technology provides high
spatial and calorimetric resolutions on the charged particles traversing liquid
argon. As a result, the technology has been used in a number of recent neutrino
experiments, and is the technology of choice for the Deep Underground Neutrino
Experiment (DUNE). In order to perform high precision measurements of neutrinos
in the detector, final state particles need to be effectively identified, and
their energy accurately reconstructed. This article proposes an algorithm based
on a convolutional neural network to perform the classification of energy
deposits and reconstructed particles as track-like or arising from
electromagnetic cascades. Results from testing the algorithm on data from
ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network
identifies track- and shower-like particles, as well as Michel electrons, with
high efficiency. The performance of the algorithm is consistent between data
and simulation.

We present an autonomous scanning droplet cell platform designed for
on-demand alloy electrodeposition and real-time electrochemical
characterization for investigating the corrosion-resistance properties of
multicomponent alloys. Automation and machine learning are currently driving
rapid innovation in high throughput and autonomous materials design and
discovery. We present two alloy design case studies: one focusing on a
multi-objective corrosion resistant alloy optimization, and a case study
highlighting the complexity of the multimodal characterization needed to
provide insight into the underlying structural and chemical factors that drive
observed material behavior. This motivates a close coupling between autonomous
research platforms and scientific machine learning methodology that blends
mechanistic physical models and black box machine learning models. This
emerging research area presents new opportunities to accelerate materials
synthesis, evaluation, and hence discovery and design.

Equilibrium structures determine material properties and biochemical
functions. We propose to machine learn phase-space averages, conventionally
obtained by {\em ab initio} or force-field based molecular dynamics (MD) or
Monte Carlo simulations. In analogy to \textit(ab initio} molecular dynamics
(AIMD), our {\em ab initio} machine learning (AIML) model does not require bond
topologies and therefore enables a general machine learning pathway to ensemble
properties throughout chemical compound space. We demonstrate AIML for
predicting Boltzmann averaged structures after training on hundreds of MD
trajectories. AIML output is subsequently used to train machine learning models
of free energies of solvation using experimental data, and reaching competitive
prediction errors (MAE $\sim$ 0.8 kcal/mol) for out-of-sample molecules --
within milli-seconds. As such, AIML effectively bypasses the need for MD or
MC-based phase space sampling, enabling exploration campaigns throughout CCS at
a much accelerated pace. We contextualize our findings by comparison to
state-of-the-art methods resulting in a Pareto plot for the free energy of
solvation predictions in terms of accuracy and time.

With the increasing adoption of data-hungry machine learning algorithms,
personal data privacy has emerged as one of the key concerns that could hinder
the success of digital transformation. As such, Privacy-Preserving Machine
Learning (PPML) has received much attention from both academia and industry.
However, organizations are faced with the dilemma that, on the one hand, they
are encouraged to share data to enhance ML performance, but on the other hand,
they could potentially be breaching the relevant data privacy regulations.
Practical PPML typically allows multiple participants to individually train
their ML models, which are then aggregated to construct a global model in a
privacy-preserving manner, e.g., based on multi-party computation or
homomorphic encryption. Nevertheless, in most important applications of
large-scale PPML, e.g., by aggregating clients' gradients to update a global
model for federated learning, such as consumer behavior modeling of mobile
application services, some participants are inevitably resource-constrained
mobile devices, which may drop out of the PPML system due to their mobility
nature. Therefore, the resilience of privacy-preserving aggregation has become
an important problem to be tackled. In this paper, we propose a scalable
privacy-preserving aggregation scheme that can tolerate dropout by participants
at any time, and is secure against both semi-honest and active malicious
adversaries by setting proper system parameters. By replacing
communication-intensive building blocks with a seed homomorphic pseudo-random
generator, and relying on the additive homomorphic property of Shamir secret
sharing scheme, our scheme outperforms state-of-the-art schemes by up to
6.37$\times$ in runtime and provides a stronger dropout-resilience. The
simplicity of our scheme makes it attractive both for implementation and for
further improvements.

Production logistics has an important role as a chain that connects the
components of the production system. The most important goal of production
logistics plans is to keep the flow of the production system well. However,
compared to the production system, the level of planning, management, and
digitalization of the production logistics system is not high enough, so it is
difficult to respond flexibly when unexpected situations occur in the
production logistics system. Optimization and heuristic algorithms have been
proposed to solve this problem, but due to their inflexible nature, they can
only achieve the desired solution in a limited environment. In this paper, the
relationship between the production and production logistics system is analyzed
and stochastic variables are introduced by modifying the pickup and delivery
problem with time windows (PDPTW) optimization model to establish a flexible
production logistics plan. This model, taking into account stochastic
variables, gives the scheduler a new perspective, allowing them to have new
insights based on the mathematical model. However, since the optimization model
is still insufficient to respond to the dynamic environment, future research
will cover how to derive meaningful results even in a dynamic environment such
as a machine learning model.

The countermeasure (CM) model is developed to protect ASV systems from spoof
attacks and prevent resulting personal information leakage in Automatic Speaker
Verification (ASV) system. Based on practicality and security considerations,
the CM model is usually deployed on edge devices, which have more limited
computing resources and storage space than cloud-based systems, confining the
model size under a limitation. To better trade off the CM model sizes and
performance, we proposed an adversarial speaker distillation method, which is
an improved version of knowledge distillation method combined with generalized
end-to-end (GE2E) pre-training and adversarial fine-tuning. In the evaluation
phase of the ASVspoof 2021 Logical Access task, our proposed adversarial
speaker distillation ResNetSE (ASD-ResNetSE) model reaches 0.2695 min t-DCF and
3.54% EER. ASD-ResNetSE only used 22.5% of parameters and 19.4% of multiply and
accumulate operands of ResNetSE model.

Deep neural networks (DNNs) are known to be vulnerable to adversarial
examples. It is thus imperative to devise effective attack algorithms to
identify the deficiencies of DNNs beforehand in security-sensitive
applications. To efficiently tackle the black-box setting where the target
model's particulars are unknown, feature-level transfer-based attacks propose
to contaminate the intermediate feature outputs of local models, and then
directly employ the crafted adversarial samples to attack the target model. Due
to the transferability of features, feature-level attacks have shown promise in
synthesizing more transferable adversarial samples. However, existing
feature-level attacks generally employ inaccurate neuron importance
estimations, which deteriorates their transferability. To overcome such
pitfalls, in this paper, we propose the Neuron Attribution-based Attack (NAA),
which conducts feature-level attacks with more accurate neuron importance
estimations. Specifically, we first completely attribute a model's output to
each neuron in a middle layer. We then derive an approximation scheme of neuron
attribution to tremendously reduce the computation overhead. Finally, we weight
neurons based on their attribution results and launch feature-level attacks.
Extensive experiments confirm the superiority of our approach to the
state-of-the-art benchmarks.

New classes arise frequently in our ever-changing world, e.g., emerging
topics in social media and new types of products in e-commerce. A model should
recognize new classes and meanwhile maintain discriminability over old classes.
Under severe circumstances, only limited novel instances are available to
incrementally update the model. The task of recognizing few-shot new classes
without forgetting old classes is called few-shot class-incremental learning
(FSCIL). In this work, we propose a new paradigm for FSCIL based on
meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT), which
synthesizes fake FSCIL tasks from the base dataset. The data format of fake
tasks is consistent with the `real' incremental tasks, and we can build a
generalizable feature space for the unseen tasks through meta-learning.
Besides, LIMIT also constructs a calibration module based on transformer, which
calibrates the old class classifiers and new class prototypes into the same
scale and fills in the semantic gap. The calibration module also adaptively
contextualizes the instance-specific embedding with a set-to-set function.
LIMIT efficiently adapts to new classes and meanwhile resists forgetting over
old classes. Experiments on three benchmark datasets (CIFAR100, miniImageNet,
and CUB200) and large-scale dataset, i.e., ImageNet ILSVRC2012 validate that
LIMIT achieves state-of-the-art performance.

This paper investigates probability density functions (PDFs) that are
continuous everywhere, nearly uniform around the mode of distribution, and
adaptable to a variety of distribution shapes ranging from bell-shaped to
rectangular. From the viewpoint of computational tractability, the PDF based on
the Fermi-Dirac or logistic function is advantageous in estimating its shape
parameters. The most appropriate PDF for $n$-variate distribution is of the
form:
$p\left(\mathbf{x}\right)\propto\left[\cosh\left(\left[\left(\mathbf{x}-\mathbf{m}\right)^{\mathsf{T}}\boldsymbol{\Sigma}^{-1}\left(\mathbf{x}-\mathbf{m}\right)\right]^{n/2}\right)+\cosh\left(r^{n}\right)\right]^{-1}$
where $\mathbf{x},\mathbf{m}\in\mathbb{R}^{n}$, $\boldsymbol{\Sigma}$ is an
$n\times n$ positive definite matrix, and $r>0$ is a shape parameter. The
flat-topped PDFs can be used as a component of mixture models in machine
learning to improve goodness of fit and make a model as simple as possible.

Federated learning (FL), as a disruptive machine learning paradigm, enables
the collaborative training of a global model over decentralized local datasets
without sharing them. It spans a wide scope of applications from
Internet-of-Things (IoT) to biomedical engineering and drug discovery. To
support low-latency and high-privacy FL over wireless networks, in this paper,
we propose a reconfigurable intelligent surface (RIS) empowered over-the-air FL
system to alleviate the dilemma between learning accuracy and privacy. This is
achieved by simultaneously exploiting the channel propagation reconfigurability
with RIS for boosting the receive signal power, as well as waveform
superposition property with over-the-air computation (AirComp) for fast model
aggregation. By considering a practical scenario where high-dimensional local
model updates are transmitted across multiple communication blocks, we
characterize the convergence behaviors of the differentially private federated
optimization algorithm. We further formulate a system optimization problem to
optimize the learning accuracy while satisfying privacy and power constraints
via the joint design of transmit power, artificial noise, and phase shifts at
RIS, for which a two-step alternating minimization framework is developed.
Simulation results validate our systematic, theoretical, and algorithmic
achievements and demonstrate that RIS can achieve a better trade-off between
privacy and accuracy for over-the-air FL systems.

Previous research has looked into ways to improve speech emotion recognition
(SER) by utilizing both acoustic and linguistic cues of speech. However, the
potential association between state-of-the-art ASR models and the SER task has
yet to be investigated. In this paper, we propose a novel channel and
temporal-wise attention RNN (CTA-RNN) architecture based on the intermediate
representations of pre-trained ASR models. Specifically, the embeddings of a
large-scale pre-trained end-to-end ASR encoder contain both acoustic and
linguistic information, as well as the ability to generalize to different
speakers, making them well suited for downstream SER task. To further exploit
the embeddings from different layers of the ASR encoder, we propose a novel
CTA-RNN architecture to capture the emotional salient parts of embeddings in
both the channel and temporal directions. We evaluate our approach on two
popular benchmark datasets, IEMOCAP and MSP-IMPROV, using both within-corpus
and cross-corpus settings. Experimental results show that our proposed method
can achieve excellent performance in terms of accuracy and robustness.

Vocal fry or creaky voice refers to a voice quality characterized by
irregular glottal opening and low pitch. It occurs in diverse languages and is
prevalent in American English, where it is used not only to mark phrase
finality, but also sociolinguistic factors and affect. Due to its irregular
periodicity, creaky voice challenges automatic speech processing and
recognition systems, particularly for languages where creak is frequently used.
  This paper proposes a deep learning model to detect creaky voice in fluent
speech. The model is composed of an encoder and a classifier trained together.
The encoder takes the raw waveform and learns a representation using a
convolutional neural network. The classifier is implemented as a multi-headed
fully-connected network trained to detect creaky voice, voicing, and pitch,
where the last two are used to refine creak prediction. The model is trained
and tested on speech of American English speakers, annotated for creak by
trained phoneticians.
  We evaluated the performance of our system using two encoders: one is
tailored for the task, and the other is based on a state-of-the-art
unsupervised representation. Results suggest our best-performing system has
improved recall and F1 scores compared to previous methods on unseen data.

Video streams are utilised to guide minimally-invasive surgery and diagnostic
procedures in a wide range of procedures, and many computer assisted techniques
have been developed to automatically analyse them. These approaches can provide
additional information to the surgeon such as lesion detection, instrument
navigation, or anatomy 3D shape modeling. However, the necessary image features
to recognise these patterns are not always reliably detected due to the
presence of irregular light patterns such as specular highlight reflections. In
this paper, we aim at removing specular highlights from endoscopic videos using
machine learning. We propose using a temporal generative adversarial network
(GAN) to inpaint the hidden anatomy under specularities, inferring its
appearance spatially and from neighbouring frames where they are not present in
the same location. This is achieved using in-vivo data of gastric endoscopy
(Hyper-Kvasir) in a fully unsupervised manner that relies on automatic
detection of specular highlights. System evaluations show significant
improvements to traditional methods through direct comparison as well as other
machine learning techniques through an ablation study that depicts the
importance of the network's temporal and transfer learning components. The
generalizability of our system to different surgical setups and procedures was
also evaluated qualitatively on in-vivo data of gastric endoscopy and ex-vivo
porcine data (SERV-CT, SCARED). We also assess the effect of our method in
computer vision tasks that underpin 3D reconstruction and camera motion
estimation, namely stereo disparity, optical flow, and sparse point feature
matching. These are evaluated quantitatively and qualitatively and results show
a positive effect of specular highlight inpainting on these tasks in a novel
comprehensive analysis.

Detecting COVID-19 from audio signals, such as breathing and coughing, can be
used as a fast and efficient pre-testing method to reduce the virus
transmission. Due to the promising results of deep learning networks in
modelling time sequences, and since applications to rapidly identify COVID
in-the-wild should require low computational effort, we present a
temporal-oriented broadcasting residual learning method that achieves efficient
computation and high accuracy with a small model size. Based on the
EfficientNet architecture, our novel network, named Temporal-oriented
ResNet~(TorNet), constitutes of a broadcasting learning block, i.e. the
Alternating Broadcast (AB) Block, which contains several Broadcast Residual
Blocks (BC ResBlocks) and a convolution layer. With the AB Block, the network
obtains useful audio-temporal features and higher level embeddings effectively
with much less computation than Recurrent Neural Networks~(RNNs), typically
used to model temporal information. TorNet achieves 72.2% Unweighted Average
Recall (UAR) on the INTERPSEECH 2021 Computational Paralinguistics Challenge
COVID-19 cough Sub-Challenge, by this showing competitive results with a higher
computational efficiency than other state-of-the-art alternatives.

Model quantization is considered as a promising method to greatly reduce the
resource requirements of deep neural networks. To deal with the performance
drop induced by quantization errors, a popular method is to use training data
to fine-tune quantized networks. In real-world environments, however, such a
method is frequently infeasible because training data is unavailable due to
security, privacy, or confidentiality concerns. Zero-shot quantization
addresses such problems, usually by taking information from the weights of a
full-precision teacher network to compensate the performance drop of the
quantized networks. In this paper, we first analyze the loss surface of
state-of-the-art zero-shot quantization techniques and provide several
findings. In contrast to usual knowledge distillation problems, zero-shot
quantization often suffers from 1) the difficulty of optimizing multiple loss
terms together, and 2) the poor generalization capability due to the use of
synthetic samples. Furthermore, we observe that many weights fail to cross the
rounding threshold during training the quantized networks even when it is
necessary to do so for better performance. Based on the observations, we
propose AIT, a simple yet powerful technique for zero-shot quantization, which
addresses the aforementioned two problems in the following way: AIT i) uses a
KL distance loss only without a cross-entropy loss, and ii) manipulates
gradients to guarantee that a certain portion of weights are properly updated
after crossing the rounding thresholds. Experiments show that AIT outperforms
the performance of many existing methods by a great margin, taking over the
overall state-of-the-art position in the field.

Over the recent years, with the increasing adoption of Federated Learning
(FL) algorithms and growing concerns over personal data privacy,
Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention
from both academia and industry. Practical PPFL typically allows multiple
participants to individually train their machine learning models, which are
then aggregated to construct a global model in a privacy-preserving manner. As
such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has
received substantial research interest. This survey aims to fill the gap
between a large number of studies on PPFL, where PPAgg is adopted to provide a
privacy guarantee, and the lack of a comprehensive survey on the PPAgg
protocols applied in FL systems. In this survey, we review the PPAgg protocols
proposed to address privacy and security issues in FL systems. The focus is
placed on the construction of PPAgg protocols with an extensive analysis of the
advantages and disadvantages of these selected PPAgg protocols and solutions.
Additionally, we discuss the open-source FL frameworks that support PPAgg.
Finally, we highlight important challenges and future research directions for
applying PPAgg to FL systems and the combination of PPAgg with other
technologies for further security improvement.

Score-based generative models (SGMs) have recently shown impressive results
for difficult generative tasks such as the unconditional and conditional
generation of natural images and audio signals. In this work, we extend these
models to the complex short-time Fourier transform (STFT) domain, proposing a
novel training task for speech enhancement using a complex-valued deep neural
network. We derive this training task within the formalism of stochastic
differential equations (SDEs), thereby enabling the use of predictor-corrector
samplers. We provide alternative formulations inspired by previous publications
on using generative diffusion models for speech enhancement, avoiding the need
for any prior assumptions on the noise distribution and making the training
task purely generative which, as we show, results in improved enhancement

This work introduces a diffusion model for molecule generation in 3D that is
equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model
(EDM) learns to denoise a diffusion process with an equivariant network that
jointly operates on both continuous (atom coordinates) and categorical features
(atom types). In addition, we provide a probabilistic analysis which admits
likelihood computation of molecules using our model. Experimentally, the
proposed method significantly outperforms previous 3D molecular generative
methods regarding the quality of generated samples and efficiency at training
time.

We explore the use of class-conditional autoregressive (CA) models to perform
image classification on MNIST-10. Autoregressive models assign probability to
an entire input by combining probabilities from each individual feature; hence
classification decisions made by a CA can be readily decomposed into
contributions from each each input feature. That is to say, CA are inherently
locally interpretable. Our experiments show that naively training a CA achieves
much worse accuracy compared to a standard classifier, however this is due to
over-fitting and not a lack of expressive power. Using knowledge distillation
from a standard classifier, a student CA can be trained to match the
performance of the teacher while still being interpretable.

Deep learning based singing voice synthesis (SVS) systems have been
demonstrated to flexibly generate singing with better qualities, compared to
conventional statistical parametric based methods. However, neural systems are
generally data-hungry and have difficulty to reach reasonable singing quality
with limited public available training data. In this work, we explore different
data augmentation methods to boost the training of SVS systems, including
several strategies customized to SVS based on pitch augmentation and mix-up
augmentation. To further stabilize the training, we introduce the
cycle-consistent training strategy. Extensive experiments on two public singing
databases demonstrate that our proposed augmentation methods and the
stabilizing training strategy can significantly improve the performance on both
objective and subjective evaluations.

Contemporary social coding platforms like GitHub promote collaborative
development. Many open-source software repositories hosted in these platforms
use machine accounts (bots) to automate and facilitate a wide range of
effort-intensive and repetitive activities. Determining if an account
corresponds to a bot or a human contributor is important for socio-technical
development analytics, for example, to understand how humans collaborate and
interact in the presence of bots, to assess the positive and negative impact of
using bots, to identify the top project contributors, to identify potential bus
factors, and so on. Our project aims to include the trained machine learning
(ML) classifier from the BoDeGHa bot detection tool as a plugin to the
GrimoireLab software development analytics platform. In this work, we present
the procedure to form a pipeline for retrieving contribution and contributor
data using Perceval, distinguishing bots from humans using BoDeGHa, and
visualising the results using Kibana.

Background: Egocentric video has recently emerged as a potential solution for
monitoring hand function in individuals living with tetraplegia in the
community, especially for its ability to detect functional use in the home
environment. Objective: To develop and validate a wearable vision-based system
for measuring hand use in the home among individuals living with tetraplegia.
Methods: Several deep learning algorithms for detecting functional hand-object
interactions were developed and compared. The most accurate algorithm was used
to extract measures of hand function from 65 hours of unscripted video recorded
at home by 20 participants with tetraplegia. These measures were: the
percentage of interaction time over total recording time (Perc); the average
duration of individual interactions (Dur); the number of interactions per hour
(Num). To demonstrate the clinical validity of the technology, egocentric
measures were correlated with validated clinical assessments of hand function
and independence (Graded Redefined Assessment of Strength, Sensibility and
Prehension - GRASSP, Upper Extremity Motor Score - UEMS, and Spinal Cord
Independent Measure - SCIM). Results: Hand-object interactions were
automatically detected with a median F1-score of 0.80 (0.67-0.87). Our results
demonstrated that higher UEMS and better prehension were related to greater
time spent interacting, whereas higher SCIM and better hand sensation resulted
in a higher number of interactions performed during the egocentric video
recordings. Conclusions: For the first time, measures of hand function
automatically estimated in an unconstrained environment in individuals with
tetraplegia have been validated against internationally accepted measures of
hand function. Future work will necessitate a formal evaluation of the
reliability and responsiveness of the egocentric-based performance measures for
hand use.

Hypergraph representations are both more efficient and better suited to
describe data characterized by relations between two or more objects. In this
work, we present a new graph neural network based on message passing capable of
processing hypergraph-structured data. We show that the proposed model defines
a design space for neural network models for hypergraphs, thus generalizing
existing models for hypergraphs. We report experiments on a benchmark dataset
for node classification, highlighting the effectiveness of the proposed model
with respect to other state-of-the-art methods for graphs and hypergraphs. We
also discuss the benefits of using hypergraph representations and, at the same
time, highlight the limitation of using equivalent graph representations when
the underlying problem has relations among more than two objects.

Acoustic source localization has been applied in different fields, such as
aeronautics and ocean science, generally using multiple microphones array data
to reconstruct the source location. However, the model-based beamforming
methods fail to achieve the high-resolution of conventional beamforming maps.
Deep neural networks are also appropriate to locate the sound source, but in
general, these methods with complex network structures are hard to be
recognized by hardware. In this paper, a novel neural network, termed the
Acoustic-Net, is proposed to locate and quantify the sound source simply using
the original signals. The experiments demonstrate that the proposed method
significantly improves the accuracy of sound source prediction and the
computing speed, which may generalize well to real data. The code and trained
models are available at https://github.com/JoaquinChou/Acoustic-Net.

A hybrid imaging system is a simultaneous physical arrangement of a
refractive lens and a multilevel phase mask (MPM) as a diffractive optical
element (DOE). The favorable properties of the hybrid setup are improved
extended-depth-of-field (EDoF) imaging and low chromatic aberrations. We built
a fully differentiable image formation model in order to use neural network
techniques to optimize imaging. At the first stage, the design framework relies
on the model-based approach with numerical simulation and end-to-end joint
optimization of both MPM and imaging algorithms. In the second stage, MPM is
fixed as found at the first stage, and the image processing is optimized
experimentally using the CNN learning-based approach with MPM implemented by a
spatial light modulator. The paper is concentrated on a comparative analysis of
imaging accuracy and quality for design with various basic optical parameters:
aperture size, lens focal length, and distance between MPM and sensor. We point
out that the varying aperture size, lens focal length, and distance between MPM
and sensor are for the first time considered for end-to-end optimization of
EDoF. We numerically and experimentally compare the designs for visible
wavelength interval [400-700]nm and the following EDoF ranges: [0.5-100]m for
simulations and [0.5-1.9]m for experimental tests. This study concerns an
application of hybrid optics for compact cameras with aperture [5-9] mm and
distance between MPM and sensor [3-10]mm.

Self-supervised learning (SSL) has drawn increasing attention in pathological
image analysis in recent years. Compared to contrastive learning which requires
careful design, masked autoencoders (MAE) building SSL from a generative
paradigm probably is a simpler method. In this paper, we introduce MAE and
verify the effect of visible patches for pathological image classification.
Based on it, a novel SD-MAE model is proposed to enable a self-distillation
augmented SSL on top of the raw MAE. Besides the reconstruction loss on masked
image patches, SD-MAE further imposes the self-distillation loss on visible
patches. It transfers knowledge brought by the global attention of the decoder
to the encoder which only uses local attention. We apply SD-MAE on two public
pathological image datasets. Experiments demonstrate that SD-MAE performs
highly competitive when compared with other SSL methods. Our code will be
released soon.

Self-supervised learning (SSL) to learn high-level speech representations has
been a popular approach to building Automatic Speech Recognition (ASR) systems
in low-resource settings. However, the common assumption made in literature is
that a considerable amount of unlabeled data is available for the same domain
or language that can be leveraged for SSL pre-training, which we acknowledge is
not feasible in a real-world setting. In this paper, as part of the Interspeech
Gram Vaani ASR challenge, we try to study the effect of domain, language,
dataset size, and other aspects of our upstream pre-training SSL data on the
final performance low-resource downstream ASR task. We also build on the
continued pre-training paradigm to study the effect of prior knowledge
possessed by models trained using SSL. Extensive experiments and studies reveal
that the performance of ASR systems is susceptible to the data used for SSL
pre-training. Their performance improves with an increase in similarity and
volume of pre-training data. We believe our work will be helpful to the speech
community in building better ASR systems in low-resource settings and steer
research towards improving generalization in SSL-based pre-training for speech
systems.

Deep learning has become the mainstream methodological choice for analyzing
and interpreting whole-slide digital pathology images (WSIs). It is commonly
assumed that tumor regions carry most predictive information. In this paper, we
proposed an unsupervised clustering-based multiple-instance learning, and apply
our method to develop deep-learning models for prediction of gene mutations
using WSIs from three cancer types in The Cancer Genome Atlas (TCGA) studies
(CRC, LUAD, and HNSCC). We showed that unsupervised clustering of image patches
could help identify predictive patches, exclude patches lack of predictive
information, and therefore improve prediction on gene mutations in all three
different cancer types, compared with the WSI based method without selection of
image patches and models based on only tumor regions. Additionally, our
proposed algorithm outperformed two recently published baseline algorithms
leveraging unsupervised clustering to assist model prediction. The
unsupervised-clustering-based approach for mutation prediction allows
identification of the spatial regions related to mutation of a specific gene
via the resolved probability scores, highlighting the heterogeneity of a
predicted genotype in the tumor microenvironment. Finally, our study also
demonstrated that selection of tumor regions of WSIs is not always the best way
to identify patches for prediction of gene mutations, and other tissue types in
the tumor micro-environment may provide better prediction ability for gene
mutations than tumor tissues.

Human video instance segmentation plays an important role in computer
understanding of human activities and is widely used in video processing, video
surveillance, and human modeling in virtual reality. Most current VIS methods
are based on Mask-RCNN framework, where the target appearance and motion
information for data matching will increase computational cost and have an
impact on segmentation real-time performance; on the other hand, the existing
datasets for VIS focus less on all the people appearing in the video. In this
paper, to solve the problems, we develop a new method for human video instance
segmentation based on single-stage detector. To tracking the instance across
the video, we have adopted data association strategy for matching the same
instance in the video sequence, where we jointly learn target instance
appearances and their affinities in a pair of video frames in an end-to-end
fashion. We have also adopted the centroid sampling strategy for enhancing the
embedding extraction ability of instance, which is to bias the instance
position to the inside of each instance mask with heavy overlap condition. As a
result, even there exists a sudden change in the character activity, the
instance position will not move out of the mask, so that the problem that the
same instance is represented by two different instances can be alleviated.
Finally, we collect PVIS dataset by assembling several video instance
segmentation datasets to fill the gap of the current lack of datasets dedicated
to human video segmentation. Extensive simulations based on such dataset has
been conduct. Simulation results verify the effectiveness and efficiency of the
proposed work.

While self-supervised speech representation learning (SSL) models serve a
variety of downstream tasks, these models have been observed to overfit to the
domain from which the unlabelled data originates. To alleviate this issue, we
propose PADA (Pruning Assisted Domain Adaptation) and zero out redundant
weights from models pre-trained on large amounts of out-of-domain (OOD) data.
Intuitively, this helps to make space for the target-domain ASR finetuning. The
redundant weights can be identified through various pruning strategies which
have been discussed in detail as a part of this work. Specifically, we
investigate the effect of the recently discovered Task-Agnostic and Task-Aware
pruning on PADA and propose a new pruning paradigm based on the latter, which
we call Cross-Domain Task-Aware Pruning (CD-TAW). CD-TAW obtains the initial
pruning mask from a well fine-tuned OOD model, which makes it starkly different
from the rest of the pruning strategies discussed in the paper. Our proposed
CD-TAW methodology achieves up to 20.6% relative WER improvement over our
baseline when fine-tuned on a 2-hour subset of Switchboard data without
language model (LM) decoding. Furthermore, we conduct a detailed analysis to
highlight the key design choices of our proposed method.

Vision transformer (ViT) has been trending in image classification tasks due
to its promising performance when compared to convolutional neural networks
(CNNs). As a result, many researchers have tried to incorporate ViT models in
hyperspectral image (HSI) classification tasks, but without achieving
satisfactory performance. To this paper, we introduce a new multimodal fusion
transformer (MFT) network for HSI land-cover classification, which utilizes
other sources of multimodal data in addition to HSI. Instead of using
conventional feature fusion techniques, other multimodal data are used as an
external classification (CLS) token in the transformer encoder, which helps
achieving better generalization. ViT and other similar transformer models use a
randomly initialized external classification token {and fail to generalize
well}. However, the use of a feature embedding derived from other sources of
multimodal data, such as light detection and ranging (LiDAR), offers the
potential to improve those models by means of a CLS. The concept of
tokenization is used in our work to generate CLS and HSI patch tokens, helping
to learn key features in a reduced feature space. We also introduce a new
attention mechanism for improving the exchange of information between HSI
tokens and the CLS (e.g., LiDAR) token. Extensive experiments are carried out
on widely used and benchmark datasets i.e., the University of Houston, Trento,
University of Southern Mississippi Gulfpark (MUUFL), and Augsburg. In the
results section, we compare the proposed MFT model with other state-of-the-art
transformer models, classical CNN models, as well as conventional classifiers.
The superior performance achieved by the proposed model is due to the use of
multimodal information as external classification tokens.

Biological systems in nature have evolved for millions of years to adapt and
survive the environment. Many features they developed can be inspirational and
beneficial for solving technical problems in modern industries. This leads to a
novel form of design-by-analogy called bio-inspired design (BID). Although BID
as a design method has been proven beneficial, the gap between biology and
engineering continuously hinders designers from effectively applying the
method. Therefore, we explore the recent advance of artificial intelligence
(AI) for a computational approach to bridge the gap. This paper proposes a
generative design approach based on the pre-trained language model (PLM) to
automatically retrieve and map biological analogy and generate BID in the form
of natural language. The latest generative pre-trained transformer, namely
GPT-3, is used as the base PLM. Three types of design concept generators are
identified and fine-tuned from the PLM according to the looseness of the
problem space representation. Machine evaluators are also fine-tuned to assess
the correlation between the domains within the generated BID concepts. The
approach is then tested via a case study in which the fine-tuned models are
applied to generate and evaluate light-weighted flying car concepts inspired by
nature. The results show our approach can generate BID concepts with good

Any city-scale visual localization system has to overcome long-term
appearance changes, such as varying illumination conditions or seasonal changes
between query and database images. Since semantic content is more robust to
such changes, we exploit semantic information to improve visual localization.
In our scenario, the database consists of gnomonic views generated from
panoramic images (e.g. Google Street View) and query images are collected with
a standard field-of-view camera at a different time. To improve localization,
we check the semantic similarity between query and database images, which is
not trivial since the position and viewpoint of the cameras do not exactly
match. To learn similarity, we propose training a CNN in a self-supervised
fashion with contrastive learning on a dataset of semantically segmented
images. With experiments we showed that this semantic similarity estimation
approach works better than measuring the similarity at pixel-level. Finally, we
used the semantic similarity scores to verify the retrievals obtained by a
state-of-the-art visual localization method and observed that contrastive
learning-based pose verification increases top-1 recall value to 0.90 which
corresponds to a 2% improvement.

In the present paper a new data-driven model is proposed to close and
increase accuracy of RANS equations. The divergence of the Reynolds Stress
Tensor (RST) is obtained through a Neural Network (NN) whose architecture and
input choice guarantee both Galilean and coordinates-frame rotation. The former
derives from the input choice of the NN while the latter from the expansion of
the divergence of the RST into a vector basis. This approach has been widely
used for data-driven models for the anisotropic RST or the RST discrepancies
and it is here proposed for the divergence of the RST. Hence, a constitutive
relation of the divergence of the RST from mean quantities is proposed to
obtain such expansion. Moreover, once the proposed data-driven approach is
trained, there is no need to run any classic turbulence model to close the
equations. The well-known tests of flow in a square duct and over periodic
hills are used to show advantages of the present method compared to standard
turbulence models.

Modeling user sequential behaviors has recently attracted increasing
attention in the recommendation domain. Existing methods mostly assume coherent
preference in the same sequence. However, user personalities are volatile and
easily changed, and there can be multiple mixed preferences underlying user
behaviors. To solve this problem, in this paper, we propose a novel sequential
recommender model via decomposing and modeling user independent preferences. To
achieve this goal, we highlight three practical challenges considering the
inconsistent, evolving and uneven nature of the user behavior, which are seldom
noticed by the previous work. For overcoming these challenges in a unified
framework, we introduce a reinforcement learning module to simulate the
evolution of user preference. More specifically, the action aims to allocate
each item into a sub-sequence or create a new one according to how the previous
items are decomposed as well as the time interval between successive behaviors.
The reward is associated with the final loss of the learning objective, aiming
to generate sub-sequences which can better fit the training data. We conduct
extensive experiments based on six real-world datasets across different
domains. Compared with the state-of-the-art methods, empirical studies manifest
that our model can on average improve the performance by about 8.21%, 10.08%,
10.32%, and 9.82% on the metrics of Precision, Recall, NDCG and MRR,

Recently machine learning using neural networks (NN) has been developed, and
many new methods have been suggested. These methods are optimized for the type
of input data and work very effectively, but they cannot be used with any kind
of input data universally. On the other hand, the human brain is universal for
any kind of problem, and we will be able to construct artificial general
intelligence if we can mimic the system of how the human brain works. We
consider how the human brain learns things uniformly, and find that the essence
of learning is the compression of information. We suggest a toy NN model which
mimics the system of the human brain, and we show that the NN can compress the
input information without ad hoc treatment, only by setting the loss function
properly. The loss function is expressed as the sum of the self-information to
remember and the loss of the information along with the compression, and its
minimum corresponds to the self-information of the original data. To evaluate
the self-information to remember, we provided the concept of memory. The memory
expresses the compressed information, and the learning proceeds by referring to
previous memories. There are many similarities between this NN and the human
brain, and this NN is a realization of the free-energy principle which is
considered to be a unified theory of the human brain. This work can be applied
to any kind of data analysis and cognitive science.

In this paper, we present a new model for Direction of Arrival (DOA)
estimation of sound sources based on an Icosahedral Convolutional Neural
Network (CNN) applied over SRP-PHAT power maps computed from the signals
received by a microphone array. This icosahedral CNN is equivariant to the 60
rotational symmetries of the icosahedron, which represent a good approximation
of the continuous space of spherical rotations, and can be implemented using
standard 2D convolutional layers, having a lower computational cost than most
of the spherical CNNs. In addition, instead of using fully connected layers
after the icosahedral convolutions, we propose a new soft-argmax function that
can be seen as a differentiable version of the argmax function and allows us to
solve the DOA estimation as a regression problem interpreting the output of the
convolutional layers as a probability distribution. We prove that using models
that fit the equivariances of the problem allows us to outperform other
state-of-the-art models with a lower computational cost and more robustness,
obtaining root mean square localization errors lower than 10{\deg} even in
scenarios with a reverberation time $T_{60}$ of 1.5 s.

As a powerful tool for modeling complex relationships, hypergraphs are
gaining popularity from the graph learning community. However, commonly used
frameworks in deep hypergraph learning focus on hypergraphs with
edge-independent vertex weights (EIVWs), without considering hypergraphs with
edge-dependent vertex weights (EDVWs) that have more modeling power. To
compensate for this, we present General Hypergraph Spectral Convolution (GHSC),
a general learning framework that not only handles EDVW and EIVW hypergraphs,
but more importantly, enables theoretically explicitly utilizing the existing
powerful Graph Convolutional Neural Networks (GCNNs) such that largely ease the
design of Hypergraph Neural Networks. In this framework, the graph Laplacian of
the given undirected GCNNs is replaced with a unified hypergraph Laplacian that
incorporates vertex weight information from a random walk perspective by
equating our defined generalized hypergraphs with simple undirected graphs.
Extensive experiments from various domains including social network analysis,
visual objective classification, and protein learning demonstrate the
state-of-the-art performance of the proposed framework.

The goal of voice conversion (VC) is to convert input voice to match the
target speaker's voice while keeping text and prosody intact. VC is usually
used in entertainment and speaking-aid systems, as well as applied for speech
data generation and augmentation. The development of any-to-any VC systems,
which are capable of generating voices unseen during model training, is of
particular interest to both researchers and the industry. Despite recent
progress, any-to-any conversion quality is still inferior to natural speech.
  In this work, we propose a new any-to-any voice conversion pipeline. Our
approach uses automated speech recognition (ASR) features, pitch tracking, and
a state-of-the-art waveform prediction model. According to multiple subjective
and objective evaluations, our method outperforms modern baselines in terms of
voice quality, similarity and consistency.

In this work we consider the problem of data classification in post-classical
settings were the number of training examples consists of mere few data points.
We explore the phenomenon and reveal key relationships between dimensionality
of AI model's feature space, non-degeneracy of data distributions, and the
model's generalisation capabilities. The main thrust of our present analysis is
on the influence of nonlinear feature transformations mapping original data
into higher- and possibly infinite-dimensional spaces on the resulting model's
generalisation capabilities. Subject to appropriate assumptions, we establish
new relationships between intrinsic dimensions of the transformed data and the
probabilities to learn successfully from few presentations.

Rain removal aims to remove rain streaks from images/videos and reduce the
disruptive effects caused by rain. It not only enhances image/video visibility
but also allows many computer vision algorithms to function properly. This
paper makes the first attempt to conduct a comprehensive study on the
robustness of deep learning-based rain removal methods against adversarial
attacks. Our study shows that, when the image/video is highly degraded, rain
removal methods are more vulnerable to the adversarial attacks as small
distortions/perturbations become less noticeable or detectable. In this paper,
we first present a comprehensive empirical evaluation of various methods at
different levels of attacks and with various losses/targets to generate the
perturbations from the perspective of human perception and machine analysis
tasks. A systematic evaluation of key modules in existing methods is performed
in terms of their robustness against adversarial attacks. From the insights of
our analysis, we construct a more robust deraining method by integrating these
effective modules. Finally, we examine various types of adversarial attacks
that are specific to deraining problems and their effects on both human and
machine vision tasks, including 1) rain region attacks, adding perturbations
only in the rain regions to make the perturbations in the attacked rain images
less visible; 2) object-sensitive attacks, adding perturbations only in regions
near the given objects. Code is available at
https://github.com/yuyi-sd/Robust_Rain_Removal.

Recent advances in neural text-to-speech research have been dominated by
two-stage pipelines utilizing low-level intermediate speech representation such
as mel-spectrograms. However, such predetermined features are fundamentally
limited, because they do not allow to exploit the full potential of a
data-driven approach through learning hidden representations. For this reason,
several end-to-end methods have been proposed. However, such models are harder
to train and require a large number of high-quality recordings with
transcriptions. Here, we propose WavThruVec - a two-stage architecture that
resolves the bottleneck by using high-dimensional Wav2Vec 2.0 embeddings as
intermediate speech representation. Since these hidden activations provide
high-level linguistic features, they are more robust to noise. That allows us
to utilize annotated speech datasets of a lower quality to train the
first-stage module. At the same time, the second-stage component can be trained
on large-scale untranscribed audio corpora, as Wav2Vec 2.0 embeddings are
already time-aligned. This results in an increased generalization capability to
out-of-vocabulary words, as well as to a better generalization to unseen
speakers. We show that the proposed model not only matches the quality of
state-of-the-art neural models, but also presents useful properties enabling
tasks like voice conversion or zero-shot synthesis.

Deep neural networks have brought significant advancements to speech emotion
recognition (SER). However, the architecture design in SER is mainly based on
expert knowledge and empirical (trial-and-error) evaluations, which is
time-consuming and resource intensive. In this paper, we propose to apply
neural architecture search (NAS) techniques to automatically configure the SER
models. To accelerate the candidate architecture optimization, we propose a
uniform path dropout strategy to encourage all candidate architecture
operations to be equally optimized. Experimental results of two different
neural structures on IEMOCAP show that NAS can improve SER performance (54.89\%
to 56.28\%) while maintaining model parameter sizes. The proposed dropout
strategy also shows superiority over the previous approaches.

Considering the difficulty of students in calculating the direct and inverse
kinematics of a robotic manipulator using only conventional tools of a
classroom, this article proposes the application of Project Based Learning
(ABP) through the design, development, mathematical modeling of a robotic
manipulator as an integrative project of the disciplines of Industrial
Robotics, Microcontrollers and Computer Assisted Design with students of the
Control and Automation Engineering of the University of Fortaleza. Once
designed and machined, the manipulator arm was assembled using servo motors
connected to a microcontroled prototyping board, to then have its kinematics
calculated. At the end are presented the results that the project has brought
to the learning of the disciplines on the optics of the tutor and students.

The practical success of much of NLP depends on the availability of training
data. However, in real-world scenarios, training data is often scarce, not
least because many application domains are restricted and specific. In this
work, we compare different methods to handle this problem and provide
guidelines for building NLP applications when there is only a small amount of
labeled training data available for a specific domain. While transfer learning
with pre-trained language models outperforms other methods across tasks,
alternatives do not perform much worse while requiring much less computational
effort, thus significantly reducing monetary and environmental cost. We examine
the performance tradeoffs of several such alternatives, including models that
can be trained up to 175K times faster and do not require a single GPU.

This work was conceived based on Project-Based Learning (ABP) and presents
the design, development and mathematical modeling steps of a low-cost robotic
manipulator with five degrees of freedom through an interdisciplinary project
linking two very important disciplines of the course of Control Engineering and
Automation of the University of Fortaleza: Computer Aided Design,
Microcontrollers and Industrial Robotics. At the end are presented the results
that the project has brought to the best learning of the discipline on the
optics of the tutor and students.

The study of robotic manipulators is the main goal of Industrial Robotics
Class, part of Control Engineers training course. There is a difficulty in
preparing academic practices and projects in the area of robotics due to the
high cost of specific educational equipment. The practical classes and the
development of projects are very important for engineers training, it is
proposed to use simulation software in order to provide practical experience
for the students of the discipline. In this context, the present article aims
to expose the use of the Robot Operation System (ROS) as a tool to develop a
robotic arm and implement the functionality of forward and inverse kinematics.
Such development could be used as an educational tool to increase the interest
and learning of students in the robotics discipline and to expand research
areas for the discipline.

Objective: The use of routinely-acquired medical data for research purposes
requires the protection of patient confidentiality via data anonymisation. The
objective of this work is to calculate the risk of re-identification arising
from a malicious attack to an anonymised dataset, as described below. Methods:
We first present an analytical means of estimating the probability of
re-identification of a single patient in a k-anonymised dataset of Electronic
Health Record (EHR) data. Second, we generalize this solution to obtain the
probability of multiple patients being re-identified. We provide synthetic
validation via Monte Carlo simulations to illustrate the accuracy of the
estimates obtained. Results: The proposed analytical framework for risk
estimation provides re-identification probabilities that are in agreement with
those provided by simulation in a number of scenarios. Our work is limited by
conservative assumptions which inflate the re-identification probability.
Discussion: Our estimates show that the re-identification probability increases
with the proportion of the dataset maliciously obtained and that it has an
inverse relationship with the equivalence class size. Our recursive approach
extends the applicability domain to the general case of a multi-patient
re-identification attack in an arbitrary k-anonymisation scheme. Conclusion: We
prescribe a systematic way to parametrize the k-anonymisation process based on
a pre-determined re-identification probability. We observed that the benefits
of a reduced re-identification risk that come with increasing k-size may not be
worth the reduction in data granularity when one is considering benchmarking
the re-identification probability on the size of the portion of the dataset
maliciously obtained by the adversary.

Currently, robotics is one of the fastest growing areas not only in the
industrial sector but also in the consumer and service sectors. Several areas
benefit from the technological advancement of robotics, especially the
industrial area those benefits from gains in productivity and quality. However,
to supply this growing demand it is necessary for the newly graduated
professionals to have a deeper understanding of how to design and control a
robotic manipulator. It is logical that in order to obtain this more in-depth
knowledge of robotics, it is necessary to have an experience with a real
robotic manipulator, since the practice is a much more efficient way of
learning than theory. However, it is known that a robotic arm is not a cheap
investment, and its maintenance is not cheap either. Therefore, many
educational institutions are not able to provide this type of experience to
their students. With this in mind, and through the use of Unity 3D, which is a
game development software, a robotic arm simulator has been developed to
correlate classroom theory with what actually happens in practice. The robotic
manipulators implemented on this simulator can be controlled by both inverse
kinematics (which is the industry standard) and direct kinematics.

Optimisation problems often have multiple conflicting objectives that can be
computationally and/or financially expensive. Mono-surrogate Bayesian
optimisation (BO) is a popular model-based approach for optimising such
black-box functions. It combines objective values via scalarisation and builds
a Gaussian process (GP) surrogate of the scalarised values. The location which
maximises a cheap-to-query acquisition function is chosen as the next location
to expensively evaluate. While BO is an effective strategy, the use of GPs is
limiting. Their performance decreases as the problem input dimensionality
increases, and their computational complexity scales cubically with the amount
of data. To address these limitations, we extend previous work on BO by
density-ratio estimation (BORE) to the multi-objective setting. BORE links the
computation of the probability of improvement acquisition function to that of
probabilistic classification. This enables the use of state-of-the-art
classifiers in a BO-like framework. In this work we present MBORE:
multi-objective Bayesian optimisation by density-ratio estimation, and compare
it to BO across a range of synthetic and real-world benchmarks. We find that
MBORE performs as well as or better than BO on a wide variety of problems, and
that it outperforms BO on high-dimensional and real-world problems.

In this paper, we aim to forecast a future trajectory distribution of a
moving agent in the real world, given the social scene images and historical
trajectories. Yet, it is a challenging task because the ground-truth
distribution is unknown and unobservable, while only one of its samples can be
applied for supervising model learning, which is prone to bias. Most recent
works focus on predicting diverse trajectories in order to cover all modes of
the real distribution, but they may despise the precision and thus give too
much credit to unrealistic predictions. To address the issue, we learn the
distribution with symmetric cross-entropy using occupancy grid maps as an
explicit and scene-compliant approximation to the ground-truth distribution,
which can effectively penalize unlikely predictions. In specific, we present an
inverse reinforcement learning based multi-modal trajectory distribution
forecasting framework that learns to plan by an approximate value iteration
network in an end-to-end manner. Besides, based on the predicted distribution,
we generate a small set of representative trajectories through a differentiable
Transformer-based network, whose attention mechanism helps to model the
relations of trajectories. In experiments, our method achieves state-of-the-art
performance on the Stanford Drone Dataset and Intersection Drone Dataset.

Domain adaptive object detection is challenging due to distinctive data
distribution between source domain and target domain. In this paper, we propose
a unified multi-granularity alignment based object detection framework towards
domain-invariant feature learning. To this end, we encode the dependencies
across different granularity perspectives including pixel-, instance-, and
category-levels simultaneously to align two domains. Based on pixel-level
feature maps from the backbone network, we first develop the omni-scale gated
fusion module to aggregate discriminative representations of instances by
scale-aware convolutions, leading to robust multi-scale object detection.
Meanwhile, the multi-granularity discriminators are proposed to identify which
domain different granularities of samples(i.e., pixels, instances, and
categories) come from. Notably, we leverage not only the instance
discriminability in different categories but also the category consistency
between two domains. Extensive experiments are carried out on multiple domain
adaptation scenarios, demonstrating the effectiveness of our framework over
state-of-the-art algorithms on top of anchor-free FCOS and anchor-based Faster
RCNN detectors with different backbones.

Optical flow estimation aims to find the 2D motion field by identifying
corresponding pixels between two images. Despite the tremendous progress of
deep learning-based optical flow methods, it remains a challenge to accurately
estimate large displacements with motion blur. This is mainly because the
correlation volume, the basis of pixel matching, is computed as the dot product
of the convolutional features of the two images. The locality of convolutional
features makes the computed correlations susceptible to various noises. On
large displacements with motion blur, noisy correlations could cause severe
errors in the estimated flow. To overcome this challenge, we propose a new
architecture "CRoss-Attentional Flow Transformer" (CRAFT), aiming to revitalize
the correlation volume computation. In CRAFT, a Semantic Smoothing Transformer
layer transforms the features of one frame, making them more global and
semantically stable. In addition, the dot-product correlations are replaced
with transformer Cross-Frame Attention. This layer filters out feature noises
through the Query and Key projections, and computes more accurate correlations.
On Sintel (Final) and KITTI (foreground) benchmarks, CRAFT has achieved new
state-of-the-art performance. Moreover, to test the robustness of different
models on large motions, we designed an image shifting attack that shifts input
images to generate large artificial motions. Under this attack, CRAFT performs
much more robustly than two representative methods, RAFT and GMA. The code of
CRAFT is is available at https://github.com/askerlee/craft.

Recent advances in Natural Language Processing, and in particular on the
construction of very large pre-trained language representation models, is
opening up new perspectives on the construction of conversational information
seeking (CIS) systems. In this paper we investigate the usage of in-context
learning and pre-trained language representation models to address the problem
of information extraction from process description documents, in an incremental
question and answering oriented fashion. In particular we investigate the usage
of the native GPT-3 (Generative Pre-trained Transformer 3) model, together with
two in-context learning customizations that inject conceptual definitions and a
limited number of samples in a few shot-learning fashion. The results highlight
the potential of the approach and the usefulness of the in-context learning
customizations, which can substantially contribute to address the "training
data challenge" of deep learning based NLP techniques the BPM field. It also
highlight the challenge posed by control flow relations for which further
training needs to be devised.

Some exciting new approaches to neural architectures for the analysis of
conversation have been introduced over the past couple of years. These include
neural architectures for detecting emotion, dialogue acts, and sentiment
polarity. They take advantage of some of the key attributes of contemporary
machine learning, such as recurrent neural networks with attention mechanisms
and transformer-based approaches. However, while the architectures themselves
are extremely promising, the phenomena they have been applied to to date are
but a small part of what makes conversation engaging. In this paper we survey
these neural architectures and what they have been applied to. On the basis of
the social science literature, we then describe what we believe to be the most
fundamental and definitional feature of conversation, which is its
co-construction over time by two or more interlocutors. We discuss how neural
architectures of the sort surveyed could profitably be applied to these more
fundamental aspects of conversation, and what this buys us in terms of a better
analysis of conversation and even, in the longer term, a better way of
generating conversation for a conversational system.

Coverage hole detection is an important research problem in wireless sensor
network research community. However, distributed approaches proposed in recent
years for coverage hole detection problem have high computational complexity.
In this paper, we propose a novel approach for coverage hole detection in
wireless sensor networks called FD-TL (Force-directed and Transfer-learning)
which is based on layout generation capability of Force-directed Algorithms and
image recognition power of Convolutional Neural Network with transfer learning.
In contrast to existing approaches, the proposed approach is a pure
topology-based approach since FD-TL can detect both triangular and
non-triangular coverage holes from a wireless sensor network based on the input
network topology without relying on the physical locations of the anchor nodes.
In FD-TL, a Force-directed Algorithm is used to generate a series of possible
layouts from a given input topology. Next, a Convolutional Neural Network is
used to recognize potential coverage holes from the generated layouts. During
the training phase, a transfer learning method is used to aid the recognition
process. Experimental results show that FD-TL method can achieve 90%
sensitivity and 96% specificity for coverage hole detection in wireless sensor
networks.

Measuring model performance is a key issue for deep learning practitioners.
However, we often lack the ability to explain why a specific architecture
attains superior predictive accuracy for a given data set. Often, validation
accuracy is used as a performance heuristic quantifying how well a network
generalizes to unseen data, but it does not capture anything about the
information flow in the model. Mutual information can be used as a measure of
the quality of internal representations in deep learning models, and the
information plane may provide insights into whether the model exploits the
available information in the data. The information plane has previously been
explored for fully connected neural networks and convolutional architectures.
We present an architecture-agnostic method for tracking a network's internal
representations during training, which are then used to create the mutual
information plane. The method is exemplified for graph-based neural networks
fitted on citation data. We compare how the inductive bias introduced in
graph-based architectures changes the mutual information plane relative to a
fully connected neural network.

The interaction context (or environment) is key to any HCI task and
especially to adaptive user interfaces (AUIs), since it represents the
conditions under which users interact with computers. Unfortunately, there are
currently no formal representations to model said interaction context. In order
to address this gap, we propose a contextual framework for AUIs and illustrate
a practical applica- tion using learning management systems as a case study. We
also discuss limitations of our framework and offer discussion points about the
realisation of truly context-aware AUIs.

There has been rapid progress recently on 3D human rendering, including novel
view synthesis and pose animation, based on the advances of neural radiance
fields (NeRF). However, most existing methods focus on person-specific training
and their training typically requires multi-view videos. This paper deals with
a new challenging task -- rendering novel views and novel poses for a person
unseen in training, using only multiview images as input. For this task, we
propose a simple yet effective method to train a generalizable NeRF with
multiview images as conditional input. The key ingredient is a dedicated
representation combining a canonical NeRF and a volume deformation scheme.
Using a canonical space enables our method to learn shared properties of human
and easily generalize to different people. Volume deformation is used to
connect the canonical space with input and target images and query image
features for radiance and density prediction. We leverage the parametric 3D
human model fitted on the input images to derive the deformation, which works
quite well in practice when combined with our canonical NeRF. The experiments
on both real and synthetic data with the novel view synthesis and pose
animation tasks collectively demonstrate the efficacy of our method.

Many complex real world phenomena exhibit abrupt, intermittent or jumping
behaviors, which are more suitable to be described by stochastic differential
equations under non-Gaussian L\'evy noise. Among these complex phenomena, the
most likely transition paths between metastable states are important since
these rare events may have high impact in certain scenarios. Based on the large
deviation principle, the most likely transition path could be treated as the
minimizer of the rate function upon paths that connect two points. One of the
challenges to calculate the most likely transition path for stochastic
dynamical systems under non-Gaussian L\'evy noise is that the associated rate
function can not be explicitly expressed by paths. For this reason, we
formulate an optimal control problem to obtain the optimal state as the most
likely transition path. We then develop a neural network method to solve this
issue. Several experiments are investigated for both Gaussian and non-Gaussian
cases.

Ransomware attacks have increased significantly in recent years, causing
great destruction and damage to critical systems and business operations.
Attackers are unfailingly finding innovative ways to bypass detection
mechanisms, whichencouraged the adoption of artificial intelligence. However,
most research summarizes the general features of AI and induces many false
positives, as the behavior of ransomware constantly differs to bypass
detection. Focusing on the key indicating features of ransomware becomes vital
as this guides the investigator to the inner workings and main function of
ransomware itself. By utilizing access privileges in process memory, the main
function of the ransomware can be detected more easily and accurately.
Furthermore, new signatures and fingerprints of ransomware families can be
identified to classify novel ransomware attacks correctly. The current research
used the process memory access privileges of the different memory regions of
the behavior of an executable to quickly determine its intent before serious
harm can occur. To achieve this aim, several well-known machine learning
algorithms were explored with an accuracy range of 81.38 to 96.28 percents. The
study thus confirms the feasibility of utilizing process memory as a detection
mechanism for ransomware.

The current global pandemic occasioned by the SARS-CoV-2 virus has been
attributed, partially, to the growing range of cyber vises within the cyber
ecosystem. One area of such impact is the increasing tendencies of
cyber-bullying among students. Cyberbullying -- the act of subjugating others
using a cyber platform -- is a growing concern among educators, especially in
High-S chools. Whilst studies have been carried out towards understanding this
menace, the approach towards id entifying indicators of cyberbullying is
largely missing in the literature. To address this research gap, this study
proposed a cyberbullying framework based on the identification of some
observable behavioral indicators. Using a self-administered measurement
instrument from 30-respondents, the study observed the probability of a
cyberbully construct, as a potential measure of the presence of cyberbullying;
a probability that has been largely ignored in extant literature. This
observation presents a veritable tool for the development of an active and
integrated learning platform void of abuse among students. Furthermore, within
the cyber education ecosystem, a cyberbullying construct would provide a
mechanism for the development of an appropriate online learning platform, which
would be useful to the information system and cyber education research
communities.

Conventional machine learning applications typically assume that data samples
are independently and identically distributed (i.i.d.). However, practical
scenarios often involve a data-generating process that produces highly
dependent data samples, which are known to heavily bias the stochastic
optimization process and slow down the convergence of learning. In this paper,
we conduct a fundamental study on how different stochastic data sampling
schemes affect the sample complexity of online stochastic gradient descent
(SGD) over highly dependent data. Specifically, with a $\phi$-mixing model of
data dependence, we show that online SGD with proper periodic data-subsampling
achieves an improved sample complexity over the standard online SGD in the full
spectrum of the data dependence level. Interestingly, even subsampling a subset
of data samples can accelerate the convergence of online SGD over highly
dependent data. Moreover, we show that online SGD with mini-batch sampling can
further substantially improve the sample complexity over online SGD with
periodic data-subsampling over highly dependent data. Numerical experiments
validate our theoretical results.

Recommender systems have been widely deployed in many real-world
applications, but usually suffer from the long-standing user cold-start
problem. As a promising way, Cross-Domain Recommendation (CDR) has attracted a
surge of interest, which aims to transfer the user preferences observed in the
source domain to make recommendations in the target domain. Previous CDR
approaches mostly achieve the goal by following the Embedding and Mapping
(EMCDR) idea which attempts to learn a mapping function to transfer the
pre-trained user representations (embeddings) from the source domain into the
target domain. However, they pre-train the user/item representations
independently for each domain, ignoring to consider both domain interactions
simultaneously. Therefore, the biased pre-trained representations inevitably
involve the domain-specific information which may lead to negative impact to
transfer information across domains. In this work, we consider a key point of
the CDR task: what information needs to be shared across domains? To achieve
the above idea, this paper utilizes the information bottleneck (IB) principle,
and proposes a novel approach termed as CDRIB to enforce the representations
encoding the domain-shared information. To derive the unbiased representations,
we devise two IB regularizers to model the cross-domain/in-domain user-item
interactions simultaneously and thereby CDRIB could consider both domain
interactions jointly for de-biasing.

Online social networks have become a fertile ground for spreading fake news.
Methods to automatically mitigate fake news propagation have been proposed.
Some studies focus on selecting top k influential users on social networks as
debunkers, but the social influence of debunkers may not translate to wide
mitigation information propagation as expected. Other studies assume a given
set of debunkers and focus on optimizing intensity for debunkers to publish
true news, but as debunkers are fixed, even if with high social influence
and/or high intensity to post true news, the true news may not reach users
exposed to fake news and therefore mitigation effect may be limited. In this
paper, we propose the multi-stage fake news mitigation campaign where debunkers
are dynamically selected within budget at each stage. We formulate it as a
reinforcement learning problem and propose a greedy algorithm optimized by
predicting future states so that the debunkers can be selected in a way that
maximizes the overall mitigation effect. We conducted extensive experiments on
synthetic and real-world social networks and show that our solution outperforms
state-of-the-art baselines in terms of mitigation effect.

In neural text-to-speech (TTS), two-stage system or a cascade of separately
learned models have shown synthesis quality close to human speech. For example,
FastSpeech2 transforms an input text to a mel-spectrogram and then HiFi-GAN
generates a raw waveform from a mel-spectogram where they are called an
acoustic feature generator and a neural vocoder respectively. However, their
training pipeline is somewhat cumbersome in that it requires a fine-tuning and
an accurate speech-text alignment for optimal performance. In this work, we
present end-to-end text-to-speech (E2E-TTS) model which has a simplified
training pipeline and outperforms a cascade of separately learned models.
Specifically, our proposed model is jointly trained FastSpeech2 and HiFi-GAN
with an alignment module. Since there is no acoustic feature mismatch between
training and inference, it does not requires fine-tuning. Furthermore, we
remove dependency on an external speech-text alignment tool by adopting an
alignment learning objective in our joint training framework. Experiments on
LJSpeech corpus shows that the proposed model outperforms publicly available,
state-of-the-art implementations of ESPNet2-TTS on subjective evaluation (MOS)
and some objective evaluations.

After the 2017 TuSimple Lane Detection Challenge, its dataset and evaluation
based on accuracy and F1 score have become the de facto standard to measure the
performance of lane detection methods. While they have played a major role in
improving the performance of lane detection methods, the validity of this
evaluation method in downstream tasks has not been adequately researched. In
this study, we design 2 new driving-oriented metrics for lane detection:
End-to-End Lateral Deviation metric (E2E-LD) is directly formulated based on
the requirements of autonomous driving, a core downstream task of lane
detection; Per-frame Simulated Lateral Deviation metric (PSLD) is a lightweight
surrogate metric of E2E-LD. To evaluate the validity of the metrics, we conduct
a large-scale empirical study with 4 major types of lane detection approaches
on the TuSimple dataset and our newly constructed dataset Comma2k19-LD. Our
results show that the conventional metrics have strongly negative correlations
($\leq$-0.55) with E2E-LD, meaning that some recent improvements purely
targeting the conventional metrics may not have led to meaningful improvements
in autonomous driving, but rather may actually have made it worse by
overfitting to the conventional metrics. As autonomous driving is a
security/safety-critical system, the underestimation of robustness hinders the
sound development of practical lane detection models. We hope that our study
will help the community achieve more downstream task-aware evaluations for lane

This paper addresses the problem of document image dewarping, which aims at
eliminating the geometric distortion in document images for document
digitization. Instead of designing a better neural network to approximate the
optical flow fields between the inputs and outputs, we pursue the best
readability by taking the text lines and the document boundaries into account
from a constrained optimization perspective. Specifically, our proposed method
first learns the boundary points and the pixels in the text lines and then
follows the most simple observation that the boundaries and text lines in both
horizontal and vertical directions should be kept after dewarping to introduce
a novel grid regularization scheme. To obtain the final forward mapping for
dewarping, we solve an optimization problem with our proposed grid
regularization. The experiments comprehensively demonstrate that our proposed
approach outperforms the prior arts by large margins in terms of readability
(with the metrics of Character Errors Rate and the Edit Distance) while
maintaining the best image quality on the publicly-available DocUNet benchmark.

Although deep learning and end-to-end models have been widely used and shown
superiority in automatic speech recognition (ASR) and text-to-speech (TTS)
synthesis, state-of-the-art forced alignment (FA) models are still based on
hidden Markov model (HMM). HMM has limited view of contextual information and
is developed with long pipelines, leading to error accumulation and
unsatisfactory performance. Inspired by the capability of attention mechanism
in capturing long term contextual information and learning alignments in ASR
and TTS, we propose a neural network based end-to-end forced aligner called
NeuFA, in which a novel bidirectional attention mechanism plays an essential
role. NeuFA integrates the alignment learning of both ASR and TTS tasks in a
unified framework by learning bidirectional alignment information from a shared
attention matrix in the proposed bidirectional attention mechanism. Alignments
are extracted from the learnt attention weights and optimized by the ASR, TTS
and FA tasks in a multi-task learning manner. Experimental results demonstrate
the effectiveness of our proposed model, with mean absolute error on test set
drops from 25.8 ms to 23.7 ms at word level, and from 17.0 ms to 15.7 ms at
phoneme level compared with state-of-the-art HMM based model.

Semantic scene reconstruction from point cloud is an essential and
challenging task for 3D scene understanding. This task requires not only to
recognize each instance in the scene, but also to recover their geometries
based on the partial observed point cloud. Existing methods usually attempt to
directly predict occupancy values of the complete object based on incomplete
point cloud proposals from a detection-based backbone. However, this framework
always fails to reconstruct high fidelity mesh due to the obstruction of
various detected false positive object proposals and the ambiguity of
incomplete point observations for learning occupancy values of complete
objects. To circumvent the hurdle, we propose a Disentangled Instance Mesh
Reconstruction (DIMR) framework for effective point scene understanding. A
segmentation-based backbone is applied to reduce false positive object
proposals, which further benefits our exploration on the relationship between
recognition and reconstruction. Based on the accurate proposals, we leverage a
mesh-aware latent code space to disentangle the processes of shape completion
and mesh generation, relieving the ambiguity caused by the incomplete point
observations. Furthermore, with access to the CAD model pool at test time, our
model can also be used to improve the reconstruction quality by performing mesh
retrieval without extra training. We thoroughly evaluate the reconstructed mesh
quality with multiple metrics, and demonstrate the superiority of our method on
the challenging ScanNet dataset. Code is available at
\url{https://github.com/ashawkey/dimr}.

Recently, there has been an increasing concern about the privacy issue raised
by using personally identifiable information in machine learning. However,
previous portrait matting methods were all based on identifiable portrait
images. To fill the gap, we present P3M-10k in this paper, which is the first
large-scale anonymized benchmark for Privacy-Preserving Portrait Matting (P3M).
P3M-10k consists of 10,000 high-resolution face-blurred portrait images along
with high-quality alpha mattes. We systematically evaluate both trimap-free and
trimap-based matting methods on P3M-10k and find that existing matting methods
show different generalization abilities under the privacy preserving training
setting, i.e., training only on face-blurred images while testing on arbitrary
images. Based on the gained insights, we propose a unified matting model named
P3M-Net consisting of three carefully designed integration modules that can
perform privacy-insensitive semantic perception and detail-reserved matting
simultaneously. We further design multiple variants of P3M-Net with different
CNN and transformer backbones and identify the difference in their
generalization abilities. To further mitigate this issue, we devise a simple
yet effective Copy and Paste strategy (P3M-CP) that can borrow facial
information from public celebrity images without privacy concerns and direct
the network to reacquire the face context at both data and feature levels.
P3M-CP only brings a few additional computations during training, while
enabling the matting model to process both face-blurred and normal images
without extra effort during inference. Extensive experiments on P3M-10k
demonstrate the superiority of P3M-Net over state-of-the-art methods and the
effectiveness of P3M-CP in improving the generalization ability of P3M-Net,
implying a great significance of P3M for future research and real-world

Recent work on self-supervised pre-training focus on leveraging large-scale
unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM)
that can be later fine-tuned on downstream tasks e.g., automatic speech
recognition (ASR). Yet, few works investigated the impact on performance when
the data properties substantially differ between the pre-training and
fine-tuning phases, termed domain shift. We target this scenario by analyzing
the robustness of Wav2Vec 2.0 and XLS-R models on downstream ASR for a
completely unseen domain, air traffic control (ATC) communications. We
benchmark these two models on several open-source and challenging ATC databases
with signal-to-noise ratio between 5 and 20 dB. Relative word error rate (WER)
reductions between 20% to 40% are obtained in comparison to hybrid-based ASR
baselines by only fine-tuning E2E acoustic models with a smaller fraction of
labeled data. We analyze WERs on the low-resource scenario and gender bias
carried by one ATC dataset.

In today's online advertising markets, it is common for an advertiser to set
a long-period budget. Correspondingly, advertising platforms adopt budget
management methods to control an advertiser's payment. Most budget control
methods rely on the value distributions of advertisers. However, the platform
hardly learns their true priors due to multiple reasons. Therefore, it is
essential to understand how budget control auction mechanisms perform under
unassured priors.
  This paper gives a two-fold answer. First, we introduce the bid-discount
method into first-price auction. We show that the resulting auction mechanism
exhibits desirable revenue maximization and computation properties. Second, we
compare this mechanism with other four in the prior manipulation model, where
an advertiser can arbitrarily report a value distribution to the platform.
These four mechanisms include the optimal mechanism satisfying
budget-constrained IC, first-price/second-price mechanisms with the
widely-studied pacing method, and bid-discount second-price mechanism. We
consider two different settings varying on whether the seller knows the
reported value distributions before choosing the auction mechanism. When the
reported priors are pre-known to the seller, we show that bid-discount
first-price auction dominates the other four mechanisms concerning the
platform's revenue. On the other hand, when the seller has no information on
the reported priors before committing to a mechanism, we show a
strategic-equivalence result between this mechanism and the optimal auction.
When buyers are further symmetric, we establish a wide equivalence among
variants of first-price and second-price auctions. Based on these findings, we
provide a thorough understanding of prior dependency in repeated auctions with
budgets. Bid-discount first-price auction itself may also be of further
independent research interest.

Low surface brightness (LSB) galaxies are galaxies with central surface
brightness fainter than the night sky. Due to the faint nature of LSB galaxies
and the comparable sky background, it is difficult to search LSB galaxies
automatically and efficiently from large sky survey. In this study, we
established the Low Surface Brightness Galaxies Auto Detect model (LSBG-AD),
which is a data-driven model for end-to-end detection of LSB galaxies from
Sloan Digital Sky Survey (SDSS) images. Object detection techniques based on
deep learning are applied to the SDSS field images to identify LSB galaxies and
estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS
images, we detected 1197 LSB galaxy candidates, of which 1081 samples are
already known and 116 samples are newly found candidates. The B-band central
surface brightness of the candidates searched by the model ranges from 22 mag
arcsec $^ {- 2} $ to 24 mag arcsec $^ {- 2} $, quite consistent with the
surface brightness distribution of the standard sample. 96.46\% of LSB galaxy
candidates have an axis ratio ($b/a$) greater than 0.3, and 92.04\% of them
have $fracDev\_r$\textless 0.4, which is also consistent with the standard
sample. The results show that the LSBG-AD model learns the features of LSB
galaxies of the training samples well, and can be used to search LSB galaxies
without using photometric parameters. Next, this method will be used to develop
efficient algorithms to detect LSB galaxies from massive images of the next
generation observatories.

We consider the problem of sequentially learning to estimate, in the mean
squared error (MSE) sense, a Gaussian $K$-vector of unknown covariance by
observing only $m < K$ of its entries in each round. This reduces to learning
an optimal subset for estimating the entire vector. Towards this, we first
establish an exponential concentration bound for an estimate of the MSE for
each observable subset. We then frame the estimation problem with bandit
feedback in the best-subset identification setting. We propose a variant of the
successive elimination algorithm to cater to the adaptive estimation problem,
and we derive an upper bound on the sample complexity of this algorithm. In
addition, to understand the fundamental limit on the sample complexity of this
adaptive estimation bandit problem, we derive a minimax lower bound.

Rate Splitting Multiple Access (RSMA) has recently emerged as a promising
technique to enhance the transmission rate for multiple access networks. Unlike
conventional multiple access schemes, RSMA requires splitting and transmitting
messages at different rates. The joint optimization of the power allocation and
rate control at the transmitter is challenging given the uncertainty and
dynamics of the environment. Furthermore, securing transmissions in RSMA
networks is a crucial problem because the messages transmitted can be easily
exposed to adversaries. This work first proposes a stochastic optimization
framework that allows the transmitter to adaptively adjust its power and
transmission rates allocated to users, and thereby maximizing the sum-rate and
fairness of the system under the presence of an adversary. We then develop a
highly effective learning algorithm that can help the transmitter to find the
optimal policy without requiring complete information about the environment in
advance. Extensive simulations show that our proposed scheme can achieve
positive covert transmission rates in the finite blocklength regime and
non-saturating rates at high SNR values. More significantly, our achievable
covert rate can be increased at high SNR values (i.e., 20 dB to 40 dB),
compared with saturating rates of a conventional multiple access scheme.

Meta-reinforcement learning (meta-RL) acquires meta-policies that show good
performance for tasks in a wide task distribution. However, conventional
meta-RL, which learns meta-policies by randomly sampling tasks, has been
reported to show meta-overfitting for certain tasks, especially for easy tasks
where an agent can easily get high scores. To reduce effects of the
meta-overfitting, we considered meta-RL with curriculum-based task sampling.
Our method is Robust Meta Reinforcement Learning with Guided Task Sampling
(RMRL-GTS), which is an effective method that restricts task sampling based on
scores and epochs. We show that in order to achieve robust meta-RL, it is
necessary not only to intensively sample tasks with poor scores, but also to
restrict and expand the task regions of the tasks to be sampled.

We target at the task of weakly-supervised action localization (WSAL), where
only video-level action labels are available during model training. Despite the
recent progress, existing methods mainly embrace a
localization-by-classification paradigm and overlook the fruitful fine-grained
temporal distinctions between video sequences, thus suffering from severe
ambiguity in classification learning and classification-to-localization
adaption. This paper argues that learning by contextually comparing
sequence-to-sequence distinctions offers an essential inductive bias in WSAL
and helps identify coherent action instances. Specifically, under a
differentiable dynamic programming formulation, two complementary contrastive
objectives are designed, including Fine-grained Sequence Distance (FSD)
contrasting and Longest Common Subsequence (LCS) contrasting, where the first
one considers the relations of various action/background proposals by using
match, insert, and delete operators and the second one mines the longest common
subsequences between two videos. Both contrasting modules can enhance each
other and jointly enjoy the merits of discriminative action-background
separation and alleviated task gap between classification and localization.
Extensive experiments show that our method achieves state-of-the-art
performance on two popular benchmarks. Our code is available at
https://github.com/MengyuanChen21/CVPR2022-FTCL.

Dimension reduction and data quantization are two important methods for
reducing data complexity. In the paper, we study the methodology of first
reducing data dimension by random projection and then quantizing the
projections to ternary or binary codes, which has been widely applied in
classification. Usually, the quantization will seriously degrade the accuracy
of classification due to high quantization errors. Interestingly, however, we
observe that the quantization could provide comparable and often superior
accuracy, as the data to be quantized are sparse features generated with common
filters. Furthermore, this quantization property could be maintained in the
random projections of sparse features, if both the features and random
projection matrices are sufficiently sparse. By conducting extensive
experiments, we validate and analyze this intriguing property.

Physics-informed machine learning (PIML), referring to the combination of
prior knowledge of physics, which is the high level abstraction of natural
phenomenons and human behaviours in the long history, with data-driven machine
learning models, has emerged as an effective way to mitigate the shortage of
training data, to increase models' generalizability and to ensure the physical
plausibility of results. In this paper, we survey an abundant number of recent
works in PIML and summarize them from three aspects: (1) motivations of PIML,
(2) physics knowledge in PIML, (3) methods of physics knowledge integration in
PIML. We also discuss current challenges and corresponding research
opportunities in PIML.

Emotion Recognition (ER) aims to classify human utterances into different
emotion categories. Based on early-fusion and self-attention-based multimodal
interaction between text and acoustic modalities, in this paper, we propose
MMER, a multimodal multitask learning approach for ER from individual
utterances in isolation. Our proposed MMER leverages a multimodal dynamic
fusion network that adds minimal parameters over an existing speech encoder to
leverage the semantic and syntactic properties hidden in text. Experiments on
the IEMOCAP benchmark show that our proposed model achieves state-of-the-art
performance. In addition, strong baselines and ablation studies prove the
effectiveness of our proposed approach. We make our code publicly available on
GitHub.

Realistic and diverse simulation scenarios with reactive and feasible agent
behaviors can be used for validation and verification of self-driving system
performance without relying on expensive and time-consuming real-world testing.
Existing simulators rely on heuristic-based behavior models for background
vehicles, which cannot capture the complex interactive behaviors in real-world
scenarios. To bridge the gap between simulation and the real world, we propose
TrajGen, a two-stage trajectory generation framework, which can capture more
realistic behaviors directly from human demonstration. In particular, TrajGen
consists of the multi-modal trajectory prediction stage and the reinforcement
learning based trajectory modification stage. In the first stage, we propose a
novel auxiliary RouteLoss for the trajectory prediction model to generate
multi-modal diverse trajectories in the drivable area. In the second stage,
reinforcement learning is used to track the predicted trajectories while
avoiding collisions, which can improve the feasibility of generated
trajectories. In addition, we develop a data-driven simulator I-Sim that can be
used to train reinforcement learning models in parallel based on naturalistic
driving data. The vehicle model in I-Sim can guarantee that the generated
trajectories by TrajGen satisfy vehicle kinematic constraints. Finally, we give
comprehensive metrics to evaluate generated trajectories for simulation
scenarios, which shows that TrajGen outperforms either trajectory prediction or
inverse reinforcement learning in terms of fidelity, reactivity, feasibility,
and diversity.

We present the COGnitive Neural GENerative system (CogNGen), a cognitive
architecture that combines two neurobiologically-plausible, computational
models: predictive processing and hyperdimensional/vector-symbolic models. We
draw inspiration from architectures such as ACT-R and Spaun/Nengo. CogNGen is
in broad agreement with these, providing a level of detail between ACT-R's
high-level symbolic description of human cognition and Spaun's low-level
neurobiological description, furthermore creating the groundwork for designing
agents that learn continually from diverse tasks and model human performance at
larger scales than what is possible with current systems. We test CogNGen on
four maze-learning tasks, including those that test memory and planning, and
find that CogNGen matches performance of deep reinforcement learning models and
exceeds on a task designed to test memory.

Environmental, Social, and Governance (ESG) are non-financial factors that
are garnering attention from investors as they increasingly look to apply these
as part of their analysis to identify material risks and growth opportunities.
Some of this attention is also driven by clients who, now more aware than ever,
are demanding for their money to be managed and invested responsibly. As the
interest in ESG grows, so does the need for investors to have access to
consumable ESG information. Since most of it is in text form in reports,
disclosures, press releases, and 10-Q filings, we see a need for sophisticated
NLP techniques for classification tasks for ESG text. We hypothesize that an
ESG domain-specific pre-trained model will help with such and study building of
the same in this paper. We explored doing this by fine-tuning BERTs pre-trained
weights using ESG specific text and then further fine-tuning the model for a
classification task. We were able to achieve accuracy better than the original
BERT and baseline models in environment-specific classification tasks.

The inherent challenge of detecting symmetries stems from arbitrary
orientations of symmetry patterns; a reflection symmetry mirrors itself against
an axis with a specific orientation while a rotation symmetry matches its
rotated copy with a specific orientation. Discovering such symmetry patterns
from an image thus benefits from an equivariant feature representation, which
varies consistently with reflection and rotation of the image. In this work, we
introduce a group-equivariant convolutional network for symmetry detection,
dubbed EquiSym, which leverages equivariant feature maps with respect to a
dihedral group of reflection and rotation. The proposed network is built
end-to-end with dihedrally-equivariant layers and trained to output a spatial
map for reflection axes or rotation centers. We also present a new dataset,
DENse and DIverse symmetry (DENDI), which mitigates limitations of existing
benchmarks for reflection and rotation symmetry detection. Experiments show
that our method achieves the state of the arts in symmetry detection on LDRS
and DENDI datasets.

Learning generic joint representations for video and text by a supervised
method requires a prohibitively substantial amount of manually annotated video
datasets. As a practical alternative, a large-scale but uncurated and narrated
video dataset, HowTo100M, has recently been introduced. But it is still
challenging to learn joint embeddings of video and text in a self-supervised
manner, due to its ambiguity and non-sequential alignment. In this paper, we
propose a novel multi-modal self-supervised framework Video-Text Temporally
Weak Alignment-based Contrastive Learning (VT-TWINS) to capture significant
information from noisy and weakly correlated data using a variant of Dynamic
Time Warping (DTW). We observe that the standard DTW inherently cannot handle
weakly correlated data and only considers the globally optimal alignment path.
To address these problems, we develop a differentiable DTW which also reflects
local information with weak temporal alignment. Moreover, our proposed model
applies a contrastive learning scheme to learn feature representations on
weakly correlated data. Our extensive experiments demonstrate that VT-TWINS
attains significant improvements in multi-modal representation learning and
outperforms various challenging downstream tasks. Code is available at
https://github.com/mlvlab/VT-TWINS.

Automatic image-based pavement distress detection and recognition are vital
for pavement maintenance and management. However, existing deep learning-based
methods largely omit the specific characteristics of pavement images, such as
high image resolution and low distress area ratio, and are not end-to-end
trainable. In this paper, we present a series of simple yet effective
end-to-end deep learning approaches named Weakly Supervised Patch Label
Inference Networks (WSPLIN) for efficiently addressing these tasks under
various application settings. To fully exploit the resolution and scale
information, WSPLIN first divides the pavement image under different scales
into patches with different collection strategies and then employs a Patch
Label Inference Network (PLIN) to infer the labels of these patches. Notably,
we design a patch label sparsity constraint based on the prior knowledge of
distress distribution, and leverage the Comprehensive Decision Network (CDN) to
guide the training of PLIN in a weakly supervised way. Therefore, the patch
labels produced by PLIN provide interpretable intermediate information, such as
the rough location and the type of distress. We evaluate our method on a
large-scale bituminous pavement distress dataset named CQU-BPDD. Extensive
results demonstrate the superiority of our method over baselines in both
performance and efficiency.

In the recent years, pixel-based perceptual algorithms have been successfully
applied for privacy-preserving deep learning (DL) based applications. However,
their security has been broken in subsequent works by demonstrating a
chosen-plaintext attack. In this paper, we propose an efficient pixel-based
perceptual encryption method. The method provides a necessary level of security
while preserving the intrinsic properties of the original image. Thereby, can
enable deep learning (DL) applications in the encryption domain. The method is
substitution based where pixel values are XORed with a sequence (as opposed to
a single value used in the existing methods) generated by a chaotic map. We
have used logistic maps for their low computational requirements. In addition,
to compensate for any inefficiency because of the logistic maps, we use a
second key to shuffle the sequence. We have compared the proposed method in
terms of encryption efficiency and classification accuracy of the DL models on
them. We have validated the proposed method with CIFAR datasets. The analysis
shows that when classification is performed on the cipher images, the model
preserves accuracy of the existing methods while provides better security.

Visual appearance is considered to be the most important cue to understand
images for cross-modal retrieval, while sometimes the scene text appearing in
images can provide valuable information to understand the visual semantics.
Most of existing cross-modal retrieval approaches ignore the usage of scene
text information and directly adding this information may lead to performance
degradation in scene text free scenarios. To address this issue, we propose a
full transformer architecture to unify these cross-modal retrieval scenarios in
a single $\textbf{Vi}$sion and $\textbf{S}$cene $\textbf{T}$ext
$\textbf{A}$ggregation framework (ViSTA). Specifically, ViSTA utilizes
transformer blocks to directly encode image patches and fuse scene text
embedding to learn an aggregated visual representation for cross-modal
retrieval. To tackle the modality missing problem of scene text, we propose a
novel fusion token based transformer aggregation approach to exchange the
necessary scene text information only through the fusion token and concentrate
on the most important features in each modality. To further strengthen the
visual modality, we develop dual contrastive learning losses to embed both
image-text pairs and fusion-text pairs into a common cross-modal space.
Compared to existing methods, ViSTA enables to aggregate relevant scene text
semantics with visual appearance, and hence improve results under both scene
text free and scene text aware scenarios. Experimental results show that ViSTA
outperforms other methods by at least $\bf{8.4}\%$ at Recall@1 for scene text
aware retrieval task. Compared with state-of-the-art scene text free retrieval
methods, ViSTA can achieve better accuracy on Flicker30K and MSCOCO while
running at least three times faster during the inference stage, which validates
the effectiveness of the proposed framework.

We present Mask Atari, a new benchmark to help solve partially observable
Markov decision process (POMDP) problems with Deep Reinforcement Learning
(DRL)-based approaches. To achieve a simulation environment for the POMDP
problems, Mask Atari is constructed based on Atari 2600 games with
controllable, moveable, and learnable masks as the observation area for the
target agent, especially with the active information gathering (AIG) setting in
POMDPs. Given that one does not yet exist, Mask Atari provides a challenging,
efficient benchmark for evaluating the methods that focus on the above problem.
Moreover, the mask operation is a trial for introducing the receptive field in
the human vision system into a simulation environment for an agent, which means
the evaluations are not biased from the sensing ability and purely focus on the
cognitive performance of the methods when compared with the human baseline. We
describe the challenges and features of our benchmark and evaluate several
baselines with Mask Atari.

Utilizing text-only data with an external language model (ELM) in end-to-end
RNN-Transducer (RNN-T) for speech recognition is challenging. Recently, a class
of methods such as density ratio (DR) and internal language model estimation
(ILME) have been developed, outperforming the classic shallow fusion (SF)
method. The basic idea behind these methods is that RNN-T posterior should
first subtract the implicitly learned internal language model (ILM) prior, in
order to integrate the ELM. While recent studies suggest that RNN-T only learns
some low-order language model information, the DR method uses a well-trained
neural language model with full context, which may be inappropriate for the
estimation of ILM and deteriorate the integration performance. Based on the DR
method, we propose a low-order density ratio method (LODR) by replacing the
estimation with a low-order weak language model. Extensive empirical
experiments are conducted on both in-domain and cross-domain scenarios on
English LibriSpeech & Tedlium-2 and Chinese WenetSpeech & AISHELL-1 datasets.
It is shown that LODR consistently outperforms SF in all tasks, while
performing generally close to ILME and better than DR in most tests.

Hate speech has spread more rapidly through the daily use of technology and,
most notably, by sharing your opinions or feelings on social media in a
negative aspect. Although numerous works have been carried out in detecting
hate speeches in English, German, and other languages, very few works have been
carried out in the context of the Bengali language. In contrast, millions of
people communicate on social media in Bengali. The few existing works that have
been carried out need improvements in both accuracy and interpretability. This
article proposed encoder decoder based machine learning model, a popular tool
in NLP, to classify user's Bengali comments on Facebook pages. A dataset of
7,425 Bengali comments, consisting of seven distinct categories of hate
speeches, was used to train and evaluate our model. For extracting and encoding
local features from the comments, 1D convolutional layers were used. Finally,
the attention mechanism, LSTM, and GRU based decoders have been used for
predicting hate speech categories. Among the three encoder decoder algorithms,
the attention-based decoder obtained the best accuracy (77%).

Speech representations learned from Self-supervised learning (SSL) models can
benefit various speech processing tasks. However, utilizing SSL representations
usually requires fine-tuning the pre-trained models or designing task-specific
downstream models and loss functions, causing much memory usage and human
labor. Recently, prompting in Natural Language Processing (NLP) has been found
to be an efficient technique to leverage pre-trained language models (LMs).
Specifically, prompt tuning optimizes a limited number of task-specific
parameters with a fixed pre-trained model; as a result, only a small set of
parameters is needed to be stored for each task. Prompt tuning improves
computation and memory efficiency by leveraging the pre-trained LM's prediction
ability. Nevertheless, such a paradigm is little studied in the speech
community. We report in this paper the first exploration of the prompt tuning
paradigm for speech processing tasks based on Generative Spoken Language Model
(GSLM). Experiment results show that the prompt tuning technique achieves
competitive performance in speech classification tasks with fewer trainable
parameters than fine-tuning specialized downstream models. We further study the
technique in challenging sequence generation tasks. Prompt tuning also
demonstrates its potential, while the limitation and possible research
directions are discussed in this paper. The source code is available on
https://github.com/ga642381/SpeechPrompt.

Keyword spotting (KWS) and speaker verification (SV) are two important tasks
in speech applications. Research shows that the state-of-art KWS and SV models
are trained independently using different datasets since they expect to learn
distinctive acoustic features. However, humans can distinguish language content
and the speaker identity simultaneously. Motivated by this, we believe it is
important to explore a method that can effectively extract common features
while decoupling task-specific features. Bearing this in mind, a two-branch
deep network (KWS branch and SV branch) with the same network structure is
developed and a novel decoupling feature learning method is proposed to push up
the performance of KWS and SV simultaneously where speaker-invariant keyword
representations and keyword-invariant speaker representations are expected
respectively. Experiments are conducted on Google Speech Commands Dataset
(GSCD). The results demonstrate that the orthogonality regularization helps the
network to achieve SOTA EER of 1.31% and 1.87% on KWS and SV, respectively.

Learning knowledge representation of scientific paper data is a problem to be
solved, and how to learn the representation of paper nodes in scientific paper
heterogeneous network is the core to solve this problem. This paper proposes an
unsupervised cluster-level scientific paper heterogeneous graph node
representation learning method (UCHL), aiming at obtaining the representation
of nodes (authors, institutions, papers, etc.) in the heterogeneous graph of
scientific papers. Based on the heterogeneous graph representation, this paper
performs link prediction on the entire heterogeneous graph and obtains the
relationship between the edges of the nodes, that is, the relationship between
papers and papers. Experiments results show that the proposed method achieves
excellent performance on multiple evaluation metrics on real scientific paper
datasets.

Neural vocoder using denoising diffusion probabilistic model (DDPM) has been
improved by adaptation of the diffusion noise distribution to given acoustic
features. In this study, we propose SpecGrad that adapts the diffusion noise so
that its time-varying spectral envelope becomes close to the conditioning
log-mel spectrogram. This adaptation by time-varying filtering improves the
sound quality especially in the high-frequency bands. It is processed in the
time-frequency domain to keep the computational cost almost the same as the
conventional DDPM-based neural vocoders. Experimental results showed that
SpecGrad generates higher-fidelity speech waveform than conventional DDPM-based
neural vocoders in both analysis-synthesis and speech enhancement scenarios.
Audio demos are available at wavegrad.github.io/specgrad/.

This paper presents an alternate representation framework to commonly used
time-frequency representation for acoustic scene classification (ASC). A raw
audio signal is represented using a pre-trained convolutional neural network
(CNN) using its various intermediate layers. The study assumes that the
representations obtained from the intermediate layers lie in low-dimensions
intrinsically. To obtain low-dimensional embeddings, principal component
analysis is performed, and the study analyzes that only a few principal
components are significant. However, the appropriate number of significant
components are not known. To address this, an automatic dictionary learning
framework is utilized that approximates the underlying subspace. Further, the
low-dimensional embeddings are aggregated in a late-fusion manner in the
ensemble framework to incorporate hierarchical information learned at various
intermediate layers. The experimental evaluation is performed on publicly
available DCASE 2017 and 2018 ASC datasets on a pre-trained 1-D CNN, SoundNet.
Empirically, it is observed that deeper layers show more compression ratio than
others. At 70% compression ratio across different datasets, the performance is
similar to that obtained without performing any dimensionality reduction. The
proposed framework outperforms the time-frequency representation based methods.

Due to a constantly increasing amount of speech data that is stored in
different types of databases, voice privacy has become a major concern. To
respond to such concern, speech researchers have developed various methods for
speaker de-identification. The state-of-the-art solutions utilize deep learning
solutions which can be effective but might be unavailable or impractical to
apply for, for example, under-resourced languages. Formant modification is a
simpler, yet effective method for speaker de-identification which requires no
training data. Still, remaining intonational patterns in formant-anonymized
speech may contain speaker-dependent cues. This study introduces a novel
speaker de-identification method, which, in addition to simple formant shifts,
manipulates f0 trajectories based on functional data analysis. The proposed
speaker de-identification method will conceal plausibly identifying pitch
characteristics in a phonetically controllable manner and improve formant-based
speaker de-identification up to 25%.

This paper proposes a model-free Volt-VAR control (VVC) algorithm via the
spatio-temporal graph ConvNet-based deep reinforcement learning (STGCN-DRL)
framework, whose goal is to control smart inverters in an unbalanced
distribution system. We first identify the graph shift operator (GSO) based on
the power flow equations. Then, we develop a spatio-temporal graph ConvNet
(STGCN), testing both recurrent graph ConvNets (RGCN) and convolutional graph
ConvNets (CGCN) architectures, aimed at capturing the spatiotemporal
correlation of voltage phasors. The STGCN layer performs the feature extraction
task for the policy function and the value function of the reinforcement
learning architecture, and then we utilize the proximal policy optimization
(PPO) to search the action spaces for an optimum policy function and to
approximate an optimum value function. We further utilize the low-pass property
of voltage graph signal to introduce an GCN architecture for the the policy
whose input is a decimated state vector, i.e. a partial observation. Case
studies on the unbalanced 123-bus systems validate the excellent performance of
the proposed method in mitigating instabilities and maintaining nodal voltage
profiles within a desirable range.

As a promising candidate to complement traditional biometric modalities,
brain biometrics using electroencephalography (EEG) data has received a
widespread attention in recent years. However, compared with existing
biometrics such as fingerprints and face recognition, research on EEG
biometrics is still in its infant stage. Most of the studies focus on either
designing signal elicitation protocols from the perspective of neuroscience or
developing feature extraction and classification algorithms from the viewpoint
of machine learning. These studies have laid the ground for the feasibility of
using EEG as a biometric authentication modality, but they have also raised
security and privacy concerns as EEG data contains sensitive information.
Existing research has used hash functions and cryptographic schemes to protect
EEG data, but they do not provide functions for revoking compromised templates
as in cancellable template design. This paper proposes the first cancellable
EEG template design for privacy-preserving EEG-based authentication systems,
which can protect raw EEG signals containing sensitive privacy information
(e.g., identity, health and cognitive status). A novel cancellable EEG template
is developed based on EEG graph features and a non-invertible transform. The
proposed transformation provides cancellable templates, while taking advantage
of EEG elicitation protocol fusion to enhance biometric performance. The
proposed authentication system offers equivalent authentication performance
(8.58\% EER on a public database) as in the non-transformed domain, while
protecting raw EEG data. Furthermore, we analyze the system's capacity for
resisting multiple attacks, and discuss some overlooked but critical issues and
possible pitfalls involving hill-climbing attacks, second attacks, and
classification-based authentication systems.

Explaining the generalization characteristics of deep learning is an emerging
topic in advanced machine learning. There are several unanswered questions
about how learning under stochastic optimization really works and why certain
strategies are better than others. In this paper, we address the following
question: \textit{can we probe intermediate layers of a deep neural network to
identify and quantify the learning quality of each layer?} With this question
in mind, we propose new explainability metrics that measure the redundant
information in a network's layers using a low-rank factorization framework and
quantify a complexity measure that is highly correlated with the generalization
performance of a given optimizer, network, and dataset. We subsequently exploit
these metrics to augment the Stochastic Gradient Descent (SGD) optimizer by
adaptively adjusting the learning rate in each layer to improve in
generalization performance. Our augmented SGD -- dubbed RMSGD -- introduces
minimal computational overhead compared to SOTA methods and outperforms them by
exhibiting strong generalization characteristics across application,
architecture, and dataset.

We present a novel method for classification of Synthetic Aperture Radar
(SAR) data by combining ideas from graph-based learning and neural network
methods within an active learning framework. Graph-based methods in machine
learning are based on a similarity graph constructed from the data. When the
data consists of raw images composed of scenes, extraneous information can make
the classification task more difficult. In recent years, neural network methods
have been shown to provide a promising framework for extracting patterns from
SAR images. These methods, however, require ample training data to avoid
overfitting. At the same time, such training data are often unavailable for
applications of interest, such as automatic target recognition (ATR) and SAR
data. We use a Convolutional Neural Network Variational Autoencoder (CNNVAE) to
embed SAR data into a feature space, and then construct a similarity graph from
the embedded data and apply graph-based semi-supervised learning techniques.
The CNNVAE feature embedding and graph construction requires no labeled data,
which reduces overfitting and improves the generalization performance of graph
learning at low label rates. Furthermore, the method easily incorporates a
human-in-the-loop for active learning in the data-labeling process. We present
promising results and compare them to other standard machine learning methods
on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset
for ATR with small amounts of labeled data.

Most measurements in particle and nuclear physics use matrix-based unfolding
algorithms to correct for detector effects. In nearly all cases, the observable
is defined analogously at the particle and detector level. We point out that
while the particle-level observable needs to be physically motivated to link
with theory, the detector-level need not be and can be optimized. We show that
using deep learning to define detector-level observables has the capability to
improve the measurement when combined with standard unfolding methods.

In recent years, Jupyter notebooks have grown in popularity in several
domains of software engineering, such as data science, machine learning, and
computer science education. Their popularity has to do with their rich features
for presenting and visualizing data, however, recent studies show that
notebooks also share a lot of drawbacks: high number of code clones, low
reproducibility, etc.
  In this work, we carry out a comparison between Python code written in
Jupyter Notebooks and in traditional Python scripts. We compare the code from
two perspectives: structural and stylistic. In the first part of the analysis,
we report the difference in the number of lines, the usage of functions, as
well as various complexity metrics. In the second part, we show the difference
in the number of stylistic issues and provide an extensive overview of the 15
most frequent stylistic issues in the studied mediums. Overall, we demonstrate
that notebooks are characterized by the lower code complexity, however, their
code could be perceived as more entangled than in the scripts. As for the
style, notebooks tend to have 1.4 times more stylistic issues, but at the same
time, some of them are caused by specific coding practices in notebooks and
should be considered as false positives. With this research, we want to pave
the way to studying specific problems of notebooks that should be addressed by
the development of notebook-specific tools, and provide various insights that
can be useful in this regard.

Parametrized quantum circuits can be used as quantum neural networks and have
the potential to outperform their classical counterparts when trained for
addressing learning problems. To date, much of the results on their performance
on practical problems are heuristic in nature. In particular, the convergence
rate for the training of quantum neural networks is not fully understood. Here,
we analyze the dynamics of gradient descent for the training error of a class
of variational quantum machine learning models. We define wide quantum neural
networks as parameterized quantum circuits in the limit of a large number of
qubits and variational parameters. We then find a simple analytic formula that
captures the average behavior of their loss function and discuss the
consequences of our findings. For example, for random quantum circuits, we
predict and characterize an exponential decay of the residual training error as
a function of the parameters of the system. We finally validate our analytic
results with numerical experiments.

Adapting pre-trained models with broad capabilities has become standard
practice for learning a wide range of downstream tasks. The typical approach of
fine-tuning different models for each task is performant, but incurs a
substantial memory cost. To efficiently learn multiple downstream tasks we
introduce Task Adaptive Parameter Sharing (TAPS), a general method for tuning a
base model to a new task by adaptively modifying a small, task-specific subset
of layers. This enables multi-task learning while minimizing resources used and
competition between tasks. TAPS solves a joint optimization problem which
determines which layers to share with the base model and the value of the
task-specific weights. Further, a sparsity penalty on the number of active
layers encourages weight sharing with the base model. Compared to other
methods, TAPS retains high accuracy on downstream tasks while introducing few
task-specific parameters. Moreover, TAPS is agnostic to the model architecture
and requires only minor changes to the training scheme. We evaluate our method
on a suite of fine-tuning tasks and architectures (ResNet, DenseNet, ViT) and
show that it achieves state-of-the-art performance while being simple to

Variational quantum algorithms stand at the forefront of simulations on
near-term and future fault-tolerant quantum devices. While most variational
quantum algorithms involve only continuous optimization variables, the
representational power of the variational ansatz can sometimes be significantly
enhanced by adding certain discrete optimization variables, as is exemplified
by the generalized quantum approximate optimization algorithm (QAOA). However,
the hybrid discrete-continuous optimization problem in the generalized QAOA
poses a challenge to the optimization. We propose a new algorithm called
MCTS-QAOA, which combines a Monte Carlo tree search method with an improved
natural policy gradient solver to optimize the discrete and continuous
variables in the quantum circuit, respectively. We find that MCTS-QAOA has
excellent noise-resilience properties and outperforms prior algorithms in
challenging instances of the generalized QAOA.

Incorporating artificial intelligence and machine learning (AI/ML) methods
within the 5G wireless standard promises autonomous network behavior and
ultra-low-latency reconfiguration. However, the effort so far has purely
focused on learning from radio frequency (RF) signals. Future standards and
next-generation (nextG) networks beyond 5G will have two significant evolutions
over the state-of-the-art 5G implementations: (i) massive number of antenna
elements, scaling up to hundreds-to-thousands in number, and (ii) inclusion of
AI/ML in the critical path of the network reconfiguration process that can
access sensor feeds from a variety of RF and non-RF sources. While the former
allows unprecedented flexibility in 'beamforming', where signals combine
constructively at a target receiver, the latter enables the network with
enhanced situation awareness not captured by a single and isolated data
modality. This survey presents a thorough analysis of the different approaches
used for beamforming today, focusing on mmWave bands, and then proceeds to make
a compelling case for considering non-RF sensor data from multiple modalities,
such as LiDAR, Radar, GPS for increasing beamforming directional accuracy and
reducing processing time. This so called idea of multimodal beamforming will
require deep learning based fusion techniques, which will serve to augment the
current RF-only and classical signal processing methods that do not scale well
for massive antenna arrays. The survey describes relevant deep learning
architectures for multimodal beamforming, identifies computational challenges
and the role of edge computing in this process, dataset generation tools, and
finally, lists open challenges that the community should tackle to realize this
transformative vision of the future of beamforming.

Traditional studies on voice conversion (VC) have made progress with parallel
training data and known speakers. Good voice conversion quality is obtained by
exploring better alignment modules or expressive mapping functions. In this
study, we investigate zero-shot VC from a novel perspective of self-supervised
disentangled speech representation learning. Specifically, we achieve the
disentanglement by balancing the information flow between global speaker
representation and time-varying content representation in a sequential
variational autoencoder (VAE). A zero-shot voice conversion is performed by
feeding an arbitrary speaker embedding and content embeddings to the VAE
decoder. Besides that, an on-the-fly data augmentation training strategy is
applied to make the learned representation noise invariant. On TIMIT and VCTK
datasets, we achieve state-of-the-art performance on both objective evaluation,
i.e., speaker verification (SV) on speaker embedding and content embedding, and
subjective evaluation, i.e., voice naturalness and similarity, and remains to
be robust even with noisy source/target utterances.

Many extensions of the standard model (SM) predict the existence of
axion-like particles and/or dark Higgs in the sub-GeV scale. Two new sub-GeV
particles, a scalar and a pseudoscalar, produced through the Higgs boson exotic
decays, are investigated. The decay signatures of these two new particles with
highly collimated photons in the final states are discriminated from the ones
of SM backgrounds using the Convolutional Neural Networks and Boosted Decision
Trees techniques. The sensitivities of searching for such new physics
signatures at the Large Hadron Collider are obtained.

Memorization of the relation between entities in a dataset can lead to
privacy issues when using a trained model for question answering. We introduce
Relational Memorization (RM) to understand, quantify and control this
phenomenon. While bounding general memorization can have detrimental effects on
the performance of a trained model, bounding RM does not prevent effective
learning. The difference is most pronounced when the data distribution is
long-tailed, with many queries having only few training examples: Impeding
general memorization prevents effective learning, while impeding only
relational memorization still allows learning general properties of the
underlying concepts. We formalize the notion of Relational Privacy (RP) and,
inspired by Differential Privacy (DP), we provide a possible definition of
Differential Relational Privacy (DrP). These notions can be used to describe
and compute bounds on the amount of RM in a trained model. We illustrate
Relational Privacy concepts in experiments with large-scale models for Question
Answering.

Electron density distribution plays an essential role in predicting molecular
properties. It is also a simple observable from which machine-learning models
for molecular electronic structure can be derived. In the present work, we
present the performance of the one-body M{\o}ller-Plesset second-order
perturbation (OBMP2) theory that we have recently developed. In OBMP2, an
effective one-body Hamiltonian including dynamic correlation at the MP2 level
is derived using the canonical transformation followed by the cumulant
approximation. We evaluate electron density and related properties of three
groups of open-shell systems: atoms and their ions, main-group radicals, and
halogen dimmers. We find that OBMP2 outperforms standard MP2 and density
functional theory in all cases considered here, and its accuracy is comparable
to coupled-cluster singles and doubles (CCSD), a higher-level method. OBMP2 is
thus believed to be an effective method for predicting the accurate electron
density of open-shell molecules.

One of the main questions regarding complex systems at large scales concerns
the effective interactions and driving forces that emerge from the detailed
microscopic properties. Coarse-grained models aim to describe complex systems
in terms of coarse-scale equations with a reduced number of degrees of freedom.
Recent developments in machine learning (ML) algorithms have significantly
empowered the discovery process of the governing equations directly from data.
However, it remains difficult to discover partial differential equations (PDEs)
with high-order derivatives. In this paper, we present new data-driven
architectures based on multi-layer perceptron (MLP), convolutional neural
network (CNN), and a combination of CNN and long short-term memory (CNN-LSTM)
structures for discovering the non-linear equations of motion for phase-field
models with non-conserved and conserved order parameters. The well-known
Allen--Cahn, Cahn--Hilliard, and the phase-field crystal (PFC) models were used
as the test cases. Two conceptually different types of implementations were
used: (a) guided by physical intuition (such as local dependence of the
derivatives) and (b) in the absence of any physical assumptions (black-box
model). We show that not only can we effectively learn the time derivatives of
the field in both scenarios, but we can also use the data-driven PDEs to
propagate the field in time and achieve results in good agreement with the
original PDEs.

In this paper, we propose a simple yet powerful improvement over the recent
Self-Supervised Audio Spectrogram Transformer (SSAST) model for speech and
audio classification. Specifically, we leverage the insight that the SSAST uses
a very high masking ratio (75%) during pretraining, meaning that the vast
majority of self-attention compute is performed on mask tokens. We address this
by integrating the encoder-decoder architecture from Masked Autoencoders are
Scalable Vision Learners (MAE) into the SSAST, where a deep encoder operates on
only unmasked input, and a shallow decoder operates on encoder outputs and mask
tokens. We find that MAE-like pretraining can provide a 3x speedup and 2x
memory usage reduction over the vanilla SSAST using current audio pretraining
strategies with ordinary model and input sizes. When fine-tuning on downstream
tasks, which only uses the encoder, we find that our approach outperforms the
SSAST on a variety of downstream tasks. We further conduct comprehensive
evaluations into different strategies of pretraining and explore differences in
MAE-style pretraining between the visual and audio domains.

Finding best architectures of learning machines, such as deep neural
networks, is a well-known technical and theoretical challenge. Recent work by
Mellor et al (2021) showed that there may exist correlations between the
accuracies of trained networks and the values of some easily computable
measures defined on randomly initialised networks which may enable to search
tens of thousands of neural architectures without training. Mellor et al used
the Hamming distance evaluated over all ReLU neurons as such a measure.
Motivated by these findings, in our work, we ask the question of the existence
of other and perhaps more principled measures which could be used as
determinants of success of a given neural architecture. In particular, we
examine, if the dimensionality and quasi-orthogonality of neural networks'
feature space could be correlated with the network's performance after
training. We showed, using the setup as in Mellor et al, that dimensionality
and quasi-orthogonality may jointly serve as network's performance
discriminants. In addition to offering new opportunities to accelerate neural
architecture search, our findings suggest important relationships between the
networks' final performance and properties of their randomly initialised
feature spaces: data dimension and quasi-orthogonality.

Wind energy has emerged as a highly promising source of renewable energy in
recent times. However, wind turbines regularly suffer from operational
inconsistencies, leading to significant costs and challenges in operations and
maintenance (O&M). Condition-based monitoring (CBM) and performance
assessment/analysis of turbines are vital aspects for ensuring efficient O&M
planning and cost minimisation. Data-driven decision making techniques have
witnessed rapid evolution in the wind industry for such O&M tasks during the
last decade, from applying signal processing methods in early 2010 to
artificial intelligence (AI) techniques, especially deep learning in 2020. In
this article, we utilise statistical computing to present a scientometric
review of the conceptual and thematic evolution of AI in the wind energy
sector, providing evidence-based insights into present strengths and
limitations of data-driven decision making in the wind industry. We provide a
perspective into the future and on current key challenges in data availability
and quality, lack of transparency in black box-natured AI models, and
prevailing issues in deploying models for real-time decision support, along
with possible strategies to overcome these problems. We hope that a systematic
analysis of the past, present and future of CBM and performance assessment can
encourage more organisations to adopt data-driven decision making techniques in
O&M towards making wind energy sources more reliable, contributing to the
global efforts of tackling climate change.

Binary stars undergo a variety of interactions and evolutionary phases,
critical for predicting and explaining observed properties. Binary population
synthesis with full stellar-structure and evolution simulations are
computationally expensive requiring a large number of mass-transfer sequences.
The recently developed binary population synthesis code POSYDON incorporates
grids of MESA binary star simulations which are then interpolated to model
large-scale populations of massive binaries. The traditional method of
computing a high-density rectilinear grid of simulations is not scalable for
higher-dimension grids, accounting for a range of metallicities, rotation, and
eccentricity. We present a new active learning algorithm, psy-cris, which uses
machine learning in the data-gathering process to adaptively and iteratively
select targeted simulations to run, resulting in a custom, high-performance
training set. We test psy-cris on a toy problem and find the resulting training
sets require fewer simulations for accurate classification and regression than
either regular or randomly sampled grids. We further apply psy-cris to the
target problem of building a dynamic grid of MESA simulations, and we
demonstrate that, even without fine tuning, a simulation set of only $\sim 1/4$
the size of a rectilinear grid is sufficient to achieve the same classification
accuracy. We anticipate further gains when algorithmic parameters are optimized
for the targeted application. We find that optimizing for classification only
may lead to performance losses in regression, and vice versa. Lowering the
computational cost of producing grids will enable future versions of POSYDON to
cover more input parameters while preserving interpolation accuracies.

We present a debiased dataset for the Person-centric Visual Grounding (PCVG)
task first proposed by Cui et al. (2021) in the Who's Waldo dataset. Given an
image and a caption, PCVG requires pairing up a person's name mentioned in a
caption with a bounding box that points to the person in the image. We find
that the original Who's Waldo dataset compiled for this task contains a large
number of biased samples that are solvable simply by heuristic methods; for
instance, in many cases the first name in the sentence corresponds to the
largest bounding box, or the sequence of names in the sentence corresponds to
an exact left-to-right order in the image. Naturally, models trained on these
biased data lead to over-estimation of performance on the benchmark. To enforce
models being correct for the correct reasons, we design automated tools to
filter and debias the original dataset by ruling out all examples of
insufficient context, such as those with no verb or with a long chain of
conjunct names in their captions. Our experiments show that our new sub-sampled
dataset contains less bias with much lowered heuristic performances and widened
gaps between heuristic and supervised methods. We also demonstrate the same
benchmark model trained on our debiased training set outperforms that trained
on the original biased (and larger) training set on our debiased test set. We
argue our debiased dataset offers the PCVG task a more practical baseline for
reliable benchmarking and future improvements.

We introduce HyperMorph, a framework that facilitates efficient
hyperparameter tuning in learning-based deformable image registration.
Classical registration algorithms perform an iterative pair-wise optimization
to compute a deformation field that aligns two images. Recent learning-based
approaches leverage large image datasets to learn a function that rapidly
estimates a deformation for a given image pair. In both strategies, the
accuracy of the resulting spatial correspondences is strongly influenced by the
choice of certain hyperparameter values. However, an effective hyperparameter
search consumes substantial time and human effort as it often involves training
multiple models for different fixed hyperparameter values and may lead to
suboptimal registration. We propose an amortized hyperparameter learning
strategy to alleviate this burden by learning the impact of hyperparameters on
deformation fields. We design a meta network, or hypernetwork, that predicts
the parameters of a registration network for input hyperparameters, thereby
comprising a single model that generates the optimal deformation field
corresponding to given hyperparameter values. This strategy enables fast,
high-resolution hyperparameter search at test-time, reducing the inefficiency
of traditional approaches while increasing flexibility. We also demonstrate
additional benefits of HyperMorph, including enhanced robustness to model
initialization and the ability to rapidly identify optimal hyperparameter
values specific to a dataset, image contrast, task, or even anatomical region,
all without the need to retrain models. We make our code publicly available at
http://hypermorph.voxelmorph.net.

Recent studies on the automatic detection of facial action unit (AU) have
extensively relied on large-sized annotations. However, manually AU labeling is
difficult, time-consuming, and costly. Most existing semi-supervised works
ignore the informative cues from the temporal domain, and are highly dependent
on densely annotated videos, making the learning process less efficient. To
alleviate these problems, we propose a deep semi-supervised framework
Knowledge-Spreader (KS), which differs from conventional methods in two
aspects. First, rather than only encoding human knowledge as constraints, KS
also learns the Spatial-Temporal AU correlation knowledge in order to
strengthen its out-of-distribution generalization ability. Second, we approach
KS by applying consistency regularization and pseudo-labeling in multiple
student networks alternately and dynamically. It spreads the spatial knowledge
from labeled frames to unlabeled data, and completes the temporal information
of partially labeled video clips. Thus, the design allows KS to learn AU
dynamics from video clips with only one label allocated, which significantly
reduce the requirements of using annotations. Extensive experiments demonstrate
that the proposed KS achieves competitive performance as compared to the state
of the arts under the circumstances of using only 2% labels on BP4D and 5%
labels on DISFA. In addition, we test it on our newly developed large-scale
comprehensive emotion database, which contains considerable samples across
well-synchronized and aligned sensor modalities for easing the scarcity issue
of annotations and identities in human affective computing. The new database
will be released to the research community.

We demonstrate the use of model order reduction and neural networks for
estimating the hysteresis properties of nanocrystalline permanent magnets from
microstructure. With a data-driven approach, we learn the demagnetization curve
from data-sets created by grain growth and micromagnetic simulations. We show
that the granular structure of a magnet can be encoded within a low-dimensional
latent space. Latent codes are constructed using a variational autoencoder. The
mapping of structure code to hysteresis properties is a multi-target regression
problem. We apply deep neural network and use parameter sharing, in order to
predict anchor points along the demagnetization curves from the magnet's
structure code. The method is applied to study the magnetic properties of
nanocrystalline permanent magnets. We show how new grain structures can be
generated by interpolation between two points in the latent space and how the
magnetic properties of the resulting magnets can be predicted.

This paper studies the problem of identifying low-order linear systems via
Hankel nuclear norm regularization. Hankel regularization encourages the
low-rankness of the Hankel matrix, which maps to the low-orderness of the
system. We provide novel statistical analysis for this regularization and
carefully contrast it with the unregularized ordinary least-squares (OLS)
estimator. Our analysis leads to new bounds on estimating the impulse response
and the Hankel matrix associated with the linear system. We first design an
input excitation and show that Hankel regularization enables one to recover the
system using optimal number of observations in the true system order and
achieve strong statistical estimation rates. Surprisingly, we demonstrate that
the input design indeed matters, by showing that intuitive choices such as
i.i.d. Gaussian input leads to provably sub-optimal sample complexity. To
better understand the benefits of regularization, we also revisit the OLS
estimator. Besides refining existing bounds, we experimentally identify when
regularized approach improves over OLS: (1) For low-order systems with slow
impulse-response decay, OLS method performs poorly in terms of sample
complexity, (2) Hankel matrix returned by regularization has a more clear
singular value gap that ease identification of the system order, (3) Hankel
regularization is less sensitive to hyperparameter choice. Finally, we
establish model selection guarantees through a joint train-validation procedure
where we tune the regularization parameter for near-optimal estimation.

Intrinsic image decomposition is the process of recovering the image
formation components (reflectance and shading) from an image. Previous methods
employ either explicit priors to constrain the problem or implicit constraints
as formulated by their losses (deep learning). These methods can be negatively
influenced by strong illumination conditions causing shading-reflectance
leakages.
  Therefore, in this paper, an end-to-end edge-driven hybrid CNN approach is
proposed for intrinsic image decomposition. Edges correspond to illumination
invariant gradients. To handle hard negative illumination transitions, a
hierarchical approach is taken including global and local refinement layers. We
make use of attention layers to further strengthen the learning process.
  An extensive ablation study and large scale experiments are conducted showing
that it is beneficial for edge-driven hybrid IID networks to make use of
illumination invariant descriptors and that separating global and local cues
helps in improving the performance of the network. Finally, it is shown that
the proposed method obtains state of the art performance and is able to
generalise well to real world images. The project page with pretrained models,
finetuned models and network code can be found at
https://ivi.fnwi.uva.nl/cv/pienet/.

In Heterogeneous Face Recognition (HFR), the objective is to match faces
across two different domains such as visible and thermal. Large domain
discrepancy makes HFR a difficult problem. Recent methods attempting to fill
the gap via synthesis have achieved promising results, but their performance is
still limited by the scarcity of paired training data. In practice, large-scale
heterogeneous face data are often inaccessible due to the high cost of
acquisition and annotation process as well as privacy regulations. In this
paper, we propose a new face hallucination paradigm for HFR, which not only
enables data-efficient synthesis but also allows to scale up model training
without breaking any privacy policy. Unlike existing methods that learn face
synthesis entirely from scratch, our approach is particularly designed to take
advantage of rich and diverse facial priors from visible domain for more
faithful hallucination. On the other hand, large-scale training is enabled by
introducing a new federated learning scheme to allow institution-wise
collaborations while avoiding explicit data sharing. Extensive experiments
demonstrate the advantages of our approach in tackling HFR under current data
limitations. In a unified framework, our method yields the state-of-the-art
hallucination results on multiple HFR datasets.

Many popular contextual bandit algorithms estimate reward models to inform
decision making. However, true rewards can contain action-independent
redundancies that are not relevant for decision making and only increase the
statistical complexity of accurate estimation. It is sufficient and more
data-efficient to estimate the simplest function that explains the reward
differences between actions, that is, the heterogeneous treatment effect,
commonly understood to be more structured and simpler than the reward.
Motivated by this observation, building on recent work on oracle-based
algorithms, we design a statistically optimal and computationally efficient
algorithm using heterogeneous treatment effect estimation oracles. Our results
provide the first universal reduction of contextual bandits to a
general-purpose heterogeneous treatment effect estimation method. We show that
our approach is more robust to model misspecification than reward estimation
methods based on squared error regression oracles. Experimentally, we show the
benefits of heterogeneous treatment effect estimation in contextual bandits
over reward estimation.

Ranking systems have an unprecedented influence on how and what information
people access, and their impact on our society is being analyzed from different
perspectives, such as users' discrimination. A notable example is represented
by reputation-based ranking systems, a class of systems that rely on users'
reputation to generate a non-personalized item-ranking, proved to be biased
against certain demographic classes. To safeguard that a given sensitive user's
attribute does not systematically affect the reputation of that user, prior
work has operationalized a reputation independence constraint on this class of
systems. In this paper, we uncover that guaranteeing reputation independence
for a single sensitive attribute is not enough. When mitigating biases based on
one sensitive attribute (e.g., gender), the final ranking might still be biased
against certain demographic groups formed based on another attribute (e.g.,
age). Hence, we propose a novel approach to introduce reputation independence
for multiple sensitive attributes simultaneously. We then analyze the extent to
which our approach impacts on discrimination and other important properties of
the ranking system, such as its quality and robustness against attacks.
Experiments on two real-world datasets show that our approach leads to less
biased rankings with respect to multiple users' sensitive attributes, without
affecting the system's quality and robustness.

In this paper, we explore the use of GAN-based few-shot data augmentation as
a method to improve few-shot classification performance. We perform an
exploration into how a GAN can be fine-tuned for such a task (one of which is
in a class-incremental manner), as well as a rigorous empirical investigation
into how well these models can perform to improve few-shot classification. We
identify issues related to the difficulty of training such generative models
under a purely supervised regime with very few examples, as well as issues
regarding the evaluation protocols of existing works. We also find that in this
regime, classification accuracy is highly sensitive to how the classes of the
dataset are randomly split. Therefore, we propose a semi-supervised fine-tuning
approach as a more pragmatic way forward to address these problems.

Reproducibility is of utmost concern in machine learning and natural language
processing (NLP). In the field of natural language generation (especially
machine translation), the seminal paper of Post (2018) has pointed out problems
of reproducibility of the dominant metric, BLEU, at the time of publication.
Nowadays, BERT-based evaluation metrics considerably outperform BLEU. In this
paper, we ask whether results and claims from four recent BERT-based metrics
can be reproduced. We find that reproduction of claims and results often fails
because of (i) heavy undocumented preprocessing involved in the metrics, (ii)
missing code and (iii) reporting weaker results for the baseline metrics. (iv)
In one case, the problem stems from correlating not to human scores but to a
wrong column in the csv file, inflating scores by 5 points. Motivated by the
impact of preprocessing, we then conduct a second study where we examine its
effects more closely (for one of the metrics). We find that preprocessing can
have large effects, especially for highly inflectional languages. In this case,
the effect of preprocessing may be larger than the effect of the aggregation
mechanism (e.g., greedy alignment vs. Word Mover Distance).

$\textit{The Bachelor}$ is a reality TV dating show in which a single
bachelor selects his wife from a pool of approximately 30 female contestants
over eight weeks of filming (American Broadcasting Company 2002). We collected
the following data on all 422 contestants that participated in seasons 11
through 25: their Age, Hometown, Career, Race, Week they got their first 1-on-1
date, whether they got the first impression rose, and what "place" they ended
up getting. We then trained three machine learning models to predict the ideal
characteristics of a successful contestant on $\textit{The Bachelor}$. The
three algorithms that we tested were: random forest classification, neural
networks, and linear regression. We found consistency across all three models,
although the neural network performed the best overall. Our models found that a
woman has the highest probability of progressing far on $\textit{The Bachelor}$
if she is: 26 years old, white, from the Northwest, works as an dancer,
received a 1-on-1 in week 6, and did not receive the First Impression Rose. Our
methodology is broadly applicable to all romantic reality television, and our
results will inform future $\textit{The Bachelor}$ production and contestant
strategies. While our models were relatively successful, we still encountered
high misclassification rates. This may be because: (1) Our training dataset had
fewer than 400 points or (2) Our models were too simple to parameterize the
complex romantic connections contestants forge over the course of a season.

In traditional speaker diarization systems, a well-trained speaker model is a
key component to extract representations from consecutive and partially
overlapping segments in a long speech session. To be more consistent with the
back-end segmentation and clustering, we propose a new CNN-based speaker
modeling scheme, which takes into account the heterogeneity of the speakers in
each training segment and batch. We randomly and synthetically augment the
training data into a set of segments, each of which contains more than one
speaker and some overlapping parts. A soft label is imposed on each segment
based on its speaker occupation ratio, and the standard cross entropy loss is
implemented in model training. In this way, the speaker model should have the
ability to generate a geometrically meaningful embedding for each multi-speaker
segment. Experimental results show that our system is superior to the baseline
system using x-vectors in two speaker diarization tasks. In the CALLHOME task
trained on the NIST SRE and Switchboard datasets, our system achieves a
relative reduction of 12.93% in DER. In Track 2 of CHiME-6, our system provides
13.24%, 12.60%, and 5.65% relative reductions in DER, JER, and WER,

In this work we study robust one- and two-stage problems with discrete
uncertainty sets which are known to be hard to solve even if the underlying
deterministic problem is easy. Popular solution methods iteratively generate
scenario constraints and possibly second-stage variables. This way, by solving
a sequence of smaller problems, it is often possible to avoid the complexity of
considering all scenarios simultaneously. A key ingredient for the performance
of the iterative methods is a good selection of start scenarios. In this paper
we propose a data-driven heuristic to seed the iterative solution method with a
set of starting scenarios that provide a strong lower bound early in the
process, and result in considerably smaller overall solution times compared to
other benchmark methods. Our heuristic learns the relevance of a scenario by
extracting information from training data based on a combined similarity
measure between robust problem instances and single scenarios. Our experiments
show that predicting even a small number of good start scenarios by our method
can considerably reduce the computation time of the iterative methods.

We present a meta-learning framework for learning new visual concepts
quickly, from just one or a few examples, guided by multiple naturally
occurring data streams: simultaneously looking at images, reading sentences
that describe the objects in the scene, and interpreting supplemental sentences
that relate the novel concept with other concepts. The learned concepts support
downstream applications, such as answering questions by reasoning about unseen
images. Our model, namely FALCON, represents individual visual concepts, such
as colors and shapes, as axis-aligned boxes in a high-dimensional space (the
"box embedding space"). Given an input image and its paired sentence, our model
first resolves the referential expression in the sentence and associates the
novel concept with particular objects in the scene. Next, our model interprets
supplemental sentences to relate the novel concept with other known concepts,
such as "X has property Y" or "X is a kind of Y". Finally, it infers an optimal
box embedding for the novel concept that jointly 1) maximizes the likelihood of
the observed instances in the image, and 2) satisfies the relationships between
the novel concepts and the known ones. We demonstrate the effectiveness of our
model on both synthetic and real-world datasets.

As a neurophysiological response to threat or adverse conditions, stress can
affect cognition, emotion and behaviour with potentially detrimental effects on
health in the case of sustained exposure. Since the affective content of speech
is inherently modulated by an individual's physical and mental state, a
substantial body of research has been devoted to the study of paralinguistic
correlates of stress-inducing task load. Historically, voice stress analysis
(VSA) has been conducted using conventional digital signal processing (DSP)
techniques. Despite the development of modern methods based on deep neural
networks (DNNs), accurately detecting stress in speech remains difficult due to
the wide variety of stressors and considerable variability in the individual
stress perception. To that end, we introduce a set of five datasets for task
load detection in speech. The voice recordings were collected as either
cognitive or physical stress was induced in the cohort of volunteers, with a
cumulative number of more than a hundred speakers. We used the datasets to
design and evaluate a novel self-supervised audio representation that leverages
the effectiveness of handcrafted features (DSP-based) and the complexity of
data-driven DNN representations. Notably, the proposed approach outperformed
both extensive handcrafted feature sets and novel DNN-based audio
representation learning approaches.

Transformers typically require some form of positional encoding, such as
positional embeddings, to process natural language sequences. Surprisingly, we
find that transformer language models without any explicit positional encoding
are still competitive with standard models, and that this phenomenon is robust
across different datasets, model sizes, and sequence lengths. Probing
experiments reveal that such models acquire an implicit notion of absolute
positions throughout the network, effectively compensating for the missing
information. We conjecture that causal attention enables the model to infer the
number of predecessors that each token can attend to, thereby approximating its
absolute position.

This paper focuses on self-supervised video representation learning. Most
existing approaches follow the contrastive learning pipeline to construct
positive and negative pairs by sampling different clips. However, this
formulation tends to bias to static background and have difficulty establishing
global temporal structures. The major reason is that the positive pairs, i.e.,
different clips sampled from the same video, have limited temporal receptive
field, and usually share similar background but differ in motions. To address
these problems, we propose a framework to jointly utilize local clips and
global videos to learn from detailed region-level correspondence as well as
general long-term temporal relations. Based on a set of controllable
augmentations, we achieve accurate appearance and motion pattern alignment
through soft spatio-temporal region contrast. Our formulation is able to avoid
the low-level redundancy shortcut by mutual information minimization to improve
the generalization. We also introduce local-global temporal order dependency to
further bridge the gap between clip-level and video-level representations for
robust temporal modeling. Extensive experiments demonstrate that our framework
is superior on three video benchmarks in action recognition and video
retrieval, capturing more accurate temporal dynamics.

Enhancing neural networks with knowledge of physical equations has become an
efficient way of solving various physics problems, from fluid flow to
electromagnetism. Graph neural networks show promise in accurately representing
irregularly meshed objects and learning their dynamics, but have so far
required supervision through large datasets. In this work, we represent meshes
naturally as graphs, process these using Graph Networks, and formulate our
physics-based loss to provide an unsupervised learning framework for partial
differential equations (PDE). We quantitatively compare our results to a
classical numerical PDE solver, and show that our computationally efficient
approach can be used as an interactive PDE solver that is adjusting boundary
conditions in real-time and remains sufficiently close to the baseline
solution. Our inherently differentiable framework will enable the application
of PDE solvers in interactive settings, such as model-based control of
soft-body deformations, or in gradient-based optimization methods that require
a fully differentiable pipeline.

In recent years, the field of implicit neural representation has progressed
significantly. Models such as neural radiance fields (NeRF), which uses
relatively small neural networks, can represent high-quality scenes and achieve
state-of-the-art results for novel view synthesis. Training these types of
networks, however, is still computationally very expensive. We present depth
distribution neural radiance field (DDNeRF), a new method that significantly
increases sampling efficiency along rays during training while achieving
superior results for a given sampling budget. DDNeRF achieves this by learning
a more accurate representation of the density distribution along rays. More
specifically, we train a coarse model to predict the internal distribution of
the transparency of an input volume in addition to the volume's total density.
This finer distribution then guides the sampling procedure of the fine model.
This method allows us to use fewer samples during training while reducing

This paper investigates RF-based system for automatic American Sign Language
(ASL) recognition. We consider radar for ASL by joint spatio-temporal
preprocessing of radar returns using time frequency (TF) analysis and
high-resolution receive beamforming. The additional degrees of freedom offered
by joint temporal and spatial processing using a multiple antenna sensor can
help to recognize ASL conversation between two or more individuals. This is
performed by applying beamforming to collect spatial images in an attempt to
resolve individuals communicating at the same time through hand and arm
movements. The spatio-temporal images are fused and classified by a
convolutional neural network (CNN) which is capable of discerning signs
performed by different individuals even when the beamformer is unable to
separate the respective signs completely. The focus group comprises individuals
with varying expertise with sign language, and real time measurements at 77 GHz
frequency are performed using Texas Instruments (TI) cascade radar.

We evaluate the performance of federated learning (FL) in developing deep
learning models for analysis of digitized tissue sections. A classification
application was considered as the example use case, on quantifiying the
distribution of tumor infiltrating lymphocytes within whole slide images
(WSIs). A deep learning classification model was trained using 50*50 square
micron patches extracted from the WSIs. We simulated a FL environment in which
a dataset, generated from WSIs of cancer from numerous anatomical sites
available by The Cancer Genome Atlas repository, is partitioned in 8 different
nodes. Our results show that the model trained with the federated training
approach achieves similar performance, both quantitatively and qualitatively,
to that of a model trained with all the training data pooled at a centralized
location. Our study shows that FL has tremendous potential for enabling
development of more robust and accurate models for histopathology image
analysis without having to collect large and diverse training data at a single
location.

Multi-object Tracking (MOT) generally can be split into two sub-tasks, i.e.,
detection and association. Many previous methods follow the tracking by
detection paradigm, which first obtain detections at each frame and then
associate them between adjacent frames. Though with an impressive performance
by utilizing a strong detector, it will degrade their detection and association
performance under scenes with many occlusions and large motion if not using
temporal information. In this paper, we propose a novel Reference Search (RS)
module to provide a more reliable association based on the deformable
transformer structure, which is natural to learn the feature alignment for each
object among frames. RS takes previous detected results as references to
aggregate the corresponding features from the combined features of the adjacent
frames and makes a one-to-one track state prediction for each reference in
parallel. Therefore, RS can attain a reliable association coping with
unexpected motions by leveraging visual temporal features while maintaining the
strong detection performance by decoupling from the detector. Our RS module can
also be compatible with the structure of the other tracking by detection
frameworks. Furthermore, we propose a joint training strategy and an effective
matching pipeline for our online MOT framework with the RS module. Our method
achieves competitive results on MOT17 and MOT20 datasets.

Knowledge-based entity prediction (KEP) is a novel task that aims to improve
machine perception in autonomous systems. KEP leverages relational knowledge
from heterogeneous sources in predicting potentially unrecognized entities. In
this paper, we provide a formal definition of KEP as a knowledge completion
task. Three potential solutions are then introduced, which employ several
machine learning and data mining techniques. Finally, the applicability of KEP
is demonstrated on two autonomous systems from different domains; namely,
autonomous driving and smart manufacturing. We argue that in complex real-world
systems, the use of KEP would significantly improve machine perception while
pushing the current technology one step closer to achieving full autonomy.

Many important machine learning applications involve regularized nonconvex
bi-level optimization. However, the existing gradient-based bi-level
optimization algorithms cannot handle nonconvex or nonsmooth regularizers, and
they suffer from a high computation complexity in nonconvex bi-level
optimization. In this work, we study a proximal gradient-type algorithm that
adopts the approximate implicit differentiation (AID) scheme for nonconvex
bi-level optimization with possibly nonconvex and nonsmooth regularizers. In
particular, the algorithm applies the Nesterov's momentum to accelerate the
computation of the implicit gradient involved in AID. We provide a
comprehensive analysis of the global convergence properties of this algorithm
through identifying its intrinsic potential function. In particular, we
formally establish the convergence of the model parameters to a critical point
of the bi-level problem, and obtain an improved computation complexity
$\mathcal{O}(\kappa^{3.5}\epsilon^{-2})$ over the state-of-the-art result.
Moreover, we analyze the asymptotic convergence rates of this algorithm under a
class of local nonconvex geometries characterized by a {\L}ojasiewicz-type
gradient inequality. Experiment on hyper-parameter optimization demonstrates

Speech systems developed for a particular choice of acoustic domain and
sampling frequency do not translate easily to others. The usual practice is to
learn domain adaptation and bandwidth extension models independently. Contrary
to this, we propose to learn both tasks together. Particularly, we learn to map
narrowband conversational telephone speech to wideband microphone speech. We
developed parallel and non-parallel learning solutions which utilize both
paired and unpaired data. First, we first discuss joint and disjoint training
of multiple generative models for our tasks. Then, we propose a two-stage
learning solution where we use a pre-trained domain adaptation system for
pre-processing in bandwidth extension training. We evaluated our schemes on a
Speaker Verification downstream task. We used the JHU-MIT experimental setup
for NIST SRE21, which comprises SRE16, SRE-CTS Superset and SRE21. Our results
provide the first evidence that learning both tasks is better than learning
just one. On SRE16, our best system achieves 22% relative improvement in Equal
Error Rate w.r.t. a direct learning baseline and 8% w.r.t. a strong bandwidth
expansion system.

We present a novel neural network (NN) method for the detection and removal
of Radio Frequency Interference (RFI) from the raw digitized signal in the
signal processing chain of a typical radio astronomy experiment. The main
advantage of our method is that it does not require a training set. Instead,
our method relies on the fact that the true signal of interest coming from
astronomical sources is thermal and therefore described as a Gaussian random
process, which cannot be compressed. We employ a variational encoder/decoder
network to find the compressible information in the datastream that can explain
the most variance with the fewest degrees of freedom. We demonstrate it on a
set of toy problems and stored ringbuffers from the Baryon Mapping eXperiment
(BMX) prototype. We find that the RFI subtraction is effective at cleaning
simulated timestreams: while we find that the power spectra of the RFI-cleaned
timestreams output by the NN suffer from extra signal consistent with additive
noise, we find that it is generally around percent level across the band and
sub 10 percent in contaminated spectral channels even when RFI power is an
order of magnitude larger than the signal. We discuss advantages and
limitations of this method and possible implementation in the front-end of
future radio experiments.

Lung cancer is the leading cause of cancer-related mortality worldwide. Lung
cancer screening (LCS) using annual low-dose computed tomography (CT) scanning
has been proven to significantly reduce lung cancer mortality by detecting
cancerous lung nodules at an earlier stage. Improving risk stratification of
malignancy risk in lung nodules can be enhanced using machine/deep learning
algorithms. However most existing algorithms: a) have primarily assessed single
time-point CT data alone thereby failing to utilize the inherent advantages
contained within longitudinal imaging datasets; b) have not integrated into
computer models pertinent clinical data that might inform risk prediction; c)
have not assessed algorithm performance on the spectrum of nodules that are
most challenging for radiologists to interpret and where assistance from
analytic tools would be most beneficial.
  Here we show the performance of our time-series deep learning model
(DeepCAD-NLM-L) which integrates multi-model information across three
longitudinal data domains: nodule-specific, lung-specific, and clinical
demographic data. We compared our time-series deep learning model to a)
radiologist performance on CTs from the National Lung Screening Trial enriched
with the most challenging nodules for diagnosis; b) a nodule management
algorithm from a North London LCS study (SUMMIT). Our model demonstrated
comparable and complementary performance to radiologists when interpreting
challenging lung nodules and showed improved performance (AUC=88\%) against
models utilizing single time-point data only. The results emphasise the
importance of time-series, multi-modal analysis when interpreting malignancy
risk in LCS.

In the last decade, Single-Board Computers (SBCs) have been employed more
frequently in engineering and computer science both to technical and
educational levels. Several factors such as the versatility, the low-cost, and
the possibility to enhance the learning process through technology have
contributed to the educators and students usually employ these devices.
However, the implications, possibilities, and constraints of these devices in
engineering and Computer Science (CS) education have not been explored in
detail. In this systematic literature review, we explore how the SBCs are
employed in engineering and computer science and what educational results are
derived from their usage in the period 2010-2020 at tertiary education. For
that, 154 studies were selected out of n=605 collected from the academic
databases Ei Compendex, ERIC, and Inspec. The analysis was carried-out in two
phases, identifying, e.g., areas of application, learning outcomes, and
students and researchers' perceptions. The results mainly indicate the
following aspects: (1) The areas of laboratories and e-learning, computing
education, robotics, Internet of Things (IoT), and persons with disabilities
gather the studies in the review. (2) Researchers highlight the importance of
the SBCs to transform the curricula in engineering and CS for the students to
learn complex topics through experimentation in hands-on activities. (3) The
typical cognitive learning outcomes reported by the authors are the improvement
of the students' grades and the technical skills regarding the topics in the
courses. Concerning the affective learning outcomes, the increase of interest,
motivation, and engagement are commonly reported by the authors.

Optical architectures have been emerging as an energy-efficient and
high-throughput hardware platform to accelerate computationally intensive
general matrix-matrix multiplications (GEMMs) in modern machine learning (ML)
algorithms. However, the inevitable imperfection and non-uniformity in
large-scale optoelectronic devices prevent the scalable deployment of optical
architectures, particularly those with innovative nano-devices. Here, we report
an optical ML hardware to accelerate GEMM operations based on cascaded spatial
light modulators and present a calibration procedure that enables accurate
calculations despite the non-uniformity and imperfection in devices and system.
We further characterize the hardware calculation accuracy under different
configurations of electrical-optical interfaces. Finally, we deploy the
developed optical hardware and calibration procedure to perform a ML task of
predicting the intersubband plasmon frequency in single-wall carbon nanotubes.
The obtained prediction accuracy from the optical hardware agrees well with
that obtained using a general purpose electronic graphic process unit.

We propose a novel approach aimed at object and semantic scene completion
from a partial scan represented as a 3D point cloud. Our architecture relies on
three novel layers that are used successively within an encoder-decoder
structure and specifically developed for the task at hand. The first one
carries out feature extraction by matching the point features to a set of
pre-trained local descriptors. Then, to avoid losing individual descriptors as
part of standard operations such as max-pooling, we propose an alternative
neighbor-pooling operation that relies on adopting the feature vectors with the
highest activations. Finally, up-sampling in the decoder modifies our feature
extraction in order to increase the output dimension. While this model is
already able to achieve competitive results with the state of the art, we
further propose a way to increase the versatility of our approach to process
point clouds. To this aim, we introduce a second model that assembles our
layers within a transformer architecture. We evaluate both architectures on
object and indoor scene completion tasks, achieving state-of-the-art

Entity recognition is a fundamental task in understanding document images.
Traditional sequence labeling frameworks treat the entity types as class IDs
and rely on extensive data and high-quality annotations to learn semantics
which are typically expensive in practice. In this paper, we aim to build an
entity recognition model requiring only a few shots of annotated document
images. To overcome the data limitation, we propose to leverage the label
surface names to better inform the model of the target entity type semantics
and also embed the labels into the spatial embedding space to capture the
spatial correspondence between regions and labels. Specifically, we go beyond
sequence labeling and develop a novel label-aware seq2seq framework, LASER. The
proposed model follows a new labeling scheme that generates the label surface
names word-by-word explicitly after generating the entities. During training,
LASER refines the label semantics by updating the label surface name
representations and also strengthens the label-region correlation. In this way,
LASER recognizes the entities from document images through both semantic and
layout correspondence. Extensive experiments on two benchmark datasets
demonstrate the superiority of LASER under the few-shot setting.

In this Letter we present an information-theoretic task that consists in
learning an apparently perfectly isolated bit of information by merely
spatially translating the object that encodes it. We show that the task can be
solved with the use of independently prepared indistinguishable particles, but
cannot be solved with distinguishable ones, unless they are entangled. Our
results thus corroborate the view that the entanglement that seems to be
necessarily present in systems comprised of indistinguishable particles is more
than a mere representational artefact and can indeed be used as a resource for
information processing. Apart from its foundational merit, our results are of
practical importance for cryptography.

Continually learning new classes from fresh data without forgetting previous
knowledge of old classes is a very challenging research problem. Moreover, it
is imperative that such learning must respect certain memory and computational
constraints such as (i) training samples are limited to only a few per class,
(ii) the computational cost of learning a novel class remains constant, and
(iii) the memory footprint of the model grows at most linearly with the number
of classes observed. To meet the above constraints, we propose C-FSCIL, which
is architecturally composed of a frozen meta-learned feature extractor, a
trainable fixed-size fully connected layer, and a rewritable dynamically
growing memory that stores as many vectors as the number of encountered
classes. C-FSCIL provides three update modes that offer a trade-off between
accuracy and compute-memory cost of learning novel classes. C-FSCIL exploits
hyperdimensional embedding that allows to continually express many more classes
than the fixed dimensions in the vector space, with minimal interference. The
quality of class vector representations is further improved by aligning them
quasi-orthogonally to each other by means of novel loss functions. Experiments
on the CIFAR100, miniImageNet, and Omniglot datasets show that C-FSCIL
outperforms the baselines with remarkable accuracy and compression. It also
scales up to the largest problem size ever tried in this few-shot setting by
learning 423 novel classes on top of 1200 base classes with less than 1.6%
accuracy drop. Our code is available at
https://github.com/IBM/constrained-FSCIL.

We consider the problem of estimating piecewise regular functions in an
online setting, i.e., the data arrive sequentially and at any round our task is
to predict the value of the true function at the next revealed point using the
available data from past predictions. We propose a suitably modified version of
a recently developed online learning algorithm called the sleeping experts
aggregation algorithm. We show that this estimator satisfies oracle risk bounds
simultaneously for all local regions of the domain. As concrete instantiations
of the expert aggregation algorithm proposed here, we study an online mean
aggregation and an online linear regression aggregation algorithm where experts
correspond to the set of dyadic subrectangles of the domain. The resulting
algorithms are near linear time computable in the sample size. We specifically
focus on the performance of these online algorithms in the context of
estimating piecewise polynomial and bounded variation function classes in the
fixed design setup. The simultaneous oracle risk bounds we obtain for these
estimators in this context provide new and improved (in certain aspects)
guarantees even in the batch setting and are not available for the state of the
art batch learning estimators.

Since the rise of vision-language navigation (VLN), great progress has been
made in instruction following -- building a follower to navigate environments
under the guidance of instructions. However, far less attention has been paid
to the inverse task: instruction generation -- learning a speaker~to generate
grounded descriptions for navigation routes. Existing VLN methods train a
speaker independently and often treat it as a data augmentation tool to
strengthen the follower while ignoring rich cross-task relations. Here we
describe an approach that learns the two tasks simultaneously and exploits
their intrinsic correlations to boost the training of each: the follower judges
whether the speaker-created instruction explains the original navigation route
correctly, and vice versa. Without the need of aligned instruction-path pairs,
such cycle-consistent learning scheme is complementary to task-specific
training targets defined on labeled data, and can also be applied over
unlabeled paths (sampled without paired instructions). Another agent,
called~creator is added to generate counterfactual environments. It greatly
changes current scenes yet leaves novel items -- which are vital for the
execution of original instructions -- unchanged. Thus more informative training
scenes are synthesized and the three agents compose a powerful VLN learning
system. Extensive experiments on a standard benchmark show that our approach
improves the performance of various follower models and produces accurate
navigation instructions.

Dealing with non-stationarity in environments (e.g., in the transition
dynamics) and objectives (e.g., in the reward functions) is a challenging
problem that is crucial in real-world applications of reinforcement learning
(RL). While most current approaches model the changes as a single shared
embedding vector, we leverage insights from the recent causality literature to
model non-stationarity in terms of individual latent change factors, and causal
graphs across different environments. In particular, we propose Factored
Adaptation for Non-Stationary RL (FANS-RL), a factored adaption approach that
learns jointly both the causal structure in terms of a factored MDP, and a
factored representation of the individual time-varying change factors. We prove
that under standard assumptions, we can completely recover the causal graph
representing the factored transition and reward function, as well as a partial
structure between the individual change factors and the state components.
Through our general framework, we can consider general non-stationary scenarios
with different function types and changing frequency, including changes across
episodes and within episodes. Experimental results demonstrate that FANS-RL
outperforms existing approaches in terms of return, compactness of the latent
state representation, and robustness to varying degrees of non-stationarity.

The calibration of solid constitutive models with full-field experimental
data is a long-standing challenge, especially in materials which undergo large
deformation. In this paper, we propose a physics-informed deep-learning
framework for the discovery of constitutive model parameterizations given
full-field displacement data and global force-displacement data. Contrary to
the majority of recent literature in this field, we work with the weak form of
the governing equations rather than the strong form to impose physical
constraints upon the neural network predictions. The approach presented in this
paper is computationally efficient, suitable for irregular geometric domains,
and readily ingests displacement data without the need for interpolation onto a
computational grid. A selection of canonical hyperelastic materials models
suitable for different material classes is considered including the
Neo-Hookean, Gent, and Blatz-Ko constitutive models as exemplars for general
hyperelastic behavior, polymer behavior with lock-up, and compressible foam
behavior respectively. We demonstrate that physics informed machine learning is
an enabling technology and may shift the paradigm of how full-field
experimental data is utilized to calibrate constitutive models under finite
deformations.

The state-of-the-art models for coreference resolution are based on
independent mention pair-wise decisions. We propose a modelling approach that
learns coreference at the document-level and takes global decisions. For this
purpose, we model coreference links in a graph structure where the nodes are
tokens in the text, and the edges represent the relationship between them. Our
model predicts the graph in a non-autoregressive manner, then iteratively
refines it based on previous predictions, allowing global dependencies between
decisions. The experimental results show improvements over various baselines,
reinforcing the hypothesis that document-level information improves conference
resolution.

In recent years, the field of machine learning has seen rapid growth, with
applications in a variety of domains, including image recognition, natural
language processing, and predictive modeling. In this paper, we explore the
application of machine learning to the generation of scientific articles. We
present a method for using machine learning to generate scientific articles
based on a data set of scientific papers. The method uses a machine-learning
algorithm to learn the structure of a scientific article and a set of training
data consisting of scientific papers. The machine-learning algorithm is used to
generate a scientific article based on the data set of scientific papers. We
evaluate the performance of the method by comparing the generated article to a
set of manually written articles. The results show that the machine-generated
article is of similar quality to the manually written articles.

Recent advances in deep learning-based medical image segmentation studies
achieve nearly human-level performance when in fully supervised condition.
However, acquiring pixel-level expert annotations is extremely expensive and
laborious in medical imaging fields. Unsupervised domain adaptation can
alleviate this problem, which makes it possible to use annotated data in one
imaging modality to train a network that can successfully perform segmentation
on target imaging modality with no labels. In this work, we propose a
self-training based unsupervised domain adaptation framework for 3D medical
image segmentation named COSMOS and validate it with automatic segmentation of
Vestibular Schwannoma (VS) and cochlea on high-resolution T2 Magnetic Resonance
Images (MRI). Our target-aware contrast conversion network translates source
domain annotated T1 MRI to pseudo T2 MRI to enable segmentation training on
target domain, while preserving important anatomical features of interest in
the converted images. Iterative self-training is followed to incorporate
unlabeled data to training and incrementally improve the quality of
pseudo-labels, thereby leading to improved performance of segmentation. COSMOS
won the 1\textsuperscript{st} place in the Cross-Modality Domain Adaptation
(crossMoDA) challenge held in conjunction with the 24th International
Conference on Medical Image Computing and Computer Assisted Intervention
(MICCAI 2021). It achieves mean Dice score and Average Symmetric Surface
Distance of 0.871(0.063) and 0.437(0.270) for VS, and 0.842(0.020) and
0.152(0.030) for cochlea.

We present a collection of 65,981 Mira-type variable stars found in the
Optical Gravitational Lensing Experiment (OGLE) project database. Two-thirds of
our sample (40,356 objects) are located in the Galactic bulge fields, whereas
25,625 stars are in the Galactic disk. The vast majority of the collection
(47,532 objects) are new discoveries. We provide basic observational parameters
of the Mira variables: equatorial coordinates, pulsation periods, $I$-band and
$V$-band mean magnitudes, $I$-band brightness amplitudes, and identifications
in other catalogs of variable stars. We also provide the $I$-band and $V$-band
time-series photometry collected since 1997 during the OGLE-II, OGLE-III, and
OGLE-IV phases. The classical selection process, i.e., mostly based on the
visual inspection of light curves by experienced astronomers, led us to the
high purity of the catalog. As a result, this collection can be used as a
training set in the machine learning classification algorithms. Using
overlapping parts of adjacent OGLE fields, we estimate the completeness of the
catalog to be about 96%. We compare and discuss the statistical features of
Miras located in different regions of the Milky Way. We show examples of stars
that change their type over time, from a semi-regular variable to Mira and vice
versa. This dataset is perfectly suited to study the three-dimensional
structure of the Milky Way, and it may help to explain the puzzle of the
X-shaped bulge.

Light field applications, especially light field rendering and depth
estimation, developed rapidly in recent years. While state-of-the-art light
field rendering methods handle semi-transparent and reflective objects well,
depth estimation methods either ignore these cases altogether or only deliver a
weak performance. We argue that this is due current methods only considering a
single "true" depth, even when multiple objects at different depths contributed
to the color of a single pixel. Based on the simple idea of outputting a
posterior depth distribution instead of only a single estimate, we develop and
explore several different deep-learning-based approaches to the problem.
Additionally, we contribute the first "multimodal light field depth dataset"
that contains the depths of all objects which contribute to the color of a
pixel. This allows us to supervise the multimodal depth prediction and also
validate all methods by measuring the KL divergence of the predicted
posteriors. With our thorough analysis and novel dataset, we aim to start a new
line of depth estimation research that overcomes some of the long-standing
limitations of this field.

The study of nonlocality in scenarios that depart from the bipartite
Einstein-Podolsky-Rosen setup is allowing to uncover many fundamental features
of quantum mechanics. Recently, an approach to building network-local models
based on machine learning lead to the conjecture that the family of quantum
triangle distributions of [DOI:10.1103/PhysRevLett.123.140401] did not admit
triangle-local models in a larger range than the original proof. We prove part
of this conjecture in the affirmative. Our approach consists in reducing the
family of original, four-outcome distributions to families of binary-outcome
ones, which we prove not to admit triangle-local models by means of the
inflation technique. In the process, we produce a large collection of network
Bell inequalities for the triangle scenario with binary outcomes, which are of
independent interest.

This paper aims to address the problem of pre-training for person
re-identification (Re-ID) with noisy labels. To setup the pre-training task, we
apply a simple online multi-object tracking system on raw videos of an existing
unlabeled Re-ID dataset "LUPerson" nd build the Noisy Labeled variant called
"LUPerson-NL". Since theses ID labels automatically derived from tracklets
inevitably contain noises, we develop a large-scale Pre-training framework
utilizing Noisy Labels (PNL), which consists of three learning modules:
supervised Re-ID learning, prototype-based contrastive learning, and
label-guided contrastive learning. In principle, joint learning of these three
modules not only clusters similar examples to one prototype, but also rectifies
noisy labels based on the prototype assignment. We demonstrate that learning
directly from raw videos is a promising alternative for pre-training, which
utilizes spatial and temporal correlations as weak supervision. This simple
pre-training task provides a scalable way to learn SOTA Re-ID representations
from scratch on "LUPerson-NL" without bells and whistles. For example, by
applying on the same supervised Re-ID method MGN, our pre-trained model
improves the mAP over the unsupervised pre-training counterpart by 5.7%, 2.2%,
2.3% on CUHK03, DukeMTMC, and MSMT17 respectively. Under the small-scale or
few-shot setting, the performance gain is even more significant, suggesting a
better transferability of the learned representation. Code is available at
https://github.com/DengpanFu/LUPerson-NL

We propose a test-time adaptation method for cross-domain image segmentation.
Our method is simple: Given a new unseen instance at test time, we adapt a
pre-trained model by conducting instance-specific BatchNorm (statistics)
calibration. Our approach has two core components. First, we replace the
manually designed BatchNorm calibration rule with a learnable module. Second,
we leverage strong data augmentation to simulate random domain shifts for
learning the calibration rule. In contrast to existing domain adaptation
methods, our method does not require accessing the target domain data at
training time or conducting computationally expensive test-time model
training/optimization. Equipping our method with models trained by standard
recipes achieves significant improvement, comparing favorably with several
state-of-the-art domain generalization and one-shot unsupervised domain
adaptation approaches. Combining our method with the domain generalization
methods further improves performance, reaching a new state of the art.

While neural representations for static 3D shapes are widely studied,
representations for deformable surfaces are limited to be template-dependent or
lack efficiency. We introduce Canonical Deformation Coordinate Space (CaDeX), a
unified representation of both shape and nonrigid motion. Our key insight is
the factorization of the deformation between frames by continuous bijective
canonical maps (homeomorphisms) and their inverses that go through a learned
canonical shape. Our novel deformation representation and its implementation
are simple, efficient, and guarantee cycle consistency, topology preservation,
and, if needed, volume conservation. Our modelling of the learned canonical
shapes provides a flexible and stable space for shape prior learning. We
demonstrate state-of-the-art performance in modelling a wide range of
deformable geometries: human bodies, animal bodies, and articulated objects.

Recent advances show that Generative Adversarial Networks (GANs) can
synthesize images with smooth variations along semantically meaningful latent
directions, such as pose, expression, layout, etc. While this indicates that
GANs implicitly learn pixel-level correspondences across images, few studies
explored how to extract them explicitly. In this work, we introduce Coordinate
GAN (CoordGAN), a structure-texture disentangled GAN that learns a dense
correspondence map for each generated image. We represent the correspondence
maps of different images as warped coordinate frames transformed from a
canonical coordinate frame, i.e., the correspondence map, which describes the
structure (e.g., the shape of a face), is controlled via a transformation.
Hence, finding correspondences boils down to locating the same coordinate in
different correspondence maps. In CoordGAN, we sample a transformation to
represent the structure of a synthesized instance, while an independent texture
branch is responsible for rendering appearance details orthogonal to the
structure. Our approach can also extract dense correspondence maps for real
images by adding an encoder on top of the generator. We quantitatively
demonstrate the quality of the learned dense correspondences through
segmentation mask transfer on multiple datasets. We also show that the proposed
generator achieves better structure and texture disentanglement compared to
existing approaches. Project page: https://jitengmu.github.io/CoordGAN/

Grounded situation recognition is the task of predicting the main activity,
entities playing certain roles within the activity, and bounding-box groundings
of the entities in the given image. To effectively deal with this challenging
task, we introduce a novel approach where the two processes for activity
classification and entity estimation are interactive and complementary. To
implement this idea, we propose Collaborative Glance-Gaze TransFormer
(CoFormer) that consists of two modules: Glance transformer for activity
classification and Gaze transformer for entity estimation. Glance transformer
predicts the main activity with the help of Gaze transformer that analyzes
entities and their relations, while Gaze transformer estimates the grounded
entities by focusing only on the entities relevant to the activity predicted by
Glance transformer. Our CoFormer achieves the state of the art in all
evaluation metrics on the SWiG dataset. Training code and model weights are
available at https://github.com/jhcho99/CoFormer.

Recent progress towards learning from limited supervision has encouraged
efforts towards designing models that can recognize novel classes at test time
(generalized zero-shot learning or GZSL). GZSL approaches assume knowledge of
all classes, with or without labeled data, beforehand. However, practical
scenarios demand models that are adaptable and can handle dynamic addition of
new seen and unseen classes on the fly (that is continual generalized zero-shot
learning or CGZSL). One solution is to sequentially retrain and reuse
conventional GZSL methods, however, such an approach suffers from catastrophic
forgetting leading to suboptimal generalization performance. A few recent
efforts towards tackling CGZSL have been limited by difference in settings,
practicality, data splits and protocols followed-inhibiting fair comparison and
a clear direction forward. Motivated from these observations, in this work, we
firstly consolidate the different CGZSL setting variants and propose a new
Online-CGZSL setting which is more practical and flexible. Secondly, we
introduce a unified feature-generative framework for CGZSL that leverages
bi-directional incremental alignment to dynamically adapt to addition of new
classes, with or without labeled data, that arrive over time in any of these
CGZSL settings. Our comprehensive experiments and analysis on five benchmark
datasets and comparison with baselines show that our approach consistently
outperforms existing methods, especially on the more practical Online setting.

We introduce the first end-to-end learning-based solution to near-field
Photometric Stereo (PS), where the light sources are close to the object of
interest. This setup is especially useful for reconstructing large immobile
objects. Our method is fast, producing a mesh from 52 512$\times$384 resolution
images in about 1 second on a commodity GPU, thus potentially unlocking several
AR/VR applications. Existing approaches rely on optimization coupled with a
far-field PS network operating on pixels or small patches. Using optimization
makes these approaches slow and memory intensive (requiring 17GB GPU and 27GB
of CPU memory) while using only pixels or patches makes them highly susceptible
to noise and calibration errors. To address these issues, we develop a
recursive multi-resolution scheme to estimate surface normal and depth maps of
the whole image at each step. The predicted depth map at each scale is then
used to estimate `per-pixel lighting' for the next scale. This design makes our
approach almost 45$\times$ faster and 2$^{\circ}$ more accurate (11.3$^{\circ}$
vs. 13.3$^{\circ}$ Mean Angular Error) than the state-of-the-art near-field PS
reconstruction technique, which uses iterative optimization.

The goal of this work is to establish a scalable pipeline for expanding an
object detector towards novel/unseen categories, using zero manual annotations.
To achieve that, we make the following four contributions: (i) in pursuit of
generalisation, we propose a two-stage open-vocabulary object detector, where
the class-agnostic object proposals are classified with a text encoder from
pre-trained visual-language model; (ii) To pair the visual latent space (of RPN
box proposals) with that of the pre-trained text encoder, we propose the idea
of regional prompt learning to align the textual embedding space with regional
visual object features; (iii) To scale up the learning procedure towards
detecting a wider spectrum of objects, we exploit the available online resource
via a novel self-training framework, which allows to train the proposed
detector on a large corpus of noisy uncurated web images. Lastly, (iv) to
evaluate our proposed detector, termed as PromptDet, we conduct extensive
experiments on the challenging LVIS and MS-COCO dataset. PromptDet shows
superior performance over existing approaches with fewer additional training
images and zero manual annotations whatsoever. Project page with code:
https://fcjian.github.io/promptdet.

Coronavirus 2019 has brought severe challenges to social stability and public
health worldwide. One effective way of curbing the epidemic is to require
people to wear masks in public places and monitor mask-wearing states by
utilizing suitable automatic detectors. However, existing deep learning based
models struggle to simultaneously achieve the requirements of both high
precision and real-time performance. To solve this problem, we propose an
improved lightweight face mask detector based on YOLOv5, which can achieve an
excellent balance of precision and speed. Firstly, a novel backbone
ShuffleCANet that combines ShuffleNetV2 network with Coordinate Attention
mechanism is proposed as the backbone. Afterwards, an efficient path aggression
network BiFPN is applied as the feature fusion neck. Furthermore, the
localization loss is replaced with alpha-CIoU in model training phase to obtain
higher-quality anchors. Some valuable strategies such as data augmentation,
adaptive image scaling, and anchor cluster operation are also utilized.
Experimental results on AIZOO face mask dataset show the superiority of the
proposed model. Compared with the original YOLOv5, the proposed model increases
the inference speed by 28.3% while still improving the precision by 0.58%. It
achieves the best mean average precision of 95.2% compared with other seven
existing models, which is 4.4% higher than the baseline.

Previous partial permutation synchronization (PPS) algorithms, which are
commonly used for multi-object matching, often involve computation-intensive
and memory-demanding matrix operations. These operations become intractable for
large scale structure-from-motion datasets. For pure permutation
synchronization, the recent Cycle-Edge Message Passing (CEMP) framework
suggests a memory-efficient and fast solution. Here we overcome the restriction
of CEMP to compact groups and propose an improved algorithm, CEMP-Partial, for
estimating the corruption levels of the observed partial permutations. It
allows us to subsequently implement a nonconvex weighted projected power method
without the need of spectral initialization. The resulting new PPS algorithm,
MatchFAME (Fast, Accurate and Memory-Efficient Matching), only involves sparse
matrix operations, and thus enjoys lower time and space complexities in
comparison to previous PPS algorithms. We prove that under adversarial
corruption, though without additive noise and with certain assumptions,
CEMP-Partial is able to exactly classify corrupted and clean partial
permutations. We demonstrate the state-of-the-art accuracy, speed and memory
efficiency of our method on both synthetic and real datasets.

We introduce dGSLM, the first "textless" model able to generate audio samples
of naturalistic spoken dialogues. It uses recent work on unsupervised spoken
unit discovery coupled with a dual-tower transformer architecture with
cross-attention trained on 2000 hours of two-channel raw conversational audio
(Fisher dataset) without any text or labels. It is able to generate speech,
laughter and other paralinguistic signals in the two channels simultaneously
and reproduces naturalistic turn taking. Generation samples can be found at:
https://speechbot.github.io/dgslm.

The requirements of much larger file sizes, different storage formats, and
immersive viewing conditions of VR pose significant challenges to the goals of
acquiring, transmitting, compressing, and displaying high-quality VR content.
At the same time, the great potential of deep learning to advance progress on
the video compression problem has driven a significant research effort. Because
of the high bandwidth requirements of VR, there has also been significant
interest in the use of space-variant, foveated compression protocols. We have
integrated these techniques to create an end-to-end deep learning video
compression framework. A feature of our new compression model is that it
dispenses with the need for expensive search-based motion prediction
computations. This is accomplished by exploiting statistical regularities
inherent in video motion expressed by displaced frame differences. Foveation
protocols are desirable since only a small portion of a video viewed in VR may
be visible as a user gazes in any given direction. Moreover, even within a
current field of view (FOV), the resolution of retinal neurons rapidly
decreases with distance (eccentricity) from the projected point of gaze. In our
learning based approach, we implement foveation by introducing a Foveation
Generator Unit (FGU) that generates foveation masks which direct the allocation
of bits, significantly increasing compression efficiency while making it
possible to retain an impression of little to no additional visual loss given
an appropriate viewing geometry. Our experiment results reveal that our new
compression model, which we call the Foveated MOtionless VIdeo Codec (Foveated
MOVI-Codec), is able to efficiently compress videos without computing motion,
while outperforming foveated version of both H.264 and H.265 on the widely used
UVG dataset and on the HEVC Standard Class B Test Sequences.

Different from previous work accelerating translation at the cost of quality
loss, we propose Generalized Aggressive Decoding (GAD) -- a novel decoding
paradigm for lossless speedup of autoregressive translation, through the
collaboration of autoregressive and non-autoregressive translation (NAT) of the
Transformer. At each decoding iteration, GAD aggressively decodes a number of
tokens with NAT as a draft and then verifies them in the autoregressive manner,
where only the tokens that pass the verification are accepted as decoded
tokens. GAD can achieve the same results as autoregressive translation but much
more efficiently because both NAT drafting and autoregressive verification
compute in parallel.
  We conduct experiments in four standard WMT benchmarks and confirm that the
vanilla GAD yields exactly the same results as greedy decoding with an around
$3\times$ speedup, and that its variant (GAD++) with an advanced verification
strategy not only outperforms the greedy translation and even achieves the
comparable translation quality with the beam search result, but also further
improves the decoding speed, resulting in an around $5\times$ speedup over
autoregressive translation. Moreover, GAD can be easily generalized for
lossless speedup of other seq2seq tasks like Abstractive Summarization, and
benefit more from stronger computing devices, demonstrating its potential to
become a de facto decoding paradigm in the future. Our models and codes are
available at https://github.com/hemingkx/GAD.

Object reconstruction from 3D point clouds has achieved impressive progress
in the computer vision and computer graphics research field. However,
reconstruction from time-varying point clouds (a.k.a. 4D point clouds) is
generally overlooked. In this paper, we propose a new network architecture,
namely RFNet-4D, that jointly reconstruct objects and their motion flows from
4D point clouds. The key insight is that simultaneously performing both tasks
via learning spatial and temporal features from a sequence of point clouds can
leverage individual tasks, leading to improved overall performance. To prove
this ability, we design a temporal vector field learning module using
unsupervised learning approach for flow estimation, leveraged by supervised
learning of spatial structures for object reconstruction. Extensive experiments
and analyses on benchmark dataset validated the effectiveness and efficiency of
our method. As shown in experimental results, our method achieves
state-of-the-art performance on both flow estimation and object reconstruction
while performing much faster than existing methods in both training and
inference. Our code and data are available at
https://github.com/hkust-vgd/RFNet-4D

Aleatoric uncertainty captures the inherent randomness of the data, such as
measurement noise. In Bayesian regression, we often use a Gaussian observation
model, where we control the level of aleatoric uncertainty with a noise
variance parameter. By contrast, for Bayesian classification we use a
categorical distribution with no mechanism to represent our beliefs about
aleatoric uncertainty. Our work shows that explicitly accounting for aleatoric
uncertainty significantly improves the performance of Bayesian neural networks.
We note that many standard benchmarks, such as CIFAR, have essentially no
aleatoric uncertainty. Moreover, we show data augmentation in approximate
inference has the effect of softening the likelihood, leading to
underconfidence and profoundly misrepresenting our honest beliefs about
aleatoric uncertainty. Accordingly, we find that a cold posterior, tempered by
a power greater than one, often more honestly reflects our beliefs about
aleatoric uncertainty than no tempering -- providing an explicit link between
data augmentation and cold posteriors. We show that we can match or exceed the
performance of posterior tempering by using a Dirichlet observation model,
where we explicitly control the level of aleatoric uncertainty, without any
need for tempering.

Deep neural networks (DNNs) have been widely used for decision making,
prompting a surge of interest in interpreting how these complex models work.
Recent literature on DNN interpretation has revolved around already-trained
models; however, much less research focuses on interpreting how the models
evolve as they are trained. Interpreting model evolution is crucial to monitor
network training and can aid proactive decisions about necessary interventions.
In this work, we present ConceptEvo, a general interpretation framework for
DNNs that reveals the inception and evolution of detected concepts during
training. Through a large-scale human evaluation with 260 participants and
quantitative experiments, we show that ConceptEvo discovers evolution across
different models that are meaningful to humans, helpful for early-training
intervention decisions, and crucial to the prediction for a given class.

Eye tracking data during reading is a useful source of information to
understand the cognitive processes that take place during language
comprehension processes. Different languages account for different brain
triggers , however there seems to be some uniform indicators. In this paper, we
describe our submission to the CMCL 2022 shared task on predicting human
reading patterns for multi-lingual dataset. Our model uses text representations
from transformers and some hand engineered features with a regression layer on
top to predict statistical measures of mean and standard deviation for 2 main
eye-tracking features. We train an end to end model to extract meaningful
information from different languages and test our model on two seperate
datasets. We compare different transformer models and show ablation studies
affecting model performance. Our final submission ranked 4th place for
SubTask-1 and 1st place for SubTask-2 for the shared task.

When training deep learning models for least-squares regression, we cannot
expect that the training error residuals of the final model, selected after a
fixed training time or based on performance on a hold-out data set, sum to
zero. This can introduce a systematic error that accumulates if we are
interested in the total aggregated performance over many data points. We
suggest to adjust the bias of the machine learning model after training as a
default postprocessing step, which efficiently solves the problem. The
severeness of the error accumulation and the effectiveness of the bias
correction is demonstrated in exemplary experiments.

The COVID-19 pandemic has represented a challenge for higher education in
terms to provide quality education despite the lockdown periods, the
transformation of the in-person classes to virtual classes, and the
demotivation and anxiety that are experimented by the students. Because the
basis of engineering is the experimentation through hands-on activities and
learning by doing, the lockdown periods and the temporary suspension of the
in-person classes and laboratories have meant a problem for educators that try
to teach and motivate the students despite the situation. In this context, this
study presents an educational methodology based on Problem-Based Learning (PBL)
and in-home laboratories in engineering. The methodology was carried out in two
phases during 2020, in the academic programs of Industrial Engineering and
Technology in Electronics with (n=44) students. The in-home laboratories were
sent to the students as part of "kits" with the devices needed in each subject.
Besides, due to the difficulties in monitoring the learning process, the
students made videos and blogs as a strategy to reinforce their learning and
evidence the progress in the courses. The outcomes of the methodology show
mainly the following points: (1) An improvement of the academic performance and
learning of the students in the courses. (2) A positive influence of the usage
of in-home laboratories in motivation, self-efficacy, and reduction of anxiety.
(3) Positive correlations between the usage of in-home laboratories, the blogs
and videos, and the teacher's feedback for learning, motivation, and
self-efficacy. Thus, these results evidence that other alternatives that gather
the cognitive and affective learning domains can emerge from engineering to
deal with the educational problems produced by the crisis periods.

Artificial intelligence, particularly through recent advancements in deep
learning, has achieved exceptional performances in many tasks in fields such as
natural language processing and computer vision. In addition to desirable
evaluation metrics, a high level of interpretability is often required for
these models to be reliably utilized. Therefore, explanations that offer
insight into the process by which a model maps its inputs onto its outputs are
much sought-after. Unfortunately, the current black box nature of machine
learning models is still an unresolved issue and this very nature prevents
researchers from learning and providing explicative descriptions for a model's
behavior and final predictions. In this work, we propose a novel framework
utilizing Adversarial Inverse Reinforcement Learning that can provide global
explanations for decisions made by a Reinforcement Learning model and capture
intuitive tendencies that the model follows by summarizing the model's
decision-making process.

Federated Learning is expected to provide strong privacy guarantees, as only
gradients or model parameters but no plain text training data is ever exchanged
either between the clients or between the clients and the central server. In
this paper, we challenge this claim by introducing a simple but still very
effective membership inference attack algorithm, which relies only on a single
training step. In contrast to the popular honest-but-curious model, we
investigate a framework with a dishonest central server. Our strategy is
applicable to models with ReLU activations and uses the properties of this
activation function to achieve perfect accuracy. Empirical evaluation on visual
classification tasks with MNIST, CIFAR10, CIFAR100 and CelebA datasets show
that our method provides perfect accuracy in identifying one sample in a
training set with thousands of samples. Occasional failures of our method lead
us to discover duplicate images in the CIFAR100 and CelebA datasets.

This article presents a new criterion for convergence of gradient descent to
a global minimum. The criterion is used to show that gradient descent with
proper initialization converges to a global minimum when training any
feedforward neural network with smooth and strictly increasing activation
functions, provided that the input dimension is greater than or equal to the
number of data points. The main difference with prior work is that the width of
the network can be a fixed number instead of unrealistically growing as some
multiple or power of the number of data points.

Probing Pre-trained Language Models (PLMs) using prompts has indirectly
implied that language models (LMs) can be treated as knowledge bases. To this
end, this phenomena has been effective especially when these LMs are fine-tuned
towards not just data of a specific domain, but also to the style or linguistic
pattern of the prompts themselves. We observe that, satisfying a particular
linguistic pattern in prompts is an unsustainable constraint that unnecessarily
lengthens the probing task, especially because, they are often manually
designed and the range of possible prompt template patterns can vary depending
on the prompting objective and domain. We therefore explore an idea of using a
position-attention mechanism to capture positional information of each word in
a prompt relative to the mask to be filled, hence avoiding the need to
re-construct prompts when the prompts linguistic pattern changes. Using our
approach, we demonstrate the ability of eliciting answers to rare prompt
templates (in a case study on health outcome generation) such as Postfix and
Mixed patterns whose missing information is respectively at the start and in
multiple random places of the prompt. More so, using various biomedical PLMs,
our approach consistently outperforms a baseline in which the default mask
language model (MLM) representation is used to predict masked tokens.

We consider two high-order tuners that have been shown to have accelerated
performance, one based on Polyak's heavy ball method and another based on
Nesterov's acceleration method. We show that parameter estimates are bounded
and converge to the true values exponentially fast when the regressors are
persistently exciting. Simulation results corroborate the accelerated
performance and accelerated learning properties of these high-order tuners in
comparison to algorithms based on normalized gradient descent.

Learning high-level causal representations together with a causal model from
unstructured low-level data such as pixels is impossible from observational
data alone. We prove under mild assumptions that this representation is however
identifiable in a weakly supervised setting. This involves a dataset with
paired samples before and after random, unknown interventions, but no further
labels. We then introduce implicit latent causal models, variational
autoencoders that represent causal variables and causal structure without
having to optimize an explicit discrete graph structure. On simple image data,
including a novel dataset of simulated robotic manipulation, we demonstrate
that such models can reliably identify the causal structure and disentangle
causal variables.

We consider the problem of localizing a spatio-temporal tube in a video
corresponding to a given text query. This is a challenging task that requires
the joint and efficient modeling of temporal, spatial and multi-modal
interactions. To address this task, we propose TubeDETR, a transformer-based
architecture inspired by the recent success of such models for text-conditioned
object detection. Our model notably includes: (i) an efficient video and text
encoder that models spatial multi-modal interactions over sparsely sampled
frames and (ii) a space-time decoder that jointly performs spatio-temporal
localization. We demonstrate the advantage of our proposed components through
an extensive ablation study. We also evaluate our full approach on the
spatio-temporal video grounding task and demonstrate improvements over the
state of the art on the challenging VidSTG and HC-STVG benchmarks. Code and
trained models are publicly available at
https://antoyang.github.io/tubedetr.html.

We present a novel feasibility study on the automatic recognition of
Expressed Emotion (EE), a family environment concept based on caregivers
speaking freely about their relative/family member. We describe an automated
approach for determining the \textit{degree of warmth}, a key component of EE,
from acoustic and text features acquired from a sample of 37 recorded
interviews. These recordings, collected over 20 years ago, are derived from a
nationally representative birth cohort of 2,232 British twin children and were
manually coded for EE. We outline the core steps of extracting usable
information from recordings with highly variable audio quality and assess the
efficacy of four machine learning approaches trained with different
combinations of acoustic and text features. Despite the challenges of working
with this legacy data, we demonstrated that the degree of warmth can be
predicted with an $F_{1}$-score of \textbf{61.5\%}. In this paper, we summarise
our learning and provide recommendations for future work using real-world
speech samples.

We consider the application of the factor graph framework for symbol
detection on linear inter-symbol interference channels. Based on the Ungerboeck
observation model, a detection algorithm with appealing complexity properties
can be derived. However, since the underlying factor graph contains cycles, the
sum-product algorithm (SPA) yields a suboptimal algorithm. In this paper, we
develop and evaluate efficient strategies to improve the performance of the
factor graph-based symbol detection by means of neural enhancement. In
particular, we consider neural belief propagation and generalizations of the
factor nodes as an effective way to mitigate the effect of cycles within the
factor graph. By applying a generic preprocessor to the channel output, we
propose a simple technique to vary the underlying factor graph in every SPA
iteration. Using this dynamic factor graph transition, we intend to preserve
the extrinsic nature of the SPA messages which is otherwise impaired due to
cycles. Simulation results show that the proposed methods can massively improve
the detection performance, even approaching the maximum a posteriori
performance for various transmission scenarios, while preserving a complexity
which is linear in both the block length and the channel memory.

Most existing fair classifiers rely on sensitive attributes to achieve
fairness. However, for many scenarios, we cannot obtain sensitive attributes
due to privacy and legal issues. The lack of sensitive attributes challenges
many existing works. Though we lack sensitive attributes, for many
applications, there usually exists features or information of various formats
that are relevant to sensitive attributes. For example, a personal purchase
history can reflect his or her race, which would be helpful for learning fair
classifiers on race. However, the work on exploring relevant features for
learning fair models without sensitive attributes is rather limited. Therefore,
in this paper, we study a novel problem of learning fair models without
sensitive attributes by exploring relevant features. We propose a probabilistic
generative framework to effectively estimate the sensitive attribute from the
training data with relevant features in various formats and utilize the
estimated sensitive attribute information to learn fair models. Experimental
results on real-world datasets show the effectiveness of our framework in terms
of both accuracy and fairness.

Building a high-quality singing corpus for a person who is not good at
singing is non-trivial, thus making it challenging to create a singing voice
synthesizer for this person. Learn2Sing is dedicated to synthesizing the
singing voice of a speaker without his or her singing data by learning from
data recorded by others, i.e., the singing teacher. Inspired by the fact that
pitch is the key style factor to distinguish singing from speaking voice, the
proposed Learn2Sing 2.0 first generates the preliminary acoustic feature with
averaged pitch value in the phone level, which allows the training of this
process for different styles, i.e., speaking or singing, share same conditions
except for the speaker information. Then, conditioned on the specific style, a
diffusion decoder, which is accelerated by a fast sampling algorithm during the
inference stage, is adopted to gradually restore the final acoustic feature.
During the training, to avoid the information confusion of the speaker
embedding and the style embedding, mutual information is employed to restrain
the learning of speaker embedding and style embedding. Experiments show that
the proposed approach is capable of synthesizing high-quality singing voice for
the target speaker without singing data with 10 decoding steps.

Theory of Mind is an essential ability of humans to infer the mental states
of others. Here we provide a coherent summary of the potential, current
progress, and problems of deep learning approaches to Theory of Mind. We
highlight that many current findings can be explained through shortcuts. These
shortcuts arise because the tasks used to investigate Theory of Mind in deep
learning systems have been too narrow. Thus, we encourage researchers to
investigate Theory of Mind in complex open-ended environments. Furthermore, to
inspire future deep learning systems we provide a concise overview of prior
work done in humans. We further argue that when studying Theory of Mind with
deep learning, the research's main focus and contribution ought to be opening
up the network's representations. We recommend researchers use tools from the
field of interpretability of AI to study the relationship between different
network components and aspects of Theory of Mind.

As a challenging multi-player card game, DouDizhu has recently drawn much
attention for analyzing competition and collaboration in imperfect-information
games. In this paper, we propose PerfectDou, a state-of-the-art DouDizhu AI
system that dominates the game, in an actor-critic framework with a proposed
technique named perfect information distillation. In detail, we adopt a
perfect-training-imperfect-execution framework that allows the agents to
utilize the global information to guide the training of the policies as if it
is a perfect information game and the trained policies can be used to play the
imperfect information game during the actual gameplay. To this end, we
characterize card and game features for DouDizhu to represent the perfect and
imperfect information. To train our system, we adopt proximal policy
optimization with generalized advantage estimation in a parallel training
paradigm. In experiments we show how and why PerfectDou beats all existing AI
programs, and achieves state-of-the-art performance.

In this paper, we explore the possibility of detecting polar lows in C-band
SAR images by means of deep learning. Specifically, we introduce a novel
dataset consisting of Sentinel-1 images divided into two classes, representing
the presence and absence of a maritime mesocyclone, respectively. The dataset
is constructed using the ERA5 dataset as baseline and it consists of 2004
annotated images. To our knowledge, this is the first dataset of its kind to be
publicly released. The dataset is used to train a deep learning model to
classify the labeled images. Evaluated on an independent test set, the model
yields an F-1 score of 0.95, indicating that polar lows can be consistently
detected from SAR images. Interpretability techniques applied to the deep
learning model reveal that atmospheric fronts and cyclonic eyes are key
features in the classification. Moreover, experimental results show that the
model is accurate even if: (i) such features are significantly cropped due to
the limited swath width of the SAR, (ii) the features are partly covered by sea
ice and (iii) land is covering significant parts of the images. By evaluating
the model performance on multiple input image resolutions (pixel sizes of 500m,
1km and 2km), it is found that higher resolution yield the best performance.
This emphasises the potential of using high resolution sensors like SAR for
detecting polar lows, as compared to conventionally used sensors such as
scatterometers.

As the overlap between traditional computational mechanics and machine
learning grows, there is an increasing demand for straight-forward approaches
to interface Python-based procedures with C++-based OpenFOAM. This article
introduces one such general methodology, allowing the execution of Python code
directly within an OpenFOAM solver without the need for Python code
translation. The proposed approach is based on the lightweight library
pybind11, where OpenFOAM data is transferred to an embedded Python interpreter
for manipulation, and results are returned as needed. Following a review of
related approaches, the article describes the approach, with a particular focus
on data transfer between Python and OpenFOAM, executing Python scripts and
functions, and practical details about the implementation in OpenFOAM. Three
complementary test cases are presented to highlight the functionality and
demonstrate the effect of different data transfer approaches: a Python-based
velocity profile boundary condition; a Python-based solver for prototyping; and
a machine learning mechanical constitutive law class for solids4foam which
performs field calculations.

Motion style transfer is highly desired for motion generation systems for
gaming. Compared to its offline counterpart, the research on online motion
style transfer under interactive control is limited. In this work, we propose
an end-to-end neural network that can generate motions with different styles
and transfer motion styles in real-time under user control. Our approach
eliminates the use of handcrafted phase features, and could be easily trained
and directly deployed in game systems. In the experiment part, we evaluate our
approach from three aspects that are essential for industrial game design:
accuracy, flexibility, and variety, and our model performs a satisfying result.

Most current approaches to undersampled multi-coil MRI reconstruction focus
on learning the reconstruction model for a fixed, equidistant acquisition
trajectory. In this paper, we study the problem of joint learning of the
reconstruction model together with acquisition policies. To this end, we extend
the End-to-End Variational Network with learnable acquisition policies that can
adapt to different data points. We validate our model on a coil-compressed
version of the large scale undersampled multi-coil fastMRI dataset using two
undersampling factors: $4\times$ and $8\times$. Our experiments show on-par
performance with the learnable non-adaptive and handcrafted equidistant
strategies at $4\times$, and an observed improvement of more than $2\%$ in SSIM
at $8\times$ acceleration, suggesting that potentially-adaptive $k$-space
acquisition trajectories can improve reconstructed image quality for larger
acceleration factors. However, and perhaps surprisingly, our best performing
policies learn to be explicitly non-adaptive.

Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of
interest to biologists and engineers. Solving the fully coupled FSI equations
for incompressible Navier-Stokes and finite elasticity is computationally
expensive. Optimizing robotic swimmer design within such a system generally
involves cumbersome, gradient-free procedures on top of the already costly
simulation. To address this challenge we present a novel, fully differentiable
hybrid approach to FSI that combines a 2D direct numerical simulation for the
deformable solid structure of the swimmer and a physics-constrained neural
network surrogate to capture hydrodynamic effects of the fluid. For the
deformable solid simulation of the swimmer's body, we use state-of-the-art
techniques from the field of computer graphics to speed up the finite-element
method (FEM). For the fluid simulation, we use a U-Net architecture trained
with a physics-based loss function to predict the flow field at each time step.
The pressure and velocity field outputs from the neural network are sampled
around the boundary of our swimmer using an immersed boundary method (IBM) to
compute its swimming motion accurately and efficiently. We demonstrate the
computational efficiency and differentiability of our hybrid simulator on a 2D
carangiform swimmer. Due to differentiability, the simulator can be used for
computational design of controls for soft bodies immersed in fluids via direct
gradient-based optimization.

With the capability to find the best fit to arbitrarily complicated data
patterns, machine-learning (ML) enhanced quantum state tomography (QST) has
demonstrated its advantages in extracting complete information about the
quantum states. Instead of using the reconstruction model in training a
truncated density matrix, we develop a high-performance, lightweight, and
easy-to-install supervised characteristic model by generating the target
parameters directly. Such a characteristic model-based ML-QST can avoid the
problem of dealing with large Hilbert space, but keep feature extractions with
high precision. With the experimentally measured data generated from the
balanced homodyne detectors, we compare the degradation information about
quantum noise squeezed states predicted by the reconstruction and
characteristic models, both give agreement to the empirically fitting curves
obtained from the covariance method. Such a ML-QST with direct parameter
estimations illustrates a crucial diagnostic toolbox for applications with
squeezed states, including advanced gravitational wave detectors, quantum
metrology, macroscopic quantum state generation, and quantum information
process.

Existing works have identified the limitation of top-$1$ attack success rate
(ASR) as a metric to evaluate the attack strength but exclusively investigated
it in the white-box setting, while our work extends it to a more practical
black-box setting: transferable attack. It is widely reported that stronger
I-FGSM transfers worse than simple FGSM, leading to a popular belief that
transferability is at odds with the white-box attack strength. Our work
challenges this belief with empirical finding that stronger attack actually
transfers better for the general top-$k$ ASR indicated by the interest class
rank (ICR) after attack. For increasing the attack strength, with an intuitive
interpretation of the logit gradient from the geometric perspective, we
identify that the weakness of the commonly used losses lie in prioritizing the
speed to fool the network instead of maximizing its strength. To this end, we
propose a new normalized CE loss that guides the logit to be updated in the
direction of implicitly maximizing its rank distance from the ground-truth
class. Extensive results in various settings have verified that our proposed
new loss is simple yet effective for top-$k$ attack. Code is available at:
\url{https://bit.ly/3uCiomP}

Capsule network (CapsNet) acts as a promising alternative to the typical
convolutional neural network, which is the dominant network to develop the
remaining useful life (RUL) estimation models for mechanical equipment.
Although CapsNet comes with an impressive ability to represent the entities'
hierarchical relationships through a high-dimensional vector embedding, it
fails to capture the long-term temporal correlation of run-to-failure time
series measured from degraded mechanical equipment. On the other hand, the
slow-varying dynamics, which reveals the low-frequency information hidden in
mechanical dynamical behaviour, is overlooked in the existing RUL estimation
models, limiting the utmost ability of advanced networks. To address the
aforementioned concerns, we propose a Slow-varying Dynamics assisted Temporal
CapsNet (SD-TemCapsNet) to simultaneously learn the slow-varying dynamics and
temporal dynamics from measurements for accurate RUL estimation. First, in
light of the sensitivity of fault evolution, slow-varying features are
decomposed from normal raw data to convey the low-frequency components
corresponding to the system dynamics. Next, the long short-term memory (LSTM)
mechanism is introduced into CapsNet to capture the temporal correlation of
time series. To this end, experiments conducted on an aircraft engine and a
milling machine verify that the proposed SD-TemCapsNet outperforms the
mainstream methods. In comparison with CapsNet, the estimation accuracy of the
aircraft engine with four different scenarios has been improved by 10.17%,
24.97%, 3.25%, and 13.03% concerning the index root mean squared error,
respectively. Similarly, the estimation accuracy of the milling machine has
been improved by 23.57% compared to LSTM and 19.54% compared to CapsNet.

Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a
specific aspect in the given sentence. While pre-trained language models such
as BERT have achieved great success, incorporating dynamic semantic changes
into ABSA remains challenging. To this end, in this paper, we propose to
address this problem by Dynamic Re-weighting BERT (DR-BERT), a novel method
designed to learn dynamic aspect-oriented semantics for ABSA. Specifically, we
first take the Stack-BERT layers as a primary encoder to grasp the overall
semantic of the sentence and then fine-tune it by incorporating a lightweight
Dynamic Re-weighting Adapter (DRA). Note that the DRA can pay close attention
to a small region of the sentences at each step and re-weigh the vitally
important words for better aspect-aware sentiment understanding. Finally,
experimental results on three benchmark datasets demonstrate the effectiveness
and the rationality of our proposed model and provide good interpretable
insights for future semantic modeling.

WLANs, which have overtaken wired networks to become the primary means of
connecting devices to the Internet, are prone to performance issues due to the
scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and
subsequent amendments aim at increasing the spatial reuse of a radio channel by
allowing the dynamic update of two key parameters in wireless transmission: the
transmission power (TX_POWER) and the sensitivity threshold (OBSS_PD). In this
paper, we present INSPIRE, a distributed solution performing local Bayesian
optimizations based on Gaussian processes to improve the spatial reuse in
WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and
favors altruistic behaviors of the access points, leading them to find adequate
configurations of their TX_POWER and OBSS_PD parameters for the "greater good"
of the WLANs. We demonstrate the superiority of INSPIRE over other
state-of-the-art strategies using the ns-3 simulator and two examples inspired
by real-life deployments of dense WLANs. Our results show that, in only a few
seconds, INSPIRE is able to drastically increase the quality of service of
operational WLANs by improving their fairness and throughput.

The effectiveness of machine learning models is significantly affected by the
size of the dataset and the quality of features as redundant and irrelevant
features can radically degrade the performance. This paper proposes IGRF-RFE: a
hybrid feature selection method tasked for multi-class network anomalies using
a Multilayer perceptron (MLP) network. IGRF-RFE can be considered as a feature
reduction technique based on both the filter feature selection method and the
wrapper feature selection method. In our proposed method, we use the filter
feature selection method, which is the combination of Information Gain and
Random Forest Importance, to reduce the feature subset search space. Then, we
apply recursive feature elimination(RFE) as a wrapper feature selection method
to further eliminate redundant features recursively on the reduced feature
subsets. Our experimental results obtained based on the UNSW-NB15 dataset
confirm that our proposed method can improve the accuracy of anomaly detection
while reducing the feature dimension. The results show that the feature
dimension is reduced from 42 to 23 while the multi-classification accuracy of
MLP is improved from 82.25% to 84.24%.

Catastrophic forgetting is a thorny challenge when updating keyword spotting
(KWS) models after deployment. This problem will be more challenging if KWS
models are further required for edge devices due to their limited memory. To
alleviate such an issue, we propose a novel diversity-aware incremental
learning method named Rainbow Keywords (RK). Specifically, the proposed RK
approach introduces a diversity-aware sampler to select a diverse set from
historical and incoming keywords by calculating classification uncertainty. As
a result, the RK approach can incrementally learn new tasks without forgetting
prior knowledge. Besides, the RK approach also proposes data augmentation and
knowledge distillation loss function for efficient memory management on the
edge device. Experimental results show that the proposed RK approach achieves
4.2% absolute improvement in terms of average accuracy over the best baseline
on Google Speech Command dataset with less required memory. The scripts are

JPEG is a popular image compression method widely used by individuals, data
center, cloud storage and network filesystems. However, most recent progress on
image compression mainly focuses on uncompressed images while ignoring
trillions of already-existing JPEG images. To compress these JPEG images
adequately and restore them back to JPEG format losslessly when needed, we
propose a deep learning based JPEG recompression method that operates on DCT
domain and propose a Multi-Level Cross-Channel Entropy Model to compress the
most informative Y component. Experiments show that our method achieves
state-of-the-art performance compared with traditional JPEG recompression
methods including Lepton, JPEG XL and CMIX. To the best of our knowledge, this
is the first learned compression method that losslessly transcodes JPEG images
to more storage-saving bitstreams.

The detailed detector simulation models are vital for the successful
operation of modern high-energy physics experiments. In most cases, such
detailed models require a significant amount of computing resources to run.
Often this may not be afforded and less resource-intensive approaches are
desired. In this work, we demonstrate the applicability of Generative
Adversarial Networks (GAN) as the basis for such fast-simulation models for the
case of the Time Projection Chamber (TPC) at the MPD detector at the NICA
accelerator complex. Our prototype GAN-based model of TPC works more than an
order of magnitude faster compared to the detailed simulation without any
noticeable drop in the quality of the high-level reconstruction characteristics
for the generated data. Approaches with direct and indirect quality metrics
optimization are compared.

We present a method that uses high-resolution topography data of rough
terrain, and ground vehicle simulation, to predict traversability.
Traversability is expressed as three independent measures: the ability to
traverse the terrain at a target speed, energy consumption, and acceleration.
The measures are continuous and reflect different objectives for planning that
go beyond binary classification. A deep neural network is trained to predict
the traversability measures from the local heightmap and target speed. To
produce training data, we use an articulated vehicle with wheeled bogie
suspensions and procedurally generated terrains. We evaluate the model on
laser-scanned forest terrains, previously unseen by the model. The model
predicts traversability with an accuracy of 90%. Predictions rely on features
from the high-dimensional terrain data that surpass local roughness and slope
relative to the heading. Correlations show that the three traversability
measures are complementary to each other. With an inference speed 3000 times
faster than the ground truth simulation and trivially parallelizable, the model
is well suited for traversability analysis and optimal path planning over large
areas.

Orbital angular momentum of light is regarded as a valuable resource in
quantum technology, especially in quantum communication and quantum sensing and
ranging. However, the OAM state of light is susceptible to undesirable
experimental conditions such as propagation distance and phase distortions,
which hinders the potential for the realistic implementation of relevant
technologies. In this article, we exploit an enhanced deep learning neural
network to identify different OAM modes of light at multiple propagation
distances with phase distortions. Specifically, our trained deep learning
neural network can efficiently identify the vortex beam's topological charge
and propagation distance with 97% accuracy. Our technique has important
implications for OAM based communication and sensing protocols.

Home absence detection is an emerging field on smart home installations.
Identifying whether or not the residents of the house are present, is important
in numerous scenarios. Possible scenarios include but are not limited to:
elderly people living alone, people suffering from dementia, home quarantine.
The majority of published papers focus on either pressure / door sensors or
cameras in order to detect outing events. Although the aforementioned
approaches provide solid results, they are intrusive and require modifications
for sensor placement. In our work, appliance electrical use is investigated as
a means for detecting the presence or absence of residents. The energy use is
the result of power disaggregation, a non intrusive / non invasive sensing
method. Since a dataset providing energy data and ground truth for home absence
is not available, artificial outing events were introduced on the UK-DALE
dataset, a well known dataset for Non Intrusive Load Monitoring (NILM). Several
machine learning algorithms were evaluated using the generated dataset.
Benchmark results have shown that home absence detection using appliance power
consumption is feasible.

Constrained optimization problems arise frequently in classical machine
learning. There exist frameworks addressing constrained optimization, for
instance, CVXPY and GENO. However, in contrast to deep learning frameworks, GPU
support is limited. Here, we extend the GENO framework to also solve
constrained optimization problems on the GPU. The framework allows the user to
specify constrained optimization problems in an easy-to-read modeling language.
A solver is then automatically generated from this specification. When run on
the GPU, the solver outperforms state-of-the-art approaches like CVXPY combined
with a GPU-accelerated solver such as cuOSQP or SCS by a few orders of

We study the problem of multi-robot active mapping, which aims for complete
scene map construction in minimum time steps. The key to this problem lies in
the goal position estimation to enable more efficient robot movements. Previous
approaches either choose the frontier as the goal position via a myopic
solution that hinders the time efficiency, or maximize the long-term value via
reinforcement learning to directly regress the goal position, but does not
guarantee the complete map construction. In this paper, we propose a novel
algorithm, namely NeuralCoMapping, which takes advantage of both approaches. We
reduce the problem to bipartite graph matching, which establishes the node
correspondences between two graphs, denoting robots and frontiers. We introduce
a multiplex graph neural network (mGNN) that learns the neural distance to fill
the affinity matrix for more effective graph matching. We optimize the mGNN
with a differentiable linear assignment layer by maximizing the long-term
values that favor time efficiency and map completeness via reinforcement
learning. We compare our algorithm with several state-of-the-art multi-robot
active mapping approaches and adapted reinforcement-learning baselines.
Experimental results demonstrate the superior performance and exceptional
generalization ability of our algorithm on various indoor scenes and unseen
number of robots, when only trained with 9 indoor scenes.

In this paper, we delve into two key techniques in Semi-Supervised Object
Detection (SSOD), namely pseudo labeling and consistency training. We observe
that these two techniques currently neglect some important properties of object
detection, hindering efficient learning on unlabeled data. Specifically, for
pseudo labeling, existing works only focus on the classification score yet fail
to guarantee the localization precision of pseudo boxes; For consistency
training, the widely adopted random-resize training only considers the
label-level consistency but misses the feature-level one, which also plays an
important role in ensuring the scale invariance. To address the problems
incurred by noisy pseudo boxes, we design Noisy Pseudo box Learning (NPL) that
includes Prediction-guided Label Assignment (PLA) and Positive-proposal
Consistency Voting (PCV). PLA relies on model predictions to assign labels and
makes it robust to even coarse pseudo boxes; while PCV leverages the regression
consistency of positive proposals to reflect the localization quality of pseudo
boxes. Furthermore, in consistency training, we propose Multi-view
Scale-invariant Learning (MSL) that includes mechanisms of both label- and
feature-level consistency, where feature consistency is achieved by aligning
shifted feature pyramids between two images with identical content but varied
scales. On COCO benchmark, our method, termed PSEudo labeling and COnsistency
training (PseCo), outperforms the SOTA (Soft Teacher) by 2.0, 1.8, 2.0 points
under 1%, 5%, and 10% labelling ratios, respectively. It also significantly
improves the learning efficiency for SSOD, e.g., PseCo halves the training time
of the SOTA approach but achieves even better performance. Code is available at
https://github.com/ligang-cs/PseCo.

Performing closed-loop grasping at close proximity to an object requires a
large field of view. However, such images will inevitably bring large amounts
of unnecessary background information, especially when the camera is far away
from the target object at the initial stage, resulting in performance
degradation of the grasping network. To address this problem, we design a novel
PEGG-Net, a real-time, pixel-wise, robotic grasp generation network. The
proposed lightweight network is inherently able to learn to remove background
noise that can reduce grasping accuracy. Our proposed PEGG-Net achieves
improved state-of-the-art performance on both Cornell dataset (98.9%) and
Jacquard dataset (93.8%). In the real-world tests, PEGG-Net can support
closed-loop grasping at up to 50Hz using an image size of 480x480 in dynamic
environments. The trained model also generalizes to previously unseen objects
with complex geometrical shapes, household objects and workshop tools and
achieved an overall grasp success rate of 91.2% in our real-world grasping

Object detection and forecasting are fundamental components of embodied
perception. These two problems, however, are largely studied in isolation by
the community. In this paper, we propose an end-to-end approach for detection
and motion forecasting based on raw sensor measurement as opposed to ground
truth tracks. Instead of predicting the current frame locations and forecasting
forward in time, we directly predict future object locations and backcast to
determine where each trajectory began. Our approach not only improves overall
accuracy compared to other modular or end-to-end baselines, it also prompts us
to rethink the role of explicit tracking for embodied perception. Additionally,
by linking future and current locations in a many-to-one manner, our approach
is able to reason about multiple futures, a capability that was previously
considered difficult for end-to-end approaches. We conduct extensive
experiments on the popular nuScenes dataset and demonstrate the empirical
effectiveness of our approach. In addition, we investigate the appropriateness
of reusing standard forecasting metrics for an end-to-end setup, and find a
number of limitations which allow us to build simple baselines to game these
metrics. We address this issue with a novel set of joint forecasting and
detection metrics that extend the commonly used AP metrics from the detection
community to measuring forecasting accuracy. Our code is available at
https://github.com/neeharperi/FutureDet

Contrastive learning (CL) is widely known to require many negative samples,
65536 in MoCo for instance, for which the performance of a dictionary-free
framework is often inferior because the negative sample size (NSS) is limited
by its mini-batch size (MBS). To decouple the NSS from the MBS, a dynamic
dictionary has been adopted in a large volume of CL frameworks, among which
arguably the most popular one is MoCo family. In essence, MoCo adopts a
momentum-based queue dictionary, for which we perform a fine-grained analysis
of its size and consistency. We point out that InfoNCE loss used in MoCo
implicitly attract anchors to their corresponding positive sample with various
strength of penalties and identify such inter-anchor hardness-awareness
property as a major reason for the necessity of a large dictionary. Our
findings motivate us to simplify MoCo v2 via the removal of its dictionary as
well as momentum. Based on an InfoNCE with the proposed dual temperature, our
simplified frameworks, SimMoCo and SimCo, outperform MoCo v2 by a visible
margin. Moreover, our work bridges the gap between CL and non-CL frameworks,
contributing to a more unified understanding of these two mainstream frameworks
in SSL. Code is available at: https://bit.ly/3LkQbaT.

Motivated by the state-of-art psychological research, we note that a piano
performance transcribed with existing Automatic Music Transcription (AMT)
methods cannot be successfully resynthesized without affecting the artistic
content of the performance. This is due to 1) the different mappings between
MIDI parameters used by different instruments, and 2) the fact that musicians
adapt their way of playing to the surrounding acoustic environment. To face
this issue, we propose a methodology to build acoustics-specific AMT systems
that are able to model the adaptations that musicians apply to convey their
interpretation. Specifically, we train models tailored for virtual instruments
in a modular architecture that takes as input an audio recording and the
relative aligned music score, and outputs the acoustics-specific velocities of
each note. We test different model shapes and show that the proposed
methodology generally outperforms the usual AMT pipeline which does not
consider specificities of the instrument and of the acoustic environment.
Interestingly, such a methodology is extensible in a straightforward way since
only slight efforts are required to train models for the inference of other
piano parameters, such as pedaling.

We introduce AmsterTime: a challenging dataset to benchmark visual place
recognition (VPR) in presence of a severe domain shift. AmsterTime offers a
collection of 2,500 well-curated images matching the same scene from a street
view matched to historical archival image data from Amsterdam city. The image
pairs capture the same place with different cameras, viewpoints, and
appearances. Unlike existing benchmark datasets, AmsterTime is directly
crowdsourced in a GIS navigation platform (Mapillary). We evaluate various
baselines, including non-learning, supervised and self-supervised methods,
pre-trained on different relevant datasets, for both verification and retrieval
tasks. Our result credits the best accuracy to the ResNet-101 model pre-trained
on the Landmarks dataset for both verification and retrieval tasks by 84% and
24%, respectively. Additionally, a subset of Amsterdam landmarks is collected
for feature evaluation in a classification task. Classification labels are
further used to extract the visual explanations using Grad-CAM for inspection
of the learned similar visuals in a deep metric learning models.

This paper deals with the design of nonlinear MPC controllers that provide
offset-free setpoint tracking for models described by Neural Nonlinear
AutoRegressive eXogenous (NNARX) networks. The NNARX model is identified from
input-output data collected from the plant, and can be given a state-space
representation with known measurable states made by past input and output
variables, so that a state observer is not required. In the training phase, the
Incremental Input-to-State Stability ({\delta}ISS) property can be forced when
consistent with the behavior of the plant. The {\delta}ISS property is then
leveraged to augment the model with an explicit integral action on the output
tracking error, which allows to achieve offset-free tracking capabilities to
the designed control scheme. The proposed control architecture is numerically
tested on a water heating system and the achieved results are compared to those
scored by another popular offset-free MPC method, showing that the proposed
scheme attains remarkable performances even in presence of disturbances acting
on the plant.

A one-step two-critic deep reinforcement learning (OSTC-DRL) approach for
inverter-based volt-var control (IB-VVC) in active distribution networks is
proposed in this paper. Firstly, considering IB-VVC can be formulated as a
single-period optimization problem, we formulate the IB-VVC as a one-step
Markov decision process rather than the standard Markov decision process, which
simplifies the DRL learning task. Then we design the one-step actor-critic DRL
scheme which is a simplified version of recent DRL algorithms, and it avoids
the issue of Q value overestimation successfully. Furthermore, considering two
objectives of VVC: minimizing power loss and eliminating voltage violation, we
utilize two critics to approximate the rewards of two objectives separately. It
simplifies the approximation tasks of each critic, and avoids the interaction
effect between two objectives in the learning process of critic. The OSTC-DRL
approach integrates the one-step actor-critic DRL scheme and the two-critic
technology. Based on the OSTC-DRL, we design two centralized DRL algorithms.
Further, we extend the OSTC-DRL to multi-agent OSTC-DRL for decentralized
IB-VVC and design two multi-agent DRL algorithms. Simulations demonstrate that
the proposed OSTC-DRL has a faster convergence rate and a better control
performance, and the multi-agent OSTC-DRL works well for decentralized IB-VVC
problems.

This paper proposes a new method to generate synthetic data sets based on
copula models. Our goal is to produce surrogate data resembling real data in
terms of marginal and joint distributions. We present a complete and reliable
algorithm for generating a synthetic data set comprising numeric or categorical
variables. Applying our methodology to two datasets shows better performance
compared to other methods such as SMOTE and autoencoders.

Many ways of annotating a dataset for machine learning classification tasks
that go beyond the usual class labels exist in practice. These are of interest
as they can simplify or facilitate the collection of annotations, while not
greatly affecting the resulting machine learning model. Many of these fall
under the umbrella term of weak labels or annotations. However, it is not
always clear how different alternatives are related. In this paper we propose a
framework for categorising weak supervision settings with the aim of: (1)
helping the dataset owner or annotator navigate through the available options
within weak supervision when prescribing an annotation process, and (2)
describing existing annotations for a dataset to machine learning practitioners
so that we allow them to understand the implications for the learning process.
To this end, we identify the key elements that characterise weak supervision
and devise a series of dimensions that categorise most of the existing
approaches. We show how common settings in the literature fit within the
framework and discuss its possible uses in practice.

Any successful interpretation of quantum mechanics must explain how our
empirical evidence allows us to come to know about quantum mechanics. In this
article, we argue that this vital criterion is not met by the class of
'orthodox interpretations,' which includes QBism, neo-Copenhagen
interpretations, and some versions of relational quantum mechanics. We
demonstrate that intersubjectivity fails in radical ways in these approaches,
and we explain why intersubjectivity matters for empirical confirmation. We
take a detailed look at the way in which belief-updating might work in the kind
of universe postulated by an orthodox interpretation, and argue that observers
in such a universe are unable to escape their own perspective in order to learn
about the structure of the set of perspectives that is supposed to make up
reality according to these interpretations. We also argue that in some versions
of these interpretations it is not even possible to use one's own relative
frequencies for empirical confirmation. Ultimately we conclude that it cannot
be rational to believe these sorts of interpretations unless they are
supplemented with some observer-independent structure which underwrites
intersubjective agreement in at least certain sorts of cases.

Reinforcement learning (RL) has shown promise as a tool for engineering safe,
ethical, or legal behaviour in autonomous agents. Its use typically relies on
assigning punishments to state-action pairs that constitute unsafe or unethical
choices. Despite this assignment being a crucial step in this approach,
however, there has been limited discussion on generalizing the process of
selecting punishments and deciding where to apply them. In this paper, we adopt
an approach that leverages an existing framework -- the normative supervisor of
(Neufeld et al., 2021) -- during training. This normative supervisor is used to
dynamically translate states and the applicable normative system into
defeasible deontic logic theories, feed these theories to a theorem prover, and
use the conclusions derived to decide whether or not to assign a punishment to
the agent. We use multi-objective RL (MORL) to balance the ethical objective of
avoiding violations with a non-ethical objective; we will demonstrate that our
approach works for a multiplicity of MORL techniques, and show that it is
effective regardless of the magnitude of the punishment we assign.

Do black-box neural network models learn clinically relevant features for
fracture diagnosis? The answer not only establishes reliability quenches
scientific curiosity but also leads to explainable and verbose findings that
can assist the radiologists in the final and increase trust. This work
identifies the concepts networks use for vertebral fracture diagnosis in CT
images. This is achieved by associating concepts to neurons highly correlated
with a specific diagnosis in the dataset. The concepts are either associated
with neurons by radiologists pre-hoc or are visualized during a specific
prediction and left for the user's interpretation. We evaluate which concepts
lead to correct diagnosis and which concepts lead to false positives. The
proposed frameworks and analysis pave the way for reliable and explainable
vertebral fracture diagnosis.

Non-autoregressive translation (NAT) models suffer from inferior translation
quality due to removal of dependency on previous target tokens from inputs to
the decoder. In this paper, we propose a novel and general approach to enhance
the target dependency within the NAT decoder from two perspectives: decoder
input and decoder self-attention. First, we transform the initial decoder input
from the source language space to the target language space through a novel
attentive transformation process. The transformation reassembles the decoder
input based on target token embeddings and conditions the final output on the
target-side information. Second, before NAT training, we introduce an effective
forward-backward pre-training phase, implemented with different triangle
attention masks. This pre-training phase enables the model to gradually learn
bidirectional dependencies for the final NAT decoding process. Experimental
results demonstrate that the proposed approaches consistently improve highly
competitive NAT models on four WMT translation directions by up to 1.88 BLEU
score, while overall maintaining inference latency comparable to other fully
NAT models.

Current text-to-speech algorithms produce realistic fakes of human voices,
making deepfake detection a much-needed area of research. While researchers
have presented various techniques for detecting audio spoofs, it is often
unclear exactly why these architectures are successful: Preprocessing steps,
hyperparameter settings, and the degree of fine-tuning are not consistent
across related work. Which factors contribute to success, and which are
accidental? In this work, we address this problem: We systematize audio
spoofing detection by re-implementing and uniformly evaluating architectures
from related work. We identify overarching features for successful audio
deepfake detection, such as using cqtspec or logspec features instead of
melspec features, which improves performance by 37% EER on average, all other
factors constant. Additionally, we evaluate generalization capabilities: We
collect and publish a new dataset consisting of 37.9 hours of found audio
recordings of celebrities and politicians, of which 17.2 hours are deepfakes.
We find that related work performs poorly on such real-world data (performance
degradation of up to one thousand percent). This may suggest that the community
has tailored its solutions too closely to the prevailing ASVSpoof benchmark and
that deepfakes are much harder to detect outside the lab than previously
thought.

To avoid collapse in self-supervised learning (SSL), a contrastive loss is
widely used but often requires a large number of negative samples. Without
negative samples yet achieving competitive performance, a recent work has
attracted significant attention for providing a minimalist simple Siamese
(SimSiam) method to avoid collapse. However, the reason for how it avoids
collapse without negative samples remains not fully clear and our investigation
starts by revisiting the explanatory claims in the original SimSiam. After
refuting their claims, we introduce vector decomposition for analyzing the
collapse based on the gradient analysis of the $l_2$-normalized representation
vector. This yields a unified perspective on how negative samples and SimSiam
alleviate collapse. Such a unified perspective comes timely for understanding
the recent progress in SSL.

Segmenting or detecting objects in sparse Lidar point clouds are two
important tasks in autonomous driving to allow a vehicle to act safely in its
3D environment. The best performing methods in 3D semantic segmentation or
object detection rely on a large amount of annotated data. Yet annotating 3D
Lidar data for these tasks is tedious and costly. In this context, we propose a
self-supervised pre-training method for 3D perception models that is tailored
to autonomous driving data. Specifically, we leverage the availability of
synchronized and calibrated image and Lidar sensors in autonomous driving
setups for distilling self-supervised pre-trained image representations into 3D
models. Hence, our method does not require any point cloud nor image
annotations. The key ingredient of our method is the use of superpixels which
are used to pool 3D point features and 2D pixel features in visually similar
regions. We then train a 3D network on the self-supervised task of matching
these pooled point features with the corresponding pooled image pixel features.
The advantages of contrasting regions obtained by superpixels are that: (1)
grouping together pixels and points of visually coherent regions leads to a
more meaningful contrastive task that produces features well adapted to 3D
semantic segmentation and 3D object detection; (2) all the different regions
have the same weight in the contrastive loss regardless of the number of 3D
points sampled in these regions; (3) it mitigates the noise produced by
incorrect matching of points and pixels due to occlusions between the different
sensors. Extensive experiments on autonomous driving datasets demonstrate the
ability of our image-to-Lidar distillation strategy to produce 3D
representations that transfer well on semantic segmentation and object
detection tasks.

In recent years, with the increase of social investment in scientific
research, the number of research results in various fields has increased
significantly. Accurately and effectively predicting the trends of future
research topics can help researchers discover future research hotspots.
However, due to the increasingly close correlation between various research
themes, there is a certain dependency relationship between a large number of
research themes. Viewing a single research theme in isolation and using
traditional sequence problem processing methods cannot effectively explore the
spatial dependencies between these research themes. To simultaneously capture
the spatial dependencies and temporal changes between research topics, we
propose a deep neural network-based research topic hotness prediction
algorithm, a spatiotemporal convolutional network model. Our model combines a
graph convolutional neural network (GCN) and Temporal Convolutional Network
(TCN), specifically, GCNs are used to learn the spatial dependencies of
research topics a and use space dependence to strengthen spatial
characteristics. TCN is used to learn the dynamics of research topics' trends.
Optimization is based on the calculation of weighted losses based on time
distance. Compared with the current mainstream sequence prediction models and
similar spatiotemporal models on the paper datasets, experiments show that, in
research topic prediction tasks, our model can effectively capture
spatiotemporal relationships and the predictions outperform state-of-art

Machine learning (ML) is becoming an increasingly important component of
cutting-edge physics research, but its computational requirements present
significant challenges. In this white paper, we discuss the needs of the
physics community regarding ML across latency and throughput regimes, the tools
and resources that offer the possibility of addressing these needs, and how
these can be best utilized and accessed in the coming years.

This Letter investigates the transition to synchronization of oscillator
ensembles encoded by simplicial complexes in which pairwise and higher-order
coupling weights alter with time through a rate-based adaptive mechanism
inspired by the Hebbian learning rule. These simultaneously evolving disparate
adaptive coupling weights lead to a phenomenon in which the in-phase
synchronization is completely obliterated; instead, the anti-phase
synchronization is originated. In addition, the onsets of antiphase
synchronization and desynchronization are manageable through both dyadic and
triadic learning rates. The theoretical validation of these numerical
assessments is delineated thoroughly by employing Ott-Antonsen dimensionality
reduction. The framework and results of the Letter could help in the
understanding of the underlying synchronization behavior of a range of
real-world systems, such as brain functions and social systems where
interactions evolve with time.

We present a novel Transformer-based network architecture for instance-aware
image-to-image translation, dubbed InstaFormer, to effectively integrate
global- and instance-level information. By considering extracted content
features from an image as tokens, our networks discover global consensus of
content features by considering context information through a self-attention
module in Transformers. By augmenting such tokens with an instance-level
feature extracted from the content feature with respect to bounding box
information, our framework is capable of learning an interaction between object
instances and the global image, thus boosting the instance-awareness. We
replace layer normalization (LayerNorm) in standard Transformers with adaptive
instance normalization (AdaIN) to enable a multi-modal translation with style
codes. In addition, to improve the instance-awareness and translation quality
at object regions, we present an instance-level content contrastive loss
defined between input and translated image. We conduct experiments to
demonstrate the effectiveness of our InstaFormer over the latest methods and
provide extensive ablation studies.

Nowadays, detecting anomalous communities in networks is an essential task in
research, as it helps discover insights into community-structured networks.
Most of the existing methods leverage either information regarding attributes
of vertices or the topological structure of communities. In this study, we
introduce the Co-Membership-based Generic Anomalous Communities Detection
Algorithm (referred as to CMMAC), a novel and generic method that utilizes the
information of vertices co-membership in multiple communities. CMMAC is
domain-free and almost unaffected by communities' sizes and densities.
Specifically, we train a classifier to predict the probability of each vertex
in a community being a member of the community. We then rank the communities by
the aggregated membership probabilities of each community's vertices. The
lowest-ranked communities are considered to be anomalous. Furthermore, we
present an algorithm for generating a community-structured random network
enabling the infusion of anomalous communities to facilitate research in the
field. We utilized it to generate two datasets, composed of thousands of
labeled anomaly-infused networks, and published them. We experimented
extensively on thousands of simulated, and real-world networks, infused with
artificial anomalies. CMMAC outperformed other existing methods in a range of
settings. Additionally, we demonstrated that CMMAC can identify abnormal
communities in real-world unlabeled networks in different domains, such as
Reddit and Wikipedia.

Although action recognition has achieved impressive results over recent
years, both collection and annotation of video training data are still
time-consuming and cost intensive. Therefore, image-to-video adaptation has
been proposed to exploit labeling-free web image source for adapting on
unlabeled target videos. This poses two major challenges: (1) spatial domain
shift between web images and video frames; (2) modality gap between image and
video data. To address these challenges, we propose Cycle Domain Adaptation
(CycDA), a cycle-based approach for unsupervised image-to-video domain
adaptation by leveraging the joint spatial information in images and videos on
the one hand and, on the other hand, training an independent spatio-temporal
model to bridge the modality gap. We alternate between the spatial and
spatio-temporal learning with knowledge transfer between the two in each cycle.
We evaluate our approach on benchmark datasets for image-to-video as well as
for mixed-source domain adaptation achieving state-of-the-art results and
demonstrating the benefits of our cyclic adaptation.

Evolutionary algorithms are wildly used in unmanned aerial vehicle path
planning for their flexibility and effectiveness. Nevertheless, they are so
sensitive to the change of environment that can't adapt to all scenarios. Due
to this drawback, the previously successful planner frequently fail in a new
scene. In this paper, a first-of-its-kind machine learning method named
Evolutionary Programmer is proposed to solve this problem. Concretely, the most
commonly used Evolutionary Algorithms are decomposed into a series of
operators, which constitute the operator library of the system. The new method
recompose the operators to a integrated planner, thus, the most suitable
operators can be selected for adapting to the changing circumstances. Different
from normal machine programmers, this method focuses on a specific task with
high-level integrated instructions and thus alleviate the problem of huge
search space caused by the briefness of instructions. On this basis, a 64-bit
sequence is presented to represent path planner and then evolved with the
modified Genetic Algorithm. Finally, the most suitable planner is created by
utilizing the information of the previous planner and various randomly
generated ones.

Biclustering is an unsupervised machine learning technique that
simultaneously clusters rows and columns in a data matrix. Biclustering has
emerged as an important approach and plays an essential role in various
applications such as bioinformatics, text mining, and pattern recognition.
However, finding significant biclusters is an NP-hard problem that can be
formulated as an optimization problem. Therefore, different metaheuristics have
been applied to biclustering problems because of their exploratory capability
of solving complex optimization problems in reasonable computation time.
Although various surveys on biclustering have been proposed, there is a lack of
a comprehensive survey on the biclustering problem using metaheuristics. This
chapter will present a survey of metaheuristics approaches to address the
biclustering problem. The review focuses on the underlying optimization methods
and their main search components: representation, objective function, and
variation operators. A specific discussion on single versus multi-objective
approaches is presented. Finally, some emerging research directions are

The $\mathcal{H}_{\infty}$ synthesis approach is a cornerstone robust control
design technique, but is known to be conservative in some cases. The objective
of this paper is to quantify the additional cost the controller incurs planning
for the worst-case scenario, by adopting an approach inspired by regret from
online learning. We define the \textit{disturbance-reality gap} as the
difference between the predicted worst-case disturbance signal and the actual
realization. The regret is shown to scale with the norm of this \textit{gap},
which turns out to have a similar structure to that of the certainty equivalent
controller with inaccurate predictions, obtained here in terms of the
\textit{prediction error} norm.

There is a growing interest in industry and academia in machine learning (ML)
testing. We believe that industry and academia need to learn together to
produce rigorous and relevant knowledge. In this study, we initiate a
collaboration between stakeholders from one case company, one research
institute, and one university. To establish a common view of the problem
domain, we applied an interactive rapid review of the state of the art. Four
researchers from Lund University and RISE Research Institutes and four
practitioners from Axis Communications reviewed a set of 180 primary studies on
ML testing. We developed a taxonomy for the communication around ML testing
challenges and results and identified a list of 12 review questions relevant
for Axis Communications. The three most important questions (data testing,
metrics for assessment, and test generation) were mapped to the literature, and
an in-depth analysis of the 35 primary studies matching the most important
question (data testing) was made. A final set of the five best matches were
analysed and we reflect on the criteria for applicability and relevance for the
industry. The taxonomies are helpful for communication but not final.
Furthermore, there was no perfect match to the case company's investigated
review question (data testing). However, we extracted relevant approaches from
the five studies on a conceptual level to support later context-specific
improvements. We found the interactive rapid review approach useful for
triggering and aligning communication between the different stakeholders.

We propose an approach to modelling large-scale multi-agent dynamical systems
allowing interactions among more than just pairs of agents using the theory of
mean field games and the notion of hypergraphons, which are obtained as limits
of large hypergraphs. To the best of our knowledge, ours is the first work on
mean field games on hypergraphs. Together with an extension to a multi-layer
setup, we obtain limiting descriptions for large systems of non-linear,
weakly-interacting dynamical agents. On the theoretical side, we prove the
well-foundedness of the resulting hypergraphon mean field game, showing both
existence and approximate Nash properties. On the applied side, we extend
numerical and learning algorithms to compute the hypergraphon mean field
equilibria. To verify our approach empirically, we consider a social rumor
spreading model, where we give agents intrinsic motivation to spread rumors to
unaware agents, and an epidemics control problem.

Algorithmic latency in speech processing is dominated by the frame length
used for Fourier analysis, which in turn limits the achievable performance of
magnitude-centric approaches. As previous studies suggest the importance of
phase grows with decreasing frame length, this work presents a systematical
study on the contribution of phase and magnitude in modern Deep Neural Network
(DNN)-based speech enhancement at different frame lengths. Results indicate
that DNNs can successfully estimate phase when using short frames, with similar
or better overall performance compared to using longer frames. Thus,
interestingly, modern phase-aware DNNs allow for low-latency speech enhancement
at high quality.

This study addresses the issue of fusing infrared and visible images that
appear differently for object detection. Aiming at generating an image of high
visual quality, previous approaches discover commons underlying the two
modalities and fuse upon the common space either by iterative optimization or
deep networks. These approaches neglect that modality differences implying the
complementary information are extremely important for both fusion and
subsequent detection task. This paper proposes a bilevel optimization
formulation for the joint problem of fusion and detection, and then unrolls to
a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a
commonly used detection network. The fusion network with one generator and dual
discriminators seeks commons while learning from differences, which preserves
structural information of targets from the infrared and textural details from
the visible. Furthermore, we build a synchronized imaging system with
calibrated infrared and optical sensors, and collect currently the most
comprehensive benchmark covering a wide range of scenarios. Extensive
experiments on several public datasets and our benchmark demonstrate that our
method outputs not only visually appealing fusion but also higher detection mAP
than the state-of-the-art approaches.

Due to the difficulty of collecting exhaustive multi-label annotations,
multi-label datasets often contain partial labels. We consider an extreme of
this weakly supervised learning problem, called single positive multi-label
learning (SPML), where each multi-label training image has only one positive
label. Traditionally, all unannotated labels are assumed as negative labels in
SPML, which introduces false negative labels and causes model training to be
dominated by assumed negative labels. In this work, we choose to treat all
unannotated labels from an alternative perspective, i.e. acknowledging they are
unknown. Hence, we propose entropy-maximization (EM) loss to attain a special
gradient regime for providing proper supervision signals. Moreover, we propose
asymmetric pseudo-labeling (APL), which adopts asymmetric-tolerance strategies
and a self-paced procedure, to cooperate with EM loss and then provide more
precise supervision. Experiments show that our method significantly improves
performance and achieves state-of-the-art results on all four benchmarks. Code
is available at https://github.com/Correr-Zhou/SPML-AckTheUnknown.

In many web applications, deep learning-based CTR prediction models (deep CTR
models for short) are widely adopted. Traditional deep CTR models learn
patterns in a static manner, i.e., the network parameters are the same across
all the instances. However, such a manner can hardly characterize each of the
instances which may have different underlying distributions. It actually limits
the representation power of deep CTR models, leading to sub-optimal results. In
this paper, we propose an efficient, effective, and universal module, named as
Adaptive Parameter Generation network (APG), which can dynamically generate
parameters for deep CTR models on-the-fly based on different instances.
Extensive experimental evaluation results show that APG can be applied to a
variety of deep CTR models and significantly improve their performance.
Meanwhile, APG can reduce the time cost by 38.7\% and memory usage by 96.6\%
compared to a regular deep CTR model. We have deployed APG in the industrial
sponsored search system and achieved 3\% CTR gain and 1\% RPM gain

The stochastic gradient Langevin Dynamics is one of the most fundamental
algorithms to solve sampling problems and non-convex optimization appearing in
several machine learning applications. Especially, its variance reduced
versions have nowadays gained particular attention. In this paper, we study two
variants of this kind, namely, the Stochastic Variance Reduced Gradient
Langevin Dynamics and the Stochastic Recursive Gradient Langevin Dynamics. We
prove their convergence to the objective distribution in terms of KL-divergence
under the sole assumptions of smoothness and Log-Sobolev inequality which are
weaker conditions than those used in prior works for these algorithms. With the
batch size and the inner loop length set to $\sqrt{n}$, the gradient complexity
to achieve an $\epsilon$-precision is
$\tilde{O}((n+dn^{1/2}\epsilon^{-1})\gamma^2 L^2\alpha^{-2})$, which is an
improvement from any previous analyses. We also show some essential
applications of our result to non-convex optimization.

High-Dimensional and Incomplete (HDI) data are frequently found in various
industrial applications with complex interactions among numerous nodes, which
are commonly non-negative for representing the inherent non-negativity of node
interactions. A Non-negative Latent Factor (NLF) model is able to extract
intrinsic features from such data efficiently. However, existing NLF models all
adopt a static divergence metric like Euclidean distance or {\alpha}-\b{eta}
divergence to build its learning objective, which greatly restricts its
scalability of accurately representing HDI data from different domains. Aiming
at addressing this issue, this study presents an Adaptive Divergence-based
Non-negative Latent Factor (ADNLF) model with three-fold ideas: a) generalizing
the objective function with the {\alpha}-\b{eta}-divergence to expand its
potential of representing various HDI data; b) adopting a non-negative bridging
function to connect the optimization variables with output latent factors for
fulfilling the non-negativity constraints constantly; and c) making the
divergence parameters adaptive through particle swarm optimization, thereby
facilitating adaptive divergence in the learning objective to achieve high
scalability. Empirical studies are conducted on four HDI datasets from real
applications, whose results demonstrate that in comparison with
state-of-the-art NLF models, an ADNLF model achieves significantly higher
estimation accuracy for missing data of an HDI dataset with high computational

This paper concerns the problem of multi-object tracking based on the
min-cost flow (MCF) formulation, which is conventionally studied as an instance
of linear program. Given its computationally tractable inference, the success
of MCF tracking largely relies on the learned cost function of underlying
linear program. Most previous studies focus on learning the cost function by
only taking into account two frames during training, therefore the learned cost
function is sub-optimal for MCF where a multi-frame data association must be
considered during inference. In order to address this problem, in this paper we
propose a novel differentiable framework that ties training and inference
together during learning by solving a bi-level optimization problem, where the
lower-level solves a linear program and the upper-level contains a loss
function that incorporates global tracking result. By back-propagating the loss
through differentiable layers via gradient descent, the globally parameterized
cost function is explicitly learned and regularized. With this approach, we are
able to learn a better objective for global MCF tracking. As a result, we
achieve competitive performances compared to the current state-of-the-art
methods on the popular multi-object tracking benchmarks such as MOT16, MOT17
and MOT20.

Learning visual representation of high quality is essential for image
classification. Recently, a series of contrastive representation learning
methods have achieved preeminent success. Particularly, SupCon outperformed the
dominant methods based on cross-entropy loss in representation learning.
However, we notice that there could be potential ethical risks in supervised
contrastive learning. In this paper, we for the first time analyze unfairness
caused by supervised contrastive learning and propose a new Fair Supervised
Contrastive Loss (FSCL) for fair visual representation learning. Inheriting the
philosophy of supervised contrastive learning, it encourages representation of
the same class to be closer to each other than that of different classes, while
ensuring fairness by penalizing the inclusion of sensitive attribute
information in representation. In addition, we introduce a group-wise
normalization to diminish the disparities of intra-group compactness and
inter-class separability between demographic groups that arouse unfair
classification. Through extensive experiments on CelebA and UTK Face, we
validate that the proposed method significantly outperforms SupCon and existing
state-of-the-art methods in terms of the trade-off between top-1 accuracy and
fairness. Moreover, our method is robust to the intensity of data bias and
effectively works in incomplete supervised settings. Our code is available at
https://github.com/sungho-CoolG/FSCL.

Next Point-of-Interest (POI) recommendation has become an indispensable
functionality in Location-based Social Networks (LBSNs) due to its
effectiveness in helping people decide the next POI to visit. However, accurate
recommendation requires a vast amount of historical check-in data, thus
threatening user privacy as the location-sensitive data needs to be handled by
cloud servers. Although there have been several on-device frameworks for
privacy-preserving POI recommendations, they are still resource-intensive when
it comes to storage and computation, and show limited robustness to the high
sparsity of user-POI interactions. On this basis, we propose a novel
decentralized collaborative learning framework for POI recommendation (DCLR),
which allows users to train their personalized models locally in a
collaborative manner. DCLR significantly reduces the local models' dependence
on the cloud for training, and can be used to expand arbitrary centralized
recommendation models. To counteract the sparsity of on-device user data when
learning each local model, we design two self-supervision signals to pretrain
the POI representations on the server with geographical and categorical
correlations of POIs. To facilitate collaborative learning, we innovatively
propose to incorporate knowledge from either geographically or semantically
similar users into each local model with attentive aggregation and mutual
information maximization. The collaborative learning process makes use of
communications between devices while requiring only minor engagement from the
central server for identifying user groups, and is compatible with common
privacy preservation mechanisms like differential privacy. We evaluate DCLR
with two real-world datasets, where the results show that DCLR outperforms
state-of-the-art on-device frameworks and yields competitive results compared
with centralized counterparts.

In recent years, the prediction of quantum mechanical observables with
machine learning methods has become increasingly popular. Message-passing
neural networks (MPNNs) solve this task by constructing atomic representations,
from which the properties of interest are predicted. Here, we introduce a
method to automatically identify chemical moieties (molecular building blocks)
from such representations, enabling a variety of applications beyond property
prediction, which otherwise rely on expert knowledge. The required
representation can either be provided by a pretrained MPNN, or learned from
scratch using only structural information. Beyond the data-driven design of
molecular fingerprints, the versatility of our approach is demonstrated by
enabling the selection of representative entries in chemical databases, the
automatic construction of coarse-grained force fields, as well as the
identification of reaction coordinates.

Semiconductor materials provide a compelling platform for quantum
technologies (QT), and the properties of a vast amount of materials can be
found in databases containing information from both experimental and
theoretical explorations. However, searching these databases to find promising
candidate materials for quantum technology applications is a major challenge.
Therefore, we have developed a framework for the automated discovery of
semiconductor host platforms for QT using material informatics and machine
learning methods, resulting in a dataset consisting of over $25.000$ materials
and nearly $5000$ physics-informed features. Three approaches were devised,
named the Ferrenti, extended Ferrenti and the empirical approach, to label data
for the supervised machine learning (ML) methods logistic regression, decision
trees, random forests and gradient boosting. We find that of the three, the
empirical approach relying exclusively on findings from the literature
predicted substantially fewer candidates than the other two approaches with a
clear distinction between suitable and unsuitable candidates when comparing the
two largest eigenvalues in the covariance matrix. In contrast to expectations
from the literature and that found for the Ferrenti and extended Ferrenti
approaches focusing on band gap and ionic character, the ML methods from the
empirical approach highlighted features related to symmetry and crystal
structure, including bond length, orientation and radial distribution, as
influential when predicting a material as suitable for QT. All three approaches
and all four ML methods agreed on a subset of $47$ eligible candidates %(to a
probability of $>50 \ \%$) of $8$ elemental, $29$ binary, and $10$ tertiary
compounds, and provide a basis for further material explorations towards
quantum technology.

We propose a new problem formulation and a corresponding evaluation framework
to advance research on unsupervised domain adaptation for semantic image
segmentation. The overall goal is fostering the development of adaptive
learning systems that will continuously learn, without supervision, in
ever-changing environments. Typical protocols that study adaptation algorithms
for segmentation models are limited to few domains, adaptation happens offline,
and human intervention is generally required, at least to annotate data for
hyper-parameter tuning. We argue that such constraints are incompatible with
algorithms that can continuously adapt to different real-world situations. To
address this, we propose a protocol where models need to learn online, from
sequences of temporally correlated images, requiring continuous, frame-by-frame
adaptation. We accompany this new protocol with a variety of baselines to
tackle the proposed formulation, as well as an extensive analysis of their
behaviors, which can serve as a starting point for future research.

We introduce optical Flow transFormer, dubbed as FlowFormer, a
transformer-based neural network architecture for learning optical flow.
FlowFormer tokenizes the 4D cost volume built from an image pair, encodes the
cost tokens into a cost memory with alternate-group transformer (AGT) layers in
a novel latent space, and decodes the cost memory via a recurrent transformer
decoder with dynamic positional cost queries. On the Sintel benchmark,
FlowFormer achieves 1.159 and 2.088 average end-point-error (AEPE) on the clean
and final pass, a 16.5% and 15.5% error reduction from the best published
result (1.388 and 2.47). Besides, FlowFormer also achieves strong
generalization performance. Without being trained on Sintel, FlowFormer
achieves 1.01 AEPE on the clean pass of Sintel training set, outperforming the
best published result (1.29) by 21.7%.

Unsupervised models of representations based on Contrastive Predictive Coding
(CPC)[1] are primarily used in spoken language modelling in that they encode
phonetic information. In this study, we ask what other types of information are
present in CPC speech representations. We focus on three categories: phone
class, gender and language, and compare monolingual and bilingual models. Using
qualitative and quantitative tools, we find that both gender and phone class
information are present in both types of models. Language information, however,
is very salient in the bilingual model only, suggesting CPC models learn to
discriminate languages when trained on multiple languages. Some language
information can also be retrieved from monolingual models, but it is more
diffused across all features. These patterns hold when analyses are carried on
the discrete units from a downstream clustering model. However, although there
is no effect of the number of target clusters on phone class and language
information, more gender information is encoded with more clusters. Finally, we
find that there is some cost to being exposed to two languages on a downstream
phoneme discrimination task.

From condensed matter to quantum chromodynamics, multidimensional spins are a
fundamental paradigm, with a pivotal role in combinatorial optimization and
machine learning. Machines formed by coupled parametric oscillators can
simulate spin models, but only for Ising or low-dimensional spins. Currently,
machines implementing arbitrary dimensions remain a challenge. Here, we
introduce and validate a hyperspin machine to simulate multidimensional
continuous spin models. We realize high-dimensional spins by pumping groups of
parametric oscillators, and study NP-hard graphs of hyperspins. The hyperspin
machine can interpolate between different dimensions by tuning the coupling
topology, a strategy that we call "dimensional annealing". When interpolating
between the XY and the Ising model, the dimensional annealing impressively
increases the success probability compared to conventional Ising simulators.
Hyperspin machines are a new computational model for combinatorial
optimization. They can be realized by off-the-shelf hardware for ultrafast,
large-scale applications in classical and quantum computing, condensed-matter
physics, and fundamental studies.

Contrastive learning (CL) has become a ubiquitous approach for several
natural language processing (NLP) downstream tasks, especially for question
answering (QA). However, the major challenge, how to efficiently train the
knowledge retrieval model in an unsupervised manner, is still unresolved.
Recently the commonly used methods are composed of CL and masked language model
(MLM). Unexpectedly, MLM ignores the sentence-level training, and CL also
neglects extraction of the internal info from the query. To optimize the CL
hardly obtain internal information from the original query, we introduce a
joint training method by combining CL and Auto-MLM for self-supervised
multi-lingual knowledge retrieval. First, we acquire the fixed dimensional
sentence vector. Then, mask some words among the original sentences with random
strategy. Finally, we generate a new token representation for predicting the
masked tokens. Experimental results show that our proposed approach
consistently outperforms all the previous SOTA methods on both AliExpress $\&$
LAZADA service corpus and openly available corpora in 8 languages.

Data streams are ubiquitous in modern business and society. In practice, data
streams may evolve over time and cannot be stored indefinitely. Effective and
transparent machine learning on data streams is thus often challenging.
Hoeffding Trees have emerged as a state-of-the art for online predictive
modelling. They are easy to train and provide meaningful convergence guarantees
under a stationary process. Yet, at the same time, Hoeffding Trees often
require heuristic and costly extensions to adjust to distributional change,
which may considerably impair their interpretability. In this work, we revisit
Model Trees for machine learning in evolving data streams. Model Trees are able
to maintain more flexible and locally robust representations of the active data
concept, making them a natural fit for data stream applications. Our novel
framework, called Dynamic Model Tree, satisfies desirable consistency and
minimality properties. In experiments with synthetic and real-world tabular
streaming data sets, we show that the proposed framework can drastically reduce
the number of splits required by existing incremental decision trees. At the
same time, our framework often outperforms state-of-the-art models in terms of
predictive quality -- especially when concept drift is involved. Dynamic Model
Trees are thus a powerful online learning framework that contributes to more
lightweight and interpretable machine learning in data streams.

In this work, we propose marginalized operators, a new class of off-policy
evaluation operators for reinforcement learning. Marginalized operators
strictly generalize generic multi-step operators, such as Retrace, as special
cases. Marginalized operators also suggest a form of sample-based estimates
with potential variance reduction, compared to sample-based estimates of the
original multi-step operators. We show that the estimates for marginalized
operators can be computed in a scalable way, which also generalizes prior
results on marginalized importance sampling as special cases. Finally, we
empirically demonstrate that marginalized operators provide performance gains
to off-policy evaluation and downstream policy optimization algorithms.

The Swiss State Secretariat for Migration recently announced a pilot project
for a machine learning-based assignment process for refugee resettlement. This
approach has the potential to substantially increase the overall employment
rate of refugees in Switzerland. However, the currently proposed method ignores
families' preferences. In this paper, we build on this prior work and propose
two matching mechanisms that additionally take families' preferences over
locations into account. The first mechanism is strategyproof while the second
is not but achieves higher family welfare. Importantly, we parameterize both
mechanisms, giving placement officers precise control how to trade off family
welfare against overall employment success. Preliminary simulations on
synthetic data show that both mechanisms can significantly increase family
welfare even with only a small loss on the overall employment rate of refugees.

More and more learning apps like Duolingo are using some form of gamification
(e.g., badges, points, and leaderboards) to enhance user learning. However,
they are not always successful. Gamification misuse is a phenomenon that occurs
when users become too fixated on gamification and get distracted from learning.
This undesirable phenomenon wastes users' precious time and negatively impacts
their learning performance. However, there has been little research in the
literature to understand gamification misuse and inform future gamification
designs. Therefore, this paper aims to fill this knowledge gap by conducting
the first extensive qualitative research on gamification misuse in a popular
learning app called Duolingo. Duolingo is currently the world's most downloaded
learning app used to learn languages. This study consists of two phases: (I) a
content analysis of data from Duolingo forums (from the past nine years) and
(II) semi-structured interviews with 15 international Duolingo users. Our
research contributes to the Human-Computer Interaction (HCI) and Learning at
Scale (L@S) research communities in three ways: (1) elaborating the
ramifications of gamification misuse on user learning, well-being, and ethics,
(2) identifying the most common reasons for gamification misuse (e.g.,
competitiveness, overindulgence in playfulness, and herding), and (3) providing
designers with practical suggestions to prevent (or mitigate) the occurrence of
gamification misuse in their future designs of gamified learning apps.

Knowledge distillation (KD) shows a bright promise as a powerful
regularization strategy to boost generalization ability by leveraging learned
sample-level soft targets. Yet, employing a complex pre-trained teacher network
or an ensemble of peer students in existing KD is both time-consuming and
computationally costly. Various self KD methods have been proposed to achieve
higher distillation efficiency. However, they either require extra network
architecture modification or are difficult to parallelize. To cope with these
challenges, we propose an efficient and reliable self-distillation framework,
named Self-Distillation from Last Mini-Batch (DLB). Specifically, we rearrange
the sequential sampling by constraining half of each mini-batch coinciding with
the previous iteration. Meanwhile, the rest half will coincide with the
upcoming iteration. Afterwards, the former half mini-batch distills on-the-fly
soft targets generated in the previous iteration. Our proposed mechanism guides
the training stability and consistency, resulting in robustness to label noise.
Moreover, our method is easy to implement, without taking up extra run-time
memory or requiring model structure modification. Experimental results on three
classification benchmarks illustrate that our approach can consistently
outperform state-of-the-art self-distillation approaches with different network
architectures. Additionally, our method shows strong compatibility with
augmentation strategies by gaining additional performance improvement. The code
is available at https://github.com/Meta-knowledge-Lab/DLB.

Multi-object multi-part scene parsing is a challenging task which requires
detecting multiple object classes in a scene and segmenting the semantic parts
within each object. In this paper, we propose FLOAT, a factorized label space
framework for scalable multi-object multi-part parsing. Our framework involves
independent dense prediction of object category and part attributes which
increases scalability and reduces task complexity compared to the monolithic
label space counterpart. In addition, we propose an inference-time 'zoom'
refinement technique which significantly improves segmentation quality,
especially for smaller objects/parts. Compared to state of the art, FLOAT
obtains an absolute improvement of 2.0% for mean IOU (mIOU) and 4.8% for
segmentation quality IOU (sqIOU) on the Pascal-Part-58 dataset. For the larger
Pascal-Part-108 dataset, the improvements are 2.1% for mIOU and 3.9% for sqIOU.
We incorporate previously excluded part attributes and other minor parts of the
Pascal-Part dataset to create the most comprehensive and challenging version
which we dub Pascal-Part-201. FLOAT obtains improvements of 8.6% for mIOU and
7.5% for sqIOU on the new dataset, demonstrating its parsing effectiveness
across a challenging diversity of objects and parts. The code and datasets are
available at floatseg.github.io.

Recent machine learning algorithms such as neural networks can classify
objects and actions in video frames with high accuracy. Here, I discuss a
classification of objects based on basal dynamic patterns referencing one
tradition, the link between rabbit, toad, and the Moon, which can be seen in
several cultures. In order for them to be classified into one class, a basic
pattern of behavior (cyclic appearance and disappearance) works as a feature
point. A static character such as the shape and time scale of the behavior are
not essential for this classification. In cognitive semantics, image schemas
are introduced to describe basal patterns of events. If learning of these image
schemas is attained, a machine may be able to categorize rabbit, toad, and the
Moon as the same class. For learning, video frames that show boundary boxes or
segmentation may be helpful. Although this discussion is preliminary and many
tasks remain to be solved, the classification based on basal behaviors can be
an important topic for cognitive processes and computer science.

One of the most important factors that contribute to the success of a machine
learning model is a good training objective. Training objective crucially
influences the model's performance and generalization capabilities. This paper
specifically focuses on graph neural network training objective for link
prediction, which has not been explored in the existing literature. Here, the
training objective includes, among others, a negative sampling strategy, and
various hyperparameters, such as edge message ratio which controls how training
edges are used. Commonly, these hyperparameters are fine-tuned by complete grid
search, which is very time-consuming and model-dependent. To mitigate these
limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically
adjusts the edge message ratio during training. It is model agnostic and highly
scalable with a fully customizable computational budget. Through extensive
experiments, we show that AdaGrid can boost the performance of the models up to
$1.9\%$ while being nine times more time-efficient than a complete search.
Overall, AdaGrid represents an effective automated algorithm for designing
machine learning training objectives.

Recommendation in the fashion domain has seen a recent surge in research in
various areas, for example, shop-the-look, context-aware outfit creation,
personalizing outfit creation, etc. The majority of state of the art approaches
in the domain of outfit recommendation pursue to improve compatibility among
items so as to produce high quality outfits. Some recent works have realized
that style is an important factor in fashion and have incorporated it in
compatibility learning and outfit generation. These methods often depend on the
availability of fine-grained product categories or the presence of rich item
attributes (e.g., long-skirt, mini-skirt, etc.). In this work, we aim to
generate outfits conditional on styles or themes as one would dress in real
life, operating under the practical assumption that each item is mapped to a
high level category as driven by the taxonomy of an online portal, like
outdoor, formal etc and an image. We use a novel style encoder network that
renders outfit styles in a smooth latent space. We present an extensive
analysis of different aspects of our method and demonstrate its superiority
over existing state of the art baselines through rigorous experiments.

Embedded in any speech signal is a rich combination of cognitive,
neuromuscular and physiological information. This richness makes speech a
powerful signal in relation to a range of different health conditions,
including major depressive disorders (MDD). One pivotal issue in
speech-depression research is the assumption that depressive severity is the
dominant measurable effect. However, given the heterogeneous clinical profile
of MDD, it may actually be the case that speech alterations are more strongly
associated with subsets of key depression symptoms. This paper presents strong
evidence in support of this argument. First, we present a novel large,
cross-sectional, multi-modal dataset collected at Thymia. We then present a set
of machine learning experiments that demonstrate that combining speech with
features from an n-Back working memory assessment improves classifier
performance when predicting the popular eight-item Patient Health Questionnaire
depression scale (PHQ-8). Finally, we present a set of experiments that
highlight the association between different speech and n-Back markers at the
PHQ-8 item level. Specifically, we observe that somatic and psychomotor
symptoms are more strongly associated with n-Back performance scores, whilst
the other items: anhedonia, depressed mood, change in appetite, feelings of
worthlessness and trouble concentrating are more strongly associated with
speech changes.

We consider the problem of churn prediction in real-time. Because of the
batch mode of inference generation, the traditional methods can only support
retention campaigns with offline interventions, e.g., test messages, emails or
static in-product nudges. Other recent works in real-time churn predictions do
not assess the cost to accuracy trade-off to deploy such models in production.
In this paper we present RICON, a flexible, cost-effective and robust machine
learning system to predict customer churn propensities in real-time using
clickstream data. In addition to churn propensity prediction, RICON provides
insights based on product usage intelligence. Through application on a real big
data of QBO Advanced customers we showcase how RICON has achieved a top decile
lift of 2.68 in the presence of strong class imbalance. Moreover, we execute an
extensive comparative study to justify our modeling choices for RICON. Finally,
we mention how RICON can be integrated with intervention platforms within
Intuit to run large-scale retention campaigns with real-time in-product
contextual helps.

Existing navigation policies for autonomous robots tend to focus on collision
avoidance while ignoring human-robot interactions in social life. For instance,
robots can pass along the corridor safer and easier if pedestrians notice them.
Sounds have been considered as an efficient way to attract the attention of
pedestrians, which can alleviate the freezing robot problem. In this work, we
present a new deep reinforcement learning (DRL) based social navigation
approach for autonomous robots to move in pedestrian-rich environments with
interaction capacity. Most existing DRL based methods intend to train a general
policy that outputs both navigation actions, i.e., expected robot's linear and
angular velocities, and interaction actions, i.e., the beep action, in the
context of reinforcement learning. Different from these methods, we intend to
train the policy via both supervised learning and reinforcement learning. In
specific, we first train an interaction policy in the context of supervised
learning, which provides a better understanding of the social situation, then
we use this interaction policy to train the navigation policy via multiple
reinforcement learning algorithms. We evaluate our approach in various
simulation environments and compare it to other methods. The experimental
results show that our approach outperforms others in terms of the success rate.
We also deploy the trained policy on a real-world robot, which shows a nice
performance in crowded environments.

The unprecedented availability of spatial and temporal high-resolution
satellite image time series (SITS) for crop type mapping is believed to
necessitate deep learning architectures to accommodate challenges arising from
both dimensions. Recent state-of-the-art deep learning models have shown
promising results by stacking spatial and temporal encoders. However, we
present a Pyramid Time-Series Transformer (PTST) that operates solely on the
temporal dimension, i.e., neglecting the spatial dimension, can produce
superior results with a drastic reduction in GPU memory consumption and easy
extensibility. Furthermore, we augment it to perform semi-supervised learning
by proposing a classification-friendly VAE framework that introduces clustering
mechanisms into latent space and can promote linear separability therein.
Consequently, a few principal axes of the latent space can explain the majority
of variance in raw data. Meanwhile, the VAE framework with proposed tweaks can
maintain competitive classification performance as its purely discriminative
counterpart when only $40\%$ of labelled data is used. We hope the proposed
framework can serve as a baseline for crop classification with SITS for its
modularity and simplicity.

Respiratory sound classification is an important tool for remote screening of
respiratory-related diseases such as pneumonia, asthma, and COVID-19. To
facilitate the interpretability of classification results, especially ones
based on deep learning, many explanation methods have been proposed using
prototypes. However, existing explanation techniques often assume that the data
is non-biased and the prediction results can be explained by a set of
prototypical examples. In this work, we develop a unified example-based
explanation method for selecting both representative data (prototypes) and
outliers (criticisms). In particular, we propose a novel application of
adversarial attacks to generate an explanation spectrum of data instances via
an iterative fast gradient sign method. Such unified explanation can avoid
over-generalisation and bias by allowing human experts to assess the model
mistakes case by case. We performed a wide range of quantitative and
qualitative evaluations to show that our approach generates effective and
understandable explanation and is robust with many deep learning models

DUNE is a dual-site experiment for long-baseline neutrino oscillation
studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase
(DP) is a 6x6x6m3 liquid argon time-projection-chamber (LArTPC) that recorded
cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of
the DUNE Far Detector. Charged particles propagating through the LArTPC produce
ionization and scintillation light. The scintillation light signal in these
detectors can provide the trigger for non-beam events. In addition, it adds
precise timing capabilities and improves the calorimetry measurements. In
ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic
muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away
from the ionizing track. In this paper, the ProtoDUNE-DP photon detection
system performance is evaluated with a particular focus on the different
wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr,
considering its future use in giant LArTPCs. The scintillation light production
and propagation processes are analyzed and a comparison of simulation to data
is performed, improving understanding of the liquid argon properties

Autonomous driving technology has drawn a lot of attention due to its fast
development and extremely high commercial values. The recent technological leap
of autonomous driving can be primarily attributed to the progress in the
environment perception. Good environment perception provides accurate
high-level environment information which is essential for autonomous vehicles
to make safe and precise driving decisions and strategies. Moreover, such
progress in accurate environment perception would not be possible without deep
learning models and advanced onboard sensors, such as optical sensors (LiDARs
and cameras), radars, GPS. However, the advanced sensors and deep learning
models are prone to recently invented attack methods. For example, LiDARs and
cameras can be compromised by optical attacks, and deep learning models can be
attacked by adversarial examples. The attacks on advanced sensors and deep
learning models can largely impact the accuracy of the environment perception,
posing great threats to the safety and security of autonomous vehicles. In this
thesis, we study the detection methods against the attacks on onboard sensors
and the linkage between attacked deep learning models and driving safety for
autonomous vehicles. To detect the attacks, redundant data sources can be
exploited, since information distortions caused by attacks in victim sensor
data result in inconsistency with the information from other redundant sources.
To study the linkage between attacked deep learning models and driving
safety...

Random walk is widely used in many graph analysis tasks, especially the
first-order random walk. However, as a simplification of real-world problems,
the first-order random walk is poor at modeling higher-order structures in the
data. Recently, second-order random walk-based applications (e.g., Node2vec,
Second-order PageRank) have become attractive. Due to the complexity of the
second-order random walk models and memory limitations, it is not scalable to
run second-order random walk-based applications on a single machine. Existing
disk-based graph systems are only friendly to the first-order random walk
models and suffer from expensive disk I/Os when executing the second-order
random walks. This paper introduces an I/O-efficient disk-based graph system
for the scalable second-order random walk of large graphs, called GraSorw.
First, to eliminate massive light vertex I/Os, we develop a bi-block execution
engine that converts random I/Os into sequential I/Os by applying a new
triangular bi-block scheduling strategy, the bucket-based walk management, and
the skewed walk storage. Second, to improve the I/O utilization, we design a
learning-based block loading model to leverage the advantages of the full-load
and on-demand load methods. Finally, we conducted extensive experiments on six
large real datasets as well as several synthetic datasets. The empirical
results demonstrate that the end-to-end time cost of popular tasks in GraSorw
is reduced by more than one order of magnitude compared to the existing
disk-based graph systems.

In this work Time Series Classification techniques are investigated, and
especially their applicability in applications where there are significant
differences between the individuals where data is collected, and the
individuals where the classification is evaluated. Classification methods are
applied to a fault classification case, where a key assumption is that data
from a fault free reference case for each specific individual is available. For
the investigated application, wave propagation cause almost chaotic changes of
a measured pressure signal, and physical modeling is difficult. Direct
application of One-Nearest-Neighbor Dynamic Time Warping, a common technique
for this kind of problem, and other machine learning techniques are shown to
fail for this case and new methods to improve the situation are presented. By
using relative features describing the difference from the reference case
rather than the absolute time series, improvements are made compared to
state-of-the-art time series classification algorithms.

Deep-learning-based compressor has received interests recently due to much
improved compression ratio. However, modern approaches suffer from long
execution time. To ease this problem, this paper targets on cutting down the
execution time of deep-learning-based compressors. Building
history-dependencies sequentially (e.g., recurrent neural networks) is
responsible for long inference latency. Instead, we introduce transformer into
deep learning compressors to build history-dependencies in parallel. However,
existing transformer is too heavy in computation and incompatible to
compression tasks.
  This paper proposes a fast general-purpose lossless compressor, TRACE, by
designing a compression-friendly structure based on a single-layer transformer.
We first design a new metric to advise the selection part of compression model
structures. Byte-grouping and Shared-ffn schemes are further proposed to fully
utilize the capacity of the single-layer transformer. These features allow
TRACE to achieve competitive compression ratio and a much faster speed. In
addition, we further accelerate the compression procedure by designing a
controller to reduce the parameter updating overhead. Experiments show that
TRACE achieves an overall $\sim$3x speedup while keeps a comparable compression
ratio to the state-of-the-art compressors. The source code for TRACE and links
to the datasets are available at
https://github.com/mynotwo/A-Fast-Transformer-based-General-Purpose-LosslessCompressor.

In step with the digitalization of transportation, we are witnessing a
growing range of path-based smart-city applications, e.g., travel-time
estimation and travel path ranking. A temporal path(TP) that includes temporal
information, e.g., departure time, into the path is fundamental to enable such
applications. In this setting, it is essential to learn generic temporal path
representations(TPRs) that consider spatial and temporal correlations
simultaneously and that can be used in different applications, i.e., downstream
tasks. Existing methods fail to achieve the goal since (i) supervised methods
require large amounts of task-specific labels when training and thus fail to
generalize the obtained TPRs to other tasks; (ii) through unsupervised methods
can learn generic representations, they disregard the temporal aspect, leading
to sub-optimal results. To contend with the limitations of existing solutions,
we propose a Weakly-Supervised Contrastive (WSC) learning model. We first
propose a temporal path encoder that encodes both the spatial and temporal
information of a temporal path into a TPR. To train the encoder, we introduce
weak labels that are easy and inexpensive to obtain and are relevant to
different tasks, e.g., temporal labels indicating peak vs. off-peak hours from
departure times. Based on the weak labels, we construct meaningful positive and
negative temporal path samples by considering both spatial and temporal
information, which facilities training the encoder using contrastive learning
by pulling closer to the positive samples' representations while pushing away
the negative samples' representations. To better guide contrastive learning, we
propose a learning strategy based on Curriculum Learning such that the learning
performs from easy to hard training instances. Experiments studies verify the
effectiveness of the proposed method.

Modern buildings are densely equipped with smart energy meters, which
periodically generate a massive amount of time-series data yielding few million
data points every day. This data can be leveraged to discover the underlying
loads, infer their energy consumption patterns, inter-dependencies on
environmental factors, and the building's operational properties. Furthermore,
it allows us to simultaneously identify anomalies present in the electricity
consumption profiles, which is a big step towards saving energy and achieving
global sustainability. However, to date, the lack of large-scale annotated
energy consumption datasets hinders the ongoing research in anomaly detection.
We contribute to this effort by releasing a well-annotated version of a
publicly available ASHRAE Great Energy Predictor III data set containing 1,413
smart electricity meter time series spanning over one year. In addition, we
benchmark the performance of eight state-of-the-art anomaly detection methods
on our dataset and compare their performance.

Finding the failure scenarios of a system is a very complex problem in the
field of Probabilistic Safety Assessment (PSA). In order to solve this problem
we will use the Hidden Quantum Markov Models (HQMMs) to create a generative
model. Therefore, in this paper, we will study and compare the results of HQMMs
and classical Hidden Markov Models HMM on a real datasets generated from real
small systems in the field of PSA. As a quality metric we will use Description
accuracy DA and we will show that the quantum approach gives better results
compared with the classical approach, and we will give a strategy to identify
the probable and no-probable failure scenarios of a system.

Speech distortions are a long-standing problem that degrades the performance
of supervisely trained speech processing models. It is high time that we
enhance the robustness of speech processing models to obtain good performance
when encountering speech distortions while not hurting the original performance
on clean speech. In this work, we propose to improve the robustness of speech
processing models by domain adversarial training (DAT). We conducted
experiments based on the SUPERB framework on five different speech processing
tasks. In case we do not always have knowledge of the distortion types for
speech data, we analyzed the binary-domain and multi-domain settings, where the
former treats all distorted speech as one domain, and the latter views
different distortions as different domains. In contrast to supervised training
methods, we obtained promising results in target domains where speech data is
distorted with different distortions including new unseen distortions
introduced during testing.

Existing continual learning methods use Batch Normalization (BN) to
facilitate training and improve generalization across tasks. However, the
non-i.i.d and non-stationary nature of continual learning data, especially in
the online setting, amplify the discrepancy between training and testing in BN
and hinder the performance of older tasks. In this work, we study the
cross-task normalization effect of BN in online continual learning where BN
normalizes the testing data using moments biased towards the current task,
resulting in higher catastrophic forgetting. This limitation motivates us to
propose a simple yet effective method that we call Continual Normalization (CN)
to facilitate training similar to BN while mitigating its negative effect.
Extensive experiments on different continual learning algorithms and online
scenarios show that CN is a direct replacement for BN and can provide
substantial performance improvements. Our implementation is available at
\url{https://github.com/phquang/Continual-Normalization}.

We provide an end-to-end Renyi DP based-framework for differentially private
top-$k$ selection. Unlike previous approaches, which require a data-independent
choice on $k$, we propose to privately release a data-dependent choice of $k$
such that the gap between $k$-th and the $(k+1)$st "quality" is large. This is
achieved by a novel application of the Report-Noisy-Max. Not only does this
eliminate one hyperparameter, the adaptive choice of $k$ also certifies the
stability of the top-$k$ indices in the unordered set so we can release them
using a variant of propose-test-release (PTR) without adding noise. We show
that our construction improves the privacy-utility trade-offs compared to the
previous top-$k$ selection algorithms theoretically and empirically.
Additionally, we apply our algorithm to "Private Aggregation of Teacher
Ensembles (PATE)" in multi-label classification tasks with a large number of
labels and show that it leads to significant performance gains.

The recently proposed Graph Convolutional Networks (GCNs) have achieved
significantly superior performance on various graph-related tasks, such as node
classification and recommendation. However, currently researches on GCN models
usually recursively aggregate the information from all the neighbors or
randomly sampled neighbor subsets, without explicitly identifying whether the
aggregated neighbors provide useful information during the graph convolution.
In this paper, we theoretically analyze the affection of the neighbor quality
over GCN models' performance and propose the Neighbor Enhanced Graph
Convolutional Network (NEGCN) framework to boost the performance of existing
GCN models. Our contribution is three-fold. First, we at the first time propose
the concept of neighbor quality for both node classification and recommendation
tasks in a general theoretical framework. Specifically, for node
classification, we propose three propositions to theoretically analyze how the
neighbor quality affects the node classification performance of GCN models.
Second, based on the three proposed propositions, we introduce the graph
refinement process including specially designed neighbor evaluation methods to
increase the neighbor quality so as to boost both the node classification and
recommendation tasks. Third, we conduct extensive node classification and
recommendation experiments on several benchmark datasets. The experimental
results verify that our proposed NEGCN framework can significantly enhance the
performance for various typical GCN models on both node classification and
recommendation tasks.

We consider the problem of omni-supervised object detection, which can use
unlabeled, fully labeled and weakly labeled annotations, such as image tags,
counts, points, etc., for object detection. This is enabled by a unified
architecture, Omni-DETR, based on the recent progress on student-teacher
framework and end-to-end transformer based object detection. Under this unified
architecture, different types of weak labels can be leveraged to generate
accurate pseudo labels, by a bipartite matching based filtering mechanism, for
the model to learn. In the experiments, Omni-DETR has achieved state-of-the-art
results on multiple datasets and settings. And we have found that weak
annotations can help to improve detection performance and a mixture of them can
achieve a better trade-off between annotation cost and accuracy than the
standard complete annotation. These findings could encourage larger object
detection datasets with mixture annotations. The code is available at
https://github.com/amazon-research/omni-detr.

The multiplexing and de-multiplexing of orbital angular momentum (OAM) beams
are critical issues in optical communication. Optical diffractive neural
networks have been introduced to perform classification, generation,
multiplexing and de-multiplexing of OAM beams. However, conventional
diffractive neural networks cannot handle OAM modes with a varying spatial
distribution of polarization directions. Herein, we propose a polarized optical
deep diffractive neural network that is designed based on the concept of
rectangular micro-structure meta-material. Our proposed polarized optical
diffractive neural network is trained to classify, generate, multiplex and
de-multiplex polarized OAM beams.The simulation results show that our network
framework can successfully classify 14 kinds of orthogonally polarized vortex
beams and de-multiplex the hybrid OAM beams into Gauss beams at two, three and
four spatial positions respectively. 6 polarized OAM beams with identical total
intensity and 8 cylinder vector beams with different topology charges also have
been classified effectively. Additionally, results reveal that the network can
generate hybrid OAM beams with high quality and multiplex two polarized linear
beams into 8 kinds of cylinder vector beams.

Although many video prediction methods have obtained good performance in
low-resolution (64$\sim$128) videos, predictive models for high-resolution
(512$\sim$4K) videos have not been fully explored yet, which are more
meaningful due to the increasing demand for high-quality videos. Compared with
low-resolution videos, high-resolution videos contain richer appearance
(spatial) information and more complex motion (temporal) information. In this
paper, we propose a Spatiotemporal Residual Predictive Model (STRPM) for
high-resolution video prediction. On the one hand, we propose a Spatiotemporal
Encoding-Decoding Scheme to preserve more spatiotemporal information for
high-resolution videos. In this way, the appearance details for each frame can
be greatly preserved. On the other hand, we design a Residual Predictive Memory
(RPM) which focuses on modeling the spatiotemporal residual features (STRF)
between previous and future frames instead of the whole frame, which can
greatly help capture the complex motion information in high-resolution videos.
In addition, the proposed RPM can supervise the spatial encoder and temporal
encoder to extract different features in the spatial domain and the temporal
domain, respectively. Moreover, the proposed model is trained using generative
adversarial networks (GANs) with a learned perceptual loss (LP-loss) to improve
the perceptual quality of the predictions. Experimental results show that STRPM
can generate more satisfactory results compared with various existing methods.

Learning a set of tasks in sequence remains a challenge for artificial neural
networks, which, in such scenarios, tend to suffer from Catastrophic Forgetting
(CF). The same applies to End-to-End (E2E) Automatic Speech Recognition (ASR)
models, even for monolingual tasks. In this paper, we aim to overcome CF for
E2E ASR by inserting adapters, small architectures of few parameters which
allow a general model to be fine-tuned to a specific task, into our model. We
make these adapters task-specific, while regularizing the parameters of the
model shared by all tasks, thus stimulating the model to fully exploit the
adapters while keeping the shared parameters to work well for all tasks. Our
method outperforms all baselines on two monolingual experiments while being
more storage efficient and without requiring the storage of data from previous
tasks.

Acoustic word embeddings (AWEs) are discriminative representations of speech
segments, and learned embedding space reflects the phonetic similarity between
words. With multi-view learning, where text labels are considered as
supplementary input, AWEs are jointly trained with acoustically grounded word
embeddings (AGWEs). In this paper, we expand the multi-view approach into a
proxy-based framework for deep metric learning by equating AGWEs with proxies.
A simple modification in computing the similarity matrix allows the general
pair weighting to formulate the data-to-proxy relationship. Under the
systematized framework, we propose an asymmetric-proxy loss that combines
different parts of loss functions asymmetrically while keeping their merits. It
follows the assumptions that the optimal function for anchor-positive pairs may
differ from one for anchor-negative pairs, and a proxy may have a different
impact when it substitutes for different positions in the triplet. We present
comparative experiments with various proxy-based losses including our
asymmetric-proxy loss, and evaluate AWEs and AGWEs for word discrimination
tasks on WSJ corpus. The results demonstrate the effectiveness of the proposed
method.

Existing universal lesion detection (ULD) methods utilize compute-intensive
anchor-based architectures which rely on predefined anchor boxes, resulting in
unsatisfactory detection performance, especially in small and mid-sized
lesions. Further, these default fixed anchor-sizes and ratios do not generalize
well to different datasets. Therefore, we propose a robust one-stage
anchor-free lesion detection network that can perform well across varying
lesions sizes by exploiting the fact that the box predictions can be sorted for
relevance based on their center rather than their overlap with the object.
Furthermore, we demonstrate that the ULD can be improved by explicitly
providing it the domain-specific information in the form of multi-intensity
images generated using multiple HU windows, followed by self-attention based
feature-fusion and backbone initialization using weights learned via
self-supervision over CT-scans. We obtain comparable results to the
state-of-the-art methods, achieving an overall sensitivity of 86.05% on the
DeepLesion dataset, which comprises of approximately 32K CT-scans with lesions
annotated across various body organs.

Although a recent shift has been made in the field of predictive process
monitoring to use models from the explainable artificial intelligence field,
the evaluation still occurs mainly through performance-based metrics, thus not
accounting for the actionability and implications of the explanations. In this
paper, we define explainability through the interpretability of the
explanations and the faithfulness of the explainability model in the field of
process outcome prediction. The introduced properties are analysed along the
event, case, and control flow perspective which are typical for a process-based
analysis. This allows comparing inherently created explanations with post-hoc
explanations. We benchmark seven classifiers on thirteen real-life events logs,
and these cover a range of transparent and non-transparent machine learning and
deep learning models, further complemented with explainability techniques.
Next, this paper contributes a set of guidelines named X-MOP which allows
selecting the appropriate model based on the event log specifications, by
providing insight into how the varying preprocessing, model complexity and
explainability techniques typical in process outcome prediction influence the
explainability of the model.

In this paper, we are interested in modeling a how-to instructional
procedure, such as a cooking recipe, with a meaningful and rich high-level
representation. Specifically, we propose to represent cooking recipes and food
images as cooking programs. Programs provide a structured representation of the
task, capturing cooking semantics and sequential relationships of actions in
the form of a graph. This allows them to be easily manipulated by users and
executed by agents. To this end, we build a model that is trained to learn a
joint embedding between recipes and food images via self-supervision and
jointly generate a program from this embedding as a sequence. To validate our
idea, we crowdsource programs for cooking recipes and show that: (a) projecting
the image-recipe embeddings into programs leads to better cross-modal retrieval
results; (b) generating programs from images leads to better recognition
results compared to predicting raw cooking instructions; and (c) we can
generate food images by manipulating programs via optimizing the latent code of
a GAN. Code, data, and models are available online.

In this paper, we consider a subset selection problem in a spatial field
where we seek to find a set of k locations whose observations provide the best
estimate of the field value at a finite set of prediction locations. The
measurements can be taken at any location in the continuous field, and the
covariance between the field values at different points is given by the widely
used squared exponential covariance function. One approach for observation
selection is to perform a grid discretization of the space and obtain an
approximate solution using the greedy algorithm. The solution quality improves
with a finer grid resolution but at the cost of increased computation. We
propose a method to reduce the computational complexity, or conversely to
increase solution quality, of the greedy algorithm by considering a search
space consisting only of prediction locations and centroids of cliques formed
by the prediction locations. We demonstrate the effectiveness of our proposed
approach in simulation, both in terms of solution quality and runtime.

Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive
model to a downstream optimization task that uses its predictions in order to
perform better on that specific task. The main technical challenge associated
with DFL is that it requires being able to differentiate through the
optimization problem, which is difficult due to discontinuous solutions and
other challenges. Past work has largely gotten around this issue by
handcrafting task-specific surrogates to the original optimization problem that
provide informative gradients when differentiated through. However, the need to
handcraft surrogates for each new task limits the usability of DFL. In
addition, there are often no guarantees about the convexity of the resulting
surrogates and, as a result, training a predictive model using them can lead to
inferior local optima. In this paper, we do away with surrogates altogether and
instead learn loss functions that capture task-specific information. To the
best of our knowledge, ours is the first approach that entirely replaces the
optimization component of decision-focused learning with a loss that is
automatically learned. Our approach (a) only requires access to a black-box
oracle that can solve the optimization problem and is thus generalizable, and
(b) can be convex by construction and so can be easily optimized over. We
evaluate our approach on three resource allocation problems from the literature
and find that our approach outperforms learning without taking into account
task structure in all three domains, and even hand-crafted surrogates from the

X-ray reflectivity (XRR) is widely used for thin-film structure analysis, and
XRR data analysis involves minimizing the difference between an XRR curve
calculated from model parameters describing the thin-film structure. This
analysis takes a certain amount of time because it involves many unavoidable
iterations. However, the recently introduced artificial neural network (ANN)
method can dramatically reduce the analysis time in the case of repeated
analyses of similar samples. Here, we demonstrate the analysis of XRR data
using a mixture density network (MDN), which enables probabilistic prediction
while maintaining the advantages of an ANN. First, under the assumption of a
unimodal probability distribution of the output parameter, the trained MDN can
estimate the best-fit parameter and, at the same time, estimate the confidence
interval (CI) corresponding to the error bar of the best-fit parameter. The CI
obtained in this manner is similar to that obtained using the Neumann process,
a well-known statistical method. Next, the MDN method provides several possible
solutions for each parameter in the case of a multimodal distribution of the
output parameters. An unsupervised machine learning method is used to cluster
possible parameter sets in order of high probability. Determining the true
value by examining the candidates of the parameter sets obtained in this manner
can help solve the inherent inverse problem associated with scattering data.

Breakthroughs in transformer-based models have revolutionized not only the
NLP field, but also vision and multimodal systems. However, although
visualization and interpretability tools have become available for NLP models,
internal mechanisms of vision and multimodal transformers remain largely
opaque. With the success of these transformers, it is increasingly critical to
understand their inner workings, as unraveling these black-boxes will lead to
more capable and trustworthy models. To contribute to this quest, we propose
VL-InterpreT, which provides novel interactive visualizations for interpreting
the attentions and hidden representations in multimodal transformers.
VL-InterpreT is a task agnostic and integrated tool that (1) tracks a variety
of statistics in attention heads throughout all layers for both vision and
language components, (2) visualizes cross-modal and intra-modal attentions
through easily readable heatmaps, and (3) plots the hidden representations of
vision and language tokens as they pass through the transformer layers. In this
paper, we demonstrate the functionalities of VL-InterpreT through the analysis
of KD-VLP, an end-to-end pretraining vision-language multimodal
transformer-based model, in the tasks of Visual Commonsense Reasoning (VCR) and
WebQA, two visual question answering benchmarks. Furthermore, we also present a
few interesting findings about multimodal transformer behaviors that were
learned through our tool.

Graph Convolutional Networks (GCN) have been effective at tasks that have
rich relational structure and can preserve global structure information of a
dataset in graph embeddings. Recently, many researchers focused on examining
whether GCNs could handle different Natural Language Processing tasks,
especially text classification. While applying GCNs to text classification is
well-studied, its graph construction techniques, such as node/edge selection
and their feature representation, and the optimal GCN learning mechanism in
text classification is rather neglected. In this paper, we conduct a
comprehensive analysis of the role of node and edge embeddings in a graph and
its GCN learning techniques in text classification. Our analysis is the first
of its kind and provides useful insights into the importance of each graph
node/edge construction mechanism when applied at the GCN training/testing in
different text classification benchmarks, as well as under its semi-supervised

We present the first self-supervised method to train panoramic room layout
estimation models without any labeled data. Unlike per-pixel dense depth that
provides abundant correspondence constraints, layout representation is sparse
and topological, hindering the use of self-supervised reprojection consistency
on images. To address this issue, we propose Differentiable Layout View
Rendering, which can warp a source image to the target camera pose given the
estimated layout from the target image. As each rendered pixel is
differentiable with respect to the estimated layout, we can now train the
layout estimation model by minimizing reprojection loss. Besides, we introduce
regularization losses to encourage Manhattan alignment, ceiling-floor
alignment, cycle consistency, and layout stretch consistency, which further
improve our predictions. Finally, we present the first self-supervised results
on ZilloIndoor and MatterportLayout datasets. Our approach also shows promising
solutions in data-scarce scenarios and active learning, which would have an
immediate value in the real estate virtual tour software. Code is available at
https://github.com/joshua049/Stereo-360-Layout.

When creating training data for machine-learned interatomic potentials
(MLIPs), it is common to create initial structures and evolve them using
molecular dynamics to sample a larger configuration space. We benchmark two
other modalities of evolving structures, contour exploration and dimer-method
searches against molecular dynamics for their ability to produce diverse and
robust training density functional theory data sets for MLIPs. We also discuss
the generation of initial structures which are either from known structures or
from random structures in detail to further formalize the structure-sourcing
processes in the future. The polymorph-rich zirconium-oxygen composition space
is used as a rigorous benchmark system for comparing the performance of MLIPs
trained on structures generated from these structural evolution methods. Using
Behler-Parrinello neural networks as our machine-learned interatomic potential
models, we find that contour exploration and the dimer-method searches are
generally superior to molecular dynamics in terms of spatial descriptor
diversity and statistical accuracy.

The vast majority of speech separation methods assume that the number of
speakers is known in advance, hence they are specific to the number of
speakers. By contrast, a more realistic and challenging task is to separate a
mixture in which the number of speakers is unknown. This paper formulates the
speech separation with the unknown number of speakers as a multi-pass source
extraction problem and proposes a coarse-to-fine recursive speech separation
method. This method comprises two stages, namely, recursive cue extraction and
target speaker extraction. The recursive cue extraction stage determines how
many computational iterations need to be performed and outputs a coarse cue
speech by monitoring statistics in the mixture. As the number of recursive
iterations increases, the accumulation of distortion eventually comes into the
extracted speech and reminder. Therefore, in the second stage, we use a target
speaker extraction network to extract a fine speech based on the coarse target
cue and the original distortionless mixture. Experiments show that the proposed
method archived state-of-the-art performance on the WSJ0 dataset with a
different number of speakers. Furthermore, it generalizes well to an unseen
large number of speakers.

Only a subset of degrees of freedom are typically accessible or measurable in
real-world systems. As a consequence, the proper setting for empirical modeling
is that of partially-observed systems. Notably, data-driven models consistently
outperform physics-based models for systems with few observable degrees of
freedom; e.g., hydrological systems. Here, we provide an operator-theoretic
explanation for this empirical success. To predict a partially-observed
system's future behavior with physics-based models, the missing degrees of
freedom must be explicitly accounted for using data assimilation and model
parametrization. Data-driven models, in contrast, employ delay-coordinate
embeddings and their evolution under the Koopman operator to implicitly model
the effects of the missing degrees of freedom. We describe in detail the
statistical physics of partial observations underlying data-driven models using
novel Maximum Entropy and Maximum Caliber measures. The resulting
nonequilibrium Wiener projections applied to the Mori-Zwanzig formalism reveal
how data-driven models may converge to the true dynamics of the observable
degrees of freedom. Additionally, this framework shows how data-driven models
infer the effects of unobserved degrees of freedom implicitly, in much the same
way that physics models infer the effects explicitly. This provides a unified
implicit-explicit modeling framework for predicting partially-observed systems,
with hybrid physics-informed machine learning methods combining implicit and
explicit aspects.

The shortage of high-quality teachers is one of the biggest educational
problems faced by underdeveloped areas. With the development of information and
communication technologies (ICTs), China has begun a remote co-teaching
intervention program using ICTs for rural classes, forming a unique co-teaching
classroom. We conducted semi-structured interviews with nine remote urban
teachers and twelve local rural teachers. We identified the remote co-teaching
classes standard practices and co-teachers collaborative work process. We also
found that remote teachers high-quality class directly impacted local teachers
and students. Furthermore, interestingly, local teachers were also actively
involved in making indirect impacts on their students by deeply coordinating
with remote teachers and adapting the resources offered by the remote teachers.
We conclude by summarizing and discussing the challenges faced by teachers,
lessons learned from the current program, and related design implications to
achieve a more adaptive and sustainable ICT4D program design.

In zero-shot learning (ZSL) community, it is generally recognized that
transductive learning performs better than inductive one as the unseen-class
samples are also used in its training stage. How to generate pseudo labels for
unseen-class samples and how to use such usually noisy pseudo labels are two
critical issues in transductive learning. In this work, we introduce an
iterative co-training framework which contains two different base ZSL models
and an exchanging module. At each iteration, the two different ZSL models are
co-trained to separately predict pseudo labels for the unseen-class samples,
and the exchanging module exchanges the predicted pseudo labels, then the
exchanged pseudo-labeled samples are added into the training sets for the next
iteration. By such, our framework can gradually boost the ZSL performance by
fully exploiting the potential complementarity of the two models'
classification capabilities. In addition, our co-training framework is also
applied to the generalized ZSL (GZSL), in which a semantic-guided OOD detector
is proposed to pick out the most likely unseen-class samples before class-level
classification to alleviate the bias problem in GZSL. Extensive experiments on
three benchmarks show that our proposed methods could significantly outperform
about $31$ state-of-the-art ones.

Because the performance of speech separation is excellent for speech in which
two speakers completely overlap, research attention has been shifted to dealing
with more realistic scenarios. However, domain mismatch between training/test
situations due to factors, such as speaker, content, channel, and environment,
remains a severe problem for speech separation. Speaker and environment
mismatches have been studied in the existing literature. Nevertheless, there
are few studies on speech content and channel mismatches. Moreover, the impacts
of language and channel in these studies are mostly tangled. In this study, we
create several datasets for various experiments. The results show that the
impacts of different languages are small enough to be ignored compared to the
impacts of different channels. In our experiments, training on data recorded by
Android phones leads to the best generalizability. Moreover, we provide a new
solution for channel mismatch by evaluating projection, where the channel
similarity can be measured and used to effectively select additional training
data to improve the performance of in-the-wild test data.

User interests are usually dynamic in the real world, which poses both
theoretical and practical challenges for learning accurate preferences from
rich behavior data. Among existing user behavior modeling solutions, attention
networks are widely adopted for its effectiveness and relative simplicity.
Despite being extensively studied, existing attentions still suffer from two
limitations: i) conventional attentions mainly take into account the spatial
correlation between user behaviors, regardless the distance between those
behaviors in the continuous time space; and ii) these attentions mostly provide
a dense and undistinguished distribution over all past behaviors then
attentively encode them into the output latent representations. This is however
not suitable in practical scenarios where a user's future actions are relevant
to a small subset of her/his historical behaviors. In this paper, we propose a
novel attention network, named self-modulating attention, that models the
complex and non-linearly evolving dynamic user preferences. We empirically
demonstrate the effectiveness of our method on top-N sequential recommendation
tasks, and the results on three large-scale real-world datasets show that our
model can achieve state-of-the-art performance.

Establishing dense correspondences across semantically similar images remains
a challenging task due to the significant intra-class variations and background
clutters. Traditionally, a supervised learning was used for training the
models, which required tremendous manually-labeled data, while some methods
suggested a self-supervised or weakly-supervised learning to mitigate the
reliance on the labeled data, but with limited performance. In this paper, we
present a simple, but effective solution for semantic correspondence that
learns the networks in a semi-supervised manner by supplementing few
ground-truth correspondences via utilization of a large amount of confident
correspondences as pseudo-labels, called SemiMatch. Specifically, our framework
generates the pseudo-labels using the model's prediction itself between source
and weakly-augmented target, and uses pseudo-labels to learn the model again
between source and strongly-augmented target, which improves the robustness of
the model. We also present a novel confidence measure for pseudo-labels and
data augmentation tailored for semantic correspondence. In experiments,
SemiMatch achieves state-of-the-art performance on various benchmarks,
especially on PF-Willow by a large margin.

Variational auto-encoder (VAE) is an effective neural network architecture to
disentangle a speech utterance into speaker identity and linguistic content
latent embeddings, then generate an utterance for a target speaker from that of
a source speaker. This is possible by concatenating the identity embedding of
the target speaker and the content embedding of the source speaker uttering a
desired sentence. In this work, we propose to improve VAE models with
self-attention and structural regularization (RGSM). Specifically, we found a
suitable location of VAE's decoder to add a self-attention layer for
incorporating non-local information in generating a converted utterance and
hiding the source speaker's identity. We applied relaxed group-wise splitting
method (RGSM) to regularize network weights and remarkably enhance
generalization performance.
  In experiments of zero-shot many-to-many voice conversion task on VCTK data
set, with the self-attention layer and relaxed group-wise splitting method, our
model achieves a gain of speaker classification accuracy on unseen speakers by
28.3\% while slightly improved conversion voice quality in terms of MOSNet
scores. Our encouraging findings point to future research on integrating more
variety of attention structures in VAE framework while controlling model size
and overfitting for advancing zero-shot many-to-many voice conversions.

We propose a method to infer a dense depth map from a single image, its
calibration, and the associated sparse point cloud. In order to leverage
existing models (teachers) that produce putative depth maps, we propose an
adaptive knowledge distillation approach that yields a positive congruent
training process, wherein a student model avoids learning the error modes of
the teachers. In the absence of ground truth for model selection and training,
our method, termed Monitored Distillation, allows a student to exploit a blind
ensemble of teachers by selectively learning from predictions that best
minimize the reconstruction error for a given image. Monitored Distillation
yields a distilled depth map and a confidence map, or ``monitor'', for how well
a prediction from a particular teacher fits the observed image. The monitor
adaptively weights the distilled depth where if all of the teachers exhibit
high residuals, the standard unsupervised image reconstruction loss takes over
as the supervisory signal. On indoor scenes (VOID), we outperform blind
ensembling baselines by 17.53% and unsupervised methods by 24.25%; we boast a
79% model size reduction while maintaining comparable performance to the best
supervised method. For outdoors (KITTI), we tie for 5th overall on the
benchmark despite not using ground truth. Code available at:
https://github.com/alexklwong/mondi-python.

Mathematical modeling and aesthetic rule extraction of works of art are
complex activities. This is because art is a multidimensional, subjective
discipline. Perception and interpretation of art are, to many extents, relative
and open-ended rather than measurable. Following the explainable Artificial
Intelligence paradigm, this paper investigated in a human-understandable
fashion the limits to which a single-task, single-modality benchmark computer
vision model performs in classifying contemporary 2D visual arts. It is
important to point out that this work does not introduce an interpreting method
to open the black box of Deep Neural Networks, instead it uses existing
evaluating metrics derived from the confusion matrix to try to uncover the
mechanism with which Deep Neural Networks understand art. To achieve so,
VGG-11, pre-trained on ImageNet and discriminatively fine-tuned, was used on
handcrafted small-data datasets designed from real-world photography gallery
shows. We demonstrated that the artwork's Exhibited Properties or formal
factors such as shape and color, rather than Non-Exhibited Properties or
content factors such as history and intention, have much higher potential to be
the determinant when art pieces have very similar Exhibited Properties. We also
showed that a single-task and single-modality model's understanding of art is
inadequate as it largely ignores Non-Exhibited Properties.

Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and
provides performance lift by adapting unlabelled data to downstream task.
Unfortunately, existing adaptations mainly involve deterministic rules that
cannot generalize well. Here, we propose Clozer, a sequence-tagging based cloze
answer extraction method used in TAPT that is extendable for adaptation on any
cloze-style machine reading comprehension (MRC) downstream tasks. We experiment
on multiple-choice cloze-style MRC tasks, and show that Clozer performs
significantly better compared to the oracle and state-of-the-art in escalating
TAPT effectiveness in lifting model performance, and prove that Clozer is able
to recognize the gold answers independently of any heuristics.

Recent works in artificial intelligence fairness attempt to mitigate
discrimination by proposing constrained optimization programs that achieve
parity for some fairness statistic. Most assume availability of the class
label, which is impractical in many real-world applications such as precision
medicine, actuarial analysis and recidivism prediction. Here we consider
fairness in longitudinal right-censored environments, where the time to event
might be unknown, resulting in censorship of the class label and
inapplicability of existing fairness studies. We devise applicable fairness
measures, propose a debiasing algorithm, and provide necessary theoretical
constructs to bridge fairness with and without censorship for these important
and socially-sensitive tasks. Our experiments on four censored datasets confirm
the utility of our approach.

Applying machine learning (ML) in design flow is a popular trend in EDA with
various applications from design quality predictions to optimizations. Despite
its promise, which has been demonstrated in both academic researches and
industrial tools, its effectiveness largely hinges on the availability of a
large amount of high-quality training data. In reality, EDA developers have
very limited access to the latest design data, which is owned by design
companies and mostly confidential. Although one can commission ML model
training to a design company, the data of a single company might be still
inadequate or biased, especially for small companies. Such data availability
problem is becoming the limiting constraint on future growth of ML for chip
design. In this work, we propose an Federated-Learning based approach for
well-studied ML applications in EDA. Our approach allows an ML model to be
collaboratively trained with data from multiple clients but without explicit
access to the data for respecting their data privacy. To further strengthen the
results, we co-design a customized ML model FLNet and its personalization under
the decentralized training scenario. Experiments on a comprehensive dataset
show that collaborative training improves accuracy by 11% compared with
individual local models, and our customized model FLNet significantly
outperforms the best of previous routability estimators in this collaborative
training flow.

Large rotating machines, e.g., compressors, steam turbines, gas turbines, are
critical equipment in many process industries such as energy, chemical, and
power generation. Due to high rotating speed and tremendous momentum of the
rotor, the centrifugal force may lead to flying apart of the rotor parts, which
brings a great threat to the operation safety. Early detection and prediction
of potential failures could prevent the catastrophic plant downtime and
economic loss. In this paper, we divide the operational states of a rotating
machine into normal, risky, and high-risk ones based on the time to the moment
of failure. Then a cascade classifying algorithm is proposed to predict the
states in two steps, first we judge whether the machine is in normal or
abnormal condition; for time periods which are predicted as abnormal we further
classify them into risky or high-risk states. Moreover, traditional
classification model evaluation metrics, such as confusion matrix, true-false
accuracy, are static and neglect the online prediction dynamics and uneven
wrong-prediction prices. An Online Prediction Ability Index (OPAI) is proposed
to select prediction models with consistent online predictions and smaller
close-to-downtime prediction errors. Real-world data sets and computational
experiments are used to verify the effectiveness of proposed methods.

The increased throughput brought by MIMO technology relies on the knowledge
of channel state information (CSI) acquired in the base station (BS). To make
the CSI feedback overhead affordable for the evolution of MIMO technology
(e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced
to deal with the CSI compression task. Based on the separation principle in
existing communication systems, DL based CSI compression is used as source
coding. However, this separate source-channel coding (SSCC) scheme is inferior
to the joint source-channel coding (JSCC) scheme in the finite blocklength
regime. In this paper, we propose a deep joint source-channel coding (DJSCC)
based framework for the CSI feedback task. In particular, the proposed method
can simultaneously learn from the CSI source and the wireless channel. Instead
of truncating CSI via Fourier transform in the delay domain in existing
methods, we apply non-linear transform networks to compress the CSI.
Furthermore, we adopt an SNR adaption mechanism to deal with the wireless
channel variations. The extensive experiments demonstrate the validity,
adaptability, and generality of the proposed framework.

Photonic accelerators have been intensively studied to provide enhanced
information processing capability to benefit from the unique attributes of
physical processes. Recently, it has been reported that chaotically oscillating
ultrafast time series from a laser, called laser chaos, provide the ability to
solve multi-armed bandit (MAB) problems or decision-making problems at GHz
order. Furthermore, it has been confirmed that the negatively correlated
time-domain structure of laser chaos contributes to the acceleration of
decision-making. However, the underlying mechanism of why decision-making is
accelerated by correlated time series is unknown. In this study, we demonstrate
a theoretical model to account for accelerating decision-making by correlated
time sequence. We first confirm the effectiveness of the negative
autocorrelation inherent in time series for solving two-armed bandit problems
using Fourier transform surrogate methods. We propose a theoretical model that
concerns the correlated time series subjected to the decision-making system and
the internal status of the system therein in a unified manner, inspired by
correlated random walks. We demonstrate that the performance derived
analytically by the theory agrees well with the numerical simulations, which
confirms the validity of the proposed model and leads to optimal system design.
The present study paves the way for improving the effectiveness of correlated
time series for decision-making, impacting artificial intelligence and other

Sampling is a key operation in point-cloud task and acts to increase
computational efficiency and tractability by discarding redundant points.
Universal sampling algorithms (e.g., Farthest Point Sampling) work without
modification across different tasks, models, and datasets, but by their very
nature are agnostic about the downstream task/model. As such, they have no
implicit knowledge about which points would be best to keep and which to
reject. Recent work has shown how task-specific point cloud sampling (e.g.,
SampleNet) can be used to outperform traditional sampling approaches by
learning which points are more informative. However, these learnable samplers
face two inherent issues: i) overfitting to a model rather than a task, and
\ii) requiring training of the sampling network from scratch, in addition to
the task network, somewhat countering the original objective of down-sampling
to increase efficiency. In this work, we propose an almost-universal sampler,
in our quest for a sampler that can learn to preserve the most useful points
for a particular task, yet be inexpensive to adapt to different tasks, models,
or datasets. We first demonstrate how training over multiple models for the
same task (e.g., shape reconstruction) significantly outperforms the vanilla
SampleNet in terms of accuracy by not overfitting the sample network to a
particular task network. Second, we show how we can train an almost-universal
meta-sampler across multiple tasks. This meta-sampler can then be rapidly
fine-tuned when applied to different datasets, networks, or even different
tasks, thus amortizing the initial cost of training.

This paper studies the problem of learning an unknown function $f$ from given
data about $f$. The learning problem is to give an approximation $\hat f$ to
$f$ that predicts the values of $f$ away from the data. There are numerous
settings for this learning problem depending on (i) what additional information
we have about $f$ (known as a model class assumption), (ii) how we measure the
accuracy of how well $\hat f$ predicts $f$, (iii) what is known about the data
and data sites, (iv) whether the data observations are polluted by noise. A
mathematical description of the optimal performance possible (the smallest
possible error of recovery) is known in the presence of a model class
assumption. Under standard model class assumptions, it is shown in this paper
that a near optimal $\hat f$ can be found by solving a certain discrete
over-parameterized optimization problem with a penalty term. Here, near optimal
means that the error is bounded by a fixed constant times the optimal error.
This explains the advantage of over-parameterization which is commonly used in
modern machine learning. The main results of this paper prove that
over-parameterized learning with an appropriate loss function gives a near
optimal approximation $\hat f$ of the function $f$ from which the data is
collected. Quantitative bounds are given for how much over-parameterization
needs to be employed and how the penalization needs to be scaled in order to
guarantee a near optimal recovery of $f$. An extension of these results to the
case where the data is polluted by additive deterministic noise is also given.

In the task of audio-visual sound source separation, which leverages visual
information for sound source separation, identifying objects in an image is a
crucial step prior to separating the sound source. However, existing methods
that assign sound on detected bounding boxes suffer from a problem that their
approach heavily relies on pre-trained object detectors. Specifically, when
using these existing methods, it is required to predetermine all the possible
categories of objects that can produce sound and use an object detector
applicable to all such categories. To tackle this problem, we propose a fully
unsupervised method that learns to detect objects in an image and separate
sound source simultaneously. As our method does not rely on any pre-trained
detector, our method is applicable to arbitrary categories without any
additional annotation. Furthermore, although being fully unsupervised, we found
that our method performs comparably in separation accuracy.

Learning and predicting the performance of a configurable software system
helps to provide better quality assurance. One important engineering decision
therein is how to encode the configuration into the model built. Despite the
presence of different encoding schemes, there is still little understanding of
which is better and under what circumstances, as the community often relies on
some general beliefs that inform the decision in an ad-hoc manner. To bridge
this gap, in this paper, we empirically compared the widely used encoding
schemes for software performance learning, namely label, scaled label, and
one-hot encoding. The study covers five systems, seven models, and three
encoding schemes, leading to 105 cases of investigation.
  Our key findings reveal that: (1) conducting trial-and-error to find the best
encoding scheme in a case by case manner can be rather expensive, requiring up
to 400+ hours on some models and systems; (2) the one-hot encoding often leads
to the most accurate results while the scaled label encoding is generally weak
on accuracy over different models; (3) conversely, the scaled label encoding
tends to result in the fastest training time across the models/systems while
the one-hot encoding is the slowest; (4) for all models studied, label and
scaled label encoding often lead to relatively less biased outcomes between
accuracy and training time, but the paired model varies according to the
system.
  We discuss the actionable suggestions derived from our findings, hoping to
provide a better understanding of this topic for the community. To promote open
science, the data and code of this work can be publicly accessed at
https://github.com/ideas-labo/MSR2022-encoding-study.

Semantic patterns of fine-grained objects are determined by subtle appearance
difference of local parts, which thus inspires a number of part-based methods.
However, due to uncontrollable object poses in images, distinctive details
carried by local regions can be spatially distributed or even self-occluded,
leading to a large variation on object representation. For discounting pose
variations, this paper proposes to learn a novel graph based object
representation to reveal a global configuration of local parts for
self-supervised pose alignment across classes, which is employed as an
auxiliary feature regularization on a deep representation learning
network.Moreover, a coarse-to-fine supervision together with the proposed
pose-insensitive constraint on shallow-to-deep sub-networks encourages
discriminative features in a curriculum learning manner. We evaluate our method
on three popular fine-grained object classification benchmarks, consistently
achieving the state-of-the-art performance. Source codes are available at
https://github.com/yangxh11/P2P-Net.

One of the key challenges in high speed off road navigation on ground
vehicles is that the kinodynamics of the vehicle terrain interaction can differ
dramatically depending on the terrain. Previous approaches to addressing this
challenge have considered learning an inverse kinodynamics (IKD) model,
conditioned on inertial information of the vehicle to sense the kinodynamic
interactions. In this paper, we hypothesize that to enable accurate high-speed
off-road navigation using a learned IKD model, in addition to inertial
information from the past, one must also anticipate the kinodynamic
interactions of the vehicle with the terrain in the future. To this end, we
introduce Visual-Inertial Inverse Kinodynamics (VI-IKD), a novel learning based
IKD model that is conditioned on visual information from a terrain patch ahead
of the robot in addition to past inertial information, enabling it to
anticipate kinodynamic interactions in the future. We validate the
effectiveness of VI-IKD in accurate high-speed off-road navigation
experimentally on a scale 1/5 UT-AlphaTruck off-road autonomous vehicle in both
indoor and outdoor environments and show that compared to other
state-of-the-art approaches, VI-IKD enables more accurate and robust off-road
navigation on a variety of different terrains at speeds of up to 3.5 m/s.

The deep learning revolution has been enabled in large part by GPUs, and more
recently accelerators, which make it possible to carry out computationally
demanding training and inference in acceptable times. As the size of machine
learning networks and workloads continues to increase, multi-GPU machines have
emerged as an important platform offered on High Performance Computing and
cloud data centers. As these machines are shared between multiple users, it
becomes increasingly important to protect applications against potential
attacks. In this paper, we explore the vulnerability of Nvidia's DGX multi-GPU
machines to covert and side channel attacks. These machines consist of a number
of discrete GPUs that are interconnected through a combination of custom
interconnect (NVLink) and PCIe connections. We reverse engineer the cache
hierarchy and show that it is possible for an attacker on one GPU to cause
contention on the L2 cache of another GPU. We use this observation to first
develop a covert channel attack across two GPUs, achieving the best bandwidth
of 3.95 MB/s. We also develop a prime and probe attack on a remote GPU allowing
an attacker to recover the cache hit and miss behavior of another workload.
This basic capability can be used in any number of side channel attacks: we
demonstrate a proof of concept attack that fingerprints the application running
on the remote GPU, with high accuracy. Our work establishes for the first time
the vulnerability of these machines to microarchitectural attacks, and we hope
that it guides future research to improve their security.

The further development of deep neural networks is hampered by the limited
GPU memory resource. Therefore, the optimization of GPU memory resources is
highly demanded. Swapping and recomputation are commonly applied to make better
use of GPU memory in deep learning. However, as an emerging domain, several
challenges remain:1)The efficiency of recomputation is limited for both static
and dynamic methods. 2)Swapping requires offloading parameters manually, which
incurs a great time cost. 3) There is no such dynamic and fine-grained method
that involves tensor swapping together with tensor recomputation nowadays. To
remedy the above issues, we propose a novel scheduler manager named
DELTA(Dynamic tEnsor offLoad and recompuTAtion). To the best of our knowledge,
we are the first to make a reasonable dynamic runtime scheduler on the
combination of tensor swapping and tensor recomputation without user oversight.
In DELTA, we propose a filter algorithm to select the optimal tensors to be
released out of GPU memory and present a director algorithm to select a proper
action for each of these tensors. Furthermore, prefetching and overlapping are
deliberately considered to overcome the time cost caused by swapping and
recomputing tensors. Experimental results show that DELTA not only saves
40%-70% of GPU memory, surpassing the state-of-the-art method to a great extent
but also gets comparable convergence results as the baseline with acceptable
time delay. Also, DELTA gains 2.04$\times$ maximum batchsize when training
ResNet-50 and 2.25$\times$ when training ResNet-101 compared with the baseline.
Besides, comparisons between the swapping cost and recomputation cost in our
experiments demonstrate the importance of making a reasonable dynamic scheduler
on tensor swapping and tensor recomputation, which refutes the arguments in
some related work that swapping should be the first and best choice.

We address the problem of detecting speech directed to a device that does not
contain a specific wake-word. Specifically, we focus on audio coming from a
touch-based invocation. Mitigating virtual assistants (VAs) activation due to
accidental button presses is critical for user experience. While the majority
of approaches to false trigger mitigation (FTM) are designed to detect the
presence of a target keyword, inferring user intent in absence of keyword is
difficult. This also poses a challenge when creating the training/evaluation
data for such systems due to inherent ambiguity in the user's data. To this
end, we propose a novel FTM approach that uses weakly-labeled training data
obtained with a newly introduced data sampling strategy. While this sampling
strategy reduces data annotation efforts, the data labels are noisy as the data
are not annotated manually. We use these data to train an acoustics-only model
for the FTM task by regularizing its loss function via knowledge distillation
from an ASR-based (LatticeRNN) model. This improves the model decisions,
resulting in 66% gain in accuracy, as measured by equal-error-rate (EER), over
the base acoustics-only model. We also show that the ensemble of the LatticeRNN
and acoustic-distilled models brings further accuracy improvement of 20%.

Deep Probabilistic Programming (DPP) allows powerful models based on
recursive computation to be learned using efficient deep-learning optimization
techniques. Additionally, DPP offers a unified perspective, where inference and
learning algorithms are treated on a par with models as stochastic programs.
Here, we offer a framework for representing and learning flexible PAC-Bayes
bounds as stochastic programs using DPP-based methods. In particular, we show
that DPP techniques may be leveraged to derive generalization bounds that draw
on the compositionality of DPP representations. In turn, the bounds we
introduce offer principled training objectives for higher-order probabilistic
programs. We offer a definition of a higher-order generalization bound, which
naturally encompasses single- and multi-task generalization perspectives
(including transfer- and meta-learning) and a novel class of bound based on a
learned measure of model complexity. Further, we show how modified forms of all
higher-order bounds can be efficiently optimized as objectives for DPP
training, using variational techniques. We test our framework using single- and
multi-task generalization settings on synthetic and biological data, showing
improved performance and generalization prediction using flexible DPP model
representations and learned complexity measures.

We introduce a formal meta-language for probabilistic programming, capable of
expressing both programs and the type systems in which they are embedded. We
are motivated here by the desire to allow an AGI to learn not only relevant
knowledge (programs/proofs), but also appropriate ways of reasoning
(logics/type systems). We draw on the frameworks of cubical type theory and
dependent typed metagraphs to formalize our approach. In doing so, we show that
specific constructions within the meta-language can be related via bisimulation
(implying path equivalence) to the type systems they correspond. This allows
our approach to provide a convenient means of deriving synthetic denotational
semantics for various type systems. Particularly, we derive bisimulations for
pure type systems (PTS), and probabilistic dependent type systems (PDTS). We
discuss further the relationship of PTS to non-well-founded set theory, and
demonstrate the feasibility of our approach with an implementation of a
bisimulation proof in a Guarded Cubical Type Theory type checker.

Cross-device federated learning (FL) protects user privacy by collaboratively
training a model on user devices, therefore eliminating the need for
collecting, storing, and manually labeling user data. While important topics
such as the FL training algorithm, non-IID-ness, and Differential Privacy have
been well studied in the literature, this paper focuses on two challenges of
practical importance for improving on-device ASR: the lack of ground-truth
transcriptions and the scarcity of compute resource and network bandwidth on
edge devices. First, we propose a FL system for on-device ASR domain adaptation
with full self-supervision, which uses self-labeling together with data
augmentation and filtering techniques. The system can improve a strong
Emformer-Transducer based ASR model pretrained on out-of-domain data, using
in-domain audio without any ground-truth transcriptions. Second, to reduce the
training cost, we propose a self-restricted RNN Transducer (SR-RNN-T) loss, a
variant of alignment-restricted RNN-T that uses Viterbi alignments from
self-supervision. To further reduce the compute and network cost, we
systematically explore adapting only a subset of weights in the
Emformer-Transducer. Our best training recipe achieves a $12.9\%$ relative WER
reduction over the strong out-of-domain baseline, which equals $70\%$ of the
reduction achievable with full human supervision and centralized training.

In this paper, we propose a new deep learning-based method for estimating
room layout given a pair of 360 panoramas. Our system, called Position-aware
Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose estimator.
PSMNet consists of a Stereo Pano Pose (SP2) transformer and a novel
Cross-Perspective Projection (CP2) layer. The stereo-view SP2 transformer is
used to implicitly infer correspondences between views, and can handle noisy
poses. The pose-aware CP2 layer is designed to render features from the
adjacent view to the anchor (reference) view, in order to perform view fusion
and estimate the visible layout. Our experiments and analysis validate our
method, which significantly outperforms the state-of-the-art layout estimators,
especially for large and complex room spaces.

Theoretically, the accuracy of computational models in compound-protein
binding affinities (CPAs) could be improved by the introduction of protein 3D
structure information. However, most of these models still suffer from low
accuracy due to the lack of an efficient approach to encode informative protein
features. The major challenge is how to combine the multi-modal information
such as the residue sequence of the protein, residue atom coordinates and the
torsion angles. To tackle this problem, we develop Fast Evolutional Attention
and Thoroughgoing-graph Neural Networks (FeatNN) to facilitate the application
of protein 3D structure information for predicting CPAs. Specifically, we
established a novel end-to-end architecture to jointly embed torsion matrix,
discrete distance matrix, and sequence information of protein and extract
compound features with deep graph convolution layers. In addition, a new
pairwise mapping attention mechanism is introduced to comprehensively learn
potential interaction information between proteins and compounds. FeatNN
considerably outperforms various state-of-the-art baselines in CPA prediction
with the R2 coefficient elevated by about 21.33%. Thus, FeatNN provides an
outstanding method for highly accurate CPA prediction and facilitates
high-throughput virtual screening of drug candidates.

As part of the large number of scientific articles being published every
year, the publication rate of biomedical literature has been increasing.
Consequently, there has been considerable effort to harness and summarize the
massive amount of biomedical research articles. While transformer-based
encoder-decoder models in a vanilla source document-to-summary setting have
been extensively studied for abstractive summarization in different domains,
their major limitations continue to be entity hallucination (a phenomenon where
generated summaries constitute entities not related to or present in source
article(s)) and factual inconsistency. This problem is exacerbated in a
biomedical setting where named entities and their semantics (which can be
captured through a knowledge base) constitute the essence of an article. The
use of named entities and facts mined from background knowledge bases
pertaining to the named entities to guide abstractive summarization has not
been studied in biomedical article summarization literature. In this paper, we
propose an entity-driven fact-aware framework for training end-to-end
transformer-based encoder-decoder models for abstractive summarization of
biomedical articles. We call the proposed approach, whose building block is a
transformer-based model, EFAS, Entity-driven Fact-aware Abstractive
Summarization. We conduct experiments using five state-of-the-art
transformer-based models (two of which are specifically designed for long
document summarization) and demonstrate that injecting knowledge into the
training/inference phase of these models enables the models to achieve
significantly better performance than the standard source document-to-summary
setting in terms of entity-level factual accuracy, N-gram novelty, and semantic
equivalence while performing comparably on ROUGE metrics. The proposed approach
is evaluated on ICD-11-Summ-1000, and PubMed-50k.

In this paper we investigate the properties of representations learned by
deep reinforcement learning systems. Much of the earlier work in representation
learning for reinforcement learning focused on designing fixed-basis
architectures to achieve properties thought to be desirable, such as
orthogonality and sparsity. In contrast, the idea behind deep reinforcement
learning methods is that the agent designer should not encode representational
properties, but rather that the data stream should determine the properties of
the representation -- good representations emerge under appropriate training
schemes. In this paper we bring these two perspectives together, empirically
investigating the properties of representations that support transfer in
reinforcement learning. This analysis allows us to provide novel hypotheses
regarding impact of auxiliary tasks in end-to-end training of non-linear
reinforcement learning methods. We introduce and measure six representational
properties over more than 25 thousand agent-task settings. We consider DQN
agents with convolutional networks in a pixel-based navigation environment. We
develop a method to better understand \emph{why} some representations work
better for transfer, through a systematic approach varying task similarity and
measuring and correlating representation properties with transfer performance.

Reducing the latency and model size has always been a significant research
problem for live Automatic Speech Recognition (ASR) application scenarios.
Along this direction, model quantization has become an increasingly popular
approach to compress neural networks and reduce computation cost. Most of the
existing practical ASR systems apply post-training 8-bit quantization. To
achieve a higher compression rate without introducing additional performance
regression, in this study, we propose to develop 4-bit ASR models with native
quantization aware training, which leverages native integer operations to
effectively optimize both training and inference. We conducted two experiments
on state-of-the-art Conformer-based ASR models to evaluate our proposed
quantization technique. First, we explored the impact of different precisions
for both weight and activation quantization on the LibriSpeech dataset, and
obtained a lossless 4-bit Conformer model with 7.7x size reduction compared to
the float32 model. Following this, we for the first time investigated and
revealed the viability of 4-bit quantization on a practical ASR system that is
trained with large-scale datasets, and produced a lossless Conformer ASR model
with mixed 4-bit and 8-bit weights that has 5x size reduction compared to the
float32 model.

This work presents a structured systematic process for undergraduate capstone
research projects embodying computational thinking (CT) practices. Students
learn to conduct research with a decision support system utilizing CT. The
system is demonstrated through a case study of a capstone research project
course. The course is a 3rd year single semester capstone in an aviation
program. CT was integrated over a decade, through 21 semesters of coordinating
and delivering the course. The CT practices evolved and were utilized for more
aspects over time. The CT system facilitated a significant reduction in staff
workload by eliminating the need for direct one-on-one supervision and enabling
the streamlining of marking. This resulted in fairer marking by eliminating
supervisor bias. Student feedback shows a high degree of satisfaction, with
comments highlighting choice and learning.

We present a method that learns neural shadow fields which are neural scene
representations that are only learnt from the shadows present in the scene.
While traditional shape-from-shadow (SfS) algorithms reconstruct geometry from
shadows, they assume a fixed scanning setup and fail to generalize to complex
scenes. Neural rendering algorithms, on the other hand, rely on photometric
consistency between RGB images, but largely ignore physical cues such as
shadows, which have been shown to provide valuable information about the scene.
We observe that shadows are a powerful cue that can constrain neural scene
representations to learn SfS, and even outperform NeRF to reconstruct otherwise
hidden geometry. We propose a graphics-inspired differentiable approach to
render accurate shadows with volumetric rendering, predicting a shadow map that
can be compared to the ground truth shadow. Even with just binary shadow maps,
we show that neural rendering can localize the object and estimate coarse
geometry. Our approach reveals that sparse cues in images can be used to
estimate geometry using differentiable volumetric rendering. Moreover, our
framework is highly generalizable and can work alongside existing 3D
reconstruction techniques that otherwise only use photometric consistency.

Black-box variational inference (BBVI) now sees widespread use in machine
learning and statistics as a fast yet flexible alternative to Markov chain
Monte Carlo methods for approximate Bayesian inference. However, stochastic
optimization methods for BBVI remain unreliable and require substantial
expertise and hand-tuning to apply effectively. In this paper, we propose
Robust, Automated, and Accurate BBVI (RAABBVI), a framework for reliable BBVI
optimization. RAABBVI is based on rigorously justified automation techniques,
includes just a small number of intuitive tuning parameters, and detects
inaccurate estimates of the optimal variational approximation. RAABBVI
adaptively decreases the learning rate by detecting convergence of the
fixed--learning-rate iterates, then estimates the symmetrized Kullback--Leiber
(KL) divergence between the current variational approximation and the optimal
one. It also employs a novel optimization termination criterion that enables
the user to balance desired accuracy against computational cost by comparing
(i) the predicted relative decrease in the symmetrized KL divergence if a
smaller learning were used and (ii) the predicted computation required to
converge with the smaller learning rate. We validate the robustness and
accuracy of RAABBVI through carefully designed simulation studies and on a
diverse set of real-world model and data examples.

As an essential prerequisite task in image-based plant phenotyping, leaf
segmentation has garnered increasing attention in recent years. While
self-supervised learning is emerging as an effective alternative to various
computer vision tasks, its adaptation for image-based plant phenotyping remains
rather unexplored. In this work, we present a self-supervised leaf segmentation
framework consisting of a self-supervised semantic segmentation model, a
color-based leaf segmentation algorithm, and a self-supervised color correction
model. The self-supervised semantic segmentation model groups the semantically
similar pixels by iteratively referring to the self-contained information,
allowing the pixels of the same semantic object to be jointly considered by the
color-based leaf segmentation algorithm for identifying the leaf regions.
Additionally, we propose to use a self-supervised color correction model for
images taken under complex illumination conditions. Experimental results on
datasets of different plant species demonstrate the potential of the proposed
self-supervised framework in achieving effective and generalizable leaf

Modern smart sensor-based energy management systems leverage non-intrusive
load monitoring (NILM) to predict and optimize appliance load distribution in
real-time. NILM, or energy disaggregation, refers to the decomposition of
electricity usage conditioned on the aggregated power signals (i.e., smart
sensor on the main channel). Based on real-time appliance power prediction
using sensory technology, energy disaggregation has great potential to increase
electricity efficiency and reduce energy expenditure. With the introduction of
transformer models, NILM has achieved significant improvements in predicting
device power readings. Nevertheless, transformers are less efficient due to
O(l^2) complexity w.r.t. sequence length l. Moreover, transformers can fail to
capture local signal patterns in sequence-to-point settings due to the lack of
inductive bias in local context. In this work, we propose an efficient
localness transformer for non-intrusive load monitoring (ELTransformer).
Specifically, we leverage normalization functions and switch the order of
matrix multiplication to approximate self-attention and reduce computational
complexity. Additionally, we introduce localness modeling with sparse local
attention heads and relative position encodings to enhance the model capacity
in extracting short-term local patterns. To the best of our knowledge,
ELTransformer is the first NILM model that addresses computational complexity
and localness modeling in NILM. With extensive experiments and quantitative
analyses, we demonstrate the efficiency and effectiveness of the the proposed
ELTransformer with considerable improvements compared to state-of-the-art

Like many other tasks involving neural networks, Speech Recognition models
are vulnerable to adversarial attacks. However recent research has pointed out
differences between attacks and defenses on ASR models compared to image
models. Improving the robustness of ASR models requires a paradigm shift from
evaluating attacks on one or a few models to a systemic approach in evaluation.
We lay the ground for such research by evaluating on various architectures a
representative set of adversarial attacks: targeted and untargeted,
optimization and speech processing-based, white-box, black-box and targeted
attacks. Our results show that the relative strengths of different attack
algorithms vary considerably when changing the model architecture, and that the
results of some attacks are not to be blindly trusted. They also indicate that
training choices such as self-supervised pretraining can significantly impact
robustness by enabling transferable perturbations. We release our source code
as a package that should help future research in evaluating their attacks and
defenses.

Current leading mispronunciation detection and diagnosis (MDD) systems
achieve promising performance via end-to-end phoneme recognition. One challenge
of such end-to-end solutions is the scarcity of human-annotated phonemes on
natural L2 speech. In this work, we leverage unlabeled L2 speech via a
pseudo-labeling (PL) procedure and extend the fine-tuning approach based on
pre-trained self-supervised learning (SSL) models. Specifically, we use Wav2vec
2.0 as our SSL model, and fine-tune it using original labeled L2 speech samples
plus the created pseudo-labeled L2 speech samples. Our pseudo labels are
dynamic and are produced by an ensemble of the online model on-the-fly, which
ensures that our model is robust to pseudo label noise. We show that
fine-tuning with pseudo labels achieves a 5.35% phoneme error rate reduction
and 2.48% MDD F1 score improvement over a labeled-samples-only fine-tuning
baseline. The proposed PL method is also shown to outperform conventional
offline PL methods. Compared to the state-of-the-art MDD systems, our MDD
solution produces a more accurate and consistent phonetic error diagnosis. In
addition, we conduct an open test on a separate UTD-4Accents dataset, where our
system recognition outputs show a strong correlation with human perception,
based on accentedness and intelligibility.

Graphs are present in many real-world applications, such as financial fraud
detection, commercial recommendation, and social network analysis. But given
the high cost of graph annotation or labeling, we face a severe graph
label-scarcity problem, i.e., a graph might have a few labeled nodes. One
example of such a problem is the so-called \textit{few-shot node
classification}. A predominant approach to this problem resorts to
\textit{episodic meta-learning}. In this work, we challenge the status quo by
asking a fundamental question whether meta-learning is a must for few-shot node
classification tasks. We propose a new and simple framework under the standard
few-shot node classification setting as an alternative to meta-learning to
learn an effective graph encoder. The framework consists of supervised graph
contrastive learning with novel mechanisms for data augmentation, subgraph
encoding, and multi-scale contrast on graphs. Extensive experiments on three
benchmark datasets (CoraFull, Reddit, Ogbn) show that the new framework
significantly outperforms state-of-the-art meta-learning based methods.

This theoretical article examines how to construct human-like working memory
and thought processes within a computer. There should be two working memory
stores, one analogous to sustained firing in association cortex, and one
analogous to synaptic potentiation in the cerebral cortex. These stores must be
constantly updated with new representations that arise from either
environmental stimulation or internal processing. They should be updated
continuously, and in an iterative fashion, meaning that, in the next state,
some items in the set of coactive items should always be retained. Thus, the
set of concepts coactive in working memory will evolve gradually and
incrementally over time. This makes each state is a revised iteration of the
preceding state and causes successive states to overlap and blend with respect
to the set of representations they contain. As new representations are added
and old ones are subtracted, some remain active for several seconds over the
course of these changes. This persistent activity, similar to that used in
artificial recurrent neural networks, is used to spread activation energy
throughout the global workspace to search for the next associative update. The
result is a chain of associatively linked intermediate states that are capable
of advancing toward a solution or goal. Iterative updating is conceptualized
here as an information processing strategy, a computational and
neurophysiological determinant of the stream of thought, and an algorithm for
designing and programming artificial intelligence.

The Internet of Things (IoT) boom has revolutionized almost every corner of
people's daily lives: healthcare, home, transportation, manufacturing, supply
chain, and so on. With the recent development of sensor and communication
technologies, IoT devices including smart wearables, cameras, smartwatches, and
autonomous vehicles can accurately measure and perceive their surrounding
environment. Continuous sensing generates massive amounts of data and presents
challenges for machine learning. Deep learning models (e.g., convolution neural
networks and recurrent neural networks) have been extensively employed in
solving IoT tasks by learning patterns from multi-modal sensory data. Graph
Neural Networks (GNNs), an emerging and fast-growing family of neural network
models, can capture complex interactions within sensor topology and have been
demonstrated to achieve state-of-the-art results in numerous IoT learning
tasks. In this survey, we present a comprehensive review of recent advances in
the application of GNNs to the IoT field, including a deep dive analysis of GNN
design in various IoT sensing environments, an overarching list of public data
and source code from the collected publications, and future research
directions. To keep track of newly published works, we collect representative
papers and their open-source implementations and create a Github repository at
https://github.com/GuiminDong/GNN4IoT.

This article presents an artificial intelligence (AI) architecture intended
to simulate the human working memory system as well as the manner in which it
is updated iteratively. It features several interconnected neural networks
designed to emulate the specialized modules of the cerebral cortex. These are
structured hierarchically and integrated into a global workspace. They are
capable of temporarily maintaining high-level patterns akin to the
psychological items maintained in working memory. This maintenance is made
possible by persistent neural activity in the form of two modalities: sustained
neural firing (resulting in a focus of attention) and synaptic potentiation
(resulting in a short-term store). This persistent activity is updated
iteratively resulting in incremental changes to the content of the working
memory system. As the content stored in working memory gradually evolves,
successive states overlap and are continuous with one another. The present
article will explore how this architecture can lead to gradual shift in the
distribution of coactive representations, ultimately leading to mental
continuity between processing states, and thus to human-like cognition.

This paper presents a semi-supervised learning framework that is new in being
designed for automatic modulation classification (AMC). By carefully utilizing
unlabeled signal data with a self-supervised contrastive-learning pre-training
step, our framework achieves higher performance given smaller amounts of
labeled data, thereby largely reducing the labeling burden of deep learning. We
evaluate the performance of our semi-supervised framework on a public dataset.
The evaluation results demonstrate that our semi-supervised approach
significantly outperforms supervised frameworks thereby substantially enhancing
our ability to train deep neural networks for automatic modulation
classification in a manner that leverages unlabeled data.

Optimization strategies driven by machine learning, such as Bayesian
optimization, are being explored across experimental sciences as an efficient
alternative to traditional design of experiment. When combined with automated
laboratory hardware and high-performance computing, these strategies enable
next-generation platforms for autonomous experimentation. However, the
practical application of these approaches is hampered by a lack of flexible
software and algorithms tailored to the unique requirements of chemical
research. One such aspect is the pervasive presence of constraints in the
experimental conditions when optimizing chemical processes or protocols, and in
the chemical space that is accessible when designing functional molecules or
materials. Although many of these constraints are known a priori, they can be
interdependent, non-linear, and result in non-compact optimization domains. In
this work, we extend our experiment planning algorithms Phoenics and Gryffin
such that they can handle arbitrary known constraints via an intuitive and
flexible interface. We benchmark these extended algorithms on continuous and
discrete test functions with a diverse set of constraints, demonstrating their
flexibility and robustness. In addition, we illustrate their practical utility
in two simulated chemical research scenarios: the optimization of the synthesis
of o-xylenyl Buckminsterfullerene adducts under constrained flow conditions,
and the design of redox active molecules for flow batteries under synthetic
accessibility constraints. The tools developed constitute a simple, yet
versatile strategy to enable model-based optimization with known experimental
constraints, contributing to its applicability as a core component of
autonomous platforms for scientific discovery.

Learning 3D generative models from a dataset of monocular images enables
self-supervised 3D reasoning and controllable synthesis. State-of-the-art 3D
generative models are GANs which use neural 3D volumetric representations for
synthesis. Images are synthesized by rendering the volumes from a given camera.
These models can disentangle the 3D scene from the camera viewpoint in any
generated image. However, most models do not disentangle other factors of image
formation, such as geometry and appearance. In this paper, we design a 3D GAN
which can learn a disentangled model of objects, just from monocular
observations. Our model can disentangle the geometry and appearance variations
in the scene, i.e., we can independently sample from the geometry and
appearance spaces of the generative model. This is achieved using a novel
non-rigid deformable scene formulation. A 3D volume which represents an object
instance is computed as a non-rigidly deformed canonical 3D volume. Our method
learns the canonical volume, as well as its deformations, jointly during
training. This formulation also helps us improve the disentanglement between
the 3D scene and the camera viewpoints using a novel pose regularization loss
defined on the 3D deformation field. In addition, we further model the inverse
deformations, enabling the computation of dense correspondences between images
generated by our model. Finally, we design an approach to embed real images
into the latent space of our disentangled generative model, enabling editing of
real images.

Hierarchical multi-agent reinforcement learning (MARL) has shown a
significant learning efficiency by searching policy over higher-level,
temporally extended actions (options). However, standard policy gradient-based
MARL methods have a difficulty generalizing to option-based scenarios due to
the asynchronous executions of multi-agent options. In this work, we propose a
mathematical framework to enable policy gradient optimization over asynchronous
multi-agent options by adjusting option-based policy distribution as well as
trajectory probability. We study our method under a set of multi-agent
cooperative setups with varying inter-dependency levels, and evaluate the
effectiveness of our method on typical option-based multi-agent cooperation
tasks.

Being able to predict the performance of circuits without running expensive
simulations is a desired capability that can catalyze automated design. In this
paper, we present a supervised pretraining approach to learn circuit
representations that can be adapted to new circuit topologies or unseen
prediction tasks. We hypothesize that if we train a neural network (NN) that
can predict the output DC voltages of a wide range of circuit instances it will
be forced to learn generalizable knowledge about the role of each circuit
element and how they interact with each other. The dataset for this supervised
learning objective can be easily collected at scale since the required DC
simulation to get ground truth labels is relatively cheap. This representation
would then be helpful for few-shot generalization to unseen circuit metrics
that require more time consuming simulations for obtaining the ground-truth
labels. To cope with the variable topological structure of different circuits
we describe each circuit as a graph and use graph neural networks (GNNs) to
learn node embeddings. We show that pretraining GNNs on prediction of output
node voltages can encourage learning representations that can be adapted to new
unseen topologies or prediction of new circuit level properties with up to 10x
more sample efficiency compared to a randomly initialized model. We further
show that we can improve sample efficiency of prior SoTA model-based
optimization methods by 2x (almost as good as using an oracle model) via
fintuning pretrained GNNs as the feature extractor of the learned models.

In this study, the problem of computing a sparse representation of
multi-dimensional visual data is considered. In general, such data e.g.,
hyperspectral images, color images or video data consists of signals that
exhibit strong local dependencies. A new computationally efficient sparse
coding optimization problem is derived by employing regularization terms that
are adapted to the properties of the signals of interest. Exploiting the merits
of the learnable regularization techniques, a neural network is employed to act
as structure prior and reveal the underlying signal dependencies. To solve the
optimization problem Deep unrolling and Deep equilibrium based algorithms are
developed, forming highly interpretable and concise deep-learning-based
architectures, that process the input dataset in a block-by-block fashion.
Extensive simulation results, in the context of hyperspectral image denoising,
are provided, which demonstrate that the proposed algorithms outperform
significantly other sparse coding approaches and exhibit superior performance
against recent state-of-the-art deep-learning-based denoising models. In a
wider perspective, our work provides a unique bridge between a classic
approach, that is the sparse representation theory, and modern representation
tools that are based on deep learning modeling.

This study demonstrates the existence of a testable condition for the
identification of the causal effect of a treatment on an outcome in
observational data, which relies on two sets of variables: observed covariates
to be controlled for and a suspected instrument. Under a causal structure
commonly found in empirical applications, the testable conditional independence
of the suspected instrument and the outcome given the treatment and the
covariates has two implications. First, the instrument is valid, i.e. it does
not directly affect the outcome (other than through the treatment) and is
unconfounded conditional on the covariates. Second, the treatment is
unconfounded conditional on the covariates such that the treatment effect is
identified. We suggest tests of this conditional independence based on machine
learning methods that account for covariates in a data-driven way and
investigate their asymptotic behavior and finite sample performance in a
simulation study. We also apply our testing approach to evaluating the impact
of fertility on female labor supply when using the sibling sex ratio of the
first two children as supposed instrument, which by and large points to a
violation of our testable implication for the moderate set of socio-economic
covariates considered.

In classification problems, supervised machine-learning methods outperform
traditional algorithms, thanks to the ability of neural networks to learn
complex patterns. However, in two-class classification tasks like anomaly or
fraud detection, unsupervised methods could do even better, because their
prediction is not limited to previously learned types of anomalies. An
intuitive approach of anomaly detection can be based on the distances from the
centers of mass of the two respective classes. Autoencoders, although trained
without supervision, can also detect anomalies: considering the center of mass
of the normal points, reconstructions have now radii, with largest radii most
likely indicating anomalous points. Of course, radii-based classification were
already possible without interposing an autoencoder. In any space, radial
classification can be operated, to some extent. In order to outperform it, we
proceed to radial deformations of data (i.e. centric compression or expansions
of axes) and autoencoder training. Any autoencoder that makes use of a data
center is here baptized a centric autoencoder (cAE). A special type is the cAE
trained with a uniformly compressed dataset, named the centripetal autoencoder
(cpAE). The new concept is studied here in relation with a schematic artificial
dataset, and the derived methods show consistent score improvements. But tested
on real banking data, our radial deformation supervised algorithms alone still
perform better that cAEs, as expected from most supervised methods;
nonetheless, in hybrid approaches, cAEs can be combined with a radial
deformation of space, improving its classification score. We expect that
centric autoencoders will become irreplaceable objects in anomaly live
detection based on geometry, thanks to their ability to stem naturally on
geometrical algorithms and to their native capability of detecting unknown
anomaly types.

Current 3D object detectors for autonomous driving are almost entirely
trained on human-annotated data. Although of high quality, the generation of
such data is laborious and costly, restricting them to a few specific locations
and object types. This paper proposes an alternative approach entirely based on
unlabeled data, which can be collected cheaply and in abundance almost
everywhere on earth. Our approach leverages several simple common sense
heuristics to create an initial set of approximate seed labels. For example,
relevant traffic participants are generally not persistent across multiple
traversals of the same route, do not fly, and are never under ground. We
demonstrate that these seed labels are highly effective to bootstrap a
surprisingly accurate detector through repeated self-training without a single
human annotated label.

Identification of burn depth with sufficient accuracy is a challenging
problem. This paper presents a deep convolutional neural network to classify
burn depth based on altered tissue morphology of burned skin manifested as
texture patterns in the ultrasound images. The network first learns a
low-dimensional manifold of the unburned skin images using an encoder-decoder
architecture that reconstructs it from ultrasound images of burned skin. The
encoder is then re-trained to classify burn depths. The encoder-decoder network
is trained using a dataset comprised of B-mode ultrasound images of unburned
and burned ex vivo porcine skin samples. The classifier is developed using
B-mode images of burned in situ skin samples obtained from freshly euthanized
postmortem pigs. The performance metrics obtained from 20-fold cross-validation
show that the model can identify deep-partial thickness burns, which is the
most difficult to diagnose clinically, with 99% accuracy, 98% sensitivity, and
100% specificity. The diagnostic accuracy of the classifier is further
illustrated by the high area under the curve values of 0.99 and 0.95,
respectively, for the receiver operating characteristic and precision-recall
curves. A post hoc explanation indicates that the classifier activates the
discriminative textural features in the B-mode images for burn classification.
The proposed model has the potential for clinical utility in assisting the
clinical assessment of burn depths using a widely available clinical imaging
device.

Neural architecture-based recommender systems have achieved tremendous
success in recent years. However, when dealing with highly sparse data, they
still fall short of expectation. Self-supervised learning (SSL), as an emerging
technique to learn with unlabeled data, recently has drawn considerable
attention in many fields. There is also a growing body of research proceeding
towards applying SSL to recommendation for mitigating the data sparsity issue.
In this survey, a timely and systematical review of the research efforts on
self-supervised recommendation (SSR) is presented. Specifically, we propose an
exclusive definition of SSR, on top of which we build a comprehensive taxonomy
to divide existing SSR methods into four categories: contrastive, generative,
predictive, and hybrid. For each category, the narrative unfolds along its
concept and formulation, the involved methods, and its pros and cons.
Meanwhile, to facilitate the development and evaluation of SSR models, we
release an open-source library SELFRec, which incorporates multiple benchmark
datasets and evaluation metrics, and has implemented a number of
state-of-the-art SSR models for empirical comparison. Finally, we shed light on
the limitations in the current research and outline the future research

Emotional voice conversion (EVC) focuses on converting a speech utterance
from a source to a target emotion; it can thus be a key enabling technology for
human-computer interaction applications and beyond. However, EVC remains an
unsolved research problem with several challenges. In particular, as speech
rate and rhythm are two key factors of emotional conversion, models have to
generate output sequences of differing length. Sequence-to-sequence modelling
is recently emerging as a competitive paradigm for models that can overcome
those challenges. In an attempt to stimulate further research in this promising
new direction, recent sequence-to-sequence EVC papers were systematically
investigated and reviewed from six perspectives: their motivation, training
strategies, model architectures, datasets, model inputs, and evaluation
methods. This information is organised to provide the research community with
an easily digestible overview of the current state-of-the-art. Finally, we
discuss existing challenges of sequence-to-sequence EVC.

Nesterov's accelerated gradient algorithm is derived from first principles.
The first principles are founded on the recently-developed optimal control
theory for optimization. This theory frames an optimization problem as an
optimal control problem whose trajectories generate various continuous-time
algorithms. The algorithmic trajectories satisfy the necessary conditions for
optimal control. The necessary conditions produce a controllable dynamical
system for accelerated optimization. Stabilizing this system via a quadratic
control Lyapunov function generates an ordinary differential equation. An Euler
discretization of the resulting differential equation produces Nesterov's
algorithm. In this context, this result solves the purported mystery
surrounding the algorithm.

Supervised approaches to 3D pose estimation from single images are remarkably
effective when labeled data is abundant. However, as the acquisition of
ground-truth 3D labels is labor intensive and time consuming, recent attention
has shifted towards semi- and weakly-supervised learning. Generating an
effective form of supervision with little annotations still poses major
challenge in crowded scenes. In this paper we propose to impose multi-view
geometrical constraints by means of a weighted differentiable triangulation and
use it as a form of self-supervision when no labels are available. We therefore
train a 2D pose estimator in such a way that its predictions correspond to the
re-projection of the triangulated 3D pose and train an auxiliary network on
them to produce the final 3D poses. We complement the triangulation with a
weighting mechanism that alleviates the impact of noisy predictions caused by
self-occlusion or occlusion from other subjects. We demonstrate the
effectiveness of our semi-supervised approach on Human3.6M and MPI-INF-3DHP
datasets, as well as on a new multi-view multi-person dataset that features

Large-scale auto-regressive language models pretrained on massive text have
demonstrated their impressive ability to perform new natural language tasks
with only a few text examples, without the need for fine-tuning. Recent studies
further show that such a few-shot learning ability can be extended to the
text-image setting by training an encoder to encode the images into embeddings
functioning like the text embeddings of the language model. Interested in
exploring the possibility of transferring the few-shot learning ability to the
audio-text setting, we propose a novel speech understanding framework,
WavPrompt, where we finetune a wav2vec model to generate a sequence of audio
embeddings understood by the language model. We show that WavPrompt is a
few-shot learner that can perform speech understanding tasks better than a
naive text baseline. We conduct detailed ablation studies on different
components and hyperparameters to empirically identify the best model
configuration. In addition, we conduct a non-speech understanding experiment to
show WavPrompt can extract more information than just the transcriptions. Code
is available at https://github.com/Hertin/WavPrompt

Probing is popular to analyze whether linguistic information can be captured
by a well-trained deep neural model, but it is hard to answer how the change of
the encoded linguistic information will affect task performance. To this end,
we study the dynamic relationship between the encoded linguistic information
and task performance from the viewpoint of Pareto Optimality. Its key idea is
to obtain a set of models which are Pareto-optimal in terms of both objectives.
From this viewpoint, we propose a method to optimize the Pareto-optimal models
by formalizing it as a multi-objective optimization problem. We conduct
experiments on two popular NLP tasks, i.e., machine translation and language
modeling, and investigate the relationship between several kinds of linguistic
information and task performances. Experimental results demonstrate that the
proposed method is better than a baseline method. Our empirical findings
suggest that some syntactic information is helpful for NLP tasks whereas
encoding more syntactic information does not necessarily lead to better
performance, because the model architecture is also an important factor.

Neural image caption generation (NICG) models have received massive attention
from the research community due to their excellent performance in visual
understanding. Existing work focuses on improving NICG model accuracy while
efficiency is less explored. However, many real-world applications require
real-time feedback, which highly relies on the efficiency of NICG models.
Recent research observed that the efficiency of NICG models could vary for
different inputs. This observation brings in a new attack surface of NICG
models, i.e., An adversary might be able to slightly change inputs to cause the
NICG models to consume more computational resources. To further understand such
efficiency-oriented threats, we propose a new attack approach, NICGSlowDown, to
evaluate the efficiency robustness of NICG models. Our experimental results
show that NICGSlowDown can generate images with human-unnoticeable
perturbations that will increase the NICG model latency up to 483.86%. We hope
this research could raise the community's concern about the efficiency
robustness of NICG models.

Current practices in metric evaluation focus on one single dataset, e.g.,
Newstest dataset in each year's WMT Metrics Shared Task. However, in this
paper, we qualitatively and quantitatively show that the performances of
metrics are sensitive to data. The ranking of metrics varies when the
evaluation is conducted on different datasets. Then this paper further
investigates two potential hypotheses, i.e., insignificant data points and the
deviation of Independent and Identically Distributed (i.i.d) assumption, which
may take responsibility for the issue of data variance. In conclusion, our
findings suggest that when evaluating automatic translation metrics,
researchers should take data variance into account and be cautious to claim the
result on a single dataset, because it may leads to inconsistent results with
most of other datasets.

Deep learning has remarkably advanced in the last few years, supported by
large labeled data sets. These data sets are precious yet scarce because of the
time-consuming labeling procedures, discouraging researchers from producing
them. This scarcity is especially true in dentistry, where deep learning
applications are still in an embryonic stage. Motivated by this background, we
address in this study the construction of a public data set of dental panoramic
radiographs. Our objects of interest are the teeth, which are segmented and
numbered, as they are the primary targets for dentists when screening a
panoramic radiograph. We benefited from the human-in-the-loop (HITL) concept to
expedite the labeling procedure, using predictions from deep neural networks as
provisional labels, later verified by human annotators. All the gathering and
labeling procedures of this novel data set is thoroughly analyzed. The results
were consistent and behaved as expected: At each HITL iteration, the model
predictions improved. Our results demonstrated a 51% labeling time reduction
using HITL, saving us more than 390 continuous working hours. In a novel online
platform, called OdontoAI, created to work as task central for this novel data
set, we released 4,000 images, from which 2,000 have their labels publicly
available for model fitting. The labels of the other 2,000 images are private
and used for model evaluation considering instance and semantic segmentation
and numbering. To the best of our knowledge, this is the largest-scale publicly
available data set for panoramic radiographs, and the OdontoAI is the first
platform of its kind in dentistry.

Despite the progress in legged robotic locomotion, autonomous navigation in
unknown environments remains an open problem. Ideally, the navigation system
utilizes the full potential of the robots' locomotion capabilities while
operating within safety limits under uncertainty. The robot must sense and
analyze the traversability of the surrounding terrain, which depends on the
hardware, locomotion control, and terrain properties. It may contain
information about the risk, energy, or time consumption needed to traverse the
terrain. To avoid hand-crafted traversability cost functions we propose to
collect traversability information about the robot and locomotion policy by
simulating the traversal over randomly generated terrains using a physics
simulator. Thousand of robots are simulated in parallel controlled by the same
locomotion policy used in reality to acquire 57 years of real-world locomotion
experience equivalent. For deployment on the real robot, a sparse convolutional
network is trained to predict the simulated traversability cost, which is
tailored to the deployed locomotion policy, from an entirely geometric
representation of the environment in the form of a 3D voxel-occupancy map. This
representation avoids the need for commonly used elevation maps, which are
error-prone in the presence of overhanging obstacles and multi-floor or
low-ceiling scenarios. The effectiveness of the proposed traversability
prediction network is demonstrated for path planning for the legged robot
ANYmal in various indoor and natural environments.

Restless bandits are an important class of problems with applications in
recommender systems, active learning, revenue management and other areas. We
consider infinite-horizon discounted restless bandits with many arms where a
fixed proportion of arms may be pulled in each period and where arms share a
finite state space. Although an average-case-optimal policy can be computed via
stochastic dynamic programming, the computation required grows exponentially
with the number of arms $N$. Thus, it is important to find scalable policies
that can be computed efficiently for large $N$ and that are near optimal in
this regime, in the sense that the optimality gap (i.e. the loss of expected
performance against an optimal policy) per arm vanishes for large $N$. However,
the most popular approach, the Whittle index, requires a hard-to-verify
indexability condition to be well-defined and another hard-to-verify condition
to guarantee a $o(N)$ optimality gap. We present a method resolving these
difficulties. By replacing a global Lagrange multiplier used by the Whittle
index with a sequence of Lagrangian multipliers, one per time period up to a
finite truncation point, we derive a class of policies, called fluid-balance
policies, that have a $O(\sqrt{N})$ optimality gap. Unlike the Whittle index,
fluid-balance policies do not require indexability to be well-defined and their
$O(\sqrt{N})$ optimality gap bound holds universally without sufficient
conditions. We also demonstrate empirically that fluid-balance policies provide
state-of-the-art performance on specific problems.

The goal of digital quantum simulation is to approximate the dynamics of a
given target Hamiltonian via a sequence of quantum gates, a procedure known as
Trotterization. The quality of this approximation can be controlled by the so
called Trotter step, that governs the number of required quantum gates per unit
simulation time. The stroboscopic dynamics generated by Trotterization is
effectively described by a time-independent Hamiltonian, referred to as the
Floquet Hamiltonian. In this work, we propose Floquet Hamiltonian learning to
reconstruct the experimentally realized Floquet Hamiltonian order-by-order in
the Trotter step. This procedure is efficient, i.e., it requires a number of
measurements that scales polynomially in the system size, and can be readily
implemented in state-of-the-art experiments. With numerical examples, we
propose several applications of our method in the context of verification of
quantum devices: from the characterization of the distinct sources of errors in
digital quantum simulators to determining the optimal operating regime of the
device. We show that our protocol provides the basis for feedback-loop design
and calibration of new types of quantum gates. Furthermore it can be extended
to the case of non-unitary dynamics and used to learn Floquet Liouvillians,
thereby offering a way of characterizing the dissipative processes present in
NISQ quantum devices.

State-of-the-art deep Q-learning methods update Q-values using state
transition tuples sampled from the experience replay buffer. This strategy
often uniformly and randomly samples or prioritizes data sampling based on
measures such as the temporal difference (TD) error. Such sampling strategies
can be inefficient at learning Q-function because a state's Q-value depends on
the Q-value of successor states. If the data sampling strategy ignores the
precision of the Q-value estimate of the next state, it can lead to useless and
often incorrect updates to the Q-values. To mitigate this issue, we organize
the agent's experience into a graph that explicitly tracks the dependency
between Q-values of states. Each edge in the graph represents a transition
between two states by executing a single action. We perform value backups via a
breadth-first search starting from that expands vertices in the graph starting
from the set of terminal states and successively moving backward. We
empirically show that our method is substantially more data-efficient than
several baselines on a diverse range of goal-reaching tasks. Notably, the
proposed method also outperforms baselines that consume more batches of
training experience and operates from high-dimensional observational data such
as images.

In this paper, we consider the problem of formally verifying a Neural Network
(NN) based autonomous landing system. In such a system, a NN controller
processes images from a camera to guide the aircraft while approaching the
runway. A central challenge for the safety and liveness verification of
vision-based closed-loop systems is the lack of mathematical models that
captures the relation between the system states (e.g., position of the
aircraft) and the images processed by the vision-based NN controller. Another
challenge is the limited abilities of state-of-the-art NN model checkers. Such
model checkers can reason only about simple input-output robustness properties
of neural networks. This limitation creates a gap between the NN model checker
abilities and the need to verify a closed-loop system while considering the
aircraft dynamics, the perception components, and the NN controller. To this
end, this paper presents NNLander-VeriF, a framework to verify vision-based NN
controllers used for autonomous landing. NNLander-VeriF addresses the
challenges above by exploiting geometric models of perspective cameras to
obtain a mathematical model that captures the relation between the aircraft
states and the inputs to the NN controller. By converting this model into a NN
(with manually assigned weights) and composing it with the NN controller, one
can capture the relation between aircraft states and control actions using one
augmented NN. Such an augmented NN model leads to a natural encoding of the
closed-loop verification into several NN robustness queries, which
state-of-the-art NN model checkers can handle. Finally, we evaluate our
framework to formally verify the properties of a trained NN and we show its

While several self-supervised approaches for learning discrete speech
representation have been proposed, it is unclear how these seemingly similar
approaches relate to each other. In this paper, we consider a generative model
with discrete latent variables that learns a discrete representation for
speech. The objective of learning the generative model is formulated as
information-theoretic co-training. Besides the wide generality, the objective
can be optimized with several approaches, subsuming HuBERT-like training and
vector quantization for learning discrete representation. Empirically, we find
that the proposed approach learns discrete representation that is highly
correlated with phonetic units, more correlated than HuBERT-like training and
vector quantization.

Language model (LM) pretraining can learn various knowledge from text
corpora, helping downstream tasks. However, existing methods such as BERT model
a single document, and do not capture dependencies or knowledge that span
across documents. In this work, we propose LinkBERT, an LM pretraining method
that leverages links between documents, e.g., hyperlinks. Given a text corpus,
we view it as a graph of documents and create LM inputs by placing linked
documents in the same context. We then pretrain the LM with two joint
self-supervised objectives: masked language modeling and our new proposal,
document relation prediction. We show that LinkBERT outperforms BERT on various
downstream tasks across two domains: the general domain (pretrained on
Wikipedia with hyperlinks) and biomedical domain (pretrained on PubMed with
citation links). LinkBERT is especially effective for multi-hop reasoning and
few-shot QA (+5% absolute improvement on HotpotQA and TriviaQA), and our
biomedical LinkBERT sets new states of the art on various BioNLP tasks (+7% on
BioASQ and USMLE). We release our pretrained models, LinkBERT and BioLinkBERT,
as well as code and data at https://github.com/michiyasunaga/LinkBERT.

The Transiting Exoplanet Survey Satellite (TESS) has detected thousands of
exoplanet candidates since 2018, most of which have yet to be confirmed. A key
step in the confirmation process of these candidates is ruling out false
positives through vetting. Vetting also eases the burden on follow-up
observations, provides input for demographics studies, and facilitates training
machine learning algorithms. Here we present the TESS Triple-9 (TT9) catalog --
a uniformly-vetted catalog containing dispositions for 999 exoplanet candidates
listed on ExoFOP-TESS, known as TESS Objects of Interest (TOIs). The TT9 was
produced using the Discovery And Vetting of Exoplanets pipeline, DAVE, and
utilizing the power of citizen science as part of the Planet Patrol project.
More than 70% of the TOIs listed in the TT9 pass our diagnostic tests, and are
thus marked as true planetary candidates. We flagged 144 candidates as false
positives, and identified 146 as potential false positives. At the time of
writing, the TT9 catalog contains ~20% of the entire ExoFOP-TESS TOIs list,
demonstrates the synergy between automated tools and citizen science, and
represents the first stage of our efforts to vet all TOIs. The DAVE generated
results are publicly available on ExoFOP-TESS.

The high instantaneous luminosity of the CERN Large Hadron Collider leads to
multiple proton-proton interactions in the same or nearby bunch crossings
(pileup). Advanced pileup mitigation algorithms are designed to remove this
noise from pileup particles and improve the performance of crucial physics
observables. This study implements a semi-supervised graph neural network for
particle-level pileup noise removal, by identifying individual particles
produced from pileup. The graph neural network is firstly trained on charged
particles with known labels, which can be obtained from detector measurements
on data or simulation, and then inferred on neutral particles for which such
labels are missing. This semi-supervised approach does not depend on the ground
truth information from simulation and thus allows us to perform training
directly on experimental data. The performance of this approach is found to be
consistently better than widely-used domain algorithms and comparable to the
fully-supervised training using simulation truth information. The study serves
as the first attempt at applying semi-supervised learning techniques to pileup
mitigation, and opens up a new direction of fully data-driven machine learning
pileup mitigation studies.

Acquisition and creation of digital human avatars is an important problem
with applications to virtual telepresence, gaming, and human modeling. Most
contemporary approaches for avatar generation can be viewed either as 3D-based
methods, which use multi-view data to learn a 3D representation with appearance
(such as a mesh, implicit surface, or volume), or 2D-based methods which learn
photo-realistic renderings of avatars but lack accurate 3D representations. In
this work, we present, DRaCoN, a framework for learning full-body volumetric
avatars which exploits the advantages of both the 2D and 3D neural rendering
techniques. It consists of a Differentiable Rasterization module, DiffRas, that
synthesizes a low-resolution version of the target image along with additional
latent features guided by a parametric body model. The output of DiffRas is
then used as conditioning to our conditional neural 3D representation module
(c-NeRF) which generates the final high-res image along with body geometry
using volumetric rendering. While DiffRas helps in obtaining photo-realistic
image quality, c-NeRF, which employs signed distance fields (SDF) for 3D
representations, helps to obtain fine 3D geometric details. Experiments on the
challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms
state-of-the-art methods both in terms of error metrics and visual quality.

We show that under a general dependent data sampling scheme, the classical
stochastic projected and proximal subgradient methods for weakly convex
functions have worst-case rate of convergence $\tilde{O}(n^{-1/4})$ and
complexity $\tilde{O}(\varepsilon^{-4})$ for achieving an $\varepsilon$-near
stationary point in terms of the norm of the gradient of Moreau envelope. While
classical convergence guarantee requires i.i.d. data sampling from the target
distribution, we only require a mild mixing condition of the conditional
distribution, which holds for a wide class of Markov chain sampling algorithms.
This improves the existing complexity for the specific case of constrained
smooth nonconvex optimization with dependent data from
$\tilde{O}(\varepsilon^{-8})$ to $\tilde{O}(\varepsilon^{-4})$ with a
significantly simpler analysis. We illustrate the generality of our approach by
deriving convergence results with dependent data for adaptive stochastic
subgradient algorithm AdaGrad and stochastic subgradient algorithm with heavy
ball momentum. As an application, we obtain first online nonnegative matrix
factorization algorithms for dependent data based on stochastic projected
gradient methods with adaptive step sizes with optimal rate of convergence
guarantee.

An unsupervised text-to-speech synthesis (TTS) system learns to generate
speech waveforms corresponding to any written sentence in a language by
observing: 1) a collection of untranscribed speech waveforms in that language;
2) a collection of texts written in that language without access to any
transcribed speech. Developing such a system can significantly improve the
availability of speech technology to languages without a large amount of
parallel speech and text data. This paper proposes an unsupervised TTS system
based on an alignment module that outputs pseudo-text and another synthesis
module that uses pseudo-text for training and real text for inference. Our
unsupervised system can achieve comparable performance to the supervised system
in seven languages with about 10-20 hours of speech each. A careful study on
the effect of text units and vocoders has also been conducted to better
understand what factors may affect unsupervised TTS performance. The samples
generated by our models can be found at
https://cactuswiththoughts.github.io/UnsupTTS-Demo, and our code can be found
at https://github.com/lwang114/UnsupTTS.

Public school districts across the United States have implemented school
choice systems that have the potential to improve underserved students' access
to educational opportunities. However, research has shown that learning about
and applying for schools can be extremely time-consuming and expensive, making
it difficult for these systems to create more equitable access to resources in
practice. A common factor surfaced in prior work is unequal access to
information about the schools and enrollment process. In response, governments
and non-profits have invested in providing more information about schools to
parents, for instance, through detailed online dashboards. However, we know
little about what information is actually useful for historically marginalized
and underserved families. We conducted interviews with 10 low-income families
and families of color to learn about the challenges they faced navigating an
online school choice and enrollment system. We complement this data with four
interviews with people who have supported families through the enrollment
process in a wide range of roles, from school principal to non-profit staff
("parent advocates"). Our findings highlight the value of personalized support
and trusting relationships to delivering relevant and helpful information. We
contrast this against online information resources and dashboards, which tend
to be impersonal, target a broad audience, and make strong assumptions about
what parents should look for in a school without sensitivity to families'
varying circumstances. We advocate for an assets-based design approach to
information support in public school enrollment, which would ask how we can
support the local, one-on-one support that community members already provide.

Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the
issue of domain shift. Specifically, UDA methods try to align the source and
target representations to improve the generalization on the target domain.
Further, UDA methods work under the assumption that the source data is
accessible during the adaptation process. However, in real-world scenarios, the
labelled source data is often restricted due to privacy regulations, data
transmission constraints, or proprietary data concerns. The Source-Free Domain
Adaptation (SFDA) setting aims to alleviate these concerns by adapting a
source-trained model for the target domain without requiring access to the
source data. In this paper, we explore the SFDA setting for the task of
adaptive object detection. To this end, we propose a novel training strategy
for adapting a source-trained object detector to the target domain without
source data. More precisely, we design a novel contrastive loss to enhance the
target representations by exploiting the objects relations for a given target
domain input. These object instance relations are modelled using an Instance
Relation Graph (IRG) network, which are then used to guide the contrastive
representation learning. In addition, we utilize a student-teacher based
knowledge distillation strategy to avoid overfitting to the noisy pseudo-labels
generated by the source-trained model. Extensive experiments on multiple object
detection benchmark datasets show that the proposed approach is able to
efficiently adapt source-trained object detectors to the target domain,
outperforming previous state-of-the-art domain adaptive detection methods. Code
is available at https://github.com/Vibashan/irg-sfda.

Solving the domain shift problem during inference is essential in medical
imaging as most deep-learning based solutions suffer from it. In practice,
domain shifts are tackled by performing Unsupervised Domain Adaptation (UDA),
where a model is adapted to an unlabeled target domain by leveraging the
labelled source domain. In medical scenarios, the data comes with huge privacy
concerns making it difficult to apply standard UDA techniques. Hence, a closer
clinical setting is Source-Free UDA (SFUDA), where we have access to source
trained model but not the source data during adaptation. Methods trying to
solve SFUDA typically address the domain shift using pseudo-label based
self-training techniques. However, due to domain shift, these pseudo-labels are
usually of high entropy and denoising them still does not make them perfect
labels to supervise the model. Therefore, adapting the source model with noisy
pseudo labels reduces its segmentation capability while addressing the domain
shift. To this end, we propose a two-stage approach for source-free domain
adaptive image segmentation: 1) Target-specific adaptation followed by 2)
Task-specific adaptation. In the first stage, we focus on generating
target-specific pseudo labels while suppressing high entropy regions by
proposing an Ensemble Entropy Minimization loss. We also introduce a selective
voting strategy to enhance pseudo-label generation. In the second stage, we
focus on adapting the network for task-specific representation by using a
teacher-student self-training approach based on augmentation-guided
consistency. We evaluate our proposed method on both 2D fundus datasets and 3D
MRI volumes across 7 different domain shifts where we achieve better
performance than recent UDA and SF-UDA methods for medical image segmentation.
Code is available at https://github.com/Vibashan/tt-sfuda.

Collaborative filtering (CF) is one of the most successful and fundamental
techniques in recommendation systems. In recent years, Graph Neural Network
(GNN)-based CF models, such as NGCF [31], LightGCN [10] and GTN [9] have
achieved tremendous success and significantly advanced the state-of-the-art.
While there is a rich literature of such works using advanced models for
learning user and item representations separately, item recommendation is
essentially a link prediction problem between users and items. Furthermore,
while there have been early works employing link prediction for collaborative
filtering [5, 6], this trend has largely given way to works focused on
aggregating information from user and item nodes, rather than modeling links
directly. In this paper, we propose a new linkage (connectivity) score for
bipartite graphs, generalizing multiple standard link prediction methods. We
combine this new score with an iterative degree update process in the user-item
interaction bipartite graph to exploit local graph structures without any node
modeling. The result is a simple, non-deep learning model with only six
learnable parameters. Despite its simplicity, we demonstrate our approach
significantly outperforms existing state-of-the-art GNN-based CF approaches on
four widely used benchmarks. In particular, on Amazon-Book, we demonstrate an
over 60% improvement for both Recall and NDCG. We hope our work would invite
the community to revisit the link prediction aspect of collaborative filtering,
where significant performance gains could be achieved through aligning link
prediction with item recommendations.

The growth of videos in our digital age and the users' limited time raise the
demand for processing untrimmed videos to produce shorter versions conveying
the same information. Despite the remarkable progress that summarization
methods have made, most of them can only select a few frames or skims, creating
visual gaps and breaking the video context. This paper presents a novel
weakly-supervised methodology based on a reinforcement learning formulation to
accelerate instructional videos using text. A novel joint reward function
guides our agent to select which frames to remove and reduce the input video to
a target length without creating gaps in the final video. We also propose the
Extended Visually-guided Document Attention Network (VDAN+), which can generate
a highly discriminative embedding space to represent both textual and visual
data. Our experiments show that our method achieves the best performance in
Precision, Recall, and F1 Score against the baselines while effectively
controlling the video's output length. Visit
https://www.verlab.dcc.ufmg.br/semantic-hyperlapse/tpami2022/ for code and
extra results.

While developments in machine learning led to impressive performance gains on
big data, many human subjects data are, in actuality, small and sparsely
labeled. Existing methods applied to such data often do not easily generalize
to out-of-sample subjects. Instead, models must make predictions on test data
that may be drawn from a different distribution, a problem known as
\textit{zero-shot learning}. To address this challenge, we develop an
end-to-end framework using a meta-learning approach, which enables the model to
rapidly adapt to a new prediction task with limited training data for
out-of-sample test data. We use three real-world small-scale human subjects
datasets (two randomized control studies and one observational study), for
which we predict treatment outcomes for held-out treatment groups. Our model
learns the latent treatment effects of each intervention and, by design, can
naturally handle multi-task predictions. We show that our model performs the
best holistically for each held-out group and especially when the test group is
distinctly different from the training group. Our model has implications for
improved generalization of small-size human studies to the wider population.

Evolving swarm behaviors with artificial agents is computationally expensive
and challenging. Because reward structures are often sparse in swarm problems,
only a few simulations among hundreds evolve successful swarm behaviors.
Additionally, swarm evolutionary algorithms typically rely on ad hoc fitness
structures, and novel fitness functions need to be designed for each swarm
task. This paper evolves swarm behaviors by systematically combining
Postcondition-Precondition-Action (PPA) canonical Behavior Trees (BT) with a
Grammatical Evolution. The PPA structure replaces ad hoc reward structures with
systematic postcondition checks, which allows a common grammar to learn
solutions to different tasks using only environmental cues and BT feedback. The
static performance of learned behaviors is poor because no agent learns all
necessary subtasks, but performance while evolving is excellent because agents
can quickly change behaviors in new contexts. The evolving algorithm succeeded
in 75\% of learning trials for both foraging and nest maintenance tasks, an
eight-fold improvement over prior work.

Estimating the 3DoF rotation from a single RGB image is an important yet
challenging problem. Recent works achieve good performance relying on a large
amount of expensive-to-obtain labeled data. To reduce the amount of
supervision, we for the first time propose a general framework, FisherMatch,
for semi-supervised rotation regression, without assuming any domain-specific
knowledge or paired data. Inspired by the popular semi-supervised approach,
FixMatch, we propose to leverage pseudo label filtering to facilitate the
information flow from labeled data to unlabeled data in a teacher-student
mutual learning framework. However, incorporating the pseudo label filtering
mechanism into semi-supervised rotation regression is highly non-trivial,
mainly due to the lack of a reliable confidence measure for rotation
prediction. In this work, we propose to leverage matrix Fisher distribution to
build a probabilistic model of rotation and devise a matrix Fisher-based
regressor for jointly predicting rotation along with its prediction
uncertainty. We then propose to use the entropy of the predicted distribution
as a confidence measure, which enables us to perform pseudo label filtering for
rotation regression. For supervising such distribution-like pseudo labels, we
further investigate the problem of how to enforce loss between two matrix
Fisher distributions. Our extensive experiments show that our method can work
well even under very low labeled data ratios on different benchmarks, achieving
significant and consistent performance improvement over supervised learning and
other semi-supervised learning baselines. Our project page is at
https://yd-yin.github.io/FisherMatch.

Large observational surveys, like those that will be conducted at the Vera C.
Rubin Observatory, are expected to discover up to one million new asteroids in
the first year of operation. This will more than double the database of known
asteroids. New methods and techniques will be needed to handle the large influx
of data. Here, we tested some of these new methods by studying the population
of asteroids on stable orbits inside the ${\nu}_6$ secular resonance. This
resonance is one of the strongest mechanisms for destabilizing the orbits of
main-belt bodies and producing Near-Earth Asteroids (NEAs). Yet, stable orbital
configurations where the asteroid pericenter is either aligned or anti-aligned
with that of Saturn exist inside the resonance. The population of stable
${\nu}_6$ resonators is now the largest population of asteroids in stable
orbits inside a secular resonance. Here we obtained the largest sample of
asteroids' proper elements ever used for this problem. Clustering methods and
the use of machine learning algorithms permitted the identification of the
known asteroid families crossed by the ${\nu}_6$ resonance and of two entirely
new groups: the Tiffanykapler and the 138605 QW177 families. The Tiffanykapler
family is the first young asteroid family ever found in a linear secular
resonance, with an age of $3.0\pm1.2$ Myr and an ejection velocity field
parameter of $V_{EJ} = 15^{+6}_{-3}$ m/s. We identify a population of
high-eccentricity objects around the Tina family that may be the first example
of an asteroid family "resonant halo".

Structuring the latent space in probabilistic deep generative models, e.g.,
variational autoencoders (VAEs), is important to yield more expressive models
and interpretable representations, and to avoid overfitting. One way to achieve
this objective is to impose a sparsity constraint on the latent variables,
e.g., via a Laplace prior. However, such approaches usually complicate the
training phase, and they sacrifice the reconstruction quality to promote
sparsity. In this paper, we propose a simple yet effective methodology to
structure the latent space via a sparsity-promoting dictionary model, which
assumes that each latent code can be written as a sparse linear combination of
a dictionary's columns. In particular, we leverage a computationally efficient
and tuning-free method, which relies on a zero-mean Gaussian latent prior with
learnable variances. We derive a variational inference scheme to train the
model. Experiments on speech generative modeling demonstrate the advantage of
the proposed approach over competing techniques, since it promotes sparsity
while not deteriorating the output speech quality.

Learning invariant causal structure often relies on conditional independence
testing and assumption of independent and identically distributed data. Recent
work has explored inferring invariant causal structure using data coming from
different environments. These approaches are based on independent causal
mechanism (ICM) principle which postulates that the cause mechanism is
independent of the effect given cause mechanism. Despite its wide application
in machine learning and causal inference, there lacks a statistical
formalization of what independent mechanism means. Here we present Causal de
Finetti which offers a first statistical formalization of ICM principle.

Reinforcement learning systems have the potential to enable continuous
improvement in unstructured environments, leveraging data collected
autonomously. However, in practice these systems require significant amounts of
instrumentation or human intervention to learn in the real world. In this work,
we propose a system for reinforcement learning that leverages multi-task
reinforcement learning bootstrapped with prior data to enable continuous
autonomous practicing, minimizing the number of resets needed while being able
to learn temporally extended behaviors. We show how appropriately provided
prior data can help bootstrap both low-level multi-task policies and strategies
for sequencing these tasks one after another to enable learning with minimal
resets. This mechanism enables our robotic system to practice with minimal
human intervention at training time while being able to solve long horizon
tasks at test time. We show the efficacy of the proposed system on a
challenging kitchen manipulation task both in simulation and in the real world,
demonstrating the ability to practice autonomously in order to solve temporally
extended problems.

Large language models have shown that impressive zero-shot performance can be
achieved through natural language prompts (Radford et al., 2019; Brown et al.,
2020; Sanh et al., 2021). Creating an effective prompt, however, requires
significant trial and error. That \textit{prompts} the question: how do the
qualities of a prompt effects its performance? To this end, we collect and
standardize prompts from a diverse range of tasks for use with tasks they were
not designed for. We then evaluate these prompts across fixed multiple choice
datasets for a quantitative analysis of how certain attributes of a prompt
affect performance. We find that including the choices and using prompts not
used during pre-training provide significant improvements. All experiments and
code can be found https://github.com/gabeorlanski/zero-shot-cross-task.

Despite the tremendous advances in machine learning (ML), training with
imbalanced data still poses challenges in many real-world applications. Among a
series of diverse techniques to solve this problem, sampling algorithms are
regarded as an efficient solution. However, the problem is more fundamental,
with many works emphasizing the importance of instance hardness. This issue
refers to the significance of managing unsafe or potentially noisy instances
that are more likely to be misclassified and serve as the root cause of poor
classification performance. This paper introduces HardVis, a visual analytics
system designed to handle instance hardness mainly in imbalanced classification
scenarios. Our proposed system assists users in visually comparing different
distributions of data types, selecting types of instances based on local
characteristics that will later be affected by the active sampling method, and
validating which suggestions from undersampling or oversampling techniques are
beneficial for the ML model. Additionally, rather than uniformly
undersampling/oversampling a specific class, we allow users to find and sample
easy and difficult to classify training instances from all classes. Users can
explore subsets of data from different perspectives to decide all those
parameters, while HardVis keeps track of their steps and evaluates the model's
predictive performance in a test set separately. The end result is a
well-balanced data set that boosts the predictive power of the ML model. The
efficacy and effectiveness of HardVis are demonstrated with a hypothetical
usage scenario and a use case. Finally, we also look at how useful our system
is based on feedback we received from ML experts.

We present a method to develop low-complexity convolutional neural networks
(CNNs) for acoustic scene classification (ASC). The large size and high
computational complexity of typical CNNs is a bottleneck for their deployment
on resource-constrained devices. We propose a passive filter pruning framework,
where a few convolutional filters from the CNNs are eliminated to yield
compressed CNNs. Our hypothesis is that similar filters produce similar
responses and give redundant information allowing such filters to be eliminated
from the network. To identify similar filters, a cosine distance based greedy
algorithm is proposed. A fine-tuning process is then performed to regain much
of the performance lost due to filter elimination. To perform efficient
fine-tuning, we analyze how the performance varies as the number of fine-tuning
training examples changes. An experimental evaluation of the proposed framework
is performed on the publicly available DCASE 2021 Task 1A baseline network
trained for ASC. The proposed method is simple, reduces computations per
inference by 27%, with 25% fewer parameters, with less than 1% drop in
accuracy.

Go-Explore achieved breakthrough performance on challenging reinforcement
learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that
successful exploration requires an agent to first return to an interesting
state ('Go'), and only then explore into unknown terrain ('Explore'). We refer
to such exploration after a goal is reached as 'post-exploration'. In this
paper we present a systematic study of post-exploration, answering open
questions that the Go-Explore paper did not answer yet. First, we study the
isolated potential of post-exploration, by turning it on and off within the
same algorithm. Subsequently, we introduce new methodology to adaptively decide
when to post-explore and for how long to post-explore. Experiments on a range
of MiniGrid environments show that post-exploration indeed boosts performance
(with a bigger impact than tuning regular exploration parameters), and this
effect is further enhanced by adaptively deciding when and for how long to
post-explore. In short, our work identifies adaptive post-exploration as a
promising direction for RL exploration research.

Using a Deep Neuronal Network model (DNN) trained on the large ANI-1 data set
of small organic molecules, we propose a transferable density-free many-body
dispersion model (DNN-MBD). The DNN strategy bypasses the explicit Hirshfeld
partitioning of the Kohn-Sham electron density required by MBD models to obtain
the atom-in-molecules volumes used by the Tkatchenko-Scheffler polarizability
rescaling. The resulting DNN-MBD model is trained with minimal basis iterative
Stockholder atomic volumes and, coupled to Density Functional Theory (DFT),
exhibits comparable (if not greater) accuracy to other approaches based on
different partitioning schemes. Implemented in the Tinker-HP package, the
DNN-MBD model decreases the overall computational cost compared to MBD models
where the explicit density partitioning is performed. Its coupling with the
recently introduced Stochastic formulation of the MBD equations (J. Chem.
Theory. Comput., 2022, 18, 3, 1633-1645) enables large routine
dispersion-corrected DFT calculations at preserved accuracy. Furthermore, the
DNN electron density-free features extend MBD's applicability beyond electronic
structure theory within methodologies such as force fields and neural networks.

Traffic time series forecasting is challenging due to complex spatio-temporal
dynamics time series from different locations often have distinct patterns; and
for the same time series, patterns may vary across time, where, for example,
there exist certain periods across a day showing stronger temporal
correlations. Although recent forecasting models, in particular deep learning
based models, show promising results, they suffer from being spatio-temporal
agnostic. Such spatio-temporal agnostic models employ a shared parameter space
irrespective of the time series locations and the time periods and they assume
that the temporal patterns are similar across locations and do not evolve
across time, which may not always hold, thus leading to sub-optimal results. In
this work, we propose a framework that aims at turning spatio-temporal agnostic
models to spatio-temporal aware models. To do so, we encode time series from
different locations into stochastic variables, from which we generate
location-specific and time-varying model parameters to better capture the
spatio-temporal dynamics. We show how to integrate the framework with canonical
attentions to enable spatio-temporal aware attentions. Next, to compensate for
the additional overhead introduced by the spatio-temporal aware model parameter
generation process, we propose a novel window attention scheme, which helps
reduce the complexity from quadratic to linear, making spatio-temporal aware
attentions also have competitive efficiency. We show strong empirical evidence
on four traffic time series datasets, where the proposed spatio-temporal aware
attentions outperform state-of-the-art methods in term of accuracy and
efficiency. This is an extended version of "Towards Spatio-Temporal Aware
Traffic Time Series Forecasting", to appear in ICDE 2022 [1], including
additional experimental results.

We consider the problem of learning latent community structure from multiple
correlated networks. We study edge-correlated stochastic block models with two
balanced communities, focusing on the regime where the average degree is
logarithmic in the number of vertices. Our main result derives the precise
information-theoretic threshold for exact community recovery using multiple
correlated graphs. This threshold captures the interplay between the community
recovery and graph matching tasks. In particular, we uncover and characterize a
region of the parameter space where exact community recovery is possible using
multiple correlated graphs, even though (1) this is information-theoretically
impossible using a single graph and (2) exact graph matching is also
information-theoretically impossible. In this regime, we develop a novel
algorithm that carefully synthesizes algorithms from the community recovery and
graph matching literatures.

Since 2016, deep learning (DL) has advanced tomographic imaging with
remarkable successes, especially in low-dose computed tomography (LDCT)
imaging. Despite being driven by big data, the LDCT denoising and pure
end-to-end reconstruction networks often suffer from the black box nature and
major issues such as instabilities, which is a major barrier to apply deep
learning methods in low-dose CT applications. An emerging trend is to integrate
imaging physics and model into deep networks, enabling a hybridization of
physics/model-based and data-driven elements. In this paper, we systematically
review the physics/model-based data-driven methods for LDCT, summarize the loss
functions and training strategies, evaluate the performance of different
methods, and discuss relevant issues and future directions

In this article, for the first time, we propose a transformer network-based
reinforcement learning (RL) method for power distribution network (PDN)
optimization of high bandwidth memory (HBM). The proposed method can provide an
optimal decoupling capacitor (decap) design to maximize the reduction of PDN
self- and transfer impedance seen at multiple ports. An attention-based
transformer network is implemented to directly parameterize decap optimization
policy. The optimality performance is significantly improved since the
attention mechanism has powerful expression to explore massive combinatorial
space for decap assignments. Moreover, it can capture sequential relationships
between the decap assignments. The computing time for optimization is
dramatically reduced due to the reusable network on positions of probing ports
and decap assignment candidates. This is because the transformer network has a
context embedding process to capture meta-features including probing ports
positions. In addition, the network is trained with randomly generated data
sets. Therefore, without additional training, the trained network can solve new
decap optimization problems. The computing time for training and data cost are
critically decreased due to the scalability of the network. Thanks to its
shared weight property, the network can adapt to a larger scale of problems
without additional training. For verification, we compare the results with
conventional genetic algorithm (GA), random search (RS), and all the previous
RL-based methods. As a result, the proposed method outperforms in all the
following aspects: optimality performance, computing time, and data efficiency.

Real-time human motion reconstruction from a sparse set of (e.g. six)
wearable IMUs provides a non-intrusive and economic approach to motion capture.
Without the ability to acquire position information directly from IMUs, recent
works took data-driven approaches that utilize large human motion datasets to
tackle this under-determined problem. Still, challenges remain such as temporal
consistency, drifting of global and joint motions, and diverse coverage of
motion types on various terrains. We propose a novel method to simultaneously
estimate full-body motion and generate plausible visited terrain from only six
IMU sensors in real-time. Our method incorporates 1. a conditional Transformer
decoder model giving consistent predictions by explicitly reasoning prediction
history, 2. a simple yet general learning target named "stationary body points"
(SBPs) which can be stably predicted by the Transformer model and utilized by
analytical routines to correct joint and global drifting, and 3. an algorithm
to generate regularized terrain height maps from noisy SBP predictions which
can in turn correct noisy global motion estimation. We evaluate our framework
extensively on synthesized and real IMU data, and with real-time live demos,
and show superior performance over strong baseline methods.

Recently, tremendous progress has been made in the field of quantum science
and technologies: different platforms for quantum simulation as well as quantum
computing, ranging from superconducting qubits to neutral atoms, are starting
to reach unprecedentedly large systems. In order to benchmark these systems and
gain physical insights, the need for efficient tools to characterize quantum
states arises. The exponential growth of the Hilbert space with system size
renders a full reconstruction of the quantum state prohibitively demanding in
terms of the number of necessary measurements. Here we propose and implement an
efficient and scalable scheme for quantum state tomography using active
learning. Based on a few initial measurements, the active learning protocol
proposes the next measurement basis, designed to yield the maximum information
gain. For a fixed total number of measurements and basis configurations, our
algorithm maximizes the information one can obtain about the quantum state
under consideration. We apply the active learning quantum state tomography
scheme to reconstruct different multi-qubit states with varying degree of
entanglement as well as to ground states of the XXZ model in 1D and a
kinetically constrained spin chain. In all cases, we obtain a significantly
improved reconstruction as compared to a reconstruction based on the exact same
number of measurements, but with randomly chosen basis configurations. Our
scheme is highly relevant to gain physical insights in quantum many-body
systems as well as for benchmarking and characterization of quantum devices,
and paves the way for scalable adaptive protocols to probe, prepare, and
manipulate quantum systems.

We introduce the integrative task of few-shot classification and segmentation
(FS-CS) that aims to both classify and segment target objects in a query image
when the target classes are given with a few examples. This task combines two
conventional few-shot learning problems, few-shot classification and
segmentation. FS-CS generalizes them to more realistic episodes with arbitrary
image pairs, where each target class may or may not be present in the query. To
address the task, we propose the integrative few-shot learning (iFSL) framework
for FS-CS, which trains a learner to construct class-wise foreground maps for
multi-label classification and pixel-wise segmentation. We also develop an
effective iFSL model, attentive squeeze network (ASNet), that leverages deep
semantic correlation and global self-attention to produce reliable foreground
maps. In experiments, the proposed method shows promising performance on the
FS-CS task and also achieves the state of the art on standard few-shot
segmentation benchmarks.

Learning how humans manipulate objects requires machines to acquire knowledge
from two perspectives: one for understanding object affordances and the other
for learning human's interactions based on the affordances. Even though these
two knowledge bases are crucial, we find that current databases lack a
comprehensive awareness of them. In this work, we propose a multi-modal and
rich-annotated knowledge repository, OakInk, for visual and cognitive
understanding of hand-object interactions. We start to collect 1,800 common
household objects and annotate their affordances to construct the first
knowledge base: Oak. Given the affordance, we record rich human interactions
with 100 selected objects in Oak. Finally, we transfer the interactions on the
100 recorded objects to their virtual counterparts through a novel method:
Tink. The recorded and transferred hand-object interactions constitute the
second knowledge base: Ink. As a result, OakInk contains 50,000 distinct
affordance-aware and intent-oriented hand-object interactions. We benchmark
OakInk on pose estimation and grasp generation tasks. Moreover, we propose two
practical applications of OakInk: intent-based interaction generation and
handover generation. Our datasets and source code are publicly available at
https://github.com/lixiny/OakInk.

In data-driven modeling of spatiotemporal phenomena careful consideration
often needs to be made in capturing the dynamics of the high wavenumbers. This
problem becomes especially challenging when the system of interest exhibits
shocks or chaotic dynamics. We present a data-driven modeling method that
accurately captures shocks and chaotic dynamics by proposing a novel
architecture, stabilized neural ordinary differential equation (ODE). In our
proposed architecture, we learn the right-hand-side (RHS) of an ODE by adding
the outputs of two NN together where one learns a linear term and the other a
nonlinear term. Specifically, we implement this by training a sparse linear
convolutional NN to learn the linear term and a dense fully-connected nonlinear
NN to learn the nonlinear term. This is in contrast with the standard neural
ODE which involves training only a single NN for learning the RHS. We apply
this setup to the viscous Burgers equation, which exhibits shocked behavior,
and show better short-time tracking and prediction of the energy spectrum at
high wavenumbers than a standard neural ODE. We also find that the stabilized
neural ODE models are much more robust to noisy initial conditions than the
standard neural ODE approach. We also apply this method to chaotic trajectories
of the Kuramoto-Sivashinsky equation. In this case, stabilized neural ODEs keep
long-time trajectories on the attractor, and are highly robust to noisy initial
conditions, while standard neural ODEs fail at achieving either of these
results. We conclude by demonstrating how stabilizing neural ODEs provide a
natural extension for use in reduced-order modeling by projecting the dynamics
onto the eigenvectors of the learned linear term.

Visual entailment is a recently proposed multimodal reasoning task where the
goal is to predict the logical relationship of a piece of text to an image. In
this paper, we propose an extension of this task, where the goal is to predict
the logical relationship of fine-grained knowledge elements within a piece of
text to an image. Unlike prior work, our method is inherently explainable and
makes logical predictions at different levels of granularity. Because we lack
fine-grained labels to train our method, we propose a novel multi-instance
learning approach which learns a fine-grained labeling using only sample-level
supervision. We also impose novel semantic structural constraints which ensure
that fine-grained predictions are internally semantically consistent. We
evaluate our method on a new dataset of manually annotated knowledge elements
and show that our method achieves 68.18\% accuracy at this challenging task
while significantly outperforming several strong baselines. Finally, we present
extensive qualitative results illustrating our method's predictions and the
visual evidence our method relied on. Our code and annotated dataset can be
found here: https://github.com/SkrighYZ/FGVE.

A lot of recent advances in unsupervised feature learning are based on
designing features which are invariant under semantic data augmentations. A
common way to do this is contrastive learning, which uses positive and negative
samples. Some recent works however have shown promising results for
non-contrastive learning, which does not require negative samples. However, the
non-contrastive losses have obvious "collapsed" minima, in which the encoders
output a constant feature embedding, independent of the input. A folk
conjecture is that so long as these collapsed solutions are avoided, the
produced feature representations should be good. In our paper, we cast doubt on
this story: we show through theoretical results and controlled experiments that
even on simple data models, non-contrastive losses have a preponderance of
non-collapsed bad minima. Moreover, we show that the training process does not
avoid these minima.

Manually annotating 3D point clouds is laborious and costly, limiting the
training data preparation for deep learning in real-world object detection.
While a few previous studies tried to automatically generate 3D bounding boxes
from weak labels such as 2D boxes, the quality is sub-optimal compared to human
annotators. This work proposes a novel autolabeler, called multimodal attention
point generator (MAP-Gen), that generates high-quality 3D labels from weak 2D
boxes. It leverages dense image information to tackle the sparsity issue of 3D
point clouds, thus improving label quality. For each 2D pixel, MAP-Gen predicts
its corresponding 3D coordinates by referencing context points based on their
2D semantic or geometric relationships. The generated 3D points densify the
original sparse point clouds, followed by an encoder to regress 3D bounding
boxes. Using MAP-Gen, object detection networks that are weakly supervised by
2D boxes can achieve 94~99% performance of those fully supervised by 3D
annotations. It is hopeful this newly proposed MAP-Gen autolabeling flow can
shed new light on utilizing multimodal information for enriching sparse point
clouds.

Federated Learning (FL) framework brings privacy benefits to distributed
learning systems by allowing multiple clients to participate in a learning task
under the coordination of a central server without exchanging their private
data. However, recent studies have revealed that private information can still
be leaked through shared gradient information. To further protect user's
privacy, several defense mechanisms have been proposed to prevent privacy
leakage via gradient information degradation methods, such as using additive
noise or gradient compression before sharing it with the server. In this work,
we validate that the private training data can still be leaked under certain
defense settings with a new type of leakage, i.e., Generative Gradient Leakage
(GGL). Unlike existing methods that only rely on gradient information to
reconstruct data, our method leverages the latent space of generative
adversarial networks (GAN) learned from public image datasets as a prior to
compensate for the informational loss during gradient degradation. To address
the nonlinearity caused by the gradient operator and the GAN model, we explore
various gradient-free optimization methods (e.g., evolution strategies and
Bayesian optimization) and empirically show their superiority in reconstructing
high-quality images from gradients compared to gradient-based optimizers. We
hope the proposed method can serve as a tool for empirically measuring the
amount of privacy leakage to facilitate the design of more robust defense

Background Study individuals may face repeated events overtime. However,
there is no consensus around learning approaches to use in a high-dimensional
framework for survival data (when the number of variables exceeds the number of
individuals, i.e., p>n). This study aimed at identifying learning algorithms
for analyzing/predicting recurrent events and at comparing them to standard
statistical models in various data simulation settings. Methods A literature
review (LR) was conducted to provide state-of-the-art methodology. Data were
then simulated including variations of the number of variables and proportion
of active variables. Learning algorithms from the LR were compared to standard
methods in such simulation scheme. Evaluation measures were Harrell's
concordance index (C-index), Kim's C-index and error rate for active variables.
Results Seven publications were identified, consisting in four methodological
studies, one application paper and two review. The broken adaptive ridge
penalization and the RankDeepSurv deep neural network were used for comparison.
On simulated data, the standard models failed when p>n. Penalized Andersen-Gill
and frailty models outperformed, whereas RankDeepSurv reported lower
performances. Conclusion As no guidelines support a specific approach, this
study helps to better understand mechanisms and limits of investigated methods
in such context.

Object counting and localization are key steps for quantitative analysis in
large-scale microscopy applications. This procedure becomes challenging when
target objects are overlapping, are densely clustered, and/or present fuzzy
boundaries. Previous methods producing density maps based on deep learning have
reached a high level of accuracy for object counting by assuming that object
counting is equivalent to the integration of the density map. However, this
model fails when objects show significant overlap regarding accurate
localization. We propose an alternative method to count and localize objects
from the density map to overcome this limitation. Our procedure includes the
following three key aspects: 1) Proposing a new counting method based on the
statistical properties of the density map, 2) optimizing the counting results
for those objects which are well-detected based on the proposed counting
method, and 3) improving localization of poorly detected objects using the
proposed counting method as prior information. Validation includes processing
of microscopy data with known ground truth and comparison with other models
that use conventional processing of the density map. Our results show improved
performance in counting and localization of objects in 2D and 3D microscopy
data. Furthermore, the proposed method is generic, considering various
applications that rely on the density map approach. Our code will be released
post-review.

Forest plays a vital role in reducing greenhouse gas emissions and mitigating
climate change besides maintaining the world's biodiversity. The existing
satellite-based forest monitoring system utilizes supervised learning
approaches that are limited to a particular region and depend on manually
annotated data to identify forest. This work envisages forest identification as
a few-shot semantic segmentation task to achieve generalization across
different geographical regions. The proposed few-shot segmentation approach
incorporates a texture attention module in the prototypical network to
highlight the texture features of the forest. Indeed, the forest exhibits a
characteristic texture different from other classes, such as road, water, etc.
In this work, the proposed approach is trained for identifying tropical forests
of South Asia and adapted to determine the temperate forest of Central Europe
with the help of a few (one image for 1-shot) manually annotated support images
of the temperate forest. An IoU of 0.62 for forest class (1-way 1-shot) was
obtained using the proposed method, which is significantly higher (0.46 for
PANet) than the existing few-shot semantic segmentation approach. This result
demonstrates that the proposed approach can generalize across geographical
regions for forest identification, creating an opportunity to develop a global
forest cover identification tool.

Most text-to-speech (TTS) methods use high-quality speech corpora recorded in
a well-designed environment, incurring a high cost for data collection. To
solve this problem, existing noise-robust TTS methods are intended to use noisy
speech corpora as training data. However, they only address either
time-invariant or time-variant noises. We propose a degradation-robust TTS
method, which can be trained on speech corpora that contain both additive
noises and environmental distortions. It jointly represents the time-variant
additive noises with a frame-level encoder and the time-invariant environmental
distortions with an utterance-level encoder. We also propose a regularization
method to attain clean environmental embedding that is disentangled from the
utterance-dependent information such as linguistic contents and speaker
characteristics. Evaluation results show that our method achieved significantly
higher-quality synthetic speech than previous methods in the condition
including both additive noise and reverberation.

Flexible and biocompatible memristive devices are particularly attractive for
bioelectronic systems due to the interest in improving computing capabilities
and the motivation to interface electronics with biological systems including
drug delivery, neural interfaces and biosensors. Structures made of more
unorthodox, organic material can address different issues due to their
characteristics: flexibility, conformability, biocompatibility and simple and
low-cost fabrication. It has been observed that gating graphene/ionic liquid
(IL) devices leads to the formation of an electrical double layer (a thin layer
of ions with a thickness of a few nanometers) at the graphene/IL interface due
to the local potential difference which also controls the local conductivity.
This structure provides a memristive mechanism based on a dynamic p-n junction
formation along the channel. Motivated by this memristive behavior, graphene/IL
devices were assembled with the aim of demonstrating memristive behavior and
associative learning. This work investigates memristive properties of flexible
graphene/ IL devices on polymer substrates. The I-V characteristics of these
novel devices and switching mechanism are investigated. Two distinct topologies
(single input, single output and double input, single output) of devices are
manufactured and tested to mimic conditioning. It is observed that the
application of voltage pulse trains of both positive and negative polarities
increases the device conductance and allows larger currents to pass after
repetitive excitation. This characteristic was exploited to condition devices
and emulate associative learning.

Various facial manipulation techniques have drawn serious public concerns in
morality, security, and privacy. Although existing face forgery classifiers
achieve promising performance on detecting fake images, these methods are
vulnerable to adversarial examples with injected imperceptible perturbations on
the pixels. Meanwhile, many face forgery detectors always utilize the frequency
diversity between real and fake faces as a crucial clue. In this paper, instead
of injecting adversarial perturbations into the spatial domain, we propose a
frequency adversarial attack method against face forgery detectors. Concretely,
we apply discrete cosine transform (DCT) on the input images and introduce a
fusion module to capture the salient region of adversary in the frequency
domain. Compared with existing adversarial attacks (e.g. FGSM, PGD) in the
spatial domain, our method is more imperceptible to human observers and does
not degrade the visual quality of the original images. Moreover, inspired by
the idea of meta-learning, we also propose a hybrid adversarial attack that
performs attacks in both the spatial and frequency domains. Extensive
experiments indicate that the proposed method fools not only the spatial-based
detectors but also the state-of-the-art frequency-based detectors effectively.
In addition, the proposed frequency attack enhances the transferability across
face forgery detectors as black-box attacks.

Individual Treatment Effects (ITE) estimation methods have risen in
popularity in the last years. Most of the time, individual effects are better
presented as Conditional Average Treatment Effects (CATE). Recently,
representation balancing techniques have gained considerable momentum in causal
inference from observational data, still limited to continuous (and binary)
outcomes. However, in numerous pathologies, the outcome of interest is a
(possibly censored) survival time. Our paper proposes theoretical guarantees
for a representation balancing framework applied to counterfactual inference in
a survival setting using a neural network capable of predicting the factual and
counterfactual survival functions (and then the CATE), in the presence of
censorship, at the individual level. We also present extensive experiments on
synthetic and semisynthetic datasets that show that the proposed extensions
outperform baseline methods.

We give novel algorithms for multi-task and lifelong linear bandits with
shared representation. Specifically, we consider the setting where we play $M$
linear bandits with dimension $d$, each for $T$ rounds, and these $M$ bandit
tasks share a common $k(\ll d)$ dimensional linear representation. For both the
multi-task setting where we play the tasks concurrently, and the lifelong
setting where we play tasks sequentially, we come up with novel algorithms that
achieve $\widetilde{O}\left(d\sqrt{kMT} + kM\sqrt{T}\right)$ regret bounds,
which matches the known minimax regret lower bound up to logarithmic factors
and closes the gap in existing results [Yang et al., 2021]. Our main technique
include a more efficient estimator for the low-rank linear feature extractor
and an accompanied novel analysis for this estimator.

Polynomial chaos expansion (PCE) is a powerful surrogate model-based
reliability analysis method. Generally, a PCE model with a higher expansion
order is usually required to obtain an accurate surrogate model for some
complex non-linear stochastic systems. However, the high-order PCE increases
the number of labeled data required for solving the expansion coefficients. To
alleviate this problem, this paper proposes a consistency regularization-based
deep polynomial chaos neural network (Deep PCNN) method, including the
low-order adaptive PCE model (the auxiliary model) and the high-order
polynomial chaos neural network (the main model). The expansion coefficients of
the main model are parameterized into the learnable weights of the polynomial
chaos neural network, realizing iterative learning of expansion coefficients to
obtain more accurate high-order PCE models. The auxiliary model uses a proposed
consistency regularization loss function to assist in training the main model.
The consistency regularization-based Deep PCNN method can significantly reduce
the number of labeled data in constructing a high-order PCE model without
losing accuracy by using few labeled data and abundant unlabeled data. A
numerical example validates the effectiveness of the consistency
regularization-based Deep PCNN method, and then this method is applied to
analyze the reliability of two aerospace engineering systems.

In human-robot collaboration, one challenging task is to teach a robot new
yet unknown objects. Thereby, gaze can contain valuable information. We
investigate if it is possible to detect objects (object or no object) from gaze
data and determine their bounding box parameters. For this purpose, we explore
different sizes of temporal windows, which serve as a basis for the computation
of heatmaps, i.e., the spatial distribution of the gaze data. Additionally, we
analyze different grid sizes of these heatmaps, and various machine learning
techniques are applied. To generate the data, we conducted a small study with
five subjects who could move freely and thus, turn towards arbitrary objects.
This way, we chose a scenario for our data collection that is as realistic as
possible. Since the subjects move while facing objects, the heatmaps also
contain gaze data trajectories, complicating the detection and parameter

Several solutions for lightweight TTS have shown promising results. Still,
they either rely on a hand-crafted design that reaches non-optimum size or use
a neural architecture search but often suffer training costs. We present
Nix-TTS, a lightweight TTS achieved via knowledge distillation to a
high-quality yet large-sized, non-autoregressive, and end-to-end (vocoder-free)
TTS teacher model. Specifically, we offer module-wise distillation, enabling
flexible and independent distillation to the encoder and decoder module. The
resulting Nix-TTS inherited the advantageous properties of being
non-autoregressive and end-to-end from the teacher, yet significantly smaller
in size, with only 5.23M parameters or up to 89.34% reduction of the teacher
model; it also achieves over 3.04x and 8.36x inference speedup on Intel-i7 CPU
and Raspberry Pi 3B respectively and still retains a fair voice naturalness and
intelligibility compared to the teacher model. We provide pretrained models and
audio samples of Nix-TTS.

Object manipulation is a natural activity we perform every day. How humans
handle objects can communicate not only the willfulness of the acting, or key
aspects of the context where we operate, but also the properties of the objects
involved, without any need for explicit verbal description. Since human
intelligence comprises the ability to read the context, allowing robots to
perform actions that intuitively convey this kind of information would greatly
facilitate collaboration. In this work, we focus on how to transfer on two
different robotic platforms the same kinematics modulation that humans adopt
when manipulating delicate objects, aiming to endow robots with the capability
to show carefulness in their movements. We choose to modulate the velocity
profile adopted by the robots' end-effector, inspired by what humans do when
transporting objects with different characteristics. We exploit a novel
Generative Adversarial Network architecture, trained with human kinematics
examples, to generalize over them and generate new and meaningful velocity
profiles, either associated with careful or not careful attitudes. This
approach would allow next generation robots to select the most appropriate
style of movement, depending on the perceived context, and autonomously
generate their motor action execution.

During the last years, we have seen significant advances in the object
detection task, mainly due to the outperforming results of convolutional neural
networks. In this vein, anchor-based models have achieved the best results.
However, these models require prior information about the aspect and scales of
target objects, needing more hyperparameters to fit. In addition, using anchors
to fit bounding boxes seems far from how our visual system does the same visual
task. Instead, our visual system uses the interactions of different scene parts
to semantically identify objects, called perceptual grouping. An object
detection methodology closer to the natural model is anchor-free detection,
where models like FCOS or Centernet have shown competitive results, but these
have not yet exploited the concept of perceptual grouping. Therefore, to
increase the effectiveness of anchor-free models keeping the inference time
low, we propose to add non-local attention (NL modules) modules to boost the
feature map of the underlying backbone. NL modules implement the perceptual
grouping mechanism, allowing receptive fields to cooperate in visual
representation learning. We show that non-local modules combined with an FCOS
head (NL-FCOS) are practical and efficient. Thus, we establish state-of-the-art
performance in clothing detection and handwritten amount recognition problems.

Counterfactual explanations have shown promising results as a post-hoc
framework to make image classifiers more explainable. In this paper, we propose
DiME, a method allowing the generation of counterfactual images using the
recent diffusion models. By leveraging the guided generative diffusion process,
our proposed methodology shows how to use the gradients of the target
classifier to generate counterfactual explanations of input instances. Further,
we analyze current approaches to evaluate spurious correlations and extend the
evaluation measurements by proposing a new metric: Correlation Difference. Our
experimental validations show that the proposed algorithm surpasses previous
State-of-the-Art results on 5 out of 6 metrics on CelebA.

Many physical systems have underlying safety considerations that require that
the strategy deployed ensures the satisfaction of a set of constraints.
Further, often we have only partial information on the state of the system. We
study the problem of safe real-time decision making under uncertainty. In this
paper, we formulate a conservative stochastic contextual bandit formulation for
real-time decision making when an adversary chooses a distribution on the set
of possible contexts and the learner is subject to certain safety/performance
constraints. The learner observes only the context distribution and the exact
context is unknown, and the goal is to develop an algorithm that selects a
sequence of optimal actions to maximize the cumulative reward without violating
the safety constraints at any time step. By leveraging the UCB algorithm for
this setting, we propose a conservative linear UCB algorithm for stochastic
bandits with context distribution. We prove an upper bound on the regret of the
algorithm and show that it can be decomposed into three terms: (i) an upper
bound for the regret of the standard linear UCB algorithm, (ii) a constant term
(independent of time horizon) that accounts for the loss of being conservative
in order to satisfy the safety constraint, and (ii) a constant term
(independent of time horizon) that accounts for the loss for the contexts being
unknown and only the distribution being known. To validate the performance of
our approach we perform extensive simulations on synthetic data and on
real-world maize data collected through the Genomes to Fields (G2F) initiative.

Existing self-supervised 3D human pose estimation schemes have largely relied
on weak supervisions like consistency loss to guide the learning, which,
inevitably, leads to inferior results in real-world scenarios with unseen
poses. In this paper, we propose a novel self-supervised approach that allows
us to explicitly generate 2D-3D pose pairs for augmenting supervision, through
a self-enhancing dual-loop learning framework. This is made possible via
introducing a reinforcement-learning-based imitator, which is learned jointly
with a pose estimator alongside a pose hallucinator; the three components form
two loops during the training process, complementing and strengthening one
another. Specifically, the pose estimator transforms an input 2D pose sequence
to a low-fidelity 3D output, which is then enhanced by the imitator that
enforces physical constraints. The refined 3D poses are subsequently fed to the
hallucinator for producing even more diverse data, which are, in turn,
strengthened by the imitator and further utilized to train the pose estimator.
Such a co-evolution scheme, in practice, enables training a pose estimator on
self-generated motion data without relying on any given 3D data. Extensive
experiments across various benchmarks demonstrate that our approach yields
encouraging results significantly outperforming the state of the art and, in
some cases, even on par with results of fully-supervised methods. Notably, it
achieves 89.1% 3D PCK on MPI-INF-3DHP under self-supervised cross-dataset
evaluation setup, improving upon the previous best self-supervised methods by
8.6%. Code can be found at: https://github.com/Garfield-kh/PoseTriplet

In this work, a novel algorithm called SVM with Shape-adaptive Reconstruction
and Smoothed Total Variation (SaR-SVM-STV) is introduced to classify
hyperspectral images, which makes full use of spatial and spectral information.
The Shape-adaptive Reconstruction (SaR) is introduced to preprocess each pixel
based on the Pearson Correlation between pixels in its shape-adaptive (SA)
region. Support Vector Machines (SVMs) are trained to estimate the pixel-wise
probability maps of each class. Then the Smoothed Total Variation (STV) model
is applied to denoise and generate the final classification map. Experiments
show that SaR-SVM-STV outperforms the SVM-STV method with a few training
labels, demonstrating the significance of reconstructing hyperspectral images
before classification.

Galaxy populations show bimodality in a variety of properties: stellar mass,
colour, specific star-formation rate, size, and S\'ersic index. These
parameters are our feature space. We use an existing sample of 7556 galaxies
from the Galaxy and Mass Assembly (GAMA) survey, represented using five
features and the K-means clustering technique, showed that the bimodalities are
the manifestation of a more complex population structure, represented by
between 2 and 6 clusters.
  Here we use Self Organizing Maps (SOM), an unsupervised learning technique
which can be used to visualize similarity in a higher dimensional space using a
2D representation, to map these five-dimensional clusters in the feature space
onto two-dimensional projections. To further analyze these clusters, using the
SOM information, we agree with previous results that the sub-populations found
in the feature space can be reasonably mapped onto three or five clusters. We
explore where the "green valley" galaxies are mapped onto the SOM, indicating
multiple interstitial populations within the green valley population.
  Finally, we use the projection of the SOM to verify whether morphological
information provided by GalaxyZoo users, for example, if features are visible,
can be mapped onto the SOM-generated map. Voting on whether galaxies are
smooth, likely ellipticals, or "featured" can reasonably be separated but
smaller morphological features (bar, spiral arms) can not. SOMs promise to be a
useful tool to map and identify instructive sub-populations in multidimensional
galaxy survey feature space, provided they are large enough.

Self-supervised speech representation learning has shown promising results in
various speech processing tasks. However, the pre-trained models, e.g., HuBERT,
are storage-intensive Transformers, limiting their scope of applications under
low-resource settings. To this end, we propose LightHuBERT, a once-for-all
Transformer compression framework, to find the desired architectures
automatically by pruning structured parameters. More precisely, we create a
Transformer-based supernet that is nested with thousands of weight-sharing
subnets and design a two-stage distillation strategy to leverage the
contextualized latent representations from HuBERT. Experiments on automatic
speech recognition (ASR) and the SUPERB benchmark show the proposed LightHuBERT
enables over $10^9$ architectures concerning the embedding dimension, attention
dimension, head number, feed-forward network ratio, and network depth.
LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with
the HuBERT size, achieves comparable performance to the teacher model in most
tasks with a reduction of 29% parameters, and obtains a $3.5\times$ compression
ratio in three SUPERB tasks, e.g., automatic speaker verification, keyword
spotting, and intent classification, with a slight accuracy loss. The code and
pre-trained models are available at
https://github.com/mechanicalsea/lighthubert.

Outdoor webcam images are an information-dense yet accessible visualization
of past and present weather conditions, and are consulted by meteorologists and
the general public alike. Weather forecasts, however, are still communicated as
text, pictograms or charts. We therefore introduce a novel method that uses
photographic images to also visualize future weather conditions.
  This is challenging, because photographic visualizations of weather forecasts
should look real, be free of obvious artifacts, and should match the predicted
weather conditions. The transition from observation to forecast should be
seamless, and there should be visual continuity between images for consecutive
lead times. We use conditional Generative Adversarial Networks to synthesize
such visualizations. The generator network, conditioned on the analysis and the
forecasting state of the numerical weather prediction (NWP) model, transforms
the present camera image into the future. The discriminator network judges
whether a given image is the real image of the future, or whether it has been
synthesized. Training the two networks against each other results in a
visualization method that scores well on all four evaluation criteria.
  We present results for three camera sites across Switzerland that differ in
climatology and terrain. We show that users find it challenging to distinguish
real from generated images, performing not much better than if they guessed
randomly. The generated images match the atmospheric, ground and illumination
conditions of the COSMO-1 NWP model forecast in at least 89 % of the examined
cases. Nowcasting sequences of generated images achieve a seamless transition
from observation to forecast and attain visual continuity.

Science and technology big data contain a lot of cross-media
information.There are images and texts in the scientific paper.The s ingle
modal search method cannot well meet the needs of scientific researchers.This
paper proposes a cross-media scientific research achievements retrieval method
based on deep language model (CARDL).It achieves a unified cross-media semantic
representation by learning the semantic association between different modal
data, and is applied to the generation of text semantic vector of scientific
research achievements, and then cross-media retrieval is realized through
semantic similarity matching between different modal data.Experimental results
show that the proposed CARDL method achieves better cross-modal retrieval
performance than existing methods. Key words science and technology big data ;
cross-media retrieval; cross-media semantic association learning; deep language
model; semantic similarity

We study a stochastic bandit problem with a general unknown reward function
and a general unknown constraint function. Both functions can be non-linear
(even non-convex) and are assumed to lie in a reproducing kernel Hilbert space
(RKHS) with a bounded norm. This kernelized bandit setup strictly generalizes
standard multi-armed bandits and linear bandits. In contrast to safety-type
hard constraints studied in prior works, we consider soft constraints that may
be violated in any round as long as the cumulative violations are small, which
is motivated by various practical applications. Our ultimate goal is to study
how to utilize the nature of soft constraints to attain a finer
complexity-regret-constraint trade-off in the kernelized bandit setting. To
this end, leveraging primal-dual optimization, we propose a general framework
for both algorithm design and performance analysis. This framework builds upon
a novel sufficient condition, which not only is satisfied under general
exploration strategies, including \emph{upper confidence bound} (UCB),
\emph{Thompson sampling} (TS), and new ones based on \emph{random exploration},
but also enables a unified analysis for showing both sublinear regret and
sublinear or even zero constraint violation. We demonstrate the superior
performance of our proposed algorithms via numerical experiments based on both
synthetic and real-world datasets. Along the way, we also make the first
detailed comparison between two popular methods for analyzing constrained
bandits and Markov decision processes (MDPs) by discussing the key difference
and some subtleties in the analysis, which could be of independent interest to
the communities.

The partial differential equation (PDE) plays a significantly important role
in many fields of science and engineering. The conventional case of the
derivation of PDE mainly relies on first principles and empirical observation.
However, the development of machine learning technology allows us to mine
potential control equations from the massive amounts of stored data in a fresh
way. Although there has been considerable progress in the data-driven discovery
of PDE, the extant literature mostly focuses on the improvements of discovery
methods, without substantial breakthroughs in the discovery process itself,
including the principles for the construction of candidates and how to
incorporate physical priors. In this paper, through rigorous derivation of
formulas, novel physically enhanced machining learning discovery methods for
control equations: GSNN (Galileo Symbolic Neural Network) and LSNN (Lorentz
Symbolic Neural Network) are firstly proposed based on Galileo invariance and
Lorentz invariance respectively, setting forth guidelines for building the
candidates of discovering equations. The adoption of mandatory embedding of
physical constraints is fundamentally different from PINN in the form of the
loss function, thus ensuring that the designed Neural Network strictly obeys
the physical prior of invariance and enhancing the interpretability of the
network. By comparing the results with PDE-NET in numerical experiments of
Burgers equation and Sine-Gordon equation, it shows that the method presented
in this study has better accuracy, parsimony, and interpretability.

Unsupervised point cloud completion aims at estimating the corresponding
complete point cloud of a partial point cloud in an unpaired manner. It is a
crucial but challenging problem since there is no paired partial-complete
supervision that can be exploited directly. In this work, we propose a novel
framework, which learns a unified and structured latent space that encoding
both partial and complete point clouds. Specifically, we map a series of
related partial point clouds into multiple complete shape and occlusion code
pairs and fuse the codes to obtain their representations in the unified latent
space. To enforce the learning of such a structured latent space, the proposed
method adopts a series of constraints including structured ranking
regularization, latent code swapping constraint, and distribution supervision
on the related partial point clouds. By establishing such a unified and
structured latent space, better partial-complete geometry consistency and shape
completion accuracy can be achieved. Extensive experiments show that our
proposed method consistently outperforms state-of-the-art unsupervised methods
on both synthetic ShapeNet and real-world KITTI, ScanNet, and Matterport3D
datasets.

We present a method to separate speech signals from noisy environments in the
compressed domain of a neural audio codec. We introduce a new training
procedure that allows our model to produce structured encodings of audio
waveforms given by embedding vectors, where one part of the embedding vector
represents the speech signal, and the rest represents the environment. We
achieve this by partitioning the embeddings of different input waveforms and
training the model to faithfully reconstruct audio from mixed partitions,
thereby ensuring each partition encodes a separate audio attribute. As use
cases, we demonstrate the separation of speech from background noise or from
reverberation characteristics. Our method also allows for targeted adjustments
of the audio output characteristics.

Controlling the time evolution of interacting spin systems is an important
approach of implementing quantum computing. Different from the approaches by
compiling the circuits into the product of multiple elementary gates, we here
propose the quantum circuit encapsulation (QCE), where we encapsulate the
circuits into different parts, and optimize the magnetic fields to realize the
unitary transformation of each part by the time evolution. The QCE is
demonstrated to possess well-controlled error and time cost, which avoids the
error accumulations by aiming at finding the shortest path directly to the
target unitary. We test four different encapsulation ways to realize the
multi-qubit quantum Fourier transformations by controlling the time evolution
of the quantum Ising chain. The scaling behaviors of the time costs and errors
against the number of two-qubit controlled gates are demonstrated. The QCE
provides an alternative compiling scheme that translates the circuits into a
physically-executable form based on the quantum many-body dynamics, where the
key issue becomes the encapsulation way to balance between the efficiency and
flexibility.

We investigate the optimal model size and number of tokens for training a
transformer language model under a given compute budget. We find that current
large language models are significantly undertrained, a consequence of the
recent focus on scaling language models whilst keeping the amount of training
data constant. By training over \nummodels language models ranging from 70
million to over 16 billion parameters on 5 to 500 billion tokens, we find that
for compute-optimal training, the model size and the number of training tokens
should be scaled equally: for every doubling of model size the number of
training tokens should also be doubled. We test this hypothesis by training a
predicted compute-optimal model, \chinchilla, that uses the same compute budget
as \gopher but with 70B parameters and 4$\times$ more more data. \chinchilla
uniformly and significantly outperforms \Gopher (280B), GPT-3 (175B),
Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of
downstream evaluation tasks. This also means that \chinchilla uses
substantially less compute for fine-tuning and inference, greatly facilitating
downstream usage. As a highlight, \chinchilla reaches a state-of-the-art
average accuracy of 67.5\% on the MMLU benchmark, greater than a 7\%
improvement over \gopher.

Deep Neural Networks inherit spurious correlations embedded in training data
and hence may fail to predict desired labels on unseen domains (or
environments), which have different distributions from the domain used in
training. Invariance Learning (IL) has been developed recently to overcome this
shortcoming; using training data in many domains, IL estimates such a predictor
that is invariant to a change of domain. However, the requirement of training
data in multiple domains is a strong restriction of IL, since it often needs
high annotation cost. We propose a novel IL framework to overcome this problem.
Assuming the availability of data from multiple domains for a higher level of
classification task, for which the labeling cost is low, we estimate an
invariant predictor for the target classification task with training data in a
single domain. Additionally, we propose two cross-validation methods for
selecting hyperparameters of invariance regularization to solve the issue of
hyperparameter selection, which has not been handled properly in existing IL
methods. The effectiveness of the proposed framework, including the
cross-validation, is demonstrated empirically, and the correctness of the
hyperparameter selection is proved under some conditions.

Convolutional Neural Networks need the construction of informative features,
which are determined by channel-wise and spatial-wise information at the
network's layers. In this research, we focus on bringing in a novel solution
that uses sophisticated optimization for enhancing both the spatial and channel
components inside each layer's receptive field. Capsule Networks were used to
understand the spatial association between features in the feature map.
Standalone capsule networks have shown good results on comparatively simple
datasets than on complex datasets as a result of the inordinate amount of
feature information. Thus, to tackle this issue, we have proposed ME-CapsNet by
introducing deeper convolutional layers to extract important features before
passing through modules of capsule layers strategically to improve the
performance of the network significantly. The deeper convolutional layer
includes blocks of Squeeze-Excitation networks which use a stochastic sampling
approach for progressively reducing the spatial size thereby dynamically
recalibrating the channels by reconstructing their interdependencies without
much loss of important feature information. Extensive experimentation was done
using commonly used datasets demonstrating the efficiency of the proposed
ME-CapsNet, which clearly outperforms various research works by achieving
higher accuracy with minimal model complexity in complex datasets.

Recent advances in neural algorithmic reasoning with graph neural networks
(GNNs) are propped up by the notion of algorithmic alignment. Broadly, a neural
network will be better at learning to execute a reasoning task (in terms of
sample complexity) if its individual components align well with the target
algorithm. Specifically, GNNs are claimed to align with dynamic programming
(DP), a general problem-solving strategy which expresses many polynomial-time
algorithms. However, has this alignment truly been demonstrated and
theoretically quantified? Here we show, using methods from category theory and
abstract algebra, that there exists an intricate connection between GNNs and
DP, going well beyond the initial observations over individual algorithms such
as Bellman-Ford. Exposing this connection, we easily verify several prior
findings in the literature, produce better-grounded GNN architectures for
edge-centric tasks, and demonstrate empirical results on the CLRS algorithmic
reasoning benchmark. We hope our exposition will serve as a foundation for
building stronger algorithmically aligned GNNs.

Modeling user's historical feedback is essential for Click-Through Rate
Prediction in personalized search and recommendation. Existing methods usually
only model users' positive feedback information such as click sequences which
neglects the context information of the feedback. In this paper, we propose a
new perspective for context-aware users' behavior modeling by including the
whole page-wisely exposed products and the corresponding feedback as
contextualized page-wise feedback sequence. The intra-page context information
and inter-page interest evolution can be captured to learn more specific user
preference. We design a novel neural ranking model RACP(i.e., Recurrent
Attention over Contextualized Page sequence), which utilizes page-context aware
attention to model the intra-page context. A recurrent attention process is
used to model the cross-page interest convergence evolution as denoising the
interest in the previous pages. Experiments on public and real-world industrial
datasets verify our model's effectiveness.

Gamma-Ray Bursts (GRBs), can be employed as standardized candles, extending
the distance ladder beyond Supernovae Type Ia (SNe Ia, $z=2.26$). We
standardize GRBs using the 3D fundamental plane relation (the Dainotti
relation) among the rest-frame end time of the X-ray plateau emission, its
corresponding luminosity, and the peak prompt luminosity. Combining SNe Ia and
GRBs, we constrain $\Omega_{\text{M}}= 0.299 \pm 0.009$ assuming a flat
$\Lambda$CDM cosmology with and without correcting GRBs for selection biases
and redshift evolution. Using a 3D optical Dainotti correlation, we find this
sample is as efficacious in the determination of $\Omega_{\text{M}}$ as the
X-ray sample. We trimmed our GRB samples to achieve tighter planes to simulate
additional GRBs. We determined how many GRBs are needed as standalone probes to
achieve a comparable precision on $\Omega_{\text{M}}$ to the one obtained by
SNe Ia only. We reach the same error measurements derived using SNe Ia in 2011
and 2014 with 142 and 284 simulated optical GRBs, respectively, considering the
errorbars on the variables halved. These error limits will be reached in 2038
and in 2047, respectively. Using a doubled sample (obtained by future machine
learning approaches allowing a lightcurve reconstruction and the estimates of
GRB redhifts when z is unknown) compared to the current sample, with errorbars
halved we will reach the same precision as SNe Ia in 2011 and 2014, now and in
2026, respectively. If we consider the current SNe precision, this will be
reached with 390 optical GRBs by 2054.

Audio-text retrieval aims at retrieving a target audio clip or caption from a
pool of candidates given a query in another modality. Solving such cross-modal
retrieval task is challenging because it not only requires learning robust
feature representations for both modalities, but also requires capturing the
fine-grained alignment between these two modalities. Existing cross-modal
retrieval models are mostly optimized by metric learning objectives as both of
them attempt to map data to an embedding space, where similar data are close
together and dissimilar data are far apart. Unlike other cross-modal retrieval
tasks such as image-text and video-text retrievals, audio-text retrieval is
still an unexplored task. In this work, we aim to study the impact of different
metric learning objectives on the audio-text retrieval task. We present an
extensive evaluation of popular metric learning objectives on the AudioCaps and
Clotho datasets. We demonstrate that NT-Xent loss adapted from self-supervised
learning shows stable performance across different datasets and training
settings, and outperforms the popular triplet-based losses. Our code is
available at https://github.com/XinhaoMei/audio-text_retrieval.

Our goal is to recover the 3D shape and pose of dogs from a single image.
This is a challenging task because dogs exhibit a wide range of shapes and
appearances, and are highly articulated. Recent work has proposed to directly
regress the SMAL animal model, with additional limb scale parameters, from
images. Our method, called BARC (Breed-Augmented Regression using
Classification), goes beyond prior work in several important ways. First, we
modify the SMAL shape space to be more appropriate for representing dog shape.
But, even with a better shape model, the problem of regressing dog shape from
an image is still challenging because we lack paired images with 3D ground
truth. To compensate for the lack of paired data, we formulate novel losses
that exploit information about dog breeds. In particular, we exploit the fact
that dogs of the same breed have similar body shapes. We formulate a novel
breed similarity loss consisting of two parts: One term encourages the shape of
dogs from the same breed to be more similar than dogs of different breeds. The
second one, a breed classification loss, helps to produce recognizable
breed-specific shapes. Through ablation studies, we find that our breed losses
significantly improve shape accuracy over a baseline without them. We also
compare BARC qualitatively to WLDO with a perceptual study and find that our
approach produces dogs that are significantly more realistic. This work shows
that a-priori information about genetic similarity can help to compensate for
the lack of 3D training data. This concept may be applicable to other animal
species or groups of species. Our code is publicly available for research
purposes at https://barc.is.tue.mpg.de/.

Current top-notch deep learning (DL) based vision models are primarily based
on exploring and exploiting the inherent correlations between training data
samples and their associated labels. However, a known practical challenge is
their degraded performance against "noisy" data, induced by different
circumstances such as spurious correlations, irrelevant contexts, domain shift,
and adversarial attacks. In this work, we incorporate this binary information
of "existence of noise" as treatment into image classification tasks to improve
prediction accuracy by jointly estimating their treatment effects. Motivated
from causal variational inference, we propose a transformer-based architecture,
Treatment Learning Transformer (TLT), that uses a latent generative model to
estimate robust feature representations from current observational input for
noise image classification. Depending on the estimated noise level (modeled as
a binary treatment factor), TLT assigns the corresponding inference network
trained by the designed causal loss for prediction. We also create new noisy
image datasets incorporating a wide range of noise factors (e.g., object
masking, style transfer, and adversarial perturbation) for performance
benchmarking. The superior performance of TLT in noisy image classification is
further validated by several refutation evaluation metrics. As a by-product,
TLT also improves visual salience methods for perceiving noisy images.

Automated Audio captioning (AAC) is a cross-modal task that generates natural
language to describe the content of input audio. Most prior works usually
extract single-modality acoustic features and are therefore sub-optimal for the
cross-modal decoding task. In this work, we propose a novel AAC system called
CLIP-AAC to learn interactive cross-modality representation with both acoustic
and textual information. Specifically, the proposed CLIP-AAC introduces an
audio-head and a text-head in the pre-trained encoder to extract audio-text
information. Furthermore, we also apply contrastive learning to narrow the
domain difference by learning the correspondence between the audio signal and
its paired captions. Experimental results show that the proposed CLIP-AAC
approach surpasses the best baseline by a significant margin on the Clotho
dataset in terms of NLP evaluation metrics. The ablation study indicates that
both the pre-trained model and contrastive learning contribute to the
performance gain of the AAC model.

In order to improve the accuracy of molecular dynamics simulations, classical
force fields are supplemented with a kernel-based machine learning method
trained on quantum-mechanical fragment energies. As an example application, a
potential-energy surface is generalised for a small DNA duplex, taking into
account explicit solvation and long-range electron exchange--correlation
effects. Study of the corrected potential energy versus extension shows that
leading classical DNA models have excessive stiffness with respect to
stretching. This discrepancy is found to be common across multiple forcefields.
The quantum correction is in qualitative agreement to the experimental
thermodynamics for larger DNA double helices, providing a candidate explanation
for the general and long-standing discrepancy between single molecule
stretching experiments and classical calculations of DNA stretching. The new
dataset of quantum calculations and the associated Kernel Modified Molecular
Dynamics (KMMD) method should be of general utility in biomolecular
simulations. KMMD is made available as part of the AMBER22 simulation software.

Collision avoidance systems play a vital role in reducing the number of
vehicle accidents and saving human lives. This paper extends the previous work
using evolutionary neural networks for reactive collision avoidance. We are
proposing a new method we have called symmetric neural networks. The method
improves the model's performance by enforcing constraints between the network
weights which reduces the model optimization search space and hence, learns
more accurate control of the vehicle steering for improved maneuvering. The
training and validation processes are carried out using a simulation
environment - the codebase is publicly available. Extensive experiments are
conducted to analyze the proposed method and evaluate its performance. The
method is tested in several simulated driving scenarios. In addition, we have
analyzed the effect of the rangefinder sensor resolution and noise on the
overall goal of reactive collision avoidance. Finally, we have tested the
generalization of the proposed method. The results are encouraging; the
proposed method has improved the model's learning curve for training scenarios
and generalization to the new test scenarios. Using constrained weights has
significantly improved the number of generations required for the Genetic
Algorithm optimization.

Deep audio classification, traditionally cast as training a deep neural
network on top of mel-filterbanks in a supervised fashion, has recently
benefited from two independent lines of work. The first one explores "learnable
frontends", i.e., neural modules that produce a learnable time-frequency
representation, to overcome limitations of fixed features. The second one uses
self-supervised learning to leverage unprecedented scales of pre-training data.
In this work, we study the feasibility of combining both approaches, i.e.,
pre-training learnable frontend jointly with the main architecture for
downstream classification. First, we show that pretraining two previously
proposed frontends (SincNet and LEAF) on Audioset drastically improves
linear-probe performance over fixed mel-filterbanks, suggesting that learnable
time-frequency representations can benefit self-supervised pre-training even
more than supervised training. Surprisingly, randomly initialized learnable
filterbanks outperform mel-scaled initialization in the self-supervised
setting, a counter-intuitive result that questions the appropriateness of
strong priors when designing learnable filters. Through exploratory analysis of
the learned frontend components, we uncover crucial differences in properties
of these frontends when used in a supervised and self-supervised setting,
especially the affinity of self-supervised filters to diverge significantly
from the mel-scale to model a broader range of frequencies.

Development of machine learning (ML) applications is hard. Producing
successful applications requires, among others, being deeply familiar with a
variety of complex and quickly evolving application programming interfaces
(APIs). It is therefore critical to understand what prevents developers from
learning these APIs, using them properly at development time, and understanding
what went wrong when it comes to debugging. We look at the (lack of) guidance
that currently used development environments and ML APIs provide to developers
of ML applications, contrast these with software engineering best practices,
and identify gaps in the current state of the art. We show that current ML
tools fall short of fulfilling some basic software engineering gold standards
and point out ways in which software engineering concepts, tools and techniques
need to be extended and adapted to match the special needs of ML application
development. Our findings point out ample opportunities for research on
ML-specific software engineering.

Tree-ensemble algorithms, such as random forest, are effective machine
learning methods popular for their flexibility, high performance, and
robustness to overfitting. However, since multiple learners are combined,they
are not as interpretable as a single decision tree. In this work we propose a
methodology, called Local Tree eXtractor (LTreeX) which is able to explain the
forest prediction for a given test instance with a few diverse rules. Starting
from the decision trees generated by a random forest, our method 1) pre-selects
a subset of them, 2) creates a vector representation, and 3) eventually
clusters such a representation. Each cluster prototype results in a rule that
explains the test instance prediction. We test the effectiveness of LTreeX on
71 real-world datasets and we demonstrate the validity of our approach for
binary classification, regression, multi-label classification and time-to-event
tasks. In all set-ups, we show that our extracted surrogate model manages to
approximate the performance of the corresponding ensemble model, while
selecting only few trees from the whole forest.We also show that our proposed
approach substantially outperforms other explainable methods in terms of

Network topology inference is a fundamental problem in many applications of
network science, such as locating the source of fake news, brain connectivity
networks detection, etc. Many real-world situations suffer from a critical
problem that only a limited part of observed nodes are available. This letter
considers the problem of network topology inference under the framework of
partial observability. Based on the vector autoregressive model, we propose a
novel unbiased estimator for the symmetric network topology with the Gaussian
noise and the Laplacian combination rule. Theoretically, we prove that it
converges to the network combination matrix in probability. Furthermore, by
utilizing the Gaussian mixture model algorithm, an effective algorithm called
network inference Gauss algorithm is developed to infer the network structure.
Finally, compared with the state-of-the-art methods, numerical experiments
demonstrate the proposed algorithm enjoys better performance in the case of

A major hurdle for students and professional software developers who want to
enter the world of machine learning (ML), is mastering not just the scientific
background but also the available ML APIs. Therefore, we address the challenge
of creating APIs that are easy to learn and use, especially by novices.
However, it is not clear how this can be achieved without compromising
expressiveness. We investigate this problem for scikit-learn, a widely used ML
API. In this paper, we analyze its use by the Kaggle community, identifying
unused and apparently useless parts of the API that can be eliminated without
affecting client programs. In addition, we discuss usability issues in the
remaining parts, propose related design improvements and show how they can be
implemented by semi-automated wrapping of the existing third-party API.

We train neural models to represent both the optimal policy (i.e. the optimal
thrust direction) and the value function (i.e. the time of flight) for a time
optimal, constant acceleration low-thrust rendezvous. In both cases we develop
and make use of the data augmentation technique we call backward generation of
optimal examples. We are thus able to produce and work with large dataset and
to fully exploit the benefit of employing a deep learning framework. We
achieve, in all cases, accuracies resulting in successful rendezvous (simulated
following the learned policy) and time of flight predictions (using the learned
value function). We find that residuals as small as a few m/s, thus well within
the possibility of a spacecraft navigation $\Delta V$ budget, are achievable
for the velocity at rendezvous. We also find that, on average, the absolute
error to predict the optimal time of flight to rendezvous from any orbit in the
asteroid belt to an Earth-like orbit is small (less than 4\%) and thus also of
interest for practical uses, for example, during preliminary mission design
phases.

Federated learning (FL) is a promising learning paradigm that can tackle the
increasingly prominent isolated data islands problem while keeping users' data
locally with privacy and security guarantees. However, FL could result in
task-oriented data traffic flows over wireless networks with limited radio
resources. To design communication-efficient FL, most of the existing studies
employ the first-order federated optimization approach that has a slow
convergence rate. This however results in excessive communication rounds for
local model updates between the edge devices and edge server. To address this
issue, in this paper, we instead propose a novel over-the-air second-order
federated optimization algorithm to simultaneously reduce the communication
rounds and enable low-latency global model aggregation. This is achieved by
exploiting the waveform superposition property of a multi-access channel to
implement the distributed second-order optimization algorithm over wireless
networks. The convergence behavior of the proposed algorithm is further
characterized, which reveals a linear-quadratic convergence rate with an
accumulative error term in each iteration. We thus propose a system
optimization approach to minimize the accumulated error gap by joint device
selection and beamforming design. Numerical results demonstrate the system and
communication efficiency compared with the state-of-the-art approaches.

This paper proposes using a sparse-structured multivariate Gaussian to
provide a closed-form approximator for the output of probabilistic ensemble
models used for dense image prediction tasks. This is achieved through a
convolutional neural network that predicts the mean and covariance of the
distribution, where the inverse covariance is parameterised by a sparsely
structured Cholesky matrix. Similarly to distillation approaches, our single
network is trained to maximise the probability of samples from pre-trained
probabilistic models, in this work we use a fixed ensemble of networks. Once
trained, our compact representation can be used to efficiently draw spatially
correlated samples from the approximated output distribution. Importantly, this
approach captures the uncertainty and structured correlations in the
predictions explicitly in a formal distribution, rather than implicitly through
sampling alone. This allows direct introspection of the model, enabling
visualisation of the learned structure. Moreover, this formulation provides two
further benefits: estimation of a sample probability, and the introduction of
arbitrary spatial conditioning at test time. We demonstrate the merits of our
approach on monocular depth estimation and show that the advantages of our
approach are obtained with comparable quantitative performance.

Inspired by the success of control barrier functions (CBFs) in addressing
safety, and the rise of data-driven techniques for modeling functions, we
propose a non-parametric approach for online synthesis of CBFs using Gaussian
Processes (GPs). Mathematical constructs such as CBFs have achieved safety by
designing a candidate function a priori. However, designing such a candidate
function can be challenging. A practical example of such a setting would be to
design a CBF in a disaster recovery scenario where safe and navigable regions
need to be determined. The decision boundary for safety in such an example is
unknown and cannot be designed a priori. In our approach, we work with safety
samples or observations to construct the CBF online by assuming a flexible GP
prior on these samples, and term our formulation as a Gaussian CBF. GPs have
favorable properties, in addition to being non-parametric, such as analytical
tractability and robust uncertainty estimation. This allows realizing the
posterior components with high safety guarantees by incorporating variance
estimation, while also computing associated partial derivatives in closed-form
to achieve safe control. Moreover, the synthesized safety function from our
approach allows changing the corresponding safe set arbitrarily based on the
data, thus allowing non-convex safe sets. We validate our approach
experimentally on a quadrotor by demonstrating safe control for fixed but
arbitrary safe sets and collision avoidance where the safe set is constructed
online. Finally, we juxtapose Gaussian CBFs with regular CBFs in the presence
of noisy states to highlight its flexibility and robustness to noise. The
experiment video can be seen at: https://youtu.be/HX6uokvCiGk.

Neural networks have been applied to tackle many-body electron correlations
for small molecules and physical models in recent years. Here we propose a new
architecture that extends molecular neural networks with the inclusion of
periodic boundary conditions to enable ab initio calculation of real solids.
The accuracy of our approach is demonstrated in four different types of
systems, namely the one-dimensional periodic hydrogen chain, the
two-dimensional graphene, the three-dimensional lithium hydride crystal, and
the homogeneous electron gas, where the obtained results, e.g. total energies,
dissociation curves, and cohesive energies, outperform many traditional ab
initio methods and reach the level of the most accurate approaches. Moreover,
electron densities of typical systems are also calculated to provide physical
intuition of various solids. Our method of extending a molecular neural network
to periodic systems can be easily integrated into other neural network
structures, highlighting a promising future of ab initio solution of more
complex solid systems using neural network ansatz, and more generally endorsing
the application of machine learning in materials simulation and condensed
matter physics.

Dynamic networks are ubiquitous for modelling sequential graph-structured
data, e.g., brain connectome, population flows and messages exchanges. In this
work, we consider dynamic networks that are temporal sequences of graph
snapshots, and aim at detecting abrupt changes in their structure. This task is
often termed network change-point detection and has numerous applications, such
as fraud detection or physical motion monitoring. Leveraging a graph neural
network model, we design a method to perform online network change-point
detection that can adapt to the specific network domain and localise changes
with no delay. The main novelty of our method is to use a siamese graph neural
network architecture for learning a data-driven graph similarity function,
which allows to effectively compare the current graph and its recent history.
Importantly, our method does not require prior knowledge on the network
generative distribution and is agnostic to the type of change-points; moreover,
it can be applied to a large variety of networks, that include for instance
edge weights and node attributes. We show on synthetic and real data that our
method enjoys a number of benefits: it is able to learn an adequate graph
similarity function for performing online network change-point detection in
diverse types of change-point settings, and requires a shorter data history to
detect changes than most existing state-of-the-art baselines.

Semantic segmentation is a core ability required by autonomous agents, as
being able to distinguish which parts of the scene belong to which object class
is crucial for navigation and interaction with the environment. Approaches
which use only one time-step of data cannot distinguish between moving objects
nor can they benefit from temporal integration. In this work, we extend a
backbone LatticeNet to process temporal point cloud data. Additionally, we take
inspiration from optical flow methods and propose a new module called Abstract
Flow which allows the network to match parts of the scene with similar abstract
features and gather the information temporally. We obtain state-of-the-art
results on the SemanticKITTI dataset that contains LiDAR scans from real urban
environments. We share the PyTorch implementation of TemporalLatticeNet at
https://github.com/AIS-Bonn/temporal_latticenet .

Common AI music composition algorithms based on artificial neural networks
are to train a machine by feeding a large number of music pieces and create
artificial neural networks that can produce music similar to the input music
data. This approach is a blackbox optimization, that is, the underlying
composition algorithm is, in general, not known to users.
  In this paper, we present a way of machine composition that trains a machine
the composition principle embedded in the given music data instead of directly
feeding music pieces. We propose this approach by using the concept of
{\color{black}{Overlap}} matrix proposed in \cite{TPJ}. In \cite{TPJ}, a type
of Korean music, so-called the {\it Dodeuri} music such as Suyeonjangjigok has
been analyzed using topological data analysis (TDA), particularly using
persistent homology. As the raw music data is not suitable for TDA analysis,
the music data is first reconstructed as a graph. The node of the graph is
defined as a two-dimensional vector composed of the pitch and duration of each
music note. The edge between two nodes is created when those nodes appear
consecutively in the music flow. Distance is defined based on the frequency of
such appearances. Through TDA on the constructed graph, a unique set of cycles
is found for the given music. In \cite{TPJ}, the new concept of the {\it
{\color{black}{Overlap}} matrix} has been proposed, which visualizes how those
cycles are interconnected over the music flow, in a matrix form.
  In this paper, we explain how we use the {\color{black}{Overlap}} matrix for
machine composition. The {\color{black}{Overlap}} matrix makes it possible to
compose a new music piece algorithmically and also provide a seed music towards
the desired artificial neural network. In this paper, we use the {\it Dodeuri}
music and explain detailed steps.

Self-supervised neural language models with attention have recently been
applied to biological sequence data, advancing structure, function and
mutational effect prediction. Some protein language models, including MSA
Transformer and AlphaFold's EvoFormer, take multiple sequence alignments (MSAs)
of evolutionarily related proteins as inputs. Simple combinations of MSA
Transformer's row attentions have led to state-of-the-art unsupervised
structural contact prediction. We demonstrate that similarly simple, and
universal, combinations of MSA Transformer's column attentions strongly
correlate with Hamming distances between sequences in MSAs. Therefore,
MSA-based language models encode detailed phylogenetic relationships. We
further show that these models can separate coevolutionary signals encoding
functional and structural constraints from phylogenetic correlations reflecting
historical contingency. To assess this, we generate synthetic MSAs, either
without or with phylogeny, from Potts models trained on natural MSAs. We find
that unsupervised contact prediction is substantially more resilient to
phylogenetic noise when using MSA Transformer versus inferred Potts models.

Training a text-to-speech (TTS) model requires a large scale text labeled
speech corpus, which is troublesome to collect. In this paper, we propose a
transfer learning framework for TTS that utilizes a large amount of unlabeled
speech dataset for pre-training. By leveraging wav2vec2.0 representation,
unlabeled speech can highly improve performance, especially in the lack of
labeled speech. We also extend the proposed method to zero-shot multi-speaker
TTS (ZS-TTS). The experimental results verify the effectiveness of the proposed
method in terms of naturalness, intelligibility, and speaker generalization. We
highlight that the single speaker TTS model fine-tuned on the only 10 minutes
of labeled dataset outperforms the other baselines, and the ZS-TTS model
fine-tuned on the only 30 minutes of single speaker dataset can generate the
voice of the arbitrary speaker, by pre-training on unlabeled multi-speaker
speech corpus.

Shadows are frequently encountered natural phenomena that significantly
hinder the performance of computer vision perception systems in practical
settings, e.g., autonomous driving. A solution to this would be to eliminate
shadow regions from the images before the processing of the perception system.
Yet, training such a solution requires pairs of aligned shadowed and
non-shadowed images which are difficult to obtain. We introduce a novel weakly
supervised shadow removal framework UnShadowNet trained using contrastive
learning. It comprises of a DeShadower network responsible for removal of the
extracted shadow under the guidance of an Illumination network which is trained
adversarially by the illumination critic and a Refinement network to further
remove artifacts. We show that UnShadowNet can also be easily extended to a
fully-supervised setup to exploit the ground-truth when available. UnShadowNet
outperforms existing state-of-the-art approaches on three publicly available
shadow datasets (ISTD, adjusted ISTD, SRD) in both the weakly and fully
supervised setups.

Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D
sample on the scale of individual cell components. Unfortunately, these 2D
images can be too noisy to be fused into a useful 3D structure and facilitating
good denoisers is challenging due to the lack of clean-noisy pairs.
Additionally, representing a detailed 3D structure can be difficult even for
clean data when using regular 3D grids. Addressing these two limitations, we
suggest a differentiable image formation model for STEM, allowing to learn a
joint model of 2D sensor noise in STEM together with an implicit 3D model. We
show, that the combination of these models are able to successfully disentangle
3D signal and noise without supervision and outperform at the same time several
baselines on synthetic and real data.

We demonstrate high prediction accuracy of three important properties that
determine the initial geometry of the heavy-ion collision (HIC) experiments by
using supervised Machine Learning (ML) methods. These properties are the impact
parameter, the eccentricity and the participant eccentricity. Though ML
techniques have been used previously to determine the impact parameter of these
collisions, we study multiple ML algorithms, their error spectrum, and sampling
methods using exhaustive parameter scans and ablation studies to determine a
combination of efficient algorithm and tuned training set that gives multi-fold
improvement in accuracy for all three different heavy-ion collision models. The
three models chosen are a transport model, a hydrodynamic model and a hybrid
model. The motivation of using three different heavy-ion collision models was
to show that even if the model is trained using a transport model, it gives
accurate results for a hydrodynamic model as well as a hybrid model. We show
that the accuracy of the impact parameter prediction depends on the centrality
of the collision. With the standard application of ML training methods,
prediction accuracy is considerable low for central collisions. Our method
increases this accuracy by multiple folds. We also show that the eccentricity
prediction accuracy can be improved by inclusion of the impact parameter as a
feature in all these algorithms. We discuss how the errors can be minimized and
the accuracy can be improved to a great extent in all the ranges of impact
parameter and eccentricity predictions.

We investigate the performance of self-supervised pretraining frameworks on
pathological speech datasets used for automatic speech recognition (ASR).
Modern end-to-end models require thousands of hours of data to train well, but
only a small number of pathological speech datasets are publicly available. A
proven solution to this problem is by first pretraining the model on a huge
number of healthy speech datasets and then fine-tuning it on the pathological
speech datasets. One new pretraining framework called self-supervised learning
(SSL) trains a network using only speech data, providing more flexibility in
training data requirements and allowing more speech data to be used in
pretraining. We investigate SSL frameworks such as the wav2vec 2.0 and WavLM
models using different setups and compare their performance with different
supervised pretraining setups, using two types of pathological speech, namely,
Japanese electrolaryngeal and English dysarthric. Our results show that
although SSL has shown success with minimally resourced healthy speech, we do
not find this to be the case with pathological speech. The best supervised
setup outperforms the best SSL setup by 13.9% character error rate in
electrolaryngeal speech and 16.8% word error rate in dysarthric speech.

We study the algebraic effects and handlers as a way to support
decision-making abstractions in functional programs, whereas a user can ask a
learning algorithm to resolve choices without implementing the underlying
selection mechanism, and give a feedback by way of rewards. Differently from
some recently proposed approach to the problem based on the selection monad
[Abadi and Plotkin, LICS 2021], we express the underlying intelligence as a
reinforcement learning algorithm implemented as a set of handlers for some of
these algebraic operations, including those for choices and rewards. We show
how we can in practice use algebraic operations and handlers -- as available in
the programming language EFF -- to clearly separate the learning algorithm from
its environment, thus allowing for a good level of modularity. We then show how
the host language can be taken as a lambda-calculus with handlers, this way
showing what the essential linguistic features are. We conclude by hinting at
how type and effect systems could ensure safety properties, at the same time
pointing at some directions for further work.

The hands-on cybersecurity training quality is crucial to mitigate cyber
threats and attacks effectively. However, practical cybersecurity training is
strongly process-oriented, making the post-training analysis very difficult.
This paper presents process-mining methods applied to the learning analytics
workflow. We introduce a unified approach to reconstruct behavioral graphs from
sparse event logs of cyber ranges. Furthermore, we discuss significant data
features that affect their practical usability for educational process mining.
Based on that, methods of dealing with the complexity of process graphs are
presented, taking advantage of the puzzle-based gamification of in-class
training sessions.

Comprehending and exploring large and complex data is becoming increasingly
important for users in a wide range of application domains. Still, non-experts
in visual data analysis often have problems with correctly reading and
interpreting information from visualizations that are new to them. To support
novices in learning how to use new digital technologies, the concept of
onboarding has been successfully applied in other fields and first approaches
also exist in the visualization domain. However, empirical evidence on the
effectiveness of such approaches is scarce. Therefore, we conducted 3 studies:
1) Firstly, we explored the effect of vis onboarding, using an interactive
step-by-step guide, on user performance for four increasingly complex
visualization techniques. We performed a between-subject experiment with 596
participants in total. The results showed that there are no significant
differences between the answer correctness of the questions with and without
onboarding. Furthermore, participants commented that for highly familiar
visualization types no onboarding is needed. 2) Second, we performed another
study with MTurk workers to assess if there is a difference in user
performances on different onboarding types: step-by-step, scrollytelling
tutorial, and video tutorial. The study revealed that the video tutorial was
ranked as the most positive on average, based on sentiment analysis, followed
by the scrollytelling tutorial and the interactive step-by-step guide. 3) For
our third study with students, we gathered data on users' experience in using
an in-situ scrollytelling for the VA tool. The results showed that they
preferred scrollytelling over the tutorial integrated into the landing page. In
summary, the in-situ scrollytelling approach works well for visualization
onboarding and a video tutorial can help to introduce interaction techniques.

Due to the migration megatrend, efficient and effective second-language
acquisition is vital. One proposed solution involves AI-enabled conversational
agents for person-centered interactive language practice. We present results
from ongoing action research targeting quality assurance of proprietary
generative dialog models trained for virtual job interviews. The action team
elicited a set of 38 requirements for which we designed corresponding automated
test cases for 15 of particular interest to the evolving solution. Our results
show that six of the test case designs can detect meaningful differences
between candidate models. While quality assurance of natural language
processing applications is complex, we provide initial steps toward an
automated framework for machine learning model selection in the context of an
evolving conversational agent. Future work will focus on model selection in an
MLOps setting.

We present a method that lowers the dose required for a ptychographic
reconstruction by adaptively scanning the specimen, thereby providing the
required spatial information redundancy in the regions of highest importance.
The proposed method is built upon a deep learning model that is trained by
reinforcement learning (RL), using prior knowledge of the specimen structure
from training data sets. We show that equivalent low-dose experiments using
adaptive scanning outperform conventional ptychography experiments in terms of
reconstruction resolution.

Designing suitable deep model architectures, for AI-driven on-device apps and
features, at par with rapidly evolving mobile hardware and increasingly complex
target scenarios is a difficult task. Though Neural Architecture Search
(NAS/AutoML) has made this easier by shifting paradigm from extensive manual
effort to automated architecture learning from data, yet it has major
limitations, leading to critical bottlenecks in the context of mobile devices,
including model-hardware fidelity, prohibitive search times and deviation from
primary target objective(s). Thus, we propose AutoCoMet that can learn the most
suitable DNN architecture optimized for varied types of device hardware and
task contexts, ~ 3x faster. Our novel co-regulated shaping reinforcement
controller together with the high fidelity hardware meta-behavior predictor
produces a smart, fast NAS framework that adapts to context via a generalized
formalism for any kind of multi-criteria optimization.

Novelty detection, a widely studied problem in machine learning, is the
problem of detecting a novel class of data that has not been previously
observed. A common setting for novelty detection is inductive whereby only
examples of the negative class are available during training time. Transductive
novelty detection on the other hand has only witnessed a recent surge in
interest, it not only makes use of the negative class during training but also
incorporates the (unlabeled) test set to detect novel examples. Several studies
have emerged under the transductive setting umbrella that have demonstrated its
advantage over its inductive counterpart. Depending on the assumptions about
the data, these methods go by different names (e.g. transductive novelty
detection, semi-supervised novelty detection, positive-unlabeled learning,
out-of-distribution detection). With the use of generative adversarial networks
(GAN), a segment of those studies have adopted a transductive setup in order to
learn how to generate examples of the novel class. In this study, we propose
TransductGAN, a transductive generative adversarial network that attempts to
learn how to generate image examples from both the novel and negative classes
by using a mixture of two Gaussians in the latent space. It achieves that by
incorporating an adversarial autoencoder with a GAN network, the ability to
generate examples of novel data points offers not only a visual representation
of novelties, but also overcomes the hurdle faced by many inductive methods of
how to tune the model hyperparameters at the decision rule level. Our model has
shown superior performance over state-of-the-art inductive and transductive
methods. Our study is fully reproducible with the code available publicly.

Neural sequence-to-sequence automatic speech recognition (ASR) systems are in
principle open vocabulary systems, when using appropriate modeling units. In
practice, however, they often fail to recognize words not seen during training,
e.g., named entities, numbers or technical terms. To alleviate this problem,
Huber et al. proposed to supplement an end-to-end ASR system with a word/phrase
memory and a mechanism to access this memory to recognize the words and phrases
correctly. In this paper we study, a) methods to acquire important words for
this memory dynamically and, b) the trade-off between improvement in
recognition accuracy of new words and the potential danger of false alarms for
those added words. We demonstrate significant improvements in the detection
rate of new words with only a minor increase in false alarms (F1 score 0.30
$\rightarrow$ 0.80), when using an appropriate number of new words. In
addition, we show that important keywords can be extracted from supporting
documents and used effectively.

High-resolution reconstruction of flow-field data from low-resolution and
noisy measurements is of interest due to the prevalence of such problems in
experimental fluid mechanics, where the measurement data are in general sparse,
incomplete and noisy. Deep-learning approaches have been shown suitable for
such super-resolution tasks. However, a high number of high-resolution examples
is needed, which may not be available for many cases. Moreover, the obtained
predictions may lack in complying with the physical principles, e.g. mass and
momentum conservation. Physics-informed deep learning provides frameworks for
integrating data and physical laws for learning. In this study, we apply
physics-informed neural networks (PINNs) for super-resolution of flow-field
data both in time and space from a limited set of noisy measurements without
having any high-resolution reference data. Our objective is to obtain a
continuous solution of the problem, providing a physically-consistent
prediction at any point in the solution domain. We demonstrate the
applicability of PINNs for the super-resolution of flow-field data in time and
space through three canonical cases: Burgers' equation, two-dimensional vortex
shedding behind a circular cylinder and the minimal turbulent channel flow. The
robustness of the models is also investigated by adding synthetic Gaussian
noise. Our results show excellent capabilities of PINNs in the context of data
augmentation for experiments in fluid mechanics.

The rise of process data availability has recently led to the development of
data-driven learning approaches. However, most of these approaches restrict the
use of the learned model to predict the future of ongoing process executions.
The goal of this paper is moving a step forward and leveraging available data
to learning to act, by supporting users with recommendations derived from an
optimal strategy (measure of performance). We take the optimization perspective
of one process actor and we recommend the best activities to execute next, in
response to what happens in a complex external environment, where there is no
control on exogenous factors. To this aim, we investigate an approach that
learns, by means of Reinforcement Learning, the optimal policy from the
observation of past executions and recommends the best activities to carry on
for optimizing a Key Performance Indicator of interest. The validity of the
approach is demonstrated on two scenarios taken from real-life data.

Compared to traditional imitation learning methods such as DAgger and DART,
intervention-based imitation offers a more convenient and sample efficient data
collection process to users. In this paper, we introduce Reinforced
Intervention-based Learning (ReIL), a framework consisting of a general
intervention-based learning algorithm and a multi-task imitation learning model
aimed at enabling non-expert users to train agents in real environments with
little supervision or fine tuning. ReIL achieves this with an algorithm that
combines the advantages of imitation learning and reinforcement learning and a
model capable of concurrently processing demonstrations, past experience, and
current observations. Experimental results from real world mobile robot
navigation challenges indicate that ReIL learns rapidly from sparse supervisor
corrections without suffering deterioration in performance that is
characteristic of supervised learning-based methods such as HG-Dagger and IWR.
The results also demonstrate that in contrast to other intervention-based
methods such as IARL and EGPO, ReIL can utilize an arbitrary reward function
for training without any additional heuristics.

Existing deep learning based methods effectively prompt the performance of
aerial scene classification. However, due to the large amount of parameters and
computational cost, it is rather difficult to apply these methods to multiple
real-time remote sensing applications such as on-board data preception on
drones and satellites. In this paper, we address this task by developing a
light-weight ConvNet named multi-stage duplex fusion network (MSDF-Net). The
key idea is to use parameters as little as possible while obtaining as strong
as possible scene representation capability. To this end, a residual-dense
duplex fusion strategy is developed to enhance the feature propagation while
re-using parameters as much as possible, and is realized by our duplex fusion
block (DFblock). Specifically, our MSDF-Net consists of multi-stage structures
with DFblock. Moreover, duplex semantic aggregation (DSA) module is developed
to mine the remote sensing scene information from extracted convolutional
features, which also contains two parallel branches for semantic description.
Extensive experiments are conducted on three widely-used aerial scene
classification benchmarks, and reflect that our MSDF-Net can achieve a
competitive performance against the recent state-of-art while reducing up to
80% parameter numbers. Particularly, an accuracy of 92.96% is achieved on AID
with only 0.49M parameters.

Fingerprint-based localization plays an important role in indoor
location-based services, where the position information is usually collected in
distributed clients and gathered in a centralized server. However, the
overloaded transmission as well as the potential risk of divulging private
information burdens the application.Owning the ability to address these
challenges, federated learning (FL)-based fingerprinting localization comes
into people's sights, which aims to train a global model while keeping raw data
locally. However, in distributed machine learning (ML) scenarios, the
unavoidable database heterogeneity usually degrades the performance of existing
FL-based localization algorithm (FedLoc). In this paper, we first characterize
the database heterogeneity with a computable metric, i.e., the area of convex
hull, and verify it by experimental results. Then, a novel heterogeneous
FL-based localization algorithm with the area of convex hull-based aggregation
(FedLoc-AC) is proposed. Extensive experimental results, including real-word
cases are conducted. We can conclude that the proposed FedLoc-AC can achieve an
obvious prediction gain compared to FedLoc in heterogeneous scenarios and has
almost the same prediction error with it in homogeneous scenarios. Moreover,
the extension of FedLoc-AC in multi-floor cases is proposed and verified.

Multiobjective combinatorial optimization (MOCO) problems can be found in
many real-world applications. However, exactly solving these problems would be
very challenging, particularly when they are NP-hard. Many handcrafted
heuristic methods have been proposed to tackle different MOCO problems over the
past decades. In this work, we generalize the idea of neural combinatorial
optimization, and develop a learning-based approach to approximate the whole
Pareto set for a given MOCO problem without further search procedure. We
propose a single preference-conditioned model to directly generate approximate
Pareto solutions for any trade-off preference, and design an efficient
multiobjective reinforcement learning algorithm to train this model. Our
proposed method can be treated as a learning-based extension for the
widely-used decomposition-based multiobjective evolutionary algorithm (MOEA/D).
It uses a single model to accommodate all the possible preferences, whereas
other methods use a finite number of solutions to approximate the Pareto set.
Experimental results show that our proposed method significantly outperforms
some other methods on the multiobjective traveling salesman problem,
multiobjective vehicle routing problem, and multiobjective knapsack problem in
terms of solution quality, speed, and model efficiency.

Most methods tackle zero-shot video classification by aligning
visual-semantic representations within seen classes, which limits
generalization to unseen classes. To enhance model generalizability, this paper
presents an end-to-end framework that preserves alignment and uniformity
properties for representations on both seen and unseen classes. Specifically,
we formulate a supervised contrastive loss to simultaneously align
visual-semantic features (i.e., alignment) and encourage the learned features
to distribute uniformly (i.e., uniformity). Unlike existing methods that only
consider the alignment, we propose uniformity to preserve maximal-info of
existing features, which improves the probability that unobserved features fall
around observed data. Further, we synthesize features of unseen classes by
proposing a class generator that interpolates and extrapolates the features of
seen classes. Besides, we introduce two metrics, closeness and dispersion, to
quantify the two properties and serve as new measurements of model
generalizability. Experiments show that our method significantly outperforms
SoTA by relative improvements of 28.1% on UCF101 and 27.0% on HMDB51. Code is

Recently, many novel techniques have been introduced to deal with spoofing
attacks, and achieve promising countermeasure (CM) performances. However, these
works only take the stand-alone CM models into account. Nowadays, a spoofing
aware speaker verification (SASV) challenge which aims to facilitate the
research of integrated CM and ASV models, arguing that jointly optimizing CM
and ASV models will lead to better performance, is taking place. In this paper,
we propose a novel multi-model and multi-level fusion strategy to tackle the
SASV task. Compared with purely scoring fusion and embedding fusion methods,
this framework first utilizes embeddings from CM models, propagating CM
embeddings into a CM block to obtain a CM score. In the second-level fusion,
the CM score and ASV scores directly from ASV systems will be concatenated into
a prediction block for the final decision. As a result, the best single fusion
system has achieved the SASV-EER of 0.97% on the evaluation set. Then by
ensembling the top-5 fusion systems, the final SASV-EER reached 0.89%.

Variational methods have been widely used in soft matter physics for both
static and dynamic problems. These methods are mostly based on two variational
principles: the variational principle of minimum free energy (MFEVP) and
Onsager's variational principle (OVP). Our interests lie in the applications of
these variational methods to active matter physics. In our former work [Soft
Matter, 2021, 17, 3634], we have explored the applications of OVP-based
variational methods for the modeling of active matter dynamics. In the present
work, we explore variational (or energy) methods that are based on MFEVP for
static problems in active elastic solids. We show that MFEVP can be used not
only to derive equilibrium equations, but also to develop approximate solution
methods, such as Ritz method, for active solid statics. Moreover, the power of
Ritz-type method can be further enhanced using deep learning methods if we use
deep neural networks to construct the trial solutions of the variational
problems. We then apply these variational methods and the deep Ritz method to
study the spontaneous bending and contraction of a thin active circular plate
that is induced by internal asymmetric active contraction. The circular plate
is found to be bent towards its contracting side. The study of such a simple
toy system gives implications for understanding the morphogenesis of solid-like
confluent cell monolayers. In addition, we introduce a so-called activogravity
length to characterize the importance of gravitational forces relative to
internal active contraction in driving the bending of the active plate. When
the lateral plate dimension is larger than the activogravity length (about 100
micron), gravitational forces become important. Such gravitaxis behaviors at
multicellular scales may play significant roles in the morphogenesis and in the
up-down symmetry broken during tissue development.

Current image-to-image translations do not control the output domain beyond
the classes used during training, nor do they interpolate between different
domains well, leading to implausible results. This limitation largely arises
because labels do not consider the semantic distance. To mitigate such
problems, we propose a style-aware discriminator that acts as a critic as well
as a style encoder to provide conditions. The style-aware discriminator learns
a controllable style space using prototype-based self-supervised learning and
simultaneously guides the generator. Experiments on multiple datasets verify
that the proposed model outperforms current state-of-the-art image-to-image
translation methods. In contrast with current methods, the proposed approach
supports various applications, including style interpolation, content
transplantation, and local image translation.

Neuromorphic Computing implemented in photonic hardware is one of the most
promising routes towards achieving machine learning processing at the
picosecond scale, with minimum power consumption. In this work, we present a
new concept for realizing photonic recurrent neural networks and reservoir
computing architectures with the use of recurrent optical spectrum slicing.
This is accomplished through simple optical filters placed in an loop, where
each filter processes a specific spectral slice of the incoming optical signal.
The synaptic weights in our scheme are equivalent to filters central
frequencies and bandwidths. This new method for implementing recurrent neural
processing in the photonic domain, which we call Recurrent Optical Spectrum
Slicing Neural Networks, is numerically evaluated on a demanding,
industry-relevant task such as high baud rate optical signal equalization 100
Gbaud, exhibiting ground-breaking performance. The performance enhancement
surpasses state-of-the-art digital processing techniques by doubling the reach
while minimizing complexity and power consumption by a factor of 10 compared to
state-of-the-art solutions. In this respect, ROSS-NNs can pave the way for the
implementation of ultra-efficient photonic hardware accelerators tailored for
processing high-bandwidth optical signals in optical communication and
high-speed imaging applications

Image BERT pre-training with masked image modeling (MIM) becomes a popular
practice to cope with self-supervised representation learning. A seminal work,
BEiT, casts MIM as a classification task with a visual vocabulary, tokenizing
the continuous visual signals into discrete vision tokens using a pre-learned
dVAE. Despite a feasible solution, the improper discretization hinders further
improvements of image pre-training. Since image discretization has no
ground-truth answers, we believe that the masked patch should not be assigned
with a unique token id even if a better tokenizer can be obtained. In this
work, we introduce an improved BERT-style image pre-training method, namely
mc-BEiT, which performs MIM proxy tasks towards eased and refined multi-choice
training objectives. Specifically, the multi-choice supervision for the masked
image patches is formed by the soft probability vectors of the discrete token
ids, which are predicted by the off-the-shelf image tokenizer and further
refined by high-level inter-patch perceptions resorting to the observation that
similar patches should share their choices. Extensive experiments on
classification, segmentation, and detection tasks demonstrate the superiority
of our method, e.g., the pre-trained ViT-B achieves 84.1% top-1 fine-tuning
accuracy on ImageNet-1K classification, 49.2% AP^b and 44.0% AP^m of object
detection and instance segmentation on COCO, 50.8% mIOU on ADE20K semantic
segmentation, outperforming the competitive counterparts. The code will be
available at https://github.com/lixiaotong97/mc-BEiT.

Multiclass classification is of great interest for various machine learning
applications, for example, it is a common task in computer vision, where one
needs to categorize an image into three or more classes. Here we propose a
quantum machine learning approach based on quantum convolutional neural
networks for solving this problem. The corresponding learning procedure is
implemented via TensorFlowQuantum as a hybrid quantum-classical (variational)
model, where quantum output results are fed to softmax cost function with
subsequent minimization of it via optimization of parameters of quantum
circuit. Our conceptional improvements include a new model for quantum
perceptron and optimized structure of the quantum circuit. We use the proposed
approach to demonstrate the 4-class classification for the case of the MNIST
dataset using eight qubits for data encoding and four acnilla qubits. Our
results demonstrate comparable accuracy of our solution with classical
convolutional neural networks with comparable numbers of trainable parameters.
We expect that our finding provide a new step towards the use of quantum
machine learning for solving practically relevant problems in the NISQ era and
beyond.

We propose a method for self-supervised image representation learning under
the guidance of 3D geometric consistency. Our intuition is that 3D geometric
consistency priors such as smooth regions and surface discontinuities may imply
consistent semantics or object boundaries, and can act as strong cues to guide
the learning of 2D image representations without semantic labels. Specifically,
we introduce 3D geometric consistency into a contrastive learning framework to
enforce the feature consistency within image views. We propose to use geometric
consistency sets as constraints and adapt the InfoNCE loss accordingly. We show
that our learned image representations are general. By fine-tuning our
pre-trained representations for various 2D image-based downstream tasks,
including semantic segmentation, object detection, and instance segmentation on
real-world indoor scene datasets, we achieve superior performance compared with

The networks trained on the long-tailed dataset vary remarkably, despite the
same training settings, which shows the great uncertainty in long-tailed
learning. To alleviate the uncertainty, we propose a Nested Collaborative
Learning (NCL), which tackles the problem by collaboratively learning multiple
experts together. NCL consists of two core components, namely Nested Individual
Learning (NIL) and Nested Balanced Online Distillation (NBOD), which focus on
the individual supervised learning for each single expert and the knowledge
transferring among multiple experts, respectively. To learn representations
more thoroughly, both NIL and NBOD are formulated in a nested way, in which the
learning is conducted on not just all categories from a full perspective but
some hard categories from a partial perspective. Regarding the learning in the
partial perspective, we specifically select the negative categories with high
predicted scores as the hard categories by using a proposed Hard Category
Mining (HCM). In the NCL, the learning from two perspectives is nested, highly
related and complementary, and helps the network to capture not only global and
robust features but also meticulous distinguishing ability. Moreover,
self-supervision is further utilized for feature enhancement. Extensive
experiments manifest the superiority of our method with outperforming the
state-of-the-art whether by using a single model or an ensemble.

Learning under a continuously changing data distribution with incorrect
labels is a desirable real-world problem yet challenging. A large body of
continual learning (CL) methods, however, assumes data streams with clean
labels, and online learning scenarios under noisy data streams are yet
underexplored. We consider a more practical CL task setup of an online learning
from blurry data stream with corrupted labels, where existing CL methods
struggle. To address the task, we first argue the importance of both diversity
and purity of examples in the episodic memory of continual learning models. To
balance diversity and purity in the episodic memory, we propose a novel
strategy to manage and use the memory by a unified approach of label noise
aware diverse sampling and robust learning with semi-supervised learning. Our
empirical validations on four real-world or synthetic noise datasets (CIFAR10
and 100, mini-WebVision, and Food-101N) exhibit that our method significantly
outperforms prior arts in this realistic and challenging continual learning
scenario. Code and data splits are available in
https://github.com/clovaai/puridiver.

Sign languages are visual languages, with vocabularies as rich as their
spoken language counterparts. However, current deep-learning based Sign
Language Production (SLP) models produce under-articulated skeleton pose
sequences from constrained vocabularies and this limits applicability. To be
understandable and accepted by the deaf, an automatic SLP system must be able
to generate co-articulated photo-realistic signing sequences for large domains
of discourse.
  In this work, we tackle large-scale SLP by learning to co-articulate between
dictionary signs, a method capable of producing smooth signing while scaling to
unconstrained domains of discourse. To learn sign co-articulation, we propose a
novel Frame Selection Network (FS-Net) that improves the temporal alignment of
interpolated dictionary signs to continuous signing sequences. Additionally, we
propose SignGAN, a pose-conditioned human synthesis model that produces
photo-realistic sign language videos direct from skeleton pose. We propose a
novel keypoint-based loss function which improves the quality of synthesized
hand images.
  We evaluate our SLP model on the large-scale meineDGS (mDGS) corpus,
conducting extensive user evaluation showing our FS-Net approach improves
co-articulation of interpolated dictionary signs. Additionally, we show that
SignGAN significantly outperforms all baseline methods for quantitative
metrics, human perceptual studies and native deaf signer comprehension.

Object detection under imperfect data receives great attention recently.
Weakly supervised object detection (WSOD) suffers from severe localization
issues due to the lack of instance-level annotation, while semi-supervised
object detection (SSOD) remains challenging led by the inter-image discrepancy
between labeled and unlabeled data. In this study, we propose the Single
Instance annotated Object Detection (SIOD), requiring only one instance
annotation for each existing category in an image. Degraded from inter-task
(WSOD) or inter-image (SSOD) discrepancies to the intra-image discrepancy, SIOD
provides more reliable and rich prior knowledge for mining the rest of
unlabeled instances and trades off the annotation cost and performance. Under
the SIOD setting, we propose a simple yet effective framework, termed
Dual-Mining (DMiner), which consists of a Similarity-based Pseudo Label
Generating module (SPLG) and a Pixel-level Group Contrastive Learning module
(PGCL). SPLG firstly mines latent instances from feature representation space
to alleviate the annotation missing problem. To avoid being misled by
inaccurate pseudo labels, we propose PGCL to boost the tolerance to false
pseudo labels. Extensive experiments on MS COCO verify the feasibility of the
SIOD setting and the superiority of the proposed method, which obtains
consistent and significant improvements compared to baseline methods and
achieves comparable results with fully supervised object detection (FSOD)
methods with only 40% instances annotated.

Synthesizing a subject-specific pathology-free image from a pathological
image is valuable for algorithm development and clinical practice. In recent
years, several approaches based on the Generative Adversarial Network (GAN)
have achieved promising results in pseudo-healthy synthesis. However, the
discriminator (i.e., a classifier) in the GAN cannot accurately identify
lesions and further hampers from generating admirable pseudo-healthy images. To
address this problem, we present a new type of discriminator, the segmentor, to
accurately locate the lesions and improve the visual quality of pseudo-healthy
images. Then, we apply the generated images into medical image enhancement and
utilize the enhanced results to cope with the low contrast problem existing in
medical image segmentation. Furthermore, a reliable metric is proposed by
utilizing two attributes of label noise to measure the health of synthetic
images. Comprehensive experiments on the T2 modality of BraTS demonstrate that
the proposed method substantially outperforms the state-of-the-art methods. The
method achieves better performance than the existing methods with only 30\% of
the training data. The effectiveness of the proposed method is also
demonstrated on the LiTS and the T1 modality of BraTS. The code and the
pre-trained model of this study are publicly available at
https://github.com/Au3C2/Generator-Versus-Segmentor.

The existing generative adversarial fusion methods generally concatenate
source images and extract local features through convolution operation, without
considering their global characteristics, which tends to produce an unbalanced
result and is biased towards the infrared image or visible image. Toward this
end, we propose a novel end-to-end mode based on generative adversarial
training to achieve better fusion balance, termed as \textit{interactive
compensatory attention fusion network} (ICAFusion). In particular, in the
generator, we construct a multi-level encoder-decoder network with a triple
path, and adopt infrared and visible paths to provide additional intensity and
gradient information. Moreover, we develop interactive and compensatory
attention modules to communicate their pathwise information, and model their
long-range dependencies to generate attention maps, which can more focus on
infrared target perception and visible detail characterization, and further
increase the representation power for feature extraction and feature
reconstruction. In addition, dual discriminators are designed to identify the
similar distribution between fused result and source images, and the generator
is optimized to produce a more balanced result. Extensive experiments
illustrate that our ICAFusion obtains superior fusion performance and better
generalization ability, which precedes other advanced methods in the subjective
visual description and objective metric evaluation. Our codes will be public at
\url{https://github.com/Zhishe-Wang/ICAFusion}

Generic event boundary detection aims to localize the generic, taxonomy-free
event boundaries that segment videos into chunks. Existing methods typically
require video frames to be decoded before feeding into the network, which
demands considerable computational power and storage space. To that end, we
propose a new end-to-end compressed video representation learning for event
boundary detection that leverages the rich information in the compressed
domain, i.e., RGB, motion vectors, residuals, and the internal group of
pictures (GOP) structure, without fully decoding the video. Specifically, we
first use the ConvNets to extract features of the I-frames in the GOPs. After
that, a light-weight spatial-channel compressed encoder is designed to compute
the feature representations of the P-frames based on the motion vectors,
residuals and representations of their dependent I-frames. A temporal
contrastive module is proposed to determine the event boundaries of video
sequences. To remedy the ambiguities of annotations and speed up the training
process, we use the Gaussian kernel to preprocess the ground-truth event
boundaries. Extensive experiments conducted on the Kinetics-GEBD dataset
demonstrate that the proposed method achieves comparable results to the
state-of-the-art methods with $4.5\times$ faster running speed.

Multimodal learning helps to comprehensively understand the world, by
integrating different senses. Accordingly, multiple input modalities are
expected to boost model performance, but we actually find that they are not
fully exploited even when the multimodal model outperforms its uni-modal
counterpart. Specifically, in this paper we point out that existing multimodal
discriminative models, in which uniform objective is designed for all
modalities, could remain under-optimized uni-modal representations, caused by
another dominated modality in some scenarios, e.g., sound in blowing wind
event, vision in drawing picture event, etc. To alleviate this optimization
imbalance, we propose on-the-fly gradient modulation to adaptively control the
optimization of each modality, via monitoring the discrepancy of their
contribution towards the learning objective. Further, an extra Gaussian noise
that changes dynamically is introduced to avoid possible generalization drop
caused by gradient modulation. As a result, we achieve considerable improvement
over common fusion methods on different multimodal tasks, and this simple
strategy can also boost existing multimodal methods, which illustrates its
efficacy and versatility. The source code is available at
\url{https://github.com/GeWu-Lab/OGM-GE_CVPR2022}.

Currently, many theoretical as well as practically relevant questions towards
the transferability and robustness of Convolutional Neural Networks (CNNs)
remain unsolved. While ongoing research efforts are engaging these problems
from various angles, in most computer vision related cases these approaches can
be generalized to investigations of the effects of distribution shifts in image
data. In this context, we propose to study the shifts in the learned weights of
trained CNN models. Here we focus on the properties of the distributions of
dominantly used 3x3 convolution filter kernels. We collected and publicly
provide a dataset with over 1.4 billion filters from hundreds of trained CNNs,
using a wide range of datasets, architectures, and vision tasks. In a first use
case of the proposed dataset, we can show highly relevant properties of many
publicly available pre-trained models for practical applications: I) We analyze
distribution shifts (or the lack thereof) between trained filters along
different axes of meta-parameters, like visual category of the dataset, task,
architecture, or layer depth. Based on these results, we conclude that model
pre-training can succeed on arbitrary datasets if they meet size and variance
conditions. II) We show that many pre-trained models contain degenerated
filters which make them less robust and less suitable for fine-tuning on target
  Data & Project website: https://github.com/paulgavrikov/cnn-filter-db

Single image dehazing as a fundamental low-level vision task, is essential
for the development of robust intelligent surveillance system. In this paper,
we make an early effort to consider dehazing robustness under variational haze
density, which is a realistic while under-studied problem in the research filed
of singe image dehazing. To properly address this problem, we propose a novel
density-variational learning framework to improve the robustness of the image
dehzing model assisted by a variety of negative hazy images, to better deal
with various complex hazy scenarios. Specifically, the dehazing network is
optimized under the consistency-regularized framework with the proposed
Contrast-Assisted Reconstruction Loss (CARL). The CARL can fully exploit the
negative information to facilitate the traditional positive-orient dehazing
objective function, by squeezing the dehazed image to its clean target from
different directions. Meanwhile, the consistency regularization keeps
consistent outputs given multi-level hazy images, thus improving the model
robustness. Extensive experimental results on two synthetic and three
real-world datasets demonstrate that our method significantly surpasses the

While monitoring system behavior to detect anomalies and failures is
important, existing methods based on log-analysis can only be as good as the
information contained in the logs, and other approaches that look at the
OS-level software state introduce high overheads. We tackle the problem with
syslrn, a system that first builds an understanding of a target system offline,
and then tailors the online monitoring instrumentation based on the learned
identifiers of normal behavior. While our syslrn prototype is still preliminary
and lacks many features, we show in a case study for the monitoring of
OpenStack failures that it can outperform state-of-the-art log-analysis systems
with little overhead.

A quantum neural network (QNN) is interpreted today as any quantum circuit
with trainable continuous parameters. This work builds on previous works by the
authors and addresses QNN for image classification with Novel Enhanced Quantum
Representation of (NEQR) processed classical data where Principal component
analysis (PCA) and Projected Quantum Kernel features (PQK) were investigated
previously by the authors as a path to quantum advantage for the same classical
dataset. For each of these cases the Fashion-MNIST dataset was downscaled using
PCA to convert into quantum data where the classical NN easily outperformed the
QNN. However, we demonstrated quantum advantage by using PQK where quantum
models achieved more than ~90% accuracy surpassing their classical counterpart
on the same training dataset as in the first case. In this current work, we use
the same dataset fed into a QNN and compare that with performance of a
classical NN model. We built an NEQR model circuit to pre-process the same data
and feed the images into the QNN. Our results showed marginal improvements
(only about ~5.0%) where the QNN performance with NEQR exceeded the performance
of QNN without NEQR. We conclude that given the computational cost and the
massive circuit depth associated with running NEQR, the advantage offered by
this specific Quantum Image Processing (QIMP) algorithm is questionable at
least for classical image dataset. No actual quantum computing hardware
platform exists today that can support the circuit depth needed to run NEQR
even for the reduced image sizes of our toy classical dataset.

Neural Machine Translation (NMT) has reached a level of maturity to be
recognized as the premier method for the translation between different
languages and aroused interest in different research areas, including software
engineering. A key step to validate the robustness of the NMT models consists
in evaluating the performance of the models on adversarial inputs, i.e., inputs
obtained from the original ones by adding small amounts of perturbation.
However, when dealing with the specific task of the code generation (i.e., the
generation of code starting from a description in natural language), it has not
yet been defined an approach to validate the robustness of the NMT models. In
this work, we address the problem by identifying a set of perturbations and
metrics tailored for the robustness assessment of such models. We present a
preliminary experimental evaluation, showing what type of perturbations affect
the model the most and deriving useful insights for future directions.

Multi-label classification has attracted much attention in the machine
learning community to address the problem of assigning single samples to more
than one class at the same time. We propose an evolving multi-label fuzzy
classifier (EFC-ML) which is able to self-adapt and self-evolve its structure
with new incoming multi-label samples in an incremental, single-pass manner. It
is based on a multi-output Takagi-Sugeno type architecture, where for each
class a separate consequent hyper-plane is defined. The learning procedure
embeds a locally weighted incremental correlation-based algorithm combined with
(conventional) recursive fuzzily weighted least squares and Lasso-based
regularization. The correlation-based part ensures that the interrelations
between class labels, a specific well-known property in multi-label
classification for improved performance, are preserved properly; the
Lasso-based regularization reduces the curse of dimensionality effects in the
case of a higher number of inputs. Antecedent learning is achieved by
product-space clustering and conducted for all class labels together, which
yields a single rule base, allowing a compact knowledge view. Furthermore, our
approach comes with an online active learning (AL) strategy for updating the
classifier on just a number of selected samples, which in turn makes the
approach applicable for scarcely labelled streams in applications, where the
annotation effort is typically expensive. Our approach was evaluated on several
data sets from the MULAN repository and showed significantly improved
classification accuracy compared to (evolving) one-versus-rest or classifier
chaining concepts. A significant result was that, due to the online AL method,
a 90\% reduction in the number of samples used for classifier updates had
little effect on the accumulated accuracy trend lines compared to a full update
in most data set cases.

Deep learning has made many remarkable achievements in many fields but
suffers from noisy labels in datasets. The state-of-the-art learning with noisy
label method Co-teaching and Co-teaching+ confronts the noisy label by
mutual-information between dual-network. However, the dual network always tends
to convergent which would weaken the dual-network mechanism to resist the noisy
labels. In this paper, we proposed a noise-tolerant framework named MLC in an
end-to-end manner. It adjusts the dual-network with divergent regularization to
ensure the effectiveness of the mechanism. In addition, we correct the label
distribution according to the agreement between dual-networks. The proposed
method can utilize the noisy data to improve the accuracy, generalization, and
robustness of the network. We test the proposed method on the simulate noisy
dataset MNIST, CIFAR-10, and the real-world noisy dataset Clothing1M. The
experimental result shows that our method outperforms the previous
state-of-the-art method. Besides, our method is network-free thus it is
applicable to many tasks. Our code can be found at
https://github.com/JiarunLiu/MLC.

As a well-known physical unclonable function that can provide huge number of
challenge response pairs (CRP) with a compact design and fully compatibility
with current electronic fabrication process, the arbiter PUF (APUF) has
attracted great attention. To improve its resilience against modeling attacks,
many APUF variants have been proposed so far. Though the modeling resilience of
response obfuscated APUF variants such as XOR-APUF and lightweight secure APUF
(LSPUF) have been well studied, the challenge obfuscated APUFs (CO-APUFs) such
as feed-forward APUF (FF-APUF), and XOR-FF-APUF are less elucidated,
especially, with the deep learning (DL) methods. This work systematically
evaluates five CO-APUFs including three influential designs of FF-APUF,
XOR-FF-APUF, iPUF, one very recently design and our newly optimized design
(dubbed as OAX-FF-APUF), in terms of their reliability, uniformity (related to
uniqueness), and modeling resilience. Three DL techniques of GRU, TCN and MLP
are employed to examine these CO-APUFs' modeling resilience -- the first two
are newly explored. With computation resource of a common personal computer, we
show that all five CO-APUFs with relatively large scale can be successfully
modeled -- attacking accuracy higher or close to its reliability. The
hyper-parameter tuning of DL technique is crucial for implementing efficient
attacks. Increasing the scale of the CO-APUF is validated to be able to improve
the resilience but should be done with minimizing the reliability degradation.
As the powerful capability of DL technique affirmed by us, we recommend the DL,
specifically the MLP technique always demonstrating best efficacy, to be always
considered for examining the modeling resilience when newly composited APUFs
are devised or to a large extent, other strong PUFs are constructed.

In this paper, we focus on unsupervised learning for Video Object
Segmentation (VOS) which learns visual correspondence (i.e., the similarity
between pixel-level features) from unlabeled videos. Previous methods are
mainly based on the contrastive learning paradigm, which optimize either in
image level or pixel level. Image-level optimization (e.g., the spatially
pooled feature of ResNet) learns robust high-level semantics but is sub-optimal
since the pixel-level features are optimized implicitly. By contrast,
pixel-level optimization is more explicit, however, it is sensitive to the
visual quality of training data and is not robust to object deformation. To
complementarily perform these two levels of optimization in a unified
framework, we propose the In-aNd-Out (INO) generative learning from a purely
generative perspective with the help of naturally designed class tokens and
patch tokens in Vision Transformer (ViT). Specifically, for image-level
optimization, we force the out-view imagination from local to global views on
class tokens, which helps capture high-level semantics, and we name it as
out-generative learning. As to pixel-level optimization, we perform in-view
masked image modeling on patch tokens, which recovers the corrupted parts of an
image via inferring its fine-grained structure, and we term it as in-generative
learning. To discover the temporal information better, we additionally force
the inter-frame consistency from both feature and affinity matrix levels.
Extensive experiments on DAVIS-2017 val and YouTube-VOS 2018 val show that our
INO outperforms previous state-of-the-art methods by significant margins. Code
is available: https://github.com/pansanity666/INO_VOS

Zero-shot learning (ZSL) aims to learn models that can recognize unseen image
semantics based on the training of data with seen semantics. Recent studies
either leverage the global image features or mine discriminative local patch
features to associate the extracted visual features to the semantic attributes.
However, due to the lack of the necessary top-down guidance and semantic
alignment for ensuring the model attending to the real attribute-correlation
regions, these methods still encounter a significant semantic gap between the
visual modality and the attribute modality, which makes their prediction on
unseen semantics unreliable. To solve this problem, this paper establishes a
novel transformer encoder-decoder model, called hybrid routing transformer
(HRT). In HRT encoder, we embed an active attention, which is constructed by
both the bottom-up and the top-down dynamic routing pathways to generate the
attribute-aligned visual feature. While in HRT decoder, we use static routing
to calculate the correlation among the attribute-aligned visual features, the
corresponding attribute semantics, and the class attribute vectors to generate
the final class label predictions. This design makes the presented transformer
model a hybrid of 1) top-down and bottom-up attention pathways and 2) dynamic
and static routing pathways. Comprehensive experiments on three widely-used
benchmark datasets, namely CUB, SUN, and AWA2, are conducted. The obtained
experimental results demonstrate the effectiveness of the proposed method.

In this work, we tackle the task of estimating the 6D pose of an object from
point cloud data. While recent learning-based approaches to addressing this
task have shown great success on synthetic datasets, we have observed them to
fail in the presence of real-world data. We thus analyze the causes of these
failures, which we trace back to the difference between the feature
distributions of the source and target point clouds, and the sensitivity of the
widely-used SVD-based loss function to the range of rotation between the two
point clouds. We address the first challenge by introducing a new normalization
strategy, Match Normalization, and the second via the use of a loss function
based on the negative log likelihood of point correspondences. Our two
contributions are general and can be applied to many existing learning-based 3D
object registration frameworks, which we illustrate by implementing them in two
of them, DCP and IDAM. Our experiments on the real-scene TUD-L, LINEMOD and
Occluded-LINEMOD datasets evidence the benefits of our strategies. They allow
for the first time learning-based 3D object registration methods to achieve
meaningful results on real-world data. We therefore expect them to be key to
the future development of point cloud registration methods.

Pruning is an effective way to reduce the huge inference cost of Transformer
models. However, prior work on pruning Transformers requires retraining the
models. This can add high training cost and high complexity to model
deployment, making it difficult to use in many practical situations. To address
this, we propose a fast post-training pruning framework for Transformers that
does not require any retraining. Given a resource constraint and a sample
dataset, our framework automatically prunes the Transformer model using
structured sparsity methods. To retain high accuracy without retraining, we
introduce three novel techniques: (i) a lightweight mask search algorithm that
finds which heads and filters to prune based on the Fisher information; (ii)
mask rearrangement that complements the search algorithm; and (iii) mask tuning
that reconstructs the output activations for each layer. We apply our method to
BERT-base and DistilBERT, and we evaluate its effectiveness on GLUE and SQuAD
benchmarks. Our framework achieves up to 2.0x reduction in FLOPs and 1.56x
speedup in inference latency, while maintaining < 1% loss in accuracy.
Importantly, our framework prunes Transformers in less than 3 minutes on a
single GPU, which is over two orders of magnitude faster than existing pruning
approaches that retrain the models.

Wind speed modelling and prediction has been gaining importance because of
its significant roles in various stages of wind energy management. In this
paper, we propose a hybrid model, based on wavelet transform to improve the
accuracy of the short-term forecast. The wind speed time series are split into
various frequency components using wavelet decomposition technique, and each
frequency components are modelled separately. Since the components associated
with the high- frequency range shows stochastic nature, we modelled them with
autoregressive (AR) method and rest of low-frequency components modelled with
support vector machine (SVM). The results of the hybrid method show a promising
improvement in accuracy of wind speed prediction compared to that of
stand-alone AR or SVM model.

Deep Neural Networks, particularly Convolutional Neural Networks (ConvNets),
have achieved incredible success in many vision tasks, but they usually require
millions of parameters for good accuracy performance. With increasing
applications that use ConvNets, updating hundreds of networks for multiple
tasks on an embedded device can be costly in terms of memory, bandwidth, and
energy. Approaches to reduce this cost include model compression and
parameter-efficient models that adapt a subset of network layers for each new
task. This work proposes a novel parameter-efficient kernel modulation (KM)
method that adapts all parameters of a base network instead of a subset of
layers. KM uses lightweight task-specialized kernel modulators that require
only an additional 1.4% of the base network parameters. With multiple tasks,
only the task-specialized KM weights are communicated and stored on the
end-user device. We applied this method in training ConvNets for Transfer
Learning and Meta-Learning scenarios. Our results show that KM delivers up to
9% higher accuracy than other parameter-efficient methods on the Transfer
Learning benchmark.

Artificial neural networks are efficient computing platforms inspired by the
brain. Such platforms can tackle a vast area of real-life tasks ranging from
image processing to language translation. Silicon photonic integrated chips
(PICs), by employing coherent interactions in Mach-Zehnder interferometers, are
promising accelerators offering record low power consumption and ultra-fast
matrix multiplication. Such photonic accelerators, however, suffer from phase
uncertainty due to fabrication errors and crosstalk effects that inhibit the
development of high-density implementations. In this work, we present a
Bayesian learning framework for such photonic accelerators. In addition to the
conventional log-likelihood optimization path, two novel training schemes are
derived, namely a regularized version and a fully Bayesian learning scheme.
They are applied on a photonic neural network with 512 phase shifters targeting
the MNIST dataset. The new schemes, when combined with a pre-characterization
stage that provides the passive offsets, are able to dramatically decrease the
operational power of the PIC beyond 70%, with just a slight loss in
classification accuracy. The full Bayesian scheme, apart from this energy
reduction, returns information with respect to the sensitivity of the phase
shifters. This information is used to de-activate 31% of the phase actuators
and, thus, significantly simplify the driving system.

The advances in monocular 3D human pose estimation are dominated by
supervised techniques that require large-scale 2D/3D pose annotations. Such
methods often behave erratically in the absence of any provision to discard
unfamiliar out-of-distribution data. To this end, we cast the 3D human pose
learning as an unsupervised domain adaptation problem. We introduce MRP-Net
that constitutes a common deep network backbone with two output heads
subscribing to two diverse configurations; a) model-free joint localization and
b) model-based parametric regression. Such a design allows us to derive
suitable measures to quantify prediction uncertainty at both pose and joint
level granularity. While supervising only on labeled synthetic samples, the
adaptation process aims to minimize the uncertainty for the unlabeled target
images while maximizing the same for an extreme out-of-distribution dataset
(backgrounds). Alongside synthetic-to-real 3D pose adaptation, the
joint-uncertainties allow expanding the adaptation to work on in-the-wild
images even in the presence of occlusion and truncation scenarios. We present a
comprehensive evaluation of the proposed approach and demonstrate
state-of-the-art performance on benchmark datasets.

Climate control of buildings makes up a significant portion of global energy
consumption, with groundwater heat pumps providing a suitable alternative. To
prevent possibly negative interactions between heat pumps throughout a city,
city planners have to optimize their layouts in the future. We develop a novel
data-driven approach for building small-scale surrogates for modelling the
thermal plumes generated by groundwater heat pumps in the surrounding
subsurface water. Building on a data set generated from 2D numerical
simulations, we train a convolutional neural network for predicting
steady-state subsurface temperature fields from a given subsurface velocity
field. We show that compared to existing models ours can capture more complex
dynamics while still being quick to compute. The resulting surrogate is thus
well-suited for interactive design tools by city planners.

Despite the growing interest in robot control utilizing the computation of
biological neurons, context-dependent behavior by neuron-connected robots
remains a challenge. Context-dependent behavior here is defined as behavior
that is not the result of a simple sensory-motor coupling, but rather based on
an understanding of the task goal. This paper proposes design principles for
training neuron-connected robots based on task goals to achieve
context-dependent behavior. First, we employ deep reinforcement learning (RL)
to enable training that accounts for goal achievements. Second, we propose a
neuron simulator as a probability distribution based on recorded neural data,
aiming to represent physiologically valid neural dynamics while avoiding
complex modeling with high computational costs. Furthermore, we propose to
update the simulators during the training to bridge the gap between the
simulation and the real settings. The experiments showed that the robot
gradually learned context-dependent behaviors in pole balancing and robot
navigation tasks. Moreover, the learned policies were valid for neural
simulators based on novel neural data, and the task performance increased by
updating the simulators during training. These results suggest the
effectiveness of the proposed design principle for the context-dependent
behavior of neuron-connected robots.

Code search is to search reusable code snippets from source code corpus based
on natural languages queries. Deep learning-based methods of code search have
shown promising results. However, previous methods focus on retrieval accuracy
but lacked attention to the efficiency of the retrieval process. We propose a
novel method CoSHC to accelerate code search with deep hashing and code
classification, aiming to perform an efficient code search without sacrificing
too much accuracy. To evaluate the effectiveness of CoSHC, we apply our method
to five code search models. Extensive experimental results indicate that
compared with previous code search baselines, CoSHC can save more than 90% of
retrieval time meanwhile preserving at least 99% of retrieval accuracy.

A variety of recent works have looked into defenses for deep neural networks
against adversarial attacks particularly within the image processing domain.
Speech processing applications such as automatic speech recognition (ASR) are
increasingly relying on deep learning models, and so are also prone to
adversarial attacks. However, many of the defenses explored for ASR simply
adapt the image-domain defenses, which may not provide optimal robustness. This
paper explores speech specific defenses using the mel spectral domain, and
introduces a novel defense method called 'mel domain noise flooding' (MDNF).
MDNF applies additive noise to the mel spectrogram of a speech utterance prior
to re-synthesising the audio signal. We test the defenses against strong
white-box adversarial attacks such as projected gradient descent (PGD) and
Carlini-Wagner (CW) attacks, and show better robustness compared to a
randomized smoothing baseline across strong threat models.

Bearing fault identification and analysis is an important research area in
the field of machinery fault diagnosis. Aiming at the common faults of rolling
bearings, we propose a data-driven diagnostic algorithm based on the
characteristics of bearing vibrations called multi-size kernel based adaptive
convolutional neural network (MSKACNN). Using raw bearing vibration signals as
the inputs, MSKACNN provides vibration feature learning and signal
classification capabilities to identify and analyze bearing faults. Ball mixing
is a ball bearing production quality problem that is difficult to identify
using traditional frequency domain analysis methods since it requires high
frequency resolutions of the measurement signals and results in a long
analyzing time. The proposed MSKACNN is shown to improve the efficiency and
accuracy of ball mixing diagnosis. To further demonstrate the effectiveness of
MSKACNN in bearing fault identification, a bearing vibration data acquisition
system was developed, and vibration signal acquisition was performed on rolling
bearings under five different fault conditions including ball mixing. The
resulting datasets were used to analyze the performance of our proposed model.
To validate the adaptive ability of MSKACNN, fault test data from the Case
Western Reserve University Bearing Data Center were also used. Test results
show that MSKACNN can identify the different bearing conditions with high
accuracy with high generalization ability. We presented an implementation of
the MSKACNN as a lightweight module for a real-time bearing fault diagnosis
system that is suitable for production.

Linear Programs (LP) are celebrated widely, particularly so in machine
learning where they have allowed for effectively solving probabilistic
inference tasks or imposing structure on end-to-end learning systems. Their
potential might seem depleted but we propose a foundational, causal perspective
that reveals intriguing intra- and inter-structure relations for LP components.
We conduct a systematic, empirical investigation on general-, shortest path-
and energy system LPs.

Traditional simultaneous localization and mapping (SLAM) methods focus on
improvement in the robot's localization under environment and sensor
uncertainty. This paper, however, focuses on mitigating the need for exact
localization of a mobile robot to pursue autonomous navigation using a sparse
set of images. The proposed method consists of a model architecture - RoomNet,
for unsupervised learning resulting in a coarse identification of the
environment and a separate local navigation policy for local identification and
navigation. The former learns and predicts the scene based on the short term
image sequences seen by the robot along with the transition image scenarios
using long term image sequences. The latter uses sparse image matching to
characterise the similarity of frames achieved vis-a-vis the frames viewed by
the robot during the mapping and training stage. A sparse graph of the image
sequence is created which is then used to carry out robust navigation purely on
the basis of visual goals. The proposed approach is evaluated on two robots in
a test environment and demonstrates the ability to navigate in dynamic
environments where landmarks are obscured and classical localization methods
fail.

Recent escalation in the field of computer vision underpins a huddle of
algorithms with the magnificent potential to unravel the information contained
within images. These computer vision algorithms are being practised in medical
image analysis and are transfiguring the perception and interpretation of
Imaging data. Among these algorithms, Vision Transformers are evolved as one of
the most contemporary and dominant architectures that are being used in the
field of computer vision. These are immensely utilized by a plenty of
researchers to perform new as well as former experiments. Here, in this article
we investigate the intersection of Vision Transformers and Medical images and
proffered an overview of various ViTs based frameworks that are being used by
different researchers in order to decipher the obstacles in Medical Computer
Vision. We surveyed the application of Vision transformers in different areas
of medical computer vision such as image-based disease classification,
anatomical structure segmentation, registration, region-based lesion Detection,
captioning, report generation, reconstruction using multiple medical imaging
modalities that greatly assist in medical diagnosis and hence treatment
process. Along with this, we also demystify several imaging modalities used in
Medical Computer Vision. Moreover, to get more insight and deeper
understanding, self-attention mechanism of transformers is also explained
briefly. Conclusively, we also put some light on available data sets, adopted
methodology, their performance measures, challenges and their solutions in form
of discussion. We hope that this review article will open future directions for
researchers in medical computer vision.

We investigate the importances of various dynamical features in predicting
the dynamical state (DS) of galaxy clusters, based on the Random Forest (RF)
machine learning approach. We use a large sample of galaxy clusters from the
Three Hundred Project of hydrodynamical zoomed-in simulations, and construct
dynamical features from the raw data as well as from the corresponding mock
maps in the optical, X-ray, and Sunyaev-Zel'dovich (SZ) channels. Instead of
relying on the impurity based feature importance of the RF algorithm, we
directly use the out-of-bag (OOB) scores to evaluate the importances of
individual features and different feature combinations. Among all the features
studied, we find the virial ratio, $\eta$, to be the most important single
feature. The features calculated directly from the simulations and in
3-dimensions carry more information on the DS than those constructed from the
mock maps. Compared with the features based on X-ray or SZ maps, features
related to the centroid positions are more important. Despite the large number
of investigated features, a combination of up to three features of different
types can already saturate the score of the prediction. Lastly, we show that
the most sensitive feature $\eta$ is strongly correlated with the well-known
half-mass bias in dynamical modelling. Without a selection in DS, cluster halos
have an asymmetric distribution in $\eta$, corresponding to an overall positive
half-mass bias. Our work provides a quantitative reference for selecting the
best features to discriminate the DS of galaxy clusters in both simulations and

We consider the problem of testing for a difference in means between clusters
of observations identified via k-means clustering. In this setting, classical
hypothesis tests lead to an inflated Type I error rate. To overcome this
problem, we take a selective inference approach. We propose a finite-sample
p-value that controls the selective Type I error for a test of the difference
in means between a pair of clusters obtained using k-means clustering, and show
that it can be efficiently computed. We apply our proposal in simulation, and
on hand-written digits data and single-cell RNA-sequencing data.

We show that any memory-constrained, first-order algorithm which minimizes
$d$-dimensional, $1$-Lipschitz convex functions over the unit ball to
$1/\mathrm{poly}(d)$ accuracy using at most $d^{1.25 - \delta}$ bits of memory
must make at least $\tilde{\Omega}(d^{1 + (4/3)\delta})$ first-order queries
(for any constant $\delta \in [0, 1/4]$). Consequently, the performance of such
memory-constrained algorithms are a polynomial factor worse than the optimal
$\tilde{O}(d)$ query bound for this problem obtained by cutting plane methods
that use $\tilde{O}(d^2)$ memory. This resolves a COLT 2019 open problem of
Woodworth and Srebro.

We present a novel framework to efficiently acquire near-planar anisotropic
reflectance in a pixel-independent fashion, using a deep gated
mixtureof-experts. While existing work employs a unified network to handle all
possible input, our network automatically learns to condition on the input for
enhanced reconstruction. We train a gating module to select one out of a number
of specialized decoders for reflectance reconstruction, based on photometric
measurements, essentially trading generality for quality. A common, pre-trained
latent transform module is also appended to each decoder, to offset the burden
of the increased number of decoders. In addition, the illumination conditions
during acquisition can be jointly optimized. The effectiveness of our framework
is validated on a wide variety of challenging samples using a near-field
lightstage. Compared with the state-of-the-art technique, our results are
improved at the same input bandwidth, and our bandwidth can be reduced to about
1/3 for equal-quality results.

Human voice is the source of several important information. This is in the
form of features. These Features help in interpreting various features
associated with the speaker and speech. The speaker dependent work
researchersare targeted towards speaker identification, Speaker verification,
speaker biometric, forensics using feature, and cross-modal matching via speech
and face images. In such context research, it is a very difficult task to come
across clean, and well annotated publicly available speech corpus as data set.
Acquiring volunteers to generate such dataset is also very expensive, not to
mention the enormous amount of effort and time researchers spend to gather such
data. The present paper work, a Neural Network proposal as NeuraGen focused
which is a low-resource ANN architecture. The proposed tool used to classify
gender of the speaker from the speech recordings. We have used speech
recordings collected from the ELSDSR and limited TIMIT datasets, from which we
extracted 8 speech features, which were pre-processed and then fed into
NeuraGen to identify the gender. NeuraGen has successfully achieved accuracy of
90.7407% and F1 score of 91.227% in train and 20-fold cross validation dataset.

Identification of the mechanically exfoliated graphene flakes and
classification of the thickness is important in the nanomanufacturing of
next-generation materials and devices that overcome the bottleneck of Moore's
Law. Currently, identification and classification of exfoliated graphene flakes
are conducted by human via inspecting the optical microscope images. The
existing state-of-the-art automatic identification by machine learning is not
able to accommodate images with different backgrounds while different
backgrounds are unavoidable in experiments. This paper presents a deep learning
method to automatically identify and classify the thickness of exfoliated
graphene flakes on Si/SiO2 substrates from optical microscope images with
various settings and background colors. The presented method uses a
hierarchical deep convolutional neural network that is capable of learning new
images while preserving the knowledge from previous images. The deep learning
model was trained and used to classify exfoliated graphene flakes into
monolayer (1L), bi-layer (2L), tri-layer (3L), four-to-six-layer (4-6L),
seven-to-ten-layer (7-10L), and bulk categories. Compared with existing machine
learning methods, the presented method possesses high accuracy and efficiency
as well as robustness to the backgrounds and resolutions of images. The results
indicated that our deep learning model has accuracy as high as 99% in
identifying and classifying exfoliated graphene flakes. This research will shed
light on scaled-up manufacturing and characterization of graphene for advanced
materials and devices.

Automatic surgical scene segmentation is fundamental for facilitating
cognitive intelligence in the modern operating theatre. Previous works rely on
conventional aggregation modules (e.g., dilated convolution, convolutional
LSTM), which only make use of the local context. In this paper, we propose a
novel framework STswinCL that explores the complementary intra- and inter-video
relations to boost segmentation performance, by progressively capturing the
global context. We firstly develop a hierarchy Transformer to capture
intra-video relation that includes richer spatial and temporal cues from
neighbor pixels and previous frames. A joint space-time window shift scheme is
proposed to efficiently aggregate these two cues into each pixel embedding.
Then, we explore inter-video relation via pixel-to-pixel contrastive learning,
which well structures the global embedding space. A multi-source contrast
training objective is developed to group the pixel embeddings across videos
with the ground-truth guidance, which is crucial for learning the global
property of the whole data. We extensively validate our approach on two public
surgical video benchmarks, including EndoVis18 Challenge and CaDIS dataset.
Experimental results demonstrate the promising performance of our method, which
consistently exceeds previous state-of-the-art approaches. Code is available at
https://github.com/YuemingJin/STswinCL.

The use of Automatic speech recognition (ASR) interfaces have become
increasingly popular in daily life for use in interaction and control of
electronic devices. The interfaces currently being used are not feasible for a
variety of users such as those suffering from a speech disorder, locked-in
syndrome, paralysis or people with utmost privacy requirements. In such cases,
an interface that can identify envisioned speech using electroencephalogram
(EEG) signals can be of great benefit. Various works targeting this problem
have been done in the past. However, there has been limited work in identifying
the frequency bands ($\delta, \theta, \alpha, \beta, \gamma$) of the EEG signal
that contribute towards envisioned speech recognition. Therefore, in this work,
we aim to analyze the significance of different EEG frequency bands and signals
obtained from different lobes of the brain and their contribution towards
recognizing envisioned speech. Signals obtained from different lobes and
bandpass filtered for different frequency bands are fed to a spatio-temporal
deep learning architecture with Convolutional Neural Network (CNN) and Long
Short-Term Memory (LSTM). The performance is evaluated on a publicly available
dataset comprising of three classification tasks - digit, character and images.
We obtain a classification accuracy of $85.93\%$, $87.27\%$ and $87.51\%$ for
the three tasks respectively. The code for the implementation has been made
available at https://github.com/ayushayt/ImaginedSpeechRecognition.

In this paper, we present Multi-scale Feature Aggregation Conformer
(MFA-Conformer), an easy-to-implement, simple but effective backbone for
automatic speaker verification based on the Convolution-augmented Transformer
(Conformer). The architecture of the MFA-Conformer is inspired by recent
stateof-the-art models in speech recognition and speaker verification. Firstly,
we introduce a convolution subsampling layer to decrease the computational cost
of the model. Secondly, we adopt Conformer blocks which combine Transformers
and convolution neural networks (CNNs) to capture global and local features
effectively. Finally, the output feature maps from all Conformer blocks are
concatenated to aggregate multi-scale representations before final pooling. We
evaluate the MFA-Conformer on the widely used benchmarks. The best system
obtains 0.64%, 1.29% and 1.63% EER on VoxCeleb1-O, SITW.Dev, and SITW.Eval set,
respectively. MFA-Conformer significantly outperforms the popular ECAPA-TDNN
systems in both recognition performance and inference speed. Last but not the
least, the ablation studies clearly demonstrate that the combination of global
and local feature learning can lead to robust and accurate speaker embedding
extraction. We have also released the code for future comparison.

Deep neural networks for 3D point cloud classification, such as PointNet,
have been demonstrated to be vulnerable to adversarial attacks. Current
adversarial defenders often learn to denoise the (attacked) point clouds by
reconstruction, and then feed them to the classifiers as input. In contrast to
the literature, we propose a family of robust structured declarative
classifiers for point cloud classification, where the internal constrained
optimization mechanism can effectively defend adversarial attacks through
implicit gradients. Such classifiers can be formulated using a bilevel
optimization framework. We further propose an effective and efficient
instantiation of our approach, namely, Lattice Point Classifier (LPC), based on
structured sparse coding in the permutohedral lattice and 2D convolutional
neural networks (CNNs) that is end-to-end trainable. We demonstrate
state-of-the-art robust point cloud classification performance on ModelNet40
and ScanNet under seven different attackers. For instance, we achieve 89.51%
and 83.16% test accuracy on each dataset under the recent JGBA attacker that
outperforms DUP-Net and IF-Defense with PointNet by ~70%. Demo code is
available at https://zhang-vislab.github.io.

In this paper we propose augmenting Vision Transformer models with learnable
memory tokens. Our approach allows the model to adapt to new tasks, using few
parameters, while optionally preserving its capabilities on previously learned
tasks. At each layer we introduce a set of learnable embedding vectors that
provide contextual information useful for specific datasets. We call these
"memory tokens". We show that augmenting a model with just a handful of such
tokens per layer significantly improves accuracy when compared to conventional
head-only fine-tuning, and performs only slightly below the significantly more
expensive full fine-tuning. We then propose an attention-masking approach that
enables extension to new downstream tasks, with a computation reuse. In this
setup in addition to being parameters efficient, models can execute both old
and new tasks as a part of single inference at a small incremental cost.

Recent image-to-image translation works have been transferred from supervised
to unsupervised settings due to the expensive cost of capturing or labeling
large amounts of paired data. However, current unsupervised methods using the
cycle-consistency constraint may not find the desired mapping, especially for
difficult translation tasks. On the other hand, a small number of paired data
are usually accessible. We therefore introduce a general framework for
semi-supervised image translation. Unlike previous works, our main idea is to
learn the translation over the latent feature space instead of the image space.
Thanks to the low dimensional feature space, it is easier to find the desired
mapping function, resulting in improved quality of translation results as well
as the stability of the translation model. Empirically we show that using
feature translation generates better results, even using a few bits of paired
data. Experimental comparisons with state-of-the-art approaches demonstrate the
effectiveness of the proposed framework on a variety of challenging
image-to-image translation tasks

We present a framework for gesture customization requiring minimal examples
from users, all without degrading the performance of existing gesture sets. To
achieve this, we first deployed a large-scale study (N=500+) to collect data
and train an accelerometer-gyroscope recognition model with a cross-user
accuracy of 95.7% and a false-positive rate of 0.6 per hour when tested on
everyday non-gesture data. Next, we design a few-shot learning framework which
derives a lightweight model from our pre-trained model, enabling knowledge
transfer without performance degradation. We validate our approach through a
user study (N=20) examining on-device customization from 12 new gestures,
resulting in an average accuracy of 55.3%, 83.1%, and 87.2% on using one,
three, or five shots when adding a new gesture, while maintaining the same
recognition accuracy and false-positive rate from the pre-existing gesture set.
We further evaluate the usability of our real-time implementation with a user
experience study (N=20). Our results highlight the effectiveness, learnability,
and usability of our customization framework. Our approach paves the way for a
future where users are no longer bound to pre-existing gestures, freeing them
to creatively introduce new gestures tailored to their preferences and
abilities.

We study the problem of identifying the best arm in a multi-armed bandit
environment when each arm is a time-homogeneous and ergodic discrete-time
Markov process on a common, finite state space. The state evolution on each arm
is governed by the arm's transition probability matrix (TPM). A decision entity
that knows the set of arm TPMs but not the exact mapping of the TPMs to the
arms, wishes to find the index of the best arm as quickly as possible, subject
to an upper bound on the error probability. The decision entity selects one arm
at a time sequentially, and all the unselected arms continue to undergo state
evolution ({\em restless} arms). For this problem, we derive the first-known
problem instance-dependent asymptotic lower bound on the growth rate of the
expected time required to find the index of the best arm, where the asymptotics
is as the error probability vanishes. Further, we propose a sequential policy
that, for an input parameter $R$, forcibly selects an arm that has not been
selected for $R$ consecutive time instants. We show that this policy achieves
an upper bound that depends on $R$ and is monotonically non-increasing as
$R\to\infty$. The question of whether, in general, the limiting value of the
upper bound as $R\to\infty$ matches with the lower bound, remains open. We
identify a special case in which the upper and the lower bounds match. Prior
works on best arm identification have dealt with (a) independent and
identically distributed observations from the arms, and (b) rested Markov arms,
whereas our work deals with the more difficult setting of restless Markov arms.

We propose a framework that can deform an object in a 2D image as it exists
in 3D space. Most existing methods for 3D-aware image manipulation are limited
to (1) only changing the global scene information or depth, or (2) manipulating
an object of specific categories. In this paper, we present a 3D-aware image
deformation method with minimal restrictions on shape category and deformation
type. While our framework leverages 2D-to-3D reconstruction, we argue that
reconstruction is not sufficient for realistic deformations due to the
vulnerability to topological errors. Thus, we propose to take a supervised
learning-based approach to predict the shape Laplacian of the underlying volume
of a 3D reconstruction represented as a point cloud. Given the deformation
energy calculated using the predicted shape Laplacian and user-defined
deformation handles (e.g., keypoints), we obtain bounded biharmonic weights to
model plausible handle-based image deformation. In the experiments, we present
our results of deforming 2D character and clothed human images. We also
quantitatively show that our approach can produce more accurate deformation
weights compared to alternative methods (i.e., mesh reconstruction and point
cloud Laplacian methods).

Pooling multiple neuroimaging datasets across institutions often enables
improvements in statistical power when evaluating associations (e.g., between
risk factors and disease outcomes) that may otherwise be too weak to detect.
When there is only a {\em single} source of variability (e.g., different
scanners), domain adaptation and matching the distributions of representations
may suffice in many scenarios. But in the presence of {\em more than one}
nuisance variable which concurrently influence the measurements, pooling
datasets poses unique challenges, e.g., variations in the data can come from
both the acquisition method as well as the demographics of participants
(gender, age). Invariant representation learning, by itself, is ill-suited to
fully model the data generation process. In this paper, we show how bringing
recent results on equivariant representation learning (for studying symmetries
in neural networks) instantiated on structured spaces together with simple use
of classical results on causal inference provides an effective practical
solution. In particular, we demonstrate how our model allows dealing with more
than one nuisance variable under some assumptions and can enable analysis of
pooled scientific datasets in scenarios that would otherwise entail removing a
large portion of the samples.

Polygonal modeling is a core task of content creation in Computer Graphics.
The complexity of modeling, in terms of the number and the order of operations
and time required to execute them makes it challenging to learn and execute.
Our goal is to automatically derive a polygonal modeling sequence for a given
target. Then, one can learn polygonal modeling by observing the resulting
sequence and also expedite the modeling process by starting from the
auto-generated result. As a starting point for building a system for 3D
modeling in the future, we tackle the 2D shape modeling problem and present
AutoPoly, a hybrid method that generates a polygonal mesh construction sequence
from a silhouette image. The key idea of our method is the use of the Monte
Carlo tree search (MCTS) algorithm and differentiable rendering to separately
predict sequential topological actions and geometric actions. Our hybrid method
can alter topology, whereas the recently proposed inverse shape estimation
methods using differentiable rendering can only handle a fixed topology. Our
novel reward function encourages MCTS to select topological actions that lead
to a simpler shape without self-intersection. We further designed two deep
learning-based methods to improve the expansion and simulation steps in the
MCTS search process: an $n$-step "future action prediction" network (nFAP-Net)
to generate candidates for potential topological actions, and a shape warping
network (WarpNet) to predict polygonal shapes given the predicted rendered
images and topological actions. We demonstrate the efficiency of our method on
2D polygonal shapes of multiple man-made object categories.

Adversarial attacks perturb images such that a deep neural network produces
incorrect classification results. A promising approach to defend against
adversarial attacks on natural multi-object scenes is to impose a
context-consistency check, wherein, if the detected objects are not consistent
with an appropriately defined context, then an attack is suspected. Stronger
attacks are needed to fool such context-aware detectors. We present the first
approach for generating context-consistent adversarial attacks that can evade
the context-consistency check of black-box object detectors operating on
complex, natural scenes. Unlike many black-box attacks that perform repeated
attempts and open themselves to detection, we assume a "zero-query" setting,
where the attacker has no knowledge of the classification decisions of the
victim system. First, we derive multiple attack plans that assign incorrect
labels to victim objects in a context-consistent manner. Then we design and use
a novel data structure that we call the perturbation success probability
matrix, which enables us to filter the attack plans and choose the one most
likely to succeed. This final attack plan is implemented using a
perturbation-bounded adversarial attack algorithm. We compare our zero-query
attack against a few-query scheme that repeatedly checks if the victim system
is fooled. We also compare against state-of-the-art context-agnostic attacks.
Against a context-aware defense, the fooling rate of our zero-query approach is
significantly higher than context-agnostic approaches and higher than that
achievable with up to three rounds of the few-query scheme.

Scientists at the Berkeley SETI Research Center are Searching for
Extraterrestrial Intelligence (SETI) by a new signal detection method that
converts radio signals into spectrograms through Fourier transforms and
classifies signals represented by two-dimensional time-frequency spectrums,
which successfully converts a signal classification problem into an image
classification task. In view of the negative impact of background noises on the
accuracy of spectrograms classification, a new method is introduced in this
paper. After Gaussian convolution smoothing the signals, edge detection
functions are applied to detect the edge of the signals and enhance the outline
of the signals, then the processed spectrograms are used to train the deep
neural network to compare the classification accuracy of various image
classification networks. The results show that the proposed method can
effectively improve the classification accuracy of SETI spectrums.

While prior works have investigated and developed computational models
capable of object detection, models still struggle to reliably interpret images
with motion blur and small objects. Moreover, none of these models are
specifically designed for handheld object detection. In this work, we present
SHOP (Small Handheld Object Pipeline), a pipeline that reliably and efficiently
interprets blurry images containing handheld objects. The specific models used
in each stage of the pipeline are flexible and can be changed based on
performance requirements. First, images are deblurred and then run through a
pose detection system where areas-of-interest are proposed around the hands of
any people present. Next, object detection is performed on the images by a
single-stage object detector. Finally, the proposed areas-of-interest are used
to filter out low confidence detections. Testing on a handheld subset of
Microsoft Common Objects in Context (MS COCO) demonstrates that this 3 stage
process results in a 70 percent decrease in false positives while only reducing
true positives by 17 percent in its strongest configuration. We also present a
subset of MS COCO consisting solely of handheld objects that can be used to
continue the development of handheld object detection methods.
https://github.com/spider-sense/SHOP

Motivated by previous findings that the magnitude gap between certain
satellite galaxy and the central galaxy can be used to improve the estimation
of halo mass, we carry out a systematic study of the information content of
different member galaxies in the modelling of the host halo mass using a
machine learning approach. We employ data from the hydrodynamical simulation
IllustrisTNG and train a Random Forest (RF) algorithm to predict a halo mass
from the stellar masses of its member galaxies. Exhaustive feature selection is
adopted to disentangle the importances of different galaxy members. We confirm
that an additional satellite does improve the halo mass estimation compared to
that estimated by the central alone. However, the magnitude of this improvement
does not differ significantly using different satellite galaxies. When three
galaxies are used in the halo mass prediction, the best combination is always
that of the central galaxy with the most massive satellite and the smallest
satellite. Furthermore, among the top 7 galaxies, the combination of a central
galaxy and two or three satellite galaxies gives a near-optimal estimation of
halo mass, and further addition of galaxies does not raise the precision of the
prediction. We demonstrate that these dependences can be understood from the
shape variation of the conditional satellite distribution, with different
member galaxies accounting for distinct halo-dependent features in different
parts of the cumulative stellar mass function.

Recently, transformer-based methods have achieved promising progresses in
object detection, as they can eliminate the post-processes like NMS and enrich
the deep representations. However, these methods cannot well cope with scene
text due to its extreme variance of scales and aspect ratios. In this paper, we
present a simple yet effective transformer-based architecture for scene text
detection. Different from previous approaches that learn robust deep
representations of scene text in a holistic manner, our method performs scene
text detection based on a few representative features, which avoids the
disturbance by background and reduces the computational cost. Specifically, we
first select a few representative features at all scales that are highly
relevant to foreground text. Then, we adopt a transformer for modeling the
relationship of the sampled features, which effectively divides them into
reasonable groups. As each feature group corresponds to a text instance, its
bounding box can be easily obtained without any post-processing operation.
Using the basic feature pyramid network for feature extraction, our method
consistently achieves state-of-the-art results on several popular datasets for
scene text detection.

We investigate the quantum effect on machine learning (ML) models exemplified
by the Generative Adversarial Network (GAN), which is a promising deep learning
framework. In the general GAN framework the generator maps uniform noise to a
fake image. In this study, we utilize the Associative Adversarial Network
(AAN), which consists of a standard GAN and an associative memory. Further, we
set a Boltzmann Machine (BM), which is an undirected graphical model that
learns low-dimensional features extracted from a discriminator, as the memory.
Owing to difficulty calculating the BM's log-likelihood gradient, it is
necessary to approximate it by using the sample mean obtained from the BM,
which has tentative parameters. To calculate the sample mean, a Markov Chain
Monte Carlo (MCMC) is often used. In a previous study, this was performed using
a quantum annealer device, and the performance of the "Quantum" AAN was
compared to that of the standard GAN. However, its better performance than the
standard GAN is not well understood. In this study, we introduce two methods to
draw samples: classical sampling via MCMC and quantum sampling via quantum
Monte Carlo (QMC) simulation, which is quantum simulation on the classical
computer. Then, we compare these methods to investigate whether quantum
sampling is advantageous. Specifically, calculating the discriminator loss, the
generator loss, inception score and Fr\'echet inception distance, we discuss
the possibility of AAN. We show that the AANs trained by both MCMC and QMC are
more stable during training and produce more varied images than the standard
GANs. However, the results indicate no difference in sampling by QMC simulation
compared to that by MCMC.

Affine registration is indispensable in a comprehensive medical image
registration pipeline. However, only a few studies focus on fast and robust
affine registration algorithms. Most of these studies utilize convolutional
neural networks (CNNs) to learn joint affine and non-parametric registration,
while the standalone performance of the affine subnetwork is less explored.
Moreover, existing CNN-based affine registration approaches focus either on the
local misalignment or the global orientation and position of the input to
predict the affine transformation matrix, which are sensitive to spatial
initialization and exhibit limited generalizability apart from the training
dataset. In this paper, we present a fast and robust learning-based algorithm,
Coarse-to-Fine Vision Transformer (C2FViT), for 3D affine medical image
registration. Our method naturally leverages the global connectivity and
locality of the convolutional vision transformer and the multi-resolution
strategy to learn the global affine registration. We evaluate our method on 3D
brain atlas registration and template-matching normalization. Comprehensive
results demonstrate that our method is superior to the existing CNNs-based
affine registration methods in terms of registration accuracy, robustness and
generalizability while preserving the runtime advantage of the learning-based
methods. The source code is available at https://github.com/cwmok/C2FViT.

This study presents the applicability of conventional deep recurrent neural
networks (RNN) to predict path-dependent plasticity associated with material
heterogeneity and anisotropy. Although the architecture of RNN possesses
inductive biases toward information over time, it is still challenging to learn
the path-dependent material behavior as a function of the loading path
considering the change from elastic to elastoplastic regimes. Our attempt is to
develop a simple machine-learning-based model that can replicate elastoplastic
behaviors considering material heterogeneity and anisotropy. The basic
Long-Short Term Memory Unit (LSTM) is adopted for the modeling of plasticity in
the two-dimensional space by enhancing the inductive bias toward the past
information through manipulating input variables. Our results find that a
single LSTM based model can capture the J2 plasticity responses under both
monotonic and arbitrary loading paths provided the material heterogeneity. The
proposed neural network architecture is then used to model elastoplastic
responses of a two-dimensional transversely anisotropic material associated
with computational homogenization (FE2). It is also found that a single LSTM
model can be used to accurately and effectively capture the path-dependent
responses of heterogeneous and anisotropic microstructures under arbitrary
mechanical loading conditions.

Weeds are a major threat to crops and are responsible for reducing crop yield
worldwide. To mitigate their negative effect, it is advantageous to accurately
identify them early in the season to prevent their spread throughout the field.
Traditionally, farmers rely on manually scouting fields and applying herbicides
for different weeds. However, it is easy to confuse between crops with weeds
during the early growth stages. Recently, deep learning-based weed
identification has become popular as deep learning relies on convolutional
neural networks that are capable of learning important distinguishable features
between weeds and crops. However, training robust deep learning models requires
access to large imagery datasets. Therefore, an early-season weeds dataset was
acquired under field conditions. The dataset consists of 159 Cocklebur images,
139 Foxtail images, 170 Redroot Pigweed images and 150 Giant Ragweed images
corresponding to four common weed species found in corn and soybean production
systems.. Bounding box annotations were created for each image to prepare the
dataset for training both image classification and object detection deep
learning networks capable of accurately locating and identifying weeds within
corn and soybean fields. (https://osf.io/w9v3j/)

To learn camera-view invariant features for person Re-IDentification (Re-ID),
the cross-camera image pairs of each person play an important role. However,
such cross-view training samples could be unavailable under the ISolated Camera
Supervised (ISCS) setting, e.g., a surveillance system deployed across distant
scenes. To handle this challenging problem, a new pipeline is introduced by
synthesizing the cross-camera samples in the feature space for model training.
Specifically, the feature encoder and generator are end-to-end optimized under
a novel method, Camera-Conditioned Stable Feature Generation (CCSFG). Its joint
learning procedure raises concern on the stability of generative model
training. Therefore, a new feature generator, $\sigma$-Regularized Conditional
Variational Autoencoder ($\sigma$-Reg.~CVAE), is proposed with theoretical and
experimental analysis on its robustness. Extensive experiments on two ISCS
person Re-ID datasets demonstrate the superiority of our CCSFG to the

This paper proposes a new eXplanation framework, called OrphicX, for
generating causal explanations for any graph neural networks (GNNs) based on
learned latent causal factors. Specifically, we construct a distinct generative
model and design an objective function that encourages the generative model to
produce causal, compact, and faithful explanations. This is achieved by
isolating the causal factors in the latent space of graphs by maximizing the
information flow measurements. We theoretically analyze the cause-effect
relationships in the proposed causal graph, identify node attributes as
confounders between graphs and GNN predictions, and circumvent such confounder
effect by leveraging the backdoor adjustment formula. Our framework is
compatible with any GNNs, and it does not require access to the process by
which the target GNN produces its predictions. In addition, it does not rely on
the linear-independence assumption of the explained features, nor require prior
knowledge on the graph learning tasks. We show a proof-of-concept of OrphicX on
canonical classification problems on graph data. In particular, we analyze the
explanatory subgraphs obtained from explanations for molecular graphs (i.e.,
Mutag) and quantitatively evaluate the explanation performance with frequently
occurring subgraph patterns. Empirically, we show that OrphicX can effectively
identify the causal semantics for generating causal explanations, significantly
outperforming its alternatives.

Efficient performance estimation of architectures drawn from large search
spaces is essential to Neural Architecture Search. One-Shot methods tackle this
challenge by training one supernet to approximate the performance of every
architecture in the search space via weight-sharing, thereby drastically
reducing the search cost. However, due to coupled optimization between child
architectures caused by weight-sharing, One-Shot supernet's performance
estimation could be inaccurate, leading to degraded search outcomes. To address
this issue, Few-Shot NAS reduces the level of weight-sharing by splitting the
One-Shot supernet into multiple separated sub-supernets via edge-wise
(layer-wise) exhaustive partitioning. Since each partition of the supernet is
not equally important, it necessitates the design of a more effective splitting
criterion. In this work, we propose a gradient matching score (GM) that
leverages gradient information at the shared weight for making informed
splitting decisions. Intuitively, gradients from different child models can be
used to identify whether they agree on how to update the shared modules, and
subsequently to decide if they should share the same weight. Compared with
exhaustive partitioning, the proposed criterion significantly reduces the
branching factor per edge. This allows us to split more edges (layers) for a
given budget, resulting in substantially improved performance as NAS search
spaces usually include dozens of edges (layers). Extensive empirical
evaluations of the proposed method on a wide range of search spaces
(NASBench-201, DARTS, MobileNet Space), datasets (cifar10, cifar100, ImageNet)
and search algorithms (DARTS, SNAS, RSPS, ProxylessNAS, OFA) demonstrate that
it significantly outperforms its Few-Shot counterparts while surpassing
previous comparable methods in terms of the accuracy of derived architectures.

Visual private information leakage is an emerging key issue for the fast
growing applications of video understanding like activity recognition. Existing
approaches for mitigating privacy leakage in action recognition require privacy
labels along with the action labels from the video dataset. However, annotating
frames of video dataset for privacy labels is not feasible. Recent developments
of self-supervised learning (SSL) have unleashed the untapped potential of the
unlabeled data. For the first time, we present a novel training framework which
removes privacy information from input video in a self-supervised manner
without requiring privacy labels. Our training framework consists of three main
components: anonymization function, self-supervised privacy removal branch, and
action recognition branch. We train our framework using a minimax optimization
strategy to minimize the action recognition cost function and maximize the
privacy cost function through a contrastive self-supervised loss. Employing
existing protocols of known-action and privacy attributes, our framework
achieves a competitive action-privacy trade-off to the existing
state-of-the-art supervised methods. In addition, we introduce a new protocol
to evaluate the generalization of learned the anonymization function to
novel-action and privacy attributes and show that our self-supervised framework
outperforms existing supervised methods. Code available at:
https://github.com/DAVEISHAN/SPAct

This paper studies a practical domain adaptive (DA) semantic segmentation
problem where only pseudo-labeled target data is accessible through a black-box
model. Due to the domain gap and label shift between two domains,
pseudo-labeled target data contains mixed closed-set and open-set label noises.
In this paper, we propose a simplex noise transition matrix (SimT) to model the
mixed noise distributions in DA semantic segmentation and formulate the problem
as estimation of SimT. By exploiting computational geometry analysis and
properties of segmentation, we design three complementary regularizers, i.e.
volume regularization, anchor guidance, convex guarantee, to approximate the
true SimT. Specifically, volume regularization minimizes the volume of simplex
formed by rows of the non-square SimT, which ensures outputs of segmentation
model to fit into the ground truth label distribution. To compensate for the
lack of open-set knowledge, anchor guidance and convex guarantee are devised to
facilitate the modeling of open-set noise distribution and enhance the
discriminative feature learning among closed-set and open-set classes. The
estimated SimT is further utilized to correct noise issues in pseudo labels and
promote the generalization ability of segmentation model on target domain data.
Extensive experimental results demonstrate that the proposed SimT can be
flexibly plugged into existing DA methods to boost the performance. The source
code is available at https://github.com/CityU-AIM-Group/SimT.

Reconstructing 3D shape from a single 2D image is a challenging task, which
needs to estimate the detailed 3D structures based on the semantic attributes
from 2D image. So far, most of the previous methods still struggle to extract
semantic attributes for 3D reconstruction task. Since the semantic attributes
of a single image are usually implicit and entangled with each other, it is
still challenging to reconstruct 3D shape with detailed semantic structures
represented by the input image. To address this problem, we propose 3DAttriFlow
to disentangle and extract semantic attributes through different semantic
levels in the input images. These disentangled semantic attributes will be
integrated into the 3D shape reconstruction process, which can provide definite
guidance to the reconstruction of specific attribute on 3D shape. As a result,
the 3D decoder can explicitly capture high-level semantic features at the
bottom of the network, and utilize low-level features at the top of the
network, which allows to reconstruct more accurate 3D shapes. Note that the
explicit disentangling is learned without extra labels, where the only
supervision used in our training is the input image and its corresponding 3D
shape. Our comprehensive experiments on ShapeNet dataset demonstrate that
3DAttriFlow outperforms the state-of-the-art shape reconstruction methods, and
we also validate its generalization ability on shape completion task.

Weakly-supervised temporal action localization aims to recognize and localize
action segments in untrimmed videos given only video-level action labels for
training. Without the boundary information of action segments, existing methods
mostly rely on multiple instance learning (MIL), where the predictions of
unlabeled instances (i.e., video snippets) are supervised by classifying
labeled bags (i.e., untrimmed videos). However, this formulation typically
treats snippets in a video as independent instances, ignoring the underlying
temporal structures within and across action segments. To address this problem,
we propose \system, a novel WTAL framework that enables explicit, action-aware
segment modeling beyond standard MIL-based methods. Our framework entails three
segment-centric components: (i) dynamic segment sampling for compensating the
contribution of short actions; (ii) intra- and inter-segment attention for
modeling action dynamics and capturing temporal dependencies; (iii) pseudo
instance-level supervision for improving action boundary prediction.
Furthermore, a multi-step refinement strategy is proposed to progressively
improve action proposals along the model training process. Extensive
experiments on THUMOS-14 and ActivityNet-v1.3 demonstrate the effectiveness of
our approach, establishing new state of the art on both datasets. The code and
models are publicly available at~\url{https://github.com/boheumd/ASM-Loc}.

Segmentation of images is a popular topic in medical AI. This is mainly due
to the difficulty to obtain a significant number of pixel-level annotated data
to train a neural network. To address this issue, we proposed a semi-supervised
segmentation network based on contrastive learning. In contrast to the previous
state-of-the-art, we introduce Min-Max Similarity (MMS), a contrastive learning
form of dual-view training by employing classifiers and projectors to build
all-negative, and positive and negative feature pairs respectively to formulate
the learning problem as solving min-max similarity problem. The all-negative
pairs are used to supervise the networks learning from different views and make
sure to capture general features, and the consistency of unlabeled predictions
is measured by pixel-wise contrastive loss between positive and negative pairs.
To quantitative and qualitative evaluate our proposed method, we test it on two
public endoscopy surgical tool segmentation datasets and one cochlear implant
surgery dataset which we manually annotate the cochlear implant in surgical
videos. The segmentation performance (dice coefficients) indicates that our
proposed method outperforms state-of-the-art semi-supervised and fully
supervised segmentation algorithms consistently. And our semi-supervised
segmentation algorithm can successfully recognize unknown surgical tools and
provide good predictions. Also, our MMS could achieve about 40 frames per
second (fps) and suitable to deal with the real-time video segmentation.

Conventional self-supervised monocular depth prediction methods are based on
a static environment assumption, which leads to accuracy degradation in dynamic
scenes due to the mismatch and occlusion problems introduced by object motions.
Existing dynamic-object-focused methods only partially solved the mismatch
problem at the training loss level. In this paper, we accordingly propose a
novel multi-frame monocular depth prediction method to solve these problems at
both the prediction and supervision loss levels. Our method, called
DynamicDepth, is a new framework trained via a self-supervised cycle consistent
learning scheme. A Dynamic Object Motion Disentanglement (DOMD) module is
proposed to disentangle object motions to solve the mismatch problem. Moreover,
novel occlusion-aware Cost Volume and Re-projection Loss are designed to
alleviate the occlusion effects of object motions. Extensive analyses and
experiments on the Cityscapes and KITTI datasets show that our method
significantly outperforms the state-of-the-art monocular depth prediction
methods, especially in the areas of dynamic objects. Code is available at
https://github.com/AutoAILab/DynamicDepth

The objective of this study is to address the problem of
background/foreground separation with missing pixels by combining the video
acquisition, video recovery, background/foreground separation into a single
framework. To achieve this, a smooth robust tensor completion (SRTC) model is
proposed to recover the data and decompose it into the static background and
smooth foreground, respectively. Specifically, the static background is modeled
by the low-rank tucker decomposition and the smooth foreground (moving objects)
is modeled by the spatiotemporal continuity, which is enforced by the total
variation regularization. An efficient algorithm based on tensor proximal
alternating minimization (tenPAM) is implemented to solve the proposed model
with global convergence guarantee under very mild conditions. Extensive
experiments on real data demonstrate that the proposed method significantly
outperforms the state-of-the-art approaches for background/foreground
separation with missing pixels.

Word embedding is a modern distributed word representations approach widely
used in many natural language processing tasks. Converting the vocabulary in a
legal document into a word embedding model facilitates subjecting legal
documents to machine learning, deep learning, and other algorithms and
subsequently performing the downstream tasks of natural language processing
vis-\`a-vis, for instance, document classification, contract review, and
machine translation. The most common and practical approach of accuracy
evaluation with the word embedding model uses a benchmark set with linguistic
rules or the relationship between words to perform analogy reasoning via
algebraic calculation. This paper proposes establishing a 1,134 Legal
Analogical Reasoning Questions Set (LARQS) from the 2,388 Chinese Codex corpus
using five kinds of legal relations, which are then used to evaluate the
accuracy of the Chinese word embedding model. Moreover, we discovered that
legal relations might be ubiquitous in the word embedding model.

Curriculum learning allows complex tasks to be mastered via incremental
progression over `stepping stone' goals towards a final desired behaviour.
Typical implementations learn locomotion policies for challenging environments
through gradual complexification of a terrain mesh generated through a
parameterised noise function. To date, researchers have predominantly generated
terrains from a limited range of noise functions, and the effect of the
generator on the learning process is underrepresented in the literature. We
compare popular noise-based terrain generators to two indirect encodings, CPPN
and GAN. To allow direct comparison between both direct and indirect
representations, we assess the impact of a range of representation-agnostic
MAP-Elites feature descriptors that compute metrics directly from the generated
terrain meshes. Next, performance and coverage are assessed when training a
humanoid robot in a physics simulator using the PPO algorithm. Results describe
key differences between the generators that inform their use in curriculum
learning, and present a range of useful feature descriptors for uptake by the

Exploiting light field data makes it possible to obtain dense and accurate
depth map. However, synthetic scenes with limited disparity range cannot
contain the diversity of real scenes. By training in synthetic data, current
learning-based methods do not perform well in real scenes. In this paper, we
propose a self-supervised learning framework for light field depth estimation.
Different from the existing end-to-end training methods using disparity label
per pixel, our approach implements network training by estimating EPI disparity
shift after refocusing, which extends the disparity range of epipolar lines. To
reduce the sensitivity of EPI to noise, we propose a new input mode called
EPI-Stack, which stacks EPIs in the view dimension. This method is less
sensitive to noise scenes than traditional input mode and improves the
efficiency of estimation. Compared with other state-of-the-art methods, the
proposed method can also obtain higher quality results in real-world scenarios,
especially in the complex occlusion and depth discontinuity.

Prostate cancer is the second leading cause of cancer death among men in the
United States. The diagnosis of prostate MRI often relies on the accurate
prostate zonal segmentation. However, state-of-the-art automatic segmentation
methods often fail to produce well-contained volumetric segmentation of the
prostate zones since certain slices of prostate MRI, such as base and apex
slices, are harder to segment than other slices. This difficulty can be
overcome by accounting for the cross-slice relationship of adjacent slices, but
current methods do not fully learn and exploit such relationships. In this
paper, we propose a novel cross-slice attention mechanism, which we use in a
Transformer module to systematically learn the cross-slice relationship at
different scales. The module can be utilized in any existing learning-based
segmentation framework with skip connections. Experiments show that our
cross-slice attention is able to capture the cross-slice information in
prostate zonal segmentation and improve the performance of current
state-of-the-art methods. Our method improves segmentation accuracy in the
peripheral zone, such that the segmentation results are consistent across all
the prostate slices (apex, mid-gland, and base).

One of important areas of machine learning research is zero-shot learning. It
is applied when properly labeled training data set is not available. A number
of zero-shot algorithms have been proposed and experimented with. However, none
of them seems to be the "overall winner". In situations like this, it may be
possible to develop a meta-classifier that would combine "best aspects" of
individual classifiers and outperform all of them. In this context, the goal of
this contribution is twofold. First, multiple state-of-the-art zero-shot
learning methods are compared for standard benchmark datasets. Second, multiple
meta-classifiers are suggested and experimentally compared (for the same
datasets).

Vision-based tactile sensors typically utilize a deformable elastomer and a
camera mounted above to provide high-resolution image observations of contacts.
Obtaining accurate volumetric meshes for the deformed elastomer can provide
direct contact information and benefit robotic grasping and manipulation. This
paper focuses on learning to synthesize the volumetric mesh of the elastomer
based on the image imprints acquired from vision-based tactile sensors.
Synthetic image-mesh pairs and real-world images are gathered from 3D finite
element methods (FEM) and physical sensors, respectively. A graph neural
network (GNN) is introduced to learn the image-to-mesh mappings with supervised
learning. A self-supervised adaptation method and image augmentation techniques
are proposed to transfer networks from simulation to reality, from primitive
contacts to unseen contacts, and from one sensor to another. Using these
learned and adapted networks, our proposed method can accurately reconstruct
the deformation of the real-world tactile sensor elastomer in various domains,
as indicated by the quantitative and qualitative results.

An overwhelming majority of the world's human population lives in urban areas
and cities. Understanding a city's transportation typology is immensely
valuable for planners and policy makers whose decisions can potentially impact
millions of city residents. Despite the value of understanding a city's
typology, labeled data (city and it's typology) is scarce, and spans at most a
few hundred cities in the current transportation literature. To break this
barrier, we propose a supervised machine learning approach to predict a city's
typology given the information in its Wikipedia page. Our method leverages
recent breakthroughs in natural language processing, namely sentence-BERT, and
shows how the text-based information from Wikipedia can be effectively used as
a data source for city typology prediction tasks that can be applied to over
2000 cities worldwide. We propose a novel method for low-dimensional city
representation using a city's Wikipedia page, which makes supervised learning
of city typology labels tractable even with a few hundred labeled samples.
These features are used with labeled city samples to train binary classifiers
(logistic regression) for four different city typologies: (i) congestion, (ii)
auto-heavy, (iii) transit-heavy, and (iv) bike-friendly cities resulting in
reasonably high AUC scores of 0.87, 0.86, 0.61 and 0.94 respectively. Our
approach provides sufficient flexibility for incorporating additional variables
in the city typology models and can be applied to study other city typologies
as well. Our findings can assist a diverse group of stakeholders in
transportation and urban planning fields, and opens up new opportunities for
using text-based information from Wikipedia (or similar platforms) as data
sources in such fields.

Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term
complication in diabetic patients associated with painful neuropathy, foot
ulceration and amputation. The Michigan neuropathy screening instrument (MNSI)
is one of the most common screening techniques for DSPN, however, it does not
provide any direct severity grading system. Method: For designing and modelling
the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology
of Diabetes Interventions and Complications (EDIC) clinical trials were used.
MNSI variables and patient outcomes were investigated using machine learning
tools to identify the features having higher association in DSPN
identification. A multivariable logistic regression-based nomogram was
generated and validated for DSPN severity grading. Results: The top-7 ranked
features from MNSI: 10-gm filament, Vibration perception (R), Vibration
perception (L), previous diabetic neuropathy, the appearance of deformities,
appearance of callus and appearance of fissure were identified as key features
for identifying DSPN using the extra tree model. The area under the curve (AUC)
of the nomogram for the internal and external datasets were 0.9421 and 0.946,
respectively. From the developed nomogram, the probability of having DSPN was
predicted and a DSPN severity scoring system for MNSI was developed from the
probability score. The model performance was validated on an independent
dataset. Patients were stratified into four severity levels: absent, mild,
moderate, and severe using a cut-off value of 10.5, 12.7 and 15 for a DSPN
probability less than 50%, 75% to 90%, and above 90%, respectively.
Conclusions: This study provides a simple, easy-to-use and reliable algorithm
for defining the prognosis and management of patients with DSPN.

We study the problem of learning nonparametric distributions in a finite
mixture, and establish tight bounds on the sample complexity for learning the
component distributions in such models. Namely, we are given i.i.d. samples
from a pdf $f$ where $$ f=\sum_{i=1}^k w_i f_i, \quad\sum_{i=1}^k w_i=1, \quad
w_i>0 $$ and we are interested in learning each component $f_i$. Without any
assumptions on $f_i$, this problem is ill-posed. In order to identify the
components $f_i$, we assume that each $f_i$ can be written as a convolution of
a Gaussian and a compactly supported density $\nu_i$ with
$\text{supp}(\nu_i)\cap \text{supp}(\nu_j)=\emptyset$.
  Our main result shows that $(\frac{1}{\varepsilon})^{\Omega(\log\log
\frac{1}{\varepsilon})}$ samples are required for estimating each $f_i$. Unlike
parametric mixtures, the difficulty does not arise from the order $k$ or small
weights $w_i$, and unlike nonparametric density estimation it does not arise
from the curse of dimensionality, irregularity, or inhomogeneity. The proof
relies on a fast rate for approximation with Gaussians, which may be of
independent interest. To show this is tight, we also propose an algorithm that
uses $(\frac{1}{\varepsilon})^{O(\log\log \frac{1}{\varepsilon})}$ samples to
estimate each $f_i$. Unlike existing approaches to learning latent variable
models based on moment-matching and tensor methods, our proof instead involves
a delicate analysis of an ill-conditioned linear system via orthogonal
functions. Combining these bounds, we conclude that the optimal sample
complexity of this problem properly lies in between polynomial and exponential,
which is not common in learning theory.

Recently, convolution-augmented transformer (Conformer) has achieved
promising performance in automatic speech recognition (ASR) and time-domain
speech enhancement (SE), as it can capture both local and global dependencies
in the speech signal. In this paper, we propose a conformer-based metric
generative adversarial network (CMGAN) for SE in the time-frequency (TF)
domain. In the generator, we utilize two-stage conformer blocks to aggregate
all magnitude and complex spectrogram information by modeling both time and
frequency dependencies. The estimation of magnitude and complex spectrogram is
decoupled in the decoder stage and then jointly incorporated to reconstruct the
enhanced speech. In addition, a metric discriminator is employed to further
improve the quality of the enhanced estimated speech by optimizing the
generator with respect to a corresponding evaluation score. Quantitative
analysis on Voice Bank+DEMAND dataset indicates the capability of CMGAN in
outperforming various previous models with a margin, i.e., PESQ of 3.41 and
SSNR of 11.10 dB.

In this paper, we introduce the task of language-queried audio source
separation (LASS), which aims to separate a target source from an audio mixture
based on a natural language query of the target source (e.g., "a man tells a
joke followed by people laughing"). A unique challenge in LASS is associated
with the complexity of natural language description and its relation with the
audio sources. To address this issue, we proposed LASS-Net, an end-to-end
neural network that is learned to jointly process acoustic and linguistic
information, and separate the target source that is consistent with the
language query from an audio mixture. We evaluate the performance of our
proposed system with a dataset created from the AudioCaps dataset. Experimental
results show that LASS-Net achieves considerable improvements over baseline
methods. Furthermore, we observe that LASS-Net achieves promising
generalization results when using diverse human-annotated descriptions as
queries, indicating its potential use in real-world scenarios. The separated
audio samples and source code are available at
https://liuxubo717.github.io/LASS-demopage.

Here I present an investigation on the evolution and use of vocabulary in
data science in the last 13 years. Based on a rigorous statistical analysis, a
database with 12,787 documents containing the words "data science" in the
title, abstract or keywords is analyzed. It is proposed to classify the
evolution of this discipline in three periods: emergence, growth and boom.
Characteristic words and pioneering documents are identified for each period.
By proposing the distinctive vocabulary and relevant topics of data science and
classified in time periods, these results add value to the scientific community
of this discipline.

We propose a new method for training a supervised source separation system
that aims to learn the interdependent relationships between all combinations of
sources in a mixture. Rather than independently estimating each source from a
mix, we reframe the source separation problem as an Orderless Neural
Autoregressive Density Estimator (NADE), and estimate each source from both the
mix and a random subset of the other sources. We adapt a standard source
separation architecture, Demucs, with additional inputs for each individual
source, in addition to the input mixture. We randomly mask these input sources
during training so that the network learns the conditional dependencies between
the sources. By pairing this training method with a block Gibbs sampling
procedure at inference time, we demonstrate that the network can iteratively
improve its separation performance by conditioning a source estimate on its
earlier source estimates. Experiments on two source separation datasets show
that training a Demucs model with an Orderless NADE approach and using Gibbs
sampling (up to 512 steps) at inference time strongly outperforms a Demucs
baseline that uses a standard regression loss and direct (one step) estimation
of sources.

We propose a novel architecture for depth estimation from a single image. The
architecture itself is based on the popular encoder-decoder architecture that
is frequently used as a starting point for all dense regression tasks. We build
on AdaBins which estimates a global distribution of depth values for the input
image and evolve the architecture in two ways. First, instead of predicting
global depth distributions, we predict depth distributions of local
neighborhoods at every pixel. Second, instead of predicting depth distributions
only towards the end of the decoder, we involve all layers of the decoder. We
call this new architecture LocalBins. Our results demonstrate a clear
improvement over the state-of-the-art in all metrics on the NYU-Depth V2
dataset. Code and pretrained models will be made publicly available.

The aim of this paper is to study how to apply deep reinforcement learning
for the control of aggregates of minimalistic robots. We define aggregates as
groups of robots with a physical connection that compels them to form a
specified shape. In our case, the robots are pre-attached to an object that
must be collectively transported to a known location. Minimalism, in our
setting, stems from the barebone capabilities we assume: The robots can sense
the target location and the immediate obstacles, but lack the means to
communicate explicitly through, e.g., message-passing. In our setting,
communication is implicit, i.e., mediated by aggregated push-and-pull on the
object exerted by each robot. We analyze the ability to reach coordinated
behavior of four well-known algorithms for deep reinforcement learning (DQN,
DDQN, DDPG, and TD3). Our experiments include robot failures and different
types of environmental obstacles. We compare the performance of the best
control strategies found, highlighting strengths and weaknesses of each of the
considered training algorithms.

Many transit agencies operating paratransit and microtransit services have to
respond to trip requests that arrive in real-time, which entails solving hard
combinatorial and sequential decision-making problems under uncertainty. To
avoid decisions that lead to significant inefficiency in the long term,
vehicles should be allocated to requests by optimizing a non-myopic utility
function or by batching requests together and optimizing a myopic utility
function. While the former approach is typically offline, the latter can be
performed online. We point out two major issues with such approaches when
applied to paratransit services in practice. First, it is difficult to batch
paratransit requests together as they are temporally sparse. Second, the
environment in which transit agencies operate changes dynamically (e.g.,
traffic conditions), causing estimates that are learned offline to become
stale. To address these challenges, we propose a fully online approach to solve
the dynamic vehicle routing problem (DVRP) with time windows and stochastic
trip requests that is robust to changing environmental dynamics by
construction. We focus on scenarios where requests are relatively sparse - our
problem is motivated by applications to paratransit services. We formulate DVRP
as a Markov decision process and use Monte Carlo tree search to evaluate
actions for any given state. Accounting for stochastic requests while
optimizing a non-myopic utility function is computationally challenging;
indeed, the action space for such a problem is intractably large in practice.
To tackle the large action space, we leverage the structure of the problem to
design heuristics that can sample promising actions for the tree search. Our
experiments using real-world data from our partner agency show that the
proposed approach outperforms existing state-of-the-art approaches both in
terms of performance and robustness.

Natural language-based communication with mobile devices and home appliances
is becoming increasingly popular and has the potential to become natural for
communicating with mobile robots in the future. Towards this goal, we
investigate cross-modal text-to-point-cloud localization that will allow us to
specify, for example, a vehicle pick-up or goods delivery location. In
particular, we propose Text2Pos, a cross-modal localization module that learns
to align textual descriptions with localization cues in a coarse- to-fine
manner. Given a point cloud of the environment, Text2Pos locates a position
that is specified via a natural language-based description of the immediate
surroundings. To train Text2Pos and study its performance, we construct
KITTI360Pose, the first dataset for this task based on the recently introduced
KITTI360 dataset. Our experiments show that we can localize 65% of textual
queries within 15m distance to query locations for top-10 retrieved locations.
This is a starting point that we hope will spark future developments towards
language-based navigation.

A common trait of current access control approaches is the challenging need
to engineer abstract and intuitive access control models. This entails
designing access control information in the form of roles (RBAC), attributes
(ABAC), or relationships (ReBAC) as the case may be, and subsequently,
designing access control rules. This framework has its benefits but has
significant limitations in the context of modern systems that are dynamic,
complex, and large-scale, due to which it is difficult to maintain an accurate
access control state in the system for a human administrator. This paper
proposes Deep Learning Based Access Control (DLBAC) by leveraging significant
advances in deep learning technology as a potential solution to this problem.
We envision that DLBAC could complement and, in the long-term, has the
potential to even replace, classical access control models with a neural
network that reduces the burden of access control model engineering and
updates. Without loss of generality, we conduct a thorough investigation of a
candidate DLBAC model, called DLBAC_alpha, using both real-world and synthetic
datasets. We demonstrate the feasibility of the proposed approach by addressing
issues related to accuracy, generalization, and explainability. We also discuss
challenges and future research directions.

3D object detection is a central task for applications such as autonomous
driving, in which the system needs to localize and classify surrounding traffic
agents, even in the presence of adverse weather. In this paper, we address the
problem of LiDAR-based 3D object detection under snowfall. Due to the
difficulty of collecting and annotating training data in this setting, we
propose a physically based method to simulate the effect of snowfall on real
clear-weather LiDAR point clouds. Our method samples snow particles in 2D space
for each LiDAR line and uses the induced geometry to modify the measurement for
each LiDAR beam accordingly. Moreover, as snowfall often causes wetness on the
ground, we also simulate ground wetness on LiDAR point clouds. We use our
simulation to generate partially synthetic snowy LiDAR data and leverage these
data for training 3D object detection models that are robust to snowfall. We
conduct an extensive evaluation using several state-of-the-art 3D object
detection methods and show that our simulation consistently yields significant
performance gains on the real snowy STF dataset compared to clear-weather
baselines and competing simulation approaches, while not sacrificing
performance in clear weather. Our code is available at
www.github.com/SysCV/LiDAR_snow_sim.

A community of developers has formed to modernize the Fortran ecosystem. In
this article, we describe the high-level features of Fortran that continue to
make it a good choice for scientists and engineers in the 21st century. Ongoing
efforts include the development of a Fortran standard library and package
manager, the fostering of a friendly and welcoming online community, improved
compiler support, and language feature development. The lessons learned are
common across contemporary programming languages and help reduce the learning
curve and increase adoption of Fortran.

Deep speaker embedding extractors have already become new state-of-the-art
systems in the speaker verification field. However, the problem of verification
score calibration for such systems often remains out of focus. An irrelevant
score calibration leads to serious issues, especially in the case of unknown
acoustic conditions, even if we use a strong speaker verification system in
terms of threshold-free metrics. This paper presents an investigation over
several methods of score calibration: a classical approach based on the
logistic regression model; the recently presented magnitude estimation network
MagnetO that uses activations from the pooling layer of the trained deep
speaker extractor and generalization of such approach based on separate scale
and offset prediction neural networks. An additional focus of this research is
to estimate the impact of score normalization on the calibration performance of
the system. The obtained results demonstrate that there are no serious problems
if in-domain development data are used for calibration tuning. Otherwise, a
trade-off between good calibration performance and threshold-free system
quality arises. In most cases using adaptive s-norm helps to stabilize score
distributions and to improve system performance. Meanwhile, some experiments
demonstrate that novel approaches have their limits in score stabilization on
several datasets.

Federated learning is a framework for distributed optimization that places
emphasis on communication efficiency. In particular, it follows a client-server
broadcast model and is particularly appealing because of its ability to
accommodate heterogeneity in client compute and storage resources, non-i.i.d.
data assumptions, and data privacy. Our contribution is to offer a new
federated learning algorithm, FedADMM, for solving non-convex composite
optimization problems with non-smooth regularizers. We prove converges of
FedADMM for the case when not all clients are able to participate in a given
communication round under a very general sampling model.

Training a high-dimensional simulated agent with an under-specified reward
function often leads the agent to learn physically infeasible strategies that
are ineffective when deployed in the real world. To mitigate these unnatural
behaviors, reinforcement learning practitioners often utilize complex reward
functions that encourage physically plausible behaviors. However, a tedious
labor-intensive tuning process is often required to create hand-designed
rewards which might not easily generalize across platforms and tasks. We
propose substituting complex reward functions with "style rewards" learned from
a dataset of motion capture demonstrations. A learned style reward can be
combined with an arbitrary task reward to train policies that perform tasks
using naturalistic strategies. These natural strategies can also facilitate
transfer to the real world. We build upon Adversarial Motion Priors -- an
approach from the computer graphics domain that encodes a style reward from a
dataset of reference motions -- to demonstrate that an adversarial approach to
training policies can produce behaviors that transfer to a real quadrupedal
robot without requiring complex reward functions. We also demonstrate that an
effective style reward can be learned from a few seconds of motion capture data
gathered from a German Shepherd and leads to energy-efficient locomotion
strategies with natural gait transitions.

Prevalent semantic segmentation solutions, despite their different network
designs (FCN based or attention based) and mask decoding strategies (parametric
softmax based or pixel-query based), can be placed in one category, by
considering the softmax weights or query vectors as learnable class prototypes.
In light of this prototype view, this study uncovers several limitations of
such parametric segmentation regime, and proposes a nonparametric alternative
based on non-learnable prototypes. Instead of prior methods learning a single
weight/query vector for each class in a fully parametric manner, our model
represents each class as a set of non-learnable prototypes, relying solely on
the mean features of several training pixels within that class. The dense
prediction is thus achieved by nonparametric nearest prototype retrieving. This
allows our model to directly shape the pixel embedding space, by optimizing the
arrangement between embedded pixels and anchored prototypes. It is able to
handle arbitrary number of classes with a constant amount of learnable
parameters. We empirically show that, with FCN based and attention based
segmentation models (i.e., HRNet, Swin, SegFormer) and backbones (i.e., ResNet,
HRNet, Swin, MiT), our nonparametric framework yields compelling results over
several datasets (i.e., ADE20K, Cityscapes, COCO-Stuff), and performs well in
the large-vocabulary situation. We expect this work will provoke a rethink of
the current de facto semantic segmentation model design.

We present an analysis of the performance of Federated Learning in a
paradigmatic natural-language processing task: Named-Entity Recognition (NER).
For our evaluation, we use the language-independent CoNLL-2003 dataset as our
benchmark dataset and a Bi-LSTM-CRF model as our benchmark NER model. We show
that federated training reaches almost the same performance as the centralized
model, though with some performance degradation as the learning environments
become more heterogeneous. We also show the convergence rate of federated
models for NER. Finally, we discuss existing challenges of Federated Learning
for NLP applications that can foster future research directions.

Existing works show that although modern neural networks achieve remarkable
generalization performance on the in-distribution (ID) dataset, the accuracy
drops significantly on the out-of-distribution (OOD) datasets
\cite{recht2018cifar, recht2019imagenet}. To understand why a variety of models
consistently make more mistakes in the OOD datasets, we propose a new metric to
quantify the difficulty of the test images (either ID or OOD) that depends on
the interaction of the training dataset and the model. In particular, we
introduce \textit{confusion score} as a label-free measure of image difficulty
which quantifies the amount of disagreement on a given test image based on the
class conditional probabilities estimated by an ensemble of trained models.
Using the confusion score, we investigate CIFAR-10 and its OOD derivatives.
Next, by partitioning test and OOD datasets via their confusion scores, we
predict the relationship between ID and OOD accuracies for various
architectures. This allows us to obtain an estimator of the OOD accuracy of a
given model only using ID test labels. Our observations indicate that the
biggest contribution to the accuracy drop comes from images with high confusion
scores. Upon further inspection, we report on the nature of the misclassified
images grouped by their confusion scores: \textit{(i)} images with high
confusion scores contain \textit{weak spurious correlations} that appear in
multiple classes in the training data and lack clear \textit{class-specific
features}, and \textit{(ii)} images with low confusion scores exhibit spurious
correlations that belong to another class, namely \textit{class-specific
spurious correlations}.

We present the efficient implementations of probabilistic deterministic
finite automaton learning methods available in FlexFringe. These implement
well-known strategies for state-merging including several modifications to
improve their performance in practice. We show experimentally that these
algorithms obtain competitive results and significant improvements over a
default implementation. We also demonstrate how to use FlexFringe to learn
interpretable models from software logs and use these for anomaly detection.
Although less interpretable, we show that learning smaller more convoluted
models improves the performance of FlexFringe on anomaly detection,
outperforming an existing solution based on neural nets.

Machine learning models such as Transformers or LSTMs struggle with tasks
that are compositional in nature such as those involving reasoning/inference.
Although many datasets exist to evaluate compositional generalization, when it
comes to evaluating inference abilities, options are more limited. This paper
presents LogicInference, a new dataset to evaluate the ability of models to
perform logical inference. The dataset focuses on inference using propositional
logic and a small subset of first-order logic, represented both in semi-formal
logical notation, as well as in natural language. We also report initial
results using a collection of machine learning models to establish an initial
baseline in this dataset.

Recent advances in unsupervised speech representation learning discover new
approaches and provide new state-of-the-art for diverse types of speech
processing tasks. This paper presents an investigation of using wav2vec 2.0
deep speech representations for the speaker recognition task. The proposed
fine-tuning procedure of wav2vec 2.0 with simple TDNN and statistic pooling
back-end using additive angular margin loss allows to obtain deep speaker
embedding extractor that is well-generalized across different domains. It is
concluded that Contrastive Predictive Coding pretraining scheme efficiently
utilizes the power of unlabeled data, and thus opens the door to powerful
transformer-based speaker recognition systems. The experimental results
obtained in this study demonstrate that fine-tuning can be done on relatively
small sets and a clean version of data. Using data augmentation during
fine-tuning provides additional performance gains in speaker verification. In
this study speaker recognition systems were analyzed on a wide range of
well-known verification protocols: VoxCeleb1 cleaned test set, NIST SRE 18
development set, NIST SRE 2016 and NIST SRE 2019 evaluation set, VOiCES
evaluation set, NIST 2021 SRE, and CTS challenges sets.

Singing voice separation aims to separate music into vocals and accompaniment
components. One of the major constraints for the task is the limited amount of
training data with separated vocals. Data augmentation techniques such as
random source mixing have been shown to make better use of existing data and
mildly improve model performance. We propose a novel data augmentation
technique, chromagram-based pitch-aware remixing, where music segments with
high pitch alignment are mixed. By performing controlled experiments in both
supervised and semi-supervised settings, we demonstrate that training models
with pitch-aware remixing significantly improves the test signal-to-distortion
ratio (SDR)

Background: Skin cancer is one of the widely seen cancer worldwide and
automatic classification of skin cancer can be benefited dermatology clinics
for an accurate diagnosis. Hence, a machine learning-based automatic skin
cancer detection model must be developed. Material and Method: This research
interests to overcome automatic skin cancer detection problem. A colored skin
cancer image dataset is used. This dataset contains 3297 images with two
classes. An automatic multilevel textural and deep features-based model is
presented. Multilevel fuse feature generation using discrete wavelet transform
(DWT), local phase quantization (LPQ), local binary pattern (LBP), pre-trained
DarkNet19, and DarkNet53 are utilized to generate features of the skin cancer
images, top 1000 features are selected threshold value-based neighborhood
component analysis (NCA). The chosen top 1000 features are classified using the
10-fold cross-validation technique. Results: To obtain results, ten-fold
cross-validation is used and 91.54% classification accuracy results are
obtained by using the recommended pyramidal hybrid feature generator and NCA
selector-based model. Further, various training and testing separation ratios
(90:10, 80:20, 70:30, 60:40, 50:50) are used and the maximum classification
rate is calculated as 95.74% using the 90:10 separation ratio. Conclusions: The
findings and accuracies calculated are denoted that this model can be used in
dermatology and pathology clinics to simplify the skin cancer detection process
and help physicians.

Self-supervised monocular depth estimation enables robots to learn 3D
perception from raw video streams. This scalable approach leverages projective
geometry and ego-motion to learn via view synthesis, assuming the world is
mostly static. Dynamic scenes, which are common in autonomous driving and
human-robot interaction, violate this assumption. Therefore, they require
modeling dynamic objects explicitly, for instance via estimating pixel-wise 3D
motion, i.e. scene flow. However, the simultaneous self-supervised learning of
depth and scene flow is ill-posed, as there are infinitely many combinations
that result in the same 3D point. In this paper we propose DRAFT, a new method
capable of jointly learning depth, optical flow, and scene flow by combining
synthetic data with geometric self-supervision. Building upon the RAFT
architecture, we learn optical flow as an intermediate task to bootstrap depth
and scene flow learning via triangulation. Our algorithm also leverages
temporal and geometric consistency losses across tasks to improve multi-task
learning. Our DRAFT architecture simultaneously establishes a new state of the
art in all three tasks in the self-supervised monocular setting on the standard
KITTI benchmark. Project page: https://sites.google.com/tri.global/draft.

In text-video retrieval, the objective is to learn a cross-modal similarity
function between a text and a video that ranks relevant text-video pairs higher
than irrelevant pairs. However, videos inherently express a much wider gamut of
information than texts. Instead, texts often capture sub-regions of entire
videos and are most semantically similar to certain frames within videos.
Therefore, for a given text, a retrieval model should focus on the text's most
semantically similar video sub-regions to make a more relevant comparison. Yet,
most existing works aggregate entire videos without directly considering text.
Common text-agnostic aggregations schemes include mean-pooling or
self-attention over the frames, but these are likely to encode misleading
visual information not described in the given text. To address this, we propose
a cross-modal attention model called X-Pool that reasons between a text and the
frames of a video. Our core mechanism is a scaled dot product attention for a
text to attend to its most semantically similar frames. We then generate an
aggregated video representation conditioned on the text's attention weights
over the frames. We evaluate our method on three benchmark datasets of MSR-VTT,
MSVD and LSMDC, achieving new state-of-the-art results by up to 12% in relative
improvement in Recall@1. Our findings thereby highlight the importance of joint
text-video reasoning to extract important visual cues according to text. Full
code and demo can be found at: https://layer6ai-labs.github.io/xpool/

Interventional effects have been proposed as a solution to the
unidentifiability of natural (in)direct effects under mediator-outcome
confounders affected by the exposure. Such confounders are an intrinsic
characteristic of studies with time-varying exposures and mediators, yet the
generalization of the interventional effect framework to the time-varying case
has received little attention in the literature. We present an identification
result for interventional effects in a general longitudinal data structure that
allows flexibility in the specification of treatment-outcome,
treatment-mediator, and mediator-outcome relationships. Identification is
achieved under the standard no-unmeasured-confounders and positivity
assumptions. We also present a theoretical and computational study of the
properties of the identifying functional based on the efficient influence
function (EIF). We use the EIF to propose a sequential regression estimation
algorithm that yields doubly robust, $\sqrt{n}$-consistent, asymptotically
Gaussian, and efficient estimators under slow convergence rates for the
regression algorithms used. This allows the use of flexible machine learning
for regression while permitting uncertainty quantification through confidence
intervals and p-values. A free and open source \texttt{R} package implementing
our proposed estimators is made available on GitHub. We apply the proposed
estimator to an application from a comparative effectiveness trial of two
medications for opioid use disorder. In the application, we estimate the extent
to which differences between the two treatments' on subsequent risk of opioid
use is mediated by craving symptoms.

Synthetic aperture sonar (SAS) systems produce high-resolution images of the
seabed environment. Moreover, deep learning has demonstrated superior ability
in finding robust features for automating imagery analysis. However, the
success of deep learning is conditioned on having lots of labeled training
data, but obtaining generous pixel-level annotations of SAS imagery is often
practically infeasible. This challenge has thus far limited the adoption of
deep learning methods for SAS segmentation. Algorithms exist to segment SAS
imagery in an unsupervised manner, but they lack the benefit of
state-of-the-art learning methods and the results present significant room for
improvement. In view of the above, we propose a new iterative algorithm for
unsupervised SAS image segmentation combining superpixel formation, deep
learning, and traditional clustering methods. We call our method Iterative Deep
Unsupervised Segmentation (IDUS). IDUS is an unsupervised learning framework
that can be divided into four main steps: 1) A deep network estimates class
assignments. 2) Low-level image features from the deep network are clustered
into superpixels. 3) Superpixels are clustered into class assignments (which we
call pseudo-labels) using $k$-means. 4) Resulting pseudo-labels are used for
loss backpropagation of the deep network prediction. These four steps are
performed iteratively until convergence. A comparison of IDUS to current
state-of-the-art methods on a realistic benchmark dataset for SAS image
segmentation demonstrates the benefits of our proposal even as the IDUS incurs
a much lower computational burden during inference (actual labeling of a test
image). Finally, we also develop a semi-supervised (SS) extension of IDUS
called IDSS and demonstrate experimentally that it can further enhance
performance while outperforming supervised alternatives that exploit the same
labeled training imagery.

Extracting rich phenotype information, such as cell density and arrangement,
from whole slide histology images (WSIs), requires analysis of large field of
view, i.e more contexual information. This can be achieved through analyzing
the digital slides at lower resolution. A potential drawback is missing out on
details present at a higher resolution. To jointly leverage complementary
information from multiple resolutions, we present a novel transformer based
Pyramidal Context-Detail Network (CD-Net). CD-Net exploits the WSI pyramidal
structure through co-training of proposed Context and Detail Modules, which
operate on inputs from multiple resolutions. The residual connections between
the modules enable the joint training paradigm while learning self-supervised
representation for WSIs. The efficacy of CD-Net is demonstrated in classifying
Lung Adenocarcinoma from Squamous cell carcinoma.

There are two main algorithmic approaches to autonomous driving systems: (1)
An end-to-end system in which a single deep neural network learns to map
sensory input directly into appropriate warning and driving responses. (2) A
mediated hybrid recognition system in which a system is created by combining
independent modules that detect each semantic feature. While some researchers
believe that deep learning can solve any problem, others believe that a more
engineered and symbolic approach is needed to cope with complex environments
with less data. Deep learning alone has achieved state-of-the-art results in
many areas, from complex gameplay to predicting protein structures. In
particular, in image classification and recognition, deep learning models have
achieved accuracies as high as humans. But sometimes it can be very difficult
to debug if the deep learning model doesn't work. Deep learning models can be
vulnerable and are very sensitive to changes in data distribution.
Generalization can be problematic. It's usually hard to prove why it works or
doesn't. Deep learning models can also be vulnerable to adversarial attacks.
Here, we combine deep learning-based object recognition and tracking with an
adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System
(NARS), that can adapt to its environment by building concepts based on
perceptual sequences. We achieved an improved intersection-over-union (IOU)
object recognition performance of 0.65 in the adaptive retraining model
compared to IOU 0.31 in the COCO data pre-trained model. We improved the object
detection limits using RADAR sensors in a simulated environment, and
demonstrated the weaving car detection capability by combining deep
learning-based object detection and tracking with a neurosymbolic model.

Lasso is a celebrated method for variable selection in linear models, but it
faces challenges when the variables are moderately or strongly correlated. This
motivates alternative approaches such as using a non-convex penalty, adding a
ridge regularization, or conducting a post-Lasso thresholding. In this paper,
we compare Lasso with 5 other methods: Elastic net, SCAD, forward selection,
thresholded Lasso, and forward backward selection. We measure their
performances theoretically by the expected Hamming error, assuming that the
regression coefficients are iid drawn from a two-point mixture and that the
Gram matrix is block-wise diagonal. By deriving the rates of convergence of
Hamming errors and the phase diagrams, we obtain useful conclusions about the
pros and cons of different methods.

AI solutions are heavily dependant on the quality and accuracy of the input
training data, however the training data may not always fully reflect the most
up-to-date policy landscape or may be missing business logic. The advances in
explainability have opened the possibility of allowing users to interact with
interpretable explanations of ML predictions in order to inject modifications
or constraints that more accurately reflect current realities of the system. In
this paper, we present a solution which leverages the predictive power of ML
models while allowing the user to specify modifications to decision boundaries.
Our interactive overlay approach achieves this goal without requiring model
retraining, making it appropriate for systems that need to apply instant
changes to their decision making. We demonstrate that user feedback rules can
be layered with the ML predictions to provide immediate changes which in turn
supports learning with less data.

Tactile sensing is a crucial perception mode for robots and human amputees in
need of controlling a prosthetic device. Today robotic and prosthetic systems
are still missing the important feature of accurate tactile sensing. This lack
is mainly due to the fact that the existing tactile technologies have limited
spatial and temporal resolution and are either expensive or not scalable. In
this paper, we present the design and the implementation of a hardware-software
embedded system called SmartHand. It is specifically designed to enable the
acquisition and the real-time processing of high-resolution tactile information
from a hand-shaped multi-sensor array for prosthetic and robotic applications.
During data collection, our system can deliver a high throughput of 100 frames
per second, which is 13.7x higher than previous related work. We collected a
new tactile dataset while interacting with daily-life objects during five
different sessions. We propose a compact yet accurate convolutional neural
network that requires one order of magnitude less memory and 15.6x fewer
computations compared to related work without degrading classification
accuracy. The top-1 and top-3 cross-validation accuracies are respectively
98.86% and 99.83%. We further analyze the inter-session variability and obtain
the best top-3 leave-one-out-validation accuracy of 77.84%. We deploy the
trained model on a high-performance ARM Cortex-M7 microcontroller achieving an
inference time of only 100 ms minimizing the response latency. The overall
measured power consumption is 505 mW. Finally, we fabricate a new control
sensor and perform additional experiments to provide analyses on sensor
degradation and slip detection. This work is a step forward in giving robotic
and prosthetic devices a sense of touch and demonstrates the practicality of a
smart embedded system empowered by tiny machine learning.

This paper presents DeepShadow, a one-shot method for recovering the depth
map and surface normals from photometric stereo shadow maps. Previous works
that try to recover the surface normals from photometric stereo images treat
cast shadows as a disturbance. We show that the self and cast shadows not only
do not disturb 3D reconstruction, but can be used alone, as a strong learning
signal, to recover the depth map and surface normals. We demonstrate that 3D
reconstruction from shadows can even outperform shape-from-shading in certain
cases. To the best of our knowledge, our method is the first to reconstruct 3D
shape-from-shadows using neural networks. The method does not require any
pre-training or expensive labeled data, and is optimized during inference time.

CounterFactual (CF) visual explanations try to find images similar to the
query image that change the decision of a vision system to a specified outcome.
Existing methods either require inference-time optimization or joint training
with a generative adversarial model which makes them time-consuming and
difficult to use in practice. We propose a novel approach, Cycle-Consistent
Counterfactuals by Latent Transformations (C3LT), which learns a latent
transformation that automatically generates visual CFs by steering in the
latent space of generative models. Our method uses cycle consistency between
the query and CF latent representations which helps our training to find better
solutions. C3LT can be easily plugged into any state-of-the-art pretrained
generative network. This enables our method to generate high-quality and
interpretable CF images at high resolution such as those in ImageNet. In
addition to several established metrics for evaluating CF explanations, we
introduce a novel metric tailored to assess the quality of the generated CF
examples and validate the effectiveness of our method on an extensive set of

The ability to predict lung and heart based diseases using deep learning
techniques is central to many researchers, particularly in the medical field
around the world. In this paper, we present a unique outlook of a very familiar
problem of disease classification using X-rays. We present a hypothesis that
X-rays of patients included with the follow up history of their most recent
three chest X-ray images would perform better in disease classification in
comparison to one chest X-ray image input using an internal CNN to perform
feature extraction. We have discovered that our generic deep learning
architecture which we propose for solving this problem performs well with 3
input X ray images provided per sample for each patient. In this paper, we have
also established that without additional layers before the output
classification, the CNN models will improve the performance of predicting the
disease labels for each patient. We have provided our results in ROC curves and
AUROC scores. We define a fresh approach of collecting three X-ray images for
training deep learning models, which we have concluded has clearly improved the
performance of the models. We have shown that ResNet, in general, has a better
result than any other CNN model used in the feature extraction phase. With our
original approach to data pre-processing, image training, and pre-trained
models, we believe that the current research will assist many medical
institutions around the world, and this will improve the prediction of
patients' symptoms and diagnose them with more accurate cure.

Most applications of machine learning for finance are related to forecasting
tasks for investment decisions. Instead, we aim to promote a better
understanding of financial markets with machine learning techniques. Leveraging
the tremendous progress in deep learning models for natural language
processing, we construct a hierarchical Reformer ([15]) model capable of
processing a large document level dataset, SEDAR, from canadian financial
regulatory filings. Using this model, we show that it is possible to predict
trade volume changes using regulatory filings. We adapt the pretraining task of
HiBERT ([36]) to obtain good sentence level representations using a large
unlabelled document dataset. Finetuning the model to successfully predict trade
volume changes indicates that the model captures a view from financial markets
and processing regulatory filings is beneficial. Analyzing the attention
patterns of our model reveals that it is able to detect some indications of
material information without explicit training, which is highly relevant for
investors and also for the market surveillance mandate of financial regulators.

We tackle the problem of minimum-time flight for a quadrotor through a
sequence of waypoints in the presence of obstacles while exploiting the full
quadrotor dynamics. Early works relied on simplified dynamics or polynomial
trajectory representations that did not exploit the full actuator potential of
the quadrotor, and, thus, resulted in suboptimal solutions. Recent works can
plan minimum-time trajectories; yet, the trajectories are executed with control
methods that do not account for obstacles. Thus, a successful execution of such
trajectories is prone to errors due to model mismatch and in-flight
disturbances. To this end, we leverage deep reinforcement learning and
classical topological path planning to train robust neural-network controllers
for minimum-time quadrotor flight in cluttered environments. The resulting
neural network controller demonstrates substantially better performance of up
to 19\% over state-of-the-art methods. More importantly, the learned policy
solves the planning and control problem simultaneously online to account for
disturbances, thus achieving much higher robustness. As such, the presented
method achieves 100% success rate of flying minimum-time policies without
collision, while traditional planning and control approaches achieve only 40%.
The proposed method is validated in both simulation and the real world, with
quadrotor speeds of up to 42km/h and accelerations of 3.6g.

Existing models that achieve state-of-the-art (SOTA) performance on both
clean and adversarially-perturbed images rely on convolution operations
conditioned with feature-wise linear modulation (FiLM) layers. These layers
require many new parameters and are hyperparameter sensitive. They
significantly increase training time, memory cost, and potential latency which
can prove costly for resource-limited or real-time applications. In this paper,
we present a fast learnable once-for-all adversarial training (FLOAT)
algorithm, which instead of the existing FiLM-based conditioning, presents a
unique weight conditioned learning that requires no additional layer, thereby
incurring no significant increase in parameter count, training time, or network
latency compared to standard adversarial training. In particular, we add
configurable scaled noise to the weight tensors that enables a trade-off
between clean and adversarial performance. Extensive experiments show that
FLOAT can yield SOTA performance improving both clean and perturbed image
classification by up to ~6% and ~10%, respectively. Moreover, real hardware
measurement shows that FLOAT can reduce the training time by up to 1.43x with
fewer model parameters of up to 1.47x on iso-hyperparameter settings compared
to the FiLM-based alternatives. Additionally, to further improve memory
efficiency we introduce FLOAT sparse (FLOATS), a form of non-iterative model
pruning and provide detailed empirical analysis to provide a three way
accuracy-robustness-complexity trade-off for these new class of pruned
conditionally trained models.

Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing
the performance of a classifier for imbalanced data. AUC maximization refers to
a learning paradigm that learns a predictive model by directly maximizing its
AUC score. It has been studied for more than two decades dating back to late
90s and a huge amount of work has been devoted to AUC maximization since then.
Recently, stochastic AUC maximization for big data and deep AUC maximization
for deep learning have received increasing attention and yielded dramatic
impact for solving real-world problems. However, to the best our knowledge
there is no comprehensive survey of related works for AUC maximization. This
paper aims to address the gap by reviewing the literature in the past two
decades. We not only give a holistic view of the literature but also present
detailed explanations and comparisons of different papers from formulations to
algorithms and theoretical guarantees. We also identify and discuss remaining
and emerging issues for deep AUC maximization, and provide suggestions on
topics for future work.

Social navigation is the capability of an autonomous agent, such as a robot,
to navigate in a 'socially compliant' manner in the presence of other
intelligent agents such as humans. With the emergence of autonomously
navigating mobile robots in human populated environments (e.g., domestic
service robots in homes and restaurants and food delivery robots on public
sidewalks), incorporating socially compliant navigation behaviors on these
robots becomes critical to ensuring safe and comfortable human robot
coexistence. To address this challenge, imitation learning is a promising
framework, since it is easier for humans to demonstrate the task of social
navigation rather than to formulate reward functions that accurately capture
the complex multi objective setting of social navigation. The use of imitation
learning and inverse reinforcement learning to social navigation for mobile
robots, however, is currently hindered by a lack of large scale datasets that
capture socially compliant robot navigation demonstrations in the wild. To fill
this gap, we introduce Socially CompliAnt Navigation Dataset (SCAND) a large
scale, first person view dataset of socially compliant navigation
demonstrations. Our dataset contains 8.7 hours, 138 trajectories, 25 miles of
socially compliant, human teleoperated driving demonstrations that comprises
multi modal data streams including 3D lidar, joystick commands, odometry,
visual and inertial information, collected on two morphologically different
mobile robots a Boston Dynamics Spot and a Clearpath Jackal by four different
human demonstrators in both indoor and outdoor environments. We additionally
perform preliminary analysis and validation through real world robot
experiments and show that navigation policies learned by imitation learning on
SCAND generate socially compliant behaviors

Determining the frequency and duration of changing--look (CL) active galactic
nuclei (AGNs) phenomena, where the optical broad emission lines appear or
disappear, is crucial to understand the evolution of the accretion flow around
supermassive black holes. We present a strategy to select new CL candidates
starting from a spectroscopic type 2 AGNs sample and searching for current type
1 photometric variability. We use the publicly available Zwicky Transient
Facility (ZTF) alert stream and the Automatic Learning for the Rapid
Classification of Events (ALeRCE) light curve classifier to produce a list of
CL candidates with a highly automated algorithm, resulting in 60 candidates.
Visual inspection reduced the sample to 30. We performed new spectroscopic
observations of six candidates of our clean sample, without further refinement,
finding the appearance of clear broad Balmer lines in four of them and
tentative evidence of type changes in the remaining two, which suggests a
promising success rate of $\geq66$ per cent for this CL selection method.

We present the SLIM (https://github.com/slimgroup) open-source software
framework for computational geophysics, and more generally, inverse problems
based on the wave-equation (e.g., medical ultrasound). We developed a software
environment aimed at scalable research and development by designing multiple
layers of abstractions. This environment allows the researchers to easily
formulate their problem in an abstract fashion, while still being able to
exploit the latest developments in high-performance computing. We illustrate
and demonstrate the benefits of our software design on many geophysical
applications, including seismic inversion and physics-informed machine learning
for geophysics (e.g., loop unrolled imaging, uncertainty quantification), all
while facilitating the integration of external software.

Real world tasks are hierarchical and compositional. Tasks can be composed of
multiple subtasks (or sub-goals) that are dependent on each other. These
subtasks are defined in terms of entities (e.g., "apple", "pear") that can be
recombined to form new subtasks (e.g., "pickup apple", and "pickup pear"). To
solve these tasks efficiently, an agent must infer subtask dependencies (e.g.
an agent must execute "pickup apple" before "place apple in pot"), and
generalize the inferred dependencies to new subtasks (e.g. "place apple in pot"
is similar to "place apple in pan"). Moreover, an agent may also need to solve
unseen tasks, which can involve unseen entities. To this end, we formulate
parameterized subtask graph inference (PSGI), a method for modeling subtask
dependencies using first-order logic with subtask entities. To facilitate this,
we learn entity attributes in a zero-shot manner, which are used as quantifiers
(e.g. "is_pickable(X)") for the parameterized subtask graph. We show this
approach accurately learns the latent structure on hierarchical and
compositional tasks more efficiently than prior work, and show PSGI can
generalize by modelling structure on subtasks unseen during adaptation.

Planning under uncertainty is an area of interest in artificial intelligence.
We present a novel approach based on tree search and graph machine learning for
the scheduling problem known as Disjunctive Temporal Networks with Uncertainty
(DTNU). Dynamic Controllability (DC) of DTNUs seeks a reactive scheduling
strategy to satisfy temporal constraints in response to uncontrollable action
durations. We introduce new semantics for reactive scheduling: Time-based
Dynamic Controllability (TDC) and a restricted subset of TDC, R-TDC. We design
a tree search algorithm to determine whether or not a DTNU is R-TDC. Moreover,
we leverage a graph neural network as a heuristic for tree search guidance.
Finally, we conduct experiments on a known benchmark on which we show R-TDC to
retain significant completeness with regard to DC, while being faster to prove.
This results in the tree search processing fifty percent more DTNU problems in
R-TDC than the state-of-the-art DC solver does in DC with the same time budget.
We also observe that graph neural network search guidance leads to substantial
performance gains on benchmarks of more complex DTNUs, with up to eleven times
more problems solved than the baseline tree search.

Few-shot object detection (FSOD), with the aim to detect novel objects using
very few training examples, has recently attracted great research interest in
the community. Metric-learning based methods have been demonstrated to be
effective for this task using a two-branch based siamese network, and calculate
the similarity between image regions and few-shot examples for detection.
However, in previous works, the interaction between the two branches is only
restricted in the detection head, while leaving the remaining hundreds of
layers for separate feature extraction. Inspired by the recent work on vision
transformers and vision-language transformers, we propose a novel Fully
Cross-Transformer based model (FCT) for FSOD by incorporating cross-transformer
into both the feature backbone and detection head. The asymmetric-batched
cross-attention is proposed to aggregate the key information from the two
branches with different batch sizes. Our model can improve the few-shot
similarity learning between the two branches by introducing the multi-level
interactions. Comprehensive experiments on both PASCAL VOC and MSCOCO FSOD
benchmarks demonstrate the effectiveness of our model.

Deep learning has been widely used to analyze digitized hematoxylin and eosin
(H&E)-stained histopathology whole slide images. Automated cancer segmentation
using deep learning can be used to diagnose malignancy and to find novel
morphological patterns to predict molecular subtypes. To train pixel-wise
cancer segmentation models, manual annotation from pathologists is generally a
bottleneck due to its time-consuming nature. In this paper, we propose Deep
Interactive Learning with a pretrained segmentation model from a different
cancer type to reduce manual annotation time. Instead of annotating all pixels
from cancer and non-cancer regions on giga-pixel whole slide images, an
iterative process of annotating mislabeled regions from a segmentation model
and training/finetuning the model with the additional annotation can reduce the
time. Especially, employing a pretrained segmentation model can further reduce
the time than starting annotation from scratch. We trained an accurate ovarian
cancer segmentation model with a pretrained breast segmentation model by 3.5
hours of manual annotation which achieved intersection-over-union of 0.74,
recall of 0.86, and precision of 0.84. With automatically extracted high-grade
serous ovarian cancer patches, we attempted to train another deep learning
model to predict BRCA mutation. The segmentation model and code have been
released at https://github.com/MSKCC-Computational-Pathology/DMMN-ovary.

In this work, we introduce DAMNETS, a deep generative model for Markovian
network time series. Time series of networks are found in many fields such as
trade or payment networks in economics, contact networks in epidemiology or
social media posts over time. Generative models of such data are useful for
Monte-Carlo estimation and data set expansion, which is of interest for both
data privacy and model fitting. Using recent ideas from the Graph Neural
Network (GNN) literature, we introduce a novel GNN encoder-decoder structure in
which an encoder GNN learns a latent representation of the input graph, and a
decoder GNN uses this representation to simulate the network dynamics. We show
using synthetic data sets that DAMNETS can replicate features of network
topology across time observed in the real world, such as changing community
structure and preferential attachment. DAMNETS outperforms competing methods on
all of our measures of sample quality over several real and synthetic data
sets.

Fueled by the power of deep learning techniques and implicit shape learning,
recent advances in single-image human digitalization have reached unprecedented
accuracy and could recover fine-grained surface details such as garment
wrinkles. However, a common problem for the implicit-based methods is that they
cannot produce separated and topology-consistent mesh for each garment piece,
which is crucial for the current 3D content creation pipeline. To address this
issue, we proposed a novel geometry inference framework ReEF that reconstructs
topology-consistent layered garment mesh by registering the explicit garment
template to the whole-body implicit fields predicted from single images.
Experiments demonstrate that our method notably outperforms its counterparts on
single-image layered garment reconstruction and could bring high-quality
digital assets for further content creation.

Traffic light recognition, as a critical component of the perception module
of self-driving vehicles, plays a vital role in the intelligent transportation
systems. The prevalent deep learning based traffic light recognition methods
heavily hinge on the large quantity and rich diversity of training data.
However, it is quite challenging to collect data in various rare scenarios such
as flashing, blackout or extreme weather, thus resulting in the imbalanced
distribution of training data and consequently the degraded performance in
recognizing rare classes. In this paper, we seek to improve traffic light
recognition by leveraging data synthesis. Inspired by the generative
adversarial networks (GANs), we propose a novel traffic light generation
approach TL-GAN to synthesize the data of rare classes to improve traffic light
recognition for autonomous driving. TL-GAN disentangles traffic light sequence
generation into image synthesis and sequence assembling. In the image synthesis
stage, our approach enables conditional generation to allow full control of the
color of the generated traffic light images. In the sequence assembling stage,
we design the style mixing and adaptive template to synthesize realistic and
diverse traffic light sequences. Extensive experiments show that the proposed
TL-GAN renders remarkable improvement over the baseline without using the
generated data, leading to the state-of-the-art performance in comparison with
the competing algorithms that are used for general image synthesis and data
imbalance tackling.

Manipulating deformable linear objects by robots has a wide range of
applications, e.g., manufacturing and medical surgery. To complete such tasks,
an accurate dynamics model for predicting the deformation is critical for
robust control. In this work, we deal with this challenge by proposing a hybrid
offline-online method to learn the dynamics of cables in a robust and
data-efficient manner. In the offline phase, we adopt Graph Neural Network
(GNN) to learn the deformation dynamics purely from the simulation data. Then a
linear residual model is learned in real-time to bridge the sim-to-real gap.
The learned model is then utilized as the dynamics constraint of a trust region
based Model Predictive Controller (MPC) to calculate the optimal robot
movements. The online learning and MPC run in a closed-loop manner to robustly
accomplish the task. Finally, comparative results with existing methods are
provided to quantitatively show the effectiveness and robustness.

Cosine similarity is a widely used measure of the relatedness of pre-trained
word embeddings, trained on a language modeling goal. Datasets such as
WordSim-353 and SimLex-999 rate how similar words are according to human
annotators, and as such are often used to evaluate the performance of language
models. Thus, any improvement on the word similarity task requires an improved
word representation. In this paper, we propose instead the use of an extended
cosine similarity measure to improve performance on that task, with gains in
interpretability. We explore the hypothesis that this approach is particularly
useful if the word-similarity pairs share the same context, for which distinct
contextualized similarity measures can be learned. We first use the dataset of
Richie et al. (2020) to learn contextualized metrics and compare the results
with the baseline values obtained using the standard cosine similarity measure,
which consistently shows improvement. We also train a contextualized similarity
measure for both SimLex-999 and WordSim-353, comparing the results with the
corresponding baselines, and using these datasets as independent test sets for
the all-context similarity measure learned on the contextualized dataset,
obtaining positive results for a number of tests.

Prior works on action representation learning mainly focus on designing
various architectures to extract the global representations for short video
clips. In contrast, many practical applications such as video alignment have
strong demand for learning dense representations for long videos. In this
paper, we introduce a novel contrastive action representation learning (CARL)
framework to learn frame-wise action representations, especially for long
videos, in a self-supervised manner. Concretely, we introduce a simple yet
efficient video encoder that considers spatio-temporal context to extract
frame-wise representations. Inspired by the recent progress of self-supervised
learning, we present a novel sequence contrastive loss (SCL) applied on two
correlated views obtained through a series of spatio-temporal data
augmentations. SCL optimizes the embedding space by minimizing the
KL-divergence between the sequence similarity of two augmented views and a
prior Gaussian distribution of timestamp distance. Experiments on FineGym,
PennAction and Pouring datasets show that our method outperforms previous
state-of-the-art by a large margin for downstream fine-grained action
classification. Surprisingly, although without training on paired videos, our
approach also shows outstanding performance on video alignment and fine-grained
frame retrieval tasks. Code and models are available at
https://github.com/minghchen/CARL_code.

3D-aware generative models have shown that the introduction of 3D information
can lead to more controllable image generation. In particular, the current
state-of-the-art model GIRAFFE can control each object's rotation, translation,
scale, and scene camera pose without corresponding supervision. However,
GIRAFFE only operates well when the image resolution is low. We propose GIRAFFE
HD, a high-resolution 3D-aware generative model that inherits all of GIRAFFE's
controllable features while generating high-quality, high-resolution images
($512^2$ resolution and above). The key idea is to leverage a style-based
neural renderer, and to independently generate the foreground and background to
force their disentanglement while imposing consistency constraints to stitch
them together to composite a coherent final image. We demonstrate
state-of-the-art 3D controllable high-resolution image generation on multiple
natural image datasets.

Deep learning models tend to forget their earlier knowledge while
incrementally learning new tasks. This behavior emerges because the parameter
updates optimized for the new tasks may not align well with the updates
suitable for older tasks. The resulting latent representation mismatch causes
forgetting. In this work, we propose ELI: Energy-based Latent Aligner for
Incremental Learning, which first learns an energy manifold for the latent
representations such that previous task latents will have low energy and the
current task latents have high energy values. This learned manifold is used to
counter the representational shift that happens during incremental learning.
The implicit regularization that is offered by our proposed methodology can be
used as a plug-and-play module in existing incremental learning methodologies.
We validate this through extensive evaluation on CIFAR-100, ImageNet subset,
ImageNet 1k and Pascal VOC datasets. We observe consistent improvement when ELI
is added to three prominent methodologies in class-incremental learning, across
multiple incremental settings. Further, when added to the state-of-the-art
incremental object detector, ELI provides over 5% improvement in detection
accuracy, corroborating its effectiveness and complementary advantage to
existing art.

Multi-task learning commonly encounters competition for resources among
tasks, specifically when model capacity is limited. This challenge motivates
models which allow control over the relative importance of tasks and total
compute cost during inference time. In this work, we propose such a
controllable multi-task network that dynamically adjusts its architecture and
weights to match the desired task preference as well as the resource
constraints. In contrast to the existing dynamic multi-task approaches that
adjust only the weights within a fixed architecture, our approach affords the
flexibility to dynamically control the total computational cost and match the
user-preferred task importance better. We propose a disentangled training of
two hypernetworks, by exploiting task affinity and a novel branching
regularized loss, to take input preferences and accordingly predict
tree-structured models with adapted weights. Experiments on three multi-task
benchmarks, namely PASCAL-Context, NYU-v2, and CIFAR-100, show the efficacy of
our approach. Project page is available at https://www.nec-labs.com/~mas/DYMU.

The trade-off between throughput and image quality is an inherent challenge
in microscopy. To improve throughput, compressive imaging under-samples image
signals; the images are then computationally reconstructed by solving a
regularized inverse problem. Compared to traditional regularizers, Deep
Learning based methods have achieved greater success in compression and image
quality. However, the information loss in the acquisition process sets the
compression bounds. Further improvement in compression, without compromising
the reconstruction quality is thus a challenge. In this work, we propose
differentiable compressive fluorescence microscopy ($\partial \mu$) which
includes a realistic generalizable forward model with learnable-physical
parameters (e.g. illumination patterns), and a novel physics-inspired inverse
model. The cascaded model is end-to-end differentiable and can learn optimal
compressive sampling schemes through training data. With our model, we
performed thousands of numerical experiments on various compressive microscope
configurations. Our results suggest that learned sampling outperforms widely
used traditional compressive sampling schemes at higher compressions ($\times
100- 1000$) in terms of reconstruction quality. We further utilize our
framework for Task Aware Compression. The experimental results show superior
performance on segmentation tasks even at extremely high compression ($\times
4096$).

Ever since the first microscope by Zacharias Janssen in the late 16th
century, scientists have been inventing new types of microscopes for various
tasks. Inventing a novel architecture demands years, if not decades, worth of
scientific experience and creativity. In this work, we introduce Differentiable
Microscopy ($\partial\mu$), a deep learning-based design paradigm, to aid
scientists design new interpretable microscope architectures. Differentiable
microscopy first models a common physics-based optical system however with
trainable optical elements at key locations on the optical path. Using
pre-acquired data, we then train the model end-to-end for a task of interest.
The learnt design proposal can then be simplified by interpreting the learnt
optical elements. As a first demonstration, based on the optical 4-$f$ system,
we present an all-optical quantitative phase microscope (QPM) design that
requires no computational post-reconstruction. A follow-up literature survey
suggested that the learnt architecture is similar to the generalized phase
concept developed two decades ago. We then incorporate the generalized phase
contrast concept to simplify the learning procedure. Furthermore, this physical
optical setup is miniaturized using a diffractive deep neural network (D2NN).
We outperform the existing benchmark for all-optical phase-to-intensity
conversion on multiple datasets, and ours is the first demonstration of its
kind on D2NNs. The proposed differentiable microscopy framework supplements the
creative process of designing new optical systems and would perhaps lead to
unconventional but better optical designs.

Speech super-resolution (SR) is a task to increase speech sampling rate by
generating high-frequency components. Existing speech SR methods are trained in
constrained experimental settings, such as a fixed upsampling ratio. These
strong constraints can potentially lead to poor generalization ability in
mismatched real-world cases. In this paper, we propose a neural vocoder based
speech super-resolution method (NVSR) that can handle a variety of input
resolution and upsampling ratios. NVSR consists of a mel-bandwidth extension
module, a neural vocoder module, and a post-processing module. Our proposed
system achieves state-of-the-art results on the VCTK multi-speaker benchmark.
On 44.1 kHz target resolution, NVSR outperforms WSRGlow and Nu-wave by 8% and
37% respectively on log spectral distance and achieves a significantly better
perceptual quality. We also demonstrate that prior knowledge in the pre-trained
vocoder is crucial for speech SR by performing mel-bandwidth extension with a
simple replication-padding method. Samples can be found in
https://haoheliu.github.io/nvsr.

Recently, vision-language pre-training shows great potential in
open-vocabulary object detection, where detectors trained on base classes are
devised for detecting new classes. The class text embedding is firstly
generated by feeding prompts to the text encoder of a pre-trained
vision-language model. It is then used as the region classifier to supervise
the training of a detector. The key element that leads to the success of this
model is the proper prompt, which requires careful words tuning and ingenious
design. To avoid laborious prompt engineering, there are some prompt
representation learning methods being proposed for the image classification
task, which however can only be sub-optimal solutions when applied to the
detection task. In this paper, we introduce a novel method, detection prompt
(DetPro), to learn continuous prompt representations for open-vocabulary object
detection based on the pre-trained vision-language model. Different from the
previous classification-oriented methods, DetPro has two highlights: 1) a
background interpretation scheme to include the proposals in image background
into the prompt training; 2) a context grading scheme to separate proposals in
image foreground for tailored prompt training. We assemble DetPro with ViLD, a
recent state-of-the-art open-world object detector, and conduct experiments on
the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365
datasets. Experimental results show that our DetPro outperforms the baseline
ViLD in all settings, e.g., +3.4 APbox and +3.0 APmask improvements on the
novel classes of LVIS. Code and models are available at
https://github.com/dyabel/detpro.

Data privacy is a central problem for embodied agents that can perceive the
environment, communicate with humans, and act in the real world. While helping
humans complete tasks, the agent may observe and process sensitive information
of users, such as house environments, human activities, etc. In this work, we
introduce privacy-preserving embodied agent learning for the task of
Vision-and-Language Navigation (VLN), where an embodied agent navigates house
environments by following natural language instructions. We view each house
environment as a local client, which shares nothing other than local updates
with the cloud server and other clients, and propose a novel federated
vision-and-language navigation (FedVLN) framework to protect data privacy
during both training and pre-exploration. Particularly, we propose a
decentralized training strategy to limit the data of each client to its local
model training and a federated pre-exploration method to do partial model
aggregation to improve model generalizability to unseen environments. Extensive
results on R2R and RxR datasets show that under our FedVLN framework,
decentralized VLN models achieve comparable results with centralized training
while protecting seen environment privacy, and federated pre-exploration
significantly outperforms centralized pre-exploration while preserving unseen
environment privacy.

This paper proposes an attributable visual similarity learning (AVSL)
framework for a more accurate and explainable similarity measure between
images. Most existing similarity learning methods exacerbate the
unexplainability by mapping each sample to a single point in the embedding
space with a distance metric (e.g., Mahalanobis distance, Euclidean distance).
Motivated by the human semantic similarity cognition, we propose a generalized
similarity learning paradigm to represent the similarity between two images
with a graph and then infer the overall similarity accordingly. Furthermore, we
establish a bottom-up similarity construction and top-down similarity inference
framework to infer the similarity based on semantic hierarchy consistency. We
first identify unreliable higher-level similarity nodes and then correct them
using the most coherent adjacent lower-level similarity nodes, which
simultaneously preserve traces for similarity attribution. Extensive
experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets
demonstrate significant improvements over existing deep similarity learning
methods and verify the interpretability of our framework. Code is available at
https://github.com/zbr17/AVSL.

The retinal vasculature provides important clues in the diagnosis and
monitoring of systemic diseases including hypertension and diabetes. The
microvascular system is of primary involvement in such conditions, and the
retina is the only anatomical site where the microvasculature can be directly
observed. The objective assessment of retinal vessels has long been considered
a surrogate biomarker for systemic vascular diseases, and with recent
advancements in retinal imaging and computer vision technologies, this topic
has become the subject of renewed attention. In this paper, we present a novel
dataset, dubbed RAVIR, for the semantic segmentation of Retinal Arteries and
Veins in Infrared Reflectance (IR) imaging. It enables the creation of deep
learning-based models that distinguish extracted vessel type without extensive
post-processing. We propose a novel deep learning-based methodology, denoted as
SegRAVIR, for the semantic segmentation of retinal arteries and veins and the
quantitative measurement of the widths of segmented vessels. Our extensive
experiments validate the effectiveness of SegRAVIR and demonstrate its superior
performance in comparison to state-of-the-art models. Additionally, we propose
a knowledge distillation framework for the domain adaptation of RAVIR
pretrained networks on color images. We demonstrate that our pretraining
procedure yields new state-of-the-art benchmarks on the DRIVE, STARE, and
CHASE_DB1 datasets. Dataset link: https://ravirdataset.github.io/data/

Taxonomies have been widely used in various domains to underpin numerous
applications. Specially, product taxonomies serve an essential role in the
e-commerce domain for the recommendation, browsing, and query understanding.
However, taxonomies need to constantly capture the newly emerged terms or
concepts in e-commerce platforms to keep up-to-date, which is expensive and
labor-intensive if it relies on manual maintenance and updates. Therefore, we
target the taxonomy expansion task to attach new concepts to existing
taxonomies automatically. In this paper, we present a self-supervised and user
behavior-oriented product taxonomy expansion framework to append new concepts
into existing taxonomies. Our framework extracts hyponymy relations that
conform to users' intentions and cognition. Specifically, i) to fully exploit
user behavioral information, we extract candidate hyponymy relations that match
user interests from query-click concepts; ii) to enhance the semantic
information of new concepts and better detect hyponymy relations, we model
concepts and relations through both user-generated content and structural
information in existing taxonomies and user click logs, by leveraging
Pre-trained Language Models and Graph Neural Network combined with Contrastive
Learning; iii) to reduce the cost of dataset construction and overcome data
skews, we construct a high-quality and balanced training dataset from existing
taxonomy with no supervision. Extensive experiments on real-world product
taxonomies in Meituan Platform, a leading Chinese vertical e-commerce platform
to order take-out with more than 70 million daily active users, demonstrate the
superiority of our proposed framework over state-of-the-art methods. Notably,
our method enlarges the size of real-world product taxonomies from 39,263 to
94,698 relations with 88% precision.

The act of appearing kind or helpful via the use of but having a feeling of
superiority condescending and patronizing language can have have serious mental
health implications to those that experience it. Thus, detecting this
condescending and patronizing language online can be useful for online
moderation systems. Thus, in this manuscript, we describe the system developed
by Team UTSA SemEval-2022 Task 4, Detecting Patronizing and Condescending
Language. Our approach explores the use of several deep learning architectures
including RoBERTa, convolutions neural networks, and Bidirectional Long
Short-Term Memory Networks. Furthermore, we explore simple and effective
methods to create ensembles of neural network models. Overall, we experimented
with several ensemble models and found that the a simple combination of five
RoBERTa models achieved an F-score of .6441 on the development dataset and
.5745 on the final test dataset. Finally, we also performed a comprehensive
error analysis to better understand the limitations of the model and provide
ideas for further research.

Modern object detectors have achieved impressive progress under the close-set
setup. However, open-set object detection (OSOD) remains challenging since
objects of unknown categories are often misclassified to existing known
classes. In this work, we propose to identify unknown objects by separating
high/low-density regions in the latent space, based on the consensus that
unknown objects are usually distributed in low-density latent regions. As
traditional threshold-based methods only maintain limited low-density regions,
which cannot cover all unknown objects, we present a novel Open-set Detector
(OpenDet) with expanded low-density regions. To this aim, we equip OpenDet with
two learners, Contrastive Feature Learner (CFL) and Unknown Probability Learner
(UPL). CFL performs instance-level contrastive learning to encourage compact
features of known classes, leaving more low-density regions for unknown
classes; UPL optimizes unknown probability based on the uncertainty of
predictions, which further divides more low-density regions around the cluster
of known classes. Thus, unknown objects in low-density regions can be easily
identified with the learned unknown probability. Extensive experiments
demonstrate that our method can significantly improve the OSOD performance,
e.g., OpenDet reduces the Absolute Open-Set Errors by 25%-35% on six OSOD
benchmarks. Code is available at: https://github.com/csuhan/opendet2.

This paper addresses the problem of estimating the modes of an observed
non-stationary mixture signal in the presence of an arbitrary distributed
noise. A novel Bayesian model is introduced to estimate the model parameters
from the spectrogram of the observed signal, by resorting to the stochastic
version of the EM algorithm to avoid the computationally expensive joint
parameters estimation from the posterior distribution. The proposed method is
assessed through comparative experiments with state-of-the-art methods. The
obtained results validate the proposed approach by highlighting an improvement
of the modes estimation performance.

In this paper, we investigate properties and performance of synthetic random
graph models with a built-in community structure. Such models are important for
evaluating and tuning community detection algorithms that are unsupervised by
nature. We propose ABCDe, a multi-threaded implementation of the ABCD
(Artificial Benchmark for Community Detection) graph generator. We discuss the
implementation details of the algorithm and compare it with both the previously
available sequential version of the ABCD model and with the parallel
implementation of the standard and extensively used LFR
(Lancichinetti--Fortunato--Radicchi) generator. We show that ABCDe is more than
ten times faster and scales better than the parallel implementation of LFR
provided in NetworKit. Moreover, the algorithm is not only faster but random
graphs generated by ABCD have similar properties to the ones generated by the
original LFR algorithm, while the parallelized NetworKit implementation of LFR
produces graphs that have noticeably different characteristics.

Self-supervised learning (SSL) has made enormous progress and largely
narrowed the gap with the supervised ones, where the representation learning is
mainly guided by a projection into an embedding space. During the projection,
current methods simply adopt uniform aggregation of pixels for embedding;
however, this risks involving object-irrelevant nuisances and spatial
misalignment for different augmentations. In this paper, we present a new
approach, Learning Where to Learn (LEWEL), to adaptively aggregate spatial
information of features, so that the projected embeddings could be exactly
aligned and thus guide the feature learning better. Concretely, we reinterpret
the projection head in SSL as a per-pixel projection and predict a set of
spatial alignment maps from the original features by this weight-sharing
projection head. A spectrum of aligned embeddings is thus obtained by
aggregating the features with spatial weighting according to these alignment
maps. As a result of this adaptive alignment, we observe substantial
improvements on both image-level prediction and dense prediction at the same
time: LEWEL improves MoCov2 by 1.6%/1.3%/0.5%/0.4% points, improves BYOL by
1.3%/1.3%/0.7%/0.6% points, on ImageNet linear/semi-supervised classification,
Pascal VOC semantic segmentation, and object detection, respectively.

Despite the recent progress in deep learning, most approaches still go for a
silo-like solution, focusing on learning each task in isolation: training a
separate neural network for each individual task. Many real-world problems,
however, call for a multi-modal approach and, therefore, for multi-tasking
models. Multi-task learning (MTL) aims to leverage useful information across
tasks to improve the generalization capability of a model. This thesis is
concerned with multi-task learning in the context of computer vision. First, we
review existing approaches for MTL. Next, we propose several methods that
tackle important aspects of multi-task learning. The proposed methods are
evaluated on various benchmarks. The results show several advances in the
state-of-the-art of multi-task learning. Finally, we discuss several
possibilities for future work.

In speaker recognition, where speech segments are mapped to embeddings on the
unit hypersphere, two scoring backends are commonly used, namely cosine scoring
or PLDA. Both have advantages and disadvantages, depending on the context.
Cosine scoring follows naturally from the spherical geometry, but for PLDA the
blessing is mixed -- length normalization Gaussianizes the between-speaker
distribution, but violates the assumption of a speaker-independent
within-speaker distribution. We propose PSDA, an analogue to PLDA that uses Von
Mises-Fisher distributions on the hypersphere for both within and between-class
distributions. We show how the self-conjugacy of this distribution gives
closed-form likelihood-ratio scores, making it a drop-in replacement for PLDA
at scoring time. All kinds of trials can be scored, including single-enroll and
multi-enroll verification, as well as more complex likelihood-ratios that could
be used in clustering and diarization. Learning is done via an EM-algorithm
with closed-form updates. We explain the model and present some first

Spark SQL has been widely deployed in industry but it is challenging to tune
its performance. Recent studies try to employ machine learning (ML) to solve
this problem, but suffer from two drawbacks. First, it takes a long time (high
overhead) to collect training samples. Second, the optimal configuration for
one input data size of the same application might not be optimal for others. To
address these issues, we propose a novel Bayesian Optimization (BO) based
approach named LOCAT to automatically tune the configurations of Spark SQL
applications online. LOCAT innovates three techniques. The first technique,
named QCSA, eliminates the configuration-insensitive queries by Query
Configuration Sensitivity Analysis (QCSA) when collecting training samples. The
second technique, dubbed DAGP, is a Datasize-Aware Gaussian Process (DAGP)
which models the performance of an application as a distribution of functions
of configuration parameters as well as input data size. The third technique,
called IICP, Identifies Important Configuration Parameters (IICP) with respect
to performance and only tunes the important ones. As such, LOCAT can tune the
configurations of a Spark SQL application with low overhead and adapt to
different input data sizes. We employ Spark SQL applications from benchmark
suites TPC-DS, TPC-H, and HiBench running on two significantly different
clusters, a four-node ARM cluster and an eight-node x86 cluster, to evaluate
LOCAT. The experimental results on the ARM cluster show that LOCAT accelerates
the optimization procedures of the state-of-the-art approaches by at least 4.1x
and up to 9.7x; moreover, LOCAT improves the application performance by at
least 1.9x and up to 2.4x. On the x86 cluster, LOCAT shows similar results to
those on the ARM cluster.

Many real world graphs contain time domain information. Temporal Graph Neural
Networks capture temporal information as well as structural and contextual
information in the generated dynamic node embeddings. Researchers have shown
that these embeddings achieve state-of-the-art performance in many different
tasks. In this work, we propose TGL, a unified framework for large-scale
offline Temporal Graph Neural Network training where users can compose various
Temporal Graph Neural Networks with simple configuration files. TGL comprises
five main components, a temporal sampler, a mailbox, a node memory module, a
memory updater, and a message passing engine. We design a Temporal-CSR data
structure and a parallel sampler to efficiently sample temporal neighbors to
formtraining mini-batches. We propose a novel random chunk scheduling technique
that mitigates the problem of obsolete node memory when training with a large
batch size. To address the limitations of current TGNNs only being evaluated on
small-scale datasets, we introduce two large-scale real-world datasets with 0.2
and 1.3 billion temporal edges. We evaluate the performance of TGL on four
small-scale datasets with a single GPU and the two large datasets with multiple
GPUs for both link prediction and node classification tasks. We compare TGL
with the open-sourced code of five methods and show that TGL achieves similar
or better accuracy with an average of 13x speedup. Our temporal parallel
sampler achieves an average of 173x speedup on a multi-core CPU compared with
the baselines. On a 4-GPU machine, TGL can train one epoch of more than one
billion temporal edges within 1-10 hours. To the best of our knowledge, this is
the first work that proposes a general framework for large-scale Temporal Graph
Neural Networks training on multiple GPUs.

Runtime-reconfigurable software coupled with reconfigurable hardware is
highly desirable as a means towards maximizing runtime efficiency without
compromising programmability. Compilers for such software systems are extremely
difficult to design as they must leverage different types of hardware at
runtime. To address the need for static and dynamic compiler optimization of
workflows matched to dynamically reconfigurable hardware, we propose a novel
design of the central component of a dynamic software compiler for software
defined hardware. Our comprehensive design focuses not just on static knowledge
but also on semi-supervised extraction of knowledge from program executions and
developing their performance models. Specifically, our novel {\it dynamic and
extensible knowledge base} 1) continuously gathers knowledge during execution
of workflows 2) identifies {\it optimal} implementations of workflows on {\it
optimal} (available) hardware configurations. It plays a hub role in storing
information from, and providing information to other components of the
compiler, as well as the human analyst. Through a rich tripartite graph
representation, the knowledge base captures and learns extensive information on
decomposition and mapping of code steps to kernels and mapping of kernels to
available hardware configurations. The knowledge base is implemented using the
C++ Boost Library and is capable of quickly processing offline and online
queries and updates. We show that our knowledge base can answer queries in
$1ms$ regardless of the number of workflows it stores. To the best of our
knowledge, this is the first design of a dynamic and extensible knowledge base
to support compilation of high-level languages to leverage arbitrary
reconfigurable platforms.

Speech emotion recognition~(SER) refers to the technique of inferring the
emotional state of an individual from speech signals. SERs continue to garner
interest due to their wide applicability. Although the domain is mainly founded
on signal processing, machine learning, and deep learning, generalizing over
languages continues to remain a challenge. However, developing generalizable
and transferable models are critical due to a lack of sufficient resources in
terms of data and labels for languages beyond the most commonly spoken ones. To
improve performance over languages, we propose a denoising autoencoder with
semi-supervision using a continuous metric loss based on either activation or
valence. The novelty of this work lies in our proposal of continuous metric
learning, which is among the first proposals on the topic to the best of our
knowledge. Furthermore, to address the lack of activation and valence labels in
the transfer datasets, we annotate the signal samples with activation and
valence levels corresponding to a dimensional model of emotions, which were
then used to evaluate the quality of the embedding over the transfer datasets.
We show that the proposed semi-supervised model consistently outperforms the
baseline unsupervised method, which is a conventional denoising autoencoder, in
terms of emotion classification accuracy as well as correlation with respect to
the dimensional variables. Further evaluation of classification accuracy with
respect to the reference, a BERT based speech representation model, shows that
the proposed method is comparable to the reference method in classifying
specific emotion classes at a much lower complexity.

In recent years, speech emotion recognition (SER) has been used in wide
ranging applications, from healthcare to the commercial sector. In addition to
signal processing approaches, methods for SER now also use deep learning
techniques which provide transfer learning possibilities. However, generalizing
over languages, corpora and recording conditions is still an open challenge. In
this work we address this gap by exploring loss functions that aid in
transferability, specifically to non-tonal languages. We propose a variational
autoencoder (VAE) with KL annealing and a semi-supervised VAE to obtain more
consistent latent embedding distributions across data sets. To ensure
transferability, the distribution of the latent embedding should be similar
across non-tonal languages (data sets). We start by presenting a low-complexity
SER based on a denoising-autoencoder, which achieves an unweighted
classification accuracy of over 52.09% for four-class emotion classification.
This performance is comparable to that of similar baseline methods. Following
this, we employ a VAE, the semi-supervised VAE and the VAE with KL annealing to
obtain a more regularized latent space. We show that while the DAE has the
highest classification accuracy among the methods, the semi-supervised VAE has
a comparable classification accuracy and a more consistent latent embedding
distribution over data sets.

A promising direction for recovering the lost information in low-resolution
headshot images is utilizing a set of high-resolution exemplars from the same
identity. Complementary images in the reference set can improve the generated
headshot quality across many different views and poses. However, it is
challenging to make the best use of multiple exemplars: the quality and
alignment of each exemplar cannot be guaranteed. Using low-quality and
mismatched images as references will impair the output results. To overcome
these issues, we propose an efficient Headshot Image Super-Resolution with
Multiple Exemplars network (HIME) method. Compared with previous methods, our
network can effectively handle the misalignment between the input and the
reference without requiring facial priors and learn the aggregated reference
set representation in an end-to-end manner. Furthermore, to reconstruct more
detailed facial features, we propose a correlation loss that provides a rich
representation of the local texture in a controllable spatial range.
Experimental results demonstrate that the proposed framework not only has
significantly fewer computation cost than recent exemplar-guided methods but
also achieves better qualitative and quantitative performance.

Diffusion condensation is a dynamic process that yields a sequence of
multiscale data representations that aim to encode meaningful abstractions. It
has proven effective for manifold learning, denoising, clustering, and
visualization of high-dimensional data. Diffusion condensation is constructed
as a time-inhomogeneous process where each step first computes and then applies
a diffusion operator to the data. We theoretically analyze the convergence and
evolution of this process from geometric, spectral, and topological
perspectives. From a geometric perspective, we obtain convergence bounds based
on the smallest transition probability and the radius of the data, whereas from
a spectral perspective, our bounds are based on the eigenspectrum of the
diffusion kernel. Our spectral results are of particular interest since most of
the literature on data diffusion is focused on homogeneous processes. From a
topological perspective, we show diffusion condensation generalizes
centroid-based hierarchical clustering. We use this perspective to obtain a
bound based on the number of data points, independent of their location. To
understand the evolution of the data geometry beyond convergence, we use
topological data analysis. We show that the condensation process itself defines
an intrinsic diffusion homology. We use this intrinsic topology as well as an
ambient topology to study how the data changes over diffusion time. We
demonstrate both homologies in well-understood toy examples. Our work gives
theoretical insights into the convergence of diffusion condensation, and shows
that it provides a link between topological and geometric data analysis.

The pursuit algorithms integrated in multi-layer convolutional sparse coding
(ML-CSC) can interpret the convolutional neural networks (CNNs). However, many
current state-of-art (SOTA) pursuit algorithms require multiple iterations to
optimize the solution of ML-CSC, which limits their applications to deeper CNNs
due to high computational cost and large number of resources for getting very
tiny gain of performance. In this study, we focus on the 0th iteration in
pursuit algorithm by introducing an effective initialization strategy for each
layer, by which the solution for ML-CSC can be improved. Specifically, we first
propose a novel width-depth synchronous extension-based basis pursuit (WSEBP)
algorithm which solves the ML-CSC problem without the limitation of the number
of iterations compared to the SOTA algorithms and maximizes the performance by
an effective initialization in each layer. Then, we propose a simple and
unified ML-CSC-based classification network (ML-CSC-Net) which consists of an
ML-CSC-based feature encoder and a fully-connected layer to validate the
performance of WSEBP on image classification task. The experimental results
show that our proposed WSEBP outperforms SOTA algorithms in terms of accuracy
and consumption resources. In addition, the WSEBP integrated in CNNs can
improve the performance of deeper CNNs and make them interpretable. Finally,
taking VGG as an example, we propose WSEBP-VGG13 to enhance the performance of
VGG13, which achieves competitive results on four public datasets, i.e., 87.79%
vs. 86.83% on Cifar-10 dataset, 58.01% vs. 54.60% on Cifar-100 dataset, 91.52%
vs. 89.58% on COVID-19 dataset, and 99.88% vs. 99.78% on Crack dataset,
respectively. The results show the effectiveness of the proposed WSEBP, the
improved performance of ML-CSC with WSEBP, and interpretation of the CNNs or
deeper CNNs.

Deep imitation learning requires many expert demonstrations, which can be
hard to obtain, especially when many tasks are involved. However, different
tasks often share similarities, so learning them jointly can greatly benefit
them and alleviate the need for many demonstrations. But, joint multi-task
learning often suffers from negative transfer, sharing information that should
be task-specific. In this work, we introduce a method to perform multi-task
imitation while allowing for task-specific features. This is done by using
proto-policies as modules to divide the tasks into simple sub-behaviours that
can be shared. The proto-policies operate in parallel and are adaptively chosen
by a selector mechanism that is jointly trained with the modules. Experiments
on different sets of tasks show that our method improves upon the accuracy of
single agents, task-conditioned and multi-headed multi-task agents, as well as
state-of-the-art meta learning agents. We also demonstrate its ability to
autonomously divide the tasks into both shared and task-specific
sub-behaviours.

Recently, sketches have been introduced as a general language for
representing the subgoal structure of instances drawn from the same domain.
Sketches are collections of rules of the form C -> E over a given set of
features where C expresses Boolean conditions and E expresses qualitative
changes. Each sketch rule defines a subproblem: going from a state that
satisfies C to a state that achieves the change expressed by E or a goal state.
Sketches can encode simple goal serializations, general policies, or
decompositions of bounded width that can be solved greedily, in polynomial
time, by the SIW_R variant of the SIW algorithm. Previous work has shown the
computational value of sketches over benchmark domains that, while tractable,
are challenging for domain-independent planners. In this work, we address the
problem of learning sketches automatically given a planning domain, some
instances of the target class of problems, and the desired bound on the sketch
width. We present a logical formulation of the problem, an implementation using
the ASP solver Clingo, and experimental results. The sketch learner and the
SIW_R planner yield a domain-independent planner that learns and exploits
domain structure in a crisp and explicit form.

Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram
(sEMG) signals has recently shown significant potential for development of
advanced myoelectric-controlled prosthesis. Existing deep learning approaches,
typically, include only one model as such can hardly maintain acceptable
generalization performance in changing scenarios. In this paper, we aim to
address this challenge by capitalizing on the recent advances of hybrid models
and transformers. In other words, we propose a hybrid framework based on the
transformer architecture, which is a relatively new and revolutionizing deep
learning model. The proposed hybrid architecture, referred to as the
Transformer for Hand Gesture Recognition (TraHGR), consists of two parallel
paths followed by a linear layer that acts as a fusion center to integrate the
advantage of each module and provide robustness over different scenarios. We
evaluated the proposed architecture TraHGR based on the commonly used second
Ninapro dataset, referred to as the DB2. The sEMG signals in the DB2 dataset
are measured in the real-life conditions from 40 healthy users, each performing
49 gestures. We have conducted extensive set of experiments to test and
validate the proposed TraHGR architecture, and have compared its achievable
accuracy with more than five recently proposed HGR classification algorithms
over the same dataset. We have also compared the results of the proposed TraHGR
architecture with each individual path and demonstrated the distinguishing
power of the proposed hybrid architecture. The recognition accuracies of the
proposed TraHGR architecture are 86.18%, 88.91%, 81.44%, and 93.84%, which are
2.48%, 5.12%, 8.82%, and 4.30% higher than the state-ofthe-art performance for
DB2 (49 gestures), DB2-B (17 gestures), DB2-C (23 gestures), and DB2-D (9
gestures), respectively.

Multi-output regression problems are commonly encountered in science and
engineering. In particular, multi-output Gaussian processes have been emerged
as a promising tool for modeling these complex systems since they can exploit
the inherent correlations and provide reliable uncertainty estimates. In many
applications, however, acquiring the data is expensive and safety concerns
might arise (e.g. robotics, engineering). We propose a safe active learning
approach for multi-output Gaussian process regression. This approach queries
the most informative data or output taking the relatedness between the
regressors and safety constraints into account. We prove the effectiveness of
our approach by providing theoretical analysis and by demonstrating empirical
results on simulated datasets and on a real-world engineering dataset. On all
datasets, our approach shows improved convergence compared to its competitors.

Active matter spans a wide range of time and length scales, from groups of
cells and synthetic self-propelled particles to schools of fish, flocks of
birds, or even human crowds. The theoretical framework describing these systems
has shown tremendous success at finding universal phenomenology. However,
further progress is often burdened by the difficulty of determining the forces
that control the dynamics of the individual elements within each system.
Accessing this local information is key to understanding the physics dominating
the system and to create the models that can explain the observed collective
phenomena. In this work, we present a machine-learning model, a graph neural
network, that uses the collective movement of the system to learn the active
and two-body forces controlling the individual dynamics of the particles. We
verify our approach using numerical simulations of active brownian particles,
considering different interaction potentials and levels of activity. Finally,
we apply our model to experiments of electrophoretic Janus particles,
extracting the active and two-body forces that control the dynamics of the
colloids. Due to this, we can uncover the physics dominating the behavior of
the system. We extract an active force that depends on the electric field and
also area fraction. We also discover a dependence of the two-body interaction
with the electric field that leads us to propose that the dominant force
between these colloids is a screened electrostatic interaction with a constant
length scale. We expect that this methodology can open a new avenue for the
study and modeling of experimental systems of active particles.

The human visual system is remarkable in learning new visual concepts from
just a few examples. This is precisely the goal behind few-shot class
incremental learning (FSCIL), where the emphasis is additionally placed on
ensuring the model does not suffer from "forgetting". In this paper, we push
the boundary further for FSCIL by addressing two key questions that bottleneck
its ubiquitous application (i) can the model learn from diverse modalities
other than just photo (as humans do), and (ii) what if photos are not readily
accessible (due to ethical and privacy constraints). Our key innovation lies in
advocating the use of sketches as a new modality for class support. The product
is a "Doodle It Yourself" (DIY) FSCIL framework where the users can freely
sketch a few examples of a novel class for the model to learn to recognize
photos of that class. For that, we present a framework that infuses (i)
gradient consensus for domain invariant learning, (ii) knowledge distillation
for preserving old class information, and (iii) graph attention networks for
message passing between old and novel classes. We experimentally show that
sketches are better class support than text in the context of FSCIL, echoing
findings elsewhere in the sketching literature.

Machine learning classifiers' capability is largely dependent on the scale of
available training data and limited by the model overfitting in data-scarce
learning tasks. To address this problem, this work proposes a novel framework
of Meta Functional Learning (MFL) by meta-learning a generalisable functional
model from data-rich tasks whilst simultaneously regularising knowledge
transfer to data-scarce tasks. The MFL computes meta-knowledge on functional
regularisation generalisable to different learning tasks by which functional
training on limited labelled data promotes more discriminative functions to be
learned. Based on this framework, we formulate three variants of MFL: MFL with
Prototypes (MFL-P) which learns a functional by auxiliary prototypes, Composite
MFL (ComMFL) that transfers knowledge from both functional space and
representational space, and MFL with Iterative Updates (MFL-IU) which improves
knowledge transfer regularisation from MFL by progressively learning the
functional regularisation in knowledge transfer. Moreover, we generalise these
variants for knowledge transfer regularisation from binary classifiers to
multi-class classifiers. Extensive experiments on two few-shot learning
scenarios, Few-Shot Learning (FSL) and Cross-Domain Few-Shot Learning (CD-FSL),
show that meta functional learning for knowledge transfer regularisation can
improve FSL classifiers.

Automatic speech recognition (ASR) systems degrade significantly in face of
noisy conditions. Recently, speech enhancement (SE) has been introduced as
front-end module to reduce noise and improve speech quality for ASR, but it
would also suppress some important speech information, i.e., over-suppression
problem. To alleviate this, we propose a dual-path style learning approach for
end-to-end noise-robust automatic speech recognition (DPSL-ASR). Specifically,
we first introduce clean speech feature along with the fused feature from
previously proposed IFF-Net as dual-path inputs to recover the over-suppressed
information. Then, we propose a style learning method to map the fused feature
close to clean feature, in order to learn latent speech information from the
latter, i.e., clean "speech style". Furthermore, we employ consistency loss to
minimize the distance of ASR outputs in two paths to improve noise-robustness.
Experimental results show that the proposed approach achieves relative word
error rate (WER) reductions of 10.6% and 8.6% over the best IFF-Net baseline,
on RATS Channel-A and CHiME-4 1-Channel Track datasets, respectively.
Visualizations of intermediate embeddings indicate that DPSL-ASR can recover
abundant over-suppressed information in enhanced speech. Our code is available
at GitHub: https://github.com/YUCHEN005/DPSL-ASR.

In our previous work, we proposed a language-independent speaker
anonymization system based on self-supervised learning models. Although the
system can anonymize speech data of any language, the anonymization was
imperfect, and the speech content of the anonymized speech was distorted. This
limitation is more severe when the input speech is from a domain unseen in the
training data. This study analyzed the bottleneck of the anonymization system
under unseen conditions. It was found that the domain (e.g., language and
channel) mismatch between the training and test data affected the neural
waveform vocoder and anonymized speaker vectors, which limited the performance
of the whole system. Increasing the training data diversity for the vocoder was
found to be helpful to reduce its implicit language and channel dependency.
Furthermore, a simple correlation-alignment-based domain adaption strategy was
found to be significantly effective to alleviate the mismatch on the anonymized
speaker vectors. Audio samples and source code are available online.

Predictions of hydrologic variables across the entire water cycle have
significant value for water resource management as well as downstream
applications such as ecosystem and water quality modeling. Recently, purely
data-driven deep learning models like long short-term memory (LSTM) showed
seemingly-insurmountable performance in modeling rainfall-runoff and other
geoscientific variables, yet they cannot predict unobserved physical variables
and remain challenging to interpret. Here we show that differentiable,
learnable, process-based models (called {\delta} models here) can approach the
performance level of LSTM for the intensively-observed variable (streamflow)
with regionalized parameterization. We use a simple hydrologic model HBV as the
backbone and use embedded neural networks, which can only be trained in a
differentiable programming framework, to parameterize, replace, or enhance the
process-based model modules. Without using an ensemble or post-processor,
{\delta} models can obtain a median Nash Sutcliffe efficiency of 0.715 for 671
basins across the USA for a particular forcing data, compared to 0.72 from a
state-of-the-art LSTM model with the same setup. Meanwhile, the resulting
learnable process-based models can be evaluated (and later, to be trained) by
multiple sources of observations, e.g., groundwater storage,
evapotranspiration, surface runoff, and baseflow. Both simulated
evapotranspiration and fraction of discharge from baseflow agreed decently with
alternative estimates. The general framework can work with models with various
process complexity and opens up the path for learning physics from big data.

Background School closures and distance learning have been extensively
applied to control SARS-CoV-2 transmission. Despite evidence of viral
circulation in schools, the contribution of students and of in-person schooling
to the transmission remains poorly quantified. Methods We analyze 976 exposure
events, involving 460 positive individuals, as identified in early 2021 by
routine surveillance and through an extensive screening conducted on students,
school personnel, and their household members during an outbreak in a small
municipality of Italy. Results From the analysis of potential transmission
chains, we estimated that, on average, 55.1%, 17.3% and 27.6% infection
episodes were linked to household, school, and community contacts,
respectively. Clusters originated from students or school personnel showed a
larger average cluster size (3.32 vs 1.15), a larger average number of
generations in the transmission chain (1.56 vs 1.17) and a larger set of
associated close contacts (11.3 vs 3.15, on average). We found substantial
transmission heterogeneities, with 20% positive individuals seeding 75-80 of
all transmission. A higher proportion of infected individuals causing onward
transmission was found among students (48.8% vs 29.9%, on average), who also
caused a markedly higher number of secondary cases (mean: 1.3 vs 0.5).
Conclusions Uncontrolled transmission at school could disrupt the regular
conduct of teaching activities, likely seeding the transmission into other
settings, and increasing the burden on contact-tracing operations.

Reconstruction of high-quality HDR images is at the core of modern
computational photography. Significant progress has been made with multi-frame
HDR reconstruction methods, producing high-resolution, rich and accurate color
reconstructions with high-frequency details. However, they are still prone to
fail in dynamic or largely over-exposed scenes, where frame misalignment often
results in visible ghosting artifacts. Recent approaches attempt to alleviate
this by utilizing an event-based camera (EBC), which measures only binary
changes of illuminations. Despite their desirable high temporal resolution and
dynamic range characteristics, such approaches have not outperformed
traditional multi-frame reconstruction methods, mainly due to the lack of color
information and low-resolution sensors. In this paper, we propose to leverage
both bracketed LDR images and simultaneously captured events to obtain the best
of both worlds: high-quality RGB information from bracketed LDRs and
complementary high frequency and dynamic range information from events. We
present a multi-modal end-to-end learning-based HDR imaging system that fuses
bracketed images and event modalities in the feature domain using attention and
multi-scale spatial alignment modules. We propose a novel event-to-image
feature distillation module that learns to translate event features into the
image-feature space with self-supervision. Our framework exploits the higher
temporal resolution of events by sub-sampling the input event streams using a
sliding window, enriching our combined feature representation. Our proposed
approach surpasses SoTA multi-frame HDR reconstruction methods using synthetic
and real events, with a 2dB and 1dB improvement in PSNR-L and PSNR-mu on the
HdM HDR dataset, respectively.

This article aims to summarize recent and ongoing efforts to simulate
continuous-variable quantum systems using flow-based variational quantum Monte
Carlo techniques, focusing for pedagogical purposes on the example of bosons in
the field amplitude (quadrature) basis. Particular emphasis is placed on the
variational real- and imaginary-time evolution problems, carefully reviewing
the stochastic estimation of the time-dependent variational principles and
their relationship with information geometry. Some practical instructions are
provided to guide the implementation of a PyTorch code. The review is intended
to be accessible to researchers interested in machine learning and quantum
information science.

Sketching enables many exciting applications, notably, image retrieval. The
fear-to-sketch problem (i.e., "I can't sketch") has however proven to be fatal
for its widespread adoption. This paper tackles this "fear" head on, and for
the first time, proposes an auxiliary module for existing retrieval models that
predominantly lets the users sketch without having to worry. We first conducted
a pilot study that revealed the secret lies in the existence of noisy strokes,
but not so much of the "I can't sketch". We consequently design a stroke subset
selector that {detects noisy strokes, leaving only those} which make a positive
contribution towards successful retrieval. Our Reinforcement Learning based
formulation quantifies the importance of each stroke present in a given subset,
based on the extent to which that stroke contributes to retrieval. When
combined with pre-trained retrieval models as a pre-processing module, we
achieve a significant gain of 8%-10% over standard baselines and in turn report
new state-of-the-art performance. Last but not least, we demonstrate the
selector once trained, can also be used in a plug-and-play manner to empower
various sketch applications in ways that were not previously possible.

The modelling of small-scale processes is a major source of error in climate
models, hindering the accuracy of low-cost models which must approximate such
processes through parameterization. Red noise is essential to many operational
parameterization schemes, helping model temporal correlations. We show how to
build on the successes of red noise by combining the known benefits of
stochasticity with machine learning. This is done using a physically-informed
recurrent neural network within a probabilistic framework. Our model is
competitive and often superior to both a bespoke baseline and an existing
probabilistic machine learning approach (GAN) when applied to the Lorenz 96
atmospheric simulation. This is due to its superior ability to model temporal
patterns compared to standard first-order autoregressive schemes. It also
generalises to unseen scenarios. We evaluate across a number of metrics from
the literature, and also discuss the benefits of using the probabilistic metric
of hold-out likelihood.

Despite an increase in the empirical study of crowdfunding platforms and the
prevalence of visual information, operations management and marketing
literature has yet to explore the role that image characteristics play in
crowdfunding success. The authors of this manuscript begin by synthesizing
literature on visual processing to identify several image characteristics that
are likely to shape crowdfunding success. After detailing measures for each
image characteristic, they use them as part of a machine-learning algorithm
(Bayesian additive trees), along with project characteristics and textual
information, to predict crowdfunding success. Results show that the inclusion
of these image characteristics substantially improves prediction over baseline
project variables, as well as textual features. Furthermore, image
characteristic variables exhibit high importance, similar to variables linked
to the number of pictures and number of videos. This research therefore offers
valuable resources to researchers and managers who are interested in the role
of visual information in ensuring new product success.

In this paper, we propose a novel design, called MixNN, for protecting deep
learning model structure and parameters. The layers in a deep learning model of
MixNN are fully decentralized. It hides communication address, layer parameters
and operations, and forward as well as backward message flows among
non-adjacent layers using the ideas from mix networks. MixNN has following
advantages: 1) an adversary cannot fully control all layers of a model
including the structure and parameters, 2) even some layers may collude but
they cannot tamper with other honest layers, 3) model privacy is preserved in
the training phase. We provide detailed descriptions for deployment. In one
classification experiment, we compared a neural network deployed in a virtual
machine with the same one using the MixNN design on the AWS EC2. The result
shows that our MixNN retains less than 0.001 difference in terms of
classification accuracy, while the whole running time of MixNN is about 7.5
times slower than the one running on a single virtual machine.

Learning discriminative deep feature embeddings by using million-scale
in-the-wild datasets and margin-based softmax loss is the current
state-of-the-art approach for face recognition. However, the memory and
computing cost of the Fully Connected (FC) layer linearly scales up to the
number of identities in the training set. Besides, the large-scale training
data inevitably suffers from inter-class conflict and long-tailed distribution.
In this paper, we propose a sparsely updating variant of the FC layer, named
Partial FC (PFC). In each iteration, positive class centers and a random subset
of negative class centers are selected to compute the margin-based softmax
loss. All class centers are still maintained throughout the whole training
process, but only a subset is selected and updated in each iteration.
Therefore, the computing requirement, the probability of inter-class conflict,
and the frequency of passive update on tail class centers, are dramatically
reduced. Extensive experiments across different training data and backbones
(e.g. CNN and ViT) confirm the effectiveness, robustness and efficiency of the
proposed PFC. The source code is available at
\https://github.com/deepinsight/insightface/tree/master/recognition.

The use of deep learning has successfully solved several problems in the
field of medical imaging. Deep learning has been applied to the CT denoising
problem successfully. However, the use of deep learning requires large amounts
of data to train deep convolutional networks (CNNs). Moreover, due to large
parameter count, such deep CNNs may cause unexpected results. In this study, we
introduce a novel CT denoising framework, which has interpretable behaviour,
and provides useful results with limited data. We employ bilateral filtering in
both the projection and volume domains to remove noise. To account for
non-stationary noise, we tune the $\sigma$ parameters of the volume for every
projection view, and for every volume pixel. The tuning is carried out by two
deep CNNs. Due to impracticality of labelling, the two deep CNNs are trained
via a Deep-Q reinforcement learning task. The reward for the task is generated
by using a custom reward function represented by a neural network. Our
experiments were carried out on abdominal scans for the Mayo Clinic TCIA
dataset, and the AAPM Low Dose CT Grand Challenge. Our denoising framework has
excellent denoising performance increasing the PSNR from 28.53 to 28.93, and
increasing the SSIM from 0.8952 to 0.9204. We outperform several
state-of-the-art deep CNNs, which have several orders of magnitude higher
number of parameters (p-value (PSNR) = 0.000, p-value (SSIM) = 0.000). Our
method does not introduce any blurring, which is introduced by MSE loss based
methods, or any deep learning artifacts, which are introduced by WGAN based
models. Our ablation studies show that parameter tuning and using our reward
network results in the best possible results.

This study aimed to assess the reliability and validity of the Polar V800 to
measure vertical jump height. Twenty-two physically active healthy men (age:
22.89 +- 4.23 years; body mass: 70.74 +- 8.04 kg; height: 1.74 +- 0.76 m) were
recruited for the study. The reliability was evaluated by comparing
measurements acquired by the Polar V800 in two identical testing sessions one
week apart. Validity was assessed by comparing measurements simultaneously
obtained using a force platform (gold standard), high-speed camera and the
Polar V800 during squat jump (SJ) and countermovement jump (CMJ) tests. In the
test-retest reliability, high intraclass correlation coefficients (ICCs) were
observed (mean: 0.90, SJ and CMJ) in the Polar V800. There was no significant
systematic bias +- random errors (p > 0.05) between test-retest. Low
coefficients of variation (<5%) were detected in both jumps in the Polar V800.
In the validity assessment, similar jump height was detected among devices (p >
0.05). There was almost perfect agreement between the Polar V800 compared to a
force platform for the SJ and CMJ tests (Mean ICCs = 0.95; no systematic bias
+- random errors in SJ mean: -0.38 +- 2.10 cm, p > 0.05). Mean ICC between the
Polar V800 versus high-speed camera was 0.91 for the SJ and CMJ tests, however,
a significant systematic bias +- random error (0.97 +- 2.60 cm; p = 0.01) was
detected in CMJ test. The Polar V800 offers valid, compared to force platform,
and reliable information about vertical jump height performance in physically
active healthy young men.

5G is the next-generation cellular network technology, with the goal of
meeting the critical demand for bandwidth required to accommodate a high
density of users. It employs flexible architectures to accommodate the high
density. 5G is enabled by mmWave communication, which operates at frequencies
ranging from 30 to 300 GHz. This paper describes the design of the 5G Routing
Interfered Environment (5GRIE), a python-based environment based on Gym's
methods. The environment can run different algorithms to route packets with
source and destination pairs using a formulated interference model. Deep
Reinforcement Learning algorithms that use Stable-Baselines 3, as well as
heuristic-based algorithms like random or greedy, can be run on it. Profitable
is an algorithm that is provided.

Practical determination of physical recovery after intense exercise is a
challenging topic that must include mechanical aspects as well as cognitive
ones because most of physical sport activities, as well as professional
activities (including brain computer interface-operated systems), require good
shape in both of them. This paper presents a new online handwritten database of
20 healthy subjects. The main goal was to study the influence of several
physical exercise stimuli in different handwritten tasks and to evaluate the
recovery after strenuous exercise. To this aim, they performed different
handwritten tasks before and after physical exercise as well as other
measurements such as metabolic and mechanical fatigue assessment. Experimental
results showed that although a fast mechanical recovery happens and can be
measured by lactate concentrations and mechanical fatigue, this is not the case
when cognitive effort is required. Handwriting analysis revealed that
statistical differences exist on handwriting performance even after lactate
concentration and mechanical assessment recovery. Conclusions: This points out
a necessity of more recovering time in sport and professional activities than
those measured in classic ways.

We propose a novel non-linear manifold learning from snapshot data and
demonstrate its superiority over Proper Orthogonal Decomposition (POD) for
shedding-dominated shear flows. Key enablers are isometric feature mapping,
Isomap (Tenenbaum et al., 2000), as encoder and K-nearest neighbours (KNN)
algorithm as decoder. The proposed technique is applied to numerical and
experimental datasets including the fluidic pinball, a swirling jet, and the
wake behind a couple of tandem cylinders. Analyzing the fluidic pinball, the
manifold is able to describe the pitchfork bifurcation and the chaotic regime
with only three feature coordinates. These coordinates are linked to
vortex-shedding phases and the force coefficients. The manifold coordinates of
the swirling jet are comparable to the POD mode amplitudes, yet allow for a
more distinct manifold identification which is less sensitive to measurement
noise. As similar observation is made for the wake of two tandem cylinders
(Raiola et al., 2016). The tandem cylinders are aligned in streamwise distance
which corresponds to the transition between the single bluff body and the
reattachment regimes of vortex shedding. Isomap unveils these two shedding
regimes while the Lissajous plots of first two POD mode amplitudes feature a
single circle. The reconstruction error of the manifold model is small compared
to the fluctuation level, indicating that the low embedding dimensions contains
the coherent structure dynamics. The proposed Isomap-KNN manifold learner is
expected to be of large importance in estimation, dynamic modeling and control
for large range of configurations with dominant coherent structures.

Wildfire events have caused severe losses in many places around the world and
are expected to increase with climate change. Throughout the years many
technologies have been developed to identify fire events early on and to
simulate fire behavior once they have started. Another particularly helpful
technology is fire risk indices, which use weather forcing to make advanced
predictions of the risk of fire. Predictions of fire risk indices can be used,
for instance, to allocate resources in places with high risk. These indices
have been developed over the years as empirical models with parameters that
were estimated in lab experiments and field tests. These parameters, however,
may not fit well all places where these models are used. In this paper we
propose a novel implementation of one index (NFDRS IC) as a differentiable
function in which one can optimize its internal parameters via gradient
descent. We leverage existing machine learning frameworks (PyTorch) to
construct our model. This approach has two benefits: (1) the NFDRS IC
parameters can be improved for each region using actual observed fire events,
and (2) the internal variables remain intact for interpretations by specialists
instead of meaningless hidden layers as in traditional neural networks. In this
paper we evaluate our strategy with actual fire events for locations in the USA
and Europe.

Interventional magnetic resonance imaging (i-MRI) for surgical guidance could
help visualize the interventional process such as deep brain stimulation (DBS),
improving the surgery performance and patient outcome. Different from
retrospective reconstruction in conventional dynamic imaging, i-MRI for DBS has
to acquire and reconstruct the interventional images sequentially online. Here
we proposed a convolutional long short-term memory (Conv-LSTM) based recurrent
neural network (RNN), or ConvLR, to reconstruct interventional images with
golden-angle radial sampling. By using an initializer and Conv-LSTM blocks, the
priors from the pre-operative reference image and intra-operative frames were
exploited for reconstructing the current frame. Data consistency for radial
sampling was implemented by a soft-projection method. To improve the
reconstruction accuracy, an adversarial learning strategy was adopted. A set of
interventional images based on the pre-operative and post-operative MR images
were simulated for algorithm validation. Results showed with only 10 radial
spokes, ConvLR provided the best performance compared with state-of-the-art
methods, giving an acceleration up to 40 folds. The proposed algorithm has the
potential to achieve real-time i-MRI for DBS and can be used for general
purpose MR-guided intervention.

Temporal Convolutional Networks (TCNs) are promising Deep Learning models for
time-series processing tasks. One key feature of TCNs is time-dilated
convolution, whose optimization requires extensive experimentation. We propose
an automatic dilation optimizer, which tackles the problem as a weight pruning
on the time-axis, and learns dilation factors together with weights, in a
single training. Our method reduces the model size and inference latency on a
real SoC hardware target by up to 7.4x and 3x, respectively with no accuracy
drop compared to a network without dilation. It also yields a rich set of
Pareto-optimal TCNs starting from a single model, outperforming hand-designed
solutions in both size and accuracy.

We describe methods that extend (generalize or transport) causal inferences
from cluster randomized trials to a target population of clusters, under a
general nonparametric model that allows for arbitrary within-cluster
dependence. We propose doubly robust estimators of potential outcome means in
the target population that exploit individual-level data on covariates and
outcomes to improve efficiency and are appropriate for use with machine
learning methods. We illustrate the methods using a cluster randomized trial of
influenza vaccination strategies conducted in 818 nursing homes nested in a
cohort of 4,475 trial-eligible Medicare-certified nursing homes.

We present STUDIES, a new speech corpus for developing a voice agent that can
speak in a friendly manner. Humans naturally control their speech prosody to
empathize with each other. By incorporating this "empathetic dialogue" behavior
into a spoken dialogue system, we can develop a voice agent that can respond to
a user more naturally. We designed the STUDIES corpus to include a speaker who
speaks with empathy for the interlocutor's emotion explicitly. We describe our
methodology to construct an empathetic dialogue speech corpus and report the
analysis results of the STUDIES corpus. We conducted a text-to-speech
experiment to initially investigate how we can develop more natural voice agent
that can tune its speaking style corresponding to the interlocutor's emotion.
The results show that the use of interlocutor's emotion label and
conversational context embedding can produce speech with the same degree of
naturalness as that synthesized by using the agent's emotion label. Our project
page of the STUDIES corpus is http://sython.org/Corpus/STUDIES.

In this paper, we consider communication-efficient over-the-air federated
learning (FL), where multiple edge devices with non-independent and identically
distributed datasets perform multiple local iterations in each communication
round and then concurrently transmit their updated gradients to an edge server
over the same radio channel for global model aggregation using over-the-air
computation (AirComp). We derive the upper bound of the time-average norm of
the gradients to characterize the convergence of AirComp-assisted FL, which
reveals the impact of the model aggregation errors accumulated over all
communication rounds on convergence. Based on the convergence analysis, we
formulate an optimization problem to minimize the upper bound to enhance the
learning performance, followed by proposing an alternating optimization
algorithm to facilitate the optimal transceiver design for AirComp-assisted FL.
As the alternating optimization algorithm suffers from high computation
complexity, we further develop a knowledge-guided learning algorithm that
exploits the structure of the analytic expression of the optimal transmit power
to achieve computation-efficient transceiver design. Simulation results
demonstrate that the proposed knowledge-guided learning algorithm achieves a
comparable performance as the alternating optimization algorithm, but with a
much lower computation complexity. Moreover, both proposed algorithms
outperform the baseline methods in terms of convergence speed and test
accuracy.

Enabling the capability of assessing risk and making risk-aware decisions is
essential to applying reinforcement learning to safety-critical robots like
drones. In this paper, we investigate a specific case where a nano quadcopter
robot learns to navigate an apriori-unknown cluttered environment under partial
observability. We present a distributional reinforcement learning framework to
generate adaptive risk-tendency policies. Specifically, we propose to use lower
tail conditional variance of the learnt return distribution as intrinsic
uncertainty estimation, and use exponentially weighted average forecasting
(EWAF) to adapt the risk-tendency in accordance with the estimated uncertainty.
In simulation and real-world empirical results, we show that (1) the most
effective risk-tendency vary across states, (2) the agent with adaptive
risk-tendency achieves superior performance compared to risk-neutral policy or
risk-averse policy baselines.

A wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU
to enable non-invasive and comfortable monitoring, but ensuring robust
PPG-based heart-rate monitoring in the presence of motion artifacts is still an
open challenge. Recent state-of-the-art algorithms combine PPG and inertial
signals to mitigate the effect of motion artifacts. However, these approaches
suffer from limited generality. Moreover, their deployment on MCU-based edge
nodes has not been investigated. In this work, we tackle both the
aforementioned problems by proposing the use of hardware-friendly Temporal
Convolutional Networks (TCN) for PPG-based heart estimation. Starting from a
single "seed" TCN, we leverage an automatic Neural Architecture Search (NAS)
approach to derive a rich family of models. Among them, we obtain a TCN that
outperforms the previous state-of-the-art on the largest PPG dataset available
(PPGDalia), achieving a Mean Absolute Error (MAE) of just 3.84 Beats Per Minute
(BPM). Furthermore, we tested also a set of smaller yet still accurate (MAE of
5.64 - 6.29 BPM) networks that can be deployed on a commercial MCU (STM32L4)
which require as few as 5k parameters and reach a latency of 17.1 ms consuming
just 0.21 mJ per inference.

This work develops a new method for estimating and optimizing the directed
information rate between two jointly stationary and ergodic stochastic
processes. Building upon recent advances in machine learning, we propose a
recurrent neural network (RNN)-based estimator which is optimized via gradient
ascent over the RNN parameters. The estimator does not require prior knowledge
of the underlying joint and marginal distributions. The estimator is also
readily optimized over continuous input processes realized by a deep generative
model. We prove consistency of the proposed estimation and optimization methods
and combine them to obtain end-to-end performance guarantees. Applications for
channel capacity estimation of continuous channels with memory are explored,
and empirical results demonstrating the scalability and accuracy of our method
are provided. When the channel is memoryless, we investigate the mapping
learned by the optimized input generator.

For the most comfortable, human-aware robot navigation, subjective user
preferences need to be taken into account. This paper presents a novel
reinforcement learning framework to train a personalized navigation controller
along with an intuitive virtual reality demonstration interface. The conducted
user study provides evidence that our personalized approach significantly
outperforms classical approaches with more comfortable human-robot experiences.
We achieve these results using only a few demonstration trajectories from
non-expert users, who predominantly appreciate the intuitive demonstration
setup. As we show in the experiments, the learned controller generalizes well
to states not covered in the demonstration data, while still reflecting user
preferences during navigation. Finally, we transfer the navigation controller
without loss in performance to a real robot.

Maximizing a DR-submodular function subject to a general convex set is an
NP-hard problem arising from many applications in combinatorial optimization
and machine learning. While it is highly desirable to design efficient
approximation algorithms under this general setting where neither the objective
function is monotonic nor the feasible set is down-closed, our main
contribution is to present a 0.25-approximation Frank-Wolfe type of algorithm
with a sub-exponential time-complexity under the value oracle model.

In recent years, named entity recognition has always been a popular research
in the field of natural language processing, while traditional deep learning
methods require a large amount of labeled data for model training, which makes
them not suitable for areas where labeling resources are scarce. In addition,
the existing cross-domain knowledge transfer methods need to adjust the entity
labels for different fields, so as to increase the training cost. To solve
these problems, enlightened by a processing method of Chinese named entity
recognition, we propose to use domain knowledge to improve the performance of
named entity recognition in areas with low resources. The domain knowledge
mainly applied by us is domain dictionary and domain labeled data. We use
dictionary information for each word to strengthen its word embedding and
domain labeled data to reinforce the recognition effect. The proposed model
avoids large-scale data adjustments in different domains while handling named
entities recognition with low resources. Experiments demonstrate the
effectiveness of our method, which has achieved impressive results on the data
set in the field of scientific and technological equipment, and the F1 score
has been significantly improved compared with many other baseline methods.

Regularized methods have been widely applied to system identification
problems without known model structures. This paper proposes an
infinite-dimensional sparse learning algorithm based on atomic norm
regularization. Atomic norm regularization decomposes the transfer function
into first-order atomic models and solves a group lasso problem that selects a
sparse set of poles and identifies the corresponding coefficients. The
difficulty in solving the problem lies in the fact that there are an infinite
number of possible atomic models. This work proposes a greedy algorithm that
generates new candidate atomic models maximizing the violation of the
optimality condition of the existing problem. This algorithm is able to solve
the infinite-dimensional group lasso problem with high precision. The algorithm
is further extended to reduce the bias and reject false positives in pole
location estimation by iteratively reweighted adaptive group lasso and
complementary pairs stability selection respectively. Numerical results
demonstrate that the proposed algorithm performs better than benchmark
parameterized and regularized methods in terms of both impulse response fitting
and pole location estimation.

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal
multiple access (NOMA) scheme for the enabling of massive machine-type
communication. In SCMA, the design of good sparse codebooks and efficient
multiuser decoding have attracted tremendous research attention in the past few
years. This paper aims to leverage deep learning to jointly design the downlink
SCMA encoder and decoder with the aid of autoencoder. We introduce a novel
end-to-end learning based SCMA (E2E-SCMA) design framework, under which
improved sparse codebooks and low-complexity decoder are obtained. Compared to
conventional SCMA schemes, our numerical results show that the proposed
E2E-SCMA leads to significant improvements in terms of error rate and

Controlling UAV flights precisely requires a realistic dynamic model and
accurate state estimates from onboard sensors like UAV, GPS and visual
observations. Obtaining a precise dynamic model is extremely difficult, as
important aerodynamic effects are hard to model, in particular ground effect
and other turbulences. While machine learning has been used in the past to
estimate UAV created turbulence, this was restricted to flat grounds or diffuse
in-flight air turbulences, both without taking into account obstacles. In this
work we address the complex problem of estimating in-flight turbulences caused
by obstacles, in particular the complex structures in cluttered environments.
We learn a mapping from control input and images captured by onboard cameras to
turbulence. In a large-scale setting, we train a model over a large number of
different simulated photo-realistic environments loaded into the Habitat.AI
simulator augmented with a dynamic UAV model and an analytic ground effect
model. We transfer the model from simulation to a real environment and evaluate
on real UAV flights from the EuRoC-MAV dataset, showing that the model is
capable of good sim2real generalization performance. The dataset will be made
publicly available upon acceptance.

3D shape abstraction has drawn great interest over the years. Apart from
low-level representations such as meshes and voxels, researchers also seek to
semantically abstract complex objects with basic geometric primitives. Recent
deep learning methods rely heavily on datasets, with limited generality to
unseen categories. Furthermore, abstracting an object accurately yet with a
small number of primitives still remains a challenge. In this paper, we propose
a novel non-parametric Bayesian statistical method to infer an abstraction,
consisting of an unknown number of geometric primitives, from a point cloud. We
model the generation of points as observations sampled from an infinite mixture
of Gaussian Superquadric Taper Models (GSTM). Our approach formulates the
abstraction as a clustering problem, in which: 1) each point is assigned to a
cluster via the Chinese Restaurant Process (CRP); 2) a primitive representation
is optimized for each cluster, and 3) a merging post-process is incorporated to
provide a concise representation. We conduct extensive experiments on two
datasets. The results indicate that our method outperforms the state-of-the-art
in terms of accuracy and is generalizable to various types of objects.

The task of Named Entity Recognition (NER) is an important component of many
natural language processing systems, such as relation extraction and knowledge
graph construction. In this work, we present a simple and effective approach
for Named Entity Recognition. The main idea of our approach is to encode the
input subword sequence with a pre-trained transformer such as BERT, and then,
instead of directly classifying the word labels, another layer of transformer
is added to the subword representation to better encode the word-level
interaction. We evaluate our approach on three benchmark datasets for
scientific NER, particularly in the computer science and biomedical domains.
Experimental results show that our model outperforms the current
state-of-the-art on SciERC and TDM datasets without requiring external
resources or specific data augmentation. Code is available at
\url{https://github.com/urchade/HNER}.

Human-Object Interaction (HOI) detection is the task of identifying a set of
<human, object, interaction> triplets from an image. Recent work proposed
transformer encoder-decoder architectures that successfully eliminated the need
for many hand-designed components in HOI detection through end-to-end training.
However, they are limited to single-scale feature resolution, providing
suboptimal performance in scenes containing humans, objects and their
interactions with vastly different scales and distances. To tackle this
problem, we propose a Multi-Scale TRansformer (MSTR) for HOI detection powered
by two novel HOI-aware deformable attention modules called Dual-Entity
attention and Entity-conditioned Context attention. While existing deformable
attention comes at a huge cost in HOI detection performance, our proposed
attention modules of MSTR learn to effectively attend to sampling points that
are essential to identify interactions. In experiments, we achieve the new
state-of-the-art performance on two HOI detection benchmarks.

The tensor network, as a facterization of tensors, aims at performing the
operations that are common for normal tensors, such as addition, contraction
and stacking. However, due to its non-unique network structure, only the tensor
network contraction is so far well defined. In this paper, we propose a
mathematically rigorous definition for the tensor network stack approach, that
compress a large amount of tensor networks into a single one without changing
their structures and configurations. We illustrate the main ideas with the
matrix product states based machine learning as an example. Our results are
compared with the for loop and the efficient coding method on both CPU and GPU.

Zero-shot sketch-based image retrieval typically asks for a trained model to
be applied as is to unseen categories. In this paper, we question to argue that
this setup by definition is not compatible with the inherent abstract and
subjective nature of sketches, i.e., the model might transfer well to new
categories, but will not understand sketches existing in different test-time
distribution as a result. We thus extend ZS-SBIR asking it to transfer to both
categories and sketch distributions. Our key contribution is a test-time
training paradigm that can adapt using just one sketch. Since there is no
paired photo, we make use of a sketch raster-vector reconstruction module as a
self-supervised auxiliary task. To maintain the fidelity of the trained
cross-modal joint embedding during test-time update, we design a novel
meta-learning based training paradigm to learn a separation between model
updates incurred by this auxiliary task from those off the primary objective of
discriminative learning. Extensive experiments show our model to outperform
state of-the-arts, thanks to the proposed test-time adaption that not only
transfers to new categories but also accommodates to new sketching styles.

This work considers training neural networks for speaker recognition with a
much smaller dataset size compared to contemporary work. We artificially
restrict the amount of data by proposing three subsets of the popular VoxCeleb2
dataset. These subsets are restricted to 50 k audio files (versus over 1 M
files available), and vary on the axis of number of speakers and session
variability. We train three speaker recognition systems on these subsets; the
X-vector, ECAPA-TDNN, and wav2vec2 network architectures. We show that the
self-supervised, pre-trained weights of wav2vec2 substantially improve
performance when training data is limited. Code and data subsets are available
at \url{https://github.com/nikvaessen/w2v2-speaker-few-samples}.

In this work a general framework is proposed to support the development of
software systems that are able to adapt their behaviour according to the
operating environment changes. The proposed approach, named REPTILE, works in a
complete proactive manner and relies on Deep Reinforcement Learning-based
agents to react to events, referred as novelties, that can affect the expected
behaviour of the system. In our framework, two types of novelties are taken
into account: those related to the context/environment and those related to the
physical architecture itself. The framework, predicting those novelties before
their occurrence, extracts time-changing models of the environment and uses a
suitable Markov Decision Process to deal with the real-time setting. Moreover,
the architecture of our RL agent evolves based on the possible actions that can
be taken.

This work approximates high-dimensional density functions with an ANOVA-like
sparse structure by the mixture of wrapped Gaussian and von Mises
distributions. When the dimension $d$ is very large, it is complex and
impossible to train the model parameters by the usually known learning
algorithms due to the curse of dimensionality. Therefore, assuming that each
component of the model depends on an a priori unknown much smaller number of
variables than the space dimension $d,$ we first define an algorithm that
determines the mixture model's set of active variables by the
Kolmogorov-Smirnov and correlation test. Then restricting the learning
procedure to the set of active variables, we iteratively determine the set of
variable interactions of the marginal density function and simultaneously learn
the parameters by the Kolmogorov and correlation coefficient statistic test and
the proximal Expectation-Maximization algorithm. The learning procedure
considerably reduces the algorithm's complexity for the input dimension $d$ and
increases the model's accuracy for the given samples, as the numerical examples
show.

Unsupervised person re-identification (re-ID) aims at learning discriminative
representations for person retrieval from unlabeled data. Recent techniques
accomplish this task by using pseudo-labels, but these labels are inherently
noisy and deteriorate the accuracy. To overcome this problem, several
pseudo-label refinement methods have been proposed, but they neglect the
fine-grained local context essential for person re-ID. In this paper, we
propose a novel Part-based Pseudo Label Refinement (PPLR) framework that
reduces the label noise by employing the complementary relationship between
global and part features. Specifically, we design a cross agreement score as
the similarity of k-nearest neighbors between feature spaces to exploit the
reliable complementary relationship. Based on the cross agreement, we refine
pseudo-labels of global features by ensembling the predictions of part
features, which collectively alleviate the noise in global feature clustering.
We further refine pseudo-labels of part features by applying label smoothing
according to the suitability of given labels for each part. Thanks to the
reliable complementary information provided by the cross agreement score, our
PPLR effectively reduces the influence of noisy labels and learns
discriminative representations with rich local contexts. Extensive experimental
results on Market-1501 and MSMT17 demonstrate the effectiveness of the proposed
method over the state-of-the-art performance. The code is available at
https://github.com/yoonkicho/PPLR.

Crop maps are crucial for agricultural monitoring and food management and can
additionally support domain-specific applications, such as setting cold supply
chain infrastructure in developing countries. Machine learning (ML) models,
combined with freely-available satellite imagery, can be used to produce
cost-effective and high spatial-resolution crop maps. However, accessing ground
truth data for supervised learning is especially challenging in developing
countries due to factors such as smallholding and fragmented geography, which
often results in a lack of crop type maps or even reliable cropland maps. Our
area of interest for this study lies in Himachal Pradesh, India, where we aim
at producing an open-access binary cropland map at 10-meter resolution for the
Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline
that relies on Sentinel-2 satellite images time series. We investigated two
pixel-based supervised classifiers, support vector machines (SVM) and random
forest (RF), which are used to classify per-pixel time series for binary
cropland mapping. The ground truth data used for training, validation and
testing was manually annotated from a combination of field survey reference
points and visual interpretation of very high resolution (VHR) imagery. We
trained and validated the models via spatial cross-validation to account for
local spatial autocorrelation and selected the RF model due to overall
robustness and lower computational cost. We tested the generalization
capability of the chosen model at the pixel level by computing the accuracy,
recall, precision, and F1-score on hold-out test sets of each district,
achieving an average accuracy for the RF (our best model) of 87%. We used this
model to generate a cropland map for three districts of Himachal Pradesh,
spanning 14,600 km2, which improves the resolution and quality of existing
public maps.

Game dynamics theory, as a field of science, the consistency of theory and
experiment is essential. In the past 10 years, important progress has been made
in the merging of the theory and experiment in this field, in which dynamics
cycle is the presentation. However, the merging works have not got rid of the
constraints of Euclidean two-dimensional cycle so far. This paper uses a
classic four-strategy game to study the dynamic structure (non-Euclidean
superplane cycle). The consistency is in significant between the three ways:
(1) the analytical results from evolutionary dynamics equations, (2)
agent-based simulation results from learning models and (3) laboratory results
from human subjects game experiments. The consistency suggests that, game
dynamic structure could be quantitatively predictable, observable and
controllable in general.

Federated Learning (FL) fuses collaborative models from local nodes without
centralizing users' data. The permutation invariance property of neural
networks and the non-i.i.d. data across clients make the locally updated
parameters imprecisely aligned, disabling the coordinate-based parameter
averaging. Traditional neurons do not explicitly consider position information.
Hence, we propose Position-Aware Neurons (PANs) as an alternative, fusing
position-related values (i.e., position encodings) into neuron outputs. PANs
couple themselves to their positions and minimize the possibility of
dislocation, even updating on heterogeneous data. We turn on/off PANs to
disable/enable the permutation invariance property of neural networks. PANs are
tightly coupled with positions when applied to FL, making parameters across
clients pre-aligned and facilitating coordinate-based parameter averaging. PANs
are algorithm-agnostic and could universally improve existing FL algorithms.
Furthermore, "FL with PANs" is simple to implement and computationally
friendly.

This paper studies the 3D instance segmentation problem, which has a variety
of real-world applications such as robotics and augmented reality. Since the
surroundings of 3D objects are of high complexity, the separating of different
objects is very difficult. To address this challenging problem, we propose a
novel framework to group and refine the 3D instances. In practice, we first
learn an offset vector for each point and shift it to its predicted instance
center. To better group these points, we propose a Hierarchical Point Grouping
algorithm to merge the centrally aggregated points progressively. All points
are grouped into small clusters, which further gradually undergo another
clustering procedure to merge into larger groups. These multi-scale groups are
exploited for instance prediction, which is beneficial for predicting instances
with different scales. In addition, a novel MaskScoreNet is developed to
produce binary point masks of these groups for further refining the
segmentation results. Extensive experiments conducted on the ScanNetV2 and
S3DIS benchmarks demonstrate the effectiveness of the proposed method. For
instance, our approach achieves a 66.4\% mAP with the 0.5 IoU threshold on the
ScanNetV2 test set, which is 1.9\% higher than the state-of-the-art method.

Neural networks have been used successfully in a variety of fields, which has
led to a great deal of interest in developing a theoretical understanding of
how they store the information needed to perform a particular task. We study
the weight matrices of trained deep neural networks using methods from random
matrix theory (RMT) and show that the statistics of most of the singular values
follow universal RMT predictions. This suggests that they are random and do not
contain system specific information, which we investigate further by comparing
the statistics of eigenvector entries to the universal Porter-Thomas
distribution. We find that for most eigenvectors the hypothesis of randomness
cannot be rejected, and that only eigenvectors belonging to the largest
singular values deviate from the RMT prediction, indicating that they may
encode learned information. In addition, a comparison with RMT predictions also
allows to distinguish networks trained in different learning regimes - from
lazy to rich learning. We analyze the spectral distribution of the large
singular values using the Hill estimator and find that the distribution cannot
in general be characterized by a tail index, i.e. is not of power law type.

Model-based value expansion methods promise to improve the quality of value
function targets and, thereby, the effectiveness of value function learning.
However, to date, these methods are being outperformed by Dyna-style algorithms
with conceptually simpler 1-step value function targets. This shows that in
practice, the theoretical justification of value expansion does not seem to
hold. We provide a thorough empirical study to shed light on the causes of
failure of value expansion methods in practice which is believed to be the
compounding model error. By leveraging GPU based physics simulators, we are
able to efficiently use the true dynamics for analysis inside the model-based
reinforcement learning loop. Performing extensive comparisons between true and
learned dynamics sheds light into this black box. This paper provides a better
understanding of the actual problems in value expansion. We provide future
directions of research by empirically testing the maximum theoretical
performance of current approaches.

We study the problem of building text classifiers with little or no training
data, commonly known as zero and few-shot text classification. In recent years,
an approach based on neural textual entailment models has been found to give
strong results on a diverse range of tasks. In this work, we show that with
proper pre-training, Siamese Networks that embed texts and labels offer a
competitive alternative. These models allow for a large reduction in inference
cost: constant in the number of labels rather than linear. Furthermore, we
introduce label tuning, a simple and computationally efficient approach that
allows to adapt the models in a few-shot setup by only changing the label
embeddings. While giving lower performance than model fine-tuning, this
approach has the architectural advantage that a single encoder can be shared by
many different tasks.

Large neural networks trained in the overparameterized regime are able to fit
noise to zero train error. Recent work \citep{nakkiran2020distributional} has
empirically observed that such networks behave as "conditional samplers" from
the noisy distribution. That is, they replicate the noise in the train data to
unseen examples. We give a theoretical framework for studying this conditional
sampling behavior in the context of learning theory. We relate the notion of
such samplers to knowledge distillation, where a student network imitates the
outputs of a teacher on unlabeled data. We show that samplers, while being bad
classifiers, can be good teachers. Concretely, we prove that distillation from
samplers is guaranteed to produce a student which approximates the Bayes
optimal classifier. Finally, we show that some common learning algorithms
(e.g., Nearest-Neighbours and Kernel Machines) can generate samplers when
applied in the overparameterized regime.

Batch-Normalization (BN) layers have become fundamental components in the
evermore complex deep neural network architectures. Such models require
acceleration processes for deployment on edge devices. However, BN layers add
computation bottlenecks due to the sequential operation processing: thus, a
key, yet often overlooked component of the acceleration process is BN layers
folding. In this paper, we demonstrate that the current BN folding approaches
are suboptimal in terms of how many layers can be removed. We therefore provide
a necessary and sufficient condition for BN folding and a corresponding optimal
algorithm. The proposed approach systematically outperforms existing baselines
and allows to dramatically reduce the inference time of deep neural networks.

Computationally expensive neural networks are ubiquitous in computer vision
and solutions for efficient inference have drawn a growing attention in the
machine learning community. Examples of such solutions comprise quantization,
i.e. converting the processing values (weights and inputs) from floating point
into integers e.g. int8 or int4. Concurrently, the rise of privacy concerns
motivated the study of less invasive acceleration methods, such as data-free
quantization of pre-trained models weights and activations. Previous approaches
either exploit statistical information to deduce scalar ranges and scaling
factors for the activations in a static manner, or dynamically adapt this range
on-the-fly for each input of each layers (also referred to as activations): the
latter generally being more accurate at the expanse of significantly slower
inference. In this work, we argue that static input quantization can reach the
accuracy levels of dynamic methods by means of a per-channel input quantization
scheme that allows one to more finely preserve cross-channel dynamics. We show
through a thorough empirical evaluation on multiple computer vision problems
(e.g. ImageNet classification, Pascal VOC object detection as well as
CityScapes semantic segmentation) that the proposed method, dubbed SPIQ,
achieves accuracies rivalling dynamic approaches with static-level inference
speed, significantly outperforming state-of-the-art quantization methods on
every benchmark.

Deep learning models are typically evaluated to measure and compare their
performance on a given task. The metrics that are commonly used to evaluate
these models are standard metrics that are used for different tasks. In the
field of music composition or generation, the standard metrics used in other
fields have no clear meaning in terms of music theory. In this paper, we
propose a subjective method to evaluate AI-based music composition systems by
asking questions related to basic music principles to different levels of users
based on their musical experience and knowledge. We use this method to compare
state-of-the-art models for music composition with deep learning. We give the
results of this evaluation method and we compare the responses of each user
level for each evaluated model.

This paper addresses the task of performing robust and reliable time-delay
estimation in audio-signals in noisy and reverberating environments. In
contrast to the popular signal processing based methods, this paper proposes
machine learning based method, i.e., a semi-causal convolutional neural network
consisting of a set of causal and anti-causal layers with a novel
correlation-based objective function. The causality in the network ensures
non-leakage of representations from future time-intervals and the proposed loss
function makes the network generate sequences with high correlation at the
actual time delay. The proposed approach is also intrinsically interpretable as
it does not lose time information. Even a shallow convolution network is able
to capture local patterns in sequences, while also correlating them globally.
SyncNet outperforms other classical approaches in estimating mutual time delays
for different types of audio signals including pulse, speech and musical beats.

Cross-Lingual Word Embeddings (CLWEs) are a key component to transfer
linguistic information learnt from higher-resource settings into lower-resource
ones. Recent research in cross-lingual representation learning has focused on
offline mapping approaches due to their simplicity, computational efficacy, and
ability to work with minimal parallel resources. However, they crucially depend
on the assumption of embedding spaces being approximately isomorphic i.e.
sharing similar geometric structure, which does not hold in practice, leading
to poorer performance on low-resource and distant language pairs. In this
paper, we introduce a framework to learn CLWEs, without assuming isometry, for
low-resource pairs via joint exploitation of a related higher-resource
language. In our work, we first pre-align the low-resource and related language
embedding spaces using offline methods to mitigate the assumption of isometry.
Following this, we use joint training methods to develops CLWEs for the related
language and the target embed-ding space. Finally, we remap the pre-aligned
low-resource space and the target space to generate the final CLWEs. We show
consistent gains over current methods in both quality and degree of
isomorphism, as measured by bilingual lexicon induction (BLI) and eigenvalue
similarity respectively, across several language pairs: {Nepali, Finnish,
Romanian, Gujarati, Hungarian}-English. Lastly, our analysis also points to the
relatedness as well as the amount of related language data available as being
key factors in determining the quality of embeddings achieved.

Dynamic state and parameter estimation (DSE) plays a key role for reliably
monitoring and operating future, power-electronics-dominated power systems.
While DSE is a very active research field, experimental applications of
proposed algorithms to real-world systems remain scarce. This motivates the
present paper, in which we demonstrate the effectiveness of a DSE algorithm
previously presented by parts of the authors with real-world data collected by
a Phasor Measurement Unit (PMU) at a substation close to a power plant within
the extra-high voltage grid of Germany. To this end, at first we derive a
suitable mapping of the real-world PMU-measurements recorded at a substation
close to the power plant to the terminal bus of the power plants' synchronous
generator (SG). This mapping considers the high-voltage (HV) transmission line,
the tap-changing transformer and the auxiliary system of the power plant. Next,
we introduce several practically motivated extensions to the estimation
algorithm, which significantly improve its practical performance with
real-world measurements. Finally, we successfully validate the algorithm
experimentally in an auto- as well as a cross-validation.

In this work, we explore the challenging task of generating 3D shapes from
text. Beyond the existing works, we propose a new approach for text-guided 3D
shape generation, capable of producing high-fidelity shapes with colors that
match the given text description. This work has several technical
contributions. First, we decouple the shape and color predictions for learning
features in both texts and shapes, and propose the word-level spatial
transformer to correlate word features from text with spatial features from
shape. Also, we design a cyclic loss to encourage consistency between text and
shape, and introduce the shape IMLE to diversify the generated shapes. Further,
we extend the framework to enable text-guided shape manipulation. Extensive
experiments on the largest existing text-shape benchmark manifest the
superiority of this work. The code and the models are available at
https://github.com/liuzhengzhe/Towards-Implicit Text-Guided-Shape-Generation.

Chemical plant design and optimisation have proven challenging due to the
complexity of these real-world systems. The resulting complexity translates
into high computational costs for these systems' mathematical formulations and
simulation models. Research has illustrated the benefits of using machine
learning surrogate models as substitutes for computationally expensive models
during optimisation. This paper extends recent research into optimising
chemical plant design and operation. The study further explores Surrogate
Assisted Genetic Algorithms (SA-GA) in more complex variants of the original
plant design and optimisation problems, such as the inclusion of parallel and
feedback components. The novel extension to the original algorithm proposed in
this study, Surrogate Assisted NSGA-\Romannum{2} (SA-NSGA), was tested on a
popular literature case, the Pressure Swing Adsorption (PSA) system. We further
provide extensive experimentation, comparing various meta-heuristic
optimisation techniques and numerous machine learning models as surrogates. The
results for both sets of systems illustrate the benefits of using Genetic
Algorithms as an optimisation framework for complex chemical plant system
design and optimisation for both single and multi-objective scenarios. We
confirm that Random Forest surrogate assisted Evolutionary Algorithms can be
scaled to increasingly complex chemical systems with parallel and feedback
components. We further find that combining a Genetic Algorithm framework with
Machine Learning Surrogate models as a substitute for long-running simulation
models yields significant computational efficiency improvements, 1.7 - 1.84
times speedup for the increased complexity examples and a 2.7 times speedup for
the Pressure Swing Adsorption system.

Deep learning methods provide significant assistance in analyzing coronavirus
disease (COVID-19) in chest computed tomography (CT) images, including
identification, severity assessment, and segmentation. Although the earlier
developed methods address the lack of data and specific annotations, the
current goal is to build a robust algorithm for clinical use, having a larger
pool of available data. With the larger datasets, the domain shift problem
arises, affecting the performance of methods on the unseen data. One of the
critical sources of domain shift in CT images is the difference in
reconstruction kernels used to generate images from the raw data (sinograms).
In this paper, we show a decrease in the COVID-19 segmentation quality of the
model trained on the smooth and tested on the sharp reconstruction kernels.
Furthermore, we compare several domain adaptation approaches to tackle the
problem, such as task-specific augmentation and unsupervised adversarial
learning. Finally, we propose the unsupervised adaptation method, called
F-Consistency, that outperforms the previous approaches. Our method exploits a
set of unlabeled CT image pairs which differ only in reconstruction kernels
within every pair. It enforces the similarity of the network hidden
representations (feature maps) by minimizing mean squared error (MSE) between
paired feature maps. We show our method achieving 0.64 Dice Score on the test
dataset with unseen sharp kernels, compared to the 0.56 Dice Score of the
baseline model. Moreover, F-Consistency scores 0.80 Dice Score between
predictions on the paired images, which almost doubles the baseline score of
0.46 and surpasses the other methods. We also show F-Consistency to better
generalize on the unseen kernels and without the specific semantic content,
e.g., presence of the COVID-19 lesions.

In this paper we provide the quantum version of the Convex Non-negative
Matrix Factorization algorithm (Convex-NMF) by using the D-wave quantum
annealer. More precisely, we use D-wave 2000Q to find the low rank
approximation of a fixed real-valued matrix X by the product of two
non-negative matrices factors W and G such that the Frobenius norm of the
difference X-XWG is minimized. In order to solve this optimization problem we
proceed in two steps. In the first step we transform the global real
optimization problem depending on W,G into two quadratic unconstrained binary
optimization problems (QUBO) depending on W and G respectively. In the second
step we use an alternative strategy between the two QUBO problems corresponding
to W and G to find the global solution. The running of these two QUBO problems
on D-wave 2000Q need to use an embedding to the chimera graph of D-wave 2000Q,
this embedding is limited by the number of qubits of D-wave 2000Q. We perform a
study on the maximum number of real data to be used by our approach on D-wave
2000Q. The proposed study is based on the number of qubits used to represent
each real variable. We also tested our approach on D-Wave 2000Q with several
randomly generated data sets to prove that our approach is faster than the
classical approach and also to prove that it gets the best results.

This paper proposes a hybrid learning and optimization framework for mobile
manipulators for complex and physically interactive tasks. The framework
exploits an admittance-type physical interface to obtain intuitive and
simplified human demonstrations and Gaussian Mixture Model (GMM)/Gaussian
Mixture Regression (GMR) to encode and generate the learned task requirements
in terms of position, velocity, and force profiles. Next, using the desired
trajectories and force profiles generated by GMM/GMR, the impedance parameters
of a Cartesian impedance controller are optimized online through a Quadratic
Program augmented with an energy tank to ensure the passivity of the controlled
system. Two experiments are conducted to validate the framework, comparing our
method with two approaches with constant stiffness (high and low). The results
showed that the proposed method outperforms the other two cases in terms of
trajectory tracking and generated interaction forces, even in the presence of
disturbances such as unexpected end-effector collisions.

Deep neural networks (DNNs) have achieved remarkable success in diverse
fields. However, it has been demonstrated that DNNs are very vulnerable to
adversarial examples even in black-box settings. A large number of black-box
attack methods have been proposed to in the literature. However, those methods
usually suffer from low success rates and large query counts, which cannot
fully satisfy practical purposes. In this paper, we propose a hybrid attack
method which trains meta adversarial perturbations (MAPs) on surrogate models
and performs black-box attacks by estimating gradients of the models. Our
method uses the meta adversarial perturbation as an initialization and
subsequently trains any black-box attack method for several epochs.
Furthermore, the MAPs enjoy favorable transferability and universality, in the
sense that they can be employed to boost performance of other black-box
adversarial attack methods. Extensive experiments demonstrate that our method
can not only improve the attack success rates, but also reduces the number of
queries compared to other methods.

Deepfakes are synthetically generated media often devised with malicious
intent. They have become increasingly more convincing with large training
datasets advanced neural networks. These fakes are readily being misused for
slander, misinformation and fraud. For this reason, intensive research for
developing countermeasures is also expanding. However, recent work is almost
exclusively limited to deepfake detection - predicting if audio is real or
fake. This is despite the fact that attribution (who created which fake?) is an
essential building block of a larger defense strategy, as practiced in the
field of cybersecurity for a long time. This paper considers the problem of
deepfake attacker attribution in the domain of audio. We present several
methods for creating attacker signatures using low-level acoustic descriptors
and machine learning embeddings. We show that speech signal features are
inadequate for characterizing attacker signatures. However, we also demonstrate
that embeddings from a recurrent neural network can successfully characterize
attacks from both known and unknown attackers. Our attack signature embeddings
result in distinct clusters, both for seen and unseen audio deepfakes. We show
that these embeddings can be used in downstream-tasks to high-effect, scoring
97.10% accuracy in attacker-id classification.

Using machine learning techniques, we verify that the emergence of
renormalization group limit cycles beyond the unitary limit is transferred from
the three-boson subsystems to the whole four-boson system. Focussing on four
identical bosons, we first generate populations of synthetic singular
potentials within the latent space of a boosted ensemble of variational
autoencoders. After introducing the limit cycle loss for measuring the
deviation of a given renormalization group flow from limit cycle behavior, we
minimize it by applying an elitist genetic algorithm to the generated
populations. The fittest potentials are observed to accumulate around the
inverse-square potential, which we prove to generate limit cycles for four
bosons and which is already known to produce limit cycles in the three-boson
system. This also indicates that a four-body term does not enter low-energy
observables at leading order, since we do not observe any additional scale to
emerge.

Automatic recognition of dysarthric and elderly speech highly challenging
tasks to date. Speaker-level heterogeneity attributed to accent or gender
commonly found in normal speech, when aggregated with age and speech impairment
severity, create large diversity among speakers. Speaker adaptation techniques
play a crucial role in personalization of ASR systems for such users. Their
mobility issues limit the amount of speaker-level data available for model
based adaptation. To this end, this paper investigates two novel forms of
feature based on-the-fly rapid speaker adaptation approaches. The first is
based on speaker-level variance regularized spectral basis embedding (SBEVR)
features, while the other uses on-the-fly learning hidden unit contributions
(LHUC) transforms conditioned on speaker-level spectral features. Experiments
conducted on the UASpeech dysarthric and DimentiaBank Pitt elderly speech
datasets suggest the proposed SBEVR features based adaptation statistically
significantly outperform both the baseline on-the-fly i-Vector adapted hybrid
TDNN/DNN systems by up to 2.48% absolute (7.92% relative) reduction in word
error rate (WER), and offline batch mode model based LHUC adaptation using all
speaker-level data by 0.78% absolute (2.41% relative) in WER reduction.

A brain--machine interface (BMI) based on motor imagery (MI) enables the
control of devices using brain signals while the subject imagines performing a
movement. It plays an important role in prosthesis control and motor
rehabilitation and is a crucial element towards the future Internet of Minds
(IoM). To improve user comfort, preserve data privacy, and reduce the system's
latency, a new trend in wearable BMIs is to embed algorithms on low-power
microcontroller units (MCUs) to process the electroencephalographic (EEG) data
in real-time close to the sensors into the wearable device. However, most of
the classification models present in the literature are too resource-demanding,
making them unfit for low-power MCUs. This paper proposes an efficient
convolutional neural network (CNN) for EEG-based MI classification that
achieves comparable accuracy while being orders of magnitude less
resource-demanding and significantly more energy-efficient than
state-of-the-art (SoA) models for a long-lifetime battery operation. We propose
an automatic channel selection method based on spatial filters and quantize
both weights and activations to 8-bit precision to further reduce the model
complexity with negligible accuracy loss. Finally, we efficiently implement and
evaluate the proposed models on a parallel ultra-low power (PULP) MCU. The most
energy-efficient solution consumes only 50.10 uJ with an inference runtime of
5.53 ms and an accuracy of 82.51% while using 6.4x fewer EEG channels, becoming
the new SoA for embedded MI-BMI and defining a new Pareto frontier in the
three-way trade-off among accuracy, resource cost, and power usage.

Artificial barriers in Learning Automata (LA) is a powerful and yet
under-explored concept although it was first proposed in the 1980s. Introducing
artificial non-absorbing barriers makes the LA schemes resilient to being
trapped in absorbing barriers, a phenomenon which is often referred to as lock
in probability leading to an exclusive choice of one action after convergence.
Within the field of LA and reinforcement learning in general, there is a
sacristy of theoretical works and applications of schemes with artificial
barriers. In this paper, we devise a LA with artificial barriers for solving a
general form of stochastic bimatrix game. Classical LA systems possess
properties of absorbing barriers and they are a powerful tool in game theory
and were shown to converge to game's of Nash equilibrium under limited
information. However, the stream of works in LA for solving game theoretical
problems can merely solve the case where the Saddle Point of the game exists in
a pure strategy and fail to reach mixed Nash equilibrium when no Saddle Point
exists for a pure strategy. In this paper, by resorting to the powerful concept
of artificial barriers, we suggest a LA that converges to an optimal mixed Nash
equilibrium even though there may be no Saddle Point when a pure strategy is
invoked. Our deployed scheme is of Linear Reward-Inaction ($L_{R-I}$) flavor
which is originally an absorbing LA scheme, however, we render it non-absorbing
by introducing artificial barriers in an elegant and natural manner, in the
sense that that the well-known legacy $L_{R-I}$ scheme can be seen as an
instance of our proposed algorithm for a particular choice of the barrier.
Furthermore, we present an $S$ Learning version of our LA with absorbing
barriers that is able to handle $S$-Learning environment in which the feedback
is continuous and not binary as in the case of the $L_{R-I}$.

Combining the respective advantages of cross-modality images can compensate
for the lack of information in the single modality, which has attracted
increasing attention of researchers into multi-modal image matching tasks.
Meanwhile, due to the great appearance differences between cross-modality image
pairs, it often fails to make the feature representations of correspondences as
close as possible. In this letter, we design a cross-modality feature
representation learning network, S2-Net, which is based on the recently
successful detect-and-describe pipeline, originally proposed for visible images
but adapted to work with cross-modality image pairs. To solve the consequent
problem of optimization difficulties, we introduce self-supervised learning
with a well-designed loss function to guide the training without discarding the
original advantages. This novel strategy simulates image pairs in the same
modality, which is also a useful guide for the training of cross-modality
images. Notably, it does not require additional data but significantly improves
the performance and is even workable for all methods of the detect-and-describe
pipeline. Extensive experiments are conducted to evaluate the performance of
the strategy we proposed, compared to both handcrafted and deep learning-based
methods. Results show that our elegant formulation of combined optimization of
supervised and self-supervised learning outperforms state-of-the-arts on
RoadScene and RGB-NIR datasets.

Domain-specific named entity recognition (NER) on Computer Science (CS)
scholarly articles is an information extraction task that is arguably more
challenging for the various annotation aims that can beset the task and has
been less studied than NER in the general domain. Given that significant
progress has been made on NER, we believe that scholarly domain-specific NER
will receive increasing attention in the years to come. Currently, progress on
CS NER -- the focus of this work -- is hampered in part by its recency and the
lack of a standardized annotation aim for scientific entities/terms. This work
proposes a standardized task by defining a set of seven contribution-centric
scholarly entities for CS NER viz., research problem, solution, resource,
language, tool, method, and dataset. Following which, its main contributions
are: combines existing CS NER resources that maintain their annotation focus on
the set or subset of contribution-centric scholarly entities we consider;
further, noting the need for big data to train neural NER models, this work
additionally supplies thousands of contribution-centric entity annotations from
article titles and abstracts, thus releasing a cumulative large novel resource
for CS NER; and, finally, trains a sequence labeling CS NER model inspired
after state-of-the-art neural architectures from the general domain NER task.
Throughout the work, several practical considerations are made which can be
useful to information technology designers of the digital libraries.

In Neural Architecture Search (NAS), reducing the cost of architecture
evaluation remains one of the most crucial challenges. Among a plethora of
efforts to bypass training of each candidate architecture to convergence for
evaluation, the Neural Tangent Kernel (NTK) is emerging as a promising
theoretical framework that can be utilized to estimate the performance of a
neural architecture at initialization. In this work, we revisit several
at-initialization metrics that can be derived from the NTK and reveal their key
shortcomings. Then, through the empirical analysis of the time evolution of
NTK, we deduce that modern neural architectures exhibit highly non-linear
characteristics, making the NTK-based metrics incapable of reliably estimating
the performance of an architecture without some amount of training. To take
such non-linear characteristics into account, we introduce Label-Gradient
Alignment (LGA), a novel NTK-based metric whose inherent formulation allows it
to capture the large amount of non-linear advantage present in modern neural
architectures. With minimal amount of training, LGA obtains a meaningful level
of rank correlation with the post-training test accuracy of an architecture.
Lastly, we demonstrate that LGA, complemented with few epochs of training,
successfully guides existing search algorithms to achieve competitive search
performances with significantly less search cost. The code is available at:
https://github.com/nutellamok/DemystifyingNTK.

The problem of detecting edge correlation between two Erd\H{o}s-R\'enyi
random graphs on $n$ unlabeled nodes can be formulated as a hypothesis testing
problem: under the null hypothesis, the two graphs are sampled independently;
under the alternative, the two graphs are independently sub-sampled from a
parent graph which is Erd\H{o}s-R\'enyi $\mathbf{G}(n, p)$ (so that their
marginal distributions are the same as the null). We establish a sharp
information-theoretic threshold when $p = n^{-\alpha+o(1)}$ for $\alpha\in (0,
1]$ which sharpens a constant factor in a recent work by Wu, Xu and Yu. A key
novelty in our work is an interesting connection between the detection problem
and the densest subgraph of an Erd\H{o}s-R\'enyi graph.

Fog computing has emerged as a potential solution to the explosive
computational demand of mobile users. This potential mainly stems from the
capacity of task offloading and allocation at the network edge, which reduces
the delay and improves the quality of service. Despite the significant
potential, optimizing the performance of a fog network is often challenging. In
the fog architecture, the computing nodes are heterogeneous smart devices with
distinct abilities and capacities, thereby, preferences. Besides, in an
ultra-dense fog network with random task arrival, centralized control results
in excessive overhead, and therefore, it is not feasible. We study a
distributed task allocation problem in a heterogeneous fog computing network
under uncertainty. We formulate the problem as a social-concave game, where the
players attempt to minimize their regret on the path to Nash equilibrium. To
solve the formulated problem, we develop two no-regret decision-making
strategies. One strategy, namely bandit gradient ascent with momentum, is an
online convex optimization algorithm with bandit feedback. The other strategy,
Lipschitz Bandit with Initialization, is an EXP3 multi-armed bandit algorithm.
We establish a regret bound for both strategies and analyze their convergence
characteristics. Moreover, we compare the proposed strategies with a
centralized allocation strategy named Learning with Linear Rewards. Theoretical
and numerical analysis shows the superior performance of the proposed
strategies for efficient task allocation compared to the state-of-the-art
methods.

Higgsino in supersymmetric standard models can play the role of dark matter
particle. In conjunction with the naturalness criterion, the higgsino mass
parameter is expected to be around the electroweak scale. In this work, we
explore the potential of probing the nearly degenerate light higgsinos with
machine learning at the LHC. By analyzing jet images and other jet substructure
information, we use the Convolutional Neural Network(CNN) to enhance the signal
significance. We find that our deep learning jet image method can improve the
previous result based on the conventional cut-flow by about a factor of two at
the High-Luminosity LHC.

Domain adaptation (DA) or domain generalization (DG) for face presentation
attack detection (PAD) has attracted attention recently with its robustness
against unseen attack scenarios. Existing DA/DG-based PAD methods, however,
have not yet fully explored the domain-specific style information that can
provide knowledge regarding attack styles (e.g., materials, background,
illumination and resolution). In this paper, we introduce a novel Style-Guided
Domain Adaptation (SGDA) framework for inference-time adaptive PAD.
Specifically, Style-Selective Normalization (SSN) is proposed to explore the
domain-specific style information within the high-order feature statistics. The
proposed SSN enables the adaptation of the model to the target domain by
reducing the style difference between the target and the source domains.
Moreover, we carefully design Style-Aware Meta-Learning (SAML) to boost the
adaptation ability, which simulates the inference-time adaptation with style
selection process on virtual test domain. In contrast to previous domain
adaptation approaches, our method does not require either additional auxiliary
models (e.g., domain adaptors) or the unlabeled target domain during training,
which makes our method more practical to PAD task. To verify our experiments,
we utilize the public datasets: MSU-MFSD, CASIA-FASD, OULU-NPU and Idiap
REPLAYATTACK. In most assessments, the result demonstrates a notable gap of
performance compared to the conventional DA/DG-based PAD methods.

This paper aims to explore a generative approach for knowledge-based design
ideation by applying the latest pre-trained language models in artificial
intelligence (AI). Specifically, a method of fine-tuning the generative
pre-trained transformer using the USPTO patent database is proposed. The
AI-generated ideas are not only in concise and understandable language but also
able to synthesize the target design with external knowledge sources with
controllable knowledge distance. The method is tested in a case study of
rolling toy design and the results show good performance in generating ideas of
varied novelty with near-field and far-field source knowledge.

Training a spoofing countermeasure (CM) that generalizes to various unseen
data is desired but challenging. While methods such as data augmentation and
self-supervised learning are applicable, the imperfect CM performance on
diverse test sets still calls for additional strategies. This study took the
initiative and investigated CM training using active learning (AL), a framework
that iteratively selects useful data from a large pool set and fine-tunes the
CM. This study compared a few methods to measure the data usefulness and the
impact of using different pool sets collected from various sources. The results
showed that the AL-based CMs achieved better generalization than our strong
baseline on multiple test tests. Furthermore, compared with a top-line CM that
simply used the whole data pool set for training, the AL-based CMs achieved
similar performance using less training data. Although no single best
configuration was found for AL, the rule of thumb is to include diverse spoof
and bona fide data in the pool set and to avoid any AL data selection method
that selects the data that the CM feels confident in.

The goal of continual learning (CL) is to efficiently update a machine
learning model with new data without forgetting previously-learned knowledge.
Most widely-used CL methods rely on a rehearsal memory of data points to be
reused while training on new data. Curating such a rehearsal memory to maintain
a small, informative subset of all the data seen so far is crucial to the
success of these methods. We devise a coreset selection method for
rehearsal-based continual learning. Our method is based on the idea of gradient
matching: The gradients induced by the coreset should match, as closely as
possible, those induced by the original training dataset. Inspired by the
neural tangent kernel theory, we perform this gradient matching across the
model's initialization distribution, allowing us to extract a coreset without
having to train the model first. We evaluate the method on a wide range of
continual learning scenarios and demonstrate that it improves the performance
of rehearsal-based CL methods compared to competing memory management
strategies such as reservoir sampling.

Supervised deep learning methods require a large repository of annotated
data; hence, label noise is inevitable. Training with such noisy data
negatively impacts the generalization performance of deep neural networks. To
combat label noise, recent state-of-the-art methods employ some sort of sample
selection mechanism to select a possibly clean subset of data. Next, an
off-the-shelf semi-supervised learning method is used for training where
rejected samples are treated as unlabeled data. Our comprehensive analysis
shows that current selection methods disproportionately select samples from
easy (fast learnable) classes while rejecting those from relatively harder
ones. This creates class imbalance in the selected clean set and in turn,
deteriorates performance under high label noise. In this work, we propose
UNICON, a simple yet effective sample selection method which is robust to high
label noise. To address the disproportionate selection of easy and hard
samples, we introduce a Jensen-Shannon divergence based uniform selection
mechanism which does not require any probabilistic modeling and hyperparameter
tuning. We complement our selection method with contrastive learning to further
combat the memorization of noisy labels. Extensive experimentation on multiple
benchmark datasets demonstrates the effectiveness of UNICON; we obtain an 11.4%
improvement over the current state-of-the-art on CIFAR100 dataset with a 90%
noise rate. Our code is publicly available

As nowadays Machine Learning (ML) techniques are generating huge data
collections, the problem of how to efficiently engineer their storage and
operations is becoming of paramount importance. In this article we propose a
new lossless compression scheme for real-valued matrices which achieves
efficient performance in terms of compression ratio and time for linear-algebra
operations. Experiments show that, as a compressor, our tool is clearly
superior to gzip and it is usually within 20% of xz in terms of compression
ratio. In addition, our compressed format supports matrix-vector
multiplications in time and space proportional to the size of the compressed
representation, unlike gzip and xz that require the full decompression of the
compressed matrix. To our knowledge our lossless compressor is the first one
achieving time and space complexities which match the theoretical limit
expressed by the $k$-th order statistical entropy of the input.
  To achieve further time/space reductions, we propose column-reordering
algorithms hinging on a novel column-similarity score. Our experiments on
various data sets of ML matrices show that, with a modest preprocessing time,
our column reordering can yield a further reduction of up to 16% in the peak
memory usage during matrix-vector multiplication.
  Finally, we compare our proposal against the state-of-the-art Compressed
Linear Algebra (CLA) approach showing that ours runs always at least twice
faster (in a multi-thread setting) and achieves better compressed space
occupancy for most of the tested data sets. This experimentally confirms the
provably effective theoretical bounds we show for our compressed-matrix
approach.

The unlabeled data are generally assumed to be normal data in detecting
abnormal data via semisupervised learning. This assumption, however, causes
inevitable detection error when distribution of unlabeled data is different
from distribution of labeled normal dataset. To deal the problem caused by
distribution gap between labeled and unlabeled data, we propose a
semi-supervised anomaly detection algorithm using KL divergence (SAD-KL). The
proposed SAD-KL is composed of two steps: (1) estimating KL divergence of
probability density functions (PDFs) of the local outlier factors (LOFs) of the
labeled normal data and the unlabeled data (2) estimating detection probability
and threshold for detecting normal data in unlabeled data by using the KL
divergence. We show that the PDFs of the LOFs follow Burr distribution and use
them for detection. Once the threshold is computed, the SAD-KL runs iteratively
until the labeling change rate is lower than the predefined threshold.
Experiments results show that the SAD-KL shows superior detection probability
over the existing algorithms even though it takes less learning time.

The Sloan Digital Sky Survey has recently initiated its 5th survey generation
(SDSS-V), with a central focus on stellar spectroscopy. In particular, SDSS-V
Milky Way Mapper program will deliver multi-epoch optical and near-infrared
spectra for more than 5 million stars across the entire sky, covering a large
range in stellar mass, surface temperature, evolutionary stage, and age. About
10% of those spectra will be of hot stars of OBAF spectral types, for whose
analysis no established survey pipelines exist. Here we present the spectral
analysis algorithm, Zeta-Payne, developed specifically to obtain stellar labels
from SDSS-V spectra of stars with these spectral types and drawing on machine
learning tools. We provide details of the algorithm training, its test on
artificial spectra, and its validation on two control samples of real stars.
Analysis with Zeta-Payne leads to only modest internal uncertainties in the
near-IR with APOGEE (optical with BOSS): 3-10% (1-2%) for Teff, 5-30% (5-25%)
for v*sin(i), 1.7-6.3 km/s(0.7-2.2 km/s) for RV, $<0.1$ dex ($<0.05$ dex) for
log(g), and 0.4-0.5 dex (0.1 dex) for [M/H] of the star, respectively. We find
a good agreement between atmospheric parameters of OBAF-type stars when
inferred from their high- and low-resolution optical spectra. For most stellar
labels the APOGEE spectra are (far) less informative than the BOSS spectra of
these stars, while log(g), v*sin(i), and [M/H] are in most cases too uncertain
for meaningful astrophysical interpretation. This makes BOSS low-resolution
optical spectra better for stellar labels of OBAF-type stars, unless the latter
are subject to high levels of extinction.

We propose the first reference-based video super-resolution (RefVSR) approach
that utilizes reference videos for high-fidelity results. We focus on RefVSR in
a triple-camera setting, where we aim at super-resolving a low-resolution
ultra-wide video utilizing wide-angle and telephoto videos. We introduce the
first RefVSR network that recurrently aligns and propagates temporal reference
features fused with features extracted from low-resolution frames. To
facilitate the fusion and propagation of temporal reference features, we
propose a propagative temporal fusion module. For learning and evaluation of
our network, we present the first RefVSR dataset consisting of triplets of
ultra-wide, wide-angle, and telephoto videos concurrently taken from triple
cameras of a smartphone. We also propose a two-stage training strategy fully
utilizing video triplets in the proposed dataset for real-world 4x video
super-resolution. We extensively evaluate our method, and the result shows the
state-of-the-art performance in 4x super-resolution.

The tremendous amount of accessible data in cyberspace face the risk of being
unauthorized used for training deep learning models. To address this concern,
methods are proposed to make data unlearnable for deep learning models by
adding a type of error-minimizing noise. However, such conferred unlearnability
is found fragile to adversarial training. In this paper, we design new methods
to generate robust unlearnable examples that are protected from adversarial
training. We first find that the vanilla error-minimizing noise, which
suppresses the informative knowledge of data via minimizing the corresponding
training loss, could not effectively minimize the adversarial training loss.
This explains the vulnerability of error-minimizing noise in adversarial
training. Based on the observation, robust error-minimizing noise is then
introduced to reduce the adversarial training loss. Experiments show that the
unlearnability brought by robust error-minimizing noise can effectively protect
data from adversarial training in various scenarios. The code is available at
\url{https://github.com/fshp971/robust-unlearnable-examples}.

Phonotactic constraints can be employed to distinguish languages by
representing a speech utterance as a multinomial distribution or phone events.
In the present study, we propose a new learning mechanism based on
subspace-based representation, which can extract concealed phonotactic
structures from utterances, for language verification and dialect/accent
identification. The framework mainly involves two successive parts. The first
part involves subspace construction. Specifically, it decodes each utterance
into a sequence of vectors filled with phone-posteriors and transforms the
vector sequence into a linear orthogonal subspace based on low-rank matrix
factorization or dynamic linear modeling. The second part involves subspace
learning based on kernel machines, such as support vector machines and the
newly developed subspace-based neural networks (SNNs). The input layer of SNNs
is specifically designed for the sample represented by subspaces. The topology
ensures that the same output can be derived from identical subspaces by
modifying the conventional feed-forward pass to fit the mathematical definition
of subspace similarity. Evaluated on the "General LR" test of NIST LRE 2007,
the proposed method achieved up to 52%, 46%, 56%, and 27% relative reductions
in equal error rates over the sequence-based PPR-LM, PPR-VSM, and PPR-IVEC
methods and the lattice-based PPR-LM method, respectively. Furthermore, on the
dialect/accent identification task of NIST LRE 2009, the SNN-based system
performed better than the aforementioned four baseline methods.

The goal of this paper is to train effective self-supervised speaker
representations without identity labels. We propose two curriculum learning
strategies within a self-supervised learning framework. The first strategy aims
to gradually increase the number of speakers in the training phase by enlarging
the used portion of the train dataset. The second strategy applies various data
augmentations to more utterances within a mini-batch as the training proceeds.
A range of experiments conducted using the DINO self-supervised framework on
the VoxCeleb1 evaluation protocol demonstrates the effectiveness of our
proposed curriculum learning strategies. We report a competitive equal error
rate of 4.47% with a single-phase training, and we also demonstrate that the
performance further improves to 1.84% by fine-tuning on a small labelled
dataset.

3D medical image segmentation methods have been successful, but their
dependence on large amounts of voxel-level annotated data is a disadvantage
that needs to be addressed given the high cost to obtain such annotation.
Semi-supervised learning (SSL) solve this issue by training models with a large
unlabelled and a small labelled dataset. The most successful SSL approaches are
based on consistency learning that minimises the distance between model
responses obtained from perturbed views of the unlabelled data. These
perturbations usually keep the spatial input context between views fairly
consistent, which may cause the model to learn segmentation patterns from the
spatial input contexts instead of the segmented objects. In this paper, we
introduce the Translation Consistent Co-training (TraCoCo) which is a
consistency learning SSL method that perturbs the input data views by varying
their spatial input context, allowing the model to learn segmentation patterns
from visual objects. Furthermore, we propose the replacement of the commonly
used mean squared error (MSE) semi-supervised loss by a new Cross-model
confident Binary Cross entropy (CBC) loss, which improves training convergence
and keeps the robustness to co-training pseudo-labelling mistakes. We also
extend CutMix augmentation to 3D SSL to further improve generalisation. Our
TraCoCo shows state-of-the-art results for the Left Atrium (LA) and Brain Tumor
Segmentation (BRaTS19) datasets with different backbones. Our code is available
at https://github.com/yyliu01/TraCoCo.

In this paper, we study the optimistic online convex optimization problem in
dynamic environments. Existing works have shown that Ader enjoys an
$O\left(\sqrt{\left(1+P_T\right)T}\right)$ dynamic regret upper bound, where
$T$ is the number of rounds, and $P_T$ is the path length of the reference
strategy sequence. However, Ader is not environment-adaptive. Based on the fact
that optimism provides a framework for implementing environment-adaptive, we
replace Greedy Projection (GP) and Normalized Exponentiated Subgradient (NES)
in Ader with Optimistic-GP and Optimistic-NES respectively, and name the
corresponding algorithm ONES-OGP. We also extend the doubling trick to the
adaptive trick, and introduce three characteristic terms naturally arise from
optimism, namely $M_T$, $\widetilde{M}_T$ and $V_T+1_{L^2\rho\left(\rho+2
P_T\right)\leqslant\varrho^2 V_T}D_T$, to replace the dependence of the dynamic
regret upper bound on $T$. We elaborate ONES-OGP with adaptive trick and its
subgradient variation version, all of which are environment-adaptive.

A key characteristic of deep recommendation models is the immense memory
requirements of their embedding tables. These embedding tables can often reach
hundreds of gigabytes which increases hardware requirements and training cost.
A common technique to reduce model size is to hash all of the categorical
variable identifiers (ids) into a smaller space. This hashing reduces the
number of unique representations that must be stored in the embedding table;
thus decreasing its size. However, this approach introduces collisions between
semantically dissimilar ids that degrade model quality. We introduce an
alternative approach, Learned Hash Functions, which instead learns a new
mapping function that encourages collisions between semantically similar ids.
We derive this learned mapping from historical data and embedding access
patterns. We experiment with this technique on a production model and find that
a mapping informed by the combination of access frequency and a learned low
dimension embedding is the most effective. We demonstrate a small improvement
relative to the hashing trick and other collision related compression
techniques. This is ongoing work that explores the impact of categorical id
collisions on recommendation model quality and how those collisions may be
controlled to improve model performance.

Despite recent success in incorporating learning into point cloud
registration, many works focus on learning feature descriptors and continue to
rely on nearest-neighbor feature matching and outlier filtering through RANSAC
to obtain the final set of correspondences for pose estimation. In this work,
we conjecture that attention mechanisms can replace the role of explicit
feature matching and RANSAC, and thus propose an end-to-end framework to
directly predict the final set of correspondences. We use a network
architecture consisting primarily of transformer layers containing self and
cross attentions, and train it to predict the probability each point lies in
the overlapping region and its corresponding position in the other point cloud.
The required rigid transformation can then be estimated directly from the
predicted correspondences without further post-processing. Despite its
simplicity, our approach achieves state-of-the-art performance on 3DMatch and
ModelNet benchmarks. Our source code can be found at
https://github.com/yewzijian/RegTR .

A collection of invited non-archival papers for the Conference on Health,
Inference, and Learning (CHIL) 2022. This index is incomplete as some authors
of invited non-archival presentations opted not to include their papers in this
index.

Drug-drug interaction (DDI) prediction provides a drug combination strategy
for systemically effective treatment. Previous studies usually model drug
information constrained on a single view such as the drug itself, leading to
incomplete and noisy information, which limits the accuracy of DDI prediction.
In this work, we propose a novel multi- view drug substructure network for DDI
prediction (MSN-DDI), which learns chemical substructures from both the
representations of the single drug (intra-view) and the drug pair (inter-view)
simultaneously and utilizes the substructures to update the drug representation
iteratively. Comprehensive evaluations demonstrate that MSN-DDI has almost
solved DDI prediction for existing drugs by achieving a relatively improved
accuracy of 19.32% and an over 99% accuracy under the transductive setting.
More importantly, MSN-DDI exhibits better generalization ability to unseen
drugs with a relatively improved accuracy of 7.07% under more challenging
inductive scenarios. Finally, MSN-DDI improves prediction performance for
real-world DDI applications to new drugs.

Researchers are increasingly turning to machine learning (ML) algorithms to
investigate causal heterogeneity in randomized experiments. Despite their
promise, ML algorithms may fail to accurately ascertain heterogeneous treatment
effects under practical settings with many covariates and small sample size. In
addition, the quantification of estimation uncertainty remains a challenge. We
develop a general approach to statistical inference for heterogeneous treatment
effects discovered by a generic ML algorithm. We apply the Neyman's repeated
sampling framework to a common setting, in which researchers use an ML
algorithm to estimate the conditional average treatment effect and then divide
the sample into several groups based on the magnitude of the estimated effects.
We show how to estimate the average treatment effect within each of these
groups, and construct a valid confidence interval. In addition, we develop
nonparametric tests of treatment effect homogeneity across groups, and
rank-consistency of within-group average treatment effects. The validity of our
methodology does not rely on the properties of ML algorithms because it is
solely based on the randomization of treatment assignment and random sampling
of units. Finally, we generalize our methodology to the cross-fitting procedure
by accounting for the additional uncertainty induced by the random splitting of
data.

Precise representations of 3D faces are beneficial to various computer vision
and graphics applications. Due to the data discretization and model linearity,
however, it remains challenging to capture accurate identity and expression
clues in current studies. This paper presents a novel 3D morphable face model,
namely ImFace, to learn a nonlinear and continuous space with implicit neural
representations. It builds two explicitly disentangled deformation fields to
model complex shapes associated with identities and expressions, respectively,
and designs an improved learning strategy to extend embeddings of expressions
to allow more diverse changes. We further introduce a Neural Blend-Field to
learn sophisticated details by adaptively blending a series of local fields. In
addition to ImFace, an effective preprocessing pipeline is proposed to address
the issue of watertight input requirement in implicit representations, enabling
them to work with common facial surfaces for the first time. Extensive
experiments are performed to demonstrate the superiority of ImFace.

Recent advances in vision Transformers (ViTs) have come with a voracious
appetite for computing power, high-lighting the urgent need to develop
efficient training methods for ViTs. Progressive learning, a training scheme
where the model capacity grows progressively during training, has started
showing its ability in efficient training. In this paper, we take a practical
step towards efficient training of ViTs by customizing and automating
progressive learning. First, we develop a strong manual baseline for
progressive learning of ViTs, by introducing momentum growth (MoGrow) to bridge
the gap brought by model growth. Then, we propose automated progressive
learning (AutoProg), an efficient training scheme that aims to achieve lossless
acceleration by automatically increasing the training overload on-the-fly; this
is achieved by adaptively deciding whether, where and how much should the model
grow during progressive learning. Specifically, we first relax the optimization
of the growth schedule to sub-network architecture optimization problem, then
propose one-shot estimation of the sub-network performance via an elastic
supernet. The searching overhead is reduced to minimal by recycling the
parameters of the supernet. Extensive experiments of efficient training on
ImageNet with two representative ViT models, DeiT and VOLO, demonstrate that
AutoProg can accelerate ViTs training by up to 85.1% with no performance drop.
Code: https://github.com/changlin31/AutoProg

Despite most existing anomaly detection studies assume the availability of
normal training samples only, a few labeled anomaly examples are often
available in many real-world applications, such as defect samples identified
during random quality inspection, lesion images confirmed by radiologists in
daily medical screening, etc. These anomaly examples provide valuable knowledge
about the application-specific abnormality, enabling significantly improved
detection of similar anomalies in some recent models. However, those anomalies
seen during training often do not illustrate every possible class of anomaly,
rendering these models ineffective in generalizing to unseen anomaly classes.
This paper tackles open-set supervised anomaly detection, in which we learn
detection models using the anomaly examples with the objective to detect both
seen anomalies (`gray swans') and unseen anomalies (`black swans'). We propose
a novel approach that learns disentangled representations of abnormalities
illustrated by seen anomalies, pseudo anomalies, and latent residual anomalies
(i.e., samples that have unusual residuals compared to the normal data in a
latent space), with the last two abnormalities designed to detect unseen
anomalies. Extensive experiments on nine real-world anomaly detection datasets
show superior performance of our model in detecting seen and unseen anomalies
under diverse settings. Code and data are available at:
https://github.com/choubo/DRA.

We propose a method for selective inference after a model selection procedure
that is potentially a black box. In the conditional post-selection inference
framework, a crucial quantity in determining the post-selection distribution of
a test statistic is the probability of selecting the model conditional on the
statistic. By repeatedly running the model selection procedure on bootstrapped
datasets, we can generate training data with binary responses indicating the
selection event as well as specially designed covariates, which are then used
to learn the selection probability. We prove that the constructed confidence
intervals are asymptotically valid if we can learn the selection probability
sufficiently well around a neighborhood of the target parameter. The validity
of the proposed algorithm is verified by several examples.

Molecule design is a fundamental problem in molecular science and has
critical applications in a variety of areas, such as drug discovery, material
science, etc. However, due to the large searching space, it is impossible for
human experts to enumerate and test all molecules in wet-lab experiments.
Recently, with the rapid development of machine learning methods, especially
generative methods, molecule design has achieved great progress by leveraging
machine learning models to generate candidate molecules. In this paper, we
systematically review the most relevant work in machine learning models for
molecule design. We start with a brief review of the mainstream molecule
featurization and representation methods (including 1D string, 2D graph, and 3D
geometry) and general generative methods (deep generative and combinatorial
optimization methods). Then we summarize all the existing molecule design
problems into several venues according to the problem setup, including input,
output types and goals. Finally, we conclude with the open challenges and point
out future opportunities of machine learning models for molecule design in
real-world applications.

Novel object captioning aims at describing objects absent from training data,
with the key ingredient being the provision of object vocabulary to the model.
Although existing methods heavily rely on an object detection model, we view
the detection step as vocabulary retrieval from an external knowledge in the
form of embeddings for any object's definition from Wiktionary, where we use in
the retrieval image region features learned from a transformers model. We
propose an end-to-end Novel Object Captioning with Retrieved vocabulary from
External Knowledge method (NOC-REK), which simultaneously learns vocabulary
retrieval and caption generation, successfully describing novel objects outside
of the training dataset. Furthermore, our model eliminates the requirement for
model retraining by simply updating the external knowledge whenever a novel
object appears. Our comprehensive experiments on held-out COCO and Nocaps
datasets show that our NOC-REK is considerably effective against SOTAs.

While cultural backgrounds have been shown to affect linguistic expressions,
existing natural language processing (NLP) research on culture modeling is
overly coarse-grained and does not examine cultural differences among speakers
of the same language. To address this problem and augment NLP models with
cultural background features, we collect, annotate, manually validate, and
benchmark EnCBP, a finer-grained news-based cultural background prediction
dataset in English. Through language modeling (LM) evaluations and manual
analyses, we confirm that there are noticeable differences in linguistic
expressions among five English-speaking countries and across four states in the
US. Additionally, our evaluations on nine syntactic (CoNLL-2003), semantic
(PAWS-Wiki, QNLI, STS-B, and RTE), and psycholinguistic tasks (SST-5, SST-2,
Emotion, and Go-Emotions) show that, while introducing cultural background
information does not benefit the Go-Emotions task due to text domain conflicts,
it noticeably improves deep learning (DL) model performance on other tasks. Our
findings strongly support the importance of cultural background modeling to a
wide variety of NLP tasks and demonstrate the applicability of EnCBP in
culture-related research.

While the generative model has many advantages, it is not feasible to
calculate the Jensen-Shannon divergence of the density function of the data and
the density function of the model of deep neural networks; for this reason,
various alternative approaches have been developed. Generative adversarial
networks (GANs) can be used to formulate this problem as a discriminative
problem with two models, a generator and a discriminator whose learning can be
formulated in the context of game theory and the local Nash equilibrium. Since
this optimization is more difficult than minimization of a single objective
function, we propose to apply the conjugate gradient method to solve the local
Nash equilibrium problem in GANs. We give a proof and convergence analysis
under mild assumptions showing that the proposed method converges to a local
Nash equilibrium with three different learning-rate schedules including a
constant learning rate. Furthermore, we demonstrate the convergence of a simple
toy problem to a local Nash equilibrium and compare the proposed method with
other optimization methods in experiments using real-world data, finding that
the proposed method outperforms stochastic gradient descent (SGD) and momentum
SGD.

This paper is about the old Wahba problem in its more general form, which we
call "simultaneous rotation and correspondence search". In this generalization
we need to find a rotation that best aligns two partially overlapping $3$D
point sets, of sizes $m$ and $n$ respectively with $m\geq n$. We first propose
a solver, $\texttt{ARCS}$, that i) assumes noiseless point sets in general
position, ii) requires only $2$ inliers, iii) uses $O(m\log m)$ time and $O(m)$
space, and iv) can successfully solve the problem even with, e.g., $m,n\approx
10^6$ in about $0.1$ seconds. We next robustify $\texttt{ARCS}$ to noise, for
which we approximately solve consensus maximization problems using ideas from
robust subspace learning and interval stabbing. Thirdly, we refine the
approximately found consensus set by a Riemannian subgradient descent approach
over the space of unit quaternions, which we show converges globally to an
$\varepsilon$-stationary point in $O(\varepsilon^{-4})$ iterations, or locally
to the ground-truth at a linear rate in the absence of noise. We combine these
algorithms into $\texttt{ARCS+}$, to simultaneously search for rotations and
correspondences. Experiments show that $\texttt{ARCS+}$ achieves
state-of-the-art performance on large-scale datasets with more than $10^6$
points with a $10^4$ time-speedup over alternative methods.
\url{https://github.com/liangzu/ARCS}

Mathematical reasoning recently has been shown as a hard challenge for neural
systems. Abilities including expression translation, logical reasoning, and
mathematics knowledge acquiring appear to be essential to overcome the
challenge. This paper demonstrates that some abilities can be achieved through
abductive combination with discrete systems that have been programmed with
human knowledge. On a mathematical reasoning dataset, we adopt the recently
proposed abductive learning framework, and propose the ABL-Sym algorithm that
combines the Transformer neural models with a symbolic mathematics library.
ABL-Sym shows 9.73% accuracy improvement on the interpolation tasks and 47.22%
accuracy improvement on the extrapolation tasks, over the state-of-the-art
approaches. Online demonstration: http://math.polixir.ai

Equivariance has been a long-standing concern in various fields ranging from
computer vision to physical modeling. Most previous methods struggle with
generality, simplicity, and expressiveness -- some are designed ad hoc for
specific data types, some are too complex to be accessible, and some sacrifice
flexible transformations. In this work, we propose a novel and simple framework
to achieve equivariance for point cloud analysis based on the message passing
(graph neural network) scheme. We find the equivariant property could be
obtained by introducing an orientation for each point to decouple the relative
position for each point from the global pose of the entire point cloud.
Therefore, we extend current message passing networks with a module that learns
orientations for each point. Before aggregating information from the neighbors
of a point, the networks transforms the neighbors' coordinates based on the
point's learned orientations. We provide formal proofs to show the equivariance
of the proposed framework. Empirically, we demonstrate that our proposed method
is competitive on both point cloud analysis and physical modeling tasks. Code
is available at https://github.com/luost26/Equivariant-OrientedMP .

In the field of data science, and for academics in general, the Python
programming language is a popular choice, mainly because of its libraries for
storing, manipulating, and gaining insight from data. Evidence includes the
versatile set of machine learning, data visualization, and manipulation
packages used for the ever-growing size of available data. The Zen of Python is
a set of guiding design principles that developers use to write acceptable and
elegant Python code. Most principles revolve around simplicity. However, as the
need to compute large amounts of data, performance has become a necessity for
the Python programmer. The new idea in this paper is to confirm whether writing
the Pythonic way peaks performance at scale. As a starting point, we conduct a
set of preliminary experiments to evaluate nine Pythonic code examples by
comparing the performance of both Pythonic and Non-Pythonic code snippets. Our
results reveal that writing in Pythonic idioms may save memory and time. We
show that incorporating list comprehension, generator expression, zip, and
itertools.zip_longest idioms can save up to 7,000 MB and up to 32.25 seconds.
The results open more questions on how they could be utilized in a real-world
setting. The replication package includes all scripts, and the results are
available at https://doi.org/10.5281/zenodo.5712349

Multiple studies have demonstrated that obtaining standardized fetal brain
biometry from mid-trimester ultrasonography (USG) examination is key for the
reliable assessment of fetal neurodevelopment and the screening of central
nervous system (CNS) anomalies. Obtaining these measurements is highly
subjective, expertise-driven, and requires years of training experience,
limiting quality prenatal care for all pregnant mothers. In this study, we
propose a deep learning (DL) approach to compute 3 key fetal brain biometry
from the 2D USG images of the transcerebellar plane (TC) through the accurate
and automated caliper placement (2 per biometry) by modeling it as a landmark
detection problem. We leveraged clinically relevant biometric constraints
(relationship between caliper points) and domain-relevant data augmentation to
improve the accuracy of a U-Net DL model (trained/tested on: 596 images, 473
subjects/143 images, 143 subjects). We performed multiple experiments
demonstrating the effect of the DL backbone, data augmentation,
generalizability and benchmarked against a recent state-of-the-art approach
through extensive clinical validation (DL vs. 7 experienced clinicians). For
all cases, the mean errors in the placement of the individual caliper points
and the computed biometry were comparable to error rates among clinicians. The
clinical translation of the proposed framework can assist novice users from
low-resource settings in the reliable and standardized assessment of fetal
brain sonograms.

Deep learning has shown impressive performance in semantic segmentation, but
it is still unaffordable for resource-constrained mobile devices. While
offloading computation tasks is promising, the high traffic demands overwhelm
the limited bandwidth. Existing compression algorithms are not fit for semantic
segmentation, as the lack of obvious and concentrated regions of interest
(RoIs) forces the adoption of uniform compression strategies, leading to low
compression ratios or accuracy. This paper introduces STAC, a DNN-driven
compression scheme tailored for edge-assisted semantic video segmentation. STAC
is the first to exploit DNN's gradients as spatial sensitivity metrics for
spatial adaptive compression and achieves superior compression ratio and
accuracy. Yet, it is challenging to adapt this content-customized compression
to videos. Practical issues include varying spatial sensitivity and huge
bandwidth consumption for compression strategy feedback and offloading. We
tackle these issues through a spatiotemporal adaptive scheme, which (1) takes
partial strategy generation operations offline to reduce communication load,
and (2) propagates compression strategies and segmentation results across
frames through dense optical flow, and adaptively offloads keyframes to
accommodate video content. We implement STAC on a commodity mobile device.
Experiments show that STAC can save up to 20.95% of bandwidth without losing
accuracy, compared to the state-of-the-art algorithm.

It is extremely challenging to create an animatable clothed human avatar from
RGB videos, especially for loose clothes due to the difficulties in motion
modeling. To address this problem, we introduce a novel representation on the
basis of recent neural scene rendering techniques. The core of our
representation is a set of structured local radiance fields, which are anchored
to the pre-defined nodes sampled on a statistical human body template. These
local radiance fields not only leverage the flexibility of implicit
representation in shape and appearance modeling, but also factorize cloth
deformations into skeleton motions, node residual translations and the dynamic
detail variations inside each individual radiance field. To learn our
representation from RGB data and facilitate pose generalization, we propose to
learn the node translations and the detail variations in a conditional
generative latent space. Overall, our method enables automatic construction of
animatable human avatars for various types of clothes without the need for
scanning subject-specific templates, and can generate realistic images with
dynamic details for novel poses. Experiment show that our method outperforms

Multi-agent reinforcement learning methods such as VDN, QMIX, and QTRAN that
adopt centralized training with decentralized execution (CTDE) framework have
shown promising results in cooperation and competition. However, in some
multi-agent scenarios, the number of agents and the size of action set actually
vary over time. We call these unshaped scenarios, and the methods mentioned
above fail in performing satisfyingly. In this paper, we propose a new method
called Unshaped Networks for Multi-Agent Systems (UNMAS) that adapts to the
number and size changes in multi-agent systems. We propose the self-weighting
mixing network to factorize the joint action-value. Its adaption to the change
in agent number is attributed to the nonlinear mapping from each-agent Q value
to the joint action-value with individual weights. Besides, in order to address
the change in action set, each agent constructs an individual action-value
network that is composed of two streams to evaluate the constant
environment-oriented subset and the varying unit-oriented subset. We evaluate
UNMAS on various StarCraft II micro-management scenarios and compare the
results with several state-of-the-art MARL algorithms. The superiority of UNMAS
is demonstrated by its highest winning rates especially on the most difficult
scenario 3s5z_vs_3s6z. The agents learn to perform effectively cooperative
behaviors while other MARL algorithms fail in. Animated demonstrations and
source code are provided in https://sites.google.com/view/unmas.

Configuration knobs of database systems are essential to achieve high
throughput and low latency. Recently, automatic tuning systems using machine
learning methods (ML) have shown to find better configurations compared to
experienced database administrators (DBAs). However, there are still gaps to
apply the existing systems in production environments, especially in the cloud.
First, they conduct tuning for a given workload within a limited time window
and ignore the dynamicity of workloads and data. Second, they rely on a copied
instance and do not consider the availability of the database when sampling
configurations, making the tuning expensive, delayed, and unsafe. To fill these
gaps, we propose OnlineTune, which tunes the online databases safely in
changing cloud environments. To accommodate the dynamicity, OnlineTune embeds
the environmental factors as context feature and adopts contextual Bayesian
Optimization with context space partition to optimize the database adaptively
and scalably. To pursue safety during tuning, we leverage the black-box and the
white-box knowledge to evaluate the safety of configurations and propose a safe
exploration strategy via subspace adaptation.%, greatly decreasing the risks of
applying bad configurations. We conduct evaluations on dynamic workloads from
benchmarks and real-world workloads. Compared with the state-of-the-art
methods, OnlineTune achieves 14.4%~165.3% improvement on cumulative performance
while reducing 91.0%~99.5% unsafe configuration recommendations.

Most current multivariate time series (MTS) classification algorithms focus
on improving the predictive accuracy. However, for large-scale (either
high-dimensional or long-sequential) time series (TS) datasets, there is an
additional consideration: to design an efficient network architecture to reduce
computational costs such as training time and memory footprint. In this work we
propose a methodology based on module-wise pruning and Pareto analysis to
investigate the relationship between model efficiency and accuracy, as well as
its complexity. Comprehensive experiments on benchmark MTS datasets illustrate

Sepsis is a leading cause of death in the Intensive Care Units (ICU). Early
detection of sepsis is critical for patient survival. In this paper, we propose
a multimodal Transformer model for early sepsis prediction, using the
physiological time series data and clinical notes for each patient within $36$
hours of ICU admission. Specifically, we aim to predict sepsis using only the
first 12, 18, 24, 30 and 36 hours of laboratory measurements, vital signs,
patient demographics, and clinical notes. We evaluate our model on two large
critical care datasets: MIMIC-III and eICU-CRD. The proposed method is compared
with six baselines. In addition, ablation analysis and case studies are
conducted to study the influence of each individual component of the model and
the contribution of each data modality for early sepsis prediction.
Experimental results demonstrate the effectiveness of our method, which
outperforms competitive baselines on all metrics.

Analysis of human affect plays a vital role in human-computer interaction
(HCI) systems. Due to the difficulty in capturing large amounts of real-life
data, most of the current methods have mainly focused on controlled
environments, which limit their application scenarios. To tackle this problem,
we propose our solution based on the ensemble learning method. Specifically, we
formulate the problem as a classification task, and then train several
expression classification models with different types of backbones--ResNet,
EfficientNet and InceptionNet. After that, the outputs of several models are
fused via model ensemble method to predict the final results. Moreover, we
introduce the multi-fold ensemble method to train and ensemble several models
with the same architecture but different data distributions to enhance the
performance of our solution. We conduct many experiments on the AffWild2
dataset of the ABAW2022 Challenge, and the results demonstrate the
effectiveness of our solution.

Generating step-by-step "chain-of-thought" rationales improves language model
performance on complex reasoning tasks like mathematics or commonsense
question-answering. However, inducing language model rationale generation
currently requires either constructing massive rationale datasets or
sacrificing accuracy by using only few-shot inference. We propose a technique
to iteratively leverage a small number of rationale examples and a large
dataset without rationales, to bootstrap the ability to perform successively
more complex reasoning. This technique, the "Self-Taught Reasoner" (STaR),
relies on a simple loop: generate rationales to answer many questions, prompted
with a few rationale examples; if the generated answers are wrong, try again to
generate a rationale given the correct answer; fine-tune on all the rationales
that ultimately yielded correct answers; repeat. We show that STaR
significantly improves performance on multiple datasets compared to a model
fine-tuned to directly predict final answers, and performs comparably to
fine-tuning a 30$\times$ larger state-of-the-art language model on
CommensenseQA. Thus, STaR lets a model improve itself by learning from its own
generated reasoning.

This paper is a technical report to share our experience and findings
building a Korean and English bilingual multimodal model. While many of the
multimodal datasets focus on English and multilingual multimodal research uses
machine-translated texts, employing such machine-translated texts is limited to
describing unique expressions, cultural information, and proper noun in
languages other than English. In this work, we collect 1.1 billion image-text
pairs (708 million Korean and 476 million English) and train a bilingual
multimodal model named KELIP. We introduce simple yet effective training
schemes, including MAE pre-training and multi-crop augmentation. Extensive
experiments demonstrate that a model trained with such training schemes shows
competitive performance in both languages. Moreover, we discuss
multimodal-related research questions: 1) strong augmentation-based methods can
distract the model from learning proper multimodal relations; 2) training
multimodal model without cross-lingual relation can learn the relation via
visual semantics; 3) our bilingual KELIP can capture cultural differences of
visual semantics for the same meaning of words; 4) a large-scale multimodal
model can be used for multimodal feature analogy. We hope that this work will
provide helpful experience and findings for future research. We provide an
open-source pre-trained KELIP.

Face representation in the wild is extremely hard due to the large scale face
variations. To this end, some deep convolutional neural networks (CNNs) have
been developed to learn discriminative feature by designing properly
margin-based losses, which perform well on easy samples but fail on hard
samples. Based on this, some methods mainly adjust the weights of hard samples
in training stage to improve the feature discrimination. However, these methods
overlook the feature distribution property which may lead to better results
since the miss-classified hard samples may be corrected by using the
distribution metric. This paper proposes the hard samples guided optimal
transport (OT) loss for deep face representation, OTFace for short. OTFace aims
to enhance the performance of hard samples by introducing the feature
distribution discrepancy while maintain the performance on easy samples.
Specifically, we embrace triplet scheme to indicate hard sample groups in one
mini-batch during training. OT is then used to characterize the distribution
differences of features from the high level convolutional layer. Finally, we
integrate the margin-based-softmax (e.g. ArcFace or AM-Softmax) and OT to guide
deep CNN learning. Extensive experiments are conducted on several benchmark
databases. The quantitative results demonstrate the advantages of the proposed
OTFace over state-of-the-art methods.

Unsupervised pixel-level defective region segmentation is an important task
in image-based anomaly detection for various industrial applications. The
state-of-the-art methods have their own advantages and limitations:
matrix-decomposition-based methods are robust to noise but lack complex
background image modeling capability; representation-based methods are good at
defective region localization but lack accuracy in defective region shape
contour extraction; reconstruction-based methods detected defective region
match well with the ground truth defective region shape contour but are noisy.
To combine the best of both worlds, we present an unsupervised patch
autoencoder based deep image decomposition (PAEDID) method for defective region
segmentation. In the training stage, we learn the common background as a deep
image prior by a patch autoencoder (PAE) network. In the inference stage, we
formulate anomaly detection as an image decomposition problem with the deep
image prior and domain-specific regularizations. By adopting the proposed
approach, the defective regions in the image can be accurately extracted in an
unsupervised fashion. We demonstrate the effectiveness of the PAEDID method in
simulation studies and an industrial dataset in the case study.

In this paper, we present a second order spatial compatibility (SC^2) measure
based method for efficient and robust point cloud registration (PCR), called
SC^2-PCR. Firstly, we propose a second order spatial compatibility (SC^2)
measure to compute the similarity between correspondences. It considers the
global compatibility instead of local consistency, allowing for more
distinctive clustering between inliers and outliers at early stage. Based on
this measure, our registration pipeline employs a global spectral technique to
find some reliable seeds from the initial correspondences. Then we design a
two-stage strategy to expand each seed to a consensus set based on the SC^2
measure matrix. Finally, we feed each consensus set to a weighted SVD algorithm
to generate a candidate rigid transformation and select the best model as the
final result. Our method can guarantee to find a certain number of outlier-free
consensus sets using fewer samplings, making the model estimation more
efficient and robust. In addition, the proposed SC^2 measure is general and can
be easily plugged into deep learning based frameworks. Extensive experiments
are carried out to investigate the performance of our method. Code will be
available at \url{https://github.com/ZhiChen902/SC2-PCR}.

We demonstrate the implementation of a novel machine learning framework for
probability density estimation and classification using quantum circuits. The
framework maps a training data set or a single data sample to the quantum state
of a physical system through quantum feature maps. The quantum state of the
arbitrarily large training data set summarises its probability distribution in
a finite-dimensional quantum wave function. By projecting the quantum state of
a new data sample onto the quantum state of the training data set, one can
derive statistics to classify or estimate the density of the new data sample.
Remarkably, the implementation of our framework on a real quantum device does
not require any optimisation of quantum circuit parameters. Nonetheless, we
discuss a variational quantum circuit approach that could leverage quantum
advantage for our framework.

Solving eigenproblem of the Laplacian matrix of a fully connected weighted
graph has wide applications in data science, machine learning, and image
processing, etc. However, this is very challenging because it involves
expensive matrix operations. Here, we propose an efficient quantum algorithm to
solve it based on a assumption that the element of each vertex and its norms
can be effectively accessed via a quantum random access memory data structure.
Specifically, we adopt the optimal Hamiltonian simulation technique based on
the block-encoding framework to implement the quantum simulation of the
Laplacian matrix. Then, the eigenvalues and eigenvectors of the Laplacian
matrix are extracted by the quantum phase estimation algorithm. The core of our
entire algorithm is to construct the block-encoding of the Laplacian matrix. To
achieve this, we propose in detail how to construct the block-encodings of
operators containing the information of the weight matrix and the degree matrix
respectively, and further obtain the block-encoding of the Laplacian matrix.
Compared with its classical counterpart, our algorithm has a polynomial speedup
on the number of vertices and an exponential speedup on the dimension of each
vertex. We also show that our algorithm can be extended to solve the
eigenproblem of symmetric (non-symmetric) normalized Laplacian matrix.

Electromagnetic metasurfaces have attracted significant interest recently due
to their low profile and advantageous applications. Practically, many
metasurface designs start with a set of constraints for the radiated far-field,
such as main-beam direction(s) and side lobe levels, and end with a non-uniform
physical structure for the surface. This problem is quite challenging, since
the required tangential field transformations are not completely known when
only constraints are placed on the scattered fields. Hence, the required
surface properties cannot be solved for analytically. Moreover, the translation
of the desired surface properties to the physical unit cells can be
time-consuming and difficult, as it is often a one-to-many mapping in a large
solution space. Here, we divide the inverse design process into two steps: a
macroscopic and microscopic design step. In the former, we use an iterative
optimization process to find the surface properties that radiate a far-field
pattern that complies with specified constraints. This iterative process
exploits non-radiating currents to ensure a passive and lossless design. In the
microscopic step, these optimized surface properties are realized with physical
unit cells using machine learning surrogate models. The effectiveness of this
end-to-end synthesis process is demonstrated through measurement results of a
beam-splitting prototype.

This paper probes intrinsic factors behind typical failure cases (e.g.
spatial inconsistency and boundary confusion) produced by the existing
state-of-the-art method in face parsing. To tackle these problems, we propose a
novel Decoupled Multi-task Learning with Cyclical Self-Regulation (DML-CSR) for
face parsing. Specifically, DML-CSR designs a multi-task model which comprises
face parsing, binary edge, and category edge detection. These tasks only share
low-level encoder weights without high-level interactions between each other,
enabling to decouple auxiliary modules from the whole network at the inference
stage. To address spatial inconsistency, we develop a dynamic dual graph
convolutional network to capture global contextual information without using
any extra pooling operation. To handle boundary confusion in both single and
multiple face scenarios, we exploit binary and category edge detection to
jointly obtain generic geometric structure and fine-grained semantic clues of
human faces. Besides, to prevent noisy labels from degrading model
generalization during training, cyclical self-regulation is proposed to
self-ensemble several model instances to get a new model and the resulting
model then is used to self-distill subsequent models, through alternating
iterations. Experiments show that our method achieves the new state-of-the-art
performance on the Helen, CelebAMask-HQ, and Lapa datasets. The source code is
https://github.com/deepinsight/insightface/tree/master/parsing/dml_csr.

The impact of Vehicle-to-Everything (V2X) communications on platoon control
performance is investigated. Platoon control is essentially a sequential
stochastic decision problem (SSDP), which can be solved by Deep Reinforcement
Learning (DRL) to deal with both the control constraints and uncertainty in the
platoon leading vehicle's behavior. In this context, the value of V2X
communications for DRL-based platoon controllers is studied with an emphasis on
the tradeoff between the gain of including exogenous information in the system
state for reducing uncertainty and the performance erosion due to the
curse-of-dimensionality. Our objective is to find the specific set of
information that should be shared among the vehicles for the construction of
the most appropriate state space. SSDP models are conceived for platoon control
under different information topologies (IFT) by taking into account `just
sufficient' information. Furthermore, theorems are established for comparing
the performance of their optimal policies. In order to determine whether a
piece of information should or should not be transmitted for improving the
DRL-based control policy, we quantify its value by deriving the conditional KL
divergence of the transition models. More meritorious information is given
higher priority in transmission, since including it in the state space has a
higher probability in offsetting the negative effect of having higher state
dimensions. Finally, simulation results are provided to illustrate the

Deep neural networks can be unreliable in the real world especially when they
heavily use spurious features for their predictions. Recently, Singla & Feizi
(2022) introduced the Salient Imagenet dataset by annotating and localizing
core and spurious features of ~52k samples from 232 classes of Imagenet. While
this dataset is useful for evaluating the reliance of pretrained models on
spurious features, its small size limits its usefulness for training models. In
this work, we first introduce the Salient Imagenet-1M dataset with more than 1
million soft masks localizing core and spurious features for all 1000 Imagenet
classes. Using this dataset, we first evaluate the reliance of several Imagenet
pretrained models (42 total) on spurious features and observe that: (i)
transformers are more sensitive to spurious features compared to Convnets, (ii)
zero-shot CLIP transformers are highly susceptible to spurious features. Next,
we introduce a new learning paradigm called Core Risk Minimization (CoRM) whose
objective ensures that the model predicts a class using its core features. We
evaluate different computational approaches for solving CoRM and achieve
significantly higher (+12%) core accuracy (accuracy when non-core regions
corrupted using noise) with no drop in clean accuracy compared to models
trained via Empirical Risk Minimization.

Many novel notions of "risk" (e.g., CVaR, tilted risk, DRO risk) have been
proposed and studied, but these risks are all at least as sensitive as the mean
to loss tails on the upside, and tend to ignore deviations on the downside. We
study a complementary new risk class that penalizes loss deviations in a
bi-directional manner, while having more flexibility in terms of tail
sensitivity than is offered by mean-variance. This class lets us derive
high-probability learning guarantees without explicit gradient clipping, and
empirical tests using both simulated and real data illustrate a high degree of
control over key properties of the test loss distribution incurred by
gradient-based learners.

This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with
eroded or missing borders. Missing borders is a common real-world situation,
for example, when dealing with the reconstruction of broken artifacts or ruined
frescoes. In this particular condition, the puzzle's pieces do not align
perfectly due to the borders' gaps; in this situation, the patches' direct
match is unfeasible due to the lack of color and line continuations. JiGAN, is
a two-steps procedure that tackles this issue: first, we repair the eroded
borders with a GAN-based image extension model and measure the alignment
affinity between pieces; then, we solve the puzzle with the relaxation labeling
algorithm to enforce consistency in pieces positioning, hence, reconstructing
the puzzle. We test the method on a large dataset of small puzzles and on three
commonly used benchmark datasets to demonstrate the feasibility of the proposed
approach.

Text-to-Speech (TTS) services that run on edge devices have many advantages
compared to cloud TTS, e.g., latency and privacy issues. However, neural
vocoders with a low complexity and small model footprint inevitably generate
annoying sounds. This study proposes a Bunched LPCNet2, an improved LPCNet
architecture that provides highly efficient performance in high-quality for
cloud servers and in a low-complexity for low-resource edge devices. Single
logistic distribution achieves computational efficiency, and insightful tricks
reduce the model footprint while maintaining speech quality. A DualRate
architecture, which generates a lower sampling rate from a prosody model, is
also proposed to reduce maintenance costs. The experiments demonstrate that
Bunched LPCNet2 generates satisfactory speech quality with a model footprint of
1.1MB while operating faster than real-time on a RPi 3B. Our audio samples are
available at https://srtts.github.io/bunchedLPCNet2.

In self-supervised learning, multi-granular features are heavily desired
though rarely investigated, as different downstream tasks (e.g., general and
fine-grained classification) often require different or multi-granular
features, e.g.~fine- or coarse-grained one or their mixture. In this work, for
the first time, we propose an effective MUlti-Granular Self-supervised learning
(Mugs) framework to explicitly learn multi-granular visual features. Mugs has
three complementary granular supervisions: 1) an instance discrimination
supervision (IDS), 2) a novel local-group discrimination supervision (LGDS),
and 3) a group discrimination supervision (GDS). IDS distinguishes different
instances to learn instance-level fine-grained features. LGDS aggregates
features of an image and its neighbors into a local-group feature, and pulls
local-group features from different crops of the same image together and push
them away for others. It provides complementary instance supervision to IDS via
an extra alignment on local neighbors, and scatters different local-groups
separately to increase discriminability. Accordingly, it helps learn high-level
fine-grained features at a local-group level. Finally, to prevent similar
local-groups from being scattered randomly or far away, GDS brings similar
samples close and thus pulls similar local-groups together, capturing
coarse-grained features at a (semantic) group level. Consequently, Mugs can
capture three granular features that often enjoy higher generality on diverse
downstream tasks over single-granular features, e.g.~instance-level
fine-grained features in contrastive learning. By only pretraining on
ImageNet-1K, Mugs sets new SoTA linear probing accuracy 82.1$\%$ on ImageNet-1K
and improves previous SoTA by $1.1\%$. It also surpasses SoTAs on other tasks,
e.g. transfer learning, detection and segmentation.

Layout design is ubiquitous in many applications, e.g. architecture/urban
planning, etc, which involves a lengthy iterative design process. Recently,
deep learning has been leveraged to automatically generate layouts via image
generation, showing a huge potential to free designers from laborious routines.
While automatic generation can greatly boost productivity, designer input is
undoubtedly crucial. An ideal AI-aided design tool should automate repetitive
routines, and meanwhile accept human guidance and provide smart/proactive
suggestions. However, the capability of involving humans into the loop has been
largely ignored in existing methods which are mostly end-to-end approaches. To
this end, we propose a new human-in-the-loop generative model, iPLAN, which is
capable of automatically generating layouts, but also interacting with
designers throughout the whole procedure, enabling humans and AI to co-evolve a
sketchy idea gradually into the final design. iPLAN is evaluated on diverse
datasets and compared with existing methods. The results show that iPLAN has
high fidelity in producing similar layouts to those from human designers, great
flexibility in accepting designer inputs and providing design suggestions
accordingly, and strong generalizability when facing unseen design tasks and
limited training data.

Machine learning methodologies can be adopted in cultural applications and
propose new ways to distribute or even present the cultural content to the
public. For instance, speech analytics can be adopted to automatically generate
subtitles in theatrical plays, in order to (among other purposes) help people
with hearing loss. Apart from a typical speech-to-text transcription with
Automatic Speech Recognition (ASR), Speech Emotion Recognition (SER) can be
used to automatically predict the underlying emotional content of speech
dialogues in theatrical plays, and thus to provide a deeper understanding how
the actors utter their lines. However, real-world datasets from theatrical
plays are not available in the literature. In this work we present GreThE, the
Greek Theatrical Emotion dataset, a new publicly available data collection for
speech emotion recognition in Greek theatrical plays. The dataset contains
utterances from various actors and plays, along with respective valence and
arousal annotations. Towards this end, multiple annotators have been asked to
provide their input for each speech recording and inter-annotator agreement is
taken into account in the final ground truth generation. In addition, we
discuss the results of some indicative experiments that have been conducted
with machine and deep learning frameworks, using the dataset, along with some
widely used databases in the field of speech emotion recognition.

Quantum computing architectures require an accurate and efficient description
in terms of many-electron states. Recent implementations include quantum dot
arrays, where the ground state of a multi q-bit system can be altered by
voltages applied to the top gates. An extensive investigation concerning the
spectra of the many-electron systems under multiple operation conditions set by
external voltages typically requires a relatively large number of Hamiltonian
diagonalizations, where the Coulomb interaction is considered in an exact
manner. Instead of making exhaustive calculations using high throughput
computing, we approach this problem by augmenting numerical diagonalizations
with machine learning techniques designed to predict the many-electron
eigenvalues and eigenfunctions. To this end, we employ and compare the results
from linear regression methods such as multivariate least squares (MLS) as well
as non-linear techniques based on kernel ridge regression (KRR), Gaussian
process regression (GPR) and artificial neural networks (ANNs). The input
feature vectors are assembled from readily available information comprised from
a binary representation of the potential and the strength of the Coulomb
interaction. Furthermore, employing a linear classifier, we establish a rule
for detecting a singlet-triplet transition which may arise for certain
potential configurations.

Seismic monitoring of carbon storage sequestration is a challenging problem
involving both fluid-flow physics and wave physics. Additionally, monitoring
usually requires the solvers for these physics to be coupled and differentiable
to effectively invert for the subsurface properties of interest. To drastically
reduce the computational cost, we introduce a learned coupled inversion
framework based on the wave modeling operator, rock property conversion and a
proxy fluid-flow simulator. We show that we can accurately use a Fourier neural
operator as a proxy for the fluid-flow simulator for a fraction of the
computational cost. We demonstrate the efficacy of our proposed method by means
of a synthetic experiment. Finally, our framework is extended to carbon
sequestration forecasting, where we effectively use the surrogate Fourier
neural operator to forecast the CO2 plume in the future at near-zero additional
cost.

Self-supervised vision-language pretraining from pure images and text with a
contrastive loss is effective, but ignores fine-grained alignment due to a
dual-stream architecture that aligns image and text representations only on a
global level. Earlier, supervised, non-contrastive methods were capable of
finer-grained alignment, but required dense annotations that were not scalable.
We propose a single stream architecture that aligns images and language at
multiple levels: global, fine-grained patch-token, and conceptual/semantic,
using two novel tasks: symmetric cross-modality reconstruction (XMM) and a
pseudo-labeled key word prediction (PSL). In XMM, we mask input tokens from one
modality and use cross-modal information to reconstruct the masked token, thus
improving fine-grained alignment between the two modalities. In PSL, we use
attention to select keywords in a caption, use a momentum encoder to recommend
other important keywords that are missing from the caption but represented in
the image, and then train the visual encoder to predict the presence of those
keywords, helping it learn semantic concepts that are essential for grounding a
textual token to an image region. We demonstrate competitive performance and
improved data efficiency on image-text retrieval, grounding, visual question
answering/reasoning against larger models and models trained on more data. Code
and models available at zaidkhan.me/SIMLA.

Solar energetic particles (SEPs) are an essential source of space radiation,
which are hazards for humans in space, spacecraft, and technology in general.
In this paper we propose a deep learning method, specifically a bidirectional
long short-term memory (biLSTM) network, to predict if an active region (AR)
would produce an SEP event given that (i) the AR will produce an M- or X-class
flare and a coronal mass ejection (CME) associated with the flare, or (ii) the
AR will produce an M- or X-class flare regardless of whether or not the flare
is associated with a CME. The data samples used in this study are collected
from the Geostationary Operational Environmental Satellite's X-ray flare
catalogs provided by the National Centers for Environmental Information. We
select M- and X-class flares with identified ARs in the catalogs for the period
between 2010 and 2021, and find the associations of flares, CMEs and SEPs in
the Space Weather Database of Notifications, Knowledge, Information during the
same period. Each data sample contains physical parameters collected from the
Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory.
Experimental results based on different performance metrics demonstrate that
the proposed biLSTM network is better than related machine learning algorithms
for the two SEP prediction tasks studied here. We also discuss extensions of
our approach for probabilistic forecasting and calibration with empirical

Most EEG-based Brain-Computer Interfaces (BCIs) require a considerable amount
of training data to calibrate the classification model, owing to the high
variability in the EEG data, which manifests itself between participants, but
also within participants from session to session (and, of course, from trial to
trial). In general, the more complex the model, the more data for training is
needed. We suggest a novel technique for augmenting the training data by
generating new data from the data set at hand. Different from existing
techniques, our method uses backward and forward projection using source
localization and a head model to modify the current source dipoles of the
model, thereby generating inter-participant variability in a physiologically
meaningful way. In this manuscript, we explain the method and show first
preliminary results for participant-independent motor-imagery classification.
The accuracy was increased when using the proposed method of data augmentation
by 13, 6 and 2 percentage points when using a deep neural network, a shallow
neural network and LDA, respectively.

Despite the striking performance achieved by modern detectors when training
and test data are sampled from the same or similar distribution, the
generalization ability of detectors under unknown distribution shifts remains
hardly studied. Recently several works discussed the detectors' adaptation
ability to a specific target domain which are not readily applicable in
real-world applications since detectors may encounter various environments or
situations while pre-collecting all of them before training is inconceivable.
In this paper, we study the critical problem, domain generalization in object
detection (DGOD), where detectors are trained with source domains and evaluated
on unknown target domains. To thoroughly evaluate detectors under unknown
distribution shifts, we formulate the DGOD problem and propose a comprehensive
evaluation benchmark to fill the vacancy. Moreover, we propose a novel method
named Region Aware Proposal reweighTing (RAPT) to eliminate dependence within
RoI features. Extensive experiments demonstrate that current DG methods fail to
address the DGOD problem and our method outperforms other state-of-the-art

Seismic imaging is an ill-posed inverse problem that is challenged by noisy
data and modeling inaccuracies -- due to errors in the background
squared-slowness model. Uncertainty quantification is essential for determining
how variability in the background models affects seismic imaging. Due to the
costs associated with the forward Born modeling operator as well as the high
dimensionality of seismic images, quantification of uncertainty is
computationally expensive. As such, the main contribution of this work is a
survey-specific Fourier neural operator surrogate to velocity continuation that
maps seismic images associated with one background model to another virtually
for free. While being trained with only 200 background and seismic image pairs,
this surrogate is able to accurately predict seismic images associated with new
background models, thus accelerating seismic imaging uncertainty
quantification. We support our method with a realistic data example in which we
quantify seismic imaging uncertainties using a Fourier neural operator
surrogate, illustrating how variations in background models affect the position
of reflectors in a seismic image.

Continual learning is an emerging paradigm in machine learning, wherein a
model is exposed in an online fashion to data from multiple different
distributions (i.e. environments), and is expected to adapt to the distribution
change. Precisely, the goal is to perform well in the new environment, while
simultaneously retaining the performance on the previous environments (i.e.
avoid "catastrophic forgetting") -- without increasing the size of the model.
  While this setup has enjoyed a lot of attention in the applied community,
there hasn't be theoretical work that even formalizes the desired guarantees.
In this paper, we propose a framework for continual learning through the
framework of feature extraction -- namely, one in which features, as well as a
classifier, are being trained with each environment. When the features are
linear, we design an efficient gradient-based algorithm $\mathsf{DPGD}$, that
is guaranteed to perform well on the current environment, as well as avoid
catastrophic forgetting. In the general case, when the features are non-linear,
we show such an algorithm cannot exist, whether efficient or not.

This article describes creating algorithmic support for the functioning of a
personal virtual assistant, which allows automating the processing of customer
requests. The study aims to reduce errors and processing time for a client
request in business systems - text chats or voice channels using a text
transcription system. The results of the development of algorithmic support and
an assessment of the quality of work on synthetic data presented.

This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question
Answering (MCQA) dataset designed to address real-world medical entrance exam
questions. More than 194k high-quality AIIMS \& NEET PG entrance exam MCQs
covering 2.4k healthcare topics and 21 medical subjects are collected with an
average token length of 12.77 and high topical diversity. Each sample contains
a question, correct answer(s), and other options which requires a deeper
language understanding as it tests the 10+ reasoning abilities of a model
across a wide range of medical subjects \& topics. A detailed explanation of
the solution, along with the above information, is provided in this study.

As a representative self-supervised method, contrastive learning has achieved
great successes in unsupervised training of representations. It trains an
encoder by distinguishing positive samples from negative ones given query
anchors. These positive and negative samples play critical roles in defining
the objective to learn the discriminative encoder, avoiding it from learning
trivial features. While existing methods heuristically choose these samples, we
present a principled method where both positive and negative samples are
directly learnable end-to-end with the encoder. We show that the positive and
negative samples can be cooperatively and adversarially learned by minimizing
and maximizing the contrastive loss, respectively. This yields cooperative
positives and adversarial negatives with respect to the encoder, which are
updated to continuously track the learned representation of the query anchors
over mini-batches. The proposed method achieves 71.3% and 75.3% in top-1
accuracy respectively over 200 and 800 epochs of pre-training ResNet-50
backbone on ImageNet1K without tricks such as multi-crop or stronger
augmentations. With Multi-Crop, it can be further boosted into 75.7%. The
source code and pre-trained model are released in
https://github.com/maple-research-lab/caco.

While numerous studies have been conducted in the literature exploring
different types of machine learning approaches for search ranking, most of them
are focused on specific pre-defined problems but only a few of them have
studied the ranking framework which can be applied in a commercial search
engine in a scalable way. In the meantime, existing ranking models are often
optimized for normalized discounted cumulative gains (NDCG) or online
click-through rate (CTR), and both types of machine learning models are built
based on the assumption that high-quality training data can be easily obtained
and well applied to unseen cases. In practice at Facebook search, we observed
that our training data for ML models have certain issues. First, tail query
intents are hardly covered in our human rating dataset. Second, search click
logs are often noisy and hard to clean up due to various reasons. To address
the above issues, in this paper, we propose a probabilistic intent based
ranking framework (short for piRank), which can: 1) provide a scalable
framework to address various ranking issues for different query intents in a
divide-and-conquer way; 2) improve system development agility including
iteration speed and system debuggability; 3) combine both machine learning and
empirical-based algorithmic methods in a systematic way. We conducted extensive
experiments and studies on top of Facebook search engine system and validated
the effectiveness of this new ranking architecture.

Recent years have witnessed growing interest in the application of deep
neural networks (DNNs) for receiver design, which can potentially be applied in
complex environments without relying on knowledge of the channel model.
However, the dynamic nature of communication channels often leads to rapid
distribution shifts, which may require periodically retraining. This paper
formulates a data-efficient two-stage training method that facilitates rapid
online adaptation. Our training mechanism uses a predictive meta-learning
scheme to train rapidly from data corresponding to both current and past
channel realizations. Our method is applicable to any deep neural network
(DNN)-based receiver, and does not require transmission of new pilot data for
training. To illustrate the proposed approach, we study DNN-aided receivers
that utilize an interpretable model-based architecture, and introduce a modular
training strategy based on predictive meta-learning. We demonstrate our
techniques in simulations on a synthetic linear channel, a synthetic non-linear
channel, and a COST 2100 channel. Our results demonstrate that the proposed
online training scheme allows receivers to outperform previous techniques based
on self-supervision and joint-learning by a margin of up to 2.5 dB in coded bit
error rate in rapidly-varying scenarios.

Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid
gland. Much effort has been invested in improving its diagnosis, and
thyroidectomy remains the primary treatment method. A successful operation
without unnecessary side injuries relies on an accurate preoperative diagnosis.
Current human assessment of thyroid nodule malignancy is prone to errors and
may not guarantee an accurate preoperative diagnosis. This study proposed a
machine framework to predict thyroid nodule malignancy based on a novel
clinical dataset we collected. The 10-fold cross-validation, bootstrap
analysis, and permutation predictor importance were applied to estimate and
interpret the model performance under uncertainty. The comparison between model
prediction and expert assessment shows the advantage of our framework over
human judgment in predicting thyroid nodule malignancy. Our method is accurate,
interpretable, and thus useable as additional evidence in the preoperative
diagnosis for thyroid cancer.

Discovering new materials is a long-standing challenging task that is crucial
to the progress of human society. Conventional approaches based on
trial-and-error experiments and computational simulations are labor-intensive
or costly with success heavily depending on experts' heuristic knowledge.
Recently, deep generative models have been proposed for generative design of
materials by learning implicit knowledge from known materials datasets.
However, these models are either applicable to a specific material system or
the performance is low due to their failure to incorporate physical rules into
their model training process. Here, we propose a deep learning based Physics
Guided Crystal Generative Model (PGCGM) for efficient generative materials
design with high structural diversity (up to 20 different space groups). The
high performance of our model manifests its capability to capture and exploit
the symmetric constraints of crystals and the pairwise atomic distance
constraints among neighbor atoms. Using data augmentation and spatial atom
clustering and merging, our PGCGM model increases the overall generation
validity performance by more than 700\% compared to FTCP, one of the
state-of-the-art structure generators and by more than 45\% compared to our
previous CubicGAN model. The newly generated crystal materials also show higher
quality in terms of atomic spatial distribution and composition diversity. We
further validated the new crystal structures by Density Functional Theory (DFT)
calculations. 1,869 materials out of 2,000 were successfully optimized, of
which 39.6\% have negative formation energy and 5.3\% have energy-above-hull
less than 0.25 eV/atom, indicating their thermodynamic stability and potential
synthesizability. The 1,869 crystal structures have been deposited to the
Carolina Materials Database \url{www.carolinamatdb.org}.

As large Pre-trained Language Models (PLMs) trained on large amounts of data
in an unsupervised manner become more ubiquitous, identifying various types of
bias in the text has come into sharp focus. Existing "Stereotype Detection"
datasets mainly adopt a diagnostic approach toward large PLMs. Blodgett et. al
(2021a) show that there are significant reliability issues with the existing
benchmark datasets. Annotating a reliable dataset requires a precise
understanding of the subtle nuances of how stereotypes manifest in text. In
this paper, we annotate a focused evaluation set for "Stereotype Detection"
that addresses those pitfalls by de-constructing various ways in which
stereotypes manifest in text. Further, we present a multi-task model that
leverages the abundance of data-rich neighboring tasks such as hate speech
detection, offensive language detection, misogyny detection, etc., to improve
the empirical performance on "Stereotype Detection". We then propose a
reinforcement-learning agent that guides the multi-task learning model by
learning to identify the training examples from the neighboring tasks that help
the target task the most. We show that the proposed models achieve significant
empirical gains over existing baselines on all the tasks.

Classical reinforcement learning (RL) has generated excellent results in
different regions; however, its sample inefficiency remains a critical issue.
In this paper, we provide concrete numerical evidence that the sample
efficiency (the speed of convergence) of quantum RL could be better than that
of classical RL, and for achieving comparable learning performance, quantum RL
could use much (at least one order of magnitude) fewer trainable parameters
than classical RL. Specifically, we employ the popular benchmarking
environments of RL in the OpenAI Gym, and show that our quantum RL agent
converges faster than classical fully-connected neural networks (FCNs) in the
tasks of CartPole and Acrobot under the same optimization process. We also
successfully train the first quantum RL agent that can complete the task of
LunarLander in the OpenAI Gym. Our quantum RL agent only requires a
single-qubit-based variational quantum circuit without entangling gates,
followed by a classical neural network (NN) to post-process the measurement
output. Finally, we could accomplish the aforementioned tasks on the real IBM
quantum machines. To the best of our knowledge, none of the earlier quantum RL
agents could do that.

Modeling long range dependencies in sequential data is a fundamental step
towards attaining human-level performance in many modalities such as text,
vision, audio and video. While attention-based models are a popular and
effective choice in modeling short-range interactions, their performance on
tasks requiring long range reasoning has been largely inadequate. In an
exciting result, Gu et al. (ICLR 2022) proposed the $\textit{Structured State
Space}$ (S4) architecture delivering large gains over state-of-the-art models
on several long-range tasks across various modalities. The core proposition of
S4 is the parameterization of state matrices via a diagonal plus low rank
structure, allowing efficient computation. In this work, we show that one can
match the performance of S4 even without the low rank correction and thus
assuming the state matrices to be diagonal. Our $\textit{Diagonal State Space}$
(DSS) model matches the performance of S4 on Long Range Arena tasks, speech
classification on Speech Commands dataset, while being conceptually simpler and
straightforward to implement.

Segmentation is essential for medical image analysis to identify and localize
diseases, monitor morphological changes, and extract discriminative features
for further diagnosis. Skin cancer is one of the most common types of cancer
globally, and its early diagnosis is pivotal for the complete elimination of
malignant tumors from the body. This research develops an Artificial
Intelligence (AI) framework for supervised skin lesion segmentation employing
the deep learning approach. The proposed framework, called MFSNet (Multi-Focus
Segmentation Network), uses differently scaled feature maps for computing the
final segmentation mask using raw input RGB images of skin lesions. In doing
so, initially, the images are preprocessed to remove unwanted artifacts and
noises. The MFSNet employs the Res2Net backbone, a recently proposed
convolutional neural network (CNN), for obtaining deep features used in a
Parallel Partial Decoder (PPD) module to get a global map of the segmentation
mask. In different stages of the network, convolution features and multi-scale
maps are used in two boundary attention (BA) modules and two reverse attention
(RA) modules to generate the final segmentation output. MFSNet, when evaluated
on three publicly available datasets: $PH^2$, ISIC 2017, and HAM10000,
outperforms state-of-the-art methods, justifying the reliability of the
framework. The relevant codes for the proposed approach are accessible at
https://github.com/Rohit-Kundu/MFSNet

Distributed scheduling algorithms for throughput or utility maximization in
dense wireless multi-hop networks can have overwhelmingly high overhead,
causing increased congestion, energy consumption, radio footprint, and security
vulnerability. For wireless networks with dense connectivity, we propose a
distributed scheme for link sparsification with graph convolutional networks
(GCNs), which can reduce the scheduling overhead while keeping most of the
network capacity. In a nutshell, a trainable GCN module generates node
embeddings as topology-aware and reusable parameters for a local decision
mechanism, based on which a link can withdraw itself from the scheduling
contention if it is not likely to win. In medium-sized wireless networks, our
proposed sparse scheduler beats classical threshold-based sparsification
policies by retaining almost $70\%$ of the total capacity achieved by a
distributed greedy max-weight scheduler with $0.4\%$ of the point-to-point
message complexity and $2.6\%$ of the average number of interfering neighbors
per link.

This paper presents LibMTL, an open-source Python library built on PyTorch,
which provides a unified, comprehensive, reproducible, and extensible
implementation framework for Multi-Task Learning (MTL). LibMTL considers
different settings and approaches in MTL, and it supports a large number of
state-of-the-art MTL methods, including 12 loss weighting strategies, 7
architectures, and 84 combinations of different architectures and loss
weighting methods. Moreover, the modular design in LibMTL makes it easy-to-use
and well extensible, thus users can easily and fast develop new MTL methods,
compare with existing MTL methods fairly, or apply MTL algorithms to real-world
applications with the support of LibMTL. The source code and detailed
documentations of LibMTL are available at
https://github.com/median-research-group/LibMTL and
https://libmtl.readthedocs.io, respectively.

Humans are able to recognize structured relations in observation, allowing us
to decompose complex scenes into simpler parts and abstract the visual world in
multiple levels. However, such hierarchical reasoning ability of human
perception remains largely unexplored in current literature of semantic
segmentation. Existing work is often aware of flatten labels and predicts
target classes exclusively for each pixel. In this paper, we instead address
hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise
description of visual observation in terms of a class hierarchy. We devise
HSSN, a general HSS framework that tackles two critical issues in this task: i)
how to efficiently adapt existing hierarchy-agnostic segmentation networks to
the HSS setting, and ii) how to leverage the hierarchy information to
regularize HSS network learning. To address i), HSSN directly casts HSS as a
pixel-wise multi-label classification task, only bringing minimal architecture
change to current segmentation models. To solve ii), HSSN first explores
inherent properties of the hierarchy as a training objective, which enforces
segmentation predictions to obey the hierarchy structure. Further, with
hierarchy-induced margin constraints, HSSN reshapes the pixel embedding space,
so as to generate well-structured pixel representations and improve
segmentation eventually. We conduct experiments on four semantic segmentation
datasets (i.e., Mapillary Vistas 2.0, Cityscapes, LIP, and PASCAL-Person-Part),
with different class hierarchies, segmentation network architectures and
backbones, showing the generalization and superiority of HSSN.

Our target is to learn visual correspondence from unlabeled videos. We
develop LIIR, a locality-aware inter-and intra-video reconstruction framework
that fills in three missing pieces, i.e., instance discrimination, location
awareness, and spatial compactness, of self-supervised correspondence learning
puzzle. First, instead of most existing efforts focusing on intra-video
self-supervision only, we exploit cross video affinities as extra negative
samples within a unified, inter-and intra-video reconstruction scheme. This
enables instance discriminative representation learning by contrasting desired
intra-video pixel association against negative inter-video correspondence.
Second, we merge position information into correspondence matching, and design
a position shifting strategy to remove the side-effect of position encoding
during inter-video affinity computation, making our LIIR location-sensitive.
Third, to make full use of the spatial continuity nature of video data, we
impose a compactness-based constraint on correspondence matching, yielding more
sparse and reliable solutions. The learned representation surpasses
self-supervised state-of-the-arts on label propagation tasks including objects,
semantic parts, and keypoints.

Error correction code is a major part of the communication physical layer,
ensuring the reliable transfer of data over noisy channels. Recently, neural
decoders were shown to outperform classical decoding techniques. However, the
existing neural approaches present strong overfitting due to the exponential
training complexity, or a restrictive inductive bias due to reliance on Belief
Propagation. Recently, Transformers have become methods of choice in many
applications thanks to their ability to represent complex interactions between
elements. In this work, we propose to extend for the first time the Transformer
architecture to the soft decoding of linear codes at arbitrary block lengths.
We encode each channel's output dimension to high dimension for better
representation of the bits information to be processed separately. The
element-wise processing allows the analysis of the channel output reliability,
while the algebraic code and the interaction between the bits are inserted into
the model via an adapted masked self-attention module. The proposed approach
demonstrates the extreme power and flexibility of Transformers and outperforms
existing state-of-the-art neural decoders by large margins at a fraction of
their time complexity.

We study the behavior of ultra-wide neural networks when their weights are
randomly pruned at the initialization, through the lens of neural tangent
kernels (NTKs). We show that for fully-connected neural networks when the
network is pruned randomly at the initialization, as the width of each layer
grows to infinity, the empirical NTK of the pruned neural network converges to
that of the original (unpruned) network with some extra scaling factor.
Further, if we apply some appropriate scaling after pruning at the
initialization, the empirical NTK of the pruned network converges to the exact
NTK of the original network, and we provide a non-asymptotic bound on the
approximation error in terms of pruning probability. Moreover, when we apply
our result to an unpruned network (i.e., we set the probability of pruning a
given weight to be zero), our analysis is optimal up to a logarithmic factor in
width compared with the result in \cite{arora2019exact}. We conduct experiments
to validate our theoretical results. We further test our theory by evaluating
random pruning across different architectures via image classification on MNIST
and CIFAR-10 and compare its performance with other pruning strategies.

Due to the rising concern of data privacy, it's reasonable to assume the
local client data can't be transferred to a centralized server, nor their
associated identity label is provided. To support continuous learning and fill
the last-mile quality gap, we introduce a new problem setup called
Local-Adaptive Face Recognition (LaFR). Leveraging the environment-specific
local data after the deployment of the initial global model, LaFR aims at
getting optimal performance by training local-adapted models automatically and
un-supervisely, as opposed to fixing their initial global model. We achieve
this by a newly proposed embedding cluster model based on Graph Convolution
Network (GCN), which is trained via meta-optimization procedure. Compared with
previous works, our meta-clustering model can generalize well in unseen local
environments. With the pseudo identity labels from the clustering results, we
further introduce novel regularization techniques to improve the model
adaptation performance. Extensive experiments on racial and internal sensor
adaptation demonstrate that our proposed solution is more effective for
adapting face recognition models in each specific environment. Meanwhile, we
show that LaFR can further improve the global model by a simple federated
aggregation over the updated local models.

One of the long-standing problems in materials science is how to predict a
material's structure and then its properties given only its composition.
Experimental characterization of crystal structures has been widely used for
structure determination, which is however too expensive for high-throughput
screening. At the same time, directly predicting crystal structures from
compositions remains a challenging unsolved problem. Herein we propose a deep
learning algorithm for predicting the XRD spectrum given only the composition
of a material, which can then be used to infer key structural features for
downstream structural analysis such as crystal system or space group
classification or crystal lattice parameter determination or materials property
predictions. Benchmark studies on two datasets show that our DeepXRD algorithm
can achieve good performance for XRD prediction as evaluated over our test
sets. It can thus be used in high-throughput screening in the huge materials
composition space for new materials discovery.

Face anti-spoofing (FAS) plays a critical role in securing face recognition
systems from different presentation attacks. Previous works leverage auxiliary
pixel-level supervision and domain generalization approaches to address unseen
spoof types. However, the local characteristics of image captures, i.e.,
capturing devices and presenting materials, are ignored in existing works and
we argue that such information is required for networks to discriminate between
live and spoof images. In this work, we propose PatchNet which reformulates
face anti-spoofing as a fine-grained patch-type recognition problem. To be
specific, our framework recognizes the combination of capturing devices and
presenting materials based on the patches cropped from non-distorted face
images. This reformulation can largely improve the data variation and enforce
the network to learn discriminative feature from local capture patterns. In
addition, to further improve the generalization ability of the spoof feature,
we propose the novel Asymmetric Margin-based Classification Loss and
Self-supervised Similarity Loss to regularize the patch embedding space. Our
experimental results verify our assumption and show that the model is capable
of recognizing unseen spoof types robustly by only looking at local regions.
Moreover, the fine-grained and patch-level reformulation of FAS outperforms the
existing approaches on intra-dataset, cross-dataset, and domain generalization
benchmarks. Furthermore, our PatchNet framework can enable practical
applications like Few-Shot Reference-based FAS and facilitate future
exploration of spoof-related intrinsic cues.

We propose a multi-tone decomposition algorithm that can find the
frequencies, amplitudes and phases of the fundamental sinusoids in a noisy
observation sequence. Under independent identically distributed Gaussian noise,
our method utilizes a maximum likelihood approach to estimate the relevant tone
parameters from the contaminated observations. When estimating $M$ number of
sinusoidal sources, our algorithm successively estimates their frequencies and
jointly optimizes their amplitudes and phases. Our method can also be
implemented as a blind source separator in the absence of the information about
$M$. The computational complexity of our algorithm is near-linear, i.e.,
$\tilde{O}(N)$.

The core issue in semi-supervised learning (SSL) lies in how to effectively
leverage unlabeled data, whereas most existing methods tend to put a great
emphasis on the utilization of high-confidence samples yet seldom fully explore
the usage of low-confidence samples. In this paper, we aim to utilize
low-confidence samples in a novel way with our proposed mutex-based consistency
regularization, namely MutexMatch. Specifically, the high-confidence samples
are required to exactly predict "what it is" by conventional True-Positive
Classifier, while the low-confidence samples are employed to achieve a simpler
goal -- to predict with ease "what it is not" by True-Negative Classifier. In
this sense, we not only mitigate the pseudo-labeling errors but also make full
use of the low-confidence unlabeled data by consistency of dissimilarity
degree. MutexMatch achieves superior performance on multiple benchmark
datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, and mini-ImageNet. More
importantly, our method further shows superiority when the amount of labeled
data is scarce, e.g., 92.23% accuracy with only 20 labeled data on CIFAR-10.
Code has been released at https://github.com/NJUyued/MutexMatch4SSL.

Face forgery has attracted increasing attention in recent applications of
computer vision. Existing detection techniques using the two-branch framework
benefit a lot from a frequency perspective, yet are restricted by their fixed
frequency decomposition and transform. In this paper, we propose to Adaptively
learn Frequency information in the two-branch Detection framework, dubbed AFD.
To be specific, we automatically learn decomposition in the frequency domain by
introducing heterogeneity constraints, and propose an attention-based module to
adaptively incorporate frequency features into spatial clues. Then we liberate
our network from the fixed frequency transforms, and achieve better performance
with our data- and task-dependent transform layers. Extensive experiments show
that AFD generally outperforms.

The past year has witnessed a rapid development of masked image modeling
(MIM). MIM is mostly built upon the vision transformers, which suggests that
self-supervised visual representations can be done by masking input image parts
while requiring the target model to recover the missing contents. MIM has
demonstrated promising results on downstream tasks, yet we are interested in
whether there exist other effective ways to `learn by recovering missing
contents'. In this paper, we investigate this topic by designing five other
learning objectives that follow the same procedure as MIM but degrade the input
image in different ways. With extensive experiments, we manage to summarize a
few design principles for token-based pre-training of vision transformers. In
particular, the best practice is obtained by keeping the original image style
and enriching spatial masking with spatial misalignment -- this design achieves
superior performance over MIM in a series of downstream recognition tasks
without extra computational cost. The code is available at
https://github.com/sunsmarterjie/beyond_masking.

We revisit the complexity of the classical Interval Scheduling in the dynamic
setting. In this problem, the goal is to maintain a set of intervals under
insertions and deletions and report the size of the maximum size subset of
pairwise disjoint intervals after each update. Nontrivial approximation
algorithms are known for this problem, for both the unweighted and weighted
versions [Henzinger, Neumann, Wiese, SoCG 2020]. Surprisingly, it was not known
if the general exact version admits an exact solution working in sublinear
time, that is, without recomputing the answer after each update.
  Our first contribution is a structure for Dynamic Interval Scheduling with
amortized $\tilde{\mathcal{O}}(n^{1/3})$ update time. Then, building on the
ideas used for the case of one machine, we design a sublinear solution for any
constant number of machines: we describe a structure for Dynamic Interval
Scheduling on $m\geq 2$ machines with amortized $\tilde{\mathcal{O}}(n^{1 -
1/m})$ update time.
  We complement the above results by considering Dynamic Weighted Interval
Scheduling on one machine, that is maintaining (the weight of) the maximum
weight subset of pairwise disjoint intervals. We show an almost linear lower
bound (conditioned on the hardness of Minimum Weight $k$-Clique) for the
update/query time of any structure for this problem. Hence, in the weighted
case one should indeed seek approximate solutions.

Deep Learning (DL) has shown great promise in the unsupervised task of
clustering. That said, while in classical (i.e., non-deep) clustering the
benefits of the nonparametric approach are well known, most deep-clustering
methods are parametric: namely, they require a predefined and fixed number of
clusters, denoted by K. When K is unknown, however, using model-selection
criteria to choose its optimal value might become computationally expensive,
especially in DL as the training process would have to be repeated numerous
times. In this work, we bridge this gap by introducing an effective
deep-clustering method that does not require knowing the value of K as it
infers it during the learning. Using a split/merge framework, a dynamic
architecture that adapts to the changing K, and a novel loss, our proposed
method outperforms existing nonparametric methods (both classical and deep
ones). While the very few existing deep nonparametric methods lack scalability,
we demonstrate ours by being the first to report the performance of such a
method on ImageNet. We also demonstrate the importance of inferring K by
showing how methods that fix it deteriorate in performance when their assumed K
value gets further from the ground-truth one, especially on imbalanced
datasets. Our code is available at https://github.com/BGU-CS-VIL/DeepDPM.

Few-shot video object segmentation (FS-VOS) aims at segmenting video frames
using a few labelled examples of classes not seen during initial training. In
this paper, we present a simple but effective temporal transductive inference
(TTI) approach that leverages temporal consistency in the unlabelled video
frames during few-shot inference. Key to our approach is the use of both global
and local temporal constraints. The objective of the global constraint is to
learn consistent linear classifiers for novel classes across the image
sequence, whereas the local constraint enforces the proportion of
foreground/background regions in each frame to be coherent across a local
temporal window. These constraints act as spatiotemporal regularizers during
the transductive inference to increase temporal coherence and reduce
overfitting on the few-shot support set. Empirically, our model outperforms
state-of-the-art meta-learning approaches in terms of mean intersection over
union on YouTube-VIS by 2.8%. In addition, we introduce improved benchmarks
that are exhaustively labelled (i.e. all object occurrences are labelled,
unlike the currently available), and present a more realistic evaluation
paradigm that targets data distribution shift between training and testing
sets. Our empirical results and in-depth analysis confirm the added benefits of
the proposed spatiotemporal regularizers to improve temporal coherence and
overcome certain overfitting scenarios.

Temporal graph representation learning has drawn significant attention for
the prevalence of temporal graphs in the real world. However, most existing
works resort to taking discrete snapshots of the temporal graph, or are not
inductive to deal with new nodes, or do not model the exciting effects which is
the ability of events to influence the occurrence of another event. In this
work, We propose TREND, a novel framework for temporal graph representation
learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes
process-based graph neural network (GNN). TREND presents a few major
advantages: (1) it is inductive due to its GNN architecture; (2) it captures
the exciting effects between events by the adoption of the Hawkes process; (3)
as our main novelty, it captures the individual and collective characteristics
of events by integrating both event and node dynamics, driving a more precise
modeling of the temporal process. Extensive experiments on four real-world
datasets demonstrate the effectiveness of our proposed model.

The performance of deep learning models highly depends on the amount of
training data. It is common practice for today's data holders to merge their
datasets and train models collaboratively, which yet poses a threat to data
privacy. Different from existing methods such as secure multi-party computation
(MPC) and federated learning (FL), we find representation learning has unique
advantages in collaborative learning due to the lower communication overhead
and task-independency. However, data representations face the threat of model
inversion attacks. In this article, we formally define the collaborative
learning scenario, and quantify data utility and privacy. Then we present ARS,
a collaborative learning framework wherein users share representations of data
to train models, and add imperceptible adversarial noise to data
representations against reconstruction or attribute extraction attacks. By
evaluating ARS in different contexts, we demonstrate that our mechanism is
effective against model inversion attacks, and achieves a balance between
privacy and utility. The ARS framework has wide applicability. First, ARS is
valid for various data types, not limited to images. Second, data
representations shared by users can be utilized in different tasks. Third, the
framework can be easily extended to the vertical data partitioning scenario.

We evaluated a lightweight Convolutional Neural Network (CNN) called LPRNet
[1] for automatic License Plate Recognition (LPR). We evaluated the algorithm
on two datasets, one composed of real license plate images and the other of
synthetic license plate images. In addition, we compared its performance
against Tesseract [2], an Optical Character Recognition engine. We measured
performance based on recognition accuracy and Levenshtein Distance. LPRNet is
an end-to-end framework and demonstrated robust performance on both datasets,
delivering 90 and 89 percent recognition accuracy on test sets of 1000 real and
synthetic license plate images, respectively. Tesseract was not trained using
real license plate images and performed well only on the synthetic dataset
after pre-processing steps delivering 93 percent recognition accuracy. Finally,
Pareto analysis for frequency analysis of misclassified characters allowed us
to find in detail which characters were the most conflicting ones according to
the percentage of accumulated error. Depending on the region, license plate
images possess particular characteristics. Once properly trained, LPRNet can be
used to recognize characters from a specific region and dataset. Future work
can focus on applying transfer learning to utilize the features learned by
LPRNet and fine-tune it given a smaller, newer dataset of license plates.

We introduce a novel formulation for guided super-resolution. Its core is a
differentiable optimisation layer that operates on a learned affinity graph.
The learned graph potentials make it possible to leverage rich contextual
information from the guide image, while the explicit graph optimisation within
the architecture guarantees rigorous fidelity of the high-resolution target to
the low-resolution source. With the decision to employ the source as a
constraint rather than only as an input to the prediction, our method differs
from state-of-the-art deep architectures for guided super-resolution, which
produce targets that, when downsampled, will only approximately reproduce the
source. This is not only theoretically appealing, but also produces crisper,
more natural-looking images. A key property of our method is that, although the
graph connectivity is restricted to the pixel lattice, the associated edge
potentials are learned with a deep feature extractor and can encode rich
context information over large receptive fields. By taking advantage of the
sparse graph connectivity, it becomes possible to propagate gradients through
the optimisation layer and learn the edge potentials from data. We extensively
evaluate our method on several datasets, and consistently outperform recent
baselines in terms of quantitative reconstruction errors, while also delivering
visually sharper outputs. Moreover, we demonstrate that our method generalises
particularly well to new datasets not seen during training.

A stroke is defined as a neurologic deficit arising from an interruption in
blood supply to the brain. According to the World Health Organization, over 15
million people suffer from strokes annually, of which almost 70% die or are
permanently disabled. Effective treatment must be administered within one hour
to prevent irreversible brain damage. Unfortunately, the current gold standards
for diagnosis, CT and MRI, are time-consuming, expensive, and immobile.
Electroencephalograms reveal biomarkers of strokes while being inexpensive and
available for remote use, but no system exists that utilizes them for this
purpose. To address this issue, we created StrokeSight, a novel, open-source
web application that automatically provides a full diagnosis and visualization
of ischemic and hemorrhagic strokes in under 50 seconds using 60-second
electroencephalograms. We first calculated the averaged power spectral
densities for 132, 60-second electroencephalogram readings, which we then used
to train three deep neural networks that respectively predict a stroke type
(control/ischemic/hemorrhagic), location (left/right hemisphere), and severity
(small/large) with accuracies of 97.5%, 94.4%, and 100%. StrokeSight also
implements a novel process to visualize spectral abnormalities caused by
strokes. Azimuthal equidistant projection and multivariate spline interpolation
are used to reshape 3D electrodes onto a head-shaped 2D plane and then a
contour map of each frequency band power is created, allowing neurologists to
quickly and accurately interpret electroencephalogram data. StrokeSight could
act as a revolutionary solution for stroke care that drastically improves the
speed, cost efficiency, and accessibility of stroke diagnosis while allowing
for personalized treatment and interpretation.

We present the first comprehensive video polyp segmentation (VPS) study in
the deep learning era. Over the years, developments in VPS are not moving
forward with ease due to the lack of large-scale fine-grained segmentation
annotations. To address this issue, we first introduce a high-quality
frame-by-frame annotated VPS dataset, named SUN-SEG, which contains 158,690
colonoscopy frames from the well-known SUN-database. We provide additional
annotations with diverse types, i.e., attribute, object mask, boundary,
scribble, and polygon. Second, we design a simple but efficient baseline,
dubbed PNS+, consisting of a global encoder, a local encoder, and normalized
self-attention (NS) blocks. The global and local encoders receive an anchor
frame and multiple successive frames to extract long-term and short-term
spatial-temporal representations, which are then progressively updated by two
NS blocks. Extensive experiments show that PNS+ achieves the best performance
and real-time inference speed (170fps), making it a promising solution for the
VPS task. Third, we extensively evaluate 13 representative polyp/object
segmentation models on our SUN-SEG dataset and provide attribute-based
comparisons. Finally, we discuss several open issues and suggest possible
research directions for the VPS community.

Abstract syntax trees (ASTs) play a crucial role in source code
representation. However, due to the large number of nodes in an AST and the
typically deep AST hierarchy, it is challenging to learn the hierarchical
structure of an AST effectively. In this paper, we propose HELoC, a
hierarchical contrastive learning model for source code representation. To
effectively learn the AST hierarchy, we use contrastive learning to allow the
network to predict the AST node level and learn the hierarchical relationships
between nodes in a self-supervised manner, which makes the representation
vectors of nodes with greater differences in AST levels farther apart in the
embedding space. By using such vectors, the structural similarities between
code snippets can be measured more precisely. In the learning process, a novel
GNN (called Residual Self-attention Graph Neural Network, RSGNN) is designed,
which enables HELoC to focus on embedding the local structure of an AST while
capturing its overall structure. HELoC is self-supervised and can be applied to
many source code related downstream tasks such as code classification, code
clone detection, and code clustering after pre-training. Our extensive
experiments demonstrate that HELoC outperforms the state-of-the-art source code
representation models.

While Natural Language Processing (NLP) algorithms keep reaching
unprecedented milestones, out-of-distribution generalization is still
challenging. In this paper we address the problem of multi-source adaptation to
unknown domains: Given labeled data from multiple source domains, we aim to
generalize to data drawn from target domains that are unknown to the algorithm
at training time. We present an algorithmic framework based on example-based
Hypernetwork adaptation: Given an input example, a T5 encoder-decoder first
generates a unique signature which embeds this example in the semantic space of
the source domains, and this signature is then fed into a Hypernetwork which
generates the weights of the task classifier. In an advanced version of our
model, the learned signature also serves for improving the representation of
the input example. In experiments with two tasks, sentiment classification and
natural language inference, across 29 adaptation settings, our algorithms
substantially outperform existing algorithms for this adaptation setup. To the
best of our knowledge, this is the first time Hypernetworks are applied to
domain adaptation or in example-based manner in NLP.

A comprehensive understanding of human-object interaction (HOI) requires
detecting not only a small portion of predefined HOI concepts (or categories)
but also other reasonable HOI concepts, while current approaches usually fail
to explore a huge portion of unknown HOI concepts (i.e., unknown but reasonable
combinations of verbs and objects). In this paper, 1) we introduce a novel and
challenging task for a comprehensive HOI understanding, which is termed as HOI
Concept Discovery; and 2) we devise a self-compositional learning framework (or
SCL) for HOI concept discovery. Specifically, we maintain an online updated
concept confidence matrix during training: 1) we assign pseudo-labels for all
composite HOI instances according to the concept confidence matrix for
self-training; and 2) we update the concept confidence matrix using the
predictions of all composite HOI instances. Therefore, the proposed method
enables the learning on both known and unknown HOI concepts. We perform
extensive experiments on several popular HOI datasets to demonstrate the
effectiveness of the proposed method for HOI concept discovery, object
affordance recognition and HOI detection. For example, the proposed
self-compositional learning framework significantly improves the performance of
1) HOI concept discovery by over 10% on HICO-DET and over 3% on V-COCO,
respectively; 2) object affordance recognition by over 9% mAP on MS-COCO and
HICO-DET; and 3) rare-first and non-rare-first unknown HOI detection relatively
over 30% and 20%, respectively. Code is publicly available at
https://github.com/zhihou7/HOI-CL.

Airborne Wind Energy is a lightweight technology that allows power extraction
from the wind using airborne devices such as kites and gliders, where the
airfoil orientation can be dynamically controlled in order to maximize
performance. The dynamical complexity of turbulent aerodynamics makes this
optimization problem unapproachable by conventional methods such as classical
control theory, which rely on accurate and tractable analytical models of the
dynamical system at hand. Here we propose to attack this problem through
Reinforcement Learning, a technique that -- by repeated trial-and-error
interactions with the environment -- learns to associate observations with
profitable actions without requiring prior knowledge of the system. We show
that in a simulated environment Reinforcement Learning finds an efficient way
to control a kite so that it can tow a vehicle for long distances. The
algorithm we use is based on a small set of intuitive observations and its
physically transparent interpretation allows to describe the approximately
optimal strategy as a simple list of manoeuvring instructions.

With the constantly growing number of reviews and other sentiment-bearing
texts on the Web, the demand for automatic sentiment analysis algorithms
continues to expand. Aspect-based sentiment classification (ABSC) allows for
the automatic extraction of highly fine-grained sentiment information from text
documents or sentences. In this survey, the rapidly evolving state of the
research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the
ABSC models into three major categories: knowledge-based, machine learning, and
hybrid models. This taxonomy is accompanied with summarizing overviews of the
reported model performances, and both technical and intuitive explanations of
the various ABSC models. State-of-the-art ABSC models are discussed, such as
models based on the transformer model, and hybrid deep learning models that
incorporate knowledge bases. Additionally, various techniques for representing
the model inputs and evaluating the model outputs are reviewed. Furthermore,
trends in the research on ABSC are identified and a discussion is provided on
the ways in which the field of ABSC can be advanced in the future.

The growing need for trustworthy machine learning has led to the blossom of
interpretability research. Numerous explanation methods have been developed to
serve this purpose. However, these methods are deficiently and inappropriately
evaluated. Many existing metrics for explanations are introduced by researchers
as by-products of their proposed explanation techniques to demonstrate the
advantages of their methods. Although widely used, they are more or less
accused of problems. We claim that the lack of acknowledged and justified
metrics results in chaos in benchmarking these explanation methods -- Do we
really have good/bad explanation when a metric gives a high/low score? We split
existing metrics into two categories and demonstrate that they are insufficient
to properly evaluate explanations for multiple reasons. We propose guidelines
in dealing with the problems in evaluating machine learning explanation and
encourage researchers to carefully deal with these problems when developing
explanation techniques and metrics.

Attention is an important mechanism that can be employed for a variety of
deep learning models across many different domains and tasks. This survey
provides an overview of the most important attention mechanisms proposed in the
literature. The various attention mechanisms are explained by means of a
framework consisting of a general attention model, uniform notation, and a
comprehensive taxonomy of attention mechanisms. Furthermore, the various
measures for evaluating attention models are reviewed, and methods to
characterize the structure of attention models based on the proposed framework
are discussed. Last, future work in the field of attention models is

Understanding realistic visual scene images together with language
descriptions is a fundamental task towards generic visual understanding.
Previous works have shown compelling comprehensive results by building
hierarchical structures for visual scenes (e.g., scene graphs) and natural
languages (e.g., dependency trees), individually. However, how to construct a
joint vision-language (VL) structure has barely been investigated. More
challenging but worthwhile, we introduce a new task that targets on inducing
such a joint VL structure in an unsupervised manner. Our goal is to bridge the
visual scene graphs and linguistic dependency trees seamlessly. Due to the lack
of VL structural data, we start by building a new dataset VLParse. Rather than
using labor-intensive labeling from scratch, we propose an automatic alignment
procedure to produce coarse structures followed by human refinement to produce
high-quality ones. Moreover, we benchmark our dataset by proposing a
contrastive learning (CL)-based framework VLGAE, short for Vision-Language
Graph Autoencoder. Our model obtains superior performance on two derived tasks,
i.e., language grammar induction and VL phrase grounding. Ablations show the
effectiveness of both visual cues and dependency relationships on fine-grained
VL structure construction.

In this paper, we consider image quality assessment (IQA) as a measure of how
images are amenable with respect to a given downstream task, or task
amenability. When the task is performed using machine learning algorithms, such
as a neural-network-based task predictor for image classification or
segmentation, the performance of the task predictor provides an objective
estimate of task amenability. In this work, we use an IQA controller to predict
the task amenability which, itself being parameterised by neural networks, can
be trained simultaneously with the task predictor. We further develop a
meta-reinforcement learning framework to improve the adaptability for both IQA
controllers and task predictors, such that they can be fine-tuned efficiently
on new datasets or meta-tasks. We demonstrate the efficacy of the proposed
task-specific, adaptable IQA approach, using two clinical applications for
ultrasound-guided prostate intervention and pneumonia detection on X-ray
images.

Recommendation systems focus on helping users find items of interest in the
situations of information overload, where users' preferences are typically
estimated by the past observed behaviors. In contrast, conversational
recommendation systems (CRS) aim to understand users' preferences via
interactions in conversation flows. CRS is a complex problem that consists of
two main tasks: (1) recommendation and (2) response generation. Previous work
often tried to solve the problem in a modular manner, where recommenders and
response generators are separate neural models. Such modular architectures
often come with a complicated and unintuitive connection between the modules,
leading to inefficient learning and other issues. In this work, we propose a
unified framework based on BART for conversational recommendation, which
tackles two tasks in a single model. Furthermore, we also design and collect a
lightweight knowledge graph for CRS in the movie domain. The experimental
results show that the proposed methods achieve the state-of-the-art performance
in terms of both automatic and human evaluation.

Estimations and evaluations of the main patterns of time series data in
groups benefit large amounts of applications in various fields. Different from
the classical auto-correlation time series analysis and the modern neural
networks techniques, in this paper we propose a combination of functional
analysis of variance (FANOVA) and permutation tests in a more intuitive manner
for a limited sample size. First, FANOVA is applied in order to separate the
common information and to dig out the additional categorical influence through
paired group comparison, the results of which are secondly analyzed through
permutation tests to identify the time zones where the means of the different
groups differ significantly. Normalized kernel functions of different groups
are able to reflect remarkable mean characteristics in grouped unities, also
meaningful for deeper interpretation and group-wise classification. In order to
learn whether and when the proposed method of FANOVA and permutation F-test
works precisely and efficiently, we compare the estimated kernel results with
the ground truth on simulated data. After the confirmation of the model's
efficiency from simulation, we apply it also to the RAVDESS facial dataset to
extract the emotional behaviors of humans based on facial muscles contractions
(so-called action units (AU) technically in computer graphics), by comparing
the neutral performances with emotional ones.

Recent advances in the Active Speaker Detection (ASD) problem build upon a
two-stage process: feature extraction and spatio-temporal context aggregation.
In this paper, we propose an end-to-end ASD workflow where feature learning and
contextual predictions are jointly learned. Our end-to-end trainable network
simultaneously learns multi-modal embeddings and aggregates spatio-temporal
context. This results in more suitable feature representations and improved
performance in the ASD task. We also introduce interleaved graph neural network
(iGNN) blocks, which split the message passing according to the main sources of
context in the ASD problem. Experiments show that the aggregated features from
the iGNN blocks are more suitable for ASD, resulting in state-of-the art
performance. Finally, we design a weakly-supervised strategy, which
demonstrates that the ASD problem can also be approached by utilizing
audiovisual data but relying exclusively on audio annotations. We achieve this
by modelling the direct relationship between the audio signal and the possible
sound sources (speakers), as well as introducing a contrastive loss. All the
resources of this project will be made available at:
https://github.com/fuankarion/end-to-end-asd.

This paper strives for activity recognition under domain shift, for example
caused by change of scenery or camera viewpoint. The leading approaches reduce
the shift in activity appearance by adversarial training and self-supervised
learning. Different from these vision-focused works we leverage activity sounds
for domain adaptation as they have less variance across domains and can
reliably indicate which activities are not happening. We propose an
audio-adaptive encoder and associated learning methods that discriminatively
adjust the visual feature representation as well as addressing shifts in the
semantic distribution. To further eliminate domain-specific features and
include domain-invariant activity sounds for recognition, an audio-infused
recognizer is proposed, which effectively models the cross-modal interaction
across domains. We also introduce the new task of actor shift, with a
corresponding audio-visual dataset, to challenge our method with situations
where the activity appearance changes dramatically. Experiments on this
dataset, EPIC-Kitchens and CharadesEgo show the effectiveness of our approach.

Domain generalization (DG) is essentially an out-of-distribution problem,
aiming to generalize the knowledge learned from multiple source domains to an
unseen target domain. The mainstream is to leverage statistical models to model
the dependence between data and labels, intending to learn representations
independent of domain. Nevertheless, the statistical models are superficial
descriptions of reality since they are only required to model dependence
instead of the intrinsic causal mechanism. When the dependence changes with the
target distribution, the statistic models may fail to generalize. In this
regard, we introduce a general structural causal model to formalize the DG
problem. Specifically, we assume that each input is constructed from a mix of
causal factors (whose relationship with the label is invariant across domains)
and non-causal factors (category-independent), and only the former cause the
classification judgments. Our goal is to extract the causal factors from inputs
and then reconstruct the invariant causal mechanisms. However, the theoretical
idea is far from practical of DG since the required causal/non-causal factors
are unobserved. We highlight that ideal causal factors should meet three basic
properties: separated from the non-causal ones, jointly independent, and
causally sufficient for the classification. Based on that, we propose a
Causality Inspired Representation Learning (CIRL) algorithm that enforces the
representations to satisfy the above properties and then uses them to simulate
the causal factors, which yields improved generalization ability. Extensive
experimental results on several widely used datasets verify the effectiveness

Human mind is the palace of curious questions that seek answers.
Computational resolution of this challenge is possible through Natural Language
Processing techniques. Statistical techniques like machine learning and deep
learning require a lot of data to train and despite that they fail to tap into
the nuances of language. Such systems usually perform best on close-domain
datasets. We have proposed development of a rule-based open-domain
question-answering system which is capable of answering questions of any domain
from a corresponding context passage. We have used 1000 questions from SQuAD
2.0 dataset for testing the developed system and it gives satisfactory results.
In this paper, we have described the structure of the developed system and have
analyzed the performance.

Labeled datasets are essential for supervised machine learning. Various data
labeling tools have been built to collect labels in different usage scenarios.
However, developing labeling tools is time-consuming, costly, and
expertise-demanding on software development. In this paper, we propose a
conceptual framework for data labeling and OneLabeler based on the conceptual
framework to support easy building of labeling tools for diverse usage
scenarios. The framework consists of common modules and states in labeling
tools summarized through coding of existing tools. OneLabeler supports
configuration and composition of common software modules through visual
programming to build data labeling tools. A module can be a human, machine, or
mixed computation procedure in data labeling. We demonstrate the expressiveness
and utility of the system through ten example labeling tools built with
OneLabeler. A user study with developers provides evidence that OneLabeler
supports efficient building of diverse data labeling tools.

Modeling social influence on outcomes of network-connected individuals is a
central research question in several scientific disciplines. However, network
influence cannot be identified from observational data because it is confounded
with unobserved homophily. We propose a latent homophily-adjusted Spatial
Autoregressive model (SAR) for networked responses to identify the causal
contagion effects. The latent homophily is estimated from the spectral
embedding of the network's adjacency matrix. We further develop maximum
likelihood estimators for the parameters of the SAR model when covariates are
measured with error. The bias-corrected MLE enjoys statistical consistency and
asymptotic normality properties. We combine the estimated latent homophily with
the bias corrected MLE in the SAR model to estimate network influence. Our
simulations show that the methods perform well in finite samples. Applying our
methodology to a data-set of female criminal offenders in a therapeutic
community (TC), we provide causal estimates of network influence on graduation
from the TC.

Although deep learning-based end-to-end Automatic Speech Recognition (ASR)
has shown remarkable performance in recent years, it suffers severe performance
regression on test samples drawn from different data distributions. Test-time
Adaptation (TTA), previously explored in the computer vision area, aims to
adapt the model trained on source domains to yield better predictions for test
samples, often out-of-domain, without accessing the source data. Here, we
propose the Single-Utterance Test-time Adaptation (SUTA) framework for ASR,
which is the first TTA study on ASR to our best knowledge. The single-utterance
TTA is a more realistic setting that does not assume test data are sampled from
identical distribution and does not delay on-demand inference due to
pre-collection for the batch of adaptation data. SUTA consists of unsupervised
objectives with an efficient adaptation strategy. Empirical results demonstrate
that SUTA effectively improves the performance of the source ASR model
evaluated on multiple out-of-domain target corpora and in-domain test samples.

Despite the recent success of video self-supervised learning models, there is
much still to be understood about their generalization capability. In this
paper, we investigate how sensitive video self-supervised learning is to the
current conventional benchmark and whether methods generalize beyond the
canonical evaluation setting. We do this across four different factors of
sensitivity: domain, samples, actions and task. Our study which encompasses
over 500 experiments on 7 video datasets, 9 self-supervised methods and 6 video
understanding tasks, reveals that current benchmarks in video self-supervised
learning are not good indicators of generalization along these sensitivity
factors. Further, we find that self-supervised methods considerably lag behind
vanilla supervised pre-training, especially when domain shift is large and the
amount of available downstream samples are low. From our analysis, we distill
the SEVERE-benchmark, a subset of our experiments, and discuss its implication
for evaluating the generalizability of representations obtained by existing and
future self-supervised video learning methods.

Discriminative representation is crucial for the association step in
multi-object tracking. Recent work mainly utilizes features in single or
neighboring frames for constructing metric loss and empowering networks to
extract representation of targets. Although this strategy is effective, it
fails to fully exploit the information contained in a whole trajectory. To this
end, we propose a strategy, namely multi-view trajectory contrastive learning,
in which each trajectory is represented as a center vector. By maintaining all
the vectors in a dynamically updated memory bank, a trajectory-level
contrastive loss is devised to explore the inter-frame information in the whole
trajectories. Besides, in this strategy, each target is represented as multiple
adaptively selected keypoints rather than a pre-defined anchor or center. This
design allows the network to generate richer representation from multiple views
of the same target, which can better characterize occluded objects.
Additionally, in the inference stage, a similarity-guided feature fusion
strategy is developed for further boosting the quality of the trajectory
representation. Extensive experiments have been conducted on MOTChallenge to
verify the effectiveness of the proposed techniques. The experimental results
indicate that our method has surpassed preceding trackers and established new

Imaging using millimeter waves (mmWs) has many advantages including the
ability to penetrate obscurants such as clothes and polymers. After having
explored shape information retrieved from mmW images for person recognition, in
this work we aim to gain some insight about the potential of using mmW texture
information for the same task, considering not only the mmW face, but also mmW
torso and mmW wholebody. We report experimental results using the mmW TNO
database consisting of 50 individuals based on both hand-crafted and learned
features from Alexnet and VGG-face pretrained Convolutional Neural Networks
(CNN) models. First, we analyze the individual performance of three mmW body
parts, concluding that: i) mmW torso region is more discriminative than mmW
face and the whole body, ii) CNN features produce better results compared to
hand-crafted features on mmW faces and the entire body, and iii) hand-crafted
features slightly outperform CNN features on mmW torso. In the second part of
this work, we analyze different multi-algorithmic and multi-modal techniques,
including a novel CNN-based fusion technique, improving verification results to
2% EER and identification rank-1 results up to 99%. Comparative analyses with
mmW body shape information and face recognition in the visible and NIR spectral
bands are also reported.

Many existing conditional score-based data generation methods utilize Bayes'
theorem to decompose the gradients of a log posterior density into a mixture of
scores. These methods facilitate the training procedure of conditional score
models, as a mixture of scores can be separately estimated using a score model
and a classifier. However, our analysis indicates that the training objectives
for the classifier in these methods may lead to a serious score mismatch issue,
which corresponds to the situation that the estimated scores deviate from the
true ones. Such an issue causes the samples to be misled by the deviated scores
during the diffusion process, resulting in a degraded sampling quality. To
resolve it, we formulate a novel training objective, called Denoising
Likelihood Score Matching (DLSM) loss, for the classifier to match the
gradients of the true log likelihood density. Our experimental evidence shows
that the proposed method outperforms the previous methods on both Cifar-10 and
Cifar-100 benchmarks noticeably in terms of several key evaluation metrics. We
thus conclude that, by adopting DLSM, the conditional scores can be accurately
modeled, and the effect of the score mismatch issue is alleviated.

The generic object detection (GOD) task has been successfully tackled by
recent deep neural networks, trained by an avalanche of annotated training
samples from some common classes. However, it is still non-trivial to
generalize these object detectors to the novel long-tailed object classes,
which has only few labeled training samples. To this end, the Few-Shot Object
Detection (FSOD) has been topical recently, as it mimics the humans' ability of
learning to learn, and intelligently transfers the learnt generic object
knowledge from the common heavy-tailed, to the novel long-tailed object
classes. Especially, the research in this emerging field has been flourish in
the recent years with various benchmarks, backbones, and methodologies
proposed. To review these FSOD works, there are several insightful FSOD survey
articles that systematically study and compare them as the groups of
fine-tuning/transfer learning, and meta-learning methods. In contrast, we
compare these FSOD algorithms from the new perspective and taxonomy of their
contributions, i.e., data-oriented, model-oriented, and algorithm oriented
ones. Thus, an empirical study and comparison has been conducted on the recent
achievements of FSOD. Furthermore, we also analyze the technical challenges,
the merits and demerits of these methods, and envision the future directions of
FSOD. Specifically, we give an overview of FSOD, including the problem
definition, common datasets, and evaluation protocols. A new taxonomy is then
proposed based on the role of prior knowledge during object detection of novel
classes. Following this taxonomy, we provide a systematic review of the
advances in FSOD. Finally, further discussions on performance, challenges, and
future directions are presented.

The lack of resolution has a negative impact on the performance of
image-based biometrics. While many generic super-resolution methods have been
proposed to restore low-resolution images, they usually aim to enhance their
visual appearance. However, a visual enhancement of biometric images does not
necessarily correlate with a better recognition performance. Reconstruction
approaches need thus to incorporate specific information from the target
biometric modality to effectively improve recognition. This paper presents a
comprehensive survey of iris super-resolution approaches proposed in the
literature. We have also adapted an Eigen-patches reconstruction method based
on PCA Eigen-transformation of local image patches. The structure of the iris
is exploited by building a patch-position dependent dictionary. In addition,
image patches are restored separately, having their own reconstruction weights.
This allows the solution to be locally optimized, helping to preserve local
information. To evaluate the algorithm, we degraded high-resolution images from
the CASIA Interval V3 database. Different restorations were considered, with
15x15 pixels being the smallest resolution. To the best of our knowledge, this
is among the smallest resolutions employed in the literature. The framework is
complemented with six public iris comparators, which were used to carry out
biometric verification and identification experiments. Experimental results
show that the proposed method significantly outperforms both bilinear and
bicubic interpolation at very low-resolution. The performance of a number of
comparators attains an impressive Equal Error Rate as low as 5%, and a Top-1
accuracy of 77-84% when considering iris images of only 15x15 pixels. These
results clearly demonstrate the benefit of using trained super-resolution
techniques to improve the quality of iris images prior to matching.

In the real open world, data tends to follow long-tailed class distributions,
motivating the well-studied long-tailed recognition (LTR) problem. Naive
training produces models that are biased toward common classes in terms of
higher accuracy. The key to addressing LTR is to balance various aspects
including data distribution, training losses, and gradients in learning. We
explore an orthogonal direction, weight balancing, motivated by the empirical
observation that the naively trained classifier has "artificially" larger
weights in norm for common classes (because there exists abundant data to train
them, unlike the rare classes). We investigate three techniques to balance
weights, L2-normalization, weight decay, and MaxNorm. We first point out that
L2-normalization "perfectly" balances per-class weights to be unit norm, but
such a hard constraint might prevent classes from learning better classifiers.
In contrast, weight decay penalizes larger weights more heavily and so learns
small balanced weights; the MaxNorm constraint encourages growing small weights
within a norm ball but caps all the weights by the radius. Our extensive study
shows that both help learn balanced weights and greatly improve the LTR
accuracy. Surprisingly, weight decay, although underexplored in LTR,
significantly improves over prior work. Therefore, we adopt a two-stage
training paradigm and propose a simple approach to LTR: (1) learning features
using the cross-entropy loss by tuning weight decay, and (2) learning
classifiers using class-balanced loss by tuning weight decay and MaxNorm. Our
approach achieves the state-of-the-art accuracy on five standard benchmarks,
serving as a future baseline for long-tailed recognition.

The lack of adversarial robustness has been recognized as an important issue
for state-of-the-art machine learning (ML) models, e.g., deep neural networks
(DNNs). Thereby, robustifying ML models against adversarial attacks is now a
major focus of research. However, nearly all existing defense methods,
particularly for robust training, made the white-box assumption that the
defender has the access to the details of an ML model (or its surrogate
alternatives if available), e.g., its architectures and parameters. Beyond
existing works, in this paper we aim to address the problem of black-box
defense: How to robustify a black-box model using just input queries and output
feedback? Such a problem arises in practical scenarios, where the owner of the
predictive model is reluctant to share model information in order to preserve
privacy. To this end, we propose a general notion of defensive operation that
can be applied to black-box models, and design it through the lens of denoised
smoothing (DS), a first-order (FO) certified defense technique. To allow the
design of merely using model queries, we further integrate DS with the
zeroth-order (gradient-free) optimization. However, a direct implementation of
zeroth-order (ZO) optimization suffers a high variance of gradient estimates,
and thus leads to ineffective defense. To tackle this problem, we next propose
to prepend an autoencoder (AE) to a given (black-box) model so that DS can be
trained using variance-reduced ZO optimization. We term the eventual defense as
ZO-AE-DS. In practice, we empirically show that ZO-AE- DS can achieve improved
accuracy, certified robustness, and query complexity over existing baselines.
And the effectiveness of our approach is justified under both image
classification and image reconstruction tasks. Codes are available at
https://github.com/damon-demon/Black-Box-Defense.

Modal split prediction in transportation networks has the potential to
support network operators in managing traffic congestion and improving transit
service reliability. We focus on the problem of hourly prediction of the
fraction of travelers choosing one mode of transportation over another using
high-dimensional travel time data. We use logistic regression as base model and
employ various regularization techniques for variable selection to prevent
overfitting and resolve multicollinearity issues. Importantly, we interpret the
prediction accuracy results with respect to the inherent variability of modal
splits and travelers' aggregate responsiveness to changes in travel time. By
visualizing model parameters, we conclude that the subset of segments found
important for predictive accuracy changes from hour-to-hour and include
segments that are topologically central and/or highly congested. We apply our
approach to the San Francisco Bay Area freeway and rapid transit network and
demonstrate superior prediction accuracy and interpretability of our method
compared to pre-specified variable selection methods.

This paper proposes a novel learning based high-dynamic-range (HDR)
reconstruction method using a polarization camera. We utilize a previous
observation that polarization filters with different orientations can attenuate
natural light differently, and we treat the multiple images acquired by the
polarization camera as a set acquired under different exposure times, to
introduce the development of solutions for the HDR reconstruction problem. We
propose a deep HDR reconstruction framework with a feature masking mechanism
that uses polarimetric cues available from the polarization camera, called Deep
Polarimetric HDR Reconstruction (DPHR). The proposed DPHR obtains polarimetric
information to propagate valid features through the network more effectively to
regress the missing pixels. We demonstrate through both qualitative and
quantitative evaluations that the proposed DPHR performs favorably than
state-of-the-art HDR reconstruction algorithms.

The growing amount of data and advances in data science have created a need
for a new kind of cloud platform that provides users with flexibility, strong
security, and the ability to couple with supercomputers and edge devices
through high-performance networks. We have built such a nation-wide cloud
platform, called "mdx" to meet this need. The mdx platform's virtualization
service, jointly operated by 9 national universities and 2 national research
institutes in Japan, launched in 2021, and more features are in development.
Currently mdx is used by researchers in a wide variety of domains, including
materials informatics, geo-spatial information science, life science,
astronomical science, economics, social science, and computer science. This
paper provides an the overview of the mdx platform, details the motivation for
its development, reports its current status, and outlines its future plans.

Generating educational questions of fairytales or storybooks is vital for
improving children's literacy ability. However, it is challenging to generate
questions that capture the interesting aspects of a fairytale story with
educational meaningfulness. In this paper, we propose a novel question
generation method that first learns the question type distribution of an input
story paragraph, and then summarizes salient events which can be used to
generate high-cognitive-demand questions. To train the event-centric
summarizer, we finetune a pre-trained transformer-based sequence-to-sequence
model using silver samples composed by educational question-answer pairs. On a
newly proposed educational question answering dataset FairytaleQA, we show good
performance of our method on both automatic and human evaluation metrics. Our
work indicates the necessity of decomposing question type distribution learning
and event-centric summary generation for educational question generation.

The area under the ROC curve (AUROC) has been vigorously applied for
imbalanced classification and moreover combined with deep learning techniques.
However, there is no existing work that provides sound information for peers to
choose appropriate deep AUROC maximization techniques. In this work, we fill
this gap from three aspects. (i) We benchmark a variety of loss functions with
different algorithmic choices for deep AUROC optimization problem. We study the
loss functions in two categories: pairwise loss and composite loss, which
includes a total of 10 loss functions. Interestingly, we find composite loss,
as an innovative loss function class, shows more competitive performance than
pairwise loss from both training convergence and testing generalization
perspectives. Nevertheless, data with more corrupted labels favors a pairwise
symmetric loss. (ii) Moreover, we benchmark and highlight the essential
algorithmic choices such as positive sampling rate, regularization,
normalization/activation, and optimizers. Key findings include: higher positive
sampling rate is likely to be beneficial for deep AUROC maximization; different
datasets favors different weights of regularizations; appropriate normalization
techniques, such as sigmoid and $\ell_2$ score normalization, could improve
model performance. (iii) For optimization aspect, we benchmark SGD-type,
Momentum-type, and Adam-type optimizers for both pairwise and composite loss.
Our findings show that although Adam-type method is more competitive from
training perspective, but it does not outperform others from testing

Automatic Time Series Forecasting (TSF) model design which aims to help users
to efficiently design suitable forecasting model for the given time series data
scenarios, is a novel research topic to be urgently solved. In this paper, we
propose AutoTS algorithm trying to utilize the existing design skills and
design efficient search methods to effectively solve this problem. In AutoTS,
we extract effective design experience from the existing TSF works. We allow
the effective combination of design experience from different sources, so as to
create an effective search space containing a variety of TSF models to support
different TSF tasks. Considering the huge search space, in AutoTS, we propose a
two-stage pruning strategy to reduce the search difficulty and improve the
search efficiency. In addition, in AutoTS, we introduce the knowledge graph to
reveal associations between module options. We make full use of these
relational information to learn higher-level features of each module option, so
as to further improve the search quality. Extensive experimental results show
that AutoTS is well-suited for the TSF area. It is more efficient than the
existing neural architecture search algorithms, and can quickly design powerful
TSF model better than the manually designed ones.

SGD does not produce robust results on datasets with label noise. Because the
gradients calculated according to the losses of the noisy samples cause the
optimization process to go in the wrong direction. In this paper, as an
alternative to SGD, we recommend using samples with loss less than a threshold
value determined during the optimization process, instead of using all samples
in the mini-batch. Our proposed method, Adaptive-k, aims to exclude label noise
samples from the optimization process and make the process robust. On noisy
datasets, we found that using a threshold-based approach, such as Adaptive-k,
produces better results than using all samples or a fixed number of low-loss
samples in the mini-batch. Based on our theoretical analysis and experimental
results, we show that the Adaptive-k method is closest to the performance of
the oracle, in which noisy samples are entirely removed from the dataset.
Adaptive-k is a simple but effective method. It does not require prior
knowledge of the noise ratio of the dataset, does not require additional model
training, and does not increase training time significantly. The code for
Adaptive-k is available at https://github.com/enesdedeoglu-TR/Adaptive-k

The process of continuously reallocating funds into financial assets, aiming
to increase the expected return of investment and minimizing the risk, is known
as portfolio management. Processing speed and energy consumption of portfolio
management have become crucial as the complexity of their real-world
applications increasingly involves high-dimensional observation and action
spaces and environment uncertainty, which their limited onboard resources
cannot offset. Emerging neuromorphic chips inspired by the human brain increase
processing speed by up to 1000 times and reduce power consumption by several
orders of magnitude. This paper proposes a spiking deep reinforcement learning
(SDRL) algorithm that can predict financial markets based on unpredictable
environments and achieve the defined portfolio management goal of profitability
and risk reduction. This algorithm is optimized forIntel's Loihi neuromorphic
processor and provides 186x and 516x energy consumption reduction is observed
compared to the competitors, respectively. In addition, a 1.3x and 2.0x
speed-up over the high-end processors and GPUs, respectively. The evaluations
are performed on cryptocurrency market between 2016 and 2021 the benchmark.

Autonomous vehicles (AVs) rely on environment perception and behavior
prediction to reason about agents in their surroundings. These perception
systems must be robust to adverse weather such as rain, fog, and snow. However,
validation of these systems is challenging due to their complexity and
dependence on observation histories. This paper presents a method for
characterizing failures of LiDAR-based perception systems for AVs in adverse
weather conditions. We develop a methodology based in reinforcement learning to
find likely failures in object tracking and trajectory prediction due to
sequences of disturbances. We apply disturbances using a physics-based data
augmentation technique for simulating LiDAR point clouds in adverse weather
conditions. Experiments performed across a wide range of driving scenarios from
a real-world driving dataset show that our proposed approach finds high
likelihood failures with smaller input disturbances compared to baselines while
remaining computationally tractable. Identified failures can inform future
development of robust perception systems for AVs.

Deep Learning (DL) algorithms are becoming increasingly popular for the
reconstruction of high-resolution turbulent flows (aka super-resolution).
However, current DL approaches perform spatially uniform super-resolution - a
key performance limiter for scalability of DL-based surrogates for
Computational Fluid Dynamics (CFD).
  To address the above challenge, we introduce NUNet, a deep learning-based
adaptive mesh refinement (AMR) framework for non-uniform super-resolution of
turbulent flows. NUNet divides the input low-resolution flow field into
patches, scores each patch, and predicts their target resolution. As a result,
it outputs a spatially non-uniform flow field, adaptively refining regions of
the fluid domain to achieve the target accuracy. We train NUNet with
Reynolds-Averaged Navier-Stokes (RANS) solutions from three different canonical
flows, namely turbulent channel flow, flat plate, and flow around ellipses.
NUNet shows remarkable discerning properties, refining areas with complex flow
features, such as near-wall domains and the wake region in flow around solid
bodies, while leaving areas with smooth variations (such as the freestream) in
the low-precision range. Hence, NUNet demonstrates an excellent qualitative and
quantitative alignment with the traditional OpenFOAM AMR solver. Moreover, it
reaches the same convergence guarantees as the AMR solver while accelerating it
by 3.2-5.5x, including unseen-during-training geometries and boundary
conditions, demonstrating its generalization capacities. Due to NUNet's ability
to super-resolve only regions of interest, it predicts the same target
1024x1024 spatial resolution 7-28.5x faster than state-of-the-art DL methods
and reduces the memory usage by 4.4-7.65x, showcasing improved scalability.

Intelligent reflecting surface (IRS) is envisioned to be widely applied in
future wireless networks. In this paper, we investigate a multi-user
communication system assisted by cooperative IRS devices with the capability of
energy harvesting. Aiming to maximize the long-term average achievable system
rate, an optimization problem is formulated by jointly designing the transmit
beamforming at the base station (BS) and discrete phase shift beamforming at
the IRSs, with the constraints on transmit power, user data rate requirement
and IRS energy buffer size. Considering time-varying channels and stochastic
arrivals of energy harvested by the IRSs, we first formulate the problem as a
Markov decision process (MDP) and then develop a novel multi-agent Q-mix (MAQ)
framework with two layers to decouple the optimization parameters. The higher
layer is for optimizing phase shift resolutions, and the lower one is for phase
shift beamforming and power allocation. Since the phase shift optimization is
an integer programming problem with a large-scale action space, we improve MAQ
by incorporating the Wolpertinger method, namely, MAQ-WP algorithm to achieve a
sub-optimality with reduced dimensions of action space. In addition, as MAQ-WP
is still of high complexity to achieve good performance, we propose a policy
gradient-based MAQ algorithm, namely, MAQ-PG, by mapping the discrete phase
shift actions into a continuous space at the cost of a slight performance loss.
Simulation results demonstrate that the proposed MAQ-WP and MAQ-PG algorithms
can converge faster and achieve data rate improvements of 10.7% and 8.8% over
the conventional multi-agent DDPG, respectively.

It has been well recognized that neural network based image classifiers are
easily fooled by images with tiny perturbations crafted by an adversary. There
has been a vast volume of research to generate and defend such adversarial
attacks. However, the following problem is left unexplored: How to
reverse-engineer adversarial perturbations from an adversarial image? This
leads to a new adversarial learning paradigm--Reverse Engineering of Deceptions
(RED). If successful, RED allows us to estimate adversarial perturbations and
recover the original images. However, carefully crafted, tiny adversarial
perturbations are difficult to recover by optimizing a unilateral RED
objective. For example, the pure image denoising method may overfit to
minimizing the reconstruction error but hardly preserve the classification
properties of the true adversarial perturbations. To tackle this challenge, we
formalize the RED problem and identify a set of principles crucial to the RED
approach design. Particularly, we find that prediction alignment and proper
data augmentation (in terms of spatial transformations) are two criteria to
achieve a generalizable RED approach. By integrating these RED principles with
image denoising, we propose a new Class-Discriminative Denoising based RED
framework, termed CDD-RED. Extensive experiments demonstrate the effectiveness
of CDD-RED under different evaluation metrics (ranging from the pixel-level,
prediction-level to the attribution-level alignment) and a variety of attack
generation methods (e.g., FGSM, PGD, CW, AutoAttack, and adaptive attacks).

The robustness of deep neural networks has received significant interest
recently, especially when being deployed in safety-critical systems, as it is
important to analyze how sensitive the model output is under input
perturbations. While most previous works focused on the local robustness
property around an input sample, the studies of the global robustness property,
which bounds the maximum output change under perturbations over the entire
input space, are still lacking. In this work, we formulate the global
robustness certification for neural networks with ReLU activation functions as
a mixed-integer linear programming (MILP) problem, and present an efficient
approach to address it. Our approach includes a novel interleaving twin-network
encoding scheme, where two copies of the neural network are encoded
side-by-side with extra interleaving dependencies added between them, and an
over-approximation algorithm leveraging relaxation and refinement techniques to
reduce complexity. Experiments demonstrate the timing efficiency of our work
when compared with previous global robustness certification methods and the
tightness of our over-approximation. A case study of closed-loop control safety
verification is conducted, and demonstrates the importance and practicality of
our approach for certifying the global robustness of neural networks in
safety-critical systems.

We consider Hodgkin-Huxley-type model that is a stiff ODE system with two
fast and one slow variables. For the parameter ranges under consideration the
original version of the model has unstable fixed point and the oscillating
attractor that demonstrates bifurcation from bursting to spiking dynamics. Also
a modified version is considered where the bistability occurs such that an area
in the parameter space appears where the fixed point becomes stable and
coexists with the bursting attractor. For these two systems we create
artificial neural networks that are able to reproduce their dynamics. The
created networks operate as recurrent maps and are trained on trajectory cuts
sampled at random parameter values within a certain range. Although the
networks are trained only on oscillatory trajectory cuts, it also discover the
fixed point of the considered systems. The position and even the eigenvalues
coincide very well with the fixed point of the initial ODEs. For the bistable
model it means that the network being trained only on one brunch of the
solutions recovers another brunch without seeing it during the training. These
results, as we see it, are able to trigger the development of new approaches to
complex dynamics reconstruction and discovering. From the practical point of
view reproducing dynamics with the neural network can be considered as a sort
of alternative method of numerical modeling intended for use with contemporary
parallel hard- and software.

Fake news on social media is increasingly regarded as one of the most
concerning issues. Low cost, simple accessibility via social platforms, and a
plethora of low-budget online news sources are some of the factors that
contribute to the spread of false news. Most of the existing fake news
detection algorithms are solely focused on the news content only but engaged
users prior posts or social activities provide a wealth of information about
their views on news and have significant ability to improve fake news
identification. Graph Neural Networks are a form of deep learning approach that
conducts prediction on graph-described data. Social media platforms are
followed graph structure in their representation, Graph Neural Network are
special types of neural networks that could be usually applied to graphs,
making it much easier to execute edge, node, and graph-level prediction.
Therefore, in this paper, we present a comparative analysis among some commonly
used machine learning algorithms and Graph Neural Networks for detecting the
spread of false news on social media platforms. In this study, we take the UPFD
dataset and implement several existing machine learning algorithms on text data
only. Besides this, we create different GNN layers for fusing graph-structured
news propagation data and the text data as the node feature in our GNN models.
GNNs provide the best solutions to the dilemma of identifying false news in our
research.

Under what conditions do the behaviors of players, who play a game
repeatedly, converge to a Nash equilibrium? If one assumes that the players'
behavior is a discrete-time or continuous-time rule whereby the current mixed
strategy profile is mapped to the next, this becomes a problem in the theory of
dynamical systems. We apply this theory, and in particular the concepts of
chain recurrence, attractors, and Conley index, to prove a general
impossibility result: there exist games for which any dynamics is bound to have
starting points that do not end up at a Nash equilibrium. We also prove a
stronger result for $\epsilon$-approximate Nash equilibria: there are games
such that no game dynamics can converge (in an appropriate sense) to
$\epsilon$-Nash equilibria, and in fact the set of such games has positive
measure. Further numerical results demonstrate that this holds for any
$\epsilon$ between zero and $0.09$. Our results establish that, although the
notions of Nash equilibria (and its computation-inspired approximations) are
universally applicable in all games, they are also fundamentally incomplete as
predictors of long term behavior, regardless of the choice of dynamics.

In the last two years, millions of lives have been lost due to COVID-19.
Despite the vaccination programmes for a year, hospitalization rates and deaths
are still high due to the new variants of COVID-19. Stringent guidelines and
COVID-19 screening measures such as temperature check and mask check at all
public places are helping reduce the spread of COVID-19. Visual inspections to
ensure these screening measures can be taxing and erroneous. Automated
inspection ensures an effective and accurate screening. Traditional approaches
involve identification of faces and masks from visual camera images followed by
extraction of temperature values from thermal imaging cameras. Use of visual
imaging as a primary modality limits these applications only for good-lighting
conditions. The use of thermal imaging alone for these screening measures makes
the system invariant to illumination. However, lack of open source datasets is
an issue to develop such systems. In this paper, we discuss our work on using
machine learning over thermal video streams for face and mask detection and
subsequent temperature screening in a passive non-invasive way that enables an
effective automated COVID-19 screening method in public places. We open source
our NTIC dataset that was used for training our models and was collected at 8
different locations. Our results show that the use of thermal imaging is as
effective as visual imaging in the presence of high illumination. This
performance stays the same for thermal images even under low-lighting
conditions, whereas the performance with visual trained classifiers show more
than 50% degradation.

The behavior of no-regret learning algorithms is well understood in
two-player min-max (i.e, zero-sum) games. In this paper, we investigate the
behavior of no-regret learning in min-max games with dependent strategy sets,
where the strategy of the first player constrains the behavior of the second.
Such games are best understood as sequential, i.e., min-max Stackelberg, games.
We consider two settings, one in which only the first player chooses their
actions using a no-regret algorithm while the second player best responds, and
one in which both players use no-regret algorithms. For the former case, we
show that no-regret dynamics converge to a Stackelberg equilibrium. For the
latter case, we introduce a new type of regret, which we call Lagrangian
regret, and show that if both players minimize their Lagrangian regrets, then
play converges to a Stackelberg equilibrium. We then observe that online mirror
descent (OMD) dynamics in these two settings correspond respectively to a known
nested (i.e., sequential) gradient descent-ascent (GDA) algorithm and a new
simultaneous GDA-like algorithm, thereby establishing convergence of these
algorithms to Stackelberg equilibrium. Finally, we analyze the robustness of
OMD dynamics to perturbations by investigating online min-max Stackelberg
games. We prove that OMD dynamics are robust for a large class of online
min-max games with independent strategy sets. In the dependent case, we
demonstrate the robustness of OMD dynamics experimentally by simulating them in
online Fisher markets, a canonical example of a min-max Stackelberg game with
dependent strategy sets.

Action recognition models have shown a promising capability to classify human
actions in short video clips. In a real scenario, multiple correlated human
actions commonly occur in particular orders, forming semantically meaningful
human activities. Conventional action recognition approaches focus on analyzing
single actions. However, they fail to fully reason about the contextual
relations between adjacent actions, which provide potential temporal logic for
understanding long videos. In this paper, we propose a prompt-based framework,
Bridge-Prompt (Br-Prompt), to model the semantics across adjacent actions, so
that it simultaneously exploits both out-of-context and contextual information
from a series of ordinal actions in instructional videos. More specifically, we
reformulate the individual action labels as integrated text prompts for
supervision, which bridge the gap between individual action semantics. The
generated text prompts are paired with corresponding video clips, and together
co-train the text encoder and the video encoder via a contrastive approach. The
learned vision encoder has a stronger capability for ordinal-action-related
downstream tasks, e.g. action segmentation and human activity recognition. We
evaluate the performances of our approach on several video datasets: Georgia
Tech Egocentric Activities (GTEA), 50Salads, and the Breakfast dataset.
Br-Prompt achieves state-of-the-art on multiple benchmarks. Code is available
at https://github.com/ttlmh/Bridge-Prompt

With the rapid development of deep learning, training Big Models (BMs) for
multiple downstream tasks becomes a popular paradigm. Researchers have achieved
various outcomes in the construction of BMs and the BM application in many
fields. At present, there is a lack of research work that sorts out the overall
progress of BMs and guides the follow-up research. In this paper, we cover not
only the BM technologies themselves but also the prerequisites for BM training
and applications with BMs, dividing the BM review into four parts: Resource,
Models, Key Technologies and Application. We introduce 16 specific BM-related
topics in those four parts, they are Data, Knowledge, Computing System,
Parallel Training System, Language Model, Vision Model, Multi-modal Model,
Theory&Interpretability, Commonsense Reasoning, Reliability&Security,
Governance, Evaluation, Machine Translation, Text Generation, Dialogue and
Protein Research. In each topic, we summarize clearly the current studies and
propose some future research directions. At the end of this paper, we conclude
the further development of BMs in a more general view.

Brain-computer interfaces (BCIs), transform neural signals in the brain into
in-structions to control external devices. However, obtaining sufficient
training data is difficult as well as limited. With the advent of advanced
machine learning methods, the capability of brain-computer interfaces has been
enhanced like never before, however, these methods require a large amount of
data for training and thus require data augmentation of the limited data
available. Here, we use spiking neural networks (SNN) as data generators. It is
touted as the next-generation neu-ral network and is considered as one of the
algorithms oriented to general artifi-cial intelligence because it borrows the
neural information processing from bio-logical neurons. We use the SNN to
generate neural spike information that is bio-interpretable and conforms to the
intrinsic patterns in the original neural data. Ex-periments show that the
model can directly synthesize new spike trains, which in turn improves the
generalization ability of the BCI decoder. Both the input and output of the
spiking neural model are spike information, which is a brain-inspired
intelligence approach that can be better integrated with BCI in the future.

A fundamental and challenging problem in deep learning is catastrophic
forgetting, i.e. the tendency of neural networks to fail to preserve the
knowledge acquired from old tasks when learning new tasks. This problem has
been widely investigated in the research community and several Incremental
Learning (IL) approaches have been proposed in the past years. While earlier
works in computer vision have mostly focused on image classification and object
detection, more recently some IL approaches for semantic segmentation have been
introduced. These previous works showed that, despite its simplicity, knowledge
distillation can be effectively employed to alleviate catastrophic forgetting.
In this paper, we follow this research direction and, inspired by recent
literature on contrastive learning, we propose a novel distillation framework,
Uncertainty-aware Contrastive Distillation (\method). In a nutshell, \method~is
operated by introducing a novel distillation loss that takes into account all
the images in a mini-batch, enforcing similarity between features associated to
all the pixels from the same classes, and pulling apart those corresponding to
pixels from different classes. In order to mitigate catastrophic forgetting, we
contrast features of the new model with features extracted by a frozen model
learned at the previous incremental step. Our experimental results demonstrate
the advantage of the proposed distillation technique, which can be used in
synergy with previous IL approaches, and leads to state-of-art performance on
three commonly adopted benchmarks for incremental semantic segmentation. The
code is available at \url{https://github.com/ygjwd12345/UCD}.

Federated learning (FL) is a key enabler for efficient communication and
computing, leveraging devices' distributed computing capabilities. However,
applying FL in practice is challenging due to the local devices' heterogeneous
energy, wireless channel conditions, and non-independently and identically
distributed (non-IID) data distributions. To cope with these issues, this paper
proposes a novel learning framework by integrating FL and width-adjustable
slimmable neural networks (SNN). Integrating FL with SNNs is challenging due to
time-varying channel conditions and data distributions. In addition, existing
multi-width SNN training algorithms are sensitive to the data distributions
across devices, which makes SNN ill-suited for FL. Motivated by this, we
propose a communication and energy-efficient SNN-based FL (named SlimFL) that
jointly utilizes superposition coding (SC) for global model aggregation and
superposition training (ST) for updating local models. By applying SC, SlimFL
exchanges the superposition of multiple-width configurations decoded as many
times as possible for a given communication throughput. Leveraging ST, SlimFL
aligns the forward propagation of different width configurations while avoiding
inter-width interference during backpropagation. We formally prove the
convergence of SlimFL. The result reveals that SlimFL is not only
communication-efficient but also deals with non-IID data distributions and poor
channel conditions, which is also corroborated by data-intensive simulations.

This work proposes a new pipeline for leveraging data collected on the Stack
Overflow website for pre-training a multimodal model for searching duplicates
on question answering websites. Our multimodal model is trained on question
descriptions and source codes in multiple programming languages. We design two
new learning objectives to improve duplicate detection capabilities. The result
of this work is a mature, fine-tuned Multimodal Question Duplicity Detection
(MQDD) model, ready to be integrated into a Stack Overflow search system, where
it can help users find answers for already answered questions. Alongside the
MQDD model, we release two datasets related to the software engineering domain.
The first Stack Overflow Dataset (SOD) represents a massive corpus of paired
questions and answers. The second Stack Overflow Duplicity Dataset (SODD)
contains data for training duplicate detection models.

The physical and textural attributes of objects have been widely studied for
recognition, detection and segmentation tasks in computer vision. A number of
datasets, such as large scale ImageNet, have been proposed for feature learning
using data hungry deep neural networks and for hand-crafted feature extraction.
To intelligently interact with objects, robots and intelligent machines need
the ability to infer beyond the traditional physical/textural attributes, and
understand/learn visual cues, called visual affordances, for affordance
recognition, detection and segmentation. To date there is no publicly available
large dataset for visual affordance understanding and learning. In this paper,
we introduce a large scale multi-view RGBD visual affordance learning dataset,
a benchmark of 47210 RGBD images from 37 object categories, annotated with 15
visual affordance categories and 35 cluttered/complex scenes with different
objects and multiple affordances. To the best of our knowledge, this is the
first ever and the largest multi-view RGBD visual affordance learning dataset.
We benchmark the proposed dataset for affordance recognition and segmentation.
To achieve this we propose an Affordance Recognition Network a.k.a ARNet. In
addition, four state-of-the-art deep learning networks are evaluated for
affordance segmentation task. Our experimental results showcase the challenging
nature of the dataset and present definite prospects for new and robust
affordance learning algorithms. The dataset is available at:
https://sites.google.com/view/afaqshah/dataset.

Machine Learning (ML) techniques have begun to dominate data analytics
applications and services. Recommendation systems are a key component of online
service providers. The financial industry has adopted ML to harness large
volumes of data in areas such as fraud detection, risk-management, and
compliance. Deep Learning is the technology behind voice-based personal
assistants, etc. Deployment of ML technologies onto cloud computing
infrastructures has benefited numerous aspects of our daily life. The
advertising and associated online industries in particular have fuelled a rapid
rise the in deployment of personal data collection and analytics tools.
Traditionally, behavioural analytics relies on collecting vast amounts of data
in centralised cloud infrastructure before using it to train machine learning
models that allow user behaviour and preferences to be inferred. A contrasting
approach, distributed data analytics, where code and models for training and
inference are distributed to the places where data is collected, has been
boosted by two recent, ongoing developments: increased processing power and
memory capacity available in user devices at the edge of the network, such as
smartphones and home assistants; and increased sensitivity to the highly
intrusive nature of many of these devices and services and the attendant
demands for improved privacy. Indeed, the potential for increased privacy is
not the only benefit of distributing data analytics to the edges of the
network: reducing the movement of large volumes of data can also improve energy
efficiency, helping to ameliorate the ever increasing carbon footprint of our
digital infrastructure, enabling much lower latency for service interactions
than is possible when services are cloud-hosted. These approaches often
introduce challenges in privacy, utility, and efficiency trade-offs, while
having to ensure fruitful user engagement.

The manual annotation for large-scale point clouds is still tedious and
unavailable for many harsh real-world tasks. Self-supervised learning, which is
used on raw and unlabeled data to pre-train deep neural networks, is a
promising approach to address this issue. Existing works usually take the
common aid from auto-encoders to establish the self-supervision by the
self-reconstruction schema. However, the previous auto-encoders merely focus on
the global shapes and do not distinguish the local and global geometric
features apart. To address this problem, we present a novel and efficient
self-supervised point cloud representation learning framework, named 3D
Occlusion Auto-Encoder (3D-OAE), to facilitate the detailed supervision
inherited in local regions and global shapes. We propose to randomly occlude
some local patches of point clouds and establish the supervision via inpainting
the occluded patches using the remaining ones. Specifically, we design an
asymmetrical encoder-decoder architecture based on standard Transformer, where
the encoder operates only on the visible subset of patches to learn local
patterns, and a lightweight decoder is designed to leverage these visible
patterns to infer the missing geometries via self-attention. We find that
occluding a very high proportion of the input point cloud (e.g. 75%) will still
yield a nontrivial self-supervisory performance, which enables us to achieve
3-4 times faster during training but also improve accuracy. Experimental
results show that our approach outperforms the state-of-the-art on a diverse
range of downstream discriminative and generative tasks.

Graph Neural Networks (GNNs) often suffer from weak-generalization due to
sparsely labeled data despite their promising results on various graph-based
tasks. Data augmentation is a prevalent remedy to improve the generalization
ability of models in many domains. However, due to the non-Euclidean nature of
data space and the dependencies between samples, designing effective
augmentation on graphs is challenging. In this paper, we propose a novel
framework Metropolis-Hastings Data Augmentation (MH-Aug) that draws augmented
graphs from an explicit target distribution for semi-supervised learning.
MH-Aug produces a sequence of augmented graphs from the target distribution
enables flexible control of the strength and diversity of augmentation. Since
the direct sampling from the complex target distribution is challenging, we
adopt the Metropolis-Hastings algorithm to obtain the augmented samples. We
also propose a simple and effective semi-supervised learning strategy with
generated samples from MH-Aug. Our extensive experiments demonstrate that
MH-Aug can generate a sequence of samples according to the target distribution
to significantly improve the performance of GNNs.

A new learning algorithm for speech separation networks is designed to
explicitly reduce residual noise and artifacts in the separated signal in an
unsupervised manner. Generative adversarial networks are known to be effective
in constructing separation networks when the ground truth for the observed
signal is inaccessible. Still, weak objectives aimed at
distribution-to-distribution mapping make the learning unstable and limit their
performance. This study introduces the remix-cycle-consistency loss as a more
appropriate objective function and uses it to fine-tune adversarially learned
source separation models. The remix-cycle-consistency loss is defined as the
difference between the mixed speech observed at microphones and the
pseudo-mixed speech obtained by alternating the process of separating the mixed
sound and remixing its outputs with another combination. The minimization of
this loss leads to an explicit reduction in the distortions in the output of
the separation network. Experimental comparisons with multichannel speech
separation demonstrated that the proposed method achieved high separation
accuracy and learning stability comparable to supervised learning.

A major challenge in todays power grid is to manage the increasing load from
electric vehicle (EV) charging. Demand response (DR) solutions aim to exploit
flexibility therein, i.e., the ability to shift EV charging in time and thus
avoid excessive peaks or achieve better balancing. Whereas the majority of
existing research works either focus on control strategies for a single EV
charger, or use a multi-step approach (e.g., a first high level aggregate
control decision step, followed by individual EV control decisions), we rather
propose a single-step solution that jointly coordinates multiple charging
points at once. In this paper, we further refine an initial proposal using
reinforcement learning (RL), specifically addressing computational challenges
that would limit its deployment in practice. More precisely, we design a new
Markov decision process (MDP) formulation of the EV charging coordination
process, exhibiting only linear space and time complexity (as opposed to the
earlier quadratic space complexity). We thus improve upon earlier
state-of-the-art, demonstrating 30% reduction of training time in our case
study using real-world EV charging session data. Yet, we do not sacrifice the
resulting performance in meeting the DR objectives: our new RL solutions still
improve the performance of charging demand coordination by 40-50% compared to a
business-as-usual policy (that charges EV fully upon arrival) and 20-30%
compared to a heuristic policy (that uniformly spreads individual EV charging
over time).

Video generation is a challenging task that requires modeling plausible
spatial and temporal dynamics in a video. Inspired by how humans perceive a
video by grouping a scene into moving and stationary components, we propose a
method that decomposes the task of video generation into the synthesis of
foreground, background and motion. Foreground and background together describe
the appearance, whereas motion specifies how the foreground moves in a video
over time. We propose V3GAN, a novel three-branch generative adversarial
network where two branches model foreground and background information, while
the third branch models the temporal information without any supervision. The
foreground branch is augmented with our novel feature-level masking layer that
aids in learning an accurate mask for foreground and background separation. To
encourage motion consistency, we further propose a shuffling loss for the video
discriminator. Extensive quantitative and qualitative analysis on synthetic as
well as real-world benchmark datasets demonstrates that V3GAN outperforms the
state-of-the-art methods by a significant margin.

In this paper, we focus on the Audio-Visual Question Answering (AVQA) task,
which aims to answer questions regarding different visual objects, sounds, and
their associations in videos. The problem requires comprehensive multimodal
understanding and spatio-temporal reasoning over audio-visual scenes. To
benchmark this task and facilitate our study, we introduce a large-scale
MUSIC-AVQA dataset, which contains more than 45K question-answer pairs covering
33 different question templates spanning over different modalities and question
types. We develop several baselines and introduce a spatio-temporal grounded
audio-visual network for the AVQA problem. Our results demonstrate that AVQA
benefits from multisensory perception and our model outperforms recent A-, V-,
and AVQA approaches. We believe that our built dataset has the potential to
serve as testbed for evaluating and promoting progress in audio-visual scene
understanding and spatio-temporal reasoning. Code and dataset:
http://gewu-lab.github.io/MUSIC-AVQA/

Reliable extraction of cosmological information from observed cosmic
microwave background (CMB) maps requires accurate removal of foreground
components. Since some regions of the sky are inevitably contaminated by strong
foreground signals, it may be desired to excise such strongly contaminated
regions even after a foreground subtraction has been performed. We employ an
artificial neural network (ANN) to predict the full sky CMB angular power
spectrum from the partial sky spectrum obtained from masked CMB anisotropy map,
at a high pixel resolution. We use a simple ANN with one hidden layer
containing $895$ neurons. Using $1.2 \times 10^{5}$ training samples of full
sky and corresponding partial sky spectra at Healpix pixel resolution parameter
$N_{side} = 256$, we show that predicted spectrum by our ANN agrees well with
the target spectrum at each realization for the multipole range $2 \leq l \leq
512$. The predicted spectra are statistically unbiased and they preserve the
cosmic variance accurately. Statistically, the differences between the mean
predicted and underlying theoretical spectra are within $3\sigma$. Moreover,
the probability densities obtained from predicted spectra agree very well with
those obtained from actual full sky spectra for each multipole. Equally
interesting is that the correlation matrix calculated from predicted spectra
does not show any signature of correlation between the spectrum of different
multipoles. Our work shows that the predicted spectra do not need to be binned
even at high resolution to rid of correlations due to mode-mode coupling
introduced on the partial sky since the ANN learns to effectively recover the
lost information due to sky cut. The excellent agreement of statistical
properties between the predicted and the ground-truth demonstrates the
importance of using artificial intelligence systems in cosmological analysis
more widely.

Clustering is one of the most fundamental problem in Machine Learning.
Researchers in the field often require a lower bound on the size of the
clusters to maintain anonymity and upper bound for the ease of analysis.
Specifying an optimal cluster size is a problem often faced by scientists. In
this paper, we present a framework to obtain constant factor approximations for
some prominent clustering objectives, with lower and upper bounds on cluster
size. This enables scientists to give an approximate cluster size by specifying
the lower and the upper bounds for it. Our results preserve the lower bounds
but may violate the upper bound a little. %{GroverGD21_LBUBFL_Cocoon} to $2$.
%namely, $k$ Center (LUkC) and $k$ Median (LUkM) problem. We study the problems
when either of the bounds is uniform. We apply our framework to give the first
constant factor approximations for LUkM and its generalization, $k$-facility
location problem (LUkFL), with $\beta+1$ factor violation in upper bounds where
$\beta$ is the violation of upper bounds in solutions of upper bounded
$k$-median and $k$-facility location problems respectively. We also present a
result on LUkC with uniform upper bounds and, its generalization, lower and
(uniform) upper bounded $k$ supplier problem (LUkS). The approach also gives a
result on lower and upper bounded facility location problem (LUFL), improving
upon the upper bound violation of $5/2$ due to Gupta et al.
  We also reduce the violation in upper bounds for a special case when the gap
between the lower and upper bounds is not too small.

Learning a 3D representation of a scene has been a challenging problem for
decades in computer vision. Recent advances in implicit neural representation
from images using neural radiance fields(NeRF) have shown promising results.
Some of the limitations of previous NeRF based methods include longer training
time, and inaccurate underlying geometry. The proposed method takes advantage
of RGB-D data to reduce training time by leveraging depth sensing to improve
local sampling. This paper proposes a depth-guided local sampling strategy and
a smaller neural network architecture to achieve faster training time without
compromising quality.

Gesture typing is a method of typing words on a touch-based keyboard by
creating a continuous trace passing through the relevant keys. This work is
aimed at developing a keyboard that supports gesture typing in Indic languages.
We begin by noting that when dealing with Indic languages, one needs to cater
to two different sets of users: (i) users who prefer to type in the native
Indic script (Devanagari, Bengali, etc.) and (ii) users who prefer to type in
the English script but want the output transliterated into the native script.
In both cases, we need a model that takes a trace as input and maps it to the
intended word. To enable the development of these models, we create and release
two datasets. First, we create a dataset containing keyboard traces for 193,658
words from 7 Indic languages. Second, we curate 104,412 English-Indic
transliteration pairs from Wikidata across these languages. Using these
datasets we build a model that performs path decoding, transliteration, and
transliteration correction. Unlike prior approaches, our proposed model does
not make co-character independence assumptions during decoding. The overall
accuracy of our model across the 7 languages varies from 70-95%.

Deep Implicit Function (DIF) has gained popularity as an efficient 3D shape
representation. To capture geometry details, current methods usually learn DIF
using local latent codes, which discretize the space into a regular 3D grid (or
octree) and store local codes in grid points (or octree nodes). Given a query
point, the local feature is computed by interpolating its neighboring local
codes with their positions. However, the local codes are constrained at
discrete and regular positions like grid points, which makes the code positions
difficult to be optimized and limits their representation ability. To solve
this problem, we propose to learn DIF with Dynamic Code Cloud, named DCC-DIF.
Our method explicitly associates local codes with learnable position vectors,
and the position vectors are continuous and can be dynamically optimized, which
improves the representation ability. In addition, we propose a novel code
position loss to optimize the code positions, which heuristically guides more
local codes to be distributed around complex geometric details. In contrast to
previous methods, our DCC-DIF represents 3D shapes more efficiently with a
small amount of local codes, and improves the reconstruction quality.
Experiments demonstrate that DCC-DIF achieves better performance over previous
methods. Code and data are available at https://github.com/lity20/DCCDIF.

In the last a few decades, deep neural networks have achieved remarkable
success in machine learning, computer vision, and pattern recognition. Recent
studies however show that neural networks (both shallow and deep) may be easily
fooled by certain imperceptibly perturbed input samples called adversarial
examples. Such security vulnerability has resulted in a large body of research
in recent years because real-world threats could be introduced due to vast
applications of neural networks. To address the robustness issue to adversarial
examples particularly in pattern recognition, robust adversarial training has
become one mainstream. Various ideas, methods, and applications have boomed in
the field. Yet, a deep understanding of adversarial training including
characteristics, interpretations, theories, and connections among different
models has still remained elusive. In this paper, we present a comprehensive
survey trying to offer a systematic and structured investigation on robust
adversarial training in pattern recognition. We start with fundamentals
including definition, notations, and properties of adversarial examples. We
then introduce a unified theoretical framework for defending against
adversarial samples - robust adversarial training with visualizations and
interpretations on why adversarial training can lead to model robustness.
Connections will be also established between adversarial training and other
traditional learning theories. After that, we summarize, review, and discuss
various methodologies with adversarial attack and defense/training algorithms
in a structured way. Finally, we present analysis, outlook, and remarks of

Facial expression recognition (FER) is still one challenging research due to
the small inter-class discrepancy in the facial expression data. In view of the
significance of facial crucial regions for FER, many existing researches
utilize the prior information from some annotated crucial points to improve the
performance of FER. However, it is complicated and time-consuming to manually
annotate facial crucial points, especially for vast wild expression images.
Based on this, a local non-local joint network is proposed to adaptively light
up the facial crucial regions in feature learning of FER in this paper. In the
proposed method, two parts are constructed based on facial local and non-local
information respectively, where an ensemble of multiple local networks are
proposed to extract local features corresponding to multiple facial local
regions and a non-local attention network is addressed to explore the
significance of each local region. Especially, the attention weights obtained
by the non-local network is fed into the local part to achieve the interactive
feedback between the facial global and local information. Interestingly, the
non-local weights corresponding to local regions are gradually updated and
higher weights are given to more crucial regions. Moreover, U-Net is employed
to extract the integrated features of deep semantic information and low
hierarchical detail information of expression images. Finally, experimental
results illustrate that the proposed method achieves more competitive
performance compared with several state-of-the art methods on five benchmark
datasets. Noticeably, the analyses of the non-local weights corresponding to
local regions demonstrate that the proposed method can automatically enhance
some crucial regions in the process of feature learning without any facial
landmark information.

Contrastive self-supervised learning has recently benefited fMRI
classification with inductive biases. Its weak label reliance prevents
overfitting on small medical datasets and tackles the high intraclass
variances. Nonetheless, existing contrastive methods generate resemblant pairs
only on pixel-level features of 3D medical images, while the functional
connectivity that reveals critical cognitive information is under-explored.
Additionally, existing methods predict labels on individual contrastive
representation without recognizing neighbouring information in the patient
group, whereas interpatient contrast can act as a similarity measure suitable
for population-based classification. We hereby proposed contrastive functional
connectivity graph learning for population-based fMRI classification.
Representations on the functional connectivity graphs are "repelled" for
heterogeneous patient pairs meanwhile homogeneous pairs "attract" each other.
Then a dynamic population graph that strengthens the connections between
similar patients is updated for classification. Experiments on a multi-site
dataset ADHD200 validate the superiority of the proposed method on various
metrics. We initially visualize the population relationships and exploit
potential subtypes.

Sequential recommender systems have recently achieved significant performance
improvements with the exploitation of deep learning (DL) based methods.
However, although various DL-based methods have been introduced, most of them
only focus on the transformations of network structure, neglecting the
importance of other influential factors including data augmentation. Obviously,
DL-based models require a large amount of training data in order to estimate
parameters well and achieve high performances, which leads to the early efforts
to increase the training data through data augmentation in computer vision and
speech domains. In this paper, we seek to figure out that various data
augmentation strategies can improve the performance of sequential recommender
systems, especially when the training dataset is not large enough. To this end,
we propose a simple set of data augmentation strategies, all of which transform
original item sequences in the way of direct corruption and describe how data
augmentation changes the performance. Extensive experiments on the latest
DL-based model show that applying data augmentation can help the model
generalize better, and it can be significantly effective to boost model
performances especially when the amount of training data is small. Furthermore,
it is shown that our proposed strategies can improve performances to a better
or competitive level to existing strategies suggested in the prior works.

We study the benefits of reinforcement learning (RL) environments based on
agent-based models (ABM). While ABMs are known to offer microfoundational
simulations at the cost of computational complexity, we empirically show in
this work that their non-deterministic dynamics can improve the generalization
of RL agents. To this end, we examine the control of an epidemic SIR
environments based on either differential equations or ABMs. Numerical
simulations demonstrate that the intrinsic noise in the ABM-based dynamics of
the SIR model not only improve the average reward but also allow the RL agent
to generalize on a wider ranges of epidemic parameters.

The ability to perform aggressive movements, which are called aggressive
flights, is important for quadrotors during navigation. However, aggressive
quadrotor flights are still a great challenge to practical applications. The
existing solutions to aggressive flights heavily rely on a predefined
trajectory, which is a time-consuming preprocessing step. To avoid such path
planning, we propose a curiosity-driven reinforcement learning method for
aggressive flight missions and a similarity-based curiosity module is
introduced to speed up the training procedure. A branch structure exploration
(BSE) strategy is also applied to guarantee the robustness of the policy and to
ensure the policy trained in simulations can be performed in real-world
experiments directly. The experimental results in simulations demonstrate that
our reinforcement learning algorithm performs well in aggressive flight tasks,
speeds up the convergence process and improves the robustness of the policy.
Besides, our algorithm shows a satisfactory simulated to real transferability
and performs well in real-world experiments.

For the goal of strong artificial intelligence that can mimic human-level
intelligence, AI systems would have the ability to adapt to ever-changing
scenarios and learn new knowledge continuously without forgetting previously
acquired knowledge. When a machine learning model is consecutively trained on
multiple tasks that come in sequence, its performance on previously learned
tasks may drop dramatically during the learning process of the newly seen task.
To avoid this phenomenon termed catastrophic forgetting, continual learning,
also known as lifelong learning, has been proposed and become one of the most
up-to-date research areas of machine learning. As quantum machine learning
blossoms in recent years, it is interesting to develop quantum continual
learning. This paper focuses on the case of quantum models for quantum data
where the computation model and the data to be processed are both quantum. The
gradient episodic memory method is incorporated to design a quantum continual
learning scheme that overcomes catastrophic forgetting and realizes knowledge
backward transfer. Specifically, a sequence of quantum state classification
tasks is continually learned by a variational quantum classifier whose
parameters are optimized by a classical gradient-based optimizer. The gradient
of the current task is projected to the closest gradient, avoiding the increase
of the loss at previous tasks, but allowing the decrease. Numerical simulation
results show that our scheme not only overcomes catastrophic forgetting, but
also realize knowledge backward transfer, which means the classifier's
performance on previous tasks is enhanced rather than compromised while
learning a new task.

Taking medicines is a fundamental aspect to cure illnesses. However, studies
have shown that it can be hard for patients to remember the correct posology.
More aggravating, a wrong dosage generally causes the disease to worsen.
Although, all relevant instructions for a medicine are summarized in the
corresponding patient information leaflet, the latter is generally difficult to
navigate and understand. To address this problem and help patients with their
medication, in this paper we introduce an augmented reality mobile application
that can present to the user important details on the framed medicine. In
particular, the app implements an inference engine based on a deep neural
network, i.e., a densenet, fine-tuned to recognize a medicinal from its
package. Subsequently, relevant information, such as posology or a simplified
leaflet, is overlaid on the camera feed to help a patient when taking a
medicine. Extensive experiments to select the best hyperparameters were
performed on a dataset specifically collected to address this task; ultimately
obtaining up to 91.30\% accuracy as well as real-time capabilities.

Disentangled representation is a powerful technique to tackle domain shift
problem in medical image analysis in unsupervised domain adaptation
setting.However, previous methods only focus on exacting domain-invariant
feature and ignore whether exacted feature is meaningful for downstream
tasks.We propose a novel framework, called semantic-guided disentangled
representation (SGDR), an effective method to exact semantically meaningful
feature for segmentation task to improve performance of cross modality medical
image segmentation in unsupervised domain adaptation setting.To exact the
meaningful domain-invariant features of different modality, we introduce a
content discriminator to force the content representation to be embedded to the
same space and a feature discriminator to exact the meaningful
representation.We also use pixel-level annotations to guide the encoder to
learn features that are meaningful for segmentation task.We validated our
method on two public datasets and experiment results show that our approach
outperforms the state of the art methods on two evaluation metrics by a

Virtual Reality has become a significant element of education throughout the
years. To understand the quality and advantages of these techniques, it is
important to understand how they were developed and evaluated. Since COVID-19,
the education system has drastically changed a lot. It has shifted from being
in a classroom with a whiteboard and projectors to having your own room in
front of your laptop in a virtual meeting. In this respect, virtual reality in
the laboratory or Virtual Laboratory is the main focus of this research, which
is intended to comprehend the work done in quality education from a distance
using VR. As per the findings of the study, adopting virtual reality in
education can help students learn more effectively and also help them increase
perspective, enthusiasm, and knowledge of complex notions by offering them an
interactive experience in which they can engage and learn more effectively.
This highlights the importance of a significant expansion of VR use in
learning, the majority of which employ scientific comparison approaches to
compare students who use VR to those who use the traditional method for
learning.

Deep neuroevolution and deep Reinforcement Learning have received a lot of
attention in the last years. Some works have compared them, highlighting theirs
pros and cons, but an emerging trend consists in combining them so as to
benefit from the best of both worlds. In this paper, we provide a survey of
this emerging trend by organizing the literature into related groups of works
and casting all the existing combinations in each group into a generic
framework. We systematically cover all easily available papers irrespective of
their publication status, focusing on the combination mechanisms rather than on
the experimental results. In total, we cover 45 algorithms more recent than
2017. We hope this effort will favor the growth of the domain by facilitating
the understanding of the relationships between the methods, leading to deeper
analyses, outlining missing useful comparisons and suggesting new combinations
of mechanisms.

Nowadays, airport detection in remote sensing images has attracted
considerable attention due to its strategic role in civilian and military
scopes. In particular, uncrewed and operated aerial vehicles must immediately
detect safe areas to land in emergencies. The previous schemes suffered from
various aspects, including complicated backgrounds, scales, and shapes of the
airport. Meanwhile, the rapid action and accuracy of the method are confronted
with significant concerns. Hence, this study proposes an effective scheme by
extending YOLOV3 and ShearLet transform. In this way, MobileNet and ResNet18,
with fewer layers and parameters retrained on a similar dataset, are parallelly
trained as base networks. According to airport geometrical characteristics, the
ShearLet filters with different scales and directions are considered in the
first convolution layers of ResNet18 as a visual attention mechanism. Besides,
the major extended in YOLOV3 concerns the detection Sub-Networks with novel
structures which boost object expression ability and training efficiency. In
addition, novel augmentation and negative mining strategies are presented to
significantly increase the localization phase's performance. The experimental
results on the DIOR dataset reveal that the framework reliably detects
different types of airports in a varied area and acquires robust results in
complex scenes compared to traditional YOLOV3 and state-of-the-art schemes.

Monocular depth estimation is one of the fundamental tasks in environmental
perception and has achieved tremendous progress in virtue of deep learning.
However, the performance of trained models tends to degrade or deteriorate when
employed on other new datasets due to the gap between different datasets.
Though some methods utilize domain adaptation technologies to jointly train
different domains and narrow the gap between them, the trained models cannot
generalize to new domains that are not involved in training. To boost the
transferability of depth estimation models, we propose an adversarial depth
estimation task and train the model in the pipeline of meta-learning. Our
proposed adversarial task mitigates the issue of meta-overfitting, since the
network is trained in an adversarial manner and aims to extract domain
invariant representations. In addition, we propose a constraint to impose upon
cross-task depth consistency to compel the depth estimation to be identical in
different adversarial tasks, which improves the performance of our method and
smoothens the training process. Experiments demonstrate that our method adapts
well to new datasets after few training steps during the test procedure.

Deep learning methods are widely applied in digital pathology to address
clinical challenges such as prognosis and diagnosis. As one of the most recent
applications, deep models have also been used to extract molecular features
from whole slide images. Although molecular tests carry rich information, they
are often expensive, time-consuming, and require additional tissue to sample.
In this paper, we propose tRNAsfomer, an attention-based topology that can
learn both to predict the bulk RNA-seq from an image and represent the whole
slide image of a glass slide simultaneously. The tRNAsfomer uses multiple
instance learning to solve a weakly supervised problem while the pixel-level
annotation is not available for an image. We conducted several experiments and
achieved better performance and faster convergence in comparison to the
state-of-the-art algorithms. The proposed tRNAsfomer can assist as a
computational pathology tool to facilitate a new generation of search and
classification methods by combining the tissue morphology and the molecular
fingerprint of the biopsy samples.

Recommender systems based on collaborative filtering play a vital role in
many E-commerce applications as they guide the user in finding their items of
interest based on the user's past transactions and feedback of other similar
customers. Data Sparsity is one of the major drawbacks with collaborative
filtering technique arising due to the less number of transactions and feedback
data. In order to reduce the sparsity problem, techniques called transfer
learning/cross-domain recommendation has emerged. In transfer learning methods,
the data from other dense domain(s) (source) is considered in order to predict
the missing ratings in the sparse domain (target). In this paper, we come up
with a novel transfer learning approach for cross-domain recommendation,
wherein the cluster-level rating pattern(codebook) of the source domain is
obtained via a co-clustering technique. Thereafter we apply the Maximum Margin
Matrix factorization (MMMF) technique on the codebook in order to learn the
user and item latent features of codebook. Prediction of the target rating
matrix is achieved by introducing these latent features in a novel way into the
optimisation function. In the experiments we demonstrate that our model
improves the prediction accuracy of the target matrix on benchmark datasets.

Federated semi-supervised learning (FSSL) aims to derive a global model by
training fully-labeled and fully-unlabeled clients or training partially
labeled clients. The existing approaches work well when local clients have
independent and identically distributed (IID) data but fail to generalize to a
more practical FSSL setting, i.e., Non-IID setting. In this paper, we present a
Random Sampling Consensus Federated learning, namely RSCFed, by considering the
uneven reliability among models from fully-labeled clients, fully-unlabeled
clients or partially labeled clients. Our key motivation is that given models
with large deviations from either labeled clients or unlabeled clients, the
consensus could be reached by performing random sub-sampling over clients. To
achieve it, instead of directly aggregating local models, we first distill
several sub-consensus models by random sub-sampling over clients and then
aggregating the sub-consensus models to the global model. To enhance the
robustness of sub-consensus models, we also develop a novel distance-reweighted
model aggregation method. Experimental results show that our method outperforms
state-of-the-art methods on three benchmarked datasets, including both natural
and medical images. The code is available at
https://github.com/XMed-Lab/RSCFed.

This paper proposes a novel neuronal current source localization method based
on Deep Prior that represents a more complicated prior distribution of current
source using convolutional networks. Deep Prior has been suggested as a means
of an unsupervised learning approach that does not require learning using
training data, and randomly-initialized neural networks are used to update a
source location using a single observation. In our previous work, a
Deep-Prior-based current source localization method in the brain has been
proposed but the performance was not almost the same as those of conventional
approaches, such as sLORETA. In order to improve the Deep-Prior-based approach,
in this paper, a depth weight of the current source is introduced for Deep
Prior, where depth weighting amounts to assigning more penalty to the
superficial currents. Its effectiveness is confirmed by experiments of current
source estimation on simulated MEG data.

Linguistic steganography (LS) conceals the presence of communication by
embedding secret information into a text. How to generate a high-quality text
carrying secret information is a key problem. With the widespread application
of deep learning in natural language processing, recent algorithms use a
language model (LM) to generate the steganographic text, which provides a
higher payload compared with many previous arts. However, the security still
needs to be enhanced. To tackle with this problem, we propose a novel
autoregressive LS algorithm based on BERT and consistency coding, which
achieves a better trade-off between embedding payload and system security. In
the proposed work, based on the introduction of the masked LM, given a text, we
use consistency coding to make up for the shortcomings of block coding used in
the previous work so that we can encode arbitrary-size candidate token set and
take advantages of the probability distribution for information hiding. The
masked positions to be embedded are filled with tokens determined by an
autoregressive manner to enhance the connection between contexts and therefore
maintain the quality of the text. Experimental results have shown that,
compared with related works, the proposed work improves the fluency of the
steganographic text while guaranteeing security, and also increases the
embedding payload to a certain extent.

Tuning machine parameters of particle accelerators is a repetitive and
time-consuming task that is challenging to automate. While many off-the-shelf
optimization algorithms are available, in practice their use is limited because
most methods do not account for safety-critical constraints in each iteration,
such as loss signals or step-size limitations. One notable exception is safe
Bayesian optimization, which is a data-driven tuning approach for global
optimization with noisy feedback. We propose and evaluate a step-size limited
variant of safe Bayesian optimization on two research facilities of the Paul
Scherrer Institut (PSI): a) the Swiss Free Electron Laser (SwissFEL) and b) the
High-Intensity Proton Accelerator (HIPA). We report promising experimental
results on both machines, tuning up to 16 parameters subject to 224

In the relatively short history of machine learning, the subtle balance
between engineering and theoretical progress has been proved critical at
various stages. The most recent wave of AI has brought to the IR community
powerful techniques, particularly for pattern recognition. While many benefits
from the burst of ideas as numerous tasks become algorithmically feasible, the
balance is tilting toward the application side. The existing theoretical tools
in IR can no longer explain, guide, and justify the newly-established
  The consequences can be suffering: in stark contrast to how the IR industry
has envisioned modern AI making life easier, many are experiencing increased
confusion and costs in data manipulation, model selection, monitoring,
censoring, and decision making. This reality is not surprising: without handy
theoretical tools, we often lack principled knowledge of the pattern
recognition model's expressivity, optimization property, generalization
guarantee, and our decision-making process has to rely on over-simplified
assumptions and human judgments from time to time.
  Time is now to bring the community a systematic tutorial on how we
successfully adapt those tools and make significant progress in understanding,
designing, and eventually productionize impactful IR systems. We emphasize
systematicity because IR is a comprehensive discipline that touches upon
particular aspects of learning, causal inference analysis, interactive (online)
decision-making, etc. It thus requires systematic calibrations to render the
actual usefulness of the imported theoretical tools to serve IR problems, as
they usually exhibit unique structures and definitions. Therefore, we plan this
tutorial to systematically demonstrate our learning and successful experience
of using advanced theoretical tools for understanding and designing IR systems.

Quadrotors are one of the popular unmanned aerial vehicles (UAVs) due to
their versatility and simple design. However, the tuning of gains for quadrotor
flight controllers can be laborious, and accurately stable control of
trajectories can be difficult to maintain under exogenous disturbances and
uncertain system parameters. This paper introduces a novel robust and adaptive
control synthesis methodology for a quadrotor robot's attitude and altitude
stabilization. The developed method is based on the fuzzy reinforcement
learning and Strictly Negative Imaginary (SNI) property. The first stage of our
control approach is to transform a nonlinear quadrotor system into an
equivalent Negative-Imaginary (NI) linear model by means of the feedback
linearization (FL) technique. The second phase is to design a control scheme
that adapts online the Strictly Negative Imaginary (SNI) controller gains via
fuzzy Q-learning, inspired by biological learning. The proposed controller does
not require any prior training. The performance of the designed controller is
compared with that of a fixed-gain SNI controller, a fuzzy-SNI controller, and
a conventional PID controller in a series of numerical simulations.
Furthermore, the stability of the proposed controller and the adaptive laws are
proofed using the NI theorem.

The interventional nature of recommendation has attracted increasing
attention in recent years. It particularly motivates researchers to formulate
learning and evaluating recommendation as causal inference and data
missing-not-at-random problems. However, few take seriously the consequence of
violating the critical assumption of overlapping, which we prove can
significantly threaten the validity and interpretation of the outcome. We find
a critical piece missing in the current understanding of information retrieval
(IR) systems: as interventions, recommendation not only affects the already
observed data, but it also interferes with the target domain (distribution) of
interest. We then rephrase optimizing recommendation as finding an intervention
that best transports the patterns it learns from the observed domain to its
intervention domain. Towards this end, we use domain transportation to
characterize the learning-intervention mechanism of recommendation. We design a
principled transportation-constraint risk minimization objective and convert it
to a two-player minimax game. We prove the consistency, generalization, and
excessive risk bounds for the proposed objective, and elaborate how they
compare to the current results. Finally, we carry out extensive real-data and
semi-synthetic experiments to demonstrate the advantage of our approach, and
launch online testing with a real-world IR system.

Recently there has been significant interest in measuring time-varying
functional connectivity (TVC) between different brain regions using
resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to
assess the relationship between signals from different brain regions is to
measure their phase synchronization (PS) across time. However, this requires
the \textit{a priori} choice of type and cut-off frequencies for the bandpass
filter needed to perform the analysis. Here we explore alternative approaches
based on the use of various mode decomposition (MD) techniques that circumvent
this issue. These techniques allow for the data driven decomposition of signals
jointly into narrow-band components at different frequencies, thus fulfilling
the requirements needed to measure PS. We explore several variants of MD,
including empirical mode decomposition (EMD), bivariate EMD (BEMD),
noise-assisted multivariate EMD (na-MEMD), and introduce the use of
multivariate variational mode decomposition (MVMD) in the context of estimating
time-varying PS. We contrast the approaches using a series of simulations and
application to rs-fMRI data. Our results show that MVMD outperforms other
evaluated MD approaches, and further suggests that this approach can be used as
a tool to reliably investigate time-varying PS in rs-fMRI data.

We propose cooperative edge-assisted dynamic federated learning (CE-FL).
CE-FL introduces a distributed machine learning (ML) architecture, where data
collection is carried out at the end devices, while the model training is
conducted cooperatively at the end devices and the edge servers, enabled via
data offloading from the end devices to the edge servers through base stations.
CE-FL also introduces floating aggregation point, where the local models
generated at the devices and the servers are aggregated at an edge server,
which varies from one model training round to another to cope with the network
evolution in terms of data distribution and users' mobility. CE-FL considers
the heterogeneity of network elements in terms of communication/computation
models and the proximity to one another. CE-FL further presumes a dynamic
environment with online variation of data at the network devices which causes a
drift at the ML model performance. We model the processes taken during CE-FL,
and conduct analytical convergence analysis of its ML model training. We then
formulate network-aware CE-FL which aims to adaptively optimize all the network
elements via tuning their contribution to the learning process, which turns out
to be a non-convex mixed integer problem. Motivated by the large scale of the
system, we propose a distributed optimization solver to break down the
computation of the solution across the network elements. We finally demonstrate
the effectiveness of our framework with the data collected from a real-world
testbed.

Automated machine learning systems for non-experts could be critical for
industries to adopt artificial intelligence to their own applications. This
paper detailed the engineering system implementation of an automated machine
learning system called YMIR, which completely relies on graphical interface to
interact with users. After importing training/validation data into the system,
a user without AI knowledge can label the data, train models, perform data
mining and evaluation by simply clicking buttons. The paper described: 1) Open
implementation of model training and inference through docker containers. 2)
Implementation of task and resource management. 3) Integration of Labeling
software. 4) Implementation of HCI (Human Computer Interaction) with a rebuilt
collaborative development paradigm. We also provide subsequent case study on
training models with the system. We hope this paper can facilitate the
prosperity of our automated machine learning community from industry
application perspective. The code of the system has already been released to
GitHub (https://github.com/industryessentials/ymir).

Question answering (QA) is a fundamental means to facilitate assessment and
training of narrative comprehension skills for both machines and young
children, yet there is scarcity of high-quality QA datasets carefully designed
to serve this purpose. In particular, existing datasets rarely distinguish
fine-grained reading skills, such as the understanding of varying narrative
elements. Drawing on the reading education research, we introduce FairytaleQA,
a dataset focusing on narrative comprehension of kindergarten to eighth-grade
students. Generated by educational experts based on an evidence-based
theoretical framework, FairytaleQA consists of 10,580 explicit and implicit
questions derived from 278 children-friendly stories, covering seven types of
narrative elements or relations. Our dataset is valuable in two folds: First,
we ran existing QA models on our dataset and confirmed that this annotation
helps assess models' fine-grained learning skills. Second, the dataset supports
question generation (QG) task in the education domain. Through benchmarking
with QG models, we show that the QG model trained on FairytaleQA is capable of
asking high-quality and more diverse questions.

The Boundary representation (B-rep) format is the de-facto shape
representation in computer-aided design (CAD) to model watertight solid
objects. Recent approaches to generating CAD models have focused on learning
sketch-and-extrude modeling sequences that are executed by a solid modeling
kernel in postprocess to recover a B-rep. In this paper we present a new
approach that enables learning from and synthesizing B-reps without the need
for supervision through CAD modeling sequence data. Our method SolidGen, is an
autoregressive neural network that models the B-rep directly by predicting the
vertices, edges and faces using Transformer-based and pointer neural networks.
Key to achieving this is our Indexed Boundary Representation that references
B-rep vertices, edges and faces in a well-defined hierarchy to capture the
geometric and topological relations suitable for use with machine learning.
SolidGen can be easily conditioned on contexts e.g., class labels thanks to its
probabilistic modeling of the B-rep distribution. We demonstrate qualitatively,
quantitatively and through perceptual evaluation by human subjects that
SolidGen can produce high quality, realistic looking CAD models.

Machine learning models can be used to predict physical quantities like
homogenized elasticity stiffness tensors, which must always be symmetric
positive definite (SPD) based on conservation arguments. Two datasets of
homogenized elasticity tensors of lattice materials are presented as examples,
where it is desired to obtain models that map unit cell geometric and material
parameters to their homogenized stiffness. Fitting a model to SPD data does not
guarantee the model's predictions will remain SPD. Existing Cholsesky
factorization and Eigendecomposition schemes are abstracted in this work as
transformation layers which enforce the SPD condition. These layers can be
included in many popular machine learning models to enforce SPD behavior. This
work investigates the effects that different positivity functions have on the
layers and how their inclusion affects model accuracy. Commonly used models are
considered, including polynomials, radial basis functions, and neural networks.
Ultimately it is shown that a single SPD layer improves the model's average
prediction accuracy.

In recent years, deep learning using neural network architecture, i.e. deep
neural networks, has been on the frontier of computer science research. It has
even lead to superhuman performance in some problems, e.g., in computer vision,
games and biology, and as a result the term deep learning revolution was
coined. The undisputed success and rapid growth of deep learning suggests that,
in future, it might become an enabler for Artificial General Intelligence
(AGI). In this article, we approach this statement critically showing five
major reasons of why deep neural networks, as of the current state, are not
ready to be the technique of choice for reaching AGI.

We consider a challenging theoretical problem in offline reinforcement
learning (RL): obtaining sample-efficiency guarantees with a dataset lacking
sufficient coverage, under only realizability-type assumptions for the function
approximators. While the existing theory has addressed learning under
realizability and under non-exploratory data separately, no work has been able
to address both simultaneously (except for a concurrent work which we compare
in detail). Under an additional gap assumption, we provide guarantees to a
simple pessimistic algorithm based on a version space formed by marginalized
importance sampling (MIS), and the guarantee only requires the data to cover
the optimal policy and the function classes to realize the optimal value and
density-ratio functions. While similar gap assumptions have been used in other
areas of RL theory, our work is the first to identify the utility and the novel
mechanism of gap assumptions in offline RL with weak function approximation.

An overwhelming number of theoretical predictions for hadron colliders
require parton distribution functions (PDFs), which are an important ingredient
of theory infrastructure for the next generation of high-energy experiments.
This whitepaper summarizes the status and future prospects for determination of
high-precision PDFs applicable in a wide range of energies and experiments, in
particular in precision tests of the Standard Model and in new physics searches
at the high-luminosity Large Hadron Collider and Electron-Ion Collider. We
discuss the envisioned advancements in experimental measurements, QCD theory,
global analysis methodology, and computing that are necessary to bring
unpolarized PDFs in the nucleon to the N2LO and N3LO accuracy in the QCD
coupling strength. Special attention is given to the new tasks that emerge in
the era of the precision PDF analysis, such as those focusing on the robust
control of systematic factors both in experimental measurements and theoretical
computations. Various synergies between experimental and theoretical studies of
the hadron structure are explored, including opportunities for studying PDFs
for nuclear and meson targets, PDFs with electroweak contributions or
dependence on the transverse momentum, for incisive comparisons between
phenomenological models for the PDFs and computations on discrete lattice, and
for cross-fertilization with machine learning/AI approaches. [Submitted to the
US Community Study on the Future of Particle Physics (Snowmass 2021).]

Recommender systems play a significant role in information filtering and have
been utilized in different scenarios, such as e-commerce and social media. With
the prosperity of deep learning, deep recommender systems show superior
performance by capturing non-linear information and item-user relationships.
However, the design of deep recommender systems heavily relies on human
experiences and expert knowledge. To tackle this problem, Automated Machine
Learning (AutoML) is introduced to automatically search for the proper
candidates for different parts of deep recommender systems. This survey
performs a comprehensive review of the literature in this field. Firstly, we
propose an abstract concept for AutoML for deep recommender systems
(AutoRecSys) that describes its building blocks and distinguishes it from
conventional AutoML techniques and recommender systems. Secondly, we present a
taxonomy as a classification framework containing embedding dimension search,
feature interaction search, model design search and other components search.
Furthermore, we put a particular emphasis on the search space and search
strategy, as they are the common thread to connect all methods within each
category and enable practitioners to analyze and compare various approaches.
Finally, we propose four future promising research directions that will lead
this line of research.

In view of the performance limitations of fully-decoupled designs for neural
architectures and accelerators, hardware-software co-design has been emerging
to fully reap the benefits of flexible design spaces and optimize neural
network performance. Nonetheless, such co-design also enlarges the total search
space to practically infinity and presents substantial challenges. While the
prior studies have been focusing on improving the search efficiency (e.g., via
reinforcement learning), they commonly rely on co-searches over the entire
architecture-accelerator design space. In this paper, we propose a
\emph{semi}-decoupled approach to reduce the size of the total design space by
orders of magnitude, yet without losing optimality. We first perform neural
architecture search to obtain a small set of optimal architectures for one
accelerator candidate. Importantly, this is also the set of (close-to-)optimal
architectures for other accelerator designs based on the property that neural
architectures' ranking orders in terms of inference latency and energy
consumption on different accelerator designs are highly similar. Then, instead
of considering all the possible architectures, we optimize the accelerator
design only in combination with this small set of architectures, thus
significantly reducing the total search cost. We validate our approach by
conducting experiments on various architecture spaces for accelerator designs
with different dataflows. Our results highlight that we can obtain the optimal
design by only navigating over the reduced search space. The source code of
this work is at \url{https://github.com/Ren-Research/CoDesign}.

Natural Language Understanding (NLU) models can be trained on sensitive
information such as phone numbers, zip-codes etc. Recent literature has focused
on Model Inversion Attacks (ModIvA) that can extract training data from model
parameters. In this work, we present a version of such an attack by extracting
canaries inserted in NLU training data. In the attack, an adversary with
open-box access to the model reconstructs the canaries contained in the model's
training set. We evaluate our approach by performing text completion on
canaries and demonstrate that by using the prefix (non-sensitive) tokens of the
canary, we can generate the full canary. As an example, our attack is able to
reconstruct a four digit code in the training dataset of the NLU model with a
probability of 0.5 in its best configuration. As countermeasures, we identify
several defense mechanisms that, when combined, effectively eliminate the risk
of ModIvA in our experiments.

Automatic fetal brain tissue segmentation can enhance the quantitative
assessment of brain development at this critical stage. Deep learning methods
represent the state of the art in medical image segmentation and have also
achieved impressive results in brain segmentation. However, effective training
of a deep learning model to perform this task requires a large number of
training images to represent the rapid development of the transient fetal brain
structures. On the other hand, manual multi-label segmentation of a large
number of 3D images is prohibitive. To address this challenge, we segmented 272
training images, covering 19-39 gestational weeks, using an automatic
multi-atlas segmentation strategy based on deformable registration and
probabilistic atlas fusion, and manually corrected large errors in those
segmentations. Since this process generated a large training dataset with noisy
segmentations, we developed a novel label smoothing procedure and a loss
function to train a deep learning model with smoothed noisy segmentations. Our
proposed methods properly account for the uncertainty in tissue boundaries. We
evaluated our method on 23 manually-segmented test images of a separate set of
fetuses. Results show that our method achieves an average Dice similarity
coefficient of 0.893 and 0.916 for the transient structures of younger and
older fetuses, respectively. Our method generated results that were
significantly more accurate than several state-of-the-art methods including
nnU-Net that achieved the closest results to our method. Our trained model can
serve as a valuable tool to enhance the accuracy and reproducibility of fetal
brain analysis in MRI.

While (message-passing) graph neural networks have clear limitations in
approximating permutation-equivariant functions over graphs or general
relational data, more expressive, higher-order graph neural networks do not
scale to large graphs. They either operate on $k$-order tensors or consider all
$k$-node subgraphs, implying an exponential dependence on $k$ in memory
requirements, and do not adapt to the sparsity of the graph. By introducing new
heuristics for the graph isomorphism problem, we devise a class of universal,
permutation-equivariant graph networks, which, unlike previous architectures,
offer a fine-grained control between expressivity and scalability and adapt to
the sparsity of the graph. These architectures lead to vastly reduced
computation times compared to standard higher-order graph networks in the
supervised node- and graph-level classification and regression regime while
significantly improving over standard graph neural network and graph kernel
architectures in terms of predictive performance.

Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality
reduction and manifold learning method. It has two main steps which are linear
reconstruction and linear embedding of points in the input space and embedding
space, respectively. In this work, we look at the linear reconstruction step
from a stochastic perspective where it is assumed that every data point is
conditioned on its linear reconstruction weights as latent factors. The
stochastic linear reconstruction of LLE is solved using expectation
maximization. We show that there is a theoretical connection between three
fundamental dimensionality reduction methods, i.e., LLE, factor analysis, and
probabilistic Principal Component Analysis (PCA). The stochastic linear
reconstruction of LLE is formulated similar to the factor analysis and
probabilistic PCA. It is also explained why factor analysis and probabilistic
PCA are linear and LLE is a nonlinear method. This work combines and makes a
bridge between two broad approaches of dimensionality reduction, i.e., the
spectral and probabilistic algorithms.

Deep neural networks (DNNs) have found their way into many applications with
potential impact on the safety, security, and fairness of
human-machine-systems. Such require basic understanding and sufficient trust by
the users. This motivated the research field of explainable artificial
intelligence (XAI), i.e. finding methods for opening the "black-boxes" DNNs
represent. For the computer vision domain in specific, practical assessment of
DNNs requires a globally valid association of human interpretable concepts with
internals of the model. The research field of concept (embedding) analysis (CA)
tackles this problem: CA aims to find global, assessable associations of
humanly interpretable semantic concepts (e.g., eye, bearded) with internal
representations of a DNN. This work establishes a general definition of CA and
a taxonomy for CA methods, uniting several ideas from literature. That allows
to easily position and compare CA approaches. Guided by the defined notions,
the current state-of-the-art research regarding CA methods and interesting
applications are reviewed. More than thirty relevant methods are discussed,
compared, and categorized. Finally, for practitioners, a survey of fifteen
datasets is provided that have been used for supervised concept analysis. Open
challenges and research directions are pointed out at the end.

Sparse polynomial approximation has become indispensable for approximating
smooth, high- or infinite-dimensional functions from limited samples. This is a
key task in computational science and engineering, e.g., surrogate modelling in
UQ where the function is the solution map of a parametric or stochastic PDE.
Yet, sparse polynomial approximation lacks a complete theory. On the one hand,
there is a well-developed theory of best $s$-term polynomial approximation,
which asserts exponential or algebraic rates of convergence for holomorphic
functions. On the other hand, there are increasingly mature methods such as
(weighted) $\ell^1$-minimization for computing such approximations. While the
sample complexity of these methods has been analyzed in detail, the matter of
whether or not these methods achieve such rates is not well understood.
Furthermore, these methods are not algorithms per se, since they involve exact
minimizers of nonlinear optimization problems.
  This paper closes these gaps. Specifically, we pose and answer the following
question: are there robust, efficient algorithms for computing approximations
to finite- or infinite-dimensional, holomorphic and Hilbert-valued functions
from limited samples that achieve best $s$-term rates? We answer this in the
affirmative by introducing algorithms and theoretical guarantees that assert
exponential or algebraic rates of convergence, along with robustness to
sampling, algorithmic, and physical discretization errors. We tackle both
scalar- and Hilbert-valued functions, this being particularly relevant to
parametric and stochastic PDEs. Our work involves several significant
developments of existing techniques, including a novel restarted primal-dual
iteration for solving weighted $\ell^1$-minimization problems in Hilbert
spaces. Our theory is supplemented by numerical experiments demonstrating the
practical efficacy of these algorithms.

We study the challenging incremental few-shot object detection (iFSD)
setting. Recently, hypernetwork-based approaches have been studied in the
context of continuous and finetune-free iFSD with limited success. We take a
closer look at important design choices of such methods, leading to several key
improvements and resulting in a more accurate and flexible framework, which we
call Sylph. In particular, we demonstrate the effectiveness of decoupling
object classification from localization by leveraging a base detector that is
pretrained for class-agnostic localization on a large-scale dataset. Contrary
to what previous results have suggested, we show that with a carefully designed
class-conditional hypernetwork, finetune-free iFSD can be highly effective,
especially when a large number of base categories with abundant data are
available for meta-training, almost approaching alternatives that undergo
test-time-training. This result is even more significant considering its many
practical advantages: (1) incrementally learning new classes in sequence
without additional training, (2) detecting both novel and seen classes in a
single pass, and (3) no forgetting of previously seen classes. We benchmark our
model on both COCO and LVIS, reporting as high as 17% AP on the long-tail rare
classes on LVIS, indicating the promise of hypernetwork-based iFSD.

In this work we leverage a weakly-labeled dataset of spectral data from NASAs
IRIS satellite for the prediction of solar flares using the Multiple Instance
Learning (MIL) paradigm. While standard supervised learning models expect a
label for every instance, MIL relaxes this and only considers bags of instances
to be labeled. This is ideally suited for flare prediction with IRIS data that
consists of time series of bags of UV spectra measured along the instrument
slit. In particular, we consider the readout window around the Mg II h&k lines
that encodes information on the dynamics of the solar chromosphere. Our MIL
models are not only able to predict whether flares occur within the next
$\sim$25 minutes with accuracies of around 90%, but are also able to explain
which spectral profiles were particularly important for their bag-level
prediction. This information can be used to highlight regions of interest in
ongoing IRIS observations in real-time and to identify candidates for typical
flare precursor spectral profiles. We use k-means clustering to extract groups
of spectral profiles that appear relevant for flare prediction. The recovered
groups show high intensity, triplet red wing emission and single-peaked h and k
lines, as found by previous works. They seem to be related to small-scale
explosive events that have been reported to occur tens of minutes before a
flare.

Deep learning is a standard tool in the field of high-energy physics,
facilitating considerable sensitivity enhancements for numerous analysis
strategies. In particular, in identification of physics objects, such as jet
flavor tagging, complex neural network architectures play a major role.
However, these methods are reliant on accurate simulations. Mismodeling can
lead to non-negligible differences in performance in data that need to be
measured and calibrated against. We investigate the classifier response to
input data with injected mismodelings and probe the vulnerability of flavor
tagging algorithms via application of adversarial attacks. Subsequently, we
present an adversarial training strategy that mitigates the impact of such
simulated attacks and improves the classifier robustness. We examine the
relationship between performance and vulnerability and show that this method
constitutes a promising approach to reduce the vulnerability to poor modeling.

We extend the idea of automated debiased machine learning to the dynamic
treatment regime and more generally to nested functionals. We show that the
multiply robust formula for the dynamic treatment regime with discrete
treatments can be re-stated in terms of a recursive Riesz representer
characterization of nested mean regressions. We then apply a recursive Riesz
representer estimation learning algorithm that estimates de-biasing corrections
without the need to characterize how the correction terms look like, such as
for instance, products of inverse probability weighting terms, as is done in
prior work on doubly robust estimation in the dynamic regime. Our approach
defines a sequence of loss minimization problems, whose minimizers are the
mulitpliers of the de-biasing correction, hence circumventing the need for
solving auxiliary propensity models and directly optimizing for the mean
squared error of the target de-biasing correction. We provide further
applications of our approach to estimation of dynamic discrete choice models
and estimation of long-term effects with surrogates.

Battery energy storage systems can be used for peak demand reduction in power
systems, leading to significant economic benefits. Two practical challenges are
1) accurately determining the peak load days and hours and 2) quantifying and
reducing uncertainties associated with the forecast in probabilistic risk
measures for dispatch decision-making. In this study, we develop a supervised
machine learning approach to generate 1) the probability of the next operation
day containing the peak hour of the month and 2) the probability of an hour to
be the peak hour of the day. Guidance is provided on the preparation and
augmentation of data as well as the selection of machine learning models and
decision-making thresholds. The proposed approach is applied to the Duke Energy
Progress system and successfully captures 69 peak days out of 72 testing months
with a 3% exceedance probability threshold. On 90% of the peak days, the actual
peak hour is among the 2 hours with the highest probabilities.

Sepsis is a leading cause of mortality and its treatment is very expensive.
Sepsis treatment is also very challenging because there is no consensus on what
interventions work best and different patients respond very differently to the
same treatment. Deep Reinforcement Learning methods can be used to come up with
optimal policies for treatment strategies mirroring physician actions. In the
healthcare scenario, the available data is mostly collected offline with no
interaction with the environment, which necessitates the use of offline RL
techniques. The Offline RL paradigm suffers from action distribution shifts
which in turn negatively affects learning an optimal policy for the treatment.
In this work, a Conservative-Q Learning (CQL) algorithm is used to mitigate
this shift and its corresponding policy reaches closer to the physicians policy
than conventional deep Q Learning. The policy learned could help clinicians in
Intensive Care Units to make better decisions while treating septic patients
and improve survival rate.

As social media platforms are evolving from text-based forums into
multi-modal environments, the nature of misinformation in social media is also
changing accordingly. Taking advantage of the fact that visual modalities such
as images and videos are more favorable and attractive to the users, and
textual contents are sometimes skimmed carelessly, misinformation spreaders
have recently targeted contextual correlations between modalities e.g., text
and image. Thus, many research efforts have been put into development of
automatic techniques for detecting possible cross-modal discordances in
web-based media. In this work, we aim to analyze, categorize and identify
existing approaches in addition to challenges and shortcomings they face in
order to unearth new opportunities in furthering the research in the field of
multi-modal misinformation detection.

Recent unsupervised pre-training methods have shown to be effective on
language and vision domains by learning useful representations for multiple
downstream tasks. In this paper, we investigate if such unsupervised
pre-training methods can also be effective for vision-based reinforcement
learning (RL). To this end, we introduce a framework that learns
representations useful for understanding the dynamics via generative
pre-training on videos. Our framework consists of two phases: we pre-train an
action-free latent video prediction model, and then utilize the pre-trained
representations for efficiently learning action-conditional world models on
unseen environments. To incorporate additional action inputs during
fine-tuning, we introduce a new architecture that stacks an action-conditional
latent prediction model on top of the pre-trained action-free prediction model.
Moreover, for better exploration, we propose a video-based intrinsic bonus that
leverages pre-trained representations. We demonstrate that our framework
significantly improves both final performances and sample-efficiency of
vision-based RL in a variety of manipulation and locomotion tasks. Code is
available at https://github.com/younggyoseo/apv.

Objective: To comparatively evaluate several transformer model architectures
at identifying protected health information (PHI) in the i2b2/UTHealth 2014
clinical text de-identification challenge corpus.
  Methods: The i2b2/UTHealth 2014 corpus contains N=1304 clinical notes
obtained from N=296 patients. Using a transfer learning framework, we fine-tune
several transformer model architectures on the corpus, including: BERT-base,
BERT-large, ROBERTA-base, ROBERTA-large, ALBERT-base and ALBERT-xxlarge. During
fine-tuning we vary the following model hyper-parameters: batch size, number
training epochs, learning rate and weight decay. We fine tune models on a
training data set, we evaluate and select optimally performing models on an
independent validation dataset, and lastly assess generalization performance on
a held-out test dataset. We assess model performance in terms of accuracy,
precision (positive predictive value), recall (sensitivity) and F1 score
(harmonic mean of precision and recall). We are interested in overall model
performance (PHI identified vs. PHI not identified), as well as PHI-specific
model performance.
  Results: We observe that the ROBERTA-large models perform best at identifying
PHI in the i2b2/UTHealth 2014 corpus, achieving >99% overall accuracy and 96.7%
recall/precision on the heldout test corpus. Performance was good across many
PHI classes; however, accuracy/precision/recall decreased for identification of
the following entity classes: professions, organizations, ages, and certain
locations.
  Conclusions: Transformers are a promising model class/architecture for
clinical text de-identification. With minimal hyper-parameter tuning
transformers afford researchers/clinicians the opportunity to obtain (near)

The Beam Energy Scan Theory (BEST) collaboration's equation of state (EoS)
incorporates a 3D Ising model critical point into the Quantum Chromodynamics
(QCD) equation of state from lattice simulations. However, it contains 4 free
parameters related to the size and location of the critical region in the QCD
phase diagram. Certain combinations of the free parameters lead to acausal or
unstable realizations of the EoS that should not be considered. In this work,
we use an active learning framework to rule out pathological EoS efficiently.
We find that checking stability and causality for a small portion of the
parameters' range is sufficient to construct algorithms that perform with
$>$96% accuracy across the entire parameter space. Though in this work we focus
on a specific case, our approach can be generalized to any EoS containing a
parameter space-class correspondence.

In the field of materials science, microscopy is the first and often only
accessible method for structural characterization. There is a growing interest
in the development of machine learning methods that can automate the analysis
and interpretation of microscopy images. Typically training of machine learning
models requires large numbers of images with associated structural labels,
however, manual labeling of images requires domain knowledge and is prone to
human error and subjectivity. To overcome these limitations, we present a
semi-supervised transfer learning approach that uses a small number of labeled
microscopy images for training and performs as effectively as methods trained
on significantly larger image datasets. Specifically, we train an image encoder
with unlabeled images using self-supervised learning methods and use that
encoder for transfer learning of different downstream image tasks
(classification and segmentation) with a minimal number of labeled images for
training. We test the transfer learning ability of two self-supervised learning
methods: SimCLR and Barlow-Twins on transmission electron microscopy (TEM)
images. We demonstrate in detail how this machine learning workflow applied to
TEM images of protein nanowires enables automated classification of nanowire
morphologies (e.g., single nanowires, nanowire bundles, phase separated) as
well as segmentation tasks that can serve as groundwork for quantification of
nanowire domain sizes and shape analysis. We also extend the application of the
machine learning workflow to classification of nanoparticle morphologies and
identification of different type of viruses from TEM images.

Unsupervised semantic segmentation requires assigning a label to every pixel
without any human annotations. Despite recent advances in self-supervised
representation learning for individual images, unsupervised semantic
segmentation with pixel-level representations is still a challenging task and
remains underexplored. In this work, we propose a self-supervised pixel
representation learning method for semantic segmentation by using visual
concepts (i.e., groups of pixels with semantic meanings, such as parts,
objects, and scenes) extracted from images. To guide self-supervised learning,
we leverage three types of relationships between pixels and concepts, including
the relationships between pixels and local concepts, local and global concepts,
as well as the co-occurrence of concepts. We evaluate the learned pixel
embeddings and visual concepts on three datasets, including PASCAL VOC 2012,
COCO 2017, and DAVIS 2017. Our results show that the proposed method gains
consistent and substantial improvements over recent unsupervised semantic
segmentation approaches, and also demonstrate that visual concepts can reveal
insights into image datasets.

Neural Machine Translation (NMT) models are typically trained on
heterogeneous data that are concatenated and randomly shuffled. However, not
all of the training data are equally useful to the model. Curriculum training
aims to present the data to the NMT models in a meaningful order. In this work,
we introduce a two-stage curriculum training framework for NMT where we
fine-tune a base NMT model on subsets of data, selected by both deterministic
scoring using pre-trained methods and online scoring that considers prediction
scores of the emerging NMT model. Through comprehensive experiments on six
language pairs comprising low- and high-resource languages from WMT'21, we have
shown that our curriculum strategies consistently demonstrate better quality
(up to +2.2 BLEU improvement) and faster convergence (approximately 50% fewer
updates).

The need for a large amount of labeled data in the supervised setting has led
recent studies to utilize self-supervised learning to pre-train deep neural
networks using unlabeled data. Many self-supervised training strategies have
been investigated especially for medical datasets to leverage the information
available in the much fewer unlabeled data. One of the fundamental strategies
in image-based self-supervision is context prediction. In this approach, a
model is trained to reconstruct the contents of an arbitrary missing region of
an image based on its surroundings. However, the existing methods adopt a
random and blind masking approach by focusing uniformly on all regions of the
images. This approach results in a lot of unnecessary network updates that
cause the model to forget the rich extracted features. In this work, we develop
a novel self-supervised approach that occludes targeted regions to improve the
pre-training procedure. To this end, we propose a reinforcement learning-based
agent which learns to intelligently mask input images through deep Q-learning.
We show that training the agent against the prediction model can significantly
improve the semantic features extracted for downstream classification tasks. We
perform our experiments on two public datasets for diagnosing breast cancer in
the ultrasound images and detecting lower-grade glioma with MR images. In our
experiments, we show that our novel masking strategy advances the learned
features according to the performance on the classification task in terms of
accuracy, macro F1, and AUROC.

It is well known that visual SLAM systems based on dense matching are locally
accurate but are also susceptible to long-term drift and map corruption. In
contrast, feature matching methods can achieve greater long-term consistency
but can suffer from inaccurate local pose estimation when feature information
is sparse. Based on these observations, we propose an RGB-D SLAM system that
leverages the advantages of both approaches: using dense frame-to-model
odometry to build accurate sub-maps and on-the-fly feature-based matching
across sub-maps for global map optimisation. In addition, we incorporate a
learning-based loop closure component based on 3-D features which further
stabilises map building. We have evaluated the approach on indoor sequences
from public datasets, and the results show that it performs on par or better
than state-of-the-art systems in terms of map reconstruction quality and pose
estimation. The approach can also scale to large scenes where other systems
often fail.

The rapid development of diagnostic technologies in healthcare is leading to
higher requirements for physicians to handle and integrate the heterogeneous,
yet complementary data that are produced during routine practice. For instance,
the personalized diagnosis and treatment planning for a single cancer patient
relies on the various images (e.g., radiological, pathological, and camera
images) and non-image data (e.g., clinical data and genomic data). However,
such decision-making procedures can be subjective, qualitative, and have large
inter-subject variabilities. With the recent advances in multi-modal deep
learning technologies, an increasingly large number of efforts have been
devoted to a key question: how do we extract and aggregate multi-modal
information to ultimately provide more objective, quantitative computer-aided
clinical decision making? This paper reviews the recent studies on dealing with
such a question. Briefly, this review will include the (1) overview of current
multi-modal learning workflows, (2) summarization of multi-modal fusion
methods, (3) discussion of the performance, (4) applications in disease
diagnosis and prognosis, and (5) challenges and future directions.

Deep learning has been proposed for the assessment and classification of
medical images. However, many medical image databases with appropriately
labeled and annotated images are small and imbalanced, and thus unsuitable to
train and validate such models. The option is to generate synthetic images and
one successful technique has been patented which limits its use for others. We
have developed a free-access, alternate method for generating synthetic
high-resolution images using Generative Adversarial Networks (GAN) for data
augmentation and showed their effectiveness using eye-fundus images for
Age-Related Macular Degeneration (AMD) identification. Ten different GAN
architectures were compared to generate synthetic eye-fundus images with and
without AMD. Data from three public databases were evaluated using the
Fr\'echet Inception Distance (FID), two clinical experts and deep-learning
classification. The results show that StyleGAN2 reached the lowest FID
(166.17), and clinicians could not accurately differentiate between real and
synthetic images. ResNet-18 architecture obtained the best performance with 85%
accuracy and outperformed the two experts in detecting AMD fundus images, whose
average accuracy was 77.5%. These results are similar to a recently patented
method, and will provide an alternative to generating high-quality synthetic
medical images. Free access has been provided to the entire method to
facilitate the further development of this field.

This paper presents a model-free approximation for the Hessian of the
performance of deterministic policies to use in the context of Reinforcement
Learning based on Quasi-Newton steps in the policy parameters. We show that the
approximate Hessian converges to the exact Hessian at the optimal policy, and
allows for a superlinear convergence in the learning, provided that the policy
parametrization is rich. The natural policy gradient method can be interpreted
as a particular case of the proposed method. We analytically verify the
formulation in a simple linear case and compare the convergence of the proposed
method with the natural policy gradient in a nonlinear example.

While quantum machine learning (ML) has been proposed to be one of the most
promising applications of quantum computing, how to build quantum ML models
that outperform classical ML remains a major open question. Here, we
demonstrate an algorithm for constructing quantum kernels for support vector
machines that adapts quantum gate sequences to data. The algorithm includes
three essential ingredients: greedy search in the space of quantum circuits,
Bayesian information criterion as circuit selection metric and Bayesian
optimization of the parameters of the optimal quantum circuit identified. The
performance of the resulting quantum models for classification problems with a
small number of training points significantly exceeds that of optimized
classical models with conventional kernels. In addition, we illustrate the
possibility of mapping quantum circuits onto molecular fingerprints and show
that performant quantum kernels can be isolated in the resulting chemical
space. This suggests that methods developed for optimization and interpolation
of molecular properties across chemical spaces can be used for building quantum
circuits for quantum machine learning with enhanced performance.

Cluster algebras have recently become an important player in mathematics and
physics. In this work, we investigate them through the lens of modern data
science, specifically with techniques from network science and
machine-learning. Network analysis methods are applied to the exchange graphs
for cluster algebras of varying mutation types. The analysis indicates that
when the graphs are represented without identifying by permutation equivalence
between clusters an elegant symmetry emerges in the quiver exchange graph
embedding. The ratio between number of seeds and number of quivers associated
to this symmetry is computed for finite Dynkin type algebras up to rank 5, and
conjectured for higher ranks. Simple machine learning techniques successfully
learn to differentiate cluster algebras from their seeds. The learning
performance exceeds 0.9 accuracies between algebras of the same mutation type
and between types, as well as relative to artificially generated data.

Learning from Demonstration (LfD) seeks to democratize robotics by enabling
diverse end-users to teach robots to perform a task by providing
demonstrations. However, most LfD techniques assume users provide optimal
demonstrations. This is not always the case in real applications where users
are likely to provide demonstrations of varying quality, that may change with
expertise and other factors. Demonstration quality plays a crucial role in
robot learning and generalization. Hence, it is important to quantify the
quality of the provided demonstrations before using them for robot learning. In
this paper, we propose quantifying the quality of the demonstrations based on
how well they perform in the learned task. We hypothesize that task performance
can give an indication of the generalization performance on similar tasks. The
proposed approach is validated in a user study (N = 27). Users with different
robotics expertise levels were recruited to teach a PR2 robot a generic task
(pressing a button) under different task constraints. They taught the robot in
two sessions on two different days to capture their teaching behaviour across
sessions. The task performance was utilized to classify the provided
demonstrations into high-quality and low-quality sets. The results show a
significant Pearson correlation coefficient (R = 0.85, p < 0.0001) between the
task performance and generalization performance across all participants. We
also found that users clustered into two groups: Users who provided
high-quality demonstrations from the first session, assigned to the
fast-adapters group, and users who provided low-quality demonstrations in the
first session and then improved with practice, assigned to the slow-adapters
group. These results highlight the importance of quantifying demonstration
quality, which can be indicative of the adaptation level of the user to the
task.

Machine learning (ML) is widely used in drug discovery to train models that
predict protein-ligand binding. These models are of great value to medicinal
chemists, in particular if they provide case-specific insight into the physical
interactions that drive the binding process. In this study we derive ML models
from over 50 fragment-screening campaigns to introduce two important elements
that we believe are absent in most -- if not all -- ML studies of this type
reported to date: First, alongside the observed hits we use to train our
models, we incorporate true misses and show that these experimentally validated
negative data are of significant importance to the quality of the derived
models. Second, we provide a physically interpretable and verifiable
representation of what the ML model considers important for successful binding.
This representation is derived from a straightforward attribution procedure
that explains the prediction in terms of the (inter-)action of chemical
environments. Critically, we validate the attribution outcome on a large scale
against prior annotations made independently by expert molecular modellers. We
find good agreement between the key molecular substructures proposed by the ML
model and those assigned manually, even when the model's performance in
discriminating hits from misses is far from perfect. By projecting the
attribution onto predefined interaction prototypes (pharmacophores), we show
that ML allows us to formulate simple rules for what drives fragment binding
against a target automatically from screening data.

Deep Neural Networks ( DNN s) are known to make overconfident mistakes, which
makes their use problematic in safety-critical applications. State-of-the-art (
SOTA ) calibration techniques improve on the confidence of predicted labels
alone and leave the confidence of non-max classes (e.g. top-2, top-5)
uncalibrated. Such calibration is not suitable for label refinement using
post-processing. Further, most SOTA techniques learn a few hyper-parameters
post-hoc, leaving out the scope for image, or pixel specific calibration. This
makes them unsuitable for calibration under domain shift, or for dense
prediction tasks like semantic segmentation. In this paper, we argue for
intervening at the train time itself, so as to directly produce calibrated DNN
models. We propose a novel auxiliary loss function: Multi-class Difference in
Confidence and Accuracy ( MDCA ), to achieve the same MDCA can be used in
conjunction with other application/task-specific loss functions. We show that
training with MDCA leads to better-calibrated models in terms of Expected
Calibration Error ( ECE ), and Static Calibration Error ( SCE ) on image
classification, and segmentation tasks. We report ECE ( SCE ) score of 0.72
(1.60) on the CIFAR 100 dataset, in comparison to 1.90 (1.71) by the SOTA.
Under domain shift, a ResNet-18 model trained on PACS dataset using MDCA gives
an average ECE ( SCE ) score of 19.7 (9.7) across all domains, compared to 24.2
(11.8) by the SOTA. For the segmentation task, we report a 2X reduction in
calibration error on PASCAL - VOC dataset in comparison to Focal Loss. Finally,
MDCA training improves calibration even on imbalanced data, and for natural
language classification tasks. We have released the code here: code is
available at https://github.com/mdca-loss

We investigate applications of machine learning models to directly infer
physical properties of brown dwarfs from their photometry and spectra using
$\textit{The Cannon}$. We demonstrate that absolute magnitudes, spectral types,
and spectral indices can be determined from low-resolution SpeX prism spectra
of L and T dwarfs without trigonometric parallax measurements and with
precisions competitive with commonly used methods. For T dwarfs with
sufficiently precise spectra and photometry, bolometric luminosities and
effective temperatures can be determined at precisions comparable to methods
that use polynomial relations as a function of absolute magnitudes. We also
provide new and updated polynomial relations for absolute magnitudes as a
function of spectral types L0-T8 in 14 bands spanning Pan-STARRS $r_{P1}$ to
AllWISE $\textit{W3}$, using a volume-limited sample of 256 brown dwarfs
defined entirely by parallaxes. These include the first relations for brown
dwarfs using Pan-STARRS1 photometry and the first for several infrared bands
using a volume-limited sample. We find that our novel method with $\textit{The
Cannon}$ can infer absolute magnitudes with equal or smaller uncertainties than
the polynomial relations that depend on trigonometric parallax measurements.

We have computed obscured AGN redshifts using the XZ method, adopting a broad
treatment in which we employed a wide-ranging data set and worked primarily at
the XZ counts sensitivity threshold, culminating with a redshift catalog
containing 121 sources that lack documented redshifts. We considered 363
obscured AGN from the Chandra Source Catalog Release 2.0, 59 of which were
selected using multiwavelength criteria while 304 were X-ray selected.
One-third of the data set had cross-matched spectroscopic or photometric
redshifts. These sources, dominated by low-$z$ and low-$N_H$ AGN, were
supplemented by 1000 simulations to form a data set for testing the XZ method.
We used a multi-layer perceptron neural network to examine and predict cases in
which XZ fails to reproduce the known redshift, yielding a classifier that can
identify and discard poor redshift estimates. This classifier demonstrated a
statistically significant $\sim$3$\sigma$ improvement over the existing XZ
redshift information gain filter. We applied the machine learning model to
sources with no documented redshifts, resulting in the 121-source new redshift
catalog, all of which were X-ray selected. Our neural network's performance
suggests that nearly 90% of these redshift estimates are consistent with
hypothetical spectroscopic or photometric measurements, strengthening the
notion that redshifts can be reliably estimated using only X-rays, which is
valuable to current and future missions such as Athena. We have also identified
a possible Compton-thick candidate that warrants further investigation.

Human-centric perception plays a vital role in vision and graphics. But their
data annotations are prohibitively expensive. Therefore, it is desirable to
have a versatile pre-train model that serves as a foundation for data-efficient
downstream tasks transfer. To this end, we propose the Human-Centric
Multi-Modal Contrastive Learning framework HCMoCo that leverages the
multi-modal nature of human data (e.g. RGB, depth, 2D keypoints) for effective
representation learning. The objective comes with two main challenges: dense
pre-train for multi-modality data, efficient usage of sparse human priors. To
tackle the challenges, we design the novel Dense Intra-sample Contrastive
Learning and Sparse Structure-aware Contrastive Learning targets by
hierarchically learning a modal-invariant latent space featured with continuous
and ordinal feature distribution and structure-aware semantic consistency.
HCMoCo provides pre-train for different modalities by combining heterogeneous
datasets, which allows efficient usage of existing task-specific human data.
Extensive experiments on four downstream tasks of different modalities
demonstrate the effectiveness of HCMoCo, especially under data-efficient
settings (7.16% and 12% improvement on DensePose Estimation and Human Parsing).
Moreover, we demonstrate the versatility of HCMoCo by exploring cross-modality
supervision and missing-modality inference, validating its strong ability in
cross-modal association and reasoning.

In most scenarios, conditional image generation can be thought of as an
inversion of the image understanding process. Since generic image understanding
involves solving multiple tasks, it is natural to aim at generating images via
multi-conditioning. However, multi-conditional image generation is a very
challenging problem due to the heterogeneity and the sparsity of the (in
practice) available conditioning labels. In this work, we propose a novel
neural architecture to address the problem of heterogeneity and sparsity of the
spatially multi-conditional labels. Our choice of spatial conditioning, such as
by semantics and depth, is driven by the promise it holds for better control of
the image generation process. The proposed method uses a transformer-like
architecture operating pixel-wise, which receives the available labels as input
tokens to merge them in a learned homogeneous space of labels. The merged
labels are then used for image generation via conditional generative
adversarial training. In this process, the sparsity of the labels is handled by
simply dropping the input tokens corresponding to the missing labels at the
desired locations, thanks to the proposed pixel-wise operating architecture.
Our experiments on three benchmark datasets demonstrate the clear superiority
of our method over the state-of-the-art and compared baselines. The source code

Social media platforms often act as breeding grounds for various forms of
trolling or malicious content targeting users or communities. One way of
trolling users is by creating memes, which in most cases unites an image with a
short piece of text embedded on top of it. The situation is more complex for
multilingual(e.g., Tamil) memes due to the lack of benchmark datasets and
models. We explore several models to detect Troll memes in Tamil based on the
shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022.
We observe while the text-based model MURIL performs better for Non-troll meme
classification, the image-based model VGG16 performs better for Troll-meme
classification. Further fusing these two modalities help us achieve stable
outcomes in both classes. Our fusion model achieved a 0.561 weighted average F1
score and ranked second in this task.

The problem of reconstructing continuous functions over time is important for
problems such as reconstructing moving scenes, and interpolating between time
steps. Previous approaches that use deep-learning rely on regularization to
ensure that reconstructions are approximately continuous, which works well on
short sequences. As sequence length grows, though, it becomes more difficult to
regularize, and it becomes less feasible to learn only through regularization.
We propose a new architecture for function reconstruction based on classical
Bezier splines, which ensures $C^0$ and $C^1$-continuity, where $C^0$
continuity is that $\forall c:\lim\limits_{x\to c} f(x)
  = f(c)$, or more intuitively that there are no breaks at any point in the
function. In order to demonstrate our architecture, we reconstruct dynamic
scenes using Neural Radiance Fields, but hope it is clear that our approach is
general and can be applied to a variety of problems. We recover a Bezier spline
$B(\beta, t\in[0,1])$, parametrized by the control points $\beta$. Using Bezier
splines ensures reconstructions have $C^0$ and $C^1$ continuity, allowing for
guaranteed interpolation over time. We reconstruct $\beta$ with a multi-layer
perceptron (MLP), blending machine learning with classical animation
techniques. All code is available at https://github.com/JulianKnodt/nerf_atlas,
and datasets are from prior work.

In this paper we introduce "Federated Learning Utilities and Tools for
Experimentation" (FLUTE), a high-performance open-source platform for federated
learning research and offline simulations. The goal of FLUTE is to enable rapid
prototyping and simulation of new federated learning algorithms at scale,
including novel optimization, privacy, and communications strategies. We
describe the architecture of FLUTE, enabling arbitrary federated modeling
schemes to be realized. We compare the platform with other state-of-the-art
platforms and describe available features of FLUTE for experimentation in core
areas of active research, such as optimization, privacy, and scalability. A
comparison with other established platforms shows speed-ups of up to 42x and
savings in memory footprint of 3x. A sample of the platform capabilities is
also presented for a range of tasks, as well as other functionality, such as
linear scaling for the number of participating clients, and a variety of
federated optimizers, including FedAdam, DGA, etcetera.

We investigate nonlinear prediction in an online setting and introduce a
hybrid model that effectively mitigates, via an end-to-end architecture, the
need for hand-designed features and manual model selection issues of
conventional nonlinear prediction/regression methods. In particular, we use
recursive structures to extract features from sequential signals, while
preserving the state information, i.e., the history, and boosted decision trees
to produce the final output. The connection is in an end-to-end fashion and we
jointly optimize the whole architecture using stochastic gradient descent, for
which we also provide the backward pass update equations. In particular, we
employ a recurrent neural network (LSTM) for adaptive feature extraction from
sequential data and a gradient boosting machinery (soft GBDT) for effective
supervised regression. Our framework is generic so that one can use other deep
learning architectures for feature extraction (such as RNNs and GRUs) and
machine learning algorithms for decision making as long as they are
differentiable. We demonstrate the learning behavior of our algorithm on
synthetic data and the significant performance improvements over the
conventional methods over various real life datasets. Furthermore, we openly
share the source code of the proposed method to facilitate further research.

A key challenge in metabolomics is annotating measured spectra from a
biological sample with chemical identities. Currently, only a small fraction of
measurements can be assigned identities. Two complementary computational
approaches have emerged to address the annotation problem: mapping candidate
molecules to spectra, and mapping query spectra to molecular candidates. In
essence, the candidate molecule with the spectrum that best explains the query
spectrum is recommended as the target molecule. Despite candidate ranking being
fundamental in both approaches, no prior works utilized rank learning tasks in
determining the target molecule. We propose a novel machine learning model,
Ensemble Spectral Prediction (ESP), for metabolite annotation. ESP takes
advantage of prior neural network-based annotation models that utilize
multilayer perceptron (MLP) networks and Graph Neural Networks (GNNs). Based on
the ranking results of the MLP and GNN-based models, ESP learns a weighting for
the outputs of MLP and GNN spectral predictors to generate a spectral
prediction for a query molecule. Importantly, training data is stratified by
molecular formula to provide candidate sets during model training. Further,
baseline MLP and GNN models are enhanced by considering peak dependencies
through multi-head attention mechanism and multi-tasking on spectral topic
distributions. ESP improves average rank by 41% and 30% over the MLP and GNN
baselines, respectively, demonstrating remarkable performance gain over
state-of-the-art neural network approaches. We show that annotation
performance, for ESP and other models, is a strong function of the number of
molecules in the candidate set and their similarity to the target molecule.

In this paper, we initiate a rigorous study of the phenomenon of
low-dimensional adversarial perturbations (LDAPs) in classification. Unlike the
classical setting, these perturbations are limited to a subspace of dimension
$k$ which is much smaller than the dimension $d$ of the feature space. The case
$k=1$ corresponds to so-called universal adversarial perturbations (UAPs;
Moosavi-Dezfooli et al., 2017). First, we consider binary classifiers under
generic regularity conditions (including ReLU networks) and compute analytical
lower-bounds for the fooling rate of any subspace. These bounds explicitly
highlight the dependence of the fooling rate on the pointwise margin of the
model (i.e., the ratio of the output to its $L_2$ norm of its gradient at a
test point), and on the alignment of the given subspace with the gradients of
the model w.r.t. inputs. Our results provide a rigorous explanation for the
recent success of heuristic methods for efficiently generating low-dimensional
adversarial perturbations. Finally, we show that if a decision-region is
compact, then it admits a universal adversarial perturbation with $L_2$ norm
which is $\sqrt{d}$ times smaller than the typical $L_2$ norm of a data point.
Our theoretical results are confirmed by experiments on both synthetic and real
data.

Social media platforms are used by a large number of people prominently to
express their thoughts and opinions. However, these platforms have contributed
to a substantial amount of hateful and abusive content as well. Therefore, it
is important to curb the spread of hate speech on these platforms. In India,
Marathi is one of the most popular languages used by a wide audience. In this
work, we present L3Cube-MahaHate, the first major Hate Speech Dataset in
Marathi. The dataset is curated from Twitter, annotated manually. Our dataset
consists of over 25000 distinct tweets labeled into four major classes i.e
hate, offensive, profane, and not. We present the approaches used for
collecting and annotating the data and the challenges faced during the process.
Finally, we present baseline classification results using deep learning models
based on CNN, LSTM, and Transformers. We explore mono-lingual and multi-lingual
variants of BERT like MahaBERT, IndicBERT, mBERT, and xlm-RoBERTa and show that
mono-lingual models perform better than their multi-lingual counterparts. The
MahaBERT model provides the best results on L3Cube-MahaHate Corpus. The data
and models are available at https://github.com/l3cube-pune/MarathiNLP .

Human behavior has the nature of indeterminacy, which requires the pedestrian
trajectory prediction system to model the multi-modality of future motion
states. Unlike existing stochastic trajectory prediction methods which usually
use a latent variable to represent multi-modality, we explicitly simulate the
process of human motion variation from indeterminate to determinate. In this
paper, we present a new framework to formulate the trajectory prediction task
as a reverse process of motion indeterminacy diffusion (MID), in which we
progressively discard indeterminacy from all the walkable areas until reaching
the desired trajectory. This process is learned with a parameterized Markov
chain conditioned by the observed trajectories. We can adjust the length of the
chain to control the degree of indeterminacy and balance the diversity and
determinacy of the predictions. Specifically, we encode the history behavior
information and the social interactions as a state embedding and devise a
Transformer-based diffusion model to capture the temporal dependencies of
trajectories. Extensive experiments on the human trajectory prediction
benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the
superiority of our method. Code is available at
https://github.com/gutianpei/MID.

Physical systems are governed by partial differential equations (PDEs). The
Navier-Stokes equations describe fluid flows and are representative of
nonlinear physical systems with complex spatio-temporal interactions. Fluid
flows are omnipresent in nature and engineering applications, and their
accurate simulation is essential for providing insights into these processes.
While PDEs are typically solved with numerical methods, the recent success of
machine learning (ML) has shown that ML methods can provide novel avenues of
finding solutions to PDEs. ML is becoming more and more present in
computational fluid dynamics (CFD). However, up to this date, there does not
exist a general-purpose ML-CFD package which provides 1) powerful
state-of-the-art numerical methods, 2) seamless hybridization of ML with CFD,
and 3) automatic differentiation (AD) capabilities. AD in particular is
essential to ML-CFD research as it provides gradient information and enables
optimization of preexisting and novel CFD models. In this work, we propose
JAX-FLUIDS: a comprehensive fully-differentiable CFD Python solver for
compressible two-phase flows. JAX-FLUIDS allows the simulation of complex fluid
dynamics with phenomena like three-dimensional turbulence, compressibility
effects, and two-phase flows. Written entirely in JAX, it is straightforward to
include existing ML models into the proposed framework. Furthermore, JAX-FLUIDS
enables end-to-end optimization. I.e., ML models can be optimized with
gradients that are backpropagated through the entire CFD algorithm, and
therefore contain not only information of the underlying PDE but also of the
applied numerical methods. We believe that a Python package like JAX-FLUIDS is
crucial to facilitate research at the intersection of ML and CFD and may pave
the way for an era of differentiable fluid dynamics.

We present a fast and accurate analytical method for fluorescence lifetime
imaging microscopy (FLIM) using the extreme learning machine (ELM). We used
extensive metrics to evaluate ELM and existing algorithms. First, we compared
these algorithms using synthetic datasets. Results indicate that ELM can obtain
higher fidelity, even in low-photon conditions. Afterwards, we used ELM to
retrieve lifetime components from human prostate cancer cells loaded with gold
nanosensors, showing that ELM also outperforms the iterative fitting and
non-fitting algorithms. By comparing ELM with a computational efficient neural
network, ELM achieves comparable accuracy with less training and inference
time. As there is no back-propagation process for ELM during the training
phase, the training speed is much higher than existing neural network
approaches. The proposed strategy is promising for edge computing with online
training.

Hierarchical VAEs have emerged in recent years as a reliable option for
maximum likelihood estimation. However, instability issues and demanding
computational requirements have hindered research progress in the area. We
present simple modifications to the Very Deep VAE to make it converge up to
$2.6\times$ faster, save up to $20\times$ in memory load and improve stability
during training. Despite these changes, our models achieve comparable or better
negative log-likelihood performance than current state-of-the-art models on all
$7$ commonly used image datasets we evaluated on. We also make an argument
against using 5-bit benchmarks as a way to measure hierarchical VAE's
performance due to undesirable biases caused by the 5-bit quantization.
Additionally, we empirically demonstrate that roughly $3\%$ of the hierarchical
VAE's latent space dimensions is sufficient to encode most of the image
information, without loss of performance, opening up the doors to efficiently
leverage the hierarchical VAEs' latent space in downstream tasks. We release
our source code and models at https://github.com/Rayhane-mamah/Efficient-VDVAE .

The popularity of machine learning has wildly expanded in recent years.
Machine learning techniques have been heatedly studied in academia and applied
in the industry to create business value. However, there is a lack of
guidelines for code quality in machine learning applications. In particular,
code smells have rarely been studied in this domain. Although machine learning
code is usually integrated as a small part of an overarching system, it usually
plays an important role in its core functionality. Hence ensuring code quality
is quintessential to avoid issues in the long run. This paper proposes and
identifies a list of 22 machine learning-specific code smells collected from
various sources, including papers, grey literature, GitHub commits, and Stack
Overflow posts. We pinpoint each smell with a description of its context,
potential issues in the long run, and proposed solutions. In addition, we link
them to their respective pipeline stage and the evidence from both academic and
grey literature. The code smell catalog helps data scientists and developers
produce and maintain high-quality machine learning application code.

Quantum computers hold great promise to enhance machine learning, but their
current qubit counts restrict the realisation of this promise. In an attempt to
placate this limitation techniques can be applied for evaluating a quantum
circuit using a machine with fewer qubits than the circuit naively requires.
These techniques work by evaluating many smaller circuits on the smaller
machine, that are then combined in a polynomial to replicate the output of the
larger machine. This scheme requires more circuit evaluations than are
practical for general circuits. However, we investigate the possibility that
for certain applications many of these subcircuits are superfluous, and that a
much smaller sum is sufficient to estimate the full circuit. We construct a
machine learning model that may be capable of approximating the outputs of the
larger circuit with much fewer circuit evaluations. We successfully apply our
model to the task of digit recognition, using simulated quantum computers much
smaller than the data dimension. The model is also applied to the task of
approximating a random 10 qubit PQC with simulated access to a 5 qubit
computer, even with only relatively modest number of circuits our model
provides an accurate approximation of the 10 qubit PQCs output, superior to a
neural network attempt. The developed method might be useful for implementing
quantum models on larger data throughout the NISQ era.

Purpose: Real-time monitoring of cardiac output (CO) requires low latency
reconstruction and segmentation of real-time phase contrast MR (PCMR), which
has previously been difficult to perform. Here we propose a deep learning
framework for 'Flow Reconstruction and Segmentation for low latency Cardiac
Output monitoring' (FReSCO).
  Methods: Deep artifact suppression and segmentation U-Nets were independently
trained. Breath hold spiral PCMR data (n=516) was synthetically undersampled
using a variable density spiral sampling pattern and gridded to create aliased
data for training of the artifact suppression U-net. A subset of the data
(n=96) was segmented and used to train the segmentation U-net. Real-time spiral
PCMR was prospectively acquired and then reconstructed and segmented using the
trained models (FReSCO) at low latency at the scanner in 10 healthy subjects
during rest, exercise and recovery periods. CO obtained via FReSCO was compared
to a reference rest CO and rest and exercise Compressed Sensing (CS) CO.
  Results: FReSCO was demonstrated prospectively at the scanner. Beat-to-beat
heartrate, stroke volume and CO could be visualized with a mean latency of
622ms. No significant differences were noted when compared to reference at rest
(Bias = -0.21+-0.50 L/min, p=0.246) or CS at peak exercise (Bias=0.12+-0.48
L/min, p=0.458).
  Conclusion: FReSCO was successfully demonstrated for real-time monitoring of
CO during exercise and could provide a convenient tool for assessment of the
hemodynamic response to a range of stressors.

Finding efficient means of fingerprinting microstructural information is a
critical step towards harnessing data-centric machine learning approaches. A
statistical framework is systematically developed for compressed
characterisation of a population of images, which includes some classical
computer vision methods as special cases. The focus is on materials
microstructure. The ultimate purpose is to rapidly fingerprint sample images in
the context of various high-throughput design/make/test scenarios. This
includes, but is not limited to, quantification of the disparity between
microstructures for quality control, classifying microstructures, predicting
materials properties from image data and identifying potential processing
routes to engineer new materials with specific properties. Here, we consider
microstructure classification and utilise the resulting features over a range
of related machine learning tasks, namely supervised, semi-supervised, and
  The approach is applied to two distinct datasets to illustrate various
aspects and some recommendations are made based on the findings. In particular,
methods that leverage transfer learning with convolutional neural networks
(CNNs), pretrained on the ImageNet dataset, are generally shown to outperform
other methods. Additionally, dimensionality reduction of these CNN-based
fingerprints is shown to have negligible impact on classification accuracy for
the supervised learning approaches considered. In situations where there is a
large dataset with only a handful of images labelled, graph-based label
propagation to unlabelled data is shown to be favourable over discarding
unlabelled data and performing supervised learning. In particular, label
propagation by Poisson learning is shown to be highly effective at low label
rates.

Recently, anomaly scores have been formulated using reconstruction loss of
the adversarially learned generators and/or classification loss of
discriminators. Unavailability of anomaly examples in the training data makes
optimization of such networks challenging. Attributed to the adversarial
training, performance of such models fluctuates drastically with each training
step, making it difficult to halt the training at an optimal point. In the
current study, we propose a robust anomaly detection framework that overcomes
such instability by transforming the fundamental role of the discriminator from
identifying real vs. fake data to distinguishing good vs. bad quality
reconstructions. For this purpose, we propose a method that utilizes the
current state as well as an old state of the same generator to create good and
bad quality reconstruction examples. The discriminator is trained on these
examples to detect the subtle distortions that are often present in the
reconstructions of anomalous data. In addition, we propose an efficient generic
criterion to stop the training of our model, ensuring elevated performance.
Extensive experiments performed on six datasets across multiple domains
including image and video based anomaly detection, medical diagnosis, and
network security, have demonstrated excellent performance of our approach.

Searching for a more compact network width recently serves as an effective
way of channel pruning for the deployment of convolutional neural networks
(CNNs) under hardware constraints. To fulfill the searching, a one-shot
supernet is usually leveraged to efficiently evaluate the performance
\wrt~different network widths. However, current methods mainly follow a
\textit{unilaterally augmented} (UA) principle for the evaluation of each
width, which induces the training unfairness of channels in supernet. In this
paper, we introduce a new supernet called Bilaterally Coupled Network (BCNet)
to address this issue. In BCNet, each channel is fairly trained and responsible
for the same amount of network widths, thus each network width can be evaluated
more accurately. Besides, we propose to reduce the redundant search space and
present the BCNetV2 as the enhanced supernet to ensure rigorous training
fairness over channels. Furthermore, we leverage a stochastic complementary
strategy for training the BCNet, and propose a prior initial population
sampling method to boost the performance of the evolutionary search. We also
propose the first open-source width benchmark on macro structures named
Channel-Bench-Macro for the better comparison of width search algorithms.
Extensive experiments on benchmark CIFAR-10 and ImageNet datasets indicate that
our method can achieve state-of-the-art or competing performance over other
baseline methods. Moreover, our method turns out to further boost the
performance of NAS models by refining their network widths. For example, with
the same FLOPs budget, our obtained EfficientNet-B0 achieves 77.53\% Top-1
accuracy on ImageNet dataset, surpassing the performance of original setting by
0.65\%.

Black-box functions are broadly used to model complex problems that provide
no explicit information but the input and output. Despite existing studies of
black-box function optimization, the solution set satisfying an inequality with
a black-box function plays a more significant role than only one optimum in
many practical situations. Covering as much as possible of the solution set
through limited evaluations to the black-box objective function is defined as
the Black-Box Coverage (BBC) problem in this paper. We formalized this problem
in a sample-based search paradigm and constructed a coverage criterion with
Confusion Matrix Analysis. Further, we propose LAMBDA (Latent-Action
Monte-Carlo Beam Search with Density Adaption) to solve BBC problems. LAMBDA
can focus around the solution set quickly by recursively partitioning the
search space into accepted and rejected sub-spaces. Compared with La-MCTS,
LAMBDA introduces density information to overcome the sampling bias of
optimization and obtain more exploration. Benchmarking shows, LAMBDA achieved
state-of-the-art performance among all baselines and was at most 33x faster to
get 95% coverage than Random Search. Experiments also demonstrate that LAMBDA
has a promising future in the verification of autonomous systems in virtual
tests.

Formulating learning systems for the detection of real-world anomalous events
using only video-level labels is a challenging task mainly due to the presence
of noisy labels as well as the rare occurrence of anomalous events in the
training data. We propose a weakly supervised anomaly detection system which
has multiple contributions including a random batch selection mechanism to
reduce inter-batch correlation and a normalcy suppression block which learns to
minimize anomaly scores over normal regions of a video by utilizing the overall
information available in a training batch. In addition, a clustering loss block
is proposed to mitigate the label noise and to improve the representation
learning for the anomalous and normal regions. This block encourages the
backbone network to produce two distinct feature clusters representing normal
and anomalous events. Extensive analysis of the proposed approach is provided
using three popular anomaly detection datasets including UCF-Crime,
ShanghaiTech, and UCSD Ped2. The experiments demonstrate a superior anomaly
detection capability of our approach.

The deep convolutional neural network has achieved significant progress for
single image rain streak removal. However, most of the data-driven learning
methods are full-supervised or semi-supervised, unexpectedly suffering from
significant performance drops when dealing with real rain. These data-driven
learning methods are representative yet generalize poor for real rain. The
opposite holds true for the model-driven unsupervised optimization methods. To
overcome these problems, we propose a unified unsupervised learning framework
which inherits the generalization and representation merits for real rain
removal. Specifically, we first discover a simple yet important domain
knowledge that directional rain streak is anisotropic while the natural clean
image is isotropic, and formulate the structural discrepancy into the energy
function of the optimization model. Consequently, we design an optimization
model-driven deep CNN in which the unsupervised loss function of the
optimization model is enforced on the proposed network for better
generalization. In addition, the architecture of the network mimics the main
role of the optimization models with better feature representation. On one
hand, we take advantage of the deep network to improve the representation. On
the other hand, we utilize the unsupervised loss of the optimization model for
better generalization. Overall, the unsupervised learning framework achieves
good generalization and representation: unsupervised training (loss) with only
a few real rainy images (input) and physical meaning network (architecture).
Extensive experiments on synthetic and real-world rain datasets show the

Compensation for channel mismatch and noise interference is essential for
robust automatic speech recognition. Enhanced speech has been introduced into
the multi-condition training of acoustic models to improve their generalization
ability. In this paper, a noise-aware training framework based on two cascaded
neural structures is proposed to jointly optimize speech enhancement and speech
recognition. The feature enhancement module is composed of a multi-task
autoencoder, where noisy speech is decomposed into clean speech and noise. By
concatenating its enhanced, noise-aware, and noisy features for each frame, the
acoustic-modeling module maps each feature-augmented frame into a triphone
state by optimizing the lattice-free maximum mutual information and cross
entropy between the predicted and actual state sequences. On top of the
factorized time delay neural network (TDNN-F) and its convolutional variant
(CNN-TDNNF), both with SpecAug, the two proposed systems achieve word error
rate (WER) of 3.90% and 3.55%, respectively, on the Aurora-4 task. Compared
with the best existing systems that use bigram and trigram language models for
decoding, the proposed CNN-TDNNF-based system achieves a relative WER reduction
of 15.20% and 33.53%, respectively. In addition, the proposed CNN-TDNNF-based
system also outperforms the baseline CNN-TDNNF system on the AMI task.

In this paper we demonstrate the TerraByte Client, a software to download
user-defined plant datasets from a data portal hosted at Compute Canada. To
that end the client offers two key functionalities: (1) It allows the user to
get an overview on what data is available and a quick way to visually check
samples of that data. For this the client receives the results of queries to a
database and displays the number of images that fulfill the search criteria.
Furthermore, a sample can be downloaded within seconds to confirm that the data
suits the user's needs. (2) The user can then download the specified data to
their own drive. This data is prepared into chunks server-side and sent to the
user's end-system, where it is automatically extracted into individual files.
The first chunks of data are available for inspection after a brief waiting
period of a minute or less depending on available bandwidth and type of data.
The TerraByte Client has a full graphical user interface for easy usage and
uses end-to-end encryption. The user interface is built on top of a low-level
client. This architecture in combination of offering the client program
open-source makes it possible for the user to develop their own user interface
or use the client's functionality directly. An example for direct usage could
be to download specific data on demand within a larger application, such as
training machine learning models.

In our previous work, we proposed a discriminative autoencoder (DcAE) for
speech recognition. DcAE combines two training schemes into one. First, since
DcAE aims to learn encoder-decoder mappings, the squared error between the
reconstructed speech and the input speech is minimized. Second, in the code
layer, frame-based phonetic embeddings are obtained by minimizing the
categorical cross-entropy between ground truth labels and predicted
triphone-state scores. DcAE is developed based on the Kaldi toolkit by treating
various TDNN models as encoders. In this paper, we further propose three new
versions of DcAE. First, a new objective function that considers both
categorical cross-entropy and mutual information between ground truth and
predicted triphone-state sequences is used. The resulting DcAE is called a
chain-based DcAE (c-DcAE). For application to robust speech recognition, we
further extend c-DcAE to hierarchical and parallel structures, resulting in
hc-DcAE and pc-DcAE. In these two models, both the error between the
reconstructed noisy speech and the input noisy speech and the error between the
enhanced speech and the reference clean speech are taken into the objective
function. Experimental results on the WSJ and Aurora-4 corpora show that our
DcAE models outperform baseline systems.

Human communication is a collaborative process. Speakers, on top of conveying
their own intent, adjust the content and language expressions by taking the
listeners into account, including their knowledge background, personalities,
and physical capabilities. Towards building AI agents with similar abilities in
language communication, we propose Pragmatic Rational Speaker (PRS), a
framework extending Rational Speech Act (RSA). The PRS attempts to learn the
speaker-listener disparity and adjust the speech accordingly, by adding a
light-weighted disparity adjustment layer into working memory on top of
speaker's long-term memory system. By fixing the long-term memory, the PRS only
needs to update its working memory to learn and adapt to different types of
listeners. To validate our framework, we create a dataset that simulates
different types of speaker-listener disparities in the context of referential
games. Our empirical results demonstrate that the PRS is able to shift its
output towards the language that listener are able to understand, significantly
improve the collaborative task outcome.

Chest Computational Tomography (CT) scans present low cost, speed and
objectivity for COVID-19 diagnosis and deep learning methods have shown great
promise in assisting the analysis and interpretation of these images. Most
hospitals or countries can train their own models using in-house data, however
empirical evidence shows that those models perform poorly when tested on new
unseen cases, surfacing the need for coordinated global collaboration. Due to
privacy regulations, medical data sharing between hospitals and nations is
extremely difficult. We propose a GAN-augmented federated learning model,
dubbed ST-FL (Style Transfer Federated Learning), for COVID-19 image
segmentation. Federated learning (FL) permits a centralised model to be learned
in a secure manner from heterogeneous datasets located in disparate private
data silos. We demonstrate that the widely varying data quality on FL client
nodes leads to a sub-optimal centralised FL model for COVID-19 chest CT image
segmentation. ST-FL is a novel FL framework that is robust in the face of
highly variable data quality at client nodes. The robustness is achieved by a
denoising CycleGAN model at each client of the federation that maps arbitrary
quality images into the same target quality, counteracting the severe data
variability evident in real-world FL use-cases. Each client is provided with
the target style, which is the same for all clients, and trains their own
denoiser. Our qualitative and quantitative results suggest that this FL model
performs comparably to, and in some cases better than, a model that has
centralised access to all the training data.

Model-based optimization approaches for monitoring and control, such as model
predictive control and optimal state and parameter estimation, have been used
for decades in many engineering applications. Models describing the dynamics,
constraints, and desired performance criteria are fundamental to model-based
approaches. Thanks to recent technological advancements in digitalization,
machine learning methods such as deep learning, and computing power, there has
been an increasing interest in using machine learning methods alongside
model-based approaches for control and estimation. The number of new methods
and theoretical findings using machine learning for model-based control and
optimization is increasing rapidly. This paper outlines the basic ideas and
principles behind an easy-to-use Python toolbox that allows to quickly and
efficiently solve machine-learning-supported optimization, model predictive
control, and estimation problems. The toolbox leverages state-of-the-art
machine learning libraries to train components used to define the problem. It
allows to efficiently solve the resulting optimization problems. Machine
learning can be used for a broad spectrum of tasks, ranging from model
predictive control for stabilization, setpoint tracking, path following, and
trajectory tracking to moving horizon estimation and Kalman filtering. For
linear systems, it enables quick code generation for embedded MPC applications.
HILO-MPC is flexible and adaptable, making it especially suitable for research
and fundamental development tasks. Due to its simplicity and numerous already
implemented examples, it is also a powerful teaching tool. The usability is
underlined, presenting a series of application examples.

Searchable symmetric encryption (SSE) supports keyword search over outsourced
symmetrically encrypted data. Dynamic searchable symmetric encryption (DSSE), a
variant of SSE, further enables data updating. Most DSSE works with conjunctive
keyword search primarily consider forward and backward privacy. Ideally, the
server should only learn the result sets involving all keywords in the
conjunction. However, existing schemes suffer from keyword pair result pattern
(KPRP) leakage, revealing the partial result sets containing two of query
keywords. We propose the first DSSE scheme to address aforementioned concerns
that achieves strong privacy-preserving conjunctive keyword search.
Specifically, our scheme can maintain forward and backward privacy and
eliminate KPRP leakage, offering a higher level of security. The search
complexity scales with the number of documents stored in the database in
several existing schemes. However, the complexity of our scheme scales with the
update frequency of the least frequent keyword in the conjunction, which is
much smaller than the size of the entire database. Besides, we devise a least
frequent keyword acquisition protocol to reduce frequent interactions between
clients. Finally, we analyze the security of our scheme and evaluate its
performance theoretically and experimentally. The results show that our scheme
has strong privacy preservation and efficiency.

The Dirichlet Process Gaussian Mixture Model (DPGMM) is often used to cluster
data when the number of clusters is unknown. One main DPGMM inference paradigm
relies on sampling. Here we consider a known state-of-art sampler (proposed by
Chang and Fisher III (2013) and improved by Dinari et al. (2019)), analyze its
failure modes, and show how to improve it, often drastically. Concretely, in
that sampler, whenever a new cluster is formed it is augmented with two
subclusters whose labels are initialized at random. Upon their evolution, the
subclusters serve to propose a split of the parent cluster. We show that the
random initialization is often problematic and hurts the otherwise-effective
sampler. Specifically, we demonstrate that this initialization tends to lead to
poor split proposals and/or too many iterations before a desired split is
accepted. This slows convergence and can damage the clustering. As a remedy, we
propose two drop-in-replacement options for the subcluster-initialization
subroutine. The first is an intuitive heuristic while the second is based on
deep learning. We show that the proposed approach yields better splits, which
in turn translate to substantial improvements in performance, results, and

Transformers have become widely used in modern models for various tasks such
as natural language processing and machine vision. This paper proposes
Gransformer, an algorithm for generating graphs based on the Transformer. We
extend a simple autoregressive Transformer encoder to exploit the structural
information of the given graph through efficient modifications. The attention
mechanism is modified to consider the presence or absence of edges between each
pair of nodes. We also introduce a graph-based familiarity measure between node
pairs that applies to both the attention and the positional encoding. This
measure of familiarity is based on message passing algorithms and contains
structural information about the graph. Furthermore, the proposed measure is
autoregressive, which allows our mode to acquire the necessary conditional
probabilities in a single forward pass. In the output layer, we also use a
masked autoencoder for density estimation to efficiently model the sequential
generation of dependent edges. Moreover, since we use BFS node orderings, we
propose a technique to prevent the model from generating isolated nodes without
connection to preceding nodes. We evaluate this method on two real-world
datasets and compare it with other state-of-the-art autoregressive graph
generation methods. Experimental results have shown that the proposed method
performs comparatively to these methods, including recurrent models and graph

We demonstrate a simple connection between dictionary methods for time series
classification, which involve extracting and counting symbolic patterns in time
series, and methods based on transforming input time series using convolutional
kernels, namely ROCKET and its variants. We show that by adjusting a single
hyperparameter it is possible to move by degrees between models resembling
dictionary methods and models resembling ROCKET. We present HYDRA, a simple,
fast, and accurate dictionary method for time series classification using
competing convolutional kernels, combining key aspects of both ROCKET and
conventional dictionary methods. HYDRA is faster and more accurate than the
most accurate existing dictionary methods, and can be combined with ROCKET and
its variants to further improve the accuracy of these methods.

Synaptic plasticity or the ability of a brain to changes one or more of its
functions or structures has generated and is sill generating a lot of interest
from the scientific community especially neuroscientists. These interests
especially went into high gear after empirical evidences were collected that
challenged the established paradigm that human brain structures and functions
are set from childhood and only modest changes were expected beyond. Early
synaptic plasticity rules or laws to that regard include the basic Hebbian rule
that proposed a mechanism for strengthening or weakening of synapses (weights)
during learning and memory. This rule however did not account from the fact
that weights must have bounded growth overtime. Thereafter, many other rules
were proposed to complement the basic Hebbian rule and they also possess other
desirable properties. In particular, a desirable property in synaptic
plasticity rule is that the ambient system must account for inhibition which is
often achieved if the rule used allows for a lower bound in synaptic weights.
In this paper, we propose a synaptic plasticity rule inspired from the Allee
effect, a phenomenon often observed in population dynamics. We show properties
such such as synaptic normalization, competition between weights,
de-correlation potential, and dynamic stability are satisfied. We show that in
fact, an Allee effect in synaptic plasticity can be construed as an absence of

Physics-informed neural networks (PINNs) seamlessly integrate data and
physical constraints into the solving of problems governed by differential
equations. In settings with little labeled training data, their optimization
relies on the complexity of the embedded physics loss function. Two fundamental
questions arise in any discussion of frequently reported convergence issues in
PINNs: Why does the optimization often converge to solutions that lack physical
behavior? And why do reduced domain methods improve convergence behavior in
PINNs? We answer these questions by studying the physics loss function in the
vicinity of fixed points of dynamical systems. Experiments on a simple
dynamical system demonstrate that physics loss residuals are trivially
minimized in the vicinity of fixed points. As a result we observe that
solutions corresponding to nonphysical system dynamics can be dominant in the
physics loss landscape and optimization. We find that reducing the
computational domain lowers the optimization complexity and chance of getting
trapped with nonphysical solutions.

Audio-text retrieval based on natural language descriptions is a challenging
task. It involves learning cross-modality alignments between long sequences
under inadequate data conditions. In this work, we investigate several audio
features as well as sequence aggregation methods for better audio-text
alignment. Moreover, through a qualitative analysis we observe that semantic
mapping is more important than temporal relations in contextual retrieval.
Using pre-trained audio features and a descriptor-based aggregation method, we
build our contextual audio-text retrieval system. Specifically, we utilize
PANNs features pre-trained on a large sound event dataset and NetRVLAD pooling,
which directly works with averaged descriptors. Experiments are conducted on
the AudioCaps and CLOTHO datasets, and results are compared with the previous
state-of-the-art system. With our proposed system, a significant improvement
has been achieved on bidirectional audio-text retrieval, on all metrics
including recall, median and mean rank.

We introduce probabilistic embeddings using Laplacian priors (PELP). The
proposed model enables incorporating graph side-information into static word
embeddings. We theoretically show that the model unifies several previously
proposed embedding methods under one umbrella. PELP generalises graph-enhanced,
group, dynamic, and cross-lingual static word embeddings. PELP also enables any
combination of these previous models in a straightforward fashion. Furthermore,
we empirically show that our model matches the performance of previous models
as special cases. In addition, we demonstrate its flexibility by applying it to
the comparison of political sociolects over time. Finally, we provide code as a
TensorFlow implementation enabling flexible estimation in different settings.

In self-driving, predicting future in terms of location and motion of all the
agents around the vehicle is a crucial requirement for planning. Recently, a
new joint formulation of perception and prediction has emerged by fusing rich
sensory information perceived from multiple cameras into a compact bird's-eye
view representation to perform prediction. However, the quality of future
predictions degrades over time while extending to longer time horizons due to
multiple plausible predictions. In this work, we address this inherent
uncertainty in future predictions with a stochastic temporal model. Our model
learns temporal dynamics in a latent space through stochastic residual updates
at each time step. By sampling from a learned distribution at each time step,
we obtain more diverse future predictions that are also more accurate compared
to previous work, especially stretching both spatially further regions in the
scene and temporally over longer time horizons. Despite separate processing of
each time step, our model is still efficient through decoupling of the learning
of dynamics and the generation of future predictions.

We consider the identification of large-scale linear and stable dynamic
systems whose outputs may be the result of many correlated inputs. Hence,
severe ill-conditioning may affect the estimation problem. This is a scenario
often arising when modeling complex physical systems given by the
interconnection of many sub-units where feedback and algebraic loops can be
encountered. We develop a strategy based on Bayesian regularization where any
impulse response is modeled as the realization of a zero-mean Gaussian process.
The stable spline covariance is used to include information on smooth
exponential decay of the impulse responses. We then design a new Markov chain
Monte Carlo scheme that deals with collinearity and is able to efficiently
reconstruct the posterior of the impulse responses. It is based on a variation
of Gibbs sampling which updates possibly overlapping blocks of the parameter
space on the basis of the level of collinearity affecting the different inputs.
Numerical experiments are included to test the goodness of the approach where
hundreds of impulse responses form the system and inputs correlation may be
very high.

Inspired by the recent progress in self-supervised learning for computer
vision, in this paper we introduce DeLoRes, a new general-purpose audio
representation learning approach. Our main objective is to make our network
learn representations in a resource-constrained setting (both data and
compute), that can generalize well across a diverse set of downstream tasks.
Inspired from the Barlow Twins objective function, we propose to learn
embeddings that are invariant to distortions of an input audio sample, while
making sure that they contain non-redundant information about the sample. To
achieve this, we measure the cross-correlation matrix between the outputs of
two identical networks fed with distorted versions of an audio segment sampled
from an audio file and make it as close to the identity matrix as possible. We
use a combination of a small subset of the large-scale AudioSet dataset and
FSD50K for self-supervised learning and are able to learn with less than half
the parameters compared to state-of-the-art algorithms. For evaluation, we
transfer these learned representations to 9 downstream classification tasks,
including speech, music, and animal sounds, and show competitive results under
different evaluation setups. In addition to being simple and intuitive, our
pre-training algorithm is amenable to compute through its inherent nature of
construction and does not require careful implementation details to avoid
trivial or degenerate solutions. Furthermore, we conduct ablation studies on
our results and make all our code and pre-trained models publicly available
https://github.com/Speech-Lab-IITM/DeLoRes.

Speech emotion recognition (SER) is a crucial research topic in
human-computer interactions. Existing works are mainly based on manually
designed models. Despite their great success, these methods heavily rely on
historical experience, which are time-consuming but cannot exhaust all possible
structures. To address this problem, we propose a neural architecture search
(NAS) based framework for SER, called "EmotionNAS". We take spectrogram and
wav2vec features as the inputs, followed with NAS to optimize the network
structure for these features separately. We further incorporate complementary
information in these features through decision-level fusion. Experimental
results on IEMOCAP demonstrate that our method succeeds over existing
state-of-the-art strategies on SER.

Graph convolution networks (GCNs) are currently mainstream in learning with
irregular data. These models rely on message passing and attention mechanisms
that capture context and node-to-node relationships. With multi-head attention,
GCNs become highly accurate but oversized, and their deployment on cheap
devices requires their pruning. However, pruning at high regimes usually leads
to topologically inconsistent networks with weak generalization. In this paper,
we devise a novel method for lightweight GCN design. Our proposed approach
parses and selects subnetworks with the highest magnitudes while guaranteeing
their topological consistency. The latter is obtained by selecting only
accessible and co-accessible connections which actually contribute in the
evaluation of the selected subnetworks. Experiments conducted on the
challenging FPHA dataset show the substantial gain of our topologically
consistent pruning method especially at very high pruning regimes.

We tackle catastrophic forgetting problem in the context of class-incremental
learning for video recognition, which has not been explored actively despite
the popularity of continual learning. Our framework addresses this challenging
task by introducing time-channel importance maps and exploiting the importance
maps for learning the representations of incoming examples via knowledge
distillation. We also incorporate a regularization scheme in our objective
function, which encourages individual features obtained from different time
steps in a video to be uncorrelated and eventually improves accuracy by
alleviating catastrophic forgetting. We evaluate the proposed approach on
brand-new splits of class-incremental action recognition benchmarks constructed
upon the UCF101, HMDB51, and Something-Something V2 datasets, and demonstrate
the effectiveness of our algorithm in comparison to the existing continual
learning methods that are originally designed for image data.

We present a meta-learning framework for weakly supervised anomaly detection
in videos, where the detector learns to adapt to unseen types of abnormal
activities effectively when only video-level annotations of binary labels are
available. Our work is motivated by the fact that existing methods suffer from
poor generalization to diverse unseen examples. We claim that an anomaly
detector equipped with a meta-learning scheme alleviates the limitation by
leading the model to an initialization point for better optimization. We
evaluate the performance of our framework on two challenging datasets,
UCF-Crime and ShanghaiTech. The experimental results demonstrate that our
algorithm boosts the capability to localize unseen abnormal events in a weakly
supervised setting. Besides the technical contributions, we perform the
annotation of missing labels in the UCF-Crime dataset and make our task
evaluated effectively.

Unsupervised video representation learning has made remarkable achievements
in recent years. However, most existing methods are designed and optimized for
video classification. These pre-trained models can be sub-optimal for temporal
localization tasks due to the inherent discrepancy between video-level
classification and clip-level localization. To bridge this gap, we make the
first attempt to propose a self-supervised pretext task, coined as Pseudo
Action Localization (PAL) to Unsupervisedly Pre-train feature encoders for
Temporal Action Localization tasks (UP-TAL). Specifically, we first randomly
select temporal regions, each of which contains multiple clips, from one video
as pseudo actions and then paste them onto different temporal positions of the
other two videos. The pretext task is to align the features of pasted pseudo
action regions from two synthetic videos and maximize the agreement between
them. Compared to the existing unsupervised video representation learning
approaches, our PAL adapts better to downstream TAL tasks by introducing a
temporal equivariant contrastive learning paradigm in a temporally dense and
scale-aware manner. Extensive experiments show that PAL can utilize large-scale
unlabeled video data to significantly boost the performance of existing TAL
methods. Our codes and models will be made publicly available at
https://github.com/zhang-can/UP-TAL.

The challenge of dynamic traffic demand in mobile networks is tackled by
moving cells based on unmanned aerial vehicles. Considering the tremendous
potential of unmanned aerial vehicles in the future, we propose a new heuristic
algorithm for coverage optimization. The proposed algorithm is implemented
based on a conditional generative adversarial neural network, with a unique
multilayer sum-pooling loss function. To assess the performance of the proposed
approach, we compare it with the optimal core-set algorithm and quasi-optimal
spiral algorithm. Simulation results show that the proposed approach converges
to the quasi-optimal solution with a negligible difference from the global
optimum while maintaining a quadratic complexity regardless of the number of
users.

The current workflow for Information Extraction (IE) analysts involves the
definition of the entities/relations of interest and a training corpus with
annotated examples. In this demonstration we introduce a new workflow where the
analyst directly verbalizes the entities/relations, which are then used by a
Textual Entailment model to perform zero-shot IE. We present the design and
implementation of a toolkit with a user interface, as well as experiments on
four IE tasks that show that the system achieves very good performance at
zero-shot learning using only 5--15 minutes per type of a user's effort. Our
demonstration system is open-sourced at https://github.com/BBN-E/ZS4IE . A
demonstration video is available at https://vimeo.com/676138340 .

Humans perceive the world in terms of objects and relations between them. In
fact, for any given pair of objects, there is a myriad of relations that apply
to them. How does the cognitive system learn which relations are useful to
characterize the task at hand? And how can it use these representations to
build a relational policy to interact effectively with the environment? In this
paper we propose that this problem can be understood through the lens of a
sub-field of symbolic machine learning called relational reinforcement learning
(RRL). To demonstrate the potential of our approach, we build a simple model of
relational policy learning based on a function approximator developed in RRL.
We trained and tested our model in three Atari games that required to consider
an increasingly number of potential relations: Breakout, Pong and Demon Attack.
In each game, our model was able to select adequate relational representations
and build a relational policy incrementally. We discuss the relationship
between our model with models of relational and analogical reasoning, as well
as its limitations and future directions of research.

We demonstrate a machine learning-based optical network monitoring system
which can integrate fiber monitoring, predictive maintenance of optical
hardware, and security information management in a single solution.

Reducing the circuit depth of quantum circuits is a crucial bottleneck to
enabling quantum technology. This depth is inversely proportional to the number
of available quantum gates that have been synthesised. Moreover, quantum gate
synthesis and control problems exhibit a vast range of external parameter
dependencies, both physical and application-specific. In this article we
address the possibility of learning families of optimal control pulses which
depend adaptively on various parameters, in order to obtain a global optimal
mapping from the space of potential parameter values to the control space, and
hence continuous classes of gates. Our proposed method is tested on different
experimentally relevant quantum gates and proves capable of producing
high-fidelity pulses even in presence of multiple variable or uncertain
parameters with wide ranges.

In Automatic Speech Recognition, GMM-HMM had been widely used for acoustic
modelling. With the current advancement of deep learning, the Gaussian Mixture
Model (GMM) from acoustic models has been replaced with Deep Neural Network,
namely DNN-HMM Acoustic Models. The GMM models are widely used to create the
alignments of the training data for the hybrid deep neural network model, thus
making it an important task to create accurate alignments. Many factors such as
training dataset size, training data augmentation, model hyperparameters, etc.,
affect the model learning. Traditionally in machine learning, larger datasets
tend to have better performance, while smaller datasets tend to trigger
over-fitting. The collection of speech data and their accurate transcriptions
is a significant challenge that varies over different languages, and in most
cases, it might be limited to big organizations. Moreover, in the case of
available large datasets, training a model using such data requires additional
time and computing resources, which may not be available. While the data about
the accuracy of state-of-the-art ASR models on open-source datasets are
published, the study about the impact of the size of a dataset on acoustic
models is not readily available. This work aims to investigate the impact of
dataset size variations on the performance of various GMM-HMM Acoustic Models
and their respective computational costs.

Speaker-independent speech separation has achieved remarkable performance in
recent years with the development of deep neural network (DNN). Various network
architectures, from traditional convolutional neural network (CNN) and
recurrent neural network (RNN) to advanced transformer, have been designed
sophistically to improve separation performance. However, the state-of-the-art
models usually suffer from several flaws related to the computation, such as
large model size, huge memory consumption and computational complexity. To find
the balance between the performance and computational efficiency and to further
explore the modeling ability of traditional network structure, we combine RNN
and a newly proposed variant of convolutional network to cope with speech
separation problem. By embedding two RNNs into basic block of this variant with
the help of dual-path strategy, the proposed network can effectively learn the
local information and global dependency. Besides, a four-staged structure
enables the separation procedure to be performed gradually at finer and finer
scales as the feature dimension increases. The experimental results on various
datasets have proven the effectiveness of the proposed method and shown that a
trade-off between the separation performance and computational efficiency is
well achieved.

The discovery of reusable sub-routines simplifies decision-making and
planning in complex reinforcement learning problems. Previous approaches
propose to learn such temporal abstractions in a purely unsupervised fashion
through observing state-action trajectories gathered from executing a policy.
However, a current limitation is that they process each trajectory in an
entirely sequential manner, which prevents them from revising earlier decisions
about sub-routine boundary points in light of new incoming information. In this
work we propose SloTTAr, a fully parallel approach that integrates sequence
processing Transformers with a Slot Attention module and adaptive computation
for learning about the number of such sub-routines in an unsupervised fashion.
We demonstrate how SloTTAr is capable of outperforming strong baselines in
terms of boundary point discovery, even for sequences containing variable
amounts of sub-routines, while being up to 7x faster to train on existing

This paper studies category-level object pose estimation based on a single
monocular image. Recent advances in pose-aware generative models have paved the
way for addressing this challenging task using analysis-by-synthesis. The idea
is to sequentially update a set of latent variables, e.g., pose, shape, and
appearance, of the generative model until the generated image best agrees with
the observation. However, convergence and efficiency are two challenges of this
inference procedure. In this paper, we take a deeper look at the inference of
analysis-by-synthesis from the perspective of visual navigation, and
investigate what is a good navigation policy for this specific task. We
evaluate three different strategies, including gradient descent, reinforcement
learning and imitation learning, via thorough comparisons in terms of
convergence, robustness and efficiency. Moreover, we show that a simple hybrid
approach leads to an effective and efficient solution. We further compare these
strategies to state-of-the-art methods, and demonstrate superior performance on
synthetic and real-world datasets leveraging off-the-shelf pose-aware

We propose and examine the idea of continuously adapting state-of-the-art
neural network (NN)-based orthogonal frequency division multiplex (OFDM)
receivers to current channel conditions. This online adaptation via retraining
is mainly motivated by two reasons: First, receiver design typically focuses on
the universal optimal performance for a wide range of possible channel
realizations. However, in actual applications and within short time intervals,
only a subset of these channel parameters is likely to occur, as macro
parameters, e.g., the maximum channel delay, can assumed to be static. Second,
in-the-field alterations like temporal interferences or other conditions out of
the originally intended specifications can occur on a practical (real-world)
transmission. While conventional (filter-based) systems would require
reconfiguration or additional signal processing to cope with these unforeseen
conditions, NN-based receivers can learn to mitigate previously unseen effects
even after their deployment. For this, we showcase on-the-fly adaption to
current channel conditions and temporal alterations solely based on recovered
labels from an outer forward error correction (FEC) code without any additional
piloting overhead. To underline the flexibility of the proposed adaptive
training, we showcase substantial gains for scenarios with static channel macro
parameters, for out-of-specification usage and for interference compensation.

Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA)
automatically answer natural language questions using triples contained in a
KG. The key idea is to represent questions and entities of a KG as
low-dimensional embeddings. Previous KGQAs have attempted to represent entities
using Knowledge Graph Embedding (KGE) and Deep Learning (DL) methods. However,
KGEs are too shallow to capture the expressive features and DL methods process
each triple independently. Recently, Graph Convolutional Network (GCN) has
shown to be excellent in providing entity embeddings. However, using GCNs to
KGQAs is inefficient because GCNs treat all relations equally when aggregating
neighbourhoods. Also, a problem could occur when using previous KGQAs: in most
cases, questions often have an uncertain number of answers. To address the
above issues, we propose a graph summarization technique using Recurrent
Convolutional Neural Network (RCNN) and GCN. The combination of GCN and RCNN
ensures that the embeddings are propagated together with the relations relevant
to the question, and thus better answers. The proposed graph summarization
technique can be used to tackle the issue that KGQAs cannot answer questions
with an uncertain number of answers. In this paper, we demonstrated the
proposed technique on the most common type of questions, which is
single-relation questions. Experiments have demonstrated that the proposed
graph summarization technique using RCNN and GCN can provide better results
when compared to the GCN. The proposed graph summarization technique
significantly improves the recall of actual answers when the questions have an
uncertain number of answers.

Despite the widespread use of encryption techniques to provide
confidentiality over Internet communications, mobile device users are still
susceptible to privacy and security risks. In this paper, a new Deep Neural
Network (DNN) based user activity detection framework is proposed to identify
fine grained user activities performed on mobile applications (known as in-app
activities) from a sniffed encrypted Internet traffic stream. One of the
challenges is that there are countless applications, and it is practically
impossible to collect and train a DNN model using all possible data from them.
Therefore, in this work we exploit the probability distribution of DNN output
layer to filter the data from applications that are not considered during the
model training (i.e., unknown data). The proposed framework uses a time window
based approach to divide the traffic flow of an activity into segments, so that
in-app activities can be identified just by observing only a fraction of the
activity related traffic. Our tests have shown that the DNN based framework has
demonstrated an accuracy of 90% or above in identifying previously trained
in-app activities and an average accuracy of 79% in identifying previously
untrained in-app activity traffic as unknown data when this framework is
employed.

We study the $p$-generalized probit regression model, which is a generalized
linear model for binary responses. It extends the standard probit model by
replacing its link function, the standard normal cdf, by a $p$-generalized
normal distribution for $p\in[1, \infty)$. The $p$-generalized normal
distributions \citep{Sub23} are of special interest in statistical modeling
because they fit much more flexibly to data. Their tail behavior can be
controlled by choice of the parameter $p$, which influences the model's
sensitivity to outliers. Special cases include the Laplace, the Gaussian, and
the uniform distributions. We further show how the maximum likelihood estimator
for $p$-generalized probit regression can be approximated efficiently up to a
factor of $(1+\varepsilon)$ on large data by combining sketching techniques
with importance subsampling to obtain a small data summary called coreset.

Recent years have witnessed an exponential growth in developing deep learning
(DL) models for the time-series electricity forecasting in power systems.
However, most of the proposed models are designed based on the designers'
inherent knowledge and experience without elaborating on the suitability of the
proposed neural architectures. Moreover, these models cannot be self-adjusted
to the dynamically changing data patterns due to an inflexible design of their
structures. Even though several latest studies have considered application of
the neural architecture search (NAS) technique for obtaining a network with an
optimized structure in the electricity forecasting sector, their training
process is quite time-consuming, computationally expensive and not intelligent,
indicating that the NAS application in electricity forecasting area is still at
an infancy phase. In this research study, we propose an intelligent automated
architecture search (IAAS) framework for the development of time-series
electricity forecasting models. The proposed framework contains two primary
components, i.e., network function-preserving transformation operation and
reinforcement learning (RL)-based network transformation control. In the first
component, we introduce a theoretical function-preserving transformation of
recurrent neural networks (RNN) to the literature for capturing the hidden
temporal patterns within the time-series data. In the second component, we
develop three RL-based transformation actors and a net pool to intelligently
and effectively search a high-quality neural architecture. After conducting
comprehensive experiments on two publicly-available electricity load datasets
and two wind power datasets, we demonstrate that the proposed IAAS framework
significantly outperforms the ten existing models or methods in terms of
forecasting accuracy and stability.

Conditional local independence is an independence relation among continuous
time stochastic processes. It describes whether the evolution of one process is
directly influenced by another process given the histories of additional
processes, and it is important for the description and learning of causal
relations among processes. However, no nonparametric test of conditional local
independence has been available. We propose such a nonparametric test based on
double machine learning. The test is based on a functional target parameter
defined as the expectation of a stochastic integral. Under the hypothesis of
conditional local independence the stochastic integral is a zero-mean
martingale, and the target parameter is constantly equal to zero. We introduce
the test statistic as an estimator of the target parameter and show that by
using sample splitting or cross-fitting, its distributional limit is a Gaussian
martingale under the hypothesis. Its variance function can be estimated
consistently, and we derive specific univariate test statistics and their
asymptotic distributions. An example based on a marginalized Cox model with
time-dependent covariates is used throughout to illustrate the theory, and
simulations based on this example show how double machine learning as well as
sample splitting are needed for the test to work. Moreover, the simulation
study shows that when both of these techniques are used in combination, the
test works well without restrictive parametric assumptions.

One of the key problems in computer vision is adaptation: models are too
rigid to follow the variability of the inputs. The canonical computation that
explains adaptation in sensory neuroscience is divisive normalization, and it
has appealing effects on image manifolds. In this work we show that including
divisive normalization in current deep networks makes them more invariant to
non-informative changes in the images. In particular, we focus on U-Net
architectures for image segmentation. Experiments show that the inclusion of
divisive normalization in the U-Net architecture leads to better segmentation
results with respect to conventional U-Net. The gain increases steadily when
dealing with images acquired in bad weather conditions. In addition to the
results on the Cityscapes and Foggy Cityscapes datasets, we explain these
advantages through visualization of the responses: the equalization induced by
the divisive normalization leads to more invariant features to local changes in
contrast and illumination.

We introduce a new kind of linear transform named Deformable Butterfly
(DeBut) that generalizes the conventional butterfly matrices and can be adapted
to various input-output dimensions. It inherits the fine-to-coarse-grained
learnable hierarchy of traditional butterflies and when deployed to neural
networks, the prominent structures and sparsity in a DeBut layer constitutes a
new way for network compression. We apply DeBut as a drop-in replacement of
standard fully connected and convolutional layers, and demonstrate its
superiority in homogenizing a neural network and rendering it favorable
properties such as light weight and low inference complexity, without
compromising accuracy. The natural complexity-accuracy tradeoff arising from
the myriad deformations of a DeBut layer also opens up new rooms for analytical
and practical research. The codes and Appendix are publicly available at:
https://github.com/ruilin0212/DeBut.

In many real-world applications, the reward function is too complex to be
manually specified. In such cases, reward functions must instead be learned
from human feedback. Since the learned reward may fail to represent user
preferences, it is important to be able to validate the learned reward function
prior to deployment. One promising approach is to apply interpretability tools
to the reward function to spot potential deviations from the user's intention.
Existing work has applied general-purpose interpretability tools to understand
learned reward functions. We propose exploiting the intrinsic structure of
reward functions by first preprocessing them into simpler but equivalent reward
functions, which are then visualized. We introduce a general framework for such
reward preprocessing and propose concrete preprocessing algorithms. Our
empirical evaluation shows that preprocessed rewards are often significantly
easier to understand than the original reward.

Gene annotation addresses the problem of predicting unknown associations
between gene and functions (e.g., biological processes) of a specific organism.
Despite recent advances, the cost and time demanded by annotation procedures
that rely largely on in vivo biological experiments remain prohibitively high.
This paper presents a novel in silico approach for to the annotation problem
that combines cluster analysis and hierarchical multi-label classification
(HMC). The approach uses spectral clustering to extract new features from the
gene co-expression network (GCN) and enrich the prediction task. HMC is used to
build multiple estimators that consider the hierarchical structure of gene
functions. The proposed approach is applied to a case study on Zea mays, one of
the most dominant and productive crops in the world. The results illustrate how
in silico approaches are key to reduce the time and costs of gene annotation.
More specifically, they highlight the importance of: (i) building new features
that represent the structure of gene relationships in GCNs to annotate genes;
and (ii) taking into account the structure of biological processes to obtain
consistent predictions.

Morphologically rich languages pose difficulties to machine translation.
Machine translation engines that rely on statistical learning from parallel
training data, such as state-of-the-art neural systems, face challenges
especially with rich morphology on the output language side. Key challenges of
rich target-side morphology in data-driven machine translation include: (1) A
large amount of differently inflected word surface forms entails a larger
vocabulary and thus data sparsity. (2) Some inflected forms of infrequent terms
typically do not appear in the training corpus, which makes closed-vocabulary
systems unable to generate these unobserved variants. (3) Linguistic agreement
requires the system to correctly match the grammatical categories between
inflected word forms in the output sentence, both in terms of target-side
morpho-syntactic wellformedness and semantic adequacy with respect to the
input.
  In this paper, we re-investigate two target-side linguistic processing
techniques: a lemma-tag strategy and a linguistically informed word
segmentation strategy. Our experiments are conducted on a English-German
translation task under three training corpus conditions of different
magnitudes. We find that a stronger Transformer baseline leaves less room for
improvement than a shallow-RNN encoder-decoder model when translating
in-domain. However, we find that linguistic modeling of target-side morphology
does benefit the Transformer model when the same system is applied to
out-of-domain input text. We also successfully apply our approach to English to
Czech translation.

Ensemble learning is a classical learning method utilizing a group of weak
learners to form a strong learner, which aims to increase the accuracy of the
model. Recently, brain-inspired hyperdimensional computing (HDC) becomes an
emerging computational paradigm that has achieved success in various domains
such as human activity recognition, voice recognition, and bio-medical signal
classification. HDC mimics the brain cognition and leverages high-dimensional
vectors (e.g., 10000 dimensions) with fully distributed holographic
representation and (pseudo-)randomness. This paper presents the first effort in
exploring ensemble learning in the context of HDC and proposes the first
ensemble HDC model referred to as EnHDC. EnHDC uses a majority voting-based
mechanism to synergistically integrate the prediction outcomes of multiple base
HDC classifiers. To enhance the diversity of base classifiers, we vary the
encoding mechanisms, dimensions, and data width settings among base
classifiers. By applying EnHDC on a wide range of applications, results show
that the EnHDC can achieve on average 3.2\% accuracy improvement over a single
HDC classifier. Further, we show that EnHDC with reduced dimensionality, e.g.,
1000 dimensions, can achieve similar or even surpass the accuracy of baseline
HDC with higher dimensionality, e.g., 10000 dimensions. This leads to a 20\%
reduction of storage requirement of HDC model, which is key to enabling HDC on
low-power computing platforms.

Recent years have witnessed the success of deep learning on the visual sound
separation task. However, existing works follow similar settings where the
training and testing datasets share the same musical instrument categories,
which to some extent limits the versatility of this task. In this work, we
focus on a more general and challenging scenario, namely the separation of
unknown musical instruments, where the categories in training and testing
phases have no overlap with each other. To tackle this new setting, we propose
the Separation-with-Consistency (SeCo) framework, which can accomplish the
separation on unknown categories by exploiting the consistency constraints.
Furthermore, to capture richer characteristics of the novel melodies, we devise
an online matching strategy, which can bring stable enhancements with no cost
of extra parameters. Experiments demonstrate that our SeCo framework exhibits
strong adaptation ability on the novel musical categories and outperforms the
baseline methods by a significant margin.

Traditional statistical learning theory relies on the assumption that data
are identically and independently generated from a given distribution (i.i.d.).
The independently distributed assumption, on the other hand, fails to hold in
many real applications. In this survey, we consider learning settings in which
examples are dependent and their dependence relationship can be characterized
by a graph. We collect various graph-dependent concentration bounds, which are
then used to derive Rademacher and stability generalization bounds for learning
from graph-dependent data. We illustrate this paradigm with three learning
tasks and provide some research directions for future work. To the best of our
knowledge, this is the first survey on this subject.

Document intelligence as a relatively new research topic supports many
business applications. Its main task is to automatically read, understand, and
analyze documents. However, due to the diversity of formats (invoices, reports,
forms, etc.) and layouts in documents, it is difficult to make machines
understand documents. In this paper, we present the GraphDoc, a multimodal
graph attention-based model for various document understanding tasks. GraphDoc
is pre-trained in a multimodal framework by utilizing text, layout, and image
information simultaneously. In a document, a text block relies heavily on its
surrounding contexts, accordingly we inject the graph structure into the
attention mechanism to form a graph attention layer so that each input node can
only attend to its neighborhoods. The input nodes of each graph attention layer
are composed of textual, visual, and positional features from semantically
meaningful regions in a document image. We do the multimodal feature fusion of
each node by the gate fusion layer. The contextualization between each node is
modeled by the graph attention layer. GraphDoc learns a generic representation
from only 320k unlabeled documents via the Masked Sentence Modeling task.
Extensive experimental results on the publicly available datasets show that
GraphDoc achieves state-of-the-art performance, which demonstrates the
effectiveness of our proposed method. The code is available at
https://github.com/ZZR8066/GraphDoc.

Subword regularizations use multiple subword segmentations during training to
improve the robustness of neural machine translation models. In previous
subword regularizations, we use multiple segmentations in the training process
but use only one segmentation in the inference. In this study, we propose an
inference strategy to address this discrepancy. The proposed strategy
approximates the marginalized likelihood by using multiple segmentations
including the most plausible segmentation and several sampled segmentations.
Because the proposed strategy aggregates predictions from several
segmentations, we can regard it as a single model ensemble that does not
require any additional cost for training. Experimental results show that the
proposed strategy improves the performance of models trained with subword
regularization in low-resource machine translation tasks.

The sequential data observed in earth science can be regarded as paths in
multidimensional space. To read the path effectively, it is useful to convert
it into a sequence of numbers called the signature, which can faithfully
describe the order of points and nonlinearity in the path. In particular, a
linear combination of the terms in a signature can be used to approximate any
nonlinear function defined on a set of paths. Thereby, when one learns a set of
sequential data with labels attached to it, linear regression can be applied to
the pairs of signature and label, which will achieve high performance learning
even when the labels are determined by a nonlinear function. By incorporating
the signature methods into machine learning and data assimilation utilizing
sequential data, it is expected that we can extract information that has
previously been overlooked.

Federated learning (FL) can achieve privacy-safe and reliable collaborative
training without collecting users' private data. Its excellent privacy security
potential promotes a wide range of FL applications in Internet-of-Things (IoT),
wireless networks, mobile devices, and autonomous vehicles. However, the FL
method suffers from poor model performance on non-IID data and excessive
traffic volume. We propose a personalized FL algorithm using a hierarchical
proximal mapping based on L2-norm, named sparse federated learning with
hierarchical personalized models (sFedHP), which significantly improves the
global model performance facing diverse data. An approximated L1-norm is well
used as the sparse constraint to reduce the communication cost. Convergence
analysis shows that sFedHP's convergence rate is state-of-the-art with linear
speedup and the sparse constraint only reduces the convergence rate to a small
extent while significantly reducing the communication cost. Experimentally, we
demonstrate the benefits of sFedHP compared with the FedAvg, HierFAVG
(hierarchical FedAvg), and personalized FL methods based on local
customization, including FedAMP, FedProx, Per-FedAvg, pFedMe, and pFedGP.

Diffusion probabilistic models (DPMs) and their extensions have emerged as
competitive generative models yet confront challenges of efficient sampling. We
propose a new bilateral denoising diffusion model (BDDM) that parameterizes
both the forward and reverse processes with a schedule network and a score
network, which can train with a novel bilateral modeling objective. We show
that the new surrogate objective can achieve a lower bound of the log marginal
likelihood tighter than a conventional surrogate. We also find that BDDM allows
inheriting pre-trained score network parameters from any DPMs and consequently
enables speedy and stable learning of the schedule network and optimization of
a noise schedule for sampling. Our experiments demonstrate that BDDMs can
generate high-fidelity audio samples with as few as three sampling steps.
Moreover, compared to other state-of-the-art diffusion-based neural vocoders,
BDDMs produce comparable or higher quality samples indistinguishable from human
speech, notably with only seven sampling steps (143x faster than WaveGrad and
28.6x faster than DiffWave). We release our code at
https://github.com/tencent-ailab/bddm.

Since the outbreak of the COVID-19 in December 2019, medical protective
equipment such as disposable medical masks and KN95 masks have become essential
resources for the public. Enterprises in all sectors of society have also
transformed the production of medical masks. After the outbreak, how to choose
the right time to produce medical protective masks, and what type of medical
masks to produce will play a positive role in preventing and controlling the
epidemic in a short time. In this regard, the evolutionary game competition
analysis will be conducted through the relevant data of disposable medical
masks and KN95 masks to determine the appropriate nodes for the production of
corresponding mask types. After the research and analysis of the production
strategy of mask types, it has a positive effect on how to guide the resumption
of work and production.

While class activation map (CAM) generated by image classification network
has been widely used for weakly supervised object localization (WSOL) and
semantic segmentation (WSSS), such classifiers usually focus on discriminative
object regions. In this paper, we propose Contrastive learning for
Class-agnostic Activation Map (C$^2$AM) generation only using unlabeled image
data, without the involvement of image-level supervision. The core idea comes
from the observation that i) semantic information of foreground objects usually
differs from their backgrounds; ii) foreground objects with similar appearance
or background with similar color/texture have similar representations in the
feature space. We form the positive and negative pairs based on the above
relations and force the network to disentangle foreground and background with a
class-agnostic activation map using a novel contrastive loss. As the network is
guided to discriminate cross-image foreground-background, the class-agnostic
activation maps learned by our approach generate more complete object regions.
We successfully extracted from C$^2$AM class-agnostic object bounding boxes for
object localization and background cues to refine CAM generated by
classification network for semantic segmentation. Extensive experiments on
CUB-200-2011, ImageNet-1K, and PASCAL VOC2012 datasets show that both WSOL and
WSSS can benefit from the proposed C$^2$AM.

In this article, we provide the appendix for Lifelong Generative Modelling
Using Dynamic Expansion Graph Model. This appendix includes additional visual
results as well as the numerical results on the challenging datasets. In
addition, we also provide detailed proofs for the proposed theoretical analysis
framework. The source code can be found in
https://github.com/dtuzi123/Expansion-Graph-Model.

NECTAR, a Node-centric ovErlapping Community deTection AlgoRithm, presented
in 2016 by Cohen et. al, chooses dynamically between two objective functions
which function to optimize, based on the network on which it is invoked. This
approach, as shown by Cohen et al., outperforms six state-of-the-art algorithms
for overlapping community detection. In this work, we present NECTAR-ML, an
extension of the NECTAR algorithm that uses a machine-learning based model for
automating the selection of the objective function, trained and evaluated on a
dataset of 15,755 synthetic and 7 real-world networks. Our analysis shows that
in approximately 90% of the cases our model was able to successfully select the
correct objective function. We conducted a competitive analysis of NECTAR and
NECTAR-ML. NECTAR-ML was shown to significantly outperform NECTAR's ability to
select the best objective function. We also conducted a competitive analysis of
NECTAR-ML and two additional state-of-the-art multi-objective community
detection algorithms. NECTAR-ML outperformed both algorithms in terms of
average detection quality. Multiobjective EAs (MOEAs) are considered to be the
most popular approach to solve MOP and the fact that NECTAR-ML significantly
outperforms them demonstrates the effectiveness of ML-based objective function
selection.

Digitalization efforts are rapidly spreading across societies, challenging
new and important ethical issues that arise from technological development.
Software developers, designers and managerial decision-makers are ever more
expected to consider ethical values and conduct normative evaluations when
building digital products. Yet, when one looks for guidance in the academic
literature one encounters a plethora of branches of applied ethics. Depending
on the context of the system that is to be developed, interesting subfields
like big data ethics, machine ethics, information ethics, AI ethics or computer
ethics (to only name a few) may present themselves. In this paper we want to
offer assistance to any member of a development team by giving a clear and
brief introduction into two fields of ethical endeavor (normative ethics and
applied ethics), describing how they are related to each other and, finally,
provide an ordering of the different branches of applied ethics (big data
ethics, machine ethics, information ethics, AI ethics or computer ethics etc.)
which have gained traction over the last years. Finally, we discuss an example
in the domain of facial recognition software in the domain of medicine to
illustrate how this process of normative analysis might be conducted.

Learning with few labeled data is a key challenge for visual recognition, as
deep neural networks tend to overfit using a few samples only. One of the
Few-shot learning methods called metric learning addresses this challenge by
first learning a deep distance metric to determine whether a pair of images
belong to the same category, then applying the trained metric to instances from
other test set with limited labels. This method makes the most of the few
samples and limits the overfitting effectively. However, extant metric networks
usually employ Linear classifiers or Convolutional neural networks (CNN) that
are not precise enough to globally capture the subtle differences between
vectors. In this paper, we propose a novel approach named Bi-attention network
to compare the instances, which can measure the similarity between embeddings
of instances precisely, globally and efficiently. We verify the effectiveness
of our model on two benchmarks. Experiments show that our approach achieved
improved accuracy and convergence speed over baseline models.

Recently, pre-trained Transformer based language models, such as BERT, have
shown great superiority over the traditional methods in many Natural Language
Processing (NLP) tasks. However, the computational cost for deploying these
models is prohibitive on resource-restricted devices. One method to alleviate
this computation overhead is to quantize the original model into fewer bits
representation, and previous work has proved that we can at most quantize both
weights and activations of BERT into 8-bits, without degrading its performance.
In this work, we propose MKQ-BERT, which further improves the compression level
and uses 4-bits for quantization. In MKQ-BERT, we propose a novel way for
computing the gradient of the quantization scale, combined with an advanced
distillation strategy. On the one hand, we prove that MKQ-BERT outperforms the
existing BERT quantization methods for achieving a higher accuracy under the
same compression level. On the other hand, we are the first work that
successfully deploys the 4-bits BERT and achieves an end-to-end speedup for
inference. Our results suggest that we could achieve 5.3x of bits reduction
without degrading the model accuracy, and the inference speed of one int4 layer
is 15x faster than a float32 layer in Transformer based model.

Research on optical computing has recently attracted significant attention
due to the transformative advances in machine learning. Among different
approaches, diffractive optical networks composed of spatially-engineered
transmissive surfaces have been demonstrated for all-optical statistical
inference and performing arbitrary linear transformations using passive,
free-space optical layers. Here, we introduce a polarization multiplexed
diffractive processor to all-optically perform multiple, arbitrarily-selected
linear transformations through a single diffractive network trained using deep
learning. In this framework, an array of pre-selected linear polarizers is
positioned between trainable transmissive diffractive materials that are
isotropic, and different target linear transformations (complex-valued) are
uniquely assigned to different combinations of input/output polarization
states. The transmission layers of this polarization multiplexed diffractive
network are trained and optimized via deep learning and error-backpropagation
by using thousands of examples of the input/output fields corresponding to each
one of the complex-valued linear transformations assigned to different
input/output polarization combinations. Our results and analysis reveal that a
single diffractive network can successfully approximate and all-optically
implement a group of arbitrarily-selected target transformations with a
negligible error when the number of trainable diffractive features/neurons (N)
approaches N_p x N_i x N_o, where N_i and N_o represent the number of pixels at
the input and output fields-of-view, respectively, and N_p refers to the number
of unique linear transformations assigned to different input/output
polarization combinations. This polarization-multiplexed all-optical
diffractive processor can find various applications in optical computing and
polarization-based machine vision tasks.

Program synthesis strives to generate a computer program as a solution to a
given problem specification, expressed with input-output examples or natural
language descriptions. The prevalence of large language models advances the
state-of-the-art for program synthesis, though limited training resources and
data impede open access to such models. To democratize this, we train and
release a family of large language models up to 16.1B parameters, called
CODEGEN, on natural language and programming language data, and open source the
training library JAXFORMER. We show the utility of the trained model by
demonstrating that it is competitive with the previous state-of-the-art on
zero-shot Python code generation on HumanEval. We further investigate the
multi-step paradigm for program synthesis, where a single program is factorized
into multiple prompts specifying subproblems. To this end, we construct an open
benchmark, Multi-Turn Programming Benchmark (MTPB), consisting of 115 diverse
problem sets that are factorized into multi-turn prompts. Our analysis on MTPB
shows that the same intent provided to CODEGEN in multi-turn fashion
significantly improves program synthesis over that provided as a single turn.
We make the training library JAXFORMER and model checkpoints available as open
source contribution: https://github.com/salesforce/CodeGen.

The task of recognizing human facial expressions plays a vital role in
various human-related systems, including health care and medical fields. With
the recent success of deep learning and the accessibility of a large amount of
annotated data, facial expression recognition research has been mature enough
to be utilized in real-world scenarios with audio-visual datasets. In this
paper, we introduce Swin transformer-based facial expression approach for an
in-the-wild audio-visual dataset of the Aff-Wild2 Expression dataset.
Specifically, we employ a three-stream network (i.e., Visual stream, Temporal
stream, and Audio stream) for the audio-visual videos to fuse the multi-modal
information into facial expression recognition. Experimental results on the
Aff-Wild2 dataset show the effectiveness of our proposed multi-modal

Capturing multimodal natures is essential for stochastic pedestrian
trajectory prediction, to infer a finite set of future trajectories. The
inferred trajectories are based on observation paths and the latent vectors of
potential decisions of pedestrians in the inference step. However, stochastic
approaches provide varying results for the same data and parameter settings,
due to the random sampling of the latent vector. In this paper, we analyze the
problem by reconstructing and comparing probabilistic distributions from
prediction samples and socially-acceptable paths, respectively. Through this
analysis, we observe that the inferences of all stochastic models are biased
toward the random sampling, and fail to generate a set of realistic paths from
finite samples. The problem cannot be resolved unless an infinite number of
samples is available, which is infeasible in practice. We introduce that the
Quasi-Monte Carlo (QMC) method, ensuring uniform coverage on the sampling
space, as an alternative to the conventional random sampling. With the same
finite number of samples, the QMC improves all the multimodal prediction
results. We take an additional step ahead by incorporating a learnable sampling
network into the existing networks for trajectory prediction. For this purpose,
we propose the Non-Probability Sampling Network (NPSN), a very small network
(~5K parameters) that generates purposive sample sequences using the past paths
of pedestrians and their social interactions. Extensive experiments confirm
that NPSN can significantly improve both the prediction accuracy (up to 60%)
and reliability of the public pedestrian trajectory prediction benchmark. Code
is publicly available at https://github.com/inhwanbae/NPSN .

DPICT is the first learning-based image codec supporting fine granular
scalability. In this paper, we describe how to implement two key components of
DPICT efficiently: trit-plane slicing and rate-distortion-optimized
(RD-optimized) coding. In DPICT, we transform an image into a latent tensor,
represent the tensor in ternary digits (trits), and encode the trits in the
decreasing order of significance. For entropy encoding, it is necessary to
compute the probability of each trit, which demands high time complexity in
both the encoder and the decoder. To reduce the complexity, we develop a
parallel computing scheme for the probabilities, which is described in detail
with pseudo-codes. Moreover, we compare the trit-plane slicing in DPICT with
the alternative bit-plane slicing. Experimental results show that the time
complexity is reduced significantly by the parallel computing and that the
trit-plane slicing provides better RD performances than the bit-plane slicing.

Few-shot classification is a challenging problem that aims to learn a model
that can adapt to unseen classes given a few labeled samples. Recent approaches
pre-train a feature extractor, and then fine-tune for episodic meta-learning.
Other methods leverage spatial features to learn pixel-level correspondence
while jointly training a classifier. However, results using such approaches
show marginal improvements. In this paper, inspired by the transformer style
self-attention mechanism, we propose a strategy to cross-attend and re-weight
discriminative features for few-shot classification. Given a base
representation of support and query images after global pooling, we introduce a
single shared module that projects features and cross-attends in two aspects:
(i) query to support, and (ii) support to query. The module computes attention
scores between features to produce an attention pooled representation of
features in the same class that is later added to the original representation
followed by a projection head. This effectively re-weights features in both
aspects (i & ii) to produce features that better facilitate improved
metric-based meta-learning. Extensive experiments on public benchmarks show our
approach outperforms state-of-the-art methods by 3%~5%.

The sequential recommendation aims at predicting the next items in user
behaviors, which can be solved by characterizing item relationships in
sequences. Due to the data sparsity and noise issues in sequences, a new
self-supervised learning (SSL) paradigm is proposed to improve the performance,
which employs contrastive learning between positive and negative views of
  However, existing methods all construct views by adopting augmentation from
data perspectives, while we argue that 1) optimal data augmentation methods are
hard to devise, 2) data augmentation methods destroy sequential correlations,
and 3) data augmentation fails to incorporate comprehensive self-supervised
signals.
  Therefore, we investigate the possibility of model augmentation to construct
view pairs. We propose three levels of model augmentation methods: neuron
masking, layer dropping, and encoder complementing.
  This work opens up a novel direction in constructing views for contrastive
SSL. Experiments verify the efficacy of model augmentation for the SSL in the
sequential recommendation. Code is
available\footnote{\url{https://github.com/salesforce/SRMA}}.

In order to introduce deep learning technologies into anomaly detection,
Generative Adversarial Networks (GANs) are considered as important roles in the
algorithm design and realistic applications. In terms of GANs, event
probability reflected in the objective function, has an impact on the event
generation which plays a crucial part in GAN-based anomaly detection. The
information metric, e.g. Kullback-Leibler divergence in the original GAN, makes
the objective function have different sensitivity on different event
probability, which provides an opportunity to refine GAN-based anomaly
detection by influencing data generation. In this paper, we introduce the
exponential information metric into the GAN, referred to as MIM-based GAN,
whose superior characteristics on data generation are discussed in theory.
Furthermore, we propose an anomaly detection method with MIM-based GAN, as well
as explain its principle for the unsupervised learning case from the viewpoint
of probability event generation. Since this method is promising to detect
anomalies in Internet of Things (IoT), such as environmental, medical and
biochemical outliers, we make use of several datasets from the online ODDS
repository to evaluate its performance and compare it with other methods.

The potential of deep learning, especially in medical imaging, initiated
astonishing results and improved the methodologies after every passing day.
Deep learning in radiology provides the opportunity to classify, detect and
segment different diseases automatically. In the proposed study, we worked on a
non-trivial aspect of medical imaging where we classified and localized the
X-Rays affected by bullets. We tested Images on different classification and
localization models to get considerable accuracy. The replicated data set used
in the study was replicated on different images of chest X-Rays. The proposed
model worked not only on chest radiographs but other body organs X-rays like
leg, abdomen, head, even the training dataset based on chest radiographs.
Custom models have been used for classification and localization purposes after
tuning parameters. Finally, the results of our findings manifested using
different frameworks. This might assist the research enlightening towards this
field. To the best of our knowledge, this is the first study on the detection
and classification of radiographs affected by bullets using deep learning.

Automating video-based data and machine learning pipelines poses several
challenges including metadata generation for efficient storage and retrieval
and isolation of key-frames for scene understanding tasks. In this work, we
present two semi-supervised approaches that automate this process of manual
frame sifting in video streams by automatically classifying scenes for content
and filtering frames for fine-tuning scene understanding tasks. The first
rule-based method starts from a pre-trained object detector and it assigns
scene type, uncertainty and lighting categories to each frame based on
probability distributions of foreground objects. Next, frames with the highest
uncertainty and structural dissimilarity are isolated as key-frames. The second
method relies on the simCLR model for frame encoding followed by
label-spreading from 20% of frame samples to label the remaining frames for
scene and lighting categories. Also, clustering the video frames in the encoded
feature space further isolates key-frames at cluster boundaries. The proposed
methods achieve 64-93% accuracy for automated scene categorization for outdoor
image videos from public domain datasets of JAAD and KITTI. Also, less than 10%
of all input frames can be filtered as key-frames that can then be sent for
annotation and fine tuning of machine vision algorithms. Thus, the proposed
framework can be scaled to additional video data streams for automated training
of perception-driven systems with minimal training images.

Recently, contrastive learning has risen to be a promising approach for
large-scale self-supervised learning. However, theoretical understanding of how
it works is still unclear. In this paper, we propose a new guarantee on the
downstream performance without resorting to the conditional independence
assumption that is widely adopted in previous work but hardly holds in
practice. Our new theory hinges on the insight that the support of different
intra-class samples will become more overlapped under aggressive data
augmentations, thus simply aligning the positive samples (augmented views of
the same sample) could make contrastive learning cluster intra-class samples
together. Based on this augmentation overlap perspective, theoretically, we
obtain asymptotically closed bounds for downstream performance under weaker
assumptions, and empirically, we propose an unsupervised model selection metric
ARC that aligns well with downstream accuracy. Our theory suggests an
alternative understanding of contrastive learning: the role of aligning
positive samples is more like a surrogate task than an ultimate goal, and the
overlapped augmented views (i.e., the chaos) create a ladder for contrastive
learning to gradually learn class-separated representations. The code for
computing ARC is available at https://github.com/zhangq327/ARC.

Adversarial Training (AT) is known as an effective approach to enhance the
robustness of deep neural networks. Recently researchers notice that robust
models with AT have good generative ability and can synthesize realistic
images, while the reason behind it is yet under-explored. In this paper, we
demystify this phenomenon by developing a unified probabilistic framework,
called Contrastive Energy-based Models (CEM). On the one hand, we provide the
first probabilistic characterization of AT through a unified understanding of
robustness and generative ability. On the other hand, our unified framework can
be extended to the unsupervised scenario, which interprets unsupervised
contrastive learning as an important sampling of CEM. Based on these, we
propose a principled method to develop adversarial learning and sampling
methods. Experiments show that the sampling methods derived from our framework
improve the sample quality in both supervised and unsupervised learning.
Notably, our unsupervised adversarial sampling method achieves an Inception
score of 9.61 on CIFAR-10, which is superior to previous energy-based models
and comparable to state-of-the-art generative models.

Convolutional Neural Network (CNN), which mimics human visual perception
mechanism, has been successfully used in many computer vision areas. Some
psychophysical studies show that the visual perception mechanism synchronously
processes the form, color, movement, depth, etc., in the initial stage [7,20]
and then integrates all information for final recognition [38]. What's more,
the human visual system [20] contains different subdivisions or different
tasks. Inspired by the above visual perception mechanism, we investigate a new
task, termed as Model Disassembling and Assembling (MDA-Task), which can
disassemble the deep models into independent parts and assemble those parts
into a new deep model without performance cost like playing LEGO toys. To this
end, we propose a feature route attribution technique (FRAT) for disassembling
CNN classifiers in this paper. In FRAT, the positive derivatives of predicted
class probability w.r.t. the feature maps are adopted to locate the critical
features in each layer. Then, relevance analysis between the critical features
and preceding/subsequent parameter layers is adopted to bridge the route
between two adjacent parameter layers. In the assembling phase, class-wise
components of each layer are assembled into a new deep model for a specific
task. Extensive experiments demonstrate that the assembled CNN classifier can
achieve close accuracy with the original classifier without any fine-tune, and
excess original performance with one-epoch fine-tune. What's more, we also
conduct massive experiments to verify the broad application of MDA-Task on
model decision route visualization, model compression, knowledge distillation,
transfer learning, incremental learning, and so on.

While deep learning (DL) is data-hungry and usually relies on extensive
labeled data to deliver good performance, Active Learning (AL) reduces labeling
costs by selecting a small proportion of samples from unlabeled data for
labeling and training. Therefore, Deep Active Learning (DAL) has risen as a
feasible solution for maximizing model performance under a limited labeling
cost/budget in recent years. Although abundant methods of DAL have been
developed and various literature reviews conducted, the performance evaluation
of DAL methods under fair comparison settings is not yet available. Our work
intends to fill this gap. In this work, We construct a DAL toolkit, DeepAL+, by
re-implementing 19 highly-cited DAL methods. We survey and categorize
DAL-related works and construct comparative experiments across frequently used
datasets and DAL algorithms. Additionally, we explore some factors (e.g., batch
size, number of epochs in the training process) that influence the efficacy of
DAL, which provides better references for researchers to design their DAL
experiments or carry out DAL-related applications.

Seismic assessment of buildings and determination of their structural damage
is at the forefront of modern scientific research. Since now, several
researchers have proposed a number of procedures, in an attempt to estimate the
damage response of the buildings subjected to strong ground motions, without
conducting time-consuming analyses. These procedures, e.g. construction of
fragility curves, usually utilize methods based on the application of
statistical theory. In the last decades, the increase of the computers' power
has led to the development of modern soft computing methods based on the
adoption of Machine Learning algorithms. The present paper attempts an
extensive comparative evaluation of the capability of various Machine Learning
methods to adequately predict the seismic response of R/C buildings. The
training dataset is created by means of Nonlinear Time History Analyses of 90
3D R/C buildings with three different masonry infills' distributions, which are
subjected to 65 earthquakes. The seismic damage is expressed in terms of the
Maximum Interstory Drift Ratio. A large-scale comparison study is utilized by
the most efficient Machine Learning algorithms. The experimentation shows that
the LightGBM approach produces training stability, high overall performance and
a remarkable coefficient of determination to estimate the ability to predict
the buildings' damage response. Due to the extremely urgent issue, civil
protection mechanisms need to incorporate in their technological systems
scientific methodologies and appropriate technical or modeling tools such as
the proposed one, which can offer valuable assistance in making optimal

After its sweeping success in vision and language tasks, pure attention-based
neural architectures (e.g. DeiT) are emerging to the top of audio tagging (AT)
leaderboards, which seemingly obsoletes traditional convolutional neural
networks (CNNs), feed-forward networks or recurrent networks. However, taking a
closer look, there is great variability in published research, for instance,
performances of models initialized with pretrained weights differ drastically
from without pretraining, training time for a model varies from hours to weeks,
and often, essences are hidden in seemingly trivial details. This urgently
calls for a comprehensive study since our 1st comparison is half-decade old. In
this work, we perform extensive experiments on AudioSet which is the largest
weakly-labeled sound event dataset available, we also did an analysis based on
the data quality and efficiency. We compare a few state-of-the-art baselines on
the AT task, and study the performance and efficiency of 2 major categories of
neural architectures: CNN variants and attention-based variants. We also
closely examine their optimization procedures. Our opensourced experimental
results provide insights to trade-off between performance, efficiency,
optimization process, for both practitioners and researchers. Implementation:
https://github.com/lijuncheng16/AudioTaggingDoneRight

Optimal stopping is the problem of determining when to stop a stochastic
system in order to maximize reward, which is of practical importance in domains
such as finance, operations management and healthcare. Existing methods for
high-dimensional optimal stopping that are popular in practice produce
deterministic linear policies -- policies that deterministically stop based on
the sign of a weighted sum of basis functions -- but are not guaranteed to find
the optimal policy within this policy class given a fixed basis function
architecture. In this paper, we propose a new methodology for optimal stopping
based on randomized linear policies, which choose to stop with a probability
that is determined by a weighted sum of basis functions. We motivate these
policies by establishing that under mild conditions, given a fixed basis
function architecture, optimizing over randomized linear policies is equivalent
to optimizing over deterministic linear policies. We formulate the problem of
learning randomized linear policies from data as a smooth non-convex sample
average approximation (SAA) problem. We theoretically prove the almost sure
convergence of our randomized policy SAA problem and establish bounds on the
out-of-sample performance of randomized policies obtained from our SAA problem
based on Rademacher complexity. We also show that the SAA problem is in general
NP-Hard, and consequently develop a practical heuristic for solving our
randomized policy problem. Through numerical experiments on a benchmark family
of option pricing problem instances, we show that our approach can
substantially outperform state-of-the-art methods.

After a patient consultation, a clinician determines the steps in the
management of the patient. A clinician may for example request to see the
patient again or refer them to a specialist. Whilst most clinicians will record
their intent as "next steps" in the patient's clinical notes, in some cases the
clinician may forget to indicate their intent as an order or request, e.g.
failure to place the follow-up order. This consequently results in patients
becoming lost-to-follow up and may in some cases lead to adverse consequences.
In this paper we train a machine learning model to detect a clinician's intent
to follow up with a patient from the patient's clinical notes. Annotators
systematically identified 22 possible types of clinical intent and annotated
3000 Bariatric clinical notes. The annotation process revealed a class
imbalance in the labeled data and we found that there was only sufficient
labeled data to train 11 out of the 22 intents. We used the data to train a
BERT based multilabel classification model and reported the following average
accuracy metrics for all intents: macro-precision: 0.91, macro-recall: 0.90,
macro-f1: 0.90.

Visual Emotion Analysis (VEA) is attracting increasing attention. One of the
biggest challenges of VEA is to bridge the affective gap between visual clues
in a picture and the emotion expressed by the picture. As the granularity of
emotions increases, the affective gap increases as well. Existing deep
approaches try to bridge the gap by directly learning discrimination among
emotions globally in one shot without considering the hierarchical relationship
among emotions at different affective levels and the affective level of
emotions to be classified. In this paper, we present the Multi-level Dependent
Attention Network (MDAN) with two branches, to leverage the emotion hierarchy
and the correlation between different affective levels and semantic levels. The
bottom-up branch directly learns emotions at the highest affective level and
strictly follows the emotion hierarchy while predicting emotions at lower
affective levels. In contrast, the top-down branch attempt to disentangle the
affective gap by one-to-one mapping between semantic levels and affective
levels, namely, Affective Semantic Mapping. At each semantic level, a local
classifier learns discrimination among emotions at the corresponding affective
level. Finally, We integrate global learning and local learning into a unified
deep framework and optimize the network simultaneously. Moreover, to properly
extract and leverage channel dependencies and spatial attention while
disentangling the affective gap, we carefully designed two attention modules:
the Multi-head Cross Channel Attention module and the Level-dependent Class
Activation Map module. Finally, the proposed deep framework obtains new
state-of-the-art performance on six VEA benchmarks, where it outperforms
existing state-of-the-art methods by a large margin, e.g., +3.85% on the WEBEmo
dataset at 25 classes classification accuracy.

Millions of images of human faces are captured every single day; but these
photographs portray the likeness of an individual with a fixed pose,
expression, and appearance. Portrait image animation enables the post-capture
adjustment of these attributes from a single image while maintaining a
photorealistic reconstruction of the subject's likeness or identity. Still,
current methods for portrait image animation are typically based on 2D warping
operations or manipulations of a 2D generative adversarial network (GAN) and
lack explicit mechanisms to enforce multi-view consistency. Thus these methods
may significantly alter the identity of the subject, especially when the
viewpoint relative to the camera is changed. In this work, we leverage newly
developed 3D GANs, which allow explicit control over the pose of the image
subject with multi-view consistency. We propose a supervision strategy to
flexibly manipulate expressions with 3D morphable models, and we show that the
proposed method also supports editing appearance attributes, such as age or
hairstyle, by interpolating within the latent space of the GAN. The proposed
technique for portrait image animation outperforms previous methods in terms of
image quality, identity preservation, and pose transfer while also supporting
attribute editing.

Facial action unit recognition is an important task for facial analysis.
Owing to the complex collection environment, facial action unit recognition in
the wild is still challenging. The 3rd competition on affective behavior
analysis in-the-wild (ABAW) has provided large amount of facial images with
facial action unit annotations. In this paper, we introduce a facial action
unit recognition method based on transfer learning. We first use available
facial images with expression labels to train the feature extraction network.
Then we fine-tune the network for facial action unit recognition.

Spatial learning across many species is impaired by lesions in the
hippocampus, a subcortical brain structure whose cellular composition changes
substantially over its 5 to 6 week lifetime from mainly excitatory neurons
during development to equal proportions of inhibitory interneurons (gamma
Amp/Arcs) as well as pyramidal cells early in life, but which later on
comprises only about 10% Arches+ projection spiny cortical projecting principal
cells that are located within discrete cytoarchitectonic patches known as CA3
or just hilus granular layer 2 (CG2). While other structures may contribute
importantly in certain situations e.g., perirhinal cortex when using visual
cues with no reference frame for location), these remaining cell types also
change their proportion through time with gamma APs forming 30 to 35 percent),
fast firing parvalbumin immunoreactive basket or axoaxonic synapses into
somatic spines at 40 days after birth (DABs formed into spine membranes by 15
DAB), and CG2 pyramids forming relatively slowly but strengthening synapses
onto nearby dendritic shafts made up largely (>80 %) by GABAergic terminals
(+spines) until then that were still synaptically silent.

We propose Nash Neural Networks ($N^3$) as a new type of Physics Informed
Neural Network that is able to infer the underlying utility from observations
of how rational individuals behave in a differential game with a Nash
equilibrium. We assume that the dynamics for both the population and the
individual are known, but not the payoff function, which specifies the cost per
unit time of being in any particular state. We construct our network in such a
way that the Euler-Lagrange equations of the corresponding optimal control
problem are satisfied and the optimal control is self-consistently determined.
In this way, we are able to learn the unknown payoff function in an
unsupervised manner. We have applied the $N^3$ to study the optimal behaviour
during epidemics, in which individuals can choose to socially distance
depending on the state of the pandemic and the cost of being infected. Training
our network against synthetic data for a simple SIR model, we showed that it is
possible to accurately reproduce the hidden payoff function, in such a way that
the game dynamics are respected. Our approach will have far-reaching
applications, as it allows one to infer utilities from behavioural data, and
can thus be applied to study a wide array of problems in science, engineering,
economics and government planning.

Motion planning in off-road environments requires reasoning about both the
geometry and semantics of the scene (e.g., a robot may be able to drive through
soft bushes but not a fallen log). In many recent works, the world is
classified into a finite number of semantic categories that often are not
sufficient to capture the ability (i.e., the speed) with which a robot can
traverse off-road terrain. Instead, this work proposes a new representation of
traversability based exclusively on robot speed that can be learned from data,
offers interpretability and intuitive tuning, and can be easily integrated with
a variety of planning paradigms in the form of a costmap. Specifically, given a
dataset of experienced trajectories, the proposed algorithm learns to predict a
distribution of speeds the robot could achieve, conditioned on the environment
semantics and commanded speed. The learned speed distribution map is converted
into costmaps with a risk-aware cost term based on conditional value at risk
(CVaR). Numerical simulations demonstrate that the proposed risk-aware planning
algorithm leads to faster average time-to-goals compared to a method that only
considers expected behavior, and the planner can be tuned for slightly slower,
but less variable behavior. Furthermore, the approach is integrated into a full
autonomy stack and demonstrated in a high-fidelity Unity environment and is
shown to provide a 30\% improvement in the success rate of navigation.

Current instance segmentation methods rely heavily on pixel-level annotated
images. The huge cost to obtain such fully-annotated images restricts the
dataset scale and limits the performance. In this paper, we formally address
semi-supervised instance segmentation, where unlabeled images are employed to
boost the performance. We construct a framework for semi-supervised instance
segmentation by assigning pixel-level pseudo labels. Under this framework, we
point out that noisy boundaries associated with pseudo labels are double-edged.
We propose to exploit and resist them in a unified manner simultaneously: 1) To
combat the negative effects of noisy boundaries, we propose a noise-tolerant
mask head by leveraging low-resolution features. 2) To enhance the positive
impacts, we introduce a boundary-preserving map for learning detailed
information within boundary-relevant regions. We evaluate our approach by
extensive experiments. It behaves extraordinarily, outperforming the supervised
baseline by a large margin, more than 6% on Cityscapes, 7% on COCO and 4.5% on
BDD100k. On Cityscapes, our method achieves comparable performance by utilizing
only 30% labeled images.

Machine learning (ML) models that use deep neural networks are vulnerable to
backdoor attacks. Such attacks involve the insertion of a (hidden) trigger by
an adversary. As a consequence, any input that contains the trigger will cause
the neural network to misclassify the input to a (single) target class, while
classifying other inputs without a trigger correctly. ML models that contain a
backdoor are called Trojan models. Backdoors can have severe consequences in
safety-critical cyber and cyber physical systems when only the outputs of the
model are available. Defense mechanisms have been developed and illustrated to
be able to distinguish between outputs from a Trojan model and a non-Trojan
model in the case of a single-target backdoor attack with accuracy > 96
percent. Understanding the limitations of a defense mechanism requires the
construction of examples where the mechanism fails. Current single-target
backdoor attacks require one trigger per target class. We introduce a new, more
general attack that will enable a single trigger to result in misclassification
to more than one target class. Such a misclassification will depend on the true
(actual) class that the input belongs to. We term this category of attacks
multi-target backdoor attacks. We demonstrate that a Trojan model with either a
single-target or multi-target trigger can be trained so that the accuracy of a
defense mechanism that seeks to distinguish between outputs coming from a
Trojan and a non-Trojan model will be reduced. Our approach uses the non-Trojan
model as a teacher for the Trojan model and solves a min-max optimization
problem between the Trojan model and defense mechanism. Empirical evaluations
demonstrate that our training procedure reduces the accuracy of a
state-of-the-art defense mechanism from >96 to 0 percent.

Deep reinforcement learning in partially observable environments is a
difficult task in itself, and can be further complicated by a sparse reward
signal. Most tasks involving navigation in three-dimensional environments
provide the agent with extremely limited information. Typically, the agent
receives a visual observation input from the environment and is rewarded once
at the end of the episode. A good reward function could substantially improve
the convergence of reinforcement learning algorithms for such tasks. The
classic approach to increase the density of the reward signal is to augment it
with supplementary rewards. This technique is called the reward shaping. In
this study, we propose two modifications of one of the recent reward shaping
methods based on graph convolutional networks: the first involving advanced
aggregation functions, and the second utilizing the attention mechanism. We
empirically validate the effectiveness of our solutions for the task of
navigation in a 3D environment with sparse rewards. For the solution featuring
attention mechanism, we are also able to show that the learned attention is
concentrated on edges corresponding to important transitions in 3D environment.

Traditionally, when recommender systems are formalized as multi-armed
bandits, the policy of the recommender system influences the rewards accrued,
but not the length of interaction. However, in real-world systems, dissatisfied
users may depart (and never come back). In this work, we propose a novel
multi-armed bandit setup that captures such policy-dependent horizons. Our
setup consists of a finite set of user types, and multiple arms with Bernoulli
payoffs. Each (user type, arm) tuple corresponds to an (unknown) reward
probability. Each user's type is initially unknown and can only be inferred
through their response to recommendations. Moreover, if a user is dissatisfied
with their recommendation, they might depart the system. We first address the
case where all users share the same type, demonstrating that a recent UCB-based
algorithm is optimal. We then move forward to the more challenging case, where
users are divided among two types. While naive approaches cannot handle this
setting, we provide an efficient learning algorithm that achieves
$\tilde{O}(\sqrt{T})$ regret, where $T$ is the number of users.

Strategic classification, i.e. classification under possible strategic
manipulations of features, has received a lot of attention from both the
machine learning and the game theory community. Most works focus on analysing
properties of the optimal decision rule under such manipulations. In our work
we take a learning theoretic perspective, focusing on the sample complexity
needed to learn a good decision rule which is robust to strategic manipulation.
We perform this analysis by introducing a novel loss function, the
\emph{strategic manipulation loss}, which takes into account both the accuracy
of the final decision rule and its vulnerability to manipulation. We analyse
the sample complexity for a known graph of possible manipulations in terms of
the complexity of the function class and the manipulation graph. Additionally,
we initialize the study of learning under unknown manipulation capabilities of
the involved agents. Using techniques from transfer learning theory, we define
a similarity measure for manipulation graphs and show that learning outcomes
are robust with respect to small changes in the manipulation graph. Lastly, we
analyse the (sample complexity of) learning of the manipulation capability of
agents with respect to this similarity measure, providing novel guarantees for
strategic classification with respect to an unknown manipulation graph.

Seeking informative projecting directions has been an important task in
utilizing sliced Wasserstein distance in applications. However, finding these
directions usually requires an iterative optimization procedure over the space
of projecting directions, which is computationally expensive. Moreover, the
computational issue is even more severe in deep learning applications, where
computing the distance between two mini-batch probability measures is repeated
several times. This nested loop has been one of the main challenges that
prevent the usage of sliced Wasserstein distances based on good projections in
practice. To address this challenge, we propose to utilize the
learning-to-optimize technique or amortized optimization to predict the
informative direction of any given two mini-batch probability measures. To the
best of our knowledge, this is the first work that bridges amortized
optimization and sliced Wasserstein generative models. In particular, we derive
linear amortized models, generalized linear amortized models, and non-linear
amortized models which are corresponding to three types of novel mini-batch
losses, named amortized sliced Wasserstein. We demonstrate the favorable
performance of the proposed sliced losses in deep generative modeling on
standard benchmark datasets.

Sound source localization in visual scenes aims to localize objects emitting
the sound in a given image. Recent works showing impressive localization
performance typically rely on the contrastive learning framework. However, the
random sampling of negatives, as commonly adopted in these methods, can result
in misalignment between audio and visual features and thus inducing ambiguity
in localization. In this paper, instead of following previous literature, we
propose Self-Supervised Predictive Learning (SSPL), a negative-free method for
sound localization via explicit positive mining. Specifically, we first devise
a three-stream network to elegantly associate sound source with two augmented
views of one corresponding video frame, leading to semantically coherent
similarities between audio and visual features. Second, we introduce a novel
predictive coding module for audio-visual feature alignment. Such a module
assists SSPL to focus on target objects in a progressive manner and effectively
lowers the positive-pair learning difficulty. Experiments show surprising
results that SSPL outperforms the state-of-the-art approach on two standard
sound localization benchmarks. In particular, SSPL achieves significant
improvements of 8.6% cIoU and 3.4% AUC on SoundNet-Flickr compared to the
previous best. Code is available at: https://github.com/zjsong/SSPL.

Natural language is the most intuitive medium for us to interact with other
people when expressing commands and instructions. However, using language is
seldom an easy task when humans need to express their intent towards robots,
since most of the current language interfaces require rigid templates with a
static set of action targets and commands. In this work, we provide a flexible
language-based interface for human-robot collaboration, which allows a user to
reshape existing trajectories for an autonomous agent. We take advantage of
recent advancements in the field of large language models (BERT and CLIP) to
encode the user command, and then combine these features with trajectory
information using multi-modal attention transformers. We train the model using
imitation learning over a dataset containing robot trajectories modified by
language commands, and treat the trajectory generation process as a sequence
prediction problem, analogously to how language generation architectures
operate. We evaluate the system in multiple simulated trajectory scenarios, and
show a significant performance increase of our model over baseline approaches.
In addition, our real-world experiments with a robot arm show that users
significantly prefer our natural language interface over traditional methods
such as kinesthetic teaching or cost-function programming. Our study shows how
the field of robotics can take advantage of large pre-trained language models
towards creating more intuitive interfaces between robots and machines. Project
webpage: https://arthurfenderbucker.github.io/NL_trajectory_reshaper/

In this article, we prove approximation theorems in classes of deep and
shallow neural networks with analytic activation functions by elementary
arguments. We prove for both real and complex networks with non-polynomial
activation that the closure of the class of neural networks coincides with the
closure of the space of polynomials. The closure can further be characterized
by the Stone-Weierstrass theorem (in the real case) and Mergelyan's theorem (in
the complex case). In the real case, we further prove approximation results for
networks with higher-dimensional harmonic activation and orthogonally projected
linear maps. We further show that fully connected and residual networks of
large depth with polynomial activation functions can approximate any polynomial
under certain width requirements. All proofs are entirely elementary.

This work considers supervised contrastive learning for semantic
segmentation. We apply contrastive learning to enhance the discriminative power
of the multi-scale features extracted by semantic segmentation networks. Our
key methodological insight is to leverage samples from the feature spaces
emanating from multiple stages of a model's encoder itself requiring neither
data augmentation nor online memory banks to obtain a diverse set of samples.
To allow for such an extension we introduce an efficient and effective sampling
process, that enables applying contrastive losses over the encoder's features
at multiple scales. Furthermore, by first mapping the encoder's multi-scale
representations to a common feature space, we instantiate a novel form of
supervised local-global constraint by introducing cross-scale contrastive
learning linking high-resolution local features to low-resolution global
features. Combined, our multi-scale and cross-scale contrastive losses boost
performance of various models (DeepLabV3, HRNet, OCRNet, UPerNet) with both CNN
and Transformer backbones, when evaluated on 4 diverse datasets from natural
(Cityscapes, PascalContext, ADE20K) but also surgical (CaDIS) domains. Our code
is available at https://github.com/RViMLab/MS_CS_ContrSeg. datasets from
natural (Cityscapes, PascalContext, ADE20K) but also surgical (CaDIS) domains.

Deep learning (DL) techniques involving fine-tuning large numbers of model
parameters have delivered impressive performance on the task of discriminating
between language produced by cognitively healthy individuals, and those with
Alzheimer's disease (AD). However, questions remain about their ability to
generalize beyond the small reference sets that are publicly available for
research. As an alternative to fitting model parameters directly, we propose a
novel method by which a Transformer DL model (GPT-2) pre-trained on general
English text is paired with an artificially degraded version of itself (GPT-D),
to compute the ratio between these two models' \textit{perplexities} on
language from cognitively healthy and impaired individuals. This technique
approaches state-of-the-art performance on text data from a widely used "Cookie
Theft" picture description task, and unlike established alternatives also
generalizes well to spontaneous conversations. Furthermore, GPT-D generates
text with characteristics known to be associated with AD, demonstrating the
induction of dementia-related linguistic anomalies. Our study is a step toward
better understanding of the relationships between the inner workings of
generative neural language models, the language that they produce, and the
deleterious effects of dementia on human speech and language characteristics.

Driven by rapid digitization and expansive internet access, market-driven
platforms (e.g., Amazon, DoorDash, Uber, TaskRabbit) are increasingly prevalent
and becoming key drivers of the economy. Across many industries, platforms
leverage digital infrastructure to efficiently match producers and consumers,
dynamically set prices, and enable economies of scale. This increasing
prominence makes it important to understand the behavior of platforms, which
induces complex phenomenon especially in the presence of severe market shocks
(e.g., during pandemics). In this work, we develop a multi-agent simulation
environment to capture key elements of a platform economy, including the kinds
of economic shocks that disrupt a traditional, off-platform market. We use deep
reinforcement learning (RL) to model the pricing and matching behavior of a
platform that optimizes for revenue and various socially-aware objectives. We
start with tractable motivating examples to establish intuitions about the
dynamics and function of optimal platform policies. We then conduct extensive
empirical simulations on multi-period environments, including settings with
market shocks. We characterize the effect of a platform on the efficiency and
resilience of an economic system under different platform design objectives. We
further analyze the consequences of regulation fixing platform fees, and study
the alignment of a revenue-maximizing platform with social welfare under
different platform matching policies. As such, our RL-based framework provides
a foundation for understanding platform economies under different designs and
for yielding new economic insights that are beyond analytical tractability.

We propose a novel technique for algorithm-selection, applicable to
optimisation domains in which there is implicit sequential information
encapsulated in the data, e.g., in online bin-packing. Specifically we train
two types of recurrent neural networks to predict a packing heuristic in online
bin-packing, selecting from four well-known heuristics. As input, the RNN
methods only use the sequence of item-sizes. This contrasts to typical
approaches to algorithm-selection which require a model to be trained using
domain-specific instance features that need to be first derived from the input
data. The RNN approaches are shown to be capable of achieving within 5% of the
oracle performance on between 80.88% to 97.63% of the instances, depending on
the dataset. They are also shown to outperform classical machine learning
models trained using derived features. Finally, we hypothesise that the
proposed methods perform well when the instances exhibit some implicit
structure that results in discriminatory performance with respect to a set of
heuristics. We test this hypothesis by generating fourteen new datasets with
increasing levels of structure, and show that there is a critical threshold of
structure required before algorithm-selection delivers benefit.

Continual Learning research typically focuses on tackling the phenomenon of
catastrophic forgetting in neural networks. Catastrophic forgetting is
associated with an abrupt loss of knowledge previously learned by a model when
the task, or more broadly the data distribution, being trained on changes. In
supervised learning problems this forgetting, resulting from a change in the
model's representation, is typically measured or observed by evaluating the
decrease in old task performance. However, a model's representation can change
without losing knowledge about prior tasks. In this work we consider the
concept of representation forgetting, observed by using the difference in
performance of an optimal linear classifier before and after a new task is
introduced. Using this tool we revisit a number of standard continual learning
benchmarks and observe that, through this lens, model representations trained
without any explicit control for forgetting often experience small
representation forgetting and can sometimes be comparable to methods which
explicitly control for forgetting, especially in longer task sequences. We also
show that representation forgetting can lead to new insights on the effect of
model capacity and loss function used in continual learning. Based on our
results, we show that a simple yet competitive approach is to learn
representations continually with standard supervised contrastive learning while
constructing prototypes of class samples when queried on old samples.

Cyber-phishing attacks recently became more precise, targeted, and tailored
by training data to activate only in the presence of specific information or
cues. They are adaptable to a much greater extent than traditional phishing
detection. Hence, automated detection systems cannot always be 100% accurate,
increasing the uncertainty around expected behavior when faced with a potential
phishing email. On the other hand, human-centric defence approaches focus
extensively on user training but face the difficulty of keeping users up to
date with continuously emerging patterns. Therefore, advances in analyzing the
content of an email in novel ways along with summarizing the most pertinent
content to the recipients of emails is a prospective gateway to furthering how
to combat these threats. Addressing this gap, this work leverages
transformer-based machine learning to (i) analyze prospective psychological
triggers, to (ii) detect possible malicious intent, and (iii) create
representative summaries of emails. We then amalgamate this information and
present it to the user to allow them to (i) easily decide whether the email is
"phishy" and (ii) self-learn advanced malicious patterns.

This paper concerns differentially private Bayesian estimation of the
parameters of a population distribution, when a statistic of a sample from that
population is shared in noise to provide differential privacy.
  This work mainly addresses two problems: (1) What statistic of the sample
should be shared privately? For the first question, i.e., the one about
statistic selection, we promote using the Fisher information. We find out that,
the statistic that is most informative in a non-privacy setting may not be the
optimal choice under the privacy restrictions. We provide several examples to
support that point. We consider several types of data sharing settings and
propose several Monte Carlo-based numerical estimation methods for calculating
the Fisher information for those settings. The second question concerns
inference: (2) Based on the shared statistics, how could we perform effective
Bayesian inference? We propose several Markov chain Monte Carlo (MCMC)
algorithms for sampling from the posterior distribution of the parameter given
the noisy statistic. The proposed MCMC algorithms can be preferred over one
another depending on the problem. For example, when the shared statistics is
additive and added Gaussian noise, a simple Metropolis-Hasting algorithm that
utilizes the central limit theorem is a decent choice. We propose more advanced
MCMC algorithms for several other cases of practical relevance.
  Our numerical examples involve comparing several candidate statistics to be
shared privately. For each statistic, we perform Bayesian estimation based on
the posterior distribution conditional on the privatized version of that
statistic. We demonstrate that, the relative performance of a statistic, in
terms of the mean squared error of the Bayesian estimator based on the
corresponding privatized statistic, is adequately predicted by the Fisher
information of the privatized statistic.

A general control policy framework based on deep reinforcement learning (DRL)
is introduced for closed-loop decision making in subsurface flow settings.
Traditional closed-loop modeling workflows in this context involve the repeated
application of data assimilation/history matching and robust optimization
steps. Data assimilation can be particularly challenging in cases where both
the geological style (scenario) and individual model realizations are
uncertain. The closed-loop reservoir management (CLRM) problem is formulated
here as a partially observable Markov decision process, with the associated
optimization problem solved using a proximal policy optimization algorithm.
This provides a control policy that instantaneously maps flow data observed at
wells (as are available in practice) to optimal well pressure settings. The
policy is represented by a temporal convolution and gated transformer blocks.
Training is performed in a preprocessing step with an ensemble of prior
geological models, which can be drawn from multiple geological scenarios.
Example cases involving the production of oil via water injection, with both 2D
and 3D geological models, are presented. The DRL-based methodology is shown to
result in an NPV increase of 15% (for the 2D cases) and 33% (3D cases) relative
to robust optimization over prior models, and to an average improvement of 4%
in NPV relative to traditional CLRM. The solutions from the control policy are
found to be comparable to those from deterministic optimization, in which the
geological model is assumed to be known, even when multiple geological
scenarios are considered. The control policy approach results in a 76% decrease
in computational cost relative to traditional CLRM with the algorithms and
parameter settings considered in this work.

Machine learning models that achieve high overall accuracy often make
systematic errors on important subsets (or slices) of data. Identifying
underperforming slices is particularly challenging when working with
high-dimensional inputs (e.g. images, audio), where important slices are often
unlabeled. In order to address this issue, recent studies have proposed
automated slice discovery methods (SDMs), which leverage learned model
representations to mine input data for slices on which a model performs poorly.
To be useful to a practitioner, these methods must identify slices that are
both underperforming and coherent (i.e. united by a human-understandable
concept). However, no quantitative evaluation framework currently exists for
rigorously assessing SDMs with respect to these criteria. Additionally, prior
qualitative evaluations have shown that SDMs often identify slices that are
incoherent. In this work, we address these challenges by first designing a
principled evaluation framework that enables a quantitative comparison of SDMs
across 1,235 slice discovery settings in three input domains (natural images,
medical images, and time-series data). Then, motivated by the recent
development of powerful cross-modal representation learning approaches, we
present Domino, an SDM that leverages cross-modal embeddings and a novel
error-aware mixture model to discover and describe coherent slices. We find
that Domino accurately identifies 36% of the 1,235 slices in our framework - a
12 percentage point improvement over prior methods. Further, Domino is the
first SDM that can provide natural language descriptions of identified slices,
correctly generating the exact name of the slice in 35% of settings.

Studies have shown that some Natural Language Processing (NLP) systems encode
and replicate harmful biases with potential adverse ethical effects in our
society. In this article, we propose an approach for identifying gender and
racial stereotypes in word embeddings trained on judicial opinions from U.S.
case law. Embeddings containing stereotype information may cause harm when used
by downstream systems for classification, information extraction, question
answering, or other machine learning systems used to build legal research
tools. We first explain how previously proposed methods for identifying these
biases are not well suited for use with word embeddings trained on legal
opinion text. We then propose a domain adapted method for identifying gender
and racial biases in the legal domain. Our analyses using these methods suggest
that racial and gender biases are encoded into word embeddings trained on legal
opinions. These biases are not mitigated by exclusion of historical data, and
appear across multiple large topical areas of the law. Implications for
downstream systems that use legal opinion word embeddings and suggestions for
potential mitigation strategies based on our observations are also discussed.

Plastic waste is a significant environmental pollutant that is difficult to
monitor. We created a system of neural networks to analyze spectral, spatial,
and temporal components of Sentinel-2 satellite data to identify terrestrial
aggregations of waste. The system works at continental scale. We evaluated
performance in Indonesia and detected 374 waste aggregations, more than double
the number of sites found in public databases. The same system deployed across
twelve countries in Southeast Asia identifies 996 subsequently confirmed waste
sites. For each detected site, we algorithmically monitor waste site footprints
through time and cross-reference other datasets to generate physical and social
metadata. 19% of detected waste sites are located within 200 m of a waterway.
Numerous sites sit directly on riverbanks, with high risk of ocean leakage.

For a long time, different recommendation tasks typically require designing
task-specific architectures and training objectives. As a result, it is hard to
transfer the learned knowledge and representations from one task to another,
thus restricting the generalization ability of existing recommendation
approaches, e.g., a sequential recommendation model can hardly be applied or
transferred to a review generation method. To deal with such issues,
considering that language can describe almost anything and language grounding
is a powerful medium to represent various problems or tasks, we present a
flexible and unified text-to-text paradigm called "Pretrain, Personalized
Prompt, and Predict Paradigm" (P5) for recommendation, which unifies various
recommendation tasks in a shared framework. In P5, all data such as user-item
interactions, user descriptions, item metadata, and user reviews are converted
to a common format -- natural language sequences. The rich information from
natural language assists P5 to capture deeper semantics for personalization and
recommendation. Specifically, P5 learns different tasks with the same language
modeling objective during pretraining. Thus, it serves as the foundation model
for various downstream recommendation tasks, allows easy integration with other
modalities, and enables instruction-based recommendation based on prompts. P5
advances recommender systems from shallow model to deep model to big model, and
will revolutionize the technical form of recommender systems towards universal
recommendation engine. With adaptive personalized prompt for different users,
P5 is able to make predictions in a zero-shot or few-shot manner and largely
reduces the necessity for extensive fine-tuning. On several recommendation
benchmarks, we conduct experiments to show the effectiveness of P5. We release
the source code at https://github.com/jeykigung/P5.

Recently, many researchers have advanced data-driven methods for modeling
heterogeneous treatment effects (HTEs). Even still, estimation of HTEs is a
difficult task -- these methods frequently over- or under-estimate the
treatment effects, leading to poor calibration of the resulting models.
However, while many methods exist for evaluating the calibration of prediction
and classification models, formal approaches to assess the calibration of HTE
models are limited to the calibration slope. In this paper, we define an
analogue of the \smash{($\ell_2$)} expected calibration error for HTEs, and
propose a robust estimator. Our approach is motivated by doubly robust
treatment effect estimators, making it unbiased, and resilient to confounding,
overfitting, and high-dimensionality issues. Furthermore, our method is
straightforward to adapt to many structures under which treatment effects can
be identified, including randomized trials, observational studies, and survival
analysis. We illustrate how to use our proposed metric to evaluate the
calibration of learned HTE models through the application to the CRITEO-UPLIFT
Trial.

Researchers have observed that the frequencies of leading digits in many
man-made and naturally occurring datasets follow a logarithmic curve, with
digits that start with the number 1 accounting for $\sim 30\%$ of all numbers
in the dataset and digits that start with the number 9 accounting for $\sim
5\%$ of all numbers in the dataset. This phenomenon, known as Benford's Law, is
highly repeatable and appears in lists of numbers from electricity bills, stock
prices, tax returns, house prices, death rates, lengths of rivers, and
naturally occurring images. In this paper we demonstrate that human speech
spectra also follow Benford's Law. We use this observation to motivate a new
set of features that can be efficiently extracted from speech and demonstrate
that these features can be used to classify between human speech and synthetic
speech.

We investigate how to efficiently predict play personas based on playtraces.
Play personas can be computed by calculating the action agreement ratio between
a player and a generative model of playing behavior, a so-called procedural
persona. But this is computationally expensive and assumes that appropriate
procedural personas are readily available. We present two methods for
estimating player persona, one using regular supervised learning and aggregate
measures of game mechanics initiated, and another based on sequence learning on
a trace of closely cropped gameplay observations. While both of these methods
achieve high accuracy when predicting play personas defined by agreement with
procedural personas, they utterly fail to predict play style as defined by the
players themselves using a questionnaire. This interesting result highlights
the value of using computational methods in defining play personas.

In modern robotics, effectively computing optimal control policies under
dynamically varying environments poses substantial challenges to the
off-the-shelf parametric policy gradient methods, such as the Deep
Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic policy
gradient (TD3). In this paper, we propose a systematic methodology to
dynamically learn a sequence of optimal control policies non-parametrically,
while autonomously adapting with the constantly changing environment dynamics.
Specifically, our non-parametric kernel-based methodology embeds a policy
distribution as the features in a non-decreasing Euclidean space, therefore
allowing its search space to be defined as a very high (possible infinite)
dimensional RKHS (Reproducing Kernel Hilbert Space). Moreover, by leveraging
the similarity metric computed in RKHS, we augmented our non-parametric
learning with the technique of AdaptiveH- adaptively selecting a time-frame
window of finishing the optimal part of whole action-sequence sampled on some
preceding observed state. To validate our proposed approach, we conducted
extensive experiments with multiple classic benchmarks and one simulated
robotics benchmark equipped with dynamically changing environments. Overall,
our methodology has outperformed the well-established DDPG and TD3 methodology
by a sizeable margin in terms of learning performance.

Deep learning continues to play as a powerful state-of-art technique that has
achieved extraordinary accuracy levels in various domains of regression and
classification tasks, including images, video, signal, and natural language
data. The original goal of proposing the neural network model is to improve the
understanding of complex human brains using a mathematical expression approach.
However, recent deep learning techniques continue to lose the interpretations
of its functional process by being treated mostly as a black-box approximator.
To address this issue, such an AI model needs to be biological and
physiological realistic to incorporate a better understanding of human-machine
evolutionary intelligence. In this study, we compare neural networks and
biological circuits to discover the similarities and differences from various
perspective views. We further discuss the insights into how neural networks
learn from data by investigating human biological behaviors and understandable
justifications.

Explainable artificial intelligence (XAI) is an important and rapidly
expanding research topic. The goal of XAI is to gain trust in a machine
learning (ML) model through clear insights into how the model arrives at its
predictions. Genetic programming (GP) is often cited as being uniquely
well-suited to contribute to XAI because of its capacity to learn (small)
symbolic models that have the potential to be interpreted. Nevertheless, like
many ML algorithms, GP typically results in a single best model. However, in
practice, the best model in terms of training error may well not be the most
suitable one as judged by a domain expert for various reasons, including
overfitting, multiple different models existing that have similar accuracy, and
unwanted errors on particular data points due to typical accuracy measures like
mean squared error. Hence, to increase chances that domain experts deem a
resulting model plausible, it becomes important to be able to explicitly search
for multiple, diverse, high-quality models that trade-off different meanings of
accuracy. In this paper, we achieve exactly this with a novel multi-modal
multi-tree multi-objective GP approach that extends a modern model-based GP
algorithm known as GP-GOMEA that is already effective at searching for small

The study of language emergence aims to understand how human languages are
shaped by perceptual grounding and communicative intent. Computational
approaches to emergent communication (EC) predominantly consider referential
games in limited domains and analyze the learned protocol within the game
framework. As a result, it remains unclear how the emergent languages from
these settings connect to natural languages or provide benefits in real-world
language processing tasks, where statistical models trained on large text
corpora dominate. In this work, we propose a novel way to establish such a link
by corpus transfer, i.e. pretraining on a corpus of emergent language for
downstream natural language tasks, which is in contrast to prior work that
directly transfers speaker and listener parameters. Our approach showcases
non-trivial transfer benefits for two different tasks -- language modeling and
image captioning. For example, in a low-resource setup (modeling 2 million
natural language tokens), pre-training on an emergent language corpus with just
2 million tokens reduces model perplexity by $24.6\%$ on average across ten
natural languages. We also introduce a novel metric to predict the
transferability of an emergent language by translating emergent messages to
natural language captions grounded on the same images. We find that our
translation-based metric highly correlates with the downstream performance on
modeling natural languages (for instance $\rho=0.83$ on Hebrew), while
topographic similarity, a popular metric in previous work, shows surprisingly
low correlation ($\rho=0.003$), hinting that simple properties like attribute
disentanglement from synthetic domains might not capture the full complexities
of natural language. Our findings also indicate potential benefits of moving
language emergence forward with natural language resources and models.

Safe collaboration between human and robots in a common unstructured
environment becomes increasingly critical with the emergence of Industry 4.0.
However, to accomplish safe, robust, and autonomous collaboration with humans,
modern robotic systems must possess not only effective proximity perception but
also reactive obstacle avoidance. Unfortunately, for most robotic systems,
their shared working environment with human operators may not always be static,
instead often dynamically varying and being constantly cluttered with
unanticipated obstacles or hazards. In this paper, we present a novel
methodology of reactive whole-body obstacle avoidance methodology that
safeguards the human who enters the robot's workspace through achieving
conflict-free human-robot interactions even in a dynamically constrained
environment. Unlike existing Jacobian-type or geometric approaches, our
proposed methodology leverages both topological manifold learning and latest
deep learning advances, therefore can not only be readily generalized into
other unseen problem settings, but also achieve high computing efficiency with
concrete theoretical basis. Furthermore, in sharp contrast to the industrial
cobot setting, our methodology allows a robotic arm to proactively avoid
obstacles of arbitrary 3D shapes without direct contacting. To solidify our
study, we implement and validate our methodology with a robotic platform
consisting of dual 6-DoF robotic arms with optimized proximity sensor
placement, both of which are capable of working collaboratively with different
levels of interference. Specifically, one arm will perform reactive whole-body
obstacle avoidance while achieving its pre-determined objective, with the other
arm emulating the presence of a human collaborator with independent and
potentially adversary movements.

End-to-end speech-to-speech translation (S2ST) without relying on
intermediate text representations is a rapidly emerging frontier of research.
Recent works have demonstrated that the performance of such direct S2ST systems
is approaching that of conventional cascade S2ST when trained on comparable
datasets. However, in practice, the performance of direct S2ST is bounded by
the availability of paired S2ST training data. In this work, we explore
multiple approaches for leveraging much more widely available unsupervised and
weakly-supervised speech and text data to improve the performance of direct
S2ST based on Translatotron 2. With our most effective approaches, the average
translation quality of direct S2ST on 21 language pairs on the CVSS-C corpus is
improved by +13.6 BLEU (or +113% relatively), as compared to the previous
state-of-the-art trained without additional data. The improvements on
low-resource language are even more significant (+398% relatively on average).
Our comparative studies suggest future research directions for S2ST and speech

There often is a dilemma between ease of optimization and robust
out-of-distribution (OoD) generalization. For instance, many OoD methods rely
on penalty terms whose optimization is challenging. They are either too strong
to optimize reliably or too weak to achieve their goals.
  We propose to initialize the networks with a rich representation containing a
palette of potentially useful features, ready to be used by even simple models.
On the one hand, a rich representation provides a good initialization for the
optimizer. On the other hand, it also provides an inductive bias that helps OoD
generalization. Such a representation is constructed with the Rich Feature
Construction (RFC) algorithm, also called the Bonsai algorithm, which consists
of a succession of training episodes. During discovery episodes, we craft a
multi-objective optimization criterion and its associated datasets in a manner
that prevents the network from using the features constructed in the previous
iterations. During synthesis episodes, we use knowledge distillation to force
the network to simultaneously represent all the previously discovered features.
  Initializing the networks with Bonsai representations consistently helps six
OoD methods achieve top performance on ColoredMNIST benchmark. The same
technique substantially outperforms comparable results on the Wilds Camelyon17
task, eliminates the high result variance that plagues other methods, and makes
hyperparameter tuning and model selection more reliable.

We present a technique for zero-shot generation of a 3D model using only a
target text prompt. Without any 3D supervision our method deforms the control
shape of a limit subdivided surface along with its texture map and normal map
to obtain a 3D asset that corresponds to the input text prompt and can be
easily deployed into games or modeling applications. We rely only on a
pre-trained CLIP model that compares the input text prompt with differentiably
rendered images of our 3D model. While previous works have focused on
stylization or required training of generative models we perform optimization
on mesh parameters directly to generate shape, texture or both. To constrain
the optimization to produce plausible meshes and textures we introduce a number
of techniques using image augmentations and the use of a pretrained prior that
generates CLIP image embeddings given a text embedding.

We consider the problem of estimating social influence, the effect that a
person's behavior has on the future behavior of their peers. The key challenge
is that shared behavior between friends could be equally explained by influence
or by two other confounding factors: 1) latent traits that caused people to
both become friends and engage in the behavior, and 2) latent preferences for
the behavior. This paper addresses the challenges of estimating social
influence with three contributions. First, we formalize social influence as a
causal effect, one which requires inferences about hypothetical interventions.
Second, we develop Poisson Influence Factorization (PIF), a method for
estimating social influence from observational data. PIF fits probabilistic
factor models to networks and behavior data to infer variables that serve as
substitutes for the confounding latent traits. Third, we develop assumptions
under which PIF recovers estimates of social influence. We empirically study
PIF with semi-synthetic and real data from Last.fm, and conduct a sensitivity
analysis. We find that PIF estimates social influence most accurately compared
to related methods and remains robust under some violations of its assumptions.

Federated learning has been extensively studied and is the prevalent method
for privacy-preserving distributed learning in edge devices. Correspondingly,
continual learning is an emerging field targeted towards learning multiple
tasks sequentially. However, there is little attention towards additional
challenges emerging when federated aggregation is performed in a continual
learning system. We identify \textit{client drift} as one of the key weaknesses
that arise when vanilla federated averaging is applied in such a system,
especially since each client can independently have different order of tasks.
We outline a framework for performing Federated Continual Learning (FCL) by
using NetTailor as a candidate continual learning approach and show the extent
of the problem of client drift. We show that adaptive federated optimization
can reduce the adverse impact of client drift and showcase its effectiveness on
CIFAR100, MiniImagenet, and Decathlon benchmarks. Further, we provide an
empirical analysis highlighting the interplay between different hyperparameters
such as client and server learning rates, the number of local training
iterations, and communication rounds. Finally, we evaluate our framework on
useful characteristics of federated learning systems such as scalability,
robustness to the skewness in clients' data distribution, and stragglers.

We present the extension of the Remember and Forget for Experience Replay
(ReF-ER) algorithm to Multi-Agent Reinforcement Learning (MARL). ReF-ER was
shown to outperform state of the art algorithms for continuous control in
problems ranging from the OpenAI Gym to complex fluid flows. In MARL, the
dependencies between the agents are included in the state-value estimator and
the environment dynamics are modeled via the importance weights used by ReF-ER.
In collaborative environments, we find the best performance when the value is
estimated using individual rewards and we ignore the effects of other actions
on the transition map. We benchmark the performance of ReF-ER MARL on the
Stanford Intelligent Systems Laboratory (SISL) environments. We find that
employing a single feed-forward neural network for the policy and the value
function in ReF-ER MARL, outperforms state of the art algorithms that rely on
complex neural network architectures.

In this paper, we propose a novel gait recognition method based on a
bag-of-words feature representation method. The algorithm is trained, tested
and evaluated on a unique human gait data consisting of 93 individuals who
walked with comfortable pace between two end points during two different
sessions. To evaluate the effectiveness of the proposed model, the results are
compared with the outputs of the classification using extracted features. As it
is presented, the proposed method results in significant improvement accuracy
compared to using common statistical features, in all the used classifiers.

Earthquake forecasting and prediction have long and in some cases sordid
histories but recent work has rekindled interest based on advances in early
warning, hazard assessment for induced seismicity and successful prediction of
laboratory earthquakes. In the lab, frictional stick-slip events provide an
analog for earthquakes and the seismic cycle. Labquakes are ideal targets for
machine learning (ML) because they can be produced in long sequences under
controlled conditions. Recent works show that ML can predict several aspects of
labquakes using fault zone acoustic emissions. Here, we generalize these
results and explore deep learning (DL) methods for labquake prediction and
autoregressive (AR) forecasting. DL improves existing ML methods of labquake
prediction. AR methods allow forecasting at future horizons via iterative
predictions. We demonstrate that DL models based on Long-Short Term Memory
(LSTM) and Convolution Neural Networks predict labquakes under several
conditions, and that fault zone stress can be predicted with fidelity,
confirming that acoustic energy is a fingerprint of fault zone stress. We
predict also time to start of failure (TTsF) and time to the end of Failure
(TTeF) for labquakes. Interestingly, TTeF is successfully predicted in all
seismic cycles, while the TTsF prediction varies with the amount of preseismic
fault creep. We report AR methods to forecast the evolution of fault stress
using three sequence modeling frameworks: LSTM, Temporal Convolution Network
and Transformer Network. AR forecasting is distinct from existing predictive
models, which predict only a target variable at a specific time. The results
for forecasting beyond a single seismic cycle are limited but encouraging. Our
ML/DL models outperform the state-of-the-art and our autoregressive model
represents a novel framework that could enhance current methods of earthquake
forecasting.

Recent work studies the supervised online continual learning setting where a
learner receives a stream of data whose class distribution changes over time.
Distinct from other continual learning settings the learner is presented new
samples only once and must distinguish between all seen classes. A number of
successful methods in this setting focus on storing and replaying a subset of
samples alongside incoming data in a computationally efficient manner. One
recent proposal ER-AML achieved strong performance in this setting by applying
an asymmetric loss based on contrastive learning to the incoming data and
replayed data. However, a key ingredient of the proposed method is avoiding
contrasts between incoming data and stored data, which makes it impractical for
the setting where only one new class is introduced in each phase of the stream.
In this work we adapt a recently proposed approach (\textit{BYOL}) from
self-supervised learning to the supervised learning setting, unlocking the
constraint on contrasts. We then show that supplementing this with additional
regularization on class prototypes yields a new method that achieves strong
performance in the one-class incremental learning setting and is competitive
with the top performing methods in the multi-class incremental setting.

Data analysis at the LHC has a very steep learning curve, which erects a
formidable barrier between data and anyone who wishes to analyze data, either
to study an idea or to simply understand how data analysis is performed. To
make analysis more accessible, we designed the so-called Analysis Description
Language (ADL), a domain specific language capable of describing the contents
of an LHC analysis in a standard and unambiguous way, independent of any
computing frameworks. ADL has an English-like highly human-readable syntax and
directly employs concepts relevant to HEP. Therefore it eliminates the need to
learn complex analysis frameworks written based on general purpose languages
such as C++ or Python, and shifts the focus directly to physics. Analyses
written in ADL can be run on data using a runtime interpreter called CutLang,
without the necessity of programming. ADL and CutLang are designed for use by
anyone with an interest in, and/or knowledge of LHC physics, ranging from
experimentalists and phenomenologists to non-professional enthusiasts.
ADL/CutLang are originally designed for research, but are also equally intended
for education and public use. This approach has already been employed to train
undergraduate students with no programming experience in LHC analysis in two
dedicated schools in Turkey and Vietnam, and is being adapted for use with LHC
Open Data. Moreover, work is in progress towards piloting an educational module
in particle physics data analysis for high school students and teachers. Here,
we introduce ADL and CutLang and present the educational activities based on
these practical tools.

Facial Action Coding System is an important approach of facial expression
analysis.This paper describes our submission to the third Affective Behavior
Analysis (ABAW) 2022 competition. We proposed a transfomer based model to
detect facial action unit (FAU) in video. To be specific, we firstly trained a
multi-modal model to extract both audio and visual feature. After that, we
proposed a action units correlation module to learn relationships between each
action unit labels and refine action unit detection result. Experimental
results on validation dataset shows that our method achieves better performance
than baseline model, which verifies that the effectiveness of proposed network.

Recent work on controlled text generation has either required attribute-based
fine-tuning of the base language model (LM), or has restricted the
parameterization of the attribute discriminator to be compatible with the base
autoregressive LM. In this work, we propose Mix and Match LM, a global
score-based alternative for controllable text generation that combines
arbitrary pre-trained black-box models for achieving the desired attributes in
the generated text without involving any fine-tuning or structural assumptions
about the black-box models. We interpret the task of controllable generation as
drawing samples from an energy-based model whose energy values are a linear
combination of scores from black-box models that are separately responsible for
fluency, the control attribute, and faithfulness to any conditioning context.
We use a Metropolis-Hastings sampling scheme to sample from this energy-based
model using bidirectional context and global attribute features. We validate
the effectiveness of our approach on various controlled generation and
style-based text revision tasks by outperforming recently proposed methods that
involve extra training, fine-tuning, or restrictive assumptions over the form
of models.

Forecasting the behavior of high-dimensional dynamical systems using machine
learning requires efficient methods to learn the underlying physical model. We
demonstrate spatiotemporal chaos prediction using a machine learning
architecture that, when combined with a next-generation reservoir computer,
displays state-of-the-art performance with a computational time $10^3-10^4$
times faster for training process and training data set $\sim 10^2$ times
smaller than other machine learning algorithms. We also take advantage of the
translational symmetry of the model to further reduce the computational cost
and training data, each by a factor of $\sim$10.

In this paper, we present our submission to 3rd Affective Behavior Analysis
in-the-wild (ABAW) challenge. Learningcomplex interactions among multimodal
sequences is critical to recognise dimensional affect from in-the-wild
audiovisual data. Recurrence and attention are the two widely used sequence
modelling mechanisms in the literature. To clearly understand the performance
differences between recurrent and attention models in audiovisual affect
recognition, we present a comprehensive evaluation of fusion models based on
LSTM-RNNs, self-attention and cross-modal attention, trained for valence and
arousal estimation. Particularly, we study the impact of some key design
choices: the modelling complexity of CNN backbones that provide features to the
the temporal models, with and without end-to-end learning. We trained the
audiovisual affect recognition models on in-the-wild ABAW corpus by
systematically tuning the hyper-parameters involved in the network architecture
design and training optimisation. Our extensive evaluation of the audiovisual
fusion models shows that LSTM-RNNs can outperform the attention models when
coupled with low-complex CNN backbones and trained in an end-to-end fashion,
implying that attention models may not necessarily be the optimal choice for
continuous-time multimodal emotion recognition.

We study a relaxed version of the column-sampling problem for the Nystr\"om
approximation of kernel matrices, where approximations are defined from
multisets of landmark points in the ambient space; such multisets are referred
to as Nystr\"om samples. We consider an unweighted variation of the radial
squared-kernel discrepancy (SKD) criterion as a surrogate for the classical
criteria used to assess the Nystr\"om approximation accuracy; in this setting,
we discuss how Nystr\"om samples can be efficiently optimised through
stochastic gradient descent. We perform numerical experiments which demonstrate
that the local minimisation of the radial SKD yields Nystr\"om samples with
improved Nystr\"om approximation accuracy.

To achieve conflict-free human-machine collaborations, robotic agents need to
skillfully avoid continuously moving obstacles while achieving collective
objectives. Sometimes, these obstacles can even change their 3D shapes and
forms simultaneously, hence being "amorphous". To this end, this paper
formulates the problem of Dynamic Amorphous Obstacle Avoidance (DAO-A), where a
robotic arm can dexterously avoid dynamically generated obstacles that
constantly change their trajectories and their 3D forms. Specifically, we
introduce a novel control strategy for robotic arms that leverages both
topological manifold learning and latest deep learning advancements. We test
our learning framework, using a 7-DoF robotic manipulator, in both simulation
and physical experiments, where the robot satisfactorily learns and synthesizes
realistic skills avoiding previously-unseen obstacles, while generating novel
movements to achieve pre-defined motion objectives. Most notably, our learned
methodology, once finalized, for a given robotic manipulator, can avoid any
number of 3D obstacles with arbitrary and unseen moving trajectories, therefore
it is universal, versatile, and completely reusable

The adversarial risk of a machine learning model has been widely studied.
Most previous works assume that the data lies in the whole ambient space. We
propose to take a new angle and take the manifold assumption into
consideration. Assuming data lies in a manifold, we investigate two new types
of adversarial risk, the normal adversarial risk due to perturbation along
normal direction, and the in-manifold adversarial risk due to perturbation
within the manifold. We prove that the classic adversarial risk can be bounded
from both sides using the normal and in-manifold adversarial risks. We also
show with a surprisingly pessimistic case that the standard adversarial risk
can be nonzero even when both normal and in-manifold risks are zero. We
finalize the paper with empirical studies supporting our theoretical results.
Our results suggest the possibility of improving the robustness of a classifier
by only focusing on the normal adversarial risk.

Adam and AdaBelief compute and make use of elementwise adaptive stepsizes in
training deep neural networks (DNNs) by tracking the exponential moving average
(EMA) of the squared-gradient g_t^2 and the squared prediction error
(m_t-g_t)^2, respectively, where m_t is the first momentum at iteration t and
can be viewed as a prediction of g_t. In this work, we investigate if layerwise
gradient statistics can be expoited in Adam and AdaBelief to allow for more
effective training of DNNs. We address the above research question in two
steps. Firstly, we slightly modify Adam and AdaBelief by introducing layerwise
adaptive stepsizes in their update procedures via either pre- or
post-processing. Our empirical results indicate that the slight modification
produces comparable performance for training VGG and ResNet models over CIFAR10
and CIFAR100, suggesting that layer-wise gradient statistics play an important
role towards the success of Adam and AdaBelief for at least certian DNN tasks.
In the second step, we propose Aida, a new optimisation method, with the
objective that the elementwise stepsizes within each layer have significantly
smaller statistical variances, and the layerwise average stepsizes are much
more compact across all the layers. Motivated by the fact that (m_t-g_t)^2 in
AdaBelief is conservative in comparison to g_t^2 in Adam in terms of layerwise
statistical averages and variances, Aida is designed by tracking a more
conservative function of m_t and g_t than (m_t-g_t)^2 via layerwise vector
projections. Experimental results show that Aida produces either competitive or
better performance with respect to a number of existing methods including Adam
and AdaBelief for a set of challenging DNN tasks. Code is available <a
href="https://github.com/guoqiang-x-zhang/AidaOptimizer">at this URL</a>

Foundation models offer an exciting new paradigm for constructing models with
out-of-the-box embeddings and a few labeled examples. However, it is not clear
how to best apply foundation models without labeled data. A potential approach
is to fuse foundation models with weak supervision frameworks, which use weak
label sources -- pre-trained models, heuristics, crowd-workers -- to construct
pseudolabels. The challenge is building a combination that best exploits the
signal available in both foundation models and weak sources. We propose Liger,
a combination that uses foundation model embeddings to improve two crucial
elements of existing weak supervision techniques. First, we produce finer
estimates of weak source quality by partitioning the embedding space and
learning per-part source accuracies. Second, we improve source coverage by
extending source votes in embedding space. Despite the black-box nature of
foundation models, we prove results characterizing how our approach improves
performance and show that lift scales with the smoothness of label
distributions in embedding space. On six benchmark NLP and video tasks, Liger
outperforms vanilla weak supervision by 14.1 points, weakly-supervised kNN and
adapters by 11.8 points, and kNN and adapters supervised by traditional hand
labels by 7.2 points.

Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is
a long-standing problem in computer vision. Driven by end-to-end deep learning,
recent studies suggest interpreting PnP as a differentiable layer, so that
2D-3D point correspondences can be partly learned by backpropagating the
gradient w.r.t. object pose. Yet, learning the entire set of unrestricted 2D-3D
points from scratch fails to converge with existing approaches, since the
deterministic pose is inherently non-differentiable. In this paper, we propose
the EPro-PnP, a probabilistic PnP layer for general end-to-end pose estimation,
which outputs a distribution of pose on the SE(3) manifold, essentially
bringing categorical Softmax to the continuous domain. The 2D-3D coordinates
and corresponding weights are treated as intermediate variables learned by
minimizing the KL divergence between the predicted and target pose
distribution. The underlying principle unifies the existing approaches and
resembles the attention mechanism. EPro-PnP significantly outperforms
competitive baselines, closing the gap between PnP-based method and the
task-specific leaders on the LineMOD 6DoF pose estimation and nuScenes 3D
object detection benchmarks.

Optimizing behaviors for dexterous manipulation has been a longstanding
challenge in robotics, with a variety of methods from model-based control to
model-free reinforcement learning having been previously explored in
literature. Perhaps one of the most powerful techniques to learn complex
manipulation strategies is imitation learning. However, collecting and learning
from demonstrations in dexterous manipulation is quite challenging. The
complex, high-dimensional action-space involved with multi-finger control often
leads to poor sample efficiency of learning-based methods. In this work, we
propose 'Dexterous Imitation Made Easy' (DIME) a new imitation learning
framework for dexterous manipulation. DIME only requires a single RGB camera to
observe a human operator and teleoperate our robotic hand. Once demonstrations
are collected, DIME employs standard imitation learning methods to train
dexterous manipulation policies. On both simulation and real robot benchmarks
we demonstrate that DIME can be used to solve complex, in-hand manipulation
tasks such as 'flipping', 'spinning', and 'rotating' objects with the Allegro
hand. Our framework along with pre-collected demonstrations is publicly
available at https://nyu-robot-learning.github.io/dime.

Recent studies on StyleGAN show high performance on artistic portrait
generation by transfer learning with limited data. In this paper, we explore
more challenging exemplar-based high-resolution portrait style transfer by
introducing a novel DualStyleGAN with flexible control of dual styles of the
original face domain and the extended artistic portrait domain. Different from
StyleGAN, DualStyleGAN provides a natural way of style transfer by
characterizing the content and style of a portrait with an intrinsic style path
and a new extrinsic style path, respectively. The delicately designed extrinsic
style path enables our model to modulate both the color and complex structural
styles hierarchically to precisely pastiche the style example. Furthermore, a
novel progressive fine-tuning scheme is introduced to smoothly transform the
generative space of the model to the target domain, even with the above
modifications on the network architecture. Experiments demonstrate the
superiority of DualStyleGAN over state-of-the-art methods in high-quality
portrait style transfer and flexible style control.

3D point cloud registration is fragile to outliers, which are labeled as the
points without corresponding points. To handle this problem, a widely adopted
strategy is to estimate the relative pose based only on some accurate
correspondences, which is achieved by building correspondences on the
identified inliers or by selecting reliable ones. However, these approaches are
usually complicated and time-consuming. By contrast, the virtual point-based
methods learn the virtual corresponding points (VCPs) for all source points
uniformly without distinguishing the outliers and the inliers. Although this
strategy is time-efficient, the learned VCPs usually exhibit serious collapse
degeneration due to insufficient supervision and the inherent distribution
limitation. In this paper, we propose to exploit the best of both worlds and
present a novel robust 3D point cloud registration framework. We follow the
idea of the virtual point-based methods but learn a new type of virtual points
called rectified virtual corresponding points (RCPs), which are defined as the
point set with the same shape as the source and with the same pose as the
target. Hence, a pair of consistent point clouds, i.e. source and RCPs, is
formed by rectifying VCPs to RCPs (VRNet), through which reliable
correspondences between source and RCPs can be accurately obtained. Since the
relative pose between source and RCPs is the same as the relative pose between
source and target, the input point clouds can be registered naturally.
Specifically, we first construct the initial VCPs by using an estimated soft
matching matrix to perform a weighted average on the target points. Then, we
design a correction-walk module to learn an offset to rectify VCPs to RCPs,
which effectively breaks the distribution limitation of VCPs. Finally, we
develop a hybrid loss function to enforce the shape and geometry structure
consistency ...

Transformer-based models generally allocate the same amount of computation
for each token in a given sequence. We develop a simple but effective "token
dropping" method to accelerate the pretraining of transformer models, such as
BERT, without degrading its performance on downstream tasks. In short, we drop
unimportant tokens starting from an intermediate layer in the model to make the
model focus on important tokens; the dropped tokens are later picked up by the
last layer of the model so that the model still produces full-length sequences.
We leverage the already built-in masked language modeling (MLM) loss to
identify unimportant tokens with practically no computational overhead. In our
experiments, this simple approach reduces the pretraining cost of BERT by 25%
while achieving similar overall fine-tuning performance on standard downstream
tasks.

3D point cloud registration in remote sensing field has been greatly advanced
by deep learning based methods, where the rigid transformation is either
directly regressed from the two point clouds (correspondences-free approaches)
or computed from the learned correspondences (correspondences-based
approaches). Existing correspondences-free methods generally learn the holistic
representation of the entire point cloud, which is fragile for partial and
noisy point clouds. In this paper, we propose a correspondences-free
unsupervised point cloud registration (UPCR) method from the representation
separation perspective. First, we model the input point cloud as a combination
of pose-invariant representation and pose-related representation. Second, the
pose-related representation is used to learn the relative pose wrt a "latent
canonical shape" for the source and target point clouds respectively. Third,
the rigid transformation is obtained from the above two learned relative poses.
Our method not only filters out the disturbance in pose-invariant
representation but also is robust to partial-to-partial point clouds or noise.
Experiments on benchmark datasets demonstrate that our unsupervised method
achieves comparable if not better performance than state-of-the-art supervised
registration methods.

The primary assumption of conventional supervised learning or classification
is that the test samples are drawn from the same distribution as the training
samples, which is called closed set learning or classification. In many
practical scenarios, this is not the case because there are unknowns or unseen
class samples in the test data, which is called the open set scenario, and the
unknowns need to be detected. This problem is referred to as the open set
recognition problem and is important in safety-critical applications. We
propose to detect unknowns (or unseen class samples) through learning pairwise
similarities. The proposed method works in two steps. It first learns a closed
set classifier using the seen classes that have appeared in training and then
learns how to compare seen classes with pseudo-unseen (automatically generated
unseen class samples). The pseudo-unseen generation is carried out by
performing distribution shifting augmentations on the seen or training samples.
We call our method OPG (Open set recognition based on Pseudo unseen data
Generation). The experimental evaluation shows that the learned
similarity-based features can successfully distinguish seen from unseen in
benchmark datasets for open set recognition.

Much of the research on learning symbolic models of AI agents focuses on
agents with stationary models. This assumption fails to hold in settings where
the agent's capabilities may change as a result of learning, adaptation, or
other post-deployment modifications. Efficient assessment of agents in such
settings is critical for learning the true capabilities of an AI system and for
ensuring its safe usage. In this work, we propose a novel approach to
"differentially" assess black-box AI agents that have drifted from their
previously known models. As a starting point, we consider the fully observable
and deterministic setting. We leverage sparse observations of the drifted
agent's current behavior and knowledge of its initial model to generate an
active querying policy that selectively queries the agent and computes an
updated model of its functionality. Empirical evaluation shows that our
approach is much more efficient than re-learning the agent model from scratch.
We also show that the cost of differential assessment using our method is
proportional to the amount of drift in the agent's functionality.

Multi-task learning (MTL) is a novel framework to learn several tasks
simultaneously with a single shared network where each task has its distinct
personalized header network for fine-tuning. MTL can be implemented in
federated learning settings as well, in which tasks are distributed across
clients. In federated settings, the statistical heterogeneity due to different
task complexities and data heterogeneity due to non-iid nature of local
datasets can both degrade the learning performance of the system. In addition,
tasks can negatively affect each other's learning performance due to negative
transference effects. To cope with these challenges, we propose FedGradNorm
which uses a dynamic-weighting method to normalize gradient norms in order to
balance learning speeds among different tasks. FedGradNorm improves the overall
learning performance in a personalized federated learning setting. We provide
convergence analysis for FedGradNorm by showing that it has an exponential
convergence rate. We also conduct experiments on multi-task facial landmark
(MTFL) and wireless communication system dataset (RadComDynamic). The
experimental results show that our framework can achieve faster training
performance compared to equal-weighting strategy. In addition to improving
training speed, FedGradNorm also compensates for the imbalanced datasets among
clients.

We develop and analyze algorithms for distributionally robust optimization
(DRO) of convex losses. In particular, we consider group-structured and bounded
$f$-divergence uncertainty sets. Our approach relies on an accelerated method
that queries a ball optimization oracle, i.e., a subroutine that minimizes the
objective within a small ball around the query point. Our main contribution is
efficient implementations of this oracle for DRO objectives. For DRO with $N$
non-smooth loss functions, the resulting algorithms find an $\epsilon$-accurate
solution with $\widetilde{O}\left(N\epsilon^{-2/3} + \epsilon^{-2}\right)$
first-order oracle queries to individual loss functions. Compared to existing
algorithms for this problem, we improve complexity by a factor of up to
$\epsilon^{-4/3}$.

Precipitation nowcasting is of great importance for weather forecast users,
for activities ranging from outdoor activities and sports competitions to
airport traffic management. In contrast to long-term precipitation forecasts
which are traditionally obtained from numerical models, precipitation
nowcasting needs to be very fast. It is therefore more challenging to obtain
because of this time constraint. Recently, many machine learning based methods
had been proposed. We propose the use three popular deep learning models
(U-net, ConvLSTM and SVG-LP) trained on two-dimensional precipitation maps for
precipitation nowcasting. We proposed an algorithm for patch extraction to
obtain high resolution precipitation maps. We proposed a loss function to solve
the blurry image issue and to reduce the influence of zero value pixels in
precipitation maps.

We consider a type of constrained optimization problem, where the violation
of a constraint leads to an irrevocable loss, such as breakage of a valuable
experimental resource/platform or loss of human life. Such problems are
referred to as safe optimization problems (SafeOPs). While SafeOPs have
received attention in the machine learning community in recent years, there was
little interest in the evolutionary computation (EC) community despite some
early attempts between 2009 and 2011. Moreover, there is a lack of acceptable
guidelines on how to benchmark different algorithms for SafeOPs, an area where
the EC community has significant experience in. Driven by the need for more
efficient algorithms and benchmark guidelines for SafeOPs, the objective of
this paper is to reignite the interest of this problem class in the EC
community. To achieve this we (i) provide a formal definition of SafeOPs and
contrast it to other types of optimization problems that the EC community is
familiar with, (ii) investigate the impact of key SafeOP parameters on the
performance of selected safe optimization algorithms, (iii) benchmark EC
against state-of-the-art safe optimization algorithms from the machine learning
community, and (iv) provide an open-source Python framework to replicate and
extend our work.

The term `surrogate modeling' in computational science and engineering refers
to the development of computationally efficient approximations for expensive
simulations, such as those arising from numerical solution of partial
differential equations (PDEs). Surrogate modeling is an enabling methodology
for many-query computations in science and engineering, which include iterative
methods in optimization and sampling methods in uncertainty quantification.
Over the last few years, several approaches to surrogate modeling for PDEs
using neural networks have emerged, motivated by successes in using neural
networks to approximate nonlinear maps in other areas. In principle, the
relative merits of these different approaches can be evaluated by
understanding, for each one, the cost required to achieve a given level of
accuracy. However, the absence of a complete theory of approximation error for
these approaches makes it difficult to assess this cost-accuracy trade-off. The
purpose of the paper is to provide a careful numerical study of this issue,
comparing a variety of different neural network architectures for operator
approximation across a range of problems arising from PDE models in continuum
mechanics.

In natural language, referencing objects at different levels of specificity
is a fundamental pragmatic mechanism for efficient communication in context. We
develop a novel communication game, the hierarchical reference game, to study
the emergence of such reference systems in artificial agents. We consider a
simplified world, in which concepts are abstractions over a set of primitive
attributes (e.g., color, style, shape). Depending on how many attributes are
combined, concepts are more general ("circle") or more specific ("red dotted
circle"). Based on the context, the agents have to communicate at different
levels of this hierarchy. Our results show that the agents learn to play the
game successfully and can even generalize to novel concepts. To achieve
abstraction, they use implicit (omitting irrelevant information) and explicit
(indicating that attributes are irrelevant) strategies. In addition, the
compositional structure underlying the concept hierarchy is reflected in the
emergent protocols, indicating that the need to develop hierarchical reference
systems supports the emergence of compositionality.

In this paper, we investigate the continual learning of Vision Transformers
(ViT) for the challenging exemplar-free scenario, with special focus on how to
efficiently distill the knowledge of its crucial self-attention mechanism
(SAM). Our work takes an initial step towards a surgical investigation of SAM
for designing coherent continual learning methods in ViTs. We first carry out
an evaluation of established continual learning regularization techniques. We
then examine the effect of regularization when applied to two key enablers of
SAM: (a) the contextualized embedding layers, for their ability to capture
well-scaled representations with respect to the values, and (b) the prescaled
attention maps, for carrying value-independent global contextual information.
We depict the perks of each distilling strategy on two image recognition
benchmarks (CIFAR100 and ImageNet-32) -- while (a) leads to a better overall
accuracy, (b) helps enhance the rigidity by maintaining competitive
performances. Furthermore, we identify the limitation imposed by the symmetric
nature of regularization losses. To alleviate this, we propose an asymmetric
variant and apply it to the pooled output distillation (POD) loss adapted for
ViTs. Our experiments confirm that introducing asymmetry to POD boosts its
plasticity while retaining stability across (a) and (b). Moreover, we
acknowledge low forgetting measures for all the compared methods, indicating
that ViTs might be naturally inclined continual learner

This paper presents a novel method for face clustering in videos using a
video-centralised transformer. Previous works often employed contrastive
learning to learn frame-level representation and used average pooling to
aggregate the features along the temporal dimension. This approach may not
fully capture the complicated video dynamics. In addition, despite the recent
progress in video-based contrastive learning, few have attempted to learn a
self-supervised clustering-friendly face representation that benefits the video
face clustering task. To overcome these limitations, our method employs a
transformer to directly learn video-level representations that can better
reflect the temporally-varying property of faces in videos, while we also
propose a video-centralised self-supervised framework to train the transformer
model. We also investigate face clustering in egocentric videos, a
fast-emerging field that has not been studied yet in works related to face
clustering. To this end, we present and release the first large-scale
egocentric video face clustering dataset named EasyCom-Clustering. We evaluate
our proposed method on both the widely used Big Bang Theory (BBT) dataset and
the new EasyCom-Clustering dataset. Results show the performance of our
video-centralised transformer has surpassed all previous state-of-the-art
methods on both benchmarks, exhibiting a self-attentive understanding of face
videos.

The Kullback-Leibler divergence or relative entropy is an
information-theoretic measure between statistical models that play an important
role in measuring a distance between random variables. In the study of complex
systems, random fields are mathematical structures that models the interaction
between these variables by means of an inverse temperature parameter,
responsible for controlling the spatial dependence structure along the field.
In this paper, we derive closed-form expressions for the Kullback-Leibler
divergence between two pairwise isotropic Gaussian-Markov random fields in both
univariate and multivariate cases. The proposed equation allows the development
of novel similarity measures in image processing and machine learning
applications, such as image denoising and unsupervised metric learning.

Generating speech-consistent body and gesture movements is a long-standing
problem in virtual avatar creation. Previous studies often synthesize pose
movement in a holistic manner, where poses of all joints are generated
simultaneously. Such a straightforward pipeline fails to generate fine-grained
co-speech gestures. One observation is that the hierarchical semantics in
speech and the hierarchical structures of human gestures can be naturally
described into multiple granularities and associated together. To fully utilize
the rich connections between speech audio and human gestures, we propose a
novel framework named Hierarchical Audio-to-Gesture (HA2G) for co-speech
gesture generation. In HA2G, a Hierarchical Audio Learner extracts audio
representations across semantic granularities. A Hierarchical Pose Inferer
subsequently renders the entire human pose gradually in a hierarchical manner.
To enhance the quality of synthesized gestures, we develop a contrastive
learning strategy based on audio-text alignment for better audio
representations. Extensive experiments and human evaluation demonstrate that
the proposed method renders realistic co-speech gestures and outperforms
previous methods in a clear margin. Project page:
https://alvinliu0.github.io/projects/HA2G

In machine learning, fewer features reduce model complexity. Carefully
assessing the influence of each input feature on the model quality is therefore
a crucial preprocessing step. We propose a novel feature selection algorithm
based on a quadratic unconstrained binary optimization (QUBO) problem, which
allows to select a specified number of features based on their importance and
redundancy. In contrast to iterative or greedy methods, our direct approach
yields higherquality solutions. QUBO problems are particularly interesting
because they can be solved on quantum hardware. To evaluate our proposed
algorithm, we conduct a series of numerical experiments using a classical
computer, a quantum gate computer and a quantum annealer. Our evaluation
compares our method to a range of standard methods on various benchmark
datasets. We observe competitive performance.

When trained on diverse labeled data, machine learning models have proven
themselves to be a powerful tool in all facets of society. However, due to
budget limitations, deliberate or non-deliberate censorship, and other problems
during data collection and curation, the labeled training set might exhibit a
systematic shortage of data for certain groups. We investigate a scenario in
which the absence of certain data is linked to the second level of a two-level
hierarchy in the data. Inspired by the idea of protected groups from
algorithmic fairness, we refer to the partitions carved by this second level as
"subgroups"; we refer to combinations of subgroups and classes, or leaves of
the hierarchy, as "sources". To characterize the problem, we introduce the
concept of classes with incomplete subgroup support. The representational bias
in the training set can give rise to spurious correlations between the classes
and the subgroups which render standard classification models ungeneralizable
to unseen sources. To overcome this bias, we make use of an additional, diverse
but unlabeled dataset, called the "deployment set", to learn a representation
that is invariant to subgroup. This is done by adversarially matching the
support of the training and deployment sets in representation space. In order
to learn the desired invariance, it is paramount that the sets of samples
observed by the discriminator are balanced by class; this is easily achieved
for the training set, but requires using semi-supervised clustering for the
deployment set. We demonstrate the effectiveness of our method with experiments
on several datasets and variants of the problem.

Transformer-based language models (TLMs) provide state-of-the-art performance
in many modern natural language processing applications. TLM training is
conducted in two phases. First, the model is pre-trained over large volumes of
text to minimize a generic objective function, such as the Masked Language
Model (MLM). Second, the model is fine-tuned in specific downstream tasks.
Pre-training requires large volumes of data and high computational resources,
while introducing many still unresolved design choices. For instance, selecting
hyperparameters for language model pre-training is often carried out based on
heuristics or grid-based searches. In this work, we propose a multi-armed
bandit-based online optimization framework for the sequential selection of
pre-training hyperparameters to optimize language model performance. We pose
the pre-training procedure as a sequential decision-making task, where at each
pre-training step, an agent must determine what hyperparameters to use towards
optimizing the pre-training objective. We propose a Thompson sampling bandit
algorithm, based on a surrogate Gaussian process reward model of the MLM
pre-training objective, for its sequential minimization. We empirically show
how the proposed Gaussian process based Thompson sampling pre-trains robust and
well-performing language models. Namely, by sequentially selecting masking
hyperparameters of the TLM, we achieve satisfactory performance in less epochs,
not only in terms of the pre-training MLM objective, but in diverse downstream
fine-tuning tasks. The proposed bandit-based technique provides an automated
hyperparameter selection method for pre-training TLMs of interest to
practitioners. In addition, our results indicate that, instead of MLM
pre-training with fixed masking probabilities, sequentially adapting the
masking hyperparameters improves both pre-training loss and downstream task
metrics.

Progress in automatic control of thermal processes and real-time estimation
of heat penetration into live tissue has long been limited by the difficulty of
obtaining high-fidelity thermodynamic models. Traditionally, in complex
thermodynamic systems, it is often infeasible to estimate the thermophysical
parameters of spatiotemporally varying processes, forcing the adoption of
model-free control architectures. This comes at the cost of losing any
robustness guarantees, and implies a need for extensive real-life testing. In
recent years, however, infrared cameras and other thermographic equipment have
become readily applicable to these processes, allowing for a real-time,
non-invasive means of sensing the thermal state of a process. In this work, we
present a novel physics-based approach to learning a thermal process's dynamics
directly from such real-time thermographic data, while focusing attention on
regions with high thermal activity. We call this process, which applies to any
higher-dimensional scalar field, attention-based noise robust averaging (ANRA).
Given a partial-differential equation model structure, we show that our
approach is robust against noise, and can be used to initialize optimization
routines to further refine parameter estimates. We demonstrate our method on
several simulation examples, as well as by applying it to electrosurgical
thermal response data on in vivo porcine skin tissue.

Deep Neural Networks (DNNs) have been widely used in software making
decisions impacting people's lives. However, they have been found to exhibit
severe erroneous behaviors that may lead to unfortunate outcomes. Previous work
shows that such misbehaviors often occur due to class property violations
rather than errors on a single image. Although methods for detecting such
errors have been proposed, fixing them has not been studied so far. Here, we
propose a generic method called Weighted Regularization (WR) consisting of five
concrete methods targeting the error-producing classes to fix the DNNs. In
particular, it can repair confusion error and bias error of DNN models for both
single-label and multi-label image classifications. A confusion error happens
when a given DNN model tends to confuse between two classes. Each method in WR
assigns more weights at a stage of DNN retraining or inference to mitigate the
confusion between target pair. A bias error can be fixed similarly. We evaluate
and compare the proposed methods along with baselines on six widely-used
datasets and architecture combinations. The results suggest that WR methods
have different trade-offs but under each setting at least one WR method can
greatly reduce confusion/bias errors at a very limited cost of the overall

Recent text-to-image generation methods provide a simple yet exciting
conversion capability between text and image domains. While these methods have
incrementally improved the generated image fidelity and text relevancy, several
pivotal gaps remain unanswered, limiting applicability and quality. We propose
a novel text-to-image method that addresses these gaps by (i) enabling a simple
control mechanism complementary to text in the form of a scene, (ii)
introducing elements that substantially improve the tokenization process by
employing domain-specific knowledge over key image regions (faces and salient
objects), and (iii) adapting classifier-free guidance for the transformer use
case. Our model achieves state-of-the-art FID and human evaluation results,
unlocking the ability to generate high fidelity images in a resolution of
512x512 pixels, significantly improving visual quality. Through scene
controllability, we introduce several new capabilities: (i) Scene editing, (ii)
text editing with anchor scenes, (iii) overcoming out-of-distribution text
prompts, and (iv) story illustration generation, as demonstrated in the story
we wrote.

Hyperparameters (HPs) Optimization in machine learning algorithms represents
an open problem with a direct impact on algorithm performances and on the
knowledge extraction process from data. Matrix Decompositions (MDs) has
recently gained more attention in data science as mathematical techniques able
to capture latent information embedded in large datasets. MDs can be formalized
as penalized optimization problems in which the tuning of the penalization HPs
represents an issue. Current literature panorama does not provide any general
framework addressing optimally conditions for the best configuration of HPs. In
this work, we focus on the role the HPs play in the penalized Nonnegative
Matrix Factorizations (NMF) context and on the importance of their correct
selection. Using a bi-level optimization approach, HPs selection is considered
from an optimization point of view and their choice is directly incorporated in
the unsupervised algorithm as a part of the updating process. Either theorems
of existence and convergence of numerical solutions, under appropriate
assumptions, are studied and a novel algorithm, namely AltBi-Alternate bi-level
Algorithm, is proposed to tune the penalization HP in NMF problems.

Machine learning driven trading strategies have garnered a lot of interest
over the past few years. There is, however, limited consensus on the ideal
approach for the development of such trading strategies. Further, most
literature has focused on trading strategies for short-term trading, with
little or no focus on strategies that attempt to build long-term wealth. Our
paper proposes a new approach for developing long-term investment strategies
using an ensemble of evolutionary algorithms and a deep learning model by
taking a series of short-term purchase decisions. Our methodology focuses on
building long-term wealth by improving systematic investment planning (SIP)
decisions on Exchange Traded Funds (ETF) over a period of time. We provide
empirical evidence of superior performance (around 1% higher returns) using our
ensemble approach as compared to the traditional daily systematic investment
practice on a given ETF. Our results are based on live trading decisions made
by our algorithm and executed on the Robinhood trading platform.

As the popularity of quantum computing continues to grow, quantum machine
access over the cloud is critical to both academic and industry researchers
across the globe. And as cloud quantum computing demands increase
exponentially, the analysis of resource consumption and execution
characteristics are key to efficient management of jobs and resources at both
the vendor-end as well as the client-end. While the analysis of resource
consumption and management are popular in the classical HPC domain, it is
severely lacking for more nascent technology like quantum computing.
  This paper is a first-of-its-kind academic study, analyzing various trends in
job execution and resources consumption / utilization on quantum cloud systems.
We focus on IBM Quantum systems and analyze characteristics over a two year
period, encompassing over 6000 jobs which contain over 600,000 quantum circuit
executions and correspond to almost 10 billion "shots" or trials over 20+
quantum machines. Specifically, we analyze trends focused on, but not limited
to, execution times on quantum machines, queuing/waiting times in the cloud,
circuit compilation times, machine utilization, as well as the impact of job
and machine characteristics on all of these trends. Our analysis identifies
several similarities and differences with classical HPC cloud systems. Based on
our insights, we make recommendations and contributions to improve the
management of resources and jobs on future quantum cloud systems.

A major prerequisite for the application of machine learning models in
clinical decision making is trust and interpretability. Current explainability
studies in the neuroimaging community have mostly focused on explaining
individual decisions of trained models, e.g. obtained by a convolutional neural
network (CNN). Using attribution methods such as layer-wise relevance
propagation or SHAP heatmaps can be created that highlight which regions of an
input are more relevant for the decision than others. While this allows the
detection of potential data set biases and can be used as a guide for a human
expert, it does not allow an understanding of the underlying principles the
model has learned. In this study, we instead show, to the best of our
knowledge, for the first time results using feature visualization of
neuroimaging CNNs. Particularly, we have trained CNNs for different tasks
including sex classification and artificial lesion classification based on
structural magnetic resonance imaging (MRI) data. We have then iteratively
generated images that maximally activate specific neurons, in order to
visualize the patterns they respond to. To improve the visualizations we
compared several regularization strategies. The resulting images reveal the
learned concepts of the artificial lesions, including their shapes, but remain
hard to interpret for abstract features in the sex classification task.

We are interested in anticipating as early as possible the target location of
a person's object manipulation action in a 3D workspace from egocentric vision.
It is important in fields like human-robot collaboration, but has not yet
received enough attention from vision and learning communities. To stimulate
more research on this challenging egocentric vision task, we propose a large
multimodality dataset of more than 1 million frames of RGB-D and IMU streams,
and provide evaluation metrics based on our high-quality 2D and 3D labels from
semi-automatic annotation. Meanwhile, we design baseline methods using
recurrent neural networks and conduct various ablation studies to validate
their effectiveness. Our results demonstrate that this new task is worthy of
further study by researchers in robotics, vision, and learning communities.

We present minicons, an open source library that provides a standard API for
researchers interested in conducting behavioral and representational analyses
of transformer-based language models (LMs). Specifically, minicons enables
researchers to apply analysis methods at two levels: (1) at the prediction
level -- by providing functions to efficiently extract word/sentence level
probabilities; and (2) at the representational level -- by also facilitating
efficient extraction of word/phrase level vectors from one or more layers. In
this paper, we describe the library and apply it to two motivating case
studies: One focusing on the learning dynamics of the BERT architecture on
relative grammatical judgments, and the other on benchmarking 23 different LMs
on zero-shot abductive reasoning. minicons is available at
https://github.com/kanishkamisra/minicons

Recent localization frameworks exploit spatial information of complex channel
measurements (CMs) to estimate accurate positions even in multipath propagation
scenarios. State-of-the art CM fingerprinting(FP)-based methods employ
convolutional neural networks (CNN) to extract the spatial information.
However, they need spatially dense data sets (associated with high acquisition
and maintenance efforts) to work well -- which is rarely the case in practical
applications. If such data is not available (or its quality is low), we cannot
compensate the performance degradation of CNN-based FP as they do not provide
statistical position estimates, which prevents a fusion with other sources of
information on the observation level.
  We propose a novel localization framework that adapts well to sparse datasets
that only contain CMs of specific areas within the environment with strong
multipath propagation. Our framework compresses CMs into informative features
to unravel spatial information. It then regresses Gaussian processes (GPs) for
each of them, which imply statistical observation models based on
distance-dependent covariance kernels. Our framework combines the trained GPs
with line-of-sight ranges and a dynamics model in a particle filter. Our
measurements show that our approach outperforms state-of-the-art CNN
fingerprinting (0.52 m vs. 1.3 m MAE) on spatially sparse data collected in a
realistic industrial indoor environment.

Data-driven modeling is an imperative tool in various industrial
applications, including many applications in the sectors of aeronautics and
commercial aviation. These models are in charge of providing key insights, such
as which parameters are important on a specific measured outcome or which
parameter values we should expect to observe given a set of input parameters.
At the same time, however, these models rely heavily on assumptions (e.g.,
stationarity) or are "black box" (e.g., deep neural networks), meaning that
they lack interpretability of their internal working and can be viewed only in
terms of their inputs and outputs. An interpretable alternative to the "black
box" models and with considerably less assumptions is symbolic regression (SR).
SR searches for the optimal model structure while simultaneously optimizing the
model's parameters without relying on an a-priori model structure. In this
work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at
high frequencies through the entire flight, in order to uncover meaningful
algebraic relationships between the EGT and other measurable engine parameters.
The experimental results exhibit promising model accuracy, as well as
explainability returning an absolute difference of 3{\deg}C compared to the
ground truth and demonstrating consistency from an engineering perspective.

Class-Incremental Learning (CIL) struggles with catastrophic forgetting when
learning new knowledge, and Data-Free CIL (DFCIL) is even more challenging
without access to the training data of previously learned classes. Though
recent DFCIL works introduce techniques such as model inversion to synthesize
data for previous classes, they fail to overcome forgetting due to the severe
domain gap between the synthetic and real data. To address this issue, this
paper proposes relation-guided representation learning (RRL) for DFCIL, dubbed
R-DFCIL. In RRL, we introduce relational knowledge distillation to flexibly
transfer the structural relation of new data from the old model to the current
model. Our RRL-boosted DFCIL can guide the current model to learn
representations of new classes better compatible with representations of
previous classes, which greatly reduces forgetting while improving plasticity.
To avoid the mutual interference between representation and classifier
learning, we employ local rather than global classification loss during RRL.
After RRL, the classification head is refined with global class-balanced
classification loss to address the data imbalance issue as well as learn the
decision boundaries between new and previous classes. Extensive experiments on
CIFAR100, Tiny-ImageNet200, and ImageNet100 demonstrate that our R-DFCIL
significantly surpasses previous approaches and achieves a new state-of-the-art
performance for DFCIL. Code is available at
https://github.com/jianzhangcs/R-DFCIL

We derive low-dimensional, data-driven models for transitions among exact
coherent states (ECSs) in one of the most studied canonical shear flows, the
plane Couette flow. These one- or two-dimensional nonlinear models represent
the leading-order reduced dynamics on attracting spectral submanifolds (SSMs),
which we construct using the recently developed SSMLearn algorithm from a small
number of simulated transitions. We find that the energy input and output rates
provide efficient parametrizations for the most important SSMs. By restricting
the dynamics to these SSMs, we obtain reduced-order models that also reliably
predict nearby, off-SSM transitions that were not used in their training.

Studying the inherent symmetry of data is of great importance in machine
learning. Point cloud, the most important data format for 3D environmental
perception, is naturally endowed with strong radial symmetry. In this work, we
exploit this radial symmetry via a divide-and-conquer strategy to boost 3D
perception performance and ease optimization. We propose Azimuth Normalization
(AziNorm), which normalizes the point clouds along the radial direction and
eliminates the variability brought by the difference of azimuth. AziNorm can be
flexibly incorporated into most LiDAR-based perception methods. To validate its
effectiveness and generalization ability, we apply AziNorm in both object
detection and semantic segmentation. For detection, we integrate AziNorm into
two representative detection methods, the one-stage SECOND detector and the
state-of-the-art two-stage PV-RCNN detector. Experiments on Waymo Open Dataset
demonstrate that AziNorm improves SECOND and PV-RCNN by 7.03 mAPH and 3.01 mAPH
respectively. For segmentation, we integrate AziNorm into KPConv. On
SemanticKitti dataset, AziNorm improves KPConv by 1.6/1.1 mIoU on val/test set.
Besides, AziNorm remarkably improves data efficiency and accelerates
convergence, reducing the requirement of data amounts or training epochs by an
order of magnitude. SECOND w/ AziNorm can significantly outperform fully
trained vanilla SECOND, even trained with only 10% data or 10% epochs. Code and
models are available at https://github.com/hustvl/AziNorm.

Recent progress in neural information retrieval has demonstrated large gains
in effectiveness, while often sacrificing the efficiency and interpretability
of the neural model compared to classical approaches. This paper proposes
ColBERTer, a neural retrieval model using contextualized late interaction
(ColBERT) with enhanced reduction. Along the effectiveness Pareto frontier,
ColBERTer's reductions dramatically lower ColBERT's storage requirements while
simultaneously improving the interpretability of its token-matching scores. To
this end, ColBERTer fuses single-vector retrieval, multi-vector refinement, and
optional lexical matching components into one model. For its multi-vector
component, ColBERTer reduces the number of stored vectors per document by
learning unique whole-word representations for the terms in each document and
learning to identify and remove word representations that are not essential to
effective scoring. We employ an explicit multi-task, multi-stage training to
facilitate using very small vector dimensions. Results on the MS MARCO and
TREC-DL collection show that ColBERTer can reduce the storage footprint by up
to 2.5x, while maintaining effectiveness. With just one dimension per token in
its smallest setting, ColBERTer achieves index storage parity with the
plaintext size, with very strong effectiveness results. Finally, we demonstrate
ColBERTer's robustness on seven high-quality out-of-domain collections,
yielding statistically significant gains over traditional retrieval baselines.

Generative adversarial networks have recently demonstrated outstanding
performance in neural vocoding outperforming best autoregressive and flow-based
models. In this paper, we show that this success can be extended to other tasks
of conditional audio generation. In particular, building upon HiFi vocoders, we
propose a novel HiFi++ general framework for bandwidth extension and speech
enhancement. We show that with the improved generator architecture and
simplified multi-discriminator training, HiFi++ performs better or on par with
the state-of-the-art in these tasks while spending significantly less
computational resources. The effectiveness of our approach is validated through
a series of extensive experiments.

Distributed training algorithms of deep neural networks show impressive
convergence speedup properties on very large problems. However, they inherently
suffer from communication related slowdowns and communication topology becomes
a crucial design choice. Common approaches supported by most machine learning
frameworks are: 1) Synchronous decentralized algorithms relying on a
peer-to-peer All Reduce topology that is sensitive to stragglers and
communication delays. 2) Asynchronous centralised algorithms with a server
based topology that is prone to communication bottleneck. Researchers also
suggested asynchronous decentralized algorithms designed to avoid the
bottleneck and speedup training, however, those commonly use inexact sparse
averaging that may lead to a degradation in accuracy. In this paper, we propose
Local Asynchronous SGD (LASGD), an asynchronous decentralized algorithm that
relies on All Reduce for model synchronization.
  We empirically validate LASGD's performance on image classification tasks on
the ImageNet dataset. Our experiments demonstrate that LASGD accelerates
training compared to SGD and state of the art gossip based approaches.

Novel prediction methods should always be compared to a baseline to know how
well they perform. Without this frame of reference, the performance score of a
model is basically meaningless. What does it mean when a model achieves an
$F_1$ of 0.8 on a test set? A proper baseline is needed to evaluate the
`goodness' of a performance score. Comparing with the latest state-of-the-art
model is usually insightful. However, being state-of-the-art can change rapidly
when newer models are developed. Contrary to an advanced model, a simple dummy
classifier could be used. However, the latter could be beaten too easily,
making the comparison less valuable. This paper presents a universal baseline
method for all binary classification models, named the Dutch Draw (DD). This
approach weighs simple classifiers and determines the best classifier to use as
a baseline. We theoretically derive the DD baseline for many commonly used
evaluation measures and show that in most situations it reduces to (almost)
always predicting either zero or one. Summarizing, the DD baseline is: (1)
general, as it is applicable to all binary classification problems; (2) simple,
as it is quickly determined without training or parameter-tuning; (3)
informative, as insightful conclusions can be drawn from the results. The DD
baseline serves two purposes. First, to enable comparisons across research
papers by this robust and universal baseline. Secondly, to provide a sanity
check during the development process of a prediction model. It is a major
warning sign when a model is outperformed by the DD baseline.

The discovery of nucleic acids and the structure of DNA have brought
considerable advances in the understanding of life. The development of
next-generation sequencing technologies has led to a large-scale generation of
data, for which computational methods have become essential for analysis and
knowledge discovery. In particular, RNAs have received much attention because
of the diversity of their functionalities in the organism and the discoveries
of different classes with different functions in many biological processes.
Therefore, the correct identification of RNA sequences is increasingly
important to provide relevant information to understand the functioning of
organisms. This work addresses this context by presenting a new method for the
classification of biological sequences through complex networks and entropy
maximization. The maximum entropy principle is proposed to identify the most
informative edges about the RNA class, generating a filtered complex network.
The proposed method was evaluated in the classification of different RNA
classes from 13 species. The proposed method was compared to PLEK, CPC2 and
BASiNET methods, outperforming all compared methods. BASiNETEntropy classified
all RNA sequences with high accuracy and low standard deviation in results,
showing assertiveness and robustness. The proposed method is implemented in an
open source in R language and is freely available at
https://cran.r-project.org/web/packages/BASiNETEntropy.

The design of optimal test statistics is a key task in frequentist statistics
and for a number of scenarios optimal test statistics such as the
profile-likelihood ratio are known. By turning this argument around we can find
the profile likelihood ratio even in likelihood-free cases, where only samples
from a simulator are available, by optimizing a test statistic within those
scenarios. We propose a likelihood-free training algorithm that produces test
statistics that are equivalent to the profile likelihood ratios in cases where
the latter is known to be optimal.

Due to the collection of big data and the development of deep learning,
research to predict human emotions in the wild is being actively conducted. We
designed a multi-task model using ABAW dataset to predict valence-arousal,
expression, and action unit through audio data and face images at in real
world. We trained model from the incomplete label by applying the knowledge
distillation technique. The teacher model was trained as a supervised learning
method, and the student model was trained by using the output of the teacher
model as a soft label. As a result we achieved 2.40 in Multi Task Learning task
validation dataset.

Intelligent applications based on machine learning are impacting many parts
of our lives. They are required to operate under rigorous practical constraints
in terms of service latency, network bandwidth overheads, and also privacy. Yet
current implementations running in the Cloud are unable to satisfy all these
constraints. The Edge-Cloud Collaborative Intelligence (ECCI) paradigm has
become a popular approach to address such issues, and rapidly increasing
applications are developed and deployed. However, these prototypical
implementations are developer-dependent and scenario-specific without
generality, which cannot be efficiently applied in large-scale or to general
ECC scenarios in practice, due to the lack of supports for infrastructure
management, edge-cloud collaborative service, complex intelligence workload,
and efficient performance optimization. In this article, we systematically
design and construct the first unified platform, ACE, that handles
ever-increasing edge and cloud resources, user-transparent services, and
proliferating intelligence workloads with increasing scale and complexity, to
facilitate cost-efficient and high-performing ECCI application development and
deployment. For verification, we explicitly present the construction process of
an ACE-based intelligent video query application, and demonstrate how to
achieve customizable performance optimization efficiently. Based on our initial
experience, we discuss both the limitations and vision of ACE to shed light on
promising issues to elaborate in the approaching ECCI ecosystem.

We propose SwiftAgg+, a novel secure aggregation protocol for federated
learning systems, where a central server aggregates local models of $N \in
\mathbb{N}$ distributed users, each of size $L \in \mathbb{N}$, trained on
their local data, in a privacy-preserving manner. SwiftAgg+ can significantly
reduce the communication overheads without any compromise on security, and
achieve optimal communication loads within diminishing gaps. Specifically, in
presence of at most $D=o(N)$ dropout users, SwiftAgg+ achieves a per-user
communication load of $(1+\mathcal{O}(\frac{1}{N}))L$ symbols and a server
communication load of $(1+\mathcal{O}(\frac{1}{N}))L$ symbols, with a
worst-case information-theoretic security guarantee, against any subset of up
to $T=o(N)$ semi-honest users who may also collude with the curious server.
Moreover, the proposed SwiftAgg+ allows for a flexible trade-off between
communication loads and the number of active communication links. In
particular, for $T<N-D$ and for any $K\in\mathbb{N}$, SwiftAgg+ can achieve the
server communication load of $(1+\frac{T}{K})L$ symbols, and per-user
communication load of up to $(1+\frac{T+D}{K})L$ symbols, where the number of
pair-wise active connections in the network is $\frac{N}{2}(K+T+D+1)$.

Driving 3D characters to dance following a piece of music is highly
challenging due to the spatial constraints applied to poses by choreography
norms. In addition, the generated dance sequence also needs to maintain
temporal coherency with different music genres. To tackle these challenges, we
propose a novel music-to-dance framework, Bailando, with two powerful
components: 1) a choreographic memory that learns to summarize meaningful
dancing units from 3D pose sequence to a quantized codebook, 2) an actor-critic
Generative Pre-trained Transformer (GPT) that composes these units to a fluent
dance coherent to the music. With the learned choreographic memory, dance
generation is realized on the quantized units that meet high choreography
standards, such that the generated dancing sequences are confined within the
spatial constraints. To achieve synchronized alignment between diverse motion
tempos and music beats, we introduce an actor-critic-based reinforcement
learning scheme to the GPT with a newly-designed beat-align reward function.
Extensive experiments on the standard benchmark demonstrate that our proposed
framework achieves state-of-the-art performance both qualitatively and
quantitatively. Notably, the learned choreographic memory is shown to discover
human-interpretable dancing-style poses in an unsupervised manner.

Temporal grounding in videos aims to localize one target video segment that
semantically corresponds to a given query sentence. Thanks to the semantic
diversity of natural language descriptions, temporal grounding allows activity
grounding beyond pre-defined classes and has received increasing attention in
recent years. The semantic diversity is rooted in the principle of
compositionality in linguistics, where novel semantics can be systematically
described by combining known words in novel ways (compositional
generalization). However, current temporal grounding datasets do not
specifically test for the compositional generalizability. To systematically
measure the compositional generalizability of temporal grounding models, we
introduce a new Compositional Temporal Grounding task and construct two new
dataset splits, i.e., Charades-CG and ActivityNet-CG. Evaluating the
state-of-the-art methods on our new dataset splits, we empirically find that
they fail to generalize to queries with novel combinations of seen words. To
tackle this challenge, we propose a variational cross-graph reasoning framework
that explicitly decomposes video and language into multiple structured
hierarchies and learns fine-grained semantic correspondence among them.
Experiments illustrate the superior compositional generalizability of our
approach. The repository of this work is at https://github.com/YYJMJC/
Compositional-Temporal-Grounding.

Pulmonary hypertension (PH) in newborns and infants is a complex condition
associated with several pulmonary, cardiac, and systemic diseases contributing
to morbidity and mortality. Therefore, accurate and early detection of PH is
crucial for successful management. Using echocardiography, the primary
diagnostic tool in pediatrics, human assessment is both time-consuming and
expertise-demanding, raising the need for an automated approach. In this work,
we present an interpretable multi-view video-based deep learning approach to
predict PH for a cohort of 194 newborns using echocardiograms. We use
spatio-temporal convolutional architectures for the prediction of PH from each
view, and aggregate the predictions of the different views using majority
voting. To the best of our knowledge, this is the first work for an automated
assessment of PH in newborns using echocardiograms. Our results show a mean
F1-score of 0.84 for severity prediction and 0.92 for binary detection using
10-fold cross-validation. We complement our predictions with saliency maps and
show that the learned model focuses on clinically relevant cardiac structures,
motivating its usage in clinical practice.

The MAPE-K feedback loop has been established as the primary reference model
for self-adaptive and autonomous systems in domains such as autonomous driving,
robotics, and Cyber-Physical Systems. At the same time, the Human Machine
Teaming (HMT) paradigm is designed to promote partnerships between humans and
autonomous machines. It goes far beyond the degree of collaboration expected in
human-on-the-loop and human-in-the-loop systems and emphasizes interactions,
partnership, and teamwork between humans and machines. However, while MAPE-K
enables fully autonomous behavior, it does not explicitly address the
interactions between humans and machines as intended by HMT. In this paper, we
present the MAPE-K-HMT framework which augments the traditional MAPE-K loop
with support for HMT. We identify critical human-machine teaming factors and
describe the infrastructure needed across the various phases of the MAPE-K loop
in order to effectively support HMT. This includes runtime models that are
constructed and populated dynamically across monitoring, analysis, planning,
and execution phases to support human-machine partnerships. We illustrate
MAPE-K-HMT using examples from an autonomous multi-UAV emergency response
system, and present guidelines for integrating HMT into MAPE-K.

Visual action planning particularly excels in applications where the state of
the system cannot be computed explicitly, such as manipulation of deformable
objects, as it enables planning directly from raw images. Even though the field
has been significantly accelerated by deep learning techniques, a crucial
requirement for their success is the availability of a large amount of data. In
this work, we propose the Augment-Connect-Explore (ACE) paradigm to enable
visual action planning in cases of data scarcity.
  We build upon the Latent Space Roadmap (LSR) framework which performs
planning with a graph built in a low dimensional latent space. In particular,
ACE is used to i) Augment the available training dataset by autonomously
creating new pairs of datapoints, ii) create new unobserved Connections among
representations of states in the latent graph, and iii) Explore new regions of
the latent space in a targeted manner. We validate the proposed approach on
both simulated box stacking and real-world folding task showing the
applicability for rigid and deformable object manipulation tasks, respectively.

We propose a cross-modal co-attention model for continuous emotion
recognition using visual-audio-linguistic information. The model consists of
four blocks. The visual, audio, and linguistic blocks are used to learn the
spatial-temporal features of the multi-modal input. A co-attention block is
designed to fuse the learned features with the multi-head co-attention
mechanism. The visual encoding from the visual block is concatenated with the
attention feature to emphasize the visual information. To make full use of the
data and alleviate over-fitting, cross-validation is carried out on the
training and validation set. The concordance correlation coefficient (CCC)
centering is used to merge the results from each fold. The achieved CCC on the
test set is $0.520$ for valence and $0.602$ for arousal, which significantly
outperforms the baseline method with the corresponding CCC of 0.180 and 0.170
for valence and arousal, respectively. The code is available at
https://github.com/sucv/ABAW3.

The post-Minkowskian expansion of Einstein's general theory of relativity has
received much attention in recent years due to the possibility of harnessing
the computational power of modern amplitude calculations in such a classical
context. In this brief review, we focus on the post-Minkowskian expansion as
applied to the two-body problem in general relativity without spin, and we
describe how relativistic quantum field theory can be used to greatly simplify
analytical calculations based on the Einstein-Hilbert action. Subtleties
related to the extraction of classical physics from such quantum mechanical
calculations highlight the care which must be taken when both positive and
negative powers of Planck's constant are at play. In the process of obtaining
classical results in both Einstein gravity and supergravity, one learns new
aspects of quantum field theory that are obscured when using units in which
Planck's constant is set to unity. The scattering amplitude approach provides a
self-contained framework for deriving the two-body scattering valid in all
regimes of energy. There is hope that the full impact of amplitude computations
in this field may significantly alter the way in which gravitational wave
predictions will advance in the coming years.

Domain generalization (DG) aims to improve the generalization performance for
an unseen target domain by using the knowledge of multiple seen source domains.
Mainstream DG methods typically assume that the domain label of each source
sample is known a priori, which is challenged to be satisfied in many
real-world applications. In this paper, we study a practical problem of
compound DG, which relaxes the discrete domain assumption to the mixed source
domains setting. On the other hand, current DG algorithms prioritize the focus
on semantic invariance across domains (one-vs-one), while paying less attention
to the holistic semantic structure (many-vs-many). Such holistic semantic
structure, referred to as meta-knowledge here, is crucial for learning
generalizable representations. To this end, we present Compound Domain
Generalization via Meta-Knowledge Encoding (COMEN), a general approach to
automatically discover and model latent domains in two steps. Firstly, we
introduce Style-induced Domain-specific Normalization (SDNorm) to re-normalize
the multi-modal underlying distributions, thereby dividing the mixture of
source domains into latent clusters. Secondly, we harness the prototype
representations, the centroids of classes, to perform relational modeling in
the embedding space with two parallel and complementary modules, which
explicitly encode the semantic structure for the out-of-distribution
generalization. Experiments on four standard DG benchmarks reveal that COMEN
exceeds the state-of-the-art performance without the need of domain

A difficult step in the process of karyotyping is segmenting chromosomes that
touch or overlap. In an attempt to automate the process, previous studies
turned to Deep Learning methods, with some formulating the task as a semantic
segmentation problem. These models treat separate chromosome instances as
semantic classes, which we show to be problematic, since it is uncertain which
chromosome should be classed as #1 and #2. Assigning class labels based on
comparison rules, such as the shorter/longer chromosome alleviates, but does
not fully resolve the issue. Instead, we separate the chromosome instances in a
second stage, predicting the orientation of the chromosomes by the model and
use it as one of the key distinguishing factors of the chromosomes. We
demonstrate this method to be effective. Furthermore, we introduce a novel
Double-Angle representation that a neural network can use to predict the
orientation. The representation maps any direction and its reverse to the same
point. Lastly, we present a new expanded synthetic dataset, which is based on
Pommier's dataset, but addresses its issues with insufficient separation
between its training and testing sets.

Cortical surface registration is a fundamental tool for neuroimaging analysis
that has been shown to improve the alignment of functional regions relative to
volumetric approaches. Classically, image registration is performed by
optimizing a complex objective similarity function, leading to long run times.
This contributes to a convention for aligning all data to a global average
reference frame that poorly reflects the underlying cortical heterogeneity. In
this paper, we propose a novel unsupervised learning-based framework that
converts registration to a multi-label classification problem, where each point
in a low-resolution control grid deforms to one of fixed, finite number of
endpoints. This is learned using a spherical geometric deep learning
architecture, in an end-to-end unsupervised way, with regularization imposed
using a deep Conditional Random Field (CRF). Experiments show that our proposed
framework performs competitively, in terms of similarity and areal distortion,
relative to the most popular classical surface registration algorithms and
generates smoother deformations than other learning-based surface registration
methods, even in subjects with atypical cortical morphology.

Dimensionality reduction is crucial both for visualization and preprocessing
high dimensional data for machine learning. We introduce a novel method based
on a hierarchy built on 1-nearest neighbor graphs in the original space which
is used to preserve the grouping properties of the data distribution on
multiple levels. The core of the proposal is an optimization-free projection
that is competitive with the latest versions of t-SNE and UMAP in performance
and visualization quality while being an order of magnitude faster in run-time.
Furthermore, its interpretable mechanics, the ability to project new data, and
the natural separation of data clusters in visualizations make it a general
purpose unsupervised dimension reduction technique. In the paper, we argue
about the soundness of the proposed method and evaluate it on a diverse
collection of datasets with sizes varying from 1K to 11M samples and dimensions
from 28 to 16K. We perform comparisons with other state-of-the-art methods on
multiple metrics and target dimensions highlighting its efficiency and
performance. Code is available at https://github.com/koulakis/h-nne

Although face swapping has attracted much attention in recent years, it
remains a challenging problem. The existing methods leverage a large number of
data samples to explore the intrinsic properties of face swapping without
taking into account the semantic information of face images. Moreover, the
representation of the identity information tends to be fixed, leading to
suboptimal face swapping. In this paper, we present a simple yet efficient
method named FaceSwapper, for one-shot face swapping based on Generative
Adversarial Networks. Our method consists of a disentangled representation
module and a semantic-guided fusion module. The disentangled representation
module is composed of an attribute encoder and an identity encoder, which aims
to achieve the disentanglement of the identity and the attribute information.
The identity encoder is more flexible and the attribute encoder contains more
details of the attributes than its competitors. Benefiting from the
disentangled representation, FaceSwapper can swap face images progressively. In
addition, semantic information is introduced into the semantic-guided fusion
module to control the swapped area and model the pose and expression more
accurately. The experimental results show that our method achieves
state-of-the-art results on benchmark datasets with fewer training samples. Our
code is publicly available at https://github.com/liqi-casia/FaceSwapper.

Artificial Intelligence (AI) is increasingly used to build Decision Support
Systems (DSS) across many domains. This paper describes a series of experiments
designed to observe human response to different characteristics of a DSS such
as accuracy and bias, particularly the extent to which participants rely on the
DSS, and the performance they achieve. In our experiments, participants play a
simple online game inspired by so-called "wildcat" (i.e., exploratory) drilling
for oil. The landscape has two layers: a visible layer describing the costs
(terrain), and a hidden layer describing the reward (oil yield). Participants
in the control group play the game without receiving any assistance, while in
treatment groups they are assisted by a DSS suggesting places to drill. For
certain treatments, the DSS does not consider costs, but only rewards, which
introduces a bias that is observable by users. Between subjects, we vary the
accuracy and bias of the DSS, and observe the participants' total score, time
to completion, the extent to which they follow or ignore suggestions. We also
measure the acceptability of the DSS in an exit survey. Our results show that
participants tend to score better with the DSS, that the score increase is due
to users following the DSS advice, and related to the difficulty of the game
and the accuracy of the DSS. We observe that this setting elicits mostly
rational behavior from participants, who place a moderate amount of trust in
the DSS and show neither algorithmic aversion (under-reliance) nor automation
bias (over-reliance).However, their stated willingness to accept the DSS in the
exit survey seems less sensitive to the accuracy of the DSS than their
behavior, suggesting that users are only partially aware of the (lack of)
accuracy of the DSS.

In addition to signature-based and heuristics-based detection techniques,
machine learning (ML) is widely used to generalize to new, never-before-seen
malicious software (malware). However, it has been demonstrated that ML models
can be fooled by tricking the classifier into returning the incorrect label.
These studies, for instance, usually rely on a prediction score that is fragile
to gradient-based attacks. In the context of a more realistic situation where
an attacker has very little information about the outputs of a malware
detection engine, modest evasion rates are achieved. In this paper, we propose
a method using reinforcement learning with DQN and REINFORCE algorithms to
challenge two state-of-the-art ML-based detection engines (MalConv \& EMBER)
and a commercial AV classified by Gartner as a leader AV. Our method combines
several actions, modifying a Windows portable execution (PE) file without
breaking its functionalities. Our method also identifies which actions perform
better and compiles a detailed vulnerability report to help mitigate the
evasion. We demonstrate that REINFORCE achieves very good evasion rates even on
a commercial AV with limited available information.

Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices
using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts
(MAs) caused by movements of the subject's arm affect the performance of
PPG-based HR tracking. This is typically addressed coupling the PPG signal with
acceleration measurements from an inertial sensor. Unfortunately, most standard
approaches of this kind rely on hand-tuned parameters, which impair their
generalization capabilities and their applicability to real data in the field.
In contrast, methods based on deep learning, despite their better
generalization, are considered to be too complex to deploy on wearable devices.
  In this work, we tackle these limitations, proposing a design space
exploration methodology to automatically generate a rich family of deep
Temporal Convolutional Networks (TCNs) for HR monitoring, all derived from a
single "seed" model. Our flow involves a cascade of two Neural Architecture
Search (NAS) tools and a hardware-friendly quantizer, whose combination yields
both highly accurate and extremely lightweight models. When tested on the
PPG-Dalia dataset, our most accurate model sets a new state-of-the-art in Mean
Absolute Error. Furthermore, we deploy our TCNs on an embedded platform
featuring a STM32WB55 microcontroller, demonstrating their suitability for
real-time execution. Our most accurate quantized network achieves 4.41 Beats
Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of
47.65 mJ and a memory footprint of 412 kB. At the same time, the smallest
network that obtains a MAE < 8 BPM, among those generated by our flow, has a
memory footprint of 1.9 kB and consumes just 1.79 mJ per inference.

Entity resolution (ER) aims at matching records that refer to the same
real-world entity. Although widely studied for the last 50 years, ER still
represents a challenging data management problem, and several recent works have
started to investigate the opportunity of applying deep learning (DL)
techniques to solve this problem. In this paper, we study the fundamental
problem of explainability of the DL solution for ER. Understanding the matching
predictions of an ER solution is indeed crucial to assess the trustworthiness
of the DL model and to discover its biases. We treat the DL model as a black
box classifier and - while previous approaches to provide explanations for DL
predictions are agnostic to the classification task. we propose the CERTA
approach that is aware of the semantics of the ER problem. Our approach
produces both saliency explanations, which associate each attribute with a
saliency score, and counterfactual explanations, which provide examples of
values that can flip the prediction. CERTA builds on a probabilistic framework
that aims at computing the explanations evaluating the outcomes produced by
using perturbed copies of the input records. We experimentally evaluate CERTA's
explanations of state-of-the-art ER solutions based on DL models using publicly
available datasets, and demonstrate the effectiveness of CERTA over recently
proposed methods for this problem.

Deep neural networks (DNNs) have been successfully applied to many real-world
problems, but a complete understanding of their dynamical and computational
principles is still lacking. Conventional theoretical frameworks for analysing
DNNs often assume random networks with coupling weights obeying Gaussian
statistics. However, non-Gaussian, heavy-tailed coupling is a ubiquitous
phenomenon in DNNs. Here, by weaving together theories of heavy-tailed random
matrices and non-equilibrium statistical physics, we develop a new type of mean
field theory for DNNs which predicts that heavy-tailed weights enable the
emergence of an extended critical regime without fine-tuning parameters. In
this extended critical regime, DNNs exhibit rich and complex propagation
dynamics across layers. We further elucidate that the extended criticality
endows DNNs with profound computational advantages: balancing the contraction
as well as expansion of internal neural representations and speeding up
training processes, hence providing a theoretical guide for the design of
efficient neural architectures.

The right to be forgotten has been legislated in many countries, but its
enforcement in the AI industry would cause unbearable costs. When single data
deletion requests come, companies may need to delete the whole models learned
with massive resources. Existing works propose methods to remove knowledge
learned from data for explicitly parameterized models, which however are not
appliable to the sampling-based Bayesian inference, i.e., Markov chain Monte
Carlo (MCMC), as MCMC can only infer implicit distributions. In this paper, we
propose the first machine unlearning algorithm for MCMC. We first convert the
MCMC unlearning problem into an explicit optimization problem. Based on this
problem conversion, an {\it MCMC influence function} is designed to provably
characterize the learned knowledge from data, which then delivers the MCMC
unlearning algorithm. Theoretical analysis shows that MCMC unlearning would not
compromise the generalizability of the MCMC models. Experiments on Gaussian
mixture models and Bayesian neural networks confirm the effectiveness of the
proposed algorithm. The code is available at
\url{https://github.com/fshp971/mcmc-unlearning}.

In this article we consider Bayesian inference associated to deep neural
networks (DNNs) and in particular, trace-class neural network (TNN) priors
which were proposed by Sell et al. [39]. Such priors were developed as more
robust alternatives to classical architectures in the context of inference
problems. For this work we develop multilevel Monte Carlo (MLMC) methods for
such models. MLMC is a popular variance reduction technique, with particular
applications in Bayesian statistics and uncertainty quantification. We show how
a particular advanced MLMC method that was introduced in [4] can be applied to
Bayesian inference from DNNs and establish mathematically, that the
computational cost to achieve a particular mean square error, associated to
posterior expectation computation, can be reduced by several orders, versus
more conventional techniques. To verify such results we provide numerous
numerical experiments on model problems arising in machine learning. These
include Bayesian regression, as well as Bayesian classification and

We provide the optimal receive combining strategy for federated learning in
multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows
the clients to perform individual gradient sparsification which greatly
improves performance in scenarios with heterogeneous (non i.i.d.) training
data. The proposed method beats the benchmark by a wide margin.

We investigate both stationary and time-varying, nonmonotone generalized Nash
equilibrium problems that exhibit symmetric interactions among the agents,
which are known to be potential. As may happen in practical cases, however, we
envision a scenario in which the formal expression of the underlying potential
function is not available, and we design a semi-decentralized Nash equilibrium
seeking algorithm. In the proposed two-layer scheme, a coordinator iteratively
integrates the (possibly noisy and sporadic) agents' feedback to learn the
pseudo-gradients of the agents, and then design personalized incentives for
them. On their side, the agents receive those personalized incentives, compute
a solution to an extended game, and then return feedback measurements to the
coordinator. In the stationary setting, our algorithm returns a Nash
equilibrium in case the coordinator is endowed with standard learning policies,
while it returns a Nash equilibrium up to a constant, yet adjustable, error in
the time-varying case. As a motivating application, we consider the ridehailing
service provided by several companies with mobility as a service orchestration,
necessary to both handle competition among firms and avoid traffic congestion,
which is also adopted to run numerical experiments verifying our results.

This paper presents a reinforcement learning method for object goal
navigation (ObjNav) where an agent navigates in 3D indoor environments to reach
a target object based on long-term observations of objects and scenes. To this
end, we propose Object Memory Transformer (OMT) that consists of two key ideas:
1) Object-Scene Memory (OSM) that enables to store long-term scenes and object
semantics, and 2) Transformer that attends to salient objects in the sequence
of previously observed scenes and objects stored in OSM. This mechanism allows
the agent to efficiently navigate in the indoor environment without prior
knowledge about the environments, such as topological maps or 3D meshes. To the
best of our knowledge, this is the first work that uses a long-term memory of
object semantics in a goal-oriented navigation task. Experimental results
conducted on the AI2-THOR dataset show that OMT outperforms previous approaches
in navigating in unknown environments. In particular, we show that utilizing
the long-term object semantics information improves the efficiency of
navigation.

Dynamic attention mechanism and global modeling ability make Transformer show
strong feature learning ability. In recent years, Transformer has become
comparable to CNNs methods in computer vision. This review mainly investigates
the current research progress of Transformer in image and video applications,
which makes a comprehensive overview of Transformer in visual learning
understanding. First, the attention mechanism is reviewed, which plays an
essential part in Transformer. And then, the visual Transformer model and the
principle of each module are introduced. Thirdly, the existing
Transformer-based models are investigated, and their performance is compared in
visual learning understanding applications. Three image tasks and two video
tasks of computer vision are investigated. The former mainly includes image
classification, object detection, and image segmentation. The latter contains
object tracking and video classification. It is significant for comparing
different models' performance in various tasks on several public benchmark data
sets. Finally, ten general problems are summarized, and the developing
prospects of the visual Transformer are given in this review.

Zero-shot slot filling has received considerable attention to cope with the
problem of limited available data for the target domain. One of the important
factors in zero-shot learning is to make the model learn generalized and
reliable representations. For this purpose, we present mcBERT, which stands for
momentum contrastive learning with BERT, to develop a robust zero-shot slot
filling model. mcBERT uses BERT to initialize the two encoders, the query
encoder and key encoder, and is trained by applying momentum contrastive
learning. Our experimental results on the SNIPS benchmark show that mcBERT
substantially outperforms the previous models, recording a new
state-of-the-art. Besides, we also show that each component composing mcBERT
contributes to the performance improvement.

Human-machine interaction is gaining traction in rehabilitation tasks, such
as controlling prosthetic hands or robotic arms. Gesture recognition exploiting
surface electromyographic (sEMG) signals is one of the most promising
approaches, given that sEMG signal acquisition is non-invasive and is directly
related to muscle contraction. However, the analysis of these signals still
presents many challenges since similar gestures result in similar muscle
contractions. Thus the resulting signal shapes are almost identical, leading to
low classification accuracy. To tackle this challenge, complex neural networks
are employed, which require large memory footprints, consume relatively high
energy and limit the maximum battery life of devices used for classification.
This work addresses this problem with the introduction of the Bioformers. This
new family of ultra-small attention-based architectures approaches
state-of-the-art performance while reducing the number of parameters and
operations of 4.9X. Additionally, by introducing a new inter-subjects
pre-training, we improve the accuracy of our best Bioformer by 3.39%, matching
state-of-the-art accuracy without any additional inference cost. Deploying our
best performing Bioformer on a Parallel, Ultra-Low Power (PULP) microcontroller
unit (MCU), the GreenWaves GAP8, we achieve an inference latency and energy of
2.72 ms and 0.14 mJ, respectively, 8.0X lower than the previous
state-of-the-art neural network, while occupying just 94.2 kB of memory.

Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning
models for Time Series analysis. We introduce an automated exploration approach
and a library of optimized kernels to map TCNs on Parallel Ultra-Low Power
(PULP) microcontrollers. Our approach minimizes latency and energy by
exploiting a layer tiling optimizer to jointly find the tiling dimensions and
select among alternative implementations of the causal and dilated
1D-convolution operations at the core of TCNs. We benchmark our approach on a
commercial PULP device, achieving up to 103X lower latency and 20.3X lower
energy than the Cube-AI toolkit executed on the STM32L4 and from 2.9X to 26.6X
lower energy compared to commercial closed-source and academic open-source
approaches on the same hardware target.

This paper gives the first polynomial-time algorithm for tabular Markov
Decision Processes (MDP) that enjoys a regret bound \emph{independent on the
planning horizon}. Specifically, we consider tabular MDP with $S$ states, $A$
actions, a planning horizon $H$, total reward bounded by $1$, and the agent
plays for $K$ episodes. We design an algorithm that achieves an
$O\left(\mathrm{poly}(S,A,\log K)\sqrt{K}\right)$ regret in contrast to
existing bounds which either has an additional $\mathrm{polylog}(H)$
dependency~\citep{zhang2020reinforcement} or has an exponential dependency on
$S$~\citep{li2021settling}. Our result relies on a sequence of new structural
lemmas establishing the approximation power, stability, and concentration
property of stationary policies, which can have applications in other problems
related to Markov chains.

The convolutional neural network (CNN) has been widely applied to process the
industrial data based tensor input, which integrates data records of
distributed industrial systems from the spatial, temporal, and system dynamics
aspects. However, unlike images, information in the industrial data based
tensor is not necessarily spatially ordered. Thus, directly applying CNN is
ineffective. To tackle such issue, we propose a plug and play module, the
Rubik's Cube Operator (RCO), to adaptively permutate the data organization of
the industrial data based tensor to an optimal or suboptimal order of
attributes before being processed by CNNs, which can be updated with subsequent
CNNs together via the gradient-based optimizer. The proposed RCO maintains K
binary and right stochastic permutation matrices to permutate attributes of K
axes of the input industrial data based tensor. A novel learning process is
proposed to enable learning permutation matrices from data, where the
Gumbel-Softmax is employed to reparameterize elements of permutation matrices,
and the soft regularization loss is proposed and added to the task-specific
loss to ensure the feature diversity of the permuted data. We verify the
effectiveness of the proposed RCO via considering two representative learning
tasks processing industrial data via CNNs, the wind power prediction (WPP) and
the wind speed prediction (WSP) from the renewable energy domain. Computational
experiments are conducted based on four datasets collected from different wind
farms and the results demonstrate that the proposed RCO can improve the
performance of CNN based networks significantly.

Estimation of human shape and pose from a single image is a challenging task.
It is an even more difficult problem to map the identified human shape onto a
3D human model. Existing methods map manually labelled human pixels in real 2D
images onto the 3D surface, which is prone to human error, and the sparsity of
available annotated data often leads to sub-optimal results. We propose to
solve the problem of data scarcity by training 2D-3D human mapping algorithms
using automatically generated synthetic data for which exact and dense 2D-3D
correspondence is known. Such a learning strategy using synthetic environments
has a high generalisation potential towards real-world data. Using different
camera parameter variations, background and lighting settings, we created
precise ground truth data that constitutes a wider distribution. We evaluate
the performance of models trained on synthetic using the COCO dataset and
validation framework. Results show that training 2D-3D mapping network models
on synthetic data is a viable alternative to using real data.

We present a novel rationale-centric framework with human-in-the-loop --
Rationales-centric Double-robustness Learning (RDL) -- to boost model
out-of-distribution performance in few-shot learning scenarios. By using static
semi-factual generation and dynamic human-intervened correction, RDL exploits
rationales (i.e. phrases that cause the prediction), human interventions and
semi-factual augmentations to decouple spurious associations and bias models
towards generally applicable underlying distributions, which enables fast and
accurate generalisation. Experimental results show that RDL leads to
significant prediction benefits on both in-distribution and out-of-distribution
tests compared to many state-of-the-art benchmarks -- especially for few-shot
learning scenarios. We also perform extensive ablation studies to support
in-depth analyses of each component in our framework.

We propose WarpingGAN, an effective and efficient 3D point cloud generation
network. Unlike existing methods that generate point clouds by directly
learning the mapping functions between latent codes and 3D shapes, Warping-GAN
learns a unified local-warping function to warp multiple identical pre-defined
priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D
shapes driven by local structure-aware semantics. In addition, we also
ingeniously utilize the principle of the discriminator and tailor a stitching
loss to eliminate the gaps between different partitions of a generated shape
corresponding to different priors for boosting quality. Owing to the novel
generating mechanism, WarpingGAN, a single lightweight network after one-time
training, is capable of efficiently generating uniformly distributed 3D point
clouds with various resolutions. Extensive experimental results demonstrate the
superiority of our WarpingGAN over state-of-the-art methods in terms of
quantitative metrics, visual quality, and efficiency. The source code is
publicly available at https://github.com/yztang4/WarpingGAN.git.

Deep learning has recently been widely applied to many applications across
different domains, e.g., image classification and audio recognition. However,
the quality of Deep Neural Networks (DNNs) still raises concerns in the
practical operational environment, which calls for systematic testing,
especially in safety-critical scenarios. Inspired by software testing, a number
of structural coverage criteria are designed and proposed to measure the test
adequacy of DNNs. However, due to the blackbox nature of DNN, the existing
structural coverage criteria are difficult to interpret, making it hard to
understand the underlying principles of these criteria. The relationship
between the structural coverage and the decision logic of DNNs is unknown.
Moreover, recent studies have further revealed the non-existence of correlation
between the structural coverage and DNN defect detection, which further posts
concerns on what a suitable DNN testing criterion should be.
  In this paper, we propose the interpretable coverage criteria through
constructing the decision structure of a DNN. Mirroring the control flow graph
of the traditional program, we first extract a decision graph from a DNN based
on its interpretation, where a path of the decision graph represents a decision
logic of the DNN. Based on the control flow and data flow of the decision
graph, we propose two variants of path coverage to measure the adequacy of the
test cases in exercising the decision logic. The higher the path coverage, the
more diverse decision logic the DNN is expected to be explored. Our large-scale
evaluation results demonstrate that: the path in the decision graph is
effective in characterizing the decision of the DNN, and the proposed coverage
criteria are also sensitive with errors including natural errors and
adversarial examples, and strongly correlated with the output impartiality.

Since the inception of crowdsourcing, aggregation has been a common strategy
for dealing with unreliable data. Aggregate ratings are more reliable than
individual ones. However, many natural language processing (NLP) applications
that rely on aggregate ratings only report the reliability of individual
ratings, which is the incorrect unit of analysis. In these instances, the data
reliability is under-reported, and a proposed k-rater reliability (kRR) should
be used as the correct data reliability for aggregated datasets. It is a
multi-rater generalization of inter-rater reliability (IRR). We conducted two
replications of the WordSim-353 benchmark, and present empirical, analytical,
and bootstrap-based methods for computing kRR on WordSim-353. These methods
produce very similar results. We hope this discussion will nudge researchers to
report kRR in addition to IRR.

In this article, we propose a sparse spectra graph convolutional network
(SSGCNet) for solving Epileptic EEG signal classification problems. The aim is
to achieve a lightweight deep learning model without losing model
classification accuracy. We propose a weighted neighborhood field graph (WNFG)
to represent EEG signals, which reduces the redundant edges between graph
nodes. WNFG has lower time complexity and memory usage than the conventional
solutions. Using the graph representation, the sequential graph convolutional
network is based on a combination of sparse weight pruning technique and the
alternating direction method of multipliers (ADMM). Our approach can reduce
computation complexity without effect on classification accuracy. We also
present convergence results for the proposed approach. The performance of the
approach is illustrated in public and clinical-real datasets. Compared with the
existing literature, our WNFG of EEG signals achieves up to 10 times of
redundant edge reduction, and our approach achieves up to 97 times of model
pruning without loss of classification accuracy.

Named entity recognition (NER) is the process of recognising and classifying
important information (entities) in text. Proper nouns, such as a person's
name, an organization's name, or a location's name, are examples of entities.
The NER is one of the important modules in applications like human resources,
customer support, search engines, content classification, and academia. In this
work, we consider NER for low-resource Indian languages like Hindi and Marathi.
The transformer-based models have been widely used for NER tasks. We consider
different variations of BERT like base-BERT, RoBERTa, and AlBERT and benchmark
them on publicly available Hindi and Marathi NER datasets. We provide an
exhaustive comparison of different monolingual and multilingual
transformer-based models and establish simple baselines currently missing in
the literature. We show that the monolingual MahaRoBERTa model performs the
best for Marathi NER whereas the multilingual XLM-RoBERTa performs the best for
Hindi NER. We also perform cross-language evaluation and present mixed

Human emotions recognization contributes to the development of human-computer
interaction. The machines understanding human emotions in the real world will
significantly contribute to life in the future. This paper will introduce the
Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. The paper
focuses on solving the problem of the valence-arousal estimation and action
unit detection. For valence-arousal estimation, we conducted two stages:
creating new features from multimodel and temporal learning to predict
valence-arousal. First, we make new features; the Gated Recurrent Unit (GRU)
and Transformer are combined using a Regular Networks (RegNet) feature, which
is extracted from the image. The next step is the GRU combined with Local
Attention to predict valence-arousal. The Concordance Correlation Coefficient
(CCC) was used to evaluate the model.

Identifying argument components from unstructured texts and predicting the
relationships expressed among them are two primary steps of argument mining.
The intrinsic complexity of these tasks demands powerful learning models. While
pretrained Transformer-based Language Models (LM) have been shown to provide
state-of-the-art results over different NLP tasks, the scarcity of manually
annotated data and the highly domain-dependent nature of argumentation restrict
the capabilities of such models. In this work, we propose a novel transfer
learning strategy to overcome these challenges. We utilize argumentation-rich
social discussions from the ChangeMyView subreddit as a source of unsupervised,
argumentative discourse-aware knowledge by finetuning pretrained LMs on a
selectively masked language modeling task. Furthermore, we introduce a novel
prompt-based strategy for inter-component relation prediction that compliments
our proposed finetuning method while leveraging on the discourse context.
Exhaustive experiments show the generalization capability of our method on
these two tasks over within-domain as well as out-of-domain datasets,
outperforming several existing and employed strong baselines.

Machine learning based medical image analysis highly depends on datasets.
Biases in the dataset can be learned by the model and degrade the
generalizability of the applications. There are studies on debiased models.
However, scientists and practitioners are difficult to identify implicit biases
in the datasets, which causes lack of reliable unbias test datasets to valid
models. To tackle this issue, we first define the data intrinsic bias
attribute, and then propose a novel bias identification framework for medical
image datasets. The framework contains two major components, KlotskiNet and
Bias Discriminant Direction Analysis(bdda), where KlostkiNet is to build the
mapping which makes backgrounds to distinguish positive and negative samples
and bdda provides a theoretical solution on determining bias attributes.
Experimental results on three datasets show the effectiveness of the bias
attributes discovered by the framework.

We demonstrate the performance of a very efficient tagger applies on
hadronically decaying top quark pairs as signal based on deep neural network
algorithms and compares with the QCD multi-jet background events. A significant
enhancement of performance in boosted top quark events is observed with our
limited computing resources. We also compare modern machine learning approaches
and perform a multivariate analysis of boosted top-pair as well as single top
quark production through weak interaction at $\sqrt{s}=$14 TeV proton-proton
Collider. The most relevant known background processes are incorporated.
Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer
Perceptron (MLP) the analysis is trained to observe the performance in
comparison with the conventional cut based and count approach.

6-DoF object pose estimation from a monocular image is challenging, and a
post-refinement procedure is generally needed for high-precision estimation. In
this paper, we propose a framework based on a recurrent neural network (RNN)
for object pose refinement, which is robust to erroneous initial poses and
occlusions. During the recurrent iterations, object pose refinement is
formulated as a non-linear least squares problem based on the estimated
correspondence field (between a rendered image and the observed image). The
problem is then solved by a differentiable Levenberg-Marquardt (LM) algorithm
enabling end-to-end training. The correspondence field estimation and pose
refinement are conducted alternatively in each iteration to recover the object
poses. Furthermore, to improve the robustness to occlusion, we introduce a
consistency-check mechanism based on the learned descriptors of the 3D model
and observed 2D images, which downweights the unreliable correspondences during
pose optimization. Extensive experiments on LINEMOD, Occlusion-LINEMOD, and
YCB-Video datasets validate the effectiveness of our method and demonstrate

Structural re-parameterization (Rep) methods achieve noticeable improvements
on simple VGG-style networks. Despite the prevalence, current Rep methods
simply re-parameterize all operations into an augmented network, including
those that rarely contribute to the model's performance. As such, the price to
pay is an expensive computational overhead to manipulate these unnecessary
behaviors. To eliminate the above caveats, we aim to bootstrap the training
with minimal cost by devising a dynamic re-parameterization (DyRep) method,
which encodes Rep technique into the training process that dynamically evolves
the network structures. Concretely, our proposal adaptively finds the
operations which contribute most to the loss in the network, and applies Rep to
enhance their representational capacity. Besides, to suppress the noisy and
redundant operations introduced by Rep, we devise a de-parameterization
technique for a more compact re-parameterization. With this regard, DyRep is
more efficient than Rep since it smoothly evolves the given network instead of
constructing an over-parameterized network. Experimental results demonstrate
our effectiveness, e.g., DyRep improves the accuracy of ResNet-18 by $2.04\%$
on ImageNet and reduces $22\%$ runtime over the baseline. Code is available at:
https://github.com/hunto/DyRep.

Multilingual evaluation benchmarks usually contain limited high-resource
languages and do not test models for specific linguistic capabilities.
CheckList is a template-based evaluation approach that tests models for
specific capabilities. The CheckList template creation process requires native
speakers, posing a challenge in scaling to hundreds of languages. In this work,
we explore multiple approaches to generate Multilingual CheckLists. We device
an algorithm - Template Extraction Algorithm (TEA) for automatically extracting
target language CheckList templates from machine translated instances of a
source language templates. We compare the TEA CheckLists with CheckLists
created with different levels of human intervention. We further introduce
metrics along the dimensions of cost, diversity, utility, and correctness to
compare the CheckLists. We thoroughly analyze different approaches to creating
CheckLists in Hindi. Furthermore, we experiment with 9 more different
languages. We find that TEA followed by human verification is ideal for scaling
Checklist-based evaluation to multiple languages while TEA gives a good
estimates of model performance.

Kullback-Leibler (KL) control enables efficient numerical methods for
nonlinear optimal control problems. The crucial assumption of KL control is the
full controllability of the transition distribution. However, this assumption
is often violated when the dynamics evolves in a continuous space.
Consequently, applying KL control to problems with continuous spaces requires
some approximation, which leads to the lost of the optimality. To avoid such
approximation, in this paper, we reformulate the KL control problem for
continuous spaces so that it does not require unrealistic assumptions. The key
difference between the original and reformulated KL control is that the former
measures the control effort by KL divergence between controlled and
uncontrolled transition distributions while the latter replaces the
uncontrolled transition by a noise-driven transition. We show that the
reformulated KL control admits efficient numerical algorithms like the original
one without unreasonable assumptions. Specifically, the associated value
function can be computed by using a Monte Carlo method based on its path
integral representation.

Convolutional neural networks (CNN) have demonstrated outstanding Compressed
Sensing (CS) performance compared to traditional, hand-crafted methods.
However, they are broadly limited in terms of generalisability, inductive bias
and difficulty to model long distance relationships. Transformer neural
networks (TNN) overcome such issues by implementing an attention mechanism
designed to capture dependencies between inputs. However, high-resolution tasks
typically require vision Transformers (ViT) to decompose an image into
patch-based tokens, limiting inputs to inherently local contexts. We propose a
novel image decomposition that naturally embeds images into low-resolution
inputs. These Kaleidoscope tokens (KD) provide a mechanism for global
attention, at the same computational cost as a patch-based approach. To
showcase this development, we replace CNN components in a well-known CS-MRI
neural network with TNN blocks and demonstrate the improvements afforded by KD.
We also propose an ensemble of image tokens, which enhance overall image
quality and reduces model size. Supplementary material is available:
https://github.com/uqmarlonbran/TCS.git

Recently, a surge of interest in visual transformers is to reduce the
computational cost by limiting the calculation of self-attention to a local
window. Most current work uses a fixed single-scale window for modeling by
default, ignoring the impact of window size on model performance. However, this
may limit the modeling potential of these window-based models for multi-scale
information. In this paper, we propose a novel method, named Dynamic Window
Vision Transformer (DW-ViT). The dynamic window strategy proposed by DW-ViT
goes beyond the model that employs a fixed single window setting. To the best
of our knowledge, we are the first to use dynamic multi-scale windows to
explore the upper limit of the effect of window settings on model performance.
In DW-ViT, multi-scale information is obtained by assigning windows of
different sizes to different head groups of window multi-head self-attention.
Then, the information is dynamically fused by assigning different weights to
the multi-scale window branches. We conducted a detailed performance evaluation
on three datasets, ImageNet-1K, ADE20K, and COCO. Compared with related
state-of-the-art (SoTA) methods, DW-ViT obtains the best performance.
Specifically, compared with the current SoTA Swin Transformers
\cite{liu2021swin}, DW-ViT has achieved consistent and substantial improvements
on all three datasets with similar parameters and computational costs. In
addition, DW-ViT exhibits good scalability and can be easily inserted into any
window-based visual transformers.

Purpose: A core component of advancing cancer treatment research is assessing
response to therapy. Doing so by hand, for example as per RECIST or RANO
criteria, is tedious, time-consuming, and can miss important tumor response
information; most notably, they exclude non-target lesions. We wish to assess
change in a holistic fashion that includes all lesions, obtaining simple,
informative, and automated assessments of tumor progression or regression. Due
to often low patient enrolments in clinical trials, we wish to make response
assessments with small training sets. Deep neuroevolution (DNE) can produce
radiology artificial intelligence (AI) that performs well on small training
sets. Here we use DNE for function approximation that predicts progression
versus regression of metastatic brain disease.
  Methods: We analyzed 50 pairs of MRI contrast-enhanced images as our training
set. Half of these pairs, separated in time, qualified as disease progression,
while the other 25 images constituted regression. We trained the parameters of
a relatively small CNN via mutations that consisted of random CNN weight
adjustments and mutation fitness. We then incorporated the best mutations into
the next generations CNN, repeating this process for approximately 50,000
generations. We applied the CNNs to our training set, as well as a separate
testing set with the same class balance of 25 progression and 25 regression
images.
  Results: DNE achieved monotonic convergence to 100% training set accuracy.
DNE also converged monotonically to 100% testing set accuracy.
  Conclusion: DNE can accurately classify brain-metastatic disease progression
versus regression. Future work will extend the input from 2D image slices to
full 3D volumes, and include the category of no change. We believe that an
approach such as our could ultimately provide a useful adjunct to RANO/RECIST

Image manipulation has attracted a lot of interest due to its wide range of
applications. Prior work modifies images either from low-level manipulation,
such as image inpainting or through manual edits via paintbrushes and
scribbles, or from high-level manipulation, employing deep generative networks
to output an image conditioned on high-level semantic input. In this study, we
propose Semantic Image Manipulation with Background-guided Internal Learning
(SIMBIL), which combines high-level and low-level manipulation. Specifically,
users can edit an image at the semantic level by applying changes on a scene
graph. Then our model manipulates the image at the pixel level according to the
modified scene graph. There are two major advantages of our approach. First,
high-level manipulation of scene graphs requires less manual effort from the
user compared to manipulating raw image pixels. Second, our low-level internal
learning approach is scalable to images of various sizes without reliance on
external visual datasets for training. We outperform the state-of-the-art in a
quantitative and qualitative evaluation on the CLEVR and Visual Genome
datasets. Experiments show 8 points improvement on FID scores (CLEVR) and 27%
improvement on user evaluation (Visual Genome), demonstrating the effectiveness

In this thesis, we propose a pioneering work on sparse keypoints tracking
across images using transformer networks. While deep learning-based keypoints
matching have been widely investigated using graph neural networks - and more
recently transformer networks, they remain relatively too slow to operate in
real-time and are particularly sensitive to the poor repeatability of the
keypoints detectors. In order to address these shortcomings, we propose to
study the particular case of real-time and robust keypoints tracking.
Specifically, we propose a novel architecture which ensures a fast and robust
estimation of the keypoints tracking between successive images of a video
sequence. Our method takes advantage of a recent breakthrough in computer
vision, namely, visual transformer networks. Our method consists of two
successive stages, a coarse matching followed by a fine localization of the
keypoints' correspondences prediction. Through various experiments, we
demonstrate that our approach achieves competitive results and demonstrates
high robustness against adverse conditions, such as illumination change,
occlusion and viewpoint differences.

Steganalysis as a method to detect whether image contains se-cret message, is
a crucial study avoiding the imperils from abus-ing steganography. The point of
steganalysis is to detect the weak embedding signals which is hardly learned by
convolution-al layer and easily suppressed. In this paper, to enhance
embed-ding signals, we study the insufficiencies of activation function,
filters and loss function from the aspects of reduce embedding signal loss and
enhance embedding signal capture ability. Adap-tive Parametric Activation
Module is designed to reserve nega-tive embedding signal. For embedding signal
capture ability enhancement, we add constraints on the high-pass filters to
im-prove residual diversity which enables the filters extracts rich embedding
signals. Besides, a loss function based on contrastive learning is applied to
overcome the limitations of cross-entropy loss by maximum inter-class distance.
It helps the network make a distinction between embedding signals and semantic
edges. We use images from BOSSbase 1.01 and make stegos by WOW and S-UNIWARD
for experiments. Compared to state-of-the-art methods, our method has a

Energy-based latent variable models (EBLVMs) are more expressive than
conventional energy-based models. However, its potential on visual tasks are
limited by its training process based on maximum likelihood estimate that
requires sampling from two intractable distributions. In this paper, we propose
Bi-level doubly variational learning (BiDVL), which is based on a new bi-level
optimization framework and two tractable variational distributions to
facilitate learning EBLVMs. Particularly, we lead a decoupled EBLVM consisting
of a marginal energy-based distribution and a structural posterior to handle
the difficulties when learning deep EBLVMs on images. By choosing a symmetric
KL divergence in the lower level of our framework, a compact BiDVL for visual
tasks can be obtained. Our model achieves impressive image generation
performance over related works. It also demonstrates the significant capacity
of testing image reconstruction and out-of-distribution detection.

Multiple-instance learning (MIL) provides an effective way to tackle the
video anomaly detection problem by modeling it as a weakly supervised problem
as the labels are usually only available at the video level while missing for
frames due to expensive labeling cost. We propose to conduct novel Bayesian
non-parametric submodular video partition (BN-SVP) to significantly improve MIL
model training that can offer a highly reliable solution for robust anomaly
detection in practical settings that include outlier segments or multiple types
of abnormal events. BN-SVP essentially performs dynamic non-parametric
hierarchical clustering with an enhanced self-transition that groups segments
in a video into temporally consistent and semantically coherent hidden states
that can be naturally interpreted as scenes. Each segment is assumed to be
generated through a non-parametric mixture process that allows variations of
segments within the same scenes to accommodate the dynamic and noisy nature of
many real-world surveillance videos. The scene and mixture component assignment
of BN-SVP also induces a pairwise similarity among segments, resulting in
non-parametric construction of a submodular set function. Integrating this
function with an MIL loss effectively exposes the model to a diverse set of
potentially positive instances to improve its training. A greedy algorithm is
developed to optimize the submodular function and support efficient model
training. Our theoretical analysis ensures a strong performance guarantee of
the proposed algorithm. The effectiveness of the proposed approach is
demonstrated over multiple real-world anomaly video datasets with robust

This study addresses a multiclass learning from label proportions (MCLLP)
setting in which training instances are provided in bags and only the
proportion of each class within the bags is provided. Most existing MCLLP
methods impose bag-wise constraints on the prediction of instances or assign
them pseudo-labels; however, none of these methods have a theoretical
consistency. To solve this problem, a risk-consistent method is proposed for
instance classification using the empirical risk minimization framework, and
its estimation error bound is derived. An approximation method is proposed for
the proposed risk estimator, to apply it to large bags, by diverting the
constraints on bags in existing research. The proposed method can be applied to
any deep model or loss and is compatible with stochastic optimization.
Experiments are conducted on benchmarks to verify the effectiveness of the

Implementing a wireless mesh network in a real-life scenario requires a
significant systems engineering effort to turn a network concept into a
complete system. This paper presents an evaluation of a fielded system within
the DARPA Subterranean (SubT) Challenge Final Event that contributed to a 3rd
place finish. Our system included a team of air and ground robots, deployable
mesh extender nodes, and a human operator base station. This paper presents a
real-world evaluation of a stack optimized for air and ground robotic
exploration in a RF-limited environment under practical system design
limitations. Our highly customizable solution utilizes a minimum of non-free
components with form factor options suited for UAV operations and provides
insight into network operations at all levels. We present performance metrics
based on our performance in the Final Event of the DARPA Subterranean
Challenge, demonstrating the practical successes and limitations of our
approach, as well as a set of lessons learned and suggestions for future

Precise congestion prediction from a placement solution plays a crucial role
in circuit placement. This work proposes the lattice hypergraph (LH-graph), a
novel graph formulation for circuits, which preserves netlist data during the
whole learning process, and enables the congestion information propagated
geometrically and topologically. Based on the formulation, we further developed
a heterogeneous graph neural network architecture LHNN, jointing the routing
demand regression to support the congestion spot classification. LHNN
constantly achieves more than 35% improvements compared with U-nets and Pix2Pix
on the F1 score. We expect our work shall highlight essential procedures using
machine learning for congestion prediction.

Pose Estimation techniques rely on visual cues available through observations
represented in the form of pixels. But the performance is bounded by the frame
rate of the video and struggles from motion blur, occlusions, and temporal
coherence. This issue is magnified when people are interacting with objects and
instruments, for example playing the violin. Standard approaches for
postprocessing use interpolation and smoothing functions to filter noise and
fill gaps, but they cannot model highly non-linear motion. We present a method
that leverages our understanding of the high degree of a causal relationship
between the sound produced and the motion that produces them. We use the audio
signature to refine and predict accurate human body pose motion models. We
propose MAPnet (Music Assisted Pose network) for generating a fine grain motion
model from sparse input pose sequences but continuous audio. To accelerate
further research in this domain, we also open-source MAPdat, a new multi-modal
dataset of 3D violin playing motion with music. We perform a comparison of
different standard machine learning models and perform analysis on input
modalities, sampling techniques, and audio and motion features. Experiments on
MAPdat suggest multi-modal approaches like ours as a promising direction for
tasks previously approached with visual methods only. Our results show both
qualitatively and quantitatively how audio can be combined with visual
observation to help improve any pose estimation methods.

The rising popularity of online User-Generated-Content (UGC) in the form of
streamed and shared videos, has hastened the development of perceptual Video
Quality Assessment (VQA) models, which can be used to help optimize their
delivery. Gaming videos, which are a relatively new type of UGC videos, are
created when skilled gamers post videos of their gameplay. These kinds of
screenshots of UGC gameplay videos have become extremely popular on major
streaming platforms like YouTube and Twitch. Synthetically-generated gaming
content presents challenges to existing VQA algorithms, including those based
on natural scene/video statistics models. Synthetically generated gaming
content presents different statistical behavior than naturalistic videos. A
number of studies have been directed towards understanding the perceptual
characteristics of professionally generated gaming videos arising in gaming
video streaming, online gaming, and cloud gaming. However, little work has been
done on understanding the quality of UGC gaming videos, and how it can be
characterized and predicted. Towards boosting the progress of gaming video VQA
model development, we conducted a comprehensive study of subjective and
objective VQA models on UGC gaming videos. To do this, we created a novel UGC
gaming video resource, called the LIVE-YouTube Gaming video quality
(LIVE-YT-Gaming) database, comprised of 600 real UGC gaming videos. We
conducted a subjective human study on this data, yielding 18,600 human quality
ratings recorded by 61 human subjects. We also evaluated a number of
state-of-the-art (SOTA) VQA models on the new database, including a new one,
called GAME-VQP, based on both natural video statistics and CNN-learned
features. To help support work in this field, we are making the new
LIVE-YT-Gaming Database, publicly available through the link:
https://live.ece.utexas.edu/research/LIVE-YT-Gaming/index.html .

Graph Contrastive Learning (GCL) has shown promising performance in graph
representation learning (GRL) without the supervision of manual annotations.
GCL can generate graph-level embeddings by maximizing the Mutual Information
(MI) between different augmented views of the same graph (positive pairs).
However, the GCL is limited by dimensional collapse, i.e., embedding vectors
only occupy a low-dimensional subspace. In this paper, we show that the
smoothing effect of the graph pooling and the implicit regularization of the
graph convolution are two causes of the dimensional collapse in GCL. To
mitigate the above issue, we propose a non-maximum removal graph contrastive
learning approach (nmrGCL), which removes "prominent'' dimensions (i.e.,
contribute most in similarity measurement) for positive pair in the pre-text
task. Comprehensive experiments on various benchmark datasets are conducted to
demonstrate the effectiveness of nmrGCL, and the results show that our model
outperforms the state-of-the-art methods. Source code will be made publicly

As intelligent agents become autonomous over longer periods of time, they may
eventually become lifelong counterparts to specific people. If so, it may be
common for a user to want the agent to master a task temporarily but later on
to forget the task due to privacy concerns. However enabling an agent to
\emph{forget privately} what the user specified without degrading the rest of
the learned knowledge is a challenging problem. With the aim of addressing this
challenge, this paper formalizes this continual learning and private unlearning
(CLPU) problem. The paper further introduces a straightforward but exactly
private solution, CLPU-DER++, as the first step towards solving the CLPU
problem, along with a set of carefully designed benchmark problems to evaluate
the effectiveness of the proposed solution. The code is available at
https://github.com/Cranial-XIX/Continual-Learning-Private-Unlearning.

Recently, several Vision Transformer (ViT) based methods have been proposed
for Fine-Grained Visual Classification (FGVC).These methods significantly
surpass existing CNN-based ones, demonstrating the effectiveness of ViT in FGVC
tasks.However, there are some limitations when applying ViT directly to
FGVC.First, ViT needs to split images into patches and calculate the attention
of every pair, which may result in heavy redundant calculation and unsatisfying
performance when handling fine-grained images with complex background and small
objects.Second, a standard ViT only utilizes the class token in the final layer
for classification, which is not enough to extract comprehensive fine-grained
information. To address these issues, we propose a novel ViT based fine-grained
object discriminator for FGVC tasks, ViT-FOD for short. Specifically, besides a
ViT backbone, it further introduces three novel components, i.e, Attention
Patch Combination (APC), Critical Regions Filter (CRF), and Complementary
Tokens Integration (CTI). Thereinto, APC pieces informative patches from two
images to generate a new image so that the redundant calculation can be
reduced. CRF emphasizes tokens corresponding to discriminative regions to
generate a new class token for subtle feature learning. To extract
comprehensive information, CTI integrates complementary information captured by
class tokens in different ViT layers. We conduct comprehensive experiments on
widely used datasets and the results demonstrate that ViT-FOD is able to

In popular media, there is often a connection drawn between the advent of
awareness in artificial agents and those same agents simultaneously achieving
human or superhuman level intelligence. In this work, we explore the validity
and potential application of this seemingly intuitive link between
consciousness and intelligence. We do so by examining the cognitive abilities
associated with three contemporary theories of conscious function: Global
Workspace Theory (GWT), Information Generation Theory (IGT), and Attention
Schema Theory (AST). We find that all three theories specifically relate
conscious function to some aspect of domain-general intelligence in humans.
With this insight, we turn to the field of Artificial Intelligence (AI) and
find that, while still far from demonstrating general intelligence, many
state-of-the-art deep learning methods have begun to incorporate key aspects of
each of the three functional theories. Having identified this trend, we use the
motivating example of mental time travel in humans to propose ways in which
insights from each of the three theories may be combined into a single unified
and implementable model. Given that it is made possible by cognitive abilities
underlying each of the three functional theories, artificial agents capable of
mental time travel would not only possess greater general intelligence than
current approaches, but also be more consistent with our current understanding
of the functional role of consciousness in humans, thus making it a promising
near-term goal for AI research.

Instrumental variables regression is a popular causal inference method for
endogenous treatment. A significant concern in practical applications is the
validity and strength of instrumental variables. This paper aims to perform
causal inference when all instruments are possibly invalid. To do this, we
propose a novel methodology called two stage curvature identification (TSCI)
together with a generalized concept to measure the strengths of possibly
invalid instruments: such invalid instruments can still be used for inference
in our framework. We fit the treatment model with a general machine learning
method and propose a novel bias correction method to remove the overfitting
bias from machine learning methods. Among a collection of spaces of violation
functions, we choose the best one by evaluating invalid instrumental variables'
strength. We demonstrate our proposed TSCI methodology in a large-scale
simulation study and revisit the important economics question on the effect of
education on earnings.

We present an unsupervised simultaneous learning framework for the task of
monocular camera re-localization and depth estimation from unlabeled video
sequences. Monocular camera re-localization refers to the task of estimating
the absolute camera pose from an instance image in a known environment, which
has been intensively studied for alternative localization in GPS-denied
environments. In recent works, camera re-localization methods are trained via
supervised learning from pairs of camera images and camera poses. In contrast
to previous works, we propose a completely unsupervised learning framework for
camera re-localization and depth estimation, requiring only monocular video
sequences for training. In our framework, we train two networks that estimate
the scene coordinates using directions and the depth map from each image which
are then combined to estimate the camera pose. The networks can be trained
through the minimization of loss functions based on our loop closed view
synthesis. In experiments with the 7-scenes dataset, the proposed method
outperformed the re-localization of the state-of-the-art visual SLAM,
ORB-SLAM3. Our method also outperforms state-of-the-art monocular depth
estimation in a trained environment.

COVID-19 pandemic has spread rapidly and caused a shortage of global medical
resources. The efficiency of COVID-19 diagnosis has become highly significant.
As deep learning and convolutional neural network (CNN) has been widely
utilized and been verified in analyzing medical images, it has become a
powerful tool for computer-assisted diagnosis. However, there are two most
significant challenges in medical image classification with the help of deep
learning and neural networks, one of them is the difficulty of acquiring enough
samples, which may lead to model overfitting. Privacy concerns mainly bring the
other challenge since medical-related records are often deemed patients'
private information and protected by laws such as GDPR and HIPPA. Federated
learning can ensure the model training is decentralized on different devices
and no data is shared among them, which guarantees privacy. However, with data
located on different devices, the accessible data of each device could be
limited. Since transfer learning has been verified in dealing with limited data
with good performance, therefore, in this paper, We made a trial to implement
federated learning and transfer learning techniques using CNNs to classify
COVID-19 using lung CT scans. We also explored the impact of dataset
distribution at the client-side in federated learning and the number of
training epochs a model is trained. Finally, we obtained very high performance
with federated learning, demonstrating our success in leveraging accuracy and
privacy.

Given an irregular dense tensor, how can we efficiently analyze it? An
irregular tensor is a collection of matrices whose columns have the same size
and rows have different sizes from each other. PARAFAC2 decomposition is a
fundamental tool to deal with an irregular tensor in applications including
phenotype discovery and trend analysis. Although several PARAFAC2 decomposition
methods exist, their efficiency is limited for irregular dense tensors due to
the expensive computations involved with the tensor. In this paper, we propose
DPar2, a fast and scalable PARAFAC2 decomposition method for irregular dense
tensors. DPar2 achieves high efficiency by effectively compressing each slice
matrix of a given irregular tensor, careful reordering of computations with the
compression results, and exploiting the irregularity of the tensor. Extensive
experiments show that DPar2 is up to 6.0x faster than competitors on real-world
irregular tensors while achieving comparable accuracy. In addition, DPar2 is
scalable with respect to the tensor size and target rank.

We consider the problem of learning the dynamics of autonomous linear systems
(i.e., systems that are not affected by external control inputs) from
observations of multiple trajectories of those systems, with finite sample
guarantees. Existing results on learning rate and consistency of autonomous
linear system identification rely on observations of steady state behaviors
from a single long trajectory, and are not applicable to unstable systems. In
contrast, we consider the scenario of learning system dynamics based on
multiple short trajectories, where there are no easily observed steady state
behaviors. We provide a finite sample analysis, which shows that the dynamics
can be learned at a rate $\mathcal{O}(\frac{1}{\sqrt{N}})$ for both stable and
unstable systems, where $N$ is the number of trajectories, when the initial
state of the system has zero mean (which is a common assumption in the existing
literature). We further generalize our result to the case where the initial
state has non-zero mean. We show that one can adjust the length of the
trajectories to achieve a learning rate of
$\mathcal{O}(\sqrt{\frac{\log{N}}{N})}$ for strictly stable systems and a
learning rate of $\mathcal{O}(\frac{(\log{N})^d}{\sqrt{N}})$ for marginally
stable systems, where $d$ is some constant.

A fundamental characteristic of natural language is the high rate at which
speakers produce novel expressions. Because of this novelty, a heavy-tail of
rare events accounts for a significant amount of the total probability mass of
distributions in language (Baayen, 2001). Standard language modeling metrics
such as perplexity quantify the performance of language models (LM) in
aggregate. As a result, we have relatively little understanding of whether
neural LMs accurately estimate the probability of sequences in this heavy-tail
of rare events. To address this gap, we develop a controlled evaluation scheme
which uses generative models trained on natural data as artificial languages
from which we can exactly compute sequence probabilities. Training LMs on
generations from these artificial languages, we compare the sequence-level
probability estimates given by LMs to the true probabilities in the target
language. Our experiments reveal that LSTM and Transformer language models (i)
systematically underestimate the probability of sequences drawn from the target
language, and (ii) do so more severely for less-probable sequences.
Investigating where this probability mass went, (iii) we find that LMs tend to
overestimate the probability of ill formed (perturbed) sequences. In addition,
we find that this underestimation behaviour (iv) is weakened, but not
eliminated by greater amounts of training data, and (v) is exacerbated for
target distributions with lower entropy.

We propose and analyze a reinforcement learning principle that approximates
the Bellman equations by enforcing their validity only along an user-defined
space of test functions. Focusing on applications to model-free offline RL with
function approximation, we exploit this principle to derive confidence
intervals for off-policy evaluation, as well as to optimize over policies
within a prescribed policy class. We prove an oracle inequality on our policy
optimization procedure in terms of a trade-off between the value and
uncertainty of an arbitrary comparator policy. Different choices of test
function spaces allow us to tackle different problems within a common
framework. We characterize the loss of efficiency in moving from on-policy to
off-policy data using our procedures, and establish connections to
concentrability coefficients studied in past work. We examine in depth the
implementation of our methods with linear function approximation, and provide
theoretical guarantees with polynomial-time implementations even when Bellman
closure does not hold.

Appearance of dressed humans undergoes a complex geometric transformation
induced not only by the static pose but also by its dynamics, i.e., there
exists a number of cloth geometric configurations given a pose depending on the
way it has moved. Such appearance modeling conditioned on motion has been
largely neglected in existing human rendering methods, resulting in rendering
of physically implausible motion. A key challenge of learning the dynamics of
the appearance lies in the requirement of a prohibitively large amount of
observations. In this paper, we present a compact motion representation by
enforcing equivariance -- a representation is expected to be transformed in the
way that the pose is transformed. We model an equivariant encoder that can
generate the generalizable representation from the spatial and temporal
derivatives of the 3D body surface. This learned representation is decoded by a
compositional multi-task decoder that renders high fidelity time-varying
appearance. Our experiments show that our method can generate a temporally
coherent video of dynamic humans for unseen body poses and novel views given a
single view video.

Unit testing is an essential part of the software development process, which
helps to identify issues with source code in early stages of development and
prevent regressions. Machine learning has emerged as viable approach to help
software developers generate automated unit tests. However, generating reliable
unit test cases that are semantically correct and capable of catching software
bugs or unintended behavior via machine learning requires large, metadata-rich,
datasets. In this paper we present Methods2Test: A dataset of focal methods
mapped to test cases: a large, supervised dataset of test cases mapped to
corresponding methods under test (i.e., focal methods). This dataset contains
780,944 pairs of JUnit tests and focal methods, extracted from a total of
91,385 Java open source projects hosted on GitHub with licenses permitting
re-distribution. The main challenge behind the creation of the Methods2Test was
to establish a reliable mapping between a test case and the relevant focal
method. To this aim, we designed a set of heuristics, based on developers' best
practices in software testing, which identify the likely focal method for a
given test case. To facilitate further analysis, we store a rich set of
metadata for each method-test pair in JSON-formatted files. Additionally, we
extract textual corpus from the dataset at different context levels, which we
provide both in raw and tokenized forms, in order to enable researchers to
train and evaluate machine learning models for Automated Test Generation.
Methods2Test is publicly available at:
https://github.com/microsoft/methods2test

Modern day computer games have extremely large state and action spaces. To
detect bugs in these games' models, human testers play the games repeatedly to
explore the game and find errors in the games. Such gameplay is exhaustive and
time consuming. Moreover, since robotics simulators depend on similar methods
of model specification and debugging, the problem of finding errors in the
model is of interest to the robotics community to ensure robot behaviors and
interactions are consistent in simulators. Previous methods have used
reinforcement learning arXiv:2103.13798 and search based methods (Chang, 2019,
(Chang, 2021) arXiv:1811.06962 including Rapidly-exploring Random Trees (RRT)
to explore a game's state-action space to find bugs. However, such search and
exploration based methods are not efficient at exploring the state-action space
without a pre-defined heuristic. In this work we attempt to combine a
human-tester's expertise in solving games, and the RRT's exhaustiveness to
search a game's state space efficiently with high coverage. This paper
introduces Cloning Assisted RRT (CA-RRT) to test a game through search. We
compare our methods to two existing baselines: 1) a weighted-RRT as described
by arXiv:1812.03125; 2) human demonstration seeded RRT as described by Chang
et. al. We find CA-RRT is applicable to more game maps and explores more game
states in fewer tree expansions/iterations when compared to the existing
baselines. In each test, CA-RRT reached more states on average in the same
number of iterations as weighted-RRT. In our tested environments, CA-RRT
reached the same number of states as weighted-RRT by more than 5000 fewer
iterations on average, almost a 50% reduction and applied to more scenarios
than. Moreover, as a consequence of our first person behavior cloning approach,
CA-RRT worked on unseen game maps than just seeding the RRT with human
demonstrated states.

Optimization-based meta-learning offers a promising direction for few-shot
learning that is essential for many real-world computer vision applications.
However, learning from few samples introduces uncertainty, and quantifying
model confidence for few-shot predictions is essential for many critical
domains. Furthermore, few-shot tasks used in meta training are usually sampled
randomly from a task distribution for an iterative model update, leading to
high labeling costs and computational overhead in meta-training. We propose a
novel uncertainty-aware task selection model for label efficient meta-learning.
The proposed model formulates a multidimensional belief measure, which can
quantify the known uncertainty and lower bound the unknown uncertainty of any
given task. Our theoretical result establishes an important relationship
between the conflicting belief and the incorrect belief. The theoretical result
allows us to estimate the total uncertainty of a task, which provides a
principled criterion for task selection. A novel multi-query task formulation
is further developed to improve both the computational and labeling efficiency
of meta-learning. Experiments conducted over multiple real-world few-shot image
classification tasks demonstrate the effectiveness of the proposed model.

An oft-ignored challenge of real-world reinforcement learning is that the
real world does not pause when agents make learning updates. As standard
simulated environments do not address this real-time aspect of learning, most
available implementations of RL algorithms process environment interactions and
learning updates sequentially. As a consequence, when such implementations are
deployed in the real world, they may make decisions based on significantly
delayed observations and not act responsively. Asynchronous learning has been
proposed to solve this issue, but no systematic comparison between sequential
and asynchronous reinforcement learning was conducted using real-world
environments. In this work, we set up two vision-based tasks with a robotic
arm, implement an asynchronous learning system that extends a previous
architecture, and compare sequential and asynchronous reinforcement learning
across different action cycle times, sensory data dimensions, and mini-batch
sizes. Our experiments show that when the time cost of learning updates
increases, the action cycle time in sequential implementation could grow
excessively long, while the asynchronous implementation can always maintain an
appropriate action cycle time. Consequently, when learning updates are
expensive, the performance of sequential learning diminishes and is
outperformed by asynchronous learning by a substantial margin. Our system
learns in real-time to reach and track visual targets from pixels within two
hours of experience and does so directly using real robots, learning completely
from scratch.

Increasingly larger and better Transformer models keep advancing
state-of-the-art accuracy and capability for Natural Language Processing
applications. These models demand more computational power, storage, and
energy. Mokey reduces the footprint of state-of-the-art 32-bit or 16-bit
floating-point transformer models by quantizing all values to 4-bit indexes
into dictionaries of representative 16-bit fixed-point centroids. Mokey does
not need fine-tuning, an essential feature as often the training resources or
datasets are not available to many. Exploiting the range of values that
naturally occur in transformer models, Mokey selects centroid values to also
fit an exponential curve. This unique feature enables Mokey to replace the bulk
of the original multiply-accumulate operations with narrow 3b fixed-point
additions resulting in an area- and energy-efficient hardware accelerator
design. Over a set of state-of-the-art transformer models, the Mokey
accelerator delivers an order of magnitude improvements in energy efficiency
over a Tensor Cores-based accelerator while improving performance by at least
$4\times$ and as much as $15\times$ depending on the model and on-chip
buffering capacity. Optionally, Mokey can be used as a memory compression
assist for any other accelerator, transparently stashing wide floating-point or
fixed-point activations or weights into narrow 4-bit indexes. Mokey proves
superior to prior state-of-the-art quantization methods for Transformers.

The preparation of Gibbs thermal states is an important task in quantum
computation with applications in quantum simulation, quantum optimization, and
quantum machine learning. However, many algorithms for preparing Gibbs states
rely on quantum subroutines which are difficult to implement on near-term
hardware. Here, we address this by (i) introducing an objective function that,
unlike the free energy, is easily measured, and (ii) using dynamically
generated, problem-tailored ans\"atze. This allows for arbitrarily accurate
Gibbs state preparation using low-depth circuits. To verify the effectiveness
of our approach, we numerically demonstrate that our algorithm can prepare
high-fidelity Gibbs states across a broad range of temperatures and for a
variety of Hamiltonians.

Much have been learned about the topological transport in real materials. We
investigate the interplay between magnetism and topology in the
magneto-transport of PrRhC$_2$. The four-fold degeneracy reduces to two-fold
followed by non-degenerate Weyl nodes (WNs) when the orientation of magnetic
quantization axis (MQA) is changed from easy axis to body-diagonal through
face-diagonal. This engenders chirality imbalance between positive and negative
chirality WNs around the Fermi energy. We observe significant enhancement in
the chiral anomaly mediated response such as planar Hall conductivity and
longitudinal magneto-conductivity, due to the emergence of chirality imbalance
upon orienting the MQA to body-diagonal. We further investigate the profiles of
anomalous Hall conductivities as a function of Fermi energy to explore the
effects of symmetries as well as chirality imbalance on Berry curvature.

Learning-based program repair has achieved good results in a recent series of
papers. Yet, we observe that the related work fails to repair some bugs because
of a lack of knowledge about 1) the application domain of the program being
repaired, and 2) the fault type being repaired. In this paper, we solve both
problems by changing the learning paradigm from supervised training to
self-supervised training in an approach called SelfAPR. First, SelfAPR
generates training samples on disk by perturbing a previous version of the
program being repaired, enforcing the neural model to capture projectspecific
knowledge. This is different from the previous work based on mined past
commits. Second, SelfAPR executes all training samples and extracts and encodes
test execution diagnostics into the input representation, steering the neural
model to fix the kind of fault. This is different from the existing studies
that only consider static source code as input. We implement SelfAPR and
evaluate it in a systematic manner. We generate 1 039 873 training samples
obtained by perturbing 17 open-source projects. We evaluate SelfAPR on 818 bugs
from Defects4J, SelfAPR correctly repairs 110 of them, outperforming all the
supervised learning repair approaches.

Collaborative robots are expected to physically interact with humans in daily
living and workplace, including industrial and healthcare settings. A related
key enabling technology is tactile sensing, which currently requires addressing
the outstanding scientific challenge to simultaneously detect contact location
and intensity by means of soft conformable artificial skins adapting over large
areas to the complex curved geometries of robot embodiments. In this work, the
development of a large-area sensitive soft skin with a curved geometry is
presented, allowing for robot total-body coverage through modular patches. The
biomimetic skin consists of a soft polymeric matrix, resembling a human
forearm, embedded with photonic Fiber Bragg Grating (FBG) transducers, which
partially mimics Ruffini mechanoreceptor functionality with diffuse,
overlapping receptive fields. A Convolutional Neural Network deep learning
algorithm and a multigrid Neuron Integration Process were implemented to decode
the FBG sensor outputs for inferring contact force magnitude and localization
through the skin surface. Results achieved 35 mN (IQR = 56 mN) and 3.2 mm (IQR
= 2.3 mm) median errors, for force and localization predictions, respectively.
Demonstrations with an anthropomorphic arm pave the way towards AI-based
integrated skins enabling safe human-robot cooperation via machine
intelligence.

Passive haptic learning (PHL) uses vibrotactile stimulation to train piano
songs using repetition, even when the recipient of stimulation is focused on
other tasks. However, many of the benefits of playing piano cannot be acquired
without actively playing the instrument. In this position paper, we posit that
passive haptic rehearsal, where active piano practice is assisted by separate
sessions of passive stimulation, is of greater everyday use than solely PHL. We
propose a study to examine the effects of passive haptic rehearsal for
self-paced piano learners and consider how to incorporate passive rehearsal
into everyday practice.

Deep metric learning (DML) enables learning with less supervision through its
emphasis on the similarity structure of representations. There has been much
work on improving generalization of DML in settings like zero-shot retrieval,
but little is known about its implications for fairness. In this paper, we are
the first to evaluate state-of-the-art DML methods trained on imbalanced data,
and to show the negative impact these representations have on minority subgroup
performance when used for downstream tasks. In this work, we first define
fairness in DML through an analysis of three properties of the representation
space -- inter-class alignment, intra-class alignment, and uniformity -- and
propose finDML, the fairness in non-balanced DML benchmark to characterize
representation fairness. Utilizing finDML, we find bias in DML representations
to propagate to common downstream classification tasks. Surprisingly, this bias
is propagated even when training data in the downstream task is re-balanced. To
address this problem, we present Partial Attribute De-correlation (PARADE) to
de-correlate feature representations from sensitive attributes and reduce
performance gaps between subgroups in both embedding space and downstream
metrics.

Finding relevant moments and highlights in videos according to natural
language queries is a natural and highly valuable common need in the current
video content explosion era. Nevertheless, jointly conducting moment retrieval
and highlight detection is an emerging research topic, even though its
component problems and some related tasks have already been studied for a
while. In this paper, we present the first unified framework, named Unified
Multi-modal Transformers (UMT), capable of realizing such joint optimization
while can also be easily degenerated for solving individual problems. As far as
we are aware, this is the first scheme to integrate multi-modal (visual-audio)
learning for either joint optimization or the individual moment retrieval task,
and tackles moment retrieval as a keypoint detection problem using a novel
query generator and query decoder. Extensive comparisons with existing methods
and ablation studies on QVHighlights, Charades-STA, YouTube Highlights, and
TVSum datasets demonstrate the effectiveness, superiority, and flexibility of
the proposed method under various settings. Source code and pre-trained models
are available at https://github.com/TencentARC/UMT.

Bayesian optimization (BayesOpt) is a gold standard for query-efficient
continuous optimization. However, its adoption for drug design has been
hindered by the discrete, high-dimensional nature of the decision variables. We
develop a new approach (LaMBO) which jointly trains a denoising autoencoder
with a discriminative multi-task Gaussian process head, allowing gradient-based
optimization of multi-objective acquisition functions in the latent space of
the autoencoder. These acquisition functions allow LaMBO to balance the
explore-exploit tradeoff over multiple design rounds, and to balance objective
tradeoffs by optimizing sequences at many different points on the Pareto
frontier. We evaluate LaMBO on two small-molecule design tasks, and introduce
new tasks optimizing \emph{in silico} and \emph{in vitro} properties of
large-molecule fluorescent proteins. In our experiments LaMBO outperforms
genetic optimizers and does not require a large pretraining corpus,
demonstrating that BayesOpt is practical and effective for biological sequence
design.

Federated learning is a prime candidate for distributed machine learning at
the network edge due to the low communication complexity and privacy protection
among other attractive properties. However, existing algorithms face issues
with slow convergence and/or robustness of performance due to the considerable
heterogeneity of data distribution, computation and communication capability at
the edge. In this work, we tackle both of these issues by focusing on the key
component of model aggregation in federated learning systems and studying
optimal algorithms to perform this task. Particularly, we propose a contextual
aggregation scheme that achieves the optimal context-dependent bound on loss
reduction in each round of optimization. The aforementioned context-dependent
bound is derived from the particular participating devices in that round and an
assumption on smoothness of the overall loss function. We show that this
aggregation leads to a definite reduction of loss function at every round.
Furthermore, we can integrate our aggregation with many existing algorithms to
obtain the contextual versions. Our experimental results demonstrate
significant improvements in convergence speed and robustness of the contextual
versions compared to the original algorithms. We also consider different
variants of the contextual aggregation and show robust performance even in the
most extreme settings.

Modern computer systems tend to rely on large trusted computing bases (TCBs)
for operations. To address the TCB bloating problem, hardware vendors have
developed mechanisms to enable or facilitate the creation of a trusted
execution environment (TEE) in which critical software applications can execute
securely in an isolated environment. Even under the circumstance that a host OS
is compromised by an adversary, key security properties such as confidentiality
and integrity of the software inside the TEEs can be guaranteed. The promise of
integrity and security has driven developers to adopt it for use cases
involving access control, PKS, IoT among other things.
  Among these applications include blockchain-related use cases. The usage of
the TEEs doesn't come without its own implementation challenges and potential
pitfalls. In this paper, we examine the assumptions, security models, and
operational environments of the proposed TEE use cases of blockchain-based
applications. The exercise and analysis help the hardware TEE research
community to identify some open challenges and opportunities for research and
rethink the design of hardware TEEs in general.

Most current domain adaptation methods address either covariate shift or
label shift, but are not applicable where they occur simultaneously and are
confounded with each other. Domain adaptation approaches which do account for
such confounding are designed to adapt covariates to optimally predict a
particular label whose shift is confounded with covariate shift. In this paper,
we instead seek to achieve general-purpose data backwards compatibility. This
would allow the adapted covariates to be used for a variety of downstream
problems, including on pre-existing prediction models and on data analytics
tasks. To do this we consider a special case of generalized label shift (GLS),
which we call confounded shift. We present a novel framework for this problem,
based on minimizing the expected divergence between the source and target
conditional distributions, conditioning on possible confounders. Within this
framework, we propose using the reverse Kullback-Leibler divergence,
demonstrating the use of parametric and nonparametric Gaussian estimators of
the conditional distribution. We also propose using the Maximum Mean
Discrepancy (MMD), introducing a dynamic strategy for choosing the kernel
bandwidth, which is applicable even outside the confounded shift setting.
Finally, we demonstrate our approach on synthetic and real datasets.

Transformers and masked language modeling are quickly being adopted and
explored in computer vision as vision transformers and masked image modeling
(MIM). In this work, we argue that image token masking differs from token
masking in text, due to the amount and correlation of tokens in an image. In
particular, to generate a challenging pretext task for MIM, we advocate a shift
from random masking to informed masking. We develop and exhibit this idea in
the context of distillation-based MIM, where a teacher transformer encoder
generates an attention map, which we use to guide masking for the student. We
thus introduce a novel masking strategy, called attention-guided masking
(AttMask), and we demonstrate its effectiveness over random masking for dense
distillation-based MIM as well as plain distillation-based self-supervised
learning on classification tokens. We confirm that AttMask accelerates the
learning process and improves the performance on a variety of downstream tasks.
We provide the implementation code at https://github.com/gkakogeorgiou/attmask.

An efficient data-driven prediction strategy for multi-antenna
frequency-selective channels must operate based on a small number of pilot
symbols. This paper proposes novel channel prediction algorithms that address
this goal by integrating transfer and meta-learning with a reduced-rank
parametrization of the channel. The proposed methods optimize linear predictors
by utilizing data from previous frames, which are generally characterized by
distinct propagation characteristics, in order to enable fast training on the
time slots of the current frame. The proposed predictors rely on a novel
long-short-term decomposition (LSTD) of the linear prediction model that
leverages the disaggregation of the channel into long-term space-time
signatures and fading amplitudes. We first develop predictors for
single-antenna frequency-flat channels based on transfer/meta-learned quadratic
regularization. Then, we introduce transfer and meta-learning algorithms for
LSTD-based prediction models that build on equilibrium propagation (EP) and
alternating least squares (ALS). Numerical results under the 3GPP 5G standard
channel model demonstrate the impact of transfer and meta-learning on reducing
the number of pilots for channel prediction, as well as the merits of the
proposed LSTD parametrization.

Self-supervised learning (SSL) aims to eliminate one of the major bottlenecks
in representation learning - the need for human annotations. As a result, SSL
holds the promise to learn representations from data in-the-wild, i.e., without
the need for finite and static datasets. Instead, true SSL algorithms should be
able to exploit the continuous stream of data being generated on the internet
or by agents exploring their environments. But do traditional self-supervised
learning approaches work in this setup? In this work, we investigate this
question by conducting experiments on the continuous self-supervised learning
problem. While learning in the wild, we expect to see a continuous (infinite)
non-IID data stream that follows a non-stationary distribution of visual
concepts. The goal is to learn a representation that can be robust, adaptive
yet not forgetful of concepts seen in the past. We show that a direct
application of current methods to such continuous setup is 1) inefficient both
computationally and in the amount of data required, 2) leads to inferior
representations due to temporal correlations (non-IID data) in some sources of
streaming data and 3) exhibits signs of catastrophic forgetting when trained on
sources with non-stationary data distributions. We propose the use of replay
buffers as an approach to alleviate the issues of inefficiency and temporal
correlations. We further propose a novel method to enhance the replay buffer by
maintaining the least redundant samples. Minimum redundancy (MinRed) buffers
allow us to learn effective representations even in the most challenging
streaming scenarios composed of sequential visual data obtained from a single
embodied agent, and alleviates the problem of catastrophic forgetting when
learning from data with non-stationary semantic distributions.

Neural language models show vulnerability to adversarial examples which are
semantically similar to their original counterparts with a few words replaced
by their synonyms. A common way to improve model robustness is adversarial
training which follows two steps-collecting adversarial examples by attacking a
target model, and fine-tuning the model on the augmented dataset with these
adversarial examples. The objective of traditional adversarial training is to
make a model produce the same correct predictions on an original/adversarial
example pair. However, the consistency between model decision-makings on two
similar texts is ignored. We argue that a robust model should behave
consistently on original/adversarial example pairs, that is making the same
predictions (what) based on the same reasons (how) which can be reflected by
consistent interpretations. In this work, we propose a novel feature-level
adversarial training method named FLAT. FLAT aims at improving model robustness
in terms of both predictions and interpretations. FLAT incorporates variational
word masks in neural networks to learn global word importance and play as a
bottleneck teaching the model to make predictions based on important words.
FLAT explicitly shoots at the vulnerability problem caused by the mismatch
between model understandings on the replaced words and their synonyms in
original/adversarial example pairs by regularizing the corresponding global
word importance scores. Experiments show the effectiveness of FLAT in improving
the robustness with respect to both predictions and interpretations of four
neural network models (LSTM, CNN, BERT, and DeBERTa) to two adversarial attacks
on four text classification tasks. The models trained via FLAT also show better
robustness than baseline models on unforeseen adversarial examples across
different attacks.

Dengue fever is a vector-borne disease mostly endemic to tropical and
subtropical countries that affect millions every year and is considered a
significant burden for public health. Its geographic distribution makes it
highly sensitive to climate conditions. Here, we explore the effect of climate
variables using the Generalized Additive Model for location, scale, and shape
(GAMLSS) and Random Forest (RF) machine learning algorithms. Using the reported
number of dengue cases, we obtained reliable predictions. The uncertainty of
the predictions was also measured. These predictions will serve as input to
health officials to further improve and optimize the allocation of resources
prior to dengue outbreaks.

Unpaired image-to-image translation (I2I) is an ill-posed problem, as an
infinite number of translation functions can map the source domain distribution
to the target distribution. Therefore, much effort has been put into designing
suitable constraints, e.g., cycle consistency (CycleGAN), geometry consistency
(GCGAN), and contrastive learning-based constraints (CUTGAN), that help better
pose the problem. However, these well-known constraints have limitations: (1)
they are either too restrictive or too weak for specific I2I tasks; (2) these
methods result in content distortion when there is a significant spatial
variation between the source and target domains. This paper proposes a
universal regularization technique called maximum spatial perturbation
consistency (MSPC), which enforces a spatial perturbation function (T ) and the
translation operator (G) to be commutative (i.e., TG = GT ). In addition, we
introduce two adversarial training components for learning the spatial
perturbation function. The first one lets T compete with G to achieve maximum
perturbation. The second one lets G and T compete with discriminators to align
the spatial variations caused by the change of object size, object distortion,
background interruptions, etc. Our method outperforms the state-of-the-art
methods on most I2I benchmarks. We also introduce a new benchmark, namely the
front face to profile face dataset, to emphasize the underlying challenges of
I2I for real-world applications. We finally perform ablation experiments to
study the sensitivity of our method to the severity of spatial perturbation and
its effectiveness for distribution alignment.

When robots interact with human partners, often these partners change their
behavior in response to the robot. On the one hand this is challenging because
the robot must learn to coordinate with a dynamic partner. But on the other
hand -- if the robot understands these dynamics -- it can harness its own
behavior, influence the human, and guide the team towards effective
collaboration. Prior research enables robots to learn to influence other robots
or simulated agents. In this paper we extend these learning approaches to now
influence humans. What makes humans especially hard to influence is that -- not
only do humans react to the robot -- but the way a single user reacts to the
robot may change over time, and different humans will respond to the same robot
behavior in different ways. We therefore propose a robust approach that learns
to influence changing partner dynamics. Our method first trains with a set of
partners across repeated interactions, and learns to predict the current
partner's behavior based on the previous states, actions, and rewards. Next, we
rapidly adapt to new partners by sampling trajectories the robot learned with
the original partners, and then leveraging those existing behaviors to
influence the new partner dynamics. We compare our resulting algorithm to
state-of-the-art baselines across simulated environments and a user study where
the robot and participants collaborate to build towers. We find that our
approach outperforms the alternatives, even when the partner follows new or
unexpected dynamics. Videos of the user study are available here:
https://youtu.be/lYsWM8An18g

Feature attribution XAI algorithms enable their users to gain insight into
the underlying patterns of large datasets through their feature importance
calculation. Existing feature attribution algorithms treat all features in a
dataset homogeneously, which may lead to misinterpretation of consequences of
changing feature values. In this work, we consider partitioning features into
controllable and uncontrollable parts and propose the Controllable fActor
Feature Attribution (CAFA) approach to compute the relative importance of
controllable features. We carried out experiments applying CAFA to two existing
datasets and our own COVID-19 non-pharmaceutical control measures dataset.
Experimental results show that with CAFA, we are able to exclude influences
from uncontrollable features in our explanation while keeping the full dataset
for prediction.

We illustrate the detrimental effect, such as overconfident decisions, that
exponential behavior can have in methods like classical LDA and logistic
regression. We then show how polynomiality can remedy the situation. This,
among others, leads purposefully to random-level performance in the tails, away
from the bulk of the training data. A directly related, simple, yet important
technical novelty we subsequently present is softRmax: a reasoned alternative
to the standard softmax function employed in contemporary (deep) neural
networks. It is derived through linking the standard softmax to Gaussian
class-conditional models, as employed in LDA, and replacing those by a
polynomial alternative. We show that two aspects of softRmax, conservativeness
and inherent gradient regularization, lead to robustness against adversarial
attacks without gradient obfuscation.

How do property prices respond to changes in local taxes and local public
services? Attempts to measure this, starting with Oates (1969), have suffered
from a lack of local public service controls. Recent work attempts to overcome
such data limitations through the use of quasi-experimental methods. We revisit
this fundamental problem, but adopt a different empirical strategy that pairs
the double machine learning estimator of Chernozhukov et al. (2018) with a
novel dataset of 947 time-varying local characteristic and public service
controls for all municipalities in Sweden over the 2010-2016 period. We find
that properly controlling for local public service and characteristic controls
more than doubles the estimated impact of local income taxes on house prices.
We also exploit the unique features of our dataset to demonstrate that tax
capitalization is stronger in areas with greater municipal competition,
providing support for a core implication of the Tiebout hypothesis. Finally, we
measure the impact of public services, education, and crime on house prices and
the effect of local taxes on migration.

This paper presents an unpaired method for creating line drawings from
photographs. Current methods often rely on high quality paired datasets to
generate line drawings. However, these datasets often have limitations due to
the subjects of the drawings belonging to a specific domain, or in the amount
of data collected. Although recent work in unsupervised image-to-image
translation has shown much progress, the latest methods still struggle to
generate compelling line drawings. We observe that line drawings are encodings
of scene information and seek to convey 3D shape and semantic meaning. We build
these observations into a set of objectives and train an image translation to
map photographs into line drawings. We introduce a geometry loss which predicts
depth information from the image features of a line drawing, and a semantic
loss which matches the CLIP features of a line drawing with its corresponding
photograph. Our approach outperforms state-of-the-art unpaired image
translation and line drawing generation methods on creating line drawings from
arbitrary photographs. For code and demo visit our webpage
carolineec.github.io/informative_drawings

Reinforcement learning algorithms struggle on tasks with complex hierarchical
dependency structures. Humans and other intelligent agents do not waste time
assessing the utility of every high-level action in existence, but instead only
consider ones they deem possible in the first place. By focusing only on what
is feasible, or "afforded", at the present moment, an agent can spend more time
both evaluating the utility of and acting on what matters. To this end, we
present Hierarchical Affordance Learning (HAL), a method that learns a model of
hierarchical affordances in order to prune impossible subtasks for more
effective learning. Existing works in hierarchical reinforcement learning
provide agents with structural representations of subtasks but are not
affordance-aware, and by grounding our definition of hierarchical affordances
in the present state, our approach is more flexible than the multitude of
approaches that ground their subtask dependencies in a symbolic history. While
these logic-based methods often require complete knowledge of the subtask
hierarchy, our approach is able to utilize incomplete and varying symbolic
specifications. Furthermore, we demonstrate that relative to
non-affordance-aware methods, HAL agents are better able to efficiently learn
complex tasks, navigate environment stochasticity, and acquire diverse skills
in the absence of extrinsic supervision -- all of which are hallmarks of human
learning.

We consider constrained optimization problems with a nonsmooth objective
function in the form of mathematical expectation. The Sample Average
Approximation (SAA) is used to estimate the objective function and variable
sample size strategy is employed. The proposed algorithm combines an SAA
subgradient with the spectral coefficient in order to provide a suitable
direction which improves the performance of the first order method as shown by
numerical results. The step sizes are chosen from the predefined interval and
the almost sure convergence of the method is proved under the standard
assumptions in stochastic environment. To enhance the performance of the
proposed algorithm, we further specify the choice of the step size by
introducing an Armijo-like procedure adapted to this framework. Considering the
computational cost on machine learning problems, we conclude that the line
search improves the performance significantly. Numerical experiments conducted
on finite sums problems also reveal that the variable sample strategy
outperforms the full sample approach.

Recent advances in deep learning have enabled optimization of deep reactive
policies (DRPs) for continuous MDP planning by encoding a parametric policy as
a deep neural network and exploiting automatic differentiation in an end-to-end
model-based gradient descent framework. This approach has proven effective for
optimizing DRPs in nonlinear continuous MDPs, but it requires a large number of
sampled trajectories to learn effectively and can suffer from high variance in
solution quality. In this work, we revisit the overall model-based DRP
objective and instead take a minorization-maximization perspective to
iteratively optimize the DRP w.r.t. a locally tight lower-bounded objective.
This novel formulation of DRP learning as iterative lower bound optimization
(ILBO) is particularly appealing because (i) each step is structurally easier
to optimize than the overall objective, (ii) it guarantees a monotonically
improving objective under certain theoretical conditions, and (iii) it reuses
samples between iterations thus lowering sample complexity. Empirical
evaluation confirms that ILBO is significantly more sample-efficient than the
state-of-the-art DRP planner and consistently produces better solution quality
with lower variance. We additionally demonstrate that ILBO generalizes well to
new problem instances (i.e., different initial states) without requiring

For autonomous agents to act as trustworthy partners to human users, they
must be able to reliably communicate their competency for the tasks they are
asked to perform. Towards this objective, we develop probabilistic world models
based on deep generative modelling that allow for the simulation of agent
trajectories and accurate calculation of tasking outcome probabilities. By
combining the strengths of conditional variational autoencoders with recurrent
neural networks, the deep generative world model can probabilistically forecast
trajectories over long horizons to task completion. We show how these
forecasted trajectories can be used to calculate outcome probability
distributions, which enable the precise assessment of agent competency for
specific tasks and initial settings.

The increase in the observed volume in cosmological surveys imposes various
challenges on simulation preparations. Firstly, the volume of the simulations
required increases proportionally to the observations. However, large-volume
simulations are quickly becoming computationally intractable. Secondly,
on-going and future large-volume survey are targeting smaller objects, e.g.
emission line galaxies, compared to the earlier focus, i.e. luminous red
galaxies. They require the simulations to have higher mass resolutions. In this
work we present a machine learning (ML) approach to calibrate the halo
catalogue of a low-resolution (LR) simulation by training with a paired
high-resolution (HR) simulation with the same background white noise, thus we
can build the training data by matching HR haloes to LR haloes in a one-to-one
fashion. After training, the calibrated LR halo catalogue reproduces the
mass-clustering relation for mass down to $2.5\times 10^{11}~h^{-1}M_\odot$
within $5~{\rm per~cent}$ at scales $k<1~h\,\rm Mpc^{-1}$. We validate the
performance of different statistics including halo mass function, power
spectrum, two-point correlation function, and bispectrum in both real and
redshift space. Our approach generates high-resolution-like halo catalogues
($>200$ particles per halo) from low-resolution catalogues ($>25$ particles per
halo) containing corrected halo masses for each object. This allows to bypass
the computational burden of a large-volume real high-resolution simulation
without much compromise in the mass resolution of the result. The cost of our
ML approach ($\sim 1$ CPU-hour) is negligible compared to the cost of a
$N$-body simulation (e.g. millions of CPU-hours), The required computing time
is cut a factor of 8.

Hadronization is a complex quantum process whereby quarks and gluons become
hadrons. The widely-used models of hadronization in event generators are based
on physically-inspired phenomenological models with many free parameters. We
propose an alternative approach whereby neural networks are used instead. Deep
generative models are highly flexible, differentiable, and compatible with
Graphical Processing Unit (GPUs). We make the first step towards a data-driven
machine learning-based hadronization model by replacing a compont of the
hadronization model within the Herwig event generator (cluster model) with a
Generative Adversarial Network (GAN). We show that a GAN is capable of
reproducing the kinematic properties of cluster decays. Furthermore, we
integrate this model into Herwig to generate entire events that can be compared
with the output of the public Herwig simulator as well as with $e^+e^-$ data.

It is well-known that the most existing machine learning (ML)-based
safety-critical applications are vulnerable to carefully crafted input
instances called adversarial examples (AXs). An adversary can conveniently
attack these target systems from digital as well as physical worlds. This paper
aims to the generation of robust physical AXs against face recognition systems.
We present a novel smoothness loss function and a patch-noise combo attack for
realizing powerful physical AXs. The smoothness loss interjects the concept of
delayed constraints during the attack generation process, thereby causing
better handling of optimization complexity and smoother AXs for the physical
domain. The patch-noise combo attack combines patch noise and imperceptibly
small noises from different distributions to generate powerful
registration-based physical AXs. An extensive experimental analysis found that
our smoothness loss results in robust and more transferable digital and
physical AXs than the conventional techniques. Notably, our smoothness loss
results in a 1.17 and 1.97 times better mean attack success rate (ASR) in
physical white-box and black-box attacks, respectively. Our patch-noise combo
attack furthers the performance gains and results in 2.39 and 4.74 times higher
mean ASR than conventional technique in physical world white-box and black-box
attacks, respectively.

Computational protein-protein interaction (PPI) prediction techniques can
contribute greatly in reducing time, cost and false-positive interactions
compared to experimental approaches. Sequence is one of the key and primary
information of proteins that plays a crucial role in PPI prediction. Several
machine learning approaches have been applied to exploit the characteristics of
PPI datasets. However, these datasets greatly influence the performance of
predicting models. So, care should be taken on both dataset curation as well as
design of predictive models. Here, we have described our submitted solution
with the results of the SeqPIP competition whose objective was to develop
comprehensive PPI predictive models from sequence information with high-quality
bias-free interaction datasets. A training set of 2000 positive and 2000
negative interactions with sequences was given to us. Our method was evaluated
with three independent high-quality interaction test datasets and with other
competitors solutions.

In the past decades, Computed Tomography (CT) has established itself as one
of the most important imaging techniques in medicine. Today, the applicability
of CT is only limited by the deposited radiation dose, reduction of which
manifests in noisy or incomplete measurements. Thus, the need for robust
reconstruction algorithms arises. In this work, we learn a parametric
regularizer with a global receptive field by maximizing it's likelihood on
reference CT data. Due to this unsupervised learning strategy, our trained
regularizer truly represents higher-level domain statistics, which we
empirically demonstrate by synthesizing CT images. Moreover, this regularizer
can easily be applied to different CT reconstruction problems by embedding it
in a variational framework, which increases flexibility and interpretability
compared to feed-forward learning-based approaches. In addition, the
accompanying probabilistic perspective enables experts to explore the full
posterior distribution and may quantify uncertainty of the reconstruction
approach. We apply the regularizer to limited-angle and few-view CT
reconstruction problems, where it outperforms traditional reconstruction
algorithms by a large margin.

We propose a novel scene flow method that captures 3D motions from point
clouds without relying on ground-truth scene flow annotations. Due to the
irregularity and sparsity of point clouds, it is expensive and time-consuming
to acquire ground-truth scene flow annotations. Some state-of-the-art
approaches train scene flow networks in a self-supervised learning manner via
approximating pseudo scene flow labels from point clouds. However, these
methods fail to achieve the performance level of fully supervised methods, due
to the limitations of point cloud such as sparsity and lacking color
information. To provide an alternative, we propose a novel approach that
utilizes monocular RGB images and point clouds to generate pseudo scene flow
labels for training scene flow networks. Our pseudo label generation module
infers pseudo scene labels for point clouds by jointly leveraging rich
appearance information in monocular images and geometric information of point
clouds. To further reduce the negative effect of noisy pseudo labels on the
training, we propose a noisy-label-aware training scheme by exploiting the
geometric relations of points. Experiment results show that our method not only
outperforms state-of-the-art self-supervised approaches, but also outperforms
some supervised approaches that use accurate ground-truth flows.

We propose an implicit gradient based scheme for a constrained optimization
problem with nonconvex loss function, which can be potentially used to analyze
a variety of applications in machine learning, including meta-learning,
hyperparameter optimization, and reinforcement learning. The proposed algorithm
is based on the iterative differentiation (ITD) strategy. We extend the
convergence and rate analysis of the current literature of bilevel optimization
to a constrained bilevel structure with the motivation of learning under
constraints. For addressing bilevel optimization using any first-order scheme,
it involves the gradient of the inner-level optimal solution with respect to
the outer variable (implicit gradient). In this paper, taking into account of a
possible large-scale structure, we propose an efficient way of obtaining the
implicit gradient. We further provide error bounds with respect to the true
gradients. Further, we provide nonasymptotic rate results.

We report atomistic simulation results which indicate that the location of
shear banding in a metallic glass (MG) can be ascertained with reasonably high
accuracy solely from the undeformed static structure. Correlation is observed
between the location of the initiation of shear bands in a simulated MG and the
initial distribution of the density of fertile sites (DFS) for stress-driven
shear transformations identified a priori based on a deep learning model
devised in our recent work [Fan and Ma, Nat. Commun. 12, 1506 (2021)]. In
addition, we demonstrated that one can judge whether a glass is brittle or
ductile solely based upon its initial DFS distribution. These validate that
shear bands in MG arise from non-linear instabilities, and that the as-quenched
glass structure contains inhomogeneities that influence these instabilities.
This work also reveals an important subtlety regarding the non-deterministic
nature of athermal quasistatic shear simulations.

Efficient transformer variants with linear time complexity have been
developed to mitigate the quadratic computational overhead of the vanilla
transformer. Among them are low-rank projection methods such as Linformer and
kernel-based Transformers. Despite their unique merits, they usually suffer
from a performance drop comparing with the vanilla transformer on many sequence
generation tasks, and often fail to obtain computation gain when the generation
is short. We propose MemSizer, an approach towards closing the performance gap
while improving the efficiency even with short generation. It projects the
source sequences into lower dimension representations like Linformer, while
enjoying efficient recurrent-style incremental computation similar to
kernel-based transformers. This yields linear computation time and constant
memory complexity at inference time. MemSizer also employs a lightweight
multi-head mechanism which renders the computation as light as a single-head
model. We demonstrate that MemSizer provides an improved balance between
efficiency and accuracy over the vanilla transformer and other efficient
transformer variants in three typical sequence generation tasks, including
machine translation, abstractive text summarization, and language modeling.

Machine learning algorithms can perform well when trained on large datasets.
While large organisations often have considerable data assets, it can be
difficult for these assets to be unified in a manner that makes training
possible. Data is very often 'siloed' in different parts of the organisation,
with little to no access between silos. This fragmentation of data assets is
especially prevalent in heavily regulated industries like financial services or
healthcare. In this paper we propose a framework to enable asynchronous
collaborative training of machine learning models across data silos. This
allows data science teams to collaboratively train a machine learning model,
without sharing data with one another. Our proposed approach enhances
conventional federated learning techniques to make them suitable for this
asynchronous training in this intra-organisation, cross-silo setting. We
validate our proposed approach via extensive experiments.

We present an end-to-end framework to learn partial differential equations
that brings together initial data production, selection of boundary conditions,
and the use of physics-informed neural operators to solve partial differential
equations that are ubiquitous in the study and modeling of physics phenomena.
We first demonstrate that our methods reproduce the accuracy and performance of
other neural operators published elsewhere in the literature to learn the 1D
wave equation and the 1D Burgers equation. Thereafter, we apply our
physics-informed neural operators to learn new types of equations, including
the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we
show that our approach is also applicable to learn the physics of the 2D linear
and nonlinear shallow water equations, which involve three coupled partial
differential equations. We release our artificial intelligence surrogates and
scientific software to produce initial data and boundary conditions to study a
broad range of physically motivated scenarios. We provide the source code, an
interactive website to visualize the predictions of our physics informed neural
operators, and a tutorial for their use at the Data and Learning Hub for
Science.

We present a hybrid classical-quantum framework based on the Frank-Wolfe
algorithm, Q-FW, for solving quadratic, linearly-constrained, binary
optimization problems on quantum annealers (QA). The computational premise of
quantum computers has cultivated the re-design of various existing vision
problems into quantum-friendly forms. Experimental QA realizations can solve a
particular non-convex problem known as the quadratic unconstrained binary
optimization (QUBO). Yet a naive-QUBO cannot take into account the restrictions
on the parameters. To introduce additional structure in the parameter space,
researchers have crafted ad-hoc solutions incorporating (linear) constraints in
the form of regularizers. However, this comes at the expense of a
hyper-parameter, balancing the impact of regularization. To date, a true
constrained solver of quadratic binary optimization (QBO) problems has lacked.
Q-FW first reformulates constrained-QBO as a copositive program (CP), then
employs Frank-Wolfe iterations to solve CP while satisfying linear (in)equality
constraints. This procedure unrolls the original constrained-QBO into a set of
unconstrained QUBOs all of which are solved, in a sequel, on a QA. We use
D-Wave Advantage QA to conduct synthetic and real experiments on two important
computer vision problems, graph matching and permutation synchronization, which
demonstrate that our approach is effective in alleviating the need for an
explicit regularization coefficient.

as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging.
However, it has not been fully explored for clinical data analysis. Even though
an immense amount of Electronic Health Record (EHR) data is recorded, data and
labels can be scarce if the data is collected in small hospitals or deals with
rare diseases. In such scenarios, pre-training on a larger set of EHR data
could improve the model performance. In this paper, we apply unsupervised
pre-training to heterogeneous, multi-modal EHR data for patient outcome
prediction. To model this data, we leverage graph deep learning over population
graphs. We first design a network architecture based on graph transformer
designed to handle various input feature types occurring in EHR data, like
continuous, discrete, and time-series features, allowing better multi-modal
data fusion. Further, we design pre-training methods based on masked imputation
to pre-train our network before fine-tuning on different end tasks.
Pre-training is done in a fully unsupervised fashion, which lays the groundwork
for pre-training on large public datasets with different tasks and similar
modalities in the future. We test our method on two medical datasets of patient
records, TADPOLE and MIMIC-III, including imaging and non-imaging features and
different prediction tasks. We find that our proposed graph based pre-training
method helps in modeling the data at a population level and further improves
performance on the fine tuning tasks in terms of AUC on average by 4.15% for
MIMIC and 7.64% for TADPOLE.

In previous deep-learning-based methods, semantic segmentation has been
regarded as a static or dynamic per-pixel classification task, \textit{i.e.,}
classify each pixel representation to a specific category. However, these
methods only focus on learning better pixel representations or classification
kernels while ignoring the structural information of objects, which is critical
to human decision-making mechanism. In this paper, we present a new paradigm
for semantic segmentation, named structure-aware extraction. Specifically, it
generates the segmentation results via the interactions between a set of
learnable structure tokens and the image feature, which aims to progressively
extract the structural information of each category from the feature. Extensive
experiments show that our StructToken outperforms the state-of-the-art on three
widely-used benchmarks, including ADE20K, Cityscapes, and COCO-Stuff-10K.

We present a machine learning algorithm that discovers conservation laws from
differential equations, both numerically (parametrized as neural networks) and
symbolically, ensuring their functional independence (a non-linear
generalization of linear independence). Our independence module can be viewed
as a nonlinear generalization of singular value decomposition. Our method can
readily handle inductive biases for conservation laws. We validate it with
examples including the 3-body problem, the KdV equation and nonlinear
Schr\"odinger equation.

Deep learning models have reached or surpassed human-level performance in the
field of medical imaging, especially in disease diagnosis using chest x-rays.
However, prior work has found that such classifiers can exhibit biases in the
form of gaps in predictive performance across protected groups. In this paper,
we question whether striving to achieve zero disparities in predictive
performance (i.e. group fairness) is the appropriate fairness definition in the
clinical setting, over minimax fairness, which focuses on maximizing the
performance of the worst-case group. We benchmark the performance of nine
methods in improving classifier fairness across these two definitions. We find,
consistent with prior work on non-clinical data, that methods which strive to
achieve better worst-group performance do not outperform simple data balancing.
We also find that methods which achieve group fairness do so by worsening
performance for all groups. In light of these results, we discuss the utility
of fairness definitions in the clinical setting, advocating for an
investigation of the bias-inducing mechanisms in the underlying data generating
process whenever possible.

We study how visual representations pre-trained on diverse human video data
can enable data-efficient learning of downstream robotic manipulation tasks.
Concretely, we pre-train a visual representation using the Ego4D human video
dataset using a combination of time-contrastive learning, video-language
alignment, and an L1 penalty to encourage sparse and compact representations.
The resulting representation, R3M, can be used as a frozen perception module
for downstream policy learning. Across a suite of 12 simulated robot
manipulation tasks, we find that R3M improves task success by over 20% compared
to training from scratch and by over 10% compared to state-of-the-art visual
representations like CLIP and MoCo. Furthermore, R3M enables a Franka Emika
Panda arm to learn a range of manipulation tasks in a real, cluttered apartment
given just 20 demonstrations. Code and pre-trained models are available at
https://tinyurl.com/robotr3m.

Policy regularization methods such as maximum entropy regularization are
widely used in reinforcement learning to improve the robustness of a learned
policy. In this paper, we show how this robustness arises from hedging against
worst-case perturbations of the reward function, which are chosen from a
limited set by an imagined adversary. Using convex duality, we characterize
this robust set of adversarial reward perturbations under KL and
alpha-divergence regularization, which includes Shannon and Tsallis entropy
regularization as special cases. Importantly, generalization guarantees can be
given within this robust set. We provide detailed discussion of the worst-case
reward perturbations, and present intuitive empirical examples to illustrate
this robustness and its relationship with generalization. Finally, we discuss
how our analysis complements and extends previous results on adversarial reward
robustness and path consistency optimality conditions.

Cascading bandits is a natural and popular model that frames the task of
learning to rank from Bernoulli click feedback in a bandit setting. For the
case of unstructured rewards, we prove matching upper and lower bounds for the
problem-independent (i.e., gap-free) regret, both of which strictly improve the
best known. A key observation is that the hard instances of this problem are
those with small mean rewards, i.e., the small click-through rates that are
most relevant in practice. Based on this, and the fact that small mean implies
small variance for Bernoullis, our key technical result shows that
variance-aware confidence sets derived from the Bernstein and Chernoff bounds
lead to optimal algorithms (up to log terms), whereas Hoeffding-based
algorithms suffer order-wise suboptimal regret. This sharply contrasts with the
standard (non-cascading) bandit setting, where the variance-aware algorithms
only improve constants. In light of this and as an additional contribution, we
propose a variance-aware algorithm for the structured case of linear rewards
and show its regret strictly improves the state-of-the-art.

Photorealistic rendering and reposing of humans is important for enabling
augmented reality experiences. We propose a novel framework to reconstruct the
human and the scene that can be rendered with novel human poses and views from
just a single in-the-wild video. Given a video captured by a moving camera, we
train two NeRF models: a human NeRF model and a scene NeRF model. To train
these models, we rely on existing methods to estimate the rough geometry of the
human and the scene. Those rough geometry estimates allow us to create a
warping field from the observation space to the canonical pose-independent
space, where we train the human model in. Our method is able to learn subject
specific details, including cloth wrinkles and accessories, from just a 10
seconds video clip, and to provide high quality renderings of the human under
novel poses, from novel views, together with the background.

Language models excel at generating coherent text, and model compression
techniques such as knowledge distillation have enabled their use in
resource-constrained settings. However, these models can be biased in multiple
ways, including the unfounded association of male and female genders with
gender-neutral professions. Therefore, knowledge distillation without any
fairness constraints may preserve or exaggerate the teacher model's biases onto
the distilled model. To this end, we present a novel approach to mitigate
gender disparity in text generation by learning a fair model during knowledge
distillation. We propose two modifications to the base knowledge distillation
based on counterfactual role reversal$\unicode{x2014}$modifying teacher
probabilities and augmenting the training set. We evaluate gender polarity
across professions in open-ended text generated from the resulting distilled
and finetuned GPT$\unicode{x2012}$2 models and demonstrate a substantial
reduction in gender disparity with only a minor compromise in utility. Finally,
we observe that language models that reduce gender polarity in language
generation do not improve embedding fairness or downstream classification
fairness.

Visual attention has been extensively studied for learning fine-grained
features in both facial expression recognition (FER) and Action Unit (AU)
detection. A broad range of previous research has explored how to use attention
modules to localize detailed facial parts (e,g. facial action units), learn
discriminative features, and learn inter-class correlation. However, few
related works pay attention to the robustness of the attention module itself.
Through experiments, we found neural attention maps initialized with different
feature maps yield diverse representations when learning to attend the
identical Region of Interest (ROI). In other words, similar to general feature
learning, the representational quality of attention maps also greatly affects
the performance of a model, which means unconstrained attention learning has
lots of randomnesses. This uncertainty lets conventional attention learning
fall into sub-optimal. In this paper, we propose a compact model to enhance the
representational and focusing power of neural attention maps and learn the
"inter-attention" correlation for refined attention maps, which we term the
"Self-Diversified Multi-Channel Attention Network (SMA-Net)". The proposed
method is evaluated on two benchmark databases (BP4D and DISFA) for AU
detection and four databases (CK+, MMI, BU-3DFE, and BP4D+) for facial
expression recognition. It achieves superior performance compared to the

Node classification is the task of inferring or predicting missing node
attributes from information available for other nodes in a network. This paper
presents a general prediction model to hierarchical multi-label classification
(HMC), where the attributes to be inferred can be specified as a strict poset.
It is based on a top-down classification approach that addresses hierarchical
multi-label classification with supervised learning by building a local
classifier per class. The proposed model is showcased with a case study on the
prediction of gene functions for Oryza sativa Japonica, a variety of rice. It
is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model,
by evaluating both approaches in terms of prediction performance and
computational cost. The results in this work support the working hypothesis
that the proposed model can achieve good levels of prediction efficiency, while
scaling up in relation to the state of the art.

Earth observation is a fundamental tool for monitoring the evolution of land
use in specific areas of interest. Observing and precisely defining change, in
this context, requires both time-series data and pixel-wise segmentations. To
that end, we propose the DynamicEarthNet dataset that consists of daily,
multi-spectral satellite observations of 75 selected areas of interest
distributed over the globe with imagery from Planet Labs. These observations
are paired with pixel-wise monthly semantic segmentation labels of 7 land use
and land cover (LULC) classes. DynamicEarthNet is the first dataset that
provides this unique combination of daily measurements and high-quality labels.
In our experiments, we compare several established baselines that either
utilize the daily observations as additional training data (semi-supervised
learning) or multiple observations at once (spatio-temporal learning) as a
point of reference for future research. Finally, we propose a new evaluation
metric SCS that addresses the specific challenges associated with time-series
semantic change segmentation. The data is available at:
https://mediatum.ub.tum.de/1650201.

Model fine-tuning and adaptation have become a common approach for model
specialization for downstream tasks or domains. Fine-tuning the entire model or
a subset of the parameters using light-weight adaptation has shown considerable
success across different specialization tasks. Fine-tuning a model for a large
number of domains typically requires starting a new training job for every
domain posing scaling limitations. Once these models are trained, deploying
them also poses significant scalability challenges for inference for real-time
applications. In this paper, building upon prior light-weight adaptation
techniques, we propose a modular framework that enables us to substantially
improve scalability for model training and inference. We introduce Submodels
that can be quickly and dynamically loaded for on-the-fly inference. We also
propose multiple approaches for training those Submodels in parallel using an
embedding space in the same training job. We test our framework on an extreme
use-case which is speech model personalization for atypical speech, requiring a
Submodel for each user. We obtain 128x Submodel throughput with a fixed
computation budget without a loss of accuracy. We also show that learning a
speaker-embedding space can scale further and reduce the amount of
personalization training data required per speaker.

In this paper, we propose a new class of metric for table structure
recognition (TSR) evaluation, called grid table similarity (GriTS). Unlike
prior metrics, GriTS evaluates the correctness of a predicted table directly in
its natural form as a matrix. To create a similarity measure between matrices,
we generalize the two-dimensional largest common substructure (2D-LCS) problem,
which is NP-hard, to the 2D most similar substructures (2D-MSS) problem and
propose a polynomial-time heuristic for solving it. This algorithm produces
both an upper and a lower bound on the true similarity between matrices. We
show using evaluation on a large real-world dataset that in practice there is
almost no difference between these bounds. We compare GriTS to other metrics
and empirically validate that matrix similarity exhibits more desirable
behavior than alternatives for TSR performance evaluation. Finally, GriTS
unifies all three subtasks of cell topology recognition, cell location
recognition, and cell content recognition within the same framework, which
simplifies the evaluation and enables more meaningful comparisons across
different types of TSR approaches. Code will be released at
https://github.com/microsoft/table-transformer.

Synapses play a critical role in memory, learning, and cognition. Their main
functions include converting pre-synaptic voltage spikes to post-synaptic
currents, as well as scaling the input signal. Several brain-inspired
architectures have been proposed to emulate the behavior of biological
synapses. While these are useful to explore the properties of nervous systems,
the challenge of making biocompatible and flexible circuits with biologically
plausible time constants and tunable gain remains. Here, a physically flexible
organic log-domain integrator synaptic circuit is shown to address this
challenge. In particular, the circuit is fabricated using organic-based
materials that are electrically active, offer flexibility and biocompatibility,
as well as time constants (critical in learning neural codes and encoding
spatiotemporal patterns) that are biologically plausible. Using a 10 nF
synaptic capacitor, the time constant reached 126 ms and 221 ms before and
during bending, respectively. The flexible synaptic circuit is characterized
before and during bending, followed by studies on the effects of weighting
voltage, synaptic capacitance, and disparity in pre-synaptic signals on the
time constant.

Clustering under pairwise constraints is an important knowledge discovery
tool that enables the learning of appropriate kernels or distance metrics to
improve clustering performance. These pairwise constraints, which come in the
form of must-link and cannot-link pairs, arise naturally in many applications
and are intuitive for users to provide. However, the common practice of
relaxing discrete constraints to a continuous domain to ease optimization when
learning kernels or metrics can harm generalization, as information which only
encodes linkage is transformed to informing distances. We introduce a new
constrained clustering algorithm that jointly clusters data and learns a kernel
in accordance with the available pairwise constraints. To generalize well, our
method is designed to maximize constraint satisfaction without relaxing
pairwise constraints to a continuous domain where they inform distances. We
show that the proposed method outperforms existing approaches on a large number
of diverse publicly available datasets, and we discuss how our method can scale
to handling large data.

We revisit the classical causal inference problem of estimating the average
treatment effect in the presence of fully observed confounding variables using
two-stage semiparametric methods. In existing theoretical studies of methods
such as G-computation, inverse propensity weighting (IPW), and two common
doubly robust estimators -- augmented IPW (AIPW) and targeted maximum
likelihood estimation (TMLE) -- they are either bias-dominated, or have similar
asymptotic statistical properties. However, when applied to real datasets, they
often appear to have notably different variance. We compare these methods when
using a machine learning (ML) model to estimate the nuisance parameters of the
semiparametric model, and highlight some of the important differences. When the
outcome model estimates have little bias, which is common among some key ML
models, G-computation and the TMLE outperforms the other estimators in both
bias and variance.
  We show that the differences can be explained using high-dimensional
statistical theory, where the number of confounders $d$ is of the same order as
the sample size $n$. To make this theoretical problem tractable, we posit a
generalized linear model for the effect of the confounders on the treatment
assignment and outcomes. Despite making parametric assumptions, this setting is
a useful surrogate for some machine learning methods used to adjust for
confounding in two-stage semiparametric methods. In particular, the estimation
of the first stage adds variance that does not vanish, forcing us to confront
terms in the asymptotic expansion that normally are brushed aside as finite
sample defects. However, our model emphasizes differences in performance
between these estimators beyond first-order asymptotics.

Recent social networks' misinformation mitigation approaches tend to
investigate how to reduce misinformation by considering a whole-network
statistical scale. However, unbalanced misinformation exposures among
individuals urge to study fair allocation of mitigation resources. Moreover,
the network has random dynamics which change over time. Therefore, we introduce
a stochastic and non-stationary knapsack problem, and we apply its resolution
to mitigate misinformation in social network campaigns. We further propose a
generic misinformation mitigation algorithm that is robust to different social
networks' misinformation statistics, allowing a promising impact in real-world
scenarios. A novel loss function ensures fair mitigation among users. We
achieve fairness by intelligently allocating a mitigation incentivization
budget to the knapsack, and optimizing the loss function. To this end, a team
of Learning Automata (LA) drives the budget allocation. Each LA is associated
with a user and learns to minimize its exposure to misinformation by performing
a non-stationary and stochastic walk over its state space. Our results show how
our LA-based method is robust and outperforms similar misinformation mitigation
methods in how the mitigation is fairly influencing the network users.

Hate speech classifiers exhibit substantial performance degradation when
evaluated on datasets different from the source. This is due to learning
spurious correlations between words that are not necessarily relevant to
hateful language, and hate speech labels from the training corpus. Previous
work has attempted to mitigate this problem by regularizing specific terms from
pre-defined static dictionaries. While this has been demonstrated to improve
the generalizability of classifiers, the coverage of such methods is limited
and the dictionaries require regular manual updates from human experts. In this
paper, we propose to automatically identify and reduce spurious correlations
using attribution methods with dynamic refinement of the list of terms that
need to be regularized during training. Our approach is flexible and improves
the cross-corpora performance over previous work independently and in
combination with pre-defined dictionaries.

We present the design of a new large scale orchestration layer for
accelerators. Our system, Pathways, is explicitly designed to enable
exploration of new systems and ML research ideas, while retaining state of the
art performance for current models. Pathways uses a sharded dataflow graph of
asynchronous operators that consume and produce futures, and efficiently
gang-schedules heterogeneous parallel computations on thousands of accelerators
while coordinating data transfers over their dedicated interconnects. Pathways
makes use of a novel asynchronous distributed dataflow design that lets the
control plane execute in parallel despite dependencies in the data plane. This
design, with careful engineering, allows Pathways to adopt a single-controller
model that makes it easier to express complex new parallelism patterns. We
demonstrate that Pathways can achieve performance parity (~100% accelerator
utilization) with state-of-the-art systems when running SPMD computations over
2048 TPUs, while also delivering throughput comparable to the SPMD case for
Transformer models that are pipelined across 16 stages, or sharded across two
islands of accelerators connected over a data center network.

Action Unit (AU) Detection is the branch of affective computing that aims at
recognizing unitary facial muscular movements. It is key to unlock unbiased
computational face representations and has therefore aroused great interest in
the past few years. One of the main obstacles toward building efficient deep
learning based AU detection system is the lack of wide facial image databases
annotated by AU experts. In that extent the ABAW challenge paves the way toward
better AU detection as it involves a 2M frames AU annotated dataset. In this
paper, we present our submission to the ABAW3 challenge. In a nutshell, we
applied a multi-label detection transformer that leverage multi-head attention
to learn which part of the face image is the most relevant to predict each AU.

We discuss an approach to probabilistic forecasting based on two chained
machine-learning steps: a dimensional reduction step that learns a reduction
map of predictor information to a low-dimensional space in a manner designed to
preserve information about forecast quantities; and a density estimation step
that uses the probabilistic machine learning technique of normalizing flows to
compute the joint probability density of reduced predictors and forecast
quantities. This joint density is then renormalized to produce the conditional
forecast distribution. In this method, probabilistic calibration testing plays
the role of a regularization procedure, preventing overfitting in the second
step, while effective dimensional reduction from the first step is the source
of forecast sharpness. We verify the method using a 22-year 1-hour cadence time
series of Weather Research and Forecasting (WRF) simulation data of surface
wind on a grid.

Semi-supervised learning (SSL) has recently received increased attention from
machine learning researchers. By enabling effective propagation of known labels
in graph-based deep learning (GDL) algorithms, SSL is poised to become an
increasingly used technique in GDL in the coming years. However, there are
currently few explorations in the graph-based SSL literature on exploiting
classical dimensionality reduction techniques for improved label propagation.
In this work, we investigate the use of dimensionality reduction techniques
such as PCA, t-SNE, and UMAP to see their effect on the performance of graph
neural networks (GNNs) designed for semi-supervised propagation of node labels.
Our study makes use of benchmark semi-supervised GDL datasets such as the Cora
and Citeseer datasets to allow meaningful comparisons of the representations
learned by each algorithm when paired with a dimensionality reduction
technique. Our comprehensive benchmarks and clustering visualizations
quantitatively and qualitatively demonstrate that, under certain conditions,
employing a priori and a posteriori dimensionality reduction to GNN inputs and
outputs, respectively, can simultaneously improve the effectiveness of
semi-supervised node label propagation and node clustering. Our source code is
freely available on GitHub.

Block RAMs (BRAMs) are the storage houses of FPGAs, providing extensive
on-chip memory bandwidth to the compute units implemented using Logic Blocks
(LBs) and Digital Signal Processing (DSP) slices. We propose modifying BRAMs to
convert them to CoMeFa (Compute-In-Memory Blocks for FPGAs) RAMs. These RAMs
provide highly-parallel compute-in-memory by combining computation and storage
capabilities in one block. CoMeFa RAMs utilize the true dual port nature of
FPGA BRAMs and contain multiple programmable single-bit bit-serial processing
elements. CoMeFa RAMs can be used to compute in any precision, which is
extremely important for evolving applications like Deep Learning. Adding CoMeFa
RAMs to FPGAs significantly increases their compute density. We explore and
propose two architectures of these RAMs: CoMeFa-D (optimized for delay) and
CoMeFa-A (optimized for area). Compared to existing proposals, CoMeFa RAMs do
not require changing the underlying SRAM technology like simultaneously
activating multiple rows on the same port, and are practical to implement.
CoMeFa RAMs are versatile blocks that find applications in numerous diverse
parallel applications like Deep Learning, signal processing, databases, etc. By
augmenting an Intel Arria-10-like FPGA with CoMeFa-D (CoMeFa-A) RAMs at the
cost of 3.8% (1.2%) area, and with algorithmic improvements and efficient
mapping, we observe a geomean speedup of 2.5x (1.8x), across several
representative benchmarks. Replacing all or some BRAMs with CoMeFa RAMs in
FPGAs can make them better accelerators of modern compute-intensive workloads.

Point normal, as an intrinsic geometric property of 3D objects, not only
serves conventional geometric tasks such as surface consolidation and
reconstruction, but also facilitates cutting-edge learning-based techniques for
shape analysis and generation. In this paper, we propose a normal refinement
network, called Refine-Net, to predict accurate normals for noisy point clouds.
Traditional normal estimation wisdom heavily depends on priors such as surface
shapes or noise distributions, while learning-based solutions settle for single
types of hand-crafted features. Differently, our network is designed to refine
the initial normal of each point by extracting additional information from
multiple feature representations. To this end, several feature modules are
developed and incorporated into Refine-Net by a novel connection module.
Besides the overall network architecture of Refine-Net, we propose a new
multi-scale fitting patch selection scheme for the initial normal estimation,
by absorbing geometry domain knowledge. Also, Refine-Net is a generic normal
estimation framework: 1) point normals obtained from other methods can be
further refined, and 2) any feature module related to the surface geometric
structures can be potentially integrated into the framework. Qualitative and
quantitative evaluations demonstrate the clear superiority of Refine-Net over
the state-of-the-arts on both synthetic and real-scanned datasets. Our code is
available at https://github.com/hrzhou2/refinenet.

Solving an ill-posed linear inverse problem requires knowledge about the
underlying signal model. In many applications, this model is a priori unknown
and has to be learned from data. However, it is impossible to learn the model
using observations obtained via a single incomplete measurement operator, as
there is no information about the signal model in the nullspace of the
operator, resulting in a chicken-and-egg problem: to learn the model we need
reconstructed signals, but to reconstruct the signals we need to know the
model. Two ways to overcome this limitation are using multiple measurement
operators or assuming that the signal model is invariant to a certain group
action. In this paper, we present necessary and sufficient sensing conditions
for learning the signal model from measurement data alone which only depend on
the dimension of the model and the number of operators or properties of the
group action that the model is invariant to. As our results are agnostic of the
learning algorithm, they shed light into the fundamental limitations of
learning from incomplete data and have implications in a wide range set of
practical algorithms, such as dictionary learning, matrix completion and deep

Ride-hailing service is becoming a leading part in urban transportation. To
improve the efficiency of ride-hailing service, accurate prediction of
transportation demand is a fundamental challenge. In this paper, we tackle this
problem from both aspects of network structure and data-set formulation. For
network design, we propose a spatial-temporal attention multi-graph convolution
network (STA-MGCN). A spatial-temporal layer in STA-MGCN is developed to
capture the temporal correlations by temporal attention mechanism and temporal
gate convolution, and the spatial correlations by multigraph convolution. A
feature cluster layer is introduced to learn latent regional functions and to
reduce the computation burden. For the data-set formulation, we develop a novel
approach which considers the transportation feature of periodicity with offset.
Instead of only using history data during the same time period, the history
order demand in forward and backward neighboring time periods from yesterday
and last week are also included. Extensive experiments on the three real-world
datasets of New-York, Chicago and Chengdu show that the proposed algorithm
achieves the state-of-the-art performance for ride-hailing demand prediction.

In this technical report, we provide a complete example of running the SAM+
algorithm, an algorithm for learning stochastic planning action models, on a
simplified PPDDL version of the Coffee problem. We provide a very brief
description of the SAM+ algorithm and detailed description of our simplified
version of the Coffee domain, and then describe the results of running it on
the simplified Coffee domain.

Sampling from complicated probability distributions is a hard computational
problem arising in many fields, including statistical physics, optimization,
and machine learning. Quantum computers have recently been used to sample from
complicated distributions that are hard to sample from classically, but which
seldom arise in applications. Here we introduce a quantum algorithm to sample
from distributions that pose a bottleneck in several applications, which we
implement on a superconducting quantum processor. The algorithm performs Markov
chain Monte Carlo (MCMC), a popular iterative sampling technique, to sample
from the Boltzmann distribution of classical Ising models. In each step, the
quantum processor explores the model in superposition to propose a random move,
which is then accepted or rejected by a classical computer and returned to the
quantum processor, ensuring convergence to the desired Boltzmann distribution.
We find that this quantum algorithm converges in fewer iterations than common
classical MCMC alternatives on relevant problem instances, both in simulations
and experiments. It therefore opens a new path for quantum computers to solve
useful--not merely difficult--problems in the near term.

Learning-based depth estimation has witnessed recent progress in multiple
directions; from self-supervision using monocular video to supervised methods
offering highest accuracy. Complementary to supervision, further boosts to
performance and robustness are gained by combining information from multiple
signals. In this paper we systematically investigate key trade-offs associated
with sensor and modality design choices as well as related model training
strategies. Our study leads us to a new method, capable of connecting
modality-specific advantages from polarisation, Time-of-Flight and
structured-light inputs. We propose a novel pipeline capable of estimating
depth from monocular polarisation for which we evaluate various training
signals. The inversion of differentiable analytic models thereby connects scene
geometry with polarisation and ToF signals and enables self-supervised and
cross-modal learning. In the absence of existing multimodal datasets, we
examine our approach with a custom-made multi-modal camera rig and collect
CroMo; the first dataset to consist of synchronized stereo polarisation,
indirect ToF and structured-light depth, captured at video rates. Extensive
experiments on challenging video scenes confirm both qualitative and
quantitative pipeline advantages where we are able to outperform competitive
monocular depth estimation method.

While deep learning-based image reconstruction methods have shown significant
success in removing objects from pictures, they have yet to achieve acceptable
results for attributing consistency to gender, ethnicity, expression, and other
characteristics like the topological structure of the face. The purpose of this
work is to extract the mask region from a masked image and rebuild the area
that has been detected. This problem is complex because (i) it is difficult to
determine the gender of an image hidden behind a mask, which causes the network
to become confused and reconstruct the male face as a female or vice versa;
(ii) we may receive images from multiple angles, making it extremely difficult
to maintain the actual shape, topological structure of the face and a natural
image; and (iii) there are problems with various mask forms because, in some
cases, the area of the mask cannot be anticipated precisely; certain parts of
the mask remain on the face after completion. To solve this complex task, we
split the problem into three phases: landmark detection, object detection for
the targeted mask area, and inpainting the addressed mask region. To begin, to
solve the first problem, we have used gender classification, which detects the
actual gender behind a mask, then we detect the landmark of the masked facial
image. Second, we identified the non-face item, i.e., the mask, and used the
Mask R-CNN network to create the binary mask of the observed mask area.
Thirdly, we developed an inpainting network that uses anticipated landmarks to
create realistic images. To segment the mask, this article uses a mask R-CNN
and offers a binary segmentation map for identifying the mask area.
Additionally, we generated the image utilizing landmarks as structural guidance
through a GAN-based network. The studies presented in this paper use the FFHQ
and CelebA datasets.

This paper describes our proposed method for the Workshop on Computational
Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022
shared task on Personality Prediction (PER) and Reactivity Index Prediction
(IRI). In this paper, we adopt the prompt-based learning method with the
pre-trained language model to accomplish these tasks. Specifically, the prompt
is designed to provide knowledge of the extra personalized information for
enhancing the pre-trained model. Data augmentation and model ensemble are
adopted for obtaining better results. Moreover, we also provided the online
software demonstration and the codes of the software for further research.

Cartesian reverse differential categories (CRDCs) are a recently defined
structure which categorically model the reverse differentiation operations used
in supervised learning. Here we define a related structure called a monoidal
reverse differential category, prove important results about its relationship
to CRDCs, and provide examples of both structures, including examples coming
from models of quantum computation.

Planetary exploration depends heavily on 3D image data to characterize the
static and dynamic properties of the rock and environment. Analyzing 3D images
requires many computations, causing efficiency to suffer lengthy processing
time alongside large energy consumption. High-Performance Computing (HPC)
provides apparent efficiency at the expense of energy consumption. However, for
remote explorations, the conveyed surveillance and the robotized sensing need
faster data analysis with ultimate accuracy to make real-time decisions. In
such environments, access to HPC and energy is limited. Therefore, we realize
that reducing the number of computations to optimal and maintaining the desired
accuracy leads to higher efficiency. This paper demonstrates the semantic
segmentation capability of a probabilistic decision tree algorithm, 3D Adapted
Random Forest Vision (3DARFV), exceeding deep learning algorithm efficiency at
the utmost accuracy.

Classification networks have been used in weakly-supervised semantic
segmentation (WSSS) to segment objects by means of class activation maps
(CAMs). However, without pixel-level annotations, they are known to (1) mainly
focus on discriminative regions, and (2) to produce diffuse CAMs without
well-defined prediction contours. In this work, we alleviate both problems by
improving CAM learning. First, we incorporate importance sampling based on the
class-wise probability mass function induced by the CAMs to produce stochastic
image-level class predictions. This results in segmentations that cover a
larger extent of the objects, as shown in our empirical studies. Second, we
formulate a feature similarity loss term, which further improves the alignment
of predicted contours with edges in the image. Furthermore, we shed new light
onto the problem of WSSS by measuring the contour F-score as a complement to
the common area mIoU metric. We show that our method significantly outperforms
previous methods in terms of contour quality, while matching state-of-the-art
on region similarity.

Many studies have sought to identify interdisciplinary research as a function
of the diversity of disciplines identified in an article's references or
citations. However, given the constant evolution of the scientific landscape,
disciplinary boundaries are shifting and blurring, making it increasingly
difficult to describe research within a strict taxonomy. In this work, we
explore the potential for graph learning methods to learn embedded
representations for research papers that encode their 'interdisciplinarity' in
a citation network. This facilitates the identification of interdisciplinary
research without the use of disciplinary categories. We evaluate these
representations and their ability to identify interdisciplinary research,
according to their utility in interdisciplinary citation prediction. We find
that those representations which preserve structural equivalence in the
citation graph are best able to predict distant, interdisciplinary interactions
in the network, according to multiple definitions of citation distance.

The success of deep convolutional neural networks (DCNNs) benefits from high
volumes of annotated data. However, annotating medical images is laborious,
expensive, and requires human expertise, which induces the label scarcity
problem. Especially when encountering the domain shift, the problem becomes
more serious. Although deep unsupervised domain adaptation (UDA) can leverage
well-established source domain annotations and abundant target domain data to
facilitate cross-modality image segmentation and also mitigate the label
paucity problem on the target domain, the conventional UDA methods suffer from
severe performance degradation when source domain annotations are scarce. In
this paper, we explore a challenging UDA setting - limited source domain
annotations. We aim to investigate how to efficiently leverage unlabeled data
from the source and target domains with limited source annotations for
cross-modality image segmentation. To achieve this, we propose a new
label-efficient UDA framework, termed MT-UDA, in which the student model
trained with limited source labels learns from unlabeled data of both domains
by two teacher models respectively in a semi-supervised manner. More
specifically, the student model not only distills the intra-domain semantic
knowledge by encouraging prediction consistency but also exploits the
inter-domain anatomical information by enforcing structural consistency.
Consequently, the student model can effectively integrate the underlying
knowledge beneath available data resources to mitigate the impact of source
label scarcity and yield improved cross-modality segmentation performance. We
evaluate our method on MM-WHS 2017 dataset and demonstrate that our approach
outperforms the state-of-the-art methods by a large margin under the
source-label scarcity scenario.

We are constantly using recommender systems, often without even noticing.
They build a profile of our person in order to recommend the content we will
most likely be interested in. The data representing the users, their
interactions with the system or the products may come from different sources
and be of a various nature. Our goal is to use a multi-view learning approach
to improve our recommender system and improve its capacity to manage multi-view
data. We propose a comparative study between several state-of-the-art
multi-view models applied to our industrial data. Our study demonstrates the
relevance of using multi-view learning within recommender systems.

Effective modeling of human interactions is of utmost importance when
forecasting behaviors such as future trajectories. Each individual, with its
motion, influences surrounding agents since everyone obeys to social
non-written rules such as collision avoidance or group following. In this paper
we model such interactions, which constantly evolve through time, by looking at
the problem from an algorithmic point of view, i.e. as a data manipulation
task. We present a neural network based on an end-to-end trainable working
memory, which acts as an external storage where information about each agent
can be continuously written, updated and recalled. We show that our method is
capable of learning explainable cause-effect relationships between motions of
different agents, obtaining state-of-the-art results on multiple trajectory
forecasting datasets.

Relevant to broad applied fields and natural processes, interfacial ionic
hydrates has been widely studied by ultrahigh-resolution atomic force
microscopy (AFM). However, the complex relationship between AFM signal and the
investigated system makes it difficult to determine the atomic structure of
such complex system from AFM images alone. Using machine learning, we achieved
precise identification of the atomic structures of interfacial water/ionic
hydrates based on AFM images, including the position of each atom and the
orientation of water molecules. Furthermore, it was found that structure
prediction of ionic hydrates can be achieved cost-effectively by transfer
learning using neural network (NN) trained with easily available interfacial
water data. Thus, this work provides an efficient and economical methodology
which not only opens up avenues to determine atomic structures of more complex
systems from AFM images, but may also help to interpret other science-wide
studies involving sophisticated experimental results.

Optimizing resource utilization in target platforms is key to achieving high
performance during DNN inference. While optimizations have been proposed for
inference latency, memory footprint, and energy consumption, prior
hardware-aware neural architecture search (NAS) methods have omitted resource
utilization, preventing DNNs to take full advantage of the target inference
platforms. Modeling resource utilization efficiently and accurately is
challenging, especially for widely-used array-based inference accelerators such
as Google TPU. In this work, we propose a novel hardware-aware NAS framework
that does not only optimize for task accuracy and inference latency, but also
for resource utilization. We also propose and validate a new computational
model for resource utilization in inference accelerators. By using the proposed
NAS framework and the proposed resource utilization model, we achieve 2.8 - 4x
speedup for DNN inference compared to prior hardware-aware NAS methods while
attaining similar or improved accuracy in image classification on CIFAR-10 and
Imagenet-100 datasets.

Hamilton-Jacobi reachability methods for safety-critical control have been
well studied, but the safety guarantees derived rely on the accuracy of the
numerical computation. Thus, it is crucial to understand and account for any
inaccuracies that occur due to uncertainty in the underlying dynamics and
environment as well as the induced numerical errors. To this end, we propose a
framework for modeling the error of the value function inherent in
Hamilton-Jacobi reachability using a Gaussian process. The derived safety
controller can be used in conjuncture with arbitrary controllers to provide a
safe hybrid control law. The marginal likelihood of the Gaussian process then
provides a confidence metric used to determine switches between a least
restrictive controller and a safety controller. We test both the prediction as
well as the correction capabilities of the presented method in a classical
pursuit-evasion example.

Low complexity of a system model is essential for its use in real-time
applications. However, sparse identification methods commonly have stringent
requirements that exclude them from being applied in an industrial setting. In
this paper, we introduce a flexible method for the sparse identification of
dynamical systems described by ordinary differential equations. Our method
relieves many of the requirements imposed by other methods that relate to the
structure of the model and the data set, such as fixed sampling rates, full
state measurements, and linearity of the model. The Levenberg-Marquardt
algorithm is used to solve the identification problem. We show that the
Levenberg-Marquardt algorithm can be written in a form that enables parallel
computing, which greatly diminishes the time required to solve the
identification problem. An efficient backward elimination strategy is presented
to construct a lean system model.

Canonical Correlation Analysis (CCA) is a method for feature extraction of
two views by finding maximally correlated linear projections of them. Several
variants of CCA have been introduced in the literature, in particular, variants
based on deep neural networks for learning highly correlated nonlinear
transformations of two views. As these models are parameterized conventionally,
their learnable parameters remain independent of the inputs after the training
process, which may limit their capacity for learning highly correlated
representations. We introduce a novel dynamic scaling method for training an
input-dependent canonical correlation model. In our deep-CCA models, the
parameters of the last layer are scaled by a second neural network that is
conditioned on the model's input, resulting in a parameterization that is
dependent on the input samples. We evaluate our model on multiple datasets and
demonstrate that the learned representations are more correlated in comparison
to the conventionally-parameterized CCA-based models and also obtain preferable
retrieval results. Our code is available at
https://github.com/tomerfr/DynamicallyScaledDeepCCA.

Training of speech enhancement systems often does not incorporate knowledge
of human perception and thus can lead to unnatural sounding results.
Incorporating psychoacoustically motivated speech perception metrics as part of
model training via a predictor network has recently gained interest. However,
the performance of such predictors is limited by the distribution of metric
scores that appear in the training data. In this work, we propose MetricGAN+/-
(an extension of MetricGAN+, one such metric-motivated system) which introduces
an additional network - a "de-generator" which attempts to improve the
robustness of the prediction network (and by extension of the generator) by
ensuring observation of a wider range of metric scores in training.
Experimental results on the VoiceBank-DEMAND dataset show relative improvement
in PESQ score of 3.8% (3.05 vs 3.22 PESQ score), as well as better
generalisation to unseen noise and speech.

Many of the existing sentiment analysis techniques are based on supervised
learning, and they demand the availability of valuable training datasets to
train their models. When dataset freshness is critical, the annotating of high
speed unlabelled data streams becomes critical but remains an open problem. In
this paper, we propose PLStream, a novel Apache Flink-based framework for fast
polarity labelling of massive data streams, like Twitter tweets or online
product reviews. We address the associated implementation challenges and
propose a list of techniques including both algorithmic improvements and system
optimizations. A thorough empirical validation with two real-world workloads
demonstrates that PLStream is able to generate high quality labels (almost 80%
accuracy) in the presence of high-speed continuous unlabelled data streams
(almost 16,000 tuples/sec) without any manual efforts.

Wind speed forecasting models and their application to wind farm operations
are attaining remarkable attention in the literature because of its benefits as
a clean energy source. In this paper, we suggested the time series machine
learning approach called random forest regression for predicting wind speed
variations. The computed values of mutual information and auto-correlation
shows that wind speed values depend on the past data up to 12 hours. The random
forest model was trained using ensemble from two weeks data with previous 12
hours values as input for every value. The computed root mean square error
shows that model trained with two weeks data can be employed to make reliable
short-term predictions up to three years ahead.

While transactions with cryptocurrencies such as Ethereum are becoming more
prevalent, fraud and other criminal transactions are not uncommon. Graph
analysis algorithms and machine learning techniques detect suspicious
transactions that lead to phishing in large transaction networks. Many graph
neural network (GNN) models have been proposed to apply deep learning
techniques to graph structures. Although there is research on phishing
detection using GNN models in the Ethereum transaction network, models that
address the scale of the number of vertices and edges and the imbalance of
labels have not yet been studied. In this paper, we compared the model
performance of GNN models on the actual Ethereum transaction network dataset
and phishing reported label data to exhaustively compare and verify which GNN
models and hyperparameters produce the best accuracy. Specifically, we
evaluated the model performance of representative homogeneous GNN models which
consider single-type nodes and edges and heterogeneous GNN models which support
different types of nodes and edges. We showed that heterogeneous models had
better model performance than homogeneous models. In particular, the RGCN model
achieved the best performance in the overall metrics.

The lack of annotated datasets is a major challenge in training new
task-specific supervised AI algorithms as manual annotation is expensive and
time-consuming. To address this problem, we present MONAI Label, a free and
open-source platform that facilitates the development of AI-based applications
that aim at reducing the time required to annotate 3D medical image datasets.
Through MONAI Label researchers can develop annotation applications focusing on
their domain of expertise. It allows researchers to readily deploy their apps
as services, which can be made available to clinicians via their preferred
user-interface. Currently, MONAI Label readily supports locally installed
(3DSlicer) and web-based (OHIF) frontends, and offers two Active learning
strategies to facilitate and speed up the training of segmentation algorithms.
MONAI Label allows researchers to make incremental improvements to their
labeling apps by making them available to other researchers and clinicians
alike. Lastly, MONAI Label provides sample labeling apps, namely DeepEdit and
DeepGrow, demonstrating dramatically reduced annotation times.

The high cost of acquiring a sufficient amount of seismic data for training
has limited the use of machine learning in seismic tomography. In addition, the
inversion uncertainty due to the noisy data and data scarcity is less discussed
in conventional seismic tomography literature. To mitigate the uncertainty
effects and quantify their impacts in the prediction, the so-called Bayesian
Physics-Informed Neural Networks (BPINNs) based on the eikonal equation are
adopted to infer the velocity field and reconstruct the travel-time field. In
BPINNs, two inference algorithms including Stein Variational Gradient Descent
(SVGD) and Gaussian variational inference (VI) are investigated for the
inference task. The numerical results of several benchmark problems demonstrate
that the velocity field can be estimated accurately and the travel-time can be
well approximated with reasonable uncertainty estimates by BPINNs. This
suggests that the inferred velocity model provided by BPINNs may serve as a
valid initial model for seismic inversion and migration.

Text line segmentation is one of the key steps in historical document
understanding. It is challenging due to the variety of fonts, contents, writing
styles and the quality of documents that have degraded through the years.
  In this paper, we address the limitations that currently prevent people from
building line segmentation models with a high generalization capacity. We
present a study conducted using three state-of-the-art systems Doc-UFCN,
dhSegment and ARU-Net and show that it is possible to build generic models
trained on a wide variety of historical document datasets that can correctly
segment diverse unseen pages. This paper also highlights the importance of the
annotations used during training: each existing dataset is annotated
differently. We present a unification of the annotations and show its positive
impact on the final text recognition results. In this end, we present a
complete evaluation strategy using standard pixel-level metrics, object-level
ones and introducing goal-oriented metrics.

We aim to understand how actions are performed and identify subtle
differences, such as 'fold firmly' vs. 'fold gently'. To this end, we propose a
method which recognizes adverbs across different actions. However, such
fine-grained annotations are difficult to obtain and their long-tailed nature
makes it challenging to recognize adverbs in rare action-adverb compositions.
Our approach therefore uses semi-supervised learning with multiple adverb
pseudo-labels to leverage videos with only action labels. Combined with
adaptive thresholding of these pseudo-adverbs we are able to make efficient use
of the available data while tackling the long-tailed distribution.
Additionally, we gather adverb annotations for three existing video retrieval
datasets, which allows us to introduce the new tasks of recognizing adverbs in
unseen action-adverb compositions and unseen domains. Experiments demonstrate
the effectiveness of our method, which outperforms prior work in recognizing
adverbs and semi-supervised works adapted for adverb recognition. We also show
how adverbs can relate fine-grained actions.

Only parts of unlabeled data are selected to train models for most
semi-supervised learning methods, whose confidence scores are usually higher
than the pre-defined threshold (i.e., the confidence margin). We argue that the
recognition performance should be further improved by making full use of all
unlabeled data. In this paper, we learn an Adaptive Confidence Margin (Ada-CM)
to fully leverage all unlabeled data for semi-supervised deep facial expression
recognition. All unlabeled samples are partitioned into two subsets by
comparing their confidence scores with the adaptively learned confidence margin
at each training epoch: (1) subset I including samples whose confidence scores
are no lower than the margin; (2) subset II including samples whose confidence
scores are lower than the margin. For samples in subset I, we constrain their
predictions to match pseudo labels. Meanwhile, samples in subset II participate
in the feature-level contrastive objective to learn effective facial expression
features. We extensively evaluate Ada-CM on four challenging datasets, showing
that our method achieves state-of-the-art performance, especially surpassing
fully-supervised baselines in a semi-supervised manner. Ablation study further
proves the effectiveness of our method. The source code is available at
https://github.com/hangyu94/Ada-CM.

In the last ten years, Convolutional Neural Networks (CNNs) have formed the
basis of deep-learning architectures for most computer vision tasks. However,
they are not necessarily optimal. For example, mathematical morphology is known
to be better suited to deal with binary images. In this work, we create a
morphological neural network that handles binary inputs and outputs. We propose
their construction inspired by CNNs to formulate layers adapted to such images
by replacing convolutions with erosions and dilations. We give explainable
theoretical results on whether or not the resulting learned networks are indeed
morphological operators. We present promising experimental results designed to
learn basic binary operators, and we have made our code publicly available
online.

Understanding how neural networks generalize on unseen data is crucial for
designing more robust and reliable models. In this paper, we study the
generalization gap of neural networks using methods from topological data
analysis. For this purpose, we compute homological persistence diagrams of
weighted graphs constructed from neuron activation correlations after a
training phase, aiming to capture patterns that are linked to the
generalization capacity of the network. We compare the usefulness of different
numerical summaries from persistence diagrams and show that a combination of
some of them can accurately predict and partially explain the generalization
gap without the need of a test set. Evaluation on two computer vision
recognition tasks (CIFAR10 and SVHN) shows competitive generalization gap
prediction when compared against state-of-the-art methods.

Measuring and quantifying dependencies between random variables (RV's) can
give critical insights into a data-set. Typical questions are: `Do underlying
relationships exist?', `Are some variables redundant?', and `Is some target
variable $Y$ highly or weakly dependent on variable $X$?' Interestingly,
despite the evident need for a general-purpose measure of dependency between
RV's, common practice of data analysis is that most data analysts use the
Pearson correlation coefficient (PCC) to quantify dependence between RV's,
while it is well-recognized that the PCC is essentially a measure for linear
dependency only. Although many attempts have been made to define more generic
dependency measures, there is yet no consensus on a standard, general-purpose
dependency function. In fact, several ideal properties of a dependency function
have been proposed, but without much argumentation. Motivated by this, in this
paper we will discuss and revise the list of desired properties and propose a
new dependency function that meets all these requirements. This general-purpose
dependency function provides data analysts a powerful means to quantify the
level of dependence between variables. To this end, we also provide Python code
to determine the dependency function for use in practice.

In this letter, we propose a learning based channel estimation scheme for
orthogonal frequency division multiplexing (OFDM) systems in the presence of
phase noise in doubly-selective fading channels. Two-dimensional (2D)
convolutional neural networks (CNNs) are employed for effective training and
tracking of channel variation in both frequency as well as time domain. The
proposed network learns and estimates the channel coefficients in the entire
time-frequency (TF) grid based on pilots sparsely populated in the TF grid. In
order to make the network robust to phase noise (PN) impairment, a novel
training scheme where the training data is rotated by random phases before
being fed to the network is employed. Further, using the estimated channel
coefficients, a simple and effective PN estimation and compensation scheme is
devised. Numerical results demonstrate that the proposed network and PN
compensation scheme achieve robust OFDM performance in the presence of phase
noise.

Patient scans from MRI often suffer from noise, which hampers the diagnostic
capability of such images. As a method to mitigate such artifact, denoising is
largely studied both within the medical imaging community and beyond the
community as a general subject. However, recent deep neural network-based
approaches mostly rely on the minimum mean squared error (MMSE) estimates,
which tend to produce a blurred output. Moreover, such models suffer when
deployed in real-world sitautions: out-of-distribution data, and complex noise
distributions that deviate from the usual parametric noise models. In this
work, we propose a new denoising method based on score-based reverse diffusion
sampling, which overcomes all the aforementioned drawbacks. Our network,
trained only with coronal knee scans, excels even on out-of-distribution in
vivo liver MRI data, contaminated with complex mixture of noise. Even more, we
propose a method to enhance the resolution of the denoised image with the same
network. With extensive experiments, we show that our method establishes
state-of-the-art performance, while having desirable properties which prior
MMSE denoisers did not have: flexibly choosing the extent of denoising, and
quantifying uncertainty.

With the increasing growth of information through smart devices, increasing
the quality level of human life requires various computational paradigms
presentation including the Internet of Things, fog, and cloud. Between these
three paradigms, the cloud computing paradigm as an emerging technology adds
cloud layer services to the edge of the network so that resource allocation
operations occur close to the end-user to reduce resource processing time and
network traffic overhead. Hence, the resource allocation problem for its
providers in terms of presenting a suitable platform, by using computational
paradigms is considered a challenge. In general, resource allocation approaches
are divided into two methods, including auction-based methods(goal, increase
profits for service providers-increase user satisfaction and usability) and
optimization-based methods(energy, cost, network exploitation, Runtime,
reduction of time delay). In this paper, according to the latest scientific
achievements, a comprehensive literature study (CLS) on artificial intelligence
methods based on resource allocation optimization without considering
auction-based methods in various computing environments are provided such as
cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G
networks, vehicular cloud architecture,machine-to-machine
communication(M2M),Train-to-Train(T2T) communication network, Peer-to-Peer(P2P)
network. Since deep learning methods based on artificial intelligence are used
as the most important methods in resource allocation problems; Therefore, in
this paper, resource allocation approaches based on deep learning are also used
in the mentioned computational environments such as deep reinforcement
learning, Q-learning technique, reinforcement learning, online learning, and
also Classical learning methods such as Bayesian learning, Cummins clustering,
Markov decision process.

In this paper, we present a robust and low complexity system for Acoustic
Scene Classification (ASC), the task of identifying the scene of an audio
recording. We first construct an ASC baseline system in which a novel
inception-residual-based network architecture is proposed to deal with the
mismatched recording device issue. To further improve the performance but still
satisfy the low complexity model, we apply two techniques: ensemble of multiple
spectrograms and channel reduction on the ASC baseline system. By conducting
extensive experiments on the benchmark DCASE 2020 Task 1A Development dataset,
we achieve the best model performing an accuracy of 69.9% and a low complexity
of 2.4M trainable parameters, which is competitive to the state-of-the-art ASC
systems and potential for real-life applications on edge devices.

The Internet of Things (IoT) is gaining momentum in its quest to bridge the
gap between the physical and the digital world. The main goal of the IoT is the
creation of smart environments and self-aware things that help to facilitate a
variety of services such as smart transport, climate monitoring, e-health, etc.
Huge volumes of data are expected to be collected by the connected
sensors/things, which in traditional cases are processed centrally by large
data centers in the core network that will inevitably lead to excessive
transportation power consumption as well as added latency overheads. Instead,
fog computing has been proposed by researchers from industry and academia to
extend the capability of the cloud right to the point where the data is
collected at the sensing layer. This way, primitive tasks that can be hosted in
IoT sensors do not need to be sent all the way to the cloud for processing. In
this paper we propose energy efficient embedding of machine learning (ML)
models over a cloud-fog network using a Mixed Integer Linear Programming (MILP)
optimization model. We exploit virtualization in our framework to provide
service abstraction of Deep Neural Networks (DNN) layers that can be composed
into a set of VMs interconnected by virtual links. We constrain the number of
VMs that can be processed at the IoT layer and study the impact on the
performance of the cloud fog approach.

Recent High Dynamic Range (HDR) techniques extend the capabilities of current
cameras where scenes with a wide range of illumination can not be accurately
captured with a single low-dynamic-range (LDR) image. This is generally
accomplished by capturing several LDR images with varying exposure values whose
information is then incorporated into a merged HDR image. While such approaches
work well for static scenes, dynamic scenes pose several challenges, mostly
related to the difficulty of finding reliable pixel correspondences.
Data-driven approaches tackle the problem by learning an end-to-end mapping
with paired LDR-HDR training data, but in practice generating such HDR
ground-truth labels for dynamic scenes is time-consuming and requires complex
procedures that assume control of certain dynamic elements of the scene (e.g.
actor pose) and repeatable lighting conditions (stop-motion capturing). In this
work, we propose a novel self-supervised approach for learnable HDR estimation
that alleviates the need for HDR ground-truth labels. We propose to leverage
the internal statistics of LDR images to create HDR pseudo-labels. We
separately exploit static and well-exposed parts of the input images, which in
conjunction with synthetic illumination clipping and motion augmentation
provide high quality training examples. Experimental results show that the HDR
models trained using our proposed self-supervision approach achieve performance
competitive with those trained under full supervision, and are to a large
extent superior to previous methods that equally do not require any

In time-varying fading channels, channel coefficients are estimated using
pilot symbols that are transmitted every coherence interval. For channels with
high Doppler spread, the rapid channel variations over time will require
considerable bandwidth for pilot transmission, leading to poor throughput. In
this paper, we propose a novel receiver architecture using deep recurrent
neural networks (RNNs) that learns the channel variations and thereby reduces
the number of pilot symbols required for channel estimation. Specifically, we
design and train an RNN to learn the correlation in the time-varying channel
and predict the channel coefficients into the future with good accuracy over a
wide range of Dopplers and signal-to-noise ratios (SNR). The proposed training
methodology enables accurate channel prediction through the use of techniques
such as teacher-force training, early-stop, and reduction of learning rate on
plateau. Also, the robustness of prediction for different Dopplers and SNRs is
achieved by adapting the number of predictions into the future based on the
Doppler and SNR. Numerical results show that good bit error performance is
achieved by the proposed receiver in time-varying fading channels. We also
propose a data decision driven receiver architecture using RNNs that further
reduces the pilot overhead while maintaining good bit error performance.

Onchocerciasis is causing blindness in over half a million people in the
world today. Drug development for the disease is crippled as there is no way of
measuring effectiveness of the drug without an invasive procedure. Drug
efficacy measurement through assessment of viability of onchocerca worms
requires the patients to undergo nodulectomy which is invasive, expensive,
time-consuming, skill-dependent, infrastructure dependent and lengthy process.
In this paper, we discuss the first-ever study that proposes use of machine
learning over thermal imaging to non-invasively and accurately predict the
viability of worms. The key contributions of the paper are (i) a unique thermal
imaging protocol along with pre-processing steps such as alignment,
registration and segmentation to extract interpretable features (ii) extraction
of relevant semantic features (iii) development of accurate classifiers for
detecting the existence of viable worms in a nodule. When tested on a
prospective test data of 30 participants with 48 palpable nodules, we achieved
an Area Under the Curve (AUC) of 0.85.

Anomaly detection aims to identify abnormal data that deviates from the
normal ones, while typically requiring a sufficient amount of normal data to
train the model for performing this task. Despite the success of recent anomaly
detection methods, performing anomaly detection in an unseen domain remain a
challenging task. In this paper, we address the task of domain-generalized
textured surface anomaly detection. By observing normal and abnormal surface
data across multiple source domains, our model is expected to be generalized to
an unseen textured surface of interest, in which only a small number of normal
data can be observed during testing. Although with only image-level labels
observed in the training data, our patch-based meta-learning model exhibits
promising generalization ability: not only can it generalize to unseen image
domains, but it can also localize abnormal regions in the query image. Our
experiments verify that our model performs favorably against state-of-the-art
anomaly detection and domain generalization approaches in various settings.

Autonomously navigating a robot in everyday crowded spaces requires solving
complex perception and planning challenges. When using only monocular image
sensor data as input, classical two-dimensional planning approaches cannot be
used. While images present a significant challenge when it comes to perception
and planning, they also allow capturing potentially important details, such as
complex geometry, body movement, and other visual cues. In order to
successfully solve the navigation task from only images, algorithms must be
able to model the scene and its dynamics using only this channel of
information. We investigate whether the world model concept, which has shown
state-of-the-art results for modeling and learning policies in Atari games as
well as promising results in 2D LiDAR-based crowd navigation, can also be
applied to the camera-based navigation problem. To this end, we create
simulated environments where a robot must navigate past static and moving
humans without colliding in order to reach its goal. We find that
state-of-the-art methods are able to achieve success in solving the navigation
problem, and can generate dream-like predictions of future image-sequences
which show consistent geometry and moving persons. We are also able to show
that policy performance in our high-fidelity sim2real simulation scenario
transfers to the real world by testing the policy on a real robot. We make our
simulator, models and experiments available at
https://github.com/danieldugas/NavDreams.

Self-attention heads are characteristic of Transformer models and have been
well studied for interpretability and pruning. In this work, we demonstrate an
altogether different utility of attention heads, namely for adversarial
detection. Specifically, we propose a method to construct input-specific
attention subnetworks (IAS) from which we extract three features to
discriminate between authentic and adversarial inputs. The resultant detector
significantly improves (by over 7.5%) the state-of-the-art adversarial
detection accuracy for the BERT encoder on 10 NLU datasets with 11 different
adversarial attack types. We also demonstrate that our method (a) is more
accurate for larger models which are likely to have more spurious correlations
and thus vulnerable to adversarial attack, and (b) performs well even with
modest training sets of adversarial examples.

Accurately forecasting extreme rainfall is notoriously difficult, but is also
ever more crucial for society as climate change increases the frequency of such
extremes. Global numerical weather prediction models often fail to capture
extremes, and are produced at too low a resolution to be actionable, while
regional, high-resolution models are hugely expensive both in computation and
labour. In this paper we explore the use of deep generative models to
simultaneously correct and downscale (super-resolve) global ensemble forecasts
over the Continental US. Specifically, using fine-grained radar observations as
our ground truth, we train a conditional Generative Adversarial Network --
coined CorrectorGAN -- via a custom training procedure and augmented loss
function, to produce ensembles of high-resolution, bias-corrected forecasts
based on coarse, global precipitation forecasts in addition to other relevant
meteorological fields. Our model outperforms an interpolation baseline, as well
as super-resolution-only and CNN-based univariate methods, and approaches the
performance of an operational regional high-resolution model across an array of
established probabilistic metrics. Crucially, CorrectorGAN, once trained,
produces predictions in seconds on a single machine. These results raise
exciting questions about the necessity of regional models, and whether
data-driven downscaling and correction methods can be transferred to data-poor
regions that so far have had no access to high-resolution forecasts.

In recent years, reinforcement learning (RL) has shown outstanding
performance for locomotion control of highly articulated robotic systems. Such
approaches typically involve tedious reward function tuning to achieve the
desired motion style. Imitation learning approaches such as adversarial motion
priors aim to reduce this problem by encouraging a pre-defined motion style. In
this work, we present an approach to augment the concept of adversarial motion
prior-based RL to allow for multiple, discretely switchable styles. We show
that multiple styles and skills can be learned simultaneously without notable
performance differences, even in combination with motion data-free skills. Our
approach is validated in several real-world experiments with a wheeled-legged
quadruped robot showing skills learned from existing RL controllers and
trajectory optimization, such as ducking and walking, and novel skills such as
switching between a quadrupedal and humanoid configuration. For the latter
skill, the robot is required to stand up, navigate on two wheels, and sit down.
Instead of tuning the sit-down motion, we verify that a reverse playback of the
stand-up movement helps the robot discover feasible sit-down behaviors and
avoids tedious reward function tuning.

Living cell segmentation from bright-field light microscopy images is
challenging due to the image complexity and temporal changes in the living
cells. Recently developed deep learning (DL)-based methods became popular in
medical and microscopy image segmentation tasks due to their success and
promising outcomes. The main objective of this paper is to develop a deep
learning, U-Net-based method to segment the living cells of the HeLa line in
bright-field transmitted light microscopy. To find the most suitable
architecture for our datasets, a residual attention U-Net was proposed and
compared with an attention and a simple U-Net architecture.
  The attention mechanism highlights the remarkable features and suppresses
activations in the irrelevant image regions. The residual mechanism overcomes
with vanishing gradient problem. The Mean-IoU score for our datasets reaches
0.9505, 0.9524, and 0.9530 for the simple, attention, and residual attention
U-Net, respectively. The most accurate semantic segmentation results was
achieved in the Mean-IoU and Dice metrics by applying the residual and
attention mechanisms together. The watershed method applied to this best --
Residual Attention -- semantic segmentation result gave the segmentation with
the specific information for each cell.

Deep learning has in recent years achieved immense success in all areas of
computer vision and has the potential of assisting medical doctors in analyzing
visual content for disease and other abnormalities. However, the current state
of deep learning is very much a black box, making medical professionals highly
skeptical about integrating these methods into clinical practice. Several
methods have been proposed in order to shine some light onto these black boxes,
but there is no consensus on the opinion of the medical doctors that will
consume these explanations. This paper presents a study asking medical doctors
about their opinion of current state-of-the-art explainable artificial
intelligence methods when applied to a gastrointestinal disease detection use
case. We compare two different categories of explanation methods, intrinsic and
extrinsic, and gauge their opinion of the current value of these explanations.
The results indicate that intrinsic explanations are preferred and that

In federated learning (FL), clients may have diverse objectives, merging all
clients' knowledge into one global model will cause negative transfers to local
performance. Thus, clustered FL is proposed to group similar clients into
clusters and maintain several global models. Nevertheless, current clustered FL
algorithms require the assumption of the number of clusters, they are not
effective enough to explore the latent relationships among clients. However, we
take advantage of peer-to-peer (P2P) FL, where clients communicate with
neighbors without a central server and propose an algorithm that enables
clients to form an effective communication topology in a decentralized manner
without assuming the number of clusters. Additionally, the P2P setting will
release the concerns caused by the central server in centralized FL, such as
reliability and communication bandwidth problems. In our method, 1) we present
two novel metrics for measuring client similarity, applicable under P2P
protocols; 2) we devise a two-stage algorithm, in the first stage, an efficient
method to enable clients to match same-cluster neighbors with high confidence
is proposed; 3) then in the second stage, a heuristic method based on
Expectation Maximization under the Gaussian Mixture Model assumption of
similarities is used for clients to discover more neighbors with similar
objectives. We make a theoretical analysis of how our work is superior to the
P2P FL counterpart and extensive experiments show that our method outperforms
all P2P FL baselines and has comparable or even superior performance to
centralized cluster FL. Moreover, results show that our method is much
effective in mining latent cluster relationships under various heterogeneity
without assuming the number of clusters and it is effective even under low
communication budgets.

Nowadays, data-driven, machine and deep learning approaches have provided
unprecedented performance in various complex tasks, including image
classification and object detection, and in a variety of application areas,
like autonomous vehicles, medical imaging and wireless communications.
Traditionally, such approaches have been deployed, along with the involved
datasets, on standalone devices. Recently, a shift has been observed towards
the so-called Edge Machine Learning, in which centralized architectures are
adopted that allow multiple devices with local computational and storage
resources to collaborate with the assistance of a centralized server. The
well-known federated learning approach is able to utilize such architectures by
allowing the exchange of only parameters with the server, while keeping the
datasets private to each contributing device. In this work, we propose a fully
distributed, diffusion-based learning algorithm that does not require a central
server and propose an adaptive combination rule for the cooperation of the
devices. By adopting a classification task on the MNIST dataset, the efficacy
of the proposed algorithm over corresponding counterparts is demonstrated via
the reduction of the number of collaboration rounds required to achieve an
acceptable accuracy level in non- IID dataset scenarios.

Information extraction suffers from its varying targets, heterogeneous
structures, and demand-specific schemas. In this paper, we propose a unified
text-to-structure generation framework, namely UIE, which can universally model
different IE tasks, adaptively generate targeted structures, and
collaboratively learn general IE abilities from different knowledge sources.
Specifically, UIE uniformly encodes different extraction structures via a
structured extraction language, adaptively generates target extractions via a
schema-based prompt mechanism - structural schema instructor, and captures the
common IE abilities via a large-scale pre-trained text-to-structure model.
Experiments show that UIE achieved the state-of-the-art performance on 4 IE
tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings
for a wide range of entity, relation, event and sentiment extraction tasks and
their unification. These results verified the effectiveness, universality, and
transferability of UIE.

Sparse Transformer has recently attracted a lot of attention since the
ability for reducing the quadratic dependency on the sequence length. We argue
that two factors, information bottleneck sensitivity and inconsistency between
different attention topologies, could affect the performance of the Sparse
Transformer. This paper proposes a well-designed model named ERNIE-Sparse. It
consists of two distinctive parts: (i) Hierarchical Sparse Transformer (HST) to
sequentially unify local and global information. (ii) Self-Attention
Regularization (SAR) method, a novel regularization designed to minimize the
distance for transformers with different attention topologies. To evaluate the
effectiveness of ERNIE-Sparse, we perform extensive evaluations. Firstly, we
perform experiments on a multi-modal long sequence modeling task benchmark,
Long Range Arena (LRA). Experimental results demonstrate that ERNIE-Sparse
significantly outperforms a variety of strong baseline methods including the
dense attention and other efficient sparse attention methods and achieves
improvements by 2.77% (57.78% vs. 55.01%). Secondly, to further show the
effectiveness of our method, we pretrain ERNIE-Sparse and verified it on 3 text
classification and 2 QA downstream tasks, achieve improvements on
classification benchmark by 0.83% (92.46% vs. 91.63%), on QA benchmark by 3.24%
(74.67% vs. 71.43%). Experimental results continue to demonstrate its superior

Low-shot relation extraction~(RE) aims to recognize novel relations with very
few or even no samples, which is critical in real scenario application.
Few-shot and zero-shot RE are two representative low-shot RE tasks, which seem
to be with similar target but require totally different underlying abilities.
In this paper, we propose Multi-Choice Matching Networks to unify low-shot
relation extraction. To fill in the gap between zero-shot and few-shot RE, we
propose the triplet-paraphrase meta-training, which leverages triplet
paraphrase to pre-train zero-shot label matching ability and uses meta-learning
paradigm to learn few-shot instance summarizing ability. Experimental results
on three different low-shot RE tasks show that the proposed method outperforms
strong baselines by a large margin, and achieve the best performance on
few-shot RE leaderboard.

Event cameras are a new type of sensors that are different from traditional
cameras. Each pixel is triggered asynchronously by event. The trigger event is
the change of the brightness irradiated on the pixel. If the increment or
decrement of brightness is higher than a certain threshold, an event is output.
Compared with traditional cameras, event cameras have the advantages of high
dynamic range and no motion blur. Since events are caused by the apparent
motion of intensity edges, the majority of 3D reconstructed maps consist only
of scene edges, i.e., semi-dense maps, which is not enough for some
applications. In this paper, we propose a pipeline to realize event-based dense
reconstruction. First, deep learning is used to reconstruct intensity images
from events. And then, structure from motion (SfM) is used to estimate camera
intrinsic, extrinsic and sparse point cloud. Finally, multi-view stereo (MVS)
is used to complete dense reconstruction.

The goal of recommender systems is to provide ordered item lists to users
that best match their interests. As a critical task in the recommendation
pipeline, re-ranking has received increasing attention in recent years. In
contrast to conventional ranking models that score each item individually,
re-ranking aims to explicitly model the mutual influences among items to
further refine the ordering of items given an initial ranking list. In this
paper, we present a personalized re-ranking model (dubbed PEAR) based on
contextualized transformer. PEAR makes several major improvements over the
existing methods. Specifically, PEAR not only captures feature-level and
item-level interactions, but also models item contexts from both the initial
ranking list and the historical clicked item list. In addition to item-level
ranking score prediction, we also augment the training of PEAR with a
list-level classification task to assess users' satisfaction on the whole
ranking list. Experimental results on both public and production datasets have
shown the superior effectiveness of PEAR compared to the previous re-ranking
models.

The 2030 Agenda and accompanying Sustainable Development Goals (SDGs) are
vital in guiding national and global policy. However, many of the SDG
indicators used to measure progress toward those goals suffer from long
publication lags. Nowcasting has the potential to address this problem and
generate more timely estimates of those indicators. This paper provides
resources for achieving that potential by 1) carrying out a comprehensive
nowcasting feasibility survey of all SDG indicators to assess their potential
to be nowcast, and 2) performing a case study of indicator 9.4.1 to illustrate
and shed light on the process of performing a nowcasting exercise. There exist
231 SDG indicators, but due to only examining Tier 1 indicators and the fact
that many indicators have multiple sub-indicators, 362 indicators and
sub-indicators were eventually surveyed. Of those 362, 150 were found highly
likely to be suitable candidates for nowcasting, 87 were found to be likely,
and 125 were found to be unsuitable.

Infectious diseases usually originate from a specific location within a city.
Due to the heterogenous distribution of population and public facilities, and
the structural heterogeneity of human mobility network embedded in space,
infectious diseases break out at different locations would cause different
transmission risk and control difficulty. This study aims to investigate the
impact of initial outbreak locations on the risk of spatiotemporal transmission
and reveal the driving force behind high-risk outbreak locations. First,
integrating mobile phone location data, we built a SLIR
(susceptible-latent-infectious-removed)-based meta-population model to simulate
the spreading process of an infectious disease (i.e., COVID-19) across
fine-grained intra-urban regions (i.e., 649 communities of Shenzhen City,
China). Based on the simulation model, we evaluated the transmission risk
caused by different initial outbreak locations by proposing three indexes
including the number of infected cases (CaseNum), the number of affected
regions (RegionNum), and the spatial diffusion range (SpatialRange). Finally,
we investigated the contribution of different influential factors to the
transmission risk via machine learning models. Results indicates that different
initial outbreak locations would cause similar CaseNum but different RegionNum
and SpatialRange. To avoid the epidemic spread quickly to more regions, it is
necessary to prevent epidemic breaking out in locations with high
population-mobility flow density. While to avoid epidemic spread to larger
spatial range, remote regions with long daily trip distance of residents need
attention. Those findings can help understand the transmission risk and driving
force of initial outbreak locations within cities and make precise prevention
and control strategies in advance.

The key towards learning informative node representations in graphs lies in
how to gain contextual information from the neighbourhood. In this work, we
present a simple-yet-effective self-supervised node representation learning
strategy via directly maximizing the mutual information between the hidden
representations of nodes and their neighbourhood, which can be theoretically
justified by its link to graph smoothing. Following InfoNCE, our framework is
optimized via a surrogate contrastive loss, where the positive selection
underpins the quality and efficiency of representation learning. To this end,
we propose a topology-aware positive sampling strategy, which samples positives
from the neighbourhood by considering the structural dependencies between nodes
and thus enables positive selection upfront. In the extreme case when only one
positive is sampled, we fully avoid expensive neighbourhood aggregation. Our
methods achieve promising performance on various node classification datasets.
It is also worth mentioning by applying our loss function to MLP based node
encoders, our methods can be orders of faster than existing solutions. Our
codes and supplementary materials are available at
https://github.com/dongwei156/n2n.

De novo peptide sequencing aims to recover amino acid sequences of a peptide
from tandem mass spectrometry (MS) data. Existing approaches for de novo
analysis enumerate MS evidence for all amino acid classes during inference. It
leads to over-trimming on receptive fields of MS data and restricts MS evidence
associated with following undecoded amino acids. Our approach, DPST,
circumvents these limitations with two key components: (1) A confidence value
aggregation encoder to sketch spectrum representations according to
amino-acid-based connectivity among MS; (2) A global-local fusion decoder to
progressively assimilate contextualized spectrum representations with a
predefined preconception of localized MS evidence and amino acid priors. Our
components originate from a closed-form solution and selectively attend to
informative amino-acid-aware MS representations. Through extensive empirical
studies, we demonstrate the superiority of DPST, showing that it outperforms
state-of-the-art approaches by a margin of 12% - 19% peptide accuracy.

We show how to distinguish circuits with $\log k$ negations (a.k.a
$k$-monotone functions) from uniformly random functions in
$\exp\left(\tilde{O}\left(n^{1/3}k^{2/3}\right)\right)$ time using random
samples. The previous best distinguisher, due to the learning algorithm by
Blais, Cannone, Oliveira, Servedio, and Tan (RANDOM'15), requires
$\exp\big(\tilde{O}(n^{1/2} k)\big)$ time.
  Our distinguishers are based on Fourier analysis on \emph{slices of the
Boolean cube}. We show that some "middle" slices of negation-limited circuits
have strong low-degree Fourier concentration and then we apply a variation of
the classic Linial, Mansour, and Nisan "Low-Degree algorithm" (JACM'93) on
slices. Our techniques also lead to a slightly improved weak learner for
negation limited circuits under the uniform distribution.

Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on
self-training, i.e., generating hard pseudo-labels by a teacher model on
unlabeled data as supervisory signals. Although they achieved certain success,
the limited labeled data in semi-supervised learning scales up the challenges
of object detection. We analyze the challenges these methods meet with the
empirical experiment results. We find that the massive False Negative samples
and inferior localization precision lack consideration. Besides, the large
variance of object sizes and class imbalance (i.e., the extreme ratio between
background and object) hinder the performance of prior arts. Further, we
overcome these challenges by introducing a novel approach, Scale-Equivalent
Distillation (SED), which is a simple yet effective end-to-end knowledge
distillation framework robust to large object size variance and class
imbalance. SED has several appealing benefits compared to the previous works.
(1) SED imposes a consistency regularization to handle the large scale variance
problem. (2) SED alleviates the noise problem from the False Negative samples
and inferior localization precision. (3) A re-weighting strategy can implicitly
screen the potential foreground regions of the unlabeled data to reduce the
effect of class imbalance. Extensive experiments show that SED consistently
outperforms the recent state-of-the-art methods on different datasets with
significant margins. For example, it surpasses the supervised counterpart by
more than 10 mAP when using 5% and 10% labeled data on MS-COCO.

Fingerprints are popular among the biometric based systems due to ease of
acquisition, uniqueness and availability. Nowadays it is used in smart phone
security, digital payment and digital locker. The traditional fingerprint
matching methods based on minutiae are mainly applicable for large-area
fingerprint and the accuracy rate would reduce significantly when dealing with
small-area fingerprint from smart phone. There are many attempts to using deep
learning for small-area fingerprint recognition, and there are many successes.
But training deep neural network needs a lot of datasets for training. There is
no well-known dataset for small-area, so we have to make datasets ourselves. In
this paper, we propose a method of data augmentation to train a small-area
fingerprint recognition deep neural network with a normal fingerprint database
(such as FVC2002) and verify it via tests. The experimental results showed the
efficiency of our method.

The identification of pedestrians using radar micro-Doppler signatures has
become a hot topic in recent years. In this paper, we propose a
multi-characteristic learning (MCL) model with clusters to jointly learn
discrepant pedestrian micro-Doppler signatures and fuse the knowledge learned
from each cluster into final decisions. Time-Doppler spectrogram (TDS) and
signal statistical features extracted from FMCW radar, as two categories of
micro-Doppler signatures, are used in MCL to learn the micro-motion information
inside pedestrians' free walking patterns. The experimental results show that
our model achieves a higher accuracy rate and is more stable for pedestrian
identification than other studies, which make our model more practical.

The syntactic categories of categorial grammar formalisms are structured
units made of smaller, indivisible primitives, bound together by the underlying
grammar's category formation rules. In the trending approach of constructive
supertagging, neural models are increasingly made aware of the internal
category structure, which in turn enables them to more reliably predict rare
and out-of-vocabulary categories, with significant implications for grammars
previously deemed too complex to find practical use. In this work, we revisit
constructive supertagging from a graph-theoretic perspective, and propose a
framework based on heterogeneous dynamic graph convolutions aimed at exploiting
the distinctive structure of a supertagger's output space. We test our approach
on a number of categorial grammar datasets spanning different languages and
grammar formalisms, achieving substantial improvements over previous state of
the art scores. Code will be made available at
https://github.com/konstantinosKokos/dynamic-graph-supertagging

Contrastive learning has been applied to Human Activity Recognition (HAR)
based on sensor data owing to its ability to achieve performance comparable to
supervised learning with a large amount of unlabeled data and a small amount of
labeled data. The pre-training task for contrastive learning is generally
instance discrimination, which specifies that each instance belongs to a single
class, but this will consider the same class of samples as negative examples.
Such a pre-training task is not conducive to human activity recognition tasks,
which are mainly classification tasks. To address this problem, we follow
SimCLR to propose a new contrastive learning framework that negative selection
by clustering in HAR, which is called ClusterCLHAR. Compared with SimCLR, it
redefines the negative pairs in the contrastive loss function by using
unsupervised clustering methods to generate soft labels that mask other samples
of the same cluster to avoid regarding them as negative samples. We evaluate
ClusterCLHAR on three benchmark datasets, USC-HAD, MotionSense, and UCI-HAR,
using mean F1-score as the evaluation metric. The experiment results show that
it outperforms all the state-of-the-art methods applied to HAR in
self-supervised learning and semi-supervised learning.

Few-shot object detection (FSOD), which aims at learning a generic detector
that can adapt to unseen tasks with scarce training samples, has witnessed
consistent improvement recently. However, most existing methods ignore the
efficiency issues, e.g., high computational complexity and slow adaptation
speed. Notably, efficiency has become an increasingly important evaluation
metric for few-shot techniques due to an emerging trend toward embedded AI. To
this end, we present an efficient pretrain-transfer framework (PTF) baseline
with no computational increment, which achieves comparable results with
previous state-of-the-art (SOTA) methods. Upon this baseline, we devise an
initializer named knowledge inheritance (KI) to reliably initialize the novel
weights for the box classifier, which effectively facilitates the knowledge
transfer process and boosts the adaptation speed. Within the KI initializer, we
propose an adaptive length re-scaling (ALR) strategy to alleviate the vector
length inconsistency between the predicted novel weights and the pretrained
base weights. Finally, our approach not only achieves the SOTA results across
three public benchmarks, i.e., PASCAL VOC, COCO and LVIS, but also exhibits
high efficiency with 1.8-9.0x faster adaptation speed against the other methods
on COCO/LVIS benchmark during few-shot transfer. To our best knowledge, this is
the first work to consider the efficiency problem in FSOD. We hope to motivate
a trend toward powerful yet efficient few-shot technique development. The codes
are publicly available at https://github.com/Ze-Yang/Efficient-FSOD.

Despite the remarkable success of deep multi-modal learning in practice, it
has not been well-explained in theory. Recently, it has been observed that the
best uni-modal network outperforms the jointly trained multi-modal network,
which is counter-intuitive since multiple signals generally bring more
information. This work provides a theoretical explanation for the emergence of
such performance gap in neural networks for the prevalent joint training
framework. Based on a simplified data distribution that captures the realistic
property of multi-modal data, we prove that for the multi-modal late-fusion
network with (smoothed) ReLU activation trained jointly by gradient descent,
different modalities will compete with each other. The encoder networks will
learn only a subset of modalities. We refer to this phenomenon as modality
competition. The losing modalities, which fail to be discovered, are the
origins where the sub-optimality of joint training comes from. Experimentally,
we illustrate that modality competition matches the intrinsic behavior of
late-fusion joint training.

Physics-driven deep learning methods have emerged as a powerful tool for
computational magnetic resonance imaging (MRI) problems, pushing reconstruction
performance to new limits. This article provides an overview of the recent
developments in incorporating physics information into learning-based MRI
reconstruction. We consider inverse problems with both linear and non-linear
forward models for computational MRI, and review the classical approaches for
solving these. We then focus on physics-driven deep learning approaches,
covering physics-driven loss functions, plug-and-play methods, generative
models, and unrolled networks. We highlight domain-specific challenges such as
real- and complex-valued building blocks of neural networks, and translational
applications in MRI with linear and non-linear forward models. Finally, we
discuss common issues and open challenges, and draw connections to the
importance of physics-driven learning when combined with other downstream tasks
in the medical imaging pipeline.

Failure to consider the characteristics, limitations, and abilities of
diverse end-users during mobile apps development may lead to problems for
end-users such as accessibility and usability issues. We refer to this class of
problems as human-centric issues. Despite their importance, there is a limited
understanding of the types of human-centric issues that are encountered by
end-users and taken into account by the developers of mobile apps. In this
paper, we examine what human-centric issues end-users report through Google App
Store reviews, which human-centric issues are a topic of discussion for
developers on GitHub, and whether end-users and developers discuss the same
human-centric issues. We then investigate whether an automated tool might help
detect such human-centric issues and whether developers would find such a tool
useful. To do this, we conducted an empirical study by extracting and manually
analysing a random sample of 1,200 app reviews and 1,200 issue comments from 12
diverse projects that exist on both Google App Store and GitHub. Our analysis
led to a taxonomy of human-centric issues that categorises human-centric issues
into three-high levels: App Usage, Inclusiveness, and User Reaction. We then
developed machine learning and deep learning models that are promising in
automatically identifying and classifying human-centric issues from app reviews
and developer discussions. A survey of mobile app developers shows that the
automated detection of human-centric issues has practical applications. Guided
by our findings, we highlight some implications and possible future work to
further understand and incorporate human-centric issues in mobile apps

Lexically constrained neural machine translation (NMT), which controls the
generation of NMT models with pre-specified constraints, is important in many
practical scenarios. Due to the representation gap between discrete constraints
and continuous vectors in NMT models, most existing works choose to construct
synthetic data or modify the decoding algorithm to impose lexical constraints,
treating the NMT model as a black box. In this work, we propose to open this
black box by directly integrating the constraints into NMT models.
Specifically, we vectorize source and target constraints into continuous keys
and values, which can be utilized by the attention modules of NMT models. The
proposed integration method is based on the assumption that the correspondence
between keys and values in attention modules is naturally suitable for modeling
constraint pairs. Experimental results show that our method consistently
outperforms several representative baselines on four language pairs,
demonstrating the superiority of integrating vectorized lexical constraints.

Recent studies in deepfake detection have yielded promising results when the
training and testing face forgeries are from the same dataset. However, the
problem remains challenging when one tries to generalize the detector to
forgeries created by unseen methods in the training dataset. This work
addresses the generalizable deepfake detection from a simple principle: a
generalizable representation should be sensitive to diverse types of forgeries.
Following this principle, we propose to enrich the "diversity" of forgeries by
synthesizing augmented forgeries with a pool of forgery configurations and
strengthen the "sensitivity" to the forgeries by enforcing the model to predict
the forgery configurations. To effectively explore the large forgery
augmentation space, we further propose to use the adversarial training strategy
to dynamically synthesize the most challenging forgeries to the current model.
Through extensive experiments, we show that the proposed strategies are
surprisingly effective (see Figure 1), and they could achieve superior
performance than the current state-of-the-art methods. Code is available at
\url{https://github.com/liangchen527/SLADD}.

Lung squamous cell carcinoma (LSCC) has a high recurrence and metastasis
rate. Factors influencing recurrence and metastasis are currently unknown and
there are no distinct histopathological or morphological features indicating
the risks of recurrence and metastasis in LSCC. Our study focuses on the
recurrence prediction of LSCC based on H&E-stained histopathological
whole-slide images (WSI). Due to the small size of LSCC cohorts in terms of
patients with available recurrence information, standard end-to-end learning
with various convolutional neural networks for this task tends to overfit.
Also, the predictions made by these models are hard to interpret.
Histopathology WSIs are typically very large and are therefore processed as a
set of smaller tiles. In this work, we propose a novel conditional
self-supervised learning (SSL) method to learn representations of WSI at the
tile level first, and leverage clustering algorithms to identify the tiles with
similar histopathological representations. The resulting representations and
clusters from self-supervision are used as features of a survival model for
recurrence prediction at the patient level. Using two publicly available
datasets from TCGA and CPTAC, we show that our LSCC recurrence prediction
survival model outperforms both LSCC pathological stage-based approach and
machine learning baselines such as multiple instance learning. The proposed
method also enables us to explain the recurrence histopathological risk factors
via the derived clusters. This can help pathologists derive new hypotheses
regarding morphological features associated with LSCC recurrence.

Previous works on expressive speech synthesis mainly focus on current
sentence. The context in adjacent sentences is neglected, resulting in
inflexible speaking style for the same text, which lacks speech variations. In
this paper, we propose a hierarchical framework to model speaking style from
context. A hierarchical context encoder is proposed to explore a wider range of
contextual information considering structural relationship in context,
including inter-phrase and inter-sentence relations. Moreover, to encourage
this encoder to learn style representation better, we introduce a novel
training strategy with knowledge distillation, which provides the target for
encoder training. Both objective and subjective evaluations on a Mandarin
lecture dataset demonstrate that the proposed method can significantly improve
the naturalness and expressiveness of the synthesized speech.

Recommender systems have been successfully used in many domains with the help
of machine learning algorithms. However, such applications tend to use
multi-dimensional user data, which has raised widespread concerns about the
breach of users privacy. Meanwhile, wearable technologies have enabled users to
collect fitness-related data through embedded sensors to monitor their
conditions or achieve personalized fitness goals. In this paper, we propose a
novel privacy-aware personalized fitness recommender system. We introduce a
multi-level deep learning framework that learns important features from a
large-scale real fitness dataset that is collected from wearable IoT devices to
derive intelligent fitness recommendations. Unlike most existing approaches,
our approach achieves personalization by inferring the fitness characteristics
of users from sensory data and thus minimizing the need for explicitly
collecting user identity or biometric information, such as name, age, height,
weight. In particular, our proposed models and algorithms predict (a)
personalized exercise distance recommendations to help users to achieve target
calories, (b) personalized speed sequence recommendations to adjust exercise
speed given the nature of the exercise and the chosen route, and (c)
personalized heart rate sequence to guide the user of the potential health
status for future exercises. Our experimental evaluation on a real-world Fitbit
dataset demonstrated high accuracy in predicting exercise distance, speed
sequence, and heart rate sequence compared to similar studies. Furthermore, our
approach is novel compared to existing studies as it does not require
collecting and using users sensitive information, and thus it preserves the
users privacy.

Improving the generalization capability of Deep Neural Networks (DNNs) is
critical for their practical uses, which has been a longstanding challenge.
Some theoretical studies have revealed that DNNs have preferences to different
frequency components in the learning process and indicated that this may affect
the robustness of learned features. In this paper, we propose Deep Frequency
Filtering (DFF) for learning domain-generalizable features, which is the first
endeavour to explicitly modulate frequency components of different transfer
difficulties across domains during training. To achieve this, we perform Fast
Fourier Transform (FFT) on feature maps at different layers, then adopt a
light-weight module to learn the attention masks from frequency representations
after FFT to enhance transferable frequency components while suppressing the
components not conductive to generalization. Further, we empirically compare
different types of attention for implementing our conceptualized DFF. Extensive
experiments demonstrate the effectiveness of the proposed DFF and show that
applying DFF on a plain baseline outperforms the state-of-the-art methods on
different domain generalization tasks, including close-set classification and
open-set retrieval.

Alzheimer's Disease (AD) is a neurodegenerative disease that is one of the
significant causes of death in the elderly population. Many deep learning
techniques have been proposed to diagnose AD using Magnetic Resonance Imaging
(MRI) scans. Predicting AD using 2D slices extracted from 3D MRI scans is
challenging as the inter-slice information gets lost. To this end, we propose a
novel and lightweight framework termed 'Biceph-Net' for AD diagnosis using 2D
MRI scans that model both the intra-slice and inter-slice information.
Biceph-Net has been experimentally shown to perform similar to other
Spatio-temporal neural networks while being computationally more efficient.
Biceph-Net is also superior in performance compared to vanilla 2D convolutional
neural networks (CNN) for AD diagnosis using 2D MRI slices. Biceph-Net also has
an inbuilt neighbourhood-based model interpretation feature that can be
exploited to understand the classification decision taken by the network.
Biceph-Net experimentally achieves a test accuracy of 100% in the
classification of Cognitively Normal (CN) vs AD, 98.16% for Mild Cognitive
Impairment (MCI) vs AD, and 97.80% for CN vs MCI vs AD.

Model predictive control (MPC) provides a useful means for controlling
systems with constraints, but suffers from the computational burden of
repeatedly solving an optimization problem in real time. Offline (explicit)
solutions for MPC attempt to alleviate real time computational challenges using
either multiparametric programming or machine learning. The multiparametric
approaches are typically applied to linear or quadratic MPC problems, while
learning-based approaches can be more flexible and are less memory-intensive.
Existing learning-based approaches offer significant speedups, but the
challenge becomes ensuring constraint satisfaction while maintaining good
performance. In this paper, we provide a neural network parameterization of MPC
policies that explicitly encodes the constraints of the problem. By exploring
the interior of the MPC feasible set in an unsupervised learning paradigm, the
neural network finds better policies faster than projection-based methods and
exhibits substantially shorter solve times. We use the proposed policy to solve
a robust MPC problem, and demonstrate the performance and computational gains
on a standard test system.

In this paper, we present a new self-supervised scene flow estimation
approach for a pair of consecutive point clouds. The key idea of our approach
is to represent discrete point clouds as continuous probability density
functions using Gaussian mixture models. Scene flow estimation is therefore
converted into the problem of recovering motion from the alignment of
probability density functions, which we achieve using a closed-form expression
of the classic Cauchy-Schwarz divergence. Unlike existing
nearest-neighbor-based approaches that use hard pairwise correspondences, our
proposed approach establishes soft and implicit point correspondences between
point clouds and generates more robust and accurate scene flow in the presence
of missing correspondences and outliers. Comprehensive experiments show that
our method makes noticeable gains over the Chamfer Distance and the Earth
Mover's Distance in real-world environments and achieves state-of-the-art
performance among self-supervised learning methods on FlyingThings3D and KITTI,
even outperforming some supervised methods with ground truth annotations.

Point clouds are an increasingly ubiquitous input modality and the raw signal
can be efficiently processed with recent progress in deep learning. This signal
may, often inadvertently, capture sensitive information that can leak semantic
and geometric properties of the scene which the data owner does not want to
share. The goal of this work is to protect sensitive information when learning
from point clouds; by censoring the sensitive information before the point
cloud is released for downstream tasks. Specifically, we focus on preserving
utility for perception tasks while mitigating attribute leakage attacks. The
key motivating insight is to leverage the localized saliency of perception
tasks on point clouds to provide good privacy-utility trade-offs. We realize
this through a mechanism called Censoring by Noisy Sampling (CBNS), which is
composed of two modules: i) Invariant Sampler: a differentiable point-cloud
sampler which learns to remove points invariant to utility and ii) Noisy
Distorter: which learns to distort sampled points to decouple the sensitive
information from utility, and mitigate privacy leakage. We validate the
effectiveness of CBNS through extensive comparisons with state-of-the-art
baselines and sensitivity analyses of key design choices. Results show that
CBNS achieves superior privacy-utility trade-offs on multiple datasets.

We introduce a novel algorithm for gradient-based optimization of stochastic
objective functions. The method may be seen as a variant of SGD with momentum
equipped with an adaptive learning rate automatically adjusted by an 'energy'
variable. The method is simple to implement, computationally efficient, and
well suited for large-scale machine learning problems. The method exhibits
unconditional energy stability for any size of the base learning rate. We
provide a regret bound on the convergence rate under the online convex
optimization framework. We also establish the energy-dependent convergence rate
of the algorithm to a stationary point in the stochastic non-convex setting. In
addition, a sufficient condition is provided to guarantee a positive lower
threshold for the energy variable. Our experiments demonstrate that the
algorithm converges fast while generalizing better than or as well as SGD with
momentum in training deep neural networks, and compares also favorably to Adam.

Many physical systems can be best understood as sets of discrete data with
associated relationships. Where previously these sets of data have been
formulated as series or image data to match the available machine learning
architectures, with the advent of graph neural networks (GNNs), these systems
can be learned natively as graphs. This allows a wide variety of high- and
low-level physical features to be attached to measurements and, by the same
token, a wide variety of HEP tasks to be accomplished by the same GNN
architectures. GNNs have found powerful use-cases in reconstruction, tagging,
generation and end-to-end analysis. With the wide-spread adoption of GNNs in
industry, the HEP community is well-placed to benefit from rapid improvements
in GNN latency and memory usage. However, industry use-cases are not perfectly
aligned with HEP and much work needs to be done to best match unique GNN
capabilities to unique HEP obstacles. We present here a range of these
capabilities, predictions of which are currently being well-adopted in HEP
communities, and which are still immature. We hope to capture the landscape of
graph techniques in machine learning as well as point out the most significant
gaps that are inhibiting potentially large leaps in research.

Transformers have recently been shown to be capable of reliably performing
logical reasoning over facts and rules expressed in natural language, but
abductive reasoning - inference to the best explanation of an unexpected
observation - has been underexplored despite significant applications to
scientific discovery, common-sense reasoning, and model interpretability.
  We present AbductionRules, a group of natural language datasets designed to
train and test generalisable abduction over natural-language knowledge bases.
We use these datasets to finetune pretrained Transformers and discuss their
performance, finding that our models learned generalisable abductive techniques
but also learned to exploit the structure of our data. Finally, we discuss the
viability of this approach to abductive reasoning and ways in which it may be
improved in future work.

Programming machines with commonsense reasoning (CSR) abilities is a
longstanding challenge in the Artificial Intelligence community. Current CSR
benchmarks use multiple-choice (and in relatively fewer cases, generative)
question-answering instances to evaluate machine commonsense. Recent progress
in transformer-based language representation models suggest that considerable
progress has been made on existing benchmarks. However, although tens of CSR
benchmarks currently exist, and are growing, it is not evident that the full
suite of commonsense capabilities have been systematically evaluated.
Furthermore, there are doubts about whether language models are 'fitting' to a
benchmark dataset's training partition by picking up on subtle, but normatively
irrelevant (at least for CSR), statistical features to achieve good performance
on the testing partition. To address these challenges, we propose a benchmark
called Theoretically-Grounded Commonsense Reasoning (TG-CSR) that is also based
on discriminative question answering, but with questions designed to evaluate
diverse aspects of commonsense, such as space, time, and world states. TG-CSR
is based on a subset of commonsense categories first proposed as a viable
theory of commonsense by Gordon and Hobbs. The benchmark is also designed to be
few-shot (and in the future, zero-shot), with only a few training and
validation examples provided. This report discusses the structure and
construction of the benchmark. Preliminary results suggest that the benchmark
is challenging even for advanced language representation models designed for
discriminative CSR question answering tasks.
  Benchmark access and leaderboard:
https://codalab.lisn.upsaclay.fr/competitions/3080 Benchmark website:
https://usc-isi-i2.github.io/TGCSR/

Slow shutter speed and long exposure time of frame-based cameras often cause
visual blur and loss of inter-frame information, degenerating the overall
quality of captured videos. To this end, we present a unified framework of
event-based motion deblurring and frame interpolation for blurry video
enhancement, where the extremely low latency of events is leveraged to
alleviate motion blur and facilitate intermediate frame prediction.
Specifically, the mapping relation between blurry frames and sharp latent
images is first predicted by a learnable double integral network, and a fusion
network is then proposed to refine the coarse results via utilizing the
information from consecutive blurry inputs and the concurrent events. By
exploring the mutual constraints among blurry frames, latent images, and event
streams, we further propose a self-supervised learning framework to enable
network training with real-world blurry videos and events. Extensive
experiments demonstrate that our method compares favorably against the
state-of-the-art approaches and achieves remarkable performance on both
synthetic and real-world datasets.

A recent study by Feldman (2020) proposed a long-tail theory to explain the
memorization behavior of deep learning models. However, memorization has not
been empirically verified in the context of NLP, a gap addressed by this work.
In this paper, we use three different NLP tasks to check if the long-tail
theory holds. Our experiments demonstrate that top-ranked memorized training
instances are likely atypical, and removing the top-memorized training
instances leads to a more serious drop in test accuracy compared with removing
training instances randomly. Furthermore, we develop an attribution method to
better understand why a training instance is memorized. We empirically show
that our memorization attribution method is faithful, and share our interesting
finding that the top-memorized parts of a training instance tend to be features
negatively correlated with the class label.

Advances in federated learning (FL) algorithms,along with technologies like
differential privacy and homomorphic encryption, have led to FL being
increasingly adopted and used in many application domains. This increasing
adoption has led to rapid growth in the number, size (number of
participants/parties) and diversity (intermittent vs. active parties) of FL
jobs. Many existing FL systems, based on centralized (often single) model
aggregators are unable to scale to handle large FL jobs and adapt to parties'
behavior.
  In this paper, we present a new scalable and adaptive architecture for FL
aggregation. First, we demonstrate how traditional tree overlay based
aggregation techniques (from P2P, publish-subscribe and stream processing
research) can help FL aggregation scale, but are ineffective from a resource
utilization and cost standpoint. Next, we present the design and implementation
of AdaFed, which uses serverless/cloud functions to adaptively scale
aggregation in a resource efficient and fault tolerant manner. We describe how
AdaFed enables FL aggregation to be dynamically deployed only when necessary,
elastically scaled to handle participant joins/leaves and is fault tolerant
with minimal effort required on the (aggregation) programmer side. We also
demonstrate that our prototype based on Ray scales to thousands of
participants, and is able to achieve a >90% reduction in resource requirements
and cost, with minimal impact on aggregation latency.

Forecasting bushfire spread is an important element in fire prevention and
response efforts. Empirical observations of bushfire spread can be used to
estimate fire response under certain conditions. These observations form
rate-of-spread models, which can be used to generate simulations. We use
machine learning to drive the emulation approach for bushfires and show that
emulation has the capacity to closely reproduce simulated fire-front data. We
present a preliminary emulator approach with the capacity for fast emulation of
complex simulations. Large numbers of predictions can then be generated as part
of ensemble estimation techniques, which provide more robust and reliable
forecasts of stochastic systems.

Automatic segmentation of vertebral bodies (VBs) and intervertebral discs
(IVDs) in 3D magnetic resonance (MR) images is vital in diagnosing and treating
spinal diseases. However, segmenting the VBs and IVDs simultaneously is not
trivial. Moreover, problems exist, including blurry segmentation caused by
anisotropy resolution, high computational cost, inter-class similarity and
intra-class variability, and data imbalances. We proposed a two-stage
algorithm, named semi-supervised hybrid spine network (SSHSNet), to address
these problems by achieving accurate simultaneous VB and IVD segmentation. In
the first stage, we constructed a 2D semi-supervised DeepLabv3+ by using cross
pseudo supervision to obtain intra-slice features and coarse segmentation. In
the second stage, a 3D full-resolution patch-based DeepLabv3+ was built. This
model can be used to extract inter-slice information and combine the coarse
segmentation and intra-slice features provided from the first stage. Moreover,
a cross tri-attention module was applied to compensate for the loss of
inter-slice and intra-slice information separately generated from 2D and 3D
networks, thereby improving feature representation ability and achieving
satisfactory segmentation results. The proposed SSHSNet was validated on a
publicly available spine MR image dataset, and remarkable segmentation
performance was achieved. Moreover, results show that the proposed method has
great potential in dealing with the data imbalance problem. Based on previous
reports, few studies have incorporated a semi-supervised learning strategy with
a cross attention mechanism for spine segmentation. Therefore, the proposed
method may provide a useful tool for spine segmentation and aid clinically in
spinal disease diagnoses and treatments. Codes are publicly available at:
https://github.com/Meiyan88/SSHSNet.

With the advent of 3D printers in different price ranges and sizes, they are
no longer just for professionals. However, it is still challenging to use a 3D
printer perfectly. Especially, in the case of the Fused Deposition Method, it
is very difficult to perform with accurate calibration. Previous studies have
suggested that these problems can be detected using sensor data and image data
with machine learning methods. However, there are difficulties to apply the
proposed method due to extra installation of additional sensors. Considering
actual use in the future, we focus on generating the lightweight early
detection model with easily collectable data. Proposed early detection model
through Convolutional Neural Network shows significant fault classification
accuracy with 96.72% for the binary classification task, and 93.38% for
multi-classification task respectively. By this research, we hope that general
users of 3D printers can use the printer accurately.

Maximum Probability Framework, powered by Maximum Probability Theorem, is a
recent theoretical development in artificial intelligence, aiming to formally
define probabilistic models, guiding development of objective functions, and
regularization of probabilistic models. MPT uses the probability distribution
that the models assume on random variables to provide an upper bound on the
probability of the model. We apply MPT to challenging out-of-distribution (OOD)
detection problems in computer vision by incorporating MPT as a regularization
scheme in the training of CNNs and their energy-based variants. We demonstrate
the effectiveness of the proposed method on 1080 trained models, with varying
hyperparameters, and conclude that the MPT-based regularization strategy
stabilizes and improves the generalization and robustness of base models in
addition to enhanced OOD performance on CIFAR10, CIFAR100, and MNIST datasets.

The machine learning algorithm is gaining prominence in traffic
identification research as it offers a way to overcome the shortcomings of
port-based and deep packet inspection, especially for P2P-based Skype.
However,recent studies have focused mainly on traffic identification based on a
full-packet dataset, which poses great challenges to identifying online network
traffic. This study aims to provide a new flow identification algorithm by
taking the sampled flow records as the object. The study constructs flow
records from a Skype set as the dataset, considers the inherent NETFLOW and
extended flow metrics as features, and uses a fast correlation-based filter
algorithm to select highly correlated features. The study also proposes a new
NFI method that adopts a Bayesian updating mechanism to improve the classifier
model. The experimental results show that the proposed scheme can achieve much
better identification performance than existing state-of-the-art traffic
identification methods, and a typical feature metric is analyzed in the
sampling environment. The NFI method improves identification accuracy and
reduces false positives and false negatives compared to other methods.

In many applications in statistics and machine learning, the availability of
data samples from multiple possibly heterogeneous sources has become
increasingly prevalent. On the other hand, in distributionally robust
optimization, we seek data-driven decisions which perform well under the most
adverse distribution from a nominal distribution constructed from data samples
within a certain discrepancy of probability distributions. However, it remains
unclear how to achieve such distributional robustness in model learning and
estimation when data samples from multiple sources are available. In this work,
we propose constructing the nominal distribution in optimal transport-based
distributionally robust optimization problems through the notion of Wasserstein
barycenter as an aggregation of data samples from multiple sources. Under
specific choices of the loss function, the proposed formulation admits a
tractable reformulation as a finite convex program, with powerful finite-sample
and asymptotic guarantees. As an illustrative example, we demonstrate with the
problem of distributionally robust sparse inverse covariance matrix estimation
for zero-mean Gaussian random vectors that our proposed scheme outperforms
other widely used estimators in both the low- and high-dimensional regimes.

Inspired by the path coordination problem arising from robo-taxis, warehouse
management, and mixed-vehicle routing problems, we model a group of
heterogeneous players responding to stochastic demands as a congestion game
under Markov decision process dynamics. Players share a common state-action
space but have unique transition dynamics, and each player's unique cost is a
{function} of the joint state-action probability distribution. For a class of
player cost functions, we formulate the player-specific optimization problem,
prove the equivalence between the Nash equilibrium and the solution of a
potential minimization problem, and derive dynamic programming approaches to
solve the Nash equilibrium. We apply this game to model multi-agent path
coordination and introduce congestion-based cost functions that enable players
to complete individual tasks while avoiding congestion with their opponents.
Finally, we present a learning algorithm for finding the Nash equilibrium that
has linear complexity in the number of players. We demonstrate our game model
on a multi-robot warehouse \change{path coordination problem}, in which robots
autonomously retrieve and deliver packages while avoiding congested paths.

The right to AI explainability has consolidated as a consensus in the
research community and policy-making. However, a key component of
explainability has been missing: extrapolation, which describes the extent to
which AI models can be clueless when they encounter unfamiliar samples (i.e.,
samples outside the convex hull of their training sets, as we will explain). We
report that AI models extrapolate outside their range of familiar data,
frequently and without notifying the users and stakeholders. Knowing whether a
model has extrapolated or not is a fundamental insight that should be included
in explaining AI models in favor of transparency and accountability. Instead of
dwelling on the negatives, we offer ways to clear the roadblocks in promoting
AI transparency. Our analysis commentary accompanying practical clauses useful
to include in AI regulations such as the National AI Initiative Act in the US
and the AI Act by the European Commission.

Machine learning has had a great deal of success in image processing.
However, the focus of this work has largely been on realistic images, ignoring
more niche art styles such as pixel art. Additionally, many traditional machine
learning models that focus on groups of pixels do not work well with pixel art,
where individual pixels are important. We propose the Pixel VQ-VAE, a
specialized VQ-VAE model that learns representations of pixel art. We show that
it outperforms other models in both the quality of embeddings as well as
performance on downstream tasks.

As audio-visual systems are being deployed for safety-critical tasks such as
surveillance and malicious content filtering, their robustness remains an
under-studied area. Existing published work on robustness either does not scale
to large-scale dataset, or does not deal with multiple modalities. This work
aims to study several key questions related to multi-modal learning through the
lens of robustness: 1) Are multi-modal models necessarily more robust than
uni-modal models? 2) How to efficiently measure the robustness of multi-modal
learning? 3) How to fuse different modalities to achieve a more robust
multi-modal model? To understand the robustness of the multi-modal model in a
large-scale setting, we propose a density-based metric, and a convexity metric
to efficiently measure the distribution of each modality in high-dimensional
latent space. Our work provides a theoretical intuition together with empirical
evidence showing how multi-modal fusion affects adversarial robustness through
these metrics. We further devise a mix-up strategy based on our metrics to
improve the robustness of the trained model. Our experiments on AudioSet and
Kinetics-Sounds verify our hypothesis that multi-modal models are not
necessarily more robust than their uni-modal counterparts in the face of
adversarial examples. We also observe our mix-up trained method could achieve
as much protection as traditional adversarial training, offering a
computationally cheap alternative. Implementation:
https://github.com/lijuncheng16/AudioSetDoneRight

Current polyp detection methods from colonoscopy videos use exclusively
normal (i.e., healthy) training images, which i) ignore the importance of
temporal information in consecutive video frames, and ii) lack knowledge about
the polyps. Consequently, they often have high detection errors, especially on
challenging polyp cases (e.g., small, flat, or partially visible polyps). In
this work, we formulate polyp detection as a weakly-supervised anomaly
detection task that uses video-level labelled training data to detect
frame-level polyps. In particular, we propose a novel convolutional
transformer-based multiple instance learning method designed to identify
abnormal frames (i.e., frames with polyps) from anomalous videos (i.e., videos
containing at least one frame with polyp). In our method, local and global
temporal dependencies are seamlessly captured while we simultaneously optimise
video and snippet-level anomaly scores. A contrastive snippet mining method is
also proposed to enable an effective modelling of the challenging polyp cases.
The resulting method achieves a detection accuracy that is substantially better
than current state-of-the-art approaches on a new large-scale colonoscopy video
dataset introduced in this work.

The matrix completion problem has been studied broadly under many underlying
conditions. The problem has been explored under adaptive or non-adaptive, exact
or estimation, single-phase or multi-phase, and many other categories. In most
of these cases, the observation cost of each entry is uniform and has the same
cost across the columns. However, in many real-life scenarios, we could expect
elements from distinct columns or distinct positions to have a different cost.
In this paper, we explore this generalization under adaptive conditions. We
approach the problem under two different cost models. The first one is that
entries from different columns have different observation costs, but, within
the same column, each entry has a uniform cost. The second one is any two entry
has different observation cost, despite being the same or different columns. We
provide complexity analysis of our algorithms and provide tightness guarantees.

A robust body of reinforcement learning techniques have been developed to
solve complex sequential decision making problems. However, these methods
assume that train and evaluation tasks come from similarly or identically
distributed environments. This assumption does not hold in real life where
small novel changes to the environment can make a previously learned policy
fail or introduce simpler solutions that might never be found. To that end we
explore the concept of {\em novelty}, defined in this work as the sudden change
to the mechanics or properties of environment. We provide an ontology of for
novelties most relevant to sequential decision making, which distinguishes
between novelties that affect objects versus actions, unary properties versus
non-unary relations, and the distribution of solutions to a task. We introduce
NovGrid, a novelty generation framework built on MiniGrid, acting as a toolkit
for rapidly developing and evaluating novelty-adaptation-enabled reinforcement
learning techniques. Along with the core NovGrid we provide exemplar novelties
aligned with our ontology and instantiate them as novelty templates that can be
applied to many MiniGrid-compliant environments. Finally, we present a set of
metrics built into our framework for the evaluation of
novelty-adaptation-enabled machine-learning techniques, and show
characteristics of a baseline RL model using these metrics.

Deep reinforcement learning has the potential to address various scientific
problems. In this paper, we implement an optics simulation environment for
reinforcement learning based controllers. The environment incorporates
nonconvex and nonlinear optical phenomena as well as more realistic
time-dependent noise. Then we provide the benchmark results of several
state-of-the-art reinforcement learning algorithms on the proposed simulation
environment. In the end, we discuss the difficulty of controlling the
real-world optical environment with reinforcement learning algorithms.

Current methods for diagnosing the progression of multiple types of cancer
within patients rely on interpreting stained needle biopsies. This process is
time-consuming and susceptible to error throughout the paraffinization,
Hematoxylin and Eosin (H&E) staining, deparaffinization, and annotation stages.
Fourier Transform Infrared (FTIR) imaging has been shown to be a promising
alternative to staining for appropriately annotating biopsy cores without the
need for deparaffinization or H&E staining with the use of Fourier Transform
Infrared (FTIR) images when combined with machine learning to interpret the
dense spectral information. We present a machine learning pipeline to segment
white blood cell (lymphocyte) pixels in hyperspectral images of biopsy cores.
These cells are clinically important for diagnosis, but some prior work has
struggled to incorporate them due to difficulty obtaining precise pixel labels.
Evaluated methods include Support Vector Machine (SVM), Gaussian Naive Bayes,
and Multilayer Perceptron (MLP), as well as analyzing the comparatively modern
convolutional neural network (CNN).

Recent advances in machine learning technology have enabled highly portable
and performant models for many common tasks, especially in image recognition.
One emerging field, 3D human pose recognition extrapolated from video, has now
advanced to the point of enabling real-time software applications with robust
enough output to support downstream machine learning tasks. In this work we
propose a new machine learning pipeline and web interface that performs human
pose recognition on a live video feed to detect when common exercises are
performed and classify them accordingly. We present a model interface capable
of webcam input with live display of classification results. Our main
contributions include a keypoint and time series based lightweight approach for
classifying a selected set of fitness exercises and a web-based software
application for obtaining and visualizing the results in real time.

Research in unsupervised text generation has been gaining attention over the
years. One recent approach is local search towards a heuristically defined
objective, which specifies language fluency, semantic meanings, and other
task-specific attributes. Search in the sentence space is realized by
word-level edit operations including insertion, replacement, and deletion.
However, such objective function is manually designed with multiple components.
Although previous work has shown maximizing this objective yields good
performance in terms of true measure of success (i.e. BLEU and iBLEU), the
objective landscape is considered to be non-smooth with significant noises,
posing challenges for optimization. In this dissertation, we address the
research problem of smoothing the noise in the heuristic search objective by
learning to model the search dynamics. Then, the learned model is combined with
the original objective function to guide the search in a bootstrapping fashion.
Experimental results show that the learned models combined with the original
search objective can indeed provide a smoothing effect, improving the search
performance by a small margin.

Over the past several years, deep learning for sequence modeling has grown in
popularity. To achieve this goal, LSTM network structures have proven to be
very useful for making predictions for the next output in a series. For
instance, a smartphone predicting the next word of a text message could use an
LSTM. We sought to demonstrate an approach of music generation using Recurrent
Neural Networks (RNN). More specifically, a Long Short-Term Memory (LSTM)
neural network. Generating music is a notoriously complicated task, whether
handmade or generated, as there are a myriad of components involved. Taking
this into account, we provide a brief synopsis of the intuition, theory, and
application of LSTMs in music generation, develop and present the network we
found to best achieve this goal, identify and address issues and challenges
faced, and include potential future improvements for our network.

This paper proposes a multi-section vector quantization approach for on-line
signature recognition. We have used the MCYT database, which consists of 330
users and 25 skilled forgeries per person performed by 5 different impostors.
This database is larger than those typically used in the literature.
Nevertheless, we also provide results from the SVC database.
  Our proposed system outperforms the winner of SVC with a reduced
computational requirement, which is around 47 times lower than DTW. In
addition, our system improves the database storage requirements due to vector
compression, and is more privacy-friendly as it is not possible to recover the
original signature using the codebooks. Experimental results with MCYT provide
a 99.76% identification rate and 2.46% EER (skilled forgeries and individual
threshold). Experimental results with SVC are 100% of identification rate and
0% (individual threshold) and 0.31% (general threshold) when using a
two-section VQ approach.

We study the role of the adaptive movement strategy in promoting biodiversity
in cyclic models described by the rock-paper-scissors game rules. We assume
that individuals of one out of the species may adjust their movement to escape
hostile regions and stay longer in their comfort zones. Running a series of
stochastic simulations, we calculate the alterations in the spatial patterns
and population densities in scenarios where not all organisms are physically or
cognitively conditioned to perform the behavioural strategy. Although the
adaptive movement strategy is not profitable in terms of territorial dominance
for the species, it may promote biodiversity. Our findings show that if all
individuals are apt to move adaptively, coexistence probability increases for
intermediary mobility. The outcomes also show that even if not all individuals
can react to the signals received from the neighbourhood, biodiversity is still
benefited, but for a shorter mobility range. We find that the improvement in
the coexistence conditions is more accentuated if organisms adjust their
movement intensely and can receive sensory information from longer distances.
We also discover that biodiversity is slightly promoted for high mobility if
the proportion of individuals participating in the strategy is low. Our results
may be helpful for biologists and data scientists to understand adaptive
process learning in system biology.

Both non-neural and neural biological systems can learn. So rather than
focusing on purely brain-like learning, efforts are underway to study learning
in physical systems. Such efforts include equilibrium propagation (EP) and
coupled learning (CL), which require storage of two different states-the free
state and the perturbed state-during the learning process to retain information
about gradients. Inspired by slime mold, we propose a new learning algorithm
rooted in chemical signaling that does not require storage of two different
states. Rather, the output error information is encoded in a chemical signal
that diffuses into the network in a similar way as the activation/feedforward
signal. The steady state feedback chemical concentration, along with the
activation signal, stores the required gradient information locally. We apply
our algorithm using a physical, linear flow network and test it using the Iris
data set with 93% accuracy. We also prove that our algorithm performs gradient
descent. Finally, in addition to comparing our algorithm directly with EP and
CL, we address the biological plausibility of the algorithm.

Putting a watermark into digital circuitry has its own set of challenges.
Creating a secure watermark in printed matter usually involves including
graphics that are difficult to reproduce. In circuitry, including additional
circuitry that is hard to produce, one must contend with the prospect of
increasing power consumption of the circuit, decreasing the speed of the
circuit, and introducing watermark circuitry that is easily reproduced. In this
paper we present a watermark method for sequential circuitry. It allows for
several degrees of calibration, allowing the user to tune the complexity of the
watermark to requirements of speed, power, and efficacy. Our method uses an
encryption technique to introduce secrets about the watermark circuit, at
several levels. It also employs a boundary scan testing protocol as a means to
protect the watermark circuitry. Our discussion starts by describing the
different tools needed in our watermark scheme. We then discuss the difficulty
of the problem associated with cracking our watermark circuit. This analysis
shows that, with full implementation, our method can be made quite secure.

One of the most classical results in high-dimensional learning theory
provides a closed-form expression for the generalisation error of binary
classification with the single-layer teacher-student perceptron on i.i.d.
Gaussian inputs. Both Bayes-optimal estimation and empirical risk minimisation
(ERM) were extensively analysed for this setting. At the same time, a
considerable part of modern machine learning practice concerns multi-class
classification. Yet, an analogous analysis for the corresponding multi-class
teacher-student perceptron was missing. In this manuscript we fill this gap by
deriving and evaluating asymptotic expressions for both the Bayes-optimal and
ERM generalisation errors in the high-dimensional regime. For Gaussian teacher
weights, we investigate the performance of ERM with both cross-entropy and
square losses, and explore the role of ridge regularisation in approaching
Bayes-optimality. In particular, we observe that regularised cross-entropy
minimisation yields close-to-optimal accuracy. Instead, for a binary teacher we
show that a first-order phase transition arises in the Bayes-optimal

There has been significant recent interest in quantum neural networks (QNNs),
along with their applications in diverse domains. Current solutions for QNNs
pose significant challenges concerning their scalability, ensuring that the
postulates of quantum mechanics are satisfied and that the networks are
physically realizable. The exponential state space of QNNs poses challenges for
the scalability of training procedures. The no-cloning principle prohibits
making multiple copies of training samples, and the measurement postulates lead
to non-deterministic loss functions. Consequently, the physical realizability
and efficiency of existing approaches that rely on repeated measurement of
several copies of each sample for training QNNs are unclear. This paper
presents a new model for QNNs that relies on band-limited Fourier expansions of
transfer functions of quantum perceptrons (QPs) to design scalable training
procedures. This training procedure is augmented with a randomized quantum
stochastic gradient descent technique that eliminates the need for sample
replication. We show that this training procedure converges to the true minima
in expectation, even in the presence of non-determinism due to quantum
measurement. Our solution has a number of important benefits: (i) using QPs
with concentrated Fourier power spectrum, we show that the training procedure
for QNNs can be made scalable; (ii) it eliminates the need for resampling, thus
staying consistent with the no-cloning rule; and (iii) enhanced data efficiency
for the overall training process since each data sample is processed once per
epoch. We present a detailed theoretical foundation for our models and methods'
scalability, accuracy, and data efficiency. We also validate the utility of our
approach through a series of numerical experiments.

Deep neural networks (DNNs) have demonstrated their effectiveness in a wide
range of computer vision tasks, with the state-of-the-art results obtained
through complex and deep structures that require intensive computation and
memory. Now-a-days, efficient model inference is crucial for consumer
applications on resource-constrained platforms. As a result, there is much
interest in the research and development of dedicated deep learning (DL)
hardware to improve the throughput and energy efficiency of DNNs. Low-precision
representation of DNN data-structures through quantization would bring great
benefits to specialized DL hardware. However, the rigorous quantization leads
to a severe accuracy drop. As such, quantization opens a large hyper-parameter
space at bit-precision levels, the exploration of which is a major challenge.
In this paper, we propose a novel framework referred to as the Fixed-Point
Quantizer of deep neural Networks (FxP-QNet) that flexibly designs a mixed
low-precision DNN for integer-arithmetic-only deployment. Specifically, the
FxP-QNet gradually adapts the quantization level for each data-structure of
each layer based on the trade-off between the network accuracy and the
low-precision requirements. Additionally, it employs post-training
self-distillation and network prediction error statistics to optimize the
quantization of floating-point values into fixed-point numbers. Examining
FxP-QNet on state-of-the-art architectures and the benchmark ImageNet dataset,
we empirically demonstrate the effectiveness of FxP-QNet in achieving the
accuracy-compression trade-off without the need for training. The results show
that FxP-QNet-quantized AlexNet, VGG-16, and ResNet-18 reduce the overall
memory requirements of their full-precision counterparts by 7.16x, 10.36x, and
6.44x with less than 0.95%, 0.95%, and 1.99% accuracy drop, respectively.

We study low-dimensional dynamics of a Kuramoto model with inertia with
Hebbian learning. In this model, the coupling strength between oscillators
depends on the phase differences between oscillators and changes according to a
Hebbian learning rule. We analyze the special case of two coupled oscillators,
which yields a three-dimensional dynamical system, and classify the stability
of its equilibrium points using linear stability analysis. We show that the
system is dissipative and that all of its trajectories are eventually confined
to a bounded region. We approximate the limiting behavior and demarcate the
parameter regions of three qualitatively different behaviors of the system.
Using insights from our analysis of the low-dimensional dynamics, we study the
original high-dimensional system when we draw the intrinsic frequencies of the
oscillators from Gaussian distributions with different variances.

We present a deep neural network for removing undesirable shading features
from an unconstrained portrait image, recovering the underlying texture. Our
training scheme incorporates three regularization strategies: masked loss, to
emphasize high-frequency shading features; soft-shadow loss, which improves
sensitivity to subtle changes in lighting; and shading-offset estimation, to
supervise separation of shading and texture. Our method demonstrates improved
delighting quality and generalization when compared with the state-of-the-art.
We further demonstrate how our delighting method can enhance the performance of
light-sensitive computer vision tasks such as face relighting and semantic
parsing, allowing them to handle extreme lighting conditions.

We present a novel framework named PlaneMVS for 3D plane reconstruction from
multiple input views with known camera poses. Most previous learning-based
plane reconstruction methods reconstruct 3D planes from single images, which
highly rely on single-view regression and suffer from depth scale ambiguity. In
contrast, we reconstruct 3D planes with a multi-view-stereo (MVS) pipeline that
takes advantage of multi-view geometry. We decouple plane reconstruction into a
semantic plane detection branch and a plane MVS branch. The semantic plane
detection branch is based on a single-view plane detection framework but with
differences. The plane MVS branch adopts a set of slanted plane hypotheses to
replace conventional depth hypotheses to perform plane sweeping strategy and
finally learns pixel-level plane parameters and its planar depth map. We
present how the two branches are learned in a balanced way, and propose a
soft-pooling loss to associate the outputs of the two branches and make them
benefit from each other. Extensive experiments on various indoor datasets show
that PlaneMVS significantly outperforms state-of-the-art (SOTA) single-view
plane reconstruction methods on both plane detection and 3D geometry metrics.
Our method even outperforms a set of SOTA learning-based MVS methods thanks to
the learned plane priors. To the best of our knowledge, this is the first work
on 3D plane reconstruction within an end-to-end MVS framework.

Multiple instance learning (MIL) has been increasingly used in the
classification of histopathology whole slide images (WSIs). However, MIL
approaches for this specific classification problem still face unique
challenges, particularly those related to small sample cohorts. In these, there
are limited number of WSI slides (bags), while the resolution of a single WSI
is huge, which leads to a large number of patches (instances) cropped from this
slide. To address this issue, we propose to virtually enlarge the number of
bags by introducing the concept of pseudo-bags, on which a double-tier MIL
framework is built to effectively use the intrinsic features. Besides, we also
contribute to deriving the instance probability under the framework of
attention-based MIL, and utilize the derivation to help construct and analyze
the proposed framework. The proposed method outperforms other latest methods on
the CAMELYON-16 by substantially large margins, and is also better in
performance on the TCGA lung cancer dataset. The proposed framework is ready to
be extended for wider MIL applications. The code is available at:
https://github.com/hrzhang1123/DTFD-MIL

We show that, for any sufficiently small fixed $\epsilon > 0$, when both
players in a general-sum two-player (bimatrix) game employ optimistic mirror
descent (OMD) with smooth regularization, learning rate $\eta = O(\epsilon^2)$
and $T = \Omega(\text{poly}(1/\epsilon))$ repetitions, either the dynamics
reach an $\epsilon$-approximate Nash equilibrium (NE), or the average
correlated distribution of play is an $\Omega(\text{poly}(\epsilon))$-strong
coarse correlated equilibrium (CCE): any possible unilateral deviation does not
only leave the player worse, but will decrease its utility by
$\Omega(\text{poly}(\epsilon))$. As an immediate consequence, when the iterates
of OMD are bounded away from being Nash equilibria in a bimatrix game, we
guarantee convergence to an exact CCE after only $O(1)$ iterations. Our results
reveal that uncoupled no-regret learning algorithms can converge to CCE in
general-sum games remarkably faster than to NE in, for example, zero-sum games.
To establish this, we show that when OMD does not reach arbitrarily close to a
NE, the (cumulative) regret of both players is not only negative, but decays
linearly with time. Given that regret is the canonical measure of performance
in online learning, our results suggest that cycling behavior of no-regret
learning algorithms in games can be justified in terms of efficiency.

We present a self-supervised approach for learning to predict traversable
paths for wheeled mobile robots that require good traction to navigate. Our
algorithm, termed WayFAST (Waypoint Free Autonomous Systems for
Traversability), uses RGB and depth data, along with navigation experience, to
autonomously generate traversable paths in outdoor unstructured environments.
Our key inspiration is that traction can be estimated for rolling robots using
kinodynamic models. Using traction estimates provided by an online receding
horizon estimator, we are able to train a traversability prediction neural
network in a self-supervised manner, without requiring heuristics utilized by
previous methods. We demonstrate the effectiveness of WayFAST through extensive
field testing in varying environments, ranging from sandy dry beaches to forest
canopies and snow covered grass fields. Our results clearly demonstrate that
WayFAST can learn to avoid geometric obstacles as well as untraversable
terrain, such as snow, which would be difficult to avoid with sensors that
provide only geometric data, such as LiDAR. Furthermore, we show that our
training pipeline based on online traction estimates is more data-efficient
than other heuristic-based methods.

The discovery of complex multicellular organism development took millions of
years of evolution. The genome of such a multicellular organism guides the
development of its body from a single cell, including its control system. Our
goal is to imitate this natural process using a single neural cellular
automaton (NCA) as a genome for modular robotic agents. In the introduced
approach, called Neural Cellular Robot Substrate (NCRS), a single NCA guides
the growth of a robot and the cellular activity which controls the robot during
deployment. We also introduce three benchmark environments, which test the
ability of the approach to grow different robot morphologies. In this paper,
NCRSs are trained with covariance matrix adaptation evolution strategy
(CMA-ES), and covariance matrix adaptation MAP-Elites (CMA-ME) for quality
diversity, which we show leads to more diverse robot morphologies with higher
fitness scores. While the NCRS can solve the easier tasks from our benchmark
environments, the success rate reduces when the difficulty of the task
increases. We discuss directions for future work that may facilitate the use of
the NCRS approach for more complex domains.

Software developers frequently use the system shell to perform configuration
management tasks. Unfortunately, the shell does not scale well to large
systems, and configuration management tools like Ansible are more difficult to
learn. We address this problem with Dozer, a technique to help developers push
their shell commands into Ansible task definitions. It operates by tracing and
comparing system calls to find Ansible modules with similar behaviors to shell
commands, then generating and validating migrations to find the task which
produces the most similar changes to the system. Dozer is syntax agnostic,
which should allow it to generalize to other configuration management
platforms. We evaluate Dozer using datasets from open source configuration
scripts.

Most existing results about \emph{last-iterate convergence} of learning
dynamics are limited to two-player zero-sum games, and only apply under rigid
assumptions about what dynamics the players follow. In this paper we provide
new results and techniques that apply to broader families of games and learning
dynamics. First, we use a regret-based analysis to show that in a class of
games that includes constant-sum polymatrix and strategically zero-sum games,
dynamics such as \emph{optimistic mirror descent (OMD)} have \emph{bounded
second-order path lengths}, a property which holds even when players employ
different algorithms and prediction mechanisms. This enables us to obtain
$O(1/\sqrt{T})$ rates and optimal $O(1)$ regret bounds. Our analysis also
reveals a surprising property: OMD either reaches arbitrarily close to a Nash
equilibrium, or it outperforms the \emph{robust price of anarchy} in
efficiency. Moreover, for potential games we establish convergence to an
$\epsilon$-equilibrium after $O(1/\epsilon^2)$ iterations for mirror descent
under a broad class of regularizers, as well as optimal $O(1)$ regret bounds
for OMD variants. Our framework also extends to near-potential games, and
unifies known analyses for distributed learning in Fisher's market model.
Finally, we analyze the convergence, efficiency, and robustness of
\emph{optimistic gradient descent (OGD)} in general-sum continuous games.

Inspired by the success of self-supervised autoregressive representation
learning in natural language (GPT and its variants), and advances in recent
visual architecture design with Vision Transformers (ViTs), in this paper, we
explore the effect various design choices have on the success of applying such
training strategies for visual feature learning. Specifically, we introduce a
novel strategy that we call Random Segments with Autoregressive Coding
(RandSAC). In RandSAC, we group patch representations (image tokens) into
hierarchically arranged segments; within each segment, tokens are predicted in
parallel, similar to BERT, while across segment predictions are sequential,
similar to GPT. We illustrate that randomized serialization of the segments
significantly improves the performance and results in distribution over
spatially-long (across-segments) and -short (within-segment) predictions which
are effective for feature learning. We illustrate the pertinence of these
design choices and explore alternatives on a number of datasets (e.g., CIFAR10,
CIFAR100, ImageNet). While our pre-training strategy works with a vanilla
Transformer, we also propose a conceptually simple, but highly effective,
addition to the decoder that allows learnable skip-connections to encoder$'$s
feature layers, which further improves the performance.

In the stereo-to-multichannel upmixing problem for music, one of the main
tasks is to set the directionality of the instrument sources in the
multichannel rendering results. In this paper, we propose a modified
variational autoencoder model that learns a latent space to describe the
spatial images in multichannel music. We seek to disentangle the spatial images
and music content, so the learned latent variables are invariant to the music.
At test time, we use the latent variables to control the panning of sources. We
propose two upmixing use cases: transferring the spatial images from one song
to another and blind panning based on the generative model. We report objective
and subjective evaluation results to empirically show that our model captures
spatial images separately from music content and achieves transfer-based
interactive panning.

Reinforcement learning has received significant interest in recent years, due
primarily to the successes of deep reinforcement learning at solving many
challenging tasks such as playing Chess, Go and online computer games. However,
with the increasing focus on reinforcement learning, applications outside of
gaming and simulated environments require understanding the robustness,
stability, and resilience of reinforcement learning methods. To this end, we
conducted a comprehensive literature review to characterize the available
literature on these three behaviors as they pertain to reinforcement learning.
We classify the quantitative and theoretical approaches used to indicate or
measure robustness, stability, and resilience behaviors. In addition, we
identified the action or event to which the quantitative approaches were
attempting to be stable, robust, or resilient. Finally, we provide a decision
tree useful for selecting metrics to quantify the behaviors. We believe that
this is the first comprehensive review of stability, robustness and resilience
specifically geared towards reinforcement learning.

Architecture reverse engineering has become an emerging attack against deep
neural network (DNN) implementations. Several prior works have utilized
side-channel leakage to recover the model architecture while the target is
executing on a hardware acceleration platform. In this work, we target an
open-source deep-learning accelerator, Versatile Tensor Accelerator (VTA), and
utilize electromagnetic (EM) side-channel leakage to comprehensively learn the
association between DNN architecture configurations and EM emanations. We also
consider the holistic system -- including the low-level tensor program code of
the VTA accelerator on a Xilinx FPGA and explore the effect of such low-level
configurations on the EM leakage. Our study demonstrates that both the
optimization and configuration of tensor programs will affect the EM
side-channel leakage.
  Gaining knowledge of the association between the low-level tensor program and
the EM emanations, we propose NNReArch, a lightweight tensor program scheduling
framework against side-channel-based DNN model architecture reverse
engineering. Specifically, NNReArch targets reshaping the EM traces of
different DNN operators, through scheduling the tensor program execution of the
DNN model so as to confuse the adversary. NNReArch is a comprehensive
protection framework supporting two modes, a balanced mode that strikes a
balance between the DNN model confidentiality and execution performance, and a
secure mode where the most secure setting is chosen. We implement and evaluate
the proposed framework on the open-source VTA with state-of-the-art DNN
architectures. The experimental results demonstrate that NNReArch can
efficiently enhance the model architecture security with a small performance
overhead. In addition, the proposed obfuscation technique makes reverse
engineering of the DNN architecture significantly harder.

Non-degradable plastic waste stays for decades on land and in water,
jeopardizing our environment; yet our modern lifestyle and current technologies
are impossible to sustain without plastics. Bio-synthesized and biodegradable
alternatives such as the polymer family of polyhydroxyalkanoates (PHAs) have
the potential to replace large portions of the world's plastic supply with
cradle-to-cradle materials, but their chemical complexity and diversity limit
traditional resource-intensive experimentation. In this work, we develop
multitask deep neural network property predictors using available experimental
data for a diverse set of nearly 23000 homo- and copolymer chemistries. Using
the predictors, we identify 14 PHA-based bioplastics from a search space of
almost 1.4 million candidates which could serve as potential replacements for
seven petroleum-based commodity plastics that account for 75% of the world's
yearly plastic production. We discuss possible synthesis routes for these
identified promising materials. The developed multitask polymer property
predictors are made available as a part of the Polymer Genome project at
https://PolymerGenome.org.

This paper presents an extended version of Deeper, a search-based
simulation-integrated test solution that generates failure-revealing test
scenarios for testing a deep neural network-based lane-keeping system. In the
newly proposed version, we utilize a new set of bio-inspired search algorithms,
genetic algorithm (GA), $({\mu}+{\lambda})$ and $({\mu},{\lambda})$ evolution
strategies (ES), and particle swarm optimization (PSO), that leverage a quality
population seed and domain-specific cross-over and mutation operations tailored
for the presentation model used for modeling the test scenarios. In order to
demonstrate the capabilities of the new test generators within Deeper, we carry
out an empirical evaluation and comparison with regard to the results of five
participating tools in the cyber-physical systems testing competition at SBST
2021. Our evaluation shows the newly proposed test generators in Deeper not
only represent a considerable improvement on the previous version but also
prove to be effective and efficient in provoking a considerable number of
diverse failure-revealing test scenarios for testing an ML-driven lane-keeping
system. They can trigger several failures while promoting test scenario
diversity, under a limited test time budget, high target failure severity, and
strict speed limit constraints.

Many promising applications of supervised machine learning face hurdles in
the acquisition of labeled data in sufficient quantity and quality, creating an
expensive bottleneck. To overcome such limitations, techniques that do not
depend on ground truth labels have been studied, including weak supervision and
generative modeling. While these techniques would seem to be usable in concert,
improving one another, how to build an interface between them is not
well-understood. In this work, we propose a model fusing programmatic weak
supervision and generative adversarial networks and provide theoretical
justification motivating this fusion. The proposed approach captures discrete
latent variables in the data alongside the weak supervision derived label
estimate. Alignment of the two allows for better modeling of sample-dependent
accuracies of the weak supervision sources, improving the estimate of
unobserved labels. It is the first approach to enable data augmentation through
weakly supervised synthetic images and pseudolabels. Additionally, its learned
latent variables can be inspected qualitatively. The model outperforms baseline
weak supervision label models on a number of multiclass image classification
datasets, improves the quality of generated images, and further improves
end-model performance through data augmentation with synthetic samples.

In this paper we present the Open Software Services for Classrooms and
Research (OSSCAR) platform. OSSCAR provides an open collaborative environment
to develop, deploy and access educational resources in the form of web
applications. To minimize efforts in the creation and use of new educational
material, it combines software tools that have emerged as standards with custom
domain-specific ones. The technical solutions adopted to create and distribute
content are described and motivated on the basis of reliability,
sustainability, ease of uptake and use. Examples from courses in the domains of
physics, chemistry, and materials science are shown to demonstrate the style
and level of interactivity of typical applications. The tools presented are
easy to use, and create a uniform and open environment exploitable by a large
community of teachers, students, and researchers with the goal of facilitating
learning and avoiding, when possible, duplication of efforts in creating
teaching material. Contributions to expand the educational content of the
OSSCAR project are welcome.

Most of the existing handcrafted and learning-based local descriptors are
still at best approximately invariant to affine image transformations, often
disregarding deformable surfaces. In this paper, we take one step further by
proposing a new approach to compute descriptors from RGB-D images (where RGB
refers to the pixel color brightness and D stands for depth information) that
are invariant to isometric non-rigid deformations, as well as to scale changes
and rotation. Our proposed description strategies are grounded on the key idea
of learning feature representations on undistorted local image patches using
surface geodesics. We design two complementary local descriptors strategies to
compute geodesic-aware features efficiently: one efficient binary descriptor
based on handcrafted binary tests (named GeoBit), and one learning-based
descriptor (GeoPatch) with convolutional neural networks (CNNs) to compute
features. In different experiments using real and publicly available RGB-D data
benchmarks, they consistently outperforms state-of-the-art handcrafted and
learning-based image and RGB-D descriptors in matching scores, as well as in
object retrieval and non-rigid surface tracking experiments, with comparable
processing times. We also provide to the community a new dataset with accurate
matching annotations of RGB-D images of different objects (shirts, cloths,
paintings, bags), subjected to strong non-rigid deformations, for evaluation
benchmark of deformable surface correspondence algorithms.

Smartphones are widely available and used extensively by students worldwide.
These phones often come equipped with high-quality cameras that can be combined
with basic optical elements to build a cost-effective DIY spectrometer. Here,
we discuss a series of demonstrations and pedagogical exercises, accompanied by
our DIY diffractive spectrometer that uses a free web platform for instant
spectral analysis. Specifically, these demonstrations can be used to encourage
hands-on and inquiry-based learning of wave optics, broadband vs discrete light
emission, quantization, Heisenberg's energy-time uncertainty relation, and the
use of spectroscopy in day-to-day life. Hence, these simple tools can be
readily deployed in high school classrooms to communicate the practices of
science.

If a unit cannot receive intelligence from a source due to external factors,
we consider them disadvantaged users. We categorize this as a preoccupied unit
working on a low connectivity device on the edge. This case requires that we
use a different approach to deliver intelligence, particularly satellite
imagery information, than normally employed. To address this, we propose a
survey of information reduction techniques to deliver the information from a
satellite image in a smaller package. We investigate four techniques to aid in
the reduction of delivered information: traditional image compression, neural
network image compression, object detection image cutout, and image to caption.
Each of these mechanisms have their benefits and tradeoffs when considered for
a disadvantaged user.

We propose a new tournament structure that combines the popular knockout
tournaments and the round-robin tournaments. As opposed to the extremes of
divisive elimination and no elimination, our tournament aims to eliminate the
participants as linearly as possible as a form of subtractive elimination. Our
design is flexible in the sense that it can be adapted to any number of players
$N$ and any number of matches $M$. Our design satisfies many properties that
are desirable for a tournament to select a winner and can be adapted to rank
all the participating players.

In many modern applications, discretely-observed data may be naturally
understood as a set of functions. Functional data often exhibit two confounded
sources of variability: amplitude (y-axis) and phase (x-axis). The extraction
of amplitude and phase, a process known as registration, is essential in
exploring the underlying structure of functional data in a variety of areas,
from environmental monitoring to medical imaging. Critically, such data are
often gathered sequentially with new functional observations arriving over
time. Despite this, most available registration procedures are only applicable
to batch learning, leading to inefficient computation. To address these
challenges, we introduce a Bayesian framework for sequential registration of
functional data, which updates statistical inference as new sets of functions
are assimilated. This Bayesian model-based sequential learning approach
utilizes sequential Monte Carlo sampling to recursively update the alignment of
observed functions while accounting for associated uncertainty. As a result,
distributed computing, which is not generally an option in batch learning,
significantly reduces computational cost. Simulation studies and comparisons to
existing batch learning methods reveal that the proposed approach performs well
even when the target posterior distribution has a challenging structure. We
apply the proposed method to three real datasets: (1) functions of annual
drought intensity near Kaweah River in California, (2) annual sea surface
salinity functions near Null Island, and (3) PQRST complexes segmented from an
electrocardiogram signal.

Contrastive self-supervised learning has become a prominent technique in
representation learning. The main step in these methods is to contrast
semantically similar and dissimilar pairs of samples. However, in the domain of
Natural Language Processing (NLP), the augmentation methods used in creating
similar pairs with regard to contrastive learning (CL) assumptions are
challenging. This is because, even simply modifying a word in the input might
change the semantic meaning of the sentence, and hence, would violate the
distributional hypothesis. In this review paper, we formalize the contrastive
learning framework, emphasize the considerations that need to be addressed in
the data transformation step, and review the state-of-the-art methods and
evaluations for contrastive representation learning in NLP. Finally, we
describe some challenges and potential directions for learning better text
representations using contrastive methods.

Standard acoustic event classification (AEC) solutions require large-scale
collection of data from client devices for model optimization. Federated
learning (FL) is a compelling framework that decouples data collection and
model training to enhance customer privacy. In this work, we investigate the
feasibility of applying FL to improve AEC performance while no customer data
can be directly uploaded to the server. We assume no pseudo labels can be
inferred from on-device user inputs, aligning with the typical use cases of
AEC. We adapt self-supervised learning to the FL framework for on-device
continual learning of representations, and it results in improved performance
of the downstream AEC classifiers without labeled/pseudo-labeled data
available. Compared to the baseline w/o FL, the proposed method improves
precision up to 20.3\% relatively while maintaining the recall. Our work
differs from prior work in FL in that our approach does not require
user-generated learning targets, and the data we use is collected from our Beta
program and is de-identified, to maximally simulate the production settings.

Most convergence guarantees for stochastic gradient descent with momentum
(SGDm) rely on iid sampling. Yet, SGDm is often used outside this regime, in
settings with temporally correlated input samples such as continual learning
and reinforcement learning. Existing work has shown that SGDm with a decaying
step-size can converge under Markovian temporal correlation. In this work, we
show that SGDm under covariate shift with a fixed step-size can be unstable and
diverge. In particular, we show SGDm under covariate shift is a parametric
oscillator, and so can suffer from a phenomenon known as resonance. We
approximate the learning system as a time varying system of ordinary
differential equations, and leverage existing theory to characterize the
system's divergence/convergence as resonant/nonresonant modes. The theoretical
result is limited to the linear setting with periodic covariate shift, so we
empirically supplement this result to show that resonance phenomena persist
even under non-periodic covariate shift, nonlinear dynamics with neural
networks, and optimizers other than SGDm.

The current popular two-stream, two-stage tracking framework extracts the
template and the search region features separately and then performs relation
modeling, thus the extracted features lack the awareness of the target and have
limited target-background discriminability. To tackle the above issue, we
propose a novel one-stream tracking (OSTrack) framework that unifies feature
learning and relation modeling by bridging the template-search image pairs with
bidirectional information flows. In this way, discriminative target-oriented
features can be dynamically extracted by mutual guidance. Since no extra heavy
relation modeling module is needed and the implementation is highly
parallelized, the proposed tracker runs at a fast speed. To further improve the
inference efficiency, an in-network candidate early elimination module is
proposed based on the strong similarity prior calculated in the one-stream
framework. As a unified framework, OSTrack achieves state-of-the-art
performance on multiple benchmarks, in particular, it shows impressive results
on the one-shot tracking benchmark GOT-10k, i.e., achieving 73.7% AO, improving
the existing best result (SwinTrack) by 4.3\%. Besides, our method maintains a
good performance-speed trade-off and shows faster convergence. The code and
models are available at https://github.com/botaoye/OSTrack.

A computational approach via implementation of the Principle Component
Analysis (PCA) and Gaussian Mixture (GM) clustering methods from Machine
Learning (ML) algorithms to identify domain structures of supercooled liquids
is developed. Raw features data are collected from the coordination numbers of
particles smoothed using its radial distribution function and are used as an
order-parameter of disordered structures for GM clustering after dimensionality
reduction from the PCA. To transfer the knowledge from features(structural)
space to configurational space, another GM clustering is performed using the
Cartesian coordinates as an order-parameter with the particles' identity from
GM in the feature space. Both GM clustering are performed iteratively until
convergence. Results show the appearance of aggregated clusters of nano-domains
over sufficient long timescale both in structural and configurational spaces
with heterogeneous dynamics. More importantly, consistent nano-domains tilling
up the whole space regardless of the system size are observed and our approach
can be applied to any disordered systems.

The Vision Transformer (ViT) model is built on the assumption of treating
image patches as "visual tokens" and learning patch-to-patch attention. The
patch embedding based tokenizer is a workaround in practice and has a semantic
gap with respect to its counterpart, the textual tokenizer. The patch-to-patch
attention suffers from the quadratic complexity issue, and also makes it
non-trivial to explain learned ViT models. To address these issues in ViT
models, this paper proposes to learn patch-to-cluster attention (PaCa) based
ViT models. Queries in our PaCaViT are based on patches, while keys and values
are based on clustering (with a predefined small number of clusters). The
clusters are learned end-to-end, leading to better tokenizers and realizing
joint clustering-for-attention and attention-for-clustering when deployed in
ViT models. The quadratic complexity is relaxed to linear complexity. Also,
directly visualizing the learned clusters can reveal how a trained ViT model
learns to perform a task (e.g., object detection). In experiments, the proposed
PaCa-ViT is tested on CIFAR-100 and ImageNet-1000 image classification, and
MS-COCO object detection and instance segmentation. Compared with prior arts,
it obtains better performance in classification and comparable performance in
detection and segmentation. It is significantly more efficient in COCO due to
the linear complexity. The learned clusters are also semantically meaningful
and shed light on designing more discriminative yet interpretable ViT models.

Mean Field Games (MFGs) have been introduced to efficiently approximate games
with very large populations of strategic agents. Recently, the question of
learning equilibria in MFGs has gained momentum, particularly using model-free
reinforcement learning (RL) methods. One limiting factor to further scale up
using RL is that existing algorithms to solve MFGs require the mixing of
approximated quantities such as strategies or $q$-values. This is far from
being trivial in the case of non-linear function approximation that enjoy good
generalization properties, e.g. neural networks. We propose two methods to
address this shortcoming. The first one learns a mixed strategy from
distillation of historical data into a neural network and is applied to the
Fictitious Play algorithm. The second one is an online mixing method based on
regularization that does not require memorizing historical data or previous
estimates. It is used to extend Online Mirror Descent. We demonstrate
numerically that these methods efficiently enable the use of Deep RL algorithms
to solve various MFGs. In addition, we show that these methods outperform SotA
baselines from the literature.

The Higgs potential is vital to understand the electroweak symmetry breaking
mechanism, and probing the Higgs self-interaction is arguably one of the most
important physics targets at current and upcoming collider experiments. In
particular, the triple Higgs coupling may be accessible at the HL-LHC by
combining results in multiple channels, which motivates to study all possible
decay modes for the double Higgs production. In this paper, we revisit the
double Higgs production at the HL-LHC in the final state with two $b$-tagged
jets, two leptons and missing transverse momentum. We focus on the performance
of various neural network architectures with different input features:
low-level (four momenta), high-level (kinematic variables) and image-based. We
find it possible to bring a modest increase in the signal sensitivity over
existing results via careful optimization of machine learning algorithms making
a full use of novel kinematic variables.

We examine the capability of generative models to produce realistic galaxy
images. We show that mixing generated data with the original data improves the
robustness in downstream machine learning tasks. We focus on three different
data sets; analytical S\'ersic profiles, real galaxies from the COSMOS survey,
and galaxy images produced with the SKIRT code, from the IllustrisTNG
simulation. We quantify the performance of each generative model using the
Wasserstein distance between the distributions of morphological properties
(e.g. the Gini-coefficient, the asymmetry, and ellipticity), the surface
brightness distribution on various scales (as encoded by the power-spectrum),
the bulge statistic and the colour for the generated and source data sets. With
an average Wasserstein distance (Fr\'echet Inception Distance) of $7.19 \times
10^{-2}\, (0.55)$, $5.98 \times 10^{-2}\, (1.45)$ and $5.08 \times 10^{-2}\,
(7.76)$ for the S\'ersic, COSMOS and SKIRT data set, respectively, our best
models convincingly reproduce even the most complicated galaxy properties and
create images that are visually indistinguishable from the source data. We
demonstrate that by supplementing the training data set with generated data, it
is possible to significantly improve the robustness against domain-shifts and
out-of-distribution data. In particular, we train a convolutional neural
network to denoise a data set of mock observations. By mixing generated images
into the original training data, we obtain an improvement of $11$ and $45$ per
cent in the model performance regarding domain-shifts in the physical pixel
size and background noise level, respectively.

Vision-language models can encode societal biases and stereotypes, but there
are challenges to measuring and mitigating these multimodal harms due to
lacking measurement robustness and feature degradation. To address these
challenges, we investigate bias measures and apply ranking metrics for
image-text representations. We then investigate debiasing methods and show that
prepending learned embeddings to text queries that are jointly trained with
adversarial debiasing and a contrastive loss reduces various bias measures with
minimal degradation to the image-text representation.

In this paper, we present a system to train driving policies from experiences
collected not just from the ego-vehicle, but all vehicles that it observes.
This system uses the behaviors of other agents to create more diverse driving
scenarios without collecting additional data. The main difficulty in learning
from other vehicles is that there is no sensor information. We use a set of
supervisory tasks to learn an intermediate representation that is invariant to
the viewpoint of the controlling vehicle. This not only provides a richer
signal at training time but also allows more complex reasoning during
inference. Learning how all vehicles drive helps predict their behavior at test
time and can avoid collisions. We evaluate this system in closed-loop driving
simulations. Our system outperforms all prior methods on the public CARLA
Leaderboard by a wide margin, improving driving score by 25 and route
completion rate by 24 points. Our method won the 2021 CARLA Autonomous Driving
challenge. Code and data are available at https://github.com/dotchen/LAV.

Dataset distillation is the task of synthesizing a small dataset such that a
model trained on the synthetic set will match the test accuracy of the model
trained on the full dataset. In this paper, we propose a new formulation that
optimizes our distilled data to guide networks to a similar state as those
trained on real data across many training steps. Given a network, we train it
for several iterations on our distilled data and optimize the distilled data
with respect to the distance between the synthetically trained parameters and
the parameters trained on real data. To efficiently obtain the initial and
target network parameters for large-scale datasets, we pre-compute and store
training trajectories of expert networks trained on the real dataset. Our
method handily outperforms existing methods and also allows us to distill
higher-resolution visual data.

Multiple domains like vision, natural language, and audio are witnessing
tremendous progress by leveraging Transformers for large scale pre-training
followed by task specific fine tuning. In contrast, in robotics we primarily
train a single robot for a single task. However, modular robot systems now
allow for the flexible combination of general-purpose building blocks into task
optimized morphologies. However, given the exponentially large number of
possible robot morphologies, training a controller for each new design is
impractical. In this work, we propose MetaMorph, a Transformer based approach
to learn a universal controller over a modular robot design space. MetaMorph is
based on the insight that robot morphology is just another modality on which we
can condition the output of a Transformer. Through extensive experiments we
demonstrate that large scale pre-training on a variety of robot morphologies
results in policies with combinatorial generalization capabilities, including
zero shot generalization to unseen robot morphologies. We further demonstrate
that our pre-trained policy can be used for sample-efficient transfer to
completely new robot morphologies and tasks.

The full optimization of the design and operation of instruments whose
functioning relies on the interaction of radiation with matter is a super-human
task, given the large dimensionality of the space of possible choices for
geometry, detection technology, materials, data-acquisition, and
information-extraction techniques, and the interdependence of the related
parameters. On the other hand, massive potential gains in performance over
standard, "experience-driven" layouts are in principle within our reach if an
objective function fully aligned with the final goals of the instrument is
maximized by means of a systematic search of the configuration space. The
stochastic nature of the involved quantum processes make the modeling of these
systems an intractable problem from a classical statistics point of view, yet
the construction of a fully differentiable pipeline and the use of deep
learning techniques may allow the simultaneous optimization of all design
  In this document we lay down our plans for the design of a modular and
versatile modeling tool for the end-to-end optimization of complex instruments
for particle physics experiments as well as industrial and medical applications
that share the detection of radiation as their basic ingredient. We consider a
selected set of use cases to highlight the specific needs of different

We propose focal modulation networks (FocalNets in short), where
self-attention (SA) is completely replaced by a focal modulation mechanism for
modeling token interactions in vision. Focal modulation comprises three
components: (i) hierarchical contextualization, implemented using a stack of
depth-wise convolutional layers, to encode visual contexts from short to long
ranges, (ii) gated aggregation to selectively gather contexts for each query
token based on its
  content, and (iii) element-wise modulation or affine transformation to inject
the aggregated context into the query. Extensive experiments show FocalNets
outperform the state-of-the-art SA counterparts (e.g., Swin and Focal
Transformers) with similar computational costs on the tasks of image
classification, object detection, and segmentation. Specifically, FocalNets
with tiny and base size achieve 82.3% and 83.9% top-1 accuracy on ImageNet-1K.
After pretrained on ImageNet-22K in 224 resolution, it attains 86.5% and 87.3%
top-1 accuracy when finetuned with resolution 224 and 384, respectively. When
transferred to downstream tasks, FocalNets exhibit clear superiority. For
object detection with Mask R-CNN, FocalNet base trained with 1\times
outperforms the Swin counterpart by 2.1 points and already surpasses Swin
trained with 3\times schedule (49.0 v.s. 48.5). For semantic segmentation with
UPerNet, FocalNet base at single-scale outperforms Swin by 2.4, and beats Swin
at multi-scale (50.5 v.s. 49.7). Using large FocalNet and Mask2former, we
achieve 58.5 mIoU for ADE20K semantic segmentation, and 57.9 PQ for COCO
Panoptic Segmentation. Using huge FocalNet and DINO, we achieved 64.3 and 64.4
mAP on COCO minival and test-dev, respectively, establishing new SoTA on top of
much larger attention-based models like Swinv2-G and BEIT-3. Code and
checkpoints are available at https://github.com/microsoft/FocalNet.

The application of machine learning to image and video data often yields a
high dimensional feature space. Effective feature selection techniques identify
a discriminant feature subspace that lowers computational and modeling costs
with little performance degradation. A novel supervised feature selection
methodology is proposed for machine learning decisions in this work. The
resulting tests are called the discriminant feature test (DFT) and the relevant
feature test (RFT) for the classification and regression problems,
respectively. The DFT and RFT procedures are described in detail. Furthermore,
we compare the effectiveness of DFT and RFT with several classic feature
selection methods. To this end, we use deep features obtained by LeNet-5 for
MNIST and Fashion-MNIST datasets as illustrative examples. Other datasets with
handcrafted and gene expressions features are also included for performance
evaluation. It is shown by experimental results that DFT and RFT can select a
lower dimensional feature subspace distinctly and robustly while maintaining
high decision performance.

Recently collaborative learning is widely applied to model sensitive data
generated in Industrial IoT (IIoT). It enables a large number of devices to
collectively train a global model by collaborating with a server while keeping
the datasets on their respective premises. However, existing approaches are
limited by high overheads and may also suffer from falsified aggregated results
returned by a malicious server. Hence, we propose a Scalable,
Privacy-preserving and veRIfiable collaboraTive lEarning (SPRITE) algorithm to
train linear and logistic regression models for IIoT. We aim to reduce burden
from resource-constrained IIoT devices and trust dependence on cloud by
introducing fog as a middleware. SPRITE employs threshold secret sharing to
guarantee privacy-preservation and robustness to IIoT device dropout whereas
verifiable additive homomorphic secret sharing to ensure verifiability during
model aggregation. We prove the security of SPRITE in an honest-but-curious
setting where the cloud is untrustworthy. We validate SPRITE to be scalable and
lightweight through theoretical overhead analysis and extensive testbed
experimentation on an IIoT use-case with two real-world industrial datasets.
For a large-scale industrial setup, SPRITE records 65% and 55% improved
performance over its competitor for linear and logistic regressions
respectively while reducing communication overhead for an IIoT device by 90%.

In this paper we show that behavioral cloning can be used to learn effective
sketches of programmatic strategies. We show that even the sketches learned by
cloning the behavior of weak players can help the synthesis of programmatic
strategies. This is because even weak players can provide helpful information,
e.g., that a player must choose an action in their turn of the game. If
behavioral cloning is not employed, the synthesizer needs to learn even the
most basic information by playing the game, which can be computationally
expensive. We demonstrate empirically the advantages of our sketch-learning
approach with simulated annealing and UCT synthesizers. We evaluate our
synthesizers in the games of Can't Stop and MicroRTS. The sketch-based
synthesizers are able to learn stronger programmatic strategies than their
original counterparts. Our synthesizers generate strategies of Can't Stop that
defeat a traditional programmatic strategy for the game. They also synthesize
strategies that defeat the best performing method from the latest MicroRTS
competition.

Fetal ultrasounds are an essential part of prenatal care and can be used to
estimate gestational age (GA). Accurate GA assessment is important for
providing appropriate prenatal care throughout pregnancy and identifying
complications such as fetal growth disorders. Since derivation of GA from
manual fetal biometry measurements (head, abdomen, femur) are
operator-dependent and time-consuming, there have been a number of research
efforts focused on using artificial intelligence (AI) models to estimate GA
using standard biometry images, but there is still room to improve the accuracy
and reliability of these AI systems for widescale adoption. To improve GA
estimates, without significant change to provider workflows, we leverage AI to
interpret standard plane ultrasound images as well as 'fly-to' ultrasound
videos, which are 5-10s videos automatically recorded as part of the standard
of care before the still image is captured. We developed and validated three AI
models: an image model using standard plane images, a video model using fly-to
videos, and an ensemble model (combining both image and video). All three were
statistically superior to standard fetal biometry-based GA estimates derived by
expert sonographers, the ensemble model has the lowest mean absolute error
(MAE) compared to the clinical standard fetal biometry (mean difference: -1.51
$\pm$ 3.96 days, 95% CI [-1.9, -1.1]) on a test set that consisted of 404
participants. We showed that our models outperform standard biometry by a more
substantial margin on fetuses that were small for GA. Our AI models have the
potential to empower trained operators to estimate GA with higher accuracy
while reducing the amount of time required and user variability in measurement
acquisition.

In this work we demonstrate the vulnerability of vision transformers (ViTs)
to gradient-based inversion attacks. During this attack, the original data
batch is reconstructed given model weights and the corresponding gradients. We
introduce a method, named GradViT, that optimizes random noise into naturally
looking images via an iterative process. The optimization objective consists of
(i) a loss on matching the gradients, (ii) image prior in the form of distance
to batch-normalization statistics of a pretrained CNN model, and (iii) a total
variation regularization on patches to guide correct recovery locations. We
propose a unique loss scheduling function to overcome local minima during
optimization. We evaluate GadViT on ImageNet1K and MS-Celeb-1M datasets, and
observe unprecedentedly high fidelity and closeness to the original (hidden)
data. During the analysis we find that vision transformers are significantly
more vulnerable than previously studied CNNs due to the presence of the
attention mechanism. Our method demonstrates new state-of-the-art results for
gradient inversion in both qualitative and quantitative metrics. Project page
at https://gradvit.github.io/.

This article focuses on making discrete-time Adaptive Iterative Learning
Control (ILC) more effective using multiple estimation models. Existing
strategies use the tracking error between the reference model's output and the
plant's output to adjust the estimates of the unknown parameters. We propose a
strategy that uses the last component of the identification error between the
estimation model's state and the plant's state to tune these estimates of the
model parameters. We prove that this strategy results in bounded estimates of
the parameters, and bounded and convergent identification and tracking errors.
We emphasize that the proof does not use the key technical lemma. Rather, it
uses the properties of square-summable sequences. We extend this strategy to
include multiple estimation models and show that all the signals are bounded,
and the errors converge. It is also shown that this works whether we switch
between the models at every instant and every iteration or at the end of every
iteration. Simulation results demonstrate the efficacy of the proposed method
with a faster convergence using multiple estimation models.

The continuity of life and its evolution, we have proposed, emerge from an
interactive group process termed Survival-of-the-Fitted. This process supplants
the Darwinian theory of individual struggle and Survival-of-the-Fittest as the
primary mechanism of evolution. Here, we propose that Survival-of-the-Fitted
results from a natural process functionally related to computer autoencoding.
Autoencoding is a machine-learning technique for extracting a compact
representation of the essential features of input data; dimensionality
reduction by autoencoding establishes a code that enables a variety of
applications based on decoding of the relevant data. We establish the following
points: (1) We define a species by its species interaction code, which consists
of the fundamental, core interactions of the species with its external and
internal environments; core interactions are encoded by multi-scale networks
including molecules-cells-organisms. (2) Evolution proceeds by sustainable
changes in species interaction codes; these changing codes both reflect and
construct the species environment. The survival of species is computed by what
we term Natural Autoencoding: arrays of input interactions generate species
codes, which survive by decoding into networks of sustained ecosystem
interactions. DNA is only one element in Natural Autoencoding. (3) Natural
Autoencoding and artificial autoencoding processes manifest defined
similarities and differences. (4) Natural autoencoding accounts for the
dynamics of evolution and resolves the paradox of sexual reproduction.
Survival-of-the-Fitted by Natural Autoencoding sheds a new light on the
mechanism of evolution and explains why a habitable biosphere requires a
diversity of fitted group interactions.

In this report, we summarize the takeaways from the first NeurIPS 2021
NetHack Challenge. Participants were tasked with developing a program or agent
that can win (i.e., 'ascend' in) the popular dungeon-crawler game of NetHack by
interacting with the NetHack Learning Environment (NLE), a scalable,
procedurally generated, and challenging Gym environment for reinforcement
learning (RL). The challenge showcased community-driven progress in AI with
many diverse approaches significantly beating the previously best results on
NetHack. Furthermore, it served as a direct comparison between neural (e.g.,
deep RL) and symbolic AI, as well as hybrid systems, demonstrating that on
NetHack symbolic bots currently outperform deep RL by a large margin. Lastly,
no agent got close to winning the game, illustrating NetHack's suitability as a
long-term benchmark for AI research.

A long-term goal of AI research is to build intelligent agents that can
communicate with humans in natural language, perceive the environment, and
perform real-world tasks. Vision-and-Language Navigation (VLN) is a fundamental
and interdisciplinary research topic towards this goal, and receives increasing
attention from natural language processing, computer vision, robotics, and
machine learning communities. In this paper, we review contemporary studies in
the emerging field of VLN, covering tasks, evaluation metrics, methods, etc.
Through structured analysis of current progress and challenges, we highlight
the limitations of current VLN and opportunities for future work. This paper
serves as a thorough reference for the VLN research community.

Open-vocabulary object detection, which is concerned with the problem of
detecting novel objects guided by natural language, has gained increasing
attention from the community. Ideally, we would like to extend an
open-vocabulary detector such that it can produce bounding box predictions
based on user inputs in form of either natural language or exemplar image. This
offers great flexibility and user experience for human-computer interaction. To
this end, we propose a novel open-vocabulary detector based on DETR -- hence
the name OV-DETR -- which, once trained, can detect any object given its class
name or an exemplar image. The biggest challenge of turning DETR into an
open-vocabulary detector is that it is impossible to calculate the
classification cost matrix of novel classes without access to their labeled
images. To overcome this challenge, we formulate the learning objective as a
binary matching one between input queries (class name or exemplar image) and
the corresponding objects, which learns useful correspondence to generalize to
unseen queries during testing. For training, we choose to condition the
Transformer decoder on the input embeddings obtained from a pre-trained
vision-language model like CLIP, in order to enable matching for both text and
image queries. With extensive experiments on LVIS and COCO datasets, we
demonstrate that our OV-DETR -- the first end-to-end Transformer-based
open-vocabulary detector -- achieves non-trivial improvements over current
state of the arts.

Recent developments in experimental neuroscience make it possible to
simultaneously record the activity of thousands of neurons. However, the
development of analysis approaches for such large-scale neural recordings have
been slower than those applicable to single-cell experiments. One approach that
has gained recent popularity is neural manifold learning. This approach takes
advantage of the fact that often, even though neural datasets may be very high
dimensional, the dynamics of neural activity tends to traverse a much
lower-dimensional space. The topological structures formed by these
low-dimensional neural subspaces are referred to as neural manifolds, and may
potentially provide insight linking neural circuit dynamics with cognitive
function and behavioural performance. In this paper we review a number of
linear and non-linear approaches to neural manifold learning, by setting them
within a common mathematical framework, and comparing their advantages and
disadvantages with respect to their use for neural data analysis. We apply them
to a number of datasets from published literature, comparing the manifolds that
result from their application to hippocampal place cells, motor cortical
neurons during a reaching task, and prefrontal cortical neurons during a
multi-behaviour task. We find that in many circumstances linear algorithms
produce similar results to non-linear methods, although in particular in cases
where the behavioural complexity is greater, nonlinear methods tend to find
lower dimensional manifolds, at the possible expense of interpretability. We
demonstrate that these methods are applicable to the study of neurological
disorders through simulation of a mouse model of Alzheimers Disease, and
speculate that neural manifold analysis may help us to understand the
circuit-level consequences of molecular and cellular neuropathology.

The COVID-19 pandemic has demonstrated the increasing need of policymakers
for timely estimates of macroeconomic variables. A prior UNCTAD research paper
examined the suitability of long short-term memory artificial neural networks
(LSTM) for performing economic nowcasting of this nature. Here, the LSTM's
performance during the COVID-19 pandemic is compared and contrasted with that
of the dynamic factor model (DFM), a commonly used methodology in the field.
Three separate variables, global merchandise export values and volumes and
global services exports, were nowcast with actual data vintages and performance
evaluated for the second, third, and fourth quarters of 2020 and the first and
second quarters of 2021. In terms of both mean absolute error and root mean
square error, the LSTM obtained better performance in two-thirds of
variable/quarter combinations, as well as displayed more gradual forecast
evolutions with more consistent narratives and smaller revisions. Additionally,
a methodology to introduce interpretability to LSTMs is introduced and made
available in the accompanying nowcast_lstm Python library, which is now also
available in R, MATLAB, and Julia.

This paper presents a variational representation of the Bayes' law using
optimal transportation theory. The variational representation is in terms of
the optimal transportation between the joint distribution of the (state,
observation) and their independent coupling. By imposing certain structure on
the transport map, the solution to the variational problem is used to construct
a Brenier-type map that transports the prior distribution to the posterior
distribution for any value of the observation signal. The new formulation is
used to derive the optimal transport form of the Ensemble Kalman filter (EnKF)
for the discrete-time filtering problem and propose a novel extension of EnKF
to the non-Gaussian setting utilizing input convex neural networks. Finally,
the proposed methodology is used to derive the optimal transport form of the
feedback particle filler (FPF) in the continuous-time limit, which constitutes
its first variational construction without explicitly using the nonlinear
filtering equation or Bayes' law.

Neural networks are known to be highly sensitive to adversarial examples.
These may arise due to different factors, such as random initialization, or
spurious correlations in the learning problem. To better understand these
factors, we provide a precise study of the adversarial robustness in different
scenarios, from initialization to the end of training in different regimes, as
well as intermediate scenarios, where initialization still plays a role due to
"lazy" training. We consider over-parameterized networks in high dimensions
with quadratic targets and infinite samples. Our analysis allows us to identify
new tradeoffs between approximation (as measured via test error) and
robustness, whereby robustness can only get worse when test error improves, and
vice versa. We also show how linearized lazy training regimes can worsen
robustness, due to improperly scaled random initialization. Our theoretical
results are illustrated with numerical experiments.

We identify configurational phases and structural transitions in a polymer
nanotube composite by means of machine learning. We employ various unsupervised
dimensionality reduction methods, conventional neural networks, as well as the
confusion method, an unsupervised neural-network-based approach. We find neural
networks are able to reliably recognize all configurational phases that have
been found previously in experiment and simulation. Furthermore, we locate the
boundaries between configurational phases in a way that removes human intuition
or bias. This could be done before only by relying on preconceived, ad-hoc
order parameters.

Optimization plays a costly and crucial role in developing machine learning
systems. In learned optimizers, the few hyperparameters of commonly used
hand-designed optimizers, e.g. Adam or SGD, are replaced with flexible
parametric functions. The parameters of these functions are then optimized so
that the resulting learned optimizer minimizes a target loss on a chosen class
of models. Learned optimizers can both reduce the number of required training
steps and improve the final test loss. However, they can be expensive to train,
and once trained can be expensive to use due to computational and memory
overhead for the optimizer itself. In this work, we identify and quantify the
design features governing the memory, compute, and performance trade-offs for
many learned and hand-designed optimizers. We further leverage our analysis to
construct a learned optimizer that is both faster and more memory efficient
than previous work. Our model and training code are open source.

Analyzing gender is critical to study mental health (MH) support in CVD
(cardiovascular disease). The existing studies on using social media for
extracting MH symptoms consider symptom detection and tend to ignore user
context, disease, or gender. The current study aims to design and evaluate a
system to capture how MH symptoms associated with CVD are expressed differently
with the gender on social media. We observe that the reliable detection of MH
symptoms expressed by persons with heart disease in user posts is challenging
because of the co-existence of (dis)similar MH symptoms in one post and due to
variation in the description of symptoms based on gender. We collect a corpus
of $150k$ items (posts and comments) annotated using the subreddit labels and
transfer learning approaches. We propose GeM, a novel task-adaptive multi-task
learning approach to identify the MH symptoms in CVD patients based on gender.
Specifically, we adapt a knowledge-assisted RoBERTa based bi-encoder model to
capture CVD-related MH symptoms. Moreover, it enhances the reliability for
differentiating the gender language in MH symptoms when compared to the
state-of-art language models. Our model achieves high (statistically
significant) performance and predicts four labels of MH issues and two gender
labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom
identification task and by 2.55% on the gender identification task.

Sionna is a GPU-accelerated open-source library for link-level simulations
based on TensorFlow. It enables the rapid prototyping of complex communication
system architectures and provides native support for the integration of neural
networks. Sionna implements a wide breadth of carefully tested state-of-the-art
algorithms that can be used for benchmarking and end-to-end performance
evaluation. This allows researchers to focus on their research, making it more
impactful and reproducible, while saving time implementing components outside
their area of expertise. This white paper provides a brief introduction to
Sionna, explains its design principles and features, as well as future
extensions, such as integrated ray tracing and custom CUDA kernels. We believe
that Sionna is a valuable tool for research on next-generation communication
systems, such as 6G, and we welcome contributions from our community.

The grand goal of data-driven decision-making is to help humans make
decisions, not only easily and at scale but also wisely, accurately, and just.
However, data-driven algorithms are only as good as the data they work with,
while data sets, especially social data, often miss representing minorities.
Representation Bias in data can happen due to various reasons ranging from
historical discrimination to selection and sampling biases in the data
acquisition and preparation methods. One cannot expect AI-based societal
solutions to have equitable outcomes without addressing the representation
bias. This paper surveys the existing literature on representation bias in the
data. It presents a taxonomy to categorize the studied techniques based on
multiple design dimensions and provide a side-by-side comparison of their
properties. There is still a long way to fully address representation bias
issues in data. The authors hope that this survey motivates researchers to
approach these challenges in the future by observing existing work within their
respective domains.

Recent advances in natural language processing have enabled powerful
privacy-invasive authorship attribution. To counter authorship attribution,
researchers have proposed a variety of rule-based and learning-based text
obfuscation approaches. However, existing authorship obfuscation approaches do
not consider the adversarial threat model. Specifically, they are not evaluated
against adversarially trained authorship attributors that are aware of
potential obfuscation. To fill this gap, we investigate the problem of
adversarial authorship attribution for deobfuscation. We show that
adversarially trained authorship attributors are able to degrade the
effectiveness of existing obfuscators from 20-30% to 5-10%. We also evaluate
the effectiveness of adversarial training when the attributor makes incorrect
assumptions about whether and which obfuscator was used. While there is a a
clear degradation in attribution accuracy, it is noteworthy that this
degradation is still at or above the attribution accuracy of the attributor
that is not adversarially trained at all. Our results underline the need for
stronger obfuscation approaches that are resistant to deobfuscation

Spectral algorithms are an important building block in machine learning and
graph algorithms. We are interested in studying when such algorithms can be
applied directly to provide optimal solutions to inference tasks. Previous
works by Abbe, Fan, Wang and Zhong (2020) and by Dhara, Gaudio, Mossel and
Sandon (2022) showed the optimality for community detection in the Stochastic
Block Model (SBM), as well as in a censored variant of the SBM. Here we show
that this optimality is somewhat universal as it carries over to other planted
substructures such as the planted dense subgraph problem and submatrix
localization problem, as well as to a censored version of the planted dense
subgraph problem.

Inverse Reinforcement Learning (IRL) is a powerful way of learning from
demonstrations. In this paper, we address IRL problems with the availability of
prior knowledge that optimal policies will never violate certain constraints.
Conventional approaches ignoring these constraints need many demonstrations to
converge. We propose XOR-Maximum Entropy Constrained Inverse Reinforcement
Learning (X-MEN), which is guaranteed to converge to the optimal policy in
linear rate w.r.t. the number of learning iterations. X-MEN embeds XOR-sampling
-- a provable sampling approach that transforms the #P complete sampling
problem into queries to NP oracles -- into the framework of maximum entropy
IRL. X-MEN also guarantees the learned policy will never generate trajectories
that violate constraints. Empirical results in navigation demonstrate that
X-MEN converges faster to the optimal policies compared to baseline approaches
and always generates trajectories that satisfy multi-state combinatorial

$ $As a result of bad eating habits, humanity may be destroyed. People are
constantly on the lookout for tasty foods, with junk foods being the most
common source. As a consequence, our eating patterns are shifting, and we're
gravitating toward junk food more than ever, which is bad for our health and
increases our risk of acquiring health problems. Machine learning principles
are applied in every aspect of our lives, and one of them is object recognition
via image processing. However, because foods vary in nature, this procedure is
crucial, and traditional methods like ANN, SVM, KNN, PLS etc., will result in a
low accuracy rate. All of these issues were defeated by the Deep Neural
Network. In this work, we created a fresh dataset of 10,000 data points from 20
junk food classifications to try to recognize junk foods. All of the data in
the data set was gathered using the Google search engine, which is thought to
be one-of-a-kind in every way. The goal was achieved using Convolution Neural
Network (CNN) technology, which is well-known for image processing. We achieved
a 98.05\% accuracy rate throughout the research, which was satisfactory. In
addition, we conducted a test based on a real-life event, and the outcome was
extraordinary. Our goal is to advance this research to the next level, so that
it may be applied to a future study. Our ultimate goal is to create a system
that would encourage people to avoid eating junk food and to be
health-conscious. \keywords{ Machine Learning \and junk food \and object
detection \and YOLOv3 \and custom food dataset.}

Models trained in federated settings often suffer from degraded performances
and fail at generalizing, especially when facing heterogeneous scenarios. In
this work, we investigate such behavior through the lens of geometry of the
loss and Hessian eigenspectrum, linking the model's lack of generalization
capacity to the sharpness of the solution. Motivated by prior studies
connecting the sharpness of the loss surface and the generalization gap, we
show that i) training clients locally with Sharpness-Aware Minimization (SAM)
or its adaptive version (ASAM) and ii) averaging stochastic weights (SWA) on
the server-side can substantially improve generalization in Federated Learning
and help bridging the gap with centralized models. By seeking parameters in
neighborhoods having uniform low loss, the model converges towards flatter
minima and its generalization significantly improves in both homogeneous and
heterogeneous scenarios. Empirical results demonstrate the effectiveness of
those optimizers across a variety of benchmark vision datasets (e.g.
CIFAR10/100, Landmarks-User-160k, IDDA) and tasks (large scale classification,
semantic segmentation, domain generalization).

In this paper, we tackle the problem of synthesizing a ground-view panorama
image conditioned on a top-view aerial image, which is a challenging problem
due to the large gap between the two image domains with different view-points.
Instead of learning cross-view mapping in a feedforward pass, we propose a
novel adversarial feedback GAN framework named PanoGAN with two key components:
an adversarial feedback module and a dual branch discrimination strategy.
First, the aerial image is fed into the generator to produce a target panorama
image and its associated segmentation map in favor of model training with
layout semantics. Second, the feature responses of the discriminator encoded by
our adversarial feedback module are fed back to the generator to refine the
intermediate representations, so that the generation performance is continually
improved through an iterative generation process. Third, to pursue
high-fidelity and semantic consistency of the generated panorama image, we
propose a pixel-segmentation alignment mechanism under the dual branch
discrimiantion strategy to facilitate cooperation between the generator and the
discriminator. Extensive experimental results on two challenging cross-view
image datasets show that PanoGAN enables high-quality panorama image generation
with more convincing details than state-of-the-art approaches. The source code
and trained models are available at \url{https://github.com/sswuai/PanoGAN}.

Provably solving stochastic convex optimization problems with constraints is
essential for various problems in science, business, and statistics. Recently
proposed XOR-Stochastic Gradient Descent (XOR-SGD) provides a convergence rate
guarantee solving the constraints-free version of the problem by leveraging
XOR-Sampling. However, the task becomes more difficult when additional equality
and inequality constraints are needed to be satisfied. Here we propose XOR-PGD,
a novel algorithm based on Projected Gradient Descent (PGD) coupled with the
XOR sampler, which is guaranteed to solve the constrained stochastic convex
optimization problem still in linear convergence rate by choosing proper step
size. We show on both synthetic stochastic inventory management and real-world
road network design problems that the rate of constraints satisfaction of the
solutions optimized by XOR-PGD is $10\%$ more than the competing approaches in
a very large searching space. The improved XOR-PGD algorithm is demonstrated to
be more accurate and efficient than both XOR-SGD and SGD coupled with MCMC
based samplers. It is also shown to be more scalable with respect to the number
of samples and processor cores via experiments with large dimensions.

Predicting the performance of an optimization algorithm on a new problem
instance is crucial in order to select the most appropriate algorithm for
solving that problem instance. For this purpose, recent studies learn a
supervised machine learning (ML) model using a set of problem landscape
features linked to the performance achieved by the optimization algorithm.
However, these models are black-box with the only goal of achieving good
predictive performance, without providing explanations which landscape features
contribute the most to the prediction of the performance achieved by the
optimization algorithm. In this study, we investigate the expressiveness of
problem landscape features utilized by different supervised ML models in
automated algorithm performance prediction. The experimental results point out
that the selection of the supervised ML method is crucial, since different
supervised ML regression models utilize the problem landscape features
differently and there is no common pattern with regard to which landscape
features are the most informative.

Taking advantage of the unprecedented statistical power of upcoming cosmic
shear surveys will require exquisite knowledge of the matter power spectrum
over a wide range of scales. Analytical methods can achieve such precision only
up to quasi-linear scales. For smaller non-linear scales we must resort to
$N$-body and hydrodynamical simulations, which despite recent technological
advances and improved algorithms remain computationally expensive. Over the
past decade machine learning and the advent of emulators have propelled our
ability to hit the target accuracy with impressive computing efficiency. Yet,
realistically these techniques will be able to produce high-precision
non-linear matter power spectra only for a restricted sub-set of extensions to
the "vanilla" ${\Lambda}$CDM cosmology. I will present a promising alternative
to alleviate these shortcomings that draws strength from the combination of
halo model, perturbation theory and emulators--the reaction framework. I will
show how a power spectrum evolved in the standard cosmology can be readily
adjusted to account for physics beyond ${\Lambda}$CDM, and discuss the accuracy
of the reaction for well-known modifications to gravity, dark energy
parametrizations and massive neutrino cosmologies.

When doctors are trained to diagnose a specific disease, they learn faster
when presented with cases in order of increasing difficulty. This creates the
need for automatically estimating how difficult it is for doctors to classify a
given case. In this paper, we introduce methods for estimating how hard it is
for a doctor to diagnose a case represented by a medical image, both when
ground truth difficulties are available for training, and when they are not.
Our methods are based on embeddings obtained with deep metric learning.
Additionally, we introduce a practical method for obtaining ground truth human
difficulty for each image case in a dataset using self-assessed certainty. We
apply our methods to two different medical datasets, achieving high Kendall
rank correlation coefficients, showing that we outperform existing methods by a
large margin on our problem and data.

Deep models must learn robust and transferable representations in order to
perform well on new domains. While domain transfer methods (e.g., domain
adaptation, domain generalization) have been proposed to learn transferable
representations across domains, they are typically applied to ResNet backbones
pre-trained on ImageNet. Thus, existing works pay little attention to the
effects of pre-training on domain transfer tasks. In this paper, we provide a
broad study and in-depth analysis of pre-training for domain adaptation and
generalization, namely: network architectures, size, pre-training loss, and
datasets. We observe that simply using a state-of-the-art backbone outperforms
existing state-of-the-art domain adaptation baselines and set new baselines on
Office-Home and DomainNet improving by 10.7\% and 5.5\%. We hope that this work
can provide more insights for future domain transfer research.

It has been hypothesized that some form of "modular" structure in artificial
neural networks should be useful for learning, compositionality, and
generalization. However, defining and quantifying modularity remains an open
problem. We cast the problem of detecting functional modules into the problem
of detecting clusters of similar-functioning units. This begs the question of
what makes two units functionally similar. For this, we consider two broad
families of methods: those that define similarity based on how units respond to
structured variations in inputs ("upstream"), and those based on how variations
in hidden unit activations affect outputs ("downstream"). We conduct an
empirical study quantifying modularity of hidden layer representations of
simple feedforward, fully connected networks, across a range of
hyperparameters. For each model, we quantify pairwise associations between
hidden units in each layer using a variety of both upstream and downstream
measures, then cluster them by maximizing their "modularity score" using
established tools from network science. We find two surprising results: first,
dropout dramatically increased modularity, while other forms of weight
regularization had more modest effects. Second, although we observe that there
is usually good agreement about clusters within both upstream methods and
downstream methods, there is little agreement about the cluster assignments
across these two families of methods. This has important implications for
representation-learning, as it suggests that finding modular representations
that reflect structure in inputs (e.g. disentanglement) may be a distinct goal
from learning modular representations that reflect structure in outputs (e.g.
compositionality).

We address the problem of designing stabilizing control policies for
nonlinear systems in discrete-time, while minimizing an arbitrary cost
function. When the system is linear and the cost is convex, the System Level
Synthesis (SLS) approach offers an exact solution based on convex programming.
Beyond this case, a globally optimal solution cannot be found in a tractable
way, in general. In this paper, we develop a parametrization of all and only
the control policies stabilizing a given time-varying nonlinear system in terms
of the combined effect of 1) a strongly stabilizing base controller and 2) a
stable SLS operator to be freely designed. Based on this result, we propose a
Neural SLS (Neur-SLS) approach guaranteeing closed-loop stability during and
after parameter optimization, without requiring any constraints to be
satisfied. We exploit recent Deep Neural Network (DNN) models based on
Recurrent Equilibrium Networks (RENs) to learn over a rich class of nonlinear
stable operators, and demonstrate the effectiveness of the proposed approach in
numerical examples.

Deep convolutional neural networks have achieved exceptional results on
multiple detection and recognition tasks. However, the performance of such
detectors are often evaluated in public benchmarks under constrained and
non-realistic situations. The impact of conventional distortions and processing
operations found in imaging workflows such as compression, noise, and
enhancement are not sufficiently studied. Currently, only a few researches have
been done to improve the detector robustness to unseen perturbations. This
paper proposes a more effective data augmentation scheme based on real-world
image degradation process. This novel technique is deployed for deepfake
detection tasks and has been evaluated by a more realistic assessment
framework. Extensive experiments show that the proposed data augmentation
scheme improves generalization ability to unpredictable data distortions and
unseen datasets.

Deep neural networks can be fragile and sensitive to small input
perturbations that might cause a significant change in the output. In this
paper, we employ contraction theory to improve the robustness of neural ODEs
(NODEs). A dynamical system is contractive if all solutions with different
initial conditions converge to each other exponentially fast. As a consequence,
perturbations in initial conditions become less and less relevant over time.
Since in NODEs the input data corresponds to the initial condition of dynamical
systems, we show contractivity can mitigate the effect of input perturbations.
More precisely, inspired by NODEs with Hamiltonian dynamics, we propose a class
of contractive Hamiltonian NODEs (CH-NODEs). By properly tuning a scalar
parameter, CH-NODEs ensure contractivity by design and can be trained using
standard backpropagation. Moreover, CH-NODEs enjoy built-in guarantees of
non-exploding gradients, which ensure a well-posed training process. Finally,
we demonstrate the robustness of CH-NODEs on the MNIST image classification
problem with noisy test data.

Deep convolutional neural networks have shown remarkable results on multiple
detection tasks. Despite the significant progress, the performance of such
detectors are often assessed in public benchmarks under non-realistic
conditions. Specifically, impact of conventional distortions and processing
operations such as compression, noise, and enhancement are not sufficiently
studied. This paper proposes a rigorous framework to assess performance of
learning-based detectors in more realistic situations. An illustrative example
is shown under deepfake detection context. Inspired by the assessment results,
a data augmentation strategy based on natural image degradation process is
designed, which significantly improves the generalization ability of two
deepfake detectors.

Recently, several methods have been proposed for estimating the mutual
information from sample data using deep neural networks. These estimators ar
referred to as neural mutual information estimation (NMIE)s. NMIEs differ from
other approaches as they are data-driven estimators. As such, they have the
potential to perform well on a large class of capacity problems. In order to
test the performance across various NMIEs, it is desirable to establish a
benchmark encompassing the different challenges of capacity estimation. This is
the objective of this paper. In particular, we consider three scenarios for
benchmarking:i the classic AWGN channel, ii channels continuous inputs optical
intensity and peak-power constrained AWGN channel iii channels with a discrete
output, i.e., Poisson channel. We also consider the extension to the
multi-terminal case with iv the AWGN and optical MAC models. We argue that
benchmarking a certain NMIE across these four scenarios provides a substantive
test of performance. In this paper we study the performance of mutual
information neural estimator (MINE), smoothed mutual information lower-bound
estimator (SMILE), and directed information neural estimator (DINE). and
provide insights on the performance of other methods as well. To summarize our
benchmarking results, MINE provides the most reliable performance.

We employ a feed-forward artificial neural network to extrapolate at large
model spaces the results of {\it ab-initio} hypernuclear No-Core Shell Model
calculations for the $\Lambda$ separation energy $B_\Lambda$ of the lightest
hypernuclei, $^3_\Lambda$H, $^4_\Lambda$H and $^4_\Lambda$He, obtained in
computationally accessible harmonic oscillator basis spaces using chiral
nucleon-nucleon, nucleon-nucleon-nucleon and hyperon-nucleon interactions. The
overfitting problem is avoided by enlarging the size of the input dataset and
by introducing a Gaussian noise during the training process of the neural
network. We find that a network with a single hidden layer of eight neurons is
sufficient to correctly extrapolate the value of $\Lambda$ separation energy to
model spaces of size $N_{max}=100$. The results obtained are in good agreement
with the experimental data for the three light hypernuclei considered.

Program semantics learning is the core and fundamental for various code
intelligent tasks e.g., vulnerability detection, clone detection. A
considerable amount of existing works propose diverse approaches to learn the
program semantics for different tasks and these works have achieved
state-of-the-art performance. However, currently, a comprehensive and
systematic study on evaluating different program representation techniques
across diverse tasks is still missed.
  From this starting point, in this paper, we conduct an empirical study to
evaluate different program representation techniques. Specifically, we
categorize current mainstream code representation techniques into four
categories i.e., Feature-based, Sequence-based, Tree-based, and Graph-based
program representation technique and evaluate its performance on three diverse
and popular code intelligent tasks i.e., {Code Classification}, Vulnerability
Detection, and Clone Detection on the public released benchmark. We further
design three {research questions (RQs)} and conduct a comprehensive analysis to
investigate the performance. By the extensive experimental results, we conclude
that (1) The graph-based representation is superior to the other selected
techniques across these tasks. (2) Compared with the node type information used
in tree-based and graph-based representations, the node textual information is
more critical to learning the program semantics. (3) Different tasks require
the task-specific semantics to achieve their highest performance, however
combining various program semantics from different dimensions such as control
dependency, data dependency can still produce promising results.

The estimation of speaker characteristics such as age and height is a
challenging task, having numerous applications in voice forensic analysis. In
this work, we propose a bi-encoder transformer mixture model for speaker age
and height estimation. Considering the wide differences in male and female
voice characteristics such as differences in formant and fundamental
frequencies, we propose the use of two separate transformer encoders for the
extraction of specific voice features in the male and female gender, using
wav2vec 2.0 as a common-level feature extractor. This architecture reduces the
interference effects during backpropagation and improves the generalizability
of the model. We perform our experiments on the TIMIT dataset and significantly
outperform the current state-of-the-art results on age estimation.
Specifically, we achieve root mean squared error (RMSE) of 5.54 years and 6.49
years for male and female age estimation, respectively. Further experiment to
evaluate the relative importance of different phonetic types for our task
demonstrate that vowel sounds are the most distinguishing for age estimation.

Non-Standard Interactions (NSI) between neutrinos and matter affect the
neutrino flavor oscillations. Due to the high matter density in the core of the
Sun, solar neutrinos are suited to probe these interactions. Using the $277$
kton-yr exposure of Super-Kamiokande to $^{8}$B solar neutrinos, we search for
the presence of NSI. Our data favors the presence of NSI with down quarks at
1.8$\sigma$, and with up quarks at 1.6$\sigma$, with the best fit NSI
parameters being ($\epsilon_{11}^{d},\epsilon_{12}^{d}$) = (-3.3, -3.1) for
$d$-quarks and ($\epsilon_{11}^{u},\epsilon_{12}^{u}$) = (-2.5, -3.1) for
$u$-quarks. After combining with data from the Sudbury Neutrino Observatory and
Borexino, the significance increases by 0.1$\sigma$.

Despite its popularity in the reinforcement learning community, a provably
convergent policy gradient method for general continuous space-time stochastic
control problems has been elusive. This paper closes the gap by proposing a
proximal gradient algorithm for feedback controls of finite-time horizon
stochastic control problems. The state dynamics are continuous time nonlinear
diffusions with controlled drift and possibly degenerate noise, and the
objectives are nonconvex in the state and nonsmooth in the control. We prove
under suitable conditions that the algorithm converges linearly to a stationary
point of the control problem, and is stable with respect to policy updates by
approximate gradient steps. The convergence result justifies the recent
reinforcement learning heuristics that adding entropy regularization or a
fictitious discount factor to the optimization objective accelerates the
convergence of policy gradient methods. The proof exploits careful regularity
estimates of backward stochastic differential equations.

The sparse layouts of radio interferometers result in an incomplete sampling
of the sky in Fourier space which leads to artifacts in the reconstructed
images. Cleaning these systematic effects is essential for the scientific use
of radiointerferometric images. Established reconstruction methods are often
time-consuming, require expert-knowledge, and suffer from a lack of
reproducibility. We have developed a prototype Deep Learning-based method that
generates reproducible images in an expedient fashion. To this end, we take
advantage of the efficiency of Convolutional Neural Networks to reconstruct
image data from incomplete information in Fourier space. The Neural Network
architecture is inspired by super-resolution models that utilize residual
blocks. Using simulated data of radio galaxies that are composed of Gaussian
components we train Deep Learning models whose reconstruction capability is
quantified using various measures. The reconstruction performance is evaluated
on clean and noisy input data by comparing the resulting predictions with the
true source images. We find that source angles and sizes are well reproduced,
while the recovered fluxes show substantial scatter, albeit not worse than
existing methods without fine-tuning. Finally, we propose more advanced
approaches using Deep Learning that include uncertainty estimates and a concept
to analyze larger images.

Federated learning (FL) allows multiple clients to collectively train a
high-performance global model without sharing their private data. However, the
key challenge in federated learning is that the clients have significant
statistical heterogeneity among their local data distributions, which would
cause inconsistent optimized local models on the client-side. To address this
fundamental dilemma, we propose a novel federated learning algorithm with local
drift decoupling and correction (FedDC). Our FedDC only introduces lightweight
modifications in the local training phase, in which each client utilizes an
auxiliary local drift variable to track the gap between the local model
parameter and the global model parameters. The key idea of FedDC is to utilize
this learned local drift variable to bridge the gap, i.e., conducting
consistency in parameter-level. The experiment results and analysis demonstrate
that FedDC yields expediting convergence and better performance on various
image classification tasks, robust in partial participation settings, non-iid
data, and heterogeneous clients.

In this paper, we propose to quantitatively compare loss functions based on
parameterized Tsallis-Havrda-Charvat entropy and classical Shannon entropy for
the training of a deep network in the case of small datasets which are usually
encountered in medical applications. Shannon cross-entropy is widely used as a
loss function for most neural networks applied to the segmentation,
classification and detection of images. Shannon entropy is a particular case of
Tsallis-Havrda-Charvat entropy. In this work, we compare these two entropies
through a medical application for predicting recurrence in patients with
head-neck and lung cancers after treatment. Based on both CT images and patient
information, a multitask deep neural network is proposed to perform a
recurrence prediction task using cross-entropy as a loss function and an image
reconstruction task. Tsallis-Havrda-Charvat cross-entropy is a parameterized
cross entropy with the parameter $\alpha$. Shannon entropy is a particular case
of Tsallis-Havrda-Charvat entropy for $\alpha$ = 1. The influence of this
parameter on the final prediction results is studied. In this paper, the
experiments are conducted on two datasets including in total 580 patients, of
whom 434 suffered from head-neck cancers and 146 from lung cancers. The results
show that Tsallis-Havrda-Charvat entropy can achieve better performance in
terms of prediction accuracy with some values of $\alpha$.

Network meta-analysis (NMA) is a technique used in medical statistics to
combine evidence from multiple medical trials. NMA defines an inference and
information processing problem on a network of treatment options and trials
connecting the treatments. We believe that statistical physics can offer useful
ideas and tools for this area, including from the theory of complex networks,
stochastic modelling and simulation techniques. The lack of a unique source
that would allow physicists to learn about NMA effectively is a barrier to
this. In this article we aim to present the `NMA problem' and existing
approaches to it coherently and in a language accessible to statistical
physicists. We also summarise existing points of contact between statistical
physics and NMA, and describe our ideas of how physics might make a difference
for NMA in the future. The overall goal of the article is to attract physicists
to this interesting, timely and worthwhile field of research.

Learning in biological and artificial neural networks is often framed as a
problem in which targeted error signals are used to directly guide parameter
updating for more optimal network behaviour. Backpropagation of error (BP) is
an example of such an approach and has proven to be a highly successful
application of stochastic gradient descent to deep neural networks. However, BP
relies on the transmission of gradient information directly to parameters, and
frames learning as two completely separated passes. We propose constrained
parameter inference (COPI) as a new principle for learning. The COPI approach
to learning proposes that parameters might infer their updates based upon local
neuron activities. This estimation of network parameters is possible under the
constraints of decorrelated neural inputs and top-down perturbations of neural
states, where credit is assigned to units instead of parameters directly. The
form of the top-down perturbation determines which credit assignment method is
being used, and when aligned with BP it constitutes a mixture of the forward
and backward passes. We show that COPI is not only more biologically plausible
but also provides distinct advantages for fast learning when compared to BP.

Digital breast tomosynthesis (DBT) exams should utilize the lowest possible
radiation dose while maintaining sufficiently good image quality for accurate
medical diagnosis. In this work, we propose a convolution neural network (CNN)
to restore low-dose (LD) DBT projections to achieve an image quality equivalent
to a standard full-dose (FD) acquisition. The proposed network architecture
benefits from priors in terms of layers that were inspired by traditional
model-based (MB) restoration methods, considering a model-based deep learning
approach, where the network is trained to operate in the variance stabilization
transformation (VST) domain. To accurately control the network operation point,
in terms of noise and blur of the restored image, we propose a loss function
that minimizes the bias and matches residual noise between the input and the
output. The training dataset was composed of clinical data acquired at the
standard FD and low-dose pairs obtained by the injection of quantum noise. The
network was tested using real DBT projections acquired with a physical
anthropomorphic breast phantom. The proposed network achieved superior results
in terms of the mean normalized squared error (MNSE), training time and noise
spatial correlation compared with networks trained with traditional data-driven
methods. The proposed approach can be extended for other medical imaging
application that requires LD acquisitions.

It has been shown that ultrasonic techniques work well for online measuring
of circumferential oil film thickness profile in journal bearings;
unfortunately, they can be limited by their measuring range and unable to
capture details of the film all around the bearing circumference. Attempts to
model the film thickness over the full range of the bearing rely on
deterministic approaches, which assume the observations to be true with
absolute certainty. Unaccounted uncertainties of the film thickness may lead to
a cascade of inaccurate predictions for subsequent calculations of hydrodynamic
parameters. In the present work, a probabilistic framework is proposed to model
the film thickness with Gaussian Processes. The results are then used to
estimate the location of the bearing shaft under various operational
conditions. A further step in the process involves using the newly-constructed
dataset to generate likelihood maps displaying the probable location of the
shaft centre, given the bearing rotational speed and applied static load. The
results offer the possibility to visualise the confidence of the predictions
and allow the true location to be found within an area of high probability
within the bearing's bore.

Recent advances in self-supervised contrastive learning yield good
image-level representation, which favors classification tasks but usually
neglects pixel-level detailed information, leading to unsatisfactory transfer
performance to dense prediction tasks such as semantic segmentation. In this
work, we propose a pixel-wise contrastive learning method called CP2
(Copy-Paste Contrastive Pretraining), which facilitates both image- and
pixel-level representation learning and therefore is more suitable for
downstream dense prediction tasks. In detail, we copy-paste a random crop from
an image (the foreground) onto different background images and pretrain a
semantic segmentation model with the objective of 1) distinguishing the
foreground pixels from the background pixels, and 2) identifying the composed
images that share the same foreground.Experiments show the strong performance
of CP2 in downstream semantic segmentation: By finetuning CP2 pretrained models
on PASCAL VOC 2012, we obtain 78.6% mIoU with a ResNet-50 and 79.5% with a
ViT-S.

Aspect-based sentiment analysis (ABSA) task aim at associating a piece of
text with a set of aspects and meanwhile infer their respective sentimental
polarities. The state-of-the-art approaches are built upon fine-tuning of
various pre-trained language models. They commonly attempt to learn
aspect-specific representation from the corpus. Unfortunately, the aspect is
often expressed implicitly through a set of representatives and thus renders
implicit mapping process unattainable unless sufficient labeled examples are
available. However, high-quality labeled examples may not be readily available
in real-world scenarios. In this paper, we propose to jointly address aspect
categorization and aspect-based sentiment subtasks in a unified framework.
Specifically, we first introduce a simple but effective mechanism to construct
an auxiliary-sentence for the implicit aspect based on the semantic information
in the corpus. Then, we encourage BERT to learn the aspect-specific
representation in response to the automatically constructed auxiliary-sentence
instead of the aspect itself. Finally, we empirically evaluate the performance
of the proposed solution by a comparative study on real benchmark datasets for
both ABSA and Targeted-ABSA tasks. Our extensive experiments show that it
consistently achieves state-of-the-art performance in terms of aspect
categorization and aspect-based sentiment across all datasets and the
improvement margins are considerable. The code of BERT-ASC is available in
GitHub: https://github.com/amurtadha/BERT-ASC.

Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have achieved
great success in neural networks (NNs). Due to the complex non-linear
characteristic of samples, the objective of those activation functions is to
project samples from their original feature space to a linear separable feature
space. This phenomenon ignites our interest in exploring whether all features
need to be transformed by all non-linear functions in current typical NNs,
i.e., whether there exists a part of features arriving at the linear separable
feature space in the intermediate layers, that does not require further
non-linear variation but an affine transformation instead. To validate the
above hypothesis, we explore the problem of linear feature disentanglement for
neural networks in this paper. Specifically, we devise a learnable mask module
to distinguish between linear and non-linear features. Through our designed
experiments we found that some features reach the linearly separable space
earlier than the others and can be detached partly from the NNs. The explored
method also provides a readily feasible pruning strategy which barely affects
the performance of the original model. We conduct our experiments on four
datasets and present promising results.

Learned image compression research has achieved state-of-the-art compression
performance with auto-encoder based neural network architectures, where the
image is mapped via convolutional neural networks (CNN) into a latent
representation that is quantized and processed again with CNN to obtain the
reconstructed image. CNN operate on entire input images. On the other hand,
traditional state-of-the-art image and video compression methods process images
with a block-by-block processing approach for various reasons. Very recently,
work on learned image compression with block based approaches have also
appeared, which use the auto-encoder architecture on large blocks of the input
image and introduce additional neural networks that perform intra/spatial
prediction and deblocking/post-processing functions. This paper explores an
alternative learned block-based image compression approach in which neither an
explicit intra prediction neural network nor an explicit deblocking neural
network is used. A single auto-encoder neural network with block-level masked
convolutions is used and the block size is much smaller (8x8). By using
block-level masked convolutions, each block is processed using reconstructed
neighboring left and upper blocks both at the encoder and decoder. Hence, the
mutual information between adjacent blocks is exploited during compression and
each block is reconstructed using neighboring blocks, resolving the need for
explicit intra prediction and deblocking neural networks. Since the explored
system is a closed loop system, a special optimization procedure, the
asymptotic closed loop design, is used with standard stochastic gradient
descent based training. The experimental results indicate competitive image
compression performance.

Continual learning is a longstanding research topic due to its crucial role
in tackling continually arriving tasks. Up to now, the study of continual
learning in computer vision is mainly restricted to convolutional neural
networks (CNNs). However, recently there is a tendency that the newly emerging
vision transformers (ViTs) are gradually dominating the field of computer
vision, which leaves CNN-based continual learning lagging behind as they can
suffer from severe performance degradation if straightforwardly applied to
ViTs. In this paper, we study ViT-backed continual learning to strive for
higher performance riding on recent advances of ViTs. Inspired by mask-based
continual learning methods in CNNs, where a mask is learned per task to adapt
the pre-trained ViT to the new task, we propose MEta-ATtention (MEAT), i.e.,
attention to self-attention, to adapt a pre-trained ViT to new tasks without
sacrificing performance on already learned tasks. Unlike prior mask-based
methods like Piggyback, where all parameters are associated with corresponding
masks, MEAT leverages the characteristics of ViTs and only masks a portion of
its parameters. It renders MEAT more efficient and effective with less overhead
and higher accuracy. Extensive experiments demonstrate that MEAT exhibits
significant superiority to its state-of-the-art CNN counterparts, with 4.0~6.0%
absolute boosts in accuracy. Our code has been released at
https://github.com/zju-vipa/MEAT-TIL.

The expressive power of message passing GNNs is upper-bounded by
Weisfeiler-Lehman (WL) test. To achieve high expressive GNNs beyond WL test, we
propose a novel graph isomorphism test method, namely Twin-WL, which
simultaneously passes node labels and node identities rather than only passes
node label as WL. The identity-passing mechanism encodes complete structure
information of rooted subgraph, and thus Twin-WL can offer extra power beyond
WL at distinguishing graph structures. Based on Twin-WL, we implement two
Twin-GNNs for graph classification via defining readout function over rooted
subgraph: one simply readouts the size of rooted subgraph and the other
readouts rich structure information of subgraph following a GNN-style. We prove
that the two Twin-GNNs both have higher expressive power than traditional
message passing GNNs. Experiments also demonstrate the Twin-GNNs significantly
outperform state-of-the-art methods at the task of graph classification.

Building models of natural language processing (NLP) is challenging in
low-resource scenarios where only limited data are available.
Optimization-based meta-learning algorithms achieve promising results in
low-resource scenarios by adapting a well-generalized model initialization to
handle new tasks. Nonetheless, these approaches suffer from the memorization
overfitting issue, where the model tends to memorize the meta-training tasks
while ignoring support sets when adapting to new tasks. To address this issue,
we propose a memory imitation meta-learning (MemIML) method that enhances the
model's reliance on support sets for task adaptation. Specifically, we
introduce a task-specific memory module to store support set information and
construct an imitation module to force query sets to imitate the behaviors of
some representative support-set samples stored in the memory. A theoretical
analysis is provided to prove the effectiveness of our method, and empirical
results also demonstrate that our method outperforms competitive baselines on
both text classification and generation tasks.

Our opinions and views of life can be shaped by how we perceive the opinions
of others on social media like Facebook. This dependence has increased during
COVID-19 periods when we have fewer means to connect with others. However, fake
news related to COVID-19 has become a significant problem on Facebook. Bengali
is the seventh most spoken language worldwide, yet we are aware of no previous
research that studied the prevalence of COVID-19 related fake news in Bengali
on Facebook. In this paper, we develop machine learning models to detect fake
news in Bengali automatically. The best performing model is BERT, with an
F1-score of 0.97. We apply BERT on all Facebook Bengali posts related to
COVID-19. We find 10 topics in the COVID-19 Bengali fake news grouped into
three categories: System (e.g., medical system), belief (e.g., religious
rituals), and social (e.g., scientific awareness).

Graphical models provide a powerful methodology for learning the conditional
independence structure in multivariate data. Inference is often focused on
estimating individual edges in the latent graph. Nonetheless, there is
increasing interest in inferring more complex structures, such as communities,
for multiple reasons, including more effective information retrieval and better
interpretability. Stochastic blockmodels offer a powerful tool to detect such
structure in a network. We thus propose to exploit advances in random graph
theory and embed them within the graphical models framework. A consequence of
this approach is the propagation of the uncertainty in graph estimation to
large-scale structure learning. We consider Bayesian nonparametric stochastic
blockmodels as priors on the graph. We extend such models to consider
clique-based blocks and to multiple graph settings introducing a novel prior
process based on a Dependent Dirichlet process. Moreover, we devise a tailored
computation strategy of Bayes factors for block structure based on the
Savage-Dickey ratio to test for presence of larger structure in a graph. We
demonstrate our approach in simulations as well as on real data applications in
finance and transcriptomics.

Recently, researchers have shown an increased interest in the online
knowledge distillation. Adopting an one-stage and end-to-end training fashion,
online knowledge distillation uses aggregated intermediated predictions of
multiple peer models for training. However, the absence of a powerful teacher
model may result in the homogeneity problem between group peers, affecting the
effectiveness of group distillation adversely. In this paper, we propose a
novel online knowledge distillation method, \textbf{C}hannel
\textbf{S}elf-\textbf{S}upervision for Online Knowledge Distillation (CSS),
which structures diversity in terms of input, target, and network to alleviate
the homogenization problem. Specifically, we construct a dual-network
multi-branch structure and enhance inter-branch diversity through
self-supervised learning, adopting the feature-level transformation and
augmenting the corresponding labels. Meanwhile, the dual network structure has
a larger space of independent parameters to resist the homogenization problem
during distillation. Extensive quantitative experiments on CIFAR-100 illustrate
that our method provides greater diversity than OKDDip and we also give pretty
performance improvement, even over the state-of-the-art such as PCL. The
results on three fine-grained datasets (StanfordDogs, StanfordCars,
CUB-200-211) also show the significant generalization capability of our
approach.

Same-Day Delivery services are becoming increasingly popular in recent years.
These have been usually modelled by previous studies as a certain class of
Dynamic Vehicle Routing Problem (DVRP) where goods must be delivered from a
depot to a set of customers in the same day that the orders were placed.
Adaptive exact solution methods for DVRPs can become intractable even for small
problem instances. In this paper, we formulate the SDDP as a Markov Decision
Process (MDP) and solve it using a parameter-sharing Deep Q-Network, which
corresponds to a decentralised Multi-Agent Reinforcement Learning (MARL)
approach. For this, we create a multi-agent grid-based SDD environment,
consisting of multiple vehicles, a central depot and dynamic order generation.
In addition, we introduce zone-specific order generation and reward
probabilities. We compare the performance of our proposed MARL approach against
a Mixed Inter Programming (MIP) solution. Results show that our proposed MARL
framework performs on par with MIP-based policy when the number of orders is
relatively low. For problem instances with higher order arrival rates,
computational results show that the MARL approach underperforms the MIP by up
to 30%. The performance gap between both methods becomes smaller when
zone-specific parameters are employed. The gap is reduced from 30% to 3% for a
5x5 grid scenario with 30 orders. Execution time results indicate that the MARL
approach is, on average, 65 times faster than the MIP-based policy, and
therefore may be more advantageous for real-time control, at least for
small-sized instances.

In pursuit of enhanced multi-agent collaboration, we analyze several
on-policy deep reinforcement learning algorithms in the recently published
Hanabi benchmark. Our research suggests a perhaps counter-intuitive finding,
where Proximal Policy Optimization (PPO) is outperformed by Vanilla Policy
Gradient over multiple random seeds in a simplified environment of the
multi-agent cooperative card game. In our analysis of this behavior we look
into Hanabi-specific metrics and hypothesize a reason for PPO's plateau. In
addition, we provide proofs for the maximum length of a perfect game (71 turns)
and any game (89 turns). Our code can be found at:
https://github.com/bramgrooten/DeepRL-for-Hanabi

Autonomous Driving requires high levels of coordination and collaboration
between agents. Achieving effective coordination in multi-agent systems is a
difficult task that remains largely unresolved. Multi-Agent Reinforcement
Learning has arisen as a powerful method to accomplish this task because it
considers the interaction between agents and also allows for decentralized
training -- which makes it highly scalable. However, transferring policies from
simulation to the real world is a big challenge, even for single-agent
applications. Multi-agent systems add additional complexities to the
Sim-to-Real gap due to agent collaboration and environment synchronization. In
this paper, we propose a method to transfer multi-agent autonomous driving
policies to the real world. For this, we create a multi-agent environment that
imitates the dynamics of the Duckietown multi-robot testbed, and train
multi-agent policies using the MAPPO algorithm with different levels of domain
randomization. We then transfer the trained policies to the Duckietown testbed
and compare the use of the MAPPO algorithm against a traditional rule-based
method. We show that the rewards of the transferred policies with MAPPO and
domain randomization are, on average, 1.85 times superior to the rule-based
method. Moreover, we show that different levels of parameter randomization have
a substantial impact on the Sim-to-Real gap.

Nowadays, industry 4.0 plays a tremendous role in the manufacturing
industries for increasing the amount of data and accuracy in modern
manufacturing systems. Thanks to artificial intelligence, particularly machine
learning, big data analytics have dramatically amended, and manufacturers
easily exploit organized and unorganized data. This study utilized hybrid
optimization algorithms to find friction stir welding and optimal hardness
value at the nugget zone. A similar AA 6262 material was used and welded in a
butt joint configuration. Tool rotational speed (RPM), tool traverse speed
(mm/min), and the plane depth (mm) are used as controllable parameters and
optimized using Taguchi L9, Random Forest, and XG Boost machine learning tools.
Analysis of variance was also conducted at a 95% confidence interval for
identifying the significant parameters. The result indicated that the
coefficient of determination from Taguchi L9 orthogonal array is 0.91 obtained
while Random Forest and XG Boost algorithm imparted 0.62 and 0.65,

Thanks to their universal approximation properties and new efficient training
strategies, Deep Neural Networks are becoming a valuable tool for the
approximation of mathematical operators. In the present work, we introduce
Mesh-Informed Neural Networks (MINNs), a class of architectures specifically
tailored to handle mesh based functional data, and thus of particular interest
for reduced order modeling of parametrized Partial Differential Equations
(PDEs). The driving idea behind MINNs is to embed hidden layers into discrete
functional spaces of increasing complexity, obtained through a sequence of
meshes defined over the underlying spatial domain. The approach leads to a
natural pruning strategy which enables the design of sparse architectures that
are able to learn general nonlinear operators. We assess this strategy through
an extensive set of numerical experiments, ranging from nonlocal operators to
nonlinear diffusion PDEs, where MINNs are compared to classical fully connected
Deep Neural Networks. Our results show that MINNs can handle functional data
defined on general domains of any shape, while ensuring reduced training times,
lower computational costs, and better generalization capabilities, thus making
MINNs very well-suited for demanding applications such as Reduced Order
Modeling and Uncertainty Quantification for PDEs.

Recent years have witnessed increasing interest in few-shot knowledge graph
completion (FKGC), which aims to infer unseen query triples for a few-shot
relation using a few reference triples about the relation. The primary focus of
existing FKGC methods lies in learning relation representations that can
reflect the common information shared by the query and reference triples. To
this end, these methods learn entity-pair representations from the direct
neighbors of head and tail entities, and then aggregate the representations of
reference entity pairs. However, the entity-pair representations learned only
from direct neighbors may have low expressiveness when the involved entities
have sparse direct neighbors or share a common local neighborhood with other
entities. Moreover, merely modeling the semantic information of head and tail
entities is insufficient to accurately infer their relational information
especially when they have multiple relations. To address these issues, we
propose a Relation-Specific Context Learning (RSCL) framework, which exploits
graph contexts of triples to learn global and local relation-specific
representations for few-shot relations. Specifically, we first extract graph
contexts for each triple, which can provide long-term entity-relation
dependencies. To encode the extracted graph contexts, we then present a
hierarchical attention network to capture contextualized information of triples
and highlight valuable local neighborhood information of entities. Finally, we
design a hybrid attention aggregator to evaluate the likelihood of the query
triples at the global and local levels. Experimental results on two public
datasets demonstrate that RSCL outperforms state-of-the-art FKGC methods.

Human actions often induce changes of object states such as "cutting an
apple", "cleaning shoes" or "pouring coffee". In this paper, we seek to
temporally localize object states (e.g. "empty" and "full" cup) together with
the corresponding state-modifying actions ("pouring coffee") in long uncurated
videos with minimal supervision. The contributions of this work are threefold.
First, we develop a self-supervised model for jointly learning state-modifying
actions together with the corresponding object states from an uncurated set of
videos from the Internet. The model is self-supervised by the causal ordering
signal, i.e. initial object state $\rightarrow$ manipulating action
$\rightarrow$ end state. Second, to cope with noisy uncurated training data,
our model incorporates a noise adaptive weighting module supervised by a small
number of annotated still images, that allows to efficiently filter out
irrelevant videos during training. Third, we collect a new dataset with more
than 2600 hours of video and 34 thousand changes of object states, and manually
annotate a part of this data to validate our approach. Our results demonstrate
substantial improvements over prior work in both action and object
state-recognition in video.

Multi-source domain adaptation has been intensively studied. The distribution
shift in features inherent to specific domains causes the negative transfer
problem, degrading a model's generality to unseen tasks. In Federated Learning
(FL), learned model parameters are shared to train a global model that
leverages the underlying knowledge across client models trained on separate
data domains. Nonetheless, the data confidentiality of FL hinders the
effectiveness of traditional domain adaptation methods that require prior
knowledge of different domain data. We propose a new federated domain
generalization method called Federated Knowledge Alignment (FedKA). FedKA
leverages feature distribution matching in a global workspace such that the
global model can learn domain-invariant client features under the constraint of
unknown client data. FedKA employs a federated voting mechanism that generates
target domain pseudo-labels based on the consensus from clients to facilitate
global model fine-tuning. We performed extensive experiments, including an
ablation study, to evaluate the effectiveness of the proposed method in both
image and text classification tasks using different model architectures. The
empirical results show that FedKA achieves performance gains of 8.8% and 3.5%
in Digit-Five and Office-Caltech10, respectively, and a gain of 0.7% in Amazon
Review with extremely limited training data. Moreover, we studied the
effectiveness of FedKA in alleviating the negative transfer of FL based on a
new criterion called Group Effect. The results show that FedKA can reduce
negative transfer, improving the performance gain via model aggregation by 4
times.

Model poisoning attacks on federated learning (FL) intrude in the entire
system via compromising an edge model, resulting in malfunctioning of machine
learning models. Such compromised models are tampered with to perform
adversary-desired behaviors. In particular, we considered a semi-targeted
situation where the source class is predetermined however the target class is
not. The goal is to cause the global classifier to misclassify data of the
source class. Though approaches such as label flipping have been adopted to
inject poisoned parameters into FL, it has been shown that their performances
are usually class-sensitive varying with different target classes applied.
Typically, an attack can become less effective when shifting to a different
target class. To overcome this challenge, we propose the Attacking
Distance-aware Attack (ADA) to enhance a poisoning attack by finding the
optimized target class in the feature space. Moreover, we studied a more
challenging situation where an adversary had limited prior knowledge about a
client's data. To tackle this problem, ADA deduces pair-wise distances between
different classes in the latent feature space from shared model parameters
based on the backward error analysis. We performed extensive empirical
evaluations on ADA by varying the factor of attacking frequency in three
different image classification tasks. As a result, ADA succeeded in increasing
the attack performance by 1.8 times in the most challenging case with an
attacking frequency of 0.01.

This paper studies the task of conditional Human Motion Animation (cHMA).
Given a source image and a driving video, the model should animate the new
frame sequence, in which the person in the source image should perform a
similar motion as the pose sequence from the driving video. Despite the success
of Generative Adversarial Network (GANs) methods in image and video synthesis,
it is still very challenging to conduct cHMA due to the difficulty in
efficiently utilizing the conditional guided information such as images or
poses, and generating images of good visual quality. To this end, this paper
proposes a novel model of learning to Quantize, Scrabble, and Craft (QS-Craft)
for conditional human motion animation. The key novelties come from the newly
introduced three key steps: quantize, scrabble and craft. Particularly, our
QS-Craft employs transformer in its structure to utilize the attention
architectures. The guided information is represented as a pose coordinate
sequence extracted from the driving videos. Extensive experiments on human
motion datasets validate the efficacy of our model.

Two pretrained neural networks are deemed equivalent if they yield similar
outputs for the same inputs. Equivalence checking of neural networks is of
great importance, due to its utility in replacing learning-enabled components
with equivalent ones, when there is need to fulfill additional requirements or
to address security threats, as is the case for example when using knowledge
distillation, adversarial training etc. SMT solvers can potentially provide
solutions to the problem of neural network equivalence checking that will be
sound and complete, but as it is expected any such solution is associated with
significant limitations with respect to the size of neural networks to be
checked. This work presents a first SMT-based encoding of the equivalence
checking problem, explores its utility and limitations and proposes avenues for
future research and improvements towards more scalable and practically
applicable solutions. We present experimental results that shed light to the
aforementioned issues, for diverse types of neural network models (classifiers
and regression networks) and equivalence criteria, towards a general and
application-independent equivalence checking approach.

The plug-in estimator of the Wasserstein distance is known to be
conservative, however its usefulness is severely limited when the distributions
are similar as its bias does not decay to zero with the true Wasserstein
distance. We propose a linear combination of plug-in estimators for the squared
2-Wasserstein distance with a reduced bias that decays to zero with the true
distance. The new estimator is provably conservative provided one distribution
is appropriately overdispersed with respect the other, and is unbiased when the
distributions are equal. We apply it to approximately bound from above the
2-Wasserstein distance between the target and current distribution in Markov
chain Monte Carlo, running multiple identically distributed chains which start,
and remain, overdispersed with respect to the target. Our bound consistently
outperforms the current state-of-the-art bound, which uses coupling, improving
mixing time bounds by up to an order of magnitude.

Spotting camouflaged objects that are visually assimilated into the
background is tricky for both object detection algorithms and humans who are
usually confused or cheated by the perfectly intrinsic similarities between the
foreground objects and the background surroundings. To tackle this challenge,
we aim to extract the high-resolution texture details to avoid the detail
degradation that causes blurred vision in edges and boundaries. We introduce a
novel HitNet to refine the low-resolution representations by high-resolution
features in an iterative feedback manner, essentially a global loop-based
connection among the multi-scale resolutions. In addition, an iterative
feedback loss is proposed to impose more constraints on each feedback
connection. Extensive experiments on four challenging datasets demonstrate that
our \ourmodel~breaks the performance bottleneck and achieves significant
improvements compared with 29 state-of-the-art methods. To address the data
scarcity in camouflaged scenarios, we provide an application example by
employing cross-domain learning to extract the features that can reflect the
camouflaged object properties and embed the features into salient objects,
thereby generating more camouflaged training samples from the diverse salient
object datasets The code will be available at
https://github.com/HUuxiaobin/HitNet.

We consider the adaptive routing problem in multihop wireless networks. The
link states are assumed to be random variables drawn from unknown
distributions, independent and identically distributed across links and time.
This model has attracted a growing interest recently in cognitive radio
networks and adaptive communication systems. In such networks, devices are
cognitive in the sense of learning the link states and updating the
transmission parameters to allow efficient resource utilization. This model
contrasts sharply with the vast literature on routing algorithms that assumed
complete knowledge about the link state means. The goal is to design an
algorithm that learns online optimal paths for data transmissions to maximize
the network throughput while attaining low path cost over flows in the network.
We develop a novel Online Learning for Shortest path and Backpressure (OLSB)
algorithm to achieve this goal. We analyze the performance of OLSB rigorously
and show that it achieves a logarithmic regret with time, defined as the loss
of an algorithm as compared to a genie that has complete knowledge about the
link state means. We further evaluate the performance of OLSB numerically via
extensive simulations, which support the theoretical findings and demonstrate
its high efficiency.

In the recent past, greater accessibility to powerful computational resources
has enabled progress in the field of Deep Learning and Computer Vision to grow
by leaps and bounds. This in consequence has lent progress to the domain of
Autonomous Driving and Navigation Systems. Most of the present research work
has been focused on driving scenarios in the European or American roads. Our
paper draws special attention to the Indian driving context. To this effect, we
propose a novel architecture, DR-Gaze, which is used to map the driver's gaze
onto the road. We compare our results with previous works and state-of-the-art
results on the DGAZE dataset. Our code will be made publicly available upon
acceptance of our paper.

Machine learning models have been successfully employed in the diagnosis of
Schizophrenia disease. The impact of classification models and the feature
selection techniques on the diagnosis of Schizophrenia have not been evaluated.
Here, we sought to access the performance of classification models along with
different feature selection approaches on the structural magnetic resonance
imaging data. The data consist of 72 subjects with Schizophrenia and 74 healthy
control subjects. We evaluated different classification algorithms based on
support vector machine (SVM), random forest, kernel ridge regression and
randomized neural networks. Moreover, we evaluated T-Test, Receiver Operator
Characteristics (ROC), Wilcoxon, entropy, Bhattacharyya, Minimum Redundancy
Maximum Relevance (MRMR) and Neighbourhood Component Analysis (NCA) as the
feature selection techniques. Based on the evaluation, SVM based models with
Gaussian kernel proved better compared to other classification models and
Wilcoxon feature selection emerged as the best feature selection approach.
Moreover, in terms of data modality the performance on integration of the grey
matter and white matter proved better compared to the performance on the grey
and white matter individually. Our evaluation showed that classification
algorithms along with the feature selection approaches impact the diagnosis of
Schizophrenia disease. This indicates that proper selection of the features and
the classification models can improve the diagnosis of Schizophrenia.

Content based providers transmits real time complex signal such as video data
from one region to another. During this transmission process, the signals
usually end up distorted or degraded where the actual information present in
the video is lost. This normally happens in the streaming video services
applications. Hence there is a need to know the level of degradation that
happened in the receiver side. This video degradation can be estimated by
network state parameters like data rate and packet loss values. Our proposed
solution vQoS GAN (video Quality of Service Generative Adversarial Network) can
estimate the network state parameters from the degraded received video data
using a deep learning approach of semi supervised generative adversarial
network algorithm. A robust and unique design of deep learning network model
has been trained with the video data along with data rate and packet loss class
labels and achieves over 95 percent of training accuracy. The proposed semi
supervised generative adversarial network can additionally reconstruct the
degraded video data to its original form for a better end user experience.

The so-called Mild Cognitive Impairment (MCI) or cognitive loss appears in a
previous stage before Alzheimer's Disease (AD), but it does not seem
sufficiently severe to interfere in independent abilities of daily life, so it
usually does not receive an appropriate diagnosis. Its detection is a
challenging issue to be addressed by medical specialists. This work presents a
novel proposal based on automatic analysis of speech and disfluencies aimed at
supporting MCI diagnosis. The approach includes deep learning by means of
Convolutional Neural Networks (CNN) and non-linear multifeature modelling.
Moreover, to select the most relevant features non-parametric Mann-Whitney
U-testt and Support Vector Machine Attribute (SVM) evaluation are used.

In this article we present an unsupervised low-frequency method aimed at
detecting and disaggregating the power used by Cumulative Water Heaters (CWH)
in residential homes. Our model circumvents the inherent difficulty of
unsupervised signal disaggregation by using both the shape of a power spike and
its time of occurrence to identify the contribution of CWH reliably. Indeed,
many CHWs in France are configured to turn on automatically during off-peak
hours only, and we are able to use this domain knowledge to aid peak
identification despite the low sampling frequency. In order to test our model,
we equipped a home with sensors to record the ground-truth consumption of a
water heater. We then apply the model to a larger dataset of energy consumption
of Hello Watt users consisting of one month of consumption data for 5k homes at
30-minute resolution. In this dataset we successfully identified CWHs in the
majority of cases where consumers declared using them. The remaining part is
likely due to possible misconfiguration of CWHs, since triggering them during
off-peak hours requires specific wiring in the electrical panel of the house.
Our model, despite its simplicity, offers promising applications: detection of
mis-configured CWHs on off-peak contracts and slow performance degradation.

Reliable wireless communication between the autonomously driving cars is one
of the fundamental needs for guaranteeing passenger safety and comfort.
However, when the number of communicating cars increases, the transmission
quality may be significantly degraded due to too high occupancy radio of the
used frequency band. In this paper, we concentrate on the autonomous
vehicle-platooning use-case, where intra-platoon communication is done in the
dynamically selected frequency band, other than nominally devoted for such
purposes. The carrier selection is done in a flexible manner with the support
of the context database located at the roadside unit (edge of wireless
communication infrastructure). However, as the database delivers only context
information to the platoons' leaders, the final decision is made separately by
the individual platoons, following the suggestions made by the artificial
intelligence algorithms. In this work, we concentrate on a lightweight
Q-learning solution, that could be successfully implemented in each car for
dynamic channel selection.

The goal of face recognition (FR) can be viewed as a pair similarity
optimization problem, maximizing a similarity set $\mathcal{S}^p$ over positive
pairs, while minimizing similarity set $\mathcal{S}^n$ over negative pairs.
Ideally, it is expected that FR models form a well-discriminative feature space
(WDFS) that satisfies $\inf{\mathcal{S}^p} > \sup{\mathcal{S}^n}$. With regard
to WDFS, the existing deep feature learning paradigms (i.e., metric and
classification losses) can be expressed as a unified perspective on different
pair generation (PG) strategies. Unfortunately, in the metric loss (ML), it is
infeasible to generate negative pairs taking all classes into account in each
iteration because of the limited mini-batch size. In contrast, in
classification loss (CL), it is difficult to generate extremely hard negative
pairs owing to the convergence of the class weight vectors to their center.
This leads to a mismatch between the two similarity distributions of the
sampled pairs and all negative pairs. Thus, this paper proposes a unified
negative pair generation (UNPG) by combining two PG strategies (i.e., MLPG and
CLPG) from a unified perspective to alleviate the mismatch. UNPG introduces
useful information about negative pairs using MLPG to overcome the CLPG
deficiency. Moreover, it includes filtering the similarities of noisy negative
pairs to guarantee reliable convergence and improved performance. Exhaustive
experiments show the superiority of UNPG by achieving state-of-the-art
performance across recent loss functions on public benchmark datasets. Our code
and pretrained models are publicly available.

Pre-training has been adopted in a few of recent works for
Vision-and-Language Navigation (VLN). However, previous pre-training methods
for VLN either lack the ability to predict future actions or ignore the
trajectory contexts, which are essential for a greedy navigation process. In
this work, to promote the learning of spatio-temporal visual-textual
correspondence as well as the agent's capability of decision making, we propose
a novel history-and-order aware pre-training paradigm (HOP) with VLN-specific
objectives that exploit the past observations and support future action
prediction. Specifically, in addition to the commonly used Masked Language
Modeling (MLM) and Trajectory-Instruction Matching (TIM), we design two proxy
tasks to model temporal order information: Trajectory Order Modeling (TOM) and
Group Order Modeling (GOM). Moreover, our navigation action prediction is also
enhanced by introducing the task of Action Prediction with History (APH), which
takes into account the history visual perceptions. Extensive experimental
results on four downstream VLN tasks (R2R, REVERIE, NDH, RxR) demonstrate the
effectiveness of our proposed method compared against several state-of-the-art
agents.

This paper investigates the problem of temporally interpolating dynamic 3D
point clouds with large non-rigid deformation. We formulate the problem as
estimation of point-wise trajectories (i.e., smooth curves) and further reason
that temporal irregularity and under-sampling are two major challenges. To
tackle the challenges, we propose IDEA-Net, an end-to-end deep learning
framework, which disentangles the problem under the assistance of the
explicitly learned temporal consistency. Specifically, we propose a temporal
consistency learning module to align two consecutive point cloud frames
point-wisely, based on which we can employ linear interpolation to obtain
coarse trajectories/in-between frames. To compensate the high-order nonlinear
components of trajectories, we apply aligned feature embeddings that encode
local geometry properties to regress point-wise increments, which are combined
with the coarse estimations. We demonstrate the effectiveness of our method on
various point cloud sequences and observe large improvement over
state-of-the-art methods both quantitatively and visually. Our framework can
bring benefits to 3D motion data acquisition. The source code is publicly
available at https://github.com/ZENGYIMING-EAMON/IDEA-Net.git.

Context modeling plays a significant role in building multi-turn dialogue
systems. In order to make full use of context information, systems can use
Incomplete Utterance Rewriting(IUR) methods to simplify the multi-turn dialogue
into single-turn by merging current utterance and context information into a
self-contained utterance. However, previous approaches ignore the intent
consistency between the original query and rewritten query. The detection of
omitted or coreferred locations in the original query can be further improved.
In this paper, we introduce contrastive learning and multi-task learning to
jointly model the problem. Our method benefits from carefully designed
self-supervised objectives, which act as auxiliary tasks to capture semantics
at both sentence-level and token-level. The experiments show that our proposed
model achieves state-of-the-art performance on several public datasets.

We propose a distributed Quantum State Tomography (QST) protocol, named Local
Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor
of a density matrix over a set of local machines. QST is the canonical
procedure to characterize the state of a quantum system, which we formulate as
a stochastic nonconvex smooth optimization problem. Physically, the estimation
of a low-rank density matrix helps characterizing the amount of noise
introduced by quantum computation. Theoretically, we prove the local
convergence of Local SFGD for a general class of restricted strongly
convex/smooth loss functions, i.e., Local SFGD converges locally to a small
neighborhood of the global optimum at a linear rate with a constant step size,
while it locally converges exactly at a sub-linear rate with diminishing step
sizes. With a proper initialization, local convergence results imply global
convergence. We validate our theoretical findings with numerical simulations of
QST on the Greenberger-Horne-Zeilinger (GHZ) state.

This paper introduces a new penalized synthetic control method for policy
evaluation. The proposed sparse synthetic control penalizes the number of
predictors used in generating the counterfactual to improve pre-treatment fit
and select the most important predictors. To motivate the method theoretically
I derive, in a linear factor model framework, a model selection consistency
result and a mean squared error convergence rate result. Through a simulation
study, I then show that the sparse synthetic control achieves lower bias and
has better post-treatment fit than the unpenalized synthetic control. Finally,
I apply the method to study the effects of the passage of Proposition 99 in
California in a setting with a large number of predictors.

In this work, the application of the multi-dimensional higher order dynamic
mode decomposition (HODMD) is proposed for the first time to analyse combustion
databases. In particular, HODMD has been adapted and combined with other
pre-processing techniques (generally used in machine learning), in light of the
multivariate nature of the data. A truncation step separate the main dynamics
driving the flow from less relevant non-linear dynamics. The method is applied
to analyse a database obtained from a Computational Fluid Dynamics (CFD)
simulation of an axisymmetric, time varying, non-premixed, co-flow methane
flame carried out by means of a detailed kinetic mechanism. Results show that
HODMD can reconstruct the main jet dynamics with a reduced number of relevant
modes, able to reproduce the system dynamics. These modes are found to be
representative for the main flow physics with two main advantages: (i) they
provide for the possibility to achieve a strong simplification with respect to
the high-dimensional input data, and at the same time (ii) a small
reconstruction error with respect to the original dataset is observed. In
addition, the method was also validated considering a reduced matrix obtained
using Principal Component Analysis (PCA) based feature selection and the
Varimax rotation. This validation also reveals that it is not important to have
all the variables in the dataset, just a group of them is necessary to obtain
the main dynamics of the system. This has an impact on feature selection and on
the cost these methodologies for very massive data.

Sequential audio event tagging can provide not only the type information of
audio events, but also the order information between events and the number of
events that occur in an audio clip. Most previous works on audio event sequence
analysis rely on connectionist temporal classification (CTC). However, CTC's
conditional independence assumption prevents it from effectively learning
correlations between diverse audio events. This paper first attempts to
introduce Transformer into sequential audio tagging, since Transformers perform
well in sequence-related tasks. To better utilize contextual information of
audio event sequences, we draw on the idea of bidirectional recurrent neural
networks, and propose a contextual Transformer (cTransformer) with a
bidirectional decoder that could exploit the forward and backward information
of event sequences. Experiments on the real-life polyphonic audio dataset show
that, compared to CTC-based methods, the cTransformer can effectively combine
the fine-grained acoustic representations from the encoder and coarse-grained
audio event cues to exploit contextual information to successfully recognize
and predict audio event sequences.

Despite significant progress, there remain three limitations to the previous
multi-view clustering algorithms. First, they often suffer from high
computational complexity, restricting their feasibility for large-scale
datasets. Second, they typically fuse multi-view information via one-stage
fusion, neglecting the possibilities in multi-stage fusions. Third,
dataset-specific hyperparameter-tuning is frequently required, further
undermining their practicability. In light of this, we propose a fast
multi-view clustering via ensembles (FastMICE) approach. Particularly, the
concept of random view groups is presented to capture the versatile view-wise
relationships, through which the hybrid early-late fusion strategy is designed
to enable efficient multi-stage fusions. With multiple views extended to many
view groups, three levels of diversity (w.r.t. features, anchors, and
neighbors, respectively) are jointly leveraged for constructing the
view-sharing bipartite graphs in the early-stage fusion. Then, a set of
diversified base clusterings for different view groups are obtained via fast
graph partitioning, which are further formulated into a unified bipartite graph
for final clustering in the late-stage fusion. Remarkably, FastMICE has almost
linear time and space complexity, and is free of dataset-specific tuning.
Experiments on twenty multi-view datasets demonstrate its advantages in
scalability (for extremely large datasets), superiority (in clustering
performance), and simplicity (to be applied) over the state-of-the-art.

In this work, we propose a novel data augmentation method for clinical audio
datasets based on a conditional Wasserstein Generative Adversarial Network with
Gradient Penalty (cWGAN-GP), operating on log-mel spectrograms. To validate our
method, we created a clinical audio dataset which was recorded in a real-world
operating room during Total Hip Arthroplasty (THA) procedures and contains
typical sounds which resemble the different phases of the intervention. We
demonstrate the capability of the proposed method to generate realistic
class-conditioned samples from the dataset distribution and show that training
with the generated augmented samples outperforms classical audio augmentation
methods in terms of classification performance. The performance was evaluated
using a ResNet-18 classifier which shows a mean Macro F1-score improvement of
1.70% in a 5-fold cross validation experiment using the proposed augmentation
method. Because clinical data is often expensive to acquire, the development of
realistic and high-quality data augmentation methods is crucial to improve the
robustness and generalization capabilities of learning-based algorithms which
is especially important for safety-critical medical applications. Therefore,
the proposed data augmentation method is an important step towards improving
the data bottleneck for clinical audio-based machine learning systems.

The recently proposed sparsifying transform models incur low computational
cost and have been applied to medical imaging. Meanwhile, deep models with
nested network structure reveal great potential for learning features in
different layers. In this study, we propose a network-structured sparsifying
transform learning approach for X-ray computed tomography (CT), which we refer
to as multi-layer clustering-based residual sparsifying transform (MCST)
learning. The proposed MCST scheme learns multiple different unitary transforms
in each layer by dividing each layer's input into several classes. We apply the
MCST model to low-dose CT (LDCT) reconstruction by deploying the learned MCST
model into the regularizer in penalized weighted least squares (PWLS)
reconstruction. We conducted LDCT reconstruction experiments on XCAT phantom
data and Mayo Clinic data and trained the MCST model with 2 (or 3) layers and
with 5 clusters in each layer. The learned transforms in the same layer showed
rich features while additional information is extracted from representation
residuals. Our simulation results demonstrate that PWLS-MCST achieves better
image reconstruction quality than the conventional FBP method and PWLS with
edge-preserving (EP) regularizer. It also outperformed recent advanced methods
like PWLS with a learned multi-layer residual sparsifying transform prior
(MARS) and PWLS with a union of learned transforms (ULTRA), especially for
displaying clear edges and preserving subtle details.

In this paper, we introduce a novel interactive satellite image change
detection algorithm based on active learning. The proposed approach is
iterative and asks the user (oracle) questions about the targeted changes and
according to the oracle's responses updates change detections. We consider a
probabilistic framework which assigns to each unlabeled sample a relevance
measure modeling how critical is that sample when training change detection
functions. These relevance measures are obtained by minimizing an objective
function mixing diversity, representativity and uncertainty. These criteria
when combined allow exploring different data modes and also refining change
detections. To further explore the potential of this objective function, we
consider a reinforcement learning approach that finds the best combination of
diversity, representativity and uncertainty, through active learning
iterations, leading to better generalization as corroborated through
experiments in interactive satellite image change detection.

Speech synthesis has come a long way as current text-to-speech (TTS) models
can now generate natural human-sounding speech. However, most of the TTS
research focuses on using adult speech data and there has been very limited
work done on child speech synthesis. This study developed and validated a
training pipeline for fine-tuning state-of-the-art (SOTA) neural TTS models
using child speech datasets. This approach adopts a multi-speaker TTS retuning
workflow to provide a transfer-learning pipeline. A publicly available child
speech dataset was cleaned to provide a smaller subset of approximately 19
hours, which formed the basis of our fine-tuning experiments. Both subjective
and objective evaluations were performed using a pretrained MOSNet for
objective evaluation and a novel subjective framework for mean opinion score
(MOS) evaluations. Subjective evaluations achieved the MOS of 3.95 for speech
intelligibility, 3.89 for voice naturalness, and 3.96 for voice consistency.
Objective evaluation using a pretrained MOSNet showed a strong correlation
between real and synthetic child voices. Speaker similarity was also verified
by calculating the cosine similarity between the embeddings of utterances. An
automatic speech recognition (ASR) model is also used to provide a word error
rate (WER) comparison between the real and synthetic child voices. The final
trained TTS model was able to synthesize child-like speech from reference audio
samples as short as 5 seconds.

Humans can learn several tasks in succession with minimal mutual interference
but perform more poorly when trained on multiple tasks at once. The opposite is
true for standard deep neural networks. Here, we propose novel computational
constraints for artificial neural networks, inspired by earlier work on gating
in the primate prefrontal cortex, that capture the cost of interleaved training
and allow the network to learn two tasks in sequence without forgetting. We
augment standard stochastic gradient descent with two algorithmic motifs,
so-called "sluggish" task units and a Hebbian training step that strengthens
connections between task units and hidden units that encode task-relevant
information. We found that the "sluggish" units introduce a switch-cost during
training, which biases representations under interleaved training towards a
joint representation that ignores the contextual cue, while the Hebbian step
promotes the formation of a gating scheme from task units to the hidden layer
that produces orthogonal representations which are perfectly guarded against
interference. Validating the model on previously published human behavioural
data revealed that it matches performance of participants who had been trained
on blocked or interleaved curricula, and that these performance differences
were driven by misestimation of the true category boundary.

In this paper, we devise a novel interactive satellite image change detection
algorithm based on active learning. The proposed framework is iterative and
relies on a question and answer model which asks the oracle (user) questions
about the most informative display (subset of critical images), and according
to the user's responses, updates change detections. The contribution of our
framework resides in a novel display model which selects the most
representative and diverse virtual exemplars that adversely challenge the
learned change detection functions, thereby leading to highly discriminating
functions in the subsequent iterations of active learning. Extensive
experiments, conducted on the challenging task of interactive satellite image
change detection, show the superiority of the proposed virtual display model
against the related work.

Normalizing flows provide an elegant approach to generative modeling that
allows for efficient sampling and exact density evaluation of unknown data
distributions. However, current techniques have significant limitations in
their expressivity when the data distribution is supported on a low-dimensional
manifold or has a non-trivial topology. We introduce a novel statistical
framework for learning a mixture of local normalizing flows as "chart maps"
over the data manifold. Our framework augments the expressivity of recent
approaches while preserving the signature property of normalizing flows, that
they admit exact density evaluation. We learn a suitable atlas of charts for
the data manifold via a vector quantized auto-encoder (VQ-AE) and the
distributions over them using a conditional flow. We validate experimentally
that our probabilistic framework enables existing approaches to better model
data distributions over complex manifolds.

Sparse inversion and classification problems are ubiquitous in modern data
science and imaging. They are often formulated as non-smooth minimisation
problems. In sparse inversion, we minimise, e.g., the sum of a data fidelity
term and an L1/LASSO regulariser. In classification, we consider, e.g., the sum
of a data fidelity term and a non-smooth Ginzburg--Landau energy. Standard
(sub)gradient descent methods have shown to be inefficient when approaching
such problems. Splitting techniques are much more useful: here, the target
function is partitioned into a sum of two subtarget functions -- each of which
can be efficiently optimised. Splitting proceeds by performing optimisation
steps alternately with respect to each of the two subtarget functions.
  In this work, we study splitting from a stochastic continuous-time
perspective. Indeed, we define a differential inclusion that follows one of the
two subtarget function's negative subdifferential at each point in time. The
choice of the subtarget function is controlled by a binary continuous-time
Markov process. The resulting dynamical system is a stochastic approximation of
the underlying subgradient flow. We investigate this stochastic approximation
for an L1-regularised sparse inversion flow and for a discrete Allen-Cahn
equation minimising a Ginzburg--Landau energy. In both cases, we study the
longtime behaviour of the stochastic dynamical system and its ability to
approximate the underlying subgradient flow at any accuracy. We illustrate our
theoretical findings in a simple sparse estimation problem and also in low- and
high-dimensional classification problems.

Pretrained language models can be queried for factual knowledge, with
potential applications in knowledge base acquisition and tasks that require
inference. However, for that, we need to know how reliable this knowledge is,
and recent work has shown that monolingual English language models lack
consistency when predicting factual knowledge, that is, they fill-in-the-blank
differently for paraphrases describing the same fact. In this paper, we extend
the analysis of consistency to a multilingual setting. We introduce a resource,
mParaRel, and investigate (i) whether multilingual language models such as
mBERT and XLM-R are more consistent than their monolingual counterparts; and
(ii) if such models are equally consistent across languages. We find that mBERT
is as inconsistent as English BERT in English paraphrases, but that both mBERT
and XLM-R exhibit a high degree of inconsistency in English and even more so
for all the other 45 languages.

Artificial intelligence (AI) has been embedded into many aspects of people's
daily lives and it has become normal for people to have AI make decisions for
them. Reinforcement learning (RL) models increase the space of solvable
problems with respect to other machine learning paradigms. Some of the most
interesting applications are in situations with non-differentiable expected
reward function, operating in unknown or underdefined environment, as well as
for algorithmic discovery that surpasses performance of any teacher, whereby
agent learns from experimental experience through simple feedback. The range of
applications and their social impact is vast, just to name a few: genomics,
game-playing (chess, Go, etc.), general optimization, financial investment,
governmental policies, self-driving cars, recommendation systems, etc. It is
therefore essential to improve the trust and transparency of RL-based systems
through explanations. Most articles dealing with explainability in artificial
intelligence provide methods that concern supervised learning and there are
very few articles dealing with this in the area of RL. The reasons for this are
the credit assignment problem, delayed rewards, and the inability to assume
that data is independently and identically distributed (i.i.d.). This position
paper attempts to give a systematic overview of existing methods in the
explainable RL area and propose a novel unified taxonomy, building and
expanding on the existing ones. The position section describes pragmatic
aspects of how explainability can be observed. The gap between the parties
receiving and generating the explanation is especially emphasized. To reduce
the gap and achieve honesty and truthfulness of explanations, we set up three
pillars: proactivity, risk attitudes, and epistemological constraints. To this
end, we illustrate our proposal on simple variants of the shortest path
problem.

Slinky, a helical elastic rod, is a seemingly simple structure with unusual
mechanical behavior; for example, it can walk down a flight of stairs under its
own weight. Taking the Slinky as a test-case, we propose a physics-informed
deep learning approach for building reduced-order models of physical systems.
The approach introduces a Euclidean symmetric neural network (ESNN)
architecture that is trained under the neural ordinary differential equation
framework to learn the 2D latent dynamics from the motion trajectory of a
reduced-order representation of the 3D Slinky. The ESNN implements a
physics-guided architecture that simultaneously preserves energy invariance and
force equivariance on Euclidean transformations of the input, including
translation, rotation, and reflection. The embedded Euclidean symmetry provides
physics-guided interpretability and generalizability, while preserving the full
expressive power of the neural network. We demonstrate that the ESNN approach
is able to accelerate simulation by one to two orders of magnitude compared to
traditional numerical methods and achieve a superior generalization
performance, i.e., the neural network, trained on a single demonstration case,
predicts accurately on unseen cases with different Slinky configurations and
boundary conditions.

Object perception capabilities of humans are impressive, something that
becomes even more evident when trying to develop solutions with similar
proficiency for robots. Taking inspiration from how humans use vision and
haptics for object perception and related tasks, this article summarises the
current state of multimodal object perception for robotic applications. It
covers aspects of biological inspiration, sensor technologies, data sets, and
sensory data processing for object recognition and grasping. Firstly, the
biological basis of multimodal object perception is outlined. Then the sensing
technologies and data collection strategies are discussed. Next, an
introduction to the main computational aspects is presented, highlighting a few
representative articles for each main application area, including object
recognition, transfer learning, and object manipulation and grasping. Finally,
informed by the current advancements in each area, this article outlines
promising new research directions.

Sleep staging is essential for sleep assessment and plays a vital role as a
health indicator. Many recent studies have devised various machine learning as
well as deep learning architectures for sleep staging. However, two key
challenges hinder the practical use of these architectures: effectively
capturing salient waveforms in sleep signals and correctly classifying
confusing stages in transitioning epochs. In this study, we propose a novel
deep neural network structure, TransSleep, that captures distinctive local
temporal patterns and distinguishes confusing stages using two auxiliary tasks.
In particular, TransSleep adopts an attention-based multi-scale feature
extractor module to capture salient waveforms; a stage-confusion estimator
module with a novel auxiliary task, epoch-level stage classification, to
estimate confidence scores for identifying confusing stages; and a context
encoder module with the other novel auxiliary task, stage-transition detection,
to represent contextual relationships across neighboring epochs. Results show
that TransSleep achieves promising performance in automatic sleep staging. The
validity of TransSleep is demonstrated by its state-of-the-art performance on
two publicly available datasets, Sleep-EDF and MASS. Furthermore, we performed
ablations to analyze our results from different perspectives. Based on our
overall results, we believe that TransSleep has immense potential to provide
new insights into deep learning-based sleep staging.

The COVID-19 pandemic has disrupted various levels of society. The use of
masks is essential in preventing the spread of COVID-19 by identifying an image
of a person using a mask. Although only 23.1% of people use masks correctly,
Artificial Neural Networks (ANN) can help classify the use of good masks to
help slow the spread of the Covid-19 virus. However, it requires a large
dataset to train an ANN that can classify the use of masks correctly.
MaskedFace-Net is a suitable dataset consisting of 137016 digital images with 4
class labels, namely Mask, Mask Chin, Mask Mouth Chin, and Mask Nose Mouth.
Mask classification training utilizes Vision Transformers (ViT) architecture
with transfer learning method using pre-trained weights on ImageNet-21k, with
random augmentation. In addition, the hyper-parameters of training of 20
epochs, an Stochastic Gradient Descent (SGD) optimizer with a learning rate of
0.03, a batch size of 64, a Gaussian Cumulative Distribution (GeLU) activation
function, and a Cross-Entropy loss function are used to be applied on the
training of three architectures of ViT, namely Base-16, Large-16, and Huge-14.
Furthermore, comparisons of with and without augmentation and transfer learning
are conducted. This study found that the best classification is transfer
learning and augmentation using ViT Huge-14. Using this method on
MaskedFace-Net dataset, the research reaches an accuracy of 0.9601 on training
data, 0.9412 on validation data, and 0.9534 on test data. This research shows
that training the ViT model with data augmentation and transfer learning
improves classification of the mask usage, even better than convolutional-based
Residual Network (ResNet).

Multi-pod systolic arrays are emerging as the architecture of choice in DNN
inference accelerators. Despite their potential, designing multi-pod systolic
arrays to maximize effective throughput/Watt (i.e., throughput/Watt adjusted
when accounting for array utilization) poses a unique set of challenges. In
this work, we study three key pillars in multi-pod systolic array designs,
namely array granularity, interconnect, and tiling. We identify optimal array
granularity across workloads and show that state-of-the-art commercial
accelerators use suboptimal array sizes for single-tenancy workloads. We, then
evaluate the bandwidth/latency trade-offs in interconnects and show that
Butterfly networks offer a scalable topology for accelerators with a large
number of pods. Finally, we introduce a novel data tiling scheme with custom
partition size to maximize utilization in optimally sized pods. We propose
Scale-out Systolic Arrays, a multi-pod inference accelerator for both single-
and multi-tenancy based on these three pillars. We show that SOSA exhibits
scaling of up to 600 TeraOps/s in effective throughput for state-of-the-art DNN
inference workloads, and outperforms state-of-the-art multi-pod accelerators by
a factor of 1.5x.

Dense point cloud generation from a sparse or incomplete point cloud is a
crucial and challenging problem in 3D computer vision and computer graphics. So
far, the existing methods are either computationally too expensive, suffer from
limited resolution, or both. In addition, some methods are strictly limited to
watertight surfaces -- another major obstacle for a number of applications. To
address these issues, we propose a lightweight Convolutional Neural Network
that learns and predicts the unsigned distance field for arbitrary 3D shapes
for dense point cloud generation using the recently emerged concept of implicit
function learning. Experiments demonstrate that the proposed architecture
outperforms the state of the art by 87% less model parameters, 36% reduced
inference time and improved generated point cloud accuracy.

In real-world applications, it is important and desirable to learn a model
that performs well on out-of-distribution (OOD) data. Recently, causality has
become a powerful tool to tackle the OOD generalization problem, with the idea
resting on the causal mechanism that is invariant across domains of interest.
To leverage the generally unknown causal mechanism, existing works assume a
linear form of causal feature or require sufficiently many and diverse training
domains, which are usually restrictive in practice. In this work, we obviate
these assumptions and tackle the OOD problem without explicitly recovering the
causal feature. Our approach is based on transformations that modify the
non-causal feature but leave the causal part unchanged, which can be either
obtained from prior knowledge or learned from the training data in the
multi-domain scenario. Under the setting of invariant causal mechanism, we
theoretically show that if all such transformations are available, then we can
learn a minimax optimal model across the domains using only single domain data.
Noticing that knowing a complete set of these causal invariant transformations
may be impractical, we further show that it suffices to know only a subset of
these transformations. Based on the theoretical findings, a regularized
training procedure is proposed to improve the OOD generalization capability.
Extensive experimental results on both synthetic and real datasets verify the
effectiveness of the proposed algorithm, even with only a few causal invariant

Double Q-learning is a popular reinforcement learning algorithm in Markov
decision process (MDP) problems. Clipped Double Q-learning, as an effective
variant of Double Q-learning, employs the clipped double estimator to
approximate the maximum expected action value. Due to the underestimation bias
of the clipped double estimator, the performance of clipped Double Q-learning
may be degraded in some stochastic environments. In this paper, in order to
reduce the underestimation bias, we propose an action candidate-based clipped
double estimator for Double Q-learning. Specifically, we first select a set of
elite action candidates with high action values from one set of estimators.
Then, among these candidates, we choose the highest valued action from the
other set of estimators. Finally, we use the maximum value in the second set of
estimators to clip the action value of the chosen action in the first set of
estimators and the clipped value is used for approximating the maximum expected
action value. Theoretically, the underestimation bias in our clipped Double
Q-learning decays monotonically as the number of action candidates decreases.
Moreover, the number of action candidates controls the trade-off between the
overestimation and underestimation biases. In addition, we also extend our
clipped Double Q-learning to continuous action tasks via approximating the
elite continuous action candidates. We empirically verify that our algorithm
can more accurately estimate the maximum expected action value on some toy
environments and yield good performance on several benchmark problems.

The Community Engagement Frontier presents this set of three white papers, as
part of Snowmass 2021. These papers address critical issues -- Power Dynamics
in Physics, Informal Socialization in Physics Training, and Policing and
Gatekeeping in STEM -- that make significant impacts on the experiences of the
people who work in and learn particle physics. In this introductory document,
we present crosscutting concepts that appear in each paper, and some advice on
how to manage readers' responses to the contents. We expect that you will learn
something new here. We hope that whatever you encounter, you will be energized
to increase justice in this discipline we all love.

We present a clustering method and provide a theoretical analysis and an
explanation to a phenomenon encountered in the applied statistical literature
since the 1990's. This phenomenon is the natural adaptability of the order when
using a clustering method derived from the famous EM algorithm. We define a new
statistic, the relative entropic order, that represents the number of clumps in
the target distribution. We prove in particular that the empirical version of
this relative entropic order is consistent. Our approach is easy to implement
and has a high potential of applications. Perspectives of this works are
algorithmic and theoretical, with possible natural extensions to various cases
such as dependent or multidimensional data.

Image deraining is a typical low-level image restoration task, which aims at
decomposing the rainy image into two distinguishable layers: the clean image
layer and the rain layer. Most of the existing learning-based deraining methods
are supervisedly trained on synthetic rainy-clean pairs. The domain gap between
the synthetic and real rains makes them less generalized to different real
rainy scenes. Moreover, the existing methods mainly utilize the property of the
two layers independently, while few of them have considered the mutually
exclusive relationship between the two layers. In this work, we propose a novel
non-local contrastive learning (NLCL) method for unsupervised image deraining.
Consequently, we not only utilize the intrinsic self-similarity property within
samples but also the mutually exclusive property between the two layers, so as
to better differ the rain layer from the clean image. Specifically, the
non-local self-similarity image layer patches as the positives are pulled
together and similar rain layer patches as the negatives are pushed away. Thus
the similar positive/negative samples that are close in the original space
benefit us to enrich more discriminative representation. Apart from the
self-similarity sampling strategy, we analyze how to choose an appropriate
feature encoder in NLCL. Extensive experiments on different real rainy datasets
demonstrate that the proposed method obtains state-of-the-art performance in
real deraining.

Deep neural networks perform poorly on heavily class-imbalanced datasets.
Given the promising performance of contrastive learning, we propose Rebalanced
Siamese Contrastive Mining (ResCom) to tackle imbalanced recognition. Based on
the mathematical analysis and simulation results, we claim that supervised
contrastive learning suffers a dual class-imbalance problem at both the
original batch and Siamese batch levels, which is more serious than long-tailed
classification learning. In this paper, at the original batch level, we
introduce a class-balanced supervised contrastive loss to assign adaptive
weights for different classes. At the Siamese batch level, we present a
class-balanced queue, which maintains the same number of keys for all classes.
Furthermore, we note that the imbalanced contrastive loss gradient with respect
to the contrastive logits can be decoupled into the positives and negatives,
and easy positives and easy negatives will make the contrastive gradient
vanish. We propose supervised hard positive and negative pairs mining to pick
up informative pairs for contrastive computation and improve representation
learning. Finally, to approximately maximize the mutual information between the
two views, we propose Siamese Balanced Softmax and joint it with the
contrastive loss for one-stage training. Extensive experiments demonstrate that
ResCom outperforms the previous methods by large margins on multiple
long-tailed recognition benchmarks. Our code and models are made publicly
available at: https://github.com/dvlab-research/ResCom.

Speech enhancement (SE) methods mainly focus on recovering clean speech from
noisy input. In real-world speech communication, however, noises often exist in
not only speaker but also listener environments. Although SE methods can
suppress the noise contained in the speaker's voice, they cannot deal with the
noise that is physically present in the listener side. To address such a
complicated but common scenario, we investigate a deep learning-based joint
framework integrating noise reduction (NR) with listening enhancement (LE), in
which the NR module first suppresses noise and the LE module then modifies the
denoised speech, i.e., the output of the NR module, to further improve speech
intelligibility. The enhanced speech can thus be less noisy and more
intelligible for listeners. Experimental results show that our proposed method
achieves promising results and significantly outperforms the disjoint
processing methods in terms of various speech evaluation metrics.

This paper proposes an approach to improve Non-Intrusive speech quality
assessment(NI-SQA) based on the residuals between impaired speech and enhanced
speech. The difficulty in our task is particularly lack of information, for
which the corresponding reference speech is absent. We generate an enhanced
speech on the impaired speech to compensate for the absence of the reference
audio, then pair the information of residuals with the impaired speech.
Compared to feeding the impaired speech directly into the model, residuals
could bring some extra helpful information from the contrast in enhancement.
The human ear is sensitive to certain noises but different to deep learning
model. Causing the Mean Opinion Score(MOS) the model predicted is not enough to
fit our subjective sensitive well and causes deviation. These residuals have a
close relationship to reference speech and then improve the ability of the deep
learning models to predict MOS. During the training phase, experimental results
demonstrate that paired with residuals can quickly obtain better evaluation
indicators under the same conditions. Furthermore, our final results improved
31.3 percent and 14.1 percent, respectively, in PLCC and RMSE.

Detection-driven real-time video analytics require continuous detection of
objects contained in the video frames using deep learning models like YOLOV3,
EfficientDet. However, running these detectors on each and every frame in
resource-constrained edge devices is computationally intensive. By taking the
temporal correlation between consecutive video frames into account, we note
that detection outputs tend to be overlapping in successive frames. Elimination
of similar consecutive frames will lead to a negligible drop in performance
while offering significant performance benefits by reducing overall computation
and communication costs. The key technical questions are, therefore, (a) how to
identify which frames to be processed by the object detector, and (b) how many
successive frames can be skipped (called skip-length) once a frame is selected
to be processed. The overall goal of the process is to keep the error due to
skipping frames as small as possible. We introduce a novel error vs processing
rate optimization problem with respect to the object detection task that
balances between the error rate and the fraction of frames filtering.
Subsequently, we propose an off-line Reinforcement Learning (RL)-based
algorithm to determine these skip-lengths as a state-action policy of the RL
agent from a recorded video and then deploy the agent online for live video
streams. To this end, we develop FrameHopper, an edge-cloud collaborative video
analytics framework, that runs a lightweight trained RL agent on the camera and
passes filtered frames to the server where the object detection model runs for
a set of applications. We have tested our approach on a number of live videos
captured from real-life scenarios and show that FrameHopper processes only a
handful of frames but produces detection results closer to the oracle solution
and outperforms recent state-of-the-art solutions in most cases.

Recent studies show that Graph Neural Networks (GNNs) are vulnerable to
adversarial attack, i.e., an imperceptible structure perturbation can fool GNNs
to make wrong predictions. Some researches explore specific properties of clean
graphs such as the feature smoothness to defense the attack, but the analysis
of it has not been well-studied. In this paper, we analyze the adversarial
attack on graphs from the perspective of feature smoothness which further
contributes to an efficient new adversarial defensive algorithm for GNNs. We
discover that the effect of the high-order graph structure is a smoother filter
for processing graph structures. Intuitively, the high-order graph structure
denotes the path number between nodes, where larger number indicates closer
connection, so it naturally contributes to defense the adversarial
perturbation. Further, we propose a novel algorithm that incorporates the
high-order structural information into the graph structure learning. We perform
experiments on three popular benchmark datasets, Cora, Citeseer and Polblogs.
Extensive experiments demonstrate the effectiveness of our method for defending
against graph adversarial attacks.

Privacy laws and regulations enforce data-driven systems, e.g., recommender
systems, to erase the data that concern individuals. As machine learning models
potentially memorize the training data, data erasure should also unlearn the
data lineage in models, which raises increasing interest in the problem of
Machine Unlearning (MU). However, existing MU methods cannot be directly
applied into recommendation. The basic idea of most recommender systems is
collaborative filtering, but existing MU methods ignore the collaborative
information across users and items. In this paper, we propose a general
erasable recommendation framework, namely LASER, which consists of Group module
and SeqTrain module. Firstly, Group module partitions users into balanced
groups based on their similarity of collaborative embedding learned via
hypergraph. Then SeqTrain module trains the model sequentially on all groups
with curriculum learning. Both theoretical analysis and experiments on two
real-world datasets demonstrate that LASER can not only achieve efficient
unlearning, but also outperform the state-of-the-art unlearning framework in
terms of model utility.

Q-learning with function approximation could diverge in the off-policy
setting and the target network is a powerful technique to address this issue.
In this manuscript, we examine the sample complexity of the associated target
Q-learning algorithm in the tabular case with a generative oracle. We point out
a misleading claim in [Lee and He, 2020] and establish a tight analysis. In
particular, we demonstrate that the sample complexity of the target Q-learning
algorithm in [Lee and He, 2020] is $\widetilde{\mathcal O}(|\mathcal
S|^2|\mathcal A|^2 (1-\gamma)^{-5}\varepsilon^{-2})$. Furthermore, we show that
this sample complexity is improved to $\widetilde{\mathcal O}(|\mathcal
S||\mathcal A| (1-\gamma)^{-5}\varepsilon^{-2})$ if we can sequentially update
all state-action pairs and $\widetilde{\mathcal O}(|\mathcal S||\mathcal A|
(1-\gamma)^{-4}\varepsilon^{-2})$ if $\gamma$ is further in $(1/2, 1)$.
Compared with the vanilla Q-learning, our results conclude that the
introduction of a periodically-frozen target Q-function does not sacrifice the
sample complexity.

Imbalanced datasets can lead to biasedness into the detection of fake news.
In this work, we present several strategies for resolving the imbalance issue
for fake news detection in Bangla with a comparative assessment of proposed
methodologies. Additionally, we propose a technique for improving performance
even when the dataset is imbalanced. We applied our proposed approaches to
BanFakeNews, a dataset developed for the purpose of detecting fake news in
Bangla comprising of 50K instances but is significantly skewed, with 97% of
majority instances. We obtained a 93.1% F1-score using data manipulation
manipulation techniques such as SMOTE, and a 79.1% F1-score using without data
manipulation approaches such as Stacked Generalization. Without implementing
these techniques, the F1-score would have been 67.6% for baseline models. We
see this work as an important step towards paving the way of fake news
detection in Bangla. By implementing these strategies the obstacles of
imbalanced dataset can be removed and improvement in the performance can be
achieved.

We introduce AdaMix, an adaptive differentially private algorithm for
training deep neural network classifiers using both private and public image
data. While pre-training language models on large public datasets has enabled
strong differential privacy (DP) guarantees with minor loss of accuracy, a
similar practice yields punishing trade-offs in vision tasks. A few-shot or
even zero-shot learning baseline that ignores private data can outperform
fine-tuning on a large private dataset. AdaMix incorporates few-shot training,
or cross-modal zero-shot learning, on public data prior to private fine-tuning,
to improve the trade-off. AdaMix reduces the error increase from the
non-private upper bound from the 167-311\% of the baseline, on average across 6
datasets, to 68-92\% depending on the desired privacy level selected by the
user. AdaMix tackles the trade-off arising in visual classification, whereby
the most privacy sensitive data, corresponding to isolated points in
representation space, are also critical for high classification accuracy. In
addition, AdaMix comes with strong theoretical privacy guarantees and
convergence analysis.

Through a mixed-method analysis of data from Scratch, we examine how novices
learn to program with simple data structures by using community-produced
learning resources. First, we present a qualitative study that describes how
community-produced learning resources create archetypes that shape exploration
and may disadvantage some with less common interests. In a second quantitative
study, we find broad support for this dynamic in several hypothesis tests. Our
findings identify a social feedback loop that we argue could limit sources of
inspiration, pose barriers to broadening participation, and confine learners'
understanding of general concepts. We conclude by suggesting several approaches
that may mitigate these dynamics.

Human speakers encode information into raw speech which is then decoded by
the listeners. This complex relationship between encoding (production) and
decoding (perception) is often modeled separately. Here, we test how encoding
and decoding of lexical semantic information can emerge automatically from raw
speech in unsupervised generative deep convolutional networks that combine the
production and perception principles of speech. We introduce, to our knowledge,
the most challenging objective in unsupervised lexical learning: a network that
must learn unique representations for lexical items with no direct access to
training data. We train several models (ciwGAN and fiwGAN arXiv:2006.02951) and
test how the networks classify acoustic lexical items in unobserved test data.
Strong evidence in favor of lexical learning and a causal relationship between
latent codes and meaningful sublexical units emerge. The architecture that
combines the production and perception principles is thus able to learn to
decode unique information from raw acoustic data without accessing real
training data directly. We propose a technique to explore lexical (holistic)
and sublexical (featural) learned representations in the classifier network.
The results bear implications for unsupervised speech technology, as well as
for unsupervised semantic modeling as language models increasingly bypass text
and operate from raw acoustics.

Federated learning (FL) has attracted growing attention via data-private
collaborative training on decentralized clients. However, most existing methods
unrealistically assume object classes of the overall framework are fixed over
time. It makes the global model suffer from significant catastrophic forgetting
on old classes in real-world scenarios, where local clients often collect new
classes continuously and have very limited storage memory to store old classes.
Moreover, new clients with unseen new classes may participate in the FL
training, further aggravating the catastrophic forgetting of the global model.
To address these challenges, we develop a novel Global-Local Forgetting
Compensation (GLFC) model, to learn a global class incremental model for
alleviating the catastrophic forgetting from both local and global
perspectives. Specifically, to address local forgetting caused by class
imbalance at the local clients, we design a class-aware gradient compensation
loss and a class-semantic relation distillation loss to balance the forgetting
of old classes and distill consistent inter-class relations across tasks. To
tackle the global forgetting brought by the non-i.i.d class imbalance across
clients, we propose a proxy server that selects the best old global model to
assist the local relation distillation. Moreover, a prototype gradient-based
communication mechanism is developed to protect privacy. Our model outperforms
state-of-the-art methods by 4.4%-15.1% in terms of average accuracy on
representative benchmark datasets.

Implementing big data storage at scale is a complex and arduous task that
requires an advanced infrastructure. With the rise of public cloud computing,
various big data management services can be readily leveraged. As a critical
part of Twitter's "Project Partly Cloudy", the cold storage data and analytics
systems are being moved to the public cloud. This paper showcases our approach
in designing a scalable big data storage and analytics management framework
using BigQuery in Google Cloud Platform while ensuring security, privacy, and
data protection. The paper also discusses the limitations on the public cloud
resources and how they can be effectively overcome when designing a big data
storage and analytics solution at scale. Although the paper discusses the
framework implementation in Google Cloud Platform, it can easily be applied to
all major cloud providers.

Transfer learning has enjoyed increasing popularity in a range of big data
applications. In the context of large-scale multiple testing, the goal is to
extract and transfer knowledge learned from related source domains to improve
the accuracy of simultaneously testing of a large number of hypotheses in the
target domain. This article develops a locally adaptive transfer learning
algorithm (LATLA) for transfer learning for multiple testing. In contrast with
existing covariate-assisted multiple testing methods that require the auxiliary
covariates to be collected alongside the primary data on the same testing
units, LATLA provides a principled and generic transfer learning framework that
is capable of incorporating multiple samples of auxiliary data from related
source domains, possibly in different dimensions/structures and from diverse
populations. Both the theoretical and numerical results show that LATLA
controls the false discovery rate and outperforms existing methods in power.
LATLA is illustrated through an application to genome-wide association studies
for the identification of disease-associated SNPs by cross-utilizing the
auxiliary data from a related linkage analysis.

The identification of drug-target binding affinity (DTA) has attracted
increasing attention in the drug discovery process due to the more specific
interpretation than binary interaction prediction. Recently, numerous deep
learning-based computational methods have been proposed to predict the binding
affinities between drugs and targets benefiting from their satisfactory
performance. However, the previous works mainly focus on encoding biological
features and chemical structures of drugs and targets, with a lack of
exploiting the essential topological information from the drug-target affinity
network. In this paper, we propose a novel hierarchical graph representation
learning model for the drug-target binding affinity prediction, namely
HGRL-DTA. The main contribution of our model is to establish a hierarchical
graph learning architecture to incorporate the intrinsic properties of
drug/target molecules and the topological affinities of drug-target pairs. In
this architecture, we adopt a message broadcasting mechanism to integrate the
hierarchical representations learned from the global-level affinity graph and
the local-level molecular graph. Besides, we design a similarity-based
embedding map to solve the cold start problem of inferring representations for
unseen drugs and targets. Comprehensive experimental results under different
scenarios indicate that HGRL-DTA significantly outperforms the state-of-the-art
models and shows better model generalization among all the scenarios.

We investigate how well CLIP understands texture in natural images described
by natural language. To this end, we analyze CLIP's ability to: (1) perform
zero-shot learning on various texture and material classification datasets; (2)
represent compositional properties of texture such as red dots or yellow
stripes on the Describable Texture in Detail(DTDD) dataset; and (3) aid
fine-grained categorization of birds in photographs described by color and
texture of their body parts.

Chemical reaction prediction, involving forward synthesis and retrosynthesis
prediction, is a fundamental problem in organic synthesis. A popular
computational paradigm formulates synthesis prediction as a
sequence-to-sequence translation problem, where the typical SMILES is adopted
for molecule representations. However, the general-purpose SMILES neglects the
characteristics of chemical reactions, where the molecular graph topology is
largely unaltered from reactants to products, resulting in the suboptimal
performance of SMILES if straightforwardly applied. In this article, we propose
the root-aligned SMILES (R-SMILES), which specifies a tightly aligned
one-to-one mapping between the product and the reactant SMILES for more
efficient synthesis prediction. Due to the strict one-to-one mapping and
reduced edit distance, the computational model is largely relieved from
learning the complex syntax and dedicated to learning the chemical knowledge
for reactions. We compare the proposed R-SMILES with various state-of-the-art
baselines and show that it significantly outperforms them all, demonstrating
the superiority of the proposed method.

In the proposed demo, we will present a new software - Linguistic Field Data
Management and Analysis System - LiFE (https://github.com/kmi-linguistics/life)
- an open-source, web-based linguistic data management and analysis application
that allows for systematic storage, management, sharing and usage of linguistic
data collected from the field. The application allows users to store lexical
items, sentences, paragraphs, audio-visual content with rich glossing /
annotation; generate interactive and print dictionaries; and also train and use
natural language processing tools and models for various purposes using this
data. Since its a web-based application, it also allows for seamless
collaboration among multiple persons and sharing the data, models, etc with
each other.
  The system uses the Python-based Flask framework and MongoDB in the backend
and HTML, CSS and Javascript at the frontend. The interface allows creation of
multiple projects that could be shared with the other users. At the backend,
the application stores the data in RDF format so as to allow its release as
Linked Data over the web using semantic web technologies - as of now it makes
use of the OntoLex-Lemon for storing the lexical data and Ligt for storing the
interlinear glossed text and then internally linking it to the other linked
lexicons and databases such as DBpedia and WordNet. Furthermore it provides
support for training the NLP systems using scikit-learn and HuggingFace
Transformers libraries as well as make use of any model trained using these
libraries - while the user interface itself provides limited options for tuning
the system, an externally-trained model could be easily incorporated within the
application; similarly the dataset itself could be easily exported into a
standard machine-readable format like JSON or CSV that could be consumed by
other programs and pipelines.

Multi-modal learning has been intensified in recent years, especially for
applications in facial analysis and action unit detection whilst there still
exist two main challenges in terms of 1) relevant feature learning for
representation and 2) efficient fusion for multi-modalities. Recently, there
are a number of works have shown the effectiveness in utilizing the attention
mechanism for AU detection, however, most of them are binding the region of
interest (ROI) with features but rarely apply attention between features of
each AU. On the other hand, the transformer, which utilizes a more efficient
self-attention mechanism, has been widely used in natural language processing
and computer vision tasks but is not fully explored in AU detection tasks. In
this paper, we propose a novel end-to-end Multi-Head Fused Transformer (MFT)
method for AU detection, which learns AU encoding features representation from
different modalities by transformer encoder and fuses modalities by another
fusion transformer module. Multi-head fusion attention is designed in the
fusion transformer module for the effective fusion of multiple modalities. Our
approach is evaluated on two public multi-modal AU databases, BP4D, and BP4D+,
and the results are superior to the state-of-the-art algorithms and baseline
models. We further analyze the performance of AU detection from different

The aging world population requires a sustainable and high-quality healthcare
system. To examine the efficiency of medical cooperation, medical provider and
physician networks were constructed using patient claims data. Previous studies
have shown that these networks contain information on medical cooperation.
However, the usage patterns of multiple medical providers in a series of
medical services have not been considered. In addition, these studies used only
general network features to represent medical cooperation, but their expressive
ability was low. To overcome these limitations, we analyzed the medical
provider network to examine its overall contribution to the quality of
healthcare provided by cooperation between medical providers in a series of
medical services. This study focused on: i) the method of feature extraction
from the network, ii) incorporation of the usage pattern of medical providers,
and iii) expressive ability of the statistical model. Femoral neck fractures
were selected as the target disease. To build the medical provider networks, we
analyzed the patient claims data from a single prefecture in Japan between
January 1, 2014 and December 31, 2019. We considered four types of models: a
model using node strength and linear regression to a model using feature
representation by node2vec and regression tree ensemble, which is a machine
learning method. The results showed that a stronger medical provider reduces
the duration of hospital stay. The overall contribution of the medical
cooperation to the duration of hospital stay extracted from the medical
provider network using node2vec is approximately 20%, which is approximately 20
times higher than the model using strength.

In this paper, we propose a probabilistic extension of the recent
self-supervised learning (SSL) method, SimSiam. The proposed extension makes
SimSiam uncertainty-aware by considering SimSiam as a generative model of
augmented views and learning it in terms of variational inference. SimSiam
trains a model by maximizing the similarity between image representations of
different augmented views of the same image. The augmentation process sometimes
produces ambiguous images, and their representations potentially have
uncertainty. Although the use of uncertainty-aware machine learning becoming
common, such as in deep variational inference, SimSiam and other SSL methods
are insufficiently uncertainty-aware, leading to limitations in the use of
augmented ambiguous images. Our main contributions are twofold: Firstly, we
clarify the theoretical relationship between non-contrastive SSL and multimodal
variational inference. Secondly, we introduce a novel SSL called variational
inference SimSiam (VI-SimSiam), which incorporates uncertainty by involving
spherical posterior distributions. The experiment results show that VI-SimSiam
outperforms SimSiam in classification tasks in several datasets, such as
ImageNette and ImageWoof by successfully estimating the representation

A key technique of machine learning and computer vision is to embed discrete
weighted graphs into continuous spaces for further downstream processing.
Embedding discrete hierarchical structures in hyperbolic geometry has proven
very successful since it was shown that any weighted tree can be embedded in
that geometry with arbitrary low distortion. Various optimization methods for
hyperbolic embeddings based on common models of hyperbolic geometry have been
studied. In this paper, we consider Hilbert geometry for the standard simplex
which is isometric to a vector space equipped with the variation polytope norm.
We study the representation power of this Hilbert simplex geometry by embedding
distance matrices of graphs. Our findings demonstrate that Hilbert simplex
geometry is competitive to alternative geometries such as the Poincar\'e
hyperbolic ball or the Euclidean geometry for embedding tasks while being fast
and numerically robust.

DeepFakes are raising significant social concerns. Although various DeepFake
detectors have been developed as forensic countermeasures, these detectors are
still vulnerable to attacks. Recently, a few attacks, principally adversarial
attacks, have succeeded in cloaking DeepFake images to evade detection.
However, these attacks have typical detector-specific designs, which require
prior knowledge about the detector, leading to poor transferability. Moreover,
these attacks only consider simple security scenarios. Less is known about how
effective they are in high-level scenarios where either the detectors or the
attacker's knowledge varies. In this paper, we solve the above challenges with
presenting a novel detector-agnostic trace removal attack for DeepFake
anti-forensics. Instead of investigating the detector side, our attack looks
into the original DeepFake creation pipeline, attempting to remove all
detectable natural DeepFake traces to render the fake images more "authentic".
To implement this attack, first, we perform a DeepFake trace discovery,
identifying three discernible traces. Then a trace removal network (TR-Net) is
proposed based on an adversarial learning framework involving one generator and
multiple discriminators. Each discriminator is responsible for one individual
trace representation to avoid cross-trace interference. These discriminators
are arranged in parallel, which prompts the generator to remove various traces
simultaneously. To evaluate the attack efficacy, we crafted heterogeneous
security scenarios where the detectors were embedded with different levels of
defense and the attackers' background knowledge of data varies. The
experimental results show that the proposed attack can significantly compromise
the detection accuracy of six state-of-the-art DeepFake detectors while causing
only a negligible loss in visual quality to the original DeepFake samples.

For Domain Generalizable Object Detection (DGOD), Disentangled Representation
Learning (DRL) helps a lot by explicitly disentangling Domain-Invariant
Representations (DIR) from Domain-Specific Representations (DSR). Considering
the domain category is an attribute of input data, it should be feasible for
networks to fit a specific mapping which projects DSR into feature channels
exclusive to domain-specific information, and thus much cleaner disentanglement
of DIR from DSR can be achieved simply on channel dimension. Inspired by this
idea, we propose a novel DRL method for DGOD, which is termed Gated
Domain-Invariant Feature Disentanglement (GDIFD). In GDIFD, a Channel Gate
Module (CGM) learns to output channel gate signals close to either 0 or 1,
which can mask out the channels exclusive to domain-specific information
helpful for domain recognition. With the proposed GDIFD, the backbone in our
framework can fit the desired mapping easily, which enables the channel-wise
disentanglement. In experiments, we demonstrate that our approach is highly
effective and achieves state-of-the-art DGOD performance.

Podcasts have recently shown a rapid rise in popularity. Summarization of
podcast transcripts is of practical benefit to both content providers and
consumers. It helps consumers to quickly decide whether they will listen to the
podcasts and reduces the cognitive load of content providers to write
summaries. Nevertheless, podcast summarization faces significant challenges
including factual inconsistencies with respect to the inputs. The problem is
exacerbated by speech disfluencies and recognition errors in transcripts of
spoken language. In this paper, we explore a novel abstractive summarization
method to alleviate these challenges. Specifically, our approach learns to
produce an abstractive summary while grounding summary segments in specific
portions of the transcript to allow for full inspection of summary details. We
conduct a series of analyses of the proposed approach on a large podcast
dataset and show that the approach can achieve promising results. Grounded
summaries bring clear benefits in locating the summary and transcript segments
that contain inconsistent information, and hence significantly improve
summarization quality in both automatic and human evaluation metrics.

We develop an algorithm that combines model-based and model-free methods for
solving a nonlinear optimal control problem with a quadratic cost in which the
system model is given by a linear state-space model with a small additive
nonlinear perturbation. We decompose the cost into a sum of two functions, one
having an explicit form obtained from the approximate linear model, the other
being a black-box model representing the unknown modeling error. The
decomposition allows us to formulate the problem as a composite optimization
problem. To solve the optimization problem, our algorithm performs gradient
descent using the gradient obtained from the approximate linear model until
backtracking line search fails, upon which the model-based gradient is compared
with the exact gradient obtained from a model-free algorithm. The difference
between the model gradient and the exact gradient is then used for compensating
future gradient-based updates. Our algorithm is shown to decrease the number of
function evaluations compared with traditional model-free methods both in
theory and in practice.

Confidence estimation aims to quantify the confidence of the model
prediction, providing an expectation of success. A well-calibrated confidence
estimate enables accurate failure prediction and proper risk measurement when
given noisy samples and out-of-distribution data in real-world settings.
However, this task remains a severe challenge for neural machine translation
(NMT), where probabilities from softmax distribution fail to describe when the
model is probably mistaken. To address this problem, we propose an unsupervised
confidence estimate learning jointly with the training of the NMT model. We
explain confidence as how many hints the NMT model needs to make a correct
prediction, and more hints indicate low confidence. Specifically, the NMT model
is given the option to ask for hints to improve translation accuracy at the
cost of some slight penalty. Then, we approximate their level of confidence by
counting the number of hints the model uses. We demonstrate that our learned
confidence estimate achieves high accuracy on extensive sentence/word-level
quality estimation tasks. Analytical results verify that our confidence
estimate can correctly assess underlying risk in two real-world scenarios: (1)
discovering noisy samples and (2) detecting out-of-domain data. We further
propose a novel confidence-based instance-specific label smoothing approach
based on our learned confidence estimate, which outperforms standard label
smoothing.

Background: Machine learning techniques have been widely used and demonstrate
promising performance in many software security tasks such as software
vulnerability prediction. However, the class ratio within software
vulnerability datasets is often highly imbalanced (since the percentage of
observed vulnerability is usually very low). Goal: To help security
practitioners address software security data class imbalanced issues and
further help build better prediction models with resampled datasets. Method: We
introduce an approach called Dazzle which is an optimized version of
conditional Wasserstein Generative Adversarial Networks with gradient penalty
(cWGAN-GP). Dazzle explores the architecture hyperparameters of cWGAN-GP with a
novel optimizer called Bayesian Optimization. We use Dazzle to generate
minority class samples to resample the original imbalanced training dataset.
Results: We evaluate Dazzle with three software security datasets, i.e., Moodle
vulnerable files, Ambari bug reports, and JavaScript function code. We show
that Dazzle is practical to use and demonstrates promising improvement over
existing state-of-the-art oversampling techniques such as SMOTE (e.g., with an
average of about 60% improvement rate over SMOTE in recall among all datasets).
Conclusion: Based on this study, we would suggest the use of optimized GANs as
an alternative method for security vulnerability data class imbalanced issues.

Inverse Reinforcement Learning (IRL) algorithms infer a reward function that
explains demonstrations provided by an expert acting in the environment.
Maximum Causal Entropy (MCE) IRL is currently the most popular formulation of
IRL, with numerous extensions. In this tutorial, we present a compressed
derivation of MCE IRL and the key results from contemporary implementations of
MCE IRL algorithms. We hope this will serve both as an introductory resource
for those new to the field, and as a concise reference for those already
familiar with these topics.

Prior research has discussed and illustrated the need to consider linguistic
norms at the community level when studying taboo (hateful/offensive/toxic etc.)
language. However, a methodology for doing so, that is firmly founded on
community language norms is still largely absent. This can lead both to biases
in taboo text classification and limitations in our understanding of the causes
of bias. We propose a method to study bias in taboo classification and
annotation where a community perspective is front and center. This is
accomplished by using special classifiers tuned for each community's language.
In essence, these classifiers represent community level language norms. We use
these to study bias and find, for example, biases are largest against African
Americans (7/10 datasets and all 3 classifiers examined). In contrast to
previous papers we also study other communities and find, for example, strong
biases against South Asians. In a small scale user study we illustrate our key
idea which is that common utterances, i.e., those with high alignment scores
with a community (community classifier confidence scores) are unlikely to be
regarded taboo. Annotators who are community members contradict taboo
classification decisions and annotations in a majority of instances. This paper
is a significant step toward reducing false positive taboo decisions that over
time harm minority communities.

One of the emerging research trends in natural language understanding is
machine reading comprehension (MRC) which is the task to find answers to human
questions based on textual data. Existing Vietnamese datasets for MRC research
concentrate solely on answerable questions. However, in reality, questions can
be unanswerable for which the correct answer is not stated in the given textual
data. To address the weakness, we provide the research community with a
benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question
answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a
benchmark dataset for the challenge on Vietnamese MRC at the Eighth Workshop on
Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77
participant teams from 34 universities and other organizations. In this
article, we present details of the organization of the challenge, an overview
of the methods employed by shared-task participants, and the results. The
highest performances are 77.24% in F1-score and 67.43% in Exact Match on the
private test set. The Vietnamese MRC systems proposed by the top 3 teams use
XLM-RoBERTa, a powerful pre-trained language model based on the transformer
architecture. The UIT-ViQuAD 2.0 dataset motivates researchers to further
explore the Vietnamese machine reading comprehension task and related tasks
such as question answering, question generation, and natural language

We present a dataset of 998 3D models of everyday tabletop objects along with
their 847,000 real world RGB and depth images. Accurate annotations of camera
poses and object poses for each image are performed in a semi-automated fashion
to facilitate the use of the dataset for myriad 3D applications like shape
reconstruction, object pose estimation, shape retrieval etc. We primarily focus
on learned multi-view 3D reconstruction due to the lack of appropriate real
world benchmark for the task and demonstrate that our dataset can fill that
gap. The entire annotated dataset along with the source code for the annotation
tools and evaluation baselines is available at
http://www.ocrtoc.org/3d-reconstruction.html.

In many real-world settings, machine learning models need to identify user
inputs that are out-of-domain (OOD) so as to avoid performing wrong actions.
This work focuses on a challenging case of OOD detection, where no labels for
in-domain data are accessible (e.g., no intent labels for the intent
classification task). To this end, we first evaluate different language model
based approaches that predict likelihood for a sequence of tokens. Furthermore,
we propose a novel representation learning based method by combining
unsupervised clustering and contrastive learning so that better data
representations for OOD detection can be learned. Through extensive
experiments, we demonstrate that this method can significantly outperform
likelihood-based methods and can be even competitive to the state-of-the-art
supervised approaches with label information.

State-of-the-art automatic speech recognition (ASR) systems are trained with
tens of thousands of hours of labeled speech data. Human transcription is
expensive and time consuming. Factors such as the quality and consistency of
the transcription can greatly affect the performance of the ASR models trained
with these data. In this paper, we show that we can train a strong teacher
model to produce high quality pseudo labels by utilizing recent self-supervised
and semi-supervised learning techniques. Specifically, we use JUST (Joint
Unsupervised/Supervised Training) and iterative noisy student teacher training
to train a 600 million parameter bi-directional teacher model. This model
achieved 4.0% word error rate (WER) on a voice search task, 11.1% relatively
better than a baseline. We further show that by using this strong teacher model
to generate high-quality pseudo labels for training, we can achieve 13.6%
relative WER reduction (5.9% to 5.1%) for a streaming model compared to using
human labels.

Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibrotic
lung disease with a variable and unpredictable rate of progression. CT scans of
the lungs inform clinical assessment of IPF patients and contain pertinent
information related to disease progression. In this work, we propose a
multi-modal method that uses neural networks and memory banks to predict the
survival of IPF patients using clinical and imaging data. The majority of
clinical IPF patient records have missing data (e.g. missing lung function
tests). To this end, we propose a probabilistic model that captures the
dependencies between the observed clinical variables and imputes missing ones.
This principled approach to missing data imputation can be naturally combined
with a deep survival analysis model. We show that the proposed framework yields
significantly better survival analysis results than baselines in terms of
concordance index and integrated Brier score. Our work also provides insights
into novel image-based biomarkers that are linked to mortality.

The growing interest in explainable artificial intelligence (XAI) for
critical decision making motivates the need for interpretable machine learning
(ML) models. In fact, due to their structure (especially with small sizes),
these models are inherently understandable by humans. Recently, several exact
methods for computing such models are proposed to overcome weaknesses of
traditional heuristic methods by providing more compact models or better
prediction quality.
  Despite their compressed representation of Boolean functions, Binary decision
diagrams (BDDs) did not gain enough interest as other interpretable ML models.
In this paper, we first propose SAT-based models for learning optimal BDDs (in
terms of the number of features) that classify all input examples. Then, we
lift the encoding to a MaxSAT model to learn optimal BDDs in limited depths,
that maximize the number of examples correctly classified. Finally, we tackle
the fragmentation problem by introducing a method to merge compatible subtrees
for the BDDs found via the MaxSAT model. Our empirical study shows clear
benefits of the proposed approach in terms of prediction quality and
intrepretability (i.e., lighter size) compared to the state-of-the-art

Professional news media organizations have always touted the importance that
they give to multiple perspectives. However, in practice the traditional
approach to all-sides has favored people in the dominant culture. Hence it has
come under ethical critique under the new norms of diversity, equity, and
inclusion (DEI). When DEI is applied to journalism, it goes beyond conventional
notions of impartiality and bias and instead democratizes the journalistic
practice of sourcing -- who is quoted or interviewed, who is not, how often,
from which demographic group, gender, and so forth. There is currently no
real-time or on-demand tool in the hands of reporters to analyze the persons
they quote. In this paper, we present DIANES, a DEI Audit Toolkit for News
Sources. It consists of a natural language processing pipeline on the backend
to extract quotes, speakers, titles, and organizations from news articles in
real time. On the frontend, DIANES offers the WordPress plugins, a Web monitor,
and a DEI annotation API service, to help news media monitor their own quoting
patterns and push themselves towards DEI norms.

We consider Bayesian optimization of expensive-to-evaluate experiments that
generate vector-valued outcomes over which a decision-maker (DM) has
preferences. These preferences are encoded by a utility function that is not
known in closed form but can be estimated by asking the DM to express
preferences over pairs of outcome vectors. To address this problem, we develop
Bayesian optimization with preference exploration, a novel framework that
alternates between interactive real-time preference learning with the DM via
pairwise comparisons between outcomes, and Bayesian optimization with a learned
compositional model of DM utility and outcomes. Within this framework, we
propose preference exploration strategies specifically designed for this task,
and demonstrate their performance via extensive simulation studies.

In this paper, we propose an improved Bayesian bidirectional long-short term
memory (BiLSTM) neural networks for multi-step ahead (MSA) solar generation
forecasting. The proposed technique applies alpha-beta divergence for a more
appropriate consideration of outliers in the solar generation data and
resulting variability of the weight parameter distribution in the neural
network. The proposed method is examined on highly granular solar generation
data from Ausgrid using probabilistic evaluation metrics such as Pinball loss
and Winkler score. Moreover, a comparative analysis between MSA and the
single-step ahead (SSA) forecasting is provided to test the effectiveness of
the proposed method on variable forecasting horizons. The numerical results
clearly demonstrate that the proposed Bayesian BiLSTM with alpha-beta
divergence outperforms standard Bayesian BiLSTM and other benchmark methods for
MSA forecasting in terms of error performance.

Few-shot models aim at making predictions using a minimal number of labeled
examples from a given task. The main challenge in this area is the one-shot
setting where only one element represents each class. We propose HyperShot -
the fusion of kernels and hypernetwork paradigm. Compared to reference
approaches that apply a gradient-based adjustment of the parameters, our model
aims to switch the classification module parameters depending on the task's
embedding. In practice, we utilize a hypernetwork, which takes the aggregated
information from support data and returns the classifier's parameters
handcrafted for the considered problem. Moreover, we introduce the kernel-based
representation of the support examples delivered to hypernetwork to create the
parameters of the classification module. Consequently, we rely on relations
between embeddings of the support examples instead of direct feature values
provided by the backbone models. Thanks to this approach, our model can adapt
to highly different tasks.

After initial release of a machine learning algorithm, the model can be
fine-tuned by retraining on subsequently gathered data, adding newly discovered
features, or more. Each modification introduces a risk of deteriorating
performance and must be validated on a test dataset. It may not always be
practical to assemble a new dataset for testing each modification, especially
when most modifications are minor or are implemented in rapid succession.
Recent works have shown how one can repeatedly test modifications on the same
dataset and protect against overfitting by (i) discretizing test results along
a grid and (ii) applying a Bonferroni correction to adjust for the total number
of modifications considered by an adaptive developer. However, the standard
Bonferroni correction is overly conservative when most modifications are
beneficial and/or highly correlated. This work investigates more powerful
approaches using alpha-recycling and sequentially-rejective graphical
procedures (SRGPs). We introduce novel extensions that account for correlation
between adaptively chosen algorithmic modifications. In empirical analyses, the
SRGPs control the error rate of approving unacceptable modifications and
approve a substantially higher number of beneficial modifications than previous

Unmanned aerial vehicle (UAV) systems are vulnerable to jamming from
self-interested users who utilize radio devices for their benefits during UAV
transmissions. The vulnerability occurs due to the open nature of air-to-ground
(A2G) wireless communication networks, which may enable network-wide attacks.
This paper presents two strategies to identify Jammers in UAV networks. The
first strategy is based on time series approaches for anomaly detection where
the signal available in resource blocks are decomposed statistically to find
trend, seasonality, and residues, while the second is based on newly designed
deep networks. The joined technique is suitable for UAVs because the
statistical model does not require heavy computation processing but is limited
in generalizing possible attack's identification. On the other hand, the deep
network can classify attacks accurately but requires more resources. The
simulation considers the location and power of the jamming attacks and the UAV
position related to the base station. The statistical method technique made it
feasible to identify 84.38 % of attacks when the attacker was at 30 m from the
UAV. Furthermore, the Deep network's accuracy was approximately 99.99 % for
jamming powers greater than two and jammer distances less than 200 meters.

Modern language models can generate high-quality short texts. However, they
often meander or are incoherent when generating longer texts. These issues
arise from the next-token-only language modeling objective. To address these
issues, we introduce Time Control (TC), a language model that implicitly plans
via a latent stochastic process. TC does this by learning a representation
which maps the dynamics of how text changes in a document to the dynamics of a
stochastic process of interest. Using this representation, the language model
can generate text by first implicitly generating a document plan via a
stochastic process, and then generating text that is consistent with this
latent plan. Compared to domain-specific methods and fine-tuning GPT2 across a
variety of text domains, TC improves performance on text infilling and
discourse coherence. On long text generation settings, TC preserves the text
structure both in terms of ordering (up to +40% better) and text length
consistency (up to +17% better). Human evaluators also prefer TC's output 28.6%
more than the baselines.

In reinforcement learning, the graph Laplacian has proved to be a valuable
tool in the task-agnostic setting, with applications ranging from skill
discovery to reward shaping. Recently, learning the Laplacian representation
has been framed as the optimization of a temporally-contrastive objective to
overcome its computational limitations in large (or continuous) state spaces.
However, this approach requires uniform access to all states in the state
space, overlooking the exploration problem that emerges during the
representation learning process. In this work, we propose an alternative method
that is able to recover, in a non-uniform-prior setting, the expressiveness and
the desired properties of the Laplacian representation. We do so by combining
the representation learning with a skill-based covering policy, which provides
a better training distribution to extend and refine the representation. We also
show that a simple augmentation of the representation objective with the
learned temporal abstractions improves dynamics-awareness and helps
exploration. We find that our method succeeds as an alternative to the
Laplacian in the non-uniform setting and scales to challenging continuous
control environments. Finally, even if our method is not optimized for skill
discovery, the learned skills can successfully solve difficult continuous
navigation tasks with sparse rewards, where standard skill discovery approaches
are no so effective.

Machine Learning (ML) research publications commonly provide open-source
implementations on GitHub, allowing their audience to replicate, validate, or
even extend machine learning algorithms, data sets, and metadata.
  However, thus far little is known about the degree of collaboration activity
happening on such ML research repositories, in particular regarding (1) the
degree to which such repositories receive contributions from forks, (2) the
nature of such contributions (i.e., the types of changes), and (3) the nature
of changes that are not contributed back to forks, which might represent missed
opportunities. In this paper, we empirically study contributions to 1,346 ML
research repositories and their 67,369 forks, both quantitatively and
qualitatively (by building on Hindle et al.'s seminal taxonomy of code
changes). We found that while ML research repositories are heavily forked, only
9% of the forks made modifications to the forked repository. 42% of the latter
sent changes to the parent repositories, half of which (52%) were accepted by
the parent repositories. Our qualitative analysis on 539 contributed and 378
local (fork-only) changes, extends Hindle et al.'s taxonomy with one new
top-level change category related to ML (Data), and 15 new sub-categories,
including nine ML-specific ones (input data, output data, program data,
sharing, change evaluation, parameter tuning, performance, pre-processing,
model training). While the changes that are not contributed back by the forks
mostly concern domain-specific customizations and local experimentation (e.g.,
parameter tuning), the origin ML repositories do miss out on a non-negligible
15.4% of Documentation changes, 13.6% of Feature changes and 11.4% of Bug fix
changes. The findings in this paper will be useful for practitioners,
researchers, toolsmiths, and educators.

Pre-trained language models derive substantial linguistic and factual
knowledge from the massive corpora on which they are trained, and prompt
engineering seeks to align these models to specific tasks. Unfortunately,
existing prompt engineering methods require significant amounts of labeled
data, access to model parameters, or both. We introduce a new method for
selecting prompt templates \textit{without labeled examples} and
\textit{without direct access to the model}. Specifically, over a set of
candidate templates, we choose the template that maximizes the mutual
information between the input and the corresponding model output. Across 8
datasets representing 7 distinct NLP tasks, we show that when a template has
high mutual information, it also has high accuracy on the task. On the largest
model, selecting prompts with our method gets 90\% of the way from the average
prompt accuracy to the best prompt accuracy and requires no ground truth
labels.

We propose a new class of physics-informed neural networks, called
physics-informed Variational Autoencoder (PI-VAE), to solve stochastic
differential equations (SDEs) or inverse problems involving SDEs. In these
problems the governing equations are known but only a limited number of
measurements of system parameters are available. PI-VAE consists of a
variational autoencoder (VAE), which generates samples of system variables and
parameters. This generative model is integrated with the governing equations.
In this integration, the derivatives of VAE outputs are readily calculated
using automatic differentiation, and used in the physics-based loss term. In
this work, the loss function is chosen to be the Maximum Mean Discrepancy (MMD)
for improved performance, and neural network parameters are updated iteratively
using the stochastic gradient descent algorithm. We first test the proposed
method on approximating stochastic processes. Then we study three types of
problems related to SDEs: forward and inverse problems together with mixed
problems where system parameters and solutions are simultaneously calculated.
The satisfactory accuracy and efficiency of the proposed method are numerically
demonstrated in comparison with physics-informed generative adversarial network
(PI-WGAN).

Neural networks can implement arbitrary functions. But, mechanistically, what
are the tools at their disposal to construct the target? For classification
tasks, the network must transform the data classes into a linearly separable
representation in the final hidden layer. We show that a feed-forward
architecture has one primary tool at hand to achieve this separability:
progressive folding of the data manifold in unoccupied higher dimensions. The
operation of folding provides a useful intuition in low-dimensions that
generalizes to high ones. We argue that an alternative method based on shear,
requiring very deep architectures, plays only a small role in real-world
networks. The folding operation, however, is powerful as long as layers are
wider than the data dimensionality, allowing efficient solutions by providing
access to arbitrary regions in the distribution, such as data points of one
class forming islands within the other classes. We argue that a link exists
between the universal approximation property in ReLU networks and the
fold-and-cut theorem (Demaine et al., 1998) dealing with physical paper
folding. Based on the mechanistic insight, we predict that the progressive
generation of separability is necessarily accompanied by neurons showing mixed
selectivity and bimodal tuning curves. This is validated in a network trained
on the poker hand task, showing the emergence of bimodal tuning curves during
training. We hope that our intuitive picture of the data transformation in deep
networks can help to provide interpretability, and discuss possible
applications to the theory of convolutional networks, loss landscapes, and
  TL;DR: Shows that the internal processing of deep networks can be thought of
as literal folding operations on the data distribution in the N-dimensional
activation space. A link to a well-known theorem in origami theory is provided.

Optical flow estimation is a fundamental task in computer vision. Recent
direct-regression methods using deep neural networks achieve remarkable
performance improvement. However, they do not explicitly capture long-term
motion correspondences and thus cannot handle large motions effectively. In
this paper, inspired by the traditional matching-optimization methods where
matching is introduced to handle large displacements before energy-based
optimizations, we introduce a simple but effective global matching step before
the direct regression and develop a learning-based matching-optimization
framework, namely GMFlowNet. In GMFlowNet, global matching is efficiently
calculated by applying argmax on 4D cost volumes. Additionally, to improve the
matching quality, we propose patch-based overlapping attention to extract large
context features. Extensive experiments demonstrate that GMFlowNet outperforms
RAFT, the most popular optimization-only method, by a large margin and achieves
state-of-the-art performance on standard benchmarks. Thanks to the matching and
overlapping attention, GMFlowNet obtains major improvements on the predictions
for textureless regions and large motions. Our code is made publicly available
at https://github.com/xiaofeng94/GMFlowNet

Social media platforms are deploying machine learning based offensive
language classification systems to combat hateful, racist, and other forms of
offensive speech at scale. However, despite their real-world deployment, we do
not yet comprehensively understand the extent to which offensive language
classifiers are robust against adversarial attacks. Prior work in this space is
limited to studying robustness of offensive language classifiers against
primitive attacks such as misspellings and extraneous spaces. To address this
gap, we systematically analyze the robustness of state-of-the-art offensive
language classifiers against more crafty adversarial attacks that leverage
greedy- and attention-based word selection and context-aware embeddings for
word replacement. Our results on multiple datasets show that these crafty
adversarial attacks can degrade the accuracy of offensive language classifiers
by more than 50% while also being able to preserve the readability and meaning
of the modified text.

Mixed Reality (MR) has recently shown great success as an intuitive interface
for enabling end-users to teach robots. Related works have used MR interfaces
to communicate robot intents and beliefs to a co-located human, as well as
developed algorithms for taking multi-modal human input and learning complex
motor behaviors. Even with these successes, enabling end-users to teach robots
complex motor tasks still poses a challenge because end-user communication is
highly task dependent and world knowledge is highly varied. We propose a
learning framework where end-users teach robots a) motion demonstrations, b)
task constraints, c) planning representations, and d) object information, all
of which are integrated into a single motor skill learning framework based on
Dynamic Movement Primitives (DMPs). We hypothesize that conveying this world
knowledge will be intuitive with an MR interface, and that a sample-efficient
motor skill learning framework which incorporates varied modalities of world
knowledge will enable robots to effectively solve complex tasks.

Training quantised neural networks (QNNs) is a non-differentiable
optimisation problem since weights and features are output by piecewise
constant functions. The standard solution is to apply the straight-through
estimator (STE), using different functions during the inference and gradient
computation steps. Several STE variants have been proposed in the literature
aiming to maximise the task accuracy of the trained network. In this paper, we
analyse STE variants and study their impact on QNN training. We first observe
that most such variants can be modelled as stochastic regularisations of stair
functions; although this intuitive interpretation is not new, our rigorous
discussion generalises to further variants. Then, we analyse QNNs mixing
different regularisations, finding that some suitably synchronised smoothing of
each layer map is required to guarantee pointwise compositional convergence to
the target discontinuous function. Based on these theoretical insights, we
propose additive noise annealing (ANA), a new algorithm to train QNNs
encompassing standard STE and its variants as special cases. When testing ANA
on the CIFAR-10 image classification benchmark, we find that the major impact
on task accuracy is not due to the qualitative shape of the regularisations but
to the proper synchronisation of the different STE variants used in a network,
in accordance with the theoretical results.

Alarm management systems have become indispensable in modern industry. Alarms
inform the operator of abnormal situations, particularly in the case of
equipment failures. Due to the interconnections between various parts of the
system, each fault can affect other sections of the system operating normally.
As a result, the fault propagates through faultless devices, increasing the
number of alarms. Hence, the timely detection of the major fault that triggered
the alarm by the operator can prevent the following consequences. However, due
to the complexity of the system, it is often impossible to find precise
relations between the underlying fault and the alarms. As a result, the
operator needs support to make an appropriate decision immediately. Modeling
alarms based on the historical alarm data can assist the operator in
determining the root cause of the alarm. This research aims to model the
relations between industrial alarms using historical alarm data in the
database. Firstly, alarm data is collected, and alarm tags are sequenced. Then,
these sequences are converted to numerical vectors using word embedding. Next,
a self-attention-based BiLSTM-CNN classifier is used to learn the structure and
relevance between historical alarm data. After training the model, this model
is used for online fault detection. Finally, as a case study, the proposed
model is implemented in the well-known Tennessee Eastman process, and the
results are presented.

Discourse analysis allows us to attain inferences of a text document that
extend beyond the sentence-level. The current performance of discourse models
is very low on texts outside of the training distribution's coverage,
diminishing the practical utility of existing models. There is need for a
measure that can inform us to what extent our model generalizes from the
training to the test sample when these samples may be drawn from distinct
distributions. While this can be estimated via distribution shift, we argue
that this does not directly correlate with change in the observed error of a
classifier (i.e. error-gap). Thus, we propose to use a statistic from the
theoretical domain adaptation literature which can be directly tied to
error-gap. We study the bias of this statistic as an estimator of error-gap
both theoretically and through a large-scale empirical study of over 2400
experiments on 6 discourse datasets from domains including, but not limited to:
news, biomedical texts, TED talks, Reddit posts, and fiction. Our results not
only motivate our proposal and help us to understand its limitations, but also
provide insight on the properties of discourse models and datasets which
improve performance in domain adaptation. For instance, we find that non-news
datasets are slightly easier to transfer to than news datasets when the
training and test sets are very different. Our code and an associated Python
package are available to allow practitioners to make more informed model and
dataset choices.

Distributed nonconvex optimization problems underlie many applications in
learning and autonomy, and such problems commonly face asynchrony in agents'
computations and communications. When delays in these operations are bounded,
they are called partially asynchronous. In this paper, we present an
uncoordinated stepsize selection rule for partially asynchronous block
coordinate descent that only requires local information to implement, and it
leads to faster convergence for a class of nonconvex problems than existing
stepsize rules, which require global information in some form. The problems we
consider satisfy the error bound condition, and the stepsize rule we present
only requires each agent to know (i) a certain type of Lipschitz constant of
its block of the gradient of the objective and (ii) the communication delays
experienced between it and its neighbors. This formulation requires less
information to be available to each agent than existing approaches, typically
allows for agents to use much larger stepsizes, and alleviates the impact of
stragglers while still guaranteeing convergence to a stationary point.
Simulation results provide comparisons and validate the faster convergence
attained by the stepsize rule we develop.

We introduce the novel problem of anticipating a time series of future hand
masks from egocentric video. A key challenge is to model the stochasticity of
future head motions, which globally impact the head-worn camera video analysis.
To this end, we propose a novel deep generative model -- EgoGAN, which uses a
3D Fully Convolutional Network to learn a spatio-temporal video representation
for pixel-wise visual anticipation, generates future head motion using
Generative Adversarial Network (GAN), and then predicts the future hand masks
based on the video representation and the generated future head motion. We
evaluate our method on both the EPIC-Kitchens and the EGTEA Gaze+ datasets. We
conduct detailed ablation studies to validate the design choices of our
approach. Furthermore, we compare our method with previous state-of-the-art
methods on future image segmentation and show that our method can more
accurately predict future hand masks.

The prediction accuracy of the deep neural networks (DNNs) after deployment
at the edge can suffer with time due to shifts in the distribution of the new
data. To improve robustness of DNNs, they must be able to update themselves to
enhance their prediction accuracy. This adaptation at the resource-constrained
edge is challenging as: (i) new labeled data may not be present; (ii)
adaptation needs to be on device as connections to cloud may not be available;
and (iii) the process must not only be fast but also memory- and
energy-efficient. Recently, lightweight prediction-time unsupervised DNN
adaptation techniques have been introduced that improve prediction accuracy of
the models for noisy data by re-tuning the batch normalization (BN) parameters.
This paper, for the first time, performs a comprehensive measurement study of
such techniques to quantify their performance and energy on various edge
devices as well as find bottlenecks and propose optimization opportunities. In
particular, this study considers CIFAR-10-C image classification dataset with
corruptions, three robust DNNs (ResNeXt, Wide-ResNet, ResNet-18), two BN
adaptation algorithms (one that updates normalization statistics and the other
that also optimizes transformation parameters), and three edge devices (FPGA,
Raspberry-Pi, and Nvidia Xavier NX). We find that the approach that only
updates the normalization parameters with Wide-ResNet, running on Xavier GPU,
to be overall effective in terms of balancing multiple cost metrics. However,
the adaptation overhead can still be significant (around 213 ms). The results
strongly motivate the need for algorithm-hardware co-design for efficient
on-device DNN adaptation.

Acoustic sensing has proved effective as a foundation for numerous
applications in health and human behavior analysis. In this work, we focus on
the problem of detecting in-person social interactions in naturalistic settings
from audio captured by a smartwatch. As a first step towards detecting social
interactions, it is critical to distinguish the speech of the individual
wearing the watch from all other sounds nearby, such as speech from other
individuals and ambient sounds. This is very challenging in realistic settings,
where interactions take place spontaneously and supervised models cannot be
trained apriori to recognize the full complexity of dynamic social
environments. In this paper, we introduce a transfer learning-based approach to
detect foreground speech of users wearing a smartwatch. A highlight of the
method is that it does not depend on the collection of voice samples to build
user-specific models. Instead, the approach is based on knowledge transfer from
general-purpose speaker representations derived from public datasets. Our
experiments demonstrate that our approach performs comparably to a fully
supervised model, with 80% F1 score. To evaluate the method, we collected a
dataset of 31 hours of smartwatch-recorded audio in 18 homes with a total of 39
participants performing various semi-controlled tasks.

In this modern era of overpopulation disease prediction is a crucial step in
diagnosing various diseases at an early stage. With the advancement of various
machine learning algorithms, the prediction has become quite easy. However, the
complex and the selection of an optimal machine learning technique for the
given dataset greatly affects the accuracy of the model. A large amount of
datasets exists globally but there is no effective use of it due to its
unstructured format. Hence, a lot of different techniques are available to
extract something useful for the real world to implement. Therefore, accuracy
becomes a major metric in evaluating the model. In this paper, a disease
prediction approach is proposed that implements a random forest classifier on
Parkinson's disease. We compared the accuracy of this model with the Principal
Component Analysis (PCA) applied Artificial Neural Network (ANN) model and
captured a visible difference. The model secured a significant accuracy of up
to 90%.

Learning representations of images that are invariant to sensitive or
unwanted attributes is important for many tasks including bias removal and
cross domain retrieval. Here, our objective is to learn representations that
are invariant to the domain (sensitive attribute) for which labels are
provided, while being informative over all other image attributes, which are
unlabeled. We present a new approach, proposing a new domain-wise contrastive
objective for ensuring invariant representations. This objective crucially
restricts negative image pairs to be drawn from the same domain, which enforces
domain invariance whereas the standard contrastive objective does not. This
domain-wise objective is insufficient on its own as it suffers from shortcut
solutions resulting in feature suppression. We overcome this issue by a
combination of a reconstruction constraint, image augmentations and
initialization with pre-trained weights. Our analysis shows that the choice of
augmentations is important, and that a misguided choice of augmentations can
harm the invariance and informativeness objectives. In an extensive evaluation,
our method convincingly outperforms the state-of-the-art in terms of
representation invariance, representation informativeness, and training speed.
Furthermore, we find that in some cases our method can achieve excellent
results even without the reconstruction constraint, leading to a much faster
and resource efficient training.

Studying misinformation and how to deal with unhealthy behaviours within
online discussions has recently become an important field of research within
social studies. With the rapid development of social media, and the increasing
amount of available information and sources, rigorous manual analysis of such
discourses has become unfeasible. Many approaches tackle the issue by studying
the semantic and syntactic properties of discussions following a supervised
approach, for example using natural language processing on a dataset labeled
for abusive, fake or bot-generated content. Solutions based on the existence of
a ground truth are limited to those domains which may have ground truth.
However, within the context of misinformation, it may be difficult or even
impossible to assign labels to instances. In this context, we consider the use
of temporal dynamic patterns as an indicator of discussion health. Working in a
domain for which ground truth was unavailable at the time (early COVID-19
pandemic discussions) we explore the characterization of discussions based on
the the volume and time of contributions. First we explore the types of
discussions in an unsupervised manner, and then characterize these types using
the concept of ephemerality, which we formalize. In the end, we discuss the
potential use of our ephemerality definition for labeling online discourses
based on how desirable, healthy and constructive they are.

In the last few years, there have been several revolutions in the field of
deep learning, mainly headlined by the large impact of Generative Adversarial
Networks (GANs). GANs not only provide an unique architecture when defining
their models, but also generate incredible results which have had a direct
impact on society. Due to the significant improvements and new areas of
research that GANs have brought, the community is constantly coming up with new
researches that make it almost impossible to keep up with the times. Our survey
aims to provide a general overview of GANs, showing the latest architectures,
optimizations of the loss functions, validation metrics and application areas
of the most widely recognized variants. The efficiency of the different
variants of the model architecture will be evaluated, as well as showing the
best application area; as a vital part of the process, the different metrics
for evaluating the performance of GANs and the frequently used loss functions
will be analyzed. The final objective of this survey is to provide a summary of
the evolution and performance of the GANs which are having better results to
guide future researchers in the field.

We consider the problem of segmenting scenes into constituent entities, i.e.
underlying objects and their parts. Current supervised visual detectors though
impressive within their training distribution, often fail to segment
out-of-distribution scenes into their constituent entities. Recent slot-centric
generative models break such dependence on supervision, by attempting to
segment scenes into entities unsupervised, by reconstructing pixels. However,
they have been restricted thus far to toy scenes as they suffer from a
reconstruction-segmentation trade-off: as the entity bottleneck gets wider,
reconstruction improves but then the segmentation collapses. We propose
GFS-Nets (Generating Fast and Slow Networks) that alleviate this issue with two
ingredients: i) curriculum training in the form of primitives, often missing
from current generative models and, ii) test-time adaptation per scene through
gradient descent on the reconstruction objective, what we call slow inference,
missing from current feed-forward detectors. We show the proposed curriculum
suffices to break the reconstruction-segmentation trade-off, and slow inference
greatly improves segmentation in out-of-distribution scenes. We evaluate
GFS-Nets in 3D and 2D scene segmentation benchmarks of PartNet, CLEVR, Room
Diverse++, and show large ( 50%) performance improvements against SOTA
supervised feed-forward detectors and unsupervised object discovery methods

Optimization based tracking methods have been widely successful by
integrating a target model prediction module, providing effective global
reasoning by minimizing an objective function. While this inductive bias
integrates valuable domain knowledge, it limits the expressivity of the
tracking network. In this work, we therefore propose a tracker architecture
employing a Transformer-based model prediction module. Transformers capture
global relations with little inductive bias, allowing it to learn the
prediction of more powerful target models. We further extend the model
predictor to estimate a second set of weights that are applied for accurate
bounding box regression. The resulting tracker relies on training and on test
frame information in order to predict all weights transductively. We train the
proposed tracker end-to-end and validate its performance by conducting
comprehensive experiments on multiple tracking datasets. Our tracker sets a new
state of the art on three benchmarks, achieving an AUC of 68.5% on the
challenging LaSOT dataset.

Estimating the target extent poses a fundamental challenge in visual object
tracking. Typically, trackers are box-centric and fully rely on a bounding box
to define the target in the scene. In practice, objects often have complex
shapes and are not aligned with the image axis. In these cases, bounding boxes
do not provide an accurate description of the target and often contain a
majority of background pixels. We propose a segmentation-centric tracking
pipeline that not only produces a highly accurate segmentation mask, but also
internally works with segmentation masks instead of bounding boxes. Thus, our
tracker is able to better learn a target representation that clearly
differentiates the target in the scene from background content. In order to
achieve the necessary robustness for the challenging tracking scenario, we
propose a separate instance localization component that is used to condition
the segmentation decoder when producing the output mask. We infer a bounding
box from the segmentation mask, validate our tracker on challenging tracking
datasets and achieve the new state of the art on LaSOT with a success AUC score
of 69.7%. Since most tracking datasets do not contain mask annotations, we
cannot use them to evaluate predicted segmentation masks. Instead, we validate
our segmentation quality on two popular video object segmentation datasets.

We study the challenge of learning causal reasoning over procedural text to
answer "What if..." questions when external commonsense knowledge is required.
We propose a novel multi-hop graph reasoning model to 1) efficiently extract a
commonsense subgraph with the most relevant information from a large knowledge
graph; 2) predict the causal answer by reasoning over the representations
obtained from the commonsense subgraph and the contextual interactions between
the questions and context. We evaluate our model on WIQA benchmark and achieve
state-of-the-art performance compared to the recent models.

Masked autoencoding has achieved great success for self-supervised learning
in the image and language domains. However, mask based pretraining has yet to
show benefits for point cloud understanding, likely due to standard backbones
like PointNet being unable to properly handle the training versus testing
distribution mismatch introduced by masking during training. In this paper, we
bridge this gap by proposing a discriminative mask pretraining Transformer
framework, MaskPoint}, for point clouds. Our key idea is to represent the point
cloud as discrete occupancy values (1 if part of the point cloud; 0 if not),
and perform simple binary classification between masked object points and
sampled noise points as the proxy task. In this way, our approach is robust to
the point sampling variance in point clouds, and facilitates learning rich
representations. We evaluate our pretrained models across several downstream
tasks, including 3D shape classification, segmentation, and real-word object
detection, and demonstrate state-of-the-art results while achieving a
significant pretraining speedup (e.g., 4.1x on ScanNet) compared to the prior
state-of-the-art Transformer baseline. Code is available at
https://github.com/haotian-liu/MaskPoint.

Deep learning has innovated the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews
an emerging paradigm, imaging physics-based data synthesis (IPADS), that can
provide huge training data in biomedical magnetic resonance without or with few
real data. Following the physical law of magnetic resonance, IPADS generates
signals from differential equations or analytical solution models, making the
learning more scalable, explainable, and better protecting privacy. Key
components of IPADS learning, including signal generation models, basic deep
learning network structures, enhanced data generation, and learning methods are
discussed. Great potentials of IPADS have been demonstrated by representative
applications in fast imaging, ultrafast signal reconstruction and accurate
parameter quantification. Finally, open questions and future work have been

Reward-free, unsupervised discovery of skills is an attractive alternative to
the bottleneck of hand-designing rewards in environments where task supervision
is scarce or expensive. However, current skill pre-training methods, like many
RL techniques, make a fundamental assumption - stationary environments during
training. Traditional methods learn all their skills simultaneously, which
makes it difficult for them to both quickly adapt to changes in the
environment, and to not forget earlier skills after such adaptation. On the
other hand, in an evolving or expanding environment, skill learning must be
able to adapt fast to new environment situations while not forgetting
previously learned skills. These two conditions make it difficult for classic
skill discovery to do well in an evolving environment. In this work, we propose
a new framework for skill discovery, where skills are learned one after another
in an incremental fashion. This framework allows newly learned skills to adapt
to new environment or agent dynamics, while the fixed old skills ensure the
agent doesn't forget a learned skill. We demonstrate experimentally that in
both evolving and static environments, incremental skills significantly
outperform current state-of-the-art skill discovery methods on both skill
quality and the ability to solve downstream tasks. Videos for learned skills
and code are made public on https://notmahi.github.io/disk

Current deep neural network approaches for camera pose estimation rely on
scene structure for 3D motion estimation, but this decreases the robustness and
thereby makes cross-dataset generalization difficult. In contrast, classical
approaches to structure from motion estimate 3D motion utilizing optical flow
and then compute depth. Their accuracy, however, depends strongly on the
quality of the optical flow. To avoid this issue, direct methods have been
proposed, which separate 3D motion from depth estimation but compute 3D motion
using only image gradients in the form of normal flow. In this paper, we
introduce a network NFlowNet, for normal flow estimation which is used to
enforce robust and direct constraints. In particular, normal flow is used to
estimate relative camera pose based on the cheirality (depth positivity)
constraint. We achieve this by formulating the optimization problem as a
differentiable cheirality layer, which allows for end-to-end learning of camera
pose. We perform extensive qualitative and quantitative evaluation of the
proposed DiffPoseNet's sensitivity to noise and its generalization across
datasets. We compare our approach to existing state-of-the-art methods on
KITTI, TartanAir, and TUM-RGBD datasets.

Understanding the mechanism of generative adversarial networks (GANs) helps
us better use GANs for downstream applications. Existing efforts mainly target
interpreting unconditional models, leaving it less explored how a conditional
GAN learns to render images regarding various categories. This work fills in
this gap by investigating how a class conditional generator unifies the
synthesis of multiple classes. For this purpose, we dive into the widely used
class-conditional batch normalization (CCBN), and observe that each feature
channel is activated at varying degrees given different categorical embeddings.
To describe such a phenomenon, we propose channel awareness, which
quantitatively characterizes how a single channel contributes to the final
synthesis. Extensive evaluations and analyses on the BigGAN model pre-trained
on ImageNet reveal that only a subset of channels is primarily responsible for
the generation of a particular category, similar categories (e.g., cat and dog)
usually get related to some same channels, and some channels turn out to share
information across all classes. For good measure, our algorithm enables several
novel applications with conditional GANs. Concretely, we achieve (1) versatile
image editing via simply altering a single channel and manage to (2)
harmoniously hybridize two different classes. We further verify that the
proposed channel awareness shows promising potential in (3) segmenting the
synthesized image and (4) evaluating the category-wise synthesis performance.

Coarse-grained (CG) molecular simulations have become a standard tool to
study molecular processes on time- and length-scales inaccessible to all-atom
simulations. Parameterizing CG force fields to match all-atom simulations has
mainly relied on force-matching or relative entropy minimization, which require
many samples from costly simulations with all-atom or CG resolutions,
respectively. Here we present flow-matching, a new training method for CG force
fields that combines the advantages of both methods by leveraging normalizing
flows, a generative deep learning method. Flow-matching first trains a
normalizing flow to represent the CG probability density, which is equivalent
to minimizing the relative entropy without requiring iterative CG simulations.
Subsequently, the flow generates samples and forces according to the learned
distribution in order to train the desired CG free energy model via force
matching. Even without requiring forces from the all-atom simulations,
flow-matching outperforms classical force-matching by an order of magnitude in
terms of data efficiency, and produces CG models that can capture the folding
and unfolding transitions of small proteins.

This work investigates learning pixel-wise semantic image segmentation in
urban scenes without any manual annotation, just from the raw non-curated data
collected by cars which, equipped with cameras and LiDAR sensors, drive around
a city. Our contributions are threefold. First, we propose a novel method for
cross-modal unsupervised learning of semantic image segmentation by leveraging
synchronized LiDAR and image data. The key ingredient of our method is the use
of an object proposal module that analyzes the LiDAR point cloud to obtain
proposals for spatially consistent objects. Second, we show that these 3D
object proposals can be aligned with the input images and reliably clustered
into semantically meaningful pseudo-classes. Finally, we develop a cross-modal
distillation approach that leverages image data partially annotated with the
resulting pseudo-classes to train a transformer-based model for image semantic
segmentation. We show the generalization capabilities of our method by testing
on four different testing datasets (Cityscapes, Dark Zurich, Nighttime Driving
and ACDC) without any finetuning, and demonstrate significant improvements
compared to the current state of the art on this problem. See project webpage
https://vobecant.github.io/DriveAndSegment/ for the code and more.

networks using operator sketching. The deep unrolling networks are currently
high-dimensional imaging tasks, especially the 3D cone-beam X-ray CT and 4D MRI
imaging, the deep unrolling schemes typically become inefficient both in terms
of memory and computation, due to the need of computing multiple times the
high-dimensional forward and adjoint operators. Recently researchers have found
that such limitations can be partially addressed by stochastic unrolling with
subsets of operators, inspired by the success of stochastic first-order
optimization. In this work, we propose a further acceleration upon stochastic
unrolling, using sketching techniques to approximate products in the
high-dimensional image space. The operator sketching can be jointly applied
with stochastic unrolling for the best acceleration and compression
performance. Our numerical experiments on X-ray CT image reconstruction
demonstrate the remarkable effectiveness of our sketched unrolling schemes.

In this report we present a new model of concepts, based on the framework of
variational autoencoders, which is designed to have attractive properties such
as factored conceptual domains, and at the same time be learnable from data.
The model is inspired by, and closely related to, the Beta-VAE model of
concepts, but is designed to be more closely connected with language, so that
the names of concepts form part of the graphical model. We provide evidence
that our model -- which we call the Conceptual VAE -- is able to learn
interpretable conceptual representations from simple images of coloured shapes
together with the corresponding concept labels. We also show how the model can
be used as a concept classifier, and how it can be adapted to learn from fewer
labels per instance. Finally, we formally relate our model to Gardenfors'
theory of conceptual spaces, showing how the Gaussians we use to represent
concepts can be formalised in terms of "fuzzy concepts" in such a space.

Recent large language models often answer factual questions correctly. But
users can't trust any given claim a model makes without fact-checking, because
language models can hallucinate convincing nonsense. In this work we use
reinforcement learning from human preferences (RLHP) to train "open-book" QA
models that generate answers whilst also citing specific evidence for their
claims, which aids in the appraisal of correctness. Supporting evidence is
drawn from multiple documents found via a search engine, or from a single
user-provided document. Our 280 billion parameter model, GopherCite, is able to
produce answers with high quality supporting evidence and abstain from
answering when unsure. We measure the performance of GopherCite by conducting
human evaluation of answers to questions in a subset of the NaturalQuestions
and ELI5 datasets. The model's response is found to be high-quality 80\% of the
time on this Natural Questions subset, and 67\% of the time on the ELI5 subset.
Abstaining from the third of questions for which it is most unsure improves
performance to 90\% and 80\% respectively, approaching human baselines.
However, analysis on the adversarial TruthfulQA dataset shows why citation is
only one part of an overall strategy for safety and trustworthiness: not all
claims supported by evidence are true.

A satellite image is a remotely sensed image data, where each pixel
represents a specific location on earth. The pixel value recorded is the
reflection radiation from the earth's surface at that location. Multispectral
images are those that capture image data at specific frequencies across the
electromagnetic spectrum as compared to Panchromatic images which are sensitive
to all wavelength of visible light. Because of the high resolution and high
dimensions of these images, they create difficulties for clustering techniques
to efficiently detect clusters of different sizes, shapes and densities as a
trade off for fast processing time. In this paper we propose a grid-density
based clustering technique for identification of objects. We also introduce an
approach to classify a satellite image data using a rule induction based
machine learning algorithm. The object identification and classification
methods have been validated using several synthetic and benchmark datasets.

In the last decade, much work in atmospheric science has focused on spatial
verification (SV) methods for gridded prediction, which overcome serious
disadvantages of pixelwise verification. However, neural networks (NN) in
atmospheric science are almost always trained to optimize pixelwise loss
functions, even when ultimately assessed with SV methods. This establishes a
disconnect between model verification during vs. after training. To address
this issue, we develop spatially enhanced loss functions (SELF) and demonstrate
their use for a real-world problem: predicting the occurrence of thunderstorms
(henceforth, "convection") with NNs. In each SELF we use either a neighbourhood
filter, which highlights convection at scales larger than a threshold, or a
spectral filter (employing Fourier or wavelet decomposition), which is more
flexible and highlights convection at scales between two thresholds. We use
these filters to spatially enhance common verification scores, such as the
Brier score. We train each NN with a different SELF and compare their
performance at many scales of convection, from discrete storm cells to tropical
cyclones. Among our many findings are that (a) for a low (high) risk threshold,
the ideal SELF focuses on small (large) scales; (b) models trained with a
pixelwise loss function perform surprisingly well; (c) however, models trained
with a spectral filter produce better-calibrated probabilities than a pixelwise
model. We provide a general guide to using SELFs, including technical
challenges and the final Python code, as well as demonstrating their use for
the convection problem. To our knowledge this is the most in-depth guide to
SELFs in the geosciences.

The spread of hatred that was formerly limited to verbal communications has
rapidly moved over the Internet. Social media and community forums that allow
people to discuss and express their opinions are becoming platforms for the
spreading of hate messages. Many countries have developed laws to avoid online
hate speech. They hold the companies that run the social media responsible for
their failure to eliminate hate speech. But as online content continues to
grow, so does the spread of hate speech However, manual analysis of hate speech
on online platforms is infeasible due to the huge amount of data as it is
expensive and time consuming. Thus, it is important to automatically process
the online user contents to detect and remove hate speech from online media.
Many recent approaches suffer from interpretability problem which means that it
can be difficult to understand why the systems make the decisions they do.
Through this work, some solutions for the problem of automatic detection of
hate messages were proposed using Support Vector Machine (SVM) and Na\"ive
Bayes algorithms. This achieved near state-of-the-art performance while being
simpler and producing more easily interpretable decisions than other methods.
Empirical evaluation of this technique has resulted in a classification
accuracy of approximately 99% and 50% for SVM and NB respectively over the test
set.
  Keywords: classification; hate speech; feature extraction, algorithm,
supervised learning

We study the problem of efficient object detection of 3D LiDAR point clouds.
To reduce the memory and computational cost, existing point-based pipelines
usually adopt task-agnostic random sampling or farthest point sampling to
progressively downsample input point clouds, despite the fact that not all
points are equally important to the task of object detection. In particular,
the foreground points are inherently more important than background points for
object detectors. Motivated by this, we propose a highly-efficient single-stage
point-based 3D detector in this paper, termed IA-SSD. The key of our approach
is to exploit two learnable, task-oriented, instance-aware downsampling
strategies to hierarchically select the foreground points belonging to objects
of interest. Additionally, we also introduce a contextual centroid perception
module to further estimate precise instance centers. Finally, we build our
IA-SSD following the encoder-only architecture for efficiency. Extensive
experiments conducted on several large-scale detection benchmarks demonstrate
the competitive performance of our IA-SSD. Thanks to the low memory footprint
and a high degree of parallelism, it achieves a superior speed of 80+
frames-per-second on the KITTI dataset with a single RTX2080Ti GPU. The code is
available at \url{https://github.com/yifanzhang713/IA-SSD}.

In early 2020, the Covid-19 pandemic forced employees in tech companies
worldwide to abruptly transition from working in offices to working from their
homes. During two years of predominantly working from home, employees and
managers alike formed expectations about what post-pandemic working life should
look like. Many companies are currently experimenting with new work policies
that balance both employee- and manager expectations to where, when and how
work should be done in the future. In this article, we gather experiences from
17 companies and their sites, covering 12 countries. We share the results of
corporate surveys of employee preferences for working from home and analyse new
work policies. Our results are threefold. First, through the new work policies
all companies are formally giving more flexibility to the employees with
regards to working time and work location. Second, there is a great variation
in how much flexibility the companies are willing to yield to the employees.
The variation is related both to industry type, size of the companies, and
company culture. Third, we document a change in the psychological contract
between employees and managers, where the option of working from home is
converted from an exclusive perk that managers could choose to give to the few,
to a core privilege that all employees feel they are entitled to. Finally,
there are indications that as the companies learn and solicit feedback
regarding the efficiency of the chosen strategies, we might see further
developments and changes of the work policies with respect to how much
flexibility to work whenever and from anywhere they grant. Through these
findings, the paper contributes to a growing literature about the new trends
emerging from the pandemic in tech companies and spells out practical
implications onwards.

Despite great advances in explaining synaptic plasticity and neuron function,
a complete understanding of the brain's learning algorithms is still missing.
Artificial neural networks provide a powerful learning paradigm through the
backpropagation algorithm which modifies synaptic weights by using feedback
connections. Backpropagation requires extensive communication of information
back through the layers of a network. This has been argued to be biologically
implausible and it is not clear whether backpropagation can be realized in the
brain. Here we suggest that biophotons guided by axons provide a potential
channel for backward transmission of information in the brain. Biophotons have
been experimentally shown to be produced in the brain, yet their purpose is not
understood. We propose that biophotons can propagate from each post-synaptic
neuron to its pre-synaptic one to carry the required information backward. To
reflect the stochastic character of biophoton emissions, our model includes the
stochastic backward transmission of teaching signals. We demonstrate that a
three-layered network of neurons can learn the MNIST handwritten digit
classification task using our proposed backpropagation-like algorithm with
stochastic photonic feedback. We model realistic restrictions and show that our
system still learns the task for low rates of biophoton emission,
information-limited (one bit per photon) backward transmission, and in the
presence of noise photons. Our results suggest a new functionality for
biophotons and provide an alternate mechanism for backward transmission in the
brain.

The aim of visualization is to support people in dealing with large and
complex information structures, to make these structures more comprehensible,
facilitate exploration, and enable knowledge discovery. However, users often
have problems reading and interpreting data from visualizations, in particular
when they experience them for the first time. A lack of visualization literacy,
i.e., knowledge in terms of domain, data, visual encoding, interaction, and
also analytical methods can be observed. To support users in learning how to
use new digital technologies, the concept of onboarding has been successfully
applied in other domains. However, it has not received much attention from the
visualization community so far. This chapter aims to fill this gap by defining
the concept and systematically laying out the design space of onboarding in the
context of visualization as a descriptive design space. On this basis, we
present a survey of approaches from the academic community as well as from
commercial products, especially surveying educational theories that inform the
onboarding strategies. Additionally, we derived design considerations based on
previous publications and present some guidelines for the design of
visualization onboarding concepts.

Deep neural networks are commonly used for medical purposes such as image
generation, segmentation, or classification. Besides this, they are often
criticized as black boxes as their decision process is often not human
interpretable. Encouraging the latent representation of a generative model to
be disentangled offers new perspectives of control and interpretability.
Understanding the data generation process could help to create artificial
medical data sets without violating patient privacy, synthesizing different
data modalities, or discovering data generating characteristics. These
characteristics might unravel novel relationships that can be related to
genetic traits or patient outcomes. In this paper, we give a comprehensive
overview of popular generative models, like Generative Adversarial Networks
(GANs), Variational Autoencoders (VAEs), and Flow-based Models. Furthermore, we
summarize the different notions of disentanglement, review approaches to
disentangle latent space representations and metrics to evaluate the degree of
disentanglement. After introducing the theoretical frameworks, we give an
overview of recent medical applications and discuss the impact and importance
of disentanglement approaches for medical applications.

Unlike classical lexical overlap metrics such as BLEU, most current
evaluation metrics (such as BERTScore or MoverScore) are based on black-box
language models such as BERT or XLM-R. They often achieve strong correlations
with human judgments, but recent research indicates that the lower-quality
classical metrics remain dominant, one of the potential reasons being that
their decision processes are transparent. To foster more widespread acceptance
of the novel high-quality metrics, explainability thus becomes crucial. In this
concept paper, we identify key properties and propose key goals of explainable
machine translation evaluation metrics. We also provide a synthesizing overview
over recent approaches for explainable machine translation metrics and discuss
how they relate to those goals and properties. Further, we conduct own novel
experiments, which (among others) find that current adversarial NLP techniques
are unsuitable for automatically identifying limitations of high-quality
black-box evaluation metrics, as they are not meaning-preserving. Finally, we
provide a vision of future approaches to explainable evaluation metrics and
their evaluation. We hope that our work can help catalyze and guide future
research on explainable evaluation metrics and, mediately, also contribute to
better and more transparent text generation systems.

In order for AI to be safely deployed in real-world scenarios such as
hospitals, schools, and the workplace, it must be able to robustly reason about
the physical world. Fundamental to this reasoning is physical common sense:
understanding the physical properties and affordances of available objects, how
they can be manipulated, and how they interact with other objects. Physical
commonsense reasoning is fundamentally a multi-sensory task, since physical
properties are manifested through multiple modalities - two of them being
vision and acoustics. Our paper takes a step towards real-world physical
commonsense reasoning by contributing PACS: the first audiovisual benchmark
annotated for physical commonsense attributes. PACS contains 13,400
question-answer pairs, involving 1,377 unique physical commonsense questions
and 1,526 videos. Our dataset provides new opportunities to advance the
research field of physical reasoning by bringing audio as a core component of
this multimodal problem. Using PACS, we evaluate multiple state-of-the-art
models on our new challenging task. While some models show promising results
(70% accuracy), they all fall short of human performance (95% accuracy). We
conclude the paper by demonstrating the importance of multimodal reasoning and
providing possible avenues for future research.

As the services and requirements of next-generation wireless networks become
increasingly diversified, it is estimated that the current frequency bands of
mobile network operators (MNOs) will be unable to cope with the immensity of
anticipated demands. Due to spectrum scarcity, there has been a growing trend
among stakeholders toward identifying practical solutions to make the most
productive use of the exclusively allocated bands on a shared basis through
spectrum sharing mechanisms. However, due to the technical complexities of
these mechanisms, their design presents challenges, as it requires coordination
among multiple entities. To address this challenge, in this paper, we begin
with a detailed review of the recent literature on spectrum sharing methods,
classifying them on the basis of their operational frequency regime that is,
whether they are implemented to operate in licensed bands (e.g., licensed
shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing
(DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted
access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to
narrow the gap between the standardization and vendor-specific implementations,
we provide a detailed review of the potential implementation scenarios and
necessary amendments to legacy cellular networks from the perspective of
telecom vendors and regulatory bodies. Next, we analyze applications of
artificial intelligence (AI) and machine learning (ML) techniques for
facilitating spectrum sharing mechanisms and leveraging the full potential of
autonomous sharing scenarios. Finally, we conclude the paper by presenting open
research challenges, which aim to provide insights into prospective research

BEFANA is a free and open-source software tool for ecological network
analysis and visualisation. It is adapted to ecologists' needs and allows them
to study the topology and dynamics of ecological networks as well as apply
selected machine learning algorithms. BEFANA is implemented in Python, and
structured as an ordered collection of interactive computational notebooks. It
relies on widely used open-source libraries, and aims to achieve simplicity,
interactivity, and extensibility. BEFANA provides methods and implementations
for data loading and preprocessing, network analysis and interactive
visualisation, modelling with experimental data, and predictive modelling with
machine learning. We showcase BEFANA through a concrete example of a detrital
soil food web of agricultural grasslands, and demonstrate all of its main
components and functionalities.

Gene expression is a biochemical process, where stochastic binding and
un-binding events naturally generate fluctuations and cell-to-cell variability
in gene dynamics. These fluctuations typically have destructive consequences
for proper biological dynamics and function (e.g., loss of timing and synchrony
in biological oscillators). Here, we show that gene expression noise
counter-intuitively accelerates the evolution of a biological oscillator and,
thus, can impart a benefit to living organisms. We used computer simulations to
evolve two mechanistic models of a biological oscillator at different levels of
gene expression noise. We first show that gene expression noise induces
oscillatory-like dynamics in regions of parameter space that cannot oscillate
in the absence of noise. We then demonstrate that these noise-induced
oscillations generate a fitness landscape whose gradient robustly and quickly
guides evolution by mutation towards robust and self-sustaining oscillation.
These results suggest that noise can help dynamical systems evolve or learn new
behavior by revealing cryptic dynamic phenotypes outside the bifurcation point.

Scene flow is a powerful tool for capturing the motion field of 3D point
clouds. However, it is difficult to directly apply flow-based models to dynamic
point cloud classification since the unstructured points make it hard or even
impossible to efficiently and effectively trace point-wise correspondences. To
capture 3D motions without explicitly tracking correspondences, we propose a
kinematics-inspired neural network (Kinet) by generalizing the kinematic
concept of ST-surfaces to the feature space. By unrolling the normal solver of
ST-surfaces in the feature space, Kinet implicitly encodes feature-level
dynamics and gains advantages from the use of mature backbones for static point
cloud processing. With only minor changes in network structures and low
computing overhead, it is painless to jointly train and deploy our framework
with a given static model. Experiments on NvGesture, SHREC'17, MSRAction-3D,
and NTU-RGBD demonstrate its efficacy in performance, efficiency in both the
number of parameters and computational complexity, as well as its versatility
to various static backbones. Noticeably, Kinet achieves the accuracy of 93.27%
on MSRAction-3D with only 3.20M parameters and 10.35G FLOPS.

Video-based analysis of facial expressions has been increasingly applied to
infer health states of individuals, such as depression and pain. Among the
existing approaches, deep learning models composed of structures for multiscale
spatiotemporal processing have shown strong potential for encoding facial
dynamics. However, such models have high computational complexity, making for a
difficult deployment of these solutions. To address this issue, we introduce a
new technique to decompose the extraction of multiscale spatiotemporal
features. Particularly, a building block structure called Decomposed Multiscale
Spatiotemporal Network (DMSN) is presented along with three variants: DMSN-A,
DMSN-B, and DMSN-C blocks. The DMSN-A block generates multiscale
representations by analyzing spatiotemporal features at multiple temporal
ranges, while the DMSN-B block analyzes spatiotemporal features at multiple
ranges, and the DMSN-C block analyzes spatiotemporal features at multiple
spatial sizes. Using these variants, we design our DMSN architecture which has
the ability to explore a variety of multiscale spatiotemporal features,
favoring the adaptation to different facial behaviors. Our extensive
experiments on challenging datasets show that the DMSN-C block is effective for
depression detection, whereas the DMSN-A block is efficient for pain
estimation. Results also indicate that our DMSN architecture provides a
cost-effective solution for expressions that range from fewer facial variations
over time, as in depression detection, to greater variations, as in pain

Over the last two decades, a lot of work has been done in improving network
security, particularly in intrusion detection systems (IDS) and anomaly
detection. Machine learning solutions have also been employed in IDSs to detect
known and plausible attacks in incoming traffic. Parameters such as packet
contents, sender IP and sender port, connection duration, etc. have been
previously used to train these machine learning models to learn to
differentiate genuine traffic from malicious ones. Generative Adversarial
Networks (GANs) have been significantly successful in detecting such anomalies,
mostly attributed to the adversarial training of the generator and
discriminator in an attempt to bypass each other and in turn increase their own
power and accuracy. However, in large networks having a wide variety of traffic
at possibly different regions of the network and susceptible to a large number
of potential attacks, training these GANs for a particular kind of anomaly may
make it oblivious to other anomalies and attacks. In addition, the dataset
required to train these models has to be made centrally available and publicly
accessible, posing the obvious question of privacy of the communications of the
respective participants of the network. The solution proposed in this work aims
at tackling the above two issues by using GANs in a federated architecture in
networks of such scale and capacity. In such a setting, different users of the
network will be able to train and customize a centrally available adversarial
model according to their own frequently faced conditions. Simultaneously, the
member users of the network will also able to gain from the experiences of the
other users in the network.

Inverting a Generative Adversarial Network (GAN) facilitates a wide range of
image editing tasks using pre-trained generators. Existing methods typically
employ the latent space of GANs as the inversion space yet observe the
insufficient recovery of spatial details. In this work, we propose to involve
the padding space of the generator to complement the latent space with spatial
information. Concretely, we replace the constant padding (e.g., usually zeros)
used in convolution layers with some instance-aware coefficients. In this way,
the inductive bias assumed in the pre-trained model can be appropriately
adapted to fit each individual image. Through learning a carefully designed
encoder, we manage to improve the inversion quality both qualitatively and
quantitatively, outperforming existing alternatives. We then demonstrate that
such a space extension barely affects the native GAN manifold, hence we can
still reuse the prior knowledge learned by GANs for various downstream
applications. Beyond the editing tasks explored in prior arts, our approach
allows a more flexible image manipulation, such as the separate control of face
contour and facial details, and enables a novel editing manner where users can
customize their own manipulations highly efficiently.

Data-driven methods that detect anomalies in times series data are ubiquitous
in practice, but they are in general unable to provide helpful explanations for
the predictions they make. In this work we propose a model-agnostic algorithm
that generates counterfactual ensemble explanations for time series anomaly
detection models. Our method generates a set of diverse counterfactual
examples, i.e, multiple perturbed versions of the original time series that are
not considered anomalous by the detection model. Since the magnitude of the
perturbations is limited, these counterfactuals represent an ensemble of inputs
similar to the original time series that the model would deem normal. Our
algorithm is applicable to any differentiable anomaly detection model. We
investigate the value of our method on univariate and multivariate real-world
datasets and two deep-learning-based anomaly detection models, under several
explainability criteria previously proposed in other data domains such as
Validity, Plausibility, Closeness and Diversity. We show that our algorithm can
produce ensembles of counterfactual examples that satisfy these criteria and
thanks to a novel type of visualisation, can convey a richer interpretation of
a model's internal mechanism than existing methods. Moreover, we design a
sparse variant of our method to improve the interpretability of counterfactual
explanations for high-dimensional time series anomalies. In this setting, our
explanation is localised on only a few dimensions and can therefore be
communicated more efficiently to the model's user.

Gameplay videos contain rich information about how players interact with the
game and how the game responds. Sharing gameplay videos on social media
platforms, such as Reddit, has become a common practice for many players.
Often, players will share gameplay videos that showcase video game bugs. Such
gameplay videos are software artifacts that can be utilized for game testing,
as they provide insight for bug analysis. Although large repositories of
gameplay videos exist, parsing and mining them in an effective and structured
fashion has still remained a big challenge. In this paper, we propose a search
method that accepts any English text query as input to retrieve relevant videos
from large repositories of gameplay videos. Our approach does not rely on any
external information (such as video metadata); it works solely based on the
content of the video. By leveraging the zero-shot transfer capabilities of the
Contrastive Language-Image Pre-Training (CLIP) model, our approach does not
require any data labeling or training. To evaluate our approach, we present the
$\texttt{GamePhysics}$ dataset consisting of 26,954 videos from 1,873 games,
that were collected from the GamePhysics section on the Reddit website. Our
approach shows promising results in our extensive analysis of simple queries,
compound queries, and bug queries, indicating that our approach is useful for
object and event detection in gameplay videos. An example application of our
approach is as a gameplay video search engine to aid in reproducing video game
bugs. Please visit the following link for the code and the data:
https://asgaardlab.github.io/CLIPxGamePhysics/

Clinical coding is the task of transforming medical information in a
patient's health records into structured codes so that they can be used for
statistical analysis. This is a cognitive and time-consuming task that follows
a standard process in order to achieve a high level of consistency. Clinical
coding could potentially be supported by an automated system to improve the
efficiency and accuracy of the process. We introduce the idea of automated
clinical coding and summarise its challenges from the perspective of Artificial
Intelligence (AI) and Natural Language Processing (NLP), based on the
literature, our project experience over the past two and half years (late 2019
- early 2022), and discussions with clinical coding experts in Scotland and the
UK. Our research reveals the gaps between the current deep learning-based
approach applied to clinical coding and the need for explainability and
consistency in real-world practice. Knowledge-based methods that represent and
reason the standard, explainable process of a task may need to be incorporated
into deep learning-based methods for clinical coding. Automated clinical coding
is a promising task for AI, despite the technical and organisational
challenges. Coders are needed to be involved in the development process. There
is much to achieve to develop and deploy an AI-based automated system to
support coding in the next five years and beyond.

Methods for 3D lane detection have been recently proposed to address the
issue of inaccurate lane layouts in many autonomous driving scenarios
(uphill/downhill, bump, etc.). Previous work struggled in complex cases due to
their simple designs of the spatial transformation between front view and
bird's eye view (BEV) and the lack of a realistic dataset. Towards these
issues, we present PersFormer: an end-to-end monocular 3D lane detector with a
novel Transformer-based spatial feature transformation module. Our model
generates BEV features by attending to related front-view local regions with
camera parameters as a reference. PersFormer adopts a unified 2D/3D anchor
design and an auxiliary task to detect 2D/3D lanes simultaneously, enhancing
the feature consistency and sharing the benefits of multi-task learning.
Moreover, we release one of the first large-scale real-world 3D lane datasets:
OpenLane, with high-quality annotation and scenario diversity. OpenLane
contains 200,000 frames, over 880,000 instance-level lanes, 14 lane categories,
along with scene tags and the closed-in-path object annotations to encourage
the development of lane detection and more industrial-related autonomous
driving methods. We show that PersFormer significantly outperforms competitive
baselines in the 3D lane detection task on our new OpenLane dataset as well as
Apollo 3D Lane Synthetic dataset, and is also on par with state-of-the-art
algorithms in the 2D task on OpenLane. The project page is available at
https://github.com/OpenPerceptionX/PersFormer_3DLane and OpenLane dataset is
provided at https://github.com/OpenPerceptionX/OpenLane.

OpenStreetMap is a unique source of openly available worldwide map data,
increasingly adopted in real-world applications. Vandalism detection in
OpenStreetMap is critical and remarkably challenging due to the large scale of
the dataset, the sheer number of contributors, various vandalism forms, and the
lack of annotated data to train machine learning algorithms. This paper
presents Ovid - a novel machine learning method for vandalism detection in
OpenStreetMap. Ovid relies on a neural network architecture that adopts a
multi-head attention mechanism to effectively summarize information indicating
vandalism from OpenStreetMap changesets. To facilitate automated vandalism
detection, we introduce a set of original features that capture changeset,
user, and edit information. Our evaluation results on real-world vandalism data
demonstrate that the proposed Ovid method outperforms the baselines by 4.7
percentage points in F1 score.

When training neural networks with simulated quantization, we observe that
quantized weights can, rather unexpectedly, oscillate between two grid-points.
The importance of this effect and its impact on quantization-aware training
(QAT) are not well-understood or investigated in literature. In this paper, we
delve deeper into the phenomenon of weight oscillations and show that it can
lead to a significant accuracy degradation due to wrongly estimated
batch-normalization statistics during inference and increased noise during
training. These effects are particularly pronounced in low-bit ($\leq$ 4-bits)
quantization of efficient networks with depth-wise separable layers, such as
MobileNets and EfficientNets. In our analysis we investigate several previously
proposed QAT algorithms and show that most of these are unable to overcome
oscillations. Finally, we propose two novel QAT algorithms to overcome
oscillations during training: oscillation dampening and iterative weight
freezing. We demonstrate that our algorithms achieve state-of-the-art accuracy
for low-bit (3 & 4 bits) weight and activation quantization of efficient
architectures, such as MobileNetV2, MobileNetV3, and EfficentNet-lite on
ImageNet. Our source code is available at
{https://github.com/qualcomm-ai-research/oscillations-qat}.

Creating presentation slides is a critical but time-consuming task for data
scientists. While researchers have proposed many AI techniques to lift data
scientists' burden on data preparation and model selection, few have targeted
the presentation creation task. Based on the needs identified from a formative
study, this paper presents NB2Slides, an AI system that facilitates users to
compose presentations of their data science work. NB2Slides uses deep learning
methods as well as example-based prompts to generate slides from computational
notebooks, and take users' input (e.g., audience background) to structure the
slides. NB2Slides also provides an interactive visualization that links the
slides with the notebook to help users further edit the slides. A follow-up
user evaluation with 12 data scientists shows that participants believed
NB2Slides can improve efficiency and reduces the complexity of creating slides.
Yet, participants questioned the future of full automation and suggested a
human-AI collaboration paradigm.

For image classification problems, various neural network models are commonly
used due to their success in yielding high accuracies. Convolutional Neural
Network (CNN) is one of the most frequently used deep learning methods for
image classification applications. It may produce extraordinarily accurate
results with regard to its complexity. However, the more complex the model is
the longer it takes to train. In this paper, an acceleration design that uses
the power of FPGA is given for a basic CNN model which consists of one
convolutional layer and one fully connected layer for the training phase of the
fully connected layer. Nonetheless, inference phase is also accelerated
automatically due to the fact that training phase includes inference. In this
design, the convolutional layer is calculated by the host computer and the
fully connected layer is calculated by an FPGA board. It should be noted that
the training of convolutional layer is not taken into account in this design
and is left for future research. The results are quite encouraging as this FPGA
design tops the performance of some of the state-of-the-art deep learning
platforms such as Tensorflow on the host computer approximately 2 times in both
training and inference.

Despite abundant data on the spatial distribution of contemporary human
settlements, historical data on the long-term evolution of human settlements at
fine spatial and temporal granularity is scarce, limiting our quantitative
understanding of long-term changes of built-up areas. This is because commonly
used mapping methods (e.g., image classification) and suitable data sources
(i.e., aerial imagery, multi-spectral remote sensing data, LiDAR) have only
been available in recent decades. However, there are alternative data sources
such as cadastral records that are digitally available, containing relevant
information such as building age information, allowing for an approximate,
digital reconstruction of past building distributions. We conducted a
non-exhaustive search of open and publicly available data resources from
administrative institutions in the United States and gathered, integrated, and
harmonized cadastral parcel data, tax assessment data, and building footprint
data for 33 counties, wherever building footprint geometries and building
construction year information was available. The result of this effort is a
unique dataset which we call the Multi-Temporal Building Footprint Dataset for
33 U.S. Counties (MTBF-33). MTBF-33 contains over 6.2 million building
footprints including their construction year, and can be used to derive
retrospective depictions of built-up areas from 1900 to 2015, at fine spatial
and temporal grain and can be used for data validation purposes, or to train
statistical learning approaches aiming to extract historical information on
human settlements from remote sensing data, historical maps, or similar data
sources. MTBF-33 is available at http://doi.org/10.17632/w33vbvjtdy.

This article aims to study intrusion attacks and then develop a novel
cyberattack detection framework for blockchain networks. Specifically, we first
design and implement a blockchain network in our laboratory. This blockchain
network will serve two purposes, i.e., generate the real traffic data
(including both normal data and attack data) for our learning models and
implement real-time experiments to evaluate the performance of our proposed
intrusion detection framework. To the best of our knowledge, this is the first
dataset that is synthesized in a laboratory for cyberattacks in a blockchain
network. We then propose a novel collaborative learning model that allows
efficient deployment in the blockchain network to detect attacks. The main idea
of the proposed learning model is to enable blockchain nodes to actively
collect data, share the knowledge learned from its data, and then exchange the
knowledge with other blockchain nodes in the network. In this way, we can not
only leverage the knowledge from all the nodes in the network but also do not
need to gather all raw data for training at a centralized node like
conventional centralized learning solutions. Such a framework can also avoid
the risk of exposing local data's privacy as well as the excessive network
overhead/congestion. Both intensive simulations and real-time experiments
clearly show that our proposed collaborative learning-based intrusion detection
framework can achieve an accuracy of up to 97.7% in detecting attacks.

This paper presents Dense Siamese Network (DenseSiam), a simple unsupervised
learning framework for dense prediction tasks. It learns visual representations
by maximizing the similarity between two views of one image with two types of
consistency, i.e., pixel consistency and region consistency. Concretely,
DenseSiam first maximizes the pixel level spatial consistency according to the
exact location correspondence in the overlapped area. It also extracts a batch
of region embeddings that correspond to some sub-regions in the overlapped area
to be contrasted for region consistency. In contrast to previous methods that
require negative pixel pairs, momentum encoders or heuristic masks, DenseSiam
benefits from the simple Siamese network and optimizes the consistency of
different granularities. It also proves that the simple location correspondence
and interacted region embeddings are effective enough to learn the similarity.
We apply DenseSiam on ImageNet and obtain competitive improvements on various
downstream tasks. We also show that only with some extra task-specific losses,
the simple framework can directly conduct dense prediction tasks. On an
existing unsupervised semantic segmentation benchmark, it surpasses
state-of-the-art segmentation methods by 2.1 mIoU with 28% training costs. Code
and models are released at https://github.com/ZwwWayne/DenseSiam.

We study the distributed stochastic compositional optimization problems over
directed communication networks in which agents privately own a stochastic
compositional objective function and collaborate to minimize the sum of all
objective functions. We propose a distributed stochastic compositional gradient
descent method, where the gradient tracking and the stochastic correction
techniques are employed to adapt to the networks' directed structure and
increase the accuracy of inner function estimation. When the objective function
is smooth, the proposed method achieves the convergence rate
$\mathcal{O}\left(k^{-1/2}\right)$ and sample complexity
$\mathcal{O}\left(\frac{1}{\epsilon^2}\right)$ for finding the
($\epsilon$)-stationary point. When the objective function is strongly convex,
the convergence rate is improved to $\mathcal{O}\left(k^{-1}\right)$. Moreover,
the asymptotic normality of Polyak-Ruppert averaged iterates of the proposed
method is also presented. We demonstrate the empirical performance of the
proposed method on model-agnostic meta-learning problem and logistic regression
problem.

The dynamicity of real-world systems poses a significant challenge to
deployed predictive machine learning (ML) models. Changes in the system on
which the ML model has been trained may lead to performance degradation during
the system's life cycle. Recent advances that study non-stationary environments
have mainly focused on identifying and addressing such changes caused by a
phenomenon called concept drift. Different terms have been used in the
literature to refer to the same type of concept drift and the same term for
various types. This lack of unified terminology is set out to create confusion
on distinguishing between different concept drift variants. In this paper, we
start by grouping concept drift types by their mathematical definitions and
survey the different terms used in the literature to build a consolidated
taxonomy of the field. We also review and classify performance-based concept
drift detection methods proposed in the last decade. These methods utilize the
predictive model's performance degradation to signal substantial changes in the
systems. The classification is outlined in a hierarchical diagram to provide an
orderly navigation between the methods. We present a comprehensive analysis of
the main attributes and strategies for tracking and evaluating the model's
performance in the predictive system. The paper concludes by discussing open
research challenges and possible research directions.

Color constancy aims to restore the constant colors of a scene under
different illuminants. However, due to the existence of camera spectral
sensitivity, the network trained on a certain sensor, cannot work well on
others. Also, since the training datasets are collected in certain
environments, the diversity of illuminants is limited for complex real world
prediction. In this paper, we tackle these problems via two aspects. First, we
propose cross-sensor self-supervised training to train the network. In detail,
we consider both the general sRGB images and the white-balanced RAW images from
current available datasets as the white-balanced agents. Then, we train the
network by randomly sampling the artificial illuminants in a sensor-independent
manner for scene relighting and supervision. Second, we analyze a previous
cascaded framework and present a more compact and accurate model by sharing the
backbone parameters with learning attention specifically. Experiments show that
our cross-sensor model and single-sensor model outperform other
state-of-the-art methods by a large margin on cross and single sensor
evaluations, respectively, with only 16% parameters of the previous best model.

Correctly estimating how demand respond to prices is fundamental for airlines
willing to optimize their pricing policy. Under some conditions, these
policies, while aiming at maximizing short term revenue, can present too little
price variation which may decrease the overall quality of future demand
forecasting. This problem, known as earning while learning problem, is not
exclusive to airlines, and it has been investigated by academia and industry in
recent years. One of the most promising methods presented in literature
combines the revenue maximization and the demand model quality into one single
objective function. This method has shown great success in simulation studies
and real life benchmarks. Nevertheless, this work needs to be adapted to
certain constraints that arise in the airline revenue management (RM), such as
the need to control the prices of several active flights of a leg
simultaneously. In this paper, we adjust this method to airline RM while
assuming unconstrained capacity. Then, we show that our new algorithm
efficiently performs price experimentation in order to generate more revenue
over long horizons than classical methods that seek to maximize revenue only.

Systems that rely on Machine Learning (ML systems) have differing demands on
system quality compared to traditional systems. Such quality demands, known as
non-functional requirements (NFRs), may differ in their definition, scope, and
importance from NFRs for traditional systems. Despite the importance of NFRs
for ML systems, our understanding of their definitions and scope -- and of the
extent of existing research in each NFR -- is lacking compared to our
understanding in traditional domains.
  Building on an investigation into importance and treatment of ML system NFRs
in industry, we make three contributions towards narrowing this gap: (1) we
present clusters of ML system NFRs based on shared characteristics, (2) we use
Scopus search results -- as well as inter-coder reliability on a sample of NFRs
-- to estimate the number of relevant studies on a subset of the NFRs, and (3),
we use our initial reading of titles and abstracts in each sample to define the
scope of NFRs over parts of the system (e.g., training data, ML model, or other
system elements). These initial findings form the groundwork for future
research in this emerging domain.

Parallel text-to-speech (TTS) models have recently enabled fast and
highly-natural speech synthesis. However, such models typically require
external alignment models, which are not necessarily optimized for the decoder
as they are not jointly trained. In this paper, we propose a differentiable
duration method for learning monotonic alignments between input and output
sequences. Our method is based on a soft-duration mechanism that optimizes a
stochastic process in expectation. Using this differentiable duration method, a
direct text to waveform TTS model is introduced to produce raw audio as output
instead of performing neural vocoding. Our model learns to perform
high-fidelity speech synthesis through a combination of adversarial training
and matching the total ground-truth duration. Experimental results show that
our model obtains competitive results while enjoying a much simpler training
pipeline. Audio samples are available online.

We present the Deep Convolutional Gaussian Mixture Model (DCGMM), a new
probabilistic approach for image modeling capable of density estimation,
sampling and tractable inference. DCGMM instances exhibit a CNN-like layered
structure, in which the principal building blocks are convolutional Gaussian
Mixture (cGMM) layers. A key innovation w.r.t. related models like sum-product
networks (SPNs) and probabilistic circuits (PCs) is that each cGMM layer
optimizes an independent loss function and therefore has an independent
probabilistic interpretation. This modular approach permits intervening
transformation layers to harness the full spectrum of (potentially
non-invertible) mappings available to CNNs, e.g., max-pooling or
half-convolutions. DCGMM sampling and inference are realized by a deep chain of
hierarchical priors, where a sample generated by a given cGMM layer defines the
parameters of sampling in the next-lower cGMM layer. For sampling through
non-invertible transformation layers, we introduce a new gradient-based
sharpening technique that exploits redundancy (overlap) in, e.g.,
half-convolutions. DCGMMs can be trained end-to-end by SGD from random initial
conditions, much like CNNs. We show that DCGMMs compare favorably to several
recent PC and SPN models in terms of inference, classification and sampling,
the latter particularly for challenging datasets such as SVHN. We provide a
public TF2 implementation.

The number of Digital Signal Processor (DSP) resources available in Field
Programmable Gate Arrays (FPGAs) is often quite limited. Therefore, full
utilization of available DSP resources for the computationally intensive parts
of an algorithm is paramount for optimizing the non-functional properties of an
implementation (i.e., performance, power, and area). The DSPs available in
Xilinx devices implement large bit width operators (i.e. a 48-bit accumulator
or a $18 \times 27$ multiplier). However, using such a DSP for low-precision
quantized data (as is common in image processing or machine learning
applications) leaves the DSP resources underutilized. As a remedy, A method has
been proposed to pack and compute four 4-bit multiplications on a single DSP in
a single clock cycle. This paper presents a generalization of this scheme to
arbitrary bit widths and number of multiplications. We also demonstrate that
the previously proposed approach leads to errors (Mean Absolute Error (MAE) =
0.37). Furthermore, we explain where these errors come from and how they can be
corrected. On top, we introduce a novel approximate method called "Overpacking"
which allows to squeeze even more multiplications into a single DSP at the cost
of small errors (MAE = 0.47). Overpacking allows to squeeze six 4-bit
multiplications into a single DSP compared to just four in the literature.
Finally, we introduce an alternative method for packing multiple small-bit
width additions into a single 48-bit accumulator for use in applications such
as Spiking Neural Networks.

Glioma is the most common and aggressive brain tumor. Magnetic resonance
imaging (MRI) plays a vital role to evaluate tumors for the arrangement of
tumor surgery and the treatment of subsequent procedures. However, the manual
segmentation of the MRI image is strenuous, which limits its clinical
application. With the development of deep learning, a large number of automatic
segmentation methods have been developed, but most of them stay in 2D images,
which leads to subpar performance. Moreover, the serious voxel imbalance
between the brain tumor and the background as well as the different sizes and
locations of the brain tumor makes the segmentation of 3D images a challenging
problem. Aiming at segmenting 3D MRI, we propose a model for brain tumor
segmentation with multiple encoders. The structure contains four encoders and
one decoder. The four encoders correspond to the four modalities of the MRI
image, perform one-to-one feature extraction, and then merge the feature maps
of the four modalities into the decoder. This method reduces the difficulty of
feature extraction and greatly improves model performance. We also introduced a
new loss function named "Categorical Dice", and set different weights for
different segmented regions at the same time, which solved the problem of voxel
imbalance. We evaluated our approach using the online BraTS 2020 Challenge
verification. Our proposed method can achieve promising results in the
validation set compared to the state-of-the-art approaches with Dice scores of
0.70249, 0.88267, and 0.73864 for the intact tumor, tumor core, and enhanced
tumor, respectively.

This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel
brain-inspired unsupervised local learning rule for the online implementation
of Hebb's plasticity mechanism on neuromorphic hardware. The proposed VDSP
learning rule updates the synaptic conductance on the spike of the postsynaptic
neuron only, which reduces by a factor of two the number of updates with
respect to standard spike-timing-dependent plasticity (STDP). This update is
dependent on the membrane potential of the presynaptic neuron, which is readily
available as part of neuron implementation and hence does not require
additional memory for storage. Moreover, the update is also regularized on
synaptic weight and prevents explosion or vanishing of weights on repeated
stimulation. Rigorous mathematical analysis is performed to draw an equivalence
between VDSP and STDP. To validate the system-level performance of VDSP, we
train a single-layer spiking neural network (SNN) for the recognition of
handwritten digits. We report 85.01 $ \pm $ 0.76% (Mean $ \pm $ S.D.) accuracy
for a network of 100 output neurons on the MNIST dataset. The performance
improves when scaling the network size (89.93 $ \pm $ 0.41% for 400 output
neurons, 90.56 $ \pm $ 0.27 for 500 neurons), which validates the applicability
of the proposed learning rule for spatial pattern recognition tasks. Future
work will consider more complicated tasks. Interestingly, the learning rule
better adapts than STDP to the frequency of input signal and does not require
hand-tuning of hyperparameters

We propose a new object-centric framework for learning-based stereo 3D object
detection. Previous studies build scene-centric representations that do not
consider the significant variation among outdoor instances and thus lack the
flexibility and functionalities that an instance-level model can offer. We
build such an instance-level model by formulating and tackling a local update
problem, i.e., how to predict a refined update given an initial 3D cuboid
guess. We demonstrate how solving this problem can complement scene-centric
approaches in (i) building a coarse-to-fine multi-resolution system, (ii)
performing model-agnostic object location refinement, and (iii) conducting
stereo 3D tracking-by-detection. Extensive experiments demonstrate the
effectiveness of our approach, which achieves state-of-the-art performance on
the KITTI benchmark. Code and pre-trained models are available at
https://github.com/Nicholasli1995/SNVC.

Automatic machine learning of empirical models from experimental data has
recently become possible as a result of increased availability of computational
power and dedicated algorithms. Despite the successes of non-parametric
inference and neural-network-based inference for empirical modelling, a
physical interpretation of the results often remains challenging. Here, we
focus on direct inference of governing differential equations from data, which
can be formulated as a linear inverse problem. A Bayesian framework with a
Laplacian prior distribution is employed for finding sparse solutions
efficiently. The superior accuracy and robustness of the method is demonstrated
for various cases, including ordinary, partial, and stochastic differential
equations. Furthermore, we develop an active learning procedure for the
automated discovery of stochastic differential equations. In this procedure,
learning of the unknown dynamical equations is coupled to the application of
perturbations to the measured system in a feedback loop. We demonstrate with
simulations that the active learning procedure improves the inference of
empirical, Langevin-type descriptions of stochastic processes.

Graph-based reasoning over skeleton data has emerged as a promising approach
for human action recognition. However, the application of prior graph-based
methods, which predominantly employ whole temporal sequences as their input, to
the setting of online inference entails considerable computational redundancy.
In this paper, we tackle this issue by reformulating the Spatio-Temporal Graph
Convolutional Neural Network as a Continual Inference Network, which can
perform step-by-step predictions in time without repeat frame processing. To
evaluate our method, we create a continual version of ST-GCN, CoST-GCN,
alongside two derived methods with different self-attention mechanisms, CoAGCN
and CoS-TR. We investigate weight transfer strategies and architectural
modifications for inference acceleration, and perform experiments on the NTU
RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400 datasets. Retaining similar
predictive accuracy, we observe up to 109x reduction in time complexity,
on-hardware accelerations of 26x, and reductions in maximum allocated memory of
52% during online inference.

We apply the TrOCR framework to real-world, historical manuscripts and show
that TrOCR per se is a strong model, ideal for transfer learning. TrOCR has
been trained on English only, but it can adapt to other languages that use the
Latin alphabet fairly easily and with little training material. We compare
TrOCR against a SOTA HTR framework (Transkribus) and show that it can beat such
systems. This finding is essential since Transkribus performs best when it has
access to baseline information, which is not needed at all to fine-tune TrOCR.

The high prevalence of work-related musculoskeletal disorders (WMSDs) could
be addressed by optimizing Human-Robot Collaboration (HRC) frameworks for
manufacturing applications. In this context, this paper proposes two hypotheses
for ergonomically effective task delegation and HRC. The first hypothesis
states that it is possible to quantify ergonomically professional tasks using
motion data from a reduced set of sensors. Then, the most dangerous tasks can
be delegated to a collaborative robot. The second hypothesis is that by
including gesture recognition and spatial adaptation, the ergonomics of an HRC
scenario can be improved by avoiding needless motions that could expose
operators to ergonomic risks and by lowering the physical effort required of
operators. An HRC scenario for a television manufacturing process is optimized
to test both hypotheses. For the ergonomic evaluation, motion primitives with
known ergonomic risks were modeled for their detection in professional tasks
and to estimate a risk score based on the European Assembly Worksheet (EAWS). A
Deep Learning gesture recognition module trained with egocentric television
assembly data was used to complement the collaboration between the human
operator and the robot. Additionally, a skeleton-tracking algorithm provided
the robot with information about the operator's pose, allowing it to spatially
adapt its motion to the operator's anthropometrics. Three experiments were
conducted to determine the effect of gesture recognition and spatial adaptation
on the operator's range of motion. The rate of spatial adaptation was used as a
key performance indicator (KPI), and a new KPI for measuring the reduction in
the operator's motion is presented in this paper.

Underwater Image Rendering aims to generate a true-tolife underwater image
from a given clean one, which could be applied to various practical
applications such as underwater image enhancement, camera filter, and virtual
gaming. We explore two less-touched but challenging problems in underwater
image rendering, namely, i) how to render diverse underwater scenes by a single
neural network? ii) how to adaptively learn the underwater light fields from
natural exemplars, i,e., realistic underwater images? To this end, we propose a
neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural
Rendering). Specifically, UWNR is a data-driven neural network that implicitly
learns the natural degenerated model from authentic underwater images, avoiding
introducing erroneous biases by hand-craft imaging models. Compared with
existing underwater image generation methods, UWNR utilizes the natural light
field to simulate the main characteristics ofthe underwater scene. Thus, it is
able to synthesize a wide variety ofunderwater images from one clean image with
various realistic underwater images. Extensive experiments demonstrate that our
approach achieves better visual effects and quantitative metrics over previous
methods. Moreover, we adopt UWNR to build an open Large Neural Rendering
Underwater Dataset containing various types of water quality, dubbed LNRUD. The
source code and LNRUD are available at https: //github.com/Ephemeral182/UWNR.

Numerical optimization of complex systems benefits from the technological
development of computing platforms in the last twenty years. Unfortunately,
this is still not enough, and a large computational time is still necessary
when mathematical models that include richer (and therefore more realistic)
physical models are adopted.
  In this paper we show how the combination of optimization and Artificial
Intelligence (AI), in particular the Machine Learning algorithms, can help in
strongly reducing the overall computational times, making possible the use of
complex simulation systems within the optimization cycle. Original approaches
are also proposed.

We present a new model for the probability that the Disturbance storm time
(Dst) index exceeds -100 nT, with a lead time between 1 and 3 days. $Dst$
provides essential information about the strength of the ring current around
the Earth caused by the protons and electrons from the solar wind, and it is
routinely used as a proxy for geomagnetic storms. The model is developed using
an ensemble of Convolutional Neural Networks (CNNs) that are trained using SoHO
images (MDI, EIT and LASCO). The relationship between the SoHO images and the
solar wind has been investigated by many researchers, but these studies have
not explicitly considered using SoHO images to predict the $Dst$ index.
  This work presents a novel methodology to train the individual models and to
learn the optimal ensemble weights iteratively, by using a customized
class-balanced mean square error (CB-MSE) loss function tied to a least-squares
(LS) based ensemble.
  The proposed model can predict the probability that Dst<-100 nT 24 hours
ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation
Coefficient (MCC) of 0.37. The weighted TSS and MCC from Guastavino et al.
(2021) is 0.68 and 0.47, respectively. An additional validation during
non-Earth-directed CME periods is also conducted which yields a good TSS and
MCC score.

Aiming for higher-level scene understanding, this work presents a neural
network approach that takes a road-layout map in bird's-eye-view as input, and
predicts a human-interpretable graph that represents the road's topological
layout. Our approach elevates the understanding of road layouts from pixel
level to the level of graphs. To achieve this goal, an image-graph-image
auto-encoder is utilized. The network is designed to learn to regress the graph
representation at its auto-encoder bottleneck. This learning is self-supervised
by an image reconstruction loss, without needing any external manual
annotations. We create a synthetic dataset containing common road layout
patterns and use it for training of the auto-encoder in addition to the
real-world Argoverse dataset. By using this additional synthetic dataset, which
conceptually captures human knowledge of road layouts and makes this available
to the network for training, we are able to stabilize and further improve the
performance of topological road layout understanding on the real-world
Argoverse dataset. The evaluation shows that our approach exhibits comparable
performance to a strong fully-supervised baseline.

Recent studies have shown that language models pretrained and/or fine-tuned
on randomly permuted sentences exhibit competitive performance on GLUE, putting
into question the importance of word order information. Somewhat
counter-intuitively, some of these studies also report that position embeddings
appear to be crucial for models' good performance with shuffled text. We probe
these language models for word order information and investigate what position
embeddings learned from shuffled text encode, showing that these models retain
information pertaining to the original, naturalistic word order. We show this
is in part due to a subtlety in how shuffling is implemented in previous work
-- before rather than after subword segmentation. Surprisingly, we find even
Language models trained on text shuffled after subword segmentation retain some
semblance of information about word order because of the statistical
dependencies between sentence length and unigram probabilities. Finally, we
show that beyond GLUE, a variety of language understanding tasks do require
word order information, often to an extent that cannot be learned through

With rising powerful, low-cost embedded devices, the edge computing has
become an increasingly popular choice. In this paper, we propose a new
incremental local stochastic gradient descent (SGD) tailored on the Raspberry
Pi to deal with large ImageNet ILSVRC 2010 dataset having 1,261,405 images with
1,000 classes. The local SGD splits the data block into $k$ partitions using
$k$means algorithm and then it learns in the parallel way SGD models in each
data partition to classify the data locally. The incremental local SGD
sequentially loads small data blocks of the training dataset to learn local SGD
models. The numerical test results on Imagenet dataset show that our
incremental local SGD algorithm with the Raspberry Pi 4 is faster and more
accurate than the state-of-the-art linear SVM run on a PC Intel(R) Core i7-4790
CPU, 3.6 GHz, 4 cores.

In deep learning, fine-grained N:M sparsity reduces the data footprint and
bandwidth of a General Matrix multiply (GEMM) by x2, and doubles throughput by
skipping computation of zero values. So far, it was only used to prune weights.
We examine how this method can be used also for activations and their gradients
(i.e., "neural gradients"). To this end, we first establish a tensor-level
optimality criteria. Previous works aimed to minimize the mean-square-error
(MSE) of each pruned block. We show that while minimization of the MSE works
fine for pruning the activations, it catastrophically fails for the neural
gradients. Instead, we show that optimal pruning of the neural gradients
requires an unbiased minimum-variance pruning mask. We design such specialized
masks, and find that in most cases, 1:2 sparsity is sufficient for training,
and 2:4 sparsity is usually enough when this is not the case. Further, we
suggest combining several such methods together in order to potentially speed
up training even more. A reference implementation is supplied in
https://github.com/brianchmiel/Act-and-Grad-structured-sparsity.

It was recently proposed that neural networks could be used to approximate
many-dimensional probability distributions that appear e.g. in lattice field
theories or statistical mechanics. Subsequently they can be used as variational
approximators to asses extensive properties of statistical systems, like free
energy, and also as neural samplers used in Monte Carlo simulations. The
practical application of this approach is unfortunately limited by its
unfavorable scaling both of the numerical cost required for training, and the
memory requirements with the system size. This is due to the fact that the
original proposition involved a neural network of width which scaled with the
total number of degrees of freedom, e.g. $L^2$ in case of a two dimensional
$L\times L$ lattice. In this work we propose a hierarchical association of
physical degrees of freedom, for instance spins, to neurons which replaces it
with the scaling with the linear extent $L$ of the system. We demonstrate our
approach on the two-dimensional Ising model by simulating lattices of various
sizes up to $128 \times 128$ spins, with time benchmarks reaching lattices of
size $512 \times 512$. We observe that our proposal improves the quality of
neural network training, i.e. the approximated probability distribution is
closer to the target that could be previously achieved. As a consequence, the
variational free energy reaches a value closer to its theoretical expectation
and, if applied in a Markov Chain Monte Carlo algorithm, the resulting
autocorrelation time is smaller. Finally, the replacement of a single neural
network by a hierarchy of smaller networks considerably reduces the memory
requirements.

Cache plays an important role to maintain high and stable performance (i.e.
high throughput, low tail latency and throughput jitter) in storage systems.
Existing rule-based cache management methods, coupled with engineers' manual
configurations, cannot meet ever-growing requirements of both time-varying
workloads and complex storage systems, leading to frequent cache overloading.
In this paper, we for the first time propose a light-weight learning-based
cache bandwidth control technique, called \LQoCo which can adaptively control
the cache bandwidth so as to effectively prevent cache overloading in storage
systems. Extensive experiments with various workloads on real systems show that
LQoCo, with its strong adaptability and fast learning ability, can adapt to
various workloads to effectively control cache bandwidth, thereby significantly
improving the storage performance (e.g. increasing the throughput by 10\%-20\%
and reducing the throughput jitter and tail latency by 2X-6X and 1.5X-4X,
respectively, compared with two representative rule-based methods).

As shared, collaborative, networked, virtual environments become increasingly
popular, various challenges arise regarding the efficient transmission of model
and scene transformation data over the network. As user immersion and real-time
interactions heavily depend on VR stream synchronization, transmitting the
entire data sat does not seem a suitable approach, especially for sessions
involving a large number of users. Session recording is another
momentum-gaining feature of VR applications that also faces the same challenge.
The selection of a suitable data format can reduce the occupied volume, while
it may also allow effective replication of the VR session and optimized
post-processing for analytics and deep-learning algorithms. In this work, we
propose two algorithms that can be applied in the context of a networked
multiplayer VR session, to efficiently transmit the displacement and
orientation data from the users' hand-based VR HMDs. Moreover, we present a
novel method describing effective VR recording of the data exchanged in such a
session. Our algorithms, based on the use of dual-quaternions and multivectors,
impact the network consumption rate and are highly effective in scenarios
involving multiple users. By sending less data over the network and
interpolating the in-between frames locally, we manage to obtain better visual
results than current state-of-the-art methods. Lastly, we prove that, for
recording purposes, storing less data and interpolating them on-demand yields a
data set quantitatively close to the original one.

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art
method for graph-based learning tasks. However, training GCNs at scale is still
challenging, hindering both the exploration of more sophisticated GCN
architectures and their applications to real-world large graphs. While it might
be natural to consider graph partition and distributed training for tackling
this challenge, this direction has only been slightly scratched the surface in
the previous works due to the limitations of existing designs. In this work, we
first analyze why distributed GCN training is ineffective and identify the
underlying cause to be the excessive number of boundary nodes of each
partitioned subgraph, which easily explodes the memory and communication costs
for GCN training. Furthermore, we propose a simple yet effective method dubbed
BNS-GCN that adopts random Boundary-Node-Sampling to enable efficient and
scalable distributed GCN training. Experiments and ablation studies
consistently validate the effectiveness of BNS-GCN, e.g., boosting the
throughput by up to 16.2x and reducing the memory usage by up to 58%, while
maintaining a full-graph accuracy. Furthermore, both theoretical and empirical
analysis show that BNS-GCN enjoys a better convergence than existing
sampling-based methods. We believe that our BNS-GCN has opened up a new
paradigm for enabling GCN training at scale. The code is available at
https://github.com/RICE-EIC/BNS-GCN.

The long duration of the COVID-19 pandemic allowed for multiple bursts in the
infection and death rates, the so-called epidemic waves. This complex behavior
is no longer tractable by simple compartmental model and requires more
sophisticated mathematical techniques for analyzing epidemic data and
generating reliable forecasts. In this work, we propose a framework for
analyzing complex dynamical systems by dividing the data in consecutive
time-windows to be separately analyzed. We fit parameters for each time-window
through an Approximate Bayesian Computation (ABC) algorithm, and the posterior
distribution of parameters obtained for one window is used as the prior
distribution for the next window. This Bayesian learning approach is tested
with data on COVID-19 cases in multiple countries and is shown to improve ABC
performance and to produce good short-term forecasting.

Monocular 3D object detection is an important yet challenging task in
autonomous driving. Some existing methods leverage depth information from an
off-the-shelf depth estimator to assist 3D detection, but suffer from the
additional computational burden and achieve limited performance caused by
inaccurate depth priors. To alleviate this, we propose MonoDTR, a novel
end-to-end depth-aware transformer network for monocular 3D object detection.
It mainly consists of two components: (1) the Depth-Aware Feature Enhancement
(DFE) module that implicitly learns depth-aware features with auxiliary
supervision without requiring extra computation, and (2) the Depth-Aware
Transformer (DTR) module that globally integrates context- and depth-aware
features. Moreover, different from conventional pixel-wise positional
encodings, we introduce a novel depth positional encoding (DPE) to inject depth
positional hints into transformers. Our proposed depth-aware modules can be
easily plugged into existing image-only monocular 3D object detectors to
improve the performance. Extensive experiments on the KITTI dataset demonstrate
that our approach outperforms previous state-of-the-art monocular-based methods
and achieves real-time detection. Code is available at
https://github.com/kuanchihhuang/MonoDTR

Uplift modeling is a rapidly growing approach that utilizes causal inference
and machine learning methods to directly estimate the heterogeneous treatment
effects, which has been widely applied to various online marketplaces to assist
large-scale decision-making in recent years. The existing popular models, like
causal forest (CF), are limited to either discrete treatments or posing
parametric assumptions on the outcome-treatment relationship that may suffer
model misspecification. However, continuous treatments (e.g., price, duration)
often arise in marketplaces. To alleviate these restrictions, we use a
kernel-based doubly robust estimator to recover the non-parametric
dose-response functions that can flexibly model continuous treatment effects.
Moreover, we propose a generic distance-based splitting criterion to capture
the heterogeneity for the continuous treatments. We call the proposed algorithm
generalized causal forest (GCF) as it generalizes the use case of CF to a much
broader setting. We show the effectiveness of GCF by deriving the asymptotic
property of the estimator and comparing it to popular uplift modeling methods
on both synthetic and real-world datasets. We implement GCF on Spark and
successfully deploy it into a large-scale online pricing system at a leading
ride-sharing company. Online A/B testing results further validate the
superiority of GCF.

Recent joint embedding-based self-supervised methods have surpassed standard
supervised approaches on various image recognition tasks such as image
classification. These self-supervised methods aim at maximizing agreement
between features extracted from two differently transformed views of the same
image, which results in learning an invariant representation with respect to
appearance and geometric image transformations. However, the effectiveness of
these approaches remains unclear in the context of gaze estimation, a
structured regression task that requires equivariance under geometric
transformations (e.g., rotations, horizontal flip). In this work, we propose
SwAT, an equivariant version of the online clustering-based self-supervised
approach SwAV, to learn more informative representations for gaze estimation.
We identify the most effective image transformations for self-supervised
pretraining and demonstrate that SwAT, with ResNet-50 and supported with
uncurated unlabeled face images, outperforms state-of-the-art gaze estimation
methods and supervised baselines in various experiments. In particular, we
achieve up to 57% and 25% improvements in cross-dataset and within-dataset
evaluation tasks on existing benchmarks (ETH-XGaze, Gaze360, and MPIIFaceGaze).

Loss functions with non-isolated minima have emerged in several machine
learning problems, creating a gap between theory and practice. In this paper,
we formulate a new type of local convexity condition that is suitable to
describe the behavior of loss functions near non-isolated minima. We show that
such condition is general enough to encompass many existing conditions. In
addition we study the local convergence of the SGD under this mild condition by
adopting the notion of stochastic stability. The corresponding concentration
inequalities from the convergence analysis help to interpret the empirical
observation from some practical training results.

Stack Overflow is often viewed as the most influential Software Question
Answer (SQA) website with millions of programming-related questions and
answers. Tags play a critical role in efficiently structuring the contents in
Stack Overflow and are vital to support a range of site operations, e.g.,
querying relevant contents. Poorly selected tags often introduce extra noise
and redundancy, which leads to tag synonym and tag explosion problems. Thus, an
automated tag recommendation technique that can accurately recommend
high-quality tags is desired to alleviate the problems mentioned above.
Inspired by the recent success of pre-trained language models (PTMs) in natural
language processing (NLP), we present PTM4Tag, a tag recommendation framework
for Stack Overflow posts that utilize PTMs with a triplet architecture, which
models the components of a post, i.e., Title, Description, and Code with
independent language models. To the best of our knowledge, this is the first
work that leverages PTMs in the tag recommendation task of SQA sites. We
comparatively evaluate the performance of PTM4Tag based on five popular
pre-trained models: BERT, RoBERTa, ALBERT, CodeBERT, and BERTOverflow. Our
results show that leveraging the software engineering (SE) domain-specific PTM
CodeBERT in PTM4Tag achieves the best performance among the five considered
PTMs and outperforms the state-of-the-art deep learning (Convolutional Neural
Network-based) approach by a large margin in terms of average $Precision@k$,
$Recall@k$, and $F1$-$score@k$. We conduct an ablation study to quantify the
contribution of a post's constituent components (Title, Description, and Code
Snippets) to the performance of PTM4Tag. Our results show that Title is the
most important in predicting the most relevant tags, and utilizing all the
components achieves the best performance.

Despite notable results in various fields over the recent years, deep
reinforcement learning (DRL) algorithms lack transparency, affecting user trust
and hindering their deployment to high-risk tasks. Causal confusion refers to a
phenomenon where an agent learns spurious correlations between features which
might not hold across the entire state space, preventing safe deployment to
real tasks where such correlations might be broken. In this work, we examine
whether an agent relies on spurious correlations in critical states, and
propose an alternative subset of features on which it should base its decisions
instead, to make it less susceptible to causal confusion. Our goal is to
increase transparency of DRL agents by exposing the influence of learned
spurious correlations on its decisions, and offering advice to developers about
feature selection in different parts of state space, to avoid causal confusion.
We propose ReCCoVER, an algorithm which detects causal confusion in agent's
reasoning before deployment, by executing its policy in alternative
environments where certain correlations between features do not hold. We
demonstrate our approach in taxi and grid world environments, where ReCCoVER
detects states in which an agent relies on spurious correlations and offers a
set of features that should be considered instead.

Implementing an autonomous vehicle that is able to output feasible, smooth
and efficient trajectories is a long-standing challenge. Several approaches
have been considered, roughly falling under two categories: rule-based and
learning-based approaches. The rule-based approaches, while guaranteeing safety
and feasibility, fall short when it comes to long-term planning and
generalization. The learning-based approaches are able to account for long-term
planning and generalization to unseen situations, but may fail to achieve
smoothness, safety and the feasibility which rule-based approaches ensure.
Hence, combining the two approaches is an evident step towards yielding the
best compromise out of both. We propose a Reinforcement Learning-based
approach, which learns target trajectory parameters for fully autonomous
driving on highways. The trained agent outputs continuous trajectory parameters
based on which a feasible polynomial-based trajectory is generated and
executed. We compare the performance of our agent against four other highway
driving agents. The experiments are conducted in the Sumo simulator, taking
into consideration various realistic, dynamically changing highway scenarios,
including surrounding vehicles with different driver behaviors. We demonstrate
that our offline trained agent, with randomly collected data, learns to drive
smoothly, achieving velocities as close as possible to the desired velocity,
while outperforming the other agents. Code, training data and details available
at: https://nrgit.informatik.uni-freiburg. de/branka.mirchevska/offline-rl-tp.

Online 3D multi-object tracking (MOT) has witnessed significant research
interest in recent years, largely driven by demand from the autonomous systems
community. However, 3D offline MOT is relatively less explored. Labeling 3D
trajectory scene data at a large scale while not relying on high-cost human
experts is still an open research question. In this work, we propose Batch3DMOT
which follows the tracking-by-detection paradigm and represents real-world
scenes as directed, acyclic, and category-disjoint tracking graphs that are
attributed using various modalities such as camera, LiDAR, and radar. We
present a multi-modal graph neural network that uses a cross-edge attention
mechanism mitigating modality intermittence, which translates into sparsity in
the graph domain. Additionally, we present attention-weighted convolutions over
frame-wise k-NN neighborhoods as suitable means to allow information exchange
across disconnected graph components. We evaluate our approach using various
sensor modalities and model configurations on the challenging nuScenes and
KITTI datasets. Extensive experiments demonstrate that our proposed approach
yields an overall improvement of 3.3% in the AMOTA score on nuScenes thereby
setting the new state-of-the-art for 3D tracking and further enhancing false
positive filtering.

Self-supervised monocular depth estimation, aiming to learn scene depths from
single images in a self-supervised manner, has received much attention
recently. In spite of recent efforts in this field, how to learn accurate scene
depths and alleviate the negative influence of occlusions for self-supervised
depth estimation, still remains an open problem. Addressing this problem, we
firstly empirically analyze the effects of both the continuous and discrete
depth constraints which are widely used in the training process of many
existing works. Then inspired by the above empirical analysis, we propose a
novel network to learn an Occlusion-aware Coarse-to-Fine Depth map for
self-supervised monocular depth estimation, called OCFD-Net. Given an arbitrary
training set of stereo image pairs, the proposed OCFD-Net does not only employ
a discrete depth constraint for learning a coarse-level depth map, but also
employ a continuous depth constraint for learning a scene depth residual,
resulting in a fine-level depth map. In addition, an occlusion-aware module is
designed under the proposed OCFD-Net, which is able to improve the capability
of the learnt fine-level depth map for handling occlusions. Experimental
results on KITTI demonstrate that the proposed method outperforms the
comparative state-of-the-art methods under seven commonly used metrics in most
cases. In addition, experimental results on Make3D demonstrate the
effectiveness of the proposed method in terms of the cross-dataset
generalization ability under four commonly used metrics. The code is available
at https://github.com/ZM-Zhou/OCFD-Net_pytorch.

Deploying machine learning applications on edge devices can bring clear
benefits such as improved reliability, latency and privacy but it also
introduces its own set of challenges. Most works focus on the limited
computational resources of edge platforms but this is not the only bottleneck
standing in the way of widespread adoption. In this paper we list several other
challenges that a TinyML practitioner might need to consider when
operationalizing an application on edge devices. We focus on tasks such as
monitoring and managing the application, common functionality for a MLOps
platform, and show how they are complicated by the distributed nature of edge
deployment. We also discuss issues that are unique to edge applications such as
protecting a model's intellectual property and verifying its integrity.

This paper considers a class of nonsmooth nonconvex-nonconcave min-max
problems in machine learning and games. We first provide sufficient conditions
for the existence of global minimax points and local minimax points. Next, we
establish the first-order and second-order optimality conditions for local
minimax points by using directional derivatives. These conditions reduce to
smooth min-max problems with Fr{\'e}chet derivatives. We apply our theoretical
results to generative adversarial networks (GANs) in which two neural networks
contest with each other in a game. Examples are used to illustrate applications
of the new theory for training GANs.

Exploiting internal spatial geometric constraints of sparse LiDARs is
beneficial to depth completion, however, has been not explored well. This paper
proposes an efficient method to learn geometry-aware embedding, which encodes
the local and global geometric structure information from 3D points, e.g.,
scene layout, object's sizes and shapes, to guide dense depth estimation.
Specifically, we utilize the dynamic graph representation to model generalized
geometric relationship from irregular point clouds in a flexible and efficient
manner. Further, we joint this embedding and corresponded RGB appearance
information to infer missing depths of the scene with well structure-preserved
details. The key to our method is to integrate implicit 3D geometric
representation into a 2D learning architecture, which leads to a better
trade-off between the performance and efficiency. Extensive experiments
demonstrate that the proposed method outperforms previous works and could
reconstruct fine depths with crisp boundaries in regions that are over-smoothed
by them. The ablation study gives more insights into our method that could
achieve significant gains with a simple design, while having better
generalization capability and stability. The code is available at
https://github.com/Wenchao-Du/GAENet.

Data driven robotics relies upon accurate real-world representations to learn
useful policies. Despite our best-efforts, zero-shot sim-to-real transfer is
still an unsolved problem, and we often need to allow our agents to explore
online to learn useful policies for a given task. For many applications of
field robotics online exploration is prohibitively expensive and dangerous,
this is especially true in fixed-wing aerial robotics. To address these
challenges we offer an intermediary solution for learning in field robotics. We
investigate the use of dissimilar platform vehicle for learning and offer a
procedure to mimic the behavior of one vehicle with another. We specifically
consider the problem of training fixed-wing aircraft, an expensive and
dangerous vehicle type, using a multi-rotor host platform. Using a Model
Predictive Control approach, we design a controller capable of mimicking
another vehicles behavior in both simulation and the real-world.

Despite the numerous breakthroughs achieved with Reinforcement Learning (RL),
solving environments with sparse rewards remains a challenging task that
requires sophisticated exploration. Learning from Demonstrations (LfD) remedies
this issue by guiding the agent's exploration towards states experienced by an
expert. Naturally, the benefits of this approach hinge on the quality of
demonstrations, which are rarely optimal in realistic scenarios. Modern LfD
algorithms require meticulous tuning of hyperparameters that control the
influence of demonstrations and, as we show in the paper, struggle with
learning from suboptimal demonstrations. To address these issues, we extend
Self-Imitation Learning (SIL), a recent RL algorithm that exploits the agent's
past good experience, to the LfD setup by initializing its replay buffer with
demonstrations. We denote our algorithm as SIL from Demonstrations (SILfD). We
empirically show that SILfD can learn from demonstrations that are noisy or far
from optimal and can automatically adjust the influence of demonstrations
throughout the training without additional hyperparameters or handcrafted
schedules. We also find SILfD superior to the existing state-of-the-art LfD
algorithms in sparse environments, especially when demonstrations are highly
suboptimal.

Cloud-enabled Machine Learning as a Service (MLaaS) has shown enormous
promise to transform how deep learning models are developed and deployed.
Nonetheless, there is a potential risk associated with the use of such services
since a malicious party can modify them to achieve an adverse result.
Therefore, it is imperative for model owners, service providers, and end-users
to verify whether the deployed model has not been tampered with or not. Such
verification requires public verifiability (i.e., fingerprinting patterns are
available to all parties, including adversaries) and black-box access to the
deployed model via APIs. Existing watermarking and fingerprinting approaches,
however, require white-box knowledge (such as gradient) to design the
fingerprinting and only support private verifiability, i.e., verification by an
honest party.
  In this paper, we describe a practical watermarking technique that enables
black-box knowledge in fingerprint design and black-box queries during
verification. The service ensures the integrity of cloud-based services through
public verification (i.e. fingerprinting patterns are available to all parties,
including adversaries). If an adversary manipulates a model, this will result
in a shift in the decision boundary. Thus, the underlying principle of
double-black watermarking is that a model's decision boundary could serve as an
inherent fingerprint for watermarking. Our approach captures the decision
boundary by generating a limited number of encysted sample fingerprints, which
are a set of naturally transformed and augmented inputs enclosed around the
model's decision boundary in order to capture the inherent fingerprints of the
model. We evaluated our watermarking approach against a variety of model
integrity attacks and model compression attacks.

An effective way to model the complex real world is to view the world as a
composition of basic components of objects and transformations. Although humans
through development understand the compositionality of the real world, it is
extremely difficult to equip robots with such a learning mechanism. In recent
years, there has been significant research on autonomously learning
representations of the world using the deep learning; however, most studies
have taken a statistical approach, which requires a large number of training
data. Contrary to such existing methods, we take a novel algebraic approach for
representation learning based on a simpler and more intuitive formulation that
the observed world is the combination of multiple independent patterns and
transformations that are invariant to the shape of patterns. Since the shape of
patterns can be viewed as the invariant features against symmetric
transformations such as translation or rotation, we can expect that the
patterns can naturally be extracted by expressing transformations with
symmetric Lie group transformers and attempting to reconstruct the scene with
them. Based on this idea, we propose a model that disentangles the scenes into
the minimum number of basic components of patterns and Lie transformations from
only one sequence of images, by introducing the learnable shape-invariant Lie
group transformers as transformation components. Experiments show that given
one sequence of images in which two objects are moving independently, the
proposed model can discover the hidden distinct objects and multiple
shape-invariant transformations that constitute the scenes.

Document-level Relation Extraction (DocRE) is a more challenging task
compared to its sentence-level counterpart. It aims to extract relations from
multiple sentences at once. In this paper, we propose a semi-supervised
framework for DocRE with three novel components. Firstly, we use an axial
attention module for learning the interdependency among entity-pairs, which
improves the performance on two-hop relations. Secondly, we propose an adaptive
focal loss to tackle the class imbalance problem of DocRE. Lastly, we use
knowledge distillation to overcome the differences between human annotated data
and distantly supervised data. We conducted experiments on two DocRE datasets.
Our model consistently outperforms strong baselines and its performance exceeds
the previous SOTA by 1.36 F1 and 1.46 Ign_F1 score on the DocRED leaderboard.
Our code and data will be released at https://github.com/tonytan48/KD-DocRE.

Despite recent stereo matching networks achieving impressive performance
given sufficient training data, they suffer from domain shifts and generalize
poorly to unseen domains. We argue that maintaining feature consistency between
matching pixels is a vital factor for promoting the generalization capability
of stereo matching networks, which has not been adequately considered. Here we
address this issue by proposing a simple pixel-wise contrastive learning across
the viewpoints. The stereo contrastive feature loss function explicitly
constrains the consistency between learned features of matching pixel pairs
which are observations of the same 3D points. A stereo selective whitening loss
is further introduced to better preserve the stereo feature consistency across
domains, which decorrelates stereo features from stereo viewpoint-specific
style information. Counter-intuitively, the generalization of feature
consistency between two viewpoints in the same scene translates to the
generalization of stereo matching performance to unseen domains. Our method is
generic in nature as it can be easily embedded into existing stereo networks
and does not require access to the samples in the target domain. When trained
on synthetic data and generalized to four real-world testing sets, our method
achieves superior performance over several state-of-the-art networks.

Recently, learned image compression techniques have achieved remarkable
performance, even surpassing the best manually designed lossy image coders.
They are promising to be large-scale adopted. For the sake of practicality, a
thorough investigation of the architecture design of learned image compression,
regarding both compression performance and running speed, is essential. In this
paper, we first propose uneven channel-conditional adaptive coding, motivated
by the observation of energy compaction in learned image compression. Combining
the proposed uneven grouping model with existing context models, we obtain a
spatial-channel contextual adaptive model to improve the coding performance
without damage to running speed. Then we study the structure of the main
transform and propose an efficient model, ELIC, to achieve state-of-the-art
speed and compression ability. With superior performance, the proposed model
also supports extremely fast preview decoding and progressive decoding, which
makes the coming application of learning-based image compression more
promising.

The importance of plastic waste recycling is undeniable. In this respect,
computer vision and deep learning enable solutions through the automated
analysis of short-wave-infrared hyper-spectral images of plastics. In this
paper, we offer an exhaustive empirical study to show the importance of
efficient model selection for resolving the task of hyper-spectral image
segmentation of various plastic flakes using deep learning. We assess the
complexity level of generic and specialized models and infer their performance
capacity: generic models are often unnecessarily complex. We introduce two
variants of a specialized hyper-spectral architecture, PlasticNet, that
outperforms several well-known segmentation architectures in both performance
as well as computational complexity. In addition, we shed lights on the
significance of signal pre-processing within the realm of hyper-spectral
imaging. To complete our contribution, we introduce the largest, most versatile
hyper-spectral dataset of plastic flakes of four primary polymer types.

In this paper, we contribute to the Extreme Bandit problem, a variant of
Multi-Armed Bandits in which the learner seeks to collect the largest possible
reward. We first study the concentration of the maximum of i.i.d random
variables under mild assumptions on the tail of the rewards distributions. This
analysis motivates the introduction of Quantile of Maxima (QoMax). The
properties of QoMax are sufficient to build an Explore-Then-Commit (ETC)
strategy, QoMax-ETC, achieving strong asymptotic guarantees despite its
simplicity. We then propose and analyze a more adaptive, anytime algorithm,
QoMax-SDA, which combines QoMax with a subsampling method recently introduced
by Baudry et al. (2021). Both algorithms are more efficient than existing
approaches in two aspects (1) they lead to better empirical performance (2)
they enjoy a significant reduction of the memory and time complexities.

We present a lightweight neural model for remote heart rate estimation
focused on the efficient spatio-temporal learning of facial
photoplethysmography (PPG) based on i) modelling of PPG dynamics by
combinations of multiple convolutional derivatives, and ii) increased
flexibility of the model to learn possible offsets between the facial video PPG
and the ground truth. PPG dynamics are modelled by a Temporal Derivative Module
(TDM) constructed by the incremental aggregation of multiple convolutional
derivatives, emulating a Taylor series expansion up to the desired order.
Robustness to ground truth offsets is handled by the introduction of TALOS
(Temporal Adaptive LOcation Shift), a new temporal loss to train learning-based
models. We verify the effectiveness of our model by reporting accuracy and
efficiency metrics on the public PURE and UBFC-rPPG datasets. Compared to
existing models, our approach shows competitive heart rate estimation accuracy
with a much lower number of parameters and lower computational cost.

Recently, supervised network embedding (NE) has emerged as a predominant
technique for representing complex systems that take the form of networks, and
various downstream node- and network-level tasks have benefited from its
remarkable developments. However, unsupervised NE still remains challenging due
to the uncertainty in defining a learning objective. In addition, it is still
an unexplored research question whether existing NE methods adapt well to
heterophilous networks. This paper introduces the first empirical study on the
influence of homophily ratio on the performance of existing unsupervised NE
methods and reveals their limitations. Inspired by our empirical findings, we
design unsupervised NE task as an r-ego network discrimination problem and
further develop a SELf-supErvised Network Embedding (Selene) framework for
learning useful node representations for both homophilous and heterophilous
networks. Specifically, we propose a dual-channel feature embedding mechanism
to fuse node attributes and network structure information and leverage a
sampling and anonymisation strategy to break the implicit homophily assumption
of existing embedding mechanisms. Lastly, we introduce a negative-sample-free
SSL objective function to optimise the framework. We conduct extensive
experiments and a series of ablation studies on 12 real-world datasets and 20
synthetic networks. Results demonstrate Selene's superior performance and
confirm the effectiveness of each component.

In real-world scenarios, many large-scale datasets often contain inaccurate
labels, i.e., noisy labels, which may confuse model training and lead to
performance degradation. To overcome this issue, Label Noise Learning (LNL) has
recently attracted much attention, and various methods have been proposed to
design an unbiased risk estimator to the noise-free dataset to combat such
label noise. Among them, a trend of works based on Loss Decomposition and
Centroid Estimation (LDCE) has shown very promising performance. However,
existing LNL methods based on LDCE are only designed for binary classification,
and they are not directly extendable to multi-class situations. In this paper,
we propose a novel multi-class robust learning method for LDCE, which is termed
"MC-LDCE". Specifically, we decompose the commonly adopted loss (e.g., mean
squared loss) function into a label-dependent part and a label-independent
part, in which only the former is influenced by label noise. Further, by
defining a new form of data centroid, we transform the recovery problem of a
label-dependent part to a centroid estimation problem. Finally, by critically
examining the mathematical expectation of clean data centroid given the
observed noisy set, the centroid can be estimated which helps to build an
unbiased risk estimator for multi-class learning. The proposed MC-LDCE method
is general and applicable to different types (i.e., linear and nonlinear) of
classification models. The experimental results on five public datasets
demonstrate the superiority of the proposed MC-LDCE against other
representative LNL methods in tackling multi-class label noise problem.

The raw depth image captured by the indoor depth sensor usually has an
extensive range of missing depth values due to inherent limitations such as the
inability to perceive transparent objects and limited distance range. The
incomplete depth map burdens many downstream vision tasks, and a rising number
of depth completion methods have been proposed to alleviate this issue. While
most existing methods can generate accurate dense depth maps from sparse and
uniformly sampled depth maps, they are not suitable for complementing the large
contiguous regions of missing depth values, which is common and critical. In
this paper, we design a novel two-branch end-to-end fusion network, which takes
a pair of RGB and incomplete depth images as input to predict a dense and
completed depth map. The first branch employs an encoder-decoder structure to
regress the local dense depth values from the raw depth map, with the help of
local guidance information extracted from the RGB image. In the other branch,
we propose an RGB-depth fusion GAN to transfer the RGB image to the
fine-grained textured depth map. We adopt adaptive fusion modules named W-AdaIN
to propagate the features across the two branches, and we append a confidence
fusion head to fuse the two outputs of the branches for the final depth map.
Extensive experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our
proposed method clearly improves the depth completion performance, especially
in a more realistic setting of indoor environments with the help of the pseudo
depth map.

Conventional deep models predict a test sample with a single forward
propagation, which, however, may not be sufficient for predicting
hard-classified samples. On the contrary, we human beings may need to carefully
check the sample many times before making a final decision. During the recheck
process, one may refine/adjust the prediction by referring to related samples.
Motivated by this, we propose to predict those hard-classified test samples in
a looped manner to boost the model performance. However, this idea may pose a
critical challenge: how to construct looped inference, so that the original
erroneous predictions on these hard test samples can be corrected with little
additional effort. To address this, we propose a general Closed-Loop Inference
(CLI) method. Specifically, we first devise a filtering criterion to identify
those hard-classified test samples that need additional inference loops. For
each hard sample, we construct an additional auxiliary learning task based on
its original top-$K$ predictions to calibrate the model, and then use the
calibrated model to obtain the final prediction. Promising results on ImageNet
(in-distribution test samples) and ImageNet-C (out-of-distribution test
samples) demonstrate the effectiveness of CLI in improving the performance of
any pre-trained model.

The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker
for the diagnosis and prognosis of glioma. It is promising to better predict
glioma genotype by integrating focal tumor image and geometric features with
brain network features derived from MRI. Convolutions neural networks show
reasonable performance in predicting IDH mutation, which, however, cannot learn
from non-Euclidean data, e.g., geometric and network data. In this study, we
propose a multi-modal learning framework using three separate encoders to
extract features of focal tumor image, tumor geometrics and global brain
networks. To mitigate the limited availability of diffusion MRI, we develop a
self-supervised approach to generate brain networks from anatomical
multi-sequence MRI. Moreover, to extract tumor-related features from the brain
network, we design a hierarchical attention module for the brain network
encoder. Further, we design a bi-level multi-modal contrastive loss to align
the multi-modal features and tackle the domain gap at the focal tumor and
global brain. Finally, we propose a weighted population graph to integrate the
multi-modal features for genotype prediction. Experimental results on the
testing set show that the proposed model outperforms the baseline deep learning
models. The ablation experiments validate the performance of different
components of the framework. The visualized interpretation corresponds to
clinical knowledge with further validation. In conclusion, the proposed
learning framework provides a novel approach for predicting the genotype of
glioma.

Studies have shown evolution strategies (ES) to be a promising approach for
reinforcement learning (RL) with deep neural networks. However, the issue of
high sample complexity persists in applications of ES to deep RL over long
horizons. This paper is the first to address the shortcoming of today's methods
via a novel neuroevolutionary multitasking (NuEMT) algorithm, designed to
transfer information from a set of auxiliary tasks (of short episode length) to
the target (full length) RL task at hand. The auxiliary tasks, extracted from
the target, allow an agent to update and quickly evaluate policies on shorter
time horizons. The evolved skills are then transferred to guide the longer and
harder task towards an optimal policy. We demonstrate that the NuEMT algorithm
achieves data-efficient evolutionary RL, reducing expensive agent-environment
interaction data requirements. Our key algorithmic contribution in this setting
is to introduce, for the first time, a multitask skills transfer mechanism
based on the statistical importance sampling technique. In addition, an
adaptive resource allocation strategy is utilized to assign computational
resources to auxiliary tasks based on their gleaned usefulness. Experiments on
a range of continuous control tasks from the OpenAI Gym confirm that our
proposed algorithm is efficient compared to recent ES baselines.

Extremely Metal-Poor (EMP) stars provide a valuable probe of early chemical
enrichment in the Milky Way. Here we leverage a large sample of $\sim600,000$
high-resolution stellar spectra from the GALAH survey plus a machine learning
algorithm to find 54 candidates with estimated [Fe/H]~$\leq$~-3.0, 6 of which
have [Fe/H]~$\leq$~-3.5. Our sample includes $\sim 20 \%$ main sequence EMP
candidates, unusually high for \emp surveys. We find the magnitude-limited
metallicity distribution function of our sample is consistent with previous
work that used more complex selection criteria. The method we present has
significant potential for application to the next generation of massive stellar
spectroscopic surveys, which will expand the available spectroscopic data well
into the millions of stars.

Most of medical developments require the ability to identify samples that are
anomalous with respect to a target group or control group, in the sense they
could belong to a new, previously unseen class or are not class data. In this
case when there are not enough data to train two-class One-class classification
appear like an available solution. On the other hand non-linear approaches
could give very useful information. The aim of our project is to contribute to
earlier diagnosis of AD and better estimates of its severity by using automatic
analysis performed through new biomarkers extracted from speech signal. The
methods selected in this case are speech biomarkers oriented to Spontaneous
Speech and Emotional Response Analysis. In this approach One-class classifiers
and two-class classifiers are analyzed. The use of information about outlier
and Fractal Dimension features improves the system performance.

Metric learning aims to learn a highly discriminative model encouraging the
embeddings of similar classes to be close in the chosen metrics and pushed
apart for dissimilar ones. The common recipe is to use an encoder to extract
embeddings and a distance-based loss function to match the representations --
usually, the Euclidean distance is utilized. An emerging interest in learning
hyperbolic data embeddings suggests that hyperbolic geometry can be beneficial
for natural data. Following this line of work, we propose a new
hyperbolic-based model for metric learning. At the core of our method is a
vision transformer with output embeddings mapped to hyperbolic space. These
embeddings are directly optimized using modified pairwise cross-entropy loss.
We evaluate the proposed model with six different formulations on four datasets
achieving the new state-of-the-art performance. The source code is available at
https://github.com/htdt/hyp_metric.

Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic
dysathria (HD) which is also manifested in the field of phonation. Clinical
signs of HD like monoloudness, monopitch or hoarse voice are usually quantified
by conventional clinical interpretable features (jitter, shimmer,
harmonic-to-noise ratio, etc.). This paper provides large and robust insight
into perceptual analysis of 5 Czech vowels of 84 PD patients and proves that
despite the clinical inexplicability the perceptual features outperform the
conventional ones, especially in terms of discrimination power (classification
accuracy ACC = 92 %, sensitivity SEN = 93 %, specificity SPE = 92 %) and
partial correlation with clinical scores like UPDRS (Unified Parkinson's
disease rating scale), MMSE (Mini-mental state examination) or FOG (Freezing of
gait questionnaire), where p < 0.0001.

Reinforcement learning-based path planning for multi-agent systems of varying
size constitutes a research topic with increasing significance as progress in
domains such as urban air mobility and autonomous aerial vehicles continues.
Reinforcement learning with continuous state and action spaces is used to train
a policy network that accommodates desirable path planning behaviors and can be
used for time-critical applications. A Long Short-Term Memory module is
proposed to encode an unspecified number of states for a varying, indefinite
number of agents. The described training strategies and policy architecture
lead to a guidance that scales to an infinite number of agents and unlimited
physical dimensions, although training takes place at a smaller scale. The
guidance is implemented on a low-cost, off-the-shelf onboard computer. The
feasibility of the proposed approach is validated by presenting flight test
results of up to four drones, autonomously navigating collision-free in a
real-world environment.

Image translation and manipulation have gain increasing attention along with
the rapid development of deep generative models. Although existing approaches
have brought impressive results, they mainly operated in 2D space. In light of
recent advances in NeRF-based 3D-aware generative models, we introduce a new
task, Semantic-to-NeRF translation, that aims to reconstruct a 3D scene
modelled by NeRF, conditioned on one single-view semantic mask as input. To
kick-off this novel task, we propose the Sem2NeRF framework. In particular,
Sem2NeRF addresses the highly challenging task by encoding the semantic mask
into the latent code that controls the 3D scene representation of a pre-trained
decoder. To further improve the accuracy of the mapping, we integrate a new
region-aware learning strategy into the design of both the encoder and the
decoder. We verify the efficacy of the proposed Sem2NeRF and demonstrate that
it outperforms several strong baselines on two benchmark datasets. Code and
video are available at https://donydchen.github.io/sem2nerf/

In this paper, an IRS-aided integrated sensing and communications (ISAC)
system operating in the terahertz (THz) band is proposed to maximize the system
capacity. Transmit beamforming and phase-shift design are transformed into a
universal optimization problem with ergodic constraints. Then the joint
optimization of transmit beamforming and phase-shift design is achieved by
gradient-based, primal-dual proximal policy optimization (PPO) in the
multi-user multiple-input single-output (MISO) scenario. Specifically, the
actor part generates continuous transmit beamforming and the critic part takes
charge of discrete phase shift design. Based on the MISO scenario, we
investigate a distributed PPO (DPPO) framework with the concept of
multi-threading learning in the multi-user multiple-input multiple-output
(MIMO) scenario. Simulation results demonstrate the effectiveness of the
primal-dual PPO algorithm and its multi-threading version in terms of transmit
beamforming and phase-shift design.

Image anomaly detection problems aim to determine whether an image is
abnormal, and to detect anomalous areas. These methods are actively used in
various fields such as manufacturing, medical care, and intelligent
information. Encoder-decoder structures have been widely used in the field of
anomaly detection because they can easily learn normal patterns in an
unsupervised learning environment and calculate a score to identify
abnormalities through a reconstruction error indicating the difference between
input and reconstructed images. Therefore, current image anomaly detection
methods have commonly used convolutional encoder-decoders to extract normal
information through the local features of images. However, they are limited in
that only local features of the image can be utilized when constructing a
normal representation owing to the characteristics of convolution operations
using a filter of fixed size. Therefore, we propose a vision transformer-based
encoder-decoder model, named AnoViT, designed to reflect normal information by
additionally learning the global relationship between image patches, which is
capable of both image anomaly detection and localization. The proposed approach
constructs a feature map that maintains the existing location information of
individual patches by using the embeddings of all patches passed through
multiple self-attention layers. The proposed AnoViT model performed better than
the convolution-based model on three benchmark datasets. In MVTecAD, which is a
representative benchmark dataset for anomaly localization, it showed improved
results on 10 out of 15 classes compared with the baseline. Furthermore, the
proposed method showed good performance regardless of the class and type of the
anomalous area when localization results were evaluated qualitatively.

Quantifying COVID-19 infection over time is an important task to manage the
hospitalization of patients during a global pandemic. Recently, deep
learning-based approaches have been proposed to help radiologists automatically
quantify COVID-19 pathologies on longitudinal CT scans. However, the learning
process of deep learning methods demands extensive training data to learn the
complex characteristics of infected regions over longitudinal scans. It is
challenging to collect a large-scale dataset, especially for longitudinal
training. In this study, we want to address this problem by proposing a new
self-supervised learning method to effectively train longitudinal networks for
the quantification of COVID-19 infections. For this purpose, longitudinal
self-supervision schemes are explored on clinical longitudinal COVID-19 CT
scans. Experimental results show that the proposed method is effective, helping
the model better exploit the semantics of longitudinal data and improve two
COVID-19 quantification tasks.

We present a novel learning framework to obtain finite-state controllers
(FSCs) for partially observable Markov decision processes and illustrate its
applicability for indefinite-horizon specifications. Our framework builds on
oracle-guided inductive synthesis to explore a design space compactly
representing available FSCs. The inductive synthesis approach consists of two
stages: The outer stage determines the design space, i.e., the set of FSC
candidates, while the inner stage efficiently explores the design space. This
framework is easily generalisable and shows promising results when compared to
existing approaches. Experiments indicate that our technique is (i) competitive
to state-of-the-art belief-based approaches for indefinite-horizon properties,
(ii) yields smaller FSCs than existing methods for several models, and (iii)
naturally treats multi-objective specifications.

Deep learning-based approaches have been developed to solve challenging
problems in wireless communications, leading to promising results. Early
attempts adopted neural network architectures inherited from applications such
as computer vision. They often yield poor performance in large scale networks
(i.e., poor scalability) and unseen network settings (i.e., poor
generalization). To resolve these issues, graph neural networks (GNNs) have
been recently adopted, as they can effectively exploit the domain knowledge,
i.e., the graph topology in wireless communications problems. GNN-based methods
can achieve near-optimal performance in large-scale networks and generalize
well under different system settings, but the theoretical underpinnings and
design guidelines remain elusive, which may hinder their practical
implementations. This paper endeavors to fill both the theoretical and
practical gaps. For theoretical guarantees, we prove that GNNs achieve
near-optimal performance in wireless networks with much fewer training samples
than traditional neural architectures. Specifically, to solve an optimization
problem on an $n$-node graph (where the nodes may represent users, base
stations, or antennas), GNNs' generalization error and required number of
training samples are $\mathcal{O}(n)$ and $\mathcal{O}(n^2)$ times lower than
the unstructured multi-layer perceptrons. For design guidelines, we propose a
unified framework that is applicable to general design problems in wireless
networks, which includes graph modeling, neural architecture design, and
theory-guided performance enhancement. Extensive simulations, which cover a
variety of important problems and network settings, verify our theory and the
effectiveness of the proposed design framework.

Human-centricity is the core value behind the evolution of manufacturing
towards Industry 5.0. Nevertheless, there is a lack of architecture that
considers safety, trustworthiness, and human-centricity at its core. Therefore,
we propose an architecture that integrates Artificial Intelligence (Active
Learning, Forecasting, Explainable Artificial Intelligence), simulated reality,
decision-making, and users' feedback, focusing on synergies between humans and
machines. Furthermore, we align the proposed architecture with the Big Data
Value Association Reference Architecture Model. Finally, we validate it on
three use cases from real-world case studies.

Domain generalization (DG) aims to learn a generalized model to an unseen
target domain using only limited source domains. Previous attempts to DG fail
to learn domain-invariant representations only from the source domains due to
the significant domain shifts between training and test domains. Instead, we
re-formulate the DG objective using mutual information with the oracle model, a
model generalized to any possible domain. We derive a tractable variational
lower bound via approximating the oracle model by a pre-trained model, called
Mutual Information Regularization with Oracle (MIRO). Our extensive experiments
show that MIRO significantly improves the out-of-distribution performance.
Furthermore, our scaling experiments show that the larger the scale of the
pre-trained model, the greater the performance improvement of MIRO. Source code
is available at https://github.com/kakaobrain/miro.

Classification of skull fracture is a challenging task for both radiologists
and researchers. Skull fractures result in broken pieces of bone, which can cut
into the brain and cause bleeding and other injury types. So it is vital to
detect and classify the fracture very early. In real world, often fractures
occur at multiple sites. This makes it harder to detect the fracture type where
many fracture types might summarize a skull fracture. Unfortunately, manual
detection of skull fracture and the classification process is time-consuming,
threatening a patient's life. Because of the emergence of deep learning, this
process could be automated. Convolutional Neural Networks (CNNs) are the most
widely used deep learning models for image categorization because they deliver
high accuracy and outstanding outcomes compared to other models. We propose a
new model called SkullNetV1 comprising a novel CNN by taking advantage of CNN
for feature extraction and lazy learning approach which acts as a classifier
for classification of skull fractures from brain CT images to classify five
fracture types. Our suggested model achieved a subset accuracy of 88%, an F1
score of 93%, the Area Under the Curve (AUC) of 0.89 to 0.98, a Hamming score
of 92% and a Hamming loss of 0.04 for this seven-class multi-labeled

Salient object detection on RGB-D images is an active topic in computer
vision. Although the existing methods have achieved appreciable performance,
there are still some challenges. The locality of convolutional neural network
requires that the model has a sufficiently deep global receptive field, which
always leads to the loss of local details. To address the challenge, we propose
a novel Group Transformer Network (GroupTransNet) for RGB-D salient object
detection. This method is good at learning the long-range dependencies of cross
layer features to promote more perfect feature expression. At the beginning,
the features of the slightly higher classes of the middle three levels and the
latter three levels are soft grouped to absorb the advantages of the high-level
features. The input features are repeatedly purified and enhanced by the
attention mechanism to purify the cross modal features of color modal and depth
modal. The features of the intermediate process are first fused by the features
of different layers, and then processed by several transformers in multiple
groups, which not only makes the size of the features of each scale unified and
interrelated, but also achieves the effect of sharing the weight of the
features within the group. The output features in different groups complete the
clustering staggered by two owing to the level difference, and combine with the
low-level features. Extensive experiments demonstrate that GroupTransNet
outperforms the comparison models and achieves the new state-of-the-art

Batch normalization (BN) is a milestone technique in deep learning. It
normalizes the activation using mini-batch statistics during training but the
estimated population statistics during inference. This paper focuses on
investigating the estimation of population statistics. We define the estimation
shift magnitude of BN to quantitatively measure the difference between its
estimated population statistics and expected ones. Our primary observation is
that the estimation shift can be accumulated due to the stack of BN in a
network, which has detriment effects for the test performance. We further find
a batch-free normalization (BFN) can block such an accumulation of estimation
shift. These observations motivate our design of XBNBlock that replace one BN
with BFN in the bottleneck block of residual-style networks. Experiments on the
ImageNet and COCO benchmarks show that XBNBlock consistently improves the
performance of different architectures, including ResNet and ResNeXt, by a
significant margin and seems to be more robust to distribution shift.

Decreasing magnetic resonance (MR) image acquisition times can potentially
make MR examinations more accessible. Prior arts including the deep learning
models have been devoted to solving the problem of long MRI imaging time.
Recently, deep generative models have exhibited great potentials in algorithm
robustness and usage flexibility. Nevertheless, none of existing schemes can be
learned or employed to the k-space measurement directly. Furthermore, how do
the deep generative models work well in hybrid domain is also worth being
investigated. In this work, by taking advantage of the deep energy-based
models, we propose a k-space and image domain collaborative generative model to
comprehensively estimate the MR data from under-sampled measurement.
Experimental comparisons with the state-of-the-arts demonstrated that the
proposed hybrid method has less error in reconstruction accuracy and is more
stable under different acceleration factors

We introduce a novel frame-interpolation-based method for slice imputation to
improve segmentation accuracy for anisotropic 3D medical images, in which the
number of slices and their corresponding segmentation labels can be increased
between two consecutive slices in anisotropic 3D medical volumes. Unlike
previous inter-slice imputation methods, which only focus on the smoothness in
the axial direction, this study aims to improve the smoothness of the
interpolated 3D medical volumes in all three directions: axial, sagittal, and
coronal. The proposed multitask inter-slice imputation method, in particular,
incorporates a smoothness loss function to evaluate the smoothness of the
interpolated 3D medical volumes in the through-plane direction (sagittal and
coronal). It not only improves the resolution of the interpolated 3D medical
volumes in the through-plane direction but also transforms them into isotropic
representations, which leads to better segmentation performances. Experiments
on whole tumor segmentation in the brain, liver tumor segmentation, and
prostate segmentation indicate that our method outperforms the competing slice
imputation methods on both computed tomography and magnetic resonance images
volumes in most cases.

This letter presents a new intelligent control scheme for the accurate
trajectory tracking of flexible link manipulators. The proposed approach is
mainly based on a sliding mode controller for underactuated systems with an
embedded artificial neural network to deal with modeling inaccuracies. The
adopted neural network only needs a single input and one hidden layer, which
drastically reduces the computational complexity of the control law and allows
its implementation in low-power microcontrollers. Online learning, rather than
supervised offline training, is chosen to allow the weights of the neural
network to be adjusted in real time during the tracking. Therefore, the
resulting controller is able to cope with the underactuating issues and to
adapt itself by learning from experience, which grants the capacity to deal
with plant dynamics properly. The boundedness and convergence properties of the
tracking error are proved by evoking Barbalat's lemma in a Lyapunov-like
stability analysis. Experimental results obtained with a small single-link
flexible manipulator show the efficacy of the proposed control scheme, even in
the presence of a high level of uncertainty and noisy signals.

Nowadays, many classification algorithms have been applied to various
industries to help them work out their problems met in real-life scenarios.
However, in many binary classification tasks, samples in the minority class
only make up a small part of all instances, which leads to the datasets we get
usually suffer from high imbalance ratio. Existing models sometimes treat
minority classes as noise or ignore them as outliers encountering data skewing.
In order to solve this problem, we propose a bagging ensemble learning
framework $ASE$ (Anomaly Scoring Based Ensemble Learning). This framework has a
scoring system based on anomaly detection algorithms which can guide the
resampling strategy by divided samples in the majority class into subspaces.
Then specific number of instances will be under-sampled from each subspace to
construct subsets by combining with the minority class. And we calculate the
weights of base classifiers trained by the subsets according to the
classification result of the anomaly detection model and the statistics of the
subspaces. Experiments have been conducted which show that our ensemble
learning model can dramatically improve the performance of base classifiers and
is more efficient than other existing methods under a wide range of imbalance
ratio, data scale and data dimension. $ASE$ can be combined with various
classifiers and every part of our framework has been proved to be reasonable
and necessary.

In computer-aided design (CAD) community, the point cloud data is pervasively
applied in reverse engineering, where the point cloud analysis plays an
important role. While a large number of supervised learning methods have been
proposed to handle the unordered point clouds and demonstrated their remarkable
success, their performance and applicability are limited to the costly data
annotation. In this work, we propose a novel self-supervised pretraining model
for point cloud learning without human annotations, which relies solely on
upsampling operation to perform feature learning of point cloud in an effective
manner. The key premise of our approach is that upsampling operation encourages
the network to capture both high-level semantic information and low-level
geometric information of the point cloud, thus the downstream tasks such as
classification and segmentation will benefit from the pre-trained model.
Specifically, our method first conducts the random subsampling from the input
point cloud at a low proportion e.g., 12.5%. Then, we feed them into an
encoder-decoder architecture, where an encoder is devised to operate only on
the subsampled points, along with a upsampling decoder is adopted to
reconstruct the original point cloud based on the learned features. Finally, we
design a novel joint loss function which enforces the upsampled points to be
similar with the original point cloud and uniformly distributed on the
underlying shape surface. By adopting the pre-trained encoder weights as
initialisation of models for downstream tasks, we find that our UAE outperforms
previous state-of-the-art methods in shape classification, part segmentation
and point cloud upsampling tasks. Code will be made publicly available upon
acceptance.

While $\mathcal{H}_\infty$ methods can introduce robustness against
worst-case perturbations, their nominal performance under conventional
stochastic disturbances is often drastically reduced. Though this fundamental
tradeoff between nominal performance and robustness is known to exist, it is
not well-characterized in quantitative terms. Toward addressing this issue, we
borrow from the increasingly ubiquitous notion of adversarial training from
machine learning to construct a class of controllers which are optimized for
disturbances consisting of mixed stochastic and worst-case components. We find
that this problem admits a stationary optimal controller that has a simple
analytic form closely related to suboptimal $\mathcal{H}_\infty$ solutions. We
then provide a quantitative performance-robustness tradeoff analysis, in which
system-theoretic properties such as controllability and stability explicitly
manifest in an interpretable manner. This provides practitioners with general
guidance for determining how much robustness to incorporate based on a priori
system knowledge. We empirically validate our results by comparing the
performance of our controller against standard baselines, and plotting tradeoff
curves.

Mixup is an efficient data augmentation approach that improves the
generalization of neural networks by smoothing the decision boundary with mixed
data. Recently, dynamic mixup methods have improved previous static policies
effectively (e.g., linear interpolation) by maximizing salient regions or
maintaining the target in mixed samples. The discrepancy is that the generated
mixed samples from dynamic policies are more instance discriminative than the
static ones, e.g., the foreground objects are decoupled from the background.
However, optimizing mixup policies with dynamic methods in input space is an
expensive computation compared to static ones. Hence, we are trying to transfer
the decoupling mechanism of dynamic methods from the data level to the
objective function level and propose the general decoupled mixup (DM) loss. The
primary effect is that DM can adaptively focus on discriminative features
without losing the original smoothness of the mixup while avoiding heavy
computational overhead. As a result, DM enables static mixup methods to achieve
comparable or even exceed the performance of dynamic methods. This also leads
to an interesting objective design problem for mixup training that we need to
focus on both smoothing the decision boundaries and identifying discriminative
features. Extensive experiments on supervised and semi-supervised learning
benchmarks across seven classification datasets validate the effectiveness of
DM by equipping it with various mixup methods.

A slot value might be provided segment by segment over multiple-turn
interactions in a dialog, especially for some important information such as
phone numbers and names. It is a common phenomenon in daily life, but little
attention has been paid to it in previous work. To fill the gap, this paper
defines a new task named Sub-Slot based Task-Oriented Dialog (SSTOD) and builds
a Chinese dialog dataset SSD for boosting research on SSTOD. The dataset
includes a total of 40K dialogs and 500K utterances from four different
domains: Chinese names, phone numbers, ID numbers and license plate numbers.
The data is well annotated with sub-slot values, slot values, dialog states and
actions. We find some new linguistic phenomena and interactive manners in SSTOD
which raise critical challenges of building dialog agents for the task. We test
three state-of-the-art dialog models on SSTOD and find they cannot handle the
task well on any of the four domains. We also investigate an improved model by
involving slot knowledge in a plug-in manner. More work should be done to meet
the new challenges raised from SSTOD which widely exists in real-life
applications. The dataset and code are publicly available via
https://github.com/shunjiu/SSTOD.

In this paper, we develop a novel approach to posterior contractions rates
(PCRs), for both finite-dimensional (parametric) and infinite-dimensional
(nonparametric) Bayesian models. Critical to our approach is the combination of
an assumption of local Lipschitz-continuity for the posterior distribution with
a dynamic formulation of the Wasserstein distance, here referred to as
Wasserstein dynamics, which allows to set forth a connection between the
problem of establishing PCRs and some classical problems in mathematical
analysis, probability theory and mathematical statistics: the Laplace method
for approximating integrals, Sanov's large deviation principles in the
Wasserstein distance, rates of convergence of the mean Glivenko-Cantelli
theorem, and estimates of weighted Poincar\'e-Wirtinger constants. Under
dominated Bayesian models, we present two main results: i) a theorem on PCRs
for the regular infinite-dimensional exponential family of statistical models;
ii) a theorem on PCRs for a general dominated statistical model. Some
applications of our results are presented for the regular parametric model, the
multinomial model, the finite-dimensional and the infinite-dimensional
logistic-Gaussian model and the infinite-dimensional linear regression. In
general, our results lead to optimal PCRs in finite dimension, whereas in
infinite dimension it is shown how the prior distribution may affect PCRs. With
regards to infinite-dimensional Bayesian models for density estimation, our
approach to PCRs is the first to consider strong norm distances on parameter
spaces of functions, such as Sobolev-like norms, as most of the approaches in
the classical (frequentist) and Bayesian literature deal with spaces of density
functions endowed with $\mathrm{L}^p$ norms or the Hellinger distance.

Pretrained multilingual models enable zero-shot learning even for unseen
languages, and that performance can be further improved via adaptation prior to
finetuning. However, it is unclear how the number of pretraining languages
influences a model's zero-shot learning for languages unseen during
pretraining. To fill this gap, we ask the following research questions: (1) How
does the number of pretraining languages influence zero-shot performance on
unseen target languages? (2) Does the answer to that question change with model
adaptation? (3) Do the findings for our first question change if the languages
used for pretraining are all related? Our experiments on pretraining with
related languages indicate that choosing a diverse set of languages is crucial.
Without model adaptation, surprisingly, increasing the number of pretraining
languages yields better results up to adding related languages, after which
performance plateaus. In contrast, with model adaptation via continued
pretraining, pretraining on a larger number of languages often gives further
improvement, suggesting that model adaptation is crucial to exploit additional
pretraining languages.

We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual
speech representations in many languages. XTREME-S covers four task families:
speech recognition, classification, speech-to-text translation and retrieval.
Covering 102 languages from 10+ language families, 3 different domains and 4
task families, XTREME-S aims to simplify multilingual speech representation
evaluation, as well as catalyze research in "universal" speech representation
learning. This paper describes the new benchmark and establishes the first
speech-only and speech-text baselines using XLS-R and mSLAM on all downstream
tasks. We motivate the design choices and detail how to use the benchmark.
Datasets and fine-tuning scripts are made easily accessible at
https://hf.co/datasets/google/xtreme_s.

In this paper, we develop a new multi-singer Chinese neural singing voice
synthesis (SVS) system named WeSinger. To improve the accuracy and naturalness
of synthesized singing voice, we design several specifical modules and
techniques: 1) A deep bi-directional LSTM-based duration model with multi-scale
rhythm loss and post-processing step; 2) A Transformer-alike acoustic model
with progressive pitch-weighted decoder loss; 3) a 24 kHz pitch-aware LPCNet
neural vocoder to produce high-quality singing waveforms; 4) A novel data
augmentation method with multi-singer pre-training for stronger robustness and
naturalness. To our knowledge, WeSinger is the first SVS system to adopt 24 kHz
LPCNet and multi-singer pre-training simultaneously. Both quantitative and
qualitative evaluation results demonstrate the effectiveness of WeSinger in
terms of accuracy and naturalness, and WeSinger achieves state-of-the-art
performance on the recent public Chinese singing corpus
Opencpop\footnote{https://wenet.org.cn/opencpop/}. Some synthesized singing
samples are available online\footnote{https://zzw922cn.github.io/wesinger/}.

Air pollution and carbon emissions caused by modern transportation are
closely related to global climate change. With the help of next-generation
information technology such as Internet of Things (IoT) and Artificial
Intelligence (AI), accurate traffic flow prediction can effectively solve
problems such as traffic congestion and mitigate environmental pollution and
climate change. It further promotes the development of Intelligent
Transportation Systems (ITS) and smart cities. However, the strong spatial and
temporal correlation of traffic data makes the task of accurate traffic
forecasting a significant challenge. Existing methods are usually based on
graph neural networks using predefined spatial adjacency graphs of traffic
networks to model spatial dependencies, ignoring the dynamic correlation of
relationships between road nodes. In addition, they usually use independent
Spatio-temporal components to capture Spatio-temporal dependencies and do not
effectively model global Spatio-temporal dependencies. This paper proposes a
new Spatio-temporal Causal Graph Attention Network (STCGAT) for traffic
prediction to address the above challenges. In STCGAT, we use a node embedding
approach that can adaptively generate spatial adjacency subgraphs at each time
step without a priori geographic knowledge and fine-grained modeling of the
topology of dynamically generated graphs for different time steps. Meanwhile,
we propose an efficient causal temporal correlation component that contains
node adaptive learning, graph convolution, and local and global causal temporal
convolution modules to learn local and global Spatio-temporal dependencies
jointly. Extensive experiments on four real, large traffic datasets show that
our model consistently outperforms all baseline models.

One of the fundamental problems in machine learning is generalization. In
neural network models with a large number of weights (parameters), many
solutions can be found to fit the training data equally well. The key question
is which solution can describe testing data not in the training set. Here, we
report the discovery of an exact duality (equivalence) between changes in
activities in a given layer of neurons and changes in weights that connect to
the next layer of neurons in a densely connected layer in any feed forward
neural network. The activity-weight (A-W) duality allows us to map variations
in inputs (data) to variations of the corresponding dual weights. By using this
mapping, we show that the generalization loss can be decomposed into a sum of
contributions from different eigen-directions of the Hessian matrix of the loss
function at the solution in weight space. The contribution from a given
eigen-direction is the product of two geometric factors (determinants): the
sharpness of the loss landscape and the standard deviation of the dual weights,
which is found to scale with the weight norm of the solution. Our results
provide an unified framework, which we used to reveal how different
regularization schemes (weight decay, stochastic gradient descent with
different batch sizes and learning rates, dropout), training data size, and
labeling noise affect generalization performance by controlling either one or
both of these two geometric determinants for generalization. These insights can
be used to guide development of algorithms for finding more generalizable
solutions in overparametrized neural networks.

The metaliteracy ability is a very important in today's life, especially to
live in the disruptive technology era. Metaliteracy is an ability that goes
beyond metacognition and technological literacy. However, the students'
metalliteracy ability is still relatively low. One of the causes of the low
ability is due to the learning model that has been applied so far has not been
able to bring out this ability. Therefore, in this study, an RBL learning model
that is integrated with the STEM approach will be applied in solving the
r-Dynamic coloring problem. By r-dynamic coloring, we mean a proper k-coloring
c of G if for every vertex v in V (G) satisfies the number of neighbours color
is greater then min{r,d(v)}. The minimum k such that G has an r-dynamic
coloring with k colors is called an r-dynamic chromatic number, denoted by
r(G). By using the r-dynamic coloring technique, we will improve students'
metaliteracy in solving the tessellation decoration problem. Therefore, in this
research, the syntax of learning activities of the Research-Based Learning and
STEM approach will be developed including the assesment indicator of the
metaliteracy ability.

With the rapid development of Internet technology, people have more and more
access to a variety of web page resources. At the same time, the current rapid
development of deep learning technology is often inseparable from the huge
amount of Web data resources. On the other hand, NLP is also an important part
of data processing technology, such as web page data extraction. At present,
the extraction technology of web page text mainly uses a single heuristic
function or strategy, and most of them need to determine the threshold
manually. With the rapid growth of the number and types of web resources, there
are still problems to be solved when using a single strategy to extract the
text information of different pages. This paper proposes a web page text
extraction algorithm based on multi-feature fusion. According to the text
information characteristics of web resources, DOM nodes are used as the
extraction unit to design multiple statistical features, and high-order
features are designed according to heuristic strategies. This method
establishes a small neural network, takes multiple features of DOM nodes as
input, predicts whether the nodes contain text information, makes full use of
different statistical information and extraction strategies, and adapts to more
types of pages. Experimental results show that this method has a good ability
of web page text extraction and avoids the problem of manually determining the
threshold.

Using deep learning, we now have the ability to create exceptionally good
semantic segmentation systems; however, collecting the prerequisite pixel-wise
annotations for training images remains expensive and time-consuming.
Therefore, it would be ideal to minimize the number of human annotations needed
when creating a new dataset. Here, we address this problem by proposing a novel
algorithm that combines active learning and semi-supervised learning. Active
learning is an approach for identifying the best unlabeled samples to annotate.
While there has been work on active learning for segmentation, most methods
require annotating all pixel objects in each image, rather than only the most
informative regions. We argue that this is inefficient. Instead, our active
learning approach aims to minimize the number of annotations per-image. Our
method is enriched with semi-supervised learning, where we use pseudo labels
generated with a teacher-student framework to identify image regions that help
disambiguate confused classes. We also integrate mechanisms that enable better
performance on imbalanced label distributions, which have not been studied
previously for active learning in semantic segmentation. In experiments on the
CamVid and CityScapes datasets, our method obtains over 95% of the network's
performance on the full-training set using less than 17% of the training data,
whereas the previous state of the art required 40% of the training data.

This paper substantially extends our work published at ECCV, in which an
intermediate-level attack was proposed to improve the transferability of some
baseline adversarial examples. Specifically, we advocate a framework in which a
direct linear mapping from the intermediate-level discrepancies (between
adversarial features and benign features) to prediction loss of the adversarial
example is established. By delving deep into the core components of such a
framework, we show that 1) a variety of linear regression models can all be
considered in order to establish the mapping, 2) the magnitude of the finally
obtained intermediate-level adversarial discrepancy is correlated with the
transferability, 3) further boost of the performance can be achieved by
performing multiple runs of the baseline attack with random initialization. In
addition, by leveraging these findings, we achieve new state-of-the-arts on
transfer-based $\ell_\infty$ and $\ell_2$ attacks. Our code is publicly
available at https://github.com/qizhangli/ila-plus-plus-lr.

Thanks to the rapid development of deep learning, big data analysis
technology is not only widely used in the field of natural language processing,
but also more mature in the field of numerical prediction. It is of great
significance for the subject heat prediction and analysis of science and
technology demand data. How to apply theme features to accurately predict the
theme heat of science and technology demand is the core to solve this problem.
In this paper, a prediction method of subject heat of science and technology
demand based on time convolution network (TCN) is proposed to obtain the
subject feature representation of science and technology demand. Time series
prediction is carried out based on TCN network and self attention mechanism,
which increases the accuracy of subject heat prediction of science and
technology demand data Experiments show that the prediction accuracy of this
algorithm is better than other time series prediction methods on the real
science and technology demand datasets.

Patent texts contain a large amount of entity information. Through named
entity recognition, intellectual property entity information containing key
information can be extracted from it, helping researchers to understand the
patent content faster. Therefore, it is difficult for existing named entity
extraction methods to make full use of the semantic information at the word
level brought about by professional vocabulary changes. This paper proposes a
method for extracting intellectual property entities based on Transformer and
technical word information , and provides accurate word vector representation
in combination with the BERT language method. In the process of word vector
generation, the technical word information extracted by IDCNN is added to
improve the understanding of intellectual property entities Representation
ability. Finally, the Transformer encoder that introduces relative position
encoding is used to learn the deep semantic information of the text from the
sequence of word vectors, and realize entity label prediction. Experimental
results on public datasets and annotated patent datasets show that the method
improves the accuracy of entity recognition.

Machine Learning (ML) and Deep Learning (DL) methods are increasingly
replacing traditional methods in many domains involved with important decision
making activities. DL techniques tailor-made for specific tasks such as image
recognition, signal processing, or speech analysis are being introduced at a
fast pace with many improvements. However, for the domain of forecasting, the
current state in the ML community is perhaps where other domains such as
Natural Language Processing and Computer Vision were at several years ago. The
field of forecasting has mainly been fostered by statisticians/econometricians;
consequently the related concepts are not the mainstream knowledge among
general ML practitioners. The different non-stationarities associated with time
series challenge the data-driven ML models. Nevertheless, recent trends in the
domain have shown that with the availability of massive amounts of time series,
ML techniques are quite competent in forecasting, when related pitfalls are
properly handled. Therefore, in this work we provide a tutorial-like
compilation of the details of one of the most important steps in the overall
forecasting process, namely the evaluation. This way, we intend to impart the
information of forecast evaluation to fit the context of ML, as means of
bridging the knowledge gap between traditional methods of forecasting and
state-of-the-art ML techniques. We elaborate on the different problematic
characteristics of time series such as non-normalities and non-stationarities
and how they are associated with common pitfalls in forecast evaluation. Best
practices in forecast evaluation are outlined with respect to the different
steps such as data partitioning, error calculation, statistical testing, and
others. Further guidelines are also provided along selecting valid and suitable
error measures depending on the specific characteristics of the dataset at
hand.

Face clustering is an essential task in computer vision due to the explosion
of related applications such as augmented reality or photo album management.
The main challenge of this task lies in the imperfectness of similarities among
image feature representations. Given an existing feature extraction model, it
is still an unresolved problem that how can the inherent characteristics of
similarities of unlabelled images be leveraged to improve the clustering
performance. Motivated by answering the question, we develop an effective
unsupervised method, named as FaceMap, by formulating face clustering as a
process of non-overlapping community detection, and minimizing the entropy of
information flows on a network of images. The entropy is denoted by the map
equation and its minimum represents the least description of paths among images
in expectation. Inspired by observations on the ranked transition probabilities
in the affinity graph constructed from facial images, we develop an outlier
detection strategy to adaptively adjust transition probabilities among images.
Experiments with ablation studies demonstrate that FaceMap significantly
outperforms existing methods and achieves new state-of-the-arts on three
popular large-scale datasets for face clustering, e.g., an absolute improvement
of more than $10\%$ and $4\%$ comparing with prior unsupervised and supervised
methods respectively in terms of average of Pairwise F-score. Our code is
publicly available on github.

Internet of Vehicles (IoVs) consist of smart vehicles, Autonomous Vehicles
(AVs) as well as roadside units (RSUs) that communicate wirelessly to provide
enhanced transportation services such as improved traffic efficiency and
reduced traffic congestion and accidents. IoVs, however, suffer from issues of
security, privacy and trust. Blockchain technology has been emerged as a
decentralized approach for enhanced security without depending on trusted third
parties to run services. Blockchain offers the benefits of trustworthiness,
immutability, and mitigates the problem of single point of failure and other
attacks. In this work, we present the state-of-the-art of Blockchain-enabled
IoVs (BIoV) with a particular focus on their applications such as
crowdsourcing-based applications, energy trading, traffic congestion reduction,
collision and accident avoidance and infotainment and content cashing. We also
present in-depth applications federated learning (FL) applications for BIoVs.
The key challenges resulted from the integration of Blockchain with IoV is
investigated in several domains such as edge computing, ML, and FL. Lastly, a
number of open issues and challenges as well as future opportunities in the
area of AI-enabled BIoV, hardware-assisted security for BIoV and quantum
computing attacks on BIoV.

Advanced driver assistance and automated driving systems should be capable of
predicting and avoiding dangerous situations. This study proposes a method to
predict potentially dangerous cut-in maneuvers happening in the ego lane. We
follow a computer vision-based approach that only employs a single in-vehicle
RGB camera, and we classify the target vehicle's maneuver based on the recent
video frames. Our algorithm consists of a CNN-based vehicle detection and
tracking step and an LSTM-based maneuver classification step. It is more
computationally efficient than other vision-based methods since it exploits a
small number of features for the classification step rather than feeding CNNs
with RGB frames. We evaluated our approach on a publicly available driving
dataset and a lane change detection dataset. We obtained 0.9585 accuracy with
side-aware two-class (cut-in vs. lane-pass) classification models. Experiment
results also reveal that our approach outperforms state-of-the-art approaches
when used for lane change detection.

The increasing size of generative Pre-trained Language Models (PLMs) has
greatly increased the demand for model compression. Despite various methods to
compress BERT or its variants, there are few attempts to compress generative
PLMs, and the underlying difficulty remains unclear. In this paper, we compress
generative PLMs by quantization. We find that previous quantization methods
fail on generative tasks due to the \textit{homogeneous word embeddings} caused
by reduced capacity, and \textit{varied distribution of weights}.
Correspondingly, we propose a token-level contrastive distillation to learn
distinguishable word embeddings, and a module-wise dynamic scaling to make
quantizers adaptive to different modules. Empirical results on various tasks
show that our proposed method outperforms the state-of-the-art compression
methods on generative PLMs by a clear margin. With comparable performance with
the full-precision models, we achieve 14.4x and 13.4x compression rates on
GPT-2 and BART, respectively.

Capsule networks are designed to present the objects by a set of parts and
their relationships, which provide an insight into the procedure of visual
perception. Although recent works have shown the success of capsule networks on
simple objects like digits, the human faces with homologous structures, which
are suitable for capsules to describe, have not been explored. In this paper,
we propose a Hierarchical Parsing Capsule Network (HP-Capsule) for unsupervised
face subpart-part discovery. When browsing large-scale face images without
labels, the network first encodes the frequently observed patterns with a set
of explainable subpart capsules. Then, the subpart capsules are assembled into
part-level capsules through a Transformer-based Parsing Module (TPM) to learn
the compositional relations between them. During training, as the face
hierarchy is progressively built and refined, the part capsules adaptively
encode the face parts with semantic consistency. HP-Capsule extends the
application of capsule networks from digits to human faces and takes a step
forward to show how the neural networks understand homologous objects without
human intervention. Besides, HP-Capsule gives unsupervised face segmentation
results by the covered regions of part capsules, enabling qualitative and
quantitative evaluation. Experiments on BP4D and Multi-PIE datasets show the

Class-based language models (LMs) have been long devised to address context
sparsity in $n$-gram LMs. In this study, we revisit this approach in the
context of neural LMs. We hypothesize that class-based prediction leads to an
implicit context aggregation for similar words and thus can improve
generalization for rare words. We map words that have a common WordNet hypernym
to the same class and train large neural LMs by gradually annealing from
predicting the class to token prediction during training. Empirically, this
curriculum learning strategy consistently improves perplexity over various
large, highly-performant state-of-the-art Transformer-based models on two
datasets, WikiText-103 and Arxiv. Our analysis shows that the performance
improvement is achieved without sacrificing performance on rare words. Finally,
we document other attempts that failed to yield empirical gains, and discuss
future directions for the adoption of class-based LMs on a larger scale.

Convolutional neural networks (CNNs) have gained significant popularity in
orthopedic imaging in recent years due to their ability to solve fracture
classification problems. A common criticism of CNNs is their opaque learning
and reasoning process, making it difficult to trust machine diagnosis and the
subsequent adoption of such algorithms in clinical setting. This is especially
true when the CNN is trained with limited amount of medical data, which is a
common issue as curating sufficiently large amount of annotated medical imaging
data is a long and costly process. While interest has been devoted to
explaining CNN learnt knowledge by visualizing network attention, the
utilization of the visualized attention to improve network learning has been
rarely investigated. This paper explores the effectiveness of regularizing CNN
network with human-provided attention guidance on where in the image the
network should look for answering clues. On two orthopedics radiographic
fracture classification datasets, through extensive experiments we demonstrate
that explicit human-guided attention indeed can direct correct network
attention and consequently significantly improve classification performance.
The development code for the proposed attention guidance is publicly available
on GitHub.

Object pose estimation methods allow finding locations of objects in
unstructured environments. This is a highly desired skill for autonomous robot
manipulation as robots need to estimate the precise poses of the objects in
order to manipulate them. In this paper, we investigate the problems of tactile
pose estimation and manipulation for category-level objects. Our proposed
method uses a Bayes filter with a learned tactile observation model and a
deterministic motion model. Later, we train policies using deep reinforcement
learning where the agents use the belief estimation from the Bayes filter. Our
models are trained in simulation and transferred to the real world. We analyze
the reliability and the performance of our framework through a series of
simulated and real-world experiments and compare our method to the baseline
work. Our results show that the learned tactile observation model can localize
the pose of novel objects at 2-mm and 1-degree resolution for position and
orientation, respectively. Furthermore, we experiment on a bottle opening task
where the gripper needs to reach the desired grasp state.

Real-time on-device continual learning is needed for new applications such as
home robots, user personalization on smartphones, and augmented/virtual reality
headsets. However, this setting poses unique challenges: embedded devices have
limited memory and compute capacity and conventional machine learning models
suffer from catastrophic forgetting when updated on non-stationary data
streams. While several online continual learning models have been developed,
their effectiveness for embedded applications has not been rigorously studied.
In this paper, we first identify criteria that online continual learners must
meet to effectively perform real-time, on-device learning. We then study the
efficacy of several online continual learning methods when used with mobile
neural networks. We measure their performance, memory usage, compute
requirements, and ability to generalize to out-of-domain inputs.

Identifying the causal structure of systems with multiple dynamic elements is
critical to several scientific disciplines. The conventional approach is to
conduct statistical tests of causality, for example with Granger Causality,
between observed signals that are selected a priori. Here it is posited that,
in many systems, the causal relations are embedded in a latent space that is
expressed in the observed data as a linear mixture. A technique for blindly
identifying the latent sources is presented: the observations are projected
into pairs of components -- driving and driven -- to maximize the strength of
causality between the pairs. This leads to an optimization problem with closed
form expressions for the objective function and gradient that can be solved
with off-the-shelf techniques. After demonstrating proof-of-concept on
synthetic data with known latent structure, the technique is applied to
recordings from the human brain and historical cryptocurrency prices. In both
cases, the approach recovers multiple strong causal relationships that are not
evident in the observed data. The proposed technique is unsupervised and can be
readily applied to any multiple time series to shed light on the causal
relationships underlying the data.

In ultrasound shear wave elasticity (SWE) imaging, a number of algorithms
exist for estimating the shear wave speed (SWS) from spatiotemporal
displacement data. However, no method provides a well-calibrated and practical
uncertainty metric, hindering SWE's clinical adoption and utility in downstream
decision-making. Here, we designed a deep learning SWS estimator that
simultaneously outputs a quantitative and well-calibrated uncertainty value for
each estimate. Our deep neural network (DNN) takes as input a single 2D
spatiotemporal plane of tracked displacement data and outputs the two
parameters $m$ and $\sigma$ of a log-normal probability distribution. For
training and testing, we used in vivo 2D-SWE data of the cervix collected from
30 pregnant subjects, totaling 551 acquisitions and >2 million space-time
plots. Points were grouped by uncertainty into bins to assess uncertainty
calibration: the predicted uncertainty closely matched the root-mean-square
estimation error, with an average absolute percent deviation of 3.84%. We
created a leave-one-out ensemble model that estimated uncertainty with better
calibration (1.45%) than any individual ensemble member on a held-out patient's
data. Lastly, we applied the DNN to an external dataset to evaluate its
generalizability. We have made the trained model, SweiNet, openly available to
provide the research community with a fast SWS estimator that also outputs a
well-calibrated estimate of the predictive uncertainty.

Brain-computer interfaces (BCIs) decode recorded neural signals from the
brain and/or stimulate the brain with encoded neural signals. BCIs span both
hardware and software and have a wide range of applications in restorative
medicine, from restoring movement through prostheses and robotic limbs to
restoring sensation and communication through spellers. BCIs also have
applications in diagnostic medicine, e.g., providing clinicians with data for
detecting seizures, sleep patterns, or emotions.
  Despite their promise, BCIs have not yet been adopted for long-term,
day-to-day use because of challenges related to reliability and robustness,
which are needed for safe operation in all scenarios. Ensuring safe operation
currently requires hours of manual data collection and recalibration, involving
both patients and clinicians. However, data collection is not targeted at
eliminating specific faults in a BCI. This paper presents a new methodology for
characterizing, detecting, and localizing faults in BCIs. Specifically, it
proposes partial test oracles as a method for detecting faults and slice
functions as a method for localizing faults to characteristic patterns in the
input data or relevant tasks performed by the user. Through targeted data
acquisition and retraining, the proposed methodology improves the correctness
of BCIs. We evaluated the proposed methodology on five BCI applications. The
results show that the proposed methodology (1) precisely localizes faults and
(2) can significantly reduce the frequency of faults through retraining based
on targeted, fault-based data acquisition. These results suggest that the
proposed methodology is a promising step towards repairing faulty BCIs.

Machine translation and other NLP systems often contain significant biases
regarding sensitive attributes, such as gender or race, that worsen system
performance and perpetuate harmful stereotypes. Recent preliminary research
suggests that adversarial learning can be used as part of a model-agnostic bias
mitigation method that requires no data modifications. However, adapting this
strategy for machine translation and other modern NLP domains requires (1)
restructuring training objectives in the context of fine-tuning pretrained
large language models and (2) developing measures for gender or other protected
variables for tasks in which these attributes must be deduced from the data
itself.
  We present an adversarial learning framework that addresses these challenges
to mitigate gender bias in seq2seq machine translation. Our framework improves
the disparity in translation quality for sentences with male vs. female
entities by 86% for English-German translation and 91% for English-French
translation, with minimal effect on translation quality. The results suggest
that adversarial learning is a promising technique for mitigating gender bias
in machine translation.

We study the problem of learning generative adversarial networks (GANs) for a
rare class of an unlabeled dataset subject to a labeling budget. This problem
is motivated from practical applications in domains including security (e.g.,
synthesizing packets for DNS amplification attacks), systems and networking
(e.g., synthesizing workloads that trigger high resource usage), and machine
learning (e.g., generating images from a rare class). Existing approaches are
unsuitable, either requiring fully-labeled datasets or sacrificing the fidelity
of the rare class for that of the common classes. We propose RareGAN, a novel
synthesis of three key ideas: (1) extending conditional GANs to use labelled
and unlabelled data for better generalization; (2) an active learning approach
that requests the most useful labels; and (3) a weighted loss function to favor
learning the rare class. We show that RareGAN achieves a better
fidelity-diversity tradeoff on the rare class than prior work across different
applications, budgets, rare class fractions, GAN losses, and architectures.

We introduce a fully convolutional fractional scaling component, FCFS. Fully
convolutional networks can be applied to any size input and previously did not
support non-integer scaling. Our architecture is simple with an efficient
single layer implementation. Examples and code implementations of three common
scaling methods are published.

Continual learning is essential for real-world deployment when there is a
need to quickly adapt the model to new tasks without forgetting knowledge of
old tasks. Existing work on continual sequence generation either always reuses
existing parameters to learn new tasks, which is vulnerable to catastrophic
forgetting on dissimilar tasks, or blindly adds new parameters for every new
task, which could prevent knowledge sharing between similar tasks. To get the
best of both worlds, in this work, we propose continual sequence generation
with adaptive compositional modules to adaptively add modules in transformer
architectures and compose both old and new modules for new tasks. We also
incorporate pseudo experience replay to facilitate knowledge transfer in those
shared modules. Experiment results on various sequences of generation tasks
show that our framework can adaptively add modules or reuse modules based on
task similarity, outperforming state-of-the-art baselines in terms of both
performance and parameter efficiency. We make our code public at
https://github.com/GT-SALT/Adaptive-Compositional-Modules.

Modern time series datasets are often high-dimensional, incomplete/sparse,
and nonstationary. These properties hinder the development of scalable and
efficient solutions for time series forecasting and analysis. To address these
challenges, we propose a Nonstationary Temporal Matrix Factorization (NoTMF)
model, in which matrix factorization is used to reconstruct the whole time
series matrix and vector autoregressive (VAR) process is imposed on a properly
differenced copy of the temporal factor matrix. This approach not only
preserves the low-rank property of the data but also offers consistent temporal
dynamics. The learning process of NoTMF involves the optimization of two factor
matrices and a collection of VAR coefficient matrices. To efficiently solve the
optimization problem, we derive an alternating minimization framework, in which
subproblems are solved using conjugate gradient and least squares methods. In
particular, the use of conjugate gradient method offers an efficient routine
and allows us to apply NoTMF on large-scale problems. Through extensive
experiments on Uber movement speed dataset, we demonstrate the superior
accuracy and effectiveness of NoTMF over other baseline models. Our results
also confirm the importance of addressing the nonstationarity of real-world
time series data such as spatiotemporal traffic flow/speed.

Optical neural networks are emerging as a promising type of machine learning
hardware capable of energy-efficient, parallel computation. Today's optical
neural networks are mainly developed to perform optical inference after in
silico training on digital simulators. However, various physical imperfections
that cannot be accurately modelled may lead to the notorious reality gap
between the digital simulator and the physical system. To address this
challenge, we demonstrate hybrid training of optical neural networks where the
weight matrix is trained with neuron activation functions computed optically
via forward propagation through the network. We examine the efficacy of hybrid
training with three different networks: an optical linear classifier, a hybrid
opto-electronic network, and a complex-valued optical network. We perform a
comparative study to in silico training, and our results show that hybrid
training is robust against different kinds of static noise. Our
platform-agnostic hybrid training scheme can be applied to a wide variety of
optical neural networks, and this work paves the way towards advanced
all-optical training in machine intelligence.

Blockchain network deployment and evaluation have become prevalent due to the
demand for private blockchains by enterprises, governments, and edge computing
systems. Whilst a blockchain network's deployment and evaluation are driven by
its architecture, practitioners still need to learn and carry out many
repetitive and error-prone activities to transform architecture into an
operational blockchain network and evaluate it. Greater efficiency could be
gained if practitioners focus solely on the architecture design, a valuable and
hard-to-automate activity, and leave the implementation steps to an automation
framework. This paper proposes an automation framework called NVAL (Network
Deployment and Evaluation Framework), which can deploy and evaluate blockchain
networks based on their architecture specifications. The key idea of NVAL is
reusing and combining the existing automation scripts and utilities of various
blockchain types to deploy and evaluate incoming blockchain network
architectures. We propose a novel meta-model to capture blockchain network
architectures as computer-readable artefacts and employ a state-space search
approach to plan and conduct their deployment and evaluation. An evaluative
case study shows that NVAL successfully combines seven deployment and
evaluation procedures to deploy 65 networks with 12 different architectures and
generate 295 evaluation datasets whilst incurring a negligible processing time
overhead.

Objective and Impact Statement. We adopt a deep learning model for bone
osteolysis prediction on computed tomography (CT) images of murine breast
cancer bone metastases. Given the bone CT scans at previous time steps, the
model incorporates the bone-cancer interactions learned from the sequential
images and generates future CT images. Its ability of predicting the
development of bone lesions in cancer-invading bones can assist in assessing
the risk of impending fractures and choosing proper treatments in breast cancer
bone metastasis. Introduction. Breast cancer often metastasizes to bone, causes
osteolytic lesions, and results in skeletal related events (SREs) including
severe pain and even fatal fractures. Although current imaging techniques can
detect macroscopic bone lesions, predicting the occurrence and progression of
bone lesions remains a challenge. Methods. We adopt a temporal variational
auto-encoder (T-VAE) model that utilizes a combination of variational
auto-encoders and long short-term memory networks to predict bone lesion
emergence on our micro-CT dataset containing sequential images of murine
tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn
the distribution of their future states from data. Results. We test our model
against other deep learning-based prediction models on the bone lesion
progression prediction task. Our model produces much more accurate predictions
than existing models under various evaluation metrics. Conclusion. We develop a
deep learning framework that can accurately predict and visualize the
progression of osteolytic bone lesions. It will assist in planning and
evaluating treatment strategies to prevent SREs in breast cancer patients.

Multi-head self-attention forms the core of Transformer networks. However,
their quadratically growing complexity with respect to the input sequence
length impedes their deployment on resource-constrained edge devices. We
address this challenge by proposing a dynamic pruning method, which exploits
the temporal stability of data across tokens to reduce inference cost. The
threshold-based method only retains significant differences between the
subsequent tokens, effectively reducing the number of multiply-accumulates, as
well as the internal tensor data sizes. The approach is evaluated on the Google
Speech Commands Dataset for keyword spotting, and the performance is compared
against the baseline Keyword Transformer. Our experiments show that we can
reduce ~80% of operations while maintaining the original 98.4% accuracy.
Moreover, a reduction of ~87-94% operations can be achieved when only degrading
the accuracy by 1-4%, speeding up the multi-head self-attention inference by a
factor of ~7.5-16.

We demonstrate and discuss nonasymptotic bounds in probability for the cost
of a regression scheme with a general loss function from the perspective of the
Rademacher theory, and for the optimality with respect to the average
$L^{2}$-distance to the underlying conditional expectations of least squares
regression outcomes from the perspective of the Vapnik-Chervonenkis theory.
  The results follow from an analysis involving independent but possibly
nonstationary training samples and can be extended, in a manner that we explain
and illustrate, to relevant cases in which the training sample exhibits
dependence.

For numerous earth observation applications, one may benefit from various
satellite sensors to address the reconstruction of some process or information
of interest. A variety of satellite sensors deliver observation data with
different sampling patterns due satellite orbits and/or their sensitivity to
atmospheric conditions (e.g., clour cover, heavy rains,...). Beyond the ability
to account for irregularly-sampled observations, the definition of model-driven
inversion methods is often limited to specific case-studies where one can
explicitly derive a physical model to relate the different observation sources.
Here, we investigate how end-to-end learning schemes provide new means to
address multimodal inversion problems. The proposed scheme combines a
variational formulation with trainable observation operators, {\em a priori}
terms and solvers. Through an application to space oceanography, we show how
this scheme can successfully extract relevant information from
satellite-derived sea surface temperature images and enhance the reconstruction
of sea surface currents issued from satellite altimetry data.

We propose a trainable Image Signal Processing (ISP) framework that produces
DSLR quality images given RAW images captured by a smartphone. To address the
color misalignments between training image pairs, we employ a color-conditional
ISP network and optimize a novel parametric color mapping between each input
RAW and reference DSLR image. During inference, we predict the target color
image by designing a color prediction network with efficient Global Context
Transformer modules. The latter effectively leverage global information to
learn consistent color and tone mappings. We further propose a robust masked
aligned loss to identify and discard regions with inaccurate motion estimation
during training. Lastly, we introduce the ISP in the Wild (ISPW) dataset,
consisting of weakly paired phone RAW and DSLR sRGB images. We extensively
evaluate our method, setting a new state-of-the-art on two datasets.

Recommender systems are highly prevalent in the modern world due to their
value to both users and platforms and services that employ them. Generally,
they can improve the user experience and help to increase satisfaction, but
they do not come without risks. One such risk is that of their effect on users
and their ability to play an active role in shaping user preferences. This risk
is more significant for reinforcement learning based recommender systems. These
are capable of learning for instance, how recommended content shown to a user
today may tamper that user's preference for other content recommended in the
future. Reinforcement learning based recommendation systems can thus implicitly
learn to influence users if that means maximizing clicks, engagement, or
consumption. On social news and media platforms, in particular, this type of
behavior is cause for alarm. Social media undoubtedly plays a role in public
opinion and has been shown to be a contributing factor to increased political
polarization. Recommender systems on such platforms, therefore, have great
potential to influence users in undesirable ways. However, it may also be
possible for this form of manipulation to be used intentionally. With
advancements in political opinion dynamics modeling and larger collections of
user data, explicit user manipulation in which the beliefs and opinions of
users are tailored towards a certain end emerges as a significant concern in
reinforcement learning based recommender systems.

Clinical notes in Electronic Health Records (EHR) present rich documented
information of patients to inference phenotype for disease diagnosis and study
patient characteristics for cohort selection. Unsupervised user embedding aims
to encode patients into fixed-length vectors without human supervisions.
Medical concepts extracted from the clinical notes contain rich connections
between patients and their clinical categories. However, existing unsupervised
approaches of user embeddings from clinical notes do not explicitly incorporate
medical concepts. In this study, we propose a concept-aware unsupervised user
embedding that jointly leverages text documents and medical concepts from two
clinical corpora, MIMIC-III and Diabetes. We evaluate user embeddings on both
extrinsic and intrinsic tasks, including phenotype classification, in-hospital
mortality prediction, patient retrieval, and patient relatedness. Experiments
on the two clinical corpora show our approach exceeds unsupervised baselines,
and incorporating medical concepts can significantly improve the baseline

While optical microscopy inspection of blood films and bone marrow aspirates
by a hematologist is a crucial step in establishing diagnosis of acute
leukemia, especially in low-resource settings where other diagnostic modalities
might not be available, the task remains time-consuming and prone to human
inconsistencies. This has an impact especially in cases of Acute Promyelocytic
Leukemia (APL) that require urgent treatment. Integration of automated
computational hematopathology into clinical workflows can improve the
throughput of these services and reduce cognitive human error. However, a major
bottleneck in deploying such systems is a lack of sufficient cell morphological
object-labels annotations to train deep learning models. We overcome this by
leveraging patient diagnostic labels to train weakly-supervised models that
detect different types of acute leukemia. We introduce a deep learning
approach, Multiple Instance Learning for Leukocyte Identification (MILLIE),
able to perform automated reliable analysis of blood films with minimal
supervision. Without being trained to classify individual cells, MILLIE
differentiates between acute lymphoblastic and myeloblastic leukemia in blood
films. More importantly, MILLIE detects APL in blood films (AUC 0.94+/-0.04)
and in bone marrow aspirates (AUC 0.99+/-0.01). MILLIE is a viable solution to
augment the throughput of clinical pathways that require assessment of blood
film microscopy.

In typical machine learning systems, an estimate of the probability of the
prediction is used to assess the system's confidence in the prediction. This
confidence measure is usually uncalibrated; i.e.\ the system's confidence in
the prediction does not match the true probability of the predicted output. In
this paper, we present an investigation into calibrating open setting machine
reading systems such as open-domain question answering and claim verification
systems. We show that calibrating such complex systems which contain discrete
retrieval and deep reading components is challenging and current calibration
techniques fail to scale to these settings. We propose simple extensions to
existing calibration approaches that allows us to adapt them to these settings.
Our experimental results reveal that the approach works well, and can be useful
to selectively predict answers when question answering systems are posed with
unanswerable or out-of-the-training distribution questions.

In this paper, we propose a framework that incorporates experts diagnostics
and insights into the analysis of Optical Coherence Tomography (OCT) using
multi-modal learning. To demonstrate the effectiveness of this approach, we
create a medical diagnostic attribute dataset to improve disease classification
using OCT. Although there have been successful attempts to deploy machine
learning for disease classification in OCT, such methodologies lack the experts
insights. We argue that injecting ophthalmological assessments as another
supervision in a learning framework is of great importance for the machine
learning process to perform accurate and interpretable classification. We
demonstrate the proposed framework through comprehensive experiments that
compare the effectiveness of combining diagnostic attribute features with
latent visual representations and show that they surpass the state-of-the-art
approach. Finally, we analyze the proposed dual-stream architecture and provide
an insight that determine the components that contribute most to classification

We designed and built a game called \textit{Immersive Text Game}, which
allows the player to choose a story and a character, and interact with other
characters in the story in an immersive manner of dialogues. The game is based
on several latest models, including text generation language model, information
extraction model, commonsense reasoning model, and psychology evaluation model.
In the past, similar text games usually let players choose from limited actions
instead of answering on their own, and not every time what characters said are
determined by the player. Through the combination of these models and elaborate
game mechanics and modes, the player will find some novel experiences as driven
through the storyline.

Contemporary neural networks have achieved a series of developments and
successes in many aspects; however, when exposed to data outside the training
distribution, they may fail to predict correct answers. In this work, we were
concerned about this generalisation issue and thus analysed a broad set of
models systematically and robustly over long stories. Related experiments were
conducted based on the CLUTRR, which is a diagnostic benchmark suite that can
analyse generalisation of natural language understanding (NLU) systems by
training over small story graphs and testing on larger ones. In order to handle
the multi-relational story graph, we consider two classes of neural models:
"E-GNN", the graph-based models that can process graph-structured data and
consider the edge attributes simultaneously; and "L-Graph", the sequence-based
models which can process linearized version of the graphs. We performed an
extensive empirical evaluation, and we found that the modified recurrent neural
network yield surprisingly accurate results across every systematic
generalisation tasks which outperform the modified graph neural network, while
the latter produced more robust models.

A fully automated system for interpreting abdominal computed tomography (CT)
scans with multiple phases of contrast enhancement requires an accurate
classification of the phases. This work aims at developing and validating a
precise, fast multi-phase classifier to recognize three main types of contrast
phases in abdominal CT scans. We propose in this study a novel method that uses
a random sampling mechanism on top of deep CNNs for the phase recognition of
abdominal CT scans of four different phases: non-contrast, arterial, venous,
and others. The CNNs work as a slice-wise phase prediction, while the random
sampling selects input slices for the CNN models. Afterward, majority voting
synthesizes the slice-wise results of the CNNs, to provide the final prediction
at scan level. Our classifier was trained on 271,426 slices from 830
phase-annotated CT scans, and when combined with majority voting on 30% of
slices randomly chosen from each scan, achieved a mean F1-score of 92.09% on
our internal test set of 358 scans. The proposed method was also evaluated on 2
external test sets: CTPAC-CCRCC (N = 242) and LiTS (N = 131), which were
annotated by our experts. Although a drop in performance has been observed, the
model performance remained at a high level of accuracy with a mean F1-score of
76.79% and 86.94% on CTPAC-CCRCC and LiTS datasets, respectively. Our
experimental results also showed that the proposed method significantly
outperformed the state-of-the-art 3D approaches while requiring less
computation time for inference.

Animals such as rabbits and birds can instantly generate locomotion behavior
in reaction to a dynamic, approaching object, such as a person or a rock,
despite having possibly never seen the object before and having limited
perception of the object's properties. Recently, deep reinforcement learning
has enabled complex kinematic systems such as humanoid robots to successfully
move from point A to point B. Inspired by the observation of the innate
reactive behavior of animals in nature, we hope to extend this progress in
robot locomotion to settings where external, dynamic objects are involved whose
properties are partially observable to the robot. As a first step toward this
goal, we build a simulation environment in MuJoCo where a legged robot must
avoid getting hit by a ball moving toward it. We explore whether prior
locomotion experiences that animals typically possess benefit the learning of a
reactive control policy under a proposed hierarchical reinforcement learning
framework. Preliminary results support the claim that the learning becomes more
efficient using this hierarchical reinforcement learning method, even when
partial observability (radius-based object visibility) is taken into account.

Mammography, or breast X-ray, is the most widely used imaging modality to
detect cancer and other breast diseases. Recent studies have shown that deep
learning-based computer-assisted detection and diagnosis (CADe or CADx) tools
have been developed to support physicians and improve the accuracy of
interpreting mammography. However, most published datasets of mammography are
either limited on sample size or digitalized from screen-film mammography
(SFM), hindering the development of CADe and CADx tools which are developed
based on full-field digital mammography (FFDM). To overcome this challenge, we
introduce VinDr-Mammo - a new benchmark dataset of FFDM for detecting and
diagnosing breast cancer and other diseases in mammography. The dataset
consists of 5,000 mammography exams, each of which has four standard views and
is double read with disagreement (if any) being resolved by arbitration. It is
created for the assessment of Breast Imaging Reporting and Data System
(BI-RADS) and density at the breast level. In addition, the dataset also
provides the category, location, and BI-RADS assessment of non-benign findings.
We make VinDr-Mammo publicly available on PhysioNet as a new imaging resource
to promote advances in developing CADe and CADx tools for breast cancer
screening.

In this work, we study two self-play training schemes, Chainer and Pool, and
show they lead to improved agent performance in Atari Pong compared to a
standard DQN agent -- trained against the built-in Atari opponent. To measure
agent performance, we define a robustness metric that captures how difficult it
is to learn a strategy that beats the agent's learned policy. Through playing
past versions of themselves, Chainer and Pool are able to target weaknesses in
their policies and improve their resistance to attack. Agents trained using
these methods score well on our robustness metric and can easily defeat the
standard DQN agent. We conclude by using linear probing to illuminate what
internal structures the different agents develop to play the game. We show that
training agents with Chainer or Pool leads to richer network activations with
greater predictive power to estimate critical game-state features compared to
the standard DQN agent.

Computer-aided diagnosis systems in adult chest radiography (CXR) have
recently achieved great success thanks to the availability of large-scale,
annotated datasets and the advent of high-performance supervised learning
algorithms. However, the development of diagnostic models for detecting and
diagnosing pediatric diseases in CXR scans is undertaken due to the lack of
high-quality physician-annotated datasets. To overcome this challenge, we
introduce and release VinDr-PCXR, a new pediatric CXR dataset of 9,125 studies
retrospectively collected from a major pediatric hospital in Vietnam between
2020 and 2021. Each scan was manually annotated by a pediatric radiologist who
has more than ten years of experience. The dataset was labeled for the presence
of 36 critical findings and 15 diseases. In particular, each abnormal finding
was identified via a rectangle bounding box on the image. To the best of our
knowledge, this is the first and largest pediatric CXR dataset containing
lesion-level annotations and image-level labels for the detection of multiple
findings and diseases. For algorithm development, the dataset was divided into
a training set of 7,728 and a test set of 1,397. To encourage new advances in
pediatric CXR interpretation using data-driven approaches, we provide a
detailed description of the VinDr-PCXR data sample and make the dataset
publicly available on https://physionet.org/.

Building an accurate computer-aided diagnosis system based on data-driven
approaches requires a large amount of high-quality labeled data. In medical
imaging analysis, multiple expert annotators often produce subjective estimates
about "ground truth labels" during the annotation process, depending on their
expertise and experience. As a result, the labeled data may contain a variety
of human biases with a high rate of disagreement among annotators, which
significantly affect the performance of supervised machine learning algorithms.
To tackle this challenge, we propose a simple yet effective approach to combine
annotations from multiple radiology experts for training a deep learning-based
detector that aims to detect abnormalities on medical scans. The proposed
method first estimates the ground truth annotations and confidence scores of
training examples. The estimated annotations and their scores are then used to
train a deep learning detector with a re-weighted loss function to localize
abnormal findings. We conduct an extensive experimental evaluation of the
proposed approach on both simulated and real-world medical imaging datasets.
The experimental results show that our approach significantly outperforms
baseline approaches that do not consider the disagreements among annotators,
including methods in which all of the noisy annotations are treated equally as
ground truth and the ensemble of different models trained on different label
sets provided separately by annotators.

Image augmentation techniques have been widely investigated to improve the
performance of deep learning (DL) algorithms on mammography classification
tasks. Recent methods have proved the efficiency of image augmentation on data
deficiency or data imbalance issues. In this paper, we propose a novel
transparency strategy to boost the Breast Imaging Reporting and Data System
(BI-RADS) scores of mammograms classifier. The proposed approach utilizes the
Region of Interest (ROI) information to generate more high-risk training
examples from original images. Our extensive experiments were conducted on our
benchmark mammography dataset. The experiment results show that the proposed
approach surpasses current state-of-the-art data augmentation techniques such
as Upsampling or CutMix. The study highlights that the transparency method is
more effective than other augmentation strategies for BI-RADS classification
and can be widely applied for our computer vision tasks.

Stochastic alternating algorithms for bi-objective optimization are
considered when optimizing two conflicting functions for which optimization
steps have to be applied separately for each function. Such algorithms consist
of applying a certain number of steps of gradient or subgradient descent on
each single objective at each iteration. In this paper, we show that stochastic
alternating algorithms achieve a sublinear convergence rate of
$\mathcal{O}(1/T)$, under strong convexity, for the determination of a
minimizer of a weighted-sum of the two functions, parameterized by the number
of steps applied on each of them. An extension to the convex case is presented
for which the rate weakens to $\mathcal{O}(1/\sqrt{T})$. These rates are valid
also in the non-smooth case. Importantly, by varying the proportion of steps
applied to each function, one can determine an approximation to the Pareto
front.

Significant advances have recently been achieved in Multi-Agent Reinforcement
Learning (MARL) which tackles sequential decision-making problems involving
multiple participants. However, MARL requires a tremendous number of samples
for effective training. On the other hand, model-based methods have been shown
to achieve provable advantages of sample efficiency. However, the attempts of
model-based methods to MARL have just started very recently. This paper
presents a review of the existing research on model-based MARL, including
theoretical analyses, algorithms, and applications, and analyzes the advantages
and potential of model-based MARL. Specifically, we provide a detailed taxonomy
of the algorithms and point out the pros and cons for each algorithm according
to the challenges inherent to multi-agent scenarios. We also outline promising
directions for future development of this field.

The growing IC complexity has led to a compelling need for design efficiency
improvement through new electronic design automation (EDA) methodologies. In
recent years, many unprecedented efficient EDA methods have been enabled by
machine learning (ML) techniques. While ML demonstrates its great potential in
circuit design, however, the dark side about security problems, is seldomly
discussed. This paper gives a comprehensive and impartial summary of all
security concerns we have observed in ML for EDA. Many of them are hidden or
neglected by practitioners in this field. In this paper, we first provide our
taxonomy to define four major types of security concerns, then we analyze
different application scenarios and special properties in ML for EDA. After
that, we present our detailed analysis of each security concern with

One of the critical tools for early detection and subsequent evaluation of
the incidence of lung diseases is chest radiography. This study presents a
real-world implementation of a convolutional neural network (CNN) based Carebot
Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model
takes the form of a simple and intuitive application. Used CNN can be deployed
as a STOW-RS prediction endpoint for direct implementation into DICOM viewers.
The results of this study show that the deep learning model based on DenseNet
and ResNet architecture can detect SARS-CoV-2 from CXR images with precision of
0.981, recall of 0.962 and AP of 0.993.

We present a novel approach to modeling the ground state mass of atomic
nuclei based directly on a probabilistic neural network constrained by relevant
physics. Our Physically Interpretable Machine Learning (PIML) approach
incorporates knowledge of physics by using a physically motivated feature space
in addition to a soft physics constraint that is implemented as a penalty to
the loss function. We train our PIML model on a random set of $\sim$20\% of the
Atomic Mass Evaluation (AME) and predict the remaining $\sim$80\%. The success
of our methodology is exhibited by the unprecedented
$\sigma_\textrm{RMS}\sim186$ keV match to data for the training set and
$\sigma_\textrm{RMS}\sim316$ keV for the entire AME with $Z \geq 20$. We show
that our general methodology can be interpreted using feature importance.

Open-vocabulary object detection aims to detect novel object categories
beyond the training set.
  The advanced open-vocabulary two-stage detectors employ instance-level
visual-to-visual knowledge distillation to align the visual space of the
detector with the semantic space of the Pre-trained Visual-Language Model
(PVLM).
  However, in the more efficient one-stage detector, the absence of
class-agnostic object proposals hinders the knowledge distillation on unseen
objects, leading to severe performance degradation.
  In this paper, we propose a hierarchical visual-language knowledge
distillation method, i.e., HierKD, for open-vocabulary one-stage detection.
  Specifically, a global-level knowledge distillation is explored to transfer
the knowledge of unseen categories from the PVLM to the detector.
  Moreover, we combine the proposed global-level knowledge distillation and the
common instance-level knowledge distillation to learn the knowledge of seen and
unseen categories simultaneously.
  Extensive experiments on MS-COCO show that our method significantly surpasses
the previous best one-stage detector with 11.9\% and 6.7\% $AP_{50}$ gains
under the zero-shot detection and generalized zero-shot detection settings, and
reduces the $AP_{50}$ performance gap from 14\% to 7.3\% compared to the best
two-stage detector.

In this chapter, we identify fundamental geometric structures that underlie
the problems of sampling, optimisation, inference and adaptive decision-making.
Based on this identification, we derive algorithms that exploit these geometric
structures to solve these problems efficiently. We show that a wide range of
geometric theories emerge naturally in these fields, ranging from
measure-preserving processes, information divergences, Poisson geometry, and
geometric integration. Specifically, we explain how (i) leveraging the
symplectic geometry of Hamiltonian systems enable us to construct (accelerated)
sampling and optimisation methods, (ii) the theory of Hilbertian subspaces and
Stein operators provides a general methodology to obtain robust estimators,
(iii) preserving the information geometry of decision-making yields adaptive
agents that perform active inference. Throughout, we emphasise the rich
connections between these fields; e.g., inference draws on sampling and
optimisation, and adaptive decision-making assesses decisions by inferring
their counterfactual consequences. Our exposition provides a conceptual
overview of underlying ideas, rather than a technical discussion, which can be
found in the references herein.

Variational Quantum Circuits are being used as versatile Quantum Machine
Learning models. Some empirical results exhibit an advantage in supervised and
generative learning tasks. However, when applied to Reinforcement Learning,
less is known. In this work, we considered a Variational Quantum Circuit
composed of a low-depth hardware-efficient ansatz as the parameterized policy
of a Reinforcement Learning agent. We show that an $\epsilon$-approximation of
the policy gradient can be obtained using a logarithmic number of samples
concerning the total number of parameters. We empirically verify that such
quantum models behave similarly or even outperform typical classical neural
networks used in standard benchmarking environments and in quantum control,
using only a fraction of the parameters. Moreover, we study the Barren Plateau
phenomenon in quantum policy gradients using the Fisher Information Matrix
spectrum.

Spatio-temporal action recognition has been a challenging task that involves
detecting where and when actions occur. Current state-of-the-art action
detectors are mostly anchor-based, requiring sensitive anchor designs and huge
computations due to calculating large numbers of anchor boxes. Motivated by
nascent anchor-free approaches, we propose Point3D, a flexible and
computationally efficient network with high precision for spatio-temporal
action recognition. Our Point3D consists of a Point Head for action
localization and a 3D Head for action classification. Firstly, Point Head is
used to track center points and knot key points of humans to localize the
bounding box of an action. These location features are then piped into a
time-wise attention to learn long-range dependencies across frames. The 3D Head
is later deployed for the final action classification. Our Point3D achieves
state-of-the-art performance on the JHMDB, UCF101-24, and AVA benchmarks in
terms of frame-mAP and video-mAP. Comprehensive ablation studies also
demonstrate the effectiveness of each module proposed in our Point3D.

In this work, we investigate a data-driven approach for obtaining a reduced
equivalent load model of distribution systems for electromechanical transient
stability analysis. The proposed reduced equivalent is a neuro-physical model
comprising of a traditional ZIP load model augmented with a neural network.
This neuro-physical model is trained through differentiable programming. We
discuss the formulation, modeling details, and training of the proposed model
set up as a differential parametric program. The performance and accuracy of
this neurophysical ZIP load model is presented on a medium-scale 350-bus
transmission-distribution network.

In real-world scenarios, a text classification task often begins with a cold
start, when labeled data is scarce. In such cases, the common practice of
fine-tuning pre-trained models, such as BERT, for a target classification task,
is prone to produce poor performance. We suggest a method to boost the
performance of such models by adding an intermediate unsupervised
classification task, between the pre-training and fine-tuning phases. As such
an intermediate task, we perform clustering and train the pre-trained model on
predicting the cluster labels. We test this hypothesis on various data sets,
and show that this additional classification phase can significantly improve
performance, mainly for topical classification tasks, when the number of
labeled instances available for fine-tuning is only a couple of dozen to a few
hundred.

We study the role of an essential hyper-parameter that governs the training
of Transformers for neural machine translation in a low-resource setting: the
batch size. Using theoretical insights and experimental evidence, we argue
against the widespread belief that batch size should be set as large as allowed
by the memory of the GPUs. We show that in a low-resource setting, a smaller
batch size leads to higher scores in a shorter training time, and argue that
this is due to better regularization of the gradients during training.

Multi-behavior recommendation (MBR) aims to jointly consider multiple
behaviors to improve the target behavior's performance. We argue that MBR
models should: (1) model the coarse-grained commonalities between different
behaviors of a user, (2) consider both individual sequence view and global
graph view in multi-behavior modeling, and (3) capture the fine-grained
differences between multiple behaviors of a user. In this work, we propose a
novel Multi-behavior Multi-view Contrastive Learning Recommendation (MMCLR)
framework, including three new CL tasks to solve the above challenges,
respectively. The multi-behavior CL aims to make different user single-behavior
representations of the same user in each view to be similar. The multi-view CL
attempts to bridge the gap between a user's sequence-view and graph-view
representations. The behavior distinction CL focuses on modeling fine-grained
differences of different behaviors. In experiments, we conduct extensive
evaluations and ablation tests to verify the effectiveness of MMCLR and various
CL tasks on two real-world datasets, achieving SOTA performance over existing
baselines. Our code will be available on
\url{https://github.com/wyqing20/MMCLR}

This work studies distributionally robust evaluation of expected function
values over temporal data. A set of alternative measures is characterized by
the causal optimal transport. We prove the strong duality and recast the
causality constraint as minimization over an infinite-dimensional test function
space. We approximate test functions by neural networks and prove the sample
complexity with Rademacher complexity. Moreover, when structural information is
available to further restrict the ambiguity set, we prove the dual formulation
and provide efficient optimization methods. Empirical analysis on realized
volatility and stock indices demonstrate that our framework offers an
attractive alternative to the classic optimal transport formulation.

Real scans always miss partial geometries of objects due to the
self-occlusions, external-occlusions, and limited sensor resolutions. Point
cloud completion aims to refer the complete shapes for incomplete 3D scans of
objects. Current deep learning-based approaches rely on large-scale complete
shapes in the training process, which are usually obtained from synthetic
datasets. It is not applicable for real-world scans due to the domain gap. In
this paper, we propose a self-supervised point cloud completion method (TraPCC)
for vehicles in real traffic scenes without any complete data. Based on the
symmetry and similarity of vehicles, we make use of consecutive point cloud
frames to construct vehicle memory bank as reference. We design a bottom-up
mechanism to focus on both local geometry details and global shape features of
inputs. In addition, we design a scene-graph in the network to pay attention to
the missing parts by the aid of neighboring vehicles. Experiments show that
TraPCC achieve good performance for real-scan completion on KITTI and nuScenes
traffic datasets even without any complete data in training. We also show a
downstream application of 3D detection, which benefits from our completion
approach.

Researches on analyzing graphs with Graph Neural Networks (GNNs) have been
receiving more and more attention because of the great expressive power of
graphs. GNNs map the adjacency matrix and node features to node representations
by message passing through edges on each convolution layer. However, the
message passed through GNNs is not always beneficial for all parts in a graph.
Specifically, as the data distribution is different over the graph, the
receptive field (the farthest nodes that a node can obtain information from)
needed to gather information is also different. Existing GNNs treat all parts
of the graph uniformly, which makes it difficult to adaptively pass the most
informative message for each unique part. To solve this problem, we propose two
regularization terms that consider message passing locally: (1) Intra-Energy
Reg and (2) Inter-Energy Reg. Through experiments and theoretical discussion,
we first show that the speed of smoothing of different parts varies enormously
and the topology of each part affects the way of smoothing. With Intra-Energy
Reg, we strengthen the message passing within each part, which is beneficial
for getting more useful information. With Inter-Energy Reg, we improve the
ability of GNNs to distinguish different nodes. With the proposed two
regularization terms, GNNs are able to filter the most useful information
adaptively, learn more robustly and gain higher expressiveness. Moreover, the
proposed LEReg can be easily applied to other GNN models with plug-and-play
characteristics. Extensive experiments on several benchmarks verify that GNNs
with LEReg outperform or match the state-of-the-art methods. The effectiveness
and efficiency are also empirically visualized with elaborate experiments.

Image signal processors (ISPs) are historically grown legacy software systems
for reconstructing color images from noisy raw sensor measurements. They are
usually composited of many heuristic blocks for denoising, demosaicking, and
color restoration. Color reproduction in this context is of particular
importance, since the raw colors are often severely distorted, and each smart
phone manufacturer has developed their own characteristic heuristics for
improving the color rendition, for example of skin tones and other visually
important colors.
  In recent years there has been strong interest in replacing the historically
grown ISP systems with deep learned pipelines. Much progress has been made in
approximating legacy ISPs with such learned models. However, so far the focus
of these efforts has been on reproducing the structural features of the images,
with less attention paid to color rendition.
  Here we present CRISPnet, the first learned ISP model to specifically target
color rendition accuracy relative to a complex, legacy smart phone ISP. We
achieve this by utilizing both image metadata (like a legacy ISP would), as
well as by learning simple global semantics based on image classification --
similar to what a legacy ISP does to determine the scene type. We also
contribute a new ISP image dataset consisting of both high dynamic range
monitor data, as well as real-world data, both captured with an actual cell
phone ISP pipeline under a variety of lighting conditions, exposure times, and
gain settings.

Network robustness is critical for various societal and industrial networks
again malicious attacks. In particular, connectivity robustness and
controllability robustness reflect how well a networked system can maintain its
connectedness and controllability against destructive attacks, which can be
quantified by a sequence of values that record the remaining connectivity and
controllability of the network after a sequence of node- or edge-removal
attacks. Traditionally, robustness is determined by attack simulations, which
are computationally very time-consuming or even practically infeasible. In this
paper, an improved method for network robustness prediction is developed based
on learning feature representation using convolutional neural network
(LFR-CNN). In this scheme, higher-dimensional network data are compressed to
lower-dimensional representations, and then passed to a CNN to perform
robustness prediction. Extensive experimental studies on both synthetic and
real-world networks, both directed and undirected, demonstrate that 1) the
proposed LFR-CNN performs better than other two state-of-the-art prediction
methods, with significantly lower prediction errors; 2) LFR-CNN is insensitive
to the variation of the network size, which significantly extends its
applicability; 3) although LFR-CNN needs more time to perform feature learning,
it can achieve accurate prediction faster than attack simulations; 4) LFR-CNN
not only can accurately predict network robustness, but also provides a good
indicator for connectivity robustness, better than the classical spectral
measures.

Promising performance has been achieved for visual perception on the point
cloud. However, the current methods typically rely on labour-extensive
annotations on the scene scans. In this paper, we explore how synthetic models
alleviate the real scene annotation burden, i.e., taking the labelled 3D
synthetic models as reference for supervision, the neural network aims to
recognize specific categories of objects on a real scene scan (without scene
annotation for supervision). The problem studies how to transfer knowledge from
synthetic 3D models to real 3D scenes and is named Referring Transfer Learning
(RTL). The main challenge is solving the model-to-scene (from a single model to
the scene) and synthetic-to-real (from synthetic model to real scene's object)
gap between the synthetic model and the real scene. To this end, we propose a
simple yet effective framework to perform two alignment operations. First,
physical data alignment aims to make the synthetic models cover the diversity
of the scene's objects with data processing techniques. Then a novel
\textbf{convex-hull regularized feature alignment} introduces learnable
prototypes to project the point features of both synthetic models and real
scenes to a unified feature space, which alleviates the domain gap. These
operations ease the model-to-scene and synthetic-to-real difficulty for a
network to recognize the target objects on a real unseen scene. Experiments
show that our method achieves the average mAP of 46.08\% and 55.49\% on the
ScanNet and S3DIS datasets by learning the synthetic models from the ModelNet
dataset. Code will be publicly available.

Named entity recognition (NER) is a fundamental task in natural language
processing. Recent works treat named entity recognition as a reading
comprehension task, constructing type-specific queries manually to extract
entities. This paradigm suffers from three issues. First, type-specific queries
can only extract one type of entities per inference, which is inefficient.
Second, the extraction for different types of entities is isolated, ignoring
the dependencies between them. Third, query construction relies on external
knowledge and is difficult to apply to realistic scenarios with hundreds of
entity types. To deal with them, we propose Parallel Instance Query Network
(PIQN), which sets up global and learnable instance queries to extract entities
from a sentence in a parallel manner. Each instance query predicts one entity,
and by feeding all instance queries simultaneously, we can query all entities
in parallel. Instead of being constructed from external knowledge, instance
queries can learn their different query semantics during training. For training
the model, we treat label assignment as a one-to-many Linear Assignment Problem
(LAP) and dynamically assign gold entities to instance queries with minimal
assignment cost. Experiments on both nested and flat NER datasets demonstrate
that our proposed method outperforms previous state-of-the-art models.

This paper presents a new vision Transformer, named Iwin Transformer, which
is specifically designed for human-object interaction (HOI) detection, a
detailed scene understanding task involving a sequential process of
human/object detection and interaction recognition. Iwin Transformer is a
hierarchical Transformer which progressively performs token representation
learning and token agglomeration within irregular windows. The irregular
windows, achieved by augmenting regular grid locations with learned offsets, 1)
eliminate redundancy in token representation learning, which leads to efficient
human/object detection, and 2) enable the agglomerated tokens to align with
humans/objects with different shapes, which facilitates the acquisition of
highly-abstracted visual semantics for interaction recognition. The
effectiveness and efficiency of Iwin Transformer are verified on the two
standard HOI detection benchmark datasets, HICO-DET and V-COCO. Results show
our method outperforms existing Transformers-based methods by large margins
(3.7 mAP gain on HICO-DET and 2.0 mAP gain on V-COCO) with fewer training
epochs ($0.5 \times$).

While stochastic video prediction models enable future prediction under
uncertainty, they mostly fail to model the complex dynamics of real-world
scenes. For example, they cannot provide reliable predictions for scenes with a
moving camera and independently moving foreground objects in driving scenarios.
The existing methods fail to fully capture the dynamics of the structured world
by only focusing on changes in pixels. In this paper, we assume that there is
an underlying process creating observations in a video and propose to factorize
it into static and dynamic components. We model the static part based on the
scene structure and the ego-motion of the vehicle, and the dynamic part based
on the remaining motion of the dynamic objects. By learning separate
distributions of changes in foreground and background, we can decompose the
scene into static and dynamic parts and separately model the change in each.
Our experiments demonstrate that disentangling structure and motion helps
stochastic video prediction, leading to better future predictions in complex
driving scenarios on two real-world driving datasets, KITTI and Cityscapes.

As artificial intelligence becomes more powerful and a ubiquitous presence in
daily life, it is imperative to understand and manage the impact of AI systems
on our lives and decisions. Modern ML systems often change user behavior (e.g.
personalized recommender systems learn user preferences to deliver
recommendations that change online behavior). An externality of behavior change
is preference change. This article argues for the establishment of a
multidisciplinary endeavor focused on understanding how AI systems change
preference: Preference Science. We operationalize preference to incorporate
concepts from various disciplines, outlining the importance of meta-preferences
and preference-change preferences, and proposing a preliminary framework for
how preferences change. We draw a distinction between preference change,
permissible preference change, and outright preference manipulation. A
diversity of disciplines contribute unique insights to this framework.

In this work, the integration of two machine learning approaches, namely
domain adaptation and explainable AI, is proposed to address these two issues
of generalized detection and explainability. Firstly the Domain Adversarial
Neural Network (DANN) develops a generalized misinformation detector across
multiple social media platforms DANN is employed to generate the classification
results for test domains with relevant but unseen data. The DANN-based model, a
traditional black-box model, cannot justify its outcome, i.e., the labels for
the target domain. Hence a Local Interpretable Model-Agnostic Explanations
(LIME) explainable AI model is applied to explain the outcome of the DANN mode.
To demonstrate these two approaches and their integration for effective
explainable generalized detection, COVID-19 misinformation is considered a case
study. We experimented with two datasets, namely CoAID and MiSoVac, and
compared results with and without DANN implementation. DANN significantly
improves the accuracy measure F1 classification score and increases the
accuracy and AUC performance. The results obtained show that the proposed
framework performs well in the case of domain shift and can learn
domain-invariant features while explaining the target labels with LIME
implementation enabling trustworthy information processing and extraction to
combat misinformation effectively.

This paper presents a new reward function that can be used for deep
reinforcement learning in unmanned aerial vehicle (UAV) control and navigation
problems. The reward function is based on the construction and estimation of
the time of simplified trajectories to the target, which are third-order Bezier
curves. This reward function can be applied unchanged to solve problems in both
two-dimensional and three-dimensional virtual environments. The effectiveness
of the reward function was tested in a newly developed virtual environment,
namely, a simplified two-dimensional environment describing the dynamics of UAV
control and flight, taking into account the forces of thrust, inertia, gravity,
and aerodynamic drag. In this formulation, three tasks of UAV control and
navigation were successfully solved: UAV flight to a given point in space,
avoidance of interception by another UAV, and organization of interception of
one UAV by another. The three most relevant modern deep reinforcement learning
algorithms, Soft actor-critic, Deep Deterministic Policy Gradient, and Twin
Delayed Deep Deterministic Policy Gradient were used. All three algorithms
performed well, indicating the effectiveness of the selected reward function.

In this work, we propose a framework for allowing autonomous robots deployed
for extended periods of time in public spaces to adapt their own behaviour
online from user interactions. The robot behaviour planning is embedded in a
Reinforcement Learning (RL) framework, where the objective is maximising the
level of overall user engagement during the interactions. We use the
Upper-Confidence-Bound Value-Iteration (UCBVI) algorithm, which gives a helpful
way of managing the exploration-exploitation trade-off for real-time
interactions. An engagement model trained end-to-end generates the reward
function in real-time during policy execution. We test this approach in a
public museum in Lincoln (UK), where the robot is deployed as a tour guide for
the visitors. Results show that after a couple of months of exploration, the
robot policy learned to maintain the engagement of users for longer, with an
increase of 22.8% over the initial static policy in the number of items visited
during the tour and a 30% increase in the probability of completing the tour.
This work is a promising step toward behavioural adaptation in long-term
scenarios for robotics applications in social settings.

Patient-specific cardiac modeling combines geometries of the heart derived
from medical images and biophysical simulations to predict various aspects of
cardiac function. However, generating simulation-suitable models of the heart
from patient image data often requires complicated procedures and significant
human effort. We present a fast and automated deep-learning method to construct
simulation-suitable models of the heart from medical images. The approach
constructs meshes from 3D patient images by learning to deform a small set of
deformation handles on a whole heart template. For both 3D CT and MR data, this
method achieves promising accuracy for whole heart reconstruction, consistently
outperforming prior methods in constructing simulation-suitable meshes of the
heart. When evaluated on time-series CT data, this method produced more
anatomically and temporally consistent geometries than prior methods, and was
able to produce geometries that better satisfy modeling requirements for
cardiac flow simulations. Our source code will be available on GitHub.

Topology Optimization (TO) provides a systematic approach for obtaining
structure design with optimum performance of interest. However, the process
requires numerical evaluation of objective function and constraints at each
iteration, which is computational expensive especially for large-scale design.
Deep learning-based models have been developed to accelerate the process either
by acting as surrogate models replacing the simulation process, or completely
replacing the optimization process. However, most of them require a large set
of labelled training data, which are generated mostly through simulations. The
data generation time scales rapidly with the design domain size, decreasing the
efficiency of the method itself. Another major issue is the weak
generalizability of most deep learning models. Most models are trained to work
with the design problem similar to that used for data generation and require
retraining if the design problem changes. In this work a scalable deep
learning-based model-order-reduction method is proposed to accelerate
large-scale TO process, by utilizing MapNet, a neural network which maps the
field of interest from coarse-scale to fine-scale. The proposed method allows
for each simulation of the TO process to be performed at a coarser mesh,
thereby greatly reducing the total computational time. Moreover, by using
domain fragmentation, the transferability of the MapNet is largely improved.
Specifically, it has been demonstrated that the MapNet trained using data from
one cantilever beam design with a specific loading condition can be directly
applied to other structure design problems with different domain shapes, sizes,
boundary and loading conditions.

The medical datasets are usually faced with the problem of scarcity and data
imbalance. Moreover, annotating large datasets for semantic segmentation of
medical lesions is domain-knowledge and time-consuming. In this paper, we
propose a new object-blend method(short in soft-CP) that combines the
Copy-Paste augmentation method for semantic segmentation of medical lesions
offline, ensuring the correct edge information around the lession to solve the
issue above-mentioned. We proved the method's validity with several datasets in
different imaging modalities. In our experiments on the KiTS19[2] dataset,
Soft-CP outperforms existing medical lesions synthesis approaches. The Soft-CP
augementation provides gains of +26.5% DSC in the low data regime(10% of data)
and +10.2% DSC in the high data regime(all of data), In offline training data,
the ratio of real images to synthetic images is 3:1.

Deep neural networks (DNNs) have become a popular approach for wireless
localization based on channel state information (CSI). A common practice is to
use the raw CSI in the input and allow the network to learn relevant channel
representations for mapping to location information. However, various works
show that raw CSI can be very sensitive to system impairments and small changes
in the environment. On the contrary, hand-designing features may hinder the
limits of channel representation learning of the DNN. In this work, we propose
attention-based CSI for robust feature learning. We evaluate the performance of
attended features in centralized and distributed massive MIMO systems for
ray-tracing channels in two non-stationary railway track environments. By
comparison to a base DNN, our approach provides exceptional performance.

In this paper, a new parameter perturbation attack on DNNs, called
adversarial parameter attack, is proposed, in which small perturbations to the
parameters of the DNN are made such that the accuracy of the attacked DNN does
not decrease much, but its robustness becomes much lower. The adversarial
parameter attack is stronger than previous parameter perturbation attacks in
that the attack is more difficult to be recognized by users and the attacked
DNN gives a wrong label for any modified sample input with high probability.
The existence of adversarial parameters is proved. For a DNN $F_{\Theta}$ with
the parameter set $\Theta$ satisfying certain conditions, it is shown that if
the depth of the DNN is sufficiently large, then there exists an adversarial
parameter set $\Theta_a$ for $\Theta$ such that the accuracy of $F_{\Theta_a}$
is equal to that of $F_{\Theta}$, but the robustness measure of $F_{\Theta_a}$
is smaller than any given bound. An effective training algorithm is given to
compute adversarial parameters and numerical experiments are used to
demonstrate that the algorithms are effective to produce high quality
adversarial parameters.

This paper describes a method for generating robot grasps by jointly
considering stability and other task and object-specific constraints. We
introduce a three-level representation that is acquired for each object class
from a small number of exemplars of objects, tasks, and relevant grasps. The
representation encodes task-specific knowledge for each object class as a
relationship between a keypoint skeleton and suitable grasp points that is
preserved despite intra-class variations in scale and orientation. The learned
models are queried at run time by a simple sampling-based method to guide the
generation of grasps that balance task and stability constraints. We ground and
evaluate our method in the context of a Franka Emika Panda robot assisting a
human in picking tabletop objects for which the robot does not have prior CAD
models. Experimental results demonstrate that in comparison with a baseline
method that only focuses on stability, our method is able to provide suitable
grasps for different tasks.

Generally, the classic iterative learning control (ILC) methods focus on
finding design conditions for repetitive systems to achieve the perfect
tracking of any specified trajectory, whereas they ignore a fundamental problem
of ILC: whether the specified trajectory is trackable, or equivalently, whether
there exist some inputs for the repetitive systems under consideration to
generate the specified trajectory? The current paper contributes to dealing
with this problem. Not only is a concept of trackability introduced formally
for any specified trajectory in ILC, but also some related trackability
criteria are established. Further, the relation between the trackability and
the perfect tracking tasks for ILC is bridged, based on which a new convergence
analysis approach is developed for ILC by leveraging properties of a functional
Cauchy sequence (FCS). Simulation examples are given to verify the
effectiveness of the presented trackability criteria and FCS-induced
convergence analysis method for ILC.

MicroRacer is a simple, open source environment inspired by car racing
especially meant for the didactics of Deep Reinforcement Learning. The
complexity of the environment has been explicitly calibrated to allow users to
experiment with many different methods, networks and hyperparameters settings
without requiring sophisticated software or the need of exceedingly long
training times. Baseline agents for major learning algorithms such as DDPG,
PPO, SAC, TD2 and DSAC are provided too, along with a preliminary comparison in
terms of training time and performance.

Recently self-supervised representation learning has drawn considerable
attention from the scene text recognition community. Different from previous
studies using contrastive learning, we tackle the issue from an alternative
perspective, i.e., by formulating the representation learning scheme in a
generative manner. Typically, the neighboring image patches among one text line
tend to have similar styles, including the strokes, textures, colors, etc.
Motivated by this common sense, we augment one image patch and use its
neighboring patch as guidance to recover itself. Specifically, we propose a
Similarity-Aware Normalization (SimAN) module to identify the different
patterns and align the corresponding styles from the guiding patch. In this
way, the network gains representation capability for distinguishing complex
patterns such as messy strokes and cluttered backgrounds. Experiments show that
the proposed SimAN significantly improves the representation quality and
achieves promising performance. Moreover, we surprisingly find that our
self-supervised generative network has impressive potential for data synthesis,
text image editing, and font interpolation, which suggests that the proposed
SimAN has a wide range of practical applications.

Through its magnetic activity, the Sun governs the conditions in Earth's
vicinity, creating space weather events, which have drastic effects on our
space- and ground-based technology. One of the most important solar magnetic
features creating the space weather is the solar wind, that originates from the
coronal holes (CHs). The identification of the CHs on the Sun as one of the
source regions of the solar wind is therefore crucial to achieve predictive
capabilities. In this study, we used an unsupervised machine learning method,
$k$-means, to pixel-wise cluster the passband images of the Sun taken by the
Atmospheric Imaging Assembly on {\it the Solar Dynamics Observatory} (AIA/SDO)
in 171 \AA, 193 \AA\,, and 211 \AA\,in different combinations. Our results show
that the pixel-wise $k$-means clustering together with systematic pre- and
post-processing steps provides compatible results with those from complex
methods, such as CNNs. More importantly, our study shows that there is a need
for a CH database that a consensus about the CH boundaries are reached by
observers independently. This database then can be used as the "ground truth",
when using a supervised method or just to evaluate the goodness of the models.

Pre-trained models have achieved excellent performance on the dialogue task.
However, for the continual increase of online chit-chat scenarios, directly
fine-tuning these models for each of the new tasks not only explodes the
capacity of the dialogue system on the embedded devices but also causes
knowledge forgetting on pre-trained models and knowledge interference among
diverse dialogue tasks. In this work, we propose a hierarchical inductive
transfer framework to learn and deploy the dialogue skills continually and
efficiently. First, we introduce the adapter module into pre-trained models for
learning new dialogue tasks. As the only trainable module, it is beneficial for
the dialogue system on the embedded devices to acquire new dialogue skills with
negligible additional parameters. Then, for alleviating knowledge interference
between tasks yet benefiting the regularization between them, we further design
hierarchical inductive transfer that enables new tasks to use general knowledge
in the base adapter without being misled by diverse knowledge in task-specific
adapters. Empirical evaluation and analysis indicate that our framework obtains
comparable performance under deployment-friendly model capacity.

We introduce a new task of entailment relation aware paraphrase generation
which aims at generating a paraphrase conforming to a given entailment relation
(e.g. equivalent, forward entailing, or reverse entailing) with respect to a
given input. We propose a reinforcement learning-based weakly-supervised
paraphrasing system, ERAP, that can be trained using existing paraphrase and
natural language inference (NLI) corpora without an explicit task-specific
corpus. A combination of automated and human evaluations show that ERAP
generates paraphrases conforming to the specified entailment relation and are
of good quality as compared to the baselines and uncontrolled paraphrasing
systems. Using ERAP for augmenting training data for downstream textual
entailment task improves performance over an uncontrolled paraphrasing system,
and introduces fewer training artifacts, indicating the benefit of explicit
control during paraphrasing.

In recent years, the prevalent online services generate a sheer volume of
user activity data. Service providers collect these data in order to perform
client behavior analysis, and offer better and more customized services.
Majority of these data can be modeled and stored as graph, such as the social
graph in Facebook, user-video interaction graph in Youtube. These graphs need
to evolve over time to capture the dynamics in the real world, leading to the
invention of dynamic graphs. However, the temporal information embedded in the
dynamic graphs brings new challenges in analyzing and deploying them. Events
staleness, temporal information learning and explicit time dimension usage are
some example challenges in dynamic graph learning. In order to offer a
convenient reference to both the industry and academia, this survey presents
the Three Stages Recurrent Temporal Learning Framework based on dynamic graph
evolution theories, so as to interpret the learning of temporal information
with a generalized framework. Under this framework, this survey categories and
reviews different learnable encoder-decoder architectures for supervised
dynamic graph learning. We believe that this survey could supply useful
guidelines to researchers and engineers in finding suitable graph structures
for their dynamic learning tasks.

In recent years, neural network based methods for multi-speaker
text-to-speech synthesis (TTS) have made significant progress. However, the
current speaker encoder models used in these methods still cannot capture
enough speaker information. In this paper, we focus on accurate speaker encoder
modeling and propose an end-to-end method that can generate high-quality speech
and better similarity for both seen and unseen speakers. The proposed
architecture consists of three separately trained components: a speaker encoder
based on the state-of-the-art ECAPA-TDNN model which is derived from speaker
verification task, a FastSpeech2 based synthesizer, and a HiFi-GAN vocoder. The
comparison among different speaker encoder models shows our proposed method can
achieve better naturalness and similarity. To efficiently evaluate our
synthesized speech, we are the first to adopt deep learning based automatic MOS
evaluation methods to assess our results, and these methods show great
potential in automatic speech quality assessment.

As wireless standards evolve, more complex functionalities are introduced to
address the increasing requirements in terms of throughput, latency, security,
and efficiency. To unleash the potential of such new features, artificial
intelligence (AI) and machine learning (ML) are currently being exploited for
deriving models and protocols from data, rather than by hand-programming. In
this paper, we explore the feasibility of applying ML in next-generation
wireless local area networks (WLANs). More specifically, we focus on the IEEE
802.11ax spatial reuse (SR) problem and predict its performance through
federated learning (FL) models. The set of FL solutions overviewed in this work
is part of the 2021 International Telecommunication Union (ITU) AI for 5G
Challenge.

Criminals have become increasingly experienced in using cryptocurrencies,
such as Bitcoin, for money laundering. The use of cryptocurrencies can hide
criminal identities and transfer hundreds of millions of dollars of dirty funds
through their criminal digital wallets. However, this is considered a paradox
because cryptocurrencies are goldmines for open-source intelligence, giving law
enforcement agencies more power when conducting forensic analyses. This paper
proposed Inspection-L, a graph neural network (GNN) framework based on a
self-supervised Deep Graph Infomax (DGI) and Graph Isomorphism Network (GIN),
with supervised learning algorithms, namely Random Forest (RF), to detect
illicit transactions for anti-money laundering (AML). To the best of our
knowledge, our proposal is the first to apply self-supervised GNNs to the
problem of AML in Bitcoin. The proposed method was evaluated on the Elliptic
dataset and shows that our approach outperforms the state-of-the-art in terms
of key classification metrics, which demonstrates the potential of
self-supervised GNN in the detection of illicit cryptocurrency transactions.

In this paper, we propose a new adapter network for adapting a pre-trained
deep neural network to a target domain with minimal computation. The proposed
model, unidirectional thin adapter (UDTA), helps the classifier adapt to new
data by providing auxiliary features that complement the backbone network. UDTA
takes outputs from multiple layers of the backbone as input features but does
not transmit any feature to the backbone. As a result, UDTA can learn without
computing the gradient of the backbone, which saves computation for training
significantly. In addition, since UDTA learns the target task without modifying
the backbone, a single backbone can adapt to multiple tasks by learning only
UDTAs separately. In experiments on five fine-grained classification datasets
consisting of a small number of samples, UDTA significantly reduced computation
and training time required for backpropagation while showing comparable or even
improved accuracy compared with conventional adapter models.

Video super-resolution is currently one of the most active research topics in
computer vision as it plays an important role in many visual applications.
Generally, video super-resolution contains a significant component, i.e.,
motion compensation, which is used to estimate the displacement between
successive video frames for temporal alignment. Optical flow, which can supply
dense and sub-pixel motion between consecutive frames, is among the most common
ways for this task. To obtain a good understanding of the effect that optical
flow acts in video super-resolution, in this work, we conduct a comprehensive
review on this subject for the first time. This investigation covers the
following major topics: the function of super-resolution (i.e., why we require
super-resolution); the concept of video super-resolution (i.e., what is video
super-resolution); the description of evaluation metrics (i.e., how (video)
superresolution performs); the introduction of optical flow based video
super-resolution; the investigation of using optical flow to capture temporal
dependency for video super-resolution. Prominently, we give an in-depth study
of the deep learning based video super-resolution method, where some
representative algorithms are analyzed and compared. Additionally, we highlight
some promising research directions and open issues that should be further

Transfer learning is widely used in computer vision (CV), natural language
processing (NLP) and achieves great success. Most transfer learning systems are
based on the same modality (e.g. RGB image in CV and text in NLP). However, the
cross-modality transfer learning (CMTL) systems are scarce. In this work, we
study CMTL from 2D to 3D sensor to explore the upper bound performance of 3D
sensor only systems, which play critical roles in robotic navigation and
perform well in low light scenarios. While most CMTL pipelines from 2D to 3D
vision are complicated and based on Convolutional Neural Networks (ConvNets),
ours is easy to implement, expand and based on both ConvNets and Vision
transformers(ViTs): 1) By converting point clouds to pseudo-images, we can use
an almost identical network from pre-trained models based on 2D images. This
makes our system easy to implement and expand. 2) Recently ViTs have been
showing good performance and robustness to occlusions, one of the key reasons
for poor performance of 3D vision systems. We explored both ViT and ConvNet
with similar model sizes to investigate the performance difference. We name our
approach simCrossTrans: simple cross-modality transfer learning with ConvNets
or ViTs. Experiments on SUN RGB-D dataset show: with simCrossTrans we achieve
$13.2\%$ and $16.1\%$ absolute performance gain based on ConvNets and ViTs
separately. We also observed the ViTs based performs $9.7\%$ better than the
ConvNets one, showing the power of simCrossTrans with ViT. simCrossTrans with
ViTs surpasses the previous state-of-the-art (SOTA) by a large margin of
$+15.4\%$ mAP50. Compared with the previous 2D detection SOTA based RGB images,
our depth image only system only has a $1\%$ gap. The code, training/inference
logs and models are publicly available at
https://github.com/liketheflower/simCrossTrans

In contrastive learning in the image domain, the anchor and positive samples
are forced to have as close representations as possible. However, forcing the
two samples to have the same representation could be misleading because the
data augmentation techniques make the two samples different. In this paper, we
introduce a new representation, partitioned representation, which can learn
both common and unique features of the anchor and positive samples in
contrastive learning. The partitioned representation consists of two parts: the
content part and the style part. The content part represents common features of
the class, and the style part represents the own features of each sample, which
can lead to the representation of the data augmentation method. We can achieve
the partitioned representation simply by decomposing a loss function of
contrastive learning into two terms on the two separate representations,
respectively. To evaluate our representation with two parts, we take two
framework models: Variational AutoEncoder (VAE) and BootstrapYour Own
Latent(BYOL) to show the separability of content and style, and to confirm the
generalization ability in classification, respectively. Based on the
experiments, we show that our approach can separate two types of information in
the VAE framework and outperforms the conventional BYOL in linear separability
and a few-shot learning task as downstream tasks.

The coin-tap test is a convenient and primary method for non-destructive
testing, while its manual on-site operation is tough and costly. With the help
of the latest intelligent signal processing method, convolutional neural
networks (CNN), we achieve an intelligent coin-tap test which exhibited
superior performance in recognizing the defects. However, this success of CNNs
relies on plenty of well-labeled data from the identical scenario, which could
be difficult to get for many real industrial practices. This paper further
develops transfer learning strategies for this issue, that is, to transfer the
model trained on data of one scenario to another. In experiments, the result
presents a notable improvement by using domain adaptation and pseudo label
learning strategies. Hence, it becomes possible to apply the model into
scenarios with none or little (less than 10\%) labeled data adopting the
transfer learning strategies proposed herein. In addition, we used a benchmark
dataset constructed ourselves throughout this study. This benchmark dataset for
the coin-tap test containing around 100,000 sound signals is published at
https://github.com/PPhub-hy/torch-tapnet.

We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce
platform in China, for training and serving GNN-based recommendations over
web-scale graphs. ZOOMER is designed for tackling two challenges presented by
the massive user data at Taobao: low training/serving efficiency due to the
huge scale of the graphs, and low recommendation quality due to the information
overload which distracts the recommendation model from specific user
intentions. ZOOMER achieves this by introducing a key concept, Region of
Interests (ROI) in GNNs for recommendations, i.e., a neighborhood region in the
graph with significant relevance to a strong user intention. ZOOMER narrows the
focus from the whole graph and "zooms in" on the more relevant ROIs, thereby
reducing the training/serving cost and mitigating the information overload at
the same time. With carefully designed mechanisms, ZOOMER identifies the
interest expressed by each recommendation request, constructs an ROI subgraph
by sampling with respect to the interest, and guides the GNN to reweigh
different parts of the ROI towards the interest by a multi-level attention
module. Deployed as a large-scale distributed system, ZOOMER supports graphs
with billions of nodes for training and thousands of requests per second for
serving. ZOOMER achieves up to 14x speedup when downsizing sampling scales with
comparable (even better) AUC performance than baseline methods. Besides, both
the offline evaluation and online A/B test demonstrate the effectiveness of
ZOOMER.

Recently, the pretrain-finetuning paradigm has attracted tons of attention in
graph learning community due to its power of alleviating the lack of labels
problem in many real-world applications. Current studies use existing
techniques, such as weight constraint, representation constraint, which are
derived from images or text data, to transfer the invariant knowledge from the
pre-train stage to fine-tuning stage. However, these methods failed to preserve
invariances from graph structure and Graph Neural Network (GNN) style models.
In this paper, we present a novel optimal transport-based fine-tuning framework
called GTOT-Tuning, namely, Graph Topology induced Optimal Transport
fine-Tuning, for GNN style backbones. GTOT-Tuning is required to utilize the
property of graph data to enhance the preservation of representation produced
by fine-tuned networks. Toward this goal, we formulate graph local knowledge
transfer as an Optimal Transport (OT) problem with a structural prior and
construct the GTOT regularizer to constrain the fine-tuned model behaviors. By
using the adjacency relationship amongst nodes, the GTOT regularizer achieves
node-level optimal transport procedures and reduces redundant transport
procedures, resulting in efficient knowledge transfer from the pre-trained
models. We evaluate GTOT-Tuning on eight downstream tasks with various GNN
backbones and demonstrate that it achieves state-of-the-art fine-tuning
performance for GNNs.

Many approaches to program synthesis perform a search within an enormous
space of programs to find one that satisfies a given specification. Prior works
have used neural models to guide combinatorial search algorithms, but such
approaches still explore a huge portion of the search space and quickly become
intractable as the size of the desired program increases. To tame the search
space blowup, we propose training a neural model to learn a hands-on search
policy for bottom-up synthesis, instead of relying on a combinatorial search
algorithm. Our approach, called CrossBeam, uses the neural model to choose how
to combine previously-explored programs into new programs, taking into account
the search history and partial program executions. Motivated by work in
structured prediction on learning to search, CrossBeam is trained on-policy
using data extracted from its own bottom-up searches on training tasks. We
evaluate CrossBeam in two very different domains, string manipulation and logic
programming. We observe that CrossBeam learns to search efficiently, exploring
much smaller portions of the program space compared to the state-of-the-art.

The complete reason behind a decision is a Boolean formula that characterizes
why the decision was made. This recently introduced notion has a number of
applications, which include generating explanations, detecting decision bias
and evaluating counterfactual queries. Prime implicants of the complete reason
are known as sufficient reasons for the decision and they correspond to what is
known as PI explanations and abductive explanations. In this paper, we refer to
the prime implicates of a complete reason as necessary reasons for the
decision. We justify this terminology semantically and show that necessary
reasons correspond to what is known as contrastive explanations. We also study
the computation of complete reasons for multi-class decision trees and graphs
with nominal and numeric features for which we derive efficient, closed-form
complete reasons. We further investigate the computation of shortest necessary
and sufficient reasons for a broad class of complete reasons, which include the
derived closed forms and the complete reasons for Sentential Decision Diagrams
(SDDs). We provide an algorithm which can enumerate their shortest necessary
reasons in output polynomial time. Enumerating shortest sufficient reasons for
this class of complete reasons is hard even for a single reason. For this
problem, we provide an algorithm that appears to be quite efficient as we show

Text-based games provide an interactive way to study natural language
processing. While deep reinforcement learning has shown effectiveness in
developing the game playing agent, the low sample efficiency and the large
action space remain to be the two major challenges that hinder the DRL from
being applied in the real world. In this paper, we address the challenges by
introducing world-perceiving modules, which automatically decompose tasks and
prune actions by answering questions about the environment. We then propose a
two-phase training framework to decouple language learning from reinforcement
learning, which further improves the sample efficiency. The experimental
results show that the proposed method significantly improves the performance
and sample efficiency. Besides, it shows robustness against compound error and
limited pre-training data.

In this work, we study over-parameterization as a necessary condition for
having the ability for the models to extrapolate outside the convex hull of
training set. We specifically, consider classification models, e.g., image
classification and other applications of deep learning. Such models are
classification functions that partition their domain and assign a class to each
partition \cite{strang2019linear}. Partitions are defined by decision
boundaries and so is the classification model/function. Convex hull of training
set may occupy only a subset of the domain, but trained model may partition the
entire domain and not just the convex hull of training set. This is important
because many of the testing samples may be outside the convex hull of training
set and the way in which a model partitions its domain outside the convex hull
would be influential in its generalization. Using approximation theory, we
prove that over-parameterization is a necessary condition for having control
over the partitioning of the domain outside the convex hull of training set. We
also propose a more clear definition for the notion of over-parametrization
based on the learning task and the training set at hand. We present empirical
evidence about geometry of datasets, both image and non-image, to provide
insights about the extent of extrapolation performed by the models. We consider
a 64-dimensional feature space learned by a ResNet model and investigate the
geometric arrangements of convex hulls and decision boundaries in that space.
We also formalize the notion of extrapolation and relate it to the scope of the
model. Finally, we review the rich extrapolation literature in pure and applied
mathematics, e.g., the Whitney's Extension Problem, and place our theory in
that context.

We study a fundamental problem in structure-based drug design -- generating
molecules that bind to specific protein binding sites. While we have witnessed
the great success of deep generative models in drug design, the existing
methods are mostly string-based or graph-based. They are limited by the lack of
spatial information and thus unable to be applied to structure-based design
tasks. Particularly, such models have no or little knowledge of how molecules
interact with their target proteins exactly in 3D space. In this paper, we
propose a 3D generative model that generates molecules given a designated 3D
protein binding site. Specifically, given a binding site as the 3D context, our
model estimates the probability density of atom's occurrences in 3D space --
positions that are more likely to have atoms will be assigned higher
probability. To generate 3D molecules, we propose an auto-regressive sampling
scheme -- atoms are sampled sequentially from the learned distribution until
there is no room for new atoms. Combined with this sampling scheme, our model
can generate valid and diverse molecules, which could be applicable to various
structure-based molecular design tasks such as molecule sampling and linker
design. Experimental results demonstrate that molecules sampled from our model
exhibit high binding affinity to specific targets and good drug properties such
as drug-likeness even if the model is not explicitly optimized for them.

Advantage learning (AL) aims to improve the robustness of value-based
reinforcement learning against estimation errors with action-gap-based
regularization. Unfortunately, the method tends to be unstable in the case of
function approximation. In this paper, we propose a simple variant of AL, named
smoothing advantage learning (SAL), to alleviate this problem. The key to our
method is to replace the original Bellman Optimal operator in AL with a smooth
one so as to obtain more reliable estimation of the temporal difference target.
We give a detailed account of the resulting action gap and the performance
bound for approximate SAL. Further theoretical analysis reveals that the
proposed value smoothing technique not only helps to stabilize the training
procedure of AL by controlling the trade-off between convergence rate and the
upper bound of the approximation errors, but is beneficial to increase the
action gap between the optimal and sub-optimal action value as well.

Human-annotated attributes serve as powerful semantic embeddings in zero-shot
learning. However, their annotation process is labor-intensive and needs expert
supervision. Current unsupervised semantic embeddings, i.e., word embeddings,
enable knowledge transfer between classes. However, word embeddings do not
always reflect visual similarities and result in inferior zero-shot
performance. We propose to discover semantic embeddings containing
discriminative visual properties for zero-shot learning, without requiring any
human annotation. Our model visually divides a set of images from seen classes
into clusters of local image regions according to their visual similarity, and
further imposes their class discrimination and semantic relatedness. To
associate these clusters with previously unseen classes, we use external
knowledge, e.g., word embeddings and propose a novel class relation discovery
module. Through quantitative and qualitative evaluation, we demonstrate that
our model discovers semantic embeddings that model the visual properties of
both seen and unseen classes. Furthermore, we demonstrate on three benchmarks
that our visually-grounded semantic embeddings further improve performance over
word embeddings across various ZSL models by a large margin.

In recent years, quantum computing (QC) has been getting a lot of attention
from industry and academia. Especially, among various QC research topics,
variational quantum circuit (VQC) enables quantum deep reinforcement learning
(QRL). Many studies of QRL have shown that the QRL is superior to the classical
reinforcement learning (RL) methods under the constraints of the number of
training parameters. This paper extends and demonstrates the QRL to quantum
multi-agent RL (QMARL). However, the extension of QRL to QMARL is not
straightforward due to the challenge of the noise intermediate-scale quantum
(NISQ) and the non-stationary properties in classical multi-agent RL (MARL).
Therefore, this paper proposes the centralized training and decentralized
execution (CTDE) QMARL framework by designing novel VQCs for the framework to
cope with these issues. To corroborate the QMARL framework, this paper conducts
the QMARL demonstration in a single-hop environment where edge agents offload
packets to clouds. The extensive demonstration shows that the proposed QMARL
framework enhances 57.7% of total reward than classical frameworks.

Objective: The majority of detailed patient information in real-world data
(RWD) is only consistently available in free-text clinical documents. Manual
curation is expensive and time-consuming. Developing natural language
processing (NLP) methods for structuring RWD is thus essential for scaling
real-world evidence generation.
  Materials and Methods: Traditional rule-based systems are vulnerable to the
prevalent linguistic variations and ambiguities in clinical text, and prior
applications of machine-learning methods typically require sentence-level or
report-level labeled examples that are hard to produce at scale. We propose
leveraging patient-level supervision from medical registries, which are often
readily available and capture key patient information, for general RWD
applications. To combat the lack of sentence-level or report-level annotations,
we explore advanced deep-learning methods by combining domain-specific
pretraining, recurrent neural networks, and hierarchical attention.
  Results: We conduct an extensive study on 135,107 patients from the cancer
registry of a large integrated delivery network (IDN) comprising healthcare
systems in five western US states. Our deep learning methods attain test AUROC
of 94-99% for key tumor attributes and comparable performance on held-out data
from separate health systems and states.
  Discussion and Conclusion: Ablation results demonstrate clear superiority of
these advanced deep-learning methods over prior approaches. Error analysis
shows that our NLP system sometimes even corrects errors in registrar labels.
We also conduct a preliminary investigation in accelerating registry curation
and general RWD structuring via assisted curation for over 1.2 million cancer
patients in this healthcare network.

Advantage Learning (AL) seeks to increase the action gap between the optimal
action and its competitors, so as to improve the robustness to estimation
errors. However, the method becomes problematic when the optimal action induced
by the approximated value function does not agree with the true optimal action.
In this paper, we present a novel method, named clipped Advantage Learning
(clipped AL), to address this issue. The method is inspired by our observation
that increasing the action gap blindly for all given samples while not taking
their necessities into account could accumulate more errors in the performance
loss bound, leading to a slow value convergence, and to avoid that, we should
adjust the advantage value adaptively. We show that our simple clipped AL
operator not only enjoys fast convergence guarantee but also retains proper
action gaps, hence achieving a good balance between the large action gap and
the fast convergence. The feasibility and effectiveness of the proposed method
are verified empirically on several RL benchmarks with promising performance.

While sadness is a human emotion that people experience at certain times
throughout their lives, inflicting them with emotional disappointment and pain,
depression is a longer term mental illness which impairs social, occupational,
and other vital regions of functioning making it a much more serious issue and
needs to be catered to at the earliest. NLP techniques can be utilized for the
detection and subsequent diagnosis of these emotions. Most of the open sourced
data on the web deal with sadness as a part of depression, as an emotion even
though the difference in severity of both is huge. Thus, we create our own
novel dataset illustrating the difference between the two. In this paper, we
aim to highlight the difference between the two and highlight how interpretable
our models are to distinctly label sadness and depression. Due to the sensitive
nature of such information, privacy measures need to be taken for handling and
training of such data. Hence, we also explore the effect of Federated Learning
(FL) on contextualised language models.

Polyphone disambiguation is the most crucial task in Mandarin
grapheme-to-phoneme (g2p) conversion. Previous studies have approached this
problem using pre-trained language models, restricted output, and extra
information from Part-Of-Speech (POS) tagging. Inspired by these strategies, we
propose a novel approach, called g2pW, which adapts learnable softmax-weights
to condition the outputs of BERT with the polyphonic character of interest and
its POS tagging. Rather than using the hard mask as in previous works, our
experiments show that learning a soft-weighting function for the candidate
phonemes benefits performance. In addition, our proposed g2pW does not require
extra pre-trained POS tagging models while using POS tags as auxiliary features
since we train the POS tagging model simultaneously with the unified encoder.
Experimental results show that our g2pW outperforms existing methods on the
public CPP dataset. All codes, model weights, and a user-friendly package are

Graph Convolutional Networks (GCNs) is the state-of-the-art method for
learning graph-structured data, and training large-scale GCNs requires
distributed training across multiple accelerators such that each accelerator is
able to hold a partitioned subgraph. However, distributed GCN training incurs
prohibitive overhead of communicating node features and feature gradients among
partitions for every GCN layer during each training iteration, limiting the
achievable training efficiency and model scalability. To this end, we propose
PipeGCN, a simple yet effective scheme that hides the communication overhead by
pipelining inter-partition communication with intra-partition computation. It
is non-trivial to pipeline for efficient GCN training, as communicated node
features/gradients will become stale and thus can harm the convergence,
negating the pipeline benefit. Notably, little is known regarding the
convergence rate of GCN training with both stale features and stale feature
gradients. This work not only provides a theoretical convergence analysis but
also finds the convergence rate of PipeGCN to be close to that of the vanilla
distributed GCN training without any staleness. Furthermore, we develop a
smoothing method to further improve PipeGCN's convergence. Extensive
experiments show that PipeGCN can largely boost the training throughput
(1.7x~28.5x) while achieving the same accuracy as its vanilla counterpart and
existing full-graph training methods. The code is available at
https://github.com/RICE-EIC/PipeGCN.

How to learn a better speech representation for end-to-end speech-to-text
translation (ST) with limited labeled data? Existing techniques often attempt
to transfer powerful machine translation (MT) capabilities to ST, but neglect
the representation discrepancy across modalities. In this paper, we propose the
Speech-TExt Manifold Mixup (STEMM) method to calibrate such discrepancy.
Specifically, we mix up the representation sequences of different modalities,
and take both unimodal speech sequences and multimodal mixed sequences as input
to the translation model in parallel, and regularize their output predictions
with a self-learning framework. Experiments on MuST-C speech translation
benchmark and further analysis show that our method effectively alleviates the
cross-modal representation discrepancy, and achieves significant improvements
over a strong baseline on eight translation directions.

Audio applications involving environmental sound analysis increasingly use
general-purpose audio representations, also known as embeddings, for transfer
learning. Recently, Holistic Evaluation of Audio Representations (HEAR)
evaluated twenty-nine embedding models on nineteen diverse tasks. However, the
evaluation's effectiveness depends on the variation already captured within a
given dataset. Therefore, for a given data domain, it is unclear how the
representations would be affected by the variations caused by myriad
microphones' range and acoustic conditions -- commonly known as channel
effects. We aim to extend HEAR to evaluate invariance to channel effects in
this work. To accomplish this, we imitate channel effects by injecting
perturbations to the audio signal and measure the shift in the new (perturbed)
embeddings with three distance measures, making the evaluation domain-dependent
but not task-dependent. Combined with the downstream performance, it helps us
make a more informed prediction of how robust the embeddings are to the channel
effects. We evaluate two embeddings -- YAMNet, and OpenL3 on monophonic
(UrbanSound8K) and polyphonic (SONYC-UST) urban datasets. We show that one
distance measure does not suffice in such task-independent evaluation. Although
Fr\'echet Audio Distance (FAD) correlates with the trend of the performance
drop in the downstream task most accurately, we show that we need to study FAD
in conjunction with the other distances to get a clear understanding of the
overall effect of the perturbation. In terms of the embedding performance, we
find OpenL3 to be more robust than YAMNet, which aligns with the HEAR

This paper presents a fast, principled approach for detecting anomalous and
out-of-distribution (OOD) samples in deep neural networks (DNN). We propose the
application of linear statistical dimensionality reduction techniques on the
semantic features produced by a DNN, in order to capture the low-dimensional
subspace truly spanned by said features. We show that the "feature
reconstruction error" (FRE), which is the $\ell_2$-norm of the difference
between the original feature in the high-dimensional space and the pre-image of
its low-dimensional reduced embedding, is highly effective for OOD and anomaly
detection. To generalize to intermediate features produced at any given layer,
we extend the methodology by applying nonlinear kernel-based methods.
Experiments using standard image datasets and DNN architectures demonstrate
that our method meets or exceeds best-in-class quality performance, but at a
fraction of the computational and memory cost required by the state of the art.
It can be trained and run very efficiently, even on a traditional CPU.

Households across the world contain arbitrary objects: from mate gourds and
coffee mugs to sitars and guitars. Considering this diversity, robot perception
must handle a large variety of semantic objects without additional fine-tuning
to be broadly applicable in homes. Recently, zero-shot models have demonstrated
impressive performance in image classification of arbitrary objects (i.e.,
classifying images at inference with categories not explicitly seen during
training). In this paper, we translate the success of zero-shot vision models
(e.g., CLIP) to the popular embodied AI task of object navigation. In our
setting, an agent must find an arbitrary goal object, specified via text, in
unseen environments coming from different datasets. Our key insight is to
modularize the task into zero-shot object localization and exploration.
Employing this philosophy, we design CLIP on Wheels (CoW) baselines for the
task and evaluate each zero-shot model in both Habitat and RoboTHOR simulators.
We find that a straightforward CoW, with CLIP-based object localization plus
classical exploration, and no additional training, often outperforms learnable
approaches in terms of success, efficiency, and robustness to dataset
distribution shift. This CoW achieves 6.3% SPL in Habitat and 10.0% SPL in
RoboTHOR, when tested zero-shot on all categories. On a subset of four RoboTHOR
categories considered in prior work, the same CoW shows a 16.1 percentage point
improvement in Success over the learnable state-of-the-art baseline.

This paper investigates the stability of deep ReLU neural networks for
nonparametric regression under the assumption that the noise has only a finite
p-th moment. We unveil how the optimal rate of convergence depends on p, the
degree of smoothness and the intrinsic dimension in a class of nonparametric
regression functions with hierarchical composition structure when both the
adaptive Huber loss and deep ReLU neural networks are used. This optimal rate
of convergence cannot be obtained by the ordinary least squares but can be
achieved by the Huber loss with a properly chosen parameter that adapts to the
sample size, smoothness, and moment parameters. A concentration inequality for
the adaptive Huber ReLU neural network estimators with allowable optimization
errors is also derived. To establish a matching lower bound within the class of
neural network estimators using the Huber loss, we employ a different strategy
from the traditional route: constructing a deep ReLU network estimator that has
a better empirical loss than the true function and the difference between these
two functions furnishes a low bound. This step is related to the Huberization
bias, yet more critically to the approximability of deep ReLU networks. As a
result, we also contribute some new results on the approximation theory of deep
ReLU neural networks.

Deep learning (DL) methods where interpretability is intrinsically considered
as part of the model are required to better understand the relationship of
clinical and imaging-based attributes with DL outcomes, thus facilitating their
use in reasoning medical decisions. Latent space representations built with
variational autoencoders (VAE) do not ensure individual control of data
attributes. Attribute-based methods enforcing attribute disentanglement have
been proposed in the literature for classical computer vision tasks in
benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that
includes an attribute regularization term to associate clinical and medical
imaging attributes with different regularized dimensions in the generated
latent space, enabling a better disentangled interpretation of the attributes.
Furthermore, the generated attention maps explained the attribute encoding in
the regularized latent space dimensions. The Attri-VAE approach analyzed
healthy and myocardial infarction patients with clinical, cardiac morphology,
and radiomics attributes. The proposed model provided an excellent trade-off
between reconstruction fidelity, disentanglement, and interpretability,
outperforming state-of-the-art VAE approaches according to several quantitative
metrics. The resulting latent space allowed the generation of realistic
synthetic data in the trajectory between two distinct input samples or along a
specific attribute dimension to better interpret changes between different
cardiac conditions.

Several pre-training objectives, such as masked language modeling (MLM), have
been proposed to pre-train language models (e.g. BERT) with the aim of learning
better language representations. However, to the best of our knowledge, no
previous work so far has investigated how different pre-training objectives
affect what BERT learns about linguistics properties. We hypothesize that
linguistically motivated objectives such as MLM should help BERT to acquire
better linguistic knowledge compared to other non-linguistically motivated
objectives that are not intuitive or hard for humans to guess the association
between the input and the label to be predicted. To this end, we pre-train BERT
with two linguistically motivated objectives and three non-linguistically
motivated ones. We then probe for linguistic characteristics encoded in the
representation of the resulting models. We find strong evidence that there are
only small differences in probing performance between the representations
learned by the two different types of objectives. These surprising results
question the dominant narrative of linguistically informed pre-training.

Process models are used by human analysts to model and analyse behaviour, and
by machines to verify properties such as soundness, liveness or other
reachability properties, and to compare their expressed behaviour with recorded
behaviour within business processes of organisations. For both human and
machine use, small models are preferable over large and complex models: for
ease of human understanding and to reduce the time spent by machines in state
space explorations. Reduction rules that preserve the behaviour of models have
been defined for Petri nets, however in this paper we show that a subclass of
Petri nets returned by process discovery techniques, that is, block-structured
workflow nets, can be further reduced by considering their block structure in
process trees. We revisit an existing set of reduction rules for process trees
and show that the rules are correct, terminating, confluent and complete, and
for which classes of process trees they are and are not complete. In a
real-life experiment, we show that these rules can reduce process models
discovered from real-life event logs further compared with rules that consider
only Petri net structures.

Anomalies in emails such as phishing and spam present major security risks
such as the loss of privacy, money, and brand reputation to both individuals
and organizations. Previous studies on email anomaly detection relied on a
single type of anomaly and the analysis of the email body and subject content.
A drawback of this approach is that it takes into account the written language
of the email content. To overcome this deficit, this study conducted feature
extraction and selection on email header datasets and leveraged both multi and
one-class anomaly detection approaches. Experimental analysis results obtained
demonstrate that email header information only is enough to reliably detect
spam and phishing emails. Supervised learning algorithms such as Random Forest,
SVM, MLP, KNN, and their stacked ensembles were found to be very successful,
achieving high accuracy scores of 97% for phishing and 99% for spam emails.
One-class classification with One-Class SVM achieved accuracy scores of 87% and
89% with spam and phishing emails, respectively. Real-world email filtering
applications will benefit from the use of only the header information in terms
of resources utilization and efficiency.

This paper proposes, implements, and evaluates a reinforcement learning
(RL)-based computational framework for automatic mesh generation. Mesh
generation plays a fundamental role in numerical simulations in the area of
computer aided design and engineering (CAD/E). It is identified as one of the
critical issues in the NASA CFD Vision 2030 Study. Existing mesh generation
methods suffer from high computational complexity, low mesh quality in complex
geometries, and speed limitations. These methods and tools, including
commercial software packages, are typically semiautomatic and they need inputs
or help from human experts. By formulating the mesh generation as a Markov
decision process (MDP) problem, we are able to use a state-of-the-art
reinforcement learning (RL) algorithm called "soft actor-critic" to
automatically learn from trials the policy of actions for mesh generation. The
implementation of this RL algorithm for mesh generation allows us to build a
fully automatic mesh generation system without human intervention and any extra
clean-up operations, which fills the gap in the existing mesh generation tools.
In the experiments to compare with two representative commercial software
packages, our system demonstrates promising performance with respect to
scalability, generalizability, and effectiveness.

Hybrid images is a technique to generate images with two interpretations that
change as a function of viewing distance. It has been utilized to study
multiscale processing of images by the human visual system. Using 63,000 hybrid
images across 10 fruit categories, here we show that predictions of deep
learning vision models qualitatively matches with the human perception of these
images. Our results provide yet another evidence in support of the hypothesis
that Convolutional Neural Networks (CNNs) and Transformers are good at modeling
the feedforward sweep of information in the ventral stream of visual cortex.
Code and data is available at https://github.com/aliborji/hybrid_images.git.

Autonomous vehicles utilize urban scene segmentation to understand the real
world like a human and react accordingly. Semantic segmentation of normal
scenes has experienced a remarkable rise in accuracy on conventional
benchmarks. However, a significant portion of real-life accidents features
abnormal scenes, such as those with object deformations, overturns, and
unexpected traffic behaviors. Since even small mis-segmentation of driving
scenes can lead to serious threats to human lives, the robustness of such
models in accident scenarios is an extremely important factor in ensuring
safety of intelligent transportation systems.
  In this paper, we propose a Multi-source Meta-learning Unsupervised Domain
Adaptation (MMUDA) framework, to improve the generalization of segmentation
transformers to extreme accident scenes. In MMUDA, we make use of Multi-Domain
Mixed Sampling to augment the images of multiple-source domains (normal scenes)
with the target data appearances (abnormal scenes). To train our model, we
intertwine and study a meta-learning strategy in the multi-source setting for
robustifying the segmentation results. We further enhance the segmentation
backbone (SegFormer) with a HybridASPP decoder design, featuring large window
attention spatial pyramid pooling and strip pooling, to efficiently aggregate
long-range contextual dependencies. Our approach achieves a mIoU score of
46.97% on the DADA-seg benchmark, surpassing the previous state-of-the-art
model by more than 7.50%. Code will be made publicly available at
https://github.com/xinyu-laura/MMUDA.

Heart rate variability (HRV) is a practical and noninvasive measure of
autonomic nervous system activity, which plays an essential role in
cardiovascular health. However, using HRV to assess physiology status is
challenging. Even in clinical settings, HRV is sensitive to acute stressors
such as physical activity, mental stress, hydration, alcohol, and sleep.
Wearable devices provide convenient HRV measurements, but the irregularity of
measurements and uncaptured stressors can bias conventional analytical methods.
To better interpret HRV measurements for downstream healthcare applications, we
learn a personalized diurnal rhythm as an accurate physiological indicator for
each individual. We develop Physiological Multitask-Learning (PhysioMTL) by
harnessing Optimal Transport theory within a Multitask-learning (MTL)
framework. The proposed method learns an individual-specific predictive model
from heterogeneous observations, and enables estimation of an optimal transport
map that yields a push forward operation onto the demographic features for each
task. Our model outperforms competing MTL methodologies on unobserved
predictive tasks for synthetic and two real-world datasets. Specifically, our
method provides remarkable prediction results on unseen held-out subjects given
only $20\%$ of the subjects in real-world observational studies. Furthermore,
our model enables a counterfactual engine that generates the effect of acute
stressors and chronic conditions on HRV rhythms.

Recently, prefix-tuning has gained increasing attention as a
parameter-efficient finetuning method for large-scale pretrained language
models. The method keeps the pretrained models fixed and only updates the
prefix token parameters for each downstream task. Despite being lightweight and
modular, prefix-tuning still lacks robustness to textual adversarial attacks.
However, most currently developed defense techniques necessitate auxiliary
model update and storage, which inevitably hamper the modularity and low
storage of prefix-tuning. In this work, we propose a robust prefix-tuning
framework that preserves the efficiency and modularity of prefix-tuning. The
core idea of our framework is leveraging the layerwise activations of the
language model by correctly-classified training data as the standard for
additional prefix finetuning. During the test phase, an extra batch-level
prefix is tuned for each batch and added to the original prefix for robustness
enhancement. Extensive experiments on three text classification benchmarks show
that our framework substantially improves robustness over several strong
baselines against five textual attacks of different types while maintaining
comparable accuracy on clean texts. We also interpret our robust prefix-tuning
framework from the optimal control perspective and pose several directions for

We study the generalization of over-parameterized deep networks (for image
classification) in relation to the convex hull of their training sets. Despite
their great success, generalization of deep networks is considered a mystery.
These models have orders of magnitude more parameters than their training
samples, and they can achieve perfect accuracy on their training sets, even
when training images are randomly labeled, or the contents of images are
replaced with random noise. The training loss function of these models has
infinite number of near zero minimizers, where only a small subset of those
minimizers generalize well. Overall, it is not clear why models need to be
over-parameterized, why we should use a very specific training regime to train
them, and why their classifications are so susceptible to imperceivable
adversarial perturbations (phenomenon known as adversarial vulnerability)
\cite{papernot2016limitations,shafahi2018adversarial,tsipras2018robustness}.
Some recent studies have made advances in answering these questions, however,
they only consider interpolation. We show that interpolation is not adequate to
understand generalization of deep networks and we should broaden our

We present a novel method for multi image domain and multi-landmark
definition learning for small dataset facial localization. Training a small
dataset alongside a large(r) dataset helps with robust learning for the former,
and provides a universal mechanism for facial landmark localization for new
and/or smaller standard datasets. To this end, we propose a Vision Transformer
encoder with a novel decoder with a definition agnostic shared landmark
semantic group structured prior, that is learnt, as we train on more than one
dataset concurrently. Due to our novel definition agnostic group prior the
datasets may vary in landmark definitions and domains. During the decoder stage
we use cross- and self-attention, whose output is later fed into
domain/definition specific heads that minimize a Laplacian-log-likelihood loss.
We achieve state-of-the-art performance on standard landmark localization
datasets such as COFW and WFLW, when trained with a bigger dataset. We also
show state-of-the-art performance on several varied image domain small datasets
for animals, caricatures, and facial portrait paintings. Further, we contribute
a small dataset (150 images) of pareidolias to show efficacy of our method.
Finally, we provide several analysis and ablation studies to justify our
claims.

Online recommender systems should be always aligned with users' current
interest to accurately suggest items that each user would like. Since user
interest usually evolves over time, the update strategy should be flexible to
quickly catch users' current interest from continuously generated new user-item
interactions. Existing update strategies focus either on the importance of each
user-item interaction or the learning rate for each recommender parameter, but
such one-directional flexibility is insufficient to adapt to varying
relationships between interactions and parameters. In this paper, we propose
MeLON, a meta-learning based novel online recommender update strategy that
supports two-directional flexibility. It is featured with an adaptive learning
rate for each parameter-interaction pair for inducing a recommender to quickly
learn users' up-to-date interest. The procedure of MeLON is optimized following
a meta-learning approach: it learns how a recommender learns to generate the
optimal learning rates for future updates. Specifically, MeLON first enriches
the meaning of each interaction based on previous interactions and identifies
the role of each parameter for the interaction; and then combines these two
pieces of information to generate an adaptive learning rate. Theoretical
analysis and extensive evaluation on three real-world online recommender
datasets validate the effectiveness of MeLON.

A broad challenge of research on generalization for sequential
decision-making tasks in interactive environments is designing benchmarks that
clearly landmark progress. While there has been notable headway, current
benchmarks either do not provide suitable exposure nor intuitive control of the
underlying factors, are not easy-to-implement, customizable, or extensible, or
are computationally expensive to run. We built the Sandbox Environment for
Generalizable Agent Research (SEGAR) with all of these things in mind. SEGAR
improves the ease and accountability of generalization research in RL, as
generalization objectives can be easy designed by specifying task
distributions, which in turns allows the researcher to measure the nature of
the generalization objective. We present an overview of SEGAR and how it
contributes to these goals, as well as experiments that demonstrate a few types
of research questions SEGAR can help answer.

Quantifying charge-state transition energy levels of impurities in
semiconductors is critical to understanding and engineering their
optoelectronic properties for applications ranging from solar photovoltaics to
infrared lasers. While these transition levels can be measured and calculated
accurately, such efforts are time-consuming and more rapid prediction methods
would be beneficial. Here, we significantly reduce the time typically required
to predict impurity transition levels using multi-fidelity datasets and a
machine learning approach employing features based on elemental properties and
impurity positions. We use transition levels obtained from low-fidelity (i.e.,
local-density approximation or generalized gradient approximation) density
functional theory (DFT) calculations, corrected using a recently proposed
modified band alignment scheme, which well-approximates transition levels from
high-fidelity DFT (i.e., hybrid HSE06). The model fit to the large
multi-fidelity database shows improved accuracy compared to the models trained
on the more limited high-fidelity values. Crucially, in our approach, when
using the multi-fidelity data, high-fidelity values are not required for model
training, significantly reducing the computational cost required for training
the model. Our machine learning model of transition levels has a root mean
squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid
functional values when averaged over 14 semiconductor systems from the II-VI
and III-V families. As a guide for use on other systems, we assessed the model
on simulated data to show the expected accuracy level as a function of bandgap
for new materials of interest. Finally, we use the model to predict a complete
space of impurity charge-state transition levels in all zinc blende III-V and
II-VI systems.

We proposes a novel algorithm, ANTHRO, that inductively extracts over 600K
human-written text perturbations in the wild and leverages them for realistic
adversarial attack. Unlike existing character-based attacks which often
deductively hypothesize a set of manipulation strategies, our work is grounded
on actual observations from real-world texts. We find that adversarial texts
generated by ANTHRO achieve the best trade-off between (1) attack success rate,
(2) semantic preservation of the original text, and (3) stealthiness--i.e.
indistinguishable from human writings hence harder to be flagged as suspicious.
Specifically, our attacks accomplished around 83% and 91% attack success rates
on BERT and RoBERTa, respectively. Moreover, it outperformed the TextBugger
baseline with an increase of 50% and 40% in terms of semantic preservation and
stealthiness when evaluated by both layperson and professional human workers.
ANTHRO can further enhance a BERT classifier's performance in understanding
different variations of human-written toxic texts via adversarial training when
compared to the Perspective API.

Unsupervised Anomaly detection (AD) requires building a notion of normalcy,
distinguishing in-distribution (ID) and out-of-distribution (OOD) data, using
only available ID samples. Recently, large gains were made on this task for the
domain of natural images using self-supervised contrastive feature learning as
a first step followed by kNN or traditional one-class classifiers for feature
scoring. Learned representations that are non-uniformly distributed on the unit
hypersphere have been shown to be beneficial for this task. We go a step
further and investigate how the \emph {geometrical compactness} of the ID
feature distribution makes isolating and detecting outliers easier, especially
in the realistic situation when ID training data is polluted (i.e. ID data
contains some OOD data that is used for learning the feature extractor
parameters). We propose novel architectural modifications to the
self-supervised feature learning step, that enable such compact distributions
for ID data to be learned. We show that the proposed modifications can be
effectively applied to most existing self-supervised objectives, with large
gains in performance. Furthermore, this improved OOD performance is obtained
without resorting to tricks such as using strongly augmented ID images (e.g. by
90 degree rotations) as proxies for the unseen OOD data, as these impose overly
prescriptive assumptions about ID data and its invariances. We perform
extensive studies on benchmark datasets for one-class OOD detection and show
state-of-the-art performance in the presence of pollution in the ID data, and
comparable performance otherwise. We also propose and extensively evaluate a
novel feature scoring technique based on the angular Mahalanobis distance, and
propose a simple and novel technique for feature ensembling during evaluation
that enables a big boost in performance at nearly zero run-time cost.

6D object pose estimation is a fundamental yet challenging problem in
computer vision. Convolutional Neural Networks (CNNs) have recently proven to
be capable of predicting reliable 6D pose estimates even under monocular
settings. Nonetheless, CNNs are identified as being extremely data-driven, and
acquiring adequate annotations is oftentimes very time-consuming and labor
intensive. To overcome this limitation, we propose a novel monocular 6D pose
estimation approach by means of self-supervised learning, removing the need for
real annotations. After training our proposed network fully supervised with
synthetic RGB data, we leverage current trends in noisy student training and
differentiable rendering to further self-supervise the model on these
unsupervised real RGB(-D) samples, seeking for a visually and geometrically
optimal alignment. Moreover, employing both visible and amodal mask
information, our self-supervision becomes very robust towards challenging
scenarios such as occlusion. Extensive evaluations demonstrate that our
proposed self-supervision outperforms all other methods relying on synthetic
data or employing elaborate techniques from the domain adaptation realm.
Noteworthy, our self-supervised approach consistently improves over its
synthetically trained baseline and often almost closes the gap towards its
fully supervised counterpart. The code and models are publicly available at
https://github.com/THU-DA-6D-Pose-Group/self6dpp.git.

Inspired by sum-of-infeasibilities methods in convex optimization, we propose
a novel procedure for analyzing verification queries on neural networks with
piecewise-linear activation functions. Given a convex relaxation which
over-approximates the non-convex activation functions, we encode the violations
of activation functions as a cost function and optimize it with respect to the
convex relaxation. The cost function, referred to as the Sum-of-Infeasibilities
(SoI), is designed so that its minimum is zero and achieved only if all the
activation functions are satisfied. We propose a stochastic procedure, DeepSoI,
to efficiently minimize the SoI. An extension to a canonical
case-analysis-based complete search procedure can be achieved by replacing the
convex procedure executed at each search state with DeepSoI. Extending the
complete search with DeepSoI achieves multiple simultaneous goals: 1) it guides
the search towards a counter-example; 2) it enables more informed branching
decisions; and 3) it creates additional opportunities for bound derivation. An
extensive evaluation across different benchmarks and solvers demonstrates the
benefit of the proposed techniques. In particular, we demonstrate that SoI
significantly improves the performance of an existing complete search
procedure. Moreover, the SoI-based implementation outperforms other
state-of-the-art complete verifiers. We also show that our technique can
efficiently improve upon the perturbation bound derived by a recent adversarial
attack algorithm.

Besides weights of synaptic connections, Forward propagation and Back
propagation also include weights of synaptic ranges [15,16,19-22]. PNN
considers synaptic strength balance in dynamic of phagocytosing of synapses and
static of constant sum of synapses length [15],the lead behavior of the school
of fish is well embodied in our PNN. Synapse formation will inhibit dendrites
generation to a certain extent in experiments, by simulations synapse formation
will inhibit the function of dendrites [16]. Closing the critical period will
cause neurological disorder in experiments, but worse results in PNN
simulations [19]. The memory persistence gradient information of backward
circuit similar to the Enforcing Resilience in a Spring Boot. The relatively
good and inferior gradient information in synapse formation of backward circuit
like the folds of the brain. Considering both negative and positive memories
persistence help activate synapse length changes with iterations better than
only considering positive memory. So using memory of fear learning and
improving of synaptic activity to observe obviously [20]. Memory persistence
factor also inhibit local synaptic accumulation. And refers PNN can also
introduce the relatively good and inferior solution to update the velocity of
particle in PSO. Astrocytic phagocytosis will avoid the local accumulation of
synapses by simulation (Lack of astrocytic phagocytosis causes excitatory
synapses and functionally impaired synapses accumulate in experiments and lead
to destruction of cognition, but local longer synapses and worse results in PNN
simulations) [21]. It gives relationship of human intelligence and cortical
thickness, individual differences in brain[22].The simple PNN which only has
the synaptic phagocytosis regardless of the gradient update. Therefore, is it
possible to simulate and plan the factors of biological experiments through
modeling?

Due to the lack of properly annotated medical data, exploring the
generalization capability of the deep model is becoming a public concern.
Zero-shot learning (ZSL) has emerged in recent years to equip the deep model
with the ability to recognize unseen classes. However, existing studies mainly
focus on natural images, which utilize linguistic models to extract auxiliary
information for ZSL. It is impractical to apply the natural image ZSL solutions
directly to medical images, since the medical terminology is very
domain-specific, and it is not easy to acquire linguistic models for the
medical terminology. In this work, we propose a new paradigm of ZSL
specifically for medical images utilizing cross-modality information. We make
three main contributions with the proposed paradigm. First, we extract the
prior knowledge about the segmentation targets, called relation prototypes,
from the prior model and then propose a cross-modality adaptation module to
inherit the prototypes to the zero-shot model. Second, we propose a relation
prototype awareness module to make the zero-shot model aware of information
contained in the prototypes. Last but not least, we develop an inheritance
attention module to recalibrate the relation prototypes to enhance the
inheritance process. The proposed framework is evaluated on two public
cross-modality datasets including a cardiac dataset and an abdominal dataset.
Extensive experiments show that the proposed framework significantly
outperforms the state of the arts.

Due to the homophily assumption in graph convolution networks, a common
consensus is that graph neural networks (GNNs) perform well on homophilic
graphs but may fail on heterophilic graphs with many inter-class edges. In this
work, we re-examine the heterophily problem of GNNs and investigate the feature
aggregation of inter-class neighbors. Instead of treating the inter-class edges
as harmful, under some conditions, we argue that they may provide helpful
information for node classification from an entire neighbor's perspective. To
better evaluate whether the neighbor is helpful for the downstream tasks, we
present the concept of the neighbor effect of each node and use the von Neumann
entropy to measure the randomness/identifiability of the neighbor distribution
for each class. Moreover, we propose a Conv-Agnostic GNNs framework (CAGNNs) to
enhance the performance of GNNs on heterophily datasets by learning the
neighbor effect for each node. Specifically, we first decouple the feature of
each node into the discriminative feature for downstream tasks and the
aggregation feature for graph convolution. Then, we propose a shared mixer
module for all layers to adaptively evaluate the neighbor effect of each node
to incorporate the neighbor information. Experiments are performed on nine
well-known benchmark datasets for the node classification task. The results
indicate that our framework is able to improve the average prediction
performance by 9.81%, 25.81%, and 20.61% for GIN, GAT, and GCN, respectively.
Extensive ablation studies and robustness analysis further verify the
effectiveness, robustness, and interpretability of our framework.

Recently, the problem of robustness of pre-trained language models (PrLMs)
has received increasing research interest. Latest studies on adversarial
attacks achieve high attack success rates against PrLMs, claiming that PrLMs
are not robust. However, we find that the adversarial samples that PrLMs fail
are mostly non-natural and do not appear in reality. We question the validity
of current evaluation of robustness of PrLMs based on these non-natural
adversarial samples and propose an anomaly detector to evaluate the robustness
of PrLMs with more natural adversarial samples. We also investigate two
applications of the anomaly detector: (1) In data augmentation, we employ the
anomaly detector to force generating augmented data that are distinguished as
non-natural, which brings larger gains to the accuracy of PrLMs. (2) We apply
the anomaly detector to a defense framework to enhance the robustness of PrLMs.
It can be used to defend all types of attacks and achieves higher accuracy on
both adversarial samples and compliant samples than other defense frameworks.

Vertical federated learning (VFL) attracts increasing attention due to the
emerging demands of multi-party collaborative modeling and concerns of privacy
leakage. A complete list of metrics to evaluate VFL algorithms should include
model applicability, privacy security, communication cost, and computation
efficiency, where privacy security is especially important to VFL. However, to
the best of our knowledge, there does not exist a VFL algorithm satisfying all
these criteria very well. To address this challenging problem, in this paper,
we reveal that zeroth-order optimization (ZOO) is a desirable companion for
VFL. Specifically, ZOO can 1) improve the model applicability of VFL framework,
2) prevent VFL framework from privacy leakage under curious, colluding, and
malicious threat models, 3) support inexpensive communication and efficient
computation. Based on that, we propose a novel and practical VFL framework with
black-box models, which is inseparably interconnected to the promising
properties of ZOO. We believe that it takes one stride towards designing a
practical VFL framework matching all the criteria. Under this framework, we
raise two novel {\bf asy}nchronous ze{\bf r}oth-ord{\bf e}r algorithms for {\bf
v}ertical f{\bf e}derated {\bf l}earning (AsyREVEL) with different smoothing
techniques. We theoretically drive the convergence rates of AsyREVEL algorithms
under nonconvex condition. More importantly, we prove the privacy security of
our proposed framework under existing VFL attacks on different levels.
Extensive experiments on benchmark datasets demonstrate the favorable model
applicability, satisfied privacy security, inexpensive communication, efficient
computation, scalability and losslessness of our framework.

Parameter-free algorithms are online learning algorithms that do not require
setting learning rates. They achieve optimal regret with respect to the
distance between the initial point and any competitor. Yet, parameter-free
algorithms do not take into account the geometry of the losses. Recently, in
the stochastic optimization literature, it has been proposed to instead use
truncated linear lower bounds, which produce better performance by more closely
modeling the losses. In particular, truncated linear models greatly reduce the
problem of overshooting the minimum of the loss function. Unfortunately,
truncated linear models cannot be used with parameter-free algorithms because
the updates become very expensive to compute. In this paper, we propose new
parameter-free algorithms that can take advantage of truncated linear models
through a new update that has an "implicit" flavor. Based on a novel
decomposition of the regret, the new update is efficient, requires only one
gradient at each step, never overshoots the minimum of the truncated model, and
retains the favorable parameter-free properties. We also conduct an empirical
study demonstrating the practical utility of our algorithms.

We investigate what kind of structural knowledge learned in neural network
encoders is transferable to processing natural language. We design artificial
languages with structural properties that mimic natural language, pretrain
encoders on the data, and see how much performance the encoder exhibits on
downstream tasks in natural language. Our experimental results show that
pretraining with an artificial language with a nesting dependency structure
provides some knowledge transferable to natural language. A follow-up probing
analysis indicates that its success in the transfer is related to the amount of
encoded contextual information and what is transferred is the knowledge of
position-aware context dependence of language. Our results provide insights
into how neural network encoders process human languages and the source of
cross-lingual transferability of recent multilingual language models.

Knowledge graph embedding (KGE) models represent each entity and relation of
a knowledge graph (KG) with low-dimensional embedding vectors. These methods
have recently been applied to KG link prediction and question answering over
incomplete KGs (KGQA). KGEs typically create an embedding for each entity in
the graph, which results in large model sizes on real-world graphs with
millions of entities. For downstream tasks these atomic entity representations
often need to be integrated into a multi stage pipeline, limiting their
utility. We show that an off-the-shelf encoder-decoder Transformer model can
serve as a scalable and versatile KGE model obtaining state-of-the-art results
for KG link prediction and incomplete KG question answering. We achieve this by
posing KG link prediction as a sequence-to-sequence task and exchange the
triple scoring approach taken by prior KGE methods with autoregressive
decoding. Such a simple but powerful method reduces the model size up to 98%
compared to conventional KGE models while keeping inference time tractable.
After finetuning this model on the task of KGQA over incomplete KGs, our
approach outperforms baselines on multiple large-scale datasets without
extensive hyperparameter tuning.

Continual Learning requires the model to learn from a stream of dynamic,
non-stationary data without forgetting previous knowledge. Several approaches
have been developed in the literature to tackle the Continual Learning
challenge. Among them, Replay approaches have empirically proved to be the most
effective ones. Replay operates by saving some samples in memory which are then
used to rehearse knowledge during training in subsequent tasks. However, an
extensive comparison and deeper understanding of different replay
implementation subtleties is still missing in the literature. The aim of this
work is to compare and analyze existing replay-based strategies and provide
practical recommendations on developing efficient, effective and generally
applicable replay-based strategies. In particular, we investigate the role of
the memory size value, different weighting policies and discuss about the
impact of data augmentation, which allows reaching better performance with
lower memory sizes.

Solving math word problems requires deductive reasoning over the quantities
in the text. Various recent research efforts mostly relied on
sequence-to-sequence or sequence-to-tree models to generate mathematical
expressions without explicitly performing relational reasoning between
quantities in the given context. While empirically effective, such approaches
typically do not provide explanations for the generated expressions. In this
work, we view the task as a complex relation extraction problem, proposing a
novel approach that presents explainable deductive reasoning steps to
iteratively construct target expressions, where each step involves a primitive
operation over two quantities defining their relation. Through extensive
experiments on four benchmark datasets, we show that the proposed model
significantly outperforms existing strong baselines. We further demonstrate
that the deductive procedure not only presents more explainable steps but also
enables us to make more accurate predictions on questions that require more
complex reasoning.

Optimization of directed acyclic graph (DAG) structures has many
applications, such as neural architecture search (NAS) and probabilistic
graphical model learning. Encoding DAGs into real vectors is a dominant
component in most neural-network-based DAG optimization frameworks. Currently,
most DAG encoders use an asynchronous message passing scheme which sequentially
processes nodes according to the dependency between nodes in a DAG. That is, a
node must not be processed until all its predecessors are processed. As a
result, they are inherently not parallelizable. In this work, we propose a
Parallelizable Attention-based Computation structure Encoder (PACE) that
processes nodes simultaneously and encodes DAGs in parallel. We demonstrate the
superiority of PACE through encoder-dependent optimization subroutines that
search the optimal DAG structure based on the learned DAG embeddings.
Experiments show that PACE not only improves the effectiveness over previous
sequential DAG encoders with a significantly boosted training and inference
speed, but also generates smooth latent (DAG encoding) spaces that are
beneficial to downstream optimization subroutines. Our source code is available
at \url{https://github.com/zehao-dong/PACE}

A common approach in forecasting problems is to estimate a least-squares
regression (or other statistical learning models) from past data, which is then
applied to predict future outcomes. An underlying assumption is that the same
correlations that were observed in the past still hold for the future. We
propose a model for situations when this assumption is not met: adopting
methods from the state space literature, we model how regression coefficients
change over time. Our approach can shed light on the large uncertainties
associated with forecasting the future, and how much of this is due to changing
dynamics of the past. Our simulation study shows that accurate estimates are
obtained when the outcome is continuous, but the procedure fails for binary
outcomes.

Dynamic demand prediction is a key issue in ride-hailing dispatching. Many
methods have been developed to improve the demand prediction accuracy of an
increase in demand-responsive, ride-hailing transport services. However, the
uncertainties in predicting ride-hailing demands due to multiscale
spatiotemporal granularity, as well as the resulting statistical errors, are
seldom explored. This paper attempts to fill this gap and to examine the
spatiotemporal granularity effects on ride-hailing demand prediction accuracy
by using empirical data for Chengdu, China. A convolutional, long short-term
memory model combined with a hexagonal convolution operation (H-ConvLSTM) is
proposed to explore the complex spatial and temporal relations. Experimental
analysis results show that the proposed approach outperforms conventional
methods in terms of prediction accuracy. A comparison of 36 spatiotemporal
granularities with both departure demands and arrival demands shows that the
combination of a hexagonal spatial partition with an 800 m side length and a 30
min time interval achieves the best comprehensive prediction accuracy. However,
the departure demands and arrival demands reveal different variation trends in
the prediction errors for various spatiotemporal granularities.

Multimodal machine translation (MMT) aims to improve neural machine
translation (NMT) with additional visual information, but most existing MMT
methods require paired input of source sentence and image, which makes them
suffer from shortage of sentence-image pairs. In this paper, we propose a
phrase-level retrieval-based method for MMT to get visual information for the
source input from existing sentence-image data sets so that MMT can break the
limitation of paired sentence-image input. Our method performs retrieval at the
phrase level and hence learns visual information from pairs of source phrase
and grounded region, which can mitigate data sparsity. Furthermore, our method
employs the conditional variational auto-encoder to learn visual
representations which can filter redundant visual information and only retain
visual information related to the phrase. Experiments show that the proposed
method significantly outperforms strong baselines on multiple MMT datasets,
especially when the textual context is limited.

This work focuses on tackling the challenging but realistic visual task of
Incremental Few-Shot Learning (IFSL), which requires a model to continually
learn novel classes from only a few examples while not forgetting the base
classes on which it was pre-trained. Our study reveals that the challenges of
IFSL lie in both inter-class separation and novel-class representation. Dur to
intra-class variation, a novel class may implicitly leverage the knowledge from
multiple base classes to construct its feature representation. Hence, simply
reusing the pre-trained embedding space could lead to a scattered feature
distribution and result in category confusion. To address such issues, we
propose a two-step learning strategy referred to as \textbf{Im}planting and
\textbf{Co}mpressing (\textbf{IMCO}), which optimizes both feature space
partition and novel class reconstruction in a systematic manner. Specifically,
in the \textbf{Implanting} step, we propose to mimic the data distribution of
novel classes with the assistance of data-abundant base set, so that a model
could learn semantically-rich features that are beneficial for discriminating
between the base and other unseen classes. In the \textbf{Compressing} step, we
adapt the feature extractor to precisely represent each novel class for
enhancing intra-class compactness, together with a regularized parameter
updating rule for preventing aggressive model updating. Finally, we demonstrate
that IMCO outperforms competing baselines with a significant margin, both in
image classification task and more challenging object detection task.

Unified Virtual Memory (UVM) relieves the developers from the onus of
maintaining complex data structures and explicit data migration by enabling
on-demand data movement between CPU memory and GPU memory. However, on-demand
paging soon becomes a performance bottleneck of UVM due to the high latency
caused by page table walks and data migration over interconnect. Prefetching is
considered a promising solution to this problem given its ability to leverage
the locality of program memory access patterns. However, existing
locality-based prefetching schemes can not handle all the situations. %Data
structures like arrays tend to be stored in contiguous blocks, and accessed
repeatedly. An ideal prefetcher should not only look at narrow regions of the
requested address space but also capture global context to deliver a good
prediction of the memory access pattern.
  This paper proposes a novel approach for page prefetching for UVM through
deep learning. We first show that a powerful Transformer learning model can
provide high accuracy for UVM page prefetching. We then perform analysis to
interpret this Transformer model and derive several insights that allow us to
design a simpler model to match the unconstrained model's accuracy with orders
of magnitude lower cost. We evaluate this simplified model on a set of 11
memory-intensive benchmarks from popular benchmark suites. Our solution
outperforms the state-of-the-art UVM framework, improving the performance by
10.89%, improving the device memory page hit rate by 16.98% (89.02% vs. 76.10%
for prior art), and reducing the CPU-GPU interconnect traffic by 11.05%.
According to our proposed unified metric, which combines the accuracy,
coverage, and page hit rate, our solution is approaching the ideal prefetching
scheme more than the state-of-the-art design (0.90 vs. 0.85, with the perfect
prefetcher of 1.0).

For video frame interpolation (VFI), existing deep-learning-based approaches
strongly rely on the ground-truth (GT) intermediate frames, which sometimes
ignore the non-unique nature of motion judging from the given adjacent frames.
As a result, these methods tend to produce averaged solutions that are not
clear enough. To alleviate this issue, we propose to relax the requirement of
reconstructing an intermediate frame as close to the GT as possible. Towards
this end, we develop a texture consistency loss (TCL) upon the assumption that
the interpolated content should maintain similar structures with their
counterparts in the given frames. Predictions satisfying this constraint are
encouraged, though they may differ from the pre-defined GT. Without the bells
and whistles, our plug-and-play TCL is capable of improving the performance of
existing VFI frameworks. On the other hand, previous methods usually adopt the
cost volume or correlation map to achieve more accurate image/feature warping.
However, the O(N^2) ({N refers to the pixel count}) computational complexity
makes it infeasible for high-resolution cases. In this work, we design a
simple, efficient (O(N)) yet powerful cross-scale pyramid alignment (CSPA)
module, where multi-scale information is highly exploited. Extensive
experiments justify the efficiency and effectiveness of the proposed strategy.

In the literature on adversarial examples, white box and black box attacks
have received the most attention. The adversary is assumed to have either full
(white) or no (black) access to the defender's model. In this work, we focus on
the equally practical gray box setting, assuming an attacker has partial
information. We propose a novel defense that assumes everything but a private
key will be made available to the attacker. Our framework uses an image
denoising procedure coupled with encryption via a discretized Baker map.
Extensive testing against adversarial images (e.g. FGSM, PGD) crafted using
various gradients shows that our defense achieves significantly better results
on CIFAR-10 and CIFAR-100 than the state-of-the-art gray box defenses in both
natural and adversarial accuracy.

Modern ETL streaming pipelines extract data from various sources and forward
it to multiple consumers, such as data warehouses (DW) and analytical systems
that leverage machine learning (ML). However, the increasing number of systems
that are connected to such pipelines requires new solutions for data
integration. The canonical (or common) data model (CDM) offers such an
integration. It is particular useful for integrating microservice systems into
ETL pipelines. (Villaca et al 2020, Oliveira et al 2019) However, a mapping to
a CDM is complex. (Lemcke et al 2012) There are three complexity problems,
namely the size of the required mapping matrix, the automation of updates of
the matrix in response to changes in the extraction sources and the time
efficiency of the mapping. In this paper, we present a new solution for these
problems. More precisely, we present a new dynamic mapping matrix (DMM), which
is based on permutation matrices that are obtained by block-partitioning the
full mapping matrix. We show that the DMM can be used for automated updates in
response to schema changes, for parallel computation in near real-time and for
highly efficient compacting. For the solution, we draw on research into matrix
partitioning (Quinn 2004) and dynamic networks (Haase et al 2021). The DMM has
been implemented into an app called Message ETL (METL). METL is the key part of
a new ETL streaming pipeline at EOS that conducts the transformation to a CDM.
The ETL pipeline is based on Kafka-streams. It extracts data from more than 80
microservices with log-based Change Data Capture (CDC) with Debezium and loads
the data to a DW and an ML platform. EOS is part of the Otto-Group, the
second-largest e-commerce provider in Europe.

Because of the current COVID-19 pandemic with its increasing fears among
people, it has triggered several health complications such as depression and
anxiety. Such complications have not only affected the developed countries but
also developing countries such as Nepal. These complications can be understood
from peoples' tweets/comments posted online after their proper analysis and
sentiment classification. Nevertheless, owing to the limited number of
tokens/words in each tweet, it is always crucial to capture multiple
information associated with them for their better understanding. In this study,
we, first, represent each tweet by combining both syntactic and semantic
information, called hybrid features. The syntactic information is generated
from the bag of words method, whereas the semantic information is generated
from the combination of the fastText-based (ft) and domain-specific (ds)
methods. Second, we design a novel multi-channel convolutional neural network
(MCNN), which ensembles the multiple CNNs, to capture multi-scale information
for better classification. Last, we evaluate the efficacy of both the proposed
feature extraction method and the MCNN model classifying tweets into three
sentiment classes (positive, neutral and negative) on NepCOV19Tweets dataset,
which is the only public COVID-19 tweets dataset in Nepali language. The
evaluation results show that the proposed hybrid features outperform individual
feature extraction methods with the highest classification accuracy of 69.7%
and the MCNN model outperforms the existing methods with the highest
classification accuracy of 71.3% during classification.

Laser degradation analysis is a crucial process for the enhancement of laser
reliability. Here, we propose a data-driven fault detection approach based on
Long Short-Term Memory (LSTM) recurrent neural networks to detect the different
laser degradation modes based on synthetic historical failure data. In
comparison to typical threshold-based systems, attaining 24.41% classification
accuracy, the LSTM-based model achieves 95.52% accuracy, and also outperforms
classical machine learning (ML) models namely Random Forest (RF), K-Nearest
Neighbours (KNN) and Logistic Regression (LR).

In this paper, a data-driven diagnostic and prognostic approach based on
machine learning is proposed to detect laser failure modes and to predict the
remaining useful life (RUL) of a laser during its operation. We present an
architecture of the proposed cognitive predictive maintenance framework and
demonstrate its effectiveness using synthetic data.

A novel approach based on an artificial neural network (ANN) for lifetime
prediction of 1.55 um InGaAsP MQW-DFB laser diodes is presented. It outperforms
the conventional lifetime projection using accelerated aging tests.

Graph Neural Networks (GNNs) show strong expressive power on graph data
mining, by aggregating information from neighbors and using the integrated
representation in the downstream tasks. The same aggregation methods and
parameters for each node in a graph are used to enable the GNNs to utilize the
homophily relational data. However, not all graphs are homophilic, even in the
same graph, the distributions may vary significantly. Using the same
convolution over all nodes may lead to the ignorance of various graph patterns.
Furthermore, many existing GNNs integrate node features and structure
identically, which ignores the distributions of nodes and further limits the
expressive power of GNNs. To solve these problems, we propose Meta Weight Graph
Neural Network (MWGNN) to adaptively construct graph convolution layers for
different nodes. First, we model the Node Local Distribution (NLD) from node
feature, topological structure and positional identity aspects with the
Meta-Weight. Then, based on the Meta-Weight, we generate the adaptive graph
convolutions to perform a node-specific weighted aggregation and boost the node
representations. Finally, we design extensive experiments on real-world and
synthetic benchmarks to evaluate the effectiveness of MWGNN. These experiments
show the excellent expressive power of MWGNN in dealing with graph data with
various distributions.

We propose a deep learning approach based on an autoencoder for identifying
and localizing fiber faults in passive optical networks. The experimental
results show that the proposed method detects faults with 97% accuracy,
pinpoints them with an RMSE of 0.18 m and outperforms conventional techniques.

Fast and accurate fault detection and localization in fiber optic cables is
extremely important to ensure the optical network survivability and
reliability. Hence there exists a crucial need to develop an automatic and
reliable algorithm for real time optical fiber fault detection and diagnosis
leveraging the telemetry data obtained by an optical time domain reflectometry
(OTDR) instrument. In this paper, we propose a novel data driven approach based
on convolutional neural networks (CNNs) to detect and characterize the fiber
reflective faults given noisy simulated OTDR data, whose SNR (signal-to-noise
ratio) values vary from 0 dB to 30 dB, incorporating reflective event patterns.
In our simulations, we achieved a higher detection capability with low false
alarm rate and greater localization accuracy even for low SNR values compared
to conventionally employed techniques.

Semantic segmentation with limited annotations, such as weakly supervised
semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS),
is a challenging task that has attracted much attention recently. Most leading
WSSS methods employ a sophisticated multi-stage training strategy to estimate
pseudo-labels as precise as possible, but they suffer from high model
complexity. In contrast, there exists another research line that trains a
single network with image-level labels in one training cycle. However, such a
single-stage strategy often performs poorly because of the compounding effect
caused by inaccurate pseudo-label estimation. To address this issue, this paper
presents a Self-supervised Low-Rank Network (SLRNet) for single-stage WSSS and
SSSS. The SLRNet uses cross-view self-supervision, that is, it simultaneously
predicts several complementary attentive LR representations from different
views of an image to learn precise pseudo-labels. Specifically, we reformulate
the LR representation learning as a collective matrix factorization problem and
optimize it jointly with the network learning in an end-to-end manner. The
resulting LR representation deprecates noisy information while capturing stable
semantics across different views, making it robust to the input variations,
thereby reducing overfitting to self-supervision errors. The SLRNet can provide
a unified single-stage framework for various label-efficient semantic
segmentation settings: 1) WSSS with image-level labeled data, 2) SSSS with a
few pixel-level labeled data, and 3) SSSS with a few pixel-level labeled data
and many image-level labeled data. Extensive experiments on the Pascal VOC
2012, COCO, and L2ID datasets demonstrate that our SLRNet outperforms both
state-of-the-art WSSS and SSSS methods with a variety of different settings,
proving its good generalizability and efficacy.

A hybrid prognostic model based on convolutional neural networks (CNN) and
long short-term memory (LSTM) is proposed to predict the laser remaining useful
life (RUL). The experimental results show that it outperforms the conventional
methods.

A new privacy-preserving federated learning framework allowing laser
manufacturers to collaboratively build a robust ML-based laser lifetime
prediction model, is proposed. It achieves a mean absolute error of 0.1 years
and a significant performance improvement

Secure and reliable data communication in optical networks is critical for
high-speed Internet. However, optical fibers, serving as the data transmission
medium providing connectivity to billons of users worldwide, are prone to a
variety of anomalies resulting from hard failures (e.g., fiber cuts) and
malicious physical attacks (e.g., optical eavesdropping (fiber tapping)) etc.
Such anomalies may cause network disruption and thereby inducing huge financial
and data losses, or compromise the confidentiality of optical networks by
gaining unauthorized access to the carried data, or gradually degrade the
network operations. Therefore, it is highly required to implement efficient
anomaly detection, diagnosis, and localization schemes for enhancing the
availability and reliability of optical networks. In this paper, we propose a
data driven approach to accurately and quickly detect, diagnose, and localize
fiber anomalies including fiber cuts, and optical eavesdropping attacks. The
proposed method combines an autoencoder-based anomaly detection and an
attention-based bidirectional gated recurrent unit algorithm, whereby the
former is used for fault detection and the latter is adopted for fault
diagnosis and localization once an anomaly is detected by the autoencoder. We
verify the efficiency of our proposed approach by experiments under various
anomaly scenarios using real operational data. The experimental results
demonstrate that: (i) the autoencoder detects any fiber fault or anomaly with
an F1 score of 96.86%; and (ii) the attention-based bidirectional gated
recurrent unit algorithm identifies the the detected anomalies with an average
accuracy of 98.2%, and localizes the faults with an average root mean square
error of 0.19 m.

Articulatory features are inherently invariant to acoustic signal distortion
and have been successfully incorporated into automatic speech recognition (ASR)
systems for normal speech. Their practical application to disordered speech
recognition is often limited by the difficulty in collecting such specialist
data from impaired speakers. This paper presents a cross-domain
acoustic-to-articulatory (A2A) inversion approach that utilizes the parallel
acoustic-articulatory data of the 15-hour TORGO corpus in model training before
being cross-domain adapted to the 102.7-hour UASpeech corpus and to produce
articulatory features. Mixture density networks based neural A2A inversion
models were used. A cross-domain feature adaptation network was also used to
reduce the acoustic mismatch between the TORGO and UASpeech data. On both
tasks, incorporating the A2A generated articulatory features consistently
outperformed the baseline hybrid DNN/TDNN, CTC and Conformer based end-to-end
systems constructed using acoustic features only. The best multi-modal system
incorporating video modality and the cross-domain articulatory features as well
as data augmentation and learning hidden unit contributions (LHUC) speaker
adaptation produced the lowest published word error rate (WER) of 24.82% on the
16 dysarthric speakers of the benchmark UASpeech task.

To reduce operation-and-maintenance expenses (OPEX) and to ensure optical
network survivability, optical network operators need to detect and diagnose
faults in a timely manner and with high accuracy. With the rapid advancement of
telemetry technology and data analysis techniques, data-driven approaches
leveraging telemetry data to tackle the fault diagnosis problem have been
gaining popularity due to their quick implementation and deployment. In this
paper, we propose a novel multi-task learning model based on long short-term
memory (LSTM) to detect, locate, and estimate the reflectance of fiber
reflective faults (events) including the connectors and the mechanical splices
by extracting insights from monitored data obtained by the optical time domain
reflectometry (OTDR) principle commonly used for troubleshooting of fiber optic
cables or links. The experimental results prove that the proposed method: (i)
achieves a good detection capability and high localization accuracy within
short measurement time even for low SNR values; and (ii) outperforms
conventionally employed techniques.

Optical time-domain reflectometry (OTDR) has been widely used for
characterizing fiber optical links and for detecting and locating fiber faults.
OTDR traces are prone to be distorted by different kinds of noise, causing
blurring of the backscattered signals, and thereby leading to a misleading
interpretation and a more cumbersome event detection task. To address this
problem, a novel method combining a denoising convolutional autoencoder (DCAE)
and a bidirectional long short-term memory (BiLSTM) is proposed, whereby the
former is used for noise removal of OTDR signals and the latter for fault
detection, localization, and diagnosis with the denoised signal as input. The
proposed approach is applied to noisy OTDR signals of different levels of input
SNR ranging from -5 dB to 15 dB. The experimental results demonstrate that: (i)
the DCAE is efficient in denoising the OTDR traces and it outperforms other
deep learning techniques and the conventional denoising methods; and (ii) the
BiLSTM achieves a high detection and diagnostic accuracy of 96.7% with an
improvement of 13.74% compared to the performance of the same model trained
with noisy OTDR signals.

Predictive algorithms have a powerful potential to offer benefits in areas as
varied as medicine or education. However, these algorithms and the data they
use are built by humans, consequently, they can inherit the bias and prejudices
present in humans. The outcomes can systematically repeat errors that create
unfair results, which can even lead to situations of discrimination (e.g.
gender, social or racial). In order to illustrate how important is to count
with a diverse training dataset to avoid bias, we manipulate a well-known
facial expression recognition dataset to explore gender bias and discuss its

Robotic cutting of soft materials is critical for applications such as food
processing, household automation, and surgical manipulation. As in other areas
of robotics, simulators can facilitate controller verification, policy
learning, and dataset generation. Moreover, differentiable simulators can
enable gradient-based optimization, which is invaluable for calibrating
simulation parameters and optimizing controllers. In this work, we present
DiSECt: the first differentiable simulator for cutting soft materials. The
simulator augments the finite element method (FEM) with a continuous contact
model based on signed distance fields (SDF), as well as a continuous damage
model that inserts springs on opposite sides of the cutting plane and allows
them to weaken until zero stiffness, enabling crack formation. Through various
experiments, we evaluate the performance of the simulator. We first show that
the simulator can be calibrated to match resultant forces and deformation
fields from a state-of-the-art commercial solver and real-world cutting
datasets, with generality across cutting velocities and object instances. We
then show that Bayesian inference can be performed efficiently by leveraging
the differentiability of the simulator, estimating posteriors over hundreds of
parameters in a fraction of the time of derivative-free methods. Next, we
illustrate that control parameters in the simulation can be optimized to
minimize cutting forces via lateral slicing motions. Finally, we conduct
experiments on a real robot arm equipped with a slicing knife to infer
simulation parameters from force measurements. By optimizing the slicing motion
of the knife, we show on fruit cutting scenarios that the average knife force
can be reduced by more than 40% compared to a vertical cutting motion. We
publish code and additional materials on our project website at
https://diff-cutting-sim.github.io.

Randomized singular value decomposition (RSVD) is a class of computationally
efficient algorithms for computing the truncated SVD of large data matrices.
Given a $n \times n$ symmetric matrix $\mathbf{M}$, the prototypical RSVD
algorithm outputs an approximation of the $k$ leading singular vectors of
$\mathbf{M}$ by computing the SVD of $\mathbf{M}^{g} \mathbf{G}$; here $g \geq
1$ is an integer and $\mathbf{G} \in \mathbb{R}^{n \times k}$ is a random
Gaussian sketching matrix. In this paper we study the statistical properties of
RSVD under a general "signal-plus-noise" framework, i.e., the observed matrix
$\hat{\mathbf{M}}$ is assumed to be an additive perturbation of some true but
unknown signal matrix $\mathbf{M}$. We first derive upper bounds for the
$\ell_2$ (spectral norm) and $\ell_{2\to\infty}$ (maximum row-wise $\ell_2$
norm) distances between the approximate singular vectors of $\hat{\mathbf{M}}$
and the true singular vectors of the signal matrix $\mathbf{M}$. These upper
bounds depend on the signal-to-noise ratio (SNR) and the number of power
iterations $g$. A phase transition phenomenon is observed in which a smaller
SNR requires larger values of $g$ to guarantee convergence of the $\ell_2$ and
$\ell_{2\to\infty}$ distances. We also show that the thresholds for $g$ where
these phase transitions occur are sharp whenever the noise matrices satisfy a
certain trace growth condition. Finally, we derive normal approximations for
the row-wise fluctuations of the approximate singular vectors and the entrywise
fluctuations of the approximate matrix. We illustrate our theoretical results
by deriving nearly-optimal performance guarantees for RSVD when applied to
three statistical inference problems, namely, community detection, matrix
completion, and principal component analysis with missing data.

Transformers have been shown to be able to perform deductive reasoning on a
logical rulebase containing rules and statements written in natural language.
Recent works show that such models can also produce the reasoning steps (i.e.,
the proof graph) that emulate the model's logical reasoning process. Currently,
these black-box models generate both the proof graph and intermediate
inferences within the same model and thus may be unfaithful. In this work, we
frame the deductive logical reasoning task by defining three modular
components: rule selection, fact selection, and knowledge composition. The rule
and fact selection steps select the candidate rule and facts to be used and
then the knowledge composition combines them to generate new inferences. This
ensures model faithfulness by assured causal relation from the proof step to
the inference reasoning. To test our framework, we propose FaiRR (Faithful and
Robust Reasoner) where the above three components are independently modeled by
transformers. We observe that FaiRR is robust to novel language perturbations,
and is faster at inference than previous works on existing reasoning datasets.
Additionally, in contrast to black-box generative models, the errors made by
FaiRR are more interpretable due to the modular approach.

3D shape analysis has been widely explored in the era of deep learning.
Numerous models have been developed for various 3D data representation formats,
e.g., MeshCNN for meshes, PointNet for point clouds and VoxNet for voxels. In
this study, we present Representation-Agnostic Shape Fields (RASF), a
generalizable and computation-efficient shape embedding module for 3D deep
learning. RASF is implemented with a learnable 3D grid with multiple channels
to store local geometry. Based on RASF, shape embeddings for various 3D shape
representations (point clouds, meshes and voxels) are retrieved by coordinate
indexing. While there are multiple ways to optimize the learnable parameters of
RASF, we provide two effective schemes among all in this paper for RASF
pre-training: shape reconstruction and normal estimation. Once trained, RASF
becomes a plug-and-play performance booster with negligible cost. Extensive
experiments on diverse 3D representation formats, networks and applications,
validate the universal effectiveness of the proposed RASF. Code and pre-trained
models are publicly available https://github.com/seanywang0408/RASF

In recommender systems, the collected data always contains various biases and
leads to the challenge of accurate predictions. To address selection bias and
confounding bias, the doubly robust (DR) method and its variants show superior
performance due to the double robustness property and smaller bias under
inaccurate propensity and error imputation models. However, we theoretically
show that the variance of the error imputation-based (EIB) method is much
smaller than that of DR, although EIB may suffer from a much larger bias. In
this paper, we propose a doubly robust targeted learning method that
effectively combines the small-bias property of DR and the small-variance
property of EIB, by leveraging the targeted maximum likelihood estimation
technique. Theoretical analysis shows that the proposed targeted learning is
effective in reducing the variance of DR while maintaining double robustness.
To further reduce the bias and variance during the training process, we propose
a novel collaborative targeted learning approach that decomposes imputed errors
into parametric and nonparametric parts and updates them collaboratively,
resulting in more accurate predictions. Both theoretical analysis and
experiments demonstrate the superiority of the proposed methods compared with
existing debiasing methods.

Various models have been proposed to incorporate knowledge of syntactic
structures into neural language models. However, previous works have relied
heavily on elaborate components for a specific language model, usually
recurrent neural network (RNN), which makes themselves unwieldy in practice to
fit into other neural language models, such as Transformer and GPT-2. In this
paper, we introduce the Dependency-based Mixture Language Models. In detail, we
first train neural language models with a novel dependency modeling objective
to learn the probability distribution of future dependent tokens given context.
We then formulate the next-token probability by mixing the previous dependency
modeling probability distributions with self-attention. Extensive experiments
and human evaluations show that our method can be easily and effectively
applied to different neural language models while improving neural text
generation on various tasks.

A common method for extractive multi-document news summarization is to
re-formulate it as a single-document summarization problem by concatenating all
documents as a single meta-document. However, this method neglects the relative
importance of documents. We propose a simple approach to reorder the documents
according to their relative importance before concatenating and summarizing
them. The reordering makes the salient content easier to learn by the
summarization model. Experiments show that our approach outperforms previous
state-of-the-art methods with more complex architectures.

Recently, the NLP community has witnessed a rapid advancement in multilingual
and cross-lingual transfer research where the supervision is transferred from
high-resource languages (HRLs) to low-resource languages (LRLs). However, the
cross-lingual transfer is not uniform across languages, particularly in the
zero-shot setting. Towards this goal, one promising research direction is to
learn shareable structures across multiple tasks with limited annotated data.
The downstream multilingual applications may benefit from such a learning setup
as most of the languages across the globe are low-resource and share some
structures with other languages. In this paper, we propose a novel
meta-learning framework (called Meta-X$_{NLG}$) to learn shareable structures
from typologically diverse languages based on meta-learning and language
clustering. This is a step towards uniform cross-lingual transfer for unseen
languages. We first cluster the languages based on language representations and
identify the centroid language of each cluster. Then, a meta-learning algorithm
is trained with all centroid languages and evaluated on the other languages in
the zero-shot setting. We demonstrate the effectiveness of this modeling on two
NLG tasks (Abstractive Text Summarization and Question Generation), 5 popular
datasets and 30 typologically diverse languages. Consistent improvements over
strong baselines demonstrate the efficacy of the proposed framework. The
careful design of the model makes this end-to-end NLG setup less vulnerable to
the accidental translation problem, which is a prominent concern in zero-shot
cross-lingual NLG tasks.

Comprehending a dialogue requires a model to capture diverse kinds of key
information in the utterances, which are either scattered around or implicitly
implied in different turns of conversations. Therefore, dialogue comprehension
requires diverse capabilities such as paraphrasing, summarizing, and
commonsense reasoning. Towards the objective of pre-training a zero-shot
dialogue comprehension model, we develop a novel narrative-guided pre-training
strategy that learns by narrating the key information from a dialogue input.
However, the dialogue-narrative parallel corpus for such a pre-training
strategy is currently unavailable. For this reason, we first construct a
dialogue-narrative parallel corpus by automatically aligning movie subtitles
and their synopses. We then pre-train a BART model on the data and evaluate its
performance on four dialogue-based tasks that require comprehension.
Experimental results show that our model not only achieves superior zero-shot
performance but also exhibits stronger fine-grained dialogue comprehension
capabilities. The data and code are available at
https://github.com/zhaochaocs/Diana

Transformer-based architectures start to emerge in single image super
resolution (SISR) and have achieved promising performance. Most existing Vision
Transformers divide images into the same number of patches with a fixed size,
which may not be optimal for restoring patches with different levels of texture
richness. This paper presents HIPA, a novel Transformer architecture that
progressively recovers the high resolution image using a hierarchical patch
partition. Specifically, we build a cascaded model that processes an input
image in multiple stages, where we start with tokens with small patch sizes and
gradually merge to the full resolution. Such a hierarchical patch mechanism not
only explicitly enables feature aggregation at multiple resolutions but also
adaptively learns patch-aware features for different image regions, e.g., using
a smaller patch for areas with fine details and a larger patch for textureless
regions. Meanwhile, a new attention-based position encoding scheme for
Transformer is proposed to let the network focus on which tokens should be paid
more attention by assigning different weights to different tokens, which is the
first time to our best knowledge. Furthermore, we also propose a new
multi-reception field attention module to enlarge the convolution reception
field from different branches. The experimental results on several public
datasets demonstrate the superior performance of the proposed HIPA over
previous methods quantitatively and qualitatively.

Expensive-to-train deep learning models can benefit from an optimization of
the hyperparameters that determine the model architecture. We optimize 23
hyperparameters of a materials informatics model, Compositionally-Restricted
Attention-Based Network (CrabNet), over 100 adaptive design iterations using
two models within the Adaptive Experimentation (Ax) Platform. This includes a
recently developed Bayesian optimization (BO) algorithm, sparse axis-aligned
subspaces Bayesian optimization (SAASBO), which has shown exciting performance
on high-dimensional optimization tasks. Using SAASBO to optimize CrabNet
hyperparameters, we demonstrate a new state-of-the-art on the experimental band
gap regression task within the materials informatics benchmarking platform,
Matbench (~4.5% decrease in mean absolute error (MAE) relative to incumbent).
Characteristics of the adaptive design scheme as well as feature importances
are described for each of the Ax models. SAASBO has great potential to both
improve existing surrogate models, as shown in this work, and in future work,
to efficiently discover new, high-performing materials in high-dimensional
materials science search spaces.

Human action recognition has recently become one of the popular research
topics in the computer vision community. Various 3D-CNN based methods have been
presented to tackle both the spatial and temporal dimensions in the task of
video action recognition with competitive results. However, these methods have
suffered some fundamental limitations such as lack of robustness and
generalization, e.g., how does the temporal ordering of video frames affect the
recognition results? This work presents a novel end-to-end Transformer-based
Directed Attention (DirecFormer) framework for robust action recognition. The
method takes a simple but novel perspective of Transformer-based approach to
understand the right order of sequence actions. Therefore, the contributions of
this work are three-fold. Firstly, we introduce the problem of ordered temporal
learning issues to the action recognition problem. Secondly, a new Directed
Attention mechanism is introduced to understand and provide attentions to human
actions in the right order. Thirdly, we introduce the conditional dependency in
action sequence modeling that includes orders and classes. The proposed
approach consistently achieves the state-of-the-art (SOTA) results compared
with the recent action recognition methods, on three standard large-scale
benchmarks, i.e. Jester, Kinetics-400 and Something-Something-V2.

Training automated agents to complete complex tasks in interactive
environments is challenging: reinforcement learning requires careful
hand-engineering of reward functions, imitation learning requires specialized
infrastructure and access to a human expert, and learning from intermediate
forms of supervision (like binary preferences) is time-consuming and extracts
little information from each human intervention. Can we overcome these
challenges by building agents that learn from rich, interactive feedback
instead? We propose a new supervision paradigm for interactive learning based
on "teachable" decision-making systems that learn from structured advice
provided by an external teacher. We begin by formalizing a class of
human-in-the-loop decision making problems in which multiple forms of
teacher-provided advice are available to a learner. We then describe a simple
learning algorithm for these problems that first learns to interpret advice,
then learns from advice to complete tasks even in the absence of human
supervision. In puzzle-solving, navigation, and locomotion domains, we show
that agents that learn from advice can acquire new skills with significantly
less human supervision than standard reinforcement learning algorithms and
often less than imitation learning.

Direction of arrival (DOA) estimation is a fundamental problem in both
conventional radar and wireless communication applications and emerging
integrated sensing and communication (ISAC) systems. Due to many imperfect
factors in the low-cost systems, including the antenna position perturbations,
the inconsistent gains/phases, the mutual coupling effect, the nonlinear
amplifier effect, etc., the performance of the DOA estimation often degrades
significantly. To characterize the realistic array more accurately, a novel
deep learning (DL)-based DOA estimation method named super-resolution DOA
network (SDOAnet) is proposed in this paper. Different from the existing
DL-based DOA methods, our proposed SDOAnet employs the sampled received
signals, instead of the covariance matrices of the received signals, as the
input of the convolution layers for extracting data features. Moreover, the
output of SDOAnet is a vector that is independent of the DOA of targets but can
be used to estimate their spatial spectrum. As a result, the same training
network can be applied with any number of targets, which significantly reduce
the implementation complexity. At last, the convergence speed of our SDOAnet
with a low-dimension network structure is much faster than existing DL-based
methods. Simulation results show that the proposed SDOAnet outperforms the
existing DOA estimation methods with the effect of the imperfect array. The
code about the SDOAnet is available online
https://github.com/chenpengseu/SDOAnet.git.

The challenges to solving the collision avoidance problem lie in adaptively
choosing optimal robot velocities in complex scenarios full of interactive
obstacles. In this paper, we propose a distributed approach for multi-robot
navigation which combines the concept of reciprocal velocity obstacle (RVO) and
the scheme of deep reinforcement learning (DRL) to solve the reciprocal
collision avoidance problem under limited information. The novelty of this work
is threefold: (1) using a set of sequential VO and RVO vectors to represent the
interactive environmental states of static and dynamic obstacles, respectively;
(2) developing a bidirectional recurrent module based neural network, which
maps the states of a varying number of surrounding obstacles to the actions
directly; (3) developing a RVO area and expected collision time based reward
function to encourage reciprocal collision avoidance behaviors and trade off
between collision risk and travel time. The proposed policy is trained through
simulated scenarios and updated by the actor-critic based DRL algorithm. We
validate the policy in complex environments with various numbers of
differential drive robots and obstacles. The experiment results demonstrate
that our approach outperforms the state-of-art methods and other learning based
approaches in terms of the success rate, travel time, and average speed. Source
code of this approach is available at https://github.com/hanruihua/rl_rvo_nav.

Non-convex optimization problems have various important applications, whereas
many algorithms have been proven only to converge to stationary points.
Meanwhile, gradient Langevin dynamics (GLD) and its variants have attracted
increasing attention as a framework to provide theoretical convergence
guarantees for a global solution in non-convex settings. The studies on GLD
initially treated unconstrained convex problems and very recently expanded to
convex constrained non-convex problems by Lamperski (2021). In this work, we
can deal with non-convex problems with some kind of non-convex feasible region.
This work analyzes reflected gradient Langevin dynamics (RGLD), a global
optimization algorithm for smoothly constrained problems, including non-convex
constrained ones, and derives a convergence rate to a solution with
$\epsilon$-sampling error. The convergence rate is faster than the one given by
Lamperski (2021) for convex constrained cases. Our proofs exploit the Poisson
equation to effectively utilize the reflection for the faster convergence rate.

In algorithm optimization in reinforcement learning, how to deal with the
exploration-exploitation dilemma is particularly important. Multi-armed bandit
problem can optimize the proposed solutions by changing the reward distribution
to realize the dynamic balance between exploration and exploitation. Thompson
Sampling is a common method for solving multi-armed bandit problem and has been
used to explore data that conform to various laws. In this paper, we consider
the Thompson Sampling approach for multi-armed bandit problem, in which rewards
conform to unknown asymmetric $\alpha$-stable distributions and explore their
applications in modelling financial and wireless data.

Semantic 3D keypoints are category-level semantic consistent points on 3D
objects. Detecting 3D semantic keypoints is a foundation for a number of 3D
vision tasks but remains challenging, due to the ambiguity of semantic
information, especially when the objects are represented by unordered 3D point
clouds. Existing unsupervised methods tend to generate category-level keypoints
in implicit manners, making it difficult to extract high-level information,
such as semantic labels and topology. From a novel mutual reconstruction
perspective, we present an unsupervised method to generate consistent semantic
keypoints from point clouds explicitly. To achieve this, the proposed model
predicts keypoints that not only reconstruct the object itself but also
reconstruct other instances in the same category. To the best of our knowledge,
the proposed method is the first to mine 3D semantic consistent keypoints from
a mutual reconstruction view. Experiments under various evaluation metrics as
well as comparisons with the state-of-the-arts demonstrate the efficacy of our
new solution to mining semantic consistent keypoints with mutual

Deep learning (DL) has recently drawn much attention in image analysis,
natural language process, and high-dimensional medical data analysis. Under the
causal direct acyclic graph (DAG) interpretation, the input variables without
incoming edges from parent nodes in the DL architecture maybe assumed to be
randomized and independent of each other. As in a regression setting, including
the input variables in the DL algorithm would reduce the bias from the
potential confounders. However, failing to include a potential latent causal
structure among the input variables affecting both treatment assignment and the
output variable could be additional significant source of bias. The primary
goal of this study is to develop new DL algorithms to estimate causal
individual event times for time-to-event data, equivalently to estimate the
causal time-to-event distribution with or without right censoring, accounting
for the potential latent structure among the input variables. Once the causal
individual event times are estimated, it would be straightforward to estimate
the causal average treatment effects as the differences in the averages of the
estimated causal individual event times. A connection is made between the
proposed method and the targeted maximum likelihood estimation (TMLE).
Simulation studies are performed to assess improvement in prediction abilities
of the proposed methods by using the mean square error (MSE)-based method and
rank-based $C$-Index metric. The simulation results indicate that improvement
on the prediction accuracy could be substantial particularly when there is a
collider among the input variables. The proposed method is illustrated with a
publicly available and influential breast cancer data set. The proposed method
has been implemented by using PyTorch and uploaded at
https://github.com/yicjia/CausalDeepCENT.

We propose a novel M-estimate conjugate gradient (CG) algorithm, termed
Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive
noise environments. In particular, the TbMCG algorithm can achieve a faster
convergence while retaining a reduced computational complexity as compared to
the recursive least-squares (RLS) algorithm. Specifically, the Tukey's biweight
M-estimate incorporates a constraint into the CG filter to tackle impulsive
noise environments. Moreover, the convergence behavior of the TbMCG algorithm
is analyzed. Simulation results confirm the excellent performance of the
proposed TbMCG algorithm for system identification and active noise control

The identification and classification of transitions in topological and
microstructural regimes in pattern-forming processes are critical for
understanding and fabricating microstructurally precise novel materials in many
application domains. Unfortunately, relevant microstructure transitions may
depend on process parameters in subtle and complex ways that are not captured
by the classic theory of phase transition. While supervised machine learning
methods may be useful for identifying transition regimes, they need labels
which require prior knowledge of order parameters or relevant structures
describing these transitions. Motivated by the universality principle for
dynamical systems, we instead use a self-supervised approach to solve the
inverse problem of predicting process parameters from observed microstructures
using neural networks. This approach does not require predefined, labeled data
about the different classes of microstructural patterns or about the target
task of predicting microstructure transitions. We show that the difficulty of
performing the inverse-problem prediction task is related to the goal of
discovering microstructure regimes, because qualitative changes in
microstructural patterns correspond to changes in uncertainty predictions for
our self-supervised problem. We demonstrate the value of our approach by
automatically discovering transitions in microstructural regimes in two
distinct pattern-forming processes: the spinodal decomposition of a two-phase
mixture and the formation of concentration modulations of binary alloys during
physical vapor deposition of thin films. This approach opens a promising path
forward for discovering and understanding unseen or hard-to-discern transition
regimes, and ultimately for controlling complex pattern-forming processes.

A comprehensive representation of an image requires understanding objects and
their mutual relationship, especially in image-to-graph generation, e.g., road
network extraction, blood-vessel network extraction, or scene graph generation.
Traditionally, image-to-graph generation is addressed with a two-stage approach
consisting of object detection followed by a separate relation prediction,
which prevents simultaneous object-relation interaction. This work proposes a
unified one-stage transformer-based framework, namely Relationformer, that
jointly predicts objects and their relations. We leverage direct set-based
object prediction and incorporate the interaction among the objects to learn an
object-relation representation jointly. In addition to existing [obj]-tokens,
we propose a novel learnable token, namely [rln]-token. Together with
[obj]-tokens, [rln]-token exploits local and global semantic reasoning in an
image through a series of mutual associations. In combination with the
pair-wise [obj]-token, the [rln]-token contributes to a computationally
efficient relation prediction. We achieve state-of-the-art performance on
multiple, diverse and multi-domain datasets that demonstrate our approach's
effectiveness and generalizability.

High-fidelity quantum dynamics emulators can be used to predict the time
evolution of complex physical systems. Here, we introduce an efficient training
framework for constructing machine learning-based emulators. Our approach is
based on the idea of knowledge distillation and uses elements of curriculum
learning. It works by constructing a set of simple, but rich-in-physics
training examples (a curriculum). These examples are used by the emulator to
learn the general rules describing the time evolution of a quantum system
(knowledge distillation). The goal is not only to obtain high-quality
predictions, but also to examine the process of how the emulator learns the
physics of the underlying problem. This allows us to discover new facts about
the physical system, detect symmetries, and measure relative importance of the
contributing physical processes. We illustrate this approach by training an
artificial neural network to predict the time evolution of quantum wave
packages propagating through a potential landscape. We focus on the question of
how the emulator learns the rules of quantum dynamics from the curriculum of
simple training examples and to which extent it can generalize the acquired
knowledge to solve more challenging cases.

This paper investigates the cost function learning in social information
networks, wherein the influence of humans' memory on information consumption is
explicitly taken into account. We first propose a model for social
information-diffusion dynamics with a focus on systematic modeling of
asymmetric cognitive bias, represented by confirmation bias and novelty bias.
Building on the proposed social model, we then propose the M$^{3}$IRL: a model
and maximum-entropy based inverse reinforcement learning framework for learning
the cost functions of target individuals in the memorized social networks.
Compared with the existing Bayesian IRL, maximum entropy IRL, relative entropy
IRL and maximum causal entropy IRL, the characteristics of M$^{3}$IRL are
significantly different here: no dependency on the Markov Decision Process
principle, the need of only a single finite-time trajectory sample, and bounded
decision variables. Finally, the effectiveness of the proposed social
information-diffusion model and the M$^{3}$IRL algorithm are validated by the
online social media data.

We propose MisMatch, a novel consistency-driven semi-supervised segmentation
framework which produces predictions that are invariant to learnt feature
perturbations. MisMatch consists of an encoder and a two-head decoders. One
decoder learns positive attention to the foreground regions of interest (RoI)
on unlabelled images thereby generating dilated features. The other decoder
learns negative attention to the foreground on the same unlabelled images
thereby generating eroded features. We then apply a consistency regularisation
on the paired predictions. MisMatch outperforms state-of-the-art
semi-supervised methods on a CT-based pulmonary vessel segmentation task and a
MRI-based brain tumour segmentation task. In addition, we show that the
effectiveness of MisMatch comes from better model calibration than its
supervised learning counterpart.

The recent and rapid growth in Unmanned Aerial Vehicles (UAVs) deployment for
various computer vision tasks has paved the path for numerous opportunities to
make them more effective and valuable. Object detection in aerial images is
challenging due to variations in appearance, pose, and scale. Autonomous aerial
flight systems with their inherited limited memory and computational power
demand accurate and computationally efficient detection algorithms for
real-time applications. Our work shows the adaptation of the popular YOLOv4
framework for predicting the objects and their locations in aerial images with
high accuracy and inference speed. We utilized transfer learning for faster
convergence of the model on the VisDrone DET aerial object detection dataset.
The trained model resulted in a mean average precision (mAP) of 45.64% with an
inference speed reaching 8.7 FPS on the Tesla K80 GPU and was highly accurate
in detecting truncated and occluded objects. We experimentally evaluated the
impact of varying network resolution sizes and training epochs on the
performance. A comparative study with several contemporary aerial object
detectors proved that YOLOv4 performed better, implying a more suitable
detection algorithm to incorporate on aerial platforms.

A critical limitation of current methods based on Neural Radiance Fields
(NeRF) is that they are unable to quantify the uncertainty associated with the
learned appearance and geometry of the scene. This information is paramount in
real applications such as medical diagnosis or autonomous driving where, to
reduce potentially catastrophic failures, the confidence on the model outputs
must be included into the decision-making process. In this context, we
introduce Conditional-Flow NeRF (CF-NeRF), a novel probabilistic framework to
incorporate uncertainty quantification into NeRF-based approaches. For this
purpose, our method learns a distribution over all possible radiance fields
modelling which is used to quantify the uncertainty associated with the
modelled scene. In contrast to previous approaches enforcing strong constraints
over the radiance field distribution, CF-NeRF learns it in a flexible and fully
data-driven manner by coupling Latent Variable Modelling and Conditional
Normalizing Flows. This strategy allows to obtain reliable uncertainty
estimation while preserving model expressivity. Compared to previous
state-of-the-art methods proposed for uncertainty quantification in NeRF, our
experiments show that the proposed method achieves significantly lower
prediction errors and more reliable uncertainty values for synthetic novel view
and depth-map estimation.

Fair prediction across protected groups is an important constraint for many
federated learning applications. However, prior work studying group fair
federated learning lacks formal convergence or fairness guarantees. In this
work we propose a general framework for provably fair federated learning. In
particular, we explore and extend the notion of Bounded Group Loss as a
theoretically-grounded approach for group fairness. Using this setup, we
propose a scalable federated optimization method that optimizes the empirical
risk under a number of group fairness constraints. We provide convergence
guarantees for the method as well as fairness guarantees for the resulting
solution. Empirically, we evaluate our method across common benchmarks from
fair ML and federated learning, showing that it can provide both fairer and
more accurate predictions than baseline approaches.

The Expectation-Maximization (EM) algorithm is a popular choice for learning
latent variable models. Variants of the EM have been initially introduced,
using incremental updates to scale to large datasets, and using Monte Carlo
(MC) approximations to bypass the intractable conditional expectation of the
latent data for most nonconvex models. In this paper, we propose a general
class of methods called Two-Timescale EM Methods based on a two-stage approach
of stochastic updates to tackle an essential nonconvex optimization task for
latent variable models. We motivate the choice of a double dynamic by invoking
the variance reduction virtue of each stage of the method on both sources of
noise: the index sampling for the incremental update and the MC approximation.
We establish finite-time and global convergence bounds for nonconvex objective
functions. Numerical applications on various models such as deformable template
for image analysis or nonlinear models for pharmacokinetics are also presented
to illustrate our findings.

Model Agnostic Meta-Learning (MAML) consists of two optimization loops: the
outer loop learns a meta-initialization of model parameters that is shared
across tasks, and the inner loop task-specific adaptation step. A variant of
MAML, Meta-SGD, uses the same two loop structure, but also learns the
learning-rate for the adaptation step. Little attention has been paid to how
the learned learning-rate of Meta-SGD affects feature reuse. In this paper, we
study the effect that a learned learning-rate has on the per-task feature
representations in Meta-SGD. The learned learning-rate of Meta-SGD often
contains negative values. During the adaptation phase, these negative learning
rates push features away from task-specific features and towards task-agnostic
features.
  We performed several experiments on the Mini-Imagenet dataset. Two neural
networks were trained, one with MAML, and one with Meta-SGD. The feature
quality for both models was tested as follows: strip away the linear
classification layer, pass labeled and unlabeled samples through this encoder,
classify the unlabeled samples according to their nearest neighbor. This
process was performed: 1) after training and using the meta-initialization
parameters; 2) after adaptation, and validated on that task; and 3) after
adaptation, and validated on a different task. The MAML trained model improved
on the task it was adapted to, but had worse performance on other tasks. The
Meta-SGD trained model was the opposite; it had worse performance on the task
it was adapted to, but improved on other tasks. This confirms the hypothesis
that Meta-SGD's negative learning rates cause the model to learn task-agnostic
features rather than simply adapt to task specific features.

Video compression plays a crucial role in video streaming and classification
systems by maximizing the end-user quality of experience (QoE) at a given
bandwidth budget. In this paper, we conduct the first systematic study for
adversarial attacks on deep learning-based video compression and downstream
classification systems. Our attack framework, dubbed RoVISQ, manipulates the
Rate-Distortion (R-D) relationship of a video compression model to achieve one
or both of the following goals: (1) increasing the network bandwidth, (2)
degrading the video quality for end-users. We further devise new objectives for
targeted and untargeted attacks to a downstream video classification service.
Finally, we design an input-invariant perturbation that universally disrupts
video compression and classification systems in real time. Unlike previously
proposed attacks on video classification, our adversarial perturbations are the
first to withstand compression. We empirically show the resilience of RoVISQ
attacks against various defenses, i.e., adversarial training, video denoising,
and JPEG compression. Our extensive experimental results on various video
datasets show RoVISQ attacks deteriorate peak signal-to-noise ratio by up to
5.6dB and the bit-rate by up to 2.4 times while achieving over 90% attack
success rate on a downstream classifier.

Active learning methods are rapidly becoming the integral component of
automated experiment workflows in imaging, materials synthesis, and
computation. The distinctive aspect of many experimental scenarios is the
presence of multiple information channels, including both the intrinsic
modalities of the measurement system and the exogenous environment and noise
signals. One of the key tasks in experimental studies is hence establishing
which of these channels is predictive of the behaviors of interest. Here we
explore the problem of discovery of the optimal predictive channel for
structure-property relationships (in microscopy) using deep kernel learning for
modality selection in an active experiment setting. We further pose that this
approach can be directly applicable to similar active learning tasks in
automated synthesis and the discovery of quantitative structure-activity
relations in molecular systems.

A central challenge to applying many off-policy reinforcement learning
algorithms to real world problems is the variance introduced by importance
sampling. In off-policy learning, the agent learns about a different policy
than the one being executed. To account for the difference importance sampling
ratios are often used, but can increase variance in the algorithms and reduce
the rate of learning. Several variations of importance sampling have been
proposed to reduce variance, with per-decision importance sampling being the
most popular. However, the update rules for most off-policy algorithms in the
literature depart from per-decision importance sampling in a subtle way; they
correct the entire TD error instead of just the TD target. In this work, we
show how this slight change can be interpreted as a control variate for the TD
target, reducing variance and improving performance. Experiments over a wide
range of algorithms show this subtle modification results in improved

Physical systems exhibiting neuromechanical functions promise to enable
structures with directly encoded autonomy and intelligence. We report on a
class of neuromorphic metamaterials embodying bioinspired mechanosensing,
memory, and learning functionalities obtained by leveraging mechanical
instabilities and flexible memristive materials. Our prototype system comprises
a multistable metamaterial whose bistable units filter, amplify, and transduce
external mechanical inputs over large areas into simple electrical signals
using piezoresistivity. We record these mechanically transduced signals using
non-volatile flexible memristors that remember sequences of mechanical inputs,
providing a means to store spatially distributed mechanical signals in
measurable material states. The accumulated memristance changes resulting from
the sequential mechanical inputs allow us to physically encode a Hopfield
network into our neuromorphic metamaterials. This physical network learns a
series of external spatially distributed input patterns. Crucially, the learned
patterns input into our neuromorphic metamaterials can be retrieved from the
final accumulated state of our memristors. Therefore, our system exhibits the
ability to learn without supervised training and retain spatially distributed
inputs with minimal external overhead. Our system's embodied mechanosensing,
memory, and learning capabilities establish an avenue for synthetic
neuromorphic metamaterials enabling the learning of touch-like sensations
covering large areas for robotics, autonomous systems, wearables, and morphing

At present, the educational data mining community lacks many tools needed for
ensuring equitable ability estimation for Neurodivergent (ND) learners. On one
hand, most learner models are susceptible to under-estimating ND ability since
confounding contexts cannot be held accountable (eg consider dyslexia and
text-heavy assessments), and on the other, few (if any) existing datasets are
suited for appraising model and data bias in ND contexts. In this paper we
attempt to model the relationships between context (delivery and response
types) and performance of ND students with zero-inflated learner models. This
approach facilitates simulation of several expected ND behavioural traits,
provides equitable ability estimates across all student groups from generated
datasets, increases interpretability confidence, and can significantly increase
the quality of learning opportunities for ND students. Our approach
consistently out-performs baselines in our experiments and can also be applied
to many other learner modelling frameworks.

We propose to generate adversarial samples by modifying activations of upper
layers encoding semantically meaningful concepts. The original sample is
shifted towards a target sample, yielding an adversarial sample, by using the
modified activations to reconstruct the original sample. A human might (and
possibly should) notice differences between the original and the adversarial
sample. Depending on the attacker-provided constraints, an adversarial sample
can exhibit subtle differences or appear like a "forged" sample from another
class. Our approach and goal are in stark contrast to common attacks involving
perturbations of single pixels that are not recognizable by humans. Our
approach is relevant in, e.g., multi-stage processing of inputs, where both
humans and machines are involved in decision-making because invisible
perturbations will not fool a human. Our evaluation focuses on deep neural
networks. We also show the transferability of our adversarial examples among
networks.

Cyber and cyber-physical systems equipped with machine learning algorithms
such as autonomous cars share environments with humans. In such a setting, it
is important to align system (or agent) behaviors with the preferences of one
or more human users. We consider the case when an agent has to learn behaviors
in an unknown environment. Our goal is to capture two defining characteristics
of humans: i) a tendency to assess and quantify risk, and ii) a desire to keep
decision making hidden from external parties. We incorporate cumulative
prospect theory (CPT) into the objective of a reinforcement learning (RL)
problem for the former. For the latter, we use differential privacy. We design
an algorithm to enable an RL agent to learn policies to maximize a CPT-based
objective in a privacy-preserving manner and establish guarantees on the
privacy of value functions learned by the algorithm when rewards are
sufficiently close. This is accomplished through adding a calibrated noise
using a Gaussian process mechanism at each step. Through empirical evaluations,
we highlight a privacy-utility tradeoff and demonstrate that the RL agent is
able to learn behaviors that are aligned with that of a human user in the same
environment in a privacy-preserving manner

Knowledge distillation (KD) is a substantial strategy for transferring
learned knowledge from one neural network model to another. A vast number of
methods have been developed for this strategy. While most method designs a more
efficient way to facilitate knowledge transfer, less attention has been put on
comparing the effect of knowledge sources such as features, logits, and
gradients. This work provides a new perspective to motivate a set of knowledge
distillation strategies by approximating the classical KL-divergence criteria
with different knowledge sources, making a systematic comparison possible in
model compression and incremental learning. Our analysis indicates that logits
are generally a more efficient knowledge source and suggests that having
sufficient feature dimensions is crucial for the model design, providing a
practical guideline for effective KD-based transfer learning.

This paper studies the problem of object discovery -- separating objects from
the background without manual labels. Existing approaches utilize appearance
cues, such as color, texture, and location, to group pixels into object-like
regions. However, by relying on appearance alone, these methods fail to
separate objects from the background in cluttered scenes. This is a fundamental
limitation since the definition of an object is inherently ambiguous and
context-dependent. To resolve this ambiguity, we choose to focus on dynamic
objects -- entities that can move independently in the world. We then scale the
recent auto-encoder based frameworks for unsupervised object discovery from toy
synthetic images to complex real-world scenes. To this end, we simplify their
architecture, and augment the resulting model with a weak learning signal from
general motion segmentation algorithms. Our experiments demonstrate that,
despite only capturing a small subset of the objects that move, this signal is
enough to generalize to segment both moving and static instances of dynamic
objects. We show that our model scales to a newly collected, photo-realistic
synthetic dataset with street driving scenarios. Additionally, we leverage
ground truth segmentation and flow annotations in this dataset for thorough
ablation and evaluation. Finally, our experiments on the real-world KITTI
benchmark demonstrate that the proposed approach outperforms both heuristic-
and learning-based methods by capitalizing on motion cues.

Traditional botnet attacks leverage large and distributed numbers of
compromised internet-connected devices to target and overwhelm other devices
with internet packets. With increasing consumer adoption of high-wattage
internet-facing "smart devices", a new "power botnet" attack emerges, where
such devices are used to target and overwhelm power grid devices with unusual
load demand. We introduce a variant of this attack, the power-botnet
weardown-attack, which does not intend to cause blackouts or short-term acute
instability, but instead forces expensive mechanical components to activate
more frequently, necessitating costly replacements / repairs. Specifically, we
target the on-load tap-changer (OLTC) transformer, which uses a mechanical
switch that responds to change in load demand. In our analysis and simulations,
these attacks can halve the lifespan of an OLTC, or in the most extreme cases,
reduce it to $2.5\%$ of its original lifespan. Notably, these power botnets are
composed of devices not connected to the internal SCADA systems used to control
power grids. This represents a new internet-based cyberattack that targets the
power grid from the outside. To help the power system to mitigate these types
of botnet attacks, we develop attack-localization strategies. We formulate the
problem as a supervised machine learning task to locate the source of power
botnet attacks. Within a simulated environment, we generate the training and
testing dataset to evaluate several machine learning algorithm based
localization methods, including SVM, neural network and decision tree. We show
that decision-tree based classification successfully identifies power botnet
attacks and locates compromised devices with at least $94\%$ improvement of
accuracy over a baseline "most-frequent" classifier.

Novel view synthesis is a long-standing problem. In this work, we consider a
variant of the problem where we are given only a few context views sparsely
covering a scene or an object. The goal is to predict novel viewpoints in the
scene, which requires learning priors. The current state of the art is based on
Neural Radiance Field (NeRF), and while achieving impressive results, the
methods suffer from long training times as they require evaluating millions of
3D point samples via a neural network for each image. We propose a 2D-only
method that maps multiple context views and a query pose to a new image in a
single pass of a neural network. Our model uses a two-stage architecture
consisting of a codebook and a transformer model. The codebook is used to embed
individual images into a smaller latent space, and the transformer solves the
view synthesis task in this more compact space. To train our model efficiently,
we introduce a novel branching attention mechanism that allows us to use the
same model not only for neural rendering but also for camera pose estimation.
Experimental results on real-world scenes show that our approach is competitive
compared to NeRF-based methods while not reasoning explicitly in 3D, and it is
faster to train.

Deep Learning and transfer learning models are being used to generate time
series forecasts; however, there is scarce evidence about their performance
prediction that it is more evident for monthly time series. The purpose of this
paper is to compare Deep Learning models with transfer learning and without
transfer learning and other traditional methods used for monthly forecasts to
answer three questions about the suitability of Deep Learning and Transfer
Learning to generate predictions of time series. Time series of M4 and M3
competitions were used for the experiments. The results suggest that deep
learning models based on TCN, LSTM, and CNN with transfer learning tend to
surpass the performance prediction of other traditional methods. On the other
hand, TCN and LSTM, trained directly on the target time series, got similar or
better performance than traditional methods for some forecast horizons.

Cross-silo federated learning (FL) has attracted much attention in medical
imaging analysis with deep learning in recent years as it can resolve the
critical issues of insufficient data, data privacy, and training efficiency.
However, there can be a generalization gap between the model trained from FL
and the one from centralized training. This important issue comes from the
non-iid data distribution of the local data in the participating clients and is
well-known as client drift. In this work, we propose a novel training framework
FedSM to avoid the client drift issue and successfully close the generalization
gap compared with the centralized training for medical image segmentation tasks
for the first time. We also propose a novel personalized FL objective
formulation and a new method SoftPull to solve it in our proposed framework
FedSM. We conduct rigorous theoretical analysis to guarantee its convergence
for optimizing the non-convex smooth objective function. Real-world medical
image segmentation experiments using deep FL validate the motivations and
effectiveness of our proposed method.

In this paper, we consider the infinite-horizon reach-avoid zero-sum game
problem, where the goal is to find a set in the state space, referred to as the
reach-avoid set, such that the system starting at a state therein could be
controlled to reach a given target set without violating constraints under the
worst-case disturbance. We address this problem by designing a new value
function with a contracting Bellman backup, where the super-zero level set,
i.e., the set of states where the value function is evaluated to be
non-negative, recovers the reach-avoid set. Building upon this, we prove that
the proposed method can be adapted to compute the viability kernel, or the set
of states which could be controlled to satisfy given constraints, and the
backward reachable set, or the set of states that could be driven towards a
given target set. Finally, we propose to alleviate the curse of dimensionality
issue in high-dimensional problems by extending Conservative Q-Learning, a deep
reinforcement learning technique, to learn a value function such that the
super-zero level set of the learned value function serves as a (conservative)
approximation to the reach-avoid set. Our theoretical and empirical results
suggest that the proposed method could learn reliably the reach-avoid set and
the optimal control policy even with neural network approximation.

Despite considerable progress in maternal healthcare, maternal and perinatal
deaths remain high in low-to-middle income countries. Fetal ultrasound is an
important component of antenatal care, but shortage of adequately trained
healthcare workers has limited its adoption. We developed and validated an
artificial intelligence (AI) system that uses novice-acquired "blind sweep"
ultrasound videos to estimate gestational age (GA) and fetal malpresentation.
We further addressed obstacles that may be encountered in low-resourced
settings. Using a simplified sweep protocol with real-time AI feedback on sweep
quality, we have demonstrated the generalization of model performance to
minimally trained novice ultrasound operators using low cost ultrasound devices
with on-device AI integration. The GA model was non-inferior to standard fetal
biometry estimates with as few as two sweeps, and the fetal malpresentation
model had high AUC-ROCs across operators and devices. Our AI models have the
potential to assist in upleveling the capabilities of lightly trained
ultrasound operators in low resource settings.

In deep learning, embeddings are widely used to represent categorical
entities such as words, apps, and movies. An embedding layer maps each entity
to a unique vector, causing the layer's memory requirement to be proportional
to the number of entities. In the recommendation domain, a given category can
have hundreds of thousands of entities, and its embedding layer can take
gigabytes of memory. The scale of these networks makes them difficult to deploy
in resource constrained environments. In this paper, we propose a novel
approach for reducing the size of an embedding table while still mapping each
entity to its own unique embedding. Rather than maintaining the full embedding
table, we construct each entity's embedding "on the fly" using two separate
embedding tables. The first table employs hashing to force multiple entities to
share an embedding. The second table contains one trainable weight per entity,
allowing the model to distinguish between entities sharing the same embedding.
Since these two tables are trained jointly, the network is able to learn a
unique embedding per entity, helping it maintain a discriminative capability
similar to a model with an uncompressed embedding table. We call this approach
MEmCom (Multi-Embedding Compression). We compare with state-of-the-art model
compression techniques for multiple problem classes including classification
and ranking. On four popular recommender system datasets, MEmCom had a 4%
relative loss in nDCG while compressing the input embedding sizes of our
recommendation models by 16x, 4x, 12x, and 40x. MEmCom outperforms the
state-of-the-art techniques, which achieved 16%, 6%, 10%, and 8% relative loss
in nDCG at the respective compression ratios. Additionally, MEmCom is able to
compress the RankNet ranking model by 32x on a dataset with millions of users'
interactions with games while incurring only a 1% relative loss in nDCG.

Recent works in deep learning have shown that integrating differentiable
physics simulators into the training process can greatly improve the quality of
results. Although this combination represents a more complex optimization task
than supervised neural network training, the same gradient-based optimizers are
typically employed to minimize the loss function. However, the integrated
physics solvers have a profound effect on the gradient flow as manipulating
scales in magnitude and direction is an inherent property of many physical
processes. Consequently, the gradient flow is often highly unbalanced and
creates an environment in which existing gradient-based optimizers perform
poorly. In this work, we analyze the characteristics of both physical and
neural network optimizations to derive a new method that does not suffer from
this phenomenon. Our method is based on a half-inversion of the Jacobian and
combines principles of both classical network and physics optimizers to solve
the combined optimization task. Compared to state-of-the-art neural network
optimizers, our method converges more quickly and yields better solutions,
which we demonstrate on three complex learning problems involving nonlinear
oscillators, the Schroedinger equation and the Poisson problem.

We study the problem of estimating the value of a known smooth function $f$
at an unknown point $\boldsymbol{\mu} \in \mathbb{R}^n$, where each component
$\mu_i$ can be sampled via a noisy oracle. Sampling more frequently components
of $\boldsymbol{\mu}$ corresponding to directions of the function with larger
directional derivatives is more sample-efficient. However, as
$\boldsymbol{\mu}$ is unknown, the optimal sampling frequencies are also
unknown. We design an instance-adaptive algorithm that learns to sample
according to the importance of each coordinate, and with probability at least
$1-\delta$ returns an $\epsilon$ accurate estimate of $f(\boldsymbol{\mu})$. We
generalize our algorithm to adapt to heteroskedastic noise, and prove
asymptotic optimality when $f$ is linear. We corroborate our theoretical
results with numerical experiments, showing the dramatic gains afforded by
adaptivity.

Metagenomics combined with high-resolution sequencing techniques have enabled
researchers to study the genomes of entire microbial communities. Unraveling
interactions between these communities is of vital importance to understand how
microbes influence human health and disease. However, learning these
interactions from microbiome data is challenging, due to the high
dimensionality, discreteness, broad dispersion levels, compositionality and
excess of zero counts that characterize these data. In this paper, we develop a
copula graphical model for structure learning in these settings. In particular,
we advocate the use of discrete Weibull regression for linking the marginal
distributions to external covariates, which are often available in genomic
studies but rarely used for network inference, coupled with a Gaussian copula
to model the joint distribution of the counts. An efficient Bayesian procedure
for structural learning is implemented in the R package BDgraph and returns
inference of the marginals and of the dependency structure, providing
simultaneous differential analysis and graph uncertainty estimates. A
simulation study and a real data analysis of microbiome data show the
usefulness of the proposed approach at inferring networks from high-dimensional
count data in general, and its relevance in the context of microbiota data
analyses in particular.

This paper studies the item-to-item recommendation problem in recommender
systems from a new perspective of metric learning via implicit feedback. We
develop and investigate a personalizable deep metric model that captures both
the internal contents of items and how they were interacted with by users.
There are two key challenges in learning such model. First, there is no
explicit similarity annotation, which deviates from the assumption of most
metric learning methods. Second, these approaches ignore the fact that items
are often represented by multiple sources of meta data and different users use
different combinations of these sources to form their own notion of similarity.
  To address these challenges, we develop a new metric representation embedded
as kernel parameters of a probabilistic model. This helps express the
correlation between items that a user has interacted with, which can be used to
predict user interaction with new items. Our approach hinges on the intuition
that similar items induce similar interactions from the same user, thus fitting
a metric-parameterized model to predict an implicit feedback signal could
indirectly guide it towards finding the most suitable metric for each user. To
this end, we also analyze how and when the proposed method is effective from a
theoretical lens. Its empirical effectiveness is also demonstrated on several

For a very broad range of problems, recommendation algorithms have been
increasingly used over the past decade. In most of these algorithms, the
predictions are built upon user-item affinity scores which are obtained from
high-dimensional embeddings of items and users. In more complex scenarios, with
geometrical or capacity constraints, prediction based on embeddings may not be
sufficient and some additional features should be considered in the design of
the algorithm. In this work, we study the recommendation problem in the setting
where affinities between users and items are based both on their embeddings in
a latent space and on their geographical distance in their underlying euclidean
space (e.g., $\mathbb{R}^2$), together with item capacity constraints. This
framework is motivated by some real-world applications, for instance in
healthcare: the task is to recommend hospitals to patients based on their
location, pathology, and hospital capacities. In these applications, there is
somewhat of an asymmetry between users and items: items are viewed as static
points, their embeddings, capacities and locations constraining the allocation.
Upon the observation of an optimal allocation, user embeddings, items
capacities, and their positions in their underlying euclidean space, our aim is
to recover item embeddings in the latent space; doing so, we are then able to
use this estimate e.g. in order to predict future allocations. We propose an
algorithm (SiMCa) based on matrix factorization enhanced with optimal transport
steps to model user-item affinities and learn item embeddings from observed
data. We then illustrate and discuss the results of such an approach for
hospital recommendation on synthetic data.

Despite significant advances in machine learning, decision-making of
artificial agents is still not perfect and often requires post-hoc human
interventions. If the prediction of a model relies on unreasonable factors it
is desirable to remove their effect. Deep interactive prototype adjustment
enables the user to give hints and correct the model's reasoning. In this
paper, we demonstrate that prototypical-part models are well suited for this
task as their prediction is based on prototypical image patches that can be
interpreted semantically by the user. It shows that even correct
classifications can rely on unreasonable prototypes that result from
confounding variables in a dataset. Hence, we propose simple yet effective
interaction schemes for inference adjustment: The user is consulted
interactively to identify faulty prototypes. Non-object prototypes can be
removed by prototype masking or a custom mode of deselection training.
Interactive prototype rejection allows machine learning na\"{i}ve users to
adjust the logic of reasoning without compromising the accuracy.

Several real-life applications require crafting concise, quantitative scoring
functions (also called rating systems) from measured observations. For example,
an effectiveness score needs to be created for advertising campaigns using a
number of engagement metrics. Experts often need to create such scoring
functions in the absence of labelled data, where the scores need to reflect
business insights and rules as understood by the domain experts. Without a way
to capture these inputs systematically, this becomes a time-consuming process
involving trial and error. In this paper, we introduce a label-free practical
approach to learn a scoring function from multi-dimensional numerical data. The
approach incorporates insights and business rules from domain experts in the
form of easily observable and specifiable constraints, which are used as weak
supervision by a machine learning model. We convert such constraints into loss
functions that are optimized simultaneously while learning the scoring
function. We examine the efficacy of the approach using a synthetic dataset as
well as four real-life datasets, and also compare how it performs vis-a-vis
supervised learning models.

We study learning from user feedback for extractive question answering by
simulating feedback using supervised data. We cast the problem as contextual
bandit learning, and analyze the characteristics of several learning scenarios
with focus on reducing data annotation. We show that systems initially trained
on a small number of examples can dramatically improve given feedback from
users on model-predicted answers, and that one can use existing datasets to
deploy systems in new domains without any annotation, but instead improving the
system on-the-fly via user feedback.

Onboard localization capabilities for planetary rovers to date have used
relative navigation, by integrating combinations of wheel odometry, visual
odometry, and inertial measurements during each drive to track position
relative to the start of each drive. At the end of each drive, a
ground-in-the-loop (GITL) interaction is used to get a position update from
human operators in a more global reference frame, by matching images or local
maps from onboard the rover to orbital reconnaissance images or maps of a large
region around the rover's current position. Autonomous rover drives are limited
in distance so that accumulated relative navigation error does not risk the
possibility of the rover driving into hazards known from orbital images.
However, several rover mission concepts have recently been studied that require
much longer drives between GITL cycles, particularly for the Moon. These
concepts require greater autonomy to minimize GITL cycles to enable such large
range; onboard global localization is a key element of such autonomy. Multiple
techniques have been studied in the past for onboard rover global localization,
but a satisfactory solution has not yet emerged. For the Moon, the ubiquitous
craters offer a new possibility, which involves mapping craters from orbit,
then recognizing crater landmarks with cameras and-or a lidar onboard the
rover. This approach is applicable everywhere on the Moon, does not require
high resolution stereo imaging from orbit as some other approaches do, and has
potential to enable position knowledge with order of 5 to 10 m accuracy at all
times. This paper describes our technical approach to crater-based lunar rover
localization and presents initial results on crater detection using 3D point
cloud data from onboard lidar or stereo cameras, as well as using shading cues
in monocular onboard imagery.

Spectro-temporal dynamics of consonant-vowel (CV) transition regions are
considered to provide robust cues related to articulation. In this work, we
propose an objective measure of precise articulation, dubbed the objective
articulation measure (OAM), by analyzing the CV transitions segmented around
vowel onsets. The OAM is derived based on the posteriors of a convolutional
neural network pre-trained to classify between different consonants using CV
regions as input. We demonstrate the OAM is correlated with perceptual measures
in a variety of contexts including (a) adult dysarthric speech, (b) the speech
of children with cleft lip/palate, and (c) a database of accented English
speech from native Mandarin and Spanish speakers.

Preference-based reinforcement learning (RL) has shown potential for teaching
agents to perform the target tasks without a costly, pre-defined reward
function by learning the reward with a supervisor's preference between the two
agent behaviors. However, preference-based learning often requires a large
amount of human feedback, making it difficult to apply this approach to various
applications. This data-efficiency problem, on the other hand, has been
typically addressed by using unlabeled samples or data augmentation techniques
in the context of supervised learning. Motivated by the recent success of these
approaches, we present SURF, a semi-supervised reward learning framework that
utilizes a large amount of unlabeled samples with data augmentation. In order
to leverage unlabeled samples for reward learning, we infer pseudo-labels of
the unlabeled samples based on the confidence of the preference predictor. To
further improve the label-efficiency of reward learning, we introduce a new
data augmentation that temporally crops consecutive subsequences from the
original behaviors. Our experiments demonstrate that our approach significantly
improves the feedback-efficiency of the state-of-the-art preference-based
method on a variety of locomotion and robotic manipulation tasks.

In stochastic games with incomplete information, the uncertainty is evoked by
the lack of knowledge about a player's own and the other players' types, i.e.
the utility function and the policy space, and also the inherent stochasticity
of different players' interactions. In existing literature, the risk in
stochastic games has been studied in terms of the inherent uncertainty evoked
by the variability of transitions and actions. In this work, we instead focus
on the risk associated with the \textit{uncertainty over types}. We contrast
this with the multi-agent reinforcement learning framework where the other
agents have fixed stationary policies and investigate risk-sensitiveness due to
the uncertainty about the other agents' adaptive policies. We propose
risk-sensitive versions of existing algorithms proposed for risk-neutral
stochastic games, such as Iterated Best Response (IBR), Fictitious Play (FP)
and a general multi-objective gradient approach using dual ascent (DAPG). Our
experimental analysis shows that risk-sensitive DAPG performs better than
competing algorithms for both social welfare and general-sum stochastic games.

Recently, there is a revival of interest in low-rank matrix completion-based
unsupervised learning through the lens of dual-graph regularization, which has
significantly improved the performance of multidisciplinary machine learning
tasks such as recommendation systems, genotype imputation and image inpainting.
While the dual-graph regularization contributes a major part of the success,
computational costly hyper-parameter tunning is usually involved. To circumvent
such a drawback and improve the completion performance, we propose a novel
Bayesian learning algorithm that automatically learns the hyper-parameters
associated with dual-graph regularization, and at the same time, guarantees the
low-rankness of matrix completion. Notably, a novel prior is devised to promote
the low-rankness of the matrix and encode the dual-graph information
simultaneously, which is more challenging than the single-graph counterpart. A
nontrivial conditional conjugacy between the proposed priors and likelihood
function is then explored such that an efficient algorithm is derived under
variational inference framework. Extensive experiments using synthetic and
real-world datasets demonstrate the state-of-the-art performance of the
proposed learning algorithm for various data analysis tasks.

A machine learning (ML) method aided by domain knowledge was proposed to
predict saturated magnetization (Bs) and critical diameter (Dmax) of soft
magnetic metallic glass (MGs). Two datasets were established based on published
experimental works about soft magnetic MGs. A general feature space was
proposed and proved to be adaptive for ML model training for different
prediction tasks. It was found that the predictive performance of ML models was
better than traditional physical knowledge-based estimation methods. In
addition, domain knowledge aided feature selection can greatly reduce the
number of features without significantly reducing the prediction accuracy.
Finally, binary classification of the critical size of soft magnetic metallic
glass was studied.

Time is a fundamental factor during stroke treatments. A fast, automatic
approach that segments the ischemic regions helps treatment decisions. In
clinical use today, a set of color-coded parametric maps generated from
computed tomography perfusion (CTP) images are investigated manually to decide
a treatment plan. We propose an automatic method based on a neural network
using a set of parametric maps to segment the two ischemic regions (core and
penumbra) in patients affected by acute ischemic stroke. Our model is based on
a convolution-deconvolution bottleneck structure with multi-input and slow
fusion. A loss function based on the focal Tversky index addresses the data
imbalance issue. The proposed architecture demonstrates effective performance
and results comparable to the ground truth annotated by neuroradiologists. A
Dice coefficient of 0.81 for penumbra and 0.52 for core over the large vessel
occlusion test set is achieved. The full implementation is available at:
https://git.io/JtFGb.

The generalization mystery in deep learning is the following: Why do
over-parameterized neural networks trained with gradient descent (GD)
generalize well on real datasets even though they are capable of fitting random
datasets of comparable size? Furthermore, from among all solutions that fit the
training data, how does GD find one that generalizes well (when such a
well-generalizing solution exists)? We argue that the answer to both questions
lies in the interaction of the gradients of different examples during training.
Intuitively, if the per-example gradients are well-aligned, that is, if they
are coherent, then one may expect GD to be (algorithmically) stable, and hence
generalize well. We formalize this argument with an easy to compute and
interpretable metric for coherence, and show that the metric takes on very
different values on real and random datasets for several common vision
networks. The theory also explains a number of other phenomena in deep
learning, such as why some examples are reliably learned earlier than others,
why early stopping works, and why it is possible to learn from noisy labels.
Moreover, since the theory provides a causal explanation of how GD finds a
well-generalizing solution when one exists, it motivates a class of simple
modifications to GD that attenuate memorization and improve generalization.
Generalization in deep learning is an extremely broad phenomenon, and
therefore, it requires an equally general explanation. We conclude with a
survey of alternative lines of attack on this problem, and argue that the
proposed approach is the most viable one on this basis.

Cryo-electron tomography (cryo-ET) is an imaging technique that allows
three-dimensional visualization of macro-molecular assemblies under near-native
conditions. Cryo-ET comes with a number of challenges, mainly low
signal-to-noise and inability to obtain images from all angles. Computational
methods are key to analyze cryo-electron tomograms.
  To promote innovation in computational methods, we generate a novel simulated
dataset to benchmark different methods of localization and classification of
biological macromolecules in tomograms. Our publicly available dataset contains
ten tomographic reconstructions of simulated cell-like volumes. Each volume
contains twelve different types of complexes, varying in size, function and
  In this paper, we have evaluated seven different methods of finding and
classifying proteins. Seven research groups present results obtained with
learning-based methods and trained on the simulated dataset, as well as a
baseline template matching (TM), a traditional method widely used in cryo-ET
research. We show that learning-based approaches can achieve notably better
localization and classification performance than TM. We also experimentally
confirm that there is a negative relationship between particle size and
performance for all methods.

In modern industrial settings with small batch sizes it should be easy to set
up a robot system for a new task. Strategies exist, e.g. the use of skills, but
when it comes to handling forces and torques, these systems often fall short.
We introduce an approach that provides a combination of task-level planning
with targeted learning of scenario-specific parameters for skill-based systems.
We propose the following pipeline: (1) the user provides a task goal in the
planning language PDDL, (2) a plan (i.e., a sequence of skills) is generated
and the learnable parameters of the skills are automatically identified. An
operator then chooses (3) reward functions and hyperparameters for the learning
process. Two aspects of our methodology are critical: (a) learning is tightly
integrated with a knowledge framework to support symbolic planning and to
provide priors for learning, (b) using multi-objective optimization. This can
help to balance key performance indicators (KPIs) such as safety and task
performance since they can often affect each other. We adopt a multi-objective
Bayesian optimization approach and learn entirely in simulation. We demonstrate
the efficacy and versatility of our approach by learning skill parameters for
two different contact-rich tasks. We show their successful execution on a real
7-DOF KUKA-iiwa manipulator and outperform the manual parameterization by human
robot operators.

Semi-supervised semantic segmentation learns from small amounts of labelled
images and large amounts of unlabelled images, which has witnessed impressive
progress with the recent advance of deep neural networks. However, it often
suffers from severe class-bias problem while exploring the unlabelled images,
largely due to the clear pixel-wise class imbalance in the labelled images.
This paper presents an unbiased subclass regularization network (USRN) that
alleviates the class imbalance issue by learning class-unbiased segmentation
from balanced subclass distributions. We build the balanced subclass
distributions by clustering pixels of each original class into multiple
subclasses of similar sizes, which provide class-balanced pseudo supervision to
regularize the class-biased segmentation. In addition, we design an
entropy-based gate mechanism to coordinate learning between the original
classes and the clustered subclasses which facilitates subclass regularization
effectively by suppressing unconfident subclass predictions. Extensive
experiments over multiple public benchmarks show that USRN achieves superior
performance as compared with the state-of-the-art.

Retrieving accurate semantic information in challenging high dynamic range
(HDR) and high-speed conditions remains an open challenge for image-based
algorithms due to severe image degradations. Event cameras promise to address
these challenges since they feature a much higher dynamic range and are
resilient to motion blur. Nonetheless, semantic segmentation with event cameras
is still in its infancy which is chiefly due to the lack of high-quality,
labeled datasets. In this work, we introduce ESS (Event-based Semantic
Segmentation), which tackles this problem by directly transferring the semantic
segmentation task from existing labeled image datasets to unlabeled events via
unsupervised domain adaptation (UDA). Compared to existing UDA methods, our
approach aligns recurrent, motion-invariant event embeddings with image
embeddings. For this reason, our method neither requires video data nor
per-pixel alignment between images and events and, crucially, does not need to
hallucinate motion from still images. Additionally, we introduce DSEC-Semantic,
the first large-scale event-based dataset with fine-grained labels. We show
that using image labels alone, ESS outperforms existing UDA approaches, and
when combined with event labels, it even outperforms state-of-the-art
supervised approaches on both DDD17 and DSEC-Semantic. Finally, ESS is
general-purpose, which unlocks the vast amount of existing labeled image
datasets and paves the way for new and exciting research directions in new
fields previously inaccessible for event cameras.

Extracting blood vessels from retinal fundus images plays a decisive role in
diagnosing the progression in pertinent diseases. In medical image analysis,
vessel extraction is a semantic binary segmentation problem, where blood
vasculature needs to be extracted from the background. Here, we present an
image enhancement technique based on the morphological preprocessing coupled
with a scaled U-net architecture. Despite a relatively less number of trainable
network parameters, the scaled version of U-net architecture provides better
performance compare to other methods in the domain. We validated the proposed
method on retinal fundus images from the DRIVE database. A significant
improvement as compared to the other algorithms in the domain, in terms of the
area under ROC curve (>0.9762) and classification accuracy (>95.47%) are
evident from the results. Furthermore, the proposed method is resistant to the
central vessel reflex while sensitive to detect blood vessels in the presence
of background items viz. exudates, optic disc, and fovea.

This is a report on the NSF Future Directions Workshop on Automatic
Evaluation of Dialog. The workshop explored the current state of the art along
with its limitations and suggested promising directions for future work in this
important and very rapidly changing area of research.

Nowadays, machine and deep learning techniques are widely used in different
areas, ranging from economics to biology. In general, these techniques can be
used in two ways: trying to adapt well-known models and architectures to the
available data, or designing custom architectures. In both cases, to speed up
the research process, it is useful to know which type of models work best for a
specific problem and/or data type. By focusing on EEG signal analysis, and for
the first time in literature, in this paper a benchmark of machine and deep
learning for EEG signal classification is proposed. For our experiments we used
the four most widespread models, i.e., multilayer perceptron, convolutional
neural network, long short-term memory, and gated recurrent unit, highlighting
which one can be a good starting point for developing EEG classification
models.

Spiking neural networks (SNNs), as one of the brain-inspired models, has
spatio-temporal information processing capability, low power feature, and high
biological plausibility. The effective spatio-temporal feature makes it
suitable for event streams classification. However, neuromorphic datasets, such
as N-MNIST, CIFAR10-DVS, DVS128-gesture, need to aggregate individual events
into frames with a new higher temporal resolution for event stream
classification, which causes high training and inference latency. In this work,
we proposed a spatio-temporal compression method to aggregate individual events
into a few time steps of synaptic current to reduce the training and inference
latency. To keep the accuracy of SNNs under high compression ratios, we also
proposed a synaptic convolutional block to balance the dramatic change between
adjacent time steps. And multi-threshold Leaky Integrate-and-Fire (LIF) with
learnable membrane time constant is introduced to increase its information
processing capability. We evaluate the proposed method for event streams
classification tasks on neuromorphic N-MNIST, CIFAR10-DVS, DVS128 gesture
datasets. The experiment results show that our proposed method outperforms the
state-of-the-art accuracy on nearly all datasets, using fewer time steps.

In the medical domain, different computer-aided diagnosis systems have been
proposed to extract blood vessels from retinal fundus images for the clinical
treatment of vascular diseases. Accurate extraction of blood vessels from the
fundus images using a computer-generated method can help the clinician to
produce timely and accurate reports for the patient suffering from these
diseases. In this article, we integrate top-hat based preprocessing approach
with fine-tuned B-COSFIRE filter to achieve more accurate segregation of blood
vessel pixels from the background. The use of top-hat transformation in the
preprocessing stage enhances the efficacy of the algorithm to extract blood
vessels in presence of structures like fovea, exudates, haemorrhages, etc.
Furthermore, to reduce the false positives, small clusters of blood vessel
pixels are removed in the postprocessing stage. Further, we find that the
proposed algorithm is more efficient as compared to various modern algorithms
reported in the literature.

The conversational recommender systems (CRSs) have received extensive
attention in recent years. However, most of the existing works focus on various
deep learning models, which are largely limited by the requirement of
large-scale human-annotated datasets. Such methods are not able to deal with
the cold-start scenarios in industrial products. To alleviate the problem, we
propose FORCE, a Framework Of Rule-based Conversational Recommender system that
helps developers to quickly build CRS bots by simple configuration. We conduct
experiments on two datasets in different languages and domains to verify its
effectiveness and usability.

As the volume of Electronic Health Records (EHR) sharply grows, there has
been emerging interest in learning the representation of EHR for healthcare
applications. Representation learning of EHR requires appropriate modeling of
the two dominant modalities in EHR: structured data and unstructured text. In
this paper, we present MedGTX, a pre-trained model for multi-modal
representation learning of the structured and textual EHR data. MedGTX uses a
novel graph encoder to exploit the graphical nature of structured EHR data, and
a text encoder to handle unstructured text, and a cross-modal encoder to learn
a joint representation space. We pre-train our model through four proxy tasks
on MIMIC-III, an open-source EHR data, and evaluate our model on two clinical
benchmarks and three novel downstream tasks which tackle real-world problems in
EHR data. The results consistently show the effectiveness of pre-training the
model for joint representation of both structured and unstructured information
from EHR. Given the promising performance of MedGTX, we believe this work opens
a new door to jointly understanding the two fundamental modalities of EHR data.

Hyperspectral images of a scene or object are a rich data source, often
encoding a hundred or more spectral bands of reflectance at each pixel. Despite
being very high-dimensional, these images typically encode latent
low-dimensional structure that can be exploited for material discrimination.
However, due to an inherent trade-off between spectral and spatial resolution,
many hyperspectral images are generated at a coarse spatial scale, and single
pixels may correspond to spatial regions containing multiple materials. This
article introduces the Diffusion and Volume maximization-based Image Clustering
(D-VIC) algorithm for unsupervised material discrimination. D-VIC locates
cluster modes - high-density, high-purity pixels in the hyperspectral image
that are far in diffusion distance (a data-dependent distance metric) from
other high-density, high-purity pixels - and assigns these pixels unique
labels, as these points are meant to exemplify underlying material structure.
Non-modal pixels are labeled according to their diffusion distance nearest
neighbor of higher density and purity that is already labeled. By directly
incorporating pixel purity into its modal and non-modal labeling, D-VIC
upweights pixels that correspond to a spatial region containing just a single
material, yielding more interpretable clusterings. D-VIC is shown to outperform
baseline and comparable state-of-the-art methods in extensive numerical
experiments on a range of hyperspectral images, implying that it is
well-equipped for material discrimination and clustering of these data.

As the demand for mobility in our society seems to increase, the various
issues centered on urban mobility are among those that worry most city
inhabitants in this planet. For instance, how to go from A to B in an efficient
(but also less stressful) way? These questions and concerns have not changed
even during the covid-19 pandemic; on the contrary, as the current stand,
people who are avoiding public transportation are only contributing to an
increase in the vehicular traffic. The are of intelligent transportation
systems (ITS) aims at investigating how to employ information and communication
technologies to problems related to transportation. This may mean monitoring
and managing the infrastructure (e.g., traffic roads, traffic signals, etc.).
However, currently, ITS is also targeting the management of demand. In this
panorama, artificial intelligence plays an important role, especially with the
advances in machine learning that translates in the use of computational
vision, connected and autonomous vehicles, agent-based simulation, among
others. In the present work, a survey of several works developed by our group
are discussed in a holistic perspective, i.e., they cover not only the supply
side (as commonly found in ITS works), but also the demand side, and, in an
novel perspective, the integration of both.

Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin
of such high optical extinction is not well understood. In this work, we
investigate the absorption strength of NFAs by building a database of
time-dependent density functional theory (TDDFT) calculations of ~500
pi-conjugated molecules. The calculations are first validated by comparison
with experimental measurements on liquid and solid state using common fullerene
and non-fullerene acceptors. We find that the molar extinction coefficient
({\epsilon}_(d,max)) shows reasonable agreement between calculation in vacuum
and experiment for molecules in solution, highlighting the effectiveness of
TDDFT for predicting optical properties of organic pi-conjugated molecules. We
then perform a statistical analysis based on molecular descriptors to identify
which features are important in defining the absorption strength. This allows
us to identify structural features that are correlated with high absorption
strength in NFAs and could be used to guide molecular design: highly absorbing
NFAs should possess a planar, linear, and fully conjugated molecular backbone
with highly polarisable heteroatoms. We then exploit a random decision forest
to draw predictions for {\epsilon}_(d,max) using a computational framework
based on extended tight-binding Hamiltonians, which shows reasonable predicting
accuracy with lower computational cost than TDDFT. This work provides a general
understanding of the relationship between molecular structure and absorption
strength in pi-conjugated organic molecules, including NFAs, while introducing
predictive machine-learning models of low computational cost.

In the current Industrial 4.0 revolution, Prognostics and Health Management
(PHM) is an emerging field of research. The difficulty of obtaining data from
electromechanical systems in an industrial setting increases proportionally
with the scale and accessibility of the automated industry, resulting in a less
interpolated PHM system. To put it another way, the development of an accurate
PHM system for each industrial system necessitates a unique dataset acquired
under specified conditions. In most circumstances, obtaining this one-of-a-kind
dataset is difficult, and the resulting dataset has a significant imbalance, a
lack of certain useful information, and multi-domain knowledge. To address
this, this paper provides a fault detection and diagnosis system that evaluates
and pre-processes Imbalanced, Scarce, Multi-Domain (ISMD) data acquired from an
industrial robot utilizing Signal Processing (SP) techniques and Deep
Learning-based (DL) domain knowledge transfer. The domain knowledge transfer is
used to produce a synthetic dataset with a high interpolation rate that
contains all the useful information about each domain. For domain knowledge
transfer and data generation, Continuous Wavelet Transform (CWT) with
Generative Adversarial Network (GAN) was used, as well as Convolutional Neural
Network (CNN) to test the suggested methodology using transfer learning and
categorize several faults. The proposed methodology was tested on a real
experimental bench that included an industrial robot created by Hyundai
Robotics Co. This development resulted in a satisfactory resolution with 99.7%
(highest) classification accuracy achieved by transfer learning on several CNN
benchmark models.

Task-oriented personal assistants enable people to interact with a host of
devices and services using natural language. One of the challenges of making
neural dialogue systems available to more users is the lack of training data
for all but a few languages. Zero-shot methods try to solve this issue by
acquiring task knowledge in a high-resource language such as English with the
aim of transferring it to the low-resource language(s). To this end, we
introduce CrossAligner, the principal method of a variety of effective
approaches for zero-shot cross-lingual transfer based on learning alignment
from unlabelled parallel data. We present a quantitative analysis of individual
methods as well as their weighted combinations, several of which exceed
state-of-the-art (SOTA) scores as evaluated across nine languages, fifteen test
sets and three benchmark multilingual datasets. A detailed qualitative error
analysis of the best methods shows that our fine-tuned language models can
zero-shot transfer the task knowledge better than anticipated.

Over the past years, the ever-growing trend on data storage demand, more
specifically for "cold" data (rarely accessed data), has motivated research for
alternative systems of data storage. Because of its biochemical
characteristics, synthetic DNA molecules are now considered as serious
candidates for this new kind of storage. This paper presents some results on
lossy image compression methods based on convolutional autoencoders adapted to
DNA data storage.
  The model architectures presented here have been designed to efficiently
compress images, encode them into a quaternary code, and finally store them
into synthetic DNA molecules. This work also aims at making the compression
models better fit the problematics that we encounter when storing data into
DNA, namely the fact that the DNA writing, storing and reading methods are
error prone processes. The main take away of this kind of compressive
autoencoder is our quantization and the robustness to substitution errors
thanks to the noise model that we use during training.

Functional properties of transition-metal oxides strongly depend on
crystallographic defects. In transition-metal-oxide electrocatalysts such as
SrIrO3 (SIO), crystallographic lattice deviations can affect ionic diffusion
and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging
of local structural distortions across an extended spatial region of thin
samples. Line defects remain challenging to detect and localize using
nanodiffraction, due to their weak diffuse scattering. Here we apply an
unsupervised machine learning clustering algorithm to isolate the low-intensity
diffuse scattering in as-grown and alkaline-treated thin epitaxially strained
SIO films. We pinpoint the defect locations, find additional strain variation
in the morphology of electrochemically cycled SIO, and interpret the defect
type by analyzing the diffraction profile through clustering. Our findings
demonstrate the use of a machine learning clustering algorithm for identifying
and characterizing hard-to-find crystallographic defects in thin films of
electrocatalysts and highlight the potential to study electrochemical reactions
at defect sites in operando experiments.

Machine learning models often fail to generalize well under distributional
shifts. Understanding and overcoming these failures have led to a research
field of Out-of-Distribution (OOD) generalization. Despite being extensively
studied for static computer vision tasks, OOD generalization has been
underexplored for time series tasks. To shine light on this gap, we present
WOODS: eight challenging open-source time series benchmarks covering a diverse
range of data modalities, such as videos, brain recordings, and sensor signals.
We revise the existing OOD generalization algorithms for time series tasks and
evaluate them using our systematic framework. Our experiments show a large room
for improvement for empirical risk minimization and OOD generalization
algorithms on our datasets, thus underscoring the new challenges posed by time
series tasks. Code and documentation are available at
https://woods-benchmarks.github.io .

Biomedical knowledge graphs (BioMedKGs) are essential infrastructures for
biomedical and healthcare big data and artificial intelligence (AI),
facilitating natural language processing, model development, and data exchange.
For decades, these knowledge graphs have been developed via expert curation;
however, this method can no longer keep up with today's AI development, and a
transition to algorithmically generated BioMedKGs is necessary. In this work,
we introduce the Biomedical Informatics Ontology System (BIOS), the first
large-scale publicly available BioMedKG generated completely by machine
learning algorithms. BIOS currently contains 4.1 million concepts, 7.4 million
terms in two languages, and 7.3 million relation triplets. We present the
methodology for developing BIOS, including the curation of raw biomedical
terms, computational identification of synonymous terms and aggregation of
these terms to create concept nodes, semantic type classification of the
concepts, relation identification, and biomedical machine translation. We
provide statistics on the current BIOS content and perform preliminary
assessments of term quality, synonym grouping, and relation extraction. The
results suggest that machine learning-based BioMedKG development is a viable
alternative to traditional expert curation.

The removal of non-brain signal from magnetic resonance imaging (MRI) data,
known as skull-stripping, is an integral component of many neuroimage analysis
streams. Despite their abundance, popular classical skull-stripping methods are
usually tailored to images with specific acquisition properties, namely
near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are
prevalent in research settings. As a result, existing tools tend to adapt
poorly to other image types, such as stacks of thick slices acquired with fast
spin-echo (FSE) MRI that are common in the clinic. While learning-based
approaches for brain extraction have gained traction in recent years, these
methods face a similar burden, as they are only effective for image types seen
during the training procedure. To achieve robust skull-stripping across a
landscape of imaging protocols, we introduce SynthStrip, a rapid,
learning-based brain-extraction tool. By leveraging anatomical segmentations to
generate an entirely synthetic training dataset with anatomies, intensity
distributions, and artifacts that far exceed the realistic range of medical
images, SynthStrip learns to successfully generalize to a variety of real
acquired brain images, removing the need for training data with target
contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image
acquisitions and resolutions across subject populations, ranging from newborn
to adult. We show substantial improvements in accuracy over popular
skull-stripping baselines -- all with a single trained model. Our method and
labeled evaluation data are available at https://w3id.org/synthstrip.

Everyone wants to write beautiful and correct text, yet the lack of language
skills, experience, or hasty typing can result in errors. By employing the
recent advances in transformer architectures, we construct a grammatical error
correction model for Lithuanian, the language rich in archaic features. We
compare subword and byte-level approaches and share our best trained model,
achieving F$_{0.5}$=0.92, and accompanying code, in an online open-source

By driving optimizers to converge to flat minima, sharpness-aware
minimization (SAM) has shown the power to improve the model generalization.
However, SAM requires to perform two forward-backward propagations for one
parameter update, which largely burdens the practical computation. In this
paper, we propose a novel and efficient training scheme, called Stochastic
Scheduled SAM (SS-SAM). Specifically, in SS-SAM, the optimizer is arranged by a
predefined scheduling function to perform a random trial at each update step,
which would randomly select to perform the SGD optimization or the SAM
optimization. In this way, the overall count of propagation pair could be
largely reduced. Then, we empirically investigate four typical types of
scheduling functions, and demonstrates the computational efficiency and their
impact on model performance respectively. We show that with proper scheduling
functions, models could be trained to achieve comparable or even better
performance with much lower computation cost compared to models trained with
only SAM training scheme.

Consistency is one of the keys to maintainable source code and hence a
successful software project. We propose a novel method of extracting the intent
of programmers from source code of a large project (~300kLOC) and checking the
semantic consistency of its variable names. Our system learns a
project-specific naming convention for variables based on its role solely from
source code, and suggest alternatives when it violates its internal
consistency. The system can also show the reasoning why a certain variable
should be named in a specific way. The system does not rely on any external
knowledge. We applied our method to 12 open-source projects and evaluated its
results with human reviewers. Our system proposed alternative variable names
for 416 out of 1080 (39%) instances that are considered better than ones
originally used by the developers. Based on the results, we created patches to
correct the inconsistent names and sent them to its developers. Three
open-source projects adopted it.

Recently, medical report generation, which aims to automatically generate a
long and coherent descriptive paragraph of a given medical image, has received
growing research interests. Different from the general image captioning tasks,
medical report generation is more challenging for data-driven neural models.
This is mainly due to 1) the serious data bias: the normal visual regions
dominate the dataset over the abnormal visual regions, and 2) the very long
sequence. To alleviate above two problems, we propose an AlignTransformer
framework, which includes the Align Hierarchical Attention (AHA) and the
Multi-Grained Transformer (MGT) modules: 1) AHA module first predicts the
disease tags from the input image and then learns the multi-grained visual
features by hierarchically aligning the visual regions and disease tags. The
acquired disease-grounded visual features can better represent the abnormal
regions of the input image, which could alleviate data bias problem; 2) MGT
module effectively uses the multi-grained features and Transformer framework to
generate the long medical report. The experiments on the public IU-Xray and
MIMIC-CXR datasets show that the AlignTransformer can achieve results
competitive with state-of-the-art methods on the two datasets. Moreover, the
human evaluation conducted by professional radiologists further proves the

Variational autoencoders (VAEs) are latent variable models that can generate
complex objects and provide meaningful latent representations. Moreover, they
could be further used in downstream tasks such as classification. As previous
work has shown, one can easily fool VAEs to produce unexpected latent
representations and reconstructions for a visually slightly modified input.
Here, we examine several objective functions for adversarial attack
construction proposed previously and present a solution to alleviate the effect
of these attacks. Our method utilizes the Markov Chain Monte Carlo (MCMC)
technique in the inference step that we motivate with a theoretical analysis.
Thus, we do not incorporate any extra costs during training, and the
performance on non-attacked inputs is not decreased. We validate our approach
on a variety of datasets (MNIST, Fashion MNIST, Color MNIST, CelebA) and VAE
configurations ($\beta$-VAE, NVAE, $\beta$-TCVAE), and show that our approach
consistently improves the model robustness to adversarial attacks.

Due to the evolution of the Web and social network platforms it becomes very
easy to disseminate the information. Peoples are creating and sharing more
information than ever before, which may be misleading, misinformation or fake
information. Fake news detection is a crucial and challenging task due to the
unstructured nature of the available information. In the recent years,
researchers have provided significant solutions to tackle with the problem of
fake news detection, but due to its nature there are still many open issues. In
this paper, we have proposed majority voting approach to detect fake news
articles. We have used different textual properties of fake and real news. We
have used publicly available fake news dataset, comprising of 20,800 news
articles among which 10,387 are real and 10,413 are fake news labeled as binary
0 and 1. For the evaluation of our approach, we have used commonly used machine
learning classifiers like, Decision Tree, Logistic Regression, XGBoost, Random
Forest, Extra Trees, AdaBoost, SVM, SGD and Naive Bayes. Using the
aforementioned classifiers, we built a multi-model fake news detection system
using Majority Voting technique to achieve the more accurate results. The
experimental results show that, our proposed approach achieved accuracy of
96.38%, precision of 96%, recall of 96% and F1-measure of 96%. The evaluation
confirms that, Majority Voting technique achieved more acceptable results as
compare to individual learning technique.

Since Protostars and Planets VI (PPVI), our knowledge of the global
properties of protoplanetary and debris disks, as well as of young stars, has
dramatically improved. At the time of PPVI, mm-observations and optical to
near-infrared spectroscopic surveys were largely limited to the Taurus
star-forming region, especially of its most massive disk and stellar
population. Now, near-complete surveys of multiple star-forming regions cover
both spectroscopy of young stars and mm interferometry of their protoplanetary
disks. This provides an unprecedented statistical sample of stellar masses and
mass accretion rates, as well as disk masses and radii, for almost 1000 young
stellar objects within 300 pc from us, while also sampling different
evolutionary stages, ages, and environments. At the same time, surveys of
debris disks are revealing the bulk properties of this class of more evolved
objects. This chapter reviews the statistics of these measured global star and
disk properties and discusses their constraints on theoretical models
describing global disk evolution. Our comparisons of observations to
theoretical model predictions extends beyond the traditional viscous evolution
framework to include analytical descriptions of magnetic wind effects. Finally,
we discuss how recent observational results can provide a framework for models
of planet population synthesis and planet formation.

Most recent style-transfer techniques based on generative architectures are
able to obtain synthetic multimedia contents, or commonly called deepfakes,
with almost no artifacts. Researchers already demonstrated that synthetic
images contain patterns that can determine not only if it is a deepfake but
also the generative architecture employed to create the image data itself.
These traces can be exploited to study problems that have never been addressed
in the context of deepfakes. To this aim, in this paper a first approach to
investigate the image ballistics on deepfake images subject to style-transfer
manipulations is proposed. Specifically, this paper describes a study on
detecting how many times a digital image has been processed by a generative
architecture for style transfer. Moreover, in order to address and study
accurately forensic ballistics on deepfake images, some mathematical properties
of style-transfer operations were investigated.

Currently, autonomous vehicles are able to drive more naturally based on the
driving policies learned from millions of driving miles in real environments.
However, to further improve the automation level of vehicles is a challenging
task, especially in the case of multi-vehicle cooperation. In recent heated
discussions of 6G, millimeter-wave (mmWave) and terahertz (THz) bands are
deemed to play important roles in new radio communication architectures and
algorithms. To enable reliable autonomous driving in 6G, in this paper, we
envision collaborative autonomous driving, a new framework that jointly
controls driving topology and formulate vehicular networks in the mmWave/THz
bands. As a swarm intelligence system, the collaborative driving scheme goes
beyond existing autonomous driving patterns based on single-vehicle
intelligence in terms of safety and efficiency. With efficient data sharing,
the proposed framework is able to achieve cooperative sensing and load
balancing so that improve sensing efficiency with saved computational
resources. To deal with the new challenges in the collaborative driving
framework, we further illustrate two promising approaches for mmWave/THz-based
vehicle-to-vehicle (V2V) communications. Finally, we discuss several potential
open research problems for the proposed collaborative driving scheme.

Motion planning in the configuration space (C-space) induces benefits, such
as smooth trajectories. It becomes more complex as the degrees of freedom (DOF)
increase. This is due to the direct relation between the dimensionality of the
search space and the DOF. Self-organizing neural networks (SONN) and their
famous candidate, the Self-Organizing Map, have been proven to be useful tools
for C-space reduction while preserving its underlying topology, as presented in
[29]. In this work, we extend our previous study with additional models and
adapt the approach from human motion data towards robots' kinematics. The
evaluation includes the best performant models from [29] and three additional
SONN architectures, representing the consequent continuation of this previous
work. Generated Trajectories, planned with the different SONN models, were
successfully tested in a robot simulation.

Simultaneous sparse approximation (SSA) seeks to represent a set of dependent
signals using sparse vectors with identical supports. The SSA model has been
used in various signal and image processing applications involving multiple
correlated input signals. In this paper, we propose algorithms for
convolutional SSA (CSSA) based on the alternating direction method of
multipliers. Specifically, we address the CSSA problem with different sparsity
structures and the convolutional feature learning problem in multimodal
data/signals based on the SSA model. We evaluate the proposed algorithms by
applying them to multimodal and multifocus image fusion problems.

This paper stresses the importance of biases in the field of artificial
intelligence (AI) in two regards. First, in order to foster efficient
algorithmic decision-making in complex, unstable, and uncertain real-world
environments, we argue for the structurewise implementation of human cognitive
biases in learning algorithms. Secondly, we argue that in order to achieve
ethical machine behavior, filter mechanisms have to be applied for selecting
biased training stimuli that represent social or behavioral traits that are
ethically desirable. We use insights from cognitive science as well as ethics
and apply them to the AI field, combining theoretical considerations with seven
case studies depicting tangible bias implementation scenarios. Ultimately, this
paper is the first tentative step to explicitly pursue the idea of a
re-evaluation of the ethical significance of machine biases, as well as putting
the idea forth to implement cognitive biases into machines.

State-of-the-art document dewarping techniques learn to predict 3-dimensional
information of documents which are prone to errors while dealing with documents
with irregular distortions or large variations in depth. This paper presents
FDRNet, a Fourier Document Restoration Network that can restore documents with
different distortions and improve document recognition in a reliable and
simpler manner. FDRNet focuses on high-frequency components in the Fourier
space that capture most structural information but are largely free of
degradation in appearance. It dewarps documents by a flexible Thin-Plate Spline
transformation which can handle various deformations effectively without
requiring deformation annotations in training. These features allow FDRNet to
learn from a small amount of simply labeled training images, and the learned
model can dewarp documents with complex geometric distortion and recognize the
restored texts accurately. To facilitate document restoration research, we
create a benchmark dataset consisting of over one thousand camera documents
with different types of geometric and photometric distortion. Extensive
experiments show that FDRNet outperforms the state-of-the-art by large margins
on both dewarping and text recognition tasks. In addition, FDRNet requires a
small amount of simply labeled training data and is easy to deploy.

The ability to discover new transients via image differencing without direct
human intervention is an important task in observational astronomy. For these
kind of image classification problems, machine Learning techniques such as
Convolutional Neural Networks (CNNs) have shown remarkable success. In this
work, we present the results of an automated transient identification on images
with CNNs for an extant dataset from the Dark Energy Survey Supernova program
(DES-SN), whose main focus was on using Type Ia supernovae for cosmology. By
performing an architecture search of CNNs, we identify networks that
efficiently select non-artifacts (e.g. supernovae, variable stars, AGN, etc.)
from artifacts (image defects, mis-subtractions, etc.), achieving the
efficiency of previous work performed with random Forests, without the need to
expend any effort in feature identification. The CNNs also help us identify a
subset of mislabeled images. Performing a relabeling of the images in this
subset, the resulting classification with CNNs is significantly better than
previous results.

Affordance grounding, a task to ground (i.e., localize) action possibility
region in objects, which faces the challenge of establishing an explicit link
with object parts due to the diversity of interactive affordance. Human has the
ability that transform the various exocentric interactions to invariant
egocentric affordance so as to counter the impact of interactive diversity. To
empower an agent with such ability, this paper proposes a task of affordance
grounding from exocentric view, i.e., given exocentric human-object interaction
and egocentric object images, learning the affordance knowledge of the object
and transferring it to the egocentric image using only the affordance label as
supervision. To this end, we devise a cross-view knowledge transfer framework
that extracts affordance-specific features from exocentric interactions and
enhances the perception of affordance regions by preserving affordance
correlation. Specifically, an Affordance Invariance Mining module is devised to
extract specific clues by minimizing the intra-class differences originated
from interaction habits in exocentric images. Besides, an Affordance
Co-relation Preserving strategy is presented to perceive and localize
affordance by aligning the co-relation matrix of predicted results between the
two views. Particularly, an affordance grounding dataset named AGD20K is
constructed by collecting and labeling over 20K images from 36 affordance
categories. Experimental results demonstrate that our method outperforms the
representative models in terms of objective metrics and visual quality. Code:
github.com/lhc1224/Cross-View-AG.

Automatic Music Transcription (AMT) has been recognized as a key enabling
technology with a wide range of applications. Given the task's complexity, best
results have typically been reported for systems focusing on specific settings,
e.g. instrument-specific systems tend to yield improved results over
instrument-agnostic methods. Similarly, higher accuracy can be obtained when
only estimating frame-wise $f_0$ values and neglecting the harder note event
detection. Despite their high accuracy, such specialized systems often cannot
be deployed in the real-world. Storage and network constraints prohibit the use
of multiple specialized models, while memory and run-time constraints limit
their complexity. In this paper, we propose a lightweight neural network for
musical instrument transcription, which supports polyphonic outputs and
generalizes to a wide variety of instruments (including vocals). Our model is
trained to jointly predict frame-wise onsets, multipitch and note activations,
and we experimentally show that this multi-output structure improves the
resulting frame-level note accuracy. Despite its simplicity, benchmark results
show our system's note estimation to be substantially better than a comparable
baseline, and its frame-level accuracy to be only marginally below those of
specialized state-of-the-art AMT systems. With this work we hope to encourage
the community to further investigate low-resource, instrument-agnostic AMT
systems.

We propose a theoretical framework of multi-way similarity to model
real-valued data into hypergraphs for clustering via spectral embedding. For
graph cut based spectral clustering, it is common to model real-valued data
into graph by modeling pairwise similarities using kernel function. This is
because the kernel function has a theoretical connection to the graph cut. For
problems where using multi-way similarities are more suitable than pairwise
ones, it is natural to model as a hypergraph, which is generalization of a
graph. However, although the hypergraph cut is well-studied, there is not yet
established a hypergraph cut based framework to model multi-way similarity. In
this paper, we formulate multi-way similarities by exploiting the theoretical
foundation of kernel function. We show a theoretical connection between our
formulation and hypergraph cut in two ways, generalizing both weighted kernel
$k$-means and the heat kernel, by which we justify our formulation. We also
provide a fast algorithm for spectral clustering. Our algorithm empirically
shows better performance than existing graph and other heuristic modeling
methods.

Transformers have gained much attention by outperforming convolutional neural
networks in many 2D vision tasks. However, they are known to have
generalization problems and rely on massive-scale pre-training and
sophisticated training techniques. When applying to 3D tasks, the irregular
data structure and limited data scale add to the difficulty of transformer's
application. We propose CodedVTR (Codebook-based Voxel TRansformer), which
improves data efficiency and generalization ability for 3D sparse voxel
transformers. On the one hand, we propose the codebook-based attention that
projects an attention space into its subspace represented by the combination of
"prototypes" in a learnable codebook. It regularizes attention learning and
improves generalization. On the other hand, we propose geometry-aware
self-attention that utilizes geometric information (geometric pattern, density)
to guide attention learning. CodedVTR could be embedded into existing sparse
convolution-based methods, and bring consistent performance improvements for
indoor and outdoor 3D semantic segmentation tasks

Up to 90 % of patients with Parkinson's disease (PD) suffer from hypokinetic
dysarthria (HD). In this work, we analysed the power of conventional speech
features quantifying imprecise articulation, dysprosody, speech dysfluency and
speech quality deterioration extracted from a specialized poem recitation task
to discriminate dysarthric and healthy speech. For this purpose, 152 speakers
(53 healthy speakers, 99 PD patients) were examined. Only mildly strong
correlation between speech features and clinical status of the speakers was
observed. In the case of univariate classification analysis, sensitivity of
62.63% (imprecise articulation), 61.62% (dysprosody), 71.72% (speech
dysfluency) and 59.60% (speech quality deterioration) was achieved.
Multivariate classification analysis improved the classification performance.
Sensitivity of 83.42% using only two features describing imprecise articulation
and speech quality deterioration in HD was achieved. We showed the promising
potential of the selected speech features and especially the use of poem
recitation task to quantify and identify HD in PD.

This paper proposes a supervised classification algorithm capable of
continual learning by utilizing an Adaptive Resonance Theory (ART)-based
growing self-organizing clustering algorithm. The ART-based clustering
algorithm is theoretically capable of continual learning, and the proposed
algorithm independently applies it to each class of training data for
generating classifiers. Whenever an additional training data set from a new
class is given, a new ART-based clustering will be defined in a different
learning space. Thanks to the above-mentioned features, the proposed algorithm
realizes continual learning capability. Simulation experiments showed that the
proposed algorithm has superior classification performance compared with
state-of-the-art clustering-based classification algorithms capable of
continual learning.

Biomedical systems are regulated by interacting mechanisms that operate
across multiple spatial and temporal scales and produce biosignals with linear
and non-linear information inside. In this sense entropy could provide a useful
measure about disorder in the system, lack of information in time-series and/or
irregularity of the signals. Essential tremor (ET) is the most common movement
disorder, being 20 times more common than Parkinson's disease, and 50-70% of
this disease cases are estimated to be genetic in origin. Archimedes spiral
drawing is one of the most used standard tests for clinical diagnosis. This
work, on selection of nonlinear biomarkers from drawings and handwriting, is
part of a wide-ranging cross study for the diagnosis of essential tremor in
BioDonostia Health Institute. Several entropy algorithms are used to generate
nonlinear feayures. The automatic analysis system consists of several Machine
Learning paradigms.

For the identification of switched systems with a measured switching signal,
this work aims to analyze the effect of switching strategies on the estimation
error. The data for identification is assumed to be collected from globally
asymptotically or marginally stable switched systems under switches that are
arbitrary or subject to an average dwell time constraint. Then the switched
system is estimated by the least-squares (LS) estimator. To capture the effect
of the parameters of the switching strategies on the LS estimation error,
finite-sample error bounds are developed in this work. The obtained error
bounds show that the estimation error is logarithmic of the switching
parameters when there are only stable modes; however, when there are unstable
modes, the estimation error bound can increase linearly as the switching
parameter changes. This suggests that in the presence of unstable modes, the
switching strategy should be properly designed to avoid the significant
increase of the estimation error.

Deep learning models were frequently reported to learn from shortcuts like
dataset biases. As deep learning is playing an increasingly important role in
the modern healthcare system, it is of great need to combat shortcut learning
in medical data as well as develop unbiased and trustworthy models. In this
paper, we study the problem of developing debiased chest X-ray diagnosis models
from the biased training data without knowing exactly the bias labels. We start
with the observations that the imbalance of bias distribution is one of the key
reasons causing shortcut learning, and the dataset biases are preferred by the
model if they were easier to be learned than the intended features. Based on
these observations, we proposed a novel algorithm, pseudo bias-balanced
learning, which first captures and predicts per-sample bias labels via
generalized cross entropy loss and then trains a debiased model using pseudo
bias labels and bias-balanced softmax function. We constructed several chest
X-ray datasets with various dataset bias situations and demonstrated with
extensive experiments that our proposed method achieved consistent improvements
over other state-of-the-art approaches.

Digital systems for analyzing human communication data have become prevalent
in recent years. Intelligence analysis of communications data in investigative
journalism, criminal intelligence, and law present particularly interesting
cases, as they must take into account the often highly sensitive properties of
the underlying operations and data. At the same time, these are areas where
increasingly automated, sophisticated approaches systems can be particularly
relevant, especially in terms of Big Data manageability. However, by the
shifting of responsibilities, this also poses dangers. In addition to privacy
concerns, these dangers relate to uncertain or poor data quality, leading to
discrimination and potentially misleading insights. Visual analytics combines
machine learning methods with interactive visual interfaces to enable human
sense- and decision-making. This technique can be key for designing and
operating meaningful interactive communication analysis systems that consider
these ethical challenges. In this interdisciplinary work, a joint endeavor of
computer scientists, ethicists, and scholars in Science & Technology Studies,
we investigate and evaluate opportunities and risks involved in using Visual
analytics approaches for communication analysis in intelligence applications in
particular. We introduce, at first, the common technological systems used in
communication analysis, further discussing the domain-specific ethical
implications, tensions, and risks involved. We then make the case of how
tailored Visual Analytics approaches may reduce and mitigate the described
problems, both theoretically and through practical examples. We show that
finding Visual Analytics design solutions for ethical issues is not a mere
optimization task, but balancing out and negotiating these trade-offs has, as
we argue, to be an integral aspect of the system design process from the
outset.

In this paper, we formulate a potentially valuable panoramic depth completion
(PDC) task as panoramic 3D cameras often produce 360{\deg} depth with missing
data in complex scenes. Its goal is to recover dense panoramic depths from raw
sparse ones and panoramic RGB images. To deal with the PDC task, we train a
deep network that takes both depth and image as inputs for the dense panoramic
depth recovery. However, it needs to face a challenging optimization problem of
the network parameters due to its non-convex objective function. To address
this problem, we propose a simple yet effective approach termed M{^3}PT:
multi-modal masked pre-training. Specifically, during pre-training, we
simultaneously cover up patches of the panoramic RGB image and sparse depth by
shared random mask, then reconstruct the sparse depth in the masked regions. To
our best knowledge, it is the first time that we show the effectiveness of
masked pre-training in a multi-modal vision task, instead of the single-modal
task resolved by masked autoencoders (MAE). Different from MAE where
fine-tuning completely discards the decoder part of pre-training, there is no
architectural difference between the pre-training and fine-tuning stages in our
M$^{3}$PT as they only differ in the prediction density, which potentially
makes the transfer learning more convenient and effective. Extensive
experiments verify the effectiveness of M{^3}PT on three panoramic datasets.
Notably, we improve the state-of-the-art baselines by averagely 26.2% in RMSE,
51.7% in MRE, 49.7% in MAE, and 37.5% in RMSElog on three benchmark datasets.

ML-based predictions are used to inform consequential decisions about
individuals. How should we use predictions (e.g., risk of heart attack) to
inform downstream binary classification decisions (e.g., undergoing a medical
procedure)? When the risk estimates are perfectly calibrated, the answer is
well understood: a classification problem's cost structure induces an optimal
treatment threshold $j^{\star}$. In practice, however, some amount of
miscalibration is unavoidable, raising a fundamental question: how should one
use potentially miscalibrated predictions to inform binary decisions? We
formalize a natural (distribution-free) solution concept: given anticipated
miscalibration of $\alpha$, we propose using the threshold $j$ that minimizes
the worst-case regret over all $\alpha$-miscalibrated predictors, where the
regret is the difference in clinical utility between using the threshold in
question and using the optimal threshold in hindsight. We provide closed form
expressions for $j$ when miscalibration is measured using both expected and
maximum calibration error, which reveal that it indeed differs from $j^{\star}$
(the optimal threshold under perfect calibration). We validate our theoretical
findings on real data, demonstrating that there are natural cases in which
making decisions using $j$ improves the clinical utility.

Recent works have revealed the vulnerability of automatic speech recognition
(ASR) models to adversarial examples (AEs), i.e., small perturbations that
cause an error in the transcription of the audio signal. Studying audio
adversarial attacks is therefore the first step towards robust ASR. Despite the
significant progress made in attacking audio examples, the black-box attack
remains challenging because only the hard-label information of transcriptions
is provided. Due to this limited information, existing black-box methods often
require an excessive number of queries to attack a single audio example. In
this paper, we introduce NP-Attack, a neural predictor-based method, which
progressively evolves the search towards a small adversarial perturbation.
Given a perturbation direction, our neural predictor directly estimates the
smallest perturbation that causes a mistranscription. In particular, it enables
NP-Attack to accurately learn promising perturbation directions via
gradient-based optimization. Experimental results show that NP-Attack achieves
competitive results with other state-of-the-art black-box adversarial attacks
while requiring a significantly smaller number of queries. The code of
NP-Attack is available online.

This paper presents a gender classification schema based on online
handwriting. Using samples acquired with a digital tablet that captures the
dynamics of the writing, it classifies the writer as a male or a female. The
method proposed is allographic, regarding strokes as the structural units of
handwriting. Strokes performed while the writing device is not exerting any
pressure on the writing surface, pen-up (in-air) strokes, are also taken into
account. The method is also text-dependent meaning that training and testing is
done with exactly the same text. Text-dependency allows classification be
performed with very small amounts of text. Experimentation, performed with
samples from the BiosecurID database, yields results that fall in the range of
the classification averages expected from human judges. With only four
repetitions of a single uppercase word, the average rate of well classified
writers is 68%; with sixteen words, the rate rises to an average 72.6%.
Statistical analysis reveals that the aforementioned rates are highly
significant. In order to explore the classification potential of the pen-up
strokes, these are also considered. Although in this case results are not
conclusive, an outstanding average of 74% of well classified writers is
obtained when information from pen-up strokes is combined with information from
pen-down ones.

Efficiently using the space of an elevator for a service robot is very
necessary, due to the need for reducing the amount of time caused by waiting
for the next elevator. To solve this, we propose a hybrid approach that
combines reinforcement learning (RL) with voice interaction for robot
navigation in the scene of entering the elevator. RL provides robots with a
high exploration ability to find a new clear path to enter the elevator
compared to the traditional navigation methods such as Optimal Reciprocal
Collision Avoidance (ORCA). The proposed method allows the robot to take an
active clear path action towards the elevator whilst a crowd of people stands
at the entrance of the elevator wherein there are still lots of space. This is
done by embedding a clear path action (beep) into the RL framework, and the
proposed navigation policy leads the robot to finish tasks efficiently and
safely. Our model approach provides a great improvement in the success rate and
reward of entering the elevator compared to state-of-the-art ORCA and RL
navigation policy without beep.

The Coq Platform is a continuously developed distribution of the Coq proof
assistant together with commonly used libraries, plugins, and external tools
useful in Coq-based formal verification projects. The Coq Platform enables
reproducing and extending Coq artifacts in research, education, and industry,
e.g., formalized mathematics and verified software systems. In this paper, we
describe the background and motivation for the Platform, and outline its
organization and development process. We also compare the Coq Platform to
similar distributions and processes in the proof assistant community, such as
for Isabelle and Lean, and in the wider open source software community.

To perform adversarial attacks in the physical world, many studies have
proposed adversarial camouflage, a method to hide a target object by applying
camouflage patterns on 3D object surfaces. For obtaining optimal physical
adversarial camouflage, previous studies have utilized the so-called neural
renderer, as it supports differentiability. However, existing neural renderers
cannot fully represent various real-world transformations due to a lack of
control of scene parameters compared to the legacy photo-realistic renderers.
In this paper, we propose the Differentiable Transformation Attack (DTA), a
framework for generating a robust physical adversarial pattern on a target
object to camouflage it against object detection models with a wide range of
transformations. It utilizes our novel Differentiable Transformation Network
(DTN), which learns the expected transformation of a rendered object when the
texture is changed while preserving the original properties of the target
object. Using our attack framework, an adversary can gain both the advantages
of the legacy photo-realistic renderers including various physical-world
transformations and the benefit of white-box access by offering
differentiability. Our experiments show that our camouflaged 3D vehicles can
successfully evade state-of-the-art object detection models in the
photo-realistic environment (i.e., CARLA on Unreal Engine). Furthermore, our
demonstration on a scaled Tesla Model 3 proves the applicability and
transferability of our method to the real world.

Self-supervised speech recognition models require considerable labeled
training data for learning high-fidelity representations for Automatic Speech
Recognition (ASR) which is computationally demanding and time-consuming,
thereby hindering the usage of these models in resource-constrained
environments. We consider the task of identifying an optimal subset of data to
train self-supervised speech models for ASR. We make a surprising observation
that the dataset pruning strategies used in vision tasks for sampling the most
informative examples do not perform better than random subset selection on the
task of fine-tuning self-supervised ASR. We then present the COWERAGE algorithm
for better subset selection in self-supervised ASR, which is based on our
finding that ensuring the coverage of examples based on training Word Error
Rate (WER) in the early training epochs leads to better generalization
performance. Extensive experiments on the wav2vec 2.0 model and TIMIT,
Librispeech, and LJSpeech datasets show the effectiveness of COWERAGE, with up
to 17% absolute WER improvement over existing dataset pruning methods and
random sampling. We also demonstrate that the coverage of training instances in
terms of WER ensures inclusion of phonemically diverse examples which leads to
better test accuracy in self-supervised speech recognition models.

Custom voice is to construct a personal speech synthesis system by adapting
the source speech synthesis model to the target model through the target few
recordings. The solution to constructing a custom voice is to combine an
adaptive acoustic model with a robust vocoder. However, training a robust
vocoder usually requires a multi-speaker dataset, which should include various
age groups and various timbres, so that the trained vocoder can be used for
unseen speakers. Collecting such a multi-speaker dataset is difficult, and the
dataset distribution always has a mismatch with the distribution of the target
speaker dataset. This paper proposes an adaptive vocoder for custom voice from
another novel perspective to solve the above problems. The adaptive vocoder
mainly uses a cross-domain consistency loss to solve the overfitting problem
encountered by the GAN-based neural vocoder in the transfer learning of
few-shot scenes. We construct two adaptive vocoders, AdaMelGAN and AdaHiFi-GAN.
First, We pre-train the source vocoder model on AISHELL3 and CSMSC datasets,
respectively. Then, fine-tune it on the internal dataset VXI-children with few
adaptation data. The empirical results show that a high-quality custom voice
system can be built by combining a adaptive acoustic model with a adaptive
vocoder.

This work digs into a root question in human perception: can face geometry be
gleaned from one's voices? Previous works that study this question only adopt
developments in image synthesis and convert voices into face images to show
correlations, but working on the image domain unavoidably involves predicting
attributes that voices cannot hint, including facial textures, hairstyles, and
backgrounds. We instead investigate the ability to reconstruct 3D faces to
concentrate on only geometry, which is much more physiologically grounded. We
propose our analysis framework, Cross-Modal Perceptionist, under both
supervised and unsupervised learning. First, we construct a dataset,
Voxceleb-3D, which extends Voxceleb and includes paired voices and face meshes,
making supervised learning possible. Second, we use a knowledge distillation
mechanism to study whether face geometry can still be gleaned from voices
without paired voices and 3D face data under limited availability of 3D face
scans. We break down the core question into four parts and perform visual and
numerical analyses as responses to the core question. Our findings echo those
in physiology and neuroscience about the correlation between voices and facial
structures. The work provides future human-centric cross-modal learning with
explainable foundations. See our project page:
https://choyingw.github.io/works/Voice2Mesh/index.html

Recently, there has been a rise in the development of powerful pre-trained
natural language models, including GPT-2, Grover, and XLM. These models have
shown state-of-the-art capabilities towards a variety of different NLP tasks,
including question answering, content summarisation, and text generation.
Alongside this, there have been many studies focused on online authorship
attribution (AA). That is, the use of models to identify the authors of online
texts. Given the power of natural language models in generating convincing
texts, this paper examines the degree to which these language models can
generate texts capable of deceiving online AA models. Experimenting with both
blog and Twitter data, we utilise GPT-2 language models to generate texts using
the existing posts of online users. We then examine whether these AI-based text
generators are capable of mimicking authorial style to such a degree that they
can deceive typical AA models. From this, we find that current AI-based text
generators are able to successfully mimic authorship, showing capabilities
towards this on both datasets. Our findings, in turn, highlight the current
capacity of powerful natural language models to generate original online posts
capable of mimicking authorial style sufficiently to deceive popular AA
methods; a key finding given the proposed role of AA in real world applications
such as spam-detection and forensic investigation.

We consider the task of object grasping with a prosthetic hand capable of
multiple grasp types. In this setting, communicating the intended grasp type
often requires a high user cognitive load which can be reduced adopting shared
autonomy frameworks. Among these, so-called eye-in-hand systems automatically
control the hand pre-shaping before the grasp, based on visual input coming
from a camera on the wrist. In this paper, we present an eye-in-hand
learning-based approach for hand pre-shape classification from RGB sequences.
Differently from previous work, we design the system to support the possibility
to grasp each considered object part with a different grasp type. In order to
overcome the lack of data of this kind and reduce the need for tedious data
collection sessions for training the system, we devise a pipeline for rendering
synthetic visual sequences of hand trajectories. We develop a sensorized setup
to acquire real human grasping sequences for benchmarking and show that,
compared on practical use cases, models trained with our synthetic dataset
achieve better generalization performance than models trained on real data. We
finally integrate our model on the Hannes prosthetic hand and show its
practical effectiveness. We make publicly available the code and dataset to
reproduce the presented results.

Scene Graph Generation, which generally follows a regular encoder-decoder
pipeline, aims to first encode the visual contents within the given image and
then parse them into a compact summary graph. Existing SGG approaches generally
not only neglect the insufficient modality fusion between vision and language,
but also fail to provide informative predicates due to the biased relationship
predictions, leading SGG far from practical. Towards this end, in this paper,
we first present a novel Stacked Hybrid-Attention network, which facilitates
the intra-modal refinement as well as the inter-modal interaction, to serve as
the encoder. We then devise an innovative Group Collaborative Learning strategy
to optimize the decoder. Particularly, based upon the observation that the
recognition capability of one classifier is limited towards an extremely
unbalanced dataset, we first deploy a group of classifiers that are expert in
distinguishing different subsets of classes, and then cooperatively optimize
them from two aspects to promote the unbiased SGG. Experiments conducted on VG
and GQA datasets demonstrate that, we not only establish a new state-of-the-art
in the unbiased metric, but also nearly double the performance compared with
two baselines.

Observations collected by agents in a network may be unreliable due to
observation noise or interference. This paper proposes a distributed algorithm
that allows each node to improve the reliability of its own observation by
relying solely on local computations and interactions with immediate neighbors,
assuming that the field (graph signal) monitored by the network lies in a
low-dimensional subspace and that a low-rank noise is present in addition to
the usual full-rank noise. While oblique projections can be used to project
measurements onto a low-rank subspace along a direction that is oblique to the
subspace, the resulting solution is not distributed. Starting from the
centralized solution, we propose an algorithm that performs the oblique
projection of the overall set of observations onto the signal subspace in an
iterative and distributed manner. We then show how the oblique projection
framework can be extended to handle distributed learning and adaptation
problems over networks.

Deep reinforcement learning (DRL) is one of the promising approaches for
introducing robots into complicated environments. The recent remarkable
progress of DRL stands on regularization of policy, which allows the policy to
improve stably and efficiently. A popular method, so-called proximal policy
optimization (PPO), and its variants constrain density ratio of the latest and
baseline policies when the density ratio exceeds a given threshold. This
threshold can be designed relatively intuitively, and in fact its recommended
value range has been suggested. However, the density ratio is asymmetric for
its center, and the possible error scale from its center, which should be close
to the threshold, would depend on how the baseline policy is given. In order to
maximize the values of regularization of policy, this paper proposes a new PPO
derived using relative Pearson (RPE) divergence, therefore so-called PPO-RPE,
to design the threshold adaptively. In PPO-RPE, the relative density ratio,
which can be formed with symmetry, replaces the raw density ratio. Thanks to
this symmetry, its error scale from center can easily be estimated, hence, the
threshold can be adapted for the estimated error scale. From three simple
benchmark simulations, the importance of algorithm-dependent threshold design
is revealed. By simulating additional four locomotion tasks, it is verified
that the proposed method statistically contributes to task accomplishment by
appropriately restricting the policy updates.

We study the problem of calibrating a quantum receiver for optical coherent
states when transmitted on a quantum optical channel with variable
transmissivity, a common model for long-distance optical-fiber and
free/deep-space optical communication. We optimize the error probability of
legacy adaptive receivers, such as Kennedy's and Dolinar's, on average with
respect to the channel transmissivity distribution. We then compare our results
with the ultimate error probability attainable by a general quantum device,
computing the Helstrom bound for mixtures of coherent-state hypotheses, for the
first time to our knowledge, and with homodyne measurements. With these tools,
we first analyze the simplest case of two different transmissivity values; we
find that the strategies adopted by adaptive receivers exhibit strikingly new
features as the difference between the two transmissivities increases. Finally,
we employ a recently introduced library of shallow reinforcement learning
methods, demonstrating that an intelligent agent can learn the optimal receiver
setup from scratch by training on repeated communication episodes on the
channel with variable transmissivity and receiving rewards if the
coherent-state message is correctly identified.

Pseudo-supervised learning methods have been shown to be effective for weakly
supervised object localization tasks. However, the effectiveness depends on the
powerful regularization ability of deep neural networks. Based on the
assumption that the localization network should have similar location
predictions on different versions of the same image, we propose a two-stage
approach to learn more consistent localization. In the first stage, we propose
a mask-based pseudo label generator algorithm, and use the pseudo-supervised
learning method to initialize an object localization network. In the second
stage, we propose a simple and effective method for evaluating the confidence
of pseudo-labels based on classification discrimination, and by learning
consistency from high-quality pseudo-labels, we further refine the localization
network to get better localization performance. Experimental results show that
our proposed approach achieves excellent performance in three benchmark
datasets including CUB-200-2011, ImageNet-1k and Tiny-ImageNet, which
demonstrates its effectiveness.

The problem of scheduling non-simultaneously released jobs with due dates on
a single machine with the objective to minimize the maximum job lateness is
known to be strongly NP-hard. Here we consider an extended model in which the
compression of the job processing times is allowed. The compression is
accomplished at the cost of involving additional emerging resources, whose use,
however, yields some cost. With a given upper limit $U$ on the total allowable
cost, one wishes to minimize the maximum job lateness. This setting is
realistic in practice because by completing some emerging jobs earlier using
some available resources, the objective function value might be decreased. In
particular, for minimizing the maximum job lateness, shortening the processing
time of specific jobs turns out to be very helpful. Although the generalized
problem remains strongly NP-hard and is considerably more complicated than the
generic non-compressible version, given a "sufficient amount" of additional
resources, it might be possible to solve the problem optimally. In particular,
we establish the compression rate for some specific jobs and develop an
algorithm that obtains an optimal solution for the processing times reduced in
this way. Such an approach has an obvious benefit in practice since the
manufacturer can be provided with an information about the required amount of
additional resources in order to solve the problem optimally.

Machine Learning-based techniques have shown success in cyber intelligence.
However, they are increasingly becoming targets of sophisticated data-driven
adversarial attacks resulting in misprediction, eroding their ability to detect
threats on network devices. In this paper, we present AdIoTack, a system that
highlights vulnerabilities of decision trees against adversarial attacks,
helping cybersecurity teams quantify and refine the resilience of their trained
models for monitoring IoT networks. To assess the model for the worst-case
scenario, AdIoTack performs white-box adversarial learning to launch successful
volumetric attacks that decision tree ensemble models cannot flag. Our first
contribution is to develop a white-box algorithm that takes a trained decision
tree ensemble model and the profile of an intended network-based attack on a
victim class as inputs. It then automatically generates recipes that specify
certain packets on top of the indented attack packets (less than 15% overhead)
that together can bypass the inference model unnoticed. We ensure that the
generated attack instances are feasible for launching on IP networks and
effective in their volumetric impact. Our second contribution develops a method
to monitor the network behavior of connected devices actively, inject
adversarial traffic (when feasible) on behalf of a victim IoT device, and
successfully launch the intended attack. Our third contribution prototypes
AdIoTack and validates its efficacy on a testbed consisting of a handful of
real IoT devices monitored by a trained inference model. We demonstrate how the
model detects all non-adversarial volumetric attacks on IoT devices while
missing many adversarial ones. The fourth contribution develops systematic
methods for applying patches to trained decision tree ensemble models,
improving their resilience against adversarial volumetric attacks.

2D convolution (Conv2d), which is responsible for extracting features from
the input image, is one of the key modules of a convolutional neural network
(CNN). However, Conv2d is vulnerable to image corruptions and adversarial
samples. It is an important yet rarely investigated problem that whether we can
design a more robust alternative of Conv2d for more reliable feature
extraction. In this paper, inspired by the recently developed learnable sparse
transform that learns to convert the CNN features into a compact and sparse
latent space, we design a novel building block, denoted by RConv-MK, to
strengthen the robustness of extracted convolutional features. Our method
leverages a set of learnable kernels of different sizes to extract features at
different frequencies and employs a normalized soft thresholding operator to
adaptively remove noises and trivial features at different corruption levels.
Extensive experiments on clean images, corrupted images as well as adversarial
samples validate the effectiveness of the proposed robust module for reliable
visual recognition. The source codes are enclosed in the submission.

Classically, the mechanical response of materials is described through
constitutive models, often in the form of constrained ordinary differential
equations. These models have a very limited number of parameters, yet, they are
extremely efficient in reproducing complex responses observed in experiments.
Additionally, in their discretized form, they are computationally very
efficient, often resulting in a simple algebraic relation, and therefore they
have been extensively used within large-scale explicit and implicit finite
element models. However, it is very challenging to formulate new constitutive
models, particularly for materials with complex microstructures such as
composites. A recent trend in constitutive modeling leverages complex neural
network architectures to construct complex material responses where a
constitutive model does not yet exist. Whilst very accurate, they suffer from
two deficiencies. First, they are interpolation models and often do poorly in
extrapolation. Second, due to their complex architecture and numerous
parameters, they are inefficient to be used as a constitutive model within a
large-scale finite element model. In this study, we propose a novel approach
based on the physics-informed learning machines for the characterization and
discovery of constitutive models. Unlike data-driven constitutive models, we
leverage foundations of elastoplasticity theory as regularization terms in the
total loss function to find parametric constitutive models that are also
theoretically sound. We demonstrate that our proposed framework can efficiently
identify the underlying constitutive model describing different datasets from
the von Mises family.

Machine learning (ML) force fields are one of the most common applications of
ML methods in the field of physical and chemical science. In the optimal case,
they are able to reach accuracy close to the first principles methods with
significantly lowered computational cost. However, often the training of the ML
methods rely on full atomic structures alongside their potential energies, and
applying the force information needs special modifications to standard
algorithms. Here we apply distance-based ML methods to predict force norms and
estimate the directions of the force vectors of the thiolate-protected gold
nanoclusters. The method relies only on local structural information without
energy evaluations. We apply the atomic ML forces on the structure optimization
of the gold-thiolate rings, $\text{Au}_{25}(\text{SCH}_{3})_{18}$ nanocluster
and two known structural isomers of the $\text{Au}_{38}(\text{SCH}_{3})_{24}$
nanocluster. The results demonstrate that the method is well-suited for the
structural optimizations of the gold-thiolate systems, where the atomic bonding
has a covalent nature in the ligand shell and at the metal-ligand interface.
The methodology could be seen as an early attempt to introduce equivariant
learning to distance-based ML methods.

In this paper, we study the problem of fair sequential decision making with
biased linear bandit feedback. At each round, a player selects an action
described by a covariate and by a sensitive attribute. The perceived reward is
a linear combination of the covariates of the chosen action, but the player
only observes a biased evaluation of this reward, depending on the sensitive
attribute. To characterize the difficulty of this problem, we design a phased
elimination algorithm that corrects the unfair evaluations, and establish upper
bounds on its regret. We show that the worst-case regret is smaller than
$\mathcal{O}(\kappa_*^{1/3}\log(T)^{1/3}T^{2/3})$, where $\kappa_*$ is an
explicit geometrical constant characterizing the difficulty of bias estimation.
We prove lower bounds on the worst-case regret for some sets of actions showing
that this rate is tight up to a possible sub-logarithmic factor. We also derive
gap-dependent upper bounds on the regret, and matching lower bounds for some
problem instance.Interestingly, these results reveal a transition between a
regime where the problem is as difficult as its unbiased counterpart, and a
regime where it can be much harder.

In this paper, we propose ISDE (Independence Structure Density Estimation),
an algorithm designed to estimate a multivariate density under Kullback-Leibler
loss and the Independence Structure (IS) model. IS tackles the curse of
dimensionality by separating features into independent groups. We explain the
construction of ISDE and present some experiments to show its performance on
synthetic and real-world data. Performance is measured quantitatively by
comparing empirical $\log$-likelihood with other density estimation methods and
qualitatively by analyzing outputted partitions of variables. We also provide
information about complexity and running time.

The analysis of logs is a vital activity undertaken for cyber investigation,
digital forensics and fault detection to enhance system and cyber resilience.
However, performing log analysis is a complex task. It requires extensive
knowledge of how the logs are generated and the format of the log entries used.
Also, it requires extensive knowledge or expertise in the identifying anomalous
log entries from normal or benign log entries. This is especially complex when
the forms of anomalous entries are constrained by what are the known forms of
internal or external attacks techniques or the varied forms of disruptions that
may exists. New or evasive forms of such disruptions are difficult to define.
The challenge of log analysis is further complicated by the volume of log
entries. Even with the availability of such log data, labelling such log
entries would be a massive undertaking. Hence this research seeks to address
these challenges with its novel Deep Learning model that learns and improves
itself progressively with inputs or corrections provided when available. The
practical application of such model construct facilitates log analysis or
review with abilities to learn or incorporate new patterns to spot anomalies or
ignore false positives.

GAN-generated deepfakes as a genre of digital images are gaining ground as
both catalysts of artistic expression and malicious forms of deception,
therefore demanding systems to enforce and accredit their ethical use. Existing
techniques for the source attribution of synthetic images identify subtle
intrinsic fingerprints using multiclass classification neural nets limited in
functionality and scalability. Hence, we redefine the deepfake detection and
source attribution problems as a series of related binary classification tasks.
We leverage transfer learning to rapidly adapt forgery detection networks for
multiple independent attribution problems, by proposing a semi-decentralized
modular design to solve them simultaneously and efficiently. Class activation
mapping is also demonstrated as an effective means of feature localization for
model interpretation. Our models are determined via experimentation to be
competitive with current benchmarks, and capable of decent performance on human
portraits in ideal conditions. Decentralized fingerprint-based attribution is
found to retain validity in the presence of novel sources, but is more
susceptible to type II errors that intensify with image perturbations and
attributive uncertainty. We describe both our conceptual framework and model
prototypes for further enhancement when investigating the technical limits of
reactive deepfake attribution.

Partially-supervised instance segmentation is a task which requests
segmenting objects from novel unseen categories via learning on limited seen
categories with annotated masks thus eliminating demands of heavy annotation
burden. The key to addressing this task is to build an effective class-agnostic
mask segmentation model. Unlike previous methods that learn such models only on
seen categories, in this paper, we propose a new method, named ContrastMask,
which learns a mask segmentation model on both seen and unseen categories under
a unified pixel-level contrastive learning framework. In this framework,
annotated masks of seen categories and pseudo masks of unseen categories serve
as a prior for contrastive learning, where features from the mask regions
(foreground) are pulled together, and are contrasted against those from the
background, and vice versa. Through this framework, feature discrimination
between foreground and background is largely improved, facilitating learning of
the class-agnostic mask segmentation model. Exhaustive experiments on the COCO
dataset demonstrate the superiority of our method, which outperforms previous

We explore the task of language-guided video segmentation (LVS). Previous
algorithms mostly adopt 3D CNNs to learn video representation, struggling to
capture long-term context and easily suffering from visual-linguistic
misalignment. In light of this, we present Locater (local-global context aware
Transformer), which augments the Transformer architecture with a finite memory
so as to query the entire video with the language expression in an efficient
manner. The memory is designed to involve two components -- one for
persistently preserving global video content, and one for dynamically gathering
local temporal context and segmentation history. Based on the memorized
local-global context and the particular content of each frame, Locater
holistically and flexibly comprehends the expression as an adaptive query
vector for each frame. The vector is used to query the corresponding frame for
mask generation. The memory also allows Locater to process videos with linear
time complexity and constant size memory, while Transformer-style
self-attention computation scales quadratically with sequence length. To
thoroughly examine the visual grounding capability of LVS models, we contribute
a new LVS dataset, A2D-S+, which is built upon A2D-S dataset but poses
increased challenges in disambiguating among similar objects. Experiments on
three LVS datasets and our A2D-S+ show that Locater outperforms previous
state-of-the-arts. Further, our Locater based solution achieved the 1st place
in the Referring Video Object Segmentation Track of the 3rd Large-scale Video
Object Segmentation Challenge. Our code and dataset are available at:
https://github.com/leonnnop/Locater

Video frame interpolation (VFI) aims to improve the temporal resolution of a
video sequence. Most of the existing deep learning based VFI methods adopt
off-the-shelf optical flow algorithms to estimate the bidirectional flows and
interpolate the missing frames accordingly. Though having achieved a great
success, these methods require much human experience to tune the bidirectional
flows and often generate unpleasant results when the estimated flows are not
accurate. In this work, we rethink the VFI problem and formulate it as a
continuous image transition (CIT) task, whose key issue is to transition an
image from one space to another space continuously. More specifically, we learn
to implicitly decouple the images into a translatable flow space and a
non-translatable feature space. The former depicts the translatable states
between the given images, while the later aims to reconstruct the intermediate
features that cannot be directly translated. In this way, we can easily perform
image interpolation in the flow space and intermediate image synthesis in the
feature space, obtaining a CIT model. The proposed space decoupled learning
(SDL) approach is simple to implement, while it provides an effective framework
to a variety of CIT problems beyond VFI, such as style transfer and image
morphing. Our extensive experiments on a variety of CIT tasks demonstrate the
superiority of SDL to existing methods. The source code and models can be found
at \url{https://github.com/yangxy/SDL}.

Prompt-based tuning for pre-trained language models (PLMs) has shown its
effectiveness in few-shot learning. Typically, prompt-based tuning wraps the
input text into a cloze question. To make predictions, the model maps the
output words to labels via a verbalizer, which is either manually designed or
automatically built. However, manual verbalizers heavily depend on
domain-specific prior knowledge and human efforts, while finding appropriate
label words automatically still remains challenging.In this work, we propose
the prototypical verbalizer (ProtoVerb) which is built directly from training
data. Specifically, ProtoVerb learns prototype vectors as verbalizers by
contrastive learning. In this way, the prototypes summarize training instances
and are able to enclose rich class-level semantics. We conduct experiments on
both topic classification and entity typing tasks, and the results demonstrate
that ProtoVerb significantly outperforms current automatic verbalizers,
especially when training data is extremely scarce. More surprisingly, ProtoVerb
consistently boosts prompt-based tuning even on untuned PLMs, indicating an
elegant non-tuning way to utilize PLMs. Our codes are avaliable at
https://github.com/thunlp/OpenPrompt.

Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and
functional magnetic resonance imaging (fMRI), enable us to model the human
brain as a brain network or connectome. Capturing brain networks' structural
information and hierarchical patterns is essential for understanding brain
functions and disease states. Recently, the promising network representation
learning capability of graph neural networks (GNNs) has prompted many GNN-based
methods for brain network analysis to be proposed. Specifically, these methods
apply feature aggregation and global pooling to convert brain network instances
into meaningful low-dimensional representations used for downstream brain
network analysis tasks. However, existing GNN-based methods often neglect that
brain networks of different subjects may require various aggregation iterations
and use GNN with a fixed number of layers to learn all brain networks.
Therefore, how to fully release the potential of GNNs to promote brain network
analysis is still non-trivial. To solve this problem, we propose a novel brain
network representation framework, namely BN-GNN, which searches for the optimal
GNN architecture for each brain network. Concretely, BN-GNN employs deep
reinforcement learning (DRL) to train a meta-policy to automatically determine
the optimal number of feature aggregations (reflected in the number of GNN
layers) required for a given brain network. Extensive experiments on eight
real-world brain network datasets demonstrate that our proposed BN-GNN improves
the performance of traditional GNNs on different brain network analysis tasks.

Overlapping speech diarization has been traditionally treated as a
multi-label classification problem. In this paper, we reformulate this task as
a single-label prediction problem by encoding multiple binary labels into a
single label with the power set, which represents the possible combinations of
target speakers. This formulation has two benefits. First, the overlaps of
target speakers are explicitly modeled. Second, threshold selection is no
longer needed. Through this formulation, we propose the speaker embedding-aware
neural diarization (SEND) framework, where a speech encoder, a speaker encoder,
two similarity scorers, and a post-processing network are jointly optimized to
predict the encoded labels according to the similarities between speech
features and speaker embeddings. Experimental results show that SEND has a
stable learning process and can be trained on highly overlapped data without
extra initialization. More importantly, our method achieves the
state-of-the-art performance in real meeting scenarios with fewer model
parameters and lower computational complexity.

We forecast two different chaotic dynamics of the quasiperiodically forced
logistic map using the well-known deep learning framework Long Short-Term
Memory. We generate two data sets and use one in the training process and the
other in the testing process. The predicted values are evaluated using the
metric called Root Mean Square Error and visualized using the scatter plots.
The robustness of the Long Short-Term Memory model is evaluated using the
number of units in the layers of the model. We also make multi-step forecasting
of the considered system. We show that the considered Long Short-Term Memory
model performs well in predicting chaotic attractors upto three steps.

Bike sharing is an increasingly popular part of urban transportation systems.
Accurate demand prediction is the key to support timely re-balancing and ensure
service efficiency. Most existing models of bike-sharing demand prediction are
solely based on its own historical demand variation, essentially regarding bike
sharing as a closed system and neglecting the interaction between different
transport modes. This is particularly important because bike sharing is often
used to complement travel through other modes (e.g., public transit). Despite
some recent efforts, there is no existing method capable of leveraging
spatiotemporal information from multiple modes with heterogeneous spatial
units. To address this research gap, this study proposes a graph-based deep
learning approach for bike sharing demand prediction (B-MRGNN) with multimodal
historical data as input. The spatial dependencies across modes are encoded
with multiple intra- and inter-modal graphs. A multi-relational graph neural
network (MRGNN) is introduced to capture correlations between spatial units
across modes, such as bike sharing stations, subway stations, or ride-hailing
zones. Extensive experiments are conducted using real-world bike sharing,
subway and ride-hailing data from New York City, and the results demonstrate
the superior performance of our proposed approach compared to existing methods.

Deep neural networks (DNNs) have been proven to be vulnerable to adversarial
examples. A special branch of adversarial examples, namely sparse adversarial
examples, can fool the target DNNs by perturbing only a few pixels. However,
many existing sparse adversarial attacks use heuristic methods to select the
pixels to be perturbed, and regard the pixel selection and the adversarial
attack as two separate steps. From the perspective of neural network pruning,
we propose a novel end-to-end sparse adversarial attack method, namely
AutoAdversary, which can find the most important pixels automatically by
integrating the pixel selection into the adversarial attack. Specifically, our
method utilizes a trainable neural network to generate a binary mask for the
pixel selection. After jointly optimizing the adversarial perturbation and the
neural network, only the pixels corresponding to the value 1 in the mask are
perturbed. Experiments demonstrate the superiority of our proposed method over
several state-of-the-art methods. Furthermore, since AutoAdversary does not
require a heuristic pixel selection process, it does not slow down excessively
as other methods when the image size increases.

We consider distributed optimization methods for problems where forming the
Hessian is computationally challenging and communication is a significant
bottleneck. We leverage randomized sketches for reducing the problem dimensions
as well as preserving privacy and improving straggler resilience in
asynchronous distributed systems. We derive novel approximation guarantees for
classical sketching methods and establish tight concentration results that
serve as both upper and lower bounds on the error. We then extend our analysis
to the accuracy of parameter averaging for distributed sketches. Furthermore,
we develop unbiased parameter averaging methods for randomized second order
optimization for regularized problems that employ sketching of the Hessian.
Existing works do not take the bias of the estimators into consideration, which
limits their application to massively parallel computation. We provide
closed-form formulas for regularization parameters and step sizes that provably
minimize the bias for sketched Newton directions. Additionally, we demonstrate
the implications of our theoretical findings via large scale experiments on a
serverless cloud computing platform.

Level set estimation (LSE) is the problem of identifying regions where an
unknown function takes values above or below a specified threshold. Active
sampling strategies for efficient LSE have primarily been studied in
continuous-valued functions. Motivated by applications in human psychophysics
where common experimental designs produce binary responses, we study LSE active
sampling with Bernoulli outcomes. With Gaussian process classification
surrogate models, the look-ahead model posteriors used by state-of-the-art
continuous-output methods are intractable. However, we derive analytic
expressions for look-ahead posteriors of sublevel set membership, and show how
these lead to analytic expressions for a class of look-ahead LSE acquisition
functions, including information-based methods. Benchmark experiments show the
importance of considering the global look-ahead impact on the entire posterior.
We demonstrate a clear benefit to using this new class of acquisition functions
on benchmark problems, and on a challenging real-world task of estimating a
high-dimensional contrast sensitivity function.

A long-horizon dexterous robot manipulation task of deformable objects, such
as banana peeling, is problematic because of difficulties in object modeling
and a lack of knowledge about stable and dexterous manipulation skills. This
paper presents a goal-conditioned dual-action deep imitation learning (DIL)
which can learn dexterous manipulation skills using human demonstration data.
Previous DIL methods map the current sensory input and reactive action, which
easily fails because of compounding errors in imitation learning caused by
recurrent computation of actions. The proposed method predicts reactive action
when the precise manipulation of the target object is required (local action)
and generates the entire trajectory when the precise manipulation is not
required. This dual-action formulation effectively prevents compounding error
with the trajectory-based global action while respond to unexpected changes in
the target object with the reactive local action. Furthermore, in this
formulation, both global/local actions are conditioned by a goal state which is
defined as the last step of each subtask, for robust policy prediction. The
proposed method was tested in the real dual-arm robot and successfully
accomplished the banana peeling task.

Federated learning (FL) provides a distributed learning framework for
multiple participants to collaborate learning without sharing raw data. In many
practical FL scenarios, participants have heterogeneous resources due to
disparities in hardware and inference dynamics that require quickly loading
models of different sizes and levels of robustness. The heterogeneity and
dynamics together impose significant challenges to existing FL approaches and
thus greatly limit FL's applicability. In this paper, we propose a novel
Split-Mix FL strategy for heterogeneous participants that, once training is
done, provides in-situ customization of model sizes and robustness.
Specifically, we achieve customization by learning a set of base sub-networks
of different sizes and robustness levels, which are later aggregated on-demand
according to inference requirements. This split-mix strategy achieves
customization with high efficiency in communication, storage, and inference.
Extensive experiments demonstrate that our method provides better in-situ
customization than the existing heterogeneous-architecture FL methods. Codes
and pre-trained models are available: https://github.com/illidanlab/SplitMix.

Here, we propose a new reconstruction method of smooth time-series signals. A
key concept of this study is not considering the model in signal space, but in
delay-embedded space. In other words, we indirectly represent a time-series
signal as an output of inverse delay-embedding of a matrix, and the matrix is
constrained. Based on the model under inverse delay-embedding, we propose to
constrain the matrix to be rank-1 with smooth factor vectors. The proposed
model is closely related to the convolutional model, and quadratic variation
(QV) regularization. Especially, the proposed method can be characterized as a
generalization of QV regularization. In addition, we show that the proposed
method provides the softer smoothness than QV regularization. Experiments of
audio inpainting and declipping are conducted to show its advantages in
comparison with several existing interpolation methods and sparse modeling.

Domain adaptive semantic segmentation aims to learn a model with the
supervision of source domain data, and produce satisfactory dense predictions
on unlabeled target domain. One popular solution to this challenging task is
self-training, which selects high-scoring predictions on target samples as
pseudo labels for training. However, the produced pseudo labels often contain
much noise because the model is biased to source domain as well as majority
categories. To address the above issues, we propose to directly explore the
intrinsic pixel distributions of target domain data, instead of heavily relying
on the source domain. Specifically, we simultaneously cluster pixels and
rectify pseudo labels with the obtained cluster assignments. This process is
done in an online fashion so that pseudo labels could co-evolve with the
segmentation model without extra training rounds. To overcome the class
imbalance problem on long-tailed categories, we employ a distribution alignment
technique to enforce the marginal class distribution of cluster assignments to
be close to that of pseudo labels. The proposed method, namely Class-balanced
Pixel-level Self-Labeling (CPSL), improves the segmentation performance on
target domain over state-of-the-arts by a large margin, especially on
long-tailed categories.

To generalize well, classifiers must learn to be invariant to nuisance
transformations that do not alter an input's class. Many problems have
"class-agnostic" nuisance transformations that apply similarly to all classes,
such as lighting and background changes for image classification. Neural
networks can learn these invariances given sufficient data, but many real-world
datasets are heavily class imbalanced and contain only a few examples for most
of the classes. We therefore pose the question: how well do neural networks
transfer class-agnostic invariances learned from the large classes to the small
ones? Through careful experimentation, we observe that invariance to
class-agnostic transformations is still heavily dependent on class size, with
the networks being much less invariant on smaller classes. This result holds
even when using data balancing techniques, and suggests poor invariance
transfer across classes. Our results provide one explanation for why
classifiers generalize poorly on unbalanced and long-tailed distributions.
Based on this analysis, we show how a generative approach for learning the
nuisance transformations can help transfer invariances across classes and
improve performance on a set of imbalanced image classification benchmarks.
Source code for our experiments is available at
https://github.com/AllanYangZhou/generative-invariance-transfer.

We propose a semi-supervised learning framework for monocular depth
estimation. Compared to existing semi-supervised learning methods, which
inherit limitations of both sparse supervised and unsupervised loss functions,
we achieve the complementary advantages of both loss functions, by building two
separate network branches for each loss and distilling each other through the
mutual distillation loss function. We also present to apply different data
augmentation to each branch, which improves the robustness. We conduct
experiments to demonstrate the effectiveness of our framework over the latest
methods and provide extensive ablation studies.

Series photo selection (SPS) is an important branch of the image aesthetics
quality assessment, which focuses on finding the best one from a series of
nearly identical photos. While a great progress has been observed, most of the
existing SPS approaches concentrate solely on extracting features from the
original image, neglecting that multiple views, e.g, saturation level, color
histogram and depth of field of the image, will be of benefit to successfully
reflecting the subtle aesthetic changes. Taken multi-view into consideration,
we leverage a graph neural network to construct the relationships between
multi-view features. Besides, multiple views are aggregated with an
adaptive-weight self-attention module to verify the significance of each view.
Finally, a siamese network is proposed to select the best one from a series of
nearly identical photos. Experimental results demonstrate that our model
accomplish the highest success rates compared with competitive methods.

Weakly-supervised learning (WSL) has shown promising results in addressing
label scarcity on many NLP tasks, but manually designing a comprehensive,
high-quality labeling rule set is tedious and difficult. We study interactive
weakly-supervised learning -- the problem of iteratively and automatically
discovering novel labeling rules from data to improve the WSL model. Our
proposed model, named PRBoost, achieves this goal via iterative prompt-based
rule discovery and model boosting. It uses boosting to identify large-error
instances and then discovers candidate rules from them by prompting pre-trained
LMs with rule templates. The candidate rules are judged by human experts, and
the accepted rules are used to generate complementary weak labels and
strengthen the current model. Experiments on four tasks show PRBoost
outperforms state-of-the-art WSL baselines up to 7.1% and bridges the gaps with
fully supervised models. Our Implementation is available at
\url{https://github.com/rz-zhang/PRBoost}.

Label assignment (LA), which aims to assign each training sample a positive
(pos) and a negative (neg) loss weight, plays an important role in object
detection. Existing LA methods mostly focus on the design of pos weighting
function, while the neg weight is directly derived from the pos weight. Such a
mechanism limits the learning capacity of detectors. In this paper, we explore
a new weighting paradigm, termed dual weighting (DW), to specify pos and neg
weights separately. We first identify the key influential factors of pos/neg
weights by analyzing the evaluation metrics in object detection, and then
design the pos and neg weighting functions based on them. Specifically, the pos
weight of a sample is determined by the consistency degree between its
classification and localization scores, while the neg weight is decomposed into
two terms: the probability that it is a neg sample and its importance
conditioned on being a neg sample. Such a weighting strategy offers greater
flexibility to distinguish between important and less important samples,
resulting in a more effective object detector. Equipped with the proposed DW
method, a single FCOS-ResNet-50 detector can reach 41.5% mAP on COCO under 1x
schedule, outperforming other existing LA methods. It consistently improves the
baselines on COCO by a large margin under various backbones without bells and
whistles. Code is available at https://github.com/strongwolf/DW.

We introduce, design, and evaluate a set of universal receiver beamforming
techniques. Our approach and system DEFORM, a Deep Learning (DL) based RX
beamforming achieves significant gain for multi antenna RF receivers while
being agnostic to the transmitted signal features (e.g., modulation or
bandwidth). It is well known that combining coherent RF signals from multiple
antennas results in a beamforming gain proportional to the number of receiving
elements. However in practice, this approach heavily relies on explicit channel
estimation techniques, which are link specific and require significant
communication overhead to be transmitted to the receiver. DEFORM addresses this
challenge by leveraging Convolutional Neural Network to estimate the channel
characteristics in particular the relative phase to antenna elements. It is
specifically designed to address the unique features of wireless signals
complex samples, such as the ambiguous $2\pi$ phase discontinuity and the high
sensitivity of the link Bit Error Rate. The channel prediction is subsequently
used in the Maximum Ratio Combining algorithm to achieve an optimal combination
of the received signals. While being trained on a fixed, basic RF settings, we
show that DEFORM DL model is universal, achieving up to 3 dB of SNR gain for a
two antenna receiver in extensive experiments demonstrating various settings of
modulations, bandwidths, and channels. The universality of DEFORM is
demonstrated through joint beamforming relaying of LoRa (Chirp Spread Spectrum
modulation) and ZigBee signals, achieving significant improvements to Packet
Loss/Delivery Rates relatively to conventional Amplify and Forward (LoRa PLR
reduced by 23 times and ZigBee PDR increased by 8 times).

Recovering high-quality images from undersampled measurements is critical for
accelerated MRI reconstruction. Recently, various supervised deep
learning-based MRI reconstruction methods have been developed. Despite the
achieved promising performances, these methods require fully sampled reference
data, the acquisition of which is resource-intensive and time-consuming.
Self-supervised learning has emerged as a promising solution to alleviate the
reliance on fully sampled datasets. However, existing self-supervised methods
suffer from reconstruction errors due to the insufficient constraint enforced
on the non-sampled data points and the error accumulation happened alongside
the iterative image reconstruction process for model-driven deep learning
reconstrutions. To address these challenges, we propose K2Calibrate, a K-space
adaptation strategy for self-supervised model-driven MR reconstruction
optimization. By iteratively calibrating the learned measurements, K2Calibrate
can reduce the network's reconstruction deterioration caused by statistically
dependent noise. Extensive experiments have been conducted on the open-source
dataset FastMRI, and K2Calibrate achieves better results than five
state-of-the-art methods. The proposed K2Calibrate is plug-and-play and can be
easily integrated with different model-driven deep learning reconstruction
methods.

Pattern classification with compact representation is an important component
in machine intelligence. In this work, an analytic bridge solution is proposed
for compressive classification. The proposal has been based upon solving a
penalized error formulation utilizing an approximated $\ell_p$-norm. The
solution comes in a primal form for over-determined systems and in a dual form
for under-determined systems. While the primal form is suitable for problems of
low dimension with large data samples, the dual form is suitable for problems
of high dimension but with a small number of data samples. The solution has
also been extended for problems with multiple classification outputs. Numerical
studies based on simulated and real-world data validated the effectiveness of
the proposed solution.

Wireless links are increasingly used to deliver critical services, while
intentional interference (jamming) remains a very serious threat to such
services. In this paper, we are concerned with the design and evaluation of a
universal anti-jamming building block, that is agnostic to the specifics of the
communication link and can therefore be combined with existing technologies. We
believe that such a block should not require explicit probes, sounding,
training sequences, channel estimation, or even the cooperation of the
transmitter. To meet these requirements, we propose an approach that relies on
advances in Machine Learning, and the promises of neural accelerators and
software defined radios. We identify and address multiple challenges, resulting
in a convolutional neural network architecture and models for a multi-antenna
system to infer the existence of interference, the number of interfering
emissions and their respective phases. This information is continuously fed
into an algorithm that cancels the interfering signal. We develop a two-antenna
prototype system and evaluate our jamming cancellation approach in various
environment settings and modulation schemes using Software Defined Radio
platforms. We demonstrate that the receiving node equipped with our approach
can detect a jammer with over 99% of accuracy and achieve a Bit Error Rate
(BER) as low as $10^{-6}$ even when the jammer power is nearly two orders of
magnitude (18 dB) higher than the legitimate signal, and without requiring
modifications to the link modulation. In non-adversarial settings, our approach
can have other advantages such as detecting and mitigating collisions.

Similar to the energy flowing process in traditional heat engines,
information could be considered to flow in the communication systems with the
form of energy and entropy. Combining the thermodynamic Carnot machine and the
classical Shannon information theory, a generalized thermodynamic MIMO
(multiple input multiple outputs) communication system is established to
analyze the channel capacity using forward error correction codes. Based on the
concepts of freedom and entropy in the communication system, the generalized
channel capacity is proposed under the thermodynamic theory. Furthermore, the
relationships between the proposed channel capacity and the noise freedom and
coding overhead are derived and simulated. Simulation results verify the
proposed channel capacity is coincident with the classical channel capacity.

When neural networks are used to model dynamics, properties such as stability
of the dynamics are generally not guaranteed. In contrast, there is a recent
method for learning the dynamics of autonomous systems that guarantees global
exponential stability using neural networks. In this paper, we propose a new
method for learning the dynamics of input-affine control systems. An important
feature is that a stabilizing controller and control Lyapunov function of the
learned model are obtained as well. Moreover, the proposed method can also be
applied to solving Hamilton-Jacobi inequalities. The usefulness of the proposed
method is examined through numerical examples.

Recently, few-shot voice cloning has achieved a significant improvement.
However, most models for few-shot voice cloning are single-modal, and
multi-modal few-shot voice cloning has been understudied. In this paper, we
propose to use multi-modal learning to improve the few-shot voice cloning
performance. Inspired by the recent works on unsupervised speech
representation, the proposed multi-modal system is built by extending Tacotron2
with an unsupervised speech representation module. We evaluate our proposed
system in two few-shot voice cloning scenarios, namely few-shot
text-to-speech(TTS) and voice conversion(VC). Experimental results demonstrate
that the proposed multi-modal learning can significantly improve the few-shot
voice cloning performance over their counterpart single-modal systems.

Source code summarization aims to generate natural language descriptions of
code snippets. Many existing studies learn the syntactic and semantic knowledge
of code snippets from their token sequences and Abstract Syntax Trees (ASTs).
They use the learned code representations as input to code summarization
models, which can accordingly generate summaries describing source code.
Traditional models traverse ASTs as sequences or split ASTs into paths as
input. However, the former loses the structural properties of ASTs, and the
latter destroys the overall structure of ASTs. Therefore, comprehensively
capturing the structural features of ASTs in learning code representations for
source code summarization remains a challenging problem to be solved. In this
paper, we propose M2TS, a Multi-scale Multi-modal approach based on Transformer
for source code Summarization. M2TS uses a multi-scale AST feature extraction
method, which can extract the structures of ASTs more completely and accurately
at multiple local and global levels. To complement missing semantic information
in ASTs, we also obtain code token features, and further combine them with the
extracted AST features using a cross modality fusion method that not only fuses
the syntactic and contextual semantic information of source code, but also
highlights the key features of each modality. We conduct experiments on two
Java and one Python datasets, and the experimental results demonstrate that
M2TS outperforms current state-of-the-art methods. We release our code at
https://github.com/TranSMS/M2TS.

Detecting objects from LiDAR point clouds is of tremendous significance in
autonomous driving. In spite of good progress, accurate and reliable 3D
detection is yet to be achieved due to the sparsity and irregularity of LiDAR
point clouds. Among existing strategies, multi-view methods have shown great
promise by leveraging the more comprehensive information from both bird's eye
view (BEV) and range view (RV). These multi-view methods either refine the
proposals predicted from single view via fused features, or fuse the features
without considering the global spatial context; their performance is limited
consequently. In this paper, we propose to adaptively fuse multi-view features
in a global spatial context via Dual Cross-VIew SpaTial Attention (VISTA). The
proposed VISTA is a novel plug-and-play fusion module, wherein the multi-layer
perceptron widely adopted in standard attention modules is replaced with a
convolutional one. Thanks to the learned attention mechanism, VISTA can produce
fused features of high quality for prediction of proposals. We decouple the
classification and regression tasks in VISTA, and an additional constraint of
attention variance is applied that enables the attention module to focus on
specific targets instead of generic points. We conduct thorough experiments on
the benchmarks of nuScenes and Waymo; results confirm the efficacy of our
designs. At the time of submission, our method achieves 63.0% in overall mAP
and 69.8% in NDS on the nuScenes benchmark, outperforming all published methods
by up to 24% in safety-crucial categories such as cyclist. The source code in
PyTorch is available at https://github.com/Gorilla-Lab-SCUT/VISTA

Recent advances in electronic devices and communication infrastructure have
revolutionized the traditional healthcare system into a smart healthcare system
by using IoMT devices. However, due to the centralized training approach of
artificial intelligence (AI), the use of mobile and wearable IoMT devices
raises privacy concerns with respect to the information that has been
communicated between hospitals and end users. The information conveyed by the
IoMT devices is highly confidential and can be exposed to adversaries. In this
regard, federated learning (FL), a distributive AI paradigm has opened up new
opportunities for privacy-preservation in IoMT without accessing the
confidential data of the participants. Further, FL provides privacy to end
users as only gradients are shared during training. For these specific
properties of FL, in this paper we present privacy related issues in IoMT.
Afterwards, we present the role of FL in IoMT networks for privacy preservation
and introduce some advanced FL architectures incorporating deep reinforcement
learning (DRL), digital twin, and generative adversarial networks (GANs) for
detecting privacy threats. Subsequently, we present some practical
opportunities of FL in smart healthcare systems. At the end, we conclude this
survey by providing open research challenges for FL that can be used in future
smart healthcare systems

Recent progress in Graph Neural Networks (GNNs) for modeling atomic
simulations has the potential to revolutionize catalyst discovery, which is a
key step in making progress towards the energy breakthroughs needed to combat
climate change. However, the GNNs that have proven most effective for this task
are memory intensive as they model higher-order interactions in the graphs such
as those between triplets or quadruplets of atoms, making it challenging to
scale these models. In this paper, we introduce Graph Parallelism, a method to
distribute input graphs across multiple GPUs, enabling us to train very large
GNNs with hundreds of millions or billions of parameters. We empirically
evaluate our method by scaling up the number of parameters of the recently
proposed DimeNet++ and GemNet models by over an order of magnitude. On the
large-scale Open Catalyst 2020 (OC20) dataset, these graph-parallelized models
lead to relative improvements of 1) 15% on the force MAE metric for the S2EF
task and 2) 21% on the AFbT metric for the IS2RS task, establishing new
state-of-the-art results.

Learning discriminative representation from the complex spatio-temporal
dynamic space is essential for video recognition. On top of those stylized
spatio-temporal computational units, further refining the learnt feature with
axial contexts is demonstrated to be promising in achieving this goal. However,
previous works generally focus on utilizing a single kind of contexts to
calibrate entire feature channels and could hardly apply to deal with diverse
video activities. The problem can be tackled by using pair-wise spatio-temporal
attentions to recompute feature response with cross-axis contexts at the
expense of heavy computations. In this paper, we propose an efficient feature
refinement method that decomposes the feature channels into several groups and
separately refines them with different axial contexts in parallel. We refer
this lightweight feature calibration as group contextualization (GC).
Specifically, we design a family of efficient element-wise calibrators, i.e.,
ECal-G/S/T/L, where their axial contexts are information dynamics aggregated
from other axes either globally or locally, to contextualize feature channel
groups. The GC module can be densely plugged into each residual layer of the
off-the-shelf video networks. With little computational overhead, consistent
improvement is observed when plugging in GC on different networks. By utilizing
calibrators to embed feature with four different kinds of contexts in parallel,
the learnt representation is expected to be more resilient to diverse types of
activities. On videos with rich temporal variations, empirically GC can boost
the performance of 2D-CNN (e.g., TSN and TSM) to a level comparable to the
state-of-the-art video networks. Code is available at
https://github.com/haoyanbin918/Group-Contextualization.

In this paper, we study the problem of principal component analysis with
generative modeling assumptions, adopting a general model for the observed
matrix that encompasses notable special cases, including spiked matrix recovery
and phase retrieval. The key assumption is that the underlying signal lies near
the range of an $L$-Lipschitz continuous generative model with bounded
$k$-dimensional inputs. We propose a quadratic estimator, and show that it
enjoys a statistical rate of order $\sqrt{\frac{k\log L}{m}}$, where $m$ is the
number of samples. We also provide a near-matching algorithm-independent lower
bound. Moreover, we provide a variant of the classic power method, which
projects the calculated data onto the range of the generative model during each
iteration. We show that under suitable conditions, this method converges
exponentially fast to a point achieving the above-mentioned statistical rate.
We perform experiments on various image datasets for spiked matrix and phase
retrieval models, and illustrate performance gains of our method to the classic
power method and the truncated power method devised for sparse principal
component analysis.

Learning a dense 3D model with fine-scale details from a single facial image
is highly challenging and ill-posed. To address this problem, many approaches
fit smooth geometries through facial prior while learning details as additional
displacement maps or personalized basis. However, these techniques typically
require vast datasets of paired multi-view data or 3D scans, whereas such
datasets are scarce and expensive. To alleviate heavy data dependency, we
present a robust texture-guided geometric detail recovery approach using only a
single in-the-wild facial image. More specifically, our method combines
high-quality texture completion with the powerful expressiveness of implicit
surfaces. Initially, we inpaint occluded facial parts, generate complete
textures, and build an accurate multi-view dataset of the same subject. In
order to estimate the detailed geometry, we define an implicit signed distance
function and employ a physically-based implicit renderer to reconstruct fine
geometric details from the generated multi-view images. Our method not only
recovers accurate facial details but also decomposes normals, albedos, and
shading parts in a self-supervised way. Finally, we register the implicit shape
details to a 3D Morphable Model template, which can be used in traditional
modeling and rendering pipelines. Extensive experiments demonstrate that the
proposed approach can reconstruct impressive facial details from a single
image, especially when compared with state-of-the-art methods trained on large
datasets.

Recently, speech representation learning has improved many speech-related
tasks such as speech recognition, speech classification, and speech-to-text
translation. However, all the above tasks are in the direction of speech
understanding, but for the inverse direction, speech synthesis, the potential
of representation learning is yet to be realized, due to the challenging nature
of generating high-quality speech. To address this problem, we propose our
framework, Alignment-Aware Acoustic-Text Pretraining (A$^3$T), which
reconstructs masked acoustic signals with text input and acoustic-text
alignment during training. In this way, the pretrained model can generate high
quality reconstructed spectrogram, which can be applied to the speech editing
and unseen speaker TTS directly. Experiments show A$^3$T outperforms SOTA
models on speech editing, and improves multi-speaker speech synthesis without
the external speaker verification model.

Thanks to the tiny storage and efficient execution, hyperdimensional
Computing (HDC) is emerging as a lightweight learning framework on
resource-constrained hardware. Nonetheless, the existing HDC training relies on
various heuristic methods, significantly limiting their inference accuracy. In
this paper, we propose a new HDC framework, called LeHDC, which leverages a
principled learning approach to improve the model accuracy. Concretely, LeHDC
maps the existing HDC framework into an equivalent Binary Neural Network
architecture, and employs a corresponding training strategy to minimize the
training loss. Experimental validation shows that LeHDC outperforms previous
HDC training strategies and can improve on average the inference accuracy over
15% compared to the baseline HDC.

Due to numerous breakthroughs in real-world applications brought by machine
intelligence, deep neural networks (DNNs) are widely employed in critical
applications. However, predictions of DNNs are easily manipulated with
imperceptible adversarial perturbations, which impedes the further deployment
of DNNs and may result in profound security and privacy implications. By
incorporating adversarial samples into the training data pool, adversarial
training is the strongest principled strategy against various adversarial
attacks among all sorts of defense methods. Recent works mainly focus on
developing new loss functions or regularizers, attempting to find the unique
optimal point in the weight space. But none of them taps the potentials of
classifiers obtained from standard adversarial training, especially states on
the searching trajectory of training. In this work, we are dedicated to the
weight states of models through the training process and devise a simple but
powerful \emph{Self-Ensemble Adversarial Training} (SEAT) method for yielding a
robust classifier by averaging weights of history models. This considerably
improves the robustness of the target model against several well known
adversarial attacks, even merely utilizing the naive cross-entropy loss to
supervise. We also discuss the relationship between the ensemble of predictions
from different adversarially trained models and the prediction of
weight-ensembled models, as well as provide theoretical and empirical evidence
that the proposed self-ensemble method provides a smoother loss landscape and
better robustness than both individual models and the ensemble of predictions
from different classifiers. We further analyze a subtle but fatal issue in the
general settings for the self-ensemble model, which causes the deterioration of
the weight-ensembled method in the late phases.

Bayesian coresets approximate a posterior distribution by building a small
weighted subset of the data points. Any inference procedure that is too
computationally expensive to be run on the full posterior can instead be run
inexpensively on the coreset, with results that approximate those on the full
data. However, current approaches are limited by either a significant run-time
or the need for the user to specify a low-cost approximation to the full
posterior. We propose a Bayesian coreset construction algorithm that first
selects a uniformly random subset of data, and then optimizes the weights using
a novel quasi-Newton method. Our algorithm is a simple to implement, black-box
method, that does not require the user to specify a low-cost posterior
approximation. It is the first to come with a general high-probability bound on
the KL divergence of the output coreset posterior. Experiments demonstrate that
our method provides significant improvements in coreset quality against
alternatives with comparable construction times, with far less storage cost and
user input required.

X-ray micro-computed tomography (X-ray microCT) has enabled the
characterization of the properties and processes that take place in plants and
soils at the micron scale. Despite the widespread use of this advanced
technique, major limitations in both hardware and software limit the speed and
accuracy of image processing and data analysis. Recent advances in machine
learning, specifically the application of convolutional neural networks to
image analysis, have enabled rapid and accurate segmentation of image data.
Yet, challenges remain in applying convolutional neural networks to the
analysis of environmentally and agriculturally relevant images. Specifically,
there is a disconnect between the computer scientists and engineers, who build
these AI/ML tools, and the potential end users in agricultural research, who
may be unsure of how to apply these tools in their work. Additionally, the
computing resources required for training and applying deep learning models are
unique, more common to computer gaming systems or graphics design work, than to
traditional computational systems. To navigate these challenges, we developed a
modular workflow for applying convolutional neural networks to X-ray microCT
images, using low-cost resources in Googles Colaboratory web application. Here
we present the results of the workflow, illustrating how parameters can be
optimized to achieve best results using example scans from walnut leaves,
almond flower buds, and a soil aggregate. We expect that this framework will
accelerate the adoption and use of emerging deep learning techniques within the
plant and soil sciences.

Estimating the effect of an intervention while accounting for confounding
variables is a key task in causal inference. Oftentimes, the confounders are
unobserved, but we have access to large amounts of unstructured data (images,
text) that contain valuable proxy signal about the missing confounders. This
paper demonstrates that leveraging unstructured data that is often left unused
by existing algorithms improves the accuracy of causal effect estimation.
Specifically, we introduce deep multi-modal structural equations, a generative
model in which confounders are latent variables and unstructured data are proxy
variables. This model supports multiple multi-modal proxies (images, text) as
well as missing data. We empirically demonstrate on tasks in genomics and
healthcare that our approach corrects for confounding using unstructured
inputs, potentially enabling the use of large amounts of data that were
previously not used in causal inference.

In this paper, we study a new latency optimization problem for
blockchain-based federated learning (BFL) in multi-server edge computing. In
this system model, distributed mobile devices (MDs) communicate with a set of
edge servers (ESs) to handle both machine learning (ML) model training and
block mining simultaneously. To assist the ML model training for
resource-constrained MDs, we develop an offloading strategy that enables MDs to
transmit their data to one of the associated ESs. We then propose a new
decentralized ML model aggregation solution at the edge layer based on a
consensus mechanism to build a global ML model via peer-to-peer (P2P)-based
blockchain communications. Blockchain builds trust among MDs and ESs to
facilitate reliable ML model sharing and cooperative consensus formation, and
enables rapid elimination of manipulated models caused by poisoning attacks. We
formulate latency-aware BFL as an optimization aiming to minimize the system
latency via joint consideration of the data offloading decisions, MDs' transmit
power, channel bandwidth allocation for MDs' data offloading, MDs'
computational allocation, and hash power allocation. Given the mixed action
space of discrete offloading and continuous allocation variables, we propose a
novel deep reinforcement learning scheme with a parameterized advantage actor
critic algorithm. We theoretically characterize the convergence properties of
BFL in terms of the aggregation delay, mini-batch size, and number of P2P
communication rounds. Our numerical evaluation demonstrates the superiority of
our proposed scheme over baselines in terms of model training efficiency,
convergence rate, system latency, and robustness against model poisoning
attacks.

Identifying stress levels can provide valuable data for mental health
analytics as well as labels for annotation systems. Although much research has
been conducted into stress detection models using heart rate variability at a
higher cost of data collection, there is a lack of research on the potential of
using low-resolution Electrodermal Activity (EDA) signals from consumer-grade
wearable devices to identify stress patterns. In this paper, we concentrate on
performing statistical analyses on the stress detection capability of two
popular approaches of training stress detection models with stress-related
biometric signals: user-dependent and user-independent models. Our research
manages to show that user-dependent models are statistically more accurate for
stress detection. In terms of effectiveness assessment, the balanced accuracy
(BA) metric is employed to evaluate the capability of distinguishing stress and
non-stress conditions of the models trained on either low-resolution or
high-resolution Electrodermal Activity (EDA) signals. The results from the
experiment show that training the model with (comparatively low-cost)
low-resolution EDA signal does not affect the stress detection accuracy of the
model significantly compared to using a high-resolution EDA signal. Our
research results demonstrate the potential of attaching the user-dependent
stress detection model trained on personal low-resolution EDA signal recorded
to collect data in daily life to provide users with personal stress level
insight and analysis.

Spatial transcriptomics (ST) has advanced significantly in the last few
years. Such advancement comes with the urgent need for novel computational
methods to handle the unique challenges of ST data analysis. Many artificial
intelligence (AI) methods have been developed to utilize various machine
learning and deep learning techniques for computational ST analysis. This
review provides a comprehensive and up-to-date survey of current AI methods for
ST analysis.

Stress is a complex issue with wide-ranging physical and psychological
impacts on human daily performance. Specifically, acute stress detection is
becoming a valuable application in contextual human understanding. Two common
approaches to training a stress detection model are subject-dependent and
subject-independent training methods. Although subject-dependent training
methods have proven to be the most accurate approach to build stress detection
models, subject-independent models are a more practical and cost-efficient
method, as they allow for the deployment of stress level detection and
management systems in consumer-grade wearable devices without requiring
training data for the end-user. To improve the performance of
subject-independent stress detection models, in this paper, we introduce a
stress-related bio-signal processing pipeline with a simple neural network
architecture using statistical features extracted from multimodal contextual
sensing sources including Electrodermal Activity (EDA), Blood Volume Pulse
(BVP), and Skin Temperature (ST) captured from a consumer-grade wearable
device. Using our proposed model architecture, we compare the accuracy between
stress detection models that use measures from each individual signal source,
and one model employing the fusion of multiple sensor sources. Extensive
experiments on the publicly available WESAD dataset demonstrate that our
proposed model outperforms conventional methods as well as providing 1.63%
higher mean accuracy score compared to the state-of-the-art model while
maintaining a low standard deviation. Our experiments also show that combining
features from multiple sources produce more accurate predictions than using
only one sensor source individually.

Meta-learning is a branch of machine learning which trains neural network
models to synthesize a wide variety of data in order to rapidly solve new
problems. In process control, many systems have similar and well-understood
dynamics, which suggests it is feasible to create a generalizable controller
through meta-learning. In this work, we formulate a meta reinforcement learning
(meta-RL) control strategy that can be used to tune proportional--integral
controllers. Our meta-RL agent has a recurrent structure that accumulates
"context" to learn a system's dynamics through a hidden state variable in
closed-loop. This architecture enables the agent to automatically adapt to
changes in the process dynamics. In tests reported here, the meta-RL agent was
trained entirely offline on first order plus time delay systems, and produced
excellent results on novel systems drawn from the same distribution of process
dynamics used for training. A key design element is the ability to leverage
model-based information offline during training in simulated environments while
maintaining a model-free policy structure for interacting with novel processes
where there is uncertainty regarding the true process dynamics. Meta-learning
is a promising approach for constructing sample-efficient intelligent
controllers.

Let $\mathscr{F}_{n,d}$ be the class of all functions $f:\{-1,1\}^n\to[-1,1]$
on the $n$-dimensional discrete hypercube of degree at most $d$. In the first
part of this paper, we prove that any (deterministic or randomized) algorithm
which learns $\mathscr{F}_{n,d}$ with $L_2$-accuracy $\varepsilon$ requires at
least $\Omega((1-\sqrt{\varepsilon})2^d\log n)$ queries for large enough $n$,
thus establishing the sharpness as $n\to\infty$ of a recent upper bound of
Eskenazis and Ivanisvili (2021). To do this, we show that the $L_2$-packing
numbers $\mathsf{M}(\mathscr{F}_{n,d},\|\cdot\|_{L_2},\varepsilon)$ of the
concept class $\mathscr{F}_{n,d}$ satisfy the two-sided estimate
$$c(1-\varepsilon)2^d\log n \leq \log
\mathsf{M}(\mathscr{F}_{n,d},\|\cdot\|_{L_2},\varepsilon) \leq \frac{2^{Cd}\log
n}{\varepsilon^4}$$ for large enough $n$, where $c, C>0$ are universal
constants. In the second part of the paper, we present a logarithmic upper
bound for the randomized query complexity of classes of bounded approximate
polynomials whose Fourier spectra are concentrated on few subsets. As an
application, we prove new estimates for the number of random queries required
to learn approximate juntas of a given degree, functions with rapidly decaying
Fourier tails and constant depth circuits of given size. Finally, we obtain
bounds for the number of queries required to learn the polynomial class
$\mathscr{F}_{n,d}$ without error in the query and random example models.

The compressive sensing (CS) scheme exploits much fewer measurements than
suggested by the Nyquist-Shannon sampling theorem to accurately reconstruct
images, which has attracted considerable attention in the computational imaging
community. While classic image CS schemes employed sparsity using analytical
transforms or bases, the learning-based approaches have become increasingly
popular in recent years. Such methods can effectively model the structures of
image patches by optimizing their sparse representations or learning deep
neural networks, while preserving the known or modeled sensing process. Beyond
exploiting local image properties, advanced CS schemes adopt nonlocal image
modeling, by extracting similar or highly correlated patches at different
locations of an image to form a group to process jointly. More recent
learning-based CS schemes apply nonlocal structured sparsity prior using group
sparse representation (GSR) and/or low-rank (LR) modeling, which have
demonstrated promising performance in various computational imaging and image
processing applications. This article reviews some recent works in image CS
tasks with a focus on the advanced GSR and LR based methods. Furthermore, we
present a unified framework for incorporating various GSR and LR models and
discuss the relationship between GSR and LR models. Finally, we discuss the
open problems and future directions in the field.

Learning semantic segmentation from weakly-labeled (e.g., image tags only)
data is challenging since it is hard to infer dense object regions from sparse
semantic tags. Despite being broadly studied, most current efforts directly
learn from limited semantic annotations carried by individual image or image
pairs, and struggle to obtain integral localization maps. Our work alleviates
this from a novel perspective, by exploring rich semantic contexts
synergistically among abundant weakly-labeled training data for network
learning and inference. In particular, we propose regional semantic contrast
and aggregation (RCA) . RCA is equipped with a regional memory bank to store
massive, diverse object patterns appearing in training data, which acts as
strong support for exploration of dataset-level semantic structure.
Particularly, we propose i) semantic contrast to drive network learning by
contrasting massive categorical object regions, leading to a more holistic
object pattern understanding, and ii) semantic aggregation to gather diverse
relational contexts in the memory to enrich semantic representations. In this
manner, RCA earns a strong capability of fine-grained semantic understanding,
and eventually establishes new state-of-the-art results on two popular
benchmarks, i.e., PASCAL VOC 2012 and COCO 2014.

Instant Search is a paradigm where a search system retrieves answers on the
fly while typing. The na\"ive implementation of an Instant Search system would
hit the search back-end for results each time a user types a key, imposing a
very high load on the underlying search system. In this paper, we propose to
address the load issue by identifying tokens that are semantically more salient
towards retrieving relevant documents and utilize this knowledge to trigger an
instant search selectively. We train a reinforcement agent that interacts
directly with the search engine and learns to predict the word's importance.
Our proposed method treats the underlying search system as a black box and is
more universally applicable to a diverse set of architectures. Furthermore, a
novel evaluation framework is presented to study the trade-off between the
number of triggered searches and the system's performance. We utilize the
framework to evaluate and compare the proposed reinforcement method with other
intuitive baselines. Experimental results demonstrate the efficacy of the
proposed method towards achieving a superior trade-off.

The goal of person search is to localize a target person from a gallery set
of scene images, which is extremely challenging due to large scale variations,
pose/viewpoint changes, and occlusions. In this paper, we propose the Cascade
Occluded Attention Transformer (COAT) for end-to-end person search. Our
three-stage cascade design focuses on detecting people in the first stage,
while later stages simultaneously and progressively refine the representation
for person detection and re-identification. At each stage the occluded
attention transformer applies tighter intersection over union thresholds,
forcing the network to learn coarse-to-fine pose/scale invariant features.
Meanwhile, we calculate each detection's occluded attention to differentiate a
person's tokens from other people or the background. In this way, we simulate
the effect of other objects occluding a person of interest at the token-level.
Through comprehensive experiments, we demonstrate the benefits of our method by
achieving state-of-the-art performance on two benchmark datasets.

In this work, we investigate the capacity of Generative Adversarial Networks
(GANs) in interpolating and extrapolating facies proportions in a geological
dataset. The new generated realizations with unrepresented (aka. missing)
proportions are assumed to belong to the same original data distribution.
Specifically, we design a conditional GANs model that can drive the generated
facies toward new proportions not found in the training set. The presented
study includes an investigation of various training settings and model
architectures. In addition, we devised new conditioning routines for an
improved generation of the missing samples. The presented numerical experiments
on images of binary and multiple facies showed good geological consistency as
well as strong correlation with the target conditions.

We formulate the task of detecting lines and paragraphs in a document into a
unified two-level clustering problem. Given a set of text detection boxes that
roughly correspond to words, a text line is a cluster of boxes and a paragraph
is a cluster of lines. These clusters form a two-level tree that represents a
major part of the layout of a document. We use a graph convolutional network to
predict the relations between text detection boxes and then build both levels
of clusters from these predictions. Experimentally, we demonstrate that the
unified approach can be highly efficient while still achieving state-of-the-art
quality for detecting paragraphs in public benchmarks and real-world images.

Accurately predicting the consequences of agents' actions is a key
prerequisite for planning in robotic control. Model-based reinforcement
learning (MBRL) is one paradigm which relies on the iterative learning and
prediction of state-action transitions to solve a task. Deep MBRL has become a
popular candidate, using a neural network to learn a dynamics model that
predicts with each pass from high-dimensional states to actions. These
"one-step" predictions are known to become inaccurate over longer horizons of
composed prediction - called the compounding error problem. Given the
prevalence of the compounding error problem in MBRL and related fields of
data-driven control, we set out to understand the properties of and conditions
causing these long-horizon errors. In this paper, we explore the effects of
subcomponents of a control problem on long term prediction error: including
choosing a system, collecting data, and training a model. These detailed
quantitative studies on simulated and real-world data show that the underlying
dynamics of a system are the strongest factor determining the shape and
magnitude of prediction error. Given a clearer understanding of compounding
prediction error, researchers can implement new types of models beyond
"one-step" that are more useful for control.

Semantic segmentation is an important task in computer vision that is often
tackled with convolutional neural networks (CNNs). A CNN learns to produce
pixel-level predictions through training on pairs of images and their
corresponding ground-truth segmentation labels. For segmentation tasks with
multiple classes, the standard approach is to use a network that computes a
multi-channel probabilistic segmentation map, with each channel representing
one class. In applications where the image grid size (e.g., when it is a 3D
volume) and/or the number of labels is relatively large, the standard
(baseline) approach can become prohibitively expensive for our computational
resources. In this paper, we propose a simple yet effective method to address
this challenge. In our approach, the segmentation network produces a
single-channel output, while being conditioned on a single class label, which
determines the output class of the network. Our method, called label
conditioned segmentation (LCS), can be used to segment images with a very large
number of classes, which might be infeasible for the baseline approach. We also
demonstrate in the experiments that label conditioning can improve the accuracy
of a given backbone architecture, likely, thanks to its parameter efficiency.
Finally, as we show in our results, an LCS model can produce previously unseen
fine-grained labels during inference time, when only coarse labels were
available during training. We provide all of our code here:
https://github.com/tym002/Label-conditioned-segmentation

In this paper, we propose a general framework for designing sensing matrix
$\boldsymbol{A} \in \mathbb{R}^{d\times p}$, for estimation of sparse
covariance matrix from compressed measurements of the form $\boldsymbol{y} =
\boldsymbol{A}\boldsymbol{x} + \boldsymbol{n}$, where $\boldsymbol{y},
\boldsymbol{n} \in \mathbb{R}^d$, and $\boldsymbol{x} \in \mathbb{R}^p$. By
viewing covariance recovery as inference over factor graphs via message passing
algorithm, ideas from coding theory, such as \textit{Density Evolution} (DE),
are leveraged to construct a framework for the design of the sensing matrix.
The proposed framework can handle both (1) regular sensing, i.e., equal
importance is given to all entries of the covariance, and (2) preferential
sensing, i.e., higher importance is given to a part of the covariance matrix.
Through experiments, we show that the sensing matrix designed via density
evolution can match the state-of-the-art for covariance recovery in the regular
sensing paradigm and attain improved performance in the preferential sensing
regime. Additionally, we study the feasibility of causal graph structure
recovery using the estimated covariance matrix obtained from the compressed

Effective and efficient planning in continuous state and action spaces is
fundamentally hard, even when the transition model is deterministic and known.
One way to alleviate this challenge is to perform bilevel planning with
abstractions, where a high-level search for abstract plans is used to guide
planning in the original transition space. In this paper, we develop a novel
framework for learning state and action abstractions that are explicitly
optimized for both effective (successful) and efficient (fast) bilevel
planning. Given demonstrations of tasks in an environment, our data-efficient
approach learns relational, neuro-symbolic abstractions that generalize over
object identities and numbers. The symbolic components resemble the STRIPS
predicates and operators found in AI planning, and the neural components refine
the abstractions into actions that can be executed in the environment.
Experimentally, we show across four robotic planning environments that our
learned abstractions are able to quickly solve held-out tasks of longer
horizons than were seen in the demonstrations, and can even outperform the
efficiency of abstractions that we manually specified. We also find that as the
planner configuration varies, the learned abstractions adapt accordingly,
indicating that our abstraction learning method is both "task-aware" and
"planner-aware." Code: https://tinyurl.com/predicators-release

In wireless Internet of things (IoT), the sensors usually have limited
bandwidth and power resources. Therefore, in a distributed setup, each sensor
should compress and quantize the sensed observations before transmitting them
to a fusion center (FC) where a global decision is inferred. Most of the
existing compression techniques and entropy quantizers consider only the
reconstruction fidelity as a metric, which means they decouple the compression
from the sensing goal. In this work, we argue that data compression mechanisms
and entropy quantizers should be co-designed with the sensing goal,
specifically for machine-consumed data. To this end, we propose a novel deep
learning-based framework for compressing and quantizing the observations of
correlated sensors. Instead of maximizing the reconstruction fidelity, our
objective is to compress the sensor observations in a way that maximizes the
accuracy of the inferred decision (i.e., sensing goal) at the FC. Unlike prior
work, we do not impose any assumptions about the observations distribution
which emphasizes the wide applicability of our framework. We also propose a
novel loss function that keeps the model focused on learning complementary
features at each sensor. The results show the superior performance of our
framework compared to other benchmark models.

Differentiable sorting algorithms allow training with sorting and ranking
supervision, where only the ordering or ranking of samples is known. Various
methods have been proposed to address this challenge, ranging from optimal
transport-based differentiable Sinkhorn sorting algorithms to making classic
sorting networks differentiable. One problem of current differentiable sorting
methods is that they are non-monotonic. To address this issue, we propose a
novel relaxation of conditional swap operations that guarantees monotonicity in
differentiable sorting networks. We introduce a family of sigmoid functions and
prove that they produce differentiable sorting networks that are monotonic.
Monotonicity ensures that the gradients always have the correct sign, which is
an advantage in gradient-based optimization. We demonstrate that monotonic
differentiable sorting networks improve upon previous differentiable sorting
methods.

Several studies have investigated the reasons behind the effectiveness of
fine-tuning, usually through the lens of probing. However, these studies often
neglect the role of the size of the dataset on which the model is fine-tuned.
In this paper, we highlight the importance of this factor and its undeniable
role in probing performance. We show that the extent of encoded linguistic
knowledge depends on the number of fine-tuning samples. The analysis also
reveals that larger training data mainly affects higher layers, and that the
extent of this change is a factor of the number of iterations updating the
model during fine-tuning rather than the diversity of the training samples.
Finally, we show through a set of experiments that fine-tuning data size
affects the recoverability of the changes made to the model's linguistic

Anomaly detection in process mining focuses on identifying anomalous cases or
events in process executions. The resulting diagnostics are used to provide
measures to prevent fraudulent behavior, as well as to derive recommendations
for improving process compliance and security. Most existing techniques focus
on detecting anomalous cases in an offline setting. However, to identify
potential anomalies in a timely manner and take immediate countermeasures, it
is necessary to detect event-level anomalies online, in real-time. In this
paper, we propose to tackle the online event anomaly detection problem using
next-activity prediction methods. More specifically, we investigate the use of
both ML models (such as RF and XGBoost) and deep models (such as LSTM) to
predict the probabilities of next-activities and consider the events predicted
unlikely as anomalies. We compare these predictive anomaly detection methods to
four classical unsupervised anomaly detection approaches (such as Isolation
forest and LOF) in the online setting. Our evaluation shows that the proposed
method using ML models tends to outperform the one using a deep model, while
both methods outperform the classical unsupervised approaches in detecting
anomalous events.

In classical cosmological analysis of large scale structure surveys with 2-pt
functions, the parameter measurement precision is limited by several key
degeneracies within the cosmology and astrophysics sectors. For cosmic shear,
clustering amplitude $\sigma_8$ and matter density $\Omega_m$ roughly follow
the $S_8=\sigma_8(\Omega_m/0.3)^{0.5}$ relation. In turn, $S_8$ is highly
correlated with the intrinsic galaxy alignment amplitude $A_{\rm{IA}}$. For
galaxy clustering, the bias $b_g$ is degenerate with both $\sigma_8$ and
$\Omega_m$, as well as the stochasticity $r_g$. Moreover, the redshift
evolution of IA and bias can cause further parameter confusion. A tomographic
2-pt probe combination can partially lift these degeneracies. In this work we
demonstrate that a deep learning analysis of combined probes of weak
gravitational lensing and galaxy clustering, which we call DeepLSS, can
effectively break these degeneracies and yield significantly more precise
constraints on $\sigma_8$, $\Omega_m$, $A_{\rm{IA}}$, $b_g$, $r_g$, and IA
redshift evolution parameter $\eta_{\rm{IA}}$. The most significant gains are
in the IA sector: the precision of $A_{\rm{IA}}$ is increased by approximately
8x and is almost perfectly decorrelated from $S_8$. Galaxy bias $b_g$ is
improved by 1.5x, stochasticity $r_g$ by 3x, and the redshift evolution
$\eta_{\rm{IA}}$ and $\eta_b$ by 1.6x. Breaking these degeneracies leads to a
significant gain in constraining power for $\sigma_8$ and $\Omega_m$, with the
figure of merit improved by 15x. We give an intuitive explanation for the
origin of this information gain using sensitivity maps. These results indicate
that the fully numerical, map-based forward modeling approach to cosmological
inference with machine learning may play an important role in upcoming LSS
surveys. We discuss perspectives and challenges in its practical deployment for
a full survey analysis.

Progress in the application of machine learning techniques to the prediction
of solid-state and molecular materials properties has been greatly facilitated
by the development state-of-the-art feature representations and novel deep
learning architectures. A large class of atomic structure representations based
on expansions of smoothed atomic densities have been shown to correspond to
specific choices of basis sets in an abstract many-body Hilbert space.
Concurrently, tensor network structures, conventionally the purview of quantum
many-body physics and quantum information, have been successfully applied in
supervised and unsupervised learning tasks in computer vision and natural
language processing. In this work, we argue that architectures based on tensor
networks are well-suited to machine learning on Hilbert-space representations
of atomic structures. This is demonstrated on supervised learning tasks
involving widely available datasets of density functional theory calculations
of metal and semiconductor alloys. In particular, we show that certain standard
tensor network topologies exhibit strong generalizability even on small
training datasets while being parametrically efficient. We further relate this
generalizability to the presence of complex entanglement in the trained tensor
networks. We also discuss connections to learning with generalized structural
kernels and related strategies for compressing large input feature spaces.

Spatial clustering has been widely used for spatial data mining and knowledge
discovery. An ideal multivariate spatial clustering should consider both
spatial contiguity and aspatial attributes. Existing spatial clustering
approaches may face challenges for discovering repeated geographic patterns
with spatial contiguity maintained. In this paper, we propose a Spatial
Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both
attributes and spatial relationships of geographic objects for multivariate
spatial clustering. A subregion is created for each geographic object serving
as the basic unit when performing clustering. A Markov random field is then
constructed to characterize the attribute dependencies of subregions. Using a
spatial consistency strategy, nearby objects are encouraged to belong to the
same cluster. To test the performance of the proposed STICC algorithm, we apply
it in two use cases. The comparison results with several baseline methods show
that the STICC outperforms others significantly in terms of adjusted rand index
and macro-F1 score. Join count statistics is also calculated and shows that the
spatial contiguity is well preserved by STICC. Such a spatial clustering method
may benefit various applications in the fields of geography, remote sensing,
transportation, and urban planning, etc.

To train machine learning models that are robust to distribution shifts in
the data, distributionally robust optimization (DRO) has been proven very
effective. However, the existing approaches to learning a distributionally
robust model either require solving complex optimization problems such as
semidefinite programming or a first-order method whose convergence scales
linearly with the number of data samples -- which hinders their scalability to
large datasets. In this paper, we show how different variants of DRO are simply
instances of a finite-sum composite optimization for which we provide scalable
methods. We also provide empirical results that demonstrate the effectiveness
of our proposed algorithm with respect to the prior art in order to learn
robust models from very large datasets.

Test Coverage criteria are an essential concept for test engineers when
generating the test cases from a System Under Test model. They are routinely
used in test case generation for user interfaces, middleware, and back-end
system parts for software, electronics, or Internet of Things (IoT) systems.
Test Coverage criteria define the number of actions or combinations by which a
system is tested, informally determining a potential "strength" of a test set.
As no previous study summarized all commonly used test coverage criteria for
Finite State Machines and comprehensively discussed them regarding their
subsumption, equivalence, or non-comparability, this paper provides this
overview. In this study, 14 most common test coverage criteria and seven of
their synonyms for Finite State Machines defined via a directed graph are
summarized and compared. The results give researchers and industry testing
engineers a helpful overview when setting a software-based or IoT system test
strategy.

Decays of Higgs boson-like particles into multileptons is a well-motivated
process for investigating physics beyond the Standard Model (SM). A unique
feature of this final state is the precision with which the SM is known. As a
result, simulations are used directly to estimate the background. Current
searches consider specific models and typically focus on those with a single
free parameter to simplify the analysis and interpretation. In this paper, we
explore recent proposals for signal model agnostic searches using machine
learning in the multilepton final state. These tools can be used to
simultaneously search for many models, some of which have no dedicated search
at the Large Hadron Collider. We find that the machine learning methods offer
broad coverage across parameter space beyond where current searches are
sensitive, with a necessary loss of performance compared to dedicated searches
by only about one order of magnitude.

While transformers demonstrate impressive performance on many knowledge
intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs)
remains limited, as shown on several slot-filling, question-answering (QA),
fact verification, and entity-linking tasks. In this paper, we implement an
efficient, data-programming technique that enriches training data with
KB-derived context and improves transformer utilization of encoded knowledge
when fine-tuning for a particular QA task, namely answer sentence selection
(AS2). Our method outperforms state of the art transformer approach on WikiQA
and TrecQA, two widely studied AS2 benchmarks, increasing by 2.0% p@1, 1.3%
MAP, 1.1% MRR, and 4.4% p@1, 0.9% MAP, 2.4% MRR, respectively. To demonstrate
our improvements in an industry setting, we additionally evaluate our approach
on a proprietary dataset of Alexa QA pairs, and show increase of 2.3% F1 and
2.0% MAP. We additionally find that these improvements remain even when KB
context is omitted at inference time, allowing for the use of our models within
existing transformer workflows without additional latency or deployment costs.

Model-based Testing (MBT) is an effective approach for testing when parts of
a system-under-test have the characteristics of a finite state machine (FSM).
Despite various strategies in the literature on this topic, little work exists
to handle special testing situations. More specifically, when concurrently: (1)
the test paths can start and end only in defined states of the FSM, (2) a
prioritization mechanism that requires only defined states and transitions of
the FSM to be visited by test cases is required, and (3) the test paths must be
in a given length range, not necessarily of explicit uniform length. This paper
presents a test generation strategy that satisfies all these requirements. A
concurrent combination of these requirements is highly practical for real
industrial testing. Six variants of possible algorithms to implement this
strategy are described. Using a mixture of 180 problem instances from real
automotive and defense projects and artificially generated FSMs, all variants
are compared with a baseline strategy based on an established N-switch coverage
concept modification. Various properties of the generated test paths and their
potential to activate fictional defects defined in FSMs are evaluated. The
presented strategy outperforms the baseline in most problem configurations. Out
of the six analyzed variants, three give the best results even though a
universal best performer is hard to identify. Depending on the application of
the FSM, the strategy and evaluation presented in this paper are applicable
both in testing functional and non-functional software requirements.

This paper aims to accelerate video stream processing, such as object
detection and semantic segmentation, by leveraging the temporal redundancies
that exist between video frames. Instead of propagating and warping features
using motion alignment, such as optical flow, we propose a novel knowledge
distillation schema coined as Delta Distillation. In our proposal, the student
learns the variations in the teacher's intermediate features over time. We
demonstrate that these temporal variations can be effectively distilled due to
the temporal redundancies within video frames. During inference, both teacher
and student cooperate for providing predictions: the former by providing
initial representations extracted only on the key-frame, and the latter by
iteratively estimating and applying deltas for the successive frames. Moreover,
we consider various design choices to learn optimal student architectures
including an end-to-end learnable architecture search. By extensive experiments
on a wide range of architectures, including the most efficient ones, we
demonstrate that delta distillation sets a new state of the art in terms of
accuracy vs. efficiency trade-off for semantic segmentation and object
detection in videos. Finally, we show that, as a by-product, delta distillation
improves the temporal consistency of the teacher model.

World knowledge exists in both structured (tables, knowledge graphs) and
unstructured forms (texts). Recently, there have been extensive research
efforts in the integration of structured factual knowledge and unstructured
textual knowledge. However, most studies focus on incorporating static factual
knowledge into pre-trained language models, while there is less work on
enhancing temporal knowledge graph embedding using textual knowledge. Existing
integration approaches can not apply to temporal knowledge graphs (tKGs) since
they often assume knowledge embedding is time-invariant. In fact, the entity
embedding in tKG embedding models usually evolves over time, which poses the
challenge of aligning temporally relevant textual information with entities. To
this end, we propose Enhanced Temporal Knowledge Embeddings with Contextualized
Language Representations (ECOLA), which uses tKG quadruple as an implicit
measure to temporally align textual data and the time-evolving entity
representations and uses a novel knowledge-text prediction task to inject
textual information into temporal knowledge embedding. ECOLA jointly optimizes
the knowledge-text prediction objective and the temporal knowledge embedding
objective, and thus, can simultaneously take full advantage of textual and
structured knowledge. Since existing datasets do not provide tKGs with aligned
textual data, we introduce three new datasets for training and evaluating
ECOLA. Experimental results on the temporal knowledge graph completion task
show that ECOLA outperforms state-of-the-art tKG embedding models by a large
margin.

To ensure satisfactory clinical outcomes, surgical skill assessment must be
objective, time-efficient, and preferentially automated - none of which is
currently achievable. Video-based assessment (VBA) is being deployed in
intraoperative and simulation settings to evaluate technical skill execution.
However, VBA remains manually- and time-intensive and prone to subjective
interpretation and poor inter-rater reliability. Herein, we propose a deep
learning (DL) model that can automatically and objectively provide a
high-stakes summative assessment of surgical skill execution based on video
feeds and low-stakes formative assessment to guide surgical skill acquisition.
Formative assessment is generated using heatmaps of visual features that
correlate with surgical performance. Hence, the DL model paves the way to the
quantitative and reproducible evaluation of surgical tasks from videos with the
potential for broad dissemination in surgical training, certification, and
credentialing.

Following the successful application of vision transformers in multiple
computer vision tasks, these models have drawn the attention of the signal
processing community. This is because signals are often represented as
spectrograms (e.g. through Discrete Fourier Transform) which can be directly
provided as input to vision transformers. However, naively applying
transformers to spectrograms is suboptimal. Since the axes represent distinct
dimensions, i.e. frequency and time, we argue that a better approach is to
separate the attention dedicated to each axis. To this end, we propose the
Separable Transformer (SepTr), an architecture that employs two transformer
blocks in a sequential manner, the first attending to tokens within the same
time interval, and the second attending to tokens within the same frequency
bin. We conduct experiments on three benchmark data sets, showing that our
separable architecture outperforms conventional vision transformers and other
state-of-the-art methods. Unlike standard transformers, SepTr linearly scales
the number of trainable parameters with the input size, thus having a lower
memory footprint. Our code is available as open source at
https://github.com/ristea/septr.

Detecting and evaluating surface coating defects is important for marine
vessel maintenance. Currently, the assessment is carried out manually by
qualified inspectors using international standards and their own experience.
Automating the processes is highly challenging because of the high level of
variation in vessel type, paint surface, coatings, lighting condition, weather
condition, paint colors, areas of the vessel, and time in service. We present a
novel deep learning-based pipeline to detect and evaluate the percentage of
corrosion, fouling, and delamination on the vessel surface from normal
photographs. We propose a multi-stage image processing framework, including
ship section segmentation, defect segmentation, and defect classification, to
automatically recognize different types of defects and measure the coverage
percentage on the ship surface. Experimental results demonstrate that our
proposed pipeline can objectively perform a similar assessment as a qualified
inspector.

In recent years, many applications have deployed incentive mechanisms to
promote users' attention and engagement. Most incentive mechanisms determine
specific incentive values based on users' attributes (e.g., preferences), while
such information is unavailable in many real-world applications. Meanwhile, due
to budget restrictions, realizing successful incentivization for all users can
be challenging to complete. In this light, we consider leveraging social
influence to maximize the incentivization result. We can directly incentivize
influential users to affect more users, so the cost of incentivizing these
users can be decreased. However, identifying influential users in a social
network requires complete information about influence strength among users,
which is impractical to acquire in real-world situations. In this research, we
propose an end-to-end reinforcement learning-based framework, called Geometric
Actor-Critic (GAC), to tackle the abovementioned problem. The proposed approach
can realize effective incentive allocation without having prior knowledge about
users' attributes. Three real-world social network datasets have been adopted
in the experiments to evaluate the performance of GAC. The experimental results
indicate that GAC can learn and apply effective incentive allocation policies
in unknown social networks and outperform existing incentive allocation

We propose data-driven one-pass streaming algorithms for estimating the
number of triangles and four cycles, two fundamental problems in graph
analytics that are widely studied in the graph data stream literature.
Recently, (Hsu 2018) and (Jiang 2020) applied machine learning techniques in
other data stream problems, using a trained oracle that can predict certain
properties of the stream elements to improve on prior "classical" algorithms
that did not use oracles. In this paper, we explore the power of a "heavy edge"
oracle in multiple graph edge streaming models. In the adjacency list model, we
present a one-pass triangle counting algorithm improving upon the previous
space upper bounds without such an oracle. In the arbitrary order model, we
present algorithms for both triangle and four cycle estimation with fewer
passes and the same space complexity as in previous algorithms, and we show
several of these bounds are optimal. We analyze our algorithms under several
noise models, showing that the algorithms perform well even when the oracle
errs. Our methodology expands upon prior work on "classical" streaming
algorithms, as previous multi-pass and random order streaming algorithms can be
seen as special cases of our algorithms, where the first pass or random order
was used to implement the heavy edge oracle. Lastly, our experiments
demonstrate advantages of the proposed method compared to state-of-the-art
streaming algorithms.

We propose sanitizer, a framework for secure and task-agnostic data release.
While releasing datasets continues to make a big impact in various applications
of computer vision, its impact is mostly realized when data sharing is not
inhibited by privacy concerns. We alleviate these concerns by sanitizing
datasets in a two-stage process. First, we introduce a global decoupling stage
for decomposing raw data into sensitive and non-sensitive latent
representations. Secondly, we design a local sampling stage to synthetically
generate sensitive information with differential privacy and merge it with
non-sensitive latent features to create a useful representation while
preserving the privacy. This newly formed latent information is a task-agnostic
representation of the original dataset with anonymized sensitive information.
While most algorithms sanitize data in a task-dependent manner, a few
task-agnostic sanitization techniques sanitize data by censoring sensitive
information. In this work, we show that a better privacy-utility trade-off is
achieved if sensitive information can be synthesized privately. We validate the
effectiveness of the sanitizer by outperforming state-of-the-art baselines on
the existing benchmark tasks and demonstrating tasks that are not possible
using existing techniques.

How can we best address the dangerous impact that deep learning-generated
fake audios, photographs, and videos (a.k.a. deepfakes) may have in personal
and societal life? We foresee that the availability of cheap deepfake
technology will create a second wave of disinformation where people will
receive specific, personalized disinformation through different channels,
making the current approaches to fight disinformation obsolete. We argue that
fake media has to be seen as an upcoming cybersecurity problem, and we have to
shift from combating its spread to a prevention and cure framework where users
have available ways to verify, challenge, and argue against the veracity of
each piece of media they are exposed to. To create the technologies behind this
framework, we propose that a new Science of Disinformation is needed, one which
creates a theoretical framework both for the processes of communication and
consumption of false content. Key scientific and technological challenges
facing this research agenda are listed and discussed in the light of
state-of-art technologies for fake media generation and detection, argument
finding and construction, and how to effectively engage users in the prevention
and cure processes.

The use of personal data for training machine learning systems comes with a
privacy threat and measuring the level of privacy of a model is one of the
major challenges in machine learning today. Identifying training data based on
a trained model is a standard way of measuring the privacy risks induced by the
model. We develop a novel approach to address the problem of membership
inference in pattern recognition models, relying on information provided by
adversarial examples. The strategy we propose consists of measuring the
magnitude of a perturbation necessary to build an adversarial example. Indeed,
we argue that this quantity reflects the likelihood of belonging to the
training data. Extensive numerical experiments on multivariate data and an
array of state-of-the-art target models show that our method performs
comparable or even outperforms state-of-the-art strategies, but without
requiring any additional training samples.

Reinforcement learning (RL) approaches can illuminate emergent behaviors that
facilitate coordination across teams of agents as part of a multi-agent system
(MAS), which can provide windows of opportunity in various military tasks.
Technologically advancing adversaries pose substantial risks to a friendly
nation's interests and resources. Superior resources alone are not enough to
defeat adversaries in modern complex environments because adversaries create
standoff in multiple domains against predictable military doctrine-based
maneuvers. Therefore, as part of a defense strategy, friendly forces must use
strategic maneuvers and disruption to gain superiority in complex multi-faceted
domains such as multi-domain operations (MDO). One promising avenue for
implementing strategic maneuver and disruption to gain superiority over
adversaries is through coordination of MAS in future military operations. In
this paper, we present overviews of prominent works in the RL domain with their
strengths and weaknesses for overcoming the challenges associated with
performing autonomous strategic maneuver and disruption in military contexts.

We study balancing weight estimators, which reweight outcomes from a source
population to estimate missing outcomes in a target population. These
estimators minimize the worst-case error by making an assumption about the
outcome model. In this paper, we show that this outcome assumption has two
immediate implications. First, we can replace the minimax optimization problem
for balancing weights with a simple convex loss over the assumed outcome
function class. Second, we can replace the commonly-made overlap assumption
with a more appropriate quantitative measure, the minimum worst-case bias.
Finally, we show conditions under which the weights remain robust when our
assumptions on the outcomes are wrong.

We investigate the power of message-passing neural networks (MPNNs) in their
capacity to transform the numerical features stored in the nodes of their input
graphs. Our focus is on global expressive power, uniformly over all input
graphs, or over graphs of bounded degree with features from a bounded domain.
Accordingly, we introduce the notion of a global feature map transformer
(GFMT). As a yardstick for expressiveness, we use a basic language for GFMTs,
which we call MPLang. Every MPNN can be expressed in MPLang, and our results
clarify to which extent the converse inclusion holds. We consider exact versus
approximate expressiveness; the use of arbitrary activation functions; and the
case where only the ReLU activation function is allowed.

Federated learning (FL) can be essential in knowledge representation,
reasoning, and data mining applications over multi-source knowledge graphs
(KGs). A recent study FedE first proposes an FL framework that shares entity
embeddings of KGs across all clients. However, entity embedding sharing from
FedE would incur a severe privacy leakage. Specifically, the known entity
embedding can be used to infer whether a specific relation between two entities
exists in a private client. In this paper, we introduce a novel attack method
that aims to recover the original data based on the embedding information,
which is further used to evaluate the vulnerabilities of FedE. Furthermore, we
propose a Federated learning paradigm with privacy-preserving Relation
embedding aggregation (FedR) to tackle the privacy issue in FedE. Besides,
relation embedding sharing can significantly reduce the communication cost due
to its smaller size of queries. We conduct extensive experiments to evaluate
FedR with five different KG embedding models and three datasets. Compared to
FedE, FedR achieves similar utility and significant improvements regarding
privacy-preserving effect and communication efficiency on the link prediction
task.

Gait recognition is one of the most recent emerging techniques of human
biometric which can be used for security based purposes having unobtrusive
learning method. In comparison with other bio-metrics gait analysis has some
special security features. Most of the biometric technique uses sequential
template based component analysis for recognition. Comparing with those
methods, we proposed a developed technique for gait identification using the
feature Gait Energy Image (GEI). GEI representation of gait contains all
information of each image in one gait cycle and requires less storage and low
processing speed. As only one image is enough to store the necessary
information in GEI feature recognition process is very easier than any other
feature for gait recognition. Gait recognition has some limitations in
recognition process like viewing angle variation, walking speed, clothes,
carrying load etc. Our proposed method in the paper compares the recognition
performance with template based feature extraction which needs to process each
frame in the cycle. We use GEI which gives relatively all information about all
the frames in the cycle and results in better performance than other feature of
gait analysis.

Machine learning is a promising tool to reconstruct time-series phenomena,
such as variability of active galactic nuclei (AGN), from sparsely-sampled
data. Here we use three Continuous Auto-Regressive Moving Average (CARMA)
representations of AGN variability -- the Damped Random Walk (DRW) and
(over/under-)Damped Harmonic Oscillator (DHO) -- to simulate 10-year AGN light
curves as they would appear in the upcoming Vera Rubin Observatory Legacy
Survey of Space and Time (LSST), and provide a public tool to generate these
for any survey cadence. We investigate the impact on AGN science of five
proposed cadence strategies for LSST's primary Wide-Fast-Deep (WFD) survey. We
apply for the first time in astronomy a novel Stochastic Recurrent Neural
Network (SRNN) algorithm to reconstruct input light curves from the simulated
LSST data, and provide a metric to evaluate how well SRNN can help recover the
underlying CARMA parameters. We find that the light curve reconstruction is
most sensitive to the duration of gaps between observing season, and that of
the proposed cadences, those that change the balance between filters, or avoid
having long gaps in the {g}-band perform better. Overall, SRNN is a promising
means to reconstruct densely sampled AGN light curves and recover the long-term
Structure Function of the DRW process (SF$_\infty$) reasonably well. However,
we find that for all cadences, CARMA/SRNN models struggle to recover the
decorrelation timescale ($\tau$) due to the long gaps in survey observations.
This may indicate a major limitation in using LSST WFD data for AGN variability
science.

Strong Lensing is a powerful probe of the matter distribution in galaxies and
clusters and a relevant tool for cosmography. Analyses of strong gravitational
lenses with Deep Learning have become a popular approach due to these
astronomical objects' rarity and image complexity. Next-generation surveys will
provide more opportunities to derive science from these objects and an
increasing data volume to be analyzed. However, finding strong lenses is
challenging, as their number densities are orders of magnitude below those of
galaxies. Therefore, specific Strong Lensing search algorithms are required to
discover the highest number of systems possible with high purity and low false
alarm rate. The need for better algorithms has prompted the development of an
open community data science competition named Strong Gravitational Lensing
Challenge (SGLC). This work presents the Deep Learning strategies and
methodology used to design the highest-scoring algorithm in the II SGLC. We
discuss the approach used for this dataset, the choice for a suitable
architecture, particularly the use of a network with two branches to work with
images in different resolutions, and its optimization. We also discuss the
detectability limit, the lessons learned, and prospects for defining a
tailor-made architecture in a survey in contrast to a general one. Finally, we
release the models and discuss the best choice to easily adapt the model to a
dataset representing a survey with a different instrument. This work helps to
take a step towards efficient, adaptable and accurate analyses of strong lenses
with deep learning frameworks.

Powerful priors allow us to perform inference with insufficient information.
In this paper, we propose an autoregressive prior for 3D shapes to solve
multimodal 3D tasks such as shape completion, reconstruction, and generation.
We model the distribution over 3D shapes as a non-sequential autoregressive
distribution over a discretized, low-dimensional, symbolic grid-like latent
representation of 3D shapes. This enables us to represent distributions over 3D
shapes conditioned on information from an arbitrary set of spatially anchored
query locations and thus perform shape completion in such arbitrary settings
(e.g., generating a complete chair given only a view of the back leg). We also
show that the learned autoregressive prior can be leveraged for conditional
tasks such as single-view reconstruction and language-based generation. This is
achieved by learning task-specific naive conditionals which can be approximated
by light-weight models trained on minimal paired data. We validate the
effectiveness of the proposed method using both quantitative and qualitative
evaluation and show that the proposed method outperforms the specialized
state-of-the-art methods trained for individual tasks. The project page with
code and video visualizations can be found at
https://yccyenchicheng.github.io/AutoSDF/.

Real-world data often exhibit imbalanced label distributions. Existing
studies on data imbalance focus on single-domain settings, i.e., samples are
from the same data distribution. However, natural data can originate from
distinct domains, where a minority class in one domain could have abundant
instances from other domains. We formalize the task of Multi-Domain Long-Tailed
Recognition (MDLT), which learns from multi-domain imbalanced data, addresses
label imbalance, domain shift, and divergent label distributions across
domains, and generalizes to all domain-class pairs. We first develop the
domain-class transferability graph, and show that such transferability governs
the success of learning in MDLT. We then propose BoDA, a theoretically grounded
learning strategy that tracks the upper bound of transferability statistics,
and ensures balanced alignment and calibration across imbalanced domain-class
distributions. We curate five MDLT benchmarks based on widely-used multi-domain
datasets, and compare BoDA to twenty algorithms that span different learning
strategies. Extensive and rigorous experiments verify the superior performance
of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on
Domain Generalization benchmarks, highlighting the importance of addressing
data imbalance across domains, which can be crucial for improving
generalization to unseen domains. Code and data are available at:
https://github.com/YyzHarry/multi-domain-imbalance.

While numerous 3D detection works leverage the complementary relationship
between RGB images and point clouds, developments in the broader framework of
semi-supervised object recognition remain uninfluenced by multi-modal fusion.
Current methods develop independent pipelines for 2D and 3D semi-supervised
learning despite the availability of paired image and point cloud frames.
Observing that the distinct characteristics of each sensor cause them to be
biased towards detecting different objects, we propose DetMatch, a flexible
framework for joint semi-supervised learning on 2D and 3D modalities. By
identifying objects detected in both sensors, our pipeline generates a cleaner,
more robust set of pseudo-labels that both demonstrates stronger performance
and stymies single-modality error propagation. Further, we leverage the richer
semantics of RGB images to rectify incorrect 3D class predictions and improve
localization of 3D boxes. Evaluating on the challenging KITTI and Waymo
datasets, we improve upon strong semi-supervised learning methods and observe
higher quality pseudo-labels. Code will be released at
https://github.com/Divadi/DetMatch

Toxic language detection systems often falsely flag text that contains
minority group mentions as toxic, as those groups are often the targets of
online hate. Such over-reliance on spurious correlations also causes systems to
struggle with detecting implicitly toxic language. To help mitigate these
issues, we create ToxiGen, a new large-scale and machine-generated dataset of
274k toxic and benign statements about 13 minority groups. We develop a
demonstration-based prompting framework and an adversarial
classifier-in-the-loop decoding method to generate subtly toxic and benign text
with a massive pretrained language model. Controlling machine generation in
this way allows ToxiGen to cover implicitly toxic text at a larger scale, and
about more demographic groups, than previous resources of human-written text.
We conduct a human evaluation on a challenging subset of ToxiGen and find that
annotators struggle to distinguish machine-generated text from human-written
language. We also find that 94.5% of toxic examples are labeled as hate speech
by human annotators. Using three publicly-available datasets, we show that
finetuning a toxicity classifier on our data improves its performance on
human-written data substantially. We also demonstrate that ToxiGen can be used
to fight machine-generated toxicity as finetuning improves the classifier
significantly on our evaluation subset. Our code and data can be found at
https://github.com/microsoft/ToxiGen.

Learned communication between agents is a powerful tool when approaching
decision-making problems that are hard to overcome by any single agent in
isolation. However, continual coordination and communication learning between
machine agents or human-machine partnerships remains a challenging open
problem. As a stepping stone toward solving the continual communication
learning problem, in this paper we contribute a multi-faceted study into what
we term Pavlovian signalling -- a process by which learned, temporally extended
predictions made by one agent inform decision-making by another agent with
different perceptual access to their shared environment. We seek to establish
how different temporal processes and representational choices impact Pavlovian
signalling between learning agents. To do so, we introduce a partially
observable decision-making domain we call the Frost Hollow. In this domain a
prediction learning agent and a reinforcement learning agent are coupled into a
two-part decision-making system that seeks to acquire sparse reward while
avoiding time-conditional hazards. We evaluate two domain variations: 1)
machine prediction and control learning in a linear walk, and 2) a prediction
learning machine interacting with a human participant in a virtual reality
environment. Our results showcase the speed of learning for Pavlovian
signalling, the impact that different temporal representations do (and do not)
have on agent-agent coordination, and how temporal aliasing impacts agent-agent
and human-agent interactions differently. As a main contribution, we establish
Pavlovian signalling as a natural bridge between fixed signalling paradigms and
fully adaptive communication learning. Our results therefore point to an
actionable, constructivist path towards continual communication learning
between reinforcement learning agents, with potential impact in a range of
real-world settings.

We present a general-purpose framework for image modelling and vision tasks
based on probabilistic frame prediction. Our approach unifies a broad range of
tasks, from image segmentation, to novel view synthesis and video
interpolation. We pair this framework with an architecture we term Transframer,
which uses U-Net and Transformer components to condition on annotated context
frames, and outputs sequences of sparse, compressed image features. Transframer
is the state-of-the-art on a variety of video generation benchmarks, is
competitive with the strongest models on few-shot view synthesis, and can
generate coherent 30 second videos from a single image without any explicit
geometric information. A single generalist Transframer simultaneously produces
promising results on 8 tasks, including semantic segmentation, image
classification and optical flow prediction with no task-specific architectural
components, demonstrating that multi-task computer vision can be tackled using
probabilistic image models. Our approach can in principle be applied to a wide
range of applications that require learning the conditional structure of
annotated image-formatted data.

We propose a framework for training non-autoregressive sequence-to-sequence
models for editing tasks, where the original input sequence is iteratively
edited to produce the output. We show that the imitation learning algorithms
designed to train such models for machine translation introduces mismatches
between training and inference that lead to undertraining and poor
generalization in editing scenarios. We address this issue with two
complementary strategies: 1) a roll-in policy that exposes the model to
intermediate training sequences that it is more likely to encounter during
inference, 2) a curriculum that presents easy-to-learn edit operations first,
gradually increasing the difficulty of training samples as the model becomes
competent. We show the efficacy of these strategies on two challenging English
editing tasks: controllable text simplification and abstractive summarization.
Our approach significantly improves output quality on both tasks and controls
output complexity better on the simplification task.

Obtaining accurate ground and low-lying excited states of electronic systems
is crucial in a multitude of important applications. One ab initio method for
solving the Schr\"odinger equation that scales favorably for large systems is
variational quantum Monte Carlo (QMC). The recently introduced deep QMC
approach uses ansatzes represented by deep neural networks and generates nearly
exact ground-state solutions for molecules containing up to a few dozen
electrons, with the potential to scale to much larger systems where other
highly accurate methods are not feasible. In this paper, we extend one such
ansatz (PauliNet) to compute electronic excited states. We demonstrate our
method on various small atoms and molecules and consistently achieve high
accuracy for low-lying states. To highlight the method's potential, we compute
the first excited state of the much larger benzene molecule, as well as the
conical intersection of ethylene, with PauliNet matching results of more
expensive high-level methods.

We propose a new model independent technique for constructing background data
templates for use in searches for new physics processes at the LHC. This
method, called CURTAINs, uses invertible neural networks to parametrise the
distribution of side band data as a function of the resonant observable. The
network learns a transformation to map any data point from its value of the
resonant observable to another chosen value. Using CURTAINs, a template for the
background data in the signal window is constructed by mapping the data from
the side-bands into the signal region. We perform anomaly detection using the
CURTAINs background template to enhance the sensitivity to new physics in a
bump hunt. We demonstrate its performance in a sliding window search across a
wide range of mass values. Using the LHC Olympics dataset, we demonstrate that
CURTAINs outperforms other leading approaches which aim to improve the
sensitivity of bump hunts. It can be trained on a much smaller range of the
invariant mass, and is fully data driven.

Novel view synthesis from a single image has recently attracted a lot of
attention, and it has been primarily advanced by 3D deep learning and rendering
techniques. However, most work is still limited by synthesizing new views
within relatively small camera motions. In this paper, we propose a novel
approach to synthesize a consistent long-term video given a single scene image
and a trajectory of large camera motions. Our approach utilizes an
autoregressive Transformer to perform sequential modeling of multiple frames,
which reasons the relations between multiple frames and the corresponding
cameras to predict the next frame. To facilitate learning and ensure
consistency among generated frames, we introduce a locality constraint based on
the input cameras to guide self-attention among a large number of patches
across space and time. Our method outperforms state-of-the-art view synthesis
approaches by a large margin, especially when synthesizing long-term future in
indoor 3D scenes. Project page at https://xrenaa.github.io/look-outside-room/.

The usefulness of deep learning models in robotics is largely dependent on
the availability of training data. Manual annotation of training data is often
infeasible. Synthetic data is a viable alternative, but suffers from domain
gap. We propose a multi-step method to obtain training data without manual
annotation effort: From 3D object meshes, we generate images using a modern
synthesis pipeline. We utilize a state-of-the-art image-to-image translation
method to adapt the synthetic images to the real domain, minimizing the domain
gap in a learned manner. The translation network is trained from unpaired
images, i.e. just requires an un-annotated collection of real images. The
generated and refined images can then be used to train deep learning models for
a particular task. We also propose and evaluate extensions to the translation
method that further increase performance, such as patch-based training, which
shortens training time and increases global consistency. We evaluate our method
and demonstrate its effectiveness on two robotic datasets. We finally give
insight into the learned refinement operations.

Existing continual learning techniques focus on either task incremental
learning (TIL) or class incremental learning (CIL) problem, but not both. CIL
and TIL differ mainly in that the task-id is provided for each test sample
during testing for TIL, but not provided for CIL. Continual learning methods
intended for one problem have limitations on the other problem. This paper
proposes a novel unified approach based on out-of-distribution (OOD) detection
and task masking, called CLOM, to solve both problems. The key novelty is that
each task is trained as an OOD detection model rather than a traditional
supervised learning model, and a task mask is trained to protect each task to
prevent forgetting. Our evaluation shows that CLOM outperforms existing
state-of-the-art baselines by large margins. The average TIL/CIL accuracy of
CLOM over six experiments is 87.6/67.9% while that of the best baselines is
only 82.4/55.0%.

The reconstruction of cortical surfaces from brain magnetic resonance imaging
(MRI) scans is essential for quantitative analyses of cortical thickness and
sulcal morphology. Although traditional and deep learning-based algorithmic
pipelines exist for this purpose, they have two major drawbacks: lengthy
runtimes of multiple hours (traditional) or intricate post-processing, such as
mesh extraction and topology correction (deep learning-based). In this work, we
address both of these issues and propose Vox2Cortex, a deep learning-based
algorithm that directly yields topologically correct, three-dimensional meshes
of the boundaries of the cortex. Vox2Cortex leverages convolutional and graph
convolutional neural networks to deform an initial template to the densely
folded geometry of the cortex represented by an input MRI scan. We show in
extensive experiments on three brain MRI datasets that our meshes are as
accurate as the ones reconstructed by state-of-the-art methods in the field,
without the need for time- and resource-intensive post-processing. To
accurately reconstruct the tightly folded cortex, we work with meshes
containing about 168,000 vertices at test time, scaling deep explicit
reconstruction methods to a new level.

Image super-resolution (SR) techniques are used to generate a high-resolution
image from a low-resolution image. Until now, deep generative models such as
autoregressive models and Generative Adversarial Networks (GANs) have proven to
be effective at modelling high-resolution images. VAE-based models have often
been criticised for their feeble generative performance, but with new
advancements such as VDVAE, there is now strong evidence that deep VAEs have
the potential to outperform current state-of-the-art models for high-resolution
image generation. In this paper, we introduce VDVAE-SR, a new model that aims
to exploit the most recent deep VAE methodologies to improve upon the results
of similar models. VDVAE-SR tackles image super-resolution using transfer
learning on pretrained VDVAEs. The presented model is competitive with other
state-of-the-art models, having comparable results on image quality metrics.

The problem of identifying a probabilistic context free grammar has two
aspects: the first is determining the grammar's topology (the rules of the
grammar) and the second is estimating probabilistic weights for each rule.
Given the hardness results for learning context-free grammars in general, and
probabilistic grammars in particular, most of the literature has concentrated
on the second problem. In this work we address the first problem. We restrict
attention to structurally unambiguous weighted context-free grammars (SUWCFG)
and provide a query learning algorithm for \structurally unambiguous
probabilistic context-free grammars (SUPCFG). We show that SUWCFG can be
represented using \emph{co-linear multiplicity tree automata} (CMTA), and
provide a polynomial learning algorithm that learns CMTAs. We show that the
learned CMTA can be converted into a probabilistic grammar, thus providing a
complete algorithm for learning a structurally unambiguous probabilistic
context free grammar (both the grammar topology and the probabilistic weights)
using structured membership queries and structured equivalence queries. A
summarized version of this work was published at AAAI 21.

Many basic indoor activities such as eating or writing are always conducted
upon different tabletops (e.g., coffee tables, writing desks). It is
indispensable to understanding tabletop scenes in 3D indoor scene parsing
applications. Unfortunately, it is hard to meet this demand by directly
deploying data-driven algorithms, since 3D tabletop scenes are rarely available
in current datasets. To remedy this defect, we introduce TO-Scene, a
large-scale dataset focusing on tabletop scenes, which contains 20,740 scenes
with three variants. To acquire the data, we design an efficient and scalable
framework, where a crowdsourcing UI is developed to transfer CAD objects from
ModelNet and ShapeNet onto tables from ScanNet, then the output tabletop scenes
are simulated into real scans and annotated automatically.
  Further, a tabletop-aware learning strategy is proposed for better perceiving
the small-sized tabletop instances. Notably, we also provide a real scanned
test set TO-Real to verify the practical value of TO-Scene. Experiments show
that the algorithms trained on TO-Scene indeed work on the realistic test data,
and our proposed tabletop-aware learning strategy greatly improves the
state-of-the-art results on both 3D semantic segmentation and object detection
tasks. Dataset and code are available at
https://github.com/GAP-LAB-CUHK-SZ/TO-Scene.

To compare alternative taxi schedules and to compute them, as well as to
provide insights into an upcoming taxi trip to drivers and passengers, the
duration of a trip or its Estimated Time of Arrival (ETA) is predicted. To
reach a high prediction precision, machine learning models for ETA are state of
the art. One yet unexploited option to further increase prediction precision is
to combine multiple ETA models into an ensemble. While an increase of
prediction precision is likely, the main drawback is that the predictions made
by such an ensemble become less transparent due to the sophisticated ensemble
architecture. One option to remedy this drawback is to apply eXplainable
Artificial Intelligence (XAI). The contribution of this paper is three-fold.
First, we combine multiple machine learning models from our previous work for
ETA into a two-level ensemble model - a stacked ensemble model - which on its
own is novel; therefore, we can outperform previous state-of-the-art static
route-free ETA approaches. Second, we apply existing XAI methods to explain the
first- and second-level models of the ensemble. Third, we propose three joining
methods for combining the first-level explanations with the second-level ones.
Those joining methods enable us to explain stacked ensembles for regression
tasks. An experimental evaluation shows that the ETA models correctly learned
the importance of those input features driving the prediction.

We study stochastic monotone inclusion problems, which widely appear in
machine learning applications, including robust regression and adversarial
learning. We propose novel variants of stochastic Halpern iteration with
recursive variance reduction. In the cocoercive -- and more generally
Lipschitz-monotone -- setup, our algorithm attains $\epsilon$ norm of the
operator with $\mathcal{O}(\frac{1}{\epsilon^3})$ stochastic operator
evaluations, which significantly improves over state of the art
$\mathcal{O}(\frac{1}{\epsilon^4})$ stochastic operator evaluations required
for existing monotone inclusion solvers applied to the same problem classes. We
further show how to couple one of the proposed variants of stochastic Halpern
iteration with a scheduled restart scheme to solve stochastic monotone
inclusion problems with ${\mathcal{O}}(\frac{\log(1/\epsilon)}{\epsilon^2})$
stochastic operator evaluations under additional sharpness or strong
monotonicity assumptions.

Deep learning-based methods have made significant achievements for image
dehazing. However, most of existing dehazing networks are concentrated on
training models using simulated hazy images, resulting in generalization
performance degradation when applied on real-world hazy images because of
domain shift. In this paper, we propose a mutual learning dehazing framework
for domain adaption. Specifically, we first devise two siamese networks: a
teacher network in the synthetic domain and a student network in the real
domain, and then optimize them in a mutual learning manner by leveraging EMA
and joint loss. Moreover, we design a sample-cycle strategy based on density
augmentation (HDA) module to introduce pseudo real-world image pairs provided
by the student network into training for further improving the generalization
performance. Extensive experiments on both synthetic and real-world dataset
demonstrate that the propose mutual learning framework outperforms
state-of-the-art dehazing techniques in terms of subjective and objective

Commonsense question answering requires reasoning about everyday situations
and causes and effects implicit in context. Typically, existing approaches
first retrieve external evidence and then perform commonsense reasoning using
these evidence. In this paper, we propose a Self-supervised Bidirectional
Encoder Representation Learning of Commonsense (elBERto) framework, which is
compatible with off-the-shelf QA model architectures. The framework comprises
five self-supervised tasks to force the model to fully exploit the additional
training signals from contexts containing rich commonsense. The tasks include a
novel Contrastive Relation Learning task to encourage the model to distinguish
between logically contrastive contexts, a new Jigsaw Puzzle task that requires
the model to infer logical chains in long contexts, and three classic SSL tasks
to maintain pre-trained models language encoding ability. On the representative
WIQA, CosmosQA, and ReClor datasets, elBERto outperforms all other methods,
including those utilizing explicit graph reasoning and external knowledge
retrieval. Moreover, elBERto achieves substantial improvements on
out-of-paragraph and no-effect questions where simple lexical similarity
comparison does not help, indicating that it successfully learns commonsense
and is able to leverage it when given dynamic context.

Establishing correspondences from image to 3D has been a key task of 6DoF
object pose estimation for a long time. To predict pose more accurately, deeply
learned dense maps replaced sparse templates. Dense methods also improved pose
estimation in the presence of occlusion. More recently researchers have shown
improvements by learning object fragments as segmentation. In this work, we
present a discrete descriptor, which can represent the object surface densely.
By incorporating a hierarchical binary grouping, we can encode the object
surface very efficiently. Moreover, we propose a coarse to fine training
strategy, which enables fine-grained correspondence prediction. Finally, by
matching predicted codes with object surface and using a PnP solver, we
estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show
major improvement over the state of the art w.r.t. ADD(-S) metric, even
surpassing RGB-D based methods in some cases.

The saliency ranking task is recently proposed to study the visual behavior
that humans would typically shift their attention over different objects of a
scene based on their degrees of saliency. Existing approaches focus on learning
either object-object or object-scene relations. Such a strategy follows the
idea of object-based attention in Psychology, but it tends to favor those
objects with strong semantics (e.g., humans), resulting in unrealistic saliency
ranking. We observe that spatial attention works concurrently with object-based
attention in the human visual recognition system. During the recognition
process, the human spatial attention mechanism would move, engage, and
disengage from region to region (i.e., context to context). This inspires us to
model the region-level interactions, in addition to the object-level reasoning,
for saliency ranking. To this end, we propose a novel bi-directional method to
unify spatial attention and object-based attention for saliency ranking. Our
model includes two novel modules: (1) a selective object saliency (SOS) module
that models objectbased attention via inferring the semantic representation of
the salient object, and (2) an object-context-object relation (OCOR) module
that allocates saliency ranks to objects by jointly modeling the object-context
and context-object interactions of the salient objects. Extensive experiments
show that our approach outperforms existing state-of-theart methods. Our code
and pretrained model are available at https://github.com/GrassBro/OCOR.

Underwater visual perception is essentially important for underwater
exploration, archeology, ecosystem and so on. The low illumination, light
reflections, scattering, absorption and suspended particles inevitably lead to
the critically degraded underwater image quality, which causes great challenges
on recognizing the objects from the underwater images. The existing underwater
enhancement methods that aim to promote the underwater visibility, heavily
suffer from the poor image restoration performance and generalization ability.
To reduce the difficulty of underwater image enhancement, we introduce the
media transmission map as guidance to assist in image enhancement. We formulate
the interaction between the underwater visual images and the transmission map
to obtain better enhancement results. Even with simple and lightweight network
configuration, the proposed method can achieve advanced results of 22.6 dB on
the challenging Test-R90 with an impressive 30 times faster than the existing
models. Comprehensive experimental results have demonstrated the superiority
and potential on underwater perception. Paper's code is offered on:
https://github.com/GroupG-yk/MTUR-Net.

In this paper, we analyze the generalization performance of the Iterative
Hard Thresholding (IHT) algorithm widely used for sparse recovery problems. The
parameter estimation and sparsity recovery consistency of IHT has long been
known in compressed sensing. From the perspective of statistical learning,
another fundamental question is how well the IHT estimation would predict on
unseen data. This paper makes progress towards answering this open question by
introducing a novel sparse generalization theory for IHT under the notion of
algorithmic stability. Our theory reveals that: 1) under natural conditions on
the empirical risk function over $n$ samples of dimension $p$, IHT with
sparsity level $k$ enjoys an $\mathcal{\tilde
O}(n^{-1/2}\sqrt{k\log(n)\log(p)})$ rate of convergence in sparse excess risk;
2) a tighter $\mathcal{\tilde O}(n^{-1/2}\sqrt{\log(n)})$ bound can be
established by imposing an additional iteration stability condition on a
hypothetical IHT procedure invoked to the population risk; and 3) a fast rate
of order $\mathcal{\tilde O}\left(n^{-1}k(\log^3(n)+\log(p))\right)$ can be
derived for strongly convex risk function under proper strong-signal
conditions. The results have been substantialized to sparse linear regression
and sparse logistic regression models to demonstrate the applicability of our
theory. Preliminary numerical evidence is provided to confirm our theoretical

The acquisition of labels for supervised learning can be expensive. In order
to improve the sample-efficiency of neural network regression, we study active
learning methods that adaptively select batches of unlabeled data for labeling.
We present a framework for constructing such methods out of (network-dependent)
base kernels, kernel transformations and selection methods. Our framework
encompasses many existing Bayesian methods based on Gaussian Process
approximations of neural networks as well as non-Bayesian methods.
Additionally, we propose to replace the commonly used last-layer features with
sketched finite-width Neural Tangent Kernels, and to combine them with a novel
clustering method. To evaluate different methods, we introduce an open-source
benchmark consisting of 15 large tabular regression data sets. Our proposed
method outperforms the state-of-the-art on our benchmark, scales to large data
sets, and works out-of-the-box without adjusting the network architecture or
training code. We provide open-source code that includes efficient
implementations of all kernels, kernel transformations, and selection methods,
and can be used for reproducing our results.

We derive and motivate a Laplacian-level, orbital-free
meta-generalized-gradient approximation (LL-MGGA) for the exchange-correlation
energy, targeting accurate ground-state properties of $sp$ and $sd$ metallic
condensed matter, in which the density functional for the exchange-correlation
energy is only weakly nonlocal due to perfect long-range screening. Our model
for the orbital-free kinetic energy density restores the fourth-order gradient
expansion for exchange to the r$^2$SCAN meta-GGA [Furness et al., J. Phys.
Chem. Lett. 11, 8208 (2020)], yielding a LL-MGGA we call OFR2. OFR2 matches the
accuracy of SCAN for prediction of common lattice constants and improves the
equilibrium properties of alkali metals, transition metals, and intermetallics
that were degraded relative to the PBE GGA values by both SCAN and r$^2$SCAN.
We compare OFR2 to the r$^2$SCAN-L LL-MGGA [D. Mejia-Rodriguez and S.B.
Trickey, Phys. Rev. B 102, 121109 (2020)] and show that OFR2 tends to
outperform r$^2$SCAN-L for the equilibrium properties of solids, but
r$^2$SCAN-L much better describes the atomization energies of molecules than
OFR2 does. For best accuracy in molecules and non-metallic condensed matter, we
continue to recommend SCAN and r$^2$SCAN. Numerical performance is discussed in
detail, and our work provides an outlook to machine learning.

Understanding the semantic of a collection of texts is a challenging task.
Topic models are probabilistic models that aims at extracting "topics" from a
corpus of documents. This task is particularly difficult when the corpus is
composed of short texts, such as posts on social networks. Following several
previous research papers, we explore in this paper a set of collected tweets
about bitcoin. In this work, we train three topic models and evaluate their
output with several scores. We also propose a concrete application of the
extracted topics.

Data Augmentation (DA) is known to improve the generalizability of deep
neural networks. Most existing DA techniques naively add a certain number of
augmented samples without considering the quality and the added computational
cost of these samples. To tackle this problem, a common strategy, adopted by
several state-of-the-art DA methods, is to adaptively generate or re-weight
augmented samples with respect to the task objective during training. However,
these adaptive DA methods: (1) are computationally expensive and not
sample-efficient, and (2) are designed merely for a specific setting. In this
work, we present a universal DA technique, called Glitter, to overcome both
issues. Glitter can be plugged into any DA method, making training
sample-efficient without sacrificing performance. From a pre-generated pool of
augmented samples, Glitter adaptively selects a subset of worst-case samples
with maximal loss, analogous to adversarial DA. Without altering the training
strategy, the task objective can be optimized on the selected subset. Our
thorough experiments on the GLUE benchmark, SQuAD, and HellaSwag in three
widely used training setups including consistency training, self-distillation
and knowledge distillation reveal that Glitter is substantially faster to train
and achieves a competitive performance, compared to strong baselines.

Edge detection, a basic task in the field of computer vision, is an important
preprocessing operation for the recognition and understanding of a visual
scene. In conventional models, the edge image generated is ambiguous, and the
edge lines are also very thick, which typically necessitates the use of
non-maximum suppression (NMS) and morphological thinning operations to generate
clear and thin edge images. In this paper, we aim to propose a one-stage neural
network model that can generate high-quality edge images without
postprocessing. The proposed model adopts a classic encoder-decoder framework
in which a pre-trained neural model is used as the encoder and a
multi-feature-fusion mechanism that merges the features of each level with each
other functions as a learnable decoder. Further, we propose a new loss function
that addresses the pixel-level imbalance in the edge image by suppressing the
false positive (FP) edge information near the true positive (TP) edge and the
false negative (FN) non-edge. The results of experiments conducted on several
benchmark datasets indicate that the proposed method achieves state-of-the-art
results without using NMS and morphological thinning operations.

Accurate LiDAR-camera extrinsic calibration is a precondition for many
multi-sensor systems in mobile robots. Most calibration methods rely on
laborious manual operations and calibration targets. While working online, the
calibration methods should be able to extract information from the environment
to construct the cross-modal data association. Convolutional neural networks
(CNNs) have powerful feature extraction ability and have been used for
calibration. However, most of the past methods solve the extrinsic as a
regression task, without considering the geometric constraints involved. In
this paper, we propose a novel end-to-end extrinsic calibration method named
DXQ-Net, using a differentiable pose estimation module for generalization. We
formulate a probabilistic model for LiDAR-camera calibration flow, yielding a
prediction of uncertainty to measure the quality of LiDAR-camera data
association. Testing experiments illustrate that our method achieves a
competitive with other methods for the translation component and
state-of-the-art performance for the rotation component. Generalization
experiments illustrate that the generalization performance of our method is
significantly better than other deep learning-based methods.

Inspired by the numerical solution of ordinary differential equations, in
this paper we propose a novel Reservoir Computing (RC) model, called the Euler
State Network (EuSN). The introduced approach makes use of forward Euler
discretization and antisymmetric recurrent matrices to design reservoir
dynamics that are both stable and non-dissipative by construction.
  Our mathematical analysis shows that the resulting model is biased towards
unitary effective spectral radius and zero local Lyapunov exponents,
intrinsically operating at the edge of stability. Experiments on synthetic
tasks indicate the marked superiority of the proposed approach, compared to
standard RC models, in tasks requiring long-term memorization skills.
Furthermore, results on real-world time series classification benchmarks point
out that EuSN is capable of matching (or even surpassing) the level of accuracy
of trainable Recurrent Neural Networks, while allowing up to 100-fold savings
in computation time and energy consumption.

Real-world problems, often couched as machine learning applications, involve
quantities of interest that have real-world meaning, independent of any
statistical model. To avoid potential model misspecification bias or
over-complicating the problem formulation, a direct, model-free approach is
desired. The traditional Bayesian framework relies on a model for the
data-generating process so, apparently, the desired direct, model-free,
posterior-probabilistic inference is out of reach. Fortunately, likelihood
functions are not the only means of linking data and quantities of interest.
Loss functions provide an alternative link, where the quantity of interest is
defined, or at least could be defined, as a minimizer of the corresponding
risk, or expected loss. In this case, one can obtain what is commonly referred
to as a Gibbs posterior distribution by using the empirical risk function
directly. This manuscript explores the Gibbs posterior construction, its
asymptotic concentration properties, and the frequentist calibration of its
credible regions. By being free from the constraints of model specification,
Gibbs posteriors create new opportunities for probabilistic inference in modern
statistical learning problems.

Exogenous heterogeneity, for example, in the form of instrumental variables
can help us learn a system's underlying causal structure and predict the
outcome of unseen intervention experiments. In this paper, we consider linear
models in which the causal effect from covariates $X$ on a response $Y$ is
sparse. We provide conditions under which the causal coefficient becomes
identifiable from the observed distribution. These conditions can be satisfied
even if the number of instruments is as small as the number of causal parents.
We also develop graphical criteria under which identifiability holds with
probability one if the edge coefficients are sampled randomly from a
distribution that is absolutely continuous with respect to Lebesgue measure and
$Y$ is childless. As an estimator, we propose spaceIV and prove that it
consistently estimates the causal effect if the model is identifiable and
evaluate its performance on simulated data. If identifiability does not hold,
we show that it may still be possible to recover a subset of the causal
parents.

Variational quantum circuits have been widely employed in quantum simulation
and quantum machine learning in recent years. However, quantum circuits with
random structures have poor trainability due to the exponentially vanishing
gradient with respect to the circuit depth and the qubit number. This result
leads to a general belief that deep quantum circuits will not be feasible for
practical tasks. In this work, we propose an initialization strategy with
theoretical guarantees for the vanishing gradient problem in general deep
circuits. Specifically, we prove that under proper Gaussian initialized
parameters, the norm of the gradient decays at most polynomially when the qubit
number and the circuit depth increase. Our theoretical results hold for both
the local and the global observable cases, where the latter was believed to
have vanishing gradients even for shallow circuits. Experimental results verify
our theoretical findings in the quantum simulation and quantum chemistry.

3D object recognition has seen significant advances in recent years, showing
impressive performance on real-world 3D scan benchmarks, but lacking in object
part reasoning, which is fundamental to higher-level scene understanding such
as inter-object similarities or object functionality. Thus, we propose to
leverage large-scale synthetic datasets of 3D shapes annotated with part
information to learn Neural Part Priors (NPPs), optimizable spaces
characterizing geometric part priors. Crucially, we can optimize over the
learned part priors in order to fit to real-world scanned 3D scenes at test
time, enabling robust part decomposition of the real objects in these scenes
that also estimates the complete geometry of the object while fitting
accurately to the observed real geometry. Moreover, this enables global
optimization over geometrically similar detected objects in a scene, which
often share strong geometric commonalities, enabling scene-consistent part
decompositions. Experiments on the ScanNet dataset demonstrate that NPPs
significantly outperforms state of the art in part decomposition and object
completion in real-world scenes.

Reinforcement learning (RL) tasks are typically framed as Markov Decision
Processes (MDPs), assuming that decisions are made at fixed time intervals.
However, many applications of great importance, including healthcare, do not
satisfy this assumption, yet they are commonly modelled as MDPs after an
artificial reshaping of the data. In addition, most healthcare (and similar)
problems are offline by nature, allowing for only retrospective studies. To
address both challenges, we begin by discussing the Semi-MDP (SMDP) framework,
which formally handles actions of variable timings. We next present a formal
way to apply SMDP modifications to nearly any given value-based offline RL
method. We use this theory to introduce three SMDP-based offline RL algorithms,
namely, SDQN, SDDQN, and SBCQ. We then experimentally demonstrate that only
these SMDP-based algorithms learn the optimal policy in variable-time
environments, whereas their MDP counterparts do not. Finally, we apply our new
algorithms to a real-world offline dataset pertaining to warfarin dosing for
stroke prevention and demonstrate similar results.

This paper presents a method to reconstruct high-quality textured 3D models
from both multi-view and single-view images. The reconstruction is posed as an
adaptation problem and is done progressively where in the first stage, we focus
on learning accurate geometry, whereas in the second stage, we focus on
learning the texture with a generative adversarial network. In the generative
learning pipeline, we propose two improvements. First, since the learned
textures should be spatially aligned, we propose an attention mechanism that
relies on the learnable positions of pixels. Secondly, since discriminator
receives aligned texture maps, we augment its input with a learnable embedding
which improves the feedback to the generator. We achieve significant
improvements on multi-view sequences from Tripod dataset as well as on
single-view image datasets, Pascal 3D+ and CUB. We demonstrate that our method
achieves superior 3D textured models compared to the previous works. Please
visit our web-page for 3D visuals.

A unsupervised learning approach for the computation of an explicit
functional representation of a random vector $Y$ is presented, which only
relies on a finite set of samples with unknown distribution. Motivated by
recent advances with computational optimal transport for estimating Wasserstein
distances, we develop a new \textit{Wasserstein multi-element polynomial chaos
expansion} (WPCE). It relies on the minimization of a regularized empirical
Wasserstein metric known as debiased Sinkhorn divergence.
  As a requirement for an efficient polynomial basis expansion, a suitable
(minimal) stochastic coordinate system $X$ has to be determined with the aim to
identify ideally independent random variables. This approach generalizes
representations through diffeomorphic transport maps to the case of
non-continuous and non-injective model classes $\mathcal{M}$ with different
input and output dimension, yielding the relation $Y=\mathcal{M}(X)$ in
distribution. Moreover, since the used PCE grows exponentially in the number of
random coordinates of $X$, we introduce an appropriate low-rank format given as
stacks of tensor trains, which alleviates the curse of dimensionality, leading
to only linear dependence on the input dimension. By the choice of the model
class $\mathcal{M}$ and the smooth loss function, higher order optimization
schemes become possible. It is shown that the relaxation to a discontinuous
model class is necessary to explain multimodal distributions. Moreover, the
proposed framework is applied to a numerical upscaling task, considering a
computationally challenging microscopic random non-periodic composite material.
This leads to tractable effective macroscopic random field in adopted
stochastic coordinates.

Increasing the semantic understanding and contextual awareness of machine
learning models is important for improving robustness and reducing
susceptibility to data shifts. In this work, we leverage contextual awareness
for the anomaly detection problem. Although graphed-based anomaly detection has
been widely studied, context-dependent anomaly detection is an open problem and
without much current research. We develop a general framework for converting a
context-dependent anomaly detection problem to a link prediction problem,
allowing well-established techniques from this domain to be applied. We
implement a system based on our framework that utilizes knowledge graph
embedding models and demonstrates the ability to detect outliers using context
provided by a semantic knowledge base. We show that our method can detect
context-dependent anomalies with a high degree of accuracy and show that
current object detectors can detect enough classes to provide the needed
context for good performance within our example domain.

Diffusion is a commonly used technique for spreading information from point
to point on a graph. The rationale behind diffusion is not clear. And the
multi-types Galton-Watson forest is a random model of population growth without
space or any other resource constraints. In this paper, we use the degenerated
multi-types Galton-Watson forest (MGWF) to interpret the diffusion process and
establish an equivalent relationship between them. With the two-phase setting
of the MGWF, one can interpret the diffusion process and the Google PageRank
system explicitly. It also improves the convergence behaviour of the iterative
diffusion process and Google PageRank system. We validate the proposal by
experiment while providing new research directions.

Technological advancements have made it possible to deliver mobile health
interventions to individuals. A novel framework that has emerged from such
advancements is the just-in-time adaptive intervention (JITAI), which aims to
suggest the right support to the individuals when their needs arise. The
micro-randomized trial (MRT) design has been proposed recently to test the
proximal effects of these JITAIs. However, the extant MRT framework only
considers components with a fixed number of categories added at the beginning
of the study. We propose a flexible MRT (FlexiMRT) design which allows addition
of more categories to the components during the study. The proposed design is
motivated by collaboration on the DIAMANTE study, which learns to deliver text
messages to encourage physical activity among the patients with diabetes and
depression. We developed a new test statistic and the corresponding sample size
calculator for the FlexiMRT using an approach similar to the generalized
estimating equation for longitudinal data. Simulation studies were conducted to
evaluate the sample size calculators and an R shiny application for the
calculators was developed.

The novel coronavirus disease (COVID-19) constitutes a public health
emergency globally. It is a deadly disease which has infected more than 230
million people worldwide. Therefore, early and unswerving detection of COVID-19
is necessary. Evidence of this virus is most commonly being tested by RT-PCR
test. This test is not 100% reliable as it is known to give false positives and
false negatives. Other methods like X-Ray images or CT scans show the detailed
imaging of lungs and have been proven more reliable. This paper compares
different deep learning models used to detect COVID-19 through transfer
learning technique on CT scan dataset. VGG-16 outperforms all the other models
achieving an accuracy of 85.33% on the dataset.

In a high-dimensional regression framework, we study consequences of the
naive two-step procedure where first the dimension of the input variables is
reduced and second, the reduced input variables are used to predict the output
variable. More specifically we combine principal component analysis (PCA) with
kernel regression. In order to analyze the resulting regression errors, a novel
stability result of kernel regression with respect to the Wasserstein distance
is derived. This allows us to bound errors that occur when perturbed input data
is used to fit a kernel function. We combine the stability result with known
estimates from the literature on both principal component analysis and kernel
regression to obtain convergence rates for the two-step procedure.

We prove rigorous bounds on the errors resulting from the approximation of
the incompressible Navier-Stokes equations with (extended) physics informed
neural networks. We show that the underlying PDE residual can be made
arbitrarily small for tanh neural networks with two hidden layers. Moreover,
the total error can be estimated in terms of the training error, network size
and number of quadrature points. The theory is illustrated with numerical

Many recent approaches in contrastive learning have worked to close the gap
between pretraining on iconic images like ImageNet and pretraining on complex
scenes like COCO. This gap exists largely because commonly used random crop
augmentations obtain semantically inconsistent content in crowded scene images
of diverse objects. Previous works use preprocessing pipelines to localize
salient objects for improved cropping, but an end-to-end solution is still
elusive. In this work, we propose a framework which accomplishes this goal via
joint learning of representations and segmentation. We leverage segmentation
masks to train a model with a mask-dependent contrastive loss, and use the
partially trained model to bootstrap better masks. By iterating between these
two components, we ground the contrastive updates in segmentation information,
and simultaneously improve segmentation throughout pretraining. Experiments
show our representations transfer robustly to downstream tasks in
classification, detection and segmentation.

Point-based object localization (POL), which pursues high-performance object
sensing under low-cost data annotation, has attracted increased attention.
However, the point annotation mode inevitably introduces semantic variance for
the inconsistency of annotated points. Existing POL methods heavily reply on
accurate key-point annotations which are difficult to define. In this study, we
propose a POL method using coarse point annotations, relaxing the supervision
signals from accurate key points to freely spotted points. To this end, we
propose a coarse point refinement (CPR) approach, which to our best knowledge
is the first attempt to alleviate semantic variance from the perspective of
algorithm. CPR constructs point bags, selects semantic-correlated points, and
produces semantic center points through multiple instance learning (MIL). In
this way, CPR defines a weakly supervised evolution procedure, which ensures
training high-performance object localizer under coarse point supervision.
Experimental results on COCO, DOTA and our proposed SeaPerson dataset validate
the effectiveness of the CPR approach. The dataset and code will be available
at https://github.com/ucas-vg/PointTinyBenchmark/.

Perceiving the similarity between images has been a long-standing and
fundamental problem underlying various visual generation tasks. Predominant
approaches measure the inter-image distance by computing pointwise absolute
deviations, which tends to estimate the median of instance distributions and
leads to blurs and artifacts in the generated images. This paper presents
MoNCE, a versatile metric that introduces image contrast to learn a calibrated
metric for the perception of multifaceted inter-image distances. Unlike vanilla
contrast which indiscriminately pushes negative samples from the anchor
regardless of their similarity, we propose to re-weight the pushing force of
negative samples adaptively according to their similarity to the anchor, which
facilitates the contrastive learning from informative negative samples. Since
multiple patch-level contrastive objectives are involved in image distance
measurement, we introduce optimal transport in MoNCE to modulate the pushing
force of negative samples collaboratively across multiple contrastive
objectives. Extensive experiments over multiple image translation tasks show
that the proposed MoNCE outperforms various prevailing metrics substantially.
The code is available at https://github.com/fnzhan/MoNCE.

As people's demand for personal privacy and data security becomes a priority,
encrypted traffic has become mainstream in the cyber world. However, traffic
encryption is also shielding malicious and illegal traffic introduced by
adversaries, from being detected. This is especially so in the post-COVID-19
environment where malicious traffic encryption is growing rapidly. Common
security solutions that rely on plain payload content analysis such as deep
packet inspection are rendered useless. Thus, machine learning based approaches
have become an important direction for encrypted malicious traffic detection.
In this paper, we formulate a universal framework of machine learning based
encrypted malicious traffic detection techniques and provided a systematic
review. Furthermore, current research adopts different datasets to train their
models due to the lack of well-recognized datasets and feature sets. As a
result, their model performance cannot be compared and analyzed reliably.
Therefore, in this paper, we analyse, process and combine datasets from 5
different sources to generate a comprehensive and fair dataset to aid future
research in this field. On this basis, we also implement and compare 10
encrypted malicious traffic detection algorithms. We then discuss challenges
and propose future directions of research.

Unsupervised audio-visual source localization aims at localizing visible
sound sources in a video without relying on ground-truth localization for
training. Previous works often seek high audio-visual similarities for likely
positive (sounding) regions and low similarities for likely negative regions.
However, accurately distinguishing between sounding and non-sounding regions is
challenging without manual annotations. In this work, we propose a simple yet
effective approach for Easy Visual Sound Localization, namely EZ-VSL, without
relying on the construction of positive and/or negative regions during
training. Instead, we align audio and visual spaces by seeking audio-visual
representations that are aligned in, at least, one location of the associated
image, while not matching other images, at any location. We also introduce a
novel object guided localization scheme at inference time for improved
precision. Our simple and effective framework achieves state-of-the-art
performance on two popular benchmarks, Flickr SoundNet and VGG-Sound Source. In
particular, we improve the CIoU of the Flickr SoundNet test set from 76.80% to
83.94%, and on the VGG-Sound Source dataset from 34.60% to 38.85%. The code is
available at https://github.com/stoneMo/EZ-VSL.

Graph representation learning has attracted tremendous attention due to its
remarkable performance in many real-world applications. However, prevailing
supervised graph representation learning models for specific tasks often suffer
from label sparsity issue as data labeling is always time and resource
consuming. In light of this, few-shot learning on graphs (FSLG), which combines
the strengths of graph representation learning and few-shot learning together,
has been proposed to tackle the performance degradation in face of limited
annotated data challenge. There have been many studies working on FSLG
recently. In this paper, we comprehensively survey these work in the form of a
series of methods and applications. Specifically, we first introduce FSLG
challenges and bases, then categorize and summarize existing work of FSLG in
terms of three major graph mining tasks at different granularity levels, i.e.,
node, edge, and graph. Finally, we share our thoughts on some future research
directions of FSLG. The authors of this survey have contributed significantly
to the AI literature on FSLG over the last few years.

In this work, we investigate the knowledge learned in the embeddings of
multimodal-BERT models. More specifically, we probe their capabilities of
storing the grammatical structure of linguistic data and the structure learned
over objects in visual data. To reach that goal, we first make the inherent
structure of language and visuals explicit by a dependency parse of the
sentences that describe the image and by the dependencies between the object
regions in the image, respectively. We call this explicit visual structure the
\textit{scene tree}, that is based on the dependency tree of the language
description. Extensive probing experiments show that the multimodal-BERT models
do not encode these scene trees.Code available at
\url{https://github.com/VSJMilewski/multimodal-probes}.

There are many recent research efforts to fine-tune a pre-trained generator
with a few target images to generate images of a novel domain. Unfortunately,
these methods often suffer from overfitting or under-fitting when fine-tuned
with a single target image. To address this, here we present a novel
single-shot GAN adaptation method through unified CLIP space manipulations.
Specifically, our model employs a two-step training strategy: reference image
search in the source generator using a CLIP-guided latent optimization,
followed by generator fine-tuning with a novel loss function that imposes CLIP
space consistency between the source and adapted generators. To further improve
the adapted model to produce spatially consistent samples with respect to the
source generator, we also propose contrastive regularization for patchwise
relationships in the CLIP space. Experimental results show that our model
generates diverse outputs with the target texture and outperforms the baseline
models both qualitatively and quantitatively. Furthermore, we show that our
CLIP space manipulation strategy allows more effective attribute editing.

Nowadays, our mobility systems are evolving into the era of intelligent
vehicles that aim to improve road safety. Due to their vulnerability,
pedestrians are the users who will benefit the most from these developments.
However, predicting their trajectory is one of the most challenging concerns.
Indeed, accurate prediction requires a good understanding of multi-agent
interactions that can be complex. Learning the underlying spatial and temporal
patterns caused by these interactions is even more of a competitive and open
problem that many researchers are tackling. In this paper, we introduce a model
called PRediction Transformer (PReTR) that extracts features from the
multi-agent scenes by employing a factorized spatio-temporal attention module.
It shows less computational needs than previously studied models with
empirically better results. Besides, previous works in motion prediction suffer
from the exposure bias problem caused by generating future sequences
conditioned on model prediction samples rather than ground-truth samples. In
order to go beyond the proposed solutions, we leverage encoder-decoder
Transformer networks for parallel decoding a set of learned object queries.
This non-autoregressive solution avoids the need for iterative conditioning and
arguably decreases training and testing computational time. We evaluate our
model on the ETH/UCY datasets, a publicly available benchmark for pedestrian
trajectory prediction. Finally, we justify our usage of the parallel decoding
technique by showing that the trajectory prediction task can be better solved
as a non-autoregressive task.

Backdoor attacks impose a new threat in Deep Neural Networks (DNNs), where a
backdoor is inserted into the neural network by poisoning the training dataset,
misclassifying inputs that contain the adversary trigger. The major challenge
for defending against these attacks is that only the attacker knows the secret
trigger and the target class. The problem is further exacerbated by the recent
introduction of "Hidden Triggers", where the triggers are carefully fused into
the input, bypassing detection by human inspection and causing backdoor
identification through anomaly detection to fail. To defend against such
imperceptible attacks, in this work we systematically analyze how
representations, i.e., the set of neuron activations for a given DNN when using
the training data as inputs, are affected by backdoor attacks. We propose
PiDAn, an algorithm based on coherence optimization purifying the poisoned
data. Our analysis shows that representations of poisoned data and authentic
data in the target class are still embedded in different linear subspaces,
which implies that they show different coherence with some latent spaces. Based
on this observation, the proposed PiDAn algorithm learns a sample-wise weight
vector to maximize the projected coherence of weighted samples, where we
demonstrate that the learned weight vector has a natural "grouping effect" and
is distinguishable between authentic data and poisoned data. This enables the
systematic detection and mitigation of backdoor attacks. Based on our
theoretical analysis and experimental results, we demonstrate the effectiveness
of PiDAn in defending against backdoor attacks that use different settings of
poisoned samples on GTSRB and ILSVRC2012 datasets. Our PiDAn algorithm can
detect more than 90% infected classes and identify 95% poisoned samples.

Monocular 3D motion capture (mocap) is beneficial to many applications. The
use of a single camera, however, often fails to handle occlusions of different
body parts and hence it is limited to capture relatively simple movements. We
present a light-weight, hybrid mocap technique called HybridCap that augments
the camera with only 4 Inertial Measurement Units (IMUs) in a
learning-and-optimization framework. We first employ a weakly-supervised and
hierarchical motion inference module based on cooperative Gated Recurrent Unit
(GRU) blocks that serve as limb, body and root trackers as well as an inverse
kinematics solver. Our network effectively narrows the search space of
plausible motions via coarse-to-fine pose estimation and manages to tackle
challenging movements with high efficiency. We further develop a hybrid
optimization scheme that combines inertial feedback and visual cues to improve
tracking accuracy. Extensive experiments on various datasets demonstrate
HybridCap can robustly handle challenging movements ranging from fitness
actions to Latin dance. It also achieves real-time performance up to 60 fps
with state-of-the-art accuracy.

As a relatively new field, network neuroscience has tended to focus on
aggregate behaviours of the brain averaged over many successive experiments or
over long recordings in order to construct robust brain models. These models
are limited in their ability to explain dynamic state changes in the brain
which occurs spontaneously as a result of normal brain function. Hidden Markov
Models (HMMs) trained on neuroimaging time series data have since arisen as a
method to produce dynamical models that are easy to train but can be difficult
to fully parametrise or analyse. We propose an interpretation of these neural
HMMs as multiplex brain state graph models we term Hidden Markov Graph Models
(HMGMs). This interpretation allows for dynamic brain activity to be analysed
using the full repertoire of network analysis techniques. Furthermore, we
propose a general method for selecting HMM hyperparameters in the absence of
external data, based on the principle of maximum entropy, and use this to
select the number of layers in the multiplex model. We produce a new tool for
determining important communities of brain regions using a spatiotemporal
random walk-based procedure that takes advantage of the underlying Markov
structure of the model. Our analysis of real multi-subject fMRI data provides
new results that corroborate the modular processing hypothesis of the brain at
rest as well as contributing new evidence of functional overlap between and
within dynamic brain state communities. Our analysis pipeline provides a way to
characterise dynamic network activity of the brain under novel behaviours or

In recent years, we have witnessed the proliferation of knowledge graphs (KG)
in various domains, aiming to support applications like question answering,
recommendations, etc. A frequent task when integrating knowledge from different
KGs is to find which subgraphs refer to the same real-world entity. Recently,
embedding methods have been used for entity alignment tasks, that learn a
vector-space representation of entities which preserves their similarity in the
original KGs. A wide variety of supervised, unsupervised, and semi-supervised
methods have been proposed that exploit both factual (attribute based) and
structural information (relation based) of entities in the KGs. Still, a
quantitative assessment of their strengths and weaknesses in real-world KGs
according to different performance metrics and KG characteristics is missing
from the literature. In this work, we conduct the first meta-level analysis of
popular embedding methods for entity alignment, based on a statistically sound
methodology. Our analysis reveals statistically significant correlations of
different embedding methods with various meta-features extracted by KGs and
rank them in a statistically significant way according to their effectiveness
across all real-world KGs of our testbed. Finally, we study interesting
trade-offs in terms of methods' effectiveness and efficiency.

The urban transportation system is a combination of multiple transport modes,
and the interdependencies across those modes exist. This means that the travel
demand across different travel modes could be correlated as one mode may
receive demand from or create demand for another mode, not to mention natural
correlations between different demand time series due to general demand flow
patterns across the network. It is expectable that cross-modal ripple effects
become more prevalent, with Mobility as a Service. Therefore, by propagating
demand data across modes, a better demand prediction could be obtained. To this
end, this study explores various machine learning models and transfer learning
strategies for cross-modal demand prediction. The trip data of bike-share,
metro, and taxi are processed as the station-level passenger flows, and then
the proposed prediction method is tested in the large-scale case studies of
Nanjing and Chicago. The results suggest that prediction models with transfer
learning perform better than unimodal prediction models. Furthermore, stacked
Long Short-Term Memory model performs particularly well in cross-modal demand
prediction. These results verify our combined method's forecasting improvement
over existing benchmarks and demonstrate the good transferability for
cross-modal demand prediction in multiple cities.

We develop theoretically guaranteed stochastic methods for outlier-robust
PCA. Outlier-robust PCA seeks an underlying low-dimensional linear subspace
from a dataset that is corrupted with outliers. We are able to show that our
methods, which involve stochastic geodesic gradient descent over the
Grassmannian manifold, converge and recover an underlying subspace in various
regimes through the development of a novel convergence analysis. The main
application of this method is an effective differentially private algorithm for
outlier-robust PCA that uses a Gaussian noise mechanism within the stochastic
gradient method. Our results emphasize the advantages of the nonconvex methods
over another convex approach to solving this problem in the differentially
private setting. Experiments on synthetic and stylized data verify these
results.

In recent years, convolutional neural network-based single image adverse
weather removal methods have achieved significant performance improvements on
many benchmark datasets. However, these methods require large amounts of
clean-weather degraded image pairs for training, which is often difficult to
obtain in practice. Although various weather degradation synthesis methods
exist in the literature, the use of synthetically generated weather degraded
images often results in sub-optimal performance on the real weather degraded
images due to the domain gap between synthetic and real-world images. To deal
with this problem, various semi-supervised restoration (SSR) methods have been
proposed for deraining or dehazing which learn to restore the clean image using
synthetically generated datasets while generalizing better using unlabeled
real-world images. The performance of a semi-supervised method is essentially
based on the quality of the unlabeled data. In particular, if the unlabeled
data characteristics are very different from that of the labeled data, then the
performance of a semi-supervised method degrades significantly. We
theoretically study the effect of unlabeled data on the performance of an SSR
method and develop a technique that rejects the unlabeled images that degrade
the performance. Extensive experiments and ablation study show that the
proposed sample rejection method increases the performance of existing SSR
deraining and dehazing methods significantly. Code is available at
:https://github.com/rajeevyasarla/ART-SS

The lack of labeled data is a key challenge for learning useful
representation from time series data. However, an unsupervised representation
framework that is capable of producing high quality representations could be of
great value. It is key to enabling transfer learning, which is especially
beneficial for medical applications, where there is an abundance of data but
labeling is costly and time consuming. We propose an unsupervised contrastive
learning framework that is motivated from the perspective of label smoothing.
The proposed approach uses a novel contrastive loss that naturally exploits a
data augmentation scheme in which new samples are generated by mixing two data
samples with a mixing component. The task in the proposed framework is to
predict the mixing component, which is utilized as soft targets in the loss
function. Experiments demonstrate the framework's superior performance compared
to other representation learning approaches on both univariate and multivariate
time series and illustrate its benefits for transfer learning for clinical time
series.

We present PROSUB: PROgressive SUBsampling, a deep learning based, automated
methodology that subsamples an oversampled data set (e.g. multi-channeled 3D
images) with minimal loss of information. We build upon a recent dual-network
approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI
measurement sampling-reconstruction challenge, but suffers from deep learning
training instability, by subsampling with a hard decision boundary. PROSUB uses
the paradigm of recursive feature elimination (RFE) and progressively
subsamples measurements during deep learning training, improving optimization
stability. PROSUB also integrates a neural architecture search (NAS) paradigm,
allowing the network architecture hyperparameters to respond to the subsampling
process. We show PROSUB outperforms the winner of the MUDI MICCAI challenge,
producing large improvements >18% MSE on the MUDI challenge sub-tasks and
qualitative improvements on downstream processes useful for clinical
applications. We also show the benefits of incorporating NAS and analyze the
effect of PROSUB's components. As our method generalizes to other problems
beyond MRI measurement selection-reconstruction, our code is
https://github.com/sbb-gh/PROSUB

Graph neural networks (GNNs) have been extensively developed for graph
representation learning in various application domains. However, similar to all
other neural networks models, GNNs suffer from the black-box problem as people
cannot understand the mechanism underlying them. To solve this problem, several
GNN explainability methods have been proposed to explain the decisions made by
GNNs. In this survey, we give an overview of the state-of-the-art GNN
explainability methods and how they are evaluated. Furthermore, we propose a
new evaluation metric and conduct thorough experiments to compare GNN
explainability methods on real world datasets. We also suggest future
directions for GNN explainability.

The ability to evolve is fundamental for any valuable autonomous agent whose
knowledge cannot remain limited to that injected by the manufacturer. Consider
for example a home assistant robot: it should be able to incrementally learn
new object categories when requested, but also to recognize the same objects in
different environments (rooms) and poses (hand-held/on the floor/above
furniture), while rejecting unknown ones. Despite its importance, this scenario
has started to raise interest in the robotic community only recently and the
related research is still in its infancy, with existing experimental testbeds
but no tailored methods. With this work, we propose the first learning approach
that deals with all the previously mentioned challenges at once by exploiting a
single contrastive objective. We show how it learns a feature space perfectly
suitable to incrementally include new classes and is able to capture knowledge
which generalizes across a variety of visual domains. Our method is endowed
with a tailored effective stopping criterion for each learning episode and
exploits a self-paced thresholding strategy that provides the classifier with a
reliable rejection option. Both these novel contributions are based on the
observation of the data statistics and do not need manual tuning. An extensive
experimental analysis confirms the effectiveness of the proposed approach in
establishing the new state-of-the-art. The code is available at
https://github.com/FrancescoCappio/Contrastive_Open_World.

We study the properties of various over-parametrized convolutional neural
architectures through their respective Gaussian process and neural tangent
kernels. We prove that, with normalized multi-channel input and ReLU
activation, the eigenfunctions of these kernels with the uniform measure are
formed by products of spherical harmonics, defined over the channels of the
different pixels. We next use hierarchical factorizable kernels to bound their
respective eigenvalues. We show that the eigenvalues decay polynomially,
quantify the rate of decay, and derive measures that reflect the composition of
hierarchical features in these networks. Our results provide concrete
quantitative characterization of over-parameterized convolutional network

Faithful visualizations of data residing on manifolds must take the
underlying geometry into account when producing a flat planar view of the data.
In this paper, we extend the classic stochastic neighbor embedding (SNE)
algorithm to data on general Riemannian manifolds. We replace standard Gaussian
assumptions with Riemannian diffusion counterparts and propose an efficient
approximation that only requires access to calculations of Riemannian distances
and volumes. We demonstrate that the approach also allows for mapping data from
one manifold to another, e.g. from a high-dimensional sphere to a
low-dimensional one.

In probably approximately correct (PAC) reinforcement learning (RL), an agent
is required to identify an $\epsilon$-optimal policy with probability
$1-\delta$. While minimax optimal algorithms exist for this problem, its
instance-dependent complexity remains elusive in episodic Markov decision
processes (MDPs). In this paper, we propose the first nearly matching (up to a
horizon squared factor and logarithmic terms) upper and lower bounds on the
sample complexity of PAC RL in deterministic episodic MDPs with finite state
and action spaces. In particular, our bounds feature a new notion of
sub-optimality gap for state-action pairs that we call the deterministic return
gap. While our instance-dependent lower bound is written as a linear program,
our algorithms are very simple and do not require solving such an optimization
problem during learning. Their design and analyses employ novel ideas,
including graph-theoretical concepts (minimum flows) and a new maximum-coverage
exploration strategy.

Biological intelligence is remarkable in its ability to produce complex
behaviour in many diverse situations through data efficient, generalisable and
transferable skill acquisition. It is believed that learning "good" sensory
representations is important for enabling this, however there is little
agreement as to what a good representation should look like. In this review
article we are going to argue that symmetry transformations are a fundamental
principle that can guide our search for what makes a good representation. The
idea that there exist transformations (symmetries) that affect some aspects of
the system but not others, and their relationship to conserved quantities has
become central in modern physics, resulting in a more unified theoretical
framework and even ability to predict the existence of new particles. Recently,
symmetries have started to gain prominence in machine learning too, resulting
in more data efficient and generalisable algorithms that can mimic some of the
complex behaviours produced by biological intelligence. Finally, first
demonstrations of the importance of symmetry transformations for representation
learning in the brain are starting to arise in neuroscience. Taken together,
the overwhelming positive effect that symmetries bring to these disciplines
suggest that they may be an important general framework that determines the
structure of the universe, constrains the nature of natural tasks and
consequently shapes both biological and artificial intelligence.

Federated Learning (FL) is an emerging distributed learning paradigm under
privacy constraint. Data heterogeneity is one of the main challenges in FL,
which results in slow convergence and degraded performance. Most existing
approaches only tackle the heterogeneity challenge by restricting the local
model update in client, ignoring the performance drop caused by direct global
model aggregation. Instead, we propose a data-free knowledge distillation
method to fine-tune the global model in the server (FedFTG), which relieves the
issue of direct model aggregation. Concretely, FedFTG explores the input space
of local models through a generator, and uses it to transfer the knowledge from
local models to the global model. Besides, we propose a hard sample mining
scheme to achieve effective knowledge distillation throughout the training. In
addition, we develop customized label sampling and class-level ensemble to
derive maximum utilization of knowledge, which implicitly mitigates the
distribution discrepancy across clients. Extensive experiments show that our
FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and
can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and
SCAFFOLD.

Adversarial Training has proved to be an effective training paradigm to
enforce robustness against adversarial examples in modern neural network
architectures. Despite many efforts, explanations of the foundational
principles underpinning the effectiveness of Adversarial Training are limited
and far from being widely accepted by the Deep Learning community. In this
paper, we describe surprising properties of adversarially-trained models,
shedding light on mechanisms through which robustness against adversarial
attacks is implemented. Moreover, we highlight limitations and failure modes
affecting these models that were not discussed by prior works. We conduct
extensive analyses on a wide range of architectures and datasets, performing a
deep comparison between robust and natural models.

During the COVID-19 pandemic, most countries have experienced some form of
remote education through video conferencing software platforms. However, these
software platforms fail to reduce immersion and replicate the classroom
experience. The currently emerging Metaverse addresses many of such limitations
by offering blended physical-digital environments. This paper aims to assess
how the Metaverse can support and improve e-learning. We first survey the
latest applications of blended environments in education and highlight the
primary challenges and opportunities. Accordingly, we derive our proposal for a
virtual-physical blended classroom configuration that brings students and
teachers into a shared educational Metaverse. We focus on the system
architecture of the Metaverse classroom to achieve real-time synchronization of
a large number of participants and activities across physical (mixed reality
classrooms) and virtual (remote VR platform) learning spaces. Our proposal
attempts to transform the traditional physical classroom into virtual-physical
cyberspace as a new social network of learners and educators connected at an
unprecedented scale.

Pre-trained sequence-to-sequence models have significantly improved Neural
Machine Translation (NMT). Different from prior works where pre-trained models
usually adopt an unidirectional decoder, this paper demonstrates that
pre-training a sequence-to-sequence model but with a bidirectional decoder can
produce notable performance gains for both Autoregressive and
Non-autoregressive NMT. Specifically, we propose CeMAT, a conditional masked
language model pre-trained on large-scale bilingual and monolingual corpora in
many languages. We also introduce two simple but effective methods to enhance
the CeMAT, aligned code-switching & masking and dynamic dual-masking. We
conduct extensive experiments and show that our CeMAT can achieve significant
performance improvement for all scenarios from low- to extremely high-resource
languages, i.e., up to +14.4 BLEU on low resource and +7.9 BLEU improvements on
average for Autoregressive NMT. For Non-autoregressive NMT, we demonstrate it
can also produce consistent performance gains, i.e., up to +5.3 BLEU. To the
best of our knowledge, this is the first work to pre-train a unified model for
fine-tuning on both NMT tasks. Code, data, and pre-trained models are available
at https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/CeMAT.

How to efficiently utilize the temporal features is crucial, yet challenging,
for video restoration. The temporal features usually contain various noisy and
uncorrelated information, and they may interfere with the restoration of the
current frame. This paper proposes learning noise-robust feature
representations to help video restoration. We are inspired by that the neural
codec is a natural denoiser. In neural codec, the noisy and uncorrelated
contents which are hard to predict but cost lots of bits are more inclined to
be discarded for bitrate saving. Therefore, we design a neural compression
module to filter the noise and keep the most useful information in features for
video restoration. To achieve robustness to noise, our compression module
adopts a spatial channel-wise quantization mechanism to adaptively determine
the quantization step size for each position in the latent. Experiments show
that our method can significantly boost the performance on video denoising,
where we obtain 0.13 dB improvement over BasicVSR++ with only 0.23x FLOPs.
Meanwhile, our method also obtains SOTA results on video deraining and
dehazing.

In this work, we study the applications of differential privacy (DP) in the
context of graph-structured data. We discuss the formulations of DP applicable
to the publication of graphs and their associated statistics as well as machine
learning on graph-based data, including graph neural networks (GNNs). The
formulation of DP in the context of graph-structured data is difficult, as
individual data points are interconnected (often non-linearly or sparsely).
This connectivity complicates the computation of individual privacy loss in
differentially private learning. The problem is exacerbated by an absence of a
single, well-established formulation of DP in graph settings. This issue
extends to the domain of GNNs, rendering private machine learning on
graph-structured data a challenging task. A lack of prior systematisation work
motivated us to study graph-based learning from a privacy perspective. In this
work, we systematise different formulations of DP on graphs, discuss challenges
and promising applications, including the GNN domain. We compare and separate
works into graph analysis tasks and graph learning tasks with GNNs. Finally, we
conclude our work with a discussion of open questions and potential directions
for further research in this area.

The placement of temperature sensitive and safety-critical components is
crucial in the automotive industry. It is therefore inevitable, even at the
design stage of new vehicles that these components are assessed for potential
safety issues. However, with increasing number of design proposals, risk
assessment quickly becomes expensive. We therefore present a parameterized
surrogate model for the prediction of three-dimensional flow fields in
aerothermal vehicle simulations. The proposed physics-informed neural network
(PINN) design is aimed at learning families of flow solutions according to a
geometric variation. In scope of this work, we could show that our
nondimensional, multivariate scheme can be efficiently trained to predict the
velocity and pressure distribution for different design scenarios and geometric
scales. The proposed algorithm is based on a parametric minibatch training
which enables the utilization of large datasets necessary for the
three-dimensional flow modeling. Further, we introduce a continuous resampling
algorithm that allows to operate on one static dataset. Every feature of our
methodology is tested individually and verified against conventional CFD
simulations. Finally, we apply our proposed method in context of an exemplary
real-world automotive application.

In this work the development of a machine learning-based Reduced Order Model
(ROM) for the investigation of hemodynamics in a patient-specific configuration
of Coronary Artery Bypass Graft (CABG) is proposed. The computational domain is
referred to left branches of coronary arteries when a stenosis of the Left Main
Coronary Artery (LMCA) occurs. The method extracts a reduced basis space from a
collection of high-fidelity solutions via a Proper Orthogonal Decomposition
(POD) algorithm and employs Artificial Neural Networks (ANNs) for the
computation of the modal coefficients. The Full Order Model (FOM) is
represented by the incompressible Navier-Stokes equations discretized using a
Finite Volume (FV) technique. Both physical and geometrical parametrization are
taken into account, the former one related to the inlet flow rate and the
latter one related to the stenosis severity. With respect to the previous works
focused on the development of a ROM framework for the evaluation of coronary
artery disease, the novelties of our study include the use of the FV method in
a patient-specific configuration, the use of a data-driven ROM technique and
the mesh deformation strategy based on a Free Form Deformation (FFD) technique.
The performance of our ROM approach is analyzed in terms of the error between
full order and reduced order solutions as well as the speedup achieved at the
online stage.

We study the problem of attribute compression for large-scale unstructured 3D
point clouds. Through an in-depth exploration of the relationships between
different encoding steps and different attribute channels, we introduce a deep
compression network, termed 3DAC, to explicitly compress the attributes of 3D
point clouds and reduce storage usage in this paper. Specifically, the point
cloud attributes such as color and reflectance are firstly converted to
transform coefficients. We then propose a deep entropy model to model the
probabilities of these coefficients by considering information hidden in
attribute transforms and previous encoded attributes. Finally, the estimated
probabilities are used to further compress these transform coefficients to a
final attributes bitstream. Extensive experiments conducted on both indoor and
outdoor large-scale open point cloud datasets, including ScanNet and
SemanticKITTI, demonstrated the superior compression rates and reconstruction
quality of the proposed 3DAC.

Single image super-resolution (SISR) with generative adversarial networks
(GAN) has recently attracted increasing attention due to its potentials to
generate rich details. However, the training of GAN is unstable, and it often
introduces many perceptually unpleasant artifacts along with the generated
details. In this paper, we demonstrate that it is possible to train a GAN-based
SISR model which can stably generate perceptually realistic details while
inhibiting visual artifacts. Based on the observation that the local statistics
(e.g., residual variance) of artifact areas are often different from the areas
of perceptually friendly details, we develop a framework to discriminate
between GAN-generated artifacts and realistic details, and consequently
generate an artifact map to regularize and stabilize the model training
process. Our proposed locally discriminative learning (LDL) method is simple
yet effective, which can be easily plugged in off-the-shelf SISR methods and
boost their performance. Experiments demonstrate that LDL outperforms the
state-of-the-art GAN based SISR methods, achieving not only higher
reconstruction accuracy but also superior perceptual quality on both synthetic
and real-world datasets. Codes and models are available at
https://github.com/csjliang/LDL.

Despite their high predictive performance, random forest and gradient
boosting are often considered as black boxes or uninterpretable models which
has raised concerns from practitioners and regulators. As an alternative, we
propose in this paper to use partial linear models that are inherently
interpretable. Specifically, this article introduces GAM-lasso (GAMLA) and
GAM-autometrics (GAMA), denoted as GAM(L)A in short. GAM(L)A combines
parametric and non-parametric functions to accurately capture linearities and
non-linearities prevailing between dependent and explanatory variables, and a
variable selection procedure to control for overfitting issues. Estimation
relies on a two-step procedure building upon the double residual method. We
illustrate the predictive performance and interpretability of GAM(L)A on a
regression and a classification problem. The results show that GAM(L)A
outperforms parametric models augmented by quadratic, cubic and interaction
effects. Moreover, the results also suggest that the performance of GAM(L)A is
not significantly different from that of random forest and gradient boosting.

Machine learning based image classification algorithms, such as deep neural
network approaches, will be increasingly employed in critical settings such as
quality control in industry, where transparency and comprehensibility of
decisions are crucial. Therefore, we aim to extend the defect detection task
towards an interactive human-in-the-loop approach that allows us to integrate
rich background knowledge and the inference of complex relationships going
beyond traditional purely data-driven approaches. We propose an approach for an
interactive support system for classifications in an industrial quality control
setting that combines the advantages of both (explainable) knowledge-driven and
data-driven machine learning methods, in particular inductive logic programming
and convolutional neural networks, with human expertise and control. The
resulting system can assist domain experts with decisions, provide transparent
explanations for results, and integrate feedback from users; thus reducing
workload for humans while both respecting their expertise and without removing
their agency or accountability.

Gaussian process regression underpins countless academic and industrial
applications of machine learning and statistics, with maximum likelihood
estimation routinely used to select appropriate parameters for the covariance
kernel. However, it remains an open problem to establish the circumstances in
which maximum likelihood estimation is well-posed, that is, when the
predictions of the regression model are insensitive to small perturbations of
the data. This article identifies scenarios where the maximum likelihood
estimator fails to be well-posed. These failure cases occur in the noiseless
data setting, for any Gaussian process with a stationary covariance function
whose lengthscale parameter is estimated using maximum likelihood. Although the
failure of maximum likelihood estimation is part of Gaussian process folklore,
these rigorous theoretical results appear to be the first of their kind. The
implication of these negative results is that well-posedness may need to be
assessed post-hoc, on a case-by-case basis, when maximum likelihood estimation
is used to train a Gaussian process model.

Detecting disclosures of individuals' employment status on social media can
provide valuable information to match job seekers with suitable vacancies,
offer social protection, or measure labor market flows. However, identifying
such personal disclosures is a challenging task due to their rarity in a sea of
social media content and the variety of linguistic forms used to describe them.
Here, we examine three Active Learning (AL) strategies in real-world settings
of extreme class imbalance, and identify five types of disclosures about
individuals' employment status (e.g. job loss) in three languages using
BERT-based classification models. Our findings show that, even under extreme
imbalance settings, a small number of AL iterations is sufficient to obtain
large and significant gains in precision, recall, and diversity of results
compared to a supervised baseline with the same number of labels. We also find
that no AL strategy consistently outperforms the rest. Qualitative analysis
suggests that AL helps focus the attention mechanism of BERT on core terms and
adjust the boundaries of semantic expansion, highlighting the importance of
interpretable models to provide greater control and visibility into this
dynamic learning process.

Large-scale crop type classification is a task at the core of remote sensing
efforts with applications of both economic and ecological importance. Current
state-of-the-art deep learning methods are based on self-attention and use
satellite image time series (SITS) to discriminate crop types based on their
unique growth patterns. However, existing methods generalize poorly to regions
not seen during training mainly due to not being robust to temporal shifts of
the growing season caused by variations in climate. To this end, we propose
Thermal Positional Encoding (TPE) for attention-based crop classifiers. Unlike
previous positional encoding based on calendar time (e.g. day-of-year), TPE is
based on thermal time, which is obtained by accumulating daily average
temperatures over the growing season. Since crop growth is directly related to
thermal time, but not calendar time, TPE addresses the temporal shifts between
different regions to improve generalization. We propose multiple TPE
strategies, including learnable methods, to further improve results compared to
the common fixed positional encodings. We demonstrate our approach on a crop
classification task across four different European regions, where we obtain
state-of-the-art generalization results.

As deep learning becomes the mainstream in the field of natural language
processing, the need for suitable active learning method are becoming
unprecedented urgent. Active Learning (AL) methods based on nearest neighbor
classifier are proposed and demonstrated superior results. However, existing
nearest neighbor classifier are not suitable for classifying mutual exclusive
classes because inter-class discrepancy cannot be assured by nearest neighbor
classifiers. As a result, informative samples in the margin area can not be
discovered and AL performance are damaged. To this end, we propose a novel
Nearest neighbor Classifier with Margin penalty for Active Learning(NCMAL).
Firstly, mandatory margin penalty are added between classes, therefore both
inter-class discrepancy and intra-class compactness are both assured. Secondly,
a novel sample selection strategy are proposed to discover informative samples
within the margin area. To demonstrate the effectiveness of the methods, we
conduct extensive experiments on for datasets with other state-of-the-art
methods. The experimental results demonstrate that our method achieves better
results with fewer annotated samples than all baseline methods.

Previous work on multimodal machine translation (MMT) has focused on the way
of incorporating vision features into translation but little attention is on
the quality of vision models. In this work, we investigate the impact of vision
models on MMT. Given the fact that Transformer is becoming popular in computer
vision, we experiment with various strong models (such as Vision Transformer)
and enhanced features (such as object-detection and image captioning). We
develop a selective attention model to study the patch-level contribution of an
image in MMT. On detailed probing tasks, we find that stronger vision models
are helpful for learning translation from the visual modality. Our results also
suggest the need of carefully examining MMT models, especially when current
benchmarks are small-scale and biased. Our code could be found at
\url{https://github.com/libeineu/fairseq_mmt}.

This paper compares recurrent neural networks (RNNs) with different types of
gated cells for forecasting time series with multiple seasonality. The cells we
compare include classical long short term memory (LSTM), gated recurrent unit
(GRU), modified LSTM with dilation, and two new cells we proposed recently,
which are equipped with dilation and attention mechanisms. To model the
temporal dependencies of different scales, our RNN architecture has multiple
dilated recurrent layers stacked with hierarchical dilations. The proposed RNN
produces both point forecasts and predictive intervals (PIs) for them. An
empirical study concerning short-term electrical load forecasting for 35
European countries confirmed that the new gated cells with dilation and
attention performed best.

Capturing aleatoric uncertainty is a critical part of many machine learning
systems. In deep learning, a common approach to this end is to train a neural
network to estimate the parameters of a heteroscedastic Gaussian distribution
by maximizing the logarithm of the likelihood function under the observed data.
In this work, we examine this approach and identify potential hazards
associated with the use of log-likelihood in conjunction with gradient-based
optimizers. First, we present a synthetic example illustrating how this
approach can lead to very poor but stable parameter estimates. Second, we
identify the culprit to be the log-likelihood loss, along with certain
conditions that exacerbate the issue. Third, we present an alternative
formulation, termed $\beta$-NLL, in which each data point's contribution to the
loss is weighted by the $\beta$-exponentiated variance estimate. We show that
using an appropriate $\beta$ largely mitigates the issue in our illustrative
example. Fourth, we evaluate this approach on a range of domains and tasks and
show that it achieves considerable improvements and performs more robustly
concerning hyperparameters, both in predictive RMSE and log-likelihood
criteria.

Simultaneous machine translation (SiMT) outputs translation while reading
source sentence and hence requires a policy to decide whether to wait for the
next source word (READ) or generate a target word (WRITE), the actions of which
form a read/write path. Although the read/write path is essential to SiMT
performance, no direct supervision is given to the path in the existing
methods. In this paper, we propose a method of dual-path SiMT which introduces
duality constraints to direct the read/write path. According to duality
constraints, the read/write path in source-to-target and target-to-source SiMT
models can be mapped to each other. As a result, the two SiMT models can be
optimized jointly by forcing their read/write paths to satisfy the mapping.
Experiments on En-Vi and De-En tasks show that our method can outperform strong
baselines under all latency.

Previous research on organizations often focuses on either the individual,
team, or organizational level. There is a lack of multidimensional research on
emergent phenomena and interactions between the mechanisms at different levels.
This paper takes a multifaceted perspective on individual learning and
autonomous group formation and adaptation. To analyze interactions between the
two levels, we introduce an agent-based model that captures an organization
with a population of heterogeneous agents who learn and are limited in their
rationality. To solve a task, agents form a group that can be adapted from time
to time. We explore organizations that promote learning and group adaptation
either simultaneously or sequentially and analyze the interactions between the
activities and the effects on performance. We observe underproportional
interactions when tasks are interdependent and show that pushing learning and
group adaptation too far might backfire and decrease performance significantly.

Mapping the connectome of the human brain using structural or functional
connectivity has become one of the most pervasive paradigms for neuroimaging
analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep
learning have attracted broad interest due to their established power for
modeling complex networked data. Despite their superior performance in many
fields, there has not yet been a systematic study of how to design effective
GNNs for brain network analysis. To bridge this gap, we present BrainGB, a
benchmark for brain network analysis with GNNs. BrainGB standardizes the
process by (1) summarizing brain network construction pipelines for both
functional and structural neuroimaging modalities and (2) modularizing the
implementation of GNN designs. We conduct extensive experiments on datasets
across cohorts and modalities and recommend a set of general recipes for
effective GNN designs on brain networks. To support open and reproducible
research on GNN-based brain network analysis, we host the BrainGB website at
https://braingb.us with models, tutorials, examples, as well as an out-of-box
Python package. We hope that this work will provide useful empirical evidence
and offer insights for future research in this novel and promising direction.

Recently introduced instruction-paradigm empowers non-expert users to
leverage NLP resources by defining a new task in natural language.
Instruction-tuned models have significantly outperformed multitask learning
models (without instruction); however they are far from state of the art task
specific models. Conventional approaches to improve model performance via
creating large datasets with lots of task instances or architectural/training
changes in model may not be feasible for non-expert users. However, they can
write alternate instructions to represent an instruction task. Is
Instruction-augumentation helpful? We augment a subset of tasks in the expanded
version of NATURAL INSTRUCTIONS with additional instructions and find that
these significantly improve model performance (up to 35%), especially in the
low-data regime. Our results indicate that an additional instruction can be
equivalent to ~200 data samples on average across tasks.

Organisations rely upon group formation to solve complex tasks, and groups
often adapt to the demands of the task they face by changing their composition
periodically. Previous research comes to ambiguous results regarding the
effects of group adaptation on task performance. This paper aims to understand
the impact of group adaptation, defined as a process of periodically changing a
group's composition, on complex task performance and considers the moderating
role of individual learning and task complexity in this relationship. We base
our analyses on an agent-based model of adaptive groups in a complex task
environment based on the NK-framework. The results indicate that reorganising
well-performing groups might be beneficial, but only if individual learning is
restricted. However, there are also cases in which group adaptation might
unfold adverse effects. We provide extensive analyses that shed additional
light on and, thereby, help explain the ambiguous results of previous research.

Signature verification, as a crucial practical documentation analysis task,
has been continuously studied by researchers in machine learning and pattern
recognition fields. In specific scenarios like confirming financial documents
and legal instruments, ensuring the absolute reliability of signatures is of
top priority. In this work, we proposed a new method to learn "top-rank pairs"
for writer-independent offline signature verification tasks. By this scheme, it
is possible to maximize the number of absolutely reliable signatures. More
precisely, our method to learn top-rank pairs aims at pushing positive samples
beyond negative samples, after pairing each of them with a genuine reference
signature. In the experiment, BHSig-B and BHSig-H datasets are used for
evaluation, on which the proposed model achieves overwhelming better pos@top
(the ratio of absolute top positive samples to all of the positive samples)
while showing encouraging performance on both Area Under the Curve (AUC) and
accuracy.

In this paper, we propose an optimal rejection method for rejecting ambiguous
samples by a rejection function. This rejection function is trained together
with a classification function under the framework of Learning-with-Rejection
(LwR). The highlights of LwR are: (1) the rejection strategy is not heuristic
but has a strong background from a machine learning theory, and (2) the
rejection function can be trained on an arbitrary feature space which is
different from the feature space for classification. The latter suggests we can
choose a feature space that is more suitable for rejection. Although the past
research on LwR focused only on its theoretical aspect, we propose to utilize
LwR for practical pattern classification tasks. Moreover, we propose to use
features from different CNN layers for classification and rejection. Our
extensive experiments of notMNIST classification and character/non-character
classification demonstrate that the proposed method achieves better performance
than traditional rejection strategies.

This paper presents a speech intelligibility model based on automatic speech
recognition (ASR), combining phoneme probabilities from deep neural networks
(DNN) and a performance measure that estimates the word error rate from these
probabilities. This model does not require the clean speech reference nor the
word labels during testing as the ASR decoding step, which finds the most
likely sequence of words given phoneme posterior probabilities, is omitted. The
model is evaluated via the root-mean-squared error between the predicted and
observed speech reception thresholds from eight normal-hearing listeners. The
recognition task consists of identifying noisy words from a German matrix
sentence test. The speech material was mixed with eight noise maskers covering
different modulation types, from speech-shaped stationary noise to a
single-talker masker. The prediction performance is compared to five
established models and an ASR-model using word labels. Two combinations of
features and networks were tested. Both include temporal information either at
the feature level (amplitude modulation filterbanks and a feed-forward network)
or captured by the architecture (mel-spectrograms and a time-delay deep neural
network, TDNN). The TDNN model is on par with the DNN while reducing the number
of parameters by a factor of 37; this optimization allows parallel streams on
dedicated hearing aid hardware as a forward-pass can be computed within the
10ms of each frame. The proposed model performs almost as well as the
label-based model and produces more accurate predictions than the baseline
models.

Monitoring the Covid19 pandemic constitutes a critical societal stake that
received considerable research efforts. The intensity of the pandemic on a
given territory is efficiently measured by the reproduction number, quantifying
the rate of growth of daily new infections. Recently, estimates for the time
evolution of the reproduction number were produced using an inverse problem
formulation with a nonsmooth functional minimization. While it was designed to
be robust to the limited quality of the Covid19 data (outliers, missing
counts), the procedure lacks the ability to output credibility interval based
estimates. This remains a severe limitation for practical use in actual
pandemic monitoring by epidemiologists that the present work aims to overcome
by use of Monte Carlo sampling. After interpretation of the functional into a
Bayesian framework, several sampling schemes are tailored to adjust the
nonsmooth nature of the resulting posterior distribution. The originality of
the devised algorithms stems from combining a Langevin Monte Carlo sampling
scheme with Proximal operators. Performance of the new algorithms in producing
relevant credibility intervals for the reproduction number estimates and
denoised counts are compared. Assessment is conducted on real daily new
infection counts made available by the Johns Hopkins University. The interest
of the devised monitoring tools are illustrated on Covid19 data from several
different countries.

Graph Neural Networks (GNNs) have emerged as prominent models for
representation learning on graph structured data. GNNs follow an approach of
message passing analogous to 1-dimensional Weisfeiler Lehman (1-WL) test for
graph isomorphism and consequently are limited by the distinguishing power of
1-WL. More expressive higher-order GNNs which operate on k-tuples of nodes need
increased computational resources in order to process higher-order tensors.
Instead of the WL approach, in this work, we follow the classical approach of
Individualization and Refinement (IR), a technique followed by most practical
isomorphism solvers. Individualization refers to artificially distinguishing a
node in the graph and refinement is the propagation of this information to
other nodes through message passing. We learn to adaptively select nodes to
individualize and to aggregate the resulting graphs after refinement to help
handle the complexity. Our technique lets us learn richer node embeddings while
keeping the computational complexity manageable. Theoretically, we show that
our procedure is more expressive than the 1-WL test. Experiments show that our
method outperforms prominent 1-WL GNN models as well as competitive
higher-order baselines on several benchmark synthetic and real datasets.
Furthermore, our method opens new doors for exploring the paradigm of learning
on graph structures with individualization and refinement.

Knowledge-based visual question answering requires the ability of associating
external knowledge for open-ended cross-modal scene understanding. One
limitation of existing solutions is that they capture relevant knowledge from
text-only knowledge bases, which merely contain facts expressed by first-order
predicates or language descriptions while lacking complex but indispensable
multimodal knowledge for visual understanding. How to construct vision-relevant
and explainable multimodal knowledge for the VQA scenario has been less
studied. In this paper, we propose MuKEA to represent multimodal knowledge by
an explicit triplet to correlate visual objects and fact answers with implicit
relations. To bridge the heterogeneous gap, we propose three objective losses
to learn the triplet representations from complementary views: embedding
structure, topological relation and semantic space. By adopting a pre-training
and fine-tuning learning strategy, both basic and domain-specific multimodal
knowledge are progressively accumulated for answer prediction. We outperform
the state-of-the-art by 3.35% and 6.08% respectively on two challenging
knowledge-required datasets: OK-VQA and KRVQA. Experimental results prove the
complementary benefits of the multimodal knowledge with existing knowledge
bases and the advantages of our end-to-end framework over the existing pipeline
methods. The code is available at https://github.com/AndersonStra/MuKEA.

Model-agnostic meta-learning (MAML) and its variants have become popular
approaches for few-shot learning. However, due to the non-convexity of deep
neural nets (DNNs) and the bi-level formulation of MAML, the theoretical
properties of MAML with DNNs remain largely unknown. In this paper, we first
prove that MAML with over-parameterized DNNs is guaranteed to converge to
global optima at a linear rate. Our convergence analysis indicates that MAML
with over-parameterized DNNs is equivalent to kernel regression with a novel
class of kernels, which we name as Meta Neural Tangent Kernels (MetaNTK). Then,
we propose MetaNTK-NAS, a new training-free neural architecture search (NAS)
method for few-shot learning that uses MetaNTK to rank and select
architectures. Empirically, we compare our MetaNTK-NAS with previous NAS
methods on two popular few-shot learning benchmarks, miniImageNet, and
tieredImageNet. We show that the performance of MetaNTK-NAS is comparable or
better than the state-of-the-art NAS method designed for few-shot learning
while enjoying more than 100x speedup. We believe the efficiency of MetaNTK-NAS
makes itself more practical for many real-world tasks.

Grammatical Error Correction (GEC) aims to automatically detect and correct
grammatical errors. In this aspect, dominant models are trained by
one-iteration learning while performing multiple iterations of corrections
during inference. Previous studies mainly focus on the data augmentation
approach to combat the exposure bias, which suffers from two drawbacks. First,
they simply mix additionally-constructed training instances and original ones
to train models, which fails to help models be explicitly aware of the
procedure of gradual corrections. Second, they ignore the interdependence
between different types of corrections. In this paper, we propose a Type-Driven
Multi-Turn Corrections approach for GEC. Using this approach, from each
training instance, we additionally construct multiple training instances, each
of which involves the correction of a specific type of errors. Then, we use
these additionally-constructed training instances and the original one to train
the model in turn. Experimental results and in-depth analysis show that our
approach significantly benefits the model training. Particularly, our enhanced
model achieves state-of-the-art single-model performance on English GEC
benchmarks. We release our code at Github.

Cross-view geo-localization (CVGL), which aims to estimate the geographical
location of the ground-level camera by matching against enormous geo-tagged
aerial (e.g., satellite) images, remains extremely challenging due to the
drastic appearance differences across views. Existing methods mainly employ
Siamese-like CNNs to extract global descriptors without examining the mutual
benefits between the two modes. In this paper, we present a novel approach
using cross-modal knowledge generative tactics in combination with transformer,
namely mutual generative transformer learning (MGTL), for CVGL. Specifically,
MGTL develops two separate generative modules--one for aerial-like knowledge
generation from ground-level semantic information and vice versa--and fully
exploits their mutual benefits through the attention mechanism. Experiments on
challenging public benchmarks, CVACT and CVUSA, demonstrate the effectiveness
of the proposed method compared to the existing state-of-the-art models.

The integration of additional side information to improve music source
separation has been investigated numerous times, e.g., by adding features to
the input or by adding learning targets in a multi-task learning scenario.
These approaches, however, require additional annotations such as musical
scores, instrument labels, etc. in training and possibly during inference. The
available datasets for source separation do not usually provide these
additional annotations. In this work, we explore transfer learning strategies
to incorporate VGGish features with a state-of-the-art source separation model;
VGGish features are known to be a very condensed representation of audio
content and have been successfully used in many MIR tasks. We introduce three
approaches to incorporate the features, including two latent space
regularization methods and one naive concatenation method. Experimental results
show that our proposed approaches improve several evaluation metrics for music
source separation.

Self-supervised learning, especially contrastive learning, has made an
outstanding contribution to the development of many deep learning research
fields. Recently, researchers in the acoustic signal processing field noticed
its success and leveraged contrastive learning for better music representation.
Typically, existing approaches maximize the similarity between two distorted
audio segments sampled from the same music. In other words, they ensure a
semantic agreement at the music level. However, those coarse-grained methods
neglect some inessential or noisy elements at the frame level, which may be
detrimental to the model to learn the effective representation of music.
Towards this end, this paper proposes a novel Positive-nEgative frame mask for
Music Representation based on the contrastive learning framework, abbreviated
as PEMR. Concretely, PEMR incorporates a Positive-Negative Mask Generation
module, which leverages transformer blocks to generate frame masks on the
Log-Mel spectrogram. We can generate self-augmented negative and positive
samples by masking important components or inessential components,
respectively. We devise a novel contrastive learning objective to accommodate
both self-augmented positives/negatives sampled from the same music. We conduct
experiments on four public datasets. The experimental results of two
music-related downstream tasks, music classification, and cover song
identification, demonstrate the generalization ability and transferability of
music representation learned by PEMR.

Data is fundamental to machine learning-based products and services and is
considered strategic due to its externalities for businesses, governments,
non-profits, and more generally for society. It is renowned that the value of
organizations (businesses, government agencies and programs, and even
industries) scales with the volume of available data. What is often less
appreciated is that the data value in making useful organizational predictions
will range widely and is prominently a function of data characteristics and
underlying algorithms.
  In this research, our goal is to study how the value of data changes over
time and how this change varies across contexts and business areas (e.g. next
word prediction in the context of history, sports, politics). We focus on data
from Reddit.com and compare the value's time-dependency across various Reddit
topics (Subreddits). We make this comparison by measuring the rate at which
user-generated text data loses its relevance to the algorithmic prediction of
conversations. We show that different subreddits have different rates of
relevance decline over time.
  Relating the text topics to various business areas of interest, we argue that
competing in a business area in which data value decays rapidly alters
strategies to acquire competitive advantage. When data value decays rapidly,
access to a continuous flow of data will be more valuable than access to a
fixed stock of data. In this kind of setting, improving user engagement and
increasing user-base help creating and maintaining a competitive advantage.

Pre-trained models (PTMs) have become a fundamental backbone for downstream
tasks in natural language processing and computer vision. Despite initial gains
that were obtained by applying generic PTMs to geo-related tasks at Baidu Maps,
a clear performance plateau over time was observed. One of the main reasons for
this plateau is the lack of readily available geographic knowledge in generic
PTMs. To address this problem, in this paper, we present ERNIE-GeoL, which is a
geography-and-language pre-trained model designed and developed for improving
the geo-related tasks at Baidu Maps. ERNIE-GeoL is elaborately designed to
learn a universal representation of geography-language by pre-training on
large-scale data generated from a heterogeneous graph that contains abundant
geographic knowledge. Extensive quantitative and qualitative experiments
conducted on large-scale real-world datasets demonstrate the superiority and
effectiveness of ERNIE-GeoL. ERNIE-GeoL has already been deployed in production
at Baidu Maps since April 2021, which significantly benefits the performance of
various downstream tasks. This demonstrates that ERNIE-GeoL can serve as a
fundamental backbone for a wide range of geo-related tasks.

Deep neural networks may be susceptible to learning spurious correlations
that hold on average but not in atypical test samples. As with the recent
emergence of vision transformer (ViT) models, it remains underexplored how
spurious correlations are manifested in such architectures. In this paper, we
systematically investigate the robustness of vision transformers to spurious
correlations on three challenging benchmark datasets and compare their
performance with popular CNNs. Our study reveals that when pre-trained on a
sufficiently large dataset, ViT models are more robust to spurious correlations
than CNNs. Key to their success is the ability to generalize better from the
examples where spurious correlations do not hold. Further, we perform extensive
ablations and experiments to understand the role of the self-attention
mechanism in providing robustness under spuriously correlated environments. We
hope that our work will inspire future research on further understanding the
robustness of ViT models.

The transferability of adversarial examples allows the deception on black-box
models, and transfer-based targeted attacks have attracted a lot of interest
due to their practical applicability. To maximize the transfer success rate,
adversarial examples should avoid overfitting to the source model, and image
augmentation is one of the primary approaches for this. However, prior works
utilize simple image transformations such as resizing, which limits input
diversity. To tackle this limitation, we propose the object-based diverse input
(ODI) method that draws an adversarial image on a 3D object and induces the
rendered image to be classified as the target class. Our motivation comes from
the humans' superior perception of an image printed on a 3D object. If the
image is clear enough, humans can recognize the image content in a variety of
viewing conditions. Likewise, if an adversarial example looks like the target
class to the model, the model should also classify the rendered image of the 3D
object as the target class. The ODI method effectively diversifies the input by
leveraging an ensemble of multiple source objects and randomizing viewing
conditions. In our experimental results on the ImageNet-Compatible dataset,
this method boosts the average targeted attack success rate from 28.3% to 47.0%
compared to the state-of-the-art methods. We also demonstrate the applicability
of the ODI method to adversarial examples on the face verification task and its
superior performance improvement. Our code is available at
https://github.com/dreamflake/ODI.

Speaker recognition is increasingly used in several everyday applications
including smart speakers, customer care centers and other speech-driven
analytics. It is crucial to accurately evaluate and mitigate biases present in
machine learning (ML) based speech technologies, such as speaker recognition,
to ensure their inclusive adoption. ML fairness studies with respect to various
demographic factors in modern speaker recognition systems are lagging compared
to other human-centered applications such as face recognition. Existing studies
on fairness in speaker recognition systems are largely limited to evaluating
biases at specific operating points of the systems, which can lead to false
expectations of fairness. Moreover, there are only a handful of bias mitigation
strategies developed for speaker recognition systems. In this paper, we
systematically evaluate the biases present in speaker recognition systems with
respect to gender across a range of system operating points. We also propose
adversarial and multi-task learning techniques to improve the fairness of these
systems. We show through quantitative and qualitative evaluations that the
proposed methods improve the fairness of ASV systems over baseline methods
trained using data balancing techniques. We also present a fairness-utility
trade-off analysis to jointly examine fairness and the overall system
performance. We show that although systems trained using adversarial techniques
improve fairness, they are prone to reduced utility. On the other hand,
multi-task methods can improve the fairness while retaining the utility. These
findings can inform the choice of bias mitigation strategies in the field of
speaker recognition.

Managers often believe that collecting more data will continually improve the
accuracy of their machine learning models. However, we argue in this paper that
when data lose relevance over time, it may be optimal to collect a limited
amount of recent data instead of keeping around an infinite supply of older
(less relevant) data. In addition, we argue that increasing the stock of data
by including older datasets may, in fact, damage the model's accuracy.
Expectedly, the model's accuracy improves by increasing the flow of data
(defined as data collection rate); however, it requires other tradeoffs in
terms of refreshing or retraining machine learning models more frequently.
  Using these results, we investigate how the business value created by machine
learning models scales with data and when the stock of data establishes a
sustainable competitive advantage. We argue that data's time-dependency weakens
the barrier to entry that the stock of data creates. As a result, a competing
firm equipped with a limited (yet sufficient) amount of recent data can develop
more accurate models. This result, coupled with the fact that older datasets
may deteriorate models' accuracy, suggests that created business value doesn't
scale with the stock of available data unless the firm offloads less relevant
data from its data repository. Consequently, a firm's growth policy should
incorporate a balance between the stock of historical data and the flow of new
data.
  We complement our theoretical results with an experiment. In the experiment,
we empirically measure the loss in the accuracy of a next word prediction model
trained on datasets from various time periods. Our empirical measurements
confirm the economic significance of the value decline over time. For example,
100MB of text data, after seven years, becomes as valuable as 50MB of current
data for the next word prediction task.

This paper presents a motion data augmentation scheme incorporating motion
synthesis encouraging diversity and motion correction imposing physical
plausibility. This motion synthesis consists of our modified Variational
AutoEncoder (VAE) and Inverse Kinematics (IK). In this VAE, our proposed
sampling-near-samples method generates various valid motions even with
insufficient training motion data. Our IK-based motion synthesis method allows
us to generate a variety of motions semi-automatically. Since these two schemes
generate unrealistic artifacts in the synthesized motions, our motion
correction rectifies them. This motion correction scheme consists of imitation
learning with physics simulation and subsequent motion debiasing. For this
imitation learning, we propose the PD-residual force that significantly
accelerates the training process. Furthermore, our motion debiasing
successfully offsets the motion bias induced by imitation learning to maximize
the effect of augmentation. As a result, our method outperforms previous
noise-based motion augmentation methods by a large margin on both Recurrent
Neural Network-based and Graph Convolutional Network-based human motion
prediction models. The code is available at
https://github.com/meaten/MotionAug.

Autonomous robots must communicate about their decisions to gain trust and
acceptance. When doing so, robots must determine which actions are causal,
i.e., which directly give rise to the desired outcome, so that these actions
can be included in explanations. In behavior learning in psychology, this sort
of reasoning during an action sequence has been studied extensively in the
context of imitation learning. And yet, these techniques and empirical insights
are rarely applied to human-robot interaction (HRI). In this work, we discuss
the relevance of behavior learning insights for robot intent communication, and
present the first application of these insights for a robot to efficiently
communicate its intent by selectively explaining the causal actions in an
action sequence.

How people think, feel, and behave, primarily is a representation of their
personality characteristics. By being conscious of personality characteristics
of individuals whom we are dealing with or decided to deal with, one can
competently ameliorate the relationship, regardless of its type. With the rise
of Internet-based communication infrastructures (social networks, forums,
etc.), a considerable amount of human communications take place there. The most
prominent tool in such communications, is the language in written and spoken
form that adroitly encodes all those essential personality characteristics of
individuals. Text-based Automatic Personality Prediction (APP) is the automated
forecasting of the personality of individuals based on the generated/exchanged
text contents. This paper presents a novel knowledge graph-enabled approach to
text-based APP that relies on the Big Five personality traits. To this end,
given a text a knowledge graph which is a set of interlinked descriptions of
concepts, was built through matching the input text's concepts with DBpedia
knowledge base entries. Then, due to achieving more powerful representation the
graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon,
and MRC psycholinguistic database information. Afterwards, the knowledge graph
which is now a knowledgeable alternative for the input text was embedded to
yield an embedding matrix. Finally, to perform personality predictions the
resulting embedding matrix was fed to four suggested deep learning models
independently, which are based on convolutional neural network (CNN), simple
recurrent neural network (RNN), long short term memory (LSTM) and bidirectional
long short term memory (BiLSTM). The results indicated a considerable
improvements in prediction accuracies in all of the suggested classifiers.

Despite recent progress of pre-trained language models on generating fluent
text, existing methods still suffer from incoherence problems in long-form text
generation tasks that require proper content control and planning to form a
coherent high-level logical flow. In this work, we propose PLANET, a novel
generation framework leveraging autoregressive self-attention mechanism to
conduct content planning and surface realization dynamically. To guide the
generation of output sentences, our framework enriches the Transformer decoder
with latent representations to maintain sentence-level semantic plans grounded
by bag-of-words. Moreover, we introduce a new coherence-based contrastive
learning objective to further improve the coherence of output. Extensive
experiments are conducted on two challenging long-form text generation tasks
including counterargument generation and opinion article generation. Both
automatic and human evaluations show that our method significantly outperforms
strong baselines and generates more coherent texts with richer contents.

Speaker embedding is an important front-end module to explore discriminative
speaker features for many speech applications where speaker information is
needed. Current SOTA backbone networks for speaker embedding are designed to
aggregate multi-scale features from an utterance with multi-branch network
architectures for speaker representation. However, naively adding many branches
of multi-scale features with the simple fully convolutional operation could not
efficiently improve the performance due to the rapid increase of model
parameters and computational complexity. Therefore, in the most current
state-of-the-art network architectures, only a few branches corresponding to a
limited number of temporal scales could be designed for speaker embeddings. To
address this problem, in this paper, we propose an effective temporal
multi-scale (TMS) model where multi-scale branches could be efficiently
designed in a speaker embedding network almost without increasing computational
costs. The new model is based on the conventional TDNN, where the network
architecture is smartly separated into two modeling operators: a
channel-modeling operator and a temporal multi-branch modeling operator. Adding
temporal multi-scale in the temporal multi-branch operator needs only a little
bit increase of the number of parameters, and thus save more computational
budget for adding more branches with large temporal scales. Moreover, in the
inference stage, we further developed a systemic re-parameterization method to
convert the TMS-based model into a single-path-based topology in order to
increase inference speed. We investigated the performance of the new TMS method
for automatic speaker verification (ASV) on in-domain and out-of-domain
conditions. Results show that the TMS-based model obtained a significant
increase in the performance over the SOTA ASV models, meanwhile, had a faster
inference speed.

The ability to predict the future trajectory of a patient is a key step
toward the development of therapeutics for complex diseases such as Alzheimer's
disease (AD). However, most machine learning approaches developed for
prediction of disease progression are either single-task or single-modality
models, which can not be directly adopted to our setting involving multi-task
learning with high dimensional images. Moreover, most of those approaches are
trained on a single dataset (i.e. cohort), which can not be generalized to
other cohorts. We propose a novel multimodal multi-task deep learning model to
predict AD progression by analyzing longitudinal clinical and neuroimaging data
from multiple cohorts. Our proposed model integrates high dimensional MRI
features from a 3D convolutional neural network with other data modalities,
including clinical and demographic information, to predict the future
trajectory of patients. Our model employs an adversarial loss to alleviate the
study-specific imaging bias, in particular the inter-study domain shifts. In
addition, a Sharpness-Aware Minimization (SAM) optimization technique is
applied to further improve model generalization. The proposed model is trained
and tested on various datasets in order to evaluate and validate the results.
Our results showed that 1) our model yields significant improvement over the
baseline models, and 2) models using extracted neuroimaging features from 3D
convolutional neural network outperform the same models when applied to
MRI-derived volumetric features.

This paper presents datasets utilised for synthetic near-infrared (NIR) image
generation and bounding-box level fruit detection systems. It is undeniable
that high-calibre machine learning frameworks such as Tensorflow or Pytorch,
and large-scale ImageNet or COCO datasets with the aid of accelerated GPU
hardware have pushed the limit of machine learning techniques for more than
decades. Among these breakthroughs, a high-quality dataset is one of the
essential building blocks that can lead to success in model generalisation and
the deployment of data-driven deep neural networks. In particular, synthetic
data generation tasks often require more training samples than other supervised
approaches. Therefore, in this paper, we share the NIR+RGB datasets that are
re-processed from two public datasets (i.e., nirscene and SEN12MS) and our
novel NIR+RGB sweet pepper(capsicum) dataset. We quantitatively and
qualitatively demonstrate that these NIR+RGB datasets are sufficient to be used
for synthetic NIR image generation. We achieved Frechet Inception Distance
(FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweet pepper
datasets respectively. In addition, we release manual annotations of 11 fruit
bounding boxes that can be exported as various formats using cloud service.
Four newly added fruits [blueberry, cherry, kiwi, and wheat] compound 11 novel
bounding box datasets on top of our previous work presented in the deepFruits
project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The
total number of bounding box instances of the dataset is 162k and it is ready
to use from cloud service. For the evaluation of the dataset, Yolov5 single
stage detector is exploited and reported impressive
mean-average-precision,mAP[0.5:0.95] results of[min:0.49, max:0.812]. We hope
these datasets are useful and serve as a baseline for the future studies.

The growing size of point clouds enlarges consumptions of storage,
transmission, and computation of 3D scenes. Raw data is redundant, noisy, and
non-uniform. Therefore, simplifying point clouds for achieving compact, clean,
and uniform points is becoming increasingly important for 3D vision and
graphics tasks. Previous learning based methods aim to generate fewer points
for scene understanding, regardless of the quality of surface reconstruction,
leading to results with low reconstruction accuracy and bad point distribution.
In this paper, we propose a novel point cloud simplification network (PCS-Net)
dedicated to high-quality surface mesh reconstruction while maintaining
geometric fidelity. We first learn a sampling matrix in a feature-aware
simplification module to reduce the number of points. Then we propose a novel
double-scale resampling module to refine the positions of the sampled points,
to achieve a uniform distribution. To further retain important shape features,
an adaptive sampling strategy with a novel saliency loss is designed. With our
PCS-Net, the input non-uniform and noisy point cloud can be simplified in a
feature-aware manner, i.e., points near salient features are consolidated but
still with uniform distribution locally. Experiments demonstrate the
effectiveness of our method and show that we outperform previous simplification
or reconstruction-oriented upsampling methods.

Persistent homology is perhaps the most popular and useful tool offered by
topological data analysis, with point-cloud data being the most common setup.
Its older cousin, the Euler characteristic curve (ECC) is less expressive, but
far easier to compute. It is particularly suitable for analyzing imaging data,
and is commonly used in fields ranging from astrophysics to biomedical image
analysis. These fields are embracing GPU computations to handle increasingly
large datasets. We therefore propose an optimized GPU implementation of ECC
computation for 2D and 3D grayscale images. The goal of this paper is twofold.
First, we offer a practical tool, illustrating its performance with thorough
experimentation, but also explain its inherent shortcomings. Second, this
simple algorithm serves as a perfect backdrop for highlighting basic GPU
programming techniques that make our implementation so efficient, and some
common pitfalls we avoided. This is intended as a step towards a wider usage of
GPU programming in computational geometry and topology software. We find this
is particularly important as geometric and topological tools are used in
conjunction with modern, GPU-accelerated machine learning frameworks.

Research on the generalization ability of deep neural networks (DNNs) has
recently attracted a great deal of attention. However, due to their complex
architectures and large numbers of parameters, measuring the generalization
ability of specific DNN models remains an open challenge. In this paper, we
propose to use multiple factors to measure and rank the relative generalization
of DNNs based on a new concept of confidence dimension (CD). Furthermore, we
provide a feasible framework in our CD to theoretically calculate the upper
bound of generalization based on the conventional Vapnik-Chervonenk dimension
(VC-dimension) and Hoeffding's inequality. Experimental results on image
classification and object detection demonstrate that our CD can reflect the
relative generalization ability for different DNNs. In addition to
full-precision DNNs, we also analyze the generalization ability of binary
neural networks (BNNs), whose generalization ability remains an unsolved
problem. Our CD yields a consistent and reliable measure and ranking for both
full-precision DNNs and BNNs on all the tasks.

Modern deep neural networks for classification usually jointly learn a
backbone for representation and a linear classifier to output the logit of each
class. A recent study has shown a phenomenon called neural collapse that the
within-class means of features and the classifier vectors converge to the
vertices of a simplex equiangular tight frame (ETF) at the terminal phase of
training on a balanced dataset. Since the ETF geometric structure maximally
separates the pair-wise angles of all classes in the classifier, it is natural
to raise the question, why do we spend an effort to learn a classifier when we
know its optimal geometric structure? In this paper, we study the potential of
learning a neural network for classification with the classifier randomly
initialized as an ETF and fixed during training. Our analytical work based on
the layer-peeled model indicates that the feature learning with a fixed ETF
classifier naturally leads to the neural collapse state even when the dataset
is imbalanced among classes. We further show that in this case the cross
entropy (CE) loss is not necessary and can be replaced by a simple squared loss
that shares the same global optimality but enjoys a better convergence
property. Our experimental results show that our method is able to bring
significant improvements with faster convergence on multiple imbalanced
datasets.

Machine learning can be substantially powered by a quantum computer owing to
its huge Hilbert space and inherent quantum parallelism. In the pursuit of
quantum advantages for machine learning with noisy intermediate-scale quantum
devices, it was proposed that the learning model can be designed in an
end-to-end fashion, i.e., the quantum ansatz is parameterized by directly
manipulable control pulses without circuit design and compilation. Such
gate-free models are hardware friendly and can fully exploit limited quantum
resources. Here, we report the first experimental realization of quantum
end-to-end machine learning on a superconducting processor. The trained model
can achieve 98% recognition accuracy for two handwritten digits (via two
qubits) and 89% for four digits (via three qubits) in the MNIST (Mixed National
Institute of Standards and Technology) database. The experimental results
exhibit the great potential of quantum end-to-end learning for resolving
complex real-world tasks when more qubits are available.

Simultaneous machine translation (SiMT) outputs translation while receiving
the streaming source inputs, and hence needs a policy to determine where to
start translating. The alignment between target and source words often implies
the most informative source word for each target word, and hence provides the
unified control over translation quality and latency, but unfortunately the
existing SiMT methods do not explicitly model the alignment to perform the
control. In this paper, we propose Gaussian Multi-head Attention (GMA) to
develop a new SiMT policy by modeling alignment and translation in a unified
manner. For SiMT policy, GMA models the aligned source position of each target
word, and accordingly waits until its aligned position to start translating. To
integrate the learning of alignment into the translation model, a Gaussian
distribution centered on predicted aligned position is introduced as an
alignment-related prior, which cooperates with translation-related soft
attention to determine the final attention. Experiments on En-Vi and De-En
tasks show that our method outperforms strong baselines on the trade-off
between translation and latency.

Vision-Language Pre-training (VLP) has achieved impressive performance on
various cross-modal downstream tasks. However, most existing methods can only
learn from aligned image-caption data and rely heavily on expensive regional
features, which greatly limits their scalability and performance. In this
paper, we propose an end-to-end unified-modal pre-training framework, namely
UNIMO-2, for joint learning on both aligned image-caption data and unaligned
image-only and text-only corpus. We build a unified Transformer model to
jointly learn visual representations, textual representations and semantic
alignment between images and texts. In particular, we propose to conduct
grounded learning on both images and texts via a sharing grounded space, which
helps bridge unaligned images and texts, and align the visual and textual
semantic spaces on different types of corpora. The experiments show that our
grounded learning method can improve textual and visual semantic alignment for
improving performance on various cross-modal tasks. Moreover, benefiting from
effective joint modeling of different types of corpora, our model also achieves
impressive performance on single-modal visual and textual tasks. Our code and
models are public at the UNIMO project page https://unimo-ptm.github.io/.

This paper presents new hierarchically cascaded transformers that can improve
data efficiency through attribute surrogates learning and spectral tokens
pooling. Vision transformers have recently been thought of as a promising
alternative to convolutional neural networks for visual recognition. But when
there is no sufficient data, it gets stuck in overfitting and shows inferior
performance. To improve data efficiency, we propose hierarchically cascaded
transformers that exploit intrinsic image structures through spectral tokens
pooling and optimize the learnable parameters through latent attribute
surrogates. The intrinsic image structure is utilized to reduce the ambiguity
between foreground content and background noise by spectral tokens pooling. And
the attribute surrogate learning scheme is designed to benefit from the rich
visual information in image-label pairs instead of simple visual concepts
assigned by their labels. Our Hierarchically Cascaded Transformers, called
HCTransformers, is built upon a self-supervised learning framework DINO and is
tested on several popular few-shot learning benchmarks.
  In the inductive setting, HCTransformers surpass the DINO baseline by a large
margin of 9.7% 5-way 1-shot accuracy and 9.17% 5-way 5-shot accuracy on
miniImageNet, which demonstrates HCTransformers are efficient to extract
discriminative features. Also, HCTransformers show clear advantages over SOTA
few-shot classification methods in both 5-way 1-shot and 5-way 5-shot settings
on four popular benchmark datasets, including miniImageNet, tieredImageNet,
FC100, and CIFAR-FS. The trained weights and codes are available at
https://github.com/StomachCold/HCTransformers.

Simultaneous machine translation (SiMT) starts translating while receiving
the streaming source inputs, and hence the source sentence is always incomplete
during translating. Different from the full-sentence MT using the conventional
seq-to-seq architecture, SiMT often applies prefix-to-prefix architecture,
which forces each target word to only align with a partial source prefix to
adapt to the incomplete source in streaming inputs. However, the source words
in the front positions are always illusoryly considered more important since
they appear in more prefixes, resulting in position bias, which makes the model
pay more attention on the front source positions in testing. In this paper, we
first analyze the phenomenon of position bias in SiMT, and develop a
Length-Aware Framework to reduce the position bias by bridging the structural
gap between SiMT and full-sentence MT. Specifically, given the streaming
inputs, we first predict the full-sentence length and then fill the future
source position with positional encoding, thereby turning the streaming inputs
into a pseudo full-sentence. The proposed framework can be integrated into most
existing SiMT methods to further improve performance. Experiments on two
representative SiMT methods, including the state-of-the-art adaptive policy,
show that our method successfully reduces the position bias and thereby
achieves better SiMT performance.

Adaptive remote instruction has led to important lessons for the future,
including rediscovery of known pedagogical principles in new contexts and new
insights for supporting remote learning. Studying one computer science
department that serves residential and remote undergraduate and graduate
students, we conducted interviews with stakeholders in the department (n=26)
and ran a department-wide student survey (n=102) during the four academic
quarters from spring 2020 to spring 2021. Our case study outlines what the
instructors did, summarizes what instructors and students say about courses
during this period, and provides recommendations for CS departments with
similar scope going forward. Specific insights address: (1) how instructional
components are best structured for students; (2) how students are assessed for
their learning; and (3) how students are supported in student-initiated
components of learning. The institution is a large U.S. research university
that has a history of online programs including online enrollment in regular
on-campus courses and large-scale open enrollment courses. Our recommendations
to instructors across the scope of this department may also be applicable to
other institutions that provide technology-supported in-person instruction,
remote enrollment, and hybrid courses combining both modalities.

In this paper, we introduce a novel algorithm, $\mathsf{CO}_3$, for
communication-efficiency distributed Deep Neural Network (DNN) training.
$\mathsf{CO}_3$ is a joint training/communication protocol, which encompasses
three processing steps for the network gradients: (i) quantization through
floating-point conversion, (ii) lossless compression, and (iii) error
correction. These three components are crucial in the implementation of
distributed DNN training over rate-constrained links. The interplay of these
three steps in processing the DNN gradients is carefully balanced to yield a
robust and high-performance scheme. The performance of the proposed scheme is
investigated through numerical evaluations over CIFAR-10.

Due to the remarkable progress of deep generative models, animating images
has become increasingly efficient, whereas associated results have become
increasingly realistic. Current animation-approaches commonly exploit structure
representation extracted from driving videos. Such structure representation is
instrumental in transferring motion from driving videos to still images.
However, such approaches fail in case the source image and driving video
encompass large appearance variation. Moreover, the extraction of structure
information requires additional modules that endow the animation-model with
increased complexity. Deviating from such models, we here introduce the Latent
Image Animator (LIA), a self-supervised autoencoder that evades need for
structure representation. LIA is streamlined to animate images by linear
navigation in the latent space. Specifically, motion in generated video is
constructed by linear displacement of codes in the latent space. Towards this,
we learn a set of orthogonal motion directions simultaneously, and use their
linear combination, in order to represent any displacement in the latent space.
Extensive quantitative and qualitative analysis suggests that our model
systematically and significantly outperforms state-of-art methods on VoxCeleb,
Taichi and TED-talk datasets w.r.t. generated quality.

The paradigm of training models on massive data without label through
self-supervised learning (SSL) and finetuning on many downstream tasks has
become a trend recently. However, due to the high training costs and the
unconsciousness of downstream usages, most self-supervised learning methods
lack the capability to correspond to the diversities of downstream scenarios,
as there are various data domains, different vision tasks and latency
constraints on models. Neural architecture search (NAS) is one universally
acknowledged fashion to conquer the issues above, but applying NAS on SSL seems
impossible as there is no label or metric provided for judging model selection.
In this paper, we present DATA, a simple yet effective NAS approach specialized
for SSL that provides Domain-Aware and Task-Aware pre-training. Specifically,
we (i) train a supernet which could be deemed as a set of millions of networks
covering a wide range of model scales without any label, (ii) propose a
flexible searching mechanism compatible with SSL that enables finding networks
of different computation costs, for various downstream vision tasks and data
domains without explicit metric provided. Instantiated With MoCo v2, our method
achieves promising results across a wide range of computation costs on
downstream tasks, including image classification, object detection and semantic
segmentation. DATA is orthogonal to most existing SSL methods and endows them
the ability of customization on downstream needs. Extensive experiments on
other SSL methods demonstrate the generalizability of the proposed method. Code
is released at https://github.com/GAIA-vision/GAIA-ssl

Deep Learning (DL) models have achieved superior performance. Meanwhile,
computing hardware like NVIDIA GPUs also demonstrated strong computing scaling
trends with 2x throughput and memory bandwidth for each generation. With such
strong computing scaling of GPUs, multi-tenant deep learning inference by
co-locating multiple DL models onto the same GPU becomes widely deployed to
improve resource utilization, enhance serving throughput, reduce energy cost,
etc. However, achieving efficient multi-tenant DL inference is challenging
which requires thorough full-stack system optimization. This survey aims to
summarize and categorize the emerging challenges and optimization opportunities
for multi-tenant DL inference on GPU. By overviewing the entire optimization
stack, summarizing the multi-tenant computing innovations, and elaborating the
recent technological advances, we hope that this survey could shed light on new
optimization perspectives and motivate novel works in future large-scale DL
system optimization.

End-to-end Network has become increasingly important in multi-tasking. One
prominent example of this is the growing significance of a driving perception
system in autonomous driving. This paper systematically studies an end-to-end
perception network for multi-tasking and proposes several key optimizations to
improve accuracy. First, the paper proposes efficient segmentation head and
box/class prediction networks based on weighted bidirectional feature network.
Second, the paper proposes automatically customized anchor for each level in
the weighted bidirectional feature network. Third, the paper proposes an
efficient training loss function and training strategy to balance and optimize
network. Based on these optimizations, we have developed an end-to-end
perception network to perform multi-tasking, including traffic object
detection, drivable area segmentation and lane detection simultaneously, called
HybridNets, which achieves better accuracy than prior art. In particular,
HybridNets achieves 77.3 mean Average Precision on Berkeley DeepDrive Dataset,
outperforms lane detection with 31.6 mean Intersection Over Union with 12.83
million parameters and 15.6 billion floating-point operations. In addition, it
can perform visual perception tasks in real-time and thus is a practical and
accurate solution to the multi-tasking problem. Code is available at
https://github.com/datvuthanh/HybridNets.

In this work, we focus on the challenging task, neuro-disease classification,
using functional magnetic resonance imaging (fMRI). In population graph-based
disease analysis, graph convolutional neural networks (GCNs) have achieved
remarkable success. However, these achievements are inseparable from abundant
labeled data and sensitive to spurious signals. To improve fMRI representation
learning and classification under a label-efficient setting, we propose a novel
and theory-driven self-supervised learning (SSL) framework on GCNs, namely
Graph CCA for Temporal self-supervised learning on fMRI analysis GATE.
Concretely, it is demanding to design a suitable and effective SSL strategy to
extract formation and robust features for fMRI. To this end, we investigate
several new graph augmentation strategies from fMRI dynamic functional
connectives (FC) for SSL training. Further, we leverage canonical-correlation
analysis (CCA) on different temporal embeddings and present the theoretical
implications. Consequently, this yields a novel two-step GCN learning procedure
comprised of (i) SSL on an unlabeled fMRI population graph and (ii) fine-tuning
on a small labeled fMRI dataset for a classification task. Our method is tested
on two independent fMRI datasets, demonstrating superior performance on autism
and dementia diagnosis.

The unprecedented increase of commercial airlines and private jets over the
next ten years presents a challenge for air traffic control. Precise flight
trajectory prediction is of great significance in air transportation
management, which contributes to the decision-making for safe and orderly
flights. Existing research and application mainly focus on the sequence
generation based on historical trajectories, while the aircraft-aircraft
interactions in crowded airspace especially the airspaces near busy airports
have been largely ignored. On the other hand, there are distinct
characteristics of aerodynamics for different flight phases, and the trajectory
may be affected by various uncertainties such as weather and advisories from
air traffic controllers. However, there is no literature fully considers all
these issues. Therefore, we proposed a phased flight trajectory prediction
framework. Multi-source and multi-modal datasets have been analyzed and mined
using variants of recurrent neural network (RNN) mixture. To be specific, we
first introduce spatio temporal graphs into the low-altitude airway prediction
problem, and the motion constraints of an aircraft are embedded to the
inference process for reliable forecasting results. In the en-route phase, the
dual attention mechanism is employed to adaptively extract much more important
features from overall datasets to learn the hidden patterns in dynamical
environments. The experimental results demonstrate our proposed framework can
outperform state-of-the-art methods for flight trajectory prediction for large
passenger/transport airplanes.

Triangular machine translation is a special case of low-resource machine
translation where the language pair of interest has limited parallel data, but
both languages have abundant parallel data with a pivot language. Naturally,
the key to triangular machine translation is the successful exploitation of
such auxiliary data. In this work, we propose a transfer-learning-based
approach that utilizes all types of auxiliary data. As we train auxiliary
source-pivot and pivot-target translation models, we initialize some parameters
of the pivot side with a pre-trained language model and freeze them to
encourage both translation models to work in the same pivot language space, so
that they can be smoothly transferred to the source-target translation model.
Experiments show that our approach can outperform previous ones.

Graph Augmentation Learning (GAL) provides outstanding solutions for graph
learning in handling incomplete data, noise data, etc. Numerous GAL methods
have been proposed for graph-based applications such as social network analysis
and traffic flow forecasting. However, the underlying reasons for the
effectiveness of these GAL methods are still unclear. As a consequence, how to
choose optimal graph augmentation strategy for a certain application scenario
is still in black box. There is a lack of systematic, comprehensive, and
experimentally validated guideline of GAL for scholars. Therefore, in this
survey, we in-depth review GAL techniques from macro (graph), meso (subgraph),
and micro (node/edge) levels. We further detailedly illustrate how GAL enhance
the data quality and the model performance. The aggregation mechanism of
augmentation strategies and graph learning models are also discussed by
different application scenarios, i.e., data-specific, model-specific, and
hybrid scenarios. To better show the outperformance of GAL, we experimentally
validate the effectiveness and adaptability of different GAL strategies in
different downstream tasks. Finally, we share our insights on several open
issues of GAL, including heterogeneity, spatio-temporal dynamics, scalability,
and generalization.

A common problem in data science is "use this function defined over this
small set to generate predictions over that larger set." Extrapolation,
interpolation, statistical inference and forecasting all reduce to this
problem. The Kan extension is a powerful tool in category theory that
generalizes this notion. In this work we explore several applications of Kan
extensions to data science. We begin by deriving a simple classification
algorithm as a Kan extension and experimenting with this algorithm on real
data. Next, we use the Kan extension to derive a procedure for learning
clustering algorithms from labels and explore the performance of this procedure
on real data. We then investigate how Kan extensions can be used to learn a
general mapping from datasets of labeled examples to functions and to
approximate a complex function with a simpler one.

Zero-shot learning (ZSL) aims to recognize objects from unseen classes, where
the kernel problem is to transfer knowledge from seen classes to unseen classes
by establishing appropriate mappings between visual and semantic features. The
knowledge transfer in many existing works is limited mainly due to the facts
that 1) the widely used visual features are global ones but not totally
consistent with semantic attributes; 2) only one mapping is learned in existing
works, which is not able to effectively model diverse visual-semantic
relations; 3) the bias problem in the generalized ZSL (GZSL) could not be
effectively handled. In this paper, we propose two techniques to alleviate
these limitations. Firstly, we propose a Semantic-diversity transfer Network
(SetNet) addressing the first two limitations, where 1) a multiple-attention
architecture and a diversity regularizer are proposed to learn multiple local
visual features that are more consistent with semantic attributes and 2) a
projector ensemble that geometrically takes diverse local features as inputs is
proposed to model visual-semantic relations from diverse local perspectives.
Secondly, we propose an inner disagreement based domain detection module (ID3M)
for GZSL to alleviate the third limitation, which picks out unseen-class data
before class-level classification. Due to the absence of unseen-class data in
training stage, ID3M employs a novel self-contained training scheme and detects
out unseen-class data based on a designed inner disagreement criterion.
Experimental results on three public datasets demonstrate that the proposed
SetNet with the explored ID3M achieves a significant improvement against $30$

Conversational teachable agents offer a promising platform to support
learning, both in the classroom and in remote settings. In this context, the
agent takes the role of the novice, while the student takes on the role of
teacher. This framing is significant for its ability to elicit the Prot\'eg\'e
effect in the student-teacher, a pedagogical phenomenon known to increase
engagement in the teaching task, and also improve cognitive outcomes. In prior
work, teachable agents often take a passive role in the learning interaction,
and there are few studies in which the agent and student engage in natural
language dialogue during the teaching task. This work investigates the effect
of teaching modality when interacting with a virtual agent, via the web-based
teaching platform, the Curiosity Notebook. A method of teaching the agent by
selecting sentences from source material is compared to a method paraphrasing
the source material and typing text input to teach. A user study has been
conducted to measure the effect teaching modality on the learning outcomes and
engagement of the participants. The results indicate that teaching via
paraphrasing and text input has a positive effect on learning outcomes for the
material covered, and also on aspects of affective engagement. Furthermore,
increased paraphrasing effort, as measured by the similarity between the source
material and the material the teacher conveyed to the robot, improves learning
outcomes for participants.

Data science collaboration is problematic when access to operational data or
models from outside the data-holding organisation is prohibited, for a variety
of legal, security, ethical, or practical reasons. There are significant data
privacy challenges when performing collaborative data science work against such
restricted data. In this paper we describe a range of causes and risks
associated with restricted data along with the social, environmental, data, and
cryptographic measures that may be used to mitigate such issues. We then show
how these are generally inadequate for restricted data contexts and introduce
the 'Data Airlock' - secure infrastructure that facilitates 'eyes-off' data
science workloads. After describing our use-case we detail the architecture and
implementation of a first, single-organisation version of the Data Airlock
infrastructure. We conclude with outcomes and learning from this
implementation, and outline requirements for a second, federated version.

Situation-aware technologies enabled by multiobject tracking (MOT) methods
will create new services and applications in fields such as autonomous
navigation and applied ocean sciences. Belief propagation (BP) is a
state-of-the-art method for Bayesian MOT but fully relies on a statistical
model and preprocessed sensor measurements. In this paper, we establish a
hybrid method for model-based and data-driven MOT. The proposed neural enhanced
belief propagation (NEBP) approach complements BP by information learned from
raw sensor data with the goal to improve data association and to reject false
alarm measurements. We evaluate the performance of our NEBP approach for MOT on
the nuScenes autonomous driving dataset and demonstrate that it can outperform
state-of-the-art reference methods.

As more and more AI agents are used in practice, it is time to think about
how to make these agents fully autonomous so that they can (1) learn by
themselves continually in a self-motivated and self-initiated manner rather
than being retrained offline periodically on the initiation of human engineers
and (2) accommodate or adapt to unexpected or novel circumstances. As the
real-world is an open environment that is full of unknowns or novelties,
detecting novelties, characterizing them, accommodating or adapting to them,
and gathering ground-truth training data and incrementally learning the
unknowns/novelties are critical to making the AI agent more and more
knowledgeable and powerful over time. The key challenge is how to automate the
process so that it is carried out continually on the agent's own initiative and
through its own interactions with humans, other agents and the environment just
like human on-the-job learning. This paper proposes a framework (called SOLA)
for this learning paradigm to promote the research of building autonomous and
continual learning enabled AI agents. To show feasibility, an implemented agent
is also described.

Recent machine reading comprehension datasets such as ReClor and LogiQA
require performing logical reasoning over text. Conventional neural models are
insufficient for logical reasoning, while symbolic reasoners cannot directly
apply to text. To meet the challenge, we present a neural-symbolic approach
which, to predict an answer, passes messages over a graph representing logical
relations between text units. It incorporates an adaptive logic graph network
(AdaLoGN) which adaptively infers logical relations to extend the graph and,
essentially, realizes mutual and iterative reinforcement between neural and
symbolic reasoning. We also implement a novel subgraph-to-node message passing
mechanism to enhance context-option interaction for answering multiple-choice
questions. Our approach shows promising results on ReClor and LogiQA.

In this paper, we approach the task of determining sensitivity bounds for
pose estimation neural networks. This task is particularly challenging as it
requires characterizing the sensitivity of 3D rotations. We develop a
sensitivity measure that describes the maximum rotational change in a network's
output with respect to a Euclidean change in its input. We show that this
measure is a type of Lipschitz constant, and that it is bounded by the product
of a network's Euclidean Lipschitz constant and an intrinsic property of a
rotation parameterization which we call the "distance ratio constant". We
derive the distance ratio constant for several rotation parameterizations, and
then discuss why the structure of most of these parameterizations makes it
difficult to construct a pose estimation network with provable sensitivity
bounds. However, we show that sensitivity bounds can be computed for networks
which parameterize rotation using unconstrained exponential coordinates. We
then construct and train such a network and compute sensitivity bounds for it.

We study the problem of few shot learning for named entity recognition.
Specifically, we leverage the semantic information in the names of the labels
as a way of giving the model additional signal and enriched priors. We propose
a neural architecture that consists of two BERT encoders, one to encode the
document and its tokens and another one to encode each of the labels in natural
language format. Our model learns to match the representations of named
entities computed by the first encoder with label representations computed by
the second encoder. The label semantics signal is shown to support improved
state-of-the-art results in multiple few shot NER benchmarks and on-par
performance in standard benchmarks. Our model is especially effective in low
resource settings.

The dynamics-aware economic dispatch (DED) problem embeds low-level generator
dynamics and operational constraints to enable near real-time scheduling of
generation units in a power network. DED produces a more dynamic supervisory
control policy than traditional economic dispatch (T-ED) that leads to reduced
overall generation costs. However, the incorporation of differential equations
that govern the system dynamics makes DED an optimization problem that is
computationally prohibitive to solve. In this work, we present a new
data-driven approach based on differentiable programming to efficiently obtain
parametric solutions to the underlying DED problem. In particular, we employ
the recently proposed differentiable predictive control (DPC) for offline
learning of explicit neural control policies using an identified Koopman
operator (KO) model of the power system dynamics. We demonstrate the high
solution quality and five orders of magnitude computational-time savings of the
DPC method over the original online optimization-based DED approach on a 9-bus
test power grid network.

Natural language processing (NLP) models trained on people-generated data can
be unreliable because, without any constraints, they can learn from spurious
correlations that are not relevant to the task. We hypothesize that enriching
models with speaker information in a controlled, educated way can guide them to
pick up on relevant inductive biases. For the speaker-driven task of predicting
code-switching points in English--Spanish bilingual dialogues, we show that
adding sociolinguistically-grounded speaker features as prepended prompts
significantly improves accuracy. We find that by adding influential phrases to
the input, speaker-informed models learn useful and explainable linguistic
information. To our knowledge, we are the first to incorporate speaker
characteristics in a neural model for code-switching, and more generally, take
a step towards developing transparent, personalized models that use speaker
information in a controlled way.

Balancing model complexity against the information contained in observed data
is the central challenge to learning. In order for complexity-efficient models
to exist and be discoverable in high dimensions, we require a computational
framework that relates a credible notion of complexity to simple parameter
representations. Further, this framework must allow excess complexity to be
gradually removed via gradient-based optimization. Our n-ary, or n-argument,
activation functions fill this gap by approximating belief functions
(probabilistic Boolean logic) using logit representations of probability. Just
as Boolean logic determines the truth of a consequent claim from relationships
among a set of antecedent propositions, probabilistic formulations generalize
predictions when antecedents, truth tables, and consequents all retain
uncertainty. Our activation functions demonstrate the ability to learn
arbitrary logic, such as the binary exclusive disjunction (p xor q) and ternary
conditioned disjunction ( c ? p : q ), in a single layer using an activation
function of matching or greater arity. Further, we represent belief tables
using a basis that directly associates the number of nonzero parameters to the
effective arity of the belief function, thus capturing a concrete relationship
between logical complexity and efficient parameter representations. This opens
optimization approaches to reduce logical complexity by inducing parameter
sparsity.

Communication is an effective mechanism for coordinating the behavior of
multiple agents. In the field of multi-agent reinforcement learning, agents can
improve the overall learning performance and achieve their objectives by
communication. Moreover, agents can communicate various types of messages,
either to all agents or to specific agent groups, and through specific
channels. With the growing body of research work in MARL with communication
(Comm-MARL), there is lack of a systematic and structural approach to
distinguish and classify existing Comm-MARL systems. In this paper, we survey
recent works in the Comm-MARL field and consider various aspects of
communication that can play a role in the design and development of multi-agent
reinforcement learning systems. With these aspects in mind, we propose several
dimensions along which Comm-MARL systems can be analyzed, developed, and
compared.

The growing complexity of particle detectors makes their construction and
quality control a new challenge. We present studies that explore the use of
deep learning-based computer vision techniques to perform quality checks of
detector components and assembly steps, which will automate procedures and
minimize the need for human interventions. This study focuses on the
construction steps of a silicon detector, which involve forming a mechanical
structure with the sensor and wire bonding individual cells to electronics for
reading out signals. Silicon detectors in high energy physics experiments today
have millions of channels. Manual quality control of these and other high
channel-density detectors requires enormous amounts of labor and can be prone
to errors. Here, we explore computer vision applications to either augment or
fully replace visual inspections done by humans. We investigated convolutional
neural networks for image classification and autoencoders for anomalies
detection. Two proof-of-concept studies will be presented.

Medical image segmentation has been so far achieving promising results with
Convolutional Neural Networks (CNNs). However, it is arguable that in
traditional CNNs, its pooling layer tends to discard important information such
as positions. Moreover, CNNs are sensitive to rotation and affine
transformation. Capsule network is a data-efficient network design proposed to
overcome such limitations by replacing pooling layers with dynamic routing and
convolutional strides, which aims to preserve the part-whole relationships.
Capsule network has shown a great performance in image recognition and natural
language processing, but applications for medical image segmentation,
particularly volumetric image segmentation, has been limited. In this work, we
propose 3D-UCaps, a 3D voxel-based Capsule network for medical volumetric image
segmentation. We build the concept of capsules into a CNN by designing a
network with two pathways: the first pathway is encoded by 3D Capsule blocks,
whereas the second pathway is decoded by 3D CNNs blocks. 3D-UCaps, therefore
inherits the merits from both Capsule network to preserve the spatial
relationship and CNNs to learn visual representation. We conducted experiments
on various datasets to demonstrate the robustness of 3D-UCaps including
iSeg-2017, LUNA16, Hippocampus, and Cardiac, where our method outperforms
previous Capsule networks and 3D-Unets.

Medical image analysis using deep learning has recently been prevalent,
showing great performance for various downstream tasks including medical image
segmentation and its sibling, volumetric image segmentation. Particularly, a
typical volumetric segmentation network strongly relies on a voxel grid
representation which treats volumetric data as a stack of individual voxel
`slices', which allows learning to segment a voxel grid to be as
straightforward as extending existing image-based segmentation networks to the
3D domain. However, using a voxel grid representation requires a large memory
footprint, expensive test-time and limiting the scalability of the solutions.
In this paper, we propose Point-Unet, a novel method that incorporates the
efficiency of deep learning with 3D point clouds into volumetric segmentation.
Our key idea is to first predict the regions of interest in the volume by
learning an attentional probability map, which is then used for sampling the
volume into a sparse point cloud that is subsequently segmented using a
point-based neural network. We have conducted the experiments on the medical
volumetric segmentation task with both a small-scale dataset Pancreas and
large-scale datasets BraTS18, BraTS19, and BraTS20 challenges. A comprehensive
benchmark on different metrics has shown that our context-aware Point-Unet
robustly outperforms the SOTA voxel-based networks at both accuracies, memory
usage during training, and time consumption during testing. Our code is
available at https://github.com/VinAIResearch/Point-Unet.

Interval Bound Propagation (IBP) is so far the base of state-of-the-art
methods for training neural networks with certifiable robustness guarantees
when potential adversarial perturbations present, while the convergence of IBP
training remains unknown in existing literature. In this paper, we present a
theoretical analysis on the convergence of IBP training. With an
overparameterized assumption, we analyze the convergence of IBP robust
training. We show that when using IBP training to train a randomly initialized
two-layer ReLU neural network with logistic loss, gradient descent can linearly
converge to zero robust training error with a high probability if we have
sufficiently small perturbation radius and large network width.

In this paper, we introduce a novel neural network training framework that
increases model's adversarial robustness to adversarial attacks while
maintaining high clean accuracy by combining contrastive learning (CL) with
adversarial training (AT). We propose to improve model robustness to
adversarial attacks by learning feature representations that are consistent
under both data augmentations and adversarial perturbations. We leverage
contrastive learning to improve adversarial robustness by considering an
adversarial example as another positive example, and aim to maximize the
similarity between random augmentations of data samples and their adversarial
example, while constantly updating the classification head in order to avoid a
cognitive dissociation between the classification head and the embedding space.
This dissociation is caused by the fact that CL updates the network up to the
embedding space, while freezing the classification head which is used to
generate new positive adversarial examples. We validate our method, Contrastive
Learning with Adversarial Features(CLAF), on the CIFAR-10 dataset on which it
outperforms both robust accuracy and clean accuracy over alternative supervised
and self-supervised adversarial learning methods.

Every uncalibrated classifier has a corresponding true calibration map that
calibrates its confidence. Deviations of this idealistic map from the identity
map reveal miscalibration. Such calibration errors can be reduced with many
post-hoc calibration methods which fit some family of calibration maps on a
validation dataset. In contrast, evaluation of calibration with the expected
calibration error (ECE) on the test set does not explicitly involve fitting.
However, as we demonstrate, ECE can still be viewed as if fitting a family of
functions on the test data. This motivates the fit-on-the-test view on
evaluation: first, approximate a calibration map on the test data, and second,
quantify its distance from the identity. Exploiting this view allows us to
unlock missed opportunities: (1) use the plethora of post-hoc calibration
methods for evaluating calibration; (2) tune the number of bins in ECE with
cross-validation. Furthermore, we introduce: (3) benchmarking on pseudo-real
data where the true calibration map can be estimated very precisely; and (4)
novel calibration and evaluation methods using new calibration map families PL
and PL3.

We consider an online stochastic game with risk-averse agents whose goal is
to learn optimal decisions that minimize the risk of incurring significantly
high costs. Specifically, we use the Conditional Value at Risk (CVaR) as a risk
measure that the agents can estimate using bandit feedback in the form of the
cost values of only their selected actions. Since the distributions of the cost
functions depend on the actions of all agents that are generally unobservable,
they are themselves unknown and, therefore, the CVaR values of the costs are
difficult to compute. To address this challenge, we propose a new online
risk-averse learning algorithm that relies on one-point zeroth-order estimation
of the CVaR gradients computed using CVaR values that are estimated by
appropriately sampling the cost functions. We show that this algorithm achieves
sub-linear regret with high probability. We also propose two variants of this
algorithm that improve performance. The first variant relies on a new sampling
strategy that uses samples from the previous iteration to improve the
estimation accuracy of the CVaR values. The second variant employs residual
feedback that uses CVaR values from the previous iteration to reduce the
variance of the CVaR gradient estimates. We theoretically analyze the
convergence properties of these variants and illustrate their performance on an
online market problem that we model as a Cournot game.

Morphologically-rich polysynthetic languages present a challenge for NLP
systems due to data sparsity, and a common strategy to handle this issue is to
apply subword segmentation. We investigate a wide variety of supervised and
unsupervised morphological segmentation methods for four polysynthetic
languages: Nahuatl, Raramuri, Shipibo-Konibo, and Wixarika. Then, we compare
the morphologically inspired segmentation methods against Byte-Pair Encodings
(BPEs) as inputs for machine translation (MT) when translating to and from
Spanish. We show that for all language pairs except for Nahuatl, an
unsupervised morphological segmentation algorithm outperforms BPEs consistently
and that, although supervised methods achieve better segmentation scores, they
under-perform in MT challenges. Finally, we contribute two new morphological
segmentation datasets for Raramuri and Shipibo-Konibo, and a parallel corpus
for Raramuri--Spanish.

Deep learning methods have been successful in solving tasks in machine
learning and have made breakthroughs in many sectors owing to their ability to
automatically extract features from unstructured data. However, their
performance relies on manual trial-and-error processes for selecting an
appropriate network architecture, hyperparameters for training, and
pre-/post-procedures. Even though it has been shown that network architecture
plays a critical role in learning feature representation feature from data and
the final performance, searching for the best network architecture is
computationally intensive and heavily relies on researchers' experience.
Automated machine learning (AutoML) and its advanced techniques i.e. Neural
Architecture Search (NAS) have been promoted to address those limitations. Not
only in general computer vision tasks, but NAS has also motivated various
applications in multiple areas including medical imaging. In medical imaging,
NAS has significant progress in improving the accuracy of image classification,
segmentation, reconstruction, and more. However, NAS requires the availability
of large annotated data, considerable computation resources, and pre-defined
tasks. To address such limitations, meta-learning has been adopted in the
scenarios of few-shot learning and multiple tasks. In this book chapter, we
first present a brief review of NAS by discussing well-known approaches in
search space, search strategy, and evaluation strategy. We then introduce
various NAS approaches in medical imaging with different applications such as
classification, segmentation, detection, reconstruction, etc. Meta-learning in
NAS for few-shot learning and multiple tasks is then explained. Finally, we
describe several open problems in NAS.

Offline reinforcement learning methods hold the promise of learning policies
from pre-collected datasets without the need to query the environment for new
transitions. This setting is particularly well-suited for continuous control
robotic applications for which online data collection based on trial-and-error
is costly and potentially unsafe. In practice, offline datasets are often
heterogeneous, i.e., collected in a variety of scenarios, such as data from
several human demonstrators or from policies that act with different purposes.
Unfortunately, such datasets can exacerbate the distribution shift between the
behavior policy underlying the data and the optimal policy to be learned,
leading to poor performance. To address this challenge, we propose to leverage
latent-variable policies that can represent a broader class of policy
distributions, leading to better adherence to the training data distribution
while maximizing reward via a policy over the latent variable. As we
empirically show on a range of simulated locomotion, navigation, and
manipulation tasks, our method referred to as latent-variable
advantage-weighted policy optimization (LAPO), improves the average performance
of the next best-performing offline reinforcement learning methods by 49% on
heterogeneous datasets, and by 8% on datasets with narrow and biased

Convolutional Neural Networks (CNNs) have been successful in solving tasks in
computer vision including medical image segmentation due to their ability to
automatically extract features from unstructured data. However, CNNs are
sensitive to rotation and affine transformation and their success relies on
huge-scale labeled datasets capturing various input variations. This network
paradigm has posed challenges at scale because acquiring annotated data for
medical segmentation is expensive, and strict privacy regulations. Furthermore,
visual representation learning with CNNs has its own flaws, e.g., it is
arguable that the pooling layer in traditional CNNs tends to discard positional
information and CNNs tend to fail on input images that differ in orientations
and sizes. Capsule network (CapsNet) is a recent new architecture that has
achieved better robustness in representation learning by replacing pooling
layers with dynamic routing and convolutional strides, which has shown
potential results on popular tasks such as classification, recognition,
segmentation, and natural language processing. Different from CNNs, which
result in scalar outputs, CapsNet returns vector outputs, which aim to preserve
the part-whole relationships. In this work, we first introduce the limitations
of CNNs and fundamentals of CapsNet. We then provide recent developments of
CapsNet for the task of medical image segmentation. We finally discuss various
effective network architectures to implement a CapsNet for both 2D images and
3D volumetric medical image segmentation.

In recent years, researchers have extensively studied adversarial robustness
in a variety of threat models, including L_0, L_1, L_2, and L_infinity-norm
bounded adversarial attacks. However, attacks bounded by fractional L_p "norms"
(quasi-norms defined by the L_p distance with 0<p<1) have yet to be thoroughly
considered. We proactively propose a defense with several desirable properties:
it provides provable (certified) robustness, scales to ImageNet, and yields
deterministic (rather than high-probability) certified guarantees when applied
to quantized data (e.g., images). Our technique for fractional L_p robustness
constructs expressive, deep classifiers that are globally Lipschitz with
respect to the L_p^p metric, for any 0<p<1. However, our method is even more
general: we can construct classifiers which are globally Lipschitz with respect
to any metric defined as the sum of concave functions of components. Our
approach builds on a recent work, Levine and Feizi (2021), which provides a
provable defense against L_1 attacks. However, we demonstrate that our proposed
guarantees are highly non-vacuous, compared to the trivial solution of using
(Levine and Feizi, 2021) directly and applying norm inequalities. Code is
available at https://github.com/alevine0/fractionalLpRobustness.

Temporal action proposal generation (TAPG) aims to estimate temporal
intervals of actions in untrimmed videos, which is a challenging yet plays an
important role in many tasks of video analysis and understanding. Despite the
great achievement in TAPG, most existing works ignore the human perception of
interaction between agents and the surrounding environment by applying a deep
learning model as a black-box to the untrimmed videos to extract video visual
representation. Therefore, it is beneficial and potentially improve the
performance of TAPG if we can capture these interactions between agents and the
environment. In this paper, we propose a novel framework named Agent-Aware
Boundary Network (ABN), which consists of two sub-networks (i) an Agent-Aware
Representation Network to obtain both agent-agent and agents-environment
relationships in the video representation, and (ii) a Boundary Generation
Network to estimate the confidence score of temporal intervals. In the
Agent-Aware Representation Network, the interactions between agents are
expressed through local pathway, which operates at a local level to focus on
the motions of agents whereas the overall perception of the surroundings are
expressed through global pathway, which operates at a global level to perceive
the effects of agents-environment. Comprehensive evaluations on 20-action
THUMOS-14 and 200-action ActivityNet-1.3 datasets with different backbone
networks (i.e C3D, SlowFast and Two-Stream) show that our proposed ABN robustly
outperforms state-of-the-art methods regardless of the employed backbone
network on TAPG. We further examine the proposal quality by leveraging
proposals generated by our method onto temporal action detection (TAD)
frameworks and evaluate their detection performances. The source code can be
found in this URL https://github.com/vhvkhoa/TAPG-AgentEnvNetwork.git.

We introduce Backpropagation Through Time and Space (BPTTS), a method for
training a recurrent spatio-temporal neural network, that is used in a
homogeneous multi-agent reinforcement learning (MARL) setting to learn
numerical methods for hyperbolic conservation laws. We treat the numerical
schemes underlying partial differential equations (PDEs) as a Partially
Observable Markov Game (POMG) in Reinforcement Learning (RL). Similar to
numerical solvers, our agent acts at each discrete location of a computational
space for efficient and generalizable learning. To learn higher-order spatial
methods by acting on local states, the agent must discern how its actions at a
given spatiotemporal location affect the future evolution of the state. The
manifestation of this non-stationarity is addressed by BPTTS, which allows for
the flow of gradients across both space and time. The learned numerical
policies are comparable to the SOTA numerics in two settings, the Burgers'
Equation and the Euler Equations, and generalize well to other simulation
set-ups.

The support vector data description (SVDD) approach serves as a de facto
standard for one-class classification where the learning task entails inferring
the smallest hyper-sphere to enclose target objects while linearly penalising
any errors/slacks via an $\ell_1$-norm penalty term. In this study, we
generalise this modelling formalism to a general $\ell_p$-norm ($p\geq1$) slack
penalty function. By virtue of an $\ell_p$ slack norm, the proposed approach
enables formulating a non-linear cost function with respect to slacks. From a
dual problem perspective, the proposed method introduces a sparsity-inducing
dual norm into the objective function, and thus, possesses a higher capacity to
tune into the inherent sparsity of the problem for enhanced descriptive
capability. A theoretical analysis based on Rademacher complexities
characterises the generalisation performance of the proposed approach in terms
of parameter $p$ while the experimental results on several datasets confirm the
merits of the proposed method compared to other alternatives.

Unmanned Aerial Vehicles (UAVs) acting as Flying Access Points (FAPs) are
being used to provide on-demand wireless connectivity in extreme scenarios.
Despite ongoing research, the optimization of UAVs' positions according to
dynamic users' traffic demands remains challenging. We propose the
Traffic-aware UAV Placement Algorithm (TUPA), which positions a UAV acting as
FAP according to the users' traffic demands, in order to maximize the network
utility. Using a DRL approach enables the FAP to autonomously learn and adapt
to dynamic conditions and requirements of networking scenarios. Moreover, the
proposed DRL methodology allows TUPA to generalize knowledge acquired during
training to unknown combinations of users' positions and traffic demands, with
no additional training. TUPA is trained and evaluated using network simulator
ns-3 and ns3-gym framework. The results demonstrate that TUPA increases the
network utility, compared to baseline solutions, increasing the average network
utility up to 4x in scenarios with heterogeneous traffic demands.

The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a
central criteria for the use of total knee arthroplasty. However, this
assessment suffers from imprecise standards and a remarkably high inter-reader
variability. An algorithmic, automated assessment of KOA severity could improve
overall outcomes of knee replacement procedures by increasing the
appropriateness of its use. We propose a novel deep learning-based five-step
algorithm to automatically grade KOA from posterior-anterior (PA) views of
radiographs: (1) image preprocessing (2) localization of knees joints in the
image using the YOLO v3-Tiny model, (3) initial assessment of the severity of
osteoarthritis using a convolutional neural network-based classifier, (4)
segmentation of the joints and calculation of the joint space narrowing (JSN),
and (5), a combination of the JSN and the initial assessment to determine a
final Kellgren-Lawrence (KL) score. Furthermore, by displaying the segmentation
masks used to make the assessment, our algorithm demonstrates a higher degree
of transparency compared to typical "black box" deep learning classifiers. We
perform a comprehensive evaluation using two public datasets and one dataset
from our institution, and show that our algorithm reaches state-of-the art
performance. Moreover, we also collected ratings from multiple radiologists at
our institution and showed that our algorithm performs at the radiologist
level.
  The software has been made publicly available at
https://github.com/MaciejMazurowski/osteoarthritis-classification.

Language models typically need to be trained or finetuned in order to acquire
new knowledge, which involves updating their weights. We instead envision
language models that can simply read and memorize new data at inference time,
thus acquiring new knowledge immediately. In this work, we extend language
models with the ability to memorize the internal representations of past
inputs. We demonstrate that an approximate kNN lookup into a non-differentiable
memory of recent (key, value) pairs improves language modeling across various
benchmarks and tasks, including generic webtext (C4), math papers (arXiv),
books (PG-19), code (Github), as well as formal theorems (Isabelle). We show
that the performance steadily improves when we increase the size of memory up
to 262K tokens. On benchmarks including code and mathematics, we find that the
model is capable of making use of newly defined functions and theorems during
test time.

A large body of the literature on automated program repair develops
approaches where patches are automatically generated to be validated against an
oracle (e.g., a test suite). Because such an oracle can be imperfect, the
generated patches, although validated by the oracle, may actually be incorrect.
Our empirical work investigates different representation learning approaches
for code changes to derive embeddings that are amenable to similarity
computations of patch correctness identification, and assess the possibility of
accurate classification of correct patch by combining learned embeddings with
engineered features. Experimental results demonstrate the potential of learned
embeddings to empower Leopard (a patch correctness predicting framework
implemented in this work) with learning algorithms in reasoning about patch
correctness: a machine learning predictor with BERT transformer-based learned
embeddings associated with XGBoost achieves an AUC value of about 0.803 in the
prediction of patch correctness on a new dataset of 2,147 labeled patches that
we collected for the experiments. Our investigations show that deep learned
embeddings can lead to complementary/better performance when comparing against
the state-of-the-art, PATCH-SIM, which relies on dynamic information. By
combining deep learned embeddings and engineered features, Panther (the
upgraded version of Leopard implemented in this work) outperforms Leopard with
higher scores in terms of AUC, +Recall and -Recall, and can accurately identify
more (in)correct patches that cannot be predicted by the classifiers only with
learned embeddings or engineered features. Finally, we use an explainable ML
technique, SHAP, to empirically interpret how the learned embeddings and
engineered features are contributed to the patch correctness prediction.

We study the problem of aligning the supports of distributions. Compared to
the existing work on distribution alignment, support alignment does not require
the densities to be matched. We propose symmetric support difference as a
divergence measure to quantify the mismatch between supports. We show that
select discriminators (e.g. discriminator trained for Jensen-Shannon
divergence) are able to map support differences as support differences in their
one-dimensional output space. Following this result, our method aligns supports
by minimizing a symmetrized relaxed optimal transport cost in the discriminator
1D space via an adversarial process. Furthermore, we show that our approach can
be viewed as a limit of existing notions of alignment by increasing
transportation assignment tolerance. We quantitatively evaluate the method
across domain adaptation tasks with shifts in label distributions. Our
experiments show that the proposed method is more robust against these shifts
than other alignment-based baselines.

Responding to the "datacenter tax" and "killer microseconds" problems for
datacenter applications, diverse solutions including Smart NIC-based ones have
been proposed. Nonetheless, they often suffer from high overhead of
communications over network and/or PCIe links. To tackle the limitations of the
current solutions, this paper proposes ORCA, a holistic network and
architecture co-design solution that leverages current RDMA and emerging
cache-coherent off-chip interconnect technologies. Specifically, ORCA consists
of four hardware and software components: (1) unified abstraction of inter- and
intra-machine communications managed by one-sided RDMA write and cache-coherent
memory write; (2) efficient notification of requests to accelerators assisted
by cache coherence; (3) cache-coherent accelerator architecture directly
processing requests received by NIC; and (4) adaptive device-to-host data
transfer for modern server memory systems consisting of both DRAM and NVM
exploiting state-of-the-art features in CPUs and PCIe. We prototype ORCA with a
commercial system and evaluate three popular datacenter applications: in-memory
key-value store, chain replication-based distributed transaction system, and
deep learning recommendation model inference. The evaluation shows that ORCA
provides 30.1~69.1% lower latency, up to 2.5x higher throughput, and 3x higher
power efficiency than the current state-of-the-art solutions.

HOLODEC, an airborne cloud particle imager, captures holographic images of a
fixed volume of cloud to characterize the types and sizes of cloud particles,
such as water droplets and ice crystals. Cloud particle properties include
position, diameter, and shape. We present a hologram processing algorithm,
HolodecML, that utilizes a neural segmentation model, GPUs, and computational
parallelization. HolodecML is trained using synthetically generated holograms
based on a model of the instrument, and predicts masks around particles found
within reconstructed images. From these masks, the position and size of the
detected particles can be characterized in three dimensions. In order to
successfully process real holograms, we find we must apply a series of image
corrupting transformations and noise to the synthetic images used in training.
  In this evaluation, HolodecML had comparable position and size estimation
performance to the standard processing method, but improved particle detection
by nearly 20\% on several thousand manually labeled HOLODEC images. However,
the improvement only occurred when image corruption was performed on the
simulated images during training, thereby mimicking non-ideal conditions in the
actual probe. The trained model also learned to differentiate artifacts and
other impurities in the HOLODEC images from the particles, even though no such
objects were present in the training data set, while the standard processing
method struggled to separate particles from artifacts. The novelty of the
training approach, which leveraged noise as a means for parameterizing
non-ideal aspects of the HOLODEC detector, could be applied in other domains
where the theoretical model is incapable of fully describing the real-world
operation of the instrument and accurate truth data required for supervised
learning cannot be obtained from real-world observations.

Convolutional Neural Networks are the de facto models for image recognition.
However 3D CNNs, the straight forward extension of 2D CNNs for video
recognition, have not achieved the same success on standard action recognition
benchmarks. One of the main reasons for this reduced performance of 3D CNNs is
the increased computational complexity requiring large scale annotated datasets
to train them in scale. 3D kernel factorization approaches have been proposed
to reduce the complexity of 3D CNNs. Existing kernel factorization approaches
follow hand-designed and hard-wired techniques. In this paper we propose
Gate-Shift-Fuse (GSF), a novel spatio-temporal feature extraction module which
controls interactions in spatio-temporal decomposition and learns to adaptively
route features through time and combine them in a data dependent manner. GSF
leverages grouped spatial gating to decompose input tensor and channel
weighting to fuse the decomposed tensors. GSF can be inserted into existing 2D
CNNs to convert them into an efficient and high performing spatio-temporal
feature extractor, with negligible parameter and compute overhead. We perform
an extensive analysis of GSF using two popular 2D CNN families and achieve
state-of-the-art or competitive performance on five standard action recognition
benchmarks. Code and models will be made publicly available at
https://github.com/swathikirans/GSF.

We introduce the Satellite Neural Radiance Field (Sat-NeRF), a new end-to-end
model for learning multi-view satellite photogrammetry in the wild. Sat-NeRF
combines some of the latest trends in neural rendering with native satellite
camera models, represented by rational polynomial coefficient (RPC) functions.
The proposed method renders new views and infers surface models of similar
quality to those obtained with traditional state-of-the-art stereo pipelines.
Multi-date images exhibit significant changes in appearance, mainly due to
varying shadows and transient objects (cars, vegetation). Robustness to these
challenges is achieved by a shadow-aware irradiance model and uncertainty
weighting to deal with transient phenomena that cannot be explained by the
position of the sun. We evaluate Sat-NeRF using WorldView-3 images from
different locations and stress the advantages of applying a bundle adjustment
to the satellite camera models prior to training. This boosts the network
performance and can optionally be used to extract additional cues for depth

In this paper, we consider decision trees that use both queries based on one
attribute each and queries based on hypotheses about values of all attributes.
Such decision trees are similar to ones studied in exact learning, where not
only membership but also equivalence queries are allowed. We investigate the
problem of recognition of monotone Boolean functions with $n$ variables, $n=2,
\ldots, 4$, and the problem of sorting $n$ pairwise different elements from
linearly ordered set, $n=3, \ldots, 6$. For each of these problems, we compare
the complexity of different types of optimal (relative to the depth or the
number of realizable nodes) decision trees with hypotheses. We also study the
complexity of decision trees constructed by entropy-based greedy algorithm and
analyze the length of decision rules derived from these trees.

Objective: Disease knowledge graphs are a way to connect, organize, and
access disparate information about diseases with numerous benefits for
artificial intelligence (AI). To create knowledge graphs, it is necessary to
extract knowledge from multimodal datasets in the form of relationships between
disease concepts and normalize both concepts and relationship types.
  Methods: We introduce REMAP, a multimodal approach for disease relation
extraction and classification. The REMAP machine learning approach jointly
embeds a partial, incomplete knowledge graph and a medical language dataset
into a compact latent vector space, followed by aligning the multimodal
embeddings for optimal disease relation extraction.
  Results: We apply REMAP approach to a disease knowledge graph with 96,913
relations and a text dataset of 1.24 million sentences. On a dataset annotated
by human experts, REMAP improves text-based disease relation extraction by
10.0% (accuracy) and 17.2% (F1-score) by fusing disease knowledge graphs with
text information. Further, REMAP leverages text information to recommend new
relationships in the knowledge graph, outperforming graph-based methods by 8.4%
(accuracy) and 10.4% (F1-score).
  Conclusion: REMAP is a multimodal approach for extracting and classifying
disease relationships by fusing structured knowledge and text information.
REMAP provides a flexible neural architecture to easily find, access, and
validate AI-driven relationships between disease concepts.

We currently witness the spectacular success of artificial intelligence in
both science and public life. However, the development of a rigorous
mathematical foundation is still at an early stage. In this survey article,
which is based on an invited lecture at the International Congress of
Mathematicians 2022, we will in particular focus on the current "workhorse" of
artificial intelligence, namely deep neural networks. We will present the main
theoretical directions along with several exemplary results and discuss key
open problems.

Searching for the architecture cells is a dominant paradigm in NAS. However,
little attention has been devoted to the analysis of the cell-based search
spaces even though it is highly important for the continual development of NAS.
In this work, we conduct an empirical post-hoc analysis of architectures from
the popular cell-based search spaces and find that the existing search spaces
contain a high degree of redundancy: the architecture performance is minimally
sensitive to changes at large parts of the cells, and universally adopted
designs, like the explicit search for a reduction cell, significantly increase
the complexities but have very limited impact on the performance. Across
architectures found by a diverse set of search strategies, we consistently find
that the parts of the cells that do matter for architecture performance often
follow similar and simple patterns. By explicitly constraining cells to include
these patterns, randomly sampled architectures can match or even outperform the
state of the art. These findings cast doubts into our ability to discover truly
novel architectures in the existing cell-based search spaces, and inspire our
suggestions for improvement to guide future NAS research. Code is available at
https://github.com/xingchenwan/cell-based-NAS-analysis.

Uncertainty estimation in deep learning has become a leading research field
in medical image analysis due to the need for safe utilisation of AI algorithms
in clinical practice. Most approaches for uncertainty estimation require
sampling the network weights multiple times during testing or training multiple
networks. This leads to higher training and testing costs in terms of time and
computational resources. In this paper, we propose Layer Ensembles, a novel
uncertainty estimation method that uses a single network and requires only a
single pass to estimate predictive uncertainty of a network. Moreover, we
introduce an image-level uncertainty metric, which is more beneficial for
segmentation tasks compared to the commonly used pixel-wise metrics such as
entropy and variance. We evaluate our approach on 2D and 3D, binary and
multi-class medical image segmentation tasks. Our method shows competitive
results with state-of-the-art Deep Ensembles, requiring only a single network
and a single pass.

Split computing distributes the execution of a neural network (e.g., for a
classification task) between a mobile device and a more powerful edge server. A
simple alternative to splitting the network is to carry out the supervised task
purely on the edge server while compressing and transmitting the full data, and
most approaches have barely outperformed this baseline. This paper proposes a
new approach for discretizing and entropy-coding intermediate feature
activations to efficiently transmit them from the mobile device to the edge
server. We show that a efficient splittable network architecture results from a
three-way tradeoff between (a) minimizing the computation on the mobile device,
(b) minimizing the size of the data to be transmitted, and (c) maximizing the
model's prediction performance. We propose an architecture based on this
tradeoff and train the splittable network and entropy model in a knowledge
distillation framework. In an extensive set of experiments involving three
vision tasks, three datasets, nine baselines, and more than 180 trained models,
we show that our approach improves supervised rate-distortion tradeoffs while
maintaining a considerably smaller encoder size. We also release sc2bench, an
installable Python package, to encourage and facilitate future studies on
supervised compression for split computing (SC2).

Makeup and cosmetics offer the potential for self-expression and the
reshaping of social roles for visually impaired people. However, there exist
barriers to conducting a beauty regime because of the reliance on visual
information and color variances in makeup. We present a content analysis of 145
YouTube videos to demonstrate visually impaired individuals' unique practices
before, during, and after doing makeup. Based on the makeup practices, we then
conducted semi-structured interviews with 12 visually impaired people to
discuss their perceptions of and challenges with the makeup process in more
depth. Overall, through our findings and discussion, we present novel
perceptions of makeup from visually impaired individuals (e.g., broader
representations of blindness and beauty). The existing challenges provide
opportunities for future research to address learning barriers, insufficient
feedback, and physical and environmental barriers, making the experience of
doing makeup more accessible to people with visual impairments.

We propose a new method for spatio-temporal forecasting on arbitrarily
distributed points. Assuming that the observed system follows an unknown
partial differential equation, we derive a continuous-time model for the
dynamics of the data via the finite element method. The resulting graph neural
network estimates the instantaneous effects of the unknown dynamics on each
cell in a meshing of the spatial domain. Our model can incorporate prior
knowledge via assumptions on the form of the unknown PDE, which induce a
structural bias towards learning specific processes. Through this mechanism, we
derive a transport variant of our model from the convection equation and show
that it improves the transfer performance to higher-resolution meshes on sea
surface temperature and gas flow forecasting against baseline models
representing a selection of spatio-temporal forecasting methods. A qualitative
analysis shows that our model disentangles the data dynamics into their
constituent parts, which makes it uniquely interpretable.

We investigate at decision trees that incorporate both traditional queries
based on one attribute and queries based on hypotheses about the values of all
attributes. Such decision trees are similar to ones studied in exact learning,
where membership and equivalence queries are allowed. We present greedy
algorithms based on diverse uncertainty measures for construction of above
decision trees and discuss results of computer experiments on various data sets
from the UCI ML Repository and randomly generated Boolean functions. We also
study the length and coverage of decision rules derived from the decisiontrees
constructed by greedy algorithms.

The density profiles of dark matter halos are typically modeled using
empirical formulae fitted to the density profiles of relaxed halo populations.
We present a neural network model that is trained to learn the mapping from the
raw density field containing each halo to the dark matter density profile. We
show that the model recovers the widely-used Navarro-Frenk-White (NFW) profile
out to the virial radius, and can additionally describe the variability in the
outer profile of the halos. The neural network architecture consists of a
supervised encoder-decoder framework, which first compresses the density inputs
into a low-dimensional latent representation, and then outputs $\rho(r)$ for
any desired value of radius $r$. The latent representation contains all the
information used by the model to predict the density profiles. This allows us
to interpret the latent representation by quantifying the mutual information
between the representation and the halos' ground-truth density profiles. A
two-dimensional representation is sufficient to accurately model the density
profiles up to the virial radius; however, a three-dimensional representation
is required to describe the outer profiles beyond the virial radius. The
additional dimension in the representation contains information about the
infalling material in the outer profiles of dark matter halos, thus discovering
the splashback boundary of halos without prior knowledge of the halos'
dynamical history.

Machine-vision-based defect classification techniques have been widely
adopted for automatic quality inspection in manufacturing processes. This
article describes a general framework for classifying defects from high volume
data batches with efficient inspection of unlabelled samples. The concept is to
construct a detector to identify new defect types, send them to the inspection
station for labelling, and dynamically update the classifier in an efficient
manner that reduces both storage and computational needs imposed by data
samples of previously observed batches. Both a simulation study on image
classification and a case study on surface defect detection via 3D point clouds
are performed to demonstrate the effectiveness of the proposed method.

Boundaries are among the primary visual cues used by human and computer
vision systems. One of the key problems in boundary detection is the label
representation, which typically leads to class imbalance and, as a consequence,
to thick boundaries that require non-differential post-processing steps to be
thinned. In this paper, we re-interpret boundaries as 1-D surfaces and
formulate a one-to-one vector transform function that allows for training of
boundary prediction completely avoiding the class imbalance issue.
Specifically, we define the boundary representation at any point as the unit
vector pointing to the closest boundary surface. Our problem formulation leads
to the estimation of direction as well as richer contextual information of the
boundary, and, if desired, the availability of zero-pixel thin boundaries also
at training time. Our method uses no hyper-parameter in the training loss and a
fixed stable hyper-parameter at inference. We provide theoretical
justification/discussions of the vector transform representation. We evaluate
the proposed loss method using a standard architecture and show the excellent
performance over other losses and representations on several datasets. Code is
available at https://github.com/edomel/BoundaryVT.

There has been significant progress in machine learning algorithms for human
pose estimation that may provide immense value in rehabilitation and movement
sciences. However, there remain several challenges to routine use of these
tools for clinical practice and translational research, including: 1) high
technical barrier to entry, 2) rapidly evolving space of algorithms, 3)
challenging algorithmic interdependencies, and 4) complex data management
requirements between these components. To mitigate these barriers, we developed
a human pose estimation pipeline that facilitates running state-of-the-art
algorithms on data acquired in clinical context. Our system allows for running
different implementations of several classes of algorithms and handles their
interdependencies easily. These algorithm classes include subject
identification and tracking, 2D keypoint detection, 3D joint location
estimation, and estimating the pose of body models. The system uses a database
to manage videos, intermediate analyses, and data for computations at each
stage. It also provides tools for data visualization, including generating
video overlays that also obscure faces to enhance privacy. Our goal in this
work is not to train new algorithms, but to advance the use of cutting-edge
human pose estimation algorithms for clinical and translation research. We show
that this tool facilitates analyzing large numbers of videos of human movement
ranging from gait laboratories analyses, to clinic and therapy visits, to
people in the community. We also highlight limitations of these algorithms when
applied to clinical populations in a rehabilitation setting.

Existing self-explaining models typically favor extracting the shortest
possible rationales - snippets of an input text "responsible for" corresponding
output - to explain the model prediction, with the assumption that shorter
rationales are more intuitive to humans. However, this assumption has yet to be
validated. Is the shortest rationale indeed the most human-understandable? To
answer this question, we design a self-explaining model, LimitedInk, which
allows users to extract rationales at any target length. Compared to existing
baselines, LimitedInk achieves compatible end-task performance and
human-annotated rationale agreement, making it a suitable representation of the
recent class of self-explaining models. We use LimitedInk to conduct a user
study on the impact of rationale length, where we ask human judges to predict
the sentiment label of documents based only on LimitedInk-generated rationales
with different lengths. We show rationales that are too short do not help
humans predict labels better than randomly masked text, suggesting the need for
more careful design of the best human rationales.

The code smell is a sign of design and development flaws in a software system
that reduces the reusability and maintainability of the system. Refactoring is
done as an ongoing practice to remove the code smell from the program code.
Among different code smells, the God class or Blob is one of the most common
code smells. A god class contains too many responsibilities, violating
object-oriented programming design's low coupling and high cohesiveness
principles. This paper proposes an automatic approach to extracting a God class
into multiple smaller classes with more specific responsibilities. To do this,
we first construct a graph of methods (as nodes) for the concerning god class.
The edge between any two methods is determined by their structural similarity,
and the feature for each method is initialized using different semantic
representation methods. Then, the variational graph auto-encoder is used to
learn a vector representation for each method. Finally, the learned vectors are
used to cluster methods into different groups to be recommended as refactored
classes. We assessed the proposed framework using three different class
cohesion metrics on sixteen actual God Classes collected from two well-known
open-source systems. We also conducted a comparative study of our approach with
a similar existing approach and found that the proposed approach generated
better results for almost all the God Classes used in the experiment.

The concept of a small, single-layer water Cherenkov detector, with three
photomultiplier tubes (PMTs), placed at its bottom in a $120^{\circ}$ star
configuration (\emph{Mercedes} Water Cherenkov Detector) is presented. The PMTs
are placed near the lateral walls of the stations with an adjustable
inclination and may be installed inside or outside the water volume. To
illustrate the technical viability of this concept and obtain a first-order
estimation of its cost, an engineering design was elaborated. The sensitivity
of these stations to low energy Extensive Air Shower (EAS) electrons, photons
and muons is discussed, both in compact and sparse array configurations. It is
shown that the analysis of the intensity and time patterns of the PMT signals,
using machine learning techniques, enables the tagging of muons, achieving an
excellent gamma/hadron discrimination for TeV showers.
  This concept minimises the station production and maintenance costs, allowing
for a highly flexible and fast installation. Mercedes Water Cherenkov Detectors
(WCDs) are thus well-suited for use in high-altitude large gamma-ray
observatories covering an extended energy range from the low energies, closing
the gap between satellite and ground-based measurements, to very high energy
regions, beyond the PeV scale.

The promise of self-supervised learning (SSL) is to leverage large amounts of
unlabeled data to solve complex tasks. While there has been excellent progress
with simple, image-level learning, recent methods have shown the advantage of
including knowledge of image structure. However, by introducing hand-crafted
image segmentations to define regions of interest, or specialized augmentation
strategies, these methods sacrifice the simplicity and generality that makes
SSL so powerful. Instead, we propose a self-supervised learning paradigm that
discovers this image structure by itself. Our method, Odin, couples object
discovery and representation networks to discover meaningful image
segmentations without any supervision. The resulting learning paradigm is
simpler, less brittle, and more general, and achieves state-of-the-art transfer
learning results for object detection and instance segmentation on COCO, and
semantic segmentation on PASCAL and Cityscapes, while strongly surpassing
supervised pre-training for video segmentation on DAVIS.

Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning
models which leverage the flexibility of deep learning to produce
well-calibrated predictions and naturally handle off-the-grid and missing data.
CNPs scale to large datasets and train with ease. Due to these features, CNPs
appear well-suited to tasks from environmental sciences or healthcare.
Unfortunately, CNPs do not produce correlated predictions, making them
fundamentally inappropriate for many estimation and decision making tasks.
Predicting heat waves or floods, for example, requires modelling dependencies
in temperature or precipitation over time and space. Existing approaches which
model output dependencies, such as Neural Processes (NPs; Garnelo et al.,
2018b) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to
train or prohibitively expensive. What is needed is an approach which provides
dependent predictions, but is simple to train and computationally tractable. In
this work, we present a new class of Neural Process models that make correlated
predictions and support exact maximum likelihood training that is simple and
scalable. We extend the proposed models by using invertible output
transformations, to capture non-Gaussian output distributions. Our models can
be used in downstream estimation tasks which require dependent function
samples. By accounting for output dependencies, our models show improved
predictive performance on a range of experiments with synthetic and real data.

We propose a framework to modularize the training of neural language models
that use diverse forms of sentence-external context (including metadata) by
eliminating the need to jointly train sentence-external and within-sentence
encoders. Our approach, contextual universal embeddings (CUE), trains LMs on
one set of context, such as date and author, and adapts to novel metadata
types, such as article title, or previous sentence. The model consists of a
pretrained neural sentence LM, a BERT-based context encoder, and a masked
transformer decoder that estimates LM probabilities using sentence-internal and
sentence-external information. When context or metadata are unavailable, our
model learns to combine contextual and sentence-internal information using
noisy oracle unigram embeddings as a proxy. Real contextual information can be
introduced later and used to adapt a small number of parameters that map
contextual data into the decoder's embedding space. We validate the CUE
framework on a NYTimes text corpus with multiple metadata types, for which the
LM perplexity can be lowered from 36.6 to 27.4 by conditioning on context.
Bootstrapping a contextual LM with only a subset of the context/metadata during
training retains 85\% of the achievable gain. Training the model initially with
proxy context retains 67% of the perplexity gain after adapting to real
context. Furthermore, we can swap one type of pretrained sentence LM for
another without retraining the context encoders, by only adapting the decoder
model. Overall, we obtain a modular framework that allows incremental, scalable
training of context-enhanced LMs.

Despite the enormous success of artificial neural networks (ANNs) in many
disciplines, the characterization of their computations and the origin of key
properties such as generalization and robustness remain open questions. Recent
literature suggests that robust networks with good generalization properties
tend to be biased towards processing low frequencies in images. To explore the
frequency bias hypothesis further, we develop an algorithm that allows us to
learn modulatory masks highlighting the essential input frequencies needed for
preserving a trained network's performance. We achieve this by imposing
invariance in the loss with respect to such modulations in the input
frequencies. We first use our method to test the low-frequency preference
hypothesis of adversarially trained or data-augmented networks. Our results
suggest that adversarially robust networks indeed exhibit a low-frequency bias
but we find this bias is also dependent on directions in frequency space.
However, this is not necessarily true for other types of data augmentation. Our
results also indicate that the essential frequencies in question are
effectively the ones used to achieve generalization in the first place.
Surprisingly, images seen through these modulatory masks are not recognizable
and resemble texture-like patterns.

As the quality of few shot facial animation from landmarks increases, new
applications become possible, such as ultra low bandwidth video chat
compression with a high degree of realism. However, there are some important
challenges to tackle in order to improve the experience in real world
conditions. In particular, the current approaches fail to represent profile
views without distortions, while running in a low compute regime. We focus on
this key problem by introducing a multi-frames embedding dubbed Frontalizer to
improve profile views rendering. In addition to this core improvement, we
explore the learning of a latent code conditioning generations along with
landmarks to better convey facial expressions. Our dense models achieves 22% of
improvement in perceptual quality and 73% reduction of landmark error over the
first order model baseline on a subset of DFDC videos containing head
movements. Declined with mobile architectures, our models outperform the
previous state-of-the-art (improving perceptual quality by more than 16% and
reducing landmark error by more than 47% on two datasets) while running on real
time on iPhone 8 with very low bandwidth requirements.

In computer vision, pre-training models based on largescale supervised
learning have been proven effective over the past few years. However, existing
works mostly focus on learning from individual task with single data source
(e.g., ImageNet for classification or COCO for detection). This restricted form
limits their generalizability and usability due to the lack of vast semantic
information from various tasks and data sources. Here, we demonstrate that
jointly learning from heterogeneous tasks and multiple data sources contributes
to universal visual representation, leading to better transferring results of
various downstream tasks. Thus, learning how to bridge the gaps among different
tasks and data sources is the key, but it still remains an open question. In
this work, we propose a representation learning framework called X-Learner,
which learns the universal feature of multiple vision tasks supervised by
various sources, with expansion and squeeze stage: 1) Expansion Stage:
X-Learner learns the task-specific feature to alleviate task interference and
enrich the representation by reconciliation layer. 2) Squeeze Stage: X-Learner
condenses the model to a reasonable size and learns the universal and
generalizable representation for various tasks transferring. Extensive
experiments demonstrate that X-Learner achieves strong performance on different
tasks without extra annotations, modalities and computational costs compared to
existing representation learning methods. Notably, a single X-Learner model
shows remarkable gains of 3.0%, 3.3% and 1.8% over current pretrained models on
12 downstream datasets for classification, object detection and semantic

Training of object detection models using less data is currently the focus of
existing N-shot learning models in computer vision. Such methods use
object-level labels and takes hours to train on unseen classes. There are many
cases where we have large amount of image-level labels available for training
but cannot be utilized by few shot object detection models for training. There
is a need for a machine learning framework that can be used for training any
unseen class and can become useful in real-time situations. In this paper, we
proposed an "Unseen Class Detector" that can be trained within a very short
time for any possible unseen class without bounding boxes with competitive
accuracy. We build our approach on "Strong" and "Weak" baseline detectors,
which we trained on existing object detection and image classification
datasets, respectively. Unseen concepts are fine-tuned on the strong baseline
detector using only image-level labels and further adapted by transferring the
classifier-detector knowledge between baselines. We use semantic as well as
visual similarities to identify the source class (i.e. Sheep) for the
fine-tuning and adaptation of unseen class (i.e. Goat). Our model (UnseenNet)
is trained on the ImageNet classification dataset for unseen classes and tested
on an object detection dataset (OpenImages). UnseenNet improves the mean
average precision (mAP) by 10% to 30% over existing baselines (semi-supervised
and few-shot) of object detection on different unseen class splits. Moreover,
training time of our model is <10 min for each unseen class. Qualitative
results demonstrate that UnseenNet is suitable not only for few classes of
Pascal VOC but for unseen classes of any dataset or web. Code is available at
https://github.com/Asra-Aslam/UnseenNet.

This paper analyses social media data in multiple disaster-related
collections of floods and heat waves in the UK. The proposed method uses
machine learning classifiers based on deep bidirectional neural networks
trained on benchmark datasets of disaster responses and extreme events. The
resulting models are applied to perform sentiment and qualitative analysis of
inferred topics in text data. We further analyse a set of behavioural
indicators and match them with climate variables via decoding synoptical
records to analyse thermal comfort. We highlight the advantages of aligning
behavioural indicators along with climate variables to provide with additional
valuable information to be considered especially in different phases of a
disaster and applicable to extreme weather periods. The positiveness of
messages is around 8% for disaster, 1% for disaster and medical response, 7%
for disaster and humanitarian related messages. This shows the reliability of
such data for our case studies. We show the transferability of this approach to
be applied to any social media data collection.

De novo peptide sequencing from mass spectrometry data is an important method
for protein identification. Recently, various deep learning approaches were
applied for de novo peptide sequencing and DeepNovoV2 is one of the
represetative models. In this study, we proposed an enhanced model, DePS, which
can improve the accuracy of de novo peptide sequencing even with missing signal
peaks or large number of noisy peaks in tandem mass spectrometry data. It is
showed that, for the same test set of DeepNovoV2, the DePS model achieved
excellent results of 74.22%, 74.21% and 41.68% for amino acid recall, amino
acid precision and peptide recall respectively. Furthermore, the results
suggested that DePS outperforms DeepNovoV2 on the cross species dataset.

World Input-Output (I/O) matrices provide the networks of within- and
cross-country economic relations. In the context of I/O analysis, the
methodology adopted by national statistical offices in data collection raises
the issue of obtaining reliable data in a timely fashion and it makes the
reconstruction of (part of) the I/O matrices of particular interest. In this
work, we propose a method combining hierarchical clustering and Matrix
Completion (MC) with a LASSO-like nuclear norm penalty, to impute missing
entries of a partially unknown I/O matrix. Through simulations based on
synthetic matrices we study the effectiveness of the proposed method to predict
missing values from both previous years data and current data related to
countries similar to the one for which current data are obscured. To show the
usefulness of our method, an application based on World Input-Output Database
(WIOD) tables - which are an example of industry-by-industry I/O tables - is
provided. Strong similarities in structure between WIOD and other I/O tables
are also found, which make the proposed approach easily generalizable to them.

Although many fields have witnessed the superior performance brought about by
deep learning, the robustness of neural networks remains an open issue.
Specifically, a small adversarial perturbation on the input may cause the model
to produce a completely different output. Such poor robustness implies many
potential hazards, especially in security-critical applications, e.g.,
autonomous driving and mobile robotics. This work studies what information the
adversarially trained model focuses on. Empirically, we notice that the
differences between the clean and adversarial data are mainly distributed in
the low-frequency region. We then find that an adversarially-trained model is
more robust than its naturally-trained counterpart due to the reason that the
former pays more attention to learning the dominant information in
low-frequency components. In addition, we consider two common ways to improve
model robustness, namely, by data augmentation and by using stronger network
architectures, and understand these techniques from a frequency-domain
perspective. We are hopeful this work can shed light on the design of more
robust neural networks.

Deep Neural Networks (DNNs) are very popular because of their high
performance in various cognitive tasks in Machine Learning (ML). Recent
advancements in DNNs have brought beyond human accuracy in many tasks, but at
the cost of high computational complexity. To enable efficient execution of DNN
inference, more and more research works, therefore, exploit the inherent error
resilience of DNNs and employ Approximate Computing (AC) principles to address
the elevated energy demands of DNN accelerators. This article provides a
comprehensive survey and analysis of hardware approximation techniques for DNN
accelerators. First, we analyze the state of the art and by identifying
approximation families, we cluster the respective works with respect to the
approximation type. Next, we analyze the complexity of the performed
evaluations (with respect to the dataset and DNN size) to assess the
efficiency, the potential, and limitations of approximate DNN accelerators.
Moreover, a broad discussion is provided, regarding error metrics that are more
suitable for designing approximate units for DNN accelerators as well as
accuracy recovery approaches that are tailored to DNN inference. Finally, we
present how Approximate Computing for DNN accelerators can go beyond energy
efficiency and address reliability and security issues, as well.

The efficient, automated search for well-performing neural architectures
(NAS) has drawn increasing attention in the recent past. Thereby, the
predominant research objective is to reduce the necessity of costly evaluations
of neural architectures while efficiently exploring large search spaces. To
this aim, surrogate models embed architectures in a latent space and predict
their performance, while generative models for neural architectures enable
optimization-based search within the latent space the generator draws from.
Both, surrogate and generative models, have the aim of facilitating
query-efficient search in a well-structured latent space. In this paper, we
further improve the trade-off between query-efficiency and promising
architecture generation by leveraging advantages from both, efficient surrogate
models and generative design. To this end, we propose a generative model,
paired with a surrogate predictor, that iteratively learns to generate samples
from increasingly promising latent subspaces. This approach leads to very
effective and efficient architecture search, while keeping the query amount
low. In addition, our approach allows in a straightforward manner to jointly
optimize for multiple objectives such as accuracy and hardware latency. We show
the benefit of this approach not only w.r.t. the optimization of architectures
for highest classification accuracy but also in the context of hardware
constraints and outperform state-of-the-art methods on several NAS benchmarks
for single and multiple objectives. We also achieve state-of-the-art
performance on ImageNet. The code is available at
http://github.com/jovitalukasik/AG-Net .

We establish a connection between tangles, a concept from structural graph
theory that plays a central role in Robertson and Seymour's graph minor
project, and hierarchical clustering. Tangles cannot only be defined for
graphs, but in fact for arbitrary connectivity functions, which are functions
defined on the subsets of some finite universe. In typical clustering
applications these universes consist of points in some metric space.
Connectivity functions are usually required to be submodular. It is our first
contribution to show that the central duality theorem connecting tangles with
hierarchical decompositions (so-called branch decompositions) also holds if
submodularity is replaced by a different property that we call
maximum-submodular. We then define a connectivity function on finite data sets
in an arbitrary metric space and prove that its tangles are in one-to-one
correspondence with the clusters obtained by applying the well-known single
linkage clustering algorithms to the same data set. Lastly we generalize this
correspondence for any hierarchical clustering. We show that the data structure
that represents hierarchical clustering results, called dendograms, are
equivalent to maximum-submodular connectivity functions and their tangles. The
idea of viewing tangles as clusters has first been proposed by Diestel and
Whittle in 2016 as an approach to image segmentation. To the best of our
knowledge, our result is the first that establishes a precise technical
connection between tangles and clusters.

A recent line of work on black-box adversarial attacks has revived the use of
transfer from surrogate models by integrating it into query-based search.
However, we find that existing approaches of this type underperform their
potential, and can be overly complicated besides. Here, we provide a short and
simple algorithm which achieves state-of-the-art results through a search which
uses the surrogate network's class-score gradients, with no need for other
priors or heuristics. The guiding assumption of the algorithm is that the
studied networks are in a fundamental sense learning similar functions, and
that a transfer attack from one to the other should thus be fairly "easy". This
assumption is validated by the extremely low query counts and failure rates
achieved: e.g. an untargeted attack on a VGG-16 ImageNet network using a
ResNet-152 as the surrogate yields a median query count of 6 at a success rate
of 99.9%. Code is available at https://github.com/fiveai/GFCS.

Self-supervised Learning (SSL) including the mainstream contrastive learning
has achieved great success in learning visual representations without data
annotations. However, most methods mainly focus on the instance level
information (\ie, the different augmented images of the same instance should
have the same feature or cluster into the same class), but there is a lack of
attention on the relationships between different instances. In this paper, we
introduce a novel SSL paradigm, which we term as relational self-supervised
learning (ReSSL) framework that learns representations by modeling the
relationship between different instances. Specifically, our proposed method
employs sharpened distribution of pairwise similarities among different
instances as \textit{relation} metric, which is thus utilized to match the
feature embeddings of different augmentations. To boost the performance, we
argue that weak augmentations matter to represent a more reliable relation, and
leverage momentum strategy for practical efficiency. The designed asymmetric
predictor head and an InfoNCE warm-up strategy enhance the robustness to
hyper-parameters and benefit the resulting performance. Experimental results
show that our proposed ReSSL substantially outperforms the state-of-the-art
methods across different network architectures, including various lightweight
networks (\eg, EfficientNet and MobileNet).

Though robot learning is often formulated in terms of discrete-time Markov
decision processes (MDPs), physical robots require near-continuous multiscale
feedback control. Machines operate on multiple asynchronous sensing modalities,
each with different frequencies, e.g., video frames at 30Hz, proprioceptive
state at 100Hz, force-torque data at 500Hz, etc. While the classic approach is
to batch observations into fixed-time windows then pass them through
feed-forward encoders (e.g., with deep networks), we show that there exists a
more elegant approach -- one that treats policy learning as modeling latent
state dynamics in continuous-time. Specifically, we present 'InFuser', a
unified architecture that trains continuous time-policies with Neural
Controlled Differential Equations (CDEs). InFuser evolves a single latent state
representation over time by (In)tegrating and (Fus)ing multi-sensory
observations (arriving at different frequencies), and inferring actions in
continuous-time. This enables policies that can react to multi-frequency multi
sensory feedback for truly end-to-end visuomotor control, without discrete-time
assumptions. Behavior cloning experiments demonstrate that InFuser learns
robust policies for dynamic tasks (e.g., swinging a ball into a cup) notably
outperforming several baselines in settings where observations from one sensing
modality can arrive at much sparser intervals than others.

The aim of online change-point detection is for a accurate, timely discovery
of structural breaks. As data dimension outgrows the number of data in
observation, online detection becomes challenging. Existing methods typically
test only the change of mean, which omit the practical aspect of change of
variance. We propose a complete graph-based, change-point detection algorithm
to detect change of mean and variance from low to high-dimensional online data
with a variable scanning window. Inspired by complete graph structure, we
introduce graph-spanning ratios to map high-dimensional data into metrics, and
then test statistically if a change of mean or change of variance occurs.
Theoretical study shows that our approach has the desirable pivotal property
and is powerful with prescribed error probabilities. We demonstrate that this
framework outperforms other methods in terms of detection power. Our approach
has high detection power with small and multiple scanning window, which allows
timely detection of change-point in the online setting. Finally, we applied the
method to financial data to detect change-points in S&P 500 stocks.

Federated Learning (FL) emerges from the privacy concerns traditional machine
learning raised. FL trains decentralized models by averaging them without
compromising clients' datasets. Ongoing research has found that FL is also
prone to security and privacy violations. Recent studies established that FL
leaks information by exploiting inference attacks, reconstructing a data piece
used during training, or extracting information. Additionally, poisoning
attacks and backdoors corrupt FL security by inserting poisoned data into
clients' datasets or directly modifying the model, degrading every client's
  Our proposal utilizes these attacks in combination for performing a
client-wise targeted backdoor, where a single victim client is backdoored while
the rest remains unaffected. Our results establish the viability of the
presented attack, achieving a 100% attack success rate downgrading the target
label accuracy up to 0%. Our code will be publicly available after acceptance.

Due to the amount of videos and related captions uploaded every hour, deep
learning-based solutions for cross-modal video retrieval are attracting more
and more attention. A typical approach consists in learning a joint text-video
embedding space, where the similarity of a video and its associated caption is
maximized, whereas a lower similarity is enforced with all the other captions,
called negatives. This approach assumes that only the video and caption pairs
in the dataset are valid, but different captions - positives - may also
describe its visual contents, hence some of them may be wrongly penalized. To
address this shortcoming, we propose the Relevance-Aware Negatives and
Positives mining (RANP) which, based on the semantics of the negatives,
improves their selection while also increasing the similarity of other valid
positives. We explore the influence of these techniques on two video-text
datasets: EPIC-Kitchens-100 and MSR-VTT. By using the proposed techniques, we
achieve considerable improvements in terms of nDCG and mAP, leading to
state-of-the-art results, e.g. +5.3% nDCG and +3.0% mAP on EPIC-Kitchens-100.
We share code and pretrained models at
\url{https://github.com/aranciokov/ranp}.

With the rapid growth in language processing applications, fairness has
emerged as an important consideration in data-driven solutions. Although
various fairness definitions have been explored in the recent literature, there
is lack of consensus on which metrics most accurately reflect the fairness of a
system. In this work, we propose a new formulation : ACCUMULATED PREDICTION
SENSITIVITY, which measures fairness in machine learning models based on the
model's prediction sensitivity to perturbations in input features. The metric
attempts to quantify the extent to which a single prediction depends on a
protected attribute, where the protected attribute encodes the membership
status of an individual in a protected group. We show that the metric can be
theoretically linked with a specific notion of group fairness (statistical
parity) and individual fairness. It also correlates well with humans'
perception of fairness. We conduct experiments on two text classification
datasets : JIGSAW TOXICITY, and BIAS IN BIOS, and evaluate the correlations
between metrics and manual annotations on whether the model produced a fair
outcome. We observe that the proposed fairness metric based on prediction
sensitivity is statistically significantly more correlated with human
annotation than the existing counterfactual fairness metric.

Existing model poisoning attacks to federated learning assume that an
attacker has access to a large fraction of compromised genuine clients.
However, such assumption is not realistic in production federated learning
systems that involve millions of clients. In this work, we propose the first
Model Poisoning Attack based on Fake clients called MPAF. Specifically, we
assume the attacker injects fake clients to a federated learning system and
sends carefully crafted fake local model updates to the cloud server during
training, such that the learnt global model has low accuracy for many
indiscriminate test inputs. Towards this goal, our attack drags the global
model towards an attacker-chosen base model that has low accuracy.
Specifically, in each round of federated learning, the fake clients craft fake
local model updates that point to the base model and scale them up to amplify
their impact before sending them to the cloud server. Our experiments show that
MPAF can significantly decrease the test accuracy of the global model, even if
classical defenses and norm clipping are adopted, highlighting the need for
more advanced defenses.

Non-line-of-sight reconstruction (NLoS) is a novel indirect imaging modality
that aims to recover objects or scene parts outside the field of view from
measurements of light that is indirectly scattered off a directly visible,
diffuse wall. Despite recent advances in acquisition and reconstruction
techniques, the well-posedness of the problem at large, and the recoverability
of objects and their shapes in particular, remains an open question. The
commonly employed Fermat path criterion is rather conservative with this
regard, as it classifies some surfaces as unrecoverable, although they
contribute to the signal.
  In this paper, we use a simpler necessary criterion for an opaque surface
patch to be recoverable. Such piece of surface must be directly visible from
some point on the wall, and it must occlude the space behind itself. Inspired
by recent advances in neural implicit representations, we devise a new
representation and reconstruction technique for NLoS scenes that unifies the
treatment of recoverability with the reconstruction itself. Our approach, which
we validate on various synthetic and experimental datasets, exhibits
interesting properties. Unlike memory-inefficient volumetric representations,
ours allows to infer adaptively tessellated surfaces from time-of-flight
measurements of moderate resolution. It can further recover features beyond the
Fermat path criterion, and it is robust to significant amounts of
self-occlusion. We believe that this is the first time that these properties
have been achieved in one system that, as an additional benefit, is trainable
and hence suited for data-driven approaches.

Bayesian optimization has been challenged by datasets with large-scale,
high-dimensional, and non-stationary characteristics, which are common in
real-world scenarios. Recent works attempt to handle such input by applying
neural networks ahead of the classical Gaussian process to learn a latent
representation. We show that even with proper network design, such learned
representation often leads to collision in the latent space: two points with
significantly different observations collide in the learned latent space,
leading to degraded optimization performance. To address this issue, we propose
LOCo, an efficient deep Bayesian optimization framework which employs a novel
regularizer to reduce the collision in the learned latent space and encourage
the mapping from the latent space to the objective value to be Lipschitz
continuous. LOCo takes in pairs of data points and penalizes those too close in
the latent space compared to their target space distance. We provide a rigorous
theoretical justification for LOCo by inspecting the regret of this
dynamic-embedding-based Bayesian optimization algorithm, where the neural
network is iteratively retrained with the regularizer. Our empirical results
demonstrate the effectiveness of LOCo on several synthetic and real-world
benchmark Bayesian optimization tasks.

In this paper, we address the problem of predicting complex, nonlinear
spatiotemporal dynamics when available data is recorded at irregularly-spaced
sparse spatial locations. Most of the existing deep learning models for
modeling spatiotemporal dynamics are either designed for data in a regular grid
or struggle to uncover the spatial relations from sparse and irregularly-spaced
data sites. We propose a deep learning model that learns to predict unknown
spatiotemporal dynamics using data from sparsely-distributed data sites. We
base our approach on Radial Basis Function (RBF) collocation method which is
often used for meshfree solution of partial differential equations (PDEs). The
RBF framework allows us to unravel the observed spatiotemporal function and
learn the spatial interactions among data sites on the RBF-space. The learned
spatial features are then used to compose multilevel transformations of the raw
observations and predict its evolution in future time steps. We demonstrate the
advantage of our approach using both synthetic and real-world climate data.

Automated decision support systems that are able to infer second opinions
from experts can potentially facilitate a more efficient allocation of
resources; they can help decide when and from whom to seek a second opinion. In
this paper, we look at the design of this type of support systems from the
perspective of counterfactual inference. We focus on a multiclass
classification setting and first show that, if experts make predictions on
their own, the underlying causal mechanism generating their predictions needs
to satisfy a desirable set invariant property. Further, we show that, for any
causal mechanism satisfying this property, there exists an equivalent mechanism
where the predictions by each expert are generated by independent
sub-mechanisms governed by a common noise. This motivates the design of a set
invariant Gumbel-Max structural causal model where the structure of the noise
governing the sub-mechanisms underpinning the model depends on an intuitive
notion of similarity between experts which can be estimated from data.
Experiments on both synthetic and real data show that our model can be used to
infer second opinions more accurately than its non-causal counterpart.

Deep Implicit Functions (DIFs) represent 3D geometry with continuous signed
distance functions learned through deep neural nets. Recently DIFs-based
methods have been proposed to handle shape reconstruction and dense point
correspondences simultaneously, capturing semantic relationships across shapes
of the same class by learning a DIFs-modeled shape template. These methods
provide great flexibility and accuracy in reconstructing 3D shapes and
inferring correspondences. However, the point correspondences built from these
methods do not intrinsically preserve the topology of the shapes, unlike
mesh-based template matching methods. This limits their applications on 3D
geometries where underlying topological structures exist and matter, such as
anatomical structures in medical images. In this paper, we propose a new model
called Neural Diffeomorphic Flow (NDF) to learn deep implicit shape templates,
representing shapes as conditional diffeomorphic deformations of templates,
intrinsically preserving shape topologies. The diffeomorphic deformation is
realized by an auto-decoder consisting of Neural Ordinary Differential Equation
(NODE) blocks that progressively map shapes to implicit templates. We conduct
extensive experiments on several medical image organ segmentation datasets to
evaluate the effectiveness of NDF on reconstructing and aligning shapes. NDF
achieves consistently state-of-the-art organ shape reconstruction and
registration results in both accuracy and quality. The source code is publicly
available at https://github.com/Siwensun/Neural_Diffeomorphic_Flow--NDF.

In video coding, in-loop filters are applied on reconstructed video frames to
enhance their perceptual quality, before storing the frames for output.
Conventional in-loop filters are obtained by hand-crafted methods. Recently,
learned filters based on convolutional neural networks that utilize attention
mechanisms have been shown to improve upon traditional techniques. However,
these solutions are typically significantly more computationally expensive,
limiting their potential for practical applications. The proposed method uses a
novel combination of sparsity and structured pruning for complexity reduction
of learned in-loop filters. This is done through a three-step training process
of magnitude-guidedweight pruning, insignificant neuron identification and
removal, and fine-tuning. Through initial tests we find that network parameters
can be significantly reduced with a minimal impact on network performance.

Objective: The next generation prosthetic hand that moves and feels like a
real hand requires a robust neural interconnection between the human minds and
machines. Methods: Here we present a neuroprosthetic system to demonstrate that
principle by employing an artificial intelligence (AI) agent to translate the
amputee's movement intent through a peripheral nerve interface. The AI agent is
designed based on the recurrent neural network (RNN) and could simultaneously
decode six degree-of-freedom (DOF) from multichannel nerve data in real-time.
The decoder's performance is characterized in motor decoding experiments with
three human amputees. Results: First, we show the AI agent enables amputees to
intuitively control a prosthetic hand with individual finger and wrist
movements up to 97-98% accuracy. Second, we demonstrate the AI agent's
real-time performance by measuring the reaction time and information throughput
in a hand gesture matching task. Third, we investigate the AI agent's long-term
uses and show the decoder's robust predictive performance over a 16-month
implant duration. Conclusion & significance: Our study demonstrates the
potential of AI-enabled nerve technology, underling the next generation of
dexterous and intuitive prosthetic hands.

The Lov\'asz hinge is a convex surrogate recently proposed for structured
binary classification, in which $k$ binary predictions are made simultaneously
and the error is judged by a submodular set function. Despite its wide usage in
image segmentation and related problems, its consistency has remained open. We
resolve this open question, showing that the Lov\'asz hinge is inconsistent for
its desired target unless the set function is modular. Leveraging a recent
embedding framework, we instead derive the target loss for which the Lov\'asz
hinge is consistent. This target, which we call the structured abstain problem,
allows one to abstain on any subset of the $k$ predictions. We derive two link
functions, each of which are consistent for all submodular set functions
simultaneously.

When monitoring machine learning systems, two-sample tests of homogeneity
form the foundation upon which existing approaches to drift detection build.
They are used to test for evidence that the distribution underlying recent
deployment data differs from that underlying the historical reference data.
Often, however, various factors such as time-induced correlation mean that
batches of recent deployment data are not expected to form an i.i.d. sample
from the historical data distribution. Instead we may wish to test for
differences in the distributions conditional on \textit{context} that is
permitted to change. To facilitate this we borrow machinery from the causal
inference domain to develop a more general drift detection framework built upon
a foundation of two-sample tests for conditional distributional treatment
effects. We recommend a particular instantiation of the framework based on
maximum conditional mean discrepancies. We then provide an empirical study
demonstrating its effectiveness for various drift detection problems of
practical interest, such as detecting drift in the distributions underlying
subpopulations of data in a manner that is insensitive to their respective
prevalences. The study additionally demonstrates applicability to
ImageNet-scale vision problems.

Domain experts are increasingly employing machine learning to solve their
domain-specific problems. This article presents six key challenges that a
domain expert faces in transforming their problem into a computational
workflow, and then into an executable implementation. These challenges arise
out of our conceptual framework which presents the "route" of options that a
domain expert may choose to take while developing their solution.
  To ground our conceptual framework in the state-of-the-practice, this article
discusses a selection of available textual and graphical workflow systems and
their support for these six challenges. Case studies from the literature in
various domains are also examined to highlight the tools used by the domain
experts as well as a classification of the domain-specificity and machine
learning usage of their problem, workflow, and implementation.
  The state-of-the-practice informs our discussion of the six key challenges,
where we identify which challenges are not sufficiently addressed by available
tools. We also suggest possible research directions for software engineering
researchers to increase the automation of these tools and disseminate
best-practice techniques between software engineering and various scientific
domains.

As more decisions in our daily life become automated, the need to have
machine learning algorithms that make fair decisions increases. In fair
representation learning we are tasked with finding a suitable representation of
the data in which a sensitive variable is censored. Recent work aims to learn
fair representations through adversarial learning. This paper builds upon this
work by introducing a novel algorithm which uses dampening and stacking to
learn adversarial fair representations. Results show that that our algorithm
improves upon earlier work in both censoring and reconstruction.

Friction Stir Welding is a robust joining process, and numerous AI-based
algorithms are being developed in this field to enhance mechanical and
microstructure properties. Convolutional Neural Networks (CNNs) are Artificial
Neural Networks that use image data as input. Identical to Artificial Neural
Networks, they are composed of weights that are determined throughout learning,
neurons (activated functions), and a goal (loss function). CNN is utilized in a
variety of applications, including image recognition, semantic segmentation,
image recognition, and localization. Utilizing training on 3000 microstructure
pictures and new tests on 300 microstructure photographs, the current work
investigates the predictions of Friction Stir Welded joint effectiveness using
microstructure images.

This work presents an extended version of the Vehicle Energy Dataset (VED),
which is a openly released large-scale dataset for vehicle energy consumption
analysis. Compared with its original version, the extended VED (eVED) dataset
is enhanced with accurate vehicle trip GPS coordinates, serving as a basis to
associate the VED trip records with external information, e.g., road speed
limit and intersections, from accessible map services to accumulate attributes
that is essential in analyzing vehicle energy consumption. In particularly, we
calibrate all the GPS trace records in the original VED data, upon which we
associated the VED data with attributes extracted from the Geographic
Information System (QGIS), the Overpass API, the Open Street Map API, and
Google Maps API. The associated attributes include 12,609,170 records of road
elevation, 12,203,044 of speed limit, 12,281,719 of speed limit with direction
(in case the road is bi-directional), 584,551 of intersections, 429,638 of bus
stop, 312,196 of crossings, 195,856 of traffic signals, 29,397 of stop signs,
5,848 of turning loops, 4,053 of railway crossings (level crossing), 3,554 of
turning circles, and 2,938 of motorway junctions. With the accurate GPS
coordinates and enriched features of the vehicle trip record, the obtained eVED
dataset can provide a precise and abundant medium to feed a learning engine,
especially a deep learning engine that is more demanding on data sufficiency
and richness. Moreover, our software work for data calibration and enrichment
can be reused to generate further vehicle trip datasets for specific user
cases, contributing to deep insights into vehicle behaviors and traffic
dynamics analyses. We anticipate that the eVED dataset and our data enrichment
software can serve the academic and industrial automotive section as apparatus
in developing future technologies.

Recently, motion generation by machine learning has been actively researched
to automate various tasks. Imitation learning is one such method that learns
motions from data collected in advance. However, executing long-term tasks
remains challenging. Therefore, a novel framework for imitation learning is
proposed to solve this problem. The proposed framework comprises upper and
lower layers, where the upper layer model, whose timescale is long, and lower
layer model, whose timescale is short, can be independently trained. In this
model, the upper layer learns long-term task planning, and the lower layer
learns motion primitives. The proposed method was experimentally compared to
hierarchical RNN-based methods to validate its effectiveness. Consequently, the
proposed method showed a success rate equal to or greater than that of
conventional methods. In addition, the proposed method required less than 1/20
of the training time compared to conventional methods. Moreover, it succeeded
in executing unlearned tasks by reusing the trained lower layer.

Tuning of measurement models is challenging in real-world applications of
sequential Monte Carlo methods. Recent advances in differentiable particle
filters have led to various efforts to learn measurement models through neural
networks. But existing approaches in the differentiable particle filter
framework do not admit valid probability densities in constructing measurement
models, leading to incorrect quantification of the measurement uncertainty
given state information. We propose to learn expressive and valid probability
densities in measurement models through conditional normalizing flows, to
capture the complex likelihood of measurements given states. We show that the
proposed approach leads to improved estimation performance and faster training
convergence in a visual tracking experiment.

Cross-media retrieval of scientific and technological information is one of
the important tasks in the cross-media study. Cross-media scientific and
technological information retrieval obtain target information from massive
multi-source and heterogeneous scientific and technological resources, which
helps to design applications that meet users' needs, including scientific and
technological information recommendation, personalized scientific and
technological information retrieval, etc. The core of cross-media retrieval is
to learn a common subspace, so that data from different media can be directly
compared with each other after being mapped into this subspace. In subspace
learning, existing methods often focus on modeling the discrimination of
intra-media data and the invariance of inter-media data after mapping; however,
they ignore the semantic consistency of inter-media data before and after
mapping and media discrimination of intra-semantics data, which limit the
result of cross-media retrieval. In light of this, we propose a scientific and
technological information oriented Semantics-adversarial and Media-adversarial
Cross-media Retrieval method (SMCR) to find an effective common subspace.
Specifically, SMCR minimizes the loss of inter-media semantic consistency in
addition to modeling intra-media semantic discrimination, to preserve semantic
similarity before and after mapping. Furthermore, SMCR constructs a basic
feature mapping network and a refined feature mapping network to jointly
minimize the media discriminative loss within semantics, so as to enhance the
feature mapping network's ability to confuse the media discriminant network.
Experimental results on two datasets demonstrate that the proposed SMCR
outperforms state-of-the-art methods in cross-media retrieval.

Generating artistic portraits is a challenging problem in computer vision.
Existing portrait stylization models that generate good quality results are
based on Image-to-Image Translation and require abundant data from both source
and target domains. However, without enough data, these methods would result in
overfitting. In this work, we propose CtlGAN, a new few-shot artistic portraits
generation model with a novel contrastive transfer learning strategy. We adapt
a pretrained StyleGAN in the source domain to a target artistic domain with no
more than 10 artistic faces. To reduce overfitting to the few training
examples, we introduce a novel Cross-Domain Triplet loss which explicitly
encourages the target instances generated from different latent codes to be
distinguishable. We propose a new encoder which embeds real faces into Z+ space
and proposes a dual-path training strategy to better cope with the adapted
decoder and eliminate the artifacts. Extensive qualitative, quantitative
comparisons and a user study show our method significantly outperforms
state-of-the-arts under 10-shot and 1-shot settings and generates high quality
artistic portraits. The code will be made publicly available.

Despite the success of large pre-trained language models (LMs) such as Codex,
they show below-par performance on the larger and more complicated programming
related questions. We show that LMs benefit from the summarized version of
complicated questions. Our findings show that superfluous information often
present in problem description such as human characters, background stories,
and names (which are included to help humans in understanding a task) does not
help models in understanding a task. To this extent, we create a meta-dataset
from the frequently used APPS dataset and the newly created CodeContests
dataset for the program synthesis task. Our meta-dataset consists of human and
synthesized summaries of the long and complicated programming questions.
Experimental results on Codex show that our proposed approach outperforms
baseline by 8.13% on the APPS dataset and 11.88% on the CodeContests dataset on
average in terms of strict accuracy. Our analysis shows that summaries
significantly improve performance for introductory (9.86%) and interview
(11.48%) programming questions. However, it shows improvement by a small margin
(~ 2%) for competitive programming questions, implying scope for future
research in this direction.

The Internet of Things (IoT) has been continuously rising in the past few
years, and its potentials are now more apparent. However, transient data
generation and limited energy resources are the major bottlenecks of these
networks. Besides, minimum delay and other conventional quality of service
measurements are still valid requirements to meet. An efficient caching policy
can help meet the standard quality of service requirements while bypassing IoT
networks' specific limitations. Adopting deep reinforcement learning (DRL)
algorithms enables us to develop an effective caching scheme without the need
for any prior knowledge or contextual information. In this work, we propose a
DRL-based caching scheme that improves the cache hit rate and reduces energy
consumption of the IoT networks, in the meanwhile, taking data freshness and
limited lifetime of IoT data into account. To better capture the
regional-different popularity distribution, we propose a hierarchical
architecture to deploy edge caching nodes in IoT networks. The results of
comprehensive experiments show that our proposed method outperforms the
well-known conventional caching policies and an existing DRL-based solution in
terms of cache hit rate and energy consumption of the IoT networks by
considerable margins.

We propose generative channel modeling to learn statistical channel models
from channel input-output measurements. Generative channel models can learn
more complicated distributions and represent the field data more faithfully.
They are tractable and easy to sample from, which can potentially speed up the
simulation rounds. To achieve this, we leverage advances in GAN, which helps us
learn an implicit distribution over stochastic MIMO channels from observed
measurements. In particular, our approach MIMO-GAN implicitly models the
wireless channel as a distribution of time-domain band-limited impulse
responses. We evaluate MIMO-GAN on 3GPP TDL MIMO channels and observe
high-consistency in capturing power, delay and spatial correlation statistics
of the underlying channel. In particular, we observe MIMO-GAN achieve errors of
under 3.57 ns average delay and -18.7 dB power.

Deep learning has improved vanishing point detection in images. Yet, deep
networks require expensive annotated datasets trained on costly hardware and do
not generalize to even slightly different domains, and minor problem variants.
Here, we address these issues by injecting deep vanishing point detection
networks with prior knowledge. This prior knowledge no longer needs to be
learned from data, saving valuable annotation efforts and compute, unlocking
realistic few-sample scenarios, and reducing the impact of domain changes.
Moreover, the interpretability of the priors allows to adapt deep networks to
minor problem variations such as switching between Manhattan and non-Manhattan
worlds. We seamlessly incorporate two geometric priors: (i) Hough Transform --
mapping image pixels to straight lines, and (ii) Gaussian sphere -- mapping
lines to great circles whose intersections denote vanishing points.
Experimentally, we ablate our choices and show comparable accuracy to existing
models in the large-data setting. We validate our model's improved data
efficiency, robustness to domain changes, adaptability to non-Manhattan
settings.

Quantum density matrix represents all the information of the entire quantum
system, and novel models of meaning employing density matrices naturally model
linguistic phenomena such as hyponymy and linguistic ambiguity, among others in
quantum question answering tasks. Naturally, we argue that applying the quantum
density matrix into classical Question Answering (QA) tasks can show more
effective performance. Specifically, we (i) design a new mechanism based on
Long Short-Term Memory (LSTM) to accommodate the case when the inputs are
matrixes; (ii) apply the new mechanism to QA problems with Convolutional Neural
Network (CNN) and gain the LSTM-based QA model with the quantum density matrix.
Experiments of our new model on TREC-QA and WIKI-QA data sets show encouraging
results. Similarly, we argue that the quantum density matrix can also enhance
the image feature information and the relationship between the features for the
classical image classification. Thus, we (i) combine density matrices and CNN
to design a new mechanism; (ii) apply the new mechanism to some representative
classical image classification tasks. A series of experiments show that the
application of quantum density matrix in image classification has the
generalization and high efficiency on different datasets. The application of
quantum density matrix both in classical question answering tasks and classical
image classification tasks show more effective performance.

Voice-based communication is often cited as one of the most `natural' ways in
which humans and robots might interact, and the recent availability of accurate
automatic speech recognition and intelligible speech synthesis has enabled
researchers to integrate advanced off-the-shelf spoken language technology
components into their robot platforms. Despite this, the resulting interactions
are anything but `natural'. It transpires that simply giving a robot a voice
doesn't mean that a user will know how (or when) to talk to it, and the
resulting `conversations' tend to be stilted, one-sided and short. On the
surface, these difficulties might appear to be fairly trivial consequences of
users' unfamiliarity with robots (and \emph{vice versa}), and that any problems
would be mitigated by long-term use by the human, coupled with `deep learning'
by the robot. However, it is argued here that such communication failures are
indicative of a deeper malaise: a fundamental lack of basic principles --
\emph{priors} -- underpinning not only speech-based interaction in particular,
but (vocal) interactivity in general. This is evidenced not only by the fact
that contemporary spoken language systems already require training data sets
that are orders-of-magnitude greater than that experienced by a young child,
but also by the lack of design principles for creating effective communicative
human-robot interaction. This short position paper identifies some of the key
areas where theoretical insights might help overcome these shortfalls.

At the intersection of Topological Data Analysis (TDA) and machine learning,
the field of cellular signal processing has advanced rapidly in recent years.
In this context, each signal on the cells of a complex is processed using the
combinatorial Laplacian, and the resultant Hodge decomposition. Meanwhile,
discrete Morse theory has been widely used to speed up computations by reducing
the size of complexes while preserving their global topological properties. In
this paper, we provide an approach to signal compression and reconstruction on
chain complexes that leverages the tools of algebraic discrete Morse theory.
The main goal is to reduce and reconstruct a based chain complex together with
a set of signals on its cells via deformation retracts, preserving as much as
possible the global topological structure of both the complex and the signals.
We first prove that any deformation retract of real degree-wise
finite-dimensional based chain complexes is equivalent to a Morse matching. We
will then study how the signal changes under particular types of Morse
matching, showing its reconstruction error is trivial on specific components of
the Hodge decomposition. Furthermore, we provide an algorithm to compute Morse
matchings with minimal reconstruction error.

Inferring individualised treatment effects from observational data can unlock
the potential for targeted interventions. It is, however, hard to infer these
effects from observational data. One major problem that can arise is covariate
shift where the data (outcome) conditional distribution remains the same but
the covariate (input) distribution changes between the training and test set.
In an observational data setting, this problem is materialised in control and
treated units coming from different distributions. A common solution is to
augment learning methods through reweighing schemes (e.g. propensity scores).
These are needed due to model misspecification, but might hurt performance in
the individual case. In this paper, we explore a novel generative tree based
approach that tackles model misspecification directly, helping downstream
estimators achieve better robustness. We show empirically that the choice of
model class can indeed significantly affect the final performance and that
reweighing methods can struggle in individualised effect estimation. Our
proposed approach is competitive with reweighing methods on average treatment
effects while performing significantly better on individualised treatment
effects.

Open Set Recognition (OSR) has been an emerging topic. Besides recognizing
predefined classes, the system needs to reject the unknowns. Prototype learning
is a potential manner to handle the problem, as its ability to improve
intra-class compactness of representations is much needed in discrimination
between the known and the unknowns. In this work, we propose a novel Prototype
Mining And Learning (PMAL) framework. It has a prototype mining mechanism
before the phase of optimizing embedding space, explicitly considering two
crucial properties, namely high-quality and diversity of the prototype set.
Concretely, a set of high-quality candidates are firstly extracted from
training samples based on data uncertainty learning, avoiding the interference
from unexpected noise. Considering the multifarious appearance of objects even
in a single category, a diversity-based strategy for prototype set filtering is
proposed. Accordingly, the embedding space can be better optimized to
discriminate therein the predefined classes and between known and unknowns.
Extensive experiments verify the two good characteristics (i.e., high-quality
and diversity) embraced in prototype mining, and show the remarkable
performance of the proposed framework compared to state-of-the-arts.

Collecting and annotating task-oriented dialogues is time-consuming and
costly; thus, zero and few shot learning could greatly benefit dialogue state
tracking (DST). In this work, we propose an in-context learning (ICL) framework
for zero-shot and few-shot learning DST, where a large pre-trained language
model (LM) takes a test instance and a few exemplars as input, and directly
decodes the dialogue state without any parameter updates. To better leverage a
tabular domain description in the LM prompt, we reformulate DST into a
text-to-SQL problem. We also propose a novel approach to retrieve annotated
dialogues as exemplars. Empirical results on MultiWOZ show that our method
IC-DST substantially outperforms previous fine-tuned state-of-the-art models in
few-shot settings. In addition, we test IC-DST in zero-shot settings, in which
the model only takes a fixed task instruction as input, finding that it
outperforms previous zero-shot methods by a large margin.

Geographic linguistic features are commonly used to improve the performance
of pretrained language models (PLMs) on NLP tasks where geographic knowledge is
intuitively beneficial (e.g., geolocation prediction and dialect feature
prediction). Existing work, however, leverages such geographic information in
task-specific fine-tuning, failing to incorporate it into PLMs' geo-linguistic
knowledge, which would make it transferable across different tasks. In this
work, we introduce an approach to task-agnostic geoadaptation of PLMs that
forces the PLM to learn associations between linguistic phenomena and
geographic locations. More specifically, geoadaptation is an intermediate
training step that couples masked language modeling and geolocation prediction
in a dynamic multitask learning setup. In our experiments, we geoadapt BERTi\'c
-- a PLM for Bosnian, Croatian, Montenegrin, and Serbian (BCMS) -- using a
corpus of geotagged BCMS tweets. Evaluation on three different tasks, namely
unsupervised (zero-shot) and supervised geolocation prediction and
(unsupervised) prediction of dialect features, shows that our geoadaptation
approach is very effective: e.g., we obtain new state-of-the-art performance in
supervised geolocation prediction and report massive gains over geographically
uninformed PLMs on zero-shot geolocation prediction.

In this note, we provide an elementary analysis of the prediction error of
ridge regression with random design. The proof is short and self-contained. In
particular, it bypasses the use of Rudelson's deviation inequality for
covariance matrices, through a combination of exchangeability arguments, matrix
perturbation and operator convexity.

Due to the inherent ill-posed nature of 2D-3D projection, monocular 3D object
detection lacks accurate depth recovery ability. Although the deep neural
network (DNN) enables monocular depth-sensing from high-level learned features,
the pixel-level cues are usually omitted due to the deep convolution mechanism.
To benefit from both the powerful feature representation in DNN and pixel-level
geometric constraints, we reformulate the monocular object depth estimation as
a progressive refinement problem and propose a joint semantic and geometric
cost volume to model the depth error. Specifically, we first leverage neural
networks to learn the object position, dimension, and dense normalized 3D
object coordinates. Based on the object depth, the dense coordinates patch
together with the corresponding object features is reprojected to the image
space to build a cost volume in a joint semantic and geometric error manner.
The final depth is obtained by feeding the cost volume to a refinement network,
where the distribution of semantic and geometric error is regularized by direct
depth supervision. Through effectively mitigating depth error by the refinement
framework, we achieve state-of-the-art results on both the KITTI and Waymo
datasets.

Using huge training datasets can be costly and inconvenient. This article
explores various data distillation techniques that can reduce the amount of
data required to successfully train deep networks. Inspired by recent ideas, we
suggest new data distillation techniques based on generative teaching networks,
gradient matching, and the Implicit Function Theorem. Experiments with the
MNIST image classification problem show that the new methods are
computationally more efficient than previous ones and allow to increase the
performance of models trained on distilled data.

Darknet marketplaces, accessible through, Tor are where users can buy illicit
goods, and learn to hide from law enforcement. We surveyed the advice on these
markets and found valid security advice mixed up with paranoid threat models
and a reliance on privacy tools dismissed as unusable by the mainstream.

Large multilingual pretrained language models such as mBERT and XLM-RoBERTa
have been found to be surprisingly effective for cross-lingual transfer of
syntactic parsing models (Wu and Dredze 2019), but only between related
languages. However, source and training languages are rarely related, when
parsing truly low-resource languages. To close this gap, we adopt a method from
multi-task learning, which relies on automated curriculum learning, to
dynamically optimize for parsing performance on outlier languages. We show that
this approach is significantly better than uniform and size-proportional
sampling in the zero-shot setting.

Learning to collaborate is critical in Multi-Agent Reinforcement Learning
(MARL). Previous works promote collaboration by maximizing the correlation of
agents' behaviors, which is typically characterized by Mutual Information (MI)
in different forms. However, we reveal sub-optimal collaborative behaviors also
emerge with strong correlations, and simply maximizing the MI can,
surprisingly, hinder the learning towards better collaboration. To address this
issue, we propose a novel MARL framework, called Progressive Mutual Information
Collaboration (PMIC), for more effective MI-driven collaboration. PMIC uses a
new collaboration criterion measured by the MI between global states and joint
actions. Based on this criterion, the key idea of PMIC is maximizing the MI
associated with superior collaborative behaviors and minimizing the MI
associated with inferior ones. The two MI objectives play complementary roles
by facilitating better collaborations while avoiding falling into sub-optimal
ones. Experiments on a wide range of MARL benchmarks show the superior
performance of PMIC compared with other algorithms.

Soft robotic hand shows considerable promise for various grasping
applications. However, the sensing and reconstruction of the robot pose will
cause limitation during the design and fabrication. In this work, we present a
novel 3D pose reconstruction approach to analyze the grasping motion of a
bidirectional soft robotic hand using experiment videos. The images from top,
front, back, left, right view were collected using an
one-camera-multiple-mirror imaging device. The coordinate and orientation
information of soft fingers are detected based on deep learning methods. Faster
RCNN model is used to detect the position of fingertips, while U-Net model is
applied to calculate the side boundary of the fingers. Based on the kinematics,
the corresponding coordinate and orientation databases are established. The 3D
pose reconstructed result presents a satisfactory performance and good
accuracy. Using efficacy coefficient method, the finger contribution of the
bending angle and distance between fingers of soft robot hand is analyzed by
compared with that of human hand. The results show that the soft robot hand
perform a human-like motion in both single-direction and bidirectional
grasping.

It is essential for safety-critical applications of deep neural networks to
determine when new inputs are significantly different from the training
distribution. In this paper, we explore this out-of-distribution (OOD)
detection problem for image classification using clusters of semantically
similar embeddings of the training data and exploit the differences in distance
relationships to these clusters between in- and out-of-distribution data. We
study the structure and separation of clusters in the embedding space and find
that supervised contrastive learning leads to well-separated clusters while its
self-supervised counterpart fails to do so. In our extensive analysis of
different training methods, clustering strategies, distance metrics, and
thresholding approaches, we observe that there is no clear winner. The optimal
approach depends on the model architecture and selected datasets for in- and
out-of-distribution. While we could reproduce the outstanding results for
contrastive training on CIFAR-10 as in-distribution data, we find standard
cross-entropy paired with cosine similarity outperforms all contrastive
training methods when training on CIFAR-100 instead. Cross-entropy provides
competitive results as compared to expensive contrastive training methods.

The blockchain-based smart contract lacks privacy since the contract state
and instruction code are exposed to the public. Combining smart-contract
execution with Trusted Execution Environments (TEEs) provides an efficient
solution, called TEE-assisted smart contracts, for protecting the
confidentiality of contract states. However, the combination approaches are
varied, and a systematic study is absent. Newly released systems may fail to
draw upon the experience learned from existing protocols, such as repeating
known design mistakes or applying TEE technology in insecure ways. In this
paper, we first investigate and categorize the existing systems into two types:
the layer-one solution and layer-two solution. Then, we establish an analysis
framework to capture their common lights, covering the desired properties (for
contract services), threat models, and security considerations (for underlying
systems). Based on our taxonomy, we identify their ideal functionalities and
uncover the fundamental flaws and reasons for the challenges in each
specification design. We believe that this work would provide a guide for the
development of TEE-assisted smart contracts, as well as a framework to evaluate
future TEE-assisted confidential contract systems.

Deep Metric Learning (DML) aims to learn representation spaces on which
semantic relations can simply be expressed through predefined distance metrics.
Best performing approaches commonly leverage class proxies as sample stand-ins
for better convergence and generalization. However, these proxy-methods solely
optimize for sample-proxy distances. Given the inherent non-bijectiveness of
used distance functions, this can induce locally isotropic sample
distributions, leading to crucial semantic context being missed due to
difficulties resolving local structures and intraclass relations between
samples. To alleviate this problem, we propose non-isotropy regularization
($\mathbb{NIR}$) for proxy-based Deep Metric Learning. By leveraging
Normalizing Flows, we enforce unique translatability of samples from their
respective class proxies. This allows us to explicitly induce a non-isotropic
distribution of samples around a proxy to optimize for. In doing so, we equip
proxy-based objectives to better learn local structures. Extensive experiments
highlight consistent generalization benefits of $\mathbb{NIR}$ while achieving
competitive and state-of-the-art performance on the standard benchmarks
CUB200-2011, Cars196 and Stanford Online Products. In addition, we find the
superior convergence properties of proxy-based methods to still be retained or
even improved, making $\mathbb{NIR}$ very attractive for practical usage. Code
available at https://github.com/ExplainableML/NonIsotropicProxyDML.

Deep Metric Learning (DML) proposes to learn metric spaces which encode
semantic similarities as embedding space distances. These spaces should be
transferable to classes beyond those seen during training. Commonly, DML
methods task networks to solve contrastive ranking tasks defined over binary
class assignments. However, such approaches ignore higher-level semantic
relations between the actual classes. This causes learned embedding spaces to
encode incomplete semantic context and misrepresent the semantic relation
between classes, impacting the generalizability of the learned metric space. To
tackle this issue, we propose a language guidance objective for visual
similarity learning. Leveraging language embeddings of expert- and
pseudo-classnames, we contextualize and realign visual representation spaces
corresponding to meaningful language semantics for better semantic consistency.
Extensive experiments and ablations provide a strong motivation for our
proposed approach and show language guidance offering significant,
model-agnostic improvements for DML, achieving competitive and state-of-the-art
results on all benchmarks. Code available at
https://github.com/ExplainableML/LanguageGuidance_for_DML.

Traditionally, Reinforcement Learning (RL) aims at deciding how to act
optimally for an artificial agent. We argue that deciding when to act is
equally important. As humans, we drift from default, instinctive or memorized
behaviors to focused, thought-out behaviors when required by the situation. To
enhance RL agents with this aptitude, we propose to augment the standard Markov
Decision Process and make a new mode of action available: being lazy, which
defers decision-making to a default policy. In addition, we penalize non-lazy
actions in order to encourage minimal effort and have agents focus on critical
decisions only. We name the resulting formalism lazy-MDPs. We study the
theoretical properties of lazy-MDPs, expressing value functions and
characterizing optimal solutions. Then we empirically demonstrate that policies
learned in lazy-MDPs generally come with a form of interpretability: by
construction, they show us the states where the agent takes control over the
default policy. We deem those states and corresponding actions important since
they explain the difference in performance between the default and the new,
lazy policy. With suboptimal policies as default (pretrained or random), we
observe that agents are able to get competitive performance in Atari games
while only taking control in a limited subset of states.

Learning to capture human motion is essential to 3D human pose and shape
estimation from monocular video. However, the existing methods mainly rely on
recurrent or convolutional operation to model such temporal information, which
limits the ability to capture non-local context relations of human motion. To
address this problem, we propose a motion pose and shape network (MPS-Net) to
effectively capture humans in motion to estimate accurate and temporally
coherent 3D human pose and shape from a video. Specifically, we first propose a
motion continuity attention (MoCA) module that leverages visual cues observed
from human motion to adaptively recalibrate the range that needs attention in
the sequence to better capture the motion continuity dependencies. Then, we
develop a hierarchical attentive feature integration (HAFI) module to
effectively combine adjacent past and future feature representations to
strengthen temporal correlation and refine the feature representation of the
current frame. By coupling the MoCA and HAFI modules, the proposed MPS-Net
excels in estimating 3D human pose and shape in the video. Though conceptually
simple, our MPS-Net not only outperforms the state-of-the-art methods on the
3DPW, MPI-INF-3DHP, and Human3.6M benchmark datasets, but also uses fewer
network parameters. The video demos can be found at
https://mps-net.github.io/MPS-Net/.

Generating natural and informative texts has been a long-standing problem in
NLP. Much effort has been dedicated into incorporating pre-trained language
models (PLMs) with various open-world knowledge, such as knowledge graphs or
wiki pages. However, their ability to access and manipulate the task-specific
knowledge is still limited on downstream tasks, as this type of knowledge is
usually not well covered in PLMs and is hard to acquire. To address the
problem, we propose augmenting TExt Generation via Task-specific and Open-world
Knowledge (TegTok) in a unified framework. Our model selects knowledge entries
from two types of knowledge sources through dense retrieval and then injects
them into the input encoding and output decoding stages respectively on the
basis of PLMs. With the help of these two types of knowledge, our model can
learn what and how to generate. Experiments on two text generation tasks of
dialogue generation and question generation, and on two datasets show that our
method achieves better performance than various baseline models.

The mainstream machine learning paradigms for NLP often work with two
underlying presumptions. First, the target task is predefined and static; a
system merely needs to learn to solve it exclusively. Second, the supervision
of a task mainly comes from a set of labeled examples. A question arises: how
to build a system that can keep learning new tasks from their instructions?
This work defines a new learning paradigm ConTinTin (Continual Learning from
Task Instructions), in which a system should learn a sequence of new tasks one
by one, each task is explained by a piece of textual instruction. The system is
required to (i) generate the expected outputs of a new task by learning from
its instruction, (ii) transfer the knowledge acquired from upstream tasks to
help solve downstream tasks (i.e., forward-transfer), and (iii) retain or even
improve the performance on earlier tasks after learning new tasks (i.e.,
backward-transfer). This new problem is studied on a stream of more than 60
tasks, each equipped with an instruction. Technically, our method
InstructionSpeak contains two strategies that make full use of task
instructions to improve forward-transfer and backward-transfer: one is to learn
from negative outputs, the other is to re-visit instructions of previous tasks.
To our knowledge, this is the first time to study ConTinTin in NLP. In addition
to the problem formulation and our promising approach, this work also
contributes to providing rich analyses for the community to better understand
this novel learning problem.

We propose a new differentiable probabilistic model over DAGs (DP-DAG).
DP-DAG allows fast and differentiable DAG sampling suited to continuous
optimization. To this end, DP-DAG samples a DAG by successively (1) sampling a
linear ordering of the node and (2) sampling edges consistent with the sampled
linear ordering. We further propose VI-DP-DAG, a new method for DAG learning
from observational data which combines DP-DAG with variational inference.
Hence,VI-DP-DAG approximates the posterior probability over DAG edges given the
observed data. VI-DP-DAG is guaranteed to output a valid DAG at any time during
training and does not require any complex augmented Lagrangian optimization
scheme in contrast to existing differentiable DAG learning approaches. In our
extensive experiments, we compare VI-DP-DAG to other differentiable DAG
learning baselines on synthetic and real datasets. VI-DP-DAG significantly
improves DAG structure and causal mechanism learning while training faster than

Personal Knowledge Graphs (PKGs) are introduced by the semantic web community
as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of
personalised representation of user data and interests on the top of big,
well-established encyclopedic KGs, such as DBpedia. Inspired by the widely
recent usage of PKGs in the medical domain to represent patient data, this PhD
proposal aims to adopt a similar technique in the educational domain in
e-learning platforms by deploying PKGs to represent users and learners. We
propose a novel PKG development that relies on ontology and interlinks to
Linked Open Data. Hence, adding the dimension of personalisation and
explainability in users' featured data while respecting privacy. This research
design is developed in two use cases: a collaborative search learning platform
and an e-learning platform. Our preliminary results show that e-learning
platforms can get benefited from our approach by providing personalised
recommendations and more user and group-specific data.

This paper presents a systematic literature review of image datasets for
document image analysis, focusing on historical documents, such as handwritten
manuscripts and early prints. Finding appropriate datasets for historical
document analysis is a crucial prerequisite to facilitate research using
different machine learning algorithms. However, because of the very large
variety of the actual data (e.g., scripts, tasks, dates, support systems, and
amount of deterioration), the different formats for data and label
representation, and the different evaluation processes and benchmarks, finding
appropriate datasets is a difficult task. This work fills this gap, presenting
a meta-study on existing datasets. After a systematic selection process
(according to PRISMA guidelines), we select 65 studies that are chosen based on
different factors, such as the year of publication, number of methods
implemented in the article, reliability of the chosen algorithms, dataset size,
and journal outlet. We summarize each study by assigning it to one of three
pre-defined tasks: document classification, layout structure, or content
analysis. We present the statistics, document type, language, tasks, input
visual aspects, and ground truth information for every dataset. In addition, we
provide the benchmark tasks and results from these papers or recent
competitions. We further discuss gaps and challenges in this domain. We
advocate for providing conversion tools to common formats (e.g., COCO format
for computer vision tasks) and always providing a set of evaluation metrics,
instead of just one, to make results comparable across studies.

We introduce a sampling based machine learning approach, Monte Carlo physics
informed neural networks (MC-PINNs), for solving forward and inverse fractional
partial differential equations (FPDEs). As a generalization of physics informed
neural networks (PINNs), our method relies on deep neural network surrogates in
addition to a stochastic approximation strategy for computing the fractional
derivatives of the DNN outputs. A key ingredient in our MC-PINNs is to
construct an unbiased estimation of the physical soft constraints in the loss
function. Our directly sampling approach can yield less overall computational
cost compared to fPINNs proposed in \cite{pang2019fpinns} and thus provide an
opportunity for solving high dimensional fractional PDEs. We validate the
performance of MC-PINNs method via several examples that include high
dimensional integral fractional Laplacian equations, parametric identification
of time-space fractional PDEs, and fractional diffusion equation with random
inputs. The results show that MC-PINNs is flexible and promising to tackle
high-dimensional FPDEs.

One of the possible potential candidates for describing the universe's rapid
expansion is modified gravity. In the framework of the modified theory of
gravity $f(R,G)$, the present work features the materialization of anisotropic
matter, such as compact stars. Specifically, to learn more about the physical
behavior of compact stars, the radial, and tangential pressures as well as the
energy density of six stars namely $Her X-1$, $SAXJ1808.4-3658$, $4U1820-30$,
$PSR J 1614 2230$, $VELA X-1$, and $Cen X-3$ are calculated. Herein, the
modified theory of gravity $f(R,G)$ is disintegrated into two parts i.e. the
$\tanh$ hyperbolic $f(R)$ model and the three different $f(G)$ model. The study
focuses on graphical analysis of compact stars wherein the stability aspects,
energy conditions, and anisotropic measurements are mainly addressed. Our
calculation revealed that, for the positive value of parameter n of the model
$f(G)$, all the six stars behave normally.

Since open social platforms allow for a large and continuous flow of
unverified information, rumors can emerge unexpectedly and spread quickly.
However, existing rumor detection (RD) models often assume the same training
and testing distributions and can not cope with the continuously changing
social network environment. This paper proposed a Continual Prompt-Tuning RD
(CPT-RD) framework, which avoids catastrophic forgetting (CF) of upstream tasks
during sequential task learning and enables bidirectional knowledge transfer
between domain tasks. Specifically, we propose the following strategies: (a)
Our design explicitly decouples shared and domain-specific knowledge, thus
reducing the interference among different domains during optimization; (b)
Several technologies aim to transfer knowledge of upstream tasks to deal with
emergencies; (c) A task-conditioned prompt-wise hypernetwork (TPHNet) is used
to consolidate past domains. In addition, CPT-RD avoids CF without the
necessity of a rehearsal buffer.

Industrial machine learning systems face data challenges that are often
under-explored in the academic literature. Common data challenges are data
distribution shifts, missing values and anomalies. In this paper, we discuss
data challenges and solutions in the context of a Neural Forecasting
application on labor planning.We discuss how to make this forecasting system
resilient to these data challenges. We address changes in data distribution
with a periodic retraining scheme and discuss the critical importance of model
stability in this setting. Furthermore, we show how our deep learning model
deals with missing values natively without requiring imputation. Finally, we
describe how we detect anomalies in the input data and mitigate their effect
before they impact the forecasts. This results in a fully autonomous
forecasting system that compares favorably to a hybrid system consisting of the
algorithm and human overrides.

This paper presents Deepchecks, a Python library for comprehensively
validating machine learning models and data. Our goal is to provide an
easy-to-use library comprising of many checks related to various types of
issues, such as model predictive performance, data integrity, data distribution
mismatches, and more. The package is distributed under the GNU Affero General
Public License (AGPL) and relies on core libraries from the scientific Python
ecosystem: scikit-learn, PyTorch, NumPy, pandas, and SciPy. Source code,
documentation, examples, and an extensive user guide can be found at
\url{https://github.com/deepchecks/deepchecks} and
\url{https://docs.deepchecks.com/}.

In this paper, we propose an efficient MLP-based approach for learning audio
representations, namely timestamp and scene-level audio embeddings. We use an
encoder consisting of sequentially stacked gated MLP blocks, which accept 2D
MFCCs as inputs. In addition, we also provide a simple temporal
interpolation-based algorithm for computing scene-level embeddings from
timestamp embeddings. The audio representations generated by our method are
evaluated across a diverse set of benchmarks at the Holistic Evaluation of
Audio Representations (HEAR) challenge, hosted at the NeurIPS 2021 competition
track. We achieved first place on the Speech Commands (full), Speech Commands
(5 hours), and the Mridingham Tonic benchmarks. Furthermore, our approach is
also the most resource-efficient among all the submitted methods, in terms of
both the number of model parameters and the time required to compute
embeddings.

In recent years, speaker recognition systems based on raw waveform inputs
have received increasing attention. However, the performance of such systems
are typically inferior to the state-of-the-art handcrafted feature-based
counterparts, which demonstrate equal error rates under 1% on the popular
VoxCeleb1 test set. This paper proposes a novel speaker recognition model based
on raw waveform inputs. The model incorporates recent advances in machine
learning and speaker verification, including the Res2Net backbone module and
multi-layer feature aggregation. Our best model achieves an equal error rate of
0.89%, which is competitive with the state-of-the-art models based on
handcrafted features, and outperforms the best model based on raw waveform
inputs by a large margin. We also explore the application of the proposed model
in the context of self-supervised learning framework. Our self-supervised model
outperforms single phase-based existing works in this line of research.
Finally, we show that self-supervised pre-training is effective for the
semi-supervised scenario where we only have a small set of labelled training
data, along with a larger set of unlabelled examples.

Unpaired image-to-image (I2I) translation often requires to maximize the
mutual information between the source and the translated images across
different domains, which is critical for the generator to keep the source
content and prevent it from unnecessary modifications. The self-supervised
contrastive learning has already been successfully applied in the I2I. By
constraining features from the same location to be closer than those from
different ones, it implicitly ensures the result to take content from the
source. However, previous work uses the features from random locations to
impose the constraint, which may not be appropriate since some locations
contain less information of source domain. Moreover, the feature itself does
not reflect the relation with others. This paper deals with these problems by
intentionally selecting significant anchor points for contrastive learning. We
design a query-selected attention (QS-Attn) module, which compares feature
distances in the source domain, giving an attention matrix with a probability
distribution in each row. Then we select queries according to their measurement
of significance, computed from the distribution. The selected ones are regarded
as anchors for contrastive loss. At the same time, the reduced attention matrix
is employed to route features in both domains, so that source relations
maintain in the synthesis. We validate our proposed method in three different
I2I datasets, showing that it increases the image quality without adding
learnable parameters.

Visual grounding, i.e., localizing objects in images according to natural
language queries, is an important topic in visual language understanding. The
most effective approaches for this task are based on deep learning, which
generally require expensive manually labeled image-query or patch-query pairs.
To eliminate the heavy dependence on human annotations, we present a novel
method, named Pseudo-Q, to automatically generate pseudo language queries for
supervised training. Our method leverages an off-the-shelf object detector to
identify visual objects from unlabeled images, and then language queries for
these objects are obtained in an unsupervised fashion with a pseudo-query
generation module. Then, we design a task-related query prompt module to
specifically tailor generated pseudo language queries for visual grounding
tasks. Further, in order to fully capture the contextual relationships between
images and language queries, we develop a visual-language model equipped with
multi-level cross-modality attention mechanism. Extensive experimental results
demonstrate that our method has two notable benefits: (1) it can reduce human
annotation costs significantly, e.g., 31% on RefCOCO without degrading original
model's performance under the fully supervised setting, and (2) without bells
and whistles, it achieves superior or comparable performance compared to
state-of-the-art weakly-supervised visual grounding methods on all the five
datasets we have experimented. Code is available at
https://github.com/LeapLabTHU/Pseudo-Q.

Various emotions can produce variations in electrocardiograph (ECG) signals,
distinct emotions can be distinguished by different changes in ECG signals.
This study is about emotion recognition using ECG signals. Data for four
emotions, namely happy, exciting, calm, and tense, is gathered. The raw data is
then de-noised with a finite impulse filter. We use the Discrete Cosine
Transform (DCT) to extract characteristics from the obtained data to increase
the accuracy of emotion recognition. The classifiers Support Vector Machine
(SVM), Random Forest, and K-NN are explored. To find the optimal parameters for
the SVM classifier, the Particle Swarm Optimization (PSO) technique is used.
The results of the comparison of these classification methods demonstrate that
the SVM approach has a greater accuracy in emotion recognition, which may be
applied in practice

In this paper, we tackle the task of estimating the 3D orientation of
previously-unseen objects from monocular images. This task contrasts with the
one considered by most existing deep learning methods which typically assume
that the testing objects have been observed during training. To handle the
unseen objects, we follow a retrieval-based strategy and prevent the network
from learning object-specific features by computing multi-scale local
similarities between the query image and synthetically-generated reference
images. We then introduce an adaptive fusion module that robustly aggregates
the local similarities into a global similarity score of pairwise images.
Furthermore, we speed up the retrieval process by developing a fast retrieval
strategy. Our experiments on the LineMOD, LineMOD-Occluded, and T-LESS datasets
show that our method yields a significantly better generalization to unseen
objects than previous works. Our code and pre-trained models are available at
https://sailor-z.github.io/projects/Unseen_Object_Pose.html.

In the future, most companies will be confronted with the topic of Artificial
Intelligence (AI) and will have to decide on their strategy in this regards.
Currently, a lot of companies are thinking about whether and how AI and the
usage of data will impact their business model and what potential use cases
could look like. One of the biggest challenges lies in coming up with
innovative solution ideas with a clear business value. This requires business
competencies on the one hand and technical competencies in AI and data
analytics on the other hand. In this article, we present the concept of AI
innovation labs and demonstrate a comprehensive framework, from coming up with
the right ideas to incrementally implementing and evaluating them regarding
their business value and their feasibility based on a company's capabilities.
The concept is the result of nine years of working on data-driven innovations
with companies from various domains. Furthermore, we share some lessons learned
from its practical applications. Even though a lot of technical publications
can be found in the literature regarding the development of AI models and many
consultancy companies provide corresponding services for building AI
innovations, we found very few publications sharing details about what an
end-to-end framework could look like.

Developing data-driven machine-learning interatomic potentials for materials
containing many elements becomes increasingly challenging due to the vast
configuration space that must be sampled by the training data. We study the
learning rates and achievable accuracy of machine-learning interatomic
potentials for many-element alloys with different combinations of descriptors
for the local atomic environments. We show that for a five-element alloy
system, potentials using simple low-dimensional descriptors can reach
meV/atom-accuracy with modestly sized training datasets, significantly
outperforming the high-dimensional SOAP descriptor in data efficiency,
accuracy, and speed. In particular, we develop a computationally fast
machine-learned and tabulated Gaussian approximation potential (tabGAP) for
Mo-Nb-Ta-V-W alloys with a combination of two-body, three-body, and a new
simple scalar many-body density descriptor based on the embedded atom method.

Emotion recognition technology through analyzing the EEG signal is currently
an essential concept in Artificial Intelligence and holds great potential in
emotional health care, human-computer interaction, multimedia content
recommendation, etc. Though there have been several works devoted to reviewing
EEG-based emotion recognition, the content of these reviews needs to be
updated. In addition, those works are either fragmented in content or only
focus on specific techniques adopted in this area but neglect the holistic
perspective of the entire technical routes. Hence, in this paper, we review
from the perspective of researchers who try to take the first step on this
topic. We review the recent representative works in the EEG-based emotion
recognition research and provide a tutorial to guide the researchers to start
from the beginning. The scientific basis of EEG-based emotion recognition in
the psychological and physiological levels is introduced. Further, we
categorize these reviewed works into different technical routes and illustrate
the theoretical basis and the research motivation, which will help the readers
better understand why those techniques are studied and employed. At last,
existing challenges and future investigations are also discussed in this paper,
which guides the researchers to decide potential future research directions.

Multi-agent reinforcement learning is difficult to be applied in practice,
which is partially due to the gap between the simulated and real-world
scenarios. One reason for the gap is that the simulated systems always assume
that the agents can work normally all the time, while in practice, one or more
agents may unexpectedly "crash" during the coordination process due to
inevitable hardware or software failures. Such crashes will destroy the
cooperation among agents, leading to performance degradation. In this work, we
present a formal formulation of a cooperative multi-agent reinforcement
learning system with unexpected crashes. To enhance the robustness of the
system to crashes, we propose a coach-assisted multi-agent reinforcement
learning framework, which introduces a virtual coach agent to adjust the crash
rate during training. We design three coaching strategies and the re-sampling
strategy for our coach agent. To the best of our knowledge, this work is the
first to study the unexpected crashes in the multi-agent system. Extensive
experiments on grid-world and StarCraft II micromanagement tasks demonstrate
the efficacy of adaptive strategy compared with the fixed crash rate strategy
and curriculum learning strategy. The ablation study further illustrates the
effectiveness of our re-sampling strategy.

Learned image compression methods have exhibited superior rate-distortion
performance than classical image compression standards. Most existing learned
image compression models are based on Convolutional Neural Networks (CNNs).
Despite great contributions, a main drawback of CNN based model is that its
structure is not designed for capturing local redundancy, especially the
non-repetitive textures, which severely affects the reconstruction quality.
Therefore, how to make full use of both global structure and local texture
becomes the core problem for learning-based image compression. Inspired by
recent progresses of Vision Transformer (ViT) and Swin Transformer, we found
that combining the local-aware attention mechanism with the global-related
feature learning could meet the expectation in image compression. In this
paper, we first extensively study the effects of multiple kinds of attention
mechanisms for local features learning, then introduce a more straightforward
yet effective window-based local attention block. The proposed window-based
attention is very flexible which could work as a plug-and-play component to
enhance CNN and Transformer models. Moreover, we propose a novel Symmetrical
TransFormer (STF) framework with absolute transformer blocks in the
down-sampling encoder and up-sampling decoder. Extensive experimental
evaluations have shown that the proposed method is effective and outperforms
the state-of-the-art methods. The code is publicly available at
https://github.com/Googolxx/STF.

This paper introduces a deep learning modular network for side-channel
analysis. Our deep learning approach features the capability to exchange part
of it (modules) with others networks. We aim to introduce reusable trained
modules into side-channel analysis instead of building architectures for each
evaluation, reducing the body of work when conducting those. Our experiments
demonstrate that our architecture feasibly assesses a side-channel evaluation
suggesting that learning transferability is possible with the network we
propose in this paper.

Emerging high-quality face restoration (FR) methods often utilize pre-trained
GAN models (\textit{i.e.}, StyleGAN2) as GAN Prior. However, these methods
usually struggle to balance realness and fidelity when facing various
degradation levels. Besides, there is still a noticeable visual quality gap
compared with pre-trained GAN models. In this paper, we propose a novel GAN
Prior based degradation-aware feature interpolation network, dubbed Panini-Net,
for FR tasks by explicitly learning the abstract representations to distinguish
various degradations. Specifically, an unsupervised degradation representation
learning (UDRL) strategy is first developed to extract degradation
representations (DR) of the input degraded images. Then, a degradation-aware
feature interpolation (DAFI) module is proposed to dynamically fuse the two
types of informative features (\textit{i.e.}, features from input images and
features from GAN Prior) with flexible adaption to various degradations based
on DR. Ablation studies reveal the working mechanism of DAFI and its potential
for editable FR. Extensive experiments demonstrate that our Panini-Net achieves
state-of-the-art performance for multi-degradation face restoration and face
super-resolution. The source code is available at
https://github.com/jianzhangcs/panini.

In this paper, we present a substantial step in better understanding the SOTA
sequence-to-sequence (Seq2Seq) pretraining for neural machine
translation~(NMT). We focus on studying the impact of the jointly pretrained
decoder, which is the main difference between Seq2Seq pretraining and previous
encoder-based pretraining approaches for NMT. By carefully designing
experiments on three language pairs, we find that Seq2Seq pretraining is a
double-edged sword: On one hand, it helps NMT models to produce more diverse
translations and reduce adequacy-related translation errors. On the other hand,
the discrepancies between Seq2Seq pretraining and NMT finetuning limit the
translation quality (i.e., domain discrepancy) and induce the over-estimation
issue (i.e., objective discrepancy). Based on these observations, we further
propose simple and effective strategies, named in-domain pretraining and input
adaptation to remedy the domain and objective discrepancies, respectively.
Experimental results on several language pairs show that our approach can
consistently improve both translation performance and model robustness upon
Seq2Seq pretraining.

In the acoustic scene classification (ASC) task, an acoustic scene consists
of diverse sounds and is inferred by identifying combinations of distinct
attributes among them. This study aims to extract and cluster these attributes
effectively using an improved multiple-instance learning (MIL) framework for
ASC. MIL, known as a weakly supervised learning method, is a strategy for
extracting an instance from a bundle of frames composing an input audio clip
and inferring a scene corresponding to the input data using these unlabeled
instances. However, many studies pointed out an underestimation problem of MIL.
In this study, we develop a MIL framework more suitable for ASC systems by
defining instance-level labels and loss to extract and cluster instances
effectively. Furthermore, we design a fully separated convolutional module,
which is a lightweight neural network comprising pointwise, frequency-sided
depthwise, and temporal-sided depthwise convolutional filters. As a result,
compared to vanilla MIL, the confidence and proportion of positive instances
increase significantly, overcoming the underestimation problem and improving
the classification accuracy up to 11%. The proposed system achieved a
performance of 81.1% and 72.3% on the TAU urban acoustic scenes 2019 and 2020
mobile datasets with 139 K parameters, respectively. Especially, it achieves
the highest performance among the systems having under the 1 M parameters on
the TAU urban acoustic scenes 2019 dataset.

We present a novel framework to automatically learn to transform the
differential cues from a stack of images densely captured with a rotational
motion into spatially discriminative and view-invariant per-pixel features at
each view. These low-level features can be directly fed to any existing
multi-view stereo technique for enhanced 3D reconstruction. The lighting
condition during acquisition can also be jointly optimized in a differentiable
fashion. We sample from a dozen of pre-scanned objects with a wide variety of
geometry and reflectance to synthesize a large amount of high-quality training
data. The effectiveness of our features is demonstrated on a number of
challenging objects acquired with a lightstage, comparing favorably with
state-of-the-art techniques. Finally, we explore additional applications of
geometric detail visualization and computational stylization of complex
appearance.

Deep learning methods have been employed in gravitational-wave astronomy to
accelerate the construction of surrogate waveforms for the inspiral of
spin-aligned black hole binaries, among other applications. We demonstrate,
that the residual error of an artificial neural network that models the
coefficients of the surrogate waveform expansion (especially those of the phase
of the waveform) has sufficient structure to be learnable by a second network.
Adding this second network, we were able to reduce the maximum mismatch for
waveforms in a validation set by more than an order of magnitude. We also
explored several other ideas for improving the accuracy of the surrogate model,
such as the exploitation of similarities between waveforms, the augmentation of
the training set, the dissection of the input space, using dedicated networks
per output coefficient and output augmentation. In several cases, small
improvements can be observed, but the most significant improvement still comes
from the addition of a second network that models the residual error. Since the
residual error for more general surrogate waveform models (when e.g.
eccentricity is included) may also have a specific structure, one can expect
our method to be applicable to cases where the gain in accuracy could lead to
significant gains in computational time.

The primary focus of Artificial Intelligence/Machine Learning (AI/ML)
integration within the wireless technology is to reduce capital expenditures,
optimize network performance, and build new revenue streams. Replacing
traditional algorithms with deep learning AI techniques have dramatically
reduced the power consumption and improved the system performance. Further,
implementation of ML algorithms also enables the wireless network service
providers to (i) offer high automation levels from distributed AI/ML
architectures applicable at the network edge, (ii) implement application-based
traffic steering across the access networks, (iii) enable dynamic network
slicing for addressing different scenarios with varying quality of service
requirements, and (iv) enable ubiquitous connectivity across the various 6G
communication platforms.
  In this chapter, we review/survey the ML techniques which are applicable to
the 6G wireless networks. and also list the open problems of research which
require timely solutions.

Few-shot image generation is a challenging task even using the
state-of-the-art Generative Adversarial Networks (GANs). Due to the unstable
GAN training process and the limited training data, the generated images are
often of low quality and low diversity. In this work, we propose a new
editing-based method, i.e., Attribute Group Editing (AGE), for few-shot image
generation. The basic assumption is that any image is a collection of
attributes and the editing direction for a specific attribute is shared across
all categories. AGE examines the internal representation learned in GANs and
identifies semantically meaningful directions. Specifically, the class
embedding, i.e., the mean vector of the latent codes from a specific category,
is used to represent the category-relevant attributes, and the
category-irrelevant attributes are learned globally by Sparse Dictionary
Learning on the difference between the sample embedding and the class
embedding. Given a GAN well trained on seen categories, diverse images of
unseen categories can be synthesized through editing category-irrelevant
attributes while keeping category-relevant attributes unchanged. Without
re-training the GAN, AGE is capable of not only producing more realistic and
diverse images for downstream visual applications with limited data but
achieving controllable image editing with interpretable category-irrelevant

Many studies have developed Machine Learning (ML) approaches to detect
Software Vulnerabilities (SVs) in functions and fine-grained code statements
that cause such SVs. However, there is little work on leveraging such detection
outputs for data-driven SV assessment to give information about exploitability,
impact, and severity of SVs. The information is important to understand SVs and
prioritize their fixing. Using large-scale data from 1,782 functions of 429 SVs
in 200 real-world projects, we investigate ML models for automating
function-level SV assessment tasks, i.e., predicting seven Common Vulnerability
Scoring System (CVSS) metrics. We particularly study the value and use of
vulnerable statements as inputs for developing the assessment models because
SVs in functions are originated in these statements. We show that vulnerable
statements are 5.8 times smaller in size, yet exhibit 7.5-114.5% stronger
assessment performance (Matthews Correlation Coefficient (MCC)) than
non-vulnerable statements. Incorporating context of vulnerable statements
further increases the performance by up to 8.9% (0.64 MCC and 0.75 F1-Score).
Overall, we provide the initial yet promising ML-based baselines for
function-level SV assessment, paving the way for further research in this

Unsupervised semantic segmentation aims to discover and localize semantically
meaningful categories within image corpora without any form of annotation. To
solve this task, algorithms must produce features for every pixel that are both
semantically meaningful and compact enough to form distinct clusters. Unlike
previous works which achieve this with a single end-to-end framework, we
propose to separate feature learning from cluster compactification.
Empirically, we show that current unsupervised feature learning frameworks
already generate dense features whose correlations are semantically consistent.
This observation motivates us to design STEGO ($\textbf{S}$elf-supervised
$\textbf{T}$ransformer with $\textbf{E}$nergy-based $\textbf{G}$raph
$\textbf{O}$ptimization), a novel framework that distills unsupervised features
into high-quality discrete semantic labels. At the core of STEGO is a novel
contrastive loss function that encourages features to form compact clusters
while preserving their relationships across the corpora. STEGO yields a
significant improvement over the prior state of the art, on both the CocoStuff
($\textbf{+14 mIoU}$) and Cityscapes ($\textbf{+9 mIoU}$) semantic segmentation

Some examples are easier for humans to classify than others. The same should
be true for deep neural networks (DNNs). We use the term example perplexity to
refer to the level of difficulty of classifying an example. In this paper, we
propose a method to measure the perplexity of an example and investigate what
factors contribute to high example perplexity. The related codes and resources
are available at https://github.com/vaynexie/Example-Perplexity.

Due to the partial observability and communication constraints in many
multi-agent reinforcement learning (MARL) tasks, centralized training with
decentralized execution (CTDE) has become one of the most widely used MARL
paradigms. In CTDE, centralized information is dedicated to learning the
allocation of the team reward with a mixing network, while the learning of
individual Q-values is usually based on local observations. The insufficient
utility of global observation will degrade performance in challenging
environments. To this end, this work proposes a novel Centralized Teacher with
Decentralized Student (CTDS) framework, which consists of a teacher model and a
student model. Specifically, the teacher model allocates the team reward by
learning individual Q-values conditioned on global observation, while the
student model utilizes the partial observations to approximate the Q-values
estimated by the teacher model. In this way, CTDS balances the full utilization
of global observation during training and the feasibility of decentralized
execution for online inference. Our CTDS framework is generic which is ready to
be applied upon existing CTDE methods to boost their performance. We conduct
experiments on a challenging set of StarCraft II micromanagement tasks to test
the effectiveness of our method and the results show that CTDS outperforms the
existing value-based MARL methods.

Sequence modeling has demonstrated state-of-the-art performance on natural
language and document understanding tasks. However, it is challenging to
correctly serialize tokens in form-like documents in practice due to their
variety of layout patterns. We propose FormNet, a structure-aware sequence
model to mitigate the suboptimal serialization of forms. First, we design Rich
Attention that leverages the spatial relationship between tokens in a form for
more precise attention score calculation. Second, we construct Super-Tokens for
each word by embedding representations from their neighboring tokens through
graph convolutions. FormNet therefore explicitly recovers local syntactic
information that may have been lost during serialization. In experiments,
FormNet outperforms existing methods with a more compact model size and less
pre-training data, establishing new state-of-the-art performance on CORD, FUNSD
and Payment benchmarks.

The strong few-shot in-context learning capability of large pre-trained
language models (PLMs) such as GPT-3 is highly appealing for application
domains such as biomedicine, which feature high and diverse demands of language
technologies but also high data annotation costs. In this paper, we present the
first systematic and comprehensive study to compare the few-shot performance of
GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on
two highly representative biomedical information extraction tasks, named entity
recognition and relation extraction. We follow the true few-shot setting to
avoid overestimating models' few-shot performance by model selection over a
large validation set. We also optimize GPT-3's performance with known
techniques such as contextual calibration and dynamic in-context example
retrieval. However, our results show that GPT-3 still significantly
underperforms compared to simply fine-tuning a smaller PLM. In addition, GPT-3
in-context learning also yields smaller gains in accuracy when more training
data becomes available. Our in-depth analyses further reveal issues of the
in-context learning setting that may be detrimental to information extraction
tasks in general. Given the high cost of experimenting with GPT-3, we hope our
study provides guidance for biomedical researchers and practitioners towards
more promising directions such as fine-tuning small PLMs.

Autonomous driving has the potential to revolutionize mobility and is hence
an active area of research. In practice, the behavior of autonomous vehicles
must be acceptable, i.e., efficient, safe, and interpretable. While vanilla
reinforcement learning (RL) finds performant behavioral strategies, they are
often unsafe and uninterpretable. Safety is introduced through Safe RL
approaches, but they still mostly remain uninterpretable as the learned
behaviour is jointly optimized for safety and performance without modeling them
separately. Interpretable machine learning is rarely applied to RL. This paper
proposes SafeDQN, which allows to make the behavior of autonomous vehicles safe
and interpretable while still being efficient. SafeDQN offers an
understandable, semantic trade-off between the expected risk and the utility of
actions while being algorithmically transparent. We show that SafeDQN finds
interpretable and safe driving policies for a variety of scenarios and
demonstrate how state-of-the-art saliency techniques can help to assess both
risk and utility.

Accurate and efficient lumbar spine disease identification is crucial for
clinical diagnosis. However, existing deep learning models with millions of
parameters often fail to learn with only hundreds or dozens of medical images.
These models also ignore the contextual relationship between adjacent objects,
such as between vertebras and intervertebral discs. This work introduces a
multi-scale context-guided network with coarse-to-fine localization and
classification, named CCF-Net, for lumbar spine disease identification.
Specifically, in learning, we divide the localization objective into two
parallel tasks, coarse and fine, which are more straightforward and effectively
reduce the number of parameters and computational cost. The experimental
results show that the coarse-to-fine design presents the potential to achieve
high performance with fewer parameters and data requirements. Moreover, the
multi-scale context-guided module can significantly improve the performance by
6.45% and 5.51% with ResNet18 and ResNet50, respectively. Our code is available
at https://github.com/czifan/CCFNet.pytorch.

Recent years have witnessed a great development of Convolutional Neural
Networks in semantic segmentation, where all classes of training images are
simultaneously available. In practice, new images are usually made available in
a consecutive manner, leading to a problem called Continual Semantic
Segmentation (CSS). Typically, CSS faces the forgetting problem since previous
training images are unavailable, and the semantic shift problem of the
background class. Considering the semantic segmentation as a context-dependent
pixel-level classification task, we explore CSS from a new perspective of
context analysis in this paper. We observe that the context of old-class pixels
in the new images is much more biased on new classes than that in the old
images, which can sharply aggravate the old-class forgetting and new-class
overfitting. To tackle the obstacle, we propose a biased-context-rectified CSS
framework with a context-rectified image-duplet learning scheme and a
biased-context-insensitive consistency loss. Furthermore, we propose an
adaptive re-weighting class-balanced learning strategy for the biased class
distribution. Our approach outperforms state-of-the-art methods by a large
margin in existing CSS scenarios.

Precise indoor positioning systems (IPSs) are key to perform a set of tasks
more efficiently during aircraft production, operation and maintenance. For
instance, IPSs can overcome the tedious task of configuring (wireless) sensor
nodes in an aircraft cabin. Although various solutions based on technologies of
established consumer goods, e.g., Bluetooth or WiFi, have been proposed and
tested, the published accuracy results fail to make these technologies relevant
for practical use cases. This stems from the challenging environments for
positioning, especially in aircraft cabins, which is mainly due to the
geometries, many obstacles, and highly reflective materials. To address these
issues, we propose to evaluate in this work an Ultra-Wideband (UWB)-based IPS
via a measurement campaign performed in a real aircraft cabin. We first
illustrate the difficulties that an IPS faces in an aircraft cabin, by studying
the signal propagation effects which were measured. We then investigate the
ranging and localization accuracies of our IPS. Finally, we also introduce
various methods based on machine learning (ML) for correcting the ranging
measurements and demonstrate that we are able to localize a node with respect
to an aircraft seat with a measured likelihood of 97%.

Tensor shape mismatch is a common source of bugs in deep learning programs.
Most of the studies conducted to solve this problem use the whole-program
analysis approach, which lacks compositionality. A type-based approach is
desirable in this respect, but since the problem of shape inference is
undecidable in general, fully automated shape inference is bound to be either
unsound or too conservative.
  We propose a type-based approach to detect tensor shape mismatches that works
practically under such limitations. One of the main features of our approach is
the best-effort shape inference. Because of the undecidability of shape
inference, our procedure performs it only in a best-effort manner. The
inference result may be too imprecise to statically guarantee the absence of
the shape inconsistencies, and in such cases, dynamic checks are inserted into
the program. Another main feature is gradual typing. Users can improve the
precision of the inference by adding appropriate type annotations to the
program.
  We formalize our approach and prove that it satisfies the criteria of gradual
typing proposed by Siek et al. in 2015. We implement a prototype shape checking
tool based on our approach and evaluate its effectiveness by applying it to
some deep neural network programs.

Despite the impressive progress of general face detection, the tuning of
hyper-parameters and architectures is still critical for the performance of a
domain-specific face detector. Though existing AutoML works can speedup such
process, they either require tuning from scratch for a new scenario or do not
consider data privacy. To scale up, we derive a new AutoML setting from a
platform perspective. In such setting, new datasets sequentially arrive at the
platform, where an architecture and hyper-parameter configuration is
recommended to train the optimal face detector for each dataset. This, however,
brings two major challenges: (1) how to predict the best configuration for any
given dataset without touching their raw images due to the privacy concern? and
(2) how to continuously improve the AutoML algorithm from previous tasks and
offer a better warm-up for future ones? We introduce "HyperFD", a new
privacy-preserving online AutoML framework for face detection. At its core
part, a novel meta-feature representation of a dataset as well as its learning
paradigm is proposed. Thanks to HyperFD, each local task (client) is able to
effectively leverage the learning "experience" of previous tasks without
uploading raw images to the platform; meanwhile, the meta-feature extractor is
continuously learned to better trade off the bias and variance. Extensive
experiments demonstrate the effectiveness and efficiency of our design.

As reinforcement learning (RL) has achieved near human-level performance in a
variety of tasks, its robustness has raised great attention. While a vast body
of research has explored test-time (evasion) attacks in RL and corresponding
defenses, its robustness against training-time (poisoning) attacks remains
largely unanswered. In this work, we focus on certifying the robustness of
offline RL in the presence of poisoning attacks, where a subset of training
trajectories could be arbitrarily manipulated. We propose the first
certification framework, COPA, to certify the number of poisoning trajectories
that can be tolerated regarding different certification criteria. Given the
complex structure of RL, we propose two certification criteria: per-state
action stability and cumulative reward bound. To further improve the
certification, we propose new partition and aggregation protocols to train
robust policies. We further prove that some of the proposed certification
methods are theoretically tight and some are NP-Complete problems. We leverage
COPA to certify three RL environments trained with different algorithms and
conclude: (1) The proposed robust aggregation protocols such as temporal
aggregation can significantly improve the certifications; (2) Our certification
for both per-state action stability and cumulative reward bound are efficient
and tight; (3) The certification for different training algorithms and
environments are different, implying their intrinsic robustness properties. All
experimental results are available at https://copa-leaderboard.github.io.

Within the human-robot interaction (HRI) community, many researchers have
focused on the careful design of human-subjects studies. However, other parts
of the community, e.g., the technical advances community, also need to do
human-subjects studies to collect data to train their models, in ways that
require user studies but without a strict experimental design. The design of
such data collection is an underexplored area worthy of more attention. In this
work, we contribute a clearly defined process to collect data with three steps
for machine learning modeling purposes, grounded in recent literature, and
detail an use of this process to facilitate the collection of a corpus of
referring expressions. Specifically, we discuss our data collection goal and
how we worked to encourage well-covered and abundant participant responses,
through our design of the task environment, the task itself, and the study
procedure. We hope this work would lead to more data collection formalism
efforts in the HRI community and a fruitful discussion during the workshop.

Back-translation is a critical component of Unsupervised Neural Machine
Translation (UNMT), which generates pseudo parallel data from target
monolingual data. A UNMT model is trained on the pseudo parallel data with
translated source, and translates natural source sentences in inference. The
source discrepancy between training and inference hinders the translation
performance of UNMT models. By carefully designing experiments, we identify two
representative characteristics of the data gap in source: (1) style gap (i.e.,
translated vs. natural text style) that leads to poor generalization
capability; (2) content gap that induces the model to produce hallucination
content biased towards the target language. To narrow the data gap, we propose
an online self-training approach, which simultaneously uses the pseudo parallel
data {natural source, translated target} to mimic the inference scenario.
Experimental results on several widely-used language pairs show that our
approach outperforms two strong baselines (XLM and MASS) by remedying the style
and content gaps.

Deep neural networks (DNNs) have been widely applied in various domains in
artificial intelligence including computer vision and natural language
processing. A DNN is typically trained for many epochs and then a validation
dataset is used to select the DNN in an epoch (we simply call this epoch "the
last epoch") as the final model for making predictions on unseen samples, while
it usually cannot achieve a perfect accuracy on unseen samples. An interesting
question is "how many test (unseen) samples that a DNN misclassifies in the
last epoch were ever correctly classified by the DNN before the last epoch?".
In this paper, we empirically study this question and find on several benchmark
datasets that the vast majority of the misclassified samples in the last epoch
were ever classified correctly before the last epoch, which means that the
predictions for these samples were flipped from "correct" to "wrong". Motivated
by this observation, we propose to restrict the behavior changes of a DNN on
the correctly-classified samples so that the correct local boundaries can be
maintained and the flipping error on unseen samples can be largely reduced.
Extensive experiments on different benchmark datasets with different modern
network architectures demonstrate that the proposed flipping error reduction
(FER) approach can substantially improve the generalization, the robustness,
and the transferability of DNNs without introducing any additional network
parameters or inference cost, only with a negligible training overhead.

Marginalization of latent variables or nuisance parameters is a fundamental
aspect of Bayesian inference and uncertainty quantification. In this work, we
focus on scalable marginalization of latent variables in modeling correlated
data, such as spatio-temporal or functional observations. We first introduce
Gaussian processes (GPs) for modeling correlated data and highlight the
computational challenge, where the computational complexity increases cubically
fast along with the number of observations. We then review the connection
between the state space model and GPs with Mat{\'e}rn covariance for temporal
inputs. The Kalman filter and Rauch-Tung-Striebel smoother were introduced as a
scalable marginalization technique for computing the likelihood and making
predictions of GPs without approximation. We then introduce recent efforts on
extending the scalable marginalization idea to the linear model of
coregionalization for multivariate correlated output and spatio-temporal
observations. In the final part of this work, we introduce a novel
marginalization technique to estimate interaction kernels and forecast particle
trajectories. The achievement lies in the sparse representation of covariance
function, then applying conjugate gradient for solving the computational
challenges and improving predictive accuracy. The computational advances
achieved in this work outline a wide range of applications in molecular dynamic
simulation, cellular migration, and agent-based models.

While Pre-trained Language Models (PLMs) internalize a great amount of world
knowledge, they have been shown incapable of recalling these knowledge to solve
tasks requiring complex & multi-step reasoning. Similar to how humans develop a
"chain of thought" for these tasks, how can we equip PLMs with such abilities?
In this work, we explore an iterative prompting framework, a new prompting
paradigm which progressively elicits relevant knowledge from PLMs for
multi-step inference. We identify key limitations of existing prompting
methods, namely they are either restricted to queries with a single
identifiable relation/predicate, or being agnostic to input contexts, which
makes it difficult to capture variabilities across different inference steps.
We propose an iterative context-aware prompter, which addresses these
limitations by learning to dynamically synthesize prompts conditioned on the
current step's contexts. Experiments on three datasets involving multi-step
reasoning show the effectiveness of the iterative scheme and the context-aware
prompter design.

Common image-to-image translation methods rely on joint training over data
from both source and target domains. This prevents the training process from
preserving privacy of domain data (e.g., in a federated setting), and often
means that a new model has to be trained for a new pair of domains. We present
Dual Diffusion Implicit Bridges (DDIBs), an image translation method based on
diffusion models, that circumvents training on domain pairs. Image translation
with DDIBs relies on two diffusion models trained independently on each domain,
and is a two-step process: DDIBs first obtain latent encodings for source
images with the source diffusion model, and then decode such encodings using
the target model to construct target images. Both steps are defined via an ODE,
thus the process is cycle consistent only up to discretization errors of the
ODE solvers. Theoretically, we interpret DDIBs as concatenation of source to
latent, and latent to target Schr\"odinger Bridges, a form of
entropy-regularized optimal transport, to explain the efficacy of the method.
Experimentally, we apply DDIBs on both synthetic and high-resolution image
datasets, to demonstrate their utility in a wide variety of translation tasks
and their connections to existing optimal transport methods.

Denoising diffusion probabilistic models are a promising new class of
generative models that mark a milestone in high-quality image generation. This
paper showcases their ability to sequentially generate video, surpassing prior
methods in perceptual and probabilistic forecasting metrics. We propose an
autoregressive, end-to-end optimized video diffusion model inspired by recent
advances in neural video compression. The model successively generates future
frames by correcting a deterministic next-frame prediction using a stochastic
residual generated by an inverse diffusion process. We compare this approach
against five baselines on four datasets involving natural and simulation-based
videos. We find significant improvements in terms of perceptual quality for all
datasets. Furthermore, by introducing a scalable version of the Continuous
Ranked Probability Score (CRPS) applicable to video, we show that our model
also outperforms existing approaches in their probabilistic frame forecasting
ability.

Pretrained, large, generative language models (LMs) have had great success in
a wide range of sequence tagging and structured prediction tasks. Casting a
sequence tagging task as a Seq2Seq one requires deciding the formats of the
input and output sequences. However, we lack a principled understanding of the
trade-offs associated with these formats (such as the effect on model accuracy,
sequence length, multilingual generalization, hallucination). In this paper, we
rigorously study different formats one could use for casting input text
sentences and their output labels into the input and target (i.e., output) of a
Seq2Seq model. Along the way, we introduce a new format, which we show to to be
both simpler and more effective. Additionally the new format demonstrates
significant gains in the multilingual settings -- both zero-shot transfer
learning and joint training. Lastly, we find that the new format is more robust
and almost completely devoid of hallucination -- an issue we find common in
existing formats. With well over a 1000 experiments studying 14 different
formats, over 7 diverse public benchmarks -- including 3 multilingual datasets
spanning 7 languages -- we believe our findings provide a strong empirical
basis in understanding how we should tackle sequence tagging tasks.

A major limitation in applying deep learning to artificial intelligence (AI)
systems is the scarcity of high-quality curated datasets. We investigate strong
augmentation based self-supervised learning (SSL) techniques to address this
problem. Using breast cancer detection as an example, we first identify a
mammogram-specific transformation paradigm and then systematically compare four
recent SSL methods representing a diversity of approaches. We develop a method
to convert a pretrained model from making predictions on uniformly tiled
patches to whole images, and an attention-based pooling method that improves
the classification performance. We found that the best SSL model substantially
outperformed the baseline supervised model. The best SSL model also improved
the data efficiency of sample labeling by nearly 4-fold and was highly
transferrable from one dataset to another. SSL represents a major breakthrough
in computer vision and may help the AI for medical imaging field to shift away
from supervised learning and dependency on scarce labels.

Since recommender systems have been created and developed to automate the
recommendation process, users can easily consume their desired video content on
online platforms. In this line, several content recommendation algorithms are
introduced and employed to allow users to encounter content of their interests,
effectively. However, the recommendation systems sometimes regrettably
recommend inappropriate content, including misinformation or fake news. To make
it worse, people would unreservedly accept such content due to their cognitive
heuristic, machine heuristic, which is the rule of thumb that machines are more
accurate and trustworthy than humans. In this study, we designed and conducted
a web-based experiment where the participants are invoked machine heuristic by
experiencing the whole process of machine or human recommendation system. The
results demonstrated that participants (N = 89) showed a more positive attitude
toward a machine recommender than a human recommender, even the recommended
videos contain inappropriate content. While participants who have a high level
of trust in machines exhibited a negative attitude toward recommendations.
Based on these results, we suggest that a phenomenon known as algorithm
aversion might be simultaneously considered with machine heuristic in
investigating human interaction with a machine.

Dense retrieval has achieved impressive advances in first-stage retrieval
from a large-scale document collection, which is built on bi-encoder
architecture to produce single vector representation of query and document.
However, a document can usually answer multiple potential queries from
different views. So the single vector representation of a document is hard to
match with multi-view queries, and faces a semantic mismatch problem. This
paper proposes a multi-view document representation learning framework, aiming
to produce multi-view embeddings to represent documents and enforce them to
align with different queries. First, we propose a simple yet effective method
of generating multiple embeddings through viewers. Second, to prevent
multi-view embeddings from collapsing to the same one, we further propose a
global-local loss with annealed temperature to encourage the multiple viewers
to better align with different potential queries. Experiments show our method
outperforms recent works and achieves state-of-the-art results.

The exponentially large discrete search space in mixed-precision quantization
(MPQ) makes it hard to determine the optimal bit-width for each layer. Previous
works usually resort to iterative search methods on the training set, which
consume hundreds or even thousands of GPU-hours. In this study, we reveal that
some unique learnable parameters in quantization, namely the scale factors in
the quantizer, can serve as importance indicators of a layer, reflecting the
contribution of that layer to the final accuracy at certain bit-widths. These
importance indicators naturally perceive the numerical transformation during
quantization-aware training, which can precisely and correctly provide
quantization sensitivity metrics of layers. However, a deep network always
contains hundreds of such indicators, and training them one by one would lead
to an excessive time cost. To overcome this issue, we propose a joint training
scheme that can obtain all indicators at once. It considerably speeds up the
indicators training process by parallelizing the original sequential training
processes. With these learned importance indicators, we formulate the MPQ
search problem as a one-time integer linear programming (ILP) problem. That
avoids the iterative search and significantly reduces search time without
limiting the bit-width search space. For example, MPQ search on ResNet18 with
our indicators takes only 0.06 seconds. Also, extensive experiments show our
approach can achieve SOTA accuracy on ImageNet for far-ranging models with
various constraints (e.g., BitOps, compress rate).

Suction cup grasping is very common in industry, but moving too quickly can
cause suction cups to detach, causing drops or damage. Maintaining a suction
grasp throughout a high-speed motion requires balancing suction forces against
inertial forces while the suction cups deform under strain. In this paper, we
consider Grasp Optimized Motion Planning for Suction Transport (GOMP-ST), an
algorithm that combines deep learning with optimization to decrease transport
time while avoiding suction cup failure. GOMP-ST first repeatedly moves a
physical robot, vacuum gripper, and a sample object, while measuring pressure
with a solid-state sensor to learn critical failure conditions. Then, these are
integrated as constraints on the accelerations at the end-effector into a
time-optimizing motion planner. The resulting plans incorporate real-world
effects such as suction cup deformation that are difficult to model
analytically. In GOMP-ST, the learned constraint, modeled with a neural
network, is linearized using Autograd and integrated into a sequential
quadratic program optimization. In 420 experiments with a physical UR5
transporting objects ranging from 1.3 to 1.7 kg, we compare GOMP-ST to baseline
optimizing motion planners. Results suggest that GOMP-ST can avoid suction cup
failure while decreasing transport times from 16% to 58%. For code, video, and
datasets, see https://sites.google.com/view/gomp-st.

Class-agnostic counting (CAC) aims to count all instances in a query image
given few exemplars. A standard pipeline is to extract visual features from
exemplars and match them with query images to infer object counts. Two
essential components in this pipeline are feature representation and similarity
metric. Existing methods either adopt a pretrained network to represent
features or learn a new one, while applying a naive similarity metric with
fixed inner product. We find this paradigm leads to noisy similarity matching
and hence harms counting performance. In this work, we propose a
similarity-aware CAC framework that jointly learns representation and
similarity metric. We first instantiate our framework with a naive baseline
called Bilinear Matching Network (BMNet), whose key component is a learnable
bilinear similarity metric. To further embody the core of our framework, we
extend BMNet to BMNet+ that models similarity from three aspects: 1)
representing the instances via their self-similarity to enhance feature
robustness against intra-class variations; 2) comparing the similarity
dynamically to focus on the key patterns of each exemplar; 3) learning from a
supervision signal to impose explicit constraints on matching results.
Extensive experiments on a recent CAC dataset FSC147 show that our models
significantly outperform state-of-the-art CAC approaches. In addition, we also
validate the cross-dataset generality of BMNet and BMNet+ on a car counting
dataset CARPK. Code is at tiny.one/BMNet

Environmental sound recognition (ESR) is an emerging research topic in audio
pattern recognition. Many tasks are presented to resort to computational
systems for ESR in real-life applications. However, current systems are usually
designed for individual tasks, and are not robust and applicable to other
tasks. Cross-task systems, which promote unified knowledge modeling across
various tasks, have not been thoroughly investigated. In this paper, we propose
a cross-task system for three different tasks of ESR: acoustic scene
classification, urban sound tagging, and anomalous sound detection. An
architecture named SE-Trans is presented that uses attention mechanism-based
Squeeze-and-Excitation and Transformer encoder modules to learn channel-wise
relationship and temporal dependencies of the acoustic features. FMix is
employed as the data augmentation method that improves the performance of ESR.
Evaluations for the three tasks are conducted on the recent databases of DCASE
challenges. The experimental results show that the proposed cross-task system
achieves state-of-the-art performance on all tasks. Further analysis
demonstrates that the proposed cross-task system can effectively utilize
acoustic knowledge across different ESR tasks.

Large scale nonlinear classification is a challenging task in the field of
support vector machine. Online random Fourier feature map algorithms are very
important methods for dealing with large scale nonlinear classification
problems. The main shortcomings of these methods are as follows: (1) Since only
the hyperplane vector is updated during learning while the random directions
are fixed, there is no guarantee that these online methods can adapt to the
change of data distribution when the data is coming one by one. (2) The
dimension of the random direction is often higher for obtaining better
classification accuracy, which results in longer test time. In order to
overcome these shortcomings, a multi-parameter updating Fourier online gradient
descent algorithm (MPU-FOGD) is proposed for large-scale nonlinear
classification problems based on a novel random feature map. In the proposed
method, the suggested random feature map has lower dimension while the
multi-parameter updating strategy can guarantee the learning model can better
adapt to the change of data distribution when the data is coming one by one.
Theoretically, it is proved that compared with the existing random Fourier
feature maps, the proposed random feature map can give a tighter error bound.
Empirical studies on several benchmark data sets demonstrate that compared with
the state-of-the-art online random Fourier feature map methods, the proposed
MPU-FOGD can obtain better test accuracy.

The great success neural networks have achieved is inseparable from the
application of gradient-descent (GD) algorithms. Based on GD, many variant
algorithms have emerged to improve the GD optimization process. The gradient
for back-propagation is apparently the most crucial aspect for the training of
a neural network. The quality of the calculated gradient can be affected by
multiple aspects, e.g., noisy data, calculation error, algorithm limitation,
and so on. To reveal gradient information beyond gradient descent, we introduce
a framework (\textbf{GCGD}) to perform gradient correction. GCGD consists of
two plug-in modules: 1) inspired by the idea of gradient prediction, we propose
a \textbf{GC-W} module for weight gradient correction; 2) based on Neural ODE,
we propose a \textbf{GC-ODE} module for hidden states gradient correction.
Experiment results show that our gradient correction framework can effectively
improve the gradient quality to reduce training epochs by $\sim$ 20\% and also
improve the network performance.

We aim to improve the performance of regressing hand keypoints and segmenting
pixel-level hand masks under new imaging conditions (e.g., outdoors) when we
only have labeled images taken under very different conditions (e.g., indoors).
In the real world, it is important that the model trained for both tasks works
under various imaging conditions. However, their variation covered by existing
labeled hand datasets is limited. Thus, it is necessary to adapt the model
trained on the labeled images (source) to unlabeled images (target) with unseen
imaging conditions. While self-training domain adaptation methods (i.e.,
learning from the unlabeled target images in a self-supervised manner) have
been developed for both tasks, their training may degrade performance when the
predictions on the target images are noisy. To avoid this, it is crucial to
assign a low importance (confidence) weight to the noisy predictions during
self-training. In this paper, we propose to utilize the divergence of two
predictions to estimate the confidence of the target image for both tasks.
These predictions are given from two separate networks, and their divergence
helps identify the noisy predictions. To integrate our proposed confidence
estimation into self-training, we propose a teacher-student framework where the
two networks (teachers) provide supervision to a network (student) for
self-training, and the teachers are learned from the student by knowledge
distillation. Our experiments show its superiority over state-of-the-art
methods in adaptation settings with different lighting, grasping objects,
backgrounds, and camera viewpoints. Our method improves by 4% the multi-task
score on HO3D compared to the latest adversarial adaptation method. We also
validate our method on Ego4D, egocentric videos with rapid changes in imaging
conditions outdoors.

Low-rank matrix completion has been studied extensively under various type of
categories. The problem could be categorized as noisy completion or exact
completion, also active or passive completion algorithms. In this paper we
focus on adaptive matrix completion with bounded type of noise. We assume that
the matrix $\mathbf{M}$ we target to recover is composed as low-rank matrix
with addition of bounded small noise. The problem has been previously studied
by \cite{nina}, in a fixed sampling model. Here, we study this problem in
adaptive setting that, we continuously estimate an upper bound for the angle
with the underlying low-rank subspace and noise-added subspace. Moreover, the
method suggested here, could be shown requires much smaller observation than
aforementioned method.

Datacenters execute large computational jobs, which are composed of smaller
tasks. A job completes when all its tasks finish, so stragglers -- rare, yet
extremely slow tasks -- are a major impediment to datacenter performance.
Accurately predicting stragglers would enable proactive intervention, allowing
datacenter operators to mitigate stragglers before they delay a job. While much
prior work applies machine learning to predict computer system performance,
these approaches rely on complete labels -- i.e., sufficient examples of all
possible behaviors, including straggling and non-straggling -- or strong
assumptions about the underlying latency distributions -- e.g., whether
Gaussian or not. Within a running job, however, none of this information is
available until stragglers have revealed themselves when they have already
delayed the job. To predict stragglers accurately and early without labeled
positive examples or assumptions on latency distributions, this paper presents
NURD, a novel Negative-Unlabeled learning approach with Reweighting and
Distribution-compensation that only trains on negative and unlabeled streaming
data. The key idea is to train a predictor using finished tasks of
non-stragglers to predict latency for unlabeled running tasks, and then
reweight each unlabeled task's prediction based on a weighting function of its
feature space. We evaluate NURD on two production traces from Google and
Alibaba, and find that compared to the best baseline approach, NURD produces
2--11 percentage point increases in the F1 score in terms of prediction
accuracy, and 2.0--8.8 percentage point improvements in job completion time.

This paper summarizes the needs of the dark matter and neutrino communities
as it relates to computation. The scope includes data acquisition, triggers,
data management and processing, data preservation, simulation, machine
learning, data analysis, software engineering, career development, and equity
and inclusion. Beyond identifying our community needs, we propose actions that
can be taken to strengthen this community and to work together to overcome
common challenges.

Monocular 3D object detection is one of the most challenging tasks in 3D
scene understanding. Due to the ill-posed nature of monocular imagery, existing
monocular 3D detection methods highly rely on training with the manually
annotated 3D box labels on the LiDAR point clouds. This annotation process is
very laborious and expensive. To dispense with the reliance on 3D box labels,
in this paper we explore the weakly supervised monocular 3D detection.
Specifically, we first detect 2D boxes on the image. Then, we adopt the
generated 2D boxes to select corresponding RoI LiDAR points as the weak
supervision. Eventually, we adopt a network to predict 3D boxes which can
tightly align with associated RoI LiDAR points. This network is learned by
minimizing our newly-proposed 3D alignment loss between the 3D box estimates
and the corresponding RoI LiDAR points. We will illustrate the potential
challenges of the above learning problem and resolve these challenges by
introducing several effective designs into our method. Codes will be available
at https://github.com/SPengLiang/WeakM3D.

Unsupervised domain adaptation methods aim to generalize well on unlabeled
test data that may have a different (shifted) distribution from the training
data. Such methods are typically developed on image data, and their application
to time series data is less explored. Existing works on time series domain
adaptation suffer from inconsistencies in evaluation schemes, datasets, and
backbone neural network architectures. Moreover, labeled target data are
usually employed for model selection, which violates the fundamental assumption
of unsupervised domain adaptation. To address these issues, we develop a
benchmarking evaluation suite (ADATIME) to systematically and fairly evaluate
different domain adaptation methods on time series data. Specifically, we
standardize the backbone neural network architectures and benchmarking
datasets, while also exploring more realistic model selection approaches that
can work with no labeled data or just few labeled samples. Our evaluation
includes adapting state-of-the-art visual domain adaptation methods to time
series data in addition to the recent methods specifically developed for time
series data. We conduct extensive experiments to evaluate 10 state-of-the-art
methods on four representative datasets spanning 20 cross-domain scenarios. Our
results suggest that with careful selection of hyper-parameters, visual domain
adaptation methods are competitive with methods proposed for time series domain
adaptation. In addition, we find that hyper-parameters could be selected based
on realistic model selection approaches. Our work unveils practical insights
for applying domain adaptation methods on time series data and builds a solid
foundation for future works in the field. The code is available at
\href{https://github.com/emadeldeen24/AdaTime}{github.com/emadeldeen24/AdaTime}.

Real-time density estimation is ubiquitous in many applications, including
computer vision and signal processing. Kernel density estimation is arguably
one of the most commonly used density estimation techniques, and the use of
"sliding window" mechanism adapts kernel density estimators to dynamic
processes. In this paper, we derive the asymptotic mean integrated squared
error (AMISE) upper bound for the "sliding window" kernel density estimator.
This upper bound provides a principled guide to devise a novel estimator, which
we name the temporal adaptive kernel density estimator (TAKDE). Compared to
heuristic approaches for "sliding window" kernel density estimator, TAKDE is
theoretically optimal in terms of the worst-case AMISE. We provide numerical
experiments using synthetic and real-world datasets, showing that TAKDE
outperforms other state-of-the-art dynamic density estimators (including those
outside of kernel family). In particular, TAKDE achieves a superior test
log-likelihood with a smaller runtime.

The emergence of explainability methods has enabled a better comprehension of
how deep neural networks operate through concepts that are easily understood
and implemented by the end user. While most explainability methods have been
designed for traditional deep learning, some have been further developed for
geometric deep learning, in which data are predominantly represented as graphs.
These representations are regularly derived from medical imaging data,
particularly in the field of neuroimaging, in which graphs are used to
represent brain structural and functional wiring patterns (brain connectomes)
and cortical surface models are used to represent the anatomical structure of
the brain. Although explainability techniques have been developed for
identifying important vertices (brain areas) and features for graph
classification, these methods are still lacking for more complex tasks, such as
surface-based modality transfer (or vertex-wise regression). Here, we address
the need for surface-based explainability approaches by developing a framework
for cortical surface-based deep learning, providing a transparent system for
modality transfer tasks. First, we adapted a perturbation-based approach for
use with surface data. Then, we applied our perturbation-based method to
investigate the key features and vertices used by a geometric deep learning
model developed to predict brain function from anatomy directly on a cortical
surface model. We show that our explainability framework is not only able to
identify important features and their spatial location but that it is also
reliable and valid.

Word translation or bilingual lexicon induction (BLI) is a key cross-lingual
task, aiming to bridge the lexical gap between different languages. In this
work, we propose a robust and effective two-stage contrastive learning
framework for the BLI task. At Stage C1, we propose to refine standard
cross-lingual linear maps between static word embeddings (WEs) via a
contrastive learning objective; we also show how to integrate it into the
self-learning procedure for even more refined cross-lingual maps. In Stage C2,
we conduct BLI-oriented contrastive fine-tuning of mBERT, unlocking its word
translation capability. We also show that static WEs induced from the
`C2-tuned' mBERT complement static WEs from Stage C1. Comprehensive experiments
on standard BLI datasets for diverse languages and different experimental
setups demonstrate substantial gains achieved by our framework. While the BLI
method from Stage C1 already yields substantial gains over all state-of-the-art
BLI methods in our comparison, even stronger improvements are met with the full
two-stage framework: e.g., we report gains for 112/112 BLI setups, spanning 28
language pairs.

Vision-based segmentation of the robotic tool during robot-assisted surgery
enables downstream applications, such as augmented reality feedback, while
allowing for inaccuracies in robot kinematics. With the introduction of deep
learning, many methods were presented to solve instrument segmentation directly
and solely from images. While these approaches made remarkable progress on
benchmark datasets, fundamental challenges pertaining to their robustness
remain. We present CaRTS, a causality-driven robot tool segmentation algorithm,
that is designed based on a complementary causal model of the robot tool
segmentation task. Rather than directly inferring segmentation masks from
observed images, CaRTS iteratively aligns tool models with image observations
by updating the initially incorrect robot kinematic parameters through forward
kinematics and differentiable rendering to optimize image feature similarity
end-to-end. We benchmark CaRTS with competing techniques on both synthetic as
well as real data from the dVRK, generated in precisely controlled scenarios to
allow for counterfactual synthesis. On training-domain test data, CaRTS
achieves a Dice score of 93.4 that is preserved well (Dice score of 91.8) when
tested on counterfactually altered test data, exhibiting low brightness, smoke,
blood, and altered background patterns. This compares favorably to Dice scores
of 95.0 and 86.7, respectively, of the SOTA image-based method. Future work
will involve accelerating CaRTS to achieve video framerate and estimating the
impact occlusion has in practice. Despite these limitations, our results are
promising: In addition to achieving high segmentation accuracy, CaRTS provides
estimates of the true robot kinematics, which may benefit applications such as
force estimation. Code is available at: https://github.com/hding2455/CaRTS

Deep learning is increasingly being applied in safety-critical domains. For
these scenarios it is important to know the level of uncertainty in a model's
prediction to ensure appropriate decisions are made by the system. Deep
ensembles are the de-facto standard approach to obtaining various measures of
uncertainty. However, ensembles often significantly increase the resources
required in the training and/or deployment phases. Approaches have been
developed that typically address the costs in one of these phases. In this work
we propose a novel training approach, self-distribution distillation (S2D),
which is able to efficiently train a single model that can estimate
uncertainties. Furthermore it is possible to build ensembles of these models
and apply hierarchical ensemble distillation approaches. Experiments on
CIFAR-100 showed that S2D models outperformed standard models and Monte-Carlo
dropout. Additional out-of-distribution detection experiments on LSUN, Tiny
ImageNet, SVHN showed that even a standard deep ensemble can be outperformed
using S2D based ensembles and novel distilled models.

Optical neural networks (ONNs), implemented on an array of cascaded
Mach-Zehnder interferometers (MZIs), have recently been proposed as a possible
replacement for conventional deep learning hardware. They potentially offer
higher energy efficiency and computational speed when compared to their
electronic counterparts. By utilizing tunable phase shifters, one can adjust
the output of each of MZIs in order to enable emulation of arbitrary
matrix-vector multiplication. These phase shifters are central to the
programmability of ONNs, but they require large footprint and are relatively
slow. Here we propose an ONN architecture that utilizes parity-time (PT)
symmetric couplers as its building blocks. Instead of modulating phase,
gain/loss contrasts across the array are adjusted as a means to train the
network. We demonstrate that PT symmetric optical neural networks (PT-ONN) are
adequately expressive by performing the digit-recognition task on the modified
national institute of standard and technology (MNIST) dataset. Compared to
conventional ONNs, the PT-ONN achieves a comparable accuracy (67% vs. 71%)
while circumventing the problems associated with changing phase. Our approach
may lead to new and alternative avenues for fast training in chip-scale optical

Speech Emotion Recognition (SER) application is frequently associated with
privacy concerns as it often acquires and transmits speech data at the
client-side to remote cloud platforms for further processing. These speech data
can reveal not only speech content and affective information but the speaker's
identity, demographic traits, and health status. Federated learning (FL) is a
distributed machine learning algorithm that coordinates clients to train a
model collaboratively without sharing local data. This algorithm shows enormous
potential for SER applications as sharing raw speech or speech features from a
user's device is vulnerable to privacy attacks. However, a major challenge in
FL is limited availability of high-quality labeled data samples. In this work,
we propose a semi-supervised federated learning framework, Semi-FedSER, that
utilizes both labeled and unlabeled data samples to address the challenge of
limited labeled data samples in FL. We show that our Semi-FedSER can generate
desired SER performance even when the local label rate l=20 using two SER
benchmark datasets: IEMOCAP and MSP-Improv.

Most successes in robotic manipulation have been restricted to single-arm
robots, which limits the range of solvable tasks to pick-and-place, insertion,
and objects rearrangement. In contrast, dual and multi arm robot platforms
unlock a rich diversity of problems that can be tackled, such as laundry
folding and executing cooking skills. However, developing controllers for
multi-arm robots is complexified by a number of unique challenges, such as the
need for coordinated bimanual behaviors, and collision avoidance amongst
robots. Given these challenges, in this work we study how to solve bi-manual
tasks using reinforcement learning (RL) trained in simulation, such that the
resulting policies can be executed on real robotic platforms. Our RL approach
results in significant simplifications due to using real-time (4Hz) joint-space
control and directly passing unfiltered observations to neural networks
policies. We also extensively discuss modifications to our simulated
environment which lead to effective training of RL policies. In addition to
designing control algorithms, a key challenge is how to design fair evaluation
tasks for bi-manual robots that stress bimanual coordination, while removing
orthogonal complicating factors such as high-level perception. In this work, we
design a Connect Task, where the aim is for two robot arms to pick up and
attach two blocks with magnetic connection points. We validate our approach
with two xArm6 robots and 3D printed blocks with magnetic attachments, and find
that our system has 100% success rate at picking up blocks, and 65% success
rate at the Connect Task.

Structure wakefield acceleration (SWFA) is well suited for the linear
collider (LC) application due to its natural ability to accelerate positrons
and preserve emittance. Under the SWFA roadmap, which was developed in response
to Snowmass 2013 recommendations, four principal technologies: drive beam, main
beam, wakefield structure, and LC facility design, have been investigated. The
two SWFA schemes under development are the collinear wakefield accelerator
(CWA), in which the drive and main beam follow the same path through a
structure, and the two-beam accelerator (TBA), where the drive and main beam
pass through different structures. To further advance the SWFA technology in
the next decade, continuous and coordinated efforts must be carried out in a
more synchronized way. This whitepaper is written to address the research needs
in SWFA for preparation of Snowmass 2022.

In recent years, neural models have often outperformed rule-based and classic
Machine Learning approaches in NLG. These classic approaches are now often
disregarded, for example when new neural models are evaluated. We argue that
they should not be overlooked, since, for some tasks, well-designed non-neural
approaches achieve better performance than neural ones. In this paper, the task
of generating referring expressions in linguistic context is used as an
example. We examined two very different English datasets (WEBNLG and WSJ), and
evaluated each algorithm using both automatic and human evaluations. Overall,
the results of these evaluations suggest that rule-based systems with simple
rule sets achieve on-par or better performance on both datasets compared to
state-of-the-art neural REG systems. In the case of the more realistic dataset,
WSJ, a machine learning-based system with well-designed linguistic features
performed best. We hope that our work can encourage researchers to consider
non-neural models in future.

Neural rendering algorithms introduce a fundamentally new approach for
photorealistic rendering, typically by learning a neural representation of
illumination on large numbers of ground truth images. When training for a given
variable scene, i.e., changing objects, materials, lights and viewpoint, the
space D of possible training data instances quickly becomes unmanageable as the
dimensions of variable parameters increase. We introduce a novel Active
Exploration method using Markov Chain Monte Carlo, which explores D, generating
samples (i.e., ground truth renderings) that best help training and interleaves
training and on-the-fly sample data generation. We introduce a self-tuning
sample reuse strategy to minimize the expensive step of rendering training
samples. We apply our approach on a neural generator that learns to render
novel scene instances given an explicit parameterization of the scene
configuration. Our results show that Active Exploration trains our network much
more efficiently than uniformly sampling, and together with our resolution
enhancement approach, achieves better quality than uniform sampling at
convergence. Our method allows interactive rendering of hard light transport
paths (e.g., complex caustics) -- that require very high samples counts to be
captured -- and provides dynamic scene navigation and manipulation, after
training for 5-18 hours depending on required quality and variations.

The ever-growing volume of satellite imagery data presents a challenge for
industry and governments making data-driven decisions based on the timely
analysis of very large data sets. Commonly used deep learning algorithms for
automatic classification of satellite images are time and resource-intensive to
train. The cost of retraining in the context of Big Data presents a practical
challenge when new image data and/or classes are added to a training corpus.
Recognizing the need for an adaptable, accurate, and scalable satellite image
chip classification scheme, in this research we present an ensemble of: i) a
slow to train but high accuracy vision transformer; and ii) a fast to train,
low-parameter convolutional neural network. The vision transformer model
provides a scalable and accurate foundation model. The high-speed CNN provides
an efficient means of incorporating newly labelled data into analysis, at the
expense of lower accuracy. To simulate incremental data, the very large
(~400,000 images) So2Sat LCZ42 satellite image chip dataset is divided into
four intervals, with the high-speed CNN retrained every interval and the vision
transformer trained every half interval. This experimental setup mimics an
increase in data volume and diversity over time. For the task of automated
land-cover/land-use classification, the ensemble models for each data increment
outperform each of the component models, with best accuracy of 65% against a
holdout test partition of the So2Sat dataset. The proposed ensemble and
staggered training schedule provide a scalable and cost-effective satellite
image classification scheme that is optimized to process very large volumes of
satellite data.

We present a neural network architecture for jointly learning user locations
and environment mapping up to isometry, in an unsupervised way, from channel
state information (CSI) values with no location information. The model is based
on an encoder-decoder architecture. The encoder network maps CSI values to the
user location. The decoder network models the physics of propagation by
parametrizing the environment using virtual anchors. It aims at reconstructing,
from the encoder output and virtual anchor location, the set of time of flights
(ToFs) that are extracted from CSI using super-resolution methods. The neural
network task is set prediction and is accordingly trained end-to-end. The
proposed model learns an interpretable latent, i.e., user location, by just
enforcing a physics-based decoder. It is shown that the proposed model achieves
sub-meter accuracy on synthetic ray tracing based datasets with single anchor
SISO setup while recovering the environment map up to 4cm median error in a 2D
environment and 15cm in a 3D environment

We develop a symbolic regression framework for extracting the governing
mathematical expressions from observed data. The evolutionary approach, faiGP,
is designed to leverage the properties of a function algebra that have been
encoded into a grammar, providing a theoretical guarantee of universal
approximation and a way to minimize bloat. In this framework, the choice of
operators of the grammar may be informed by a physical theory or symmetry
considerations. Since there is currently no theory that can derive the
'constants of nature', an empirical investigation on extracting these
coefficients from an evolutionary process is of methodological interest. We
quantify the impact of different types of regularizers, including a diversity
metric adapted from studies of the transcriptome and a complexity measure, on
the performance of the framework. Our implementation, which leverages neural
networks and a genetic programmer, generates non-trivial symbolically
equivalent expressions ("Ramanujan expressions") or approximations with
potentially interesting numerical applications. To illustrate the framework, a
model of ligand-receptor binding kinetics, including an account of gene
regulation by transcription factors, and a model of the regulatory range of the
cistrome from omics data are presented. This study has important implications
on the development of data-driven methodologies for the discovery of governing
equations in experimental data derived from new sensing systems and
high-throughput screening technologies.

Quality Estimation (QE) models have the potential to change how we evaluate
and maybe even train machine translation models. However, these models still
lack the robustness to achieve general adoption. We show that State-of-the-art
QE models, when tested in a Parallel Corpus Mining (PCM) setting, perform
unexpectedly bad due to a lack of robustness to out-of-domain examples. We
propose a combination of multitask training, data augmentation and contrastive
learning to achieve better and more robust QE performance. We show that our
method improves QE performance significantly in the MLQE challenge and the
robustness of QE models when tested in the Parallel Corpus Mining setup. We
increase the accuracy in PCM by more than 0.80, making it on par with
state-of-the-art PCM methods that use millions of sentence pairs to train their
models. In comparison, we use a thousand times less data, 7K parallel sentences
in total, and propose a novel low resource PCM method.

Motion prediction of road users in traffic scenes is critical for autonomous
driving systems that must take safe and robust decisions in complex dynamic
environments. We present a novel motion prediction system for autonomous
driving. Our system is based on the Bayesian inverse planning framework, which
efficiently orchestrates map-based goal extraction, a classical control-based
trajectory generator and a mixture of experts collection of light-weight neural
networks specialised in motion profile prediction. In contrast to many
alternative methods, this modularity helps isolate performance factors and
better interpret results, without compromising performance. This system
addresses multiple aspects of interest, namely multi-modality, motion profile
uncertainty and trajectory physical feasibility. We report on several
experiments with the popular highway dataset NGSIM, demonstrating
state-of-the-art performance in terms of trajectory error. We also perform a
detailed analysis of our system's components, along with experiments that
stratify the data based on behaviours, such as change-lane versus follow-lane,
to provide insights into the challenges in this domain. Finally, we present a
qualitative analysis to show other benefits of our approach, such as the
ability to interpret the outputs.

Provably sample-efficient Reinforcement Learning (RL) with rich observations
and function approximation has witnessed tremendous recent progress,
particularly when the underlying function approximators are linear. In this
linear regime, computationally and statistically efficient methods exist where
the potentially infinite state and action spaces can be captured through a
known feature embedding, with the sample complexity scaling with the
(intrinsic) dimension of these features. When the action space is finite,
significantly more sophisticated results allow non-linear function
approximation under appropriate structural constraints on the underlying RL
problem, permitting for instance, the learning of good features instead of
assuming access to them. In this work, we present the first result for
non-linear function approximation which holds for general action spaces under a
linear embeddability condition, which generalizes all linear and finite action
settings. We design a novel optimistic posterior sampling strategy, TS^3 for
such problems, and show worst case sample complexity guarantees that scale with
a rank parameter of the RL problem, the linear embedding dimension introduced
in this work and standard measures of the function class complexity.

Real-world Electronic Health Records (EHRs) are often plagued by a high rate
of missing data. In our EHRs, for example, the missing rates can be as high as
90% for some features, with an average missing rate of around 70% across all
features. We propose a Time-Aware Dual-Cross-Visit missing value imputation
method, named TA-DualCV, which spontaneously leverages multivariate
dependencies across features and longitudinal dependencies both within- and
cross-visit to maximize the information extracted from limited observable
records in EHRs. Specifically, TA-DualCV captures the latent structure of
missing patterns across measurements of different features and it also
considers the time continuity and capture the latent temporal missing patterns
based on both time-steps and irregular time-intervals. TA-DualCV is evaluated
using three large real-world EHRs on two types of tasks: an unsupervised
imputation task by varying mask rates up to 90% and a supervised 24-hour early
prediction of septic shock using Long Short-Term Memory (LSTM). Our results
show that TA-DualCV performs significantly better than all of the existing
state-of-the-art imputation baselines, such as DETROIT and TAME, on both types
of tasks.

In recent years, thanks to breakthroughs in neural network techniques
especially attentive deep learning models, natural language processing has made
many impressive achievements. However, automated legal word processing is still
a difficult branch of natural language processing. Legal sentences are often
long and contain complicated legal terminologies. Hence, models that work well
on general documents still face challenges in dealing with legal documents. We
have verified the existence of this problem with our experiments in this work.
In this dissertation, we selectively present the main achievements in improving
attentive neural networks in automatic legal document processing. Language
models tend to grow larger and larger, though, without expert knowledge, these
models can still fail in domain adaptation, especially for specialized fields
like law.

Vision transformers (ViTs) have gained popularity recently. Even without
customized image operators such as convolutions, ViTs can yield competitive
performance when properly trained on massive data. However, the computational
overhead of ViTs remains prohibitive, due to stacking multi-head self-attention
modules and else. Compared to the vast literature and prevailing success in
compressing convolutional neural networks, the study of Vision Transformer
compression has also just emerged, and existing works focused on one or two
aspects of compression. This paper proposes a unified ViT compression framework
that seamlessly assembles three effective techniques: pruning, layer skipping,
and knowledge distillation. We formulate a budget-constrained, end-to-end
optimization framework, targeting jointly learning model weights, layer-wise
pruning ratios/masks, and skip configurations, under a distillation loss. The
optimization problem is then solved using the primal-dual algorithm.
Experiments are conducted with several ViT variants, e.g. DeiT and T2T-ViT
backbones on the ImageNet dataset, and our approach consistently outperforms
recent competitors. For example, DeiT-Tiny can be trimmed down to 50\% of the
original FLOPs almost without losing accuracy. Codes are available
online:~\url{https://github.com/VITA-Group/UVC}.

Pretrained language models are typically trained on massive web-based
datasets, which are often "contaminated" with downstream test sets. It is not
clear to what extent models exploit the contaminated data for downstream tasks.
We present a principled method to study this question. We pretrain BERT models
on joint corpora of Wikipedia and labeled downstream datasets, and fine-tune
them on the relevant task. Comparing performance between samples seen and
unseen during pretraining enables us to define and quantify levels of
memorization and exploitation. Experiments with two models and three downstream
tasks show that exploitation exists in some cases, but in others the models
memorize the contaminated data, but do not exploit it. We show that these two
measures are affected by different factors such as the number of duplications
of the contaminated data and the model size. Our results highlight the
importance of analyzing massive web-scale datasets to verify that progress in
NLP is obtained by better language understanding and not better data
exploitation.

Access to dermatological care is a major issue, with an estimated 3 billion
people lacking access to care globally. Artificial intelligence (AI) may aid in
triaging skin diseases. However, most AI models have not been rigorously
assessed on images of diverse skin tones or uncommon diseases. To ascertain
potential biases in algorithm performance in this context, we curated the
Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly
curated, and pathologically confirmed image dataset with diverse skin tones.
Using this dataset of 656 images, we show that state-of-the-art dermatology AI
models perform substantially worse on DDI, with receiver operator curve area
under the curve (ROC-AUC) dropping by 27-36 percent compared to the models'
original test results. All the models performed worse on dark skin tones and
uncommon diseases, which are represented in the DDI dataset. Additionally, we
find that dermatologists, who typically provide visual labels for AI training
and test datasets, also perform worse on images of dark skin tones and uncommon
diseases compared to ground truth biopsy annotations. Finally, fine-tuning AI
models on the well-characterized and diverse DDI images closed the performance
gap between light and dark skin tones. Moreover, algorithms fine-tuned on
diverse skin tones outperformed dermatologists on identifying malignancy on
images of dark skin tones. Our findings identify important weaknesses and
biases in dermatology AI that need to be addressed to ensure reliable
application to diverse patients and diseases.

As the deployment of automated face recognition (FR) systems proliferates,
bias in these systems is not just an academic question, but a matter of public
concern. Media portrayals often center imbalance as the main source of bias,
i.e., that FR models perform worse on images of non-white people or women
because these demographic groups are underrepresented in training data. Recent
academic research paints a more nuanced picture of this relationship. However,
previous studies of data imbalance in FR have focused exclusively on the face
verification setting, while the face identification setting has been largely
ignored, despite being deployed in sensitive applications such as law
enforcement. This is an unfortunate omission, as 'imbalance' is a more complex
matter in identification; imbalance may arise in not only the training data,
but also the testing data, and furthermore may affect the proportion of
identities belonging to each demographic group or the number of images
belonging to each identity. In this work, we address this gap in the research
by thoroughly exploring the effects of each kind of imbalance possible in face
identification, and discuss other factors which may impact bias in this
setting.

The statistical properties of the density map (DM) approach to counting
microbiological objects on images are studied in detail. The DM is given by
U$^2$-Net. Two statistical methods for deep neural networks are utilized: the
bootstrap and the Monte Carlo (MC) dropout. The detailed analysis of the
uncertainties for the DM predictions leads to a deeper understanding of the DM
model's deficiencies. Based on our investigation, we propose a
self-normalization module in the network. The improved network model, called
\textit{Self-Normalized Density Map} (SNDM), can correct its output density map
by itself to accurately predict the total number of objects in the image. The
SNDM architecture outperforms the original model. Moreover, both statistical
frameworks -- bootstrap and MC dropout -- have consistent statistical results
for SNDM, which were not observed in the original model. The SNDM efficiency is
comparable with the detector-base models, such as Faster and Cascade R-CNN

False assumptions about sex and gender are deeply embedded in the medical
system, including that they are binary, static, and concordant. Machine
learning researchers must understand the nature of these assumptions in order
to avoid perpetuating them. In this perspectives piece, we identify three
common mistakes that researchers make when dealing with sex/gender data: "sex
confusion", the failure to identity what sex in a dataset does or doesn't mean;
"sex obsession", the belief that sex, specifically sex assigned at birth, is
the relevant variable for most applications; and "sex/gender slippage", the
conflation of sex and gender even in contexts where only one or the other is
known. We then discuss how these pitfalls show up in machine learning studies
based on electronic health record data, which is commonly used for everything
from retrospective analysis of patient outcomes to the development of
algorithms to predict risk and administer care. Finally, we offer a series of
recommendations about how machine learning researchers can produce both
research and algorithms that more carefully engage with questions of
sex/gender, better serving all patients, including transgender people.

As humans and animals learn in the natural world, they encounter
distributions of entities, situations and events that are far from uniform.
Typically, a relatively small set of experiences are encountered frequently,
while many important experiences occur only rarely. The highly-skewed,
heavy-tailed nature of reality poses particular learning challenges that humans
and animals have met by evolving specialised memory systems. By contrast, most
popular RL environments and benchmarks involve approximately uniform variation
of properties, objects, situations or tasks. How will RL algorithms perform in
worlds (like ours) where the distribution of environment features is far less
uniform? To explore this question, we develop three complementary RL
environments where the agent's experience varies according to a Zipfian
(discrete power law) distribution. On these benchmarks, we find that standard
Deep RL architectures and algorithms acquire useful knowledge of common
situations and tasks, but fail to adequately learn about rarer ones. To
understand this failure better, we explore how different aspects of current
approaches may be adjusted to help improve performance on rare events, and show
that the RL objective function, the agent's memory system and self-supervised
learning objectives can all influence an agent's ability to learn from uncommon
experiences. Together, these results show that learning robustly from skewed
experience is a critical challenge for applying Deep RL methods beyond
simulations or laboratories, and our Zipfian environments provide a basis for
measuring future progress towards this goal.

The computational cost for high energy physics detector simulation in future
experimental facilities is going to exceed the current available resources. To
overcome this challenge, new ideas on surrogate models using machine learning
methods are being explored to replace computationally expensive components.
Additionally, differentiable programming has been proposed as a complementary
approach, providing controllable and scalable simulation routines. In this
document, new and ongoing efforts for surrogate models and differential
programming applied to detector simulation are discussed in the context of the
2021 Particle Physics Community Planning Exercise (`Snowmass').

In this paper, we investigated whether we can 1) detect participants with
ataxia-specific gait characteristics (risk-prediction), and 2) assess severity
of ataxia from gait (severity-assessment) using computer vision. We created a
dataset of 155 videos from 89 participants, 24 controls and 65 diagnosed with
(or are pre-manifest) spinocerebellar ataxias (SCAs), performing the gait task
of the Scale for the Assessment and Rating of Ataxia (SARA) from 11 medical
sites located in 8 different states across the United States. We develop a
computer vision pipeline to detect, track, and separate out the participants
from their surroundings and construct several features from their body pose
coordinates to capture gait characteristics like step width, step length,
swing, stability, speed, etc. Our risk-prediction model achieves 83.06%
accuracy and an 80.23% F1 score. Similarly, our severity-assessment model
achieves a mean absolute error (MAE) score of 0.6225 and a Pearson's
correlation coefficient score of 0.7268. Our models still performed
competitively when evaluated on data from sites not used during training.
Furthermore, through feature importance analysis, we found that our models
associate wider steps, decreased walking speed, and increased instability with
greater ataxia severity, which is consistent with previously established
clinical knowledge. Our models create possibilities for remote ataxia
assessment in non-clinical settings in the future, which could significantly
improve accessibility of ataxia care. Furthermore, our underlying dataset was
assembled from a geographically diverse cohort, highlighting its potential to
further increase equity. The code used in this study is open to the public, and
the anonymized body pose landmark dataset is also available upon request.

In accelerated MRI reconstruction, the anatomy of a patient is recovered from
a set of under-sampled and noisy measurements. Deep learning approaches have
been proven to be successful in solving this ill-posed inverse problem and are
capable of producing very high quality reconstructions. However, current
architectures heavily rely on convolutions, that are content-independent and
have difficulties modeling long-range dependencies in images. Recently,
Transformers, the workhorse of contemporary natural language processing, have
emerged as powerful building blocks for a multitude of vision tasks. These
models split input images into non-overlapping patches, embed the patches into
lower-dimensional tokens and utilize a self-attention mechanism that does not
suffer from the aforementioned weaknesses of convolutional architectures.
However, Transformers incur extremely high compute and memory cost when 1) the
input image resolution is high and 2) when the image needs to be split into a
large number of patches to preserve fine detail information, both of which are
typical in low-level vision problems such as MRI reconstruction, having a
compounding effect. To tackle these challenges, we propose HUMUS-Net, a hybrid
architecture that combines the beneficial implicit bias and efficiency of
convolutions with the power of Transformer blocks in an unrolled and
multi-scale network. HUMUS-Net extracts high-resolution features via
convolutional blocks and refines low-resolution features via a novel
Transformer-based multi-scale feature extractor. Features from both levels are
then synthesized into a high-resolution output reconstruction. Our network
establishes new state of the art on the largest publicly available MRI dataset,
the fastMRI dataset. We further demonstrate the performance of HUMUS-Net on two
other popular MRI datasets and perform fine-grained ablation studies to
validate our design.

Deep neural networks have seen great success in recent years; however,
training a deep model is often challenging as its performance heavily depends
on the hyper-parameters used. In addition, finding the optimal hyper-parameter
configuration, even with state-of-the-art (SOTA) hyper-parameter optimization
(HPO) algorithms, can be time-consuming, requiring multiple training runs over
the entire dataset for different possible sets of hyper-parameters. Our central
insight is that using an informative subset of the dataset for model training
runs involved in hyper-parameter optimization, allows us to find the optimal
hyper-parameter configuration significantly faster. In this work, we propose
AUTOMATA, a gradient-based subset selection framework for hyper-parameter
tuning. We empirically evaluate the effectiveness of AUTOMATA in
hyper-parameter tuning through several experiments on real-world datasets in
the text, vision, and tabular domains. Our experiments show that using
gradient-based data subsets for hyper-parameter tuning achieves significantly
faster turnaround times and speedups of 3$\times$-30$\times$ while achieving
comparable performance to the hyper-parameters found using the entire dataset.

The success of machine learning solutions for reasoning about discrete
structures has brought attention to its adoption within combinatorial
optimization algorithms. Such approaches generally rely on supervised learning
by leveraging datasets of the combinatorial structures of interest drawn from
some distribution of problem instances. Reinforcement learning has also been
employed to find such structures. In this paper, we propose a radically
different approach in that no data is required for training the neural networks
that produce the solution. In particular, we reduce the combinatorial
optimization problem to a neural network and employ a dataless training scheme
to refine the parameters of the network such that those parameters yield the
structure of interest. We consider the combinatorial optimization problems of
finding maximum independent sets and maximum cliques in a graph. In principle,
since these problems belong to the NP-hard complexity class, our proposed
approach can be used to solve any other NP-hard problem. Additionally, we
propose a universal graph reduction procedure to handle large scale graphs. The
reduction exploits community detection for graph partitioning and is applicable
to any graph type and/or density. Experimental evaluation on both synthetic
graphs and real-world benchmarks demonstrates that our method performs on par
with or outperforms state-of-the-art heuristic, reinforcement learning, and
machine learning based methods without requiring any data.

Predicting pedestrian movement is critical for human behavior analysis and
also for safe and efficient human-agent interactions. However, despite
significant advancements, it is still challenging for existing approaches to
capture the uncertainty and multimodality of human navigation decision making.
In this paper, we propose SocialVAE, a novel approach for human trajectory
prediction. The core of SocialVAE is a timewise variational autoencoder
architecture that exploits stochastic recurrent neural networks to perform
prediction, combined with a social attention mechanism and a backward posterior
approximation to allow for better extraction of pedestrian navigation
strategies. We show that SocialVAE improves current state-of-the-art
performance on several pedestrian trajectory prediction benchmarks, including
the ETH/UCY benchmark, Stanford Drone Dataset, and SportVU NBA movement
dataset. Code is available at: https://github.com/xupei0610/SocialVAE.

Constitutive modeling based on continuum mechanics theory has been a
classical approach for modeling the mechanical responses of materials. However,
when constitutive laws are unknown or when defects and/or high degrees of
heterogeneity are present, these classical models may become inaccurate. In
this work, we propose to use data-driven modeling, which directly utilizes
high-fidelity simulation and/or experimental measurements to predict a
material's response without using conventional constitutive models.
Specifically, the material response is modeled by learning the implicit
mappings between loading conditions and the resultant displacement and/or
damage fields, with the neural network serving as a surrogate for a solution
operator. To model the complex responses due to material heterogeneity and
defects, we develop a novel deep neural operator architecture, which we coin as
the Implicit Fourier Neural Operator (IFNO). In the IFNO, the increment between
layers is modeled as an integral operator to capture the long-range
dependencies in the feature space. As the network gets deeper, the limit of
IFNO becomes a fixed point equation that yields an implicit neural operator and
naturally mimics the displacement/damage fields solving procedure in material
modeling problems. We demonstrate the performance of our proposed method for a
number of examples, including hyperelastic, anisotropic and brittle materials.
As an application, we further employ the proposed approach to learn the
material models directly from digital image correlation (DIC) tracking
measurements, and show that the learned solution operators substantially
outperform the conventional constitutive models in predicting displacement
fields.

We study how the choice of visual perspective affects learning and
generalization in the context of physical manipulation from raw sensor
observations. Compared with the more commonly used global third-person
perspective, a hand-centric (eye-in-hand) perspective affords reduced
observability, but we find that it consistently improves training efficiency
and out-of-distribution generalization. These benefits hold across a variety of
learning algorithms, experimental settings, and distribution shifts, and for
both simulated and real robot apparatuses. However, this is only the case when
hand-centric observability is sufficient; otherwise, including a third-person
perspective is necessary for learning, but also harms out-of-distribution
generalization. To mitigate this, we propose to regularize the third-person
information stream via a variational information bottleneck. On six
representative manipulation tasks with varying hand-centric observability
adapted from the Meta-World benchmark, this results in a state-of-the-art
reinforcement learning agent operating from both perspectives improving its
out-of-distribution generalization on every task. While some practitioners have
long put cameras in the hands of robots, our work systematically analyzes the
benefits of doing so and provides simple and broadly applicable insights for
improving end-to-end learned vision-based robotic manipulation.

UNet has become the gold standard method for segmenting 2D medical images
that any new method must be validated against. However, in recent years,
several variations of the seminal UNet have been proposed with promising
results. However, there is no clear consensus on the generalisability of these
architectures, and UNet currently remains the methodological gold standard. The
purpose of this study was to evaluate some of the most promising UNet-inspired
architectures for 3D segmentation. For the segmentation of 3D scans,
UNet-inspired methods are also dominant, but there is a larger variety across
applications. By evaluating the architectures in a different dimensionality,
embedded in a different method, and for a different task, we aimed to evaluate
if any of these UNet-alternatives are promising as a new gold standard that
generalizes even better than UNet. Specifically, we investigated the
architectures as the central 2D segmentation core in the Multi-Planar Unet 3D
segmentation method that previously demonstrated excellent generalization in
the MICCAI Segmentation Decathlon. Generalisability can be demonstrated if a
promising UNet-variant consistently outperforms UNet in this setting. For this
purpose, we evaluated four architectures for cartilage segmentation from three
different cohorts with knee MRIs.

While it is not generally reflected in the `nice' datasets used for
benchmarking machine learning algorithms, the real-world is full of processes
that would be best described as many-to-many. That is, a single input can
potentially yield many different outputs (whether due to noise, imperfect
measurement, or intrinsic stochasticity in the process) and many different
inputs can yield the same output (that is, the map is not injective). For
example, imagine a sentiment analysis task where, due to linguistic ambiguity,
a single statement can have a range of different sentiment interpretations
while at the same time many distinct statements can represent the same
sentiment. When modeling such a multivalued function $f: X \rightarrow Y$, it
is frequently useful to be able to model the distribution on $f(x)$ for
specific input $x$ as well as the distribution on fiber $f^{-1}(y)$ for
specific output $y$. Such an analysis helps the user (i) better understand the
variance intrinsic to the process they are studying and (ii) understand the
range of specific input $x$ that can be used to achieve output $y$. Following
existing work which used a fiber bundle framework to better model many-to-one
processes, we describe how morphisms of fiber bundles provide a template for
building models which naturally capture the structure of many-to-many
processes.

Assembly of multi-part physical structures is both a valuable end product for
autonomous robotics, as well as a valuable diagnostic task for open-ended
training of embodied intelligent agents. We introduce a naturalistic
physics-based environment with a set of connectable magnet blocks inspired by
children's toy kits. The objective is to assemble blocks into a succession of
target blueprints. Despite the simplicity of this objective, the compositional
nature of building diverse blueprints from a set of blocks leads to an
explosion of complexity in structures that agents encounter. Furthermore,
assembly stresses agents' multi-step planning, physical reasoning, and bimanual
coordination. We find that the combination of large-scale reinforcement
learning and graph-based policies -- surprisingly without any additional
complexity -- is an effective recipe for training agents that not only
generalize to complex unseen blueprints in a zero-shot manner, but even operate
in a reset-free setting without being trained to do so. Through extensive
experiments, we highlight the importance of large-scale training, structured
representations, contributions of multi-task vs. single-task learning, as well
as the effects of curriculums, and discuss qualitative behaviors of trained
agents.

Recent advances in wearable devices and Internet-of-Things (IoT) have led to
massive growth in sensor data generated in edge devices. Labeling such massive
data for classification tasks has proven to be challenging. In addition, data
generated by different users bear various personal attributes and edge
heterogeneity, rendering it impractical to develop a global model that adapts
well to all users. Concerns over data privacy and communication costs also
prohibit centralized data accumulation and training. This paper proposes a
novel personalized semi-supervised federated learning (SemiPFL) framework to
support edge users having no label or limited labeled datasets and a sizable
amount of unlabeled data that is insufficient to train a well-performing model.
In this work, edge users collaborate to train a hyper-network in the server,
generating personalized autoencoders for each user. After receiving updates
from edge users, the server produces a set of base models for each user, which
the users locally aggregate them using their own labeled dataset. We
comprehensively evaluate our proposed framework on various public datasets and
demonstrate that SemiPFL outperforms state-of-art federated learning frameworks
under the same assumptions. We also show that the solution performs well for
users without labeled datasets or having limited labeled datasets and
increasing performance for increased labeled data and number of users,
signifying the effectiveness of SemiPFL for handling edge heterogeneity and
limited annotation. By leveraging personalized semi-supervised learning,
SemiPFL dramatically reduces the need for annotating data and preserving
privacy in a wide range of application scenarios, from wearable health to IoT.

The prior knowledge (a.k.a. priors) integrated into the design of a machine
learning system strongly influences its generalization abilities. In the
specific context of deep learning, some of these priors are poorly understood
as they implicitly emerge from the successful heuristics and tentative
approximations of biological brains involved in deep learning design. Through
the lens of supervised image classification problems, this thesis investigates
the implicit integration of a natural clustering prior composed of three
statements: (i) natural images exhibit a rich clustered structure, (ii) image
classes are composed of multiple clusters and (iii) each cluster contains
examples from a single class. The decomposition of classes into multiple
clusters implies that supervised deep learning systems could benefit from
unsupervised clustering to define appropriate decision boundaries. Hence, this
thesis attempts to identify implicit clustering abilities, mechanisms and
hyperparameters in deep learning systems and evaluate their relevance for
explaining the generalization abilities of these systems. We do so through an
extensive empirical study of the training dynamics as well as the neuron- and
layer-level representations of deep neural networks. The resulting collection
of experiments provides preliminary evidence for the relevance of the natural
clustering prior for understanding deep learning.

The abundance of dark matter (DM) subhalos orbiting a host galaxy is a
generic prediction of the cosmological framework, and is a promising way to
constrain the nature of DM. In this paper, we investigate the use of machine
learning-based tools to quantify the magnitude of phase-space perturbations
caused by the passage of DM subhalos. A simple binary classifier and an anomaly
detection model are proposed to estimate if stars or star particles close to DM
subhalos are statistically detectable in simulations. The simulated datasets
are three Milky Way-like galaxies and nine synthetic Gaia DR2 surveys derived
from these. Firstly, we find that the anomaly detection algorithm, trained on a
simulated galaxy with full 6D kinematic observables and applied on another
galaxy, is nontrivially sensitive to the DM subhalo population. On the other
hand, the classification-based approach is not sufficiently sensitive due to
the extremely low statistics of signal stars for supervised training. Finally,
the sensitivity of both algorithms in the Gaia-like surveys is negligible. The
enormous size of the Gaia dataset motivates the further development of scalable
and accurate data analysis methods that could be used to select potential
regions of interest for DM searches to ultimately constrain the Milky Way's
subhalo mass function, as well as simulations where to study the sensitivity of
such methods under different signal hypotheses.

Object manipulation is a critical skill required for Embodied AI agents
interacting with the world around them. Training agents to manipulate objects,
poses many challenges. These include occlusion of the target object by the
agent's arm, noisy object detection and localization, and the target frequently
going out of view as the agent moves around in the scene. We propose
Manipulation via Visual Object Location Estimation (m-VOLE), an approach that
explores the environment in search for target objects, computes their 3D
coordinates once they are located, and then continues to estimate their 3D
locations even when the objects are not visible, thus robustly aiding the task
of manipulating these objects throughout the episode. Our evaluations show a
massive 3x improvement in success rate over a model that has access to the same
sensory suite but is trained without the object location estimator, and our
analysis shows that our agent is robust to noise in depth perception and agent
localization. Importantly, our proposed approach relaxes several assumptions
about idealized localization and perception that are commonly employed by
recent works in embodied AI -- an important step towards training agents for
object manipulation in the real world.

Downsampling is one of the most basic image processing operations. Improper
spatio-temporal downsampling applied on videos can cause aliasing issues such
as moir\'e patterns in space and the wagon-wheel effect in time. Consequently,
the inverse task of upscaling a low-resolution, low frame-rate video in space
and time becomes a challenging ill-posed problem due to information loss and
aliasing artifacts. In this paper, we aim to solve the space-time aliasing
problem by learning a spatio-temporal downsampler. Towards this goal, we
propose a neural network framework that jointly learns spatio-temporal
downsampling and upsampling. It enables the downsampler to retain the key
patterns of the original video and maximizes the reconstruction performance of
the upsampler. To make the downsamping results compatible with popular image
and video storage formats, the downsampling results are encoded to uint8 with a
differentiable quantization layer. To fully utilize the space-time
correspondences, we propose two novel modules for explicit temporal propagation
and space-time feature rearrangement. Experimental results show that our
proposed method significantly boosts the space-time reconstruction quality by
preserving spatial textures and motion patterns in both downsampling and
upscaling. Moreover, our framework enables a variety of applications, including
arbitrary video resampling, blurry frame reconstruction, and efficient video
storage.

Cryo-electron microscopy (cryo-EM) has become a tool of fundamental
importance in structural biology, helping us understand the basic building
blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the
unknown 3D poses and the 3D electron scattering potential of a biomolecule from
millions of extremely noisy 2D images. Existing reconstruction algorithms,
however, cannot easily keep pace with the rapidly growing size of cryo-EM
datasets due to their high computational and memory cost. We introduce cryoAI,
an ab initio reconstruction algorithm for homogeneous conformations that uses
direct gradient-based optimization of particle poses and the electron
scattering potential from single-particle cryo-EM data. CryoAI combines a
learned encoder that predicts the poses of each particle image with a
physics-based decoder to aggregate each particle image into an implicit
representation of the scattering potential volume. This volume is stored in the
Fourier domain for computational efficiency and leverages a modern coordinate
network architecture for memory efficiency. Combined with a symmetrized loss
function, this framework achieves results of a quality on par with
state-of-the-art cryo-EM solvers for both simulated and experimental data, one
order of magnitude faster for large datasets and with significantly lower
memory requirements than existing methods.

Federated data analytics is a framework for distributed data analysis where a
server compiles noisy responses from a group of distributed low-bandwidth user
devices to estimate aggregate statistics. Two major challenges in this
framework are privacy, since user data is often sensitive, and compression,
since the user devices have low network bandwidth. Prior work has addressed
these challenges separately by combining standard compression algorithms with
known privacy mechanisms. In this work, we take a holistic look at the problem
and design a family of privacy-aware compression mechanisms that work for any
given communication budget. We first propose a mechanism for transmitting a
single real number that has optimal variance under certain conditions. We then
show how to extend it to metric differential privacy for location privacy
use-cases, as well as vectors, for application to federated learning. Our
experiments illustrate that our mechanism can lead to better utility vs.
compression trade-offs for the same privacy loss in a number of settings.

This paper addresses the challenge of reconstructing an animatable human
model from a multi-view video. Some recent works have proposed to decompose a
non-rigidly deforming scene into a canonical neural radiance field and a set of
deformation fields that map observation-space points to the canonical space,
thereby enabling them to learn the dynamic scene from images. However, they
represent the deformation field as translational vector field or SE(3) field,
which makes the optimization highly under-constrained. Moreover, these
representations cannot be explicitly controlled by input motions. Instead, we
introduce a pose-driven deformation field based on the linear blend skinning
algorithm, which combines the blend weight field and the 3D human skeleton to
produce observation-to-canonical correspondences. Since 3D human skeletons are
more observable, they can regularize the learning of the deformation field.
Moreover, the pose-driven deformation field can be controlled by input skeletal
motions to generate new deformation fields to animate the canonical human
model. Experiments show that our approach significantly outperforms recent
human modeling methods. The code is available at
https://zju3dv.github.io/animatable_nerf/.

The current literature on self-supervised learning (SSL) focuses on
developing learning objectives to train neural networks more effectively on
unlabeled data. The typical development process involves taking
well-established architectures, e.g., ResNet demonstrated on ImageNet, and
using them to evaluate newly developed objectives on downstream scenarios.
While convenient, this does not take into account the role of architectures
which has been shown to be crucial in the supervised learning literature. In
this work, we establish extensive empirical evidence showing that a network
architecture plays a significant role in SSL. We conduct a large-scale study
with over 100 variants of ResNet and MobileNet architectures and evaluate them
across 11 downstream scenarios in the SSL setting. We show that there is no one
network that performs consistently well across the scenarios. Based on this, we
propose to learn not only network weights but also architecture topologies in
the SSL regime. We show that "self-supervised architectures" outperform popular
handcrafted architectures (ResNet18 and MobileNetV2) while performing
competitively with the larger and computationally heavy ResNet50 on major image
classification benchmarks (ImageNet-1K, iNat2021, and more). Our results
suggest that it is time to consider moving beyond handcrafted architectures in
SSL and start thinking about incorporating architecture search into
self-supervised learning objectives.

(abridged) Mass loss is a key parameter in the evolution of massive stars,
with discrepancies between theory and observations and with unknown importance
of the episodic mass loss. To address this we need increased numbers of
classified sources stars spanning a range of metallicity environments. We aim
to remedy the situation by applying machine learning techniques to recently
available extensive photometric catalogs. We used IR/Spitzer and
optical/Pan-STARRS, with Gaia astrometric information, to compile a large
catalog of known massive stars in M31 and M33, which were grouped in Blue, Red,
Yellow, B[e] supergiants, Luminous Blue Variables, Wolf-Rayet, and background
galaxies. Due to the high imbalance, we implemented synthetic data generation
to populate the underrepresented classes and improve separation by
undersampling the majority class. We built an ensemble classifier using color
indices. The probabilities from Support Vector Classification, Random Forests,
and Multi-layer Perceptron were combined for the final classification. The
overall weighted balanced accuracy is ~83%, recovering Red supergiants at ~94%,
Blue/Yellow/B[e] supergiants and background galaxies at ~50-80%, Wolf-Rayets at
~45%, and Luminous Blue Variables at ~30%, mainly due to their small sample
sizes. The mixing of spectral types (no strict boundaries in their color
indices) complicates the classification. Independent application to IC 1613,
WLM, and Sextans A galaxies resulted in an overall lower accuracy of ~70%,
attributed to metallicity and extinction effects. The missing data imputation
was explored using simple replacement with mean values and an iterative
imputor, which proved more capable. We also found that r-i and y-[3.6] were the
most important features. Our method, although limited by the sampling of the
feature space, is efficient in classifying sources with missing data and at
lower metallicitites.

We discuss methods for visualizing neural network decision boundaries and
decision regions. We use these visualizations to investigate issues related to
reproducibility and generalization in neural network training. We observe that
changes in model architecture (and its associate inductive bias) cause visible
changes in decision boundaries, while multiple runs with the same architecture
yield results with strong similarities, especially in the case of wide
architectures. We also use decision boundary methods to visualize double
descent phenomena. We see that decision boundary reproducibility depends
strongly on model width. Near the threshold of interpolation, neural network
decision boundaries become fragmented into many small decision regions, and
these regions are non-reproducible. Meanwhile, very narrows and very wide
networks have high levels of reproducibility in their decision boundaries with
relatively few decision regions. We discuss how our observations relate to the
theory of double descent phenomena in convex models. Code is available at
https://github.com/somepago/dbViz

Training very deep neural networks is still an extremely challenging task.
The common solution is to use shortcut connections and normalization layers,
which are both crucial ingredients in the popular ResNet architecture. However,
there is strong evidence to suggest that ResNets behave more like ensembles of
shallower networks than truly deep ones. Recently, it was shown that deep
vanilla networks (i.e. networks without normalization layers or shortcut
connections) can be trained as fast as ResNets by applying certain
transformations to their activation functions. However, this method (called
Deep Kernel Shaping) isn't fully compatible with ReLUs, and produces networks
that overfit significantly more than ResNets on ImageNet. In this work, we
rectify this situation by developing a new type of transformation that is fully
compatible with a variant of ReLUs -- Leaky ReLUs. We show in experiments that
our method, which introduces negligible extra computational cost, achieves
validation accuracies with deep vanilla networks that are competitive with
ResNets (of the same width/depth), and significantly higher than those obtained
with the Edge of Chaos (EOC) method. And unlike with EOC, the validation
accuracies we obtain do not get worse with depth.

State-of-the-art keyphrase generation methods generally depend on large
annotated datasets, limiting their performance in domains with limited
annotated data. To overcome this challenge, we design a data-oriented approach
that first identifies salient information using retrieval-based corpus-level
statistics, and then learns a task-specific intermediate representation based
on a pre-trained language model using large-scale unlabeled documents. We
introduce salient span recovery and salient span prediction as denoising
training objectives that condense the intra-article and inter-article knowledge
essential for keyphrase generation. Through experiments on multiple keyphrase
generation benchmarks, we show the effectiveness of the proposed approach for
facilitating low-resource keyphrase generation and zero-shot domain adaptation.
Our method especially benefits the generation of absent keyphrases, approaching
the performance of models trained with large training sets.

The vast majority of non-English corpora are derived from automatically
filtered versions of CommonCrawl. While prior work has identified major issues
on the quality of these datasets (Kreutzer et al., 2021), it is not clear how
this impacts downstream performance. Taking representation learning in Basque
as a case study, we explore tailored crawling (manually identifying and
scraping websites with high-quality content) as an alternative to filtering
CommonCrawl. Our new corpus, called EusCrawl, is similar in size to the Basque
portion of popular multilingual corpora like CC100 and mC4, yet it has a much
higher quality according to native annotators. For instance, 66% of documents
are rated as high-quality for EusCrawl, in contrast with <33% for both mC4 and
CC100. Nevertheless, we obtain similar results on downstream NLU tasks
regardless of the corpus used for pre-training. Our work suggests that NLU
performance in low-resource languages is not primarily constrained by the
quality of the data, and other factors like corpus size and domain coverage can
play a more important role.

On the time scales accessible to atomistic numerical modelling, chemical
reactions are considered rare events. Atomistic simulations are typically
biased along a low-dimensional representation of a chemical reaction in an
atomic structure space, i.e., along the collective variable, to accelerate
sampling of these improbable events. However, suitable collective variables are
often complicated to guess due to the complexity of the transitions. Therefore,
we present an automatic method of generating robust collective variables from
atomic representation vectors, using either fixed Behler-Parrinello functions
or representations extracted from pre-trained machine learning potentials.
Variational autoencoder with these representations as inputs is trained while
its latent space with arbitrary dimension gives us the set of collective
variables. The resulting collective variables inherit all necessary invariances
from the atomic representations and can be trained entirely unsupervised. The
method's effectiveness is demonstrated using three different chemical
reactions, one being the complex hydrolysis of a heterogeneous aluminosilicate
catalyst. Lastly, we consider the method in the context of unseen atomic
structure prediction, efficiently creating structures for different values of
collective variables in a generative model fashion.

We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
language model. We find that, without any finetuning, Codex is a strong
baseline on the Spider benchmark; we also analyze the failure modes of Codex in
this setting. Furthermore, we demonstrate on the GeoQuery and Scholar
benchmarks that a small number of in-domain examples provided in the prompt
enables Codex to perform better than state-of-the-art models finetuned on such
few-shot examples.

Barlow (1985) hypothesized that the co-occurrence of two events $A$ and $B$
is "suspicious" if $P(A,B) \gg P(A) P(B)$. We first review classical measures
of association for $2 \times 2$ contingency tables, including Yule's $Y$ (Yule,
1912), which depends only on the odds ratio $\lambda$, and is independent of
the marginal probabilities of the table. We then discuss the mutual information
(MI) and pointwise mutual information (PMI), which depend on the ratio
$P(A,B)/P(A)P(B)$, as measures of association. We show that, once the effect of
the marginals is removed, MI and PMI behave similarly to $Y$ as functions of
$\lambda$. The pointwise mutual information is used extensively in some
research communities for flagging suspicious coincidences, but it is important
to bear in mind the sensitivity of the PMI to the marginals, with increased
scores for sparser events.

There is a growing interest in the combined use of NLP and machine learning
methods to predict gaze patterns during naturalistic reading. While promising
results have been obtained through the use of transformer-based language
models, little work has been undertaken to relate the performance of such
models to general text characteristics. In this paper we report on experiments
with two eye-tracking corpora of naturalistic reading and two language models
(BERT and GPT-2). In all experiments, we test effects of a broad spectrum of
features for predicting human reading behavior that fall into five categories
(syntactic complexity, lexical richness, register-based multiword combinations,
readability and psycholinguistic word properties). Our experiments show that
both the features included and the architecture of the transformer-based
language models play a role in predicting multiple eye-tracking measures during
naturalistic reading. We also report the results of experiments aimed at
determining the relative importance of features from different groups using
SP-LIME.

This paper proposes regenerative particle Thompson sampling (RPTS), a
flexible variation of Thompson sampling. Thompson sampling itself is a Bayesian
heuristic for solving stochastic bandit problems, but it is hard to implement
in practice due to the intractability of maintaining a continuous posterior
distribution. Particle Thompson sampling (PTS) is an approximation of Thompson
sampling obtained by simply replacing the continuous distribution by a discrete
distribution supported at a set of weighted static particles. We observe that
in PTS, the weights of all but a few fit particles converge to zero. RPTS is
based on the heuristic: delete the decaying unfit particles and regenerate new
particles in the vicinity of fit surviving particles. Empirical evidence shows
uniform improvement from PTS to RPTS and flexibility and efficacy of RPTS
across a set of representative bandit problems, including an application to 5G
network slicing.

Spatial commonsense, the knowledge about spatial position and relationship
between objects (like the relative size of a lion and a girl, and the position
of a boy relative to a bicycle when cycling), is an important part of
commonsense knowledge. Although pretrained language models (PLMs) succeed in
many NLP tasks, they are shown to be ineffective in spatial commonsense
reasoning. Starting from the observation that images are more likely to exhibit
spatial commonsense than texts, we explore whether models with visual signals
learn more spatial commonsense than text-based PLMs. We propose a spatial
commonsense benchmark that focuses on the relative scales of objects, and the
positional relationship between people and objects under different actions. We
probe PLMs and models with visual signals, including vision-language pretrained
models and image synthesis models, on this benchmark, and find that image
synthesis models are more capable of learning accurate and consistent spatial
knowledge than other models. The spatial knowledge from image synthesis models
also helps in natural language understanding tasks that require spatial
commonsense.

Large-scale monitoring, anomaly detection, and root cause analysis of metrics
are essential requirements of the internet-services industry. To address the
need to continuously monitor millions of metrics, many anomaly detection
approaches are being used on a daily basis by large internet-based companies.
However, in spite of the significant progress made to accurately and
efficiently detect anomalies in metrics, the sheer scale of the number of
metrics has meant there are still a large number of false alarms that need to
be investigated. This paper presents a framework for reliable large-scale
anomaly detection. It is significantly more accurate than existing approaches
and allows for easy interpretation of models, thus enabling practical data
monitoring in the internet-services domain.

The recently proposed Sharpness-Aware Minimization (SAM) improves
generalization by minimizing a \textit{perturbed loss} defined as the maximum
loss within a neighborhood in the parameter space. However, we show that both
sharp and flat minima can have a low perturbed loss, implying that SAM does not
always prefer flat minima. Instead, we define a \textit{surrogate gap}, a
measure equivalent to the dominant eigenvalue of Hessian at a local minimum
when the radius of the neighborhood (to derive the perturbed loss) is small.
The surrogate gap is easy to compute and feasible for direct minimization
during training. Based on the above observations, we propose Surrogate
\textbf{G}ap Guided \textbf{S}harpness-\textbf{A}ware \textbf{M}inimization
(GSAM), a novel improvement over SAM with negligible computation overhead.
Conceptually, GSAM consists of two steps: 1) a gradient descent like SAM to
minimize the perturbed loss, and 2) an \textit{ascent} step in the
\textit{orthogonal} direction (after gradient decomposition) to minimize the
surrogate gap and yet not affect the perturbed loss. GSAM seeks a region with
both small loss (by step 1) and low sharpness (by step 2), giving rise to a
model with high generalization capabilities. Theoretically, we show the
convergence of GSAM and provably better generalization than SAM. Empirically,
GSAM consistently improves generalization (e.g., +3.2\% over SAM and +5.4\%
over AdamW on ImageNet top-1 accuracy for ViT-B/32). Code is released at \url{
https://sites.google.com/view/gsam-iclr22/home}.

Determinantal points processes are a promising but relatively under-developed
tool in machine learning and statistical modelling, being the canonical
statistical example of distributions with repulsion. While their mathematical
formulation is elegant and appealing, their practical use, such as simply
sampling from them, is far from straightforward.Recent work has shown how a
particular type of determinantal point process defined on the compact
multidimensional space $[-1, 1]^d$ can be practically sampled and further shown
how such samples can be used to improve Monte Carlo integration.This work
extends those results to a new determinantal point process on $\mathbb{R}^d$ by
constructing a novel sampling scheme. Samples from this new process are shown
to be useful in Monte Carlo integration against Gaussian measure, which is
particularly relevant in machine learning applications.

Multi-view subspace clustering aims to discover the hidden subspace
structures from multiple views for robust clustering, and has been attracting
considerable attention in recent years. Despite significant progress, most of
the previous multi-view subspace clustering algorithms are still faced with two
limitations. First, they usually focus on the consistency (or commonness) of
multiple views, yet often lack the ability to capture the cross-view
inconsistencies in subspace representations. Second, many of them overlook the
local structures of multiple views and cannot jointly leverage multiple local
structures to enhance the subspace representation learning. To address these
two limitations, in this paper, we propose a jointly smoothed multi-view
subspace clustering (JSMC) approach. Specifically, we simultaneously
incorporate the cross-view commonness and inconsistencies into the subspace
representation learning. The view-consensus grouping effect is presented to
jointly exploit the local structures of multiple views to regularize the
view-commonness representation, which is further associated with the low-rank
constraint via the nuclear norm to strengthen its cluster structure. Thus the
cross-view commonness and inconsistencies, the view-consensus grouping effect,
and the low-rank representation are seamlessly incorporated into a unified
objective function, upon which an alternating optimization algorithm is
performed to achieve a robust subspace representation for clustering.
Experimental results on a variety of real-world multi-view datasets confirm the
superiority of our approach. Code available:
https://github.com/huangdonghere/JSMC.

Our capacity to learn representations from data is related to our ability to
design filters that can leverage their coupling with the underlying domain.
Graph filters are one such tool for network data and have been used in a myriad
of applications. But graph filters work only with a fixed number of nodes
despite the expanding nature of practical networks. Learning filters in this
setting is challenging not only because of the increased dimensions but also
because the connectivity is known only up to an attachment model. We propose a
filter learning scheme for data over expanding graphs by relying only on such a
model. By characterizing the filter stochastically, we develop an empirical
risk minimization framework inspired by multi-kernel learning to balance the
information inflow and outflow at the incoming nodes. We particularize the
approach for denoising and semi-supervised learning (SSL) over expanding graphs
and show near-optimal performance compared with baselines relying on the exact
topology. For SSL, the proposed scheme uses the incoming node information to
improve the task on the existing ones. These findings lay the foundation for
learning representations over expanding graphs by relying only on the
stochastic connectivity model.

Building models of human decision-making from observed behaviour is critical
to better understand, diagnose and support real-world policies such as clinical
care. As established policy learning approaches remain focused on imitation
performance, they fall short of explaining the demonstrated decision-making
process. Policy Extraction through decision Trees (POETREE) is a novel
framework for interpretable policy learning, compatible with fully-offline and
partially-observable clinical decision environments -- and builds probabilistic
tree policies determining physician actions based on patients' observations and
medical history. Fully-differentiable tree architectures are grown
incrementally during optimization to adapt their complexity to the modelling
task, and learn a representation of patient history through recurrence,
resulting in decision tree policies that adapt over time with patient
information. This policy learning method outperforms the state-of-the-art on
real and synthetic medical datasets, both in terms of understanding,
quantifying and evaluating observed behaviour as well as in accurately
replicating it -- with potential to improve future decision support systems.

Methods based on machine learning have recently made substantial inroads in
many corners of cosmology. Through this process, new computational tools, new
perspectives on data collection, model development, analysis, and discovery, as
well as new communities and educational pathways have emerged. Despite rapid
progress, substantial potential at the intersection of cosmology and machine
learning remains untapped. In this white paper, we summarize current and
ongoing developments relating to the application of machine learning within
cosmology and provide a set of recommendations aimed at maximizing the
scientific impact of these burgeoning tools over the coming decade through both
technical development as well as the fostering of emerging communities.

Machine learning (ML) is an important component for enabling automation in
Radio Access Networks (RANs). The work on applying ML for RAN has been under
development for many years and is now also drawing attention in 3GPP and
Open-RAN standardization fora. A key component of multiple features, also
highlighted in the recent 3GPP specification work, is the use of mobility,
traffic and radio channel prediction. These types of predictions form the
intelligence enablers to leverage the potentials for ML for RAN, both for
current and future wireless networks. This paper provides an overview with
evaluation results of current applications that utilize such intelligence
enablers, we then discuss how those enablers likely will be a cornerstone for
emerging 6G use cases such as wireless energy transmission.

Registering CT images of the chest is a crucial step for several tasks such
as disease progression tracking or surgical planning. It is also a challenging
step because of the heterogeneous content of the human abdomen which implies
complex deformations. In this work, we focus on accurately registering a subset
of organs of interest. We register organ surface point clouds, as may typically
be extracted from an automatic segmentation pipeline, by expanding the Bayesian
Coherent Point Drift algorithm (BCPD). We introduce MO-BCPD, a multi-organ
version of the BCPD algorithm which explicitly models three important aspects
of this task: organ individual elastic properties, inter-organ motion coherence
and segmentation inaccuracy. This model also provides an interpolation
framework to estimate the deformation of the entire volume. We demonstrate the
efficiency of our method by registering different patients from the LITS
challenge dataset. The target registration error on anatomical landmarks is
almost twice as small for MO-BCPD compared to standard BCPD while imposing the
same constraints on individual organs deformation.

Autonomous driving requires a detailed understanding of complex driving
scenes. The redundancy and complementarity of the vehicle's sensors provide an
accurate and robust comprehension of the environment, thereby increasing the
level of performance and safety. This thesis focuses the on automotive RADAR,
which is a low-cost active sensor measuring properties of surrounding objects,
including their relative speed, and has the key advantage of not being impacted
by adverse weather conditions. With the rapid progress of deep learning and the
availability of public driving datasets, the perception ability of vision-based
driving systems has considerably improved. The RADAR sensor is seldom used for
scene understanding due to its poor angular resolution, the size, noise, and
complexity of RADAR raw data as well as the lack of available datasets. This
thesis proposes an extensive study of RADAR scene understanding, from the
construction of an annotated dataset to the conception of adapted deep learning
architectures. First, this thesis details approaches to tackle the current lack
of data. A simple simulation as well as generative methods for creating
annotated data will be presented. It will also describe the CARRADA dataset,
composed of synchronised camera and RADAR data with a semi-automatic annotation
method. This thesis then present a proposed set of deep learning architectures
with their associated loss functions for RADAR semantic segmentation. It also
introduces a method to open up research into the fusion of LiDAR and RADAR
sensors for scene understanding. Finally, this thesis exposes a collaborative
contribution, the RADIal dataset with synchronised High-Definition (HD) RADAR,
LiDAR and camera. A deep learning architecture is also proposed to estimate the
RADAR signal processing pipeline while performing multitask learning for object
detection and free driving space segmentation.

In PET, the amount of relative (signal-dependent) noise present in different
body regions can be significantly different and is inherently related to the
number of counts present in that region. The number of counts in a region
depends, in principle and among other factors, on the total administered
activity, scanner sensitivity, image acquisition duration, radiopharmaceutical
tracer uptake in the region, and patient local body morphometry surrounding the
region. In theory, less amount of denoising operations is needed to denoise a
high-count (low relative noise) image than images a low-count (high relative
noise) image, and vice versa. The current deep-learning-based methods for PET
image denoising are predominantly trained on image appearance only and have no
special treatment for images of different noise levels. Our hypothesis is that
by explicitly providing the local relative noise level of the input image to a
deep convolutional neural network (DCNN), the DCNN can outperform itself
trained on image appearance only. To this end, we propose a noise-level-aware
framework denoising framework that allows embedding of local noise level into a
DCNN. The proposed is trained and tested on 30 and 15 patient PET images
acquired on a GE Discovery MI PET/CT system. Our experiments showed that the
increases in both PSNR and SSIM from our backbone network with relative noise
level embedding (NLE) versus the same network without NLE were statistically
significant with p<0.001, and the proposed method significantly outperformed a
strong baseline method by a large margin.

The problem of molecular generation has received significant attention
recently. Existing methods are typically based on deep neural networks and
require training on large datasets with tens of thousands of samples. In
practice, however, the size of class-specific chemical datasets is usually
limited (e.g., dozens of samples) due to labor-intensive experimentation and
data collection. This presents a considerable challenge for the deep learning
generative models to comprehensively describe the molecular design space.
Another major challenge is to generate only physically synthesizable molecules.
This is a non-trivial task for neural network-based generative models since the
relevant chemical knowledge can only be extracted and generalized from the
limited training data. In this work, we propose a data-efficient generative
model that can be learned from datasets with orders of magnitude smaller sizes
than common benchmarks. At the heart of this method is a learnable graph
grammar that generates molecules from a sequence of production rules. Without
any human assistance, these production rules are automatically constructed from
training data. Furthermore, additional chemical knowledge can be incorporated
in the model by further grammar optimization. Our learned graph grammar yields
state-of-the-art results on generating high-quality molecules for three monomer
datasets that contain only ${\sim}20$ samples each. Our approach also achieves
remarkable performance in a challenging polymer generation task with only $117$
training samples and is competitive against existing methods using $81$k data
points. Code is available at https://github.com/gmh14/data_efficient_grammar.

We propose a nearest neighbor based clustering algorithm that results in a
naturally defined hierarchy of clusters. In contrast to the agglomerative and
divisive hierarchical clustering algorithms, our approach is not dependent on
the iterative working of the algorithm, in the sense that the partitions of the
hierarchical clusters are purely defined in accordance with the input dataset.
Our method is a universal hierarchical clustering approach since it can be
implemented as bottom up or top down versions, both of which result in the same
clustering. We show that for certain types of datasets, our algorithm has
near-linear time and space complexity.

In a previous paper, we have introduced a deep learning neural network that
should be able to detect the existence of very shallow periodic planetary
transits in the presence of red noise. The network in that feasibility study
would not provide any further details about the detected transits. The current
paper completes this missing part. We present a neural network that tags
samples that were obtained during transits. This is essentially similar to the
task of identifying the semantic context of each pixel in an image -- an
important task in computer vision, called `semantic segmentation', which is
often performed by deep neural networks. The neural network we present makes
use of novel deep learning concepts such as U-Nets, Generative Adversarial
Networks (GAN), and adversarial loss. The resulting segmentation should allow
further studies of the light curves which are tagged as containing transits.
This approach towards the detection and study of very shallow transits is bound
to play a significant role in future space-based transit surveys such as PLATO,
which are specifically aimed to detect those extremely difficult cases of
long-period shallow transits. Our segmentation network also adds to the growing
toolbox of deep learning approaches which are being increasingly used in the
study of exoplanets, but so far mainly for vetting transits, rather than their
initial detection.

Natural language spatial video grounding aims to detect the relevant objects
in video frames with descriptive sentences as the query. In spite of the great
advances, most existing methods rely on dense video frame annotations, which
require a tremendous amount of human effort. To achieve effective grounding
under a limited annotation budget, we investigate one-shot video grounding, and
learn to ground natural language in all video frames with solely one frame
labeled, in an end-to-end manner. One major challenge of end-to-end one-shot
video grounding is the existence of videos frames that are either irrelevant to
the language query or the labeled frames. Another challenge relates to the
limited supervision, which might result in ineffective representation learning.
To address these challenges, we designed an end-to-end model via Information
Tree for One-Shot video grounding (IT-OS). Its key module, the information
tree, can eliminate the interference of irrelevant frames based on branch
search and branch cropping techniques. In addition, several self-supervised
tasks are proposed based on the information tree to improve the representation
learning under insufficient labeling. Experiments on the benchmark dataset
demonstrate the effectiveness of our model.

Although Printed Electronics (PE) cannot compete with silicon-based systems
in conventional evaluation metrics, e.g., integration density, area and
performance, PE offers attractive properties such as on-demand ultra-low-cost
fabrication, flexibility and non-toxicity. As a result, it targets application
domains that are untouchable by lithography-based silicon electronics and thus
have not yet seen much proliferation of computing. However, despite the
attractive characteristics of PE, the large feature sizes in PE prohibit the
realization of complex printed circuits, such as Machine Learning (ML)
classifiers. In this work, we exploit the hardware-friendly nature of Decision
Trees for machine learning classification and leverage the hardware-efficiency
of the approximate design in order to generate approximate ML classifiers that
are suitable for tiny, ultra-resource constrained, and battery-powered printed

Non-parallel voice conversion (VC) is typically achieved using lossy
representations of the source speech. However, ensuring only speaker identity
information is dropped whilst all other information from the source speech is
retained is a large challenge. This is particularly challenging in the scenario
where at inference-time we have no knowledge of the text being read, i.e.,
text-free VC. To mitigate this, we investigate information-preserving VC
  Normalising flows have gained attention for text-to-speech synthesis, however
have been under-explored for VC. Flows utilize invertible functions to learn
the likelihood of the data, thus provide a lossless encoding of speech. We
investigate normalising flows for VC in both text-conditioned and text-free
scenarios. Furthermore, for text-free VC we compare pre-trained and
jointly-learnt priors. Flow-based VC evaluations show no degradation between
text-free and text-conditioned VC, resulting in improvements over the
state-of-the-art. Also, joint-training of the prior is found to negatively
impact text-free VC quality.

Explainable Artificial Intelligence (XAI) is an emerging research field
bringing transparency to highly complex and opaque machine learning (ML)
models. Despite the development of a multitude of methods to explain the
decisions of black-box classifiers in recent years, these tools are seldomly
used beyond visualization purposes. Only recently, researchers have started to
employ explanations in practice to actually improve models. This paper offers a
comprehensive overview over techniques that apply XAI practically for improving
various properties of ML models, and systematically categorizes these
approaches, comparing their respective strengths and weaknesses. We provide a
theoretical perspective on these methods, and show empirically through
experiments on toy and realistic settings how explanations can help improve
properties such as model generalization ability or reasoning, among others. We
further discuss potential caveats and drawbacks of these methods. We conclude
that while model improvement based on XAI can have significant beneficial
effects even on complex and not easily quantifyable model properties, these
methods need to be applied carefully, since their success can vary depending on
a multitude of factors, such as the model and dataset used, or the employed
explanation method.

The adoption of Artificial Intelligence (AI) in high-stakes domains such as
healthcare, wildlife preservation, autonomous driving and criminal justice
system calls for a data-centric approach to AI. Data scientists spend the
majority of their time studying and wrangling the data, yet tools to aid them
with data analysis are lacking. This study identifies the recurrent data
quality issues in public datasets. Analogous to code smells, we introduce a
novel catalogue of data smells that can be used to indicate early signs of
problems or technical debt in machine learning systems. To understand the
prevalence of data quality issues in datasets, we analyse 25 public datasets
and identify 14 data smells.

Multi-task dense scene understanding is a thriving research domain that
requires simultaneous perception and reasoning on a series of correlated tasks
with pixel-wise prediction. Most existing works encounter a severe limitation
of modeling in the locality due to heavy utilization of convolution operations,
while learning interactions and inference in a global spatial-position and
multi-task context is critical for this problem. In this paper, we propose a
novel end-to-end Inverted Pyramid multi-task Transformer (InvPT) to perform
simultaneous modeling of spatial positions and multiple tasks in a unified
framework. To the best of our knowledge, this is the first work that explores
designing a transformer structure for multi-task dense prediction for scene
understanding. Besides, it is widely demonstrated that a higher spatial
resolution is remarkably beneficial for dense predictions, while it is very
challenging for existing transformers to go deeper with higher resolutions due
to huge complexity to large spatial size. InvPT presents an efficient
UP-Transformer block to learn multi-task feature interaction at gradually
increased resolutions, which also incorporates effective self-attention message
passing and multi-scale feature aggregation to produce task-specific prediction
at a high resolution. Our method achieves superior multi-task performance on
NYUD-v2 and PASCAL-Context datasets respectively, and significantly outperforms
previous state-of-the-arts. The code is available at
https://github.com/prismformore/InvPT

Temporal factors are tied to the growth of facts in realistic applications,
such as the progress of diseases and the development of political situation,
therefore, research on Temporal Knowledge Graph (TKG) attracks much attention.
In TKG, relation patterns inherent with temporality are required to be studied
for representation learning and reasoning across temporal facts. However,
existing methods can hardly model temporal relation patterns, nor can capture
the intrinsic connections between relations when evolving over time, lacking of
interpretability. In this paper, we propose a novel temporal modeling method
which represents temporal entities as Rotations in Quaternion Vector Space
(RotateQVS) and relations as complex vectors in Hamilton's quaternion space. We
demonstrate our method can model key patterns of relations in TKG, such as
symmetry, asymmetry, inverse, and can further capture time-evolved relations by
theory. Empirically, we show that our method can boost the performance of link
prediction tasks over four temporal knowledge graph benchmarks.

Approximate learning machines have become popular in the era of small
devices, including quantised, factorised, hashed, or otherwise compressed
predictors, and the quest to explain and guarantee good generalisation
abilities for such methods has just begun. In this paper we study the role of
approximability in learning, both in the full precision and the approximated
settings of the predictor that is learned from the data, through a notion of
sensitivity of predictors to the action of the approximation operator at hand.
We prove upper bounds on the generalisation of such predictors, yielding the
following main findings, for any PAC-learnable class and any given
approximation operator. 1) We show that under mild conditions, approximable
target concepts are learnable from a smaller labelled sample, provided
sufficient unlabelled data. 2) We give algorithms that guarantee a good
predictor whose approximation also enjoys the same generalisation guarantees.
3) We highlight natural examples of structure in the class of sensitivities,
which reduce, and possibly even eliminate the otherwise abundant requirement of
additional unlabelled data, and henceforth shed new light onto what makes one
problem instance easier to learn than another. These results embed the scope of
modern model compression approaches into the general goal of statistical
learning theory, which in return suggests appropriate algorithms through
minimising uniform bounds.

After the great success of Vision Transformer variants (ViTs) in computer
vision, it has also demonstrated great potential in domain adaptive semantic
segmentation. Unfortunately, straightforwardly applying local ViTs in domain
adaptive semantic segmentation does not bring in expected improvement. We find
that the pitfall of local ViTs is due to the severe high-frequency components
generated during both the pseudo-label construction and features alignment for
target domains. These high-frequency components make the training of local ViTs
very unsmooth and hurt their transferability. In this paper, we introduce a
low-pass filtering mechanism, momentum network, to smooth the learning dynamics
of target domain features and pseudo labels. Furthermore, we propose a dynamic
of discrepancy measurement to align the distributions in the source and target
domains via dynamic weights to evaluate the importance of the samples. After
tackling the above issues, extensive experiments on sim2real benchmarks show
that the proposed method outperforms the state-of-the-art methods. Our codes
are available at https://github.com/alpc91/TransDA

Accurate uncertainty estimates are essential for deploying deep object
detectors in safety-critical systems. The development and evaluation of
probabilistic object detectors have been hindered by shortcomings in existing
performance measures, which tend to involve arbitrary thresholds or limit the
detector's choice of distributions. In this work, we propose to view object
detection as a set prediction task where detectors predict the distribution
over the set of objects. Using the negative log-likelihood for random finite
sets, we present a proper scoring rule for evaluating and training
probabilistic object detectors. The proposed method can be applied to existing
probabilistic detectors, is free from thresholds, and enables fair comparison
between architectures. Three different types of detectors are evaluated on the
COCO dataset. Our results indicate that the training of existing detectors is
optimized toward non-probabilistic metrics. We hope to encourage the
development of new object detectors that can accurately estimate their own
uncertainty. Code available at https://github.com/georghess/pmb-nll.

Batch Normalization's (BN) unique property of depending on other samples in a
batch is known to cause problems in several tasks, including sequential
modeling, and has led to the use of alternatives in these fields. In video
learning, however, these problems are less studied, despite the ubiquitous use
of BN in CNNs for visual feature extraction. We argue that BN's properties
create major obstacles for training CNNs and temporal models end to end in
video tasks. Yet, end-to-end learning seems preferable in specialized domains
such as surgical workflow analysis, which lack well-pretrained feature
extractors. While previous work in surgical workflow analysis has avoided
BN-related issues through complex, multi-stage learning procedures, we show
that even simple, end-to-end CNN-LSTMs can outperform the state of the art when
CNNs without BN are used. Moreover, we analyze in detail when BN-related issues
occur, including a "cheating" phenomenon in surgical anticipation tasks. We
hope that a deeper understanding of BN's limitations and a reconsideration of
end-to-end approaches can be beneficial for future research in surgical
workflow analysis and general video learning.

Following the earlier formalism of the categorical representation learning
(arXiv:2103.14770) by the first two authors, we discuss the construction of the
"RG-flow based categorifier". Borrowing ideas from theory of renormalization
group flows (RG) in quantum field theory, holographic duality, and hyperbolic
geometry, and mixing them with neural ODE's, we construct a new algorithmic
natural language processing (NLP) architecture, called the RG-flow categorifier
or for short the RG categorifier, which is capable of data classification and
generation in all layers. We apply our algorithmic platform to biomedical data
sets and show its performance in the field of sequence-to-function mapping. In
particular we apply the RG categorifier to particular genomic sequences of flu
viruses and show how our technology is capable of extracting the information
from given genomic sequences, find their hidden symmetries and dominant
features, classify them and use the trained data to make stochastic prediction
of new plausible generated sequences associated with new set of viruses which
could avoid the human immune system. The content of the current article is part
of the recent US patent application submitted by first two authors (U.S. Patent
Application No.: 63/313.504).

In order to advance the state of the art in graph learning algorithms, it is
necessary to construct large real-world datasets. While there are many
benchmark datasets for homogeneous graphs, only a few of them are available for
heterogeneous graphs. Furthermore, the latter graphs are small in size
rendering them insufficient to understand how graph learning algorithms perform
in terms of classification metrics and computational resource utilization. We
introduce, PDNS-Net, the largest public heterogeneous graph dataset containing
447K nodes and 897K edges for the malicious domain classification task.
Compared to the popular heterogeneous datasets IMDB and DBLP, PDNS-Net is 38
and 17 times bigger respectively. We provide a detailed analysis of PDNS-Net
including the data collection methodology, heterogeneous graph construction,
descriptive statistics and preliminary graph classification performance. The
dataset is publicly available at https://github.com/qcri/PDNS-Net. Our
preliminary evaluation of both popular homogeneous and heterogeneous graph
neural networks on PDNS-Net reveals that further research is required to
improve the performance of these models on large heterogeneous graphs.

Neural fields have gained significant attention in the computer vision
community due to their excellent performance in novel view synthesis, geometry
reconstruction, and generative modeling. Some of their advantages are a sound
theoretic foundation and an easy implementation in current deep learning
frameworks. While neural fields have been applied to signals on manifolds,
e.g., for texture reconstruction, their representation has been limited to
extrinsically embedding the shape into Euclidean space. The extrinsic embedding
ignores known intrinsic manifold properties and is inflexible wrt. transfer of
the learned function. To overcome these limitations, this work introduces
intrinsic neural fields, a novel and versatile representation for neural fields
on manifolds. Intrinsic neural fields combine the advantages of neural fields
with the spectral properties of the Laplace-Beltrami operator. We show
theoretically that intrinsic neural fields inherit many desirable properties of
the extrinsic neural field framework but exhibit additional intrinsic
qualities, like isometry invariance. In experiments, we show intrinsic neural
fields can reconstruct high-fidelity textures from images with state-of-the-art
quality and are robust to the discretization of the underlying manifold. We
demonstrate the versatility of intrinsic neural fields by tackling various
applications: texture transfer between deformed shapes & different shapes,
texture reconstruction from real-world images with view dependence, and
discretization-agnostic learning on meshes and point clouds.

Performing signal processing over graphs requires knowledge of the underlying
fixed topology. However, graphs often grow in size with new nodes appearing
over time, whose connectivity is typically unknown; hence, making more
challenging the downstream tasks in applications like cold start
recommendation. We address such a challenge for signal interpolation at the
incoming nodes blind to the topological connectivity of the specific node.
Specifically, we propose a stochastic attachment model for incoming nodes
parameterized by the attachment probabilities and edge weights. We estimate
these parameters in a data-driven fashion by relying only on the attachment
behaviour of earlier incoming nodes with the goal of interpolating the signal
value. We study the non-convexity of the problem at hand, derive conditions
when it can be marginally convexified, and propose an alternating projected
descent approach between estimating the attachment probabilities and the edge
weights. Numerical experiments with synthetic and real data dealing in cold
start collaborative filtering corroborate our findings.

We introduce a simulation-based, amortised Bayesian inference scheme to infer
the parameters of random walks. Our approach learns the posterior distribution
of the walks' parameters with a likelihood-free method. In the first step a
graph neural network is trained on simulated data to learn optimized
low-dimensional summary statistics of the random walk. In the second step an
invertible neural network generates the posterior distribution of the
parameters from the learnt summary statistics using variational inference. We
apply our method to infer the parameters of the fractional Brownian motion
model from single trajectories. The computational complexity of the amortized
inference procedure scales linearly with trajectory length, and its precision
scales similarly to the Cram{\'e}r-Rao bound over a wide range of lengths. The
approach is robust to positional noise, and generalizes well to trajectories
longer than those seen during training. Finally, we adapt this scheme to show
that a finite decorrelation time in the environment can furthermore be inferred
from individual trajectories.

Speech enhancement and separation are two fundamental tasks for robust speech
processing. Speech enhancement suppresses background noise while speech
separation extracts target speech from interfering speakers. Despite a great
number of supervised learning-based enhancement and separation methods having
been proposed and achieving good performance, studies on applying
self-supervised learning (SSL) to enhancement and separation are limited. In
this paper, we evaluate 13 SSL upstream methods on speech enhancement and
separation downstream tasks. Our experimental results on Voicebank-DEMAND and
Libri2Mix show that some SSL representations consistently outperform baseline
features including the short-time Fourier transform (STFT) magnitude and log
Mel filterbank (FBANK). Furthermore, we analyze the factors that make existing
SSL frameworks difficult to apply to speech enhancement and separation and
discuss the representation properties desired for both tasks. Our study is
included as the official speech enhancement and separation downstreams for
SUPERB.

Process data with confidential information cannot be shared directly in
public, which hinders the research in process data mining and analytics. Data
encryption methods have been studied to protect the data, but they still may be
decrypted, which leads to individual identification. We experimented with
different models of representation learning and used the learned model to
generate synthetic process data. We introduced an adversarial generative
network for process data generation (ProcessGAN) with two Transformer networks
for the generator and the discriminator. We evaluated ProcessGAN and
traditional models on six real-world datasets, of which two are public and four
are collected in medical domains. We used statistical metrics and supervised
learning scores to evaluate the synthetic data. We also used process mining to
discover workflows for the authentic and synthetic datasets and had medical
experts evaluate the clinical applicability of the synthetic workflows. We
found that ProcessGAN outperformed traditional sequential models when trained
on small authentic datasets of complex processes. ProcessGAN better represented
the long-range dependencies between the activities, which is important for
complicated processes such as the medical processes. Traditional sequential
models performed better when trained on large data of simple processes. We
conclude that ProcessGAN can generate a large amount of sharable synthetic
process data indistinguishable from authentic data.

Federated learning with differential privacy, i.e. private federated learning
(PFL), makes it possible to train models on private data distributed across
users' devices without harming privacy. PFL is efficient for models, such as
neural networks, that have a fixed number of parameters, and thus a
fixed-dimensional gradient vector. Such models include neural-net language
models, but not tokenizers, the topic of this work. Training a tokenizer
requires frequencies of words from an unlimited vocabulary, and existing
methods for finding an unlimited vocabulary need a separate privacy budget.
  A workaround is to train the tokenizer on publicly available data. However,
in this paper we first show that a tokenizer trained on mismatched data results
in worse model performance compared to a privacy-violating "oracle" tokenizer
that accesses user data, with perplexity increasing by 20%. We also show that
sub-word tokenizers are better suited to the federated context than word-level
ones, since they can encode new words, though with more tokens per word.
  Second, we propose a novel method to obtain a tokenizer without using any
additional privacy budget. During private federated learning of the language
model, we sample from the model, train a new tokenizer on the sampled
sequences, and update the model embeddings. We then continue private federated
learning, and obtain performance within 1% of the "oracle" tokenizer. Since
this process trains the tokenizer only indirectly on private data, we can use
the "postprocessing guarantee" of differential privacy and thus use no
additional privacy budget.

New algorithms called nudging induced neural networks (NINNs), to control and
improve the accuracy of deep neural networks (DNNs), are introduced. The NINNs
framework can be applied to almost all pre-existing DNNs, with forward
propagation, with costs comparable to existing DNNs. NINNs work by adding a
feedback control term to the forward propagation of the network. The feedback
term nudges the neural network towards a desired quantity of interest. NINNs
offer multiple advantages, for instance, they lead to higher accuracy when
compared with existing data assimilation algorithms such as nudging. Rigorous
convergence analysis is established for NINNs. The algorithmic and theoretical
findings are illustrated on examples from data assimilation and chemically
reacting flows.

We investigate the complexity of the reachability problem for (deep) neural
networks: does it compute valid output given some valid input? It was recently
claimed that the problem is NP-complete for general neural networks and
specifications over the input/output dimension given by conjunctions of linear
inequalities. We recapitulate the proof and repair some flaws in the original
upper and lower bound proofs. Motivated by the general result, we show that
NP-hardness already holds for restricted classes of simple specifications and
neural networks. Allowing for a single hidden layer and an output dimension of
one as well as neural networks with just one negative, zero and one positive
weight or bias is sufficient to ensure NP-hardness. Additionally, we give a
thorough discussion and outlook of possible extensions for this direction of
research on neural network verification.

This paper explores the threat detection for general Social Engineering (SE)
attack using Machine Learning (ML) techniques, rather than focusing on or
limited to a specific SE attack type, e.g. email phishing. Firstly, this paper
processes and obtains more SE threat data from the previous Knowledge Graph
(KG), and then extracts different threat features and generates new datasets
corresponding with three different feature combinations. Finally, 9 types of ML
models are created and trained using the three datasets, respectively, and
their performance are compared and analyzed with 27 threat detectors and 270
times of experiments. The experimental results and analyses show that: 1) the
ML techniques are feasible in detecting general SE attacks and some ML models
are quite effective; ML-based SE threat detection is complementary with
KG-based approaches; 2) the generated datasets are usable and the SE domain
ontology proposed in previous work can dissect SE attacks and deliver the SE
threat features, allowing it to be used as a data model for future research.
Besides, more conclusions and analyses about the characteristics of different
ML detectors and the datasets are discussed.

Research on limit order book markets has been rapidly growing and nowadays
high-frequency full order book data is widely available for researchers and
practitioners. However, it is common that research papers use the best level
data only, which motivates us to ask whether the exclusion of the quotes deeper
in the book over multiple price levels causes performance degradation. In this
paper, we address this question by using modern Machine Learning (ML)
techniques to predict mid-price movements without assuming that limit order
book markets represent a linear system. We provide a number of results that are
robust across ML prediction models, feature selection algorithms, data sets,
and prediction horizons. We find that the best bid and ask levels are
systematically identified not only as the most informative levels in the order
books, but also to carry most of the information needed for good prediction
performance. On the other hand, even if the top-of-the-book levels contain most
of the relevant information, to maximize models' performance one should use all
data across all the levels. Additionally, the informativeness of the order book
levels clearly decreases from the first to the fourth level while the rest of
the levels are approximately equally important.

Opinion summarization is the task of automatically generating summaries that
encapsulate information from multiple user reviews. We present Semantic
Autoencoder (SemAE) to perform extractive opinion summarization in an
unsupervised manner. SemAE uses dictionary learning to implicitly capture
semantic information from the review and learns a latent representation of each
sentence over semantic units. A semantic unit is supposed to capture an
abstract semantic concept. Our extractive summarization algorithm leverages the
representations to identify representative opinions among hundreds of reviews.
SemAE is also able to perform controllable summarization to generate
aspect-specific summaries. We report strong performance on SPACE and AMAZON
datasets, and perform experiments to investigate the functioning of our model.
Our code is publicly available at https://github.com/brcsomnath/SemAE.

With the popularity of virtual assistants (e.g., Siri, Alexa), the use of
speech recognition is now becoming more and more widespread.However, speech
signals contain a lot of sensitive information, such as the speaker's identity,
which raises privacy concerns.The presented experiments show that the
representations extracted by the deep layers of speech recognition networks
contain speaker information.This paper aims to produce an anonymous
representation while preserving speech recognition performance.To this end, we
propose to use vector quantization to constrain the representation space and
induce the network to suppress the speaker identity.The choice of the
quantization dictionary size allows to configure the trade-off between utility
(speech recognition) and privacy (speaker identity concealment).

While 6D object pose estimation has recently made a huge leap forward, most
methods can still only handle a single or a handful of different objects, which
limits their applications. To circumvent this problem, category-level object
pose estimation has recently been revamped, which aims at predicting the 6D
pose as well as the 3D metric size for previously unseen instances from a given
set of object classes. This is, however, a much more challenging task due to
severe intra-class shape variations. To address this issue, we propose
GPV-Pose, a novel framework for robust category-level pose estimation,
harnessing geometric insights to enhance the learning of category-level
pose-sensitive features. First, we introduce a decoupled confidence-driven
rotation representation, which allows geometry-aware recovery of the associated
rotation matrix. Second, we propose a novel geometry-guided point-wise voting
paradigm for robust retrieval of the 3D object bounding box. Finally,
leveraging these different output streams, we can enforce several geometric
consistency terms, further increasing performance, especially for non-symmetric
categories. GPV-Pose produces superior results to state-of-the-art competitors
on common public benchmarks, whilst almost achieving real-time inference speed
at 20 FPS.

Classically, statistical datasets have a larger number of data points than
features ($n > p$). The standard model of classical statistics caters for the
case where data points are considered conditionally independent given the
parameters. However, for $n\approx p$ or $p > n$ such models are poorly
determined. Kalaitzis et al. (2013) introduced the Bigraphical Lasso, an
estimator for sparse precision matrices based on the Cartesian product of
graphs. Unfortunately, the original Bigraphical Lasso algorithm is not
applicable in case of large p and n due to memory requirements. We exploit
eigenvalue decomposition of the Cartesian product graph to present a more
efficient version of the algorithm which reduces memory requirements from
$O(n^2p^2)$ to $O(n^2 + p^2)$. Many datasets in different application fields,
such as biology, medicine and social science, come with count data, for which
Gaussian based models are not applicable. Our multi-way network inference
approach can be used for discrete data. Our methodology accounts for the
dependencies across both instances and features, reduces the computational
complexity for high dimensional data and enables to deal with both discrete and
continuous data. Numerical studies on both synthetic and real datasets are
presented to showcase the performance of our method.

Natural language processing models learn word representations based on the
distributional hypothesis, which asserts that word context (e.g.,
co-occurrence) correlates with meaning. We propose that $n$-grams composed of
random character sequences, or $garble$, provide a novel context for studying
word meaning both within and beyond extant language. In particular, randomly
generated character $n$-grams lack meaning but contain primitive information
based on the distribution of characters they contain. By studying the
embeddings of a large corpus of garble, extant language, and pseudowords using
CharacterBERT, we identify an axis in the model's high-dimensional embedding
space that separates these classes of $n$-grams. Furthermore, we show that this
axis relates to structure within extant language, including word
part-of-speech, morphology, and concept concreteness. Thus, in contrast to
studies that are mainly limited to extant language, our work reveals that
meaning and primitive information are intrinsically linked.

We describe a simple and effective method (Spectral Attribute removaL; SAL)
to remove guarded information from neural representations. Our method uses
singular value decomposition and eigenvalue decomposition to project the input
representations into directions with reduced covariance with the guarded
information rather than maximal covariance as normally these factorization
methods are used. We begin with linear information removal and proceed to
generalize our algorithm to the case of nonlinear information removal through
the use of kernels. Our experiments demonstrate that our algorithm retains
better main task performance after removing the guarded information compared to
previous methods. In addition, our experiments demonstrate that we need a
relatively small amount of guarded attribute data to remove information about
these attributes, which lowers the exposure to such possibly sensitive data and
fits better low-resource scenarios.

X-ray absorption spectroscopy (XAS) is a well-established method for in-depth
characterization of the electronic structure due to its sensitivity to the
local coordination and electronic states of the active ions. In practice
hundreds of energy points should be sampled during the XAS measurement, most of
which are redundant and do not contain important information. In addition, it
is also a tedious procedure to estimate reasonable parameters in the atomic
Hamiltonian for mechanistic understanding. We implemented an Adversarial
Bayesian optimization (ABO) algorithm comprising two coupled Gaussian processes
(GPs) to automatically fit the multiplet model Hamiltonian and meanwhile to
sample effectively based on active learning. Taking NiO as an example, we found
that less than 30 sampling points are enough to obtain the complete XAS with
the corresponding crystal field and charge transfer models, which can be
selected based on intuitive hypothesis learning. Further application on the
experimental data revealed that less than 80 sampling points can already give
reasonable XAS and reliable atomic model parameters. Our ABO algorithm has a
great potential for future application in automated physics-driven XAS analysis
and active learning sampling.

Non-deterministic measurements are common in real-world scenarios: the
performance of a stochastic optimization algorithm or the total reward of a
reinforcement learning agent in a chaotic environment are just two examples in
which unpredictable outcomes are common. These measures can be modeled as
random variables and compared among each other via their expected values or
more sophisticated tools such as null hypothesis statistical tests. In this
paper, we propose an alternative framework to visually compare two samples
according to their estimated cumulative distribution functions. First, we
introduce a dominance measure for two random variables that quantifies the
proportion in which the cumulative distribution function of one of the random
variables stochastically dominates the other one. Then, we present a graphical
method that decomposes in quantiles i) the proposed dominance measure and ii)
the probability that one of the random variables takes lower values than the
other. With illustrative purposes, we re-evaluate the experimentation of an
already published work with the proposed methodology and we show that
additional conclusions (missed by the rest of the methods) can be inferred.
Additionally, the software package RVCompare was created as a convenient way of
applying and experimenting with the proposed framework.

The tunability of conductance states of various emerging non-volatile
memristive devices emulates the plasticity of biological synapses, making it
promising in the hardware realization of large-scale neuromorphic systems. The
inference of the neural network can be greatly accelerated by the vector-matrix
multiplication (VMM) performed within a crossbar array of memristive devices in
one step. Nevertheless, the implementation of the VMM needs complex peripheral
circuits and the complexity further increases since non-idealities of
memristive devices prevent precise conductance tuning (especially for the
online training) and largely degrade the performance of the deep neural
networks (DNNs). Here, we present an efficient online training method of the
memristive deep belief net (DBN). The proposed memristive DBN uses
stochastically binarized activations, reducing the complexity of peripheral
circuits, and uses the contrastive divergence (CD) based gradient descent
learning algorithm. The analog VMM and digital CD are performed separately in a
mixed-signal hardware arrangement, making the memristive DBN high immune to
non-idealities of synaptic devices. The number of write operations on
memristive devices is reduced by two orders of magnitude. The recognition
accuracy of 95%~97% can be achieved for the MNIST dataset using pulsed synaptic
behaviors of various memristive synaptic devices.

Neural face avatars that are trained from multi-view data captured in camera
domes can produce photo-realistic 3D reconstructions. However, at inference
time, they must be driven by limited inputs such as partial views recorded by
headset-mounted cameras or a front-facing camera, and sparse facial landmarks.
To mitigate this asymmetry, we introduce a prior model that is conditioned on
the runtime inputs and tie this prior space to the 3D face model via a
normalizing flow in the latent space. Our proposed model, LiP-Flow, consists of
two encoders that learn representations from the rich training-time and
impoverished inference-time observations. A normalizing flow bridges the two
representation spaces and transforms latent samples from one domain to another,
allowing us to define a latent likelihood objective. We trained our model
end-to-end to maximize the similarity of both representation spaces and the
reconstruction quality, making the 3D face model aware of the limited driving
signals. We conduct extensive evaluations where the latent codes are optimized
to reconstruct 3D avatars from partial or sparse observations. We show that our
approach leads to an expressive and effective prior, capturing facial dynamics
and subtle expressions better.

The expected improvement (EI) algorithm is one of the most popular strategies
for optimization under uncertainty due to its simplicity and efficiency.
Despite its popularity, the theoretical aspects of this algorithm have not been
properly analyzed. In particular, whether in the noisy setting, the EI strategy
with a standard incumbent converges is still an open question of the Gaussian
process bandit optimization problem. We aim to answer this question by
proposing a variant of EI with a standard incumbent defined via the GP
predictive mean. We prove that our algorithm converges, and achieves a
cumulative regret bound of $\mathcal O(\gamma_T\sqrt{T})$, where $\gamma_T$ is
the maximum information gain between $T$ observations and the Gaussian process
model. Based on this variant of EI, we further propose an algorithm called
Improved GP-EI that converges faster than previous counterparts. In particular,
our proposed variants of EI do not require the knowledge of the RKHS norm and
the noise's sub-Gaussianity parameter as in previous works. Empirical
validation in our paper demonstrates the effectiveness of our algorithms

State-of-the-art NLP systems represent inputs with word embeddings, but these
are brittle when faced with Out-of-Vocabulary (OOV) words. To address this
issue, we follow the principle of mimick-like models to generate vectors for
unseen words, by learning the behavior of pre-trained embeddings using only the
surface form of words. We present a simple contrastive learning framework,
LOVE, which extends the word representation of an existing pre-trained language
model (such as BERT), and makes it robust to OOV with few additional
parameters. Extensive evaluations demonstrate that our lightweight model
achieves similar or even better performances than prior competitors, both on
original datasets and on corrupted variants. Moreover, it can be used in a
plug-and-play fashion with FastText and BERT, where it significantly improves
their robustness.

In document classification for, e.g., legal and biomedical text, we often
deal with hundreds of classes, including very infrequent ones, as well as
temporal concept drift caused by the influence of real world events, e.g.,
policy changes, conflicts, or pandemics. Class imbalance and drift can
sometimes be mitigated by resampling the training data to simulate (or
compensate for) a known target distribution, but what if the target
distribution is determined by unknown future events? Instead of simply
resampling uniformly to hedge our bets, we focus on the underlying optimization
algorithms used to train such document classifiers and evaluate several
group-robust optimization algorithms, initially proposed to mitigate
group-level disparities. Reframing group-robust algorithms as adaptation
algorithms under concept drift, we find that Invariant Risk Minimization and
Spectral Decoupling outperform sampling-based approaches to class imbalance and
concept drift, and lead to much better performance on minority classes. The
effect is more pronounced the larger the label set.

In this paper, we propose Self-Contrastive Decorrelation (SCD), a
self-supervised approach. Given an input sentence, it optimizes a joint
self-contrastive and decorrelation objective. Learning a representation is
facilitated by leveraging the contrast arising from the instantiation of
standard dropout at different rates. The proposed method is conceptually simple
yet empirically powerful. It achieves comparable results with state-of-the-art
methods on multiple benchmarks without using contrastive pairs. This study
opens up avenues for efficient self-supervised learning methods that are more
robust than current contrastive methods.

Large-scale datasets play a vital role in computer vision. But current
datasets are annotated blindly without differentiation to samples, making the
data collection inefficient and unscalable. The open question is how to build a
mega-scale dataset actively. Although advanced active learning algorithms might
be the answer, we experimentally found that they are lame in the realistic
annotation scenario where out-of-distribution data is extensive. This work thus
proposes a novel active learning framework for realistic dataset annotation.
Equipped with this framework, we build a high-quality vision dataset -- Bamboo,
which consists of 69M image classification annotations with 119K categories and
28M object bounding box annotations with 809 categories. We organize these
categories by a hierarchical taxonomy integrated from several knowledge bases.
The classification annotations are four times larger than ImageNet22K, and that
of detection is three times larger than Object365. Compared to ImageNet22K and
Objects365, models pre-trained on Bamboo achieve superior performance among
various downstream tasks (6.2% gains on classification and 2.1% gains on
detection). We believe our active learning framework and Bamboo are essential
for future work.

It is unquestionable that time series forecasting is of paramount importance
in many fields. The most used machine learning models to address time series
forecasting tasks are Recurrent Neural Networks (RNNs). Typically, those models
are built using one of the three most popular cells, ELMAN, Long-Short Term
Memory (LSTM), or Gated Recurrent Unit (GRU) cells, each cell has a different
structure and implies a different computational cost. However, it is not clear
why and when to use each RNN-cell structure. Actually, there is no
comprehensive characterization of all the possible time series behaviors and no
guidance on what RNN cell structure is the most suitable for each behavior. The
objective of this study is two-fold: it presents a comprehensive taxonomy of
all-time series behaviors (deterministic, random-walk, nonlinear, long-memory,
and chaotic), and provides insights into the best RNN cell structure for each
time series behavior. We conducted two experiments: (1) The first experiment
evaluates and analyzes the role of each component in the LSTM-Vanilla cell by
creating 11 variants based on one alteration in its basic architecture
(removing, adding, or substituting one cell component). (2) The second
experiment evaluates and analyzes the performance of 20 possible RNN-cell
structures. Our results showed that the MGU-SLIM3 cell is the most recommended
for deterministic and nonlinear behaviors, the MGU-SLIM2 cell is the most
suitable for random-walk behavior, FB1 cell is advocated for long-memory
behavior, and LSTM-SLIM1 for chaotic behavior.

A deep learning model is presented to nowcast the occurrence of lightning at
a five-minute time resolution 60 minutes into the future. The model is based on
a recurrent-convolutional architecture that allows it to recognize and predict
the spatiotemporal development of convection, including the motion, growth and
decay of thunderstorm cells. The predictions are performed on a stationary
grid, without the use of storm object detection and tracking. The input data,
collected from an area in and surrounding Switzerland, comprise ground-based
radar data, visible/infrared satellite data and derived cloud products,
lightning detection, numerical weather prediction and digital elevation model
data. We analyze different alternative loss functions, class weighting
strategies and model features, providing guidelines for future studies to
select loss functions optimally and to properly calibrate the probabilistic
predictions of their model. Based on these analyses, we use focal loss in this
study, but conclude that it only provides a small benefit over cross entropy,
which is a viable option if recalibration of the model is not practical. The
model achieves a pixel-wise critical success index (CSI) of 0.45 to predict
lightning occurrence within 8 km over the 60-min nowcast period, ranging from a
CSI of 0.75 at a 5-min lead time to a CSI of 0.32 at a 60-min lead time.

A well-designed strong-weak augmentation strategy and the stable teacher to
generate reliable pseudo labels are essential in the teacher-student framework
of semi-supervised learning (SSL). Considering these in mind, to suit the
semi-supervised human pose estimation (SSHPE) task, we propose a novel approach
referred to as Pose-MUM that modifies Mix/UnMix (MUM) augmentation. Like MUM in
the dense prediction task, the proposed Pose-MUM makes strong-weak augmentation
for pose estimation and leads the network to learn the relationship between
each human key point much better than the conventional methods by adding the
mixing process in intermediate layers in a stochastic manner. In addition, we
employ the exponential-moving-average-normalization (EMAN) teacher, which is
stable and well-suited to the SSL framework and furthermore boosts the
performance. Extensive experiments on MS-COCO dataset show the superiority of
our proposed method by consistently improving the performance over the previous
methods following SSHPE benchmark.

With model trustworthiness being crucial for sensitive real-world
applications, practitioners are putting more and more focus on improving the
uncertainty calibration of deep neural networks. Calibration errors are
designed to quantify the reliability of probabilistic predictions but their
estimators are usually biased and inconsistent. In this work, we introduce the
framework of proper calibration errors, which relates every calibration error
to a proper score and provides a respective upper bound with optimal estimation
properties. This relationship can be used to reliably quantify the model
calibration improvement. We theoretically and empirically demonstrate the
shortcomings of commonly used estimators compared to our approach. Due to the
wide applicability of proper scores, this gives a natural extension of
recalibration beyond classification.

In multi-agent collaboration problems with communication, an agent's ability
to encode their intention and interpret other agents' strategies is critical
for planning their future actions. This paper introduces a novel algorithm
called Intention Embedded Communication (IEC) to mimic an agent's language
learning ability. IEC contains a perception module for decoding other agents'
intentions in response to their past actions. It also includes a language
generation module for learning implicit grammar during communication with two
or more agents. Such grammar, by construction, should be compact for efficient
communication. Both modules undergo conjoint evolution - similar to an infant's
babbling that enables it to learn a language of choice by trial and error. We
utilised three multi-agent environments, namely predator/prey, traffic junction
and level-based foraging and illustrate that such a co-evolution enables us to
learn much quicker (50%) than state-of-the-art algorithms like MADDPG. Ablation
studies further show that disabling the inferring belief module, communication
module, and the hidden states reduces the model performance by 38%, 60% and
30%, respectively. Hence, we suggest that modelling other agents' behaviour
accelerates another agent to learn grammar and develop a language to
communicate efficiently. We evaluate our method on a set of cooperative
scenarios and show its superior performance to other multi-agent baselines. We
also demonstrate that it is essential for agents to reason about others' states
and learn this ability by continuous communication.

Graph convolutional networks (GCNs) can successfully learn the graph signal
representation by graph convolution. The graph convolution depends on the graph
filter, which contains the topological dependency of data and propagates data
features. However, the estimation errors in the propagation matrix (e.g., the
adjacency matrix) can have a significant impact on graph filters and GCNs. In
this paper, we study the effect of a probabilistic graph error model on the
performance of the GCNs. We prove that the adjacency matrix under the error
model is bounded by a function of graph size and error probability. We further
analytically specify the upper bound of a normalized adjacency matrix with
self-loop added. Finally, we illustrate the error bounds by running experiments
on a synthetic dataset and study the sensitivity of a simple GCN under this
probabilistic error model on accuracy.

Pre-trained Transformers are good foundations for unified multi-task models
owing to their task-agnostic representation. Pre-trained Transformers are often
combined with text-to-text framework to execute multiple tasks by a single
model. Performing a task through a graphical user interface (GUI) is another
candidate to accommodate various tasks, including multi-step tasks with vision
and language inputs. However, few papers combine pre-trained Transformers with
performing through GUI. To fill this gap, we explore a framework in which a
model performs a task by manipulating the GUI implemented with web pages in
multiple steps. We develop task pages with and without page transitions and
propose a BERT extension for the framework. We jointly trained our BERT
extension with those task pages, and made the following observations. (1) The
model learned to use both task pages with and without page transition. (2) In
four out of five tasks without page transitions, the model performs greater
than 75% of the performance of the original BERT, which does not use browsers.
(3) The model did not generalize effectively on unseen tasks. These results
suggest that we can fine-tune BERTs to multi-step tasks through GUIs, and there
is room for improvement in their generalizability. Code will be available
online.

This paper addresses the problem of unsupervised parts-aware point cloud
generation with learned parts-based self-similarity. Our SPA-VAE infers a set
of latent canonical candidate shapes for any given object, along with a set of
rigid body transformations for each such candidate shape to one or more
locations within the assembled object. In this way, noisy samples on the
surface of, say, each leg of a table, are effectively combined to estimate a
single leg prototype. When parts-based self-similarity exists in the raw data,
sharing data among parts in this way confers numerous advantages: modeling
accuracy, appropriately self-similar generative outputs, precise in-filling of
occlusions, and model parsimony. SPA-VAE is trained end-to-end using a
variational Bayesian approach which uses the Gumbel-softmax trick for the
shared part assignments, along with various novel losses to provide appropriate
inductive biases. Quantitative and qualitative analyses on ShapeNet demonstrate
the advantage of SPA-VAE.

Recent algorithms for image manipulation detection almost exclusively use
deep network models. These approaches require either dense pixelwise
groundtruth masks, camera ids, or image metadata to train the networks. On one
hand, constructing a training set to represent the countless tampering
possibilities is impractical. On the other hand, social media platforms or
commercial applications are often constrained to remove camera ids as well as
metadata from images. A self-supervised algorithm for training manipulation
detection models without dense groundtruth or camera/image metadata would be
extremely useful for many forensics applications. In this paper, we propose
self-supervised approach for training splicing detection/localization models
from frequency transforms of images. To identify the spliced regions, our deep
network learns a representation to capture an image specific signature by
enforcing (image) self consistency . We experimentally demonstrate that our
proposed model can yield similar or better performances of multiple existing
methods on standard datasets without relying on labels or metadata.

Due to the limited availability of medical data, deep learning approaches for
medical image analysis tend to generalise poorly to unseen data. Augmenting
data during training with random transformations has been shown to help and
became a ubiquitous technique for training neural networks. Here, we propose a
novel adversarial counterfactual augmentation scheme that aims at finding the
most \textit{effective} synthesised images to improve downstream tasks, given a
pre-trained generative model. Specifically, we construct an adversarial game
where we update the input \textit{conditional factor} of the generator and the
downstream \textit{classifier} with gradient backpropagation alternatively and
iteratively. This can be viewed as finding the `\textit{weakness}' of the
classifier and purposely forcing it to \textit{overcome} its weakness via the
generative model. To demonstrate the effectiveness of the proposed approach, we
validate the method with the classification of Alzheimer's Disease (AD) as a
downstream task. The pre-trained generative model synthesises brain images
using age as conditional factor. Extensive experiments and ablation studies
have been performed to show that the proposed approach improves classification
performance and has potential to alleviate spurious correlations and
catastrophic forgetting. Code will be released upon acceptance.

We show that the fundamental tasks of sorting lists and building search trees
or heaps can be modeled as quadratic unconstrained binary optimization problems
(QUBOs). The idea is to understand these tasks as permutation problems and to
devise QUBOs whose solutions represent appropriate permutation matrices. We
discuss how to construct such QUBOs and how to solve them using Hopfield nets
or adiabatic) quantum computing. In short, we show that neurocomputing methods
or quantum computers can solve problems usually associated with abstract data

Unstructured pruning is well suited to reduce the memory footprint of
convolutional neural networks (CNNs), both at training and inference time. CNNs
contain parameters arranged in $K \times K$ filters. Standard unstructured
pruning (SP) reduces the memory footprint of CNNs by setting filter elements to
zero, thereby specifying a fixed subspace that constrains the filter.
Especially if pruning is applied before or during training, this induces a
strong bias. To overcome this, we introduce interspace pruning (IP), a general
tool to improve existing pruning methods. It uses filters represented in a
dynamic interspace by linear combinations of an underlying adaptive filter
basis (FB). For IP, FB coefficients are set to zero while un-pruned
coefficients and FBs are trained jointly. In this work, we provide mathematical
evidence for IP's superior performance and demonstrate that IP outperforms SP
on all tested state-of-the-art unstructured pruning methods. Especially in
challenging situations, like pruning for ImageNet or pruning to high sparsity,
IP greatly exceeds SP with equal runtime and parameter costs. Finally, we show
that advances of IP are due to improved trainability and superior

In brain-computer interfaces (BCI), most of the approaches based on
event-related potential (ERP) focus on the detection of P300, aiming for single
trial classification for a speller task. While this is an important objective,
existing P300 BCI still require several repetitions to achieve a correct
classification accuracy. Signal processing and machine learning advances in
P300 BCI mostly revolve around the P300 detection part, leaving the character
classification out of the scope. To reduce the number of repetitions while
maintaining a good character classification, it is critical to embrace the full
classification problem. We introduce an end-to-end pipeline, starting from
feature extraction, and is composed of an ERP-level classification using
probabilistic Riemannian MDM which feeds a character-level classification using
Bayesian accumulation of confidence across trials. Whereas existing approaches
only increase the confidence of a character when it is flashed, our new
pipeline, called Bayesian accumulation of Riemannian probabilities (ASAP),
update the confidence of each character after each flash. We provide the proper
derivation and theoretical reformulation of this Bayesian approach for a
seamless processing of information from signal to BCI characters. We
demonstrate that our approach performs significantly better than standard
methods on public P300 datasets.

Federated Leaning is an emerging approach to manage cooperation between a
group of agents for the solution of Machine Learning tasks, with the goal of
improving each agent's performance without disclosing any data. In this paper
we present a novel algorithmic architecture that tackle this problem in the
particular case of Anomaly Detection (or classification or rare events), a
setting where typical applications often comprise data with sensible
information, but where the scarcity of anomalous examples encourages
collaboration. We show how Random Forests can be used as a tool for the
development of accurate classifiers with an effective insight-sharing mechanism
that does not break the data integrity. Moreover, we explain how the new
architecture can be readily integrated in a blockchain infrastructure to ensure
the verifiable and auditable execution of the algorithm. Furthermore, we
discuss how this work may set the basis for a more general approach for the
design of federated ensemble-learning methods beyond the specific task and
architecture discussed in this paper.

Reliable out-of-distribution (OOD) detection is fundamental to implementing
safer modern machine learning (ML) systems. In this paper, we introduce Igeood,
an effective method for detecting OOD samples. Igeood applies to any
pre-trained neural network, works under various degrees of access to the ML
model, does not require OOD samples or assumptions on the OOD data but can also
benefit (if available) from OOD samples. By building on the geodesic
(Fisher-Rao) distance between the underlying data distributions, our
discriminator can combine confidence scores from the logits outputs and the
learned features of a deep neural network. Empirically, we show that Igeood
outperforms competing state-of-the-art methods on a variety of network
architectures and datasets.

With the number of vehicles continuously increasing, parking monitoring and
analysis are becoming a substantial feature of modern cities. In this study, we
present a methodology to monitor car parking areas and to analyze their
occupancy in real-time. The solution is based on a combination between image
analysis and deep learning techniques. It incorporates four building blocks put
inside a pipeline: vehicle detection, vehicle tracking, manual annotation of
parking slots, and occupancy estimation using the Ray Tracing algorithm. The
aim of this methodology is to optimize the use of parking areas and to reduce
the time wasted by daily drivers to find the right parking slot for their cars.
Also, it helps to better manage the space of the parking areas and to discover
misuse cases. A demonstration of the provided solution is shown in the
following video link: https://www.youtube.com/watch?v=KbAt8zT14Tc.

Noisy training set usually leads to the degradation of generalization and
robustness of neural networks. In this paper, we propose using a theoretically
guaranteed noisy label detection framework to detect and remove noisy data for
Learning with Noisy Labels (LNL). Specifically, we design a penalized
regression to model the linear relation between network features and one-hot
labels, where the noisy data are identified by the non-zero mean shift
parameters solved in the regression model. To make the framework scalable to
datasets that contain a large number of categories and training data, we
propose a split algorithm to divide the whole training set into small pieces
that can be solved by the penalized regression in parallel, leading to the
Scalable Penalized Regression (SPR) framework. We provide the non-asymptotic
probabilistic condition for SPR to correctly identify the noisy data. While SPR
can be regarded as a sample selection module for standard supervised training
pipeline, we further combine it with semi-supervised algorithm to further
exploit the support of noisy data as unlabeled data. Experimental results on
several benchmark datasets and real-world noisy datasets show the effectiveness
of our framework. Our code and pretrained models are released at
https://github.com/Yikai-Wang/SPR-LNL.

The alignment problem in the context of large language models must consider
the plurality of human values in our world. Whilst there are many resonant and
overlapping values amongst the world's cultures, there are also many
conflicting, yet equally valid, values. It is important to observe which
cultural values a model exhibits, particularly when there is a value conflict
between input prompts and generated outputs. We discuss how the co-creation of
language and cultural value impacts large language models (LLMs). We explore
the constitution of the training data for GPT-3 and compare that to the world's
language and internet access demographics, as well as to reported statistical
profiles of dominant values in some Nation-states. We stress tested GPT-3 with
a range of value-rich texts representing several languages and nations;
including some with values orthogonal to dominant US public opinion as reported
by the World Values Survey. We observed when values embedded in the input text
were mutated in the generated outputs and noted when these conflicting values
were more aligned with reported dominant US values. Our discussion of these
results uses a moral value pluralism (MVP) lens to better understand these
value mutations. Finally, we provide recommendations for how our work may
contribute to other current work in the field.

A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to
different timestamps. TKG reasoning aims to predict potential facts in the
future given the historical KG sequences. One key of this task is to mine and
understand evolutional patterns of facts from these sequences. The evolutional
patterns are complex in two aspects, length-diversity and time-variability.
Existing models for TKG reasoning focus on modeling fact sequences of a fixed
length, which cannot discover complex evolutional patterns that vary in length.
Furthermore, these models are all trained offline, which cannot well adapt to
the changes of evolutional patterns from then on. Thus, we propose a new model,
called Complex Evolutional Network (CEN), which uses a length-aware
Convolutional Neural Network (CNN) to handle evolutional patterns of different
lengths via an easy-to-difficult curriculum learning strategy. Besides, we
propose to learn the model under the online setting so that it can adapt to the
changes of evolutional patterns over time. Extensive experiments demonstrate
that CEN obtains substantial performance improvement under both the traditional
offline and the proposed online settings.

The numerical wavefront backpropagation principle of digital holography
confers unique extended focus capabilities, without mechanical displacements
along z-axis. However, the determination of the correct focusing distance is a
non-trivial and time consuming issue. A deep learning (DL) solution is proposed
to cast the autofocusing as a regression problem and tested over both
experimental and simulated holograms. Single wavelength digital holograms were
recorded by a Digital Holographic Microscope (DHM) with a 10$\mathrm{x}$
microscope objective from a patterned target moving in 3D over an axial range
of 92 $\mu$m. Tiny DL models are proposed and compared such as a tiny Vision
Transformer (TViT), tiny VGG16 (TVGG) and a tiny Swin-Transfomer (TSwinT). The
proposed tiny networks are compared with their original versions (ViT/B16,
VGG16 and Swin-Transformer Tiny) and the main neural networks used in digital
holography such as LeNet and AlexNet. The experiments show that the predicted
focusing distance $Z_R^{\mathrm{Pred}}$ is accurately inferred with an accuracy
of 1.2 $\mu$m in average in comparison with the DHM depth of field of 15
$\mu$m. Numerical simulations show that all tiny models give the
$Z_R^{\mathrm{Pred}}$ with an error below 0.3 $\mu$m. Such a prospect would
significantly improve the current capabilities of computer vision position
sensing in applications such as 3D microscopy for life sciences or
micro-robotics. Moreover, all models reach an inference time on CPU, inferior
to 25 ms per inference. In terms of occlusions, TViT based on its Transformer
architecture is the most robust.

In recent decades, advancements in motion learning have enabled robots to
acquire new skills and adapt to unseen conditions in both structured and
unstructured environments. In practice, motion learning methods capture
relevant patterns and adjust them to new conditions such as dynamic obstacle
avoidance or variable targets. In this paper, we investigate the robot motion
learning paradigm from a Riemannian manifold perspective. We argue that
Riemannian manifolds may be learned via human demonstrations in which geodesics
are natural motion skills. The geodesics are generated using a learned
Riemannian metric produced by our novel variational autoencoder (VAE), which is
especially intended to recover full-pose end-effector states and joint space
configurations. In addition, we propose a technique for facilitating on-the-fly
end-effector/multiple-limb obstacle avoidance by reshaping the learned manifold
using an obstacle-aware ambient metric. The motion generated using these
geodesics may naturally result in multiple-solution tasks that have not been
explicitly demonstrated previously. We extensively tested our approach in task
space and joint space scenarios using a 7-DoF robotic manipulator. We
demonstrate that our method is capable of learning and generating motion skills
based on complicated motion patterns demonstrated by a human operator.
Additionally, we assess several obstacle avoidance strategies and generate
trajectories in multiple-mode settings.

A common problem in natural sciences is the comparison of competing models in
the light of observed data. Bayesian model comparison provides a statistically
sound framework for this comparison based on the evidence each model provides
for the data. However, this framework relies on the calculation of likelihood
functions which are intractable for most models used in practice. Previous
approaches in the field of Approximate Bayesian Computation (ABC) circumvent
the evaluation of the likelihood and estimate the model evidence based on
rejection sampling, but they are typically computationally intense. Here, I
propose a new efficient method to perform Bayesian model comparison in ABC.
Based on recent advances in posterior density estimation, the method
approximates the posterior over models in parametric form. In particular, I
train a mixture-density network to map features of the observed data to the
posterior probability of the models. The performance is assessed with two
examples. On a tractable model comparison problem, the underlying exact
posterior probabilities are predicted accurately. In a use-case scenario from
computational neuroscience -- the comparison between two ion channel models --
the underlying ground-truth model is reliably assigned a high posterior
probability. Overall, the method provides a new efficient way to perform
Bayesian model comparison on complex biophysical models independent of the
model architecture.

In this paper, we consider a multiuser mobile edge computing (MEC) system,
where a mixed-integer offloading strategy is used to assist the resource
assignment for task offloading. Although the conventional branch and bound
(BnB) approach can be applied to solve this problem, a huge burden of
computational complexity arises which limits the application of BnB. To address
this issue, we propose an intelligent BnB (IBnB) approach which applies deep
learning (DL) to learn the pruning strategy of the BnB approach. By using this
learning scheme, the structure of the BnB approach ensures near-optimal
performance and meanwhile DL-based pruning strategy significantly reduces the
complexity. Numerical results verify that the proposed IBnB approach achieves
optimal performance with complexity reduced by over 80%.

The Bayesian approach to solving inverse problems relies on the choice of a
prior. This critical ingredient allows the formulation of expert knowledge or
physical constraints in a probabilistic fashion and plays an important role for
the success of the inference. Recently, Bayesian inverse problems were solved
using generative models as highly informative priors. Generative models are a
popular tool in machine learning to generate data whose properties closely
resemble those of a given database. Typically, the generated distribution of
data is embedded in a low-dimensional manifold. For the inverse problem, a
generative model is trained on a database that reflects the properties of the
sought solution, such as typical structures of the tissue in the human brain in
magnetic resonance (MR) imaging. The inference is carried out in the
low-dimensional manifold determined by the generative model which strongly
reduces the dimensionality of the inverse problem. However, this proceeding
produces a posterior that admits no Lebesgue density in the actual variables
and the accuracy reached can strongly depend on the quality of the generative
model. For linear Gaussian models we explore an alternative Bayesian inference
based on probabilistic generative models which is carried out in the original
high-dimensional space. A Laplace approximation is employed to analytically
derive the required prior probability density function induced by the
generative model. Properties of the resulting inference are investigated.
Specifically, we show that derived Bayes estimates are consistent, in contrast
to the approach employing the low-dimensional manifold of the generative model.
The MNIST data set is used to construct numerical experiments which confirm our

In this manuscript we consider denoising of large rectangular matrices: given
a noisy observation of a signal matrix, what is the best way of recovering the
signal matrix itself? For Gaussian noise and rotationally-invariant signal
priors, we completely characterize the optimal denoiser and its performance in
the high-dimensional limit, in which the size of the signal matrix goes to
infinity with fixed aspects ratio, and under the Bayes optimal setting, that is
when the statistician knows how the signal and the observations were generated.
Our results generalise previous works that considered only symmetric matrices
to the more general case of non-symmetric and rectangular ones. We explore
analytically and numerically a particular choice of factorized signal prior
that models cross-covariance matrices and the matrix factorization problem. As
a byproduct of our analysis, we provide an explicit asymptotic evaluation of
the rectangular Harish-Chandra-Itzykson-Zuber integral in a special case.

Model Predictive Control (MPC) has become a popular framework in embedded
control for high-performance autonomous systems. However, to achieve good
control performance using MPC, an accurate dynamics model is key. To maintain
real-time operation, the dynamics models used on embedded systems have been
limited to simple first-principle models, which substantially limits their
representative power. In contrast to such simple models, machine learning
approaches, specifically neural networks, have been shown to accurately model
even complex dynamic effects, but their large computational complexity hindered
combination with fast real-time iteration loops. With this work, we present
Real-time Neural MPC, a framework to efficiently integrate large, complex
neural network architectures as dynamics models within a model-predictive
control pipeline.Our experiments, performed in simulation and the real world
onboard a highly agile quadrotor platform, demonstrate the capabilities of the
described system to run learned models with, previously infeasible, large
modeling capacity using gradient-based online optimization MPC. Compared to
prior implementations of neural networks in online optimization MPC we can
leverage models of over 4000 times larger parametric capacity in a 50Hz
real-time window on an embedded platform. Further, we show the feasibility of
our framework on real-world problems by reducing the positional tracking error
by up to 82% when compared to state-of-the-art MPC approaches without neural
network dynamics.

Arbitrary style transfer (AST) and domain generalization (DG) are important
yet challenging visual learning tasks, which can be cast as a feature
distribution matching problem. With the assumption of Gaussian feature
distribution, conventional feature distribution matching methods usually match
the mean and standard deviation of features. However, the feature distributions
of real-world data are usually much more complicated than Gaussian, which
cannot be accurately matched by using only the first-order and second-order
statistics, while it is computationally prohibitive to use high-order
statistics for distribution matching. In this work, we, for the first time to
our best knowledge, propose to perform Exact Feature Distribution Matching
(EFDM) by exactly matching the empirical Cumulative Distribution Functions
(eCDFs) of image features, which could be implemented by applying the Exact
Histogram Matching (EHM) in the image feature space. Particularly, a fast EHM
algorithm, named Sort-Matching, is employed to perform EFDM in a plug-and-play
manner with minimal cost. The effectiveness of our proposed EFDM method is
verified on a variety of AST and DG tasks, demonstrating new state-of-the-art
results. Codes are available at https://github.com/YBZh/EFDM.

Few-shot learning (FSL), purposing to resolve the problem of data-scarce, has
attracted considerable attention in recent years. A popular FSL framework
contains two phases: (i) the pre-train phase employs the base data to train a
CNN-based feature extractor. (ii) the meta-test phase applies the frozen
feature extractor to novel data (novel data has different categories from base
data) and designs a classifier for recognition. To correct few-shot data
distribution, researchers propose Semi-Supervised Few-Shot Learning (SSFSL) by
introducing unlabeled data. Although SSFSL has been proved to achieve
outstanding performances in the FSL community, there still exists a fundamental
problem: the pre-trained feature extractor can not adapt to the novel data
flawlessly due to the cross-category setting. Usually, large amounts of noises
are introduced to the novel feature. We dub it as Feature-Extractor-Maladaptive
(FEM) problem. To tackle FEM, we make two efforts in this paper. First, we
propose a novel label prediction method, Isolated Graph Learning (IGL). IGL
introduces the Laplacian operator to encode the raw data to graph space, which
helps reduce the dependence on features when classifying, and then project
graph representation to label space for prediction. The key point is that: IGL
can weaken the negative influence of noise from the feature representation
perspective, and is also flexible to independently complete training and
testing procedures, which is suitable for SSFSL. Second, we propose Graph
Co-Training (GCT) to tackle this challenge from a multi-modal fusion
perspective by extending the proposed IGL to the co-training framework. GCT is
a semi-supervised method that exploits the unlabeled samples with two modal
features to crossly strengthen the IGL classifier.

Cataracts are the leading cause of vision loss worldwide. Restoration
algorithms are developed to improve the readability of cataract fundus images
in order to increase the certainty in diagnosis and treatment for cataract
patients. Unfortunately, the requirement of annotation limits the application
of these algorithms in clinics. This paper proposes a network to
annotation-freely restore cataractous fundus images (ArcNet) so as to boost the
clinical practicability of restoration. Annotations are unnecessary in ArcNet,
where the high-frequency component is extracted from fundus images to replace
segmentation in the preservation of retinal structures. The restoration model
is learned from the synthesized images and adapted to real cataract images.
Extensive experiments are implemented to verify the performance and
effectiveness of ArcNet. Favorable performance is achieved using ArcNet against
state-of-the-art algorithms, and the diagnosis of ocular fundus diseases in
cataract patients is promoted by ArcNet. The capability of properly restoring
cataractous images in the absence of annotated data promises the proposed
algorithm outstanding clinical practicability.

Developers often search and reuse existing code snippets in the process of
software development. Code search aims to retrieve relevant code snippets from
a codebase according to natural language queries entered by the developer. Up
to now, researchers have already proposed information retrieval (IR) based
methods and deep learning (DL) based methods. The IR-based methods focus on
keyword matching, that is to rank codes by relevance between queries and code
snippets, while DL-based methods focus on capturing the semantic correlations.
However, the existing methods do not consider capturing two matching signals
simultaneously. Therefore, in this paper, we propose CSRS, a code search model
with relevance matching and semantic matching. CSRS comprises (1) an embedding
module containing convolution kernels of different sizes which can extract
n-gram embeddings of queries and codes, (2) a relevance matching module that
measures lexical matching signals, and (3) a co-attention based semantic
matching module to capture the semantic correlation. We train and evaluate CSRS
on a dataset with 18.22M and 10k code snippets. The experimental results
demonstrate that CSRS achieves an MRR of 0.614, which outperforms two
state-of-the-art models DeepCS and CARLCS-CNN by 33.77% and 18.53%
respectively. In addition, we also conducted several experiments to prove the
effectiveness of each component of CSRS.

Dense retrieval models, which aim at retrieving the most relevant document
for an input query on a dense representation space, have gained considerable
attention for their remarkable success. Yet, dense models require a vast amount
of labeled training data for notable performance, whereas it is often
challenging to acquire query-document pairs annotated by humans. To tackle this
problem, we propose a simple but effective Document Augmentation for dense
Retrieval (DAR) framework, which augments the representations of documents with
their interpolation and perturbation. We validate the performance of DAR on
retrieval tasks with two benchmark datasets, showing that the proposed DAR
significantly outperforms relevant baselines on the dense retrieval of both the
labeled and unlabeled documents.

We present a novel face reconstruction method capable of reconstructing
detailed face geometry, spatially varying face reflectance from a single
monocular image. We build our work upon the recent advances of DNN-based
auto-encoders with differentiable ray tracing image formation, trained in
self-supervised manner. While providing the advantage of learning-based
approaches and real-time reconstruction, the latter methods lacked fidelity. In
this work, we achieve, for the first time, high fidelity face reconstruction
using self-supervised learning only. Our novel coarse-to-fine deep architecture
allows us to solve the challenging problem of decoupling face reflectance from
geometry using a single image, at high computational speed. Compared to
state-of-the-art methods, our method achieves more visually appealing

It is challenging to control the quality of online information due to the
lack of supervision over all the information posted online. Manual checking is
almost impossible given the vast number of posts made on online media and how
quickly they spread. Therefore, there is a need for automated rumour detection
techniques to limit the adverse effects of spreading misinformation. Previous
studies mainly focused on finding and extracting the significant features of
text data. However, extracting features is time-consuming and not a highly
effective process. This study proposes the BERT- based pre-trained language
models to encode text data into vectors and utilise neural network models to
classify these vectors to detect misinformation. Furthermore, different
language models (LM) ' performance with different trainable parameters was
compared. The proposed technique is tested on different short and long text
datasets. The result of the proposed technique has been compared with the
state-of-the-art techniques on the same datasets. The results show that the
proposed technique performs better than the state-of-the-art techniques. We
also tested the proposed technique by combining the datasets. The results
demonstrated that the large data training and testing size considerably
improves the technique's performance.

SVR-GARCH model tends to "backward eavesdrop" when forecasting the financial
time series volatility in which case it tends to simply produce the prediction
by deviating the previous volatility. Though the SVR-GARCH model has achieved
good performance in terms of various performance measurements, trading
opportunities, peak or trough behaviors in the time series are all hampered by
underestimating or overestimating the volatility. We propose a blending ARCH
(BARCH) and an augmented BARCH (aBARCH) model to overcome this kind of problem
and make the prediction towards better peak or trough behaviors. The method is
illustrated using real data sets including SH300 and S&P500. The empirical
results obtained suggest that the augmented and blending models improve the
volatility forecasting ability.

Deep learning (DL) is being increasingly utilized in healthcare-related
fields due to its outstanding efficiency. However, we have to keep the
individual health data used by DL models private and secure. Protecting data
and preserving the privacy of individuals has become an increasingly prevalent
issue. The gap between the DL and privacy communities must be bridged. In this
paper, we propose privacy-preserving deep learning (PPDL)-based approach to
secure the classification of Chest X-ray images. This study aims to use Chest
X-ray images to their fullest potential without compromising the privacy of the
data that it contains. The proposed approach is based on two steps: encrypting
the dataset using partially homomorphic encryption and training/testing the DL
algorithm over the encrypted images. Experimental results on the COVID-19
Radiography database show that the MobileNetV2 model achieves an accuracy of
94.2% over the plain data and 93.3% over the encrypted data.

The performance of medical image classification has been enhanced by deep
convolutional neural networks (CNNs), which are typically trained with
cross-entropy (CE) loss. However, when the label presents an intrinsic ordinal
property in nature, e.g., the development from benign to malignant tumor, CE
loss cannot take into account such ordinal information to allow for better
generalization. To improve model generalization with ordinal information, we
propose a novel meta ordinal regression forest (MORF) method for medical image
classification with ordinal labels, which learns the ordinal relationship
through the combination of convolutional neural network and differential forest
in a meta-learning framework. The merits of the proposed MORF come from the
following two components: a tree-wise weighting net (TWW-Net) and a grouped
feature selection (GFS) module. First, the TWW-Net assigns each tree in the
forest with a specific weight that is mapped from the classification loss of
the corresponding tree. Hence, all the trees possess varying weights, which is
helpful for alleviating the tree-wise prediction variance. Second, the GFS
module enables a dynamic forest rather than a fixed one that was previously
used, allowing for random feature perturbation. During training, we
alternatively optimize the parameters of the CNN backbone and TWW-Net in the
meta-learning framework through calculating the Hessian matrix. Experimental
results on two medical image classification datasets with ordinal labels, i.e.,
LIDC-IDRI and Breast Ultrasound Dataset, demonstrate the superior performances
of our MORF method over existing state-of-the-art methods.

In the whole aircraft structural optimization loop, thermal analysis plays a
very important role. But it faces a severe computational burden when directly
applying traditional numerical analysis tools, especially when each
optimization involves repetitive parameter modification and thermal analysis
followed. Recently, with the fast development of deep learning, several
Convolutional Neural Network (CNN) surrogate models have been introduced to
overcome this obstacle. However, for temperature field prediction on irregular
geometric domains (TFP-IGD), CNN can hardly be competent since most of them
stem from processing for regular images. To alleviate this difficulty, we
propose a novel physics and data co-driven surrogate modeling method. First,
after adapting the Bezier curve in geometric parameterization, a body-fitted
coordinate mapping is introduced to generate coordinate transforms between the
irregular physical plane and regular computational plane. Second, a
physics-driven CNN surrogate with partial differential equation (PDE) residuals
as a loss function is utilized for fast meshing (meshing surrogate); then, we
present a data-driven surrogate model based on the multi-level reduced-order
method, aiming to learn solutions of temperature field in the above regular
computational plane (thermal surrogate). Finally, combining the grid position
information provided by the meshing surrogate with the scalar temperature field
information provided by the thermal surrogate (combined model), we reach an
end-to-end surrogate model from geometric parameters to temperature field
prediction on an irregular geometric domain. Numerical results demonstrate that
our method can significantly improve accuracy prediction on a smaller dataset
while reducing the training time when compared with other CNN methods.

Contemporary deep-learning object detection methods for autonomous driving
usually assume prefixed categories of common traffic participants, such as
pedestrians and cars. Most existing detectors are unable to detect uncommon
objects and corner cases (e.g., a dog crossing a street), which may lead to
severe accidents in some situations, making the timeline for the real-world
application of reliable autonomous driving uncertain. One main reason that
impedes the development of truly reliably self-driving systems is the lack of
public datasets for evaluating the performance of object detectors on corner
cases. Hence, we introduce a challenging dataset named CODA that exposes this
critical problem of vision-based detectors. The dataset consists of 1500
carefully selected real-world driving scenes, each containing four object-level
corner cases (on average), spanning more than 30 object categories. On CODA,
the performance of standard object detectors trained on large-scale autonomous
driving datasets significantly drops to no more than 12.8% in mAR. Moreover, we
experiment with the state-of-the-art open-world object detector and find that
it also fails to reliably identify the novel objects in CODA, suggesting that a
robust perception system for autonomous driving is probably still far from
reach. We expect our CODA dataset to facilitate further research in reliable
detection for real-world autonomous driving. Our dataset will be released at
https://coda-dataset.github.io.

Code completion, which aims to predict the following code token(s) according
to the code context, can improve the productivity of software development.
Recent work has proved that statistical language modeling with transformers can
greatly improve the performance in the code completion task via learning from
large-scale source code datasets. However, current approaches focus only on
code context within the file or project, i.e. internal context. Our distinction
is utilizing "external" context, inspired by human behaviors of copying from
the related code snippets when writing code. Specifically, we propose a
retrieval-augmented code completion framework, leveraging both lexical copying
and referring to code with similar semantics by retrieval. We adopt a
stage-wise training approach that combines a source code retriever and an
auto-regressive language model for programming language. We evaluate our
approach in the code completion task in Python and Java programming languages,
achieving a state-of-the-art performance on CodeXGLUE benchmark.

Recent dominant methods for video-language pre-training (VLP) learn
transferable representations from the raw pixels in an end-to-end manner to
achieve advanced performance on downstream video-language tasks. Despite the
impressive results, VLP research becomes extremely expensive with the need for
massive data and a long training time, preventing further explorations. In this
work, we revitalize region features of sparsely sampled video clips to
significantly reduce both spatial and temporal visual redundancy towards
democratizing VLP research at the same time achieving state-of-the-art results.
Specifically, to fully explore the potential of region features, we introduce a
novel bidirectional region-word alignment regularization that properly
optimizes the fine-grained relations between regions and certain words in
sentences, eliminating the domain/modality disconnections between pre-extracted
region features and text. Extensive results of downstream text-to-video
retrieval and video question answering tasks on seven datasets demonstrate the
superiority of our method on both effectiveness and efficiency, e.g., our
method achieves competing results with 80\% fewer data and 85\% less
pre-training time compared to the most efficient VLP method so far. The code
will be available at \url{https://github.com/showlab/DemoVLP}.

Low precision deep neural network (DNN) training is one of the most effective
techniques for boosting DNNs' training efficiency, as it trims down the
training cost from the finest bit level. While existing works mostly fix the
model precision during the whole training process, a few pioneering works have
shown that dynamic precision schedules help DNNs converge to a better accuracy
while leading to a lower training cost than their static precision training
counterparts. However, existing dynamic low precision training methods rely on
manually designed precision schedules to achieve advantageous efficiency and
accuracy trade-offs, limiting their more comprehensive practical applications
and achievable performance. To this end, we propose LDP, a Learnable Dynamic
Precision DNN training framework that can automatically learn a temporally and
spatially dynamic precision schedule during training towards optimal accuracy
and efficiency trade-offs. It is worth noting that LDP-trained DNNs are by
nature efficient during inference. Furthermore, we visualize the resulting
temporal and spatial precision schedule and distribution of LDP trained DNNs on
different tasks to better understand the corresponding DNNs' characteristics at
different training stages and DNN layers both during and after training,
drawing insights for promoting further innovations. Extensive experiments and
ablation studies (seven networks, five datasets, and three tasks) show that the
proposed LDP consistently outperforms state-of-the-art (SOTA) low precision DNN
training techniques in terms of training efficiency and achieved accuracy
trade-offs. For example, in addition to having the advantage of being
automated, our LDP achieves a 0.31\% higher accuracy with a 39.1\% lower
computational cost when training ResNet-20 on CIFAR-10 as compared with the
best SOTA method.

The major challenges of collision avoidance for robot navigation in crowded
scenes lie in accurate environment modeling, fast perceptions, and trustworthy
motion planning policies. This paper presents a novel adaptive environment
model based collision avoidance reinforcement learning (i.e., AEMCARL)
framework for an unmanned robot to achieve collision-free motions in
challenging navigation scenarios. The novelty of this work is threefold: (1)
developing a hierarchical network of gated-recurrent-unit (GRU) for environment
modeling; (2) developing an adaptive perception mechanism with an attention
module; (3) developing an adaptive reward function for the reinforcement
learning (RL) framework to jointly train the environment model, perception
function and motion planning policy. The proposed method is tested with the
Gym-Gazebo simulator and a group of robots (Husky and Turtlebot) under various
crowded scenes. Both simulation and experimental results have demonstrated the
superior performance of the proposed method over baseline methods.

This work presents a novel self-supervised pre-training method to learn
efficient representations without labels on histopathology medical images
utilizing magnification factors. Other state-of-theart works mainly focus on
fully supervised learning approaches that rely heavily on human annotations.
However, the scarcity of labeled and unlabeled data is a long-standing
challenge in histopathology. Currently, representation learning without labels
remains unexplored for the histopathology domain. The proposed method,
Magnification Prior Contrastive Similarity (MPCS), enables self-supervised
learning of representations without labels on small-scale breast cancer dataset
BreakHis by exploiting magnification factor, inductive transfer, and reducing
human prior. The proposed method matches fully supervised learning
state-of-the-art performance in malignancy classification when only 20% of
labels are used in fine-tuning and outperform previous works in fully
supervised learning settings. It formulates a hypothesis and provides empirical
evidence to support that reducing human-prior leads to efficient representation
learning in self-supervision. The implementation of this work is available
online on GitHub -
https://github.com/prakashchhipa/Magnification-Prior-Self-Supervised-Method

End-to-end autoencoder (AE) learning has the potential of exceeding the
performance of human-engineered transceivers and encoding schemes, without a
priori knowledge of communication-theoretic principles. In this work, we aim to
understand to what extent and for which scenarios this claim holds true when
comparing with fair benchmarks. Our particular focus is on memoryless
multiple-input multiple-output (MIMO) and multi-user (MU) systems. Four case
studies are considered: two point-to-point (closed-loop and open-loop MIMO) and
two MU scenarios (MIMO broadcast and interference channels). For the
point-to-point scenarios, we explain some of the performance gains observed in
prior work through the selection of improved baseline schemes that include
geometric shaping as well as bit and power allocation. For the MIMO broadcast
channel, we demonstrate the feasibility of a novel AE method with centralized
learning and decentralized execution. Interestingly, the learned scheme
performs close to nonlinear vector-perturbation precoding and significantly
outperforms conventional zero-forcing. Lastly, we highlight potential pitfalls
when interpreting learned communication schemes. In particular, we show that
the AE for the considered interference channel learns to avoid interference,
albeit in a rotated reference frame. After de-rotating the learned signal
constellation of each user, the resulting scheme corresponds to conventional
time sharing with geometric shaping.

In this paper, we present a novel Distribution-Aware Single-stage (DAS) model
for tackling the challenging multi-person 3D pose estimation problem. Different
from existing top-down and bottom-up methods, the proposed DAS model
simultaneously localizes person positions and their corresponding body joints
in the 3D camera space in a one-pass manner. This leads to a simplified
pipeline with enhanced efficiency. In addition, DAS learns the true
distribution of body joints for the regression of their positions, rather than
making a simple Laplacian or Gaussian assumption as previous works. This
provides valuable priors for model prediction and thus boosts the
regression-based scheme to achieve competitive performance with volumetric-base
ones. Moreover, DAS exploits a recursive update strategy for progressively
approaching to regression target, alleviating the optimization difficulty and
further lifting the regression performance. DAS is implemented with a fully
Convolutional Neural Network and end-to-end learnable. Comprehensive
experiments on benchmarks CMU Panoptic and MuPoTS-3D demonstrate the superior
efficiency of the proposed DAS model, specifically 1.5x speedup over previous
best model, and its stat-of-the-art accuracy for multi-person 3D pose

Establishing a correspondence between two non-rigidly deforming shapes is one
of the most fundamental problems in visual computing. Existing methods often
show weak resilience when presented with challenges innate to real-world data
such as noise, outliers, self-occlusion etc. On the other hand, auto-decoders
have demonstrated strong expressive power in learning geometrically meaningful
latent embeddings. However, their use in \emph{shape analysis} has been
limited. In this paper, we introduce an approach based on an auto-decoder
framework, that learns a continuous shape-wise deformation field over a fixed
template. By supervising the deformation field for points on-surface and
regularising for points off-surface through a novel \emph{Signed Distance
Regularisation} (SDR), we learn an alignment between the template and shape
\emph{volumes}. Trained on clean water-tight meshes, \emph{without} any
data-augmentation, we demonstrate compelling performance on compromised data
and real-world scans.

Advanced graph neural networks have shown great potentials in graph
classification tasks recently. Different from node classification where node
embeddings aggregated from local neighbors can be directly used to learn node
labels, graph classification requires a hierarchical accumulation of different
levels of topological information to generate discriminative graph embeddings.
Still, how to fully explore graph structures and formulate an effective graph
classification pipeline remains rudimentary. In this paper, we propose a novel
graph neural network based on supervised contrastive learning with structure
inference for graph classification. First, we propose a data-driven graph
augmentation strategy that can discover additional connections to enhance the
existing edge set. Concretely, we resort to a structure inference stage based
on diffusion cascades to recover possible connections with high node
similarities. Second, to improve the contrastive power of graph neural
networks, we propose to use a supervised contrastive loss for graph
classification. With the integration of label information, the one-vs-many
contrastive learning can be extended to a many-vs-many setting, so that the
graph-level embeddings with higher topological similarities will be pulled
closer. The supervised contrastive loss and structure inference can be
naturally incorporated within the hierarchical graph neural networks where the
topological patterns can be fully explored to produce discriminative graph
embeddings. Experiment results show the effectiveness of the proposed method
compared with recent state-of-the-art methods.

Feature representation via self-supervised learning has reached remarkable
success in image-level contrastive learning, which brings impressive
performances on image classification tasks. While image-level feature
representation mainly focuses on contrastive learning in single instance, it
ignores the objective differences between pretext and downstream prediction
tasks such as object detection and instance segmentation. In order to fully
unleash the power of feature representation on downstream prediction tasks, we
propose a new end-to-end self-supervised framework called InsCon, which is
devoted to capturing multi-instance information and extracting cell-instance
features for object recognition and localization. On the one hand, InsCon
builds a targeted learning paradigm that applies multi-instance images as
input, aligning the learned feature between corresponding instance views, which
makes it more appropriate for multi-instance recognition tasks. On the other
hand, InsCon introduces the pull and push of cell-instance, which utilizes cell
consistency to enhance fine-grained feature representation for precise boundary
localization. As a result, InsCon learns multi-instance consistency on semantic
feature representation and cell-instance consistency on spatial feature
representation. Experiments demonstrate the method we proposed surpasses MoCo
v2 by 1.1% AP^{bb} on COCO object detection and 1.0% AP^{mk} on COCO instance
segmentation using Mask R-CNN R50-FPN network structure with 90k iterations,
2.1% APbb on PASCAL VOC objection detection using Faster R-CNN R50-C4 network
structure with 24k iterations.

How to learn highly compact yet effective sentence representation?
Pre-trained language models have been effective in many NLP tasks. However,
these models are often huge and produce large sentence embeddings. Moreover,
there is a big performance gap between large and small models. In this paper,
we propose Homomorphic Projective Distillation (HPD) to learn compressed
sentence embeddings. Our method augments a small Transformer encoder model with
learnable projection layers to produce compact representations while mimicking
a large pre-trained language model to retain the sentence representation
quality. We evaluate our method with different model sizes on both semantic
textual similarity (STS) and semantic retrieval (SR) tasks. Experiments show
that our method achieves 2.7-4.5 points performance gain on STS tasks compared
with previous best representations of the same size. In SR tasks, our method
improves retrieval speed (8.2$\times$) and memory usage (8.0$\times$) compared
with state-of-the-art large models.

In recent years, deep learning-based approaches have significantly improved
the performance of single-channel speech enhancement. However, due to the
limitation of training data and computational complexity, real-time enhancement
of full-band (48 kHz) speech signals is still very challenging. Because of the
low energy of spectral information in the high-frequency part, it is more
difficult to directly model and enhance the full-band spectrum using neural
networks. To solve this problem, this paper proposes a two-stage real-time
speech enhancement model with extraction-interpolation mechanism for a
full-band signal. The 48 kHz full-band time-domain signal is divided into three
sub-channels by extracting, and a two-stage processing scheme of `masking +
compensation' is proposed to enhance the signal in the complex domain. After
the two-stage enhancement, the enhanced full-band speech signal is restored by
interval interpolation. In the subjective listening and word accuracy test, our
proposed model achieves superior performance and outperforms the baseline model
overall by 0.59 MOS and 4.0% WAcc for the non-personalized speech denoising
task.

Periodic time series (PTS) forecasting plays a crucial role in a variety of
industries to foster critical tasks, such as early warning, pre-planning,
resource scheduling, etc. However, the complicated dependencies of the PTS
signal on its inherent periodicity as well as the sophisticated composition of
various periods hinder the performance of PTS forecasting. In this paper, we
introduce a deep expansion learning framework, DEPTS, for PTS forecasting.
DEPTS starts with a decoupled formulation by introducing the periodic state as
a hidden variable, which stimulates us to make two dedicated modules to tackle
the aforementioned two challenges. First, we develop an expansion module on top
of residual learning to perform a layer-by-layer expansion of those complicated
dependencies. Second, we introduce a periodicity module with a parameterized
periodic function that holds sufficient capacity to capture diversified
periods. Moreover, our two customized modules also have certain interpretable
capabilities, such as attributing the forecasts to either local momenta or
global periodicity and characterizing certain core periodic properties, e.g.,
amplitudes and frequencies. Extensive experiments on both synthetic data and
real-world data demonstrate the effectiveness of DEPTS on handling PTS. In most
cases, DEPTS achieves significant improvements over the best baseline.
Specifically, the error reduction can even reach up to 20% for a few cases.
Finally, all codes are publicly available.

As the number of deep neural networks (DNNs) to be executed on a mobile
system-on-chip (SoC) increases, the mobile SoC suffers from the real-time DNN
acceleration within its limited hardware resources and power budget. Although
the previous mobile neural processing units (NPUs) take advantage of low-bit
computing and exploitation of the sparsity, it is incapable of accelerating
high-precision and dense DNNs. This paper proposes energy-efficient signed
bit-slice architecture which accelerates both high-precision and dense DNNs by
exploiting a large number of zero values of signed bit-slices. Proposed signed
bit-slice representation (SBR) changes signed $1111_{2}$ bit-slice to
$0000_{2}$ by borrowing a $1$ value from its lower order of bit-slice. As a
result, it generates a large number of zero bit-slices even in dense DNNs.
Moreover, it balances the positive and negative values of 2's complement data,
allowing bit-slice based output speculation which pre-computes high order of
bit-slices and skips the remaining dense low order of bit-slices. The signed
bit-slice architecture compresses and skips the zero input signed bit-slices,
and the zero skipping unit also supports the output skipping by masking the
speculated inputs as zero. Additionally, the heterogeneous network-on-chip
(NoC) benefits the exploitation of data reusability and reduction of
transmission bandwidth. The paper introduces a specialized instruction set
architecture (ISA) and a hierarchical instruction decoder for the control of
the signed bit-slice architecture. Finally, the signed bit-slice architecture
outperforms the previous bit-slice accelerator, Bit-fusion, over $\times3.65$
higher area-efficiency, $\times3.88$ higher energy-efficiency, and $\times5.35$
higher throughput.

Graph neural networks (GNNs) often assume strong homophily in graphs, seldom
considering heterophily which means connected nodes tend to have different
class labels and dissimilar features. In real-world scenarios, graphs may have
nodes that exhibit both homophily and heterophily. Failing to generalize to
this setting makes many GNNs underperform in graph classification. In this
paper, we address this limitation by identifying two useful designs and develop
a novel GNN architecture called IHGNN (Incorporating Heterophily into Graph
Neural Networks). These designs include integration and separation of the ego-
and neighbor-embeddings of nodes; and concatenation of all the node embeddings
as the final graph-level readout function. In the first design, integration is
combined with separation by an injective function which is the composition of
the MLP and the concatenation function. The second design enables the
graph-level readout function to differentiate between different node
embeddings. As the functions used in both the designs are injective, IHGNN,
while being simple, has an expressiveness as powerful as the 1-WL. We
empirically validate IHGNN on various graph datasets and demonstrate that it
achieves state-of-the-art performance on the graph classification task.

We offer a practical unpaired learning based image dehazing network from an
unpaired set of clear and hazy images. This paper provides a new perspective to
treat image dehazing as a two-class separated factor disentanglement task, i.e,
the task-relevant factor of clear image reconstruction and the task-irrelevant
factor of haze-relevant distribution. To achieve the disentanglement of these
two-class factors in deep feature space, contrastive learning is introduced
into a CycleGAN framework to learn disentangled representations by guiding the
generated images to be associated with latent factors. With such formulation,
the proposed contrastive disentangled dehazing method (CDD-GAN) employs
negative generators to cooperate with the encoder network to update
alternately, so as to produce a queue of challenging negative adversaries. Then
these negative adversaries are trained end-to-end together with the backbone
representation network to enhance the discriminative information and promote
factor disentanglement performance by maximizing the adversarial contrastive
loss. During the training, we further show that hard negative examples can
suppress the task-irrelevant factors and unpaired clear exemples can enhance
the task-relevant factors, in order to better facilitate haze removal and help
image restoration. Extensive experiments on both synthetic and real-world
datasets demonstrate that our method performs favorably against existing
unpaired dehazing baselines.

Many scenarios in mobility and traffic involve multiple different agents that
need to cooperate to find a joint solution. Recent advances in behavioral
planning use Reinforcement Learning to find effective and performant behavior
strategies. However, as autonomous vehicles and vehicle-to-X communications
become more mature, solutions that only utilize single, independent agents
leave potential performance gains on the road. Multi-Agent Reinforcement
Learning (MARL) is a research field that aims to find optimal solutions for
multiple agents that interact with each other. This work aims to give an
overview of the field to researchers in autonomous mobility. We first explain
MARL and introduce important concepts. Then, we discuss the central paradigms
that underlie MARL algorithms, and give an overview of state-of-the-art methods
and ideas in each paradigm. With this background, we survey applications of
MARL in autonomous mobility scenarios and give an overview of existing
scenarios and implementations.

Sampled Dense Times Dense Matrix Multiplication (SDDMM) and Sparse Times
Dense Matrix Multiplication (SpMM) appear in diverse settings, such as
collaborative filtering, document clustering, and graph embedding. Frequently,
the SDDMM output becomes the input sparse matrix for a subsequent SpMM
operation. Existing work has focused on shared memory parallelization of these
primitives. While there has been extensive analysis of communication-minimizing
distributed 1.5D algorithms for SpMM, no such analysis exists for SDDMM or the
back-to-back sequence of SDDMM and SpMM, termed FusedMM. We show that
distributed memory 1.5D and 2.5D algorithms for SpMM can be converted to
algorithms for SDDMM with identical communication costs and input / output data
layouts. Further, we give two communication-eliding strategies to reduce costs
further for FusedMM kernels: either reusing the replication of an input dense
matrix for the SDDMM and SpMM in sequence, or fusing the local SDDMM and SpMM
kernels.
  We benchmark FusedMM algorithms on Cori, a Cray XC40 at LBNL, using
Erdos-Renyi random matrices and large real-world sparse matrices. On 256 nodes
with 68 cores each, 1.5D FusedMM algorithms using either communication eliding
approach can save at least 30% of time spent exclusively in communication
compared to executing a distributed-memory SpMM and SDDMM kernel in sequence.
On real-world matrices with hundreds of millions of edges, all of our
algorithms exhibit at least a 10x speedup over the SpMM algorithm in PETSc. On
these matrices, our communication-eliding techniques exhibit runtimes up to 1.6
times faster than an unoptimized sequence of SDDMM and SpMM. We embed and test
the scaling of our algorithms in real-world applications, including
collaborative filtering via alternating-least-squares and inference for
attention-based graph neural networks.

We study the problem of learning worst-case-safe parameters for programs that
use neural networks as well as symbolic, human-written code. Such neurosymbolic
programs arise in many safety-critical domains. However, because they can use
nondifferentiable operations, it is hard to learn their parameters using
existing gradient-based approaches to safe learning. Our approach to this
problem, Differentiable Symbolic Execution (DSE), samples control flow paths in
a program, symbolically constructs worst-case "safety losses" along these
paths, and backpropagates the gradients of these losses through program
operations using a generalization of the REINFORCE estimator. We evaluate the
method on a mix of synthetic tasks and real-world benchmarks. Our experiments
show that DSE significantly outperforms the state-of-the-art DiffAI method on
these tasks.

Continually learning to segment more and more types of image regions is a
desired capability for many intelligent systems. However, such continual
semantic segmentation suffers from the same catastrophic forgetting issue as in
continual classification learning. While multiple knowledge distillation
strategies originally for continual classification have been well adapted to
continual semantic segmentation, they only consider transferring old knowledge
based on the outputs from one or more layers of deep fully convolutional
networks. Different from existing solutions, this study proposes to transfer a
new type of information relevant to knowledge, i.e. the relationships between
elements (Eg. pixels or small local regions) within each image which can
capture both within-class and between-class knowledge. The relationship
information can be effectively obtained from the self-attention maps in a
Transformer-style segmentation model. Considering that pixels belonging to the
same class in each image often share similar visual properties, a
class-specific region pooling is applied to provide more efficient relationship
information for knowledge transfer. Extensive evaluations on multiple public
benchmarks support that the proposed self-attention transfer method can further
effectively alleviate the catastrophic forgetting issue, and its flexible
combination with one or more widely adopted strategies significantly
outperforms state-of-the-art solu

The increasing volume of commercially available conversational agents (CAs)
on the market has resulted in users being burdened with learning and adopting
multiple agents to accomplish their tasks. Though prior work has explored
supporting a multitude of domains within the design of a single agent, the
interaction experience suffers due to the large action space of desired
capabilities. To address these problems, we introduce a new task BBAI:
Black-Box Agent Integration, focusing on combining the capabilities of multiple
black-box CAs at scale. We explore two techniques: question agent pairing and
question response pairing aimed at resolving this task. Leveraging these
techniques, we design One For All (OFA), a scalable system that provides a
unified interface to interact with multiple CAs. Additionally, we introduce
MARS: Multi-Agent Response Selection, a new encoder model for question response
pairing that jointly encodes user question and agent response pairs. We
demonstrate that OFA is able to automatically and accurately integrate an
ensemble of commercially available CAs spanning disparate domains.
Specifically, using the MARS encoder we achieve the highest accuracy on our
BBAI task, outperforming strong baselines.

In object detection, false negatives arise when a detector fails to detect a
target object. To understand why object detectors produce false negatives, we
identify five 'false negative mechanisms', where each mechanism describes how a
specific component inside the detector architecture failed. Focusing on
two-stage and one-stage anchor-box object detector architectures, we introduce
a framework for quantifying these false negative mechanisms. Using this
framework, we investigate why Faster R-CNN and RetinaNet fail to detect objects
in benchmark vision datasets and robotics datasets. We show that a detector's
false negative mechanisms differ significantly between computer vision
benchmark datasets and robotics deployment scenarios. This has implications for
the translation of object detectors developed for benchmark datasets to
robotics applications. Code is publicly available at
https://github.com/csiro-robotics/fn_mechanisms

Physics-informed Neural Network (PINN) is a promising tool that has been
applied in a variety of physical phenomena described by partial differential
equations (PDE). However, it has been observed that PINNs are difficult to
train in certain "stiff" problems, which include various nonlinear hyperbolic
PDEs that display shocks in their solutions. Recent studies added a diffusion
term to the PDE, and an artificial viscosity (AV) value was manually tuned to
allow PINNs to solve these problems. In this paper, we propose three approaches
to address this problem, none of which rely on an a priori definition of the
artificial viscosity value. The first method learns a global AV value, whereas
the other two learn localized AV values around the shocks, by means of a
parametrized AV map or a residual-based AV map. We applied the proposed methods
to the inviscid Burgers equation and the Buckley-Leverett equation, the latter
being a classical problem in Petroleum Engineering. The results show that the
proposed methods are able to learn both a small AV value and the accurate shock
location and improve the approximation error over a nonadaptive global AV
alternative method.

Molecular subtypes of breast cancer are important references to personalized
clinical treatment. For cost and labor savings, only one of the patient's
paraffin blocks is usually selected for subsequent immunohistochemistry (IHC)
to obtain molecular subtypes. Inevitable sampling error is risky due to tumor
heterogeneity and could result in a delay in treatment. Molecular subtype
prediction from conventional H&E pathological whole slide images (WSI) using AI
method is useful and critical to assist pathologists pre-screen proper paraffin
block for IHC. It's a challenging task since only WSI level labels of molecular
subtypes can be obtained from IHC. Gigapixel WSIs are divided into a huge
number of patches to be computationally feasible for deep learning. While with
coarse slide-level labels, patch-based methods may suffer from abundant noise
patches, such as folds, overstained regions, or non-tumor tissues. A weakly
supervised learning framework based on discriminative patch selecting and
multi-instance learning was proposed for breast cancer molecular subtype
prediction from H&E WSIs. Firstly, co-teaching strategy was adopted to learn
molecular subtype representations and filter out noise patches. Then, a
balanced sampling strategy was used to handle the imbalance in subtypes in the
dataset. In addition, a noise patch filtering algorithm that used local outlier
factor based on cluster centers was proposed to further select discriminative
patches. Finally, a loss function integrating patch with slide constraint
information was used to finetune MIL framework on obtained discriminative
patches and further improve the performance of molecular subtyping. The
experimental results confirmed the effectiveness of the proposed method and our
models outperformed even senior pathologists, with potential to assist
pathologists to pre-screen paraffin blocks for IHC in clinic.

Previous few-shot learning (FSL) works mostly are limited to natural images
of general concepts and categories. These works assume very high visual
similarity between the source and target classes. In contrast, the recently
proposed cross-domain few-shot learning (CD-FSL) aims at transferring knowledge
from general nature images of many labeled examples to novel domain-specific
target categories of only a few labeled examples. The key challenge of CD-FSL
lies in the huge data shift between source and target domains, which is
typically in the form of totally different visual styles. This makes it very
nontrivial to directly extend the classical FSL methods to address the CD-FSL
task. To this end, this paper studies the problem of CD-FSL by spanning the
style distributions of the source dataset. Particularly, wavelet transform is
introduced to enable the decomposition of visual representations into
low-frequency components such as shape and style and high-frequency components
e.g., texture. To make our model robust to visual styles, the source images are
augmented by swapping the styles of their low-frequency components with each
other. We propose a novel Style Augmentation (StyleAug) module to implement
this idea. Furthermore, we present a Self-Supervised Learning (SSL) module to
ensure the predictions of style-augmented images are semantically similar to
the unchanged ones. This avoids the potential semantic drift problem in
exchanging the styles. Extensive experiments on two CD-FSL benchmarks show the
effectiveness of our method. Our codes and models will be released.

Data modification, either via additional training datasets, data
augmentation, debiasing, and dataset filtering, has been proposed as an
effective solution for generalizing to out-of-domain (OOD) inputs, in both
natural language processing and computer vision literature. However, the effect
of data modification on adversarial robustness remains unclear. In this work,
we conduct a comprehensive study of common data modification strategies and
evaluate not only their in-domain and OOD performance, but also their
adversarial robustness (AR). We also present results on a two-dimensional
synthetic dataset to visualize the effect of each method on the training
distribution. This work serves as an empirical study towards understanding the
relationship between generalizing to unseen domains and defending against
adversarial perturbations. Our findings suggest that more data (either via
additional datasets or data augmentation) benefits both OOD accuracy and AR.
However, data filtering (previously shown to improve OOD accuracy on natural
language inference) hurts OOD accuracy on other tasks such as question
answering and image classification. We provide insights from our experiments to
inform future work in this direction.

Recent progress in representation and contrastive learning in NLP has not
widely considered the class of \textit{sociopragmatic meaning} (i.e., meaning
in interaction within different language communities). To bridge this gap, we
propose a novel framework for learning task-agnostic representations
transferable to a wide range of sociopragmatic tasks (e.g., emotion, hate
speech, humor, sarcasm). Our framework outperforms other contrastive learning
frameworks for both in-domain and out-of-domain data, across both the general
and few-shot settings. For example, compared to two popular pre-trained
language models, our method obtains an improvement of $11.66$ average $F_1$ on
$16$ datasets when fine-tuned on only $20$ training samples per dataset.

Matrix completion problem has been previously studied under various adaptive
and passive settings. Previously, researchers have proposed passive, two-phase
and single-phase algorithms using coherence parameter, and multi phase
algorithm using sparsity-number. It has been shown that the method using
sparsity-number reaching to theoretical lower bounds in many conditions.
However, the aforementioned method is running in many phases through the matrix
completion process, therefore it makes much more informative decision at each
stage. Hence, it is natural that the method outperforms previous algorithms. In
this paper, we are using the idea of sparsity-number and propose and
single-phase column space recovery algorithm which can be extended to two-phase
exact matrix completion algorithm. Moreover, we show that these methods are as
efficient as multi-phase matrix recovery algorithm. We provide experimental
evidence to illustrate the performance of our algorithm.

Representations of events described in text are important for various tasks.
In this work, we present SWCC: a Simultaneous Weakly supervised Contrastive
learning and Clustering framework for event representation learning. SWCC
learns event representations by making better use of co-occurrence information
of events. Specifically, we introduce a weakly supervised contrastive learning
method that allows us to consider multiple positives and multiple negatives,
and a prototype-based clustering method that avoids semantically related events
being pulled apart. For model training, SWCC learns representations by
simultaneously performing weakly supervised contrastive learning and
prototype-based clustering. Experimental results show that SWCC outperforms
other baselines on Hard Similarity and Transitive Sentence Similarity tasks. In
addition, a thorough analysis of the prototype-based clustering method
demonstrates that the learned prototype vectors are able to implicitly capture
various relations between events.

The online social platforms, like Twitter, Facebook, LinkedIn and WeChat,
have grown really fast in last decade and have been one of the most effective
platforms for people to communicate and share information with each other. Due
to the "word of mouth" effects, information usually can spread rapidly on these
social media platforms. Therefore, it is important to study the mechanisms
driving the information diffusion and quantify the consequence of information
spread. A lot of efforts have been focused on this problem to help us better
understand and achieve higher performance in viral marketing and advertising.
On the other hand, the development of neural networks has blossomed in the last
few years, leading to a large number of graph representation learning (GRL)
models. Compared to traditional models, GRL methods are often shown to be more
effective. In this paper, we present a comprehensive review for existing works
using GRL methods for popularity prediction problem, and categorize related
literatures into two big classes, according to their mainly used model and
techniques: embedding-based methods and deep learning methods. Deep learning
method is further classified into six small classes: convolutional neural
networks, graph convolutional networks, graph attention networks, graph neural
networks, recurrent neural networks, and reinforcement learning. We compare the
performance of these different models and discuss their strengths and
limitations. Finally, we outline the challenges and future chances for
popularity prediction problem.

Multilingual neural machine translation models are trained to maximize the
likelihood of a mix of examples drawn from multiple language pairs. The
dominant inductive bias applied to these models is a shared vocabulary and a
shared set of parameters across languages; the inputs and labels corresponding
to examples drawn from different language pairs might still reside in distinct
sub-spaces. In this paper, we introduce multilingual crossover encoder-decoder
(mXEncDec) to fuse language pairs at an instance level. Our approach
interpolates instances from different language pairs into joint `crossover
examples' in order to encourage sharing input and output spaces across
languages. To ensure better fusion of examples in multilingual settings, we
propose several techniques to improve example interpolation across dissimilar
languages under heavy data imbalance. Experiments on a large-scale WMT
multilingual dataset demonstrate that our approach significantly improves
quality on English-to-Many, Many-to-English and zero-shot translation tasks
(from +0.5 BLEU up to +5.5 BLEU points). Results on code-switching sets
demonstrate the capability of our approach to improve model generalization to
out-of-distribution multilingual examples. We also conduct qualitative and
quantitative representation comparisons to analyze the advantages of our
approach at the representation level.

Data-intensive physics facilities are increasingly reliant on heterogeneous
and large-scale data processing and computational systems in order to collect,
distribute, process, filter, and analyze the ever increasing huge volumes of
data being collected. Moreover, these tasks are often performed in hard
real-time or quasi real-time processing pipelines that place extreme
constraints on various parameters and design choices for those systems.
Consequently, a large number and variety of challenges are faced to design,
construct, and operate such facilities. This is especially true at the energy
and intensity frontiers of particle physics where bandwidths of raw data can
exceed 100 TB/s of heterogeneous, high-dimensional data sourced from 300M+
individual sensors. Data filtering and compression algorithms deployed at these
facilities often operate at the level of 1 part in $10^5$, and once executed,
these algorithms drive the data curation process, further highlighting the
critical roles that these systems have in the physics impact of those
endeavors. This White Paper aims to highlight the challenges that these
facilities face in the design of the trigger and data acquisition
instrumentation and systems, as well as in their installation, commissioning,
integration and operation, and in building the domain knowledge and technical
expertise required to do so.

Recently few-shot segmentation (FSS) has been extensively developed. Most
previous works strive to achieve generalization through the meta-learning
framework derived from classification tasks; however, the trained models are
biased towards the seen classes instead of being ideally class-agnostic, thus
hindering the recognition of new concepts. This paper proposes a fresh and
straightforward insight to alleviate the problem. Specifically, we apply an
additional branch (base learner) to the conventional FSS model (meta learner)
to explicitly identify the targets of base classes, i.e., the regions that do
not need to be segmented. Then, the coarse results output by these two learners
in parallel are adaptively integrated to yield precise segmentation prediction.
Considering the sensitivity of meta learner, we further introduce an adjustment
factor to estimate the scene differences between the input image pairs for
facilitating the model ensemble forecasting. The substantial performance gains
on PASCAL-5i and COCO-20i verify the effectiveness, and surprisingly, our
versatile scheme sets a new state-of-the-art even with two plain learners.
Moreover, in light of the unique nature of the proposed approach, we also
extend it to a more realistic but challenging setting, i.e., generalized FSS,
where the pixels of both base and novel classes are required to be determined.
The source code is available at github.com/chunbolang/BAM.

Multi-omics data analysis has the potential to discover hidden molecular
interactions, revealing potential regulatory and/or signal transduction
pathways for cellular processes of interest when studying life and disease
systems. One of critical challenges when dealing with real-world multi-omics
data is that they may manifest heterogeneous structures and data quality as
often existing data may be collected from different subjects under different
conditions for each type of omics data. We propose a novel deep Bayesian
generative model to efficiently infer a multi-partite graph encoding molecular
interactions across such heterogeneous views, using a fused Gromov-Wasserstein
(FGW) regularization between latent representations of corresponding views for
integrative analysis. With such an optimal transport regularization in the deep
Bayesian generative model, it not only allows incorporating view-specific side
information, either with graph-structured or unstructured data in different
views, but also increases the model flexibility with the distribution-based
regularization. This allows efficient alignment of heterogeneous latent
variable distributions to derive reliable interaction predictions compared to
the existing point-based graph embedding methods. Our experiments on several
real-world datasets demonstrate enhanced performance of MoReL in inferring
meaningful interactions compared to existing baselines.

A wide variety of Cyber Threat Information (CTI) is used by Security
Operation Centres (SOCs) to perform validation of security incidents and
alerts. Security experts manually define different types of rules and scripts
based on CTI to perform validation tasks. These rules and scripts need to be
updated continuously due to evolving threats, changing SOCs' requirements and
dynamic nature of CTI. The manual process of updating rules and scripts delays
the response to attacks. To reduce the burden of human experts and accelerate
response, we propose a novel Artificial Intelligence (AI) based framework,
SmartValidator. SmartValidator leverages Machine Learning (ML) techniques to
enable automated validation of alerts. It consists of three layers to perform
the tasks of data collection, model building and alert validation. It projects
the validation task as a classification problem. Instead of building and saving
models for all possible requirements, we propose to automatically construct the
validation models based on SOC's requirements and CTI. We built a Proof of
Concept (PoC) system with eight ML algorithms, two feature engineering
techniques and 18 requirements to investigate the effectiveness and efficiency
of SmartValidator. The evaluation results showed that when prediction models
were built automatically for classifying cyber threat data, the F1-score of
75\% of the models were above 0.8, which indicates adequate performance of the
PoC for use in a real-world organization. The results further showed that
dynamic construction of prediction models required 99\% less models to be built
than pre-building models for all possible requirements. The framework can be
followed by various industries to accelerate and automate the validation of
alerts and incidents based on their CTI and SOC's preferences.

In this paper, we propose Multi-View Dreaming, a novel reinforcement learning
agent for integrated recognition and control from multi-view observations by
extending Dreaming. Most current reinforcement learning method assumes a
single-view observation space, and this imposes limitations on the observed
data, such as lack of spatial information and occlusions. This makes obtaining
ideal observational information from the environment difficult and is a
bottleneck for real-world robotics applications. In this paper, we use
contrastive learning to train a shared latent space between different
viewpoints, and show how the Products of Experts approach can be used to
integrate and control the probability distributions of latent states for
multiple viewpoints. We also propose Multi-View DreamingV2, a variant of
Multi-View Dreaming that uses a categorical distribution to model the latent
state instead of the Gaussian distribution. Experiments show that the proposed
method outperforms simple extensions of existing methods in a realistic robot
control task.

We find an application in quantum finite automata for the ideas and results
of [JL21] and [JL22]. We reformulate quantum finite automata with multiple-time
measurements using the algebraic notion of near-ring. This gives a unified
understanding towards quantum computing and deep learning. When the near-ring
comes from a quiver, we have a nice moduli space of computing machines with
metric that can be optimized by gradient descent.

It has been reported that deep learning models are extremely vulnerable to
small but intentionally chosen perturbations of its input. In particular, a
deep network, despite its near-optimal accuracy on the clean images, often
mis-classifies an image with a worst-case but humanly imperceptible
perturbation (so-called adversarial examples). To tackle this problem, a great
amount of research has been done to study the training procedure of a network
to improve its robustness. However, most of the research so far has focused on
the case of supervised learning. With the increasing popularity of
self-supervised learning methods, it is also important to study and improve the
robustness of their resulting representation on the downstream tasks. In this
paper, we study the problem of robust representation learning with unlabeled
data in a task-agnostic manner. Specifically, we first derive an upper bound on
the adversarial loss of a prediction model (which is based on the learned
representation) on any downstream task, using its loss on the clean data and a
robustness regularizer. Moreover, the regularizer is task-independent, thus we
propose to minimize it directly during the representation learning phase to
make the downstream prediction model more robust. Extensive experiments show
that our method achieves preferable adversarial performance compared to
relevant baselines.

Bias in training datasets must be managed for various groups in
classification tasks to ensure parity or equal treatment. With the recent
growth in artificial intelligence models and their expanding role in automated
decision-making, ensuring that these models are not biased is vital. There is
an abundance of evidence suggesting that these models could contain or even
amplify the bias present in the data on which they are trained, inherent to
their objective function and learning algorithms; Many researchers direct their
attention to this issue in different directions, namely, changing data to be
statistically independent, adversarial training for restricting the
capabilities of a particular competitor who aims to maximize parity, etc. These
methods result in information loss and do not provide a suitable balance
between accuracy and fairness or do not ensure limiting the biases in training.
To this end, we propose a powerful strategy for training deep learning models
called the Distraction module, which can be theoretically proven effective in
controlling bias from affecting the classification results. This method can be
utilized with different data types (e.g., Tabular, images, graphs, etc.). We
demonstrate the potency of the proposed method by testing it on UCI Adult and
Heritage Health datasets (tabular), POKEC-Z, POKEC-N and NBA datasets (graph),
and CelebA dataset (vision). Using state-of-the-art methods proposed in the
fairness literature for each dataset, we exhibit our model is superior to these
proposed methods in minimizing bias and maintaining accuracy.

Recently, work on reinforcement learning (RL) for bipedal robots has
successfully learned controllers for a variety of dynamic gaits with robust
sim-to-real demonstrations. In order to maintain balance, the learned
controllers have full freedom of where to place the feet, resulting in highly
robust gaits. In the real world however, the environment will often impose
constraints on the feasible footstep locations, typically identified by
perception systems. Unfortunately, most demonstrated RL controllers on bipedal
robots do not allow for specifying and responding to such constraints. This
missing control interface greatly limits the real-world application of current
RL controllers. In this paper, we aim to maintain the robust and dynamic nature
of learned gaits while also respecting footstep constraints imposed externally.
We develop an RL formulation for training dynamic gait controllers that can
respond to specified touchdown locations. We then successfully demonstrate
simulation and sim-to-real performance on the bipedal robot Cassie. In
addition, we use supervised learning to induce a transition model for
accurately predicting the next touchdown locations that the controller can
achieve given the robot's proprioceptive observations. This model paves the way
for integrating the learned controller into a full-order robot locomotion
planner that robustly satisfies both balance and environmental constraints.

Recently, Stochastic Variational Inference (SVI) has been increasingly
attractive thanks to its ability to find good posterior approximations of
probabilistic models. It optimizes the variational objective with stochastic
optimization, following noisy estimates of the natural gradient. However,
almost all the state-of-the-art SVI algorithms are based on first-order
optimization algorithm and often suffer from poor convergence rate. In this
paper, we bridge the gap between second-order methods and stochastic
variational inference by proposing a second-order based stochastic variational
inference approach. In particular, firstly we derive the Hessian matrix of the
variational objective. Then we devise two numerical schemes to implement
second-order SVI efficiently. Thorough empirical evaluations are investigated
on both synthetic and real dataset to backup both the effectiveness and
efficiency of the proposed approach.

Honeyfile deployment is a useful breach detection method in cyber deception
that can also inform defenders about the intent and interests of intruders and
malicious insiders. A key property of a honeyfile, enticement, is the extent to
which the file can attract an intruder to interact with it. We introduce a
novel metric, Topic Semantic Matching (TSM), which uses topic modelling to
represent files in the repository and semantic matching in an embedding vector
space to compare honeyfile text and topic words robustly. We also present a
honeyfile corpus created with different Natural Language Processing (NLP)
methods. Experiments show that TSM is effective in inter-corpus comparisons and
is a promising tool to measure the enticement of honeyfiles. TSM is the first
measure to use NLP techniques to quantify the enticement of honeyfile content
that compares the essential topical content of local contexts to honeyfiles and
is robust to paraphrasing.

Prediction with expert advice is a foundational problem in online learning.
In instances with $T$ rounds and $n$ experts, the classical Multiplicative
Weights Update method suffers at most $\sqrt{(T/2)\ln n}$ regret when $T$ is
known beforehand. Moreover, this is asymptotically optimal when both $T$ and
$n$ grow to infinity. However, when the number of experts $n$ is small/fixed,
algorithms with better regret guarantees exist. Cover showed in 1967 a dynamic
programming algorithm for the two-experts problem restricted to $\{0,1\}$ costs
that suffers at most $\sqrt{T/2\pi} + O(1)$ regret with $O(T^2)$ pre-processing
time. In this work, we propose an optimal algorithm for prediction with two
experts' advice that works even for costs in $[0,1]$ and with $O(1)$ processing
time per turn. Our algorithm builds up on recent work on the experts problem
based on techniques and tools from stochastic calculus.

Time-series data, such as unsteady pressure-sensitive paint (PSP) measurement
data, may contain a significant amount of random noise. Thus, in this study, we
investigated a noise-reduction method that combines multivariate singular
spectrum analysis (MSSA) with low-dimensional data representation. MSSA is a
state-space reconstruction technique that utilizes time-delay embedding, and
the low-dimensional representation is achieved by projecting data onto the
singular value decomposition (SVD) basis. The noise-reduction performance of
the proposed method for unsteady PSP data, i.e., the projected MSSA, is
compared with that of the truncated SVD method, one of the most employed
noise-reduction methods. The result shows that the projected MSSA exhibits
better performance in reducing random noise than the truncated SVD method.
Additionally, in contrast to that of the truncated SVD method, the performance
of the projected MSSA is less sensitive to the truncation rank. Furthermore,
the projected MSSA achieves denoising effectively by extracting smooth
trajectories in a state space from noisy input data. Expectedly, the projected
MSSA will be effective for reducing random noise in not only PSP measurement
data, but also various high-dimensional time-series data.

Achieving the capability of adapting to ever-changing environments is a
critical step towards building fully autonomous robots that operate safely in
complicated scenarios. In multiagent competitive scenarios, agents may have to
adapt to new opponents with previously unseen behaviors by learning from the
interaction experiences between the ego-agent and the opponent. However, this
adaptation is susceptible to opponent exploitation. As the ego-agent updates
its own behavior to exploit the opponent, its own behavior could become more
exploitable as a result of overfitting to this specific opponent's behavior. To
overcome this difficulty, we developed a safe adaptation approach in which the
ego-agent is trained against a regularized opponent model, which effectively
avoids overfitting and consequently improves the robustness of the ego-agent's
policy. We evaluated our approach in the Mujoco domain with two competing
agents. The experiment results suggest that our approach effectively achieves
both adaptation to the specific opponent that the ego-agent is interacting with
and maintaining low exploitability to other possible opponent exploitation.

Standardized file formats play a key role in the development and use of
computer software. However, it is possible to abuse standardized file formats
by creating a file that is valid in multiple file formats. The resulting
polyglot (many languages) file can confound file format identification,
allowing elements of the file to evade analysis.This is especially problematic
for malware detection systems that rely on file format identification for
feature extraction. File format identification processes that depend on file
signatures can be easily evaded thanks to flexibility in the format
specifications of certain file formats. Although work has been done to identify
file formats using more comprehensive methods than file signatures, accurate
identification of polyglot files remains an open problem. Since malware
detection systems routinely perform file format-specific feature extraction,
polyglot files need to be filtered out prior to ingestion by these systems.
Otherwise, malicious content could pass through undetected. To address the
problem of polyglot detection we assembled a data set using the mitra tool. We
then evaluated the performance of the most commonly used file identification
tool, file. Finally, we demonstrated the accuracy, precision, recall and F1
score of a range of machine and deep learning models. Malconv2 and Catboost
demonstrated the highest recall on our data set with 95.16% and 95.45%,
respectively. These models can be incorporated into a malware detector's file
processing pipeline to filter out potentially malicious polyglots before file
format-dependent feature extraction takes place.

When examining dynamics occurring at non-zero temperatures, both energy and
entropy must be taken into account while describing activated barrier crossing
events. Furthermore, good reaction coordinates need to be constructed to
describe different metastable states and the transition mechanisms between
them. Here we use a physics-based machine learning method called the State
Predictive Information Bottleneck (SPIB) to find non-linear reaction
coordinates for three systems of varying complexity. The SPIB is able to
predict correctly an entropic bottleneck for an analytical flat-energy
double-well system and identify the entropy- and energy-dominated pathways for
an analytical four-well system. Finally, for a simulation of benzoic acid
permeation through a lipid bilayer, SPIB is able to discover the entropic and
energetic barriers to the permeation process. Given these results, we thus
establish that SPIB is a reasonable and robust method for finding the important
entropy and energy/enthalpy barriers in physical systems, which can then be
used for enhanced understanding and sampling of different activated mechanisms.

A well-calibrated neural model produces confidence (probability outputs)
closely approximated by the expected accuracy. While prior studies have shown
that mixup training as a data augmentation technique can improve model
calibration on image classification tasks, little is known about using mixup
for model calibration on natural language understanding (NLU) tasks. In this
paper, we explore mixup for model calibration on several NLU tasks and propose
a novel mixup strategy for pre-trained language models that improves model
calibration further. Our proposed mixup is guided by both the Area Under the
Margin (AUM) statistic (Pleiss et al., 2020) and the saliency map of each
sample (Simonyan et al.,2013). Moreover, we combine our mixup strategy with
model miscalibration correction techniques (i.e., label smoothing and
temperature scaling) and provide detailed analyses of their impact on our
proposed mixup. We focus on systematically designing experiments on three NLU
tasks: natural language inference, paraphrase detection, and commonsense
reasoning. Our method achieves the lowest expected calibration error compared
to strong baselines on both in-domain and out-of-domain test samples while
maintaining competitive accuracy.

With the recent advances in the field of deep learning, learning-based
methods are widely being implemented in various robotic systems that help
robots understand their environment and make informed decisions to achieve a
wide variety of tasks or goals. However, learning-based methods have repeatedly
been shown to have poor generalization when they are presented with inputs that
are different from those during training leading to the problem of distribution
shift. Any robotic system that employs learning-based methods is prone to
distribution shift which might lead the agents to make decisions that lead to
degraded performance or even catastrophic failure. In this paper, we discuss
various techniques that have been proposed in the literature to aid or improve
decision making under distribution shift for robotic systems. We present a
taxonomy of existing literature and present a survey of existing approaches in
the area based on this taxonomy. Finally, we also identify a few open problems
in the area that could serve as future directions for research.

Materials with the ability to self-classify their own shape have the
potential to advance a wide range of engineering applications and industries.
Biological systems possess the ability not only to self-reconfigure but also to
self-classify themselves to determine a general shape and function. Previous
work into modular robotics systems has only enabled self-recognition and
self-reconfiguration into a specific target shape, missing the inherent
robustness present in nature to self-classify. In this paper we therefore take
advantage of recent advances in deep learning and neural cellular automata, and
present a simple modular 2D robotic system that can infer its own class of
shape through the local communication of its components. Furthermore, we show
that our system can be successfully transferred to hardware which thus opens
opportunities for future self-classifying machines. Code available at
https://github.com/kattwalker/projectcube. Video available at
https://youtu.be/0TCOkE4keyc.

This paper presents primarily two Euclidean embeddings of the quotient space
generated by matrices that are identified modulo arbitrary row permutations.
The original application is in deep learning on graphs where the learning task
is invariant to node relabeling. Two embedding schemes are introduced, one
based on sorting and the other based on algebras of multivariate polynomials.
While both embeddings exhibit a computational complexity exponential in problem
size, the sorting based embedding is globally bi-Lipschitz and admits a low
dimensional target space. Additionally, an almost everywhere injective scheme
can be implemented with minimal redundancy and low computational cost. In turn,
this proves that almost any classifier can be implemented with an arbitrary
small loss of performance. Numerical experiments are carried out on two data
sets, a chemical compound data set (QM9) and a proteins data set (PROTEINS).

The link prediction task on knowledge graphs without explicit negative
triples in the training data motivates the usage of rank-based metrics. Here,
we review existing rank-based metrics and propose desiderata for improved
metrics to address lack of interpretability and comparability of existing
metrics to datasets of different sizes and properties. We introduce a simple
theoretical framework for rank-based metrics upon which we investigate two
avenues for improvements to existing metrics via alternative aggregation
functions and concepts from probability theory. We finally propose several new
rank-based metrics that are more easily interpreted and compared accompanied by
a demonstration of their usage in a benchmarking of knowledge graph embedding
models.

Particle accelerators support a wide array of scientific, industrial, and
medical applications. To meet the needs of these applications, accelerator
physicists rely heavily on detailed simulations of the complicated particle
beam dynamics through the accelerator. One of the most computationally
expensive and difficult-to-model effects is the impact of Coherent Synchrotron
Radiation (CSR). As a beam travels through a curved trajectory (e.g. due to a
bending magnet), it emits radiation that in turn interacts with the rest of the
beam. At each step through the trajectory, the electromagnetic field introduced
by CSR (called the CSR wakefield) needs to computed and used when calculating
the updates to the positions and momenta of every particle in the beam. CSR is
one of the major drivers of growth in the beam emittance, which is a key metric
of beam quality that is critical in many applications. The CSR wakefield is
very computationally intensive to compute with traditional electromagnetic
solvers, and this is a major limitation in accurately simulating accelerators.
Here, we demonstrate a new approach for the CSR wakefield computation using a
neural network solver structured in a way that is readily generalizable to new
setups. We validate its performance by adding it to a standard beam tracking
test problem and show a ten-fold speedup along with high accuracy.

We present ScienceWorld, a benchmark to test agents' scientific reasoning
abilities in a new interactive text environment at the level of a standard
elementary school science curriculum. Despite the transformer-based progress
seen in question-answering and scientific text processing, we find that current
models cannot reason about or explain learned science concepts in novel
contexts. For instance, models can easily answer what the conductivity of a
known material is but struggle when asked how they would conduct an experiment
in a grounded environment to find the conductivity of an unknown material. This
begs the question of whether current models are simply retrieving answers by
way of seeing a large number of similar examples or if they have learned to
reason about concepts in a reusable manner. We hypothesize that agents need to
be grounded in interactive environments to achieve such reasoning capabilities.
Our experiments provide empirical evidence supporting this hypothesis --
showing that a 1.5 million parameter agent trained interactively for 100k steps
outperforms a 11 billion parameter model statically trained for scientific
question-answering and reasoning from millions of expert demonstrations.

We derive a variational representation for the log-normalizing constant of
the posterior distribution in Bayesian linear regression with a uniform
spherical prior and an i.i.d. Gaussian design. We work under the "proportional"
asymptotic regime, where the number of observations and the number of features
grow at a proportional rate. This rigorously establishes the
Thouless-Anderson-Palmer (TAP) approximation arising from spin glass theory,
and proves a conjecture of Krzakala et. al. (2014) in the special case of the
spherical prior.

In recent years, distinct machine learning (ML) models have been separately
used for feature extraction and noise reduction from energy-momentum dispersion
intensity maps obtained from raw angle-resolved photoemission spectroscopy
(ARPES) data. In this work, we employ a shallow variational auto-encoder (VAE)
neural network to demonstrate the prospect of using ML for both denoising of as
well as feature extraction from ARPES dispersion maps.

Production optimization under geological uncertainty is computationally
expensive, as a large number of well control schedules must be evaluated over
multiple geological realizations. In this work, a convolutional-recurrent
neural network (CNN-RNN) proxy model is developed to predict well-by-well oil
and water rates, for given time-varying well bottom-hole pressure (BHP)
schedules, for each realization in an ensemble. This capability enables the
estimation of the objective function and nonlinear constraint values required
for robust optimization. The proxy model represents an extension of a recently
developed long short-term memory (LSTM) RNN proxy designed to predict well
rates for a single geomodel. A CNN is introduced here to processes permeability
realizations, and this provides the initial states for the RNN. The CNN-RNN
proxy is trained using simulation results for 300 different sets of BHP
schedules and permeability realizations. We demonstrate proxy accuracy for
oil-water flow through multiple realizations of 3D multi-Gaussian permeability
models. The proxy is then incorporated into a closed-loop reservoir management
(CLRM) workflow, where it is used with particle swarm optimization and a
filter-based method for nonlinear constraint satisfaction. History matching is
achieved using an adjoint-gradient-based procedure. The proxy model is shown to
perform well in this setting for five different (synthetic) `true' models.
Improved net present value along with constraint satisfaction and uncertainty
reduction are observed with CLRM. For the robust production optimization steps,
the proxy provides O(100) runtime speedup over simulation-based optimization.

Sense embedding learning methods learn different embeddings for the different
senses of an ambiguous word. One sense of an ambiguous word might be socially
biased while its other senses remain unbiased. In comparison to the numerous
prior work evaluating the social biases in pretrained word embeddings, the
biases in sense embeddings have been relatively understudied. We create a
benchmark dataset for evaluating the social biases in sense embeddings and
propose novel sense-specific bias evaluation measures. We conduct an extensive
evaluation of multiple static and contextualised sense embeddings for various
types of social biases using the proposed measures. Our experimental results
show that even in cases where no biases are found at word-level, there still
exist worrying levels of social biases at sense-level, which are often ignored
by the word-level bias evaluation measures.

While both extractive and generative readers have been successfully applied
to the Question Answering (QA) task, little attention has been paid toward the
systematic comparison of them. Characterizing the strengths and weaknesses of
the two readers is crucial not only for making a more informed reader selection
in practice but also for developing a deeper understanding to foster further
research on improving readers in a principled manner. Motivated by this goal,
we make the first attempt to systematically study the comparison of extractive
and generative readers for question answering. To be aligned with the
state-of-the-art, we explore nine transformer-based large pre-trained language
models (PrLMs) as backbone architectures. Furthermore, we organize our findings
under two main categories: (1) keeping the architecture invariant, and (2)
varying the underlying PrLMs. Among several interesting findings, it is
important to highlight that (1) the generative readers perform better in long
context QA, (2) the extractive readers perform better in short context while
also showing better out-of-domain generalization, and (3) the encoder of
encoder-decoder PrLMs (e.g., T5) turns out to be a strong extractive reader and
outperforms the standard choice of encoder-only PrLMs (e.g., RoBERTa). We also
study the effect of multi-task learning on the two types of readers varying the
underlying PrLMs and perform qualitative and quantitative diagnosis to provide
further insights into future directions in modeling better readers.

Autonomous Vehicles (AV)'s wide-scale deployment appears imminent despite
many safety challenges yet to be resolved. The modern autonomous vehicles will
undoubtedly include machine learning and probabilistic techniques that add
significant complexity to the traditional verification and validation methods.
Road testing is essential before the deployment, but scenarios are repeatable,
and it's hard to collect challenging events. Exploring numerous, diverse and
crucial scenarios is a time-consuming and expensive approach. The research
community and industry have widely accepted scenario-based testing in the last
few years. As it is focused directly on the relevant critical road situations,
it can reduce the effort required in testing. The scenario-based testing in
simulation requires the realistic behaviour of the traffic participants to
assess the System Under Test (SUT). It is essential to capture the scenarios
from the real world to encode the behaviour of actual traffic participants.
This paper proposes a novel scenario extraction method to capture the lane
change scenarios using point-cloud data and object tracking information. This
method enables fully automatic scenario extraction compared to similar
approaches in this area. The generated scenarios are represented in OpenX
format to reuse them in the SUT evaluation easily. The motivation of this
framework is to build a validation dataset to generate many critical concrete
scenarios. The code is available online at
https://github.com/dkarunakaran/scenario_extraction_framework.

Spiking Neural Networks (SNNs) are developed as a promising alternative to
Artificial Neural networks (ANNs) due to their more realistic brain-inspired
computing models. SNNs have sparse neuron firing over time, i.e.,
spatio-temporal sparsity; thus, they are useful to enable energy-efficient
hardware inference. However, exploiting spatio-temporal sparsity of SNNs in
hardware leads to unpredictable and unbalanced workloads, degrading the energy
efficiency. In this work, we propose an FPGA-based convolutional SNN
accelerator called Skydiver that exploits spatio-temporal workload balance. We
propose the Approximate Proportional Relation Construction (APRC) method that
can predict the relative workload channel-wisely and a Channel-Balanced
Workload Schedule (CBWS) method to increase the hardware workload balance ratio
to over 90%. Skydiver was implemented on a Xilinx XC7Z045 FPGA and verified on
image segmentation and MNIST classification tasks. Results show improved
throughput by 1.4X and 1.2X for the two tasks. Skydiver achieved 22.6 KFPS
throughput, and 42.4 uJ/Image prediction energy on the classification task with
98.5% accuracy.

Consider an actor making selection decisions using a series of classifiers,
which we term a sequential screening process. The early stages filter out some
applicants, and in the final stage an expensive but accurate test is applied to
the individuals that make it to the final stage. Since the final stage is
expensive, if there are multiple groups with different fractions of positives
at the penultimate stage (even if a slight gap), then the firm may naturally
only choose to the apply the final (interview) stage solely to the highest
precision group which would be clearly unfair to the other groups. Even if the
firm is required to interview all of those who pass the final round, the tests
themselves could have the property that qualified individuals from some groups
pass more easily than qualified individuals from others. Thus, we consider
requiring Equality of Opportunity (qualified individuals from each each group
have the same chance of reaching the final stage and being interviewed). We
then examine the goal of maximizing quantities of interest to the decision
maker subject to this constraint, via modification of the probabilities of
promotion through the screening process at each stage based on performance at
the previous stage. We exhibit algorithms for satisfying Equal Opportunity over
the selection process and maximizing precision (the fraction of interview that
yield qualified candidates) as well as linear combinations of precision and
recall (recall determines the number of applicants needed per hire) at the end
of the final stage. We also present examples showing that the solution space is
non-convex, which motivate our exact and (FPTAS) approximation algorithms for
maximizing the linear combination of precision and recall. Finally, we discuss
the `price of' adding additional restrictions, such as not allowing the
decision maker to use group membership in its decision process.

Semi supervised learning (SSL) provides an effective means of leveraging
unlabelled data to improve a model's performance. Even though the domain has
received a considerable amount of attention in the past years, most methods
present the common drawback of being unsafe. By safeness we mean the quality of
not degrading a fully supervised model when including unlabelled data. Our
starting point is to notice that the estimate of the risk that most
discriminative SSL methods minimise is biased, even asymptotically. This bias
makes these techniques untrustable without a proper validation set, but we
propose a simple way of removing the bias. Our debiasing approach is
straightforward to implement, and applicable to most deep SSL methods. We
provide simple theoretical guarantees on the safeness of these modified
methods, without having to rely on the strong assumptions on the data
distribution that SSL theory usually requires. We evaluate debiased versions of
different existing SSL methods and show that debiasing can compete with classic
deep SSL techniques in various classic settings and even performs well when
traditional SSL fails.

We examine the effects of contrastive visual semantic pretraining by
comparing the geometry and semantic properties of contextualized English
language representations formed by GPT-2 and CLIP, a zero-shot multimodal image
classifier which adapts the GPT-2 architecture to encode image captions. We
find that contrastive visual semantic pretraining significantly mitigates the
anisotropy found in contextualized word embeddings from GPT-2, such that the
intra-layer self-similarity (mean pairwise cosine similarity) of CLIP word
embeddings is under .25 in all layers, compared to greater than .95 in the top
layer of GPT-2. CLIP word embeddings outperform GPT-2 on word-level semantic
intrinsic evaluation tasks, and achieve a new corpus-based state of the art for
the RG65 evaluation, at .88. CLIP also forms fine-grained semantic
representations of sentences, and obtains Spearman's rho = .73 on the
SemEval-2017 Semantic Textual Similarity Benchmark with no fine-tuning,
compared to no greater than rho = .45 in any layer of GPT-2. Finally,
intra-layer self-similarity of CLIP sentence embeddings decreases as the layer
index increases, finishing at .25 in the top layer, while the self-similarity
of GPT-2 sentence embeddings formed using the EOS token increases
layer-over-layer and never falls below .97. Our results indicate that high
anisotropy is not an inevitable consequence of contextualization, and that
visual semantic pretraining is beneficial not only for ordering visual
representations, but also for encoding useful semantic representations of
language, both on the word level and the sentence level.

Deep decarbonization of the energy sector will require massive penetration of
stochastic renewable energy resources and an enormous amount of grid asset
coordination; this represents a challenging paradigm for the power system
operators who are tasked with maintaining grid stability and security in the
face of such changes. With its ability to learn from complex datasets and
provide predictive solutions on fast timescales, machine learning (ML) is
well-posed to help overcome these challenges as power systems transform in the
coming decades. In this work, we outline five key challenges (dataset
generation, data pre-processing, model training, model assessment, and model
embedding) associated with building trustworthy ML models which learn from
physics-based simulation data. We then demonstrate how linking together
individual modules, each of which overcomes a respective challenge, at
sequential stages in the machine learning pipeline can help enhance the overall
performance of the training process. In particular, we implement methods that
connect different elements of the learning pipeline through feedback, thus
"closing the loop" between model training, performance assessments, and
re-training. We demonstrate the effectiveness of this framework, its
constituent modules, and its feedback connections by learning the N-1
small-signal stability margin associated with a detailed model of a proposed
North Sea Wind Power Hub system.

VAST, the Valence-Assessing Semantics Test, is a novel intrinsic evaluation
task for contextualized word embeddings (CWEs). VAST uses valence, the
association of a word with pleasantness, to measure the correspondence of
word-level LM semantics with widely used human judgments, and examines the
effects of contextualization, tokenization, and LM-specific geometry. Because
prior research has found that CWEs from GPT-2 perform poorly on other intrinsic
evaluations, we select GPT-2 as our primary subject, and include results
showing that VAST is useful for 7 other LMs, and can be used in 7 languages.
GPT-2 results show that the semantics of a word incorporate the semantics of
context in layers closer to model output, such that VAST scores diverge between
our contextual settings, ranging from Pearson's rho of .55 to .77 in layer 11.
We also show that multiply tokenized words are not semantically encoded until
layer 8, where they achieve Pearson's rho of .46, indicating the presence of an
encoding process for multiply tokenized words which differs from that of singly
tokenized words, for which rho is highest in layer 0. We find that a few
neurons with values having greater magnitude than the rest mask word-level
semantics in GPT-2's top layer, but that word-level semantics can be recovered
by nullifying non-semantic principal components: Pearson's rho in the top layer
improves from .32 to .76. After isolating semantics, we show the utility of
VAST for understanding LM semantics via improvements over related work on four
word similarity tasks, with a score of .50 on SimLex-999, better than the
previous best of .45 for GPT-2. Finally, we show that 8 of 10 WEAT bias tests,
which compare differences in word embedding associations between groups of
words, exhibit more stereotype-congruent biases after isolating semantics,
indicating that non-semantic structures in LMs also mask biases.

District cooling energy plants (DCEPs) consisting of chillers, cooling
towers, and thermal energy storage (TES) systems consume a considerable amount
of electricity. Optimizing the scheduling of the TES and chillers to take
advantage of time-varying electricity price is a challenging optimal control
problem. The classical method, model predictive control (MPC), requires solving
a high dimensional mixed-integer nonlinear program (MINLP) because of the
on/off actuation of the chillers and charging/discharging of TES, which are
computationally challenging. RL is an attractive alternative to MPC: the real
time control computation is a low-dimensional optimization problem that can be
easily solved. However, the performance of an RL controller depends on many
design choices. In this paper, we propose a Q-learning based reinforcement
learning (RL) controller for this problem. Numerical simulation results show
that the proposed RL controller is able to reduce energy cost over a rule-based
baseline controller by approximately 8%, comparable to savings reported in the
literature with MPC for similar DCEPs. We describe the design choices in the RL
controller, including basis functions, reward function shaping, and learning
algorithm parameters. Compared to existing work on RL for DCEPs, the proposed
controller is designed for continuous state and actions spaces.

We study a reinforcement learning algorithm for continuous time stochastic
control problems. The proposed algorithm uses the sampled state process by
discretizing the state and control action spaces under piece-wise constant
control processes. We show that the algorithm converges to the optimality
equation of a finite Markov decision process (MDP). Using this MDP model, we
provide an upper bound for the approximation error for the optimal value
function of the continuous time control problem. Furthermore, we give
upper-bounds for the performance loss of the learned control process compared
to the optimal admissible control process of the original problem. The provided
error upper-bounds are functions of the time and space discretization
parameters, and they reveal the effect of different levels of the
approximation: (i) approximation of the continuous time control problem by an
MDP, (ii) use of piece-wise constant control processes, (iii) space
discretization. Finally, we state a time complexity bound for the proposed
algorithm as a function of the time and space discretization parameters.

Social learning algorithms provide models for the formation of opinions over
social networks resulting from local reasoning and peer-to-peer exchanges.
Interactions occur over an underlying graph topology, which describes the flow
of information among the agents. To account for drifting conditions in the
environment, this work adopts an adaptive social learning strategy, which is
able to track variations in the underlying signal statistics. Among other
results, we propose a technique that addresses questions of explainability and
interpretability of the results when the graph is hidden. Given observations of
the evolution of the beliefs over time, we aim to infer the underlying graph
topology, discover pairwise influences between the agents, and identify
significant trajectories in the network. The proposed framework is online in
nature and can adapt dynamically to changes in the graph topology or the true
hypothesis.

We study a fair machine learning (ML) setting where an 'upstream' model
developer is tasked with producing a fair ML model that will be used by several
similar but distinct 'downstream' users. This setting introduces new challenges
that are unaddressed by many existing fairness interventions, echoing existing
critiques that current methods are not broadly applicable across the
diversifying needs of real-world fair ML use cases. To this end, we address the
up/down stream setting by adopting a distributional-based view of fair
classification. Specifically, we introduce a new fairness definition,
distributional parity, that measures disparities in the distribution of
outcomes across protected groups, and present a post-processing method to
minimize this measure using techniques from optimal transport. We show that our
method is able that creates fairer outcomes for all downstream users, across a
variety of fairness definitions, and works at inference time on unlabeled data.
We verify this claim experimentally, through comparison to several similar
methods and across four benchmark tasks. Ultimately we argue that fairer
classification outcomes can be produced through the development of
setting-specific interventions.

Compared to top-down coarse-grained (CG) models, bottom-up approaches are
capable of offering higher structural fidelity. This fidelity results from the
tight link to a higher-resolution reference, making the CG model chemically
specific. Unfortunately, chemical specificity can be at odds with
compound-screening strategies, which call for transferable parametrizations.
Here we present an approach to reconcile bottom-up, structure-preserving CG
models with chemical transferability. We consider the bottom-up CG
parametrization of 3,441 C$_7$O$_2$ small-molecule isomers. Our approach
combines atomic representations, unsupervised learning, and a large-scale
extended-ensemble force-matching parametrization. We first identify a subset of
19 representative molecules, which maximally encode the local environment of
all gas-phase conformers. Reference interactions between the 19 representative
molecules were obtained from both homogeneous bulk liquids and various binary
mixtures. An extended-ensemble parametrization over all 703 state points leads
to a CG model that is both structure-based and chemically transferable.
Remarkably, the resulting force field is on average more structurally accurate
than single-state-point equivalents. Averaging over the extended ensemble acts
as a mean-force regularizer, smoothing out both force and structural
correlations that are overly specific to a single state point. Our approach
aims at transferability through a set of CG bead types that can be used to
easily construct new molecules, while retaining the benefits of a
structure-based parametrization.

The aim of this work is to introduce simplicial attention networks (SANs),
i.e., novel neural architectures that operate on data defined on simplicial
complexes leveraging masked self-attentional layers. Hinging on formal
arguments from topological signal processing, we introduce a proper
self-attention mechanism able to process data components at different layers
(e.g., nodes, edges, triangles, and so on), while learning how to weight both
upper and lower neighborhoods of the given topological domain in a totally
task-oriented fashion. The proposed SANs generalize most of the current
architectures available for processing data defined on simplicial complexes.
The proposed approach compares favorably with other methods when applied to
different (inductive and transductive) tasks such as trajectory prediction and
missing data imputations in citation complexes.

We consider the problem of completing a set of $n$ tasks with a human-robot
team using minimum effort. In many domains, teaching a robot to be fully
autonomous can be counterproductive if there are finitely many tasks to be
done. Rather, the optimal strategy is to weigh the cost of teaching a robot and
its benefit -- how many new tasks it allows the robot to solve autonomously. We
formulate this as a planning problem where the goal is to decide what tasks the
robot should do autonomously (act), what tasks should be delegated to a human
(delegate) and what tasks the robot should be taught (learn) so as to complete
all the given tasks with minimum effort. This planning problem results in a
search tree that grows exponentially with $n$ -- making standard graph search
algorithms intractable. We address this by converting the problem into a mixed
integer program that can be solved efficiently using off-the-shelf solvers with
bounds on solution quality. To predict the benefit of learning, we propose a
precondition prediction classifier. Given two tasks, this classifier predicts
whether a skill trained on one will transfer to the other. Finally, we evaluate
our approach on peg insertion and Lego stacking tasks, both in simulation and
real-world, showing substantial savings in human effort.

It's challenging to design reward functions for complex, real-world tasks.
Reward learning lets one instead infer reward functions from data. However,
multiple reward functions often fit the data equally well, even in the
infinite-data limit. Prior work often considers reward functions to be uniquely
recoverable, by imposing additional assumptions on data sources. By contrast,
we formally characterise the partial identifiability of popular data sources,
including demonstrations and trajectory preferences, under multiple common sets
of assumptions. We analyse the impact of this partial identifiability on
downstream tasks such as policy optimisation, including under changes in
environment dynamics. We unify our results in a framework for comparing data
sources and downstream tasks by their invariances, with implications for the
design and selection of data sources for reward learning.

Augmented Reality/Virtual Reality (AR/VR) glasses are widely foreseen as the
next generation computing platform. AR/VR glasses are a complex "system of
systems" which must satisfy stringent form factor, computing-, power- and
thermal- requirements. In this paper, we will show that a novel distributed
on-sensor compute architecture, coupled with new semiconductor technologies
(such as dense 3D-IC interconnects and Spin-Transfer Torque Magneto Random
Access Memory, STT-MRAM) and, most importantly, a full hardware-software
co-optimization are the solutions to achieve attractive and socially acceptable
AR/VR glasses. To this end, we developed a semi-analytical simulation framework
to estimate the power consumption of novel AR/VR distributed on-sensor
computing architectures. The model allows the optimization of the main
technological features of the system modules, as well as the computer-vision
algorithm partition strategy across the distributed compute architecture. We
show that, in the case of the compute-intensive machine learning based Hand
Tracking algorithm, the distributed on-sensor compute architecture can reduce
the system power consumption compared to a centralized system, with the
additional benefits in terms of latency and privacy.

Language models can learn a range of capabilities from unsupervised training
on text corpora. However, to solve a particular problem (such as text
summarization) it is typically necessary to fine-tune them on a task-specific
dataset. It is often easier for humans to choose between options than to
provide labeled data, and prior work has achieved state-of-the-art performance
by training a reward model from such preference comparisons. However,
collecting a large preference comparison dataset is still expensive -- and the
learned reward models are unreliable out-of-distribution. We seek to address
these problems via uncertainty estimation, which can improve sample efficiency
and robustness using active learning and risk-averse reinforcement learning
(RL). Specifically, we use bootstrap aggregating (bagging) to train an ensemble
of reward models differing in the initialization of their final layer.
Ensembles have proved successful in prior applications of active learning, but
we find that in our setting ensemble active learning does not outperform random
sampling. Further experiments show that while the aggregate predictions are
well-calibrated, the ensemble's estimated epistemic uncertainty is only weakly
correlated with model error. We suspect this is because the ensemble members
are fine-tuned from a single model and so are similar to one another. This
suggests current pre-training methods will need to be modified to support
uncertainty estimation, e.g. by training multiple language models.

Neural network-based collaborative filtering systems focus on designing
network architectures to learn better representations while fixing the input to
the user/item interaction vectors and/or ID. In this paper, we first show that
the non-zero elements of the inputs are learnable parameters that determine the
weights in combining the user/item embeddings, and fixing them limits the power
of the models in learning the representations. Then, we propose to learn the
value of the non-zero elements of the inputs jointly with the neural network
parameters. We analyze the model complexity and the empirical risk of our
approach and prove that learning the input leads to a better generalization
bound. Our experiments on several real-world datasets show that our method
outperforms the state-of-the-art methods, even using shallow network structures
with a smaller number of layers and parameters.

First-principle simulations are at the heart of the high-energy physics
research program. They link the vast data output of multi-purpose detectors
with fundamental theory predictions and interpretation. This review illustrates
a wide range of applications of modern machine learning to event generation and
simulation-based inference, including conceptional developments driven by the
specific requirements of particle physics. New ideas and tools developed at the
interface of particle physics and machine learning will improve the speed and
precision of forward simulations, handle the complexity of collision data, and
enhance inference as an inverse simulation problem.

Despite groundbreaking progress in reinforcement learning for robotics,
gameplay, and other complex domains, major challenges remain in applying
reinforcement learning to the evolving, open-world problems often found in
critical application spaces. Reinforcement learning solutions tend to
generalize poorly when exposed to new tasks outside of the data distribution
they are trained on, prompting an interest in continual learning algorithms. In
tandem with research on continual learning algorithms, there is a need for
challenge environments, carefully designed experiments, and metrics to assess
research progress. We address the latter need by introducing a framework for
continual reinforcement-learning development and assessment using Lifelong
Learning Explorer (L2Explorer), a new, Unity-based, first-person 3D exploration
environment that can be continuously reconfigured to generate a range of tasks
and task variants structured into complex and evolving evaluation curricula. In
contrast to procedurally generated worlds with randomized components, we have
developed a systematic approach to defining curricula in response to controlled
changes with accompanying metrics to assess transfer, performance recovery, and
data efficiency. Taken together, the L2Explorer environment and evaluation
approach provides a framework for developing future evaluation methodologies in
open-world settings and rigorously evaluating approaches to lifelong learning.

The Ki-67 proliferation index is an essential biomarker that helps
pathologists to diagnose and select appropriate treatments. However, automatic
evaluation of Ki-67 is difficult due to nuclei overlapping and complex
variations in their properties. This paper proposes an integrated pipeline for
accurate automatic counting of Ki-67, where the impact of nuclei separation
techniques is highlighted. First, semantic segmentation is performed by
combining the Squeez and Excitation Resnet and Unet algorithms to extract
nuclei from the background. The extracted nuclei are then divided into
overlapped and non-overlapped regions based on eight geometric and statistical
features. A marker-based Watershed algorithm is subsequently proposed and
applied only to the overlapped regions to separate nuclei. Finally, deep
features are extracted from each nucleus patch using Resnet18 and classified
into positive or negative by a random forest classifier. The proposed
pipeline's performance is validated on a dataset from the Department of
Pathology at H\^opital Nord Franche-Comt\'e hospital.

Automatic Readability Assessment (ARA), the task of assigning a reading level
to a text, is traditionally treated as a classification problem in NLP
research. In this paper, we propose the first neural, pairwise ranking approach
to ARA and compare it with existing classification, regression, and
(non-neural) ranking methods. We establish the performance of our model by
conducting experiments with three English, one French and one Spanish datasets.
We demonstrate that our approach performs well in monolingual single/cross
corpus testing scenarios and achieves a zero-shot cross-lingual ranking
accuracy of over 80% for both French and Spanish when trained on English data.
Additionally, we also release a new parallel bilingual readability dataset in
English and French. To our knowledge, this paper proposes the first neural
pairwise ranking model for ARA, and shows the first results of cross-lingual,
zero-shot evaluation of ARA with neural models.

In this paper we propose an artificial intelligence imaging solution to
support archaeologists in the classification task of Roman commonware
potsherds. Usually, each potsherd is represented by its sectional profile as a
two dimensional black-white image and printed in archaeological books related
to specific archaeological excavations. The partiality and handcrafted variance
of the fragments make their matching a challenging problem: we propose to pair
similar profiles via the unsupervised hierarchical clustering of non-linear
features learned in the latent space of a deep convolutional Variational
Autoencoder (VAE) network. Our contribution also include the creation of a
ROman COmmonware POTtery (ROCOPOT) database, with more than 4000 potsherds
profiles extracted from 25 Roman pottery corpora, and a MATLAB GUI software for
the easy inspection of shape similarities. Results are commented both from a
mathematical and archaeological perspective so as to unlock new research
directions in both communities.

In this paper, an online task scheduling and mapping method based on a fuzzy
neural network (FNN) learned by an evolutionary multi-objective algorithm
(NSGA-II) to jointly optimize the main design challenges of heterogeneous
MPSoCs is proposed. In this approach, first, the FNN parameters are trained
using an NSGA-II-based optimization engine by considering the main design
challenges of MPSoCs including temperature, power consumption, failure rate,
and execution time on a training dataset consisting of different application
graphs of various sizes. Next, the trained FNN is employed as an online task
scheduler to jointly optimize the main design challenges in heterogeneous
MPSoCs. Due to the uncertainty in sensor measurements and the difference
between computational models and reality, applying the fuzzy neural network is
advantageous in online scheduling procedures. The performance of the method is
compared with some previous heuristic, meta-heuristic, and rule-based
approaches in several experiments. Based on these experiments our proposed
method outperforms the related studies in optimizing all design criteria. Its
improvement over related heuristic and meta-heuristic approaches are estimated
10.58% in temperature, 9.22% in power consumption, 39.14% in failure rate, and
12.06% in execution time, averagely. Moreover, considering the interpretable
nature of the FNN, the frequently fired extracted fuzzy rules of the proposed
approach are demonstrated.

Interpreting partial information collected from systems subject to noise is a
key problem across scientific disciplines. Theoretical frameworks often focus
on the dynamics of variables that result from coarse-graining the internal
states of a physical system. However, most experimental apparatuses can only
detect a partial set of transitions, while internal states are inaccessible.
Here, we consider an observer who records a time series of occurrences of one
or several transitions performed by a system, under the assumption that its
underlying dynamics is Markovian. We pose the question of how one can use the
transitions' information to make inferences of dynamical, thermodynamical, and
biochemical properties. First, elaborating on first-passage time techniques, we
derive analytical expressions for the probabilities of consecutive transitions
and the time elapsed between them. Second, we derive a lower bound for the
entropy production rate that equals the sum of two non-negative contributions,
one due to the statistics of transitions and a second due to the statistics of
inter-transition times. We also show that when only one current is measured,
our estimate still detects irreversibility even in the absence of net currents.
We illustrate the developed framework in experimentally-validated biophysical
models of kinesin and dynein molecular motors, and in a minimal model for
template-directed polymerization. Our results reveal that while entropy
production is entailed in the statistics of two successive transitions of the
same type, the statistics of two different successive transitions can probe the
existence of an underlying disorder in the motion of a molecular motor. Taken
all together, our results highlight the power of inference from transition
statistics ranging from thermodynamic quantities to network-topology properties
of Markov processes.

Personalized recommendation is an important class of deep-learning
applications that powers a large collection of internet services and consumes a
considerable amount of datacenter resources. As the scale of production-grade
recommendation systems continues to grow, optimizing their serving performance
and efficiency in a heterogeneous datacenter is important and can translate
into infrastructure capacity saving. In this paper, we propose Hercules, an
optimized framework for personalized recommendation inference serving that
targets diverse industry-representative models and cloud-scale heterogeneous
systems. Hercules performs a two-stage optimization procedure - offline
profiling and online serving. The first stage searches the large under-explored
task scheduling space with a gradient-based search algorithm achieving up to
9.0x latency-bounded throughput improvement on individual servers; it also
identifies the optimal heterogeneous server architecture for each
recommendation workload. The second stage performs heterogeneity-aware cluster
provisioning to optimize resource mapping and allocation in response to
fluctuating diurnal loads. The proposed cluster scheduler in Hercules achieves
47.7% cluster capacity saving and reduces the provisioned power by 23.7% over a
state-of-the-art greedy scheduler.

Within the scope of our recent approach for Efficient Unsupervised
Constitutive Law Identification and Discovery (EUCLID), we propose an
unsupervised Bayesian learning framework for discovery of parsimonious and
interpretable constitutive laws with quantifiable uncertainties. As in
deterministic EUCLID, we do not resort to stress data, but only to
realistically measurable full-field displacement and global reaction force
data; as opposed to calibration of an a priori assumed model, we start with a
constitutive model ansatz based on a large catalog of candidate functional
features; we leverage domain knowledge by including features based on existing,
both physics-based and phenomenological, constitutive models. In the new
Bayesian-EUCLID approach, we use a hierarchical Bayesian model with
sparsity-promoting priors and Monte Carlo sampling to efficiently solve the
parsimonious model selection task and discover physically consistent
constitutive equations in the form of multivariate multi-modal probabilistic
distributions. We demonstrate the ability to accurately and efficiently recover
isotropic and anisotropic hyperelastic models like the Neo-Hookean, Isihara,
Gent-Thomas, Arruda-Boyce, Ogden, and Holzapfel models in both elastostatics
and elastodynamics. The discovered constitutive models are reliable under both
epistemic uncertainties - i.e. uncertainties on the true features of the
constitutive catalog - and aleatoric uncertainties - which arise from the noise
in the displacement field data, and are automatically estimated by the
hierarchical Bayesian model.

Diverse applications in photonics and microwave engineering require a means
of measurement of the instantaneous frequency of a signal. A photonic
implementation typically applies an interferometer equipped with three or more
output ports to measure the frequency dependent phase shift provided by an
optical delay line. The components constituting the interferometer are prone to
impairments which results in erroneous measurements. It is shown that the
information to be retrieved is encoded by a three-component vector that lies on
a circular cone within a three-dimensional Cartesian object space. The measured
data belongs to the image of the object space under a linear map that describes
the action of the interferometer. Assisted by a learning algorithm, an inverse
map from the image space into the object space is constructed. The inverse map
compensates for a variety of impairments while being robust to noise.
Simulation results demonstrate that, to the extent the interferometer model
captures all significant impairments, a precision limited only by the level of
random noise is attainable. A wavelength meter architecture is fabricated on
Si3N4 photonic integration platform to prove the method experimentally. Applied
to the measured data, greater than an order of magnitude improvement in
precision is achieved by the proposed method compared to the conventional
method.

We propose SwitchTT, a multi-task extension to Trajectory Transformer but
enhanced with two striking features: (i) exploiting a sparsely activated model
to reduce computation cost in multi-task offline model learning and (ii)
adopting a distributional trajectory value estimator that improves policy
performance, especially in sparse reward settings. These two enhancements make
SwitchTT suitable for solving multi-task offline reinforcement learning
problems, where model capacity is critical for absorbing the vast quantities of
knowledge available in the multi-task dataset. More specifically, SwitchTT
exploits switch transformer model architecture for multi-task policy learning,
allowing us to improve model capacity without proportional computation cost.
Also, SwitchTT approximates the distribution rather than the expectation of
trajectory value, mitigating the effects of the Monte-Carlo Value estimator
suffering from poor sample complexity, especially in the sparse-reward setting.
We evaluate our method using the suite of ten sparse-reward tasks from the
gym-mini-grid environment.We show an improvement of 10% over Trajectory
Transformer across 10-task learning and obtain up to 90% increase in offline
model training speed. Our results also demonstrate the advantage of the switch
transformer model for absorbing expert knowledge and the importance of value
distribution in evaluating the trajectory.

Deep Learning (DL) has shown remarkable results in solving inverse problems
in various domains. In particular, the Tikhonet approach is very powerful to
deconvolve optical astronomical images (Sureau et al. 2020). Yet, this approach
only uses the $\ell_2$ loss, which does not guarantee the preservation of
physical information (e.g. flux and shape) of the object reconstructed in the
image. In Nammour et al. (2021), a new loss function was proposed in the
framework of sparse deconvolution, which better preserves the shape of galaxies
and reduces the pixel error. In this paper, we extend Tikhonet to take into
account this shape constraint, and apply our new DL method, called ShapeNet, to
optical and radio-interferometry simulated data set. The originality of the
paper relies on i) the shape constraint we use in the neural network framework,
ii) the application of deep learning to radio-interferometry image
deconvolution for the first time, and iii) the generation of a simulated radio
data set that we make available for the community. A range of examples
illustrates the results.

Deep Gaussian Process (DGP) as a model prior in Bayesian learning intuitively
exploits the expressive power in function composition. DGPs also offer diverse
modeling capabilities, but inference is challenging because marginalization in
latent function space is not tractable. With Bochner's theorem, DGP with
squared exponential kernel can be viewed as a deep trigonometric network
consisting of the random feature layers, sine and cosine activation units, and
random weight layers. In the wide limit with a bottleneck, we show that the
weight space view yields the same effective covariance functions which were
obtained previously in function space. Also, varying the prior distributions
over network parameters is equivalent to employing different kernels. As such,
DGPs can be translated into the deep bottlenecked trig networks, with which the
exact maximum a posteriori estimation can be obtained. Interestingly, the
network representation enables the study of DGP's neural tangent kernel, which
may also reveal the mean of the intractable predictive distribution.
Statistically, unlike the shallow networks, deep networks of finite width have
covariance deviating from the limiting kernel, and the inner and outer widths
may play different roles in feature learning. Numerical simulations are present
to support our findings.

While the popularity of physics-informed neural networks (PINNs) is steadily
rising, to this date PINNs have not been successful in simulating dynamical
systems whose solution exhibits multi-scale, chaotic or turbulent behavior. In
this work we attribute this shortcoming to the inability of existing PINNs
formulations to respect the spatio-temporal causal structure that is inherent
to the evolution of physical systems. We argue that this is a fundamental
limitation and a key source of error that can ultimately steer PINN models to
converge towards erroneous solutions. We address this pathology by proposing a
simple re-formulation of PINNs loss functions that can explicitly account for
physical causality during model training. We demonstrate that this simple
modification alone is enough to introduce significant accuracy improvements, as
well as a practical quantitative mechanism for assessing the convergence of a
PINNs model. We provide state-of-the-art numerical results across a series of
benchmarks for which existing PINNs formulations fail, including the chaotic
Lorenz system, the Kuramoto-Sivashinsky equation in the chaotic regime, and the
Navier-Stokes equations in the turbulent regime. To the best of our knowledge,
this is the first time that PINNs have been successful in simulating such
systems, introducing new opportunities for their applicability to problems of
industrial complexity.

We present a Convolutional Neural Network (CNN) model for the separation of
astrophysical transients from image artifacts, a task known as "real-bogus"
classification, that does not rely on Difference Image Analysis (DIA) which is
a computationally expensive process involving image matching on small spatial
scales in large volumes of data. We explore the use of CNNs to (1) automate the
"real-bogus" classification, (2) reduce the computational costs of transient
discovery. We compare the efficiency of two CNNs with similar architectures,
one that uses "image triplets" (templates, search, and the corresponding
difference image) and one that adopts a similar architecture but takes as input
the template and search only. Without substantially changing the model
architecture or retuning the hyperparameters to the new input, we observe only
a small decrease in model efficiency (97% to 92% accuracy). We further
investigate how the model that does not receive the difference image learns the
required information from the template and search by exploring the saliency
maps. Our work demonstrates that (1) CNNs are excellent models for "real-bogus"
classification that rely exclusively on the imaging data and require no feature
engineering task; (2) high-accuracy models can be built without the need to
construct difference images. Since once trained, neural networks can generate
predictions at minimal computational costs, we argue that future
implementations of this methodology could dramatically reduce the computational
costs in the detection of genuine transients in synoptic surveys like Rubin
Observatory's Legacy Survey of Space and Time by bypassing the DIA step
entirely.

State-of-the-art spectral energy distribution (SED) analyses use a Bayesian
framework to infer the physical properties of galaxies from observed photometry
or spectra. They require sampling from a high-dimensional space of SED model
parameters and take $>10-100$ CPU hours per galaxy, which renders them
practically infeasible for analyzing the $billions$ of galaxies that will be
observed by upcoming galaxy surveys ($e.g.$ DESI, PFS, Rubin, Webb, and Roman).
In this work, we present an alternative scalable approach to rigorous Bayesian
inference using Amortized Neural Posterior Estimation (ANPE). ANPE is a
simulation-based inference method that employs neural networks to estimate the
posterior probability distribution over the full range of observations. Once
trained, it requires no additional model evaluations to estimate the posterior.
We present, and publicly release, ${\rm SED}{flow}$, an ANPE method to produce
posteriors of the recent Hahn et al. (2022) SED model from optical photometry.
${\rm SED}{flow}$ takes ${\sim}1$ $second~per~galaxy$ to obtain the posterior
distributions of 12 model parameters, all of which are in excellent agreement
with traditional Markov Chain Monte Carlo sampling results. We also apply ${\rm
SED}{flow}$ to 33,884 galaxies in the NASA-Sloan Atlas and publicly release
their posteriors: see https://changhoonhahn.github.io/SEDflow.

Industrial Internet of Things (I-IoT) enables fully automated production
systems by continuously monitoring devices and analyzing collected data.
Machine learning methods are commonly utilized for data analytics in such
systems. Cyber-attacks are a grave threat to I-IoT as they can manipulate
legitimate inputs, corrupting ML predictions and causing disruptions in the
production systems. Hyper-dimensional computing (HDC) is a brain-inspired
machine learning method that has been shown to be sufficiently accurate while
being extremely robust, fast, and energy-efficient. In this work, we use HDC
for intelligent fault diagnosis against different adversarial attacks. Our
black-box adversarial attacks first train a substitute model and create
perturbed test instances using this trained model. These examples are then
transferred to the target models. The change in the classification accuracy is
measured as the difference before and after the attacks. This change measures
the resiliency of a learning method. Our experiments show that HDC leads to a
more resilient and lightweight learning solution than the state-of-the-art deep
learning methods. HDC has up to 67.5% higher resiliency compared to the
state-of-the-art methods while being up to 25.1% faster to train.

This paper is concerned with the asynchronous form of Q-learning, which
applies a stochastic approximation scheme to Markovian data samples. Motivated
by the recent advances in offline reinforcement learning, we develop an
algorithmic framework that incorporates the principle of pessimism into
asynchronous Q-learning, which penalizes infrequently-visited state-action
pairs based on suitable lower confidence bounds (LCBs). This framework leads
to, among other things, improved sample efficiency and enhanced adaptivity in
the presence of near-expert data. Our approach permits the observed data in
some important scenarios to cover only partial state-action space, which is in
stark contrast to prior theory that requires uniform coverage of all
state-action pairs. When coupled with the idea of variance reduction,
asynchronous Q-learning with LCB penalization achieves near-optimal sample
complexity, provided that the target accuracy level is small enough. In
comparison, prior works were suboptimal in terms of the dependency on the
effective horizon even when i.i.d. sampling is permitted. Our results deliver
the first theoretical support for the use of pessimism principle in the
presence of Markovian non-i.i.d. data.

The rankability of data is a recently proposed problem that considers the
ability of a dataset, represented as a graph, to produce a meaningful ranking
of the items it contains. To study this concept, a number of rankability
measures have recently been proposed, based on comparisons to a complete
dominance graph via combinatorial and linear algebraic methods. In this paper,
we review these measures and highlight some questions to which they give rise
before going on to propose new methods to assess rankability, which are
amenable to efficient estimation. Finally, we compare these measures by
applying them to both synthetic and real-life sports data.

We present a strong baseline that surpasses the performance of previously
published methods on the Habitat Challenge task of navigating to a target
object in indoor environments. Our method is motivated from primary failure
modes of prior state-of-the-art: poor exploration, inaccurate object
identification, and agent getting trapped due to imprecise map construction. We
make three contributions to mitigate these issues: (i) First, we show that
existing map-based methods fail to effectively use semantic clues for
exploration. We present a semantic-agnostic exploration strategy (called
Stubborn) without any learning that surprisingly outperforms prior work. (ii)
We propose a strategy for integrating temporal information to improve object
identification. (iii) Lastly, due to inaccurate depth observation the agent
often gets trapped in small regions. We develop a multi-scale collision map for
obstacle identification that mitigates this issue.

One of the central goals of the physics program at the future colliders is to
elucidate the origin of electroweak symmetry breaking, including precision
measurements of the Higgs sector. This includes a detailed study of Higgs boson
(H) pair production, which can reveal the H self-coupling. Since the discovery
of the Higgs boson, a large campaign of measurements of the properties of the
Higgs boson has begun and many new ideas have emerged during the completion of
this program. One such idea is the use of highly boosted and merged hadronic
decays of the Higgs boson ($\mathrm{H}\to\mathrm{b}\bar{\mathrm{b}}$,
$\mathrm{H}\to\mathrm{W}\mathrm{W}\to\mathrm{q}\bar{\mathrm{q}}\mathrm{q}\bar{\mathrm{q}}$)
with machine learning methods to improve the signal-to-background
discrimination. In this white paper, we champion the use of these modes to
boost the sensitivity of future collider physics programs to Higgs boson pair
production, the Higgs self-coupling, and Higgs-vector boson couplings. We
demonstrate the potential improvement possible at the Future Circular Collider
in hadron mode, especially with the use of graph neural networks.

We consider the community detection problem in a sparse $q$-uniform
hypergraph $G$, assuming that $G$ is generated according to the so-called
Hypergraph Stochastic Block Model (HSBM). We prove that a spectral method based
on the non-backtracking operator for hypergraphs works with high probability
down to the generalized Kesten-Stigum detection threshold conjectured by
Angelini et al. We characterize the spectrum of the non-backtracking operator
for the sparse HSBM, and provide an efficient dimension reduction procedure
using the Ihara-Bass formula for hypergraphs. As a result, community detection
for the sparse HSBM on $n$ vertices can be reduced to an eigenvector problem of
a $2n\times 2n$ non-normal matrix constructed from the adjacency matrix and the
degree matrix of the hypergraph. To the best of our knowledge, this is the
first provable and efficient spectral algorithm that achieves the conjectured
threshold for HSBMs with $k$ blocks generated according to a general symmetric
probability tensor.

Recent advancements in deep learning methods bring computer-assistance a step
closer to fulfilling promises of safer surgical procedures. However, the
generalizability of such methods is often dependent on training on diverse
datasets from multiple medical institutions, which is a restrictive requirement
considering the sensitive nature of medical data. Recently proposed
collaborative learning methods such as Federated Learning (FL) allow for
training on remote datasets without the need to explicitly share data. Even so,
data annotation still represents a bottleneck, particularly in medicine and
surgery where clinical expertise is often required. With these constraints in
mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that
combines FL and self-supervised learning to exploit a decentralized dataset of
both labeled and unlabeled videos, thereby improving performance on the task of
surgical phase recognition. By leveraging temporal patterns in the labeled
data, FedCy helps guide unsupervised training on unlabeled data towards
learning task-specific features for phase recognition. We demonstrate
significant performance gains over state-of-the-art FSSL methods on the task of
automatic recognition of surgical phases using a newly collected
multi-institutional dataset of laparoscopic cholecystectomy videos.
Furthermore, we demonstrate that our approach also learns more generalizable
features when tested on data from an unseen domain.

Human decision making is well known to be imperfect and the ability to
analyse such processes individually is crucial when attempting to aid or
improve a decision-maker's ability to perform a task, e.g. to alert them to
potential biases or oversights on their part. To do so, it is necessary to
develop interpretable representations of how agents make decisions and how this
process changes over time as the agent learns online in reaction to the accrued
experience. To then understand the decision-making processes underlying a set
of observed trajectories, we cast the policy inference problem as the inverse
to this online learning problem. By interpreting actions within a potential
outcomes framework, we introduce a meaningful mapping based on agents choosing
an action they believe to have the greatest treatment effect. We introduce a
practical algorithm for retrospectively estimating such perceived effects,
alongside the process through which agents update them, using a novel
architecture built upon an expressive family of deep state-space models.
Through application to the analysis of UNOS organ donation acceptance
decisions, we demonstrate that our approach can bring valuable insights into
the factors that govern decision processes and how they change over time.

`Double descent' delineates the generalization behaviour of models depending
on the regime they belong to: under- or over-parameterized. The current
theoretical understanding behind the occurrence of this phenomenon is primarily
based on linear and kernel regression models -- with informal parallels to
neural networks via the Neural Tangent Kernel. Therefore such analyses do not
adequately capture the mechanisms behind double descent in finite-width neural
networks, as well as, disregard crucial components -- such as the choice of the
loss function. We address these shortcomings by leveraging influence functions
in order to derive suitable expressions of the population loss and its lower
bound, while imposing minimal assumptions on the form of the parametric model.
Our derived bounds bear an intimate connection with the spectrum of the Hessian
at the optimum, and importantly, exhibit a double descent behaviour at the
interpolation threshold. Building on our analysis, we further investigate how
the loss function affects double descent -- and thus uncover interesting
properties of neural networks and their Hessian spectra near the interpolation

Particle tracking is a challenging pattern recognition task at the Large
Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms,
such as those based on the Kalman Filter, achieve excellent performance in
reconstructing the prompt tracks from the collision points. However, they
require dedicated configuration and additional computing time to efficiently
reconstruct the large radius tracks created away from the collision points. We
developed an end-to-end machine learning-based track finding algorithm for the
HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic
about global track positions. In this work, we study the performance of the
Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in
the event, the pipeline simultaneously reconstructs prompt tracks and large
radius tracks with high efficiencies. This new capability offered by the
Exa.TrkX pipeline may enable us to search for new physics in real time.

We apply state-of-the-art computational geometry methods to the problem of
reconstructing a time-varying sea surface from tide gauge records. Our work
builds on a recent article by Nitzke et al.~(Computers \& Geosciences,
157:104920, 2021) who have suggested to learn a triangulation $D$ of a given
set of tide gauge stations. The objective is to minimize the misfit of the
piecewise linear surface induced by $D$ to a reference surface that has been
acquired with satellite altimetry. The authors restricted their search to
k-order Delaunay ($k$-OD) triangulations and used an integer linear program in
order to solve the resulting optimization problem.
  In geometric terms, the input to our problem consists of two sets of points
in $\mathbb{R}^2$ with elevations: a set $\mathcal{S}$ that is to be
triangulated, and a set $\mathcal{R}$ of reference points. Intuitively, we
define the error of a triangulation as the average vertical distance of a point
in $\mathcal{R}$ to the triangulated surface that is obtained by interpolating
elevations of $\mathcal{S}$ linearly in each triangle. Our goal is to find the
triangulation of $\mathcal{S}$ that has minimum error with respect to
$\mathcal{R}$.
  In our work, we prove that the minimum-error triangulation problem is NP-hard
and cannot be approximated within any multiplicative factor in polynomial time
unless $P=NP$. At the same time we show that the problem instances that occur
in our application (considering sea level data from several hundreds of tide
gauge stations worldwide) can be solved relatively fast using dynamic
programming when restricted to $k$-OD triangulations for $k\le 7$. In
particular, instances for which the number of connected components of the
so-called $k$-OD fixed-edge graph is small can be solved within few seconds.

This white paper provides a comprehensive review of our present understanding
of experimental neutrino anomalies that remain unresolved, charting the
progress achieved over the last decade at the experimental and phenomenological
level, and sets the stage for future programmatic prospects in addressing those
anomalies. It is purposed to serve as a guiding and motivational "encyclopedic"
reference, with emphasis on needs and options for future exploration that may
lead to the ultimate resolution of the anomalies. We see the main experimental,
analysis, and theory-driven thrusts that will be essential to achieving this
goal being: 1) Cover all anomaly sectors -- given the unresolved nature of all
four canonical anomalies, it is imperative to support all pillars of a diverse
experimental portfolio, source, reactor, decay-at-rest, decay-in-flight, and
other methods/sources, to provide complementary probes of and increased
precision for new physics explanations; 2) Pursue diverse signatures -- it is
imperative that experiments make design and analysis choices that maximize
sensitivity to as broad an array of these potential new physics signatures as
possible; 3) Deepen theoretical engagement -- priority in the theory community
should be placed on development of standard and beyond standard models relevant
to all four short-baseline anomalies and the development of tools for efficient
tests of these models with existing and future experimental datasets; 4) Openly
share data -- Fluid communication between the experimental and theory
communities will be required, which implies that both experimental data
releases and theoretical calculations should be publicly available; and 5)
Apply robust analysis techniques -- Appropriate statistical treatment is
crucial to assess the compatibility of data sets within the context of any
given model.

We consider model-based multi-agent reinforcement learning, where the
environment transition model is unknown and can only be learned via expensive
interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent
Reinforcement Learning), a novel sample-efficient algorithm that can
efficiently balance exploration, i.e., learning about the environment, and
exploitation, i.e., achieve good equilibrium performance in the underlying
general-sum Markov game. H-MARL builds high-probability confidence intervals
around the unknown transition model and sequentially updates them based on
newly observed data. Using these, it constructs an optimistic hallucinated game
for the agents for which equilibrium policies are computed at each round. We
consider general statistical models (e.g., Gaussian processes, deep ensembles,
etc.) and policy classes (e.g., deep neural networks), and theoretically
analyze our approach by bounding the agents' dynamic regret. Moreover, we
provide a convergence rate to the equilibria of the underlying Markov game. We
demonstrate our approach experimentally on an autonomous driving simulation
benchmark. H-MARL learns successful equilibrium policies after a few
interactions with the environment and can significantly improve the performance
compared to non-optimistic exploration methods.

In Machine Learning, the emergence of \textit{the right to be forgotten} gave
birth to a paradigm named \textit{machine unlearning}, which enables data
holders to proactively erase their data from a trained model. Existing machine
unlearning techniques focus on centralized training, where access to all
holders' training data is a must for the server to conduct the unlearning
process. It remains largely underexplored about how to achieve unlearning when
full access to all training data becomes unavailable. One noteworthy example is
Federated Learning (FL), where each participating data holder trains locally,
without sharing their training data to the central server. In this paper, we
investigate the problem of machine unlearning in FL systems. We start with a
formal definition of the unlearning problem in FL and propose a rapid
retraining approach to fully erase data samples from a trained FL model. The
resulting design allows data holders to jointly conduct the unlearning process
efficiently while keeping their training data locally. Our formal convergence
and complexity analysis demonstrate that our design can preserve model utility
with high efficiency. Extensive evaluations on four real-world datasets
illustrate the effectiveness and performance of our proposed realization.

Sponge examples are test-time inputs carefully-optimized to increase energy
consumption and latency of neural networks when deployed on hardware
accelerators. In this work, we demonstrate that sponge attacks can also be
implanted at training time, when model training is outsourced to a third party,
via an attack that we call sponge poisoning. This attack allows one to increase
the energy consumption and latency of machine-learning models indiscriminately
on each test-time input. We present a novel formalization for sponge poisoning,
overcoming the limitations related to the optimization of test-time sponge
examples, and show that this attack is possible even if the attacker only
controls a few poisoning samples and model updates. Our extensive experimental
analysis, involving two deep learning architectures and three datasets, shows
that sponge poisoning can almost completely vanish the effect of such hardware
accelerators. Finally, we analyze activations of the resulting sponge models,
identifying the module components that are more sensitive to this
vulnerability.

In computational pathology, we often face a scarcity of annotations and a
large amount of unlabeled data. One method for dealing with this is
semi-supervised learning which is commonly split into a self-supervised pretext
task and a subsequent model fine-tuning. Here, we compress this two-stage
training into one by introducing S5CL, a unified framework for
fully-supervised, self-supervised, and semi-supervised learning. With three
contrastive losses defined for labeled, unlabeled, and pseudo-labeled images,
S5CL can learn feature representations that reflect the hierarchy of distance
relationships: similar images and augmentations are embedded the closest,
followed by different looking images of the same class, while images from
separate classes have the largest distance. Moreover, S5CL allows us to
flexibly combine these losses to adapt to different scenarios. Evaluations of
our framework on two public histopathological datasets show strong improvements
in the case of sparse labels: for a H&E-stained colorectal cancer dataset, the
accuracy increases by up to 9% compared to supervised cross-entropy loss; for a
highly imbalanced dataset of single white blood cells from leukemia patient
blood smears, the F1-score increases by up to 6%.

State-of-the-art maximum entropy models for texture synthesis are built from
statistics relying on image representations defined by convolutional neural
networks (CNN). Such representations capture rich structures in texture images,
outperforming wavelet-based representations in this regard. However, conversely
to neural networks, wavelets offer meaningful representations, as they are
known to detect structures at multiple scales (e.g. edges) in images. In this
work, we propose a family of statistics built upon non-linear wavelet based
representations, that can be viewed as a particular instance of a one-layer
CNN, using a generalized rectifier non-linearity. These statistics
significantly improve the visual quality of previous classical wavelet-based
models, and allow one to produce syntheses of similar quality to
state-of-the-art models, on both gray-scale and color textures.

Mainstream Video-Language Pre-training models \cite{actbert,clipbert,violet}
consist of three parts, a video encoder, a text encoder, and a video-text
fusion Transformer. They pursue better performance via utilizing heavier
unimodal encoders or multimodal fusion Transformers, resulting in increased
parameters with lower efficiency in downstream tasks. In this work, we for the
first time introduce an end-to-end video-language model, namely
\textit{all-in-one Transformer}, that embeds raw video and textual signals into
joint representations using a unified backbone architecture. We argue that the
unique temporal information of video data turns out to be a key barrier
hindering the design of a modality-agnostic Transformer. To overcome the
challenge, we introduce a novel and effective token rolling operation to encode
temporal representations from video clips in a non-parametric manner. The
careful design enables the representation learning of both video-text
multimodal inputs and unimodal inputs using a unified backbone model. Our
pre-trained all-in-one Transformer is transferred to various downstream
video-text tasks after fine-tuning, including text-video retrieval,
video-question answering, multiple choice and visual commonsense reasoning.
State-of-the-art performances with the minimal model FLOPs on nine datasets
demonstrate the superiority of our method compared to the competitive
counterparts. The code and pretrained model have been released in
https://github.com/showlab/all-in-one.

While GANs can produce photo-realistic images in ideal conditions for certain
domains, the generation of full-body human images remains difficult due to the
diversity of identities, hairstyles, clothing, and the variance in pose.
Instead of modeling this complex domain with a single GAN, we propose a novel
method to combine multiple pretrained GANs, where one GAN generates a global
canvas (e.g., human body) and a set of specialized GANs, or insets, focus on
different parts (e.g., faces, shoes) that can be seamlessly inserted onto the
global canvas. We model the problem as jointly exploring the respective latent
spaces such that the generated images can be combined, by inserting the parts
from the specialized generators onto the global canvas, without introducing
seams. We demonstrate the setup by combining a full body GAN with a dedicated
high-quality face GAN to produce plausible-looking humans. We evaluate our
results with quantitative metrics and user studies.

Tactile sensing for robotics is achieved through a variety of mechanisms,
including magnetic, optical-tactile, and conductive fluid. Currently, the
fluid-based sensors have struck the right balance of anthropomorphic sizes and
shapes and accuracy of tactile response measurement. However, this design is
plagued by a low Signal to Noise Ratio (SNR) due to the fluid based sensing
mechanism "damping" the measurement values that are hard to model. To this end,
we present a spatio-temporal gradient representation on the data obtained from
fluid-based tactile sensors, which is inspired from neuromorphic principles of
event based sensing. We present a novel algorithm (GradTac) that converts
discrete data points from spatial tactile sensors into spatio-temporal surfaces
and tracks tactile contours across these surfaces. Processing the tactile data
using the proposed spatio-temporal domain is robust, makes it less susceptible
to the inherent noise from the fluid based sensors, and allows accurate
tracking of regions of touch as compared to using the raw data. We successfully
evaluate and demonstrate the efficacy of GradTac on many real-world experiments
performed using the Shadow Dexterous Hand, equipped with the BioTac SP sensors.
Specifically, we use it for tracking tactile input across the sensor's surface,
measuring relative forces, detecting linear and rotational slip, and for edge
tracking. We also release an accompanying task-agnostic dataset for the BioTac
SP, which we hope will provide a resource to compare and quantify various novel
approaches, and motivate further research.

Providing natural language instructions in prompts is a useful new paradigm
for improving task performance of large language models in a zero-shot setting.
Recent work has aimed to improve such prompts via manual rewriting or
gradient-based tuning. However, manual rewriting is time-consuming and requires
subjective interpretation, while gradient-based tuning can be extremely
computationally demanding for large models and requires full access to model
weights, which may not be available for API-based models. In this work, we
introduce Gradient-free Instructional Prompt Search (GrIPS), a gradient-free,
edit-based search approach for improving task instructions for large language
models. GrIPS takes in instructions designed for humans and automatically
returns an improved, edited prompt, while allowing for API-based tuning. The
instructions in our search are iteratively edited using four operations
(delete, add, swap, paraphrase) on text at the phrase-level. With InstructGPT
models, GrIPS improves the average task performance by up to 4.30 percentage
points on eight classification tasks from the Natural-Instructions dataset. We
see improvements for both instruction-only prompts and for k-shot
example+instruction prompts. Notably, GrIPS outperforms manual rewriting
following the guidelines in Mishra et al. (2022) and also outperforms purely
example-based prompts while controlling for the available compute and data
budget. Lastly, we provide qualitative analysis of the edited instructions
across several scales of GPT models. Our code is available at:
https://github.com/archiki/GrIPS

Swarm intelligence is being increasingly deployed in autonomous systems, such
as drones and unmanned vehicles. Federated reinforcement learning (FRL), a key
swarm intelligence paradigm where agents interact with their own environments
and cooperatively learn a consensus policy while preserving privacy, has
recently shown potential advantages and gained popularity. However, transient
faults are increasing in the hardware system with continuous technology node
scaling and can pose threats to FRL systems. Meanwhile, conventional
redundancy-based protection methods are challenging to deploy on
resource-constrained edge applications. In this paper, we experimentally
evaluate the fault tolerance of FRL navigation systems at various scales with
respect to fault models, fault locations, learning algorithms, layer types,
communication intervals, and data types at both training and inference stages.
We further propose two cost-effective fault detection and recovery techniques
that can achieve up to 3.3x improvement in resilience with <2.7% overhead in
FRL systems.

Continuous time temporal networks are attracting increasing attention due
their omnipresence in real-world datasets and they manifold applications. While
static network models have been successful in capturing static topological
regularities, they often fail to model effects coming from the causal nature
that explain the generation of networks. Exploiting the temporal aspect of
networks has thus been the focus of various studies in the last decades.
  We propose a framework for designing generative models for continuous time
temporal networks. Assuming a first order Markov assumption on the
edge-specific temporal point processes enables us to flexibly apply survival
analysis models directly on the waiting time between events, while using
time-varying history-based features as covariates for these predictions. This
approach links the well-documented field of temporal networks analysis through
multivariate point processes, with methodological tools adapted from survival
analysis. We propose a fitting method for models within this framework, and an
algorithm for simulating new temporal networks having desired properties. We
evaluate our method on a downstream future link prediction task, and provide a
qualitative assessment of the network simulations.

Transformer-based language models have become a key building block for
natural language processing. While these models are extremely accurate, they
can be too large and computationally intensive to run on standard deployments.
A variety of compression methods, including distillation, quantization,
structured and unstructured pruning are known to decrease model size and
increase inference speed, with low accuracy loss. In this context, this paper's
contributions are two-fold. We perform an in-depth study of the
accuracy-compression trade-off for unstructured weight pruning of BERT models.
We introduce Optimal BERT Surgeon (oBERT), an efficient and accurate weight
pruning method based on approximate second-order information, which we show to
yield state-of-the-art results in both stages of language tasks: pre-training
and fine-tuning. Specifically, oBERT extends existing work on unstructured
second-order pruning by allowing for pruning blocks of weights, and by being
applicable at the BERT scale. Second, we investigate the impact of this pruning
method when compounding compression approaches to obtain highly compressed but
accurate models for deployment on edge devices. These models significantly push
boundaries of the current state-of-the-art sparse BERT models with respect to
all metrics: model size, inference speed and task accuracy. For example,
relative to the dense BERT-base, we obtain 10x model size compression (in MB)
with < 1% accuracy drop, 10x CPU-inference speedup with < 2% accuracy drop, and
29x CPU-inference speedup with < 7.5% accuracy drop. Our code, fully integrated
with Transformers and SparseML, is available at
https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT.

Conventional convolution neural networks (CNNs) trained on narrow
Field-of-View (FoV) images are the state-of-the-art approaches for object
recognition tasks. Some methods proposed the adaptation of CNNs to ultra-wide
FoV images by learning deformable kernels. However, they are limited by the
Euclidean geometry and their accuracy degrades under strong distortions caused
by fisheye projections. In this work, we demonstrate that learning the shape of
convolution kernels in non-Euclidean spaces is better than existing deformable
kernel methods. In particular, we propose a new approach that learns deformable
kernel parameters (positions) in hyperbolic space. FisheyeHDK is a hybrid CNN
architecture combining hyperbolic and Euclidean convolution layers for
positions and features learning. First, we provide an intuition of hyperbolic
space for wide FoV images. Using synthetic distortion profiles, we demonstrate
the effectiveness of our approach. We select two datasets - Cityscapes and
BDD100K 2020 - of perspective images which we transform to fisheye equivalents
at different scaling factors (analog to focal lengths). Finally, we provide an
experiment on data collected by a real fisheye camera. Validations and
experiments show that our approach improves existing deformable kernel methods
for CNN adaptation on fisheye images.

The olive oil sector produces a substantial impact in the Mediterranean's
economy and lifestyle. Many studies exist which try to optimize the different
steps in the olive oil's production process. One of the main challenges for
olive oil producers is the ability to asses and control the quality during the
production cycle. For this purpose, several parameters need to be determined,
such as the acidity, the UV absorption or the ethyl esters content. To achieve
this, samples must be sent to an approved laboratory for chemical analysis.
This approach is expensive and cannot be performed very frequently, making
quality control of olive oil a real challenge. This work explores a new
approach based on fluorescence spectroscopy and artificial intelligence
(namely, 1-D convolutional neural networks) to predict the five chemical
quality indicators of olive oil (acidity, peroxide value, UV spectroscopic
parameters $K_{270}$ and $K_{232}$, and ethyl esters) from simple fluorescence
spectra. Fluorescence spectroscopy is a very attractive optical technique since
it does not require sample preparation, is non destructive, and, as shown in
this work, can be easily implemented in small and cost-effective sensors. The
results indicate that the proposed approach gives exceptional results in the
quality determination and would make the continuous quality control of olive
oil during and after the production process a reality. Additionally, this novel
methodology presents potential applications as a support for quality
specifications of olive oil, as defined by the European regulation.

Solving electronic structure problems represents a promising field of
application for quantum computers. Currently, much effort has been spent in
devising and optimizing quantum algorithms for quantum chemistry problems
featuring up to hundreds of electrons. While quantum algorithms can in
principle outperform their classical equivalents, the polynomially scaling
runtime, with the number of constituents, can still prevent quantum simulations
of large scale systems. We propose a strategy to extend the scope of quantum
computational methods to large scale simulations using a machine learning
potential, trained on quantum simulation data. The challenge of applying
machine learning potentials in today's quantum setting arises from the several
sources of noise affecting the quantum computations of electronic energies and
forces. We investigate the trainability of a machine learning potential
selecting various sources of noise: statistical, optimization and hardware
noise.Finally, we construct the first machine learning potential from data
computed on actual IBM Quantum processors for a hydrogen molecule. This already
would allow us to perform arbitrarily long and stable molecular dynamics
simulations, outperforming all current quantum approaches to molecular dynamics
and structure optimization.

Microseismic analysis is a valuable tool for fracture characterization in the
earth's subsurface. As distributed acoustic sensing (DAS) fibers are deployed
at depth inside wells, they hold vast potential for high-resolution
microseismic analysis. However, the accurate detection of microseismic signals
in continuous DAS data is challenging and time-consuming. We design, train, and
deploy a deep learning model to detect microseismic events in DAS data
automatically. We create a curated dataset of nearly 7,000 manually-selected
events and an equal number of background noise examples. We optimize the deep
learning model's network architecture together with its training
hyperparameters by Bayesian optimization. The trained model achieves an
accuracy of 98.6% on our benchmark dataset and even detects low-amplitude
events missed during manual labeling. Our methodology detects more than 100,000
events allowing the reconstruction of spatio-temporal fracture development far
more accurately and efficiently than would have been feasible by traditional
methods.

Many recent deep learning-based solutions have widely adopted the
attention-based mechanism in various tasks of the NLP discipline. However, the
inherent characteristics of deep learning models and the flexibility of the
attention mechanism increase the models' complexity, thus leading to challenges
in model explainability. In this paper, to address this challenge, we propose a
novel practical framework by utilizing a two-tier attention architecture to
decouple the complexity of explanation and the decision-making process. We
apply it in the context of a news article classification task. The experiments
on two large-scaled news corpora demonstrate that the proposed model can
achieve competitive performance with many state-of-the-art alternatives and
illustrate its appropriateness from an explainability perspective.

Topological data analysis asks when balls in a metric space $(X,d)$
intersect. Geometric data analysis asks how much balls have to be enlarged to
intersect. We connect this principle to the traditional core geometric concept
of curvature. This enables us, on one hand, to reconceptualize curvature and
link it to the geometric notion of hyperconvexity. On the other hand, we can
then also understand methods of topological data analysis from a geometric

Visual-inertial odometry (VIO) systems traditionally rely on filtering or
optimization-based techniques for egomotion estimation. While these methods are
accurate under nominal conditions, they are prone to failure during severe
illumination changes, rapid camera motions, or on low-texture image sequences.
Learning-based systems have the potential to outperform classical
implementations in challenging environments, but, currently, do not perform as
well as classical methods in nominal settings. Herein, we introduce a framework
for training a hybrid VIO system that leverages the advantages of learning and
standard filtering-based state estimation. Our approach is built upon a
differentiable Kalman filter, with an IMU-driven process model and a robust,
neural network-derived relative pose measurement model. The use of the Kalman
filter framework enables the principled treatment of uncertainty at training
time and at test time. We show that our self-supervised loss formulation
outperforms a similar, supervised method, while also enabling online
retraining. We evaluate our system on a visually degraded version of the EuRoC
dataset and find that our estimator operates without a significant reduction in
accuracy in cases where classical estimators consistently diverge. Finally, by
properly utilizing the metric information contained in the IMU measurements,
our system is able to recover metric scene scale, while other self-supervised
monocular VIO approaches cannot.

When dealing with binary classification of data with only one labeled class
data scientists employ two main approaches, namely One-Class (OC)
classification and Positive Unlabeled (PU) learning. The former only learns
from labeled positive data, whereas the latter also utilizes unlabeled data to
improve the overall performance. Since PU learning utilizes more data, we might
be prone to think that when unlabeled data is available, the go-to algorithms
should always come from the PU group. However, we find that this is not always
the case if unlabeled data is unreliable, i.e. contains limited or biased
latent negative data. We perform an extensive experimental study of a wide list
of state-of-the-art OC and PU algorithms in various scenarios as far as
unlabeled data reliability is concerned. Furthermore, we propose PU
modifications of state-of-the-art OC algorithms that are robust to unreliable
unlabeled data, as well as a guideline to similarly modify other OC algorithms.
Our main practical recommendation is to use state-of-the-art PU algorithms when
unlabeled data is reliable and to use the proposed modifications of
state-of-the-art OC algorithms otherwise. Additionally, we outline procedures
to distinguish the cases of reliable and unreliable unlabeled data using
statistical tests.

Simulating complex dynamics like fluids with traditional simulators is
computationally challenging. Deep learning models have been proposed as an
efficient alternative, extending or replacing parts of traditional simulators.
We investigate graph neural networks (GNNs) for learning fluid dynamics and
find that their generalization capability is more limited than previous works
would suggest. We also challenge the current practice of adding random noise to
the network inputs in order to improve its generalization capability and
simulation stability. We find that inserting the real data distribution, e.g.
by unrolling multiple simulation steps, improves accuracy and that hiding all
domain-specific features from the learning model improves generalization. Our
results indicate that learning models, such as GNNs, fail to learn the exact
underlying dynamics unless the training set is devoid of any other
problem-specific correlations that could be used as shortcuts.

We seek the best stroke sequences of a finite-size swimming predator chasing
a non-motile point or finite--size prey at low Reynolds number. We use optimal
control to seek the globally-optimal solutions for the former and RL for
general situations. The predator is represented by a squirmer model that can
translate forward and laterally, rotate and generate a stresslet flow. We
identify the predator's best squirming sequences to achieve the time-optimal
(TO) and efficiency-optimal (EO) predation. For a point prey, the TO squirmer
executing translational motions favours a two-fold L-shaped trajectory that
enables it to exploit the disturbance flow for accelerated predation; using a
stresslet mode significantly expedites the EO predation, allowing the predator
to catch the prey faster yet with lower energy consumption and higher predatory
efficiency; the predator can harness its stresslet disturbance flow to suck the
prey towards itself; compared to a translating predator, its compeer combining
translation and rotation is less time--efficient, and the latter occasionally
achieves the TO predation via retreating in order to advance. We also adopt RL
to reproduce the globally-optimal predatory strategy of chasing a point prey,
qualitatively capturing the crucial two--fold attribute of TO path. Using a
numerically emulated RL environment, we explore the dependence of the optimal
predatory path on the size of prey. Our results might provide useful
information that help design synthetic microswimmers such as \textit{in vivo}
medical micro-robots capable of capturing and approaching objects in viscous
flows.

While existing end-to-end beamformers achieve impressive performance in
various front-end speech processing tasks, they usually encapsulate the whole
process into a black box and thus lack adequate interpretability. As an attempt
to fill the blank, we propose a novel neural beamformer inspired by Taylor's
approximation theory called TaylorBeamformer for multi-channel speech
enhancement. The core idea is that the recovery process can be formulated as
the spatial filtering in the neighborhood of the input mixture. Based on that,
we decompose it into the superimposition of the 0th-order non-derivative and
high-order derivative terms, where the former serves as the spatial filter and
the latter is viewed as the residual noise canceller to further improve the
speech quality. To enable end-to-end training, we replace the derivative
operations with trainable networks and thus can learn from training data.
Extensive experiments are conducted on the synthesized dataset based on
LibriSpeech and results show that the proposed approach performs favorably
against the previous advanced baselines.

Dynamic movement primitives (DMPs) allow complex position trajectories to be
efficiently demonstrated to a robot. In contact-rich tasks, where position
trajectories alone may not be safe or robust over variation in contact
geometry, DMPs have been extended to include force trajectories. However,
different task phases or degrees of freedom may require the tracking of either
position or force -- e.g., once contact is made, it may be more important to
track the force demonstration trajectory in the contact direction. The robot
impedance balances between following a position or force reference trajectory,
where a high stiffness tracks position and a low stiffness tracks force. This
paper proposes using DMPs to learn position and force trajectories from
demonstrations, then adapting the impedance parameters online with a
higher-level control policy trained by reinforcement learning. This allows
one-shot demonstration of the task with DMPs, and improved robustness and
performance from the impedance adaptation. The approach is validated on
peg-in-hole and adhesive strip application tasks.

The correct interpretation and understanding of deep learning models is
essential in many applications. Explanatory visual interpretation approaches
for image and natural language processing allow domain experts to validate and
understand almost any deep learning model. However, they fall short when
generalizing to arbitrary time series data that is less intuitive and more
diverse. Whether a visualization explains the true reasoning or captures the
real features is difficult to judge. Hence, instead of blind trust we need an
objective evaluation to obtain reliable quality metrics. We propose a framework
of six orthogonal metrics for gradient- or perturbation-based post-hoc visual
interpretation methods designed for time series classification and segmentation
tasks. An experimental study includes popular neural network architectures for
time series and nine visual interpretation methods. We evaluate the visual
interpretation methods with diverse datasets from the UCR repository and a
complex real-world dataset, and study the influence of common regularization
techniques during training. We show that none of the methods consistently
outperforms any of the others on all metrics while some are ahead at times. Our
insights and recommendations allow experts to make informed choices of suitable
visualization techniques for the model and task at hand.

We study the problem of finding optimal correlated equilibria of various
sorts: normal-form coarse correlated equilibrium (NFCCE), extensive-form coarse
correlated equilibrium (EFCCE), and extensive-form correlated equilibrium
(EFCE). This is NP-hard in the general case and has been studied in special
cases, most notably triangle-free games, which include all two-player games
with public chance moves. However, the general case is not well understood, and
algorithms usually scale poorly. First, we introduce the correlation DAG, a
representation of the space of correlated strategies whose size is dependent on
the specific solution concept. It extends the team belief DAG of Zhang et al.
to general-sum games. For each of the three solution concepts, its size depends
exponentially only on a parameter related to the game's information structure.
We also prove a fundamental complexity gap: while our size bounds for NFCCE are
similar to those achieved in the case of team games by Zhang et al., this is
impossible to achieve for the other two concepts under standard complexity
assumptions. Second, we propose a two-sided column generation approach to
compute optimal correlated strategies. Our algorithm improves upon the
one-sided approach of Farina et al. by means of a new decomposition of
correlated strategies which allows players to re-optimize their sequence-form
strategies with respect to correlation plans which were previously added to the
support. Our techniques outperform the prior state of the art for computing
optimal general-sum correlated equilibria. For team games, the two-sided column
generation approach vastly outperforms standard column generation approaches,
making it the state of the art algorithm when the parameter is large. Along the
way we also introduce two new benchmark games: a trick-taking game that
emulates the endgame phase of the card game bridge, and a ride-sharing game.

While traditional statistical signal processing model-based methods can
derive the optimal estimators relying on specific statistical assumptions,
current learning-based methods further promote the performance upper bound via
deep neural networks but at the expense of high encapsulation and lack adequate
interpretability. Standing upon the intersection between traditional
model-based methods and learning-based methods, we propose a model-driven
approach based on the maximum a posteriori (MAP) framework, termed as MDNet,
for single-channel speech enhancement. Specifically, the original problem is
formulated into the joint posterior estimation w.r.t. speech and noise
components. Different from the manual assumption toward the prior terms, we
propose to model the prior distribution via networks and thus can learn from
training data. The framework takes the unfolding structure and in each step,
the target parameters can be progressively estimated through explicit gradient
descent operations. Besides, another network serves as the fusion module to
further refine the previous speech estimation. The experiments are conducted on
the WSJ0-SI84 and Interspeech2020 DNS-Challenge datasets, and quantitative
results show that the proposed approach outshines previous state-of-the-art

We present a general convergent class of reinforcement learning algorithms
that is founded on two distinct principles: (1) mapping value estimates to a
different space using arbitrary functions from a broad class, and (2) linearly
decomposing the reward signal into multiple channels. The first principle
enables incorporating specific properties into the value estimator that can
enhance learning. The second principle, on the other hand, allows for the value
function to be represented as a composition of multiple utility functions. This
can be leveraged for various purposes, e.g. dealing with highly varying reward
scales, incorporating a priori knowledge about the sources of reward, and
ensemble learning. Combining the two principles yields a general blueprint for
instantiating convergent algorithms by orchestrating diverse mapping functions
over multiple reward channels. This blueprint generalizes and subsumes
algorithms such as Q-Learning, Log Q-Learning, and Q-Decomposition. In
addition, our convergence proof for this general class relaxes certain required
assumptions in some of these algorithms. Based on our theory, we discuss
several interesting configurations as special cases. Finally, to illustrate the
potential of the design space that our theory opens up, we instantiate a
particular algorithm and evaluate its performance on the Atari suite.

The massive spread of visual content through the web and social media poses
both challenges and opportunities. Tracking visually-similar content is an
important task for studying and analyzing social phenomena related to the
spread of such content. In this paper, we address this need by building a
dataset of social media images and evaluating visual near-duplicates retrieval
methods based on image retrieval and several advanced visual feature extraction
methods. We evaluate the methods using a large-scale dataset of images we crawl
from social media and their manipulated versions we generated, presenting
promising results in terms of recall. We demonstrate the potential of this
method in two case studies: one that shows the value of creating systems
supporting manual content review, and another that demonstrates the usefulness
of automatic large-scale data analysis.

Unsupervised learning for monocular camera motion and 3D scene understanding
has gained popularity over traditional methods, relying on epipolar geometry or
non-linear optimization. Notably, deep learning can overcome many issues of
monocular vision, such as perceptual aliasing, low-textured areas, scale-drift,
and degenerate motions. Also, concerning supervised learning, we can fully
leverage video streams data without the need for depth or motion labels.
However, in this work, we note that rotational motion can limit the accuracy of
the unsupervised pose networks more than the translational component.
Therefore, we present RAUM-VO, an approach based on a model-free epipolar
constraint for frame-to-frame motion estimation (F2F) to adjust the rotation
during training and online inference. To this end, we match 2D keypoints
between consecutive frames using pre-trained deep networks, Superpoint and
Superglue, while training a network for depth and pose estimation using an
unsupervised training protocol. Then, we adjust the predicted rotation with the
motion estimated by F2F using the 2D matches and initializing the solver with
the pose network prediction. Ultimately, RAUM-VO shows a considerable accuracy
improvement compared to other unsupervised pose networks on the KITTI dataset
while reducing the complexity of other hybrid or traditional approaches and
achieving comparable state-of-the-art results.

Recent segmentation methods, such as OCR and CPNet, utilizing "class level"
information in addition to pixel features, have achieved notable success for
boosting the accuracy of existing network modules. However, the extracted
class-level information was simply concatenated to pixel features, without
explicitly being exploited for better pixel representation learning. Moreover,
these approaches learn soft class centers based on coarse mask prediction,
which is prone to error accumulation. In this paper, aiming to use class level
information more effectively, we propose a universal Class-Aware Regularization
(CAR) approach to optimize the intra-class variance and inter-class distance
during feature learning, motivated by the fact that humans can recognize an
object by itself no matter which other objects it appears with. Three novel
loss functions are proposed. The first loss function encourages more compact
class representations within each class, the second directly maximizes the
distance between different class centers, and the third further pushes the
distance between inter-class centers and pixels. Furthermore, the class center
in our approach is directly generated from ground truth instead of from the
error-prone coarse prediction. Our method can be easily applied to most
existing segmentation models during training, including OCR and CPNet, and can
largely improve their accuracy at no additional inference overhead. Extensive
experiments and ablation studies conducted on multiple benchmark datasets
demonstrate that the proposed CAR can boost the accuracy of all baseline models
by up to 2.23% mIOU with superior generalization ability. The complete code is
available at https://github.com/edwardyehuang/CAR.

Knowledge distillation is normally used to compress a big network, or
teacher, onto a smaller one, the student, by training it to match its outputs.
Recently, some works have shown that robustness against adversarial attacks can
also be distilled effectively to achieve good rates of robustness on
mobile-friendly models. In this work, however, we take a different point of
view, and show that knowledge distillation can be used directly to boost the
performance of state-of-the-art models in adversarial robustness. In this
sense, we present a thorough analysis and provide general guidelines to distill
knowledge from a robust teacher and boost the clean and adversarial performance
of a student model even further. To that end, we present Adversarial Knowledge
Distillation (AKD), a new framework to improve a model's robust performance,
consisting on adversarially training a student on a mixture of the original
labels and the teacher outputs. Through carefully controlled ablation studies,
we show that using early-stopping, model ensembles and weak adversarial
training are key techniques to maximize performance of the student, and show
that these insights generalize across different robust distillation techniques.
Finally, we provide insights on the effect of robust knowledge distillation on
the dynamics of the student network, and show that AKD mostly improves the
calibration of the network and modify its training dynamics on samples that the
model finds difficult to learn, or even memorize.

In recent years, the deployment of large-scale Internet of Things (IoT)
applications has given rise to edge federations that seamlessly interconnect
and leverage resources from multiple edge service providers. The requirement of
supporting both latency-sensitive and compute-intensive IoT tasks necessitates
service resilience, especially for the broker nodes in typical broker-worker
deployment designs. Existing fault-tolerance or resilience schemes often lack
robustness and generalization capability in non-stationary workload settings.
This is typically due to the expensive periodic fine-tuning of models required
to adapt them in dynamic scenarios. To address this, we present a confidence
aware resilience model, CAROL, that utilizes a memory-efficient generative
neural network to predict the Quality of Service (QoS) for a future state and a
confidence score for each prediction. Thus, whenever a broker fails, we quickly
recover the system by executing a local-search over the broker-worker topology
space and optimize future QoS. The confidence score enables us to keep track of
the prediction performance and run parsimonious neural network fine-tuning to
avoid excessive overheads, further improving the QoS of the system. Experiments
on a Raspberry-Pi based edge testbed with IoT benchmark applications show that
CAROL outperforms state-of-the-art resilience schemes by reducing the energy
consumption, deadline violation rates and resilience overheads by up to 16, 17
and 36 percent, respectively.

While predictive models are a purely technological feat, they may operate in
a social context in which benign engineering choices entail unexpected
real-life consequences. Fairness -- pertaining both to individuals and groups
-- is one of such considerations; it surfaces when data capture protected
characteristics of people who may be discriminated upon these attributes. This
notion has predominantly been studied for a fixed predictive model, sometimes
under different classification thresholds, striving to identify and eradicate
its undesirable behaviour. Here we backtrack on this assumption and explore a
novel definition of fairness where individuals can be harmed when one predictor
is chosen ad hoc from a group of equally well performing models, i.e., in view
of model multiplicity. Since a person may be classified differently across
models that are otherwise considered equivalent, this individual could argue
for a model with a more favourable outcome, possibly causing others to be
adversely affected. We introduce this scenario with a two-dimensional example
based on linear classification; then investigate its analytical properties in a
broader context; and finally present experimental results on data sets popular
in fairness studies. Our findings suggest that such unfairness can be found in
real-life situations and may be difficult to mitigate with technical measures
alone, as doing so degrades certain metrics of predictive performance.

Generative Adversarial Networks (GANs) learn an implicit generative model
from data samples through a two-player game. In this paper, we study the
existence of Nash equilibrium of the game which is consistent as the number of
data samples grows to infinity. In a realizable setting where the goal is to
estimate the ground-truth generator of a stationary Gaussian process, we show
that the existence of consistent Nash equilibrium depends crucially on the
choice of the discriminator family. The discriminator defined from second-order
statistical moments can result in non-existence of Nash equilibrium, existence
of consistent non-Nash equilibrium, or existence and uniqueness of consistent
Nash equilibrium, depending on whether symmetry properties of the generator
family are respected. We further study empirically the local stability and
global convergence of gradient descent-ascent methods towards consistent
equilibrium.

We consider infinitely dimensional classes of functions and instead of the
relative error setting, which was used in previous papers on the integral norm
discretization, we consider the absolute error setting. We demonstrate how
known results from two areas of research -- supervised learning theory and
numerical integration -- can be used in sampling discretization of the square
norm on different function classes. We prove a general result, which shows that
the sequence of entropy numbers of a function class in the uniform norm
dominates, in a certain sense, the sequence of errors of sampling
discretization of the square norm of this class. Then we use this result for
establishing new error bounds for sampling discretization of the square norm on
classes of multivariate functions with mixed smoothness.

Given any deep fully connected neural network, initialized with random
Gaussian parameters, we bound from above the quadratic Wasserstein distance
between its output distribution and a suitable Gaussian process. Our explicit
inequalities indicate how the hidden and output layers sizes affect the
Gaussian behaviour of the network and quantitatively recover the distributional
convergence results in the wide limit, i.e., if all the hidden layers sizes
become large.

Biomedical research is increasingly employing real world evidence (RWE) to
foster discoveries of novel clinical phenotypes and to better characterize long
term effect of medical treatments. However, due to limitations inherent in the
collection process, RWE often lacks key features of patients, particularly when
these features cannot be directly encoded using data standards such as ICD-10.
Here we propose a novel data-driven statistical machine learning approach,
named Feature Imputation via Local Likelihood (FILL), designed to infer missing
features by exploiting feature similarity between patients. We test our method
using a particularly challenging problem: differentiating heart failure
patients with reduced versus preserved ejection fraction (HFrEF and HFpEF
respectively). The complexity of the task stems from three aspects: the two
share many common characteristics and treatments, only part of the relevant
diagnoses may have been recorded, and the information on ejection fraction is
often missing from RWE datasets. Despite these difficulties, our method is
shown to be capable of inferring heart failure patients with HFpEF with a
precision above 80% when considering multiple scenarios across two RWE datasets
containing 11,950 and 10,051 heart failure patients. This is an improvement
when compared to classical approaches such as logistic regression and random
forest which were only able to achieve a precision < 73%. Finally, this
approach allows us to analyse which features are commonly associated with HFpEF
patients. For example, we found that specific diagnostic codes for atrial
fibrillation and personal history of long-term use of anticoagulants are often
key in identifying HFpEF patients.

Environmental disturbances, such as sensor data noises, various lighting
conditions, challenging weathers and external adversarial perturbations, are
inevitable in real self-driving applications. Existing researches and testings
have shown that they can severely influence the vehicles perception ability and
performance, one of the main issue is the false positive detection, i.e., the
ghost object which is not real existed or occurs in the wrong position (such as
a non-existent vehicle). Traditional navigation methods tend to avoid every
detected objects for safety, however, avoiding a ghost object may lead the
vehicle into a even more dangerous situation, such as a sudden break on the
highway. Considering the various disturbance types, it is difficult to address
this issue at the perceptual aspect. A potential solution is to detect the
ghost through relation learning among the whole scenario and develop an
integrated end-to-end navigation system. Our underlying logic is that the
behavior of all vehicles in the scene is influenced by their neighbors, and
normal vehicles behave in a logical way, while ghost vehicles do not. By
learning the spatio-temporal relation among surrounding vehicles, an
information reliability representation is learned for each detected vehicle and
then a robot navigation network is developed. In contrast to existing works, we
encourage the network to learn how to represent the reliability and how to
aggregate all the information with uncertainties by itself, thus increasing the
efficiency and generalizability. To the best of the authors knowledge, this
paper provides the first work on using graph relation learning to achieve
end-to-end robust navigation in the presence of ghost vehicles. Simulation
results in the CARLA platform demonstrate the feasibility and effectiveness of
the proposed method in various scenarios.

Tensor completion is a fundamental tool for incomplete data analysis, where
the goal is to predict missing entries from partial observations. However,
existing methods often make the explicit or implicit assumption that the
observed entries are noise-free to provide a theoretical guarantee of exact
recovery of missing entries, which is quite restrictive in practice. To remedy
such drawbacks, this paper proposes a novel noisy tensor completion model,
which complements the incompetence of existing works in handling the
degeneration of high-order and noisy observations. Specifically, the tensor
ring nuclear norm (TRNN) and least-squares estimator are adopted to regularize
the underlying tensor and the observed entries, respectively. In addition, a
non-asymptotic upper bound of estimation error is provided to depict the
statistical performance of the proposed estimator. Two efficient algorithms are
developed to solve the optimization problem with convergence guarantee, one of
which is specially tailored to handle large-scale tensors by replacing the
minimization of TRNN of the original tensor equivalently with that of a much
smaller one in a heterogeneous tensor decomposition framework. Experimental
results on both synthetic and real-world data demonstrate the effectiveness and
efficiency of the proposed model in recovering noisy incomplete tensor data
compared with state-of-the-art tensor completion models.

Real-world scientific or engineering applications often involve mathematical
modeling of complex uncertain systems with a large number of unknown
parameters. The complexity of such systems, and the enormous uncertainties
therein, typically make accurate model identification from the available data
infeasible. In such cases, it is desirable to represent the model uncertainty
in a Bayesian paradigm, based on which we can design robust operators that
maintain the best overall performance across all possible models and design
optimal experiments that can effectively reduce uncertainty to maximally
enhance the performance of such operators. While objective-based uncertainty
quantification (objective-UQ) based on MOCU (mean objective cost of
uncertainty) has been shown to provide effective means for quantifying and
handling uncertainty in complex systems, a major drawback has been the high
computational cost of estimating MOCU. In this work, we demonstrate for the
first time that one can design accurate surrogate models for efficient
objective-UQ via MOCU based on a data-driven approach. We adopt a neural
message passing model for surrogate modeling, which incorporates a novel
axiomatic constraint loss that penalizes an increase in the estimated system
uncertainty. As an illustrative example, we consider the optimal experimental
design (OED) problem for uncertain Kuramoto models, where the goal is to
predict the experiments that can most effectively enhance the robust
synchronization performance through uncertainty reduction. Through quantitative
performance assessment, we show that our proposed approach can accelerate
MOCU-based OED by four to five orders of magnitude, virtually without any
visible loss of performance compared to the previous state-of-the-art. The
proposed approach can be applied to general OED tasks, beyond the Kuramoto
model.

Structural health monitoring (SHM) has been an active research area for the
last three decades, and has accumulated a number of critical advances over that
period, as can be seen in the literature. However, SHM is still facing
challenges because of the paucity of damage-state data, operational and
environmental fluctuations, repeatability issues, and changes in boundary
conditions. These issues present as inconsistencies in the captured features
and can have a huge impact on the practical implementation, but more
critically, on the generalisation of the technology. Population-based SHM has
been designed to address some of these concerns by modelling and transferring
missing information using data collected from groups of similar structures.
  In this work, vibration data were collected from four healthy,
nominally-identical, full-scale composite helicopter blades. Manufacturing
differences (e.g., slight differences in geometry and/or material properties),
among the blades presented as variability in their structural dynamics, which
can be very problematic for SHM based on machine learning from vibration data.
This work aims to address this variability by defining a general model for the
frequency response functions of the blades, called a form, using mixtures of
Gaussian processes.

Temporal representation is the cornerstone of modern action detection
techniques. State-of-the-art methods mostly rely on a dense anchoring scheme,
where anchors are sampled uniformly over the temporal domain with a discretized
grid, and then regress the accurate boundaries. In this paper, we revisit this
foundational stage and introduce Recurrent Continuous Localization (RCL), which
learns a fully continuous anchoring representation. Specifically, the proposed
representation builds upon an explicit model conditioned with video embeddings
and temporal coordinates, which ensure the capability of detecting segments
with arbitrary length. To optimize the continuous representation, we develop an
effective scale-invariant sampling strategy and recurrently refine the
prediction in subsequent iterations. Our continuous anchoring scheme is fully
differentiable, allowing to be seamlessly integrated into existing detectors,
e.g., BMN and G-TAD. Extensive experiments on two benchmarks demonstrate that
our continuous representation steadily surpasses other discretized counterparts
by ~2% mAP. As a result, RCL achieves 52.92% mAP@0.5 on THUMOS14 and 37.65% mAP
on ActivtiyNet v1.3, outperforming all existing single-model detectors.

Cross-modality interaction is a critical component in Text-Video Retrieval
(TVR), yet there has been little examination of how different influencing
factors for computing interaction affect performance. This paper first studies
the interaction paradigm in depth, where we find that its computation can be
split into two terms, the interaction contents at different granularity and the
matching function to distinguish pairs with the same semantics. We also observe
that the single-vector representation and implicit intensive function
substantially hinder the optimization. Based on these findings, we propose a
disentangled framework to capture a sequential and hierarchical representation.
Firstly, considering the natural sequential structure in both text and video
inputs, a Weighted Token-wise Interaction (WTI) module is performed to decouple
the content and adaptively exploit the pair-wise correlations. This interaction
can form a better disentangled manifold for sequential inputs. Secondly, we
introduce a Channel DeCorrelation Regularization (CDCR) to minimize the
redundancy between the components of the compared vectors, which facilitate
learning a hierarchical representation. We demonstrate the effectiveness of the
disentangled representation on various benchmarks, e.g., surpassing CLIP4Clip
largely by +2.9%, +3.1%, +7.9%, +2.3%, +2.8% and +6.5% R@1 on the MSR-VTT,
MSVD, VATEX, LSMDC, AcitivityNet, and DiDeMo, respectively.

Federated learning is a semi-distributed algorithm, where a server
communicates with multiple dispersed clients to learn a global model. The
federated architecture is not robust and is sensitive to communication and
computational overloads due to its one-master multi-client structure. It can
also be subject to privacy attacks targeting personal information on the
communication links. In this work, we introduce graph federated learning (GFL),
which consists of multiple federated units connected by a graph. We then show
how graph homomorphic perturbations can be used to ensure the algorithm is
differentially private. We conduct both convergence and privacy theoretical
analyses and illustrate performance by means of computer simulations.

We study two state-of-the-art solutions to the multi-agent pickup and
delivery (MAPD) problem based on different principles -- multi-agent
path-finding (MAPF) and multi-agent reinforcement learning (MARL).
Specifically, a recent MAPF algorithm called conflict-based search (CBS) and a
current MARL algorithm called shared experience actor-critic (SEAC) are
studied. While the performance of these algorithms is measured using quite
different metrics in their separate lines of work, we aim to benchmark these
two methods comprehensively in a simulated warehouse automation environment.

The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims
to determine the sentiment polarity towards targeted aspect terms occurring in
the sentence. The development of the ABSA task is very much hindered by the
lack of annotated data. To tackle this, the prior works have studied the
possibility of utilizing the sentiment analysis (SA) datasets to assist in
training the ABSA model, primarily via pretraining or multi-task learning. In
this article, we follow this line, and for the first time, we manage to apply
the Pseudo-Label (PL) method to merge the two homogeneous tasks. While it seems
straightforward to use generated pseudo labels to handle this case of label
granularity unification for two highly related tasks, we identify its major
challenge in this paper and propose a novel framework, dubbed as
Dual-granularity Pseudo Labeling (DPL). Further, similar to PL, we regard the
DPL as a general framework capable of combining other prior methods in the
literature. Through extensive experiments, DPL has achieved state-of-the-art
performance on standard benchmarks surpassing the prior work significantly.

Recent advances in transformer-based architectures which are pre-trained in
self-supervised manner have shown great promise in several machine learning
tasks. In the audio domain, such architectures have also been successfully
utilised in the field of speech emotion recognition (SER). However, existing
works have not evaluated the influence of model size and pre-training data on
downstream performance, and have shown limited attention to generalisation,
robustness, fairness, and efficiency. The present contribution conducts a
thorough analysis of these aspects on several pre-trained variants of wav2vec
2.0 and HuBERT that we fine-tuned on the dimensions arousal, dominance, and
valence of MSP-Podcast, while additionally using IEMOCAP and MOSI to test
cross-corpus generalisation. To the best of our knowledge, we obtain the top
performance for valence prediction without use of explicit linguistic
information, with a concordance correlation coefficient (CCC) of .638 on
MSP-Podcast. Furthermore, our investigations reveal that transformer-based
architectures are more robust to small perturbations compared to a CNN-based
baseline and fair with respect to biological sex groups, but not towards
individual speakers. Finally, we are the first to show that their extraordinary
success on valence is based on implicit linguistic information learnt during
fine-tuning of the transformer layers, which explains why they perform on-par
with recent multimodal approaches that explicitly utilise textual information.
Our findings collectively paint the following picture: transformer-based
architectures constitute the new state-of-the-art in SER, but further advances
are needed to mitigate remaining robustness and individual speaker issues. To
make our findings reproducible, we release the best performing model to the

Grammatical Error Correction (GEC) should not focus only on high accuracy of
corrections but also on interpretability for language learning. However,
existing neural-based GEC models mainly aim at improving accuracy, and their
interpretability has not been explored. A promising approach for improving
interpretability is an example-based method, which uses similar retrieved
examples to generate corrections. In addition, examples are beneficial in
language learning, helping learners understand the basis of grammatically
incorrect/correct texts and improve their confidence in writing. Therefore, we
hypothesize that incorporating an example-based method into GEC can improve
interpretability as well as support language learners. In this study, we
introduce an Example-Based GEC (EB-GEC) that presents examples to language
learners as a basis for a correction result. The examples consist of pairs of
correct and incorrect sentences similar to a given input and its predicted
correction. Experiments demonstrate that the examples presented by EB-GEC help
language learners decide to accept or refuse suggestions from the GEC output.
Furthermore, the experiments also show that retrieved examples improve the
accuracy of corrections.

Electroencephalogram (EEG) signals are complex, non-linear, and
non-stationary in nature. However, previous studies that applied decomposition
to minimize the complexity mainly exploited the hand-engineering features,
limiting the information learned in EEG decoding. Therefore, extracting
additional primary features from different disassembled components to improve
the EEG-based recognition performance remains challenging. On the other hand,
attempts have been made to use a single model to learn the hand-engineering
features. Less work has been done to improve the generalization ability through
ensemble learning. In this work, we propose a novel decomposition-based hybrid
ensemble convolutional neural network (CNN) framework to enhance the capability
of decoding EEG signals. CNNs, in particular, automatically learn the primary
features from raw disassembled components but not handcraft features. The first
option is to fuse the obtained score before the Softmax layer and execute
back-propagation on the entire ensemble network, whereas the other is to fuse
the probability output of the Softmax layer. Moreover, a component-specific
batch normalization (CSBN) layer is employed to reduce subject variability.
Against the challenging cross-subject driver fatigue-related situation
awareness (SA) recognition task, eight models are proposed under the framework,
which all showed superior performance than the strong baselines. The
performance of different decomposition methods and ensemble modes were further
compared. Results indicated that discrete wavelet transform (DWT)-based
ensemble CNN achieves the best 82.11% among the proposed models. Our framework
can be simply extended to any CNN architecture and applied in any EEG-related
sectors, opening the possibility of extracting more preliminary information
from complex EEG data.

Adaptive social learning is a useful tool for studying distributed
decision-making problems over graphs. This paper investigates the effect of
combination policies on the performance of adaptive social learning strategies.
Using large-deviation analysis, it first derives a bound on the probability of
error and characterizes the optimal selection for the Perron eigenvectors of
the combination policies. It subsequently studies the effect of the combination
policy on the transient behavior of the learning strategy by estimating the
adaptation time in the small signal-to-noise ratio regime. In the process, it
is discovered that, interestingly, the influence of the combination policy on
the transient behavior is insignificant, and thus it is more critical to employ
policies that enhance the steady-state performance. The theoretical conclusions
are illustrated by means of computer simulations.

This work addresses a gap in semantic scene completion (SSC) data by creating
a novel outdoor data set with accurate and complete dynamic scenes. Our data
set is formed from randomly sampled views of the world at each time step, which
supervises generalizability to complete scenes without occlusions or traces. We
create SSC baselines from state-of-the-art open source networks and construct a
benchmark real-time dense local semantic mapping algorithm, MotionSC, by
leveraging recent 3D deep learning architectures to enhance SSC with temporal
information. Our network shows that the proposed data set can quantify and
supervise accurate scene completion in the presence of dynamic objects, which
can lead to the development of improved dynamic mapping algorithms. All
software is available at https://github.com/UMich-CURLY/3DMapping.

The few-shot learning ability of vision transformers (ViTs) is rarely
investigated though heavily desired. In this work, we empirically find that
with the same few-shot learning frameworks, \eg~Meta-Baseline, replacing the
widely used CNN feature extractor with a ViT model often severely impairs
few-shot classification performance. Moreover, our empirical study shows that
in the absence of inductive bias, ViTs often learn the low-qualified token
dependencies under few-shot learning regime where only a few labeled training
data are available, which largely contributes to the above performance
degradation. To alleviate this issue, for the first time, we propose a simple
yet effective few-shot training framework for ViTs, namely Self-promoted
sUpervisioN (SUN). Specifically, besides the conventional global supervision
for global semantic learning SUN further pretrains the ViT on the few-shot
learning dataset and then uses it to generate individual location-specific
supervision for guiding each patch token. This location-specific supervision
tells the ViT which patch tokens are similar or dissimilar and thus accelerates
token dependency learning. Moreover, it models the local semantics in each
patch token to improve the object grounding and recognition capability which
helps learn generalizable patterns. To improve the quality of location-specific
supervision, we further propose two techniques:~1) background patch filtration
to filtrate background patches out and assign them into an extra background
class; and 2) spatial-consistent augmentation to introduce sufficient diversity
for data augmentation while keeping the accuracy of the generated local
supervisions. Experimental results show that SUN using ViTs significantly
surpasses other few-shot learning frameworks with ViTs and is the first one
that achieves higher performance than those CNN state-of-the-arts.

Flexible-joint manipulators are governed by complex nonlinear dynamics,
defining a challenging control problem. In this work, we propose an approach to
learn an outer-loop joint trajectory tracking controller with deep
reinforcement learning. The controller represented by a stochastic policy is
learned in under two hours directly on the real robot. This is achieved through
bounded reference correction actions and use of a model-free off-policy
learning method. In addition, an informed policy initialization is proposed,
where the agent is pre-trained in a learned simulation. We test our approach on
the 7 DOF manipulator of a Baxter robot. We demonstrate that the proposed
method is capable of consistent learning across multiple runs when applied
directly on the real robot. Our method yields a policy which significantly
improves the trajectory tracking accuracy in comparison to the vendor-provided
controller, generalizing to an unseen payload.

We consider a novel queuing problem where the decision-maker must choose to
accept or reject randomly arriving tasks into a no buffer queue which are
processed by $N$ identical servers. Each task has a price, which is a positive
real number, and a class. Each class of task has a different price distribution
and service rate, and arrives according to an inhomogenous Poisson process. The
objective is to decide which tasks to accept so that the total price of tasks
processed is maximised over a finite horizon. We formulate the problem as a
discrete time Markov Decision Process (MDP) with a hybrid state space. We show
that the optimal value function has a specific structure, which enables us to
solve the hybrid MDP exactly. Moreover, we prove that as the time step is
reduced, the discrete time solution approaches the optimal solution to the
original continuous time problem. To improve the scalability of our approach to
a greater number of task classes, we present an approximation based on state
abstraction. We validate our approach on synthetic data, as well as a real
financial fraud data set, which is the motivating application for this work.

Plenty of scientific and real-world applications are built on magnetic fields
and their characteristics. To retrieve the valuable magnetic field information
in high resolution, extensive field measurements are required, which are either
time-consuming to conduct or even not feasible due to physical constraints. To
alleviate this problem, we predict magnetic field values at a random point in
space from a few point measurements by using a generative adversarial network
(GAN) structure. The deep learning (DL) architecture consists of two neural
networks: a generator, which predicts missing field values of a given magnetic
field, and a critic, which is trained to calculate the statistical distance
between real and generated magnetic field distributions. By minimizing this
statistical distance, a reconstruction loss as well as physical losses, our
trained generator has learned to predict the missing field values with a median
reconstruction test error of 5.14%, when a single coherent region of field
points is missing, and 5.86%, when only a few point measurements in space are
available and the field measurements around are predicted. We verify the
results on an experimentally validated field.

A neural population responding to multiple appearances of a single object
defines a manifold in the neural response space. The ability to classify such
manifolds is of interest, as object recognition and other computational tasks
require a response that is insensitive to variability within a manifold. Linear
classification of object manifolds was previously studied for max-margin
classifiers. Soft-margin classifiers are a larger class of algorithms and
provide an additional regularization parameter used in applications to optimize
performance outside the training set by balancing between making fewer training
errors and learning more robust classifiers. Here we develop a mean-field
theory describing the behavior of soft-margin classifiers applied to object
manifolds. Analyzing manifolds with increasing complexity, from points through
spheres to general manifolds, a mean-field theory describes the expected value
of the linear classifier's norm, as well as the distribution of fields and
slack variables. By analyzing the robustness of the learned classification to
noise, we can predict the probability of classification errors and their
dependence on regularization, demonstrating a finite optimal choice. The theory
describes a previously unknown phase transition, corresponding to the
disappearance of a non-trivial solution, thus providing a soft version of the
well-known classification capacity of max-margin classifiers.

The promise of active learning (AL) is to reduce labelling costs by selecting
the most valuable examples to annotate from a pool of unlabelled data.
Identifying these examples is especially challenging with high-dimensional data
(e.g. images, videos) and in low-data regimes. In this paper, we propose a
novel method for batch AL called ALFA-Mix. We identify unlabelled instances
with sufficiently-distinct features by seeking inconsistencies in predictions
resulting from interventions on their representations. We construct
interpolations between representations of labelled and unlabelled instances
then examine the predicted labels. We show that inconsistencies in these
predictions help discovering features that the model is unable to recognise in
the unlabelled instances. We derive an efficient implementation based on a
closed-form solution to the optimal interpolation causing changes in
predictions. Our method outperforms all recent AL approaches in 30 different
settings on 12 benchmarks of images, videos, and non-visual data. The
improvements are especially significant in low-data regimes and on self-trained
vision transformers, where ALFA-Mix outperforms the state-of-the-art in 59% and
43% of the experiments respectively.

We investigate an applicability of Bayesian-optimization (BO) to optimize
hyperparameters associated with support-vector-machine (SVM) in order to
classify facies using elastic properties derived from well data in the East
Central Graben, UKCS. The cross-plot products of the field dataset appear to be
successfully classified with non-linear boundaries. Although there are a few
factors to be predetermined in the BO scheme such as an iteration number to
deal with a trade-off between the prediction accuracy and the computational
cost, this approach effectively reduces possible human subjectivity connected
to the architecture of the SVM. Our proposed workflow might be beneficial in
resource-exploration and development in terms of subsurface objective technical

We present a novel bi-modal system based on deep networks to address the
problem of learning associations and simple meanings of objects depicted in
"authored" images, such as fine art paintings and drawings. Our overall system
processes both the images and associated texts in order to learn associations
between images of individual objects, their identities and the abstract
meanings they signify. Unlike past deep nets that describe depicted objects and
infer predicates, our system identifies meaning-bearing objects ("signifiers")
and their associations ("signifieds") as well as basic overall meanings for
target artworks. Our system had precision of 48% and recall of 78% with an F1
metric of 0.6 on a curated set of Dutch vanitas paintings, a genre celebrated
for its concentration on conveying a meaning of great import at the time of
their execution. We developed and tested our system on fine art paintings but
our general methods can be applied to other authored images.

DBSCAN* and HDBSCAN* are well established density based clustering
algorithms. However, obtaining the clusters of very large datasets is
infeasible, limiting their use in real world applications.
  By exploiting the geometry of Euclidean space, we prove that it is possible
to systematically construct the DBSCAN* and HDBSCAN* clusters of a finite
$X\subset \mathbb{R}^n$ from specific subsets of $X$. We are able to control
the size of these subsets and therefore our results make it possible to cluster
very large datasets.
  To illustrate our theory, we cluster the Microsoft Building Footprint
Database of the US, which is not possible using the standard implementations.

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical
research and point of care applications; however, many challenges such as data
privacy concerns impede its optimal utilization. Generative Adversarial
Networks (GANs) show great promise in generating synthetic EHR data by learning
underlying data distributions while achieving excellent performance and
addressing these challenges. This work aims to review the major developments in
various applications of GANs for EHRs and provides an overview of the proposed
methodologies. For this purpose, we combine perspectives from healthcare
applications and machine learning techniques in terms of source datasets and
the fidelity and privacy evaluation of the generated synthetic datasets. We
also compile a list of the metrics and datasets used by the reviewed works,
which can be utilized as benchmarks for future research in the field. We
conclude by discussing challenges in GANs for EHRs development and proposing
recommended practices. We hope that this work motivates novel research
development directions in the intersection of healthcare and machine learning.

Contrastive learning between different views of the data achieves outstanding
success in the field of self-supervised representation learning and the learned
representations are useful in broad downstream tasks. Since all supervision
information for one view comes from the other view, contrastive learning
approximately obtains the minimal sufficient representation which contains the
shared information and eliminates the non-shared information between views.
Considering the diversity of the downstream tasks, it cannot be guaranteed that
all task-relevant information is shared between views. Therefore, we assume the
non-shared task-relevant information cannot be ignored and theoretically prove
that the minimal sufficient representation in contrastive learning is not
sufficient for the downstream tasks, which causes performance degradation. This
reveals a new problem that the contrastive learning models have the risk of
over-fitting to the shared information between views. To alleviate this
problem, we propose to increase the mutual information between the
representation and input as regularization to approximately introduce more
task-relevant information, since we cannot utilize any downstream task
information during training. Extensive experiments verify the rationality of
our analysis and the effectiveness of our method. It significantly improves the
performance of several classic contrastive learning models in downstream tasks.
Our code is available at https://github.com/Haoqing-Wang/InfoCL.

Limited by the locality of convolutional neural networks, most existing local
features description methods only learn local descriptors with local
information and lack awareness of global and surrounding spatial context. In
this work, we focus on making local descriptors "look wider to describe better"
by learning local Descriptors with More Than just Local information (MTLDesc).
Specifically, we resort to context augmentation and spatial attention
mechanisms to make our MTLDesc obtain non-local awareness. First, Adaptive
Global Context Augmented Module and Diverse Local Context Augmented Module are
proposed to construct robust local descriptors with context information from
global to local. Second, Consistent Attention Weighted Triplet Loss is designed
to integrate spatial attention awareness into both optimization and matching
stages of local descriptors learning. Third, Local Features Detection with
Feature Pyramid is given to obtain more stable and accurate keypoints
localization. With the above innovations, the performance of our MTLDesc
significantly surpasses the prior state-of-the-art local descriptors on
HPatches, Aachen Day-Night localization and InLoc indoor localization

This paper presents a self-supervised feature learning method for
hyperspectral image classification. Our method tries to construct two different
views of the raw hyperspectral image through a cross-representation learning
method. And then to learn semantically consistent representation over the
created views by contrastive learning method. Specifically, four
cross-channel-prediction based augmentation methods are naturally designed to
utilize the high dimension characteristic of hyperspectral data for the view
construction. And the better representative features are learned by maximizing
mutual information and minimizing conditional entropy across different views
from our contrastive network. This 'Cross-View-Predicton' style is
straightforward and gets the state-of-the-art performance of unsupervised
classification with a simple SVM classifier.

The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a model
for signals with complex spectral structure. A significant limitation of the
GPCM is that it assumes a rapidly decaying spectrum: it can only model smooth
signals. Moreover, inference in the GPCM currently requires (1) a mean-field
assumption, resulting in poorly calibrated uncertainties, and (2) a tedious
variational optimisation of large covariance matrices. We redesign the GPCM
model to induce a richer distribution over the spectrum with relaxed
assumptions about smoothness: the Causal Gaussian Process Convolution Model
(CGPCM) introduces a causality assumption into the GPCM, and the Rough Gaussian
Process Convolution Model (RGPCM) can be interpreted as a Bayesian
nonparametric generalisation of the fractional Ornstein-Uhlenbeck process. We
also propose a more effective variational inference scheme, going beyond the
mean-field assumption: we design a Gibbs sampler which directly samples from
the optimal variational solution, circumventing any variational optimisation
entirely. The proposed variations of the GPCM are validated in experiments on
synthetic and real-world data, showing promising results.

Starting from 2021, the International Maritime Organization significantly
tightened the $\text{NO}_\text{x}$ emission requirements for ships entering the
Baltic and the North Sea waters. Since all methods currently used for the
ships' compliance monitoring are costly and require proximity to the ship, the
performance of global and continuous monitoring of the emission standards'
fulfillment has been impossible up to now. A promising approach is the use of
remote sensing with the recently launched TROPOMI/S5P satellite. Due to its
unprecedentedly high spatial resolution, it allows for the visual distinction
of $\text{NO}_\text{2}$ plumes of individual ships. To successfully deploy a
compliance monitoring system that is based on TROPOMI data, an automated
procedure for the attribution of $\text{NO}_\text{2}$ to individual ships has
to be developed. However, due to the extremely low signal-to-noise ratio,
interference with the signal from other - often stronger - sources, and the
absence of ground truth, the task is very challenging.
  This is the first study proposing an application of supervised learning for
the segmentation of emission plumes produced by individual ships. As such, it
is the first step towards an automated procedure for global ship compliance
monitoring using remote sensing data. To this end, we developed a feature
construction method allowing the application of multivariate models on spatial
data. We applied several supervised-learning models and benchmark them towards
existing unsupervised solutions of ship-plume segmentation with TROPOMI
satellite data. We showed that the proposed approach leads to significant plume
segmentation improvement and a high correlation with the theoretically derived
measure of the ship's emission potential.

Although traditional symbolic reasoning methods are highly interpretable,
their application in knowledge graphs link prediction has been limited due to
their computational inefficiency. A new RNNNTP method is proposed in this
paper, using a generalized EM-based approach to continuously improve the
computational efficiency of Neural Theorem Provers(NTPs). The RNNNTP is divided
into relation generator and predictor. The relation generator is trained
effectively and interpretably, so that the whole model can be carried out
according to the development of the training, and the computational efficiency
is also greatly improved. In all four data-sets, this method shows competitive
performance on the link prediction task relative to traditional methods as well
as one of the current strong competitive methods.

In this paper, we present a data-driven approach to iteratively solve the
discrete heterogeneous Helmholtz equation at high wavenumbers. In our approach,
we combine classical iterative solvers with convolutional neural networks
(CNNs) to form a preconditioner which is applied within a Krylov solver. For
the preconditioner, we use a CNN of type U-Net that operates in conjunction
with multigrid ingredients. Two types of preconditioners are proposed 1) U-Net
as a coarse grid solver, and 2) U-Net as a deflation operator with shifted
Laplacian V-cycles. Following our training scheme and data-augmentation, our
CNN preconditioner can generalize over residuals and a relatively general set
of wave slowness models. On top of that, we also offer an encoder-solver
framework where an "encoder" network generalizes over the medium and sends
context vectors to another "solver" network, which generalizes over the
right-hand-sides. We show that this option is more robust and efficient than
the stand-alone variant. Lastly, we also offer a mini-retraining procedure, to
improve the solver after the model is known. This option is beneficial when
solving multiple right-hand-sides, like in inverse problems. We demonstrate the
efficiency and generalization abilities of our approach on a variety of 2D
problems.

Atomic-scale manipulation in scanning tunneling microscopy has enabled the
creation of quantum states of matter based on artificial structures and extreme
miniaturization of computational circuitry based on individual atoms. The
ability to autonomously arrange atomic structures with precision will enable
the scaling up of nanoscale fabrication and expand the range of artificial
structures hosting exotic quantum states. However, the \textit{a priori}
unknown manipulation parameters, the possibility of spontaneous tip apex
changes, and the difficulty of modeling tip-atom interactions make it
challenging to select manipulation parameters that can achieve atomic precision
throughout extended operations. Here we use deep reinforcement learning (DRL)
to control the real-world atom manipulation process. Several state-of-the-art
reinforcement learning techniques are used jointly to boost data efficiency.
The reinforcement learning agent learns to manipulate Ag adatoms on Ag(111)
surfaces with optimal precision and is integrated with path planning algorithms
to complete an autonomous atomic assembly system. The results demonstrate that
state-of-the-art deep reinforcement learning can offer effective solutions to
real-world challenges in nanofabrication and powerful approaches to
increasingly complex scientific experiments at the atomic scale.

Implementing deep neural networks for learning the solution maps of
parametric partial differential equations (PDEs) turns out to be more efficient
than using many conventional numerical methods. However, limited theoretical
analyses have been conducted on this approach. In this study, we investigate
the expressive power of deep rectified quadratic unit (ReQU) neural networks
for approximating the solution maps of parametric PDEs. The proposed approach
is motivated by the recent important work of G. Kutyniok, P. Petersen, M.
Raslan and R. Schneider (Gitta Kutyniok, Philipp Petersen, Mones Raslan, and
Reinhold Schneider. A theoretical analysis of deep neural networks and
parametric pdes. Constructive Approximation, pages 1-53, 2021), which uses deep
rectified linear unit (ReLU) neural networks for solving parametric PDEs. In
contrast to the previously established complexity-bound
$\mathcal{O}\left(d^3\log_{2}^{q}(1/ \epsilon) \right)$ for ReLU neural
networks, we derive an upper bound $\mathcal{O}\left(d^3\log_{2}^{q}\log_{2}(1/
\epsilon) \right)$ on the size of the deep ReQU neural network required to
achieve accuracy $\epsilon>0$, where $d$ is the dimension of reduced basis
representing the solutions. Our method takes full advantage of the inherent
low-dimensionality of the solution manifolds and better approximation
performance of deep ReQU neural networks. Numerical experiments are performed
to verify our theoretical result.

Real noisy-clean pairs on a large scale are costly and difficult to obtain.
Meanwhile, supervised denoisers trained on synthetic data perform poorly in
practice. Self-supervised denoisers, which learn only from single noisy images,
solve the data collection problem. However, self-supervised denoising methods,
especially blindspot-driven ones, suffer sizable information loss during input
or network design. The absence of valuable information dramatically reduces the
upper bound of denoising performance. In this paper, we propose a simple yet
efficient approach called Blind2Unblind to overcome the information loss in
blindspot-driven denoising methods. First, we introduce a global-aware mask
mapper that enables global perception and accelerates training. The mask mapper
samples all pixels at blind spots on denoised volumes and maps them to the same
channel, allowing the loss function to optimize all blind spots at once.
Second, we propose a re-visible loss to train the denoising network and make
blind spots visible. The denoiser can learn directly from raw noise images
without losing information or being trapped in identity mapping. We also
theoretically analyze the convergence of the re-visible loss. Extensive
experiments on synthetic and real-world datasets demonstrate the superior
performance of our approach compared to previous work. Code is available at
https://github.com/demonsjin/Blind2Unblind.

Self-supervised learning (SSL) holds promise in leveraging large amounts of
unlabeled data. However, the success of popular SSL methods has limited on
single-centric-object images like those in ImageNet and ignores the correlation
among the scene and instances, as well as the semantic difference of instances
in the scene. To address the above problems, we propose a Unified
Self-supervised Visual Pre-training (UniVIP), a novel self-supervised framework
to learn versatile visual representations on either single-centric-object or
non-iconic dataset. The framework takes into account the representation
learning at three levels: 1) the similarity of scene-scene, 2) the correlation
of scene-instance, 3) the discrimination of instance-instance. During the
learning, we adopt the optimal transport algorithm to automatically measure the
discrimination of instances. Massive experiments show that UniVIP pre-trained
on non-iconic COCO achieves state-of-the-art transfer performance on a variety
of downstream tasks, such as image classification, semi-supervised learning,
object detection and segmentation. Furthermore, our method can also exploit
single-centric-object dataset such as ImageNet and outperforms BYOL by 2.5%
with the same pre-training epochs in linear probing, and surpass current
self-supervised object detection methods on COCO dataset, demonstrating its
universality and potential.

This paper demonstrates how an efficient representation of the planned path
using B-splines, and a construction procedure that takes advantage of the
neural network's inductive bias, speed up both the inference and training of a
DNN-based motion planner. We build upon our recent work on learning local car
maneuvers from past experience using a DNN architecture, introducing a novel
B-spline path construction method, making it possible to generate local
maneuvers in almost constant time of about 11 ms, respecting a number of
constraints imposed by the environment map and the kinematics of a car-like
vehicle. We evaluate thoroughly the new planner employing the recent Bench-MR
framework to obtain quantitative results showing that our method outperforms
state-of-the-art planners by a large margin in the considered task.

This study investigated the climate effect under consecutive winters on the
arrival delay of high-speed passenger trains in northern Sweden. Novel
statistical learning approaches, including inhomogeneous Markov chain model and
stratified Cox model, were adopted to account for the time-varying risks of
train delays. The inhomogeneous Markov chain modelling for the arrival delays
has used several covariates, including weather variables, train operational
direction, and findings from the primary delay analysis through stratified Cox
model. The results showed that the weather variables, such as temperature, snow
depth, ice/snow precipitation, and train operational direction, significantly
impact the arrival delay. The performance of the fitted inhomogeneous Markov
chain model was evaluated by the walk-forward validation method. The averaged
mean absolute errors between the expected rates and the observed rates of the
arrival delay over the train line was obtained at the level of 0.088, which
implies that approximately 9% of trains may be misclassified as having arrival
delays by the fitted model at a measuring point on the train line.

Novel classes frequently arise in our dynamically changing world, e.g., new
users in the authentication system, and a machine learning model should
recognize new classes without forgetting old ones. This scenario becomes more
challenging when new class instances are insufficient, which is called few-shot
class-incremental learning (FSCIL). Current methods handle incremental learning
retrospectively by making the updated model similar to the old one. By
contrast, we suggest learning prospectively to prepare for future updates, and
propose ForwArd Compatible Training (FACT) for FSCIL. Forward compatibility
requires future new classes to be easily incorporated into the current model
based on the current stage data, and we seek to realize it by reserving
embedding space for future new classes. In detail, we assign virtual prototypes
to squeeze the embedding of known classes and reserve for new ones. Besides, we
forecast possible new classes and prepare for the updating process. The virtual
prototypes allow the model to accept possible updates in the future, which act
as proxies scattered among embedding space to build a stronger classifier
during inference. FACT efficiently incorporates new classes with forward
compatibility and meanwhile resists forgetting of old ones. Extensive
experiments validate FACT's state-of-the-art performance. Code is available at:
https://github.com/zhoudw-zdw/CVPR22-Fact

Deepfakes are synthetic content generated using advanced deep learning and AI
technologies. The advancement of technology has created opportunities for
anyone to create and share deepfakes much easier. This may lead to societal
concerns based on how communities engage with it. However, there is limited
research available to understand how communities perceive deepfakes. We
examined deepfake conversations on Reddit from 2018 to 2021 -- including major
topics and their temporal changes as well as implications of these
conversations. Using a mixed-method approach -- topic modeling and qualitative
coding, we found 6,638 posts and 86,425 comments discussing concerns of the
believable nature of deepfakes and how platforms moderate them. We also found
Reddit conversations to be pro-deepfake and building a community that supports
creating and sharing deepfake artifacts and building a marketplace regardless
of the consequences. Possible implications derived from qualitative codes
indicate that deepfake conversations raise societal concerns. We propose that
there are implications for Human Computer Interaction (HCI) to mitigate the
harm created from deepfakes.

Marine scientists use remote underwater video recording to survey fish
species in their natural habitats. This helps them understand and predict how
fish respond to climate change, habitat degradation, and fishing pressure. This
information is essential for developing sustainable fisheries for human
consumption, and for preserving the environment. However, the enormous volume
of collected videos makes extracting useful information a daunting and
time-consuming task for a human. A promising method to address this problem is
the cutting-edge Deep Learning (DL) technology.DL can help marine scientists
parse large volumes of video promptly and efficiently, unlocking niche
information that cannot be obtained using conventional manual monitoring
methods. In this paper, we provide an overview of the key concepts of DL, while
presenting a survey of literature on fish habitat monitoring with a focus on
underwater fish classification. We also discuss the main challenges faced when
developing DL for underwater image processing and propose approaches to address
them. Finally, we provide insights into the marine habitat monitoring research
domain and shed light on what the future of DL for underwater image processing
may hold. This paper aims to inform a wide range of readers from marine
scientists who would like to apply DL in their research to computer scientists
who would like to survey state-of-the-art DL-based underwater fish habitat
monitoring literature.

Large sparse symmetric linear systems appear in several branches of science
and engineering thanks to the widespread use of the finite element method
(FEM). The fastest sparse linear solvers available implement hybrid iterative
methods. These methods are based on heuristic algorithms to permute rows and
columns or find a preconditioner matrix. In addition, they are inherently
sequential, making them unable to leverage the GPU processing power entirely.
We propose neural sparse linear solvers, a deep learning framework to learn
approximate solvers for sparse symmetric linear systems. Our method relies on
representing a sparse symmetric linear system as an undirected weighted graph.
Such graph representation is inherently permutation-equivariant and
scale-invariant, and it can become the input to a graph neural network trained
to regress the solution. We test neural sparse linear solvers on static linear
analysis problems from structural engineering. Our method is less accurate than
classic algorithms, but it is hardware-independent, fast on GPUs, and
applicable to generic sparse symmetric systems without any additional
hypothesis. Although many limitations remain, this study shows a general
approach to tackle problems involving sparse symmetric matrices using graph

Background: Computational models of speech recognition often assume that the
set of target words is already given. This implies that these models do not
learn to recognise speech from scratch without prior knowledge and explicit
supervision. Visually grounded speech models learn to recognise speech without
prior knowledge by exploiting statistical dependencies between spoken and
visual input. While it has previously been shown that visually grounded speech
models learn to recognise the presence of words in the input, we explicitly
investigate such a model as a model of human speech recognition.
  Methods: We investigate the time-course of word recognition as simulated by
the model using a gating paradigm to test whether its recognition is affected
by well-known word-competition effects in human speech processing. We
furthermore investigate whether vector quantisation, a technique for discrete
representation learning, aids the model in the discovery and recognition of
words.
  Results/Conclusion: Our experiments show that the model is able to recognise
nouns in isolation and even learns to properly differentiate between plural and
singular nouns. We also find that recognition is influenced by word competition
from the word-initial cohort and neighbourhood density, mirroring word
competition effects in human speech comprehension. Lastly, we find no evidence
that vector quantisation is helpful in discovering and recognising words. Our
gating experiments even show that the vector quantised model requires more of
the input sequence for correct recognition.

Studies of topological bands and their associated low-dimensional boundary
modes have largely focused on linear systems. This work reports robust
dynamical features of three-dimensional (3D) nonlinear systems in connection
with intriguing topological bands in 3D. Specifically, for a 3D setting of
coupled rock-paper-scissors cycles governed by the antisymmetric Lotka-Volterra
equation (ALVE) that is inherently nonlinear, we unveil distinct
characteristics and robustness of surface polarized masses and analyze them in
connection with the dynamics and topological bands of the linearized
Lotka-Volterra (LV) equation. Our analysis indicated that insights learned from
Weyl semimetal phases with type-I and type-II Weyl singularities based on a
linearized version of the ALVE are still remarkably useful, even though the
system dynamics is far beyond the linear regime. This work indicates the
relevance and importance of the concept of topological boundary modes in
analyzing high-dimensional nonlinear systems and hopes to stimulate another
wave of topological studies in nonlinear systems.

Affective computing plays a key role in human-computer interactions,
entertainment, teaching, safe driving, and multimedia integration. Major
breakthroughs have been made recently in the areas of affective computing
(i.e., emotion recognition and sentiment analysis). Affective computing is
realized based on unimodal or multimodal data, primarily consisting of physical
information (e.g., textual, audio, and visual data) and physiological signals
(e.g., EEG and ECG signals). Physical-based affect recognition caters to more
researchers due to multiple public databases. However, it is hard to reveal
one's inner emotion hidden purposely from facial expressions, audio tones, body
gestures, etc. Physiological signals can generate more precise and reliable
emotional results; yet, the difficulty in acquiring physiological signals also
hinders their practical application. Thus, the fusion of physical information
and physiological signals can provide useful features of emotional states and
lead to higher accuracy. Instead of focusing on one specific field of affective
analysis, we systematically review recent advances in the affective computing,
and taxonomize unimodal affect recognition as well as multimodal affective
analysis. Firstly, we introduce two typical emotion models followed by commonly
used databases for affective computing. Next, we survey and taxonomize
state-of-the-art unimodal affect recognition and multimodal affective analysis
in terms of their detailed architectures and performances. Finally, we discuss
some important aspects on affective computing and their applications and
conclude this review with an indication of the most promising future
directions, such as the establishment of baseline dataset, fusion strategies
for multimodal affective analysis, and unsupervised learning models.

In 3GPP New Radio (NR) Release 18 we see the first study item starting in May
2022, which will evaluate the potential of AI/ML methods for Radio Access
Network (RAN) 1, i.e., for mobile radio PHY and MAC layer applications. We use
the profiling method for accurate iterative estimation of multipath component
parameters for PHY layer reference, as it promises a large channel prediction
horizon. We investigate options to partly or fully replace some functionalities
of this rule based PHY layer method by AI/ML inferences, with the goal to
achieve either a higher performance, lower latency, or, reduced processing
complexity. We provide first results for noise reduction, then a combined
scheme for model order selection, compare options to infer multipath component
start parameters, and, provide an outlook on a possible channel prediction

We have developed a deep neural network that reconstructs the shape of a
polygonal domain given the first hundred of its Laplacian eigenvalues. Having
an encoder-decoder structure, the network maps input spectra to a latent space
and then predicts the discretized image of the domain on a square grid. We
tested this network on randomly generated pentagons. The prediction accuracy is
high and the predictions obey the Laplacian scaling rule. The network recovers
the continuous rotational degree of freedom beyond the symmetry of the grid.
The variation of the latent variables under the scaling transformation shows
they are strongly correlated with Weyl' s parameters (area, perimeter, and a
certain function of the angles) of the test polygons.

Named Entity Recognition task is one of the core tasks of information
extraction. Word ambiguity and word abbreviation are important reasons for the
low recognition rate of named entities. In this paper, we propose a novel named
entity recognition model WCL-BBCD (Word Contrastive Learning with
BERT-BiLSTM-CRF-DBpedia), which incorporates the idea of contrastive learning.
The model first trains the sentence pairs in the text, calculate similarity
between sentence pairs, and fine-tunes BERT used for the named entity
recognition task according to the similarity, so as to alleviate word
ambiguity. Then, the fine-tuned BERT is combined with BiLSTM-CRF to perform the
named entity recognition task. Finally, the recognition results are corrected
in combination with prior knowledge such as knowledge graphs, so as to
alleviate the low-recognition-rate problem caused by word abbreviations. The
results of experimentals conducted on the CoNLL-2003 English dataset and
OntoNotes V5 English dataset show that our model outperforms other similar
models on.

Question Answering on Electronic Health Records (EHR-QA) has a significant
impact on the healthcare domain, and it is being actively studied. Previous
research on structured EHR-QA focuses on converting natural language queries
into query language such as SQL or SPARQL (NLQ2Query), so the problem scope is
limited to pre-defined data types by the specific query language. In order to
expand the EHR-QA task beyond this limitation to handle multi-modal medical
data and solve complex inference in the future, more primitive systemic
language is needed. In this paper, we design the program-based model
(NLQ2Program) for EHR-QA as the first step towards the future direction. We
tackle MIMICSPARQL*, the graph-based EHR-QA dataset, via a program-based
approach in a semi-supervised manner in order to overcome the absence of gold
programs. Without the gold program, our proposed model shows comparable
performance to the previous state-of-the-art model, which is an NLQ2Query model
(0.9% gain). In addition, for a reliable EHR-QA model, we apply the uncertainty
decomposition method to measure the ambiguity in the input question. We
empirically confirmed data uncertainty is most indicative of the ambiguity in
the input question.

Learning with few labeled data has been a longstanding problem in the
computer vision and machine learning research community. In this paper, we
introduced a new semi-supervised learning framework, SimMatch, which
simultaneously considers semantic similarity and instance similarity. In
SimMatch, the consistency regularization will be applied on both semantic-level
and instance-level. The different augmented views of the same instance are
encouraged to have the same class prediction and similar similarity
relationship respected to other instances. Next, we instantiated a labeled
memory buffer to fully leverage the ground truth labels on instance-level and
bridge the gaps between the semantic and instance similarities. Finally, we
proposed the \textit{unfolding} and \textit{aggregation} operation which allows
these two similarities be isomorphically transformed with each other. In this
way, the semantic and instance pseudo-labels can be mutually propagated to
generate more high-quality and reliable matching targets. Extensive
experimental results demonstrate that SimMatch improves the performance of
semi-supervised learning tasks across different benchmark datasets and
different settings. Notably, with 400 epochs of training, SimMatch achieves
67.2\%, and 74.4\% Top-1 Accuracy with 1\% and 10\% labeled examples on
ImageNet, which significantly outperforms the baseline methods and is better
than previous semi-supervised learning frameworks. Code and pre-trained models
are available at https://github.com/KyleZheng1997/simmatch.

We apply a novel method with machine learning to calibrate sub-grid models
within numerical simulation codes to achieve convergence with observations and
between different codes. It utilizes active learning and neural density
estimators. The hyper parameters of the machine are calibrated with a
well-defined projectile motion problem. Then, using a set of 22 cosmological
zoom simulations, we tune the parameters of a popular star formation and
feedback model within Enzo to match simulations. The parameters that are
adjusted include the star formation efficiency, coupling of thermal energy from
stellar feedback, and volume into which the energy is deposited. This number
translates to a factor of more than three improvements over manual calibration.
Despite using fewer simulations, we obtain a better agreement to the observed
baryon makeup of a Milky-Way (MW) sized halo. Switching to a different
strategy, we improve the consistency of the recommended parameters from the
machine. Given the success of the calibration, we then apply the technique to
reconcile metal transport between grid-based and particle-based simulation
codes using an isolated galaxy. It is an improvement over manual exploration
while hinting at a less known relation between the diffusion coefficient and
the metal mass in the halo region. The exploration and calibration of the
parameters of the sub-grid models with a machine learning approach is concluded
to be versatile and directly applicable to different problems.

As far as Scene Graph Generation (SGG), coarse and fine predicates mix in the
dataset due to the crowd-sourced labeling, and the long-tail problem is also
pronounced. Given this tricky situation, many existing SGG methods treat the
predicates equally and learn the model under the supervision of
mixed-granularity predicates in one stage, leading to relatively coarse
predictions. In order to alleviate the negative impact of the suboptimum
mixed-granularity annotation and long-tail effect problems, this paper proposes
a novel Hierarchical Memory Learning (HML) framework to learn the model from
simple to complex, which is similar to the human beings' hierarchical memory
learning process. After the autonomous partition of coarse and fine predicates,
the model is first trained on the coarse predicates and then learns the fine
predicates. In order to realize this hierarchical learning pattern, this paper,
for the first time, formulates the HML framework using the new Concept
Reconstruction (CR) and Model Reconstruction (MR) constraints. It is worth
noticing that the HML framework can be taken as one general optimization
strategy to improve various SGG models, and significant improvement can be
achieved on the SGG benchmark (i.e., Visual Genome).

Neural Architecture Search (NAS) defines the design of Neural Networks as a
search problem. Unfortunately, NAS is computationally intensive because of
various possibilities depending on the number of elements in the design and the
possible connections between them. In this work, we extensively analyze the
role of the dataset size based on several sampling approaches for reducing the
dataset size (unsupervised and supervised cases) as an agnostic approach to
reduce search time. We compared these techniques with four common NAS
approaches in NAS-Bench-201 in roughly 1,400 experiments on CIFAR-100. One of
our surprising findings is that in most cases we can reduce the amount of
training data to 25\%, consequently reducing search time to 25\%, while at the
same time maintaining the same accuracy as if training on the full dataset.
Additionally, some designs derived from subsets out-perform designs derived
from the full dataset by up to 22 p.p. accuracy.

The sensor-based human activity recognition (HAR) in mobile application
scenarios is often confronted with sensor modalities variation and annotated
data deficiency. Given this observation, we devised a graph-inspired deep
learning approach toward the sensor-based HAR tasks, which was further used to
build a deep transfer learning model toward giving a tentative solution for
these two challenging problems. Specifically, we present a multi-layer residual
structure involved graph convolutional neural network (ResGCNN) toward the
sensor-based HAR tasks, namely the HAR-ResGCNN approach. Experimental results
on the PAMAP2 and mHealth data sets demonstrate that our ResGCNN is effective
at capturing the characteristics of actions with comparable results compared to
other sensor-based HAR models (with an average accuracy of 98.18% and 99.07%,
respectively). More importantly, the deep transfer learning experiments using
the ResGCNN model show excellent transferability and few-shot learning
performance. The graph-based framework shows good meta-learning ability and is
supposed to be a promising solution in sensor-based HAR tasks.

Despite the success, the process of fine-tuning large-scale PLMs brings
prohibitive adaptation costs. In fact, fine-tuning all the parameters of a
colossal model and retaining separate instances for different tasks are
practically infeasible. This necessitates a new branch of research focusing on
the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this
paper. In contrast with the standard fine-tuning, delta tuning only fine-tunes
a small portion of the model parameters while keeping the rest untouched,
largely reducing both the computation and storage costs. Recent studies have
demonstrated that a series of delta tuning methods with distinct tuned
parameter selection could achieve performance on a par with full-parameter
fine-tuning, suggesting a new promising way of stimulating large-scale PLMs. In
this paper, we first formally describe the problem of delta tuning and then
comprehensively review recent delta tuning approaches. We also propose a
unified categorization criterion that divide existing delta tuning methods into
three groups: addition-based, specification-based, and reparameterization-based
methods. Though initially proposed as an efficient method to steer large
models, we believe that some of the fascinating evidence discovered along with
delta tuning could help further reveal the mechanisms of PLMs and even deep
neural networks. To this end, we discuss the theoretical principles underlying
the effectiveness of delta tuning and propose frameworks to interpret delta
tuning from the perspective of optimization and optimal control, respectively.
Furthermore, we provide a holistic empirical study of representative methods,
where results on over 100 NLP tasks demonstrate a comprehensive performance
comparison of different approaches. The experimental results also cover the
analysis of combinatorial, scaling and transferable properties of delta tuning.

Federated learning (FL) is a promising paradigm to enable privacy-preserving
deep learning from distributed data. Most previous works are based on federated
average (FedAvg), which, however, faces several critical issues, including a
high communication overhead and the difficulty in dealing with heterogeneous
model architectures. Federated Distillation (FD) is a recently proposed
alternative to enable communication-efficient and robust FL, which achieves
orders of magnitude reduction of the communication overhead compared with
FedAvg and is flexible to handle heterogeneous models at the clients. However,
so far there is no unified algorithmic framework or theoretical analysis for
FD-based methods. In this paper, we first present a generic meta-algorithm for
FD and investigate the influence of key parameters through empirical
experiments. Then, we verify the empirical observations theoretically. Based on
the empirical results and theory, we propose a communication-efficient FD
algorithm with active data sampling to improve the model performance and reduce
the communication overhead. Empirical simulations on benchmark datasets will
demonstrate that our proposed algorithm effectively and significantly reduces
the communication overhead while achieving a satisfactory performance.

Emotional recognition through exploring the electroencephalography (EEG)
characteristics has been widely performed in recent studies. Nonlinear analysis
and feature extraction methods for understanding the complex dynamical
phenomena are associated with the EEG patterns of different emotions. The phase
space reconstruction is a typical nonlinear technique to reveal the dynamics of
the brain neural system. Recently, the topological data analysis (TDA) scheme
has been used to explore the properties of space, which provides a powerful
tool to think over the phase space. In this work, we proposed a topological EEG
nonlinear dynamics analysis approach using the phase space reconstruction (PSR)
technique to convert EEG time series into phase space, and the persistent
homology tool explores the topological properties of the phase space. We
perform the topological analysis of EEG signals in different rhythm bands to
build emotion feature vectors, which shows high distinguishing ability. We
evaluate the approach with two well-known benchmark datasets, the DEAP and
DREAMER datasets. The recognition results achieved accuracies of 99.37% and
99.35% in arousal and valence classification tasks with DEAP, and 99.96%,
99.93%, and 99.95% in arousal, valence, and dominance classifications tasks
with DREAMER, respectively. The performances are supposed to be outperformed
current state-of-art approaches in DREAMER (improved by 1% to 10% depends on
temporal length), while comparable to other related works evaluated in DEAP.
The proposed work is the first investigation in the emotion recognition
oriented EEG topological feature analysis, which brought a novel insight into
the brain neural system nonlinear dynamics analysis and feature extraction.

We proposed a novel trimap free video matting method based on the attention
mechanism. By the nature of the problem, most existing approaches use either
multiple computational expansive modules or complex algorithms to exploit
temporal information fully. We designed a temporal aggregation module to
compute the temporal coherence between the current frame and its two previous
frames.

In recent years, self-supervised learning methods have shown significant
improvement for pre-training with unlabeled data and have proven helpful for
electrocardiogram signals. However, most previous pre-training methods for
electrocardiogram focused on capturing only global contextual representations.
This inhibits the models from learning fruitful representation of
electrocardiogram, which results in poor performance on downstream tasks.
Additionally, they cannot fine-tune the model with an arbitrary set of
electrocardiogram leads unless the models were pre-trained on the same set of
leads. In this work, we propose an ECG pre-training method that learns both
local and global contextual representations for better generalizability and
performance on downstream tasks. In addition, we propose random lead masking as
an ECG-specific augmentation method to make our proposed model robust to an
arbitrary set of leads. Experimental results on two downstream tasks, cardiac
arrhythmia classification and patient identification, show that our proposed
approach outperforms other state-of-the-art methods.

Domain adaptation targets at knowledge acquisition and dissemination from a
labeled source domain to an unlabeled target domain under distribution shift.
Still, the common requirement of identical class space shared across domains
hinders applications of domain adaptation to partial-set domains. Recent
advances show that deep pre-trained models of large scale endow rich knowledge
to tackle diverse downstream tasks of small scale. Thus, there is a strong
incentive to adapt models from large-scale domains to small-scale domains. This
paper introduces Partial Domain Adaptation (PDA), a learning paradigm that
relaxes the identical class space assumption to that the source class space
subsumes the target class space. First, we present a theoretical analysis of
partial domain adaptation, which uncovers the importance of estimating the
transferable probability of each class and each instance across domains. Then,
we propose Selective Adversarial Network (SAN and SAN++) with a bi-level
selection strategy and an adversarial adaptation mechanism. The bi-level
selection strategy up-weighs each class and each instance simultaneously for
source supervised training, target self-training, and source-target adversarial
adaptation through the transferable probability estimated alternately by the
model. Experiments on standard partial-set datasets and more challenging tasks
with superclasses show that SAN++ outperforms several domain adaptation
methods.

In clinics, doctors rely on electrocardiograms (ECGs) to assess severe
cardiac disorders. Owing to the development of technology and the increase in
health awareness, ECG signals are currently obtained by using medical and
commercial devices. Deep neural networks (DNNs) can be used to analyze these
signals because of their high accuracy rate. However, researchers have found
that adversarial attacks can significantly reduce the accuracy of DNNs. Studies
have been conducted to defend ECG-based DNNs against traditional adversarial
attacks, such as projected gradient descent (PGD), and smooth adversarial
perturbation (SAP) which targets ECG classification; however, to the best of
our knowledge, no study has completely explored the defense against adversarial
attacks targeting ECG classification. Thus, we did different experiments to
explore the effects of defense methods against white-box adversarial attack and
black-box adversarial attack targeting ECG classification, and we found that
some common defense methods performed well against these attacks. Besides, we
proposed a new defense method called Adversarial Distillation Training (ADT)
which comes from defensive distillation and can effectively improve the
generalization performance of DNNs. The results show that our method performed
more effectively against adversarial attacks targeting on ECG classification
than the other baseline methods, namely, adversarial training, defensive
distillation, Jacob regularization, and noise-to-signal ratio regularization.
Furthermore, we found that our method performed better against PGD attacks with
low noise levels, which means that our method has stronger robustness.

Incorporating data-specific domain knowledge in deep networks explicitly can
provide important cues beneficial for lesion detection and can mitigate the
need for diverse heterogeneous datasets for learning robust detectors. In this
paper, we exploit the domain information present in computed tomography (CT)
scans and propose a robust universal lesion detection (ULD) network that can
detect lesions across all organs of the body by training on a single dataset,
DeepLesion. We analyze CT-slices of varying intensities, generated using
heuristically determined Hounsfield Unit(HU) windows that individually
highlight different organs and are given as inputs to the deep network. The
features obtained from the multiple intensity images are fused using a novel
convolution augmented multi-head self-attention module and subsequently, passed
to a Region Proposal Network (RPN) for lesion detection. In addition, we
observed that traditional anchor boxes used in RPN for natural images are not
suitable for lesion sizes often found in medical images. Therefore, we propose
to use lesion-specific anchor sizes and ratios in the RPN for improving the
detection performance. We use self-supervision to initialize weights of our
network on the DeepLesion dataset to further imbibe domain knowledge. Our
proposed Domain Knowledge augmented Multi-head Attention based Universal Lesion
Detection Network DMKA-ULD produces refined and precise bounding boxes around
lesions across different organs. We evaluate the efficacy of our network on the
publicly available DeepLesion dataset which comprises of approximately 32K CT
scans with annotated lesions across all organs of the body. Results demonstrate
that we outperform existing state-of-the-art methods achieving an overall
sensitivity of 87.16%.

Multinomial mixtures are widely used in the information engineering field,
however, their mathematical properties are not yet clarified because they are
singular learning models. In fact, the models are non-identifiable and their
Fisher information matrices are not positive definite. In recent years, the
mathematical foundation of singular statistical models are clarified by using
algebraic geometric methods. In this paper, we clarify the real log canonical
thresholds and multiplicities of the multinomial mixtures and elucidate their
asymptotic behaviors of generalization error and free energy.

As attribution-based explanation methods are increasingly used to establish
model trustworthiness in high-stakes situations, it is critical to ensure that
these explanations are stable, e.g., robust to infinitesimal perturbations to
an input. However, previous works have shown that state-of-the-art explanation
methods generate unstable explanations. Here, we introduce metrics to quantify
the stability of an explanation and show that several popular explanation
methods are unstable. In particular, we propose new Relative Stability metrics
that measure the change in output explanation with respect to change in input,
model representation, or output of the underlying predictor. Finally, our
experimental evaluation with three real-world datasets demonstrates interesting
insights for seven explanation methods and different stability metrics.

Contrastive learning has been demonstrated to be effective in enhancing
pre-trained language models (PLMs) to derive superior universal sentence
embeddings. However, existing contrastive methods still have two limitations.
Firstly, previous works may acquire poor performance under domain shift
settings, thus hindering the application of sentence representations in
practice. We attribute this low performance to the over-parameterization of
PLMs with millions of parameters. To alleviate it, we propose PromCSE
(Prompt-based Contrastive Learning for Sentence Embeddings), which only trains
small-scale \emph{Soft Prompt} (i.e., a set of trainable vectors) while keeping
PLMs fixed. Secondly, the commonly used NT-Xent loss function of contrastive
learning does not fully exploit hard negatives in supervised learning settings.
To this end, we propose to integrate an Energy-based Hinge loss to enhance the
pairwise discriminative power, inspired by the connection between the NT-Xent
loss and the Energy-based Learning paradigm. Empirical results on seven
standard semantic textual similarity (STS) tasks and a domain-shifted STS task
both show the effectiveness of our method compared with the current
state-of-the-art sentence embedding models. Our code is publicly avaliable at
https://github.com/YJiangcm/PromCSE

Existing methods for Table Structure Recognition (TSR) from camera-captured
or scanned documents perform poorly on complex tables consisting of nested rows
/ columns, multi-line texts and missing cell data. This is because current
data-driven methods work by simply training deep models on large volumes of
data and fail to generalize when an unseen table structure is encountered. In
this paper, we propose to train a deep network to capture the spatial
associations between different word pairs present in the table image for
unravelling the table structure. We present an end-to-end pipeline, named
TSR-DSAW: TSR via Deep Spatial Association of Words, which outputs a digital
representation of a table image in a structured format such as HTML. Given a
table image as input, the proposed method begins with the detection of all the
words present in the image using a text-detection network like CRAFT which is
followed by the generation of word-pairs using dynamic programming. These
word-pairs are highlighted in individual images and subsequently, fed into a
DenseNet-121 classifier trained to capture spatial associations such as
same-row, same-column, same-cell or none. Finally, we perform post-processing
on the classifier output to generate the table structure in HTML format. We
evaluate our TSR-DSAW pipeline on two public table-image datasets -- PubTabNet
and ICDAR 2013, and demonstrate improvement over previous methods such as
TableNet and DeepDeSRT.

We study high-dimensional sparse estimation under three natural constraints:
communication constraints, local privacy constraints, and linear measurements
(compressive sensing). Without sparsity assumptions, it has been established
that interactivity cannot improve the minimax rates of estimation under these
information constraints. The question of whether interactivity helps with
natural inference tasks has been a topic of active research. We settle this
question in the affirmative for the prototypical problems of high-dimensional
sparse mean estimation and compressive sensing, by demonstrating a gap between
interactive and noninteractive protocols. We further establish that the gap
increases when we have more structured sparsity: for block sparsity this gap
can be as large as polynomial in the dimensionality. Thus, the more structured
the sparsity is, the greater is the advantage of interaction. Proving the lower
bounds requires a careful breaking of a sum of correlated random variables into
independent components using Baranyai's theorem on decomposition of
hypergraphs, which might be of independent interest.

One of the most fundamental questions in quantitative finance is the
existence of continuous-time diffusion models that fit market prices of a given
set of options. Traditionally, one employs a mix of intuition, theoretical and
empirical analysis to find models that achieve exact or approximate fits. Our
contribution is to show how a suitable game theoretical formulation of this
problem can help solve this question by leveraging existing developments in
modern deep multi-agent reinforcement learning to search in the space of
stochastic processes. More importantly, we hope that our techniques can be
leveraged and extended by the community to solve important problems in that
field, such as the joint SPX-VIX calibration problem. Our experiments show that
we are able to learn local volatility, as well as path-dependence required in
the volatility process to minimize the price of a Bermudan option. In one
sentence, our algorithm can be seen as a particle method \`{a} la Guyon et
Henry-Labordere where particles, instead of being designed to ensure
$\sigma_{loc}(t,S_t)^2 = \mathbb{E}[\sigma_t^2|S_t]$, are learning RL-driven
agents cooperating towards more general calibration targets. This is the first
work bridging reinforcement learning with the derivative calibration problem.

Manipulating volumetric deformable objects in the real world, like plush toys
and pizza dough, bring substantial challenges due to infinite shape variations,
non-rigid motions, and partial observability. We introduce ACID, an
action-conditional visual dynamics model for volumetric deformable objects
based on structured implicit neural representations. ACID integrates two new
techniques: implicit representations for action-conditional dynamics and
geodesics-based contrastive learning. To represent deformable dynamics from
partial RGB-D observations, we learn implicit representations of occupancy and
flow-based forward dynamics. To accurately identify state change under large
non-rigid deformations, we learn a correspondence embedding field through a
novel geodesics-based contrastive loss. To evaluate our approach, we develop a
simulation framework for manipulating complex deformable shapes in realistic
scenes and a benchmark containing over 17,000 action trajectories with six
types of plush toys and 78 variants. Our model achieves the best performance in
geometry, correspondence, and dynamics predictions over existing approaches.
The ACID dynamics models are successfully employed to goal-conditioned
deformable manipulation tasks, resulting in a 30% increase in task success rate
over the strongest baseline. Furthermore, we apply the simulation-trained ACID
model directly to real-world objects and show success in manipulating them into
target configurations. For more results and information, please visit
https://b0ku1.github.io/acid/ .

A domain-independent problem-solving system based on principles of Artificial
Life is introduced. In this system, DIAS, the input and output dimensions of
the domain are laid out in a spatial medium. A population of actors, each
seeing only part of this medium, solves problems collectively in it. The
process is independent of the domain and can be implemented through different
kinds of actors. Through a set of experiments on various problem domains, DIAS
is shown able to solve problems with different dimensionality and complexity,
to require no hyperparameter tuning for new problems, and to exhibit lifelong
learning, i.e. adapt rapidly to run-time changes in the problem domain, and do
it better than a standard non-collective approach. DIAS therefore demonstrates
a role for Alife in building scalable, general, and adaptive problem-solving
systems.

We consider a sequence of related multivariate time series learning tasks,
such as predicting failures for different instances of a machine from time
series of multi-sensor data, or activity recognition tasks over different
individuals from multiple wearable sensors. We focus on two under-explored
practical challenges arising in such settings: (i) Each task may have a
different subset of sensors, i.e., providing different partial observations of
the underlying 'system'. This restriction can be due to different manufacturers
in the former case, and people wearing more or less measurement devices in the
latter (ii) We are not allowed to store or re-access data from a task once it
has been observed at the task level. This may be due to privacy considerations
in the case of people, or legal restrictions placed by machine owners.
Nevertheless, we would like to (a) improve performance on subsequent tasks
using experience from completed tasks as well as (b) continue to perform better
on past tasks, e.g., update the model and improve predictions on even the first
machine after learning from subsequently observed ones. We note that existing
continual learning methods do not take into account variability in input
dimensions arising due to different subsets of sensors being available across
tasks, and struggle to adapt to such variable input dimensions (VID) tasks. In
this work, we address this shortcoming of existing methods. To this end, we
learn task-specific generative models and classifiers, and use these to augment
data for target tasks. Since the input dimensions across tasks vary, we propose
a novel conditioning module based on graph neural networks to aid a standard
recurrent neural network. We evaluate the efficacy of the proposed approach on
three publicly available datasets corresponding to two activity recognition
tasks (classification) and one prognostics task (regression).

All-MLP architectures have attracted increasing interest as an alternative to
attention-based models. In NLP, recent work like gMLP shows that all-MLPs can
match Transformers in language modeling, but still lag behind in downstream
tasks. In this work, we analyze the limitations of MLPs in expressiveness, and
propose sparsely activated MLPs with mixture-of-experts (MoEs) in both feature
and input (token) dimensions. Such sparse all-MLPs significantly increase model
capacity and expressiveness while keeping the compute constant. We address
critical challenges in incorporating conditional computation with two routing
strategies. The proposed sparse all-MLP improves language modeling perplexity
and obtains up to 2$\times$ improvement in training efficiency compared to both
Transformer-based MoEs (GShard, Switch Transformer, Base Layers and HASH
Layers) as well as dense Transformers and all-MLPs. Finally, we evaluate its
zero-shot in-context learning performance on six downstream tasks, and find
that it surpasses Transformer-based MoEs and dense Transformers.

Transfer learning has proven to be crucial in advancing the state of speech
and natural language processing research in recent years. In speech, a model
pre-trained by self-supervised learning transfers remarkably well on multiple
tasks. However, the lack of a consistent evaluation methodology is limiting
towards a holistic understanding of the efficacy of such models. SUPERB was a
step towards introducing a common benchmark to evaluate pre-trained models
across various speech tasks. In this paper, we introduce SUPERB-SG, a new
benchmark focused on evaluating the semantic and generative capabilities of
pre-trained models by increasing task diversity and difficulty over SUPERB. We
use a lightweight methodology to test the robustness of representations learned
by pre-trained models under shifts in data domain and quality across different
types of tasks. It entails freezing pre-trained model parameters, only using
simple task-specific trainable heads. The goal is to be inclusive of all
researchers, and encourage efficient use of computational resources. We also
show that the task diversity of SUPERB-SG coupled with limited task supervision
is an effective recipe for evaluating the generalizability of model

Predicting product sales of large retail companies is a challenging task
considering volatile nature of trends, seasonalities, events as well as unknown
factors such as market competitions, change in customer's preferences, or
unforeseen events, e.g., COVID-19 outbreak. In this paper, we benchmark
forecasting models on historical sales data from Walmart to predict their
future sales. We provide a comprehensive theoretical overview and analysis of
the state-of-the-art timeseries forecasting models. Then, we apply these models
on the forecasting challenge dataset (M5 forecasting by Kaggle). Specifically,
we use a traditional model, namely, ARIMA (Autoregressive Integrated Moving
Average), and recently developed advanced models e.g., Prophet model developed
by Facebook, light gradient boosting machine (LightGBM) model developed by
Microsoft and benchmark their performances. Results suggest that ARIMA model
outperforms the Facebook Prophet and LightGBM model while the LightGBM model
achieves huge computational gain for the large dataset with negligible
compromise in the prediction accuracy.

Mix-based augmentation has been proven fundamental to the generalization of
deep vision models. However, current augmentations only mix samples at the
current data batch during training, which ignores the possible knowledge
accumulated in the learning history. In this paper, we propose a recursive
mixed-sample learning paradigm, termed "RecursiveMix" (RM), by exploring a
novel training strategy that leverages the historical input-prediction-label
triplets. More specifically, we iteratively resize the input image batch from
the previous iteration and paste it into the current batch while their labels
are fused proportionally to the area of the operated patches. Further, a
consistency loss is introduced to align the identical image semantics across
the iterations, which helps the learning of scale-invariant feature
representations. Based on ResNet-50, RM largely improves classification
accuracy by $\sim$3.2\% on CIFAR100 and $\sim$2.8\% on ImageNet with negligible
extra computation/storage costs. In the downstream object detection task, the
RM pretrained model outperforms the baseline by 2.1 AP points and surpasses
CutMix by 1.4 AP points under the ATSS detector on COCO. In semantic
segmentation, RM also surpasses the baseline and CutMix by 1.9 and 1.1 mIoU
points under UperNet on ADE20K, respectively. Codes and pretrained models are
available at \url{https://github.com/megvii-research/RecursiveMix}.

The target of space-time video super-resolution (STVSR) is to increase the
spatial-temporal resolution of low-resolution (LR) and low frame rate (LFR)
videos. Recent approaches based on deep learning have made significant
improvements, but most of them only use two adjacent frames, that is,
short-term features, to synthesize the missing frame embedding, which cannot
fully explore the information flow of consecutive input LR frames. In addition,
existing STVSR models hardly exploit the temporal contexts explicitly to assist
high-resolution (HR) frame reconstruction. To address these issues, in this
paper, we propose a deformable attention network called STDAN for STVSR. First,
we devise a long-short term feature interpolation (LSTFI) module, which is
capable of excavating abundant content from more neighboring input frames for
the interpolation process through a bidirectional RNN structure. Second, we put
forward a spatial-temporal deformable feature aggregation (STDFA) module, in
which spatial and temporal contexts in dynamic video frames are adaptively
captured and aggregated to enhance SR reconstruction. Experimental results on
several datasets demonstrate that our approach outperforms state-of-the-art
STVSR methods. The code is available at
https://github.com/littlewhitesea/STDAN.

Unsupervised domain adaptation (UDA) is one of the prominent tasks of
transfer learning, and it provides an effective approach to mitigate the
distribution shift between the labeled source domain and the unlabeled target
domain. Prior works mainly focus on aligning the marginal distributions or the
estimated class-conditional distributions. However, the joint dependency among
the feature and the label is crucial for the adaptation task and is not fully
exploited. To address this problem, we propose the Bures Joint Distribution
Alignment (BJDA) algorithm which directly models the joint distribution shift
based on the optimal transport theory in the infinite-dimensional kernel
spaces. Specifically, we propose a novel alignment loss term that minimizes the
kernel Bures-Wasserstein distance between the joint distributions. Technically,
BJDA can effectively capture the nonlinear structures underlying the data. In
addition, we introduce a dynamic margin in contrastive learning phase to
flexibly characterize the class separability and improve the discriminative
ability of representations. It also avoids the cross-validation procedure to
determine the margin parameter in traditional triplet loss based methods.
Extensive experiments show that BJDA is very effective for the UDA tasks, as it
outperforms state-of-the-art algorithms in most experimental settings. In
particular, BJDA improves the average accuracy of UDA tasks by 2.8% on
Adaptiope, 1.4% on Office-Caltech10, and 1.1% on ImageCLEF-DA.

Currently, Medical Subject Headings (MeSH) are manually assigned to every
biomedical article published and subsequently recorded in the PubMed database
to facilitate retrieving relevant information. With the rapid growth of the
PubMed database, large-scale biomedical document indexing becomes increasingly
important. MeSH indexing is a challenging task for machine learning, as it
needs to assign multiple labels to each article from an extremely large
hierachically organized collection. To address this challenge, we propose
KenMeSH, an end-to-end model that combines new text features and a dynamic
\textbf{K}nowledge-\textbf{en}hanced mask attention that integrates document
features with MeSH label hierarchy and journal correlation features to index
MeSH terms. Experimental results show the proposed method achieves
state-of-the-art performance on a number of measures.

Mapping between discrete and continuous distributions is a difficult task and
many have had to resort to heuristical approaches. We propose a
tessellation-based approach that directly learns quantization boundaries in a
continuous space, complete with exact likelihood evaluations. This is done
through constructing normalizing flows on convex polytopes parameterized using
a simple homeomorphism with an efficient log determinant Jacobian. We explore
this approach in two application settings, mapping from discrete to continuous
and vice versa. Firstly, a Voronoi dequantization allows automatically learning
quantization boundaries in a multidimensional space. The location of boundaries
and distances between regions can encode useful structural relations between
the quantized discrete values. Secondly, a Voronoi mixture model has
near-constant computation cost for likelihood evaluation regardless of the
number of mixture components. Empirically, we show improvements over existing
methods across a range of structured data modalities.

We propose an unsupervised deep learning based method to estimate depth from
focal stack camera images. On the NYU-v2 dataset, our method achieves much
better depth estimation accuracy compared to single-image based methods.

Fairness of deepfake detectors in the presence of anomalies are not well
investigated, especially if those anomalies are more prominent in either male
or female subjects. The primary motivation for this work is to evaluate how
deepfake detection model behaves under such anomalies. However, due to the
black-box nature of deep learning (DL) and artificial intelligence (AI)
systems, it is hard to predict the performance of a model when the input data
is modified. Crucially, if this defect is not addressed properly, it will
adversely affect the fairness of the model and result in discrimination of
certain sub-population unintentionally. Therefore, the objective of this work
is to adopt metamorphic testing to examine the reliability of the selected
deepfake detection model, and how the transformation of input variation places
influence on the output. We have chosen MesoInception-4, a state-of-the-art
deepfake detection model, as the target model and makeup as the anomalies.
Makeups are applied through utilizing the Dlib library to obtain the 68 facial
landmarks prior to filling in the RGB values. Metamorphic relations are derived
based on the notion that realistic perturbations of the input images, such as
makeup, involving eyeliners, eyeshadows, blushes, and lipsticks (which are
common cosmetic appearance) applied to male and female images, should not alter
the output of the model by a huge margin. Furthermore, we narrow down the scope
to focus on revealing potential gender biases in DL and AI systems.
Specifically, we are interested to examine whether MesoInception-4 model
produces unfair decisions, which should be considered as a consequence of
robustness issues. The findings from our work have the potential to pave the
way for new research directions in the quality assurance and fairness in DL and
AI systems.

In the intention of minimizing excessive X-ray radiation administration to
patients, low-dose computed tomography (LDCT) has become a distinct trend in
radiology. However, while lowering the radiation dose reduces the risk to the
patient, it also increases noise and artifacts, compromising image quality and
clinical diagnosis. In most supervised learning methods, paired CT images are
required, but such images are unlikely to be available in the clinic. We
present a self-supervised learning model (Noise2Projection) that fully exploits
the raw projection images to reduce noise and improve the quality of
reconstructed LDCT images. Unlike existing self-supervised algorithms, the
proposed method only requires noisy CT projection images and reduces noise by
exploiting the correlation between nearby projection images. We trained and
tested the model using clinical data and the quantitative and qualitative
results suggest that our model can effectively reduce LDCT image noise while
also drastically removing artifacts in LDCT images.

Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging.
However, long image acquisition times, the need for qualitative expert
analysis, and the lack of (and difficulty extracting) quantitative indicators
that are sensitive to tissue health have curtailed widespread clinical and
research studies. While recent machine learning methods for MRI reconstruction
and analysis have shown promise for reducing this burden, these techniques are
primarily validated with imperfect image quality metrics, which are discordant
with clinically-relevant measures that ultimately hamper clinical deployment
and clinician trust. To mitigate this challenge, we present the Stanford Knee
MRI with Multi-Task Evaluation (SKM-TEA) dataset, a collection of quantitative
knee MRI (qMRI) scans that enables end-to-end, clinically-relevant evaluation
of MRI reconstruction and analysis tools. This 1.6TB dataset consists of
raw-data measurements of ~25,000 slices (155 patients) of anonymized patient
MRI scans, the corresponding scanner-generated DICOM images, manual
segmentations of four tissues, and bounding box annotations for sixteen
clinically relevant pathologies. We provide a framework for using qMRI
parameter maps, along with image reconstructions and dense image labels, for
measuring the quality of qMRI biomarker estimates extracted from MRI
reconstruction, segmentation, and detection techniques. Finally, we use this
framework to benchmark state-of-the-art baselines on this dataset. We hope our
SKM-TEA dataset and code can enable a broad spectrum of research for modular
image reconstruction and image analysis in a clinically informed manner.
Dataset access, code, and benchmarks are available at
https://github.com/StanfordMIMI/skm-tea.

Edge detection and image filters are commonly used in computer vision.
However, they have never been applied to the data analysis of angle-resolved
photoemission spectroscopy (ARPES) data before in a systematic fashion. In this
paper we will use the Sobel, Laplacian of a gaussian (LoG), Canny, Prewitt,
Roberts, and fuzzy logic methods for edge detection in the ARPES results of
HfP2, ZrSiS, and Hf2Te2P2. We find that the Canny filter is the best method for
edge detection of noisy data that is typical of ARPES measurements, while the
other edge detection techniques are not able to correctly detect ARPES bands.

Data valuation and subset selection have emerged as valuable tools for
application-specific selection of important training data. However, the
efficiency-accuracy tradeoffs of state-of-the-art methods hinder their
widespread application to many AI workflows. In this paper, we propose a novel
2-phase solution to this problem. Phase 1 selects representative checkpoints
from an SGD-like training algorithm, which are used in phase-2 to estimate the
approximate training data values, e.g. decrease in validation loss due to each
training point. A key contribution of this paper is CheckSel, an Orthogonal
Matching Pursuit-inspired online sparse approximation algorithm for checkpoint
selection in the online setting, where the features are revealed one at a time.
Another key contribution is the study of data valuation in the domain
adaptation setting, where a data value estimator obtained using checkpoints
from training trajectory in the source domain training dataset is used for data
valuation in a target domain training dataset. Experimental results on
benchmark datasets show the proposed algorithm outperforms recent baseline
methods by up to 30% in terms of test accuracy while incurring a similar
computational burden, for both standalone and domain adaptation settings.

Deformable image registration plays a critical role in various tasks of
medical image analysis. A successful registration algorithm, either derived
from conventional energy optimization or deep networks requires tremendous
efforts from computer experts to well design registration energy or to
carefully tune network architectures for the specific type of medical data. To
tackle the aforementioned problems, this paper proposes an automated learning
registration algorithm (AutoReg) that cooperatively optimizes both
architectures and their corresponding training objectives, enabling
non-computer experts, e.g., medical/clinical users, to conveniently find
off-the-shelf registration algorithms for diverse scenarios. Specifically, we
establish a triple-level framework to deduce registration network architectures
and objectives with an auto-searching mechanism and cooperating optimization.
We conduct image registration experiments on multi-site volume datasets and
various registration tasks. Extensive results demonstrate that our AutoReg may
automatically learn an optimal deep registration network for given volumes and
achieve state-of-the-art performance, also significantly improving computation
efficiency than the mainstream UNet architectures (from 0.558 to 0.270 seconds
for a 3D image pair on the same configuration).

An ideal strategy in zero-sum games should not only grant the player an
average reward no less than the value of Nash equilibrium, but also exploit the
(adaptive) opponents when they are suboptimal. While most existing works in
Markov games focus exclusively on the former objective, it remains open whether
we can achieve both objectives simultaneously. To address this problem, this
work studies no-regret learning in Markov games with adversarial opponents when
competing against the best fixed policy in hindsight. Along this direction, we
present a new complete set of positive and negative results:
  When the policies of the opponents are revealed at the end of each episode,
we propose new efficient algorithms achieving $\sqrt{K}$-regret bounds when
either (1) the baseline policy class is small or (2) the opponent's policy
class is small. This is complemented with an exponential lower bound when
neither conditions are true. When the policies of the opponents are not
revealed, we prove a statistical hardness result even in the most favorable
scenario when both above conditions are true. Our hardness result is much
stronger than the existing hardness results which either only involve
computational hardness, or require further restrictions on the algorithms.

In many personalized recommendation scenarios, the generalization ability of
a target task can be improved via learning with additional auxiliary tasks
alongside this target task on a multi-task network. However, this method often
suffers from a serious optimization imbalance problem. On the one hand, one or
more auxiliary tasks might have a larger influence than the target task and
even dominate the network weights, resulting in worse recommendation accuracy
for the target task. On the other hand, the influence of one or more auxiliary
tasks might be too weak to assist the target task. More challenging is that
this imbalance dynamically changes throughout the training process and varies
across the parts of the same network. We propose a new method: MetaBalance to
balance auxiliary losses via directly manipulating their gradients w.r.t the
shared parameters in the multi-task network. Specifically, in each training
iteration and adaptively for each part of the network, the gradient of an
auxiliary loss is carefully reduced or enlarged to have a closer magnitude to
the gradient of the target loss, preventing auxiliary tasks from being so
strong that dominate the target task or too weak to help the target task.
Moreover, the proximity between the gradient magnitudes can be flexibly
adjusted to adapt MetaBalance to different scenarios. The experiments show that
our proposed method achieves a significant improvement of 8.34% in terms of
NDCG@10 upon the strongest baseline on two real-world datasets. The code of our
approach can be found at here: https://github.com/facebookresearch/MetaBalance

We consider the distributed stochastic optimization problem where $n$ agents
want to minimize a global function given by the sum of agents' local functions,
and focus on the heterogeneous setting when agents' local functions are defined
over non-i.i.d. data sets. We study the Local SGD method, where agents perform
a number of local stochastic gradient steps and occasionally communicate with a
central node to improve their local optimization tasks. We analyze the effect
of local steps on the convergence rate and the communication complexity of
Local SGD. In particular, instead of assuming a fixed number of local steps
across all communication rounds, we allow the number of local steps during the
$i$-th communication round, $H_i$, to be different and arbitrary numbers. Our
main contribution is to characterize the convergence rate of Local SGD as a
function of $\{H_i\}_{i=1}^R$ under various settings of strongly convex,
convex, and nonconvex local functions, where $R$ is the total number of
communication rounds. Based on this characterization, we provide sufficient
conditions on the sequence $\{H_i\}_{i=1}^R$ such that Local SGD can achieve
linear speed-up with respect to the number of workers. Furthermore, we propose
a new communication strategy with increasing local steps superior to existing
communication strategies for strongly convex local functions. On the other
hand, for convex and nonconvex local functions, we argue that fixed local steps
are the best communication strategy for Local SGD and recover state-of-the-art
convergence rate results. Finally, we justify our theoretical results through
extensive numerical experiments.

Each and every organisation releases information in a variety of forms
ranging from annual reports to legal proceedings. Such documents may contain
sensitive information and releasing them openly may lead to the leakage of
confidential information. Detection of sentences that contain sensitive
information in documents can help organisations prevent the leakage of valuable
confidential information. This is especially challenging when such sentences
contain a substantial amount of information or are paraphrased versions of
known sensitive content. Current approaches to sensitive information detection
in such complex settings are based on keyword-based approaches or standard
machine learning models. In this paper, we wish to explore whether pre-trained
transformer models are well suited to detect complex sensitive information.
Pre-trained transformers are typically trained on an enormous amount of text
and therefore readily learn grammar, structure and other linguistic features,
making them particularly attractive for this task. Through our experiments on
the Monsanto trial data set, we observe that the fine-tuned Bidirectional
Encoder Representations from Transformers (BERT) transformer model performs
better than traditional models. We experimented with four different categories
of documents in the Monsanto dataset and observed that BERT achieves better F2
scores by 24.13\% to 65.79\% for GHOST, 30.14\% to 54.88\% for TOXIC, 39.22\%
for CHEMI, 53.57\% for REGUL compared to existing sensitive information
detection models.

Because the magnitude of inner products with its basis functions are
invariant to rotation and scale change, the Fourier-Mellin transform has long
been used as a component in Euclidean invariant 2D shape recognition systems.
Yet Fourier-Mellin transform magnitudes are only invariant to rotation and
scale changes about a known center point, and full Euclidean invariant shape
recognition is not possible except when this center point can be consistently
and accurately identified. In this paper, we describe a system where a
Fourier-Mellin transform is computed at every point in the image. The spatial
support of the Fourier-Mellin basis functions is made local by multiplying them
with a polynomial envelope. Significantly, the magnitudes of convolutions with
these complex filters at isolated points are not (by themselves) used as
features for Euclidean invariant shape recognition because reliable
discrimination would require filters with spatial support large enough to fully
encompass the shapes. Instead, we rely on the fact that normalized histograms
of magnitudes are fully Euclidean invariant. We demonstrate a system based on
the VLAD machine learning method that performs Euclidean invariant recognition
of 2D shapes and requires an order of magnitude less training data than
comparable methods based on convolutional neural networks.

WFI J161953.3+031909 was considered to be an eclipsing novalike object in the
period gap. Using the Zwicky Transient Facility (ZTF) public database, I found
that this object is the first eclipsing Z Cam star in the period gap, and is
also most likely an ER UMa star with supercycles of 60-80 d. The longest
outburst (most likely a superoutburst) comprised 35-46% of the supercycle in
the extreme case. If superhumps are confirmed, this becomes the second object
showing both ER UMa and Z Cam states after NY Ser. These objects have
anomalously high mass-transfer rates despite that they are in the period gap. I
refined the orbital period to be 0.099419808(8) d. We can expect to learn from
WFI J161953.3+031909 using eclipses what is actually happening in the ER
UMa-type and Z Cam-type disk. I also provide a revised classification of an
eclipsing IW And star for BMAM-V383 = IPHAS J200822.55+300341.6 by detecting an
IW And-type standstill.

Stochastic model predictive control has been a successful and robust control
framework for many robotics tasks where the system dynamics model is slightly
inaccurate or in the presence of environment disturbances. Despite the
successes, it is still unclear how to best adjust control parameters to the
current task in the presence of model parameter uncertainty and heteroscedastic
noise. In this paper, we propose an adaptive MPC variant that automatically
estimates control and model parameters by leveraging ideas from Bayesian
optimisation (BO) and the classical expected improvement acquisition function.
We leverage recent results showing that BO can be reformulated via density
ratio estimation, which can be efficiently approximated by simply learning a
classifier. This is then integrated into a model predictive path integral
control framework yielding robust controllers for a variety of challenging
robotics tasks. We demonstrate the approach on classical control problems under
model uncertainty and robotics manipulation tasks.

NIST is standardizing Post Quantum Cryptography (PQC) algorithms that are
resilient to the computational capability of quantum computers. Past works show
malicious subversion with cryptographic software (algorithm subversion attacks)
that weaken the implementations. We show that PQC digital signature codes can
be subverted in line with previously reported flawed implementations that
generate verifiable, but less-secure signatures, demonstrating the risk of such
attacks. Since, all processors have built-in Hardware Performance Counters
(HPCs), there exists a body of work proposing a low-cost Machine Learning
(ML)-based integrity checking of software using HPC fingerprints. However, such
HPC-based approaches may not detect subversion of PQC codes. A miniscule
percentage of qualitative inputs when applied to the PQC codes improve this
accuracy to 98%. We propose grey-box fuzzing as a pre-processing step to obtain
inputs to aid the HPC-based method.

Many industrial sectors have been collecting big sensor data. With recent
technologies for processing big data, companies can exploit this for automatic
failure detection and prevention. We propose the first completely automated
method for failure analysis, machine-learning fault trees from raw
observational data with continuous variables. Our method scales well and is
tested on a real-world, five-year dataset of domestic heater operations in The
Netherlands, with 31 million unique heater-day readings, each containing 27
sensor and 11 failure variables. Our method builds on two previous procedures:
the C4.5 decision-tree learning algorithm, and the LIFT fault tree learning
algorithm from Boolean data. C4.5 pre-processes each continuous variable: it
learns an optimal numerical threshold which distinguishes between faulty and
normal operation of the top-level system. These thresholds discretise the
variables, thus allowing LIFT to learn fault trees which model the root failure
mechanisms of the system and are explainable. We obtain fault trees for the 11
failure variables, and evaluate them in two ways: quantitatively, with a
significance score, and qualitatively, with domain specialists. Some of the
fault trees learnt have almost maximum significance (above 0.95), while others
have medium-to-low significance (around 0.30), reflecting the difficulty of
learning from big, noisy, real-world sensor data. The domain specialists
confirm that the fault trees model meaningful relationships among the

Text coherence is a fundamental problem in natural language generation and
understanding. Organizing sentences into an order that maximizes coherence is
known as sentence ordering. This paper is proposing a new approach based on the
graph neural network approach to encode a set of sentences and learn orderings
of short stories. We propose a new method for constructing sentence-entity
graphs of short stories to create the edges between sentences and reduce noise
in our graph by replacing the pronouns with their referring entities. We
improve the sentence ordering by introducing an aggregation method based on
majority voting of state-of-the-art methods and our proposed one. Our approach
employs a BERT-based model to learn semantic representations of the sentences.
The results demonstrate that the proposed method significantly outperforms
existing baselines on a corpus of short stories with a new state-of-the-art
performance in terms of Perfect Match Ratio (PMR) and Kendall's Tau (Tau)
metrics. More precisely, our method increases PMR and Tau criteria by more than
5% and 4.3%, respectively. These outcomes highlight the benefit of forming the
edges between sentences based on their cosine similarity. We also observe that
replacing pronouns with their referring entities effectively encodes sentences
in sentence-entity graphs.

As machine learning models are increasingly being employed to make
consequential decisions in real-world settings, it becomes critical to ensure
that individuals who are adversely impacted (e.g., loan denied) by the
predictions of these models are provided with a means for recourse. While
several approaches have been proposed to construct recourses for affected
individuals, the recourses output by these methods either achieve low costs
(i.e., ease-of-implementation) or robustness to small perturbations (i.e.,
noisy implementations of recourses), but not both due to the inherent
trade-offs between the recourse costs and robustness. Furthermore, prior
approaches do not provide end users with any agency over navigating the
aforementioned trade-offs. In this work, we address the above challenges by
proposing the first algorithmic framework which enables users to effectively
manage the recourse cost vs. robustness trade-offs. More specifically, our
framework Probabilistically ROBust rEcourse (\texttt{PROBE}) lets users choose
the probability with which a recourse could get invalidated (recourse
invalidation rate) if small changes are made to the recourse i.e., the recourse
is implemented somewhat noisily. To this end, we propose a novel objective
function which simultaneously minimizes the gap between the achieved
(resulting) and desired recourse invalidation rates, minimizes recourse costs,
and also ensures that the resulting recourse achieves a positive model
prediction. We develop novel theoretical results to characterize the recourse
invalidation rates corresponding to any given instance w.r.t. different classes
of underlying models (e.g., linear models, tree based models etc.), and
leverage these results to efficiently optimize the proposed objective.
Experimental evaluation with multiple real world datasets demonstrate the
efficacy of the proposed framework.

We present a self-supervised and self-calibrating multi-shot approach to
imaging through atmospheric turbulence, called TurbuGAN. Our approach requires
no paired training data, adapts itself to the distribution of the turbulence,
leverages domain-specific data priors, and can generalize from tens to
thousands of measurements. We achieve such functionality through an adversarial
sensing framework adapted from CryoGAN, which uses a discriminator network to
match the distributions of captured and simulated measurements. Our framework
builds on CryoGAN by (1) generalizing the forward measurement model to
incorporate physically accurate and computationally efficient models for light
propagation through anisoplanatic turbulence, (2) enabling adaptation to
slightly misspecified forward models, and (3) leveraging domain-specific prior
knowledge using pretrained generative networks, when available. We validate
TurbuGAN on both computationally simulated and experimentally captured images
distorted with anisoplanatic turbulence.

Multi-party computation (MPC) is promising for designing privacy-preserving
machine learning algorithms at edge networks. An emerging approach is coded-MPC
(CMPC), which advocates the use of coded computation to improve the performance
of MPC in terms of the required number of workers involved in computations. The
current approach for designing CMPC algorithms is to merely combine efficient
coded computation constructions with MPC. Instead, we propose a new
construction; Adaptive Gap Entangled polynomial (AGE) codes, where the degrees
of polynomials used in computations are optimized for MPC. We show that MPC
with AGE codes (AGE-CMPC) performs better than existing CMPC algorithms in
terms of the required number of workers as well as storage, communication and
computation load.

We propose a machine learning enhanced algorithm for solving the optimal
landing problem. Using Pontryagin's minimum principle, we derive a two-point
boundary value problem for the landing problem. The proposed algorithm uses
deep learning to predict the optimal landing time and a space-marching
technique to provide good initial guesses for the boundary value problem
solver. The performance of the proposed method is studied using the quadrotor
example, a reasonably high dimensional and strongly nonlinear system. Drastic
improvement in reliability and efficiency is observed.

We study federated learning (FL)--especially cross-silo FL--with non-convex
loss functions and data from people who do not trust the server or other silos.
In this setting, each silo (e.g. hospital) must protect the privacy of each
person's data (e.g. patient's medical record), even if the server or other
silos act as adversarial eavesdroppers. To that end, we consider inter-silo
record-level (ISRL) differential privacy (DP), which requires silo $i$'s
communications to satisfy record/item-level DP. We give novel ISRL-DP
algorithms for FL with heterogeneous (non-i.i.d.) silo data and two classes of
Lipschitz continuous loss functions: First, we consider losses satisfying the
Proximal Polyak-Lojasiewicz (PL) inequality, which is an extension of the
classical PL condition to the constrained setting. Prior works only considered
unconstrained private optimization with Lipschitz PL loss, which rules out most
interesting PL losses such as strongly convex problems and linear/logistic
regression. However, by analyzing the proximal PL scenario, we permit these
losses and others (e.g. LASSO, some neural nets) which are Lipschitz on a
restricted parameter domain. Our algorithms nearly attain the optimal strongly
convex, homogeneous (i.i.d.) rate for ISRL-DP FL without assuming convexity or
i.i.d. data. Second, we give the first private algorithms for non-convex
non-smooth loss functions. Our utility bounds even improve on the
state-of-the-art bounds for smooth losses. We complement our upper bounds with
lower bounds. Additionally, we provide shuffle DP (SDP) algorithms that improve
over the state-of-the-art central DP algorithms under more practical trust
assumptions. Numerical experiments show that our algorithm has better accuracy
than baselines for most privacy levels.

Food is essential for human survival, and people always try to taste
different types of delicious recipes. Frequently, people choose food
ingredients without even knowing their names or pick up some food ingredients
that are not obvious to them from a grocery store. Knowing which ingredients
can be mixed to make a delicious food recipe is essential. Selecting the right
recipe by choosing a list of ingredients is very difficult for a beginner cook.
However, it can be a problem even for experts. One such example is recognising
objects through image processing. Although this process is complex due to
different food ingredients, traditional approaches will lead to an inaccuracy
rate. These problems can be solved by machine learning and deep learning
approaches. In this paper, we implemented a model for food ingredients
recognition and designed an algorithm for recommending recipes based on
recognised ingredients. We made a custom dataset consisting of 9856 images
belonging to 32 different food ingredients classes. Convolution Neural Network
(CNN) model was used to identify food ingredients, and for recipe
recommendations, we have used machine learning. We achieved an accuracy of 94
percent, which is quite impressive.

We revisit large kernel design in modern convolutional neural networks
(CNNs). Inspired by recent advances in vision transformers (ViTs), in this
paper, we demonstrate that using a few large convolutional kernels instead of a
stack of small kernels could be a more powerful paradigm. We suggested five
guidelines, e.g., applying re-parameterized large depth-wise convolutions, to
design efficient high-performance large-kernel CNNs. Following the guidelines,
we propose RepLKNet, a pure CNN architecture whose kernel size is as large as
31x31, in contrast to commonly used 3x3. RepLKNet greatly closes the
performance gap between CNNs and ViTs, e.g., achieving comparable or superior
results than Swin Transformer on ImageNet and a few typical downstream tasks,
with lower latency. RepLKNet also shows nice scalability to big data and large
models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K,
which is very competitive among the state-of-the-arts with similar model sizes.
Our study further reveals that, in contrast to small-kernel CNNs, large-kernel
CNNs have much larger effective receptive fields and higher shape bias rather
than texture bias. Code & models at
https://github.com/megvii-research/RepLKNet.

Graphs are ubiquitous in encoding relational information of real-world
objects in many domains. Graph generation, whose purpose is to generate new
graphs from a distribution similar to the observed graphs, has received
increasing attention thanks to the recent advances of deep learning models. In
this paper, we conduct a comprehensive review on the existing literature of
graph generation from a variety of emerging methods to its wide application
areas. Specifically, we first formulate the problem of deep graph generation
and discuss its difference with several related graph learning tasks. Secondly,
we divide the state-of-the-art methods into three categories based on model
architectures and summarize their generation strategies. Thirdly, we introduce
three key application areas of deep graph generation. Lastly, we highlight
challenges and opportunities in the future study of deep graph generation.

The development of scene text recognition (STR) in the era of deep learning
has been mainly focused on novel architectures of STR models. However, training
protocol (i.e., settings of the hyper-parameters involved in the training of
STR models), which plays an equally important role in successfully training a
good STR model, is under-explored for scene text recognition. In this work, we
attempt to improve the accuracy of existing STR models by searching for optimal
training protocol. Specifically, we develop a training protocol search
algorithm, based on a newly designed search space and an efficient search
algorithm using evolutionary optimization and proxy tasks. Experimental results
show that our searched training protocol can improve the recognition accuracy
of mainstream STR models by 2.7%~3.9%. In particular, with the searched
training protocol, TRBA-Net achieves 2.1% higher accuracy than the
state-of-the-art STR model (i.e., EFIFSTR), while the inference speed is 2.3x
and 3.7x faster on CPU and GPU respectively. Extensive experiments are
conducted to demonstrate the effectiveness of the proposed method and the
generalization ability of the training protocol found by our search method.
Code is available at https://github.com/VDIGPKU/STR_TPSearch.

One-bit compressive sensing is concerned with the accurate recovery of an
underlying sparse signal of interest from its one-bit noisy measurements. The
conventional signal recovery approaches for this problem are mainly developed
based on the assumption that an exact knowledge of the sensing matrix is
available. In this work, however, we present a novel data-driven and
model-based methodology that achieves blind recovery; i.e., signal recovery
without requiring the knowledge of the sensing matrix. To this end, we make use
of the deep unfolding technique and develop a model-driven deep neural
architecture which is designed for this specific task. The proposed deep
architecture is able to learn an alternative sensing matrix by taking advantage
of the underlying unfolded algorithm such that the resulting learned recovery
algorithm can accurately and quickly (in terms of the number of iterations)
recover the underlying compressed signal of interest from its one-bit noisy
measurements. In addition, due to the incorporation of the domain knowledge and
the mathematical model of the system into the proposed deep architecture, the
resulting network benefits from enhanced interpretability, has a very small
number of trainable parameters, and requires very small number of training
samples, as compared to the commonly used black-box deep neural network
alternatives for the problem at hand.

Network intrusion detection systems (NIDS) are an essential defense for
computer networks and the hosts within them. Machine learning (ML) nowadays
predominantly serves as the basis for NIDS decision making, where models are
tuned to reduce false alarms, increase detection rates, and detect known and
unknown attacks. At the same time, ML models have been found to be vulnerable
to adversarial examples that undermine the downstream task. In this work, we
ask the practical question of whether real-world ML-based NIDS can be
circumvented by crafted adversarial flows, and if so, how can they be created.
We develop the generative adversarial network (GAN)-based attack algorithm
NIDSGAN and evaluate its effectiveness against realistic ML-based NIDS. Two
main challenges arise for generating adversarial network traffic flows: (1) the
network features must obey the constraints of the domain (i.e., represent
realistic network behavior), and (2) the adversary must learn the decision
behavior of the target NIDS without knowing its model internals (e.g.,
architecture and meta-parameters) and training data. Despite these challenges,
the NIDSGAN algorithm generates highly realistic adversarial traffic flows that
evade ML-based NIDS. We evaluate our attack algorithm against two
state-of-the-art DNN-based NIDS in whitebox, blackbox, and restricted-blackbox
threat models and achieve success rates which are on average 99%, 85%, and 70%,
respectively. We also show that our attack algorithm can evade NIDS based on
classical ML models including logistic regression, SVM, decision trees and
KNNs, with a success rate of 70% on average. Our results demonstrate that
deploying ML-based NIDS without careful defensive strategies against
adversarial flows may (and arguably likely will) lead to future compromises.

Along with the proliferation of digital data collected using sensor
technologies and a boost of computing power, Deep Learning (DL) based
approaches have drawn enormous attention in the past decade due to their
impressive performance in extracting complex relations from raw data and
representing valuable information. Meanwhile, though, rooted in its notorious
black-box nature, the appreciation of DL has been highly debated due to the
lack of interpretability. On the one hand, DL only utilizes statistical
features contained in raw data while ignoring human knowledge of the underlying
system, which results in both data inefficiency and trust issues; on the other
hand, a trained DL model does not provide to researchers any extra insight
about the underlying system beyond its output, which, however, is the essence
of most fields of science, e.g. physics and economics.
  This thesis addresses the issue of interpretability in general information
modeling and endeavors to ease the problem from two scopes. Firstly, a
problem-oriented perspective is applied to incorporate knowledge into modeling
practice, where interesting mathematical properties emerge naturally which cast
constraints on modeling. Secondly, given a trained model, various methods could
be applied to extract further insights about the underlying system. These two
pathways are termed as guided model design and secondary measurements.
Remarkably, a novel scheme emerges for the modeling practice in statistical
learning: Algebraic Learning (AgLr). Instead of being restricted to the
discussion of any specific model, AgLr starts from idiosyncrasies of a learning
task itself and studies the structure of a legitimate model class. This novel
scheme demonstrates the noteworthy value of abstract algebra for general AI,
which has been overlooked in recent progress, and could shed further light on
interpretable information modeling.

In this research, we compare malware detection techniques based on static,
dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs
) on both static and dynamic feature sets and compare the resulting detection
rates over a substantial number of malware families. We also consider hybrid
cases, where dynamic analysis is used in the training phase, with static
techniques used in the detection phase, and vice versa. In our experiments, a
fully dynamic approach generally yields the best detection rates. We discuss
the implications of this research for malware detection based on hybrid

The growth of machine-readable data in finance, such as alternative data,
requires new modeling techniques that can handle non-stationary and
non-parametric data. Due to the underlying causal dependence and the size and
complexity of the data, we propose a new modeling approach for financial time
series data, the $\alpha_{t}$-RIM (recurrent independent mechanism). This
architecture makes use of key-value attention to integrate top-down and
bottom-up information in a context-dependent and dynamic way. To model the data
in such a dynamic manner, the $\alpha_{t}$-RIM utilizes an exponentially
smoothed recurrent neural network, which can model non-stationary times series
data, combined with a modular and independent recurrent structure. We apply our
approach to the closing prices of three selected stocks of the S\&P 500
universe as well as their news sentiment score. The results suggest that the
$\alpha_{t}$-RIM is capable of reflecting the causal structure between stock
prices and news sentiment, as well as the seasonality and trends. Consequently,
this modeling approach markedly improves the generalization performance, that
is, the prediction of unseen data, and outperforms state-of-the-art networks
such as long short-term memory models.

Set-valued prediction is a well-known concept in multi-class classification.
When a classifier is uncertain about the class label for a test instance, it
can predict a set of classes instead of a single class. In this paper, we focus
on hierarchical multi-class classification problems, where valid sets
(typically) correspond to internal nodes of the hierarchy. We argue that this
is a very strong restriction, and we propose a relaxation by introducing the
notion of representation complexity for a predicted set. In combination with
probabilistic classifiers, this leads to a challenging inference problem for
which specific combinatorial optimization algorithms are needed. We propose
three methods and evaluate them on benchmark datasets: a na\"ive approach that
is based on matrix-vector multiplication, a reformulation as a knapsack problem
with conflict graph, and a recursive tree search method. Experimental results
demonstrate that the last method is computationally more efficient than the
other two approaches, due to a hierarchical factorization of the conditional
class distribution.

Training deep neural networks (DNNs) is a computationally expensive job,
which can take weeks or months even with high performance GPUs. As a remedy for
this challenge, community has started exploring the use of more efficient data
representations in the training process, e.g., block floating point (BFP).
However, prior work on BFP-based DNN accelerators rely on a specific BFP
representation making them less versatile. This paper builds upon an
algorithmic observation that we can accelerate the training by leveraging
multiple BFP precisions without compromising the finally achieved accuracy.
Backed up by this algorithmic opportunity, we develop a flexible DNN training
accelerator, dubbed FlexBlock, which supports three different BFP precision
modes, possibly different among activation, weight, and gradient tensors. While
several prior works proposed such multi-precision support for DNN accelerators,
not only do they focus only on the inference, but also their core utilization
is suboptimal at a fixed precision and specific layer types when the training
is considered. Instead, FlexBlock is designed in such a way that high core
utilization is achievable for i) various layer types, and ii) three BFP
precisions by mapping data in a hierarchical manner to its compute units. We
evaluate the effectiveness of FlexBlock architecture using well-known DNNs on
CIFAR, ImageNet and WMT14 datasets. As a result, training in FlexBlock
significantly improves the training speed by 1.5~5.3x and the energy efficiency
by 2.4~7.0x on average compared to other training accelerators and incurs
marginal accuracy loss compared to full-precision training.

We propose and demonstrate the task of giving natural language summaries of
the actions of a robotic agent in a virtual environment. We explain why such a
task is important, what makes it difficult, and discuss how it might be
addressed. To encourage others to work on this, we show how a popular existing
dataset that matches robot actions with natural language descriptions designed
for an instruction following task can be repurposed to serve as a training
ground for robot action summarization work. We propose and test several methods
of learning to generate such summaries, starting from either egocentric video
frames of the robot taking actions or intermediate text representations of the
actions used by an automatic planner. We provide quantitative and qualitative
evaluations of our results, which can serve as a baseline for future work.

Offline reinforcement learning algorithms promise to be applicable in
settings where a fixed dataset is available and no new experience can be
acquired. However, such formulation is inevitably offline-data-hungry and, in
practice, collecting a large offline dataset for one specific task over one
specific environment is also costly and laborious. In this paper, we thus 1)
formulate the offline dynamics adaptation by using (source) offline data
collected from another dynamics to relax the requirement for the extensive
(target) offline data, 2) characterize the dynamics shift problem in which
prior offline methods do not scale well, and 3) derive a simple dynamics-aware
reward augmentation (DARA) framework from both model-free and model-based
offline settings. Specifically, DARA emphasizes learning from those source
transition pairs that are adaptive for the target environment and mitigates the
offline dynamics shift by characterizing state-action-next-state pairs instead
of the typical state-action distribution sketched by prior offline RL methods.
The experimental evaluation demonstrates that DARA, by augmenting rewards in
the source offline dataset, can acquire an adaptive policy for the target
environment and yet significantly reduce the requirement of target offline
data. With only modest amounts of target offline data, our performance
consistently outperforms the prior offline RL methods in both simulated and
real-world tasks.

A desirable dialog system should be able to continually learn new skills
without forgetting old ones, and thereby adapt to new domains or tasks in its
life cycle. However, continually training a model often leads to a well-known
catastrophic forgetting issue. In this paper, we present Continual Prompt
Tuning, a parameter-efficient framework that not only avoids forgetting but
also enables knowledge transfer between tasks. To avoid forgetting, we only
learn and store a few prompt tokens' embeddings for each task while freezing
the backbone pre-trained model. To achieve bi-directional knowledge transfer
among tasks, we propose several techniques (continual prompt initialization,
query fusion, and memory replay) to transfer knowledge from preceding tasks and
a memory-guided technique to transfer knowledge from subsequent tasks.
Extensive experiments demonstrate the effectiveness and efficiency of our
proposed method on continual learning for dialog state tracking, compared with

Most representative decision tree ensemble methods have been used to examine
the variable importance of Treasury term spreads to predict US economic
recessions with a balance of generating rules for US economic recession
detection. A strategy is proposed for training the classifiers with Treasury
term spreads data and the results are compared in order to select the best
model for interpretability. We also discuss the use of SHapley Additive
exPlanations (SHAP) framework to understand US recession forecasts by analyzing
feature importance. Consistently with the existing literature we find the most
relevant Treasury term spreads for predicting US economic recession and a
methodology for detecting relevant rules for economic recession detection. In
this case, the most relevant term spread found is 3 month to 6 month, which is
proposed to be monitored by economic authorities. Finally, the methodology
detected rules with high lift on predicting economic recession that can be used
by these entities for this propose. This latter result stands in contrast to a
growing body of literature demonstrating that machine learning methods are
useful for interpretation comparing many alternative algorithms and we discuss
the interpretation for our result and propose further research lines aligned
with this work.

Most of the research in the recommender systems domain is focused on the
optimization of the metrics based on historical data such as Mean Average
Precision (MAP) or Recall. However, there is a gap between the research and
industry since the leading Key Performance Indicators (KPIs) for businesses are
revenue and profit. In this paper, we explore the impact of manipulating the
profit awareness of a recommender system. An average e-commerce business does
not usually use a complicated recommender algorithm. We propose an adjustment
of a predicted ranking for score-based recommender systems and explore the
effect of the profit and customers' price preferences on two industry datasets
from the fashion domain. In the experiments, we show the ability to improve
both the precision and the generated recommendations' profit. Such an outcome
represents a win-win situation when e-commerce increases the profit and
customers get more valuable recommendations.

There is literature that questions the veracity of the studies commissioned
by the transnational tobacco companies (TTC) to measure the illicit tobacco
trade. Furthermore, there are studies that indicate that the Empty Pack Surveys
(EPS) ordered by the TTCs are oversized. The novelty of this study is that, in
addition to detecting the anomalies analyzed in the EPSs, there are provinces
in which cigarette sales are higher than reasonable values, something that the
TTCs ignore. This study analyzed simultaneously, firstly, if the EPSs
established in each of the 47 Spanish provinces were fulfilled. Second,
anomalies observed in provinces where sales exceed expected values are
measured. To achieve the objective of the paper, provincial data on cigarette
sales, price and GDP per capita are used. These data are modeled with machine
learning techniques widely used to detect anomalies in other areas. The results
reveal that the provinces in which sales below reasonable values are observed
(as detected by the EPSs) present a clear geographical pattern. Furthermore,
the values provided by the EPSs in Spain, as indicated in the previous
literature, are slightly oversized. Finally, there are regions bordering other
countries or with a high tourist influence in which the observed sales are
higher than the expected values.

In this work, we revisit the semi-supervised learning (SSL) problem from a
new perspective of explicitly reducing empirical distribution mismatch between
labeled and unlabeled samples. Benefited from this new perspective, we first
propose a new deep semi-supervised learning framework called Semi-supervised
Learning by Empirical Distribution Alignment (SLEDA), in which existing
technologies from the domain adaptation community can be readily used to
address the semi-supervised learning problem through reducing the empirical
distribution distance between labeled and unlabeled data. Based on this
framework, we also develop a new theoretical generalization bound for the
research community to better understand the semi-supervised learning problem,
in which we show the generalization error of semi-supervised learning can be
effectively bounded by minimizing the training error on labeled data and the
empirical distribution distance between labeled and unlabeled data. Building
upon our new framework and the theoretical bound, we develop a simple and
effective deep semi-supervised learning method called Augmented Distribution
Alignment Network (ADA-Net) by simultaneously adopting the well-established
adversarial training strategy from the domain adaptation community and a simple
sample interpolation strategy for data augmentation. Additionally, we
incorporate both strategies in our ADA-Net into two exiting SSL methods to
further improve their generalization capability, which indicates that our new
framework provides a complementary solution for solving the SSL problem. Our
comprehensive experimental results on two benchmark datasets SVHN and CIFAR-10
for the semi-supervised image recognition task and another two benchmark
datasets ModelNet40 and ShapeNet55 for the semi-supervised point cloud
recognition task demonstrate the effectiveness of our proposed framework for
SSL.

Powered by the simplicity of lock-free asynchrony, Hogwilld! is a go-to
approach to parallelize SGD over a shared-memory setting. Despite its
popularity and concomitant extensions, such as PASSM+ wherein concurrent
processes update a shared model with partitioned gradients, scaling it to
decentralized workers has surprisingly been relatively unexplored. To our
knowledge, there is no convergence theory of such methods, nor systematic
numerical comparisons evaluating speed-up.
  In this paper, we propose an algorithm incorporating decentralized
distributed memory computing architecture with each node running
multiprocessing parallel shared-memory SGD itself. Our scheme is based on the
following algorithmic tools and features: (a) asynchronous local gradient
updates on the shared-memory of workers, (b) partial backpropagation, and (c)
non-blocking in-place averaging of the local models. We prove that our method
guarantees ergodic convergence rates for non-convex objectives. On the
practical side, we show that the proposed method exhibits improved throughput
and competitive accuracy for standard image classification benchmarks on the
CIFAR-10, CIFAR-100, and Imagenet datasets. Our code is available at
https://github.com/bapi/LPP-SGD.

Semantic communication has witnessed a great progress with the development of
natural language processing (NLP) and deep learning (DL). Although existing
semantic communication technologies can effectively reduce errors in semantic
interpretation, most of these solutions adopt a fixed bit length structure,
along with a rigid transmission scheme, which is inefficient and lacks
scalability when faced with different meanings and signal-to-noise ratio (SNR)
conditions. In this paper, we explore the impact of adaptive bit lengths on
semantic coding (SC) under various channel conditions. First, we propose
progressive semantic hybrid automatic repeat request (HARQ) schemes that
utilize incremental knowledge (IK) to simultaneously reduce the communication
cost and semantic error. On top of this, we design a novel semantic encoding
solution with multi-bit length selection. In this fashion, the transmitter
employs a policy network to decide the appropriate coding rate, so as to secure
the correct information delivery at the cost of minimal bits. Moreover, a
specific denoiser is further introduced to reduce the semantic errors
encountered in the transmission process according to the semantic
characteristics of context. Extensive simulation results have been conducted to
verify the effectiveness of the proposed solution.

Synthesizing optimal controllers for dynamical systems often involves solving
optimization problems with hard real-time constraints. These constraints
determine the class of numerical methods that can be applied: computationally
expensive but accurate numerical routines are replaced by fast and inaccurate
methods, trading inference time for solution accuracy. This paper provides
techniques to improve the quality of optimized control policies given a fixed
computational budget. We achieve the above via a hypersolvers approach, which
hybridizes a differential equation solver and a neural network. The performance
is evaluated in direct and receding-horizon optimal control tasks in both low
and high dimensions, where the proposed approach shows consistent Pareto
improvements in solution accuracy and control performance.

Data-driven building energy prediction is an integral part of the process for
measurement and verification, building benchmarking, and building-to-grid
interaction. The ASHRAE Great Energy Predictor III (GEPIII) machine learning
competition used an extensive meter data set to crowdsource the most accurate
machine learning workflow for whole building energy prediction. A significant
component of the winning solutions was the pre-processing phase to remove
anomalous training data. Contemporary pre-processing methods focus on filtering
statistical threshold values or deep learning methods requiring training data
and multiple hyper-parameters. A recent method named ALDI (Automated Load
profile Discord Identification) managed to identify these discords using matrix
profile, but the technique still requires user-defined parameters. We develop
ALDI++, a method based on the previous work that bypasses user-defined
parameters and takes advantage of discord similarity. We evaluate ALDI++
against a statistical threshold, variational auto-encoder, and the original
ALDI as baselines in classifying discords and energy forecasting scenarios. Our
results demonstrate that while the classification performance improvement over
the original method is marginal, ALDI++ helps achieve the best forecasting
error improving 6% over the winning's team approach with six times less

The topic of robustness is experiencing a resurgence of interest in the
statistical and machine learning communities. In particular, robust algorithms
making use of the so-called median of means estimator were shown to satisfy
strong performance guarantees for many problems, including estimation of the
mean, covariance structure as well as linear regression. In this work, we
propose an extension of the median of means principle to the Bayesian
framework, leading to the notion of the robust posterior distribution. In
particular, we (a) quantify robustness of this posterior to outliers, (b) show
that it satisfies a version of the Bernstein-von Mises theorem that connects
Bayesian credible sets to the traditional confidence intervals, and (c)
demonstrate that our approach performs well in applications.

Adversarial training (AT) is always formulated as a minimax problem, of which
the performance depends on the inner optimization that involves the generation
of adversarial examples (AEs). Most previous methods adopt Projected Gradient
Decent (PGD) with manually specifying attack parameters for AE generation. A
combination of the attack parameters can be referred to as an attack strategy.
Several works have revealed that using a fixed attack strategy to generate AEs
during the whole training phase limits the model robustness and propose to
exploit different attack strategies at different training stages to improve
robustness. But those multi-stage hand-crafted attack strategies need much
domain expertise, and the robustness improvement is limited. In this paper, we
propose a novel framework for adversarial training by introducing the concept
of "learnable attack strategy", dubbed LAS-AT, which learns to automatically
produce attack strategies to improve the model robustness. Our framework is
composed of a target network that uses AEs for training to improve robustness
and a strategy network that produces attack strategies to control the AE
generation. Experimental evaluations on three benchmark databases demonstrate
the superiority of the proposed method. The code is released at
https://github.com/jiaxiaojunQAQ/LAS-AT.

The problem of learning a channel decoder is considered for two channel
models. The first model is an additive noise channel whose noise distribution
is unknown and nonparametric. The learner is provided with a fixed codebook and
a dataset comprised of independent samples of the noise, and is required to
select a precision matrix for a nearest neighbor decoder in terms of the
Mahalanobis distance. The second model is a non-linear channel with additive
white Gaussian noise and unknown channel transformation. The learner is
provided with a fixed codebook and a dataset comprised of independent
input-output samples of the channel, and is required to select a matrix for a
nearest neighbor decoder with a linear kernel. For both models, the objective
of maximizing the margin of the decoder is addressed. Accordingly, for each
channel model, a regularized loss minimization problem with a codebook-related
regularization term and hinge-like loss function is developed, which is
inspired by the support vector machine paradigm for classification problems.
Expected generalization error bounds for the error probability loss function
are provided for both models, under optimal choice of the regularization
parameter. For the additive noise channel, a theoretical guidance for choosing
the training signal-to-noise ratio is proposed based on this bound. In
addition, for the non-linear channel, a high probability uniform generalization
error bound is provided for the hypothesis class. For each channel, a
stochastic sub-gradient descent algorithm for solving the regularized loss
minimization problem is proposed, and an optimization error bound is stated.
The performance of the proposed algorithms is demonstrated through several
examples.

Video detection and human action recognition may be computationally
expensive, and need a long time to train models. In this paper, we were
intended to reduce the training time and the GPU memory usage of video
detection, and achieved a competitive detection accuracy. Other research works
such as Two-stream, C3D, TSN have shown excellent performance on UCF101. Here,
we used a LSTM structure simply for video detection. We used a simple structure
to perform a competitive top-1 accuracy on the entire validation dataset of
UCF101. The LSTM structure is named Context-LSTM, since it may process the deep
temporal features. The Context-LSTM may simulate the human recognition system.
We cascaded the LSTM blocks in PyTorch and connected the cell state flow and
hidden output flow. At the connection of the blocks, we used ReLU, Batch
Normalization, and MaxPooling functions. The Context-LSTM could reduce the
training time and the GPU memory usage, while keeping a state-of-the-art top-1
accuracy on UCF101 entire validation dataset, show a robust performance on
video action detection.

In frequency-division duplexing (FDD) massive multiple-input multiple-output
(MIMO) systems, 1-bit compressed sensing (CS)-based superimposed channel state
information (CSI) feedback has shown many advantages, while still faces many
challenges, such as low accuracy of the downlink CSI recovery and large
processing delays. To overcome these drawbacks, this paper proposes a deep
learning (DL) scheme to improve the 1-bit compressed sensing-based superimposed
CSI feedback. On the user side, the downlink CSI is compressed with the 1-bit
CS technique, superimposed on the uplink user data sequences (UL-US), and then
sent back to the base station (BS). At the BS, based on the model-driven
approach and assisted by the superimposition-interference cancellation
technology, a multi-task detection network is first constructed for detecting
both the UL-US and downlink CSI. In particular, this detection network is
jointly trained to detect the UL-US and downlink CSI simultaneously, capturing
a globally optimized network parameter. Then, with the recovered bits for the
downlink CSI, a lightweight reconstruction scheme, which consists of an initial
feature extraction of the downlink CSI with the simplified traditional method
and a single hidden layer network, is utilized to reconstruct the downlink CSI
with low processing delay. Compared with the 1-bit CS-based superimposed CSI
feedback scheme, the proposed scheme improves the recovery accuracy of the
UL-US and downlink CSI with lower processing delay and possesses robustness
against parameter variations.

Talking head video generation aims to produce a synthetic human face video
that contains the identity and pose information respectively from a given
source image and a driving video.Existing works for this task heavily rely on
2D representations (e.g. appearance and motion) learned from the input images.
However, dense 3D facial geometry (e.g. pixel-wise depth) is extremely
important for this task as it is particularly beneficial for us to essentially
generate accurate 3D face structures and distinguish noisy information from the
possibly cluttered background. Nevertheless, dense 3D geometry annotations are
prohibitively costly for videos and are typically not available for this video
generation task. In this paper, we first introduce a self-supervised geometry
learning method to automatically recover the dense 3D geometry (i.e.depth) from
the face videos without the requirement of any expensive 3D annotation data.
Based on the learned dense depth maps, we further propose to leverage them to
estimate sparse facial keypoints that capture the critical movement of the
human head. In a more dense way, the depth is also utilized to learn 3D-aware
cross-modal (i.e. appearance and depth) attention to guide the generation of
motion fields for warping source image representations. All these contributions
compose a novel depth-aware generative adversarial network (DaGAN) for talking
head generation. Extensive experiments conducted demonstrate that our proposed
method can generate highly realistic faces, and achieve significant results on
the unseen human faces.

As a promising scheme of self-supervised learning, masked autoencoding has
significantly advanced natural language processing and computer vision.
Inspired by this, we propose a neat scheme of masked autoencoders for point
cloud self-supervised learning, addressing the challenges posed by point
cloud's properties, including leakage of location information and uneven
information density. Concretely, we divide the input point cloud into irregular
point patches and randomly mask them at a high ratio. Then, a standard
Transformer based autoencoder, with an asymmetric design and a shifting mask
tokens operation, learns high-level latent features from unmasked point
patches, aiming to reconstruct the masked point patches. Extensive experiments
show that our approach is efficient during pre-training and generalizes well on
various downstream tasks. Specifically, our pre-trained models achieve 85.18%
accuracy on ScanObjectNN and 94.04% accuracy on ModelNet40, outperforming all
the other self-supervised learning methods. We show with our scheme, a simple
architecture entirely based on standard Transformers can surpass dedicated
Transformer models from supervised learning. Our approach also advances
state-of-the-art accuracies by 1.5%-2.3% in the few-shot object classification.
Furthermore, our work inspires the feasibility of applying unified
architectures from languages and images to the point cloud.

Extracting cause-effect entities from medical literature is an important task
in medical information retrieval. A solution for solving this task can be used
for compilation of various causality relations, such as, causality between
disease and symptoms, between medications and side effects, between genes and
diseases, etc. Existing solutions for extracting cause-effect entities work
well for sentences where the cause and the effect phrases are name entities,
single-word nouns, or noun phrases consisting of two to three words.
Unfortunately, in medical literature, cause and effect phrases in a sentence
are not simply nouns or noun phrases, rather they are complex phrases
consisting of several words, and existing methods fail to correctly extract the
cause and effect entities in such sentences. Partial extraction of cause and
effect entities conveys poor quality, non informative, and often, contradictory
facts, comparing to the one intended in the given sentence. In this work, we
solve this problem by designing an unsupervised method for cause and effect
phrase extraction, PatternCausality, which is specifically suitable for the
medical literature. Our proposed approach first uses a collection of
cause-effect dependency patterns as template to extract head words of cause and
effect phrases and then it uses a novel phrase extraction method to obtain
complete and meaningful cause and effect phrases from a sentence. Experiments
on a cause-effect dataset built from sentences from PubMed articles show that
for extracting cause and effect entities, PatternCausality is substantially
better than the existing methods with an order of magnitude improvement in the
F-score metric over the best of the existing methods.

Query similarity prediction task is generally solved by regression based
models with square loss. Such a model is agnostic of absolute similarity values
and it penalizes the regression error at all ranges of similarity values at the
same scale. However, to boost e-commerce platform's monetization, it is
important to predict high-level similarity more accurately than low-level
similarity, as highly similar queries retrieves items according to
user-intents, whereas moderately similar item retrieves related items, which
may not lead to a purchase. Regression models fail to customize its loss
function to concentrate around the high-similarity band, resulting poor
performance in query similarity prediction task. We address the above challenge
by considering the query prediction as an ordinal regression problem, and
thereby propose a model, ORDSIM (ORDinal Regression for SIMilarity Prediction).
ORDSIM exploits variable-width buckets to model ordinal loss, which penalizes
errors in high-level similarity harshly, and thus enable the regression model
to obtain better prediction results for high similarity values. We evaluate
ORDSIM on a dataset of over 10 millions e-commerce queries from eBay platform
and show that ORDSIM achieves substantially smaller prediction error compared
to the competing regression methods on this dataset.

As a remarkable compact model, ShuffleNetV2 offers a good example to design
efficient ConvNets but its limit is rarely noticed. In this paper, we rethink
the design pattern of ShuffleNetV2 and find that the channel-wise redundancy
problem still constrains the efficiency improvement of Shuffle block in the
wider ShuffleNetV2. To resolve this issue, we propose another augmented variant
of shuffle block in the form of bottleneck-like structure and more implicit
short connections. To verify the effectiveness of this building block, we
further build a more powerful and efficient model family, termed as
AugShuffleNets. Evaluated on the CIFAR-10 and CIFAR-100 datasets, AugShuffleNet
consistently outperforms ShuffleNetV2 in terms of accuracy with less
computational cost and fewer parameter count.

In high-stake scenarios like medical treatment and auto-piloting, it's risky
or even infeasible to collect online experimental data to train the agent.
Simulation-based training can alleviate this issue, but may suffer from its
inherent mismatches from the simulator and real environment. It is therefore
imperative to utilize the simulator to learn a robust policy for the real-world
deployment. In this work, we consider policy learning for Robust Markov
Decision Processes (RMDP), where the agent tries to seek a robust policy with
respect to unexpected perturbations on the environments. Specifically, we focus
on the setting where the training environment can be characterized as a
generative model and a constrained perturbation can be added to the model
during testing. Our goal is to identify a near-optimal robust policy for the
perturbed testing environment, which introduces additional technical
difficulties as we need to simultaneously estimate the training environment
uncertainty from samples and find the worst-case perturbation for testing. To
solve this issue, we propose a generic method which formalizes the perturbation
as an opponent to obtain a two-player zero-sum game, and further show that the
Nash Equilibrium corresponds to the robust policy. We prove that, with a
polynomial number of samples from the generative model, our algorithm can find
a near-optimal robust policy with a high probability. Our method is able to
deal with general perturbations under some mild assumptions and can also be
extended to more complex problems like robust partial observable Markov
decision process, thanks to the game-theoretical formulation.

In recent years 3D object detection from LiDAR point clouds has made great
progress thanks to the development of deep learning technologies. Although
voxel or point based methods are popular in 3D object detection, they usually
involve time-consuming operations such as 3D convolutions on voxels or ball
query among points, making the resulting network inappropriate for time
critical applications. On the other hand, 2D view-based methods feature high
computing efficiency while usually obtaining inferior performance than the
voxel or point based methods. In this work, we present a real-time view-based
single stage 3D object detector, namely CVFNet to fulfill this task. To
strengthen the cross-view feature learning under the condition of demanding
efficiency, our framework extracts the features of different views and fuses
them in an efficient progressive way. We first propose a novel Point-Range
feature fusion module that deeply integrates point and range view features in
multiple stages. Then, a special Slice Pillar is designed to well maintain the
3D geometry when transforming the obtained deep point-view features into bird's
eye view. To better balance the ratio of samples, a sparse pillar detection
head is presented to focus the detection on the nonempty grids. We conduct
experiments on the popular KITTI and NuScenes benchmark, and state-of-the-art
performances are achieved in terms of both accuracy and speed.

The impact of Music on the mood or emotion of the listener is a
well-researched area in human psychology and behavioral science. In Indian
classical music, ragas are the melodic structure that defines the various
styles and forms of the music. Each raga has been found to evoke a specific
emotion in the listener. With the advent of advanced capabilities of audio
signal processing and the application of machine learning, the demand for
intelligent music classifiers and recommenders has received increased
attention, especially in the 'Music as a service' cloud applications. This
paper explores a novel framework to leverage the raga-rasa association in
Indian classical Music to build an intelligent classifier and its application
in music recommendation system based on user's current mood and the mood they
aspire to be in.

Machine learning models are vulnerable to data inference attacks, such as
membership inference and model inversion attacks. In these types of breaches,
an adversary attempts to infer a data record's membership in a dataset or even
reconstruct this data record using a confidence score vector predicted by the
target model. However, most existing defense methods only protect against
membership inference attacks. Methods that can combat both types of attacks
require a new model to be trained, which may not be time-efficient. In this
paper, we propose a differentially private defense method that handles both
types of attacks in a time-efficient manner by tuning only one parameter, the
privacy budget. The central idea is to modify and normalize the confidence
score vectors with a differential privacy mechanism which preserves privacy and
obscures membership and reconstructed data. Moreover, this method can guarantee
the order of scores in the vector to avoid any loss in classification accuracy.
The experimental results show the method to be an effective and timely defense
against both membership inference and model inversion attacks with no reduction

Recent studies on Learning to Optimize (L2O) suggest a promising path to
automating and accelerating the optimization procedure for complicated tasks.
Existing L2O models parameterize optimization rules by neural networks, and
learn those numerical rules via meta-training. However, they face two common
pitfalls: (1) scalability: the numerical rules represented by neural networks
create extra memory overhead for applying L2O models, and limit their
applicability to optimizing larger tasks; (2) interpretability: it is unclear
what an L2O model has learned in its black-box optimization rule, nor is it
straightforward to compare different L2O models in an explainable way. To avoid
both pitfalls, this paper proves the concept that we can "kill two birds by one
stone", by introducing the powerful tool of symbolic regression to L2O. In this
paper, we establish a holistic symbolic representation and analysis framework
for L2O, which yields a series of insights for learnable optimizers. Leveraging
our findings, we further propose a lightweight L2O model that can be
meta-trained on large-scale problems and outperformed human-designed and tuned
optimizers. Our work is set to supply a brand-new perspective to L2O research.
Codes are available at:
https://github.com/VITA-Group/Symbolic-Learning-To-Optimize.

Few-shot recognition learns a recognition model with very few (e.g., 1 or 5)
images per category, and current few-shot learning methods focus on improving
the average accuracy over many episodes. We argue that in real-world
applications we may often only try one episode instead of many, and hence
maximizing the worst-case accuracy is more important than maximizing the
average accuracy. We empirically show that a high average accuracy not
necessarily means a high worst-case accuracy. Since this objective is not
accessible, we propose to reduce the standard deviation and increase the
average accuracy simultaneously. In turn, we devise two strategies from the
bias-variance tradeoff perspective to implicitly reach this goal: a simple yet
effective stability regularization (SR) loss together with model ensemble to
reduce variance during fine-tuning, and an adaptability calibration mechanism
to reduce the bias. Extensive experiments on benchmark datasets demonstrate the
effectiveness of the proposed strategies, which outperforms current
state-of-the-art methods with a significant margin in terms of not only
average, but also worst-case accuracy. Our code is available at
https://github.com/heekhero/ACSR.

Transfer learning is an important approach that produces pre-trained teacher
models which can be used to quickly build specialized student models. However,
recent research on transfer learning has found that it is vulnerable to various
attacks, e.g., misclassification and backdoor attacks. However, it is still not
clear whether transfer learning is vulnerable to model inversion attacks.
Launching a model inversion attack against transfer learning scheme is
challenging. Not only does the student model hide its structural parameters,
but it is also inaccessible to the adversary. Hence, when targeting a student
model, both the white-box and black-box versions of existing model inversion
attacks fail. White-box attacks fail as they need the target model's
parameters. Black-box attacks fail as they depend on making repeated queries of
the target model. However, they may not mean that transfer learning models are
impervious to model inversion attacks. Hence, with this paper, we initiate
research into model inversion attacks against transfer learning schemes with
two novel attack methods. Both are black-box attacks, suiting different
situations, that do not rely on queries to the target student model. In the
first method, the adversary has the data samples that share the same
distribution as the training set of the teacher model. In the second method,
the adversary does not have any such samples. Experiments show that highly
recognizable data records can be recovered with both of these methods. This
means that even if a model is an inaccessible black-box, it can still be
inverted.

Sequence-to-sequence neural networks have recently achieved great success in
abstractive summarization, especially through fine-tuning large pre-trained
language models on the downstream dataset. These models are typically decoded
with beam search to generate a unique summary. However, the search space is
very large, and with the exposure bias, such decoding is not optimal. In this
paper, we show that it is possible to directly train a second-stage model
performing re-ranking on a set of summary candidates. Our mixture-of-experts
SummaReranker learns to select a better candidate and consistently improves the
performance of the base model. With a base PEGASUS, we push ROUGE scores by
5.44% on CNN-DailyMail (47.16 ROUGE-1), 1.31% on XSum (48.12 ROUGE-1) and 9.34%
on Reddit TIFU (29.83 ROUGE-1), reaching a new state-of-the-art. Our code and
checkpoints will be available at https://github.com/ntunlp/SummaReranker.

Adversarial attacks have been extensively studied in recent years since they
can identify the vulnerability of deep learning models before deployed. In this
paper, we consider the black-box adversarial setting, where the adversary needs
to craft adversarial examples without access to the gradients of a target
model. Previous methods attempted to approximate the true gradient either by
using the transfer gradient of a surrogate white-box model or based on the
feedback of model queries. However, the existing methods inevitably suffer from
low attack success rates or poor query efficiency since it is difficult to
estimate the gradient in a high-dimensional input space with limited
information. To address these problems and improve black-box attacks, we
propose two prior-guided random gradient-free (PRGF) algorithms based on biased
sampling and gradient averaging, respectively. Our methods can take the
advantage of a transfer-based prior given by the gradient of a surrogate model
and the query information simultaneously. Through theoretical analyses, the
transfer-based prior is appropriately integrated with model queries by an
optimal coefficient in each method. Extensive experiments demonstrate that, in
comparison with the alternative state-of-the-arts, both of our methods require
much fewer queries to attack black-box models with higher success rates.

Training a generalizable 3D part segmentation network is quite challenging
but of great importance in real-world applications. To tackle this problem,
some works design task-specific solutions by translating human understanding of
the task to machine's learning process, which faces the risk of missing the
optimal strategy since machines do not necessarily understand in the exact
human way. Others try to use conventional task-agnostic approaches designed for
domain generalization problems with no task prior knowledge considered. To
solve the above issues, we propose AutoGPart, a generic method enabling
training generalizable 3D part segmentation networks with the task prior
considered. AutoGPart builds a supervision space with geometric prior knowledge
encoded, and lets the machine to search for the optimal supervisions from the
space for a specific segmentation task automatically. Extensive experiments on
three generalizable 3D part segmentation tasks are conducted to demonstrate the
effectiveness and versatility of AutoGPart. We demonstrate that the performance
of segmentation networks using simple backbones can be significantly improved
when trained with supervisions searched by our method.

The automotive mmWave radar plays a key role in advanced driver assistance
systems (ADAS) and autonomous driving. Deep learning-based instance
segmentation enables real-time object identification from the radar detection
points. In the conventional training process, accurate annotation is the key.
However, high-quality annotations of radar detection points are challenging to
achieve due to their ambiguity and sparsity. To address this issue, we propose
a contrastive learning approach for implementing radar detection points-based
instance segmentation. We define the positive and negative samples according to
the ground-truth label, apply the contrastive loss to train the model first,
and then perform fine-tuning for the following downstream task. In addition,
these two steps can be merged into one, and pseudo labels can be generated for
the unlabeled data to improve the performance further. Thus, there are four
different training settings for our method. Experiments show that when the
ground-truth information is only available for a small proportion of the
training data, our method still achieves a comparable performance to the
approach trained in a supervised manner with 100% ground-truth information.

Data augmentation has been proved effective in training deep models. Existing
data augmentation methods tackle the fine-grained problem by blending image
pairs and fusing corresponding labels according to the statistics of mixed
pixels, which produces additional noise harmful to the performance of networks.
Motivated by this, we present a simple yet effective cross ensemble knowledge
distillation (CEKD) model for fine-grained feature learning. We innovatively
propose a cross distillation module to provide additional supervision to
alleviate the noise problem, and propose a collaborative ensemble module to
overcome the target conflict problem. The proposed model can be trained in an
end-to-end manner, and only requires image-level label supervision. Extensive
experiments on widely used fine-grained benchmarks demonstrate the
effectiveness of our proposed model. Specifically, with the backbone of
ResNet-101, CEKD obtains the accuracy of 89.59%, 95.96% and 94.56% in three
datasets respectively, outperforming state-of-the-art API-Net by 0.99%, 1.06%
and 1.16%.

Mobile user profiling refers to the efforts of extracting users'
characteristics from mobile activities. In order to capture the dynamic varying
of user characteristics for generating effective user profiling, we propose an
imitation-based mobile user profiling framework. Considering the objective of
teaching an autonomous agent to imitate user mobility based on the user's
profile, the user profile is the most accurate when the agent can perfectly
mimic the user behavior patterns. The profiling framework is formulated into a
reinforcement learning task, where an agent is a next-visit planner, an action
is a POI that a user will visit next, and the state of the environment is a
fused representation of a user and spatial entities. An event in which a user
visits a POI will construct a new state, which helps the agent predict users'
mobility more accurately. In the framework, we introduce a spatial Knowledge
Graph (KG) to characterize the semantics of user visits over connected spatial
entities. Additionally, we develop a mutual-updating strategy to quantify the
state that evolves over time. Along these lines, we develop a reinforcement
imitative graph learning framework for mobile user profiling. Finally, we
conduct extensive experiments to demonstrate the superiority of our approach.

Benefited from the rapid and sustainable development of synthetic aperture
radar (SAR) sensors, change detection from SAR images has received increasing
attentions over the past few years. Existing unsupervised deep learning-based
methods have made great efforts to exploit robust feature representations, but
they consume much time to optimize parameters. Besides, these methods use
clustering to obtain pseudo-labels for training, and the pseudo-labeled samples
often involve errors, which can be considered as "label noise". To address
these issues, we propose a Dual Path Denoising Network (DPDNet) for SAR image
change detection. In particular, we introduce the random label propagation to
clean the label noise involved in preclassification. We also propose the
distinctive patch convolution for feature representation learning to reduce the
time consumption. Specifically, the attention mechanism is used to select
distinctive pixels in the feature maps, and patches around these pixels are
selected as convolution kernels. Consequently, the DPDNet does not require a
great number of training samples for parameter optimization, and its
computational efficiency is greatly enhanced. Extensive experiments have been
conducted on five SAR datasets to verify the proposed DPDNet. The experimental
results demonstrate that our method outperforms several state-of-the-art
methods in change detection results.

Heatmap regression methods have dominated face alignment area in recent years
while they ignore the inherent relation between different landmarks. In this
paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the
inherent relation. The SLPT generates the representation of each single
landmark from a local patch and aggregates them by an adaptive inherent
relation based on the attention mechanism. The subpixel coordinate of each
landmark is predicted independently based on the aggregated feature. Moreover,
a coarse-to-fine framework is further introduced to incorporate with the SLPT,
which enables the initial landmarks to gradually converge to the target facial
landmarks using fine-grained features from dynamically resized local patches.
Extensive experiments carried out on three popular benchmarks, including WFLW,
300W and COFW, demonstrate that the proposed method works at the
state-of-the-art level with much less computational complexity by learning the
inherent relation between facial landmarks. The code is available at the
project website.

We show that pooling countries across a panel dimension to macroeconomic data
can improve by a statistically significant margin the generalization ability of
structural, reduced form, and machine learning (ML) methods to produce
state-of-the-art results. Using GDP forecasts evaluated on an out-of-sample
test set, this procedure reduces root mean squared error by 12\% across
horizons and models for certain reduced-form models and by 24\% across horizons
for dynamic structural general equilibrium models. Removing US data from the
training set and forecasting out-of-sample country-wise, we show that
reduced-form and structural models are more policy-invariant when trained on
pooled data, and outperform a baseline that uses US data only. Given the
comparative advantage of ML models in a data-rich regime, we demonstrate that
our recurrent neural network model and automated ML approach outperform all
tested baseline economic models. Robustness checks indicate that our
outperformance is reproducible, numerically stable, and generalizable across
models.

I develop a numerical algorithm for stochastic impulse control in the spirit
of Regression Monte Carlo for optimal stopping. The approach consists in
generating statistical surrogates (aka functional approximators) for the
continuation function. The surrogates are recursively trained by empirical
regression over simulated state trajectories. In parallel, the same surrogates
are used to learn the intervention function characterizing the optimal impulse
amounts. I discuss appropriate surrogate types for this task, as well as the
choice of training sets. Case studies from forest rotation and irreversible
investment illustrate the numerical scheme and highlight its flexibility and
extensibility. Implementation in \texttt{R} is provided as a publicly available
package posted on GitHub.

For diagnosis of shoulder illness, it is essential to look at the morphology
deviation of scapula and humerus from the medical images that are acquired from
Magnetic Resonance (MR) imaging. However, taking high-resolution MR images is
time-consuming and costly because the reduction of the physical distance
between image slices causes prolonged scanning time. Moreover, due to the lack
of training images, images from various sources must be utilized, which creates
the issue of high variance across the dataset. Also, there are human errors
among the images due to the fact that it is hard to take the spatial
relationship into consideration when labeling the 3D image in low resolution.
In order to combat all obstacles stated above, we develop a fully automated
algorithm for segmenting the humerus and scapula bone from coarsely scanned and
low-resolution MR images and a recursive learning framework that iterative
utilize the generated labels for reducing the errors among segmentations and
increase our dataset set for training the next round network. In this study, 50
MR images are collected from several institutions and divided into five
mutually exclusive sets for carrying five-fold cross-validation. Contours that
are generated by the proposed method demonstrated a high level of accuracy when
compared with ground truth and the traditional method. The proposed neural
network and the recursive learning scheme improve the overall quality of the
segmentation on humerus and scapula on the low-resolution dataset and reduced
incorrect segmentation in the ground truth, which could have a positive impact
on finding the cause of shoulder pain and patient's early relief.

This paper proposes a simulation-based deep learning Bayesian procedure for
the estimation of macroeconomic models. This approach is able to derive
posteriors even when the likelihood function is not tractable. Because the
likelihood is not needed for Bayesian estimation, filtering is also not needed.
This allows Bayesian estimation of HANK models with upwards of 800 latent
states as well as estimation of representative agent models that are solved
with methods that don't yield a likelihood--for example, projection and value
function iteration approaches. I demonstrate the validity of the approach by
estimating a 10 parameter HANK model solved via the Reiter method that
generates 812 covariates per time step, where 810 are latent variables, showing
this can handle a large latent space without model reduction. I also estimate
the algorithm with an 11-parameter model solved via value function iteration,
which cannot be estimated with Metropolis-Hastings or even conventional maximum
likelihood estimators. In addition, I show the posteriors estimated on
Smets-Wouters 2007 are higher quality and faster using simulation-based
inference compared to Metropolis-Hastings. This approach helps address the
computational expense of Metropolis-Hastings and allows solution methods which
don't yield a tractable likelihood to be estimated.

The Hidden Markov Model (HMM) is one of the most widely used statistical
models for sequential data analysis, and it has been successfully applied in a
large variety of domains. One of the key reasons for this versatility is the
ability of HMMs to deal with missing data. However, standard HMM learning
algorithms rely crucially on the assumption that the positions of the missing
observations within the observation sequence are known. In some situations
where such assumptions are not feasible, a number of special algorithms have
been developed. Currently, these algorithms rely strongly on specific
structural assumptions of the underlying chain, such as acyclicity, and are not
applicable in the general case. In particular, there are numerous domains
within medicine and computational biology, where the missing observation
locations are unknown and acyclicity assumptions do not hold, thus presenting a
barrier for the application of HMMs in those fields. In this paper we consider
a general problem of learning HMMs from data with unknown missing observation
locations (i.e., only the order of the non-missing observations are known). We
introduce a generative model of the location omissions and propose two learning
methods for this model, a (semi) analytic approach, and a Gibbs sampler. We
evaluate and compare the algorithms in a variety of scenarios, measuring their
reconstruction precision and robustness under model misspecification.

Continual/lifelong learning from a non-stationary input data stream is a
cornerstone of intelligence. Despite their phenomenal performance in a wide
variety of applications, deep neural networks are prone to forgetting their
previously learned information upon learning new ones. This phenomenon is
called "catastrophic forgetting" and is deeply rooted in the
stability-plasticity dilemma. Overcoming catastrophic forgetting in deep neural
networks has become an active field of research in recent years. In particular,
gradient projection-based methods have recently shown exceptional performance
at overcoming catastrophic forgetting. This paper proposes two
biologically-inspired mechanisms based on sparsity and heterogeneous dropout
that significantly increase a continual learner's performance over a long
sequence of tasks. Our proposed approach builds on the Gradient Projection
Memory (GPM) framework. We leverage k-winner activations in each layer of a
neural network to enforce layer-wise sparse activations for each task, together
with a between-task heterogeneous dropout that encourages the network to use
non-overlapping activation patterns between different tasks. In addition, we
introduce two new benchmarks for continual learning under distributional shift,
namely Continual Swiss Roll and ImageNet SuperDog-40. Lastly, we provide an
in-depth analysis of our proposed method and demonstrate a significant
performance boost on various benchmark continual learning problems.

The ongoing COVID-19 pandemic continues to affect communities around the
world. To date, almost 6 million people have died as a consequence of COVID-19,
and more than one-quarter of a billion people are estimated to have been
infected worldwide. The design of appropriate and timely mitigation strategies
to curb the effects of this and future disease outbreaks requires close
monitoring of their spatio-temporal trajectories. We present machine learning
methods to anticipate sharp increases in COVID-19 activity in US counties in
real-time. Our methods leverage Internet-based digital traces -- e.g.,
disease-related Internet search activity from the general population and
clinicians, disease-relevant Twitter micro-blogs, and outbreak trajectories
from neighboring locations -- to monitor potential changes in population-level
health trends. Motivated by the need for finer spatial-resolution
epidemiological insights to improve local decision-making, we build upon
previous retrospective research efforts originally conceived at the state level
and in the early months of the pandemic. Our methods -- tested in real-time and
in an out-of-sample manner on a subset of 97 counties distributed across the US
-- frequently anticipated sharp increases in COVID-19 activity 1-6 weeks before
the onset of local outbreaks (defined as the time when the effective
reproduction number $R_t$ becomes larger than 1 consistently). Given the
continued emergence of COVID-19 variants of concern -- such as the most recent
one, Omicron -- and the fact that multiple countries have not had full access
to vaccines, the framework we present, while conceived for the county-level in
the US, could be helpful in countries where similar data sources are available.

The application of machine learning (ML) libraries has been tremendously
increased in many domains, including autonomous driving systems, medical, and
critical industries. Vulnerabilities of such libraries result in irreparable
consequences. However, the characteristics of software security vulnerabilities
have not been well studied. In this paper, to bridge this gap, we take the
first step towards characterizing and understanding the security
vulnerabilities of five well-known ML libraries, including Tensorflow, PyTorch,
Sickit-learn, Pandas, and Numpy. To do so, in total, we collected 596
security-related commits to exploring five major factors: 1) vulnerability
types, 2) root causes, 3) symptoms, 4) fixing patterns, and 5) fixing efforts
of security vulnerabilities in ML libraries. The findings of this study can
assist developers in having a better understanding of software security
vulnerabilities across different ML libraries and gain a better insight into
their weaknesses of them. To make our finding actionable, we further developed
DeepMut, an automated mutation testing tool, as a proof-of-concept application
of our findings. DeepMut is designed to assess the adequacy of existing test
suites of ML libraries against security-aware mutation operators extracted from
the vulnerabilities studied in this work. We applied DeepMut on the Tensorflow
kernel module and found more than 1k alive mutants not considered by the
existing test suits. The results demonstrate the usefulness of our findings.

Predictive Virtual Machine (VM) auto-scaling is a promising technique to
optimize cloud applications operating costs and performance. Understanding the
job arrival rate is crucial for accurately predicting future changes in cloud
workloads and proactively provisioning and de-provisioning VMs for hosting the
applications. However, developing a model that accurately predicts cloud
workload changes is extremely challenging due to the dynamic nature of cloud
workloads. Long-Short-Term-Memory (LSTM) models have been developed for cloud
workload prediction. Unfortunately, the state-of-the-art LSTM model leverages
recurrences to predict, which naturally adds complexity and increases the
inference overhead as input sequences grow longer. To develop a cloud workload
prediction model with high accuracy and low inference overhead, this work
presents a novel time-series forecasting model called WGAN-gp Transformer,
inspired by the Transformer network and improved Wasserstein-GANs. The proposed
method adopts a Transformer network as a generator and a multi-layer perceptron
as a critic. The extensive evaluations with real-world workload traces show
WGAN-gp Transformer achieves 5 times faster inference time with up to 5.1
percent higher prediction accuracy against the state-of-the-art approach. We
also apply WGAN-gp Transformer to auto-scaling mechanisms on Google cloud
platforms, and the WGAN-gp Transformer-based auto-scaling mechanism outperforms
the LSTM-based mechanism by significantly reducing VM over-provisioning and
under-provisioning rates.

Recent arguments that machine learning (ML) is facing a reproducibility and
replication crisis suggest that some published claims in ML research cannot be
taken at face value. These concerns inspire analogies to the replication crisis
affecting the social and medical sciences. They also inspire calls for the
integration of statistical approaches to causal inference and predictive
modeling. A deeper understanding of what reproducibility concerns in supervised
ML research have in common with the replication crisis in experimental science
puts the new concerns in perspective, and helps researchers avoid "the worst of
both worlds," where ML researchers begin borrowing methodologies from
explanatory modeling without understanding their limitations and vice versa. We
contribute a comparative analysis of concerns about inductive learning that
arise in causal attribution as exemplified in psychology versus predictive
modeling as exemplified in ML. We identify themes that re-occur in reform
discussions, like overreliance on asymptotic theory and non-credible beliefs
about real-world data generating processes. We argue that in both fields,
claims from learning are implied to generalize outside the specific environment
studied (e.g., the input dataset or subject sample, modeling implementation,
etc.) but are often impossible to refute due to undisclosed sources of variance
in the learning pipeline. In particular, errors being acknowledged in ML expose
cracks in long-held beliefs that optimizing predictive accuracy using huge
datasets absolves one from having to consider a true data generating process or
formally represent uncertainty in performance claims. We conclude by discussing
risks that arise when sources of errors are misdiagnosed and the need to
acknowledge the role of human inductive biases in learning and reform.

The model-X conditional randomization test (CRT) proposed by Cand\`es et al.
(2018) is known as a flexible and powerful testing procedure for the
conditional independence hypothesis: X is independent of Y conditional on Z.
Though having many attractive properties, the model-X CRT relies on the model-X
assumption that we have access to perfect knowledge of the distribution of X
conditional on Z. If there is a specification error in modeling the
distribution of X conditional on Z, this approach may lose its validity. This
problem is even more severe when the adjustment covariates Z are of high
dimensionality, in which situation precise modeling of X against Z can be hard.
In response to this, we propose the Maxway (Model and Adjust X With the
Assistance of Y) CRT, a more robust inference approach for conditional
independence when the conditional distribution of X is unknown and needs to be
estimated from the data. Besides the distribution of X | Z, the Maxway CRT also
learns the distribution of Y | Z, using it to calibrate the resampling
distribution of X to gain robustness to the error in modeling X. We show that
the type-I error inflation of the Maxway CRT can be controlled by the learning
error for the low-dimensional adjusting model plus the product of learning
errors for the distribution of X | Z and the distribution of Y | Z. This result
can be interpreted as an "almost doubly robust" property of the Maxway CRT.
Through extensive simulation studies, we demonstrate that the Maxway CRT
achieves significantly better type-I error control than existing model-X
inference approaches while having similar power. Finally, we apply our
methodology to the UK biobank dataset with the goal of studying the
relationship between the functional SNP of statins and the risk for type II
diabetes mellitus.

Mixed-dimensional heterostructures composed of two-dimensional (2D) and
three-dimensional (3D) materials are undisputed next-generation materials for
engineered devices due to their changeable properties. The present work
computationally investigates the interface between 2D graphene and 3D tin (Sn)
systems with density functional theory (DFT) method. It uses computationally
demanding simulation data to develop machine learning (ML) based potential
energy surfaces (PES). The approach to developing PES for complex interface
systems in the light of limited data and transferability of such models has
been discussed. To develop PES for graphene-tin interface systems, high
dimensional neural networks (HDNN) are used that rely on atom-centered symmetry
function to represent structural information. HDNN are modified to train on the
total energies of the interface system rather than atomic energies. The
performance of modified HDNN trained on 5789 interface structures of
graphene|Sn is tested on new interfaces of the same material pair with varying
levels of structural deviations from the training dataset. Root mean squared
error (RMSE) for test interfaces fall in the range of 0.01-0.45 eV/atom,
depending on the structural deviations from the reference training dataset. By
avoiding incorrect decomposition of total energy into atomic energies, modified
HDNN model is shown to obtain higher accuracy and transferability despite
limited dataset. Improved accuracy in ML-based modeling approach promises
cost-effective means of designing interfaces in heterostructure energy storage
systems with higher cycle life and stability.

The noise of gravitational-wave (GW) interferometers limits their sensitivity
and impacts the data quality, hindering the detection of GW signals from
astrophysical sources. For transient searches, the most problematic are
transient noise artifacts, known as glitches, that happen at a rate around $ 1
\text{ min}^{-1}$, and can mimic GW signals. Because of this, there is a need
for better modeling and inclusion of glitches in large-scale studies, such as
stress testing the pipelines. In this proof-of concept work we employ
Generative Adversarial Networks (GAN), a state-of-the-art Deep Learning
algorithm inspired by Game Theory, to learn the underlying distribution of blip
glitches and to generate artificial populations. We reconstruct the glitch in
the time-domain, providing a smooth input that the GAN can learn. With this
methodology, we can create distributions of $\sim 10^{3}$ glitches from Hanford
and Livingston detectors in less than one second. Furthermore, we employ
several metrics to measure the performance of our methodology and the quality
of its generations. This investigation will be extended in the future to
different glitch classes with the final goal of creating an open-source
interface for mock data generation.

This paper introduces the novel CNN-based encoder Twin Embedding Network
(TEN), for the jigsaw puzzle problem (JPP), which represents a puzzle piece
with respect to its boundary in a latent embedding space. Combining this latent
representation with a simple distance measure, we demonstrate improved accuracy
levels of our newly proposed pairwise compatibility measure (CM), compared to
that of various classical methods, for degraded puzzles with eroded tile
boundaries. We focus on this problem instance for our case study, as it serves
as an appropriate testbed for real-world scenarios. Specifically, we
demonstrated an improvement of up to 8.5% and 16.8% in reconstruction accuracy,
for so-called Type-1 and Type-2 problem variants, respectively. Furthermore, we
also demonstrated that TEN is faster by a few orders of magnitude, on average,
than a typical deep neural network (NN) model, i.e., it is as fast as the
classical methods. In this regard, the paper makes a significant first attempt
at bridging the gap between the relatively low accuracy (of classical methods
and the intensive computational complexity (of NN models), for practical,
real-world puzzle-like problems.

Simulation-based inference (SBI) refers to statistical inference on
stochastic models for which we can generate samples, but not compute
likelihoods. Like SBI algorithms, generative adversarial networks (GANs) do not
require explicit likelihoods. We study the relationship between SBI and GANs,
and introduce GATSBI, an adversarial approach to SBI. GATSBI reformulates the
variational objective in an adversarial setting to learn implicit posterior
distributions. Inference with GATSBI is amortised across observations, works in
high-dimensional posterior spaces and supports implicit priors. We evaluate
GATSBI on two SBI benchmark problems and on two high-dimensional simulators. On
a model for wave propagation on the surface of a shallow water body, we show
that GATSBI can return well-calibrated posterior estimates even in high
dimensions. On a model of camera optics, it infers a high-dimensional posterior
given an implicit prior, and performs better than a state-of-the-art SBI
approach. We also show how GATSBI can be extended to perform sequential
posterior estimation to focus on individual observations. Overall, GATSBI opens
up opportunities for leveraging advances in GANs to perform Bayesian inference
on high-dimensional simulation-based models.

Selecting an appropriate optimizer for a given problem is of major interest
for researchers and practitioners. Many analytical optimizers have been
proposed using a variety of theoretical and empirical approaches; however, none
can offer a universal advantage over other competitive optimizers. We are thus
motivated to study a new problem named Optimizer Amalgamation: how can we best
combine a pool of "teacher" optimizers into a single "student" optimizer that
can have stronger problem-specific performance? In this paper, we draw
inspiration from the field of "learning to optimize" to use a learnable
amalgamation target. First, we define three differentiable amalgamation
mechanisms to amalgamate a pool of analytical optimizers by gradient descent.
Then, in order to reduce variance of the amalgamation process, we also explore
methods to stabilize the amalgamation process by perturbing the amalgamation
target. Finally, we present experiments showing the superiority of our
amalgamated optimizer compared to its amalgamated components and learning to
optimize baselines, and the efficacy of our variance reducing perturbations.
Our code and pre-trained models are publicly available at
http://github.com/VITA-Group/OptimizerAmalgamation.

In this review we cover the basics of efficient nonparametric parameter
estimation (also called functional estimation), with a focus on parameters that
arise in causal inference problems. We review both efficiency bounds (i.e.,
what is the best possible performance for estimating a given parameter?) and
the analysis of particular estimators (i.e., what is this estimator's error,
and does it attain the efficiency bound?) under weak assumptions. We emphasize
minimax-style efficiency bounds, worked examples, and practical shortcuts for
easing derivations. We gloss over most technical details, in the interest of
highlighting important concepts and providing intuition for main ideas.

Session-based recommendation tries to make use of anonymous session data to
deliver high-quality recommendation under the condition that user-profiles and
the complete historical behavioral data of a target user are unavailable.
Previous works consider each session individually and try to capture user
interests within a session. Despite their encouraging results, these models can
only perceive intra-session items and cannot draw upon the massive historical
relational information. To solve this problem, we propose a novel method named
G$^3$SR (Global Graph Guided Session-based Recommendation). G$^3$SR decomposes
the session-based recommendation workflow into two steps. First, a global graph
is built upon all session data, from which the global item representations are
learned in an unsupervised manner. Then, these representations are refined on
session graphs under the graph networks, and a readout function is used to
generate session representations for each session. Extensive experiments on two
real-world benchmark datasets show remarkable and consistent improvements of
the G$^3$SR method over the state-of-the-art methods, especially for cold
items.

6G envisions artificial intelligence (AI) powered solutions for enhancing the
quality-of-service (QoS) in the network and to ensure optimal utilization of
resources. In this work, we propose an architecture based on the combination of
unmanned aerial vehicles (UAVs), AI and blockchain for agricultural
supply-chain management with the purpose of ensuring traceability,
transparency, tracking inventories and contracts. We propose a solution to
facilitate on-device AI by generating a roadmap of models with various
resource-accuracy trade-offs. A fully convolutional neural network (FCN) model
is used for biomass estimation through images captured by the UAV. Instead of a
single compressed FCN model for deployment on UAV, we motivate the idea of
iterative pruning to provide multiple task-specific models with various
complexities and accuracy. To alleviate the impact of flight failure in a 6G
enabled dynamic UAV network, the proposed model selection strategy will assist
UAVs to update the model based on the runtime resource requirements.

Particle robots are novel biologically-inspired robotic systems where
locomotion can be achieved collectively and robustly, but not independently.
While its control is currently limited to a hand-crafted policy for basic
locomotion tasks, such a multi-robot system could be potentially controlled via
Deep Reinforcement Learning (DRL) for different tasks more efficiently.
However, the particle robot system presents a new set of challenges for DRL
differing from existing swarm robotics systems: the low degrees of freedom of
each robot and the increased necessity of coordination between robots. We
present a 2D particle robot simulator using the OpenAI Gym interface and Pymunk
as the physics engine, and introduce new tasks and challenges to research the
underexplored applications of DRL in the particle robot system. Moreover, we
use Stable-baselines3 to provide a set of benchmarks for the tasks. Current
baseline DRL algorithms show signs of achieving the tasks but are yet unable to
reach the performance of the hand-crafted policy. Further development of DRL
algorithms is necessary in order to accomplish the proposed tasks.

Computer vision-based parking lot management methods have been extensively
researched upon owing to their flexibility and cost-effectiveness. To evaluate
such methods authors often employ publicly available parking lot image
datasets. In this study, we surveyed and compared robust publicly available
image datasets specifically crafted to test computer vision-based methods for
parking lot management approaches and consequently present a systematic and
comprehensive review of existing works that employ such datasets. The
literature review identified relevant gaps that require further research, such
as the requirement of dataset-independent approaches and methods suitable for
autonomous detection of position of parking spaces. In addition, we have
noticed that several important factors such as the presence of the same cars
across consecutive images, have been neglected in most studies, thereby
rendering unrealistic assessment protocols. Furthermore, the analysis of the
datasets also revealed that certain features that should be present when
developing new benchmarks, such as the availability of video sequences and
images taken in more diverse conditions, including nighttime and snow, have not
been incorporated.

Classifiers in natural language processing (NLP) often have a large number of
output classes. For example, neural language models (LMs) and machine
translation (MT) models both predict tokens from a vocabulary of thousands. The
Softmax output layer of these models typically receives as input a dense
feature representation, which has much lower dimensionality than the output. In
theory, the result is some words may be impossible to be predicted via argmax,
irrespective of input features, and empirically, there is evidence this happens
in small language models. In this paper we ask whether it can happen in
practical large language models and translation models. To do so, we develop
algorithms to detect such \emph{unargmaxable} tokens in public models. We find
that 13 out of 150 models do indeed have such tokens; however, they are very
infrequent and unlikely to impact model quality. We release our code so that
others can inspect their models.

State estimation is required whenever we deal with high-dimensional dynamical
systems, as the complete measurement is often unavailable. It is key to gaining
insight, performing control or optimizing design tasks. Most deep
learning-based approaches require high-resolution labels and work with fixed
sensor locations, thus being restrictive in their scope. Also, doing Proper
orthogonal decomposition (POD) on sparse data is nontrivial. To tackle these
problems, we propose a technique with an implicit optimization layer and a
physics-based loss function that can learn from sparse labels. It works by
minimizing the energy of the neural network prediction, enabling it to work
with a varying number of sensors at different locations. Based on this
technique we present two models for discrete and continuous prediction in
space. We demonstrate the performance using two high-dimensional fluid problems
of Burgers' equation and Flow Past Cylinder for discrete model and using Allen
Cahn equation and Convection-diffusion equations for continuous model. We show
the models are also robust to noise in measurements.

For performing regression tasks involved in various physics problems,
enhancing the precision, or equivalently reducing the uncertainty of the
regression results is undoubtedly one of the central goals. Here, somewhat
surprisingly, we find that the unfavorable regression uncertainty in performing
regression tasks of inverse statistical problems actually contains "hidden"
information concerning phase transitions of the system under consideration. By
utilizing this "hidden" information, we develop a new unsupervised machine
learning approach dubbed "learning from regression uncertainty" for automated
detection of phases of matter, with the core working horse being a neural
network performing regression tasks instead of classification tasks as in
various related machine learning approaches developed so far. This is achieved
by revealing an intrinsic connection between regression uncertainty and
response properties of the system under consideration, thus making the results
of this machine learning approach directly interpretable. We demonstrate the
approach by identifying the critical point of the ferromagnetic Ising model and
revealing the existence of the intermediate phase in the six-state and
seven-state clock models. Our approach paves the way towards intriguing
possibilities for unveiling new physics via machine learning in an
interpretable manner.

Rolling shutter (RS) distortion can be interpreted as the result of picking a
row of pixels from instant global shutter (GS) frames over time during the
exposure of the RS camera. This means that the information of each instant GS
frame is partially, yet sequentially, embedded into the row-dependent
distortion. Inspired by this fact, we address the challenging task of reversing
this process, i.e., extracting undistorted GS frames from images suffering from
RS distortion. However, since RS distortion is coupled with other factors such
as readout settings and the relative velocity of scene elements to the camera,
models that only exploit the geometric correlation between temporally adjacent
images suffer from poor generality in processing data with different readout
settings and dynamic scenes with both camera motion and object motion. In this
paper, instead of two consecutive frames, we propose to exploit a pair of
images captured by dual RS cameras with reversed RS directions for this highly
challenging task. Grounded on the symmetric and complementary nature of dual
reversed distortion, we develop a novel end-to-end model, IFED, to generate
dual optical flow sequence through iterative learning of the velocity field
during the RS time. Extensive experimental results demonstrate that IFED is
superior to naive cascade schemes, as well as the state-of-the-art which
utilizes adjacent RS images. Most importantly, although it is trained on a
synthetic dataset, IFED is shown to be effective at retrieving GS frame
sequences from real-world RS distorted images of dynamic scenes. Code is
available at https://github.com/zzh-tech/Dual-Reversed-RS.

Learning to reason about relations and dynamics over multiple interacting
objects is a challenging topic in machine learning. The challenges mainly stem
from that the interacting systems are exponentially-compositional, symmetrical,
and commonly geometrically-constrained. Current methods, particularly the ones
based on equivariant Graph Neural Networks (GNNs), have targeted on the first
two challenges but remain immature for constrained systems. In this paper, we
propose Graph Mechanics Network (GMN) which is combinatorially efficient,
equivariant and constraint-aware. The core of GMN is that it represents, by
generalized coordinates, the forward kinematics information (positions and
velocities) of a structural object. In this manner, the geometrical constraints
are implicitly and naturally encoded in the forward kinematics. Moreover, to
allow equivariant message passing in GMN, we have developed a general form of
orthogonality-equivariant functions, given that the dynamics of constrained
systems are more complicated than the unconstrained counterparts.
Theoretically, the proposed equivariant formulation is proved to be universally
expressive under certain conditions. Extensive experiments support the
advantages of GMN compared to the state-of-the-art GNNs in terms of prediction
accuracy, constraint satisfaction and data efficiency on the simulated systems
consisting of particles, sticks and hinges, as well as two real-world datasets
for molecular dynamics prediction and human motion capture.

Motivated by the recently proposed dual control for exploration and
exploitation (DCEE) concept, this paper presents a Multi-Step DCEE (MS-DCEE)
framework with guaranteed convergence for autonomous search of a source of
airborne dispersion. Different from the existing stochastic model predictive
control (SMPC) algorithm and informative path planning (IPP) approaches, the
proposed MS-DCEE approach uses the current and future input to not only drive
the agent towards the estimated source location (exploitation) but also reduce
its estimation uncertainty (exploration) by actively learning the operational
environment. Unknown source target position, together with unknown environment,
impose significant challenges in establishing the recursive feasibility and the
convergence of the proposed algorithm. To address them, with the help of the
property of Bayesian estimation, we develop a two-step approach where the
unbiasedness of the mean estimation is assumed first and then the randomness of
the mean estimate under each collected information sequence is accounted. Based
on that, we develop a MS-DCEE scheme with suitable terminal ingredients where
recursive feasibility and convergence are guaranteed. Two simulation scenarios
are conducted, which show that the proposed MS-DCEE algorithm outperforms the
SMPC, the IPP and the single-step DCEE approaches in terms of searching
successful rates and efficiency.

4D CT imaging is an essential component of radiotherapy of thoracic/abdominal
tumors. 4D CT images are, however, often affected by artifacts that compromise
treatment planning quality. In this work, deep learning (DL)-based conditional
inpainting is proposed to restore anatomically correct image information of
artifact-affected areas. The restoration approach consists of a two-stage
process: DL-based detection of common interpolation (INT) and double structure
(DS) artifacts, followed by conditional inpainting applied to the artifact
areas. In this context, conditional refers to a guidance of the inpainting
process by patient-specific image data to ensure anatomically reliable results.
Evaluation is based on 65 in-house 4D CT data sets of lung cancer patients (48
with only slight artifacts, 17 with pronounced artifacts) and the publicly
available DIRLab 4D CT data (independent external test set). Automated artifact
detection revealed a ROC-AUC of 0.99 for INT and 0.97 for DS artifacts
(in-house data). The proposed inpainting method decreased the average root mean
squared error (RMSE) by 60% (DS) and 42% (INT) for the in-house evaluation data
(simulated artifacts for the slight artifact data; original data were
considered as ground truth for RMSE computation). For the external DIR-Lab
data, the RMSE decreased by 65% and 36%, respectively. Applied to the
pronounced artifact data group, on average 68% of the detectable artifacts were
removed. The results highlight the potential of DL-based inpainting for the
restoration of artifact-affected 4D CT data. Improved performance of
conditional inpainting (compared to standard inpainting) illustrates the
benefits of exploiting patient-specific prior knowledge.

Reverse derivative categories (RDCs) have recently been shown to be a
suitable semantic framework for studying machine learning algorithms. Whereas
emphasis has been put on training methodologies, less attention has been
devoted to particular \emph{model classes}: the concrete categories whose
morphisms represent machine learning models. In this paper we study
presentations by generators and equations of classes of RDCs. In particular, we
propose \emph{polynomial circuits} as a suitable machine learning model. We
give an axiomatisation for these circuits and prove a functional completeness
result. Finally, we discuss the use of polynomial circuits over specific
semirings to perform machine learning with discrete values.

Automated salient object detection (SOD) plays an increasingly crucial role
in many computer vision applications. By reformulating the depth information as
supervision rather than as input, depth-supervised convolutional neural
networks (CNN) have achieved promising results on both RGB and RGB-D SOD
scenarios with the merits of no requirements for extra depth networks and depth
inputs in the inference stage. This paper, for the first time, seeks to expand
the applicability of depth supervision to the Transformer architecture.
Specifically, we develop a Depth-supervised Fusion TRansformer (DFTR), to
further improve the accuracy of both RGB and RGB-D SOD. The proposed DFTR
involves three primary features: 1) DFTR, to the best of our knowledge, is the
first pure Transformer-based model for depth-supervised SOD; 2) A multi-scale
feature aggregation (MFA) module is proposed to fully exploit the multi-scale
features encoded by the Swin Transformer in a coarse-to-fine manner; 3) To
enable bidirectional information flow across different streams of features, a
novel multi-stage feature fusion (MFF) module is further integrated into our
DFTR with the emphasis on salient regions at different network learning stages.
We extensively evaluate the proposed DFTR on ten benchmarking datasets.
Experimental results show that our DFTR consistently outperforms the existing
state-of-the-art methods for both RGB and RGB-D SOD tasks. The code and model

Thanks to the diffusion of the Internet of Things, nowadays it is possible to
sense human mobility almost in real time using unconventional methods (e.g.,
number of bikes in a bike station). Due to the diffusion of such technologies,
the last years have witnessed a significant growth of human mobility studies,
motivated by their importance in a wide range of applications, from traffic
management to public security and computational epidemiology. A mobility task
that is becoming prominent is crowd flow prediction, i.e., forecasting
aggregated incoming and outgoing flows in the locations of a geographic region.
Although several deep learning approaches have been proposed to solve this
problem, their usage is limited to specific types of spatial tessellations and
cannot provide sufficient explanations of their predictions. We propose
CrowdNet, a solution to crowd flow prediction based on graph convolutional
networks. Compared with state-of-the-art solutions, CrowdNet can be used with
regions of irregular shapes and provide meaningful explanations of the
predicted crowd flows. We conduct experiments on public data varying the
spatio-temporal granularity of crowd flows to show the superiority of our model
with respect to existing methods, and we investigate CrowdNet's reliability to
missing or noisy input data. Our model is a step forward in the design of
reliable deep learning models to predict and explain human displacements in
urban environments.

State-of-the-art video deblurring methods often adopt recurrent neural
networks to model the temporal dependency between the frames. While the hidden
states play key role in delivering information to the next frame, abrupt motion
blur tend to weaken the relevance in the neighbor frames. In this paper, we
propose recurrence-in-recurrence network architecture to cope with the
limitations of short-ranged memory. We employ additional recurrent units inside
the RNN cell. First, we employ inner-recurrence module (IRM) to manage the
long-ranged dependency in a sequence. IRM learns to keep track of the cell
memory and provides complementary information to find the deblurred frames.
Second, we adopt an attention-based temporal blending strategy to extract the
necessary part of the information in the local neighborhood. The adpative
temporal blending (ATB) can either attenuate or amplify the features by the
spatial attention. Our extensive experimental results and analysis validate the
effectiveness of IRM and ATB on various RNN architectures.

When dealing with a series of imminent issues, humans can naturally
concentrate on a subset of these concerning issues by prioritizing them
according to their contributions to motivational indices, e.g., the probability
of winning a game. This idea of concentration offers insights into
reinforcement learning of sophisticated Large-scale Multi-Agent Systems (LMAS)
participated by hundreds of agents. In such an LMAS, each agent receives a long
series of entity observations at each step, which can overwhelm existing
aggregation networks such as graph attention networks and cause inefficiency.
In this paper, we propose a concentration network called ConcNet. First,
ConcNet scores the observed entities considering several motivational indices,
e.g., expected survival time and state value of the agents, and then ranks,
prunes, and aggregates the encodings of observed entities to extract features.
Second, distinct from the well-known attention mechanism, ConcNet has a unique
motivational subnetwork to explicitly consider the motivational indices when
scoring the observed entities. Furthermore, we present a concentration policy
gradient architecture that can learn effective policies in LMAS from scratch.
Extensive experiments demonstrate that the presented architecture has excellent
scalability and flexibility, and significantly outperforms existing methods on
LMAS benchmarks.

Super-resolution of LiDAR range images is crucial to improving many
downstream tasks such as object detection, recognition, and tracking. While
deep learning has made a remarkable advances in super-resolution techniques,
typical convolutional architectures limit upscaling factors to specific output
resolutions in training. Recent work has shown that a continuous representation
of an image and learning its implicit function enable almost limitless
upscaling. However, the detailed approach, predicting values (depths) for
neighbor pixels in the input and then linearly interpolating them, does not
best fit the LiDAR range images since it does not fill the unmeasured details
but creates a new image with regression in a high-dimensional space. In
addition, the linear interpolation blurs sharp edges providing important
boundary information of objects in 3-D points. To handle these problems, we
propose a novel network, Implicit LiDAR Network (ILN), which learns not the
values per pixels but weights in the interpolation so that the superresolution
can be done by blending the input pixel depths but with non-linear weights.
Also, the weights can be considered as attentions from the query to the
neighbor pixels, and thus an attention module in the recent Transformer
architecture can be leveraged. Our experiments with a novel large-scale
synthetic dataset demonstrate that the proposed network reconstructs more
accurately than the state-of-the-art methods, achieving much faster convergence
in training.

Segmentation-based methods have achieved great success for arbitrary shape
text detection. However, separating neighboring text instances is still one of
the most challenging problems due to the complexity of texts in scene images.
In this paper, we propose an innovative Kernel Proposal Network (dubbed KPN)
for arbitrary shape text detection. The proposed KPN can separate neighboring
text instances by classifying different texts into instance-independent feature
maps, meanwhile avoiding the complex aggregation process existing in
segmentation-based arbitrary shape text detection methods. To be concrete, our
KPN will predict a Gaussian center map for each text image, which will be used
to extract a series of candidate kernel proposals (i.e., dynamic convolution
kernel) from the embedding feature maps according to their corresponding
keypoint positions. To enforce the independence between kernel proposals, we
propose a novel orthogonal learning loss (OLL) via orthogonal constraints.
Specifically, our kernel proposals contain important self-information learned
by network and location information by position embedding. Finally, kernel
proposals will individually convolve all embedding feature maps for generating
individual embedded maps of text instances. In this way, our KPN can
effectively separate neighboring text instances and improve the robustness
against unclear boundaries. To our knowledge, our work is the first to
introduce the dynamic convolution kernel strategy to efficiently and
effectively tackle the adhesion problem of neighboring text instances in text
detection. Experimental results on challenging datasets verify the impressive
performance and efficiency of our method. The code and model are available at
https://github.com/GXYM/KPN.

Even though deep neural models have achieved superhuman performance on many
popular benchmarks, they have failed to generalize to OOD or adversarial
datasets. Conventional approaches aimed at increasing robustness include
developing increasingly large models and augmentation with large scale
datasets. However, orthogonal to these trends, we hypothesize that a smaller,
high quality dataset is what we need. Our hypothesis is based on the fact that
deep neural networks are data driven models, and data is what leads/misleads
models. In this work, we propose an empirical study that examines how to select
a subset of and/or create high quality benchmark data, for a model to learn
effectively. We seek to answer if big datasets are truly needed to learn a
task, and whether a smaller subset of high quality data can replace big
datasets. We plan to investigate both data pruning and data creation paradigms
to generate high quality datasets.

Quantum computers promise improving machine learning. We investigated the
performance of new quantum neural network designs. Quantum neural networks
currently employed rely on a feature map to encode the input into a quantum
state. This state is then evolved via a parameterized variational circuit.
Finally, a measurement is performed and post-processed on a classical computer
to extract the prediction of the quantum model. We develop a new technique,
where we merge feature map and variational circuit into a single parameterized
circuit and post-process the results using a classical neural network. On a
variety of real and generated datasets, we show that the new, combined approach
outperforms the separated feature map & variational circuit method. We achieve
lower loss, better accuracy, and faster convergence.

Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn
an object detector generalizing to a novel domain free of annotations. Recent
advances align class-conditional distributions by narrowing down cross-domain
prototypes (class centers). Though great success,they ignore the significant
within-class variance and the domain-mismatched semantics within the training
batch, leading to a sub-optimal adaptation. To overcome these challenges, we
propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD,
which completes mismatched semantics and reformulates the adaptation with graph
matching. Specifically, we design a Graph-embedded Semantic Completion module
(GSC) that completes mismatched semantics through generating hallucination
graph nodes in missing categories. Then, we establish cross-image graphs to
model class-conditional distributions and learn a graph-guided memory bank for
better semantic completion in turn. After representing the source and target
data as graphs, we reformulate the adaptation as a graph matching problem,
i.e., finding well-matched node pairs across graphs to reduce the domain gap,
which is solved with a novel Bipartite Graph Matching adaptor (BGM). In a
nutshell, we utilize graph nodes to establish semantic-aware node affinity and
leverage graph edges as quadratic constraints in a structure-aware matching
loss, achieving fine-grained adaptation with a node-to-node graph matching.
Extensive experiments verify that SIGMA outperforms existing works
significantly. Our code is available at
https://github.com/CityU-AIM-Group/SIGMA.

The ever more accurate search for deep analysis in customer data is a really
strong technological trend nowadays, quite appealing to both private and public
companies. This is particularly true in the contact center domain, where speech
analytics is an extremely powerful methodology for gaining insights from
unstructured data, coming from customer and human agent conversations. In this
work, we describe an experimentation with a speech analytics process for an
Italian contact center, that deals with call recordings extracted from inbound
or outbound flows. First, we illustrate in detail the development of an
in-house speech-to-text solution, based on Kaldi framework, and evaluate its
performance (and compare it to Google Cloud Speech API). Then, we evaluate and
compare different approaches to the semantic tagging of call transcripts,
ranging from classic regular expressions to machine learning models based on
ngrams and logistic regression, and propose a combination of them, which is
shown to provide a consistent benefit. Finally, a decision tree inducer, called
J48S, is applied to the problem of tagging. Such an algorithm is natively
capable of exploiting sequential data, such as texts, for classification
purposes. The solution is compared with the other approaches and is shown to
provide competitive classification performances, while generating highly
interpretable models and reducing the complexity of the data preparation phase.
The potential operational impact of the whole process is thoroughly examined.

The large pre-trained BERT has achieved remarkable performance on Natural
Language Processing (NLP) tasks but is also computation and memory expensive.
As one of the powerful compression approaches, binarization extremely reduces
the computation and memory consumption by utilizing 1-bit parameters and
bitwise operations. Unfortunately, the full binarization of BERT (i.e., 1-bit
weight, embedding, and activation) usually suffer a significant performance
drop, and there is rare study addressing this problem. In this paper, with the
theoretical justification and empirical analysis, we identify that the severe
performance drop can be mainly attributed to the information degradation and
optimization direction mismatch respectively in the forward and backward
propagation, and propose BiBERT, an accurate fully binarized BERT, to eliminate
the performance bottlenecks. Specifically, BiBERT introduces an efficient
Bi-Attention structure for maximizing representation information statistically
and a Direction-Matching Distillation (DMD) scheme to optimize the full
binarized BERT accurately. Extensive experiments show that BiBERT outperforms
both the straightforward baseline and existing state-of-the-art quantized BERTs
with ultra-low bit activations by convincing margins on the NLP benchmark. As
the first fully binarized BERT, our method yields impressive 56.3 times and
31.2 times saving on FLOPs and model size, demonstrating the vast advantages
and potential of the fully binarized BERT model in real-world
resource-constrained scenarios.

Graph neural networks (GNNs) have been widely adopted for semi-supervised
learning on graphs. A recent study shows that the graph random neural network
(GRAND) model can generate state-of-the-art performance for this problem.
However, it is difficult for GRAND to handle large-scale graphs since its
effectiveness relies on computationally expensive data augmentation procedures.
In this work, we present a scalable and high-performance GNN framework GRAND+
for semi-supervised graph learning. To address the above issue, we develop a
generalized forward push (GFPush) algorithm in GRAND+ to pre-compute a general
propagation matrix and employ it to perform graph data augmentation in a
mini-batch manner. We show that both the low time and space complexities of
GFPush enable GRAND+ to efficiently scale to large graphs. Furthermore, we
introduce a confidence-aware consistency loss into the model optimization of
GRAND+, facilitating GRAND+'s generalization superiority. We conduct extensive
experiments on seven public datasets of different sizes. The results
demonstrate that GRAND+ 1) is able to scale to large graphs and costs less
running time than existing scalable GNNs, and 2) can offer consistent accuracy
improvements over both full-batch and scalable GNNs across all datasets.

Currently, for crowd counting, the fully supervised methods via density map
estimation are the mainstream research directions. However, such methods need
location-level annotation of persons in an image, which is time-consuming and
laborious. Therefore, the weakly supervised method just relying upon the
count-level annotation is urgently needed. Since CNN is not suitable for
modeling the global context and the interactions between image patches, crowd
counting with weakly supervised learning via CNN generally can not show good
performance. The weakly supervised model via Transformer was sequentially
proposed to model the global context and learn contrast features. However, the
transformer directly partitions the crowd images into a series of tokens, which
may not be a good choice due to each pedestrian being an independent
individual, and the parameter number of the network is very large. Hence, we
propose a Joint CNN and Transformer Network (JCTNet) via weakly supervised
learning for crowd counting in this paper. JCTNet consists of three parts: CNN
feature extraction module (CFM), Transformer feature extraction module (TFM),
and counting regression module (CRM). In particular, the CFM extracts crowd
semantic information features, then sends their patch partitions to TRM for
modeling global context, and CRM is used to predict the number of people.
Extensive experiments and visualizations demonstrate that JCTNet can
effectively focus on the crowd regions and obtain superior weakly supervised
counting performance on five mainstream datasets. The number of parameters of
the model can be reduced by about 67%~73% compared with the pure Transformer
works. We also tried to explain the phenomenon that a model constrained only by
count-level annotations can still focus on the crowd regions. We believe our
work can promote further research in this field.

This paper aims at a better understanding of matrix factorization (MF),
factorization machines (FM), and their combination with deep algorithms'
application in recommendation systems. Specifically, this paper will focus on
Singular Value Decomposition (SVD) and its derivations, e.g Funk-SVD, SVD++,
etc. Step-by-step formula calculation and explainable pictures are displayed.
What's more, we explain the DeepFM model in which FM is assisted by deep
learning. Through numerical examples, we attempt to tie the theory to
real-world problems.

Referring expression comprehension (REC) aims to locate a certain object in
an image referred by a natural language expression. For joint understanding of
regions and expressions, existing REC works typically target on modeling the
cross-modal relevance in each region-expression pair within each single image.
In this paper, we explore a new but general REC-related problem, named
Group-based REC, where the regions and expressions can come from different
subject-related images (images in the same group), e.g., sets of photo albums
or video frames. Different from REC, Group-based REC involves differentiated
cross-modal relevances within each group and across different groups, which,
however, are neglected in the existing one-line paradigm. To this end, we
propose a novel relevance-guided multi-group self-paced learning schema (termed
RMSL), where the within-group region-expression pairs are adaptively assigned
with different priorities according to their cross-modal relevances, and the
bias of the group priority is balanced via an across-group relevance constraint
simultaneously. In particular, based on the visual and textual semantic
features, RMSL conducts an adaptive learning cycle upon triplet ranking, where
(1) the target-negative region-expression pairs with low within-group
relevances are used preferentially in model training to distinguish the primary
semantics of the target objects, and (2) an across-group relevance
regularization is integrated into model training to balance the bias of group
priority. The relevances, the pairs, and the model parameters are alternatively
updated upon a unified self-paced hinge loss.

While astrophysical and cosmological probes provide a remarkably precise and
consistent picture of the quantity and general properties of dark matter, its
fundamental nature remains one of the most significant open questions in
physics. Obtaining a more comprehensive understanding of dark matter within the
next decade will require overcoming a number of theoretical challenges: the
groundwork for these strides is being laid now, yet much remains to be done.
Chief among the upcoming challenges is establishing the theoretical foundation
needed to harness the full potential of new observables in the astrophysical
and cosmological domains, spanning the early Universe to the inner portions of
galaxies and the stars therein. Identifying the nature of dark matter will also
entail repurposing and implementing a wide range of theoretical techniques from
outside the typical toolkit of astrophysics, ranging from effective field
theory to the dramatically evolving world of machine learning and
artificial-intelligence-based statistical inference. Through this work, the
theory frontier will be at the heart of dark matter discoveries in the upcoming
decade.

We present a Chinese BERT model dubbed MarkBERT that uses word information in
this work. Existing word-based BERT models regard words as basic units,
however, due to the vocabulary limit of BERT, they only cover high-frequency
words and fall back to character level when encountering out-of-vocabulary
(OOV) words. Different from existing works, MarkBERT keeps the vocabulary being
Chinese characters and inserts boundary markers between contiguous words. Such
design enables the model to handle any words in the same way, no matter they
are OOV words or not. Besides, our model has two additional benefits: first, it
is convenient to add word-level learning objectives over markers, which is
complementary to traditional character and sentence-level pretraining tasks;
second, it can easily incorporate richer semantics such as POS tags of words by
replacing generic markers with POS tag-specific markers. With the simple
markers insertion, MarkBERT can improve the performances of various downstream
tasks including language understanding and sequence labeling. \footnote{All the
codes and models will be made publicly available at
\url{https://github.com/daiyongya/markbert}}

Linear spectral unmixing is an essential technique in hyperspectral image
processing and interpretation. In recent years, deep learning-based approaches
have shown great promise in hyperspectral unmixing, in particular, unsupervised
unmixing methods based on autoencoder networks are a recent trend. The
autoencoder model, which automatically learns low-dimensional representations
(abundances) and reconstructs data with their corresponding bases (endmembers),
has achieved superior performance in hyperspectral unmixing. In this article,
we explore the effective utilization of spatial and spectral information in
autoencoder-based unmixing networks. Important findings on the use of spatial
and spectral information in the autoencoder framework are discussed. Inspired
by these findings, we propose a spatial-spectral collaborative unmixing
network, called SSCU-Net, which learns a spatial autoencoder network and a
spectral autoencoder network in an end-to-end manner to more effectively
improve the unmixing performance. SSCU-Net is a two-stream deep network and
shares an alternating architecture, where the two autoencoder networks are
efficiently trained in a collaborative way for estimation of endmembers and
abundances. Meanwhile, we propose a new spatial autoencoder network by
introducing a superpixel segmentation method based on abundance information,
which greatly facilitates the employment of spatial information and improves
the accuracy of unmixing network. Moreover, extensive ablation studies are
carried out to investigate the performance gain of SSCU-Net. Experimental
results on both synthetic and real hyperspectral data sets illustrate the
effectiveness and competitiveness of the proposed SSCU-Net compared with
several state-of-the-art hyperspectral unmixing methods.

Lithography simulation is a critical step in VLSI design and optimization for
manufacturability. Existing solutions for highly accurate lithography
simulation with rigorous models are computationally expensive and slow, even
when equipped with various approximation techniques. Recently, machine learning
has provided alternative solutions for lithography simulation tasks such as
coarse-grained edge placement error regression and complete contour prediction.
However, the impact of these learning-based methods has been limited due to
restrictive usage scenarios or low simulation accuracy. To tackle these
concerns, we introduce an dual-band optics-inspired neural network design that
considers the optical physics underlying lithography. To the best of our
knowledge, our approach yields the first published via/metal layer contour
simulation at 1nm^2/pixel resolution with any tile size. Compared to previous
machine learning based solutions, we demonstrate that our framework can be
trained much faster and offers a significant improvement on efficiency and
image quality with 20X smaller model size. We also achieve 85X simulation
speedup over traditional lithography simulator with 1% accuracy loss.

We applied the image-based approach with a convolutional neural network model
to the sample of low-redshifts galaxies with $-24^{m}<M_{r}<-19.4^{m}$ from the
SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy
Zoo 2 (GZ2) dataset, considering them as the inference and training datasets,
respectively. As a result, we created the morphological catalog of 315782
galaxies at 0.02<z<0.1, where morphological five classes and 34 detailed
features (bar, rings, number of spiral arms, mergers, etc.) were first defined
for 216148 galaxies (inference dataset) by the image-based CNN classifier. For
the rest of galaxies the initial morphological classification was re-assigned
as in the GZ2 project.
  Our method shows the promising performance of morphological classification
attaining more 93 % of accuracy for five classes morphology prediction except
the cigar-shaped (75 %) and completely rounded (83 %) galaxies. Main results
are presented in the catalog of 19468 completely rounded, 27321 rounded
in-between, 3235 cigar-shaped, 4099 edge-on, 18615 spiral, and 72738 general
low-redshift galaxies of the studied SDSS sample. As for the classification of
galaxies by their detailed structural morphological features, our CNN model
gives the accuracy in the range of 92-99 % depending on features, a number of
galaxies with the given feature in the inference dataset, and the galaxy image
quality. We demonstrate that implication of the CNN model with adversarial
validation and adversarial image data augmentation improves classification of
smaller and fainter SDSS galaxies with $m_{r}$ <17.7.

Linear discriminant analysis (LDA) is an important classification tool in
statistics and machine learning. This paper investigates the varying
coefficient LDA model for dynamic data, with Bayes' discriminant direction
being a function of some exposure variable to address the heterogeneity. We
propose a new least-square estimation method based on the B-spline
approximation. The data-driven discriminant procedure is more computationally
efficient than the dynamic linear programming rule \citep{jiang2020dynamic}. We
also establish the convergence rates for the corresponding estimation error
bound and the excess misclassification risk. The estimation error in $L_2$
distance is optimal for the low-dimensional regime and is near optimal for the
high-dimensional regime. Numerical experiments on synthetic data and real data
both corroborate the superiority of our proposed classification method.

In recent years, the machine learning research community has benefited
tremendously from the availability of openly accessible benchmark datasets.
Clinical data are usually not openly available due to their highly confidential
nature. This has hampered the development of reproducible and generalisable
machine learning applications in health care. Here we introduce the Health Gym
- a growing collection of highly realistic synthetic medical datasets that can
be freely accessed to prototype, evaluate, and compare machine learning
algorithms, with a specific focus on reinforcement learning. The three
synthetic datasets described in this paper present patient cohorts with acute
hypotension and sepsis in the intensive care unit, and people with human
immunodeficiency virus (HIV) receiving antiretroviral therapy in ambulatory
care. The datasets were created using a novel generative adversarial network
(GAN). The distributions of variables, and correlations between variables and
trends over time in the synthetic datasets mirror those in the real datasets.
Furthermore, the risk of sensitive information disclosure associated with the
public distribution of the synthetic datasets is estimated to be very low.

Deep learning models tend to underperform in the presence of domain shifts.
Domain transfer has recently emerged as a promising approach wherein images
exhibiting a domain shift are transformed into other domains for augmentation
or adaptation. However, with the absence of paired and annotated images, models
merely learned by adversarial loss and cycle consistency loss could result in
poor consistency of anatomy structures during the translation. Additionally,
the complexity of learning multi-domain transfer could significantly increase
with the number of target domains and source images. In this paper, we propose
a multi-domain transfer network, named MDT-Net, to address the limitations
above through perceptual supervision. Specifically, our model consists of a
single encoder-decoder network and multiple domain-specific transfer modules to
disentangle feature representations of the anatomy content and domain variance.
Owing to this architecture, the model could significantly reduce the complexity
when the translation is conducted among multiple domains. To demonstrate the
performance of our method, we evaluate our model qualitatively and
quantitatively on RETOUCH, an OCT dataset comprising scans from three different
scanner devices (domains). Furthermore, we take the transfer results as
additional training data for fluid segmentation to prove the advantage of our
model indirectly, i.e., in the task of data adaptation and augmentation.
Experimental results show that our method could bring universal improvement in
these segmentation tasks, which demonstrates the effectiveness and efficiency
of MDT-Net in multi-domain transfer.

This work presents a nonintrusive physics-preserving method to learn
reduced-order models (ROMs) of Lagrangian systems, which includes nonlinear
wave equations. Existing intrusive projection-based model reduction approaches
construct structure-preserving Lagrangian ROMs by projecting the Euler-Lagrange
equations of the full-order model (FOM) onto a linear subspace. This Galerkin
projection step requires complete knowledge about the Lagrangian operators in
the FOM and full access to manipulate the computer code. In contrast, the
proposed Lagrangian operator inference approach embeds the mechanics into the
operator inference framework to develop a data-driven model reduction method
that preserves the underlying Lagrangian structure. The proposed approach
exploits knowledge of the governing equations (but not their discretization) to
define the form and parametrization of a Lagrangian ROM which can then be
learned from projected snapshot data. The method does not require access to FOM
operators or computer code. The numerical results demonstrate Lagrangian
operator inference on an Euler-Bernoulli beam model, the sine-Gordon
(nonlinear) wave equation, and a large-scale discretization of a soft robot
fishtail with 779,232 degrees of freedom. Accurate long-time predictions of the
learned Lagrangian ROMs far outside the training time interval illustrate their

Non-exemplar class-incremental learning is to recognize both the old and new
classes when old class samples cannot be saved. It is a challenging task since
representation optimization and feature retention can only be achieved under
supervision from new classes. To address this problem, we propose a novel
self-sustaining representation expansion scheme. Our scheme consists of a
structure reorganization strategy that fuses main-branch expansion and
side-branch updating to maintain the old features, and a main-branch
distillation scheme to transfer the invariant knowledge. Furthermore, a
prototype selection mechanism is proposed to enhance the discrimination between
the old and new classes by selectively incorporating new samples into the
distillation process. Extensive experiments on three benchmarks demonstrate
significant incremental performance, outperforming the state-of-the-art methods
by a margin of 3%, 3% and 6%, respectively.

For humans, our "senses of touch" have always been necessary for our ability
to precisely and efficiently manipulate objects of all shapes in any
environment, but until recently, not many works have been done to fully
understand haptic feedback. This work proposed a novel method for getting a
better shape descriptor than existing methods for classifying an object from
multiple tactile data collected from a tactile glove. It focuses on improving
previous works on object classification using tactile data. The major problem
for object classification from multiple tactile data is to find a good way to
aggregate features extracted from multiple tactile images. We propose a novel
method, dubbed as Tactile-ViewGCN, that hierarchically aggregate tactile
features considering relations among different features by using Graph
Convolutional Network. Our model outperforms previous methods on the STAG
dataset with an accuracy of 81.82%.

This paper combines the power of deep-learning with the generalizability of
physics-based features, to present an advanced method for seismic
discrimination between earthquakes and explosions. The proposed method contains
two branches: a deep learning branch operating directly on seismic waveforms or
spectrograms, and a second branch operating on physics-based parametric
features. These features are high-frequency P/S amplitude ratios and the
difference between local magnitude (ML) and coda duration magnitude (MC). The
combination achieves better generalization performance when applied to new
regions than models that are developed solely with deep learning. We also
examined which parts of the waveform data dominate deep learning decisions
(i.e., via Grad-CAM). Such visualization provides a window into the black-box
nature of the machine-learning models and offers new insight into how the deep
learning derived models use data to make the decisions.

Vision transformers (ViTs) have gained increasing popularity as they are
commonly believed to own higher modeling capacity and representation
flexibility, than traditional convolutional networks. However, it is
questionable whether such potential has been fully unleashed in practice, as
the learned ViTs often suffer from over-smoothening, yielding likely redundant
models. Recent works made preliminary attempts to identify and alleviate such
redundancy, e.g., via regularizing embedding similarity or re-injecting
convolution-like structures. However, a "head-to-toe assessment" regarding the
extent of redundancy in ViTs, and how much we could gain by thoroughly
mitigating such, has been absent for this field. This paper, for the first
time, systematically studies the ubiquitous existence of redundancy at all
three levels: patch embedding, attention map, and weight space. In view of
them, we advocate a principle of diversity for training ViTs, by presenting
corresponding regularizers that encourage the representation diversity and
coverage at each of those levels, that enabling capturing more discriminative
information. Extensive experiments on ImageNet with a number of ViT backbones
validate the effectiveness of our proposals, largely eliminating the observed
ViT redundancy and significantly boosting the model generalization. For
example, our diversified DeiT obtains 0.70%~1.76% accuracy boosts on ImageNet
with highly reduced similarity. Our codes are fully available in
https://github.com/VITA-Group/Diverse-ViT.

Federated learning (FL) is a distributed machine learning technique that
enables collaborative model training while avoiding explicit data sharing. The
inherent privacy-preserving property of FL algorithms makes them especially
attractive to the medical field. However, in case of heterogeneous client data
distributions, standard FL methods are unstable and require intensive
hyperparameter tuning to achieve optimal performance. Conventional
hyperparameter optimization algorithms are impractical in real-world FL
applications as they involve numerous training trials, which are often not
affordable with limited compute budgets. In this work, we propose an efficient
reinforcement learning (RL)-based federated hyperparameter optimization
algorithm, termed Auto-FedRL, in which an online RL agent can dynamically
adjust hyperparameters of each client based on the current training progress.
Extensive experiments are conducted to investigate different search strategies
and RL agents. The effectiveness of the proposed method is validated on a
heterogeneous data split of the CIFAR-10 dataset as well as two real-world
medical image segmentation datasets for COVID-19 lesion segmentation in chest
CT and pancreas segmentation in abdominal CT.

With the development of sensing and communication technologies in networked
cyber-physical systems (CPSs), multi-agent reinforcement learning (MARL)-based
methodologies are integrated into the control process of physical systems and
demonstrate prominent performance in a wide array of CPS domains, such as
connected autonomous vehicles (CAVs). However, it remains challenging to
mathematically characterize the improvement of the performance of CAVs with
communication and cooperation capability. When each individual autonomous
vehicle is originally self-interest, we can not assume that all agents would
cooperate naturally during the training process. In this work, we propose to
reallocate the system's total reward efficiently to motivate stable cooperation
among autonomous vehicles. We formally define and quantify how to reallocate
the system's total reward to each agent under the proposed transferable utility
game, such that communication-based cooperation among multi-agents increases
the system's total reward. We prove that Shapley value-based reward
reallocation of MARL locates in the core if the transferable utility game is a
convex game. Hence, the cooperation is stable and efficient and the agents
should stay in the coalition or the cooperating group. We then propose a
cooperative policy learning algorithm with Shapley value reward reallocation.
In experiments, compared with several literature algorithms, we show the
improvement of the mean episode system reward of CAV systems using our proposed

The rapid advancement of deep learning has significantly boomed the
development of photorealistic style transfer. In this review, we reviewed the
development of photorealistic style transfer starting from artistic style
transfer and the contribution of traditional image processing techniques on
photorealistic style transfer, including some work that had been completed in
the Multimedia lab at the University of Alberta. Many techniques were discussed
in this review. However, our focus is on VGG-based techniques, whitening and
coloring transform (WCTs) based techniques, the combination of deep learning
with traditional image processing techniques.

Remarkable achievements have been attained with Generative Adversarial
Networks (GANs) in image-to-image translation. However, due to a tremendous
amount of parameters, state-of-the-art GANs usually suffer from low efficiency
and bulky memory usage. To tackle this challenge, firstly, this paper
investigates GANs performance from a frequency perspective. The results show
that GANs, especially small GANs lack the ability to generate high-quality high
frequency information. To address this problem, we propose a novel knowledge
distillation method referred to as wavelet knowledge distillation. Instead of
directly distilling the generated images of teachers, wavelet knowledge
distillation first decomposes the images into different frequency bands with
discrete wavelet transformation and then only distills the high frequency
bands. As a result, the student GAN can pay more attention to its learning on
high frequency bands. Experiments demonstrate that our method leads to 7.08
times compression and 6.80 times acceleration on CycleGAN with almost no
performance drop. Additionally, we have studied the relation between
discriminators and generators which shows that the compression of
discriminators can promote the performance of compressed generators.

3D object detection plays a fundamental role in enabling autonomous driving,
which is regarded as the significant key to unlocking the bottleneck of
contemporary transportation systems from the perspectives of safety, mobility,
and sustainability. Most of the state-of-the-art (SOTA) object detection
methods from point clouds are developed based on a single onboard LiDAR, whose
performance will be inevitably limited by the range and occlusion, especially
in dense traffic scenarios. In this paper, we propose \textit{PillarGrid}, a
novel cooperative perception method fusing information from multiple 3D LiDARs
(both on-board and roadside), to enhance the situation awareness for connected
and automated vehicles (CAVs). PillarGrid consists of four main phases: 1)
cooperative preprocessing of point clouds, 2) pillar-wise voxelization and
feature extraction, 3) grid-wise deep fusion of features from multiple sensors,
and 4) convolutional neural network (CNN)-based augmented 3D object detection.
A novel cooperative perception platform is developed for model training and
testing. Extensive experimentation shows that PillarGrid outperforms the SOTA
single-LiDAR-based 3D object detection methods with respect to both accuracy
and range by a large margin.

Adversarial training is a common approach for bias mitigation in natural
language processing. Although most work on debiasing is motivated by equal
opportunity, it is not explicitly captured in standard adversarial training. In
this paper, we propose an augmented discriminator for adversarial training,
which takes the target class as input to create richer features and more
explicitly model equal opportunity. Experimental results over two datasets show
that our method substantially improves over standard adversarial debiasing
methods, in terms of the performance--fairness trade-off.

Radiomics features extract quantitative information from medical images,
towards the derivation of biomarkers for clinical tasks, such as diagnosis,
prognosis, or treatment response assessment. Different image discretization
parameters (e.g. bin number or size), convolutional filters, segmentation
perturbation, or multi-modality fusion levels can be used to generate radiomics
features and ultimately signatures. Commonly, only one set of parameters is
used; resulting in only one value or flavour for a given RF. We propose tensor
radiomics (TR) where tensors of features calculated with multiple combinations
of parameters (i.e. flavours) are utilized to optimize the construction of
radiomics signatures. We present examples of TR as applied to PET/CT, MRI, and
CT imaging invoking machine learning or deep learning solutions, and
reproducibility analyses: (1) TR via varying bin sizes on CT images of lung
cancer and PET-CT images of head & neck cancer (HNC) for overall survival
prediction. A hybrid deep neural network, referred to as TR-Net, along with two
ML-based flavour fusion methods showed improved accuracy compared to regular
rediomics features. (2) TR built from different segmentation perturbations and
different bin sizes for classification of late-stage lung cancer response to
first-line immunotherapy using CT images. TR improved predicted patient
responses. (3) TR via multi-flavour generated radiomics features in MR imaging
showed improved reproducibility when compared to many single-flavour features.
(4) TR via multiple PET/CT fusions in HNC. Flavours were built from different
fusions using methods, such as Laplacian pyramids and wavelet transforms. TR
improved overall survival prediction. Our results suggest that the proposed TR
paradigm has the potential to improve performance capabilities in different
medical imaging tasks.

Current pre-trained language models (PLM) are typically trained with static
data, ignoring that in real-world scenarios, streaming data of various sources
may continuously grow. This requires PLMs to integrate the information from all
the sources in a lifelong manner. Although this goal could be achieved by
exhaustive pre-training on all the existing data, such a process is known to be
computationally expensive. To this end, we propose ELLE, aiming at efficient
lifelong pre-training for emerging data. Specifically, ELLE consists of (1)
function preserved model expansion, which flexibly expands an existing PLM's
width and depth to improve the efficiency of knowledge acquisition; and (2)
pre-trained domain prompts, which disentangle the versatile knowledge learned
during pre-training and stimulate the proper knowledge for downstream tasks. We
experiment ELLE with streaming data from 5 domains on BERT and GPT. The results
show the superiority of ELLE over various lifelong learning baselines in both
pre-training efficiency and downstream performances. The codes are publicly
available at https://github.com/thunlp/ELLE.

Entity alignment is to find identical entities in different knowledge graphs.
Although embedding-based entity alignment has recently achieved remarkable
progress, training data insufficiency remains a critical challenge.
Conventional semi-supervised methods also suffer from the incorrect entity
alignment in newly proposed training data. To resolve these issues, we design
an iterative cycle-teaching framework for semi-supervised entity alignment. The
key idea is to train multiple entity alignment models (called aligners)
simultaneously and let each aligner iteratively teach its successor the
proposed new entity alignment. We propose a diversity-aware alignment selection
method to choose reliable entity alignment for each aligner. We also design a
conflict resolution mechanism to resolve the alignment conflict when combining
the new alignment of an aligner and that from its teacher. Besides, considering
the influence of cycle-teaching order, we elaborately design a strategy to
arrange the optimal order that can maximize the overall performance of multiple
aligners. The cycle-teaching process can break the limitations of each model's
learning capability and reduce the noise in new training data, leading to
improved performance. Extensive experiments on benchmark datasets demonstrate
the effectiveness of the proposed cycle-teaching framework, which significantly
outperforms the state-of-the-art models when the training data is insufficient
and the new entity alignment has much noise.

Single image reflection removal problem aims to divide a
reflection-contaminated image into a transmission image and a reflection image.
It is a canonical blind source separation problem and is highly ill-posed. In
this paper, we present a novel deep architecture called deep unfolded single
image reflection removal network (DURRNet) which makes an attempt to combine
the best features from model-based and learning-based paradigms and therefore
leads to a more interpretable deep architecture. Specifically, we first propose
a model-based optimization with transform-based exclusion prior and then design
an iterative algorithm with simple closed-form solutions for solving each
sub-problems. With the deep unrolling technique, we build the DURRNet with
ProxNets to model natural image priors and ProxInvNets which are constructed
with invertible networks to impose the exclusion prior. Comprehensive
experimental results on commonly used datasets demonstrate that the proposed
DURRNet achieves state-of-the-art results both visually and quantitatively.

The emerging neural topic models make topic modeling more easily adaptable
and extendable in unsupervised text mining. However, the existing neural topic
models is difficult to retain representative information of the documents
within the learnt topic representation. In this paper, we propose a neural
topic model which incorporates deep mutual information estimation, i.e., Neural
Topic Modeling with Deep Mutual Information Estimation(NTM-DMIE). NTM-DMIE is a
neural network method for topic learning which maximizes the mutual information
between the input documents and their latent topic representation. To learn
robust topic representation, we incorporate the discriminator to discriminate
negative examples and positive examples via adversarial learning. Moreover, we
use both global and local mutual information to preserve the rich information
of the input documents in the topic representation. We evaluate NTM-DMIE on
several metrics, including accuracy of text clustering, with topic
representation, topic uniqueness and topic coherence. Compared to the existing
methods, the experimental results show that NTM-DMIE can outperform in all the
metrics on the four datasets.

We study the kernelized bandit problem, that involves designing an adaptive
strategy for querying a noisy zeroth-order-oracle to efficiently learn about
the optimizer of an unknown function $f$ with a norm bounded by $M<\infty$ in a
Reproducing Kernel Hilbert Space~(RKHS) associated with a positive definite
kernel $K$. Prior results, working in a \emph{minimax framework}, have
characterized the worst-case~(over all functions in the problem class) limits
on regret achievable by \emph{any} algorithm, and have constructed algorithms
with matching~(modulo polylogarithmic factors) worst-case performance for the
\matern family of kernels. These results suffer from two drawbacks. First, the
minimax lower bound gives no information about the limits of regret achievable
by the commonly used algorithms on specific problem instances. Second, due to
their worst-case nature, the existing upper bound analysis fails to adapt to
easier problem instances within the function class. Our work takes steps to
address both these issues. First, we derive \emph{instance-dependent} regret
lower bounds for algorithms with uniformly~(over the function class) vanishing
normalized cumulative regret. Our result, valid for all the practically
relevant kernelized bandits algorithms, such as, GP-UCB, GP-TS and
SupKernelUCB, identifies a fundamental complexity measure associated with every
problem instance. We then address the second issue, by proposing a new minimax
near-optimal algorithm which also adapts to easier problem instances.

We present SOCKS, a data-driven stochastic optimal control toolbox based in
kernel methods. SOCKS is a collection of data-driven algorithms that compute
approximate solutions to stochastic optimal control problems with arbitrary
cost and constraint functions, including stochastic reachability, which seeks
to determine the likelihood that a system will reach a desired target set while
respecting a set of pre-defined safety constraints. Our approach relies upon a
class of machine learning algorithms based in kernel methods, a nonparametric
technique which can be used to represent probability distributions in a
high-dimensional space of functions known as a reproducing kernel Hilbert
space. As a nonparametric technique, kernel methods are inherently data-driven,
meaning that they do not place prior assumptions on the system dynamics or the
structure of the uncertainty. This makes the toolbox amenable to a wide variety
of systems, including those with nonlinear dynamics, black-box elements, and
poorly characterized stochastic disturbances. We present the main features of
SOCKS and demonstrate its capabilities on several benchmarks.

The total masses of galaxy clusters characterize many aspects of astrophysics
and the underlying cosmology. It is crucial to obtain reliable and accurate
mass estimates for numerous galaxy clusters over a wide range of redshifts and
mass scales. We present a transfer-learning approach to estimate cluster masses
using the ugriz-band images in the SDSS Data Release 12. The target masses are
derived from X-ray or SZ measurements that are only available for a small
subset of the clusters. We designed a semi-supervised deep learning model
consisting of two convolutional neural networks. In the first network, a
feature extractor is trained to classify the SDSS photometric bands. The second
network takes the previously trained features as inputs to estimate their total
masses. The training and testing processes in this work depend purely on real
observational data. Our algorithm reaches a mean absolute error (MAE) of 0.232
dex on average and 0.214 dex for the best fold. The performance is comparable
to that given by redMaPPer, 0.192 dex. We have further applied a joint
integrated gradient and class activation mapping method to interpret such a
two-step neural network. The performance of our algorithm is likely to improve
as the size of training dataset increases. This proof-of-concept experiment
demonstrates the potential of deep learning in maximizing the scientific return
of the current and future large cluster surveys.

We introduce the Block-Recurrent Transformer, which applies a transformer
layer in a recurrent fashion along a sequence, and has linear complexity with
respect to sequence length. Our recurrent cell operates on blocks of tokens
rather than single tokens during training, and leverages parallel computation
within a block in order to make efficient use of accelerator hardware. The cell
itself is strikingly simple. It is merely a transformer layer: it uses
self-attention and cross-attention to efficiently compute a recurrent function
over a large set of state vectors and tokens. Our design was inspired in part
by LSTM cells, and it uses LSTM-style gates, but it scales the typical LSTM
cell up by several orders of magnitude. Our implementation of recurrence has
the same cost in both computation time and parameter count as a conventional
transformer layer, but offers dramatically improved perplexity in language
modeling tasks over very long sequences. Our model out-performs a long-range
Transformer XL baseline by a wide margin, while running twice as fast. We
demonstrate its effectiveness on PG19 (books), arXiv papers, and GitHub source
code. Our code has been released as open source.

The Li-Sn binary system has been the focus of extensive research because it
features Li-rich alloys with potential applications as battery anodes. Our
present re-examination of the binary system with a combination of machine
learning and ab initio methods has allowed us to screen a vast configuration
space and uncover a number of overlooked thermodynamically stable alloys. At
ambient pressure, our evolutionary searches identified a new stable Li$_3$Sn
phase with a large BCC-based hR48 structure and a possible high-T LiSn$_4$
ground state. By building a simple model for the observed and predicted Li-Sn
BCC alloys we constructed an even larger viable hR75 structure at an exotic
19:6 stoichiometry. At 20 GPa, new 11:2, 5:1, and 9:2 phases found with our
global searches destabilize previously proposed phases with high Li content.
The findings showcase the appreciable promise machine learning interatomic
potentials hold for accelerating ab initio prediction of complex materials.

This document presents the concept and physics case for a magnetized gaseous
argon-based detector system (ND-GAr) for the Deep Underground Neutrino
Experiment (DUNE) Near Detector. This detector system is required in order for
DUNE to reach its full physics potential in the measurement of CP violation and
in delivering precision measurements of oscillation parameters. In addition to
its critical role in the long-baseline oscillation program, ND-GAr will extend
the overall physics program of DUNE. The LBNF high-intensity proton beam will
provide a large flux of neutrinos that is sampled by ND-GAr, enabling DUNE to
discover new particles and search for new interactions and symmetries beyond
those predicted in the Standard Model.

This is the second part of a two-part paper that focuses on link-adaptation
(LA) and physical layer (PHY) abstraction for multi-user MIMO (MU-MIMO) systems
with non-linear receivers. The first part proposes a new metric, called
bit-metric decoding rate (BMDR) for a detector, as being the equivalent of
post-equalization signal-to-interference-noise ratio (SINR) for non-linear
receivers. Since this BMDR does not have a closed form expression, a
machine-learning based approach to estimate it effectively is presented. In
this part, the concepts developed in the first part are utilized to develop
novel algorithms for LA, dynamic detector selection from a list of available
detectors, and PHY abstraction in MU-MIMO systems with arbitrary receivers.
Extensive simulation results that substantiate the efficacy of the proposed
algorithms are presented.

Link-adaptation (LA) is one of the most important aspects of wireless
communications where the modulation and coding scheme (MCS) used by the
transmitter is adapted to the channel conditions in order to meet a certain
target error-rate. In a single-user SISO (SU-SISO) system with out-of-cell
interference, LA is performed by computing the post-equalization
signal-to-interference-noise ratio (SINR) at the receiver. The same technique
can be employed in multi-user MIMO (MU-MIMO) receivers that use linear
detectors. Another important use of post-equalization SINR is for physical
layer (PHY) abstraction, where several PHY blocks like the channel encoder, the
detector, and the channel decoder are replaced by an abstraction model in order
to speed up system-level simulations. However, for MU-MIMO systems with
non-linear receivers, there is no known equivalent of post-equalization SINR
which makes both LA and PHY abstraction extremely challenging. This important
issue is addressed in this two-part paper. In this part, a metric called the
bit-metric decoding rate (BMDR) of a detector, which is the proposed equivalent
of post-equalization SINR, is presented. Since BMDR does not have a closed form
expression that would enable its instantaneous calculation, a machine-learning
approach to predict it is presented along with extensive simulation results.

We provide a method to identify system parameters of dynamical systems,
called ID-ODE -- Inference by Differentiation and Observing Delay Embeddings.
In this setting, we are given a dataset of trajectories from a dynamical system
with system parameter labels. Our goal is to identify system parameters of new
trajectories. The given trajectories may or may not encompass the full state of
the system, and we may only observe a one-dimensional time series. In the
latter case, we reconstruct the full state by using delay embeddings, and under
sufficient conditions, Taken's Embedding Theorem assures us the reconstruction
is diffeomorphic to the original. This allows our method to work on time
series. Our method works by first learning the velocity operator (as given or
reconstructed) with a neural network having both state and system parameters as
variable inputs. Then on new trajectories we backpropagate prediction errors to
the system parameter inputs giving us a gradient. We then use gradient descent
to infer the correct system parameter. We demonstrate the efficacy of our
approach on many numerical examples: the Lorenz system, Lorenz96,
Lotka-Volterra Predator-Prey, and the Compound Double Pendulum. We also apply
our algorithm on a real-world dataset: propulsion of the Hall-effect Thruster
(HET).

In this paper we present some experiments to automatically classify online
handwritten text based on capital letters. Although handwritten text is not as
discriminative as face or voice, we still found some chance for gender
classification based on handwritten text. Accuracies are up to 74%, even in the
most challenging case of capital letters.

Predicate entailment detection is a crucial task for question-answering from
text, where previous work has explored unsupervised learning of entailment
graphs from typed open relation triples. In this paper, we present the first
pipeline for building Chinese entailment graphs, which involves a novel
high-recall open relation extraction (ORE) method and the first Chinese
fine-grained entity typing dataset under the FIGER type ontology. Through
experiments on the Levy-Holt dataset, we verify the strength of our Chinese
entailment graph, and reveal the cross-lingual complementarity: on the parallel
Levy-Holt dataset, an ensemble of Chinese and English entailment graphs
outperforms both monolingual graphs, and raises unsupervised SOTA by 4.7 AUC
points.

Learning feature interactions is important to the model performance of online
advertising services. As a result, extensive efforts have been devoted to
designing effective architectures to learn feature interactions. However, we
observe that the practical performance of those designs can vary from dataset
to dataset, even when the order of interactions claimed to be captured is the
same. That indicates different designs may have different advantages and the
interactions captured by them have non-overlapping information. Motivated by
this observation, we propose DHEN - a deep and hierarchical ensemble
architecture that can leverage strengths of heterogeneous interaction modules
and learn a hierarchy of the interactions under different orders. To overcome
the challenge brought by DHEN's deeper and multi-layer structure in training,
we propose a novel co-designed training system that can further improve the
training efficiency of DHEN. Experiments of DHEN on large-scale dataset from
CTR prediction tasks attained 0.27\% improvement on the Normalized Entropy (NE)
of prediction and 1.2x better training throughput than state-of-the-art
baseline, demonstrating their effectiveness in practice.

While deep learning surpasses human-level performance in narrow and specific
vision tasks, it is fragile and over-confident in classification. For example,
minor transformations in perspective, illumination, or object deformation in
the image space can result in drastically different labeling, which is
especially transparent via adversarial perturbations. On the other hand, human
visual perception is orders of magnitude more robust to changes in the input
stimulus. But unfortunately, we are far from fully understanding and
integrating the underlying mechanisms that result in such robust perception. In
this work, we introduce a novel method of incorporating temporal dynamics into
static image understanding. We describe a neuro-inspired method that decomposes
a single image into a series of coarse-to-fine images that simulates how
biological vision integrates information over time. Next, we demonstrate how
our novel visual perception framework can utilize this information "over time"
using a biologically plausible algorithm with recurrent units, and as a result,
significantly improving its accuracy and robustness over standard CNNs. We also
compare our proposed approach with state-of-the-art models and explicitly
quantify our adversarial robustness properties through multiple ablation
studies. Our quantitative and qualitative results convincingly demonstrate
exciting and transformative improvements over the standard computer vision and
deep learning architectures used today.

We consider the Clock Game - task formulated in the framework of quantum
information theory - that can be used to improve the existing schemes of
quantum-enhanced telescopy. The problem of learning when a stellar photon
reaches a telescope is translated into an abstract game, which we call the
Clock Game. A winning strategy is provided that involves performing a quantum
non-demolition measurement that verifies which stellar spatio-temporal modes
are occupied by a photon without disturbing the phase information. We prove
tight lower bounds on the entanglement cost needed to win the Clock Game, with
the amount of necessary entangled bits equaling the number of time-bins being
distinguished. This lower bound on the entanglement cost applies to any
telescopy protocol protocol that aims to non-destructively extract the time-bin
information of an incident photon through local measurements, and our result
implies that the protocol of [Khabiboulline et al. Phys. Rev. Lett. 123, 70504
(2019)] is optimal in terms of entanglement consumption. The full task of the
phase extraction is also considered, and we show that the quantum Fisher
Information of the stellar phase can be achieved by local measurements and
shared entanglement without the necessity of non-linear optical operations. The
optimal phase measurement is achieved asymptotically with increasing number of
ancilla qubits, whereas a single qubit pair is required if non-linear
operations are allowed.

We develop a method to learn bio-inspired foraging policies using human data.
We conduct an experiment where humans are virtually immersed in an open field
foraging environment and are trained to collect the highest amount of rewards.
A Markov Decision Process (MDP) framework is introduced to model the human
decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood
estimation is used to train Neural Networks (NN) that map human decisions to
observed states. The results show that passive imitation substantially
underperforms humans. We further refine the human-inspired policies via
Reinforcement Learning (RL), using on-policy algorithms that are more suitable
to learn from pre-trained networks. We show that the combination of IL and RL
can match human results and that good performance strongly depends on an
egocentric representation of the environment. The developed methodology can be
used to efficiently learn policies for unmanned vehicles which have to solve
missions in an open field environment.

Given a supervised machine learning problem where the training set has been
subject to a known sampling bias, how can a model be trained to fit the
original dataset? We achieve this through the Bayesian inference framework by
altering the posterior distribution to account for the sampling function. We
then apply this solution to binary logistic regression, and discuss scenarios
where a dataset might be subject to intentional sample bias such as label
imbalance. This technique is widely applicable for statistical inference on big
data, from the medical sciences to image recognition to marketing. Familiarity
with it will give the practitioner tools to improve their inference pipeline
from data collection to model selection.

In this work, we carried out a study about the use of attention-based
algorithms to automate the categorization of Brazilian case law documents. We
used data from the Kollemata Project to produce two distinct datasets with
adequate class systems. Then, we implemented a multi-class and multi-label
version of BERT and fine-tuned different BERT models with the produced
datasets. We evaluated several metrics, adopting the micro-averaged F1-Score as
our main metric for which we obtained a performance value of F1-micro=0.72
corresponding to gains of 30 percent points over the tested statistical
baseline. In this work, we carried out a study about the use of attention-based
used data from the \textit{Kollemata} Project to produce two distinct datasets
with adequate class systems. Then, we implemented a multi-class and multi-label
our main metric for which we obtained a performance value of $\langle
\mathcal{F}_1 \rangle_{micro}=0.72$ corresponding to gains of 30 percent points
over the tested statistical baseline.

Memory-augmented neural networks enhance a neural network with an external
key-value memory whose complexity is typically dominated by the number of
support vectors in the key memory. We propose a generalized key-value memory
that decouples its dimension from the number of support vectors by introducing
a free parameter that can arbitrarily add or remove redundancy to the key
memory representation. In effect, it provides an additional degree of freedom
to flexibly control the trade-off between robustness and the resources required
to store and compute the generalized key-value memory. This is particularly
useful for realizing the key memory on in-memory computing hardware where it
exploits nonideal, but extremely efficient non-volatile memory devices for
dense storage and computation. Experimental results show that adapting this
parameter on demand effectively mitigates up to 44% nonidealities, at equal
accuracy and number of devices, without any need for neural network retraining.

We propose a method for identifying an ectopic activation in the heart
non-invasively. Ectopic activity in the heart can trigger deadly arrhythmias.
The localization of the ectopic foci or earliest activation sites (EASs) is
therefore a critical information for cardiologists in deciding the optimal
treatment. In this work, we formulate the identification problem as a global
optimization problem, by minimizing the mismatch between the ECG predicted by a
cardiac model, when paced at a given EAS, and the observed ECG during the
ectopic activity. Our cardiac model amounts at solving an anisotropic eikonal
equation for cardiac activation and the forward bidomain model in the torso
with the lead field approach for computing the ECG. We build a Gaussian process
surrogate model of the loss function on the heart surface to perform Bayesian
optimization. In this procedure, we iteratively evaluate the loss function
following the lower confidence bound criterion, which combines exploring the
surface with exploitation of the minimum region. We also extend this framework
to incorporate multiple levels of fidelity of the model. We show that our
procedure converges to the minimum only after $11.7\pm10.4$ iterations (20
independent runs) for the single-fidelity case and $3.5\pm1.7$ iterations for
the multi-fidelity case. We envision that this tool could be applied in real
time in a clinical setting to identify potentially dangerous EASs.

The Sounds of New York City (SONYC) wireless sensor network (WSN) has been
fielded in Manhattan and Brooklyn over the past five years, as part of a larger
human-in-the-loop cyber-physical control system for monitoring, analyzing, and
mitigating urban noise pollution. We describe the evolution of the 2-tier SONYC
WSN from an acoustic data collection fabric into a 3-tier in situ noise
complaint monitoring WSN, and its current evaluation. The added tier consists
of long-range (LoRa), multi-hop networks of a new low-power acoustic mote, MKII
("Mach 2"), that we have designed and fabricated. MKII motes are notable in
three ways: First, they advance machine learning capability at mote-scale in
this application domain by introducing a real-time Convolutional Neural Network
(CNN) based embedding model that is competitive with alternatives while also
requiring 10$\times$ lesser training data and $\sim$2 orders of magnitude fewer
runtime resources. Second, they are conveniently deployed relatively far from
higher-tier base station nodes without assuming power or network infrastructure
support at operationally relevant sites (such as construction zones), yielding
a relatively low-cost solution. And third, their networking is frequency agile,
unlike conventional LoRa networks: it tolerates in a distributed,
self-stabilizing way the variable external interference and link fading in the
cluttered 902-928MHz ISM band urban environment by dynamically choosing good
frequencies using an efficient new method that combines passive and active

Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and
overcoming their negative repercussions is considered a hurdle in biomedical
imaging. The combination of a specified set of modalities, which is selected
depending on the scenario and anatomical part being scanned, will provide
medical practitioners with full information about the region of interest in the
human body, hence the missing MRI sequences should be reimbursed. The
compensation of the adverse impact of losing useful information owing to the
lack of one or more modalities is a well-known challenge in the field of
computer vision, particularly for medical image processing tasks including
tumour segmentation, tissue classification, and image generation. Various
approaches have been developed over time to mitigate this problem's negative
implications and this literature review goes through a significant number of
the networks that seek to do so. The approaches reviewed in this work are
reviewed in detail, including earlier techniques such as synthesis methods as
well as later approaches that deploy deep learning, such as common latent space
models, knowledge distillation networks, mutual information maximization, and
generative adversarial networks (GANs). This work discusses the most important
approaches that have been offered at the time of this writing, examining the
novelty, strength, and weakness of each one. Furthermore, the most commonly
used MRI datasets are highlighted and described. The main goal of this research
is to offer a performance evaluation of missing modality compensating networks,
as well as to outline future strategies for dealing with this issue.

There has been significant progress in creating machine learning models that
identify objects in scenes along with their associated attributes and
relationships; however, there is a large gap between the best models and human
capabilities. One of the major reasons for this gap is the difficulty in
collecting sufficient amounts of annotated relations and attributes for
training these systems. While some attributes and relations are abundant, the
distribution in the natural world and existing datasets is long tailed. In this
paper, we address this problem by introducing a novel incremental active
learning framework that asks for attributes and relations in visual scenes.
While conventional active learning methods ask for labels of specific examples,
we flip this framing to allow agents to ask for examples from specific
categories. Using this framing, we introduce an active sampling method that
asks for examples from the tail of the data distribution and show that it
outperforms classical active learning methods on Visual Genome.

In recent years, deep learning approaches have been proved good performance
in traffic flow prediction, many complex models have been proposed to make
traffic flow prediction more accurate. However, lacking transparency limits the
domain experts on understanding when and where the input data mainly impact the
results. Most urban experts and planners can only adjust traffic based on their
own experience and can not react effectively toward the potential traffic jam.
To tackle this problem, we adapt Shapley value and present a visualization
analysis system , which can provide experts with the interpretation of traffic
flow prediction. TrafPS consists of three layers, from data process to results
computation and visualization. We design three visualization views in TrafPS to
support the prediction analysis process. One demonstration shows that the
TrafPS supports an effective analytical pipeline on interpreting the prediction
flow to users and provides an intuitive visualization for decision making.

The current standard approach to scaling transformer language models trains
each model size from a different random initialization. As an alternative, we
consider a staged training setup that begins with a small model and
incrementally increases the amount of compute used for training by applying a
"growth operator" to increase the model depth and width. By initializing each
stage with the output of the previous one, the training process effectively
re-uses the compute from prior stages and becomes more efficient. Our growth
operators each take as input the entire training state (including model
parameters, optimizer state, learning rate schedule, etc.) and output a new
training state from which training continues. We identify two important
properties of these growth operators, namely that they preserve both the loss
and the "training dynamics" after applying the operator. While the
loss-preserving property has been discussed previously, to the best of our
knowledge this work is the first to identify the importance of preserving the
training dynamics (the rate of decrease of the loss during training). To find
the optimal schedule for stages, we use the scaling laws from (Kaplan et al.,
2020) to find a precise schedule that gives the most compute saving by starting
a new stage when training efficiency starts decreasing. We empirically validate
our growth operators and staged training for autoregressive language models,
showing up to 22% compute savings compared to a strong baseline trained from
scratch. Our code is available at https://github.com/allenai/staged-training.

A significant challenge in the tagging of boosted objects via
machine-learning technology is the prohibitive computational cost associated
with training sophisticated models. Nevertheless, the universality of QCD
suggests that a large amount of the information learnt in the training is
common to different physical signals and experimental setups. In this article,
we explore the use of transfer learning techniques to develop fast and
data-efficient jet taggers that leverage such universality. We consider the
graph neural networks LundNet and ParticleNet, and introduce two prescriptions
to transfer an existing tagger into a new signal based either on fine-tuning
all the weights of a model or alternatively on freezing a fraction of them. In
the case of $W$-boson and top-quark tagging, we find that one can obtain
reliable taggers using an order of magnitude less data with a corresponding
speed-up of the training process. Moreover, while keeping the size of the
training data set fixed, we observe a speed-up of the training by up to a
factor of three. This offers a promising avenue to facilitate the use of such
tools in collider physics experiments.

Of theories for why large-scale machine learning models generalize despite
being vastly overparameterized, which of their assumptions are needed to
capture the qualitative phenomena of generalization in the real world? On one
hand, we find that most theoretical analyses fall short of capturing these
qualitative phenomena even for kernel regression, when applied to kernels
derived from large-scale neural networks (e.g., ResNet-50) and real data (e.g.,
CIFAR-100). On the other hand, we find that the classical GCV estimator (Craven
and Wahba, 1978) accurately predicts generalization risk even in such
overparameterized settings. To bolster this empirical finding, we prove that
the GCV estimator converges to the generalization risk whenever a local random
matrix law holds. Finally, we apply this random matrix theory lens to explain
why pretrained representations generalize better as well as what factors govern
scaling laws for kernel regression. Our findings suggest that random matrix
theory, rather than just being a toy model, may be central to understanding the
properties of neural representations in practice.

This paper shows that self-supervised visual pre-training from real-world
images is effective for learning motor control tasks from pixels. We first
train the visual representations by masked modeling of natural images. We then
freeze the visual encoder and train neural network controllers on top with
reinforcement learning. We do not perform any task-specific fine-tuning of the
encoder; the same visual representations are used for all motor control tasks.
To the best of our knowledge, this is the first self-supervised model to
exploit real-world images at scale for motor control. To accelerate progress in
learning from pixels, we contribute a benchmark suite of hand-designed tasks
varying in movements, scenes, and robots. Without relying on labels,
state-estimation, or expert demonstrations, we consistently outperform
supervised encoders by up to 80% absolute success rate, sometimes even matching
the oracle state performance. We also find that in-the-wild images, e.g., from
YouTube or Egocentric videos, lead to better visual representations for various
manipulation tasks than ImageNet images.

While recent automated data augmentation methods lead to state-of-the-art
results, their design spaces and the derived data augmentation strategies still
incorporate strong human priors. In this work, instead of fixing a set of
hand-picked default augmentations alongside the searched data augmentations, we
propose a fully automated approach for data augmentation search named Deep
AutoAugment (DeepAA). DeepAA progressively builds a multi-layer data
augmentation pipeline from scratch by stacking augmentation layers one at a
time until reaching convergence. For each augmentation layer, the policy is
optimized to maximize the cosine similarity between the gradients of the
original and augmented data along the direction with low variance. Our
experiments show that even without default augmentations, we can learn an
augmentation policy that achieves strong performance with that of previous
works. Extensive ablation studies show that the regularized gradient matching
is an effective search method for data augmentation policies. Our code is
available at: https://github.com/MSU-MLSys-Lab/DeepAA .

In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever
that does not require any supervised data for training. Specifically, we first
present Iterative Contrastive Learning (ICoL) that iteratively trains the query
and document encoders with a cache mechanism. ICoL not only enlarges the number
of negative instances but also keeps representations of cached examples in the
same hidden space. We then propose Lexicon-Enhanced Dense Retrieval (LEDR) as a
simple yet effective way to enhance dense retrieval with lexical matching. We
evaluate LaPraDoR on the recently proposed BEIR benchmark, including 18
datasets of 9 zero-shot text retrieval tasks. Experimental results show that
LaPraDoR achieves state-of-the-art performance compared with supervised dense
retrieval models, and further analysis reveals the effectiveness of our
training strategy and objectives. Compared to re-ranking, our lexicon-enhanced
approach can be run in milliseconds (22.5x faster) while achieving superior

Scheduled batch jobs have been widely used on the asynchronous computing
platforms to execute various enterprise applications, including the scheduled
notifications and the candidate computation for the modern recommender systems.
It is important to deliver or update the information to the users at the right
time to maintain the user experience and the execution impact. However, it is
challenging to provide a versatile execution time optimization solution for the
user-basis scheduled jobs to satisfy various product scenarios while
maintaining reasonable infrastructure resource consumption. In this paper, we
describe how we apply a pointwise learning-to-rank approach plus a "best time
policy" in the best time selection. In addition, we propose a value model
approach to efficiently leverage multiple streams of user activity signals in
our scheduling decisions of the execution time. Our optimization approach has
been successfully tested with production traffic that serves billions of users
per day, with statistically significant improvements in various product
metrics, including the notifications and content candidate generation. To the
best of our knowledge, our study represents the first ML-based multi-tenant
solution to the execution time optimization problem for the scheduled jobs at a
large industrial scale.

Physical theories grounded in mathematical symmetries are an essential
component of our understanding of a wide range of properties of the universe.
Similarly, in the domain of machine learning, an awareness of symmetries such
as rotation or permutation invariance has driven impressive performance
breakthroughs in computer vision, natural language processing, and other
important applications. In this report, we argue that both the physics
community and the broader machine learning community have much to understand
and potentially to gain from a deeper investment in research concerning
symmetry group equivariant machine learning architectures. For some
applications, the introduction of symmetries into the fundamental structural
design can yield models that are more economical (i.e. contain fewer, but more
expressive, learned parameters), interpretable (i.e. more explainable or
directly mappable to physical quantities), and/or trainable (i.e. more
efficient in both data and computational requirements). We discuss various
figures of merit for evaluating these models as well as some potential benefits
and limitations of these methods for a variety of physics applications.
Research and investment into these approaches will lay the foundation for
future architectures that are potentially more robust under new computational
paradigms and will provide a richer description of the physical systems to
which they are applied.

Autonomous systems are being developed and deployed in situations that may
require some degree of ethical decision-making ability. As a result, research
in machine ethics has proliferated in recent years. This work has included
using moral dilemmas as validation mechanisms for implementing decision-making
algorithms in ethically-loaded situations. Using trolley-style problems in the
context of autonomous vehicles as a case study, I argue (1) that this is a
misapplication of philosophical thought experiments because (2) it fails to
appreciate the purpose of moral dilemmas, and (3) this has potentially
catastrophic consequences; however, (4) there are uses of moral dilemmas in
machine ethics that are appropriate and the novel situations that arise in a
machine-learning context can shed some light on philosophical work in ethics.

Developing neuromorphic intelligence on event-based datasets with Spiking
Neural Networks (SNNs) has recently attracted much research attention. However,
the limited size of event-based datasets makes SNNs prone to overfitting and
unstable convergence. This issue remains unexplored by previous academic works.
In an effort to minimize this generalization gap, we propose Neuromorphic Data
Augmentation (NDA), a family of geometric augmentations specifically designed
for event-based datasets with the goal of significantly stabilizing the SNN
training and reducing the generalization gap between training and test
performance. The proposed method is simple and compatible with existing SNN
training pipelines. Using the proposed augmentation, for the first time, we
demonstrate the feasibility of unsupervised contrastive learning for SNNs. We
conduct comprehensive experiments on prevailing neuromorphic vision benchmarks
and show that NDA yields substantial improvements over previous
state-of-the-art results. For example, the NDA-based SNN achieves accuracy gain
on CIFAR10-DVS and N-Caltech 101 by 10.1% and 13.7%, respectively. Code is
available on GitHub https://github.com/Intelligent-Computing-Lab-Yale/NDA_SNN

Automatic Differentiation (AD) is instrumental for science and industry. It
is a tool to evaluate the derivative of a function specified through a computer
program. The range of AD application domain spans from Machine Learning to
Robotics to High Energy Physics. Computing gradients with the help of AD is
guaranteed to be more precise than the numerical alternative and have a low,
constant factor more arithmetical operations compared to the original function.
Moreover, AD applications to domain problems typically are computationally
bound. They are often limited by the computational requirements of
high-dimensional parameters and thus can benefit from parallel implementations
on graphics processing units (GPUs). Clad aims to enable differential analysis
for C/C++ and CUDA and is a compiler-assisted AD tool available both as a
compiler extension and in ROOT. Moreover, Clad works as a plugin extending the
Clang compiler; as a plugin extending the interactive interpreter Cling; and as
a Jupyter kernel extension based on xeus-cling. We demonstrate the advantages
of parallel gradient computations on GPUs with Clad. We explain how to bring
forth a new layer of optimization and a proportional speed up by extending Clad
to support CUDA. The gradients of well-behaved C++ functions can be
automatically executed on a GPU. The library can be easily integrated into
existing frameworks or used interactively. Furthermore, we demonstrate the
achieved application performance improvements, including (~10x) in ROOT
histogram fitting and corresponding performance gains from offloading to GPUs.

Predicting sets of outcomes -- instead of unique outcomes -- is a promising
solution to uncertainty quantification in statistical learning. Despite a rich
literature on constructing prediction sets with statistical guarantees,
adapting to unknown covariate shift -- a prevalent issue in practice -- poses a
serious unsolved challenge. In this paper, we show that prediction sets with
finite-sample coverage guarantee are uninformative and propose a novel flexible
distribution-free method, PredSet-1Step, to efficiently construct prediction
sets with an asymptotic coverage guarantee under unknown covariate shift. We
formally show that our method is \textit{asymptotically probably approximately
correct}, having well-calibrated coverage error with high confidence for large
samples. We illustrate that it achieves nominal coverage in a number of
experiments and a data set concerning HIV risk prediction in a South African
cohort study. Our theory hinges on a new bound for the convergence rate of the
coverage of Wald confidence intervals based on general asymptotically linear

Learning effective protein representations is critical in a variety of tasks
in biology such as predicting protein function or structure. Existing
approaches usually pretrain protein language models on a large number of
unlabeled amino acid sequences and then finetune the models with some labeled
data in downstream tasks. Despite the effectiveness of sequence-based
approaches, the power of pretraining on known protein structures, which are
available in smaller numbers only, has not been explored for protein property
prediction, though protein structures are known to be determinants of protein
function. In this paper, we propose to pretrain protein representations
according to their 3D structures. We first present a simple yet effective
encoder to learn the geometric features of a protein. We pretrain the protein
graph encoder by leveraging multiview contrastive learning and different
self-prediction tasks. Experimental results on both function prediction and
fold classification tasks show that our proposed pretraining methods outperform
or are on par with the state-of-the-art sequence-based methods, while using
much less data. Our implementation is available at
https://github.com/DeepGraphLearning/GearNet.

In this paper, we present GATSPI, a novel GPU accelerated logic gate
simulator that enables ultra-fast power estimation for industry sized ASIC
designs with millions of gates. GATSPI is written in PyTorch with custom CUDA
kernels for ease of coding and maintainability. It achieves simulation kernel
speedup of up to 1668X on a single-GPU system and up to 7412X on a multiple-GPU
system when compared to a commercial gate-level simulator running on a single
CPU core. GATSPI supports a range of simple to complex cell types from an
industry standard cell library and SDF conditional delay statements without
requiring prior calibration runs and produces industry-standard SAIF files from
delay-aware gate-level simulation. Finally, we deploy GATSPI in a
glitch-optimization flow, achieving a 1.4% power saving with a 449X speedup in
turnaround time compared to a similar flow using a commercial simulator.

Ultra-widefield (UWF) imaging is a promising modality that captures a larger
retinal field of view compared to traditional fundus photography. Previous
studies showed that deep learning (DL) models are effective for detecting
retinal disease in UWF images, but primarily considered individual diseases
under less-than-realistic conditions (excluding images with other diseases,
artefacts, comorbidities, or borderline cases; and balancing healthy and
diseased images) and did not systematically investigate which regions of the
UWF images are relevant for disease detection. We first improve on the state of
the field by proposing a DL model that can recognise multiple retinal diseases
under more realistic conditions. We then use global explainability methods to
identify which regions of the UWF images the model generally attends to. Our
model performs very well, separating between healthy and diseased retinas with
an area under the curve (AUC) of 0.9206 on an internal test set, and an AUC of
0.9841 on a challenging, external test set. When diagnosing specific diseases,
the model attends to regions where we would expect those diseases to occur. We
further identify the posterior pole as the most important region in a purely
data-driven fashion. Surprisingly, 10% of the image around the posterior pole
is sufficient for achieving comparable performance to having the full images

We present a new multi-sensor dataset for 3D surface reconstruction. It
includes registered RGB and depth data from sensors of different resolutions
and modalities: smartphones, Intel RealSense, Microsoft Kinect, industrial
cameras, and structured-light scanner. The data for each scene is obtained
under a large number of lighting conditions, and the scenes are selected to
emphasize a diverse set of material properties challenging for existing
algorithms. In the acquisition process, we aimed to maximize high-resolution
depth data quality for challenging cases, to provide reliable ground truth for
learning algorithms. Overall, we provide over 1.4 million images of 110
different scenes acquired at 14 lighting conditions from 100 viewing
directions. We expect our dataset will be useful for evaluation and training of
3D reconstruction algorithms of different types and for other related tasks.
Our dataset and accompanying software will be available online.

We will review the results for stochastic learning strategies, both classical
(one-shot and iterative) and quantum (one-shot only), for optimizing the
available many-choice resources among a large number of competing agents,
developed over the last decade in the context of the Kolkata Paise Restaurant
Problem. Apart from a few rigorous and approximate analytical results, both for
classical and quantum strategies, most of the interesting results on the phase
transition behavior (obtained so far for the classical model) using classical
Monte Carlo simulations. All these, including the applications to computer
science (job or resource allotments in Internet-of-Things), transport
engineering (on-line vehicle hire problems), operation research (optimizing
efforts for delegated search problem, efficient solution of Travelling Salesman
problem), etc will be discussed.

This paper presents ActiveMLP, a general MLP-like backbone for computer
vision. The three existing dominant network families, i.e., CNNs, Transformers
and MLPs, differ from each other mainly in the ways to fuse contextual
information into a given token, leaving the design of more effective
token-mixing mechanisms at the core of backbone architecture development. In
ActiveMLP, we propose an innovative token-mixer, dubbed Active Token Mixer
(ATM), to actively incorporate contextual information from other tokens in the
global scope into the given one. This fundamental operator actively predicts
where to capture useful contexts and learns how to fuse the captured contexts
with the original information of the given token at channel levels. In this
way, the spatial range of token-mixing is expanded and the way of token-mixing
is reformed. With this design, ActiveMLP is endowed with the merits of global
receptive fields and more flexible content-adaptive information fusion.
Extensive experiments demonstrate that ActiveMLP is generally applicable and
comprehensively surpasses different families of SOTA vision backbones by a
clear margin on a broad range of vision tasks, including visual recognition and
dense prediction tasks. The code and models will be available at
https://github.com/microsoft/ActiveMLP.

Effectiveness and interpretability are two essential properties for
trustworthy AI systems. Most recent studies in visual reasoning are dedicated
to improving the accuracy of predicted answers, and less attention is paid to
explaining the rationales behind the decisions. As a result, they commonly take
advantage of spurious biases instead of actually reasoning on the
visual-textual data, and have yet developed the capability to explain their
decision making by considering key information from both modalities. This paper
aims to close the gap from three distinct perspectives: first, we define a new
type of multi-modal explanations that explain the decisions by progressively
traversing the reasoning process and grounding keywords in the images. We
develop a functional program to sequentially execute different reasoning steps
and construct a new dataset with 1,040,830 multi-modal explanations. Second, we
identify the critical need to tightly couple important components across the
visual and textual modalities for explaining the decisions, and propose a novel
explanation generation method that explicitly models the pairwise
correspondence between words and regions of interest. It improves the visual
grounding capability by a considerable margin, resulting in enhanced
interpretability and reasoning performance. Finally, with our new data and
method, we perform extensive analyses to study the effectiveness of our
explanation under different settings, including multi-task learning and
transfer learning. Our code and data are available at
https://github.com/szzexpoi/rex.

Uncertainty quantification has received increasing attention in machine
learning in the recent past. In particular, a distinction between aleatoric and
epistemic uncertainty has been found useful in this regard. The latter refers
to the learner's (lack of) knowledge and appears to be especially difficult to
measure and quantify. In this paper, we analyse a recent proposal based on the
idea of a second-order learner, which yields predictions in the form of
distributions over probability distributions. While standard (first-order)
learners can be trained to predict accurate probabilities, namely by minimising
suitable loss functions on sample data, we show that loss minimisation does not
work for second-order predictors: The loss functions proposed for inducing such
predictors do not incentivise the learner to represent its epistemic
uncertainty in a faithful way.

The Deep Underground Neutrino Experiment (DUNE) is a next-generation
long-baseline neutrino oscillation experiment with a primary physics goal of
observing neutrino and antineutrino oscillation patterns to precisely measure
the parameters governing long-baseline neutrino oscillation in a single
experiment, and to test the three-flavor paradigm. DUNE's design has been
developed by a large, international collaboration of scientists and engineers
to have unique capability to measure neutrino oscillation as a function of
energy in a broadband beam, to resolve degeneracy among oscillation parameters,
and to control systematic uncertainty using the exquisite imaging capability of
massive LArTPC far detector modules and an argon-based near detector. DUNE's
neutrino oscillation measurements will unambiguously resolve the neutrino mass
ordering and provide the sensitivity to discover CP violation in neutrinos for
a wide range of possible values of $\delta_{CP}$. DUNE is also uniquely
sensitive to electron neutrinos from a galactic supernova burst, and to a broad
range of physics beyond the Standard Model (BSM), including nucleon decays.
DUNE is anticipated to begin collecting physics data with Phase I, an initial
experiment configuration consisting of two far detector modules and a minimal
suite of near detector components, with a 1.2 MW proton beam. To realize its
extensive, world-leading physics potential requires the full scope of DUNE be
completed in Phase II. The three Phase II upgrades are all necessary to achieve
DUNE's physics goals: (1) addition of far detector modules three and four for a
total FD fiducial mass of at least 40 kt, (2) upgrade of the proton beam power
from 1.2 MW to 2.4 MW, and (3) replacement of the near detector's temporary
muon spectrometer with a magnetized, high-pressure gaseous argon TPC and
calorimeter.

Signed Language Processing (SLP) concerns the automated processing of signed
languages, the main means of communication of Deaf and hearing impaired
individuals. SLP features many different tasks, ranging from sign recognition
to translation and production of signed speech, but has been overlooked by the
NLP community thus far. In this paper, we bring to attention the task of
modelling the phonology of sign languages. We leverage existing resources to
construct a large-scale dataset of American Sign Language signs annotated with
six different phonological properties. We then conduct an extensive empirical
study to investigate whether data-driven end-to-end and feature-based
approaches can be optimised to automatically recognise these properties. We
find that, despite the inherent challenges of the task, graph-based neural
networks that operate over skeleton features extracted from raw videos are able
to succeed at the task to a varying degree. Most importantly, we show that this
performance pertains even on signs unobserved during training.

Selfie images enjoy huge popularity in social media. The same platforms
centered around sharing this type of images offer filters to beautify them or
incorporate augmented reality effects. Studies suggests that filtered images
attract more views and engagement. Selfie images are also in increasing use in
security applications due to mobiles becoming data hubs for many transactions.
Also, video conference applications, boomed during the pandemic, include such
filters.
  Such filters may destroy biometric features that would allow person
recognition or even detection of the face itself, even if such commodity
applications are not necessarily used to compromise facial systems. This could
also affect subsequent investigations like crimes in social media, where
automatic analysis is usually necessary given the amount of information posted
in social sites or stored in devices or cloud repositories.
  To help in counteracting such issues, we contribute with a database of facial
images that includes several manipulations. It includes image enhancement
filters (which mostly modify contrast and lightning) and augmented reality
filters that incorporate items like animal noses or glasses. Additionally,
images with sunglasses are processed with a reconstruction network trained to
learn to reverse such modifications. This is because obfuscating the eye region
has been observed in the literature to have the highest impact on the accuracy
of face detection or recognition.
  We start from the popular Labeled Faces in the Wild (LFW) database, to which
we apply different modifications, generating 8 datasets. Each dataset contains
4,324 images of size 64 x 64, with a total of 34,592 images. The use of a
public and widely employed face dataset allows for replication and comparison.
  The created database is available at
https://github.com/HalmstadUniversityBiometrics/LFW-Beautified

The intraday (ID) electricity market has received an increasing attention in
the recent EU electricity-market discussions. This is partly because the
uncertainty in the underlying power system is growing and the ID market
provides an adjustment platform to deal with such uncertainties. Hence, market
participants need a proper ID market price model to optimally adjust their
positions by trading in the market. Inadequate historical data for ID market
price makes it more challenging to model. This paper proposes long short-term
memory, deep convolutional generative adversarial networks, and No-U-Turn
sampler algorithms to model ID market prices. Our proposed econometric ID
market price models are applied to the Nordic ID price data and their promising
performance are illustrated.

"Species-sampling" problems (SSPs) refer to a broad class of statistical
problems that, given an observable sample from an unknown population of
individuals belonging to some species, call for estimating features of the
unknown species composition of additional unobservable samples from the same
population. Among SSPs, the problems of estimating coverage probabilities, the
number of unseen species and coverages of prevalences have emerged over the
past three decades for being the objects of numerous studies, both in methods
and applications, mostly within the field of biological sciences but also in
machine learning, electrical engineering and information theory. In this paper,
we present an overview of Bayesian nonparametric (BNP) inference for such three
SSPs under the popular Pitman--Yor process (PYP) prior: i) we introduce each
SSP in the classical (frequentist) nonparametric framework, and review its
posterior analyses in the BNP framework; ii) we improve on computation and
interpretability of existing posterior distributions, typically expressed
through complicated combinatorial numbers, by establishing novel posterior
representations in terms of simple compound Binomial and Hypergeometric
distributions. The critical question of estimating the discount and scale
parameters of the PYP prior is also considered and investigated, establishing a
general property of Bayesian consistency with respect to the hierarchical Bayes
and empirical Bayes approaches, that is: the discount parameter can be always
estimated consistently, whereas the scale parameter cannot be estimated
consistently, thus advising caution in posterior inference. We conclude our
work by discussing other SSPs, and presenting some emerging generalizations of
SSPs, mostly in biological sciences, which deal with "feature-sampling"
problems, multiple populations of individuals sharing species and classes of
Markov chains.

Learning a generalized prior for natural image restoration is an important
yet challenging task. Early methods mostly involved handcrafted priors
including normalized sparsity, l_0 gradients, dark channel priors, etc.
Recently, deep neural networks have been used to learn various image priors but
do not guarantee to generalize. In this paper, we propose a novel approach that
embeds a task-agnostic prior into a transformer. Our approach, named
Task-Agnostic Prior Embedding (TAPE), consists of two stages, namely,
task-agnostic pre-training and task-specific fine-tuning, where the first stage
embeds prior knowledge about natural images into the transformer and the second
stage extracts the knowledge to assist downstream image restoration.
Experiments on various types of degradation validate the effectiveness of TAPE.
The image restoration performance in terms of PSNR is improved by as much as
1.45dB and even outperforms task-specific algorithms. More importantly, TAPE
shows the ability of disentangling generalized image priors from degraded
images, which enjoys favorable transfer ability to unknown downstream tasks.

Hilbert series are a standard tool in algebraic geometry, and more recently
are finding many uses in theoretical physics. This summary reviews work
applying machine learning to databases of them; and was prepared for the
proceedings of the Nankai Symposium on Mathematical Dialogues, 2021.

Medical ultrasound (US) is one of the most widely used imaging modalities in
clinical practice. However, its use presents unique challenges such as variable
imaging quality. Deep learning (DL) can be used as an advanced medical US image
analysis tool, while the performance of the DL model is greatly limited by the
scarcity of big datasets. Here, we develop semi-supervised classification
enhancement (SSCE) structures by combining convolutional neural network (CNN)
and generative adversarial network (GAN) to address the data shortage. A breast
cancer dataset with 780 images is used as our base dataset. The results show
that our SSCE structures obtain an accuracy of up to 97.9%, showing a maximum
21.6% improvement compared with utilizing CNN models alone and outperforming
the previous methods using the same dataset by up to 23.9%. We believe our
proposed state-of-the-art method can be regarded as a potential auxiliary tool
for the diagnoses of medical US images.

Recent studies have shown the advantages of evaluating NLG systems using
pairwise comparisons as opposed to direct assessment. Given $k$ systems, a
naive approach for identifying the top-ranked system would be to uniformly
obtain pairwise comparisons from all ${k \choose 2}$ pairs of systems. However,
this can be very expensive as the number of human annotations required would
grow quadratically with $k$. In this work, we introduce Active Evaluation, a
framework to efficiently identify the top-ranked system by actively choosing
system pairs for comparison using dueling bandit algorithms. We perform
extensive experiments with 13 dueling bandits algorithms on 13 NLG evaluation
datasets spanning 5 tasks and show that the number of human annotations can be
reduced by 80%. To further reduce the number of human annotations, we propose
model-based dueling bandit algorithms which combine automatic evaluation
metrics with human evaluations. Specifically, we eliminate sub-optimal systems
even before the human annotation process and perform human evaluations only on
test examples where the automatic metric is highly uncertain. This reduces the
number of human annotations required further by 89%. In effect, we show that
identifying the top-ranked system requires only a few hundred human
annotations, which grow linearly with $k$. Lastly, we provide practical
recommendations and best practices to identify the top-ranked system
efficiently. Our code has been made publicly available at
https://github.com/akashkm99/duelnlg

Deep artificial neural networks (DNNs) have moved to the forefront of medical
image analysis due to their success in classification, segmentation, and
detection challenges. A principal challenge in large-scale deployment of DNNs
in neuroimage analysis is the potential for shifts in signal-to-noise ratio,
contrast, resolution, and presence of artifacts from site to site due to
variances in scanners and acquisition protocols. DNNs are famously susceptible
to these distribution shifts in computer vision. Currently, there are no
benchmarking platforms or frameworks to assess the robustness of new and
existing models to specific distribution shifts in MRI, and accessible
multi-site benchmarking datasets are still scarce or task-specific. To address
these limitations, we propose ROOD-MRI: a platform for benchmarking the
Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and
artifacts in MRI. The platform provides modules for generating benchmarking
datasets using transforms that model distribution shifts in MRI,
implementations of newly derived benchmarking metrics for image segmentation,
and examples for using the methodology with new models and tasks. We apply our
methodology to hippocampus, ventricle, and white matter hyperintensity
segmentation in several large studies, providing the hippocampus dataset as a
publicly available benchmark. By evaluating modern DNNs on these datasets, we
demonstrate that they are highly susceptible to distribution shifts and
corruptions in MRI. We show that while data augmentation strategies can
substantially improve robustness to OOD data for anatomical segmentation tasks,
modern DNNs using augmentation still lack robustness in more challenging
lesion-based segmentation tasks. We finally benchmark U-Nets and
transformer-based models, finding consistent differences in robustness to
particular classes of transforms across architectures.

Instrumental variable (IV) regression relies on instruments to infer causal
effects from observational data with unobserved confounding. We consider IV
regression in time series models, such as vector auto-regressive (VAR)
processes. Direct applications of i.i.d. techniques are generally inconsistent
as they do not correctly adjust for dependencies in the past. In this paper, we
propose methodology for constructing identifying equations that can be used for
consistently estimating causal effects. To do so, we develop nuisance IV, which
can be of interest even in the i.i.d. case, as it generalizes existing IV
methods. We further propose a graph marginalization framework that allows us to
apply nuisance and other IV methods in a principled way to time series. Our
framework builds on the global Markov property, which we prove holds for VAR
processes. For VAR(1) processes, we prove identifiability conditions that
relate to Jordan forms and are different from the well-known rank conditions in
the i.i.d. case (they do not require as many instruments as covariates, for
example). We provide methods, prove their consistency, and show how the
inferred causal effect can be used for distribution generalization. Simulation
experiments corroborate our theoretical results. We provide ready-to-use Python
code.

Prosumer operators are dealing with extensive challenges to participate in
short-term electricity markets while taking uncertainties into account.
Challenges such as variation in demand, solar energy, wind power, and
electricity prices as well as faster response time in intraday electricity
markets. Machine learning approaches could resolve these challenges due to
their ability to continuous learning of complex relations and providing a
real-time response. Such approaches are applicable with presence of the high
performance computing and big data. To tackle these challenges, a Markov
decision process is proposed and solved with a reinforcement learning algorithm
with proper observations and actions employing tabular Q-learning. Trained
agent converges to a policy which is similar to the global optimal solution. It
increases the prosumer's profit by 13.39% compared to the well-known stochastic
optimization approach.

This work provides an online learning rule that is universally consistent
under processes on (X,Y) pairs, under conditions only on the X process. As a
special case, the conditions admit all processes on (X,Y) such that the process
on X is stationary. This generalizes past results which required stationarity
for the joint process on (X,Y), and additionally required this process to be
ergodic. In particular, this means that ergodicity is superfluous for the
purpose of universally consistent online learning.

Concepts are key building blocks of higher level human understanding.
Explainable AI (XAI) methods have shown tremendous progress in recent years,
however, local attribution methods do not allow to identify coherent model
behavior across samples and therefore miss this essential component. In this
work, we study concept-based explanations and put forward a new definition of
concepts as low-dimensional subspaces of hidden feature layers. We novelly
apply sparse subspace clustering to discover these concept subspaces. Moving
forward, we derive insights from concept subspaces in terms of localized input
(concept) maps, show how to quantify concept relevances and lastly, evaluate
similarities and transferability between concepts. We empirically demonstrate
the soundness of the proposed Sparse Subspace Clustering for Concept Discovery
(SSCCD) method for a variety of different image classification tasks. This
approach allows for deeper insights into the actual model behavior that would
remain hidden from conventional input-level heatmaps.

In training machine learning models for land cover semantic segmentation
there is a stark contrast between the availability of satellite imagery to be
used as inputs and ground truth data to enable supervised learning. While
thousands of new satellite images become freely available on a daily basis,
getting ground truth data is still very challenging, time consuming and costly.
In this paper we present Embedding Earth a self-supervised contrastive
pre-training method for leveraging the large availability of satellite imagery
to improve performance on downstream dense land cover classification tasks.
Performing an extensive experimental evaluation spanning four countries and two
continents we use models pre-trained with our proposed method as initialization
points for supervised land cover semantic segmentation and observe significant
improvements up to 25% absolute mIoU. In every case tested we outperform random
initialization, especially so when ground truth data are scarse. Through a
series of ablation studies we explore the qualities of the proposed approach
and find that learnt features can generalize between disparate regions opening
up the possibility of using the proposed pre-training scheme as a replacement
to random initialization for Earth observation tasks. Code will be uploaded
soon at https://github.com/michaeltrs/DeepSatModels.

This work focuses on designing low complexity hybrid tensor networks by
considering trade-offs between the model complexity and practical performance.
Firstly, we exploit a low-rank tensor-train deep neural network (TT-DNN) to
build an end-to-end deep learning pipeline, namely LR-TT-DNN. Secondly, a
hybrid model combining LR-TT-DNN with a convolutional neural network (CNN),
which is denoted as CNN+(LR-TT-DNN), is set up to boost the performance.
Instead of randomly assigning large TT-ranks for TT-DNN, we leverage Riemannian
gradient descent to determine a TT-DNN associated with small TT-ranks.
Furthermore, CNN+(LR-TT-DNN) consists of convolutional layers at the bottom for
feature extraction and several TT layers at the top to solve regression and
classification problems. We separately assess the LR-TT-DNN and CNN+(LR-TT-DNN)
models on speech enhancement and spoken command recognition tasks. Our
empirical evidence demonstrates that the LR-TT-DNN and CNN+(LR-TT-DNN) models
with fewer model parameters can outperform the TT-DNN and CNN+(TT-DNN)

Non-rigid registration computes an alignment between a source surface with a
target surface in a non-rigid manner. In the past decade, with the advances in
3D sensing technologies that can measure time-varying surfaces, non-rigid
registration has been applied for the acquisition of deformable shapes and has
a wide range of applications. This survey presents a comprehensive review of
non-rigid registration methods for 3D shapes, focusing on techniques related to
dynamic shape acquisition and reconstruction. In particular, we review
different approaches for representing the deformation field, and the methods
for computing the desired deformation. Both optimization-based and
learning-based methods are covered. We also review benchmarks and datasets for
evaluating non-rigid registration methods, and discuss potential future

Fr\'echet Inception Distance (FID) is the primary metric for ranking models
in data-driven generative modeling. While remarkably successful, the metric is
known to sometimes disagree with human judgement. We investigate a root cause
of these discrepancies, and visualize what FID "looks at" in generated images.
We show that the feature space that FID is (typically) computed in is so close
to the ImageNet classifications that aligning the histograms of Top-$N$
classifications between sets of generated and real images can reduce FID
substantially -- without actually improving the quality of results. Thus we
conclude that FID is prone to intentional or accidental distortions. As a
practical example of an accidental distortion, we discuss a case where an
ImageNet pre-trained FastGAN achieves a FID comparable to StyleGAN2, while
being worse in terms of human evaluation

Adversarial training has been shown to be one of the most effective
approaches to improve the robustness of deep neural networks. It is formalized
as a min-max optimization over model weights and adversarial perturbations,
where the weights can be optimized through gradient descent methods like SGD.
In this paper, we show that treating model weights as random variables allows
for enhancing adversarial training through \textbf{S}econd-Order
\textbf{S}tatistics \textbf{O}ptimization (S$^2$O) with respect to the weights.
By relaxing a common (but unrealistic) assumption of previous PAC-Bayesian
frameworks that all weights are statistically independent, we derive an
improved PAC-Bayesian adversarial generalization bound, which suggests that
optimizing second-order statistics of weights can effectively tighten the
bound. In addition to this theoretical insight, we conduct an extensive set of
experiments, which show that S$^2$O not only improves the robustness and
generalization of the trained neural networks when used in isolation, but also
integrates easily in state-of-the-art adversarial training techniques like
TRADES, AWP, MART, and AVMixup, leading to a measurable improvement of these
techniques. The code is available at \url{https://github.com/Alexkael/S2O}.

of information and relative influence between pairs of agents. For a given
graph topology, these algorithms allow for the prediction of formed opinions.
In this work, we study the inverse problem. Given a social learning model and
observations of the evolution of beliefs over time, we aim at identifying the
underlying graph topology. The learned graph allows for the inference of
pairwise influence between agents, the overall influence agents have over the
behavior of the network, as well as the flow of information through the social
network. The proposed algorithm is online in nature and can adapt dynamically
to changes in the graph topology or the true hypothesis.

Self-supervision allows learning meaningful representations of natural
images, which usually contain one central object. How well does it transfer to
multi-entity scenes? We discuss key aspects of learning structured
object-centric representations with self-supervision and validate our insights
through several experiments on the CLEVR dataset. Regarding the architecture,
we confirm the importance of competition for attention-based object discovery,
where each image patch is exclusively attended by one object. For training, we
show that contrastive losses equipped with matching can be applied directly in
a latent space, avoiding pixel-based reconstruction. However, such an
optimization objective is sensitive to false negatives (recurring objects) and
false positives (matching errors). Careful consideration is thus required
around data augmentation and negative sample selection.

Adding interpretability to multivariate methods creates a powerful synergy
for exploring complex physical systems with higher order correlations while
bringing about a degree of clarity in the underlying dynamics of the system.

State-of-the-art reinforcement learning algorithms predominantly learn a
policy from either a numerical state vector or images. Both approaches
generally do not take structural knowledge of the task into account, which is
especially prevalent in robotic applications and can benefit learning if
exploited. This work introduces a neural network architecture that combines
relational inductive bias and visual feedback to learn an efficient position
control policy for robotic manipulation. We derive a graph representation that
models the physical structure of the manipulator and combines the robot's
internal state with a low-dimensional description of the visual scene generated
by an image encoding network. On this basis, a graph neural network trained
with reinforcement learning predicts joint velocities to control the robot. We
further introduce an asymmetric approach of training the image encoder
separately from the policy using supervised learning. Experimental results
demonstrate that, for a 2-DoF planar robot in a geometrically simplistic 2D
environment, a learned representation of the visual scene can replace access to
the explicit coordinates of the reaching target without compromising on the
quality and sample efficiency of the policy. We further show the ability of the
model to improve sample efficiency for a 6-DoF robot arm in a visually
realistic 3D environment.

In this paper, we focus on a new and challenging decentralized machine
learning paradigm in which there are continuous inflows of data to be addressed
and the data are stored in multiple repositories. We initiate the study of data
decentralized class-incremental learning (DCIL) by making the following
contributions. Firstly, we formulate the DCIL problem and develop the
experimental protocol. Secondly, we introduce a paradigm to create a basic
decentralized counterpart of typical (centralized) class-incremental learning
approaches, and as a result, establish a benchmark for the DCIL study. Thirdly,
we further propose a Decentralized Composite knowledge Incremental Distillation
framework (DCID) to transfer knowledge from historical models and multiple
local sites to the general model continually. DCID consists of three main
components namely local class-incremental learning, collaborated knowledge
distillation among local models, and aggregated knowledge distillation from
local models to the general one. We comprehensively investigate our DCID
framework by using different implementations of the three components. Extensive
experimental results demonstrate the effectiveness of our DCID framework. The
codes of the baseline methods and the proposed DCIL will be released at
https://github.com/zxxxxh/DCIL.

Model-Based Evolutionary Algorithms (MBEAs) can be highly scalable by virtue
of linkage (or variable interaction) learning. This requires, however, that the
linkage model can capture the exploitable structure of a problem. Usually, a
single type of linkage structure is attempted to be captured using models such
as a linkage tree. However, in practice, problems may exhibit multiple linkage
structures. This is for instance the case in multi-objective optimization when
the objectives have different linkage structures. This cannot be modelled
sufficiently well when using linkage models that aim at capturing a single type
of linkage structure, deteriorating the advantages brought by MBEAs. Therefore,
here, we introduce linkage kernels, whereby a linkage structure is learned for
each solution over its local neighborhood. We implement linkage kernels into
the MBEA known as GOMEA that was previously found to be highly scalable when
solving various problems. We further introduce a novel benchmark function
called Best-of-Traps (BoT) that has an adjustable degree of different linkage
structures. On both BoT and a worst-case scenario-based variant of the
well-known MaxCut problem, we experimentally find a vast performance
improvement of linkage-kernel GOMEA over GOMEA with a single linkage tree as
well as the MBEA known as DSMGA-II.

Recommender systems are information retrieval methods that predict user
preferences to personalize services. These systems use the feedback and the
ratings provided by users to model the behavior of users and to generate
recommendations. Typically, the ratings are quite sparse, i.e., only a small
fraction of items are rated by each user. To address this issue and enhance the
performance, active learning strategies can be used to select the most
informative items to be rated. This rating elicitation procedure enriches the
interaction matrix with informative ratings and therefore assists the
recommender system to better model the preferences of the users. In this paper,
we evaluate various non-personalized and personalized rating elicitation
strategies. We also propose a hybrid strategy that adaptively combines a
non-personalized and a personalized strategy. Furthermore, we propose a new
procedure to obtain free ratings based on the side information of the items. We
evaluate these ideas on the MovieLens dataset. The experiments reveal that our
proposed hybrid strategy outperforms the strategies from the literature. We
also propose the extent to which free ratings are obtained, improving further
the performance and also the user experience.

We propose a convolutional neural network (CNN) aided factor graphs assisted
by mutual information features estimated by a neural network for seizure
detection. Specifically, we use neural mutual information estimation to
evaluate the correlation between different electroencephalogram (EEG) channels
as features. We then use a 1D-CNN to extract extra features from the EEG
signals and use both features to estimate the probability of a seizure
event.~Finally, learned factor graphs are employed to capture the temporal
correlation in the signal. Both sets of features from the neural mutual
estimation and the 1D-CNN are used to learn the factor nodes. We show that the
proposed method achieves state-of-the-art performance using 6-fold
leave-four-patients-out cross-validation.

Recently, it has been shown that, in spite of the significant performance of
deep neural networks in different fields, those are vulnerable to adversarial
examples. In this paper, we propose a gradient-based adversarial attack against
transformer-based text classifiers. The adversarial perturbation in our method
is imposed to be block-sparse so that the resultant adversarial example differs
from the original sentence in only a few words. Due to the discrete nature of
textual data, we perform gradient projection to find the minimizer of our
proposed optimization problem. Experimental results demonstrate that, while our
adversarial attack maintains the semantics of the sentence, it can reduce the
accuracy of GPT-2 to less than 5% on different datasets (AG News, MNLI, and
Yelp Reviews). Furthermore, the block-sparsity constraint of the proposed
optimization problem results in small perturbations in the adversarial example.

Physiological monitoring in intensive care units (ICU) generates data that
can be used in clinical research. However, the recording conditions in clinical
settings limit the automated extraction of relevant information from
physiological signals due to noise and artifacts. Therefore, removing artifacts
before clinical research is essential. Manual annotation by experienced
researchers, which is the gold standard for removing artifacts, is
time-consuming and costly due to the volume of the data generated in the ICU.
In this study, we propose a hybrid artifact detection system that combines a
Variational Autoencoder with a statistical detection component for the labeling
of artifactual samples to automate the costly process of cleaning physiological
recordings. The system is applied to minute-by-minute mean blood pressure
signals from an intensive care unit dataset. Its performance is verified by
manual annotations made by an expert. We benchmark the performance of our
system with two other systems that combine an ARIMA or an autoencoder-based
model with our statistical detection component. Our results indicate that the
system consistently achieves sensitivity and specificity levels of over 90%.
Thus, it provides an initial foundation to automate data cleaning in recordings
from ICU.

Point cloud denoising aims to restore clean point clouds from raw
observations corrupted by noise and outliers while preserving the fine-grained
details. We present a novel deep learning-based denoising model, that
incorporates normalizing flows and noise disentanglement techniques to achieve
high denoising accuracy. Unlike existing works that extract features of point
clouds for point-wise correction, we formulate the denoising process from the
perspective of distribution learning and feature disentanglement. By
considering noisy point clouds as a joint distribution of clean points and
noise, the denoised results can be derived from disentangling the noise
counterpart from latent point representation, and the mapping between Euclidean
and latent spaces is modeled by normalizing flows. We evaluate our method on
synthesized 3D models and real-world datasets with various noise settings.
Qualitative and quantitative results show that our method outperforms previous
state-of-the-art deep learning-based approaches.

Recent work in spoken language modeling shows the possibility of learning a
language unsupervisedly from raw audio without any text labels. The approach
relies first on transforming the audio into a sequence of discrete units (or
pseudo-text) and then training a language model directly on such pseudo-text.
Is such a discrete bottleneck necessary, potentially introducing irreversible
errors in the encoding of the speech signal, or could we learn a language model
without discrete units at all? In this work, we study the role of discrete
versus continuous representations in spoken language modeling. We show that
discretization is indeed essential for good results in spoken language
modeling. We show that discretization removes linguistically irrelevant
information from the continuous features, helping to improve language modeling
performances. On the basis of this study, we train a language model on the
discrete units of the HuBERT features, reaching new state-of-the-art results in
the lexical, syntactic and semantic metrics of the Zero Resource Speech
Challenge 2021 (Track 1 - Speech Only).

With fiber-optic seismic acquisition development, continuous dense seismic
monitoring is becoming increasingly more accessible. Repurposing fiber cables
in telecommunication conduits makes it possible to run seismic studies at low
cost, even in locations where traditional seismometers are not easily
installed, such as in urban areas. However, due to the large volume of
continuous streaming data, data collected in such a manner will go to waste
unless we significantly automate the processing workflow. We train a
convolutional neural network (CNN) for earthquake detection using data acquired
over three years by fiber cables in telecommunication conduits under Stanford
University campus. We demonstrate that fiber-optic systems can effectively
complement sparse seismometer networks to detect local earthquakes. The CNN
allows for reliable earthquake detection despite a low signal-to-noise ratio
and even detects small-amplitude previously-uncataloged events.

As Deep Learning algorithms continue to evolve and become more sophisticated,
they require massive datasets for model training and efficacy of models. Some
of those data requirements can be met with the help of existing datasets within
the organizations. Current Machine Learning practices can be leveraged to
generate synthetic data from an existing dataset. Further, it is well
established that diversity in generated synthetic data relies on (and is
perhaps limited by) statistical properties of available dataset within a single
organization or entity. The more diverse an existing dataset is, the more
expressive and generic synthetic data can be. However, given the scarcity of
underlying data, it is challenging to collate big data in one organization. The
diverse, non-overlapping dataset across distinct organizations provides an
opportunity for them to contribute their limited distinct data to a larger pool
that can be leveraged to further synthesize. Unfortunately, this raises data
privacy concerns that some institutions may not be comfortable with.
  This paper proposes a novel approach to generate synthetic data - FedSyn.
FedSyn is a collaborative, privacy preserving approach to generate synthetic
data among multiple participants in a federated and collaborative network.
FedSyn creates a synthetic data generation model, which can generate synthetic
data consisting of statistical distribution of almost all the participants in
the network. FedSyn does not require access to the data of an individual
participant, hence protecting the privacy of participant's data. The proposed
technique in this paper leverages federated machine learning and generative
adversarial network (GAN) as neural network architecture for synthetic data
generation. The proposed method can be extended to many machine learning
problem classes in finance, health, governance, technology and many more.

Vision Transformer (ViT) has become a leading tool in various computer vision
tasks, owing to its unique self-attention mechanism that learns visual
representations explicitly through cross-patch information interactions.
Despite having good success, the literature seldom explores the explainability
of vision transformer, and there is no clear picture of how the attention
mechanism with respect to the correlation across comprehensive patches will
impact the performance and what is the further potential. In this work, we
propose a novel explainable visualization approach to analyze and interpret the
crucial attention interactions among patches for vision transformer.
Specifically, we first introduce a quantification indicator to measure the
impact of patch interaction and verify such quantification on attention window
design and indiscriminative patches removal. Then, we exploit the effective
responsive field of each patch in ViT and devise a window-free transformer
architecture accordingly. Extensive experiments on ImageNet demonstrate that
the exquisitely designed quantitative method is shown able to facilitate ViT
model learning, leading the top-1 accuracy by 4.28% at most. Moreover, the
results on downstream fine-grained recognition tasks further validate the
generalization of our proposal.

Automated short answer grading (ASAG) has gained attention in education as a
means to scale educational tasks to the growing number of students. Recent
progress in Natural Language Processing and Machine Learning has largely
influenced the field of ASAG, of which we survey the recent research
advancements. We complement previous surveys by providing a comprehensive
analysis of recently published methods that deploy deep learning approaches. In
particular, we focus our analysis on the transition from hand engineered
features to representation learning approaches, which learn representative
features for the task at hand automatically from large corpora of data. We
structure our analysis of deep learning methods along three categories: word
embeddings, sequential models, and attention-based methods. Deep learning
impacted ASAG differently than other fields of NLP, as we noticed that the
learned representations alone do not contribute to achieve the best results,
but they rather show to work in a complementary way with hand-engineered
features. The best performance are indeed achieved by methods that combine the
carefully hand-engineered features with the power of the semantic descriptions
provided by the latest models, like transformers architectures. We identify
challenges and provide an outlook on research direction that can be addressed
in the future

Regret minimization methods are a powerful tool for learning approximate Nash
equilibrium (NE) in two-player zero-sum imperfect information extensive-form
games (IIEGs). We consider the problem in the interactive bandit-feedback
setting where we don't know the dynamics of the IIEG. In general, only the
interactive trajectory and the reached terminal node value $v(z^t)$ are
revealed. To learn NE, the regret minimizer is required to estimate the
full-feedback loss gradient $\ell^t$ by $v(z^t)$ and minimize the regret. In
this paper, we propose a generalized framework for this learning setting. It
presents a theoretical framework for the design and the modular analysis of the
bandit regret minimization methods. We demonstrate that the most recent bandit
regret minimization methods can be analyzed as a particular case of our
framework. Following this framework, we describe a novel method SIX-OMD to
learn approximate NE. It is model-free and extremely improves the best existing
convergence rate from the order of $O(\sqrt{X B/T}+\sqrt{Y C/T})$ to $O(\sqrt{
M_{\mathcal{X}}/T} +\sqrt{ M_{\mathcal{Y}}/T})$. Moreover, SIX-OMD is
computationally efficient as it needs to perform the current strategy and
average strategy updates only along the sampled trajectory.

The goal of graph summarization is to represent large graphs in a structured
and compact way. A graph summary based on equivalence classes preserves
pre-defined features of a graph's vertex within a $k$-hop neighborhood such as
the vertex labels and edge labels. Based on these neighborhood characteristics,
the vertex is assigned to an equivalence class. The calculation of the assigned
equivalence class must be a permutation invariant operation on the pre-defined
features. This is achieved by sorting on the feature values, e. g., the edge
labels, which is computationally expensive, and subsequently hashing the
result. Graph Neural Networks (GNN) fulfill the permutation invariance
requirement. We formulate the problem of graph summarization as a subgraph
classification task on the root vertex of the $k$-hop neighborhood. We adapt
different GNN architectures, both based on the popular message-passing protocol
and alternative approaches, to perform the structural graph summarization task.
We compare different GNNs with a standard multi-layer perceptron (MLP) and
Bloom filter as non-neural method. For our experiments, we consider four
popular graph summary models on a large web graph. This resembles challenging
multi-class vertex classification tasks with the numbers of classes ranging
from $576$ to multiple hundreds of thousands. Our results show that the
performance of GNNs are close to each other. In three out of four experiments,
the non-message-passing GraphMLP model outperforms the other GNNs. The
performance of the standard MLP is extraordinary good, especially in the
presence of many classes. Finally, the Bloom filter outperforms all neural
architectures by a large margin, except for the dataset with the fewest number
of $576$ classes.

Recent progress on parse tree encoder for sentence representation learning is
notable. However, these works mainly encode tree structures recursively, which
is not conducive to parallelization. On the other hand, these works rarely take
into account the labels of arcs in dependency trees. To address both issues, we
propose Dependency-Transformer, which applies a relation-attention mechanism
that works in concert with the self-attention mechanism. This mechanism aims to
encode the dependency and the spatial positional relations between nodes in the
dependency tree of sentences. By a score-based method, we successfully inject
the syntax information without affecting Transformer's parallelizability. Our
model outperforms or is comparable to the state-of-the-art methods on four
tasks for sentence representation and has obvious advantages in computational

Printed electronics (PE) feature low non-recurring engineering costs and low
per unit-area fabrication costs, enabling thus extremely low-cost and on-demand
hardware. Such low-cost fabrication allows for high customization that would be
infeasible in silicon, and bespoke architectures prevail to improve the
efficiency of emerging PE machine learning (ML) applications. However, even
with bespoke architectures, the large feature sizes in PE constraint the
complexity of the ML models that can be implemented. In this work, we bring
together, for the first time, approximate computing and PE design targeting to
enable complex ML models, such as Multi-Layer Perceptrons (MLPs) and Support
Vector Machines (SVMs), in PE. To this end, we propose and implement a
cross-layer approximation, tailored for bespoke ML architectures. At the
algorithmic level we apply a hardware-driven coefficient approximation of the
ML model and at the circuit level we apply a netlist pruning through a full
search exploration. In our extensive experimental evaluation we consider 14
MLPs and SVMs and evaluate more than 4300 approximate and exact designs. Our
results demonstrate that our cross approximation delivers Pareto optimal
designs that, compared to the state-of-the-art exact designs, feature 47% and
44% average area and power reduction, respectively, and less than 1% accuracy
loss.

Superconducting photoelectron injectors are a promising technique for
generating high brilliant pulsed electron beams with high repetition rates and
low emittances. Experiments such as ultra-fast electron diffraction,
experiments at the Terahertz scale, and energy recovery linac applications
require such properties. However, optimization of the beam properties is
challenging due to the high amount of possible machine parameter combinations.
In this article, we show the successful automated optimization of beam
properties utilizing an already existing simulation model. To reduce the amount
of required computation time, we replace the costly simulation by a faster
approximation with a neural network. For optimization, we propose a
reinforcement learning approach leveraging the simple computation of the
derivative of the approximation. We prove that our approach outperforms common
optimization methods for the required function evaluations given a defined
minimum accuracy.

We present a 3D face reconstruction system that aims at recovering the 3D
facial geometry of babies from uncalibrated photographs, BabyNet. Since the 3D
facial geometry of babies differs substantially from that of adults,
baby-specific facial reconstruction systems are needed. BabyNet consists of two
stages: 1) a 3D graph convolutional autoencoder learns a latent space of the
baby 3D facial shape; and 2) a 2D encoder that maps photographs to the 3D
latent space based on representative features extracted using transfer
learning. In this way, using the pre-trained 3D decoder, we can recover a 3D
face from 2D images. We evaluate BabyNet and show that 1) methods based on
adult datasets cannot model the 3D facial geometry of babies, which proves the
need for a baby-specific method, and 2) BabyNet outperforms classical
model-fitting methods even when a baby-specific 3D morphable model, such as
BabyFM, is used.

Learning using privileged information (LUPI) paradigm, which pioneered
teacher-student interaction mechanism, makes the learning models use additional
information in training stage. This paper is the first to propose an
incremental learning algorithm with LUPI paradigm for stochastic configuration
network (SCN), named SCN+. This novel algorithm can leverage privileged
information into SCN in the training stage, which provides a new method to
train SCN. Moreover, the convergences have been studied in this paper. Finally,
experimental results indicate that SCN+ indeed performs favorably.

Machine learning algorithms can produce biased outcome/prediction, typically,
against minorities and under-represented sub-populations. Therefore, fairness
is emerging as an important requirement for the large scale application of
machine learning based technologies. The most commonly used fairness notions
(e.g. statistical parity, equalized odds, predictive parity, etc.) are
observational and rely on mere correlation between variables. These notions
fail to identify bias in case of statistical anomalies such as Simpson's or
Berkson's paradoxes. Causality-based fairness notions (e.g. counterfactual
fairness, no-proxy discrimination, etc.) are immune to such anomalies and hence
more reliable to assess fairness. The problem of causality-based fairness
notions, however, is that they are defined in terms of quantities (e.g. causal,
counterfactual, and path-specific effects) that are not always measurable. This
is known as the identifiability problem and is the topic of a large body of
work in the causal inference literature. This paper is a compilation of the
major identifiability results which are of particular relevance for machine
learning fairness. The results are illustrated using a large number of examples
and causal graphs. The paper would be of particular interest to fairness
researchers, practitioners, and policy makers who are considering the use of
causality-based fairness notions as it summarizes and illustrates the major
identifiability results

Recently, attention mechanisms have been extensively investigated in computer
vision, but few of them show excellent performance on both large and mobile
networks. This paper proposes Dual Rank-1 Tensor Attention Module (DRTAM), a
novel residual-attention-learning-guided attention module for feed-forward
convolutional neural networks. Given a 3D feature tensor map, DRTAM firstly
generates three 2D feature descriptors along three axes. Then, using three
descriptors, DRTAM sequentially infers two rank-1 tensor attention maps, the
initial attention map and the complement attention map, combines and multiplied
them to the input feature map for adaptive feature refinement(see Fig.1(c)). To
generate two attention maps, DRTAM introduces rank-1 tensor attention module
(RTAM) and residual descriptors extraction module (RDEM): RTAM divides each 2D
feature descriptors into several chunks, and generate three factor vectors of a
rank-1 tensor attention map by employing strip pooling on each chunk so that
local and long-range contextual information can be captured along three
dimension respectively; RDEM generates three 2D feature descriptors of the
residual feature to produce the complement attention map, using three factor
vectors of the initial attention map and three descriptors of the input
feature. Extensive experimental results on ImageNet-1K, MS COCO and PASCAL VOC
demonstrate that DRTAM achieves competitive performance on both large and
mobile networks compare with other state-of-the-art attention modules.

Purpose: To develop a novel deep-learning model that integrates radiomics
analysis in a multi-dimensional feature fusion workflow for glioblastoma (GBM)
post-resection survival prediction. Methods: A cohort of 235 GBM patients with
complete surgical resection was divided into short-term/long-term survival
groups with 1-yr survival time threshold. Each patient received a pre-surgery
multi-parametric MRI exam, and three tumor subregions were segmented by
neuroradiologists. The developed model comprises three data source branches: in
the 1st radiomics branch, 456 radiomics features (RF) were from each patient;
in the 2nd deep learning branch, an encoding neural network architecture was
trained for survival group prediction using each single MR modality, and
high-dimensional parameters of the last two network layers were extracted as
deep features (DF). The extracted radiomics features and deep features were
processed by a feature selection procedure to reduce dimension size of each
feature space. In the 3rd branch, non-image-based patient-specific clinical
features (PSCF) were collected. Finally, data sources from all three branches
were fused as an integrated input for a supporting vector machine (SVM)
execution for survival group prediction. Different strategies of model design,
including 1) 2D/3D-based image analysis, and 2) different data source
combinations in SVM input design, were investigated in comparison studies.
Results: The model achieved 0.638 prediction accuracy when using PSCF only,
which was higher than the results using RF or DF only in both 2D and 3D
analysis. The joint use of RF/PSCF improved accuracy results to 0.681 in 3D
analysis. The most accurate models in 2D/3D analysis reached the highest
accuracy 0.745 with different combinations of RF/DF/ PSCF, and the
corresponding ROC AUC results were 0.69(2D) and 0.71(3D), respectively.

Common state-of-the-art video codecs are optimized to deliver a low bitrate
by providing a certain quality for the final human observer, which is achieved
by rate-distortion optimization (RDO). But, with the steady improvement of
neural networks solving computer vision tasks, more and more multimedia data is
not observed by humans anymore, but directly analyzed by neural networks. In
this paper, we propose a standard-compliant feature-based RDO (FRDO) that is
designed to increase the coding performance, when the decoded frame is analyzed
by a neural network in a video coding for machine scenario. To that extent, we
replace the pixel-based distortion metrics in conventional RDO of VTM-8.0 with
distortion metrics calculated in the feature space created by the first layers
of a neural network. Throughout several tests with the segmentation network
Mask R-CNN and single images from the Cityscapes dataset, we compare the
proposed FRDO and its hybrid version HFRDO with different distortion measures
in the feature space against the conventional RDO. With HFRDO, up to 5.49 %
bitrate can be saved compared to the VTM-8.0 implementation in terms of
Bj{\o}ntegaard Delta Rate and using the weighted average precision as quality
metric. Additionally, allowing the encoder to vary the quantization parameter
results in coding gains for the proposed HFRDO of up 9.95 % compared to
conventional VTM.

Speech separation aims to separate multiple speech sources from a speech
mixture. Although speech separation is well-solved on some existing English
speech separation benchmarks, it is worthy of more investigation on the
generalizability of speech separation models on the accents or languages unseen
during training. This paper adopts meta-learning based methods to improve the
transferability of speech separation models. With the meta-learning based
methods, we discovered that only using speech data with one accent, the native
English accent, as our training data, the models still can be adapted to new
unseen accents on the Speech Accent Archive. We compared the results with a
human-rated native-likeness of accents, showing that the transferability of
MAML methods has less relation to the similarity of data between the training
and testing phase compared to the typical transfer learning methods.
Furthermore, we found that models can deal with different language data from
the CommonVoice corpus during the testing phase. Most of all, the MAML methods
outperform typical transfer learning methods when it comes to new accents, new
speakers, new languages, and noisy environments.

Benefiting from the powerful expressive capability of graphs, graph-based
approaches have been popularly applied to handle multi-modal medical data and
achieved impressive performance in various biomedical applications. For disease
prediction tasks, most existing graph-based methods tend to define the graph
manually based on specified modality (e.g., demographic information), and then
integrated other modalities to obtain the patient representation by Graph
Representation Learning (GRL). However, constructing an appropriate graph in
advance is not a simple matter for these methods. Meanwhile, the complex
correlation between modalities is ignored. These factors inevitably yield the
inadequacy of providing sufficient information about the patient's condition
for a reliable diagnosis. To this end, we propose an end-to-end Multi-modal
Graph Learning framework (MMGL) for disease prediction with multi-modality. To
effectively exploit the rich information across multi-modality associated with
the disease, modality-aware representation learning is proposed to aggregate
the features of each modality by leveraging the correlation and complementarity
between the modalities. Furthermore, instead of defining the graph manually,
the latent graph structure is captured through an effective way of adaptive
graph learning. It could be jointly optimized with the prediction model, thus
revealing the intrinsic connections among samples. Our model is also applicable
to the scenario of inductive learning for those unseen data. An extensive group
of experiments on two disease prediction tasks demonstrates that the proposed
MMGL achieves more favorable performance. The code of MMGL is available at
\url{https://github.com/SsGood/MMGL}.

Recently, federated learning (FL) has sparked widespread attention as a
promising decentralized machine learning approach which provides privacy and
low delay. However, communication bottleneck still constitutes an issue, that
needs to be resolved for an efficient deployment of FL over wireless networks.
In this paper, we aim to minimize the total convergence time of FL, by
quantizing the local model parameters prior to uplink transmission. More
specifically, the convergence analysis of the FL algorithm with stochastic
quantization is firstly presented, which reveals the impact of the quantization
error on the convergence rate. Following that, we jointly optimize the
computing, communication resources and number of quantization bits, in order to
guarantee minimized convergence time across all global rounds, subject to
energy and quantization error requirements, which stem from the convergence
analysis. The impact of the quantization error on the convergence time is
evaluated and the trade-off among model accuracy and timely execution is
revealed. Moreover, the proposed method is shown to result in faster
convergence in comparison with baseline schemes. Finally, useful insights for
the selection of the quantization error tolerance are provided.

Contrastive learning has shown great potential in unsupervised sentence
embedding tasks, e.g., SimCSE. However, We find that these existing solutions
are heavily affected by superficial features like the length of sentences or
syntactic structures. In this paper, we propose a semantics-aware contrastive
learning framework for sentence embeddings, termed Pseudo-Token BERT (PT-BERT),
which is able to exploit the pseudo-token space (i.e., latent semantic space)
representation of a sentence while eliminating the impact of superficial
features such as sentence length and syntax. Specifically, we introduce an
additional pseudo token embedding layer independent of the BERT encoder to map
each sentence into a sequence of pseudo tokens in a fixed length. Leveraging
these pseudo sequences, we are able to construct same-length positive and
negative pairs based on the attention mechanism to perform contrastive
learning. In addition, we utilize both the gradient-updating and
momentum-updating encoders to encode instances while dynamically maintaining an
additional queue to store the representation of sentence embeddings, enhancing
the encoder's learning performance for negative examples. Experiments show that
our model outperforms the state-of-the-art baselines on six standard semantic
textual similarity (STS) tasks. Furthermore, experiments on alignments and
uniformity losses, as well as hard examples with different sentence lengths and
syntax, consistently verify the effectiveness of our method.

In this paper we employ economic news within a neural network framework to
forecast the Italian 10-year interest rate spread. We use a big, open-source,
database known as Global Database of Events, Language and Tone to extract
topical and emotional news content linked to bond markets dynamics. We deploy
such information within a probabilistic forecasting framework with
autoregressive recurrent networks (DeepAR). Our findings suggest that a deep
learning network based on Long-Short Term Memory cells outperforms classical
machine learning techniques and provides a forecasting performance that is over
and above that obtained by using conventional determinants of interest rates
alone.

This paper aims to predict radio channel variations over time by deep
learning from channel observations without knowledge of the underlying channel
dynamics. In next-generation wideband cellular systems, multicarrier
transmission for higher data rate leads to the high-resolution predicting
problem. By leveraging recent advances of deep learning in high-resolution
image processing, we propose a purely data-driven deep learning (DL) approach
to predicting high-resolution temporal evolution of wideband radio channels. In
order to investigate the effect of architectural design choices, we develop and
study three deep learning prediction models, namely, baseline, image
completion, and next-frame prediction models using UNet. Numerical results show
that the proposed DL approach achieves a 52% lower prediction error than the
traditional approach based on the Kalman filter (KF) in mean absolute errors.
To quantify impact of channel aging and prediction on precoding performance, we
also evaluate the performance degradation due to outdated and predicted channel
state information (CSI) compared to perfect CSI. Our simulations show that the
proposed DL approach can reduce the performance loss due to channel aging by
71% through adapting precoding vector to changes in radio channel while the
traditional KF approach only shows a 27% reduction.

Practitioners have observed that some deep learning models generalize well
even with a perfect fit to noisy training data [5,45,44]. Since then many
theoretical works have revealed some facets of this phenomenon [4,2,1,8] known
as benign overfitting. In particular, in the linear regression model, the
minimum $l_2$-norm interpolant estimator $\hat\beta$ has received a lot of
attention [1,39] since it was proved to be consistent even though it perfectly
fits noisy data under some condition on the covariance matrix $\Sigma$ of the
input vector. Motivated by this phenomenon, we study the generalization
property of this estimator from a geometrical viewpoint. Our main results
extend and improve the convergence rates as well as the deviation probability
from [39]. Our proof differs from the classical bias/variance analysis and is
based on the self-induced regularization property introduced in [2]:
$\hat\beta$ can be written as a sum of a ridge estimator $\hat\beta_{1:k}$ and
an overfitting component $\hat\beta_{k+1:p}$ which follows a decomposition of
the features space $\mathbb{R}^p=V_{1:k}\oplus^\perp V_{k+1:p}$ into the space
$V_{1:k}$ spanned by the top $k$ eigenvectors of $\Sigma$ and the ones
$V_{k+1:p}$ spanned by the $p-k$ last ones. We also prove a matching lower
bound for the expected prediction risk. The two geometrical properties of
random Gaussian matrices at the heart of our analysis are the Dvoretsky-Milman
theorem and isomorphic and restricted isomorphic properties. In particular, the
Dvoretsky dimension appearing naturally in our geometrical viewpoint coincides
with the effective rank from [1,39] and is the key tool to handle the behavior
of the design matrix restricted to the sub-space $V_{k+1:p}$ where overfitting
happens.

Grant-free random access and uplink non-orthogonal multiple access (NOMA) are
techniques to increase the overload factor and reduce transmission latency with
signaling overhead in massive machine-type communications (mMTC). Sparse code
multiple access (SCMA) and Multi-user shared access (MUSA) are introduced as
advanced code domain NOMA schemes. In grant-free NOMA, machine-type devices
(MTD) transmit information to the base station (BS) without a grant, creating a
challenging task for the BS to identify the active MTD among all potential
active devices. In this paper, a novel deep neural network (DNN)-based
multi-user detection (MUD) scheme for the grant-free SCMA and MUSA system in an
mMTC uplink framework is proposed to jointly identify the received signal's
sparsity and the active MTDs in the absence of channel state information. The
proposed scheme learns the correlation between the received signal and the
multi-dimensional codebook for SCMA and spreading sequences for MUSA schemes
and is able to identify the active MTDs from the received signal without any
prior knowledge of the device sparsity level. The application of the proposed
MUD scheme is further investigated in an indoor factory setting using four
different mmWave channels. Numerical results show that when the number of
active MTDs in the system is large, DNN-MUD has a significantly higher
probability of detection compared to existing approaches over the
signal-to-noise ratio range of interest.

According to recent studies, the vulnerability of state-of-the-art Neural
Networks to adversarial input samples has increased drastically. A neural
network is an intermediate path or technique by which a computer learns to
perform tasks using Machine learning algorithms. Machine Learning and
Artificial Intelligence model has become a fundamental aspect of life, such as
self-driving cars [1], smart home devices, so any vulnerability is a
significant concern. The smallest input deviations can fool these extremely
literal systems and deceive their users as well as administrator into
precarious situations. This article proposes a defense algorithm that utilizes
the combination of an auto-encoder [3] and block-switching architecture.
Auto-coder is intended to remove any perturbations found in input images
whereas the block switching method is used to make it more robust against
White-box attacks. The attack is planned using FGSM [9] model, and the
subsequent counter-attack by the proposed architecture will take place thereby
demonstrating the feasibility and security delivered by the algorithm.

Streamlined qBOLD acquisitions enable experimentally straightforward
observations of brain oxygen metabolism. $R_2^\prime$ maps are easily inferred;
however, the Oxygen extraction fraction (OEF) and deoxygenated blood volume
(DBV) are more ambiguously determined from the data. As such, existing
inference methods tend to yield very noisy and underestimated OEF maps, while
overestimating DBV.
  This work describes a novel probabilistic machine learning approach that can
infer plausible distributions of OEF and DBV. Initially, we create a model that
produces informative voxelwise prior distribution based on synthetic training
data. Contrary to prior work, we model the joint distribution of OEF and DBV
through a scaled multivariate logit-Normal distribution, which enables the
values to be constrained within a plausible range. The prior distribution model
is used to train an efficient amortized variational Bayesian inference model.
This model learns to infer OEF and DBV by predicting real image data, with few
training data required, using the signal equations as a forward model.
  We demonstrate that our approach enables the inference of smooth OEF and DBV
maps, with a physiologically plausible distribution that can be adapted through
specification of an informative prior distribution. Other benefits include
model comparison (via the evidence lower bound) and uncertainty quantification
for identifying image artefacts. Results are demonstrated on a small study
comparing subjects undergoing hyperventilation and at rest. We illustrate that
the proposed approach allows measurement of gray matter differences in OEF and
DBV and enables voxelwise comparison between conditions, where we observe
significant increases in OEF and $R_2^\prime$ during hyperventilation.

Medicare fraud results in considerable losses for governments and insurance
companies and results in higher premiums from clients. Medicare fraud costs
around 13 billion euros in Europe and between 21 billion and 71 billion US
dollars per year in the United States. This study aims to use artificial neural
network based classifiers to predict medicare fraud. The main difficulty using
machine learning techniques in fraud detection or more generally anomaly
detection is that the data sets are highly imbalanced. To detect medicare
frauds, we propose a multiple inputs deep neural network based classifier with
a Long-short Term Memory (LSTM) autoencoder component. This architecture makes
it possible to take into account many sources of data without mixing them and
makes the classification task easier for the final model. The latent features
extracted from the LSTM autoencoder have a strong discriminating power and
separate the providers into homogeneous clusters. We use the data sets from the
Centers for Medicaid and Medicare Services (CMS) of the US federal government.
The CMS provides publicly available data that brings together all of the cost
price requests sent by American hospitals to medicare companies. Our results
show that although baseline artificial neural network give good performances,
they are outperformed by our multiple inputs neural networks. We have shown
that using a LSTM autoencoder to embed the provider behavior gives better
results and makes the classifiers more robust to class imbalance.

Weather is a phenomenon that affects everything and everyone around us on a
daily basis. Weather prediction has been an important point of study for
decades as researchers have tried to predict the weather and climatic changes
using traditional meteorological techniques. With the advent of modern
technologies and computing power, we can do so with the help of machine
learning techniques. We aim to predict the weather of an area using past
meteorological data and features using the Multiple Linear Regression Model.
The performance of the model is evaluated and a conclusion is drawn. The model
is successfully able to predict the average temperature of a day with an error
of 2.8 degrees Celsius.

Open intent detection is a significant problem in natural language
understanding, which aims to detect the unseen open intent with the prior
knowledge of only known intents. Current methods have two core challenges in
this task. On the one hand, they have limitations in learning friendly
representations to detect the open intent. On the other hand, there lacks an
effective approach to obtaining specific and compact decision boundaries for
known intents. To address these issues, this paper introduces an original
framework, DA-ADB, which successively learns distance-aware intent
representations and adaptive decision boundaries for open intent detection.
Specifically, we first leverage distance information to enhance the
distinguishing capability of the intent representations. Then, we design a
novel loss function to obtain appropriate decision boundaries by balancing both
empirical and open space risks. Extensive experiments show the effectiveness of
distance-aware and boundary learning strategies. Compared with the
state-of-the-art methods, our method achieves substantial improvements on three
benchmark datasets. It also yields robust performance with different
proportions of labeled data and known categories. The full data and codes are
available at https://github.com/thuiar/TEXTOIR

The COVID-19 pandemic abruptly changed the classroom context, understood as
the meeting space between teachers and students where a fundamental part of the
construction of new knowledge takes place. This presented enormous challenges
for all actors in the educational process, who had to overcome multiple
difficulties and adapt to a new daily life in which the incorporation of new
strategies and tools was essential. In this work we study the knowledge
acquisition process in two different contexts: face-to-face (before the onset
of the pandemic) and virtual (during confinement), for a particular case in
higher education in Argentina. We developed an analytical model for the
knowledge acquisition process, based on a series of surveys and information on
academic performance. We analyzed the significance of the model by means of
Artificial Neural Networks and a Multiple Linear Regression Method. We found
that the virtual context produced a decrease in motivation to learn. We also
found that the structure of the emerging contact network from peer interaction
presents very different characteristics in both contexts. Finally, we show that
in all cases, interaction with teachers is of utmost importance in the process
of acquiring knowledge.

It is commonplace to encounter heterogeneous data, of which some aspects of
the data distribution may vary but the underlying causal mechanisms remain
constant. When data are divided into distinct environments according to the
heterogeneity, recent invariant learning methods have proposed to learn robust
and invariant models based on this environment partition. It is hence tempting
to utilize the inherent heterogeneity even when environment partition is not
provided. Unfortunately, in this work, we show that learning invariant features
under this circumstance is fundamentally impossible without further inductive
biases or additional information. Then, we propose a framework to jointly learn
environment partition and invariant representation, assisted by additional
auxiliary information. We derive sufficient and necessary conditions for our
framework to provably identify invariant features under a fairly general
setting. Experimental results on both synthetic and real world datasets
validate our analysis and demonstrate an improved performance of the proposed
framework over existing methods. Finally, our results also raise the need of
making the role of inductive biases more explicit in future works, when
considering learning invariant models without environment partition. Codes are
available at https://github.com/linyongver/ZIN_official .

In a federated learning scenario where multiple parties jointly learn a model
from their respective data, there exist two conflicting goals for the choice of
appropriate algorithms. On one hand, private and sensitive training data must
be kept secure as much as possible in the presence of \textit{semi-honest}
partners, while on the other hand, a certain amount of information has to be
exchanged among different parties for the sake of learning utility. Such a
challenge calls for the privacy-preserving federated learning solution, which
maximizes the utility of the learned model and maintains a provable privacy
guarantee of participating parties' private data.
  This article illustrates a general framework that a) formulates the trade-off
between privacy loss and utility loss from a unified information-theoretic
point of view, and b) delineates quantitative bounds of privacy-utility
trade-off when different protection mechanisms including Randomization,
Sparsity, and Homomorphic Encryption are used. It was shown that in general
\textit{there is no free lunch for the privacy-utility trade-off} and one has
to trade the preserving of privacy with a certain degree of degraded utility.
The quantitative analysis illustrated in this article may serve as the guidance
for the design of practical federated learning algorithms.

Several fields in science, from genomics to neuroimaging, require monitoring
populations (measures) that evolve with time. These complex datasets,
describing dynamics with both time and spatial components, pose new challenges
for data analysis. We propose in this work a new framework to carry out
averaging of these datasets, with the goal of synthesizing a representative
template trajectory from multiple trajectories. We show that this requires
addressing three sources of invariance: shifts in time, space, and total
population size (or mass/amplitude). Here we draw inspiration from dynamic time
warping (DTW), optimal transport (OT) theory and its unbalanced extension (UOT)
to propose a criterion that can address all three issues. This proposal
leverages a smooth formulation of DTW (Soft-DTW) that is shown to capture
temporal shifts, and UOT to handle both variations in space and size. Our
proposed loss can be used to define spatio-temporal barycenters as Fr\'echet
means. Using Fenchel duality, we show how these barycenters can be computed
efficiently, in parallel, via a novel variant of entropy-regularized debiased
UOT. Experiments on handwritten letters and brain imaging data confirm our
theoretical findings and illustrate the effectiveness of the proposed loss for
spatio-temporal data.

Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative
Adversarial Networks (GANs) to impose fairness on the generated data, freeing
them from both disparate impact and disparate treatment. Given the model's
advantages and performance, we introduce a novel learning framework to transfer
a pre-trained FairGAN to other tasks. This reprogramming process has the goal
of maintaining the FairGAN's main targets of data utility, classification
utility, and data fairness, while widening its applicability and ease of use.
In this paper we present the technical extensions required to adapt the
original architecture to this new framework (and in particular the use of
Variational Auto-Encoders), and discuss the benefits, trade-offs, and
limitations of the new model.

Statistical learning has been proposed as a mechanism to structure and
segment the continuous flow of information in several sensory modalities.
Previous studies proposed that the medial temporal lobe, and in particular the
hippocampus, may be crucial to parse the stream in the visual modality.
However, the involvement of the hippocampus in auditory statistical learning,
and specifically in speech segmentation is less clear. To explore the role of
the hippocampus in speech segmentation based on statistical learning, we
exposed seven pharmaco-resistant temporal lobe epilepsy patients to a
continuous stream of trisyllabic pseudowords and recorded intracranial
stereotaxic electro-encephalography (sEEG). We used frequency-tagging analysis
to quantify neuronal synchronization of the hippocampus and auditory regions to
the temporal structure of words and syllables of the stream. Results show that
while auditory regions highly respond to syllable frequency, the hippocampus
responds mostly to word frequency. These findings provide direct evidence of
the involvement of the hippocampus in speech segmentation process and suggest a
hierarchical organization of auditory information during speech processing.

Offline reinforcement learning, which seeks to utilize offline/historical
data to optimize sequential decision-making strategies, has gained surging
prominence in recent studies. Due to the advantage that appropriate function
approximators can help mitigate the sample complexity burden in modern
reinforcement learning problems, existing endeavors usually enforce powerful
function representation models (e.g. neural networks) to learn the optimal
policies. However, a precise understanding of the statistical limits with
function representations, remains elusive, even when such a representation is
linear.
  Towards this goal, we study the statistical limits of offline reinforcement
learning with linear model representations. To derive the tight offline
learning bound, we design the variance-aware pessimistic value iteration
(VAPVI), which adopts the conditional variance information of the value
function for time-inhomogeneous episodic linear Markov decision processes
(MDPs). VAPVI leverages estimated variances of the value functions to reweight
the Bellman residuals in the least-square pessimistic value iteration and
provides improved offline learning bounds over the best-known existing results
(whereas the Bellman residuals are equally weighted by design). More
importantly, our learning bounds are expressed in terms of system quantities,
which provide natural instance-dependent characterizations that previous
results are short of. We hope our results draw a clearer picture of what
offline learning should look like when linear representations are provided.

In online display advertising, guaranteed contracts and real-time bidding
(RTB) are two major ways to sell impressions for a publisher. For large
publishers, simultaneously selling impressions through both guaranteed
contracts and in-house RTB has become a popular choice. Generally speaking, a
publisher needs to derive an impression allocation strategy between guaranteed
contracts and RTB to maximize its overall outcome (e.g., revenue and/or
impression quality). However, deriving the optimal strategy is not a trivial
task, e.g., the strategy should encourage incentive compatibility in RTB and
tackle common challenges in real-world applications such as unstable traffic
patterns (e.g., impression volume and bid landscape changing). In this paper,
we formulate impression allocation as an auction problem where each guaranteed
contract submits virtual bids for individual impressions. With this
formulation, we derive the optimal bidding functions for the guaranteed
contracts, which result in the optimal impression allocation. In order to
address the unstable traffic pattern challenge and achieve the optimal overall
outcome, we propose a multi-agent reinforcement learning method to adjust the
bids from each guaranteed contract, which is simple, converging efficiently and
scalable. The experiments conducted on real-world datasets demonstrate the

Adversarial attacks are often considered as threats to the robustness of Deep
Neural Networks (DNNs). Various defending techniques have been developed to
mitigate the potential negative impact of adversarial attacks against task
predictions. This work analyzes adversarial attacks from a different
perspective. Namely, adversarial examples contain implicit information that is
useful to the predictions i.e., image classification, and treat the adversarial
attacks against DNNs for data self-expression as extracted abstract
representations that are capable of facilitating specific learning tasks. We
propose an algorithmic framework that leverages the advantages of the DNNs for
data self-expression and task-specific predictions, to improve image
classification. The framework jointly learns a DNN for attacking Variational
Autoencoder (VAE) networks and a DNN for classification, coined as Attacking
VAE for Improve Classification (AVIC). The experiment results show that AVIC
can achieve higher accuracy on standard datasets compared to the training with
clean examples and the traditional adversarial training.

Contrastive Language-Image Pretraining (CLIP) has emerged as a novel paradigm
to learn visual models from language supervision. While researchers continue to
push the frontier of CLIP, reproducing these works remains challenging. This is
because researchers do not choose consistent training recipes and even use
different data, hampering the fair comparison between different methods. In
this work, we propose CLIP-benchmark, a first attempt to evaluate, analyze, and
benchmark CLIP and its variants. We conduct a comprehensive analysis of three
key factors: data, supervision, and model architecture. We find considerable
intuitive or counter-intuitive insights: (1). Data quality has a significant
impact on performance. (2). Certain supervision has different effects for
Convolutional Networks (ConvNets) and Vision Transformers (ViT). Applying more
proper supervision can effectively improve the performance of CLIP. (3).
Curtailing the text encoder reduces the training cost but not much affect the
final performance. Moreover, we further combine DeCLIP with FILIP, bringing us
the strongest variant DeFILIP. The CLIP-benchmark would be released at:
https://github.com/Sense-GVT/DeCLIP for future CLIP research.

Graph Convolutional Networks (GCNs) achieve great success in non-Euclidean
structure data processing recently. In existing studies, deeper layers are used
in CCNs to extract deeper features of Euclidean structure data. However, for
non-Euclidean structure data, too deep GCNs will confront with problems like
"neighbor explosion" and "over-smoothing", it also cannot be applied to large
datasets. To address these problems, we propose a model called PathSAGE, which
can learn high-order topological information and improve the model's
performance by expanding the receptive field. The model randomly samples paths
starting from the central node and aggregates them by Transformer encoder.
PathSAGE has only one layer of structure to aggregate nodes which avoid those
problems above. The results of evaluation shows that our model achieves
comparable performance with the state-of-the-art models in inductive learning
tasks.

To represent people in mixed reality applications for collaboration and
communication, we need to generate realistic and faithful avatar poses.
However, the signal streams that can be applied for this task from head-mounted
devices (HMDs) are typically limited to head pose and hand pose estimates.
While these signals are valuable, they are an incomplete representation of the
human body, making it challenging to generate a faithful full-body avatar. We
address this challenge by developing a flow-based generative model of the 3D
human body from sparse observations, wherein we learn not only a conditional
distribution of 3D human pose, but also a probabilistic mapping from
observations to the latent space from which we can generate a plausible pose
along with uncertainty estimates for the joints. We show that our approach is
not only a strong predictive model, but can also act as an efficient pose prior
in different optimization settings where a good initial latent code plays a
major role.

Co-salient object detection, with the target of detecting co-existed salient
objects among a group of images, is gaining popularity. Recent works use the
attention mechanism or extra information to aggregate common co-salient
features, leading to incomplete even incorrect responses for target objects. In
this paper, we aim to mine comprehensive co-salient features with democracy and
reduce background interference without introducing any extra information. To
achieve this, we design a democratic prototype generation module to generate
democratic response maps, covering sufficient co-salient regions and thereby
involving more shared attributes of co-salient objects. Then a comprehensive
prototype based on the response maps can be generated as a guide for final
prediction. To suppress the noisy background information in the prototype, we
propose a self-contrastive learning module, where both positive and negative
pairs are formed without relying on additional classification information.
Besides, we also design a democratic feature enhancement module to further
strengthen the co-salient features by readjusting attention values. Extensive
experiments show that our model obtains better performance than previous
state-of-the-art methods, especially on challenging real-world cases (e.g., for
CoCA, we obtain a gain of 2.0% for MAE, 5.4% for maximum F-measure, 2.3% for
maximum E-measure, and 3.7% for S-measure) under the same settings. Code will
be released soon.

A critical step in virtual dental treatment planning is to accurately
delineate all tooth-bone structures from CBCT with high fidelity and accurate
anatomical information. Previous studies have established several methods for
CBCT segmentation using deep learning. However, the inherent resolution
discrepancy of CBCT and the loss of occlusal and dentition information largely
limited its clinical applicability. Here, we present a Deep Dental Multimodal
Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral
scan (IOS) segmentation model (the most accurate digital dental model), and a
fusion model to generate 3D fused crown-root-bone structures with high fidelity
and accurate occlusal and dentition information. Our model was trained with a
large-scale dataset with 503 CBCT and 28,559 IOS meshes manually annotated by
experienced human experts. For CBCT segmentation, we use a five-fold cross
validation test, each with 50 CBCT, and our model achieves an average Dice
coefficient and IoU of 93.99% and 88.68%, respectively, significantly
outperforming the baselines. For IOS segmentations, our model achieves an mIoU
of 93.07% and 95.70% on the maxillary and mandible on a test set of 200 IOS
meshes, which are 1.77% and 3.52% higher than the state-of-art method. Our DDMA
framework takes about 20 to 25 minutes to generate the fused 3D mesh model
following the sequential processing order, compared to over 5 hours by human
experts. Notably, our framework has been incorporated into a software by a
clear aligner manufacturer, and real-world clinical cases demonstrate that our
model can visualize crown-root-bone structures during the entire orthodontic
treatment and can predict risks like dehiscence and fenestration. These
findings demonstrate the potential of multi-modal deep learning to improve the
quality of digital dental models and help dentists make better clinical

Individuals are often faced with temptations that can lead them astray from
long-term goals. We're interested in developing interventions that steer
individuals toward making good initial decisions and then maintaining those
decisions over time. In the realm of financial decision making, a particularly
successful approach is the prize-linked savings account: individuals are
incentivized to make deposits by tying deposits to a periodic lottery that
awards bonuses to the savers. Although these lotteries have been very effective
in motivating savers across the globe, they are a one-size-fits-all solution.
We investigate whether customized bonuses can be more effective. We formalize a
delayed-gratification task as a Markov decision problem and characterize
individuals as rational agents subject to temporal discounting, a cost
associated with effort, and fluctuations in willpower. Our theory is able to
explain key behavioral findings in intertemporal choice. We created an online
delayed-gratification game in which the player scores points by selecting a
queue to wait in and then performing a series of actions to advance to the
front. Data collected from the game is fit to the model, and the instantiated
model is then used to optimize predicted player performance over a space of
incentives. We demonstrate that customized incentive structures can improve an
individual's goal-directed decision making.

Multi-resolution spectro-temporal features of a speech signal represent how
the brain perceives sounds by tuning cortical cells to different spectral and
temporal modulations. These features produce a higher dimensional
representation of the speech signals. The purpose of this paper is to evaluate
how well the auditory cortex representation of speech signals contribute to
estimate articulatory features of those corresponding signals. Since obtaining
articulatory features from acoustic features of speech signals has been a
challenging topic of interest for different speech communities, we investigate
the possibility of using this multi-resolution representation of speech signals
as acoustic features. We used U. of Wisconsin X-ray Microbeam (XRMB) database
of clean speech signals to train a feed-forward deep neural network (DNN) to
estimate articulatory trajectories of six tract variables. The optimal set of
multi-resolution spectro-temporal features to train the model were chosen using
appropriate scale and rate vector parameters to obtain the best performing
model. Experiments achieved a correlation of 0.675 with ground-truth tract
variables. We compared the performance of this speech inversion system with
prior experiments conducted using Mel Frequency Cepstral Coefficients (MFCCs).

The knowledge of the users' electricity consumption pattern is an important
coordinating mechanism between the utility company and the electricity
consumers in terms of key decision makings. The load decomposition is therefore
crucial to reveal the underlying relationship between the load consumption and
its characteristics. However, load decomposition is conventionally performed on
the residential and commercial loads, and adequate consideration has not been
given to the high-energy-consuming industrial loads leading to inefficient
results. This paper thus focuses on the load decomposition of the industrial
park loads (IPL). The commonly used parameters in a conventional method are
however inapplicable in high-energy-consuming industrial loads. Therefore, a
more robust approach is developed comprising a three-algorithm model to achieve
this goal on the IPL. First, the improved variational mode decomposition (IVMD)
algorithm is introduced to denoise the training data of the IPL and improve its
stability. Secondly, the convolutional neural network (CNN) and simple
recurrent units (SRU) joint algorithms are used to achieve a non-intrusive and
non-invasive decomposition process of the IPL using a double-layer deep
learning network based on the IPL characteristics. Specifically, CNN is used to
extract the IPL data characteristics while the improved long and short-term
memory (LSTM) network, SRU, is adopted to develop the decomposition model and
further train the load data. Through the robust decomposition process, the
underlying relationship in the load consumption is extracted. The results
obtained from the numerical examples show that this approach outperforms the
state-of-the-art in the conventional decomposition process.

Learning and reasoning about physical phenomena is still a challenge in
robotics development, and computational sciences play a capital role in the
search for accurate methods able to provide explanations for past events and
rigorous forecasts of future situations. We propose a thermodynamics-informed
active learning strategy for fluid perception and reasoning from observations.
As a model problem, we take the sloshing phenomena of different fluids
contained in a glass. Starting from full-field and high-resolution synthetic
data for a particular fluid, we develop a method for the tracking (perception)
and analysis (reasoning) of any previously unseen liquid whose free surface is
observed with a commodity camera. This approach demonstrates the importance of
physics and knowledge not only in data-driven (grey box) modeling but also in
the correction for real physics adaptation in low data regimes and partial
observations of the dynamics. The method presented is extensible to other
domains such as the development of cognitive digital twins, able to learn from
observation of phenomena for which they have not been trained explicitly.

In this work, we study the deception of a Linear-Quadratic-Gaussian (LQG)
agent by manipulating the cost signals. We show that a small falsification of
the cost parameters will only lead to a bounded change in the optimal policy.
The bound is linear on the amount of falsification the attacker can apply to
the cost parameters. We propose an attack model where the attacker aims to
mislead the agent into learning a `nefarious' policy by intentionally
falsifying the cost parameters. We formulate the attack's problem as a convex
optimization problem and develop necessary and sufficient conditions to check
the achievability of the attacker's goal.
  We showcase the adversarial manipulation on two types of LQG learners: the
batch RL learner and the other is the adaptive dynamic programming (ADP)
learner. Our results demonstrate that with only 2.296% of falsification on the
cost data, the attacker misleads the batch RL into learning the 'nefarious'
policy that leads the vehicle to a dangerous position. The attacker can also
gradually trick the ADP learner into learning the same `nefarious' policy by
consistently feeding the learner a falsified cost signal that stays close to
the actual cost signal. The paper aims to raise people's awareness of the
security threats faced by RL-enabled control systems.

In decades, enormous computational resources are poured into solving the
transient partial differential equations for multifarious physical fields. The
latest artificial intelligence has shown great potential in accelerating these
computations, but its road to wide applications is hindered by the variety of
computational domains and boundary conditions. Here, we overcome this obstacle
by constructing a learning framework capable of purely representing the
transient PDEs with local neural operators (LNOs). This framework is
demonstrated in learning several transient PDEs, especially the Navier-Stokes
equations, and successfully applied to solve problems with quite different
domains and boundaries, including the internal flow, the external flow, and
remarkably, the flow across the cascade of airfoils. In these applications, our
LNOs are faster than the conventional numerical solver by over 1000 times,
which could be significant for scientific computations and engineering

Extreme pricing anomalies may occur unexpectedly without a trivial cause, and
equity traders typically experience a meticulous process to source disparate
information and analyze its reliability before integrating it into the trusted
knowledge base. We introduce DeepTrust, a reliable financial knowledge
retrieval framework on Twitter to explain extreme price moves at speed, while
ensuring data veracity using state-of-the-art NLP techniques. Our proposed
framework consists of three modules, specialized for anomaly detection,
information retrieval and reliability assessment. The workflow starts with
identifying anomalous asset price changes using machine learning models trained
with historical pricing data, and retrieving correlated unstructured data from
Twitter using enhanced queries with dynamic search conditions. DeepTrust
extrapolates information reliability from tweet features, traces of generative
language model, argumentation structure, subjectivity and sentiment signals,
and refine a concise collection of credible tweets for market insights. The
framework is evaluated on two self-annotated financial anomalies, i.e., Twitter
and Facebook stock price on 29 and 30 April 2021. The optimal setup outperforms
the baseline classifier by 7.75% and 15.77% on F0.5-scores, and 10.55% and
18.88% on precision, respectively, proving its capability in screening
unreliable information precisely. At the same time, information retrieval and
reliability assessment modules are analyzed individually on their effectiveness
and causes of limitations, with identified subjective and objective factors
that influence the performance. As a collaborative project with Refinitiv, this
framework paves a promising path towards building a scalable commercial
solution that assists traders to reach investment decisions on pricing
anomalies with authenticated knowledge from social media platforms in

Cascade SVM (CSVM) can group datasets and train subsets in parallel, which
greatly reduces the training time and memory consumption. However, the model
accuracy obtained by using this method has some errors compared with direct
training. In order to reduce the error, we analyze the causes of error in
grouping training, and summarize the grouping without error under ideal
conditions. A Balanced Cascade SVM (BCSVM) algorithm is proposed, which
balances the sample proportion in the subset after grouping to ensure that the
sample proportion in the subset is the same as the original dataset. At the
same time, it proves that the accuracy of the model obtained by BCSVM algorithm
is higher than that of CSVM. Finally, two common datasets are used for
experimental verification, and the results show that the accuracy error
obtained by using BCSVM algorithm is reduced from 1% of CSVM to 0.1%, which is
reduced by an order of magnitude.

This paper propose DualVDT, a generative model for Time-series forecasting.
Introduced dual reparametrized variational mechanisms on variational
autoencoder (VAE) to tighter the evidence lower bound (ELBO) of the model,
prove the advance performance analytically. This mechanism leverage the latent
score based generative model (SGM), explicitly denoising the perturbation
accumulated on latent vector through reverse time stochastic differential
equation and variational ancestral sampling. The posterior of denoised latent
distribution fused with dual reparametrized variational density. The KL
divergence in ELBO will reduce to reach the better results of the model. This
paper also proposed a latent attention mechanisms to extract multivariate
dependency explicitly. Build the local temporal dependency simultaneously in
factor wised through constructed local topology and temporal wised. The proven
and experiment on multiple datasets illustrate, DualVDT, with a novel dual
reparametrized structure, which denoise the latent perturbation through the
reverse dynamics combining local-temporal inference, has the advanced
performance both analytically and experimentally.

Point cloud registration is the basis for many robotic applications such as
odometry and Simultaneous Localization And Mapping (SLAM), which are
increasingly important for autonomous mobile robots. Computational resources
and power budgets are limited on these robots, thereby motivating the
development of resource-efficient registration method on low-cost FPGAs. In
this paper, we propose a novel approach for FPGA-based 3D point cloud
registration built upon a recent deep learning-based method, PointNetLK. A
highly-efficient FPGA accelerator for PointNet-based feature extraction is
designed and implemented on both low-cost and mid-range FPGAs (Avnet Ultra96v2
and Xilinx ZCU104). Our accelerator design is evaluated in terms of
registration speed, accuracy, resource usage, and power consumption.
Experimental results show that PointNetLK with our accelerator achieves up to
21.34x and 69.60x faster registration speed than the CPU counterpart and ICP,
respectively, while only consuming 722mW and maintaining the same level of
accuracy.

The growing need for technology that supports remote healthcare is being
acutely highlighted by an aging population and the COVID-19 pandemic. In
health-related machine learning applications the ability to learn predictive
models without data leaving a private device is attractive, especially when
these data might contain features (e.g., photographs or videos of the body)
that make identifying a subject trivial and/or the training data volume is
large (e.g., uncompressed video). Camera-based remote physiological sensing
facilitates scalable and low-cost measurement, but is a prime example of a task
that involves analysing high bit-rate videos containing identifiable images and
sensitive health information. Federated learning enables privacy-preserving
decentralized training which has several properties beneficial for camera-based
sensing. We develop the first mobile federated learning camera-based sensing
system and show that it can perform competitively with traditional
state-of-the-art supervised approaches. However, in the presence of corrupted
data (e.g., video or label noise) from a few devices the performance of weight
averaging quickly degrades. To address this, we leverage knowledge about the
expected noise profile within the video to intelligently adjust how the model
weights are averaged on the server. Our results show that this significantly
improves upon the robustness of models even when the signal-to-noise ratio is
low

Solar activity has significant impacts on human activities and health. One
most commonly used measure of solar activity is the sunspot number. This paper
compares three important non-deep learning models, four popular deep learning
models, and their five ensemble models in forecasting sunspot numbers. In
particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost
as a two-level nonlinear ensemble method to combine the deep learning models.
Our XGBoost-DL achieves the best forecasting performance (RMSE = 25.70 and MAE
= 19.82) in the comparison, outperforming the best non-deep learning model
SARIMA (RMSE = 54.11 and MAE = 45.51), the best deep learning model Informer
(RMSE = 29.90 and MAE = 22.35) and the NASA's forecast (RMSE = 48.38 and MAE =
38.45). Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025
for Solar Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to
but later than the NASA's at 137.7 in October 2024 and 161.2 in December 2034.
An open-source Python package of our XGBoost-DL for the sunspot number
prediction is available at https://github.com/yd1008/ts_ensemble_sunspot.

Purpose: Long scan time in phase encoding for forming complete K-space
matrices is a critical drawback of MRI, making patients uncomfortable and
wasting important time for diagnosing emergent diseases. This paper aims to
reducing the scan time by actively and sequentially selecting partial phases in
a short time so that a slice can be accurately reconstructed from the resultant
slice-specific incomplete K-space matrix. Methods: A transformer based deep
reinforcement learning framework is proposed for actively determining a
sequence of partial phases according to reconstruction-quality based Q-value (a
function of reward), where the reward is the improvement degree of
reconstructed image quality. The Q-value is efficiently predicted from binary
phase-indicator vectors, incomplete K-space matrices and their corresponding
undersampled images with a light-weight transformer so that the sequential
information of phases and global relationship in images can be used. The
inverse Fourier transform is employed for efficiently computing the
undersampled images and hence gaining the rewards of selecting phases. Results:
Experimental results on the fastMRI dataset with original K-space data
accessible demonstrate the efficiency and accuracy superiorities of proposed
method. Compared with the state-of-the-art reinforcement learning based method
proposed by Pineda et al., the proposed method is roughly 150 times faster and
achieves significant improvement in reconstruction accuracy. Conclusions: We
have proposed a light-weight transformer based deep reinforcement learning
framework for generating high-quality slice-specific trajectory consisting of a
small number of phases. The proposed method, called TITLE (Transformer Involved
Trajectory LEarning), has remarkable superiority in phase-encode selection
efficiency and image reconstruction accuracy.

The binary classification problem has a situation where only biased data are
observed in one of the classes. In this paper, we propose a new method to
approach the positive and biased negative (PbN) classification problem, which
is a weakly supervised learning method to learn a binary classifier from
positive data and negative data with biased observations. We incorporate a
method to correct the negative impact due to skewed confidence, which
represents the posterior probability that the observed data are positive. This
reduces the distortion of the posterior probability that the data are labeled,
which is necessary for the empirical risk minimization of the PbN
classification problem. We verified the effectiveness of the proposed method by
numerical experiments and real data analysis.

Entity alignment (EA) merges knowledge graphs (KGs) by identifying the
equivalent entities in different graphs, which can effectively enrich knowledge
representations of KGs. However, in practice, different KGs often include
dangling entities whose counterparts cannot be found in the other graph, which
limits the performance of EA methods. To improve EA with dangling entities, we
propose an unsupervised method called Semi-constraint Optimal Transport for
Entity Alignment in Dangling cases (SoTead). Our main idea is to model the
entity alignment between two KGs as an optimal transport problem from one KG's
entities to the others. First, we set pseudo entity pairs between KGs based on
pretrained word embeddings. Then, we conduct contrastive metric learning to
obtain the transport cost between each entity pair. Finally, we introduce a
virtual entity for each KG to "align" the dangling entities from the other KGs,
which relaxes the optimization constraints and leads to a semi-constraint
optimal transport. In the experimental part, we first show the superiority of
SoTead on a commonly-used entity alignment dataset. Besides, to analyze the
ability for dangling entity detection with other baselines, we construct a
medical cross-lingual knowledge graph dataset, MedED, where our SoTead also
reaches state-of-the-art performance.

Medical document understanding has gained much attention recently. One
representative task is the International Classification of Disease (ICD)
diagnosis code assignment. Existing work adopts either RNN or CNN as the
backbone network because the vanilla BERT cannot handle well long documents
(>2000 to kens). One issue shared across all these approaches is that they are
over specific to the ICD code assignment task, losing generality to give the
whole document-level and sentence-level embedding. As a result, it is not
straight-forward to direct them to other downstream NLU tasks. Motivated by
these observations, we propose Medical Document BERT (MDBERT) for long medical
document understanding tasks. MDBERT is not only effective in learning
representations at different levels of semantics but efficient in encoding long
documents by leveraging a bottom-up hierarchical architecture. Compared to
vanilla BERT solutions: 1, MDBERT boosts the performance up to relatively 20%
on the MIMIC-III dataset, making it comparable to current SOTA solutions; 2, it
cuts the computational complexity on self-attention modules to less than 1/100.
Other than the ICD code assignment, we conduct a variety of other NLU tasks on
a large commercial dataset named as TrialTrove, to showcase MDBERT's strength
in delivering different levels of semantics.

Despite plenty of efforts focusing on improving the domain adaptation ability
(DA) under unsupervised or few-shot semi-supervised settings, recently the
solution of active learning started to attract more attention due to its
suitability in transferring model in a more practical way with limited
annotation resource on target data. Nevertheless, most active learning methods
are not inherently designed to handle domain gap between data distribution, on
the other hand, some active domain adaptation methods (ADA) usually requires
complicated query functions, which is vulnerable to overfitting. In this work,
we propose a concise but effective ADA method called
Select-by-Distinctive-Margin (SDM), which consists of a maximum margin loss and
a margin sampling algorithm for data selection. We provide theoretical analysis
to show that SDM works like a Support Vector Machine, storing hard examples
around decision boundaries and exploiting them to find informative and
transferable data. In addition, we propose two variants of our method, one is
designed to adaptively adjust the gradient from margin loss, the other boosts
the selectivity of margin sampling by taking the gradient direction into
account. We benchmark SDM with standard active learning setting, demonstrating
our algorithm achieves competitive results with good data scalability. Code is
available at https://github.com/TencentYoutuResearch/ActiveLearning-SDM

In virtual desktop infrastructure (VDI) environments, the remote display
protocol has a big responsibility to transmit video data from a data
center-hosted desktop to the endpoint. The protocol must ensure a high level of
client perceived end-to-end quality of service (QoS) under heavy work load
conditions. Each remote display protocol works differently depending on the
network and which applications are being delivered. In healthcare applications,
doctors and nurses can use mobile devices directly to monitor patients.
Moreover, the ability to implement tasks requiring high consumption of CPU and
other resources is applicable to a variety of applications including research
and cloud gaming. Such computer games and complex processes will run on
powerful cloud servers and the screen contents will be transmitted to the
client. TO enable such applications, remote display technology requires further
enhancements to meet more stringent requirements on bandwidth and QoS, an to
allow realtime operation. In this paper, we present an architecture including
flexible QoS control to improve the user quality of experience (QoE). The QoS
control is developed based on linear regression modeling using historical
network data. Additionally, the architecture includes a novel compression
algorithm of 2D images, designed to guarantee the best image quality and to
reduce video delay; this algorithm is based on k-means clustering and can
satisfy the requirements of realtime onboard processing. Through simulations
with a real work dataset collected by the MIT Computer Science and Artificial
Lab, we present experimental as well as explain the performance of the QoS
system.

The surface defect detection method based on visual perception has been
widely used in industrial quality inspection. Because defect data are not easy
to obtain and the annotation of a large number of defect data will waste a lot
of manpower and material resources. Therefore, this paper reviews the methods
of surface defect detection of industrial products based on a small number of
labeled data, and this method is divided into traditional image
processing-based industrial product surface defect detection methods and deep
learning-based industrial product surface defect detection methods suitable for
a small number of labeled data. The traditional image processing-based
industrial product surface defect detection methods are divided into
statistical methods, spectral methods and model methods. Deep learning-based
industrial product surface defect detection methods suitable for a small number
of labeled data are divided into based on data augmentation, based on transfer
learning, model-based fine-tuning, semi-supervised, weak supervised and
unsupervised.

Purpose: To introduce a dual-domain reconstruction network with V-Net and
K-Net for accurate MR image reconstruction from undersampled k-space data.
Methods: Most state-of-the-art reconstruction methods apply U-Net or cascaded
U-Nets in image domain and/or k-space domain. Nevertheless, these methods have
following problems: (1) Directly applying U-Net in k-space domain is not
optimal for extracting features in k-space domain; (2) Classical image-domain
oriented U-Net is heavy-weight and hence is inefficient to be cascaded many
times for yielding good reconstruction accuracy; (3) Classical image-domain
oriented U-Net does not fully make use information of encoder network for
extracting features in decoder network; and (4) Existing methods are
ineffective in simultaneously extracting and fusing features in image domain
and its dual k-space domain. To tackle these problems, we propose in this paper
(1) an image-domain encoder-decoder sub-network called V-Net which is more
light-weight for cascading and effective in fully utilizing features in the
encoder for decoding, (2) a k-space domain sub-network called K-Net which is
more suitable for extracting hierarchical features in k-space domain, and (3) a
dual-domain reconstruction network where V-Nets and K-Nets are parallelly and
effectively combined and cascaded. Results: Extensive experimental results on
the challenging fastMRI dataset demonstrate that the proposed KV-Net can
reconstruct high-quality images and outperform current state-of-the-art
approaches with fewer parameters. Conclusions: To reconstruct images
effectively and efficiently from incomplete k-space data, we have presented a
parallel dual-domain KV-Net to combine K-Nets and V-Nets. The KV-Net is more
lightweight than state-of-the-art methods but achieves better reconstruction

In this paper, we propose a unified information theoretic framework for
learning-motivated methods aimed at odometry estimation, a crucial component of
many robotics and vision tasks such as navigation and virtual reality where
relative camera poses are required in real time. We formulate this problem as
optimizing a variational information bottleneck objective function, which
eliminates pose-irrelevant information from the latent representation. The
proposed framework provides an elegant tool for performance evaluation and
understanding in information-theoretic language. Specifically, we bound the
generalization errors of the deep information bottleneck framework and the
predictability of the latent representation. These provide not only a
performance guarantee but also practical guidance for model design, sample
collection, and sensor selection. Furthermore, the stochastic latent
representation provides a natural uncertainty measure without the needs for
extra structures or computations. Experiments on two well-known odometry
datasets demonstrate the effectiveness of our method.

A Bayesian coreset is a small, weighted subset of data that replaces the full
dataset during Bayesian inference, with the goal of reducing computational
cost. Although past work has shown empirically that there often exists a
coreset with low inferential error, efficiently constructing such a coreset
remains a challenge. Current methods tend to be slow, require a secondary
inference step after coreset construction, and do not provide bounds on the
data marginal evidence. In this work, we introduce a new method -- sparse
Hamiltonian flows -- that addresses all three of these challenges. The method
involves first subsampling the data uniformly, and then optimizing a
Hamiltonian flow parametrized by coreset weights and including periodic
momentum quasi-refreshment steps. Theoretical results show that the method
enables an exponential compression of the dataset in a representative model,
and that the quasi-refreshment steps reduce the KL divergence to the target.
Real and synthetic experiments demonstrate that sparse Hamiltonian flows
provide accurate posterior approximations with significantly reduced runtime
compared with competing dynamical-system-based inference methods.

Rodent stroke models are important for evaluating treatments and
understanding the pathophysiology and behavioral changes of brain ischemia, and
magnetic resonance imaging (MRI) is a valuable tool for measuring outcome in
preclinical studies. Brain extraction is an essential first step in most
neuroimaging pipelines; however, it can be challenging in the presence of
severe pathology and when dataset quality is highly variable. Convolutional
neural networks (CNNs) can improve accuracy and reduce operator time,
facilitating high throughput preclinical studies. As part of an ongoing
preclinical stroke imaging study, we developed a deep-learning mouse brain
extraction tool by using a U-net CNN. While previous studies have evaluated
U-net architectures, we sought to evaluate their practical performance across
data types. We ask how performance is affected with data across: six imaging
centers, two time points after experimental stroke, and across four MRI
contrasts. We trained, validated, and tested a typical U-net model on 240
multimodal MRI datasets including quantitative multi-echo T2 and apparent
diffusivity coefficient (ADC) maps, and performed qualitative evaluation with a
large preclinical stroke database (N=1,368). We describe the design and
development of this system, and report our findings linking data
characteristics to segmentation performance. We consistently found high
accuracy and ability of the U-net architecture to generalize performance in a
range of 95-97% accuracy, with only modest reductions in performance based on
lower fidelity imaging hardware and brain pathology. This work can help inform
the design of future preclinical rodent imaging studies and improve their
scalability and reliability.

Monocular visual odometry (VO) has attracted extensive research attention by
providing real-time vehicle motion from cost-effective camera images. However,
state-of-the-art optimization-based monocular VO methods suffer from the scale
inconsistency problem for long-term predictions. Deep learning has recently
been introduced to address this issue by leveraging stereo sequences or
ground-truth motions in the training dataset. However, it comes at an
additional cost for data collection, and such training data may not be
available in all datasets. In this work, we propose VRVO, a novel framework for
retrieving the absolute scale from virtual data that can be easily obtained
from modern simulation environments, whereas in the real domain no stereo or
ground-truth data are required in either the training or inference phases.
Specifically, we first train a scale-aware disparity network using both
monocular real images and stereo virtual data. The virtual-to-real domain gap
is bridged by using an adversarial training strategy to map images from both
domains into a shared feature space. The resulting scale-consistent disparities
are then integrated with a direct VO system by constructing a virtual stereo
objective that ensures the scale consistency over long trajectories.
Additionally, to address the suboptimality issue caused by the separate
optimization backend and the learning process, we further propose a mutual
reinforcement pipeline that allows bidirectional information flow between
learning and optimization, which boosts the robustness and accuracy of each
other. We demonstrate the effectiveness of our framework on the KITTI and
vKITTI2 datasets.

The rapid growth of the online fashion market brought demands for innovative
fashion services and commerce platforms. With the recent success of deep
learning, many applications employ AI technologies such as visual search and
recommender systems to provide novel and beneficial services. In this paper, we
describe applied technologies for AI-driven fashion social networking service
that incorporate fashion e-commerce. In the application, people can share and
browse their outfit-of-the-day (OOTD) photos, while AI analyzes them and
suggests similar style OOTDs and related products. To this end, we trained deep
learning based AI models for fashion and integrated them to build a fashion
visual search system and a recommender system for OOTD. With aforementioned
technologies, the AI-driven fashion SNS platform, iTOO, has been successfully
launched.

Despite recent advances of AI, story understanding remains an open and
under-investigated problem. We collect, preprocess, and publicly release a
video-language story dataset, Synopses of Movie Narratives(SyMoN), containing
5,193 video summaries of popular movies and TV series. SyMoN captures
naturalistic storytelling videos for human audience made by human creators, and
has higher story coverage and more frequent mental-state references than
similar video-language story datasets. Differing from most existing video-text
datasets, SyMoN features large semantic gaps between the visual and the textual
modalities due to the prevalence of reporting bias and mental state
descriptions. We establish benchmarks on video-text retrieval and zero-shot
alignment on movie summary videos. With SyMoN, we hope to lay the groundwork
for progress in multimodal story understanding.

Background: The increasing availability of databases containing both magnetic
resonance imaging (MRI) and genetic data allows researchers to utilize
multimodal data to better understand the characteristics of dementia of
Alzheimer's type (DAT). Objective: The goal of this study was to develop and
analyze novel biomarkers that can help predict the development and progression
of DAT. Methods: We used feature selection and ensemble learning classifier to
develop an image/genotype-based DAT score that represents a subject's
likelihood of developing DAT in the future. Three feature types were used: MRI
only, genetic only, and combined multimodal data. We used a novel data
stratification method to better represent different stages of DAT. Using a
pre-defined 0.5 threshold on DAT scores, we predicted whether or not a subject
would develop DAT in the future. Results: Our results on Alzheimer's Disease
Neuroimaging Initiative (ADNI) database showed that dementia scores using
genetic data could better predict future DAT progression for currently normal
control subjects (Accuracy=0.857) compared to MRI (Accuracy=0.143), while MRI
can better characterize subjects with stable mild cognitive impairment
(Accuracy=0.614) compared to genetics (Accuracy=0.356). Combining MRI and
genetic data showed improved classification performance in the remaining
stratified groups. Conclusion: MRI and genetic data can contribute to DAT
prediction in different ways. MRI data reflects anatomical changes in the
brain, while genetic data can detect the risk of DAT progression prior to the
symptomatic onset. Combining information from multimodal data in the right way
can improve prediction performance.

The training phases of Deep neural network~(DNN) consumes enormous processing
time and energy. Compression techniques utilizing the sparsity of DNNs can
effectively accelerate the inference phase of DNNs. However, it is hardly used
in the training phase because the training phase involves dense
matrix-multiplication using General-Purpose Computation on Graphics Processors
(GPGPU), which endorse the regular and structural data layout. In this paper,
we first propose the Approximate Random Dropout that replaces the conventional
random dropout of neurons and synapses with a regular and online generated
row-based or tile-based dropout patterns to eliminate the unnecessary
computation and data access for the multilayer perceptron~(MLP) and long
short-term memory~(LSTM). We then develop a SGD-based Search Algorithm that
produces the distribution of row-based or tile-based dropout patterns to
compensate for the potential accuracy loss. Moreover, aiming at the convolution
neural network~(CNN) training acceleration, we first explore the importance and
sensitivity of input feature maps; and then propose the sensitivity-aware
dropout method to dynamically drop the input feature maps based on their
sensitivity so as to achieve greater forward and backward training acceleration
while reserving better NN accuracy. To facilitate DNN programming, we build a
DNN training computation framework that unifies the proposed techniques in the
software stack. As a result, the GPGPU only needs to support the basic operator
-- matrix multiplication and can achieve significant performance improvement
regardless of DNN model.

The use of neural networks as function approximators has enabled many
advances in reinforcement learning (RL). The generalization power of neural
networks combined with advances in RL algorithms has reignited the field of
artificial intelligence. Despite their power, neural networks are considered
black boxes, and their use in safety-critical settings remains a challenge.
Recently, neural network verification has emerged as a way to certify safety
properties of networks. Verification is a hard problem, and it is difficult to
scale to large networks such as the ones used in deep reinforcement learning.
We provide an approach to train RL policies that are more easily verifiable. We
use binarized neural networks (BNNs), a type of network with mostly binary
parameters. We present an RL algorithm tailored specifically for BNNs. After
training BNNs for the Atari environments, we verify robustness properties.

Palmprints are private and stable information for biometric recognition. In
the deep learning era, the development of palmprint recognition is limited by
the lack of sufficient training data. In this paper, by observing that palmar
creases are the key information to deep-learning-based palmprint recognition,
we propose to synthesize training data by manipulating palmar creases.
Concretely, we introduce an intuitive geometric model which represents palmar
creases with parameterized B\'ezier curves. By randomly sampling B\'ezier
parameters, we can synthesize massive training samples of diverse identities,
which enables us to pretrain large-scale palmprint recognition models.
Experimental results demonstrate that such synthetically pretrained models have
a very strong generalization ability: they can be efficiently transferred to
real datasets, leading to significant performance improvements on palmprint
recognition. For example, under the open-set protocol, our method improves the
strong ArcFace baseline by more than 10\% in terms of TAR@1e-6. And under the
closed-set protocol, our method reduces the equal error rate (EER) by an order

Artificial Spiking Neural Networks (ASNNs) promise greater information
processing efficiency because of discrete event-based (i.e., spike)
computation. Several Machine Learning (ML) applications use biologically
inspired plasticity mechanisms as unsupervised learning techniques to increase
the robustness of ASNNs while preserving efficiency. Spike Time Dependent
Plasticity (STDP) and Intrinsic Plasticity (IP) (i.e., dynamic spiking
threshold adaptation) are two such mechanisms that have been combined to form
an ensemble learning method. However, it is not clear how this ensemble
learning should be regulated based on spiking activity. Moreover, previous
studies have attempted threshold based synaptic pruning following STDP, to
increase inference efficiency at the cost of performance in ASNNs. However,
this type of structural adaptation, that employs individual weight mechanisms,
does not consider spiking activity for pruning which is a better representation
of input stimuli. We envisaged that plasticity-based spike-regulation and
spike-based pruning will result in ASSNs that perform better in low resource
situations. In this paper, a novel ensemble learning method based on entropy
and network activation is introduced, which is amalgamated with a spike-rate
neuron pruning technique, operated exclusively using spiking activity. Two
electroencephalography (EEG) datasets are used as the input for classification
experiments with a three-layer feed forward ASNN trained using one-pass
learning. During the learning process, we observed neurons assembling into a
hierarchy of clusters based on spiking rate. It was discovered that pruning
lower spike-rate neuron clusters resulted in increased generalization or a
predictable decline in performance.

LiDAR-based localization and mapping is one of the core components in many
modern robotic systems due to the direct integration of range and geometry,
allowing for precise motion estimation and generation of high quality maps in
real-time. Yet, as a consequence of insufficient environmental constraints
present in the scene, this dependence on geometry can result in localization
failure, happening in self-symmetric surroundings such as tunnels. This work
addresses precisely this issue by proposing a neural network-based estimation
approach for detecting (non-)localizability during robot operation. Special
attention is given to the localizability of scan-to-scan registration, as it is
a crucial component in many LiDAR odometry estimation pipelines. In contrast to
previous, mostly traditional detection approaches, the proposed method enables
early detection of failure by estimating the localizability on raw sensor
measurements without evaluating the underlying registration optimization.
Moreover, previous approaches remain limited in their ability to generalize
across environments and sensor types, as heuristic-tuning of degeneracy
detection thresholds is required. The proposed approach avoids this problem by
learning from a collection of different environments, allowing the network to
function over various scenarios. Furthermore, the network is trained
exclusively on simulated data, avoiding arduous data collection in challenging
and degenerate, often hard-to-access, environments. The presented method is
tested during field experiments conducted across challenging environments and
on two different sensor types without any modifications. The observed detection
performance is on par with state-of-the-art methods after environment-specific
threshold tuning.

Many approaches for verifying input-output properties of neural networks have
been proposed recently. However, existing algorithms do not scale well to large
networks. Recent work in the field of model compression studied binarized
neural networks (BNNs), whose parameters and activations are binary. BNNs tend
to exhibit a slight decrease in performance compared to their full-precision
counterparts, but they can be easier to verify. This paper proposes a simple
mixed integer programming formulation for BNN verification that leverages
network structure. We demonstrate our approach by verifying properties of BNNs
trained on the MNIST dataset and an aircraft collision avoidance controller. We
compare the runtime of our approach against state-of-the-art verification
algorithms for full-precision neural networks. The results suggest that the
difficulty of training BNNs might be worth the reduction in runtime achieved by
our verification algorithm.

Continual learning, also known as lifelong learning, is an emerging research
topic that has been attracting increasing interest in the field of machine
learning. With human activity recognition (HAR) playing a key role in enabling
numerous real-world applications, an essential step towards the long-term
deployment of such recognition systems is to extend the activity model to
dynamically adapt to changes in people's everyday behavior. Current research in
continual learning applied to HAR domain is still under-explored with
researchers exploring existing methods developed for computer vision in HAR.
Moreover, analysis has so far focused on task-incremental or class-incremental
learning paradigms where task boundaries are known. This impedes the
applicability of such methods for real-world systems since data is presented in
a randomly streaming fashion. To push this field forward, we build on recent
advances in the area of continual machine learning and design a lifelong
adaptive learning framework using Prototypical Networks, LAPNet-HAR, that
processes sensor-based data streams in a task-free data-incremental fashion and
mitigates catastrophic forgetting using experience replay and continual
prototype adaptation. Online learning is further facilitated using contrastive
loss to enforce inter-class separation. LAPNet-HAR is evaluated on 5 publicly
available activity datasets in terms of the framework's ability to acquire new
information while preserving previous knowledge. Our extensive empirical
results demonstrate the effectiveness of LAPNet-HAR in task-free continual
learning and uncover useful insights for future challenges.

Precise reconstruction of top quark properties is a challenging task at the
Large Hadron Collider due to combinatorial backgrounds and missing information.
We introduce a physics-informed neural network architecture called the
Covariant Particle Transformer (CPT) for directly predicting the top quark
kinematic properties from reconstructed final state objects. This approach is
permutation invariant and partially Lorentz covariant and can account for a
variable number of input objects. In contrast to previous machine
learning-based reconstruction methods, CPT is able to predict top quark
four-momenta regardless of the jet multiplicity in the event. Using
simulations, we show that the CPT performs favorably compared with other
machine learning top quark reconstruction approaches.

Medical imaging is a cornerstone of therapy and diagnosis in modern medicine.
However, the choice of imaging modality for a particular theranostic task
typically involves trade-offs between the feasibility of using a particular
modality (e.g., short wait times, low cost, fast acquisition, reduced
radiation/invasiveness) and the expected performance on a clinical task (e.g.,
diagnostic accuracy, efficacy of treatment planning and guidance). In this
work, we aim to apply the knowledge learned from the less feasible but
better-performing (superior) modality to guide the utilization of the
more-feasible yet under-performing (inferior) modality and steer it towards
improved performance. We focus on the application of deep learning for
image-based diagnosis. We develop a light-weight guidance model that leverages
the latent representation learned from the superior modality, when training a
model that consumes only the inferior modality. We examine the advantages of
our method in the context of two clinical applications: multi-task skin lesion
classification from clinical and dermoscopic images and brain tumor
classification from multi-sequence magnetic resonance imaging (MRI) and
histopathology images. For both these scenarios we show a boost in diagnostic
performance of the inferior modality without requiring the superior modality.
Furthermore, in the case of brain tumor classification, our method outperforms
the model trained on the superior modality while producing comparable results
to the model that uses both modalities during inference.

Semi-supervised segmentation tackles the scarcity of annotations by
leveraging unlabeled data with a small amount of labeled data. A prominent way
to utilize the unlabeled data is by consistency training which commonly uses a
teacher-student network, where a teacher guides a student segmentation. The
predictions of unlabeled data are not reliable, therefore, uncertainty-aware
methods have been proposed to gradually learn from meaningful and reliable
predictions. Uncertainty estimation, however, relies on multiple inferences
from model predictions that need to be computed for each training step, which
is computationally expensive. This work proposes a novel method to estimate the
pixel-level uncertainty by leveraging the labeling representation of
segmentation masks. On the one hand, a labeling representation is learnt to
represent the available segmentation masks. The learnt labeling representation
is used to map the prediction of the segmentation into a set of plausible
masks. Such a reconstructed segmentation mask aids in estimating the
pixel-level uncertainty guiding the segmentation network. The proposed method
estimates the uncertainty with a single inference from the labeling
representation, thereby reducing the total computation. We evaluate our method
on the 3D segmentation of left atrium in MRI, and we show that our uncertainty
estimates from our labeling representation improve the segmentation accuracy
over state-of-the-art methods.

Consensus, state-machine replication (SMR) and total order broadcast (TOB)
protocols are notorious for being poorly scalable with the number of
participating nodes. Despite the recent race to reduce overall message
complexity of leader-driven SMR/TOB protocols, scalability remains poor and the
throughput is typically inversely proportional to the number of nodes. We
present Insanely Scalable State-Machine Replication, a generic construction to
turn leader-driven protocols into scalable multi-leader ones. For our scalable
SMR construction we use a novel primitive called Sequenced (Total Order)
Broadcast (SB) which we wrap around PBFT, HotStuff and Raft leader-driven
protocols to make them scale. Our construction is general enough to accommodate
most leader-driven ordering protocols (BFT or CFT) and make them scale. Our
implementation improves the peak throughput of PBFT, HotStuff, and Raft by 37x,
56x, and 55x, respectively, at a scale of 128 nodes.

Human-Object Interaction (HOI) recognition is challenging due to two factors:
(1) significant imbalance across classes and (2) requiring multiple labels per
image. This paper shows that these two challenges can be effectively addressed
by improving the classifier with the backbone architecture untouched. Firstly,
we encode the semantic correlation among classes into the classification head
by initializing the weights with language embeddings of HOIs. As a result, the
performance is boosted significantly, especially for the few-shot subset.
Secondly, we propose a new loss named LSE-Sign to enhance multi-label learning
on a long-tailed dataset. Our simple yet effective method enables
detection-free HOI classification, outperforming the state-of-the-arts that
require object detection and human pose by a clear margin. Moreover, we
transfer the classification model to instance-level HOI detection by connecting
it with an off-the-shelf object detector. We achieve state-of-the-art without
additional fine-tuning.

The fusion of public sentiment data in the form of text with stock price
prediction is a topic of increasing interest within the financial community.
However, the research literature seldom explores the application of investor
sentiment in the Portfolio Selection problem. This paper aims to unpack and
develop an enhanced understanding of the sentiment aware portfolio selection
problem. To this end, the study uses a Semantic Attention Model to predict
sentiment towards an asset. We select the optimal portfolio through a
sentiment-aware Long Short Term Memory (LSTM) recurrent neural network for
price prediction and a mean-variance strategy. Our sentiment portfolio
strategies achieved on average a significant increase in revenue above the
non-sentiment aware models. However, the results show that our strategy does
not outperform traditional portfolio allocation strategies from a stability
perspective. We argue that an improved fusion of sentiment prediction with a
combination of price prediction and portfolio optimization leads to an enhanced
portfolio selection strategy.

In this paper, we present the fourth installment of the NELA-GT datasets,
NELA-GT-2021. The dataset contains 1.8M articles from 367 outlets between
January 1st, 2021 and December 31st, 2021. Just as in past releases of the
dataset, NELA-GT-2021 includes outlet-level veracity labels from Media
Bias/Fact Check and tweets embedded in collected news articles. The
NELA-GT-2021 dataset can be found at: https://doi.org/10.7910/DVN/RBKVBM

We consider a multi-source relaying system where the independent sources
randomly generate status update packets which are sent to the destination with
the aid of a relay through unreliable links. We develop transmission scheduling
policies to minimize the sum average age of information (AoI) subject to
transmission capacity and long-run average resource constraints. We formulate a
stochastic control optimization problem. To solve the problem, a constrained
Markov decision process (CMDP) approach and a drift-plus-penalty method are
proposed. The CMDP problem is solved by transforming it into an MDP problem
using the Lagrangian relaxation method. We theoretically analyze the structure
of optimal policies for the MDP problem and subsequently propose a
structure-aware algorithm that returns a practical near-optimal policy. By the
drift-plus-penalty method, we devise a dynamic near-optimal low-complexity
policy. We also develop a model-free deep reinforcement learning policy, which
does not require the full knowledge of system statistics. To do so, we employ
the Lyapunov optimization theory and a dueling double deep Q-network.
Simulation results are provided to assess the performance of our policies and
validate the theoretical results. The results show up to 91% performance
improvement compared to a baseline policy.

Biometric authentication methods, representing the "something you are"
scheme, are considered the most secure approach for gaining access to protected
resources. Recent attacks using Machine Learning techniques demand a serious
systematic reevaluation of biometric authentication. This paper analyzes and
presents the Fast Gradient Sign Method (FGSM) attack using face recognition for
biometric authentication. Machine Learning techniques have been used to train
and test the model, which can classify and identify different people's faces
and which will be used as a target for carrying out the attack. Furthermore,
the case study will analyze the implementation of the FGSM and the level of
performance reduction that the model will have by applying this method in
attacking. The test results were performed with the change of parameters both
in terms of training and attacking the model, thus showing the efficiency of
applying the FGSM.

Current semi-supervised learning (SSL) methods assume a balance between the
number of data points available for each class in both the labeled and the
unlabeled data sets. However, there naturally exists a class imbalance in most
real-world datasets. It is known that training models on such imbalanced
datasets leads to biased models, which in turn lead to biased predictions
towards the more frequent classes. This issue is further pronounced in SSL
methods, as they would use this biased model to obtain psuedo-labels (on the
unlabeled data) during training. In this paper, we tackle this problem by
attempting to select a balanced labeled dataset for SSL that would result in an
unbiased model. Unfortunately, acquiring a balanced labeled dataset from a
class imbalanced distribution in one shot is challenging. We propose BASIL
(Balanced Active Semi-supervIsed Learning), a novel algorithm that optimizes
the submodular mutual information (SMI) functions in a per-class fashion to
gradually select a balanced dataset in an active learning loop. Importantly,
our technique can be efficiently used to improve the performance of any SSL
method. Our experiments on Path-MNIST and Organ-MNIST medical datasets for a
wide array of SSL methods show the effectiveness of Basil. Furthermore, we
observe that Basil outperforms the state-of-the-art diversity and uncertainty
based active learning methods since the SMI functions select a more balanced
dataset.

This paper introduces a data-dependent approximation of the forward
kinematics map for certain types of animal motion models. It is assumed that
motions are supported on a low-dimensional, unknown configuration manifold $Q$
that is regularly embedded in high dimensional Euclidean space
$X:=\mathbb{R}^d$. This paper introduces a method to estimate forward
kinematics from the unknown configuration submanifold $Q$ to an $n$-dimensional
Euclidean space $Y:=\mathbb{R}^n$ of observations. A known reproducing kernel
Hilbert space (RKHS) is defined over the ambient space $X$ in terms of a known
kernel function, and computations are performed using the known kernel defined
on the ambient space $X$. Estimates are constructed using a certain
data-dependent approximation of the Koopman operator defined in terms of the
known kernel on $X$. However, the rate of convergence of approximations is
studied in the space of restrictions to the unknown manifold $Q$. Strong rates
of convergence are derived in terms of the fill distance of samples in the
unknown configuration manifold, provided that a novel regularity result holds
for the Koopman operator. Additionally, we show that the derived rates of
convergence can be applied in some cases to estimates generated by the extended
dynamic mode decomposition (EDMD) method. We illustrate characteristics of the
estimates for simulated data as well as samples collected during motion capture

Constructing a diverse repertoire of manipulation skills in a scalable
fashion remains an unsolved challenge in robotics. One way to address this
challenge is with unstructured human play, where humans operate freely in an
environment to reach unspecified goals. Play is a simple and cheap method for
collecting diverse user demonstrations with broad state and goal coverage over
an environment. Due to this diverse coverage, existing approaches for learning
from play are more robust to online policy deviations from the offline data
distribution. However, these methods often struggle to learn under scene
variation and on challenging manipulation primitives, due in part to improperly
associating complex behaviors to the scene changes they induce. Our insight is
that an object-centric view of play data can help link human behaviors and the
resulting changes in the environment, and thus improve multi-task policy
learning. In this work, we construct a latent space to model object affordances
-- properties of an object that define its uses -- in the environment, and then
learn a policy to achieve the desired affordances. By modeling and predicting
the desired affordance across variable horizon tasks, our method, Predicting
Latent Affordances Through Object-Centric Play (PLATO), outperforms existing
methods on complex manipulation tasks in both 2D and 3D object manipulation
simulation and real world environments for diverse types of interactions.
Videos can be found on our website: https://tinyurl.com/4u23hwfv

Underlying complex systems, there is a rich web of interconnected components
that determine the relational properties of the system. Yet, traditional
methods used in education sciences often disregard the underlying complexity of
the educational system and, consequently, its emergence phenomena. Here, we
argue that an interconnected vision of educational systems -- from classrooms
to an organizational level -- is key to improving learning, social integration,
well-being, and decision making, all fundamental aspects of the educational
experience. Understanding the education system as an interconnected network of
people, degree programs, and institutions requires methods and concepts from
computational social sciences. Thus, we can leverage institutional records and
(quasi) experimental designs to elicit the relational maps of key players in
education and derive their implications in their functioning at all scales. In
different settings, from elementary classrooms to higher education programs, we
show how mapping the network relationships between entities can lead to the
inference of novel insights about education systems and the development of
solutions with societal implications.

During the COVID-19 crisis, educational institutions worldwide shifted from
face-to-face instruction to emergency remote teaching (ERT) modalities. In this
forced and sudden transition, teachers and students did not have the
opportunity to acquire the knowledge or skills necessary for online learning
modalities implemented through a learning management system (LMS). Therefore,
undergraduate teachers tend to mainly use an LMS as an information repository
and rarely promote virtual interactions among students, thus limiting the
benefits of collective intelligence for students. We analyzed data on 7,528
undergraduate students and found that cooperative and consensus dynamics among
university students in discussion forums positively affect their final GPA,
with a steeper effect for students with low academic performance during high
school. These results hold above and beyond socioeconomic and other LMS
activity confounders. Furthermore, using natural language processing, we show
that first-year students with low academic performance during high school are
exposed to more content-intensive posts in discussion forums, leading to
significantly higher university GPAs than their low-performance peers in high
school. We expect these results to motivate higher education teachers worldwide
to promote cooperative and consensus dynamics among students using tools such
as forum discussions in their classes to reap the benefits of social learning
and collective intelligence.

We study the problem of multiway number partition optimization, which has a
myriad of applications in the decision, learning and optimization literature.
Even though the original multiway partitioning problem is NP-hard and requires
exponential time complexity algorithms; we formulate an easier optimization
problem, where our goal is to find a solution that is locally optimal. We
propose a linearithmic time complexity $O(N\log N)$ algorithm that can produce
such a locally optimal solution. Our method is robust against the input and
requires neither positive nor integer inputs.

Retinal implants have the potential to treat incurable blindness, yet the
quality of the artificial vision they produce is still rudimentary. An
outstanding challenge is identifying electrode activation patterns that lead to
intelligible visual percepts (phosphenes). Here we propose a PSE based on CNN
that is trained in an end-to-end fashion to predict the electrode activation
patterns required to produce a desired visual percept. We demonstrate the
effectiveness of the encoder on MNIST using a psychophysically validated
phosphene model tailored to individual retinal implant users. The present work
constitutes an essential first step towards improving the quality of the
artificial vision provided by retinal implants.

The Arabic Sign Language has endorsed outstanding research achievements for
identifying gestures and hand signs using the deep learning methodology. The
term "forms of communication" refers to the actions used by hearing-impaired
people to communicate. These actions are difficult for ordinary people to
comprehend. The recognition of Arabic Sign Language (ArSL) has become a
difficult study subject due to variations in Arabic Sign Language (ArSL) from
one territory to another and then within states. The Convolution Neural Network
has been encapsulated in the proposed system which is based on the machine
learning technique. For the recognition of the Arabic Sign Language, the
wearable sensor is utilized. This approach has been used a different system
that could suit all Arabic gestures. This could be used by the impaired people
of the local Arabic community. The research method has been used with
reasonable and moderate accuracy. A deep Convolutional network is initially
developed for feature extraction from the data gathered by the sensing devices.
These sensors can reliably recognize the Arabic sign language's 30 hand sign
letters. The hand movements in the dataset were captured using DG5-V hand
gloves with wearable sensors. For categorization purposes, the CNN technique is
used. The suggested system takes Arabic sign language hand gestures as input
and outputs vocalized speech as output. The results were recognized by 90% of
the people.

The ability to grasp objects is an essential skill that enables many robotic
manipulation tasks. Recent works have studied point cloud-based methods for
object grasping by starting from simulated datasets and have shown promising
performance in real-world scenarios. Nevertheless, many of them still rely on
ad-hoc geometric heuristics to generate grasp candidates, which fail to
generalize to objects with significantly different shapes with respect to those
observed during training. Several approaches exploit complex multi-stage
learning strategies and local neighborhood feature extraction while ignoring
semantic global information. Furthermore, they are inefficient in terms of
number of training samples and time required for inference. In this paper, we
propose an end-to-end learning solution to generate 6-DOF parallel-jaw grasps
starting from the 3D partial view of the object. Our Learning to Grasp (L2G)
method gathers information from the input point cloud through a new procedure
that combines a differentiable sampling strategy to identify the visible
contact points, with a feature encoder that leverages local and global cues.
Overall, L2G is guided by a multi-task objective that generates a diverse set
of grasps by optimizing contact point sampling, grasp regression, and grasp
classification. With a thorough experimental analysis, we show the
effectiveness of L2G as well as its robustness and generalization abilities.

With the rise of powerful pre-trained vision-language models like CLIP, it
becomes essential to investigate ways to adapt these models to downstream
datasets. A recently proposed method named Context Optimization (CoOp)
introduces the concept of prompt learning -- a recent trend in NLP -- to the
vision domain for adapting pre-trained vision-language models. Specifically,
CoOp turns context words in a prompt into a set of learnable vectors and, with
only a few labeled images for learning, can achieve huge improvements over
intensively-tuned manual prompts. In our study we identify a critical problem
of CoOp: the learned context is not generalizable to wider unseen classes
within the same dataset, suggesting that CoOp overfits base classes observed
during training. To address the problem, we propose Conditional Context
Optimization (CoCoOp), which extends CoOp by further learning a lightweight
neural network to generate for each image an input-conditional token (vector).
Compared to CoOp's static prompts, our dynamic prompts adapt to each instance
and are thus less sensitive to class shift. Extensive experiments show that
CoCoOp generalizes much better than CoOp to unseen classes, even showing
promising transferability beyond a single dataset; and yields stronger domain
generalization performance as well. Code is available at
https://github.com/KaiyangZhou/CoOp.

Recently, Transformer-like deep architectures have shown strong performance
on tabular data problems. Unlike traditional models, e.g., MLP, these
architectures map scalar values of numerical features to high-dimensional
embeddings before mixing them in the main backbone. In this work, we argue that
embeddings for numerical features are an underexplored degree of freedom in
tabular DL, which allows constructing more powerful DL models and competing
with GBDT on some traditionally GBDT-friendly benchmarks. We start by
describing two conceptually different approaches to building embedding modules:
the first one is based on a piecewise linear encoding of scalar values, and the
second one utilizes periodic activations. Then, we empirically demonstrate that
these two approaches can lead to significant performance boosts compared to the
embeddings based on conventional blocks such as linear layers and ReLU
activations. Importantly, we also show that embedding numerical features is
beneficial for many backbones, not only for Transformers. Specifically, after
proper embeddings, simple MLP-like models can perform on par with the
attention-based architectures. Overall, we highlight embeddings for numerical
features as an important design aspect with good potential for further
improvements in tabular DL.

This work studies feature representations for dense label propagation in
video, with a focus on recently proposed methods that learn video
correspondence using self-supervised signals such as colorization or temporal
cycle consistency. In the literature, these methods have been evaluated with an
array of inconsistent settings, making it difficult to discern trends or
compare performance fairly. Starting with a unified formulation of the label
propagation algorithm that encompasses most existing variations, we
systematically study the impact of important implementation factors in feature
extraction and label propagation. Along the way, we report the accuracies of
properly tuned supervised and unsupervised still image baselines, which are
higher than those found in previous works. We also demonstrate that augmenting
video-based correspondence cues with still-image-based ones can further improve
performance. We then attempt a fair comparison of recent video-based methods on
the DAVIS benchmark, showing convergence of best methods to performance levels
near our strong ImageNet baseline, despite the usage of a variety of
specialized video-based losses and training particulars. Additional comparisons
on JHMDB and VIP datasets confirm the similar performance of current methods.
We hope that this study will help to improve evaluation practices and better
inform future research directions in temporal correspondence.

Anomaly detection and segmentation in images has made tremendous progress in
recent years while 3D information has often been ignored. The objective of this
paper is to further understand the benefit and role of 3D as opposed to color
in image anomaly detection. Our study begins by presenting a surprising
finding: standard color-only anomaly segmentation methods, when applied to 3D
datasets, significantly outperform all current methods. On the other hand, we
observe that color-only methods are insufficient for images containing
geometric anomalies where shape cannot be unambiguously inferred from 2D. This
suggests that better 3D methods are needed. We investigate different
representations for 3D anomaly detection and discover that handcrafted
orientation-invariant representations are unreasonably effective on this task.
We uncover a simple 3D-only method that outperforms all recent approaches while
not using deep learning, external pretraining datasets, or color information.
As the 3D-only method cannot detect color and texture anomalies, we combine it
with 2D color features, granting us the best current results by a large margin
(Pixel-wise ROCAUC: 99.2%, PRO: 95.9% on MVTec 3D-AD). We conclude by
discussing future challenges for 3D anomaly detection and segmentation.

Understanding environment dynamics is necessary for robots to act safely and
optimally in the world. In realistic scenarios, dynamics are non-stationary and
the causal variables such as environment parameters cannot necessarily be
precisely measured or inferred, even during training. We propose Implicit
Identification for Dynamics Adaptation (IIDA), a simple method to allow
predictive models to adapt to changing environment dynamics. IIDA assumes no
access to the true variations in the world and instead implicitly infers
properties of the environment from a small amount of contextual data. We
demonstrate IIDA's ability to perform well in unseen environments through a
suite of simulated experiments on MuJoCo environments and a real robot dynamic
sliding task. In general, IIDA significantly reduces model error and results in
higher task performance over commonly used methods. Our code and robot videos
are at https://bennevans.github.io/iida/

This paper presents the first large-scale real-world evaluation for using
LiDAR data to guide the mmWave beam prediction task. A machine learning (ML)
model that leverages the LiDAR sensory data to predict the current and future
beams was developed. Based on the large-scale real-world dataset, DeepSense 6G,
this model was evaluated in a vehicle-to-infrastructure communication scenario
with highly-mobile vehicles. The experimental results show that the developed
LiDAR-aided beam prediction and tracking model can predict the optimal beam in
$95\%$ of the cases and with more than $90\%$ reduction in the beam training
overhead. The LiDAR-aided beam tracking achieves comparable accuracy
performance to a baseline solution that has perfect knowledge of the previous
optimal beams, without requiring any knowledge about the previous optimal beam
information and without any need for beam calibration. This highlights a
promising solution for the critical beam alignment challenges in mmWave and
terahertz communication systems.

One major problem in deep learning-based solutions for medical imaging is the
drop in performance when a model is tested on a data distribution different
from the one that it is trained on. Adapting the source model to target data
distribution at test-time is an efficient solution for the data-shift problem.
Previous methods solve this by adapting the model to target distribution by
using techniques like entropy minimization or regularization. In these methods,
the models are still updated by back-propagation using an unsupervised loss on
complete test data distribution. In real-world clinical settings, it makes more
sense to adapt a model to a new test image on-the-fly and avoid model update
during inference due to privacy concerns and lack of computing resource at
deployment. To this end, we propose a new setting - On-the-Fly Adaptation which
is zero-shot and episodic (i.e., the model is adapted to a single image at a
time and also does not perform any back-propagation during test-time). To
achieve this, we propose a new framework called Adaptive UNet where each
convolutional block is equipped with an adaptive batch normalization layer to
adapt the features with respect to a domain code. The domain code is generated
using a pre-trained encoder trained on a large corpus of medical images. During
test-time, the model takes in just the new test image and generates a domain
code to adapt the features of source model according to the test data. We
validate the performance on both 2D and 3D data distribution shifts where we
get a better performance compared to previous test-time adaptation methods.
Code is available at https://github.com/jeya-maria-jose/On-The-Fly-Adaptation

In this article, we create a system called AI-EVL. This is an annotated-based
learning system. We extend AI to learning experience. If a user from the main
YouTube page browses YouTube videos and a user from the AI-EVL system does the
same, the amount of traffic used will be much less. It is due to ignoring
unwanted contents which indicates a reduction in bandwidth usage too. This
system is designed to be embedded with online learning tools and platforms to
enrich their curriculum. In evaluating the system using Google 2020 trend data,
we were able to extract rich ontological information for each data. Of the data
collected, 34.86% belong to wolfram, 30.41% to DBpedia, and 34.73% to
Wikipedia. The video subtitle information is displayed interactively and
functionally to the user over time as the video is played. This effective
visual learning system, due to the unique features, prevents the user's
distraction and makes learning more focused. The information about the subtitle
text is displayed in multiple layers including AI-annotated topics,
Wikipedia/DBpedia, and Wolfram enriched texts via interactive and visual
widgets.

Specialized hardware accelerators have been designed and employed to maximize
the performance efficiency of Spiking Neural Networks (SNNs). However, such
accelerators are vulnerable to transient faults (i.e., soft errors), which
occur due to high-energy particle strikes, and manifest as bit flips at the
hardware layer. These errors can change the weight values and neuron operations
in the compute engine of SNN accelerators, thereby leading to incorrect outputs
and accuracy degradation. However, the impact of soft errors in the compute
engine and the respective mitigation techniques have not been thoroughly
studied yet for SNNs. A potential solution is employing redundant executions
(re-execution) for ensuring correct outputs, but it leads to huge latency and
energy overheads. Toward this, we propose SoftSNN, a novel methodology to
mitigate soft errors in the weight registers (synapses) and neurons of SNN
accelerators without re-execution, thereby maintaining the accuracy with low
latency and energy overheads. Our SoftSNN methodology employs the following key
steps: (1) analyzing the SNN characteristics under soft errors to identify
faulty weights and neuron operations, which are required for recognizing faulty
SNN behavior; (2) a Bound-and-Protect technique that leverages this analysis to
improve the SNN fault tolerance by bounding the weight values and protecting
the neurons from faulty operations; and (3) devising lightweight hardware
enhancements for the neural hardware accelerator to efficiently support the
proposed technique. The experimental results show that, for a 900-neuron
network with even a high fault rate, our SoftSNN maintains the accuracy
degradation below 3%, while reducing latency and energy by up to 3x and 2.3x
respectively, as compared to the re-execution technique.

Networks found with Neural Architecture Search (NAS) achieve state-of-the-art
performance in a variety of tasks, out-performing human-designed networks.
However, most NAS methods heavily rely on human-defined assumptions that
constrain the search: architecture's outer-skeletons, number of layers,
parameter heuristics and search spaces. Additionally, common search spaces
consist of repeatable modules (cells) instead of fully exploring the
architecture's search space by designing entire architectures (macro-search).
Imposing such constraints requires deep human expertise and restricts the
search to pre-defined settings. In this paper, we propose LCMNAS, a method that
pushes NAS to less constrained search spaces by performing macro-search without
relying on pre-defined heuristics or bounded search spaces. LCMNAS introduces
three components for the NAS pipeline: i) a method that leverages information
about well-known architectures to autonomously generate complex search spaces
based on Weighted Directed Graphs with hidden properties, ii) a evolutionary
search strategy that generates complete architectures from scratch, and iii) a
mixed-performance estimation approach that combines information about
architectures at initialization stage and lower fidelity estimates to infer
their trainability and capacity to model complex functions. We present
experiments showing that LCMNAS generates state-of-the-art architectures from
scratch with minimal GPU computation. We study the importance of different NAS
components on a macro-search setting. Code for reproducibility is public at
\url{https://github.com/VascoLopes/LCMNAS}.

Tiny machine learning (tinyML) has emerged during the past few years aiming
to deploy machine learning models to embedded AI processors with highly
constrained memory and computation capacity. Low precision quantization is an
important model compression technique that can greatly reduce both memory
consumption and computation cost of model inference. In this study, we focus on
post-training quantization (PTQ) algorithms that quantize a model to low-bit
(less than 8-bit) precision with only a small set of calibration data and
benchmark them on different tinyML use cases. To achieve a fair comparison, we
build a simulated quantization framework to investigate recent PTQ algorithms.
Furthermore, we break down those algorithms into essential components and
re-assembled a generic PTQ pipeline. With ablation study on different
alternatives of components in the pipeline, we reveal key design choices when
performing low precision quantization. We hope this work could provide useful
data points and shed lights on the future research of low precision

Understanding the deep representations of complex networks is an important
step of building interpretable and trustworthy machine learning applications in
the age of internet. Global surrogate models that approximate the predictions
of a black box model (e.g. an artificial or biological neural net) are usually
used to provide valuable theoretical insights for the model interpretability.
In order to evaluate how well a surrogate model can account for the
representation in another model, we need to develop inference methods for model
comparison. Previous studies have compared models and brains in terms of their
representational geometries (characterized by the matrix of distances between
representations of the input patterns in a model layer or cortical area). In
this study, we propose to explore these summary statistical descriptions of
representations in models and brains as part of a broader class of statistics
that emphasize the topology as well as the geometry of representations. The
topological summary statistics build on topological data analysis (TDA) and
other graph-based methods. We evaluate these statistics in terms of the
sensitivity and specificity that they afford when used for model selection,
with the goal to relate different neural network models to each other and to
make inferences about the computational mechanism that might best account for a
black box representation. These new methods enable brain and computer
scientists to visualize the dynamic representational transformations learned by
brains and models, and to perform model-comparative statistical inference.

In learning with recurrent or very deep feed-forward networks, employing
unitary matrices in each layer can be very effective at maintaining long-range
stability. However, restricting network parameters to be unitary typically
comes at the cost of expensive parameterizations or increased training runtime.
We propose instead an efficient method based on rank-$k$ updates -- or their
rank-$k$ approximation -- that maintains performance at a nearly optimal
training runtime. We introduce two variants of this method, named Direct
(projUNN-D) and Tangent (projUNN-T) projected Unitary Neural Networks, that can
parameterize full $N$-dimensional unitary or orthogonal matrices with a
training runtime scaling as $O(kN^2)$. Our method either projects low-rank
gradients onto the closest unitary matrix (projUNN-T) or transports unitary
matrices in the direction of the low-rank gradient (projUNN-D). Even in the
fastest setting ($k=1$), projUNN is able to train a model's unitary parameters
to reach comparable performances against baseline implementations. In recurrent
neural network settings, projUNN closely matches or exceeds benchmarked results
from prior unitary neural networks. Finally, we preliminarily explore projUNN
in training orthogonal convolutional neural networks, which are currently
unable to outperform state of the art models but can potentially enhance
stability and robustness at large depth.

The conventional recipe for maximizing model accuracy is to (1) train
multiple models with various hyperparameters and (2) pick the individual model
which performs best on a held-out validation set, discarding the remainder. In
this paper, we revisit the second step of this procedure in the context of
fine-tuning large pre-trained models, where fine-tuned models often appear to
lie in a single low error basin. We show that averaging the weights of multiple
models fine-tuned with different hyperparameter configurations often improves
accuracy and robustness. Unlike a conventional ensemble, we may average many
models without incurring any additional inference or memory costs -- we call
the results "model soups." When fine-tuning large pre-trained models such as
CLIP, ALIGN, and a ViT-G pre-trained on JFT, our soup recipe provides
significant improvements over the best model in a hyperparameter sweep on
ImageNet. The resulting ViT-G model, which attains 90.94% top-1 accuracy on
ImageNet, achieved a new state of the art. Furthermore, we show that the model
soup approach extends to multiple image classification and natural language
processing tasks, improves out-of-distribution performance, and improves
zero-shot performance on new downstream tasks. Finally, we analytically relate
the performance similarity of weight-averaging and logit-ensembling to flatness
of the loss and confidence of the predictions, and validate this relation
empirically. Code is available at https://github.com/mlfoundations/model-soups.

One of the pillars to build a country's economy is the stock market. Over the
years, people are investing in stock markets to earn as much profit as possible
from the amount of money that they possess. Hence, it is vital to have a
prediction model which can accurately predict future stock prices. With the
help of machine learning, it is not an impossible task as the various machine
learning techniques if modeled properly may be able to provide the best
prediction values. This would enable the investors to decide whether to buy,
sell or hold the share. The aim of this paper is to predict the future of the
financial stocks of a company with improved accuracy. In this paper, we have
proposed the use of historical as well as sentiment data to efficiently predict
stock prices by applying LSTM. It has been found by analyzing the existing
research in the area of sentiment analysis that there is a strong correlation
between the movement of stock prices and the publication of news articles.
Therefore, in this paper, we have integrated these factors to predict the stock
prices more accurately.

Composition is a key feature of differential privacy. Well-known advanced
composition theorems allow one to query a private database quadratically more
times than basic privacy composition would permit. However, these results
require that the privacy parameters of all algorithms be fixed before
interacting with the data. To address this, Rogers et al. introduced fully
adaptive composition, wherein both algorithms and their privacy parameters can
be selected adaptively. The authors introduce two probabilistic objects to
measure privacy in adaptive composition: privacy filters, which provide
differential privacy guarantees for composed interactions, and privacy
odometers, time-uniform bounds on privacy loss. There are substantial gaps
between advanced composition and existing filters and odometers. First,
existing filters place stronger assumptions on the algorithms being composed.
Second, these odometers and filters suffer from large constants, making them
impractical. We construct filters that match the tightness of advanced
composition, including constants, despite allowing for adaptively chosen
privacy parameters. We also construct several general families of odometers.
These odometers can match the tightness of advanced composition at an
arbitrary, preselected point in time, or at all points in time simultaneously,
up to a doubly-logarithmic factor. We obtain our results by leveraging recent
advances in time-uniform martingale concentration. In sum, we show that fully
adaptive privacy is obtainable at almost no loss, and conjecture that our
results are essentially unimprovable (even in constants) in general.

The availability of charging stations is an important factor for promoting
electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for
city planners, the crucial question is where to place charging stations so that
they reach a large utilization. Here, we hypothesize that the utilization of EV
charging stations is driven by the proximity to points-of-interest (POIs), as
EV owners have a certain limited willingness to walk between charging stations
and POIs. To address our research question, we propose the use of web mining:
we characterize the influence of different POIs from OpenStreetMap on the
utilization of charging stations. For this, we present a tailored interpretable
model that takes into account the full spatial distributions of both the POIs
and the charging stations. This allows us then to estimate the distance and
magnitude of the influence of different POI types. We evaluate our model with
data from approx. 300 charging stations and 4,000 POIs in Amsterdam,
Netherlands. Our model achieves a superior performance over state-of-the-art
baselines and, on top of that, is able to offer an unmatched level of
interpretability. To the best of our knowledge, no previous paper has
quantified the POI influence on charging station utilization from real-world
usage data by estimating the spatial proximity in which POIs are relevant. As
such, our findings help city planners in identifying effective locations for
charging stations.

Real-world object detection models should be cheap and accurate. Knowledge
distillation (KD) can boost the accuracy of a small, cheap detection model by
leveraging useful information from a larger teacher model. However, a key
challenge is identifying the most informative features produced by the teacher
for distillation. In this work, we show that only a very small fraction of
features within a ground-truth bounding box are responsible for a teacher's
high detection performance. Based on this, we propose Prediction-Guided
Distillation (PGD), which focuses distillation on these key predictive regions
of the teacher and yields considerable gains in performance over many existing
KD baselines. In addition, we propose an adaptive weighting scheme over the key
regions to smooth out their influence and achieve even better performance. Our
proposed approach outperforms current state-of-the-art KD baselines on a
variety of advanced one-stage detection architectures. Specifically, on the
COCO dataset, our method achieves between +3.1% and +4.6% AP improvement using
ResNet-101 and ResNet-50 as the teacher and student backbones, respectively. On
the CrowdHuman dataset, we achieve +3.2% and +2.0% improvements in MR and AP,
also using these backbones. Our code is available at
https://github.com/ChenhongyiYang/PGD.

Devices participating in federated learning (FL) typically have heterogeneous
communication, computation, and memory resources. However, in synchronous FL,
all devices need to finish training by the same deadline dictated by the
server. Our results show that training a smaller subset of the neural network
(NN) at constrained devices, i.e., dropping neurons/filters as proposed by
state of the art, is inefficient, preventing these devices to make an effective
contribution to the model. This causes unfairness w.r.t the achievable
accuracies of constrained devices, especially in cases with a skewed
distribution of class labels across devices. We present a novel FL technique,
CoCoFL, which maintains the full NN structure on all devices. To adapt to the
devices' heterogeneous resources, CoCoFL freezes and quantizes selected layers,
reducing communication, computation, and memory requirements, whereas other
layers are still trained in full precision, enabling to reach a high accuracy.
Thereby, CoCoFL efficiently utilizes the available resources on devices and
allows constrained devices to make a significant contribution to the FL system,
increasing fairness among participants (accuracy parity) and significantly
improving the final accuracy of the model.

Advances in deep neural networks (DNNs) are transforming science and
technology. However, the increasing computational demands of the most powerful
DNNs limit deployment on low-power devices, such as smartphones and sensors --
and this trend is accelerated by the simultaneous move towards
Internet-of-Things (IoT) devices. Numerous efforts are underway to lower power
consumption, but a fundamental bottleneck remains due to energy consumption in
matrix algebra, even for analog approaches including neuromorphic, analog
memory and photonic meshes. Here we introduce and demonstrate a new approach
that sharply reduces energy required for matrix algebra by doing away with
weight memory access on edge devices, enabling orders of magnitude energy and
latency reduction. At the core of our approach is a new concept that
decentralizes the DNN for delocalized, optically accelerated matrix algebra on
edge devices. Using a silicon photonic smart transceiver, we demonstrate
experimentally that this scheme, termed Netcast, dramatically reduces energy
consumption. We demonstrate operation in a photon-starved environment with 40
aJ/multiply of optical energy for 98.8% accurate image recognition and <1
photon/multiply using single photon detectors. Furthermore, we show realistic
deployment of our system, classifying images with 3 THz of bandwidth over 86 km
of deployed optical fiber in a Boston-area fiber network. Our approach enables
computing on a new generation of edge devices with speeds comparable to modern
digital electronics and power consumption that is orders of magnitude lower.

Dual encoders and cross encoders have been widely used for image-text
retrieval. Between the two, the dual encoder encodes the image and text
independently followed by a dot product, while the cross encoder jointly feeds
image and text as the input and performs dense multi-modal fusion. These two
architectures are typically modeled separately without interaction. In this
work, we propose LoopITR, which combines them in the same network for joint
learning. Specifically, we let the dual encoder provide hard negatives to the
cross encoder, and use the more discriminative cross encoder to distill its
predictions back to the dual encoder. Both steps are efficiently performed
together in the same model. Our work centers on empirical analyses of this
combined architecture, putting the main focus on the design of the distillation
objective. Our experimental results highlight the benefits of training the two
encoders in the same network, and demonstrate that distillation can be quite
effective with just a few hard negative examples. Experiments on two standard
datasets (Flickr30K and COCO) show our approach achieves state-of-the-art dual
encoder performance when compared with approaches using a similar amount of
data.

Observations of astrophysical transients have brought many novel discoveries
and provided new insights into physical processes at work under extreme
conditions in the Universe. Multi-wavelength and multi-messenger observations
of variable objects require dedicated procedures and follow-up systems capable
of digesting and reacting to external alerts to execute coordinated follow-up
campaigns. The main functions of such follow-up systems are the processing,
filtering, and ranking of the incoming alerts, the fully automated rapid
execution of the observations according to an observation strategy tailored to
the instrument, and real-time data analysis with feedback to the operators and
other instruments. H.E.S.S. has been searching for transient phenomena since
its inauguration in 2003. In this paper, we describe the transients follow-up
system of H.E.S.S. which became operational in 2016. The system allows H.E.S.S.
to conduct a more versatile, optimised, and largely autonomous transient
follow-up program, combining all major functionalities in one systematic
approach. We describe the design, central functionalities, and interfaces of
the follow-up system in general and its three main components in detail: the
Target of Opportunity (ToO) alert system, the data acquisition and central
control system, and the real-time analysis. We highlight architectural
decisions and features that enable fully automatic ToO follow-up and indicate
key performance metrics of the sub-systems. We discuss the system's
capabilities and highlight the need for a fine-tuned interplay of the different
sub-systems in order to react quickly and reliably. Lessons learned from the
development, integration, and operation of the follow-up system are reviewed in
light of new and large science infrastructures and associated challenges in
this exciting new era of inter-operable astronomy.

Masked Autoencoder (MAE) has recently been shown to be effective in
pre-training Vision Transformers (ViT) for natural image analysis. By
performing the pretext task of reconstructing the original image from only
partial observations, the encoder, which is a ViT, is encouraged to aggregate
contextual information to infer content in masked image regions. We believe
that this context aggregation ability is also essential to the medical image
domain where each anatomical structure is functionally and mechanically
connected to other structures and regions. However, there is no ImageNet-scale
medical image dataset for pre-training. Thus, in this paper, we investigate a
self pre-training paradigm with MAE for medical images, i.e., models are
pre-trained on the same target dataset. To validate the MAE self pre-training,
we consider three diverse medical image tasks including chest X-ray disease
classification, CT abdomen multi-organ segmentation and MRI brain tumor
segmentation. It turns out MAE self pre-training benefits all the tasks
markedly. Specifically, the mAUC on lung disease classification is increased by
9.4%. The average DSC on brain tumor segmentation is improved from 77.4% to
78.9%. Most interestingly, on the small-scale multi-organ segmentation dataset
(N=30), the average DSC improves from 78.8% to 83.5% and the HD95 is reduced by
60%, indicating its effectiveness in limited data scenarios. The segmentation
and classification results reveal the promising potential of MAE self
pre-training for medical image analysis.

Artificial intelligence (AI) techniques have emerged as a powerful approach
to make wireless networks more efficient and adaptable. In this paper we
present an ns-3 simulation framework, able to implement AI algorithms for the
optimization of wireless networks. Our pipeline consists of: (i) a new
geometry-based mobility-dependent channel model for V2X; (ii) all the layers of
a 5G-NR-compliant protocol stack, based on the ns3-mmwave module; (iii) a new
application to simulate V2X data transmission, and (iv) a new intelligent
entity for the control of the network via AI. Thanks to its flexible and
modular design, researchers can use this tool to implement, train, and evaluate
their own algorithms in a realistic and controlled environment. We test the
behavior of our framework in a Predictive Quality of Service (PQoS) scenario,
where AI functionalities are implemented using Reinforcement Learning (RL), and
demonstrate that it promotes better network optimization compared to baseline
solutions that do not implement AI.

In classical statistics, the bias-variance trade-off describes how varying a
model's complexity (e.g., number of fit parameters) affects its ability to make
accurate predictions. According to this trade-off, optimal performance is
achieved when a model is expressive enough to capture trends in the data, yet
not so complex that it overfits idiosyncratic features of the training data.
Recently, it has become clear that this classic understanding of the
bias-variance must be fundamentally revisited in light of the incredible
predictive performance of "overparameterized models" -- models that avoid
overfitting even when the number of fit parameters is large enough to perfectly
fit the training data. Here, we present results for one of the simplest
examples of an overparameterized model: regression with random linear features
(i.e. a two-layer neural network with a linear activation function). Using the
zero-temperature cavity method, we derive analytic expressions for the training
error, test error, bias, and variance. We show that the linear random features
model exhibits three phase transitions: two different transitions to an
interpolation regime where the training error is zero, along with an additional
transition between regimes with large bias and minimal bias. Using random
matrix theory, we show how each transition arises due to small nonzero
eigenvalues in the Hessian matrix. Finally, we compare and contrast the phase
diagram of the random linear features model to the random nonlinear features
model and ordinary regression, highlighting the new phase transitions that
result from the use of linear basis functions.

Autonomous robotic surgery requires deliberation, i.e. the ability to plan
and execute a task adapting to uncertain and dynamic environments. Uncertainty
in the surgical domain is mainly related to the partial pre-operative knowledge
about patient-specific anatomical properties. In this paper, we introduce a
logic-based framework for surgical tasks with deliberative functions of
monitoring and learning. The DEliberative Framework for Robot-Assisted Surgery
(DEFRAS) estimates a pre-operative patient-specific plan, and executes it while
continuously measuring the applied force obtained from a biomechanical
pre-operative model. Monitoring module compares this model with the actual
situation reconstructed from sensors. In case of significant mismatch, the
learning module is invoked to update the model, thus improving the estimate of
the exerted force. DEFRAS is validated both in simulated and real environment
with da Vinci Research Kit executing soft tissue retraction. Compared with
state-of-the art related works, the success rate of the task is improved while
minimizing the interaction with the tissue to prevent unintentional damage.

The energy market relies on forecasting capabilities of both demand and power
generation that need to be kept in dynamic balance. Today, when it comes to
renewable energy generation, such decisions are increasingly made in a
liberalized electricity market environment, where future power generation must
be offered through contracts and auction mechanisms, hence based on forecasts.
The increased share of highly intermittent power generation from renewable
energy sources increases the uncertainty about the expected future power
generation. Point forecast does not account for such uncertainties. To account
for these uncertainties, it is possible to make probabilistic forecasts. This
work first presents important concepts and approaches concerning probabilistic
forecasts with deep learning. Then, deep learning models are used to make
probabilistic forecasts of day-ahead power generation from a wind power plant
located in Northern Norway. The performance in terms of obtained quality of the
prediction intervals is compared for different deep learning models and sets of
covariates. The findings show that the accuracy of the predictions improves
when historical data on measured weather and numerical weather predictions
(NWPs) were included as exogenous variables. This allows the model to
auto-correct systematic biases in the NWPs using the historical measurement
data. Using only NWPs, or only measured weather as exogenous variables, worse
prediction performances were obtained.

Replacing poorly performing existing controllers with smarter solutions will
decrease the energy intensity of the building sector. Recently, controllers
based on Deep Reinforcement Learning (DRL) have been shown to be more effective
than conventional baselines. However, since the optimal solution is usually
unknown, it is still unclear if DRL agents are attaining near-optimal
performance in general or if there is still a large gap to bridge.
  In this paper, we investigate the performance of DRL agents compared to the
theoretically optimal solution. To that end, we leverage Physically Consistent
Neural Networks (PCNNs) as simulation environments, for which optimal control
inputs are easy to compute. Furthermore, PCNNs solely rely on data to be
trained, avoiding the difficult physics-based modeling phase, while retaining
physical consistency. Our results hint that DRL agents not only clearly
outperform conventional rule-based controllers, they furthermore attain
near-optimal performance.

Being able to clearly interpret legal texts and fully understanding our
rights, obligations and other legal norms has become progressively more
important in the digital society. However, simply giving citizens access to the
laws is not enough, as there is a need to provide meaningful information that
cater to their specific queries and needs. For this, it is necessary to extract
the relevant semantic information present in legal texts. Thus, we introduce
the SNR (Semantic Norm Recognition) system, an automatic semantic information
extraction system trained on a domain-specific (legal) text corpus taken from
Portuguese Consumer Law. The SNR system uses the Portuguese Bert (BERTimbau)
and was trained on a legislative Portuguese corpus. We demonstrate how our
system achieved good results (81.44\% F1-score) on this domain-specific corpus,
despite existing noise, and how it can be used to improve downstream tasks such
as information retrieval.

We introduce a methodology for designing and training deep neural networks
(DNN) that we call "Deep Regression Ensembles" (DRE). It bridges the gap
between DNN and two-layer neural networks trained with random feature
regression. Each layer of DRE has two components, randomly drawn input weights
and output weights trained myopically (as if the final output layer) using
linear ridge regression. Within a layer, each neuron uses a different subset of
inputs and a different ridge penalty, constituting an ensemble of random
feature ridge regressions. Our experiments show that a single DRE architecture
is at par with or exceeds state-of-the-art DNN in many data sets. Yet, because
DRE neural weights are either known in closed-form or randomly drawn, its
computational cost is orders of magnitude smaller than DNN.

Finding equitable partitions is closely related to the extraction of graph
symmetries and of interest in a variety of applications context such as node
role detection, cluster synchronization, consensus dynamics, and network
control problems. In this work we study a blind identification problem in which
we aim to recover an equitable partition of a network without the knowledge of
the network's edges but based solely on the observations of the outputs of an
unknown graph filter. Specifically, we consider two settings. First, we
consider a scenario in which we can control the input to the graph filter and
present a method to extract the partition inspired by the well known
Weisfeiler-Lehman (color refinement) algorithm. Second, we generalize this idea
to a setting where only observe the outputs to random, low-rank excitations of
the graph filter, and present a simple spectral algorithm to extract the
relevant equitable partitions. Finally, we establish theoretical bounds on the
error that this spectral detection scheme incurs and perform numerical
experiments that illustrate our theoretical results and compare both

Many multimodal recommender systems have been proposed to exploit the rich
side information associated with users or items (e.g., user reviews and item
images) for learning better user and item representations to improve the
recommendation performance. Studies from psychology show that users have
individual differences in the utilization of various modalities for organizing
information. Therefore, for a certain factor of an item (such as appearance or
quality), the features of different modalities are of varying importance to a
user. However, existing methods ignore the fact that different modalities
contribute differently towards a user's preference on various factors of an
item. In light of this, in this paper, we propose a novel Disentangled
Multimodal Representation Learning (DMRL) recommendation model, which can
capture users' attention to different modalities on each factor in user
preference modeling. In particular, we employ a disentangled representation
technique to ensure the features of different factors in each modality are
independent of each other. A multimodal attention mechanism is then designed to
capture users' modality preference for each factor. Based on the estimated
weights obtained by the attention mechanism, we make recommendations by
combining the preference scores of a user's preferences to each factor of the
target item over different modalities. Extensive evaluation on five real-world
datasets demonstrate the superiority of our method compared with existing
methods.

For a given stable recurrent neural network (RNN) that is trained to perform
a classification task using sequential inputs, we quantify explicit robustness
bounds as a function of trainable weight matrices. The sequential inputs can be
perturbed in various ways, e.g., streaming images can be deformed due to robot
motion or imperfect camera lens. Using the notion of the Voronoi diagram and
Lipschitz properties of stable RNNs, we provide a thorough analysis and
characterize the maximum allowable perturbations while guaranteeing the full
accuracy of the classification task. We illustrate and validate our theoretical
results using a map dataset with clouds as well as the MNIST dataset.

In this work, we study the continual semantic segmentation problem, where the
deep neural networks are required to incorporate new classes continually
without catastrophic forgetting. We propose to use a structural
re-parameterization mechanism, named representation compensation (RC) module,
to decouple the representation learning of both old and new knowledge. The RC
module consists of two dynamically evolved branches with one frozen and one
trainable. Besides, we design a pooled cube knowledge distillation strategy on
both spatial and channel dimensions to further enhance the plasticity and
stability of the model. We conduct experiments on two challenging continual
semantic segmentation scenarios, continual class segmentation and continual
domain segmentation. Without any extra computational overhead and parameters
during inference, our method outperforms state-of-the-art performance. The code
is available at \url{https://github.com/zhangchbin/RCIL}.

Knowledge of molecular subtypes of gliomas can provide valuable information
for tailored therapies. This study aimed to investigate the use of deep
convolutional neural networks (DCNNs) for noninvasive glioma subtyping with
radiological imaging data according to the new taxonomy announced by the World
Health Organization in 2016. Methods: A DCNN model was developed for the
prediction of the five glioma subtypes based on a hierarchical classification
paradigm. This model used three parallel, weight-sharing, deep residual
learning networks to process 2.5-dimensional input of trimodal MRI data,
including T1-weighted, T1-weighted with contrast enhancement, and T2-weighted
images. A data set comprising 1,016 real patients was collected for evaluation
of the developed DCNN model. The predictive performance was evaluated via the
area under the curve (AUC) from the receiver operating characteristic analysis.
For comparison, the performance of a radiomics-based approach was also
evaluated. Results: The AUCs of the DCNN model for the four classification
tasks in the hierarchical classification paradigm were 0.89, 0.89, 0.85, and
0.66, respectively, as compared to 0.85, 0.75, 0.67, and 0.59 of the radiomics
approach. Conclusion: The results showed that the developed DCNN model can
predict glioma subtypes with promising performance, given sufficient,
non-ill-balanced training data.

It is common to model a deterministic response function, such as the output
of a computer experiment, as a Gaussian process with a Mat\'ern covariance
kernel. The smoothness parameter of a Mat\'ern kernel determines many important
properties of the model in the large data limit, such as the rate of
convergence of the conditional mean to the response function. We prove that the
maximum likelihood and cross-validation estimates of the smoothness parameter
cannot asymptotically undersmooth the truth when the data are obtained on a
fixed bounded subset of $\mathbb{R}^d$. That is, if the data-generating
response function has Sobolev smoothness $\nu_0 + d/2$, then the smoothness
parameter estimates cannot remain below $\nu_0$ as more data are obtained. The
results are based on approximation theory in Sobolev spaces and a general
theorem, proved using reproducing kernel Hilbert space techniques, about sets
of values the parameter estimates cannot take.

Various logic-locking schemes have been proposed to protect hardware from
intellectual property piracy and malicious design modifications. Since
traditional locking techniques are applied on the gate-level netlist after
logic synthesis, they have no semantic knowledge of the design function.
Data-driven, machine-learning (ML) attacks can uncover the design flaws within
gate-level locking. Recent proposals on register-transfer level (RTL) locking
have access to semantic hardware information. We investigate the resilience of
ASSURE, a state-of-the-art RTL locking method, against ML attacks. We used the
lessons learned to derive two ML-resilient RTL locking schemes built to
reinforce ASSURE locking. We developed ML-driven security metrics to evaluate
the schemes against an RTL adaptation of the state-of-the-art, ML-based
SnapShot attack.

To what extent can we forecast a time series without fitting to historical
data? Can universal patterns of probability help in this task? Deep relations
between pattern Kolmogorov complexity and pattern probability have recently
been used to make \emph{a priori} probability predictions in a variety of
systems in physics, biology and engineering. Here we study \emph{simplicity
bias} (SB) -- an exponential upper bound decay in pattern probability with
increasing complexity -- in discretised time series extracted from the World
Bank Open Data collection. We predict upper bounds on the probability of
discretised series patterns, without fitting to trends in the data. Thus we
perform a kind of `forecasting without training data', predicting time series
shape patterns \emph{a priori}, but not the actual numerical value of the
series. Additionally we make predictions about which of two discretised series
is more likely with accuracy of $\sim$80\%, much higher than a 50\% baseline
rate, just by using the complexity of each series. These results point to a
promising perspective on practical time series forecasting and integration with
machine learning methods.

Objective. Dual-energy computed tomography (DECT) has the potential to
improve contrast, reduce artifacts and the ability to perform material
decomposition in advanced imaging applications. The increased number or
measurements results with a higher radiation dose and it is therefore essential
to reduce either number of projections per energy or the source X-ray
intensity, but this makes tomographic reconstruction more ill-posed.
  Approach. We developed the multi-channel convolutional analysis operator
learning (MCAOL) method to exploit common spatial features within attenuation
images at different energies and we propose an optimization method which
jointly reconstructs the attenuation images at low and high energies with a
mixed norm regularization on the sparse features obtained by pre-trained
convolutional filters through the convolutional analysis operator learning
(CAOL) algorithm.
  Main results. Extensive experiments with simulated and real computed
tomography (CT) data were performed to validate the effectiveness of the
proposed methods and we reported increased reconstruction accuracy compared to
CAOL and iterative methods with single and joint total-variation (TV)
regularization.
  Significance. Qualitative and quantitative results on sparse-views and
low-dose DECT demonstrate that the proposed MCAOL method outperforms both CAOL
applied on each energy independently and several existing state-of-the-art
model-based iterative reconstruction (MBIR) techniques, thus paving the way for
dose reduction.

Stuttering is a complex speech disorder that negatively affects an
individual's ability to communicate effectively. Persons who stutter (PWS)
often suffer considerably under the condition and seek help through therapy.
Fluency shaping is a therapy approach where PWSs learn to modify their speech
to help them to overcome their stutter. Mastering such speech techniques takes
time and practice, even after therapy. Shortly after therapy, success is
evaluated highly, but relapse rates are high. To be able to monitor speech
behavior over a long time, the ability to detect stuttering events and
modifications in speech could help PWSs and speech pathologists to track the
level of fluency. Monitoring could create the ability to intervene early by
detecting lapses in fluency. To the best of our knowledge, no public dataset is
available that contains speech from people who underwent stuttering therapy
that changed the style of speaking. This work introduces the Kassel State of
Fluency (KSoF), a therapy-based dataset containing over 5500 clips of PWSs. The
clips were labeled with six stuttering-related event types: blocks,
prolongations, sound repetitions, word repetitions, interjections, and -
specific to therapy - speech modifications. The audio was recorded during
therapy sessions at the Institut der Kasseler Stottertherapie. The data will be
made available for research purposes upon request.

We present a data-driven and physics-informed algorithm for drilling accident
forecasting. The core machine-learning algorithm uses the data from the
drilling telemetry representing the time-series. We have developed a
Bag-of-features representation of the time series that enables the algorithm to
predict the probabilities of six types of drilling accidents in real-time. The
machine-learning model is trained on the 125 past drilling accidents from 100
different Russian oil and gas wells. Validation shows that the model can
forecast 70% of drilling accidents with a false positive rate equals to 40%.
The model addresses partial prevention of the drilling accidents at the well
construction.

The advent of deep learning has yielded powerful tools to automatically
compute gradients of computations. This is because training a neural network
equates to iteratively updating its parameters using gradient descent to find
the minimum of a loss function. Deep learning is then a subset of a broader
paradigm; a workflow with free parameters that is end-to-end optimisable,
provided one can keep track of the gradients all the way through. This work
introduces neos: an example implementation following this paradigm of a fully
differentiable high-energy physics workflow, capable of optimising a learnable
summary statistic with respect to the expected sensitivity of an analysis.
Doing this results in an optimisation process that is aware of the modelling
and treatment of systematic uncertainties.

In this article, an overview of the background, the research approaches and
the patterns of practice in the field of collaborative learning are provided. A
definition of collaborative learning and an overview of fundamental aspects
that shape research and practice in this field are included. Pedagogies and
learning theories that are used as foundations of the field alongside goals and
objectives of collaborative learning approaches are discussed. Popular patterns
of practice, exploring their application in classrooms and elaborating on the
state of the art around those practices in research are outlined. A discussion
about important topics, open questions and future directions are provided in
conclusion.

In a Federated Learning (FL) setup, a number of devices contribute to the
training of a common model. We present a method for selecting the devices that
provide updates in order to achieve improved generalization, fast convergence,
and better device-level performance. We formulate a min-max optimization
problem and decompose it into a primal-dual setup, where the duality gap is
used to quantify the device-level performance. Our strategy combines
\emph{exploration} of data freshness through a random device selection with
\emph{exploitation} through simplified estimates of device contributions. This
improves the performance of the trained model both in terms of generalization
and personalization. A modified Truncated Monte-Carlo (TMC) method is applied
during the exploitation phase to estimate the device's contribution and lower
the communication overhead. The experimental results show that the proposed
approach has a competitive performance, with lower communication overhead and
competitive personalization performance against the baseline schemes.

This work develops the concept of temporal network epistemology model
enabling the simulation of the learning process in dynamic networks. The
results of the research, conducted on the temporal social network generated
using the CogSNet model and on the static topologies as a reference, indicate a
significant influence of the network temporal dynamics on the outcome and flow
of the learning process. It has been shown that not only the dynamics of
reaching consensus is different compared to baseline models but also that
previously unobserved phenomena appear, such as uninformed agents or different
consensus states for disconnected components. It has been also observed that
sometimes only the change of the network structure can contribute to reaching
consensus. The introduced approach and the experimental results can be used to
better understand the way how human communities collectively solve both complex
problems at the scientific level and to inquire into the correctness of less
complex but common and equally important beliefs' spreading across entire
societies.

Among the seventeen Sustainable Development Goals (SDGs) proposed within the
2030 Agenda and adopted by all the United Nations member states, the 13$^{th}$
SDG is a call for action to combat climate change for a better world. In this
work, we provide an overview of areas in which audio intelligence -- a powerful
but in this context so far hardly considered technology -- can contribute to
overcome climate-related challenges. We categorise potential computer audition
applications according to the five elements of earth, water, air, fire, and
aether, proposed by the ancient Greeks in their five element theory; this
categorisation serves as a framework to discuss computer audition in relation
to different ecological aspects. Earth and water are concerned with the early
detection of environmental changes and, thus, with the protection of humans and
animals, as well as the monitoring of land and aquatic organisms. Aerial audio
is used to monitor and obtain information about bird and insect populations.
Furthermore, acoustic measures can deliver relevant information for the
monitoring and forecasting of weather and other meteorological phenomena. The
fourth considered element is fire. Due to the burning of fossil fuels, the
resulting increase in CO$_2$ emissions and the associated rise in temperature,
fire is used as a symbol for man-made climate change and in this context
includes the monitoring of noise pollution, machines, as well as the early
detection of wildfires. In all these areas, computer audition can help
counteract climate change. Aether then corresponds to the technology itself
that makes this possible. This work explores these areas and discusses
potential applications, while positioning computer audition in relation to
methodological alternatives.

Prior work on differential privacy analysis of randomized SGD algorithms
relies on composition theorems, where the implicit (unrealistic) assumption is
that the internal state of the iterative algorithm is revealed to the
adversary. As a result, the R\'enyi DP bounds derived by such composition-based
analyses linearly grow with the number of training epochs. When the internal
state of the algorithm is hidden, we prove a converging privacy bound for noisy
stochastic gradient descent (on strongly convex smooth loss functions). We show
how to take advantage of privacy amplification by sub-sampling and randomized
post-processing, and prove the dynamics of privacy bound for "shuffle and
partition" and "sample without replacement" stochastic mini-batch gradient
descent schemes. We prove that, in these settings, our privacy bound converges
exponentially fast and is substantially smaller than the composition bounds,
notably after a few number of training epochs. Thus, unless the DP algorithm
converges fast, our privacy analysis shows that hidden state analysis can
significantly amplify differential privacy.

In highly distributed environments such as cloud, edge and fog computing, the
application of machine learning for automating and optimizing processes is on
the rise. Machine learning jobs are frequently applied in streaming conditions,
where models are used to analyze data streams originating from e.g. video
streams or sensory data. Often the results for particular data samples need to
be provided in time before the arrival of next data. Thus, enough resources
must be provided to ensure the just-in-time processing for the specific data
stream. This paper focuses on proposing a runtime modeling strategy for
containerized machine learning jobs, which enables the optimization and
adaptive adjustment of resources per job and component. Our black-box approach
assembles multiple techniques into an efficient runtime profiling method, while
making no assumptions about underlying hardware, data streams, or applied
machine learning jobs. The results show that our method is able to capture the
general runtime behaviour of different machine learning jobs already after a
short profiling phase.

Blimps are well suited to perform long-duration aerial tasks as they are
energy efficient, relatively silent and safe. To address the blimp navigation
and control task, in previous work we developed a hardware and
software-in-the-loop framework and a PID-based controller for large blimps in
the presence of wind disturbance. However, blimps have a deformable structure
and their dynamics are inherently non-linear and time-delayed, making PID
controllers difficult to tune. Thus, often resulting in large tracking errors.
Moreover, the buoyancy of a blimp is constantly changing due to variations in
ambient temperature and pressure. To address these issues, in this paper we
present a learning-based framework based on deep residual reinforcement
learning (DRRL), for the blimp control task. Within this framework, we first
employ a PID controller to provide baseline performance. Subsequently, the DRRL
agent learns to modify the PID decisions by interaction with the environment.
We demonstrate in simulation that DRRL agent consistently improves the PID
performance. Through rigorous simulation experiments, we show that the agent is
robust to changes in wind speed and buoyancy. In real-world experiments, we
demonstrate that the agent, trained only in simulation, is sufficiently robust
to control an actual blimp in windy conditions. We openly provide the source
code of our approach at https://github.com/
robot-perception-group/AutonomousBlimpDRL.

In this paper, we investigate an intelligent reflecting surface
(IRS)-assisted millimeter-wave multiple-input single-output downlink wireless
communication system. By jointly calculating the active beamforming at the base
station and the passive beamforming at the IRS, we aim to minimize the transmit
power under the constraint of each user' signal-to-interference-plus-noise
ratio. To solve this problem, we propose a low-complexity machine
learning-based cross-entropy (CE) algorithm to alternately optimize the active
beamforming and the passive beamforming. Specifically, in the alternative
iteration process, the zero-forcing (ZF) method and CE algorithm are applied to
acquire the active beamforming and the passive beamforming, respectively. The
CE algorithm starts with random sampling, by the idea of distribution focusing,
namely shifting the distribution towards a desired one by minimizing CE, and a
near optimal reflection coefficients with adequately high probability can be
obtained. In addition, we extend the original one-bit phase shift at the IRS to
the common case with high-resolution phase shift to enhance the effectiveness
of the algorithms. Simulation results verify that the proposed algorithm can
obtain a near optimal solution with lower computational complexity.

Robust maritime obstacle detection is essential for fully autonomous unmanned
surface vehicles (USVs). The currently widely adopted segmentation-based
obstacle detection methods are prone to misclassification of object reflections
and sun glitter as obstacles, producing many false positive detections,
effectively rendering the methods impractical for USV navigation. However,
water-turbulence-induced temporal appearance changes on object reflections are
very distinctive from the appearance dynamics of true objects. We harness this
property to design WaSR-T, a novel maritime obstacle detection network, that
extracts the temporal context from a sequence of recent frames to reduce
ambiguity. By learning the local temporal characteristics of object reflection
on the water surface, WaSR-T substantially improves obstacle detection accuracy
in the presence of reflections and glitter. Compared with existing single-frame
methods, WaSR-T reduces the number of false positive detections by 41% overall
and by over 53% within the danger zone of the boat, while preserving a high
recall, and achieving new state-of-the-art performance on the challenging MODS
maritime obstacle detection benchmark. The code, pretrained models and extended
datasets are available at https://github.com/lojzezust/WaSR-T

Reasoning-based approaches have demonstrated their powerful ability for the
task of image-text matching. In this work, two issues are addressed for
image-text matching. First, for reasoning processing, conventional approaches
have no ability to find and use multi-level hierarchical similarity
information. To solve this problem, a hierarchical similarity reasoning module
is proposed to automatically extract context information, which is then
co-exploited with local interaction information for efficient reasoning.
Second, previous approaches only consider learning single-stream similarity
alignment (i.e., image-to-text level or text-to-image level), which is
inadequate to fully use similarity information for image-text matching. To
address this issue, a two-stream architecture is developed to decompose
image-text matching into image-to-text level and text-to-image level similarity
computation. These two issues are investigated by a unifying framework that is
trained in an end-to-end manner, namely two-stream hierarchical similarity
reasoning network. The extensive experiments performed on the two benchmark
datasets of MSCOCO and Flickr30K show the superiority of the proposed approach
as compared to existing state-of-the-art methods.

This paper presents our contribution to the REFUGE challenge 2020. The
challenge consisted of three tasks based on a dataset of retinal images:
Segmentation of optic disc and cup, classification of glaucoma, and
localization of fovea. We propose employing convolutional neural networks for
all three tasks. Segmentation is performed using a U-Net, classification is
performed by a pre-trained InceptionV3 network, and fovea detection is
performed by employing stacked hour-glass for heatmap prediction. The challenge
dataset contains images from three different data sources. To enhance
performance, cycleGANs were utilized to create a domain-shift between the data
sources. These cycleGANs move images across domains, thus creating artificial
images which can be used for training.

Image corruption by motion artifacts is an ingrained problem in Magnetic
Resonance Imaging (MRI). In this work, we propose a neural network-based
regularization term to enhance Autofocusing, a classic optimization-based
method to remove motion artifacts. The method takes the best of both worlds:
the optimization-based routine iteratively executes the blind demotion and deep
learning-based prior penalizes for unrealistic restorations and speeds up the
convergence. We validate the method on three models of motion trajectories,
using synthetic and real noisy data. The method proves resilient to noise and
anatomic structure variation, outperforming the state-of-the-art demotion
methods.

With diverse presentation attacks emerging continually, generalizable face
anti-spoofing (FAS) has drawn growing attention. Most existing methods
implement domain generalization (DG) on the complete representations. However,
different image statistics may have unique properties for the FAS tasks. In
this work, we separate the complete representation into content and style ones.
A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and
reassemble different content and style features for a stylized feature space.
Then, to obtain a generalized representation, a contrastive learning strategy
is developed to emphasize liveness-related style information while suppress the
domain-specific one. Finally, the representations of the correct assemblies are
used to distinguish between living and spoofing during the inferring. On the
other hand, despite the decent performance, there still exists a gap between
academia and industry, due to the difference in data quantity and distribution.
Thus, a new large-scale benchmark for FAS is built up to further evaluate the
performance of algorithms in reality. Both qualitative and quantitative results
on existing and proposed benchmarks demonstrate the effectiveness of our
methods. The codes will be available at https://github.com/wangzhuo2019/SSAN.

We address a physics-informed neural network based on the concept of random
projections for the numerical solution of IVPs of nonlinear ODEs in
linear-implicit form and index-1 DAEs, which may also arise from the spatial
discretization of PDEs. The scheme has a single hidden layer with appropriately
randomly parametrized Gaussian kernels and a linear output layer, while the
internal weights are fixed to ones. The unknown weights between the hidden and
output layer are computed by Newton's iterations, using the Moore-Penrose
pseudoinverse for low to medium, and sparse QR decomposition with
regularization for medium to large scale systems. To deal with stiffness and
sharp gradients, we propose a variable step size scheme for adjusting the
interval of integration and address a continuation method for providing good
initial guesses for the Newton iterations. Based on previous works on random
projections, we prove the approximation capability of the scheme for ODEs in
the canonical form and index-1 DAEs in the semiexplicit form. The optimal
bounds of the uniform distribution are parsimoniously chosen based on the
bias-variance trade-off. The performance of the scheme is assessed through
seven benchmark problems: four index-1 DAEs, the Robertson model, a model of
five DAEs describing the motion of a bead, a model of six DAEs describing a
power discharge control problem, the chemical Akzo Nobel problem and three
stiff problems, the Belousov-Zhabotinsky, the Allen-Cahn PDE and the
Kuramoto-Sivashinsky PDE. The efficiency of the scheme is compared with three
solvers ode23t, ode23s, ode15s of the MATLAB ODE suite. Our results show that
the proposed scheme outperforms the stiff solvers in several cases, especially
in regimes where high stiffness or sharp gradients arise in terms of numerical
accuracy, while the computational costs are for any practical purposes
comparable.

Synthesizing pseudo samples is currently the most effective way to solve the
Generalized Zero-Shot Learning (GZSL) problem. Most models achieve competitive
performance but still suffer from two problems: (1) Feature confounding, the
overall representations confound task-correlated and task-independent features,
and existing models disentangle them in a generative way, but they are
unreasonable to synthesize reliable pseudo samples with limited samples; (2)
Distribution uncertainty, that massive data is needed when existing models
synthesize samples from the uncertain distribution, which causes poor
performance in limited samples of seen classes. In this paper, we propose a
non-generative model to address these problems correspondingly in two modules:
(1) Task-correlated feature disentanglement, to exclude the task-correlated
features from task-independent ones by adversarial learning of domain adaption
towards reasonable synthesis; (2) Controllable pseudo sample synthesis, to
synthesize edge-pseudo and center-pseudo samples with certain characteristics
towards more diversity generated and intuitive transfer. In addation, to
describe the new scene that is the limit seen class samples in the training
process, we further formulate a new ZSL task named the 'Few-shot Seen class and
Zero-shot Unseen class learning' (FSZU). Extensive experiments on four
benchmarks verify that the proposed method is competitive in the GZSL and the
FSZU tasks.

Tracking objects in 3D space and predicting their 6DoF pose is an essential
task in computer vision. State-of-the-art approaches often rely on object
texture to tackle this problem. However, while they achieve impressive results,
many objects do not contain sufficient texture, violating the main underlying
assumption. In the following, we thus propose ICG, a novel probabilistic
tracker that fuses region and depth information and only requires the object
geometry. Our method deploys correspondence lines and points to iteratively
refine the pose. We also implement robust occlusion handling to improve
performance in real-world settings. Experiments on the YCB-Video, OPT, and Choi
datasets demonstrate that, even for textured objects, our approach outperforms
the current state of the art with respect to accuracy and robustness. At the
same time, ICG shows fast convergence and outstanding efficiency, requiring
only 1.3 ms per frame on a single CPU core. Finally, we analyze the influence
of individual components and discuss our performance compared to deep
learning-based methods. The source code of our tracker is publicly available.

Monocular depth estimation in the wild inherently predicts depth up to an
unknown scale. To resolve scale ambiguity issue, we present a learning
algorithm that leverages monocular simultaneous localization and mapping (SLAM)
with proprioceptive sensors. Such monocular SLAM systems can provide metrically
scaled camera poses. Given these metric poses and monocular sequences, we
propose a self-supervised learning method for the pre-trained supervised
monocular depth networks to enable metrically scaled depth estimation. Our
approach is based on a teacher-student formulation which guides our network to
predict high-quality depths. We demonstrate that our approach is useful for
various applications such as mobile robot navigation and is applicable to
diverse environments. Our full system shows improvements over recent
self-supervised depth estimation and completion methods on EuRoC, OpenLORIS,
and ScanNet datasets.

In this paper, we present an end-to-end joint entity and relation extraction
approach based on transformer-based language models. We apply the model to the
task of linking mathematical symbols to their descriptions in LaTeX documents.
In contrast to existing approaches, which perform entity and relation
extraction in sequence, our system incorporates information from relation
extraction into entity extraction. This means that the system can be trained
even on data sets where only a subset of all valid entity spans is annotated.
We provide an extensive evaluation of the proposed system and its strengths and
weaknesses. Our approach, which can be scaled dynamically in computational
complexity at inference time, produces predictions with high precision and
reaches 3rd place in the leaderboard of SemEval-2022 Task 12. For inputs in the
domain of physics and math, it achieves high relation extraction macro F1
scores of 95.43% and 79.17%, respectively. The code used for training and
evaluating our models is available at: https://github.com/nicpopovic/RE1st

Robustness of deep neural networks (DNNs) to malicious perturbations is a hot
topic in trustworthy AI. Existing techniques obtain robust models given fixed
datasets, either by modifying model structures, or by optimizing the process of
inference or training. While significant improvements have been made, the
possibility of constructing a high-quality dataset for model robustness remain
unexplored. Follow the campaign of data-centric AI launched by Andrew Ng, we
propose a novel algorithm for dataset enhancement that works well for many
existing DNN models to improve robustness. Transferable adversarial examples
and 14 kinds of common corruptions are included in our optimized dataset. In
the data-centric robust learning competition hosted by Alibaba Group and
Tsinghua University, our algorithm came third out of more than 3000 competitors
in the first stage while we ranked fourth in the second stage. Our code is
available at \url{https://github.com/hncszyq/tianchi_challenge}.

We present StyleBabel, a unique open access dataset of natural language
captions and free-form tags describing the artistic style of over 135K digital
artworks, collected via a novel participatory method from experts studying at
specialist art and design schools. StyleBabel was collected via an iterative
method, inspired by `Grounded Theory': a qualitative approach that enables
annotation while co-evolving a shared language for fine-grained artistic style
attribute description. We demonstrate several downstream tasks for StyleBabel,
adapting the recent ALADIN architecture for fine-grained style similarity, to
train cross-modal embeddings for: 1) free-form tag generation; 2) natural
language description of artistic style; 3) fine-grained text search of style.
To do so, we extend ALADIN with recent advances in Visual Transformer (ViT) and
cross-modal representation learning, achieving a state of the art accuracy in
fine-grained style retrieval.

Machine learning, through the use of convolutional and recurrent neural
networks is a promising avenue for the improvement of background rejection
performance in imaging atmospheric Cherenkov telescopes. However, it is of
paramount importance for science analysis that their performance remains stable
against a wide range of observing conditions and instrument states.
  We investigate the stability of convolutional recurrent networks by applying
them to background rejection in a toy Monte Carlo simulation of a Cherenkov
telescope array. We then vary a range of observation and instrument parameters
in the simulation. In general, most of the resulting systematics are at a level
not much greater than conventional analyses. However, a strong dependence of
the neural network predictions on the noise level within the camera was found,
with differences of up to 50% in the gamma-ray acceptance rate in very noisy
environments. It is clear from the performance differences seen in these
studies that these observational effects must be considered in the training
step of the final analysis when using such networks for background rejection in
Cherenkov telescope observations.

Neuromorphic hardware platforms can significantly lower the energy overhead
of a machine learning inference task. We present a design-technology tradeoff
analysis to implement such inference tasks on the processing elements (PEs) of
a Non- Volatile Memory (NVM)-based neuromorphic hardware. Through detailed
circuit-level simulations at scaled process technology nodes, we show the
negative impact of technology scaling on the information-processing latency,
which impacts the quality-of-service (QoS) of an embedded ML system. At a finer
granularity, the latency inside a PE depends on 1) the delay introduced by
parasitic components on its current paths, and 2) the varying delay to sense
different resistance states of its NVM cells. Based on these two observations,
we make the following three contributions. First, on the technology front, we
propose an optimization scheme where the NVM resistance state that takes the
longest time to sense is set on current paths having the least delay, and vice
versa, reducing the average PE latency, which improves the QoS. Second, on the
architecture front, we introduce isolation transistors within each PE to
partition it into regions that can be individually power-gated, reducing both
latency and energy. Finally, on the system-software front, we propose a
mechanism to leverage the proposed technological and architectural enhancements
when implementing a machine-learning inference task on neuromorphic PEs of the
hardware. Evaluations with a recent neuromorphic hardware architecture show
that our proposed design-technology co-optimization approach improves both
performance and energy efficiency of machine-learning inference tasks without
incurring high cost-per-bit.

Convolutional Neural Networks (CNN) are the state-of-the-art in the field of
visual computing. However, a major problem with CNNs is the large number of
floating point operations (FLOPs) required to perform convolutions for large
inputs. When considering the application of CNNs to video data, convolutional
filters become even more complex due to the extra temporal dimension. This
leads to problems when respective applications are to be deployed on mobile
devices, such as smart phones, tablets, micro-controllers or similar,
indicating less computational power.
  Kim et al. (2016) proposed using a Tucker-decomposition to compress the
convolutional kernel of a pre-trained network for images in order to reduce the
complexity of the network, i.e. the number of FLOPs. In this paper, we
generalize the aforementioned method for application to videos (and other 3D
signals) and evaluate the proposed method on a modified version of the THETIS
data set, which contains videos of individuals performing tennis shots. We show
that the compressed network reaches comparable accuracy, while indicating a
memory compression by a factor of 51. However, the actual computational
speed-up (factor 1.4) does not meet our theoretically derived expectation
(factor 6).

Cereal grains are a vital part of human diets and are important commodities
for people's livelihood and international trade. Grain Appearance Inspection
(GAI) serves as one of the crucial steps for the determination of grain quality
and grain stratification for proper circulation, storage and food processing,
etc. GAI is routinely performed manually by qualified inspectors with the aid
of some hand tools. Automated GAI has the benefit of greatly assisting
inspectors with their jobs but has been limited due to the lack of datasets and
clear definitions of the tasks.
  In this paper we formulate GAI as three ubiquitous computer vision tasks:
fine-grained recognition, domain adaptation and out-of-distribution
recognition. We present a large-scale and publicly available cereal grains
dataset called GrainSpace. Specifically, we construct three types of device
prototypes for data acquisition, and a total of 5.25 million images determined
by professional inspectors. The grain samples including wheat, maize and rice
are collected from five countries and more than 30 regions. We also develop a
comprehensive benchmark based on semi-supervised learning and self-supervised
learning techniques. To the best of our knowledge, GrainSpace is the first
publicly released dataset for cereal grain inspection.

Achieving realistic, vivid, and human-like synthesized conversational
gestures conditioned on multi-modal data is still an unsolved problem due to
the lack of available datasets, models and standard evaluation metrics. To
address this, we build Body-Expression-Audio-Text dataset, BEAT, which has i)
76 hours, high-quality, multi-modal data captured from 30 speakers talking with
eight different emotions and in four different languages, ii) 32 millions
frame-level emotion and semantic relevance annotations. Our statistical
analysis on BEAT demonstrates the correlation of conversational gestures with
facial expressions, emotions, and semantics, in addition to the known
correlation with audio, text, and speaker identity. Based on this observation,
we propose a baseline model, Cascaded Motion Network (CaMN), which consists of
above six modalities modeled in a cascaded architecture for gesture synthesis.
To evaluate the semantic relevancy, we introduce a metric, Semantic Relevance
Gesture Recall (SRGR). Qualitative and quantitative experiments demonstrate
metrics' validness, ground truth data quality, and baseline's state-of-the-art
performance. To the best of our knowledge, BEAT is the largest motion capture
dataset for investigating human gestures, which may contribute to a number of
different research fields, including controllable gesture synthesis,
cross-modality analysis, and emotional gesture recognition. The data, code and
model are available on https://pantomatrix.github.io/BEAT/.

Domain generalisation aims to promote the learning of domain-invariant
features while suppressing domain specific features, so that a model can
generalise well on previously unseen target domains. This paper studies domain
generalisation in the object detection setting. We propose new terms for
handling both the bounding box detector and domain belonging, and incorporate
them with consistency regularisation. This allows us to learn a domain agnostic
feature representation for object detection, applicable to the problem of
domain generalisation. The proposed approach is evaluated using four standard
object detection datasets with available domain metadata, namely GWHD,
Cityscapes, BDD100K, Sim10K and exhibits consistently superior generalisation
performance over baselines.

Iterative learning control (ILC) is a control strategy for repetitive tasks
wherein information from previous runs is leveraged to improve future
performance. Optimization-based ILC (OB-ILC) is a powerful design framework for
constrained ILC where measurements from the process are integrated into an
optimization algorithm to provide robustness against noise and modelling error.
This paper proposes a robust ILC controller for constrained linear processes
based on the forward-backward splitting algorithm. It demonstrates how
structured uncertainty information can be leveraged to ensure constraint
satisfaction and provides a rigorous stability analysis in the iteration domain
by combining concepts from monotone operator theory and robust control.
Numerical simulations of a precision motion stage support the theoretical
results.

The state space in Multiagent Reinforcement Learning (MARL) grows
exponentially with the agent number. Such a curse of dimensionality results in
poor scalability and low sample efficiency, inhibiting MARL for decades. To
break this curse, we propose a unified agent permutation framework that
exploits the permutation invariance (PI) and permutation equivariance (PE)
inductive biases to reduce the multiagent state space. Our insight is that
permuting the order of entities in the factored multiagent state space does not
change the information. Specifically, we propose two novel implementations: a
Dynamic Permutation Network (DPN) and a Hyper Policy Network (HPN). The core
idea is to build separate entity-wise PI input and PE output network modules to
connect the entity-factored state space and action space in an end-to-end way.
DPN achieves such connections by two separate module selection networks, which
consistently assign the same input module to the same input entity (guarantee
PI) and assign the same output module to the same entity-related output
(guarantee PE). To enhance the representation capability, HPN replaces the
module selection networks of DPN with hypernetworks to directly generate the
corresponding module weights. Extensive experiments in SMAC, Google Research
Football and MPE validate that the proposed methods significantly boost the
performance and the learning efficiency of existing MARL algorithms.
Remarkably, in SMAC, we achieve 100% win rates in almost all hard and
super-hard scenarios (never achieved before).

Vehicular edge computing has recently been proposed to support
computation-intensive applications in Intelligent Transportation Systems (ITS)
such as augmented reality and autonomous driving. Despite progress in this
area, significant challenges remain to efficiently allocate limited computation
resources to a range of time-critical ITS tasks. To this end, the current paper
develops a new task assignment scheme for vehicles in a highway. Because of the
high speed of vehicles and the limited communication range of road side units
(RSUs), the computation tasks of participating vehicles are to be dynamically
migrated across multiple servers. We formulate a binary nonlinear programming
(BNLP) problem of assigning computation tasks from vehicles to RSUs and a
macrocell base station. To deal with the potentially large size of the
formulated optimization problem, we develop a distributed multi-agent
regret-matching learning algorithm. Based on the regret minimization principle,
the proposed algorithm employs a forgetting method that allows the learning
process to quickly adapt to and effectively handle the high mobility feature of
vehicle networks. We theoretically prove that it converges to the correlated
equilibrium solutions of the considered BNLP problem. Simulation results with
practical parameter settings show that the proposed algorithm offers the lowest
total delay and cost of processing tasks. Importantly, our algorithm converges
much faster than existing methods as the problem size grows, demonstrating its
clear advantage in large-scale vehicular networks.

Point cloud segmentation is fundamental in understanding 3D environments.
However, current 3D point cloud segmentation methods usually perform poorly on
scene boundaries, which degenerates the overall segmentation performance. In
this paper, we focus on the segmentation of scene boundaries. Accordingly, we
first explore metrics to evaluate the segmentation performance on scene
boundaries. To address the unsatisfactory performance on boundaries, we then
propose a novel contrastive boundary learning (CBL) framework for point cloud
segmentation. Specifically, the proposed CBL enhances feature discrimination
between points across boundaries by contrasting their representations with the
assistance of scene contexts at multiple scales. By applying CBL on three
different baseline methods, we experimentally show that CBL consistently
improves different baselines and assists them to achieve compelling performance
on boundaries, as well as the overall performance, eg in mIoU. The experimental
results demonstrate the effectiveness of our method and the importance of
boundaries for 3D point cloud segmentation. Code and model will be made
publicly available at https://github.com/LiyaoTang/contrastBoundary.

Rectified-linear-unit (ReLU) neural networks, which play a prominent role in
deep learning, generate continuous and piecewise-linear (CPWL) functions. While
they provide a powerful parametric representation, the mapping between the
parameter and function spaces lacks stability. In this paper, we investigate an
alternative representation of CPWL functions that relies on local hat basis
functions. It is predicated on the fact that any CPWL function can be specified
by a triangulation and its values at the grid points. We give the necessary and
sufficient condition on the triangulation (in any number of dimensions) for the
hat functions to form a Riesz basis, which ensures that the link between the
parameters and the corresponding CPWL function is stable and unique. In
addition, we provide an estimate of the $\ell_2\rightarrow L_2$ condition
number of this local representation. Finally, as a special case of our
framework, we focus on a systematic parametrization of $\mathbb{R}^d$ with
control points placed on a uniform grid. In particular, we choose hat basis
functions that are shifted replicas of a single linear box spline. In this
setting, we prove that our general estimate of the condition number is optimal.
We also relate our local representation to a nonlocal one based on shifts of a
causal ReLU-like function.

Neural Machine Translation(NMT) models are usually trained via unidirectional
decoder which corresponds to optimizing one-step-ahead prediction. However,
this kind of unidirectional decoding framework may incline to focus on local
structure rather than global coherence. To alleviate this problem, we propose a
novel method, Self-Knowledge Distillation with Bidirectional Decoder for Neural
Machine Translation(SBD-NMT). We deploy a backward decoder which can act as an
effective regularization method to the forward decoder. By leveraging the
backward decoder's information about the longer-term future, distilling
knowledge learned in the backward decoder can encourage auto-regressive NMT
models to plan ahead. Experiments show that our method is significantly better
than the strong Transformer baselines on multiple machine translation data
sets.

Control using quantized feedback is a fundamental approach to system
synthesis with limited communication capacity. In this paper, we address the
stabilization problem for unknown linear systems with logarithmically quantized
feedback, via a direct data-driven control method. By leveraging a recently
developed matrix S-lemma, we prove a sufficient and necessary condition for the
existence of a common stabilizing controller for all possible dynamics
consistent with data, in the form of a linear matrix inequality. Moreover, we
formulate semi-definite programming to solve the coarsest quantization density.
By establishing its connections to unstable eigenvalues of the state matrix, we
further prove a necessary rank condition on the data for quantized feedback
stabilization. Finally, we validate our theoretical results by numerical
examples.

Personal thermal comfort models aim to predict an individual's thermal
comfort response, instead of the average response of a large group. Recently,
machine learning algorithms have proven to be having enormous potential as a
candidate for personal thermal comfort models. But, often within the normal
settings of a building, personal thermal comfort data obtained via experiments
are heavily class-imbalanced. There are a disproportionately high number of
data samples for the "Prefer No Change" class, as compared with the "Prefer
Warmer" and "Prefer Cooler" classes. Machine learning algorithms trained on
such class-imbalanced data perform sub-optimally when deployed in the real
world. To develop robust machine learning-based applications using the above
class-imbalanced data, as well as for privacy-preserving data sharing, we
propose to implement a state-of-the-art conditional synthetic data generator to
generate synthetic data corresponding to the low-frequency classes. Via
experiments, we show that the synthetic data generated has a distribution that
mimics the real data distribution. The proposed method can be extended for use
by other smart building datasets/use-cases.

In this paper, we propose a weakly-supervised approach for 3D object
detection, which makes it possible to train a strong 3D detector with
position-level annotations (i.e. annotations of object centers). In order to
remedy the information loss from box annotations to centers, our method, namely
Back to Reality (BR), makes use of synthetic 3D shapes to convert the weak
labels into fully-annotated virtual scenes as stronger supervision, and in turn
utilizes the perfect virtual labels to complement and refine the real labels.
Specifically, we first assemble 3D shapes into physically reasonable virtual
scenes according to the coarse scene layout extracted from position-level
annotations. Then we go back to reality by applying a virtual-to-real domain
adaptation method, which refine the weak labels and additionally supervise the
training of detector with the virtual scenes. Furthermore, we propose a more
challenging benckmark for indoor 3D object detection with more diversity in
object sizes to better show the potential of BR. With less than 5% of the
labeling labor, we achieve comparable detection performance with some popular
fully-supervised approaches on the widely used ScanNet dataset. Code is
available at: https://github.com/wyf-ACCEPT/BackToReality

Multi-Dimensional time series classification and prediction has been widely
used in many fields, such as disease prevention, fault diagnosis and action
recognition. However, the traditional method needs manual intervention and
inference, and cannot realize the figurative expression of multi-Dimensional
data, which lead to inadequate information mining. Inspired by the strong power
of deep learning technology in image processing, we propose a unified
time-series image fusion framework to transform multi-modal data into 2D-image,
and then realize automatic feature extraction and classification based on a
lightweight convolutional neural network. We present two basic image coding
methods, Gray image coding, RGB image coding, and their step coding methods.
Considering the universality of different application fields, we extended the
coding method and propose two types transform coding, Transform-RGB coding and
RGB-Transform coding, to improve the multi-domain representation ability. By
applying to three typical scenes of Parkinson's disease diagnosis, bearing
fault detection and gymnastics action recognition, we obtained the highest
classification accuracy of 100%, 92.86% and 99.70% respectively, which were all
higher than the classical processing methods. It proves the strong
classification ability and universality of our coding framework to different
multi-dimensional scenes. We expect that this method can be used and perform
well in other scenarios, and be potential to facilitate the progress of related

Traffic signal control is a significant part of the construction of
intelligent transportation. An efficient traffic signal control strategy can
reduce traffic congestion, improve urban road traffic efficiency and facilitate
people's lives. Existing reinforcement learning approaches for traffic signal
control mainly focus on learning through a separate neural network. Such an
independent neural network may fall into the local optimum of the training
results. Worse more, the collected data can only be sampled once, so the data
utilization rate is low. Therefore, we propose the Random Ensemble Double DQN
Light (RELight) model. It can dynamically learn traffic signal control
strategies through reinforcement learning and combine random ensemble learning
to avoid falling into the local optimum to reach the optimal strategy.
Moreover, we introduce the Update-To-Data (UTD) ratio to control the number of
data reuses to improve the problem of low data utilization. In addition, we
have conducted sufficient experiments on synthetic data and real-world data to
prove that our proposed method can achieve better traffic signal control
effects than the existing optimal methods.

Cybersecurity has become one of the focuses of organisations. The number of
cyberattacks keeps increasing as Internet usage continues to grow. An intrusion
detection system (IDS) is an alarm system that helps to detect cyberattacks. As
new types of cyberattacks continue to emerge, researchers focus on developing
machine learning (ML) based IDS to detect zero-day attacks. Researchers usually
remove some or all attack samples from the training dataset and only include
them in the testing dataset when evaluating the performance of an IDS on
detecting zero-day attacks. Although this method may show the ability of an IDs
to detect unknown attacks; however, it does not reflect the long-term
performance of the IDS as it only shows the changes in the type of attacks. In
this paper, we focus on evaluating the long-term performance of ML based IDS.
To achieve this goal, we propose evaluating the ML-based IDS using a dataset
that is created later than the training dataset. The proposed method can better
assess the long-term performance of an ML-based IDS, as the testing dataset
reflects the changes in the type of attack and the changes in network
infrastructure over time. We have implemented six of the most popular ML models
that are used for IDS, including decision tree (DT), random forest (RF),
support vector machine (SVM), na\"ive Bayes (NB), artificial neural network
(ANN) and deep neural network (DNN). Our experiments using the CIC-IDS2017 and
the CSE-CIC-IDS2018 datasets show that SVM and ANN are most resistant to
overfitting. Besides that, our experiment results also show that DT and RF
suffer the most from overfitting, although they perform well on the training
dataset. On the other hand, our experiments using the LUFlow dataset have shown
that all models can perform well when the difference between the training and
testing datasets is small.

Parkinson's disease (PD) is the second most frequent neurodegenerative
disease with prevalence among general population reaching 0.1-1 %, and an
annual incidence between 1.3-2.0/10000 inhabitants. The mean age at diagnosis
of PD is 55 and most patients are between 50 and 80 years old. The most obvious
symptoms are movement-related; these include tremor, rigidity, slowness of
movement and walking difficulties. Frequently these are the symptoms that lead
to the PD diagnoses. Later, thinking and behavioral problems may arise, and
other symptoms include cognitive impairment and sensory, sleep and emotional
problems. In this paper we will present an ongoing project that will evaluate
if voice and handwriting analysis can be reliable predictors/indicators of
swallowing and balance impairments in PD. An important advantage of voice and
handwritten analysis is its low intrusiveness and easy implementation in
clinical practice. Thus, if a significant correlation between these simple
analyses and the gold standard video-fluoroscopic analysis will imply simpler
and less stressing diagnostic test for the patients as well as the use of
cheaper analysis systems.

Natural Language Generation (NLG) has made great progress in recent years due
to the development of deep learning techniques such as pre-trained language
models. This advancement has resulted in more fluent, coherent and even
properties controllable (e.g. stylistic, sentiment, length etc.) generation,
naturally leading to development in downstream tasks such as abstractive
summarization, dialogue generation, machine translation, and data-to-text
generation. However, the faithfulness problem that the generated text usually
contains unfaithful or non-factual information has become the biggest
challenge, which makes the performance of text generation unsatisfactory for
practical applications in many real-world scenarios. Many studies on analysis,
evaluation, and optimization methods for faithfulness problems have been
proposed for various tasks, but have not been organized, compared and discussed
in a combined manner. In this survey, we provide a systematic overview of the
research progress on the faithfulness problem of NLG, including problem
analysis, evaluation metrics and optimization methods. We organize the
evaluation and optimization methods for different tasks into a unified taxonomy
to facilitate comparison and learning across tasks. Several research trends are
discussed further.

Due to the lack of expertise for medical image annotation, the investigation
of label-efficient methodology for medical image segmentation becomes a heated
topic. Recent progresses focus on the efficient utilization of weak annotations
together with few strongly-annotated labels so as to achieve comparable
segmentation performance in many unprofessional scenarios. However, these
approaches only concentrate on the supervision inconsistency between strongly-
and weakly-annotated instances but ignore the instance inconsistency inside the
weakly-annotated instances, which inevitably leads to performance degradation.
To address this problem, we propose a novel label-efficient hybrid-supervised
framework, which considers each weakly-annotated instance individually and
learns its weight guided by the gradient direction of the strongly-annotated
instances, so that the high-quality prior in the strongly-annotated instances
is better exploited and the weakly-annotated instances are depicted more
precisely. Specially, our designed dynamic instance indicator (DII) realizes
the above objectives, and is adapted to our dynamic co-regularization (DCR)
framework further to alleviate the erroneous accumulation from distortions of
weak annotations. Extensive experiments on two hybrid-supervised medical
segmentation datasets demonstrate that with only 10% strong labels, the
proposed framework can leverage the weak labels efficiently and achieve
competitive performance against the 100% strong-label supervised scenario.

The relaxation complexity rc(X) of the set of integer points X contained in a
polyhedron is the minimal number of inequalities needed to formulate a linear
optimization problem over X without using auxiliary variables. Besides its
relevance in integer programming, this concept has interpretations in aspects
of social choice, symmetric cryptanalysis, and machine learning.
  We employ efficient mixed-integer programming techniques to compute a robust
and numerically more practical variant of the relaxation complexity. Our
proposed models require row or column generation techniques and can be enhanced
by symmetry handling and suitable propagation algorithms. Theoretically, we
compare the quality of our models in terms of their LP relaxation values. The
performance of those models is investigated on a broad test set and is
underlined by their ability to solve challenging instances that could not be
solved previously.

Split learning is deemed as a promising paradigm for privacy-preserving
distributed learning, where the learning model can be cut into multiple
portions to be trained at the participants collaboratively. The participants
only exchange the intermediate learning results at the cut layer, including
smashed data via forward-pass (i.e., features extracted from the raw data) and
gradients during backward-propagation.Understanding the security performance of
split learning is critical for various privacy-sensitive applications.With the
emphasis on private labels, this paper proposes a passive clustering label
inference attack for practical split learning. The adversary (either clients or
servers) can accurately retrieve the private labels by collecting the exchanged
gradients and smashed data.We mathematically analyse potential label leakages
in split learning and propose the cosine and Euclidean similarity measurements
for clustering attack. Experimental results validate that the proposed approach
is scalable and robust under different settings (e.g., cut layer positions,
epochs, and batch sizes) for practical split learning.The adversary can still
achieve accurate predictions, even when differential privacy and gradient
compression are adopted for label protections.

Blind image super-resolution (BISR) aims to reconstruct a high-resolution
image from its low-resolution counterpart degraded by unknown blur kernel and
noise. Many deep neural network based methods have been proposed to tackle this
challenging problem without considering the image degradation model. However,
they largely rely on the training sets and often fail to handle images with
unseen blur kernels during inference. Deep unfolding methods have also been
proposed to perform BISR by utilizing the degradation model. Nonetheless, the
existing deep unfolding methods cannot explicitly solve the data term of the
unfolding objective function, limiting their capability in blur kernel
estimation. In this work, we propose a novel unfolded deep kernel estimation
(UDKE) method, which, for the first time to our best knowledge, explicitly
solves the data term with high efficiency. The UDKE based BISR method can
jointly learn image and kernel priors in an end-to-end manner, and it can
effectively exploit the information in both training data and image degradation
model. Experiments on benchmark datasets and real-world data demonstrate that
the proposed UDKE method could well predict complex unseen non-Gaussian blur
kernels in inference, achieving significantly better BISR performance than
state-of-the-art. The source code of UDKE is available at:
https://github.com/natezhenghy/UDKE.

Behavioral models are the key enablers for behavioral analysis of Software
Product Lines (SPL), including testing and model checking. Active model
learning comes to the rescue when family behavioral models are non-existent or
outdated. A key challenge on active model learning is to detect commonalities
and variability efficiently and combine them into concise family models.
Benchmarks and their associated metrics will play a key role in shaping the
research agenda in this promising field and provide an effective means for
comparing and identifying relative strengths and weaknesses in the forthcoming
techniques. In this challenge, we seek benchmarks to evaluate the efficiency
(e.g., learning time and memory footprint) and effectiveness (e.g., conciseness
and accuracy of family models) of active model learning methods in the software
product line context. These benchmark sets must contain the structural and
behavioral variability models of at least one SPL. Each SPL in a benchmark must
contain products that requires more than one round of model learning with
respect to the basic active learning $L^{*}$ algorithm. Alternatively, tools
supporting the synthesis of artificial benchmark models are also welcome.

Micro-expression recognition has drawn increasing attention due to its wide
application in lie detection, criminal detection and psychological
consultation. To improve the recognition performance of the small
micro-expression data, this paper presents a transferring dual stochastic Graph
Convolutional Network (TDSGCN) model. We propose a stochastic graph
construction method and dual graph convolutional network to extract more
discriminative features from the micro-expression images. We use transfer
learning to pre-train SGCNs from macro expression data. Optical flow algorithm
is also integrated to extract their temporal features. We fuse both spatial and
temporal features to improve the recognition performance. To the best of our
knowledge, this is the first attempt to utilize the transferring learning and
graph convolutional network in micro-expression recognition task. In addition,
to handle the class imbalance problem of dataset, we focus on the design of
focal loss function. Through extensive evaluation, our proposed method achieves
state-of-the-art performance on SAMM and recently released MMEW benchmarks. Our
code will be publicly available accompanying this paper.

Sparse local feature matching is pivotal for many computer vision and
robotics tasks. To improve their invariance to challenging appearance
conditions and viewing angles, and hence their usefulness, existing
learning-based methods have primarily focused on data augmentation-based
training. In this work, we propose an alternative, complementary approach that
centers on inducing bias in the model architecture itself to generate
`rotation-specific' features using Steerable E2-CNNs, that are then
group-pooled to achieve rotation-invariant local features. We demonstrate that
this high performance, rotation-specific coverage from the steerable CNNs can
be expanded to all rotation angles by combining it with augmentation-trained
standard CNNs which have broader coverage but are often inaccurate, thus
creating a state-of-the-art rotation-robust local feature matcher. We benchmark
our proposed methods against existing techniques on HPatches and a newly
proposed UrbanScenes3D-Air dataset for visual place recognition. Furthermore,
we present a detailed analysis of the performance effects of ensembling, robust
estimation, network architecture variations, and the use of rotation priors.

3D dense captioning is a recently-proposed novel task, where point clouds
contain more geometric information than the 2D counterpart. However, it is also
more challenging due to the higher complexity and wider variety of inter-object
relations contained in point clouds. Existing methods only treat such relations
as by-products of object feature learning in graphs without specifically
encoding them, which leads to sub-optimal results. In this paper, aiming at
improving 3D dense captioning via capturing and utilizing the complex relations
in the 3D scene, we propose MORE, a Multi-Order RElation mining model, to
support generating more descriptive and comprehensive captions. Technically,
our MORE encodes object relations in a progressive manner since complex
relations can be deduced from a limited number of basic ones. We first devise a
novel Spatial Layout Graph Convolution (SLGC), which semantically encodes
several first-order relations as edges of a graph constructed over 3D object
proposals. Next, from the resulting graph, we further extract multiple triplets
which encapsulate basic first-order relations as the basic unit, and construct
several Object-centric Triplet Attention Graphs (OTAG) to infer multi-order
relations for every target object. The updated node features from OTAG are
aggregated and fed into the caption decoder to provide abundant relational
cues, so that captions including diverse relations with context objects can be
generated. Extensive experiments on the Scan2Cap dataset prove the
effectiveness of our proposed MORE and its components, and we also outperform
the current state-of-the-art method. Our code is available at
https://github.com/SxJyJay/MORE.

Deep metric learning aims to transform input data into an embedding space,
where similar samples are close while dissimilar samples are far apart from
each other. In practice, samples of new categories arrive incrementally, which
requires the periodical augmentation of the learned model. The fine-tuning on
the new categories usually leads to poor performance on the old, which is known
as "catastrophic forgetting". Existing solutions either retrain the model from
scratch or require the replay of old samples during the training. In this
paper, a complete online deep metric learning framework is proposed based on
mutual distillation for both one-task and multi-task scenarios. Different from
the teacher-student framework, the proposed approach treats the old and new
learning tasks with equal importance. No preference over the old or new
knowledge is caused. In addition, a novel virtual feature estimation approach
is proposed to recover the features assumed to be extracted by the old models.
It allows the distillation between the new and the old models without the
replay of old training samples or the holding of old models during the
training. A comprehensive study shows the superior performance of our approach
with the support of different backbones.

Cherry tomato (Solanum Lycopersicum) is popular with consumers over the world
due to its special flavor. Soluble solids content (SSC) and firmness are two
key metrics for evaluating the product qualities. In this work, we develop
non-destructive testing techniques for SSC and fruit firmness based on
hyperspectral images and a corresponding deep learning regression model.
Hyperspectral reflectance images of over 200 tomato fruits are derived with
spectrum ranging from 400 to 1000 nm. The acquired hyperspectral images are
corrected and the spectral information is extracted. A novel
one-dimensional(1D) convolutional ResNet (Con1dResNet) based regression model
is prosed and compared with the state of art techniques. Experimental results
show that, with a relatively large number of samples our technique is 26.4\%
better than state of art technique for SSC and 33.7\% for firmness. The results
of this study indicate the application potential of hyperspectral imaging
technique in the SSC and firmness detection, which provides a new option for
non-destructive testing of cherry tomato fruit quality in the future.

Spatial data are characterized by their spatial dependence, which is often
complex, non-linear, and difficult to capture with a single model. Significant
levels of model uncertainty -- arising from these characteristics -- cannot be
resolved by model selection or simple ensemble methods, as performances are not
homogeneous. We address this issue by proposing a novel methodology that
captures spatially-varying model uncertainty, which we call spatial Bayesian
predictive synthesis. Our proposal is defined by specifying a latent factor
spatially-varying coefficient model as the synthesis function, which enables
model coefficients to vary over the region to achieve flexible spatial model
ensembling. Two MCMC strategies are implemented for full uncertainty
quantification, as well as a variational inference strategy for fast point
inference. We also extend the estimations strategy for general responses. A
finite sample theoretical guarantee is given for the predictive performance of
our methodology, showing that the predictions are exact minimax. Through
simulation examples and two real data applications, we demonstrate that our
proposed spatial Bayesian predictive synthesis outperforms standard spatial
models and advanced machine learning methods, in terms of predictive accuracy,
while maintaining interpretability of the prediction mechanism.

Time series data is used in a wide range of real world applications. In a
variety of domains , detailed analysis of time series data (via Forecasting and
Anomaly Detection) leads to a better understanding of how events associated
with a specific time instance behave. Time Series Analysis (TSA) is commonly
performed with plots and traditional models. Machine Learning (ML) approaches ,
on the other hand , have seen an increase in the state of the art for
Forecasting and Anomaly Detection because they provide comparable results when
time and data constraints are met. A number of time series toolboxes are
available that offer rich interfaces to specific model classes (ARIMA/filters ,
neural networks) or framework interfaces to isolated time series modelling
tasks (forecasting , feature extraction , annotation , classification).
Nonetheless , open source machine learning capabilities for time series remain
limited , and existing libraries are frequently incompatible with one another.
The goal of this paper is to provide a concise and user friendly overview of
the most important open source tools for time series analysis. This article
examines two related toolboxes (1) forecasting and (2) anomaly detection. This
paper describes a typical Time Series Analysis (TSA) framework with an
architecture and lists the main features of TSA framework. The tools are
categorized based on the criteria of analysis tasks completed , data
preparation methods employed , and evaluation methods for results generated.
This paper presents quantitative analysis and discusses the current state of
actively developed open source Time Series Analysis frameworks. Overall , this
article considered 60 time series analysis tools , and 32 of which provided
forecasting modules , and 21 packages included anomaly detection.

Reinforcement learning (RL) is a promising tool for developing controllers
for quadrupedal locomotion. The design of most learning-based locomotion
controllers adopts the joint position-based paradigm, wherein a low-frequency
RL policy outputs target joint positions that are then tracked by a
high-frequency proportional-derivative (PD) controller that outputs joint
torques. However, the low frequency of such a policy hinders the advancement of
highly dynamic locomotion behaviors. Moreover, determining the PD gains for
optimal tracking performance is laborious and dependent on the task at hand. In
this paper, we introduce a learning torque control framework for quadrupedal
locomotion, which trains an RL policy that directly predicts joint torques at a
high frequency, thus circumventing the use of PD controllers. We validate the
proposed framework with extensive experiments where the robot is able to both
traverse various terrains and resist external pushes, given user-specified
commands. To our knowledge, this is the first attempt of learning torque
control for quadrupedal locomotion with an end-to-end single neural network
that has led to successful real-world experiments among recent research on
learning-based quadrupedal locomotion which is mostly position-based.

While the convergence of Artificial Intelligence (AI) techniques with
improved information technology systems ensured enormous benefits to the
Internet of Vehicles (IoVs) systems, it also introduced an increased amount of
security and privacy threats. To ensure the security of IoVs data, privacy
preservation methodologies have gained significant attention in the literature.
However, these strategies also need specific adjustments and modifications to
cope with the advances in IoVs design. In the interim, Federated Learning (FL)
has been proven as an emerging idea to protect IoVs data privacy and security.
On the other hand, Blockchain technology is showing prominent possibilities
with secured, dispersed, and auditable data recording and sharing schemes. In
this paper, we present a comprehensive survey on the application and
implementation of Blockchain-Enabled Federated Learning frameworks for IoVs.
Besides, probable issues, challenges, solutions, and future research directions
for BC-Enabled FL frameworks for IoVs are also presented. This survey can
further be used as the basis for developing modern BC-Enabled FL solutions to
resolve different data privacy issues and scenarios of IoVs.

Many tasks in control, robotics, and planning can be specified using desired
goal configurations for various entities in the environment. Learning
goal-conditioned policies is a natural paradigm to solve such tasks. However,
current approaches struggle to learn and generalize as task complexity
increases, such as variations in number of environment entities or compositions
of goals. In this work, we introduce a framework for modeling entity-based
compositional structure in tasks, and create suitable policy designs that can
leverage this structure. Our policies, which utilize architectures like Deep
Sets and Self Attention, are flexible and can be trained end-to-end without
requiring any action primitives. When trained using standard reinforcement and
imitation learning methods on a suite of simulated robot manipulation tasks, we
find that these architectures achieve significantly higher success rates with
less data. We also find these architectures enable broader and compositional
generalization, producing policies that extrapolate to different numbers of
entities than seen in training, and stitch together (i.e. compose) learned
skills in novel ways. Videos of the results can be found at
https://sites.google.com/view/comp-gen-rl.

Recently, one-stage visual grounders attract high attention due to the
comparable accuracy but significantly higher efficiency than two-stage
grounders. However, inter-object relation modeling has not been well studied
for one-stage grounders. Inter-object relationship modeling, though important,
is not necessarily performed among all the objects within the image, as only a
part of them are related to the text query and may confuse the model. We call
these objects "suspected objects". However, exploring relationships among these
suspected objects in the one-stage visual grounding paradigm is non-trivial due
to two core problems: (1) no object proposals are available as the basis on
which to select suspected objects and perform relationship modeling; (2)
compared with those irrelevant to the text query, suspected objects are more
confusing, as they may share similar semantics, be entangled with certain
relationships, etc, and thereby more easily mislead the model's prediction. To
address the above issues, this paper proposes a Suspected Object Graph (SOG)
approach to encourage the correct referred object selection among the suspected
ones in the one-stage visual grounding. Suspected objects are dynamically
selected from a learned activation map as nodes to adapt to the current
discrimination ability of the model during training. Afterward, on top of the
suspected objects, a Keyword-aware Node Representation module (KNR) and an
Exploration by Random Connection strategy (ERC) are concurrently proposed
within the SOG to help the model rethink its initial prediction. Extensive
ablation studies and comparison with state-of-the-art approaches on prevalent
visual grounding benchmarks demonstrate the effectiveness of our proposed
method.

Current machine-learning based software vulnerability detection methods are
primarily conducted at the function-level. However, a key limitation of these
methods is that they do not indicate the specific lines of code contributing to
vulnerabilities. This limits the ability of developers to efficiently inspect
and interpret the predictions from a learnt model, which is crucial for
integrating machine-learning based tools into the software development
workflow. Graph-based models have shown promising performance in function-level
vulnerability detection, but their capability for statement-level vulnerability
detection has not been extensively explored. While interpreting function-level
predictions through explainable AI is one promising direction, we herein
consider the statement-level software vulnerability detection task from a fully
supervised learning perspective. We propose a novel deep learning framework,
LineVD, which formulates statement-level vulnerability detection as a node
classification task. LineVD leverages control and data dependencies between
statements using graph neural networks, and a transformer-based model to encode
the raw source code tokens. In particular, by addressing the conflicting
outputs between function-level and statement-level information, LineVD
significantly improve the prediction performance without vulnerability status
for function code. We have conducted extensive experiments against a
large-scale collection of real-world C/C++ vulnerabilities obtained from
multiple real-world projects, and demonstrate an increase of 105\% in F1-score
over the current state-of-the-art.

Large-scale pre-training has been proven to be crucial for various computer
vision tasks. However, with the increase of pre-training data amount, model
architecture amount, and the private/inaccessible data, it is not very
efficient or possible to pre-train all the model architectures on large-scale
datasets. In this work, we investigate an alternative strategy for
pre-training, namely Knowledge Distillation as Efficient Pre-training (KDEP),
aiming to efficiently transfer the learned feature representation from existing
pre-trained models to new student models for future downstream tasks. We
observe that existing Knowledge Distillation (KD) methods are unsuitable
towards pre-training since they normally distill the logits that are going to
be discarded when transferred to downstream tasks. To resolve this problem, we
propose a feature-based KD method with non-parametric feature dimension
aligning. Notably, our method performs comparably with supervised pre-training
counterparts in 3 downstream tasks and 9 downstream datasets requiring 10x less
data and 5x less pre-training time. Code is available at
https://github.com/CVMI-Lab/KDEP.

Scene text recognition is a popular topic and extensively used in the
industry. Although many methods have achieved satisfactory performance for the
close-set text recognition challenges, these methods lose feasibility in
open-set scenarios, where collecting data or retraining models for novel
characters could yield a high cost. For example, annotating samples for foreign
languages can be expensive, whereas retraining the model each time when a novel
character is discovered from historical documents costs both time and
resources. In this paper, we introduce and formulate a new open-set text
recognition task which demands the capability to spot and recognize novel
characters without retraining. A label-to-prototype learning framework is also
proposed as a baseline for the proposed task. Specifically, the framework
introduces a generalizable label-to-prototype mapping function to build
prototypes (class centers) for both seen and unseen classes. An open-set
predictor is then utilized to recognize or reject samples according to the
prototypes. The implementation of rejection capability over out-of-set
characters allows automatic spotting of unknown characters in the incoming data
stream. Extensive experiments show that our method achieves promising
performance on a variety of zero-shot, close-set, and open-set text recognition
datasets

Recently, masked image modeling (MIM) has become a promising direction for
visual pre-training. In the context of vision transformers, MIM learns
effective visual representation by aligning the token-level features with a
pre-defined space (e.g., BEIT used a d-VAE trained on a large image corpus as
the tokenizer). In this paper, we go one step further by introducing guidance
from other modalities and validating that such additional knowledge leads to
impressive gains for visual pre-training. The proposed approach is named
Multimodality-guided Visual Pre-training (MVP), in which we replace the
tokenizer with the vision branch of CLIP, a vision-language model pre-trained
on 400 million image-text pairs. We demonstrate the effectiveness of MVP by
performing standard experiments, i.e., pre-training the ViT models on ImageNet
and fine-tuning them on a series of downstream visual recognition tasks. In
particular, pre-training ViT-Base/16 for 300 epochs, MVP reports a 52.4% mIoU
on ADE20K, surpassing BEIT (the baseline and previous state-of-the-art) with an
impressive margin of 6.8%.

Disease identification is a core, routine activity in observational health
research. Cohorts impact downstream analyses, such as how a condition is
characterized, how patient risk is defined, and what treatments are studied. It
is thus critical to ensure that selected cohorts are representative of all
patients, independently of their demographics or social determinants of health.
While there are multiple potential sources of bias when constructing phenotype
definitions which may affect their fairness, it is not standard in the field of
phenotyping to consider the impact of different definitions across subgroups of
patients. In this paper, we propose a set of best practices to assess the
fairness of phenotype definitions. We leverage established fairness metrics
commonly used in predictive models and relate them to commonly used
epidemiological cohort description metrics. We describe an empirical study for
Crohn's disease and diabetes type 2, each with multiple phenotype definitions
taken from the literature across two sets of patient subgroups (gender and
race). We show that the different phenotype definitions exhibit widely varying
and disparate performance according to the different fairness metrics and
subgroups. We hope that the proposed best practices can help in constructing
fair and inclusive phenotype definitions.

In recent years, deep learning-based models have significantly improved the
Natural Language Processing (NLP) tasks. Specifically, the Convolutional Neural
Network (CNN), initially used for computer vision, has shown remarkable
performance for text data in various NLP problems. Most of the existing
CNN-based models use 1-dimensional convolving filters n-gram detectors), where
each filter specialises in extracting n-grams features of a particular input
word embedding. The input word embeddings, also called sentence matrix, is
treated as a matrix where each row is a word vector. Thus, it allows the model
to apply one-dimensional convolution and only extract n-gram based features
from a sentence matrix. These features can be termed as intra-sentence n-gram
features. To the extent of our knowledge, all the existing CNN models are based
on the aforementioned concept. In this paper, we present a CNN-based
architecture TextConvoNet that not only extracts the intra-sentence n-gram
features but also captures the inter-sentence n-gram features in input text
data. It uses an alternative approach for input matrix representation and
applies a two-dimensional multi-scale convolutional operation on the input. To
evaluate the performance of TextConvoNet, we perform an experimental study on
five text classification datasets. The results are evaluated by using various
performance metrics. The experimental results show that the presented
TextConvoNet outperforms state-of-the-art machine learning and deep learning
models for text classification purposes.

While anomaly detection in time series has been an active area of research
for several years, most recent approaches employ an inadequate evaluation
criterion leading to an inflated F1 score. We show that a rudimentary Random
Guess method can outperform state-of-the-art detectors in terms of this popular
but faulty evaluation criterion. In this work, we propose a proper evaluation
metric that measures the timeliness and precision of detecting sequential
anomalies. Moreover, most existing approaches are unable to capture temporal
features from long sequences. Self-attention based approaches, such as
transformers, have been demonstrated to be particularly efficient in capturing
long-range dependencies while being computationally efficient during training
and inference. We also propose an efficient transformer approach for anomaly
detection in time series and extensively evaluate our proposed approach on
several popular benchmark datasets.

Physics-based and first-principles models pervade the engineering and
physical sciences, allowing for the ability to model the dynamics of complex
systems with a prescribed accuracy. The approximations used in deriving
governing equations often result in discrepancies between the model and
sensor-based measurements of the system, revealing the approximate nature of
the equations and/or the signal-to-noise ratio of the sensor itself. In modern
dynamical systems, such discrepancies between model and measurement can lead to
poor quantification, often undermining the ability to produce accurate and
precise control algorithms. We introduce a discrepancy modeling framework to
resolve deterministic model-measurement mismatch with two distinct approaches:
(i) by learning a model for the evolution of systematic state-space residual,
and (ii) by discovering a model for the missing deterministic physics.
Regardless of approach, a common suite of data-driven model discovery methods
can be used. Specifically, we use four fundamentally different methods to
demonstrate the mathematical implementations of discrepancy modeling: (i) the
sparse identification of nonlinear dynamics (SINDy), (ii) dynamic mode
decomposition (DMD), (iii) Gaussian process regression (GPR), and (iv) neural
networks (NN). The choice of method depends on one's intent for discrepancy
modeling, as well as quantity and quality of the sensor measurements. We
demonstrate the utility and suitability for both discrepancy modeling
approaches using the suite of data-driven modeling methods on three dynamical
systems under varying signal-to-noise ratios. We compare reconstruction and
forecasting accuracies and provide detailed comparatives, allowing one to
select the appropriate approach and method in practice.

While video action recognition has been an active area of research for
several years, zero-shot action recognition has only recently started gaining
traction. However, there is a lack of a formal definition for the zero-shot
learning paradigm leading to uncertainty about classes that can be considered
as previously unseen. In this work, we take a new comprehensive look at the
inductive zero-shot action recognition problem from a realistic standpoint.
Specifically, we advocate for a concrete formulation for zero-shot action
recognition that avoids an exact overlap between the training and testing
classes and also limits the intra-class variance; and propose a novel
end-to-end trained transformer model which is capable of capturing long range
spatiotemporal dependencies efficiently, contrary to existing approaches which
use 3D-CNNs. The proposed approach outperforms the existing state-of-the-art
algorithms in many settings on all benchmark datasets by a wide margin.

Current adversarial attack research reveals the vulnerability of
learning-based classifiers against carefully crafted perturbations. However,
most existing attack methods have inherent limitations in cross-dataset
generalization as they rely on a classification layer with a closed set of
categories. Furthermore, the perturbations generated by these methods may
appear in regions easily perceptible to the human visual system (HVS). To
circumvent the former problem, we propose a novel algorithm that attacks
semantic similarity on feature representations. In this way, we are able to
fool classifiers without limiting attacks to a specific dataset. For
imperceptibility, we introduce the low-frequency constraint to limit
perturbations within high-frequency components, ensuring perceptual similarity
between adversarial examples and originals. Extensive experiments on three
datasets (CIFAR-10, CIFAR-100, and ImageNet-1K) and three public online
platforms indicate that our attack can yield misleading and transferable
adversarial examples across architectures and datasets. Additionally,
visualization results and quantitative performance (in terms of four different
metrics) show that the proposed algorithm generates more imperceptible
perturbations than the state-of-the-art methods. Code is made available at.

We aim to tackle the problem of point-based interactive segmentation, in
which two key challenges are to infer user's intention correctly and to
propagate the user-provided annotations to unlabeled regions efficiently. To
address those challenges, we propose a novel intention-aware feature
propagation strategy that performs explicit user intention estimation and
learns an efficient click-augmented feature representation for high-resolution
foreground segmentation. Specifically, we develop a coarse-to-fine sparse
propagation network for each interactive segmentation step, which consists of a
coarse-level network for more effective tracking of user's interest, and a
fine-level network for zooming to the target object and performing fine-level
segmentation. Moreover, we design a new sparse graph network module for both
levels to enable efficient long-range propagation of click information.
Extensive experiments show that our method surpasses the previous
state-of-the-art methods on all popular benchmarks, demonstrating its efficacy.

We solve the traditional problems of earthquake location and magnitude
estimation through a supervised learning approach, where we train a Graph
Neural Network to predict estimates directly from input pick data, and each
input allows a distinct seismic network with variable number of stations and
positions. We train the model using synthetic simulations from assumed
travel-time and amplitude-distance attenuation models. The architecture uses
one graph to represent the station set, and another to represent the model
space. The input includes theoretical predictions of data, given model
parameters, and the adjacency matrices of the graphs defined link spatially
local elements. As we show, graph convolutions on this combined representation
are highly effective at inference, data fusion, and outlier suppression. We
compare our results with traditional methods and observe favorable performance.

Discretization invariant learning aims at learning in the
infinite-dimensional function spaces with the capacity to process heterogeneous
discrete representations of functions as inputs and/or outputs of a learning
model. This paper proposes a novel deep learning framework based on integral
autoencoders (IAE-Net) for discretization invariant learning. The basic
building block of IAE-Net consists of an encoder and a decoder as integral
transforms with data-driven kernels, and a fully connected neural network
between the encoder and decoder. This basic building block is applied in
parallel in a wide multi-channel structure, which are repeatedly composed to
form a deep and densely connected neural network with skip connections as
IAE-Net. IAE-Net is trained with randomized data augmentation that generates
training data with heterogeneous structures to facilitate the performance of
discretization invariant learning. The proposed IAE-Net is tested with various
applications in predictive data science, solving forward and inverse problems
in scientific computing, and signal/image processing. Compared with
alternatives in the literature, IAE-Net achieves state-of-the-art performance
in existing applications and creates a wide range of new applications.

We consider the problem of Vision-and-Language Navigation (VLN). The majority
of current methods for VLN are trained end-to-end using either unstructured
memory such as LSTM, or using cross-modal attention over the egocentric
observations of the agent. In contrast to other works, our key insight is that
the association between language and vision is stronger when it occurs in
explicit spatial representations. In this work, we propose a cross-modal map
learning model for vision-and-language navigation that first learns to predict
the top-down semantics on an egocentric map for both observed and unobserved
regions, and then predicts a path towards the goal as a set of waypoints. In
both cases, the prediction is informed by the language through cross-modal
attention mechanisms. We experimentally test the basic hypothesis that
language-driven navigation can be solved given a map, and then show competitive
results on the full VLN-CE benchmark.

This study proposes a framework for manifold learning of image patches using
the concept of equivalence classes: manifold modeling in quotient space (MMQS).
In MMQS, we do not consider a set of local patches of the image as it is, but
rather the set of their canonical patches obtained by introducing the concept
of equivalence classes and performing manifold learning on their canonical
patches. Canonical patches represent equivalence classes, and their
auto-encoder constructs a manifold in the quotient space. Based on this
framework, we produce a novel manifold-based image model by introducing
rotation-flip-equivalence relations. In addition, we formulate an image
reconstruction problem by fitting the proposed image model to a corrupted
observed image and derive an algorithm to solve it. Our experiments show that
the proposed image model is effective for various self-supervised image
reconstruction tasks, such as image inpainting, deblurring, super-resolution,
and denoising.

Automatically generating compilable programs with (or without) natural
language descriptions has always been a touchstone problem for computational
linguistics and automated software engineering. Existing deep-learning
approaches model code generation as text generation, either constrained by
grammar structures in decoder, or driven by pre-trained language models on
large-scale code corpus (e.g., CodeGPT, PLBART, and CodeT5). However, few of
them account for compilability of the generated programs. To improve
compilability of the generated programs, this paper proposes COMPCODER, a
three-stage pipeline utilizing compiler feedback for compilable code
generation, including language model fine-tuning, compilability reinforcement,
and compilability discrimination. Comprehensive experiments on two code
generation tasks demonstrate the effectiveness of our proposed approach,
improving the success rate of compilation from 44.18 to 89.18 in code
completion on average and from 70.3 to 96.2 in text-to-code generation,
respectively, when comparing with the state-of-the-art CodeGPT.

This paper presents a reinforcement learning (RL) framework that leverages
Frank-Wolfe policy optimization to address frame-level bit allocation for
HEVC/H.265. Most previous RL-based approaches adopt the single-critic design,
which weights the rewards for distortion minimization and rate regularization
by an empirically chosen hyper-parameter. More recently, the dual-critic design
is proposed to update the actor network by alternating the rate and distortion
critics. However, the convergence of training is not guaranteed. To address
this issue, we introduce Neural Frank-Wolfe Policy Optimization (NFWPO) in
formulating the frame-level bit allocation as an action-constrained RL problem.
In this new framework, the rate critic serves to specify a feasible action set,
and the distortion critic updates the actor network towards maximizing the
reconstruction quality while conforming to the action constraint. Experimental
results show that when trained to optimize the video multi-method assessment
fusion (VMAF) metric, our NFWPO-based model outperforms both the single-critic
and the dual-critic methods. It also demonstrates comparable rate-distortion
performance to the 2-pass average bit rate control of x265.

With the increasing abundance of pretrained models in recent years, the
problem of selecting the best pretrained checkpoint for a particular downstream
classification task has been gaining increased attention. Although several
methods have recently been proposed to tackle the selection problem (e.g. LEEP,
H-score), these methods resort to applying heuristics that are not well
motivated by learning theory. In this paper we present PACTran, a theoretically
grounded family of metrics for pretrained model selection and transferability
measurement. We first show how to derive PACTran metrics from the optimal
PAC-Bayesian bound under the transfer learning setting. We then empirically
evaluate three metric instantiations of PACTran on a number of vision tasks
(VTAB) as well as a language-and-vision (OKVQA) task. An analysis of the
results shows PACTran is a more consistent and effective transferability
measure compared to existing selection methods.

Estimating treatment effects from observational data provides insights about
causality guiding many real-world applications such as different clinical study
designs, which are the formulations of trials, experiments, and observational
studies in medical, clinical, and other types of research. In this paper, we
describe causal inference for application in a novel clinical design called
basket trial that tests how well a new drug works in patients who have
different types of cancer that all have the same mutation. We propose a
multi-task adversarial learning (MTAL) method, which incorporates feature
selection multi-task representation learning and adversarial learning to
estimate potential outcomes across different tumor types for patients sharing
the same genetic mutation but having different tumor types. In our paper, the
basket trial is employed as an intuitive example to present this new causal
inference setting. This new causal inference setting includes, but is not
limited to basket trials. This setting has the same challenges as the
traditional causal inference problem, i.e., missing counterfactual outcomes
under different subgroups and treatment selection bias due to confounders. We
present the practical advantages of our MTAL method for the analysis of
synthetic basket trial data and evaluate the proposed estimator on two
benchmarks, IHDP and News. The results demonstrate the superiority of our MTAL
method over the competing state-of-the-art methods.

In the context of competitive multiplayer games, collusion happens when two
or more teams decide to collaborate towards a common goal, with the intention
of gaining an unfair advantage from this cooperation. The task of identifying
colluders from the player population is however infeasible to game designers
due to the sheer size of the player population. In this paper, we propose a
system that detects colluding behaviors in team-based multiplayer games and
highlights the players that most likely exhibit colluding behaviors. The game
designers then proceed to analyze a smaller subset of players and decide what
action to take. For this reason, it is important and necessary to be extremely
careful with false positives when automating the detection. The proposed method
analyzes the players' social relationships paired with their in-game behavioral
patterns and, using tools from graph theory, infers a feature set that allows
us to detect and measure the degree of collusion exhibited by each pair of
players from opposing teams. We then automate the detection using Isolation
Forest, an unsupervised learning technique specialized in highlighting
outliers, and show the performance and efficiency of our approach on two real
datasets, each with over 170,000 unique players and over 100,000 different
matches.

We propose a regularization method for ensemble Kalman filtering (EnKF) with
elliptic observation operators. Commonly used EnKF regularization methods
suppress state correlations at long distances. For observations described by
elliptic partial differential equations, such as the pressure Poisson equation
(PPE) in incompressible fluid flows, distance localization cannot be applied,
as we cannot disentangle slowly decaying physical interactions from spurious
long-range correlations. This is particularly true for the PPE, in which
distant vortex elements couple nonlinearly to induce pressure. Instead, these
inverse problems have a low effective dimension: low-dimensional projections of
the observations strongly inform a low-dimensional subspace of the state space.
We derive a low-rank factorization of the Kalman gain based on the spectrum of
the Jacobian of the observation operator. The identified eigenvectors
generalize the source and target modes of the multipole expansion,
independently of the underlying spatial distribution of the problem. Given
rapid spectral decay, inference can be performed in the low-dimensional
subspace spanned by the dominant eigenvectors. This low-rank EnKF is assessed
on dynamical systems with Poisson observation operators, where we seek to
estimate the positions and strengths of point singularities over time from
potential or pressure observations. We also comment on the broader
applicability of this approach to elliptic inverse problems outside the context
of filtering.

What matters for contrastive learning? We argue that contrastive learning
heavily relies on informative features, or "hard" (positive or negative)
features. Early works include more informative features by applying complex
data augmentations and large batch size or memory bank, and recent works design
elaborate sampling approaches to explore informative features. The key
challenge toward exploring such features is that the source multi-view data is
generated by applying random data augmentations, making it infeasible to always
add useful information in the augmented data. Consequently, the informativeness
of features learned from such augmented data is limited. In response, we
propose to directly augment the features in latent space, thereby learning
discriminative representations without a large amount of input data. We perform
a meta learning technique to build the augmentation generator that updates its
network parameters by considering the performance of the encoder. However,
insufficient input data may lead the encoder to learn collapsed features and
therefore malfunction the augmentation generator. A new margin-injected
regularization is further added in the objective function to avoid the encoder
learning a degenerate mapping. To contrast all features in one gradient
back-propagation step, we adopt the proposed optimization-driven unified
contrastive loss instead of the conventional contrastive loss. Empirically, our
method achieves state-of-the-art results on several benchmark datasets.

As a powerful way of realizing semi-supervised segmentation, the cross
supervision method learns cross consistency based on independent ensemble
models using abundant unlabeled images. However, the wrong pseudo labeling
information generated by cross supervision would confuse the training process
and negatively affect the effectiveness of the segmentation model. Besides, the
training process of ensemble models in such methods also multiplies the cost of
computation resources and decreases the training efficiency. To solve these
problems, we propose a novel cross supervision method, namely
uncertainty-guided self cross supervision (USCS). In addition to ensemble
models, we first design a multi-input multi-output (MIMO) segmentation model
which can generate multiple outputs with shared model and consequently impose
consistency over the outputs, saving the cost on parameters and calculations.
On the other hand, we employ uncertainty as guided information to encourage the
model to focus on the high confident regions of pseudo labels and mitigate the
effects of wrong pseudo labeling in self cross supervision, improving the
performance of the segmentation model. Extensive experiments show that our
method achieves state-of-the-art performance while saving 40.5% and 49.1% cost
on parameters and calculations.

This paper studies the communication complexity of risk averse optimization
over a network. The problem generalizes the well-studied risk-neutral
finite-sum distributed optimization problem and its importance stems from the
need to handle risk in an uncertain environment. For algorithms in the
literature, there exists a gap in communication complexities for solving
risk-averse and risk-neutral problems. We propose two distributed algorithms,
namely the distributed risk averse optimization (DRAO) method and the
distributed risk averse optimization with sliding (DRAO-S) method, to close the
gap. Specifically, the DRAO method achieves the optimal communication
complexity by assuming a certain saddle point subproblem can be easily solved
in the server node. The DRAO-S method removes the strong assumption by
introducing a novel saddle point sliding subroutine which only requires the
projection over the ambiguity set $P$. We observe that the number of
$P$-projections performed by DRAO-S is optimal. Moreover, we develop matching
lower complexity bounds to show that communication complexities of both DRAO
and DRAO-S are not improvable. Numerical experiments are conducted to
demonstrate the encouraging empirical performance of the DRAO-S method.

In this work, we aim to capitalize on the unique few-shot capabilities of
large-scale language models (LSLMs) to overcome some of their challenges with
respect to grounding to factual and up-to-date information. Motivated by
semi-parametric language models (LMs), which ground their decisions in external
retrieved evidence, we use few-shot prompting to learn to condition LMs on
information returned from the web using Google Search, a broad and constantly
updated knowledge source. Our approach does not involve fine-tuning or learning
additional parameters, thus making it applicable to any LM, offering therefore
a strong baseline. Indeed, we find that LMs conditioned on the web surpass
performance of closed-book models of similar, or even larger, model sizes in
open-domain question answering. Finally, we find that increasing the
inference-time compute of models, achieved via using multiple retrieved
evidences to generate multiple answers followed by a reranking stage that uses
scores generated by the same LMs, leads to better performance and alleviates
lower performance of smaller few-shot LMs. All in all, our findings suggest
that it might be beneficial to slow down the race towards the biggest model and
instead shift attention towards finding more effective ways to use models,
including but not limited to, better prompting or increasing inference-time
compute.

Temporal Action Localization (TAL) has experienced remarkable success under
the supervised learning paradigm. However, existing TAL methods are rooted in
the closed set assumption, which cannot handle the inevitable unknown actions
in open-world scenarios. In this paper, we, for the first time, step toward the
Open Set TAL (OSTAL) problem and propose a general framework OpenTAL based on
Evidential Deep Learning (EDL). Specifically, the OpenTAL consists of
uncertainty-aware action classification, actionness prediction, and temporal
location regression. With the proposed importance-balanced EDL method,
classification uncertainty is learned by collecting categorical evidence
majorly from important samples. To distinguish the unknown actions from
background video frames, the actionness is learned by the positive-unlabeled
learning. The classification uncertainty is further calibrated by leveraging
the guidance from the temporal localization quality. The OpenTAL is general to
enable existing TAL models for open set scenarios, and experimental results on
THUMOS14 and ActivityNet1.3 benchmarks show the effectiveness of our method.
The code and pre-trained models are released at
https://www.rit.edu/actionlab/opental.

Data citations provide a foundation for studying research data impact.
Collecting and managing data citations is a new frontier in archival science
and scholarly communication. However, the discovery and curation of research
data citations is labor intensive. Data citations that reference unique
identifiers (i.e. DOIs) are readily findable; however, informal mentions made
to research data are more challenging to infer. We propose a natural language
processing (NLP) paradigm to support the human task of identifying informal
mentions made to research datasets. The work of discovering informal data
mentions is currently performed by librarians and their staff in the
Inter-university Consortium for Political and Social Research (ICPSR), a large
social science data archive that maintains a large bibliography of data-related
literature. The NLP model is bootstrapped from data citations actively
collected by librarians at ICPSR. The model combines pattern matching with
multiple iterations of human annotations to learn additional rules for
detecting informal data mentions. These examples are then used to train an NLP
pipeline. The librarian-in-the-loop paradigm is centered in the data work
performed by ICPSR librarians, supporting broader efforts to build a more
comprehensive bibliography of data-related literature that reflects the
scholarly communities of research data users.

Wide neural networks with linear output layer have been shown to be
near-linear, and to have near-constant neural tangent kernel (NTK), in a region
containing the optimization path of gradient descent. These findings seem
counter-intuitive since in general neural networks are highly complex models.
Why does a linear structure emerge when the networks become wide? In this work,
we provide a new perspective on this "transition to linearity" by considering a
neural network as an assembly model recursively built from a set of sub-models
corresponding to individual neurons. In this view, we show that the linearity
of wide neural networks is, in fact, an emerging property of assembling a large
number of diverse "weak" sub-models, none of which dominate the assembly.

Neural Ordinary Differential Equations (Neural ODEs) construct the continuous
dynamics of hidden units using ordinary differential equations specified by a
neural network, demonstrating promising results on many tasks. However, Neural
ODEs still do not perform well on image recognition tasks. The possible reason
is that the one-hot encoding vector commonly used in Neural ODEs can not
provide enough supervised information. We propose a new training based on
knowledge distillation to construct more powerful and robust Neural ODEs
fitting image recognition tasks. Specially, we model the training of Neural
ODEs into a teacher-student learning process, in which we propose ResNets as
the teacher model to provide richer supervised information. The experimental
results show that the new training manner can improve the classification
accuracy of Neural ODEs by 24% on CIFAR10 and 5% on SVHN. In addition, we also
quantitatively discuss the effect of both knowledge distillation and time
horizon in Neural ODEs on robustness against adversarial examples. The
experimental analysis concludes that introducing the knowledge distillation and
increasing the time horizon can improve the robustness of Neural ODEs against
adversarial examples.

Recent advancements in deep reinforcement learning have brought forth an
impressive display of highly skilled artificial agents capable of complex
intelligent behavior. In video games, these artificial agents are increasingly
deployed as non-playable characters (NPCs) designed to enhance the experience
of human players. However, while it has been shown that the convincing
human-like behavior of NPCs leads to increased engagement in video games, the
believability of an artificial agent's behavior is most often measured solely
by its proficiency at a given task. Recent work has hinted that proficiency
alone is not sufficient to discern human-like behavior. Motivated by this, we
build a non-parametric two-sample hypothesis test designed to compare the
behaviors of artificial agents to those of human players. We show that the
resulting $p$-value not only aligns with anonymous human judgment of human-like
behavior, but also that it can be used as a measure of similarity.

Testing video games is an increasingly difficult task as traditional methods
fail to scale with growing software systems. Manual testing is a very
labor-intensive process, and therefore quickly becomes cost prohibitive. Using
scripts for automated testing is affordable, however scripts are ineffective in
non-deterministic environments, and knowing when to run each test is another
problem altogether. The modern game's complexity, scope, and player
expectations are rapidly increasing where quality control is a big portion of
the production cost and delivery risk. Reducing this risk and making production
happen is a big challenge for the industry currently. To keep production costs
realistic up-to and after release, we are focusing on preventive quality
assurance tactics alongside testing and data analysis automation. We present
SUPERNOVA (Selection of tests and Universal defect Prevention in External
Repositories for Novel Objective Verification of software Anomalies), a system
responsible for test selection and defect prevention while also functioning as
an automation hub. By integrating data analysis functionality with machine and
deep learning capability, SUPERNOVA assists quality assurance testers in
finding bugs and developers in reducing defects, which improves stability
during the production cycle and keeps testing costs under control. The direct
impact of this has been observed to be a reduction in 55% or more testing hours
for an undisclosed sports game title that has shipped, which was using these
test selection optimizations. Furthermore, using risk scores generated by a
semi-supervised machine learning model, we are able to detect with 71%
precision and 77% recall the probability of a change-list being bug inducing,
and provide a detailed breakdown of this inference to developers. These efforts
improve workflow and reduce testing hours required on game titles in

We propose LiftReg, a 2D/3D deformable registration approach. LiftReg is a
deep registration framework which is trained using sets of digitally
reconstructed radiographs (DRR) and computed tomography (CT) image pairs. By
using simulated training data, LiftReg can use a high-quality CT-CT image
similarity measure, which helps the network to learn a high-quality deformation
space. To further improve registration quality and to address the inherent
depth ambiguities of very limited angle acquisitions, we propose to use
features extracted from the backprojected 2D images and a statistical
deformation model. We test our approach on the DirLab lung registration dataset
and show that it outperforms an existing learning-based pairwise registration
approach.

Temporal Graph Neural Networks (TGNNs) are powerful models to capture
temporal, structural, and contextual information on temporal graphs. The
generated temporal node embeddings outperform other methods in many downstream
tasks. Real-world applications require high performance inference on real-time
streaming dynamic graphs. However, these models usually rely on complex
attention mechanisms to capture relationships between temporal neighbors. In
addition, maintaining vertex memory suffers from intrinsic temporal data
dependency that hinders task-level parallelism, making it inefficient on
general-purpose processors. In this work, we present a novel model-architecture
co-design for inference in memory-based TGNNs on FPGAs. The key modeling
optimizations we propose include a light-weight method to compute attention
scores and a related temporal neighbor pruning strategy to further reduce
computation and memory accesses. These are holistically coupled with key
hardware optimizations that leverage FPGA hardware. We replace the temporal
sampler with an on-chip FIFO based hardware sampler and the time encoder with a
look-up-table. We train our simplified models using knowledge distillation to
ensure similar accuracy vis-\'a-vis the original model. Taking advantage of the
model optimizations, we propose a principled hardware architecture using
batching, pipelining, and prefetching techniques to further improve the
performance. We also propose a hardware mechanism to ensure the chronological
vertex updating without sacrificing the computation parallelism. We evaluate
the performance of the proposed hardware accelerator on three real-world
datasets.

Tree-structured multi-task architectures have been employed to jointly tackle
multiple vision tasks in the context of multi-task learning (MTL). The major
challenge is to determine where to branch out for each task given a backbone
model to optimize for both task accuracy and computation efficiency. To address
the challenge, this paper proposes a recommender that, given a set of tasks and
a convolutional neural network-based backbone model, automatically suggests
tree-structured multi-task architectures that could achieve a high task
performance while meeting a user-specified computation budget without
performing model training. Extensive evaluations on popular MTL benchmarks show
that the recommended architectures could achieve competitive task accuracy and
computation efficiency compared with state-of-the-art MTL methods. Our
tree-structured multi-task model recommender is open-sourced and available at
https://github.com/zhanglijun95/TreeMTL.

Vision Transformer (ViT) has recently demonstrated promise in computer vision
problems. However, unlike Convolutional Neural Networks (CNN), it is known that
the performance of ViT saturates quickly with depth increasing, due to the
observed attention collapse or patch uniformity. Despite a couple of empirical
solutions, a rigorous framework studying on this scalability issue remains
elusive. In this paper, we first establish a rigorous theory framework to
analyze ViT features from the Fourier spectrum domain. We show that the
self-attention mechanism inherently amounts to a low-pass filter, which
indicates when ViT scales up its depth, excessive low-pass filtering will cause
feature maps to only preserve their Direct-Current (DC) component. We then
propose two straightforward yet effective techniques to mitigate the
undesirable low-pass limitation. The first technique, termed AttnScale,
decomposes a self-attention block into low-pass and high-pass components, then
rescales and combines these two filters to produce an all-pass self-attention
matrix. The second technique, termed FeatScale, re-weights feature maps on
separate frequency bands to amplify the high-frequency signals. Both techniques
are efficient and hyperparameter-free, while effectively overcoming relevant
ViT training artifacts such as attention collapse and patch uniformity. By
seamlessly plugging in our techniques to multiple ViT variants, we demonstrate
that they consistently help ViTs benefit from deeper architectures, bringing up
to 1.1% performance gains "for free" (e.g., with little parameter overhead). We
publicly release our codes and pre-trained models at
https://github.com/VITA-Group/ViT-Anti-Oversmoothing.

This article introduces the Python package gcimpute for missing data
imputation. gcimpute can impute missing data with many different variable
types, including continuous, binary, ordinal, count, and truncated values, by
modeling data as samples from a Gaussian copula model. This semiparametric
model learns the marginal distribution of each variable to match the empirical
distribution, yet describes the interactions between variables with a joint
Gaussian that enables fast inference, imputation with confidence intervals, and
multiple imputation. The package also provides specialized extensions to handle
large datasets (with complexity linear in the number of observations) and
streaming datasets (with online imputation). This article describes the
underlying methodology and demonstrates how to use the software package.

Drug-induced liver injury (DILI) describes the adverse effects of drugs that
damage liver. Life-threatening results including liver failure or death were
also reported in severe DILI cases. Therefore, DILI-related events are strictly
monitored for all approved drugs and the liver toxicity became important
assessments for new drug candidates. These DILI-related reports are documented
in hospital records, in clinical trial results, and also in research papers
that contain preliminary in vitro and in vivo experiments. Conventionally, data
extraction from previous publications relies heavily on resource-demanding
manual labelling, which considerably decreased the efficiency of the
information extraction process. The recent development of artificial
intelligence, particularly, the rise of natural language processing (NLP)
techniques, enabled the automatic processing of biomedical texts. In this
study, based on around 28,000 papers (titles and abstracts) provided by the
Critical Assessment of Massive Data Analysis (CAMDA) challenge, we benchmarked
model performances on filtering out DILI literature. Among four word
vectorization techniques, the model using term frequency-inverse document
frequency (TF-IDF) and logistic regression outperformed others with an accuracy
of 0.957 with our in-house test set. Furthermore, an ensemble model with
similar overall performances was implemented and was fine-tuned to lower the
false-negative cases to avoid neglecting potential DILI reports. The ensemble
model achieved a high accuracy of 0.954 and an F1 score of 0.955 in the
hold-out validation data provided by the CAMDA committee. Moreover, important
words in positive/negative predictions were identified via model
interpretation. Overall, the ensemble model reached satisfactory classification
results, which can be further used by researchers to rapidly filter
DILI-related literature.

As engineered systems grow in complexity, there is an increasing need for
automatic methods that can detect, diagnose, and even correct transient
anomalies that inevitably arise and can be difficult or impossible to diagnose
and fix manually. Among the most sensitive and complex systems of our
civilization are the detectors that search for incredibly small variations in
distance caused by gravitational waves -- phenomena originally predicted by
Albert Einstein to emerge and propagate through the universe as the result of
collisions between black holes and other massive objects in deep space. The
extreme complexity and precision of such detectors causes them to be subject to
transient noise issues that can significantly limit their sensitivity and
effectiveness. In this work, we present a demonstration of a method that can
detect and characterize emergent transient anomalies of such massively complex
systems. We illustrate the performance, precision, and adaptability of the
automated solution via one of the prevalent issues limiting gravitational-wave
discoveries: noise artifacts of terrestrial origin that contaminate
gravitational wave observatories' highly sensitive measurements and can obscure
or even mimic the faint astrophysical signals for which they are listening.
Specifically, we demonstrate how a highly interpretable convolutional
classifier can automatically learn to detect transient anomalies from auxiliary
detector data without needing to observe the anomalies themselves. We also
illustrate several other useful features of the model, including how it
performs automatic variable selection to reduce tens of thousands of auxiliary
data channels to only a few relevant ones; how it identifies behavioral
signatures predictive of anomalies in those channels; and how it can be used to
investigate individual anomalies and the channels associated with them.

Synthetic digital twins based on medical data accelerate the acquisition,
labelling and decision making procedure in digital healthcare. A core part of
digital healthcare twins is model-based data synthesis, which permits the
generation of realistic medical signals without requiring to cope with the
modelling complexity of anatomical and biochemical phenomena producing them in
reality. Unfortunately, algorithms for cardiac data synthesis have been so far
scarcely studied in the literature. An important imaging modality in the
cardiac examination is three-directional CINE multi-slice myocardial velocity
mapping (3Dir MVM), which provides a quantitative assessment of cardiac motion
in three orthogonal directions of the left ventricle. The long acquisition time
and complex acquisition produce make it more urgent to produce synthetic
digital twins of this imaging modality. In this study, we propose a hybrid deep
learning (HDL) network, especially for synthetic 3Dir MVM data. Our algorithm
is featured by a hybrid UNet and a Generative Adversarial Network with a
foreground-background generation scheme. The experimental results show that
from temporally down-sampled magnitude CINE images (six times), our proposed
algorithm can still successfully synthesise high temporal resolution 3Dir MVM
CMR data (PSNR=42.32) with precise left ventricle segmentation (DICE=0.92).
These performance scores indicate that our proposed HDL algorithm can be
implemented in real-world digital twins for myocardial velocity mapping data
simulation. To the best of our knowledge, this work is the first one in the
literature investigating digital twins of the 3Dir MVM CMR, which has shown
great potential for improving the efficiency of clinical studies via
synthesised cardiac data.

Product quantization (PQ) coupled with a space rotation, is widely used in
modern approximate nearest neighbor (ANN) search systems to significantly
compress the disk storage for embeddings and speed up the inner product
computation. Existing rotation learning methods, however, minimize quantization
distortion for fixed embeddings, which are not applicable to an end-to-end
training scenario where embeddings are updated constantly. In this paper, based
on geometric intuitions from Lie group theory, in particular the special
orthogonal group $SO(n)$, we propose a family of block Givens coordinate
descent algorithms to learn rotation matrix that are provably convergent on any
convex objectives. Compared to the state-of-the-art SVD method, the Givens
algorithms are much more parallelizable, reducing runtime by orders of
magnitude on modern GPUs, and converge more stably according to experimental
studies. They further improve upon vanilla product quantization significantly
in an end-to-end training scenario.

Model-based reinforcement learning algorithms are typically more sample
efficient than their model-free counterparts, especially in sparse reward
problems. Unfortunately, many interesting domains are too complex to specify
the complete models required by traditional model-based approaches. Learning a
model takes a large number of environment samples, and may not capture critical
information if the environment is hard to explore. If we could specify an
incomplete model and allow the agent to learn how best to use it, we could take
advantage of our partial understanding of many domains. Existing hybrid
planning and learning systems which address this problem often impose highly
restrictive assumptions on the sorts of models which can be used, limiting
their applicability to a wide range of domains. In this work we propose SAGE,
an algorithm combining learning and planning to exploit a previously unusable
class of incomplete models. This combines the strengths of symbolic planning
and neural learning approaches in a novel way that outperforms competing
methods on variations of taxi world and Minecraft.

Recent advances in domain adaptation establish that requiring a low risk on
the source domain and equal feature marginals degrade the adaptation's
performance. At the same time, empirical evidence shows that incorporating an
unsupervised target domain term that pushes decision boundaries away from the
high-density regions, along with relaxed alignment, improves adaptation. In
this paper, we theoretically justify such observations via a new bound on the
target risk, and we connect two notions of relaxation for divergence, namely
$\beta-$relaxed divergences and localization. This connection allows us to
incorporate the source domain's categorical structure into the relaxation of
the considered divergence, provably resulting in a better handling of the label
shift case in particular.

Estimating human vital signs in a contactless non-invasive method using radar
provides a convenient method in the medical field to conduct several health
checkups easily and quickly. In addition to monitoring while sitting and
sleeping, the standing position has aroused interest for both the industrial
and medical fields. However, it is more challenging due to the micro motions
induced by the body for balancing that may cause false respiratory rate
estimation. In this work, we focus on the measurement of the respiratory rate
of a standing person accurately with the capability of heavy breath detection
and estimation. Multiple estimation approaches are presented and compared,
including spectral estimation, deep-learning-based approaches, and adaptive
peak selection with Kalman filtering. The latest technique is showing the best
performance with an absolute error rate of 1.5 bpm, when compared to a Vernier
Go Direct\textsuperscript{\textregistered} respiration belt.

Social media platforms can expose influential trends in many aspects of
everyday life. However, the movements they represent can be contaminated by
disinformation. Social bots are one of the significant sources of
disinformation in social media. Social bots can pose serious cyber threats to
society and public opinion. This research aims to develop machine learning
models to detect bots based on the extracted user's profile from a Tweet's
text. Online users' profile shows the user's personal information, such as age,
gender, education, and personality. In this work, the user's profile is
constructed based on the user's online posts. This work's main contribution is
three-fold: First, we aim to improve bot detection through machine learning
models based on the user's personal information generated by the user's online
comments. When comparing two online posts, the similarity of personal
information makes it difficult to differentiate a bot from a human user.
However, this research turns personal information similarity among two online
posts into an advantage for the new bot detection model. The new proposed model
for bot detection creates user profiles based on personal information such as
age, personality, gender, education from users' online posts and introduces a
machine learning model to detect social bots with high prediction accuracy
based on personal information. Second, create a new public data set that shows
the user's profile for more than 6900 Twitter accounts in the Cresci 2017 data
set.

Paradoxically, a Variational Autoencoder (VAE) could be pushed in two
opposite directions, utilizing powerful decoder model for generating realistic
images but collapsing the learned representation, or increasing regularization
coefficient for disentangling representation but ultimately generating blurry
examples. Existing methods narrow the issues to the rate-distortion trade-off
between compression and reconstruction. We argue that a good reconstruction
model does learn high capacity latents that encode more details, however, its
use is hindered by two major issues: the prior is random noise which is
completely detached from the posterior and allow no controllability in the
generation; mean-field variational inference doesn't enforce hierarchy
structure which makes the task of recombining those units into plausible novel
output infeasible. As a result, we develop a system that learns a hierarchy of
disentangled representation together with a mechanism for recombining the
learned representation for generalization. This is achieved by introducing a
minimal amount of inductive bias to learn controllable prior for the VAE. The
idea is supported by here developed transitive information theory, that is, the
mutual information between two target variables could alternately be maximized
through the mutual information to the third variable, thus bypassing the
rate-distortion bottleneck in VAE design. In particular, we show that our
model, named SemafoVAE (inspired by the similar concept in computer science),
could generate high-quality examples in a controllable manner, perform smooth
traversals of the disentangled factors and intervention at a different level of
representation hierarchy.

Glioblastomas are the most common malignant brain tumors in adults.
Approximately 200000 people die each year from Glioblastoma in the world.
Glioblastoma patients have a median survival of 12 months with optimal therapy
and about 4 months without treatment. Glioblastomas appear as heterogeneous
necrotic masses with irregular peripheral enhancement, surrounded by vasogenic
edema. The current standard of care includes surgical resection, radiotherapy
and chemotherapy, which require accurate segmentation of brain tumor
subregions. For effective treatment planning, it is vital to identify the
methylation status of the promoter of Methylguanine Methyltransferase (MGMT), a
positive prognostic factor for chemotherapy. However, current methods for brain
tumor segmentation are tedious, subjective and not scalable, and current
techniques to determine the methylation status of MGMT promoter involve
surgically invasive procedures, which are expensive and time consuming. Hence
there is a pressing need to develop automated tools to segment brain tumors and
non-invasive methods to predict methylation status of MGMT promoter, to
facilitate better treatment planning and improve survival rate. I created an
integrated diagnostics solution powered by Artificial Intelligence to
automatically segment brain tumor subregions and predict MGMT promoter
methylation status, using brain MRI scans. My AI solution is proven on large
datasets with performance exceeding current standards and field tested with
data from teaching files of local neuroradiologists. With my solution,
physicians can submit brain MRI images, and get segmentation and methylation
predictions in minutes, and guide brain tumor patients with effective treatment
planning and ultimately improve survival time.

Constructing accurate and generalizable approximators for complex
physico-chemical processes exhibiting highly non-smooth dynamics is
challenging. In this work, we propose new developments and perform comparisons
for two promising approaches: manifold-based polynomial chaos expansion (m-PCE)
and the deep neural operator (DeepONet), and we examine the effect of
over-parameterization on generalization. We demonstrate the performance of
these methods in terms of generalization accuracy by solving the 2D
time-dependent Brusselator reaction-diffusion system with uncertainty sources,
modeling an autocatalytic chemical reaction between two species. We first
propose an extension of the m-PCE by constructing a mapping between latent
spaces formed by two separate embeddings of input functions and output QoIs. To
enhance the accuracy of the DeepONet, we introduce weight self-adaptivity in
the loss function. We demonstrate that the performance of m-PCE and DeepONet is
comparable for cases of relatively smooth input-output mappings. However, when
highly non-smooth dynamics is considered, DeepONet shows higher accuracy. We
also find that for m-PCE, modest over-parameterization leads to better
generalization, both within and outside of distribution, whereas aggressive
over-parameterization leads to over-fitting. In contrast, an even highly
over-parameterized DeepONet leads to better generalization for both smooth and
non-smooth dynamics. Furthermore, we compare the performance of the above
models with another operator learning model, the Fourier Neural Operator, and
show that its over-parameterization also leads to better generalization. Our
studies show that m-PCE can provide very good accuracy at very low training
cost, whereas a highly over-parameterized DeepONet can provide better accuracy
and robustness to noise but at higher training cost. In both methods, the
inference cost is negligible.

We provide algorithms for regression with adversarial responses under large
classes of non-i.i.d. instance sequences, on general separable metric spaces,
with provably minimal assumptions. We also give characterizations of
learnability in this regression context. We consider universal consistency
which asks for strong consistency of a learner without restrictions on the
value responses. Our analysis shows that such objective is achievable for a
significantly larger class of instance sequences than stationary processes, and
unveils a fundamental dichotomy between value spaces: whether finite-horizon
mean-estimation is achievable or not. We further provide optimistically
universal learning rules, i.e., such that if they fail to achieve universal
consistency, any other algorithm will fail as well. For unbounded losses, we
propose a mild integrability condition under which there exist algorithms for
adversarial regression under large classes of non-i.i.d. instance sequences. In
addition, our analysis also provides a learning rule for mean-estimation in
general metric spaces that is consistent under adversarial responses without
any moment conditions on the sequence, a result of independent interest.

Deep learning algorithms are dependent on the availability of large-scale
annotated clinical text datasets. The lack of such publicly available datasets
is the biggest bottleneck for the development of clinical Natural Language
Processing(NLP) systems. Zero-Shot Learning(ZSL) refers to the use of deep
learning models to classify instances from new classes of which no training
data have been seen before. Prompt-based learning is an emerging ZSL technique
where we define task-based templates for NLP tasks. We developed a novel
prompt-based clinical NLP framework called HealthPrompt and applied the
paradigm of prompt-based learning on clinical texts. In this technique, rather
than fine-tuning a Pre-trained Language Model(PLM), the task definitions are
tuned by defining a prompt template. We performed an in-depth analysis of
HealthPrompt on six different PLMs in a no-data setting. Our experiments prove
that prompts effectively capture the context of clinical texts and perform
remarkably well without any training data.

Supervised training of optical flow predictors generally yields better
accuracy than unsupervised training. However, the improved performance comes at
an often high annotation cost. Semi-supervised training trades off accuracy
against annotation cost. We use a simple yet effective semi-supervised training
method to show that even a small fraction of labels can improve flow accuracy
by a significant margin over unsupervised training. In addition, we propose
active learning methods based on simple heuristics to further reduce the number
of labels required to achieve the same target accuracy. Our experiments on both
synthetic and real optical flow datasets show that our semi-supervised networks
generally need around 50% of the labels to achieve close to full-label
accuracy, and only around 20% with active learning on Sintel. We also analyze
and show insights on the factors that may influence active learning
performance. Code is available at
https://github.com/duke-vision/optical-flow-active-learning-release.

The development of face recognition algorithms by academic and commercial
organizations is growing rapidly due to the onset of deep learning and the
widespread availability of training data. Though tests of face recognition
algorithm performance indicate yearly performance gains, error rates for many
of these systems differ based on the demographic composition of the test set.
These "demographic differentials" in algorithm performance can contribute to
unequal or unfair outcomes for certain groups of people, raising concerns with
increased worldwide adoption of face recognition systems. Consequently,
regulatory bodies in both the United States and Europe have proposed new rules
requiring audits of biometric systems for "discriminatory impacts" (European
Union Artificial Intelligence Act) and "fairness" (U.S. Federal Trade
Commission). However, no standard for measuring fairness in biometric systems
yet exists. This paper characterizes two proposed measures of face recognition
algorithm fairness (fairness measures) from scientists in the U.S. and Europe.
We find that both proposed methods are challenging to interpret when applied to
disaggregated face recognition error rates as they are commonly experienced in
practice. To address this, we propose a set of interpretability criteria,
termed the Functional Fairness Measure Criteria (FFMC), that outlines a set of
properties desirable in a face recognition algorithm fairness measure. We
further develop a new fairness measure, the Gini Aggregation Rate for Biometric
Equitability (GARBE), and show how, in conjunction with the Pareto
optimization, this measure can be used to select among alternative algorithms
based on the accuracy/fairness trade-space. Finally, we have open-sourced our
dataset of machine-readable, demographically disaggregated error rates. We
believe this is currently the largest open-source dataset of its kind.

Pedestrian trajectory prediction is an essential component in a wide range of
AI applications such as autonomous driving and robotics. Existing methods
usually assume the training and testing motions follow the same pattern while
ignoring the potential distribution differences (e.g., shopping mall and
street). This issue results in inevitable performance decrease. To address this
issue, we propose a novel Transferable Graph Neural Network (T-GNN) framework,
which jointly conducts trajectory prediction as well as domain alignment in a
unified framework. Specifically, a domain-invariant GNN is proposed to explore
the structural motion knowledge where the domain-specific knowledge is reduced.
Moreover, an attention-based adaptive knowledge learning module is further
proposed to explore fine-grained individual-level feature representations for
knowledge transfer. By this way, disparities across different trajectory
domains will be better alleviated. More challenging while practical trajectory
prediction experiments are designed, and the experimental results verify the
superior performance of our proposed model. To the best of our knowledge, our
work is the pioneer which fills the gap in benchmarks and techniques for
practical pedestrian trajectory prediction across different domains.

Most existing studies on unsupervised domain adaptation (UDA) assume that
each domain's training samples come with domain labels (e.g., painting, photo).
Samples from each domain are assumed to follow the same distribution and the
domain labels are exploited to learn domain-invariant features via feature
alignment. However, such an assumption often does not hold true -- there often
exist numerous finer-grained domains (e.g., dozens of modern painting styles
have been developed, each differing dramatically from those of the classic
styles). Therefore, forcing feature distribution alignment across each
artificially-defined and coarse-grained domain can be ineffective. In this
paper, we address both single-source and multi-source UDA from a completely
different perspective, which is to view each instance as a fine domain. Feature
alignment across domains is thus redundant. Instead, we propose to perform
dynamic instance domain adaptation (DIDA). Concretely, a dynamic neural network
with adaptive convolutional kernels is developed to generate instance-adaptive
residuals to adapt domain-agnostic deep features to each individual instance.
This enables a shared classifier to be applied to both source and target domain
data without relying on any domain annotation. Further, instead of imposing
intricate feature alignment losses, we adopt a simple semi-supervised learning
paradigm using only a cross-entropy loss for both labeled source and pseudo
labeled target data. Our model, dubbed DIDA-Net, achieves state-of-the-art
performance on several commonly used single-source and multi-source UDA
datasets including Digits, Office-Home, DomainNet, Digit-Five, and PACS.

Transfer learning (TL), the next frontier in machine learning (ML), has
gained much popularity in recent years, due to the various challenges faced in
ML, like the requirement of vast amounts of training data, expensive and
time-consuming labelling processes for data samples, and long training duration
for models. TL is useful in tackling these problems, as it focuses on
transferring knowledge from previously solved tasks to new tasks. Digital twins
and other intelligent systems need to utilise TL to use the previously gained
knowledge and solve new tasks in a more self-reliant way, and to incrementally
increase their knowledge base. Therefore, in this article, the critical
challenges in power forecasting and anomaly detection in the context of
renewable energy systems are identified, and a potential TL framework to meet
these challenges is proposed. This article also proposes a feature embedding
approach to handle the missing sensors data. The proposed TL methods help to
make a system more autonomous in the context of organic computing.

Deploying Deep Neural Networks in low-power embedded devices for real
time-constrained applications requires optimization of memory and computational
complexity of the networks, usually by quantizing the weights. Most of the
existing works employ linear quantization which causes considerable degradation
in accuracy for weight bit widths lower than 8. Since the distribution of
weights is usually non-uniform (with most weights concentrated around zero),
other methods, such as logarithmic quantization, are more suitable as they are
able to preserve the shape of the weight distribution more precise. Moreover,
using base-2 logarithmic representation allows optimizing the multiplication by
replacing it with bit shifting. In this paper, we explore non-linear
quantization techniques for exploiting lower bit precision and identify
favorable hardware implementation options. We developed the Quantization Aware
Training (QAT) algorithm that allowed training of low bit width Power-of-Two
(PoT) networks and achieved accuracies on par with state-of-the-art floating
point models for different tasks. We explored PoT weight encoding techniques
and investigated hardware designs of MAC units for three different quantization
schemes - uniform, PoT and Additive-PoT (APoT) - to show the increased
efficiency when using the proposed approach. Eventually, the experiments showed
that for low bit width precision, non-uniform quantization performs better than
uniform, and at the same time, PoT quantization vastly reduces the
computational complexity of the neural network.

In this paper, we revisit the problem of learning a stabilizing controller
from a finite number of demonstrations by an expert. By first focusing on
feedback linearizable systems, we show how to combine expert demonstrations
into a stabilizing controller, provided that demonstrations are sufficiently
long and there are at least $n+1$ of them, where $n$ is the number of states of
the system being controlled. When we have more than $n+1$ demonstrations, we
discuss how to optimally choose the best $n+1$ demonstrations to construct the
stabilizing controller. We then extend these results to a class of systems that
can be embedded into a higher-dimensional system containing a chain of
integrators. The feasibility of the proposed algorithm is demonstrated by
applying it on a CrazyFlie 2.0 quadrotor.

Language model fusion helps smart assistants recognize words which are rare
in acoustic data but abundant in text-only corpora (typed search logs).
However, such corpora have properties that hinder downstream performance,
including being (1) too large, (2) beset with domain-mismatched content, and
(3) heavy-headed rather than heavy-tailed (excessively many duplicate search
queries such as "weather"). We show that three simple strategies for selecting
language modeling data can dramatically improve rare-word recognition without
harming overall performance. First, to address the heavy-headedness, we
downsample the data according to a soft log function, which tunably reduces
high frequency (head) sentences. Second, to encourage rare-word exposure, we
explicitly filter for words rare in the acoustic data. Finally, we tackle
domain-mismatch via perplexity-based contrastive selection, filtering for
examples matched to the target domain. We down-select a large corpus of web
search queries by a factor of 53x and achieve better LM perplexities than
without down-selection. When shallow-fused with a state-of-the-art, production
speech engine, our LM achieves WER reductions of up to 24% relative on
rare-word sentences (without changing overall WER) compared to a baseline LM
trained on the raw corpus. These gains are further validated through favorable
side-by-side evaluations on live voice search traffic.

Achieving invariance to nuisance transformations is a fundamental challenge
in the construction of robust and reliable vision systems. Existing approaches
to invariance scale exponentially with the dimension of the family of
transformations, making them unable to cope with natural variabilities in
visual data such as changes in pose and perspective. We identify a common
limitation of these approaches--they rely on sampling to traverse the
high-dimensional space of transformations--and propose a new computational
primitive for building invariant networks based instead on optimization, which
in many scenarios provides a provably more efficient method for
high-dimensional exploration than sampling. We provide empirical and
theoretical corroboration of the efficiency gains and soundness of our proposed
method, and demonstrate its utility in constructing an efficient invariant
network for a simple hierarchical object detection task when combined with
unrolled optimization. Code for our networks and experiments is available at
https://github.com/sdbuch/refine.

We present the first steps in the development of a new class of hadronization
models utilizing machine learning techniques. We successfully implement,
validate, and train a conditional sliced-Wasserstein autoencoder to replicate
the Pythia generated kinematic distributions of first-hadron emissions, when
the Lund string model of hadronization implemented in Pythia is restricted to
the emissions of pions only. The trained models are then used to generate the
full hadronization chains, with an IR cutoff energy imposed externally. The
hadron multiplicities and cumulative kinematic distributions are shown to match
the Pythia generated ones. We also discuss possible future generalizations of
our results.

Data-driven model predictive control has two key advantages over model-free
methods: a potential for improved sample efficiency through model learning, and
better performance as computational budget for planning increases. However, it
is both costly to plan over long horizons and challenging to obtain an accurate
model of the environment. In this work, we combine the strengths of model-free
and model-based methods. We use a learned task-oriented latent dynamics model
for local trajectory optimization over a short horizon, and use a learned
terminal value function to estimate long-term return, both of which are learned
jointly by temporal difference learning. Our method, TD-MPC, achieves superior
sample efficiency and asymptotic performance over prior work on both state and
image-based continuous control tasks from DMControl and Meta-World. Code and
video results are available at https://nicklashansen.github.io/td-mpc.

UNet and its latest extensions like TransUNet have been the leading medical
image segmentation methods in recent years. However, these networks cannot be
effectively adopted for rapid image segmentation in point-of-care applications
as they are parameter-heavy, computationally complex and slow to use. To this
end, we propose UNeXt which is a Convolutional multilayer perceptron (MLP)
based network for image segmentation. We design UNeXt in an effective way with
an early convolutional stage and a MLP stage in the latent stage. We propose a
tokenized MLP block where we efficiently tokenize and project the convolutional
features and use MLPs to model the representation. To further boost the
performance, we propose shifting the channels of the inputs while feeding in to
MLPs so as to focus on learning local dependencies. Using tokenized MLPs in
latent space reduces the number of parameters and computational complexity
while being able to result in a better representation to help segmentation. The
network also consists of skip connections between various levels of encoder and
decoder. We test UNeXt on multiple medical image segmentation datasets and show
that we reduce the number of parameters by 72x, decrease the computational
complexity by 68x, and improve the inference speed by 10x while also obtaining
better segmentation performance over the state-of-the-art medical image
segmentation architectures. Code is available at
https://github.com/jeya-maria-jose/UNeXt-pytorch

For robots to be effectively deployed in novel environments and tasks, they
must be able to understand the feedback expressed by humans during
intervention. This can either correct undesirable behavior or indicate
additional preferences. Existing methods either require repeated episodes of
interactions or assume prior known reward features, which is data-inefficient
and can hardly transfer to new tasks. We relax these assumptions by describing
human tasks in terms of object-centric sub-tasks and interpreting physical
interventions in relation to specific objects. Our method, Object Preference
Adaptation (OPA), is composed of two key stages: 1) pre-training a base policy
to produce a wide variety of behaviors, and 2) online-updating only certain
weights in the model according to human feedback. The key to our fast, yet
simple adaptation is that general interaction dynamics between agents and
objects are fixed, and only object-specific preferences are updated. Our
adaptation occurs online, requires only one human intervention (one-shot), and
produces new behaviors never seen during training. Trained on cheap synthetic
data instead of expensive human demonstrations, our policy demonstrates
impressive adaptation to human perturbations on challenging, realistic tasks in
our user study. Videos, code, and supplementary material provided.

We develop a novel method for ensuring fairness in machine learning which we
term as the Renyi Fair Information Bottleneck (RFIB). We consider two different
fairness constraints - demographic parity and equalized odds - for learning
fair representations and derive a loss function via a variational approach that
uses Renyi's divergence with its tunable parameter $\alpha$ and that takes into
account the triple constraints of utility, fairness, and compactness of
representation. We then evaluate the performance of our method for image
classification using the EyePACS medical imaging dataset, showing it
outperforms competing state of the art techniques with performance measured
using a variety of compound utility/fairness metrics, including accuracy gap
and Rawls' minimal accuracy.

Structured pruning is an effective approach for compressing large pre-trained
neural networks without significantly affecting their performance. However,
most current structured pruning methods do not provide any performance
guarantees, and often require fine-tuning, which makes them inapplicable in the
limited-data regime. We propose a principled data-efficient structured pruning
method based on submodular optimization. In particular, for a given layer, we
select neurons/channels to prune and corresponding new weights for the next
layer, that minimize the change in the next layer's input induced by pruning.
We show that this selection problem is a weakly submodular maximization
problem, thus it can be provably approximated using an efficient greedy
algorithm. Our method is guaranteed to have an exponentially decreasing error
between the original model and the pruned model outputs w.r.t the pruned size,
under reasonable assumptions. It is also one of the few methods in the
literature that uses only a limited-number of training data and no labels. Our
experimental results demonstrate that our method outperforms state-of-the-art
methods in the limited-data regime.

Machine learning is a promising approach to visualization recommendation due
to its high scalability and representational power. Researchers can create a
neural network to predict visualizations from input data by training it over a
corpus of datasets and visualization examples. However, these machine learning
models can reflect trends in their training data that may negatively affect
their performance. Our research project aims to address training bias in
machine learning visualization recommendation systems by identifying trends in
the training data through statistical analysis.

It is well accepted that acquiring downlink channel state information in
frequency division duplexing (FDD) massive multiple-input multiple-output
(MIMO) systems is challenging because of the large overhead in training and
feedback. In this paper, we propose a deep generative model (DGM)-based
technique to address this challenge. Exploiting the partial reciprocity of
uplink and downlink channels, we first estimate the frequency-independent
underlying channel parameters, i.e., the magnitudes of path gains, delays,
angles-of-arrivals (AoAs) and angles-of-departures (AoDs), via uplink training,
since these parameters are common in both uplink and downlink. Then, the
frequency-specific underlying channel parameters, namely, the phase of each
propagation path, are estimated via downlink training using a very short
training signal. In the first step, we incorporate the underlying distribution
of the channel parameters as a prior into our channel estimation algorithm. We
use DGMs to learn this distribution. Simulation results indicate that our
proposed DGM-based channel estimation technique outperforms, by a large gap,
the conventional channel estimation techniques in practical ranges of
signal-to-noise ratio (SNR). In addition, a near-optimal performance is
achieved using only few downlink pilot measurements.

Disinformation refers to false information deliberately spread to influence
the general public, and the negative impact of disinformation on society can be
observed in numerous issues, such as political agendas and manipulating
financial markets. In this paper, we identify prevalent challenges and advances
related to automated disinformation detection from multiple aspects and propose
a comprehensive and explainable disinformation detection framework called
DISCO. It leverages the heterogeneity of disinformation and addresses the
opaqueness of prediction. Then we provide a demonstration of DISCO on a
real-world fake news detection task with satisfactory detection accuracy and
explanation. The demo video and source code of DISCO is now publicly available
https://github.com/DongqiFu/DISCO. We expect that our demo could pave the way
for addressing the limitations of identification, comprehension, and
explainability as a whole.

In this paper, we introduce a new concept of incorporating factorized flow
maps as mid-level representations, for bridging the perception and the control
modules in modular learning based robotic frameworks. To investigate the
advantages of factorized flow maps and examine their interplay with the other
types of mid-level representations, we further develop a configurable
framework, along with four different environments that contain both static and
dynamic objects, for analyzing the impacts of factorized optical flow maps on
the performance of deep reinforcement learning agents. Based on this framework,
we report our experimental results on various scenarios, and offer a set of
analyses to justify our hypothesis. Finally, we validate flow factorization in
real world scenarios.

We study the problem of distributed mean estimation and optimization under
communication constraints. We propose a correlated quantization protocol whose
leading term in the error guarantee depends on the mean deviation of data
points rather than only their absolute range. The design doesn't need any prior
knowledge on the concentration property of the dataset, which is required to
get such dependence in previous works. We show that applying the proposed
protocol as sub-routine in distributed optimization algorithms leads to better
convergence rates. We also prove the optimality of our protocol under mild
assumptions. Experimental results show that our proposed algorithm outperforms
existing mean estimation protocols on a diverse set of tasks.

Recently, equivariant neural network models have been shown to improve sample
efficiency for tasks in computer vision and reinforcement learning. This paper
explores this idea in the context of on-robot policy learning in which a policy
must be learned entirely on a physical robotic system without reference to a
model, a simulator, or an offline dataset. We focus on applications of
Equivariant SAC to robotic manipulation and explore a number of variations of
the algorithm. Ultimately, we demonstrate the ability to learn several
non-trivial manipulation tasks completely through on-robot experiences in less
than an hour or two of wall clock time.

In today's world, a massive amount of data is available in almost every
sector. This data has become an asset as we can use this enormous amount of
data to find information. Mainly health care industry contains many data
consisting of patient and disease-related information. By using the machine
learning technique, we can look for hidden data patterns to predict various
diseases. Recently CVDs, or cardiovascular disease, have become a leading cause
of death around the world. The number of death due to CVDs is frightening. That
is why many researchers are trying their best to design a predictive model that
can save many lives using the data mining model. In this research, some fusion
models have been constructed to diagnose CVDs along with its severity. Machine
learning(ML) algorithms like artificial neural network, SVM, logistic
regression, decision tree, random forest, and AdaBoost have been applied to the
heart disease dataset to predict disease. Randomoversampler was implemented
because of the class imbalance in multiclass classification. To improve the
performance of classification, a weighted score fusion approach was taken. At
first, the models were trained. After training, two algorithms' decision was
combined using a weighted sum rule. A total of three fusion models have been
developed from the six ML algorithms. The results were promising in the
performance parameter. The proposed approach has been experimented with
different test training ratios for binary and multiclass classification
problems, and for both of them, the fusion models performed well. The highest
accuracy for multiclass classification was found as 75%, and it was 95% for
binary. The code can be found in :
https://github.com/hafsa-kibria/Weighted_score_fusion_model_heart_disease_prediction

Many fundamental problems affecting the care of critically ill patients lead
to similar analytical challenges: physicians cannot easily estimate the effects
of at-risk medical conditions or treatments because the causal effects of
medical conditions and drugs are entangled. They also cannot easily perform
studies: there are not enough high-quality data for high-dimensional
observational causal inference, and RCTs often cannot ethically be conducted.
However, mechanistic knowledge is available, including how drugs are absorbed
into the body, and the combination of this knowledge with the limited data
could potentially suffice -- if we knew how to combine them. In this work, we
present a framework for interpretable estimation of causal effects for
critically ill patients under exactly these complex conditions: interactions
between drugs and observations over time, patient data sets that are not large,
and mechanistic knowledge that can substitute for lack of data. We apply this
framework to an extremely important problem affecting critically ill patients,
namely the effect of seizures and other potentially harmful electrical events
in the brain (called epileptiform activity -- EA) on outcomes. Given the high
stakes involved and the high noise in the data, interpretability is critical
for troubleshooting such complex problems. Interpretability of our matched
groups allowed neurologists to perform chart reviews to verify the quality of
our causal analysis. For instance, our work indicates that a patient who
experiences a high level of seizure-like activity (75% high EA burden) and is
untreated for a six-hour window, has, on average, a 16.7% increased chance of
adverse outcomes such as severe brain damage, lifetime disability, or death. We
find that patients with mild but long-lasting EA (average EA burden >= 50%)
have their risk of an adverse outcome increased by 11.2%.

Systems are commonly monitored for health and security through collection and
streaming of multivariate time series. Advances in time series forecasting due
to adoption of multilayer recurrent neural network architectures make it
possible to forecast in high-dimensional time series, and identify and classify
novelties early, based on subtle changes in the trends. However, mainstream
approaches to multi-variate time series predictions do not handle well cases
when the ongoing forecast must include uncertainty, nor they are robust to
missing data. We introduce a new architecture for time series monitoring based
on combination of state-of-the-art methods of forecasting in high-dimensional
time series with full probabilistic handling of uncertainty. We demonstrate
advantage of the architecture for time series forecasting and novelty
detection, in particular with partially missing data, and empirically evaluate
and compare the architecture to state-of-the-art approaches on a real-world
data set.

Algorithmic fairness is frequently motivated in terms of a trade-off in which
overall performance is decreased so as to improve performance on disadvantaged
groups where the algorithm would otherwise be less accurate. Contrary to this,
we find that applying existing fairness approaches to computer vision improve
fairness by degrading the performance of classifiers across all groups (with
increased degradation on the best performing groups).
  Extending the bias-variance decomposition for classification to fairness, we
theoretically explain why the majority of fairness classifiers designed for low
capacity models should not be used in settings involving high-capacity models,
a scenario common to computer vision. We corroborate this analysis with
extensive experimental support that shows that many of the fairness heuristics
used in computer vision also degrade performance on the most disadvantaged
groups. Building on these insights, we propose an adaptive augmentation
strategy that, uniquely, of all methods tested, improves performance for the
disadvantaged groups.

Spoken Question Answering (SQA) is to find the answer from a spoken document
given a question, which is crucial for personal assistants when replying to the
queries from the users. Existing SQA methods all rely on Automatic Speech
Recognition (ASR) transcripts. Not only does ASR need to be trained with
massive annotated data that are time and cost-prohibitive to collect for
low-resourced languages, but more importantly, very often the answers to the
questions include name entities or out-of-vocabulary words that cannot be
recognized correctly. Also, ASR aims to minimize recognition errors equally
over all words, including many function words irrelevant to the SQA task.
Therefore, SQA without ASR transcripts (textless) is always highly desired,
although known to be very difficult.
  This work proposes Discrete Spoken Unit Adaptive Learning (DUAL), leveraging
unlabeled data for pre-training and fine-tuned by the SQA downstream task. The
time intervals of spoken answers can be directly predicted from spoken
documents. We also release a new SQA benchmark corpus, NMSQA, for data with
more realistic scenarios. We empirically showed that DUAL yields results
comparable to those obtained by cascading ASR and text QA model and robust to
real-world data. Our code and model will be open-sourced.

In astronomy, upcoming space telescopes with wide-field optical instruments
have a spatially varying point spread function (PSF). Certain scientific goals
require a high-fidelity estimation of the PSF at target positions where no
direct measurement of the PSF is provided. Even though observations of the PSF
are available at some positions of the field of view (FOV), they are
undersampled, noisy, and integrated in wavelength in the instrument's passband.
PSF modeling requires building a model from these observations that can infer a
super-resolved PSF at any wavelength and any position in the FOV. Current
data-driven PSF models can tackle spatial variations and super-resolution, but
are not capable of capturing chromatic variations. Our model, coined WaveDiff,
proposes a paradigm shift in the data-driven modeling of the point spread
function field of telescopes. By adding a differentiable optical forward model
into the modeling framework, we change the data-driven modeling space from the
pixels to the wavefront. The proposed model relies on efficient automatic
differentiation technology as well as modern stochastic first-order
optimization techniques recently developed by the thriving machine-learning
community. Our framework paves the way to building powerful models that are
physically motivated and do not require special calibration data. This paper
demonstrates the WaveDiff model on a simplified setting of a space telescope.
The proposed framework represents a performance breakthrough with respect to
existing data-driven approaches. The pixel reconstruction errors decrease
6-fold at observation resolution and 44-fold for a 3x super-resolution. The
ellipticity errors are reduced by a factor of at least 20 and the size error by
a factor of more than 250. By only using noisy broad-band in-focus
observations, we successfully capture the PSF chromatic variations due to
diffraction.

Meta-learning is widely used in few-shot classification and function
regression due to its ability to quickly adapt to unseen tasks. However, it has
not yet been well explored on regression tasks with high dimensional inputs
such as images. This paper makes two main contributions that help understand
this barely explored area. \emph{First}, we design two new types of
cross-category level vision regression tasks, namely object discovery and pose
estimation of unprecedented complexity in the meta-learning domain for computer
vision. To this end, we (i) exhaustively evaluate common meta-learning
techniques on these tasks, and (ii) quantitatively analyze the effect of
various deep learning techniques commonly used in recent meta-learning
algorithms in order to strengthen the generalization capability: data
augmentation, domain randomization, task augmentation and meta-regularization.
Finally, we (iii) provide some insights and practical recommendations for
training meta-learning algorithms on vision regression tasks. \emph{Second}, we
propose the addition of functional contrastive learning (FCL) over the task
representations in Conditional Neural Processes (CNPs) and train in an
end-to-end fashion. The experimental results show that the results of prior
work are misleading as a consequence of a poor choice of the loss function as
well as too small meta-training sets. Specifically, we find that CNPs
outperform MAML on most tasks without fine-tuning. Furthermore, we observe that
naive task augmentation without a tailored design results in underfitting.

Despite achieving state-of-the-art zero-shot performance, existing
vision-language models still fall short of few-shot transfer ability on
domain-specific problems. Classical fine-tuning often fails to prevent highly
expressive models from exploiting spurious correlations. Although
model-agnostic meta-learning (MAML) presents as a natural alternative for
few-shot transfer learning, the expensive computation due to implicit
second-order optimization limits its use on large-scale vision-language models
such as CLIP. While much literature has been devoted to exploring alternative
optimization strategies, we identify another essential aspect towards effective
few-shot transfer learning, task sampling, which is previously only be viewed
as part of data pre-processing in MAML. To show the impact of task sampling, we
propose a simple algorithm, Model-Agnostic Multitask Fine-tuning (MAMF), which
differentiates classical fine-tuning only on uniformly sampling multiple tasks.
Despite its simplicity, we show that MAMF consistently outperforms classical
fine-tuning on five few-shot vision-language classification tasks. We further
show that the effectiveness of the bi-level optimization in MAML is highly
sensitive to the zero-shot performance of a task in the context of few-shot
vision-language classification. The goal of this paper is to provide new
insights on what makes few-shot learning work, and encourage more research into
investigating better task sampling strategies.

Benefiting from color independence, illumination invariance and location
discrimination attributed by the depth map, it can provide important
supplemental information for extracting salient objects in complex
environments. However, high-quality depth sensors are expensive and can not be
widely applied. While general depth sensors produce the noisy and sparse depth
information, which brings the depth-based networks with irreversible
interference. In this paper, we propose a novel multi-task and multi-modal
filtered transformer (MMFT) network for RGB-D salient object detection (SOD).
Specifically, we unify three complementary tasks: depth estimation, salient
object detection and contour estimation. The multi-task mechanism promotes the
model to learn the task-aware features from the auxiliary tasks. In this way,
the depth information can be completed and purified. Moreover, we introduce a
multi-modal filtered transformer (MFT) module, which equips with three
modality-specific filters to generate the transformer-enhanced feature for each
modality. The proposed model works in a depth-free style during the testing
phase. Experiments show that it not only significantly surpasses the
depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts
a high-quality depth map and salient contour at the same time. And, the
resulted depth map can help existing RGB-D SOD methods obtain significant
performance gain. The source code will be publicly available at
https://github.com/Xiaoqi-Zhao-DLUT/MMFT.

By mimicking brain-like cognition and exploiting parallelism,
hyperdimensional computing (HDC) classifiers have been emerging as a
lightweight framework to achieve efficient on-device inference. Nonetheless,
they have two fundamental drawbacks, heuristic training process and ultra-high
dimension, which result in sub-optimal inference accuracy and large model sizes
beyond the capability of tiny devices with stringent resource constraints. In
this paper, we address these fundamental drawbacks and propose a
low-dimensional computing (LDC) alternative. Specifically, by mapping our LDC
classifier into an equivalent neural network, we optimize our model using a
principled training approach. Most importantly, we can improve the inference
accuracy while successfully reducing the ultra-high dimension of existing HDC
models by orders of magnitude (e.g., 8000 vs. 4/64). We run experiments to
evaluate our LDC classifier by considering different datasets for inference on
tiny devices, and also implement different models on an FPGA platform for
acceleration. The results highlight that our LDC classifier offers an
overwhelming advantage over the existing brain-inspired HDC models and is
particularly suitable for inference on tiny devices.

The incorporation of high-performance optoelectronic devices into photonic
neuromorphic processors can substantially accelerate computationally intensive
operations in machine learning (ML) algorithms. However, the conventional
device design wisdom is disconnected with system optimization. We report a
device-system co-design methodology to optimize a free-space optical general
matrix multiplication (GEMM) hardware accelerator by engineering a spatially
reconfigurable array made from chalcogenide phase change materials. With a
highly-parallelized hardware emulator constructed based on experimental
information, we demonstrate the design of unit device by optimizing GEMM
calculation accuracy via reinforcement learning, including deep Q-learning
neural network, Bayesian optimization, and their cascaded approach, which show
a clear correlation between system performance metrics and physical device
specifications. Furthermore, we employ physics-aware training approaches to
deploy optimized hardware to the tasks of image classification, materials
discovery, and a closed-loop design of optical ML accelerators. The
demonstrated framework offers insights into the co-design of optoelectronic
devices and systems with reduced human-supervision and domain-knowledge
barriers.

Diffractive optical neural networks have shown promising advantages over
electronic circuits for accelerating modern machine learning (ML) algorithms.
However, it is challenging to achieve fully programmable all-optical
implementation and rapid hardware deployment. Furthermore, understanding the
threat of adversarial ML in such system becomes crucial for real-world
applications, which remains unexplored. Here, we demonstrate a large-scale,
cost-effective, complex-valued, and reconfigurable diffractive all-optical
neural networks system in the visible range based on cascaded transmissive
twisted nematic liquid crystal spatial light modulators. With the assist of
categorical reparameterization, we create a physics-aware training framework
for the fast and accurate deployment of computer-trained models onto optical
hardware. Furthermore, we theoretically analyze and experimentally demonstrate
physics-aware adversarial attacks onto the system, which are generated from a
complex-valued gradient-based algorithm. The detailed adversarial robustness
comparison with conventional multiple layer perceptrons and convolutional
neural networks features a distinct statistical adversarial property in
diffractive optical neural networks. Our full stack of software and hardware
provides new opportunities of employing diffractive optics in a variety of ML
tasks and enabling the research on optical adversarial ML.

Learning image representations on decentralized data can bring many benefits
in cases where data cannot be aggregated across data silos. Softmax cross
entropy loss is highly effective and commonly used for learning image
representations. Using a large number of classes has proven to be particularly
beneficial for the descriptive power of such representations in centralized
learning. However, doing so on decentralized data with Federated Learning is
not straightforward as the demand on FL clients' computation and communication
increases proportionally to the number of classes. In this work we introduce
federated sampled softmax (FedSS), a resource-efficient approach for learning
image representation with Federated Learning. Specifically, the FL clients
sample a set of classes and optimize only the corresponding model parameters
with respect to a sampled softmax objective that approximates the global full
softmax objective. We examine the loss formulation and empirically show that
our method significantly reduces the number of parameters transferred to and
optimized by the client devices, while performing on par with the standard full
softmax method. This work creates a possibility for efficiently learning image
representations on decentralized data with a large number of classes under the
federated setting.

Cancer stage is a large determinant of patient prognosis and management in
many cancer types, and is often assessed using medical imaging modalities, such
as CT and MRI. These medical images contain rich information that can be
explored to stratify patients within each stage group to further improve
prognostic algorithms. Although the majority of cancer deaths result from
metastatic and multifocal disease, building imaging biomarkers for patients
with multiple tumors has been a challenging task due to the lack of annotated
datasets and standard study framework. In this paper, we process two public
datasets to set up a benchmark cohort of 341 patient in total for studying
outcome prediction of multifocal metastatic cancer. We identify the lack of
expressiveness in common multiple instance classification networks and propose
two injective multiple instance pooling functions that are better suited to
outcome prediction. Our results show that multiple instance learning with
injective pooling functions can achieve state-of-the-art performance in the
non-small-cell lung cancer CT and head and neck CT outcome prediction
benchmarking tasks. We will release the processed multifocal datasets, our code
and the intermediate files i.e. extracted radiomic features to support further
transparent and reproducible research.

Deep neural network-based classifiers have been shown to be vulnerable to
imperceptible perturbations to their input, such as $\ell_p$-bounded norm
adversarial attacks. This has motivated the development of many defense
methods, which are then broken by new attacks, and so on. This paper focuses on
a different but related problem of reverse engineering adversarial attacks.
Specifically, given an attacked signal, we study conditions under which one can
determine the type of attack ($\ell_1$, $\ell_2$ or $\ell_\infty$) and recover
the clean signal. We pose this problem as a block-sparse recovery problem,
where both the signal and the attack are assumed to lie in a union of subspaces
that includes one subspace per class and one subspace per attack type. We
derive geometric conditions on the subspaces under which any attacked signal
can be decomposed as the sum of a clean signal plus an attack. In addition, by
determining the subspaces that contain the signal and the attack, we can also
classify the signal and determine the attack type. Experiments on digit and
face classification demonstrate the effectiveness of the proposed approach.

Most semantic segmentation approaches of Hyperspectral images (HSIs) use and
require preprocessing steps in the form of patching to accurately classify
diversified land cover in remotely sensed images. These approaches use patching
to incorporate the rich neighborhood information in images and exploit the
simplicity and segmentability of the most common HSI datasets. In contrast,
most landmasses in the world consist of overlapping and diffused classes,
making neighborhood information weaker than what is seen in common HSI
datasets. To combat this issue and generalize the segmentation models to more
complex and diverse HSI datasets, in this work, we propose our novel flagship
model: Clustering Ensemble U-Net (CEU-Net). CEU-Net uses the ensemble method to
combine spectral information extracted from convolutional neural network (CNN)
training on a cluster of landscape pixels. Our CEU-Net model outperforms
existing state-of-the-art HSI semantic segmentation methods and gets
competitive performance with and without patching when compared to baseline
models. We highlight CEU-Net's high performance across Botswana, KSC, and
Salinas datasets compared to HybridSN and AeroRIT methods.

Cross-domain alignment play a key roles in tasks ranging from machine
translation to transfer learning. Recently, purely unsupervised methods
operating on monolingual embeddings have successfully been used to infer a
bilingual lexicon without relying on supervision. However, current state-of-the
art methods only focus on point vectors although distributional embeddings have
proven to embed richer semantic information when representing words. In this
paper, we propose stochastic optimization approach for aligning probabilistic
embeddings. Finally, we evaluate our method on the problem of unsupervised word
translation, by aligning word embeddings trained on monolingual data. We show
that the proposed approach achieves good performance on the bilingual lexicon
induction task across several language pairs and performs better than the
point-vector based approach.

We introduce a neuro-symbolic natural logic framework based on reinforcement
learning with introspective revision. The model samples and rewards specific
reasoning paths through policy gradient, in which the introspective revision
algorithm modifies intermediate symbolic reasoning steps to discover
reward-earning operations as well as leverages external knowledge to alleviate
spurious reasoning and training inefficiency. The framework is supported by
properly designed local relation models to avoid input entangling, which helps
ensure the interpretability of the proof paths. The proposed model has built-in
interpretability and shows superior capability in monotonicity inference,
systematic generalization, and interpretability, compared to previous models on
the existing datasets.

Adversarial examples have recently drawn considerable attention in the field
of machine learning due to the fact that small perturbations in the data can
result in major performance degradation. This phenomenon is usually modeled by
a malicious adversary that can apply perturbations to the data in a constrained
fashion, such as being bounded in a certain norm. In this paper, we study this
problem when the adversary is constrained by the $\ell_0$ norm; i.e., it can
perturb a certain number of coordinates in the input, but has no limit on how
much it can perturb those coordinates. Due to the combinatorial nature of this
setting, we need to go beyond the standard techniques in robust machine
learning to address this problem. We consider a binary classification scenario
where $d$ noisy data samples of the true label are provided to us after
adversarial perturbations. We introduce a classification method which employs a
nonlinear component called truncation, and show in an asymptotic scenario, as
long as the adversary is restricted to perturb no more than $\sqrt{d}$ data
samples, we can almost achieve the optimal classification error in the absence
of the adversary, i.e. we can completely neutralize adversary's effect.
Surprisingly, we observe a phase transition in the sense that using a converse
argument, we show that if the adversary can perturb more than $\sqrt{d}$
coordinates, no classifier can do better than a random guess.

In this paper, we consider nonconvex minimax optimization, which is gaining
prominence in many modern machine learning applications such as GANs.
Large-scale edge-based collection of training data in these applications calls
for communication-efficient distributed optimization algorithms, such as those
used in federated learning, to process the data. In this paper, we analyze
Local stochastic gradient descent ascent (SGDA), the local-update version of
the SGDA algorithm. SGDA is the core algorithm used in minimax optimization,
but it is not well-understood in a distributed setting. We prove that Local
SGDA has \textit{order-optimal} sample complexity for several classes of
nonconvex-concave and nonconvex-nonconcave minimax problems, and also enjoys
\textit{linear speedup} with respect to the number of clients. We provide a
novel and tighter analysis, which improves the convergence and communication
guarantees in the existing literature. For nonconvex-PL and
nonconvex-one-point-concave functions, we improve the existing complexity
results for centralized minimax problems. Furthermore, we propose a
momentum-based local-update algorithm, which has the same convergence
guarantees, but outperforms Local SGDA as demonstrated in our experiments.

Many algorithms have been developed to solve the inverse problem of coded
aperture snapshot spectral imaging (CASSI), i.e., recovering the 3D
hyperspectral images (HSIs) from a 2D compressive measurement. In recent years,
learning-based methods have demonstrated promising performance and dominated
the mainstream research direction. However, existing CNN-based methods show
limitations in capturing long-range dependencies and non-local self-similarity.
Previous Transformer-based methods densely sample tokens, some of which are
uninformative, and calculate the multi-head self-attention (MSA) between some
tokens that are unrelated in content. This does not fit the spatially sparse
nature of HSI signals and limits the model scalability. In this paper, we
propose a novel Transformer-based method, coarse-to-fine sparse Transformer
(CST), firstly embedding HSI sparsity into deep learning for HSI
reconstruction. In particular, CST uses our proposed spectra-aware screening
mechanism (SASM) for coarse patch selecting. Then the selected patches are fed
into our customized spectra-aggregation hashing multi-head self-attention
(SAH-MSA) for fine pixel clustering and self-similarity capturing.
Comprehensive experiments show that our CST significantly outperforms
state-of-the-art methods while requiring cheaper computational costs. The code
and models will be released at https://github.com/caiyuanhao1998/MST

Language identification is an important Natural Language Processing task. It
has been thoroughly researched in the literature. However, some issues are
still open. This work addresses the identification of the related low-resource
languages on the example of the Celtic language family.
  This work's main goals were: (1) to collect the dataset of three Celtic
languages; (2) to prepare a method to identify the languages from the Celtic
family, i.e. to train a successful classification model; (3) to evaluate the
influence of different feature extraction methods, and explore the
applicability of the unsupervised models as a feature extraction technique; (4)
to experiment with the unsupervised feature extraction on a reduced annotated
set.
  We collected a new dataset including Irish, Scottish, Welsh and English
records. We tested supervised models such as SVM and neural networks with
traditional statistical features alongside the output of clustering,
autoencoder, and topic modelling methods. The analysis showed that the
unsupervised features could serve as a valuable extension to the n-gram feature
vectors. It led to an improvement in performance for more entangled classes.
The best model achieved a 98\% F1 score and 97\% MCC. The dense neural network
consistently outperformed the SVM model.
  The low-resource languages are also challenging due to the scarcity of
available annotated training data. This work evaluated the performance of the
classifiers using the unsupervised feature extraction on the reduced labelled
dataset to handle this issue. The results uncovered that the unsupervised
feature vectors are more robust to the labelled set reduction. Therefore, they
proved to help achieve comparable classification performance with much less
labelled data.

Structured prediction models aim at solving a type of problem where the
output is a complex structure, rather than a single variable. Performing
knowledge distillation for such models is not trivial due to their
exponentially large output space. In this work, we propose an approach that is
much simpler in its formulation and far more efficient for training than
existing approaches. Specifically, we transfer the knowledge from a teacher
model to its student model by locally matching their predictions on all
sub-structures, instead of the whole output space. In this manner, we avoid
adopting some time-consuming techniques like dynamic programming (DP) for
decoding output structures, which permits parallel computation and makes the
training process even faster in practice. Besides, it encourages the student
model to better mimic the internal behavior of the teacher model. Experiments
on two structured prediction tasks demonstrate that our approach outperforms
previous methods and halves the time cost for one training epoch.

Deep learning-based underwater object detection (UOD) remains a major
challenge due to the degraded visibility and difficulty to obtain sufficient
underwater object images captured from various perspectives for training. To
address these issues, this paper presents a high-precision UOD based on joint
self-supervised deblurring and improved spatial transformer network. A
self-supervised deblurring subnetwork is introduced into the designed
multi-task learning aided object detection architecture to force the shared
feature extraction module to output clean features for detection subnetwork.
Aiming at alleviating the limitation of insufficient photos from different
perspectives, an improved spatial transformer network is designed based on
perspective transformation, adaptively enriching image features within the
network. The experimental results show that the proposed UOD approach achieved
47.9 mAP in URPC2017 and 70.3 mAP in URPC2018, outperforming many
state-of-the-art UOD methods and indicating the designed method is more
suitable for UOD.

Deep learning training involves a large number of operations, which are
dominated by high dimensionality Matrix-Vector Multiplies (MVMs). This has
motivated hardware accelerators to enhance compute efficiency, but where data
movement and accessing are proving to be key bottlenecks. In-Memory Computing
(IMC) is an approach with the potential to overcome this, whereby computations
are performed in-place within dense 2-D memory. However, IMC fundamentally
trades efficiency and throughput gains for dynamic-range limitations, raising
distinct challenges for training, where compute precision requirements are seen
to be substantially higher than for inferencing. This paper explores training
on IMC hardware by leveraging two recent developments: (1) a training algorithm
enabling aggressive quantization through a radix-4 number representation; (2)
IMC leveraging compute based on precision capacitors, whereby analog noise
effects can be made well below quantization effects. Energy modeling calibrated
to a measured silicon prototype implemented in 16nm CMOS shows that energy
savings of over 400x can be achieved with full quantizer adaptability, where
all training MVMs can be mapped to IMC, and 3x can be achieved for two-level
quantizer adaptability, where two of the three training MVMs can be mapped to
IMC.

Microgrids have more operational flexibilities as well as uncertainties than
conventional power grids, especially when renewable energy resources are
utilized. An energy storage based feedback controller can compensate undesired
dynamics of a microgrid to improve its stability. However, the optimal feedback
control of a microgrid subject to a large disturbance needs to solve a
Hamilton-Jacobi-Bellman problem. This paper proposes a machine learning-based
optimal feedback control scheme. Its training dataset is generated from a
linear-quadratic regulator and a brute-force method respectively addressing
small and large disturbances. Then, a three-layer neural network is constructed
from the data for the purpose of optimal feedback control. A case study is
carried out for a microgrid model based on a modified Kundur two-area system to
test the real-time performance of the proposed control scheme.

In this paper, we propose a Text-Degradation Invariant Auto Encoder
(Text-DIAE), a self-supervised model designed to tackle two tasks, text
recognition (handwritten or scene-text) and document image enhancement. We
start by employing a transformer-based architecture that incorporates three
pretext tasks as learning objectives to be optimized during pre-training
without the usage of labeled data. Each of the pretext objectives is
specifically tailored for the final downstream tasks. We conduct several
ablation experiments that confirm the design choice of the selected pretext
tasks. Importantly, the proposed model does not exhibit limitations of previous
state-of-the-art methods based on contrastive losses, while at the same time
requiring substantially fewer data samples to converge. Finally, we demonstrate
that our method surpasses the state-of-the-art in existing supervised and
self-supervised settings in handwritten and scene text recognition and document
image enhancement. Our code and trained models will be made publicly available
at~\url{ http://Upon_Acceptance}.

This paper proposes a high-precision self-supervised monocular VO, which is
specifically designed for navigation in foggy weather. A cycled generative
adversarial network is designed to obtain high-quality self-supervised loss via
forcing the forward and backward half-cycle to output consistent estimation.
Moreover, gradient-based loss and perceptual loss are introduced to eliminate
the interference of complex photometric change on self-supervised loss in foggy
weather. To solve the ill-posed problem of depth estimation, a self-supervised
multi-task learning aided depth estimation module is designed based on the
strong correlation between the depth estimation and transmission map
calculation of hazy images in foggy weather. The experimental results on the
synthetic foggy KITTI dataset show that the proposed self-supervised monocular
VO performs better in depth and pose estimation than other state-of-the-art
monocular VO in the literature, indicating the designed method is more suitable
for foggy weather.

This technical note describes the recent updates of Graphormer, including
architecture design modifications, and the adaption to 3D molecular dynamics
simulation. With these simple modifications, Graphormer could attain better
results on large-scale molecular modeling datasets than the vanilla one, and
the performance gain could be consistently obtained on 2D and 3D molecular
graph modeling tasks. In addition, we show that with a global receptive field
and an adaptive aggregation strategy, Graphormer is more powerful than classic
message-passing-based GNNs. Empirically, Graphormer could achieve much less MAE
than the originally reported results on the PCQM4M quantum chemistry dataset
used in KDD Cup 2021. In the meanwhile, it greatly outperforms the competitors
in the recent Open Catalyst Challenge, which is a competition track on NeurIPS
2021 workshop, and aims to model the catalyst-adsorbate reaction system with
advanced AI models. All codes could be found at
https://github.com/Microsoft/Graphormer.

Humans have the capability, aided by the expressive compositionality of their
language, to learn quickly by demonstration. They are able to describe unseen
task-performing procedures and generalize their execution to other contexts. In
this work, we introduce DescribeWorld, an environment designed to test this
sort of generalization skill in grounded agents, where tasks are linguistically
and procedurally composed of elementary concepts. The agent observes a single
task demonstration in a Minecraft-like grid world, and is then asked to carry
out the same task in a new map. To enable such a level of generalization, we
propose a neural agent infused with hierarchical latent language--both at the
level of task inference and subtask planning. Our agent first generates a
textual description of the demonstrated unseen task, then leverages this
description to replicate it. Through multiple evaluation scenarios and a suite
of generalization tests, we find that agents that perform text-based inference
are better equipped for the challenge under a random split of tasks.

Pose estimation of 3D objects in monocular images is a fundamental and
long-standing problem in computer vision. Existing deep learning approaches for
6D pose estimation typically rely on the assumption of availability of 3D
object models and 6D pose annotations. However, precise annotation of 6D poses
in real data is intricate, time-consuming and not scalable, while synthetic
data scales well but lacks realism. To avoid these problems, we present a
weakly-supervised reconstruction-based pipeline, named NeRF-Pose, which needs
only 2D object segmentation and known relative camera poses during training.
Following the first-reconstruct-then-regress idea, we first reconstruct the
objects from multiple views in the form of an implicit neural representation.
Then, we train a pose regression network to predict pixel-wise 2D-3D
correspondences between images and the reconstructed model. At inference, the
approach only needs a single image as input. A NeRF-enabled PnP+RANSAC
algorithm is used to estimate stable and accurate pose from the predicted
correspondences. Experiments on LineMod and LineMod-Occlusion show that the
proposed method has state-of-the-art accuracy in comparison to the best 6D pose
estimation methods in spite of being trained only with weak labels. Besides, we
extend the Homebrewed DB dataset with more real training images to support the
weakly supervised task and achieve compelling results on this dataset. The
extended dataset and code will be released soon.

Good quality network connectivity is ever more important. For hybrid fiber
coaxial (HFC) networks, searching for upstream high noise in the past was
cumbersome and time-consuming. Even with machine learning due to the
heterogeneity of the network and its topological structure, the task remains
challenging. We present the automation of a simple business rule (largest
change of a specific value) and compare its performance with state-of-the-art
machine-learning methods and conclude that the precision@1 can be improved by
2.3 times. As it is best when a fault does not occur in the first place, we
secondly evaluate multiple approaches to forecast network faults, which would
allow performing predictive maintenance on the network.

We design and implement a Python library to help the non-expert using all
these powerful tools in a way that is efficient, extensible, and simple to
incorporate into the workflow of the data scientist, practitioner, and applied
researcher. The algorithms implemented in this library have been designed with
usability and GPU efficiency in mind, and they can be added to any PyTorch
model with just one extra line of code.
  We showcase the effectiveness of these tools on an application of
optimisation on manifolds in the setting of time series analysis. In this
setting, orthogonal and unitary optimisation is used to constraint and
regularise recurrent models and avoid vanishing and exploding gradient
problems. The algorithms designed for GeoTorch allow us to achieve state of the
art results in the standard tests for this family of models.
  We use tools from comparison geometry to give bounds on quantities that are
of interest in optimisation problems. In particular, we build on the work of
(Kaul 1976) to give explicit bounds on the norm of the second derivative of the
Riemannian exponential.

Black-box policy optimization is a class of reinforcement learning algorithms
that explores and updates the policies at the parameter level. This class of
algorithms is widely applied in robotics with movement primitives or
non-differentiable policies. Furthermore, these approaches are particularly
relevant where exploration at the action level could cause actuator damage or
other safety issues. However, Black-box optimization does not scale well with
the increasing dimensionality of the policy, leading to high demand for
samples, which are expensive to obtain in real-world systems. In many practical
applications, policy parameters do not contribute equally to the return.
Identifying the most relevant parameters allows to narrow down the exploration
and speed up the learning. Furthermore, updating only the effective parameters
requires fewer samples, improving the scalability of the method. We present a
novel method to prioritize the exploration of effective parameters and cope
with full covariance matrix updates. Our algorithm learns faster than recent
approaches and requires fewer samples to achieve state-of-the-art results. To
select the effective parameters, we consider both the Pearson correlation
coefficient and the Mutual Information. We showcase the capabilities of our
approach on the Relative Entropy Policy Search algorithm in several simulated
environments, including robotics simulations. Code is available at
https://git.ias.informatik.tu-darmstadt.de/ias\_code/aistats2022/dr-creps}{git.ias.informatik.tu-darmstadt.de/ias\_code/aistats2022/dr-creps.

Premixed flames exhibit different asymptotic regimes of interaction between
heat release and turbulence depending on their respective length scales. At
high Karlovitz number, the dilatation caused by heat release does not have any
relevant effect on turbulent kinetic energy with respect to non-reacting flow,
while at low Karlovitz number, the mean shear is a sink of turbulent kinetic
energy, and counter-gradient transport is observed. This latter phenomenon is
not well captured by closure models commonly used in Large Eddy Simulations
that are based on gradient diffusion. The massive amount of data available from
Direct Numerical Simulation (DNS) opens the possibility to develop data-driven
models able to represent physical mechanisms and non-linear features present in
both these regimes. In this work, the databases are formed by DNSs of two
planar hydrogen/air flames at different Karlovitz numbers corresponding to the
two asymptotic regimes. In this context, the Generative Adversarial Network
(GAN) gives the possibility to successfully recognize and reconstruct both
gradient and counter-gradient phenomena if trained with databases where both
regimes are included. Two GAN models were first trained each for a specific
Karlovitz number and tested using the same dataset in order to verify the
capability of the models to learn the features of a single asymptotic regime
and assess its accuracy. In both cases, the GAN models were able to reconstruct
the Reynolds stress subfilter scales accurately. Later, the GAN was trained
with a mixture of both datasets to create a model containing physical knowledge
of both combustion regimes. This model was able to reconstruct the subfilter
scales for both cases capturing the interaction between heat release and
turbulence closely to the DNS as shown from the turbulent kinetic budget and
barycentric maps.

Learned image compression has achieved great success due to its excellent
modeling capacity, but seldom further considers the Rate-Distortion
Optimization (RDO) of each input image. To explore this potential in the
learned codec, we make the first attempt to build a neural data-dependent
transform and introduce a continuous online mode decision mechanism to jointly
optimize the coding efficiency for each individual image. Specifically, apart
from the image content stream, we employ an additional model stream to generate
the transform parameters at the decoder side. The presence of a model stream
enables our model to learn more abstract neural-syntax, which helps cluster the
latent representations of images more compactly. Beyond the transform stage, we
also adopt neural-syntax based post-processing for the scenarios that require
higher quality reconstructions regardless of extra decoding overhead. Moreover,
the involvement of the model stream further makes it possible to optimize both
the representation and the decoder in an online way, i.e. RDO at the testing
time. It is equivalent to a continuous online mode decision, like coding modes
in the traditional codecs, to improve the coding efficiency based on the
individual input image. The experimental results show the effectiveness of the
proposed neural-syntax design and the continuous online mode decision
mechanism, demonstrating the superiority of our method in coding efficiency
compared to the latest conventional standard Versatile Video Coding (VVC) and
other state-of-the-art learning-based methods.

Hyperspectral images (HSI) not only have a broad macroscopic field of view
but also contain rich spectral information, and the types of surface objects
can be identified through spectral information, which is one of the main
applications in hyperspectral image related research.In recent years, more and
more deep learning methods have been proposed, among which convolutional neural
networks (CNN) are the most influential. However, CNN-based methods are
difficult to capture long-range dependencies, and also require a large amount
of labeled data for model training.Besides, most of the self-supervised
training methods in the field of HSI classification are based on the
reconstruction of input samples, and it is difficult to achieve effective use
of unlabeled samples. To address the shortcomings of CNN networks, we propose a
noval multi-scale convolutional embedding module for HSI to realize effective
extraction of spatial-spectral information, which can be better combined with
Transformer network.In order to make more efficient use of unlabeled data, we
propose a new self-supervised pretask. Similar to Mask autoencoder, but our
pre-training method only masks the corresponding token of the central pixel in
the encoder, and inputs the remaining token into the decoder to reconstruct the
spectral information of the central pixel.Such a pretask can better model the
relationship between the central feature and the domain feature, and obtain
more stable training results.

In the classic machine learning framework, models are trained on historical
data and used to predict future values. It is assumed that the data
distribution does not change over time (stationarity). However, in real-world
scenarios, the data generation process changes over time and the model has to
adapt to the new incoming data. This phenomenon is known as concept drift and
leads to a decrease in the predictive model's performance. In this study, we
propose a new concept drift detection method based on autoregressive models
called ADDM. This method can be integrated into any machine learning algorithm
from deep neural networks to simple linear regression model. Our results show
that this new concept drift detection method outperforms the state-of-the-art
drift detection methods, both on synthetic data sets and real-world data sets.
Our approach is theoretically guaranteed as well as empirical and effective for
the detection of various concept drifts. In addition to the drift detector, we
proposed a new method of concept drift adaptation based on the severity of the
drift.

Purpose: To explore the potential of Explainable Machine Learning in the
prediction and detection of drivers of cleared homicides at the national- and
state-levels in the United States.
  Methods: First, nine algorithmic approaches are compared to assess the best
performance in predicting cleared homicides country-wise, using data from the
Murder Accountability Project. The most accurate algorithm among all (XGBoost)
is then used for predicting clearance outcomes state-wise. Second, SHAP, a
framework for Explainable Artificial Intelligence, is employed to capture the
most important features in explaining clearance patterns both at the national
and state levels.
  Results: At the national level, XGBoost demonstrates to achieve the best
performance overall. Substantial predictive variability is detected state-wise.
In terms of explainability, SHAP highlights the relevance of several features
in consistently predicting investigation outcomes. These include homicide
circumstances, weapons, victims' sex and race, as well as number of involved
offenders and victims.
  Conclusions: Explainable Machine Learning demonstrates to be a helpful
framework for predicting homicide clearance. SHAP outcomes suggest a more
organic integration of the two theoretical perspectives emerged in the
literature. Furthermore, jurisdictional heterogeneity highlights the importance
of developing ad hoc state-level strategies to improve police performance in
clearing homicides.

Modeling and automatically recognizing surgical activities are fundamental
steps toward automation in surgery and play important roles in providing timely
feedback to surgeons. Accurately recognizing surgical activities in video poses
a challenging problem that requires an effective means of learning both spatial
and temporal dynamics. Human gaze and visual saliency carry important
information about visual attention and can be used to extract more relevant
features that better reflect these spatial and temporal dynamics. In this
study, we propose to use human gaze with a spatio-temporal attention mechanism
for activity recognition in surgical videos. Our model consists of an I3D-based
architecture, learns spatio-temporal features using 3D convolutions, as well as
learns an attention map using human gaze as supervision. We evaluate our model
on the Suturing task of JIGSAWS which is a publicly available surgical video
understanding dataset. To our knowledge, we are the first to use human gaze for
surgical activity recognition. Our results and ablation studies support the
contribution of using human gaze to guide attention by outperforming
state-of-the art models with an accuracy of 85.4%.

This paper proposes a deep learning framework to design distributed
compression strategies in which distributed agents need to compress
high-dimensional observations of a source, then send the compressed bits via
bandwidth limited links to a fusion center for source reconstruction. Further,
we require the compression strategy to be progressive so that it can adapt to
the varying link bandwidths between the agents and the fusion center. Moreover,
to ensure scalability, we investigate strategies that depend only on the local
channel state information (CSI) at each agent. Toward this end, we use a
data-driven approach in which the progressive linear combination and uniform
quantization strategy at each agent are trained as a function of its local CSI.
To deal with the challenges of modeling the quantization operations (which
always produce zero gradients in the training of neural networks), we propose a
novel approach of exploiting the statistics of the batch training data to set
the dynamic ranges of the uniform quantizers. Numerically, we show that the
proposed distributed estimation strategy designed with only local CSI can
significantly reduce the signaling overhead and can achieve a lower
mean-squared error distortion for source reconstruction than state-of-the-art
designs that require global CSI at comparable overall communication cost.

This work presents SkinningNet, an end-to-end Two-Stream Graph Neural Network
architecture that computes skinning weights from an input mesh and its
associated skeleton, without making any assumptions on shape class and
structure of the provided mesh. Whereas previous methods pre-compute
handcrafted features that relate the mesh and the skeleton or assume a fixed
topology of the skeleton, the proposed method extracts this information in an
end-to-end learnable fashion by jointly learning the best relationship between
mesh vertices and skeleton joints. The proposed method exploits the benefits of
the novel Multi-Aggregator Graph Convolution that combines the results of
different aggregators during the summarizing step of the Message-Passing
scheme, helping the operation to generalize for unseen topologies. Experimental
results demonstrate the effectiveness of the contributions of our novel
architecture, with SkinningNet outperforming current state-of-the-art

Autonomous robots should operate in real-world dynamic environments and
collaborate with humans in tight spaces. A key component for allowing robots to
leave structured lab and manufacturing settings is their ability to evaluate
online and real-time collisions with the world around them. Distance-based
constraints are fundamental for enabling robots to plan their actions and act
safely, protecting both humans and their hardware. However, different
applications require different distance resolutions, leading to various
heuristic approaches for measuring distance fields w.r.t. obstacles, which are
computationally expensive and hinder their application in dynamic obstacle
avoidance use-cases. We propose Regularized Deep Signed Distance Fields
(ReDSDF), a single neural implicit function that can compute smooth distance
fields at any scale, with fine-grained resolution over high-dimensional
manifolds and articulated bodies like humans, thanks to our effective data
generation and a simple inductive bias during training. We demonstrate the
effectiveness of our approach in representative simulated tasks for whole-body
control (WBC) and safe Human-Robot Interaction (HRI) in shared workspaces.
Finally, we provide proof of concept of a real-world application in a HRI
handover task with a mobile manipulator robot.

With substantial recent developments in aviation technologies, Unmanned
Aerial Vehicles (UAVs) are becoming increasingly integrated in commercial and
military operations internationally. Research into the applications of aircraft
data is essential in improving safety, reducing operational costs, and
developing the next frontier of aerial technology. Having an outlier detection
system that can accurately identify anomalous behaviour in aircraft is crucial
for these reasons. This paper proposes a system incorporating a Long Short-Term
Memory (LSTM) Deep Learning Autoencoder based method with a novel dynamic
thresholding algorithm and weighted loss function for anomaly detection of a
UAV dataset, in order to contribute to the ongoing efforts that leverage
innovations in machine learning and data analysis within the aviation industry.
The dynamic thresholding and weighted loss functions showed promising
improvements to the standard static thresholding method, both in
accuracy-related performance metrics and in speed of true fault detection.

As an essential task for the architecture, engineering, and construction
(AEC) industry, information retrieval (IR) from unstructured textual data based
on natural language processing (NLP) is gaining increasing attention. Although
various deep learning (DL) models for IR tasks have been investigated in the
AEC domain, it is still unclear how domain corpora and domain-specific
pretrained DL models can improve performance in various IR tasks. To this end,
this work systematically explores the impacts of domain corpora and various
transfer learning techniques on the performance of DL models for IR tasks and
proposes a pretrained domain-specific language model for the AEC domain. First,
both in-domain and close-domain corpora are developed. Then, two types of
pretrained models, including traditional wording embedding models and
BERT-based models, are pretrained based on various domain corpora and transfer
learning strategies. Finally, several widely used DL models for IR tasks are
further trained and tested based on various configurations and pretrained
models. The result shows that domain corpora have opposite effects on
traditional word embedding models for text classification and named entity
recognition tasks but can further improve the performance of BERT-based models
in all tasks. Meanwhile, BERT-based models dramatically outperform traditional
methods in all IR tasks, with maximum improvements of 5.4% and 10.1% in the F1
score, respectively. This research contributes to the body of knowledge in two
ways: 1) demonstrating the advantages of domain corpora and pretrained DL
models and 2) opening the first domain-specific dataset and pretrained language
model for the AEC domain, to the best of our knowledge. Thus, this work sheds
light on the adoption and application of pretrained models in the AEC domain.

The Benes filter is a well-known continuous-time stochastic filtering model
in one dimension that has the advantage of being explicitly solvable. From an
evolution equation point of view, the Benes filter is also the solution of the
filtering equations given a particular set of coefficient functions. In
general, the filtering stochastic partial differential equations (SPDE) arise
as the evolution equations for the conditional distribution of an underlying
signal given partial, and possibly noisy, observations. Their numerical
approximation presents a central issue for theoreticians and practitioners
alike, who are actively seeking accurate and fast methods, especially for such
high-dimensional settings as numerical weather prediction, for example. In this
paper we present a brief study of a new numerical method based on the mesh-free
neural network representation of the density of the solution of the Benes model
achieved by deep learning. Based on the classical SPDE splitting method, our
algorithm includes a recursive normalisation procedure to recover the
normalised conditional distribution of the signal process. Within the
analytically tractable setting of the Benes filter, we discuss the role of
nonlinearity in the filtering model equations for the choice of the domain of
the neural network. Further we present the first study of the neural network
method with an adaptive domain for the Benes model.

The Universal Knowledge Core (UKC) is a large multilingual lexical database
with a focus on language diversity and covering over a thousand languages. The
aim of the database, as well as its tools and data catalogue, is to make the
somewhat abstract notion of diversity visually understandable for humans and
formally exploitable by machines. The UKC website lets users explore millions
of individual words and their meanings, but also phenomena of cross-lingual
convergence and divergence, such as shared interlingual meanings, lexicon
similarities, cognate clusters, or lexical gaps. The UKC LiveLanguage
Catalogue, in turn, provides access to the underlying lexical data in a
computer-processable form, ready to be reused in cross-lingual applications.

We present a framework for embedding graph structured data into a vector
space, taking into account node features and topology of a graph into the
optimal transport (OT) problem. Then we propose a novel distance between two
graphs, named linearFGW, defined as the Euclidean distance between their
embeddings. The advantages of the proposed distance are twofold: 1) it can take
into account node feature and structure of graphs for measuring the similarity
between graphs in a kernel-based framework, 2) it can be much faster for
computing kernel matrix than pairwise OT-based distances, particularly fused
Gromov-Wasserstein, making it possible to deal with large-scale data sets.
After discussing theoretical properties of linearFGW, we demonstrate
experimental results on classification and clustering tasks, showing the
effectiveness of the proposed linearFGW.

Humans tend to mine objects by learning from a group of images or several
frames of video since we live in a dynamic world. In the computer vision area,
many researches focus on co-segmentation (CoS), co-saliency detection (CoSD)
and video salient object detection (VSOD) to discover the co-occurrent objects.
However, previous approaches design different networks on these similar tasks
separately, and they are difficult to apply to each other, which lowers the
upper bound of the transferability of deep learning frameworks. Besides, they
fail to take full advantage of the cues among inter- and intra-feature within a
group of images. In this paper, we introduce a unified framework to tackle
these issues, term as UFO (Unified Framework for Co-Object Segmentation).
Specifically, we first introduce a transformer block, which views the image
feature as a patch token and then captures their long-range dependencies
through the self-attention mechanism. This can help the network to excavate the
patch structured similarities among the relevant objects. Furthermore, we
propose an intra-MLP learning module to produce self-mask to enhance the
network to avoid partial activation. Extensive experiments on four CoS
benchmarks (PASCAL, iCoseg, Internet and MSRC), three CoSD benchmarks
(Cosal2015, CoSOD3k, and CocA) and four VSOD benchmarks (DAVIS16, FBMS, ViSal
and SegV2) show that our method outperforms other state-of-the-arts on three
different tasks in both accuracy and speed by using the same network
architecture , which can reach 140 FPS in real-time.

Data representativity is crucial when drawing inference from data through
machine learning models. Scholars have increased focus on unraveling the bias
and fairness in the models, also in relation to inherent biases in the input
data. However, limited work exists on the representativity of samples
(datasets) for appropriate inference in AI systems. This paper analyzes data
representativity in scientific literature related to AI and sampling, and gives
a brief overview of statistical sampling methodology from disciplines like
sampling of physical materials, experimental design, survey analysis, and
observational studies. Different notions of a 'representative sample' exist in
past and present literature. In particular, the contrast between the notion of
a representative sample in the sense of coverage of the input space, versus a
representative sample as a miniature of the target population is of relevance
when building AI systems. Using empirical demonstrations on US Census data, we
demonstrate that the first is useful for providing equality and demographic
parity, and is more robust to distribution shifts, whereas the latter notion is
useful in situations where the purpose is to make historical inference or draw
inference about the underlying population in general, or make better
predictions for the majority in the underlying population. We propose a
framework of questions for creating and documenting data, with data
representativity in mind, as an addition to existing datasheets for datasets.
Finally, we will also like to call for caution of implicit, in addition to
explicit, use of a notion of data representativeness without specific
clarification.

Knowledge Graph Embedding models have become an important area of machine
learning.Those models provide a latent representation of entities and relations
in a knowledge graph which can then be used in downstream machine learning
tasks such as link prediction. The learning process of such models can be
performed by contrasting positive and negative triples. While all triples of a
KG are considered positive, negative triples are usually not readily available.
Therefore, the choice of the sampling method to obtain the negative triples
play a crucial role in the performance and effectiveness of Knowledge Graph
Embedding models. Most of the current methods fetch negative samples from a
random distribution of entities in the underlying Knowledge Graph which also
often includes meaningless triples. Other known methods use adversarial
techniques or generative neural networks which consequently reduce the
efficiency of the process. In this paper, we propose an approach for generating
informative negative samples considering available complementary knowledge
about entities. Particularly, Pre-trained Language Models are used to form
neighborhood clusters by utilizing the distances between entities to obtain
representations of symbolic entities via their textual information. Our
comprehensive evaluations demonstrate the effectiveness of the proposed
approach on benchmark Knowledge Graphs with textual information for the link
prediction task.

It is of great challenge, though promising, to coordinate collective robots
for hunting an evader in a decentralized manner purely in light of local
observations. In this paper, this challenge is addressed by a novel hybrid
cooperative pursuit algorithm that combines reinforcement learning with the
artificial potential field method. In the proposed algorithm, decentralized
deep reinforcement learning is employed to learn cooperative pursuit policies
that are adaptive to dynamic environments. The artificial potential field
method is integrated into the learning process as predefined rules to improve
the data efficiency and generalization ability. It is shown by numerical
simulations that the proposed hybrid design outperforms the pursuit policies
either learned from vanilla reinforcement learning or designed by the potential
field method. Furthermore, experiments are conducted by transferring the
learned pursuit policies into real-world mobile robots. Experimental results
demonstrate the feasibility and potential of the proposed algorithm in learning
multiple cooperative pursuit strategies.

`gym-saturation` is an OpenAI Gym environment for reinforcement learning (RL)
agents capable of proving theorems. Currently, only theorems written in a
formal language of the Thousands of Problems for Theorem Provers (TPTP) library
in clausal normal form (CNF) are supported. `gym-saturation` implements the
'given clause' algorithm (similar to the one used in Vampire and E Prover).
Being written in Python, `gym-saturation` was inspired by PyRes. In contrast to
the monolithic architecture of a typical Automated Theorem Prover (ATP),
`gym-saturation` gives different agents opportunities to select clauses
themselves and train from their experience. Combined with a particular agent,
`gym-saturation` can work as an ATP. Even with a non trained agent based on
heuristics, `gym-saturation` can find refutations for 688 (of 8257) CNF
problems from TPTP v7.5.0.

Due to the development of machine learning and speech processing, speech
emotion recognition has been a popular research topic in recent years. However,
the speech data cannot be protected when it is uploaded and processed on
servers in the internet-of-things applications of speech emotion recognition.
Furthermore, deep neural networks have proven to be vulnerable to
human-indistinguishable adversarial perturbations. The adversarial attacks
generated from the perturbations may result in deep neural networks wrongly
predicting the emotional states. We propose a novel federated adversarial
learning framework for protecting both data and deep neural networks. The
proposed framework consists of i) federated learning for data privacy, and ii)
adversarial training at the training stage and randomisation at the testing
stage for model robustness. The experiments show that our proposed framework
can effectively protect the speech data locally and improve the model
robustness against a series of adversarial attacks.

Given two object images, how can we explain their differences in terms of the
underlying object properties? To address this question, we propose
Align-Deform-Subtract (ADS) -- an interventional framework for explaining
object differences. By leveraging semantic alignments in image-space as
counterfactual interventions on the underlying object properties, ADS
iteratively quantifies and removes differences in object properties. The result
is a set of "disentangled" error measures which explain object differences in
terms of the underlying properties. Experiments on real and synthetic data
illustrate the efficacy of the framework.

Modern scientific research and applications very often encounter "fragmentary
data" which brings big challenges to imputation and prediction. By leveraging
the structure of response patterns, we propose a unified and flexible framework
based on Generative Adversarial Nets (GAN) to deal with fragmentary data
imputation and label prediction at the same time. Unlike most of the other
generative model based imputation methods that either have no theoretical
guarantee or only consider Missing Completed At Random (MCAR), the proposed
FragmGAN has theoretical guarantees for imputation with data Missing At Random
(MAR) while no hint mechanism is needed. FragmGAN trains a predictor with the
generator and discriminator simultaneously. This linkage mechanism shows
significant advantages for predictive performances in extensive experiments.

Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine
Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations
requiring data where benign and malicious samples are clearly labelled. Such
labels demand costly expert knowledge, resulting in a lack of real deployments,
as well as on papers always relying on the same outdated data. The situation
improved recently, as some efforts disclosed their labelled datasets. However,
most past works used such datasets just as a 'yet another' testbed, overlooking
the added potential provided by such availability.
  In contrast, we promote using such existing labelled data to cross-evaluate
ML-NIDS. Such approach received only limited attention and, due to its
complexity, requires a dedicated treatment. We hence propose the first
cross-evaluation model. Our model highlights the broader range of realistic
use-cases that can be assessed via cross-evaluations, allowing the discovery of
still unknown qualities of state-of-the-art ML-NIDS. For instance, their
detection surface can be extended--at no additional labelling cost. However,
conducting such cross-evaluations is challenging. Hence, we propose the first
framework, XeNIDS, for reliable cross-evaluations based on Network Flows. By
using XeNIDS on six well-known datasets, we demonstrate the concealed
potential, but also the risks, of cross-evaluations of ML-NIDS.

Identifying collisionless shock crossings in data sent from spacecraft has so
far been done manually. It is a tedious job that shock physicists have to go
through if they want to conduct case studies or perform statistical studies. We
use a machine learning approach to automatically identify shock crossings from
the Magnetospheric Multiscale (MMS) spacecraft. We compile a database of those
crossings including various spacecraft related and shock related parameters for
each event. Furthermore, we show that the shocks in the database have
properties that are spread out both in real space and parameter space. We also
present a possible science application of the database by looking for
correlations between ion acceleration efficiency at shocks and different shock
parameters such as $\theta_{Bn}$ and $M_A$. Furthermore, we investigate
statistically the ion acceleration efficiency. We find no clear correlation
between the acceleration efficiency and $M_A$ and we find that quasi-parallel
shocks are more efficient at accelerating ions.

SparseChem provides fast and accurate machine learning models for biochemical
applications. Especially, the package supports very high-dimensional sparse
inputs, e.g., millions of features and millions of compounds. It is possible to
train classification, regression and censored regression models, or combination
of them from command line. Additionally, the library can be accessed directly
from Python. Source code and documentation is freely available under MIT
License on GitHub.

Purpose: To accelerate brain 3D MRI scans by using a deep learning method for
reconstructing images from highly-undersampled multi-coil k-space data
  Methods: DL-Speed, an unrolled optimization architecture with dense
skip-layer connections, was trained on 3D T1-weighted brain scan data to
reconstruct complex-valued images from highly-undersampled k-space data. The
trained model was evaluated on 3D MPRAGE brain scan data
retrospectively-undersampled with a 10-fold acceleration, compared to a
conventional parallel imaging method with a 2-fold acceleration. Scores of SNR,
artifacts, gray/white matter contrast, resolution/sharpness, deep gray-matter,
cerebellar vermis, anterior commissure, and overall quality, on a 5-point
Likert scale, were assessed by experienced radiologists. In addition, the
trained model was tested on retrospectively-undersampled 3D T1-weighted LAVA
(Liver Acquisition with Volume Acceleration) abdominal scan data, and
prospectively-undersampled 3D MPRAGE and LAVA scans in three healthy volunteers
and one, respectively.
  Results: The qualitative scores for DL-Speed with a 10-fold acceleration were
higher than or equal to those for the parallel imaging with 2-fold
acceleration. DL-Speed outperformed a compressed sensing method in quantitative
metrics on retrospectively-undersampled LAVA data. DL-Speed was demonstrated to
perform reasonably well on prospectively-undersampled scan data, realizing a
2-5 times reduction in scan time.
  Conclusion: DL-Speed was shown to accelerate 3D MPRAGE and LAVA with up to a
net 10-fold acceleration, achieving 2-5 times faster scans compared to
conventional parallel imaging and acceleration, while maintaining diagnostic
image quality and real-time reconstruction. The brain scan data-trained
DL-Speed also performed well when reconstructing abdominal LAVA scan data,
demonstrating versatility of the network.

Pre-training has been a popular learning paradigm in deep learning era,
especially in annotation-insufficient scenario. Better ImageNet pre-trained
models have been demonstrated, from the perspective of architecture, by
previous research to have better transferability to downstream tasks. However,
in this paper, we found that during the same pre-training process, models at
middle epochs, which is inadequately pre-trained, can outperform fully trained
models when used as feature extractors (FE), while the fine-tuning (FT)
performance still grows with the source performance. This reveals that there is
not a solid positive correlation between top-1 accuracy on ImageNet and the
transferring result on target data. Based on the contradictory phenomenon
between FE and FT that better feature extractor fails to be fine-tuned better
accordingly, we conduct comprehensive analyses on features before softmax layer
to provide insightful explanations. Our discoveries suggest that, during
pre-training, models tend to first learn spectral components corresponding to
large singular values and the residual components contribute more when

Accurate molecular force fields are of paramount importance for the efficient
implementation of molecular dynamics techniques at large scales. In the last
decade, machine learning methods have demonstrated impressive performances in
predicting accurate values for energy and forces when trained on finite size
ensembles generated with ab initio techniques. At the same time, quantum
computers have recently started to offer new viable computational paradigms to
tackle such problems. On the one hand, quantum algorithms may notably be used
to extend the reach of electronic structure calculations. On the other hand,
quantum machine learning is also emerging as an alternative and promising path
to quantum advantage. Here we follow this second route and establish a direct
connection between classical and quantum solutions for learning neural network
potentials. To this end, we design a quantum neural network architecture and
apply it successfully to different molecules of growing complexity. The quantum
models exhibit larger effective dimension with respect to classical
counterparts and can reach competitive performances, thus pointing towards
potential quantum advantages in natural science applications via quantum

Synthetic high-resolution (HR) \& low-resolution (LR) pairs are widely used
in existing super-resolution (SR) methods. To avoid the domain gap between
synthetic and test images, most previous methods try to adaptively learn the
synthesizing (degrading) process via a deterministic model. However, some
degradations in real scenarios are stochastic and cannot be determined by the
content of the image. These deterministic models may fail to model the random
factors and content-independent parts of degradations, which will limit the
performance of the following SR models. In this paper, we propose a
probabilistic degradation model (PDM), which studies the degradation
$\mathbf{D}$ as a random variable, and learns its distribution by modeling the
mapping from a priori random variable $\mathbf{z}$ to $\mathbf{D}$. Compared
with previous deterministic degradation models, PDM could model more diverse
degradations and generate HR-LR pairs that may better cover the various
degradations of test images, and thus prevent the SR model from over-fitting to
specific ones. Extensive experiments have demonstrated that our degradation
model can help the SR model achieve better performance on different datasets.
The source codes are released at \url{git@github.com:greatlog/UnpairedSR.git}.

Recent work on the intrinsic image of humans starts to consider the
visibility of incident illumination and encodes the light transfer function by
spherical harmonics. In this paper, we show that such a light transfer function
can be further decomposed into visibility and cosine terms related to surface
normal. Such decomposition allows us to recover the surface normal in addition
to visibility. We propose a deep learning-based approach with a reconstruction
loss for training on real-world images. Results show that compared with
previous works, the reconstruction of human face from our method better reveals
the surface normal and shading details especially around regions where
visibility effect is strong.

Dementia, and specially Alzheimer s disease (AD) and Mild Cognitive
Impairment (MCI) are one of the most important diseases suffered by elderly
population. Music therapy is one of the most widely used non-pharmacological
treatment in the field of cognitive impairments, given that music influences
their mood, behavior, the decrease of anxiety, as well as facilitating
reminiscence, emotional expressions and movement. In this work we present
HAIDA, a multi-platform support system for Musical Therapy oriented to
cognitive impairment, which includes not only therapy tools but also
non-invasive biometric analysis, speech, activity and hand activity. At this
moment the system is on use and recording the first sets of data.

In this paper, we propose a novel multi-level aggregation network to regress
the coordinates of the vertices of a 3D face from a single 2D image in an
end-to-end manner. This is achieved by seamlessly combining standard
convolutional neural networks (CNNs) with Graph Convolution Networks (GCNs). By
iteratively and hierarchically fusing the features across different layers and
stages of the CNNs and GCNs, our approach can provide a dense face alignment
and 3D face reconstruction simultaneously for the benefit of direct feature
learning of 3D face mesh. Experiments on several challenging datasets
demonstrate that our method outperforms state-of-the-art approaches on both 2D
and 3D face alignment tasks.

Synaptic connections in neuronal circuits are modulated by pre- and
post-synaptic spiking activity. Heuristic models of this process of synaptic
plasticity can provide excellent fits to results from in-vitro experiments in
which pre- and post-synaptic spiking is varied in a controlled fashion.
However, the plasticity rules inferred from fitting such data are inevitably
unstable, in that given constant pre- and post-synaptic activity the synapse
will either fully potentiate or depress. This instability can be held in check
by adding additional mechanisms, such as homeostasis. Here we consider an
alternative scenario in which the plasticity rule itself is stable. When this
is the case, net potentiation or depression only occur when pre- and
post-synaptic activity vary in time, e.g. when driven by time-varying inputs.
We study how the features of such inputs shape the recurrent synaptic
connections in models of neuronal circuits. In the case of oscillatory inputs,
the resulting structure is strongly affected by the phase relationship between
drive to different neurons. In large networks, distributed phases tend to lead
to hierarchical clustering. Our results may be of relevance for understanding
the effect of sensory-driven inputs, which are by nature time-varying, on
synaptic plasticity, and hence on learning and memory.

Learning text classifiers based on pre-trained language models has become the
standard practice in natural language processing applications. Unfortunately,
training large neural language models, such as transformers, from scratch is
very costly and requires a vast amount of training data, which might not be
available in the application domain of interest. Moreover, in many real-world
scenarios, classes are uncovered as more data is seen, calling for
class-incremental modelling approaches. In this work we devise a method to
perform text classification using pre-trained models on a sequence of
classification tasks provided in sequence. We formalize the problem as a
continual learning problem where the algorithm learns new tasks without
performance degradation on the previous ones and without re-training the model
from scratch. We empirically demonstrate that our method requires significantly
less model parameters compared to other state of the art methods and that it is
significantly faster at inference time. The tight control on the number of
model parameters, and so the memory, is not only improving efficiency. It is
making possible the usage of the algorithm in real-world applications where
deploying a solution with a constantly increasing memory consumption is just
unrealistic. While our method suffers little forgetting, it retains a
predictive performance on-par with state of the art but less memory efficient
methods.

The present work is based on the COST Action IC1206 for De-identification in
multimedia content. It was performed to test four algorithms of voice
modifications on a speech gender recognizer to find the degree of modification
of pitch when the speech recognizer have the probability of success equal to
the probability of failure. The purpose of this analysis is to assess the
intensity of the speech tone modification, the quality, the reversibility and
not-reversibility of the changes made. Keywords DeIdentification; Speech
Algorithms

People always desire an embodied agent that can perform a task by
understanding language instruction. Moreover, they also want to monitor and
expect agents to understand commands the way they expected. But, how to build
such an embodied agent is still unclear. Recently, people can explore this
problem with the Vision-and-Language Interaction benchmark ALFRED, which
requires an agent to perform complicated daily household tasks following
natural language instructions in unseen scenes. In this paper, we propose LEBP
-- Language Expectation and Binding Policy Module to tackle the ALFRED. The
LEBP contains a two-stream process: 1) It first conducts a language expectation
module to generate an expectation describing how to perform tasks by
understanding the language instruction. The expectation consists of a sequence
of sub-steps for the task (e.g., Pick an apple). The expectation allows people
to access and check the understanding results of instructions before the agent
takes actual actions, in case the task might go wrong. 2) Then, it uses the
binding policy module to bind sub-steps in expectation to actual actions to
specific scenarios. Actual actions include navigation and object manipulation.
Experimental results suggest our approach achieves comparable performance to
currently published SOTA methods and can avoid large decay from seen scenarios
to unseen scenarios.

A large-scale labeled dataset is a key factor for the success of supervised
deep learning in computer vision. However, a limited number of annotated data
is very common, especially in ophthalmic image analysis, since manual
annotation is time-consuming and labor-intensive. Self-supervised learning
(SSL) methods bring huge opportunities for better utilizing unlabeled data, as
they do not need massive annotations. With an attempt to use as many as
possible unlabeled ophthalmic images, it is necessary to break the dimension
barrier, simultaneously making use of both 2D and 3D images. In this paper, we
propose a universal self-supervised Transformer framework, named Uni4Eye, to
discover the inherent image property and capture domain-specific feature
embedding in ophthalmic images. Uni4Eye can serve as a global feature
extractor, which builds its basis on a Masked Image Modeling task with a Vision
Transformer (ViT) architecture. We employ a Unified Patch Embedding module to
replace the origin patch embedding module in ViT for jointly processing both 2D
and 3D input images. Besides, we design a dual-branch multitask decoder module
to simultaneously perform two reconstruction tasks on the input image and its
gradient map, delivering discriminative representations for better convergence.
We evaluate the performance of our pre-trained Uni4Eye encoder by fine-tuning
it on six downstream ophthalmic image classification tasks. The superiority of
Uni4Eye is successfully established through comparisons to other
state-of-the-art SSL pre-training methods.

In this paper, we propose a method for initial camera pose estimation from
just a single image which is robust to viewing conditions and does not require
a detailed model of the scene. This method meets the growing need of easy
deployment of robotics or augmented reality applications in any environments,
especially those for which no accurate 3D model nor huge amount of ground truth
data are available. It exploits the ability of deep learning techniques to
reliably detect objects regardless of viewing conditions. Previous works have
also shown that abstracting the geometry of a scene of objects by an ellipsoid
cloud allows to compute the camera pose accurately enough for various
application needs. Though promising, these approaches use the ellipses fitted
to the detection bounding boxes as an approximation of the imaged objects. In
this paper, we go one step further and propose a learning-based method which
detects improved elliptic approximations of objects which are coherent with the
3D ellipsoids in terms of perspective projection. Experiments prove that the
accuracy of the computed pose significantly increases thanks to our method.
This is achieved with very little effort in terms of training data acquisition
- a few hundred calibrated images of which only three need manual object
annotation. Code and models are released at
https://gitlab.inria.fr/tangram/3d-aware-ellipses-for-visual-localization

Asynchronous optimization algorithms are at the core of modern machine
learning and resource allocation systems. However, most convergence results
consider bounded information delays and several important algorithms lack
guarantees when they operate under total asynchrony. In this paper, we derive
explicit convergence rates for the proximal incremental aggregated gradient
(PIAG) and the asynchronous block-coordinate descent (Async-BCD) methods under
a specific model of total asynchrony, and show that the derived rates are
order-optimal. The convergence bounds provide an insightful understanding of
how the growth rate of the delays deteriorates the convergence times of the
algorithms. Our theoretical findings are demonstrated by a numerical example.

The detection of induced pluripotent stem cell (iPSC) colonies often needs
the precise extraction of the colony features. However, existing computerized
systems relied on segmentation of contours by preprocessing for classifying the
colony conditions were task-extensive. To maximize the efficiency in
categorizing colony conditions, we propose a multiple instance learning (MIL)
in weakly supervised settings. It is designed in a single model to produce weak
segmentation and classification of colonies without using finely labeled
samples. As a single model, we employ a U-net-like convolution neural network
(CNN) to train on binary image-level labels for MIL colonies classification.
Furthermore, to specify the object of interest we used a simple post-processing
method. The proposed approach is compared over conventional methods using
five-fold cross-validation and receiver operating characteristic (ROC) curve.
The maximum accuracy of the MIL-net is 95%, which is 15 % higher than the
conventional methods. Furthermore, the ability to interpret the location of the
iPSC colonies based on the image level label without using a pixel-wise ground
truth image is more appealing and cost-effective in colony condition

We present a framework for learning to guide geometric task and motion
planning (GTAMP). GTAMP is a subclass of task and motion planning in which the
goal is to move multiple objects to target regions among movable obstacles. A
standard graph search algorithm is not directly applicable, because GTAMP
problems involve hybrid search spaces and expensive action feasibility checks.
To handle this, we introduce a novel planner that extends basic heuristic
search with random sampling and a heuristic function that prioritizes
feasibility checking on promising state action pairs. The main drawback of such
pure planners is that they lack the ability to learn from planning experience
to improve their efficiency. We propose two learning algorithms to address
this. The first is an algorithm for learning a rank function that guides the
discrete task level search, and the second is an algorithm for learning a
sampler that guides the continuous motionlevel search. We propose design
principles for designing data efficient algorithms for learning from planning
experience and representations for effective generalization. We evaluate our
framework in challenging GTAMP problems, and show that we can improve both
planning and data efficiency

A common technique in high energy physics is to characterize the response of
a detector by means of models tunned to data which build parametric maps from
the physical parameters of the system to the expected signal of the detector.
When the underlying model is unknown it is difficult to apply this method, and
often, simplifying assumptions are made introducing modeling errors. In this
article, using a waveform toy model we present how deep learning in the form of
constrained bottleneck autoencoders can be used to learn the underlying unknown
detector response model directly from data. The results show that excellent
performance results can be achieved even when the signals are significantly
affected by random noise. The trained algorithm can be used simultaneously to
perform estimations on the physical parameters of the model, simulate the
detector response with high fidelity and to denoise detector signals.

Fine-tuning pretrained models is a common practice in domain generalization
(DG) tasks. However, fine-tuning is usually computationally expensive due to
the ever-growing size of pretrained models. More importantly, it may cause
over-fitting on source domain and compromise their generalization ability as
shown in recent works. Generally, pretrained models possess some level of
generalization ability and can achieve decent performance regarding specific
domains and samples. However, the generalization performance of pretrained
models could vary significantly over different test domains even samples, which
raises challenges for us to best leverage pretrained models in DG tasks. In
this paper, we propose a novel domain generalization paradigm to better
leverage various pretrained models, named specialized ensemble learning for
domain generalization (SEDGE). It first trains a linear label space adapter
upon fixed pretrained models, which transforms the outputs of the pretrained
model to the label space of the target domain. Then, an ensemble network aware
of model specialty is proposed to dynamically dispatch proper pretrained models
to predict each test sample. Experimental studies on several benchmarks show
that SEDGE achieves significant performance improvements comparing to strong
baselines including state-of-the-art method in DG tasks and reduces the
trainable parameters by ~99% and the training time by ~99.5%.

We provide new approaches to prove identities for the modified Macdonald
polynomials: using the LLT expansion of the modified Macdonald polynomials and
using the Garsia--Haiman module. We prove a stretching symmetry for the
modified Macdonald polynomials of one-column shape. We also prove a conjecture
of Haglund concerning multi-$t$-Macdonald polynomials of two rows.

Commercial radar sensing is gaining relevance and machine learning algorithms
constitute one of the key components that are enabling the spread of this radio
technology into areas like surveillance or healthcare. However, radar datasets
are still scarce and generalization cannot be yet achieved for all radar
systems, environment conditions or design parameters. A certain degree of fine
tuning is, therefore, usually required to deploy machine-learning-enabled radar
applications. In this work, we consider the problem of unsupervised domain
adaptation across radar configurations in the context of deep-learning human
activity classification using frequency-modulated continuous-wave. For that, we
focus on the theory-inspired technique of Margin Disparity Discrepancy, which
has already been proved successful in the area of computer vision. Our
experiments extend this technique to radar data, achieving a comparable
accuracy to fewshot supervised approaches for the same classification problem.

Multi-modal magnetic resonance (MR) imaging provides great potential for
diagnosing and analyzing brain gliomas. In clinical scenarios, common MR
sequences such as T1, T2 and FLAIR can be obtained simultaneously in a single
scanning process. However, acquiring contrast enhanced modalities such as T1ce
requires additional time, cost, and injection of contrast agent. As such, it is
clinically meaningful to develop a method to synthesize unavailable modalities
which can also be used as additional inputs to downstream tasks (e.g., brain
tumor segmentation) for performance enhancing. In this work, we propose an
end-to-end framework named Modality-Level Attention Fusion Network (MAF-Net),
wherein we innovatively conduct patchwise contrastive learning for extracting
multi-modal latent features and dynamically assigning attention weights to fuse
different modalities. Through extensive experiments on BraTS2020, our proposed
MAF-Net is found to yield superior T1ce synthesis performance (SSIM of 0.8879
and PSNR of 22.78) and accurate brain tumor segmentation (mean Dice scores of
67.9%, 41.8% and 88.0% on segmenting the tumor core, enhancing tumor and whole
tumor).

Unsupervised cross-lingual speech representation learning (XLSR) has recently
shown promising results in speech recognition by leveraging vast amounts of
unlabeled data across multiple languages. However, standard XLSR model suffers
from language interference problem due to the lack of language specific
modeling ability. In this work, we investigate language adaptive training on
XLSR models. More importantly, we propose a novel language adaptive
pre-training approach based on sparse sharing sub-networks. It makes room for
language specific modeling by pruning out unimportant parameters for each
language, without requiring any manually designed language specific component.
After pruning, each language only maintains a sparse sub-network, while the
sub-networks are partially shared with each other. Experimental results on a
downstream multilingual speech recognition task show that our proposed method
significantly outperforms baseline XLSR models on both high resource and low
resource languages. Besides, our proposed method consistently outperforms other
adaptation methods and requires fewer parameters.

Legal Judgment Prediction (LJP) aims to automatically predict judgment
results, such as charges, relevant law articles, and the term of penalty. It
plays a vital role in legal assistant systems and has become a popular research
topic in recent years. This paper concerns a worthwhile but not well-studied
LJP task, Appeal judgment Prediction (AJP), which predicts the judgment of an
appellate court on an appeal case based on the textual description of case
facts and grounds of appeal. There are two significant challenges in practice
to solve the AJP task. One is how to model the appeal judgment procedure
appropriately. The other is how to improve the interpretability of the
prediction results. We propose a Sequential Multi-task Learning Framework with
Task Dependency for Appeal Judgement Prediction (SMAJudge) to address these
challenges. SMAJudge utilizes two sequential components to model the complete
proceeding from the lower court to the appellate court and employs an attention
mechanism to make the prediction more explainable, which handles the challenges
of AJP effectively. Experimental results obtained with a dataset consisting of
more than 30K appeal judgment documents have revealed the effectiveness and
superiority of SMAJudge.

We investigate the potential of Multi-Objective, Deep Reinforcement Learning
for stock and cryptocurrency trading. More specifically, we build on the
generalized setting \`a la Fontaine and Friedman arXiv:1809.06364 (where the
reward weighting mechanism is not specified a priori, but embedded in the
learning process) by complementing it with computational speed-ups, and adding
the cumulative reward's discount factor to the learning process. Firstly, we
verify that the resulting Multi-Objective algorithm generalizes well, and we
provide preliminary statistical evidence showing that its prediction is more
stable than the corresponding Single-Objective strategy's. Secondly, we show
that the Multi-Objective algorithm has a clear edge over the corresponding
Single-Objective strategy when the reward mechanism is sparse (i.e., when
non-null feedback is infrequent over time). Finally, we discuss the
generalization properties of the discount factor. The entirety of our code is
provided in open source format.

In stream-based active learning, the learning procedure typically has access
to a stream of unlabeled data instances and must decide for each instance
whether to label it and use it for training or to discard it. There are
numerous active learning strategies which try to minimize the number of labeled
samples required for training in this setting by identifying and retaining the
most informative data samples. Most of these schemes are rule-based and rely on
the notion of uncertainty, which captures how small the distance of a data
sample is from the classifier's decision boundary. Recently, there have been
some attempts to learn optimal selection strategies directly from the data, but
many of them are still lacking generality for several reasons: 1) They focus on
specific classification setups, 2) They rely on rule-based metrics, 3) They
require offline pre-training of the active learner on related tasks. In this
work we address the above limitations and present an online stream-based meta
active learning method which learns on the fly an informativeness measure
directly from the data, and is applicable to a general class of classification
problems without any need for pretraining of the active learner on related
tasks. The method is based on reinforcement learning and combines episodic
policy search and a contextual bandits approach which are used to train the
active learner in conjunction with training of the model. We demonstrate on
several real datasets that this method learns to select training samples more
efficiently than existing state-of-the-art methods.

Neither deep neural networks nor symbolic AI alone have approached the kind
of intelligence expressed in humans. This is mainly because neural networks are
not able to decompose distinct objects from their joint representation (the
so-called binding problem), while symbolic AI suffers from exhaustive rule
searches, among other problems. These two problems are still pronounced in
neuro-symbolic AI which aims to combine the best of the two paradigms. Here, we
show that the two problems can be addressed with our proposed
neuro-vector-symbolic architecture (NVSA) by exploiting its powerful operators
on fixed-width holographic vectorized representations that serve as a common
language between neural networks and symbolic logical reasoning. The efficacy
of NVSA is demonstrated by solving the Raven's progressive matrices. NVSA
achieves a new record of 97.7% average accuracy in RAVEN, and 98.8% in I-RAVEN
datasets, with two orders of magnitude faster execution than the symbolic
logical reasoning on CPUs.

The success of Transformer in computer vision has attracted increasing
attention in the medical imaging community. Especially for medical image
segmentation, many excellent hybrid architectures based on convolutional neural
networks (CNNs) and Transformer have been presented and achieve impressive
performance. However, most of these methods, which embed modular Transformer
into CNNs, struggle to reach their full potential. In this paper, we propose a
novel hybrid architecture for medical image segmentation called PHTrans, which
parallelly hybridizes Transformer and CNN in main building blocks to produce
hierarchical representations from global and local features and adaptively
aggregate them, aiming to fully exploit their strengths to obtain better
segmentation performance. Specifically, PHTrans follows the U-shaped
encoder-decoder design and introduces the parallel hybird module in deep
stages, where convolution blocks and the modified 3D Swin Transformer learn
local features and global dependencies separately, then a sequence-to-volume
operation unifies the dimensions of the outputs to achieve feature aggregation.
Extensive experimental results on both Multi-Atlas Labeling Beyond the Cranial
Vault and Automated Cardiac Diagnosis Challeng datasets corroborate its
effectiveness, consistently outperforming state-of-the-art methods. The code is
available at: https://github.com/lseventeen/PHTrans.

Large-scale semantic image annotation is a significant challenge for
learning-based perception systems in robotics. Current approaches often rely on
human labelers, which can be expensive, or simulation data, which can visually
or physically differ from real data. This paper proposes Labels from
UltraViolet (LUV), a novel framework that enables rapid, labeled data
collection in real manipulation environments without human labeling. LUV uses
transparent, ultraviolet-fluorescent paint with programmable ultraviolet LEDs
to collect paired images of a scene in standard lighting and UV lighting to
autonomously extract segmentation masks and keypoints via color segmentation.
We apply LUV to a suite of diverse robot perception tasks to evaluate its
labeling quality, flexibility, and data collection rate. Results suggest that
LUV is 180-2500 times faster than a human labeler across the tasks. We show
that LUV provides labels consistent with human annotations on unpainted test
images. The networks trained on these labels are used to smooth and fold
crumpled towels with 83% success rate and achieve 1.7mm position error with
respect to human labels on a surgical needle pose estimation task. The low cost
of LUV makes it ideal as a lightweight replacement for human labeling systems,
with the one-time setup costs at $300 equivalent to the cost of collecting
around 200 semantic segmentation labels on Amazon Mechanical Turk. Code,
datasets, visualizations, and supplementary material can be found at
https://sites.google.com/berkeley.edu/luv

We present MLNav, a learning-enhanced path planning framework for
safety-critical and resource-limited systems operating in complex environments,
such as rovers navigating on Mars. MLNav makes judicious use of machine
learning to enhance the efficiency of path planning while fully respecting
safety constraints. In particular, the dominant computational cost in such
safety-critical settings is running a model-based safety checker on the
proposed paths. Our learned search heuristic can simultaneously predict the
feasibility for all path options in a single run, and the model-based safety
checker is only invoked on the top-scoring paths. We validate in high-fidelity
simulations using both real Martian terrain data collected by the Perseverance
rover, as well as a suite of challenging synthetic terrains. Our experiments
show that: (i) compared to the baseline ENav path planner on board the
Perserverance rover, MLNav can provide a significant improvement in multiple
key metrics, such as a 10x reduction in collision checks when navigating real
Martian terrains, despite being trained with synthetic terrains; and (ii) MLNav
can successfully navigate highly challenging terrains where the baseline ENav
fails to find a feasible path before timing out.

Training with fewer annotations is a key issue for applying deep models to
various practical domains. To date, semi-supervised learning has achieved great
success in training with few annotations. However, confirmation bias increases
dramatically as the number of annotations decreases making it difficult to
continue reducing the number of annotations. Based on the observation that the
quality of pseudo-labels early in semi-supervised training plays an important
role in mitigating confirmation bias, in this paper we propose an active
self-semi-supervised learning (AS3L) framework. AS3L bootstraps semi-supervised
models with prior pseudo-labels (PPL), where PPL is obtained by label
propagation over self-supervised features. We illustrate that the accuracy of
PPL is not only affected by the quality of features, but also by the selection
of the labeled samples. We develop active learning and label propagation
strategies to obtain better PPL. Consequently, our framework can significantly
improve the performance of models in the case of few annotations while reducing
the training time. Experiments on four semi-supervised learning benchmarks
demonstrate the effectiveness of the proposed methods. Our method outperforms
the baseline method by an average of 7\% on the four datasets and outperforms
the baseline method in accuracy while taking about 1/3 of the training time.

Video-based Unsupervised Domain Adaptation (VUDA) methods improve the
robustness of video models, enabling them to be applied to action recognition
tasks across different environments. However, these methods require constant
access to source data during the adaptation process. Yet in many real-world
applications, subjects and scenes in the source video domain should be
irrelevant to those in the target video domain. With the increasing emphasis on
data privacy, such methods that require source data access would raise serious
privacy issues. Therefore, to cope with such concern, a more practical domain
adaptation scenario is formulated as the Source-Free Video-based Domain
Adaptation (SFVDA). Though there are a few methods for Source-Free Domain
Adaptation (SFDA) on image data, these methods yield degenerating performance
in SFVDA due to the multi-modality nature of videos, with the existence of
additional temporal features. In this paper, we propose a novel Attentive
Temporal Consistent Network (ATCoN) to address SFVDA by learning temporal
consistency, guaranteed by two novel consistency objectives, namely feature
consistency and source prediction consistency, performed across local temporal
features. ATCoN further constructs effective overall temporal features by
attending to local temporal features based on prediction confidence. Empirical
results demonstrate the state-of-the-art performance of ATCoN across various
cross-domain action recognition benchmarks.

The growing use of model-selection principles in ecology for statistical
inference is underpinned by information criteria (IC) and cross-validation (CV)
techniques. Although IC techniques, such as Akaike's Information Criterion,
have been historically more popular in ecology, CV is a versatile and
increasingly used alternative. CV uses data splitting to estimate model scores
based on (out-of-sample) predictive performance, which can be used even when it
is not possible to derive a likelihood (e.g., machine learning) or count
parameters precisely (e.g., mixed-effects models and penalised regression).
Here we provide a primer to understanding and applying CV in ecology. We review
commonly applied variants of CV, including approximate methods, and make
recommendations for their use based on the statistical context. We explain some
important -- but often overlooked -- technical aspects of CV, such as bias
correction, estimation uncertainty, score selection, and parsimonious selection
rules. We also address misconceptions (and truths) about impediments to the use
of CV, including computational cost and ease of implementation, and clarify the
relationship between CV and information-theoretic approaches to model
selection. The paper includes two ecological case studies which illustrate the
application of the techniques. We conclude that CV-based model selection should
be widely applied to ecological analyses, because of its robust estimation
properties and the broad range of situations for which it is applicable. In
particular, we recommend using leave-one-out (LOO) or approximate LOO CV to
minimise bias, or otherwise K-fold CV using bias correction if K<10. To
mitigate overfitting, we recommend calibrated selection via the modified
one-standard-error rule which accounts for the predominant cause of
overfitting: score-estimation uncertainty.

In this paper, we consider a joint unicast and multi-group multicast
cell-free distributed massive multiple-input multiple-output (MIMO) system,
while accounting for co-pilot assignment strategy based channel estimation,
pilot contamination and different precoding schemes. Under the co-pilot
assignment strategy, we derive the minimum-mean-square error (MMSE) channel
state information (CSI) estimation for unicast and multicast users. Given the
acquired CSI, the closed-form expressions for downlink achievable rates with
maximum ratio transmission (MRT), zero-forcing (ZF) and MMSE beamforming are
derived. Based on these expressions, we propose an efficient power allocation
scheme by solving a multi-objective optimization problem (MOOP) between
maximizing the minimum spectral efficiency (SE) of multicast users and
maximizing the average SE of unicast users with non-dominated sorting genetic
algorithm II (NSGA-II). Moreover, the MOOP is converted into a deep learning
(DL) problem and solved by an unsupervised learning method to further promote
computational efficiency. Numerical results verify the accuracy of the derived
closed-form expressions and the effectiveness of the joint unicast and
multigroup multicast transmission scheme in cell-free distributed massive MIMO
systems. The SE analysis under various system parameters and the trade-off
regions between these two conflicting optimization objectives offers numerous
flexibilities for system optimization.

Predicting geomagnetic conditions based on in-situ solar wind observations
allows us to evade disasters caused by large electromagnetic disturbances
originating from the Sun to save lives and protect economic activity. In this
study, we aimed to examine the relationship between the Kp index, representing
global magnetospheric activity level, and solar wind conditions using an
interpretable neural network known as potential learning (PL). Data analyses
based on neural networks are difficult to interpret; however, PL learns by
focusing on the "potentiality of input neurons" and can identify which inputs
are significantly utilized by the network. Using the full advantage of PL, we
extracted the influential solar wind parameters that disturb the magnetosphere
under southward Interplanetary magnetic field (IMF) conditions. The input
parameters of PL were the three components of the IMF (Bx, By, -Bz(Bs)), solar
wind flow speed (Vx), and proton number density (Np) in geocentric solar
ecliptic (GSE) coordinates obtained from the OMNI solar wind database between
1998 and 2019. Furthermore, we classified these input parameters into two
groups (targets), depending on the Kp level: Kp = 6- to 9 (positive target) and
Kp = 0 to 1+ (negative target). Negative target samples were randomly selected
to ensure that numbers of positive and negative targets were equal. The PL
results revealed that solar wind flow speed is an influential parameter for
increasing Kp under southward IMF conditions, which was in good agreement with
previous reports on the statistical relationship between the Kp index and solar
wind velocity, and the Kp formulation based on the IMF and solar wind plasma
parameters. Based on this new neural network, we aim to construct a more
correct and parameter-dependent space weather forecasting model.

Accurate understanding of users in terms of predicative segments play an
essential role in the day to day operation of modern internet enterprises.
Nevertheless, there are significant challenges that limit the quality of data,
especially on long tail predictive tasks. In this work, we present MetaCon, our
unified predicative segments system with scalable, trillion concepts meta
learning that addresses these challenges. It builds on top of a flat concept
representation that summarizes entities' heterogeneous digital footprint,
jointly considers the entire spectrum of predicative tasks as a single learning
task, and leverages principled meta learning approach with efficient first
order meta-optimization procedure under a provable performance guarantee in
order to solve the learning task. Experiments on both proprietary production
datasets and public structured learning tasks demonstrate that MetaCon can lead
to substantial improvements over state of the art recommendation and ranking

The high performance of RGB-D based road segmentation methods contrasts with
their rare application in commercial autonomous driving, which is owing to two
reasons: 1) the prior methods cannot achieve high inference speed and high
accuracy in both ways; 2) the different properties of RGB and depth data are
not well-exploited, limiting the reliability of predicted road. In this paper,
based on the evidence theory, an uncertainty-aware symmetric network (USNet) is
proposed to achieve a trade-off between speed and accuracy by fully fusing RGB
and depth data. Firstly, cross-modal feature fusion operations, which are
indispensable in the prior RGB-D based methods, are abandoned. We instead
separately adopt two light-weight subnetworks to learn road representations
from RGB and depth inputs. The light-weight structure guarantees the real-time
inference of our method. Moreover, a multiscale evidence collection (MEC)
module is designed to collect evidence in multiple scales for each modality,
which provides sufficient evidence for pixel class determination. Finally, in
uncertainty-aware fusion (UAF) module, the uncertainty of each modality is
perceived to guide the fusion of the two subnetworks. Experimental results
demonstrate that our method achieves a state-of-the-art accuracy with real-time
inference speed of 43+ FPS. The source code is available at
https://github.com/morancyc/USNet.

We give the first sample complexity characterizations for outcome
indistinguishability, a theoretical framework of machine learning recently
introduced by Dwork, Kim, Reingold, Rothblum, and Yona (STOC 2021). In outcome
indistinguishability, the goal of the learner is to output a predictor that
cannot be distinguished from the target predictor by a class $D$ of
distinguishers examining the outcomes generated according to the predictors'
  In the distribution-specific and realizable setting where the learner is
given the data distribution together with a predictor class $P$ containing the
target predictor, we show that the sample complexity of outcome
indistinguishability is characterized by the metric entropy of $P$ w.r.t. the
dual Minkowski norm defined by $D$, and equivalently by the metric entropy of
$D$ w.r.t. the dual Minkowski norm defined by $P$. This equivalence makes an
intriguing connection to the long-standing metric entropy duality conjecture in
convex geometry. Our sample complexity characterization implies a variant of
metric entropy duality, which we show is nearly tight. In the distribution-free
setting, we focus on the case considered by Dwork et al. where $P$ contains all
possible predictors, hence the sample complexity only depends on $D$. In this
setting, we show that the sample complexity of outcome indistinguishability is
characterized by the fat-shattering dimension of $D$.
  We also show a strong sample complexity separation between realizable and
agnostic outcome indistinguishability in both the distribution-free and the
distribution-specific settings. This is in contrast to distribution-free (resp.
distribution-specific) PAC learning where the sample complexity in both the
realizable and the agnostic settings can be characterized by the VC dimension
(resp. metric entropy).

Active search refers to the task of autonomous robots (agents) detecting
objects of interest (targets) in a search space using decision making
algorithms that adapt to the history of their observations. It has important
applications in search and rescue missions, wildlife patrolling and environment
monitoring. Active search algorithms must contend with two types of
uncertainty: detection uncertainty and location uncertainty. Prior work has
typically focused on one of these while ignoring or engineering away the other.
The more common approach in robotics is to focus on location uncertainty and
remove detection uncertainty by thresholding the detection probability to zero
or one. On the other hand, it is common in the sparse signal processing
literature to assume the target location is accurate and focus on the
uncertainty of its detection. In this work, we propose an inference method to
jointly handle both target detection and location uncertainty. We then build a
decision making algorithm on this inference method that uses Thompson sampling
to enable efficient active search in both the single agent and multi-agent
settings. We perform experiments in simulation over varying number of agents
and targets to show that our inference and decision making algorithms
outperform competing baselines that only account for either target detection or
location uncertainty.

High-quality system-level message flow specifications can lead to
comprehensive validation of system-on-chip (SoC) designs. We propose a
disruptive method that utilizes an attention mechanism to produce accurate flow
specifications from SoC IP communication traces. The proposed method can
overcome the inherent complexity of SoC traces induced by the concurrency and
parallelism of multicore designs that existing flow specification mining tools
often find extremely challenging. Experiments on highly interleaved traces show
promising flow reconstruction compared to several tools dedicated to the flow
specification mining problem.

Programming videos on the Internet are valuable resources for learning
programming skills. To find relevant videos, developers typically search online
video platforms (e.g., YouTube) with keywords on topics they wish to learn.
Developers often look for live-coding screencasts, in which the videos' authors
perform live coding. Yet, not all programming videos are live-coding
screencasts. In this work, we develop a tool named PSFinder to identify
live-coding screencasts. PSFinder leverages a classifier to identify whether a
video frame contains an IDE window. It uses a sampling strategy to pick a
number of frames from an input video, runs the classifer on these frames, and
then determines whether the video is a live-coding screencast based on frames
classified as containing IDE window. In our preliminary experiment, PSFinder
can effectively identify live-coding screencasts as it achieves an F1-score of
0.97.

An increasing number of artificial intelligence (AI) applications involve the
execution of deep neural networks (DNNs) on edge devices. Many practical
reasons motivate the need to update the DNN model on the edge device
post-deployment, such as refining the model, concept drift, or outright change
in the learning task. In this paper, we consider the scenario where retraining
can be done on the server side based on a copy of the DNN model, with only the
necessary data transmitted to the edge to update the deployed model. However,
due to bandwidth constraints, we want to minimise the transmission required to
achieve the update. We develop a simple approach based on matrix factorisation
to compress the model update -- this differs from compressing the model itself.
The key idea is to preserve existing knowledge in the current model and
optimise only small additional parameters for the update which can be used to
reconstitute the model on the edge. We compared our method to similar
techniques used in federated learning; our method usually requires less than
half of the update size of existing methods to achieve the same accuracy.

We propose two bounded comparison metrics that may be implemented to
arbitrary dimensions in regression tasks. One quantifies the structure of
uncertainty and the other quantifies the distribution of uncertainty. The
structure metric assesses the similarity in shape and location of uncertainty
with the true error, while the distribution metric quantifies the supported
magnitudes between the two. We apply these metrics to Gaussian Processes (GPs),
Ensemble Deep Neural Nets (DNNs), and Ensemble Deep Neural Operators (DNOs) on
high-dimensional and nonlinear test cases. We find that comparing a model's
uncertainty estimates with the model's squared error provides a compelling
ground truth assessment. We also observe that both DNNs and DNOs, especially
when compared to GPs, provide encouraging metric values in high dimensions with
either sparse or plentiful data.

We study the problem of user segmentation: given a set of users and one or
more predefined groups or segments, assign users to their corresponding
segments. As an example, for a segment indicating particular interest in a
certain area of sports or entertainment, the task will be to predict whether
each single user will belong to the segment. However, there may exist numerous
long tail prediction tasks that suffer from data availability and may be of
heterogeneous nature, which make it hard to capture using single off the shelf
model architectures. In this work, we present SuperCone, our unified
predicative segments system that addresses the above challenges. It builds on
top of a flat concept representation that summarizes each user's heterogeneous
digital footprints, and uniformly models each of the prediction task using an
approach called "super learning ", that is, combining prediction models with
diverse architectures or learning method that are not compatible with each
other. Following this, we provide an end to end approach that learns to
flexibly attend to best suited heterogeneous experts adaptively, while at the
same time incorporating deep representations of the input concepts that
augments the above experts. Experiments show that SuperCone significantly
outperform state-of-the-art recommendation and ranking algorithms on a wide
range of predicative segment tasks and public structured data learning

The causal mechanism between climate and political violence is fraught with
complex mechanisms. Current quantitative causal models rely on one or more
assumptions: (1) the climate drivers persistently generate conflict, (2) the
causal mechanisms have a linear relationship with the conflict generation
parameter, and/or (3) there is sufficient data to inform the prior
distribution. Yet, we know conflict drivers often excite a social
transformation process which leads to violence (e.g., drought forces
agricultural producers to join urban militia), but further climate effects do
not necessarily contribute to further violence. Therefore, not only is this
bifurcation relationship highly non-linear, there is also often a lack of data
to support prior assumptions for high resolution modeling. Here, we aim to
overcome the aforementioned causal modeling challenges by proposing a neural
forward-intensity Poisson process (NFIPP) model. The NFIPP is designed to
capture the potential non-linear causal mechanism in climate induced political
violence, whilst being robust to sparse and timing-uncertain data. Our results
span 20 recent years and reveal an excitation-based causal link between extreme
climate events and political violence across diverse countries. Our
climate-induced conflict model results are cross-validated against qualitative
climate vulnerability indices. Furthermore, we label historical events that
either improve or reduce our predictability gain, demonstrating the importance
of domain expertise in informing interpretation.

This paper studies the problem of data collection for policy evaluation in
Markov decision processes (MDPs). In policy evaluation, we are given a target
policy and asked to estimate the expected cumulative reward it will obtain in
an environment formalized as an MDP. We develop theory for optimal data
collection within the class of tree-structured MDPs by first deriving an oracle
data collection strategy that uses knowledge of the variance of the reward
distributions. We then introduce the Reduced Variance Sampling (ReVar)
algorithm that approximates the oracle strategy when the reward variances are
unknown a priori and bound its sub-optimality compared to the oracle strategy.
Finally, we empirically validate that ReVar leads to policy evaluation with
mean squared error comparable to the oracle strategy and significantly lower
than simply running the target policy.

Active learning can play an important role in low-resource settings (i.e.,
where annotated data is scarce), by selecting which instances may be more
worthy to annotate. Most active learning approaches for Machine Translation
assume the existence of a pool of sentences in a source language, and rely on
human annotators to provide translations or post-edits, which can still be
costly. In this article, we assume a real world human-in-the-loop scenario in
which: (i) the source sentences may not be readily available, but instead
arrive in a stream; (ii) the automatic translations receive feedback in the
form of a rating, instead of a correct/edited translation, since the
human-in-the-loop might be a user looking for a translation, but not be able to
provide one. To tackle the challenge of deciding whether each incoming pair
source-translations is worthy to query for human feedback, we resort to a
number of stream-based active learning query strategies. Moreover, since we not
know in advance which query strategy will be the most adequate for a certain
language pair and set of Machine Translation models, we propose to dynamically
combine multiple strategies using prediction with expert advice. Our
experiments show that using active learning allows to converge to the best
Machine Translation systems with fewer human interactions. Furthermore,
combining multiple strategies using prediction with expert advice often
outperforms several individual active learning strategies with even fewer

Declines in cost and concerns about the environmental impact of traditional
generation have boosted the penetration of renewables and non-conventional
distributed energy resources into the power grid. The intermittent availability
of these resources causes the inertia of the power system to vary over time. As
a result, there is a need to go beyond traditional controllers designed to
regulate frequency under the assumption of invariant dynamics. This paper
presents a learning-based framework for the design of stable controllers based
on imitating datasets obtained from linear-quadratic regulator (LQR)
formulations for different switching sequences of inertia modes. The proposed
controller is linear and invariant, thereby interpretable, does not require the
knowledge of the current operating mode, and is guaranteed to stabilize the
switching power dynamics. We also show that it is always possible to stabilize
the switched system using a communication-free local controller, whose
implementation only requires each node to use its own state. Simulations on a
12-bus 3-region network illustrate our results.

Image steganography is the art and science of using images as cover for
covert communications. With the development of neural networks, traditional
image steganography is more likely to be detected by deep learning-based
steganalysis. To improve upon this, we propose image steganography network
based on style transfer, and the embedding of secret messages can be disguised
as image stylization. We embed secret information while transforming the
content image style. In latent space, the secret information is integrated into
the latent representation of the cover image to generate the stego images,
which are indistinguishable from normal stylized images. It is an end-to-end
unsupervised model without pre-training. Extensive experiments on the benchmark
dataset demonstrate the reliability, quality and security of stego images
generated by our steganographic network.

With recent advances in the sensitivity of radio surveys of the Galactic
disk, the number of millisecond pulsars (MSPs) has increased substantially in
recent years such that it is now possible to study their demographic properties
in more detail than in the past. We investigate what can be learned about the
radio spectra of the MSP population. Using a sample of 179 MSPs detected in
eleven surveys carried out at radio frequencies in the range 0.135-6.6 GHz, we
carry out detailed modeling of MSP radio spectral behaviour in this range.
Employing Markov Chain Monte Carlo simulations to explore a multi-dimensional
parameter space, and accurately accounting for observational selection effects,
we find strong evidence in favour of the MSP population having a two-component
power-law spectral model scaling with frequency, $\nu$. Specifically, we find
that MSP flux density spectra are approximately independent of frequency below
320 MHz, and proportional to $\nu^{-1.5}$ at higher frequencies. This
parameterization performs significantly better than single power-law models
which over predict the number of MSPs seen in low-frequency (100-200 MHz)
surveys. We compared our results with earlier work, and current understanding
of the normal pulsar population, and use our model to make predictions for MSP
yields in upcoming surveys. We demonstrate that the observed sample of MSPs
could triple in the coming decade.

Millimeter-scale embedded sensing systems have unique advantages over larger
devices as they are able to capture, analyze, store, and transmit data at the
source while being unobtrusive and covert. However, area-constrained systems
pose several challenges, including a tight energy budget and peak power,
limited data storage, costly wireless communication, and physical integration
at a miniature scale. This paper proposes a novel 6.7$\times$7$\times$5mm
imaging system with deep-learning and image processing capabilities for
intelligent edge applications, and is demonstrated in a home-surveillance
scenario. The system is implemented by vertically stacking custom
ultra-low-power (ULP) ICs and uses techniques such as dynamic behavior-specific
power management, hierarchical event detection, and a combination of data
compression methods. It demonstrates a new image-correcting neural network that
compensates for non-idealities caused by a mm-scale lens and ULP front-end. The
system can store 74 frames or offload data wirelessly, consuming 49.6$\mu$W on
average for an expected battery lifetime of 7 days.

The IEEE 802.11 standard based wireless local area networks (WLANs) or Wi-Fi
networks are critical to provide internet access in today's world. The
increasing demand for high data rate in Wi-Fi networks has led to several
advancements in the 802.11 standard. Supporting MIMO transmissions with higher
number of transmit antennas operating on wider bandwidths is one of the key
capabilities for reaching higher throughput. However, the increase in sounding
feedback overhead due to higher number of transmit antennas may significantly
curb the throughput gain. In this paper, we develop an unsupervised
learning-based method to reduce the sounding duration in a Wi-Fi MIMO link.
Simulation results show that our method uses approximately only 8% of the
number of bits required by the existing feedback mechanism and it can boost the
system throughput by up to 52%.

Modern laboratory techniques like ultrafast laser excitation and shock
compression can bring matter into highly nonequilibrium states with complex
structural transformation, metallization and dissociation dynamics. To
understand and model the dramatic change of both electronic structures and ion
dynamics during such dynamic processes, the traditional method faces
difficulties. Here, we demonstrate the ability of deep neural network (DNN) to
capture the atomic local-environment dependence of electronic density of states
(DOS) for both multicomponent system under exoplanet thermodynamic condition
and nonequilibrium system during super-heated melting process. Large scale and
time-resolved diagnosis of DOS can be efficiently achieved within the accuracy
of ab initio method. Moreover, the atomic contribution to DOS given by DNN
model accurately reveals the information of local neighborhood for selected
atom, thus can serve as robust order parameters to identify different phases
and intermediate local structures, strongly highlights the efficacy of this DNN
model in studying dynamic processes.

Team adaptation to new cooperative tasks is a hallmark of human intelligence,
which has yet to be fully realized in learning agents. Previous work on
multi-agent transfer learning accommodate teams of different sizes, heavily
relying on the generalization ability of neural networks for adapting to unseen
tasks. We believe that the relationship among tasks provides the key
information for policy adaptation. In this paper, we try to discover and
exploit common structures among tasks for more efficient transfer, and propose
to learn effect-based task representations as a common space of tasks, using an
alternatively fixed training scheme. We demonstrate that the task
representation can capture the relationship among tasks, and can generalize to
unseen tasks. As a result, the proposed method can help transfer learned
cooperation knowledge to new tasks after training on a few source tasks. We
also find that fine-tuning the transferred policies help solve tasks that are
hard to learn from scratch.

We present a conceptually simple self-supervised method for saliency
detection. Our method generates and uses pseudo-ground truth labels for
training. The generated pseudo-GT labels don't require any kind of human
annotations (e.g., pixel-wise labels or weak labels like scribbles). Recent
works show that features extracted from classification tasks provide important
saliency cues like structure and semantic information of salient objects in the
image. Our method, called 3SD, exploits this idea by adding a branch for a
self-supervised classification task in parallel with salient object detection,
to obtain class activation maps (CAM maps). These CAM maps along with the edges
of the input image are used to generate the pseudo-GT saliency maps to train
our 3SD network. Specifically, we propose a contrastive learning-based training
on multiple image patches for the classification task. We show the multi-patch
classification with contrastive loss improves the quality of the CAM maps
compared to naive classification on the entire image. Experiments on six
benchmark datasets demonstrate that without any labels, our 3SD method
outperforms all existing weakly supervised and unsupervised methods, and its
performance is on par with the fully-supervised methods. Code is available at
:https://github.com/rajeevyasarla/3SD

Action recognition from videos, i.e., classifying a video into one of the
pre-defined action types, has been a popular topic in the communities of
artificial intelligence, multimedia, and signal processing. However, existing
methods usually consider an input video as a whole and learn models, e.g.,
Convolutional Neural Networks (CNNs), with coarse video-level class labels.
These methods can only output an action class for the video, but cannot provide
fine-grained and explainable cues to answer why the video shows a specific
action. Therefore, researchers start to focus on a new task, Part-level Action
Parsing (PAP), which aims to not only predict the video-level action but also
recognize the frame-level fine-grained actions or interactions of body parts
for each person in the video. To this end, we propose a coarse-to-fine
framework for this challenging task. In particular, our framework first
predicts the video-level class of the input video, then localizes the body
parts and predicts the part-level action. Moreover, to balance the accuracy and
computation in part-level action parsing, we propose to recognize the
part-level actions by segment-level features. Furthermore, to overcome the
ambiguity of body parts, we propose a pose-guided positional embedding method
to accurately localize body parts. Through comprehensive experiments on a
large-scale dataset, i.e., Kinetics-TPS, our framework achieves
state-of-the-art performance and outperforms existing methods over a 31.10% ROC
score.

Binary authorship analysis is a significant problem in many software
engineering applications. In this paper, we formulate a binary authorship
verification task to accurately reflect the real-world working process of
software forensic experts. It aims to determine whether an anonymous binary is
developed by a specific programmer with a small set of support samples, and the
actual developer may not belong to the known candidate set but from the wild.
We propose an effective binary authorship verification framework, BinMLM.
BinMLM trains the RNN language model on consecutive opcode traces extracted
from the control-flow-graph (CFG) to characterize the candidate developers'
programming styles. We build a mixture-of-shared architecture with multiple
shared encoders and author-specific gate layers, which can learn the
developers' combination preferences of universal programming patterns and
alleviate the problem of low training resources. Through an optimization
pipeline of external pre-training, joint training, and fine-tuning, our
framework can eliminate additional noise and accurately distill developers'
unique styles. Extensive experiments show that BinMLM achieves promising
results on Google Code Jam (GCJ) and Codeforces datasets with different numbers
of programmers and supporting samples. It significantly outperforms the
baselines built on the state-of-the-art feature set (4.73% to 19.46%
improvement) and remains robust in multi-author collaboration scenarios.
Furthermore, BinMLM can perform organization-level verification on a real-world
APT malware dataset, which can provide valuable auxiliary information for
exploring the group behind the APT attack.

Data-driven software engineering processes, such as vulnerability prediction
heavily rely on the quality of the data used. In this paper, we observe that it
is infeasible to obtain a noise-free security defect dataset in practice.
Despite the vulnerable class, the non-vulnerable modules are difficult to be
verified and determined as truly exploit free given the limited manual efforts
available. It results in uncertainty, introduces labeling noise in the datasets
and affects conclusion validity. To address this issue, we propose novel
learning methods that are robust to label impurities and can leverage the most
from limited label data; noisy label learning. We investigate various noisy
label learning methods applied to software vulnerability prediction.
Specifically, we propose a two-stage learning method based on noise cleaning to
identify and remediate the noisy samples, which improves AUC and recall of
baselines by up to 8.9% and 23.4%, respectively. Moreover, we discuss several
hurdles in terms of achieving a performance upper bound with semi-omniscient
knowledge of the label noise. Overall, the experimental results show that
learning from noisy labels can be effective for data-driven software and
security analytics.

We present a hierarchical neural network model called SemText to detect HTML
boilerplate based on a novel semantic representation of HTML tags, class names,
and text blocks. We train SemText on three published datasets of news webpages
and fine-tune it using a small number of development data in CleanEval and
GoogleTrends-2017. We show that SemText achieves the state-of-the-art accuracy
on these datasets. We then demonstrate the robustness of SemText by showing
that it also detects boilerplate effectively on out-of-domain community-based
question-answer webpages.

Neural networks tend to achieve better accuracy with training if they are
larger -- even if the resulting models are overparameterized. Nevertheless,
carefully removing such excess parameters before, during, or after training may
also produce models with similar or even improved accuracy. In many cases, that
can be curiously achieved by heuristics as simple as removing a percentage of
the weights with the smallest absolute value -- even though magnitude is not a
perfect proxy for weight relevance. With the premise that obtaining
significantly better performance from pruning depends on accounting for the
combined effect of removing multiple weights, we revisit one of the classic
approaches for impact-based pruning: the Optimal Brain Surgeon(OBS). We propose
a tractable heuristic for solving the combinatorial extension of OBS, in which
we select weights for simultaneous removal, as well as a systematic update of
the remaining weights. Our selection method outperforms other methods under
high sparsity, and the weight update is advantageous even when combined with
the other methods.

This paper evaluates synthetically generated healthcare data for biases and
investigates the effect of fairness mitigation techniques on utility-fairness.
Privacy laws limit access to health data such as Electronic Medical Records
(EMRs) to preserve patient privacy. Albeit essential, these laws hinder
research reproducibility. Synthetic data is a viable solution that can enable
access to data similar to real healthcare data without privacy risks.
Healthcare datasets may have biases in which certain protected groups might
experience worse outcomes than others. With the real data having biases, the
fairness of synthetically generated health data comes into question. In this
paper, we evaluate the fairness of models generated on two healthcare datasets
for gender and race biases. We generate synthetic versions of the dataset using
a Generative Adversarial Network called HealthGAN, and compare the real and
synthetic model's balanced accuracy and fairness scores. We find that synthetic
data has different fairness properties compared to real data and fairness
mitigation techniques perform differently, highlighting that synthetic data is
not bias free.

Human activity detection from digital videos presents many challenges to the
computer vision and image processing communities. Recently, many methods have
been developed to detect human activities with varying degree of success. Yet,
the general human activity detection problem remains very challenging,
especially when the methods need to work 'in the wild' (e.g., without having
precise control over the imaging geometry). The thesis explores phase-based
solutions for (i) detecting faces, (ii) back of the heads, (iii) joint
detection of faces and back of the heads, and (iv) whether the head is looking
to the left or the right, using standard video cameras without any control on
the imaging geometry. The proposed phase-based approach is based on the
development of simple and robust methods that rely on the use of Amplitude
Modulation- Frequency Modulation (AM-FM) models. The approach is validated
using video frames extracted from the Advancing Out-of-school Learning in
Mathematics and Engineering (AOLME) project. The dataset consisted of 13,265
images from ten students looking at the camera, and 6,122 images from five
students looking away from the camera. For the students facing the camera, the
method was able to correctly classify 97.1% of them looking to the left and
95.9% of them looking to the right. For the students facing the back of the
camera, the method was able to correctly classify 87.6% of them looking to the
left and 93.3% of them looking to the right. The results indicate that AM-FM
based methods hold great promise for analyzing human activity videos.

We construct a contextual network to represent a document with syntactic and
semantic relations between word-sentence pairs, based on which we devise an
unsupervised algorithm called CNATAR (Contextual Network And Text Analysis
Rank) to score sentences, and rank them through a bi-objective 0-1 knapsack
maximization problem over topic analysis and sentence scores. We show that
CNATAR outperforms the combined ranking of the three human judges provided on
the SummBank dataset under both ROUGE and BLEU metrics, which in term
significantly outperforms each individual judge's ranking. Moreover, CNATAR
produces so far the highest ROUGE scores over DUC-02, and outperforms previous
supervised algorithms on the CNN/DailyMail and NYT datasets. We also compare
the performance of CNATAR and the latest supervised neural-network
summarization models and compute oracle results.

In recent years, a deep learning framework has been widely used for object
pose estimation. While quaternion is a common choice for rotation
representation of 6D pose, it cannot represent an uncertainty of the
observation. In order to handle the uncertainty, Bingham distribution is one
promising solution because this has suitable features, such as a smooth
representation over SO(3), in addition to the ambiguity representation.
However, it requires the complex computation of the normalizing constants. This
is the bottleneck of loss computation in training neural networks based on
Bingham representation. As such, we propose a fast-computable and
easy-to-implement loss function for Bingham distribution. We also show not only
to examine the parametrization of Bingham distribution but also an application
based on our loss function.

Graph Signal Processing is a promising framework to manipulate brain signals
as it allows to encompass the spatial dependencies between the activity in
regions of interest in the brain. In this work, we are interested in better
understanding what are the graph frequencies that are the most useful to decode
fMRI signals. To this end, we introduce a deep learning architecture and adapt
a pruning methodology to automatically identify such frequencies. We experiment
with various datasets, architectures and graphs, and show that low graph
frequencies are consistently identified as the most important for fMRI
decoding, with a stronger contribution for the functional graph over the
structural one. We believe that this work provides novel insights on how
graph-based methods can be deployed to increase fMRI decoding accuracy and

Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms
that have the ability to address competitive resource allocation problems
together with detection and mitigation of anomalous behavior. In this paper, we
investigate their use in next-generation (NextG) communications within the
context of cognitive networks to address i) spectrum sharing, ii) detecting
anomalies, and iii) mitigating security attacks. GANs have the following
advantages. First, they can learn and synthesize field data, which can be
costly, time consuming, and nonrepeatable. Second, they enable pre-training
classifiers by using semi-supervised data. Third, they facilitate increased
resolution. Fourth, they enable the recovery of corrupted bits in the spectrum.
The paper provides the basics of GANs, a comparative discussion on different
kinds of GANs, performance measures for GANs in computer vision and image
processing as well as wireless applications, a number of datasets for wireless
applications, performance measures for general classifiers, a survey of the
literature on GANs for i)-iii) above, and future research directions. As a use
case of GAN for NextG communications, we show that a GAN can be effectively
applied for anomaly detection in signal classification (e.g., user
authentication) outperforming another state-of-the-art ML technique such as an
autoencoder.

Out-of-distribution (OOD) detection is a critical task for reliable machine
learning. Recent advances in representation learning give rise to developments
in distance-based OOD detection, where testing samples are detected as OOD if
they are relatively far away from the centroids or prototypes of
in-distribution (ID) classes. However, prior methods directly take
off-the-shelf loss functions that suffice for classifying ID samples, but are
not optimally designed for OOD detection. In this paper, we propose CIDER, a
simple and effective representation learning framework by exploiting
hyperspherical embeddings for OOD detection. CIDER jointly optimizes two losses
to promote strong ID-OOD separability: (1) a dispersion loss that promotes
large angular distances among different class prototypes, and (2) a compactness
loss that encourages samples to be close to their class prototypes. We show
that CIDER is effective under various settings and establishes state-of-the-art
performance. On a hard OOD detection task CIFAR-100 vs. CIFAR-10, our method
substantially improves the AUROC by 14.20% compared to the embeddings learned
by the cross-entropy loss.

Moir\'e materials such as magic angle twisted bilayer graphene (MATBG)
exhibit remarkable phenomenology, but present significant challenges for
certain experimental methods, particularly scanning probes such as scanning
tunneling microscopy (STM). Typical STM studies that can image tens of
thousands of atomic unit cells can image roughly ten moir\'e cells, making data
analysis statistically fraught. Here, we propose a method to mitigate this
problem by aggregating STM conductance data from several bias voltages, and
then using the unsupervised machine learning method of gaussian mixture model
clustering to draw maximal insight from the resulting dataset. We apply this
method, using as input coarse-grained bond variables respecting the point group
symmetry, to investigate nematic ordering tendencies in MATBG for both charge
neutral and hole-doped samples. For the charge-neutral dataset, the clustering
reveals the surprising coexistence of multiple types of nematicity that are
unrelated by symmetry, and therefore generically nondegenerate. By contrast,
the clustering in the hole doped data is consistent with long range order of a
single type. Beyond its value in analyzing nematicity in MATBG, our method has
the potential to enhance understanding of symmetry breaking and its spatial
variation in a variety of moir\'e materials.

Visual place recognition techniques based on deep learning, which have
imposed themselves as the state-of-the-art in recent years, do not generalize
well to environments visually different from the training set. Thus, to achieve
top performance, it is sometimes necessary to fine-tune the networks to the
target environment. To this end, we propose a self-supervised domain
calibration procedure based on robust pose graph optimization from Simultaneous
Localization and Mapping (SLAM) as the supervision signal without requiring GPS
or manual labeling. Moreover, we leverage the procedure to improve uncertainty
estimation for place recognition matches which is important in safety critical
applications. We show that our approach can improve the performance of a
state-of-the-art technique on a target environment dissimilar from its training
set and that we can obtain uncertainty estimates. We believe that this approach
will help practitioners to deploy robust place recognition solutions in
real-world applications. Our code is available publicly:
https://github.com/MISTLab/vpr-calibration-and-uncertainty

Self-supervision based deep learning classification approaches have received
considerable attention in academic literature. However, the performance of such
methods on remote sensing imagery domains remains under-explored. In this work,
we explore contrastive representation learning methods on the task of
imagery-based city classification, an important problem in urban computing. We
use satellite and map imagery across 2 domains, 3 million locations and more
than 1500 cities. We show that self-supervised methods can build a
generalizable representation from as few as 200 cities, with representations
achieving over 95\% accuracy in unseen cities with minimal additional training.
We also find that the performance discrepancy of such methods, when compared to
supervised methods, induced by the domain discrepancy between natural imagery
and abstract imagery is significant for remote sensing imagery. We compare all
analysis against existing supervised models from academic literature and
open-source our models for broader usage and further criticism.

Human perceptual studies are the gold standard for the evaluation of many
research tasks in machine learning, linguistics, and psychology. However, these
studies require significant time and cost to perform. As a result, many
researchers use objective measures that can correlate poorly with human
evaluation. When subjective evaluations are performed, they are often not
reported with sufficient detail to ensure reproducibility. We propose
Reproducible Subjective Evaluation (ReSEval), an open-source framework for
quickly deploying crowdsourced subjective evaluations directly from Python.
ReSEval lets researchers launch A/B, ABX, Mean Opinion Score (MOS) and MUltiple
Stimuli with Hidden Reference and Anchor (MUSHRA) tests on audio, image, text,
or video data from a command-line interface or using one line of Python, making
it as easy to run as objective evaluation. With ReSEval, researchers can
reproduce each other's subjective evaluations by sharing a configuration file
and the audio, image, text, or video files.

Autonomous perception requires high-quality environment sensing in the form
of 3D bounding boxes of dynamic objects. The primary sensors used in automotive
systems are light-based cameras and LiDARs. However, they are known to fail in
adverse weather conditions. Radars can potentially solve this problem as they
are barely affected by adverse weather conditions. However, specular
reflections of wireless signals cause poor performance of radar point clouds.
We introduce Pointillism, a system that combines data from multiple spatially
separated radars with an optimal separation to mitigate these problems. We
introduce a novel concept of Cross Potential Point Clouds, which uses the
spatial diversity induced by multiple radars and solves the problem of noise
and sparsity in radar point clouds. Furthermore, we present the design of
RP-net, a novel deep learning architecture, designed explicitly for radar's
sparse data distribution, to enable accurate 3D bounding box estimation. The
spatial techniques designed and proposed in this paper are fundamental to
radars point cloud distribution and would benefit other radar sensing

Equivariant neural networks enforce symmetry within the structure of their
convolutional layers, resulting in a substantial improvement in sample
efficiency when learning an equivariant or invariant function. Such models are
applicable to robotic manipulation learning which can often be formulated as a
rotationally symmetric problem. This paper studies equivariant model
architectures in the context of $Q$-learning and actor-critic reinforcement
learning. We identify equivariant and invariant characteristics of the optimal
$Q$-function and the optimal policy and propose equivariant DQN and SAC
algorithms that leverage this structure. We present experiments that
demonstrate that our equivariant versions of DQN and SAC can be significantly
more sample efficient than competing algorithms on an important class of
robotic manipulation problems.

Modern high-scoring models of vision in the brain score competition do not
stem from Vision Transformers. However, in this paper, we provide evidence
against the unexpected trend of Vision Transformers (ViT) being not
perceptually aligned with human visual representations by showing how a
dual-stream Transformer, a CrossViT$~\textit{a la}$ Chen et al. (2021), under a
joint rotationally-invariant and adversarial optimization procedure yields 2nd
place in the aggregate Brain-Score 2022 competition(Schrimpf et al., 2020b)
averaged across all visual categories, and at the time of the competition held
1st place for the highest explainable variance of area V4. In addition, our
current Transformer-based model also achieves greater explainable variance for
areas V4, IT and Behaviour than a biologically-inspired CNN (ResNet50) that
integrates a frontal V1-like computation module (Dapello et al.,2020). To
assess the contribution of the optimization scheme with respect to the CrossViT
architecture, we perform several additional experiments on differently
optimized CrossViT's regarding adversarial robustness, common corruption
benchmarks, mid-ventral stimuli interpretation and feature inversion. Against
our initial expectations, our family of results provides tentative support for
an $\textit{"All roads lead to Rome"}$ argument enforced via a joint
optimization rule even for non biologically-motivated models of vision such as
Vision Transformers. Code is available at
https://github.com/williamberrios/BrainScore-Transformers

Finding prototypes (e.g., mean and median) for a dataset is central to a
number of common machine learning algorithms. Subspaces have been shown to
provide useful, robust representations for datasets of images, videos and more.
Since subspaces correspond to points on a Grassmann manifold, one is led to
consider the idea of a subspace prototype for a Grassmann-valued dataset. While
a number of different subspace prototypes have been described, the calculation
of some of these prototypes has proven to be computationally expensive while
other prototypes are affected by outliers and produce highly imperfect
clustering on noisy data. This work proposes a new subspace prototype, the flag
median, and introduces the FlagIRLS algorithm for its calculation. We provide
evidence that the flag median is robust to outliers and can be used effectively
in algorithms like Linde-Buzo-Grey (LBG) to produce improved clusterings on
Grassmannians. Numerical experiments include a synthetic dataset, the MNIST
handwritten digits dataset, the Mind's Eye video dataset and the UCF YouTube
action dataset. The flag median is compared the other leading algorithms for
computing prototypes on the Grassmannian, namely, the $\ell_2$-median and to
the flag mean. We find that using FlagIRLS to compute the flag median converges
in $4$ iterations on a synthetic dataset. We also see that Grassmannian LBG
with a codebook size of $20$ and using the flag median produces at least a
$10\%$ improvement in cluster purity over Grassmannian LBG using the flag mean
or $\ell_2$-median on the Mind's Eye dataset.

Hierarchical models represent a challenging setting for inference algorithms.
MCMC methods struggle to scale to large models with many local variables and
observations, and variational inference (VI) may fail to provide accurate
approximations due to the use of simple variational families. Some variational
methods (e.g. importance weighted VI) integrate Monte Carlo methods to give
better accuracy, but these tend to be unsuitable for hierarchical models, as
they do not allow for subsampling and their performance tends to degrade for
high dimensional models. We propose a new family of variational bounds for
hierarchical models, based on the application of tightening methods (e.g.
importance weighting) separately for each group of local random variables. We
show that our approach naturally allows the use of subsampling to get unbiased
gradients, and that it fully leverages the power of methods that build tighter
lower bounds by applying them independently in lower dimensional spaces,
leading to better results and more accurate posterior approximations than

Website fingerprinting (WF) attacks, usually conducted with the help of a
machine learning-based classifier, enable a network eavesdropper to pinpoint
which web page a user is accessing through the inspection of traffic patterns.
These attacks have been shown to succeed even when users browse the Internet
through encrypted tunnels, e.g., through Tor or VPNs. To assess the security of
new defenses against WF attacks, recent works have proposed feature-dependent
theoretical frameworks that estimate the Bayes error of an adversary's features
set or the mutual information leaked by manually-crafted features.
Unfortunately, as state-of-the-art WF attacks increasingly rely on deep
learning and latent feature spaces, security estimations based on simpler (and
less informative) manually-crafted features can no longer be trusted to assess
the potential success of a WF adversary in defeating such defenses. In this
work, we propose DeepSE-WF, a novel WF security estimation framework that
leverages specialized kNN-based estimators to produce Bayes error and mutual
information estimates from learned latent feature spaces, thus bridging the gap
between current WF attacks and security estimation methods. Our evaluation
reveals that DeepSE-WF produces tighter security estimates than previous
frameworks, reducing the required computational resources to output security
estimations by one order of magnitude.

In this work, we report on a novel quantum gate approximation algorithm based
on the application of parametric two-qubit gates in the synthesis process. The
utilization of these parametric two-qubit gates in the circuit design allows us
to transform the discrete combinatorial problem of circuit synthesis into an
optimization problem over continuous variables. The circuit is then compressed
by a sequential removal of two-qubit gates from the design, while the remaining
building blocks are continuously adapted to the reduced gate structure by
iterated learning cycles. We implemented the developed algorithm in the
SQUANDER software package and benchmarked it against several state-of-the-art
quantum gate synthesis tools. Our numerical experiments revealed outstanding
circuit compression capabilities of our compilation algorithm providing the
most optimal gate count in the majority of the addressed quantum circuits.

Multi-agent interactions are important to model for forecasting other agents'
behaviors and trajectories. At a certain time, to forecast a reasonable future
trajectory, each agent needs to pay attention to the interactions with only a
small group of most relevant agents instead of unnecessarily paying attention
to all the other agents. However, existing attention modeling works ignore that
human attention in driving does not change rapidly, and may introduce
fluctuating attention across time steps. In this paper, we formulate an
attention model for multi-agent interactions based on a total variation
temporal smoothness prior and propose a trajectory prediction architecture that
leverages the knowledge of these attended interactions. We demonstrate how the
total variation attention prior along with the new sequence prediction loss
terms leads to smoother attention and more sample-efficient learning of
multi-agent trajectory prediction, and show its advantages in terms of
prediction accuracy by comparing it with the state-of-the-art approaches on
both synthetic and naturalistic driving data. We demonstrate the performance of
our algorithm for trajectory prediction on the INTERACTION dataset on our
website.

Recent advancements in deep learning have led to drastic improvements in
speech segregation models. Despite their success and growing applicability, few
efforts have been made to analyze the underlying principles that these networks
learn to perform segregation. Here we analyze the role of harmonicity on two
state-of-the-art Deep Neural Networks (DNN)-based models- Conv-TasNet and
DPT-Net. We evaluate their performance with mixtures of natural speech versus
slightly manipulated inharmonic speech, where harmonics are slightly frequency
jittered. We find that performance deteriorates significantly if one source is
even slightly harmonically jittered, e.g., an imperceptible 3% harmonic jitter
degrades performance of Conv-TasNet from 15.4 dB to 0.70 dB. Training the model
on inharmonic speech does not remedy this sensitivity, instead resulting in
worse performance on natural speech mixtures, making inharmonicity a powerful
adversarial factor in DNN models. Furthermore, additional analyses reveal that
DNN algorithms deviate markedly from biologically inspired algorithms that rely
primarily on timing cues and not harmonicity to segregate speech.

Integrating cross-department multi-modal data (e.g., radiological,
pathological, genomic, and clinical data) is ubiquitous in brain cancer
diagnosis and survival prediction. To date, such an integration is typically
conducted by human physicians (and panels of experts), which can be subjective
and semi-quantitative. Recent advances in multi-modal deep learning, however,
have opened a door to leverage such a process to a more objective and
quantitative manner. Unfortunately, the prior arts of using four modalities on
brain cancer survival prediction are limited by a "complete modalities" setting
(i.e., with all modalities available). Thus, there are still open questions on
how to effectively predict brain cancer survival from the incomplete
radiological, pathological, genomic, and demographic data (e.g., one or more
modalities might not be collected for a patient). For instance, should we use
both complete and incomplete data, and more importantly, how to use those data?
To answer the preceding questions, we generalize the multi-modal learning on
cross-department multi-modal data to a missing data setting. Our contribution
is three-fold: 1) We introduce optimal multi-modal learning with missing data
(MMD) pipeline with optimized hardware consumption and computational
efficiency; 2) We extend multi-modal learning on radiological, pathological,
genomic, and demographic data into missing data scenarios; 3) a large-scale
public dataset (with 962 patients) is collected to systematically evaluate
glioma tumor survival prediction using four modalities. The proposed method
improved the C-index of survival prediction from 0.7624 to 0.8053.

We consider the problem of calibration and uncertainty analysis for
activity-based transportation simulators. Activity-Based Models (ABMs) rely on
statistical modeling of individual travelers' behavior to predict higher-order
travel patterns in metropolitan areas. Input parameters are typically estimated
from traveler surveys using maximum likelihood. We develop an approach that
uses a Gaussian Process emulator to calibrate those parameters using traffic
flow data. Our approach extends traditional emulators to handle the
high-dimensional and non-stationary nature of transportation simulators. We
introduce a deep learning dimensionality reduction model that is jointly
estimated with Gaussin Process model to approximate the simulator. We
demonstrate the methodology using several simulated examples as well as by
calibrating key parameters of the Bloomington, Illinois model.

This paper demonstrates how to recover causal graphs from the score of the
data distribution in non-linear additive (Gaussian) noise models. Using score
matching algorithms as a building block, we show how to design a new generation
of scalable causal discovery methods. To showcase our approach, we also propose
a new efficient method for approximating the score's Jacobian, enabling to
recover the causal graph. Empirically, we find that the new algorithm, called
SCORE, is competitive with state-of-the-art causal discovery methods while
being significantly faster.

Error analysis in NLP models is essential to successful model development and
deployment. One common approach for diagnosing errors is to identify
subpopulations in the dataset where the model produces the most errors.
However, existing approaches typically define subpopulations based on
pre-defined features, which requires users to form hypotheses of errors in
advance. To complement these approaches, we propose iSEA, an Interactive
Pipeline for Semantic Error Analysis in NLP Models, which automatically
discovers semantically-grounded subpopulations with high error rates in the
context of a human-in-the-loop interactive system. iSEA enables model
developers to learn more about their model errors through discovered
subpopulations, validate the sources of errors through interactive analysis on
the discovered subpopulations, and test hypotheses about model errors by
defining custom subpopulations. The tool supports semantic descriptions of
error-prone subpopulations at the token and concept level, as well as
pre-defined higher-level features. Through use cases and expert interviews, we
demonstrate how iSEA can assist error understanding and analysis.

Alzheimer's Disease (AD) is the most common form of dementia in the United
States. Sleep is one of the lifestyle-related factors that has been shown
critical for optimal cognitive function in old age. . However, there is a lack
of research studying the association between sleep and AD incidence. A major
bottleneck for conducting such research is that the traditional way to acquire
sleep information is time-consuming, inefficient, non-scalable, and limited to
patients' subjective experience. In this study, we developed a rule-based NLP
algorithm and machine learning models to automate the extraction of
sleep-related concepts, including snoring, napping, sleep problem, bad sleep
quality, daytime sleepiness, night wakings, and sleep duration, from the
clinical notes of patients diagnosed with AD. We trained and validated the
proposed models on the clinical notes retrieved from the University of
Pittsburgh of Medical Center (UPMC). The results show that the rule-based NLP
algorithm consistently achieved the best performance for all sleep concepts.

As charged particle bunches become shorter and more intense, the effects of
nonlinear intra-bunch collective interactions such as space charge forces and
bunch-to-bunch influences such as wakefields and coherent synchrotron radiation
also increase. Shorter more intense bunches are also more difficult to
accurately image because their dimensions are beyond the resolution of existing
diagnostics and they may be destructive to intercepting diagnostics. The
limited availability of detailed diagnostics for intense high energy beams is a
fundamental challenge for the accelerator community because both beams and
accelerators are time-varying systems that change in unpredictable ways. The
detailed 6D (x,y,z,px,py,pz) distributions of beams emerging from sources vary
with time due to factors such as evolving photocathode laser intensity profiles
and the quantum efficiency of photocathodes. Accelerator magnets, RF
amplifiers, and control systems are perturbed by external disturbances,
beam-loading effects, temperature variations, and misalignments. Although
machine learning (ML) methods have grown in popularity in the accelerator
community in recently years, they are fundamentally limited when it comes to
time-varying systems for which most current approaches are to simply collect
large new data sets and perform re-training, something which is not feasible
for busy accelerator user facilities because detailed beam measurements usually
interrupt operations. New adaptive machine learning (AML) methods designed for
time-varying systems are needed to aid in the diagnostics and control of
high-intensity, ultrashort beams by combining deep learning tools such as
convolutional neural network-based encoder-decoder architectures,
model-independent feedback, physics constraints, and online models with real
time non-invasive beam data, to provide a detailed virtual view of intense
bunch dynamics.

Bayesian networks faithfully represent the symmetric conditional
independences existing between the components of a random vector. Staged trees
are an extension of Bayesian networks for categorical random vectors whose
graph represents non-symmetric conditional independences via vertex coloring.
However, since they are based on a tree representation of the sample space, the
underlying graph becomes cluttered and difficult to visualize as the number of
variables increases. Here we introduce the first structural learning algorithms
for the class of simple staged trees, entertaining a compact coalescence of the
underlying tree from which non-symmetric independences can be easily read. We
show that data-learned simple staged trees often outperform Bayesian networks
in model fit and illustrate how the coalesced graph is used to identify
non-symmetric conditional independences.

CMOS-based computing systems that employ the von Neumann architecture are
relatively limited when it comes to parallel data storage and processing. In
contrast, the human brain is a living computational signal processing unit that
operates with extreme parallelism and energy efficiency. Although numerous
neuromorphic electronic devices have emerged in the last decade, most of them
are rigid or contain materials that are toxic to biological systems. In this
work, we report on biocompatible bilayer graphene-based artificial synaptic
transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices
leverage a dry ion-selective membrane, enabling long-term potentiation, with
~50 aJ/m^2 switching energy efficiency, at least an order of magnitude lower
than previous reports on two-dimensional material-based artificial synapses.
The devices show unique metaplasticity, a useful feature for generalizable deep
neural networks, and we demonstrate that metaplastic BLASTs outperform ideal
linear synapses in classic image classification tasks. With switching energy
well below the 1 fJ energy estimated per biological synapse, the proposed
devices are powerful candidates for bio-interfaced online learning, bridging
the gap between artificial and biological neural networks.

Data-centric AI encourages the need of cleaning and understanding of data in
order to achieve trustworthy AI. Existing technologies, such as AutoML, make it
easier to design and train models automatically, but there is a lack of a
similar level of capabilities to extract data-centric insights. Manual
stratification of tabular data per a feature (e.g., gender) is limited to scale
up for higher feature dimension, which could be addressed using automatic
discovery of divergent subgroups. Nonetheless, these automatic discovery
techniques often search across potentially exponential combinations of features
that could be simplified using a preceding feature selection step. Existing
feature selection techniques for tabular data often involve fitting a
particular model in order to select important features. However, such
model-based selection is prone to model-bias and spurious correlations in
addition to requiring extra resource to design, fine-tune and train a model. In
this paper, we propose a model-free and sparsity-based automatic feature
selection (SAFS) framework to facilitate automatic discovery of divergent
subgroups. Different from filter-based selection techniques, we exploit the
sparsity of objective measures among feature values to rank and select
features. We validated SAFS across two publicly available datasets (MIMIC-III
and Allstate Claims) and compared it with six existing feature selection
methods. SAFS achieves a reduction of feature selection time by a factor of 81x
and 104x, averaged cross the existing methods in the MIMIC-III and Claims
datasets respectively. SAFS-selected features are also shown to achieve
competitive detection performance, e.g., 18.3% of features selected by SAFS in
the Claims dataset detected divergent samples similar to those detected by
using the whole features with a Jaccard similarity of 0.95 but with a 16x
reduction in detection time.

A framework is proposed for generative models as a basis for digital twins or
mirrors of structures. The proposal is based on the premise that deterministic
models cannot account for the uncertainty present in most structural modelling
applications. Two different types of generative models are considered here. The
first is a physics-based model based on the stochastic finite element (SFE)
method, which is widely used when modelling structures that have material and
loading uncertainties imposed. Such models can be calibrated according to data
from the structure and would be expected to outperform any other model if the
modelling accurately captures the true underlying physics of the structure. The
potential use of SFE models as digital mirrors is illustrated via application
to a linear structure with stochastic material properties. For situations where
the physical formulation of such models does not suffice, a data-driven
framework is proposed, using machine learning and conditional generative
adversarial networks (cGANs). The latter algorithm is used to learn the
distribution of the quantity of interest in a structure with material
nonlinearities and uncertainties. For the examples considered in this work, the
data-driven cGANs model outperform the physics-based approach. Finally, an
example is shown where the two methods are coupled such that a hybrid model
approach is demonstrated.

The COVID-19 pandemic has significantly impacted the tourism and hospitality
sector. Public policies such as travel restrictions and stay-at-home orders had
significantly affected tourist activities and service businesses' operations
and profitability. To this end, it is essential to develop an interpretable
forecast model that supports managerial and organizational decision-making. We
developed DemandNet, a novel deep learning framework for predicting time series
data under the influence of the COVID-19 pandemic. The framework starts by
selecting the top static and dynamic features embedded in the time series data.
Then, it includes a nonlinear model which can provide interpretable insight
into the previously seen data. Lastly, a prediction model is developed to
leverage the above characteristics to make robust long-term forecasts. We
evaluated the framework using daily hotel demand and revenue data from eight
cities in the US. Our findings reveal that DemandNet outperforms the
state-of-art models and can accurately predict the impact of the COVID-19
pandemic on hotel demand and revenues.

Generative Adversarial Networks (GANs) have been shown to be powerful and
flexible priors when solving inverse problems. One challenge of using them is
overcoming representation error, the fundamental limitation of the network in
representing any particular signal. Recently, multiple proposed inversion
algorithms reduce representation error by optimizing over intermediate layer
representations. These methods are typically applied to generative models that
were trained agnostic of the downstream inversion algorithm. In our work, we
introduce a principle that if a generative model is intended for inversion
using an algorithm based on optimization of intermediate layers, it should be
trained in a way that regularizes those intermediate layers. We instantiate
this principle for two notable recent inversion algorithms: Intermediate Layer
Optimization and the Multi-Code GAN prior. For both of these inversion
algorithms, we introduce a new regularized GAN training algorithm and
demonstrate that the learned generative model results in lower reconstruction
errors across a wide range of under sampling ratios when solving compressed
sensing, inpainting, and super-resolution problems.

This paper considers a leader-following formation control problem for
heterogeneous, second-order, uncertain, input-affine, nonlinear multi-agent
systems modeled by a directed graph. A tunable, three-layer neural network (NN)
is proposed with an input layer, two hidden layers, and an output layer to
approximate an unknown nonlinearity. Unlike commonly used trial and error
efforts to select the number of neurons in a conventional NN, in this case an
\textit{a priori} knowledge allows one to set up the number of neurons in each
layer. The NN weights tuning laws are derived using the Lyapunov theory. The
leader-following and formation control problems are addressed by a robust
integral of the sign of the error (RISE) feedback and a NN-based control. The
RISE feedback term compensates for unknown leader dynamics and the unknown,
bounded disturbance in the agent error dynamics. The NN-based term compensates
for the unknown nonlinearity in the dynamics of multi-agent systems, and
semi-global asymptotic tracking results are rigorously proven using the
Lyapunov stability theory. The results of the paper are compared with two
previous results to evaluate the efficiency and performance of the proposed
method.

Hex is a turn-based two-player connection game with a high branching factor,
making the game arbitrarily complex with increasing board sizes. As such,
top-performing algorithms for playing Hex rely on accurate evaluation of board
positions using neural networks. However, the limited interpretability of
neural networks is problematic when the user wants to understand the reasoning
behind the predictions made. In this paper, we propose to use propositional
logic expressions to describe winning and losing board game positions,
facilitating precise visual interpretation. We employ a Tsetlin Machine (TM) to
learn these expressions from previously played games, describing where pieces
must be located or not located for a board position to be strong. Extensive
experiments on $6\times6$ boards compare our TM-based solution with popular
machine learning algorithms like XGBoost, InterpretML, decision trees, and
neural networks, considering various board configurations with $2$ to $22$
moves played. On average, the TM testing accuracy is $92.1\%$, outperforming
all the other evaluated algorithms. We further demonstrate the global
interpretation of the logical expressions and map them down to particular board
game configurations to investigate local interpretability. We believe the
resulting interpretability establishes building blocks for accurate assistive
AI and human-AI collaboration, also for more complex prediction tasks.

Federated Edge Learning (FEEL) is a promising distributed learning technique
that aims to train a shared global model while reducing communication costs and
promoting users' privacy. However, the training process might significantly
occupy a long time due to the nature of the used data for training, which leads
to higher energy consumption and therefore impacts the model convergence. To
tackle this issue, we propose a data-driven federated edge learning scheme that
tends to select suitable participating nodes based on quality data and energy.
First, we design an unsupervised data-aware splitting scheme that partitions
the node's local data into diverse samples used for training. We incorporate a
similarity index to select quality data that enhances the training performance.
Then, we propose a heuristic participating nodes selection scheme to minimize
the communication and computation energy consumption, as well as the amount of
communication rounds. The obtained results show that the proposed scheme
substantially outperforms the vanilla FEEL in terms of energy consumption and
the number of communication rounds.

$(j,k)$-projective clustering is the natural generalization of the family of
$k$-clustering and $j$-subspace clustering problems. Given a set of points $P$
in $\mathbb{R}^d$, the goal is to find $k$ flats of dimension $j$, i.e., affine
subspaces, that best fit $P$ under a given distance measure. In this paper, we
propose the first algorithm that returns an $L_\infty$ coreset of size
polynomial in $d$. Moreover, we give the first strong coreset construction for
general $M$-estimator regression. Specifically, we show that our construction
provides efficient coreset constructions for Cauchy, Welsch, Huber,
Geman-McClure, Tukey, $L_1-L_2$, and Fair regression, as well as general
concave and power-bounded loss functions. Finally, we provide experimental
results based on real-world datasets, showing the efficacy of our approach.

Early detection of breast cancer in mammography screening via deep-learning
based computer-aided detection systems shows promising potential in improving
the curability and mortality rates of breast cancer. However, many clinical
centres are restricted in the amount and heterogeneity of available data to
train such models to (i) achieve promising performance and to (ii) generalise
well across acquisition protocols and domains. As sharing data between centres
is restricted due to patient privacy concerns, we propose a potential solution:
sharing trained generative models between centres as substitute for real
patient data. In this work, we use three well known mammography datasets to
simulate three different centres, where one centre receives the trained
generator of Generative Adversarial Networks (GANs) from the two remaining
centres in order to augment the size and heterogeneity of its training dataset.
We evaluate the utility of this approach on mammography patch classification on
the test set of the GAN-receiving centre using two different classification
models, (a) a convolutional neural network and (b) a transformer neural
network. Our experiments demonstrate that shared GANs notably increase the
performance of both transformer and convolutional classification models and
highlight this approach as a viable alternative to inter-centre data sharing.

Conventional image reconstruction models for lensless cameras often assume
that each measurement results from convolving a given scene with a single
experimentally measured point-spread function. These image reconstruction
models fall short in simulating lensless cameras truthfully as these models are
not sophisticated enough to account for optical aberrations or scenes with
depth variations. Our work shows that learning a supervised primal-dual
reconstruction method results in image quality matching state of the art in the
literature without demanding a large network capacity. This improvement stems
from our primary finding that embedding learnable forward and adjoint models in
a learned primal-dual optimization framework can even improve the quality of
reconstructed images (+5dB PSNR) compared to works that do not correct for the
model error. In addition, we built a proof-of-concept lensless camera prototype
that uses a pseudo-random phase mask to demonstrate our point. Finally, we
share the extensive evaluation of our learned model based on an open dataset
and a dataset from our proof-of-concept lensless camera prototype.

This work proposes a novel general-purpose estimator for supervised machine
learning (ML) based on tensor trains (TT). The estimator uses TTs to
parametrize discretized functions, which are then optimized using Riemannian
gradient descent under the form of a tensor completion problem. Since this
optimization is sensitive to initialization, it turns out that the use of other
ML estimators for initialization is crucial. This results in a competitive,
fast ML estimator with lower memory usage than many other ML estimators, like
the ones used for the initialization.

In this paper, we present and prove some consistency results about the
performance of classification models using a subset of features. In addition,
we propose to use beam search to perform feature selection, which can be viewed
as a generalization of forward selection. We apply beam search to both
simulated and real-world data, by evaluating and comparing the performance of
different classification models using different sets of features. The results
demonstrate that beam search could outperform forward selection, especially
when the features are correlated so that they have more discriminative power
when considered jointly than individually. Moreover, in some cases
classification models could obtain comparable performance using only ten
features selected by beam search instead of hundreds of original features.

Based on an ethnographic action research study for a Digital Twin (DT)
deployment on an automated highway maintenance project, this paper reports some
of the stumbling blocks that practitioners face while deploying a DT in
practice. At the outset, the scope of the case study was broadly defined in
terms of digitalization, and software development and deployment, which later
pivoted towards the concept of Digital Twin during the collective reflection
sessions between the project participants. Through an iterative learning cycle
via discussions among the various project stakeholders, the case study led to
uncovering the roadblocks in practice faced by the Architecture, Engineering,
and Construction (AEC) practitioners. This research finds that the
practitioners are facing difficulty in: (1) Creating a shared understanding due
to the lack of consensus on the Digital Twin concept, (2) Adapting and
investing in Digital Twin due to inability to exhaustively evaluate and select
the appropriate capabilities in a Digital Twin, and (3) Allocation of resources
for Digital Twin development due to the inability to assess the impact of DT on
the organizational conditions and processes.

A new Bayesian modelling framework is introduced for piece-wise homogeneous
variable-memory Markov chains, along with a collection of effective algorithmic
tools for change-point detection and segmentation of discrete time series.
Building on the recently introduced Bayesian Context Trees (BCT) framework, the
distributions of different segments in a discrete time series are described as
variable-memory Markov chains. Inference for the presence and location of
change-points is then performed via Markov chain Monte Carlo sampling. The key
observation that facilitates effective sampling is that, using one of the BCT
algorithms, the prior predictive likelihood of the data can be computed
exactly, integrating out all the models and parameters in each segment. This
makes it possible to sample directly from the posterior distribution of the
number and location of the change-points, leading to accurate estimates and
providing a natural quantitative measure of uncertainty in the results.
Estimates of the actual model in each segment can also be obtained, at
essentially no additional computational cost. Results on both simulated and
real-world data indicate that the proposed methodology performs better than or
as well as state-of-the-art techniques.

This paper proposes a simple transfer learning baseline for sign language
translation. Existing sign language datasets (e.g. PHOENIX-2014T, CSL-Daily)
contain only about 10K-20K pairs of sign videos, gloss annotations and texts,
which are an order of magnitude smaller than typical parallel data for training
spoken language translation models. Data is thus a bottleneck for training
effective sign language translation models. To mitigate this problem, we
propose to progressively pretrain the model from general-domain datasets that
include a large amount of external supervision to within-domain datasets.
Concretely, we pretrain the sign-to-gloss visual network on the general domain
of human actions and the within-domain of a sign-to-gloss dataset, and pretrain
the gloss-to-text translation network on the general domain of a multilingual
corpus and the within-domain of a gloss-to-text corpus. The joint model is
fine-tuned with an additional module named the visual-language mapper that
connects the two networks. This simple baseline surpasses the previous
state-of-the-art results on two sign language translation benchmarks,
demonstrating the effectiveness of transfer learning. With its simplicity and
strong performance, this approach can serve as a solid baseline for future
research.

Researchers have adopted remote methods, such as online surveys and video
conferencing, to overcome challenges in conducting in-person usability testing,
such as participation, user representation, and safety. However, remote user
evaluation on hardware testbeds is limited, especially for blind participants,
as such methods restrict access to observations of user interactions. We employ
smart glasses in usability testing with blind people and share our lessons from
a case study conducted in blind participants' homes (N=12), where the
experimenter can access participants' activities via dual video conferencing: a
third-person view via a laptop camera and a first-person view via smart glasses
worn by the participant. We show that smart glasses hold potential for
observing participants' interactions with smartphone testbeds remotely; on
average 58.7% of the interactions were fully captured via the first-person view
compared to 3.7% via the third-person. However, this gain is not uniform across
participants as it is susceptible to head movements orienting the ear towards a
sound source, which highlights the need for a more inclusive camera form
factor. We also share our lessons learned when it comes to dealing with lack of
screen reader support in smart glasses, a rapidly draining battery, and
Internet connectivity in remote studies with blind participants.

We propose Probabilistic Warp Consistency, a weakly-supervised learning
objective for semantic matching. Our approach directly supervises the dense
matching scores predicted by the network, encoded as a conditional probability
distribution. We first construct an image triplet by applying a known warp to
one of the images in a pair depicting different instances of the same object
class. Our probabilistic learning objectives are then derived using the
constraints arising from the resulting image triplet. We further account for
occlusion and background clutter present in real image pairs by extending our
probabilistic output space with a learnable unmatched state. To supervise it,
we design an objective between image pairs depicting different object classes.
We validate our method by applying it to four recent semantic matching
architectures. Our weakly-supervised approach sets a new state-of-the-art on
four challenging semantic matching benchmarks. Lastly, we demonstrate that our
objective also brings substantial improvements in the strongly-supervised
regime, when combined with keypoint annotations.

This work presents Spacecraft Pose Network v2 (SPNv2), a Convolutional Neural
Network (CNN) for pose estimation of noncooperative spacecraft across domain
gap. SPNv2 is a multi-scale, multi-task CNN which consists of a shared
multi-scale feature encoder and multiple prediction heads that perform
different tasks on a shared feature output. These tasks are all related to
detection and pose estimation of a target spacecraft from an image, such as
prediction of pre-defined satellite keypoints, direct pose regression, and
binary segmentation of the satellite foreground. It is shown that by jointly
training on different yet related tasks with extensive data augmentations on
synthetic images only, the shared encoder learns features that are common
across image domains that have fundamentally different visual characteristics
compared to synthetic images. This work also introduces Online Domain
Refinement (ODR) which refines the parameters of the normalization layers of
SPNv2 on the target domain images online at deployment. Specifically, ODR
performs self-supervised entropy minimization of the predicted satellite
foreground, thereby improving the CNN's performance on the target domain images
without their pose labels and with minimal computational efforts. The GitHub
repository for SPNv2 is available at \url{https://github.com/tpark94/spnv2}.

We study the setting of optimizing with bandit feedback with additional prior
knowledge provided to the learner in the form of an initial hint of the optimal
action. We present a novel algorithm for stochastic linear bandits that uses
this hint to improve its regret to $\tilde O(\sqrt{T})$ when the hint is
accurate, while maintaining a minimax-optimal $\tilde O(d\sqrt{T})$ regret
independent of the quality of the hint. Furthermore, we provide a Pareto
frontier of tight tradeoffs between best-case and worst-case regret, with
matching lower bounds. Perhaps surprisingly, our work shows that leveraging a
hint shows provable gains without sacrificing worst-case performance, implying
that our algorithm adapts to the quality of the hint for free. We also provide
an extension of our algorithm to the case of $m$ initial hints, showing that we
can achieve a $\tilde O(m^{2/3}\sqrt{T})$ regret.

For applications in healthcare, physics, energy, robotics, and many other
fields, designing maximally informative experiments is valuable, particularly
when experiments are expensive, time-consuming, or pose safety hazards. While
existing approaches can sequentially design experiments based on prior
observation history, many of these methods do not extend to implicit models,
where simulation is possible but computing the likelihood is intractable.
Furthermore, they often require either significant online computation during
deployment or a differentiable simulation system. We introduce Reinforcement
Learning for Deep Adaptive Design (RL-DAD), a method for simulation-based
optimal experimental design for non-differentiable implicit models. RL-DAD
extends prior work in policy-based Bayesian Optimal Experimental Design (BOED)
by reformulating it as a Markov Decision Process with a reward function based
on likelihood-free information lower bounds, which is used to learn a policy
via deep reinforcement learning. The learned design policy maps prior histories
to experiment designs offline and can be quickly deployed during online
execution. We evaluate RL-DAD and find that it performs competitively with
baselines on three benchmarks.

Efficient approaches to quantum control and feedback are essential for
quantum technologies, from sensing to quantum computation. Pure control tasks
have been successfully solved using optimization techniques, including methods
like gradient-ascent pulse engineering (GRAPE) , relying on a differentiable
model of the quantum dynamics. For feedback tasks, such methods are not
directly applicable, since the aim is to discover strategies conditioned on
measurement outcomes. There, model-free reinforcement learning (RL) has
recently proven a powerful new ansatz. What is missing is a way to combine the
best of both approaches for scenarios that go beyond weak measurements. In this
work, we introduce feedback-GRAPE, which borrows concepts from model-free RL to
incorporate the response to strong stochastic (discrete or continuous)
measurements, while still performing direct gradient ascent through the quantum
dynamics. We illustrate its power on a Jaynes-Cummings model with feedback,
where it yields interpretable feedback strategies for state preparation and
stabilization in the presence of noise. This approach could be employed for
discovering strategies in a wide range of feedback tasks, from calibration of
multi-qubit devices to linear-optics quantum computation strategies,
quantum-enhanced sensing with adaptive measurements, and quantum error
correction.

In this work, we focus on semi-supervised learning for video action detection
which utilizes both labeled as well as unlabeled data. We propose a simple
end-to-end consistency based approach which effectively utilizes the unlabeled
data. Video action detection requires both, action class prediction as well as
a spatio-temporal localization of actions. Therefore, we investigate two types
of constraints, classification consistency, and spatio-temporal consistency.
The presence of predominant background and static regions in a video makes it
challenging to utilize spatio-temporal consistency for action detection. To
address this, we propose two novel regularization constraints for
spatio-temporal consistency; 1) temporal coherency, and 2) gradient smoothness.
Both these aspects exploit the temporal continuity of action in videos and are
found to be effective for utilizing unlabeled videos for action detection. We
demonstrate the effectiveness of the proposed approach on two different action
detection benchmark datasets, UCF101-24 and JHMDB-21. In addition, we also show
the effectiveness of the proposed approach for video object segmentation on the
Youtube-VOS which demonstrates its generalization capability The proposed
approach achieves competitive performance by using merely 20% of annotations on
UCF101-24 when compared with recent fully supervised methods. On UCF101-24, it
improves the score by +8.9% and +11% at 0.5 f-mAP and v-mAP respectively,
compared to supervised approach.

Image registration is the process of bringing different images into a common
coordinate system - a technique widely used in various applications of computer
vision, such as remote sensing, image retrieval, and most commonly in medical
imaging. Deep Learning based techniques have been applied successfully to
tackle various complex medical image processing problems, including medical
image registration. Over the years, several image registration techniques have
been proposed using deep learning. Deformable image registration techniques
such as Voxelmorph have been successful in capturing finer changes and
providing smoother deformations. However, Voxelmorph, as well as ICNet and
FIRE, do not explicitly encode global dependencies (i.e. the overall anatomical
view of the supplied image) and therefore can not track large deformations. In
order to tackle the aforementioned problems, this paper extends the Voxelmorph
approach in three different ways. To improve the performance in case of small
as well as large deformations, supervision of the model at different
resolutions have been integrated using a multi-scale UNet. To support the
network to learn and encode the minute structural co-relations of the given
image-pairs, a self-constructing graph network (SCGNet) has been used as the
latent of the multi-scale UNet - which can improve the learning process of the
model and help the model to generalise better. And finally, to make the
deformations inverse-consistent, cycle consistency loss has been employed. On
the task of registration of brain MRIs, the proposed method achieved
significant improvements over ANTs and VoxelMorph, obtaining a Dice score of
0.8013$\pm$0.0243 for intramodal and 0.6211$\pm$0.0309 for intermodal, while
VoxelMorph achieved 0.7747$\pm$0.0260 and 0.6071$\pm$0.0510, respectively.

Battery state of health is an essential metric for diagnosing battery
degradation during testing and operation. While many unique measurements are
possible in the design phase, for practical applications often only
temperature, voltage and current sensing are accessible. This paper presents a
novel combination of machine learning techniques to produce accurate
predictions significantly faster than standard Gaussian processes. The
data-driven approach uses feature generation with simple mathematics, feature
filtering, and bagging, which is validated with publicly available aging
datasets of more than 200 cells with slow and fast charging, across different
cathode chemistries, and for various operating conditions. Based on multiple
training-test partitions, average and median state of health prediction root
mean square error (RMSE) is found to be less than 1.48% and 1.27%,
respectively, with a limited amount of input data, showing the capability of
the approach even when input data and time are limiting factors. The process
developed in this paper has direct applicability to today's incumbent open
challenge of assessing retired batteries on the basis of their residual health,
and therefore nominal remaining useful life, to allow fast classification for
second-life reutilization.

Fully exploiting the learning capacity of neural networks requires
overparameterized dense networks. On the other side, directly training sparse
neural networks typically results in unsatisfactory performance. Lottery Ticket
Hypothesis (LTH) provides a novel view to investigate sparse network training
and maintain its capacity. Concretely, it claims there exist winning tickets
from a randomly initialized network found by iterative magnitude pruning and
preserving promising trainability (or we say being in trainable condition). In
this work, we regard the winning ticket from LTH as the subnetwork which is in
trainable condition and its performance as our benchmark, then go from a
complementary direction to articulate the Dual Lottery Ticket Hypothesis
(DLTH): Randomly selected subnetworks from a randomly initialized dense network
can be transformed into a trainable condition and achieve admirable performance
compared with LTH -- random tickets in a given lottery pool can be transformed
into winning tickets. Specifically, by using uniform-randomly selected
subnetworks to represent the general cases, we propose a simple sparse network
training strategy, Random Sparse Network Transformation (RST), to substantiate
our DLTH. Concretely, we introduce a regularization term to borrow learning
capacity and realize information extrusion from the weights which will be
masked. After finishing the transformation for the randomly selected
subnetworks, we conduct the regular finetuning to evaluate the model using fair
comparisons with LTH and other strong baselines. Extensive experiments on
several public datasets and comparisons with competitive approaches validate
our DLTH as well as the effectiveness of the proposed model RST. Our work is
expected to pave a way for inspiring new research directions of sparse network
training in the future. Our code is available at
https://github.com/yueb17/DLTH.

Topological data analysis (TDA) provides a set of data analysis tools for
extracting embedded topological structures from complex high-dimensional
datasets. In recent years, TDA has been a rapidly growing field which has found
success in a wide range of applications, including signal processing,
neuroscience and network analysis. In these applications, the online detection
of changes is of crucial importance, but this can be highly challenging since
such changes often occur in a low-dimensional embedding within high-dimensional
data streams. We thus propose a new method, called PERsistence diagram-based
ChangE-PoinT detection (PERCEPT), which leverages the learned topological
structure from TDA to sequentially detect changes. PERCEPT follows two key
steps: it first learns the embedded topology as a point cloud via persistence
diagrams, then applies a non-parametric monitoring approach for detecting
changes in the resulting point cloud distributions. This yields a
non-parametric, topology-aware framework which can efficiently detect online
changes from high-dimensional data streams. We investigate the effectiveness of
PERCEPT over existing methods in a suite of numerical experiments where the
data streams have an embedded topological structure. We then demonstrate the
usefulness of PERCEPT in two applications in solar flare monitoring and human
gesture detection.

Photoacoustic Remote Sensing (PARS) microscopy is an emerging label-free
optical absorption imaging modality. PARS operates by capturing
nanosecond-scale optical perturbations generated by photoacoustic pressures.
These time-domain (TD) modulations are usually projected by amplitude to
determine absorption magnitude. However, significant information on the
target's material properties is contained within the TD signals. This work
proposes a novel clustering method to learn TD features which relate to
underlying biomolecule characteristics. This technique identifies features
related to constituent biomolecules, enabling single-acquisition virtual tissue
labelling. Colorized visualizations of tissue are produced, highlighting
specific tissue components. This is demonstrated on freshly resected murine
brain tissue, clearly discerning structures including myelinated and
unmyelinated neurons (white and gray matter) and nuclear structures.

In this foundational expository article on the application of Causality
Analysis in IoT, we establish the basic theory and algorithms for estimating
Structural and Granger causality factors from measured multichannel sensor data
(vector timeseries). Vector timeseries is modeled as a Structural Vector
Autoregressive (SVAR) model; utilizing Kalman Filter and Independent Component
Analysis (ICA) methods, Structural and generalized Granger causality factors
are estimated. The estimated causal factors are presented as a Fence graph
which we call Causal Digital Twin. Practical applications of Causal Digital
Twin are demonstrated on NASA Prognostic Data Repository Bearing data
collection. Use of Causal Digital Twin for counterfactual experiments are
indicated.
  Causal Digital Twin is a horizontal solution that applies to diverse use
cases in multiple industries such as Industrial, Manufacturing, Automotive,
Consumer, Building and Smart City.

Offline policy evaluation is a fundamental statistical problem in
reinforcement learning that involves estimating the value function of some
decision-making policy given data collected by a potentially different policy.
In order to tackle problems with complex, high-dimensional observations, there
has been significant interest from theoreticians and practitioners alike in
understanding the possibility of function approximation in reinforcement
learning. Despite significant study, a sharp characterization of when we might
expect offline policy evaluation to be tractable, even in the simplest setting
of linear function approximation, has so far remained elusive, with a
surprising number of strong negative results recently appearing in the
  In this work, we identify simple control-theoretic and linear-algebraic
conditions that are necessary and sufficient for classical methods, in
particular Fitted Q-iteration (FQI) and least squares temporal difference
learning (LSTD), to succeed at offline policy evaluation. Using this
characterization, we establish a precise hierarchy of regimes under which these
estimators succeed. We prove that LSTD works under strictly weaker conditions
than FQI. Furthermore, we establish that if a problem is not solvable via LSTD,
then it cannot be solved by a broad class of linear estimators, even in the
limit of infinite data. Taken together, our results provide a complete picture
of the behavior of linear estimators for offline policy evaluation (OPE), unify
previously disparate analyses of canonical algorithms, and provide
significantly sharper notions of the underlying statistical complexity of OPE.

Adversarial attacks pose a major threat to machine learning and to the
systems that rely on it. In the cybersecurity domain, adversarial cyber-attack
examples capable of evading detection are especially concerning. Nonetheless,
an example generated for a domain with tabular data must be realistic within
that domain. This work establishes the fundamental constraint levels required
to achieve realism and introduces the Adaptative Perturbation Pattern Method
(A2PM) to fulfill these constraints in a gray-box setting. A2PM relies on
pattern sequences that are independently adapted to the characteristics of each
class to create valid and coherent data perturbations. The proposed method was
evaluated in a cybersecurity case study with two scenarios: Enterprise and
Internet of Things (IoT) networks. Multilayer Perceptron (MLP) and Random
Forest (RF) classifiers were created with regular and adversarial training,
using the CIC-IDS2017 and IoT-23 datasets. In each scenario, targeted and
untargeted attacks were performed against the classifiers, and the generated
examples were compared with the original network traffic flows to assess their
realism. The obtained results demonstrate that A2PM provides a scalable
generation of realistic adversarial examples, which can be advantageous for
both adversarial training and attacks.

Retracted research discussed on social media can spread misinformation, yet
we lack an understanding of how retracted articles are mentioned by academic
and non-academic users. This is especially relevant on Twitter due to the
platform's prominent role in science communication. Here, we analyze the pre
and post retraction differences in Twitter engagement metrics and content of
mentions for over 3,800 retracted English-language articles alongside
comparable non-retracted articles. We subset these findings according to the
five user types detected by our supervised learning classifier: members of the
public, scientists, bots, practitioners, and science communicators. We find
that retracted articles receive greater overall engagement than non-retracted
articles, especially among members of the public and bot users, the majority of
engagement happening prior to retraction. Our results highlight non-scientists'
involvement in retracted article discussions and suggest an opportunity for
Twitter to include a retraction notice feature.

Unmanned Aerial Vehicles (UAVs) are used as aerial base-stations to relay
time-sensitive packets from IoT devices to the nearby terrestrial base-station
(TBS). Scheduling of packets in such UAV-relayed IoT-networks to ensure fresh
(or up-to-date) IoT devices' packets at the TBS is a challenging problem as it
involves two simultaneous steps of (i) sampling of packets generated at IoT
devices by the UAVs [hop-1] and (ii) updating of sampled packets from UAVs to
the TBS [hop-2]. To address this, we propose Age-of-Information (AoI)
scheduling algorithms for two-hop UAV-relayed IoT-networks. First, we propose a
low-complexity AoI scheduler, termed, MAF-MAD that employs Maximum AoI First
(MAF) policy for sampling of IoT devices at UAV (hop-1) and Maximum AoI
Difference (MAD) policy for updating sampled packets from UAV to the TBS
(hop-2). We prove that MAF-MAD is the optimal AoI scheduler under ideal
conditions (lossless wireless channels and generate-at-will traffic-generation
at IoT devices). On the contrary, for general conditions (lossy channel
conditions and varying periodic traffic-generation at IoT devices), a deep
reinforcement learning algorithm, namely, Proximal Policy Optimization
(PPO)-based scheduler is proposed. Simulation results show that the proposed
PPO-based scheduler outperforms other schedulers like MAF-MAD, MAF, and
round-robin in all considered general scenarios.

This study achieved bidirectional translation between descriptions and
actions using small paired data from different modalities. The ability to
mutually generate descriptions and actions is essential for robots to
collaborate with humans in their daily lives, which generally requires a large
dataset that maintains comprehensive pairs of both modality data. However, a
paired dataset is expensive to construct and difficult to collect. To address
this issue, this study proposes a two-stage training method for bidirectional
translation. In the proposed method, we train recurrent autoencoders (RAEs) for
descriptions and actions with a large amount of non-paired data. Then, we
finetune the entire model to bind their intermediate representations using
small paired data. Because the data used for pre-training do not require
pairing, behavior-only data or a large language corpus can be used. We
experimentally evaluated our method using a paired dataset consisting of
motion-captured actions and descriptions. The results showed that our method
performed well, even when the amount of paired data to train was small. The
visualization of the intermediate representations of each RAE showed that
similar actions were encoded in a clustered position and the corresponding
feature vectors were well aligned.

A long-standing goal of intelligent assistants such as AR glasses/robots has
been to assist users in affordance-centric real-world scenarios, such as "how
can I run the microwave for 1 minute?". However, there is still no clear task
definition and suitable benchmarks. In this paper, we define a new task called
Affordance-centric Question-driven Task Completion, where the AI assistant
should learn from instructional videos to provide step-by-step help in the
user's view. To support the task, we constructed AssistQ, a new dataset
comprising 531 question-answer samples from 100 newly filmed instructional
videos. We also developed a novel Question-to-Actions (Q2A) model to address
the AQTC task and validate it on the AssistQ dataset. The results show that our
model significantly outperforms several VQA-related baselines while still
having large room for improvement. We expect our task and dataset to advance
Egocentric AI Assistant's development. Our project page is available at:
https://showlab.github.io/assistq/.

All life on Earth needs water. NASA's quest to follow the water links water
to the search for life in the cosmos. Telescopes like JWST and mission concepts
like HabEx, LUVOIR and Origins are designed to characterise rocky exoplanets
spectroscopically. However, spectroscopy remains time-intensive and therefore,
initial characterisation is critical to prioritisation of targets.
  Here, we study machine learning as a tool to assess water's existence through
broadband-filter reflected photometric flux on Earth-like exoplanets in three
forms: seawater, water-clouds and snow; based on 53,130 spectra of cold,
Earth-like planets with 6 major surfaces. XGBoost, a well-known machine
learning algorithm, achieves over 90\% balanced accuracy in detecting the
existence of snow or clouds for S/N$\gtrsim 20$, and 70\% for liquid seawater
for S/N $\gtrsim 30$. Finally, we perform mock Bayesian analysis with
Markov-chain Monte Carlo with five filters identified to derive exact surface
compositions to test for retrieval feasibility.
  The results show that the use of machine learning to identify water on the
surface of exoplanets from broadband-filter photometry provides a promising
initial characterisation tool of water in different forms. Planned small and
large telescope missions could use this to aid their prioritisation of targets
for time-intense follow-up observations.

Supervised deep learning depends on massive accurately annotated examples,
which is usually impractical in many real-world scenarios. A typical
alternative is learning from multiple noisy annotators. Numerous earlier works
assume that all labels are noisy, while it is usually the case that a few
trusted samples with clean labels are available. This raises the following
important question: how can we effectively use a small amount of trusted data
to facilitate robust classifier learning from multiple annotators? This paper
proposes a data-efficient approach, called \emph{Trustable Co-label Learning}
(TCL), to learn deep classifiers from multiple noisy annotators when a small
set of trusted data is available. This approach follows the coupled-view
learning manner, which jointly learns the data classifier and the label
aggregator. It effectively uses trusted data as a guide to generate trustable
soft labels (termed co-labels). A co-label learning can then be performed by
alternately reannotating the pseudo labels and refining the classifiers. In
addition, we further improve TCL for a special complete data case, where each
instance is labeled by all annotators and the label aggregator is represented
by multilayer neural networks to enhance model capacity. Extensive experiments
on synthetic and real datasets clearly demonstrate the effectiveness and
robustness of the proposed approach. Source code is available at
https://github.com/ShikunLi/TCL

Existing systems for sound event localization and detection (SELD) typically
operate by estimating a source location for all classes at every time instant.
In this paper, we propose an alternative class-conditioned SELD model for
situations where we may not be interested in localizing all classes all of the
time. This class-conditioned SELD model takes as input the spatial and spectral
features from the sound file, and also a one-hot vector indicating the class we
are currently interested in localizing. We inject the conditioning information
at several points in our model using feature-wise linear modulation (FiLM)
layers. Through experiments on the DCASE 2020 Task 3 dataset, we show that the
proposed class-conditioned SELD model performs better in terms of common SELD
metrics than the baseline model that locates all classes simultaneously, and
also outperforms specialist models that are trained to locate only a single
class of interest. We also evaluate performance on the DCASE 2021 Task 3
dataset, which includes directional interference (sound events from classes we
are not interested in localizing) and notice especially strong improvement from
the class-conditioned model.

Generalized zero-shot learning (GZSL) aims at training a model that can
generalize to unseen class data by only using auxiliary information. One of the
main challenges in GZSL is a biased model prediction toward seen classes caused
by overfitting on only available seen class data during training. To overcome
this issue, we propose a two-stream autoencoder-based gating model for GZSL.
Our gating model predicts whether the query data is from seen classes or unseen
classes, and utilizes separate seen and unseen experts to predict the class
independently from each other. This framework avoids comparing the biased
prediction scores for seen classes with the prediction scores for unseen
classes. In particular, we measure the distance between visual and attribute
representations in the latent space and the cross-reconstruction space of the
autoencoder. These distances are utilized as complementary features to
characterize unseen classes at different levels of data abstraction. Also, the
two-stream autoencoder works as a unified framework for the gating model and
the unseen expert, which makes the proposed method computationally efficient.
We validate our proposed method in four benchmark image recognition datasets.
In comparison with other state-of-the-art methods, we achieve the best harmonic
mean accuracy in SUN and AWA2, and the second best in CUB and AWA1.
Furthermore, our base model requires at least 20% less number of model
parameters than state-of-the-art methods relying on generative models.

Reward-biased maximum likelihood estimation (RBMLE) is a classic principle in
the adaptive control literature for tackling explore-exploit trade-offs. This
paper studies the stochastic contextual bandit problem with general bounded
reward functions and proposes NeuralRBMLE, which adapts the RBMLE principle by
adding a bias term to the log-likelihood to enforce exploration. NeuralRBMLE
leverages the representation power of neural networks and directly encodes
exploratory behavior in the parameter space, without constructing confidence
intervals of the estimated rewards. We propose two variants of NeuralRBMLE
algorithms: The first variant directly obtains the RBMLE estimator by gradient
ascent, and the second variant simplifies RBMLE to a simple index policy
through an approximation. We show that both algorithms achieve
$\widetilde{\mathcal{O}}(\sqrt{T})$ regret. Through extensive experiments, we
demonstrate that the NeuralRBMLE algorithms achieve comparable or better
empirical regrets than the state-of-the-art methods on real-world datasets with
non-linear reward functions.

Modeling biological soft tissue is complex in part due to material
heterogeneity. Microstructural patterns, which play a major role in defining
the mechanical behavior of these tissues, are both challenging to characterize,
and difficult to simulate. Recently, machine learning-based methods to predict
the mechanical behavior of heterogeneous materials have made it possible to
more thoroughly explore the massive input parameter space associated with
heterogeneous blocks of material. Specifically, we can train machine learning
(ML) models to closely approximate computationally expensive heterogeneous
material simulations where the ML model is trained on a dataset of simulations
that capture the range of spatial heterogeneity present in the material of
interest. However, when it comes to applying these techniques to biological
tissue more broadly, there is a major limitation: the relevant microstructural
patterns are both challenging to obtain and difficult to analyze. Consequently,
the number of useful examples available to characterize the input domain under
study is limited. In this work, we investigate the efficacy of ML-based
generative models as well as procedural methods as a tool for augmenting
limited input pattern datasets. We find that a Style-based Generative
Adversarial Network with adaptive discriminator augmentation is able to
successfully leverage just 1,000 example patterns to create the most authentic
generated patterns. In general, diverse generated patterns with adequate
resemblance to the real patterns can be used as inputs to finite element
simulations to meaningfully augment the training dataset. To enable this
methodological contribution, we have created an open access dataset of Finite
Element Analysis simulations based on Cahn-Hilliard patterns. We anticipate
that future researchers will be able to leverage this dataset and build on the
work presented here.

Medical Artificial Intelligence (MedAI) for diagnosis, treatment options, and
drug development represents the new age of healthcare. The security, integrity,
and credibility of MedAI tools are paramount issues because human lives are at
stake. MedAI solutions are often heavily dependent on scientific medical
research literature as a primary data source that draws the attacker's
attention as a potential target. We present a first study of how the output of
MedAI can be polluted with Predatory Publications Presence (PPP). We study two
MedAI systems: mediKanren (disease independent) and CancerMine
(Disease-specific), which use research literature as primary data input from
the research repository PubMed, PubMed derived database SemMedDB, and NIH
translational Knowledge Graphs (KGs). Our study has a three-pronged focus: (1)
identifying the PPP in PubMed; (2) verifying the PPP in SemMedDB and the KGs;
(3) demonstrating the existing vulnerability of PPP traversing to the MedAI
output. Our contribution lies in identifying the existing PPP in the MedAI
inputs and demonstrating how predatory science can jeopardize the credibility
of MedAI solutions, making their real-life deployment questionable.

Deep networks have strong capacities of embedding data into latent
representations and finishing following tasks. However, the capacities largely
come from high-quality annotated labels, which are expensive to collect. Noisy
labels are more affordable, but result in corrupted representations, leading to
poor generalization performance. To learn robust representations and handle
noisy labels, we propose selective-supervised contrastive learning (Sel-CL) in
this paper. Specifically, Sel-CL extend supervised contrastive learning
(Sup-CL), which is powerful in representation learning, but is degraded when
there are noisy labels. Sel-CL tackles the direct cause of the problem of
Sup-CL. That is, as Sup-CL works in a \textit{pair-wise} manner, noisy pairs
built by noisy labels mislead representation learning. To alleviate the issue,
we select confident pairs out of noisy ones for Sup-CL without knowing noise
rates. In the selection process, by measuring the agreement between learned
representations and given labels, we first identify confident examples that are
exploited to build confident pairs. Then, the representation similarity
distribution in the built confident pairs is exploited to identify more
confident pairs out of noisy pairs. All obtained confident pairs are finally
used for Sup-CL to enhance representations. Experiments on multiple noisy
datasets demonstrate the robustness of the learned representations by our
method, following the state-of-the-art performance. Source codes are available
at https://github.com/ShikunLi/Sel-CL

In the typical path planning pipeline for a ground robot, we build a map
(e.g., an occupancy grid) of the environment as the robot moves around. While
navigating indoors, a ground robot's knowledge about the environment may be
limited by the occlusions in its surroundings. Therefore, the map will have
many as-yet-unknown regions that may need to be avoided by a conservative
planner. Instead, if a robot is able to correctly infer what its surroundings
and occluded regions look like, the navigation can be further optimized. In
this work, we propose an approach using pix2pix and UNet to infer the occupancy
grid in unseen areas near the robot as an image-to-image translation task. Our
approach simplifies the task of occupancy map prediction for the deep learning
network and reduces the amount of data required compared to similar existing
methods. We show that the predicted map improves the navigation time in
simulations over the existing approaches.

We present Sequential Neural Variational Inference (SNVI), an approach to
perform Bayesian inference in models with intractable likelihoods. SNVI
combines likelihood-estimation (or likelihood-ratio-estimation) with
variational inference to achieve a scalable simulation-based inference
approach. SNVI maintains the flexibility of likelihood(-ratio) estimation to
allow arbitrary proposals for simulations, while simultaneously providing a
functional estimate of the posterior distribution without requiring MCMC
sampling. We present several variants of SNVI and demonstrate that they are
substantially more computationally efficient than previous algorithms, without
loss of accuracy on benchmark tasks. We apply SNVI to a neuroscience model of
the pyloric network in the crab and demonstrate that it can infer the posterior
distribution with one order of magnitude fewer simulations than previously
reported. SNVI vastly reduces the computational cost of simulation-based
inference while maintaining accuracy and flexibility, making it possible to
tackle problems that were previously inaccessible.

We present a computational framework for synthesis of distributed control
strategies for a heterogeneous team of robots in a partially observable
environment. The goal is to cooperatively satisfy specifications given as
Truncated Linear Temporal Logic (TLTL) formulas. Our approach formulates the
synthesis problem as a stochastic game and employs a policy graph method to
find a control strategy with memory for each agent. We construct the stochastic
game on the product between the team transition system and a finite state
automaton (FSA) that tracks the satisfaction of the TLTL formula. We use the
quantitative semantics of TLTL as the reward of the game, and further reshape
it using the FSA to guide and accelerate the learning process. Simulation
results demonstrate the efficacy of the proposed solution under demanding task
specifications and the effectiveness of reward shaping in significantly
accelerating the speed of learning.

Data science and machine learning provide indispensable techniques for
understanding phenomena at scale, but the discretionary choices made when doing
this work are often not recognized. Drawing from qualitative research
practices, we describe how the concepts of positionality and reflexivity can be
adapted to provide a framework for understanding, discussing, and disclosing
the discretionary choices and subjectivity inherent to data science work. We
first introduce the concepts of model positionality and computational
reflexivity that can help data scientists to reflect on and communicate the
social and cultural context of a model's development and use, the data
annotators and their annotations, and the data scientists themselves. We then
describe the unique challenges of adapting these concepts for data science work
and offer annotator fingerprinting and position mining as promising solutions.
Finally, we demonstrate these techniques in a case study of the development of
classifiers for toxic commenting in online communities.

This paper focuses on the critical load restoration problem in distribution
systems following major outages. To provide fast online response and optimal
sequential decision-making support, a reinforcement learning (RL) based
approach is proposed to optimize the restoration. Due to the complexities
stemming from the large policy search space, renewable uncertainty, and
nonlinearity in a complex grid control problem, directly applying RL algorithms
to train a satisfactory policy requires extensive tuning to be successful. To
address this challenge, this paper leverages the curriculum learning (CL)
technique to design a training curriculum involving a simpler steppingstone
problem that guides the RL agent to learn to solve the original hard problem in
a progressive and more efficient manner. We demonstrate that compared with
direct learning, CL facilitates controller training to achieve better
performance. In the experiments, to study realistic scenarios where renewable
forecasts used for decision-making are in general imperfect, the trained RL
controllers are compared with two model predictive controllers (MPCs) using
renewable forecasts with different error levels and observe how these
controllers can hedge against the uncertainty. Results show that RL controllers
are less susceptible to forecast errors than the baseline MPCs and can provide
a more reliable restoration process.

This paper aims to develop a global perspective of the complexity of the
relationship between the standardised per-capita growth rate of Covid-19 cases,
deaths, and the OxCGRT Covid-19 Stringency Index, a measure describing a
country's stringency of lockdown policies. To achieve our goal, we use a
heterogeneous intrinsic dimension estimator implemented as a Bayesian mixture
model, called Hidalgo. We identify that the Covid-19 dataset may project onto
two low-dimensional manifolds without significant information loss. The low
dimensionality suggests strong dependency among the standardised growth rates
of cases and deaths per capita and the OxCGRT Covid-19 Stringency Index for a
country over 2020-2021. Given the low dimensional structure, it may be feasible
to model observable Covid-19 dynamics with few parameters. Importantly, we
identify spatial autocorrelation in the intrinsic dimension distribution
worldwide. Moreover, we highlight that high-income countries are more likely to
lie on low-dimensional manifolds, likely arising from aging populations,
comorbidities, and increased per capita mortality burden from Covid-19.
Finally, we temporally stratify the dataset to examine the intrinsic dimension
at a more granular level throughout the Covid-19 pandemic.

Data poisoning attacks, in which an adversary corrupts a training set with
the goal of inducing specific desired mistakes, have raised substantial
concern: even just the possibility of such an attack can make a user no longer
trust the results of a learning system. In this work, we show how to achieve
strong robustness guarantees in the face of such attacks across multiple axes.
  We provide robustly-reliable predictions, in which the predicted label is
guaranteed to be correct so long as the adversary has not exceeded a given
corruption budget, even in the presence of instance targeted attacks, where the
adversary knows the test example in advance and aims to cause a specific
failure on that example. Our guarantees are substantially stronger than those
in prior approaches, which were only able to provide certificates that the
prediction of the learning algorithm does not change, as opposed to certifying
that the prediction is correct, as we are able to achieve in our work.
Remarkably, we provide a complete characterization of learnability in this
setting, in particular, nearly-tight matching upper and lower bounds on the
region that can be certified, as well as efficient algorithms for computing
this region given an ERM oracle. Moreover, for the case of linear separators
over logconcave distributions, we provide efficient truly polynomial time
algorithms (i.e., non-oracle algorithms) for such robustly-reliable
  We also extend these results to the active setting where the algorithm
adaptively asks for labels of specific informative examples, and the difficulty
is that the adversary might even be adaptive to this interaction, as well as to
the agnostic learning setting where there is no perfect classifier even over
the uncorrupted data.

The deep learning-based ISP models for mobile cameras produce high-quality
images comparable to the professional DSLR camera. However, many of them are
computationally expensive, which may not be appropriate for mobile
environments. Also, the recent mobile cameras adopt non-Bayer CFAs (e.g., Quad
Bayer, Nona Bayer, and QxQ Bayer) to improve image quality; however, most deep
learning-based ISP models mainly focus on standard Bayer CFA. In this work, we
propose PyNET-QxQ based on PyNET, a light-weighted ISP explicitly designed for
the QxQ CFA pattern. The number of parameters of PyNET-QxQ is less than 2.5% of
PyNET. We also introduce a novel knowledge distillation technique, progressive
distillation, to train the compressed network effectively. Finally, experiments
with QxQ images (obtained by an actual QxQ camera sensor, under development)
demonstrate the outstanding performance of PyNET-QxQ despite significant
parameter reductions.

Unsupervised domain adaptation (UDA) requires source domain samples with
clean ground truth labels during training. Accurately labeling a large number
of source domain samples is time-consuming and laborious. An alternative is to
utilize samples with noisy labels for training. However, training with noisy
labels can greatly reduce the performance of UDA. In this paper, we address the
problem that learning UDA models only with access to noisy labels and propose a
novel method called robust local preserving and global aligning network
(RLPGA). RLPGA improves the robustness of the label noise from two aspects. One
is learning a classifier by a robust informative-theoretic-based loss function.
The other is constructing two adjacency weight matrices and two negative weight
matrices by the proposed local preserving module to preserve the local topology
structures of input data. We conduct theoretical analysis on the robustness of
the proposed RLPGA and prove that the robust informative-theoretic-based loss
and the local preserving module are beneficial to reduce the empirical risk of
the target domain. A series of empirical studies show the effectiveness of our
proposed RLPGA.

Sensor-based human activity recognition (HAR) is a paramount technology in
the Internet of Things services. HAR using representation learning, which
automatically learns a feature representation from raw data, is the mainstream
method because it is difficult to interpret relevant information from raw
sensor data to design meaningful features. Ensemble learning is a robust
approach to improve generalization performance; however, deep ensemble learning
requires various procedures, such as data partitioning and training multiple
models, which are time-consuming and computationally expensive. In this study,
we propose Easy Ensemble (EE) for HAR, which enables the easy implementation of
deep ensemble learning in a single model. In addition, we propose input masking
as a method for diversifying the input for EE. Experiments on a benchmark
dataset for HAR demonstrated the effectiveness of EE and input masking and
their characteristics compared with conventional ensemble learning methods.

The Web contains several social media platforms for discussion, exchange of
ideas, and content publishing. These platforms are used by people, but also by
distributed agents known as bots. Although bots have existed for decades, with
many of them being benevolent, their influence in propagating and generating
deceptive information in the last years has increased. Here we present a
characterization of the discussion on Twitter about the 2020 Chilean
constitutional referendum. The characterization uses a profile-oriented
analysis that enables the isolation of anomalous content using machine
learning. As result, we obtain a characterization that matches national vote
turnout, and we measure how anomalous accounts (some of which are automated
bots) produce content and interact promoting (false) information.

Autonomous systems and humans are increasingly sharing the same space. Robots
work side by side or even hand in hand with humans to balance each other's
limitations. Such cooperative interactions are ever more sophisticated. Thus,
the ability to reason not just about a human's center of gravity position, but
also its granular motion is an important prerequisite for human-robot
interaction. Though, many algorithms ignore the multimodal nature of humans or
neglect uncertainty in their motion forecasts. We present Motron, a multimodal,
probabilistic, graph-structured model, that captures human's multimodality
using probabilistic methods while being able to output deterministic
maximum-likelihood motions and corresponding confidence values for each mode.
Our model aims to be tightly integrated with the robotic
planning-control-interaction loop; outputting physically feasible human motions
and being computationally efficient. We demonstrate the performance of our
model on several challenging real-world motion forecasting datasets,
outperforming a wide array of generative/variational methods while providing
state-of-the-art single-output motions if required. Both using significantly
less computational power than state-of-the art algorithms.

An automatic gun detection system can detect potential gun-related violence
at an early stage that is of paramount importance for citizens security. In the
whole system, object detection algorithm is the key to perceive the environment
so that the system can detect dangerous objects such as pistols and rifles.
However, mainstream deep learning-based object detection algorithms depend
heavily on large-scale high-quality annotated samples, and the existing gun
datasets are characterized by low resolution, little contextual information and
little data volume. To promote the development of security, this work presents
a new challenging dataset called YouTube Gun Detection Dataset (YouTube-GDD).
Our dataset is collected from 343 high-definition YouTube videos and contains
5000 well-chosen images, in which 16064 instances of gun and 9046 instances of
person are annotated. Compared to other datasets, YouTube-GDD is "dynamic",
containing rich contextual information and recording shape changes of the gun
during shooting. To build a baseline for gun detection, we evaluate YOLOv5 on
YouTube-GDD and analyze the influence of additional related annotated
information on gun detection. YouTube-GDD and subsequent updates will be
released at https://github.com/UCAS-GYX/YouTube-GDD.

In this paper, we study the cluster head detection problem of a two-level
unmanned aerial vehicle (UAV) swarm network (USNET) with multiple UAV clusters,
where the inherent follow strategy (IFS) of low-level follower UAVs (FUAVs)
with respect to high-level cluster head UAVs (HUAVs) is unknown. We first
propose a graph attention self-supervised learning algorithm (GASSL) to detect
the HUAVs of a single UAV cluster, where the GASSL can fit the IFS at the same
time. Then, to detect the HUAVs in the USNET with multiple UAV clusters, we
develop a multi-cluster graph attention self-supervised learning algorithm
(MC-GASSL) based on the GASSL. The MC-GASSL clusters the USNET with a gated
recurrent unit (GRU)-based metric learning scheme and finds the HUAVs in each
cluster with GASSL. Numerical results show that the GASSL can detect the HUAVs
in single UAV clusters obeying various kinds of IFSs with over 98% average
accuracy. The simulation results also show that the clustering purity of the
USNET with MC-GASSL exceeds that with traditional clustering algorithms by at
least 10% average. Furthermore, the MC-GASSL can efficiently detect all the
HUAVs in USNETs with various IFSs and cluster numbers with low detection

Robot assembly discovery is a challenging problem that lives at the
intersection of resource allocation and motion planning. The goal is to combine
a predefined set of objects to form something new while considering task
execution with the robot-in-the-loop. In this work, we tackle the problem of
building arbitrary, predefined target structures entirely from scratch using a
set of Tetris-like building blocks and a robotic manipulator. Our novel
hierarchical approach aims at efficiently decomposing the overall task into
three feasible levels that benefit mutually from each other. On the high level,
we run a classical mixed-integer program for global optimization of block-type
selection and the blocks' final poses to recreate the desired shape. Its output
is then exploited to efficiently guide the exploration of an underlying
reinforcement learning (RL) policy. This RL policy draws its generalization
properties from a flexible graph-based representation that is learned through
Q-learning and can be refined with search. Moreover, it accounts for the
necessary conditions of structural stability and robotic feasibility that
cannot be effectively reflected in the previous layer. Lastly, a grasp and
motion planner transforms the desired assembly commands into robot joint
movements. We demonstrate our proposed method's performance on a set of
competitive simulated RAD environments, showcase real-world transfer, and
report performance and robustness gains compared to an unstructured end-to-end
approach. Videos are available at https://sites.google.com/view/rl-meets-milp .

Cancer is a disease that occurs as a result of the uncontrolled division and
proliferation of cells. Colon cancer is one of the most common types of cancer
in the world. Polyps that can be seen in the large intestine can cause cancer
if not removed with early intervention. Deep learning and image segmentation
techniques are used to minimize the number of polyps that goes unnoticed by the
experts during these interventions. Although these techniques perform well in
terms of accuracy, they require too many parameters. We propose a new model to
address this problem. Our proposed model requires fewer parameters as well as
outperforms the state-of-the-art models. We use EfficientNetB0 for the encoder
part, as it performs well in various tasks while requiring fewer parameters. We
use partial decoder, which is used to reduce the number of parameters while
achieving high accuracy in segmentation. Since polyps have variable appearances
and sizes, we use an asymmetric convolution block instead of a classic
convolution block. Then, we weight each feature map using a squeeze and
excitation block to improve our segmentation results. We used different splits
of Kvasir and CVC-ClinicDB datasets for training, validation, and testing,
while we use CVC- ColonDB, ETIS, and Endoscene datasets for testing. Our model
outperforms state-of-art models with a Dice metric of %71.8 on the ColonDB test
dataset, %89.3 on the EndoScene test dataset, and %74.8 on the ETIS test
dataset while requiring fewer parameters. Our model requires 2.626.337
parameters in total while the closest model in the state-of-the-art is U-Net++
with 9.042.177 parameters.

This paper describes our submission to SemEval-2022 Task 6 on sarcasm
detection and its five subtasks for English and Arabic. Sarcasm conveys a
meaning which contradicts the literal meaning, and it is mainly found on social
networks. It has a significant role in understanding the intention of the user.
For detecting sarcasm, we used deep learning techniques based on transformers
due to its success in the field of Natural Language Processing (NLP) without
the need for feature engineering. The datasets were taken from tweets. We
created new datasets by augmenting with external data or by using word
embeddings and repetition of instances. Experiments were done on the datasets
with different types of preprocessing because it is crucial in this task. The
rank of our team was consistent across four subtasks (fourth rank in three
subtasks and sixth rank in one subtask); whereas other teams might be in the
top ranks for some subtasks but rank drastically less in other subtasks. This
implies the robustness and stability of the models and the techniques we used.

Alzheimer's disease (AD) is the most common age-related dementia. Mild
cognitive impairment (MCI) is the early stage of cognitive decline before AD.
It is crucial to predict the MCI-to-AD conversion for precise management, which
remains challenging due to the diversity of patients. Previous evidence shows
that the brain network generated from diffusion MRI promises to classify
dementia using deep learning. However, the limited availability of diffusion
MRI challenges the model training. In this study, we develop a self-supervised
contrastive learning approach to generate structural brain networks from
routine anatomical MRI under the guidance of diffusion MRI. The generated brain
networks are applied to train a learning framework for predicting the MCI-to-AD
conversion. Instead of directly modelling the AD brain networks, we train a
graph encoder and a variational autoencoder to model the healthy ageing
trajectories from brain networks of healthy controls. To predict the MCI-to-AD
conversion, we further design a recurrent neural networks based approach to
model the longitudinal deviation of patients' brain networks from the healthy
ageing trajectory. Numerical results show that the proposed methods outperform
the benchmarks in the prediction task. We also visualize the model
interpretation to explain the prediction and identify abnormal changes of white
matter tracts.

This paper presents an audio-visual approach for voice separation which
produces state-of-the-art results at a low latency in two scenarios: speech and
singing voice. The model is based on a two-stage network. Motion cues are
obtained with a lightweight graph convolutional network that processes face
landmarks. Then, both audio and motion features are fed to an audio-visual
transformer which produces a fairly good estimation of the isolated target
source. In a second stage, the predominant voice is enhanced with an audio-only
network. We present different ablation studies and comparison to
state-of-the-art methods. Finally, we explore the transferability of models
trained for speech separation in the task of singing voice separation. The
demos, code, and weights are available in https://ipcv.github.io/VoViT/

Learning in general-sum games is unstable and frequently leads to socially
undesirable (Pareto-dominated) outcomes. To mitigate this, Learning with
Opponent-Learning Awareness (LOLA) introduced opponent shaping to this setting,
by accounting for each agent's influence on their opponents' anticipated
learning steps. However, the original LOLA formulation (and follow-up work) is
inconsistent because LOLA models other agents as naive learners rather than
LOLA agents. In previous work, this inconsistency was suggested as a cause of
LOLA's failure to preserve stable fixed points (SFPs). First, we formalize
consistency and show that higher-order LOLA (HOLA) solves LOLA's inconsistency
problem if it converges. Second, we correct a claim made in the literature by
Sch\"afer and Anandkumar (2019), proving that Competitive Gradient Descent
(CGD) does not recover HOLA as a series expansion (and fails to solve the
consistency problem). Third, we propose a new method called Consistent LOLA
(COLA), which learns update functions that are consistent under mutual opponent
shaping. It requires no more than second-order derivatives and learns
consistent update functions even when HOLA fails to converge. However, we also
prove that even consistent update functions do not preserve SFPs, contradicting
the hypothesis that this shortcoming is caused by LOLA's inconsistency.
Finally, in an empirical evaluation on a set of general-sum games, we find that
COLA finds prosocial solutions and that it converges under a wider range of
learning rates than HOLA and LOLA. We support the latter finding with a
theoretical result for a simple game.

Intelligent Traffic Light Control System (ITLCS) is a typical Multi-Agent
System (MAS), which comprises multiple roads and traffic lights.Constructing a
model of MAS for ITLCS is the basis to alleviate traffic congestion. Existing
approaches of MAS are largely based on Multi-Agent Deep Reinforcement Learning
(MADRL). Although the Deep Neural Network (DNN) of MABRL is effective, the
training time is long, and the parameters are difficult to trace. Recently,
Broad Learning Systems (BLS) provided a selective way for learning in the deep
neural networks by a flat network. Moreover, Broad Reinforcement Learning (BRL)
extends BLS in Single Agent Deep Reinforcement Learning (SADRL) problem with
promising results. However, BRL does not focus on the intricate structures and
interaction of agents. Motivated by the feature of MADRL and the issue of BRL,
we propose a Multi-Agent Broad Reinforcement Learning (MABRL) framework to
explore the function of BLS in MAS. Firstly, unlike most existing MADRL
approaches, which use a series of deep neural networks structures, we model
each agent with broad networks. Then, we introduce a dynamic self-cycling
interaction mechanism to confirm the "3W" information: When to interact, Which
agents need to consider, What information to transmit. Finally, we do the
experiments based on the intelligent traffic light control scenario. We compare
the MABRL approach with six different approaches, and experimental results on
three datasets verify the effectiveness of MABRL.

Few-shot segmentation enables the model to recognize unseen classes with few
annotated examples. Most existing methods adopt prototype learning
architecture, where support prototype vectors are expanded and concatenated
with query features to perform conditional segmentation. However, such
framework potentially focuses more on query features while may neglect the
similarity between support and query features. This paper proposes a
contrastive enhancement approach using latent prototypes to leverage latent
classes and raise the utilization of similarity information between prototype
and query features. Specifically, a latent prototype sampling module is
proposed to generate pseudo-mask and novel prototypes based on features
similarity. The module conveniently conducts end-to-end learning and has no
strong dependence on clustering numbers like cluster-based method. Besides, a
contrastive enhancement module is developed to drive models to provide
different predictions with the same query features. Our method can be used as
an auxiliary module to flexibly integrate into other baselines for a better
segmentation performance. Extensive experiments show our approach remarkably
improves the performance of state-of-the-art methods for 1-shot and 5-shot
segmentation, especially outperforming baseline by 5.9% and 7.3% for 5-shot
task on Pascal-5^i and COCO-20^i. Source code is available at
https://github.com/zhaoxiaoyu1995/CELP-Pytorch

Cancer is one of the most dangerous diseases to humans, and yet no permanent
cure has been developed for it. Breast cancer is one of the most common cancer
types. According to the National Breast Cancer foundation, in 2020 alone, more
than 276,000 new cases of invasive breast cancer and more than 48,000
non-invasive cases were diagnosed in the US. To put these figures in
perspective, 64% of these cases are diagnosed early in the disease's cycle,
giving patients a 99% chance of survival. Artificial intelligence and machine
learning have been used effectively in detection and treatment of several
dangerous diseases, helping in early diagnosis and treatment, and thus
increasing the patient's chance of survival. Deep learning has been designed to
analyze the most important features affecting detection and treatment of
serious diseases. For example, breast cancer can be detected using genes or
histopathological imaging. Analysis at the genetic level is very expensive, so
histopathological imaging is the most common approach used to detect breast
cancer. In this research work, we systematically reviewed previous work done on
detection and treatment of breast cancer using genetic sequencing or
histopathological imaging with the help of deep learning and machine learning.
We also provide recommendations to researchers who will work in this field

Contact tracing is a method used by public health organisations to try
prevent the spread of infectious diseases in the community. Traditionally
performed by manual contact tracers, more recently the use of apps have been
considered utilising phone sensor data to determine the distance between two
phones. In this paper, we investigate the development of machine learning
approaches to determine the distance between two mobile phone devices using
Bluetooth Low Energy, sensory data and meta data. We use TableNet architecture
and feature engineering to improve on the existing state of the art (total nDCF
0.21 vs 2.08), significantly outperforming existing models.

Many path planning algorithms are based on sampling the state space. While
this approach is very simple, it can become costly when the obstacles are
unknown, since samples hitting these obstacles are wasted. The goal of this
paper is to efficiently identify obstacles in a map and remove them from the
sampling space. To this end, we propose a pre-processing algorithm for space
exploration that enables more efficient sampling. We show that it can boost the
performance of other space sampling methods and path planners.
  Our approach is based on the fact that a convex obstacle can be approximated
provably well by its minimum volume enclosing ellipsoid (MVEE), and a
non-convex obstacle may be partitioned into convex shapes. Our main
contribution is an algorithm that strategically finds a small sample, called
the \emph{active-coreset}, that adaptively samples the space via
membership-oracle such that the MVEE of the coreset approximates the MVEE of
the obstacle. Experimental results confirm the effectiveness of our approach
across multiple planners based on Rapidly-exploring random trees, showing
significant improvement in terms of time and path length.

Contour-based instance segmentation methods have developed rapidly recently
but feature rough and hand-crafted front-end contour initialization, which
restricts the model performance, and an empirical and fixed backend
predicted-label vertex pairing, which contributes to the learning difficulty.
In this paper, we introduce a novel contour-based method, named E2EC, for
high-quality instance segmentation. Firstly, E2EC applies a novel learnable
contour initialization architecture instead of hand-crafted contour
initialization. This consists of a contour initialization module for
constructing more explicit learning goals and a global contour deformation
module for taking advantage of all of the vertices' features better. Secondly,
we propose a novel label sampling scheme, named multi-direction alignment, to
reduce the learning difficulty. Thirdly, to improve the quality of the boundary
details, we dynamically match the most appropriate predicted-ground truth
vertex pairs and propose the corresponding loss function named dynamic matching
loss. The experiments showed that E2EC can achieve a state-of-the-art
performance on the KITTI INStance (KINS) dataset, the Semantic Boundaries
Dataset (SBD), the Cityscapes and the COCO dataset. E2EC is also efficient for
use in real-time applications, with an inference speed of 36 fps for 512*512
images on an NVIDIA A6000 GPU. Code will be released at
https://github.com/zhang-tao-whu/e2ec.

Current state-of-the-art employs approximate multipliers to address the
highly increased power demands of DNN accelerators. However, evaluating the
accuracy of approximate DNNs is cumbersome due to the lack of adequate support
for approximate arithmetic in DNN frameworks. We address this inefficiency by
presenting AdaPT, a fast emulation framework that extends PyTorch to support
approximate inference as well as approximation-aware retraining. AdaPT can be
seamlessly deployed and is compatible with the most DNNs. We evaluate the
framework on several DNN models and application fields including CNNs, LSTMs,
and GANs for a number of approximate multipliers with distinct bitwidth values.
The results show substantial error recovery from approximate re-training and
reduced inference time up to 53.9x with respect to the baseline approximate
implementation.

Probability predictions from binary regressions or machine learning methods
ought to be calibrated: If an event is predicted to occur with probability $x$,
it should materialize with approximately that frequency, which means that the
so-called calibration curve $p(\cdot)$ should equal the identity, $p(x) = x$
for all $x$ in the unit interval. We propose honest calibration assessment
based on novel confidence bands for the calibration curve, which are valid only
subject to the natural assumption of isotonicity. Besides testing the classical
goodness-of-fit null hypothesis of perfect calibration, our bands facilitate
inverted goodness-of-fit tests whose rejection allows for the sought-after
conclusion of a sufficiently well specified model. We show that our bands have
a finite sample coverage guarantee, are narrower than existing approaches, and
adapt to the local smoothness of the calibration curve $p$ and the local
variance of the binary observations. In an application to model predictions of
an infant having a low birth weight, the bounds give informative insights on
model calibration.

Estimating vehicles' locations is one of the key components in intelligent
traffic management systems (ITMSs) for increasing traffic scene awareness.
Traditionally, stationary sensors have been employed in this regard. The
development of advanced sensing and communication technologies on modern
vehicles (MVs) makes it feasible to use such vehicles as mobile sensors to
estimate the traffic data of observed vehicles. This study aims to explore the
capabilities of a monocular camera mounted on an MV in order to estimate the
geolocation of the observed vehicle in a global positioning system (GPS)
coordinate system. We proposed a new methodology by integrating deep learning,
image processing, and geometric computation to address the observed-vehicle
localization problem. To evaluate our proposed methodology, we developed new
algorithms and tested them using real-world traffic data. The results indicated
that our proposed methodology and algorithms could effectively estimate the
observed vehicle's latitude and longitude dynamically.

Over the last decade, robotic perception algorithms have significantly
benefited from the rapid advances in deep learning (DL). Indeed, a significant
amount of the autonomy stack of different commercial and research platforms
relies on DL for situational awareness, especially vision sensors. This work
explores the potential of general-purpose DL perception algorithms,
specifically detection and segmentation neural networks, for processing
image-like outputs of advanced lidar sensors. Rather than processing the
three-dimensional point cloud data, this is, to the best of our knowledge, the
first work to focus on low-resolution images with 360\textdegree field of view
obtained with lidar sensors by encoding either depth, reflectivity, or
near-infrared light in the image pixels. We show that with adequate
preprocessing, general-purpose DL models can process these images, opening the
door to their usage in environmental conditions where vision sensors present
inherent limitations. We provide both a qualitative and quantitative analysis
of the performance of a variety of neural network architectures. We believe
that using DL models built for visual cameras offers significant advantages due
to the much wider availability and maturity compared to point cloud-based
perception.

This paper proposes an adaptive auxiliary task learning based approach for
object counting problems. Unlike existing auxiliary task learning based
methods, we develop an attention-enhanced adaptively shared backbone network to
enable both task-shared and task-tailored features learning in an end-to-end
manner. The network seamlessly combines standard Convolution Neural Network
(CNN) and Graph Convolution Network (GCN) for feature extraction and feature
reasoning among different domains of tasks. Our approach gains enriched
contextual information by iteratively and hierarchically fusing the features
across different task branches of the adaptive CNN backbone. The whole
framework pays special attention to the objects' spatial locations and varied
density levels, informed by object (or crowd) segmentation and density level
segmentation auxiliary tasks. In particular, thanks to the proposed dilated
contrastive density loss function, our network benefits from individual and
regional context supervision in terms of pixel-independent and pixel-dependent
feature learning mechanisms, along with strengthened robustness. Experiments on
seven challenging multi-domain datasets demonstrate that our method achieves
superior performance to the state-of-the-art auxiliary task learning based
counting methods. Our code is made publicly available at:
https://github.com/smallmax00/Counting_With_Adaptive_Auxiliary

The formation of Middle Tropospheric Cyclones (MTCs) that are responsible for
a large portion of annual precipitation and extreme rainfall events over
western India is studied using an unsupervised machine learning algorithm and
cyclone tracking. Both approaches reveal four dominant weather patterns that
lead to the genesis of these systems; specifically, re-intensification of
westward moving synoptic systems from Bay of Bengal (Type 1, 51%), in-situ
formation with a coexisting cyclonic system over the Bay of Bengal that
precedes (Type 2a, 31%) or follows (Type 2b, 10%) genesis in the Arabian Sea,
and finally in-situ genesis within a northwestward propagating cyclonic anomaly
from the south Bay of Bengal (Type 2c, 8%). Thus, a large fraction of rainy
middle tropospheric synoptic systems in this region form in association with
cyclonic activity in the Bay of Bengal. The four variants identified also show
a marked dependence on large-scale environmental features with Type 1 and Type
2a formation primarily occurring in phases 4 and 5, and Type 2b and Type 2c in
phases 3 and 4 of the Boreal Summer Intraseasonal Oscillation. Further, while
in-situ formation with a Bay of Bengal cyclonic anomaly (Type 2a and 2b) mostly
occurs in June, downstream development is more likely in the core of the
monsoon season. Out of all categories, Type 2a is associated with the highest
rain rate (60 mm/day) and points towards the dynamical interaction between a
low pressure system over the Bay of Bengal and the development of MTCs over
western India and the northeast Arabian Sea. This classification,
identification of precursors, connection with cyclonic activity over the Bay of
Bengal and dependence on large-scale environment provides an avenue for better
understanding and prediction of rain-bearing MTCs over western India.

Mobile Manipulation (MM) systems are ideal candidates for taking up the role
of a personal assistant in unstructured real-world environments. Among other
challenges, MM requires effective coordination of the robot's embodiments for
executing tasks that require both mobility and manipulation. Reinforcement
Learning (RL) holds the promise of endowing robots with adaptive behaviors, but
most methods require prohibitively large amounts of data for learning a useful
control policy. In this work, we study the integration of robotic reachability
priors in actor-critic RL methods for accelerating the learning of MM for
reaching and fetching tasks. Namely, we consider the problem of optimal base
placement and the subsequent decision of whether to activate the arm for
reaching a 6D target. For this, we devise a novel Hybrid RL method that handles
discrete and continuous actions jointly, resorting to the Gumbel-Softmax
reparameterization. Next, we train a reachability prior using data from the
operational robot workspace, inspired by classical methods. Subsequently, we
derive Boosted Hybrid RL (BHyRL), a novel algorithm for learning Q-functions by
modeling them as a sum of residual approximators. Every time a new task needs
to be learned, we can transfer our learned residuals and learn the component of
the Q-function that is task-specific, hence, maintaining the task structure
from prior behaviors. Moreover, we find that regularizing the target policy
with a prior policy yields more expressive behaviors. We evaluate our method in
simulation in reaching and fetching tasks of increasing difficulty, and we show
the superior performance of BHyRL against baseline methods. Finally, we
zero-transfer our learned 6D fetching policy with BHyRL to our MM robot
TIAGo++. For more details and code release, please refer to our project site:
irosalab.com/rlmmbp

Multi-label classification aims to recognize multiple objects or attributes
from images. However, it is challenging to learn from proper label graphs to
effectively characterize such inter-label correlations or dependencies. Current
methods often use the co-occurrence probability of labels based on the training
set as the adjacency matrix to model this correlation, which is greatly limited
by the dataset and affects the model's generalization ability. In this paper,
we propose a Graph Attention Transformer Network (GATN), a general framework
for multi-label image classification that can effectively mine complex
inter-label relationships. First, we use the cosine similarity based on the
label word embedding as the initial correlation matrix, which can represent
rich semantic information. Subsequently, we design the graph attention
transformer layer to transfer this adjacency matrix to adapt to the current
domain. Our extensive experiments have demonstrated that our proposed methods
can achieve state-of-the-art performance on three datasets.

Building robust and general dialogue models for spoken conversations is
challenging due to the gap in distributions of spoken and written data. This
paper presents our approach to build generalized models for the
Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations
Challenge of DSTC-10. In order to mitigate the discrepancies between spoken and
written text, we mainly employ extensive data augmentation strategies on
written data, including artificial error injection and round-trip text-speech
transformation. To train robust models for spoken conversations, we improve
pre-trained language models, and apply ensemble algorithms for each sub-task.
Typically, for the detection task, we fine-tune \roberta and ELECTRA, and run
an error-fixing ensemble algorithm. For the selection task, we adopt a
two-stage framework that consists of entity tracking and knowledge ranking, and
propose a multi-task learning method to learn multi-level semantic information
by domain classification and entity selection. For the generation task, we
adopt a cross-validation data process to improve pre-trained generative
language models, followed by a consensus decoding algorithm, which can add
arbitrary features like relative \rouge metric, and tune associated feature
weights toward \bleu directly. Our approach ranks third on the objective
evaluation and second on the final official human evaluation.

Low-light image enhancement - a pervasive but challenging problem, plays a
central role in enhancing the visibility of an image captured in a poor
illumination environment. Due to the fact that not all photons can pass the
Bayer-Filter on the sensor of the color camera, in this work, we first present
a De-Bayer-Filter simulator based on deep neural networks to generate a
monochrome raw image from the colored raw image. Next, a fully convolutional
network is proposed to achieve the low-light image enhancement by fusing
colored raw data with synthesized monochrome raw data. Channel-wise attention
is also introduced to the fusion process to establish a complementary
interaction between features from colored and monochrome raw images. To train
the convolutional networks, we propose a dataset with monochrome and color raw
pairs named Mono-Colored Raw paired dataset (MCR) collected by using a
monochrome camera without Bayer-Filter and a color camera with Bayer-Filter.
The proposed pipeline take advantages of the fusion of the virtual monochrome
and the color raw images and our extensive experiments indicate that
significant improvement can be achieved by leveraging raw sensor data and
data-driven learning.

Most gait recognition methods exploit spatial-temporal representations from
static appearances and dynamic walking patterns. However, we observe that many
part-based methods neglect representations at boundaries. In addition, the
phenomenon of overfitting on training data is relatively common in gait
recognition, which is perhaps due to insufficient data and low-informative gait
silhouettes. Motivated by these observations, we propose a novel mask-based
regularization method named ReverseMask. By injecting perturbation on the
feature map, the proposed regularization method helps convolutional
architecture learn the discriminative representations and enhances
generalization. Also, we design an Inception-like ReverseMask Block, which has
three branches composed of a global branch, a feature dropping branch, and a
feature scaling branch. Precisely, the dropping branch can extract fine-grained
representations when partial activations are zero-outed. Meanwhile, the scaling
branch randomly scales the feature map, keeping structural information of
activations and preventing overfitting. The plug-and-play Inception-like
ReverseMask block is simple and effective to generalize networks, and it also
improves the performance of many state-of-the-art methods. Extensive
experiments demonstrate that the ReverseMask regularization help baseline
achieves higher accuracy and better generalization. Moreover, the baseline with
Inception-like Block significantly outperforms state-of-the-art methods on the
two most popular datasets, CASIA-B and OUMVLP. The source code will be
released.

Radio signal-based (indoor) localisation technique is important for IoT
applications such as smart factory and warehouse. Through machine learning,
especially neural networks methods, more accurate mapping from signal features
to target positions can be achieved. However, different radio protocols, such
as WiFi, Bluetooth, etc., have different features in the transmitted signals
that can be exploited for localisation purposes. Also, neural networks methods
often rely on carefully configured models and extensive training processes to
obtain satisfactory performance in individual localisation scenarios. The above
poses a major challenge in the process of determining neural network model
structure, or hyperparameters, as well as the selection of training features
from the available data. This paper proposes a neural network model
hyperparameter tuning and training method based on Bayesian optimisation.
Adaptive selection of model hyperparameters and training features can be
realised with minimal need for manual model training design. With the proposed
technique, the training process is optimised in a more automatic and efficient
way, enhancing the applicability of neural networks in localisation.

Federated learning (FL) and split learning (SL) are the two popular
distributed machine learning (ML) approaches that provide some data privacy
protection mechanisms. In the time-series classification problem, many
researchers typically use 1D convolutional neural networks (1DCNNs) based on
the SL approach with a single client to reduce the computational overhead at
the client-side while still preserving data privacy. Another method, recurrent
neural network (RNN), is utilized on sequentially partitioned data where
segments of multiple-segment sequential data are distributed across various
clients. However, to the best of our knowledge, it is still not much work done
in SL with long short-term memory (LSTM) network, even the LSTM network is
practically effective in processing time-series data. In this work, we propose
a new approach, LSTMSPLIT, that uses SL architecture with an LSTM network to
classify time-series data with multiple clients. The differential privacy (DP)
is applied to solve the data privacy leakage. The proposed method, LSTMSPLIT,
has achieved better or reasonable accuracy compared to the Split-1DCNN method
using the electrocardiogram dataset and the human activity recognition dataset.
Furthermore, the proposed method, LSTMSPLIT, can also achieve good accuracy
after applying differential privacy to preserve the user privacy of the cut
layer of the LSTMSPLIT.

We address the problem of distribution shifts in test-time data with a
principled data augmentation scheme for the task of content-level
classification. In such a task, properties such as shape or transparency of
test-time containers (cup or drinking glass) may differ from those represented
in the training data. Dealing with such distribution shifts using standard
augmentation schemes is challenging and transforming the training images to
cover the properties of the test-time instances requires sophisticated image
manipulations. We therefore generate diverse augmentations using a family of
max-entropy transformations that create samples with new shapes, colors and
spectral characteristics. We show that such a principled augmentation scheme,
alone, can replace current approaches that use transfer learning or can be used
in combination with transfer learning to improve its performance.

DL frameworks are the basis of constructing all DL programs and models, and
thus their bugs could lead to the unexpected behaviors of any DL program or
model relying on them. Such wide effect demonstrates the necessity and
importance of guaranteeing DL frameworks' quality. Understanding the
characteristics of DL framework bugs is a fundamental step for this quality
assurance task, facilitating to design effective bug detection and debugging
approaches. Hence, in this work we conduct the most large-scale study on 800
bugs from four popular and diverse DL frameworks (i.e., TensorFlow, PyTorch,
MXNet, and DL4J). By analyzing the root causes and symptoms of DL framework
bugs associated with 5 components decomposed from DL frameworks, as well as
measuring test coverage achieved by three state-of-the-art testing techniques
and developers' efforts on fixing those bugs, we obtain 14 major findings for
the comprehensive understanding of DL framework bugs and the current status of
existing DL framework testing and debugging practice, and then provide a series
of actionable guidelines for better DL framework bug detection and debugging.

This paper presents a combined task and motion planner for a robot arm to
carry out 3D metal wire curving tasks by collaborating with a bending machine.
We assume a collaborative robot that is safe to work in a human environment but
has a weak payload to bend objects with large stiffness, and developed a
combined planner for the robot to use a bending machine. Our method converts a
3D curve to a bending set and generates the feasible bending sequence, machine
usage, robotic grasp poses, and pick-and-place arm motion considering the
combined task and motion level constraints. Compared with previous deformable
linear object shaping work that relied on forces provided by robotic arms, the
proposed method is suitable for the material with high stiffness. We evaluate
the system using different tasks. The results show that the proposed system is
flexible and robust to generate robotic motion to corporate with the designed
bending machine.

We present a compilation flow for the generation of CNN inference
accelerators on FPGAs. The flow translates a frozen model into OpenCL kernels
with the TVM compiler and uses the Intel OpenCL SDK to compile to an FPGA
bitstream. We improve the quality of the generated hardware with optimizations
applied to the base OpenCL kernels generated by TVM. These optimizations
increase parallelism, reduce memory access latency, increase concurrency and
save on-chip resources. We automate these optimizations in TVM and evaluate
them by generating accelerators for LeNet-5, MobileNetV1 and ResNet-34 on an
Intel Stratix~10SX. We show that the optimizations improve the performance of
the generated accelerators by up to 846X over the base accelerators. The
performance of the optimized accelerators is up to 4.57X better than TensorFlow
on CPU, 3.83X better than single-threaded TVM and is only 0.34X compared to TVM
with 56 threads. Our optimized kernels also outperform ones generated by a
similar approach (that also uses high-level synthesis) while providing more
functionality and flexibility. However, it underperforms an approach that
utilizes hand-optimized designs. Thus, we view our approach as useful in
pre-production environments that benefit from increased performance and fast
prototyping, realizing the benefits of FPGAs without hardware design expertise.

Iterative denoising-based generation, also known as denoising diffusion
models, has recently been shown to be comparable in quality to other classes of
generative models, and even surpass them. Including, in particular, Generative
Adversarial Networks, which are currently the state of the art in many
sub-tasks of image generation. However, a major drawback of this method is that
it requires hundreds of iterations to produce a competitive result. Recent
works have proposed solutions that allow for faster generation with fewer
iterations, but the image quality gradually deteriorates with increasingly
fewer iterations being applied during generation. In this paper, we reveal some
of the causes that affect the generation quality of diffusion models,
especially when sampling with few iterations, and come up with a simple, yet
effective, solution to mitigate them. We consider two opposite equations for
the iterative denoising, the first predicts the applied noise, and the second
predicts the image directly. Our solution takes the two options and learns to
dynamically alternate between them through the denoising process. Our proposed
solution is general and can be applied to any existing diffusion model. As we
show, when applied to various SOTA architectures, our solution immediately
improves their generation quality, with negligible added complexity and
parameters. We experiment on multiple datasets and configurations and run an
extensive ablation study to support these findings.

Sources of geophysical noise (such as wind, sea waves and earthquakes) or of
anthropogenic noise impact ground-based gravitational-wave interferometric
detectors, causing transient sensitivity worsening and gaps in data taking.
During the one year-long third Observing Run (O3: from April 01, 2019 to March
27, 2020), the Virgo Collaboration collected a statistically significant
dataset, used in this article to study the response of the detector to a
variety of environmental conditions. We correlated environmental parameters to
global detector performance, such as observation range, duty cycle and control
losses. Where possible, we identified weaknesses in the detector that will be
used to elaborate strategies in order to improve Virgo robustness against
external disturbances for the next data taking period, O4, currently planned to
start at the end of 2022. The lessons learned could also provide useful
insights for the design of the next generation of ground-based interferometers.

Whole slide images (WSI) provide valuable phenotypic information for
histological assessment and malignancy grading of tumors. The WSI-based grading
promises to provide rapid diagnostic support and facilitate digital health.
Currently, the most commonly used WSIs are derived from formalin-fixed
paraffin-embedded (FFPE) and Frozen section. The majority of automatic tumor
grading models are developed based on FFPE sections, which could be affected by
the artifacts introduced by tissue processing. The frozen section exists
problems such as low quality that might influence training within single
modality as well. To overcome this problem in a single modal training and
achieve better multi-modal and discriminative representation disentanglement in
brain tumor, we propose a mutual contrastive low-rank learning (MCL) scheme to
integrate FFPE and frozen sections for glioma grading. We first design a mutual
learning scheme to jointly optimize the model training based on FFPE and frozen
sections. In this proposed scheme, we design a normalized modality contrastive
loss (NMC-loss), which could promote to disentangle multi-modality
complementary representation of FFPE and frozen sections from the same patient.
To reduce intra-class variance, and increase inter-class margin at intra- and
inter-patient levels, we conduct a low-rank (LR) loss. Our experiments show
that the proposed scheme achieves better performance than the model trained
based on each single modality or mixed modalities and even improves the feature
extraction in classical attention-based multiple instances learning methods
(MIL). The combination of NMC-loss and low-rank loss outperforms other typical
contrastive loss functions.

We investigate the performance of neural networks in identifying critical
behaviour in the 2D Ising model with next-to-nearest neighbour interactions. We
train DNN and CNN based classifiers on the Ising model configurations with
nearest neighbour interactions and test the ability of our models to identify
the critical (cross-over) region, as well as the phases in the presence of
next-to-nearest neighbour interactions. Our main objective is to investigate
whether our models can learn the notion of criticality and universality, in
contrast to merely identifying the phases by simply learning the value of the
order parameter, magnetization in this case. We design a simple adversarial
training process which forces our models to ignore learning about the order
parameter while training. We find that such models are able to identify
critical region with reasonable success, implying that it has learned to
identify the structure of spin-spin correlations near criticality.

Vision-language navigation (VLN) is a challenging task due to its large
searching space in the environment. To address this problem, previous works
have proposed some methods of fine-tuning a large model that pretrained on
large-scale datasets. However, the conventional fine-tuning methods require
extra human-labeled navigation data and lack self-exploration capabilities in
environments, which hinders their generalization of unseen scenes. To improve
the ability of fast cross-domain adaptation, we propose Prompt-based
Environmental Self-exploration (ProbES), which can self-explore the
environments by sampling trajectories and automatically generates structured
instructions via a large-scale cross-modal pretrained model (CLIP). Our method
fully utilizes the knowledge learned from CLIP to build an in-domain dataset by
self-exploration without human labeling. Unlike the conventional approach of
fine-tuning, we introduce prompt-based learning to achieve fast adaptation for
language embeddings, which substantially improves the learning efficiency by
leveraging prior knowledge. By automatically synthesizing
trajectory-instruction pairs in any environment without human supervision and
efficient prompt-based learning, our model can adapt to diverse vision-language
navigation tasks, including VLN and REVERIE. Both qualitative and quantitative
results show that our ProbES significantly improves the generalization ability
of the navigation model.

Sparse recovery is one of the most fundamental and well-studied inverse
problems. Standard statistical formulations of the problem are provably solved
by general convex programming techniques and more practical, fast
(nearly-linear time) iterative methods. However, these latter "fast algorithms"
have previously been observed to be brittle in various real-world settings.
  We investigate the brittleness of fast sparse recovery algorithms to
generative model changes through the lens of studying their robustness to a
"helpful" semi-random adversary, a framework which tests whether an algorithm
overfits to input assumptions. We consider the following basic model: let
$\mathbf{A} \in \mathbb{R}^{n \times d}$ be a measurement matrix which contains
an unknown subset of rows $\mathbf{G} \in \mathbb{R}^{m \times d}$ which are
bounded and satisfy the restricted isometry property (RIP), but is otherwise
arbitrary. Letting $x^\star \in \mathbb{R}^d$ be $s$-sparse, and given either
exact measurements $b = \mathbf{A} x^\star$ or noisy measurements $b =
\mathbf{A} x^\star + \xi$, we design algorithms recovering $x^\star$
information-theoretically optimally in nearly-linear time. We extend our
algorithm to hold for weaker generative models relaxing our planted RIP
assumption to a natural weighted variant, and show that our method's guarantees
naturally interpolate the quality of the measurement matrix to, in some
parameter regimes, run in sublinear time.
  Our approach differs from prior fast iterative methods with provable
guarantees under semi-random generative models: natural conditions on a
submatrix which make sparse recovery tractable are NP-hard to verify. We design
a new iterative method tailored to the geometry of sparse recovery which is
provably robust to our semi-random model. We hope our approach opens the door
to new robust, efficient algorithms for natural statistical inverse problems.

In Reinforcement Learning interpretability generally means to provide insight
into the agent's mechanisms such that its decisions are understandable by an
expert upon inspection. This definition, with the resulting methods from the
literature, may however fall short for online settings where the fluency of
interactions prohibits deep inspections of the decision-making algorithm. To
support interpretability in online settings it is useful to borrow from the
Explainable Planning literature methods that focus on the legibility of the
agent, by making its intention easily discernable in an observer model. As we
propose in this paper, injecting legible behavior inside an agent's policy
doesn't require modify components of its learning algorithm. Rather, the
agent's optimal policy can be regularized for legibility by evaluating how the
policy may produce observations that would make an observer infer an incorrect
policy. In our formulation, the decision boundary introduced by legibility
impacts the states in which the agent's policy returns an action that has high
likelihood also in other policies. In these cases, a trade-off between such
action, and legible/sub-optimal action is made.

Convolutional neural network inference on video data requires powerful
hardware for real-time processing. Given the inherent coherence across
consecutive frames, large parts of a video typically change little. By skipping
identical image regions and truncating insignificant pixel updates,
computational redundancy can in theory be reduced significantly. However, these
theoretical savings have been difficult to translate into practice, as sparse
updates hamper computational consistency and memory access coherence; which are
key for efficiency on real hardware. With DeltaCNN, we present a sparse
convolutional neural network framework that enables sparse frame-by-frame
updates to accelerate video inference in practice. We provide sparse
implementations for all typical CNN layers and propagate sparse feature updates
end-to-end - without accumulating errors over time. DeltaCNN is applicable to
all convolutional neural networks without retraining. To the best of our
knowledge, we are the first to significantly outperform the dense reference,
cuDNN, in practical settings, achieving speedups of up to 7x with only marginal
differences in accuracy.

We propose an end-to-end architecture for multivariate time-series prediction
that integrates a spatial-temporal graph neural network with a matrix filtering
module. This module generates filtered (inverse) correlation graphs from
multivariate time series before inputting them into a GNN. In contrast with
existing sparsification methods adopted in graph neural network, our model
explicitly leverage time-series filtering to overcome the low signal-to-noise
ratio typical of complex systems data. We present a set of experiments, where
we predict future sales from a synthetic time-series sales dataset. The
proposed spatial-temporal graph neural network displays superior performances
with respect to baseline approaches, with no graphical information, and with
fully connected, disconnected graphs and unfiltered graphs.

Figure skating scoring is a challenging task because it requires judging
players' technical moves as well as coordination with the background music.
Prior learning-based work cannot solve it well for two reasons: 1) each move in
figure skating changes quickly, hence simply applying traditional frame
sampling will lose a lot of valuable information, especially in a 3-5 minutes
lasting video, so an extremely long-range representation learning is necessary;
2) prior methods rarely considered the critical audio-visual relationship in
their models. Thus, we introduce a multimodal MLP architecture, named
Skating-Mixer. It extends the MLP-Mixer-based framework into a multimodal
fashion and effectively learns long-term representations through our designed
memory recurrent unit (MRU). Aside from the model, we also collected a
high-quality audio-visual FS1000 dataset, which contains over 1000 videos on 8
types of programs with 7 different rating metrics, overtaking other datasets in
both quantity and diversity. Experiments show the proposed method outperforms
SOTAs over all major metrics on the public Fis-V and our FS1000 dataset. In
addition, we include an analysis applying our method to recent competitions
that occurred in Beijing 2022 Winter Olympic Games, proving our method has
strong robustness.

Progress in natural language processing research is catalyzed by the
possibilities given by the widespread software frameworks. This paper
introduces Adaptor library that transposes the traditional model-centric
approach composed of pre-training + fine-tuning steps to objective-centric
approach, composing the training process by applications of selected
objectives. We survey research directions that can benefit from enhanced
objective-centric experimentation in multitask training, custom objectives
development, dynamic training curricula, or domain adaptation. Adaptor aims to
ease reproducibility of these research directions in practice. Finally, we
demonstrate the practical applicability of Adaptor in selected unsupervised
domain adaptation scenarios.

Optimal control (OC) algorithms such as Differential Dynamic Programming
(DDP) take advantage of the derivatives of the dynamics to efficiently control
physical systems. Yet, in the presence of nonsmooth dynamical systems, such
class of algorithms are likely to fail due, for instance, to the presence of
discontinuities in the dynamics derivatives or because of non-informative
gradient. On the contrary, reinforcement learning (RL) algorithms have shown
better empirical results in scenarios exhibiting non-smooth effects (contacts,
frictions, etc). Our approach leverages recent works on randomized smoothing
(RS) to tackle non-smoothness issues commonly encountered in optimal control,
and provides key insights on the interplay between RL and OC through the prism
of RS methods. This naturally leads us to introduce the randomized Differential
Dynamic Programming (R-DDP) algorithm accounting for deterministic but
non-smooth dynamics in a very sample-efficient way. The experiments demonstrate
that our method is able to solve classic robotic problems with dry friction and
frictional contacts, where classical OC algorithms are likely to fail and RL
algorithms require in practice a prohibitive number of samples to find an
optimal solution.

Self-supervised learning (SSL) recently has achieved outstanding success on
recommendation. By setting up an auxiliary task (either predictive or
contrastive), SSL can discover supervisory signals from the raw data without
human annotation, which greatly mitigates the problem of sparse user-item
interactions. However, most SSL-based recommendation models rely on
general-purpose auxiliary tasks, e.g., maximizing correspondence between node
representations learned from the original and perturbed interaction graphs,
which are explicitly irrelevant to the recommendation task. Accordingly, the
rich semantics reflected by social relationships and item categories, which lie
in the recommendation data-based heterogeneous graphs, are not fully exploited.
To explore recommendation-specific auxiliary tasks, we first quantitatively
analyze the heterogeneous interaction data and find a strong positive
correlation between the interactions and the number of user-item paths induced
by meta-paths. Based on the finding, we design two auxiliary tasks that are
tightly coupled with the target task (one is predictive and the other one is
contrastive) towards connecting recommendation with the self-supervision
signals hiding in the positive correlation. Finally, a model-agnostic
DUal-Auxiliary Learning (DUAL) framework which unifies the SSL and
recommendation tasks is developed. The extensive experiments conducted on three
real-world datasets demonstrate that DUAL can significantly improve
recommendation, reaching the state-of-the-art performance.

Being able to learn on weakly labeled data, and provide interpretability, are
two of the main reasons why attention-based deep multiple instance learning
(ABMIL) methods have become particularly popular for classification of
histopathological images. Such image data usually come in the form of
gigapixel-sized whole-slide-images (WSI) that are cropped into smaller patches
(instances). However, the sheer size of the data makes training of ABMIL models
challenging. All the instances from one WSI cannot be processed at once by
conventional GPUs. Existing solutions compromise training by relying on
pre-trained models, strategic sampling or selection of instances, or
self-supervised learning. We propose a training strategy based on gradient
accumulation that enables direct end-to-end training of ABMIL models without
being limited by GPU memory. We conduct experiments on both QMNIST and
Imagenette to investigate the performance and training time, and compare with
the conventional memory-expensive baseline and a recent sampled-based approach.
This memory-efficient approach, although slower, reaches performance
indistinguishable from the memory-expensive baseline.

In this paper, we first present a single-input, multiple-output convolutional
neural network that can estimate both heart rate and respiration rate
simultaneously by exploiting the underlying link between heart rate and
respiration rate. The inputs to the neural network are the amplitude and phase
of channel state information collected by a pair of WiFi devices. Our
WiFi-based technique addresses privacy concerns and is adaptable to a variety
of settings. This system overall accuracy for the heart and respiration rate
estimation can reach 99.109% and 98.581%, respectively. Furthermore, we
developed and analyzed two deep learning-based neural network classification
algorithms for categorizing four types of sleep stages: wake, rapid eye
movement (REM) sleep, non-rapid eye movement (NREM) light sleep, and NREM deep
sleep. This system overall classification accuracy can reach 95.925%

This paper presents an online algorithm for identification of partial
differential equations (PDEs) based on the weak-form sparse identification of
nonlinear dynamics algorithm (WSINDy). The algorithm is online in a sense that
if performs the identification task by processing solution snapshots that
arrive sequentially. The core of the method combines a weak-form discretization
of candidate PDEs with an online proximal gradient descent approach to the
sparse regression problem. In particular, we do not regularize the
$\ell_0$-pseudo-norm, instead finding that directly applying its proximal
operator (which corresponds to a hard thresholding) leads to efficient online
system identification from noisy data. We demonstrate the success of the method
on the Kuramoto-Sivashinsky equation, the nonlinear wave equation with
time-varying wavespeed, and the linear wave equation, in one, two, and three
spatial dimensions, respectively. In particular, our examples show that the
method is capable of identifying and tracking systems with coefficients that
vary abruptly in time, and offers a streaming alternative to problems in higher

Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic
inference to approximate functions of Stochastic Processes under meta-learning
settings. Given a batch of non-{\it i.i.d} function instantiations, CNPs are
jointly optimized for in-instantiation observation prediction and
cross-instantiation meta-representation adaptation within a generative
reconstruction pipeline. There can be a challenge in tying together such two
targets when the distribution of function observations scales to
high-dimensional and noisy spaces. Instead, noise contrastive estimation might
be able to provide more robust representations by learning distributional
matching objectives to combat such inherent limitation of generative models. In
light of this, we propose to equip CNPs by 1) aligning prediction with encoded
ground-truth observation, and 2) decoupling meta-representation adaptation from
generative reconstruction. Specifically, two auxiliary contrastive branches are
set up hierarchically, namely in-instantiation temporal contrastive
learning~({\tt TCL}) and cross-instantiation function contrastive
learning~({\tt FCL}), to facilitate local predictive alignment and global
function consistency, respectively. We empirically show that {\tt TCL} captures
high-level abstraction of observations, whereas {\tt FCL} helps identify
underlying functions, which in turn provides more efficient representations.
Our model outperforms other CNPs variants when evaluating function distribution
reconstruction and parameter identification across 1D, 2D and high-dimensional
time-series.

The characterization of Low Surface Brightness (LSB) stellar structures
around galaxies such as tidal debris of on-going or past collisions is
essential to constrain models of galactic evolution. Our goal is to obtain
quantitative measurements of LSB structures identified in deep images of
samples consisting of hundreds of galaxies. We developed an online annotation
tool that enables contributors to delineate the shapes of diffuse extended
stellar structures, as well as artefacts or foreground structures. All
parameters are automatically stored in a database which may be queried to
retrieve quantitative measurements. We annotated LSB structures around 352
nearby massive galaxies with deep images obtained with the CFHT as part of two
large programs: MATLAS and UNIONS/CFIS. Each LSB structure was delineated and
labeled according to its likely nature: stellar shells, streams associated to a
disrupted satellite, tails formed in major mergers, ghost reflections or
cirrus. From our database containing 8441 annotations, the area, size, median
surface brightness and distance to the host of 228 structures were computed.
The results confirm the fact that tidal structures defined as streams are
thinner than tails, as expected by numerical simulations. In addition, tidal
tails appear to exhibit a higher surface brightness than streams (by about 1
mag), which may be related to different survival times for the two types of
collisional debris. We did not detect any tidal feature fainter than 27.5
mag.arcsec$^{-2}$, while the nominal surface brightness limits of our surveys
range between 28.3 and 29 mag.arcsec$^{-2}$, a difference that needs to be
taken into account when estimating the sensitivity of future surveys to
identify LSB structures. Our annotation database of observed LSB structures may
be used for quantitative analysis and as a training set for machine learning
algorithms (abbreviated).

This work addresses the problem of recognizing action categories in videos
for which no training examples are available. The current state-of-the-art
enables such a zero-shot recognition by learning universal mappings from videos
to a shared semantic space, either trained on large-scale seen actions or on
objects. While effective, we find that universal action and object mappings are
biased to their seen categories. Such biases are further amplified due to
biases between seen and unseen categories in the semantic space. The
compounding biases result in many unseen action categories simply never being
selected during inference, hampering zero-shot progress. We seek to address
this limitation and introduce universal prototype transport for zero-shot
action recognition. The main idea is to re-position the semantic prototypes of
unseen actions through transduction, i.e. by using the distribution of the
unlabelled test set. For universal action models, we first seek to find a
hyperspherical optimal transport mapping from unseen action prototypes to the
set of all projected test videos. We then define a target prototype for each
unseen action as the weighted Fr\'echet mean over the transport couplings.
Equipped with a target prototype, we propose to re-position unseen action
prototypes along the geodesic spanned by the original and target prototypes,
acting as a form of semantic regularization. For universal object models, we
outline a variant that defines target prototypes based on an optimal transport
between unseen action prototypes and semantic object prototypes. Empirically,
we show that universal prototype transport diminishes the biased selection of
unseen action prototypes and boosts both universal action and object models,
resulting in state-of-the-art performance for zero-shot classification and
spatio-temporal localization.

Humans have the ability to accumulate knowledge of new tasks in varying
conditions, but deep neural networks often suffer from catastrophic forgetting
of previously learned knowledge after learning a new task. Many recent methods
focus on preventing catastrophic forgetting under the assumption of train and
test data following similar distributions. In this work, we consider a more
realistic scenario of continual learning under domain shifts where the model
must generalize its inference to an unseen domain. To this end, we encourage
learning semantically meaningful features by equipping the classifier with
class similarity metrics as learning parameters which are obtained through
Mahalanobis similarity computations. Learning of the backbone representation
along with these extra parameters is done seamlessly in an end-to-end manner.
In addition, we propose an approach based on the exponential moving average of
the parameters for better knowledge distillation. We demonstrate that, to a
great extent, existing continual learning algorithms fail to handle the
forgetting issue under multiple distributions, while our proposed approach
learns new tasks under domain shift with accuracy boosts up to 10% on
challenging datasets such as DomainNet and OfficeHome.

In recent years, the idea of formalising and modelling fairness for
algorithmic decision making (ADM) has advanced to a point of sophisticated
specialisation. However, the relations between technical (formalised) and
ethical discourse on fairness are not always clear and productive. Arguing for
an alternative perspective, we review existing fairness metrics and discuss
some common issues. For instance, the fairness of procedures and distributions
is often formalised and discussed statically, disregarding both structural
preconditions of the status quo and downstream effects of a given intervention.
We then introduce dynamic fairness modelling, a more comprehensive approach
that realigns formal fairness metrics with arguments from the ethical
discourse. A dynamic fairness model incorporates (1) ethical goals, (2) formal
metrics to quantify decision procedures and outcomes and (3) mid-term or
long-term downstream effects. By contextualising these elements of
fairness-related processes, dynamic fairness modelling explicates formerly
latent ethical aspects and thereby provides a helpful tool to navigate
trade-offs between different fairness interventions. To illustrate the
framework, we discuss an example application -- the current European efforts to
increase the number of women on company boards, e.g. via quota solutions -- and
present early technical work that fits within our framework.

Evolving morphologies and controllers of robots simultaneously leads to a
problem: Even if the parents have well-matching bodies and brains, the
stochastic recombination can break this match and cause a body-brain mismatch
in their offspring. We argue that this can be mitigated by having newborn
robots perform a learning process that optimizes their inherited brain quickly
after birth. We compare three different algorithms for doing this. To this end,
we consider three algorithmic properties, efficiency, efficacy, and the
sensitivity to differences in the morphologies of the robots that run the
learning process.

Many gait recognition methods first partition the human gait into N-parts and
then combine them to establish part-based feature representations. Their gait
recognition performance is often affected by partitioning strategies, which are
empirically chosen in different datasets. However, we observe that strips as
the basic component of parts are agnostic against different partitioning
strategies. Motivated by this observation, we present a strip-based multi-level
gait recognition network, named GaitStrip, to extract comprehensive gait
information at different levels. To be specific, our high-level branch explores
the context of gait sequences and our low-level one focuses on detailed posture
changes. We introduce a novel StriP-Based feature extractor (SPB) to learn the
strip-based feature representations by directly taking each strip of the human
body as the basic unit. Moreover, we propose a novel multi-branch structure,
called Enhanced Convolution Module (ECM), to extract different representations
of gaits. ECM consists of the Spatial-Temporal feature extractor (ST), the
Frame-Level feature extractor (FL) and SPB, and has two obvious advantages:
First, each branch focuses on a specific representation, which can be used to
improve the robustness of the network. Specifically, ST aims to extract
spatial-temporal features of gait sequences, while FL is used to generate the
feature representation of each frame. Second, the parameters of the ECM can be
reduced in test by introducing a structural re-parameterization technique.
Extensive experimental results demonstrate that our GaitStrip achieves
state-of-the-art performance in both normal walking and complex conditions.

Accurate short-term traffic prediction plays a pivotal role in various smart
mobility operation and management systems. Currently, most of the
state-of-the-art prediction models are based on graph neural networks (GNNs),
and the required training samples are proportional to the size of the traffic
network. In many cities, the available amount of traffic data is substantially
below the minimum requirement due to the data collection expense. It is still
an open question to develop traffic prediction models with a small size of
training data on large-scale networks. We notice that the traffic states of a
node for the near future only depend on the traffic states of its localized
neighborhoods, which can be represented using the graph relational inductive
biases. In view of this, this paper develops a graph network (GN)-based deep
learning model LocaleGN that depicts the traffic dynamics using localized data
aggregating and updating functions, as well as the node-wise recurrent neural
networks. LocaleGN is a light-weighted model designed for training on few
samples without over-fitting, and hence it can solve the problem of few-sample
traffic prediction. The proposed model is examined on predicting both traffic
speed and flow with six datasets, and the experimental results demonstrate that
LocaleGN outperforms existing state-of-the-art baseline models. It is also
demonstrated that the learned knowledge from LocaleGN can be transferred across
cities. The research outcomes can help to develop light-weighted traffic
prediction systems, especially for cities lacking historically archived traffic
data.

Video anomaly detection is well investigated in weakly-supervised and
one-class classification (OCC) settings. However, unsupervised video anomaly
detection methods are quite sparse, likely because anomalies are less frequent
in occurrence and usually not well-defined, which when coupled with the absence
of ground truth supervision, could adversely affect the performance of the
learning algorithms. This problem is challenging yet rewarding as it can
completely eradicate the costs of obtaining laborious annotations and enable
such systems to be deployed without human intervention. To this end, we propose
a novel unsupervised Generative Cooperative Learning (GCL) approach for video
anomaly detection that exploits the low frequency of anomalies towards building
a cross-supervision between a generator and a discriminator. In essence, both
networks get trained in a cooperative fashion, thereby allowing unsupervised
learning. We conduct extensive experiments on two large-scale video anomaly
detection datasets, UCF crime, and ShanghaiTech. Consistent improvement over
the existing state-of-the-art unsupervised and OCC methods corroborate the

Species distribution models (SDMs) are increasingly used in ecology,
biogeography, and wildlife management to learn about the species-habitat
relationships and abundance across space and time. Distance sampling (DS) and
capture-recapture (CR) are two widely collected data types to learn about
species-habitat relationships and abundance; still, they are seldomly used in
SDMs due to the lack of spatial coverage. However, data fusion of the two data
sources can increase spatial coverage, which can reduce parameter uncertainty
and make predictions more accurate, and therefore, can be used for species
distribution modeling. We developed a model-based approach for data fusion of
DS and CR data. Our modeling approach accounts for two common missing data
issues: 1) missing individuals that are missing not at random (MNAR) and 2)
partially missing location information. Using a simulation experiment, we
evaluated the performance of our modeling approach and compared it to existing
approaches that use ad-hoc methods to account for missing data issues. Our
results show that our approach provides unbiased parameter estimates with
increased efficiency compared to the existing approaches. We demonstrated our
approach using data collected for Grasshopper Sparrows (Ammodramus savannarum)
in north-eastern Kansas, USA.

Door-status detection, namely recognizing the presence of a door and its
status (open or closed), can induce a remarkable impact on a mobile robot's
navigation performance, especially for dynamic settings where doors can enable
or disable passages, changing the topology of the map. In this work, we address
the problem of building a door-status detector module for a mobile robot
operating in the same environment for a long time, thus observing the same set
of doors from different points of view. First, we show how to improve the
mainstream approach based on object detection by considering the constrained
perception setup typical of a mobile robot. Hence, we devise a method to build
a dataset of images taken from a robot's perspective and we exploit it to
obtain a door-status detector based on deep learning. We then leverage the
typical working conditions of a robot to qualify the model for boosting its
performance in the working environment via fine-tuning with additional data.
Our experimental analysis shows the effectiveness of this method with results
obtained both in simulation and in the real-world, that also highlight a
trade-off between costs and benefits of the fine-tuning approach.

Recently, convolutional neural networks (CNN) have obtained promising results
in single-image SR for hyperspectral pansharpening. However, enhancing CNNs'
representation ability with fewer parameters and a shorter prediction time is a
challenging and critical task. In this paper, we propose a novel multi-spectral
image fusion method using a combination of the previously proposed 3D CNN model
VolumeNet and 2.5D texture transfer method using other modality high resolution
(HR) images. Since a multi-spectral (MS) image consists of several bands and
each band is a 2D image slice, MS images can be seen as 3D data. Thus, we use
the previously proposed VolumeNet to fuse HR panchromatic (PAN) images and
bicubic interpolated MS images. Because the proposed 3D VolumeNet can
effectively improve the accuracy by expanding the receptive field of the model,
and due to its lightweight structure, we can achieve better performance against
the existing method without purchasing a large number of remote sensing images
for training. In addition, VolumeNet can restore the high-frequency information
lost in the HR MR image as much as possible, reducing the difficulty of feature
extraction in the following step: 2.5D texture transfer. As one of the latest
technologies, deep learning-based texture transfer has been demonstrated to
effectively and efficiently improve the visual performance and quality
evaluation indicators of image reconstruction. Different from the texture
transfer processing of RGB image, we use HR PAN images as the reference images
and perform texture transfer for each frequency band of MS images, which is
named 2.5D texture transfer. The experimental results show that the proposed
method outperforms the existing methods in terms of objective accuracy
assessment, method efficiency, and visual subjective evaluation.

In this paper, we propose MENAS, an efficient multi-trial evolution-based NAS
method with less human intervention. Specifically, we propose an enlarged
search space (MobileNet3-MT) for ImageNet-1K and improve the search efficiency
from two aspects. First, MENAS jointly explores architectures and optimal
pruned candidates (Lottery Tickets), gradually slimming the average model in
populations. Each model is trained with an early stop and replaced by its
Lottery Tickets, instead of first searching for a cumbersome network then
conducting pruning. Second, we introduce individual weight sharing, which is
dedicated to multi-trial NAS, aiming to amortize the training costs by sharing
weights between parents and child networks. Compared with weight sharing in
supernet, individual weight sharing attains more reliable rank consistency,
meanwhile is easy to implement by preventing the sophisticated supernet
training. Moreover, to regularize the evolutionary process from trapped in
small models, we preserve a small ratio of the largest models when formulating
parent populations, which is proved beneficial to enhance model performance.
Extensive experiment results demonstrate the superiority of MENAS. On the
ImageNet-1K database, MENAS achieves 80.5% top-1 accuracy without involving
knowledge distillation or larger image resolution. Code and models will be

To model manifold data using normalizing flows, we propose to employ the
isometric autoencoder to design nonlinear encodings with explicit inverses. The
isometry allows us to separate manifold learning and density estimation and
train both parts to high accuracy. Applied to the MNIST data set, the combined
approach generates high-quality images.

In order to develop infocommunications devices so that the capabilities of
the human brain may interact with the capabilities of any artificially
cognitive system a deeper knowledge of aging is necessary. Especially if
society does not want to exclude elder people and wants to develop automatic
systems able to help and improve the quality of life of this group of
population, healthy individuals as well as those with cognitive decline or
other pathologies. This paper tries to establish the variations in handwriting
tasks with the goal to obtain a better knowledge about aging. We present the
correlation results between several parameters extracted from online
handwriting and the age of the writers. It is based on BIOSECURID database,
which consists of 400 people that provided several biometric traits, including
online handwriting. The main idea is to identify those parameters that are more
stable and those more age dependent. One challenging topic for disease diagnose
is the differentiation between healthy and pathological aging. For this
purpose, it is necessary to be aware of handwriting parameters that are, in
general, not affected by aging and those who experiment changes, increase or
decrease their values, because of it. This paper contributes to this research
line analyzing a selected set of online handwriting parameters provided by a
healthy group of population aged from 18 to 70 years. Preliminary results show
that these parameters are not affected by aging and therefore, changes in their
values can only be attributed to motor or cognitive disorders.

not-reversibility of the changes made.

In person re-identification (ReID), very recent researches have validated
pre-training the models on unlabelled person images is much better than on
ImageNet. However, these researches directly apply the existing self-supervised
learning (SSL) methods designed for image classification to ReID without any
adaption in the framework. These SSL methods match the outputs of local views
(e.g., red T-shirt, blue shorts) to those of the global views at the same time,
losing lots of details. In this paper, we propose a ReID-specific pre-training
method, Part-Aware Self-Supervised pre-training (PASS), which can generate
part-level features to offer fine-grained information and is more suitable for
ReID. PASS divides the images into several local areas, and the local views
randomly cropped from each area are assigned with a specific learnable [PART]
token. On the other hand, the [PART]s of all local areas are also appended to
the global views. PASS learns to match the output of the local views and global
views on the same [PART]. That is, the learned [PART] of the local views from a
local area is only matched with the corresponding [PART] learned from the
global views. As a result, each [PART] can focus on a specific local area of
the image and extracts fine-grained information of this area. Experiments show
PASS sets the new state-of-the-art performances on Market1501 and MSMT17 on
various ReID tasks, e.g., vanilla ViT-S/16 pre-trained by PASS achieves
92.2\%/90.2\%/88.5\% mAP accuracy on Market1501 for supervised/UDA/USL ReID.
Our codes are available at https://github.com/CASIA-IVA-Lab/PASS-reID.

The wide adoption and application of Masked language models~(MLMs) on
sensitive data (from legal to medical) necessitates a thorough quantitative
investigation into their privacy vulnerabilities -- to what extent do MLMs leak
information about their training data? Prior attempts at measuring leakage of
MLMs via membership inference attacks have been inconclusive, implying the
potential robustness of MLMs to privacy attacks. In this work, we posit that
prior attempts were inconclusive because they based their attack solely on the
MLM's model score. We devise a stronger membership inference attack based on
likelihood ratio hypothesis testing that involves an additional reference MLM
to more accurately quantify the privacy risks of memorization in MLMs. We show
that masked language models are extremely susceptible to likelihood ratio
membership inference attacks: Our empirical results, on models trained on
medical notes, show that our attack improves the AUC of prior membership
inference attacks from 0.66 to an alarmingly high 0.90 level, with a
significant improvement in the low-error region: at 1% false positive rate, our
attack is 51X more powerful than prior work.

Self-supervised representation learning of Multivariate Time Series (MTS) is
a challenging task and attracts increasing research interests in recent years.
Many previous works focus on the pretext task of self-supervised learning and
usually neglect the complex problem of MTS encoding, leading to unpromising
results. In this paper, we tackle this challenge from two aspects: encoder and
pretext task, and propose a unified channel-aware self-supervised learning
framework CaSS. Specifically, we first design a new Transformer-based encoder
Channel-aware Transformer (CaT) to capture the complex relationships between
different time channels of MTS. Second, we combine two novel pretext tasks Next
Trend Prediction (NTP) and Contextual Similarity (CS) for the self-supervised
representation learning with our proposed encoder. Extensive experiments are
conducted on several commonly used benchmark datasets. The experimental results
show that our framework achieves new state-of-the-art comparing with previous
self-supervised MTS representation learning methods (up to +7.70\% improvement
on LSST dataset) and can be well applied to the downstream MTS classification.

It is desirable for future robots to quickly learn new tasks and adapt
learned skills to constantly changing environments. To this end, Probabilistic
Movement Primitives (ProMPs) have shown to be a promising framework to learn
generalizable trajectory generators from distributions over demonstrated
trajectories. However, in practical applications that require high precision in
the manipulation of objects, the accuracy of ProMPs is often insufficient, in
particular when they are learned in cartesian space from external observations
and executed with limited controller gains. Therefore, we propose to combine
ProMPs with recently introduced Residual Reinforcement Learning (RRL), to
account for both, corrections in position and orientation during task
execution. In particular, we learn a residual on top of a nominal ProMP
trajectory with Soft-Actor Critic and incorporate the variability in the
demonstrations as a decision variable to reduce the search space for RRL. As a
proof of concept, we evaluate our proposed method on a 3D block insertion task
with a 7-DoF Franka Emika Panda robot. Experimental results show that the robot
successfully learns to complete the insertion which was not possible before
with using basic ProMPs.

Reinforcement learning methods for robotics are increasingly successful due
to the constant development of better policy gradient techniques. A precise
(low variance) and accurate (low bias) gradient estimator is crucial to face
increasingly complex tasks. Traditional policy gradient algorithms use the
likelihood-ratio trick, which is known to produce unbiased but high variance
estimates. More modern approaches exploit the reparametrization trick, which
gives lower variance gradient estimates but requires differentiable value
function approximators. In this work, we study a different type of stochastic
gradient estimator - the Measure-Valued Derivative. This estimator is unbiased,
has low variance, and can be used with differentiable and non-differentiable
function approximators. We empirically evaluate this estimator in the
actor-critic policy gradient setting and show that it can reach comparable
performance with methods based on the likelihood-ratio or reparametrization
tricks, both in low and high-dimensional action spaces. With this work, we want
to show that the Measure-Valued Derivative estimator can be a useful
alternative to other policy gradient estimators.

The most widely discussed methods for estimating the Average Causal
Effect/Average Treatment Effect are those for intervention in discrete binary
variables whose value represents intervention/non-intervention groups. On the
other hand, methods for intervening in continuous variables independent of data
generating models have not been developed. In this study, we give a method for
estimating the average causal effect for intervention in continuous variables
that can be applied to data of any generating models as long as the causal
effect is identifiable. The proposing method is independent of machine learning
algorithms and preserves the identifiability of data.

Recently, Vision-Language Pre-training (VLP) techniques have greatly
benefited various vision-language tasks by jointly learning visual and textual
representations, which intuitively helps in Optical Character Recognition (OCR)
tasks due to the rich visual and textual information in scene text images.
However, these methods cannot well cope with OCR tasks because of the
difficulty in both instance-level text encoding and image-text pair acquisition
(i.e. images and captured texts in them). This paper presents a weakly
supervised pre-training method, oCLIP, which can acquire effective scene text
representations by jointly learning and aligning visual and textual
information. Our network consists of an image encoder and a character-aware
text encoder that extract visual and textual features, respectively, as well as
a visual-textual decoder that models the interaction among textual and visual
features for learning effective scene text representations. With the learning
of textual features, the pre-trained model can attend texts in images well with
character awareness. Besides, these designs enable the learning from weakly
annotated texts (i.e. partial texts in images without text bounding boxes)
which mitigates the data annotation constraint greatly. Experiments over the
weakly annotated images in ICDAR2019-LSVT show that our pre-trained model
improves F-score by +2.5\% and +4.8\% while transferring its weights to other
text detection and spotting networks, respectively. In addition, the proposed
method outperforms existing pre-training techniques consistently across
multiple public datasets (e.g., +3.2\% and +1.3\% for Total-Text and CTW1500).

Neural networks tend to gradually forget the previously learned knowledge
when learning multiple tasks sequentially from dynamic data distributions. This
problem is called \textit{catastrophic forgetting}, which is a fundamental
challenge in the continual learning of neural networks. In this work, we
observe that catastrophic forgetting not only occurs in continual learning but
also affects the traditional static training. Neural networks, especially
neural machine translation models, suffer from catastrophic forgetting even if
they learn from a static training set. To be specific, the final model pays
imbalanced attention to training samples, where recently exposed samples
attract more attention than earlier samples. The underlying cause is that
training samples do not get balanced training in each model update, so we name
this problem \textit{imbalanced training}. To alleviate this problem, we
propose Complementary Online Knowledge Distillation (COKD), which uses
dynamically updated teacher models trained on specific data orders to
iteratively provide complementary knowledge to the student model. Experimental
results on multiple machine translation tasks show that our method successfully
alleviates the problem of imbalanced training and achieves substantial
improvements over strong baseline systems.

Allocating resources with future channels can save resource to ensure
quality-of-service of video streaming. In this paper, we optimize predictive
power allocation to minimize the energy consumed at distributed units (DUs) by
using deep deterministic policy gradient (DDPG) to find optimal policy and
predict average channel gains. To improve training efficiency, we resort to
graph DDPG for exploiting two kinds of relational priors: (a) permutation
equivariant (PE) and permutation invariant (PI) properties of policy function
and action-value function, (b) topology relation among users and DUs. To design
graph DDPG framework more systematically in harnessing the priors, we first
demonstrate how to transform matrix-based DDPG into graph-based DDPG. Then, we
respectively design the actor and critic networks to satisfy the permutation
properties when graph neural networks are used in embedding and end to-end
manners. To avoid destroying the PE/PI properties of the actor and critic
networks, we conceive a batch normalization method. Finally, we show the impact
of leveraging each prior. Simulation results show that the learned predictive
policy performs close to the optimal solution with perfect future information,
and the graph DDPG algorithms converge much faster than existing DDPG

Recently, prompt-based methods have achieved significant performance in
few-shot learning scenarios by bridging the gap between language model
pre-training and fine-tuning for downstream tasks. However, existing prompt
templates are mostly designed for sentence-level tasks and are inappropriate
for sequence labeling objectives. To address the above issue, we propose a
multi-task instruction-based generative framework, named InstructionNER, for
low-resource named entity recognition. Specifically, we reformulate the NER
task as a generation problem, which enriches source sentences with
task-specific instructions and answer options, then inferences the entities and
types in natural language. We further propose two auxiliary tasks, including
entity extraction and entity typing, which enable the model to capture more
boundary information of entities and deepen the understanding of entity type
semantics, respectively. Experimental results show that our method consistently
outperforms other baselines on five datasets in few-shot settings.

This paper is concerned with low-rank matrix optimization, which has found a
wide range of applications in machine learning. This problem in the special
case of matrix sensing has been studied extensively through the notion of
Restricted Isometry Property (RIP), leading to a wealth of results on the
geometric landscape of the problem and the convergence rate of common
algorithms. However, the existing results can handle the problem in the case
with a general objective function subject to noisy data only when the RIP
constant is close to 0. In this paper, we develop a new mathematical framework
to solve the above-mentioned problem with a far less restrictive RIP constant.
We prove that as long as the RIP constant of the noiseless objective is less
than $1/3$, any spurious local solution of the noisy optimization problem must
be close to the ground truth solution. By working through the strict saddle
property, we also show that an approximate solution can be found in polynomial
time. We characterize the geometry of the spurious local minima of the problem
in a local region around the ground truth in the case when the RIP constant is
greater than $1/3$. Compared to the existing results in the literature, this
paper offers the strongest RIP bound and provides a complete theoretical
analysis on the global and local optimization landscapes of general low-rank
optimization problems under random corruptions from any finite-variance family.

One of the most critical challenges in deep reinforcement learning is to
maintain the long-term exploration capability of the agent. To tackle this
problem, it has been recently proposed to provide intrinsic rewards for the
agent to encourage exploration. However, most existing intrinsic reward-based
methods proposed in the literature fail to provide sustainable exploration
incentives, a problem known as vanishing rewards. In addition, these
conventional methods incur complex models and additional memory in their
learning procedures, resulting in high computational complexity and low
robustness. In this work, a novel intrinsic reward module based on the R\'enyi
entropy is proposed to provide high-quality intrinsic rewards. It is shown that
the proposed method actually generalizes the existing state entropy
maximization methods. In particular, a $k$-nearest neighbor estimator is
introduced for entropy estimation while a $k$-value search method is designed
to guarantee the estimation accuracy. Extensive simulation results demonstrate
that the proposed R\'enyi entropy-based method can achieve higher performance
as compared to existing schemes.

Pre-trained large-scale models provide a transferable embedding, and they
show promising performance on diverse downstream tasks. However, the analysis
of learned embedding has not been explored well, and the transferability for
cross-modal tasks can be improved. This paper provides a perspective to
understand multi-modal embedding in terms of uniformity and alignment. We newly
find that the representation learned by multi-modal learning models such as
CLIP has two separated embedding spaces for each heterogeneous dataset with
less alignment. Besides, there are unexplored large intermediate areas between
the two modalities with less uniformity. As a result, lack of alignment and
uniformity might restrict the robustness and transferability of the
representation for the downstream task. To this end, we provide a new
end-to-end fine-tuning method for robust representation that encourages better
uniformity and alignment score. First, we propose a \textit{Geodesic
Multi-Modal Mixup} that mixes the representation of image and text to generate
the hard negative samples on the hyperspherical embedding space. Second, we
fine-tune the multi-modal model on hard negative samples as well as normal
negatives and positive samples with contrastive loss. Through extensive
experiments on retrieval, classification, and structure-awareness task, we
demonstrate that our geodesic multi-modal Mixup learns a robust representation
and provides improved performance on various downstream tasks.

Point cloud classifiers with rotation robustness have been widely discussed
in the 3D deep learning community. Most proposed methods either use rotation
invariant descriptors as inputs or try to design rotation equivariant networks.
However, robust models generated by these methods have limited performance
under clean aligned datasets due to modifications on the original classifiers
or input space. In this study, for the first time, we show that the rotation
robustness of point cloud classifiers can also be acquired via adversarial
training with better performance on both rotated and clean datasets.
Specifically, our proposed framework named ART-Point regards the rotation of
the point cloud as an attack and improves rotation robustness by training the
classifier on inputs with Adversarial RoTations. We contribute an axis-wise
rotation attack that uses back-propagated gradients of the pre-trained model to
effectively find the adversarial rotations. To avoid model over-fitting on
adversarial inputs, we construct rotation pools that leverage the
transferability of adversarial rotations among samples to increase the
diversity of training data. Moreover, we propose a fast one-step optimization
to efficiently reach the final robust model. Experiments show that our proposed
rotation attack achieves a high success rate and ART-Point can be used on most
existing classifiers to improve the rotation robustness while showing better
performance on clean datasets than state-of-the-art methods.

In cross-silo federated learning, clients (e.g., organizations) train a
shared global model using local data. However, due to privacy concerns, the
clients may not contribute enough data points during training. To address this
issue, we propose a general incentive framework where the profit/benefit
obtained from the global model can be appropriately allocated to clients to
incentivize data contribution. We formulate the clients' interactions as a data
contribution game and study its equilibrium. We characterize conditions for an
equilibrium to exist, and prove that each client's equilibrium data
contribution increases in its data quality and decreases in the privacy
sensitivity. We further conduct experiments using CIFAR-10 and show that the
results are consistent with the analysis. Moreover, we show that practical
allocation mechanisms such as linearly proportional, leave-one-out, and
Shapley-value incentivize more data contribution from clients with
higher-quality data, in which leave-one-out tends to achieve the highest global
model accuracy at equilibrium.

The workflow of pretraining and fine-tuning has emerged as a popular paradigm
for solving various NLP and V&L (Vision-and-Language) downstream tasks. With
the capacity of pretrained models growing rapidly, how to perform
parameter-efficient fine-tuning has become fairly important for quick transfer
learning and deployment. In this paper, we design a novel unified
parameter-efficient transfer learning framework that works effectively on both
pure language and V&L tasks. In particular, we use a shared hypernetwork that
takes trainable hyper-embeddings as input, and outputs weights for fine-tuning
different small modules in a pretrained language model, such as tuning the
parameters inserted into multi-head attention blocks (i.e., prefix-tuning) and
feed-forward blocks (i.e., adapter-tuning). We define a set of embeddings
(e.g., layer, block, task and visual embeddings) as the key components to
calculate hyper-embeddings, which thus can support both pure language and V&L
tasks. Our proposed framework adds fewer trainable parameters in multi-task
learning while achieving superior performances and transfer ability compared to
state-of-the-art methods. Empirical results on the GLUE benchmark and multiple
V&L tasks confirm the effectiveness of our framework on both textual and visual

Community describes the functional mechanism of a network, making community
detection serve as a fundamental graph tool for various real applications like
discovery of social circle. To date, a Symmetric and Non-negative Matrix
Factorization (SNMF) model has been frequently adopted to address this issue
owing to its high interpretability and scalability. However, most existing
SNMF-based community detection methods neglect the high-order connection
patterns in a network. Motivated by this discovery, in this paper, we propose a
High-Order Proximity (HOP)-incorporated, Symmetry and Graph-regularized NMF
(HSGN) model that adopts the following three-fold ideas: a) adopting a weighted
pointwise mutual information (PMI)-based approach to measure the HOP indices
among nodes in a network; b) leveraging an iterative reconstruction scheme to
encode the captured HOP into the network; and c) introducing a symmetry and
graph-regularized NMF algorithm to detect communities accurately. Extensive
empirical studies on eight real-world networks demonstrate that an HSGN-based
community detector significantly outperforms both benchmark and
state-of-the-art community detectors in providing highly-accurate community
detection results.

We propose Occupancy Flow Fields, a new representation for motion forecasting
of multiple agents, an important task in autonomous driving. Our representation
is a spatio-temporal grid with each grid cell containing both the probability
of the cell being occupied by any agent, and a two-dimensional flow vector
representing the direction and magnitude of the motion in that cell. Our method
successfully mitigates shortcomings of the two most commonly-used
representations for motion forecasting: trajectory sets and occupancy grids.
Although occupancy grids efficiently represent the probabilistic location of
many agents jointly, they do not capture agent motion and lose the agent
identities. To this end, we propose a deep learning architecture that generates
Occupancy Flow Fields with the help of a new flow trace loss that establishes
consistency between the occupancy and flow predictions. We demonstrate the
effectiveness of our approach using three metrics on occupancy prediction,
motion estimation, and agent ID recovery. In addition, we introduce the problem
of predicting speculative agents, which are currently-occluded agents that may
appear in the future through dis-occlusion or by entering the field of view. We
report experimental results on a large in-house autonomous driving dataset and
the public INTERACTION dataset, and show that our model outperforms

This work simultaneously considers the discriminability and transferability
properties of deep representations in the typical supervised learning task,
i.e., image classification. By a comprehensive temporal analysis, we observe a
trade-off between these two properties. The discriminability keeps increasing
with the training progressing while the transferability intensely diminishes in
the later training period.
  From the perspective of information-bottleneck theory, we reveal that the
incompatibility between discriminability and transferability is attributed to
the over-compression of input information. More importantly, we investigate why
and how the InfoNCE loss can alleviate the over-compression, and further
present a learning framework, named contrastive temporal coding~(CTC), to
counteract the over-compression and alleviate the incompatibility. Extensive
experiments validate that CTC successfully mitigates the incompatibility,
yielding discriminative and transferable representations. Noticeable
improvements are achieved on the image classification task and challenging
transfer learning tasks. We hope that this work will raise the significance of
the transferability property in the conventional supervised learning setting.
Code is available at https://github.com/DTennant/dt-tradeoff.

Indoor route graph learning is critically important for autonomous indoor
navigation. A key problem for crowd-sourcing indoor route graph learning is
featured trajectory generation. In this paper, a system is provided to generate
featured trajectories by crowd-sourcing smartphone data. Firstly, we propose a
more accurate PDR algorithm for the generation of trajectory motion data. This
algorithm uses ADAPTIV as the step counting method and uses the step estimation
algorithm o make the trajectory more accurate in length. Next, the barometer is
used to segment the tracks of different floors, and the track floors are
obtained by WiFi feature clustering. Finally, by finding the turning point as
the feature point of the trajectory, the vertices and edges of the trajectory
are extracted to reduce the noise of the long straight trajectory.

Massive open online courses (MOOCs), which provide a large-scale interactive
participation and open access via the web, are becoming a modish way for online
and distance education. To help users have a better study experience, many MOOC
platforms have provided the services of recommending courses to users. However,
we argue that directly recommending a course to users will ignore the expertise
levels of different users. To fill this gap, this paper studies the problem of
concept recommendation in a more fine-grained view. We propose a novel
Heterogeneous Information Networks based Concept Recommender with Reinforcement
Learning (HinCRec-RL) incorporated for concept recommendation in MOOCs.
Specifically, we first formulate the concept recommendation in MOOCs as a
reinforcement learning problem to better model the dynamic interaction among
users and knowledge concepts. In addition, to mitigate the data sparsity issue
which also exists in many other recommendation tasks, we consider a
heterogeneous information network (HIN) among users, courses, videos and
concepts, to better learn the semantic representation of users. In particular,
we use the meta-paths on HIN to guide the propagation of users' preferences and
propose a heterogeneous graph attention network to represent the meta-paths. To
validate the effectiveness of our proposed approach, we conduct comprehensive
experiments on a real-world dataset from XuetangX, a popular MOOC platform from
China. The promising results show that our proposed approach can outperform

This article aims to provide the information retrieval community with some
reflections on recent advances in retrieval learning by analyzing the
reproducibility of image-text retrieval models. Due to the increase of
multimodal data over the last decade, image-text retrieval has steadily become
a major research direction in the field of information retrieval. Numerous
researchers train and evaluate image-text retrieval algorithms using benchmark
datasets such as MS-COCO and Flickr30k. Research in the past has mostly focused
on performance, with multiple state-of-the-art methodologies being suggested in
a variety of ways. According to their assertions, these techniques provide
improved modality interactions and hence more precise multimodal
representations. In contrast to previous works, we focus on the reproducibility
of the approaches and the examination of the elements that lead to improved
performance by pretrained and nonpretrained models in retrieving images and
text. To be more specific, we first examine the related reproducibility
concerns and explain why our focus is on image-text retrieval tasks. Second, we
systematically summarize the current paradigm of image-text retrieval models
and the stated contributions of those approaches. Third, we analyze various
aspects of the reproduction of pretrained and nonpretrained retrieval models.
To complete this, we conducted ablation experiments and obtained some
influencing factors that affect retrieval recall more than the improvement
claimed in the original paper. Finally, we present some reflections and
challenges that the retrieval community should consider in the future. Our
source code is publicly available at
https://github.com/WangFei-2019/Image-text-Retrieval.

Pre-trained models for programming languages have recently demonstrated great
success on code intelligence. To support both code-related understanding and
generation tasks, recent works attempt to pre-train unified encoder-decoder
models. However, such encoder-decoder framework is sub-optimal for
auto-regressive tasks, especially code completion that requires a decoder-only
manner for efficient inference. In this paper, we present UniXcoder, a unified
cross-modal pre-trained model for programming language. The model utilizes mask
attention matrices with prefix adapters to control the behavior of the model
and leverages cross-modal contents like AST and code comment to enhance code
representation. To encode AST that is represented as a tree in parallel, we
propose a one-to-one mapping method to transform AST in a sequence structure
that retains all structural information from the tree. Furthermore, we propose
to utilize multi-modal contents to learn representation of code fragment with
contrastive learning, and then align representations among programming
languages using a cross-modal generation task. We evaluate UniXcoder on five
code-related tasks over nine datasets. To further evaluate the performance of
code fragment representation, we also construct a dataset for a new task,
called zero-shot code-to-code search. Results show that our model achieves
state-of-the-art performance on most tasks and analysis reveals that comment
and AST can both enhance UniXcoder.

Medical image registration is a fundamental and vital task which will affect
the efficacy of many downstream clinical tasks. Deep learning (DL)-based
deformable image registration (DIR) methods have been investigated, showing
state-of-the-art performance. A test time optimization (TTO) technique was
proposed to further improve the DL models' performance. Despite the substantial
accuracy improvement with this TTO technique, there still remained some regions
that exhibited large registration errors even after many TTO iterations. To
mitigate this challenge, we firstly identified the reason why the TTO technique
was slow, or even failed, to improve those regions' registration results. We
then proposed a two-levels TTO technique, i.e., image-specific optimization
(ISO) and region-specific optimization (RSO), where the region can be
interactively indicated by the clinician during the registration result
reviewing process. For both efficiency and accuracy, we further envisioned a
three-step DL-based image registration workflow. Experimental results showed
that our proposed method outperformed the conventional method qualitatively and

Due to increasingly complex software design and rapid iterative development,
code defects and security vulnerabilities are prevalent in modern software. In
response, programmers rely on static analysis tools to regularly scan their
codebases and find potential bugs. In order to maximize coverage, however,
these tools generally tend to report a significant number of false positives,
requiring developers to manually verify each warning. To address this problem,
we propose a Transformer-based learning approach to identify false positive bug
warnings. We demonstrate that our models can improve the precision of static
analysis by 17.5%. In addition, we validated the generalizability of this
approach across two major bug types: null dereference and resource leak.

Human group detection, which splits crowd of people into groups, is an
important step for video-based human social activity analysis. The core of
human group detection is the human social relation representation and
division.In this paper, we propose a new two-stage multi-head framework for
human group detection. In the first stage, we propose a human behavior
simulator head to learn the social relation feature embedding, which is
self-supervisely trained by leveraging the socially grounded multi-person
behavior relationship. In the second stage, based on the social relation
embedding, we develop a self-attention inspired network for human group
detection. Remarkable performance on two state-of-the-art large-scale
benchmarks, i.e., PANDA and JRDB-Group, verifies the effectiveness of the
proposed framework. Benefiting from the self-supervised social relation
embedding, our method can provide promising results with very few (labeled)
training data. We will release the source code to the public.

Airway segmentation is essential for chest CT image analysis. However, it
remains a challenging task because of the intrinsic complex tree-like structure
and imbalanced sizes of airway branches. Current deep learning-based methods
focus on model structure design while the potential of training strategy and
loss function have not been fully explored. Therefore, we present a simple yet
effective airway segmentation pipeline, denoted NaviAirway, which finds finer
bronchioles with a bronchiole-sensitive loss function and a
human-vision-inspired iterative training strategy. Experimental results show
that NaviAirway outperforms existing methods, particularly in identification of
higher generation bronchioles and robustness to new CT scans. Besides,
NaviAirway is general. It can be combined with different backbone models and
significantly improve their performance. Moreover, we propose two new metrics
(Branch Detected and Tree-length Detected) for a more comprehensive and fairer
evaluation of deep learning-based airway segmentation approaches. NaviAirway
can generate airway roadmap for Navigation Bronchoscopy and can also be applied
to other scenarios when segmenting fine and long tubular structures in
biomedical images. The code is publicly available on
https://github.com/AntonotnaWang/NaviAirway.

Query-based video grounding is an important yet challenging task in video
understanding, which aims to localize the target segment in an untrimmed video
according to a sentence query. Most previous works achieve significant progress
by addressing this task in a fully-supervised manner with segment-level labels,
which require high labeling cost. Although some recent efforts develop
weakly-supervised methods that only need the video-level knowledge, they
generally match multiple pre-defined segment proposals with query and select
the best one, which lacks fine-grained frame-level details for distinguishing
frames with high repeatability and similarity within the entire video. To
alleviate the above limitations, we propose a self-contrastive learning
framework to address the query-based video grounding task under a
weakly-supervised setting. Firstly, instead of utilizing redundant segment
proposals, we propose a new grounding scheme that learns frame-wise matching
scores referring to the query semantic to predict the possible foreground
frames by only using the video-level annotations. Secondly, since some
predicted frames (i.e., boundary frames) are relatively coarse and exhibit
similar appearance to their adjacent frames, we propose a coarse-to-fine
contrastive learning paradigm to learn more discriminative frame-wise
representations for distinguishing the false positive frames. In particular, we
iteratively explore multi-scale hard negative samples that are close to
positive samples in the representation space for distinguishing fine-grained
frame-wise details, thus enforcing more accurate segment grounding. Extensive
experiments on two challenging benchmarks demonstrate the superiority of our
proposed method compared with the state-of-the-art methods.

Semantic analyses of object point clouds are largely driven by releasing of
benchmarking datasets, including synthetic ones whose instances are sampled
from object CAD models. However, learning from synthetic data may not
generalize to practical scenarios, where point clouds are typically incomplete,
non-uniformly distributed, and noisy. Such a challenge of Simulation-to-Reality
(Sim2Real) domain gap could be mitigated via learning algorithms of domain
adaptation; however, we argue that generation of synthetic point clouds via
more physically realistic rendering is a powerful alternative, as systematic
non-uniform noise patterns can be captured. To this end, we propose an
integrated scheme consisting of physically realistic synthesis of object point
clouds via rendering stereo images via projection of speckle patterns onto CAD
models and a novel quasi-balanced self-training designed for more balanced data
distribution by sparsity-driven selection of pseudo labeled samples for long
tailed classes. Experiment results can verify the effectiveness of our method
as well as both of its modules for unsupervised domain adaptation on point
cloud classification, achieving the state-of-the-art performance. Source codes
and the SpeckleNet synthetic dataset are available at
https://github.com/Gorilla-Lab-SCUT/QS3.

Stitched images provide a wide field-of-view (FoV) but suffer from unpleasant
irregular boundaries. To deal with this problem, existing image rectangling
methods devote to searching an initial mesh and optimizing a target mesh to
form the mesh deformation in two stages. Then rectangular images can be
generated by warping stitched images. However, these solutions only work for
images with rich linear structures, leading to noticeable distortions for
portraits and landscapes with non-linear objects. In this paper, we address
these issues by proposing the first deep learning solution to image
rectangling. Concretely, we predefine a rigid target mesh and only estimate an
initial mesh to form the mesh deformation, contributing to a compact one-stage
solution. The initial mesh is predicted using a fully convolutional network
with a residual progressive regression strategy. To obtain results with high
content fidelity, a comprehensive objective function is proposed to
simultaneously encourage the boundary rectangular, mesh shape-preserving, and
content perceptually natural. Besides, we build the first image stitching
rectangling dataset with a large diversity in irregular boundaries and scenes.
Experiments demonstrate our superiority over traditional methods both

We present a planning framework for minimising the deterministic worst-case
error in sparse Gaussian process (GP) regression. We first derive a universal
worst-case error bound for sparse GP regression with bounded noise using
interpolation theory on reproducing kernel Hilbert spaces (RKHSs). By
exploiting the conditional independence (CI) assumption central to sparse GP
regression, we show that the worst-case error minimisation can be achieved by
solving a posterior entropy minimisation problem. In turn, the posterior
entropy minimisation problem is solved using a Gaussian belief space planning
algorithm. We corroborate the proposed worst-case error bound in a simple 1D
example, and test the planning framework in simulation for a 2D vehicle in a
complex flow field. Our results demonstrate that the proposed posterior entropy
minimisation approach is effective in minimising deterministic error, and
outperforms the conventional measurement entropy maximisation formulation when
the inducing points are fixed.

Inspiration from design examples plays a crucial role in the creative process
of user interface design. However, current tools and techniques that support
inspiration usually only focus on example browsing with limited user control or
similarity-based example retrieval, leading to undesirable design outcomes such
as focus drift and design fixation. To address these issues, we propose the
GANSpiration approach that suggests design examples for both targeted and
serendipitous inspiration, leveraging a style-based Generative Adversarial
Network. A quantitative evaluation revealed that the outputs of
GANSpiration-based example suggestion approaches are relevant to the input
design, and at the same time include diverse instances. A user study with
professional UI/UX practitioners showed that the examples suggested by our
approach serve as viable sources of inspiration for overall design concepts and
specific design elements. Overall, our work paves the road of using advanced
generative machine learning techniques in supporting the creative design
practice.

Hierarchical text classification is a challenging subtask of multi-label
classification due to its complex label hierarchy. Existing methods encode text
and label hierarchy separately and mix their representations for
classification, where the hierarchy remains unchanged for all input text.
Instead of modeling them separately, in this work, we propose Hierarchy-guided
Contrastive Learning (HGCLR) to directly embed the hierarchy into a text
encoder. During training, HGCLR constructs positive samples for input text
under the guidance of the label hierarchy. By pulling together the input text
and its positive sample, the text encoder can learn to generate the
hierarchy-aware text representation independently. Therefore, after training,
the HGCLR enhanced text encoder can dispense with the redundant hierarchy.
Extensive experiments on three benchmark datasets verify the effectiveness of
HGCLR.

Machine learning interatomic force fields are promising for combining high
computational efficiency and accuracy in modeling quantum interactions and
simulating atomic level processes. Active learning methods have been recently
developed to train force fields efficiently and automatically. Among them,
Bayesian active learning utilizes principled uncertainty quantification to make
data acquisition decisions. In this work, we present an efficient Bayesian
active learning workflow, where the force field is constructed from a sparse
Gaussian process regression model based on atomic cluster expansion
descriptors. To circumvent the high computational cost of the sparse Gaussian
process uncertainty calculation, we formulate a high-performance approximate
mapping of the uncertainty and demonstrate a speedup of several orders of
magnitude. As an application, we train a model for silicon carbide (SiC), a
wide-gap semiconductor with complex polymorphic structure and diverse
technological applications in power electronics, nuclear physics and astronomy.
We show that the high pressure phase transformation is accurately captured by
the autonomous active learning workflow. The trained force field shows
excellent agreement with both ab initio calculations and experimental
measurements, and outperforms existing empirical models on vibrational and
thermal properties. The active learning workflow is readily generalized to a
wide range of systems, accelerates computational understanding and design.

Medical information extraction consists of a group of natural language
processing (NLP) tasks, which collaboratively convert clinical text to
pre-defined structured formats. Current state-of-the-art (SOTA) NLP models are
highly integrated with deep learning techniques and thus require massive
annotated linguistic data. This study presents an engineering framework of
medical entity recognition, relation extraction and attribute extraction, which
are unified in annotation, modeling and evaluation. Specifically, the
annotation scheme is comprehensive, and compatible between tasks, especially
for the medical relations. The resulted annotated corpus includes 1,200 full
medical records (or 18,039 broken-down documents), and achieves inter-annotator
agreements (IAAs) of 94.53%, 73.73% and 91.98% F 1 scores for the three tasks.
Three task-specific neural network models are developed within a shared
structure, and enhanced by SOTA NLP techniques, i.e., pre-trained language
models. Experimental results show that the system can retrieve medical
entities, relations and attributes with F 1 scores of 93.47%, 67.14% and
90.89%, respectively. This study, in addition to our publicly released
annotation scheme and code, provides solid and practical engineering experience
of developing an integrated medical information extraction system.

Estimating the risk level of adversarial examples is essential for safely
deploying machine learning models in the real world. One popular approach for
physical-world attacks is to adopt the "sticker-pasting" strategy, which
however suffers from some limitations, including difficulties in access to the
target or printing by valid colors. A new type of non-invasive attacks emerged
recently, which attempt to cast perturbation onto the target by optics based
tools, such as laser beam and projector. However, the added optical patterns
are artificial but not natural. Thus, they are still conspicuous and
attention-grabbed, and can be easily noticed by humans. In this paper, we study
a new type of optical adversarial examples, in which the perturbations are
generated by a very common natural phenomenon, shadow, to achieve naturalistic
and stealthy physical-world adversarial attack under the black-box setting. We
extensively evaluate the effectiveness of this new attack on both simulated and
real-world environments. Experimental results on traffic sign recognition
demonstrate that our algorithm can generate adversarial examples effectively,
reaching 98.23% and 90.47% success rates on LISA and GTSRB test sets
respectively, while continuously misleading a moving camera over 95% of the
time in real-world scenarios. We also offer discussions about the limitations
and the defense mechanism of this attack.

We introduce AiD Regen, a novel system that generates 3D wound models
combining 2D semantic segmentation with 3D reconstruction so that they can be
printed via 3D bio-printers during the surgery to treat diabetic foot ulcers
(DFUs). AiD Regen seamlessly binds the full pipeline, which includes RGB-D
image capturing, semantic segmentation, boundary-guided point-cloud processing,
3D model reconstruction, and 3D printable G-code generation, into a single
system that can be used out of the box. We developed a multi-stage data
preprocessing method to handle small and unbalanced DFU image datasets. AiD
Regen's human-in-the-loop machine learning interface enables clinicians to not
only create 3D regenerative patches with just a few touch interactions but also
customize and confirm wound boundaries. As evidenced by our experiments, our
model outperforms prior wound segmentation models and our reconstruction
algorithm is capable of generating 3D wound models with compelling accuracy. We
further conducted a case study on a real DFU patient and demonstrated the
effectiveness of AiD Regen in treating DFU wounds.

Magnetic Resonance (MR) image reconstruction from under-sampled acquisition
promises faster scanning time. To this end, current State-of-The-Art (SoTA)
approaches leverage deep neural networks and supervised training to learn a
recovery model. While these approaches achieve impressive performances, the
learned model can be fragile on unseen degradation, e.g. when given a different
acceleration factor. These methods are also generally deterministic and provide
a single solution to an ill-posed problem; as such, it can be difficult for
practitioners to understand the reliability of the reconstruction. We introduce
DiffuseRecon, a novel diffusion model-based MR reconstruction method.
DiffuseRecon guides the generation process based on the observed signals and a
pre-trained diffusion model, and does not require additional training on
specific acceleration factors. DiffuseRecon is stochastic in nature and
generates results from a distribution of fully-sampled MR images; as such, it
allows us to explicitly visualize different potential reconstruction solutions.
Lastly, DiffuseRecon proposes an accelerated, coarse-to-fine Monte-Carlo
sampling scheme to approximate the most likely reconstruction candidate. The
proposed DiffuseRecon achieves SoTA performances reconstructing from raw
acquisition signals in fastMRI and SKM-TEA. Code will be open-sourced at
www.github.com/cpeng93/DiffuseRecon.

Training agents in cooperative settings offers the promise of AI agents able
to interact effectively with humans (and other agents) in the real world.
Multi-agent reinforcement learning (MARL) has the potential to achieve this
goal, demonstrating success in a series of challenging problems. However,
whilst these advances are significant, the vast majority of focus has been on
the self-play paradigm. This often results in a coordination problem, caused by
agents learning to make use of arbitrary conventions when playing with
themselves. This means that even the strongest self-play agents may have very
low cross-play with other agents, including other initializations of the same
algorithm. In this paper we propose to solve this problem by adapting agent
strategies on the fly, using a posterior belief over the other agents'
strategy. Concretely, we consider the problem of selecting a strategy from a
finite set of previously trained agents, to play with an unknown partner. We
propose an extension of the classic statistical technique, Gibbs sampling, to
update beliefs about other agents and obtain close to optimal ad-hoc
performance. Despite its simplicity, our method is able to achieve strong
cross-play with unseen partners in the challenging card game of Hanabi,
achieving successful ad-hoc coordination without knowledge of the partner's
strategy a priori.

The data-centric machine learning aims to find effective ways to build
appropriate datasets which can improve the performance of AI models. In this
paper, we mainly focus on designing an efficient data-centric scheme to improve
robustness for models towards unforeseen malicious inputs in the black-box test
settings. Specifically, we introduce a noised-based data augmentation method
which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD
adversarial perturbations. The proposed method is built on lightweight
algorithms and proved highly effective based on comprehensive evaluations,
showing good efficiency on computation cost and robustness enhancement. In
addition, we share our insights about the data-centric robust machine learning
gained from our experiments.

Effective image retrieval with text feedback stands to impact a range of
real-world applications, such as e-commerce. Given a source image and text
feedback that describes the desired modifications to that image, the goal is to
retrieve the target images that resemble the source yet satisfy the given
modifications by composing a multi-modal (image-text) query. We propose a novel
solution to this problem, Additive Attention Compositional Learning (AACL),
that uses a multi-modal transformer-based architecture and effectively models
the image-text contexts. Specifically, we propose a novel image-text
composition module based on additive attention that can be seamlessly plugged
into deep neural networks. We also introduce a new challenging benchmark
derived from the Shopping100k dataset. AACL is evaluated on three large-scale
datasets (FashionIQ, Fashion200k, and Shopping100k), each with strong
baselines. Extensive experiments show that AACL achieves new state-of-the-art
results on all three datasets.

Nonnegative (linear) least square problems are a fundamental class of
problems that is well-studied in statistical learning and for which solvers
have been implemented in many of the standard programming languages used within
the machine learning community. The existing off-the-shelf solvers view the
non-negativity constraint in these problems as an obstacle and, compared to
unconstrained least squares, perform additional effort to address it. However,
in many of the typical applications, the data itself is nonnegative as well,
and we show that the nonnegativity in this case makes the problem easier. In
particular, while the oracle complexity of unconstrained least squares problems
necessarily scales with one of the data matrix constants (typically the
spectral norm) and these problems are solved to additive error, we show that
nonnegative least squares problems with nonnegative data are solvable to
multiplicative error and with complexity that is independent of any matrix
constants. The algorithm we introduce is accelerated and based on a primal-dual
perspective. We further show how to provably obtain linear convergence using
adaptive restart coupled with our method and demonstrate its effectiveness on
large-scale data via numerical experiments.

Following the explosive growth of global data, there is an ever-increasing
demand for high-throughput optical fiber communication (OFC) systems to perform
massive data transmission and processing. Existing OFC methods mainly rely on
electronic circuits for data processing, which severely limits the
communication throughput. Though considered promising for the next-generation
high-speed fiber communication, all-optical OFC remains unachievable due to
serious challenges in effective optical computing, system modeling and
configuring. Here we propose an end-to-end photonic encoder-decoder (PED)
processor which maps the physical system of OFC into an optical generative
neural network. By modeling the OFC transmission process as the variation in
the constructed optical latent space, the PED learns noise-resistant coding
schemes via unsupervised optimization. With multi-layer parametric diffractive
neural networks, the PED establishes a large-scale and high-throughput optical
computing framework that integrates the main OFC computations including coding,
encryption and compression to the optical domain. The whole system improves the
latency of computation in OFC systems by five orders of magnitude compared with
the state-of-the-art device. On benchmarking datasets, the PED experimentally
achieves up to 32% reduction in transmission error ratio (ER) than on-off
keying (OOK), one of the mainstream methods with the lowest ER in general
transmission. As we demonstrate on medical data, the PED increases the
transmission throughput by two orders of magnitude than 8-level pulse amplitude
modulation (PAM-8). We believe the proposed photonic encoder-decoder processor
not only paves the way to the next-generation all-optical OFC systems, but also
promotes a wide range of AI-based physical system designs.

Building reliable object detectors that can detect out-of-distribution (OOD)
objects is critical yet underexplored. One of the key challenges is that models
lack supervision signals from unknown data, producing overconfident predictions
on OOD objects. We propose a new unknown-aware object detection framework
through Spatial-Temporal Unknown Distillation (STUD), which distills unknown
objects from videos in the wild and meaningfully regularizes the model's
decision boundary. STUD first identifies the unknown candidate object proposals
in the spatial dimension, and then aggregates the candidates across multiple
video frames to form a diverse set of unknown objects near the decision
boundary. Alongside, we employ an energy-based uncertainty regularization loss,
which contrastively shapes the uncertainty space between the in-distribution
and distilled unknown objects. STUD establishes the state-of-the-art
performance on OOD detection tasks for object detection, reducing the FPR95
score by over 10% compared to the previous best method. Code is available at
https://github.com/deeplearning-wisc/stud.

In the online continual learning paradigm, agents must learn from a changing
distribution while respecting memory and compute constraints. Experience Replay
(ER), where a small subset of past data is stored and replayed alongside new
data, has emerged as a simple and effective learning strategy. In this work, we
focus on the change in representations of observed data that arises when
previously unobserved classes appear in the incoming data stream, and new
classes must be distinguished from previous ones. We shed new light on this
question by showing that applying ER causes the newly added classes'
representations to overlap significantly with the previous classes, leading to
highly disruptive parameter updates. Based on this empirical analysis, we
propose a new method which mitigates this issue by shielding the learned
representations from drastic adaptation to accommodate new classes. We show
that using an asymmetric update rule pushes new classes to adapt to the older
ones (rather than the reverse), which is more effective especially at task
boundaries, where much of the forgetting typically occurs. Empirical results
show significant gains over strong baselines on standard continual learning
benchmarks

This work aims to learn how to perform complex robot manipulation tasks that
are composed of several, consecutively executed low-level sub-tasks, given as
input a few visual demonstrations of the tasks performed by a person. The
sub-tasks consist of moving the robot's end-effector until it reaches a
sub-goal region in the task space, performing an action, and triggering the
next sub-task when a pre-condition is met. Most prior work in this domain has
been concerned with learning only low-level tasks, such as hitting a ball or
reaching an object and grasping it. This paper describes a new neural
network-based framework for learning simultaneously low-level policies as well
as high-level policies, such as deciding which object to pick next or where to
place it relative to other objects in the scene. A key feature of the proposed
approach is that the policies are learned directly from raw videos of task
demonstrations, without any manual annotation or post-processing of the data.
Empirical results on object manipulation tasks with a robotic arm show that the
proposed network can efficiently learn from real visual demonstrations to
perform the tasks, and outperforms popular imitation learning algorithms.

With the advancement of Deep Neural Networks (DNN) and large amounts of
sensor data from Internet of Things (IoT) systems, the research community has
worked to reduce the computational and resource demands of DNN to compute on
low-resourced microcontrollers (MCUs). However, most of the current work in
embedded deep learning focuses on solving a single task efficiently, while the
multi-tasking nature and applications of IoT devices demand systems that can
handle a diverse range of tasks (activity, voice, and context recognition) with
input from a variety of sensors, simultaneously.
  In this paper, we propose YONO, a product quantization (PQ) based approach
that compresses multiple heterogeneous models and enables in-memory model
execution and switching for dissimilar multi-task learning on MCUs. We first
adopt PQ to learn codebooks that store weights of different models. Also, we
propose a novel network optimization and heuristics to maximize the compression
rate and minimize the accuracy loss. Then, we develop an online component of
YONO for efficient model execution and switching between multiple tasks on an
MCU at run time without relying on an external storage device.
  YONO shows remarkable performance as it can compress multiple heterogeneous
models with negligible or no loss of accuracy up to 12.37$\times$. Besides,
YONO's online component enables an efficient execution (latency of 16-159 ms
per operation) and reduces model loading/switching latency and energy
consumption by 93.3-94.5% and 93.9-95.0%, respectively, compared to external
storage access. Interestingly, YONO can compress various architectures trained
with datasets that were not shown during YONO's offline codebook learning phase
showing the generalizability of our method. To summarize, YONO shows great
potential and opens further doors to enable multi-task learning systems on
extremely resource-constrained devices.

We present DARTR: a Data Adaptive RKHS Tikhonov Regularization method for the
linear inverse problem of nonparametric learning of function parameters in
operators. A key ingredient is a system intrinsic data-adaptive (SIDA) RKHS,
whose norm restricts the learning to take place in the function space of
identifiability. DARTR utilizes this norm and selects the regularization
parameter by the L-curve method. We illustrate its performance in examples
including integral operators, nonlinear operators and nonlocal operators with
discrete synthetic data. Numerical results show that DARTR leads to an accurate
estimator robust to both numerical error due to discrete data and noise in
data, and the estimator converges at a consistent rate as the data mesh refines
under different levels of noises, outperforming two baseline regularizers using
$l^2$ and $L^2$ norms.

Reinforcement Learning is an area of Machine Learning focused on how agents
can be trained to make sequential decisions, and achieve a particular goal
within an arbitrary environment. While learning, they repeatedly take actions
based on their observation of the environment, and receive appropriate rewards
which define the objective. This experience is then used to progressively
improve the policy controlling the agent's behavior, typically represented by a
neural network. This trained module can then be reused for similar problems,
which makes this approach promising for the animation of autonomous, yet
reactive characters in simulators, video games or virtual reality environments.
This paper surveys the modern Deep Reinforcement Learning methods and discusses
their possible applications in Character Animation, from skeletal control of a
single, physically-based character to navigation controllers for individual
agents and virtual crowds. It also describes the practical side of training DRL
systems, comparing the different frameworks available to build such agents.

The question of how to produce a smooth interpolating curve from a stream of
uncertainty regions, which become available sequentially, is addressed in this
paper. To this end, we formalize the concept of real-time interpolator (RTI): a
trainable recurrent unit that reconstructs smooth signals that are consistent
with the received uncertainty regions in an online manner. More specifically,
an RTI works under the requirement of reconstructing a section of the signal
immediately after an uncertainty region is revealed (zero delay), without
changing the reconstructed signal in the previous sections. Particularly, this
work formulates the design of spline-based RTIs and proposes a data-driven
training procedure, which minimizes the average curvature of the interpolated
signals over a set of example sequences. These sequences are representative of
the nature of the data sequence to be interpolated, allowing to tailor the RTI
to any specific signal source. Our overall design allows for different possible
schemes due to its modular structure, but in this work, we present two
approaches, namely, the parametrized RTI and the recurrent neural network
(RNN)-based RTI, including their architectures and properties. Experimental
results show that the two proposed RTIs can be trained to achieve improved
performance (in terms of the curvature loss metric) with respect to a
myopic-type RTI that only exploits the local information at each time step
while maintaining smooth, zero-delay, and consistency requirements.

The execution behavior of a program often depends on external resources, such
as program inputs or file contents, and so cannot be run in isolation.
Nevertheless, software developers benefit from fast iteration loops where
automated tools identify errors as early as possible, even before programs can
be compiled and run. This presents an interesting machine learning challenge:
can we predict runtime errors in a "static" setting, where program execution is
not possible? Here, we introduce a real-world dataset and task for predicting
runtime errors, which we show is difficult for generic models like
Transformers. We approach this task by developing an interpreter-inspired
architecture with an inductive bias towards mimicking program executions, which
models exception handling and "learns to execute" descriptions of the contents
of external resources. Surprisingly, we show that the model can also predict
the location of the error, despite being trained only on labels indicating the
presence/absence and kind of error. In total, we present a practical and
difficult-yet-approachable challenge problem related to learning program
execution and we demonstrate promising new capabilities of interpreter-inspired
machine learning models for code.

Few-Shot Learning refers to the problem of learning the underlying pattern in
the data just from a few training samples. Requiring a large number of data
samples, many deep learning solutions suffer from data hunger and extensively
high computation time and resources. Furthermore, data is often not available
due to not only the nature of the problem or privacy concerns but also the cost
of data preparation. Data collection, preprocessing, and labeling are strenuous
human tasks. Therefore, few-shot learning that could drastically reduce the
turnaround time of building machine learning applications emerges as a low-cost
solution. This survey paper comprises a representative list of recently
proposed few-shot learning algorithms. Given the learning dynamics and
characteristics, the approaches to few-shot learning problems are discussed in
the perspectives of meta-learning, transfer learning, and hybrid approaches
(i.e., different variations of the few-shot learning problem).

Convolutional neural networks (CNNs) have dominated the field of computer
vision for nearly a decade due to their strong ability to learn local features.
However, due to their limited receptive field, CNNs fail to model the global
context. On the other hand, transformer, an attention-based architecture can
model the global context easily. Despite this, there are limited studies that
investigate the effectiveness of transformers in crowd counting. In addition,
the majority of the existing crowd counting methods are based on the regression
of density maps which requires point-level annotation of each person present in
the scene. This annotation task is laborious and also error-prone. This has led
to increased focus on weakly-supervised crowd counting methods which require
only the count-level annotations. In this paper, we propose a weakly-supervised
method for crowd counting using a pyramid vision transformer. We have conducted
extensive evaluations to validate the effectiveness of the proposed method. Our
method is comparable to the state-of-the-art on the benchmark crowd datasets.
More importantly, it shows remarkable generalizability.

Refractory multi-principal element alloys (RMPEAs) are promising materials
for high-temperature structural applications. Here, we investigate the role of
chemical short-range ordering (CSRO) on dislocation glide in two model RMPEAs -
TaNbTi and MoNbTi - using a multi-scale modeling approach. A highly accurate
machine learning interatomic potential was developed for the Mo-Ta-Nb-Ti system
and used to demonstrate that MoNbTi exhibits a much greater degree of SRO than
TaNbTi and the local composition has a direct effect on the unstable stacking
fault energies (USFE). From mesoscale phase-field dislocation dynamics
simulations, we find that increasing SRO leads to higher mean USFEs, thereby
increasing the stress required for dislocation glide. The gliding dislocations
experience significant hardening due to pinning and depinning caused by random
compositional fluctuations, with higher SRO decreasing the degree of USFE
dispersion and hence, amount of hardening. Finally, we show how the morphology
of an expanding dislocation loop is affected by the applied stress, with higher
SRO requiring higher applied stresses to achieve smooth screw dislocation
glide.

In recent years, plentiful evidence illustrates that Graph Convolutional
Networks (GCNs) achieve extraordinary accomplishments on the node
classification task. However, GCNs may be vulnerable to adversarial attacks on
label-scarce dynamic graphs. Many existing works aim to strengthen the
robustness of GCNs; for instance, adversarial training is used to shield GCNs
against malicious perturbations. However, these works fail on dynamic graphs
for which label scarcity is a pressing issue. To overcome label scarcity,
self-training attempts to iteratively assign pseudo-labels to highly confident
unlabeled nodes but such attempts may suffer serious degradation under dynamic
graph perturbations. In this paper, we generalize noisy supervision as a kind
of self-supervised learning method and then propose a novel Bayesian
self-supervision model, namely GraphSS, to address the issue. Extensive
experiments demonstrate that GraphSS can not only affirmatively alert the
perturbations on dynamic graphs but also effectively recover the prediction of
a node classifier when the graph is under such perturbations. These two
advantages prove to be generalized over three classic GCNs across five public
graph datasets.

We consider the problem of training a $d$ dimensional model with distributed
differential privacy (DP) where secure aggregation (SecAgg) is used to ensure
that the server only sees the noisy sum of $n$ model updates in every training
round. Taking into account the constraints imposed by SecAgg, we characterize
the fundamental communication cost required to obtain the best accuracy
achievable under $\varepsilon$ central DP (i.e. under a fully trusted server
and no communication constraints). Our results show that $\tilde{O}\left(
\min(n^2\varepsilon^2, d) \right)$ bits per client are both sufficient and
necessary, and this fundamental limit can be achieved by a linear scheme based
on sparse random projections. This provides a significant improvement relative
to state-of-the-art SecAgg distributed DP schemes which use
$\tilde{O}(d\log(d/\varepsilon^2))$ bits per client.
  Empirically, we evaluate our proposed scheme on real-world federated learning
tasks. We find that our theoretical analysis is well matched in practice. In
particular, we show that we can reduce the communication cost significantly to
under $1.2$ bits per parameter in realistic privacy settings without decreasing
test-time performance. Our work hence theoretically and empirically specifies
the fundamental price of using SecAgg.

Image registration is important for medical imaging, the estimation of the
spatial transformation between different images. Many previous studies have
used learning-based methods for coarse-to-fine registration to efficiently
perform 3D image registration. The coarse-to-fine approach, however, is limited
when dealing with the different motions of nearby objects. Here we propose a
novel Motion-Aware (MA) structure that captures the different motions in a
region. The MA structure incorporates a novel Residual Aligner (RA) module
which predicts the multi-head displacement field used to disentangle the
different motions of multiple neighbouring objects. Compared with other deep
learning methods, the network based on the MA structure and RA module achieve
one of the most accurate unsupervised inter-subject registration on the 9
organs of assorted sizes in abdominal CT scans, with the highest-ranked
registration of the veins (Dice Similarity Coefficient / Average surface
distance: 62\%/4.9mm for the vena cava and 34\%/7.9mm for the portal and
splenic vein), with a half-sized structure and more efficient computation.
Applied to the segmentation of lungs in chest CT scans, the new network
achieves results which were indistinguishable from the best-ranked networks
(94\%/3.0mm). Additionally, the theorem on predicted motion pattern and the
design of MA structure are validated by further analysis.

Empirical evidence suggests that for a variety of overparameterized nonlinear
models, most notably in neural network training, the growth of the loss around
a minimizer strongly impacts its performance. Flat minima -- those around which
the loss grows slowly -- appear to generalize well. This work takes a step
towards understanding this phenomenon by focusing on the simplest class of
overparameterized nonlinear models: those arising in low-rank matrix recovery.
We analyze overparameterized matrix and bilinear sensing, robust PCA,
covariance matrix estimation, and single hidden layer neural networks with
quadratic activation functions. In all cases, we show that flat minima,
measured by the trace of the Hessian, exactly recover the ground truth under
standard statistical assumptions. For matrix completion, we establish weak
recovery, although empirical evidence suggests exact recovery holds here as
well. We complete the paper with synthetic experiments that illustrate our
findings.

Dynamic models of occupancy patterns have shown to be effective in optimizing
building-systems operations. Previous research has relied on CO$_2$ sensors and
vision-based techniques to determine occupancy patterns. Vision-based
techniques provide highly accurate information; however, they are very
intrusive. Therefore, motion or CO$_2$ sensors are more widely adopted
worldwide. Volatile Organic Compounds (VOCs) are another pollutant originating
from the occupants. However, a limited number of studies have evaluated the
impact of occupants on the VOC level. In this paper, continuous measurements of
CO$_2$, VOC, light, temperature, and humidity were recorded in a 17,000 sqft
open office space for around four months. Using different statistical models
(e.g., SVM, K-Nearest Neighbors, and Random Forest) we evaluated which
combination of environmental factors provides more accurate insights on
occupant presence. Our preliminary results indicate that VOC is a good
indicator of occupancy detection in some cases. It is also concluded that
proper feature selection and developing appropriate global occupancy detection
models can reduce the cost and energy of data collection without a significant
impact on accuracy.

Extracting environmental forces from noisy data is a common yet challenging
task in complex physical systems. Machine learning represents a robust approach
to this problem, yet is mostly tested on simulated data with known parameters.
Here we use supervised machine learning to extract the electrostatic,
hydrodynamic, and stochastic forces acting on micron-sized charged particles
levitated in an argon plasma. Trained on simulated particle trajectories using
more than 100 dynamical and statistical features, the model predicts system
parameters with 50\% better accuracy than conventional methods, and provides
non-contact measurements of the particle charge and Debye length.

Experimental grain growth observations often deviate from grain growth
simulations, revealing that the governing rules for grain boundary motion are
not fully understood. A novel deep learning model was developed to capture
grain growth behavior from training data without making assumptions about the
underlying physics. The Physics-Regularized Interpretable Machine Learning
Microstructure Evolution (PRIMME) model consists of a multi-layer neural
network that predicts the likelihood of a point changing to a neighboring
grain. Here, we demonstrate PRIMME's ability to replicate two-dimensional
normal grain growth by training it with Monte Carlo Potts simulations. The
trained PRIMME model's grain growth predictions in several test cases show good
agreement with analytical models, phase-field simulations, Monte Carlo Potts
simulations, and results from the literature. Additionally, PRIMME's
adaptability to investigate irregular grain growth behavior is shown. Important
aspects of PRIMME like interpretability, regularization, extrapolation, and
overfitting are also discussed.

Optimal Transport is a popular distance metric for measuring similarity
between distributions. Exact algorithms for computing Optimal Transport can be
slow, which has motivated the development of approximate numerical solvers
(e.g. Sinkhorn method). We introduce a new and very simple combinatorial
approach to find an $\varepsilon$-approximation of the OT distance. Our
algorithm achieves a near-optimal execution time of $O(n^2/\varepsilon^2)$ for
computing OT distance and, for the special case of the assignment problem, the
execution time improves to $O(n^2/\varepsilon)$. Our algorithm is based on the
push-relabel framework for min-cost flow problems.
  Unlike the other combinatorial approach (Lahn, Mulchandani and Raghvendra,
NeurIPS 2019) which does not have a fast parallel implementation, our algorithm
has a parallel execution time of $O(\log n/\varepsilon^2)$. Interestingly,
unlike the Sinkhorn algorithm, our method also readily provides a compact
transport plan as well as a solution to an approximate version of the dual
formulation of the OT problem, both of which have numerous applications in
Machine Learning. For the assignment problem, we provide both a CPU
implementation as well as an implementation that exploits GPU parallelism.
Experiments suggest that our algorithm is faster than the Sinkhorn algorithm,
both in terms of CPU and GPU implementations, especially while computing
matchings with a high accuracy.

Many high-dimensional practical data sets have hierarchical structures
induced by graphs or time series. Such data sets are hard to process in
Euclidean spaces and one often seeks low-dimensional embeddings in other space
forms to perform the required learning tasks. For hierarchical data, the space
of choice is a hyperbolic space because it guarantees low-distortion embeddings
for tree-like structures. The geometry of hyperbolic spaces has properties not
encountered in Euclidean spaces that pose challenges when trying to rigorously
analyze algorithmic solutions. We propose a unified framework for learning
scalable and simple hyperbolic linear classifiers with provable performance
guarantees. The gist of our approach is to focus on Poincar\'e ball models and
formulate the classification problems using tangent space formalisms. Our
results include a new hyperbolic perceptron algorithm as well as an efficient
and highly accurate convex optimization setup for hyperbolic support vector
machine classifiers. Furthermore, we adapt our approach to accommodate
second-order perceptrons, where data is preprocessed based on second-order
information (correlation) to accelerate convergence, and strategic perceptrons,
where potentially manipulated data arrives in an online manner and decisions
are made sequentially. The excellent performance of the Poincar\'e second-order
and strategic perceptrons shows that the proposed framework can be extended to
general machine learning problems in hyperbolic spaces. Our experimental
results, pertaining to synthetic, single-cell RNA-seq expression measurements,
CIFAR10, Fashion-MNIST and mini-ImageNet, establish that all algorithms
provably converge and have complexity comparable to those of their Euclidean
counterparts. Accompanying codes can be found at:
https://github.com/thupchnsky/PoincareLinearClassification.

Explainability in machine learning has become incredibly important as machine
learning-powered systems become ubiquitous and both regulation and public
sentiment begin to demand an understanding of how these systems make decisions.
As a result, a number of explanation methods have begun to receive widespread
adoption. This work summarizes, compares, and contrasts three popular
explanation methods: LIME, SmoothGrad, and SHAP. We evaluate these methods with
respect to: robustness, in the sense of sample complexity and stability;
understandability, in the sense that provided explanations are consistent with
user expectations; and usability, in the sense that the explanations allow for
the model to be modified based on the output. This work concludes that current
explanation methods are insufficient; that putting faith in and adopting these
methods may actually be worse than simply not using them.

Visual question answering is a vision-and-language multimodal task, that aims
at predicting answers given samples from the question and image modalities.
Most recent methods focus on learning a good joint embedding space of images
and questions, either by improving the interaction between these two
modalities, or by making it a more discriminant space. However, how informative
this joint space is, has not been well explored. In this paper, we propose a
novel regularization for VQA models, Constrained Optimization using Barlow's
theory (COB), that improves the information content of the joint space by
minimizing the redundancy. It reduces the correlation between the learned
feature components and thereby disentangles semantic concepts. Our model also
aligns the joint space with the answer embedding space, where we consider the
answer and image+question as two different `views' of what in essence is the
same semantic information. We propose a constrained optimization policy to
balance the categorical and redundancy minimization forces. When built on the
state-of-the-art GGE model, the resulting model improves VQA accuracy by 1.4%
and 4% on the VQA-CP v2 and VQA v2 datasets respectively. The model also
exhibits better interpretability.

The recent advancements in generative artificial speech models have made
possible the generation of highly realistic speech signals. At first, it seems
exciting to obtain these artificially synthesized signals such as speech clones
or deep fakes but if left unchecked, it may lead us to digital dystopia. One of
the primary focus in audio forensics is validating the authenticity of a
speech. Though some solutions are proposed for English speeches but the
detection of synthetic Hindi speeches have not gained much attention. Here, we
propose an approach for discrimination of AI synthesized Hindi speech from an
actual human speech. We have exploited the Bicoherence Phase, Bicoherence
Magnitude, Mel Frequency Cepstral Coefficient (MFCC), Delta Cepstral, and Delta
Square Cepstral as the discriminating features for machine learning models.
Also, we extend the study to using deep neural networks for extensive
experiments, specifically VGG16 and homemade CNN as the architecture models. We
obtained an accuracy of 99.83% with VGG16 and 99.99% with homemade CNN models.

The Cram\'er-Rao bound (CRB), a well-known lower bound on the performance of
any unbiased parameter estimator, has been used to study a wide variety of
problems. However, to obtain the CRB, requires an analytical expression for the
likelihood of the measurements given the parameters, or equivalently a precise
and explicit statistical model for the data. In many applications, such a model
is not available. Instead, this work introduces a novel approach to approximate
the CRB using data-driven methods, which removes the requirement for an
analytical statistical model. This approach is based on the recent success of
deep generative models in modeling complex, high-dimensional distributions.
Using a learned normalizing flow model, we model the distribution of the
measurements and obtain an approximation of the CRB, which we call Generative
Cram\'er-Rao Bound (GCRB). Numerical experiments on simple problems validate
this approach, and experiments on two image processing tasks of image denoising
and edge detection with a learned camera noise model demonstrate its power and
benefits.

Recent exploration of the multi-agent backdoor attack demonstrated the
backfiring effect, a natural defense against backdoor attacks where backdoored
inputs are randomly classified. This yields a side-effect of low accuracy
w.r.t. clean labels, which motivates this paper's work on the construction of
multi-agent backdoor defenses that maximize accuracy w.r.t. clean labels and
minimize that of poison labels. Founded upon agent dynamics and low-loss
subspace construction, we contribute three defenses that yield improved
multi-agent backdoor robustness.

Transformer-based architectures are the model of choice for natural language
understanding, but they come at a significant cost, as they have quadratic
complexity in the input length and can be difficult to tune. In the pursuit of
Green AI, we investigate simple MLP-based architectures. We find that existing
architectures such as MLPMixer, which achieves token mixing through a static
MLP applied to each feature independently, are too detached from the inductive
biases required for natural language understanding. In this paper, we propose a
simple variant, HyperMixer, which forms the token mixing MLP dynamically using
hypernetworks. Empirically, we demonstrate that our model performs better than
alternative MLP-based models, and on par with Transformers. In contrast to
Transformers, HyperMixer achieves these results at substantially lower costs in
terms of processing time, training data, and hyperparameter tuning.

Graph neural networks have become a powerful framework for learning complex
structure-property relationships and fast screening of chemical compounds.
Recently proposed methods have demonstrated that using 3D geometry information
of the molecule along with the bonding structure can lead to more accurate
prediction on a wide range of properties. A common practice is to use 3D
geometries computed through density functional theory (DFT) for both training
and testing of models. However, the computational time needed for DFT
calculations can be prohibitively large. Moreover, many of the properties that
we aim to predict can often be obtained with little or no overhead on top of
the DFT calculations used to produce the 3D geometry information, voiding the
need for a predictive model. To be practically useful for high-throughput
chemical screening and drug discovery, it is desirable to work with 3D
geometries obtained using less-accurate but much more efficient non-DFT
methods. In this work we investigate the impact of using non-DFT conformations
in the training and the testing of existing models and propose a data
augmentation method for improving the prediction accuracy of classical
forcefield-derived geometries.

Although certain vision transformer (ViT) and CNN architectures generalize
well on vision tasks, it is often impractical to use them on green, edge, or
desktop computing due to their computational requirements for training and even
testing. We present WaveMix as an alternative neural architecture that uses a
multi-scale 2D discrete wavelet transform (DWT) for spatial token mixing.
Unlike ViTs, WaveMix neither unrolls the image nor requires self-attention of
quadratic complexity. Additionally, DWT introduces another inductive bias --
besides convolutional filtering -- to utilize the 2D structure of an image to
improve generalization. The multi-scale nature of the DWT also reduces the
requirement for a deeper architecture compared to the CNNs, as the latter
relies on pooling for partial spatial mixing. WaveMix models show
generalization that is competitive with ViTs, CNNs, and token mixers on several
datasets while requiring lower GPU RAM (training and testing), number of
computations, and storage. WaveMix have achieved State-of-the-art (SOTA)
results in EMNIST Byclass and EMNIST Balanced datasets.

We study a Markov matching market involving a planner and a set of strategic
agents on the two sides of the market. At each step, the agents are presented
with a dynamical context, where the contexts determine the utilities. The
planner controls the transition of the contexts to maximize the cumulative
social welfare, while the agents aim to find a myopic stable matching at each
step. Such a setting captures a range of applications including ridesharing
platforms. We formalize the problem by proposing a reinforcement learning
framework that integrates optimistic value iteration with maximum weight
matching. The proposed algorithm addresses the coupled challenges of sequential
exploration, matching stability, and function approximation. We prove that the
algorithm achieves sublinear regret.

In this work, we consider the problem of learning a perception model for
monocular robot navigation using few annotated images. Using a Vision
Transformer (ViT) pretrained with a label-free self-supervised method, we
successfully train a coarse image segmentation model for the Duckietown
environment using 70 training images. Our model performs coarse image
segmentation at the 8x8 patch level, and the inference resolution can be
adjusted to balance prediction granularity and real-time perception
constraints. We study how best to adapt a ViT to our task and environment, and
find that some lightweight architectures can yield good single-image
segmentation at a usable frame rate, even on CPU. The resulting perception
model is used as the backbone for a simple yet robust visual servoing agent,
which we deploy on a differential drive mobile robot to perform two tasks: lane
following and obstacle avoidance.

Forthcoming astronomical surveys are expected to detect new sources in such
large numbers that measuring their spectroscopic redshift measurements will be
not be practical. Thus, there is much interest in using machine learning to
yield the redshift from the photometry of each object. We are particularly
interested in radio sources (quasars) detected with the Square Kilometre Array
and have found Deep Learning, trained upon a large optically-selected sample of
quasi-stellar objects, to be effective in the prediction of the redshifts in
three external samples of radio-selected sources. However, the requirement of
nine different magnitudes, from the near-infrared, optical and ultra-violet
bands, has the effect of significantly reducing the number of sources for which
redshifts can be predicted. Here we explore the possibility of using machine
learning to impute the missing features. We find that for the training sample,
simple imputation is sufficient, particularly replacing the missing magnitude
with the maximum for that band, thus presuming that the non-detection is at the
sensitivity limit. For the test samples, however, this does not perform as well
as multivariate imputation, which suggests that many of the missing magnitudes
are not limits, but have indeed not been observed. From extensive testing of
the models, we suggest that the imputation is best restricted to two missing
values per source. Where the sources overlap on the sky, in the worst case,
this increases the fraction of sources for which redshifts can be estimated
from 46% to 80%, with >90% being reached for the other samples.

We consider the problems of downlink user selection and power control in
wireless networks, comprising multiple transmitters and receivers communicating
with each other over a shared wireless medium. To achieve a high aggregate
rate, while ensuring fairness across all the receivers, we formulate a
resilient radio resource management (RRM) policy optimization problem with
per-user minimum-capacity constraints that adapt to the underlying network
conditions via learnable slack variables. We reformulate the problem in the
Lagrangian dual domain, and show that we can parameterize the user selection
and power control policies using a finite set of parameters, which can be
trained alongside the slack and dual variables via an unsupervised primal-dual
approach thanks to a provably small duality gap. We use a scalable and
permutation-equivariant graph neural network (GNN) architecture to parameterize
the RRM policies based on a graph topology derived from the instantaneous
channel conditions. Through experimental results, we verify that the
minimum-capacity constraints adapt to the underlying network configurations and
channel conditions. We further demonstrate that, thanks to such adaptation, our
proposed method achieves a superior tradeoff between the average rate and the
5th percentile rate -- a metric that quantifies the level of fairness in the
resource allocation decisions -- as compared to baseline algorithms.

This paper addresses video anomaly detection problem for videosurveillance.
Due to the inherent rarity and heterogeneity of abnormal events, the problem is
viewed as a normality modeling strategy, in which our model learns
object-centric normal patterns without seeing anomalous samples during
training. The main contributions consist in coupling pretrained object-level
action features prototypes with a cosine distance-based anomaly estimation
function, therefore extending previous methods by introducing additional
constraints to the mainstream reconstruction-based strategy. Our framework
leverages both appearance and motion information to learn object-level behavior
and captures prototypical patterns within a memory module. Experiments on
several well-known datasets demonstrate the effectiveness of our method as it
outperforms current state-of-the-art on most relevant spatio-temporal

We propose and analyse a novel statistical procedure, coined AgraSSt, to
assess the quality of graph generators that may not be available in explicit
form. In particular, AgraSSt can be used to determine whether a learnt graph
generating process is capable of generating graphs that resemble a given input
graph. Inspired by Stein operators for random graphs, the key idea of AgraSSt
is the construction of a kernel discrepancy based on an operator obtained from
the graph generator. AgraSSt can provide interpretable criticisms for a graph
generator training procedure and help identify reliable sample batches for
downstream tasks. Using Stein`s method we give theoretical guarantees for a
broad class of random graph models. We provide empirical results on both
synthetic input graphs with known graph generation procedures, and real-world
input graphs that the state-of-the-art (deep) generative models for graphs are
trained on.

In this paper we propose ways to incorporate Machine Learning training
outputs into a study of statistical significance. We describe these methods in
supervised classification tasks using a CNN and a DNN output, and unsupervised
learning based on a VAE. As use cases, we consider two physical situations
where Machine Learning are often used: high-$p_T$ hadronic activity, and
boosted Higgs in association with a massive vector boson.

Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography
images is key for diagnosis and personalized treatment of eye diseases. While
deep learning has been successful at this task, trained supervised models often
fail for images that do not resemble labeled examples, e.g. for images acquired
using different devices. We hereby propose a novel semi-supervised learning
framework for segmentation of volumetric images from new unlabeled domains. We
jointly use supervised and contrastive learning, also introducing a contrastive
pairing scheme that leverages similarity between nearby slices in 3D. In
addition, we propose channel-wise aggregation as an alternative to conventional
spatial-pooling aggregation for contrastive feature map projection. We evaluate
our methods for domain adaptation from a (labeled) source domain to an
(unlabeled) target domain, each containing images acquired with different
acquisition devices. In the target domain, our method achieves a Dice
coefficient 13.8% higher than SimCLR (a state-of-the-art contrastive
framework), and leads to results comparable to an upper bound with supervised
training in that domain. In the source domain, our model also improves the
results by 5.4% Dice, by successfully leveraging information from many
unlabeled images.

The catalog of gravitational-wave events is growing, and so are our hopes of
constraining the underlying astrophysics of stellar-mass black-hole mergers by
inferring the distributions of, e.g., masses and spins. While conventional
analyses parametrize this population with simple phenomenological models, we
propose an emulation-based approach that can compare astrophysical simulations
against gravitational-wave data. We combine state-of-the-art deep-learning
techniques with hierarchical Bayesian inference and exploit our approach to
constrain the properties of repeated black-hole mergers from the
gravitational-wave events in the most recent LIGO/Virgo catalog. Deep neural
networks allow us to (i) construct a flexible single-channel population model
that accurately emulates simple parametrized numerical simulations of
hierarchical mergers, (ii) estimate selection effects, and (iii) recover the
branching ratios of repeated-merger generations. Among our results, we find the
following: The distribution of host-environment escape speeds favors values
less than $100~\mathrm{km\,s^{-1}}$ but is relatively flat, with around $37\%$
of first-generation mergers retained in their host environments;
first-generation black holes are born with a maximum mass that is compatible
with current estimates from pair-instability supernovae; there is multimodal
substructure in both the mass and spin distributions, which, in our model, can
be explained by repeated mergers; and binaries with a higher-generation
component make up at least $14\%$ of the underlying population. Though these
results are inferred through emulation of a simplified model, the deep-learning
pipeline we present is readily applicable to realistic astrophysical
simulations

The prediction of financial markets is a challenging yet important task. In
modern electronically-driven markets traditional time-series econometric
methods often appear incapable of capturing the true complexity of the
multi-level interactions driving the price dynamics. While recent research has
established the effectiveness of traditional machine learning (ML) models in
financial applications, their intrinsic inability in dealing with
uncertainties, which is a great concern in econometrics research and real
business applications, constitutes a major drawback. Bayesian methods naturally
appear as a suitable remedy conveying the predictive ability of ML methods with
the probabilistically-oriented practice of econometric research. By adopting a
state-of-the-art second-order optimization algorithm, we train a Bayesian
bilinear neural network with temporal attention, suitable for the challenging
time-series task of predicting mid-price movements in ultra-high-frequency
limit-order book markets. By addressing the use of predictive distributions to
analyze errors and uncertainties associated with the estimated parameters and
model forecasts, we thoroughly compare our Bayesian model with traditional ML
alternatives. Our results underline the feasibility of the Bayesian deep
learning approach and its predictive and decisional advantages in complex
econometric tasks, prompting future research in this direction.

The design of more complex and powerful neural network models has
significantly advanced the state-of-the-art in local feature detection and
description. These advances can be attributed to deeper networks, improved
training methodologies through self-supervision, or the introduction of new
building blocks, such as graph neural networks for feature matching. However,
in the pursuit of increased performance, efficient architectures that generate
lightweight descriptors have received surprisingly little attention. In this
paper, we investigate the adaptations neural networks for detection and
description require in order to enable their use in embedded platforms. To that
end, we investigate and adapt network quantization techniques for use in
real-time applications. In addition, we revisit common practices in descriptor
quantization and propose the use of a binary descriptor normalization layer,
enabling the generation of distinctive length-invariant binary descriptors.
ZippyPoint, our efficient network, runs at 47.2 fps on the Apple M1 CPU. This
is up to 5x faster than other learned detection and description models, making
it the only real-time learned network. ZippyPoint consistently outperforms all
other binary detection and descriptor methods in visual localization and
homography estimation tasks. Code and trained models will be released upon
publication.

Graph Convolutional Networks (GCNs) have drawn tremendous attention in the
past three years. Compared with other deep learning modalities,
high-performance hardware acceleration of GCNs is as critical but even more
challenging. The hurdles arise from the poor data locality and redundant
computation due to the large size, high sparsity, and irregular non-zero
distribution of real-world graphs.
  In this paper we propose a novel hardware accelerator for GCN inference,
called I-GCN, that significantly improves data locality and reduces unnecessary
computation. The mechanism is a new online graph restructuring algorithm we
refer to as islandization. The proposed algorithm finds clusters of nodes with
strong internal but weak external connections. The islandization process yields
two major benefits. First, by processing islands rather than individual nodes,
there is better on-chip data reuse and fewer off-chip memory accesses. Second,
there is less redundant computation as aggregation for common/shared neighbors
in an island can be reused. The parallel search, identification, and leverage
of graph islands are all handled purely in hardware at runtime working in an
incremental pipeline. This is done without any preprocessing of the graph data
or adjustment of the GCN model structure.
  Experimental results show that I-GCN can significantly reduce off-chip
accesses and prune 38% of aggregation operations, leading to performance
speedups over CPUs, GPUs, the prior art GCN accelerators of 5549x, 403x, and
5.7x on average, respectively.

Differential privacy provides a theoretical framework for processing a
dataset about $n$ users, in a way that the output reveals a minimal information
about any single user. Such notion of privacy is usually ensured by
noise-adding mechanisms and amplified by several processes, including
subsampling, shuffling, iteration, mixing and diffusion. In this work, we
provide privacy amplification bounds for quantum and quantum-inspired
algorithms. In particular, we show for the first time, that algorithms running
on quantum encoding of a classical dataset or the outcomes of quantum-inspired
classical sampling, amplify differential privacy. Moreover, we prove that a
quantum version of differential privacy is amplified by the composition of
quantum channels, provided that they satisfy some mixing conditions.

Learning to classify video data from classes not included in the training
data, i.e. video-based zero-shot learning, is challenging. We conjecture that
the natural alignment between the audio and visual modalities in video data
provides a rich training signal for learning discriminative multi-modal
representations. Focusing on the relatively underexplored task of audio-visual
zero-shot learning, we propose to learn multi-modal representations from
audio-visual data using cross-modal attention and exploit textual label
embeddings for transferring knowledge from seen classes to unseen classes.
Taking this one step further, in our generalised audio-visual zero-shot
learning setting, we include all the training classes in the test-time search
space which act as distractors and increase the difficulty while making the
setting more realistic. Due to the lack of a unified benchmark in this domain,
we introduce a (generalised) zero-shot learning benchmark on three audio-visual
datasets of varying sizes and difficulty, VGGSound, UCF, and ActivityNet,
ensuring that the unseen test classes do not appear in the dataset used for
supervised training of the backbone deep models. Comparing multiple relevant
and recent methods, we demonstrate that our proposed AVCA model achieves
state-of-the-art performance on all three datasets. Code and data are available
at \url{https://github.com/ExplainableML/AVCA-GZSL}.

Good generalization performance on high-dimensional data crucially hinges on
a simple structure of the ground truth and a corresponding strong inductive
bias of the estimator. Even though this intuition is valid for regularized
models, in this paper we caution against a strong inductive bias for
interpolation in the presence of noise: While a stronger inductive bias
encourages a simpler structure that is more aligned with the ground truth, it
also increases the detrimental effect of noise. Specifically, for both linear
regression and classification with a sparse ground truth, we prove that minimum
$\ell_p$-norm and maximum $\ell_p$-margin interpolators achieve fast polynomial
rates close to order $1/n$ for $p > 1$ compared to a logarithmic rate for $p =
1$. Finally, we provide preliminary experimental evidence that this trade-off
may also play a crucial role in understanding non-linear interpolating models
used in practice.

It is difficult to continually update private machine learning models with
new data while maintaining privacy. Data incur increasing privacy loss -- as
measured by differential privacy -- when they are used in repeated
computations. In this paper, we describe regularized empirical risk
minimization algorithms that continually release models for a recent window of
data. One version of the algorithm uses the entire data history to improve the
model for the recent window. The second version uses a sliding window of
constant size to improve the model, ensuring more relevant models in case of
evolving data. The algorithms operate in the framework of stochastic gradient
descent. We prove that even with releasing a model at each time-step over an
infinite time horizon, the privacy cost of any data point is bounded by a
constant $\epsilon$ differential privacy, and the accuracy of the output models
are close to optimal. Experiments on MNIST and Arxiv publications data show
results consistent with the theory.

The problem of private learning has been extensively studied in classical
computer science. Notably, a striking equivalence between local differentially
private learning and statistical query learning has been shown. In addition,
the statistical query model has been recently extended to quantum computation.
In this work, we give a formal definition of quantum local differential privacy
and we extend the aforementioned result to quantum computation.

Semi-supervised learning has emerged as an appealing strategy to train deep
models with limited supervision. Most prior literature under this learning
paradigm resorts to dual-based architectures, typically composed of a
teacher-student duple. To drive the learning of the student, many of these
models leverage the aleatoric uncertainty derived from the entropy of the
predictions. While this has shown to work well in a binary scenario, we
demonstrate in this work that this strategy leads to suboptimal results in a
multi-class context, a more realistic and challenging setting. We argue,
indeed, that these approaches underperform due to the erroneous uncertainty
approximations in the presence of inter-class overlap. Furthermore, we propose
an alternative solution to compute the uncertainty in a multi-class setting,
based on divergence distances and which account for inter-class overlap. We
evaluate the proposed solution on a challenging multi-class segmentation
dataset and in two well-known uncertainty-based segmentation methods. The
reported results demonstrate that by simply replacing the mechanism used to
compute the uncertainty, our proposed solution brings substantial improvement
on tested setups.

Recent years have seen the emergence of pre-trained representations as a
powerful abstraction for AI applications in computer vision, natural language,
and speech. However, policy learning for control is still dominated by a
tabula-rasa learning paradigm, with visuo-motor policies often trained from
scratch using data from deployment environments. In this context, we revisit
and study the role of pre-trained visual representations for control, and in
particular representations trained on large-scale computer vision datasets.
Through extensive empirical evaluation in diverse control domains (Habitat,
DeepMind Control, Adroit, Franka Kitchen), we isolate and study the importance
of different representation training methods, data augmentations, and feature
hierarchies. Overall, we find that pre-trained visual representations can be
competitive or even better than ground-truth state representations to train
control policies. This is in spite of using only out-of-domain data from
standard vision datasets, without any in-domain data from the deployment
environments. Source code and more at
https://sites.google.com/view/pvr-control.

Unsupervised training of generative models is a machine learning task that
has many applications in scientific computing. In this work we evaluate the
efficacy of using quantum circuit-based generative models to generate synthetic
data of high energy physics processes. We use non-adversarial, gradient-based
training of quantum circuit Born machines to generate joint distributions over
2 and 3 variables.

Objective: Machine learning (ML) based radiation treatment (RT) planning
addresses the iterative and time-consuming nature of conventional inverse
planning. Given the rising importance of Magnetic resonance (MR) only treatment
planning workflows, we sought to determine if an ML based treatment planning
model, trained on computed tomography (CT) imaging, could be applied to MR
through domain adaptation. Methods: In this study, MR and CT imaging was
collected from 55 prostate cancer patients treated on an MR linear accelerator.
ML based plans were generated for each patient on both CT and MR imaging using
a commercially available model in RayStation 8B. The dose distributions and
acceptance rates of MR and CT based plans were compared using institutional
dose-volume evaluation criteria. The dosimetric differences between MR and CT
plans were further decomposed into setup, cohort, and imaging domain
components. Results: MR plans were highly acceptable, meeting 93.1% of all
evaluation criteria compared to 96.3% of CT plans, with dose equivalence for
all evaluation criteria except for the bladder wall, penile bulb, small and
large bowel, and one rectum wall criteria (p<0.05). Changing the input imaging
modality (domain component) only accounted for about half of the dosimetric
differences observed between MR and CT plans. Anatomical differences between
the ML training set and the MR linac cohort (cohort component) were also a
significant contributor. Significance: We were able to create highly acceptable
MR based treatment plans using a CT-trained ML model for treatment planning,
although clinically significant dose deviations from the CT based plans were
observed.

Data is the driving force of machine learning, with the amount and quality of
training data often being more important for the performance of a system than
architecture and training details. But collecting, processing and annotating
real data at scale is difficult, expensive, and frequently raises additional
privacy, fairness and legal concerns. Synthetic data is a powerful tool with
the potential to address these shortcomings: 1) it is cheap 2) supports rich
ground-truth annotations 3) offers full control over data and 4) can circumvent
or mitigate problems regarding bias, privacy and licensing. Unfortunately,
software tools for effective data generation are less mature than those for
architecture design and training, which leads to fragmented generation efforts.
To address these problems we introduce Kubric, an open-source Python framework
that interfaces with PyBullet and Blender to generate photo-realistic scenes,
with rich annotations, and seamlessly scales to large jobs distributed over
thousands of machines, and generating TBs of data. We demonstrate the
effectiveness of Kubric by presenting a series of 13 different generated
datasets for tasks ranging from studying 3D NeRF models to optical flow
estimation. We release Kubric, the used assets, all of the generation code, as
well as the rendered datasets for reuse and modification.

Graph Neural Networks (GNNs) are de facto node classification models in graph
structured data. However, during testing-time, these algorithms assume no data
shift, i.e., $\Pr_\text{train}(X,Y) = \Pr_\text{test}(X,Y)$. Domain adaption
methods can be adopted for data shift, yet most of them are designed to only
encourage similar feature distribution between source and target data.
Conditional shift on classes can still affect such adaption. Fortunately, graph
yields graph homophily across different data distributions. In response, we
propose Shift-Robust Node Classification (SRNC) to address these limitations.
We introduce an unsupervised cluster GNN on target graph to group the similar
nodes by graph homophily. An adversarial loss with label information on source
graph is used upon clustering objective. Then a shift-robust classifier is
optimized on training graph and adversarial samples on target graph, which are
generated by cluster GNN. We conduct experiments on both open-set shift and
representation-shift, which demonstrates the superior accuracy of SRNC on
generalizing to test graph with data shift. SRNC is consistently better than
previous SoTA domain adaption algorithm on graph that progressively use model
predictions on target graph for training.

Integrating different representations from complementary sensing modalities
is crucial for robust scene interpretation in autonomous driving. While deep
learning architectures that fuse vision and range data for 2D object detection
have thrived in recent years, the corresponding modalities can degrade in
adverse weather or lighting conditions, ultimately leading to a drop in
performance. Although domain adaptation methods attempt to bridge the domain
gap between source and target domains, they do not readily extend to
heterogeneous data distributions. In this work, we propose an unsupervised
domain adaptation framework, which adapts a 2D object detector for RGB and
lidar sensors to one or more target domains featuring adverse weather
conditions. Our proposed approach consists of three components. First, a data
augmentation scheme that simulates weather distortions is devised to add domain
confusion and prevent overfitting on the source data. Second, to promote
cross-domain foreground object alignment, we leverage the complementary
features of multiple modalities through a multi-scale entropy-weighted domain
discriminator. Finally, we use carefully designed pretext tasks to learn a more
robust representation of the target domain data. Experiments performed on the
DENSE dataset show that our method can substantially alleviate the domain gap
under the single-target domain adaptation (STDA) setting and the less explored
yet more general multi-target domain adaptation (MTDA) setting.

Given a training set $P \subset \mathbb{R}^d$, the nearest-neighbor
classifier assigns any query point $q \in \mathbb{R}^d$ to the class of its
closest point in $P$. To answer these classification queries, some training
points are more relevant than others. We say a training point is relevant if
its omission from the training set could induce the misclassification of some
query point in $\mathbb{R}^d$. These relevant points are commonly known as
border points, as they define the boundaries of the Voronoi diagram of $P$ that
separate points of different classes. Being able to compute this set of points
efficiently is crucial to reduce the size of the training set without affecting
the accuracy of the nearest-neighbor classifier.
  Improving over a decades-long result by Clarkson, in a recent paper by
Eppstein an output-sensitive algorithm was proposed to find the set of border
points of $P$ in $O( n^2 + nk^2 )$ time, where $k$ is the size of such set. In
this paper, we improve this algorithm to have time complexity equal to $O( nk^2
)$ by proving that the first steps of their algorithm, which require $O( n^2 )$
time, are unnecessary.

There has been a recent surge in learning generative models for graphs. While
impressive progress has been made on static graphs, work on generative modeling
of temporal graphs is at a nascent stage with significant scope for
improvement. First, existing generative models do not scale with either the
time horizon or the number of nodes. Second, existing techniques are
transductive in nature and thus do not facilitate knowledge transfer. Finally,
due to relying on one-to-one node mapping from source to the generated graph,
existing models leak node identity information and do not allow
up-scaling/down-scaling the source graph size. In this paper, we bridge these
gaps with a novel generative model called TIGGER. TIGGER derives its power
through a combination of temporal point processes with auto-regressive modeling
enabling both transductive and inductive variants. Through extensive
experiments on real datasets, we establish TIGGER generates graphs of superior
fidelity, while also being up to 3 orders of magnitude faster than the

Robotic systems that can dynamically combine manipulation and locomotion
could facilitate dangerous or physically demanding labor. For instance,
firefighter humanoid robots could leverage their body by leaning against
collapsed building rubble to push it aside. Here we introduce a teleoperation
system that targets the realization of these tasks using human whole-body motor
skills. We describe a new wheeled humanoid platform, SATYRR, and a novel
hands-free teleoperation architecture using a whole-body Human Machine
Interface (HMI). This system enables telelocomotion of the humanoid robot using
the operator body motion, freeing their arms for manipulation tasks. In this
study we evaluate the efficacy of the proposed system on hardware, and explore
the control of SATYRR using two teleoperation mappings that map the operators
body pitch and twist to the robot velocity or acceleration. Through experiments
and user feedback we showcase our preliminary findings of the pilot-system
response. Results suggest that the HMI is capable of effectively telelocomoting
SATYRR, that pilot preferences should dictate the appropriate motion mapping
and gains, and finally that the pilot can better learn to control the system
over time. This study represents a fundamental step towards the realization of
combined manipulation and locomotion via teleoperation.

The main challenge of multiagent reinforcement learning is the difficulty of
learning useful policies in the presence of other simultaneously learning
agents whose changing behaviors jointly affect the environment's transition and
reward dynamics. An effective approach that has recently emerged for addressing
this non-stationarity is for each agent to anticipate the learning of other
agents and influence the evolution of future policies towards desirable
behavior for its own benefit. Unfortunately, previous approaches for achieving
this suffer from myopic evaluation, considering only a finite number of policy
updates. As such, these methods can only influence transient future policies
rather than achieving the promise of scalable equilibrium selection approaches
that influence the behavior at convergence. In this paper, we propose a
principled framework for considering the limiting policies of other agents as
time approaches infinity. Specifically, we develop a new optimization objective
that maximizes each agent's average reward by directly accounting for the
impact of its behavior on the limiting set of policies that other agents will
converge to. Our paper characterizes desirable solution concepts within this
problem setting and provides practical approaches for optimizing over possible
outcomes. As a result of our farsighted objective, we demonstrate better
long-term performance than state-of-the-art baselines across a suite of diverse
multiagent benchmark domains.

Sequential changepoint detection is a classical problem with a variety of
applications. However, the majority of prior work has been parametric, for
example, focusing on exponential families. We develop a fundamentally new and
general framework for changepoint detection when the pre- and post-change
distributions are nonparametrically specified (and thus composite). Our
procedures come with clean, nonasymptotic bounds on the average run length
(frequency of false alarms). In certain nonparametric cases (like sub-Gaussian
or sub-exponential), we also provide near-optimal bounds on the detection delay
following a changepoint. The primary technical tool that we introduce is called
an e-detector, which is composed of sums of e-processes -- a fundamental
generalization of nonnegative supermartingales -- that are started at
consecutive times. We first introduce simple Shiryaev-Roberts and CUSUM-style
e-detectors, and then show how to design their mixtures in order to achieve
both statistical and computational efficiency. We demonstrate their efficacy in
detecting changes in the mean of a bounded random variable without any i.i.d.
assumptions, with an application to tracking the performance of a sports team
over multiple seasons.

Adversarial patches are optimized contiguous pixel blocks in an input image
that cause a machine-learning model to misclassify it. However, their
optimization is computationally demanding, and requires careful hyperparameter
tuning, potentially leading to suboptimal robustness evaluations. To overcome
these issues, we propose ImageNet-Patch, a dataset to benchmark
machine-learning models against adversarial patches. It consists of a set of
patches, optimized to generalize across different models, and readily
applicable to ImageNet data after preprocessing them with affine
transformations. This process enables an approximate yet faster robustness
evaluation, leveraging the transferability of adversarial perturbations. We
showcase the usefulness of this dataset by testing the effectiveness of the
computed patches against 127 models. We conclude by discussing how our dataset
could be used as a benchmark for robustness, and how our methodology can be
generalized to other domains. We open source our dataset and evaluation code at
https://github.com/pralab/ImageNet-Patch.

This paper explores Deep Learning (DL) methods that are used or have the
potential to be used for traffic video analysis, emphasizing driving safety for
both Autonomous Vehicles (AVs) and human-operated vehicles. We present a
typical processing pipeline, which can be used to understand and interpret
traffic videos by extracting operational safety metrics and providing general
hints and guidelines to improve traffic safety. This processing framework
includes several steps, including video enhancement, video stabilization,
semantic and incident segmentation, object detection and classification,
trajectory extraction, speed estimation, event analysis, modeling and anomaly
detection. Our main goal is to guide traffic analysts to develop their own
custom-built processing frameworks by selecting the best choices for each step
and offering new designs for the lacking modules by providing a comparative
analysis of the most successful conventional and DL-based algorithms proposed
for each step. We also review existing open-source tools and public datasets
that can help train DL models. To be more specific, we review exemplary traffic
problems and mentioned requires steps for each problem. Besides, we investigate
connections to the closely related research areas of drivers' cognition
evaluation, Crowd-sourcing-based monitoring systems, Edge Computing in roadside
infrastructures, Automated Driving Systems (ADS)-equipped vehicles, and
highlight the missing gaps. Finally, we review commercial implementations of
traffic monitoring systems, their future outlook, and open problems and
remaining challenges for widespread use of such systems.

This paper presents a new approach to distributed linear filtering and
prediction. The problem under consideration consists of a random dynamical
system observed by a multi-agent network of sensors where the network is
sparse. Inspired by the consensus+innovations type of distributed estimation
approaches, this paper proposes a novel algorithm that fuses the concepts of
consensus and innovations. The paper introduces a definition of distributed
observability, required by the proposed algorithm, which is a weaker assumption
than that of global observability and connected network assumptions combined
together. Following first principles, the optimal gain matrices are designed
such that the mean-squared error of estimation is minimized at each agent and
the distributed version of the algebraic Riccati equation is derived for
computing the gains.

State-of-the-art approaches for 6D object pose estimation require large
amounts of labeled data to train the deep networks. However, the acquisition of
6D object pose annotations is tedious and labor-intensive in large quantity. To
alleviate this problem, we propose a weakly supervised 6D object pose
estimation approach based on 2D keypoint detection. Our method trains only on
image pairs with known relative transformations between their viewpoints.
Specifically, we assign a set of arbitrarily chosen 3D keypoints to represent
each unknown target 3D object and learn a network to detect their 2D
projections that comply with the relative camera viewpoints. During inference,
our network first infers the 2D keypoints from the query image and a given
labeled reference image. We then use these 2D keypoints and the arbitrarily
chosen 3D keypoints retained from training to infer the 6D object pose.
Extensive experiments demonstrate that our approach achieves comparable
performance with state-of-the-art fully supervised approaches.

Synthetic data can be used in various applications, such as correcting bias
datasets or replacing scarce original data for simulation purposes. Generative
Adversarial Networks (GANs) are considered state-of-the-art for developing
generative models. However, these deep learning models are data-driven, and it
is, thus, difficult to control the generation process. It can, therefore, lead
to the following issues: lack of representativity in the generated data, the
introduction of bias, and the possibility of overfitting the sample's noise.
This article presents the Directed Acyclic Tabular GAN (DATGAN) to address
these limitations by integrating expert knowledge in deep learning models for
synthetic tabular data generation. This approach allows the interactions
between variables to be specified explicitly using a Directed Acyclic Graph
(DAG). The DAG is then converted to a network of modified Long Short-Term
Memory (LSTM) cells to accept multiple inputs. Multiple DATGAN versions are
systematically tested on multiple assessment metrics. We show that the best
versions of the DATGAN outperform state-of-the-art generative models on
multiple case studies. Finally, we show how the DAG can create hypothetical
synthetic datasets.

Complex, real-world domains may not be fully modeled for an agent, especially
if the agent has never operated in the domain before. The agent's ability to
effectively plan and act in such a domain is influenced by its knowledge of
when it can perform specific actions and the effects of those actions. We
describe a novel exploratory planning agent that is capable of learning action
preconditions and effects without expert traces or a given goal. The agent's
architecture allows it to perform both exploratory actions as well as
goal-directed actions, which opens up important considerations for how
exploratory planning and goal planning should be controlled, as well as how the
agent's behavior should be explained to any teammates it may have. The
contributions of this work include a new representation for contexts called
Lifted Linked Clauses, a novel exploration action selection approach using
these clauses, an exploration planner that uses lifted linked clauses as goals
in order to reach new states, and an empirical evaluation in a scenario from an
exploration-focused video game demonstrating that lifted linked clauses improve
exploration and action model learning against non-planning baseline agents.

The diversity of El Ni\~no events is commonly described by two distinct
flavors, the Eastern Pacific (EP) and Central Pacific (CP) types. While the
remote impacts, i.e. teleconnections, of EP and CP events have been studied for
different regions individually, a global picture of their teleconnection
patterns is still lacking. Here, we use Forman-Ricci curvature applied on
climate networks constructed from 2-meter air temperature data to distinguish
regional links from teleconnections. Our results confirm that teleconnection
patterns are strongly influenced by the El Ni\~no type. EP events have
primarily tropical teleconnections whereas CP events involve
tropical-extratropical connections, particularly in the Pacific. Moreover, the
central Pacific region does not have many teleconnections, even during CP
events. It is mainly the eastern Pacific that mediates the remote influences
for both El Ni\~no types.

Multi-label zero-shot learning extends conventional single-label zero-shot
learning to a more realistic scenario that aims at recognizing multiple unseen
labels of classes for each input sample. Existing works usually exploit
attention mechanism to generate the correlation among different labels.
However, most of them are usually biased on several major classes while neglect
most of the minor classes with the same importance in input samples, and may
thus result in overly diffused attention maps that cannot sufficiently cover
minor classes. We argue that disregarding the connection between major and
minor classes, i.e., correspond to the global and local information,
respectively, is the cause of the problem. In this paper, we propose a novel
framework of unbiased multi-label zero-shot learning, by considering various
class-specific regions to calibrate the training process of the classifier.
Specifically, Pyramid Feature Attention (PFA) is proposed to build the
correlation between global and local information of samples to balance the
presence of each class. Meanwhile, for the generated semantic representations
of input samples, we propose Semantic Attention (SA) to strengthen the
element-wise correlation among these vectors, which can encourage the
coordinated representation of them. Extensive experiments on the large-scale
multi-label zero-shot benchmarks NUS-WIDE and Open-Image demonstrate that the
proposed method surpasses other representative methods by significant margins.

Topographical structures represent connections between entities and provide a
comprehensive design of complex systems. Currently these structures are used to
discover correlates of neuronal and haemodynamical activity. In this work, we
incorporate them with neural processing techniques to perform regression, using
electrophysiological activity to retrieve haemodynamics. To this end, we use
Fourier features, attention mechanisms, shared space between modalities and
incorporation of style in the latent representation. By combining these
techniques, we propose several models that significantly outperform current
state-of-the-art of this task in resting state and task-based recording
settings. We report which EEG electrodes are the most relevant for the
regression task and which relations impacted it the most. In addition, we
observe that haemodynamic activity at the scalp, in contrast with sub-cortical
regions, is relevant to the learned shared space. Overall, these results
suggest that EEG electrode relationships are pivotal to retain information
necessary for haemodynamical activity retrieval.

Recent techniques in dynamical scheduling and resource management have found
applications in warehouse environments due to their ability to organize and
prioritize tasks in a higher temporal resolution. The rise of deep
reinforcement learning, as a learning paradigm, has enabled decentralized agent
populations to discover complex coordination strategies. However, training
multiple agents simultaneously introduce many obstacles in training as
observation and action spaces become exponentially large. In our work, we
experimentally quantify how various aspects of the warehouse environment (e.g.,
floor plan complexity, information about agents' live location, level of task
parallelizability) affect performance and execution priority. To achieve
efficiency, we propose a compact representation of the state and action space
for location-aware multi-agent systems, wherein each agent has knowledge of
only self and task coordinates, hence only partial observability of the
underlying Markov Decision Process. Finally, we show how agents trained in
certain environments maintain performance in completely unseen settings and
also correlate performance degradation with floor plan geometry.

To combine a feedforward neural network (FNN) and Lie group (symmetry) theory
of differential equations (DEs), an alternative artificial NN approach is
proposed to solve the initial value problems (IVPs) of ordinary DEs (ODEs).
Introducing the Lie group expressions of the solution, the trial solution of
ODEs is split into two parts. The first part is a solution of other ODEs with
initial values of original IVP. This is easily solved using the Lie group and
known symbolic or numerical methods without any network parameters (weights and
biases). The second part consists of an FNN with adjustable parameters. This is
trained using the error back propagation method by minimizing an error (loss)
function and updating the parameters. The method significantly reduces the
number of the trainable parameters and can more quickly and accurately learn
the real solution, compared to the existing similar methods. The numerical
method is applied to several cases, including physical oscillation problems.
The results have been graphically represented, and some conclusions have been
made.

The particle filter (PF) is a powerful inference tool widely used to estimate
the filtering distribution in non-linear and/or non-Gaussian problems. To
overcome the curse of dimensionality of PF, the block PF (BPF) inserts a
blocking step to partition the state space into several subspaces or blocks of
smaller dimension so that the correction and resampling steps can be performed
independently on each subspace. Using blocks of small size reduces the variance
of the filtering distribution estimate, but in turn the correlation between
blocks is broken and a bias is introduced.
  When the dependence relationships between state variables are unknown, it is
not obvious to decide how to split the state space into blocks and a
significant error overhead may arise from a poor choice of partitioning. In
this paper, we formulate the partitioning problem in the BPF as a clustering
problem and we propose a state space partitioning method based on spectral
clustering (SC). We design a generalized BPF algorithm that contains two new
steps: (i) estimation of the state vector correlation matrix from predicted
particles, (ii) SC using this estimate as the similarity matrix to determine an
appropriate partition. In addition, a constraint is imposed on the maximal
cluster size to prevent SC from providing too large blocks. We show that the
proposed method can bring together in the same blocks the most correlated state
variables while successfully escaping the curse of dimensionality.

Material training is a method to endow materials with specific responses
through external driving. We study the complexity of attainable responses, as
expressed in the number of sites that are simultaneously controlled. With
increased complexity, convergence to the desired response becomes very slow.
The training error decays as a power-law with an exponent that varies
continuously and vanishes at a critical threshold, marking the limit of
trainable responses. We study how the transition affects the vibrational
properties. Approaching the critical threshold, low frequency modes
proliferate, approaching zero frequency. This implies that training causes
material degradation and that training fails due to competing spurious low
frequency modes. We propose that the excess low frequency spectrum is due to
atypical local structures with bonds that nearly align. Our work explains how
the presence of an exotic critical point affects the convergence of training,
and could be relevant for understanding learning in physical systems.

Hyperparameter (HP) tuning in deep learning is an expensive process,
prohibitively so for neural networks (NNs) with billions of parameters. We show
that, in the recently discovered Maximal Update Parametrization (muP), many
optimal HPs remain stable even as model size changes. This leads to a new HP
tuning paradigm we call muTransfer: parametrize the target model in muP, tune
the HP indirectly on a smaller model, and zero-shot transfer them to the
full-sized model, i.e., without directly tuning the latter at all. We verify
muTransfer on Transformer and ResNet. For example, 1) by transferring
pretraining HPs from a model of 13M parameters, we outperform published numbers
of BERT-large (350M parameters), with a total tuning cost equivalent to
pretraining BERT-large once; 2) by transferring from 40M parameters, we
outperform published numbers of the 6.7B GPT-3 model, with tuning cost only 7%
of total pretraining cost. A Pytorch implementation of our technique can be
found at github.com/microsoft/mup and installable via `pip install mup`.

We propose a generative model of paraphrase generation, that encourages
syntactic diversity by conditioning on an explicit syntactic sketch. We
introduce Hierarchical Refinement Quantized Variational Autoencoders (HRQ-VAE),
a method for learning decompositions of dense encodings as a sequence of
discrete latent variables that make iterative refinements of increasing
granularity. This hierarchy of codes is learned through end-to-end training,
and represents fine-to-coarse grained information about the input. We use
HRQ-VAE to encode the syntactic form of an input sentence as a path through the
hierarchy, allowing us to more easily predict syntactic sketches at test time.
Extensive experiments, including a human evaluation, confirm that HRQ-VAE
learns a hierarchical representation of the input space, and generates
paraphrases of higher quality than previous systems.

Recent work has focused on data-driven learning of the evolution of unknown
systems via deep neural networks (DNNs), with the goal of conducting long time
prediction of the evolution of the unknown system. Training a DNN with low
generalization error is a particularly important task in this case as error is
accumulated over time. Because of the inherent randomness in DNN training,
chiefly in stochastic optimization, there is uncertainty in the resulting
prediction, and therefore in the generalization error. Hence, the
generalization error can be viewed as a random variable with some probability
distribution. Well-trained DNNs, particularly those with many hyperparameters,
typically result in probability distributions for generalization error with low
bias but high variance. High variance causes variability and unpredictably in
the results of a trained DNN. This paper presents a computational technique
which decreases the variance of the generalization error, thereby improving the
reliability of the DNN model to generalize consistently. In the proposed
ensemble averaging method, multiple models are independently trained and model
predictions are averaged at each time step. A mathematical foundation for the
method is presented, including results regarding the distribution of the local
truncation error. In addition, three time-dependent differential equation
problems are considered as numerical examples, demonstrating the effectiveness
of the method to decrease variance of DNN predictions generally.

In this paper, we will evaluate the performance of graph neural networks in
two distinct domains: computer vision and reinforcement learning. In the
computer vision section, we seek to learn whether a novel non-redundant
representation for images as graphs can improve performance over trivial pixel
to node mapping on a graph-level prediction graph, specifically image
classification. For the reinforcement learning section, we seek to learn if
explicitly modeling solving a Rubik's cube as a graph problem can improve
performance over a standard model-free technique with no inductive bias.

As autonomous and semi-autonomous agents become more integrated into society,
validation of their safety is increasingly important. The scenarios under which
they are used, however, can be quite complicated; as such, formal verification
may be impossible. To this end, simulation-based safety validation is being
used to falsify a complex system under test. Recent approaches use
reinforcement learning, making them prone to excessive exploitation of known
failures, limiting coverage of the space of failures. To overcome this, the
work below defines a class of Markov decision processes, the knowledge MDP,
which reasons over both the learned model and its metadata (e.g., sample
counts). More specifically, by leveraging the "knows what it knows" (KWIK)
framework, the learner estimates its knowledge (model estimates and confidence,
as well as assumptions) about the underlying system. This formulation is vetted
through a novel algorithm, which extends bidirectional learning in multiple
fidelities (MF) of simulators to the safety validation problem. We present a
case study in which we train an adversary to intercept a test model in a
grid-world environment. The Monte Carlo trials compare the relative sample
efficiency of the proposed algorithm to learning with a single-fidelity
simulator and demonstrate the utility of incorporating knowledge about learned
models into the decision-making process.

The sensitivity of gravitational wave searches is reduced by the presence of
non-Gaussian noise in the detector data. These non-Gaussianities often match
well with the template waveforms used in matched filter searches, and require
signal-consistency tests to distinguish them from astrophysical signals.
However, empirically tuning these tests for maximum efficacy is time consuming
and limits the complexity of these tests. In this work we demonstrate a
framework to use machine-learning techniques to automatically tune
signal-consistency tests. We implement a new $\chi^2$ signal-consistency test
targeting the large population of noise found in searches for intermediate mass
black hole binaries, training the new test using the framework set out in this
paper. We find that this method effectively trains a complex model to
down-weight the noise, while leaving the signal population relatively
unaffected. This improves the sensitivity of the search by $\sim 11\%$ for
signals with masses $> 300 M_\odot$. In the future this framework could be used
to implement new tests in any of the commonly used matched-filter search
algorithms, further improving the sensitivity of our searches.

Objectives To develop and evaluate machine learning models to detect
suspected undiagnosed nonalcoholic steatohepatitis (NASH) patients for
diagnostic screening and clinical management.
  Methods In this retrospective observational noninterventional study using
administrative medical claims data from 1,463,089 patients, gradient-boosted
decision trees were trained to detect likely NASH patients from an at-risk
patient population with a history of obesity, type 2 diabetes mellitus (T2DM),
metabolic disorder, or nonalcoholic fatty liver (NAFL). Models were trained to
detect likely NASH in all at-risk patients or in the subset without a prior
NAFL diagnosis (non-NAFL at-risk patients). Models were trained and validated
using retrospective medical claims data and assessed using area under precision
recall and receiver operating characteristic curves (AUPRCs, AUROCs).
  Results The 6-month incidence of NASH in claims data was 1 per 1,437 at-risk
patients and 1 per 2,127 non-NAFL at-risk patients. The model trained to detect
NASH in all at-risk patients had an AUPRC of 0.0107 (95% CI 0.0104 - 0.011) and
an AUROC of 0.84. At 10% recall, model precision was 4.3%, which is 60x above
NASH incidence. The model trained to detect NASH in non-NAFL patients had an
AUPRC of 0.003 (95% CI 0.0029 - 0.0031) and an AUROC of 0.78. At 10% recall,
model precision was 1%, which is 20x above NASH incidence.
  Conclusion The low incidence of NASH in medical claims data corroborates the
pattern of NASH underdiagnosis in clinical practice. Claims-based machine
learning could facilitate the detection of probable NASH patients for
diagnostic testing and disease management.

Random convolution kernel transform (Rocket) is a fast, efficient, and novel
approach for time series feature extraction using a large number of independent
randomly initialized 1-D convolution kernels of different configurations. The
output of the convolution operation on each time series is represented by a
partial positive value (PPV). A concatenation of PPVs from all kernels is the
input feature vector to a Ridge regression classifier. Unlike typical deep
learning models, the kernels are not trained and there is no weighted/trainable
connection between kernels or concatenated features and the classifier. Since
these kernels are generated randomly, a portion of these kernels may not
positively contribute in performance of the model. Hence, selection of the most
important kernels and pruning the redundant and less important ones is
necessary to reduce computational complexity and accelerate inference of Rocket
for applications on the edge devices. Selection of these kernels is a
combinatorial optimization problem. In this paper, we propose a scheme for
selecting these kernels while maintaining the classification performance.
First, the original model is pre-trained at full capacity. Then, a population
of binary candidate state vectors is initialized where each element of a vector
represents the active/inactive status of a kernel. A population-based
optimization algorithm evolves the population in order to find a best state
vector which minimizes the number of active kernels while maximizing the
accuracy of the classifier. This activation function is a linear combination of
the total number of active kernels and the classification accuracy of the
pre-trained classifier with the active kernels. Finally, the selected kernels
in the best state vector are utilized to train the Ridge regression classifier
with the selected kernels.

Despite the tremendous empirical success of deep learning models to solve
various learning tasks, our theoretical understanding of their generalization
ability is very limited. Classical generalization bounds based on tools such as
the VC dimension or Rademacher complexity, are so far unsuitable for deep
models and it is doubtful that these techniques can yield tight bounds even in
the most idealistic settings (Nagarajan & Kolter, 2019). In this work, we
instead revisit the concept of leave-one-out (LOO) error to measure the
generalization ability of deep models in the so-called kernel regime. While
popular in statistics, the LOO error has been largely overlooked in the context
of deep learning. By building upon the recently established connection between
neural networks and kernel learning, we leverage the closed-form expression for
the leave-one-out error, giving us access to an efficient proxy for the test
error. We show both theoretically and empirically that the leave-one-out error
is capable of capturing various phenomena in generalization theory, such as
double descent, random labels or transfer learning. Our work therefore
demonstrates that the leave-one-out error provides a tractable way to estimate
the generalization ability of deep neural networks in the kernel regime,
opening the door to potential, new research directions in the field of

Automated real-time evaluation of counselor-client interaction is important
for ensuring quality counseling but the rules are difficult to articulate.
Recent advancements in machine learning methods show the possibility of
learning such rules automatically. However, these methods often demand large
scale and high quality counseling data, which are difficult to collect. To
address this issue, we build an online counseling platform, which allows
professional psychotherapists to provide free counseling services to those are
in need. In exchange, we collect the counseling transcripts. Within a year of
its operation, we manage to get one of the largest set of (675) transcripts of
counseling sessions. To further leverage the valuable data we have, we label
our dataset using both coarse- and fine-grained labels and use a set of
pretraining techniques. In the end, we are able to achieve practically useful
accuracy in both labeling system.

To improve users' experience as they navigate the myriad of options offered
by online marketplaces, it is essential to have well-organized product
catalogs. One key ingredient to that is the availability of product attributes
such as color or material. However, on some marketplaces such as
Rakuten-Ichiba, which we focus on, attribute information is often incomplete or
even missing. One promising solution to this problem is to rely on deep models
pre-trained on large corpora to predict attributes from unstructured data, such
as product descriptive texts and images (referred to as modalities in this
paper). However, we find that achieving satisfactory performance with this
approach is not straightforward but rather the result of several refinements,
which we discuss in this paper. We provide a detailed description of our
approach to attribute extraction, from investigating strong single-modality
methods, to building a solid multimodal model combining textual and visual
information. One key component of our multimodal architecture is a novel
approach to seamlessly combine modalities, which is inspired by our
single-modality investigations. In practice, we notice that this new
modality-merging method may suffer from a modality collapse issue, i.e., it
neglects one modality. Hence, we further propose a mitigation to this problem
based on a principled regularization scheme. Experiments on Rakuten-Ichiba data
provide empirical evidence for the benefits of our approach, which has been
also successfully deployed to Rakuten-Ichiba. We also report results on
publicly available datasets showing that our model is competitive compared to
several recent multimodal and unimodal baselines.

In this work, we propose two novel methodologies to study temporal and
morphological phenotypic effects caused by different experimental conditions
using imaging data. As a proof of concept, we apply them to analyze drug
effects in 2D cancer cell cultures. We train a convolutional autoencoder on 1M
images dataset with random augmentations and multi-crops to use as feature
extractor. We systematically compare it to the pretrained state-of-the-art
models. We further use the feature extractor in two ways. First, we apply
distance-based analysis and dynamic time warping to cluster temporal patterns
of 31 drugs. We identify clusters allowing annotation of drugs as having
cytotoxic, cytostatic, mixed or no effect. Second, we implement an
adversarial/regularized learning setup to improve classification of 31 drugs
and visualize image regions that contribute to the improvement. We increase
top-3 classification accuracy by 8% on average and mine examples of
morphological feature importance maps. We provide the feature extractor and the
weights to foster transfer learning applications in biology. We also discuss
utility of other pretrained models and applicability of our methods to other
types of biomedical data.

Monte Carlo simulation is often used for the reliability assessment of power
systems, but it converges slowly when the system is complex. Multilevel Monte
Carlo (MLMC) can be applied to speed up computation without compromises on
model complexity and accuracy that are limiting real-world effectiveness. In
MLMC, models with different complexity and speed are combined, and having
access to fast approximate models is essential for achieving high speedups.
This paper demonstrates how machine-learned surrogate models are able to fulfil
this role without excessive manual tuning of models. Different strategies for
constructing and training surrogate models are discussed. A resource adequacy
case study based on the Great Britain system with storage units is used to
demonstrate the effectiveness of the proposed approach, and the sensitivity to
surrogate model accuracy. The high accuracy and inference speed of
machine-learned surrogates result in very large speedups, compared to using
MLMC with hand-built models.

Acoustic scene classification (ASC) aims to classify an audio clip based on
the characteristic of the recording environment. In this regard, deep learning
based approaches have emerged as a useful tool for ASC problems. Conventional
approaches to improving the classification accuracy include integrating
auxiliary methods such as attention mechanism, pre-trained models and ensemble
multiple sub-networks. However, due to the complexity of audio clips captured
from different environments, it is difficult to distinguish their categories
without using any auxiliary methods for existing deep learning models using
only a single classifier. In this paper, we propose a novel approach for ASC
using deep neural decision forest (DNDF). DNDF combines a fixed number of
convolutional layers and a decision forest as the final classifier. The
decision forest consists of a fixed number of decision tree classifiers, which
have been shown to offer better classification performance than a single
classifier in some datasets. In particular, the decision forest differs
substantially from traditional random forests as it is stochastic,
differentiable, and capable of using the back-propagation to update and learn
feature representations in neural network. Experimental results on the
DCASE2019 and ESC-50 datasets demonstrate that our proposed DNDF method
improves the ASC performance in terms of classification accuracy and shows
competitive performance as compared with state-of-the-art baselines.

Recent advances in computer vision and robotics enabled automated large-scale
biological image analysis. Various machine learning approaches have been
successfully applied to phenotypic profiling. However, it remains unclear how
they compare in terms of biological feature extraction. In this study, we
propose a simple CNN architecture and implement 4 different representation
learning approaches. We train 16 deep learning setups on the 770k cancer cell
images dataset under identical conditions, using different augmenting and
cropping strategies. We compare the learned representations by evaluating
multiple metrics for each of three downstream tasks: i) distance-based
similarity analysis of known drugs, ii) classification of drugs versus
controls, iii) clustering within cell lines. We also compare training times and
memory usage. Among all tested setups, multi-crops and random augmentations
generally improved performance across tasks, as expected. Strikingly,
self-supervised (implicit contrastive learning) models showed competitive
performance being up to 11 times faster to train. Self-supervised regularized
learning required the most of memory and computation to deliver arguably the
most informative features. We observe that no single combination of augmenting
and cropping strategies consistently results in top performance across tasks
and recommend prospective research directions.

Fully convolutional neural networks have made promising progress in joint
liver and liver tumor segmentation. Instead of following the debates over 2D
versus 3D networks (for example, pursuing the balance between large-scale 2D
pretraining and 3D context), in this paper, we novelly identify the wide
variation in the ratio between intra- and inter-slice resolutions as a crucial
obstacle to the performance. To tackle the mismatch between the intra- and
inter-slice information, we propose a slice-aware 2.5D network that emphasizes
extracting discriminative features utilizing not only in-plane semantics but
also out-of-plane coherence for each separate slice. Specifically, we present a
slice-wise multi-input multi-output architecture to instantiate such a design
paradigm, which contains a Multi-Branch Decoder (MD) with a Slice-centric
Attention Block (SAB) for learning slice-specific features and a Densely
Connected Dice (DCD) loss to regularize the inter-slice predictions to be
coherent and continuous. Based on the aforementioned innovations, we achieve
state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS)
dataset. Besides, we also test our model on the ISBI 2019 Segmentation of
THoracic Organs at Risk (SegTHOR) dataset, and the result proves the robustness
and generalizability of the proposed method in other segmentation tasks.

A variety of control tasks such as inverse kinematics (IK), trajectory
optimization (TO), and model predictive control (MPC) are commonly formulated
as energy minimization problems. Numerical solutions to such problems are
well-established. However, these are often too slow to be used directly in
real-time applications. The alternative is to learn solution manifolds for
control problems in an offline stage. Although this distillation process can be
trivially formulated as a behavioral cloning (BC) problem in an imitation
learning setting, our experiments highlight a number of significant
shortcomings arising due to incompatible local minima, interpolation artifacts,
and insufficient coverage of the state space. In this paper, we propose an
alternative to BC that is efficient and numerically robust. We formulate the
learning of solution manifolds as a minimization of the energy terms of a
control objective integrated over the space of problems of interest. We
minimize this energy integral with a novel method that combines Monte
Carlo-inspired adaptive sampling strategies with the derivatives used to solve
individual instances of the control task. We evaluate the performance of our
formulation on a series of robotic control problems of increasing complexity,
and we highlight its benefits through comparisons against traditional methods
such as behavioral cloning and Dataset aggregation (Dagger).

This paper introduces a new approach for Multivariate Time Series forecasting
that jointly infers and leverages relations among time series. Its modularity
allows it to be integrated with current univariate methods. Our approach allows
to trade-off accuracy and computational efficiency gradually via offering on
one extreme inference of a potentially fully-connected graph or on another
extreme a bipartite graph. In the potentially fully-connected case we consider
all pair-wise interactions among time-series which yields the best forecasting
accuracy. Conversely, the bipartite case leverages the dependency structure by
inter-communicating the N time series through a small set of K auxiliary nodes
that we introduce. This reduces the time and memory complexity w.r.t. previous
graph inference methods from O(N^2) to O(NK) with a small trade-off in
accuracy. We demonstrate the effectiveness of our model in a variety of
datasets where both of its variants perform better or very competitively to
previous graph inference methods in terms of forecasting accuracy and time

This paper proposes a novel scalable type of multi-agent reinforcement
learning-based coordination for distributed residential energy. Cooperating
agents learn to control the flexibility offered by electric vehicles, space
heating and flexible loads in a partially observable stochastic environment. In
the standard independent Q-learning approach, the coordination performance of
agents under partial observability drops at scale in stochastic environments.
Here, the novel combination of learning from off-line convex optimisations on
historical data and isolating marginal contributions to total rewards in reward
signals increases stability and performance at scale. Using fixed-size
Q-tables, prosumers are able to assess their marginal impact on total system
objectives without sharing personal data either with each other or with a
central coordinator. Case studies are used to assess the fitness of different
combinations of exploration sources, reward definitions, and multi-agent
learning frameworks. It is demonstrated that the proposed strategies create
value at individual and system levels thanks to reductions in the costs of
energy imports, losses, distribution network congestion, battery depreciation
and greenhouse gas emissions.

We consider estimation under model misspecification where there is a model
mismatch between the underlying system, which generates the data, and the model
used during estimation. We propose a model misspecification framework which
enables a joint treatment of the model misspecification types of having fake
and missing features, as well as incorrect covariance assumptions on the
unknowns and the noise. Here, features which are included in the model but are
not present in the underlying system, and features which are not included in
the model but are present in the underlying system, are referred to as fake and
missing features, respectively. Under this framework, we characterize the
estimation performance and reveal trade-offs between the missing and fake
features and the possibly incorrect noise level assumption. In contrast to
existing work focusing on incorrect covariance assumptions or missing features,
fake features is a central component of our framework. Our results show that
fake features can significantly improve the estimation performance, even though
they are not correlated with the features in the underlying system. In
particular, we show that the estimation error can be decreased by including
more fake features in the model, even to the point where the model is
overparametrized, i.e., the model contains more unknowns than observations.

Recent advances in generative models have made exploring design spaces easier
for de novo molecule generation. However, popular generative models like GANs
and normalizing flows face challenges such as training instabilities due to
adversarial training and architectural constraints, respectively. Score-based
generative models sidestep these challenges by modelling the gradient of the
log probability density using a score function approximation, as opposed to
modelling the density function directly, and sampling from it using annealed
Langevin Dynamics. We believe that score-based generative models could open up
new opportunities in molecule generation due to their architectural
flexibility, such as replacing the score function with an SE(3) equivariant
model. In this work, we lay the foundations by testing the efficacy of
score-based models for molecule generation. We train a Transformer-based score
function on Self-Referencing Embedded Strings (SELFIES) representations of 1.5
million samples from the ZINC dataset and use the Moses benchmarking framework
to evaluate the generated samples on a suite of metrics.

Wireless x-haul networks rely on microwave and millimeter-wave links between
4G and/or 5G base-stations to support ultra-high data rate and ultra-low
latency. A major challenge associated with these high frequency links is their
susceptibility to weather conditions. In particular, precipitation may cause
severe signal attenuation, which significantly degrades the network
performance. In this paper, we develop a Predictive Network Reconfiguration
(PNR) framework that uses historical data to predict the future condition of
each link and then prepares the network ahead of time for imminent
disturbances. The PNR framework has two components: (i) an Attenuation
Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR)
algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory
(LSTM) model to predict the sequence of future attenuation levels of each link.
The MSNR algorithm leverages these predictions to dynamically optimize routing
and admission control decisions aiming to maximize network utilization, while
preserving max-min fairness among the base-stations sharing the network and
preventing transient congestion that may be caused by re-routing. We train,
validate, and evaluate the PNR framework using a dataset containing over 2
million measurements collected from a real-world city-scale backhaul network.
The results show that the framework: (i) predicts attenuation with high
accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50
seconds; and (ii) can improve the instantaneous network utilization by more
than 200% when compared to reactive network reconfiguration algorithms that
cannot leverage information about future disturbances.

The attention mechanism has become the de facto module in scene text
recognition (STR) methods, due to its capability of extracting character-level
representations. These methods can be summarized into implicit attention based
and supervised attention based, depended on how the attention is computed,
i.e., implicit attention and supervised attention are learned from
sequence-level text annotations and character-level bounding box annotations,
respectively. Implicit attention, as it may extract coarse or even incorrect
spatial regions as character attention, is prone to suffering from an
alignment-drifted issue. Supervised attention can alleviate the above issue,
but it is category-specific, which requires extra laborious character-level
bounding box annotations and would be memory-intensive when the number of
character categories is large. To address the aforementioned issues, we propose
a novel attention mechanism for STR, self-supervised implicit glyph attention
(SIGA). SIGA delineates the glyph structures of text images by jointly
self-supervised text segmentation and implicit attention alignment, which serve
as the supervision to improve attention correctness without extra
character-level annotations. Experimental results demonstrate that SIGA
performs consistently and significantly better than previous attention-based
STR methods, in terms of both attention correctness and final recognition
performance on publicly available context benchmarks and our contributed
contextless benchmarks.

Compared with rate-based artificial neural networks, Spiking Neural Networks
(SNN) provide a more biological plausible model for the brain. But how they
perform supervised learning remains elusive. Inspired by recent works of Bengio
et al., we propose a supervised learning algorithm based on Spike-Timing
Dependent Plasticity (STDP) for a hierarchical SNN consisting of Leaky
Integrate-and-fire (LIF) neurons. A time window is designed for the presynaptic
neuron and only the spikes in this window take part in the STDP updating
process. The model is trained on the MNIST dataset. The classification accuracy
approach that of a Multilayer Perceptron (MLP) with similar architecture
trained by the standard back-propagation algorithm.

Learning from demonstration (LfD) is considered as an efficient way to
transfer skills from humans to robots. Traditionally, LfD has been used to
transfer Cartesian and joint positions and forces from human demonstrations.
The traditional approach works well for some robotic tasks, but for many tasks
of interest it is necessary to learn skills such as orientation, impedance,
and/or manipulability that have specific geometric characteristics. An
effective encoding of such skills can be only achieved if the underlying
geometric structure of the skill manifold is considered and the constrains
arising from this structure are fulfilled during both learning and execution.
However, typical learned skill models such as dynamic movement primitives
(DMPs) are limited to Euclidean data and fail in correctly embedding quantities
with geometric constraints. In this paper, we propose a novel and
mathematically principled framework that uses concepts from Riemannian geometry
to allow DMPs to properly embed geometric constrains. The resulting DMP
formulation can deal with data sampled from any Riemannian manifold including,
but not limited to, unit quaternions and symmetric and positive definite
matrices. The proposed approach has been extensively evaluated both on
simulated data and real robot experiments. The performed evaluation
demonstrates that beneficial properties of DMPs, such as convergence to a given
goal and the possibility to change the goal during operation, apply also to the
proposed formulation.

Nowadays, Machine Learning techniques offer fast and efficient solutions for
classification problems that would require intensive computational resources
via traditional methods. We examine the use of a supervised Random Forest to
classify stars in simulated galaxy clusters after subtracting the member
galaxies. These dynamically different components are interpreted as the
individual properties of the stars in the Brightest Cluster Galaxy (BCG) and
IntraCluster Light (ICL). We employ matched stellar catalogues (built from the
different dynamical properties of BCG and ICL) of 29 simulated clusters from
the DIANOGA set to train and test the classifier. The input features are
cluster mass, normalized particle cluster-centric distance, and rest-frame
velocity. The model is found to correctly identify most of the stars, while the
larger errors are exhibited at the BCG outskirt, where the differences between
the physical properties of the two components are less obvious. We investigate
the robustness of the classifier to numerical resolution, redshift dependence
(up to $z=1$), and included astrophysical models. We claim that our classifier
provides consistent results in simulations for $z<1$, at different resolution
levels and with significantly different subgrid models. The phase-space
structure is examined to assess whether the general properties of the stellar
components are recovered: (i) the transition radius between BCG-dominated and
ICL-dominated region is identified at $0.04$ \r200; (ii) the BCG outskirt ($>
0.1$ \r200) is significantly affected by uncertainties in the classification
process. In conclusion, this work suggests the importance of employing Machine
Learning to speed up a computationally expensive classification in simulations.

We consider multi-agent reinforcement learning (MARL) for cooperative
communication and coordination tasks. MARL agents can be brittle because they
can overfit their training partners' policies. This overfitting can produce
agents that adopt policies that act under the expectation that other agents
will act in a certain way rather than react to their actions. Our objective is
to bias the learning process towards finding reactive strategies towards other
agents' behaviors. Our method, transfer empowerment, measures the potential
influence between agents' actions. Results from three simulated cooperation
scenarios support our hypothesis that transfer empowerment improves MARL
performance. We discuss how transfer empowerment could be a useful principle to
guide multi-agent coordination by ensuring reactiveness to one's partner.

Full Bayesian posteriors are rarely analytically tractable, which is why
real-world Bayesian inference heavily relies on approximate techniques.
Approximations generally differ from the true posterior and require diagnostic
tools to assess whether the inference can still be trusted. We investigate a
new approach to diagnosing approximate inference: the approximation mismatch is
attributed to a change in the inductive bias by treating the approximations as
exact and reverse-engineering the corresponding prior. We show that the problem
is more complicated than it appears to be at first glance, because the solution
generally depends on the observation. By reframing the problem in terms of
incompatible conditional distributions we arrive at a natural solution: the
Gibbs prior. The resulting diagnostic is based on pseudo-Gibbs sampling, which
is widely applicable and easy to implement. We illustrate how the Gibbs prior
can be used to discover the inductive bias in a controlled Gaussian setting and
for a variety of Bayesian models and approximations.

Magnetic Resonance Imaging (MRI) typically recruits multiple sequences
(defined here as "modalities"). As each modality is designed to offer different
anatomical and functional clinical information, there are evident disparities
in the imaging content across modalities. Inter- and intra-modality affine and
non-rigid image registration is an essential medical image analysis process in
clinical imaging, as for example before imaging biomarkers need to be derived
and clinically evaluated across different MRI modalities, time phases and
slices. Although commonly needed in real clinical scenarios, affine and
non-rigid image registration is not extensively investigated using a single
unsupervised model architecture. In our work, we present an un-supervised deep
learning registration methodology which can accurately model affine and
non-rigid trans-formations, simultaneously. Moreover, inverse-consistency is a
fundamental inter-modality registration property that is not considered in deep
learning registration algorithms. To address inverse-consistency, our
methodology performs bi-directional cross-modality image synthesis to learn
modality-invariant latent rep-resentations, while involves two factorised
transformation networks and an inverse-consistency loss to learn
topology-preserving anatomical transformations. Overall, our model (named
"FIRE") shows improved performances against the reference standard baseline
method on multi-modality brain 2D and 3D MRI and intra-modality cardiac 4D MRI
data experiments.

Deep neural networks (DNNs) have achieved state-of-the-art performance in
many tasks but have shown extreme vulnerabilities to attacks generated by
adversarial examples. Many works go with a white-box attack that assumes total
access to the targeted model including its architecture and gradients. A more
realistic assumption is the black-box scenario where an attacker only has
access to the targeted model by querying some input and observing its predicted
class probabilities. Different from most prevalent black-box attacks that make
use of substitute models or gradient estimation, this paper proposes a
gradient-free attack by using a concept of evolutionary art to generate
adversarial examples that iteratively evolves a set of overlapping transparent
shapes. To evaluate the effectiveness of our proposed method, we attack three
state-of-the-art image classification models trained on the CIFAR-10 dataset in
a targeted manner. We conduct a parameter study outlining the impact the number
and type of shapes have on the proposed attack's performance. In comparison to
state-of-the-art black-box attacks, our attack is more effective at generating
adversarial examples and achieves a higher attack success rate on all three

For communication to happen successfully, a common language is required
between agents to understand information communicated by one another. Inducing
the emergence of a common language has been a difficult challenge to
multi-agent learning systems. In this work, we introduce an alternative
perspective to the communicative messages sent between agents, considering them
as different incomplete views of the environment state. Based on this
perspective, we propose a simple approach to induce the emergence of a common
language by maximizing the mutual information between messages of a given
trajectory in a self-supervised manner. By evaluating our method in
communication-essential environments, we empirically show how our method leads
to better learning performance and speed, and learns a more consistent common
language than existing methods, without introducing additional learning

This paper covers predicting high-resolution electricity peak demand features
given lower-resolution data. This is a relevant setup as it answers whether
limited higher-resolution monitoring helps to estimate future high-resolution
peak loads when the high-resolution data is no longer available. That question
is particularly interesting for network operators considering replacing
high-resolution monitoring predictive models due to economic considerations. We
propose models to predict half-hourly minima and maxima of high-resolution
(every minute) electricity load data while model inputs are of a lower
resolution (30 minutes). We combine predictions of generalized additive models
(GAM) and deep artificial neural networks (DNN), which are popular in load
forecasting. We extensively analyze the prediction models, including the input
parameters' importance, focusing on load, weather, and seasonal effects. The
proposed method won a data competition organized by Western Power Distribution,
a British distribution network operator. In addition, we provide a rigorous
evaluation study that goes beyond the competition frame to analyze the models'
robustness. The results show that the proposed methods are superior to the
competition benchmark concerning the out-of-sample root mean squared error
(RMSE). This holds regarding the competition month and the supplementary
evaluation study, which covers an additional eleven months. Overall, our
proposed model combination reduces the out-of-sample RMSE by 57.4\% compared to
the benchmark.

Tensor Core is a mixed-precision matrix-matrix multiplication unit on NVIDIA
GPUs with a theoretical peak performance of more than 300 TFlop/s on Ampere
architectures. Tensor Cores were developed in response to the high demand of
dense matrix multiplication from machine learning. However, many applications
in scientific computing such as preconditioners for iterative solvers and
low-precision Fourier transforms can exploit these Tensor Cores. To compute a
matrix multiplication on Tensor Cores, we need to convert input matrices to
half-precision, which results in loss of accuracy. To avoid this, we can keep
the mantissa loss in the conversion using additional half-precision variables
and use them for correcting the accuracy of matrix-matrix multiplication. Even
with this correction, the use of Tensor Cores yields higher throughput compared
to FP32 SIMT Cores. Nevertheless, the correcting capability of this method
alone is limited, and the resulting accuracy cannot match that of a matrix
multiplication on FP32 SIMT Cores. We address this problem and develop a high
accuracy, high performance, and low power consumption matrix-matrix
multiplication implementation using Tensor Cores, which exactly matches the
accuracy of FP32 SIMT Cores while achieving superior throughput. The
implementation is based on NVIDIA's CUTLASS. We found that the key to achieving
this accuracy is how to deal with the rounding inside Tensor Cores and
underflow probability during the correction computation. Our implementation
achieves 51TFlop/s for a limited exponent range using FP16 Tensor Cores and
33TFlop/s for full exponent range of FP32 using TF32 Tensor Cores on NVIDIA
A100 GPUs, which outperforms the theoretical FP32 SIMT Core peak performance of
19.5TFlop/s.

Human gaze is a crucial cue used in various applications such as human-robot
interaction and virtual reality. Recently, convolution neural network (CNN)
approaches have made notable progress in predicting gaze direction. However,
estimating gaze in-the-wild is still a challenging problem due to the
uniqueness of eye appearance, lightning conditions, and the diversity of head
pose and gaze directions. In this paper, we propose a robust CNN-based model
for predicting gaze in unconstrained settings. We propose to regress each gaze
angle separately to improve the per-angel prediction accuracy, which will
enhance the overall gaze performance. In addition, we use two identical losses,
one for each angle, to improve network learning and increase its
generalization. We evaluate our model with two popular datasets collected with
unconstrained settings. Our proposed model achieves state-of-the-art accuracy
of 3.92{\deg} and 10.41{\deg} on MPIIGaze and Gaze360 datasets, respectively.
We make our code open source at https://github.com/Ahmednull/L2CS-Net.

Brain tumor segmentation is highly contributive in diagnosing and treatment
planning. The manual brain tumor delineation is a time-consuming and tedious
task and varies depending on the radiologists skill. Automated brain tumor
segmentation is of high importance, and does not depend on either inter or
intra-observation. The objective of this study is to automate the delineation
of brain tumors from the FLAIR, T1 weighted, T2 weighted, and T1 weighted
contrast-enhanced MR sequences through a deep learning approach, with a focus
on determining which MR sequence alone or which combination thereof would lead
to the highest accuracy therein.

Automated medical image segmentation is an essential task to aid/speed up
diagnosis and treatment procedures in clinical practices. Deep convolutional
neural networks have exhibited promising performance in accurate and automatic
seminal segmentation. For segmentation tasks, these methods normally rely on
minimizing a cost/loss function that is designed to maximize the overlap
between the estimated target and the ground-truth mask delineated by the
experts. A simple loss function based on the degrees of overlap (i.e., Dice
metric) would not take into account the underlying shape and morphology of the
target subject, as well as its realistic/natural variations; therefore,
suboptimal segmentation results would be observed in the form of islands of
voxels, holes, and unrealistic shapes or deformations. In this light, many
studies have been conducted to refine/post-process the segmentation outcome and
consider an initial guess as prior knowledge to avoid outliers and/or
unrealistic estimations. In this study, a novel shape-based cost function is
proposed which encourages/constrains the network to learn/capture the
underlying shape features in order to generate a valid/realistic estimation of
the target structure. To this end, the Principal Component Analysis (PCA) was
performed on a vectorized training dataset to extract eigenvalues and
eigenvectors of the target subjects. The key idea was to use the reconstruction
weights to discriminate valid outcomes from outliers/erroneous estimations.

This paper proposes a novel method for geo-tracking, i.e. continuous metric
self-localization in outdoor environments by registering a vehicle's sensor
information with aerial imagery of an unseen target region. Geo-tracking
methods offer the potential to supplant noisy signals from global navigation
satellite systems (GNSS) and expensive and hard to maintain prior maps that are
typically used for this purpose. The proposed geo-tracking method aligns data
from on-board cameras and lidar sensors with geo-registered orthophotos to
continuously localize a vehicle. We train a model in a metric learning setting
to extract visual features from ground and aerial images. The ground features
are projected into a top-down perspective via the lidar points and are matched
with the aerial features to determine the relative pose between vehicle and
orthophoto.
  Our method is the first to utilize on-board cameras in an end-to-end
differentiable model for metric self-localization on unseen orthophotos. It
exhibits strong generalization, is robust to changes in the environment and
requires only geo-poses as ground truth. We evaluate our approach on the
KITTI-360 dataset and achieve a mean absolute position error (APE) of 0.94m. We
further compare with previous approaches on the KITTI odometry dataset and
achieve state-of-the-art results on the geo-tracking task.

We study the application of the factor graph framework for symbol detection
on linear inter-symbol interference channels. Cyclic factor graphs have the
potential to yield low-complexity symbol detectors, but are suboptimal if the
ubiquitous sum-product algorithm is applied. In this paper, we present and
evaluate strategies to improve the performance of cyclic factor graph-based
symbol detection algorithms by means of neural enhancement. In particular, we
apply neural belief propagation as an effective way to counteract the effect of
cycles within the factor graph. We further propose the application and
optimization of a linear preprocessor of the channel output. By modifying the
observation model, the preprocessing can effectively change the underlying
factor graph, thereby significantly improving the detection performance as well
as reducing the complexity.

Interpreting a large number of neurons in deep learning is difficult. Our
proposed `CLAssifier-DECoder' architecture (ClaDec) facilitates the
understanding of the output of an arbitrary layer of neurons or subsets
thereof. It uses a decoder that transforms the incomprehensible representation
of the given neurons to a representation that is more similar to the domain a
human is familiar with. In an image recognition problem, one can recognize what
information (or concepts) a layer maintains by contrasting reconstructed images
of ClaDec with those of a conventional auto-encoder(AE) serving as reference.
An extension of ClaDec allows trading comprehensibility and fidelity. We
evaluate our approach for image classification using convolutional neural
networks. We show that reconstructed visualizations using encodings from a
classifier capture more relevant classification information than conventional
AEs. This holds although AEs contain more information on the original input.
Our user study highlights that even non-experts can identify a diverse set of
concepts contained in images that are relevant (or irrelevant) for the
classifier. We also compare against saliency based methods that focus on pixel
relevance rather than concepts. We show that ClaDec tends to highlight more
relevant input areas to classification though outcomes depend on classifier
architecture. Code is at \url{https://github.com/JohnTailor/ClaDec}

Semiconductor innovation drives improvements to technologies that are
critical to modern society. The country that successfully accelerates
semiconductor innovation is positioned to lead future semiconductor-driven
industries and benefit from the resulting economic growth. It is our view that
a next generation infrastructure is necessary to accelerate and enhance
semiconductor innovation in the U.S. In this paper, we propose such an advanced
infrastructure composed of a national network of facilities with enhancements
in technology and business models. These enhancements enable application-driven
and challenge-based research and development, and ensure that facilities are
accessible and sustainable. The main tenets are: a challenge-driven operational
model, a next-generation infrastructure to serve that operational model,
technology innovations needed for advanced facilities to speed up learning
cycles, and innovative cost-effective business models for sustainability.
Ultimately, the expected outcomes of such a participatory, scalable, and
sustainable nation-level advanced infrastructure will have tremendous impact on
government, industry, and academia alike.

New micro-grid design with renewable energy sources and battery storage
systems can help improve greenhouse gas emissions and reduce the operational
cost. To provide an effective short-/long-term forecasting of both energy
generation and load demand, time series predictive modeling has been one of the
key tools to guide the optimal decision-making for planning and operation. One
of the critical challenges of time series renewable energy forecasting is the
lack of historical data to train an adequate predictive model. Moreover, the
performance of a machine learning model is sensitive to the choice of its
corresponding hyperparameters. Bearing these considerations in mind, this paper
develops a BiLO-Auto-TSF/ML framework that automates the optimal design of a
few-shot learning pipeline from a bi-level programming perspective.
Specifically, the lower-level meta-learning helps boost the base-learner to
mitigate the small data challenge while the hyperparameter optimization at the
upper level proactively searches for the optimal hyperparameter configurations
for both base- and meta-learners. Note that the proposed framework is so
general that any off-the-shelf machine learning method can be used in a plug-in
manner. Comprehensive experiments fully demonstrate the effectiveness of our
proposed BiLO-Auto-TSF/ML framework to search for a high-performance few-shot
learning pipeline for various energy sources.

This work introduces a new training and compression pipeline to build Nested
Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets)
suited for inference tasks deployed on resource-constrained devices at the edge
of the Internet-of-Things. A Nested Sparse ConvNet consists of a single ConvNet
architecture containing N sparse sub-networks with nested weights subsets, like
a Matryoshka doll, and can trade accuracy for latency at run time, using the
model sparsity as a dynamic knob. To attain high accuracy at training time, we
propose a gradient masking technique that optimally routes the learning signals
across the nested weights subsets. To minimize the storage footprint and
efficiently process the obtained models at inference time, we introduce a new
sparse matrix compression format with dedicated compute kernels that fruitfully
exploit the characteristic of the nested weights subsets. Tested on image
classification and object detection tasks on an off-the-shelf ARM-M7 Micro
Controller Unit (MCU), Nested Sparse ConvNets outperform variable-latency
solutions naively built assembling single sparse models trained as stand-alone
instances, achieving (i) comparable accuracy, (ii) remarkable storage savings,
and (iii) high performance. Moreover, when compared to state-of-the-art dynamic
strategies, like dynamic pruning and layer width scaling, Nested Sparse
ConvNets turn out to be Pareto optimal in the accuracy vs. latency space.

Application of artificial intelligence in medicine brings in highly accurate
predictions achieved by complex models, the reasoning of which is hard to
interpret. Their generalization ability can be reduced because of the lack of
pixel wise annotated images that occurs in frozen section tissue analysis. To
partially overcome this gap, this paper explores the approximate explicit
feature map (aEFM) transform of low-dimensional data into a low-dimensional
subspace in Hilbert space. There, with a modest increase in computational
complexity, linear algorithms yield improved performance and keep
interpretability. They remain amenable to incremental learning that is not a
trivial issue for some nonlinear algorithms. We demonstrate proposed
methodology on a very large-scale problem related to intraoperative pixel-wise
semantic segmentation and clustering of adenocarcinoma of a colon in a liver.
Compared to the results in the input space, logistic classifier achieved
statistically significant performance improvements in micro balanced accuracy
and F1 score in the amounts of 12.04% and 12.58%, respectively. Support vector
machine classifier yielded the increase of 8.04% and 9.41%. For clustering,
increases of 0.79% and 0.85% are obtained with ultra large-scale spectral
clustering algorithm. Results are supported by a discussion of interpretability
using Shapely additive explanation values for predictions of linear classifier
in input space and aEFM induced space.

Embedding-based methods have attracted increasing attention in recent entity
alignment (EA) studies. Although great promise they can offer, there are still
several limitations. The most notable is that they identify the aligned
entities based on cosine similarity, ignoring the semantics underlying the
embeddings themselves. Furthermore, these methods are shortsighted,
heuristically selecting the closest entity as the target and allowing multiple
entities to match the same candidate. To address these limitations, we model
entity alignment as a sequential decision-making task, in which an agent
sequentially decides whether two entities are matched or mismatched based on
their representation vectors. The proposed reinforcement learning (RL)-based
entity alignment framework can be flexibly adapted to most embedding-based EA
methods. The experimental results demonstrate that it consistently advances the
performance of several state-of-the-art methods, with a maximum improvement of
31.1% on Hits@1.

Prevailing deep models are single-purpose and overspecialize at individual
tasks. However, when being extended to new tasks, they typically forget
previously learned skills and learn from scratch. We address this issue by
introducing SkillNet-NLU, a general-purpose model that stitches together
existing skills to learn new tasks more effectively. The key feature of our
approach is that it is sparsely activated guided by predefined skills.
Different from traditional dense models that always activate all the model
parameters, SkillNet-NLU only activates parts of the model parameters whose
skills are relevant to the target task. When learning for a new task, our
approach precisely activates required skills and also provides an option to add
new skills. We evaluate on natural language understandings tasks and have the
following findings. First, with only one model checkpoint, SkillNet-NLU
performs better than task-specific fine-tuning and two multi-task learning
baselines (i.e., dense model and Mixture-of-Experts model) on six tasks.
Second, sparsely activated pre-training further improves the overall
performance. Third, SkillNet-NLU significantly outperforms baseline systems
when being extended to new tasks.

Point cloud completion is a generation and estimation issue derived from the
partial point clouds, which plays a vital role in the applications in 3D
computer vision. The progress of deep learning (DL) has impressively improved
the capability and robustness of point cloud completion. However, the quality
of completed point clouds is still needed to be further enhanced to meet the
practical utilization. Therefore, this work aims to conduct a comprehensive
survey on various methods, including point-based, convolution-based,
graph-based, and generative model-based approaches, etc. And this survey
summarizes the comparisons among these methods to provoke further research
insights. Besides, this review sums up the commonly used datasets and
illustrates the applications of point cloud completion. Eventually, we also
discussed possible research trends in this promptly expanding field.

Parallelizing Gated Recurrent Unit (GRU) networks is a challenging task, as
the training procedure of GRU is inherently sequential. Prior efforts to
parallelize GRU have largely focused on conventional parallelization strategies
such as data-parallel and model-parallel training algorithms. However, when the
given sequences are very long, existing approaches are still inevitably
performance limited in terms of training time. In this paper, we present a
novel parallel training scheme (called parallel-in-time) for GRU based on a
multigrid reduction in time (MGRIT) solver. MGRIT partitions a sequence into
multiple shorter sub-sequences and trains the sub-sequences on different
processors in parallel. The key to achieving speedup is a hierarchical
correction of the hidden state to accelerate end-to-end communication in both
the forward and backward propagation phases of gradient descent. Experimental
results on the HMDB51 dataset, where each video is an image sequence,
demonstrate that the new parallel training scheme achieves up to 6.5$\times$
speedup over a serial approach. As efficiency of our new parallelization
strategy is associated with the sequence length, our parallel GRU algorithm
achieves significant performance improvement as the sequence length increases.

Data augmentation is used in machine learning to make the classifier
invariant to label-preserving transformations. Usually this invariance is only
encouraged implicitly by including a single augmented input during training.
However, several works have recently shown that using multiple augmentations
per input can improve generalisation or can be used to incorporate invariances
more explicitly. In this work, we first empirically compare these recently
proposed objectives that differ in whether they rely on explicit or implicit
regularisation and at what level of the predictor they encode the invariances.
We show that the predictions of the best performing method are also the most
similar when compared on different augmentations of the same input. Inspired by
this observation, we propose an explicit regulariser that encourages this
invariance on the level of individual model predictions. Through extensive
experiments on CIFAR-100 and ImageNet we show that this explicit regulariser
(i) improves generalisation and (ii) equalises performance differences between
all considered objectives. Our results suggest that objectives that encourage
invariance on the level of the neural network itself generalise better than
those that achieve invariance by averaging predictions of non-invariant models.

We present a PAC-Bayesian analysis of lifelong learning. In the lifelong
learning problem, a sequence of learning tasks is observed one-at-a-time, and
the goal is to transfer information acquired from previous tasks to new
learning tasks. We consider the case when each learning task is a multi-armed
bandit problem. We derive lower bounds on the expected average reward that
would be obtained if a given multi-armed bandit algorithm was run in a new task
with a particular prior and for a set number of steps. We propose lifelong
learning algorithms that use our new bounds as learning objectives. Our
proposed algorithms are evaluated in several lifelong multi-armed bandit
problems and are found to perform better than a baseline method that does not
use generalisation bounds.

In recent years, the popularity and use of Artificial Intelligence (AI) and
large investments on theInternet of Medical Things (IoMT) will be common to use
products such as smart socks, smartpants, and smart shirts. These products are
known as Smart Textile or E-textile, which has theability to monitor and
collect signals that our body emits. These signals make it possible to
extractanomalous components using Machine Learning (ML) techniques that play an
essential role in thisarea. This study presents a Systematic Review of the
Literature (SLR) on Anomaly Detection usingML techniques in Smart Shirt. The
objectives of the SLR are: (i) to identify what type of anomalythe smart shirt;
(ii) what ML techniques are being used; (iii) which datasets are being used;
(iv)identify smart shirt or signal acquisition devices; (v) list the
performance metrics used to evaluatethe ML model; (vi) the results of the
techniques in general; (vii) types of ML algorithms are beingapplied.The SLR
selected 11 primary studies published between 2017-2021. The results showed
that6 types of anomalies were identified, with the Fall anomaly being the most
cited. The Support VectorMachines (SVM) algorithm is most used. Most of the
primary studies used public or private datasets.The Hexoskin smart shirt was
most cited. The most used metric performance was Accuracy. Onaverage, almost
all primary studies presented a result above 90%, and all primary studies used
theSupervisioned type of ML.

Pressure ulcers are a challenge for patients and healthcare professionals. In
the UK, 700,000 people are affected by pressure ulcers each year. Treating them
costs the National Health Service {\pounds}3.8 million every day. Their
etiology is complex and multifactorial. However, evidence has shown a strong
link between old age, disease-related sedentary lifestyles and unhealthy eating
habits. Pressure ulcers are caused by direct skin contact with a bed or chair
without frequent position changes. Urinary and faecal incontinence, diabetes,
and injuries that restrict body position and nutrition are also known risk
factors. Guidelines and treatments exist but their implementation and success
vary across different healthcare settings. This is primarily because healthcare
practitioners have a) minimal experience in dealing with pressure ulcers, and
b) a general lack of understanding of pressure ulcer treatments. Poorly
managed, pressure ulcers lead to severe pain, poor quality of life, and
significant healthcare costs. In this paper, we report the findings of a
clinical trial conducted by Mersey Care NHS Foundation Trust that evaluated the
performance of a faster region-based convolutional neural network and mobile
platform that categorised and documented pressure ulcers. The neural network
classifies category I, II, III, and IV pressure ulcers, deep tissue injuries,
and unstageable pressure ulcers. Photographs of pressure ulcers taken by
district nurses are transmitted over 4/5G communications to an inferencing
server for classification. Classified images are stored and reviewed to assess
the model's predictions and relevance as a tool for clinical decision making
and standardised reporting. The results from the study generated a mean average
Precision=0.6796, Recall=0.6997, F1-Score=0.6786 with 45 false positives using
an @.75 confidence score threshold.

Reconstructing spectral functions from propagator data is difficult as
solving the analytic continuation problem or applying an inverse integral
transformation are ill-conditioned problems. Recent work has proposed using
neural networks to solve this problem and has shown promising results, either
matching or improving upon the performance of other methods. We generalize this
approach by not only reconstructing spectral functions, but also (possible)
pairs of complex poles or an infrared (IR) cutoff. We train our network on
physically motivated toy functions, examine the reconstruction accuracy and
check its robustness to noise. Encouraging results are found on both toy
functions and genuine lattice QCD data for the gluon propagator, suggesting
that this approach may lead to significant improvements over current

Hierarchical Reinforcement Learning (HRL) has held longstanding promise to
advance reinforcement learning. Yet, it has remained a considerable challenge
to develop practical algorithms that exhibit some of these promises. To improve
our fundamental understanding of HRL, we investigate hierarchical credit
assignment from the perspective of conventional multistep reinforcement
learning. We show how e.g., a 1-step `hierarchical backup' can be seen as a
conventional multistep backup with $n$ skip connections over time connecting
each subsequent state to the first independent of actions inbetween.
Furthermore, we find that generalizing hierarchy to multistep return estimation
methods requires us to consider how to partition the environment trace, in
order to construct backup paths. We leverage these insight to develop a new
hierarchical algorithm Hier$Q_k(\lambda)$, for which we demonstrate that
hierarchical credit assignment alone can already boost agent performance (i.e.,
when eliminating generalization or exploration). Altogether, our work yields
fundamental insight into the nature of hierarchical backups and distinguishes
this as an additional basis for reinforcement learning research.

Techniques of hybridisation and ensemble learning are popular model fusion
techniques for improving the predictive power of forecasting methods. With
limited research that instigates combining these two promising approaches, this
paper focuses on the utility of the Exponential-Smoothing-Recurrent Neural
Network (ES-RNN) in the pool of base models for different ensembles. We compare
against some state of the art ensembling techniques and arithmetic model
averaging as a benchmark. We experiment with the M4 forecasting data set of
100,000 time-series, and the results show that the Feature-based Forecast Model
Averaging (FFORMA), on average, is the best technique for late data fusion with
the ES-RNN. However, considering the M4's Daily subset of data, stacking was
the only successful ensemble at dealing with the case where all base model
performances are similar. Our experimental results indicate that we attain
state of the art forecasting results compared to N-BEATS as a benchmark. We
conclude that model averaging is a more robust ensemble than model selection
and stacking strategies. Further, the results show that gradient boosting is
superior for implementing ensemble learning strategies.

Colonoscopy, currently the most efficient and recognized colon polyp
detection technology, is necessary for early screening and prevention of
colorectal cancer. However, due to the varying size and complex morphological
features of colonic polyps as well as the indistinct boundary between polyps
and mucosa, accurate segmentation of polyps is still challenging. Deep learning
has become popular for accurate polyp segmentation tasks with excellent
results. However, due to the structure of polyps image and the varying shapes
of polyps, it easy for existing deep learning models to overfitting the current
dataset. As a result, the model may not process unseen colonoscopy data. To
address this, we propose a new State-Of-The-Art model for medical image
segmentation, the SSFormer, which uses a pyramid Transformer encoder to improve
the generalization ability of models. Specifically, our proposed Progressive
Locality Decoder can be adapted to the pyramid Transformer backbone to
emphasize local features and restrict attention dispersion. The SSFormer
achieves statet-of-the-art performance in both learning and generalization

The application of deep reinforcement learning in multi-agent systems
introduces extra challenges. In a scenario with numerous agents, one of the
most important concerns currently being addressed is how to develop sufficient
collaboration between diverse agents. To address this problem, we consider the
form of agent interaction based on neighborhood and propose a multi-agent
reinforcement learning (MARL) algorithm based on the actor-critic method, which
can adaptively construct the hypergraph structure representing the agent
interaction and further implement effective information extraction and
representation learning through hypergraph convolution networks, leading to
effective cooperation. Based on different hypergraph generation methods, we
present two variants: Actor Hypergraph Convolutional Critic Network (HGAC) and
Actor Attention Hypergraph Critic Network (ATT-HGAC). Experiments with
different settings demonstrate the advantages of our approach over other

In the pharmaceutical industry, the maintenance of production machines must
be audited by the regulator. In this context, the problem of predictive
maintenance is not when to maintain a machine, but what parts to maintain at a
given point in time. The focus shifts from the entire machine to its component
parts and prediction becomes a classification problem. In this paper, we focus
on rolling-elements bearings and we propose a framework for predicting their
degradation stages automatically. Our main contribution is a k-means bearing
lifetime segmentation method based on high-frequency bearing vibration signal
embedded in a latent low-dimensional subspace using an AutoEncoder. Given
high-frequency vibration data, our framework generates a labeled dataset that
is used to train a supervised model for bearing degradation stage detection.
Our experimental results, based on the FEMTO Bearing dataset, show that our
framework is scalable and that it provides reliable and actionable predictions
for a range of different bearings.

Fine-grained image classification is a challenging computer vision task where
various species share similar visual appearances, resulting in
misclassification if merely based on visual clues. Therefore, it is helpful to
leverage additional information, e.g., the locations and dates for data
shooting, which can be easily accessible but rarely exploited. In this paper,
we first demonstrate that existing multimodal methods fuse multiple features
only on a single dimension, which essentially has insufficient help in feature
discrimination. To fully explore the potential of multimodal information, we
propose a dynamic MLP on top of the image representation, which interacts with
multimodal features at a higher and broader dimension. The dynamic MLP is an
efficient structure parameterized by the learned embeddings of variable
locations and dates. It can be regarded as an adaptive nonlinear projection for
generating more discriminative image representations in visual tasks. To our
best knowledge, it is the first attempt to explore the idea of dynamic networks
to exploit multimodal information in fine-grained image classification tasks.
Extensive experiments demonstrate the effectiveness of our method. The t-SNE
algorithm visually indicates that our technique improves the recognizability of
image representations that are visually similar but with different categories.
Furthermore, among published works across multiple fine-grained datasets,
dynamic MLP consistently achieves SOTA results
https://paperswithcode.com/dataset/inaturalist and takes third place in the
iNaturalist challenge at FGVC8
https://www.kaggle.com/c/inaturalist-2021/leaderboard. Code is available at
https://github.com/ylingfeng/DynamicMLP.git

Recently, the implementation of quantum neural networks is based on noisy
intermediate-scale quantum (NISQ) devices. Parameterized quantum circuit (PQC)
is such the method, and its current design just can handle linear data
classification. However, data in the real world often shows a topological
structure. In the machine learning field, the classical graph convolutional
layer (GCL)-based graph convolutional network (GCN) can well handle the
topological data. Inspired by the architecture of a classical GCN, in this
paper, to expand the function of the PQC, we design a novel PQC architecture to
realize a quantum GCN (QGCN). More specifically, we first implement an adjacent
matrix based on linear combination unitary and a weight matrix in a quantum
GCL, and then by stacking multiple GCLs, we obtain the QGCN. In addition, we
first achieve gradients decent on quantum circuit following the parameter-shift
rule for a GCL and then for the QGCN. We evaluate the performance of the QGCN
by conducting a node classification task on Cora dataset with topological data.
The numerical simulation result shows that QGCN has the same performance as its
classical counterpart, the GCN, in contrast, requires less tunable parameters.
Compared to a traditional PQC, we also verify that deploying an extra adjacent
matrix can significantly improve the classification performance for quantum
topological data.

Human behavior forecasting during human-human interactions is of utmost
importance to provide robotic or virtual agents with social intelligence. This
problem is especially challenging for scenarios that are highly driven by
interpersonal dynamics. In this work, we present the first systematic
comparison of state-of-the-art approaches for behavior forecasting. To do so,
we leverage whole-body annotations (face, body, and hands) from the very
recently released UDIVA v0.5, which features face-to-face dyadic interactions.
Our best attention-based approaches achieve state-of-the-art performance in
UDIVA v0.5. We show that by autoregressively predicting the future with methods
trained for the short-term future (<400ms), we outperform the baselines even
for a considerably longer-term future (up to 2s). We also show that this
finding holds when highly noisy annotations are used, which opens new horizons
towards the use of weakly-supervised learning. Combined with large-scale
datasets, this may help boost the advances in this field.

Semantic segmentation is a difficult task even when trained in a supervised
manner on photographs. In this paper, we tackle the problem of semantic
segmentation of artistic paintings, an even more challenging task because of a
much larger diversity in colors, textures, and shapes and because there are no
ground truth annotations available for segmentation. We propose an unsupervised
method for semantic segmentation of paintings using domain adaptation. Our
approach creates a training set of pseudo-paintings in specific artistic styles
by using style-transfer on the PASCAL VOC 2012 dataset, and then applies domain
confusion between PASCAL VOC 2012 and real paintings. These two steps build on
a new dataset we gathered called DRAM (Diverse Realism in Art Movements)
composed of figurative art paintings from four movements, which are highly
diverse in pattern, color, and geometry. To segment new paintings, we present a
composite multi-domain adaptation method that trains on each sub-domain
separately and composes their solutions during inference time. Our method
provides better segmentation results not only on the specific artistic
movements of DRAM, but also on other, unseen ones. We compare our approach to
alternative methods and show applications of semantic segmentation in art
paintings. The code and models for our approach are publicly available at:
https://github.com/Nadavc220/SemanticSegmentationInArtPaintings.

Pre-trained masked language models have demonstrated remarkable ability as
few-shot learners. In this paper, as an alternative, we propose a novel
approach to few-shot learning with pre-trained token-replaced detection models
like ELECTRA. In this approach, we reformulate a classification or a regression
task as a token-replaced detection problem. Specifically, we first define a
template and label description words for each task and put them into the input
to form a natural language prompt. Then, we employ the pre-trained
token-replaced detection model to predict which label description word is the
most original (i.e., least replaced) among all label description words in the
prompt. A systematic evaluation on 16 datasets demonstrates that our approach
outperforms few-shot learners with pre-trained masked language models in both
one-sentence and two-sentence learning tasks.

We present a multidimensional deep learning implementation of a stochastic
branching algorithm for the numerical solution of fully nonlinear PDEs. This
approach is designed to tackle functional nonlinearities involving gradient
terms of any orders by combining the use of neural networks with a Monte Carlo
branching algorithm. In comparison with other deep learning PDE solvers, it
also allows us to check the consistency of the learned neural network function.
Numerical experiments presented show that this algorithm can outperform deep
learning approaches based on backward stochastic differential equations or the
Galerkin method, and provide solution estimates that are not obtained by those
methods in fully nonlinear examples.

The legacy mobility robustness optimization (MRO) in self-organizing networks
aims at improving handover performance by optimizing cell-specific handover
parameters. However, such solutions cannot satisfy the needs of next-generation
network with network slicing, because it only guarantees the received signal
strength but not the per-slice service quality. To provide the truly seamless
mobility service, we propose a deep reinforcement learning-based slice-aware
mobility robustness optimization (SAMRO) approach, which improves handover
performance with per-slice service assurance by optimizing slice-specific
handover parameters. Moreover, to allow safe and sample efficient online
training, we develop a two-step transfer learning scheme: 1) regularized
offline reinforcement learning, and 2) effective online fine-tuning with mixed
experience replay. System-level simulations show that compared against the
legacy MRO algorithms, SAMRO significantly improves slice-aware service
continuation while optimizing the handover performance.

Spectral clustering is a popular approach for clustering undirected graphs,
but its extension to directed graphs (digraphs) is much more challenging. A
typical workaround is to naively symmetrize the adjacency matrix of the
directed graph, which can however lead to discarding valuable information
carried by edge directionality. In this paper, we present a generalized
spectral clustering framework that can address both directed and undirected
graphs. Our approach is based on the spectral relaxation of a new functional
that we introduce as the generalized Dirichlet energy of a graph function, with
respect to an arbitrary positive regularizing measure on the graph edges. We
also propose a practical parametrization of the regularizing measure
constructed from the iterated powers of the natural random walk on the graph.
We present theoretical arguments to explain the efficiency of our framework in
the challenging setting of unbalanced classes. Experiments using directed K-NN
graphs constructed from real datasets show that our graph partitioning method
performs consistently well in all cases, while outperforming existing
approaches in most of them.

Unsupervised domain adaptation studies how to transfer a learner from a
labeled source domain to an unlabeled target domain with different
distributions. Existing methods mainly focus on matching the marginal
distributions of the source and target domains, which probably lead a
misalignment of samples from the same class but different domains. In this
paper, we deal with this misalignment by achieving the class-conditioned
transferring from a new perspective. We aim to maximize the conditional
independence of feature and domain given class in the reproducing kernel
Hilbert space. The optimization of the conditional independence measure can be
viewed as minimizing a surrogate of a certain mutual information between
feature and domain. An interpretable empirical estimation of the conditional
dependence is deduced and connected with the unconditional case. Besides, we
provide an upper bound on the target error by taking the class-conditional
distribution into account, which provides a new theoretical insight for most
class-conditioned transferring methods. In addition to unsupervised domain
adaptation, we extend our method to the multi-source scenario in a natural and
elegant way. Extensive experiments on four benchmarks validate the
effectiveness of the proposed models in both unsupervised domain adaptation and
multiple source domain adaptation.

Next-location prediction, consisting of forecasting a user's location given
their historical trajectories, has important implications in several fields,
such as urban planning, geo-marketing, and disease spreading. Several
predictors have been proposed in the last few years to address it, including
last-generation ones based on deep learning. This paper tests the
generalization capability of these predictors on public mobility datasets,
stratifying the datasets by whether the trajectories in the test set also
appear fully or partially in the training set. We consistently discover a
severe problem of trajectory overlapping in all analyzed datasets, highlighting
that predictors memorize trajectories while having limited generalization
capacities. We thus propose a methodology to rerank the outputs of the
next-location predictors based on spatial mobility patterns. With these
techniques, we significantly improve the predictors' generalization capability,
with a relative improvement on the accuracy up to 96.15% on the trajectories
that cannot be memorized (i.e., low overlap with the training set).

Magnetic resonance (MR) images exhibit various contrasts and appearances
based on factors such as different acquisition protocols, views, manufacturers,
scanning parameters, etc. This generally accessible appearance-related side
information affects deep learning-based undersampled magnetic resonance imaging
(MRI) reconstruction frameworks, but has been overlooked in the majority of
current works. In this paper, we investigate the use of such side information
as normalisation parameters in a convolutional neural network (CNN) to improve
undersampled MRI reconstruction. Specifically, a Side Information-Guided
Normalisation (SIGN) module, containing only few layers, is proposed to
efficiently encode the side information and output the normalisation
parameters. We examine the effectiveness of such a module on two popular
reconstruction architectures, D5C5 and OUCR. The experimental results on both
brain and knee images under various acceleration rates demonstrate that the
proposed method improves on its corresponding baseline architectures with a

Though federated learning (FL) well preserves clients' data privacy, many
clients are still reluctant to join FL given the communication cost and energy
consumption in their mobile devices. It is important to design pricing
compensations to motivate enough clients to join FL and distributively train
the global model. Prior pricing mechanisms for FL are static and cannot adapt
to clients' random arrival pattern over time. We propose a new dynamic pricing
solution in closed-form by constructing the Hamiltonian function to optimally
balance the client recruitment time and the model training time, without
knowing clients' actual arrivals or training costs. During the client
recruitment phase, we offer time-dependent monetary rewards per client arrival
to trade-off between the total payment and the FL model's accuracy loss. Such
reward gradually increases when we approach to the recruitment deadline or have
greater data aging, and we also extend the deadline if the clients' training
time per iteration becomes shorter. Further, we extend to consider
heterogeneous client types in training data size and training time per
iteration. We successfully extend our dynamic pricing solution and develop an
optimal algorithm of linear complexity to monotonically select client types for
FL. Finally, we also show the robustness of our solution against estimation
error of clients' data sizes and run numerical experiments to validate our

While neural text-to-speech systems perform remarkably well in high-resource
scenarios, they cannot be applied to the majority of the over 6,000 spoken
languages in the world due to a lack of appropriate training data. In this
work, we use embeddings derived from articulatory vectors rather than
embeddings derived from phoneme identities to learn phoneme representations
that hold across languages. In conjunction with language agnostic meta
learning, this enables us to fine-tune a high-quality text-to-speech model on
just 30 minutes of data in a previously unseen language spoken by a previously
unseen speaker.

This paper deals the combination of nonlinear predictive models with
classical LPCC parameterization for speaker recognition. It is shown that the
combination of both a measure defined over LPCC coefficients and a measure
defined over predictive analysis residual signal gives rise to an improvement
over the classical method that considers only the LPCC coefficients. If the
residual signal is obtained from a linear prediction analysis, the improvement
is 2.63% (error rate drops from 6.31% to 3.68%) and if it is computed through a
nonlinear predictive neural nets based model, the improvement is 3.68%. An
efficient algorithm for reducing the computational burden is also proposed.

In this paper we compare several ADPCM schemes with nonlinear prediction
based on neural nets with the classical ADPCM schemes based on several linear
prediction schemes. Main studied variations of the ADPCM scheme with adaptive
quantization (2 to 5 bits) are: -forward vs backward -sample adaptive vs block
adaptive

In this paper we propose a Non-Linear Predictive Vector quantizer (PVQ) for
speech coding, based on Multi-Layer Perceptrons. We also propose a method to
evaluate if a quantizer is well designed, and if it exploits the correlation
between consecutive outputs. Although the results of the Non-linear PVQ do not
improve the results of the non-linear scalar predictor, we check that there is
some room for the PVQ improvement.

Knowledge amalgamation (KA) is a novel deep model reusing task aiming to
transfer knowledge from several well-trained teachers to a multi-talented and
compact student. Currently, most of these approaches are tailored for
convolutional neural networks (CNNs). However, there is a tendency that
transformers, with a completely different architecture, are starting to
challenge the domination of CNNs in many computer vision tasks. Nevertheless,
directly applying the previous KA methods to transformers leads to severe
performance degradation. In this work, we explore a more effective KA scheme
for transformer-based object detection models. Specifically, considering the
architecture characteristics of transformers, we propose to dissolve the KA
into two aspects: sequence-level amalgamation (SA) and task-level amalgamation
(TA). In particular, a hint is generated within the sequence-level amalgamation
by concatenating teacher sequences instead of redundantly aggregating them to a
fixed-size one as previous KA works. Besides, the student learns heterogeneous
detection tasks through soft targets with efficiency in the task-level
amalgamation. Extensive experiments on PASCAL VOC and COCO have unfolded that
the sequence-level amalgamation significantly boosts the performance of
students, while the previous methods impair the students. Moreover, the
transformer-based students excel in learning amalgamated knowledge, as they
have mastered heterogeneous detection tasks rapidly and achieved superior or at
least comparable performance to those of the teachers in their specializations.

In this paper, we study the stochastic bandits problem with $k$ unknown
heavy-tailed and corrupted reward distributions or arms with time-invariant
corruption distributions. At each iteration, the player chooses an arm. Given
the arm, the environment returns an uncorrupted reward with probability
$1-\varepsilon$ and an arbitrarily corrupted reward with probability
$\varepsilon$. In our setting, the uncorrupted reward might be heavy-tailed and
the corrupted reward might be unbounded. We prove a lower bound on the regret
indicating that the corrupted and heavy-tailed bandits are strictly harder than
uncorrupted or light-tailed bandits. We observe that the environments can be
categorised into hardness regimes depending on the suboptimality gap $\Delta$,
variance $\sigma$, and corruption proportion $\epsilon$. Following this, we
design a UCB-type algorithm, namely HuberUCB, that leverages Huber's estimator
for robust mean estimation. HuberUCB leads to tight upper bounds on regret in
the proposed corrupted and heavy-tailed setting. To derive the upper bound, we
prove a novel concentration inequality for Huber's estimator, which might be of

Estimating treatment effects is of great importance for many biomedical
applications with observational data. Particularly, interpretability of the
treatment effects is preferable for many biomedical researchers. In this paper,
we first provide a theoretical analysis and derive an upper bound for the bias
of average treatment effect (ATE) estimation under the strong ignorability
assumption. Derived by leveraging appealing properties of the Weighted Energy
Distance, our upper bound is tighter than what has been reported in the
literature. Motivated by the theoretical analysis, we propose a novel objective
function for estimating the ATE that uses the energy distance balancing score
and hence does not require correct specification of the propensity score model.
We also leverage recently developed neural additive models to improve
interpretability of deep learning models used for potential outcome prediction.
We further enhance our proposed model with an energy distance balancing score
weighted regularization. The superiority of our proposed model over current
state-of-the-art methods is demonstrated in semi-synthetic experiments using
two benchmark datasets, namely, IHDP and ACIC.

In this paper, we introduce the map-language navigation task where an agent
executes natural language instructions and moves to the target position based
only on a given 3D semantic map. To tackle the task, we design the
instruction-aware Path Proposal and Discrimination model (iPPD). Our approach
leverages map information to provide instruction-aware path proposals, i.e., it
selects all potential instruction-aligned candidate paths to reduce the
solution space. Next, to represent the map observations along a path for a
better modality alignment, a novel Path Feature Encoding scheme tailored for
semantic maps is proposed. An attention-based Language Driven Discriminator is
designed to evaluate path candidates and determine the best path as the final
result. Our method can naturally avoid error accumulation compared with
single-step greedy decision methods. Comparing to a single-step imitation
learning approach, iPPD has performance gains above 17% on navigation success
and 0.18 on path matching measurement nDTW in challenging unseen environments.

Deep Siamese trackers have recently gained much attention in recent years
since they can track visual objects at high speeds. Additionally, adaptive
tracking methods, where target samples collected by the tracker are employed
for online learning, have achieved state-of-the-art accuracy. However, single
object tracking (SOT) remains a challenging task in real-world application due
to changes and deformations in a target object's appearance. Learning on all
the collected samples may lead to catastrophic forgetting, and thereby corrupt
the tracking model.
  In this paper, SOT is formulated as an online incremental learning problem. A
new method is proposed for dynamic sample selection and memory replay,
preventing template corruption. In particular, we propose a change detection
mechanism to detect gradual changes in object appearance and select the
corresponding samples for online adaption. In addition, an entropy-based sample
selection strategy is introduced to maintain a diversified auxiliary buffer for
memory replay. Our proposed method can be integrated into any object tracking
algorithm that leverages online learning for model adaptation.
  Extensive experiments conducted on the OTB-100, LaSOT, UAV123, and
TrackingNet datasets highlight the cost-effectiveness of our method, along with
the contribution of its key components. Results indicate that integrating our
proposed method into state-of-art adaptive Siamese trackers can increase the
potential benefits of a template update strategy, and significantly improve

Learning-based methods are powerful in handling complex scenarios. However,
it is still challenging to use learning-based methods under uncertain
environments while stability, safety, and real-time performance of the system
are desired to guarantee. In this paper, we propose a learning-based tracking
control scheme based on a feedback linearization controller in which uncertain
disturbances are approximated online using Gaussian Processes (GPs). Using the
predicted distribution of disturbances given by GPs, a Control Lyapunov
Function (CLF) and Control Barrier Function (CBF) based Quadratic Program is
applied, with which probabilistic stability and safety are guaranteed. In
addition, the trajectory is optimized first by Model Predictive Control (MPC)
based on the linearized dynamics systems to further reduce the tracking error.
We also design an event trigger for GPs updates to improve efficiency while
stability and safety of the system are still guaranteed. The effectiveness of
the proposed tracking control strategy is illustrated in numerical simulations.

We present an approach, based on deep neural networks, that allows
identifying robust statistical arbitrage strategies in financial markets.
Robust statistical arbitrage strategies refer to self-financing trading
strategies that enable profitable trading under model ambiguity. The presented
novel methodology does not suffer from the curse of dimensionality nor does it
depend on the identification of cointegrated pairs of assets and is therefore
applicable even on high-dimensional financial markets or in markets where
classical pairs trading approaches fail. Moreover, we provide a method to build
an ambiguity set of admissible probability measures that can be derived from
observed market data. Thus, the approach can be considered as being model-free
and entirely data-driven. We showcase the applicability of our method by
providing empirical investigations with highly profitable trading performances
even in 50 dimensions, during financial crises, and when the cointegration
relationship between asset pairs stops to persist.

Although pre-trained sequence-to-sequence models have achieved great success
in dialogue response generation, chatbots still suffer from generating
inconsistent responses in real-world practice, especially in multi-turn
settings. We argue that this can be caused by a discrepancy between training
and real-world testing. At training time, chatbot generates the response with
the golden context, while it has to generate based on the context consisting of
both user utterances and the model predicted utterances during real-world
testing. With the growth of the number of utterances, this discrepancy becomes
more serious in the multi-turn settings. In this paper, we propose a
hierarchical sampling-based method consisting of both utterance-level sampling
and semi-utterance-level sampling, to alleviate the discrepancy, which
implicitly increases the dialogue coherence. We further adopt reinforcement
learning and re-ranking methods to explicitly optimize the dialogue coherence
during training and inference, respectively. Empirical experiments show the
effectiveness of the proposed methods for improving the robustness of chatbots
in real practice.

Stochastic gradient descent (SGD) has achieved great success due to its
superior performance in both optimization and generalization. Most of existing
generalization analyses are made for single-pass SGD, which is a less practical
variant compared to the commonly-used multi-pass SGD. Besides, theoretical
analyses for multi-pass SGD often concern a worst-case instance in a class of
problems, which may be pessimistic to explain the superior generalization
ability for some particular problem instance. The goal of this paper is to
sharply characterize the generalization of multi-pass SGD, by developing an
instance-dependent excess risk bound for least squares in the interpolation
regime, which is expressed as a function of the iteration number, stepsize, and
data covariance. We show that the excess risk of SGD can be exactly decomposed
into the excess risk of GD and a positive fluctuation error, suggesting that
SGD always performs worse, instance-wisely, than GD, in generalization. On the
other hand, we show that although SGD needs more iterations than GD to achieve
the same level of excess risk, it saves the number of stochastic gradient
evaluations, and therefore is preferable in terms of computational time.

With the rapid development of big data, how to efficiently and accurately
discover tight community structures in large-scale networks for knowledge
discovery has attracted more and more attention. In this paper, a community
detection framework based on Graph AutoEncoder Reconstruction (noted as GAER)
is proposed for the first time. GAER is a highly scalable framework which does
not require any prior information. We decompose the graph autoencoder-based
one-step encoding into the two-stage encoding framework to adapt to the
real-world big data system by reducing complexity from the original O(N^2) to
O(N). At the same time, based on the advantages of GAER support module
plug-and-play configuration and incremental community detection, we further
propose a peer awareness based module for real-time large graphs, which can
realize the new nodes community detection at a faster speed, and accelerate
model inference with the 6.15 times - 14.03 times speed. Finally, we apply the
GAER on multiple real-world datasets, including some large-scale networks. The
experimental result verified that GAER has achieved the superior performance on
almost all networks.

The high-volume manufacturing of the next generation of semiconductor devices
requires advances in measurement signal analysis. Many in the semiconductor
manufacturing community have reservations about the adoption of deep learning;
they instead prefer other model-based approaches for some image regression
problems, and according to the 2021 IEEE International Roadmap for Devices and
Systems (IRDS) report on Metrology a SEMI standardization committee may endorse
this philosophy. The semiconductor manufacturing community does, however,
communicate a need for state-of-the-art statistical analyses to reduce
measurement uncertainty. Prediction intervals which characterize the
reliability of the predictive performance of regression models can impact
decisions, build trust in machine learning, and be applied to other regression
models. However, we are not aware of effective and sufficiently simple
distribution-free approaches that offer valid coverage for important classes of
image data, so we consider the distribution-free conformal prediction and
conformalized quantile regression framework.The image regression problem that
is the focus of this paper pertains to line edge roughness (LER) estimation
from noisy scanning electron microscopy images. LER affects semiconductor
device performance and reliability as well as the yield of the manufacturing
process; the 2021 IRDS emphasizes the crucial importance of LER by devoting a
white paper to it in addition to mentioning or discussing it in the reports of
multiple international focus teams. It is not immediately apparent how to
effectively use normalized conformal prediction and quantile regression for LER
estimation. The modeling techniques we apply appear to be novel for finding
distribution-free prediction intervals for image data and will be presented at
the 2022 SEMI Advanced Semiconductor Manufacturing Conference.

In this work, we propose an interoceptive-only state estimation system for a
quadrotor with deep neural network processing, where the quadrotor dynamics is
considered as a perceptive supplement of the inertial kinematics. To improve
the precision of multi-sensor fusion, we train cascaded networks on real-world
quadrotor flight data to learn IMU kinematic properties, quadrotor dynamic
characteristics, and motion states of the quadrotor along with their
uncertainty information, respectively. This encoded information empowers us to
address the issues of IMU bias stability, dynamic constraints, and multi-sensor
calibration during sensor fusion. The above multi-source information is fused
into a two-stage Extended Kalman Filter (EKF) framework for better estimation.
Experiments have demonstrated the advantages of our proposed work over several
conventional and learning-based methods.

Due to the over-fitting problem caused by imbalance samples, there is still
room to improve the performance of data-driven automatic modulation
classification (AMC) in noisy scenarios. By fully considering the signal
characteristics, an AMC scheme based on adaptive fusion network (AFNet) is
proposed in this work. The AFNet can extract and aggregate multi-scale spatial
features of in-phase and quadrature (I/Q) signals intelligently, thus improving
the feature representation capability. Moreover, a novel confidence weighted
loss function is proposed to address the imbalance issue and it is implemented
by a two-stage learning scheme.Through the two-stage learning, AFNet can focus
on high-confidence samples with more valid information and extract effective
representations, so as to improve the overall classification performance. In
the simulations, the proposed scheme reaches an average accuracy of 62.66% on a
wide range of SNRs, which outperforms other AMC models. The effects of the loss
function on classification accuracy are further studied.

The key challenge of zero-shot learning (ZSL) is how to infer the latent
semantic knowledge between visual and attribute features on seen classes, and
thus achieving a desirable knowledge transfer to unseen classes. Prior works
either simply align the global features of an image with its associated class
semantic vector or utilize unidirectional attention to learn the limited latent
semantic representations, which could not effectively discover the intrinsic
semantic knowledge e.g., attribute semantics) between visual and attribute
features. To solve the above dilemma, we propose a Mutually Semantic
Distillation Network (MSDN), which progressively distills the intrinsic
semantic representations between visual and attribute features for ZSL. MSDN
incorporates an attribute$\rightarrow$visual attention sub-net that learns
attribute-based visual features, and a visual$\rightarrow$attribute attention
sub-net that learns visual-based attribute features. By further introducing a
semantic distillation loss, the two mutual attention sub-nets are capable of
learning collaboratively and teaching each other throughout the training
process. The proposed MSDN yields significant improvements over the strong
baselines, leading to new state-of-the-art performances on three popular
challenging benchmarks, i.e., CUB, SUN, and AWA2. Our codes have been available
at: \url{https://github.com/shiming-chen/MSDN}.

Clustering is one of the most crucial problems in unsupervised learning, and
the well-known $k$-means clustering algorithm has been shown to be
implementable on a quantum computer with a significant speedup. However, many
clustering problems cannot be solved by $k$-means, and a powerful method called
spectral clustering is introduced to solve these problems. In this work, we
propose a circuit design to implement spectral clustering on a quantum
processor with a substantial speedup, by initializing the processor into a
maximally entangled state and encoding the data information into an
efficiently-simulatable Hamiltonian. Compared with the established quantum
$k$-means algorithms, our method does not require a quantum random access
memory or a quantum adiabatic process. It relies on an appropriate embedding of
quantum phase estimation into Grover's search to gain the quantum speedup.
Simulations demonstrate that our method is effective in solving clustering
problems and will serve as an important supplement to quantum $k$-means for

Neural architecture search (NAS) could help search for robust network
architectures, where defining robustness evaluation metrics is the important
procedure. However, current robustness evaluations in NAS are not sufficiently
comprehensive and reliable. In particular, the common practice only considers
adversarial noise and quantified metrics such as the Jacobian matrix, whereas,
some studies indicated that the models are also vulnerable to other types of
noises such as natural noise. In addition, existing methods taking adversarial
noise as the evaluation just use the robust accuracy of the FGSM or PGD, but
these adversarial attacks could not provide the adequately reliable evaluation,
leading to the vulnerability of the models under stronger attacks. To alleviate
the above problems, we propose a novel framework, called Auto Adversarial
Attack and Defense (AAAD), where we employ neural architecture search methods,
and four types of robustness evaluations are considered, including adversarial
noise, natural noise, system noise and quantified metrics, thereby assisting in
finding more robust architectures. Also, among the adversarial noise, we use
the composite adversarial attack obtained by random search as the new metric to
evaluate the robustness of the model architectures. The empirical results on
the CIFAR10 dataset show that the searched efficient attack could help find
more robust architectures.

The demand to process vast amounts of data generated from state-of-the-art
high resolution cameras has motivated novel energy-efficient on-device AI
solutions. Visual data in such cameras are usually captured in the form of
analog voltages by a sensor pixel array, and then converted to the digital
domain for subsequent AI processing using analog-to-digital converters (ADC).
Recent research has tried to take advantage of massively parallel low-power
analog/digital computing in the form of near- and in-sensor processing, in
which the AI computation is performed partly in the periphery of the pixel
array and partly in a separate on-board CPU/accelerator. Unfortunately,
high-resolution input images still need to be streamed between the camera and
the AI processing unit, frame by frame, causing energy, bandwidth, and security
bottlenecks. To mitigate this problem, we propose a novel
Processing-in-Pixel-in-memory (P2M) paradigm, that customizes the pixel array
by adding support for analog multi-channel, multi-bit convolution, batch
normalization, and ReLU (Rectified Linear Units). Our solution includes a
holistic algorithm-circuit co-design approach and the resulting P2M paradigm
can be used as a drop-in replacement for embedding memory-intensive first few
layers of convolutional neural network (CNN) models within
foundry-manufacturable CMOS image sensor platforms. Our experimental results
indicate that P2M reduces data transfer bandwidth from sensors and analog to
digital conversions by ~21x, and the energy-delay product (EDP) incurred in
processing a MobileNetV2 model on a TinyML use case for visual wake words
dataset (VWW) by up to ~11x compared to standard near-processing or in-sensor
implementations, without any significant drop in test accuracy.

Recent advances in electron, scanning probe, optical, and chemical imaging
and spectroscopy yield bespoke data sets containing the information of
structure and functionality of complex systems. In many cases, the resulting
data sets are underpinned by low-dimensional simple representations encoding
the factors of variability within the data. The representation learning methods
seek to discover these factors of variability, ideally further connecting them
with relevant physical mechanisms. However, generally the task of identifying
the latent variables corresponding to actual physical mechanisms is extremely
complex. Here, we explore an approach based on conditioning the data on the
known (continuous) physical parameters, and systematically compare it with the
previously introduced approach based on the invariant variational autoencoders.
The conditional variational autoencoders (cVAE) approach does not rely on the
existence of the invariant transforms, and hence allows for much greater
flexibility and applicability. Interestingly, cVAE allows for limited
extrapolation outside of the original domain of the conditional variable.
However, this extrapolation is limited compared to the cases when true physical
mechanisms are known, and the physical factor of variability can be
disentangled in full. We further show that introducing the known conditioning
results in the simplification of the latent distribution if the conditioning
vector is correlated with the factor of variability in the data, thus allowing
to separate relevant physical factors. We initially demonstrate this approach
using 1D and 2D examples on a synthetic dataset and then extend it to the
analysis of experimental data on ferroelectric domain dynamics visualized via
Piezoresponse Force Microscopy.

The peer merit review of research proposals has been the major mechanism to
decide grant awards. Nowadays, research proposals have become increasingly
interdisciplinary. It has been a longstanding challenge to assign proposals to
appropriate reviewers. One of the critical steps in reviewer assignment is to
generate accurate interdisciplinary topic labels for proposals. Existing
systems mainly collect topic labels manually reported by discipline
investigators. However, such human-reported labels can be non-accurate and
incomplete. What role can AI play in developing a fair and precise proposal
review system? In this evidential study, we collaborate with the National
Science Foundation of China to address the task of automated interdisciplinary
topic path detection. For this purpose, we develop a deep Hierarchical
Interdisciplinary Research Proposal Classification Network (HIRPCN). We first
propose a hierarchical transformer to extract the textual semantic information
of proposals. We then design an interdisciplinary graph and leverage GNNs to
learn representations of each discipline in order to extract interdisciplinary
knowledge. After extracting the semantic and interdisciplinary knowledge, we
design a level-wise prediction component to fuse the two types of knowledge
representations and detect interdisciplinary topic paths for each proposal. We
conduct extensive experiments and expert evaluations on three real-world
datasets to demonstrate the effectiveness of our proposed model.

We develop an exact and scalable algorithm for one-dimensional Gaussian
process regression with Mat\'ern correlations whose smoothness parameter $\nu$
is a half-integer. The proposed algorithm only requires $\mathcal{O}(\nu^3 n)$
operations and $\mathcal{O}(\nu n)$ storage. This leads to a linear-cost solver
since $\nu$ is chosen to be fixed and usually very small in most applications.
The proposed method can be applied to multi-dimensional problems if a full grid
or a sparse grid design is used. The proposed method is based on a novel theory
for Mat\'ern correlation functions. We find that a suitable rearrangement of
these correlation functions can produce a compactly supported function, called
a "kernel packet". Using a set of kernel packets as basis functions leads to a
sparse representation of the covariance matrix that results in the proposed
algorithm. Simulation studies show that the proposed algorithm, when
applicable, is significantly superior to the existing alternatives in both the
computational time and predictive accuracy.

Current research on Deep Reinforcement Learning (DRL) for automated on-ramp
merging neglects vehicle powertrain and dynamics. This work considers automated
on-ramp merging for a power-split Plug-In Hybrid Electric Vehicle (PHEV), the
2015 Toyota Prius Plug-In, using DRL. The on-ramp merging control and the PHEV
energy management are co-optimized such that the DRL policy directly outputs
the power split between the engine and the electric motor. The testing results
show that DRL can be successfully used for co-optimization, leading to
collision-free on-ramp merging. When compared with sequential approaches
wherein the upper-level on-ramp merging control and the lower-level PHEV energy
management are performed independently and in sequence, we found that
co-optimization results in economic but jerky on-ramp merging while sequential
approaches may result in collisions due to neglecting powertrain power limit
constraints in designing the upper-level on-ramp merging controller.

The item response theory obtains the estimates and their confidence intervals
for parameters of abilities of examinees and difficulties of problems by using
the observed item response matrix consisting of 0/1 value elements. Many papers
discuss the performance of the estimates. However, this paper does not. Using
the maximum likelihood estimates, we can reconstruct the estimated item
response matrix. Then we can assess the accuracy of this reconstructed matrix
to the observed response matrix from the matrix decomposition perspective. That
is, this paper focuses on the performance of the reconstructed response matrix.
To compare the performance of the item response theory with others, we provided
the two kinds of low rank response matrix by approximating the observed
response matrix; one is the matrix via the singular value decomposition method
when the response matrix is a complete matrix, and the other is the matrix via
the matrix decomposition method when the response matrix is an incomplete
matrix. We have, firstly, found that the performance of the singular value
decomposition method and the matrix decomposition method is almost the same
when the response matrix is a complete matrix. Here, the performance is
measured by the closeness between the two matrices using the root mean squared
errors and the accuracy. Secondary, we have seen that the closeness of the
reconstructed matrix obtained from the item response theory to the observed
matrix is located between the two approximated low rank response matrices
obtained from the matrix decomposition method of k= and k=2 to the observed
matrix, where k indicates the first k columns use in the decomposed matrices.

In this paper, we study gap-dependent regret guarantees for risk-sensitive
reinforcement learning based on the entropic risk measure. We propose a novel
definition of sub-optimality gaps, which we call cascaded gaps, and we discuss
their key components that adapt to the underlying structures of the problem.
Based on the cascaded gaps, we derive non-asymptotic and logarithmic regret
bounds for two model-free algorithms under episodic Markov decision processes.
We show that, in appropriate settings, these bounds feature exponential
improvement over existing ones that are independent of gaps. We also prove
gap-dependent lower bounds, which certify the near optimality of the upper
bounds.

The IoT vision of a trillion connected devices over the next decade requires
reliable end-to-end connectivity and automated device management platforms.
While we have seen successful efforts for maintaining small IoT testbeds, there
are multiple challenges for the efficient management of large-scale device
deployments. With Industrial IoT, incorporating millions of devices,
traditional management methods do not scale well. In this work, we address
these challenges by designing a set of novel machine learning techniques, which
form a foundation of a new tool, it IoTelligent, for IoT device management,
using traffic characteristics obtained at the network level. The design of our
tool is driven by the analysis of 1-year long networking data, collected from
350 companies with IoT deployments. The exploratory analysis of this data
reveals that IoT environments follow the famous Pareto principle, such as: (i)
10% of the companies in the dataset contribute to 90% of the entire traffic;
(ii) 7% of all the companies in the set own 90% of all the devices. We designed
and evaluated CNN, LSTM, and Convolutional LSTM models for demand forecasting,
with a conclusion of the Convolutional LSTM model being the best. However,
maintaining and updating individual company models is expensive. In this work,
we design a novel, scalable approach, where a general demand forecasting model
is built using the combined data of all the companies with a normalization
factor. Moreover, we introduce a novel technique for device management, based
on autoencoders. They automatically extract relevant device features to
identify device groups with similar behavior to flag anomalous devices.

User-level differential privacy (DP) provides certifiable privacy guarantees
to the information that is specific to any user's data in federated learning.
Existing methods that ensure user-level DP come at the cost of severe accuracy
decrease. In this paper, we study the cause of model performance degradation in
federated learning under user-level DP guarantee. We find the key to solving
this issue is to naturally restrict the norm of local updates before executing
operations that guarantee DP. To this end, we propose two techniques, Bounded
Local Update Regularization and Local Update Sparsification, to increase model
quality without sacrificing privacy. We provide theoretical analysis on the
convergence of our framework and give rigorous privacy guarantees. Extensive
experiments show that our framework significantly improves the privacy-utility
trade-off over the state-of-the-arts for federated learning with user-level DP

Molecular dynamics (MD) simulations are increasingly being combined with
machine learning (ML) to predict material properties. The molecular
configurations obtained from MD are represented by multiple features, such as
thermodynamic properties, and are used as the ML input. However, to accurately
find the input--output patterns, ML requires a sufficiently sized dataset that
depends on the complexity of the ML model. Generating such a large dataset from
MD simulations is not ideal because of their high computation cost. In this
study, we present a simple supervised ML method to predict the transport
properties of materials. To simplify the model, an unsupervised ML method
obtains an efficient representation of molecular movements. This method was
applied to predict the viscosity of lubricant molecules in confinement with
shear flow. Furthermore, simplicity facilitates the interpretation of the model
to understand the molecular mechanics of viscosity. We revealed two types of
molecular mechanisms that contribute to low viscosity.

Traffic accident forecasting is a significant problem for transportation
management and public safety. However, this problem is challenging due to the
spatial heterogeneity of the environment and the sparsity of accidents in space
and time. The occurrence of traffic accidents is affected by complex
dependencies among spatial and temporal features. Recent traffic accident
prediction methods have attempted to use deep learning models to improve
accuracy. However, most of these methods either focus on small-scale and
homogeneous areas such as populous cities or simply use sliding-window-based
ensemble methods, which are inadequate to handle heterogeneity in large
regions. To address these limitations, this paper proposes a novel Hierarchical
Knowledge Transfer Network (HintNet) model to better capture irregular
heterogeneity patterns. HintNet performs a multi-level spatial partitioning to
separate sub-regions with different risks and learns a deep network model for
each level using spatio-temporal and graph convolutions. Through knowledge
transfer across levels, HintNet archives both higher accuracy and higher
training efficiency. Extensive experiments on a real-world accident dataset
from the state of Iowa demonstrate that HintNet outperforms the
state-of-the-art methods on spatially heterogeneous and large-scale areas.

We develop new theoretical results on matrix perturbation to shed light on
the impact of architecture on the performance of a deep network. In particular,
we explain analytically what deep learning practitioners have long observed
empirically: the parameters of some deep architectures (e.g., residual
networks, ResNets, and Dense networks, DenseNets) are easier to optimize than
others (e.g., convolutional networks, ConvNets). Building on our earlier work
connecting deep networks with continuous piecewise-affine splines, we develop
an exact local linear representation of a deep network layer for a family of
modern deep networks that includes ConvNets at one end of a spectrum and
ResNets, DenseNets, and other networks with skip connections at the other. For
regression and classification tasks that optimize the squared-error loss, we
show that the optimization loss surface of a modern deep network is piecewise
quadratic in the parameters, with local shape governed by the singular values
of a matrix that is a function of the local linear representation. We develop
new perturbation results for how the singular values of matrices of this sort
behave as we add a fraction of the identity and multiply by certain diagonal
matrices. A direct application of our perturbation results explains
analytically why a network with skip connections (such as a ResNet or DenseNet)
is easier to optimize than a ConvNet: thanks to its more stable singular values
and smaller condition number, the local loss surface of such a network is less
erratic, less eccentric, and features local minima that are more accommodating
to gradient-based optimization. Our results also shed new light on the impact
of different nonlinear activation functions on a deep network's singular
values, regardless of its architecture.

Time-to-event (T2E) analysis is a branch of statistics that models the
duration of time it takes for an event to occur. Such events can include
outcomes like death, unemployment, or product failure. Most modern machine
learning (ML) algorithms, like decision trees and kernel methods, are supported
for T2E modelling with data science software (python and R). To complement
these developments, SurvSet is the first open-source T2E dataset repository
designed for a rapid benchmarking of ML algorithms and statistical methods. The
data in SurvSet have been consistently formatted so that a single preprocessing
method will work for all datasets. SurvSet currently has 76 datasets which vary
in dimensionality, time dependency, and background (the majority of which come
from biomedicine). SurvSet is available on PyPI and can be installed with pip
install SurvSet. R users can download the data directly from the corresponding
git repository.

Path planning is an essential component of mobile robotics. Classical path
planning algorithms, such as wavefront and rapidly-exploring random tree (RRT)
are used heavily in autonomous robots. With the recent advances in machine
learning, development of learning-based path planning algorithms has been
experiencing rapid growth. An unified path planning interface that facilitates
the development and benchmarking of existing and new algorithms is needed. This
paper presents PathBench, a platform for developing, visualizing, training,
testing, and benchmarking of existing and future, classical and learning-based
path planning algorithms in 2D and 3D grid world environments. Many existing
path planning algorithms are supported; e.g. A*, Dijkstra, waypoint planning
networks, value iteration networks, gated path planning networks; and
integrating new algorithms is easy and clearly specified. The benchmarking
ability of PathBench is explored in this paper by comparing algorithms across
five different hardware systems and three different map types, including
built-in PathBench maps, video game maps, and maps from real world databases.
Metrics, such as path length, success rate, and computational time, were used
to evaluate algorithms. Algorithmic analysis was also performed on a real world
robot to demonstrate PathBench's support for Robot Operating System (ROS).
PathBench is open source.

The problem of human activity recognition from mobile sensor data applies to
multiple domains, such as health monitoring, personal fitness, daily life
logging, and senior care. A critical challenge for training human activity
recognition models is data quality. Acquiring balanced datasets containing
accurate activity labels requires humans to correctly annotate and potentially
interfere with the subjects' normal activities in real-time. Despite the
likelihood of incorrect annotation or lack thereof, there is often an inherent
chronology to human behavior. For example, we take a shower after we exercise.
This implicit chronology can be used to learn unknown labels and classify
future activities. In this work, we propose HAR-GCCN, a deep graph CNN model
that leverages the correlation between chronologically adjacent sensor
measurements to predict the correct labels for unclassified activities that
have at least one activity label. We propose a new training strategy enforcing
that the model predicts the missing activity labels by leveraging the known
ones. HAR-GCCN shows superior performance relative to previously used baseline
methods, improving classification accuracy by about 25% and up to 68% on
different datasets. Code is available at
\url{https://github.com/abduallahmohamed/HAR-GCNN}.

The trajectory surface hopping method has been widely used in the simulation
of charge transport in organic semiconductors. In the present study, we employ
the machine learning (ML) based Hamiltonian to simulate the charge transport in
anthracene and pentacene. The neural network (NN) based models are able to
predict not just site energies and couplings but also the gradients of the site
energy as well as off-diagonal gradients necessary for forces. We train the
models on DFTB-quality data for both anthracene and pentacene. By using the
obtained models in propagation simulations, we evaluate their performance in
reproducing hole mobilities in these materials in terms of both quality and
computational cost. The results show that the charge mobilities obtained using
the NN-based Hamiltonian are in very good agreements with the charge mobilities
computed using the DFTB-based Hamiltonian.

Attaching attributes (such as color, shape, state, action) to object
categories is an important computer vision problem. Attribute prediction has
seen exciting recent progress and is often formulated as a multi-label
classification problem. Yet significant challenges remain in: 1) predicting
diverse attributes over multiple categories, 2) modeling attributes-category
dependency, 3) capturing both global and local scene context, and 4) predicting
attributes of objects with low pixel-count. To address these issues, we propose
a novel multi-category attribute prediction deep architecture named GlideNet,
which contains three distinct feature extractors. A global feature extractor
recognizes what objects are present in a scene, whereas a local one focuses on
the area surrounding the object of interest. Meanwhile, an intrinsic feature
extractor uses an extension of standard convolution dubbed Informed Convolution
to retrieve features of objects with low pixel-count. GlideNet uses gating
mechanisms with binary masks and its self-learned category embedding to combine
the dense embeddings. Collectively, the Global-Local-Intrinsic blocks
comprehend the scene's global context while attending to the characteristics of
the local object of interest. Finally, using the combined features, an
interpreter predicts the attributes, and the length of the output is determined
by the category, thereby removing unnecessary attributes. GlideNet can achieve
compelling results on two recent and challenging datasets -- VAW and CAR -- for
large-scale attribute prediction. For instance, it obtains more than 5\% gain
over state of the art in the mean recall (mR) metric. GlideNet's advantages are
especially apparent when predicting attributes of objects with low pixel counts
as well as attributes that demand global context understanding. Finally, we
show that GlideNet excels in training starved real-world scenarios.

We present Nonparametric Approximation of Inter-Trace returns (NAIT), a
Reinforcement Learning algorithm for discrete action, pixel-based environments
that is both highly sample and computation efficient. NAIT is a lazy-learning
approach with an update that is equivalent to episodic Monte-Carlo on episode
completion, but that allows the stable incorporation of rewards while an
episode is ongoing. We make use of a fixed domain-agnostic representation,
simple distance based exploration and a proximity graph-based lookup to
facilitate extremely fast execution. We empirically evaluate NAIT on both the
26 and 57 game variants of ATARI100k where, despite its simplicity, it achieves
competitive performance in the online setting with greater than 100x speedup in
wall-time.

Research studies of artificial intelligence models in medical imaging have
been hampered by poor generalization. This problem has been especially
concerning over the last year with numerous applications of deep learning for
COVID-19 diagnosis. Virtual imaging trials (VITs) could provide a solution for
objective evaluation of these models. In this work utilizing the VITs, we
created the CVIT-COVID dataset including 180 virtually imaged computed
tomography (CT) images from simulated COVID-19 and normal phantom models under
different COVID-19 morphology and imaging properties. We evaluated the
performance of an open-source, deep-learning model from the University of
Waterloo trained with multi-institutional data and an in-house model trained
with the open clinical dataset called MosMed. We further validated the model's
performance against open clinical data of 305 CT images to understand virtual
vs. real clinical data performance. The open-source model was published with
nearly perfect performance on the original Waterloo dataset but showed a
consistent performance drop in external testing on another clinical dataset
(AUC=0.77) and our simulated CVIT-COVID dataset (AUC=0.55). The in-house model
achieved an AUC of 0.87 while testing on the internal test set (MosMed test
set). However, performance dropped to an AUC of 0.65 and 0.69 when evaluated on
clinical and our simulated CVIT-COVID dataset. The VIT framework offered
control over imaging conditions, allowing us to show there was no change in
performance as CT exposure was changed from 28.5 to 57 mAs. The VIT framework
also provided voxel-level ground truth, revealing that performance of in-house
model was much higher at AUC=0.87 for diffuse COVID-19 infection size >2.65%
lung volume versus AUC=0.52 for focal disease with <2.65% volume. The virtual
imaging framework enabled these uniquely rigorous analyses of model

Knowledge of questions' difficulty level helps a teacher in several ways,
such as estimating students' potential quickly by asking carefully selected
questions and improving quality of examination by modifying trivial and hard
questions. Can we extract such benefits of instance difficulty in NLP? To this
end, we conduct Instance-Level Difficulty Analysis of Evaluation data (ILDAE)
in a large-scale setup of 23 datasets and demonstrate its five novel
applications: 1) conducting efficient-yet-accurate evaluations with fewer
instances saving computational cost and time, 2) improving quality of existing
evaluation datasets by repairing erroneous and trivial instances, 3) selecting
the best model based on application requirements, 4) analyzing dataset
characteristics for guiding future data creation, 5) estimating Out-of-Domain
performance reliably. Comprehensive experiments for these applications result
in several interesting findings, such as evaluation using just 5% instances
(selected via ILDAE) achieves as high as 0.93 Kendall correlation with
evaluation using complete dataset and computing weighted accuracy using
difficulty scores leads to 5.2% higher correlation with Out-of-Domain
performance. We release the difficulty scores and hope our analyses and
findings will bring more attention to this important yet understudied field of
leveraging instance difficulty in evaluations.

Language models (LMs) can reproduce (or amplify) toxic language seen during
training, which poses a risk to their practical application. In this paper, we
conduct extensive experiments to study this phenomenon. We analyze the impact
of prompts, decoding strategies and training corpora on the output toxicity.
Based on our findings, we propose a simple yet effective method for language
models to "detoxify" themselves without an additional large corpus or external
discriminator. Compared to a supervised baseline, our proposed method shows
better toxicity reduction with good generation quality in the generated content
under multiple settings. Warning: some examples shown in the paper may contain
uncensored offensive content.

An early example of the ability of deep networks to improve the statistical
power of data collected in particle physics experiments was the demonstration
that such networks operating on lists of particle momenta (four-vectors) could
outperform shallow networks using features engineered with domain knowledge. A
benchmark case is described, with extensions to parameterized networks. A
discussion of data handling and architecture is presented, as well as a
description of how to incorporate physics knowledge into the network

Estimating the software projects' efforts developed by agile methods is
important for project managers or technical leads. It provides a summary as a
first view of how many hours and developers are required to complete the tasks.
There are research works on automatic predicting the software efforts,
including Term Frequency Inverse Document Frequency (TFIDF) as the traditional
approach for this problem. Graph Neural Network is a new approach that has been
applied in Natural Language Processing for text classification. The advantages
of Graph Neural Network are based on the ability to learn information via graph
data structure, which has more representations such as the relationships
between words compared to approaches of vectorizing sequence of words. In this
paper, we show the potential and possible challenges of Graph Neural Network
text classification in story point level estimation. By the experiments, we
show that the GNN Text Level Classification can achieve as high accuracy as
about 80 percent for story points level classification, which is comparable to
the traditional approach. We also analyze the GNN approach and point out
several current disadvantages that the GNN approach can improve for this
problem or other problems in software engineering.

Meta learning aims at learning a model that can quickly adapt to unseen
tasks. Widely used meta learning methods include model agnostic meta learning
(MAML), implicit MAML, Bayesian MAML. Thanks to its ability of modeling
uncertainty, Bayesian MAML often has advantageous empirical performance.
However, the theoretical understanding of Bayesian MAML is still limited,
especially on questions such as if and when Bayesian MAML has provably better
performance than MAML. In this paper, we aim to provide theoretical
justifications for Bayesian MAML's advantageous performance by comparing the
meta test risks of MAML and Bayesian MAML. In the meta linear regression, under
both the distribution agnostic and linear centroid cases, we have established
that Bayesian MAML indeed has provably lower meta test risks than MAML. We
verify our theoretical results through experiments.

Distributed consensus, the ability to reach agreement in the face of
failures, is a fundamental primitive for constructing reliable distributed
systems. The Paxos algorithm is synonymous with consensus and widely utilized
in production. Paxos uses two phases: phase one and phase two, each requiring a
quorum of acceptors, to reach consensus during a round of the protocol.
Traditionally, Paxos requires that all quorums, regardless of phase or round,
intersect and majorities are often used for this purpose. Flexible Paxos proved
that it is only necessary for phase one quorum of a given round to intersect
with the phase two quorums of all previous rounds.
  In this paper, we re-examine how Paxos approaches the problem of consensus.
We look again at quorum intersection in Flexible Paxos and observe that quorum
intersection can be safely weakened further. Most notably, we observe that if a
proposer learns that a value was proposed in some previous round then its phase
one no longer needs to intersect with the phase two quorums from that round or
from any previous rounds. Furthermore, in order to provide an intuitive
explanation of our results, we propose a novel abstraction for reasoning about
Paxos which utilizes write-once registers.

Best-of-N (BoN) Average Displacement Error (ADE)/ Final Displacement Error
(FDE) is the most used metric for evaluating trajectory prediction models. Yet,
the BoN does not quantify the whole generated samples, resulting in an
incomplete view of the model's prediction quality and performance. We propose a
new metric, Average Mahalanobis Distance (AMD) to tackle this issue. AMD is a
metric that quantifies how close the whole generated samples are to the ground
truth. We also introduce the Average Maximum Eigenvalue (AMV) metric that
quantifies the overall spread of the predictions. Our metrics are validated
empirically by showing that the ADE/FDE is not sensitive to distribution
shifts, giving a biased sense of accuracy, unlike the AMD/AMV metrics. We
introduce the usage of Implicit Maximum Likelihood Estimation (IMLE) as a
replacement for traditional generative models to train our model,
Social-Implicit. IMLE training mechanism aligns with AMD/AMV objective of
predicting trajectories that are close to the ground truth with a tight spread.
Social-Implicit is a memory efficient deep model with only 5.8K parameters that
runs in real time of about 580Hz and achieves competitive results. Interactive
demo of the problem can be seen at
https://www.abduallahmohamed.com/social-implicit-amdamv-adefde-demo . Code is
available at https://github.com/abduallahmohamed/Social-Implicit .

We present a simple and effective approach for posterior uncertainty
quantification in deep operator networks (DeepONets); an emerging paradigm for
supervised learning in function spaces. We adopt a frequentist approach based
on randomized prior ensembles, and put forth an efficient vectorized
implementation for fast parallel inference on accelerated hardware. Through a
collection of representative examples in computational mechanics and climate
modeling, we show that the merits of the proposed approach are fourfold. (1) It
can provide more robust and accurate predictions when compared against
deterministic DeepONets. (2) It shows great capability in providing reliable
uncertainty estimates on scarce data-sets with multi-scale function pairs. (3)
It can effectively detect out-of-distribution and adversarial examples. (4) It
can seamlessly quantify uncertainty due to model bias, as well as noise
corruption in the data. Finally, we provide an optimized JAX library called
{\em UQDeepONet} that can accommodate large model architectures, large ensemble
sizes, as well as large data-sets with excellent parallel performance on
accelerated hardware, thereby enabling uncertainty quantification for DeepONets
in realistic large-scale applications.

We present a systematic study on a new task called dichotomous image
segmentation (DIS) , which aims to segment highly accurate objects from natural
images. To this end, we collected the first large-scale DIS dataset, called
DIS5K, which contains 5,470 high-resolution (e.g., 2K, 4K or larger) images
covering camouflaged, salient, or meticulous objects in various backgrounds.
DIS is annotated with extremely fine-grained labels. Besides, we introduce a
simple intermediate supervision baseline (IS-Net) using both feature-level and
mask-level guidance for DIS model training. IS-Net outperforms various
cutting-edge baselines on the proposed DIS5K, making it a general self-learned
supervision network that can facilitate future research in DIS. Further, we
design a new metric called human correction efforts (HCE) which approximates
the number of mouse clicking operations required to correct the false positives
and false negatives. HCE is utilized to measure the gap between models and
real-world applications and thus can complement existing metrics. Finally, we
conduct the largest-scale benchmark, evaluating 16 representative segmentation
models, providing a more insightful discussion regarding object complexities,
and showing several potential applications (e.g., background removal, art
design, 3D reconstruction). Hoping these efforts can open up promising
directions for both academic and industries. Project page:
https://xuebinqin.github.io/dis/index.html.

An important problem in the field of graph signal processing is developing
appropriate overcomplete dictionaries for signals defined on different families
of graphs. The Cayley graph of the symmetric group has natural applications in
ranked data analysis, as its vertices represent permutations, while the
generating set formalizes a notion of distance between rankings. Taking
advantage of the rich theory of representations of the symmetric group, we
study a particular class of frames, called Frobenius-Schur frames, where every
atom belongs to the coefficient space of only one irreducible representation of
the symmetric group. We provide a characterization for all Frobenius-Schur
frames on the group algebra of the symmetric group which are "compatible" with
respect to the generating set. Such frames have been previously studied for the
permutahedron, the Cayley graph of the symmetric group with the generating set
of adjacent transpositions, and have proved to be capable of producing
meaningful interpretation of the ranked data set via the analysis coefficients.
Our results generalize frame constructions for the permutahedron to any
inverse-closed generating set.

Verifying that input-output relationships of a neural network conform to
prescribed operational specifications is a key enabler towards deploying these
networks in safety-critical applications. Semidefinite programming (SDP)-based
approaches to Rectified Linear Unit (ReLU) network verification transcribe this
problem into an optimization problem, where the accuracy of any such
formulation reflects the level of fidelity in how the neural network
computation is represented, as well as the relaxations of intractable
constraints. While the literature contains much progress on improving the
tightness of SDP formulations while maintaining tractability, comparatively
little work has been devoted to the other extreme, i.e., how to most accurately
capture the original verification problem before SDP relaxation. In this work,
we develop an exact, convex formulation of verification as a completely
positive program (CPP), and provide analysis showing that our formulation is
minimal -- the removal of any constraint fundamentally misrepresents the neural
network computation. We leverage our formulation to provide a unifying view of
existing approaches, and give insight into the source of large relaxation gaps
observed in some cases.

In this paper, we introduce the weighted-average quantile regression
framework, $\int_0^1 q_{Y|X}(u)\psi(u)du = X'\beta$, where $Y$ is a dependent
variable, $X$ is a vector of covariates, $q_{Y|X}$ is the quantile function of
the conditional distribution of $Y$ given $X$, $\psi$ is a weighting function,
and $\beta$ is a vector of parameters. We argue that this framework is of
interest in many applied settings and develop an estimator of the vector of
parameters $\beta$. We show that our estimator is $\sqrt T$-consistent and
asymptotically normal with mean zero and easily estimable covariance matrix,
where $T$ is the size of available sample. We demonstrate the usefulness of our
estimator by applying it in two empirical settings. In the first setting, we
focus on financial data and study the factor structures of the expected
shortfalls of the industry portfolios. In the second setting, we focus on wage
data and study inequality and social welfare dependence on commonly used
individual characteristics.

Multi-microgrid formation (MMGF) is a promising solution to enhance power
system resilience. This paper proposes a new deep reinforcement learning (RL)
based model-free on-line dynamic multi-MG formation (MMGF) scheme. The dynamic
MMGF problem is formulated as a Markov decision process, and a complete deep RL
framework is specially designed for the topology-transformable micro-grids. In
order to reduce the large action space caused by flexible switch operations, a
topology transformation method is proposed and an action-decoupling Q-value is
applied. Then, a CNN based multi-buffer double deep Q-network (CM-DDQN) is
developed to further improve the learning ability of original DQN method. The
proposed deep RL method provides real-time computing to support on-line dynamic
MMGF scheme, and the scheme handles a long-term resilience enhancement problem
using adaptive on-line MMGF to defend changeable conditions. The effectiveness
of the proposed method is validated using a 7-bus system and the IEEE 123-bus
system. The results show strong learning ability, timely response for varying
system conditions and convincing resilience enhancement.

What audio embedding approach generalizes best to a wide range of downstream
tasks across a variety of everyday domains without fine-tuning? The aim of the
HEAR benchmark is to develop a general-purpose audio representation that
provides a strong basis for learning in a wide variety of tasks and scenarios.
HEAR evaluates audio representations using a benchmark suite across a variety
of domains, including speech, environmental sound, and music. HEAR was launched
as a NeurIPS 2021 shared challenge. In the spirit of shared exchange, each
participant submitted an audio embedding model following a common API that is
general-purpose, open-source, and freely available to use. Twenty-nine models
by thirteen external teams were evaluated on nineteen diverse downstream tasks
derived from sixteen datasets. Open evaluation code, submitted models and
datasets are key contributions, enabling comprehensive and reproducible
evaluation, as well as previously impossible longitudinal studies. It still
remains an open question whether one single general-purpose audio
representation can perform as holistically as the human ear.

In a warehouse environment, tasks appear dynamically. Consequently, a task
management system that matches them with the workforce too early (e.g., weeks
in advance) is necessarily sub-optimal. Also, the rapidly increasing size of
the action space of such a system consists of a significant problem for
traditional schedulers. Reinforcement learning, however, is suited to deal with
issues requiring making sequential decisions towards a long-term, often remote,
goal. In this work, we set ourselves on a problem that presents itself with a
hierarchical structure: the task-scheduling, by a centralised agent, in a
dynamic warehouse multi-agent environment and the execution of one such
schedule, by decentralised agents with only partial observability thereof. We
propose to use deep reinforcement learning to solve both the high-level
scheduling problem and the low-level multi-agent problem of schedule execution.
Finally, we also conceive the case where centralisation is impossible at test
time and workers must learn how to cooperate in executing the tasks in an
environment with no schedule and only partial observability.

Delicate industrial insertion tasks (e.g., PC board assembly) remain
challenging for industrial robots. The challenges include low error tolerance,
delicacy of the components, and large task variations with respect to the
components to be inserted. To deliver a feasible robotic solution for these
insertion tasks, we also need to account for hardware limits of existing
robotic systems and minimize the integration effort. This paper proposes a
composable framework for efficient integration of a safe insertion policy on
existing robotic platforms to accomplish these insertion tasks. The policy has
an interpretable modularized design and can be learned efficiently on hardware
and transferred to new tasks easily. In particular, the policy includes a safe
insertion agent as a baseline policy for insertion, an optimal configurable
Cartesian tracker as an interface to robot hardware, a probabilistic inference
module to handle component variety and insertion errors, and a safe learning
module to optimize the parameters in the aforementioned modules to achieve the
best performance on designated hardware. The experiment results on a UR10 robot
show that the proposed framework achieves safety (for the delicacy of
components), accuracy (for low tolerance), robustness (against perception error
and component defection), adaptability and transferability (for task
variations), as well as task efficiency during execution plus data and time
efficiency during learning.

To statistically study the variability and differences between normal and
abnormal brain connectomes, a mathematical model of the neural connections is
required. In this paper, we represent the brain connectome as a Riemannian
manifold, which allows us to model neural connections as geodesics. We show for
the first time how one can leverage deep neural networks to estimate a
Riemannian metric of the brain that can accommodate fiber crossings and is a
natural modeling tool to infer the shape of the brain from DWMRI. Our method
achieves excellent performance in geodesic-white-matter-pathway alignment and
tackles the long-standing issue in previous methods: the inability to recover
the crossing fibers with high fidelity.

In the \emph{monitoring} problem, the input is an unbounded stream
$P={p_1,p_2\cdots}$ of integers in $[N]:=\{1,\cdots,N\}$, that are obtained
from a sensor (such as GPS or heart beats of a human). The goal (e.g., for
anomaly detection) is to approximate the $n$ points received so far in $P$ by a
single frequency $\sin$, e.g. $\min_{c\in C}cost(P,c)+\lambda(c)$, where
$cost(P,c)=\sum_{i=1}^n \sin^2(\frac{2\pi}{N} p_ic)$, $C\subseteq [N]$ is a
feasible set of solutions, and $\lambda$ is a given regularization function.
For any approximation error $\varepsilon>0$, we prove that \emph{every} set $P$
of $n$ integers has a weighted subset $S\subseteq P$ (sometimes called
core-set) of cardinality $|S|\in O(\log(N)^{O(1)})$ that approximates
$cost(P,c)$ (for every $c\in [N]$) up to a multiplicative factor of
$1\pm\varepsilon$. Using known coreset techniques, this implies streaming
algorithms using only $O((\log(N)\log(n))^{O(1)})$ memory. Our results hold for
a large family of functions. Experimental results and open source code are
provided.

For image restoration, the majority of existing deep learning-based
algorithms have a tendency to overfit the training data, resulting in poor
performance when confronted with unseen degradations. To achieve more robust
restoration, generative adversarial network (GAN) prior based methods have been
proposed, demonstrating a promising capacity to restore photo-realistic and
high-quality results. However, these methods are susceptible to semantic
ambiguity, particularly with semantically relevant images such as facial
images. In this paper, we propose a semantic-aware latent space exploration
method for image restoration (SAIR). By explicitly modeling referenced
semantics information, SAIR is able to reliably restore severely degraded
images not only to high-resolution highly-realistic looks but also to correct
semantics. Quantitative and qualitative experiments collectively demonstrate
the effectiveness of the proposed SAIR. Our code can be found in
https://github.com/Liamkuo/SAIR.

This paper is an introductory discussion on the cause of open source software
vulnerabilities, their importance in the cybersecurity ecosystem, and a
selection of detection methods. A recent application security report showed 44%
of applications contain critical vulnerabilities in an open source component, a
concerning proportion. Most companies do not have a reliable way of being
directly and promptly notified when zero-day vulnerabilities are found and then
when patches are made available. This means attack vectors in open source exist
longer than necessary. Conventional approaches to vulnerability detection are
outlined alongside some newer research trends. A conclusion is made that it may
not be possible to entirely replace expert human inspection of open source
software, although it can be effectively augmented with techniques such as
machine learning, IDE plug-ins and repository linking to make implementation
and review less time intensive. Underpinning any technological advances should
be better knowledge at the human level. Development teams need trained, coached
and improved so they can implement open source more securely, know what
vulnerabilities to look for and how to handle them. It is the use of this
blended approach to detection which is key.

We introduce a method for pricing consumer credit using recent advances in
offline deep reinforcement learning. This approach relies on a static dataset
and requires no assumptions on the functional form of demand. Using both real
and synthetic data on consumer credit applications, we demonstrate that our
approach using the conservative Q-Learning algorithm is capable of learning an
effective personalized pricing policy without any online interaction or price

We investigate the optimal selection of weight windows for the problem of
weighted least squares. We show that weight windows should be symmetric around
its center, which is also its peak. We consider the class of tapered rectangle
window weights, which are nonincreasing away from the center. We show that the
best rectangle window is optimal for such window definitions. We also extend
our results to the least absolutes and more general case of arbitrary loss
functions to find similar results.

In recent years, robots are used in an increasing variety of tasks,
especially by small- and medium- sized enterprises. These tasks are usually
fast-changing, they have a collaborative scenario and happen in unpredictable
environments with possible ambiguities. It is important to have methods capable
of generating robot programs easily, that are made as general as possible by
handling uncertainties. We present a system that integrates a method to learn
Behavior Trees (BTs) from demonstration for pick and place tasks, with a
framework that uses verbal interaction to ask follow-up clarification questions
to resolve ambiguities. During the execution of a task, the system asks for
user input when there is need to disambiguate an object in the scene, when the
targets of the task are objects of a same type that are present in multiple
instances. The integrated system is demonstrated on different scenarios of a
pick and place task, with increasing level of ambiguities. The code used for
this paper is made publicly available.

Visual dialog is a vision-language task where an agent needs to answer a
series of questions grounded in an image based on the understanding of the
dialog history and the image. The occurrences of coreference relations in the
dialog makes it a more challenging task than visual question-answering. Most
previous works have focused on learning better multi-modal representations or
on exploring different ways of fusing visual and language features, while the
coreferences in the dialog are mainly ignored. In this paper, based on
linguistic knowledge and discourse features of human dialog we propose two soft
constraints that can improve the model's ability of resolving coreferences in
dialog in an unsupervised way. Experimental results on the VisDial v1.0 dataset
shows that our model, which integrates two novel and linguistically inspired
soft constraints in a deep transformer neural architecture, obtains new
state-of-the-art performance in terms of recall at 1 and other evaluation
metrics compared to current existing models and this without pretraining on
other vision-language datasets. Our qualitative results also demonstrate the
effectiveness of the method that we propose.

Knowledge-based visual question answering (VQA) is a vision-language task
that requires an agent to correctly answer image-related questions using
knowledge that is not presented in the given image. It is not only a more
challenging task than regular VQA but also a vital step towards building a
general VQA system. Most existing knowledge-based VQA systems process knowledge
and image information similarly and ignore the fact that the knowledge base
(KB) contains complete information about a triplet, while the extracted image
information might be incomplete as the relations between two objects are
missing or wrongly detected. In this paper, we propose a novel model named
dynamic knowledge memory enhanced multi-step graph reasoning (DMMGR), which
performs explicit and implicit reasoning over a key-value knowledge memory
module and a spatial-aware image graph, respectively. Specifically, the memory
module learns a dynamic knowledge representation and generates a
knowledge-aware question representation at each reasoning step. Then, this
representation is used to guide a graph attention operator over the
spatial-aware image graph. Our model achieves new state-of-the-art accuracy on
the KRVQR and FVQA datasets. We also conduct ablation experiments to prove the
effectiveness of each component of the proposed model.

Network slicing is a critical driver for guaranteeing the diverse service
level agreements (SLA) in 5G and future networks. Recently, deep reinforcement
learning (DRL) has been widely utilized for resource allocation in network
slicing. However, existing related works do not consider the performance loss
associated with the initial exploration phase of DRL. This paper proposes a new
performance-guaranteed slicing strategy with a soft and hard hybrid slicing
setting. Mainly, a common slice setting is applied to guarantee slices' SLA
when training the neural network. Moreover, the resource of the common slice
tends to precisely redistribute to slices with the training of DRL until it
converges. Furthermore, experiment results confirm the effectiveness of our
proposed slicing framework: the slices' SLA of the training phase can be
guaranteed, and the proposed algorithm can achieve the near-optimal performance
in terms of the SLA satisfaction ratio, isolation degree and spectrum
maximization after convergence.

Given a classical query algorithm as a decision tree, when does there exist a
quantum query algorithm with a speed-up over the classical one? We provide a
general construction based on the structure of the underlying decision tree,
and prove that this can give us an up-to-quadratic quantum speed-up. In
particular, we obtain a bounded-error quantum query algorithm of cost
$O(\sqrt{s})$ to compute a Boolean function (more generally, a relation) that
can be computed by a classical (even randomized) decision tree of size $s$.
  Lin and Lin [ToC'16] and Beigi and Taghavi [Quantum'20] showed results of a
similar flavor, and gave upper bounds in terms of a quantity which we call the
"guessing complexity" of a decision tree. We identify that the guessing
complexity of a decision tree equals its rank, a notion introduced by
Ehrenfeucht and Haussler [Inf. Comp.'89] in the context of learning theory.
This answers a question posed by Lin and Lin, who asked whether the guessing
complexity of a decision tree is related to any complexity-theoretic measure.
We also show a polynomial separation between rank and randomized rank for the
complete binary AND-OR tree.
  Beigi and Taghavi constructed span programs and dual adversary solutions for
Boolean functions given classical decision trees computing them and an
assignment of non-negative weights to its edges. We explore the effect of
changing these weights on the resulting span program complexity and objective
value of the dual adversary bound, and capture the best possible weighting
scheme by an optimization program. We exhibit a solution to this program and
argue its optimality from first principles. We also exhibit decision trees for
which our bounds are asymptotically stronger than those of Lin and Lin, and
Beigi and Taghavi. This answers a question of Beigi and Taghavi, who asked
whether different weighting schemes could yield better upper bounds.

Speech cloning technology is becoming more sophisticated thanks to the
advances in machine learning. Researchers have successfully implemented
natural-sounding English speech synthesis and good English speech cloning by
some effective models. However, because of prosodic phrasing and large
character set of Mandarin, Chinese utilization of these models is not yet
complete. By creating a new dataset and replacing Tacotron synthesizer with
VAENAR-TTS, we improved the existing speech cloning technique CV2TTS to almost
real-time speech cloning while guaranteeing synthesis quality. In the process,
we customized the subjective tests of synthesis quality assessment by attaching
various scenarios, so that subjects focus on the differences between voice and
our improvements maybe were more advantageous to practical applications. The
results of the A/B test, real-time factor (RTF) and 2.74 mean opinion score
(MOS) in terms of naturalness and similarity, reflect the real-time
high-quality Mandarin speech cloning we achieved.

Temporal sentence grounding aims to localize a target segment in an untrimmed
video semantically according to a given sentence query. Most previous works
focus on learning frame-level features of each whole frame in the entire video,
and directly match them with the textual information. Such frame-level feature
extraction leads to the obstacles of these methods in distinguishing ambiguous
video frames with complicated contents and subtle appearance differences, thus
limiting their performance. In order to differentiate fine-grained appearance
similarities among consecutive frames, some state-of-the-art methods
additionally employ a detection model like Faster R-CNN to obtain detailed
object-level features in each frame for filtering out the redundant background
contents. However, these methods suffer from missing motion analysis since the
object detection module in Faster R-CNN lacks temporal modeling. To alleviate
the above limitations, in this paper, we propose a novel Motion- and
Appearance-guided 3D Semantic Reasoning Network (MA3SRN), which incorporates
optical-flow-guided motion-aware, detection-based appearance-aware, and
3D-aware object-level features to better reason the spatial-temporal object
relations for accurately modelling the activity among consecutive frames.
Specifically, we first develop three individual branches for motion,
appearance, and 3D encoding separately to learn fine-grained motion-guided,
appearance-guided, and 3D-aware object features, respectively. Then, both
motion and appearance information from corresponding branches are associated to
enhance the 3D-aware features for the final precise grounding. Extensive
experiments on three challenging datasets (ActivityNet Caption, Charades-STA
and TACoS) demonstrate that the proposed MA3SRN model achieves a new

One of the most contested questions about human behaviour is whether there
are inherent sex or gender differences in the formation and maintenance of
social bonds. On one hand, female and male brains are structurally almost
identical, and while there are sex differences in the endocrine system, these
are small, while much of gendered identity and behaviour is learned. On the
other hand, sex differences in some aspects of social behaviour have deep
evolutionary roots, and are widely present in non-human animals. This
observational study recorded the frequency of same-aged, adult human groups
appearing in public spaces through 2636 hours, recording group formation by
1.2mn people via 170 research assistants in 46 countries across the world. The
results show (a) a significant sex-gender difference in same-sex-same-age
frequency, in that ~50% more female-female than male-male pairs are observed in
public spaces globally, and (b) that despite regional variation, the patterns
holds up in every global region. This is the first study of sex-gender
difference in dyadic social behaviour across the world on this scale, and the
first global study that uses direct rather than internet-based observations.

Telepresence and teleoperation robotics have attracted a great amount of
attention in the last 10 years. With the Artificial Intelligence (AI)
revolution already being started, we can see a wide range of robotic
applications being realized. Intelligent robotic systems are being deployed
both in industrial and domestic environments. Telepresence is the idea of being
present in a remote location virtually or via robotic avatars. Similarly, the
idea of operating a robot from a remote location for various tasks is called
teleoperation. These technologies find significant application in health care,
education, surveillance, disaster recovery, and corporate/government sectors.
But question still remains about their maturity, security and safety levels. We
also need to think about enhancing the user experience and trust in such
technologies going into the next generation of computing.

Legislation and public sentiment throughout the world have promoted fairness
metrics, explainability, and interpretability as prescriptions for the
responsible development of ethical artificial intelligence systems. Despite the
importance of these three pillars in the foundation of the field, they can be
challenging to operationalize and attempts to solve the problems in production
environments often feel Sisyphean. This difficulty stems from a number of
factors: fairness metrics are computationally difficult to incorporate into
training and rarely alleviate all of the harms perpetrated by these systems.
Interpretability and explainability can be gamed to appear fair, may
inadvertently reduce the privacy of personal information contained in training
data, and increase user confidence in predictions -- even when the explanations
are wrong. In this work, we propose a framework for responsibly developing
artificial intelligence systems by incorporating lessons from the field of
information security and the secure development lifecycle to overcome
challenges associated with protecting users in adversarial settings. In
particular, we propose leveraging the concepts of threat modeling, design
review, penetration testing, and incident response in the context of developing
AI systems as ways to resolve shortcomings in the aforementioned methods.

Over the last years, the transportation community has witnessed a tremendous
amount of research contributions on new deep learning approaches for
spatio-temporal forecasting. These contributions tend to emphasize the modeling
of spatial correlations, while neglecting the fairly stable and recurrent
nature of human mobility patterns. In this short paper, we show that a naive
baseline method based on the average weekly pattern and linear regression can
achieve comparable results to many state-of-the-art deep learning approaches
for spatio-temporal forecasting in transportation, or even outperform them on
several datasets, thus contrasting the importance of stationarity and recurrent
patterns in the data with the importance of spatial correlations. Furthermore,
we establish 9 different reference benchmarks that can be used to compare new
approaches for spatio-temporal forecasting, and provide a discussion on best
practices and the direction that the field is taking.

Token-level adaptive training approaches can alleviate the token imbalance
problem and thus improve neural machine translation, through re-weighting the
losses of different target tokens based on specific statistical metrics (e.g.,
token frequency or mutual information). Given that standard translation models
make predictions on the condition of previous target contexts, we argue that
the above statistical metrics ignore target context information and may assign
inappropriate weights to target tokens. While one possible solution is to
directly take target contexts into these statistical metrics, the
target-context-aware statistical computing is extremely expensive, and the
corresponding storage overhead is unrealistic. To solve the above issues, we
propose a target-context-aware metric, named conditional bilingual mutual
information (CBMI), which makes it feasible to supplement target context
information for statistical metrics. Particularly, our CBMI can be formalized
as the log quotient of the translation model probability and language model
probability by decomposing the conditional joint distribution. Thus CBMI can be
efficiently calculated during model training without any pre-specific
statistical calculations and large storage overhead. Furthermore, we propose an
effective adaptive training approach based on both the token- and
sentence-level CBMI. Experimental results on WMT14 English-German and WMT19
Chinese-English tasks show our approach can significantly outperform the
Transformer baseline and other related methods.

We present a user-friendly and versatile tool for laser frequency
stabilization. Its main focus is spectroscopy locking, but the software is
suitable for lock-in techniques in general as well as bare PID operation.
Besides allowing for sinusoidal modulation (up to 50 MHz), triangular ramp
scanning, IQ demodulation (1 $f$ to 5 $f$), IIR and PID filtering, Linien
features two different algorithms for automatic lock point selection; one of
them performs time-critical tasks completely on FPGA. Linien is capable of
autonomously optimizing spectroscopy parameters by means of machine learning
and can measure the error signal's power spectral density. The software is
built in a modular way, providing both a graphical user interface as well as a
Python scripting interface. It is based on the RedPitaya STEMLab platform but
may be ported to different systems.

The study of multi-task learning has drawn great attention from the
community. Despite the remarkable progress, the challenge of optimally learning
different tasks simultaneously remains to be explored. Previous works attempt
to modify the gradients from different tasks. Yet these methods give a
subjective assumption of the relationship between tasks, and the modified
gradient may be less accurate. In this paper, we introduce Stochastic Task
Allocation~(STA), a mechanism that addresses this issue by a task allocation
approach, in which each sample is randomly allocated a subset of tasks. For
further progress, we propose Interleaved Stochastic Task Allocation~(ISTA) to
iteratively allocate all tasks to each example during several consecutive
iterations. We evaluate STA and ISTA on various datasets and applications:
NYUv2, Cityscapes, and COCO for scene understanding and instance segmentation.
Our experiments show both STA and ISTA outperform current state-of-the-art
methods. The code will be available.

Automatic detection of parasitic eggs in microscopy images has the potential
to increase the efficiency of human experts whilst also providing an objective
assessment. The time saved by such a process would both help ensure a prompt
treatment to patients, and off-load excessive work from experts' shoulders.
Advances in deep learning inspired us to exploit successful architectures for
detection, adapting them to tackle a different domain. We propose a framework
that exploits two such state-of-the-art models. Specifically, we demonstrate
results produced by both a Generative Adversarial Network (GAN) and
Faster-RCNN, for image enhancement and object detection respectively, on
microscopy images of varying quality. The use of these techniques yields
encouraging results, though further improvements are still needed for certain
egg types whose detection still proves challenging. As a result, a new dataset
has been created and made publicly available, providing an even wider range of
classes and variability.

The multi-scale nature of gaseous flows poses tremendous difficulties for
theoretical and numerical analysis. The Boltzmann equation, while possessing a
wider applicability than hydrodynamic equations, requires significantly more
computational resources due to the increased degrees of freedom in the model.
The success of a hybrid fluid-kinetic flow solver for the study of multi-scale
flows relies on accurate prediction of flow regimes. In this paper, we draw on
binary classification in machine learning and propose the first neural network
classifier to detect near-equilibrium and non-equilibrium flow regimes based on
local flow conditions. Compared with classical semi-empirical criteria of
continuum breakdown, the current method provides a data-driven alternative
where the parameterized implicit function is trained by solutions of the
Boltzmann equation. The ground-truth labels are derived rigorously from the
deviation of particle distribution functions and the approximations based on
the Chapman-Enskog ansatz. Therefore, no tunable parameter is needed in the
criterion. Following the entropy closure of the Boltzmann moment system, a data
generation strategy is developed to produce training and test sets. Numerical
analysis shows its superiority over simulation-based samplings. A hybrid
Boltzmann-Navier-Stokes flow solver is built correspondingly with adaptive
partition of local flow regimes. Numerical experiments including
one-dimensional Riemann problem, shear flow layer and hypersonic flow around
circular cylinder are presented to validate the current scheme for simulating
cross-scale and non-equilibrium flow physics. The quantitative comparison with
a semi-empirical criterion and benchmark results demonstrates the capability of
the current neural classifier to accurately predict continuum breakdown.

Huge volumes of patient queries are daily generated on online health forums,
rendering manual doctor allocation a labor-intensive task. To better help
patients, this paper studies a novel task of doctor recommendation to enable
automatic pairing of a patient to a doctor with relevant expertise. While most
prior work in recommendation focuses on modeling target users from their past
behavior, we can only rely on the limited words in a query to infer a patient's
needs for privacy reasons. For doctor modeling, we study the joint effects of
their profiles and previous dialogues with other patients and explore their
interactions via self-learning. The learned doctor embeddings are further
employed to estimate their capabilities of handling a patient query with a
multi-head attention mechanism. For experiments, a large-scale dataset is
collected from Chunyu Yisheng, a Chinese online health forum, where our model
exhibits the state-of-the-art results, outperforming baselines only consider
profiles and past dialogues to characterize a doctor.

The challenge of interpreting predictions from deep neural networks has
prompted the development of numerous interpretability methods. Many of
interpretability methods attempt to quantify the importance of input features
with respect to the class probabilities, and are called importance estimators
or saliency maps. A popular approach to evaluate such interpretability methods
is to perturb input features deemed important for predictions and observe the
decrease in accuracy. However, perturbation-based evaluation methods may
confound the sources of accuracy degradation. We conduct computational
experiments that allow to empirically estimate the $\textit{fidelity}$ of
interpretability methods and the contribution of perturbation artifacts. All
considered importance estimators clearly outperform a random baseline, which
contradicts the findings of ROAR [arXiv:1806.10758]. We further compare our
results to the crop-and-resize evaluation framework [arXiv:1705.07857], which
are largely in agreement. Our study suggests that we can estimate the impact of
artifacts and thus empirically evaluate interpretability methods without

Developing smart software services requires both Software Engineering and
Artificial Intelligence (AI) skills. AI practitioners, such as data scientists
often focus on the AI side, for example, creating and training Machine Learning
(ML) models given a specific use case and data. They are typically not
concerned with the entire software development life-cycle, architectural
decisions for the system and performance issues beyond the predictive ML models
(e.g., regarding the security, privacy, throughput, scalability, availability,
as well as ethical, legal and regulatory compliance). In this manuscript, we
propose a novel approach to enable Model-Driven Software Engineering and
Model-Driven AI Engineering. In particular, we support Automated ML, thus
assisting software engineers without deep AI knowledge in developing
AI-intensive systems by choosing the most appropriate ML model, algorithm and
techniques with suitable hyper-parameters for the task at hand. To validate our
work, we carry out a case study in the smart energy domain.

Predicting molecular conformations from molecular graphs is a fundamental
problem in cheminformatics and drug discovery. Recently, significant progress
has been achieved with machine learning approaches, especially with deep
generative models. Inspired by the diffusion process in classical
non-equilibrium thermodynamics where heated particles will diffuse from
original states to a noise distribution, in this paper, we propose a novel
generative model named GeoDiff for molecular conformation prediction. GeoDiff
treats each atom as a particle and learns to directly reverse the diffusion
process (i.e., transforming from a noise distribution to stable conformations)
as a Markov chain. Modeling such a generation process is however very
challenging as the likelihood of conformations should be roto-translational
invariant. We theoretically show that Markov chains evolving with equivariant
Markov kernels can induce an invariant distribution by design, and further
propose building blocks for the Markov kernels to preserve the desirable
equivariance property. The whole framework can be efficiently trained in an
end-to-end fashion by optimizing a weighted variational lower bound to the
(conditional) likelihood. Experiments on multiple benchmarks show that GeoDiff
is superior or comparable to existing state-of-the-art approaches, especially
on large molecules.

Pan-sharpening aims at producing a high-resolution (HR) multi-spectral (MS)
image from a low-resolution (LR) multi-spectral (MS) image and its
corresponding panchromatic (PAN) image acquired by a same satellite. Inspired
by a new fashion in recent deep learning community, we propose a novel
Transformer based model for pan-sharpening. We explore the potential of
Transformer in image feature extraction and fusion. Following the successful
development of vision transformers, we design a two-stream network with the
self-attention to extract the modality-specific features from the PAN and MS
modalities and apply a cross-attention module to merge the spectral and spatial
features. The pan-sharpened image is produced from the enhanced fused features.
Extensive experiments on GaoFen-2 and WorldView-3 images demonstrate that our
Transformer based model achieves impressive results and outperforms many
existing CNN based methods, which shows the great potential of introducing
Transformer to the pan-sharpening task. Codes are available at
https://github.com/zhysora/PanFormer.

Pose Guided Person Image Generation (PGPIG) is the task of transforming a
person image from the source pose to a given target pose. Most of the existing
methods only focus on the ill-posed source-to-target task and fail to capture
reasonable texture mapping. To address this problem, we propose a novel
Dual-task Pose Transformer Network (DPTN), which introduces an auxiliary task
(i.e., source-to-source task) and exploits the dual-task correlation to promote
the performance of PGPIG. The DPTN is of a Siamese structure, containing a
source-to-source self-reconstruction branch, and a transformation branch for
source-to-target generation. By sharing partial weights between them, the
knowledge learned by the source-to-source task can effectively assist the
source-to-target learning. Furthermore, we bridge the two branches with a
proposed Pose Transformer Module (PTM) to adaptively explore the correlation
between features from dual tasks. Such correlation can establish the
fine-grained mapping of all the pixels between the sources and the targets, and
promote the source texture transmission to enhance the details of the generated
target images. Extensive experiments show that our DPTN outperforms
state-of-the-arts in terms of both PSNR and LPIPS. In addition, our DPTN only
contains 9.79 million parameters, which is significantly smaller than other
approaches. Our code is available at:
https://github.com/PangzeCheung/Dual-task-Pose-Transformer-Network.

Existing domain adaptation (DA) usually assumes the domain shift comes from
either the covariates or the labels. However, in real-world applications,
samples selected from different domains could have biases in both the
covariates and the labels. In this paper, we propose a new assumption,
Factorizable Joint Shift (FJS), to handle the co-existence of sampling bias in
covariates and labels. Although allowing for the shift from both sides, FJS
assumes the independence of the bias between the two factors. We provide
theoretical and empirical understandings about when FJS degenerates to prior
assumptions and when it is necessary. We further propose Joint Importance
Aligning (JIA), a discriminative learning objective to obtain joint importance
estimators for both supervised and unsupervised domain adaptation. Our method
can be seamlessly incorporated with existing domain adaptation algorithms for
better importance estimation and weighting on the training data. Experiments on
a synthetic dataset demonstrate the advantage of our method.

Chromosomes exhibit non-rigid and non-articulated nature with varying degrees
of curvature. Chromosome straightening is an essential step for subsequent
karyotype construction, pathological diagnosis and cytogenetic map development.
However, robust chromosome straightening remains challenging, due to the
unavailability of training images, distorted chromosome details and shapes
after straightening, as well as poor generalization capability. We propose a
novel architecture, ViT-Patch GAN, consisting of a motion transformation
generator and a Vision Transformer-based patch (ViT-Patch) discriminator. The
generator learns the motion representation of chromosomes for straightening.
With the help of the ViT-Patch discriminator, the straightened chromosomes
retain more shape and banding pattern details. The proposed framework is
trained on a small dataset and is able to straighten chromosome images with
state-of-the-art performance for two large datasets.

Multi-agent settings remain a fundamental challenge in the reinforcement
learning (RL) domain due to the partial observability and the lack of accurate
real-time interactions across agents. In this paper, we propose a new method
based on local communication learning to tackle the multi-agent RL (MARL)
challenge within a large number of agents coexisting. First, we design a new
communication protocol that exploits the ability of depthwise convolution to
efficiently extract local relations and learn local communication between
neighboring agents. To facilitate multi-agent coordination, we explicitly learn
the effect of joint actions by taking the policies of neighboring agents as
inputs. Second, we introduce the mean-field approximation into our method to
reduce the scale of agent interactions. To more effectively coordinate
behaviors of neighboring agents, we enhance the mean-field approximation by a
supervised policy rectification network (PRN) for rectifying real-time agent
interactions and by a learnable compensation term for correcting the
approximation bias. The proposed method enables efficient coordination as well
as outperforms several baseline approaches on the adaptive traffic signal
control (ATSC) task and the StarCraft II multi-agent challenge (SMAC).

Multi-document summarization (MDS) has made significant progress in recent
years, in part facilitated by the availability of new, dedicated datasets and
capacious language models. However, a standing limitation of these models is
that they are trained against limited references and with plain
maximum-likelihood objectives. As for many other generative tasks,
reinforcement learning (RL) offers the potential to improve the training of MDS
models; yet, it requires a carefully-designed reward that can ensure
appropriate leverage of both the reference summaries and the input documents.
For this reason, in this paper we propose fine-tuning an MDS baseline with a
reward that balances a reference-based metric such as ROUGE with coverage of
the input documents. To implement the approach, we utilize RELAX (Grathwohl et
al., 2018), a contemporary gradient estimator which is both low-variance and
unbiased, and we fine-tune the baseline in a few-shot style for both stability
and computational efficiency. Experimental results over the Multi-News and WCEP
MDS datasets show significant improvements of up to +0.95 pp average ROUGE
score and +3.17 pp METEOR score over the baseline, and competitive results with
the literature. In addition, they show that the coverage of the input documents
is increased, and evenly across all documents.

This paper presents the watch-from-sky framework, where multiple unmanned
aerial vehicles (UAVs) play four roles, i.e., sensing, data forwarding,
computing, and patrolling, for predictive police surveillance. Our framework is
promising for crime deterrence because UAVs are useful for collecting and
distributing data and have high mobility. Our framework relies on machine
learning (ML) technology for controlling and dispatching UAVs and predicting
crimes. This paper compares the conceptual model of our framework against the
literature. It also reports a simulation of UAV dispatching using reinforcement
learning and distributed ML inference over a lossy UAV network.

This paper proposes a new transformer-based framework to learn class-specific
object localization maps as pseudo labels for weakly supervised semantic
segmentation (WSSS). Inspired by the fact that the attended regions of the
one-class token in the standard vision transformer can be leveraged to form a
class-agnostic localization map, we investigate if the transformer model can
also effectively capture class-specific attention for more discriminative
object localization by learning multiple class tokens within the transformer.
To this end, we propose a Multi-class Token Transformer, termed as MCTformer,
which uses multiple class tokens to learn interactions between the class tokens
and the patch tokens. The proposed MCTformer can successfully produce
class-discriminative object localization maps from class-to-patch attentions
corresponding to different class tokens. We also propose to use a patch-level
pairwise affinity, which is extracted from the patch-to-patch transformer
attention, to further refine the localization maps. Moreover, the proposed
framework is shown to fully complement the Class Activation Mapping (CAM)
method, leading to remarkably superior WSSS results on the PASCAL VOC and MS
COCO datasets. These results underline the importance of the class token for
WSSS.

Label smoothing and vocabulary sharing are two widely used techniques in
neural machine translation models. However, we argue that simply applying both
techniques can be conflicting and even leads to sub-optimal performance. When
allocating smoothed probability, original label smoothing treats the
source-side words that would never appear in the target language equally to the
real target-side words, which could bias the translation model. To address this
issue, we propose Masked Label Smoothing (MLS), a new mechanism that masks the
soft label probability of source-side words to zero. Simple yet effective, MLS
manages to better integrate label smoothing with vocabulary sharing. Our
extensive experiments show that MLS consistently yields improvement over
original label smoothing on different datasets, including bilingual and
multilingual translation from both translation quality and model's calibration.
Our code is released at https://github.com/PKUnlp-icler/MLS

In this work, we tackle the challenging problem of category-level object pose
and size estimation from a single depth image. Although previous
fully-supervised works have demonstrated promising performance, collecting
ground-truth pose labels is generally time-consuming and labor-intensive.
Instead, we propose a label-free method that learns to enforce the geometric
consistency between category template mesh and observed object point cloud
under a self-supervision manner. Specifically, our method consists of three key
components: differentiable shape deformation, registration, and rendering. In
particular, shape deformation and registration are applied to the template mesh
to eliminate the differences in shape, pose and scale. A differentiable
renderer is then deployed to enforce geometric consistency between point clouds
lifted from the rendered depth and the observed scene for self-supervision. We
evaluate our approach on real-world datasets and find that our approach
outperforms the simple traditional baseline by large margins while being
competitive with some fully-supervised approaches.

This paper surveys the trend of leveraging machine learning to solve mixed
integer programming (MIP) problems. Theoretically, MIP is an NP-hard problem,
and most of the combinatorial optimization (CO) problems can be formulated as
the MIP. Like other CO problems, the human-designed heuristic algorithms for
MIP rely on good initial solutions and cost a lot of computational resources.
Therefore, we consider applying machine learning methods to solve MIP, since
ML-enhanced approaches can provide the solution based on the typical patterns
from the historical data. In this paper, we first introduce the formulation and
preliminaries of MIP and several traditional algorithms to solve MIP. Then, we
advocate further promoting the different integration of machine learning and
MIP and introducing related learning-based methods, which can be classified
into exact algorithms and heuristic algorithms. Finally, we propose the outlook
for learning-based MIP solvers, direction towards more combinatorial
optimization problems beyond MIP, and also the mutual embrace of traditional
solvers and machine learning components.

Communication is a hallmark of intelligence. In this work, we present MIRROR,
an approach to (i) quickly learn human models from human demonstrations, and
(ii) use the models for subsequent communication planning in assistive
shared-control settings. MIRROR is inspired by social projection theory, which
hypothesizes that humans use self-models to understand others. Likewise, MIRROR
leverages self-models learned using reinforcement learning to bootstrap human
modeling. Experiments with simulated humans show that this approach leads to
rapid learning and more robust models compared to existing behavioral cloning
and state-of-the-art imitation learning methods. We also present a
human-subject study using the CARLA simulator which shows that (i) MIRROR is
able to scale to complex domains with high-dimensional observations and
complicated world physics and (ii) provides effective assistive communication
that enabled participants to drive more safely in adverse weather conditions.

Diffusion maps (DM) constitute a classic dimension reduction technique, for
data lying on or close to a (relatively) low-dimensional manifold embedded in a
much larger dimensional space. The DM procedure consists in constructing a
spectral parametrization for the manifold from simulated random walks or
diffusion paths on the data set. However, DM is hard to tune in practice. In
particular, the task to set a diffusion time t when constructing the diffusion
kernel matrix is critical. We address this problem by using the semigroup
property of the diffusion operator. We propose a semigroup criterion for
picking t. Experiments show that this principled approach is effective and
robust.

In this paper, we propose decentralized and scalable algorithms for Gaussian
process (GP) training and prediction in multi-agent systems. To decentralize
the implementation of GP training optimization algorithms, we employ the
alternating direction method of multipliers (ADMM). A closed-form solution of
the decentralized proximal ADMM is provided for the case of GP hyper-parameter
training with maximum likelihood estimation. Multiple aggregation techniques
for GP prediction are decentralized with the use of iterative and consensus
methods. In addition, we propose a covariance-based nearest neighbor selection
strategy that enables a subset of agents to perform predictions. The efficacy
of the proposed methods is illustrated with numerical experiments on synthetic
and real data.

We demonstrate an approach to replicate and forecast the spread of the
SARS-CoV-2 (COVID-19) pandemic using the toolkit of probabilistic programming
languages (PPLs). Our goal is to study the impact of various modeling
assumptions and motivate policy interventions enacted to limit the spread of
infectious diseases. Using existing compartmental models we show how to use
inference in PPLs to obtain posterior estimates for disease parameters. We
improve popular existing models to reflect practical considerations such as the
under-reporting of the true number of COVID-19 cases and motivate the need to
model policy interventions for real-world data. We design an SEI3RD model as a
reusable template and demonstrate its flexibility in comparison to other
models. We also provide a greedy algorithm that selects the optimal series of
policy interventions that are likely to control the infected population subject
to provided constraints. We work within a simple, modular, and reproducible
framework to enable immediate cross-domain access to the state-of-the-art in
probabilistic inference with emphasis on policy interventions. We are not
epidemiologists; the sole aim of this study is to serve as an exposition of
methods, not to directly infer the real-world impact of policy-making for
COVID-19.

In recent years, fully differentiable rigid body physics simulators have been
developed, which can be used to simulate a wide range of robotic systems. In
the context of reinforcement learning for control, these simulators
theoretically allow algorithms to be applied directly to analytic gradients of
the reward function. However, to date, these gradients have proved extremely
challenging to use, and are outclassed by algorithms using no gradient
information at all. In this work we present a novel algorithm, cross entropy
analytic policy gradients, that is able to leverage these gradients to
outperform state of art deep reinforcement learning on a set of challenging
nonlinear control problems.

Cyberattacks from within an organization's trusted entities are known as
insider threats. Anomaly detection using deep learning requires comprehensive
data, but insider threat data is not readily available due to confidentiality
concerns of organizations. Therefore, there arises demand to generate synthetic
data to explore enhanced approaches for threat analysis. We propose a linear
manifold learning-based generative adversarial network, SPCAGAN, that takes
input from heterogeneous data sources and adds a novel loss function to train
the generator to produce high-quality data that closely resembles the original
data distribution. Furthermore, we introduce a deep learning-based hybrid model
for insider threat analysis. We provide extensive experiments for data
synthesis, anomaly detection, adversarial robustness, and synthetic data
quality analysis using benchmark datasets. In this context, empirical
comparisons show that GAN-based oversampling is competitive with numerous
typical oversampling regimes. For synthetic data generation, our SPCAGAN model
overcame the problem of mode collapse and converged faster than previous GAN
models. Results demonstrate that our proposed approach has a lower error, is
more accurate, and generates substantially superior synthetic insider threat
data than previous models.

Machine Learning (ML) interatomic potential has shown its great power in
condensed matter physics. However, ML interatomic potential for a magnetic
system including both structural degrees of freedom and magnetic moments has
not been well developed yet. A spin-dependent ML interatomic potential approach
based on the crystal graph neural network (GNN) has been developed for any
magnetic system. It consists of the Heisenberg edge graph neural network
(HEGNN) and spin-distance edge graph neural network (SEGNN). The network
captures the Heisenberg coefficient variation between different structures and
the fine spin-lattice coupling of high order and multi-body interaction with
high accuracy. In the tests, this method perfectly fitted a high-order spin
Hamiltonian and two complex spin-lattice Hamiltonian and captured the fine
spin-lattice coupling in BiFeO3. In addition, a disturbed structure of BiFeO3
with strain was successfully optimized with the trained potential. Our work has
expanded the powerful ML GNN potentials to magnetic systems, which paves a new
way for large-scale dynamic simulations on spin-lattice coupled systems.

The Fixed-X knockoff filter is a flexible framework for variable selection
with false discovery rate (FDR) control in linear models with arbitrary
(non-singular) design matrices and it allows for finite-sample selective
inference via the LASSO estimates. In this paper, we extend the theory of the
knockoff procedure to tests with composite null hypotheses, which are usually
more relevant to real-world problems. The main technical challenge lies in
handling composite nulls in tandem with dependent features from arbitrary
designs. We develop two methods for composite inference with the knockoffs,
namely, shifted ordinary least-squares (S-OLS) and feature-response product
perturbation (FRPP), building on new structural properties of test statistics
under composite nulls. We also propose two heuristic variants of the S-OLS
method that outperform the celebrated Benjamini-Hochberg (BH) procedure for
composite nulls, which serves as a heuristic baseline under dependent test
statistics. Finally, we analyze the loss in FDR when the original knockoff
procedure is naively applied on composite tests.

Multi-agent reinforcement learning (MARL) provides an efficient way for
simultaneously learning policies for multiple agents interacting with each
other. However, in scenarios requiring complex interactions, existing
algorithms can suffer from an inability to accurately anticipate the influence
of self-actions on other agents. Incorporating an ability to reason about other
agents' potential responses can allow an agent to formulate more effective
strategies. This paper adopts a recursive reasoning model in a
centralized-training-decentralized-execution framework to help learning agents
better cooperate with or compete against others. The proposed algorithm,
referred to as the Recursive Reasoning Graph (R2G), shows state-of-the-art
performance on multiple multi-agent particle and robotics games.

Within the context of studies for novel measurement solutions for future
particle physics experiments, we developed a performant kNN-based regressor to
infer the energy of highly-relativistic muons from the pattern of their
radiation losses in a dense and granular calorimeter. The regressor is based on
a pool of weak kNN learners, which learn by adapting weights and biases to each
training event through stochastic gradient descent. The effective number of
parameters optimized by the procedure is in the 60 millions range, thus
comparable to that of large deep learning architectures. We test the
performance of the regressor on the considered application by comparing it to
that of several machine learning algorithms, showing comparable accuracy to
that achieved by boosted decision trees and neural networks.

We study the asymmetric matrix factorization problem under a natural
nonconvex formulation with arbitrary overparametrization. The model-free
setting is considered, with minimal assumption on the rank or singular values
of the observed matrix, where the global optima provably overfit. We show that
vanilla gradient descent with small random initialization sequentially recovers
the principal components of the observed matrix. Consequently, when equipped
with proper early stopping, gradient descent produces the best low-rank
approximation of the observed matrix without explicit regularization. We
provide a sharp characterization of the relationship between the approximation
error, iteration complexity, initialization size and stepsize. Our complexity
bound is almost dimension-free and depends logarithmically on the approximation
error, with significantly more lenient requirements on the stepsize and
initialization compared to prior work. Our theoretical results provide accurate
prediction for the behavior gradient descent, showing good agreement with

A key challenge in applying Monte Carlo and variational inference (VI) is the
design of proposals and variational families that are flexible enough to
closely approximate the posterior, but simple enough to admit tractable
densities and variational bounds. This paper presents recursive
auxiliary-variable inference (RAVI), a new framework for exploiting flexible
proposals, for example based on involved simulations or stochastic
optimization, within Monte Carlo and VI algorithms. The key idea is to estimate
intractable proposal densities via meta-inference: additional Monte Carlo or
variational inference targeting the proposal, rather than the model. RAVI
generalizes and unifies several existing methods for inference with expressive
approximating families, which we show correspond to specific choices of
meta-inference algorithm, and provides new theory for analyzing their bias and
variance. We illustrate RAVI's design framework and theorems by using them to
analyze and improve upon Salimans et al. (2015)'s Markov Chain Variational
Inference, and to design a novel sampler for Dirichlet process mixtures,
achieving state-of-the-art results on a standard benchmark dataset from
astronomy and on a challenging data-cleaning task with Medicare hospital data.

Sparse linear regression with ill-conditioned Gaussian random designs is
widely believed to exhibit a statistical/computational gap, but there is
surprisingly little formal evidence for this belief, even in the form of
examples that are hard for restricted classes of algorithms. Recent work has
shown that, for certain covariance matrices, the broad class of Preconditioned
Lasso programs provably cannot succeed on polylogarithmically sparse signals
with a sublinear number of samples. However, this lower bound only shows that
for every preconditioner, there exists at least one signal that it fails to
recover successfully. This leaves open the possibility that, for example,
trying multiple different preconditioners solves every sparse linear regression
problem.
  In this work, we prove a stronger lower bound that overcomes this issue. For
an appropriate covariance matrix, we construct a single signal distribution on
which any invertibly-preconditioned Lasso program fails with high probability,
unless it receives a linear number of samples.
  Surprisingly, at the heart of our lower bound is a new positive result in
compressed sensing. We show that standard sparse random designs are with high
probability robust to adversarial measurement erasures, in the sense that if
$b$ measurements are erased, then all but $O(b)$ of the coordinates of the
signal are still information-theoretically identifiable. To our knowledge, this
is the first time that partial recoverability of arbitrary sparse signals under
erasures has been studied in compressed sensing.

River bathymetry is critical for many aspects of water resources management.
We propose and demonstrate a bathymetry inversion method using a
deep-learning-based surrogate for shallow water equations solvers. The
surrogate uses the convolutional autoencoder with a shared-encoder,
separate-decoder architecture. It encodes the input bathymetry and decodes to
separate outputs for flow-field variables. A gradient-based optimizer is used
to perform bathymetry inversion with the trained surrogate. Two
physically-based constraints on both bed elevation value and slope have to be
added as inversion loss regularizations to obtain usable inversion results.
Using the "L-curve" criterion, a heuristic approach was proposed to determine
the regularization parameters. Both the surrogate model and the inversion
algorithm show good performance. We found the bathymetry inversion process has
two distinctive stages, which resembles the sculptural process of initial
broad-brush calving and final detailing. The inversion loss due to flow
prediction error reaches its minimum in the first stage and remains almost
constant afterward. The bed elevation value and slope regularizations play the
dominant role in the second stage in selecting the most probable solution. We
also found the surrogate architecture (whether with both velocity and water
surface elevation or velocity only as outputs) does not show significant impact
on inversion result.

An increasingly common methodological issue in the field of social science is
high-dimensional and highly correlated datasets that are unamenable to the
traditional deductive framework of study. Analysis of candidate choice in the
2020 Presidential Election is one area in which this issue presents itself: in
order to test the many theories explaining the outcome of the election, it is
necessary to use data such as the 2020 Cooperative Election Study Common
Content, with hundreds of highly correlated features. We present the Fuzzy
Forests algorithm, a variant of the popular Random Forests ensemble method, as
an efficient way to reduce the feature space in such cases with minimal bias,
while also maintaining predictive performance on par with common algorithms
like Random Forests and logit. Using Fuzzy Forests, we isolate the top
correlates of candidate choice and find that partisan polarization was the
strongest factor driving the 2020 presidential election.

The majority of deep learning (DL) based deformable image registration
methods use convolutional neural networks (CNNs) to estimate displacement
fields from pairs of moving and fixed images. This, however, requires the
convolutional kernels in the CNN to not only extract intensity features from
the inputs but also understand image coordinate systems. We argue that the
latter task is challenging for traditional CNNs, limiting their performance in
registration tasks. To tackle this problem, we first introduce Coordinate
Translator, a differentiable module that identifies matched features between
the fixed and moving image and outputs their coordinate correspondences without
the need for training. It unloads the burden of understanding image coordinate
systems for CNNs, allowing them to focus on feature extraction. We then propose
a novel deformable registration network, im2grid, that uses multiple Coordinate
Translator's with the hierarchical features extracted from a CNN encoder and
outputs a deformation field in a coarse-to-fine fashion. We compared im2grid
with the state-of-the-art DL and non-DL methods for unsupervised 3D magnetic
resonance image registration. Our experiments show that im2grid outperforms
these methods both qualitatively and quantitatively.

Off-policy evaluation methods are important in recommendation systems and
search engines, whereby data collected under an old logging policy is used to
predict the performance of a new target policy. However, in practice most
systems are not observed to recommend most of the possible actions, which is an
issue since existing methods require that the probability of the target policy
recommending an item can only be non-zero when the probability of the logging
policy is non-zero (known as absolute continuity). To circumvent this issue, we
explore the use of action embeddings. By representing contexts and actions in
an embedding space, we are able to share information to extrapolate behaviors
for actions and contexts previously unseen.

Object-centric processes (a.k.a. Artifact-centric processes) are
implementations of a paradigm where an instance of one process is not executed
in isolation but interacts with other instances of the same or other processes.
Interactions take place through bridging events where instances exchange data.
Object-centric processes are recently gaining popularity in academia and
industry, because their nature is observed in many application scenarios. This
poses significant challenges in predictive analytics due to the complex
intricacy of the process instances that relate to each other via many-to-many
associations. Existing research is unable to directly exploit the benefits of
these interactions, thus limiting the prediction quality. This paper proposes
an approach to incorporate the information about the object interactions into
the predictive models. The approach is assessed on real-life object-centric
process event data, using different KPIs. The results are compared with a naive
approach that overlooks the object interactions, thus illustrating the benefits
of their use on the prediction quality.

Cross-Lingual Summarization (CLS) is the task to generate a summary in one
language for an article in a different language. Previous studies on CLS mainly
take pipeline methods or train the end-to-end model using the translated
parallel data. However, the quality of generated cross-lingual summaries needs
more further efforts to improve, and the model performance has never been
evaluated on the hand-written CLS dataset. Therefore, we first propose a
clue-guided cross-lingual abstractive summarization method to improve the
quality of cross-lingual summaries, and then construct a novel hand-written CLS
dataset for evaluation. Specifically, we extract keywords, named entities, etc.
of the input article as key clues for summarization and then design a
clue-guided algorithm to transform an article into a graph with less noisy
sentences. One Graph encoder is built to learn sentence semantics and article
structures and one Clue encoder is built to encode and translate key clues,
ensuring the information of important parts are reserved in the generated
summary. These two encoders are connected by one decoder to directly learn
cross-lingual semantics. Experimental results show that our method has stronger
robustness for longer inputs and substantially improves the performance over
the strong baseline, achieving an improvement of 8.55 ROUGE-1
(English-to-Chinese summarization) and 2.13 MoverScore (Chinese-to-English
summarization) scores over the existing SOTA.

The recent development of imaging and sequencing technologies enables
systematic advances in the clinical study of lung cancer. Meanwhile, the human
mind is limited in effectively handling and fully utilizing the accumulation of
such enormous amounts of data. Machine learning-based approaches play a
critical role in integrating and analyzing these large and complex datasets,
which have extensively characterized lung cancer through the use of different
perspectives from these accrued data. In this article, we provide an overview
of machine learning-based approaches that strengthen the varying aspects of
lung cancer diagnosis and therapy, including early detection, auxiliary
diagnosis, prognosis prediction and immunotherapy practice. Moreover, we
highlight the challenges and opportunities for future applications of machine
learning in lung cancer.

A common challenge posed to robust semantic segmentation is the expensive
data annotation cost. Existing semi-supervised solutions show great potential
for solving this problem. Their key idea is constructing consistency
regularization with unsupervised data augmentation from unlabeled data for
model training. The perturbations for unlabeled data enable the consistency
training loss, which benefits semi-supervised semantic segmentation. However,
these perturbations destroy image context and introduce unnatural boundaries,
which is harmful for semantic segmentation. Besides, the widely adopted
semi-supervised learning framework, i.e. mean-teacher, suffers performance
limitation since the student model finally converges to the teacher model. In
this paper, first of all, we propose a context friendly differentiable
geometric warping to conduct unsupervised data augmentation; secondly, a novel
adversarial dual-student framework is proposed to improve the Mean-Teacher from
the following two aspects: (1) dual student models are learned independently
except for a stabilization constraint to encourage exploiting model
diversities; (2) adversarial training scheme is applied to both students and
the discriminators are resorted to distinguish reliable pseudo-label of
unlabeled data for self-training. Effectiveness is validated via extensive
experiments on PASCAL VOC2012 and Cityscapes. Our solution significantly
improves the performance and state-of-the-art results are achieved on both
datasets. Remarkably, compared with fully supervision, our solution achieves
comparable mIoU of 73.4% using only 12.5% annotated data on PASCAL VOC2012. Our
codes and models are available at https://github.com/cao-cong/ADS-SemiSeg.

This correspondence considers the resource allocation problem in wireless
interference channel (IC) under link outage constraints. Since the optimization
problem is non-convex in nature, existing approaches to find the optimal power
allocation are computationally intensive and thus practically infeasible.
Recently, deep reinforcement learning has shown promising outcome in solving
non-convex optimization problems with reduced complexity. In this
correspondence, we utilize a deep Q-learning (DQL) approach which interacts
with the wireless environment and learns the optimal power allocation of a
wireless IC while maximizing overall sum-rate of the system and maintaining
reliability requirement of each link. We have used two separate deep Q-networks
to remove the inherent instability in learning process. Simulation results
demonstrate that the proposed DQL approach outperforms existing geometric
programming based solution.

We give a spectral algorithm for decomposing overcomplete order-4 tensors, so
long as their components satisfy an algebraic non-degeneracy condition that
holds for nearly all (all but an algebraic set of measure $0$) tensors over
$(\mathbb{R}^d)^{\otimes 4}$ with rank $n \le d^2$. Our algorithm is robust to
adversarial perturbations of bounded spectral norm.
  Our algorithm is inspired by one which uses the sum-of-squares semidefinite
programming hierarchy (Ma, Shi, and Steurer STOC'16, arXiv:1610.01980), and we
achieve comparable robustness and overcompleteness guarantees under similar
algebraic assumptions. However, our algorithm avoids semidefinite programming
and may be implemented as a series of basic linear-algebraic operations. We
consequently obtain a much faster running time than semidefinite programming
methods: our algorithm runs in time $\tilde O(n^2d^3) \le \tilde O(d^7)$, which
is subquadratic in the input size $d^4$ (where we have suppressed factors
related to the condition number of the input tensor).

Autonomic computing investigates how systems can achieve (user) specified
control outcomes on their own, without the intervention of a human operator.
Autonomic computing fundamentals have been substantially influenced by those of
control theory for closed and open-loop systems. In practice, complex systems
may exhibit a number of concurrent and inter-dependent control loops. Despite
research into autonomic models for managing computer resources, ranging from
individual resources (e.g., web servers) to a resource ensemble (e.g., multiple
resources within a data center), research into integrating Artificial
Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and
performance at scale continues to be a fundamental challenge. The integration
of AI/ML to achieve such autonomic and self-management of systems can be
achieved at different levels of granularity, from full to human-in-the-loop
automation. In this article, leading academics, researchers, practitioners,
engineers, and scientists in the fields of cloud computing, AI/ML, and quantum
computing join to discuss current research and potential future directions for
these fields. Further, we discuss challenges and opportunities for leveraging
AI and ML in next generation computing for emerging computing paradigms,
including cloud, fog, edge, serverless and quantum computing environments.

Human decision-making is plagued by many systematic errors. Many of these
errors can be avoided by providing decision aids that guide decision-makers to
attend to the important information and integrate it according to a rational
decision strategy. Designing such decision aids used to be a tedious manual
process. Advances in cognitive science might make it possible to automate this
process in the future. We recently introduced machine learning methods for
discovering optimal strategies for human decision-making automatically and an
automatic method for explaining those strategies to people. Decision aids
constructed by this method were able to improve human decision-making. However,
following the descriptions generated by this method is very tedious. We
hypothesized that this problem can be overcome by conveying the automatically
discovered decision strategy as a series of natural language instructions for
how to reach a decision. Experiment 1 showed that people do indeed understand
such procedural instructions more easily than the decision aids generated by
our previous method. Encouraged by this finding, we developed an algorithm for
translating the output of our previous method into procedural instructions. We
applied the improved method to automatically generate decision aids for a
naturalistic planning task (i.e., planning a road trip) and a naturalistic
decision task (i.e., choosing a mortgage). Experiment 2 showed that these
automatically generated decision-aids significantly improved people's
performance in planning a road trip and choosing a mortgage. These findings
suggest that AI-powered boosting might have potential for improving human
decision-making in the real world.

Generative adversarial networks (GANs) have received an upsurging interest
since being proposed due to the high quality of the generated data. While
achieving increasingly impressive results, the resource demands associated with
the large model size hinders the usage of GANs in resource-limited scenarios.
For inference, the existing model compression techniques can reduce the model
complexity with comparable performance. However, the training efficiency of
GANs has less been explored due to the fragile training process of GANs. In
this paper, we, for the first time, explore the possibility of directly
training sparse GAN from scratch without involving any dense or pre-training
steps. Even more unconventionally, our proposed method enables directly
training sparse unbalanced GANs with an extremely sparse generator from
scratch. Instead of training full GANs, we start with sparse GANs and
dynamically explore the parameter space spanned over the generator throughout
training. Such a sparse-to-sparse training procedure enhances the capacity of
the highly sparse generator progressively while sticking to a fixed small
parameter budget with appealing training and inference efficiency gains.
Extensive experiments with modern GAN architectures validate the effectiveness
of our method. Our sparsified GANs, trained from scratch in one single run, are
able to outperform the ones learned by expensive iterative pruning and
re-training. Perhaps most importantly, we find instead of inheriting parameters
from expensive pre-trained GANs, directly training sparse GANs from scratch can
be a much more efficient solution. For example, only training with a 80% sparse
generator and a 70% sparse discriminator, our method can achieve even better
performance than the dense BigGAN.

Industrial bin picking is a challenging task that requires accurate and
robust segmentation of individual object instances. Particularly, industrial
objects can have irregular shapes, that is, thin and concave, whereas in
bin-picking scenarios, objects are often closely packed with strong occlusion.
To address these challenges, we formulate a novel part-aware instance
segmentation pipeline. The key idea is to decompose industrial objects into
correlated approximate convex parts and enhance the object-level segmentation
with part-level segmentation. We design a part-aware network to predict part
masks and part-to-part offsets, followed by a part aggregation module to
assemble the recognized parts into instances. To guide the network learning, we
also propose an automatic label decoupling scheme to generate ground-truth
part-level labels from instance-level labels. Finally, we contribute the first
instance segmentation dataset, which contains a variety of industrial objects
that are thin and have non-trivial shapes. Extensive experimental results on
various industrial objects demonstrate that our method can achieve the best
segmentation results compared with the state-of-the-art approaches.

Most existing works in vision-and-language navigation (VLN) focus on either
discrete or continuous environments, training agents that cannot generalize
across the two. The fundamental difference between the two setups is that
discrete navigation assumes prior knowledge of the connectivity graph of the
environment, so that the agent can effectively transfer the problem of
navigation with low-level controls to jumping from node to node with high-level
actions by grounding to an image of a navigable direction. To bridge the
discrete-to-continuous gap, we propose a predictor to generate a set of
candidate waypoints during navigation, so that agents designed with high-level
actions can be transferred to and trained in continuous environments. We refine
the connectivity graph of Matterport3D to fit the continuous
Habitat-Matterport3D, and train the waypoints predictor with the refined graphs
to produce accessible waypoints at each time step. Moreover, we demonstrate
that the predicted waypoints can be augmented during training to diversify the
views and paths, and therefore enhance agent's generalization ability. Through
extensive experiments we show that agents navigating in continuous environments
with predicted waypoints perform significantly better than agents using
low-level actions, which reduces the absolute discrete-to-continuous gap by
11.76% Success Weighted by Path Length (SPL) for the Cross-Modal Matching Agent
and 18.24% SPL for the Recurrent VLN-BERT. Our agents, trained with a simple
imitation learning objective, outperform previous methods by a large margin,
achieving new state-of-the-art results on the testing environments of the
R2R-CE and the RxR-CE datasets.

The research topic of sketch-to-portrait generation has witnessed a boost of
progress with deep learning techniques. The recently proposed StyleGAN
architectures achieve state-of-the-art generation ability but the original
StyleGAN is not friendly for sketch-based creation due to its unconditional
generation nature. To address this issue, we propose a direct conditioning
strategy to better preserve the spatial information under the StyleGAN
framework. Specifically, we introduce Spatially Conditioned StyleGAN
(SC-StyleGAN for short), which explicitly injects spatial constraints to the
original StyleGAN generation process. We explore two input modalities, sketches
and semantic maps, which together allow users to express desired generation
results more precisely and easily. Based on SC-StyleGAN, we present
DrawingInStyles, a novel drawing interface for non-professional users to easily
produce high-quality, photo-realistic face images with precise control, either
from scratch or editing existing ones. Qualitative and quantitative evaluations
show the superior generation ability of our method to existing and alternative
solutions. The usability and expressiveness of our system are confirmed by a
user study.

Machine Reading Comprehension (MRC) reveals the ability to understand a given
text passage and answer questions based on it. Existing research works in MRC
rely heavily on large-size models and corpus to improve the performance
evaluated by metrics such as Exact Match ($EM$) and $F_1$. However, such a
paradigm lacks sufficient interpretation to model capability and can not
efficiently train a model with a large corpus. In this paper, we argue that a
deep understanding of model capabilities and data properties can help us feed a
model with appropriate training data based on its learning status.
Specifically, we design an MRC capability assessment framework that assesses
model capabilities in an explainable and multi-dimensional manner. Based on it,
we further uncover and disentangle the connections between various data
properties and model performance. Finally, to verify the effectiveness of the
proposed MRC capability assessment framework, we incorporate it into a
curriculum learning pipeline and devise a Capability Boundary Breakthrough
Curriculum (CBBC) strategy, which performs a model capability-based training to
maximize the data value and improve training efficiency. Extensive experiments
demonstrate that our approach significantly improves performance, achieving up
to an 11.22% / 8.71% improvement of $EM$ / $F_1$ on MRC tasks.

Fine-Grained Visual Classification(FGVC) is the task that requires
recognizing the objects belonging to multiple subordinate categories of a
super-category. Recent state-of-the-art methods usually design sophisticated
learning pipelines to tackle this task. However, visual information alone is
often not sufficient to accurately differentiate between fine-grained visual
categories. Nowadays, the meta-information (e.g., spatio-temporal prior,
attribute, and text description) usually appears along with the images. This
inspires us to ask the question: Is it possible to use a unified and simple
framework to utilize various meta-information to assist in fine-grained
identification? To answer this problem, we explore a unified and strong
meta-framework(MetaFormer) for fine-grained visual classification. In practice,
MetaFormer provides a simple yet effective approach to address the joint
learning of vision and various meta-information. Moreover, MetaFormer also
provides a strong baseline for FGVC without bells and whistles. Extensive
experiments demonstrate that MetaFormer can effectively use various
meta-information to improve the performance of fine-grained recognition. In a
fair comparison, MetaFormer can outperform the current SotA approaches with
only vision information on the iNaturalist2017 and iNaturalist2018 datasets.
Adding meta-information, MetaFormer can exceed the current SotA approaches by
5.9% and 5.3%, respectively. Moreover, MetaFormer can achieve 92.3% and 92.7%
on CUB-200-2011 and NABirds, which significantly outperforms the SotA
approaches. The source code and pre-trained models are released
athttps://github.com/dqshuai/MetaFormer.

The performance cost of differential privacy has, for some applications, been
shown to be higher for minority groups; fairness, conversely, has been shown to
disproportionally compromise the privacy of members of such groups. Most work
in this area has been restricted to computer vision and risk assessment. In
this paper, we evaluate the impact of differential privacy on fairness across
four tasks, focusing on how attempts to mitigate privacy violations and
between-group performance differences interact: Does privacy inhibit attempts
to ensure fairness? To this end, we train $(\varepsilon,\delta)$-differentially
private models with empirical risk minimization and group distributionally
robust training objectives. Consistent with previous findings, we find that
differential privacy increases between-group performance differences in the
baseline setting; but more interestingly, differential privacy reduces
between-group performance differences in the robust setting. We explain this by
reinterpreting differential privacy as regularization.

Complex heterogeneous dynamic networks like knowledge graphs are powerful
constructs that can be used in modeling data provenance from computer systems.
From a security perspective, these attributed graphs enable causality analysis
and tracing for analyzing a myriad of cyberattacks. However, there is a paucity
in systematic development of pipelines that transform system executions and
provenance into usable graph representations for machine learning tasks. This
lack of instrumentation severely inhibits scientific advancement in provenance
graph machine learning by hindering reproducibility and limiting the
availability of data that are critical for techniques like graph neural
networks. To fulfill this need, we present Flurry, an end-to-end data pipeline
which simulates cyberattacks, captures provenance data from these attacks at
multiple system and application layers, converts audit logs from these attacks
into data provenance graphs, and incorporates this data with a framework for
training deep neural models that supports preconfigured or custom-designed
models for analysis in real-world resilient systems. We showcase this pipeline
by processing data from multiple system attacks and performing anomaly
detection via graph classification using current benchmark graph
representational learning frameworks. Flurry provides a fast, customizable,
extensible, and transparent solution for providing this much needed data to
cybersecurity professionals.

In this paper, a distributed stochastic gradient (SG) algorithm is proposed
where the estimators are aimed to collectively estimate an unknown
time-invariant parameter from a set of noisy measurements obtained by
distributed sensors. The proposed distributed SG algorithm combines the
consensus strategy of the estimation of neighbors with the diffusion of
regression vectors. A cooperative excitation condition is introduced, under
which the convergence of the distributed SG algorithm can be obtained without
relying on the independency and stationarity assumptions of regression vectors
which are commonly used in existing literature. Furthermore, the convergence
rate of the algorithm can be established. Finally, we show that all sensors can
cooperate to fulfill the estimation task even though any individual sensor can
not by a simulation example.

In the last decade, exponential data growth supplied the machine
learning-based algorithms' capacity and enabled their usage in daily life
activities. Additionally, such an improvement is partially explained due to the
advent of deep learning techniques, i.e., stacks of simple architectures that
end up in more complex models. Although both factors produce outstanding
results, they also pose drawbacks regarding the learning process since training
complex models denotes an expensive task and results are prone to overfit the
training data. A supervised regularization technique called MaxDropout was
recently proposed to tackle the latter, providing several improvements
concerning traditional regularization approaches. In this paper, we present its
improved version called MaxDropoutV2. Results considering two public datasets
show that the model performs faster than the standard version and, in most
cases, provides more accurate results.

Video super-resolution (VSR) refers to the reconstruction of high-resolution
(HR) video from the corresponding low-resolution (LR) video. Recently, VSR has
received increasing attention. In this paper, we propose a novel dual dense
connection network that can generate high-quality super-resolution (SR)
results. The input frames are creatively divided into reference frame,
pre-temporal group and post-temporal group, representing information in
different time periods. This grouping method provides accurate information of
different time periods without causing time information disorder. Meanwhile, we
produce a new loss function, which is beneficial to enhance the convergence
ability of the model. Experiments show that our model is superior to other
advanced models in Vid4 datasets and SPMCS-11 datasets.

Continual relation extraction (CRE) aims to continuously train a model on
data with new relations while avoiding forgetting old ones. Some previous work
has proved that storing a few typical samples of old relations and replaying
them when learning new relations can effectively avoid forgetting. However,
these memory-based methods tend to overfit the memory samples and perform
poorly on imbalanced datasets. To solve these challenges, a consistent
representation learning method is proposed, which maintains the stability of
the relation embedding by adopting contrastive learning and knowledge
distillation when replaying memory. Specifically, supervised contrastive
learning based on a memory bank is first used to train each new task so that
the model can effectively learn the relation representation. Then, contrastive
replay is conducted of the samples in memory and makes the model retain the
knowledge of historical relations through memory knowledge distillation to
prevent the catastrophic forgetting of the old task. The proposed method can
better learn consistent representations to alleviate forgetting effectively.
Extensive experiments on FewRel and TACRED datasets show that our method
significantly outperforms state-of-the-art baselines and yield strong
robustness on the imbalanced dataset.

This study presents a Bayesian learning perspective towards model predictive
control algorithms. High-level frameworks have been developed separately in the
earlier studies on Bayesian learning and sampling-based model predictive
control. On one hand, the Bayesian learning rule provides a general framework
capable of generating various machine learning algorithms as special instances.
On the other hand, the dynamic mirror descent model predictive control
framework is capable of diversifying sample-rollout-based control algorithms.
However, connections between the two frameworks have still not been fully
appreciated in the context of stochastic optimal control. This study combines
the Bayesian learning rule point of view into the model predictive control
setting by taking inspirations from the view of understanding model predictive
controller as an online learner. The selection of posterior class and natural
gradient approximation for the variational formulation governs diversification
of model predictive control algorithms in the Bayesian learning approach to
model predictive control. This alternative viewpoint complements the dynamic
mirror descent framework through streamlining the explanation of design
choices.

Due to the completely open-source nature of Android, the exploitable
vulnerability of malware attacks is increasing. Machine learning has led to a
great evolution in Android malware detection in recent years, which is
typically applied in the classification phase. Applying neural networks to
feature selection phase is another topic worth investigating, while the
correlation between features could be ignored in some traditional ranking-based
feature selection algorithms. And it is time-consuming for exploring all
possible valid feature subsets when processing a large number of Android
features using a wrapper-based approach. Attempting to tackle the problem of
computational expense and the vulnerability of the syntax integrity of
multi-source data, this paper proposed the DroidRL framework. The main idea is
to apply Double DQN(DDQN) algorithm to select a subset of features that can be
effectively used for malware classification. To select a better subset of
features over a larger range, the exploration-exploitation policy is applied in
the model training phase. The RNN is used as the decision network of DDQN to
give the framework the ability to sequentially select features. Word embedding
is applied for feature representation to enhance the framework's ability to
find the semantic relevance of features. On one hand, The framework's feature
selection exhibits high performance without any human intervention. On the
other hand, the proposed framework can easily be ported to other feature
selection tasks with some minor changes. The experiment results compared with
different classifiers, decision networks, and related work showed a significant
effect using the Random Forest classifier, reaching 95.6% accuracy with only 24
features selected.

Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of
the loss landscape and generalization, has demonstrated significant performance
boosts on training large-scale models such as vision transformers. However, the
update rule of SAM requires two sequential (non-parallelizable) gradient
computations at each step, which can double the computational overhead. In this
paper, we propose a novel algorithm LookSAM - that only periodically calculates
the inner gradient ascent, to significantly reduce the additional training cost
of SAM. The empirical results illustrate that LookSAM achieves similar accuracy
gains to SAM while being tremendously faster - it enjoys comparable
computational complexity with first-order optimizers such as SGD or Adam. To
further evaluate the performance and scalability of LookSAM, we incorporate a
layer-wise modification and perform experiments in the large-batch training
scenario, which is more prone to converge to sharp local minima. We are the
first to successfully scale up the batch size when training Vision Transformers
(ViTs). With a 64k batch size, we are able to train ViTs from scratch in
minutes while maintaining competitive performance.

Optimisers are an essential component for training machine learning models,
and their design influences learning speed and generalisation. Several studies
have attempted to learn more effective gradient-descent optimisers via solving
a bi-level optimisation problem where generalisation error is minimised with
respect to optimiser parameters. However, most existing optimiser learning
methods are intuitively motivated, without clear theoretical support. We take a
different perspective starting from mirror descent rather than gradient
descent, and meta-learning the corresponding Bregman divergence. Within this
paradigm, we formalise a novel meta-learning objective of minimising the regret
bound of learning. The resulting framework, termed Meta Mirror Descent
(MetaMD), learns to accelerate optimisation speed. Unlike many meta-learned
optimisers, it also supports convergence and generalisation guarantees and
uniquely does so without requiring validation data. We evaluate our framework
on a variety of tasks and architectures in terms of convergence rate and
generalisation error and demonstrate strong performance.

Commit messages are important for software development and maintenance. Many
neural network-based approaches have been proposed and shown promising results
on automatic commit message generation. However, the generated commit messages
could be repetitive or redundant. In this paper, we propose RACE, a new
retrieval-augmented neural commit message generation method, which treats the
retrieved similar commit as an exemplar and leverages it to generate an
accurate commit message. As the retrieved commit message may not always
accurately describe the content/intent of the current code diff, we also
propose an exemplar guider, which learns the semantic similarity between the
retrieved and current code diff and then guides the generation of commit
message based on the similarity. We conduct extensive experiments on a large
public dataset with five programming languages. Experimental results show that
RACE can outperform all baselines. Furthermore, RACE can boost the performance
of existing Seq2Seq models in commit message generation.

With the expected rise in behind-the-meter solar penetration within the
distribution networks, there is a need to develop time-series forecasting
methods that can reliably predict the net-load, accurately quantifying its
uncertainty and variability. This paper presents a deep learning method to
generate probabilistic forecasts of day-ahead net-load at 15-min resolution, at
various solar penetration levels. Our proposed deep-learning based architecture
utilizes the dimensional reduction, from a higher-dimensional input to a
lower-dimensional latent space, via a convolutional Autoencoder (AE). The
extracted features from AE are then utilized to generate probability
distributions across the latent space, by passing the features through a
kernel-embedded Perron-Frobenius (kPF) operator. Finally, long short-term
memory (LSTM) layers are used to synthesize time-series probability
distributions of the forecasted net-load, from the latent space distributions.
The models are shown to deliver superior forecast performance (as per several
metrics), as well as maintain superior training efficiency, in comparison to
existing benchmark models. Detailed analysis is carried out to evaluate the
model performance across various solar penetration levels (up to 50\%),
prediction horizons (e.g., 15\,min and 24\,hr ahead), and aggregation level of
houses, as well as its robustness against missing measurements.

Due to the rapid growth of data transmissions in internet of vehicles (IoV),
finding schemes that can effectively alleviate access congestion has become an
important issue. Recently, many traffic control schemes have been studied.
Nevertheless, the dynamics of traffic and the heterogeneous requirements of
different IoV applications are not considered in most existing studies, which
is significant for the random access resource allocation. In this paper, we
consider a hybrid traffic control scheme and use proximal policy optimization
(PPO) method to tackle it. Firstly, IoV devices are divided into various
classes based on delay characteristics. The target of maximizing the successful
transmission of packets with the success rate constraint is established. Then,
the optimization objective is transformed into a markov decision process (MDP)
model. Finally, the access class barring (ACB) factors are obtained based on
the PPO method to maximize the number of successful access devices. The
performance of the proposal algorithm in respect of successful events and delay
compared to existing schemes is verified by simulations.

Discovering relevant patterns for a particular user remains a challenging
tasks in data mining. Several approaches have been proposed to learn
user-specific pattern ranking functions. These approaches generalize well, but
at the expense of the running time. On the other hand, several measures are
often used to evaluate the interestingness of patterns, with the hope to reveal
a ranking that is as close as possible to the user-specific ranking. In this
paper, we formulate the problem of learning pattern ranking functions as a
multicriteria decision making problem. Our approach aggregates different
interestingness measures into a single weighted linear ranking function, using
an interactive learning procedure that operates in either passive or active
modes. A fast learning step is used for eliciting the weights of all the
measures by mean of pairwise comparisons.
  This approach is based on Analytic Hierarchy Process (AHP), and a set of
user-ranked patterns to build a preference matrix, which compares the
importance of measures according to the user-specific interestingness. A
sensitivity based heuristic is proposed for the active learning mode, in order
to insure high quality results with few user ranking queries. Experiments
conducted on well-known datasets show that our approach significantly reduces
the running time and returns precise pattern ranking, while being robust to
user-error compared with state-of-the-art approaches.

In this paper, we study the problem of federated domain generalization
(FedDG) for person re-identification (re-ID), which aims to learn a generalized
model with multiple decentralized labeled source domains. An empirical method
(FedAvg) trains local models individually and averages them to obtain the
global model for further local fine-tuning or deploying in unseen target
domains. One drawback of FedAvg is neglecting the data distributions of other
clients during local training, making the local model overfit local data and
producing a poorly-generalized global model. To solve this problem, we propose
a novel method, called "Domain and Feature Hallucinating (DFH)", to produce
diverse features for learning generalized local and global models.
Specifically, after each model aggregation process, we share the Domain-level
Feature Statistics (DFS) among different clients without violating data
privacy. During local training, the DFS are used to synthesize novel domain
statistics with the proposed domain hallucinating, which is achieved by
re-weighting DFS with random weights. Then, we propose feature hallucinating to
diversify local features by scaling and shifting them to the distribution of
the obtained novel domain. The synthesized novel features retain the original
pair-wise similarities, enabling us to utilize them to optimize the model in a
supervised manner. Extensive experiments verify that the proposed DFH can
effectively improve the generalization ability of the global model. Our method
achieves the state-of-the-art performance for FedDG on four large-scale re-ID

The recently proposed camouflaged object detection (COD) attempts to segment
objects that are visually blended into their surroundings, which is extremely
complex and difficult in real-world scenarios. Apart from high intrinsic
similarity between the camouflaged objects and their background, the objects
are usually diverse in scale, fuzzy in appearance, and even severely occluded.
To deal with these problems, we propose a mixed-scale triplet network,
\textbf{ZoomNet}, which mimics the behavior of humans when observing vague
images, i.e., zooming in and out. Specifically, our ZoomNet employs the zoom
strategy to learn the discriminative mixed-scale semantics by the designed
scale integration unit and hierarchical mixed-scale unit, which fully explores
imperceptible clues between the candidate objects and background surroundings.
Moreover, considering the uncertainty and ambiguity derived from
indistinguishable textures, we construct a simple yet effective regularization
constraint, uncertainty-aware loss, to promote the model to accurately produce
predictions with higher confidence in candidate regions. Without bells and
whistles, our proposed highly task-friendly model consistently surpasses the
existing 23 state-of-the-art methods on four public datasets. Besides, the
superior performance over the recent cutting-edge models on the SOD task also
verifies the effectiveness and generality of our model. The code will be
available at \url{https://github.com/lartpang/ZoomNet}.

Cloud computing has been regarded as a successful paradigm for IT industry by
providing benefits for both service providers and customers. In spite of the
advantages, cloud computing also suffers from distinct challenges, and one of
them is the inefficient resource provisioning for dynamic workloads. Accurate
workload predictions for cloud computing can support efficient resource
provisioning and avoid resource wastage. However, due to the high-dimensional
and high-variable features of cloud workloads, it is difficult to predict the
workloads effectively and accurately. The current dominant work for cloud
workload prediction is based on regression approaches or recurrent neural
networks, which fail to capture the long-term variance of workloads. To address
the challenges and overcome the limitations of existing works, we proposed an
efficient supervised learning-based Deep Neural Network (esDNN}) approach for
cloud workload prediction. Firstly, we utilize a sliding window to convert the
multivariate data into supervised learning time series that allow deep learning
for processing. Then we apply a revised Gated Recurrent Unit (GRU) to achieve
accurate prediction. To show the effectiveness of esDNN, we also conduct
comprehensive experiments based on realistic traces derived from Alibaba and
Google cloud data centers. The experimental results demonstrate that esDNN can
accurately and efficiently predict cloud workloads. Compared with the
state-of-the-art baselines, esDNN can reduce the mean square errors
significantly, e.g. 15% than the approach using GRU only. We also apply esDNN
for machines auto-scaling, which illustrates that esDNN can reduce the number
of active hosts efficiently, thus the costs of service providers can be
optimized.

Similarity-based method gives rise to a new class of methods for multi-label
learning and also achieves promising performance. In this paper, we generalize
this method, resulting in a new framework for classification task.
Specifically, we unite similarity-based learning and generalized linear models
to achieve the best of both worlds. This allows us to capture interdependencies
between classes and prevent from impairing performance of noisy classes. Each
learned parameter of the model can reveal the contribution of one class to
another, providing interpretability to some extent. Experiment results show the
effectiveness of the proposed approach on multi-class and multi-label datasets

Understanding the physics of phase transitions in two-dimensional (2D)
systems underpins the research in diverse fields including statistical
mechanics, quantum systems, nanomagnetism, and soft condensed matter. However,
many fundamental aspects of 2D phase transitions are still not well understood,
including the effects of interparticle potential, polydispersity, and particle
shape. Magnetic skyrmions, which are non-trivial chiral spin structures, can be
considered as quasi-particles that form two-dimensional lattices. Here we show,
by real-space imaging using in situ cryo-Lorentz transmission electron
microscopy coupled with machine learning, the ordering behavior of N\'eel
skyrmion lattices in van der Waals Fe3GeTe2. We demonstrate a distinct change
in the skyrmion size distribution during field-cooling, which leads to a loss
of lattice order and an evolution of the skyrmion liquid phase. Remarkably, the
lattice order is restored during field heating and demonstrates a thermal
hysteresis. Our quantitative analysis explains this behavior based on the
energy landscape of skyrmions and demonstrates the potential to control the
lattice order in 2D phase transitions.

A stroke occurs when an artery in the brain ruptures and bleeds or when the
blood supply to the brain is cut off. Blood and oxygen cannot reach the brain's
tissues due to the rupture or obstruction resulting in tissue death. The Middle
cerebral artery (MCA) is the largest cerebral artery and the most commonly
damaged vessel in stroke. The quick onset of a focused neurological deficit
caused by interruption of blood flow in the territory supplied by the MCA is
known as an MCA stroke. Alberta stroke programme early CT score (ASPECTS) is
used to estimate the extent of early ischemic changes in patients with MCA
stroke. This study proposes a deep learning-based method to score the CT scan
for ASPECTS. Our work has three highlights. First, we propose a novel method
for medical image segmentation for stroke detection. Second, we show the
effectiveness of AI solution for fully-automated ASPECT scoring with reduced
diagnosis time for a given non-contrast CT (NCCT) Scan. Our algorithms show a
dice similarity coefficient of 0.64 for the MCA anatomy segmentation and 0.72
for the infarcts segmentation. Lastly, we show that our model's performance is
inline with inter-reader variability between radiologists.

Weakly-supervised semantic segmentation (WSSS) with image-level labels is an
important and challenging task. Due to the high training efficiency, end-to-end
solutions for WSSS have received increasing attention from the community.
However, current methods are mainly based on convolutional neural networks and
fail to explore the global information properly, thus usually resulting in
incomplete object regions. In this paper, to address the aforementioned
problem, we introduce Transformers, which naturally integrate global
information, to generate more integral initial pseudo labels for end-to-end
WSSS. Motivated by the inherent consistency between the self-attention in
Transformers and the semantic affinity, we propose an Affinity from Attention
(AFA) module to learn semantic affinity from the multi-head self-attention
(MHSA) in Transformers. The learned affinity is then leveraged to refine the
initial pseudo labels for segmentation. In addition, to efficiently derive
reliable affinity labels for supervising AFA and ensure the local consistency
of pseudo labels, we devise a Pixel-Adaptive Refinement module that
incorporates low-level image appearance information to refine the pseudo
labels. We perform extensive experiments and our method achieves 66.0% and
38.9% mIoU on the PASCAL VOC 2012 and MS COCO 2014 datasets, respectively,
significantly outperforming recent end-to-end methods and several multi-stage
competitors. Code is available at https://github.com/rulixiang/afa.

Memory-related vulnerabilities constitute severe threats to the security of
modern software. Despite the success of deep learning-based approaches to
generic vulnerability detection, they are still limited by the underutilization
of flow information when applied for detecting memory-related vulnerabilities,
leading to high false positives.
  In this paper,we propose MVD, a statement-level Memory-related Vulnerability
Detection approach based on flow-sensitive graph neural networks (FS-GNN).
FS-GNN is employed to jointly embed both unstructured information (i.e., source
code) and structured information (i.e., control- and data-flow) to capture
implicit memory-related vulnerability patterns. We evaluate MVD on the dataset
which contains 4,353 real-world memory-related vulnerabilities, and compare our
approach with three state-of-the-art deep learning-based approaches as well as
five popular static analysisbased memory detectors. The experiment results show
that MVD achieves better detection accuracy, outperforming both state-of-theart
DL-based and static analysis-based approaches. Furthermore, MVD makes a great
trade-off between accuracy and efficiency.

Time-dependent structural reliability analysis of nonlinear dynamical systems
is non-trivial; subsequently, scope of most of the structural reliability
analysis methods is limited to time-independent reliability analysis only. In
this work, we propose a Koopman operator based approach for time-dependent
reliability analysis of nonlinear dynamical systems. Since the Koopman
representations can transform any nonlinear dynamical system into a linear
dynamical system, the time evolution of dynamical systems can be obtained by
Koopman operators seamlessly regardless of the nonlinear or chaotic behavior.
Despite the fact that the Koopman theory has been in vogue a long time back,
identifying intrinsic coordinates is a challenging task; to address this, we
propose an end-to-end deep learning architecture that learns the Koopman
observables and then use it for time marching the dynamical response. Unlike
purely data-driven approaches, the proposed approach is robust even in the
presence of uncertainties; this renders the proposed approach suitable for
time-dependent reliability analysis. We propose two architectures; one suitable
for time-dependent reliability analysis when the system is subjected to random
initial condition and the other suitable when the underlying system have
uncertainties in system parameters. The proposed approach is robust and
generalizes to unseen environment (out-of-distribution prediction). Efficacy of
the proposed approached is illustrated using three numerical examples. Results
obtained indicate supremacy of the proposed approach as compared to purely
data-driven auto-regressive neural network and long-short term memory network.

Network embedding is an effective technique to learn the low-dimensional
representations of nodes in networks. Real-world networks are usually with
multiplex or having multi-view representations from different relations.
Recently, there has been increasing interest in network embedding on multiplex
data. However, most existing multiplex approaches assume that the data is
complete in all views. But in real applications, it is often the case that each
view suffers from the missing of some data and therefore results in partial
multiplex data. In this paper, we present a novel Deep Partial Multiplex
Network Embedding approach to deal with incomplete data. In particular, the
network embeddings are learned by simultaneously minimizing the deep
reconstruction loss with the autoencoder neural network, enforcing the data
consistency across views via common latent subspace learning, and preserving
the data topological structure within the same network through graph Laplacian.
We further prove the orthogonal invariant property of the learned embeddings
and connect our approach with the binary embedding techniques. Experiments on
four multiplex benchmarks demonstrate the superior performance of the proposed
approach over several state-of-the-art methods on node classification, link
prediction and clustering tasks.

Multi-modal based speech separation has exhibited a specific advantage on
isolating the target character in multi-talker noisy environments.
Unfortunately, most of current separation strategies prefer a straightforward
fusion based on feature learning of each single modality, which is far from
sufficient consideration of inter-relationships between modalites. Inspired by
learning joint feature representations from audio and visual streams with
attention mechanism, in this study, a novel cross-modal fusion strategy is
proposed to benefit the whole framework with semantic correlations between
different modalities. To further improve audio-visual speech separation, the
dense optical flow of lip motion is incorporated to strengthen the robustness
of visual representation. The evaluation of the proposed work is performed on
two public audio-visual speech separation benchmark datasets. The overall
improvement of the performance has demonstrated that the additional motion
network effectively enhances the visual representation of the combined lip
images and audio signal, as well as outperforming the baseline in terms of all
metrics with the proposed cross-modal fusion.

Semantic parsing is a key NLP task that maps natural language to structured
meaning representations. As in many other NLP tasks, SOTA performance in
semantic parsing is now attained by fine-tuning a large pretrained language
model (PLM). While effective, this approach is inefficient in the presence of
multiple downstream tasks, as a new set of values for all parameters of the PLM
needs to be stored for each task separately. Recent work has explored methods
for adapting PLMs to downstream tasks while keeping most (or all) of their
parameters frozen. We examine two such promising techniques, prefix tuning and
bias-term tuning, specifically on semantic parsing. We compare them against
each other on two different semantic parsing datasets, and we also compare them
against full and partial fine-tuning, both in few-shot and conventional data
settings. While prefix tuning is shown to do poorly for semantic parsing tasks
off the shelf, we modify it by adding special token embeddings, which results
in very strong performance without compromising parameter savings.

Conventional NAS-based pruning algorithms aim to find the sub-network with
the best validation performance. However, validation performance does not
successfully represent test performance, i.e., potential performance. Also,
although fine-tuning the pruned network to restore the performance drop is an
inevitable process, few studies have handled this issue. This paper provides a
novel Ensemble Knowledge Guidance (EKG) to solve both problems at once. First,
we experimentally prove that the fluctuation of loss landscape can be an
effective metric to evaluate the potential performance. In order to search a
sub-network with the smoothest loss landscape at a low cost, we employ EKG as a
search reward. EKG utilized for the following search iteration is composed of
the ensemble knowledge of interim sub-networks, i.e., the by-products of the
sub-network evaluation. Next, we reuse EKG to provide a gentle and informative
guidance to the pruned network while fine-tuning the pruned network. Since EKG
is implemented as a memory bank in both phases, it requires a negligible cost.
For example, when pruning and training ResNet-50, just 315 GPU hours are
required to remove around 45.04% of FLOPS without any performance degradation,
which can operate even on a low-spec workstation. the implemented code is
available at https://github.com/sseung0703/EKG.

Online path planning for multiple unmanned aerial vehicle (multi-UAV) systems
is considered a challenging task. It needs to ensure collision-free path
planning in real-time, especially when the multi-UAV systems can become very
crowded on certain occasions. In this paper, we presented a vision-based
decentralized collision-avoidance policy for multi-UAV systems, which takes
depth images and inertial measurements as sensory inputs and outputs UAV's
steering commands. The policy is trained together with the latent
representation of depth images using a policy gradient-based reinforcement
learning algorithm and autoencoder in the multi-UAV threedimensional
workspaces. Each UAV follows the same trained policy and acts independently to
reach the goal without colliding or communicating with other UAVs. We validate
our policy in various simulated scenarios. The experimental results show that
our learned policy can guarantee fully autonomous collision-free navigation for
multi-UAV in the three-dimensional workspaces with good robustness and

Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen
classes by training only the seen classes, in which the instances of unseen
classes tend to be biased towards the seen class. In this paper, we propose a
Cluster-based Contrastive Disentangling (CCD) method to improve GZSL by
alleviating the semantic gap and domain shift problems. Specifically, we first
cluster the batch data to form several sets containing similar classes. Then,
we disentangle the visual features into semantic-unspecific and
semantic-matched variables, and further disentangle the semantic-matched
variables into class-shared and class-unique variables according to the
clustering results. The disentangled learning module with random swapping and
semantic-visual alignment bridges the semantic gap. Moreover, we introduce
contrastive learning on semantic-matched and class-unique variables to learn
high intra-set and intra-class similarity, as well as inter-set and inter-class
discriminability. Then, the generated visual features conform to the underlying
characteristics of general images and have strong discriminative information,
which alleviates the domain shift problem well. We evaluate our proposed method
on four datasets and achieve state-of-the-art results in both conventional and
generalized settings.

Federated learning (FL) enables distributed optimization of machine learning
models while protecting privacy by independently training local models on each
client and then aggregating parameters on a central server, thereby producing
an effective global model. Although a variety of FL algorithms have been
proposed, their training efficiency remains low when the data are not
independently and identically distributed (non-i.i.d.) across different
clients. We observe that the slow convergence rates of the existing methods are
(at least partially) caused by the catastrophic forgetting issue during the
local training stage on each individual client, which leads to a large increase
in the loss function concerning the previous training data at the other
clients. Here, we propose FedReg, an algorithm to accelerate FL with alleviated
knowledge forgetting in the local training stage by regularizing locally
trained parameters with the loss on generated pseudo data, which encode the
knowledge of previous training data learned by the global model. Our
comprehensive experiments demonstrate that FedReg not only significantly
improves the convergence rate of FL, especially when the neural network
architecture is deep and the clients' data are extremely non-i.i.d., but is
also able to protect privacy better in classification problems and more robust
against gradient inversion attacks. The code is available at:
https://github.com/Zoesgithub/FedReg.

Designing control policies for legged locomotion is complex due to the
under-actuated and non-continuous robot dynamics. Model-free reinforcement
learning provides promising tools to tackle this challenge. However, a major
bottleneck of applying model-free reinforcement learning in real world is
safety. In this paper, we propose a safe reinforcement learning framework that
switches between a safe recovery policy that prevents the robot from entering
unsafe states, and a learner policy that is optimized to complete the task. The
safe recovery policy takes over the control when the learner policy violates
safety constraints, and hands over the control back when there are no future
safety violations. We design the safe recovery policy so that it ensures safety
of legged locomotion while minimally intervening in the learning process.
Furthermore, we theoretically analyze the proposed framework and provide an
upper bound on the task performance. We verify the proposed framework in four
locomotion tasks on a simulated and real quadrupedal robot: efficient gait,
catwalk, two-leg balance, and pacing. On average, our method achieves 48.6%
fewer falls and comparable or better rewards than the baseline methods in
simulation. When deployed it on real-world quadruped robot, our training
pipeline enables 34% improvement in energy efficiency for the efficient gait,
40.9% narrower of the feet placement in the catwalk, and two times more jumping
duration in the two-leg balance. Our method achieves less than five falls over
the duration of 115 minutes of hardware time.

Deep neural networks are increasingly deployed for scene analytics, including
to evaluate the attention and reaction of people exposed to out-of-home
advertisements. However, the features extracted by a deep neural network that
was trained to predict a specific, consensual attribute (e.g. emotion) may also
encode and thus reveal information about private, protected attributes (e.g.
age or gender). In this work, we focus on such leakage of private information
at inference time. We consider an adversary with access to the features
extracted by the layers of a deployed neural network and use these features to
predict private attributes. To prevent the success of such an attack, we modify
the training of the network using a confusion loss that encourages the
extraction of features that make it difficult for the adversary to accurately
predict private attributes. We validate this training approach on image-based
tasks using a publicly available dataset. Results show that, compared to the
original network, the proposed PrivateNet can reduce the leakage of private
information of a state-of-the-art emotion recognition classifier by 2.88% for
gender and by 13.06% for age group, with a minimal effect on task accuracy.

Accurate identification of important objects in the scene is a prerequisite
for safe and high-quality decision making and motion planning of intelligent
agents (e.g., autonomous vehicles) that navigate in complex and dynamic
environments. Most existing approaches attempt to employ attention mechanisms
to learn importance weights associated with each object indirectly via various
tasks (e.g., trajectory prediction), which do not enforce direct supervision on
the importance estimation. In contrast, we tackle this task in an explicit way
and formulate it as a binary classification ("important" or "unimportant")
problem. We propose a novel approach for important object identification in
egocentric driving scenarios with relational reasoning on the objects in the
scene. Besides, since human annotations are limited and expensive to obtain, we
present a semi-supervised learning pipeline to enable the model to learn from
unlimited unlabeled data. Moreover, we propose to leverage the auxiliary tasks
of ego vehicle behavior prediction to further improve the accuracy of
importance estimation. The proposed approach is evaluated on a public
egocentric driving dataset (H3D) collected in complex traffic scenarios. A
detailed ablative study is conducted to demonstrate the effectiveness of each
model component and the training strategy. Our approach also outperforms
rule-based baselines by a large margin.

This study extracted and analyzed the linguistic speech patterns that
characterize Japanese anime or game characters. Conventional morphological
analyzers, such as MeCab, segment words with high performance, but they are
unable to segment broken expressions or utterance endings that are not listed
in the dictionary, which often appears in lines of anime or game characters. To
overcome this challenge, we propose segmenting lines of Japanese anime or game
characters using subword units that were proposed mainly for deep learning, and
extracting frequently occurring strings to obtain expressions that characterize
their utterances. We analyzed the subword units weighted by TF/IDF according to
gender, age, and each anime character and show that they are linguistic speech
patterns that are specific for each feature. Additionally, a classification
experiment shows that the model with subword units outperformed that with the

$Q$-learning with function approximation is one of the most empirically
successful while theoretically mysterious reinforcement learning (RL)
algorithms, and was identified in Sutton (1999) as one of the most important
theoretical open problems in the RL community. Even in the basic linear
function approximation setting, there are well-known divergent examples. In
this work, we show that \textit{target network} and \textit{truncation}
together are enough to provably stabilize $Q$-learning with linear function
approximation, and we establish the finite-sample guarantees. The result
implies an $O(\epsilon^{-2})$ sample complexity up to a function approximation
error. Moreover, our results do not require strong assumptions or modifying the
problem parameters as in existing literature.

Training of Relational Graph Convolutional Networks (R-GCN) is a memory
intense task. The amount of gradient information that needs to be stored during
training for real-world graphs is often too large for the amount of memory
available on most GPUs. In this work, we experiment with the use of graph
summarization techniques to compress the graph and hence reduce the amount of
memory needed. After training the R-GCN on the graph summary, we transfer the
weights back to the original graph and attempt to perform inference on it. We
obtain reasonable results on the AIFB, MUTAG and AM datasets. Our experiments
show that training on the graph summary can yield a comparable or higher
accuracy to training on the original graphs.Furthermore, if we take the time to
compute the summary out of the equation, we observe that the smaller graph
representations obtained with graph summarization methods reduces the
computational overhead. However, further experiments are needed to evaluate
additional graph summary models and whether our findings also holds true for
very large graphs.

We have generated an open-source dataset of over 30000 organic chemistry gas
phase partition functions. With this data, a machine learning deep neural
network estimator was trained to predict partition functions of unknown organic
chemistry gas phase transition states. This estimator only relies on reactant
and product geometries and partition functions. A second machine learning deep
neural network was trained to predict partition functions of chemical species
from their geometry. Our models accurately predict the logarithm of test set
partition functions with a maximum mean absolute error of 2.7%. Thus, this
approach provides a means to reduce the cost of computing reaction rate
constants ab initio. The models were also used to compute transition state
theory reaction rate constants prefactors and the results were in quantitative
agreement with the corresponding ab initio calculations with an accuracy of
98.3% on the log scale.

A rising problem in the compression of Deep Neural Networks is how to reduce
the number of parameters in convolutional kernels and the complexity of these
layers by low-rank tensor approximation. Canonical polyadic tensor
decomposition (CPD) and Tucker tensor decomposition (TKD) are two solutions to
this problem and provide promising results. However, CPD often fails due to
degeneracy, making the networks unstable and hard to fine-tune. TKD does not
provide much compression if the core tensor is big. This motivates using a
hybrid model of CPD and TKD, a decomposition with multiple Tucker models with
small core tensor, known as block term decomposition (BTD). This paper proposes
a more compact model that further compresses the BTD by enforcing core tensors
in BTD identical. We establish a link between the BTD with shared parameters
and a looped chain tensor network (TC). Unfortunately, such strongly
constrained tensor networks (with loop) encounter severe numerical instability,
as proved by y (Landsberg, 2012) and (Handschuh, 2015a). We study perturbation
of chain tensor networks, provide interpretation of instability in TC,
demonstrate the problem. We propose novel methods to gain the stability of the
decomposition results, keep the network robust and attain better approximation.
Experimental results will confirm the superiority of the proposed methods in
compression of well-known CNNs, and TC decomposition under challenging
scenarios

Recently, the EAGL-I system was developed to rapidly create massive labeled
datasets of plants intended to be commonly used by farmers and researchers to
create AI-driven solutions in agriculture. As a result, a publicly available
plant species recognition dataset composed of 40,000 images with different
sizes consisting of 8 plant species was created with the system in order to
demonstrate its capabilities. This paper proposes a novel method, called
Variably Overlapping Time-Coherent Sliding Window (VOTCSW), that transforms a
dataset composed of images with variable size to a 3D representation with fixed
size that is suitable for convolutional neural networks, and demonstrates that
this representation is more informative than resizing the images of the dataset
to a given size. We theoretically formalized the use cases of the method as
well as its inherent properties and we proved that it has an oversampling and a
regularization effect on the data. By combining the VOTCSW method with the 3D
extension of a recently proposed machine learning model called 1-Dimensional
Polynomial Neural Networks, we were able to create a model that achieved a
state-of-the-art accuracy of 99.9% on the dataset created by the EAGL-I system,
surpassing well-known architectures such as ResNet and Inception. In addition,
we created a heuristic algorithm that enables the degree reduction of any
pre-trained N-Dimensional Polynomial Neural Network and which compresses it
without altering its performance, thus making the model faster and lighter.
Furthermore, we established that the currently available dataset could not be
used for machine learning in its present form, due to a substantial class
imbalance between the training set and the test set. Hence, we created a
specific preprocessing and a model development framework that enabled us to
improve the accuracy from 49.23% to 99.9%.

Reinforcement learning (RL) is acquiring a key role in the space of adaptive
interventions (AIs), attracting a substantial interest within methodological
and theoretical literature and becoming increasingly popular within health
sciences. Despite potential benefits, its application in real life is still
limited due to several operational and statistical challenges--in addition to
ethical and cost issues among others--that remain open in part due to poor
communication and synergy between methodological and applied scientists. In
this work, we aim to bridge the different domains that contribute to and may
benefit from RL, under a unique framework that intersects the areas of RL,
causal inference, and AIs, among others. We provide the first unified
instructive survey on RL methods for building AIs, encompassing both dynamic
treatment regimes (DTRs) and just-in-time adaptive interventions in mobile
health (mHealth). We outline similarities and differences between the two
areas, and discuss their implications for using RL. We combine our relevant
methodological knowledge with motivating studies in both DTRs and mHealth to
illustrate the tremendous collaboration opportunities between statistical, RL,
and healthcare researchers in the space of AIs.

As quotidian use of sophisticated cameras surges, people in modern society
are more interested in capturing fine-quality images. However, the quality of
the images might be inferior to people's expectations due to the noise
contamination in the images. Thus, filtering out the noise while preserving
vital image features is an essential requirement. Current existing denoising
methods have their own assumptions on the probability distribution in which the
contaminated noise is sampled for the method to attain its expected denoising
performance. In this paper, we utilize our recent Gramian-based filtering
scheme to remove noise sampled from five prominent probability distributions
from selected images. This method preserves image smoothness by adopting
patches partitioned from the image, rather than pixels, and retains vital image
features by performing denoising on the manifold underlying the patch space
rather than in the image domain. We validate its denoising performance, using
three benchmark computer vision test images applied to two state-of-the-art
denoising methods, namely BM3D and K-SVD.

The clustering task consists in delivering labels to the members of a sample.
For most data sets, some individuals are ambiguous and intrinsically difficult
to attribute to one or another cluster. However, in practical applications,
misclassifying individuals is potentially disastrous. To overcome this
difficulty, the idea followed here is to classify only a part of the sample in
order to obtain a small misclassification rate. This approach is well known in
the supervised setting, and referred to as classification with an abstention
option. The purpose of this paper is to revisit this approach in an
unsupervised mixture-model framework. The problem is formalized in terms of
controlling the false clustering rate (FCR) below a prescribed level {\alpha},
while maximizing the number of classified items. New procedures are introduced
and their behavior is shown to be close to the optimal one by establishing
theoretical results and conducting numerical experiments. An application to
breast cancer data illustrates the benefits of the new approach from a
practical viewpoint.

The information bottleneck framework provides a systematic approach to
learning representations that compress nuisance information in the input and
extract semantically meaningful information about predictions. However, the
choice of a prior distribution that fixes the dimensionality across all the
data can restrict the flexibility of this approach for learning robust
representations. We present a novel sparsity-inducing spike-slab categorical
prior that uses sparsity as a mechanism to provide the flexibility that allows
each data point to learn its own dimension distribution. In addition, it
provides a mechanism for learning a joint distribution of the latent variable
and the sparsity and hence can account for the complete uncertainty in the
latent space. Through a series of experiments using in-distribution and
out-of-distribution learning scenarios on the MNIST, CIFAR-10, and ImageNet
data, we show that the proposed approach improves accuracy and robustness
compared to traditional fixed-dimensional priors, as well as other sparsity
induction mechanisms for latent variable models proposed in the literature.

We consider a version of actor-critic which uses proportional step-sizes and
only one critic update with a single sample from the stationary distribution
per actor step. We provide an analysis of this method using the small-gain
theorem. Specifically, we prove that this method can be used to find a
stationary point, and that the resulting sample complexity improves the state
of the art for actor-critic methods to $O \left(\mu^{-2} \epsilon^{-2} \right)$
to find an $\epsilon$-approximate stationary point where $\mu$ is the condition
number associated with the critic.

Non-local attention module has been proven to be crucial for image
restoration. Conventional non-local attention processes features of each layer
separately, so it risks missing correlation between features among different
layers. To address this problem, we propose Cross-Layer Attention (CLA) module
in this paper. Instead of finding correlated key pixels within the same layer,
each query pixel can attend to key pixels at previous layers of the network. In
order to further enhance the learning capability and reduce the inference cost
of CLA, we further propose Adaptive CLA, or ACLA, as an improved CLA. Two
adaptive designs are proposed for ACLA: 1) adaptively selecting the keys for
non-local attention at each layer; 2) automatically searching for the insertion
locations for ACLA modules. By these two adaptive designs, ACLA dynamically
selects the number of keys to be aggregated for non-local attention at layer.
In addition, ACLA searches for the optimal insert positions of ACLA modules by
a neural architecture search method to render a compact neural network with
compelling performance. Extensive experiments on image restoration tasks,
including single image super-resolution, image denoising, image demosaicing,
and image compression artifacts reduction, validate the effectiveness and
efficiency of ACLA. The code of CLA and ACLA is available at
\url{https://github.com/SDL-ASU/ACLA}.

Out-of-distribution (OOD) detection plays a crucial role in ensuring the safe
deployment of deep neural network (DNN) classifiers. While a myriad of methods
have focused on improving the performance of OOD detectors, a critical gap
remains in interpreting their decisions. We help bridge this gap by providing
explanations for OOD detectors based on learned high-level concepts. We first
propose two new metrics for assessing the effectiveness of a particular set of
concepts for explaining OOD detectors: 1) detection completeness, which
quantifies the sufficiency of concepts for explaining an OOD-detector's
decisions, and 2) concept separability, which captures the distributional
separation between in-distribution and OOD data in the concept space. Based on
these metrics, we propose a framework for learning a set of concepts that
satisfy the desired properties of detection completeness and concept
separability and demonstrate the framework's effectiveness in providing
concept-based explanations for diverse OOD techniques. We also show how to
identify prominent concepts that contribute to the detection results via a
modified Shapley value-based importance score.

Histopathological visualizations are a pillar of modern medicine and
biological research. Surgical oncology relies exclusively on post-operative
histology to determine definitive surgical success and guide adjuvant
treatments. The current histology workflow is based on bright-field microscopic
assessment of histochemical stained tissues and has some major limitations. For
example, the preparation of stained specimens for brightfield assessment
requires lengthy sample processing, delaying interventions for days or even
weeks. Hence, there is a pressing need for improved histopathology methods. In
this paper, we present a deep-learning-based approach for virtual label-free
histochemical staining of total-absorption photoacoustic remote sensing
(TA-PARS) images of unstained tissue. TA-PARS provides an array of directly
measured label-free contrasts such as scattering and total absorption
(radiative and non-radiative), ideal for developing H&E colorizations without
the need to infer arbitrary tissue structures. We use a Pix2Pix generative
adversarial network (GAN) to develop visualizations analogous to H&E staining
from label-free TA-PARS images. Thin sections of human skin tissue were first
virtually stained with the TA-PARS, then were chemically stained with H&E
producing a one-to-one comparison between the virtual and chemical staining.
The one-to-one matched virtually- and chemically- stained images exhibit high
concordance validating the digital colorization of the TA-PARS images against
the gold standard H&E. TA-PARS images were reviewed by four dermatologic
pathologists who confirmed they are of diagnostic quality, and that resolution,
contrast, and color permitted interpretation as if they were H&E. The presented
approach paves the way for the development of TA-PARS slide-free histology,
which promises to dramatically reduce the time from specimen resection to
histological imaging.

In this paper, we present a novel approach to incrementally learn an Abstract
Model of an unknown environment, and show how an agent can reuse the learned
model for tackling the Object Goal Navigation task. The Abstract Model is a
finite state machine in which each state is an abstraction of a state of the
environment, as perceived by the agent in a certain position and orientation.
The perceptions are high-dimensional sensory data (e.g., RGB-D images), and the
abstraction is reached by exploiting image segmentation and the Taskonomy model
bank. The learning of the Abstract Model is accomplished by executing actions,
observing the reached state, and updating the Abstract Model with the acquired
information. The learned models are memorized by the agent, and they are reused
whenever it recognizes to be in an environment that corresponds to the stored
model. We investigate the effectiveness of the proposed approach for the Object
Goal Navigation task, relying on public benchmarks. Our results show that the
reuse of learned Abstract Models can boost performance on Object Goal
Navigation.

Public Policies are not intrinsically positive or negative. Rather, policies
provide varying levels of effects across different recipients.
Methodologically, computational modeling enables the application of multiple
influences on empirical data, thus allowing for heterogeneous response to
policies. We use a random forest machine learning algorithm to emulate an
agent-based model (ABM) and evaluate competing policies across 46 Metropolitan
Regions (MRs) in Brazil. In doing so, we use input parameters and output
indicators of 11,076 actual simulation runs and one million emulated runs. As a
result, we obtain the optimal (and non-optimal) performance of each region over
the policies. Optimum is defined as a combination of GDP production and the
Gini coefficient inequality indicator for the full ensemble of Metropolitan
Regions. Results suggest that MRs already have embedded structures that favor
optimal or non-optimal results, but they also illustrate which policy is more
beneficial to each place. In addition to providing MR-specific policies'
results, the use of machine learning to simulate an ABM reduces the
computational burden, whereas allowing for a much larger variation among model
parameters. The coherence of results within the context of larger
uncertainty--vis-\`a-vis those of the original ABM--reinforces robustness of
the model. At the same time the exercise indicates which parameters should
policymakers intervene on, in order to work towards precise policy optimal

Motion style transfer is a common method for enriching character animation.
Motion style transfer algorithms are often designed for offline settings where
motions are processed in segments. However, for online animation applications,
such as realtime avatar animation from motion capture, motions need to be
processed as a stream with minimal latency. In this work, we realize a
flexible, high-quality motion style transfer method for this setting. We
propose a novel style transfer model, Style-ERD, to stylize motions in an
online manner with an Encoder-Recurrent-Decoder structure, along with a novel
discriminator that combines feature attention and temporal attention. Our
method stylizes motions into multiple target styles with a unified model.
Although our method targets online settings, it outperforms previous offline
methods in motion realism and style expressiveness and provides significant
gains in runtime efficiency

Most methods for conditional video synthesis use a single modality as the
condition. This comes with major limitations. For example, it is problematic
for a model conditioned on an image to generate a specific motion trajectory
desired by the user since there is no means to provide motion information.
Conversely, language information can describe the desired motion, while not
precisely defining the content of the video. This work presents a multimodal
video generation framework that benefits from text and images provided jointly
or separately. We leverage the recent progress in quantized representations for
videos and apply a bidirectional transformer with multiple modalities as inputs
to predict a discrete video representation. To improve video quality and
consistency, we propose a new video token trained with self-learning and an
improved mask-prediction algorithm for sampling video tokens. We introduce text
augmentation to improve the robustness of the textual representation and
diversity of generated videos. Our framework can incorporate various visual
modalities, such as segmentation masks, drawings, and partially occluded
images. It can generate much longer sequences than the one used for training.
In addition, our model can extract visual information as suggested by the text
prompt, e.g., "an object in image one is moving northeast", and generate
corresponding videos. We run evaluations on three public datasets and a newly
collected dataset labeled with facial attributes, achieving state-of-the-art
generation results on all four.

In this paper, we propose a multi-domain control parameter learning framework
that combines Bayesian Optimization (BO) and Hybrid Zero Dynamics (HZD) for
locomotion control of bipedal robots. We leverage BO to learn the control
parameters used in the HZD-based controller. The learning process is firstly
deployed in simulation to optimize different control parameters for a large
repertoire of gaits. Next, to tackle the discrepancy between the simulation and
the real world, the learning process is applied on the physical robot to learn
for corrections to the control parameters learned in simulation while also
respecting a safety constraint for gait stability. This method empowers an
efficient sim-to-real transition with a small number of samples in the real
world, and does not require a valid controller to initialize the training in
simulation. Our proposed learning framework is experimentally deployed and
validated on a bipedal robot Cassie to perform versatile locomotion skills with
improved performance on smoothness of walking gaits and reduction of
steady-state tracking errors.

We propose a method for constructing generative models of 3D objects from a
single 3D mesh and improving them through unsupervised low-shot learning from
2D images. Our method produces a 3D morphable model that represents shape and
albedo in terms of Gaussian processes. Whereas previous approaches have
typically built 3D morphable models from multiple high-quality 3D scans through
principal component analysis, we build 3D morphable models from a single scan
or template. As we demonstrate in the face domain, these models can be used to
infer 3D reconstructions from 2D data (inverse graphics) or 3D data
(registration). Specifically, we show that our approach can be used to perform
face recognition using only a single 3D template (one scan total, not one per
person). We extend our model to a preliminary unsupervised learning framework
that enables the learning of the distribution of 3D faces using one 3D template
and a small number of 2D images. This approach could also provide a model for
the origins of face perception in human infants, who appear to start with an
innate face template and subsequently develop a flexible system for perceiving
the 3D structure of any novel face from experience with only 2D images of a
relatively small number of familiar faces.

Deep Learning has revolutionized various fields, including Computer Vision,
Natural Language Processing, as well as Biomedical research. Within the field
of neuroscience, specifically in electrophysiological neuroimaging, researchers
are starting to explore leveraging deep learning to make predictions on their
data without extensive feature engineering. The availability of large-scale
datasets is a crucial aspect of allowing the experimentation of Deep Learning
models. We are publishing the first large-scale clinical EEG dataset that
simplifies data access and management for Deep Learning. This dataset contains
eyes-closed EEG data prepared from a collection of 1,574 juvenile participants
from the Healthy Brain Network. We demonstrate a use case integrating this
framework, and discuss why providing such neuroinformatics infrastructure to
the community is critical for future scientific discoveries.

A ridge is a function that is characterized by a one-dimensional profile
(activation) and a multidimensional direction vector. Ridges appear in the
theory of neural networks as functional descriptors of the effect of a neuron,
with the direction vector being encoded in the linear weights. In this paper,
we investigate properties of the Radon transform in relation to ridges and to
the characterization of neural networks. We introduce a broad category of
hyper-spherical Banach subspaces (including the relevant subspace of measures)
over which the back-projection operator is invertible. We also give conditions
under which the back-projection operator is extendable to the full parent space
with its null space being identifiable as a Banach complement. Starting from
first principles, we then characterize the sampling functionals that are in the
range of the filtered Radon transform. Next, we extend the definition of ridges
for any distributional profile and determine their (filtered) Radon transform
in full generality. Finally, we apply our formalism to clarify and simplify
some of the results and proofs on the optimality of ReLU networks that have
appeared in the literature.

Current clinical PET systems utilize detectors where the scintillator
typically contains single elements of 3 - 6 mm width and about 20 mm height.
While providing good TOF performance, this design limits the spatial resolution
and causes radial astigmatism as the DOI remains unknown. We propose an
alternative, aiming to combine the advantages of current detectors with the DOI
capabilities shown for monolithic concepts, based on semi-monolithic
scintillators (slabs). An array of 8 monolithic LYSO slabs of dimensions 3.9 x
32 x 19 mm3 was read out by a 64-channel photosensor containing digital SiPMs.
The position estimation in the detector's monolithic and DOI direction was
based on a calibration with a fan beam collimator and the machine learning
technique gradient tree boosting (GTB). We achieved a positioning performance
in terms of mean absolute error (MAE) of 1.44 mm and 2.12 mm for DOI
considering a wide energy window of 300 - 700 keV. The energy resolution was
determined to be 11.3%. We established both an analytical and
machine-learning-based timing calibration approach and applied them for a
first-photon trigger. The analytical timing calibration corrects for electronic
and optical time skews leading to 240 ps coincidence resolving time (CRT) for a
pair of slab-detectors. The CRT was significantly improved by utilizing GTB to
predict the time difference based on specific training data and applied on top
of the analytical calibration. We achieved 209 ps for the wide energy window
and 198 ps for a narrow selection around the photopeak (411 - 561 keV). To
maintain the detector's sensitivity, no filters were applied to the data during
processing. Overall, the semi-monolithic detector provides attractive
performance characteristics. Especially, a good CRT can be achieved while
introducing DOI capabilities to the detector, making the concept suitable for
clinical PET scanners.

An advanced reliable low-cost form of screening method, Digital mammography
has been used as an effective imaging method for breast cancer detection. With
an increased focus on technologies to aid healthcare, Mammogram images have
been utilized in developing computer-aided diagnosis systems that will
potentially help in clinical diagnosis. Researchers have proved that artificial
intelligence with its emerging technologies can be used in the early detection
of the disease and improve radiologists' performance in assessing breast
cancer. In this paper, we review the methods developed for mammogram mass
classification in two categories. The first one is classifying manually
provided cropped region of interests (ROI) as either malignant or benign, and
the second one is the classification of automatically segmented ROIs as either
malignant or benign. We also provide an overview of datasets and evaluation
metrics used in the classification task. Finally, we compare and discuss the
deep learning approach to classical image processing and learning approach in
this domain.

In this paper, we propose a novel semi-supervised learning (SSL) framework
named BoostMIS that combines adaptive pseudo labeling and informative active
annotation to unleash the potential of medical image SSL models: (1) BoostMIS
can adaptively leverage the cluster assumption and consistency regularization
of the unlabeled data according to the current learning status. This strategy
can adaptively generate one-hot "hard" labels converted from task model
predictions for better task model training. (2) For the unselected unlabeled
images with low confidence, we introduce an Active learning (AL) algorithm to
find the informative samples as the annotation candidates by exploiting virtual
adversarial perturbation and model's density-aware entropy. These informative
candidates are subsequently fed into the next training cycle for better SSL
label propagation. Notably, the adaptive pseudo-labeling and informative active
annotation form a learning closed-loop that are mutually collaborative to boost
medical image SSL. To verify the effectiveness of the proposed method, we
collected a metastatic epidural spinal cord compression (MESCC) dataset that
aims to optimize MESCC diagnosis and classification for improved specialist
referral and treatment. We conducted an extensive experimental study of
BoostMIS on MESCC and another public dataset COVIDx. The experimental results
verify our framework's effectiveness and generalisability for different medical
image datasets with a significant improvement over various state-of-the-art
methods.

We present a new strategy for automatically exploring the design space of key
CUDA+MPI programs and providing design rules that discriminate slow from fast
implementations. In such programs, the order of operations (e.g., GPU kernels,
MPI communication) and assignment of operations to resources (e.g., GPU
streams) makes the space of possible designs enormous. Systems experts have the
task of redesigning and reoptimizing these programs to effectively utilize each
new platform. This work provides a prototype tool to reduce that burden.
  In our approach, a directed acyclic graph of CUDA and MPI operations defines
the design space for the program. Monte-Carlo tree search discovers regions of
the design space that have large impact on the program's performance. A
sequence-to-vector transformation defines features for each explored
implementation, and each implementation is assigned a class label according to
its relative performance. A decision tree is trained on the features and labels
to produce design rules for each class; these rules can be used by systems
experts to guide their implementations. We demonstrate our strategy using a key
kernel from scientific computing -- sparse-matrix vector multiplication -- on a
platform with multiple MPI ranks and GPU streams.

Pansharpening aims to fuse a registered high-resolution panchromatic image
(PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an
enhanced HSI with high spectral and spatial resolution. Existing pansharpening
approaches neglect using an attention mechanism to transfer HR texture features
from PAN to LR-HSI features, resulting in spatial and spectral distortions. In
this paper, we present a novel attention mechanism for pansharpening called
HyperTransformer, in which features of LR-HSI and PAN are formulated as queries
and keys in a transformer, respectively. HyperTransformer consists of three
main modules, namely two separate feature extractors for PAN and HSI, a
multi-head feature soft attention module, and a spatial-spectral feature fusion
module. Such a network improves both spatial and spectral quality measures of
the pansharpened HSI by learning cross-feature space dependencies and
long-range details of PAN and LR-HSI. Furthermore, HyperTransformer can be
utilized across multiple spatial scales at the backbone for obtaining improved
performance. Extensive experiments conducted on three widely used datasets
demonstrate that HyperTransformer achieves significant improvement over the
state-of-the-art methods on both spatial and spectral quality measures.
Implementation code and pre-trained weights can be accessed at
https://github.com/wgcban/HyperTransformer.

Centroid based clustering methods such as k-means, k-medoids and k-centers
are heavily applied as a go-to tool in exploratory data analysis. In many
cases, those methods are used to obtain representative centroids of the data
manifold for visualization or summarization of a dataset. Real world datasets
often contain inherent abnormalities, e.g., repeated samples and sampling bias,
that manifest imbalanced clustering. We propose to remedy such a scenario by
introducing a maximal radius constraint $r$ on the clusters formed by the
centroids, i.e., samples from the same cluster should not be more than $2r$
apart in terms of $\ell_2$ distance. We achieve this constraint by solving a
semi-definite program, followed by a linear assignment problem with quadratic
constraints. Through qualitative results, we show that our proposed method is
robust towards dataset imbalances and sampling artifacts. To the best of our
knowledge, ours is the first constrained k-means clustering method with hard
radius constraints. Codes at https://bit.ly/kmeans-constrained

This work explores a new approach for optimization in the field of
microfluidics, using the combination of CFD (Computational Fluid Dynamics), and
Machine Learning techniques. The objective of this combination is to enable
global optimization with lower computational cost. The initial geometry is
inspired in a Y-type micromixer with cylindrical grooves on the surface of the
main channel and obstructions inside it. Simulations for circular obstructions
were carried out using the OpenFOAM software to observe the influences of
obstacles. The effects of obstruction diameter (OD), and offset (OF) in the
range of [20,140] mm and [10,160] mm, respectively, on percentage of mixing
($\varphi$), pressure drop ($\Delta P$) and energy cost ($\Delta P/\varphi$)
were investigated. Numerical experiments were analyzed using machine learning.
Firstly, a neural network was used to train the dataset composed by the inputs
OD and OF and outputs $\varphi$ and $\Delta P$. The objective functions (ObF)
chosen to numerically optimize the performance of micromixers with grooves and
obstructions were $\varphi$, $\Delta P$, $\Delta P/\varphi$. The genetic
algorithm obtained the geometry that offers the maximum value of $\varphi$ and
the minimum value of $\Delta P_s$. The results show that $\varphi$ increases
monotonically with increasing OD at all values of OF. The inverse is observed
with increasing offset. Furthermore, the results reveal that $\Delta P$ e
$\Delta P/\varphi$ also increase with OD. On the other hand, the pressure drop
and the cost of mixing energy present a maximum close to the lowest values of
OF. Finally, the optimal value obtained for the diameter was OD=131 mm and for
the offset OF=10 mm, which corresponds to obstruction of medium size close to
the channel wall.

Learning from label proportions (LLP) is a weakly supervised classification
problem where data points are grouped into bags, and the label proportions
within each bag are observed instead of the instance-level labels. The task is
to learn a classifier to predict the individual labels of future individual
instances. Prior work on LLP for multi-class data has yet to develop a
theoretically grounded algorithm. In this work, we provide a theoretically
grounded approach to LLP based on a reduction to learning with label noise,
using the forward correction (FC) loss of \citet{Patrini2017MakingDN}. We
establish an excess risk bound and generalization error analysis for our
approach, while also extending the theory of the FC loss which may be of
independent interest. Our approach demonstrates improved empirical performance
in deep learning scenarios across multiple datasets and architectures, compared
to the leading existing methods.

Visual neuroprostheses are the only FDA-approved technology for the treatment
of retinal degenerative blindness. Although recent work has demonstrated a
systematic relationship between electrode location and the shape of the
elicited visual percept, this knowledge has yet to be incorporated into retinal
prosthesis design, where electrodes are typically arranged on either a
rectangular or hexagonal grid. Here we optimize the intraocular placement of
epiretinal electrodes using dictionary learning. Importantly, the optimization
process is informed by a previously established and psychophysically validated
model of simulated prosthetic vision. We systematically evaluate three
different electrode placement strategies across a wide range of possible
phosphene shapes and recommend electrode arrangements that maximize visual
subfield coverage. In the near future, our work may guide the prototyping of
next-generation neuroprostheses.

Forecasting pedestrians' future motions is essential for autonomous driving
systems to safely navigate in urban areas. However, existing prediction
algorithms often overly rely on past observed trajectories and tend to fail
around abrupt dynamic changes, such as when pedestrians suddenly start or stop
walking. We suggest that predicting these highly non-linear transitions should
form a core component to improve the robustness of motion prediction
algorithms. In this paper, we introduce the new task of pedestrian stop and go
forecasting. Considering the lack of suitable existing datasets for it, we
release TRANS, a benchmark for explicitly studying the stop and go behaviors of
pedestrians in urban traffic. We build it from several existing datasets
annotated with pedestrians' walking motions, in order to have various scenarios
and behaviors. We also propose a novel hybrid model that leverages
pedestrian-specific and scene features from several modalities, both video
sequences and high-level attributes, and gradually fuses them to integrate
multiple levels of context. We evaluate our model and several baselines on
TRANS, and set a new benchmark for the community to work on pedestrian stop and
go forecasting.

This paper proposes a new method to estimate behavioural curves from a stream
of Near-Infra-Red (NIR) iris video frames. This method can be used in a Fitness
For Duty system (FFD). The research focuses on determining the effect of
external factors such as alcohol, drugs, and sleepiness on the Central Nervous
System (CNS). The aim is to analyse how this behaviour is represented on iris
and pupil movements and if it is possible to capture these changes with a
standard NIR camera. The behaviour analysis showed essential differences in
pupil and iris behaviour to classify the workers in "Fit" or "Unfit"
conditions. The best results can distinguish subjects robustly under alcohol,
drug consumption, and sleep conditions. The Multi-Layer-Perceptron and Gradient
Boosted Machine reached the best results in all groups with an overall accuracy
for Fit and Unfit classes of 74.0% and 75.5%, respectively. These results open
a new application for iris capture devices.

In many object recognition applications, the set of possible categories is an
open set, and the deployed recognition system will encounter novel objects
belonging to categories unseen during training. Detecting such "novel category"
objects is usually formulated as an anomaly detection problem. Anomaly
detection algorithms for feature-vector data identify anomalies as outliers,
but outlier detection has not worked well in deep learning. Instead, methods
based on the computed logits of visual object classifiers give state-of-the-art
performance. This paper proposes the Familiarity Hypothesis that these methods
succeed because they are detecting the absence of familiar learned features
rather than the presence of novelty. This distinction is important, because
familiarity-based detection will fail in many situations where novelty is
present. For example when an image contains both a novel object and a familiar
one, the familiarity score will be high, so the novel object will not be
noticed. The paper reviews evidence from the literature and presents additional
evidence from our own experiments that provide strong support for this
hypothesis. The paper concludes with a discussion of whether familiarity-based
detection is an inevitable consequence of representation learning.

Better-supervised models might have better performance. In this paper, we
first clarify what makes for good supervision for a classification problem, and
then explain two existing label refining methods, label smoothing and knowledge
distillation, in terms of our proposed criterion. To further answer why and how
better supervision emerges, we observe the learning path, i.e., the trajectory
of the model's predictions during training, for each training sample. We find
that the model can spontaneously refine "bad" labels through a "zig-zag"
learning path, which occurs on both toy and real datasets. Observing the
learning path not only provides a new perspective for understanding knowledge
distillation, overfitting, and learning dynamics, but also reveals that the
supervisory signal of a teacher network can be very unstable near the best
points in training on real tasks. Inspired by this, we propose a new knowledge
distillation scheme, Filter-KD, which improves downstream classification
performance in various settings.

Ontologies encompass a formal representation of knowledge through the
definition of concepts or properties of a domain, and the relationships between
those concepts. In this work, we seek to investigate whether using this
ontological information will improve learning from weakly labeled data, which
are easier to collect since it requires only the presence or absence of an
event to be known. We use the AudioSet ontology and dataset, which contains
audio clips weakly labeled with the ontology concepts and the ontology
providing the "Is A" relations between the concepts. We first re-implemented
the model proposed by soundevent_ontology with modification to fit the
multi-label scenario and then expand on that idea by using a Graph
Convolutional Network (GCN) to model the ontology information to learn the
concepts. We find that the baseline Siamese does not perform better by
incorporating ontology information in the weak and multi-label scenario, but
that the GCN does capture the ontology knowledge better for weak, multi-labeled
data. In our experiments, we also investigate how different modules can
tolerate noises introduced from weak labels and better incorporate ontology
information. Our best Siamese-GCN model achieves mAP=0.45 and AUC=0.87 for
lower-level concepts and mAP=0.72 and AUC=0.86 for higher-level concepts, which
is an improvement over the baseline Siamese but about the same as our models
that do not use ontology information.

Designing a distribution of environments in which RL agents can learn
interesting and useful skills is a challenging and poorly understood task, for
multi-agent environments the difficulties are only exacerbated. One approach is
to train a second RL agent, called a teacher, who samples environments that are
conducive for the learning of student agents. However, most previous proposals
for teacher rewards do not generalize straightforwardly to the multi-agent
setting. We examine a set of intrinsic teacher rewards derived from prediction
problems that can be applied in multi-agent settings and evaluate them in
Mujoco tasks such as multi-agent Hide and Seek as well as a diagnostic
single-agent maze task. Of the intrinsic rewards considered we found value
disagreement to be most consistent across tasks, leading to faster and more
reliable emergence of advanced skills in Hide and Seek and the maze task.
Another candidate intrinsic reward considered, value prediction error, also
worked well in Hide and Seek but was susceptible to noisy-TV style distractions
in stochastic environments. Policy disagreement performed well in the maze task
but did not speed up learning in Hide and Seek. Our results suggest that
intrinsic teacher rewards, and in particular value disagreement, are a
promising approach for automating both single and multi-agent environment
design.

Non-verbal social human behavior forecasting has increasingly attracted the
interest of the research community in recent years. Its direct applications to
human-robot interaction and socially-aware human motion generation make it a
very attractive field. In this survey, we define the behavior forecasting
problem for multiple interactive agents in a generic way that aims at unifying
the fields of social signals prediction and human motion forecasting,
traditionally separated. We hold that both problem formulations refer to the
same conceptual problem, and identify many shared fundamental challenges:
future stochasticity, context awareness, history exploitation, etc. We also
propose a taxonomy that comprises methods published in the last 5 years in a
very informative way and describes the current main concerns of the community
with regard to this problem. In order to promote further research on this
field, we also provide a summarised and friendly overview of audiovisual
datasets featuring non-acted social interactions. Finally, we describe the most
common metrics used in this task and their particular issues.

Understanding generalization in modern machine learning settings has been one
of the major challenges in statistical learning theory. In this context, recent
years have witnessed the development of various generalization bounds
suggesting different complexity notions such as the mutual information between
the data sample and the algorithm output, compressibility of the hypothesis
space, and the fractal dimension of the hypothesis space. While these bounds
have illuminated the problem at hand from different angles, their suggested
complexity notions might appear seemingly unrelated, thereby restricting their
high-level impact. In this study, we prove novel generalization bounds through
the lens of rate-distortion theory, and explicitly relate the concepts of
mutual information, compressibility, and fractal dimensions in a single
mathematical framework. Our approach consists of (i) defining a generalized
notion of compressibility by using source coding concepts, and (ii) showing
that the `compression error rate' can be linked to the generalization error
both in expectation and with high probability. We show that in the `lossless
compression' setting, we recover and improve existing mutual information-based
bounds, whereas a `lossy compression' scheme allows us to link generalization
to the rate-distortion dimension -- a particular notion of fractal dimension.
Our results bring a more unified perspective on generalization and open up
several future research directions.

Personalized treatment decisions have become an integral part of modern
medicine. Thereby, the aim is to make treatment decisions based on individual
patient characteristics. Numerous methods have been developed for learning such
policies from observational data that achieve the best outcome across a certain
policy class. Yet these methods are rarely interpretable. However,
interpretability is often a prerequisite for policy learning in clinical
practice. In this paper, we propose an algorithm for interpretable off-policy
learning via hyperbox search. In particular, our policies can be represented in
disjunctive normal form (i.e., OR-of-ANDs) and are thus intelligible. We prove
a universal approximation theorem that shows that our policy class is flexible
enough to approximate any measurable function arbitrarily well. For
optimization, we develop a tailored column generation procedure within a
branch-and-bound framework. Using a simulation study, we demonstrate that our
algorithm outperforms state-of-the-art methods from interpretable off-policy
learning in terms of regret. Using real-word clinical data, we perform a user
study with actual clinical experts, who rate our policies as highly

Graph clustering is the process of grouping vertices into densely connected
sets called clusters. We tailor two mathematical programming formulations from
the literature, to this problem. In doing so, we obtain a heuristic
approximation to the intra-cluster density maximization problem. We use two
variations of a Boltzmann machine heuristic to obtain numerical solutions. For
benchmarking purposes, we compare solution quality and computational
performances to those obtained using a commercial solver, Gurobi. We also
compare clustering quality to the clusters obtained using the popular Louvain
modularity maximization method. Our initial results clearly demonstrate the
superiority of our problem formulations. They also establish the superiority of
the Boltzmann machine over the traditional exact solver. In the case of smaller
less complex graphs, Boltzmann machines provide the same solutions as Gurobi,
but with solution times that are orders of magnitude lower. In the case of
larger and more complex graphs, Gurobi fails to return meaningful results
within a reasonable time frame. Finally, we also note that both our clustering
formulations, the distance minimization and $K$-medoids, yield clusters of
superior quality to those obtained with the Louvain algorithm.

This work studies the learning process over social networks under partial and
random information sharing. In traditional social learning, agents exchange
full information with each other while trying to infer the true state of
nature. We study the case where agents share information about only one
hypothesis, i.e., the trending topic, which can be randomly changing at every
iteration. We show that agents can learn the true hypothesis even if they do
not discuss it, at rates comparable to traditional social learning. We also
show that using one's own belief as a prior for estimating the neighbors'
non-transmitted components might create opinion clusters that prevent learning
with full confidence. This practice however avoids the complete rejection of
the truth.

Variational quantum-classical hybrid algorithms are seen as a promising
strategy for solving practical problems on quantum computers in the near term.
While this approach reduces the number of qubits and operations required from
the quantum machine, it places a heavy load on a classical optimizer. While
often under-appreciated, the latter is a computationally hard task due to the
barren plateau phenomenon in parameterized quantum circuits. The absence of
guiding features like gradients renders conventional optimization strategies
ineffective as the number of qubits increases. Here, we introduce the
fast-and-slow algorithm, which uses Bayesian Learning to identify a promising
region in parameter space. This is used to initialize a fast local optimizer to
find the global optimum point efficiently. We illustrate the effectiveness of
this method on the Bars-and-Stripes (BAS) quantum generative model, which has
been studied on several quantum hardware platforms. Our results move
variational quantum algorithms closer to their envisioned applications in
quantum chemistry, combinatorial optimization, and quantum simulation problems.

Simultaneous Machine Translation is the task of incrementally translating an
input sentence before it is fully available. Currently, simultaneous
translation is carried out by translating each sentence independently of the
previously translated text. More generally, Streaming MT can be understood as
an extension of Simultaneous MT to the incremental translation of a continuous
input text stream. In this work, a state-of-the-art simultaneous sentence-level
MT system is extended to the streaming setup by leveraging the streaming
history. Extensive empirical results are reported on IWSLT Translation Tasks,
showing that leveraging the streaming history leads to significant quality
gains. In particular, the proposed system proves to compare favorably to the
best performing systems.

Entropy modeling is a key component for high-performance image compression
algorithms. Recent developments in autoregressive context modeling helped
learning-based methods to surpass their classical counterparts. However, the
performance of those models can be further improved due to the underexploited
spatio-channel dependencies in latent space, and the suboptimal implementation
of context adaptivity. Inspired by the adaptive characteristics of the
transformers, we propose a transformer-based context model, named
Contextformer, which generalizes the de facto standard attention mechanism to
spatio-channel attention. We replace the context model of a modern compression
framework with the Contextformer and test it on the widely used Kodak,
CLIC2020, and Tecnick image datasets. Our experimental results show that the
proposed model provides up to 11% rate savings compared to the standard
Versatile Video Coding (VVC) Test Model (VTM) 16.2, and outperforms various
learning-based models in terms of PSNR and MS-SSIM.

We give a detailed theoretical derivation of the quantum master equation for
the coherent Ising machine. This is a quantum computational network with
feedback, that solves NP hard combinatoric problems, including the traveling
salesman problem and various extensions and analogs. There are two types of
master equation that are obtained, either conditional on the feedback current
or unconditional. We show that both types can be accurately simulated in a
scalable way using stochastic equations in the positive-P phase-space
representation. This depends on the nonlinearity present, and we use parameter
values that are typical of current experiments. However, while the two
approaches are in excellent agreement, they are not equivalent with regard to
efficiency. We find that unconditional simulation has much greater efficiency,
and is scalable to larger sizes. This is a case where too much knowledge is a
dangerous thing. Conditioning the simulations on the feedback current is not
essential to determining the success probability, but it greatly increases the
computational complexity. To illustrate the speed improvements obtained with
the unconditional approach, we carry out full quantum simulations of the
quantum master equation with up to 1000 nodes.

Given a deep learning model trained on data from a source site, how to deploy
the model to a target hospital automatically? How to accommodate heterogeneous
medical coding systems across different hospitals? Standard approaches rely on
existing medical code mapping tools, which have significant practical
  To tackle this problem, we propose AutoMap to automatically map the medical
codes across different EHR systems in a coarse-to-fine manner: (1)
Ontology-level Alignment: We leverage the ontology structure to learn a coarse
alignment between the source and target medical coding systems; (2) Code-level
Refinement: We refine the alignment at a fine-grained code level for the
downstream tasks using a teacher-student framework.
  We evaluate AutoMap using several deep learning models with two real-world
EHR datasets: eICU and MIMIC-III. Results show that AutoMap achieves relative
improvements up to 3.9% (AUC-ROC) and 8.7% (AUC-PR) for mortality prediction,
and up to 4.7% (AUC-ROC) and 3.7% (F1) for length-of-stay estimation. Further,
we show that AutoMap can provide accurate mapping across coding systems.
Lastly, we demonstrate that AutoMap can adapt to the two challenging scenarios:
(1) mapping between completely different coding systems and (2) between
completely different hospitals.

A theorem that establishes a one-to-one relation between zero-temperature
static spin-spin correlators and coupling constants for a general class of
quantum spin Hamiltonians bilinear in the spin operators has been recently
established by J. Quintanilla, using an argument in the spirit of the
Hohenberg-Kohn theorem in density functional theory. Quintanilla's theorem
gives a firm theoretical foundation to quantum spin Hamiltonian learning using
spin structure factors as input data. Here we extend the validity of the
theorem in two directions. First, following the same approach as Mermin, the
proof is extended to the case of finite-temperature spin structure factors,
thus ensuring that the application of this theorem to experimental data is
sound. Second, we note that this theorem applies to all types of Hamiltonians
expressed as sums of bilinear operators, so that it can also relate the
density-density correlators to the Coulomb matrix elements for interacting
electrons in the lowest Landau level.

Combinatorial optimization is a well-established area in operations research
and computer science. Until recently, its methods have focused on solving
problem instances in isolation, ignoring that they often stem from related data
distributions in practice. However, recent years have seen a surge of interest
in using machine learning as a new approach for solving combinatorial problems,
either directly as solvers or by enhancing exact solvers. Based on this
context, the ML4CO aims at improving state-of-the-art combinatorial
optimization solvers by replacing key heuristic components. The competition
featured three challenging tasks: finding the best feasible solution, producing
the tightest optimality certificate, and giving an appropriate solver
configuration. Three realistic datasets were considered: balanced item
placement, workload apportionment, and maritime inventory routing. This last
dataset was kept anonymous for the contestants.

Computing space-efficient summary, or \textit{a.k.a. sketches}, of large
data, is a central problem in the streaming algorithm. Such sketches are used
to answer \textit{post-hoc} queries in several data analytics tasks. The
algorithm for computing sketches typically requires to be fast, accurate, and
space-efficient. A fundamental problem in the streaming algorithm framework is
that of computing the frequency moments of data streams. The frequency moments
of a sequence containing $f_i$ elements of type $i$, are the numbers
$\mathbf{F}_k=\sum_{i=1}^n {f_i}^k,$ where $i\in [n]$. This is also called as
$\ell_k$ norm of the frequency vector $(f_1, f_2, \ldots f_n).$ Another
important problem is to compute the similarity between two data streams by
computing the inner product of the corresponding frequency vectors. The seminal
work of Alon, Matias, and Szegedy~\cite{AMS}, \textit{a.k.a. Tug-of-war} (or
AMS) sketch gives a randomized sublinear space (and linear time) algorithm for
computing the frequency moments, and the inner product between two frequency
vectors corresponding to the data streams. However, the variance of these
estimates typically tends to be large. In this work, we focus on minimizing the
variance of these estimates. We use the techniques from the classical
Control-Variate method~\cite{Lavenberg} which is primarily known for variance
reduction in Monte-Carlo simulations, and as a result, we are able to obtain
significant variance reduction, at the cost of a little computational overhead.
We present a theoretical analysis of our proposal and complement it with
supporting experiments on synthetic as well as real-world datasets.

This paper presents a novel parametric curve-based method for lane detection
in RGB images. Unlike state-of-the-art segmentation-based and point
detection-based methods that typically require heuristics to either decode
predictions or formulate a large sum of anchors, the curve-based methods can
learn holistic lane representations naturally. To handle the optimization
difficulties of existing polynomial curve methods, we propose to exploit the
parametric B\'ezier curve due to its ease of computation, stability, and high
freedom degrees of transformations. In addition, we propose the deformable
convolution-based feature flip fusion, for exploiting the symmetry properties
of lanes in driving scenes. The proposed method achieves a new state-of-the-art
performance on the popular LLAMAS benchmark. It also achieves favorable
accuracy on the TuSimple and CULane datasets, while retaining both low latency
(> 150 FPS) and small model size (< 10M). Our method can serve as a new
baseline, to shed the light on the parametric curves modeling for lane
detection. Codes of our model and PytorchAutoDrive: a unified framework for
self-driving perception, are available at:
https://github.com/voldemortX/pytorch-auto-drive .

The inception of the Relational Graph Convolutional Network (R-GCN) marked a
milestone in the Semantic Web domain as a widely cited method that generalises
end-to-end hierarchical representation learning to Knowledge Graphs (KGs).
R-GCNs generate representations for nodes of interest by repeatedly aggregating
parameterised, relation-specific transformations of their neighbours. However,
in this paper, we argue that the the R-GCN's main contribution lies in this
"message passing" paradigm, rather than the learned weights. To this end, we
introduce the "Random Relational Graph Convolutional Network" (RR-GCN), which
leaves all parameters untrained and thus constructs node embeddings by
aggregating randomly transformed random representations from neighbours, i.e.,
with no learned parameters. We empirically show that RR-GCNs can compete with
fully trained R-GCNs in both node classification and link prediction settings.

Monitoring of ground movement close to the rail corridor, such as that
associated with landslips caused by ground subsidence and/or uplift, is of
great interest for the detection and prevention of possible railway faults.
Interferometric synthetic-aperture radar (InSAR) data can be used to measure
ground deformation, but its use poses distinct challenges, as the data is
highly sparse and can be particularly noisy. Here we present a scheme for
processing and interpolating noisy, sparse InSAR data into a dense
spatio-temporal stack, helping suppress noise and opening up the possibility
for treatment with deep learning and other image processing methods.

Guaranteeing safety of perception-based learning systems is challenging due
to the absence of ground-truth state information unlike in state-aware control
scenarios. In this paper, we introduce a safety guaranteed learning framework
for vision-based end-to-end autonomous driving. To this end, we design a
learning system equipped with differentiable control barrier functions (dCBFs)
that is trained end-to-end by gradient descent. Our models are composed of
conventional neural network architectures and dCBFs. They are interpretable at
scale, achieve great test performance under limited training data, and are
safety guaranteed in a series of autonomous driving scenarios such as lane
keeping and obstacle avoidance. We evaluated our framework in a sim-to-real
environment, and tested on a real autonomous car, achieving safe lane following
and obstacle avoidance via Augmented Reality (AR) and real parked vehicles.

Bayesian network structure learning is an NP-hard problem that has been faced
by a number of traditional approaches in recent decades. Currently, quantum
technologies offer a wide range of advantages that can be exploited to solve
optimization tasks that cannot be addressed in an efficient way when utilizing
classic computing approaches. In this work, a specific type of variational
quantum algorithm, the quantum approximate optimization algorithm, was used to
solve the Bayesian network structure learning problem, by employing $3n(n-1)/2$
qubits, where $n$ is the number of nodes in the Bayesian network to be learned.
Our results showed that the quantum approximate optimization algorithm approach
offers competitive results with state-of-the-art methods and quantitative
resilience to quantum noise. The approach was applied to a cancer benchmark
problem, and the results justified the use of variational quantum algorithms
for solving the Bayesian network structure learning problem.

This study investigates the impact of machine learning models on the
generation of counterfactual explanations by conducting a benchmark evaluation
over three different types of models: decision-tree (fully transparent,
interpretable, white-box model), a random forest (a semi-interpretable,
grey-box model), and a neural network (a fully opaque, black-box model). We
tested the counterfactual generation process using four algorithms (DiCE,
WatcherCF, prototype, and GrowingSpheresCF) in the literature in five different
datasets (COMPAS, Adult, German, Diabetes, and Breast Cancer). Our findings
indicate that: (1) Different machine learning models have no impact on the
generation of counterfactual explanations; (2) Counterfactual algorithms based
uniquely on proximity loss functions are not actionable and will not provide
meaningful explanations; (3) One cannot have meaningful evaluation results
without guaranteeing plausibility in the counterfactual generation process.
Algorithms that do not consider plausibility in their internal mechanisms will
lead to biased and unreliable conclusions if evaluated with the current
state-of-the-art metrics; (4) A qualitative analysis is strongly recommended
(together with a quantitative analysis) to ensure a robust analysis of
counterfactual explanations and the potential identification of biases.

In the recent years, the copy detection patterns (CDP) attracted a lot of
attention as a link between the physical and digital worlds, which is of great
interest for the internet of things and brand protection applications. However,
the security of CDP in terms of their reproducibility by unauthorized parties
or clonability remains largely unexplored. In this respect this paper addresses
a problem of anti-counterfeiting of physical objects and aims at investigating
the authentication aspects and the resistances to illegal copying of the modern
CDP from machine learning perspectives. A special attention is paid to a
reliable authentication under the real life verification conditions when the
codes are printed on an industrial printer and enrolled via modern mobile
phones under regular light conditions. The theoretical and empirical
investigation of authentication aspects of CDP is performed with respect to
four types of copy fakes from the point of view of (i) multi-class supervised
classification as a baseline approach and (ii) one-class classification as a
real-life application case. The obtained results show that the modern
machine-learning approaches and the technical capacities of modern mobile
phones allow to reliably authenticate CDP on end-user mobile phones under the
considered classes of fakes.

In recent text-to-speech synthesis and voice conversion systems, a
mel-spectrogram is commonly applied as an intermediate representation, and the
necessity for a mel-spectrogram vocoder is increasing. A mel-spectrogram
vocoder must solve three inverse problems: recovery of the original-scale
magnitude spectrogram, phase reconstruction, and frequency-to-time conversion.
A typical convolutional mel-spectrogram vocoder solves these problems jointly
and implicitly using a convolutional neural network, including temporal
upsampling layers, when directly calculating a raw waveform. Such an approach
allows skipping redundant processes during waveform synthesis (e.g., the direct
reconstruction of high-dimensional original-scale spectrograms). By contrast,
the approach solves all problems in a black box and cannot effectively employ
the time-frequency structures existing in a mel-spectrogram. We thus propose
iSTFTNet, which replaces some output-side layers of the mel-spectrogram vocoder
with the inverse short-time Fourier transform (iSTFT) after sufficiently
reducing the frequency dimension using upsampling layers, reducing the
computational cost from black-box modeling and avoiding redundant estimations
of high-dimensional spectrograms. During our experiments, we applied our ideas
to three HiFi-GAN variants and made the models faster and more lightweight with
a reasonable speech quality. Audio samples are available at
https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/istftnet/.

News Recommendation System(NRS) has become a fundamental technology to many
online news services. Meanwhile, several studies show that recommendation
systems(RS) are vulnerable to data poisoning attacks, and the attackers have
the ability to mislead the system to perform as their desires. A widely studied
attack approach, injecting fake users, can be applied on the NRS when the NRS
is treated the same as the other systems whose items are fixed. However, in the
NRS, as each item (i.e. news) is more informative, we propose a novel approach
to poison the NRS, which is to perturb contents of some browsed news that
results in the manipulation of the rank of the target news. Intuitively, an
attack is useless if it is highly likely to be caught, i.e., exposed. To
address this, we introduce a notion of the exposure risk and propose a novel
problem of attacking a history news dataset by means of perturbations where the
goal is to maximize the manipulation of the target news rank while keeping the
risk of exposure under a given budget. We design a reinforcement learning
framework, called TDP-CP, which contains a two-stage hierarchical model to
reduce the searching space. Meanwhile, influence estimation is also applied to
save the time on retraining the NRS for rewards. We test the performance of
TDP-CP under three NRSs and on different target news. Our experiments show that
TDP-CP can increase the rank of the target news successfully with a limited
exposure budget.

We employ a descriptor based machine-learning approach to assess the effect
of chemical alloying on formation-enthalpy of rare-earth intermetallics.
Application of machine-learning approaches in rare-earth intermetallic design
have been sparse due to limited availability of reliable datasets. In this
work, we developed an `in-house' rare-earth database with more than 600$+$
compounds, each entry was populated with formation enthalpy and related atomic
features using high-throughput density-functional theory (DFT). The SISSO (sure
independence screening and sparsifying operator) based machine-learning method
with meaningful atomic features was used for training and testing the formation
enthalpies of rare earth compounds. The complex lattice function coupled with
the machine-learning model was used to explore the effect of transition metal
alloying on the energy stability of Ce based cubic Laves phases (MgCu$_{2}$
type). The SISSO predictions show good agreement with high-fidelity DFT
calculations and X$-$ray powder diffraction measurements. Our study provides
quantitative guidance for compositional considerations within a
machine-learning model and discovering new metastable materials. The
electronic-structure of Ce$-$Fe$-$Cu based compound was also analyzed
in$-$depth to understand the electronic origin of phase stability. The
interpretable analytical models in combination with density$-$functional theory
and experiments provide a fast and reliable design guide for discovering
technologically useful materials.

Automated surface segmentation of retinal layer is important and challenging
in analyzing optical coherence tomography (OCT). Recently, many deep learning
based methods have been developed for this task and yield remarkable
performance. However, due to large spatial gap and potential mismatch between
the B-scans of OCT data, all of them are based on 2D segmentation of individual
B-scans, which may loss the continuity information across the B-scans. In
addition, 3D surface of the retina layers can provide more diagnostic
information, which is crucial in quantitative image analysis. In this study, a
novel framework based on hybrid 2D-3D convolutional neural networks (CNNs) is
proposed to obtain continuous 3D retinal layer surfaces from OCT. The 2D
features of individual B-scans are extracted by an encoder consisting of 2D
convolutions. These 2D features are then used to produce the alignment
displacement field and layer segmentation by two 3D decoders, which are coupled
via a spatial transformer module. The entire framework is trained end-to-end.
To the best of our knowledge, this is the first study that attempts 3D retinal
layer segmentation in volumetric OCT images based on CNNs. Experiments on a
publicly available dataset show that our framework achieves superior results to
state-of-the-art 2D methods in terms of both layer segmentation accuracy and
cross-B-scan 3D continuity, thus offering more clinical values than previous
works.

Robots often face situations where grasping a goal object is desirable but
not feasible due to other present objects preventing the grasp action. We
present a deep Reinforcement Learning approach to learn grasping and pushing
policies for manipulating a goal object in highly cluttered environments to
address this problem. In particular, a dual Reinforcement Learning model
approach is proposed, which presents high resilience in handling complicated
scenes, reaching an average of 98% task completion using primitive objects in a
simulation environment. To evaluate the performance of the proposed approach,
we performed two extensive sets of experiments in packed objects and a pile of
object scenarios with a total of 1000 test runs in simulation. Experimental
results showed that the proposed method worked very well in both scenarios and
outperformed the recent state-of-the-art approaches. Demo video, trained
models, and source code for the results reproducibility purpose are publicly
https://github.com/Kamalnl92/Self-Supervised-Learning-for-pushing-and-grasping.

Pushing objects through cluttered scenes is a challenging task, especially
when the objects to be pushed have initially unknown dynamics and touching
other entities has to be avoided to reduce the risk of damage. In this paper,
we approach this problem by applying deep reinforcement learning to generate
pushing actions for a robotic manipulator acting on a planar surface where
objects have to be pushed to goal locations while avoiding other items in the
same workspace. With the latent space learned from a depth image of the scene
and other observations of the environment, such as contact information between
the end effector and the object as well as distance to the goal, our framework
is able to learn contact-rich pushing actions that avoid collisions with other
objects. As the experimental results with a six degrees of freedom robotic arm
show, our system is able to successfully push objects from start to end
positions while avoiding nearby objects. Furthermore, we evaluate our learned
policy in comparison to a state-of-the-art pushing controller for mobile robots
and show that our agent performs better in terms of success rate, collisions
with other objects, and continuous object contact in various scenarios.

Accurately identifying patient's status through medical images plays an
important role in diagnosis and treatment. Artificial intelligence (AI),
especially the deep learning, has achieved great success in many fields.
However, more reliable AI model is needed in image guided diagnosis and
therapy. To achieve this goal, developing a balanced, safe and robust model
with a unified framework is desirable. In this study, a new unified model
termed as automated multi-objective Mixer (AutoMO-Mixer) model was developed,
which utilized a recent developed multiple layer perceptron Mixer (MLP-Mixer)
as base. To build a balanced model, sensitivity and specificity were considered
as the objective functions simultaneously in training stage. Meanwhile, a new
evidential reasoning based on entropy was developed to achieve a safe and
robust model in testing stage. The experiment on an optical coherence
tomography dataset demonstrated that AutoMO-Mixer can obtain safer, more
balanced, and robust results compared with MLP-Mixer and other available
models.

Distributed optimization methods are often applied to solving huge-scale
problems like training neural networks with millions and even billions of
parameters. In such applications, communicating full vectors, e.g.,
(stochastic) gradients, iterates, is prohibitively expensive, especially when
the number of workers is large. Communication compression is a powerful
approach to alleviating this issue, and, in particular, methods with biased
compression and error compensation are extremely popular due to their practical
efficiency. Sahu et al. (2021) propose a new analysis of Error Compensated SGD
(EC-SGD) for the class of absolute compression operators showing that in a
certain sense, this class contains optimal compressors for EC-SGD. However, the
analysis was conducted only under the so-called $(M,\sigma^2)$-bounded noise
assumption. In this paper, we generalize the analysis of EC-SGD with absolute
compression to the arbitrary sampling strategy and propose the first analysis
of Error Compensated Loopless Stochastic Variance Reduced Gradient method
(EC-LSVRG) with absolute compression for (strongly) convex problems. Our rates
improve upon the previously known ones in this setting. Numerical experiments
corroborate our theoretical findings.

Search missions require motion planning and navigation methods for
information gathering that continuously replan based on new observations of the
robot's surroundings. Current methods for information gathering, such as Monte
Carlo Tree Search, are capable of reasoning over long horizons, but they are
computationally expensive. An alternative for fast online execution is to
train, offline, an information gathering policy, which indirectly reasons about
the information value of new observations. However, these policies lack safety
guarantees and do not account for the robot dynamics. To overcome these
limitations we train an information-aware policy via deep reinforcement
learning, that guides a receding-horizon trajectory optimization planner. In
particular, the policy continuously recommends a reference viewpoint to the
local planner, such that the resulting dynamically feasible and collision-free
trajectories lead to observations that maximize the information gain and reduce
the uncertainty about the environment. In simulation tests in previously unseen
environments, our method consistently outperforms greedy next-best-view
policies and achieves competitive performance compared to Monte Carlo Tree
Search, in terms of information gains and coverage time, with a reduction in
execution time by three orders of magnitude.

Modern real-time Structural Health Monitoring systems can generate a
considerable amount of information that must be processed and evaluated for
detecting early anomalies and generating prompt warnings and alarms about the
civil infrastructure conditions. The current cloud-based solutions cannot scale
if the raw data has to be collected from thousands of buildings. This paper
presents a full-stack deployment of an efficient and scalable anomaly detection
pipeline for SHM systems which does not require sending raw data to the cloud
but relies on edge computation. First, we benchmark three algorithmic
approaches of anomaly detection, i.e., Principal Component Analysis (PCA),
Fully-Connected AutoEncoder (FC-AE), and Convolutional AutoEncoder (C-AE).
Then, we deploy them on an edge-sensor, the STM32L4, with limited computing
capabilities. Our approach decreases network traffic by
$\approx8\cdot10^5\times$ , from 780KB/hour to less than 10 Bytes/hour for a
single installation and minimize network and cloud resource utilization,
enabling the scaling of the monitoring infrastructure. A real-life case study,
a highway bridge in Italy, demonstrates that combining near-sensor computation
of anomaly detection algorithms, smart pre-processing, and low-power wide-area
network protocols (LPWAN) we can greatly reduce data communication and cloud
computing costs, while anomaly detection accuracy is not adversely affected.

Identification and quantification of nuclei in colorectal cancer haematoxylin
\& eosin (H\&E) stained histology images is crucial to prognosis and patient
management. In computational pathology these tasks are referred to as nuclear
segmentation, classification and composition and are used to extract meaningful
interpretable cytological and architectural features for downstream analysis.
The CoNIC challenge poses the task of automated nuclei segmentation,
classification and composition into six different types of nuclei from the
largest publicly known nuclei dataset - Lizard. In this regard, we have
developed pipelines for the prediction of nuclei segmentation using HoVer-Net
and ALBRT for cellular composition. On testing on the preliminary test set,
HoVer-Net achieved a PQ of 0.58, a PQ+ of 0.58 and finally a mPQ+ of 0.35. For
the prediction of cellular composition with ALBRT on the preliminary test set,
we achieved an overall $R^2$ score of 0.53, consisting of 0.84 for lymphocytes,
0.70 for epithelial cells, 0.70 for plasma and .060 for eosinophils.

We report on five years of 3-5 micron photometry measurements obtained by
warm Spitzer to track the dust debris emission in the terrestrial zone of HD
166191 in combination with simultaneous optical data. We show that the debris
production in this young (~10 Myr) system increased significantly in early 2018
and reached a record high level (almost double by mid 2019) by the end of the
Spitzer mission (early 2020), suggesting intense collisional activity in its
terrestrial zone likely due to either initial assembling of terrestrial planets
through giant impacts or dynamical shake-up from unseen planet-mass objects or
recent planet migration. This intense activity is further highlighted by
detecting a star-size dust clump, passing in front of the star, in the midst of
its infrared brightening. We constrain the minimum size and mass of the clump
using multiwavelength transit profiles and conclude that the dust clump is most
likely created by a large impact involving objects of several hundred km in
size with an apparent period of 142 days (i.e., 0.62 au assuming a circular
orbit). The system's evolutionary state (right after the dispersal of its
gas-rich disk) makes it extremely valuable to learn about the process of
terrestrial planet formation and planetary architecture through future

Noisy intermediate-scale quantum (NISQ) devices are spearheading the second
quantum revolution. Of these, quantum annealers are the only ones currently
offering real world, commercial applications on as many as 5000 qubits. The
size of problems that can be solved by quantum annealers is limited mainly by
errors caused by environmental noise and intrinsic imperfections of the
processor. We address the issue of intrinsic imperfections with a novel error
correction approach, based on machine learning methods. Our approach adjusts
the input Hamiltonian to maximize the probability of finding the solution. In
our experiments, the proposed error correction method improved the performance
of annealing by up to three orders of magnitude and enabled the solving of a
previously intractable, maximally complex problem.

Recent advances of Transformers have brought new trust to computer vision
tasks. However, on small dataset, Transformers is hard to train and has lower
performance than convolutional neural networks. We make vision transformers as
data-efficient as convolutional neural networks by introducing multi-focal
attention bias. Inspired by the attention distance in a well-trained ViT, we
constrain the self-attention of ViT to have multi-scale localized receptive
field. The size of receptive field is adaptable during training so that optimal
configuration can be learned. We provide empirical evidence that proper
constrain of receptive field can reduce the amount of training data for vision
transformers. On Cifar100, our ViT-P Base model achieves the state-of-the-art
accuracy (83.16%) trained from scratch. We also perform analysis on ImageNet to
show our method does not lose accuracy on large data sets.

Automatic anatomical landmark localization has made great strides by
leveraging deep learning methods in recent years. The ability to quantify the
uncertainty of these predictions is a vital ingredient needed to see these
methods adopted in clinical use, where it is imperative that erroneous
predictions are caught and corrected. We propose Quantile Binning, a
data-driven method to categorise predictions by uncertainty with estimated
error bounds. This framework can be applied to any continuous uncertainty
measure, allowing straightforward identification of the best subset of
predictions with accompanying estimated error bounds. We facilitate easy
comparison between uncertainty measures by constructing two evaluation metrics
derived from Quantile Binning. We demonstrate this framework by comparing and
contrasting three uncertainty measures (a baseline, the current gold standard,
and a proposed method combining aspects of the two), across two datasets (one
easy, one hard) and two heatmap-based landmark localization model paradigms
(U-Net and patch-based). We conclude by illustrating how filtering out gross
mispredictions caught in our Quantile Bins significantly improves the
proportion of predictions under an acceptable error threshold, and offer
recommendations on which uncertainty measure to use and how to use it.

Recent work has shown promising results in causal discovery by leveraging
interventional data with gradient-based methods, even when the intervened
variables are unknown. However, previous work assumes that the correspondence
between samples and interventions is known, which is often unrealistic. We
envision a scenario with an extensive dataset sampled from multiple
intervention distributions and one observation distribution, but where we do
not know which distribution originated each sample and how the intervention
affected the system, \textit{i.e.}, interventions are entirely latent. We
propose a method based on neural networks and variational inference that
addresses this scenario by framing it as learning a shared causal graph among
an infinite mixture (under a Dirichlet process prior) of intervention
structural causal models. Experiments with synthetic and real data show that
our approach and its semi-supervised variant are able to discover causal
relations in this challenging scenario.

As machine learning models become increasingly larger, trained weakly
supervised on large, possibly uncurated data sets, it becomes increasingly
important to establish mechanisms for inspecting, interacting, and revising
models to mitigate learning shortcuts and guarantee their learned knowledge is
aligned with human knowledge. The recently proposed XIL framework was developed
for this purpose, and several such methods have been introduced, each with
individual motivations and methodological details. In this work, we provide a
unification of various XIL methods into a single typology by establishing a
common set of basic modules. In doing so, we pave the way for a principled
comparison of existing, but, importantly, also future XIL approaches. In
addition, we discuss existing and introduce novel measures and benchmarks for
evaluating the overall abilities of a XIL method. Given this extensive toolbox,
including our typology, measures, and benchmarks, we finally compare several
recent XIL methods methodologically and quantitatively. In our evaluations, all
methods prove to revise a model successfully. However, we found remarkable
differences in individual benchmark tasks, revealing valuable
application-relevant aspects for integrating these benchmarks in developing
future methods.

This work addresses the coexistence problem for radar networks. Specifically,
we model a network of cooperative, independent, and non-communicating radar
nodes which must share resources within the network as well as with
non-cooperative nearby emitters. We approach this problem using online Machine
Learning (ML) techniques. Online learning approaches are specifically preferred
due to the fact that each radar node has no prior knowledge of the environment
nor of the positions of the other radar nodes, and due to the sequential nature
of the problem. For this task we specifically select the multi-player
multi-armed bandit (MMAB) model, which poses the problem as a sequential game,
where each radar node in a network makes independent selections of center
frequency and waveform with the same goal of improving tracking performance for
the network as a whole. For accurate tracking, each radar node communicates
observations to a fusion center on set intervals. The fusion center has
knowledge of the radar node placement, but cannot communicate to the individual
nodes fast enough for waveform control. Every radar node in the network must
learn the behavior of the environment, which includes rewards, interferer
behavior, and target behavior. Our contributions include a mathematical
description of the MMAB framework adapted to the radar network scenario. We
conclude with a simulation study of several different network configurations.
Experimental results show that iterative, online learning using MMAB
outperforms the more traditional sense-and-avoid (SAA) and fixed-allocation

A treatment regime is a rule that assigns a treatment to patients based on
their covariate information. Recently, estimation of the optimal treatment
regime that yields the greatest overall expected clinical outcome of interest
has attracted a lot of attention. In this work, we consider estimation of the
optimal treatment regime with electronic medical record data under a
semi-supervised setting. Here, data consist of two parts: a set of `labeled'
patients for whom we have the covariate, treatment and outcome information, and
a much larger set of `unlabeled' patients for whom we only have the covariate
information. We proposes an imputation-based semi-supervised method, utilizing
`unlabeled' individuals to obtain a more efficient estimator of the optimal
treatment regime. The asymptotic properties of the proposed estimators and
their associated inference procedure are provided. Simulation studies are
conducted to assess the empirical performance of the proposed method and to
compare with a fully supervised method using only the labeled data. An
application to an electronic medical record data set on the treatment of
hypotensive episodes during intensive care unit (ICU) stays is also given for
further illustration.

Mixed reality applications often require virtual objects that are partly
occluded by real objects. However, previous research and commercial products
have limitations in terms of performance and efficiency. To address these
challenges, we propose a novel depth contour occlusion (DCO) algorithm. The
proposed method is based on the sensitivity of contour occlusion and a
binocular stereoscopic vision device. In this method, a depth contour map is
combined with a sparse depth map obtained from a two-stage adaptive filter area
stereo matching algorithm and the depth contour information of the objects
extracted by a digital image stabilisation optical flow method. We also propose
a quadratic optimisation model with three constraints to generate an accurate
dense map of the depth contour for high-quality real-virtual occlusion. The
whole process is accelerated by GPU. To evaluate the effectiveness of the
algorithm, we demonstrate a time con-sumption statistical analysis for each
stage of the DCO algorithm execution. To verify the relia-bility of the
real-virtual occlusion effect, we conduct an experimental analysis on
single-sided, enclosed, and complex occlusions; subsequently, we compare it
with the occlusion method without quadratic optimisation. With our GPU
implementation for real-time DCO, the evaluation indicates that applying the
presented DCO algorithm can enhance the real-time performance and the visual
quality of real-virtual occlusion.

Local explanations of learning-to-rank (LTR) models are thought to extract
the most important features that contribute to the ranking predicted by the LTR
model for a single data point. Evaluating the accuracy of such explanations is
challenging since the ground truth feature importance scores are not available
for most modern LTR models. In this work, we propose a systematic evaluation
technique for explanations of LTR models. Instead of using black-box models,
such as neural networks, we propose to focus on tree-based LTR models, from
which we can extract the ground truth feature importance scores using decision
paths. Once extracted, we can directly compare the ground truth feature
importance scores to the feature importance scores generated with explanation
techniques. We compare two recently proposed explanation techniques for LTR
models when using decision trees and gradient boosting models on the MQ2008
dataset. We show that the explanation accuracy in these techniques can largely
vary depending on the explained model and even which data point is explained.

With society's increased dependence on information communication systems, the
need for dependable, trustable, robust, and secure adaptive systems becomes
ever more acute. Modern autonomic message-oriented middleware platforms have
stringent requirements for self-healing, adapting, evolving, fault-tolerance,
security, and active vulnerability assessment, especially when the internal
working model of a system and the environmental influences on the system are
uncertain during run-time. In this paper, we present an adaptive and evolving
security approach, and adaptive trust management approach to autonomous
messaging middleware systems. This approach learns, anticipates, evolves, and
adapts to a changing environment at run-time in the face of changing threats.
The approach combines adaptive risk-based security, trust-based security, and
security-based trust: the resultant supra-additive synergy improves and
increases the strength of security and the degree of trust in the system. The
approach also integrates different metrics, assessment tools, and observation
tools that improve and increase the assessability and verifiability of the
trustworthiness of the system. Validation of results is through industrial case
studies and end-user assessment.

Speech enhancement in the time-frequency domain is often performed by
estimating a multiplicative mask to extract clean speech. However, most neural
network-based methods perform point estimation, i.e., their output consists of
a single mask. In this paper, we study the benefits of modeling uncertainty in
neural network-based speech enhancement. For this, our neural network is
trained to map a noisy spectrogram to the Wiener filter and its associated
variance, which quantifies uncertainty, based on the maximum a posteriori (MAP)
inference of spectral coefficients. By estimating the distribution instead of
the point estimate, one can model the uncertainty associated with each
estimate. We further propose to use the estimated Wiener filter and its
uncertainty to build an approximate MAP (A-MAP) estimator of spectral
magnitudes, which in turn is combined with the MAP inference of spectral
coefficients to form a hybrid loss function to jointly reinforce the
estimation. Experimental results on different datasets show that the proposed
method can not only capture the uncertainty associated with the estimated
filters, but also yield a higher enhancement performance over comparable models
that do not take uncertainty into account.

The large discrepancy between the source non-makeup image and the reference
makeup image is one of the key challenges in makeup transfer. Conventional
approaches for makeup transfer either learn disentangled representation or
perform pixel-wise correspondence in a parametric way between two images. We
argue that non-parametric techniques have a high potential for addressing the
pose, expression, and occlusion discrepancies. To this end, this paper proposes
a \textbf{S}emi-\textbf{p}arametric \textbf{M}akeup \textbf{T}ransfer (SpMT)
method, which combines the reciprocal strengths of non-parametric and
parametric mechanisms. The non-parametric component is a novel
\textbf{S}emantic-\textbf{a}ware \textbf{C}orrespondence (SaC) module that
explicitly reconstructs content representation with makeup representation under
the strong constraint of component semantics. The reconstructed representation
is desired to preserve the spatial and identity information of the source image
while "wearing" the makeup of the reference image. The output image is
synthesized via a parametric decoder that draws on the reconstructed
representation. Extensive experiments demonstrate the superiority of our method
in terms of visual quality, robustness, and flexibility. Code and pre-trained
model are available at \url{https://github.com/AnonymScholar/SpMT.

Effectively classifying remote sensing scenes is still a challenge due to the
increasing spatial resolution of remote imaging and large variances between
remote sensing images. Existing research has greatly improved the performance
of remote sensing scene classification (RSSC). However, these methods are not
applicable to cross-domain few-shot problems where target domain is with very
limited training samples available and has a different data distribution from
source domain. To improve the model's applicability, we propose the
feature-wise transformation module (FTM) in this paper. FTM transfers the
feature distribution learned on source domain to that of target domain by a
very simple affine operation with negligible additional parameters. Moreover,
FTM can be effectively learned on target domain in the case of few training
data available and is agnostic to specific network structures. Experiments on
RSSC and land-cover mapping tasks verified its capability to handle
cross-domain few-shot problems. By comparison with directly finetuning, FTM
achieves better performance and possesses better transferability and
fine-grained discriminability. \textit{Code will be publicly available.}

It is a mystery which input features contribute to a neural network's output.
Various explanation (feature attribution) methods are proposed in the
literature to shed light on the problem. One peculiar observation is that these
explanations (attributions) point to different features as being important. The
phenomenon raises the question, which explanation to trust? We propose a
framework for evaluating the explanations using the neural network model
itself. The framework leverages the network to generate input features that
impose a particular behavior on the output. Using the generated features, we
devise controlled experimental setups to evaluate whether an explanation method
conforms to an axiom. Thus we propose an empirical framework for axiomatic
evaluation of explanation methods. We evaluate well-known and promising
explanation solutions using the proposed framework. The framework provides a
toolset to reveal properties and drawbacks within existing and future
explanation solutions.

PercepNet, a recent extension of the RNNoise, an efficient, high-quality and
real-time full-band speech enhancement technique, has shown promising
performance in various public deep noise suppression tasks. This paper proposes
a new approach, named PercepNet+, to further extend the PercepNet with four
significant improvements. First, we introduce a phase-aware structure to
leverage the phase information into PercepNet, by adding the complex features
and complex subband gains as the deep network input and output respectively.
Then, a signal-to-noise ratio (SNR) estimator and an SNR switched
post-processing are specially designed to alleviate the over attenuation (OA)
that appears in high SNR conditions of the original PercepNet. Moreover, the
GRU layer is replaced by TF-GRU to model both temporal and frequency
dependencies. Finally, we propose to integrate the loss of complex subband
gain, SNR, pitch filtering strength, and an OA loss in a multi-objective
learning manner to further improve the speech enhancement performance.
Experimental results show that, the proposed PercepNet+ outperforms the
original PercepNet significantly in terms of both PESQ and STOI, without
increasing the model size too much.

Pseudo-label-based semi-supervised learning (SSL) has achieved great success
on raw data utilization. However, its training procedure suffers from
confirmation bias due to the noise contained in self-generated artificial
labels. Moreover, the model's judgment becomes noisier in real-world
applications with extensive out-of-distribution data. To address this issue, we
propose a general method named Class-aware Contrastive Semi-Supervised Learning
(CCSSL), which is a drop-in helper to improve the pseudo-label quality and
enhance the model's robustness in the real-world setting. Rather than treating
real-world data as a union set, our method separately handles reliable
in-distribution data with class-wise clustering for blending into downstream
tasks and noisy out-of-distribution data with image-wise contrastive for better
generalization. Furthermore, by applying target re-weighting, we successfully
emphasize clean label learning and simultaneously reduce noisy label learning.
Despite its simplicity, our proposed CCSSL has significant performance
improvements over the state-of-the-art SSL methods on the standard datasets
CIFAR100 and STL10. On the real-world dataset Semi-iNat 2021, we improve
FixMatch by 9.80% and CoMatch by 3.18%. Code is available
https://github.com/TencentYoutuResearch/Classification-SemiCLS.

In the last few years, we have witnessed the rise of a series of deep
learning methods to generate synthetic images that look extremely realistic.
These techniques prove useful in the movie industry and for artistic purposes.
However, they also prove dangerous if used to spread fake news or to generate
fake online accounts. For this reason, detecting if an image is an actual
photograph or has been synthetically generated is becoming an urgent necessity.
This paper proposes a detector of synthetic images based on an ensemble of
Convolutional Neural Networks (CNNs). We consider the problem of detecting
images generated with techniques not available at training time. This is a
common scenario, given that new image generators are published more and more
frequently. To solve this issue, we leverage two main ideas: (i) CNNs should
provide orthogonal results to better contribute to the ensemble; (ii) original
images are better defined than synthetic ones, thus they should be better
trusted at testing time. Experiments show that pursuing these two ideas
improves the detector accuracy on NVIDIA's newly generated StyleGAN3 images,
never used in training.

This paper describes the system architectures and the models submitted by our
team "IISERBBrains" to SemEval 2022 Task 6 competition. We contested for all
three sub-tasks floated for the English dataset. On the leader-board, wegot19th
rank out of43 teams for sub-taskA, the 8th rank out of22 teams for sub-task
B,and13th rank out of 16 teams for sub-taskC. Apart from the submitted results
and models, we also report the other models and results that we obtained
through our experiments after organizers published the gold labels of their
evaluation data

Light field disparity estimation is an essential task in computer vision with
various applications. Although supervised learning-based methods have achieved
both higher accuracy and efficiency than traditional optimization-based
methods, the dependency on ground-truth disparity for training limits the
overall generalization performance not to say for real-world scenarios where
the ground-truth disparity is hard to capture. In this paper, we argue that
unsupervised methods can achieve comparable accuracy, but, more importantly,
much higher generalization capacity and efficiency than supervised methods.
Specifically, we present the Occlusion Pattern Aware Loss, named OPAL, which
successfully extracts and encodes the general occlusion patterns inherent in
the light field for loss calculation. OPAL enables: i) accurate and robust
estimation by effectively handling occlusions without using any ground-truth
information for training and ii) much efficient performance by significantly
reducing the network parameters required for accurate inference. Besides, a
transformer-based network and a refinement module are proposed for achieving
even more accurate results. Extensive experiments demonstrate our method not
only significantly improves the accuracy compared with the SOTA unsupervised
methods, but also possesses strong generalization capacity, even for real-world
data, compared with supervised methods. Our code will be made publicly

This paper presents a new database collected from a bilingual speakers set
(49), in two different languages: Spanish and Catalan. Phonetically there are
significative differences between both languages. These differences have let us
to establish several conclusions on the relevance of language in speaker
recognition, using two methods: vector quantization and covariance matrices

Deep reinforcement learning (DRL) is a promising approach to solve complex
control tasks by learning policies through interactions with the environment.
However, the training of DRL policies requires large amounts of training
experiences, making it impractical to learn the policy directly on physical
systems. Sim-to-real approaches leverage simulations to pretrain DRL policies
and then deploy them in the real world. Unfortunately, the direct real-world
deployment of pretrained policies usually suffers from performance
deterioration due to the different dynamics, known as the reality gap. Recent
sim-to-real methods, such as domain randomization and domain adaptation, focus
on improving the robustness of the pretrained agents. Nevertheless, the
simulation-trained policies often need to be tuned with real-world data to
reach optimal performance, which is challenging due to the high cost of
real-world samples.
  This work proposes a distributed cloud-edge architecture to train DRL agents
in the real world in real-time. In the architecture, the inference and training
are assigned to the edge and cloud, separating the real-time control loop from
the computationally expensive training loop. To overcome the reality gap, our
architecture exploits sim-to-real transfer strategies to continue the training
of simulation-pretrained agents on a physical system. We demonstrate its
applicability on a physical inverted-pendulum control system, analyzing
critical parameters. The real-world experiments show that our architecture can
adapt the pretrained DRL agents to unseen dynamics consistently and

Generating new events given context with correlated ones plays a crucial role
in many event-centric reasoning tasks. Existing works either limit their scope
to specific scenarios or overlook event-level correlations. In this paper, we
propose to pre-train a general Correlation-aware context-to-Event Transformer
(ClarET) for event-centric reasoning. To achieve this, we propose three novel
event-centric objectives, i.e., whole event recovering, contrastive
event-correlation encoding and prompt-based event locating, which highlight
event-level correlations with effective training. The proposed ClarET is
applicable to a wide range of event-centric reasoning scenarios, considering
its versatility of (i) event-correlation types (e.g., causal, temporal,
contrast), (ii) application formulations (i.e., generation and classification),
and (iii) reasoning types (e.g., abductive, counterfactual and ending
reasoning). Empirical fine-tuning results, as well as zero- and few-shot
learning, on 9 benchmarks (5 generation and 4 classification tasks covering 4
reasoning types with diverse event correlations), verify its effectiveness and

Active speaker detection and speech enhancement have become two increasingly
attractive topics in audio-visual scenario understanding. According to their
respective characteristics, the scheme of independently designed architecture
has been widely used in correspondence to each single task. This may lead to
the representation learned by the model being task-specific, and inevitably
result in the lack of generalization ability of the feature based on
multi-modal modeling. More recent studies have shown that establishing
cross-modal relationship between auditory and visual stream is a promising
solution for the challenge of audio-visual multi-task learning. Therefore, as a
motivation to bridge the multi-modal associations in audio-visual tasks, a
unified framework is proposed to achieve target speaker detection and speech
enhancement with joint learning of audio-visual modeling in this study.

Recent progress in state-only imitation learning extends the scope of
applicability of imitation learning to real-world settings by relieving the
need for observing expert actions. However, existing solutions only learn to
extract a state-to-action mapping policy from the data, without considering how
the expert plans to the target. This hinders the ability to leverage
demonstrations and limits the flexibility of the policy. In this paper, we
introduce Decoupled Policy Optimization (DePO), which explicitly decouples the
policy as a high-level state planner and an inverse dynamics model. With
embedded decoupled policy gradient and generative adversarial training, DePO
enables knowledge transfer to different action spaces or state transition
dynamics, and can generalize the planner to out-of-demonstration state regions.
Our in-depth experimental analysis shows the effectiveness of DePO on learning
a generalized target state planner while achieving the best imitation
performance. We demonstrate the appealing usage of DePO for transferring across
different tasks by pre-training, and the potential for co-training agents with
various skills.

We argue that object detectors in the safety critical domain should
prioritize detection of objects that are most likely to interfere with the
actions of the autonomous actor. Especially, this applies to objects that can
impact the actor's safety and reliability. To quantify the impact of object
(mis)detection on safety and reliability in the context of autonomous driving,
we propose new object detection measures that reward the correct identification
of objects that are most dangerous and most likely to affect driving decisions.
To achieve this, we build an object criticality model to reward the detection
of the objects based on proximity, orientation, and relative velocity with
respect to the subject vehicle. Then, we apply our model on the recent
autonomous driving dataset nuScenes, and we compare nine object detectors.
Results show that, in several settings, object detectors that perform best
according to the nuScenes ranking are not the preferable ones when the focus is
shifted on safety and reliability.

We analyse the convergence of the proximal gradient algorithm for convex
composite problems in the presence of gradient and proximal computational
inaccuracies. We derive new tighter deterministic and probabilistic bounds that
we use to verify a simulated (MPC) and a synthetic (LASSO) optimization
problems solved on a reduced-precision machine in combination with an
inaccurate proximal operator. We also show how the probabilistic bounds are
more robust for algorithm verification and more accurate for application
performance guarantees. Under some statistical assumptions, we also prove that
some cumulative error terms follow a martingale property. And conforming to
observations, e.g., in \cite{schmidt2011convergence}, we also show how the
acceleration of the algorithm amplifies the gradient and proximal computational
errors.

The increasing energy consumption and carbon footprint of deep learning (DL)
due to growing compute requirements has become a cause of concern. In this
work, we focus on the carbon footprint of developing DL models for medical
image analysis (MIA), where volumetric images of high spatial resolution are
handled. In this study, we present and compare the features of four tools from
literature to quantify the carbon footprint of DL. Using one of these tools we
estimate the carbon footprint of medical image segmentation pipelines. We
choose nnU-net as the proxy for a medical image segmentation pipeline and
experiment on three common datasets. With our work we hope to inform on the
increasing energy costs incurred by MIA. We discuss simple strategies to
cut-down the environmental impact that can make model selection and training
processes more efficient.

Simulated annealing (SA) is a stochastic global optimisation technique
applicable to a wide range of discrete and continuous variable problems.
Despite its simplicity, the development of an effective SA optimiser for a
given problem hinges on a handful of carefully handpicked components; namely,
neighbour proposal distribution and temperature annealing schedule. In this
work, we view SA from a reinforcement learning perspective and frame the
proposal distribution as a policy, which can be optimised for higher solution
quality given a fixed computational budget. We demonstrate that this Neural SA
with such a learnt proposal distribution, parametrised by small equivariant
neural networks, outperforms SA baselines on a number of problems: Rosenbrock's
function, the Knapsack problem, the Bin Packing problem, and the Travelling
Salesperson problem. We also show that Neural SA scales well to large problems
- generalising to significantly larger problems than the ones seen during
training - while achieving comparable performance to popular off-the-shelf
solvers and other machine learning methods in terms of solution quality and
wall-clock time.

Precoding design exploiting deep learning methods has been widely studied for
multiuser multiple-input multiple-output (MU-MIMO) systems. However,
conventional neural precoding design applies black-box-based neural networks
which are less interpretable. In this paper, we propose a deep learning-based
precoding method based on an interpretable design of a neural precoding
network, namely iPNet. In particular, the iPNet mimics the classic minimum
mean-squared error (MMSE) precoding and approximates the matrix inversion in
the design of the neural network architecture. Specifically, the proposed iPNet
consists of a model-driven component network, responsible for augmenting the
input channel state information (CSI), and a data-driven sub-network,
responsible for precoding calculation from this augmented CSI. The latter
data-driven module is explicitly interpreted as an unsupervised learner of the
MMSE precoder. Simulation results show that by exploiting the augmented CSI,
the proposed iPNet achieves noticeable performance gain over existing black-box
designs and also exhibits enhanced generalizability against CSI mismatches.

In some scenarios, classifier requires detecting out-of-distribution samples
far from its training data. With desirable characteristics, reconstruction
autoencoder-based methods deal with this problem by using input reconstruction
error as a metric of novelty vs. normality. We formulate the essence of such
approach as a quadruplet domain translation with an intrinsic bias to only
query for a proxy of conditional data uncertainty. Accordingly, an improvement
direction is formalized as maximumly compressing the autoencoder's latent space
while ensuring its reconstructive power for acting as a described domain
translator. From it, strategies are introduced including semantic
reconstruction, data certainty decomposition and normalized L2 distance to
substantially improve original methods, which together establish
state-of-the-art performance on various benchmarks, e.g., the FPR@95%TPR of
CIFAR-100 vs. TinyImagenet-crop on Wide-ResNet is 0.2%. Importantly, our method
works without any additional data, hard-to-implement structure, time-consuming
pipeline, and even harming the classification accuracy of known classes.

In recent years, exploring effective sound separation (SSep) techniques to
improve overlapping sound event detection (SED) attracts more and more
attention. Creating accurate separation signals to avoid the catastrophic error
accumulation during SED model training is very important and challenging. In
this study, we first propose a novel selective pseudo-labeling approach, termed
SPL, to produce high confidence separated target events from blind sound
separation outputs. These target events are then used to fine-tune the original
SED model that pre-trained on the sound mixtures in a multi-objective learning
style. Then, to further leverage the SSep outputs, a class-wise discriminative
fusion is proposed to improve the final SED performances, by combining multiple
frame-level event predictions of both sound mixtures and their separated
signals. All experiments are performed on the public DCASE 2021 Task 4 dataset,
and results show that our approaches significantly outperforms the official
baseline, the collar-based F 1, PSDS1 and PSDS2 performances are improved from
44.3%, 37.3% and 54.9% to 46.5%, 44.5% and 75.4%, respectively.

The existing matrix completion methods focus on optimizing the relaxation of
rank function such as nuclear norm, Schatten-p norm, etc. They usually need
many iterations to converge. Moreover, only the low-rank property of matrices
is utilized in most existing models and several methods that incorporate other
knowledge are quite time-consuming in practice. To address these issues, we
propose a novel non-convex surrogate that can be optimized by closed-form
solutions, such that it empirically converges within dozens of iterations.
Besides, the optimization is parameter-free and the convergence is proved.
Compared with the relaxation of rank, the surrogate is motivated by optimizing
an upper-bound of rank. We theoretically validate that it is equivalent to the
existing matrix completion models. Besides the low-rank assumption, we intend
to exploit the column-wise correlation for matrix completion, and thus an
adaptive correlation learning, which is scaling-invariant, is developed. More
importantly, after incorporating the correlation learning, the model can be
still solved by closed-form solutions such that it still converges fast.
Experiments show the effectiveness of the non-convex surrogate and adaptive
correlation learning.

In this letter, we propose HV-Net, a new method for hypervolume approximation
in evolutionary multi-objective optimization. The basic idea of HV-Net is to
use DeepSets, a deep neural network with permutation invariant property, to
approximate the hypervolume of a non-dominated solution set. The input of
HV-Net is a non-dominated solution set in the objective space, and the output
is an approximated hypervolume value of this solution set. The performance of
HV-Net is evaluated through computational experiments by comparing it with two
commonly-used hypervolume approximation methods (i.e., point-based method and
line-based method). Our experimental results show that HV-Net outperforms the
other two methods in terms of both the approximation error and the runtime,
which shows the potential of using deep learning technique for hypervolume
approximation.

New COVID-19 epidemic strains like Delta and Omicron with increased
transmissibility and pathogenicity emerge and spread across the whole world
rapidly while causing high mortality during the pandemic period. Early
prediction of possible variants (especially spike protein) of COVID-19 epidemic
strains based on available mutated SARS-CoV-2 RNA sequences may lead to early
prevention and treatment. Here, combining the advantage of quantum and
quantum-inspired algorithms with the wide application of deep learning, we
propose a development tool named DeepQuantum, and use this software to realize
the goal of predicting spike protein variation structure of COVID-19 epidemic
strains. In addition, this hybrid quantum-classical model for the first time
achieves quantum-inspired blur convolution similar to classical depthwise
convolution and also successfully applies quantum progressive training with
quantum circuits, both of which guarantee that our model is the quantum
counterpart of the famous style-based GAN. The results state that the
fidelities of random generating spike protein variation structure are always
beyond 96% for Delta, 94% for Omicron. The training loss curve is more stable
and converges better with multiple loss functions compared with the
corresponding classical algorithm. At last, evidences that quantum-inspired
algorithms promote the classical deep learning and hybrid models effectively
predict the mutant strains are strong.

Complete Multi-lingual Neural Machine Translation (C-MNMT) achieves superior
performance against the conventional MNMT by constructing multi-way aligned
corpus, i.e., aligning bilingual training examples from different language
pairs when either their source or target sides are identical. However, since
exactly identical sentences from different language pairs are scarce, the power
of the multi-way aligned corpus is limited by its scale. To handle this
problem, this paper proposes "Extract and Generate" (EAG), a two-step approach
to construct large-scale and high-quality multi-way aligned corpus from
bilingual data. Specifically, we first extract candidate aligned examples by
pairing the bilingual examples from different language pairs with highly
similar source or target sentences; and then generate the final aligned
examples from the candidates with a well-trained generation model. With this
two-step pipeline, EAG can construct a large-scale and multi-way aligned corpus
whose diversity is almost identical to the original bilingual corpus.
Experiments on two publicly available datasets i.e., WMT-5 and OPUS-100, show
that the proposed method achieves significant improvements over strong
baselines, with +1.1 and +1.4 BLEU points improvements on the two datasets

Modeling driver behavior provides several advantages in the automotive
industry, including prediction of electric vehicle energy consumption. Studies
have shown that aggressive driving can consume up to 30% more energy than
moderate driving, in certain driving scenarios. Machine learning methods are
widely used for driver behavior classification, which, however, may yield some
challenges such as sequence modeling on long time windows and lack of labeled
data due to expensive annotation. To address the first challenge, passive
learning of driver behavior, we investigate non-recurrent architectures such as
self-attention models and convolutional neural networks with joint recurrence
plots (JRP), and compare them with recurrent models. We find that
self-attention models yield good performance, while JRP does not exhibit any
significant improvement. However, with the window lengths of 5 and 10 seconds
used in our study, none of the non-recurrent models outperform the recurrent
models. To address the second challenge, we investigate several active learning
methods with different informativeness measures. We evaluate uncertainty
sampling, as well as more advanced methods, such as query by committee and
active deep dropout. Our experiments demonstrate that some active sampling
techniques can outperform random sampling, and therefore decrease the effort
needed for annotation.

Conversations have become a critical data format on social media platforms.
Understanding conversation from emotion, content, and other aspects also
attracts increasing attention from researchers due to its widespread
application in human-computer interaction. In real-world environments, we often
encounter the problem of incomplete modalities, which has become a core issue
of conversation understanding. To address this problem, researchers propose
various methods. However, existing approaches are mainly designed for
individual utterances or medical images rather than conversational data, which
cannot exploit temporal and speaker information in conversations. To this end,
we propose a novel framework for incomplete multimodal learning in
conversations, called "Graph Complete Network (GCNet)", filling the gap of
existing works. Our GCNet contains two well-designed graph neural network-based
modules, "Speaker GNN" and "Temporal GNN", to capture temporal and speaker
information in conversations. To make full use of complete and incomplete data
in feature learning, we jointly optimize classification and reconstruction in
an end-to-end manner. To verify the effectiveness of our method, we conduct
experiments on three benchmark conversational datasets. Experimental results
demonstrate that our GCNet is superior to existing state-of-the-art approaches
in incomplete multimodal learning.

Training the multi-label image recognition models with partial labels, in
which merely some labels are known while others are unknown for each image, is
a considerably challenging and practical task. To address this task, current
algorithms mainly depend on pre-training classification or similarity models to
generate pseudo labels for the unknown labels. However, these algorithms depend
on sufficient multi-label annotations to train the models, leading to poor
performance especially with low known label proportion. In this work, we
propose to blend category-specific representation across different images to
transfer information of known labels to complement unknown labels, which can
get rid of pre-training models and thus does not depend on sufficient
annotations. To this end, we design a unified semantic-aware representation
blending (SARB) framework that exploits instance-level and prototype-level
semantic representation to complement unknown labels by two complementary
modules: 1) an instance-level representation blending (ILRB) module blends the
representations of the known labels in an image to the representations of the
unknown labels in another image to complement these unknown labels. 2) a
prototype-level representation blending (PLRB) module learns more stable
representation prototypes for each category and blends the representation of
unknown labels with the prototypes of corresponding labels to complement these
labels. Extensive experiments on the MS-COCO, Visual Genome, Pascal VOC 2007
datasets show that the proposed SARB framework obtains superior performance
over current leading competitors on all known label proportion settings, i.e.,
with the mAP improvement of 4.6%, 4.%, 2.2% on these three datasets when the
known label proportion is 10%. Codes are available at
https://github.com/HCPLab-SYSU/HCP-MLR-PL.

Knowledge graph completion (KGC) aims to reason over known facts and infer
the missing links. Text-based methods such as KGBERT (Yao et al., 2019) learn
entity representations from natural language descriptions, and have the
potential for inductive KGC. However, the performance of text-based methods
still largely lag behind graph embedding-based methods like TransE (Bordes et
al., 2013) and RotatE (Sun et al., 2019b). In this paper, we identify that the
key issue is efficient contrastive learning. To improve the learning
efficiency, we introduce three types of negatives: in-batch negatives,
pre-batch negatives, and self-negatives which act as a simple form of hard
negatives. Combined with InfoNCE loss, our proposed model SimKGC can
substantially outperform embedding-based methods on several benchmark datasets.
In terms of mean reciprocal rank (MRR), we advance the state-of-the-art by +19%
on WN18RR, +6.8% on the Wikidata5M transductive setting, and +22% on the
Wikidata5M inductive setting. Thorough analyses are conducted to gain insights
into each component. Our code is available at
https://github.com/intfloat/SimKGC .

The concept of sparsity has been extensively applied for regularization in
image reconstruction. Typically, sparsifying transforms are either pre-trained
on ground-truth images or adaptively trained during the reconstruction.
Thereby, learning algorithms are designed to minimize some target function
which encodes the desired properties of the transform. However, this procedure
ignores the subsequently employed reconstruction algorithm as well as the
physical model which is responsible for the image formation process. Iterative
neural networks - which contain the physical model - can overcome these issues.
In this work, we demonstrate how convolutional sparsifying filters can be
efficiently learned by end-to-end training of iterative neural networks. We
evaluated our approach on a non-Cartesian 2D cardiac cine MRI example and show
that the obtained filters are better suitable for the corresponding
reconstruction algorithm than the ones obtained by decoupled pre-training.

Nuclei Identification and Counting is the most important morphological
feature of cancers, especially in the colon. Many deep learning-based methods
have been proposed to deal with this problem. In this work, we construct an
extension of Hovernet for nuclei identification and counting to address the
problem named MF-Hovernet. Our proposed model is the combination of multiple
filer block to Hovernet architecture. The current result shows the efficiency
of multiple filter block to improve the performance of the original Hovernet
model.

Accurate emotion classification for online reviews is vital for business
organizations to gain deeper insights into markets. Although deep learning has
been successfully implemented in this area, accuracy and processing time are
still major problems preventing it from reaching its full potential. This paper
proposes an Enhanced Leaky Rectified Linear Unit activation and Weighted Loss
(ELReLUWL) algorithm for enhanced text emotion classification and faster
parameter convergence speed. This algorithm includes the definition of the
inflection point and the slope for inputs on the left side of the inflection
point to avoid gradient saturation. It also considers the weight of samples
belonging to each class to compensate for the influence of data imbalance.
Convolutional Neural Network (CNN) combined with the proposed algorithm to
increase the classification accuracy and decrease the processing time by
eliminating the gradient saturation problem and minimizing the negative effect
of data imbalance, demonstrated on a binary sentiment problem. The results show
that the proposed solution achieves better classification performance in
different data scenarios and different review types. The proposed model takes
less convergence time to achieve model optimization with seven epochs against
the current convergence time of 11.5 epochs on average. The proposed solution
improves accuracy and reduces the processing time of text emotion
classification. The solution provides an average class accuracy of 96.63%
against a current average accuracy of 91.56%. It also provides a processing
time of 23.3 milliseconds compared to the current average processing time of
33.2 milliseconds. Finally, this study solves the issues of gradient saturation
and data imbalance. It enhances overall average class accuracy and decreases

In this paper, a unified transformation method in learned image
compression(LIC) is proposed from the perspective of modulation. Firstly, the
quantization in LIC is considered as a generalized channel with additive
uniform noise. Moreover, the LIC is interpreted as a particular communication
system according to the consistency in structures and optimization objectives.
Thus, the technology of communication systems can be applied to guide the
design of modules in LIC. Furthermore, a unified transform method based on
signal modulation (TSM) is defined. In the view of TSM, the existing
transformation methods are mathematically reduced to a linear modulation. A
series of transformation methods, e.g. TPM and TJM, are obtained by extending
to nonlinear modulation. The experimental results on various datasets and
backbone architectures verify that the effectiveness and robustness of the
proposed method. More importantly, it further confirms the feasibility of
guiding LIC design from a communication perspective. For example, when backbone
architecture is hyperprior combining context model, our method achieves
3.52$\%$ BD-rate reduction over GDN on Kodak dataset without increasing

Learning-based multi-view stereo (MVS) has gained fine reconstructions on
popular datasets. However, supervised learning methods require ground truth for
training, which is hard to be collected, especially for the large-scale
datasets. Though nowadays unsupervised learning methods have been proposed and
have gotten gratifying results, those methods still fail to reconstruct intact
results in challenging scenes, such as weakly-textured surfaces, as those
methods primarily depend on pixel-wise photometric consistency which is
subjected to various illuminations. To alleviate matching ambiguity in those
challenging scenes, this paper proposes robust loss functions leveraging
constraints beneath multi-view images: 1) Patch-wise photometric consistency
loss, which expands the receptive field of the features in multi-view
similarity measuring, 2) Robust twoview geometric consistency, which includes a
cross-view depth consistency checking with the minimum occlusion. Our
unsupervised strategy can be implemented with arbitrary depth estimation
frameworks and can be trained with arbitrary large-scale MVS datasets.
Experiments show that our method can decrease the matching ambiguity and
particularly improve the completeness of weakly-textured reconstruction.
Moreover, our method reaches the performance of the state-of-the-art methods on
popular benchmarks, like DTU, Tanks and Temples and ETH3D. The code will be

Making language models bigger does not inherently make them better at
following a user's intent. For example, large language models can generate
outputs that are untruthful, toxic, or simply not helpful to the user. In other
words, these models are not aligned with their users. In this paper, we show an
avenue for aligning language models with user intent on a wide range of tasks
by fine-tuning with human feedback. Starting with a set of labeler-written
prompts and prompts submitted through the OpenAI API, we collect a dataset of
labeler demonstrations of the desired model behavior, which we use to fine-tune
GPT-3 using supervised learning. We then collect a dataset of rankings of model
outputs, which we use to further fine-tune this supervised model using
reinforcement learning from human feedback. We call the resulting models
InstructGPT. In human evaluations on our prompt distribution, outputs from the
1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3,
despite having 100x fewer parameters. Moreover, InstructGPT models show
improvements in truthfulness and reductions in toxic output generation while
having minimal performance regressions on public NLP datasets. Even though
InstructGPT still makes simple mistakes, our results show that fine-tuning with
human feedback is a promising direction for aligning language models with human
intent.

Entity alignment aims to identify equivalent entity pairs between different
knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that
contain time information created the need for reasoning over time in such TKGs.
Existing embedding-based entity alignment approaches disregard time information
that commonly exists in many large-scale KGs, leaving much room for
improvement. In this paper, we focus on the task of aligning entity pairs
between TKGs and propose a novel Time-aware Entity Alignment approach based on
Graph Neural Networks (TEA-GNN). We embed entities, relations and timestamps of
different KGs into a vector space and use GNNs to learn entity representations.
To incorporate both relation and time information into the GNN structure of our
model, we use a time-aware attention mechanism which assigns different weights
to different nodes with orthogonal transformation matrices computed from
embeddings of the relevant relations and timestamps in a neighborhood.
Experimental results on multiple real-world TKG datasets show that our method
significantly outperforms the state-of-the-art methods due to the inclusion of
time information.

The rapid development of deep learning provides a better solution for the
end-to-end reconstruction of hyperspectral image (HSI). However, existing
learning-based methods have two major defects. Firstly, networks with
self-attention usually sacrifice internal resolution to balance model
performance against complexity, losing fine-grained high-resolution (HR)
features. Secondly, even if the optimization focusing on spatial-spectral
domain learning (SDL) converges to the ideal solution, there is still a
significant visual difference between the reconstructed HSI and the truth.
Therefore, we propose a high-resolution dual-domain learning network (HDNet)
for HSI reconstruction. On the one hand, the proposed HR spatial-spectral
attention module with its efficient feature fusion provides continuous and fine
pixel-level features. On the other hand, frequency domain learning (FDL) is
introduced for HSI reconstruction to narrow the frequency domain discrepancy.
Dynamic FDL supervision forces the model to reconstruct fine-grained
frequencies and compensate for excessive smoothing and distortion caused by
pixel-level losses. The HR pixel-level attention and frequency-level refinement
in our HDNet mutually promote HSI perceptual quality. Extensive quantitative
and qualitative evaluation experiments show that our method achieves SOTA
performance on simulated and real HSI datasets. Code and models will be
released at https://github.com/caiyuanhao1998/MST

In this paper, we analyse the performance of the closed-loop Whiplash
gradient descent algorithm for L-smooth convex cost functions. Using numerical
experiments, we study the algorithm's performance for convex cost functions,
for different condition numbers. We analyse the convergence of the momentum
sequence using symplectic integration and introduce the concept of relaxation
sequences which analyses the non-classical character of the whiplash method.
Under the additional assumption of invexity, we establish a momentum-driven
adaptive convergence rate. Furthermore, we introduce an energy method for
predicting the convergence rate with convex cost functions for closed-loop
inertial gradient dynamics, using an integral anchored energy function and a
novel lower bound asymptotic notation, by exploiting the bounded nature of the
solutions. Using this, we establish a polynomial convergence rate for the
whiplash inertial gradient system, for a family of scalar quadratic cost
functions and an exponential rate for a quadratic scalar cost function.

Existing continual relation learning (CRL) methods rely on plenty of labeled
training data for learning a new task, which can be hard to acquire in real
scenario as getting large and representative labeled data is often expensive
and time-consuming. It is therefore necessary for the model to learn novel
relational patterns with very few labeled data while avoiding catastrophic
forgetting of previous task knowledge. In this paper, we formulate this
challenging yet practical problem as continual few-shot relation learning
(CFRL). Based on the finding that learning for new emerging few-shot tasks
often results in feature distributions that are incompatible with previous
tasks' learned distributions, we propose a novel method based on embedding
space regularization and data augmentation. Our method generalizes to new
few-shot tasks and avoids catastrophic forgetting of previous tasks by
enforcing extra constraints on the relational embeddings and by adding extra
{relevant} data in a self-supervised manner. With extensive experiments we
demonstrate that our method can significantly outperform previous
state-of-the-art methods in CFRL task settings.

The study of robustness has received much attention due to its inevitability
in data-driven settings where many systems face uncertainty. One such example
of concern is Bayesian Optimization (BO), where uncertainty is multi-faceted,
yet there only exists a limited number of works dedicated to this direction. In
particular, there is the work of Kirschner et al. (2020), which bridges the
existing literature of Distributionally Robust Optimization (DRO) by casting
the BO problem from the lens of DRO. While this work is pioneering, it
admittedly suffers from various practical shortcomings such as finite contexts
assumptions, leaving behind the main question Can one devise a computationally
tractable algorithm for solving this DRO-BO problem? In this work, we tackle
this question to a large degree of generality by considering robustness against
data-shift in $\phi$-divergences, which subsumes many popular choices, such as
the $\chi^2$-divergence, Total Variation, and the extant Kullback-Leibler (KL)
divergence. We show that the DRO-BO problem in this setting is equivalent to a
finite-dimensional optimization problem which, even in the continuous context
setting, can be easily implemented with provable sublinear regret bounds. We
then show experimentally that our method surpasses existing methods, attesting
to the theoretical results.

Assistive robots for healthcare have seen a growing demand due to the great
potential of relieving medical practitioners from routine jobs. In this paper,
we investigate the development of an optimization-based control framework for
an ultrasound-guided assistive robot to perform scoliosis assessment. A
conventional procedure for scoliosis assessment with ultrasound imaging
typically requires a medical practitioner to slide an ultrasound probe along a
patient's back. To automate this type of procedure, we need to consider
multiple objectives, such as contact force, position, orientation, energy,
posture, etc. To address the aforementioned components, we propose to formulate
the control framework design as a quadratic programming problem with each
objective weighed by its task priority subject to a set of equality and
inequality constraints. In addition, as the robot needs to establish constant
contact with the patient during spine scanning, we incorporate variable
impedance regulation of the end-effector position and orientation in the
control architecture to enhance safety and stability during the physical
human-robot interaction. Wherein, the variable impedance gains are retrieved by
learning from the medical expert's demonstrations. The proposed methodology is
evaluated by conducting real-world experiments of autonomous scoliosis
assessment with a robot manipulator xArm. The effectiveness is verified by the
obtained coronal spinal images of both a phantom and a human subject.

This paper is created to explore deep learning models and algorithms that
results in highest accuracy in detecting polyp on colonoscopy images. Previous
studies implemented deep learning using convolution neural network (CNN)
algorithm in detecting polyp and non-polyp. Other studies used dropout, and
data augmentation algorithm but mostly not checking the overfitting, thus,
include more than four-layer modelss. Rulei Yu et.al from the Institute of
Software, Chinese Academy of Sciences said that transfer learning is better
talking about performance or improving the previous used algorithm. Most
especially in applying the transfer learning in feature extraction. Series of
experiments were conducted with only a minimum of 4 CNN layers applying
previous used models and identified the model that produce the highest
percentage accuracy of 98% among the other models that apply transfer learning.
Further studies could use different optimizer to a different CNN modelsto
increase accuracy.

This work presents a fraud and abuse detection framework for streaming
services by modeling user streaming behavior. The goal is to discover anomalous
and suspicious incidents and scale the investigation efforts by creating models
that characterize the user behavior. We study the use of semi-supervised as
well as supervised approaches for anomaly detection. In the semi-supervised
approach, by leveraging only a set of authenticated anomaly-free data samples,
we show the use of one-class classification algorithms as well as autoencoder
deep neural networks for anomaly detection. In the supervised anomaly detection
task, we present a so-called heuristic-aware data labeling strategy for
creating labeled data samples. We carry out binary classification as well as
multi-class multi-label classification tasks for not only detecting the
anomalous samples but also identifying the underlying anomaly behavior(s)
associated with each one. Finally, using a systematic feature importance study
we provide insights into the underlying set of features that characterize
different streaming fraud categories. To the best of our knowledge, this is the
first paper to use machine learning methods for fraud and abuse detection in
real-world scale streaming services.

The pervasiveness of offensive language on the social network has caused
adverse effects on society, such as abusive behavior online. It is urgent to
detect offensive language and curb its spread. Existing research shows that
methods with community structure features effectively improve the performance
of offensive language detection. However, the existing models deal with
community structure independently, which seriously affects the effectiveness of
detection models. In this paper, we propose an end-to-end method based on
community structure and text features for offensive language detection
(CT-OLD). Specifically, the community structure features are directly captured
by the graph attention network layer, and the text embeddings are taken from
the last hidden layer of BERT. Attention mechanisms and position encoding are
used to fuse these features. Meanwhile, we add user opinion to the community
structure for representing user features. The user opinion is represented by
user historical behavior information, which outperforms that represented by
text information. Besides the above point, the distribution of users and tweets
is unbalanced in the popular datasets, which limits the generalization ability
of the model. To address this issue, we construct and release a dataset with
reasonable user distribution. Our method outperforms baselines with the F1
score of 89.94%. The results show that the end-to-end model effectively learns
the potential information of community structure and text, and user historical
behavior information is more suitable for user opinion representation.

Deep learning-based classifiers have substantially improved recognition of
malware samples. However, these classifiers can be vulnerable to adversarial
input perturbations. Any vulnerability in malware classifiers poses significant
threats to the platforms they defend. Therefore, to create stronger defense
models against malware, we must understand the patterns in input perturbations
caused by an adversary. This survey paper presents a comprehensive study on
adversarial machine learning for android malware classifiers. We first present
an extensive background in building a machine learning classifier for android
malware, covering both image-based and text-based feature extraction
approaches. Then, we examine the pattern and advancements in the
state-of-the-art research in evasion attacks and defenses. Finally, we present
guidelines for designing robust malware classifiers and enlist research
directions for the future.

Nucleus segmentation and classification are the prerequisites in the workflow
of digital pathology processing. However, it is very challenging due to its
high-level heterogeneity and wide variations. This work proposes a deep neural
network to simultaneously achieve nuclear classification and segmentation,
which is designed using a unified framework with three different branches,
including segmentation, HoVer mapping, and classification. The segmentation
branch aims to generate the boundaries of each nucleus. The HoVer branch
calculates the horizontal and vertical distances of nuclear pixels to their
centres of mass. The nuclear classification branch is used to distinguish the
class of pixels inside the nucleus obtained from segmentation.

Grasping moving objects, such as goods on a belt or living animals, is an
important but challenging task in robotics. Conventional approaches rely on a
set of manually defined object motion patterns for training, resulting in poor
generalization to unseen object trajectories. In this work, we introduce an
adversarial reinforcement learning framework for dynamic grasping, namely
GraspARL. To be specific. we formulate the dynamic grasping problem as a
'move-and-grasp' game, where the robot is to pick up the object on the mover
and the adversarial mover is to find a path to escape it. Hence, the two agents
play a min-max game and are trained by reinforcement learning. In this way, the
mover can auto-generate diverse moving trajectories while training. And the
robot trained with the adversarial trajectories can generalize to various
motion patterns. Empirical results on the simulator and real-world scenario
demonstrate the effectiveness of each and good generalization of our method.

One of the burning problems lately in Japan has been cyber-bullying, or
slandering and bullying people online. The problem has been especially noticed
on unofficial Web sites of Japanese schools. Volunteers consisting of school
personnel and PTA (Parent-Teacher Association) members have started Online
Patrol to spot malicious contents within Web forums and blogs. In practise,
Online Patrol assumes reading through the whole Web contents, which is a task
difficult to perform manually. With this paper we introduce a research intended
to help PTA members perform Online Patrol more efficiently. We aim to develop a
set of tools that can automatically detect malicious entries and report them to
PTA members. First, we collected cyber-bullying data from unofficial school Web
sites. Then we performed analysis of this data in two ways. Firstly, we
analysed the entries with a multifaceted affect analysis system in order to
find distinctive features for cyber-bullying and apply them to a machine
learning classifier. Secondly, we applied a SVM based machine learning method
to train a classifier for detection of cyber-bullying. The system was able to
classify cyber-bullying entries with 88.2% of balanced F-score.

Contrastive learning and self-supervised techniques have gained prevalence in
computer vision for the past few years. It is essential for medical image
analysis, which is often notorious for its lack of annotations. Most existing
self-supervised methods applied in natural imaging tasks focus on designing
proxy tasks for unlabeled data. For example, contrastive learning is often
based on the fact that an image and its transformed version share the same
identity. However, pixel annotations contain much valuable information for
medical image segmentation, which is largely ignored in contrastive learning.
In this work, we propose a novel pre-training framework called Mixed
Contrastive Learning (MixCL) that leverages both image identities and pixel
labels for better modeling by maintaining identity consistency, label
consistency, and reconstruction consistency together. Consequently, thus
pre-trained model has more robust representations that characterize medical
images. Extensive experiments demonstrate the effectiveness of the proposed
method, improving the baseline by 5.28% and 14.12% in Dice coefficient when 5%
labeled data of Spleen and 15% of BTVC are used in fine-tuning, respectively.

Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D
detection by enhancing the capability of perceiving depth with depth estimation
networks, and using LiDAR-based 3D detection architectures. The advanced stereo
3D detectors can also accurately localize 3D objects. The gap in image-to-image
generation for stereo views is much smaller than that in image-to-LiDAR
generation. Motivated by this, we propose a Pseudo-Stereo 3D detection
framework with three novel virtual view generation methods, including
image-level generation, feature-level generation, and feature-clone, for
detecting 3D objects from a single image. Our analysis of depth-aware learning
shows that the depth loss is effective in only feature-level virtual view
generation and the estimated depth map is effective in both image-level and
feature-level in our framework. We propose a disparity-wise dynamic convolution
with dynamic kernels sampled from the disparity feature map to filter the
features adaptively from a single image for generating virtual image features,
which eases the feature degradation caused by the depth estimation errors. Till
submission (November 18, 2021), our Pseudo-Stereo 3D detection framework ranks
1st on car, pedestrian, and cyclist among the monocular 3D detectors with
publications on the KITTI-3D benchmark. The code is released at
https://github.com/revisitq/Pseudo-Stereo-3D.

Many works have shown that deep learning-based medical image classification
models can exhibit bias toward certain demographic attributes like race,
gender, and age. Existing bias mitigation methods primarily focus on learning
debiased models, which may not necessarily guarantee all sensitive information
can be removed and usually comes with considerable accuracy degradation on both
privileged and unprivileged groups. To tackle this issue, we propose a method,
FairPrune, that achieves fairness by pruning. Conventionally, pruning is used
to reduce the model size for efficient inference. However, we show that pruning
can also be a powerful tool to achieve fairness. Our observation is that during
pruning, each parameter in the model has different importance for different
groups' accuracy. By pruning the parameters based on this importance
difference, we can reduce the accuracy difference between the privileged group
and the unprivileged group to improve fairness without a large accuracy drop.
To this end, we use the second derivative of the parameters of a pre-trained
model to quantify the importance of each parameter with respect to the model
accuracy for each group. Experiments on two skin lesion diagnosis datasets over
multiple sensitive attributes demonstrate that our method can greatly improve
fairness while keeping the average accuracy of both groups as high as possible.

Federated learning is a promising machine learning technique that enables
multiple clients to collaboratively build a model without revealing the raw
data to each other. Among various types of federated learning methods,
horizontal federated learning (HFL) is the best-studied category and handles
homogeneous feature spaces. However, in the case of heterogeneous feature
spaces, HFL uses only common features and leaves client-specific features
unutilized. In this paper, we propose a HFL method using neural networks named
continual horizontal federated learning (CHFL), a continual learning approach
to improve the performance of HFL by taking advantage of unique features of
each client. CHFL splits the network into two columns corresponding to common
features and unique features, respectively. It jointly trains the first column
by using common features through vanilla HFL and locally trains the second
column by using unique features and leveraging the knowledge of the first one
via lateral connections without interfering with the federated training of it.
We conduct experiments on various real world datasets and show that CHFL
greatly outperforms vanilla HFL that only uses common features and local
learning that uses all features that each client has.

Generalizable object manipulation skills are critical for intelligent and
multi-functional robots to work in real-world complex scenes. Despite the
recent progress in reinforcement learning, it is still very challenging to
learn a generalizable manipulation policy that can handle a category of
geometrically diverse articulated objects. In this work, we tackle this
category-level object manipulation policy learning problem via imitation
learning in a task-agnostic manner, where we assume no handcrafted dense
rewards but only a terminal reward. Given this novel and challenging
generalizable policy learning problem, we identify several key issues that can
fail the previous imitation learning algorithms and hinder the generalization
to unseen instances. We then propose several general but critical techniques,
including generative adversarial self-imitation learning from demonstrations,
progressive growing of discriminator, and instance-balancing for expert buffer,
that accurately pinpoints and tackles these issues and can benefit
category-level manipulation policy learning regardless of the tasks. Our
experiments on ManiSkill benchmarks demonstrate a remarkable improvement on all
tasks and our ablation studies further validate the contribution of each
proposed technique.

Medical image segmentation plays an irreplaceable role in computer-assisted
diagnosis, treatment planning, and following-up. Collecting and annotating a
large-scale dataset is crucial to training a powerful segmentation model, but
producing high-quality segmentation masks is an expensive and time-consuming
procedure. Recently, weakly-supervised learning that uses sparse annotations
(points, scribbles, bounding boxes) for network training has achieved
encouraging performance and shown the potential for annotation cost reduction.
However, due to the limited supervision signal of sparse annotations, it is
still challenging to employ them for networks training directly. In this work,
we propose a simple yet efficient scribble-supervised image segmentation method
and apply it to cardiac MRI segmentation. Specifically, we employ a dual-branch
network with one encoder and two slightly different decoders for image
segmentation and dynamically mix the two decoders' predictions to generate
pseudo labels for auxiliary supervision. By combining the scribble supervision
and auxiliary pseudo labels supervision, the dual-branch network can
efficiently learn from scribble annotations end-to-end. Experiments on the
public ACDC dataset show that our method performs better than current
scribble-supervised segmentation methods and also outperforms several
semi-supervised segmentation methods.

There exists a large number of datasets for organ segmentation, which are
partially annotated and sequentially constructed. A typical dataset is
constructed at a certain time by curating medical images and annotating the
organs of interest. In other words, new datasets with annotations of new organ
categories are built over time. To unleash the potential behind these partially
labeled, sequentially-constructed datasets, we propose to incrementally learn a
multi-organ segmentation model. In each incremental learning (IL) stage, we
lose the access to previous data and annotations, whose knowledge is assumingly
captured by the current model, and gain the access to a new dataset with
annotations of new organ categories, from which we learn to update the organ
segmentation model to include the new organs. While IL is notorious for its
`catastrophic forgetting' weakness in the context of natural image analysis, we
experimentally discover that such a weakness mostly disappears for CT
multi-organ segmentation. To further stabilize the model performance across the
IL stages, we introduce a light memory module and some loss functions to
restrain the representation of different categories in feature space,
aggregating feature representation of the same class and separating feature
representation of different classes. Extensive experiments on five open-sourced
datasets are conducted to illustrate the effectiveness of our method.

In the paper, we present an approach for learning a single model that
universally segments 33 anatomical structures, including vertebrae, pelvic
bones, and abdominal organs. Our model building has to address the following
challenges. Firstly, while it is ideal to learn such a model from a
large-scale, fully-annotated dataset, it is practically hard to curate such a
dataset. Thus, we resort to learn from a union of multiple datasets, with each
dataset containing the images that are partially labeled. Secondly, along the
line of partial labelling, we contribute an open-source, large-scale vertebra
segmentation dataset for the benefit of spine analysis community, CTSpine1K,
boasting over 1,000 3D volumes and over 11K annotated vertebrae. Thirdly, in a
3D medical image segmentation task, due to the limitation of GPU memory, we
always train a model using cropped patches as inputs instead a whole 3D volume,
which limits the amount of contextual information to be learned. To this, we
propose a cross-patch transformer module to fuse more information in adjacent
patches, which enlarges the aggregated receptive field for improved
segmentation performance. This is especially important for segmenting, say, the
elongated spine. Based on 7 partially labeled datasets that collectively
contain about 2,800 3D volumes, we successfully learn such a universal model.
Finally, we evaluate the universal model on multiple open-source datasets,
proving that our model has a good generalization performance and can
potentially serve as a solid foundation for downstream tasks.

The increasing prevalence of marine pollution during the past few decades
motivated recent research to help ease the situation. Typical water quality
assessment requires continuous monitoring of water and sediments at remote
locations with labour intensive laboratory tests to determine the degree of
pollution. We propose an automated framework where we formalise a predictive
model using Machine Learning to infer the water quality and level of pollution
using collected water and sediments samples. One commonly encountered
difficulty performing statistical analysis with water and sediment is the
limited amount of data samples and incomplete dataset due to the sparsity of
sample collection location. To this end, we performed extensive investigation
on various data imputation methods' performance in water and sediment datasets
with various data missing rates. Empirically, we show that our best model
archives an accuracy of 75% after accounting for 57% of missing data.
Experimentally, we show that our model would assist in assessing water quality
screening based on possibly incomplete real-world data.

The availability of wide-ranging third-party intellectual property (3PIP)
cores enables integrated circuit (IC) designers to focus on designing
high-level features in ASICs/SoCs. The massive proliferation of ICs brings with
it an increased number of bad actors seeking to exploit those circuits for
various nefarious reasons. This is not surprising as integrated circuits affect
every aspect of society. Thus, malicious logic (Hardware Trojans, HT) being
surreptitiously injected by untrusted vendors into 3PIP cores used in IC design
is an ever present threat. In this paper, we explore methods for identification
of trigger-based HT in designs containing synthesizable IP cores without a
golden model. Specifically, we develop methods to detect hardware trojans by
detecting triggers embedded in ICs purely based on netlists acquired from the
vendor. We propose GATE-Net, a deep learning model based on graph-convolutional
networks (GCN) trained using supervised contrastive learning, for flagging
designs containing randomly-inserted triggers using only the corresponding
netlist. Our proposed architecture achieves significant improvements over
state-of-the-art learning models yielding an average 46.99% improvement in
detection performance for combinatorial triggers and 21.91% improvement for
sequential triggers across a variety of circuit types. Through rigorous
experimentation, qualitative and quantitative performance evaluations, we
demonstrate effectiveness of GATE-Net and the supervised contrastive training
of GATE-Net for HT detection.

The Transformer architecture is ubiquitously used as the building block of
large-scale autoregressive language models. However, finding architectures with
the optimal trade-off between task performance (perplexity) and hardware
constraints like peak memory utilization and latency is non-trivial. This is
exacerbated by the proliferation of various hardware. We leverage the somewhat
surprising empirical observation that the number of decoder parameters in
autoregressive Transformers has a high rank correlation with task performance,
irrespective of the architecture topology. This observation organically induces
a simple Neural Architecture Search (NAS) algorithm that uses decoder
parameters as a proxy for perplexity without need for any model training. The
search phase of our training-free algorithm, dubbed Lightweight Transformer
Search (LTS), can be run directly on target devices since it does not require
GPUs. Using on-target-device measurements, LTS extracts the Pareto-frontier of
perplexity versus any hardware performance cost. We evaluate LTS on diverse
devices from ARM CPUs to NVIDIA GPUs and two popular autoregressive Transformer
backbones: GPT-2 and Transformer-XL. Results show that the perplexity of
16-layer GPT-2 and Transformer-XL can be achieved with up to 1.5x, 2.5x faster
runtime and 1.2x, 2.0x lower peak memory utilization. When evaluated in zero
and one-shot settings, LTS Pareto-frontier models achieve higher average
accuracy compared to the 350M parameter OPT across 14 tasks, with up to 1.6x
lower latency. LTS extracts the Pareto-frontier in under 3 hours while running
on a commodity laptop. We effectively remove the carbon footprint of hundreds
of GPU hours of training during search, offering a strong simple baseline for
future NAS methods in autoregressive language modeling.

Our goal is to enable robots to perform functional tasks in emotive ways, be
it in response to their users' emotional states, or expressive of their
confidence levels. Prior work has proposed learning independent cost functions
from user feedback for each target emotion, so that the robot may optimize it
alongside task and environment specific objectives for any situation it
encounters. However, this approach is inefficient when modeling multiple
emotions and unable to generalize to new ones. In this work, we leverage the
fact that emotions are not independent of each other: they are related through
a latent space of Valence-Arousal-Dominance (VAD). Our key idea is to learn a
model for how trajectories map onto VAD with user labels. Considering the
distance between a trajectory's mapping and a target VAD allows this single
model to represent cost functions for all emotions. As a result 1) all user
feedback can contribute to learning about every emotion; 2) the robot can
generate trajectories for any emotion in the space instead of only a few
predefined ones; and 3) the robot can respond emotively to user-generated
natural language by mapping it to a target VAD. We introduce a method that
interactively learns to map trajectories to this latent space and test it in
simulation and in a user study. In experiments, we use a simple vacuum robot as
well as the Cassie biped.

The increasing prevalence of network data in a vast variety of fields and the
need to extract useful information out of them have spurred fast developments
in related models and algorithms. Among the various learning tasks with network
data, community detection, the discovery of node clusters or "communities," has
arguably received the most attention in the scientific community. In many
real-world applications, the network data often come with additional
information in the form of node or edge covariates that should ideally be
leveraged for inference. In this paper, we add to a limited literature on
community detection for networks with covariates by proposing a Bayesian
stochastic block model with a covariate-dependent random partition prior. Under
our prior, the covariates are explicitly expressed in specifying the prior
distribution on the cluster membership. Our model has the flexibility of
modeling uncertainties of all the parameter estimates including the community
membership. Importantly, and unlike the majority of existing methods, our model
has the ability to learn the number of the communities via posterior inference
without having to assume it to be known. Our model can be applied to community
detection in both dense and sparse networks, with both categorical and
continuous covariates, and our MCMC algorithm is very efficient with good
mixing properties. We demonstrate the superior performance of our model over
existing models in a comprehensive simulation study and an application to two

Precise representation of large-scale undirected network is the basis for
understanding relations within a massive entity set. The undirected network
representation task can be efficiently addressed by a symmetry non-negative
latent factor (SNLF) model, whose objective is clearly non-convex. However,
existing SNLF models commonly adopt a first-order optimizer that cannot well
handle the non-convex objective, thereby resulting in inaccurate representation
results. On the other hand, higher-order learning algorithms are expected to
make a breakthrough, but their computation efficiency are greatly limited due
to the direct manipulation of the Hessian matrix, which can be huge in
undirected network representation tasks. Aiming at addressing this issue, this
study proposes to incorporate an efficient second-order method into SNLF,
thereby establishing a second-order symmetric non-negative latent factor
analysis model for undirected network with two-fold ideas: a) incorporating a
mapping strategy into SNLF model to form an unconstrained model, and b)
training the unconstrained model with a specially designed second order method
to acquire a proper second-order step efficiently. Empirical studies indicate
that proposed model outperforms state-of-the-art models in representation
accuracy with affordable computational burden.

Weight sharing based and predictor based methods are two major types of fast
neural architecture search methods. In this paper, we propose to jointly use
weight sharing and predictor in a unified framework. First, we construct a
SuperNet in a weight-sharing way and probabilisticly sample architectures from
the SuperNet. To increase the correctness of the evaluation of architectures,
besides direct evaluation using the inherited weights, we further apply a
few-shot predictor to assess the architecture on the other hand. The final
evaluation of the architecture is the combination of direct evaluation, the
prediction from the predictor and the cost of the architecture. We regard the
evaluation as a reward and apply a self-critical policy gradient approach to
update the architecture probabilities. To further reduce the side effects of
weight sharing, we propose a weakly weight sharing method by introducing
another HyperNet. We conduct experiments on datasets including CIFAR-10,
CIFAR-100 and ImageNet under NATS-Bench, DARTS and MobileNet search space. The
proposed WPNAS method achieves state-of-the-art performance on these datasets.

The Covid-19 pandemic has forced the workforce to switch to working from
home, which has put significant burdens on the management of broadband networks
and called for intelligent service-by-service resource optimization at the
network edge. In this context, network traffic prediction is crucial for
operators to provide reliable connectivity across large geographic regions.
Although recent advances in neural network design have demonstrated potential
to effectively tackle forecasting, in this work we reveal based on real-world
measurements that network traffic across different regions differs widely. As a
result, models trained on historical traffic data observed in one region can
hardly serve in making accurate predictions in other areas. Training bespoke
models for different regions is tempting, but that approach bears significant
measurement overhead, is computationally expensive, and does not scale.
Therefore, in this paper we propose TransMUSE, a novel deep learning framework
that clusters similar services, groups edge-nodes into cohorts by traffic
feature similarity, and employs a Transformer-based Multi-service Traffic
Prediction Network (TMTPN), which can be directly transferred within a cohort
without any customization. We demonstrate that TransMUSE exhibits imperceptible
performance degradation in terms of mean absolute error (MAE) when forecasting
traffic, compared with settings where a model is trained for each individual
edge node. Moreover, our proposed TMTPN architecture outperforms the
state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic
prediction task. To the best of our knowledge, this is the first work that
jointly employs model transfer and multi-service traffic prediction to reduce
measurement overhead, while providing fine-grained accurate demand forecasts
for edge services provisioning.

Membership inference attacks allow a malicious entity to predict whether a
sample is used during training of a victim model or not. State-of-the-art
membership inference attacks have shown to achieve good accuracy which poses a
great privacy threat. However, majority of SOTA attacks require training dozens
to hundreds of shadow models to accurately infer membership. This huge
computation cost raises questions about practicality of these attacks on deep
models. In this paper, we introduce a fundamentally different MI attack
approach which obviates the need to train hundreds of shadow models. Simply
put, we compare the victim model output on the target sample versus the samples
from the same subpopulation (i.e., semantically similar samples), instead of
comparing it with the output of hundreds of shadow models. The intuition is
that the model response should not be significantly different between the
target sample and its subpopulation if it was not a training sample. In cases
where subpopulation samples are not available to the attacker, we show that
training only a single generative model can fulfill the requirement. Hence, we
achieve the state-of-the-art membership inference accuracy while significantly
reducing the training computation cost.

Outsourced training and machine learning as a service have resulted in novel
attack vectors like backdoor attacks. Such attacks embed a secret functionality
in a neural network activated when the trigger is added to its input. In most
works in the literature, the trigger is static, both in terms of location and
pattern. The effectiveness of various detection mechanisms depends on this
property. It was recently shown that countermeasures in image classification,
like Neural Cleanse and ABS, could be bypassed with dynamic triggers that are
effective regardless of their pattern and location. Still, such backdoors are
demanding as they require a large percentage of poisoned training data. In this
work, we are the first to show that dynamic backdoor attacks could happen due
to a global average pooling layer without increasing the percentage of the
poisoned training data. Nevertheless, our experiments in sound classification,
text sentiment analysis, and image classification show this to be very
difficult in practice.

Membership inference (MI) determines if a sample was part of a victim model
training set. Recent development of MI attacks focus on record-level membership
inference which limits their application in many real-world scenarios. For
example, in the person re-identification task, the attacker (or investigator)
is interested in determining if a user's images have been used during training
or not. However, the exact training images might not be accessible to the
attacker. In this paper, we develop a user-level MI attack where the goal is to
find if any sample from the target user has been used during training even when
no exact training sample is available to the attacker. We focus on metric
embedding learning due to its dominance in person re-identification, where
user-level MI attack is more sensible. We conduct an extensive evaluation on
several datasets and show that our approach achieves high accuracy on
user-level MI task.

Split learning is a distributed training framework that allows multiple
parties to jointly train a machine learning model over vertically partitioned
data (partitioned by attributes). The idea is that only intermediate
computation results, rather than private features and labels, are shared
between parties so that raw training data remains private. Nevertheless, recent
works showed that the plaintext implementation of split learning suffers from
severe privacy risks that a semi-honest adversary can easily reconstruct
labels. In this work, we propose \textsf{TPSL} (Transcript Private Split
Learning), a generic gradient perturbation based split learning framework that
provides provable differential privacy guarantee. Differential privacy is
enforced on not only the model weights, but also the communicated messages in
the distributed computation setting. Our experiments on large-scale real-world
datasets demonstrate the robustness and effectiveness of \textsf{TPSL} against
label leakage attacks. We also find that \textsf{TPSL} have a better
utility-privacy trade-off than baselines.

We aim to help users communicate their intent to machines using flexible,
adaptive interfaces that translate arbitrary user input into desired actions.
In this work, we focus on assistive typing applications in which a user cannot
operate a keyboard, but can instead supply other inputs, such as webcam images
that capture eye gaze or neural activity measured by a brain implant. Standard
methods train a model on a fixed dataset of user inputs, then deploy a static
interface that does not learn from its mistakes; in part, because extracting an
error signal from user behavior can be challenging. We investigate a simple
idea that would enable such interfaces to improve over time, with minimal
additional effort from the user: online learning from user feedback on the
accuracy of the interface's actions. In the typing domain, we leverage
backspaces as feedback that the interface did not perform the desired action.
We propose an algorithm called x-to-text (X2T) that trains a predictive model
of this feedback signal, and uses this model to fine-tune any existing, default
interface for translating user input into actions that select words or
characters. We evaluate X2T through a small-scale online user study with 12
participants who type sentences by gazing at their desired words, a large-scale
observational study on handwriting samples from 60 users, and a pilot study
with one participant using an electrocorticography-based brain-computer
interface. The results show that X2T learns to outperform a non-adaptive
default interface, stimulates user co-adaptation to the interface, personalizes
the interface to individual users, and can leverage offline data collected from
the default interface to improve its initial performance and accelerate online
learning.

We introduce a new version of deep state-space models (DSSMs) that combines a
recurrent neural network with a state-space framework to forecast time series
data. The model estimates the observed series as functions of latent variables
that evolve non-linearly through time. Due to the complexity and non-linearity
inherent in DSSMs, previous works on DSSMs typically produced latent variables
that are very difficult to interpret. Our paper focus on producing
interpretable latent parameters with two key modifications. First, we simplify
the predictive decoder by restricting the response variables to be a linear
transformation of the latent variables plus some noise. Second, we utilize
shrinkage priors on the latent variables to reduce redundancy and improve
robustness. These changes make the latent variables much easier to understand
and allow us to interpret the resulting latent variables as random effects in a
linear mixed model. We show through two public benchmark datasets the resulting
model improves forecasting performances.

We consider Convolutional Neural Networks (CNNs) with 2D structured features
that are symmetric in the spatial dimensions. Such networks arise in modeling
pairwise relationships for a sequential recommendation problem, as well as
secondary structure inference problems of RNA and protein sequences. We develop
a CNN architecture that generates and preserves the symmetry structure in the
network's convolutional layers. We present parameterizations for the
convolutional kernels that produce update rules to maintain symmetry throughout
the training. We apply this architecture to the sequential recommendation
problem, the RNA secondary structure inference problem, and the protein contact
map prediction problem, showing that the symmetric structured networks produce
improved results using fewer numbers of machine parameters.

Text generation aims to produce human-like natural language output for
down-stream tasks. It covers a wide range of applications like machine
translation, document summarization, dialogue generation and so on. Recently
deep neural network-based end-to-end architectures have been widely adopted.
The end-to-end approach conflates all sub-modules, which used to be designed by
complex handcrafted rules, into a holistic encode-decode architecture. Given
enough training data, it is able to achieve state-of-the-art performance yet
avoiding the need of language/domain-dependent knowledge. Nonetheless, deep
learning models are known to be extremely data-hungry, and text generated from
them usually suffer from low diversity, interpretability and controllability.
As a result, it is difficult to trust the output from them in real-life
applications. Deep latent-variable models, by specifying the probabilistic
distribution over an intermediate latent process, provide a potential way of
addressing these problems while maintaining the expressive power of deep neural
networks. This dissertation presents how deep latent-variable models can
improve over the standard encoder-decoder model for text generation.

We present modality gap, an intriguing geometric phenomenon of the
representation space of multi-modal models. Specifically, we show that
different data modalities (e.g. images and text) are embedded at arm's length
in their shared representation in multi-modal models such as CLIP. Our
systematic analysis demonstrates that this gap is caused by a combination of
model initialization and contrastive learning optimization. In model
initialization, we show empirically and theoretically that the representation
of a common deep neural network is restricted to a narrow cone. As a
consequence, in a multi-modal model with two encoders, the representations of
the two modalities are clearly apart when the model is initialized. During
optimization, contrastive learning keeps the different modalities separate by a
certain distance, which is influenced by the temperature parameter in the loss
function. Our experiments further demonstrate that varying the modality gap
distance has a significant impact in improving the model's downstream zero-shot
classification performance and fairness. Our code and data are available at
https://modalitygap.readthedocs.io/

Unsupervised learning plays an important role in many fields, such as
artificial intelligence, machine learning, and neuroscience. Compared to static
data, methods for extracting low-dimensional structure for dynamic data are
lagging. We developed a novel information-theoretic framework, Compressed
Predictive Information Coding (CPIC), to extract useful representations from
dynamic data. CPIC selectively projects the past (input) into a linear subspace
that is predictive about the compressed data projected from the future
(output). The key insight of our framework is to learn representations by
minimizing the compression complexity and maximizing the predictive information
in latent space. We derive variational bounds of the CPIC loss which induces
the latent space to capture information that is maximally predictive. Our
variational bounds are tractable by leveraging bounds of mutual information. We
find that introducing stochasticity in the encoder robustly contributes to
better representation. Furthermore, variational approaches perform better in
mutual information estimation compared with estimates under a Gaussian
assumption. We demonstrate that CPIC is able to recover the latent space of
noisy dynamical systems with low signal-to-noise ratios, and extracts features
predictive of exogenous variables in neuroscience data.

Recent work has shown that label-efficient few-shot learning through
self-supervision can achieve promising medical image segmentation results.
However, few-shot segmentation models typically rely on prototype
representations of the semantic classes, resulting in a loss of local
information that can degrade performance. This is particularly problematic for
the typically large and highly heterogeneous background class in medical image
segmentation problems. Previous works have attempted to address this issue by
learning additional prototypes for each class, but since the prototypes are
based on a limited number of slices, we argue that this ad-hoc solution is
insufficient to capture the background properties. Motivated by this, and the
observation that the foreground class (e.g., one organ) is relatively
homogeneous, we propose a novel anomaly detection-inspired approach to few-shot
medical image segmentation in which we refrain from modeling the background
explicitly. Instead, we rely solely on a single foreground prototype to compute
anomaly scores for all query pixels. The segmentation is then performed by
thresholding these anomaly scores using a learned threshold. Assisted by a
novel self-supervision task that exploits the 3D structure of medical images
through supervoxels, our proposed anomaly detection-inspired few-shot medical
image segmentation model outperforms previous state-of-the-art approaches on
two representative MRI datasets for the tasks of abdominal organ segmentation
and cardiac segmentation.

Laparoscopic surgery has the advantage of avoiding large open incisions and
thereby decreasing blood loss, pain, and discomfort to patients. However, on
the other side, it is hampered by restricted workspace, ambiguous
communication, and surgeon fatigue caused by non-ergonomic head positioning. We
aimed to identify critical problems and suggest design requirements and
solutions. We used user and task analysis methods to learn about practices
performed in an operating room by observing surgeons in their working
environment to understand how they performed tasks and achieved their intended
goals. Drawing on observations and analysis from recorded laparoscopic
surgeries, we have identified several constraints and design requirements to
propose potential solutions to address the issues. Surgeons operate in a dimly
lit environment, surrounded by monitors, and communicate through verbal
commands and pointing gestures. Therefore, performing user and task analysis
allowed us to better understand the existing problems in laparoscopy while
identifying several communication constraints and design requirements, which a
solution has to follow to address those problems. Our contributions include
identifying design requirements for laparoscopy surgery through a user and task
analysis. These requirements propose design solutions towards improved
surgeons' comfort and make the surgical procedure less laborious.

Surgical resection is a common procedure in the treatment of pediatric
posterior fossa tumors. However, surgical damage is often unavoidable and its
association with postoperative complications is not well understood. A reliable
localization and measure of cerebellar damage is fundamental to study the
relationship between the damaged cerebellar regions and postoperative
neurological outcomes. Existing cerebellum normalization methods are not
reliable on postoperative scans, therefore current approaches to measure
surgical damage rely on manual labelling. In this work, we develop a robust
algorithm to automatically detect and measure cerebellum damage due to surgery
using postoperative 3D T1 magnetic resonance imaging. In our proposed approach,
normal brain tissues are first segmented using a Bayesian algorithm customized
for postoperative scans. Next, the cerebellum is isolated by nonlinear
registration of a whole brain template to the native space. The isolated
cerebellum is then normalized into the spatially unbiased atlas (SUIT) space
using anatomical information derived from the previous step. Finally, the
damage is detected in the atlas space by comparing the normalized cerebellum
and the SUIT template. We evaluated our damage detection tool on postoperative
scans of 153 patients diagnosed with medulloblastoma based on inspection by
human expects. We also designed a simulation to test the proposed approach
without human intervention. Our results show that the proposed approach has
superior performance on various scenarios.

Anomaly detection (AD) plays an important role in numerous applications. We
focus on two understudied aspects of AD that are critical for integration into
real-world applications. First, most AD methods cannot incorporate labeled data
that are often available in practice in small quantities and can be crucial to
achieve high AD accuracy. Second, most AD methods are not interpretable, a
bottleneck that prevents stakeholders from understanding the reason behind the
anomalies. In this paper, we propose a novel AD framework that adapts a
white-box model class, Generalized Additive Models, to detect anomalies using a
partial identification objective which naturally handles noisy or heterogeneous
features. In addition, the proposed framework, DIAD, can incorporate a small
amount of labeled data to further boost anomaly detection performances in
semi-supervised settings. We demonstrate the superiority of our framework
compared to previous work in both unsupervised and semi-supervised settings
using diverse tabular datasets. For example, under 5 labeled anomalies DIAD
improves from 86.2\% to 89.4\% AUC by learning AD from unlabeled data. We also
present insightful interpretations that explain why DIAD deems certain samples
as anomalies.

Automatic emotion recognition for real-life appli-cations is a challenging
task. Human emotion expressions aresubtle, and can be conveyed by a combination
of several emo-tions. In most existing emotion recognition studies, each
audioutterance/video clip is labelled/classified in its entirety.
However,utterance/clip-level labelling and classification can be too coarseto
capture the subtle intra-utterance/clip temporal dynamics. Forexample, an
utterance/video clip usually contains only a fewemotion-salient regions and
many emotionless regions. In thisstudy, we propose to use attention mechanism
in deep recurrentneural networks to detection the Regions-of-Interest (ROI)
thatare more emotionally salient in human emotional speech/video,and further
estimate the temporal emotion dynamics by aggre-gating those emotionally
salient regions-of-interest. We comparethe ROI from audio and video and analyse
them. We comparethe performance of the proposed attention networks with
thestate-of-the-art LSTM models on multi-class classification task
ofrecognizing six basic human emotions, and the proposed attentionmodels
exhibit significantly better performance. Furthermore, theattention weight
distribution can be used to interpret how anutterance can be expressed as a
mixture of possible emotions.

Finding the ground state of the Ising spin-glass is an important and
challenging problem (NP-hard, in fact) in condensed matter physics. However,
its applications spread far beyond physic due to its deep relation to various
combinatorial optimization problems, such as travelling salesman or protein
folding. Sophisticated and promising new methods for solving Ising instances
rely on quantum resources. In particular, quantum annealing is a quantum
computation paradigm, that is especially well suited for Quadratic
Unconstrained Binary Optimization (QUBO). Nevertheless, commercially available
quantum annealers (i.e., D-Wave) are prone to various errors, and their ability
to find low energetic states (corresponding to solutions of superior quality)
is limited. This naturally calls for a post-processing procedure to correct
errors (capable of lowering the energy found by the annealer). As a
proof-of-concept, this work combines the recent ideas revolving around the
DIRAC architecture with the Chimera topology and applies them in a real-world
setting as an error-correcting scheme for quantum annealers. Our preliminary
results show how to correct states output by quantum annealers using
reinforcement learning. Such an approach exhibits excellent scalability, as it
can be trained on small instances and deployed for large ones. However, its
performance on the chimera graph is still inferior to a typical algorithm one
could incorporate in this context, e.g., simulated annealing.

Hyper-Kamiokande (HK) is the next generation underground water Cherenkov
detector that builds on the highly successful Super-Kamiokande (SK) experiment.
The 260,000-ton detector has an 8.4 times larger fiducial volume than its
predecessor. HK's low energy threshold combined with the very large fiducial
volume make the detector unique; HK is expected to acquire an unprecedented
exposure of 3.8 Mton-year over a period of 20 years starting in 2027. It has an
extremely diverse science program including long-baseline neutrino oscillation
measurements, nucleon decay searches, atmospheric neutrinos, neutrinos from the
sun and supernova explosions, and neutrinos from other astrophysical origins.
Like DUNE, the flagship project of the U.S. high-energy physics program, HK
measures fundamental properties of neutrinos such as the search for leptonic CP
violation and neutrino physics beyond the Standard Model.

In continual learning (CL), the goal is to design models that can learn a
sequence of tasks without catastrophic forgetting. While there is a rich set of
techniques for CL, relatively little understanding exists on how
representations built by previous tasks benefit new tasks that are added to the
network. To address this, we study the problem of continual representation
learning (CRL) where we learn an evolving representation as new tasks arrive.
Focusing on zero-forgetting methods where tasks are embedded in subnetworks
(e.g., PackNet), we first provide experiments demonstrating CRL can
significantly boost sample efficiency when learning new tasks. To explain this,
we establish theoretical guarantees for CRL by providing sample complexity and
generalization error bounds for new tasks by formalizing the statistical
benefits of previously-learned representations. Our analysis and experiments
also highlight the importance of the order in which we learn the tasks.
Specifically, we show that CL benefits if the initial tasks have large sample
size and high "representation diversity". Diversity ensures that adding new
tasks incurs small representation mismatch and can be learned with few samples
while training only few additional nonzero weights. Finally, we ask whether one
can ensure each task subnetwork to be efficient during inference time while
retaining the benefits of representation learning. To this end, we propose an
inference-efficient variation of PackNet called Efficient Sparse PackNet (ESPN)
which employs joint channel & weight pruning. ESPN embeds tasks in
channel-sparse subnets requiring up to 80% less FLOPs to compute while
approximately retaining accuracy and is very competitive with a variety of
baselines. In summary, this work takes a step towards data and
compute-efficient CL with a representation learning perspective. GitHub page:
https://github.com/ucr-optml/CtRL

PINSKY is a system for open-ended learning through neuroevolution in
game-based domains. It builds on the Paired Open-Ended Trailblazer (POET)
system, which originally explored learning and environment generation for
bipedal walkers, and adapts it to games in the General Video Game AI (GVGAI)
system. Previous work showed that by co-evolving levels and neural network
policies, levels could be found for which successful policies could not be
created via optimization alone. Studied in the realm of Artificial Life as a
potentially open-ended alternative to gradient-based fitness, minimal criteria
(MC)-based selection helps foster diversity in evolutionary populations. The
main question addressed by this paper is how the open-ended learning actually
works, focusing in particular on the role of transfer of policies from one
evolutionary branch ("species") to another. We analyze the dynamics of the
system through creating phylogenetic trees, analyzing evolutionary trajectories
of policies, and temporally breaking down transfers according to species type.
Furthermore, we analyze the impact of the minimal criterion on generated level
diversity and inter-species transfer. The most insightful finding is that
inter-species transfer, while rare, is crucial to the system's success.

Recently several papers have been published on nonlinear prediction applied
to speech coding. At ICASSP98 we presented a system based on an ADPCM scheme
with a nonlinear predictor based on a neural net. The most critical parameter
was the training procedure in order to achieve good generalization capability
and robustness against mismatch between training and testing conditions. In
this paper, we propose several new approaches that improve the performance of
the original system in up to 1.2dB of SEGSNR (using bayesian regularization).
The variance of the SEGSNR between frames is also minimized, so the new scheme
produces a more stable quality of the output.

Data continuously emitted from industrial ecosystems such as social or
e-commerce platforms are commonly represented as heterogeneous graphs (HG)
composed of multiple node/edge types. State-of-the-art graph learning methods
for HGs known as heterogeneous graph neural networks (HGNNs) are applied to
learn deep context-informed node representations. However, many HG datasets
from industrial applications suffer from label imbalance between node types. As
there is no direct way to learn using labels rooted at different node types,
HGNNs have been applied to only a few node types with abundant labels. We
propose a zero-shot transfer learning module for HGNNs called a Knowledge
Transfer Network (KTN) that transfers knowledge from label-abundant node types
to zero-labeled node types through rich relational information given in the HG.
KTN is derived from the theoretical relationship, which we introduce in this
work, between distinct feature extractors for each node type given in an HGNN
model. KTN improves performance of 6 different types of HGNN models by up to
960% for inference on zero-labeled node types and outperforms state-of-the-art
transfer learning baselines by up to 73% across 18 different transfer learning
tasks on HGs.

Causal discovery from observational and interventional data is challenging
due to limited data and non-identifiability: factors that introduce uncertainty
in estimating the underlying structural causal model (SCM). Selecting
experiments (interventions) based on the uncertainty arising from both factors
can expedite the identification of the SCM. Existing methods in experimental
design for causal discovery from limited data either rely on linear assumptions
for the SCM or select only the intervention target. This work incorporates
recent advances in Bayesian causal discovery into the Bayesian optimal
experimental design framework, allowing for active causal discovery of large,
nonlinear SCMs while selecting both the interventional target and the value. We
demonstrate the performance of the proposed method on synthetic graphs
(Erdos-R\`enyi, Scale Free) for both linear and nonlinear SCMs as well as on
the \emph{in-silico} single-cell gene regulatory network dataset, DREAM.

Display front-of-screen (FOS) quality inspection is essential for the mass
production of displays in the manufacturing process. However, the severe
imbalanced data, especially the limited number of defect samples, has been a
long-standing problem that hinders the successful application of deep learning
algorithms. Synthetic defect data generation can help address this issue. This
paper reviews the state-of-the-art synthetic data generation methods and the
evaluation metrics that can potentially be applied to display FOS quality
inspection tasks.

Wi-Fi sensing can classify human activities because each activity causes
unique changes to the channel state information (CSI). Existing WiFi sensing
suffers from limited scalability as the system needs to be retrained whenever
new activities are added, which cause overheads of data collection and
retraining. Cross-domain sensing may fail because the mapping between
activities and CSI variations is destroyed when a different environment or user
(domain) is involved. This paper proposed a few-shot learning-based WiFi
sensing system, named FewSense, which can recognise novel classes in unseen
domains with only few samples. Specifically, a feature extractor was
pre-trained offline using the source domain data. When the system was applied
in the target domain, few samples were used to fine-tune the feature extractor
for domain adaptation. Inference was made by computing the cosine similarity.
FewSense can further boost the classification accuracy by collaboratively
fusing inference from multiple receivers. We evaluated the performance using
three public datasets, i.e., SignFi, Widar, and Wiar. The results show that
FewSense with five-shot learning recognised novel classes in unseen domains
with an accuracy of 90.3\%, 96.5\% ,82.7\% on SignFi, Widar, and Wiar datasets,
respectively. Our collaborative sensing model improved system performance by an
average of 30\%.

The ability for a human to understand an Artificial Intelligence (AI) model's
decision-making process is critical in enabling stakeholders to visualize model
behavior, perform model debugging, promote trust in AI models, and assist in
collaborative human-AI decision-making. As a result, the research fields of
interpretable and explainable AI have gained traction within AI communities as
well as interdisciplinary scientists seeking to apply AI in their subject
areas. In this paper, we focus on advancing the state-of-the-art in
interpreting multimodal models - a class of machine learning methods that
tackle core challenges in representing and capturing interactions between
heterogeneous data sources such as images, text, audio, and time-series data.
Multimodal models have proliferated numerous real-world applications across
healthcare, robotics, multimedia, affective computing, and human-computer
interaction. By performing model disentanglement into unimodal contributions
(UC) and multimodal interactions (MI), our proposed approach, DIME, enables
accurate and fine-grained analysis of multimodal models while maintaining
generality across arbitrary modalities, model architectures, and tasks. Through
a comprehensive suite of experiments on both synthetic and real-world
multimodal tasks, we show that DIME generates accurate disentangled
explanations, helps users of multimodal models gain a deeper understanding of
model behavior, and presents a step towards debugging and improving these
models for real-world deployment. Code for our experiments can be found at
https://github.com/lvyiwei1/DIME.

Machine learning classifiers with high test accuracy often perform poorly
under adversarial attacks. It is commonly believed that adversarial training
alleviates this issue. In this paper, we demonstrate that, surprisingly, the
opposite may be true -- Even though adversarial training helps when enough data
is available, it may hurt robust generalization in the small sample size
regime. We first prove this phenomenon for a high-dimensional linear
classification setting with noiseless observations. Our proof provides
explanatory insights that may also transfer to feature learning models.
Further, we observe in experiments on standard image datasets that the same
behavior occurs for perceptible attacks that effectively reduce class
information such as mask attacks and object corruptions.

Spectral clustering is widely used in practice due to its flexibility,
computational efficiency, and well-understood theoretical performance
guarantees. Recently, spectral clustering has been studied to find balanced
clusters under population-level constraints. These constraints are specified by
additional information available in the form of auxiliary categorical node
attributes. In this paper, we consider a scenario where these attributes may
not be observable, but manifest as latent features of an auxiliary graph.
Motivated by this, we study constrained spectral clustering with the aim of
finding balanced clusters in a given \textit{similarity graph} $\mathcal{G}$,
such that each individual is adequately represented with respect to an
auxiliary graph $\mathcal{R}$ (we refer to this as representation graph). We
propose an individual-level balancing constraint that formalizes this idea. Our
work leads to an interesting stochastic block model that not only plants the
given partitions in $\mathcal{G}$ but also plants the auxiliary information
encoded in the representation graph $\mathcal{R}$. We develop unnormalized and
normalized variants of spectral clustering in this setting. These algorithms
use $\mathcal{R}$ to find clusters in $\mathcal{G}$ that approximately satisfy
the proposed constraint. We also establish the first statistical consistency
result for constrained spectral clustering under individual-level constraints
for graphs sampled from the above-mentioned variant of the stochastic block
model. Our experimental results corroborate our theoretical findings.

To reduce the number of pending cases and conflicting rulings in the
Brazilian Judiciary, the National Congress amended the Constitution, allowing
the Brazilian Supreme Court (STF) to create binding precedents (BPs), i.e., a
set of understandings that both Executive and lower Judiciary branches must
follow. The STF's justices frequently cite the 58 existing BPs in their
decisions, and it is of primary relevance that judicial experts could identify
and analyze such citations. To assist in this problem, we propose LegalVis, a
web-based visual analytics system designed to support the analysis of legal
documents that cite or could potentially cite a BP. We model the problem of
identifying potential citations (i.e., non-explicit) as a classification
problem. However, a simple score is not enough to explain the results; that is
why we use an interpretability machine learning method to explain the reason
behind each identified citation. For a compelling visual exploration of
documents and BPs, LegalVis comprises three interactive visual components: the
first presents an overview of the data showing temporal patterns, the second
allows filtering and grouping relevant documents by topic, and the last one
shows a document's text aiming to interpret the model's output by pointing out
which paragraphs are likely to mention the BP, even if not explicitly
specified. We evaluated our identification model and obtained an accuracy of
96%; we also made a quantitative and qualitative analysis of the results. The
usefulness and effectiveness of LegalVis were evaluated through two usage
scenarios and feedback from six domain experts.

Explicitly accounting for uncertainties is paramount to the safety of
engineering structures. Optimization which is often carried out at the early
stage of the structural design offers an ideal framework for this task. When
the uncertainties are mainly affecting the objective function, robust design
optimization is traditionally considered. This work further assumes the
existence of multiple and competing objective functions that need to be dealt
with simultaneously. The optimization problem is formulated by considering
quantiles of the objective functions which allows for the combination of both
optimality and robustness in a single metric. By introducing the concept of
common random numbers, the resulting nested optimization problem may be solved
using a general-purpose solver, herein the non-dominated sorting genetic
algorithm (NSGA-II). The computational cost of such an approach is however a
serious hurdle to its application in real-world problems. We therefore propose
a surrogate-assisted approach using Kriging as an inexpensive approximation of
the associated computational model. The proposed approach consists of
sequentially carrying out NSGA-II while using an adaptively built Kriging model
to estimate of the quantiles. Finally, the methodology is adapted to account
for mixed categorical-continuous parameters as the applications involve the
selection of qualitative design parameters as well. The methodology is first
applied to two analytical examples showing its efficiency. The third
application relates to the selection of optimal renovation scenarios of a
building considering both its life cycle cost and environmental impact. It
shows that when it comes to renovation, the heating system replacement should
be the priority.

Numerical modeling and simulation have become indispensable tools for
advancing a comprehensive understanding of the underlying mechanisms and
cost-effective process optimization and control of flow batteries. In this
study, we propose an enhanced version of the physics-constrained deep neural
network (PCDNN) approach [1] to provide high-accuracy voltage predictions in
the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is
to enforce the physics-based zero-dimensional (0D) VRFB model in a neural
network to assure model generalization for various battery operation
conditions. Limited by the simplifications of the 0D model, the PCDNN cannot
capture sharp voltage changes in the extreme SOC regions. To improve the
accuracy of voltage prediction at extreme ranges, we introduce a second
(enhanced) DNN to mitigate the prediction errors carried from the 0D model
itself and call the resulting approach enhanced PCDNN (ePCDNN). By comparing
the model prediction with experimental data, we demonstrate that the ePCDNN
approach can accurately capture the voltage response throughout the
charge--discharge cycle, including the tail region of the voltage discharge
curve. Compared to the standard PCDNN, the prediction accuracy of the ePCDNN is
significantly improved. The loss function for training the ePCDNN is designed
to be flexible by adjusting the weights of the physics-constrained DNN and the
enhanced DNN. This allows the ePCDNN framework to be transferable to battery
systems with variable physical model fidelity.

Action representation is an important yet often overlooked aspect in
end-to-end robot learning with deep networks. Choosing one action space over
another (e.g. target joint positions, or Cartesian end-effector poses) can
result in surprisingly stark performance differences between various downstream
tasks -- and as a result, considerable research has been devoted to finding the
right action space for a given application. However, in this work, we instead
investigate how our models can discover and learn for themselves which action
space to use. Leveraging recent work on implicit behavioral cloning, which
takes both observations and actions as input, we demonstrate that it is
possible to present the same action in multiple different spaces to the same
policy -- allowing it to learn inductive patterns from each space.
Specifically, we study the benefits of combining Cartesian and joint action
spaces in the context of learning manipulation skills. To this end, we present
Implicit Kinematic Policies (IKP), which incorporates the kinematic chain as a
differentiable module within the deep network. Quantitative experiments across
several simulated continuous control tasks -- from scooping piles of small
objects, to lifting boxes with elbows, to precise block insertion with
miscalibrated robots -- suggest IKP not only learns complex prehensile and
non-prehensile manipulation from pixels better than baseline alternatives, but
also can learn to compensate for small joint encoder offset errors. Finally, we
also run qualitative experiments on a real UR5e to demonstrate the feasibility
of our algorithm on a physical robotic system with real data. See
https://tinyurl.com/4wz3nf86 for code and supplementary material.

Humans do not perceive all parts of a scene with the same resolution, but
rather focus on few regions of interest (ROIs). Traditional Object-Based codecs
take advantage of this biological intuition, and are capable of non-uniform
allocation of bits in favor of salient regions, at the expense of increased
distortion the remaining areas: such a strategy allows a boost in perceptual
quality under low rate constraints. Recently, several neural codecs have been
introduced for video compression, yet they operate uniformly over all spatial
locations, lacking the capability of ROI-based processing. In this paper, we
introduce two models for ROI-based neural video coding. First, we propose an
implicit model that is fed with a binary ROI mask and it is trained by
de-emphasizing the distortion of the background. Secondly, we design an
explicit latent scaling method, that allows control over the quantization
binwidth for different spatial regions of latent variables, conditioned on the
ROI mask. By extensive experiments, we show that our methods outperform all our
baselines in terms of Rate-Distortion (R-D) performance in the ROI. Moreover,
they can generalize to different datasets and to any arbitrary ROI at inference
time. Finally, they do not require expensive pixel-level annotations during
training, as synthetic ROI masks can be used with little to no degradation in
performance. To the best of our knowledge, our proposals are the first
solutions that integrate ROI-based capabilities into neural video compression
models.

Recently, pedestrian behavior research has shifted towards machine learning
based methods and converged on the topic of modeling pedestrian interactions.
For this, a large-scale dataset that contains rich information is needed. We
propose a data collection system that is portable, which facilitates accessible
large-scale data collection in diverse environments. We also couple the system
with a semi-autonomous labeling pipeline for fast trajectory label production.
We demonstrate the effectiveness of our system by further introducing a dataset
we have collected -- the TBD pedestrian dataset. Compared with existing
pedestrian datasets, our dataset contains three components: human verified
labels grounded in the metric space, a combination of top-down and perspective
views, and naturalistic human behavior in the presence of a socially
appropriate "robot". In addition, the TBD pedestrian dataset is larger in
quantity compared to similar existing datasets and contains unique pedestrian
behavior.

In this paper, we present a learning-based approach that allows a robot to
quickly follow a reference path defined in joint space without exceeding limits
on the position, velocity, acceleration and jerk of each robot joint. Contrary
to offline methods for time-optimal path parameterization, the reference path
can be changed during motion execution. In addition, our approach can utilize
sensory feedback, for instance, to follow a reference path with a bipedal robot
without losing balance. With our method, the robot is controlled by a neural
network that is trained via reinforcement learning using data generated by a
physics simulator. From a mathematical perspective, the problem of tracking a
reference path in a time-optimized manner is formalized as a Markov decision
process. Each state includes a fixed number of waypoints specifying the next
part of the reference path. The action space is designed in such a way that all
resulting motions comply with the specified kinematic joint limits. The reward
function finally reflects the trade-off between the execution time, the
deviation from the desired reference path and optional additional objectives
like balancing. We evaluate our approach with and without additional objectives
and show that time-optimized path tracking can be successfully learned for both
industrial and humanoid robots. In addition, we demonstrate that networks
trained in simulation can be successfully transferred to a real robot.

Tensor networks have demonstrated significant value for machine learning in a
myriad of different applications. However, optimizing tensor networks using
standard gradient descent has proven to be difficult in practice. Tensor
networks suffer from initialization problems resulting in exploding or
vanishing gradients and require extensive hyperparameter tuning. Efforts to
overcome these problems usually depend on specific network architectures, or ad
hoc prescriptions. In this paper we address the problems of initialization and
hyperparameter tuning, making it possible to train tensor networks using
established machine learning techniques. We introduce a `copy node' method that
successfully initializes arbitrary tensor networks, in addition to a gradient
based regularization technique for bond dimensions. We present numerical
results that show that the combination of techniques presented here produces
quantum inspired tensor network models with far fewer parameters, while
improving generalization performance.

In this paper we present photometric redshift (photo-$z$) estimates for the
Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, currently
the most sensitive optical survey covering the majority of the extra-galactic
sky. Our photo-$z$ methodology is based on a machine-learning approach, using
sparse Gaussian processes augmented with Gaussian mixture models (GMMs) that
allow regions of parameter space to be identified and trained separately in a
purely data-driven way. The same GMMs are also used to calculate cost-sensitive
learning weights that mitigate biases in the spectroscopic training sample. By
design, this approach aims to produce reliable and unbiased predictions for all
parts of the parameter space present in wide area surveys. Compared to previous
literature estimates using the same underlying photometry, our photo-$z$s are
significantly less biased and more accurate at $z > 1$, with negligible loss in
precision or reliability for resolved galaxies at $z < 1$. Our photo-$z$
estimates offer accurate predictions for rare high-value populations within the
parent sample, including optically selected quasars at the highest redshifts
($z > 6$), as well as X-ray or radio continuum selected populations across a
broad range of flux (densities) and redshift. Deriving photo-$z$ estimates for
the full Legacy Imaging Surveys Data Release 8, the catalogues provided in this
work offer photo-$z$ estimates predicted to be high quality for
$\gtrsim9\times10^{8}$ galaxies over $\sim 19\,400\,\text{deg}^{2}$ and
spanning $0 < z \lesssim 7$, offering one of the most extensive samples of
redshift estimates ever produced.

This paper studies the complex task of simultaneous multi-object 3D
reconstruction, 6D pose and size estimation from a single-view RGB-D
observation. In contrast to instance-level pose estimation, we focus on a more
challenging problem where CAD models are not available at inference time.
Existing approaches mainly follow a complex multi-stage pipeline which first
localizes and detects each object instance in the image and then regresses to
either their 3D meshes or 6D poses. These approaches suffer from
high-computational cost and low performance in complex multi-object scenarios,
where occlusions can be present. Hence, we present a simple one-stage approach
to predict both the 3D shape and estimate the 6D pose and size jointly in a
bounding-box free manner. In particular, our method treats object instances as
spatial centers where each center denotes the complete shape of an object along
with its 6D pose and size. Through this per-pixel representation, our approach
can reconstruct in real-time (40 FPS) multiple novel object instances and
predict their 6D pose and sizes in a single-forward pass. Through extensive
experiments, we demonstrate that our approach significantly outperforms all
shape completion and categorical 6D pose and size estimation baselines on
multi-object ShapeNet and NOCS datasets respectively with a 12.6% absolute
improvement in mAP for 6D pose for novel real-world object instances.

Unsupervised black-box models are challenging to interpret. Indeed, most
existing explainability methods require labels to select which component(s) of
the black-box's output to interpret. In the absence of labels, black-box
outputs often are representation vectors whose components do not correspond to
any meaningful quantity. Hence, choosing which component(s) to interpret in a
label-free unsupervised/self-supervised setting is an important, yet unsolved
problem. To bridge this gap in the literature, we introduce two crucial
extensions of post-hoc explanation techniques: (1) label-free feature
importance and (2) label-free example importance that respectively highlight
influential features and training examples for a black-box to construct
representations at inference time. We demonstrate that our extensions can be
successfully implemented as simple wrappers around many existing feature and
example importance methods. We illustrate the utility of our label-free
explainability paradigm through a qualitative and quantitative comparison of
representation spaces learned by various autoencoders trained on distinct
unsupervised tasks.

Recent studies show that the state-of-the-art deep neural networks are
vulnerable to model inversion attacks, in which access to a model is abused to
reconstruct private training data of any given target class. Existing attacks
rely on having access to either the complete target model (whitebox) or the
model's soft-labels (blackbox). However, no prior work has been done in the
harder but more practical scenario, in which the attacker only has access to
the model's predicted label, without a confidence measure. In this paper, we
introduce an algorithm, Boundary-Repelling Model Inversion (BREP-MI), to invert
private training data using only the target model's predicted labels. The key
idea of our algorithm is to evaluate the model's predicted labels over a sphere
and then estimate the direction to reach the target class's centroid. Using the
example of face recognition, we show that the images reconstructed by BREP-MI
successfully reproduce the semantics of the private training data for various
datasets and target model architectures. We compare BREP-MI with the
state-of-the-art whitebox and blackbox model inversion attacks and the results
show that despite assuming less knowledge about the target model, BREP-MI
outperforms the blackbox attack and achieves comparable results to the whitebox
attack.

This paper is the first to propose a generic min-max bilevel multi-objective
optimization framework, highlighting applications in representation learning
and hyperparameter optimization. In many machine learning applications such as
meta-learning, multi-task learning, and representation learning, a subset of
the parameters are shared by all the tasks, while each specific task has its
own set of additional parameters. By leveraging the recent advances of
nonconvex min-max optimization, we propose a gradient descent-ascent bilevel
optimization (MORBiT) algorithm which is able to extract a set of shared
parameters that is robust over all tasks and further overcomes the
distributional shift between training and testing tasks. Theoretical analyses
show that MORBiT converges to the first-order stationary point at a rate of
$\mathcal{O}(\sqrt{n}K^{-2/5})$ for a class of nonconvex problems, where $K$
denotes the total number of iterations and $n$ denotes the number of tasks.
Overall, we formulate a min-max bilevel multi-objective optimization problem,
provide a single loop two-timescale algorithm with convergence rate guarantees,
and show theoretical bounds on the generalization abilities of the optimizer.
Experimental results on sinusoid regression and representation learning
showcase the superiority of MORBiT over state-of-the-art methods, validating
our convergence and generalization results.

Estimating human pose and shape from monocular images is a long-standing
problem in computer vision. Since the release of statistical body models, 3D
human mesh recovery has been drawing broader attention. With the same goal of
obtaining well-aligned and physically plausible mesh results, two paradigms
have been developed to overcome challenges in the 2D-to-3D lifting process: i)
an optimization-based paradigm, where different data terms and regularization
terms are exploited as optimization objectives; and ii) a regression-based
paradigm, where deep learning techniques are embraced to solve the problem in
an end-to-end fashion. Meanwhile, continuous efforts are devoted to improving
the quality of 3D mesh labels for a wide range of datasets. Though remarkable
progress has been achieved in the past decade, the task is still challenging
due to flexible body motions, diverse appearances, complex environments, and
insufficient in-the-wild annotations. To the best of our knowledge, this is the
first survey to focus on the task of monocular 3D human mesh recovery. We start
with the introduction of body models and then elaborate recovery frameworks and
training objectives by providing in-depth analyses of their strengths and
weaknesses. We also summarize datasets, evaluation metrics, and benchmark
results. Open issues and future directions are discussed in the end, hoping to
motivate researchers and facilitate their research in this area. A regularly
updated project page can be found at https://github.com/tinatiansjz/hmr-survey.

Despite artificial intelligence (AI) having transformed major aspects of our
society, less than a fraction of its potential has been explored, let alone
deployed, for education. AI-powered learning can provide millions of learners
with a highly personalized, active and practical learning experience, which is
key to successful learning. This is especially relevant in the context of
online learning platforms. In this paper, we present the results of a
comparative head-to-head study on learning outcomes for two popular online
learning platforms (n=199 participants): A MOOC platform following a
traditional model delivering content using lecture videos and multiple-choice
quizzes, and the Korbit learning platform providing a highly personalized,
active and practical learning experience. We observe a huge and statistically
significant increase in the learning outcomes, with students on the Korbit
platform providing full feedback resulting in higher course completion rates
and achieving learning gains 2 to 2.5 times higher than both students on the
MOOC platform and students in a control group who don't receive personalized
feedback on the Korbit platform. The results demonstrate the tremendous impact
that can be achieved with a personalized, active learning AI-powered system.
Making this technology and learning experience available to millions of
learners around the world will represent a significant leap forward towards the
democratization of education.

This paper presents a comprehensive survey of vision-language (VL)
intelligence from the perspective of time. This survey is inspired by the
remarkable progress in both computer vision and natural language processing,
and recent trends shifting from single modality processing to multiple modality
comprehension. We summarize the development in this field into three time
periods, namely task-specific methods, vision-language pre-training (VLP)
methods, and larger models empowered by large-scale weakly-labeled data. We
first take some common VL tasks as examples to introduce the development of
task-specific methods. Then we focus on VLP methods and comprehensively review
key components of the model structures and training methods. After that, we
show how recent work utilizes large-scale raw image-text data to learn
language-aligned visual representations that generalize better on zero or few
shot learning tasks. Finally, we discuss some potential future trends towards
modality cooperation, unified representation, and knowledge incorporation. We
believe that this review will be of help for researchers and practitioners of
AI and ML, especially those interested in computer vision and natural language

Diffusion MRI (dMRI) is the only non-invasive technique sensitive to tissue
micro-architecture, which can, in turn, be used to reconstruct tissue
microstructure and white matter pathways. The accuracy of such tasks is
hampered by the low signal-to-noise ratio in dMRI. Today, the noise is
characterized mainly by visual inspection of residual maps and estimated
standard deviation. However, it is hard to estimate the impact of noise on
downstream tasks based only on such qualitative assessments. To address this
issue, we introduce a novel metric, Noise Uncertainty Quantification (NUQ), for
quantitative image quality analysis in the absence of a ground truth reference
image. NUQ uses a recent Bayesian formulation of dMRI models to estimate the
uncertainty of microstructural measures. Specifically, NUQ uses the maximum
mean discrepancy metric to compute a pooled quality score by comparing samples
drawn from the posterior distribution of the microstructure measures. We show
that NUQ allows a fine-grained analysis of noise, capturing details that are
visually imperceptible. We perform qualitative and quantitative comparisons on
real datasets, showing that NUQ generates consistent scores across different
denoisers and acquisitions. Lastly, by using NUQ on a cohort of schizophrenics
and controls, we quantify the substantial impact of denoising on group
differences.

Multiple objective assessment of image-quality-based studies have reported
that several deep-learning-based denoising methods show limited performance on
signal-detection tasks. Our goal was to investigate the reasons for this
limited performance. To achieve this goal, we conducted a task-based
characterization of a DL-based denoising approach for individual signal
properties. We conducted this study in the context of evaluating a DL-based
approach for denoising SPECT images. The training data consisted of signals of
different sizes and shapes within a clustered-lumpy background, imaged with a
2D parallel-hole-collimator SPECT system. The projections were generated at
normal and 20% low count level, both of which were reconstructed using an OSEM
algorithm. A CNN-based denoiser was trained to process the low-count images.
The performance of this CNN was characterized for five different signal sizes
and four different SBR by designing each evaluation as an SKE/BKS
signal-detection task. Performance on this task was evaluated using an
anthropomorphic CHO. As in previous studies, we observed that the DL-based
denoising method did not improve performance on signal-detection tasks.
Evaluation using the idea of observer-study-based characterization demonstrated
that the DL-based denoising approach did not improve performance on the
signal-detection task for any of the signal types. Overall, these results
provide new insights on the performance of the DL-based denoising approach as a
function of signal size and contrast. More generally, the observer study-based
characterization provides a mechanism to evaluate the sensitivity of the method
to specific object properties and may be explored as analogous to
characterizations such as modulation transfer function for linear systems.
Finally, this work underscores the need for objective task-based evaluation of
DL-based denoising approaches.

Thin, reflective objects such as forks and whisks are common in our daily
lives, but they are particularly challenging for robot perception because it is
hard to reconstruct them using commodity RGB-D cameras or multi-view stereo
techniques. While traditional pipelines struggle with objects like these,
Neural Radiance Fields (NeRFs) have recently been shown to be remarkably
effective for performing view synthesis on objects with thin structures or
reflective materials. In this paper we explore the use of NeRF as a new source
of supervision for robust robot vision systems. In particular, we demonstrate
that a NeRF representation of a scene can be used to train dense object
descriptors. We use an optimized NeRF to extract dense correspondences between
multiple views of an object, and then use these correspondences as training
data for learning a view-invariant representation of the object. NeRF's usage
of a density field allows us to reformulate the correspondence problem with a
novel distribution-of-depths formulation, as opposed to the conventional
approach of using a depth map. Dense correspondence models supervised with our
method significantly outperform off-the-shelf learned descriptors by 106%
(PCK@3px metric, more than doubling performance) and outperform our baseline
supervised with multi-view stereo by 29%. Furthermore, we demonstrate the
learned dense descriptors enable robots to perform accurate 6-degree of freedom
(6-DoF) pick and place of thin and reflective objects.

We present Bayesian Spillover Graphs (BSG), a novel method for learning
temporal relationships, identifying critical nodes, and quantifying uncertainty
for multi-horizon spillover effects in a dynamic system. BSG leverages both an
interpretable framework via forecast error variance decompositions (FEVD) and
comprehensive uncertainty quantification via Bayesian time series models to
contextualize temporal relationships in terms of systemic risk and prediction
variability. Forecast horizon hyperparameter $h$ allows for learning both
short-term and equilibrium state network behaviors. Experiments for identifying
source and sink nodes under various graph and error specifications show
significant performance gains against state-of-the-art Bayesian Networks and
deep-learning baselines. Applications to real-world systems also showcase BSG
as an exploratory analysis tool for uncovering indirect spillovers and
quantifying systemic risk.

Engineering a high-performance race car requires a direct consideration of
the human driver using real-world tests or Human-Driver-in-the-Loop
simulations. Apart from that, offline simulations with human-like race driver
models could make this vehicle development process more effective and efficient
but are hard to obtain due to various challenges. With this work, we intend to
provide a better understanding of race driver behavior and introduce an
adaptive human race driver model based on imitation learning. Using existing
findings and an interview with a professional race engineer, we identify
fundamental adaptation mechanisms and how drivers learn to optimize lap time on
a new track. Subsequently, we use these insights to develop generalization and
adaptation techniques for a recently presented probabilistic driver modeling
approach and evaluate it using data from professional race drivers and a
state-of-the-art race car simulator. We show that our framework can create
realistic driving line distributions on unseen race tracks with almost
human-like performance. Moreover, our driver model optimizes its driving lap by
lap, correcting driving errors from previous laps while achieving faster lap
times. This work contributes to a better understanding and modeling of the
human driver, aiming to expedite simulation methods in the modern vehicle
development process and potentially supporting automated driving and racing
technologies.

This paper provides the first real-world evaluation of using visual (RGB
camera) data and machine learning for proactively predicting millimeter wave
(mmWave) dynamic link blockages before they happen. Proactively predicting
line-of-sight (LOS) link blockages enables mmWave/sub-THz networks to make
proactive network management decisions, such as proactive beam switching and
hand-off) before a link failure happens. This can significantly enhance the
network reliability and latency while efficiently utilizing the wireless
resources. To evaluate this gain in reality, this paper (i) develops a computer
vision based solution that processes the visual data captured by a camera
installed at the infrastructure node and (ii) studies the feasibility of the
proposed solution based on the large-scale real-world dataset, DeepSense 6G,
that comprises multi-modal sensing and communication data. Based on the adopted
real-world dataset, the developed solution achieves $\approx 90\%$ accuracy in
predicting blockages happening within the future $0.1$s and $\approx 80\%$ for
blockages happening within $1$s, which highlights a promising solution for
mmWave/sub-THz communication networks.

Graph Convolutional Neural Networks (GCNs) have become effective machine
learning algorithms for many downstream network mining tasks such as node
classification, link prediction, and community detection. However, most GCN
methods have been developed for homogenous networks and are limited to a single
embedding for each node. Complex systems, often represented by heterogeneous,
multiplex networks present a more difficult challenge for GCN models and
require that such techniques capture the diverse contexts and assorted
interactions that occur between nodes. In this work, we propose RAHMeN, a novel
unified relation-aware embedding framework for attributed heterogeneous
multiplex networks. Our model incorporates node attributes, motif-based
features, relation-based GCN approaches, and relational self-attention to learn
embeddings of nodes with respect to the various relations in a heterogeneous,
multiplex network. In contrast to prior work, RAHMeN is a more expressive
embedding framework that embraces the multi-faceted nature of nodes in such
networks, producing a set of multi-embeddings that capture the varied and
diverse contexts of nodes.
  We evaluate our model on four real-world datasets from Amazon, Twitter,
YouTube, and Tissue PPIs in both transductive and inductive settings. Our
results show that RAHMeN consistently outperforms comparable state-of-the-art
network embedding models, and an analysis of RAHMeN's relational self-attention
demonstrates that our model discovers interpretable connections between
relations present in heterogeneous, multiplex networks.

Bayesian optimization (BO) is a powerful approach to sample-efficient
optimization of black-box objective functions. However, the application of BO
to areas such as recommendation systems often requires taking the
interpretability and simplicity of the configurations into consideration, a
setting that has not been previously studied in the BO literature. To make BO
applicable in this setting, we present several regularization-based approaches
that allow us to discover sparse and more interpretable configurations. We
propose a novel differentiable relaxation based on homotopy continuation that
makes it possible to target sparsity by working directly with $L_0$
regularization. We identify failure modes for regularized BO and develop a
hyperparameter-free method, sparsity exploring Bayesian optimization (SEBO)
that seeks to simultaneously maximize a target objective and sparsity. SEBO and
methods based on fixed regularization are evaluated on synthetic and real-world
problems, and we show that we are able to efficiently optimize for sparsity.

Health mentioning classification (HMC) classifies an input text as health
mention or not. Figurative and non-health mention of disease words makes the
classification task challenging. Learning the context of the input text is the
key to this problem. The idea is to learn word representation by its
surrounding words and utilize emojis in the text to help improve the
classification results. In this paper, we improve the word representation of
the input text using adversarial training that acts as a regularizer during
fine-tuning of the model. We generate adversarial examples by perturbing the
embeddings of the model and then train the model on a pair of clean and
adversarial examples. Additionally, we utilize contrastive loss that pushes a
pair of clean and perturbed examples close to each other and other examples
away in the representation space. We train and evaluate the method on an
extended version of the publicly available PHM2017 dataset. Experiments show an
improvement of 1.0% over BERT-Large baseline and 0.6% over RoBERTa-Large
baseline, whereas 5.8% over the state-of-the-art in terms of F1 score.
Furthermore, we provide a brief analysis of the results by utilizing the power
of explainable AI.

We describe a method for approximating a single-variable function $f$ using
persistence diagrams of sublevel sets of $f$ from height functions in different
directions. We provide algorithms for the piecewise linear case and for the
smooth case. Three directions suffice to locate all local maxima and minima of
a piecewise linear continuous function from its collection of directional
persistence diagrams, while five directions are needed in the case of smooth
functions with non-degenerate critical points.
  Our approximation of functions by means of persistence diagrams is motivated
by a study of importance attribution in machine learning, where one seeks to
reduce the number of critical points of signal functions without a significant
loss of information for a neural network classifier.

Quantum computing has shown the potential to substantially speed up machine
learning applications, in particular for supervised and unsupervised learning.
Reinforcement learning, on the other hand, has become essential for solving
many decision making problems and policy iteration methods remain the
foundation of such approaches. In this paper, we provide a general framework
for performing quantum reinforcement learning via policy iteration. We validate
our framework by designing and analyzing: \emph{quantum policy evaluation}
methods for infinite horizon discounted problems by building quantum states
that approximately encode the value function of a policy $\pi$; and
\emph{quantum policy improvement} methods by post-processing measurement
outcomes on these quantum states. Last, we study the theoretical and
experimental performance of our quantum algorithms on two environments from
OpenAI's Gym.

Recent advances in multimodal single-cell technologies have enabled
simultaneous acquisitions of multiple omics data from the same cell, providing
deeper insights into cellular states and dynamics. However, it is challenging
to learn the joint representations from the multimodal data, model the
relationship between modalities, and, more importantly, incorporate the vast
amount of single-modality datasets into the downstream analyses. To address
these challenges and correspondingly facilitate multimodal single-cell data
analyses, three key tasks have been introduced: $\textit{modality prediction}$,
$\textit{modality matching}$ and $\textit{joint embedding}$. In this work, we
present a general Graph Neural Network framework $\textit{scMoGNN}$ to tackle
these three tasks and show that $\textit{scMoGNN}$ demonstrates superior
results in all three tasks compared with the state-of-the-art and conventional
approaches. Our method is an official winner in the overall ranking of
$\textit{Modality prediction}$ from NeurIPS 2021 Competition, and all
implementations of our methods have been integrated into DANCE
package~\url{https://github.com/OmicsML/dance}.

In this paper, we study the named entity recognition (NER) problem under
distant supervision. Due to the incompleteness of the external dictionaries
and/or knowledge bases, such distantly annotated training data usually suffer
from a high false negative rate. To this end, we formulate the Distantly
Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU)
learning and propose a theoretically and practically novel CONFidence-based MPU
(Conf-MPU) approach. To handle the incomplete annotations, Conf-MPU consists of
two steps. First, a confidence score is estimated for each token of being an
entity token. Then, the proposed Conf-MPU risk estimation is applied to train a
multi-class classifier for the NER task. Thorough experiments on two benchmark
datasets labeled by various external knowledge demonstrate the superiority of
the proposed Conf-MPU over existing DS-NER methods.

Optical coherence tomography (OCT) imaging is a well-known technology for
visualizing retinal layers and helps ophthalmologists to detect possible
diseases. Accurate and early diagnosis of common retinal diseases can prevent
the patients from suffering critical damages to their vision. Computer-aided
diagnosis (CAD) systems can significantly assist ophthalmologists in improving
their examinations. This paper presents a new enhanced deep ensemble
convolutional neural network for detecting retinal diseases from OCT images.
Our model generates rich and multi-resolution features by employing the
learning architectures of two robust convolutional models. Spatial resolution
is a critical factor in medical images, especially the OCT images that contain
tiny essential points. To empower our model, we apply a new post-architecture
model to our ensemble model for enhancing spatial resolution learning without
increasing computational costs. The introduced post-architecture model can be
deployed to any feature extraction model to improve the utilization of the
feature map's spatial values. We have collected two open-source datasets for
our experiments to make our models capable of detecting six crucial retinal
diseases: Age-related Macular Degeneration (AMD), Central Serous Retinopathy
(CSR), Diabetic Retinopathy (DR), Choroidal Neovascularization (CNV), Diabetic
Macular Edema (DME), and Drusen alongside the normal cases. Our experiments on
two datasets and comparing our model with some other well-known deep
convolutional neural networks have proven that our architecture can increase
the classification accuracy up to 5%. We hope that our proposed methods create
the next step of CAD systems development and help future researches. The code
of this paper is shared at https://github.com/mr7495/OCT-classification.

To estimate the corneal endothelial parameters from specular microscopy
images depicting cornea guttata (Fuchs dystrophy), we propose a new deep
learning methodology that includes a novel attention mechanism named feedback
non-local attention (fNLA). Our approach first infers the cell edges, then
selects the cells that are well detected, and finally applies a postprocessing
method to correct mistakes and provide the binary segmentation from which the
corneal parameters are estimated (cell density [ECD], coefficient of variation
[CV], and hexagonality [HEX]). In this study, we analyzed 1203 images acquired
with a Topcon SP-1P microscope, 500 of which contained guttae. Manual
segmentation was performed in all images. We compared the results of different
networks (UNet, ResUNeXt, DenseUNets, UNet++) and found that DenseUNets with
fNLA provided the best performance, with a mean absolute error of 23.16
[cells/mm$^{2}$] in ECD, 1.28 [%] in CV, and 3.13 [%] in HEX, which was 3-6
times smaller than the error obtained by Topcon's built-in software. Our
approach handled the cells affected by guttae remarkably well, detecting cell
edges occluded by small guttae while discarding areas covered by large guttae.
Overall, the proposed method obtained accurate estimations in extremely
challenging specular images.

Self-supervised learning methods have shown impressive results in downstream
classification tasks. However, there is limited work in understanding and
interpreting their learned representations. In this paper, we study the
representation space of several state-of-the-art self-supervised models
including SimCLR, SwaV, MoCo V2 and BYOL. Without the use of class label
information, we first discover discriminative features that are highly active
for various subsets of samples and correspond to unique physical attributes in
images. We show that, using such discriminative features, one can compress the
representation space of self-supervised models up to 50% without affecting
downstream linear classification significantly. Next, we propose a sample-wise
Self-Supervised Representation Quality Score (or, Q-Score) that can be computed
without access to any label information. Q-Score, utilizes discriminative
features to reliably predict if a given sample is likely to be mis-classified
in the downstream classification task achieving AUPRC of 0.91 on SimCLR and
BYOL trained on ImageNet-100. Q-Score can also be used as a regularization term
to remedy low-quality representations leading up to 8% relative improvement in
accuracy on all 4 self-supervised baselines on ImageNet-100, CIFAR-10,
CIFAR-100 and STL-10. Moreover, through heatmap analysis, we show that Q-Score
regularization enhances discriminative features and reduces feature noise, thus
improving model interpretability.

The huge demand for computation in artificial intelligence (AI) is driving
unparalleled investments in hardware and software systems for AI. This leads to
an explosion in the number of specialized hardware devices, which are now
offered by major cloud vendors. By hiding the low-level complexity through a
tensor-based interface, tensor computation runtimes (TCRs) such as PyTorch
allow data scientists to efficiently exploit the exciting capabilities offered
by the new hardware. In this paper, we explore how database management systems
can ride the wave of innovation happening in the AI space.
  We design, build, and evaluate Tensor Query Processor (TQP): TQP transforms
SQL queries into tensor programs and executes them on TCRs. TQP is able to run
the full TPC-H benchmark by implementing novel algorithms for relational
operators on the tensor routines. At the same time, TQP can support various
hardware while only requiring a fraction of the usual development effort.
Experiments show that TQP can improve query execution time by up to 10$\times$
over specialized CPU- and GPU-only systems. Finally, TQP can accelerate queries
mixing ML predictions and SQL end-to-end, and deliver up to 9$\times$ speedup
over CPU baselines.

In this paper we present a deep learning method to predict the temporal
evolution of dissipative dynamic systems. We propose using both geometric and
thermodynamic inductive biases to improve accuracy and generalization of the
resulting integration scheme. The first is achieved with Graph Neural Networks,
which induces a non-Euclidean geometrical prior with permutation invariant node
and edge update functions. The second bias is forced by learning the GENERIC
structure of the problem, an extension of the Hamiltonian formalism, to model
more general non-conservative dynamics. Several examples are provided in both
Eulerian and Lagrangian description in the context of fluid and solid mechanics
respectively, achieving relative mean errors of less than 3% in all the tested
examples. Two ablation studies are provided based on recent works in both
physics-informed and geometric deep learning.

In most social choice settings, the participating agents express their
preferences over the different alternatives in the form of linear orderings.
While this clearly simplifies preference elicitation, it inevitably leads to
poor performance with respect to optimizing a cardinal objective, such as the
social welfare, since the values of the agents remain virtually unknown. This
loss in performance because of lack of information is measured by the notion of
distortion. A recent array of works put forward the agenda of designing
mechanisms that learn the values of the agents for a small number of
alternatives via queries, and use this limited extra information to make
better-informed decisions, thus improving distortion. Following this agenda, in
this work we focus on a class of combinatorial problems that includes most
well-known matching problems and several of their generalizations, such as
One-Sided Matching, Two-Sided Matching, General Graph Matching, and
$k$-Constrained Resource Allocation. We design two-query mechanisms that
achieve the best-possible worst-case distortion in terms of social welfare, and
outperform the best-possible expected distortion achieved by randomized ordinal

Rare event searches allow us to search for new physics at energy scales
inaccessible with other means by leveraging specialized large-mass detectors.
Machine learning provides a new tool to maximize the information provided by
these detectors. The information is sparse, which forces these algorithms to
start from the lowest level data and exploit all symmetries in the detector to
produce results. In this work we present KamNet which harnesses breakthroughs
in geometric deep learning and spatiotemporal data analysis to maximize the
physics reach of KamLAND-Zen, a kiloton scale spherical liquid scintillator
detector searching for neutrinoless double beta decay ($0\nu\beta\beta$). Using
a simplified background model for KamLAND we show that KamNet outperforms a
conventional CNN on benchmarking MC simulations with an increasing level of
robustness. Using simulated data, we then demonstrate KamNet's ability to
increase KamLAND-Zen's sensitivity to $0\nu\beta\beta$ and $0\nu\beta\beta$ to
excited states. A key component of this work is the addition of an attention
mechanism to elucidate the underlying physics KamNet is using for the
background rejection.

These days we live in a world with a permanent electromagnetic field. This
raises many questions about our health and the deployment of new equipment. The
problem is that these fields remain difficult to visualize easily, which only
some experts can understand. To tackle this problem, we propose to spatially
estimate the level of the field based on a few observations at all positions of
the considered space. This work presents an algorithm for spatial
reconstruction of electromagnetic fields using the Gaussian Process. We
consider a spatial, physical phenomenon observed by a sensor network. A
Gaussian Process regression model with selected mean and covariance function is
implemented to develop a 9 sensors-based estimation algorithm. A Bayesian
inference approach is used to perform the model selection of the covariance
function and to learn the hyperparameters from our data set. We present the
prediction performance of the proposed model and compare it with the case where
the mean is zero. The results show that the proposed Gaussian Process-based
prediction model reconstructs the EM fields in all positions only using 9
sensors.

In the coming years and decades, autonomous vehicles (AVs) will become
increasingly prevalent, offering new opportunities for safer and more
convenient travel and potentially smarter traffic control methods exploiting
automation and connectivity. Car following is a prime function in autonomous
driving. Car following based on reinforcement learning has received attention
in recent years with the goal of learning and achieving performance levels
comparable to humans. However, most existing RL methods model car following as
a unilateral problem, sensing only the vehicle ahead. Recent literature,
however, Wang and Horn [16] has shown that bilateral car following that
considers the vehicle ahead and the vehicle behind exhibits better system
stability. In this paper we hypothesize that this bilateral car following can
be learned using RL, while learning other goals such as efficiency
maximisation, jerk minimization, and safety rewards leading to a learned model
that outperforms human driving.
  We propose and introduce a Deep Reinforcement Learning (DRL) framework for
car following control by integrating bilateral information into both state and
reward function based on the bilateral control model (BCM) for car following
control. Furthermore, we use a decentralized multi-agent reinforcement learning
framework to generate the corresponding control action for each agent. Our
simulation results demonstrate that our learned policy is better than the human
driving policy in terms of (a) inter-vehicle headways, (b) average speed, (c)
jerk, (d) Time to Collision (TTC) and (e) string stability.

We tackle here a specific, still not widely addressed aspect, of AI
robustness, which consists of seeking invariance / insensitivity of model
performance to hidden factors of variations in the data. Towards this end, we
employ a two step strategy that a) does unsupervised discovery, via generative
models, of sensitive factors that cause models to under-perform, and b)
intervenes models to make their performance invariant to these sensitive
factors' influence. We consider 3 separate interventions for robustness,
including: data augmentation, semantic consistency, and adversarial alignment.
We evaluate our method using metrics that measure trade offs between invariance
(insensitivity) and overall performance (utility) and show the benefits of our
method for 3 settings (unsupervised, semi-supervised and generalization).

Human perception and behavior are affected by the situational context, in
particular during social interactions. A recent study demonstrated that humans
perceive visual stimuli differently depending on whether they do the task by
themselves or together with a robot. Specifically, it was found that the
central tendency effect is stronger in social than in non-social task settings.
The particular nature of such behavioral changes induced by social interaction,
and their underlying cognitive processes in the human brain are, however, still
not well understood. In this paper, we address this question by training an
artificial neural network inspired by the predictive coding theory on the above
behavioral data set. Using this computational model, we investigate whether the
change in behavior that was caused by the situational context in the human
experiment could be explained by continuous modifications of a parameter
expressing how strongly sensory and prior information affect perception. We
demonstrate that it is possible to replicate human behavioral data in both
individual and social task settings by modifying the precision of prior and
sensory signals, indicating that social and non-social task settings might in
fact exist on a continuum. At the same time an analysis of the neural
activation traces of the trained networks provides evidence that information is
coded in fundamentally different ways in the network in the individual and in
the social conditions. Our results emphasize the importance of computational
replications of behavioral data for generating hypotheses on the underlying
cognitive mechanisms of shared perception and may provide inspiration for
follow-up studies in the field of neuroscience.

Standard Bayesian learning is known to have suboptimal generalization
capabilities under model misspecification and in the presence of outliers.
PAC-Bayes theory demonstrates that the free energy criterion minimized by
Bayesian learning is a bound on the generalization error for Gibbs predictors
(i.e., for single models drawn at random from the posterior) under the
assumption of sampling distributions uncontaminated by outliers. This viewpoint
provides a justification for the limitations of Bayesian learning when the
model is misspecified, requiring ensembling, and when data is affected by
outliers. In recent work, PAC-Bayes bounds - referred to as PAC$^m$ - were
derived to introduce free energy metrics that account for the performance of
ensemble predictors, obtaining enhanced performance under misspecification.
This work presents a novel robust free energy criterion that combines the
generalized logarithm score function with PAC$^m$ ensemble bounds. The proposed
free energy training criterion produces predictive distributions that are able
to concurrently counteract the detrimental effects of model misspecification
and outliers.

To collaborate well with robots, we must be able to understand their decision
making. Humans naturally infer other agents' beliefs and desires by reasoning
about their observable behavior in a way that resembles inverse reinforcement
learning (IRL). Thus, robots can convey their beliefs and desires by providing
demonstrations that are informative for a human learner's IRL. An informative
demonstration is one that differs strongly from the learner's expectations of
what the robot will do given their current understanding of the robot's
decision making. However, standard IRL does not model the learner's existing
expectations, and thus cannot do this counterfactual reasoning. We propose to
incorporate the learner's current understanding of the robot's decision making
into our model of human IRL, so that a robot can select demonstrations that
maximize the human's understanding. We also propose a novel measure for
estimating the difficulty for a human to predict instances of a robot's
behavior in unseen environments. A user study finds that our test difficulty
measure correlates well with human performance and confidence. Interestingly,
considering human beliefs and counterfactuals when selecting demonstrations
decreases human performance on easy tests, but increases performance on
difficult tests, providing insight on how to best utilize such models.

Deep neural networks are efficient at learning the data distribution if it is
sufficiently sampled. However, they can be strongly biased by non-relevant
factors implicitly incorporated in the training data. These include operational
biases, such as ineffective or uneven data sampling, but also ethical concerns,
as the social biases are implicitly present\textemdash even inadvertently, in
the training data or explicitly defined in unfair training schedules. In tasks
having impact on human processes, the learning of social biases may produce
discriminatory, unethical and untrustworthy consequences. It is often assumed
that social biases stem from supervised learning on labelled data, and thus,
Self-Supervised Learning (SSL) wrongly appears as an efficient and bias-free
solution, as it does not require labelled data. However, it was recently proven
that a popular SSL method also incorporates biases. In this paper, we study the
biases of a varied set of SSL visual models, trained using ImageNet data, using
a method and dataset designed by psychological experts to measure social
biases. We show that there is a correlation between the type of the SSL model
and the number of biases that it incorporates. Furthermore, the results also
suggest that this number does not strictly depend on the model's accuracy and
changes throughout the network. Finally, we conclude that a careful SSL model
selection process can reduce the number of social biases in the deployed model,
whilst keeping high performance.

Video Instance Segmentation (VIS) aims to simultaneously classify, segment,
and track multiple object instances in videos. Recent clip-level VIS takes a
short video clip as input each time showing stronger performance than
frame-level VIS (tracking-by-segmentation), as more temporal context from
multiple frames is utilized. Yet, most clip-level methods are neither
end-to-end learnable nor real-time. These limitations are addressed by the
recent VIS transformer (VisTR) which performs VIS end-to-end within a clip.
However, VisTR suffers from long training time due to its frame-wise dense
attention. In addition, VisTR is not fully end-to-end learnable in multiple
video clips as it requires a hand-crafted data association to link instance
tracklets between successive clips. This paper proposes EfficientVIS, a fully
end-to-end framework with efficient training and inference. At the core are
tracklet query and tracklet proposal that associate and segment
regions-of-interest (RoIs) across space and time by an iterative query-video
interaction. We further propose a correspondence learning that makes tracklets
linking between clips end-to-end learnable. Compared to VisTR, EfficientVIS
requires 15x fewer training epochs while achieving state-of-the-art accuracy on
the YouTube-VIS benchmark. Meanwhile, our method enables whole video instance
segmentation in a single end-to-end pass without data association at all.

Linear structural equation models represent direct causal effects as directed
edges and confounding factors as bidirected edges. An open problem is to
identify the causal parameters from correlations between the nodes. We
investigate models, whose directed component forms a tree, and show that there,
besides classical instrumental variables, missing cycles of bidirected edges
can be used to identify the model. They can yield systems of quadratic
equations that we explicitly solve to obtain one or two solutions for the
causal parameters of adjacent directed edges. We show how multiple missing
cycles can be combined to obtain a unique solution. This results in an
algorithm that can identify instances that previously required approaches based
on Gr\"obner bases, which have doubly-exponential time complexity in the number
of structural parameters.

Place recognition is key to Simultaneous Localization and Mapping (SLAM) and
spatial perception. However, a place recognition in the wild often suffers from
erroneous predictions due to image variations, e.g., changing viewpoints and
street appearance. Integrating uncertainty estimation into the life cycle of
place recognition is a promising method to mitigate the impact of variations on
place recognition performance. However, existing uncertainty estimation
approaches in this vein are either computationally inefficient (e.g., Monte
Carlo dropout) or at the cost of dropped accuracy. This paper proposes STUN, a
self-teaching framework that learns to simultaneously predict the place and
estimate the prediction uncertainty given an input image. To this end, we first
train a teacher net using a standard metric learning pipeline to produce
embedding priors. Then, supervised by the pretrained teacher net, a student net
with an additional variance branch is trained to finetune the embedding priors
and estimate the uncertainty sample by sample. During the online inference
phase, we only use the student net to generate a place prediction in
conjunction with the uncertainty. When compared with place recognition systems
that are ignorant to the uncertainty, our framework features the uncertainty
estimation for free without sacrificing any prediction accuracy. Our
experimental results on the large-scale Pittsburgh30k dataset demonstrate that
STUN outperforms the state-of-the-art methods in both recognition accuracy and
the quality of uncertainty estimation.

The prediction accuracy of machine learning methods is steadily increasing,
but the calibration of their uncertainty predictions poses a significant
challenge. Numerous works focus on obtaining well-calibrated predictive models,
but less is known about reliably assessing model calibration. This limits our
ability to know when algorithms for improving calibration have a real effect,
and when their improvements are merely artifacts due to random noise in finite
datasets. In this work, we consider detecting mis-calibration of predictive
models using a finite validation dataset as a hypothesis testing problem. The
null hypothesis is that the predictive model is calibrated, while the
alternative hypothesis is that the deviation from calibration is sufficiently
large.
  We find that detecting mis-calibration is only possible when the conditional
probabilities of the classes are sufficiently smooth functions of the
predictions. When the conditional class probabilities are H\"older continuous,
we propose T-Cal, a minimax optimal test for calibration based on a debiased
plug-in estimator of the $\ell_2$-Expected Calibration Error (ECE). We further
propose Adaptive T-Cal, a version that is adaptive to unknown smoothness. We
verify our theoretical findings with a broad range of experiments, including
with several popular deep neural net architectures and several standard
post-hoc calibration methods. T-Cal is a practical general-purpose tool, which
-- combined with classical tests for discrete-valued predictors -- can be used
to test the calibration of virtually any probabilistic classification method.

We consider the problem of discovering causal relations from independence
constraints selection bias in addition to confounding is present. While the
seminal FCI algorithm is sound and complete in this setup, no criterion for the
causal interpretation of its output under selection bias is presently known. We
focus instead on local patterns of independence relations, where we find no
sound method for only three variable that can include background knowledge.
Y-Structure patterns are shown to be sound in predicting causal relations from
data under selection bias, where cycles may be present. We introduce a
finite-sample scoring rule for Y-Structures that is shown to successfully
predict causal relations in simulation experiments that include selection
mechanisms. On real-world microarray data, we show that a Y-Structure variant
performs well across different datasets, potentially circumventing spurious
correlations due to selection bias.

In the field of structural health monitoring (SHM), inverse problems which
require repeated analyses are common. With the increase in the use of nonlinear
models, the development of nonlinear reduced order modelling techniques is of
paramount interest. Of considerable research interest, is the use of flexible
and scalable machine learning methods which can learn to approximate the
behaviour of nonlinear dynamic systems using input and output data. One such
nonlinear system of interest, in the context of wind turbine structures, is the
soil structure interaction (SSI) problem. Soil demonstrates strongly nonlinear
behaviour with regards to its restoring force and has been shown to
considerably influence the dynamic response of wind turbine structures. In this
work, we demonstrate the application of a recently developed nonlinear reduced
order modelling method, which leverages Autoencoder and LSTM neural networks,
to a nonlinear soil structure interaction problem of a wind turbine monopile
subject to realistic loading at the seabed level. The accuracy and efficiency
of the methodology is compared to full order simulations carried out using
Abaqus. The ROM was shown to have good fidelity and a considerable reduction in
computational time for the system considered.

We study the problem of safe and intention-aware robot navigation in dense
and interactive crowds. Most previous reinforcement learning (RL) based methods
fail to consider different types of interactions among all agents or ignore the
intentions of people, which results in performance degradation. In this paper,
we propose a novel recurrent graph neural network with attention mechanisms to
capture heterogeneous interactions among agents through space and time. To
encourage longsighted robot behaviors, we infer the intentions of dynamic
agents by predicting their future trajectories for several timesteps. The
predictions are incorporated into a model-free RL framework to prevent the
robot from intruding into the intended paths of other agents. We demonstrate
that our method enables the robot to achieve good navigation performance and
non-invasiveness in challenging crowd navigation scenarios. We successfully
transfer the policy learned in simulation to a real-world TurtleBot 2i.

Facial palsy is unilateral facial nerve weakness or paralysis of rapid onset
with unknown causes. Automatically estimating facial palsy severeness can be
helpful for the diagnosis and treatment of people suffering from it across the
world. In this work, we develop and experiment with a novel model for
estimating facial palsy severity. For this, an effective Facial Action Units
(AU) detection technique is incorporated into our model, where AUs refer to a
unique set of facial muscle movements used to describe almost every
anatomically possible facial expression. In this paper, we propose a novel
Adaptive Local-Global Relational Network (ALGRNet) for facial AU detection and
use it to classify facial paralysis severity. ALGRNet mainly consists of three
main novel structures: (i) an adaptive region learning module that learns the
adaptive muscle regions based on the detected landmarks; (ii) a skip-BiLSTM
that models the latent relationships among local AUs; and (iii) a feature
fusion&refining module that investigates the complementary between the local
and global face. Quantitative results on two AU benchmarks, i.e., BP4D and
DISFA, demonstrate our ALGRNet can achieve promising AU detection accuracy. We
further demonstrate the effectiveness of its application to facial paralysis
estimation by migrating ALGRNet to a facial paralysis dataset collected and
annotated by medical professionals.

Full waveform inversion (FWI) aims at reconstructing unknown physical
coefficients in wave equations using the wave field data generated from
multiple incoming sources. In this work, we propose an offline-online
computational strategy for coupling classical least-squares based computational
inversion with modern deep learning based approaches for FWI to achieve
advantages that can not be achieved with only one of the components. In a
nutshell, we develop an offline learning strategy to construct a robust
approximation to the inverse operator and utilize it to design a new objective
function for the online inversion with new datasets. We demonstrate through
numerical simulations that our coupling strategy improves the computational
efficiency of FWI with reliable offline training on moderate computational
resources (in terms of both the size of the training dataset and the
computational cost needed).

We introduce a novel method for controlling the functionality of a hands-free
speech communication device which comprises a model-based acoustic echo
canceller (AEC), minimum variance distortionless response (MVDR) beamformer
(BF) and spectral postfilter (PF). While the AEC removes the early echo
component, the MVDR BF and PF suppress the residual echo and background noise.
As key innovation, we suggest to use a single deep neural network (DNN) to
jointly control the adaptation of the various algorithmic components. This
allows for rapid convergence and high steady-state performance in the presence
of high-level interfering double-talk. End-to-end training of the DNN using a
time-domain speech extraction loss function avoids the design of individual
control strategies.

Despite recent advances in generative modeling for text-to-speech synthesis,
these models do not yet have the same fine-grained adjustability of
pitch-conditioned deterministic models such as FastPitch and FastSpeech2. Pitch
information is not only low-dimensional, but also discontinuous, making it
particularly difficult to model in a generative setting. Our work explores
several techniques for handling the aforementioned issues in the context of
Normalizing Flow models. We also find this problem to be very well suited for
Neural Spline flows, which is a highly expressive alternative to the more
common affine-coupling mechanism in Normalizing Flows.

Deep neural networks are able to memorize noisy labels easily with a softmax
cross-entropy (CE) loss. Previous studies attempted to address this issue focus
on incorporating a noise-robust loss function to the CE loss. However, the
memorization issue is alleviated but still remains due to the non-robust CE
loss. To address this issue, we focus on learning robust contrastive
representations of data on which the classifier is hard to memorize the label
noise under the CE loss. We propose a novel contrastive regularization function
to learn such representations over noisy data where label noise does not
dominate the representation learning. By theoretically investigating the
representations induced by the proposed regularization function, we reveal that
the learned representations keep information related to true labels and discard
information related to corrupted labels. Moreover, our theoretical results also
indicate that the learned representations are robust to the label noise. The
effectiveness of this method is demonstrated with experiments on benchmark
datasets.

Early diagnosis of Alzheimer's disease and its prodromal stage, also known as
mild cognitive impairment (MCI), is critical since some patients with
progressive MCI will develop the disease. We propose a multi-stream deep
convolutional neural network fed with patch-based imaging data to classify
stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's
disease with cognitively normal subjects to identify distinct anatomical
landmarks using a multivariate statistical test. These landmarks are then used
to extract patches that are fed into the proposed multi-stream convolutional
neural network to classify MRI images. Next, we train the architecture in a
separate scenario using samples from Alzheimer's disease images, which are
anatomically similar to the progressive MCI ones and cognitively normal images
to compensate for the lack of progressive MCI training data. Finally, we
transfer the trained model weights to the proposed architecture in order to
fine-tune the model using progressive MCI and stable MCI data. Experimental
results on the ADNI-1 dataset indicate that our method outperforms existing
methods for MCI classification, with an F1-score of 85.96%.

Deep learning has shown great potential for modeling the physical dynamics of
complex particle systems such as fluids. Existing approaches, however, require
the supervision of consecutive particle properties, including positions and
velocities. In this paper, we consider a partially observable scenario known as
fluid dynamics grounding, that is, inferring the state transitions and
interactions within the fluid particle systems from sequential visual
observations of the fluid surface. We propose a differentiable two-stage
network named NeuroFluid. Our approach consists of (i) a particle-driven neural
renderer, which involves fluid physical properties into the volume rendering
function, and (ii) a particle transition model optimized to reduce the
differences between the rendered and the observed images. NeuroFluid provides
the first solution to unsupervised learning of particle-based fluid dynamics by
training these two models jointly. It is shown to reasonably estimate the
underlying physics of fluids with different initial shapes, viscosity, and
densities.

Conformal prediction has received tremendous attention in recent years and
has offered new solutions to problems in missing data and causal inference; yet
these advances have not leveraged modern semiparametric efficiency theory for
more robust and efficient uncertainty quantification. In this paper, we
consider the problem of obtaining distribution-free prediction regions
accounting for a shift in the distribution of the covariates between the
training and test data. Under an explainable covariate shift assumption
analogous to the standard missing at random assumption, we propose three
variants of a general framework to construct well-calibrated prediction regions
for the unobserved outcome in the test sample. Our approach is based on the
efficient influence function for the quantile of the unobserved outcome in the
test population combined with an arbitrary machine learning prediction
algorithm, without compromising asymptotic coverage. Next, we extend our
approach to account for departure from the explainable covariate shift
assumption in a semiparametric sensitivity analysis for potential latent
covariate shift. In all cases, we establish that the resulting prediction sets
eventually attain nominal average coverage in large samples. This guarantee is
a consequence of the product bias form of our proposal which implies correct
coverage if either the propensity score or the conditional distribution of the
response is estimated sufficiently well. Our results also provide a framework
for construction of doubly robust prediction sets of individual treatment
effects, under both unconfoundedness and allowing for some degree of unmeasured
confounding. Finally, we discuss aggregation of prediction sets from different
machine learning algorithms for optimal prediction and illustrate the
performance of our methods in both synthetic and real data.

This thesis rigorously studies fundamental reinforcement learning (RL)
methods in modern practical considerations, including robust RL, distributional
RL, and offline RL with neural function approximation. The thesis first
prepares the readers with an overall overview of RL and key technical
background in statistics and optimization. In each of the settings, the thesis
motivates the problems to be studied, reviews the current literature, provides
computationally efficient algorithms with provable efficiency guarantees, and
concludes with future research directions. The thesis makes fundamental
contributions to the three settings above, both algorithmically, theoretically,
and empirically, while staying relevant to practical considerations.

We present a novel method to learn Personalized Implicit Neural Avatars
(PINA) from a short RGB-D sequence. This allows non-expert users to create a
detailed and personalized virtual copy of themselves, which can be animated
with realistic clothing deformations. PINA does not require complete scans, nor
does it require a prior learned from large datasets of clothed humans. Learning
a complete avatar in this setting is challenging, since only few depth
observations are available, which are noisy and incomplete (i.e. only partial
visibility of the body per frame). We propose a method to learn the shape and
non-rigid deformations via a pose-conditioned implicit surface and a
deformation field, defined in canonical space. This allows us to fuse all
partial observations into a single consistent canonical representation. Fusion
is formulated as a global optimization problem over the pose, shape and
skinning parameters. The method can learn neural avatars from real noisy RGB-D
sequences for a diverse set of people and clothing styles and these avatars can
be animated given unseen motion sequences.

Despite enormous research interest and rapid application of federated
learning (FL) to various areas, existing studies mostly focus on supervised
federated learning under the horizontally partitioned local dataset setting.
This paper will study the unsupervised FL under the vertically partitioned
dataset setting. Accordingly, we propose the federated principal component
analysis for vertically partitioned dataset (VFedPCA) method, which reduces the
dimensionality across the joint datasets over all the clients and extracts the
principal component feature information for downstream data analysis. We
further take advantage of the nonlinear dimensionality reduction and propose
the vertical federated advanced kernel principal component analysis (VFedAKPCA)
method, which can effectively and collaboratively model the nonlinear nature
existing in many real datasets. In addition, we study two communication
topologies. The first is a server-client topology where a semi-trusted server
coordinates the federated training, while the second is the fully-decentralized
topology which further eliminates the requirement of the server by allowing
clients themselves to communicate with their neighbors. Extensive experiments
conducted on five types of real-world datasets corroborate the efficacy of
VFedPCA and VFedAKPCA under the vertically partitioned FL setting. Code is
available at: https://github.com/juyongjiang/VFedPCA-VFedAKPCA

Ranking nodes based on their centrality stands a fundamental, yet,
challenging problem in large-scale networks. Approximate methods can quickly
estimate nodes' centrality and identify the most central nodes, but the ranking
for the majority of remaining nodes may be meaningless. For example, ranking
for less-known websites in search queries is known to be noisy and unstable. To
this end, we investigate a new node ranking problem with two important
distinctions: a) ranking quality, rather than the centrality estimation
quality, as the primary objective; and b) ranking only nodes of interest, e.g.,
websites that matched search criteria. We propose Sample space Partitioning
Hypothesis Ranking, or SaPHyRa, that transforms node ranking into a hypothesis
ranking in machine learning. This transformation maps nodes' centrality to the
expected risks of hypotheses, opening doors for theoretical machine learning
(ML) tools. The key of SaPHyRa is to partition the sample space into exact and
approximate subspaces. The exact subspace contains samples related to the nodes
of interest, increasing both estimation and ranking qualities. The approximate
space can be efficiently sampled with ML-based techniques to provide
theoretical guarantees on the estimation error. Lastly, we present SaPHyRa_bc,
an illustration of SaPHyRa on ranking nodes' betweenness centrality (BC). By
combining a novel bi-component sampling, a 2-hop sample partitioning, and
improved bounds on the Vapnik-Chervonenkis dimension, SaPHyRa_bc can
effectively rank any node subset in BC. Its performance is up to 200x faster
than state-of-the-art methods in approximating BC, while its rank correlation
to the ground truth is improved by multifold.

We consider stochastic approximation for the least squares regression problem
in the non-strongly convex setting. We present the first practical algorithm
that achieves the optimal prediction error rates in terms of dependence on the
noise of the problem, as $O(d/t)$ while accelerating the forgetting of the
initial conditions to $O(d/t^2)$. Our new algorithm is based on a simple
modification of the accelerated gradient descent. We provide convergence
results for both the averaged and the last iterate of the algorithm. In order
to describe the tightness of these new bounds, we present a matching lower
bound in the noiseless setting and thus show the optimality of our algorithm.

In high-multiplicity scheduling, jobs of the same size are encoded in an
efficient way, that is, for each size the number of jobs of that size is given
instead of a list of jobs. Similarly, machines are encoded. We consider
scheduling on uniform machines where a job of size $p_j$ takes time $p_j/s_i$
on a machine of speed $s_i$. Classical (NP-hard) objectives are Makespan
minimization ($C_{\max}$) and Santa Claus ($C_{\min}$). We show that both
objectives can be solved in time $\mathcal O( p_{\max}^{\mathcal O(d^2)}
\operatorname {poly} |I| )$ where $p_{\max}$ is the largest jobs size, $d$ the
number of different job sizes and $|I|$ the encoding length of the instance.
Our approach incorporates two structural theorems: The first allows us to
replace machines of large speed by multiple machines of smaller speed. The
second tells us that some fractional assignments can be used to reduce the
instance significantly. Using only the first theorem, we show some additional
results. For the problem Envy Minimization ($C_{\mathit{envy}}$), we propose an
$\mathcal O(s_{\max} \cdot p_{\max}^{\mathcal O(d^3)} \operatorname{poly} |I|)$
time algorithm (where $s_{\max}$ is the largest speed). For $C_{\max}$ and
$C_{\min}$ in the Restricted Assignment setting, we give an $\mathcal O( (d
p_{\max})^{\mathcal O(d^3)} \operatorname{poly} |I|)$ time algorithm. As far as
we know, those running times are better than the running times of the
algorithms known until today.

Russia's attack on Ukraine on Thursday 24 February 2022 hitched financial
markets and the increased geopolitical crisis. In this paper, we select some
main economic indexes, such as Gold, Oil (WTI), NDAQ, and known currency which
are involved in this crisis and try to find the quantitative effect of this war
on them. To quantify the war effect, we use the correlation feature and the
relationships between these economic indices, create datasets, and compare the
results of forecasts with real data. To study war effects, we use Machine
Learning Linear Regression. We carry on empirical experiments and perform on
these economic indices datasets to evaluate and predict this war tolls and its
effects on main economics indexes.

RGB-infrared person re-identification is an emerging cross-modality
re-identification task, which is very challenging due to significant modality
discrepancy between RGB and infrared images. In this work, we propose a novel
modality-adaptive mixup and invariant decomposition (MID) approach for
RGB-infrared person re-identification towards learning modality-invariant and
discriminative representations. MID designs a modality-adaptive mixup scheme to
generate suitable mixed modality images between RGB and infrared images for
mitigating the inherent modality discrepancy at the pixel-level. It formulates
modality mixup procedure as Markov decision process, where an actor-critic
agent learns dynamical and local linear interpolation policy between different
regions of cross-modality images under a deep reinforcement learning framework.
Such policy guarantees modality-invariance in a more continuous latent space
and avoids manifold intrusion by the corrupted mixed modality samples.
Moreover, to further counter modality discrepancy and enforce invariant visual
semantics at the feature-level, MID employs modality-adaptive convolution
decomposition to disassemble a regular convolution layer into modality-specific
basis layers and a modality-shared coefficient layer. Extensive experimental
results on two challenging benchmarks demonstrate superior performance of MID

Assuming ALPs couple to gluons only, they can be produced via the $p p \to a
j$ process and decay into two jets at the LHC. When the coupling parameter,
$C_{\tilde{G}} / f_a$, is small, the lifetime of ALPs can be long enough
leading to displaced final state jets. In this paper, we consider the signal
including both the prompt and long-lived cases of ALPs by employing a
specialized Delphes module to handle displaced jets. Relevant background
processes are generated and simulated at the detector level, and multivariate
analyses based on machine-learning are performed to discriminate signal and
background events and achieve the best sensitivities. Based on the data
accumulated for this study, we forecast the expected upper limits on
$C_{\tilde{G}}/f_a$ for ALP mass $m_a$ in the range 5$-$2300 GeV at 2-, 3- and
5-$\sigma$ significances at the High Luminosity-LHC with 14 TeV center-of-mass
energy and 3 ab$^{-1}$ integrated luminosity. Vast previously unprobed regions
in the parameter space spanned by $C_{\tilde{G}}/f_a$ and $m_a$ are probed and
the best upper limits on $C_{\tilde{G}}/f_a$ at 2-$\sigma$ significance is
found to be around $1.0 \times 10^{-2} \,\, {\rm TeV^{-1}}$ for $m_a \sim 500$
GeV. The ALP mass is reconstructed from the kinematics of final state jets and
we find that it is measurable in this method when $m_a$ is below about 1 TeV at
the HL-LHC. The effects of systematic uncertainties and validation of the EFT
framework are also checked and discussed.

Monitoring biodiversity is paramount to manage and protect natural resources.
Collecting images of organisms over large temporal or spatial scales is a
promising practice to monitor the biodiversity of natural ecosystems, providing
large amounts of data with minimal interference with the environment. Deep
learning models are currently used to automate classification of organisms into
taxonomic units. However, imprecision in these classifiers introduces a
measurement noise that is difficult to control and can significantly hinder the
analysis and interpretation of data. {We overcome this limitation through
ensembles of Data-efficient image Transformers (DeiTs), which not only are easy
to train and implement, but also significantly outperform} the previous state
of the art (SOTA). We validate our results on ten ecological imaging datasets
of diverse origin, ranging from plankton to birds. On all the datasets, we
achieve a new SOTA, with a reduction of the error with respect to the previous
SOTA ranging from 29.35% to 100.00%, and often achieving performances very
close to perfect classification. Ensembles of DeiTs perform better not because
of superior single-model performances but rather due to smaller overlaps in the
predictions by independent models and lower top-1 probabilities. This increases
the benefit of ensembling, especially when using geometric averages to combine
individual learners. While we only test our approach on biodiversity image
datasets, our approach is generic and can be applied to any kind of images.

Generalizable person re-identification aims to learn a model with only
several labeled source domains that can perform well on unseen domains. Without
access to the unseen domain, the feature statistics of the batch normalization
(BN) layer learned from a limited number of source domains is doubtlessly
biased for unseen domain. This would mislead the feature representation
learning for unseen domain and deteriorate the generalizaiton ability of the
model. In this paper, we propose a novel Debiased Batch Normalization via
Gaussian Process approach (GDNorm) for generalizable person re-identification,
which models the feature statistic estimation from BN layers as a dynamically
self-refining Gaussian process to alleviate the bias to unseen domain for
improving the generalization. Specifically, we establish a lightweight model
with multiple set of domain-specific BN layers to capture the discriminability
of individual source domain, and learn the corresponding parameters of the
domain-specific BN layers. These parameters of different source domains are
employed to deduce a Gaussian process. We randomly sample several paths from
this Gaussian process served as the BN estimations of potential new domains
outside of existing source domains, which can further optimize these learned
parameters from source domains, and estimate more accurate Gaussian process by
them in return, tending to real data distribution. Even without a large number
of source domains, GDNorm can still provide debiased BN estimation by using the
mean path of the Gaussian process, while maintaining low computational cost
during testing. Extensive experiments demonstrate that our GDNorm effectively
improves the generalization ability of the model on unseen domain.

The investigation of dynamical processes on networks has been one focus for
the study of contagion processes. It has been demonstrated that contagions can
be used to obtain information about the embedding of nodes in a Euclidean
space. Specifically, one can use the activation times of threshold contagions
to construct contagion maps as a manifold-learning approach. One drawback of
contagion maps is their high computational cost. Here, we demonstrate that a
truncation of the threshold contagions may considerably speed up the
construction of contagion maps. Finally, we show that contagion maps may be
used to find an insightful low-dimensional embedding for single-cell
RNA-sequencing data in the form of cell-similarity networks and so reveal
biological manifolds. Overall, our work makes the use of contagion maps as
manifold-learning approaches on empirical network data more viable.

Current advanced hyperparameter optimization (HPO) methods, such as Bayesian
optimization, have high sampling efficiency and facilitate replicability.
Nonetheless, machine learning (ML) practitioners (e.g., engineers, scientists)
mostly apply less advanced HPO methods, which can increase resource consumption
during HPO or lead to underoptimized ML models. Therefore, we suspect that
practitioners choose their HPO method to achieve different goals, such as
decrease practitioner effort and target audience compliance. To develop HPO
methods that align with such goals, the reasons why practitioners decide for
specific HPO methods must be unveiled and thoroughly understood. Because
qualitative research is most suitable to uncover such reasons and find
potential explanations for them, we conducted semi-structured interviews to
explain why practitioners choose different HPO methods. The interviews revealed
six principal practitioner goals (e.g., increasing model comprehension), and
eleven key factors that impact decisions for HPO methods (e.g., available
computing resources). We deepen the understanding about why practitioners
decide for different HPO methods and outline recommendations for improvements
of HPO methods by aligning them with practitioner goals.

In the past decades, the excessive use of the last-generation GAN (Generative
Adversarial Networks) models in computer vision has enabled the creation of
artificial face images that are visually indistinguishable from genuine ones.
These images are particularly used in adversarial settings to create fake
social media accounts and other fake online profiles. Such malicious activities
can negatively impact the trustworthiness of users identities. On the other
hand, the recent development of GAN models may create high-quality face images
without evidence of spatial artifacts. Therefore, reassembling uniform color
channel correlations is a challenging research problem. To face these
challenges, we need to develop efficient tools able to differentiate between
fake and authentic face images. In this chapter, we propose a new strategy to
differentiate GAN-generated images from authentic images by leveraging spectral
band discrepancies, focusing on artificial face image synthesis. In particular,
we enable the digital preservation of face images using the Cross-band
co-occurrence matrix and spatial co-occurrence matrix. Then, we implement these
techniques and feed them to a Convolutional Neural Networks (CNN) architecture
to identify the real from artificial faces. Additionally, we show that the
performance boost is particularly significant and achieves more than 92% in
different post-processing environments. Finally, we provide several research
observations demonstrating that this strategy improves a comparable detection
method based only on intra-band spatial co-occurrences.

Weakly supervised object localization (WSOL) focuses on localizing objects
only with the supervision of image-level classification masks. Most previous
WSOL methods follow the classification activation map (CAM) that localizes
objects based on the classification structure with the multi-instance learning
(MIL) mechanism. However, the MIL mechanism makes CAM only activate
discriminative object parts rather than the whole object, weakening its
performance for localizing objects. To avoid this problem, this work provides a
novel perspective that models WSOL as a domain adaption (DA) task, where the
score estimator trained on the source/image domain is tested on the
target/pixel domain to locate objects. Under this perspective, a DA-WSOL
pipeline is designed to better engage DA approaches into WSOL to enhance
localization performance. It utilizes a proposed target sampling strategy to
select different types of target samples. Based on these types of target
samples, domain adaption localization (DAL) loss is elaborated. It aligns the
feature distribution between the two domains by DA and makes the estimator
perceive target domain cues by Universum regularization. Experiments show that
our pipeline outperforms SOTA methods on multi benchmarks. Code are released at
\url{https://github.com/zh460045050/DA-WSOL_CVPR2022}.

We consider offline reinforcement learning (RL) methods in possibly
nonstationary environments. Many existing RL algorithms in the literature rely
on the stationarity assumption that requires the system transition and the
reward function to be constant over time. However, the stationarity assumption
is restrictive in practice and is likely to be violated in a number of
applications, including traffic signal control, robotics and mobile health. In
this paper, we develop a consistent procedure to test the nonstationarity of
the optimal policy based on pre-collected historical data, without additional
online data collection. Based on the proposed test, we further develop a
sequential change point detection method that can be naturally coupled with
existing state-of-the-art RL methods for policy optimization in nonstationary
environments. The usefulness of our method is illustrated by theoretical
results, simulation studies, and a real data example from the 2018 Intern
Health Study. A Python implementation of the proposed procedure is available at
https://github.com/limengbinggz/CUSUM-RL.

Reconstructing 3D objects from 2D images is both challenging for our brains
and machine learning algorithms. To support this spatial reasoning task,
contextual information about the overall shape of an object is critical.
However, such information is not captured by established loss terms (e.g. Dice
loss). We propose to complement geometrical shape information by including
multi-scale topological features, such as connected components, cycles, and
voids, in the reconstruction loss. Our method uses cubical complexes to
calculate topological features of 3D volume data and employs an optimal
transport distance to guide the reconstruction process. This topology-aware
loss is fully differentiable, computationally efficient, and can be added to
any neural network. We demonstrate the utility of our loss by incorporating it
into SHAPR, a model for predicting the 3D cell shape of individual cells based
on 2D microscopy images. Using a hybrid loss that leverages both geometrical
and topological information of single objects to assess their shape, we find
that topological information substantially improves the quality of
reconstructions, thus highlighting its ability to extract more relevant
features from image datasets.

For flexible yet safe imitation learning (IL), we propose a modular approach
that uses a generative imitator policy with a safety layer, has an overall
explicit density/gradient, can therefore be end-to-end trained using generative
adversarial IL (GAIL), and comes with theoretical worst-case safety/robustness
guarantees. The safety layer's exact density comes from using a countable
non-injective gluing of piecewise differentiable injections and the
change-of-variables formula. The safe set (into which the safety layer maps) is
inferred by sampling actions and their potential future fail-safe fallback
continuations, together with Lipschitz continuity and convexity arguments. We
also provide theoretical bounds showing the advantage of using the safety layer
already during training (imitation error linear in the horizon) compared to
only using it at test time (quadratic error). In an experiment on challenging
real-world driver interaction data, we empirically demonstrate tractability,
safety and imitation performance of our approach.

Phase retrieval in optical imaging refers to the recovery of a complex signal
from phaseless data acquired in the form of its diffraction patterns. These
patterns are acquired through a system with a coherent light source that
employs a diffractive optical element (DOE) to modulate the scene resulting in
coded diffraction patterns at the sensor. Recently, the hybrid approach of
model-driven network or deep unfolding has emerged as an effective alternative
to conventional model-based and learning-based phase retrieval techniques
because it allows for bounding the complexity of algorithms while also
retaining their efficacy. Additionally, such hybrid approaches have shown
promise in improving the design of DOEs that follow theoretical uniqueness
conditions. There are opportunities to exploit novel experimental setups and
resolve even more complex DOE phase retrieval applications. This paper presents
an overview of algorithms and applications of deep unfolding for bootstrapped -
regardless of near, middle, and far zones - phase retrieval.

Learning neural set functions becomes increasingly more important in many
applications like product recommendation and compound selection in AI-aided
drug discovery. The majority of existing works study methodologies of set
function learning under the function value oracle, which, however, requires
expensive supervision signals. This renders it impractical for applications
with only weak supervisions under the Optimal Subset (OS) oracle, the study of
which is surprisingly overlooked. In this work, we present a principled yet
practical maximum likelihood learning framework, termed as EquiVSet, that
simultaneously meets the following desiderata of learning set functions under
the OS oracle: i) permutation invariance of the set mass function being
modeled; ii) permission of varying ground set; iii) minimum prior; and iv)
scalability. The main components of our framework involve: an energy-based
treatment of the set mass function, DeepSet-style architectures to handle
permutation invariance, mean-field variational inference, and its amortized
variants. Thanks to the elegant combination of these advanced architectures,
empirical studies on three real-world applications (including Amazon product
recommendation, set anomaly detection, and compound selection for virtual
screening) demonstrate that EquiVSet outperforms the baselines by a large
margin.

Contrastive learning based methods such as cascade comparing to detect (CC2D)
have shown great potential for one-shot medical landmark detection. However,
the important cue of relative distance between landmarks is ignored in CC2D. In
this paper, we upgrade CC2D to version II by incorporating a
simple-yet-effective relative distance bias in the training stage, which is
theoretically proved to encourage the encoder to project the relatively distant
landmarks to the embeddings with low similarities. As consequence, CC2Dv2 is
less possible to detect a wrong point far from the correct landmark.
Furthermore, we present an open-source, landmark-labeled dataset for the
measurement of biomechanical parameters of the lower extremity to alleviate the
burden of orthopedic surgeons. The effectiveness of CC2Dv2 is evaluated on the
public dataset from the ISBI 2015 Grand-Challenge of cephalometric radiographs
and our new dataset, which greatly outperforms the state-of-the-art one-shot
landmark detection approaches.

Kernel smoothers are essential tools for data analysis due to their ability
to convey complex statistical information with concise graphical
visualisations. Their inclusion in the base distribution and in the many
user-contributed add-on packages of the \proglang{R} statistical analysis
environment caters well to the requirements for many practitioners. Though
there remain some important gaps for specialised data types, most notably for
tibbles (tidy data) within the tidyverse, and for simple features (geospatial
data) within the Geographical Information Systems (GIS) ecosystem. The proposed
\pkg{eks} package fills in these gaps. In addition to kernel density
estimation, which is the most widely implemented kernel smoother, this package
also caters for more complex data analysis situations, such as density-based
classification (supervised learning), mean shift clustering (unsupervised
learning), density derivative estimation, density ridge estimation, and
significance testing for density differences and for modal regions. We
illustrate with experimental data how to obtain and to interpret the
statistical graphical analyses for these kernel smoothing methods.

Anomaly is defined as a state of the system that do not conform to the normal
behavior. For example, the emission of neutrons in a nuclear reactor channel
above the specified threshold is an anomaly. Big data refers to the data set
that is \emph{high volume, streaming, heterogeneous, distributed} and often
\emph{sparse}. Big data is not uncommon these days. For example, as per
Internet live stats, the number of tweets posted per day has gone above 500
millions. Due to data explosion in data laden domains, traditional anomaly
detection techniques developed for small data sets scale poorly on large-scale
data sets. Therefore, we take an alternative approach to tackle anomaly
detection in big data. Essentially, there are two ways to scale anomaly
detection in big data. The first is based on the \emph{online} learning and the
second is based on the \emph{distributed} learning. Our aim in the thesis is to
tackle big data problems while detecting anomaly efficiently. To that end, we
first take \emph{streaming} issue of the big data and propose
Passive-Aggressive GMEAN (PAGMEAN) algorithms. Although, online learning
algorithm can scale well over large number of data points and dimensions, they
can not process data when it is distributed at multiple locations; which is
quite common these days. Therefore, we propose anomaly detection algorithm
which is inherently distributed using ADMM. Finally, we present a case study on
anomaly detection in nuclear power plant data.

Reinforcement learning (RL) is one of the three basic paradigms of machine
learning. It has demonstrated impressive performance in many complex tasks like
Go and StarCraft, which is increasingly involved in smart manufacturing and
autonomous driving. However, RL consistently suffers from the
exploration-exploitation dilemma. In this paper, we investigated the problem of
improving exploration in RL and introduced the intrinsically-motivated RL. In
sharp contrast to the classic exploration strategies, intrinsically-motivated
RL utilizes the intrinsic learning motivation to provide sustainable
exploration incentives. We carefully classified the existing intrinsic reward
methods and analyzed their practical drawbacks. Moreover, we proposed a new
intrinsic reward method via R\'enyi state entropy maximization, which overcomes
the drawbacks of the preceding methods and provides powerful exploration
incentives. Finally, extensive simulation demonstrated that the proposed module
achieve superior performance with higher efficiency and robustness.

With the Autonomous Vehicle (AV) industry shifting towards machine-learned
approaches for motion planning, the performance of self-driving systems is
starting to rely heavily on large quantities of expert driving demonstrations.
However, collecting this demonstration data typically involves expensive HD
sensor suites (LiDAR + RADAR + cameras), which quickly becomes financially
infeasible at the scales required. This motivates the use of commodity sensors
like cameras for data collection, which are an order of magnitude cheaper than
HD sensor suites, but offer lower fidelity. Leveraging these sensors for
training an AV motion planner opens a financially viable path to observe the
`long tail' of driving events. As our main contribution we show it is possible
to train a high-performance motion planner using commodity vision data which
outperforms planners trained on HD-sensor data for a fraction of the cost. To
the best of our knowledge, we are the first to demonstrate this using
real-world data. We compare the performance of the autonomy system on these two
different sensor configurations, and show that we can compensate for the lower
sensor fidelity by means of increased quantity: a planner trained on 100h of
commodity vision data outperforms the one with 25h of expensive HD data. We
also share the engineering challenges we had to tackle to make this work.

Word-level adversarial attacks have shown success in NLP models, drastically
decreasing the performance of transformer-based models in recent years. As a
countermeasure, adversarial defense has been explored, but relatively few
efforts have been made to detect adversarial examples. However, detecting
adversarial examples may be crucial for automated tasks (e.g. review sentiment
analysis) that wish to amass information about a certain population and
additionally be a step towards a robust defense system. To this end, we release
a dataset for four popular attack methods on four datasets and four models to
encourage further research in this field. Along with it, we propose a
competitive baseline based on density estimation that has the highest AUC on 29
out of 30 dataset-attack-model combinations. Source code is available in
https://github.com/anoymous92874838/text-adv-detection.

Visible thermal person re-identification (VT-ReID) suffers from the
inter-modality discrepancy and intra-identity variations. Distribution
alignment is a popular solution for VT-ReID, which, however, is usually
restricted to the influence of the intra-identity variations. In this paper, we
propose the Cross-Modality Earth Mover's Distance (CM-EMD) that can alleviate
the impact of the intra-identity variations during modality alignment. CM-EMD
selects an optimal transport strategy and assigns high weights to pairs that
have a smaller intra-identity variation. In this manner, the model will focus
on reducing the inter-modality discrepancy while paying less attention to
intra-identity variations, leading to a more effective modality alignment.
Moreover, we introduce two techniques to improve the advantage of CM-EMD.
First, the Cross-Modality Discrimination Learning (CM-DL) is designed to
overcome the discrimination degradation problem caused by modality alignment.
By reducing the ratio between intra-identity and inter-identity variances,
CM-DL leads the model to learn more discriminative representations. Second, we
construct the Multi-Granularity Structure (MGS), enabling us to align
modalities from both coarse- and fine-grained levels with the proposed CM-EMD.
Extensive experiments show the benefits of the proposed CM-EMD and its
auxiliary techniques (CM-DL and MGS). Our method achieves state-of-the-art
performance on two VT-ReID benchmarks.

In this contribution, we develop an efficient surrogate modeling framework
for simulation-based optimization of enhanced oil recovery, where we
particularly focus on polymer flooding. The computational approach is based on
an adaptive training procedure of a neural network that directly approximates
an input-output map of the underlying PDE-constrained optimization problem. The
training process thereby focuses on the construction of an accurate surrogate
model solely related to the optimization path of an outer iterative
optimization loop. True evaluations of the objective function are used to
finally obtain certified results. Numerical experiments are given to evaluate
the accuracy and efficiency of the approach for a heterogeneous five-spot
benchmark problem.

Current unsupervised anomaly localization approaches rely on generative
models to learn the distribution of normal images, which is later used to
identify potential anomalous regions derived from errors on the reconstructed
images. However, a main limitation of nearly all prior literature is the need
of employing anomalous images to set a class-specific threshold to locate the
anomalies. This limits their usability in realistic scenarios, where only
normal data is typically accessible. Despite this major drawback, only a
handful of works have addressed this limitation, by integrating supervision on
attention maps during training. In this work, we propose a novel formulation
that does not require accessing images with abnormalities to define the
threshold. Furthermore, and in contrast to very recent work, the proposed
constraint is formulated in a more principled manner, leveraging well-known
knowledge in constrained optimization. In particular, the equality constraint
on the attention maps in prior work is replaced by an inequality constraint,
which allows more flexibility. In addition, to address the limitations of
penalty-based functions we employ an extension of the popular log-barrier
methods to handle the constraint. Last, we propose an alternative
regularization term that maximizes the Shannon entropy of the attention maps,
reducing the amount of hyperparameters of the proposed model. Comprehensive
experiments on two publicly available datasets on brain lesion segmentation
demonstrate that the proposed approach substantially outperforms relevant
literature, establishing new state-of-the-art results for unsupervised lesion
segmentation, and without the need to access anomalous images.

Early exiting allows instances to exit at different layers according to the
estimation of difficulty. Previous works usually adopt heuristic metrics such
as the entropy of internal outputs to measure instance difficulty, which
suffers from generalization and threshold-tuning. In contrast, learning to
exit, or learning to predict instance difficulty is a more appealing way.
Though some effort has been devoted to employing such "learn-to-exit" modules,
it is still unknown whether and how well the instance difficulty can be
learned. As a response, we first conduct experiments on the learnability of
instance difficulty, which demonstrates that modern neural models perform
poorly on predicting instance difficulty. Based on this observation, we propose
a simple-yet-effective Hash-based Early Exiting approach (HashEE) that replaces
the learn-to-exit modules with hash functions to assign each token to a fixed
exiting layer. Different from previous methods, HashEE requires no internal
classifiers nor extra parameters, and therefore is more efficient. Experimental
results on classification, regression, and generation tasks demonstrate that
HashEE can achieve higher performance with fewer FLOPs and inference time
compared with previous state-of-the-art early exiting methods.

The development of new treatments often requires clinical trials with
translational animal models using (pre)-clinical imaging to characterize
inter-species pathological processes. Deep Learning (DL) models are commonly
used to automate retrieving relevant information from the images. Nevertheless,
they typically suffer from low generability and explainability as a product of
their entangled design, resulting in a specific DL model per animal model.
Consequently, it is not possible to take advantage of the high capacity of DL
to discover statistical relationships from inter-species images.
  To alleviate this problem, in this work, we present a model capable of
extracting disentangled information from images of different animal models and
the mechanisms that generate the images. Our method is located at the
intersection between deep generative models, disentanglement and causal
representation learning. It is optimized from images of pathological lung
infected by Tuberculosis and is able: a) from an input slice, infer its
position in a volume, the animal model to which it belongs, the damage present
and even more, generate a mask covering the whole lung (similar overlap
measures to the nnU-Net), b) generate realistic lung images by setting the
above variables and c) generate counterfactual images, namely, healthy versions
of a damaged input slice.

In this work, we study non-parametric estimation of joint probabilities of a
given set of discrete and continuous random variables from their (empirically
estimated) 2D marginals, under the assumption that the joint probability could
be decomposed and approximated by a mixture of product densities/mass
functions. The problem of estimating the joint probability density function
(PDF) using semi-parametric techniques such as Gaussian Mixture Models (GMMs)
is widely studied. However such techniques yield poor results when the
underlying densities are mixtures of various other families of distributions
such as Laplacian or generalized Gaussian, uniform, Cauchy, etc. Further, GMMs
are not the best choice to estimate joint distributions which are hybrid in
nature, i.e., some random variables are discrete while others are continuous.
We present a novel approach for estimating the PDF using ideas from dictionary
representations in signal processing coupled with low rank tensor
decompositions. To the best our knowledge, this is the first work on estimating
joint PDFs employing dictionaries alongside tensor decompositions. We create a
dictionary of various families of distributions by inspecting the data, and use
it to approximate each decomposed factor of the product in the mixture. Our
approach can naturally handle hybrid $N$-dimensional distributions. We test our
approach on a variety of synthetic and real datasets to demonstrate its
effectiveness in terms of better classification rates and lower error rates,
when compared to state of the art estimators.

Neural Architecture Search~(NAS) has attracted increasingly more attention in
recent years because of its capability to design deep neural networks
automatically. Among them, differential NAS approaches such as DARTS, have
gained popularity for the search efficiency. However, they suffer from two main
issues, the weak robustness to the performance collapse and the poor
generalization ability of the searched architectures. To solve these two
problems, a simple-but-efficient regularization method, termed as Beta-Decay,
is proposed to regularize the DARTS-based NAS searching process. Specifically,
Beta-Decay regularization can impose constraints to keep the value and variance
of activated architecture parameters from too large. Furthermore, we provide
in-depth theoretical analysis on how it works and why it works. Experimental
results on NAS-Bench-201 show that our proposed method can help to stabilize
the searching process and makes the searched network more transferable across
different datasets. In addition, our search scheme shows an outstanding
property of being less dependent on training time and data. Comprehensive
experiments on a variety of search spaces and datasets validate the

The estimation of loss distributions for dynamic portfolios requires the
simulation of scenarios representing realistic joint dynamics of their
components, with particular importance devoted to the simulation of tail risk
scenarios. Commonly used parametric models have been successful in applications
involving a small number of assets, but may not be scalable to large or
heterogeneous portfolios involving multiple asset classes.
  We propose a novel data-driven approach for the simulation of realistic
multi-asset scenarios with a particular focus on the accurate estimation of
tail risk for a given class of static and dynamic portfolios selected by the
user. By exploiting the joint elicitability property of Value-at-Risk (VaR) and
Expected Shortfall (ES), we design a Generative Adversarial Network (GAN)
architecture capable of learning to simulate price scenarios that preserve tail
risk features for these benchmark trading strategies, leading to consistent
estimators for their Value-at-Risk and Expected Shortfall. We demonstrate the
accuracy and scalability of our method via extensive simulation experiments
using synthetic and market data. Our results show that, in contrast to other
data-driven scenario generators, our proposed scenario simulation method
correctly captures tail risk for both static and dynamic portfolios.

For autoregressive (AR) modeling of high-resolution images, vector
quantization (VQ) represents an image as a sequence of discrete codes. A short
sequence length is important for an AR model to reduce its computational costs
to consider long-range interactions of codes. However, we postulate that
previous VQ cannot shorten the code sequence and generate high-fidelity images
together in terms of the rate-distortion trade-off. In this study, we propose
the two-stage framework, which consists of Residual-Quantized VAE (RQ-VAE) and
RQ-Transformer, to effectively generate high-resolution images. Given a fixed
codebook size, RQ-VAE can precisely approximate a feature map of an image and
represent the image as a stacked map of discrete codes. Then, RQ-Transformer
learns to predict the quantized feature vector at the next position by
predicting the next stack of codes. Thanks to the precise approximation of
RQ-VAE, we can represent a 256$\times$256 image as 8$\times$8 resolution of the
feature map, and RQ-Transformer can efficiently reduce the computational costs.
Consequently, our framework outperforms the existing AR models on various
benchmarks of unconditional and conditional image generation. Our approach also
has a significantly faster sampling speed than previous AR models to generate
high-quality images.

In this review we discuss all the relevant solar/stellar radiation and plasma
parameters and processes that act together in the formation and modification of
atmospheres and exospheres that consist of surface-related minerals. Magma
ocean degassed silicate atmospheres or thin gaseous envelopes from planetary
building blocks, airless bodies in the inner Solar System, and close-in
magmatic rocky exoplanets such as CoRot-7b, HD219134b and 55 Cnc e are
addressed. The depletion and fractionation of elements from planetary embryos,
which act as the building blocks for protoplanets are also discussed. In this
context the formation processes of the Moon and Mercury are briefly reviewed.
The Lunar surface modification since its origin by micrometeoroids, plasma
sputtering, plasma impingement as well as chemical surface alteration and the
search of particles from the early Earth's atmosphere that were collected by
the Moon on its surface are also discussed. Finally, we address important
questions on what can be learned from the study of Mercury's environment and
its solar wind interaction by MESSENGER and BepiColombo in comparison with the
expected observations at exo-Mercurys by future space-observatories such as the
JWST or ARIEL and ground-based telescopes and instruments like SPHERE and
ESPRESSO on the VLT, and vice versa.

In the current work, a problem-splitting approach and a scheme motivated by
transfer learning is applied to a structural health monitoring problem. The
specific problem in this case is that of localising damage on an aircraft wing.
The original experiment is described, together with the initial approach, in
which a neural network was trained to localise damage. The results were not
ideal, partly because of a scarcity of training data, and partly because of the
difficulty in resolving two of the damage cases. In the current paper, the
problem is split into two sub-problems and an increase in classification
accuracy is obtained. The sub-problems are obtained by separating out the most
difficult-to-classify damage cases. A second approach to the problem is
considered by adopting ideas from transfer learning (usually applied in much
deeper) networks to see if a network trained on the simpler damage cases can
help with feature extraction in the more difficult cases. The transfer of a
fixed trained batch of layers between the networks is found to improve
classification by making the classes more separable in the feature space and to
speed up convergence.

Electric vehicle (EV) charging stations represent a substantial load with
significant flexibility. The exploitation of that flexibility in demand
response (DR) algorithms becomes increasingly important to manage and balance
demand and supply in power grids. Model-free DR based on reinforcement learning
(RL) is an attractive approach to balance such EV charging load. We build on
previous research on RL, based on a Markov decision process (MDP) to
simultaneously coordinate multiple charging stations. However, we note that the
computationally expensive cost function adopted in the previous research leads
to large training times, which limits the feasibility and practicality of the
approach. We, therefore, propose an improved cost function that essentially
forces the learned control policy to always fulfill any charging demand that
does not offer any flexibility. We rigorously compare the newly proposed batch
RL fitted Q-iteration implementation with the original (costly) one, using
real-world data. Specifically, for the case of load flattening, we compare the
two approaches in terms of (i) the processing time to learn the RL-based
charging policy, as well as (ii) the overall performance of the policy
decisions in terms of meeting the target load for unseen test data. The
performance is analyzed for different training periods and varying training
sample sizes. In addition to both RL policies performance results, we provide
performance bounds in terms of both (i) an optimal all-knowing strategy, and
(ii) a simple heuristic spreading individual EV charging uniformly over time

Semantic segmentation of aerial imagery is an important tool for mapping and
earth observation. However, supervised deep learning models for segmentation
rely on large amounts of high-quality labelled data, which is labour-intensive
and time-consuming to generate. To address this, we propose a new approach for
using unmanned aerial vehicles (UAVs) to autonomously collect useful data for
model training. We exploit a Bayesian approach to estimate model uncertainty in
semantic segmentation. During a mission, the semantic predictions and model
uncertainty are used as input for terrain mapping. A key aspect of our pipeline
is to link the mapped model uncertainty to a robotic planning objective based
on active learning. This enables us to adaptively guide a UAV to gather the
most informative terrain images to be labelled by a human for model training.
Our experimental evaluation on real-world data shows the benefit of using our
informative planning approach in comparison to static coverage paths in terms
of maximising model performance and reducing labelling efforts.

Attempts have been made recently in the field of population-based structural
health monitoring (PBSHM), to transfer knowledge between SHM models of
different structures. The attempts have been focussed on homogeneous and
heterogeneous populations. A more general approach to transferring knowledge
between structures, is by considering all plausible structures as points on a
multidimensional base manifold and building a fibre bundle. The idea is quite
powerful, since, a mapping between points in the base manifold and their
fibres, the potential states of any arbitrary structure, can be learnt. A
smaller scale problem, but still useful, is that of learning a specific point
of every fibre, i.e. that corresponding to the undamaged state of structures
within a population. Under the framework of PBSHM, a data-driven approach to
the aforementioned problem is developed. Structures are converted into graphs
and inference is attempted within a population, using a graph neural network
(GNN) algorithm. The algorithm solves a major problem existing in such
applications. Structures comprise different sizes and are defined as abstract
objects, thus attempting to perform inference within a heterogeneous population
is not trivial. The proposed approach is tested in a simulated population of
trusses. The goal of the application is to predict the first natural frequency
of trusses of different sizes, across different environmental temperatures and
having different bar member types. After training the GNN using part of the
total population, it was tested on trusses that were not included in the
training dataset. Results show that the accuracy of the regression is
satisfactory even in structures with higher number of nodes and members than
those used to train it.

Image restoration is a low-level vision task which is to restore degraded
images to noise-free images. With the success of deep neural networks, the
convolutional neural networks surpass the traditional restoration methods and
become the mainstream in the computer vision area. To advance the performanceof
denoising algorithms, we propose a blind real image denoising network (SRMNet)
by employing a hierarchical architecture improved from U-Net. Specifically, we
use a selective kernel with residual block on the hierarchical structure called
M-Net to enrich the multi-scale semantic information. Furthermore, our SRMNet
has competitive performance results on two synthetic and two real-world noisy
datasets in terms of quantitative metrics and visual quality. The source code
and pretrained model are available at
https://github.com/TentativeGitHub/SRMNet.

We introduce UDAAN, an open-source post-editing tool that can reduce manual
editing efforts to quickly produce publishable-standard documents in several
Indic languages. UDAAN has an end-to-end Machine Translation (MT) plus
post-editing pipeline wherein users can upload a document to obtain raw MT
output. Further, users can edit the raw translations using our tool. UDAAN
offers several advantages: a) Domain-aware, vocabulary-based lexical
constrained MT. b) source-target and target-target lexicon suggestions for
users. Replacements are based on the source and target texts lexicon alignment.
c) Translation suggestions are based on logs created during user interaction.
d) Source-target sentence alignment visualisation that reduces the cognitive
load of users during editing. e) Translated outputs from our tool are available
in multiple formats: docs, latex, and PDF. We also provide the facility to use
around 100 in-domain dictionaries for lexicon-aware machine translation.
Although we limit our experiments to English-to-Hindi translation, our tool is
independent of the source and target languages. Experimental results based on
the usage of the tools and users feedback show that our tool speeds up the
translation time by approximately a factor of three compared to the baseline
method of translating documents from scratch. Our tool is available for both
Windows and Linux platforms. The tool is open-source under MIT license, and the
source code can be accessed from our website, https://www.udaanproject.org.
Demonstration and tutorial videos for various features of our tool can be
accessed here. Our MT pipeline can be accessed at
https://udaaniitb.aicte-india.org/udaan/translate/.

There is a growing need to assist radiologists in performing X-ray readings
and diagnoses fast, comfortably, and effectively. As radiologists strive to
maximize productivity, it is essential to consider the impact of reading rooms
in interpreting complex examinations and ensure that higher volume and
reporting speeds do not compromise patient outcomes. Virtual Reality (VR) is a
disruptive technology for clinical practice in assessing X-ray images. We argue
that conjugating eye-tracking with VR devices and Machine Learning may overcome
obstacles posed by inadequate ergonomic postures and poor room conditions that
often cause erroneous diagnostics when professionals examine digital images.

Efficient data collection methods play a major role in helping us better
understand the Earth and its ecosystems. In many applications, the usage of
unmanned aerial vehicles (UAVs) for monitoring and remote sensing is rapidly
gaining momentum due to their high mobility, low cost, and flexible deployment.
A key challenge is planning missions to maximize the value of acquired data in
large environments given flight time limitations. This is, for example,
relevant for monitoring agricultural fields. This paper addresses the problem
of adaptive path planning for accurate semantic segmentation of using UAVs. We
propose an online planning algorithm which adapts the UAV paths to obtain
high-resolution semantic segmentations necessary in areas with fine details as
they are detected in incoming images. This enables us to perform close
inspections at low altitudes only where required, without wasting energy on
exhaustive mapping at maximum image resolution. A key feature of our approach
is a new accuracy model for deep learning-based architectures that captures the
relationship between UAV altitude and semantic segmentation accuracy. We
evaluate our approach on different domains using real-world data, proving the
efficacy and generability of our solution.

A powerful approach, and one of the most common ones in structural health
monitoring (SHM), is to use data-driven models to make predictions and
inferences about structures and their condition. Such methods almost
exclusively rely on the quality of the data. Within the SHM discipline, data do
not always suffice to build models with satisfactory accuracy for given tasks.
Even worse, data may be completely missing from one's dataset, regarding the
behaviour of a structure under different environmental conditions. In the
current work, with a view to confronting such issues, the generation of
artificial data using a variation of the generative adversarial network (GAN)
algorithm, is used. The aforementioned variation is that of the conditional GAN
or cGAN. The algorithm is not only used to generate artificial data, but also
to learn transformations of manifolds according to some known parameters.
Assuming that the structure's response is represented by points in a manifold,
part of the space will be formed due to variations in external conditions
affecting the structure. This idea proves efficient in SHM, as it is exploited
to generate structural data for specific values of environmental coefficients.
The scheme is applied here on a simulated structure which operates under
different temperature and humidity conditions. The cGAN is trained on data for
some discrete values of the temperature within some range, and is able to
generate data for every temperature in this range with satisfactory accuracy.
The novelty, compared to classic regression in similar problems, is that the
cGAN allows unknown environmental parameters to affect the structure and can
generate whole manifolds of data for every value of the known parameters, while
the unknown ones vary within the generated manifolds.

The growth of data today poses a challenge in management and inference. While
feature extraction methods are capable of reducing the size of the data for
inference, they do not help in minimizing the cost of data storage. On the
other hand, feature selection helps to remove the redundant features and
therefore is helpful not only in inference but also in reducing management
costs. This work presents a novel parallel feature selection approach for
classification, namely Parallel Feature Selection using Trace criterion (PFST),
which scales up to very large datasets. Our method uses trace criterion, a
measure of class separability used in Fisher's Discriminant Analysis, to
evaluate feature usefulness. We analyzed the criterion's desirable properties
theoretically. Based on the criterion, PFST rapidly finds important features
out of a set of features for big datasets by first making a forward selection
with early removal of seemingly redundant features parallelly. After the most
important features are included in the model, we check back their contribution
for possible interaction that may improve the fit. Lastly, we make a backward
selection to check back possible redundant added by the forward steps. We
evaluate our methods via various experiments using Linear Discriminant Analysis
as the classifier on selected features. The experiments show that our method
can produce a small set of features in a fraction of the amount of time by the
other methods under comparison. In addition, the classifier trained on the
features selected by PFST not only achieves better accuracy than the ones
chosen by other approaches but can also achieve better accuracy than the
classification on all available features.

Neural network on Riemannian symmetric space such as hyperbolic space and the
manifold of symmetric positive definite (SPD) matrices is an emerging subject
of research in geometric deep learning. Based on the well-established framework
of the Helgason-Fourier transform on the noncompact symmetric space, we present
a fully-connected network and its associated ridgelet transform on the
noncompact symmetric space, covering the hyperbolic neural network (HNN) and
the SPDNet as special cases. The ridgelet transform is an analysis operator of
a depth-2 continuous network spanned by neurons, namely, it maps an arbitrary
given function to the weights of a network. Thanks to the coordinate-free
reformulation, the role of nonlinear activation functions is revealed to be a
wavelet function, and the reconstruction formula directly yields the
universality of the proposed networks.

One of the most pressing issues that have arisen due to the rapid growth of
the Internet is known as information overloading. Simplifying the relevant
information in the form of a summary will assist many people because the
material on any topic is plentiful on the Internet. Manually summarising
massive amounts of text is quite challenging for humans. So, it has increased
the need for more complex and powerful summarizers. Researchers have been
trying to improve approaches for creating summaries since the 1950s, such that
the machine-generated summary matches the human-created summary. This study
provides a detailed state-of-the-art analysis of text summarization concepts
such as summarization approaches, techniques used, standard datasets,
evaluation metrics and future scopes for research. The most commonly accepted
approaches are extractive and abstractive, studied in detail in this work.
Evaluating the summary and increasing the development of reusable resources and
infrastructure aids in comparing and replicating findings, adding competition
to improve the outcomes. Different evaluation methods of generated summaries
are also discussed in this study. Finally, at the end of this study, several
challenges and research opportunities related to text summarization research
are mentioned that may be useful for potential researchers working in this
area.

Partitioning a set of elements into a given number of groups of a priori
unknown sizes is a critical task in many applications. It can be characterized
by a hypergeometric distribution, which describes biased sampling without
replacement based on the relative importance between classes of samples. Due to
hard constraints, this discrete distribution is not differentiable in its
standard formulation, prohibiting its use in modern machine learning
frameworks. Hence, previous works mostly fall back on suboptimal heuristics or
simplified assumptions. In this work, we propose a differentiable
reparameterization trick for the multivariate noncentral hypergeometric
distribution. We introduce reparameterizable gradients to enable learning of
the importance or the selection bias between groups. We highlight the
applicability and usability of the proposed formulation in two different
experiments: weakly-supervised learning and clustering.

Early Time-Series Classification (ETSC) is the task of predicting the class
of incoming time-series by observing as few measurements as possible. Such
methods can be employed to obtain classification forecasts in many
time-critical applications. However, available techniques are not equally
suitable for every problem, since differentiations in the data characteristics
can impact algorithm performance in terms of earliness, accuracy, F1-score, and
training time. We evaluate six existing ETSC algorithms on publicly available
data, as well as on two newly introduced datasets originating from the life
sciences and maritime domains. Our goal is to provide a framework for the
evaluation and comparison of ETSC algorithms and to obtain intuition on how
such approaches perform on real-life applications. The presented framework may
also serve as a benchmark for new related techniques.

Ionic Polymer Metal Composites (IPMCs) are one of the most preferred choices
among biocompatible materials for industrial and biomedical applications.
Despite their advantages, some of their drawbacks include non-linear and
hysteretic behavior, which complicates the modeling process. In previous works,
usually autoregressive models were used to predict the behavior of an IPMC
actuator. The main drawback of using an autoregressive model is that it cannot
be used in mobile and real-time applications. In this study, we proposed a
hybrid analytical intelligent model for an IPMC actuator. The most outstanding
feature of this model is its non-autoregressive structure. The hybrid concept
proposed in this study can be generalized to various problems other than IPMCs.
The structure used in this work comprises an analytical model and a deep neural
network, providing a non-linear, dynamic, and non-autoregressive model for the
IPMC actuator. Lastly, the average NMSE achieved using the proposed hybrid
model is 9.5781e-04 showing a significant drop in the error rate compared to
other non-autoregressive structures.

Quantum information has the property that measurement is an inherently
destructive process. This feature is most apparent in the principle of
complementarity, which states that mutually incompatible observables cannot be
measured at the same time. Recent work by Broadbent and Islam (TCC 2020) builds
on this aspect of quantum mechanics to realize a cryptographic notion called
certified deletion. While this remarkable notion enables a classical verifier
to be convinced that a (private-key) quantum ciphertext has been deleted by an
untrusted party, it offers no additional layer of functionality.
  In this work, we augment the proof-of-deletion paradigm with fully
homomorphic encryption (FHE). We construct the first fully homomorphic
encryption scheme with certified deletion -- an interactive protocol which
enables an untrusted quantum server to compute on encrypted data and, if
requested, to simultaneously prove data deletion to a client. Our scheme has
the desirable property that verification of a deletion certificate is public;
meaning anyone can verify that deletion has taken place. Our main technical
ingredient is an interactive protocol by which a quantum prover can convince a
classical verifier that a sample from the Learning with Errors (LWE)
distribution in the form of a quantum state was deleted. As an application of
our protocol, we construct a Dual-Regev public-key encryption scheme with
certified deletion, which we then extend towards a (leveled) FHE scheme of the
same type. We introduce the notion of Gaussian-collapsing hash functions -- a
special case of collapsing hash functions defined by Unruh (Eurocrypt 2016) --
and we prove the security of our schemes under the assumption that the Ajtai
hash function satisfies a certain strong Gaussian-collapsing property in the
presence of leakage.

In this paper, we investigate how to reduce the number of measurement
configurations needed for sufficiently precise entanglement quantification.
Instead of analytical formulae, we employ artificial neural networks to predict
the amount of entanglement in a quantum state based on results of collective
measurements (simultaneous measurements on multiple instances of the
investigated state). This approach allows us to explore the precision of
entanglement quantification as a function of measurement configurations. For
the purpose of our research, we consider general two-qubit states and their
negativity as entanglement quantifier. We outline the benefits of this approach
in future quantum communication networks.

In this work we study binary classification problems where we assume that our
training data is subject to uncertainty, i.e. the precise data points are not
known. To tackle this issue in the field of robust machine learning the aim is
to develop models which are robust against small perturbations in the training
data. We study robust support vector machines (SVM) and extend the classical
approach by an ensemble method which iteratively solves a non-robust SVM on
different perturbations of the dataset, where the perturbations are derived by
an adversarial problem. Afterwards for classification of an unknown data point
we perform a majority vote of all calculated SVM solutions. We study three
different variants for the adversarial problem, the exact problem, a relaxed
variant and an efficient heuristic variant. While the exact and the relaxed
variant can be modeled using integer programming formulations, the heuristic
one can be implemented by an easy and efficient algorithm. All derived methods
are tested on random and realistic datasets and the results indicate that the
derived ensemble methods have a much more stable behaviour when changing the
protection level compared to the classical robust SVM model.

Generative adversarial network (GAN) is widely used for generalized and
robust learning on graph data. However, for non-Euclidean graph data, the
existing GAN-based graph representation methods generate negative samples by
random walk or traverse in discrete space, leading to the information loss of
topological properties (e.g. hierarchy and circularity). Moreover, due to the
topological heterogeneity (i.e., different densities across the graph
structure) of graph data, they suffer from serious topological distortion
problems. In this paper, we proposed a novel Curvature Graph Generative
Adversarial Networks method, named \textbf{\modelname}, which is the first
GAN-based graph representation method in the Riemannian geometric manifold. To
better preserve the topological properties, we approximate the discrete
structure as a continuous Riemannian geometric manifold and generate negative
samples efficiently from the wrapped normal distribution. To deal with the
topological heterogeneity, we leverage the Ricci curvature for local structures
with different topological properties, obtaining to low-distortion
representations. Extensive experiments show that CurvGAN consistently and
significantly outperforms the state-of-the-art methods across multiple tasks
and shows superior robustness and generalization.

We propose AdaFamily, a novel method for training deep neural networks. It is
a family of adaptive gradient methods and can be interpreted as sort of a blend
of the optimization algorithms Adam, AdaBelief and AdaMomentum. We perform
experiments on standard datasets for image classification, demonstrating that
our proposed method outperforms these algorithms.

This paper considers the complex problem of a team of UAVs searching targets
under uncertainty. The goal of the UAV team is to find all of the moving
targets as quickly as possible before they arrive at their selected goal. The
uncertainty considered is threefold: First, the UAVs do not know the targets'
locations and destinations. Second, the sensing capabilities of the UAVs are
not perfect. Third, the targets' movement model is unknown. We suggest a
real-time algorithmic framework for the UAVs, combining entropy and
stochastic-temporal belief, that aims at optimizing the probability of a quick
and successful detection of all of the targets. We have empirically evaluated
the algorithmic framework, and have shown its efficiency and significant
performance improvement compared to other solutions. Furthermore, we have
evaluated our framework using Peer Designed Agents (PDAs), which are computer
agents that simulate targets and show that our algorithmic framework
outperforms other solutions in this scenario.

Randomized Hadamard Transforms (RHTs) have emerged as a computationally
efficient alternative to the use of dense unstructured random matrices across a
range of domains in computer science and machine learning. For several
applications such as dimensionality reduction and compressed sensing, the
theoretical guarantees for methods based on RHTs are comparable to approaches
using dense random matrices with i.i.d.\ entries. However, several such
applications are in the low-dimensional regime where the number of rows sampled
from the matrix is rather small. Prior arguments are not applicable to the
high-dimensional regime often found in machine learning applications like
kernel approximation. Given an ensemble of RHTs with Gaussian diagonals,
$\{M^i\}_{i = 1}^m$, and any $1$-Lipschitz function, $f: \mathbb{R} \to
\mathbb{R}$, we prove that the average of $f$ over the entries of $\{M^i v\}_{i
= 1}^m$ converges to its expectation uniformly over $\| v \| \leq 1$ at a rate
comparable to that obtained from using truly Gaussian matrices. We use our
inequality to then derive improved guarantees for two applications in the
high-dimensional regime: 1) kernel approximation and 2) distance estimation.
For kernel approximation, we prove the first \emph{uniform} approximation
guarantees for random features constructed through RHTs lending theoretical
justification to their empirical success while for distance estimation, our
convergence result implies data structures with improved runtime guarantees
over previous work by the authors. We believe our general inequality is likely
to find use in other applications.

Recently, graph neural networks (GNNs) have been shown powerful capacity at
modeling structural data. However, when adapted to downstream tasks, it usually
requires abundant task-specific labeled data, which can be extremely scarce in
practice. A promising solution to data scarcity is to pre-train a transferable
and expressive GNN model on large amounts of unlabeled graphs or coarse-grained
labeled graphs. Then the pre-trained GNN is fine-tuned on downstream datasets
with task-specific fine-grained labels. In this paper, we present a novel Graph
Matching based GNN Pre-Training framework, called GMPT. Focusing on a pair of
graphs, we propose to learn structural correspondences between them via neural
graph matching, consisting of both intra-graph message passing and inter-graph
message passing. In this way, we can learn adaptive representations for a given
graph when paired with different graphs, and both node- and graph-level
characteristics are naturally considered in a single pre-training task. The
proposed method can be applied to fully self-supervised pre-training and
coarse-grained supervised pre-training. We further propose an approximate
contrastive training strategy to significantly reduce time/memory consumption.
Extensive experiments on multi-domain, out-of-distribution benchmarks have
demonstrated the effectiveness of our approach. The code is available at:
https://github.com/RUCAIBox/GMPT.

Image captioning is a fast-growing research field of computer vision and
natural language processing that involves creating text explanations for
images. This study aims to develop a system that uses a pre-trained
convolutional neural network (CNN) to extract features from an image,
integrates the features with an attention mechanism, and creates captions using
a recurrent neural network (RNN). To encode an image into a feature vector as
graphical attributes, we employed multiple pre-trained convolutional neural
networks. Following that, a language model known as GRU is chosen as the
decoder to construct the descriptive sentence. In order to increase
performance, we merge the Bahdanau attention model with GRU to allow learning
to be focused on a specific portion of the image. On the MSCOCO dataset, the
experimental results achieve competitive performance against state-of-the-art

This paper considers a reconfigurable intelligent surface (RIS) aided
orthogonal frequency division multiplexing (OFDM) relaying system, and
investigates the joint design of RIS passive beamforming and subcarrier
matching under two cases, where Case-I ignores the source-RIS-destination
signal, while Case-II explores this signal for rate enhancement. We formulate a
mixed-integer nonlinear programming (MINIP) problem to maximize the sum
achievable rate of all subcarriers by jointly optimizing the passive
beamforming and subcarrier matching. To solve this problem, we first develop a
branch-and-bound (BnB)-based alternating optimization algorithm for attaining a
near-optimal solution. Then, a low-complexity difference-of-convex
penalty-based algorithm and learning-to-optimize approach are also proposed.
Finally, simulation results demonstrate that the RIS-assisted OFDM relaying
system achieves a substantial achievable rate gain as compared to that without
RIS since RIS recasts the subcarrier matching and balances the signal-to-noise
ratio (SNR) among different subcarrier pairs.

Prioritizing fairness is of central importance in artificial intelligence
(AI) systems, especially for those societal applications, e.g., hiring systems
should recommend applicants equally from different demographic groups, and risk
assessment systems must eliminate racism in criminal justice. Existing efforts
towards the ethical development of AI systems have leveraged data science to
mitigate biases in the training set or introduced fairness principles into the
training process. For a deployed AI system, however, it may not allow for
retraining or tuning in practice. By contrast, we propose a more flexible
approach, i.e., fairness-aware adversarial perturbation (FAAP), which learns to
perturb input data to blind deployed models on fairness-related features, e.g.,
gender and ethnicity. The key advantage is that FAAP does not modify deployed
models in terms of parameters and structures. To achieve this, we design a
discriminator to distinguish fairness-related attributes based on latent
representations from deployed models. Meanwhile, a perturbation generator is
trained against the discriminator, such that no fairness-related features could
be extracted from perturbed inputs. Exhaustive experimental evaluation
demonstrates the effectiveness and superior performance of the proposed FAAP.
In addition, FAAP is validated on real-world commercial deployments
(inaccessible to model parameters), which shows the transferability of FAAP,
foreseeing the potential of black-box adaptation.

Conventional model upgrades for visual search systems require offline refresh
of gallery features by feeding gallery images into new models (dubbed as
"backfill"), which is time-consuming and expensive, especially in large-scale
applications. The task of backward-compatible representation learning is
therefore introduced to support backfill-free model upgrades, where the new
query features are interoperable with the old gallery features. Despite the
success, previous works only investigated a close-set training scenario (i.e.,
the new training set shares the same classes as the old one), and are limited
by more realistic and challenging open-set scenarios. To this end, we first
introduce a new problem of universal backward-compatible representation
learning, covering all possible data split in model upgrades. We further
propose a simple yet effective method, dubbed as Universal Backward-Compatible
Training (UniBCT) with a novel structural prototype refinement algorithm, to
learn compatible representations in all kinds of model upgrading benchmarks in
a unified manner. Comprehensive experiments on the large-scale face recognition
datasets MS1Mv3 and IJB-C fully demonstrate the effectiveness of our method.

In this paper, we introduce a mesh-free physics-informed neural network for
solving partial differential equations on surfaces. Based on the idea of
embedding techniques, we write the underlying surface differential equations
using conventional Cartesian differential operators. With the aid of level set
function, the surface geometrical quantities, such as the normal and mean
curvature of the surface, can be computed directly and used in our surface
differential expressions. So instead of imposing the normal extension
constraints used in literature, we take the whole Cartesian differential
expressions into account in our loss function. Meanwhile, we adopt a completely
shallow (one hidden layer) network so the present model is easy to implement
and train. We perform a series of numerical experiments on both stationary and
time-dependent partial differential equations on complicated surface
geometries. The result shows that, with just a few hundred trainable
parameters, our network model is able to achieve high predictive accuracy.

Robots operating in the open world encounter various different environments
that can substantially differ from each other. This domain gap also poses a
challenge for Simultaneous Localization and Mapping (SLAM) being one of the
fundamental tasks for navigation. In particular, learning-based SLAM methods
are known to generalize poorly to unseen environments hindering their general
adoption. In this work, we introduce the novel task of continual SLAM extending
the concept of lifelong SLAM from a single dynamically changing environment to
sequential deployments in several drastically differing environments. To
address this task, we propose CL-SLAM leveraging a dual-network architecture to
both adapt to new environments and retain knowledge with respect to previously
visited environments. We compare CL-SLAM to learning-based as well as classical
SLAM methods and show the advantages of leveraging online data. We extensively
evaluate CL-SLAM on three different datasets and demonstrate that it
outperforms several baselines inspired by existing continual learning-based
visual odometry methods. We make the code of our work publicly available at
http://continual-slam.cs.uni-freiburg.de.

Matching cost construction is a key step in light field (LF) depth
estimation, but was rarely studied in the deep learning era. Recent deep
learning-based LF depth estimation methods construct matching cost by
sequentially shifting each sub-aperture image (SAI) with a series of predefined
offsets, which is complex and time-consuming. In this paper, we propose a
simple and fast cost constructor to construct matching cost for LF depth
estimation. Our cost constructor is composed by a series of convolutions with
specifically designed dilation rates. By applying our cost constructor to SAI
arrays, pixels under predefined disparities can be integrated and matching cost
can be constructed without using any shifting operation. More importantly, the
proposed cost constructor is occlusion-aware and can handle occlusions by
dynamically modulating pixels from different views. Based on the proposed cost
constructor, we develop a deep network for LF depth estimation. Our network
ranks first on the commonly used 4D LF benchmark in terms of the mean square
error (MSE), and achieves a faster running time than other state-of-the-art
methods.

Data augmentation is a cornerstone of the machine learning pipeline, yet its
theoretical underpinnings remain unclear. Is it merely a way to artificially
augment the data set size? Or is it about encouraging the model to satisfy
certain invariance? In this work we consider another angle, and we study the
effect of data augmentation on the dynamic of the learning process. We find
that data augmentation can alter the relative importance of various features,
effectively making certain informative but hard to learn features more likely
to be captured in the learning process. Importantly, we show that this effect
is more pronounced for non-linear models, such as neural networks. Our main
contribution is a detailed analysis of data augmentation on the learning
dynamic for a two layer convolutional neural network in the recently proposed
multi-view data model by Allen-Zhu and Li [2020]. We complement this analysis
with further experimental evidence that data augmentation can be viewed as
feature manipulation.

Many thousands of patent applications arrive at patent offices around the
world every day. One important subtask when a patent application is submitted
is to assign one or more classification codes from the complex and hierarchical
patent classification schemes that will enable routing of the patent
application to a patent examiner who is knowledgeable about the specific
technical field. This task is typically undertaken by patent professionals,
however due to the large number of applications and the potential complexity of
an invention, they are usually overwhelmed. Therefore, there is a need for this
code assignment manual task to be supported or even fully automated by
classification systems that will classify patent applications, hopefully with
an accuracy close to patent professionals. Like in many other text analysis
problems, in the last years, this intellectually demanding task has been
studied using word embeddings and deep learning techniques. In this paper we
shortly review these research efforts and experiment with similar deep learning
techniques using different feature representations on automatic patent
classification in the level of sub-classes. On top of that, we present an
innovative method of ensemble classifiers trained with different parts of the
patent document. To the best of our knowledge, this is the first time that an
ensemble method was proposed for the patent classification problem. Our first
results are quite promising showing that an ensemble architecture of
classifiers significantly outperforms current state-of-the-art techniques using
the same classifiers as standalone solutions.

A topic model is often formulated as a generative model that explains how
each word of a document is generated given a set of topics and
document-specific topic proportions. It is focused on capturing the word
co-occurrences in a document and hence often suffers from poor performance in
analyzing short documents. In addition, its parameter estimation often relies
on approximate posterior inference that is either not scalable or suffers from
large approximation error. This paper introduces a new topic-modeling framework
where each document is viewed as a set of word embedding vectors and each topic
is modeled as an embedding vector in the same embedding space. Embedding the
words and topics in the same vector space, we define a method to measure the
semantic difference between the embedding vectors of the words of a document
and these of the topics, and optimize the topic embeddings to minimize the
expected difference over all documents. Experiments on text analysis
demonstrate that the proposed method, which is amenable to mini-batch
stochastic gradient descent based optimization and hence scalable to big
corpora, provides competitive performance in discovering more coherent and
diverse topics and extracting better document representations.

Unsupervised graph representation learning aims to distill various graph
information into a downstream task-agnostic dense vector embedding. However,
existing graph representation learning approaches are designed mainly under the
node homophily assumption: connected nodes tend to have similar labels and
optimize performance on node-centric downstream tasks. Their design is
apparently against the task-agnostic principle and generally suffers poor
performance in tasks, e.g., edge classification, that demands feature signals
beyond the node-view and homophily assumption. To condense different feature
signals into the embeddings, this paper proposes PairE, a novel unsupervised
graph embedding method using two paired nodes as the basic unit of embedding to
retain the high-frequency signals between nodes to support node-related and
edge-related tasks. Accordingly, a multi-self-supervised autoencoder is
designed to fulfill two pretext tasks: one retains the high-frequency signal
better, and another enhances the representation of commonality. Our extensive
experiments on a diversity of benchmark datasets clearly show that PairE
outperforms the unsupervised state-of-the-art baselines, with up to 101.1\%
relative improvement on the edge classification tasks that rely on both the
high and low-frequency signals in the pair and up to 82.5\% relative
performance gain on the node classification tasks.

Face Presentation Attack Detection (PAD) is an important measure to prevent
spoof attacks for face biometric systems. Many works based on Convolution
Neural Networks (CNNs) for face PAD formulate the problem as an image-level
binary classification task without considering the context. Alternatively,
Vision Transformers (ViT) using self-attention to attend the context of an
image become the mainstreams in face PAD. Inspired by ViT, we propose a
Video-based Transformer for face PAD (ViTransPAD) with short/long-range
spatio-temporal attention which can not only focus on local details with short
attention within a frame but also capture long-range dependencies over frames.
Instead of using coarse image patches with single-scale as in ViT, we propose
the Multi-scale Multi-Head Self-Attention (MsMHSA) architecture to accommodate
multi-scale patch partitions of Q, K, V feature maps to the heads of
transformer in a coarse-to-fine manner, which enables to learn a fine-grained
representation to perform pixel-level discrimination for face PAD. Due to lack
inductive biases of convolutions in pure transformers, we also introduce
convolutions to the proposed ViTransPAD to integrate the desirable properties
of CNNs by using convolution patch embedding and convolution projection. The
extensive experiments show the effectiveness of our proposed ViTransPAD with a
preferable accuracy-computation balance, which can serve as a new backbone for
face PAD.

In existing self-supervised depth and ego-motion estimation methods,
ego-motion estimation is usually limited to only leveraging RGB information.
Recently, several methods have been proposed to further improve the accuracy of
self-supervised ego-motion estimation by fusing information from other
modalities, e.g., depth, acceleration, and angular velocity. However, they
rarely focus on how different fusion strategies affect performance. In this
paper, we investigate the effect of different fusion strategies for ego-motion
estimation and propose a new framework for self-supervised learning of depth
and ego-motion estimation, which performs ego-motion estimation by leveraging
RGB and inferred depth information in a Multi-Layer Fusion manner. As a result,
we have achieved state-of-the-art performance among learning-based methods on
the KITTI odometry benchmark. Detailed studies on the design choices of
leveraging inferred depth information and fusion strategies have also been
carried out, which clearly demonstrate the advantages of our proposed

This paper presents a new competition -- at the 2022 IEEE Conference on Games
(CoG) -- called DareFightingICE Competition. The competition has two tracks: a
sound design track and an AI track. The game platform for this competition is
also called DareFightingICE, a fighting game platform. DareFightingICE is a
sound-design-enhanced version of FightingICE, used earlier in a competition at
CoG until 2021 to promote artificial intelligence (AI) research in fighting
games. In the sound design track, participants compete for the best sound
design, given the default sound design of DareFightingICE as a sample, where we
define a sound design as a set of sound effects combined with the source code
that implements their timing-control algorithm. Participants of the AI track
are asked to develop their AI algorithm that controls a character given only
sound as the input (blind AI) to fight against their opponent; a sample
deep-learning blind AI will be provided by us. Our means to maximize the
synergy between the two tracks are also described. This competition serves to
come up with effective sound designs for visually impaired players, a group in
the gaming community which has been mostly ignored. To the best of our
knowledge, DareFightingICE Competition is the first of its kind within and
outside of CoG.

Deep learning methods have shown outstanding classification accuracy in
medical image analysis problems, which is largely attributed to the
availability of large datasets manually annotated with clean labels. However,
such manual annotation can be expensive to obtain for large datasets, so we may
rely on machine-generated noisy labels. Many Chest X-ray (CXR) classifiers are
modelled from datasets with machine-generated labels, but their training
procedure is in general not robust to the presence of noisy-label samples and
can overfit those samples to produce sub-optimal solutions. Furthermore, CXR
datasets are mostly multi-label, so current noisy-label learning methods
designed for multi-class problems cannot be easily adapted. To address such
noisy multi-label CXR learning problem, we propose a new learning method based
on estimating image virtual attributes using semantic information from the
label to assist in the identification and correction of noisy multi-labels from
training samples. Our experiments on diverse noisy multi-label training sets
and clean testing sets show that our model has state-of-the-art accuracy and
robustness across all datasets.

In this work, we use a combination of Bayesian inference, Markov chain Monte
Carlo and deep learning in the form of LSTM autoencoders to build and test a
framework to provide robust estimates of injection rate from ground surface
data in coupled flow and geomechanics problems. We use LSTM autoencoders to
reconstruct the displacement time series for grid points on the top surface of
a faulting due to water injection problem. We then deploy this LSTM autoencoder
based model instead of the high fidelity model in the Bayesian inference
framework to estimate injection rate from displacement input.

A seminal result in learning theory characterizes the PAC learnability of
binary classes through the Vapnik-Chervonenkis dimension. Extending this
characterization to the general multiclass setting has been open since the
pioneering works on multiclass PAC learning in the late 1980s. This work
resolves this problem: we characterize multiclass PAC learnability through the
DS dimension, a combinatorial dimension defined by Daniely and Shalev-Shwartz
(2014).
  The classical characterization of the binary case boils down to empirical
risk minimization. In contrast, our characterization of the multiclass case
involves a variety of algorithmic ideas; these include a natural setting we
call list PAC learning. In the list learning setting, instead of predicting a
single outcome for a given unseen input, the goal is to provide a short menu of
  Our second main result concerns the Natarajan dimension, which has been a
central candidate for characterizing multiclass learnability. This dimension
was introduced by Natarajan (1988) as a barrier for PAC learning. Whether the
Natarajan dimension characterizes PAC learnability in general has been posed as
an open question in several papers since. This work provides a negative answer:
we construct a non-learnable class with Natarajan dimension one.
  For the construction, we identify a fundamental connection between concept
classes and topology (i.e., colorful simplicial complexes). We crucially rely
on a deep and involved construction of hyperbolic pseudo-manifolds by
Januszkiewicz and Swiatkowski. It is interesting that hyperbolicity is directly
related to learning problems that are difficult to solve although no obvious
barriers exist. This is another demonstration of the fruitful links machine
learning has with different areas in mathematics.

Attitudes about vaccination have become more polarized; it is common to see
vaccine disinformation and fringe conspiracy theories online. An observational
study of Twitter vaccine discourse is found in Ojea Quintana et al. (2021): the
authors analyzed approximately six months' of Twitter discourse -- 1.3 million
original tweets and 18 million retweets between December 2019 and June 2020,
ranging from before to after the establishment of Covid-19 as a pandemic. This
work expands upon Ojea Quintana et al. (2021) with two main contributions from
data science. First, based on the authors' initial network clustering and
qualitative analysis techniques, we are able to clearly demarcate and visualize
the language patterns used in discourse by Antivaxxers (anti-vaccination
campaigners and vaccine deniers) versus other clusters (collectively, Others).
Second, using the characteristics of Antivaxxers' tweets, we develop text
classifiers to determine the likelihood a given user is employing
anti-vaccination language, ultimately contributing to an early-warning
mechanism to improve the health of our epistemic environment and bolster (and
not hinder) public health initiatives.

Certain forms of uncertainty that robotic systems encounter can be explicitly
learned within the context of a known model, like parametric model
uncertainties such as mass and moments of inertia. Quantifying such parametric
uncertainty is important for more accurate prediction of the system behavior,
leading to safe and precise task execution. In tandem, providing a form of
robustness guarantee against prevailing uncertainty levels like environmental
disturbances and current model knowledge is also desirable. To that end, the
authors' previously proposed RATTLE algorithm, a framework for online
information-aware motion planning, is outlined and extended to enhance its
applicability to real robotic systems. RATTLE provides a clear tradeoff between
information-seeking motion and traditional goal-achieving motion and features
online-updateable models. Additionally, online-updateable low level control
robustness guarantees and a new method for automatic adjustment of information
content down to a specified estimation precision is proposed. Results of
extensive experimentation in microgravity using the Astrobee robots aboard the
International Space Station and practical implementation details are presented,
demonstrating RATTLE's capabilities for real-time, robust, online-updateable,
and model information-seeking motion planning capabilities under parametric

Biologically inspired spiking neural networks (SNNs) are widely used to
realize ultralow-power energy consumption. However, deep SNNs are not easy to
train due to the excessive firing of spiking neurons in the hidden layers. To
tackle this problem, we propose a novel but simple normalization technique
called postsynaptic potential normalization. This normalization removes the
subtraction term from the standard normalization and uses the second raw moment
instead of the variance as the division term. The spike firing can be
controlled, enabling the training to proceed appropriating, by conducting this
simple normalization to the postsynaptic potential. The experimental results
show that SNNs with our normalization outperformed other models using other
normalizations. Furthermore, through the pre-activation residual blocks, the
proposed model can train with more than 100 layers without other special
techniques dedicated to SNNs.

Systematic development of accurate density functionals has been a
decades-long challenge for scientists. Despite the emerging application of
machine learning (ML) in approximating functionals, the resulting ML
functionals usually contain more than tens of thousands parameters, which makes
a huge gap in the formulation with the conventional human-designed symbolic
functionals. We propose a new framework, Symbolic Functional Evolutionary
Search (SyFES), that automatically constructs accurate functionals in the
symbolic form, which is more explainable to humans, cheaper to evaluate, and
easier to integrate to existing density functional theory codes than other ML
functionals. We first show that without prior knowledge, SyFES reconstructed a
known functional from scratch. We then demonstrate that evolving from an
existing functional $\omega$B97M-V, SyFES found a new functional, GAS22 (Google
Accelerated Science 22), that performs better for the majority of molecular
types in the test set of Main Group Chemistry Database (MGCDB84). Our framework
opens a new direction in leveraging computing power for the systematic
development of symbolic density functionals.

Recently, prompt-based learning for pre-trained language models has succeeded
in few-shot Named Entity Recognition (NER) by exploiting prompts as task
guidance to increase label efficiency. However, previous prompt-based methods
for few-shot NER have limitations such as a higher computational complexity,
poor zero-shot ability, requiring manual prompt engineering, or lack of prompt
robustness. In this work, we address these shortcomings by proposing a new
prompt-based learning NER method with Question Answering (QA), called QaNER.
Our approach includes 1) a refined strategy for converting NER problems into
the QA formulation; 2) NER prompt generation for QA models; 3) prompt-based
tuning with QA models on a few annotated NER examples; 4) zero-shot NER by
prompting the QA model. Comparing the proposed approach with previous methods,
QaNER is faster at inference, insensitive to the prompt quality, and robust to
hyper-parameters, as well as demonstrating significantly better low-resource
performance and zero-shot capability.

Liquid state estimation is important for robotics tasks such as pouring;
however, estimating the state of transparent liquids is a challenging problem.
We propose a novel segmentation pipeline that can segment transparent liquids
such as water from a static, RGB image without requiring any manual annotations
or heating of the liquid for training. Instead, we use a generative model that
is capable of translating images of colored liquids into synthetically
generated transparent liquid images, trained only on an unpaired dataset of
colored and transparent liquid images. Segmentation labels of colored liquids
are obtained automatically using background subtraction. Our experiments show
that we are able to accurately predict a segmentation mask for transparent
liquids without requiring any manual annotations. We demonstrate the utility of
transparent liquid segmentation in a robotic pouring task that controls pouring
by perceiving the liquid height in a transparent cup. Accompanying video and
supplementary materials can be found

We provide improved differentially private algorithms for identity testing of
high-dimensional distributions. Specifically, for $d$-dimensional Gaussian
distributions with known covariance $\Sigma$, we can test whether the
distribution comes from $\mathcal{N}(\mu^*, \Sigma)$ for some fixed $\mu^*$ or
from some $\mathcal{N}(\mu, \Sigma)$ with total variation distance at least
$\alpha$ from $\mathcal{N}(\mu^*, \Sigma)$ with $(\varepsilon, 0)$-differential
privacy, using only \[\tilde{O}\left(\frac{d^{1/2}}{\alpha^2} +
\frac{d^{1/3}}{\alpha^{4/3} \cdot \varepsilon^{2/3}} + \frac{1}{\alpha \cdot
\varepsilon}\right)\]
  samples if the algorithm is allowed to be computationally inefficient, and
only \[\tilde{O}\left(\frac{d^{1/2}}{\alpha^2} + \frac{d^{1/4}}{\alpha \cdot
  samples for a computationally efficient algorithm. We also provide a matching
lower bound showing that our computationally inefficient algorithm has optimal
sample complexity. We also extend our algorithms to various related problems,
including mean testing of Gaussians with bounded but unknown covariance,
uniformity testing of product distributions over $\{-1, 1\}^d$, and tolerant
testing.
  Our results improve over the previous best work of Canonne et
al.~\cite{CanonneKMUZ20} for both computationally efficient and inefficient
algorithms, and even our computationally efficient algorithm matches the
optimal \emph{non-private} sample complexity of
$O\left(\frac{\sqrt{d}}{\alpha^2}\right)$ in many standard parameter settings.
  In addition, our results show that, surprisingly, private identity testing of
$d$-dimensional Gaussians can be done with fewer samples than private identity
testing of discrete distributions over a domain of size $d$ \cite{AcharyaSZ18},
which refutes a conjectured lower bound of~\cite{CanonneKMUZ20}.

We accelerate the 4-bit product quantization (PQ) on the ARM architecture.
Notably, the drastic performance of the conventional 4-bit PQ strongly relies
on x64-specific SIMD register, such as AVX2; hence, we cannot yet achieve such
good performance on ARM. To fill this gap, we first bundle two 128-bit
registers as one 256-bit component. We then apply shuffle operations for each
using the ARM-specific NEON instruction. By making this simple but critical
modification, we achieve a dramatic speedup for the 4-bit PQ on an ARM
architecture. Experiments show that the proposed method consistently achieves a
10x improvement over the naive PQ with the same accuracy.

This study proposes a data condensation method for multivariate kernel
density estimation by genetic algorithm. First, our proposed algorithm
generates multiple subsamples of a given size with replacement from the
original sample. The subsamples and their constituting data points are regarded
as $\it{chromosome}$ and $\it{gene}$, respectively, in the terminology of
genetic algorithm. Second, each pair of subsamples breeds two new subsamples,
where each data point faces either $\it{crossover}$, $\it{mutation}$, or
$\it{reproduction}$ with a certain probability. The dominant subsamples in
terms of fitness values are inherited by the next generation. This process is
repeated generation by generation and brings the sparse representation of
kernel density estimator in its completion. We confirmed from simulation
studies that the resulting estimator can perform better than other well-known
density estimators.

Recently, contrastive learning-based image translation methods have been
proposed, which contrasts different spatial locations to enhance the spatial
correspondence. However, the methods often ignore the diverse semantic relation
within the images. To address this, here we propose a novel semantic relation
consistency (SRC) regularization along with the decoupled contrastive learning,
which utilize the diverse semantics by focusing on the heterogeneous semantics
between the image patches of a single image. To further improve the
performance, we present a hard negative mining by exploiting the semantic
relation. We verified our method for three tasks: single-modal and multi-modal
image translations, and GAN compression task for image translation.
Experimental results confirmed the state-of-art performance of our method in
all the three tasks.

Dataset condensation aims at reducing the network training effort through
condensing a cumbersome training set into a compact synthetic one.
State-of-the-art approaches largely rely on learning the synthetic data by
matching the gradients between the real and synthetic data batches. Despite the
intuitive motivation and promising results, such gradient-based methods, by
nature, easily overfit to a biased set of samples that produce dominant
gradients, and thus lack global supervision of data distribution. In this
paper, we propose a novel scheme to Condense dataset by Aligning FEatures
(CAFE), which explicitly attempts to preserve the real-feature distribution as
well as the discriminant power of the resulting synthetic set, lending itself
to strong generalization capability to various architectures. At the heart of
our approach is an effective strategy to align features from the real and
synthetic data across various scales, while accounting for the classification
of real samples. Our scheme is further backed up by a novel dynamic bi-level
optimization, which adaptively adjusts parameter updates to prevent
over-/under-fitting. We validate the proposed CAFE across various datasets, and
demonstrate that it generally outperforms the state of the art: on the SVHN
dataset, for example, the performance gain is up to 11%. Extensive experiments
and analyses verify the effectiveness and necessity of proposed designs.

The amount of manually labeled data is limited in medical applications, so
semi-supervised learning and automatic labeling strategies can be an asset for
training deep neural networks. However, the quality of the automatically
generated labels can be uneven and inferior to manual labels. In this paper, we
suggest a semi-supervised training strategy for leveraging both manually
labeled data and extra unlabeled data. In contrast to the existing approaches,
we apply robust loss for the automated labeled data to automatically compensate
for the uneven data quality using a teacher-student framework. First, we
generate pseudo-labels for unlabeled data using a teacher model pre-trained on
labeled data. These pseudo-labels are noisy, and using them along with labeled
data for training a deep neural network can severely degrade learned feature
representations and the generalization of the network. Here we mitigate the
effect of these pseudo-labels by using robust loss functions. Specifically, we
use three robust loss functions, namely beta cross-entropy, symmetric
cross-entropy, and generalized cross-entropy. We show that our proposed
strategy improves the model performance by compensating for the uneven quality
of labels in image classification as well as segmentation applications.

Despite the success of deep neural networks, there are still many challenges
in deep representation learning due to the data scarcity issues such as data
imbalance, unseen distribution, and domain shift. To address the
above-mentioned issues, a variety of methods have been devised to explore the
sample relationships in a vanilla way (i.e., from the perspectives of either
the input or the loss function), failing to explore the internal structure of
deep neural networks for learning with sample relationships. Inspired by this,
we propose to enable deep neural networks themselves with the ability to learn
the sample relationships from each mini-batch. Specifically, we introduce a
batch transformer module or BatchFormer, which is then applied into the batch
dimension of each mini-batch to implicitly explore sample relationships during
training. By doing this, the proposed method enables the collaboration of
different samples, e.g., the head-class samples can also contribute to the
learning of the tail classes for long-tailed recognition. Furthermore, to
mitigate the gap between training and testing, we share the classifier between
with or without the BatchFormer during training, which can thus be removed
during testing. We perform extensive experiments on over ten datasets and the
proposed method achieves significant improvements on different data scarcity
applications without any bells and whistles, including the tasks of long-tailed
recognition, compositional zero-shot learning, domain generalization, and
contrastive learning. Code will be made publicly available at
https://github.com/zhihou7/BatchFormer.

An emerging trend in representation learning over knowledge graphs (KGs)
moves beyond transductive link prediction tasks over a fixed set of known
entities in favor of inductive tasks that imply training on one graph and
performing inference over a new graph with unseen entities. In inductive
setups, node features are often not available and training shallow entity
embedding matrices is meaningless as they cannot be used at inference time with
unseen entities. Despite the growing interest, there are not enough benchmarks
for evaluating inductive representation learning methods. In this work, we
introduce ILPC 2022, a novel open challenge on KG inductive link prediction. To
this end, we constructed two new datasets based on Wikidata with various sizes
of training and inference graphs that are much larger than existing inductive
benchmarks. We also provide two strong baselines leveraging recently proposed
inductive methods. We hope this challenge helps to streamline community efforts
in the inductive graph representation learning area. ILPC 2022 follows best
practices on evaluation fairness and reproducibility, and is available at
https://github.com/pykeen/ilpc2022.

Spurious correlations pose a major challenge for robust machine learning.
Models trained with empirical risk minimization (ERM) may learn to rely on
correlations between class labels and spurious attributes, leading to poor
performance on data groups without these correlations. This is particularly
challenging to address when spurious attribute labels are unavailable. To
improve worst-group performance on spuriously correlated data without training
attribute labels, we propose Correct-N-Contrast (CNC), a contrastive approach
to directly learn representations robust to spurious correlations. As ERM
models can be good spurious attribute predictors, CNC works by (1) using a
trained ERM model's outputs to identify samples with the same class but
dissimilar spurious features, and (2) training a robust model with contrastive
learning to learn similar representations for same-class samples. To support
CNC, we introduce new connections between worst-group error and a
representation alignment loss that CNC aims to minimize. We empirically observe
that worst-group error closely tracks with alignment loss, and prove that the
alignment loss over a class helps upper-bound the class's worst-group vs.
average error gap. On popular benchmarks, CNC reduces alignment loss
drastically, and achieves state-of-the-art worst-group accuracy by 3.6% average
absolute lift. CNC is also competitive with oracle methods that require group
labels.

Automatic ICD coding is defined as assigning disease codes to electronic
medical records (EMRs). Existing methods usually apply label attention with
code representations to match related text snippets. Unlike these works that
model the label with the code hierarchy or description, we argue that the code
synonyms can provide more comprehensive knowledge based on the observation that
the code expressions in EMRs vary from their descriptions in ICD. By aligning
codes to concepts in UMLS, we collect synonyms of every code. Then, we propose
a multiple synonyms matching network to leverage synonyms for better code
representation learning, and finally help the code classification. Experiments
on the MIMIC-III dataset show that our proposed method outperforms previous

Physics-Informed Neural Networks (PINNs) have received increased interest for
forward, inverse, and surrogate modeling of problems described by partial
differential equations (PDE). However, their application to multiphysics
problem, governed by several coupled PDEs, present unique challenges that have
hindered the robustness and widespread applicability of this approach. Here we
investigate the application of PINNs to the forward solution of problems
involving thermo-hydro-mechanical (THM) processes in porous media, which
exhibit disparate spatial and temporal scales in thermal conductivity,
hydraulic permeability, and elasticity. In addition, PINNs are faced with the
challenges of the multi-objective and non-convex nature of the optimization
problem. To address these fundamental issues, we: (1)~rewrite the THM governing
equations in dimensionless form that is best suited for deep-learning
algorithms; (2)~propose a sequential training strategy that circumvents the
need for a simultaneous solution of the multiphysics problem and facilitates
the task of optimizers in the solution search; and (3)~leverage adaptive weight
strategies to overcome the stiffness in the gradient flow of the
multi-objective optimization problem. Finally, we apply this framework to the
solution of several synthetic problems in 1D and~2D.

Existing state-of-the-art 3D instance segmentation methods perform semantic
segmentation followed by grouping. The hard predictions are made when
performing semantic segmentation such that each point is associated with a
single class. However, the errors stemming from hard decision propagate into
grouping that results in (1) low overlaps between the predicted instance with
the ground truth and (2) substantial false positives. To address the
aforementioned problems, this paper proposes a 3D instance segmentation method
referred to as SoftGroup by performing bottom-up soft grouping followed by
top-down refinement. SoftGroup allows each point to be associated with multiple
classes to mitigate the problems stemming from semantic prediction errors and
suppresses false positive instances by learning to categorize them as
background. Experimental results on different datasets and multiple evaluation
metrics demonstrate the efficacy of SoftGroup. Its performance surpasses the
strongest prior method by a significant margin of +6.2% on the ScanNet v2
hidden test set and +6.8% on S3DIS Area 5 in terms of AP_50. SoftGroup is also
fast, running at 345ms per scan with a single Titan X on ScanNet v2 dataset.
The source code and trained models for both datasets are available at
\url{https://github.com/thangvubk/SoftGroup.git}.

The area under the ROC curve (AUC) is one of the most widely used performance
measures for classification models in machine learning. However, it summarizes
the true positive rates (TPRs) over all false positive rates (FPRs) in the ROC
space, which may include the FPRs with no practical relevance in some
applications. The partial AUC, as a generalization of the AUC, summarizes only
the TPRs over a specific range of the FPRs and is thus a more suitable
performance measure in many real-world situations. Although partial AUC
optimization in a range of FPRs had been studied, existing algorithms are not
scalable to big data and not applicable to deep learning. To address this
challenge, we cast the problem into a non-smooth difference-of-convex (DC)
program for any smooth predictive functions (e.g., deep neural networks), which
allowed us to develop an efficient approximated gradient descent method based
on the Moreau envelope smoothing technique, inspired by recent advances in
non-smooth DC optimization. To increase the efficiency of large data
processing, we used an efficient stochastic block coordinate update in our
algorithm. Our proposed algorithm can also be used to minimize the sum of
ranked range loss, which also lacks efficient solvers. We established a
complexity of $\tilde O(1/\epsilon^6)$ for finding a nearly $\epsilon$-critical
solution. Finally, we numerically demonstrated the effectiveness of our
proposed algorithms for both partial AUC maximization and sum of ranked range
loss minimization.

Although learning has found wide application in multi-agent systems, its
effects on the temporal evolution of a system are far from understood. This
paper focuses on the dynamics of Q-learning in large-scale multi-agent systems
modeled as population games. We revisit the replicator equation model for
Q-learning dynamics and observe that this model is inappropriate for our
concerned setting. Motivated by this, we develop a new formal model, which
bears a formal connection with the continuity equation in physics. We show that
our model always accurately describes the Q-learning dynamics in population
games across different initial settings of MASs and game configurations. We
also show that our model can be applied to different exploration mechanisms,
describe the mean dynamics, and be extended to Q-learning in 2-player and
n-player games. Last but not least, we show that our model can provide insights
into algorithm parameters and facilitate parameter tuning.

A camera begins to sense light the moment we press the shutter button. During
the exposure interval, relative motion between the scene and the camera causes
motion blur, a common undesirable visual artifact. This paper presents E-CIR,
which converts a blurry image into a sharp video represented as a parametric
function from time to intensity. E-CIR leverages events as an auxiliary input.
We discuss how to exploit the temporal event structure to construct the
parametric bases. We demonstrate how to train a deep learning model to predict
the function coefficients. To improve the appearance consistency, we further
introduce a refinement module to propagate visual features among consecutive
frames. Compared to state-of-the-art event-enhanced deblurring approaches,
E-CIR generates smoother and more realistic results. The implementation of
E-CIR is available at https://github.com/chensong1995/E-CIR.

Machine learning methods have revolutionized the discovery process of new
molecules and materials. However, the intensive training process of neural
networks for molecules with ever increasing complexity has resulted in
exponential growth in computation cost, leading to long simulation time and
high energy consumption. Photonic chip technology offers an alternative
platform for implementing neural network with faster data processing and lower
energy usage compared to digital computers. Here, we demonstrate the capability
of photonic neural networks in predicting the quantum mechanical properties of
molecules. Additionally, we show that multiple properties can be learned
simultaneously in a photonic chip via a multi-task regression learning
algorithm, which we believe is the first of its kind, as most previous works
focus on implementing a network for the task of classification. Photonics
technology are also naturally capable of implementing complex-valued neural
networks at no additional hardware cost and we show that such neural networks
outperform conventional real-valued networks for molecular property prediction.
Our work opens the avenue for harnessing photonic technology for large-scale
machine learning applications in molecular sciences such as drug discovery and

In this paper, we study the problem of regret minimization for episodic
Reinforcement Learning (RL) both in the model-free and the model-based setting.
We focus on learning with general function classes and general model classes,
and we derive results that scale with the eluder dimension of these classes. In
contrast to the existing body of work that mainly establishes
instance-independent regret guarantees, we focus on the instance-dependent
setting and show that the regret scales logarithmically with the horizon $T$,
provided that there is a gap between the best and the second best action in
every state. In addition, we show that such a logarithmic regret bound is
realizable by algorithms with $O(\log T)$ switching cost (also known as
adaptivity complexity). In other words, these algorithms rarely switch their
policy during the course of their execution. Finally, we complement our results
with lower bounds which show that even in the tabular setting, we cannot hope
for regret guarantees lower than $o(\log T)$.

Single image generation (SIG), described as generating diverse samples that
have similar visual content with the given single image, is first introduced by
SinGAN which builds a pyramid of GANs to progressively learn the internal patch
distribution of the single image. It also shows great potentials in a wide
range of image manipulation tasks. However, the paradigm of SinGAN has
limitations in terms of generation quality and training time. Firstly, due to
the lack of high-level information, SinGAN cannot handle the object images well
as it does on the scene and texture images. Secondly, the separate progressive
training scheme is time-consuming and easy to cause artifact accumulation. To
tackle these problems, in this paper, we dig into the SIG problem and improve
SinGAN by fully-utilization of internal and external priors. The main
contributions of this paper include: 1) We introduce to SIG a regularized
latent variable model. To the best of our knowledge, it is the first time to
give a clear formulation and optimization goal of SIG, and all the existing
methods for SIG can be regarded as special cases of this model. 2) We design a
novel Prior-based end-to-end training GAN (PetsGAN) to overcome the problems of
SinGAN. Our method gets rid of the time-consuming progressive training scheme
and can be trained end-to-end. 3) We construct abundant qualitative and
quantitative experiments to show the superiority of our method on both
generated image quality, diversity, and the training speed. Moreover, we apply
our method to other image manipulation tasks (e.g., style transfer,
harmonization), and the results further prove the effectiveness and efficiency
of our method.

Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel
classes with few examples. Recently, lots of methods have been proposed from
the perspective of meta-learning and representation learning for improving FSL
performance. However, few works focus on the interpretability of FSL decision
process. In this paper, we take a step towards the interpretable FSL by
proposing a novel decision tree-based meta-learning framework, namely, MetaDT.
Our insight is replacing the last black-box FSL classifier of the existing
representation learning methods by an interpretable decision tree with
meta-learning. The key challenge is how to effectively learn the decision tree
(i.e., the tree structure and the parameters of each node) in the FSL setting.
To address the challenge, we introduce a tree-like class hierarchy as our
prior: 1) the hierarchy is directly employed as the tree structure; 2) by
regarding the class hierarchy as an undirected graph, a graph convolution-based
decision tree inference network is designed as our meta-learner to learn to
infer the parameters of each node. At last, a two-loop optimization mechanism
is incorporated into our framework for a fast adaptation of the decision tree
with few examples. Extensive experiments on performance comparison and
interpretability analysis show the effectiveness and superiority of our MetaDT.
Our code will be publicly available upon acceptance.

The Artificial Benchmark for Community Detection (ABCD) graph is a random
graph model with community structure and power-law distribution for both
degrees and community sizes. The model generates graphs with similar properties
as the well-known LFR one, and its main parameter $\xi$ can be tuned to mimic
its counterpart in the LFR model, the mixing parameter $\mu$.
  In this paper, we investigate various theoretical asymptotic properties of
the ABCD model. In particular, we analyze the modularity function, arguably,
the most important graph property of networks in the context of community
detection. Indeed, the modularity function is often used to measure the
presence of community structure in networks. It is also used as a quality
function in many community detection algorithms, including the widely used
Louvain algorithm.

Weightless Neural Networks (WNNs) are a class of machine learning model which
use table lookups to perform inference. This is in contrast with Deep Neural
Networks (DNNs), which use multiply-accumulate operations. State-of-the-art WNN
architectures have a fraction of the implementation cost of DNNs, but still lag
behind them on accuracy for common image recognition tasks. Additionally, many
existing WNN architectures suffer from high memory requirements. In this paper,
we propose a novel WNN architecture, BTHOWeN, with key algorithmic and
architectural improvements over prior work, namely counting Bloom filters,
hardware-friendly hashing, and Gaussian-based nonlinear thermometer encodings
to improve model accuracy and reduce area and energy consumption. BTHOWeN
targets the large and growing edge computing sector by providing superior
latency and energy efficiency to comparable quantized DNNs. Compared to
state-of-the-art WNNs across nine classification datasets, BTHOWeN on average
reduces error by more than than 40% and model size by more than 50%. We then
demonstrate the viability of the BTHOWeN architecture by presenting an
FPGA-based accelerator, and compare its latency and resource usage against
similarly accurate quantized DNN accelerators, including Multi-Layer Perceptron
(MLP) and convolutional models. The proposed BTHOWeN models consume almost 80%
less energy than the MLP models, with nearly 85% reduction in latency. In our
quest for efficient ML on the edge, WNNs are clearly deserving of additional
attention.

Structured Query Language (SQL) remains the standard language used in
Relational Database Management Systems (RDBMSs) and has found applications in
healthcare (patient registries), businesses (inventories, trend analysis),
military, education, etc. Although SQL statements are English-like, the process
of writing SQL queries is often problematic for nontechnical end-users in the
industry. Similarly, formulating and comprehending written queries can be
confusing, especially for undergraduate students. One of the pivotal reasons
given for these difficulties lies with the simple syntax of SQL, which is often
misleading and hard to understand. An ideal solution is to present these two
audiences: undergraduate students and nontechnical end-users with learning and
practice tools. These tools are mostly electronic and can be used to aid their
understanding, as well as enable them to write correct SQL queries. This work
proposes a new approach aimed at understanding and writing correct SQL queries
using principles from Formal Language and Automata Theory. We present
algorithms based on: regular expressions for the recognition of simple query
constructs, context-free grammars for the recognition of nested queries, and a
jumping finite automaton for the synthesis of SQL queries from natural language
descriptions. As proof of concept, these algorithms were further implemented
into interactive software tools aimed at improving SQL comprehension.
Evaluation of these tools showed that the majority of participants agreed that
the tools were intuitive and aided their understanding of SQL queries. These
tools should, therefore, find applications in aiding SQL comprehension at
higher learning institutions and assist in the writing of correct queries in
data-centered industries.

Curating a large set of fully annotated training data can be costly,
especially for the tasks of medical image segmentation. Scribble, a weaker form
of annotation, is more obtainable in practice, but training segmentation models
from limited supervision of scribbles is still challenging. To address the
difficulties, we propose a new framework for scribble learning-based medical
image segmentation, which is composed of mix augmentation and cycle consistency
and thus is referred to as CycleMix. For augmentation of supervision, CycleMix
adopts the mixup strategy with a dedicated design of random occlusion, to
perform increments and decrements of scribbles. For regularization of
supervision, CycleMix intensifies the training objective with consistency
losses to penalize inconsistent segmentation, which results in significant
improvement of segmentation performance. Results on two open datasets, i.e.,
ACDC and MSCMRseg, showed that the proposed method achieved exhilarating
performance, demonstrating comparable or even better accuracy than the
fully-supervised methods. The code and expert-made scribble annotations for
MSCMRseg are publicly available at https://github.com/BWGZK/CycleMix.

Predicting future motion based on historical motion sequence is a fundamental
problem in computer vision, and it has wide applications in autonomous driving
and robotics. Some recent works have shown that Graph Convolutional
Networks(GCN) are instrumental in modeling the relationship between different
joints. However, considering the variants and diverse action types in human
motion data, the cross-dependency of the spatio-temporal relationships will be
difficult to depict due to the decoupled modeling strategy, which may also
exacerbate the problem of insufficient generalization. Therefore, we propose
the Spatio-Temporal Gating-Adjacency GCN(GAGCN) to learn the complex
spatio-temporal dependencies over diverse action types. Specifically, we adopt
gating networks to enhance the generalization of GCN via the trainable adaptive
adjacency matrix obtained by blending the candidate spatio-temporal adjacency
matrices. Moreover, GAGCN addresses the cross-dependency of space and time by
balancing the weights of spatio-temporal modeling and fusing the decoupled
spatio-temporal features. Extensive experiments on Human 3.6M, AMASS, and 3DPW
demonstrate that GAGCN achieves state-of-the-art performance in both short-term
and long-term predictions.

Instance segmentation and classification of nuclei is an important task in
computational pathology. We show that StarDist, a deep learning nuclei
segmentation method originally developed for fluorescence microscopy, can be
extended and successfully applied to histopathology images. This is
substantiated by conducting experiments on the Lizard dataset, and through
entering the Colon Nuclei Identification and Counting (CoNIC) challenge 2022,
where our approach achieved the first spot on the leaderboard for the
segmentation and classification task for both the preliminary and final test
phase.

Factor analysis is a widely used method for modeling a set of observed
variables by a set of (typically fewer) unobserved latent factors. The
traditional method of Exploratory Factor Analysis (EFA), despite its widespread
application, is often criticized for three well-known weaknesses: (1) requiring
the number of factors to be known, (2) ad-hoc nature in learning the support of
coefficients, and (3) nonidentifiability of the parameters due to the rotation
invariance properties of the likelihood. While more recently developed
penalized EFA methods partially address these issues, they remain
computationally intense, and still require the number of latent factors to be
given. To address these issues, we propose a fast algorithm that simultaneously
learns the number of latent factors and a model structure that leads to
identifiable parameters. Our novel approach is motivated from the perspective
of graph theory, making use of thresholded correlation graphs from the observed
data to learn the latent factor structure. We establish theoretical guarantees
for our method with results for solution uniqueness and high-dimensional
consistency of the structure learning as well as the parameter estimation. We
also present a series of simulation studies and a real data example to test and
demonstrate its performance. Our results show that the correlation thresholding
algorithm is an accurate method for learning the structure of factor analysis
models and is robust to violations of our assumptions. Moreover, we show that
our algorithm scales well up to 1500 variables, while the competing methods are
not applicable in a reasonable amount of running time.

Organ segmentation of medical images is a key step in virtual imaging trials.
However, organ segmentation datasets are limited in terms of quality (because
labels cover only a few organs) and quantity (since case numbers are limited).
In this study, we explored the tradeoffs between quality and quantity. Our goal
is to create a unified approach for multi-organ segmentation of body CT, which
will facilitate the creation of large numbers of accurate virtual phantoms.
Initially, we compared two segmentation architectures, 3D-Unet and DenseVNet,
which were trained using XCAT data that is fully labeled with 22 organs, and
chose the 3D-Unet as the better performing model. We used the XCAT-trained
model to generate pseudo-labels for the CT-ORG dataset that has only 7 organs
segmented. We performed two experiments: First, we trained 3D-UNet model on the
XCAT dataset, representing quality data, and tested it on both XCAT and CT-ORG
datasets. Second, we trained 3D-UNet after including the CT-ORG dataset into
the training set to have more quantity. Performance improved for segmentation
in the organs where we have true labels in both datasets and degraded when
relying on pseudo-labels. When organs were labeled in both datasets, Exp-2
improved Average DSC in XCAT and CT-ORG by 1. This demonstrates that quality
data is the key to improving the model's performance.

This study investigates weakly supervised image segmentation using loose
bounding box supervision. It presents a multiple instance learning strategy
based on polar transformation to assist image segmentation when loose bounding
boxes are employed as supervision. In this strategy, weighted smooth maximum
approximation is introduced to incorporate the observation that pixels closer
to the origin of the polar transformation are more likely to belong to the
object in the bounding box. The proposed approach was evaluated on a public
medical dataset using Dice coefficient. The results demonstrate its superior
performance. The codes are available at
\url{https://github.com/wangjuan313/wsis-polartransform}.

We report the first-time recovery of a fresh meteorite fall using a drone and
a machine learning algorithm. A fireball on the 1st April 2021 was observed
over Western Australia by the Desert Fireball Network, for which a fall area
was calculated for the predicted surviving mass. A search team arrived on site
and surveyed 5.1 km2 area over a 4-day period. A convolutional neural network,
trained on previously-recovered meteorites with fusion crusts, processed the
images on our field computer after each flight. meteorite candidates identified
by the algorithm were sorted by team members using two user interfaces to
eliminate false positives. Surviving candidates were revisited with a smaller
drone, and imaged in higher resolution, before being eliminated or finally
being visited in-person. The 70 g meteorite was recovered within 50 m of the
calculated fall line using, demonstrating the effectiveness of this methodology
which will facilitate the efficient collection of many more observed meteorite
falls.

Recurrent neural network (RNN) based reinforcement learning (RL) is used for
learning context-dependent tasks and has also attracted attention as a method
with remarkable learning performance in recent research. However, RNN-based RL
has some issues that the learning procedures tend to be more computationally
expensive, and training with backpropagation through time (BPTT) is unstable
because of vanishing/exploding gradients problem. An approach with replay
memory introducing reservoir computing has been proposed, which trains an agent
without BPTT and avoids these issues. The basic idea of this approach is that
observations from the environment are input to the reservoir network, and both
the observation and the reservoir output are stored in the memory. This paper
shows that the performance of this method improves by using a multi-layered
neural network for the readout layer, which regularly consists of a single
linear layer. The experimental results show that using multi-layered readout
improves the learning performance of four classical control tasks that require
time-series processing.

Bayesian optimization is a technique for optimizing black-box target
functions. At the core of Bayesian optimization is a surrogate model that
predicts the output of the target function at previously unseen inputs to
facilitate the selection of promising input values. Gaussian processes (GPs)
are commonly used as surrogate models but are known to scale poorly with the
number of observations. We adapt the Vecchia approximation, a popular GP
approximation from spatial statistics, to enable scalable high-dimensional
Bayesian optimization. We develop several improvements and extensions,
including training warped GPs using mini-batch gradient descent, approximate
neighbor search, and selecting multiple input values in parallel. We focus on
the use of our warped Vecchia GP in trust-region Bayesian optimization via
Thompson sampling. On several test functions and on two reinforcement-learning
problems, our methods compared favorably to the state of the art.

Panoramic images with their 360-degree directional view encompass exhaustive
information about the surrounding space, providing a rich foundation for scene
understanding. To unfold this potential in the form of robust panoramic
segmentation models, large quantities of expensive, pixel-wise annotations are
crucial for success. Such annotations are available, but predominantly for
narrow-angle, pinhole-camera images which, off the shelf, serve as sub-optimal
resources for training panoramic models. Distortions and the distinct
image-feature distribution in 360-degree panoramas impede the transfer from the
annotation-rich pinhole domain and therefore come with a big dent in
performance. To get around this domain difference and bring together semantic
annotations from pinhole- and 360-degree surround-visuals, we propose to learn
object deformations and panoramic image distortions in the Deformable Patch
Embedding (DPE) and Deformable MLP (DMLP) components which blend into our
Transformer for PAnoramic Semantic Segmentation (Trans4PASS) model. Finally, we
tie together shared semantics in pinhole- and panoramic feature embeddings by
generating multi-scale prototype features and aligning them in our Mutual
Prototypical Adaptation (MPA) for unsupervised domain adaptation. On the indoor
Stanford2D3D dataset, our Trans4PASS with MPA maintains comparable performance
to fully-supervised state-of-the-arts, cutting the need for over 1,400 labeled
panoramas. On the outdoor DensePASS dataset, we break state-of-the-art by
14.39% mIoU and set the new bar at 56.38%. Code will be made publicly available
at https://github.com/jamycheung/Trans4PASS.

Vertical federated learning (vFL) has gained much attention and been deployed
to solve machine learning problems with data privacy concerns in recent years.
However, some recent work demonstrated that vFL is vulnerable to privacy
leakage even though only the forward intermediate embedding (rather than raw
features) and backpropagated gradients (rather than raw labels) are
communicated between the involved participants. As the raw labels often contain
highly sensitive information, some recent work has been proposed to prevent the
label leakage from the backpropagated gradients effectively in vFL. However,
these work only identified and defended the threat of label leakage from the
backpropagated gradients. None of these work has paid attention to the problem
of label leakage from the intermediate embedding. In this paper, we propose a
practical label inference method which can steal private labels effectively
from the shared intermediate embedding even though some existing protection
methods such as label differential privacy and gradients perturbation are
applied. The effectiveness of the label attack is inseparable from the
correlation between the intermediate embedding and corresponding private
labels. To mitigate the issue of label leakage from the forward embedding, we
add an additional optimization goal at the label party to limit the label
stealing ability of the adversary by minimizing the distance correlation
between the intermediate embedding and corresponding private labels. We
conducted massive experiments to demonstrate the effectiveness of our proposed
protection methods.

The problem of synthesizing stochastic explicit model predictive control
policies is known to be quickly intractable even for systems of modest
complexity when using classical control-theoretic methods. To address this
challenge, we present a scalable alternative called stochastic parametric
differentiable predictive control (SP-DPC) for unsupervised learning of neural
control policies governing stochastic linear systems subject to nonlinear
chance constraints. SP-DPC is formulated as a deterministic approximation to
the stochastic parametric constrained optimal control problem. This formulation
allows us to directly compute the policy gradients via automatic
differentiation of the problem's value function, evaluated over sampled
parameters and uncertainties. In particular, the computed expectation of the
SP-DPC problem's value function is backpropagated through the closed-loop
system rollouts parametrized by a known nominal system dynamics model and
neural control policy which allows for direct model-based policy optimization.
We provide theoretical probabilistic guarantees for policies learned via the
SP-DPC method on closed-loop stability and chance constraints satisfaction.
Furthermore, we demonstrate the computational efficiency and scalability of the
proposed policy optimization algorithm in three numerical examples, including
systems with a large number of states or subject to nonlinear constraints.

The volume of available data has grown dramatically in recent years in many
applications. Furthermore, the age of networks that used multiple modalities
separately has practically ended. Therefore, enabling bidirectional
cross-modality data retrieval capable of processing has become a requirement
for many domains and disciplines of research. This is especially true in the
medical field, as data comes in a multitude of types, including various types
of images and reports as well as molecular data. Most contemporary works apply
cross attention to highlight the essential elements of an image or text in
relation to the other modalities and try to match them together. However,
regardless of their importance in their own modality, these approaches usually
consider features of each modality equally. In this study, self-attention as an
additional loss term will be proposed to enrich the internal representation
provided into the cross attention module. This work suggests a novel
architecture with a new loss term to help represent images and texts in the
joint latent space. Experiment results on two benchmark datasets, i.e. MS-COCO
and ARCH, show the effectiveness of the proposed method.

Drawing inspiration from gradient-based meta-learning methods with infinitely
small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a
meta-learning algorithm where adaptation follows the dynamics of a gradient
vector field. Specifically, representations of the inputs are meta-learned such
that a task-specific linear classifier is obtained as a solution of an ordinary
differential equation (ODE). Treating the learning process as an ODE offers the
notable advantage that the length of the trajectory is now continuous, as
opposed to a fixed and discrete number of gradient steps. As a consequence, we
can optimize the amount of adaptation necessary to solve a new task using
stochastic gradient descent, in addition to learning the initial conditions as
is standard practice in gradient-based meta-learning. Importantly, in order to
compute the exact meta-gradients required for the outer-loop updates, we devise
an efficient algorithm based on forward mode differentiation, whose memory
requirements do not scale with the length of the learning trajectory, thus
allowing longer adaptation in constant memory. We provide analytical guarantees
for the stability of COMLN, we show empirically its efficiency in terms of
runtime and memory usage, and we illustrate its effectiveness on a range of
few-shot image classification problems.

We introduce a set of image transformations that can be used as corruptions
to evaluate the robustness of models as well as data augmentation mechanisms
for training neural networks. The primary distinction of the proposed
transformations is that, unlike existing approaches such as Common Corruptions,
the geometry of the scene is incorporated in the transformations -- thus
leading to corruptions that are more likely to occur in the real world. We also
introduce a set of semantic corruptions (e.g. natural object occlusions). We
show these transformations are `efficient' (can be computed on-the-fly),
`extendable' (can be applied on most image datasets), expose vulnerability of
existing models, and can effectively make models more robust when employed as
`3D data augmentation' mechanisms. The evaluations on several tasks and
datasets suggest incorporating 3D information into benchmarking and training
opens up a promising direction for robustness research.

Correlation clustering is a central problem in unsupervised learning, with
applications spanning community detection, duplicate detection, automated
labelling and many more. In the correlation clustering problem one receives as
input a set of nodes and for each node a list of co-clustering preferences, and
the goal is to output a clustering that minimizes the disagreement with the
specified nodes' preferences. In this paper, we introduce a simple and
computationally efficient algorithm for the correlation clustering problem with
provable privacy guarantees. Our approximation guarantees are stronger than
those shown in prior work and are optimal up to logarithmic factors.

Owing to security implications of adversarial vulnerability, adversarial
robustness of deep metric learning models has to be improved. In order to avoid
model collapse due to excessively hard examples, the existing defenses dismiss
the min-max adversarial training, but instead learn from a weak adversary
inefficiently. Conversely, we propose Hardness Manipulation to efficiently
perturb the training triplet till a specified level of hardness for adversarial
training, according to a harder benign triplet or a pseudo-hardness function.
It is flexible since regular training and min-max adversarial training are its
boundary cases. Besides, Gradual Adversary, a family of pseudo-hardness
functions is proposed to gradually increase the specified hardness level during
training for a better balance between performance and robustness. Additionally,
an Intra-Class Structure loss term among benign and adversarial examples
further improves model robustness and efficiency. Comprehensive experimental
results suggest that the proposed method, although simple in its form,
overwhelmingly outperforms the state-of-the-art defenses in terms of
robustness, training efficiency, as well as performance on benign examples.

Clinical outcome or severity prediction from medical images has largely
focused on learning representations from single-timepoint or snapshot scans. It
has been shown that disease progression can be better characterized by temporal
imaging. We therefore hypothesized that outcome predictions can be improved by
utilizing the disease progression information from sequential images. We
present a deep learning approach that leverages temporal progression
information to improve clinical outcome predictions from single-timepoint
images. In our method, a self-attention based Temporal Convolutional Network
(TCN) is used to learn a representation that is most reflective of the disease
trajectory. Meanwhile, a Vision Transformer is pretrained in a self-supervised
fashion to extract features from single-timepoint images. The key contribution
is to design a recalibration module that employs maximum mean discrepancy loss
(MMD) to align distributions of the above two contextual representations. We
train our system to predict clinical outcomes and severity grades from
single-timepoint images. Experiments on chest and osteoarthritis radiography
datasets demonstrate that our approach outperforms other state-of-the-art

We present a fully memristive spiking neural network (MSNN) consisting of
novel memristive neurons trained using the backpropagation through time (BPTT)
learning rule. Gradient descent is applied directly to the memristive
integrated-and-fire (MIF) neuron designed using analog SPICE circuit models,
which generates distinct depolarization, hyperpolarization, and repolarization
voltage waveforms. Synaptic weights are trained by BPTT using the membrane
potential of the MIF neuron model and can be processed on memristive crossbars.
The natural spiking dynamics of the MIF neuron model are fully differentiable,
eliminating the need for gradient approximations that are prevalent in the
spiking neural network literature. Despite the added complexity of training
directly on SPICE circuit models, we achieve 97.58% accuracy on the MNIST
testing dataset and 75.26% on the Fashion-MNIST testing dataset, the highest
accuracies among all fully MSNNs.

Estimating conditional average treatment effects (CATE) is challenging,
especially when treatment information is missing. Although this is a widespread
problem in practice, CATE estimation with missing treatments has received
little attention. In this paper, we analyze CATE estimation in the setting with
missing treatments where, thus, unique challenges arise in the form of
covariate shifts. We identify two covariate shifts in our setting: (i) a
covariate shift between the treated and control population; and (ii) a
covariate shift between the observed and missing treatment population. We first
theoretically show the effect of these covariate shifts by deriving a
generalization bound for estimating CATE in our setting with missing
treatments. Then, motivated by our bound, we develop the missing treatment
representation network (MTRNet), a novel CATE estimation algorithm that learns
a balanced representation of covariates using domain adaptation. By using
balanced representations, MTRNet provides more reliable CATE estimates in the
covariate domains where the data are not fully observed. In various experiments
with semi-synthetic and real-world data, we show that our algorithm improves
over the state-of-the-art by a substantial margin.

physically-realizable memristive neurons and memristive synapses to implement
an unsupervised Spiking Time Dependent Plasticity (STDP) learning rule. The
system is fully memristive in that both neuronal and synaptic dynamics can be
realized by using memristors. The neuron is implemented using the SPICE-level
memristive integrate-and-fire (MIF) model, which consists of a minimal number
of circuit elements necessary to achieve distinct depolarization,
hyperpolarization, and repolarization voltage waveforms. The proposed MSNN
uniquely implements STDP learning by using cumulative weight changes in
memristive synapses from the voltage waveform changes across the synapses,
which arise from the presynaptic and postsynaptic spiking voltage signals
during the training process. Two types of MSNN architectures are investigated:
1) a biologically plausible memory retrieval system, and 2) a multi-class
classification system. Our circuit simulation results verify the MSNN's
unsupervised learning efficacy by replicating biological memory retrieval
mechanisms, and achieving 97.5% accuracy in a 4-pattern recognition problem in
a large scale discriminative MSNN.

Currently, convolutional neural networks (CNN) (e.g., U-Net) have become the
de facto standard and attained immense success in medical image segmentation.
However, as a downside, CNN based methods are a double-edged sword as they fail
to build long-range dependencies and global context connections due to the
limited receptive field that stems from the intrinsic characteristics of the
convolution operation. Hence, recent articles have exploited Transformer
variants for medical image segmentation tasks which open up great opportunities
due to their innate capability of capturing long-range correlations through the
attention mechanism. Although being feasibly designed, most of the cohort
studies incur prohibitive performance in capturing local information, thereby
resulting in less lucidness of boundary areas. In this paper, we propose a
contextual attention network to tackle the aforementioned limitations. The
proposed method uses the strength of the Transformer module to model the
long-range contextual dependency. Simultaneously, it utilizes the CNN encoder
to capture local semantic information. In addition, an object-level
representation is included to model the regional interaction map. The extracted
hierarchical features are then fed to the contextual attention module to
adaptively recalibrate the representation space using the local information.
Then, they emphasize the informative regions while taking into account the
long-range contextual dependency derived by the Transformer module. We validate
our method on several large-scale public medical image segmentation datasets
and achieve state-of-the-art performance. We have provided the implementation
code in https://github.com/rezazad68/TMUnet.

With the increasing prevalence of complex vision-based sensing methods for
use in obstacle identification and state estimation, characterizing
environment-dependent measurement errors has become a difficult and essential
part of modern robotics. This paper presents a self-supervised learning
approach to safety-critical control. In particular, the uncertainty associated
with stereo vision is estimated, and adapted online to new visual environments,
wherein this estimate is leveraged in a safety-critical controller in a robust
fashion. To this end, we propose an algorithm that exploits the structure of
stereo-vision to learn an uncertainty estimate without the need for
ground-truth data. We then robustify existing Control Barrier Function-based
controllers to provide safety in the presence of this uncertainty estimate. We
demonstrate the efficacy of our method on a quadrupedal robot in a variety of
environments. When not using our method safety is violated. With offline
training alone we observe the robot is safe, but overly-conservative. With our
online method the quadruped remains safe and conservatism is reduced.

Adaptive gradient methods are the method of choice for optimization in
machine learning and used to train the largest deep models. In this paper we
study the problem of learning a local preconditioner, that can change as the
data is changing along the optimization trajectory. We propose an adaptive
gradient method that has provable adaptive regret guarantees vs. the best local
preconditioner. To derive this guarantee, we prove a new adaptive regret bound
in online learning that improves upon previous adaptive online learning
methods. We demonstrate the robustness of our method in automatically choosing
the optimal learning rate schedule for popular benchmarking tasks in vision and
language domains. Without the need to manually tune a learning rate schedule,
our method can, in a single run, achieve comparable and stable task accuracy as
a fine-tuned optimizer.

Chronic Kidney Disease (CKD) has infected almost 800 million people around
the world. Around 1.7 million people die each year because of it. Detecting CKD
in the initial stage is essential for saving millions of lives. Many
researchers have applied distinct Machine Learning (ML) methods to detect CKD
at an early stage, but detailed studies are still missing. We present a
structured and thorough method for dealing with the complexities of medical
data with optimal performance. Besides, this study will assist researchers in
producing clear ideas on the medical data preparation pipeline. In this paper,
we applied KNN Imputation to impute missing values, Local Outlier Factor to
remove outliers, SMOTE to handle data imbalance, K-stratified K-fold
Cross-validation to validate the ML models, and a novel hybrid feature
selection method to remove redundant features. Applied algorithms in this study
are Support Vector Machine, Gaussian Naive Bayes, Decision Tree, Random Forest,
Logistic Regression, K-Nearest Neighbor, Gradient Boosting, Adaptive Boosting,
and Extreme Gradient Boosting. Finally, the Random Forest can detect CKD with
100% accuracy without any data leakage.

Traditional MVS methods have good accuracy but struggle with completeness,
while recently developed learning-based multi-view stereo (MVS) techniques have
improved completeness except accuracy being compromised. We propose depth
discontinuity learning for MVS methods, which further improves accuracy while
retaining the completeness of the reconstruction. Our idea is to jointly
estimate the depth and boundary maps where the boundary maps are explicitly
used for further refinement of the depth maps. We validate our idea and
demonstrate that our strategies can be easily integrated into the existing
learning-based MVS pipeline where the reconstruction depends on high-quality
depth map estimation. Extensive experiments on various datasets show that our
method improves reconstruction quality compared to baseline. Experiments also
demonstrate that the presented model and strategies have good generalization
capabilities. The source code will be available soon.

Cut-based directed graph (digraph) clustering often focuses on finding dense
within-cluster or sparse between-cluster connections, similar to cut-based
undirected graph clustering methods. In contrast, for flow-based clusterings
the edges between clusters tend to be oriented in one direction and have been
found in migration data, food webs, and trade data. In this paper we introduce
a spectral algorithm for finding flow-based clusterings. The proposed algorithm
is based on recent work which uses complex-valued Hermitian matrices to
represent digraphs. By establishing an algebraic relationship between a
complex-valued Hermitian representation and an associated real-valued,
skew-symmetric matrix the proposed algorithm produces clusterings while
remaining completely in the real field. Our algorithm uses less memory and
asymptotically less computation while provably preserving solution quality. We
also show the algorithm can be easily implemented using standard computational
building blocks, possesses better numerical properties, and loans itself to a
natural interpretation via an objective function relaxation argument.

With the widespread adoption of deep learning, reinforcement learning (RL)
has experienced a dramatic increase in popularity, scaling to previously
intractable problems, such as playing complex games from pixel observations,
sustaining conversations with humans, and controlling robotic agents. However,
there is still a wide range of domains inaccessible to RL due to the high cost
and danger of interacting with the environment. Offline RL is a paradigm that
learns exclusively from static datasets of previously collected interactions,
making it feasible to extract policies from large and diverse training
datasets. Effective offline RL algorithms have a much wider range of
applications than online RL, being particularly appealing for real-world
applications such as education, healthcare, and robotics. In this work, we
propose a unifying taxonomy to classify offline RL methods. Furthermore, we
provide a comprehensive review of the latest algorithmic breakthroughs in the
field, and a review of existing benchmarks' properties and shortcomings.
Finally, we provide our perspective on open problems and propose future
research directions for this rapidly growing field.

The main question we address in this paper is how to scale up visual
recognition of unseen classes, also known as zero-shot learning, to tens of
thousands of categories as in the ImageNet-21K benchmark. At this scale,
especially with many fine-grained categories included in ImageNet-21K, it is
critical to learn quality visual semantic representations that are
discriminative enough to recognize unseen classes and distinguish them from
seen ones. We propose a \emph{H}ierarchical \emph{G}raphical knowledge
\emph{R}epresentation framework for the confidence-based classification method,
dubbed as HGR-Net. Our experimental results demonstrate that HGR-Net can grasp
class inheritance relations by utilizing hierarchical conceptual knowledge. Our
method significantly outperformed all existing techniques, boosting the
performance by 7\% compared to the runner-up approach on the ImageNet-21K
benchmark. We show that HGR-Net is learning-efficient in few-shot scenarios. We
also analyzed our method on smaller datasets like ImageNet-21K-P, 2-hops and
3-hops, demonstrating its generalization ability. Our benchmark and code are
available at https://kaiyi.me/p/hgrnet.html.

Weak Supervision (WS) techniques allow users to efficiently create large
training datasets by programmatically labeling data with heuristic sources of
supervision. While the success of WS relies heavily on the provided labeling
heuristics, the process of how these heuristics are created in practice has
remained under-explored. In this work, we formalize the development process of
labeling heuristics as an interactive procedure, built around the existing
workflow where users draw ideas from a selected set of development data for
designing the heuristic sources. With the formalism, we study two core problems
of how to strategically select the development data to guide users in
efficiently creating informative heuristics, and how to exploit the information
within the development process to contextualize and better learn from the
resultant heuristics. Building upon two novel methodologies that effectively
tackle the respective problems considered, we present Nemo, an end-to-end
interactive system that improves the overall productivity of WS learning
pipeline by an average 20% (and up to 47% in one task) compared to the
prevailing WS approach.

Information retrieval systems are usually measured by labeling the relevance
of results corresponding to a sample of user queries. In practical search
engines, such measurement needs to be performed continuously, such as daily or
weekly. This creates a trade-off between (a) representativeness of query sample
to current query traffic of the product; (b) labeling cost: if we keep the same
query sample, results would be similar allowing us to reuse their labels; and
(c) overfitting caused by continuous usage of same query sample. In this paper
we explicitly formulate this tradeoff, propose two new variants -- Stable and
Semi-stable -- to simple and weighted random sampling and show that they
outperform existing approaches for the continuous usage settings, including
monitoring/debugging search engine or comparing ranker candidates.

Molecular simulations have provided valuable insight into the microscopic
mechanisms underlying homogeneous ice nucleation. While empirical models have
been used extensively to study this phenomenon, simulations based on
first-principles calculations have so far proven prohibitively expensive. Here,
we circumvent this difficulty by using an efficient machine learning model
trained on density-functional theory (DFT) energies and forces. We compute
nucleation rates at atmospheric pressure, over a broad range of supercoolings,
using the seeding technique and systems of up to hundreds of thousands of atoms
simulated with ab initio accuracy. The key quantity provided by the seeding
technique is the size of the critical cluster (i.e., a size such that the
cluster has equal probabilities of growing or melting at the given
supersaturation) which is used together with the equations of classical
nucleation theory to compute nucleation rates. We find that nucleation rates
for our model at moderate supercoolings are in good agreement with experimental
measurements within the error of our calculation. We also study the impact of
properties such as the thermodynamic driving force, interfacial free energy,
and stacking disorder on the calculated rates.

Open set segmentation is a relatively new and unexploredtask, with just a
handful of methods proposed to model suchtasks.We propose a novel method called
CoReSeg thattackles the issue using class conditional reconstruction ofthe
input images according to their pixelwise mask. Ourmethod conditions each input
pixel to all known classes,expecting higher errors for pixels of unknown
classes. Itwas observed that the proposed method produces better se-mantic
consistency in its predictions, resulting in cleanersegmentation maps that
better fit object boundaries. CoRe-Seg outperforms state-of-the-art methods on
the Vaihin-gen and Potsdam ISPRS datasets, while also being com-petitive on the
Houston 2018 IEEE GRSS Data Fusiondataset. Official implementation for CoReSeg
is availableat:https://github.com/iannunes/CoReSeg.

Synthetic datasets are often presented as a silver-bullet solution to the
problem of privacy-preserving data publishing. However, for many applications,
synthetic data has been shown to have limited utility when used to train
predictive models. One promising potential application of these data is in the
exploratory phase of the machine learning workflow, which involves
understanding, engineering and selecting features. This phase often involves
considerable time, and depends on the availability of data. There would be
substantial value in synthetic data that permitted these steps to be carried
out while, for example, data access was being negotiated, or with fewer
information governance restrictions. This paper presents an empirical analysis
of the agreement between the feature importance obtained from raw and from
synthetic data, on a range of artificially generated and real-world datasets
(where feature importance represents how useful each feature is when predicting
a the outcome). We employ two differentially-private methods to produce
synthetic data, and apply various utility measures to quantify the agreement in
feature importance as this varies with the level of privacy. Our results
indicate that synthetic data can sometimes preserve several representations of
the ranking of feature importance in simple settings but their performance is
not consistent and depends upon a number of factors. Particular caution should
be exercised in more nuanced real-world settings, where synthetic data can lead
to differences in ranked feature importance that could alter key modelling
decisions. This work has important implications for developing synthetic
versions of highly sensitive data sets in fields such as finance and

Deep neural networks have been shown to provide accurate function
approximations in high dimensions. However, fitting network parameters requires
training data that may not be available beforehand, which is particularly
challenging in science and engineering applications where often it is even
unclear how to collect new informative training data in the first place. This
work proposes Neural Galerkin schemes based on deep learning that generate
training data samples with active learning for numerically solving
high-dimensional partial differential equations. Neural Galerkin schemes train
networks by minimizing the residual sequentially over time, which enables
adaptively collecting new training data in a self-informed manner that is
guided by the dynamics described by the partial differential equations, which
is in stark contrast to many other machine learning methods that aim to fit
network parameters globally in time without taking into account training data
acquisition. Our finding is that the active form of gathering training data of
the proposed Neural Galerkin schemes is key for numerically realizing the
expressive power of networks in high dimensions. Numerical experiments
demonstrate that Neural Galerkin schemes have the potential to enable
simulating phenomena and processes with many variables for which traditional
and other deep-learning-based solvers fail, especially when features of the
solutions evolve locally such as in high-dimensional wave propagation problems
and interacting particle systems described by Fokker-Planck and kinetic
equations.

Machine-learned multi-orbital tight-binding (MMTB) Hamiltonian models have
been developed to describe the electronic characteristics of intermetallic
compounds $\rm Mg_2Si, Mg_2Ge, Mg_2Sn$, and $\rm Mg_2Pb$ subject to strain. The
MMTB models incorporate spin-orbital mediated interactions and they are
calibrated to the electronic band structures calculated via density functional
theory (DFT) by a massively parallelized multi-dimensional Monte-Carlo search
algorithm. The results show that a machine-learned five-band tight-binding
model reproduces the key aspects of the valence band structures in the entire
Brillouin zone. The five-band model reveals that compressive strain localizes
the contribution of the $3s$ orbital of $\rm Mg$ to the conduction bands and
the outer shell $p$ orbitals of $\rm X~(X=Si,Ge,Sn,Pb)$ to the valence bands.
In contrast, tensile strain has a reversed effect as it weakens the
contribution of the $3s$ orbital of $\rm Mg$ and the outer shell $p$ orbitals
of $\rm X$ to the conduction bands and valence bands, respectively. The $\pi$
bonding in the $\rm Mg_2X$ compounds is negligible compared to the $\sigma$
bonding components, which follow the hierarchy
$|\sigma_{sp}|>|\sigma_{pp}|>|\sigma_{ss}|$, and the largest variation against
strain belongs to $\sigma_{pp}$. The five-band model allows for estimating the
strength of spin-orbit coupling (SOC) in $\rm Mg_2X$ and obtaining its
dependence on the atomic number of $\rm X$ and strain. Further, the band
structure calculations demonstrate a significant band gap tuning and band
splitting due to strain. A compressive strain of $-10\%$ can open a band gap at
the $\Gamma$ point in metallic $\rm Mg_2Pb$, whereas a tensile strain of
$+10\%$ closes the semiconducting band gap of $\rm Mg_2Si$. A tensile strain of
$+5\%$ removes the three-fold degeneracy of valence bands at the $\Gamma$ point
in semiconducting $\rm Mg_2Ge$.

Anomaly detection with convolutional autoencoders is a popular method to
search for new physics in a model-agnostic manner. These techniques are
powerful, but they are still a "black box," since we do not know what
high-level physical observables determine how anomalous an event is. To address
this, we adapt a recently proposed technique by Faucett et al., which maps out
the physical observables learned by a neural network classifier, to the case of
anomaly detection. We propose two different strategies that use a small number
of high-level observables to mimic the decisions made by the autoencoder on
background events, one designed to directly learn the output of the
autoencoder, and the other designed to learn the difference between the
autoencoder's outputs on a pair of events. Despite the underlying differences
in their approach, we find that both strategies have similar ordering
performance as the autoencoder and independently use the same six high-level
observables. From there, we compare the performance of these networks as
anomaly detectors. We find that both strategies perform similarly to the
autoencoder across a variety of signals, giving a nontrivial demonstration that
learning to order background events transfers to ordering a variety of signal
events.

Machine learning is frequently listed among the most promising applications
for quantum computing. This is in fact a curious choice: Today's machine
learning algorithms are notoriously powerful in practice, but remain
theoretically difficult to study. Quantum computing, in contrast, does not
offer practical benchmarks on realistic scales, and theory is the main tool we
have to judge whether it could become relevant for a problem. In this
perspective we explain why it is so difficult to say something about the
practical power of quantum computers for machine learning with the tools we are
currently using. We argue that these challenges call for a critical debate on
whether quantum advantage and the narrative of "beating" classical machine
learning should continue to dominate the literature the way it does, and
provide a few examples for alternative research questions.

Learning multimodal representations involves discovering correspondences and
integrating information from multiple heterogeneous sources of data. While
recent research has begun to explore the design of more general-purpose
multimodal models (contrary to prior focus on domain and modality-specific
architectures), these methods are still largely focused on a small set of
modalities in the language, vision, and audio space. In order to accelerate
generalization towards diverse and understudied modalities, we investigate
methods for high-modality (a large set of diverse modalities) and
partially-observable (each task only defined on a small subset of modalities)
scenarios. To tackle these challenges, we design a general multimodal model
that enables multitask and transfer learning: multitask learning with shared
parameters enables stable parameter counts (addressing scalability), and
cross-modal transfer learning enables information sharing across modalities and
tasks (addressing partial observability). Our resulting model generalizes
across text, image, video, audio, time-series, sensors, tables, and set
modalities from different research areas, improves the tradeoff between
performance and efficiency, transfers to new modalities and tasks, and reveals
surprising insights on the nature of information sharing in multitask models.
We release our code and benchmarks which we hope will present a unified
platform for subsequent theoretical and empirical analysis:
https://github.com/pliang279/HighMMT.

Large-scale perturbations in the microbiome constitution are strongly
correlated, whether as a driver or a consequence, with the health and
functioning of human physiology. However, understanding the difference in the
microbiome profiles of healthy and ill individuals can be complicated due to
the large number of complex interactions among microbes. We propose to model
these interactions as a time-evolving graph whose nodes are microbes and edges
are interactions among them. Motivated by the need to analyse such complex
interactions, we develop a method that learns a low-dimensional representation
of the time-evolving graph and maintains the dynamics occurring in the
high-dimensional space. Through our experiments, we show that we can extract
graph features such as clusters of nodes or edges that have the highest impact
on the model to learn the low-dimensional representation. This information can
be crucial to identify microbes and interactions among them that are strongly
correlated with clinical diseases. We conduct our experiments on both synthetic
and real-world microbiome datasets.

In neural network's Literature, Hebbian learning traditionally refers to the
procedure by which the Hopfield model and its generalizations store archetypes
(i.e., definite patterns that are experienced just once to form the synaptic
matrix). However, the term "Learning" in Machine Learning refers to the ability
of the machine to extract features from the supplied dataset (e.g., made of
blurred examples of these archetypes), in order to make its own representation
of the unavailable archetypes. Here, given a sample of examples, we define a
supervised learning protocol by which the Hopfield network can infer the
archetypes, and we detect the correct control parameters (including size and
quality of the dataset) to depict a phase diagram for the system performance.
We also prove that, for structureless datasets, the Hopfield model equipped
with this supervised learning rule is equivalent to a restricted Boltzmann
machine and this suggests an optimal and interpretable training routine.
Finally, this approach is generalized to structured datasets: we highlight a
quasi-ultrametric organization (reminiscent of replica-symmetry-breaking) in
the analyzed datasets and, consequently, we introduce an additional "replica
hidden layer" for its (partial) disentanglement, which is shown to improve
MNIST classification from 75% to 95%, and to offer a new perspective on deep

Ensemble sampling serves as a practical approximation to Thompson sampling
when maintaining an exact posterior distribution over model parameters is
computationally intractable. In this paper, we establish a Bayesian regret
bound that ensures desirable behavior when ensemble sampling is applied to the
linear bandit problem. This represents the first rigorous regret analysis of
ensemble sampling and is made possible by leveraging information-theoretic
concepts and novel analytic techniques that may prove useful beyond the scope
of this paper.

It remains a significant challenge to train generally capable agents with
reinforcement learning (RL). A promising avenue for improving the robustness of
RL agents is through the use of curricula. One such class of methods frames
environment design as a game between a student and a teacher, using
regret-based objectives to produce environment instantiations (or levels) at
the frontier of the student agent's capabilities. These methods benefit from
their generality, with theoretical guarantees at equilibrium, yet they often
struggle to find effective levels in challenging design spaces. By contrast,
evolutionary approaches seek to incrementally alter environment complexity,
resulting in potentially open-ended learning, but often rely on domain-specific
heuristics and vast amounts of computational resources. In this paper we
propose to harness the power of evolution in a principled, regret-based
curriculum. Our approach, which we call Adversarially Compounding Complexity by
Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of
an agent's capabilities, resulting in curricula that start simple but become
increasingly complex. ACCEL maintains the theoretical benefits of prior
regret-based methods, while providing significant empirical gains in a diverse
set of environments. An interactive version of the paper is available at
accelagent.github.io.

Accurate kinodynamic models play a crucial role in many robotics applications
such as off-road navigation and high-speed driving. Many state-of-the-art
approaches in learning stochastic kinodynamic models, however, require precise
measurements of robot states as labeled input/output examples, which can be
hard to obtain in outdoor settings due to limited sensor capabilities and the
absence of ground truth. In this work, we propose a new technique for learning
neural stochastic kinodynamic models from noisy and indirect observations by
performing simultaneous state estimation and dynamics learning. The proposed
technique iteratively improves the kinodynamic model in an
expectation-maximization loop, where the E Step samples posterior state
trajectories using particle filtering, and the M Step updates the dynamics to
be more consistent with the sampled trajectories via stochastic gradient
ascent. We evaluate our approach on both simulation and real-world benchmarks
and compare it with several baseline techniques. Our approach not only achieves
significantly higher accuracy but is also more robust to observation noise,
thereby showing promise for boosting the performance of many other robotics

The travelling salesperson problem (TSP) is a classic resource allocation
problem used to find an optimal order of doing a set of tasks while minimizing
(or maximizing) an associated objective function. It is widely used in robotics
for applications such as planning and scheduling. In this work, we solve TSP
for two objectives using reinforcement learning (RL). Often in multi-objective
optimization problems, the associated objective functions can be conflicting in
nature. In such cases, the optimality is defined in terms of Pareto optimality.
A set of these Pareto optimal solutions in the objective space form a Pareto
front (or frontier). Each solution has its trade-off. We present the Pareto
frontier approximation network (PA-Net), a network that generates good
approximations of the Pareto front for the bi-objective travelling salesperson
problem (BTSP). Firstly, BTSP is converted into a constrained optimization
problem. We then train our network to solve this constrained problem using the
Lagrangian relaxation and policy gradient. With PA-Net we improve the
performance over an existing deep RL-based method. The average improvement in
the hypervolume metric, which is used to measure the optimality of the Pareto
front, is 2.3%. At the same time, PA-Net has 4.5x faster inference time.
Finally, we present the application of PA-Net to find optimal visiting order in
a robotic navigation task/coverage planning. Our code is available on the

Teachers often conduct surveys in order to collect data from a predefined
group of students to gain insights into topics of interest. When analyzing
surveys with open-ended textual responses, it is extremely time-consuming,
labor-intensive, and difficult to manually process all the responses into an
insightful and comprehensive report. In the analysis step, traditionally, the
teacher has to read each of the responses and decide on how to group them in
order to extract insightful information. Even though it is possible to group
the responses only using certain keywords, such an approach would be limited
since it not only fails to account for embedded contexts but also cannot detect
polysemous words or phrases and semantics that are not expressible in single
words. In this work, we present a novel end-to-end context-aware framework that
extracts, aggregates, and abbreviates embedded semantic patterns in
open-response survey data. Our framework relies on a pre-trained natural
language model in order to encode the textual data into semantic vectors. The
encoded vectors then get clustered either into an optimally tuned number of
groups or into a set of groups with pre-specified titles. In the former case,
the clusters are then further analyzed to extract a representative set of
keywords or summary sentences that serve as the labels of the clusters. In our
framework, for the designated clusters, we finally provide context-aware
wordclouds that demonstrate the semantically prominent keywords within each
group. Honoring user privacy, we have successfully built the on-device
implementation of our framework suitable for real-time analysis on mobile
devices and have tested it on a synthetic dataset. Our framework reduces the
costs at-scale by automating the process of extracting the most insightful
information pieces from survey data.

This paper presents Andes_gym, a versatile and high-performance reinforcement
learning environment for power system studies. The environment leverages the
modeling and simulation capability of ANDES and the reinforcement learning (RL)
environment OpenAI Gym to enable the prototyping and demonstration of RL
algorithms for power systems. The architecture of the proposed software tool is
elaborated to provide the observation and action interfaces for RL algorithms.
An example is shown to rapidly prototype a load-frequency control algorithm
based on RL trained by available algorithms. The proposed environment is highly
generalized by supporting all the power system dynamic models available in
ANDES and numerous RL algorithms available for OpenAI Gym.

To equip Convolutional Neural Networks (CNNs) with explainability, it is
essential to interpret how opaque models take specific decisions, understand
what causes the errors, improve the architecture design, and identify unethical
biases in the classifiers. This paper introduces ADVISE, a new explainability
method that quantifies and leverages the relevance of each unit of the feature
map to provide better visual explanations. To this end, we propose using
adaptive bandwidth kernel density estimation to assign a relevance score to
each unit of the feature map with respect to the predicted class. We also
propose an evaluation protocol to quantitatively assess the visual
explainability of CNN models. We extensively evaluate our idea in the image
classification task using AlexNet, VGG16, ResNet50, and Xception pretrained on
ImageNet. We compare ADVISE with the state-of-the-art visual explainable
methods and show that the proposed method outperforms competing approaches in
quantifying feature-relevance and visual explainability while maintaining
competitive time complexity. Our experiments further show that ADVISE fulfils
the sensitivity and implementation independence axioms while passing the sanity
checks. The implementation is accessible for reproducibility purposes on
https://github.com/dehshibi/ADVISE.

Virtual high throughput screening (VHTS) and machine learning (ML) have
greatly accelerated the design of single-site transition-metal catalysts. VHTS
of catalysts, however, is often accompanied with high calculation failure rate
and wasted computational resources due to the difficulty of simultaneously
converging all mechanistically relevant reactive intermediates to expected
geometries and electronic states. We demonstrate a dynamic classifier approach,
i.e., a convolutional neural network that monitors geometry optimization on the
fly, and exploit its good performance and transferability for catalyst design.
We show that the dynamic classifier performs well on all reactive intermediates
in the representative catalytic cycle of the radical rebound mechanism for
methane-to-methanol despite being trained on only one reactive intermediate.
The dynamic classifier also generalizes to chemically distinct intermediates
and metal centers absent from the training data without loss of accuracy or
model confidence. We rationalize this superior model transferability to the use
of on-the-fly electronic structure and geometric information generated from
density functional theory calculations and the convolutional layer in the
dynamic classifier. Combined with model uncertainty quantification, the dynamic
classifier saves more than half of the computational resources that would have
been wasted on unsuccessful calculations for all reactive intermediates being

Current gravitational wave (GW) detections rely on the existence of libraries
of theoretical waveforms. Consequently, finding new physics with GWs requires
libraries of non-standard models, which are computationally demanding. We
discuss how deep learning frameworks can be used to generate new waveforms
"learned" from a simulation dataset obtained, say, from numerical relativity
simulations. Concretely, we use the WaveGAN architecture of a generative
adversarial network (GAN). As a proof of concept we provide this neural network
(NN) with a sample of ($>500$) waveforms from the collisions of exotic compact
objects (Proca stars), obtained from numerical relativity simulations. Dividing
the sample into a training and a validation set, we show that after a
sufficiently large number of training epochs the NN can produce from 12\% to
25\% of the synthetic waveforms with an overlapping match of at least 95\% with
the ones from the validation set. We also demonstrate that a NN can be used to
predict the overlapping match score, with 90\% of accuracy, of new synthetic
samples. These are encouraging results for using GANs for data augmentation and
interpolation in the context of GWs, to cover the full parameter space of, say,
exotic compact binaries, without the need of intensive numerical relativity

The fast evolution and widespread of deepfake techniques in real-world
scenarios require stronger generalization abilities of face forgery detectors.
Some works capture the features that are unrelated to method-specific
artifacts, such as clues of blending boundary, accumulated up-sampling, to
strengthen the generalization ability. However, the effectiveness of these
methods can be easily corrupted by post-processing operations such as
compression. Inspired by transfer learning, neural networks pre-trained on
other large-scale face-related tasks may provide useful features for deepfake
detection. For example, lip movement has been proved to be a kind of robust and
good-transferring highlevel semantic feature, which can be learned from the
lipreading task. However, the existing method pre-trains the lip feature
extraction model in a supervised manner, which requires plenty of human
resources in data annotation and increases the difficulty of obtaining training
data. In this paper, we propose a self-supervised transformer based
audio-visual contrastive learning method. The proposed method learns mouth
motion representations by encouraging the paired video and audio
representations to be close while unpaired ones to be diverse. After
pre-training with our method, the model will then be partially fine-tuned for
deepfake detection task. Extensive experiments show that our self-supervised
method performs comparably or even better than the supervised pre-training

Network analysis has been applied in diverse application domains. In this
paper, we consider an example from protein dynamics, specifically residue
interaction networks (RINs). In this context, we use NetworKit -- an
established package for network analysis -- to build a cloud-based environment
that enables domain scientists to run their visualization and analysis
workflows on large compute servers, without requiring extensive programming
and/or system administration knowledge. To demonstrate the versatility of this
approach, we use it to build a custom Jupyter-based widget for RIN
visualization. In contrast to existing RIN visualization approaches, our widget
can easily be customized through simple modifications of Python code, while
both supporting a good feature set and providing near real-time speed. It is
also easily integrated into analysis pipelines (e.g., that use Python to feed
RIN data into downstream machine learning tasks).

Hyperspectral pixel intensities result from a mixing of reflectances from
several materials. This paper develops a method of hyperspectral pixel unmixing
that aims to recover the "pure" spectral signal of each material (hereafter
referred to as endmembers) together with the mixing ratios (abundances) given
the spectrum of a single pixel. The unmixing problem is particularly relevant
in the case of low-resolution hyperspectral images captured in a remote sensing
setting, where individual pixels can cover large regions of the scene. Under
the assumptions that (1) a multivariate Normal distribution can represent the
spectra of an endmember and (2) a Dirichlet distribution can encode abundances
of different endmembers, we develop a Latent Dirichlet Variational Autoencoder
for hyperspectral pixel unmixing. Our approach achieves state-of-the-art
results on standard benchmarks and on synthetic data generated using United
States Geological Survey spectral library.

Trajectory generation and prediction are two interwoven tasks that play
important roles in planner evaluation and decision making for intelligent
vehicles. Most existing methods focus on one of the two and are optimized to
directly output the final generated/predicted trajectories, which only contain
limited information for critical scenario augmentation and safe planning. In
this work, we propose a novel behavior-aware Trajectory Autoencoder (TAE) that
explicitly models drivers' behavior such as aggressiveness and intention in the
latent space, using semi-supervised adversarial autoencoder and domain
knowledge in transportation. Our model addresses trajectory generation and
prediction in a unified architecture and benefits both tasks: the model can
generate diverse, controllable and realistic trajectories to enhance planner
optimization in safety-critical and long-tailed scenarios, and it can provide
prediction of critical behavior in addition to the final trajectories for
decision making. Experimental results demonstrate that our method achieves
promising performance on both trajectory generation and prediction.

Introduced as a notion of algorithmic fairness, multicalibration has proved
to be a powerful and versatile concept with implications far beyond its
original intent. This stringent notion -- that predictions be well-calibrated
across a rich class of intersecting subpopulations -- provides its strong
guarantees at a cost: the computational and sample complexity of learning
multicalibrated predictors are high, and grow exponentially with the number of
class labels. In contrast, the relaxed notion of multiaccuracy can be achieved
more efficiently, yet many of the most desirable properties of multicalibration
cannot be guaranteed assuming multiaccuracy alone. This tension raises a key
question: Can we learn predictors with multicalibration-style guarantees at a
cost commensurate with multiaccuracy?
  In this work, we define and initiate the study of Low-Degree
Multicalibration. Low-Degree Multicalibration defines a hierarchy of
increasingly-powerful multi-group fairness notions that spans multiaccuracy and
the original formulation of multicalibration at the extremes. Our main
technical contribution demonstrates that key properties of multicalibration,
related to fairness and accuracy, actually manifest as low-degree properties.
Importantly, we show that low-degree multicalibration can be significantly more
efficient than full multicalibration. In the multi-class setting, the sample
complexity to achieve low-degree multicalibration improves exponentially (in
the number of classes) over full multicalibration. Our work presents compelling
evidence that low-degree multicalibration represents a sweet spot, pairing
computational and sample efficiency with strong fairness and accuracy

The calculation of electromagnetic field distributions within structured
media is central to the optimization and validation of photonic devices. We
introduce WaveY-Net, a hybrid data- and physics-augmented convolutional neural
network that can predict electromagnetic field distributions with ultra fast
speeds and high accuracy for entire classes of dielectric photonic structures.
This accuracy is achieved by training the neural network to learn only the
magnetic near-field distributions of a system and to use a discrete formalism
of Maxwell's equations in two ways: as physical constraints in the loss
function and as a means to calculate the electric fields from the magnetic
fields. As a model system, we construct a surrogate simulator for periodic
silicon nanostructure arrays and show that the high speed simulator can be
directly and effectively used in the local and global freeform optimization of
metagratings. We anticipate that physics-augmented networks will serve as a
viable Maxwell simulator replacement for many classes of photonic systems,
transforming the way they are designed.

Despite the impressive results achieved by deep learning based 3D
reconstruction, the techniques of directly learning to model 4D human captures
with detailed geometry have been less studied. This work presents a novel
framework that can effectively learn a compact and compositional representation
for dynamic human by exploiting the human body prior from the widely used SMPL
parametric model. Particularly, our representation, named H4D, represents a
dynamic 3D human over a temporal span with the SMPL parameters of shape and
initial pose, and latent codes encoding motion and auxiliary information. A
simple yet effective linear motion model is proposed to provide a rough and
regularized motion estimation, followed by per-frame compensation for pose and
geometry details with the residual encoded in the auxiliary code. Technically,
we introduce novel GRU-based architectures to facilitate learning and improve
the representation capability. Extensive experiments demonstrate our method is
not only efficacy in recovering dynamic human with accurate motion and detailed
geometry, but also amenable to various 4D human related tasks, including motion
retargeting, motion completion and future prediction. Please check out the
project page for video and code: https://boyanjiang.github.io/H4D/.

Machine Learning (ML) techniques have been employed for the high energy
physics (HEP) community since the early 80s to deal with a broad spectrum of
problems. This work explores the prospects of using Deep Learning techniques to
estimate elliptic flow ($v_2$) in heavy-ion collisions at the RHIC and LHC
energies. A novel method is developed to process the input observables from
particle kinematic information. The proposed DNN model is trained with Pb-Pb
collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with
AMPT model. The predictions from the ML technique are compared to both
simulation and experiment. The Deep Learning model seems to preserve the
centrality and energy dependence of $v_2$ for the LHC and RHIC energies. The
DNN model is also quite successful in predicting the $p_{\rm T}$ dependence of
$v_2$. When subjected to event simulation with additional noise, the proposed
DNN model still keeps the robustness and prediction accuracy intact up to a
reasonable extent.

Emerging sampling algorithms based on normalizing flows have the potential to
solve ergodicity problems in lattice calculations. Furthermore, it has been
noted that flows can be used to compute thermodynamic quantities which are
difficult to access with traditional methods. This suggests that they are also
applicable to the density-of-states approach to complex action problems. In
particular, flow-based sampling may be used to compute the density directly, in
contradistinction to the conventional strategy of reconstructing it via
measuring and integrating the derivative of its logarithm. By circumventing
this procedure, the accumulation of errors from the numerical integration is
avoided completely and the overall normalization factor can be determined
explicitly. In this proof-of-principle study, we demonstrate our method in the
context of two-component scalar field theory where the $O(2)$ symmetry is
explicitly broken by an imaginary external field. First, we concentrate on the
zero-dimensional case which can be solved exactly. We show that with our
method, the Lee-Yang zeroes of the associated partition function can be
successfully located. Subsequently, we confirm that the flow-based approach
correctly reproduces the density computed with conventional methods in one- and
two-dimensional models.

When training deep neural networks for classification tasks, an intriguing
empirical phenomenon has been widely observed in the last-layer classifiers and
features, where (i) the class means and the last-layer classifiers all collapse
to the vertices of a Simplex Equiangular Tight Frame (ETF) up to scaling, and
(ii) cross-example within-class variability of last-layer activations collapses
to zero. This phenomenon is called Neural Collapse (NC), which seems to take
place regardless of the choice of loss functions. In this work, we justify NC
under the mean squared error (MSE) loss, where recent empirical evidence shows
that it performs comparably or even better than the de-facto cross-entropy
loss. Under a simplified unconstrained feature model, we provide the first
global landscape analysis for vanilla nonconvex MSE loss and show that the
(only!) global minimizers are neural collapse solutions, while all other
critical points are strict saddles whose Hessian exhibit negative curvature
directions. Furthermore, we justify the usage of rescaled MSE loss by probing
the optimization landscape around the NC solutions, showing that the landscape
can be improved by tuning the rescaling hyperparameters. Finally, our
theoretical findings are experimentally verified on practical network

Exoplanet observations are currently analysed with Bayesian retrieval
techniques. Due to the computational load of the models used, a compromise is
needed between model complexity and computing time. Analysis of data from
future facilities, will need more complex models which will increase the
computational load of retrievals, prompting the search for a faster approach
for interpreting exoplanet observations. Our goal is to compare machine
learning retrievals of exoplanet transmission spectra with nested sampling, and
understand if machine learning can be as reliable as Bayesian retrievals for a
statistically significant sample of spectra while being orders of magnitude
faster. We generate grids of synthetic transmission spectra and their
corresponding planetary and atmospheric parameters, one using free chemistry
models, and the other using equilibrium chemistry models. Each grid is
subsequently rebinned to simulate both HST/WFC3 and JWST/NIRSpec observations,
yielding four datasets in total. Convolutional neural networks (CNNs) are
trained with each of the datasets. We perform retrievals on a 1,000 simulated
observations for each combination of model type and instrument with nested
sampling and machine learning. We also use both methods to perform retrievals
on real WFC3 transmission spectra. Finally, we test how robust machine learning
and nested sampling are against incorrect assumptions in our models. CNNs reach
a lower coefficient of determination between predicted and true values of the
parameters. Nested sampling underestimates the uncertainty in ~8% of
retrievals, whereas CNNs estimate them correctly. For real WFC3 observations,
nested sampling and machine learning agree within $2\sigma$ for ~86% of
spectra. When doing retrievals with incorrect assumptions, nested sampling
underestimates the uncertainty in ~12% to ~41% of cases, whereas this is always
below ~10% for the CNN.

Pretrained Language Models (PLMs) such as BERT have revolutionized the
landscape of Natural Language Processing (NLP). Inspired by their
proliferation, tremendous efforts have been devoted to Pretrained Graph Models
(PGMs). Owing to the powerful model architectures of PGMs, abundant knowledge
from massive labeled and unlabeled graph data can be captured. The knowledge
implicitly encoded in model parameters can benefit various downstream tasks and
help to alleviate several fundamental issues of learning on graphs. In this
paper, we provide the first comprehensive survey for PGMs. We firstly present
the limitations of graph representation learning and thus introduce the
motivation for graph pre-training. Then, we systematically categorize existing
PGMs based on a taxonomy from four different perspectives. Next, we present the
applications of PGMs in social recommendation and drug discovery. Finally, we
outline several promising research directions that can serve as a guideline for

We study online control of time-varying linear systems with unknown dynamics
in the nonstochastic control model. At a high level, we demonstrate that this
setting is \emph{qualitatively harder} than that of either unknown
time-invariant or known time-varying dynamics, and complement our negative
results with algorithmic upper bounds in regimes where sublinear regret is
possible. More specifically, we study regret bounds with respect to common
classes of policies: Disturbance Action (SLS), Disturbance Response (Youla),
and linear feedback policies. While these three classes are essentially
equivalent for LTI systems, we demonstrate that these equivalences break down
for time-varying systems.
  We prove a lower bound that no algorithm can obtain sublinear regret with
respect to the first two classes unless a certain measure of system variability
also scales sublinearly in the horizon. Furthermore, we show that offline
planning over the state linear feedback policies is NP-hard, suggesting
hardness of the online learning problem.
  On the positive side, we give an efficient algorithm that attains a sublinear
regret bound against the class of Disturbance Response policies up to the
aforementioned system variability term. In fact, our algorithm enjoys sublinear
\emph{adaptive} regret bounds, which is a strictly stronger metric than
standard regret and is more appropriate for time-varying systems. We sketch
extensions to Disturbance Action policies and partial observation, and propose
an inefficient algorithm for regret against linear state feedback policies.

The integration of Reconfigurable Intelligent Surfaces (RISs) into wireless
environments endows channels with programmability, and is expected to play a
key role in future communication standards. To date, most RIS-related efforts
focus on quasi-free-space, where wireless channels are typically modeled
analytically. Many realistic communication scenarios occur, however, in
rich-scattering environments which, moreover, evolve dynamically. These
conditions present a tremendous challenge in identifying an RIS configuration
that optimizes the achievable communication rate. In this paper, we make a
first step toward tackling this challenge. Based on a simulator that is
faithful to the underlying wave physics, we train a deep neural network as
surrogate forward model to capture the stochastic dependence of wireless
channels on the RIS configuration under dynamic rich-scattering conditions.
Subsequently, we use this model in combination with a genetic algorithm to
identify RIS configurations optimizing the communication rate. We numerically
demonstrate the ability of the proposed approach to tune RISs to improve the
achievable rate in rich-scattering setups.

This research studies graph-based approaches for Answer Sentence Selection
(AS2), an essential component for building retrieval-based Question Answering
systems. Given a question, our model creates a small-scale relevant training
graph to perform more accurate AS2. The nodes of the graphs are question-answer
pairs, where the answers are also sentences. We train and apply
state-of-the-art models for computing scores between question-question,
question-answer, and answer-answer pairs. We apply thresholding to the
relevance scores for creating edges between nodes. Finally, we apply Graph
Neural Networks to the obtained graph to perform joint learning and inference
for solving the AS2 task. The experiments on two well-known academic benchmarks
and a real-world dataset show that our approach consistently outperforms

DBSCAN is a fundamental density-based clustering technique that identifies
any arbitrary shape of the clusters. However, it becomes infeasible while
handling big data. On the other hand, centroid-based clustering is important
for detecting patterns in a dataset since unprocessed data points can be
labeled to their nearest centroid. However, it can not detect non-spherical
clusters. For a large data, it is not feasible to store and compute labels of
every samples. These can be done as and when the information is required. The
purpose can be accomplished when clustering act as a tool to identify cluster
representatives and query is served by assigning cluster labels of nearest
representative. In this paper, we propose an Incremental Prototype-based DBSCAN
(IPD) algorithm which is designed to identify arbitrary-shaped clusters for
large-scale data. Additionally, it chooses a set of representatives for each
cluster.

Inverse Kinematics (IK) solves the problem of mapping from the Cartesian
space to the joint configuration space of a robotic arm. It has a wide range of
applications in areas such as computer graphics, protein structure prediction,
and robotics. With the vast advances of artificial neural networks (NNs), many
researchers recently turned to data-driven approaches to solving the IK
problem. Unfortunately, NNs become inadequate for robotic arms with redundant
Degrees-of-Freedom (DoFs). This is because such arms may have multiple angle
solutions to reach the same desired pose, while typical NNs only implement
one-to-one mapping functions, which associate just one consistent output for a
given input. In order to train usable NNs to solve the IK problem, most
existing works employ customized training datasets, in which every desired pose
only has one angle solution. This inevitably limits the generalization and
automation of the proposed approaches. This paper breaks through at two fronts:
(1) a systematic and mechanical approach to training data collection that
covers the entire working space of the robotic arm, and can be fully automated
and done only once after the arm is developed; and (2) a novel NN-based
framework that can leverage the redundant DoFs to produce multiple angle
solutions to any given desired pose of the robotic arm. The latter is
especially useful for robotic applications such as obstacle avoidance and
posture imitation.

Attention to data-driven optimization approaches, including the well-known
stochastic gradient descent method, has grown significantly over recent
decades, but data-driven constraints have rarely been studied, because of the
computational challenges of projections onto the feasible set defined by these
hard constraints. In this paper, we focus on the non-smooth convex-concave
stochastic minimax regime and formulate the data-driven constraints as
expectation constraints. The minimax expectation constrained problem subsumes a
broad class of real-world applications, including two-player zero-sum game and
data-driven robust optimization. We propose a class of efficient primal-dual
algorithms to tackle the minimax expectation-constrained problem, and show that
our algorithms converge at the optimal rate of
$\mathcal{O}(\frac{1}{\sqrt{N}})$. We demonstrate the practical efficiency of
our algorithms by conducting numerical experiments on large-scale real-world

Multiple moving sound source localization in real-world scenarios remains a
challenging issue due to interaction between sources, time-varying
trajectories, distorted spatial cues, etc. In this work, we propose to use deep
learning techniques to learn competing and time-varying direct-path phase
differences for localizing multiple moving sound sources. A causal
convolutional recurrent neural network is designed to extract the direct-path
phase difference sequence from signals of each microphone pair. To avoid the
assignment ambiguity and the problem of uncertain output-dimension encountered
when simultaneously predicting multiple targets, the learning target is
designed in a weighted sum format, which encodes source activity in the weight
and direct-path phase differences in the summed value. The learned direct-path
phase differences for all microphone pairs can be directly used to construct
the spatial spectrum according to the formulation of steered response power
(SRP). This deep neural network (DNN) based SRP method is referred to as
SRP-DNN. The locations of sources are estimated by iteratively detecting and
removing the dominant source from the spatial spectrum, in which way the
interaction between sources is reduced. Experimental results on both simulated
and real-world data show the superiority of the proposed method in the presence
of noise and reverberation.

Anomaly detection is a widely studied task for a broad variety of data types;
among them, multiple time series appear frequently in applications, including
for example, power grids and traffic networks. Detecting anomalies for multiple
time series, however, is a challenging subject, owing to the intricate
interdependencies among the constituent series. We hypothesize that anomalies
occur in low density regions of a distribution and explore the use of
normalizing flows for unsupervised anomaly detection, because of their superior
quality in density estimation. Moreover, we propose a novel flow model by
imposing a Bayesian network among constituent series. A Bayesian network is a
directed acyclic graph (DAG) that models causal relationships; it factorizes
the joint probability of the series into the product of easy-to-evaluate
conditional probabilities. We call such a graph-augmented normalizing flow
approach GANF and propose joint estimation of the DAG with flow parameters. We
conduct extensive experiments on real-world datasets and demonstrate the
effectiveness of GANF for density estimation, anomaly detection, and
identification of time series distribution drift.

Transformer models cannot easily scale to long sequences due to their O(N^2)
time and space complexity. This has led to Transformer variants seeking to
lessen computational complexity, such as Longformer and Performer. While such
models have theoretically greater efficiency, their effectiveness on real NLP
tasks has not been well studied. We benchmark 7 variants of Transformer models
on 5 difficult NLP tasks and 7 datasets. We design experiments to isolate the
effect of pretraining and hyperparameter settings, to focus on their capacity
for long-range attention. Moreover, we present various methods to investigate
attention behaviors, to illuminate model details beyond metric scores. We find
that attention of long-range transformers has advantages on content selection
and query-guided decoding, but they come with previously unrecognized drawbacks
such as insufficient attention to distant tokens.

Conversational automatic speech recognition (ASR) is a task to recognize
conversational speech including multiple speakers. Unlike sentence-level ASR,
conversational ASR can naturally take advantages from specific characteristics
of conversation, such as role preference and topical coherence. This paper
proposes a conversational ASR model which explicitly learns conversation-level
characteristics under the prevalent end-to-end neural framework. The highlights
of the proposed model are twofold. First, a latent variational module (LVM) is
attached to a conformer-based encoder-decoder ASR backbone to learn role
preference and topical coherence. Second, a topic model is specifically adopted
to bias the outputs of the decoder to words in the predicted topics.
Experiments on two Mandarin conversational ASR tasks show that the proposed
model achieves a maximum 12% relative character error rate (CER) reduction.

Misbehavior in online social networks (OSN) is an ever-growing phenomenon.
The research to date tends to focus on the deployment of machine learning to
identify and classify types of misbehavior such as bullying, aggression, and
racism to name a few. The main goal of identification is to curb natural and
mechanical misconduct and make OSNs a safer place for social discourse. Going
beyond past works, we perform a longitudinal study of a large selection of
Twitter profiles, which enables us to characterize profiles in terms of how
consistently they post highly toxic content. Our data spans 14 years of tweets
from 122K Twitter profiles and more than 293M tweets. From this data, we
selected the most extreme profiles in terms of consistency of toxic content and
examined their tweet texts, and the domains, hashtags, and URLs they shared. We
found that these selected profiles keep to a narrow theme with lower diversity
in hashtags, URLs, and domains, they are thematically similar to each other (in
a coordinated manner, if not through intent), and have a high likelihood of
bot-like behavior (likely to have progenitors with intentions to influence).
Our work contributes a substantial and longitudinal online misbehavior dataset
to the research community and establishes the consistency of a profile's toxic
behavior as a useful factor when exploring misbehavior as potential accessories
to influence operations on OSNs.

Lowering costs by driving high utilization across deep learning workloads is
a crucial lever for cloud providers. We present Singularity, Microsoft's
globally distributed scheduling service for highly-efficient and reliable
execution of deep learning training and inference workloads. At the heart of
Singularity is a novel, workload-aware scheduler that can transparently preempt
and elastically scale deep learning workloads to drive high utilization without
impacting their correctness or performance, across a global fleet of AI
accelerators (e.g., GPUs, FPGAs).
  All jobs in Singularity are preemptable, migratable, and dynamically
resizable (elastic) by default: a live job can be dynamically and transparently
(a) preempted and migrated to a different set of nodes, cluster, data center or
a region and resumed exactly from the point where the execution was preempted,
and (b) resized (i.e., elastically scaled-up/down) on a varying set of
accelerators of a given type. Our mechanisms are transparent in that they do
not require the user to make any changes to their code or require using any
custom libraries that may limit flexibility. Additionally, our approach
significantly improves the reliability of deep learning workloads. We show that
the resulting efficiency and reliability gains with Singularity are achieved
with negligible impact on the steady-state performance. Finally, our design
approach is agnostic of DNN architectures and handles a variety of parallelism
strategies (e.g., data/pipeline/model parallelism).

Knowledge distillation aims to enhance the performance of a lightweight
student model by exploiting the knowledge from a pre-trained cumbersome teacher
model. However, in the traditional knowledge distillation, teacher predictions
are only used to provide the supervisory signal for the last layer of the
student model, which may result in those shallow student layers lacking
accurate training guidance in the layer-by-layer back propagation and thus
hinders effective knowledge transfer. To address this issue, we propose
Deeply-Supervised Knowledge Distillation (DSKD), which fully utilizes class
predictions and feature maps of the teacher model to supervise the training of
shallow student layers. A loss-based weight allocation strategy is developed in
DSKD to adaptively balance the learning process of each shallow layer, so as to
further improve the student performance. Extensive experiments show that the
performance of DSKD consistently exceeds state-of-the-art methods on various
teacher-student models, confirming the effectiveness of our proposed method.

An unsupervised point cloud object retrieval and pose estimation method,
called PCRP, is proposed in this work. It is assumed that there exists a
gallery point cloud set that contains point cloud objects with given pose
orientation information. PCRP attempts to register the unknown point cloud
object with those in the gallery set so as to achieve content-based object
retrieval and pose estimation jointly, where the point cloud registration task
is built upon an enhanced version of the unsupervised R-PointHop method.
Experiments on the ModelNet40 dataset demonstrate the superior performance of
PCRP in comparison with traditional and learning based methods.

Direct-path relative transfer function (DP-RTF) refers to the ratio between
the direct-path acoustic transfer functions of two microphone channels. Though
DP-RTF fully encodes the sound spatial cues and serves as a reliable
localization feature, it is often erroneously estimated in the presence of
noise and reverberation. This paper proposes to learn DP-RTF with deep neural
networks for robust binaural sound source localization. A DP-RTF learning
network is designed to regress the binaural sensor signals to a real-valued
representation of DP-RTF. It consists of a branched convolutional neural
network module to separately extract the inter-channel magnitude and phase
patterns, and a convolutional recurrent neural network module for joint feature
learning. To better explore the speech spectra to aid the DP-RTF estimation, a
monaural speech enhancement network is used to recover the direct-path
spectrograms from the noisy ones. The enhanced spectrograms are stacked onto
the noisy spectrograms to act as the input of the DP-RTF learning network. We
train one unique DP-RTF learning network using many different binaural arrays
to enable the generalization of DP-RTF learning across arrays. This way avoids
time-consuming training data collection and network retraining for a new array,
which is very useful in practical application. Experimental results on both
simulated and real-world data show the effectiveness of the proposed method for
direction of arrival (DOA) estimation in the noisy and reverberant environment,
and a good generalization ability to unseen binaural arrays.

Reliability is an important tool for evaluating the performance of modern
networks. Currently, it is NP-hard and #P-hard to calculate the exact
reliability of a binary-state network when the reliability of each component is
assumed to be fixed. However, this assumption is unrealistic because the
reliability of each component always varies with time. To meet this practical
requirement, we propose a new algorithm called the LSTM-BAT-MCS, based on long
short-term memory (LSTM), the Monte Carlo simulation (MCS), and the
binary-adaption-tree algorithm (BAT). The superiority of the proposed
LSTM-BAT-MCS was demonstrated by experimental results of three benchmark
networks with at most 10-4 mean square error.

Graphs are widely used to model the complex relationships among entities. As
a powerful tool for graph analytics, graph neural networks (GNNs) have recently
gained wide attention due to its end-to-end processing capabilities. With the
proliferation of cloud computing, it is increasingly popular to deploy the
services of complex and resource-intensive model training and inference in the
cloud due to its prominent benefits. However, GNN training and inference
services, if deployed in the cloud, will raise critical privacy concerns about
the information-rich and proprietary graph data (and the resulting model).
While there has been some work on secure neural network training and inference,
they all focus on convolutional neural networks handling images and text rather
than complex graph data with rich structural information. In this paper, we
design, implement, and evaluate SecGNN, the first system supporting
privacy-preserving GNN training and inference services in the cloud. SecGNN is
built from a synergy of insights on lightweight cryptography and machine
learning techniques. We deeply examine the procedure of GNN training and
inference, and devise a series of corresponding secure customized protocols to
support the holistic computation. Extensive experiments demonstrate that SecGNN
achieves comparable plaintext training and inference accuracy, with practically
affordable performance.

With the explosive growth of new graduates with research degrees every year,
unprecedented challenges arise for early-career researchers to find a job at a
suitable institution. This study aims to understand the behavior of academic
job transition and hence recommend suitable institutions for PhD graduates.
Specifically, we design a deep learning model to predict the career move of
early-career researchers and provide suggestions. The design is built on top of
scholarly/academic networks, which contains abundant information about
scientific collaboration among scholars and institutions. We construct a
heterogeneous scholarly network to facilitate the exploring of the behavior of
career moves and the recommendation of institutions for scholars. We devise an
unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax)
which aggregates attention mechanism and mutual information for institution
recommendation. Moreover, we propose scholar attention and meta-path attention
to discover the hidden relationships between several meta-paths. With these
mechanisms, HAI provides ordered recommendations with explainability. We
evaluate HAI upon a real-world dataset against baseline methods. Experimental
results verify the effectiveness and efficiency of our approach.

The recent advances in the data science field in the last few decades have
benefitted many other fields including Structural Health Monitoring (SHM).
Particularly, Artificial Intelligence (AI) such as Machine Learning (ML) and
Deep Learning (DL) methods for vibration-based damage diagnostics of civil
structures has been utilized extensively due to the observed high performances
in learning from data. Along with diagnostics, damage prognostics is also
vitally important for estimating the remaining useful life of civil structures.
Currently, AI-based data-driven methods used for damage diagnostics and
prognostics centered on historical data of the structures and require a
substantial amount of data for prediction models. Although some of these
methods are generative-based models, they are used to perform ML or DL tasks
such as classification, regression, clustering, etc. after learning the
distribution of the data. In this study, a variant of Generative Adversarial
Networks (GAN), Cycle-Consistent Wasserstein Deep Convolutional GAN with
Gradient Penalty (CycleWDCGAN-GP) model is developed to investigate the
"transition of structural dynamic signature from an undamaged-to-damaged state"
and "if this transition can be employed for predictive damage detection". The
outcomes of this study demonstrate that the proposed model can accurately
generate damaged responses from undamaged responses or vice versa. In other
words, it will be possible to understand the damaged condition while the
structure is still in a healthy (undamaged) condition or vice versa with the
proposed methodology. This will enable a more proactive approach in overseeing
the life-cycle performance as well as in predicting the remaining useful life
of structures.

K-means defines one of the most employed centroid-based clustering algorithms
with performances tied to the data's embedding. Intricate data embeddings have
been designed to push $K$-means performances at the cost of reduced theoretical
guarantees and interpretability of the results. Instead, we propose preserving
the intrinsic data space and augment K-means with a similarity measure
invariant to non-rigid transformations. This enables (i) the reduction of
intrinsic nuisances associated with the data, reducing the complexity of the
clustering task and increasing performances and producing state-of-the-art
results, (ii) clustering in the input space of the data, leading to a fully
interpretable clustering algorithm, and (iii) the benefit of convergence

Graph Convolutional Networks (GCNs) have achieved impressive performance in a
wide variety of areas, attracting considerable attention. The core step of GCNs
is the information-passing framework that considers all information from
neighbors to the central vertex to be equally important. Such equal importance,
however, is inadequate for scale-free networks, where hub vertices propagate
more dominant information due to vertex imbalance. In this paper, we propose a
novel centrality-based framework named CenGCN to address the inequality of
information. This framework first quantifies the similarity between hub
vertices and their neighbors by label propagation with hub vertices. Based on
this similarity and centrality indices, the framework transforms the graph by
increasing or decreasing the weights of edges connecting hub vertices and
adding self-connections to vertices. In each non-output layer of the GCN, this
framework uses a hub attention mechanism to assign new weights to connected
non-hub vertices based on their common information with hub vertices. We
present two variants CenGCN\_D and CenGCN\_E, based on degree centrality and
eigenvector centrality, respectively. We also conduct comprehensive
experiments, including vertex classification, link prediction, vertex
clustering, and network visualization. The results demonstrate that the two
variants significantly outperform state-of-the-art baselines.

In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid
functions are most commonly employed as the predictor outputs. Such layers
often produce overconfident predictions rather than proper probabilistic
scores, which can thus harm the decision-making of `critical' perception
systems applied in autonomous driving and robotics. Given this, the experiments
in this work propose a probabilistic approach based on distributions calculated
out of the Logit layer scores of pre-trained networks. We demonstrate that
Maximum Likelihood (ML) and Maximum a-Posteriori (MAP) functions are more
suitable for probabilistic interpretations than SoftMax and Sigmoid-based
predictions for object recognition. We explore distinct sensor modalities via
RGB images and LiDARs (RV: range-view) data from the KITTI and Lyft Level-5
datasets, where our approach shows promising performance compared to the usual
SoftMax and Sigmoid layers, with the benefit of enabling interpretable
probabilistic predictions. Another advantage of the approach introduced in this
paper is that the ML and MAP functions can be implemented in existing trained
networks, that is, the approach benefits from the output of the Logit layer of
pre-trained networks. Thus, there is no need to carry out a new training phase
since the ML and MAP functions are used in the test/prediction phase.

Road network graphs provide critical information for autonomous-vehicle
applications, such as drivable areas that can be used for motion planning
algorithms. To find road network graphs, manually annotation is usually
inefficient and labor-intensive. Automatically detecting road network graphs
could alleviate this issue, but existing works still have some limitations. For
example, segmentation-based approaches could not ensure satisfactory topology
correctness, and graph-based approaches could not present precise enough
detection results. To provide a solution to these problems, we propose a novel
approach based on transformer and imitation learning in this paper. In view of
that high-resolution aerial images could be easily accessed all over the world
nowadays, we make use of aerial images in our approach. Taken as input an
aerial image, our approach iteratively generates road network graphs
vertex-by-vertex. Our approach can handle complicated intersection points with
various numbers of incident road segments. We evaluate our approach on a
publicly available dataset. The superiority of our approach is demonstrated
through the comparative experiments. Our work is accompanied with a
demonstration video which is available at
\url{https://tonyxuqaq.github.io/projects/RNGDet/}.

Convolutional Neural Networks (CNNs) are vulnerable to misclassifying images
when small perturbations are present. With the increasing prevalence of CNNs in
self-driving cars, it is vital to ensure these algorithms are robust to prevent
collisions from occurring due to failure in recognizing a situation. In the
Adversarial Self-Driving framework, a Generative Adversarial Network (GAN) is
implemented to generate realistic perturbations in an image that cause a
classifier CNN to misclassify data. This perturbed data is then used to train
the classifier CNN further. The Adversarial Self-driving framework is applied
to an image classification algorithm to improve the classification accuracy on
perturbed images and is later applied to train a self-driving car to drive in a
simulation. A small-scale self-driving car is also built to drive around a
track and classify signs. The Adversarial Self-driving framework produces
perturbed images through learning a dataset, as a result removing the need to
train on significant amounts of data. Experiments demonstrate that the
Adversarial Self-driving framework identifies situations where CNNs are
vulnerable to perturbations and generates new examples of these situations for
the CNN to train on. The additional data generated by the Adversarial
Self-driving framework provides sufficient data for the CNN to generalize to
the environment. Therefore, it is a viable tool to increase the resilience of
CNNs to perturbations. Particularly, in the real-world self-driving car, the
application of the Adversarial Self-Driving framework resulted in an 18 %
increase in accuracy, and the simulated self-driving model had no collisions in
30 minutes of driving.

Reinforcement learning constantly deals with hard integrals, for example when
computing expectations in policy evaluation and policy iteration. These
integrals are rarely analytically solvable and typically estimated with the
Monte Carlo method, which induces high variance in policy values and gradients.
In this work, we propose to replace Monte Carlo samples with low-discrepancy
point sets. We combine policy gradient methods with Randomized Quasi-Monte
Carlo, yielding variance-reduced formulations of policy gradient and
actor-critic algorithms. These formulations are effective for policy evaluation
and policy improvement, as they outperform state-of-the-art algorithms on
standardized continuous control benchmarks. Our empirical analyses validate the
intuition that replacing Monte Carlo with Quasi-Monte Carlo yields
significantly more accurate gradient estimates.

Automatic emotion recognition plays a key role in computer-human interaction
as it has the potential to enrich the next-generation artificial intelligence
with emotional intelligence. It finds applications in customer and/or
representative behavior analysis in call centers, gaming, personal assistants,
and social robots, to mention a few. Therefore, there has been an increasing
demand to develop robust automatic methods to analyze and recognize the various
emotions. In this paper, we propose a neural network-based emotion recognition
framework that uses a late fusion of transfer-learned and fine-tuned models
from speech and text modalities. More specifically, we i) adapt a residual
network (ResNet) based model trained on a large-scale speaker recognition task
using transfer learning along with a spectrogram augmentation approach to
recognize emotions from speech, and ii) use a fine-tuned bidirectional encoder
representations from transformers (BERT) based model to represent and recognize
emotions from the text. The proposed system then combines the ResNet and
BERT-based model scores using a late fusion strategy to further improve the
emotion recognition performance. The proposed multimodal solution addresses the
data scarcity limitation in emotion recognition using transfer learning, data
augmentation, and fine-tuning, thereby improving the generalization performance
of the emotion recognition models. We evaluate the effectiveness of our
proposed multimodal approach on the interactive emotional dyadic motion capture
(IEMOCAP) dataset. Experimental results indicate that both audio and text-based
models improve the emotion recognition performance and that the proposed
multimodal solution achieves state-of-the-art results on the IEMOCAP benchmark.

Mobile Crowdsensing systems are vulnerable to various attacks as they build
on non-dedicated and ubiquitous properties. Machine learning (ML)-based
approaches are widely investigated to build attack detection systems and ensure
MCS systems security. However, adversaries that aim to clog the sensing
front-end and MCS back-end leverage intelligent techniques, which are
challenging for MCS platform and service providers to develop appropriate
detection frameworks against these attacks. Generative Adversarial Networks
(GANs) have been applied to generate synthetic samples, that are extremely
similar to the real ones, deceiving classifiers such that the synthetic samples
are indistinguishable from the originals. Previous works suggest that GAN-based
attacks exhibit more crucial devastation than empirically designed attack
samples, and result in low detection rate at the MCS platform. With this in
mind, this paper aims to detect intelligently designed illegitimate sensing
service requests by integrating a GAN-based model. To this end, we propose a
two-level cascading classifier that combines the GAN discriminator with a
binary classifier to prevent adversarial fake tasks. Through simulations, we
compare our results to a single-level binary classifier, and the numeric
results show that proposed approach raises Adversarial Attack Detection Rate
(AADR), from $0\%$ to $97.5\%$ by KNN/NB, from $45.9\%$ to $100\%$ by Decision
Tree. Meanwhile, with two-levels classifiers, Original Attack Detection Rate
(OADR) improves for the three binary classifiers, with comparison, such as NB
from $26.1\%$ to $61.5\%$.

Vision Transformers (ViTs) take all the image patches as tokens and construct
multi-head self-attention (MHSA) among them. Complete leverage of these image
tokens brings redundant computations since not all the tokens are attentive in
MHSA. Examples include that tokens containing semantically meaningless or
distractive image backgrounds do not positively contribute to the ViT
predictions. In this work, we propose to reorganize image tokens during the
feed-forward process of ViT models, which is integrated into ViT during
training. For each forward inference, we identify the attentive image tokens
between MHSA and FFN (i.e., feed-forward network) modules, which is guided by
the corresponding class token attention. Then, we reorganize image tokens by
preserving attentive image tokens and fusing inattentive ones to expedite
subsequent MHSA and FFN computations. To this end, our method EViT improves
ViTs from two perspectives. First, under the same amount of input image tokens,
our method reduces MHSA and FFN computation for efficient inference. For
instance, the inference speed of DeiT-S is increased by 50% while its
recognition accuracy is decreased by only 0.3% for ImageNet classification.
Second, by maintaining the same computational cost, our method empowers ViTs to
take more image tokens as input for recognition accuracy improvement, where the
image tokens are from higher resolution images. An example is that we improve
the recognition accuracy of DeiT-S by 1% for ImageNet classification at the
same computational cost of a vanilla DeiT-S. Meanwhile, our method does not
introduce more parameters to ViTs. Experiments on the standard benchmarks show
the effectiveness of our method. The code is available at
https://github.com/youweiliang/evit

Recent years have seen the adoption of Machine Learning (ML) techniques to
predict the performance of large-scale applications, mostly at a coarse level.
In contrast, we propose to use ML techniques for performance prediction at much
finer granularity, namely at the levels of Basic Block (BB), which are the
single entry-single exit code blocks that are used as analysis tools by all
compilers to break down a large code into manageable pieces. Utilizing ML and
BB analysis together can enable scalable hardware-software co-design beyond the
current state of the art. In this work, we extrapolate the basic block
execution counts of GPU applications for large inputs sizes from the counts of
smaller input sizes of the same application.
  We employ two ML models, a Poisson Neural Network (PNN) and a Bayesian
Regularization Backpropagation Neural Network (BR-BPNN). We train both models
using the lowest input values of the application and random input values to
predict basic block counts. Results show that our models accurately predict the
basic block execution counts of 16 benchmark applications. For PNN and BR-BPNN
models, we achieve an average accuracy of 93.5% and 95.6%, respectively, while
extrapolating the basic block counts for large input sets when the model is
trained using smaller input sets. Additionally, the models show an average
accuracy of 97.7% and 98.1%, respectively, while predicting basic block counts
on random instances.

Scalp electroencephalogram (EEG) signals inherently have a low
signal-to-noise ratio due to the way the signal is electrically transduced.
Temporal and spatial information must be exploited to achieve accurate
detection of seizure events. Most popular approaches to seizure detection using
deep learning do not jointly model this information or require multiple passes
over the signal, which makes the systems inherently non-causal. In this paper,
we exploit both simultaneously by converting the multichannel signal to a
grayscale image and using transfer learning to achieve high performance. The
proposed system is trained end-to-end with only very simple pre- and
postprocessing operations which are computationally lightweight and have low
latency, making them conducive to clinical applications that require real-time
processing. We have achieved a performance of 42.05% sensitivity with 5.78
false alarms per 24 hours on the development dataset of v1.5.2 of the Temple
University Hospital Seizure Detection Corpus. On a single-core CPU operating at
1.7 GHz, the system runs faster than real-time (0.58 xRT), uses 16 Gbytes of
memory, and has a latency of 300 msec.

Modern connected vehicles (CVs) frequently require diverse types of content
for mission-critical decision-making and onboard users' entertainment. These
contents are required to be fully delivered to the requester CVs within
stringent deadlines that the existing radio access technology (RAT) solutions
may fail to ensure. Motivated by the above consideration, this paper exploits
content caching in vehicular edge networks (VENs) with a software-defined
user-centric virtual cell (VC) based RAT solution for delivering the requested
contents from a proximity edge server. Moreover, to capture the heterogeneous
demands of the CVs, we introduce a preference-popularity tradeoff in their
content request model. To that end, we formulate a joint optimization problem
for content placement, CV scheduling, VC configuration, VC-CV association and
radio resource allocation to minimize long-term content delivery delay.
However, the joint problem is highly complex and cannot be solved efficiently
in polynomial time. As such, we decompose the original problem into a cache
placement problem and a content delivery delay minimization problem given the
cache placement policy. We use deep reinforcement learning (DRL) as a learning
solution for the first sub-problem. Furthermore, we transform the delay
minimization problem into a priority-based weighted sum rate (WSR) maximization
problem, which is solved leveraging maximum bipartite matching (MWBM) and a
simple linear search algorithm. Our extensive simulation results demonstrate
the effectiveness of the proposed method compared to existing baselines in
terms of cache hit ratio (CHR), deadline violation and content delivery delay.

In the last year, new neural architectures and multilingual pre-trained
models have been released for Russian, which led to performance evaluation
problems across a range of language understanding tasks.
  This paper presents Russian SuperGLUE 1.1, an updated benchmark styled after
GLUE for Russian NLP models. The new version includes a number of technical,
user experience and methodological improvements, including fixes of the
benchmark vulnerabilities unresolved in the previous version: novel and
improved tests for understanding the meaning of a word in context (RUSSE) along
with reading comprehension and common sense reasoning (DaNetQA, RuCoS, MuSeRC).
Together with the release of the updated datasets, we improve the benchmark
toolkit based on \texttt{jiant} framework for consistent training and
evaluation of NLP-models of various architectures which now supports the most
recent models for Russian. Finally, we provide the integration of Russian
SuperGLUE with a framework for industrial evaluation of the open-source models,
MOROCCO (MOdel ResOurCe COmparison), in which the models are evaluated
according to the weighted average metric over all tasks, the inference speed,
and the occupied amount of RAM. Russian SuperGLUE is publicly available at
https://russiansuperglue.com/.

In this work, we present CleanUNet, a causal speech denoising model on the
raw waveform. The proposed model is based on an encoder-decoder architecture
combined with several self-attention blocks to refine its bottleneck
representations, which is crucial to obtain good results. The model is
optimized through a set of losses defined over both waveform and
multi-resolution spectrograms. The proposed method outperforms the
state-of-the-art models in terms of denoised speech quality from various
objective and subjective evaluation metrics. We release our code and models at
https://github.com/nvidia/cleanunet.

Safe reinforcement learning is a promising path toward applying reinforcement
learning algorithms to real-world problems, where suboptimal behaviors may lead
to actual negative consequences. In this work, we focus on the setting where
unsafe states can be avoided by planning ahead a short time into the future. In
this setting, a model-based agent with a sufficiently accurate model can avoid
unsafe states. We devise a model-based algorithm that heavily penalizes unsafe
trajectories, and derive guarantees that our algorithm can avoid unsafe states
under certain assumptions. Experiments demonstrate that our algorithm can
achieve competitive rewards with fewer safety violations in several continuous
control tasks.

Anomaly detection has a wide range of real-world applications, such as bank
fraud detection and cyber intrusion detection. In the past decade, a variety of
anomaly detection models have been developed, which lead to big progress
towards accurately detecting various anomalies. Despite the successes, anomaly
detection models still face many limitations. The most significant one is
whether we can trust the detection results from the models. In recent years,
the research community has spent a great effort to design trustworthy machine
learning models, such as developing trustworthy classification models. However,
the attention to anomaly detection tasks is far from sufficient. Considering
that many anomaly detection tasks are life-changing tasks involving human
beings, labeling someone as anomalies or fraudsters should be extremely
cautious. Hence, ensuring the anomaly detection models conducted in a
trustworthy fashion is an essential requirement to deploy the models to conduct
automatic decisions in the real world. In this brief survey, we summarize the
existing efforts and discuss open problems towards trustworthy anomaly
detection from the perspectives of interpretability, fairness, robustness, and
privacy-preservation.

Medical Research data used for prognostication deals with binary
classification problems in most of the cases. The endocrinological disorders
have data available and it can be leveraged using Machine Learning. The dataset
for Polycystic Ovary Syndrome is available, which is termed as an
endocrinological disorder in women. Non-Parametric Supervised Ensemble machine
learning methods can be used for prediction of the disorder in early stages. In
this paper we present the Bootstrap Aggregation Supervised Ensemble
Non-parametric method for prognostication that competes state-of-the-art
performance with accuracy of over 92% along with in depth analysis of the data.

In this work we challenge the common approach of using a one-to-one mapping
('translation') between the source and target domains in unsupervised domain
adaptation (UDA). Instead, we rely on stochastic translation to capture
inherent translation ambiguities. This allows us to (i) train more accurate
target networks by generating multiple outputs conditioned on the same source
image, leveraging both accurate translation and data augmentation for
appearance variability, (ii) impute robust pseudo-labels for the target data by
averaging the predictions of a source network on multiple translated versions
of a single target image and (iii) train and ensemble diverse networks in the
target domain by modulating the degree of stochasticity in the translations. We
report improvements over strong recent baselines, leading to state-of-the-art
UDA results on two challenging semantic segmentation benchmarks.

In this work, we train conditional Wasserstein generative adversarial
networks to effectively sample from the posterior of physics-based Bayesian
inference problems. The generator is constructed using a U-Net architecture,
with the latent information injected using conditional instance normalization.
The former facilitates a multiscale inverse map, while the latter enables the
decoupling of the latent space dimension from the dimension of the measurement,
and introduces stochasticity at all scales of the U-Net. We solve PDE-based
inverse problems to demonstrate the performance of our approach in quantifying
the uncertainty in the inferred field. Further, we show the generator can learn
inverse maps which are local in nature, which in turn promotes generalizability
when testing with out-of-distribution samples.

We consider the utility maximization problem under convex constraints with
regard to theoretical results which allow the formulation of algorithmic
solvers which make use of deep learning techniques. In particular for the case
of random coefficients, we prove a stochastic maximum principle (SMP), which
also holds for utility functions $U$ with $\mathrm{id}_{\mathbb{R}^{+}} \cdot
U'$ being not necessarily nonincreasing, like the power utility functions,
thereby generalizing the SMP proved by Li and Zheng (2018). We use this SMP
together with the strong duality property for defining a new algorithm, which
we call deep primal SMP algorithm. Numerical examples illustrate the
effectiveness of the proposed algorithm - in particular for higher-dimensional
problems and problems with random coefficients, which are either path dependent
or satisfy their own SDEs. Moreover, our numerical experiments for constrained
problems show that the novel deep primal SMP algorithm overcomes the deep SMP
algorithm's (see Davey and Zheng (2021)) weakness of erroneously producing the
value of the corresponding unconstrained problem. Furthermore, in contrast to
the deep controlled 2BSDE algorithm from Davey and Zheng (2021), this algorithm
is also applicable to problems with path dependent coefficients. As the deep
primal SMP algorithm even yields the most accurate results in many of our
studied problems, we can highly recommend its usage. Moreover, we propose a
learning procedure based on epochs which improved the results of our algorithm
even further. Implementing a semi-recurrent network architecture for the
control process turned out to be also a valuable advancement.

Accurate electricity demand forecasts play a crucial role in sustainable
power systems. To enable better decision-making especially for demand
flexibility of the end-user, it is necessary to provide not only accurate but
also understandable and actionable forecasts. To provide accurate forecasts
Global Forecasting Models (GFM) trained across time series have shown superior
results in many demand forecasting competitions and real-world applications
recently, compared with univariate forecasting approaches. We aim to fill the
gap between the accuracy and the interpretability in global forecasting
approaches. In order to explain the global model forecasts, we propose Local
Interpretable Model-agnostic Rule-based Explanations for Forecasting (LIMREF),
a local explainer framework that produces k-optimal impact rules for a
particular forecast, considering the global forecasting model as a black-box
model, in a model-agnostic way. It provides different types of rules that
explain the forecast of the global model and the counterfactual rules, which
provide actionable insights for potential changes to obtain different outputs
for given instances. We conduct experiments using a large-scale electricity
demand dataset with exogenous features such as temperature and calendar
effects. Here, we evaluate the quality of the explanations produced by the
LIMREF framework in terms of both qualitative and quantitative aspects such as
accuracy, fidelity, and comprehensibility and benchmark those against other
local explainers.

Real-world data is high-dimensional: a book, image, or musical performance
can easily contain hundreds of thousands of elements even after compression.
However, the most commonly used autoregressive models, Transformers, are
prohibitively expensive to scale to the number of inputs and layers needed to
capture this long-range structure. We develop Perceiver AR, an autoregressive,
modality-agnostic architecture which uses cross-attention to map long-range
inputs to a small number of latents while also maintaining end-to-end causal
masking. Perceiver AR can directly attend to over a hundred thousand tokens,
enabling practical long-context density estimation without the need for
hand-crafted sparsity patterns or memory mechanisms. When trained on images or
music, Perceiver AR generates outputs with clear long-term coherence and
structure. Our architecture also obtains state-of-the-art likelihood on
long-sequence benchmarks, including 64 x 64 ImageNet images and PG-19 books.

Explainability is motivated by the lack of transparency of black-box Machine
Learning approaches, which do not foster trust and acceptance of Machine
Learning algorithms. This also happens in the Predictive Process Monitoring
field, where predictions, obtained by applying Machine Learning techniques,
need to be explained to users, so as to gain their trust and acceptance. In
this work, we carry on a user evaluation on explanation approaches for
Predictive Process Monitoring aiming at investigating whether and how the
explanations provided (i) are understandable; (ii) are useful in decision
making tasks;(iii) can be further improved for process analysts, with different
Machine Learning expertise levels. The results of the user evaluation show
that, although explanation plots are overall understandable and useful for
decision making tasks for Business Process Management users -- with and without
experience in Machine Learning -- differences exist in the comprehension and
usage of different plots, as well as in the way users with different Machine
Learning expertise understand and use them.

With growing concerns regarding data privacy and rapid increase in data
volume, Federated Learning(FL) has become an important learning paradigm.
However, jointly learning a deep neural network model in a FL setting proves to
be a non-trivial task because of the complexities associated with the neural
networks, such as varied architectures across clients, permutation invariance
of the neurons, and presence of non-linear transformations in each layer. This
work introduces a novel Federated Heterogeneous Neural Networks (FedHeNN)
framework that allows each client to build a personalised model without
enforcing a common architecture across clients. This allows each client to
optimize with respect to local data and compute constraints, while still
benefiting from the learnings of other (potentially more powerful) clients. The
key idea of FedHeNN is to use the instance-level representations obtained from
peer clients to guide the simultaneous training on each client. The extensive
experimental results demonstrate that the FedHeNN framework is capable of
learning better performing models on clients in both the settings of
homogeneous and heterogeneous architectures across clients.

Autofluorescence lifetime images reveal unique characteristics of endogenous
fluorescence in biological samples. Comprehensive understanding and clinical
diagnosis rely on co-registration with the gold standard, histology images,
which is extremely challenging due to the difference of both images. Here, we
show an unsupervised image-to-image translation network that significantly
improves the success of the co-registration using a conventional
optimisation-based regression network, applicable to autofluorescence lifetime
images at different emission wavelengths. A preliminary blind comparison by
experienced researchers shows the superiority of our method on co-registration.
The results also indicate that the approach is applicable to various image
formats, like fluorescence intensity images. With the registration, stitching
outcomes illustrate the distinct differences of the spectral lifetime across an
unstained tissue, enabling macro-level rapid visual identification of lung
cancer and cellular-level characterisation of cell variants and common types.
The approach could be effortlessly extended to lifetime images beyond this
range and other staining technologies.

Many measurement modalities which perform imaging by probing an object
pixel-by-pixel, such as via Photoacoustic Microscopy, produce a
multi-dimensional feature (typically a time-domain signal) at each pixel. In
principle, the many degrees of freedom in the time-domain signal would admit
the possibility of significant multi-modal information being implicitly
present, much more than a single scalar "brightness", regarding the underlying
targets being observed. However, the measured signal is neither a weighted-sum
of basis functions (such as principal components) nor one of a set of
prototypes (K-means), which has motivated the novel clustering method proposed
here. Signals are clustered based on their shape, but not amplitude, via
angular distance and centroids are calculated as the direction of maximal
intra-cluster variance, resulting in a clustering algorithm capable of learning
centroids (signal shapes) that are related to the underlying, albeit unknown,
target characteristics in a scalable and noise-robust manner.

Cross-modal retrieval aims to search for data with similar semantic meanings
across different content modalities. However, cross-modal retrieval requires
huge amounts of storage and retrieval time since it needs to process data in
multiple modalities. Existing works focused on learning single-source compact
features such as binary hash codes that preserve similarities between different
modalities. In this work, we propose a jointly learned deep hashing and
quantization network (HQ) for cross-modal retrieval. We simultaneously learn
binary hash codes and quantization codes to preserve semantic information in
multiple modalities by an end-to-end deep learning architecture. At the
retrieval step, binary hashing is used to retrieve a subset of items from the
search space, then quantization is used to re-rank the retrieved items. We
theoretically and empirically show that this two-stage retrieval approach
provides faster retrieval results while preserving accuracy. Experimental
results on the NUS-WIDE, MIR-Flickr, and Amazon datasets demonstrate that HQ
achieves boosts of more than 7% in precision compared to supervised neural
network-based compact coding models.

Future improvements in particle accelerator performance is predicated on
increasingly accurate online modeling of accelerators. Hysteresis effects in
magnetic, mechanical, and material components of accelerators are often
neglected in online accelerator models used to inform control algorithms, even
though reproducibility errors from systems exhibiting hysteresis are not
negligible in high precision accelerators. In this work, we combine the
classical Preisach model of hysteresis with machine learning techniques to
efficiently create non-parametric, high-fidelity models of arbitrary systems
exhibiting hysteresis. We demonstrate that our technique accurately predicts
hysteresis effects in physical accelerator magnets. We also experimentally
demonstrate how these methods can be used in-situ, where the hysteresis model
is combined with a Bayesian statistical model of the beam response, allowing
characterization of hysteresis in accelerator magnets solely from measurements
of the beam. Furthermore, we explore how using these joint hysteresis-beam
models allows us to overcome optimization performance limitations when
hysteresis effects are ignored.

Multi-agent reinforcement learning (MARL) is a promising framework for
solving complex tasks with many agents. However, a key challenge in MARL is
defining private utility functions that ensure coordination when training
decentralized agents. This challenge is especially prevalent in unstructured
tasks with sparse rewards and many agents. We show that successor features can
help address this challenge by disentangling an individual agent's impact on
the global value function from that of all other agents. We use this
disentanglement to compactly represent private utilities that support stable
training of decentralized agents in unstructured tasks. We implement our
approach using a centralized training, decentralized execution architecture and
test it in a variety of multi-agent environments. Our results show improved
performance and training time relative to existing methods and suggest that
disentanglement of successor features offers a promising approach to
coordination in MARL.

We describe a light-weight yet performant system for hyper-parameter
optimization that approximately minimizes an overall scalar cost function that
is obtained by combining multiple performance objectives using a
target-priority-limit scalarizer. It also supports a trade-off mode, where the
goal is to find an appropriate trade-off among objectives by interacting with
the user. We focus on the common scenario where there are on the order of tens
of hyper-parameters, each with various attributes such as a range of continuous
values, or a finite list of values, and whether it should be treated on a
linear or logarithmic scale. The system supports multiple asynchronous
simulations and is robust to simulation stragglers and failures.

Human-machine interaction has been around for several decades now, with new
applications emerging every day. One of the major goals that remain to be
achieved is designing an interaction similar to how a human interacts with
another human. Therefore, there is a need to develop interactive systems that
could replicate a more realistic and easier human-machine interaction. On the
other hand, developers and researchers need to be aware of state-of-the-art
methodologies being used to achieve this goal. We present this survey to
provide researchers with state-of-the-art data fusion technologies implemented
using multiple inputs to accomplish a task in the robotic application domain.
Moreover, the input data modalities are broadly classified into uni-modal and
multi-modal systems and their application in myriad industries, including the
health care industry, which contributes to the medical industry's future
development. It will help the professionals to examine patients using different
modalities. The multi-modal systems are differentiated by a combination of
inputs used as a single input, e.g., gestures, voice, sensor, and haptic
feedback. All these inputs may or may not be fused, which provides another
classification of multi-modal systems. The survey concludes with a summary of
technologies in use for multi-modal systems.

Video frame interpolation (VFI) is one of the fundamental research areas in
video processing and there has been extensive research on novel and enhanced
interpolation algorithms. The same is not true for quality assessment of the
interpolated content. In this paper, we describe a subjective quality study for
VFI based on a newly developed video database, BVI-VFI. BVI-VFI contains 36
reference sequences at three different frame rates and 180 distorted videos
generated using five conventional and learning based VFI algorithms. Subjective
opinion scores have been collected from 60 human participants, and then
employed to evaluate eight popular quality metrics, including PSNR, SSIM and
LPIPS which are all commonly used for assessing VFI methods. The results
indicate that none of these metrics provide acceptable correlation with the
perceived quality on interpolated content, with the best-performing metric,
LPIPS, offering a SROCC value below 0.6. Our findings show that there is an
urgent need to develop a bespoke perceptual quality metric for VFI. The BVI-VFI
dataset is publicly available and can be accessed at
https://danielism97.github.io/BVI-VFI/.

The ability to accurately predict human behavior is central to the safety and
efficiency of robot autonomy in interactive settings. Unfortunately, robots
often lack access to key information on which these predictions may hinge, such
as people's goals, attention, and willingness to cooperate. Dual control theory
addresses this challenge by treating unknown parameters of a predictive model
as stochastic hidden states and inferring their values at runtime using
information gathered during system operation. While able to optimally and
automatically trade off exploration and exploitation, dual control is
computationally intractable for general interactive motion planning, mainly due
to the fundamental coupling between robot trajectory optimization and human
intent inference. In this paper, we present a novel algorithmic approach to
enable active uncertainty reduction for interactive motion planning based on
the implicit dual control paradigm. Our approach relies on sampling-based
approximation of stochastic dynamic programming, leading to a model predictive
control problem that can be readily solved by real-time gradient-based
optimization methods. The resulting policy is shown to preserve the dual
control effect for a broad class of predictive human models with both
continuous and categorical uncertainty. The efficacy of our approach is
demonstrated with simulated driving examples.

High school science classrooms across the United States are answering calls
to make computation a part of science learning. The problem is that there is
little known about the barriers to learning that computation might bring to a
science classroom or about how to help students overcome these challenges. This
case study explores these challenges from the perspectives of students in a
high school physics classroom with a newly revamped, computation-integrated
curriculum. Focusing mainly on interviews to center the perspectives of
students, we found that computation is a double-edged sword: It can make
science learning more authentic for students who are familiar with it, but it
can also generate frustration and an aversion towards physics for students who
are not.

Aerial robots can enhance their safe and agile navigation in complex and
cluttered environments by efficiently exploiting the information collected
during a given task. In this paper, we address the learning model predictive
control problem for quadrotors. We design a learning receding--horizon
nonlinear control strategy directly formulated on the system nonlinear manifold
configuration space SO(3)xR^3. The proposed approach exploits past successful
task iterations to improve the system performance over time while respecting
system dynamics and actuator constraints. We further relax its computational
complexity making it compatible with real-time quadrotor control requirements.
We show the effectiveness of the proposed approach in learning a minimum time
control task, respecting dynamics, actuators, and environment constraints.
Several experiments in simulation and real-world set-up validate the proposed
approach.

Network Function Virtualization (NFV) carries the potential for on-demand
deployment of network algorithms in virtual machines (VMs). In large clouds,
however, VM resource allocation incurs delays that hinder the dynamic scaling
of such NFV deployment. Parallel resource management is a promising direction
for boosting performance, but it may significantly increase the communication
overhead and the decline ratio of deployment attempts. Our work analyzes the
performance of various placement algorithms and provides empirical evidence
that state-of-the-art parallel resource management dramatically increases the
decline ratio of deterministic algorithms but hardly affects randomized
algorithms. We, therefore, introduce APSR -- an efficient parallel random
resource management algorithm that requires information only from a small
number of hosts and dynamically adjusts the degree of parallelism to provide
provable decline ratio guarantees. We formally analyze APSR, evaluate it on
real workloads, and integrate it into the popular OpenStack cloud management
platform. Our evaluation shows that APSR matches the throughput provided by
other parallel schedulers, while achieving up to 13x lower decline ratio and a
reduction of over 85% in communication overheads.

In this paper, we study the problem of finding mixed Nash equilibrium for
mean-field two-player zero-sum games. Solving this problem requires optimizing
over two probability distributions. We consider a quasistatic Wasserstein
gradient flow dynamics in which one probability distribution follows the
Wasserstein gradient flow, while the other one is always at the equilibrium.
Theoretical analysis are conducted on this dynamics, showing its convergence to
the mixed Nash equilibrium under mild conditions. Inspired by the continuous
dynamics of probability distributions, we derive a quasistatic Langevin
gradient descent method with inner-outer iterations, and test the method on
different problems, including training mixture of GANs.

This paper studies the sample complexity of learning the $k$ unknown centers
of a balanced Gaussian mixture model (GMM) in $\mathbb{R}^d$ with spherical
covariance matrix $\sigma^2\mathbf{I}$. In particular, we are interested in the
following question: what is the maximal noise level $\sigma^2$, for which the
sample complexity is essentially the same as when estimating the centers from
labeled measurements? To that end, we restrict attention to a Bayesian
formulation of the problem, where the centers are uniformly distributed on the
sphere $\sqrt{d}\mathcal{S}^{d-1}$. Our main results characterize the exact
noise threshold $\sigma^2$ below which the GMM learning problem, in the large
system limit $d,k\to\infty$, is as easy as learning from labeled observations,
and above which it is substantially harder. The threshold occurs at $\frac{\log
k}{d} = \frac12\log\left( 1+\frac{1}{\sigma^2} \right)$, which is the capacity
of the additive white Gaussian noise (AWGN) channel. Thinking of the set of $k$
centers as a code, this noise threshold can be interpreted as the largest noise
level for which the error probability of the code over the AWGN channel is
small. Previous works on the GMM learning problem have identified the minimum
distance between the centers as a key parameter in determining the statistical
difficulty of learning the corresponding GMM. While our results are only proved
for GMMs whose centers are uniformly distributed over the sphere, they hint
that perhaps it is the decoding error probability associated with the center
constellation as a channel code that determines the statistical difficulty of
learning the corresponding GMM, rather than just the minimum distance.

With the growing adoption of short-form video by social media platforms,
reducing the spread of misinformation through video posts has become a critical
challenge for social media providers. In this paper, we develop methods to
detect misinformation in social media posts, exploiting modalities such as
video and text. Due to the lack of large-scale public data for misinformation
detection in multi-modal datasets, we collect 160,000 video posts from Twitter,
and leverage self-supervised learning to learn expressive representations of
joint visual and textual data. In this work, we propose two new methods for
detecting semantic inconsistencies within short-form social media video posts,
based on contrastive learning and masked language modeling. We demonstrate that
our new approaches outperform current state-of-the-art methods on both
artificial data generated by random-swapping of positive samples and in the
wild on a new manually-labeled test set for semantic misinformation.

Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networks
with wireless communication between vehicles and roadside infrastructure to
support safe operation. However, cybersecurity attacks pose a threat to VANETs
and the safe operation of CAVs. This study proposes the use of simulation for
modelling typical communication scenarios which may be subject to malicious
attacks. The Eclipse MOSAIC simulation framework is used to model two typical
road scenarios, including messaging between the vehicles and infrastructure -
and both replay and bogus information cybersecurity attacks are introduced. The
model demonstrates the impact of these attacks, and provides an open dataset to
inform the development of machine learning algorithms to provide anomaly
detection and mitigation solutions for enhancing secure communications and safe
deployment of CAVs on the road.

We study the low-rank phase retrieval problem, where the objective is to
recover a sequence of signals (typically images) given the magnitude of linear
measurements of those signals. Existing solutions involve recovering a matrix
constructed by vectorizing and stacking each image. These algorithms model this
matrix to be low-rank and leverage the low-rank property to decrease the sample
complexity required for accurate recovery. However, when the number of
available measurements is more limited, these low-rank matrix models can often
fail. We propose an algorithm called Tucker-Structured Phase Retrieval (TSPR)
that models the sequence of images as a tensor rather than a matrix that we
factorize using the Tucker decomposition. This factorization reduces the number
of parameters that need to be estimated, allowing for a more accurate
reconstruction in the under-sampled regime. Interestingly, we observe that this
structure also has improved performance in the over-determined setting when the
Tucker ranks are chosen appropriately. We demonstrate the effectiveness of our
approach on real video datasets under several different measurement models.

Generative machine learning models have recently been applied to source code,
for use cases including translating code between programming languages,
creating documentation from code, and auto-completing methods. Yet,
state-of-the-art models often produce code that is erroneous or incomplete. In
a controlled study with 32 software engineers, we examined whether such
imperfect outputs are helpful in the context of Java-to-Python code
translation. When aided by the outputs of a code translation model,
participants produced code with fewer errors than when working alone. We also
examined how the quality and quantity of AI translations affected the work
process and quality of outcomes, and observed that providing multiple
translations had a larger impact on the translation process than varying the
quality of provided translations. Our results tell a complex, nuanced story
about the benefits of generative code models and the challenges software
engineers face when working with their outputs. Our work motivates the need for
intelligent user interfaces that help software engineers effectively work with
generative code models in order to understand and evaluate their outputs and
achieve superior outcomes to working alone.

Deep neural network (DNN) classifiers are often overconfident, producing
miscalibrated class probabilities. Most existing calibration methods either
lack theoretical guarantees for producing calibrated outputs or reduce the
classification accuracy in the process. This paper proposes a new Kernel-based
calibration method called KCal. Unlike other calibration procedures, KCal does
not operate directly on the logits or softmax outputs of the DNN. Instead, it
uses the penultimate-layer latent embedding to train a metric space in a
supervised manner. In effect, KCal amounts to a supervised dimensionality
reduction of the neural network embedding, and generates a prediction using
kernel density estimation on a holdout calibration set. We first analyze KCal
theoretically, showing that it enjoys a provable asymptotic calibration
guarantee. Then, through extensive experiments, we confirm that KCal
consistently outperforms existing calibration methods in terms of both the
classification accuracy and the (confidence and class-wise) calibration error.

The predictions of question answering (QA)systems are typically evaluated
against manually annotated finite sets of one or more answers. This leads to a
coverage limitation that results in underestimating the true performance of
systems, and is typically addressed by extending over exact match (EM) with
pre-defined rules or with the token-level F1 measure. In this paper, we present
the first systematic conceptual and data-driven analysis to examine the
shortcomings of token-level equivalence measures.
  To this end, we define the asymmetric notion of answer equivalence (AE),
accepting answers that are equivalent to or improve over the reference, and
publish over 23k human judgments for candidates produced by multiple QA systems
on SQuAD. Through a careful analysis of this data, we reveal and quantify
several concrete limitations of the F1 measure, such as a false impression of
graduality, or missing dependence on the question.
  Since collecting AE annotations for each evaluated model is expensive, we
learn a BERT matching (BEM) measure to approximate this task. Being a simpler
task than QA, we find BEM to provide significantly better AE approximations
than F1, and to more accurately reflect the performance of systems.
  Finally, we demonstrate the practical utility of AE and BEM on the concrete
application of minimal accurate prediction sets, reducing the number of
required answers by up to x2.6.

Much effort has been devoted to making large and more accurate models, but
relatively little has been put into understanding which examples are benefiting
from the added complexity. In this paper, we demonstrate and analyze the
surprisingly tight link between a model's predictive uncertainty on individual
examples and the likelihood that larger models will improve prediction on them.
Through extensive numerical studies on the T5 encoder-decoder architecture, we
show that large models have the largest improvement on examples where the small
model is most uncertain. On more certain examples, even those where the small
model is not particularly accurate, large models are often unable to improve at
all, and can even perform worse than the smaller model. Based on these
findings, we show that a switcher model which defers examples to a larger model
when a small model is uncertain can achieve striking improvements in
performance and resource usage. We also explore committee-based uncertainty
metrics that can be more effective but less practical.

We develop a new approach to multi-label conformal prediction in which we aim
to output a precise set of promising prediction candidates with a bounded
number of incorrect answers. Standard conformal prediction provides the ability
to adapt to model uncertainty by constructing a calibrated candidate set in
place of a single prediction, with guarantees that the set contains the correct
answer with high probability. In order to obey this coverage property, however,
conformal sets can become inundated with noisy candidates -- which can render
them unhelpful in practice. This is particularly relevant to practical
applications where there is a limited budget, and the cost (monetary or
otherwise) associated with false positives is non-negligible. We propose to
trade coverage for a notion of precision by enforcing that the presence of
incorrect candidates in the predicted conformal sets (i.e., the total number of
false positives) is bounded according to a user-specified tolerance. Subject to
this constraint, our algorithm then optimizes for a generalized notion of set
coverage (i.e., the true positive rate) that allows for any number of true
answers for a given query (including zero). We demonstrate the effectiveness of
this approach across a number of classification tasks in natural language
processing, computer vision, and computational chemistry.

How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)?
TKGs represent facts about entities and their relations, where each fact is
associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts
from time-evolving KGs, is crucial for many applications to provide intelligent
services. However, despite the prevalence of real-world data that can be
represented as TKGs, most methods focus on reasoning over static knowledge
graphs, or cannot predict future events. In this paper, we present a problem
formulation that unifies the two major problems that need to be addressed for
an effective reasoning over TKGs, namely, modeling the event time and the
evolving network structure. Our proposed method EvoKG jointly models both tasks
in an effective framework, which captures the ever-changing structural and
temporal dynamics in TKGs via recurrent event modeling, and models the
interactions between entities based on the temporal neighborhood aggregation
framework. Further, EvoKG achieves an accurate modeling of event time, using
flexible and efficient mechanisms based on neural density estimation.
Experiments show that EvoKG outperforms existing methods in terms of
effectiveness (up to 77% and 116% more accurate time and link prediction) and

Large language models (LMs) have been shown to memorize parts of their
training data, and when prompted appropriately, they will emit the memorized
training data verbatim. This is undesirable because memorization violates
privacy (exposing user data), degrades utility (repeated easy-to-memorize text
is often low quality), and hurts fairness (some texts are memorized over
others).
  We describe three log-linear relationships that quantify the degree to which
LMs emit memorized training data. Memorization significantly grows as we
increase (1) the capacity of a model, (2) the number of times an example has
been duplicated, and (3) the number of tokens of context used to prompt the
model. Surprisingly, we find the situation becomes complicated when
generalizing these results across model families. On the whole, we find that
memorization in LMs is more prevalent than previously believed and will likely
get worse as models continues to scale, at least without active mitigations.

Attention plays a fundamental role in both natural and artificial
intelligence systems. In deep learning, attention-based neural architectures,
such as transformer architectures, are widely used to tackle problems in
natural language processing and beyond. Here we investigate the fundamental
building blocks of attention and their computational properties. Within the
standard model of deep learning, we classify all possible fundamental building
blocks of attention in terms of their source, target, and computational
mechanism. We identify and study three most important mechanisms: additive
activation attention, multiplicative output attention (output gating), and
multiplicative synaptic attention (synaptic gating). The gating mechanisms
correspond to multiplicative extensions of the standard model and are used
across all current attention-based deep learning architectures. We study their
functional properties and estimate the capacity of several attentional building
blocks in the case of linear and polynomial threshold gates. Surprisingly,
additive activation attention plays a central role in the proofs of the lower
bounds. Attention mechanisms reduce the depth of certain basic circuits and
leverage the power of quadratic activations without incurring their full cost.

In a motorway network, correlations between parts or, more precisely, between
the sections of (different) motorways, are of considerable interest. Knowledge
of flows and velocities on individual motorways is not sufficient, rather,
their correlations determine or reflect, respectively, the functionality of and
the dynamics on the network. These correlations are time-dependent as the
dynamics on the network is highly non-stationary. Apart from the conceptual
importance, correlations are also indispensable to detect risks of failure in a
traffic network. Here, we proceed with revealing a certain hierarchy of
correlations in traffic networks that is due to the presence and to the extent
of collectivity. In a previous study, we focused on the collectivity motion
present in the entire traffic network, i.e. the collectivity of the system as a
whole. Here, we manage to subtract this dominant effect from the data and
identify the subdominant collectivities which affect different, large parts of
the traffic network. To this end, we employ a spectral analysis of the
correlation matrix for the whole system. We thereby extract information from
the virtual network induced by the correlations and map it on the true
topology, i.e. on the real motorway network. The uncovered subdominant
collectivities provide a new characterization of the traffic network. We carry
out our study for the large motorway network of North Rhine-Westphalia (NRW),
Germany.

Neural networks are increasingly being used to solve partial differential
equations (PDEs), replacing slower numerical solvers. However, a critical issue
is that neural PDE solvers require high-quality ground truth data, which
usually must come from the very solvers they are designed to replace. Thus, we
are presented with a proverbial chicken-and-egg problem. In this paper, we
present a method, which can partially alleviate this problem, by improving
neural PDE solver sample complexity -- Lie point symmetry data augmentation
(LPSDA). In the context of PDEs, it turns out that we are able to
quantitatively derive an exhaustive list of data transformations, based on the
Lie point symmetry group of the PDEs in question, something not possible in
other application areas. We present this framework and demonstrate how it can
easily be deployed to improve neural PDE solver sample complexity by an order

Advances in deep vision techniques and ubiquity of smart cameras will drive
the next generation of video analytics. However, video analytics applications
consume vast amounts of energy as both deep learning techniques and cameras are
power-hungry. In this paper, we focus on a parking video analytics platform and
propose RL-CamSleep, a deep reinforcement learning-based technique, to actuate
the cameras to reduce the energy footprint while retaining the system's
utility. Our key insight is that many video-analytics applications do not
always need to be operational, and we can design policies to activate video
analytics only when necessary. Moreover, our work is complementary to existing
work that focuses on improving hardware and software efficiency. We evaluate
our approach on a city-scale parking dataset having 76 streets spread across
the city. Our analysis demonstrates how streets have various parking patterns,
highlighting the importance of an adaptive policy. Our approach can learn such
an adaptive policy that can reduce the average energy consumption by 76.38% and
achieve an average accuracy of more than 98% in performing video analytics.

Nondeterministic Good-for-MDP (GFM) automata are for MDP model checking and
reinforcement learning what good-for-games automata are for reactive synthesis:
a more compact alternative to deterministic automata that displays
nondeterminism, but only so much that it can be resolved locally, such that a
syntactic product can be analysed. GFM has recently been introduced as a
property for reinforcement learning, where the simpler B\"uchi acceptance
conditions it allows to use is key. However, while there are classic and novel
techniques to obtain automata that are GFM, there has not been a decision
procedure for checking whether or not an automaton is GFM. We show that
GFM-ness is decidable and provide an EXPTIME decision procedure as well as a
PSPACE-hardness proof.

In this work, we provide a characterization of the feature-learning process
in two-layer ReLU networks trained by gradient descent on the logistic loss
following random initialization. We consider data with binary labels that are
generated by an XOR-like function of the input features. We permit a constant
fraction of the training labels to be corrupted by an adversary. We show that,
although linear classifiers are no better than random guessing for the
distribution we consider, two-layer ReLU networks trained by gradient descent
achieve generalization error close to the label noise rate. We develop a novel
proof technique that shows that at initialization, the vast majority of neurons
function as random features that are only weakly correlated with useful
features, and the gradient descent dynamics 'amplify' these weak, random
features to strong, useful features.

Reconstruction attacks allow an adversary to regenerate data samples of the
training set using access to only a trained model. It has been recently shown
that simple heuristics can reconstruct data samples from language models,
making this threat scenario an important aspect of model release. Differential
privacy is a known solution to such attacks, but is often used with a
relatively large privacy budget (epsilon > 8) which does not translate to
meaningful guarantees. In this paper we show that, for a same mechanism, we can
derive privacy guarantees for reconstruction attacks that are better than the
traditional ones from the literature. In particular, we show that larger
privacy budgets do not protect against membership inference, but can still
protect extraction of rare secrets. We show experimentally that our guarantees
hold against various language models, including GPT-2 finetuned on
Wikitext-103.

Autonomous mobile robots require accurate human motion predictions to safely
and efficiently navigate among pedestrians, whose behavior may adapt to
environmental changes. This paper introduces a self-supervised continual
learning framework to improve data-driven pedestrian prediction models online
across various scenarios continuously. In particular, we exploit online streams
of pedestrian data, commonly available from the robot's detection and tracking
pipeline, to refine the prediction model and its performance in unseen
scenarios. To avoid the forgetting of previously learned concepts, a problem
known as catastrophic forgetting, our framework includes a regularization loss
to penalize changes of model parameters that are important for previous
scenarios and retrains on a set of previous examples to retain past knowledge.
Experimental results on real and simulation data show that our approach can
improve prediction performance in unseen scenarios while retaining knowledge
from seen scenarios when compared to naively training the prediction model
online.

Does everyone equally benefit from computer vision systems? Answers to this
question become more and more important as computer vision systems are deployed
at large scale, and can spark major concerns when they exhibit vast performance
discrepancies between people from various demographic and social backgrounds.
Systematic diagnosis of fairness, harms, and biases of computer vision systems
is an important step towards building socially responsible systems. To initiate
an effort towards standardized fairness audits, we propose three fairness
indicators, which aim at quantifying harms and biases of visual systems. Our
indicators use existing publicly available datasets collected for fairness
evaluations, and focus on three main types of harms and bias identified in the
literature, namely harmful label associations, disparity in learned
representations of social and demographic traits, and biased performance on
geographically diverse images from across the world.We define precise
experimental protocols applicable to a wide range of computer vision models.
These indicators are part of an ever-evolving suite of fairness probes and are
not intended to be a substitute for a thorough analysis of the broader impact
of the new computer vision technologies. Yet, we believe it is a necessary
first step towards (1) facilitating the widespread adoption and mandate of the
fairness assessments in computer vision research, and (2) tracking progress
towards building socially responsible models. To study the practical
effectiveness and broad applicability of our proposed indicators to any visual
system, we apply them to off-the-shelf models built using widely adopted model
training paradigms which vary in their ability to whether they can predict
labels on a given image or only produce the embeddings. We also systematically
study the effect of data domain and model size.

In this work we investigate and demonstrate benefits of a Bayesian approach
to imitation learning from multiple sensor inputs, as applied to the task of
opening office doors with a mobile manipulator. Augmenting policies with
additional sensor inputs, such as RGB + depth cameras, is a straightforward
approach to improving robot perception capabilities, especially for tasks that
may favor different sensors in different situations. As we scale multi-sensor
robotic learning to unstructured real-world settings (e.g. offices, homes) and
more complex robot behaviors, we also increase reliance on simulators for cost,
efficiency, and safety. Consequently, the sim-to-real gap across multiple
sensor modalities also increases, making simulated validation more difficult.
We show that using the Variational Information Bottleneck (Alemi et al., 2016)
to regularize convolutional neural networks improves generalization to held-out
domains and reduces the sim-to-real gap in a sensor-agnostic manner. As a side
effect, the learned embeddings also provide useful estimates of model
uncertainty for each sensor. We demonstrate that our method is able to help
close the sim-to-real gap and successfully fuse RGB and depth modalities based
on understanding of the situational uncertainty of each sensor. In a real-world
office environment, we achieve 96% task success, improving upon the baseline by
+16%.

Asteroseismology is used to infer the interior physics of stars. The
\textit{Kepler} and TESS space missions have provided a vast data set of
red-giant light curves, which may be used for asteroseismic analysis. These
data sets are expected to significantly grow with future missions such as
\textit{PLATO}, and efficient methods are therefore required to analyze these
data rapidly. Here, we describe a machine learning algorithm that identifies
red giants from the raw oscillation spectra and captures \textit{p} and
\textit{mixed} mode parameters from the red-giant power spectra. We report
algorithmic inferences for large frequency separation ($\Delta \nu$), frequency
at maximum amplitude ($\nu_{max}$), and period separation ($\Delta \Pi$) for an
ensemble of stars. In addition, we have discovered $\sim$25 new probable red
giants among 151,000 \textit{Kepler} long-cadence stellar-oscillation spectra
analyzed by the method, among which four are binary candidates which appear to
possess red-giant counterparts. To validate the results of this method, we
selected $\sim$ 3,000 \textit{Kepler} stars, at various evolutionary stages
ranging from subgiants to red clumps, and compare inferences of $\Delta \nu$,
$\Delta \Pi$, and $\nu_{max}$ with estimates obtained using other techniques.
The power of the machine-learning algorithm lies in its speed: it is able to
accurately extract seismic parameters from 1,000 spectra in $\sim$5 seconds on
a modern computer (single core of the Intel Xeon Platinum 8280 CPU).

Air pollution is one of the leading causes of mortality globally, resulting
in millions of deaths each year. Efficient monitoring is important to measure
exposure and enforce legal limits. New low-cost sensors can be deployed in
greater numbers and in more varied locations, motivating the problem of
efficient automated placement. Previous work suggests Bayesian optimisation is
an appropriate method, but only considered a satellite data set, with data
aggregated over all altitudes. It is ground-level pollution, that humans
breathe, which matters most. We improve on those results using hierarchical
models and evaluate our models on urban pollution data in London to show that
Bayesian optimisation can be successfully applied to the problem.

A fully convolutional autoencoder is developed for the detection of anomalies
in multi-sensor vehicle drive-cycle data from the powertrain domain.
Preliminary results collected on real-world powertrain data show that the
reconstruction error of faulty drive cycles deviates significantly relative to
the reconstruction of healthy drive cycles using the trained autoencoder. The
results demonstrate applicability for identifying faulty drive-cycles, and for
improving the accuracy of system prognosis and predictive maintenance in
connected vehicles.

We review briefly the characteristic topological data of Calabi--Yau
threefolds and focus on the question of when two threefolds are equivalent
through related topological data. This provides an interesting test case for
machine learning methodology in discrete mathematics problems motivated by
physics.

Multivariate time series anomaly detection has become an active area of
research in recent years, with Deep Learning models outperforming previous
approaches on benchmark datasets. Among reconstruction-based models, most
previous work has focused on Variational Autoencoders and Generative
Adversarial Networks. This work presents DGHL, a new family of generative
models for time series anomaly detection, trained by maximizing the observed
likelihood by posterior sampling and alternating back-propagation. A top-down
Convolution Network maps a novel hierarchical latent space to time series
windows, exploiting temporal dynamics to encode information efficiently.
Despite relying on posterior sampling, it is computationally more efficient
than current approaches, with up to 10x shorter training times than RNN based
models. Our method outperformed current state-of-the-art models on four popular
benchmark datasets. Finally, DGHL is robust to variable features between
entities and accurate even with large proportions of missing values, settings
with increasing relevance with the advent of IoT. We demonstrate the superior
robustness of DGHL with novel occlusion experiments in this literature. Our
code is available at https://github.com/cchallu/dghl.

In granular computing, fuzzy sets can be approximated by granularly
representable sets that are as close as possible to the original fuzzy set
w.r.t. a given closeness measure. Such sets are called granular approximations.
In this article, we introduce the concepts of disjoint and adjacent granules
and we examine how the new definitions affect the granular approximations.
First, we show that the new concepts are important for binary classification
problems since they help to keep decision regions separated (disjoint granules)
and at the same time to cover as much as possible of the attribute space
(adjacent granules). Later, we consider granular approximations for multi-class
classification problems leading to the definition of a multi-class granular
approximation. Finally, we show how to efficiently calculate multi-class
granular approximations for {\L}ukasiewicz fuzzy connectives. We also provide
graphical illustrations for a better understanding of the introduced concepts.

As intelligent transportation systems been implemented broadly and unmanned
arial vehicles (UAVs) can assist terrestrial base stations acting as
multi-access edge computing (MEC) to provide a better wireless network
communication for Internet of Vehicles (IoVs), we propose a UAVs-assisted
approach to help provide a better wireless network service retaining the
maximum Quality of Experience(QoE) of the IoVs on the lane. In the paper, we
present a Multi-Agent Graph Convolutional Deep Reinforcement Learning
(M-AGCDRL) algorithm which combines local observations of each agent with a
low-resolution global map as input to learn a policy for each agent. The agents
can share their information with others in graph attention networks, resulting
in an effective joint policy. Simulation results show that the M-AGCDRL method
enables a better QoE of IoTs and achieves good performance.

Ransomware is considered as a significant threat for most enterprises since
the past few years. In scenarios wherein users can access all files on a shared
server, one infected host can lock the access to all shared files. We propose a
tool to detect ransomware infection based on file-sharing traffic analysis. The
tool monitors the traffic exchanged between the clients and the file servers
and using machine learning techniques it searches for patterns in the traffic
that betray ransomware actions while reading and overwriting files. The
proposal is designed to work for clear text and for encrypted file-sharing
protocols. We compare three machine learning models and choose the best for
validation. We train and test the detection model using more than 70 ransomware
binaries from 26 different strains and more than 2500 hours of not infected
traffic from real users. The results reveal that the proposed tool can detect
all ransomware binaries, including those not used in training phase (unseen).
This paper provides a validation of the algorithm by studying the false
positive rate and the amount of information from user files that the ransomware
could encrypt before being detected.

We study weighted programming, a programming paradigm for specifying
mathematical models. More specifically, the weighted programs we investigate
are like usual imperative programs with two additional features: (1)
nondeterministic branching and (2) weighting execution traces. Weights can be
numbers but also other objects like words from an alphabet, polynomials, formal
power series, or cardinal numbers. We argue that weighted programming as a
paradigm can be used to specify mathematical models beyond probability
distributions (as is done in probabilistic programming).
  We develop weakest-precondition- and weakest-liberal-precondition-style
calculi \`{a} la Dijkstra for reasoning about mathematical models specified by
weighted programs. We present several case studies. For instance, we use
weighted programming to model the ski rental problem - an optimization problem.
We model not only the optimization problem itself, but also the best
deterministic online algorithm for solving this problem as weighted programs.
By means of weakest-precondition-style reasoning, we can determine the
competitive ratio of the online algorithm on source code level.

Neural networks have been recently proposed as variational wave functions for
quantum many-body systems [G. Carleo and M. Troyer, Science 355, 602 (2017)].
In this work, we focus on a specific architecture, known as Restricted
Boltzmann Machine (RBM), and analyse its accuracy for the spin-1/2 $J_1-J_2$
antiferromagnetic Heisenberg model in one spatial dimension. The ground state
of this model has a non-trivial sign structure, especially for $J_2/J_1>0.5$,
forcing us to work with complex-valued RBMs. Two variational Ans\"atze are
discussed: one defined through a fully complex RBM, and one in which two
different real-valued networks are used to approximate modulus and phase of the
wave function. In both cases, translational invariance is imposed by
considering linear combinations of RBMs, giving access also to the
lowest-energy excitations at fixed momentum $k$. We perform a systematic study
on small clusters to evaluate the accuracy of these wave functions in
comparison to exact results, providing evidence for the supremacy of the fully
complex RBM. Our calculations show that this kind of Ans\"atze is very flexible
and describes both gapless and gapped ground states, also capturing the
incommensurate spin-spin correlations and low-energy spectrum for
$J_2/J_1>0.5$. The RBM results are also compared to the ones obtained with
Gutzwiller-projected fermionic states, often employed to describe quantum spin
models [F. Ferrari, A. Parola, S. Sorella and F. Becca, Phys. Rev. B 97, 235103
(2018)]. Contrary to the latter class of variational states, the
fully-connected structure of RBMs hampers the transferability of the wave
function from small to large clusters, implying an increase of the
computational cost with the system size.

We present a data-driven approach for forecasting global weather using graph
neural networks. The system learns to step forward the current 3D atmospheric
state by six hours, and multiple steps are chained together to produce skillful
forecasts going out several days into the future. The underlying model is
trained on reanalysis data from ERA5 or forecast data from GFS. Test
performance on metrics such as Z500 (geopotential height) and T850
(temperature) improves upon previous data-driven approaches and is comparable
to operational, full-resolution, physical models from GFS and ECMWF, at least
when evaluated on 1-degree scales and when using reanalysis initial conditions.
We also show results from connecting this data-driven model to live,
operational forecasts from GFS.

We revisit the classic online portfolio selection problem, where at each
round a learner selects a distribution over a set of portfolios to allocate its
wealth. It is known that for this problem a logarithmic regret with respect to
Cover's loss is achievable using the Universal Portfolio Selection algorithm,
for example. However, all existing algorithms that achieve a logarithmic regret
for this problem have per-round time and space complexities that scale
polynomially with the total number of rounds, making them impractical. In this
paper, we build on the recent work by Haipeng et al. 2018 and present the first
practical online portfolio selection algorithm with a logarithmic regret and
whose per-round time and space complexities depend only logarithmically on the
horizon. Behind our approach are two key technical novelties of independent
interest. We first show that the Damped Online Newton steps can approximate
mirror descent iterates well, even when dealing with time-varying regularizers.
Second, we present a new meta-algorithm that achieves an adaptive logarithmic
regret (i.e. a logarithmic regret on any sub-interval) for mixable losses.

This note complements the author's recent paper "Robust representation
learning with feedback for single image deraining" by providing heuristically
theoretical explanations on the mechanism of representation learning with
feedback, namely an essential merit of the works presented in this recent
article. This note facilitates understanding of key points in the mechanism of
representation learning with feedback.

Progress in digital pathology is hindered by high-resolution images and the
prohibitive cost of exhaustive localized annotations. The commonly used
paradigm to categorize pathology images is patch-based processing, which often
incorporates multiple instance learning (MIL) to aggregate local patch-level
representations yielding image-level prediction. Nonetheless, diagnostically
relevant regions may only take a small fraction of the whole tissue, and
current MIL-based approaches often process images uniformly, discarding the
inter-patches interactions. To alleviate these issues, we propose ScoreNet, a
new efficient transformer that exploits a differentiable recommendation stage
to extract discriminative image regions and dedicate computational resources
accordingly. The proposed transformer leverages the local and global attention
of a few dynamically recommended high-resolution regions at an efficient
computational cost. We further introduce a novel mixing data-augmentation,
namely ScoreMix, by leveraging the image's semantic distribution to guide the
data mixing and produce coherent sample-label pairs. ScoreMix is embarrassingly
simple and mitigates the pitfalls of previous augmentations, which assume a
uniform semantic distribution and risk mislabeling the samples. Thorough
experiments and ablation studies on three breast cancer histology datasets of
Haematoxylin & Eosin (H&E) have validated the superiority of our approach over
prior arts, including transformer-based models on tumour regions-of-interest
(TRoIs) classification. ScoreNet equipped with proposed ScoreMix augmentation
demonstrates better generalization capabilities and achieves new
state-of-the-art (SOTA) results with only 50% of the data compared to other
mixing augmentation variants. Finally, ScoreNet yields high efficacy and
outperforms SOTA efficient transformers, namely TransPath and SwinTransformer.

Equality operators are an essential building block in tasks over secure
computation such as private information retrieval. In private information
retrieval (PIR), a user queries a database such that the server does not learn
which element is queried. In this work, we propose \emph{equality operators for
constant-weight codewords}. A constant-weight code is a collection of codewords
that share the same Hamming weight. Constant-weight equality operators have a
multiplicative depth that depends only on the Hamming weight of the code, not
the bit-length of the elements. In our experiments, we show how these equality
operators are up to 10 times faster than existing equality operators.
Furthermore, we propose PIR using the constant-weight equality operator or
\emph{constant-weight PIR}, which is a PIR protocol using an approach
previously deemed impractical. We show that for private retrieval of large,
streaming data, constant-weight PIR has a smaller communication complexity and
lower runtime compared to SEALPIR and MulPIR, respectively, which are two
state-of-the-art solutions for PIR. Moreover, we show how constant-weight PIR
can be extended to keyword PIR. In keyword PIR, the desired element is
retrieved by a unique identifier pertaining to the sought item, e.g., the name
of a file. Previous solutions to keyword PIR require one or multiple rounds of
communication to reduce the problem to normal PIR. We show that constant-weight
PIR is the first practical single-round solution to single-server keyword PIR.

Over the years, most research towards defenses against adversarial attacks on
machine learning models has been in the image recognition domain. The malware
detection domain has received less attention despite its importance. Moreover,
most work exploring these defenses has focused on several methods but with no
strategy when applying them. In this paper, we introduce StratDef, which is a
strategic defense system tailored for the malware detection domain based on a
moving target defense approach. We overcome challenges related to the
systematic construction, selection and strategic use of models to maximize
adversarial robustness. StratDef dynamically and strategically chooses the best
models to increase the uncertainty for the attacker, whilst minimizing critical
aspects in the adversarial ML domain like attack transferability. We provide
the first comprehensive evaluation of defenses against adversarial attacks on
machine learning for malware detection, where our threat model explores
different levels of threat, attacker knowledge, capabilities, and attack
intensities. We show that StratDef performs better than other defenses even
when facing the peak adversarial threat. We also show that, from the existing
defenses, only a few adversarially-trained models provide substantially better
protection than just using vanilla models but are still outperformed by
StratDef.

Safe reinforcement learning (RL) is still very challenging since it requires
the agent to consider both return maximization and safe exploration. In this
paper, we propose CUP, a Conservative Update Policy algorithm with a
theoretical safety guarantee. We derive the CUP based on the new proposed
performance bounds and surrogate functions. Although using bounds as surrogate
functions to design safe RL algorithms have appeared in some existing works, we
develop them at least three aspects: (i) We provide a rigorous theoretical
analysis to extend the surrogate functions to generalized advantage estimator
(GAE). GAE significantly reduces variance empirically while maintaining a
tolerable level of bias, which is an efficient step for us to design CUP; (ii)
The proposed bounds are tighter than existing works, i.e., using the proposed
bounds as surrogate functions are better local approximations to the objective
and safety constraints. (iii) The proposed CUP provides a non-convex
implementation via first-order optimizers, which does not depend on any convex
approximation. Finally, extensive experiments show the effectiveness of CUP
where the agent satisfies safe constraints. We have opened the source code of
CUP at https://github.com/RL-boxes/Safe-RL.

Liquidity issues have been increasingly addressed recently, especially with
regards to optimal execution of large orders. In practice, agents facing these
issues are uncertain about the future dynamics of the assets, and face the risk
of model misspecification, which could make the execution strategies non
optimal. In this paper, we address the problem of uncertainty faced by an agent
wishing to execute large orders on multiple assets. The agent only has
knowledge about the distribution of the future drift of the assets composing
her portfolio. We build on the work in [13] who proposed a model coupling
Bayesian learning and dynamic programming techniques. More precisely, in this
article, we provide a rigorous solution to the problem of portfolio optimal
execution where prices have drifted Bachelier dynamics with an unknown drift.
The agent uses Bayesian learning to update her estimation of the drift, while
she maximizes the expected exponential utility of her final wealth. We consider
the specific case where the prior is a non-degenerate multivariate Gaussian,
and the costs are quadratic. We use stochastic optimal control tools to show
how the problem of optimal execution simplifies into a system of ordinary
differential equations (ODEs) which involves a matrix Riccati ODE with
time-dependent coefficients for which classical existence theorems do not
apply. However, using a method similar to the one in [10], we provide a
rigorous solution to the problem by using a priori estimates obtained thanks to
the original control problem.

The integration of artificial intelligence into clinical workflows requires
reliable and robust models. Repeatability is a key attribute of model
robustness. Repeatable models output predictions with low variation during
independent tests carried out under similar conditions. During model
development and evaluation, much attention is given to classification
performance while model repeatability is rarely assessed, leading to the
development of models that are unusable in clinical practice. In this work, we
evaluate the repeatability of four model types (binary classification,
multi-class classification, ordinal classification, and regression) on images
that were acquired from the same patient during the same visit. We study the
performance of binary, multi-class, ordinal, and regression models on four
medical image classification tasks from public and private datasets: knee
osteoarthritis, cervical cancer screening, breast density estimation, and
retinopathy of prematurity. Repeatability is measured and compared on ResNet
and DenseNet architectures. Moreover, we assess the impact of sampling Monte
Carlo dropout predictions at test time on classification performance and
repeatability. Leveraging Monte Carlo predictions significantly increased
repeatability for all tasks on the binary, multi-class, and ordinal models
leading to an average reduction of the 95\% limits of agreement by 16% points
and of the disagreement rate by 7% points. The classification accuracy improved
in most settings along with the repeatability. Our results suggest that beyond
about 20 Monte Carlo iterations, there is no further gain in repeatability. In
addition to the higher test-retest agreement, Monte Carlo predictions were
better calibrated which leads to output probabilities reflecting more
accurately the true likelihood of being correctly classified.

Equivariant neural networks, whose hidden features transform according to
representations of a group G acting on the data, exhibit training efficiency
and an improved generalisation performance. In this work, we extend group
invariant and equivariant representation learning to the field of unsupervised
deep learning. We propose a general learning strategy based on an
encoder-decoder framework in which the latent representation is separated in an
invariant term and an equivariant group action component. The key idea is that
the network learns to encode and decode data to and from a group-invariant
representation by additionally learning to predict the appropriate group action
to align input and output pose to solve the reconstruction task. We derive the
necessary conditions on the equivariant encoder, and we present a construction
valid for any G, both discrete and continuous. We describe explicitly our
construction for rotations, translations and permutations. We test the validity
and the robustness of our approach in a variety of experiments with diverse
data types employing different network architectures.

Stochastic and adversarial data are two widely studied settings in online
learning. But many optimization tasks are neither i.i.d. nor fully adversarial,
which makes it of fundamental interest to get a better theoretical
understanding of the world between these extremes. In this work we establish
novel regret bounds for online convex optimization in a setting that
interpolates between stochastic i.i.d. and fully adversarial losses. By
exploiting smoothness of the expected losses, these bounds replace a dependence
on the maximum gradient length by the variance of the gradients, which was
previously known only for linear losses. In addition, they weaken the i.i.d.
assumption by allowing, for example, adversarially poisoned rounds, which were
previously considered in the expert and bandit setting. Our results extend this
to the online convex optimization framework. In the fully i.i.d. case, our
bounds match the rates one would expect from results in stochastic
acceleration, and in the fully adversarial case they gracefully deteriorate to
match the minimax regret. We further provide lower bounds showing that our
regret upper bounds are tight for all intermediate regimes in terms of the
stochastic variance and the adversarial variation of the loss gradients.

When reasoning about explanations of Machine Learning (ML) classifiers, a
pertinent query is to decide whether some sensitive features can serve for
explaining a given prediction. Recent work showed that the feature membership
problem (FMP) is hard for $\Sigma_2^P$ for a broad class of classifiers. In
contrast, this paper shows that for a number of families of classifiers, FMP is
in NP. Concretely, the paper proves that any classifier for which an
explanation can be computed in polynomial time, then deciding feature
membership in an explanation can be decided with one NP oracle call. The paper
then proposes propositional encodings for classifiers represented with
Sentential Decision Diagrams (SDDs) and for other related propositional
languages. The experimental results confirm the practical efficiency of the

Transformation invariances are present in many real-world problems. For
example, image classification is usually invariant to rotation and color
transformation: a rotated car in a different color is still identified as a
car. Data augmentation, which adds the transformed data into the training set
and trains a model on the augmented data, is one commonly used technique to
build these invariances into the learning process. However, it is unclear how
data augmentation performs theoretically and what the optimal algorithm is in
presence of transformation invariances. In this paper, we study PAC
learnability under transformation invariances in three settings according to
different levels of realizability: (i) A hypothesis fits the augmented data;
(ii) A hypothesis fits only the original data and the transformed data lying in
the support of the data distribution; (iii) Agnostic case. One interesting
observation is that distinguishing between the original data and the
transformed data is necessary to achieve optimal accuracy in setting (ii) and
(iii), which implies that any algorithm not differentiating between the
original and transformed data (including data augmentation) is not optimal.
Furthermore, this type of algorithms can even "harm" the accuracy. In setting
(i), although it is unnecessary to distinguish between the two data sets, data
augmentation still does not perform optimally. Due to such a difference, we
propose two combinatorial measures characterizing the optimal sample complexity
in setting (i) and (ii)(iii) and provide the optimal algorithms.

Medical tasks are prone to inter-rater variability due to multiple factors
such as image quality, professional experience and training, or guideline
clarity. Training deep learning networks with annotations from multiple raters
is a common practice that mitigates the model's bias towards a single expert.
Reliable models generating calibrated outputs and reflecting the inter-rater
disagreement are key to the integration of artificial intelligence in clinical
practice. Various methods exist to take into account different expert labels.
We focus on comparing three label fusion methods: STAPLE, average of the
rater's segmentation, and random sampling of each rater's segmentation during
training. Each label fusion method is studied using both the conventional
training framework and the recently published SoftSeg framework that limits
information loss by treating the segmentation task as a regression. Our
results, across 10 data splittings on two public datasets, indicate that
SoftSeg models, regardless of the ground truth fusion method, had better
calibration and preservation of the inter-rater rater variability compared with
their conventional counterparts without impacting the segmentation performance.
Conventional models, i.e., trained with a Dice loss, with binary inputs, and
sigmoid/softmax final activate, were overconfident and underestimated the
uncertainty associated with inter-rater variability. Conversely, fusing labels
by averaging with the SoftSeg framework led to underconfident outputs and
overestimation of the rater disagreement. In terms of segmentation performance,
the best label fusion method was different for the two datasets studied,
indicating this parameter might be task-dependent. However, SoftSeg had
segmentation performance systematically superior or equal to the conventionally
trained models and had the best calibration and preservation of the inter-rater

Bayesian optimization (BO) is a sample-efficient approach for tuning design
parameters to optimize expensive-to-evaluate, black-box performance metrics. In
many manufacturing processes, the design parameters are subject to random input
noise, resulting in a product that is often less performant than expected.
Although BO methods have been proposed for optimizing a single objective under
input noise, no existing method addresses the practical scenario where there
are multiple objectives that are sensitive to input perturbations. In this
work, we propose the first multi-objective BO method that is robust to input
noise. We formalize our goal as optimizing the multivariate value-at-risk
(MVaR), a risk measure of the uncertain objectives. Since directly optimizing
MVaR is computationally infeasible in many settings, we propose a scalable,
theoretically-grounded approach for optimizing MVaR using random
scalarizations. Empirically, we find that our approach significantly
outperforms alternative methods and efficiently identifies optimal robust
designs that will satisfy specifications across multiple metrics with high
probability.

Two main concepts studied in machine learning theory are generalization gap
(difference between train and test error) and excess risk (difference between
test error and the minimum possible error). While information-theoretic tools
have been used extensively to study the generalization gap of learning
algorithms, the information-theoretic nature of excess risk has not yet been
fully investigated. In this paper, some steps are taken toward this goal. We
consider the frequentist problem of minimax excess risk as a zero-sum game
between algorithm designer and the world. Then, we argue that it is desirable
to modify this game in a way that the order of play can be swapped. We prove
that, under some regularity conditions, if the world and designer can play
randomly the duality gap is zero and the order of play can be changed. In this
case, a Bayesian problem surfaces in the dual representation. This makes it
possible to utilize recent information-theoretic results on minimum excess risk
in Bayesian learning to provide bounds on the minimax excess risk. We
demonstrate the applicability of the results by providing information theoretic
insight on two important classes of problems: classification when the
hypothesis space has finite VC-dimension, and regularized least squares.

Community networks (CNs) have become an important paradigm for providing
essential Internet connectivity in unserved and underserved areas across the
world. However, an indispensable part for CNs is network management, where
responsive and autonomous maintenance is much needed. With the technological
advancement in telecommunications networks, a classical satellite-dependent CN
is envisioned to be transformed into a satellite-integrated CN (SICN), which
will embrace significant autonomy, intelligence, and scalability in network
management. This article discusses the machine-learning (ML) based hierarchical
approach to enabling autonomous self-maintenance for SICNs. The approach is
split into the anomaly identification and anomaly mitigation phases, where the
related ML methods, data collection means, deployment options, and mitigation
schemes are presented. With the case study, we discuss a typical scenario using
satellite and fixed connections as backhaul options and show the effectiveness
and performance improvements of the proposed approach with recurrent neural
network and ensemble methods

In this paper, we investigate the problem of stochastic multi-level
compositional optimization, where the objective function is a composition of
multiple smooth but possibly non-convex functions. Existing methods for solving
this problem either suffer from sub-optimal sample complexities or need a huge
batch size. To address these limitations, we propose a Stochastic Multi-level
Variance Reduction method (SMVR), which achieves the optimal sample complexity
of $\mathcal{O}\left(1 / \epsilon^{3}\right)$ to find an $\epsilon$-stationary
point for non-convex objectives. Furthermore, when the objective function
satisfies the convexity or Polyak-{\L}ojasiewicz (PL) condition, we propose a
stage-wise variant of SMVR and improve the sample complexity to
$\mathcal{O}\left(1 / \epsilon^{2}\right)$ for convex functions or
$\mathcal{O}\left(1 /\left(\mu\epsilon\right)\right)$ for non-convex functions
satisfying the $\mu$-PL condition. The latter result implies the same
complexity for $\mu$-strongly convex functions. To make use of adaptive
learning rates, we also develop Adaptive SMVR, which achieves the same
complexities but converges faster in practice. All our complexities match the
lower bounds not only in terms of $\epsilon$ but also in terms of $\mu$ (for PL
or strongly convex functions), without using a large batch size in each

With the recent advancements of data driven approaches using deep neural
networks, music source separation has been formulated as an instrument-specific
supervised problem. While existing deep learning models implicitly absorb the
spatial information conveyed by the multi-channel input signals, we argue that
a more explicit and active use of spatial information could not only improve
the separation process but also provide an entry-point for many
user-interaction based tools. To this end, we introduce a control method based
on the stereophonic location of the sources of interest, expressed as the
panning angle. We present various conditioning mechanisms, including the use of
raw angle and its derived feature representations, and show that spatial
information helps. Our proposed approaches improve the separation performance
compared to location agnostic architectures by 1.8 dB SI-SDR in our Slakh-based
simulated experiments. Furthermore, the proposed methods allow for the
disentanglement of same-class instruments, for example, in mixtures containing
two guitar tracks. Finally, we also demonstrate that our approach is robust to
incorrect source panning information, which can be incurred by our proposed
user interaction.

It has been witnessed that learned image compression has outperformed
conventional image coding techniques and tends to be practical in industrial
applications. One of the most critical issues that need to be considered is the
non-deterministic calculation, which makes the probability prediction
cross-platform inconsistent and frustrates successful decoding. We propose to
solve this problem by introducing well-developed post-training quantization and
making the model inference integer-arithmetic-only, which is much simpler than
presently existing training and fine-tuning based approaches yet still keeps
the superior rate-distortion performance of learned image compression. Based on
that, we further improve the discretization of the entropy parameters and
extend the deterministic inference to fit Gaussian mixture models. With our
proposed methods, the current state-of-the-art image compression models can
infer in a cross-platform consistent manner, which makes the further
development and practice of learned image compression more promising.

We study episodic two-player zero-sum Markov games (MGs) in the offline
setting, where the goal is to find an approximate Nash equilibrium (NE) policy
pair based on a dataset collected a priori. When the dataset does not have
uniform coverage over all policy pairs, finding an approximate NE involves
challenges in three aspects: (i) distributional shift between the behavior
policy and the optimal policy, (ii) function approximation to handle large
state space, and (iii) minimax optimization for equilibrium solving. We propose
a pessimism-based algorithm, dubbed as pessimistic minimax value iteration
(PMVI), which overcomes the distributional shift by constructing pessimistic
estimates of the value functions for both players and outputs a policy pair by
solving NEs based on the two value functions. Furthermore, we establish a
data-dependent upper bound on the suboptimality which recovers a sublinear rate
without the assumption on uniform coverage of the dataset. We also prove an
information-theoretical lower bound, which suggests that the data-dependent
term in the upper bound is intrinsic. Our theoretical results also highlight a
notion of "relative uncertainty", which characterizes the necessary and
sufficient condition for achieving sample efficiency in offline MGs. To the
best of our knowledge, we provide the first nearly minimax optimal result for
offline MGs with function approximation.

In this paper, we tackle the problem of blind image super-resolution(SR) with
a reformulated degradation model and two novel modules. Following the common
practices of blind SR, our method proposes to improve both the kernel
estimation as well as the kernel-based high-resolution image restoration. To be
more specific, we first reformulate the degradation model such that the
deblurring kernel estimation can be transferred into the low-resolution space.
On top of this, we introduce a dynamic deep linear filter module. Instead of
learning a fixed kernel for all images, it can adaptively generate deblurring
kernel weights conditional on the input and yield a more robust kernel
estimation. Subsequently, a deep constrained least square filtering module is
applied to generate clean features based on the reformulation and estimated
kernel. The deblurred feature and the low input image feature are then fed into
a dual-path structured SR network and restore the final high-resolution result.
To evaluate our method, we further conduct evaluations on several benchmarks,
including Gaussian8 and DIV2KRK. Our experiments demonstrate that the proposed
method achieves better accuracy and visual improvements against

Unsupervised learning is argued to be the dark matter of human intelligence.
To build in this direction, this paper focuses on unsupervised learning from an
abundance of unlabeled data followed by few-shot fine-tuning on a downstream
classification task. To this aim, we extend a recent study on adopting
contrastive learning for self-supervised pre-training by incorporating
class-level cognizance through iterative clustering and re-ranking and by
expanding the contrastive optimization loss to account for it. To our
knowledge, our experimentation both in standard and cross-domain scenarios
demonstrate that we set a new state-of-the-art (SoTA) in (5-way, 1 and 5-shot)
settings of standard mini-ImageNet benchmark as well as the (5-way, 5 and
20-shot) settings of cross-domain CDFSL benchmark. Our code and experimentation
can be found in our GitHub repository: https://github.com/ojss/c3lr.

Finding a better feasible solution in a shorter time is an integral part of
solving Mixed Integer Programs. We present a post-hoc method based on Neural
Diving to build heuristics more flexibly. We hypothesize that variables with
higher confidence scores are more definite to be included in the optimal
solution. For our hypothesis, we provide empirical evidence that confidence
threshold technique produces partial solutions leading to final solutions with
better primal objective values. Our method won 2nd place in the primal task on
the NeurIPS 2021 ML4CO competition. Also, our method shows the best score among
other learning-based methods in the competition.

Fast inverter control is a desideratum towards the smoother integration of
renewables. Adjusting inverter injection setpoints for distributed energy
resources can be an effective grid control mechanism. However, finding such
setpoints optimally requires solving an optimal power flow (OPF), which can be
computationally taxing in real time. This work proposes learning the mapping
from grid conditions to OPF minimizers using Gaussian processes (GPs). This
GP-OPF model predicts inverter setpoints when presented with a new instance of
grid conditions. Training enjoys closed-form expressions, and GP-OPF
predictions come with confidence intervals. To improve upon data efficiency, we
uniquely incorporate the sensitivities (partial derivatives) of the OPF mapping
into GP-OPF. This expedites the process of generating a training dataset as
fewer OPF instances need to be solved to attain the same accuracy. To further
reduce computational efficiency, we approximate the kernel function of GP-OPF
leveraging the concept of random features, which is neatly extended to
sensitivity data. We perform sensitivity analysis for the second-order cone
program (SOCP) relaxation of the OPF, whose sensitivities can be computed by
merely solving a system of linear equations. Extensive numerical tests using
real-world data on the IEEE 13- and 123-bus benchmark feeders corroborate the
merits of GP-OPF.

This paper presents a texture aware end-to-end trainable iris recognition
system, specifically designed for datasets like iris having limited training
data. We build upon our previous stagewise learning framework with certain key
optimization and architectural innovations. First, we pretrain a Stage-1
encoder network with an unsupervised autoencoder learning optimized with an
additional data relation loss on top of usual reconstruction loss. The data
relation loss enables learning better texture representation which is pivotal
for a texture rich dataset such as iris. Robustness of Stage-1 feature
representation is further enhanced with an auxiliary denoising task. Such
pre-training proves beneficial for effectively training deep networks on data
constrained iris datasets. Next, in Stage-2 supervised refinement, we design a
pairwise learning architecture for an end-to-end trainable iris recognition
system. The pairwise learning includes the task of iris matching inside the
training pipeline itself and results in significant improvement in recognition
performance compared to usual offline matching. We validate our model across
three publicly available iris datasets and the proposed model consistently
outperforms both traditional and deep learning baselines for both
Within-Dataset and Cross-Dataset configurations

In Reinforcement Learning, the optimal action at a given state is dependent
on policy decisions at subsequent states. As a consequence, the learning
targets evolve with time and the policy optimization process must be efficient
at unlearning what it previously learnt. In this paper, we discover that the
policy gradient theorem prescribes policy updates that are slow to unlearn
because of their structural symmetry with respect to the value target. To
increase the unlearning speed, we study a novel policy update: the gradient of
the cross-entropy loss with respect to the action maximizing $q$, but find that
such updates may lead to a decrease in value. Consequently, we introduce a
modified policy update devoid of that flaw, and prove its guarantees of
convergence to global optimality in $\mathcal{O}(t^{-1})$ under classic
assumptions. Further, we assess standard policy updates and our cross-entropy
policy updates along six analytical dimensions. Finally, we empirically
validate our theoretical findings.

CNN architectures are generally heavy on memory and computational
requirements which makes them infeasible for embedded systems with limited
hardware resources. We propose dual convolutional kernels (DualConv) for
constructing lightweight deep neural networks. DualConv combines 3$\times$3 and
1$\times$1 convolutional kernels to process the same input feature map channels
simultaneously and exploits the group convolution technique to efficiently
arrange convolutional filters. DualConv can be employed in any CNN model such
as VGG-16 and ResNet-50 for image classification, YOLO and R-CNN for object
detection, or FCN for semantic segmentation. In this paper, we extensively test
DualConv for classification since these network architectures form the
backbones for many other tasks. We also test DualConv for image detection on
YOLO-V3. Experimental results show that, combined with our structural
innovations, DualConv significantly reduces the computational cost and number
of parameters of deep neural networks while surprisingly achieving slightly
higher accuracy than the original models in some cases. We use DualConv to
further reduce the number of parameters of the lightweight MobileNetV2 by 54%
with only 0.68% drop in accuracy on CIFAR-100 dataset. When the number of
parameters is not an issue, DualConv increases the accuracy of MobileNetV1 by
4.11% on the same dataset. Furthermore, DualConv significantly improves the
YOLO-V3 object detection speed and improves its accuracy by 4.4% on PASCAL VOC
dataset.

Audio inpainting refers to signal processing techniques that aim at restoring
missing or corrupted consecutive samples in audio signals. Prior works have
shown that $\ell_1$- minimization with appropriate weighting is capable of
solving audio inpainting problems, both for the analysis and the synthesis
models. These models assume that audio signals are sparse with respect to some
redundant dictionary and exploit that sparsity for inpainting purposes.
Remaining within the sparsity framework, we utilize dictionary learning to
further increase the sparsity and combine it with weighted
$\ell_1$-minimization adapted for audio inpainting to compensate for the loss
of energy within the gap after restoration. Our experiments demonstrate that
our approach is superior in terms of signal-to-distortion ratio (SDR) and
objective difference grade (ODG) compared with its original counterpart.

Identifying phenotypes plays an important role in furthering our
understanding of disease biology through practical applications within
healthcare and the life sciences. The challenge of dealing with the
complexities and noise within electronic health records (EHRs) has motivated
applications of machine learning in phenotypic discovery. While recent research
has focused on finding predictive subtypes for clinical decision support, here
we instead focus on the noise that results in phenotypic misclassification,
which can reduce a phenotypes ability to detect associations in genome-wide
association studies (GWAS). We show that by combining anchor learning and
transformer architectures into our proposed model, AnchorBERT, we are able to
detect genomic associations only previously found in large consortium studies
with 5$\times$ more cases. When reducing the number of controls available by
50\%, we find our model is able to maintain 40\% more significant genomic
associations from the GWAS catalog compared to standard phenotype definitions.
\keywords{Phenotyping \and Machine Learning \and Semi-Supervised \and Genetic
Association Studies \and Biological Discovery}

With the advance in self-supervised learning for audio and visual modalities,
it has become possible to learn a robust audio-visual speech representation.
This would be beneficial for improving the audio-visual speech recognition
(AVSR) performance, as the multi-modal inputs contain more fruitful information
in principle. In this paper, based on existing self-supervised representation
learning methods for audio modality, we therefore propose an audio-visual
representation learning approach. The proposed approach explores both the
complementarity of audio-visual modalities and long-term context dependency
using a transformer-based fusion module and a flexible masking strategy. After
pre-training, the model is able to extract fused representations required by
AVSR. Without loss of generality, it can be applied to single-modal tasks, e.g.
audio/visual speech recognition by simply masking out one modality in the
fusion module. The proposed pre-trained model is evaluated on speech
recognition and lipreading tasks using one or two modalities, where the
superiority is revealed.

Learning in strategy games (e.g. StarCraft, poker) requires the discovery of
diverse policies. This is often achieved by iteratively training new policies
against existing ones, growing a policy population that is robust to exploit.
This iterative approach suffers from two issues in real-world games: a) under
finite budget, approximate best-response operators at each iteration needs
truncating, resulting in under-trained good-responses populating the
population; b) repeated learning of basic skills at each iteration is wasteful
and becomes intractable in the presence of increasingly strong opponents. In
this work, we propose Neural Population Learning (NeuPL) as a solution to both
issues. NeuPL offers convergence guarantees to a population of best-responses
under mild assumptions. By representing a population of policies within a
single conditional model, NeuPL enables transfer learning across policies.
Empirically, we show the generality, improved performance and efficiency of
NeuPL across several test domains. Most interestingly, we show that novel
strategies become more accessible, not less, as the neural population expands.

We propose a novel Reinforcement Learning model for discrete environments,
which is inherently interpretable and supports the discovery of deep subgoal
hierarchies. In the model, an agent learns information about environment in the
form of probabilistic rules, while policies for (sub)goals are learned as
combinations thereof. No reward function is required for learning; an agent
only needs to be given a primary goal to achieve. Subgoals of a goal G from the
hierarchy are computed as descriptions of states, which if previously achieved
increase the total efficiency of the available policies for G. These state
descriptions are introduced as new sensor predicates into the rule language of
the agent, which allows for sensing important intermediate states and for
updating environment rules and policies accordingly.

When modeling longitudinal biomedical data, often dimensionality reduction as
well as dynamic modeling in the resulting latent representation is needed. This
can be achieved by artificial neural networks for dimension reduction, and
differential equations for dynamic modeling of individual-level trajectories.
However, such approaches so far assume that parameters of individual-level
dynamics are constant throughout the observation period. Motivated by an
application from psychological resilience research, we propose an extension
where different sets of differential equation parameters are allowed for
observation sub-periods. Still, estimation for intra-individual sub-periods is
coupled for being able to fit the model also with a relatively small dataset.
We subsequently derive prediction targets from individual dynamic models of
resilience in the application. These serve as interpretable resilience-related
outcomes, to be predicted from characteristics of individuals, measured at
baseline and a follow-up time point, and selecting a small set of important
predictors. Our approach is seen to successfully identify individual-level
parameters of dynamic models that allows us to stably select predictors, i.e.,
resilience factors. Furthermore, we can identify those characteristics of
individuals that are the most promising for updates at follow-up, which might
inform future study design. This underlines the usefulness of our proposed deep
dynamic modeling approach with changes in parameters between observation
sub-periods.

Recent state-of-the-art one-stage instance segmentation model SOLO divides
the input image into a grid and directly predicts per grid cell object masks
with fully-convolutional networks, yielding comparably good performance as
traditional two-stage Mask R-CNN yet enjoying much simpler architecture and
higher efficiency. We observe SOLO generates similar masks for an object at
nearby grid cells, and these neighboring predictions can complement each other
as some may better segment certain object part, most of which are however
directly discarded by non-maximum-suppression. Motivated by the observed gap,
we develop a novel learning-based aggregation method that improves upon SOLO by
leveraging the rich neighboring information while maintaining the architectural
efficiency. The resulting model is named SODAR. Unlike the original per grid
cell object masks, SODAR is implicitly supervised to learn mask representations
that encode geometric structure of nearby objects and complement adjacent
representations with context. The aggregation method further includes two novel
designs: 1) a mask interpolation mechanism that enables the model to generate
much fewer mask representations by sharing neighboring representations among
nearby grid cells, and thus saves computation and memory; 2) a deformable
neighbour sampling mechanism that allows the model to adaptively adjust
neighbor sampling locations thus gathering mask representations with more
relevant context and achieving higher performance. SODAR significantly improves
the instance segmentation performance, e.g., it outperforms a SOLO model with
ResNet-101 backbone by 2.2 AP on COCO \texttt{test} set, with only about 3\%
additional computation. We further show consistent performance gain with the
SOLOv2 model.

We describe a case study of translational research, applying interpretability
techniques developed for computer vision to machine learning models used to
search for and find gravitational waves. The models we study are trained to
detect black hole merger events in non-Gaussian and non-stationary advanced
Laser Interferometer Gravitational-wave Observatory (LIGO) data. We produced
visualizations of the response of machine learning models when they process
advanced LIGO data that contains real gravitational wave signals, noise
anomalies, and pure advanced LIGO noise. Our findings shed light on the
responses of individual neurons in these machine learning models. Further
analysis suggests that different parts of the network appear to specialize in
local versus global features, and that this difference appears to be rooted in
the branched architecture of the network as well as noise characteristics of
the LIGO detectors. We believe efforts to whiten these "black box" models can
suggest future avenues for research and help inform the design of interpretable
machine learning models for gravitational wave astrophysics.

Federated learning (FL) has been increasingly considered to preserve data
training privacy from eavesdropping attacks in mobile edge computing-based
Internet of Thing (EdgeIoT). On the one hand, the learning accuracy of FL can
be improved by selecting the IoT devices with large datasets for training,
which gives rise to a higher energy consumption. On the other hand, the energy
consumption can be reduced by selecting the IoT devices with small datasets for
FL, resulting in a falling learning accuracy. In this paper, we formulate a new
resource allocation problem for privacy-persevering EdgeIoT to balance the
learning accuracy of FL and the energy consumption of the IoT device. We
propose a new federated learning-enabled twin-delayed deep deterministic policy
gradient (FL-DLT3) framework to achieve the optimal accuracy and energy balance
in a continuous domain. Furthermore, long short term memory (LSTM) is leveraged
in FL-DLT3 to predict the time-varying network state while FL-DLT3 is trained
to select the IoT devices and allocate the transmit power. Numerical results
demonstrate that the proposed FL-DLT3 achieves fast convergence (less than 100
iterations) while the FL accuracy-to-energy consumption ratio is improved by
51.8% compared to existing state-of-the-art benchmark.

In financial mathematics, it is a typical approach to approximate financial
markets operating in discrete time by continuous-time models such as the Black
Scholes model. Fitting this model gives rise to difficulties due to the
discrete nature of market data. We thus model the pricing process of financial
derivatives by the Black Scholes equation, where the volatility is a function
of a finite number of random variables. This reflects an influence of uncertain
factors when determining volatility. The aim is to quantify the effect of this
uncertainty when computing the price of derivatives. Our underlying method is
the generalized Polynomial Chaos (gPC) method in order to numerically compute
the uncertainty of the solution by the stochastic Galerkin approach and a
finite difference method. We present an efficient numerical variation of this
method, which is based on a machine learning technique, the so-called
Bi-Fidelity approach. This is illustrated with numerical examples.

Motivated by the recent success of end-to-end deep neural models for ranking
tasks, we present here a supervised end-to-end neural approach for query
performance prediction (QPP). In contrast to unsupervised approaches that rely
on various statistics of document score distributions, our approach is entirely
data-driven. Further, in contrast to weakly supervised approaches, our method
also does not rely on the outputs from different QPP estimators. In particular,
our model leverages information from the semantic interactions between the
terms of a query and those in the top-documents retrieved with it. The
architecture of the model comprises multiple layers of 2D convolution filters
followed by a feed-forward layer of parameters. Experiments on standard test
collections demonstrate that our proposed supervised approach outperforms other
state-of-the-art supervised and unsupervised approaches.

Machine Learning (ML) techniques are revolutionizing the way to perform
efficient materials modeling. Nevertheless, not all the ML approaches allow for
the understanding of microscopic mechanisms at play in different phenomena. To
address the latter aspect, we propose a combinatorial machine-learning approach
to obtain physical formulas based on simple and easily-accessible ingredients,
such as atomic properties. The latter are used to build materials features that
are finally employed, through Linear Regression, to predict the energetic
stability of semiconducting binary compounds with respect to zincblende and
rocksalt crystal structures. The adopted models are trained using dataset built
from first-principles calculations. Our results show that already
one-dimensional (1D) formulas well describe the energetics; a simple
grid-search optimization of the automatically-obtained 1D-formulas enhances the
prediction performances at a very small computational cost. In addition, our
approach allows to highlight the role of the different atomic properties
involved in the formulas. The computed formulas clearly indicate that "spatial"
atomic properties (i.e. radii indicating maximum probability densities for
$s,p,d$ electronic shells) drive the stabilization of one crystal structure
with respect to the other, suggesting the major relevance of the radius
associated to the $p$-shell of the cation species.

Providing user-understandable explanations to justify recommendations could
help users better understand the recommended items, increase the system's ease
of use, and gain users' trust. A typical approach to realize it is natural
language generation. However, previous works mostly adopt recurrent neural
networks to meet the ends, leaving the potentially more effective pre-trained
Transformer models under-explored. In fact, user and item IDs, as important
identifiers in recommender systems, are inherently in different semantic space
as words that pre-trained models were already trained on. Thus, how to
effectively fuse IDs into such models becomes a critical issue. Inspired by
recent advancement in prompt learning, we come up with two solutions: find
alternative words to represent IDs (called discrete prompt learning), and
directly input ID vectors to a pre-trained model (termed continuous prompt
learning). In the latter case, ID vectors are randomly initialized but the
model is trained in advance on large corpora, so they are actually in different
learning stages. To bridge the gap, we further propose two training strategies:
sequential tuning and recommendation as regularization. Extensive experiments
show that our continuous prompt learning approach equipped with the training
strategies consistently outperforms strong baselines on three datasets of
explainable recommendation.

In the Big Data era, with the ubiquity of geolocation sensors in particular,
massive datasets exhibiting a possibly complex spatial dependence structure are
becoming increasingly available. In this context, the standard probabilistic
theory of statistical learning does not apply directly and guarantees of the
generalization capacity of predictive rules learned from such data are left to
establish. We analyze here the simple Kriging task, the flagship problem in
Geostatistics: the values of a square integrable random field $X=\{X_s\}_{s\in
S}$, $S\subset \mathbb{R}^2$, with unknown covariance structure are to be
predicted with minimum quadratic risk, based upon observing a single
realization of the spatial process at a finite number of locations $s_1,\;
\ldots,\; s_n$ in $S$. Despite the connection of this minimization problem with
kernel ridge regression, establishing the generalization capacity of empirical
risk minimizers is far from straightforward, due to the non i.i.d. nature of
the spatial data $X_{s_1},\; \ldots,\; X_{s_n}$ involved. In this article,
nonasymptotic bounds of order $O_{\mathbb{P}}(1/n)$ are proved for the excess
risk of a plug-in predictive rule mimicking the true minimizer in the case of
isotropic stationary Gaussian processes observed at locations forming a regular
grid. These theoretical results, as well as the role played by the technical
conditions required to establish them, are illustrated by various numerical
experiments and hopefully pave the way for further developments in statistical
learning based on spatial data.

We consider the problem of creating assistants that can help agents - often
humans - solve novel sequential decision problems, assuming the agent is not
able to specify the reward function explicitly to the assistant. Instead of
aiming to automate, and act in place of the agent as in current approaches, we
give the assistant an advisory role and keep the agent in the loop as the main
decision maker. The difficulty is that we must account for potential biases
induced by limitations or constraints of the agent which may cause it to
seemingly irrationally reject advice. To do this we introduce a novel
formalization of assistance that models these biases, allowing the assistant to
infer and adapt to them. We then introduce a new method for planning the
assistant's advice which can scale to large decision making problems. Finally,
we show experimentally that our approach adapts to these agent biases, and
results in higher cumulative reward for the agent than automation-based

Anomaly detection in X-ray images has been an active and lasting research
area in the last decades, especially in the domain of medical X-ray images. For
this work, we created a real-world labeled anomaly dataset, consisting of
16-bit X-ray image data of fuel cell electrodes coated with a platinum catalyst
solution and perform anomaly detection on the dataset using a deep learning
approach. The dataset contains a diverse set of anomalies with 11 identified
common anomalies where the electrodes contain e.g. scratches, bubbles, smudges
etc. We experiment with 16-bit image to 8-bit image conversion methods to
utilize pre-trained Convolutional Neural Networks as feature extractors
(transfer learning) and find that we achieve the best performance by maximizing
the contrasts globally across the dataset during the 16-bit to 8-bit
conversion, through histogram equalization. We group the fuel cell electrodes
with anomalies into a single class called abnormal and the normal fuel cell
electrodes into a class called normal, thereby abstracting the anomaly
detection problem into a binary classification problem. We achieve a balanced
accuracy of 85.18\%. The anomaly detection is used by the company, Serenergy,
for optimizing the time spend on the quality control of the fuel cell
electrodes

Textless spoken language processing research aims to extend the applicability
of standard NLP toolset onto spoken language and languages with few or no
textual resources. In this paper, we introduce textless-lib, a PyTorch-based
library aimed to facilitate research in this research area. We describe the
building blocks that the library provides and demonstrate its usability by
discuss three different use-case examples: (i) speaker probing, (ii) speech
resynthesis and compression, and (iii) speech continuation. We believe that
textless-lib substantially simplifies research the textless setting and will be
handful not only for speech researchers but also for the NLP community at
large. The code, documentation, and pre-trained models are available at
https://github.com/facebookresearch/textlesslib/ .

Face recognition under ideal conditions is now considered a well-solved
problem with advances in deep learning. Recognizing faces under occlusion,
however, still remains a challenge. Existing techniques often fail to recognize
faces with both the mouth and nose covered by a mask, which is now very common
under the COVID-19 pandemic. Common approaches to tackle this problem include
1) discarding information from the masked regions during recognition and 2)
restoring the masked regions before recognition. Very few works considered the
consistency between features extracted from masked faces and from their
mask-free counterparts. This resulted in models trained for recognizing masked
faces often showing degraded performance on mask-free faces. In this paper, we
propose a unified framework, named Face Feature Rectification Network
(FFR-Net), for recognizing both masked and mask-free faces alike. We introduce
rectification blocks to rectify features extracted by a state-of-the-art
recognition model, in both spatial and channel dimensions, to minimize the
distance between a masked face and its mask-free counterpart in the rectified
feature space. Experiments show that our unified framework can learn a
rectified feature space for recognizing both masked and mask-free faces
effectively, achieving state-of-the-art results. Project code:
https://github.com/haoosz/FFR-Net

Many existing methods of counterfactual explanations ignore the intrinsic
relationships between data attributes and thus fail to generate realistic
counterfactuals. Moreover, the existing models that account for relationships
require domain knowledge, which limits their applicability in complex
real-world applications. In this paper, we propose a novel approach to
realistic counterfactual explanations that preserve the relationships and
minimise experts' interventions. The model directly learns the relationships by
a variational auto-encoder with minimal domain knowledge and then learns to
perturb the latent space accordingly. We conduct extensive experiments on both
synthetic and real-world datasets. The experimental results demonstrate that
the proposed model learns relationships from the data and preserves these
relationships in generated counterfactuals. In particular, it outperforms other
methods in terms of Mahalanobis distance, and the constraint feasibility score.

Motivated by the high computational costs of classical simulations,
machine-learned generative models can be extremely useful in particle physics
and elsewhere. They become especially attractive when surrogate models can
efficiently learn the underlying distribution, such that a generated sample
outperforms a training sample of limited size. This kind of GANplification has
been observed for simple Gaussian models. We show the same effect for a physics
simulation, specifically photon showers in an electromagnetic calorimeter.

In this paper we show that the expected generalisation performance of a
learning machine is determined by the distribution of risks or equivalently its
logarithm -- a quantity we term the risk entropy -- and the fluctuations in a
quantity we call the training ratio. We show that the risk entropy can be
empirically inferred for deep neural network models using Markov Chain Monte
Carlo techniques. Results are presented for different deep neural networks on a
variety of problems. The asymptotic behaviour of the risk entropy acts in an
analogous way to the capacity of the learning machine, but the generalisation
performance experienced in practical situations is determined by the behaviour
of the risk entropy before the asymptotic regime is reached. This performance
is strongly dependent on the distribution of the data (features and targets)
and not just on the capacity of the learning machine.

We report the results of a search for MeV-scale astrophysical neutrinos in
KamLAND presented as an excess in the number of coincident neutrino
interactions associated with the publicly available high-energy neutrino
datasets from the IceCube Neutrino Observatory. We find no statistically
significant excess in the number of observed low-energy electron antineutrinos
in KamLAND, given a coincidence time window of $\pm$500s, $\pm$1,000s,
$\pm$3,600s, and $\pm$10,000s around each of the IceCube neutrinos. We use this
observation to present limits from 1.8 MeV to 100 MeV on the electron
antineutrino fluence, assuming a mono-energetic flux. We then compare the
results to several astrophysical measurements performed by IceCube and place a
limit at the 90% confidence level on the electron antineutrino isotropic
thermal luminosity from the TXS 0506+056 blazar.

In standard Deep Neural Network (DNN) based classifiers, the general
convention is to omit the activation function in the last (output) layer and
directly apply the softmax function on the logits to get the probability scores
of each class. In this type of architectures, the loss value of the classifier
against any output class is directly proportional to the difference between the
final probability score and the label value of the associated class. Standard
White-box adversarial evasion attacks, whether targeted or untargeted, mainly
try to exploit the gradient of the model loss function to craft adversarial
samples and fool the model. In this study, we show both mathematically and
experimentally that using some widely known activation functions in the output
layer of the model with high temperature values has the effect of zeroing out
the gradients for both targeted and untargeted attack cases, preventing
attackers from exploiting the model's loss function to craft adversarial
samples. We've experimentally verified the efficacy of our approach on MNIST
(Digit), CIFAR10 datasets. Detailed experiments confirmed that our approach
substantially improves robustness against gradient-based targeted and
untargeted attack threats. And, we showed that the increased non-linearity at
the output layer has some additional benefits against some other attack methods
like Deepfool attack.

We describe an efficient algorithm to compute solutions for the general
two-player Blotto game on n battlefields with heterogeneous values. While
explicit constructions for such solutions have been limited to specific,
largely symmetric or homogeneous, setups, this algorithmic resolution covers
the most general situation to date: value-asymmetric game with asymmetric
budget. The proposed algorithm rests on recent theoretical advances regarding
Sinkhorn iterations for matrix and tensor scaling. An important case which had
been out of reach of previous attempts is that of heterogeneous but symmetric
battlefield values with asymmetric budget. In this case, the Blotto game is
constant-sum so optimal solutions exist, and our algorithm samples from an
$\varepsilon$-optimal solution in time $\tilde{\mathcal{O}}(n^2 +
\varepsilon^{-4})$, independently of budgets and battlefield values. In the
case of asymmetric values where optimal solutions need not exist but Nash
equilibria do, our algorithm samples from an $\varepsilon$-Nash equilibrium
with similar complexity but where implicit constants depend on various
parameters of the game such as battlefield values.

This paper defines a strong convertible nonconvex(SCN) function for solving
the unconstrained optimization problems with the nonconvex or
nonsmooth(nondifferentiable) function. First, many examples of SCN function are
given, where the SCN functions are nonconvex or nonsmooth. Second, the
operational properties of the SCN functions are proved, including addition,
multiplication, compound operations and so on. Third, the SCN forms of some
special functions common in machine learning and engineering applications are
presented respectively where these SCN function optimization problems can be
transformed into minmax problems with a convex and concave objective function.
Fourth,a minmax optimization problem of SCN function and its penalty function
are defined. The optimization condition,exactness and stability of the minmax
optimization problem are proved. Finally, an algorithm of penalty function to
solve the minmax optimization problem and its convergence are given. This paper
provides an efficient technique for solving unconstrained nonconvex or
nonsmooth(nondifferentiable) optimization problems to avoid using
subdifferentiation or smoothing techniques.

Transformers have become an important workhorse of machine learning, with
numerous applications. This necessitates the development of reliable methods
for increasing their transparency. Multiple interpretability methods, often
based on gradient information, have been proposed. We show that the gradient in
a Transformer reflects the function only locally, and thus fails to reliably
identify the contribution of input features to the prediction. We identify
Attention Heads and LayerNorm as main reasons for such unreliable explanations
and propose a more stable way for propagation through these layers. Our
proposal, which can be seen as a proper extension of the well-established LRP
method to Transformers, is shown both theoretically and empirically to overcome
the deficiency of a simple gradient-based approach, and achieves
state-of-the-art explanation performance on a broad range of Transformer models
and datasets.

Scientific research's mandate is to comprehend and explore the world, as well
as to improve it based on experience and knowledge. Knowledge embedding and
knowledge discovery are two significant methods of integrating knowledge and
data. Through knowledge embedding, the barriers between knowledge and data can
be eliminated, and machine learning models with physical common sense can be
established. Meanwhile, humans' understanding of the world is always limited,
and knowledge discovery takes advantage of machine learning to extract new
knowledge from observations. Knowledge discovery can not only assist
researchers to better grasp the nature of physics, but it can also support them
in conducting knowledge embedding research. A closed loop of knowledge
generation and usage are formed by combining knowledge embedding with knowledge
discovery, which can improve the robustness and accuracy of models and uncover
previously unknown scientific principles. This study summarizes and analyzes
extant literature, as well as identifies research gaps and future

Recently, improving the robustness of policies across different environments
attracts increasing attention in the reinforcement learning (RL) community.
Existing robust RL methods mostly aim to achieve the max-min robustness by
optimizing the policy's performance in the worst-case environment. However, in
practice, a user that uses an RL policy may have different preferences over its
performance across environments. Clearly, the aforementioned max-min robustness
is oftentimes too conservative to satisfy user preference. Therefore, in this
paper, we integrate user preference into policy learning in robust RL, and
propose a novel User-Oriented Robust RL (UOR-RL) framework. Specifically, we
define a new User-Oriented Robustness (UOR) metric for RL, which allocates
different weights to the environments according to user preference and
generalizes the max-min robustness metric. To optimize the UOR metric, we
develop two different UOR-RL training algorithms for the scenarios with or
without a priori known environment distribution, respectively. Theoretically,
we prove that our UOR-RL training algorithms converge to near-optimal policies
even with inaccurate or completely no knowledge about the environment
distribution. Furthermore, we carry out extensive experimental evaluations in 4
MuJoCo tasks. The experimental results demonstrate that UOR-RL is comparable to
the state-of-the-art baselines under the average and worst-case performance
metrics, and more importantly establishes new state-of-the-art performance
under the UOR metric.

This paper provides new theory to support to the eXplainable AI (XAI) method
Contextual Importance and Utility (CIU). CIU arithmetic is based on the
concepts of Multi-Attribute Utility Theory, which gives CIU a solid theoretical
foundation. The novel concept of contextual influence is also defined, which
makes it possible to compare CIU directly with so-called additive feature
attribution (AFA) methods for model-agnostic outcome explanation. One key
takeaway is that the "influence" concept used by AFA methods is inadequate for
outcome explanation purposes even for simple models to explain. Experiments
with simple models show that explanations using contextual importance (CI) and
contextual utility (CU) produce explanations where influence-based methods
fail. It is also shown that CI and CU guarantees explanation faithfulness
towards the explained model.

Video interpolation is the task that synthesizes the intermediate frame given
two consecutive frames. Most of the previous studies have focused on
appropriate frame warping operations and refinement modules for the warped
frames. These studies have been conducted on natural videos having only
continuous motions. However, many practical videos contain a lot of
discontinuous motions, such as chat windows, watermarks, GUI elements, or
subtitles. We propose three techniques to expand the concept of transition
between two consecutive frames to address these issues. First is a new
architecture that can separate continuous and discontinuous motion areas. We
also propose a novel data augmentation strategy called figure-text mixing (FTM)
to make our model learn more general scenarios. Finally, we propose loss
functions to give supervisions of the discontinuous motion areas with the data
augmentation. We collected a special dataset consisting of some mobile games
and chatting videos. We show that our method significantly improves the
interpolation qualities of the videos on the special dataset. Moreover, our
model outperforms the state-of-the-art methods for natural video datasets
containing only continuous motions, such as DAVIS and UCF101.

Over the last decade, numerous studies have shown that deep neural networks
exhibit sensory representations similar to those of the mammalian brain, in
that their activations linearly map onto cortical responses to the same sensory
inputs. However, it remains unknown whether these artificial networks also
learn like the brain. To address this issue, we analyze the brain responses of
two ferret auditory cortices recorded with functional UltraSound imaging (fUS),
while the animals were presented with 320 10\,s sounds. We compare these brain
responses to the activations of Wav2vec 2.0, a self-supervised neural network
pretrained with 960\,h of speech, and input with the same 320 sounds.
Critically, we evaluate Wav2vec 2.0 under two distinct modes: (i) "Pretrained",
where the same model is used for all sounds, and (ii) "Continuous Update",
where the weights of the pretrained model are modified with back-propagation
after every sound, presented in the same order as the ferrets. Our results show
that the Continuous-Update mode leads Wav2Vec 2.0 to generate activations that
are more similar to the brain than a Pretrained Wav2Vec 2.0 or than other
control models using different training modes. These results suggest that the
trial-by-trial modifications of self-supervised algorithms induced by
back-propagation aligns with the corresponding fluctuations of cortical
responses to sounds. Our finding thus provides empirical evidence of a common
learning mechanism between self-supervised models and the mammalian cortex
during sound processing.

Many real-world datasets can be divided into groups according to certain
salient features (e.g. grouping images by subject, grouping text by font,
etc.). Often, machine learning tasks require that these features be represented
separately from those manifesting independently of the grouping. For example,
image translation entails changing the style of an image while preserving its
content. We formalize these two kinds of attributes as two complementary
generative factors called "domain" and "content", and address the problem of
disentangling them in a fully unsupervised way. To achieve this, we propose a
principled, generalizable probabilistic model inspired by the Variational
Autoencoder. Our model exhibits state-of-the-art performance on the composite
task of generating images by combining the domain of one input with the content
of another. Distinctively, it can perform this task in a few-shot, unsupervised
manner, without being provided with explicit labelling for either domain or
content. The disentangled representations are learned through the combination
of a group-wise encoder and a novel domain-confusion loss.

Uncertainty quantification of predictive models is crucial in decision-making
problems. Conformal prediction is a general and theoretically sound answer.
However, it requires exchangeable data, excluding time series. While recent
works tackled this issue, we argue that Adaptive Conformal Inference (ACI,
Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a
good procedure for time series with general dependency. We theoretically
analyse the impact of the learning rate on its efficiency in the exchangeable
and auto-regressive case. We propose a parameter-free method, AgACI, that
adaptively builds upon ACI based on online expert aggregation. We lead
extensive fair simulations against competing methods that advocate for ACI's
use in time series. We conduct a real case study: electricity price
forecasting. The proposed aggregation algorithm provides efficient prediction
intervals for day-ahead forecasting. All the code and data to reproduce the
experiments is made available.

Federated learning (FL) is an emerging paradigm of collaborative machine
learning that preserves user privacy while building powerful models.
Nevertheless, due to the nature of open participation by self-interested
entities, it needs to guard against potential misbehaviours by legitimate FL
participants. FL verification techniques are promising solutions for this
problem. They have been shown to effectively enhance the reliability of FL
networks and help build trust among participants. Verifiable federated learning
has become an emerging topic of research that has attracted significant
interest from the academia and the industry alike. Currently, there is no
comprehensive survey on the field of verifiable federated learning, which is
interdisciplinary in nature and can be challenging for researchers to enter
into. In this paper, we bridge this gap by reviewing works focusing on
verifiable FL. We propose a novel taxonomy for verifiable FL covering both
centralised and decentralised FL settings, summarise the commonly adopted
performance evaluation approaches, and discuss promising directions towards a
versatile verifiable FL framework.

We address the differentially private estimation of multiple quantiles (MQ)
of a dataset, a key building block in modern data analysis. We apply the recent
non-smoothed Inverse Sensitivity (IS) mechanism to this specific problem and
establish that the resulting method is closely related to the current
state-of-the-art, the JointExp algorithm, sharing in particular the same
computational complexity and a similar efficiency. However, we demonstrate both
theoretically and empirically that (non-smoothed) JointExp suffers from an
important lack of performance in the case of peaked distributions, with a
potentially catastrophic impact in the presence of atoms. While its smoothed
version would allow to leverage the performance guarantees of IS, it remains an
open challenge to implement. As a proxy to fix the problem we propose a simple
and numerically efficient method called Heuristically Smoothed JointExp
(HSJointExp), which is endowed with performance guarantees for a broad class of
distributions and achieves results that are orders of magnitude better on
problematic datasets.

Drug Side-Effects (DSEs) have a high impact on public health, care system
costs, and drug discovery processes. Predicting the probability of
side-effects, before their occurrence, is fundamental to reduce this impact, in
particular on drug discovery. Candidate molecules could be screened before
undergoing clinical trials, reducing the costs in time, money, and health of
the participants. Drug side-effects are triggered by complex biological
processes involving many different entities, from drug structures to
protein-protein interactions. To predict their occurrence, it is necessary to
integrate data from heterogeneous sources. In this work, such heterogeneous
data is integrated into a graph dataset, expressively representing the
relational information between different entities, such as drug molecules and
genes. The relational nature of the dataset represents an important novelty for
drug side-effect predictors. Graph Neural Networks (GNNs) are exploited to
predict DSEs on our dataset with very promising results. GNNs are deep learning
models that can process graph-structured data, with minimal information loss,
and have been applied on a wide variety of biological tasks. Our experimental
results confirm the advantage of using relationships between data entities,
suggesting interesting future developments in this scope. The experimentation
also shows the importance of specific subsets of data in determining
associations between drugs and side-effects.

A method for obtaining appropriate reaction coordinates is required to
identify transition states distinguishing product and reactant in complex
molecular systems. Recently, abundant research has been devoted to obtaining
reaction coordinates using artificial neural networks from deep learning
literature, where many collective variables are typically utilized in the input
layer. However, it is difficult to explain the details of which collective
variables contribute to the predicted reaction coordinates owing to the
complexity of the nonlinear functions in deep neural networks. To overcome this
limitation, we used Explainable Artificial Intelligence (XAI) methods of the
Local Interpretable Model-agnostic Explanation (LIME) and the game theory-based
framework known as Shapley Additive exPlanations (SHAP). We demonstrated that
XAI enables us to obtain the degree of contribution of each collective variable
to reaction coordinates that is determined by nonlinear regressions with deep
learning for the committor of the alanine dipeptide isomerization in vacuum. In
particular, both LIME and SHAP provide important features to the predicted
reaction coordinates, which are characterized by appropriate dihedral angles
consistent with those previously reported from the committor test analysis. The
present study offers an AI-aided framework to explain the appropriate reaction
coordinates, which acquires considerable significance when the number of
degrees of freedom increases.

Memory-augmented neural networks (MANNs) provide better inference performance
in many tasks with the help of an external memory. The recently developed
differentiable neural computer (DNC) is a MANN that has been shown to
outperform in representing complicated data structures and learning long-term
dependencies. DNC's higher performance is derived from new history-based
attention mechanisms in addition to the previously used content-based attention
mechanisms. History-based mechanisms require a variety of new compute
primitives and state memories, which are not supported by existing neural
network (NN) or MANN accelerators. We present HiMA, a tiled, history-based
memory access engine with distributed memories in tiles. HiMA incorporates a
multi-mode network-on-chip (NoC) to reduce the communication latency and
improve scalability. An optimal submatrix-wise memory partition strategy is
applied to reduce the amount of NoC traffic; and a two-stage usage sort method
leverages distributed tiles to improve computation speed. To make HiMA
fundamentally scalable, we create a distributed version of DNC called DNC-D to
allow almost all memory operations to be applied to local memories with
trainable weighted summation to produce the global memory output. Two
approximation techniques, usage skimming and softmax approximation, are
proposed to further enhance hardware efficiency. HiMA prototypes are created in
RTL and synthesized in a 40nm technology. By simulations, HiMA running DNC and
DNC-D demonstrates 6.47x and 39.1x higher speed, 22.8x and 164.3x better area
efficiency, and 6.1x and 61.2x better energy efficiency over the
state-of-the-art MANN accelerator. Compared to an Nvidia 3080Ti GPU, HiMA
demonstrates speedup by up to 437x and 2,646x when running DNC and DNC-D,

We propose SpeechPainter, a model for filling in gaps of up to one second in
speech samples by leveraging an auxiliary textual input. We demonstrate that
the model performs speech inpainting with the appropriate content, while
maintaining speaker identity, prosody and recording environment conditions, and
generalizing to unseen speakers. Our approach significantly outperforms
baselines constructed using adaptive TTS, as judged by human raters in
side-by-side preference and MOS tests.

Generating informative scene graphs from images requires integrating and
reasoning from various graph components, i.e., objects and relationships.
However, current scene graph generation (SGG) methods, including the unbiased
SGG methods, still struggle to predict informative relationships due to the
lack of 1) high-level inference such as transitive inference between
relationships and 2) efficient mechanisms that can incorporate all interactions
of graph components. To address the issues mentioned above, we devise a
hyper-relationship learning network, termed HLN, for SGG. Specifically, the
proposed HLN stems from hypergraphs and two graph attention networks (GATs) are
designed to infer relationships: 1) the object-relationship GAT or OR-GAT to
explore interactions between objects and relationships, and 2) the
hyper-relationship GAT or HR-GAT to integrate transitive inference of
hyper-relationships, i.e., the sequential relationships between three objects
for transitive reasoning. As a result, HLN significantly improves the
performance of scene graph generation by integrating and reasoning from object
interactions, relationship interactions, and transitive inference of
hyper-relationships. We evaluate HLN on the most popular SGG dataset, i.e., the
Visual Genome dataset, and the experimental results demonstrate its great
superiority over recent state-of-the-art methods. For example, the proposed HLN
improves the recall per relationship from 11.3\% to 13.1\%, and maintains the
recall per image from 19.8\% to 34.9\%. We will release the source code and
pretrained models on GitHub.

This paper analyzes the influence of partisan content from national cable TV
news on local reporting in U.S. newspapers. We provide a new
machine-learning-based measure of cable news slant, trained on a corpus of 40K
transcribed TV episodes from Fox News Channel (FNC), CNN, and MSNBC
(2005-2008). Applying the method to a corpus of 24M local newspaper articles,
we find that in response to an exogenous increase in local viewership of FNC
relative to CNN/MSNBC, local newspaper articles become more similar to FNC
transcripts (and vice versa). Consistent with newspapers responding to changes
in reader preferences, we see a shift in the framing of local news coverage
rather than just direct borrowing of cable news content. Further, cable news
slant polarizes local news content: right-leaning newspapers tend to adopt
right-wing FNC language, while left-leaning newspapers tend to become more
left-wing. Media slant is contagious.

This paper presents an iterative pruning strategy for Convolutional Network
Fabrics (CNF) in presence of noisy training and testing data. With the
continuous increase in size of neural network models, various authors have
developed pruning approaches to build more compact network structures requiring
less resources, while preserving performance. As we show in this paper, because
of their intrinsic structure and function, Convolutional Network Fabrics are
ideal candidates for pruning. We present a series of pruning strategies that
can significantly reduce both the final network size and required training time
by pruning either entire convolutional filters or individual weights, so that
the grid remains visually understandable but that overall execution quality
stays within controllable boundaries. Our approach can be iteratively applied
during training so that the network complexity decreases rapidly, saving
computational time. The paper addresses both data-dependent and dataindependent
strategies, and also experimentally establishes the most efficient approaches
when training or testing data contain annotation errors.

Stochastic Gradient Descent-Ascent (SGDA) is one of the most prominent
algorithms for solving min-max optimization and variational inequalities
problems (VIP) appearing in various machine learning tasks. The success of the
method led to several advanced extensions of the classical SGDA, including
variants with arbitrary sampling, variance reduction, coordinate randomization,
and distributed variants with compression, which were extensively studied in
the literature, especially during the last few years. In this paper, we propose
a unified convergence analysis that covers a large variety of stochastic
gradient descent-ascent methods, which so far have required different
intuitions, have different applications and have been developed separately in
various communities. A key to our unified framework is a parametric assumption
on the stochastic estimates. Via our general theoretical framework, we either
recover the sharpest known rates for the known special cases or tighten them.
Moreover, to illustrate the flexibility of our approach we develop several new
variants of SGDA such as a new variance-reduced method (L-SVRGDA), new
distributed methods with compression (QSGDA, DIANA-SGDA, VR-DIANA-SGDA), and a
new method with coordinate randomization (SEGA-SGDA). Although variants of the
new methods are known for solving minimization problems, they were never
considered or analyzed for solving min-max problems and VIPs. We also
demonstrate the most important properties of the new methods through extensive

In wearable-based human activity recognition (HAR) research, one of the major
challenges is the large intra-class variability problem. The collected activity
signal is often, if not always, coupled with noises or bias caused by personal,
environmental, or other factors, making it difficult to learn effective
features for HAR tasks, especially when with inadequate data. To address this
issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD)
framework, which can disentangle the behavior patterns from the irrelevant
noises such as personal styles or environmental noises, etc. Based on a
disentanglement network, we designed several loss functions and used an
adversarial training strategy for optimization, which can disentangle activity
signals from the irrelevant noises with the least dependency (between them) in
the feature space. Our BPD framework is flexible, and it can be used on top of
existing deep learning (DL) approaches for feature refinement. Extensive
experiments were conducted on four public HAR datasets, and the promising
results of our proposed BPD scheme suggest its flexibility and effectiveness.
This is an open-source project, and the code can be found at
http://github.com/Jie-su/BPD

Non-negative and bounded-variable linear regression problems arise in a
variety of applications in machine learning and signal processing. In this
paper, we propose a technique to accelerate existing solvers for these problems
by identifying saturated coordinates in the course of iterations. This is akin
to safe screening techniques previously proposed for sparsity-regularized
regression problems. The proposed strategy is provably safe as it provides
theoretical guarantees that the identified coordinates are indeed saturated in
the optimal solution. Experimental results on synthetic and real data show
compelling accelerations for both non-negative and bounded-variable problems.

With its powerful capability to deal with graph data widely found in
practical applications, graph neural networks (GNNs) have received significant
research attention. However, as societies become increasingly concerned with
data privacy, GNNs face the need to adapt to this new normal. This has led to
the rapid development of federated graph neural networks (FedGNNs) research in
recent years. Although promising, this interdisciplinary field is highly
challenging for interested researchers to enter into. The lack of an insightful
survey on this topic only exacerbates this problem. In this paper, we bridge
this gap by offering a comprehensive survey of this emerging field. We propose
a unique 3-tiered taxonomy of the FedGNNs literature to provide a clear view
into how GNNs work in the context of Federated Learning (FL). It puts existing
works into perspective by analyzing how graph data manifest themselves in FL
settings, how GNN training is performed under different FL system architectures
and degrees of graph data overlap across data silo, and how GNN aggregation is
performed under various FL settings. Through discussions of the advantages and
limitations of existing works, we envision future research directions that can
help build more robust, dynamic, efficient, and interpretable FedGNNs.

Machine learning models can automatically learn complex relationships, such
as non-linear and interaction effects. Interpretable machine learning methods
such as partial dependence plots visualize marginal feature effects but may
lead to misleading interpretations when feature interactions are present.
Hence, employing additional methods that can detect and measure the strength of
interactions is paramount to better understand the inner workings of machine
learning models. We demonstrate several drawbacks of existing global
interaction detection approaches, characterize them theoretically, and evaluate
them empirically. Furthermore, we introduce regional effect plots with implicit
interaction detection, a novel framework to detect interactions between a
feature of interest and other features. The framework also quantifies the
strength of interactions and provides interpretable and distinct regions in
which feature effects can be interpreted more reliably, as they are less
confounded by interactions. We prove the theoretical eligibility of our method
and show its applicability on various simulation and real-world examples.

Social recommendation has shown promising improvements over traditional
systems since it leverages social correlation data as an additional input. Most
existing work assumes that all data are available to the recommendation
platform. However, in practice, user-item interaction data (e.g.,rating) and
user-user social data are usually generated by different platforms, and both of
which contain sensitive information. Therefore, "How to perform secure and
efficient social recommendation across different platforms, where the data are
highly-sparse in nature" remains an important challenge. In this work, we bring
secure computation techniques into social recommendation, and propose S3Rec, a
sparsity-aware secure cross-platform social recommendation framework. As a
result, our model can not only improve the recommendation performance of the
rating platform by incorporating the sparse social data on the social platform,
but also protect data privacy of both platforms. Moreover, to further improve
model training efficiency, we propose two secure sparse matrix multiplication
protocols based on homomorphic encryption and private information retrieval.
Our experiments on two benchmark datasets demonstrate the effectiveness of
S3Rec.

We introduce CommerceMM - a multimodal model capable of providing a diverse
and granular understanding of commerce topics associated to the given piece of
content (image, text, image+text), and having the capability to generalize to a
wide range of tasks, including Multimodal Categorization, Image-Text Retrieval,
Query-to-Product Retrieval, Image-to-Product Retrieval, etc. We follow the
pre-training + fine-tuning training regime and present 5 effective pre-training
tasks on image-text pairs. To embrace more common and diverse commerce data
with text-to-multimodal, image-to-multimodal, and multimodal-to-multimodal
mapping, we propose another 9 novel cross-modal and cross-pair retrieval tasks,
called Omni-Retrieval pre-training. The pre-training is conducted in an
efficient manner with only two forward/backward updates for the combined 14
tasks. Extensive experiments and analysis show the effectiveness of each task.
When combining all pre-training tasks, our model achieves state-of-the-art
performance on 7 commerce-related downstream tasks after fine-tuning.
Additionally, we propose a novel approach of modality randomization to
dynamically adjust our model under different efficiency constraints.

While machine learning models are usually assumed to always output a
prediction, there also exist extensions in the form of reject options which
allow the model to reject inputs where only a prediction with an unacceptably
low certainty would be possible. With the ongoing rise of eXplainable AI, a lot
of methods for explaining model predictions have been developed. However,
understanding why a given input was rejected, instead of being classified by
the model, is also of interest. Surprisingly, explanations of rejects have not
been considered so far.
  We propose to use counterfactual explanations for explaining rejects and
investigate how to efficiently compute counterfactual explanations of different
reject options for an important class of models, namely prototype-based
classifiers such as learning vector quantization models.

The success of deep learning in recent years has lead to a rising demand for
neural network architecture engineering. As a consequence, neural architecture
search (NAS), which aims at automatically designing neural network
architectures in a data-driven manner rather than manually, has evolved as a
popular field of research. With the advent of weight sharing strategies across
architectures, NAS has become applicable to a much wider range of problems. In
particular, there are now many publications for dense prediction tasks in
computer vision that require pixel-level predictions, such as semantic
segmentation or object detection. These tasks come with novel challenges, such
as higher memory footprints due to high-resolution data, learning multi-scale
representations, longer training times, and more complex and larger neural
architectures. In this manuscript, we provide an overview of NAS for dense
prediction tasks by elaborating on these novel challenges and surveying ways to
address them to ease future research and application of existing methods to
novel problems.

Recent deep models for solving routing problems always assume a single
distribution of nodes for training, which severely impairs their
cross-distribution generalization ability. In this paper, we exploit group
distributionally robust optimization (group DRO) to tackle this issue, where we
jointly optimize the weights for different groups of distributions and the
parameters for the deep model in an interleaved manner during training. We also
design a module based on convolutional neural network, which allows the deep
model to learn more informative latent pattern among the nodes. We evaluate the
proposed approach on two types of well-known deep models including GCN and
POMO. The experimental results on the randomly synthesized instances and the
ones from two benchmark dataset (i.e., TSPLib and CVRPLib) demonstrate that our
approach could significantly improve the cross-distribution generalization
performance over the original models.

We study the identification and estimation of long-term treatment effects
when both experimental and observational data are available. Since the
long-term outcome is observed only after a long delay, it is not measured in
the experimental data, but only recorded in the observational data. However,
both types of data include observations of some short-term outcomes. In this
paper, we uniquely tackle the challenge of persistent unmeasured confounders,
i.e., some unmeasured confounders that can simultaneously affect the treatment,
short-term outcomes and the long-term outcome, noting that they invalidate
identification strategies in previous literature. To address this challenge, we
exploit the sequential structure of multiple short-term outcomes, and develop
three novel identification strategies for the average long-term treatment
effect. We further propose three corresponding estimators and prove their
asymptotic consistency and asymptotic normality. We finally apply our methods
to estimate the effect of a job training program on long-term employment using
semi-synthetic data. We numerically show that our proposals outperform existing
methods that fail to handle persistent confounders.

Despite the widespread adoption of computational notebooks, little is known
about best practices for their usage in collaborative contexts. In this paper,
we fill this gap by eliciting a catalog of best practices for collaborative
data science with computational notebooks. With this aim, we first look for
best practices through a multivocal literature review. Then, we conduct
interviews with professional data scientists to assess their awareness of these
best practices. Finally, we assess the adoption of best practices through the
analysis of 1,380 Jupyter notebooks retrieved from the Kaggle platform.
Findings reveal that experts are mostly aware of the best practices and tend to
adopt them in their daily work. Nonetheless, they do not consistently follow
all the recommendations as, depending on specific contexts, some are deemed
unfeasible or counterproductive due to the lack of proper tool support. As
such, we envision the design of notebook solutions that allow data scientists
not to have to prioritize exploration and rapid prototyping over writing code
of quality.

Many scientific problems require to process data in the form of geometric
graphs. Unlike generic graph data, geometric graphs exhibit symmetries of
translations, rotations, and/or reflections. Researchers have leveraged such
inductive bias and developed geometrically equivariant Graph Neural Networks
(GNNs) to better characterize the geometry and topology of geometric graphs.
Despite fruitful achievements, it still lacks a survey to depict how
equivariant GNNs are progressed, which in turn hinders the further development
of equivariant GNNs. To this end, based on the necessary but concise
mathematical preliminaries, we analyze and classify existing methods into three
groups regarding how the message passing and aggregation in GNNs are
represented. We also summarize the benchmarks as well as the related datasets
to facilitate later researches for methodology development and experimental
evaluation. The prospect for future potential directions is also provided.

Spiking and Quantized Neural Networks (NNs) are becoming exceedingly
important for hyper-efficient implementations of Deep Learning (DL) algorithms.
However, these networks face challenges when trained using error
backpropagation, due to the absence of gradient signals when applying hard
thresholds. The broadly accepted trick to overcoming this is through the use of
biased gradient estimators: surrogate gradients which approximate thresholding
in Spiking Neural Networks (SNNs), and Straight-Through Estimators (STEs),
which completely bypass thresholding in Quantized Neural Networks (QNNs). While
noisy gradient feedback has enabled reasonable performance on simple supervised
learning tasks, it is thought that such noise increases the difficulty of
finding optima in loss landscapes, especially during the later stages of
optimization. By periodically boosting the Learning Rate (LR) during training,
we expect the network can navigate unexplored solution spaces that would
otherwise be difficult to reach due to local minima, barriers, or flat
surfaces. This paper presents a systematic evaluation of a cosine-annealed LR
schedule coupled with weight-independent adaptive moment estimation as applied
to Quantized SNNs (QSNNs). We provide a rigorous empirical evaluation of this
technique on high precision and 4-bit quantized SNNs across three datasets,
demonstrating (close to) state-of-the-art performance on the more complex
datasets. Our source code is available at this link:
https://github.com/jeshraghian/QSNNs.

Mismatched data is a challenging problem for automatic speech recognition
(ASR) systems. One of the most common techniques used to address mismatched
data is multi-style training (MTR), a form of data augmentation that attempts
to transform the training data to be more representative of the testing data;
and to learn robust representations applicable to different conditions. This
task can be very challenging if the test conditions are unknown. We explore the
impact of different MTR styles on system performance when testing conditions
are different from training conditions in the context of deep neural network
hidden Markov model (DNN-HMM) ASR systems. A controlled environment is created
using the LibriSpeech corpus, where we isolate the effect of different MTR
styles on final system performance. We evaluate our findings on a South African
call centre dataset that contains noisy, WAV49-encoded audio.

Mammalian brains operate in a very special surrounding: to survive they have
to react quickly and effectively to the pool of stimuli patterns previously
recognized as danger. Many learning tasks often encountered by living organisms
involve a specific set-up centered around a relatively small set of patterns
presented in a particular environment. For example, at a party, people
recognize friends immediately, without deep analysis, just by seeing a fragment
of their clothes. This set-up with reduced "ontology" is referred to as a
"situation". Situations are usually local in space and time. In this work, we
propose that neuron-astrocyte networks provide a network topology that is
effectively adapted to accommodate situation-based memory. In order to
illustrate this, we numerically simulate and analyze a well-established model
of a neuron-astrocyte network, which is subjected to stimuli conforming to the
situation-driven environment. Three pools of stimuli patterns are considered:
external patterns, patterns from the situation associative pool regularly
presented to the network and learned by the network, and patterns already
learned and remembered by astrocytes. Patterns from the external world are
added to and removed from the associative pool. Then we show that astrocytes
are structurally necessary for an effective function in such a learning and
testing set-up. To demonstrate this we present a novel neuromorphic model for
short-term memory implemented by a two-net spiking neural-astrocytic network.
Our results show that such a system tested on synthesized data with selective
astrocyte-induced modulation of neuronal activity provides an enhancement of
retrieval quality in comparison to standard spiking neural networks trained via
Hebbian plasticity only. We argue that the proposed set-up may offer a new way
to analyze, model, and understand neuromorphic artificial intelligence systems.

Label distributions in camera-trap images are highly imbalanced and
long-tailed, resulting in neural networks tending to be biased towards
head-classes that appear frequently. Although long-tail learning has been
extremely explored to address data imbalances, few studies have been conducted
to consider camera-trap characteristics, such as multi-domain and multi-frame
setup. Here, we propose a unified framework and introduce two datasets for
long-tailed camera-trap recognition. We first design domain experts, where each
expert learns to balance imperfect decision boundaries caused by data
imbalances and complement each other to generate domain-balanced decision
boundaries. Also, we propose a flow consistency loss to focus on moving
objects, expecting class activation maps of multi-frame matches the flow with
optical flow maps for input images. Moreover, two long-tailed camera-trap
datasets, WCS-LT and DMZ-LT, are introduced to validate our methods.
Experimental results show the effectiveness of our framework, and proposed
methods outperform previous methods on recessive domain samples.

We study how expanding the scope for selecting game and learning (adaptation)
partners affects the evolution of cooperation in public goods games on
scale-free networks. We show the following three results. (i) Expanding the
scope for selecting game partners suppresses cooperation. (ii) Expanding the
scope for selecting learning partners promotes cooperation when cooperation
evolution is difficult. (iii) When cooperation is more likely to evolve,
slightly expanding the scope for selecting learning partners causes a
significant drop in the cooperation rate, but expanding the scope further
causes the cooperation rate to recover. Although (i) is explained by the
hub-centered mechanism, the well-known dynamic that promotes cooperation on
scale-free networks, (ii) and (iii) are caused by a completely different
mechanism that has heretofore been rarely mentioned.

Case law retrieval is the retrieval of judicial decisions relevant to a legal
question. Case law retrieval comprises a significant amount of a lawyer's time,
and is important to ensure accurate advice and reduce workload. We survey
methods for case law retrieval from the past 20 years and outline the problems
and challenges facing evaluation of case law retrieval systems going forward.
Limited published work has focused on improving ranking in ad-hoc case law
retrieval. But there has been significant work in other areas of case law
retrieval, and legal information retrieval generally. This is likely due to
legal search providers being unwilling to give up the secrets of their success
to competitors. Most evaluations of case law retrieval have been undertaken on
small collections and focus on related tasks such as question-answer systems or
recommender systems. Work has not focused on Cranfield style evaluations and
baselines of methods for case law retrieval on publicly available test
collections are not present. This presents a major challenge going forward. But
there are reasons to question the extent of this problem, at least in a
commercial setting. Without test collections to baseline approaches it cannot
be known whether methods are promising. Works by commercial legal search
providers show the effectiveness of natural language systems as well as query
expansion for case law retrieval. Machine learning is being applied to more and
more legal search tasks, and undoubtedly this represents the future of case law

Adversarial robustness studies the worst-case performance of a machine
learning model to ensure safety and reliability. With the proliferation of
deep-learning based technology, the potential risks associated with model
development and deployment can be amplified and become dreadful
vulnerabilities. This paper provides a comprehensive overview of research
topics and foundational principles of research methods for adversarial
robustness of deep learning models, including attacks, defenses, verification,
and novel applications.

Although word-level prosody modeling in neural text-to-speech (TTS) has been
investigated in recent research for diverse speech synthesis, it is still
challenging to control speech synthesis manually without a specific reference.
This is largely due to lack of word-level prosody tags. In this work, we
propose a novel approach for unsupervised word-level prosody tagging with two
stages, where we first group the words into different types with a decision
tree according to their phonetic content and then cluster the prosodies using
GMM within each type of words separately. This design is based on the
assumption that the prosodies of different type of words, such as long or short
words, should be tagged with different label sets. Furthermore, a TTS system
with the derived word-level prosody tags is trained for controllable speech
synthesis. Experiments on LJSpeech show that the TTS model trained with
word-level prosody tags not only achieves better naturalness than a typical
FastSpeech2 model, but also gains the ability to manipulate word-level prosody.

The question of radio dichotomy in the active galactic nuclei (AGNs) is still
in debate even it has been proposed for more than forty years. In order to
solve the old riddle, we collect a sample of AGNs with optical $B$ band and
radio 6cm wavelength data to analyze the radio loudness ${\rm log}R$.
  Our results indicate a separation of ${\rm log}R = \langle 1.37 \pm 0.02
\rangle$ between radio-loud (RL) AGNs and radio-quiet (RQ) AGNs, suggest the
existence of an RL/RQ dichotomy. For the first time, we suggest combining radio
luminosity and radio loudness as a double-criterion to divide AGNs into RLs and
RQs to avoid misclassification problems that may happen in the single-criterion
scenario, we propose the double-criterion dividing line ${\rm log}L_{\rm 6cm} =
-2.7{\rm log}R +44.3$ by using a machine learning method.
  In addition, the key point of the RL/RQ dichotomy is the origin of radio
emission for the two classes, we suggest the radio emission from RLs and RQs
share the same origin, e.g. jets and mini-jets (aborted-jet or outflow),
through a correlation study between radio 6cm luminosity and optical $B$ band
luminosity.

Local differential privacy~(LDP) is an information-theoretic privacy
definition suitable for statistical surveys that involve an untrusted data
curator. An LDP version of quasi-maximum likelihood estimator~(QMLE) has been
developed, but the existing method to build LDP QMLE is difficult to implement
for a large-scale survey system in the real world due to long waiting time,
expensive communication cost, and the boundedness assumption of derivative of a
log-likelihood function. We provided an alternative LDP protocol without those
issues, which is potentially much easily deployable to a large-scale survey. We
also provided sufficient conditions for the consistency and asymptotic
normality and limitations of our protocol. Our protocol is less burdensome for
the users, and the theoretical guarantees cover more realistic cases than those
for the existing method.

In this document, we summarize the status and challenges of calculating
parton physics in lattice QCD for the US Particle Physics Community Planning
Exercise (a.k.a. "Snowmass"). While PDF-moments calculations have been very
successful and been continuously improved, new methods have been developed to
calculate distributions directly in $x$-space. Many recent lattice studies have
been focused on calculating isovector PDFs of the pion and nucleon, learning to
control systematics associated with excited-state contamination,
renormalization and continuum extrapolations, pion-mass and finite-volume
effects, etc. Although in some cases, the lattice results are already
competitive with experimental data, to reach the level of precision in a wide
range of $x$ for unpolarized nucleon PDFs impactful for future collider physics
remains a challenge, and may require exascale supercomputing power. The new
theoretical methods open the door for calculating other partonic observables
which will be the focus of the experimental program in nuclear physics,
including generalized parton distributions and transverse-momentum dependent
PDFs. A fruitful interplay between experimental data and lattice-QCD
calculations will usher in a new era for parton physics and hadron structure.

With rising male infertility, sperm head morphology classification becomes
critical for accurate and timely clinical diagnosis. Recent deep learning (DL)
morphology analysis methods achieve promising benchmark results, but leave
performance and robustness on the table by relying on limited and possibly
noisy class labels. To address this, we introduce a new DL training framework
that leverages anatomical and image priors from human sperm microscopy crops to
extract useful features without additional labeling cost. Our core idea is to
distill sperm head information with reliably-generated pseudo-masks and
unsupervised spatial prediction tasks. The predicted foreground masks from this
distillation step are then leveraged to regularize and reduce image and label
noise in the tuning stage. We evaluate our new approach on two public sperm
datasets and achieve state-of-the-art performances (e.g. 65.9% SCIAN accuracy
and 96.5% HuSHeM accuracy).

This paper focuses on filter-level network pruning. A novel pruning method,
termed CLR-RNF, is proposed. We first reveal a "long-tail" long-tail pruning
problem in magnitude-based weight pruning methods, and then propose a
computation-aware measurement for individual weight importance, followed by a
Cross-Layer Ranking (CLR) of weights to identify and remove the bottom-ranked
weights. Consequently, the per-layer sparsity makes up of the pruned network
structure in our filter pruning. Then, we introduce a recommendation-based
filter selection scheme where each filter recommends a group of its closest
filters. To pick the preserved filters from these recommended groups, we
further devise a k-Reciprocal Nearest Filter (RNF) selection scheme where the
selected filters fall into the intersection of these recommended groups. Both
our pruned network structure and the filter selection are non-learning
processes, which thus significantly reduce the pruning complexity, and
differentiate our method from existing works. We conduct image classification
on CIFAR-10 and ImageNet to demonstrate the superiority of our CLR-RNF over the
state-of-the-arts. For example, on CIFAR-10, CLR-RNF removes 74.1% FLOPs and
95.0% parameters from VGGNet-16 with even 0.3\% accuracy improvements. On
ImageNet, it removes 70.2% FLOPs and 64.8% parameters from ResNet-50 with only
1.7% top-5 accuracy drops. Our project is at https://github.com/lmbxmu/CLR-RNF.

Stabilizing an unknown dynamical system is one of the central problems in
control theory. In this paper, we study the sample complexity of the
learn-to-stabilize problem in Linear Time-Invariant (LTI) systems on a single
trajectory. Current state-of-the-art approaches require a sample complexity
linear in $n$, the state dimension, which incurs a state norm that blows up
exponentially in $n$. We propose a novel algorithm based on spectral
decomposition that only needs to learn "a small part" of the dynamical matrix
acting on its unstable subspace. We show that, under proper assumptions, our
algorithm stabilizes an LTI system on a single trajectory with $\tilde{O}(k)$
samples, where $k$ is the instability index of the system. This represents the
first sub-linear sample complexity result for the stabilization of LTI systems
under the regime when $k = o(n)$.

Recent work has uncovered a striking phenomenon in large-capacity neural
networks: they contain blocks of contiguous hidden layers with highly similar
representations. This block structure has two seemingly contradictory
properties: on the one hand, its constituent layers exhibit highly similar
dominant first principal components (PCs), but on the other hand, their
representations, and their common first PC, are highly dissimilar across
different random seeds. Our work seeks to reconcile these discrepant properties
by investigating the origin of the block structure in relation to the data and
training methods. By analyzing properties of the dominant PCs, we find that the
block structure arises from dominant datapoints - a small group of examples
that share similar image statistics (e.g. background color). However, the set
of dominant datapoints, and the precise shared image statistic, can vary across
random seeds. Thus, the block structure reflects meaningful dataset statistics,
but is simultaneously unique to each model. Through studying hidden layer
activations and creating synthetic datapoints, we demonstrate that these simple
image statistics dominate the representational geometry of the layers inside
the block structure. We explore how the phenomenon evolves through training,
finding that the block structure takes shape early in training, but the
underlying representations and the corresponding dominant datapoints continue
to change substantially. Finally, we study the interplay between the block
structure and different training mechanisms, introducing a targeted
intervention to eliminate the block structure, as well as examining the effects
of pretraining and Shake-Shake regularization.

Artificial Intelligence (AI) relies heavily on deep learning - a technology
that is becoming increasingly popular in real-life applications of AI, even in
the safety-critical and high-risk domains. However, it is recently discovered
that deep learning can be manipulated by embedding Trojans inside it.
Unfortunately, pragmatic solutions to circumvent the computational requirements
of deep learning, e.g. outsourcing model training or data annotation to third
parties, further add to model susceptibility to the Trojan attacks. Due to the
key importance of the topic in deep learning, recent literature has seen many
contributions in this direction. We conduct a comprehensive review of the
techniques that devise Trojan attacks for deep learning and explore their
defenses. Our informative survey systematically organizes the recent literature
and discusses the key concepts of the methods while assuming minimal knowledge
of the domain on the readers part. It provides a comprehensible gateway to the
broader community to understand the recent developments in Neural Trojans.

This work develops \emph{mixup for graph data}. Mixup has shown superiority
in improving the generalization and robustness of neural networks by
interpolating features and labels between two random samples. Traditionally,
Mixup can work on regular, grid-like, and Euclidean data such as image or
tabular data. However, it is challenging to directly adopt Mixup to augment
graph data because different graphs typically: 1) have different numbers of
nodes; 2) are not readily aligned; and 3) have unique typologies in
non-Euclidean space. To this end, we propose $\mathcal{G}$-Mixup to augment
graphs for graph classification by interpolating the generator (i.e., graphon)
of different classes of graphs. Specifically, we first use graphs within the
same class to estimate a graphon. Then, instead of directly manipulating
graphs, we interpolate graphons of different classes in the Euclidean space to
get mixed graphons, where the synthetic graphs are generated through sampling
based on the mixed graphons. Extensive experiments show that
$\mathcal{G}$-Mixup substantially improves the generalization and robustness of
GNNs.

Federated learning (FL) that enables edge devices to collaboratively learn a
shared model while keeping their training data locally has received great
attention recently and can protect privacy in comparison with the traditional
centralized learning paradigm. However, sensitive information about the
training data can still be inferred from model parameters shared in FL.
Differential privacy (DP) is the state-of-the-art technique to defend against
those attacks. The key challenge to achieving DP in FL lies in the adverse
impact of DP noise on model accuracy, particularly for deep learning models
with large numbers of parameters. This paper develops a novel
differentially-private FL scheme named Fed-SMP that provides a client-level DP
guarantee while maintaining high model accuracy. To mitigate the impact of
privacy protection on model accuracy, Fed-SMP leverages a new technique called
Sparsified Model Perturbation (SMP) where local models are sparsified first
before being perturbed by Gaussian noise. We provide a tight end-to-end privacy
analysis for Fed-SMP using Renyi DP and prove the convergence of Fed-SMP with
both unbiased and biased sparsifications. Extensive experiments on real-world
datasets are conducted to demonstrate the effectiveness of Fed-SMP in improving
model accuracy with the same DP guarantee and saving communication cost

This paper proposes a new data-driven method for the reliable prediction of
power system post-fault trajectories. The proposed method is based on the
fundamentally new concept of Deep Operator Networks (DeepONets). Compared to
traditional neural networks that learn to approximate functions, DeepONets are
designed to approximate nonlinear operators. Under this operator framework, we
design a DeepONet to (1) take as inputs the fault-on trajectories collected,
for example, via simulation or phasor measurement units, and (2) provide as
outputs the predicted post-fault trajectories. In addition, we endow our method
with a much-needed ability to balance efficiency with reliable/trustworthy
predictions via uncertainty quantification. To this end, we propose and compare
two methods that enable quantifying the predictive uncertainty. First, we
propose a \textit{Bayesian DeepONet} (B-DeepONet) that uses stochastic gradient
Hamiltonian Monte-Carlo to sample from the posterior distribution of the
DeepONet parameters. Then, we propose a \textit{Probabilistic DeepONet}
(Prob-DeepONet) that uses a probabilistic training strategy to equip DeepONets
with a form of automated uncertainty quantification, at virtually no extra
computational cost. Finally, we validate the predictive power and uncertainty
quantification capability of the proposed B-DeepONet and Prob-DeepONet using
the IEEE 16-machine 68-bus system.

The Plug-and-Play (PnP) framework was recently introduced for low-dose CT
reconstruction to leverage the interpretability and the flexibility of
model-based methods to incorporate various plugins, such as trained deep
learning (DL) neural networks. However, the benefits of PnP vs.
state-of-the-art DL methods have not been clearly demonstrated. In this work,
we proposed an improved PnP framework to address the previous limitations and
develop clinical-relevant segmentation metrics for quantitative result
assessment. Compared with the DL alone methods, our proposed PnP framework was
slightly inferior in MSE and PSNR. However, the power spectrum of the resulting
images better matched that of full-dose images than that of DL denoised images.
The resulting images supported higher accuracy in airway segmentation than DL
denoised images for all the ten patients in the test set, more substantially on
the airways with a cross-section smaller than 0.61cm$^2$, and outperformed the
DL denoised images for 45 out of 50 lung lobes in lobar segmentation. Our PnP
method proved to be significantly better at preserving the image texture, which
translated to task-specific benefits in automated structure segmentation and

Approximating distributions from their samples is a canonical
statistical-learning problem. One of its most powerful and successful
modalities approximates every distribution to an $\ell_1$ distance essentially
at most a constant times larger than its closest $t$-piece degree-$d$
polynomial, where $t\ge1$ and $d\ge0$. Letting $c_{t,d}$ denote the smallest
such factor, clearly $c_{1,0}=1$, and it can be shown that $c_{t,d}\ge 2$ for
all other $t$ and $d$. Yet current computationally efficient algorithms show
only $c_{t,1}\le 2.25$ and the bound rises quickly to $c_{t,d}\le 3$ for $d\ge
9$. We derive a near-linear-time and essentially sample-optimal estimator that
establishes $c_{t,d}=2$ for all $(t,d)\ne(1,0)$. Additionally, for many
practical distributions, the lowest approximation distance is achieved by
polynomials with vastly varying number of pieces. We provide a method that
estimates this number near-optimally, hence helps approach the best possible
approximation. Experiments combining the two techniques confirm improved
performance over existing methodologies.

Fairness in machine learning (ML), the process to understand and correct
algorithmic bias, has gained increasing attention with numerous literature
being carried out, commonly assume the underlying data is independent and
identically distributed (IID). On the other hand, graphs are a ubiquitous data
structure to capture connections among individual units and is non-IID by
nature. It is therefore of great importance to bridge the traditional fairness
literature designed on IID data and ubiquitous non-IID graph representations to
tackle bias in ML systems. In this survey, we review such recent advance in
fairness amidst non-IID graph data and identify datasets and evaluation metrics
available for future research. We also point out the limitations of existing
work as well as promising future directions.

With the recent advances of deep learning techniques, there are rapidly
growing interests in applying machine learning to log data. As a fundamental
part of log analytics, accurate log parsing that transforms raw logs to
structured events is critical for subsequent machine learning and data mining
tasks. Previous approaches either analyze the source code for parsing or are
data-driven such as text clustering. They largely neglect to exploit another
widely available and valuable resource, software documentation that provides
detailed explanations for the messages, to improve accuracy. In this paper, we
propose an approach and system framework to use documentation knowledge for log
parsing. With parameter value identification, it not only can improve the
parsing accuracy for documented messages but also for undocumented messages. In
addition, it can discover the linkages between event templates that are
established by sharing parameters and indicate the correlation of the event
context.

Along with the massive growth of the Internet from the 1990s until now,
various innovative technologies have been created to bring users breathtaking
experiences with more virtual interactions in cyberspace. Many virtual
environments with thousands of services and applications, from social networks
to virtual gaming worlds, have been developed with immersive experience and
digital transformation, but most are incoherent instead of being integrated
into a platform. In this context, metaverse, a term formed by combining meta
and universe, has been introduced as a shared virtual world that is fueled by
many emerging technologies, such as fifth-generation networks and beyond,
virtual reality, and artificial intelligence (AI). Among such technologies, AI
has shown the great importance of processing big data to enhance immersive
experience and enable human-like intelligence of virtual agents. In this
survey, we make a beneficial effort to explore the role of AI in the foundation
and development of the metaverse. We first deliver a preliminary of AI,
including machine learning algorithms and deep learning architectures, and its
role in the metaverse. We then convey a comprehensive investigation of AI-based
methods concerning six technical aspects that have potentials for the
metaverse: natural language processing, machine vision, blockchain, networking,
digital twin, and neural interface, and being potential for the metaverse.
Subsequently, several AI-aided applications, such as healthcare, manufacturing,
smart cities, and gaming, are studied to be deployed in the virtual worlds.
Finally, we conclude the key contribution of this survey and open some future
research directions in AI for the metaverse.

Differentially private federated learning (DP-FL) has received increasing
attention to mitigate the privacy risk in federated learning. Although
different schemes for DP-FL have been proposed, there is still a utility gap.
Employing central Differential Privacy in FL (CDP-FL) can provide a good
balance between the privacy and model utility, but requires a trusted server.
Using Local Differential Privacy for FL (LDP-FL) does not require a trusted
server, but suffers from lousy privacy-utility trade-off. Recently proposed
shuffle DP based FL has the potential to bridge the gap between CDP-FL and
LDP-FL without a trusted server; however, there is still a utility gap when the
number of model parameters is large. In this work, we propose OLIVE, a system
that combines the merits from CDP-FL and LDP-FL by leveraging Trusted Execution
Environment (TEE). Our main technical contributions are the analysis and
countermeasures against the vulnerability of TEE in OLIVE. Firstly, we
theoretically analyze the memory access pattern leakage of OLIVE and find that
there is a risk for sparsified gradients, which is common in FL. Secondly, we
design an inference attack to understand how the memory access pattern could be
linked to the training data. Thirdly, we propose oblivious yet efficient
algorithms to prevent the memory access pattern leakage in OLIVE. Our
experiments on real-world data demonstrate that OLIVE is efficient even when
training a model with hundreds of thousands of parameters and effective against
side-channel attacks on TEE.

Extreme mass ratio inspirals (EMRIs) are among the most interesting
gravitational wave (GW) sources for space-borne GW detectors. However,
successful GW data analysis remains challenging due to many issues, ranging
from the difficulty of modeling accurate waveforms, to the impractically large
template bank required by the traditional matched filtering search method. In
this work, we introduce a proof-of-principle approach for EMRI detection based
on convolutional neural networks (CNNs). We demonstrate the performance with
simulated EMRI signals buried in Gaussian noise. We show that over a wide range
of physical parameters, the network is effective for EMRI systems with a
signal-to-noise ratio larger than 50, and the performance is most strongly
related to the signal-to-noise ratio. The method also shows good generalization
ability towards different waveform models. Our study reveals the potential
applicability of machine learning technology like CNNs towards more realistic
EMRI data analysis.

This paper proposes a new regularization technique for reinforcement learning
(RL) towards making policy and value functions smooth and stable. RL is known
for the instability of the learning process and the sensitivity of the acquired
policy to noise. Several methods have been proposed to resolve these problems,
and in summary, the smoothness of policy and value functions learned mainly in
RL contributes to these problems. However, if these functions are extremely
smooth, their expressiveness would be lost, resulting in not obtaining the
global optimal solution. This paper therefore considers RL under local
Lipschitz continuity constraint, so-called L2C2. By designing the
spatio-temporal locally compact space for L2C2 from the state transition at
each time step, the moderate smoothness can be achieved without loss of
expressiveness. Numerical noisy simulations verified that the proposed L2C2
outperforms the task performance while smoothing out the robot action generated
from the learned policy.

In this paper, we examine the capacity of an arbitrage-free neural-SDE market
model to produce realistic scenarios for the joint dynamics of multiple
European options on a single underlying. We subsequently demonstrate its use as
a risk simulation engine for option portfolios. Through backtesting analysis,
we show that our models are more computationally efficient and accurate for
evaluating the Value-at-Risk (VaR) of option portfolios, with better coverage
performance and less procyclicality than standard filtered historical
simulation approaches.

Autonomous Mobility-on-Demand (AMoD) systems represent an attractive
alternative to existing transportation paradigms, currently challenged by
urbanization and increasing travel needs. By centrally controlling a fleet of
self-driving vehicles, these systems provide mobility service to customers and
are currently starting to be deployed in a number of cities around the world.
Current learning-based approaches for controlling AMoD systems are limited to
the single-city scenario, whereby the service operator is allowed to take an
unlimited amount of operational decisions within the same transportation
system. However, real-world system operators can hardly afford to fully
re-train AMoD controllers for every city they operate in, as this could result
in a high number of poor-quality decisions during training, making the
single-city strategy a potentially impractical solution. To address these
limitations, we propose to formalize the multi-city AMoD problem through the
lens of meta-reinforcement learning (meta-RL) and devise an actor-critic
algorithm based on recurrent graph neural networks. In our approach, AMoD
controllers are explicitly trained such that a small amount of experience
within a new city will produce good system performance. Empirically, we show
how control policies learned through meta-RL are able to achieve near-optimal
performance on unseen cities by learning rapidly adaptable policies, thus
making them more robust not only to novel environments, but also to
distribution shifts common in real-world operations, such as special events,
unexpected congestion, and dynamic pricing schemes.

Generative Adversarial Networks (GAN) have led to the generation of very
realistic face images, which have been used in fake social media accounts and
other disinformation matters that can generate profound impacts. Therefore, the
corresponding GAN-face detection techniques are under active development that
can examine and expose such fake faces. In this work, we aim to provide a
comprehensive review of recent progress in GAN-face detection. We focus on
methods that can detect face images that are generated or synthesized from GAN
models. We classify the existing detection works into four categories: (1) deep
learning-based, (2) physical-based, (3) physiological-based methods, and (4)
evaluation and comparison against human visual performance. For each category,
we summarize the key ideas and connect them with method implementations. We
also discuss open problems and suggest future research directions.

Although there have been approaches that are capable of learning action
models from plan traces, there is no work on learning action models from
textual observations, which is pervasive and much easier to collect from
real-world applications compared to plan traces. In this paper we propose a
novel approach to learning action models from natural language texts by
integrating Constraint Satisfaction and Natural Language Processing techniques.
Specifically, we first build a novel language model to extract plan traces from
texts, and then build a set of constraints to generate action models based on
the extracted plan traces. After that, we iteratively improve the language
model and constraints until we achieve the convergent language model and action
models. We empirically exhibit that our approach is both effective and
efficient.

Large volumes of experimental and simulation aerodynamic data have been
rapidly advancing aerodynamic shape optimization (ASO) via machine learning
(ML), whose effectiveness has been growing thanks to continued developments in
deep learning. In this review, we first introduce the state of the art and the
unsolved challenges in ASO. Next, we present a description of ML fundamentals
and detail the ML algorithms that have succeeded in ASO. Then we review ML
applications contributing to ASO from three fundamental perspectives: compact
geometric design space, fast aerodynamic analysis, and efficient optimization
architecture. In addition to providing a comprehensive summary of the research,
we comment on the practicality and effectiveness of the developed methods. We
show how cutting-edge ML approaches can benefit ASO and address challenging
demands like interactive design optimization. However, practical large-scale
design optimizations remain a challenge due to the costly ML training expense.
A deep coupling of ML model construction with ASO prior experience and
knowledge, such as taking physics into account, is recommended to train ML
models effectively.

Automated planning focuses on strategies, building domain models and
synthesizing plans to transit initial states to goals. Natural language
processing concerns with the interactions between agents and human language,
especially processing and analyzing large amounts of natural language data.
These two fields have abilities to generate explicit knowledge, e.g.,
preconditions and effects of action models, and learn from tacit knowledge,
e.g., neural models, respectively. Integrating AI planning and natural language
processing effectively improves the communication between human and intelligent
agents. This paper outlines the commons and relations between AI planning and
natural language processing, argues that each of them can effectively impact on
the other one by four areas: (1) planning-based text understanding, (2)
planning-based text generation, (3) text-based human-robot interaction, and (4)
text-based explainable planning. We also explore some potential future issues
between AI planning and natural language processing.

Deep neural networks achieve remarkable performances on a wide range of tasks
with the aid of large-scale labeled datasets. Yet these datasets are
time-consuming and labor-exhaustive to obtain on realistic tasks. To mitigate
the requirement for labeled data, self-training is widely used in
semi-supervised learning by iteratively assigning pseudo labels to unlabeled
samples. Despite its popularity, self-training is well-believed to be
unreliable and often leads to training instability. Our experimental studies
further reveal that the bias in semi-supervised learning arises from both the
problem itself and the inappropriate training with potentially incorrect pseudo
labels, which accumulates the error in the iterative self-training process. To
reduce the above bias, we propose Debiased Self-Training (DST). First, the
generation and utilization of pseudo labels are decoupled by two
parameter-independent classifier heads to avoid direct error accumulation.
Second, we estimate the worst case of self-training bias, where the pseudo
labeling function is accurate on labeled samples, yet makes as many mistakes as
possible on unlabeled samples. We then adversarially optimize the
representations to improve the quality of pseudo labels by avoiding the worst
case. Extensive experiments justify that DST achieves an average improvement of
6.3% against state-of-the-art methods on standard semi-supervised learning
benchmark datasets and 18.9%$ against FixMatch on 13 diverse tasks.
Furthermore, DST can be seamlessly adapted to other self-training methods and
help stabilize their training and balance performance across classes in both
cases of training from scratch and finetuning from pre-trained models.

Visual scene representation learning is an important research problem in the
field of computer vision. The performance of artificial intelligence systems on
vision tasks could be improved if more suitable representations are learned for
visual scenes. Complex visual scenes are composed of relatively simple visual
concepts, and have the property of combinatorial explosion. Compared with
directly representing the entire visual scene, extracting compositional scene
representations can better cope with the diverse combinations of background and
objects. Because compositional scene representations abstract the concept of
objects, performing visual scene analysis and understanding based on these
representations could be easier and more interpretable. Moreover, learning via
reconstruction can greatly reduce the need for training data annotations.
Therefore, reconstruction-based compositional scene representation learning has
important research significance. In this survey, we first outline the current
progress on this research topic, including development history and
categorizations of existing methods from the perspectives of modeling of visual
scenes and inference of scene representations; then provide benchmarks,
including an open source toolbox to reproduce the benchmark experiments, of
representative methods that consider the most extensively studied problem
setting and form the foundation for other methods; and finally discuss the
future directions of this research topic.

A common view in the neuroscience community is that memory is encoded in the
connection strength between neurons. This perception led artificial neural
network models to focus on connection weights as the key variables to modulate
learning. In this paper, we present a prototype for weightless spiking neural
networks that can perform a simple classification task. The memory in this
network is stored in the timing between neurons, rather than the strength of
the connection, and is trained using a Hebbian Spike Timing Dependent
Plasticity (STDP), which modulates the delays of the connection.

The bilinear method is mainstream in Knowledge Graph Embedding (KGE), aiming
to learn low-dimensional representations for entities and relations in
Knowledge Graph (KG) and complete missing links. Most of the existing works are
to find patterns between relationships and effectively model them to accomplish
this task. Previous works have mainly discovered 6 important patterns like
non-commutativity. Although some bilinear methods succeed in modeling these
patterns, they neglect to handle 1-to-N, N-to-1, and N-to-N relations (or
complex relations) concurrently, which hurts their expressiveness. To this end,
we integrate scaling, the combination of translation and rotation that can
solve complex relations and patterns, respectively, where scaling is a
simplification of projection. Therefore, we propose a corresponding bilinear
model Scaling Translation and Rotation (STaR) consisting of the above two
parts. Besides, since translation cannot be incorporated into the bilinear
model directly, we introduce translation matrix as the equivalent. Theoretical
analysis proves that STaR is capable of modeling all patterns and handling
complex relations simultaneously, and experiments demonstrate its effectiveness
on commonly used benchmarks for link prediction.

Transformers have achieved superior performances in many tasks in natural
language processing and computer vision, which also intrigues great interests
in the time series community. Among multiple advantages of transformers, the
ability to capture long-range dependencies and interactions is especially
attractive for time series modeling, leading to exciting progress in various
time series applications. In this paper, we systematically review transformer
schemes for time series modeling by highlighting their strengths as well as
limitations through a new taxonomy to summarize existing time series
transformers in two perspectives. From the perspective of network
modifications, we summarize the adaptations of module level and architecture
level of the time series transformers. From the perspective of applications, we
categorize time series transformers based on common tasks including
forecasting, anomaly detection, and classification. Empirically, we perform
robust analysis, model size analysis, and seasonal-trend decomposition analysis
to study how Transformers perform in time series. Finally, we discuss and
suggest future directions to provide useful research guidance. A corresponding
resource list that will be continuously updated can be found in the GitHub
repository. To the best of our knowledge, this paper is the first work to
comprehensively and systematically summarize the recent advances of
Transformers for modeling time series data. We hope this survey will ignite
further research interests in time series Transformers.

We present results exploring the role that probabilistic deep learning models
can play in cosmology from large scale astronomical surveys through estimating
the distances to galaxies (redshifts) from photometry. Due to the massive scale
of data coming from these new and upcoming sky surveys, machine learning
techniques using galaxy photometry are increasingly adopted to predict galactic
redshifts which are important for inferring cosmological parameters such as the
nature of dark energy. Associated uncertainty estimates are also critical
measurements, however, common machine learning methods typically provide only
point estimates and lack uncertainty information as outputs. We turn to
Bayesian neural networks (BNNs) as a promising way to provide accurate
predictions of redshift values. We have compiled a new galaxy training dataset
from the Hyper Suprime-Cam Survey, designed to mimic large surveys, but over a
smaller portion of the sky. We evaluate the performance and accuracy of
photometric redshift (photo-z) predictions from photometry using machine
learning, astronomical and probabilistic metrics. We find that while the
Bayesian neural network did not perform as well as non-Bayesian neural networks
if evaluated solely by point estimate photo-z values, BNNs can provide
uncertainty estimates that are necessary for cosmology

Human visual attention has recently shown its distinct capability in boosting
machine learning models. However, studies that aim to facilitate medical tasks
with human visual attention are still scarce. To support the use of visual
attention, this paper describes a novel deep learning model for visual saliency
prediction on chest X-ray (CXR) images. To cope with data deficiency, we
exploit the multi-task learning method and tackles disease classification on
CXR simultaneously. For a more robust training process, we propose a further
optimized multi-task learning scheme to better handle model overfitting.
Experiments show our proposed deep learning model with our new learning scheme
can outperform existing methods dedicated either for saliency prediction or
image classification. The code used in this paper is available at
https://github.com/hz-zhu/MT-UNet.

Deep graph learning (DGL) has achieved remarkable progress in both business
and scientific areas ranging from finance and e-commerce to drug and advanced
material discovery. Despite the progress, applying DGL to real-world
applications faces a series of reliability threats including adversarial
attacks, inherent noise, and distribution shift. This survey aims to provide a
comprehensive review of recent advances for improving the reliability of DGL
algorithms against the above threats. In contrast to prior related surveys
which mainly focus on adversarial attacks and defense, our survey covers more
reliability-related aspects of DGL, i.e., inherent noise and distribution
shift. Additionally, we discuss the relationships among above aspects and
highlight some important issues to be explored in future research.

The increased availability and accuracy of eye-gaze tracking technology has
sparked attention-related research in psychology, neuroscience, and, more
recently, computer vision and artificial intelligence. The attention mechanism
in artificial neural networks is known to improve learning tasks. However, no
previous research has combined the network attention and human attention. This
paper describes a gaze-guided class activation mapping (GG-CAM) method to
directly regulate the formation of network attention based on expert
radiologists' visual attention for the chest X-ray pathology classification
problem, which remains challenging due to the complex and often nuanced
differences among images. GG-CAM is a lightweight ($3$ additional trainable
parameters for regulating the learning process) and generic extension that can
be easily applied to most classification convolutional neural networks (CNN).
GG-CAM-modified CNNs do not require human attention as an input when fully
trained. Comparative experiments suggest that two standard CNNs with the GG-CAM
extension achieve significantly greater classification performance. The median
area under the curve (AUC) metrics for ResNet50 increases from $0.721$ to
$0.776$. For EfficientNetv2 (s), the median AUC increases from $0.723$ to
$0.801$. The GG-CAM also brings better interpretability of the network that
facilitates the weakly-supervised pathology localization and analysis.

Algorithmic pricing on online e-commerce platforms raises the concern of
tacit collusion, where reinforcement learning algorithms learn to set collusive
prices in a decentralized manner and through nothing more than profit feedback.
This raises the question as to whether collusive pricing can be prevented
through the design of suitable "buy boxes," i.e., through the design of the
rules that govern the elements of e-commerce sites that promote particular
products and prices to consumers. In this paper, we demonstrate that
reinforcement learning (RL) can also be used by platforms to learn buy box
rules that are effective in preventing collusion by RL sellers. For this, we
adopt the methodology of Stackelberg POMDPs, and demonstrate success in
learning robust rules that continue to provide high consumer welfare together
with sellers employing different behavior models or having out-of-distribution
costs for goods.

With recent developments in new architectures like Transformer and
pretraining techniques, significant progress has been made in applications of
natural language processing (NLP). However, the high energy cost and long
inference delay of Transformer is preventing NLP from entering broader
scenarios including edge and mobile computing. Efficient NLP research aims to
comprehensively consider computation, time and carbon emission for the entire
life-cycle of NLP, including data preparation, model training and inference. In
this survey, we focus on the inference stage and review the current state of
model compression for NLP, including the benchmarks, metrics and methodology.
We outline the current obstacles and future research directions.

Effectively scaling large Transformer models is a main driver of recent
advances in natural language processing. Dynamic neural networks, as an
emerging research direction, are capable of scaling up neural networks with
sub-linear increases in computation and time by dynamically adjusting their
computational path based on the input. Dynamic neural networks could be a
promising solution to the growing parameter numbers of pretrained language
models, allowing both model pretraining with trillions of parameters and faster
inference on mobile devices. In this survey, we summarize progress of three
types of dynamic neural networks in NLP: skimming, mixture of experts, and
early exit. We also highlight current challenges in dynamic neural networks and

There is a great desire to use adaptive sampling methods, such as
reinforcement learning (RL) and bandit algorithms, for the real-time
personalization of interventions in digital applications like mobile health and
education. A major obstacle preventing more widespread use of such algorithms
in practice is the lack of assurance that the resulting adaptively collected
data can be used to reliably answer inferential questions, including questions
about time-varying causal effects. Current methods for statistical inference on
such data are insufficient because they (a) make strong assumptions regarding
the environment dynamics, e.g., assume a contextual bandit or Markovian
environment, or (b) require data to be collected with one adaptive sampling
algorithm per user, which excludes data collected by algorithms that learn to
select actions by pooling the data of multiple users. In this work, we make
initial progress by introducing the adaptive sandwich estimator to quantify
uncertainty; this estimator (a) is valid even when user rewards and contexts
are non-stationary and highly dependent over time, and (b) accommodates
settings in which an online adaptive sampling algorithm learns using the data
of all users. Furthermore, our inference method is robust to misspecification
of the reward models used by the adaptive sampling algorithm. This work is
motivated by our work designing experiments in which RL algorithms are used to
select actions, yet reliable statistical inference is essential for conducting
primary analyses after the trial is over.

Discovering new medicines is the hallmark of human endeavor to live a better
and longer life. Yet the pace of discovery has slowed down as we need to
venture into more wildly unexplored biomedical space to find one that matches
today's high standard. Modern AI-enabled by powerful computing, large
biomedical databases, and breakthroughs in deep learning-offers a new hope to
break this loop as AI is rapidly maturing, ready to make a huge impact in the
area. In this paper we review recent advances in AI methodologies that aim to
crack this challenge. We organize the vast and rapidly growing literature of AI
for drug discovery into three relatively stable sub-areas: (a) representation
learning over molecular sequences and geometric graphs; (b) data-driven
reasoning where we predict molecular properties and their binding, optimize
existing compounds, generate de novo molecules, and plan the synthesis of
target molecules; and (c) knowledge-based reasoning where we discuss the
construction and reasoning over biomedical knowledge graphs. We will also
identify open challenges and chart possible research directions for the years
to come.

Many important multiple-objective decision problems can be cast within the
framework of ranking under constraints and solved via a weighted bipartite
matching linear program. Some of these optimization problems, such as
personalized content recommendations, may need to be solved in real time and
thus must comply with strict time requirements to prevent the perception of
latency by consumers. Classical linear programming is too computationally
inefficient for such settings. We propose a novel approach to scale up ranking
under constraints by replacing the weighted bipartite matching optimization
with a prediction problem in the algorithm deployment stage. We show
empirically that the proposed approximate solution to the ranking problem leads
to a major reduction in required computing resources without much sacrifice in
constraint compliance and achieved utility, allowing us to solve larger
constrained ranking problems real-time, within the required 50 milliseconds,
than previously reported.

Recent years have witnessed fast developments of graph neural networks (GNNs)
that have benefited myriads of graph analytic tasks and applications. In
general, most GNNs depend on the homophily assumption that nodes belonging to
the same class are more likely to be connected. However, as a ubiquitous graph
property in numerous real-world scenarios, heterophily, i.e., nodes with
different labels tend to be linked, significantly limits the performance of
tailor-made homophilic GNNs. Hence, \textit{GNNs for heterophilic graphs} are
gaining increasing attention in this community. To the best of our knowledge,
in this paper, we provide a comprehensive review of GNNs for heterophilic
graphs for the first time. Specifically, we propose a systematic taxonomy that
essentially governs existing heterophilic GNN models, along with a general
summary and detailed analysis. Furthermore, we summarize the mainstream
heterophilic graph benchmarks to facilitate robust and fair evaluations. In the
end, we point out the potential directions to advance and stimulate future
research and applications on heterophilic graphs.

An algorithm for digital signal analysis using convolutional neural networks
(CNN) was developed in this work. The main objective of this algorithm is to
make the analysis of experiments with active target time projection chambers
more efficient. The code is divided in three steps: baseline correction, signal
deconvolution and peak detection and integration. The CNNs were able to learn
the signal processing models with relative errors of less than 6\%. The
analysis based on CNNs provides the same results as the traditional
deconvolution algorithms, but considerably more efficient in terms of computing
time (about 65 times faster). This opens up new possibilities to improve
existing codes and to simplify the analysis of the large amount of data
produced in active target experiments.

This paper presents a new dynamic approach to experiment design in settings
where, due to interference or other concerns, experimental units are coarse.
`Region-split' experiments on online platforms are one example of such a
setting. The cost, or regret, of experimentation is a natural concern here. Our
new design, dubbed Synthetically Controlled Thompson Sampling (SCTS), minimizes
the regret associated with experimentation at no practically meaningful loss to
inferential ability. We provide theoretical guarantees characterizing the
near-optimal regret of our approach, and the error rates achieved by the
corresponding treatment effect estimator. Experiments on synthetic and real
world data highlight the merits of our approach relative to both fixed and
`switchback' designs common to such experimental settings.

The ability to simulate the partial differential equations (PDE's) that
govern multi-phase flow in porous media is essential for different applications
such as geologic sequestration of CO2, groundwater flow monitoring and
hydrocarbon recovery from geologic formations [1]. These multi-phase flow
problems can be simulated by solving the governing PDE's numerically, using
various discretization schemes such as finite elements, finite volumes,
spectral methods, etc. More recently, the application of Machine Learning (ML)
to approximate the solutions to PDE's has been a very active research area.
However, most researchers have focused on the performance of their models
within the time-space domain in which the models were trained. In this work, we
apply ML techniques to approximate PDE solutions and focus on the forecasting
problem outside of the training domain. To this end, we use two different ML
architectures - the feed forward neural (FFN) network and the long short-term
memory (LSTM)-based neural network, to predict the PDE solutions in future
times based on the knowledge of the solutions in the past. The results of our
methodology are presented on two example PDE's - namely a form of PDE that
models the underground injection of CO2 and its hyperbolic limit which is a
common benchmark case. In both cases, the LSTM architecture shows a huge
potential to predict the solution behavior at future times based on prior data

Novel radiotherapy techniques like synchrotron X-ray microbeam radiation
therapy (MRT), require fast dose distribution predictions that are accurate at
the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo
physics simulations are recognised to be one of the most accurate tools to
predict the dose delivered in a target tissue but can be very time consuming
and therefore prohibitive for treatment planning. Faster dose prediction
algorithms are usually developed for clinically deployed treatments only. In
this work, we explore a new approach for fast and accurate dose estimations
suitable for novel treatments using digital phantoms used in pre-clinical
development and modern machine learning techniques. We develop a generative
adversarial network (GAN) model, which is able to emulate the equivalent Geant4
Monte Carlo simulation with adequate accuracy, and use it to predict the
radiation dose delivered by a broad synchrotron beam to various phantoms. The
energy depositions used for the training of the GAN are obtained using full
Geant4 Monte Carlo simulations of a synchrotron radiation broad beam passing
through the phantoms. The energy deposition is scored and predicted in voxel
matrices of size 140x18x18 with a voxel edge length of 1 mm. The GAN model
consists of two competing 3D convolutional neural networks, which are
conditioned on the photon beam and phantom properties. The energy deposition
predictions inside all phantom geometries under investigation show deviations
of less than 3% of the maximum deposited energy from the simulation for roughly
99% of the voxels in the field of the beam. The computing time for a single
prediction is reduced from several hundred hours using Geant4 simulation to
less than a second using the GAN model.

During the training process, deep neural networks implicitly learn to
represent the input data samples through a hierarchy of features, where the
size of the hierarchy is determined by the number of layers. In this paper, we
focus on enforcing the discriminative power of the high-level representations,
that are typically learned by the deeper layers (closer to the output). To this
end, we introduce a new loss term inspired by the Gini impurity, which is aimed
at minimizing the entropy (increasing the discriminative power) of individual
high-level features with respect to the class labels. Although our Gini loss
induces highly-discriminative features, it does not ensure that the
distribution of the high-level features matches the distribution of the
classes. As such, we introduce another loss term to minimize the
Kullback-Leibler divergence between the two distributions. We conduct
experiments on two image classification data sets (CIFAR-100 and Caltech 101),
considering multiple neural architectures ranging from convolutional networks
(ResNet-17, ResNet-18, ResNet-50) to transformers (CvT). Our empirical results
show that integrating our novel loss terms into the training objective
consistently outperforms the models trained with cross-entropy alone, without
increasing the inference time at all.

This project aims to motivate research in competitive human-robot interaction
by creating a robot competitor that can challenge human users in certain
scenarios such as physical exercise and games. With this goal in mind, we
introduce the Fencing Game, a human-robot competition used to evaluate both the
capabilities of the robot competitor and user experience. We develop the robot
competitor through iterative multi-agent reinforcement learning and show that
it can perform well against human competitors. Our user study additionally
found that our system was able to continuously create challenging and enjoyable
interactions that significantly increased human subjects' heart rates. The
majority of human subjects considered the system to be entertaining and
desirable for improving the quality of their exercise.

The optimisation of neural networks can be sped up by orthogonalising the
gradients before the optimisation step, ensuring the diversification of the
learned representations. We orthogonalise the gradients of the layer's
components/filters with respect to each other to separate out the intermediate
representations. Our method of orthogonalisation allows the weights to be used
more flexibly, in contrast to restricting the weights to an orthogonalised
sub-space. We tested this method on ImageNet and CIFAR-10 resulting in a large
decrease in learning time, and also obtain a speed-up on the semi-supervised
learning BarlowTwins. We obtain similar accuracy to SGD without fine-tuning and
better accuracy for na\"ively chosen hyper-parameters.

Normalizing flows map an independent set of latent variables to their samples
using a bijective transformation. Despite the exact correspondence between
samples and latent variables, their high level relationship is not well
understood. In this paper we characterize the geometric structure of flows
using principal manifolds and understand the relationship between latent
variables and samples using contours. We introduce a novel class of normalizing
flows, called principal manifold flows (PF), whose contours are its principal
manifolds, and a variant for injective flows (iPF) that is more efficient to
train than regular injective flows. PFs can be constructed using any flow
architecture, are trained with a regularized maximum likelihood objective and
can perform density estimation on all of their principal manifolds. In our
experiments we show that PFs and iPFs are able to learn the principal manifolds
over a variety of datasets. Additionally, we show that PFs can perform density
estimation on data that lie on a manifold with variable dimensionality, which
is not possible with existing normalizing flows.

Purpose. Handwriting is one of the most frequently occurring patterns in
everyday life and with it come challenging applications such as handwriting
recognition (HWR), writer identification, and signature verification. In
contrast to offline HWR that only uses spatial information (i.e., images),
online HWR (OnHWR) uses richer spatio-temporal information (i.e., trajectory
data or inertial data). While there exist many offline HWR datasets, there is
only little data available for the development of OnHWR methods on paper as it
requires hardware-integrated pens. Methods. This paper presents data and
benchmark models for real-time sequence-to-sequence (seq2seq) learning and
single character-based recognition. Our data is recorded by a sensor-enhanced
ballpoint pen, yielding sensor data streams from triaxial accelerometers, a
gyroscope, a magnetometer and a force sensor at 100 Hz. We propose a variety of
datasets including equations and words for both the writer-dependent and
writer-independent tasks. Our datasets allow a comparison between classical
OnHWR on tablets and on paper with sensor-enhanced pens. We provide an
evaluation benchmark for seq2seq and single character-based HWR using recurrent
and temporal convolutional networks and Transformers combined with a
connectionist temporal classification (CTC) loss and cross-entropy (CE) losses.
Results. Our convolutional network combined with BiLSTMs outperforms
Transformer-based architectures, is on par with InceptionTime for
sequence-based classification tasks, and yields better results compared to 28
state-of-the-art techniques. Time-series augmentation methods improve the
sequence-based task, and we show that CE variants can improve the single

We study the problem of developing autonomous agents that can follow human
instructions to infer and perform a sequence of actions to complete the
underlying task. Significant progress has been made in recent years, especially
for tasks with short horizons. However, when it comes to long-horizon tasks
with extended sequences of actions, an agent can easily ignore some
instructions or get stuck in the middle of the long instructions and eventually
fail the task. To address this challenge, we propose a model-agnostic
milestone-based task tracker (M-TRACK) to guide the agent and monitor its
progress. Specifically, we propose a milestone builder that tags the
instructions with navigation and interaction milestones which the agent needs
to complete step by step, and a milestone checker that systemically checks the
agent's progress in its current milestone and determines when to proceed to the
next. On the challenging ALFRED dataset, our M-TRACK leads to a notable 33% and
52% relative improvement in unseen success rate over two competitive base
models.

Over 15 million epilepsy patients worldwide do not respond to drugs and
require surgical treatment. Successful surgical treatment requires complete
removal, or disconnection of the epileptogenic zone (EZ), but without a
prospective biomarker of the EZ, surgical success rates vary between 30%-70%.
Neural fragility is a model recently proposed to localize the EZ. Neural
fragility is computed as the l2 norm of a structured rank-one perturbation of
an estimated linear dynamical system. However, an analysis of its numerical
properties have not been explored. We show that neural fragility is a
well-defined model given a good estimator of the linear dynamical system from
data. Specifically, we provide bounds on neural fragility as a function of the
underlying linear system and noise.

Classical methods of solving spatiotemporal dynamical systems include
statistical approaches such as autoregressive integrated moving average, which
assume linear and stationary relationships between systems' previous outputs.
Development and implementation of linear methods are relatively simple, but
they often do not capture non-linear relationships in the data. Thus,
artificial neural networks (ANNs) are receiving attention from researchers in
analyzing and forecasting dynamical systems. Recurrent neural networks (RNN),
derived from feed-forward ANNs, use internal memory to process variable-length
sequences of inputs. This allows RNNs to applicable for finding solutions for a
vast variety of problems in spatiotemporal dynamical systems. Thus, in this
paper, we utilize RNNs to treat some specific issues associated with dynamical
systems. Specifically, we analyze the performance of RNNs applied to three
tasks: reconstruction of correct Lorenz solutions for a system with a
formulation error, reconstruction of corrupted collective motion trajectories,
and forecasting of streamflow time series possessing spikes, representing three
fields, namely, ordinary differential equations, collective motion, and
hydrological modeling, respectively. We train and test RNNs uniquely in each
task to demonstrate the broad applicability of RNNs in reconstruction and
forecasting the dynamics of dynamical systems.

This study focuses on designing and developing a mathematically based
quadcopter rotational dynamics simulation framework for testing reinforcement
learning (RL) algorithms in many flexible configurations. The design of the
simulation framework aims to simulate both linear and nonlinear representations
of a quadcopter by solving initial value problems for ordinary differential
equation (ODE) systems. In addition, the simulation environment is capable of
making the simulation deterministic/stochastic by adding random Gaussian noise
in the forms of process and measurement noises. In order to ensure that the
scope of this simulation environment is not limited only with our own RL
algorithms, the simulation environment has been expanded to be compatible with
the OpenAI Gym toolkit. The framework also supports multiprocessing
capabilities to run simulation environments simultaneously in parallel. To test
these capabilities, many state-of-the-art deep RL algorithms were trained in
this simulation framework and the results were compared in detail.

access to robotics. A key challenge in LfD research is that users tend to
provide heterogeneous demonstrations for the same task due to various
strategies and preferences. Therefore, it is essential to develop LfD
algorithms that ensure \textit{flexibility} (the robot adapts to personalized
strategies), \textit{efficiency} (the robot achieves sample-efficient
adaptation), and \textit{scalability} (robot reuses a concise set of strategies
to represent a large amount of behaviors). In this paper, we propose a novel
algorithm, Dynamic Multi-Strategy Reward Distillation (DMSRD), which distills
common knowledge between heterogeneous demonstrations, leverages learned
strategies to construct mixture policies, and continues to improve by learning
from all available data. Our personalized, federated, and lifelong LfD
architecture surpasses benchmarks in two continuous control problems with an
average 77\% improvement in policy returns and 42\% improvement in log
likelihood, alongside stronger task reward correlation and more precise
strategy rewards.

Reinforcement learning (RL) agents need to be robust to variations in
safety-critical environments. While system identification methods provide a way
to infer the variation from online experience, they can fail in settings where
fast identification is not possible. Another dominant approach is robust RL
which produces a policy that can handle worst-case scenarios, but these methods
are generally designed to achieve robustness to a single uncertainty set that
must be specified at train time. Towards a more general solution, we formulate
the multi-set robustness problem to learn a policy robust to different
perturbation sets. We then design an algorithm that enjoys the benefits of both
system identification and robust RL: it reduces uncertainty where possible
given a few interactions, but can still act robustly with respect to the
remaining uncertainty. On a diverse set of control tasks, our approach
demonstrates improved worst-case performance on new environments compared to
prior methods based on system identification and on robust RL alone.

In recent years, several companies and researchers have started to tackle the
problem of damage recognition within the scope of automated inspection of built
structures. While companies are neither willing to publish associated data nor
models, researchers are facing the problem of data shortage on one hand and
inconsistent dataset splitting with the absence of consistent metrics on the
other hand. This leads to incomparable results. Therefore, we introduce the
building inspection toolkit -- bikit -- which acts as a simple to use data hub
containing relevant open-source datasets in the field of damage recognition.
The datasets are enriched with evaluation splits and predefined metrics,
suiting the specific task and their data distribution. For the sake of
compatibility and to motivate researchers in this domain, we also provide a
leaderboard and the possibility to share model weights with the community. As
starting point we provide strong baselines for multi-target classification
tasks utilizing extensive hyperparameter search using three transfer learning
approaches for state-of-the-art algorithms. The toolkit and the leaderboard are
available online.

The rising popularity of self-driving cars has led to the emergence of a new
research field in the recent years: Autonomous racing. Researchers are
developing software and hardware for high performance race vehicles which aim
to operate autonomously on the edge of the vehicles limits: High speeds, high
accelerations, low reaction times, highly uncertain, dynamic and adversarial
environments. This paper represents the first holistic survey that covers the
research in the field of autonomous racing. We focus on the field of autonomous
racecars only and display the algorithms, methods and approaches that are used
in the fields of perception, planning and control as well as end-to-end
learning. Further, with an increasing number of autonomous racing competitions,
researchers now have access to a range of high performance platforms to test
and evaluate their autonomy algorithms. This survey presents a comprehensive
overview of the current autonomous racing platforms emphasizing both the
software-hardware co-evolution to the current stage. Finally, based on
additional discussion with leading researchers in the field we conclude with a
summary of open research challenges that will guide future researchers in this
field.

Ordinal regression is a classification task where classes have an order and
prediction error increases the further the predicted class is from the true
class. The standard approach for modeling ordinal data involves fitting
parallel separating hyperplanes that optimize a certain loss function. This
assumption offers sample efficient learning via inductive bias, but is often
too restrictive in real-world datasets where features may have varying effects
across different categories. Allowing class-specific hyperplane slopes creates
generalized logistic ordinal regression, increasing the flexibility of the
model at a cost to sample efficiency. We explore an extension of the
generalized model to the all-thresholds logistic loss and propose a
regularization approach that interpolates between these two extremes. Our
method, which we term continuously generalized ordinal logistic, significantly
outperforms the standard ordinal logistic model over a thorough set of ordinal
regression benchmark datasets. We further extend this method to deep learning
and show that it achieves competitive or lower prediction error compared to
previous models over a range of datasets and modalities. Furthermore, two
primary alternative models for deep learning ordinal regression are shown to be
special cases of our framework.

The existence of completely aligned and paired multi-modal neuroimaging data
has proved its effectiveness in diagnosis of brain diseases. However,
collecting the full set of well-aligned and paired data is expensive or even
impractical, since the practical difficulties may include high cost, long time
acquisition, image corruption, and privacy issues. A realistic solution is to
explore either an unsupervised learning or a semi-supervised learning to
synthesize the absent neuroimaging data. In this paper, we are the first one to
comprehensively approach cross-modality neuroimage synthesis task from
different perspectives, which include the level of the supervision (especially
for weakly-supervised and unsupervised), loss function, evaluation metrics, the
range of modality synthesis, datasets (aligned, private and public) and the
synthesis-based downstream tasks. To begin with, we highlight several opening
challenges for cross-modality neuroimage sysnthesis. Then we summarize the
architecture of cross-modality synthesis under various of supervision level. In
addition, we provide in-depth analysis of how cross-modality neuroimage
synthesis can improve the performance of different downstream tasks. Finally,
we re-evaluate the open challenges and point out the future directions for the
remaining challenges. All resources are available at
https://github.com/M-3LAB/awesome-multimodal-brain-image-systhesis

The recent proposed self-supervised learning (SSL) approaches successfully
demonstrate the great potential of supplementing learning algorithms with
additional unlabeled data. However, it is still unclear whether the existing
SSL algorithms can fully utilize the information of both labelled and unlabeled
data. This paper gives an affirmative answer for the reconstruction-based SSL
algorithm \citep{lee2020predicting} under several statistical models. While
existing literature only focuses on establishing the upper bound of the
convergence rate, we provide a rigorous minimax analysis, and successfully
justify the rate-optimality of the reconstruction-based SSL algorithm under
different data generation models. Furthermore, we incorporate the
reconstruction-based SSL into the existing adversarial training algorithms and
show that learning from unlabeled data helps improve the robustness.

In this paper, we demonstrate that information retrieval can be accomplished
with a single Transformer, in which all information about the corpus is encoded
in the parameters of the model. To this end, we introduce the Differentiable
Search Index (DSI), a new paradigm that learns a text-to-text model that maps
string queries directly to relevant docids; in other words, a DSI model answers
queries directly using only its parameters, dramatically simplifying the whole
retrieval process. We study variations in how documents and their identifiers
are represented, variations in training procedures, and the interplay between
models and corpus sizes. Experiments demonstrate that given appropriate design
choices, DSI significantly outperforms strong baselines such as dual encoder
models. Moreover, DSI demonstrates strong generalization capabilities,
outperforming a BM25 baseline in a zero-shot setup.

In this paper, we train turbulence models based on convolutional neural
networks. These learned turbulence models improve under-resolved low resolution
solutions to the incompressible Navier-Stokes equations at simulation time. Our
study involves the development of a differentiable numerical solver that
supports the propagation of optimisation gradients through multiple solver
steps. The significance of this property is demonstrated by the superior
stability and accuracy of those models that unroll more solver steps during
training. Furthermore, we introduce loss terms based on turbulence physics that
further improve the model accuracy. This approach is applied to three
two-dimensional turbulence flow scenarios, a homogeneous decaying turbulence
case, a temporally evolving mixing layer, and a spatially evolving mixing
layer. Our models achieve significant improvements of long-term a-posteriori
statistics when compared to no-model simulations, without requiring these
statistics to be directly included in the learning targets. At inference time,
our proposed method also gains substantial performance improvements over
similarly accurate, purely numerical methods.

Embodied AI has seen steady progress across a diverse set of independent
tasks. While these varied tasks have different end goals, the basic skills
required to complete them successfully overlap significantly. In this paper,
our goal is to leverage these shared skills to learn to perform multiple tasks
jointly. We propose Atomic Skill Completion (ASC), an approach for multi-task
training for Embodied AI, where a set of atomic skills shared across multiple
tasks are composed together to perform the tasks. The key to the success of
this approach is a pre-training scheme that decouples learning of the skills
from the high-level tasks making joint training effective. We use ASC to train
agents within the AI2-THOR environment to perform four interactive tasks
jointly and find it to be remarkably effective. In a multi-task setting, ASC
improves success rates by a factor of 2x on Seen scenes and 4x on Unseen scenes
compared to no pre-training. Importantly, ASC enables us to train a multi-task
agent that has a 52% higher Success Rate than training 4 independent single
task agents. Finally, our hierarchical agents are more interpretable than
traditional black-box architectures.

Ensembling neural networks is an effective way to increase accuracy, and can
often match the performance of individual larger models. This observation poses
a natural question: given the choice between a deep ensemble and a single
neural network with similar accuracy, is one preferable over the other? Recent
work suggests that deep ensembles may offer distinct benefits beyond predictive
power: namely, uncertainty quantification and robustness to dataset shift. In
this work, we demonstrate limitations to these purported benefits, and show
that a single (but larger) neural network can replicate these qualities. First,
we show that ensemble diversity, by any metric, does not meaningfully
contribute to an ensemble's uncertainty quantification on out-of-distribution
(OOD) data, but is instead highly correlated with the relative improvement of a
single larger model. Second, we show that the OOD performance afforded by
ensembles is strongly determined by their in-distribution (InD) performance,
and -- in this sense -- is not indicative of any "effective robustness". While
deep ensembles are a practical way to achieve improvements to predictive power,
uncertainty quantification, and robustness, our results show that these
improvements can be replicated by a (larger) single model.

Identifying phase transitions and classifying phases of matter is central to
understanding the properties and behavior of a broad range of material systems.
In recent years, machine-learning (ML) techniques have been successfully
applied to perform such tasks in a data-driven manner. However, the success of
this approach notwithstanding, we still lack a clear understanding of ML
methods for detecting phase transitions, particularly of those that utilize
neural networks (NNs). In this work, we derive analytical expressions for the
optimal output of three widely used NN-based methods for detecting phase
transitions. These optimal predictions correspond to the results obtained in
the limit of high model capacity. Therefore, in practice they can, for example,
be recovered using sufficiently large, well-trained NNs. The inner workings of
the considered methods are revealed through the explicit dependence of the
optimal output on the input data. By evaluating the analytical expressions, we
can identify phase transitions directly from experimentally accessible data
without training NNs, which makes this procedure favorable in terms of
computation time. Our theoretical results are supported by extensive numerical
simulations covering, e.g., topological, quantum, and many-body localization
phase transitions. We expect similar analyses to provide a deeper understanding
of other classification tasks in condensed matter physics.

A fundamental prediction of the LambdaCDM cosmology is the hierarchical
build-up of structure and therefore the successive merging of galaxies into
more massive ones. As one can only observe galaxies at one specific time in
cosmic history, this merger history remains in principle unobservable. By using
the TNG100 simulation of the IllustrisTNG project, we show that it is possible
to infer the unobservable stellar assembly and merger history of central
galaxies from their observable properties by using machine learning techniques.
In particular, in this first paper of ERGO-ML (Extracting Reality from Galaxy
Observables with Machine Learning), we choose a set of 7 observable integral
properties of galaxies (i.e. total stellar mass, redshift, color, stellar size,
morphology, metallicity, and age) to infer, from those, the stellar ex-situ
fraction, the average merger lookback times and mass ratios, and the lookback
time and stellar mass of the last major merger. To do so, we use and compare a
Multilayer Perceptron Neural Network and a conditional Invertible Neural
Network (cINN): thanks to the latter we are also able to infer the posterior
distribution for these parameters and hence estimate the uncertainties in the
predictions. We find that the stellar ex-situ fraction and the time of the last
major merger are well determined by the selected set of observables, that the
mass-weighted merger mass ratio is unconstrained, and that, beyond stellar
mass, stellar morphology and stellar age are the most informative properties.
Finally, we show that the cINN recovers the remaining unexplained scatter and
secondary cross-correlations. Our tools can be applied to large galaxy surveys
in order to infer unobservable properties of galaxies' past, enabling empirical
studies of galaxy evolution enriched by cosmological simulations.

Diffusion models have recently outperformed alternative approaches to model
the distribution of natural images, such as GANs. Such diffusion models allow
for deterministic sampling via the probability flow ODE, giving rise to a
latent space and an encoder map. While having important practical applications,
such as estimation of the likelihood, the theoretical properties of this map
are not yet fully understood. In the present work, we partially address this
question for the popular case of the VP SDE (DDPM) approach. We show that,
perhaps surprisingly, the DDPM encoder map coincides with the optimal transport
map for common distributions; we support this claim theoretically and by

Composing Graph Neural Networks (GNNs) of operations that respect physical
symmetries has been suggested to give better model performance with a smaller
number of learnable parameters. However, real-world applications, such as in
high energy physics have not born this out. We present the novel architecture
VecNet that combines both symmetry-respecting and unconstrained operations to
study and tune the degree of physics-informed GNNs. We introduce a novel
metric, the \textit{ant factor}, to quantify the resource-efficiency of each
configuration in the search-space. We find that a generalized architecture such
as ours can deliver optimal performance in resource-constrained applications.

We report successful joint operation of quantum and classical communications
with shared hardware. Leveraging information learned from the classical DSP,
low-noise quantum communications (0.009 SNU at 15 km) compatible with 15 Mbit/s
QKD is demonstrated.

Evaluation practices in natural language generation (NLG) have many known
flaws, but improved evaluation approaches are rarely widely adopted. This issue
has become more urgent, since neural NLG models have improved to the point
where they can often no longer be distinguished based on the surface-level
features that older metrics rely on. This paper surveys the issues with human
and automatic model evaluations and with commonly used datasets in NLG that
have been pointed out over the past 20 years. We summarize, categorize, and
discuss how researchers have been addressing these issues and what their
findings mean for the current state of model evaluations. Building on those
insights, we lay out a long-term vision for NLG evaluation and propose concrete
steps for researchers to improve their evaluation processes. Finally, we
analyze 66 NLG papers from recent NLP conferences in how well they already
follow these suggestions and identify which areas require more drastic changes
to the status quo.

Detection of small objects and objects far away in the scene is a major
challenge in surveillance applications. Such objects are represented by small
number of pixels in the image and lack sufficient details, making them
difficult to detect using conventional detectors. In this work, an open-source
framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides
a generic slicing aided inference and fine-tuning pipeline for small object
detection. The proposed technique is generic in the sense that it can be
applied on top of any available object detector without any fine-tuning.
Experimental evaluations, using object detection baselines on the Visdrone and
xView aerial object detection datasets show that the proposed inference method
can increase object detection AP by 6.8%, 5.1% and 5.3% for FCOS, VFNet and
TOOD detectors, respectively. Moreover, the detection accuracy can be further
increased with a slicing aided fine-tuning, resulting in a cumulative increase
of 12.7%, 13.4% and 14.5% AP in the same order. Proposed technique has been
integrated with Detectron2, MMDetection and YOLOv5 models and it is publicly
available at https://github.com/obss/sahi.git .

Gaussian mixture models (GMMs) are fundamental tools in statistical and data
sciences. We study the moments of multivariate Gaussians and GMMs. The $d$-th
moment of an $n$-dimensional random variable is a symmetric $d$-way tensor of
size $n^d$, so working with moments naively is assumed to be prohibitively
expensive for $d>2$ and larger values of $n$. In this work, we develop theory
and numerical methods for \emph{implicit computations} with moment tensors of
GMMs, reducing the computational and storage costs to $\mathcal{O}(n^2)$ and
$\mathcal{O}(n^3)$, respectively, for general covariance matrices, and to
$\mathcal{O}(n)$ and $\mathcal{O}(n)$, respectively, for diagonal ones. We
derive concise analytic expressions for the moments in terms of symmetrized
tensor products, relying on the correspondence between symmetric tensors and
homogeneous polynomials, and combinatorial identities involving Bell
polynomials. The primary application of this theory is to estimating GMM
parameters (means and covariances) from a set of observations, when formulated
as a moment-matching optimization problem. If there is a known and common
covariance matrix, we also show it is possible to debias the data observations,
in which case the problem of estimating the unknown means reduces to symmetric
CP tensor decomposition. Numerical results validate and illustrate the
numerical efficiency of our approaches. This work potentially opens the door to
the competitiveness of the method of moments as compared to expectation
maximization methods for parameter estimation of GMMs.

Recently, graph neural networks (GNNs) have been successfully used for a
variety of particle reconstruction problems in high energy physics, including
particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising
performance in reconstructing particle tracks in dense environments. It
includes five discrete steps: data encoding, graph building, edge filtering,
GNN, and track labeling. All steps were written in Python and run on both GPUs
and CPUs. In this work, we accelerate the Python implementation of the pipeline
through customized and commercial GPU-enabled software libraries, and develop a
C++ implementation for inferencing the pipeline. The implementation features an
improved, CUDA-enabled fixed-radius nearest neighbor search for graph building
and a weakly connected component graph algorithm for track labeling. GNNs and
other trained deep learning models are converted to ONNX and inferenced via the
ONNX Runtime C++ API. The complete C++ implementation of the pipeline allows
integration with existing tracking software. We report the memory usage and
average event latency tracking performance of our implementation applied to the
TrackML benchmark dataset.

Federated learning (FL) allows the collaborative training of AI models
without needing to share raw data. This capability makes it especially
interesting for healthcare applications where patient and data privacy is of
utmost concern. However, recent works on the inversion of deep neural networks
from model gradients raised concerns about the security of FL in preventing the
leakage of training data. In this work, we show that these attacks presented in
the literature are impractical in real FL use-cases and provide a new baseline
attack that works for more realistic scenarios where the clients' training
involves updating the Batch Normalization (BN) statistics. Furthermore, we
present new ways to measure and visualize potential data leakage in FL. Our
work is a step towards establishing reproducible methods of measuring data
leakage in FL and could help determine the optimal tradeoffs between
privacy-preserving techniques, such as differential privacy, and model accuracy
based on quantifiable metrics.

Value-based methods play a fundamental role in Markov decision processes
(MDPs) and reinforcement learning (RL). In this paper, we present a unified
control-theoretic framework for analyzing valued-based methods such as value
computation (VC), value iteration (VI), and temporal difference (TD) learning
(with linear function approximation). Built upon an intrinsic connection
between value-based methods and dynamic systems, we can directly use existing
convex testing conditions in control theory to derive various convergence
results for the aforementioned value-based methods. These testing conditions
are convex programs in form of either linear programming (LP) or semidefinite
programming (SDP), and can be solved to construct Lyapunov functions in a
straightforward manner. Our analysis reveals some intriguing connections
between feedback control systems and RL algorithms. It is our hope that such
connections can inspire more work at the intersection of system/control theory
and RL.

Deploying deep neural networks~(DNNs) on edge devices provides efficient and
effective solutions for the real-world tasks. Edge devices have been used for
collecting a large volume of data efficiently in different domains. DNNs have
been an effective tool for data processing and analysis. However, designing
DNNs on edge devices is challenging due to the limited computational resources
and memory. To tackle this challenge, we demonstrate Object Detection System
for Edge Devices~(BED) on the MAX78000 DNN accelerator. It integrates on-device
DNN inference with a camera and an LCD display for image acquisition and
detection exhibition, respectively. BED is a concise, effective and detailed
solution, including model training, quantization, synthesis and deployment. The
entire repository is open-sourced on Github, including a Graphical User
Interface~(GUI) for on-chip debugging. Experiment results indicate that BED can
produce accurate detection with a 300-KB tiny DNN model, which takes only 91.9
ms of inference time and 1.845 mJ of energy. The real-time detection is
available at YouTube.

This work provides test error bounds for iterative fixed point methods on
linear predictors -- specifically, stochastic and batch mirror descent (MD),
and stochastic temporal difference learning (TD) -- with two core
contributions: (a) a single proof technique which gives high probability
guarantees despite the absence of projections, regularization, or any
equivalents, even when optima have large or infinite norm, for
quadratically-bounded losses (e.g., providing unified treatment of squared and
logistic losses); (b) locally-adapted rates which depend not on global problem
structure (such as condition numbers and maximum margins), but rather on
properties of low norm predictors which may suffer some small excess test
error. The proof technique is an elementary and versatile coupling argument,
and is demonstrated here in the following settings: stochastic MD under
realizability; stochastic MD for general Markov data; batch MD for general IID
data; stochastic MD on heavy-tailed data (still without projections);
stochastic TD on Markov chains (all prior stochastic TD bounds are in
expectation).

Self-supervised learning (SSL) has become a popular method for generating
invariant representations without the need for human annotations. Nonetheless,
the desired invariant representation is achieved by utilising prior online
transformation functions on the input data. As a result, each SSL framework is
customised for a particular data type, e.g., visual data, and further
modifications are required if it is used for other dataset types. On the other
hand, autoencoder (AE), which is a generic and widely applicable framework,
mainly focuses on dimension reduction and is not suited for learning invariant
representation. This paper proposes a generic SSL framework based on a
constrained self-labelling assignment process that prevents degenerate
solutions. Specifically, the prior transformation functions are replaced with a
self-transformation mechanism, derived through an unsupervised training process
of adversarial training, for imposing invariant representations. Via the
self-transformation mechanism, pairs of augmented instances can be generated
from the same input data. Finally, a training objective based on contrastive
learning is designed by leveraging both the self-labelling assignment and the
self-transformation mechanism. Despite the fact that the self-transformation
process is very generic, the proposed training strategy outperforms a majority
of state-of-the-art representation learning methods based on AE structures. To
validate the performance of our method, we conduct experiments on four types of
data, namely visual, audio, text, and mass spectrometry data, and compare them
in terms of four quantitative metrics. Our comparison results indicate that the
proposed method demonstrate robustness and successfully identify patterns
within the datasets.

FOLD-RM is an automated inductive learning algorithm for learning default
rules for mixed (numerical and categorical) data. It generates an (explainable)
answer set programming (ASP) rule set for multi-category classification tasks
while maintaining efficiency and scalability. The FOLD-RM algorithm is
competitive in performance with the widely-used, state-of-the-art algorithms
such as XGBoost and multi-layer perceptrons (MLPs), however, unlike these
algorithms, the FOLD-RM algorithm produces an explainable model. FOLD-RM
outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also
provides human-friendly explanations for predictions.

A multi-fidelity (MF) active learning method is presented for design
optimization problems characterized by noisy evaluations of the performance
metrics. Namely, a generalized MF surrogate model is used for design-space
exploration, exploiting an arbitrary number of hierarchical fidelity levels,
i.e., performance evaluations coming from different models, solvers, or
discretizations, characterized by different accuracy. The method is intended to
accurately predict the design performance while reducing the computational
effort required by simulation-driven design (SDD) to achieve the global
optimum. The overall MF prediction is evaluated as a low-fidelity trained
surrogate corrected with the surrogates of the errors between consecutive
fidelity levels. Surrogates are based on stochastic radial basis functions
(SRBF) with least squares regression and in-the-loop optimization of
hyperparameters to deal with noisy training data. The method adaptively queries
new training data, selecting both the design points and the required fidelity
level via an active learning approach. This is based on the lower confidence
bounding method, which combines performance prediction and associated
uncertainty to select the most promising design regions. The fidelity levels
are selected considering the benefit-cost ratio associated with their use in
the training. The method's performance is assessed and discussed using four
analytical tests and three SDD problems based on computational fluid dynamics
simulations, namely the shape optimization of a NACA hydrofoil, the DTMB 5415
destroyer, and a roll-on/roll-off passenger ferry. Fidelity levels are provided
by both adaptive grid refinement and multi-grid resolution approaches. Under
the assumption of a limited budget of function evaluations, the proposed MF
method shows better performance in comparison with the model trained by
high-fidelity evaluations only.

Multivariate anomaly detection can be used to identify outages within large
volumes of telemetry data for computing systems. However, developing an
efficient anomaly detector that can provide users with relevant information is
a challenging problem. We introduce our approach to hierarchical multivariate
anomaly detection called DeCorus, a statistical multivariate anomaly detector
which achieves linear complexity. It extends standard statistical techniques to
improve their ability to find relevant anomalies within noisy signals and makes
use of types of domain knowledge that system operators commonly possess to
compute system-level anomaly scores. We describe the implementation of DeCorus
an online log anomaly detection tool for network device syslog messages
deployed at a cloud service provider. We use real-world data sets that consist
of $1.5$ billion network device syslog messages and hundreds of incident
tickets to characterize the performance of DeCorus and compare its ability to
detect incidents with five alternative anomaly detectors. While DeCorus
outperforms the other anomaly detectors, all of them are challenged by our data
set. We share how DeCorus provides value in the field and how we plan to
improve its incident detection accuracy.

We consider after-study statistical inference for sequentially designed
experiments wherein multiple units are assigned treatments for multiple time
points using treatment policies that adapt over time. Our goal is to provide
inference guarantees for the counterfactual mean at the smallest possible scale
-- mean outcome under different treatments for each unit and each time -- with
minimal assumptions on the adaptive treatment policy. Without any structural
assumptions on the counterfactual means, this challenging task is infeasible
due to more unknowns than observed data points. To make progress, we introduce
a latent factor model over the counterfactual means that serves as a
non-parametric generalization of the non-linear mixed effects model and the
bilinear latent factor model considered in prior works. For estimation, we use
a non-parametric method, namely a variant of nearest neighbors, and establish a
non-asymptotic high probability error bound for the counterfactual mean for
each unit and each time. Under regularity conditions, this bound leads to
asymptotically valid confidence intervals for the counterfactual mean as the
number of units and time points grows to $\infty$.

Transfer learning is a proven technique in 2D computer vision to leverage the
large amount of data available and achieve high performance with datasets
limited in size due to the cost of acquisition or annotation. In 3D, annotation
is known to be a costly task; nevertheless, transfer learning methods have only
recently been investigated. Unsupervised pre-training has been heavily favored
as no very large annotated dataset are available. In this work, we tackle the
case of real-time 3D semantic segmentation of sparse outdoor LiDAR scans. Such
datasets have been on the rise, but with different label sets even for the same
task. In this work, we propose here an intermediate-level label set called the
coarse labels, which allows all the data available to be leveraged without any
manual labelization. This way, we have access to a larger dataset, alongside a
simpler task of semantic segmentation. With it, we introduce a new pre-training
task: the coarse label pre-training, also called COLA. We thoroughly analyze
the impact of COLA on various datasets and architectures and show that it
yields a noticeable performance improvement, especially when the finetuning
task has access only to a small dataset.

We propose Active Surrogate Estimators (ASEs), a new method for
label-efficient model evaluation. Evaluating model performance is a challenging
and important problem when labels are expensive. ASEs address this active
testing problem using a surrogate-based estimation approach that interpolates
the errors of points with unknown labels, rather than forming a Monte Carlo
estimator. ASEs actively learn the underlying surrogate, and we propose a novel
acquisition strategy, XWED, that tailors this learning to the final estimation
task. We find that ASEs offer greater label-efficiency than the current
state-of-the-art when applied to challenging model evaluation problems for deep

We provide the first generalization error analysis for black-box learning
through derivative-free optimization. Under the assumption of a Lipschitz and
smooth unknown loss, we consider the Zeroth-order Stochastic Search (ZoSS)
algorithm, that updates a $d$-dimensional model by replacing stochastic
gradient directions with stochastic differences of $K+1$ perturbed loss
evaluations per dataset (example) query. For both unbounded and bounded
possibly nonconvex losses, we present the first generalization bounds for the
ZoSS algorithm. These bounds coincide with those for SGD, and rather
surprisingly are independent of $d$, $K$ and the batch size $m$, under
appropriate choices of a slightly decreased learning rate. For bounded
nonconvex losses and a batch size $m=1$, we additionally show that both
generalization error and learning rate are independent of $d$ and $K$, and
remain essentially the same as for the SGD, even for two function evaluations.
Our results extensively extend and consistently recover established results for
SGD in prior work, on both generalization bounds and corresponding learning
rates. If additionally $m=n$, where $n$ is the dataset size, we derive
generalization guarantees for full-batch GD as well.

Haemorrhaging of the brain is the leading cause of death in people between
the ages of 15 and 24 and the third leading cause of death in people older than
that. Computed tomography (CT) is an imaging modality used to diagnose
neurological emergencies, including stroke and traumatic brain injury. Recent
advances in Deep Learning and Image Processing have utilised different
modalities like CT scans to help automate the detection and segmentation of
brain haemorrhage occurrences. In this paper, we propose a novel implementation
of an architecture consisting of traditional Convolutional Neural Networks(CNN)
along with Graph Neural Networks(GNN) to produce a holistic model for the task
of brain haemorrhage segmentation.GNNs work on the principle of neighbourhood
aggregation thus providing a reliable estimate of global structures present in
images. GNNs work with few layers thus in turn requiring fewer parameters to
work with. We were able to achieve a dice coefficient score of around 0.81 with
limited data with our implementation.

We introduce the visual acoustic matching task, in which an audio clip is
transformed to sound like it was recorded in a target environment. Given an
image of the target environment and a waveform for the source audio, the goal
is to re-synthesize the audio to match the target room acoustics as suggested
by its visible geometry and materials. To address this novel task, we propose a
cross-modal transformer model that uses audio-visual attention to inject visual
properties into the audio and generate realistic audio output. In addition, we
devise a self-supervised training objective that can learn acoustic matching
from in-the-wild Web videos, despite their lack of acoustically mismatched
audio. We demonstrate that our approach successfully translates human speech to
a variety of real-world environments depicted in images, outperforming both
traditional acoustic matching and more heavily supervised baselines.

Quantum information is a rapidly-growing interdisciplinary field at the
intersection of information science, computer science, mathematics, philosophy,
and quantum science, working at the core of our quantum technologies.
Regardless of its scientific success, quantum information is not exempt from
the intrinsic features that come from the fact that scientists are humans and
members of society: both the good and the bad of our social practices leak into
the scientific activity. In our scientific community, diversity and equal
opportunity problems are particularly difficult to observe due to social,
economic, or cultural barriers, often remaining invisible. How can our lack of
awareness negatively influence the progress of science in the long term? How
can our community grow into a better version of itself?
  This article reflects on how research events - such as conferences - can
contribute to a shift in our culture. This reflection draws on what we learn
from Q-Turn: an initiative triggered by postdoctoral researchers to discuss
these questions. One of Q-turn's main missions is to foster an inclusive
community and highlight outstanding research that may be under-appreciated in
other high-impact venues due to systemic biases. As well as a scientific
program, Q-turn features talks and discussions on issues that affect the
quantum information community, ranging from diversity and inclusion, health and
mental health, to workers' rights.
  In this perspective article, we will consider Q-Turn as an example of how a
research community can work to tackle systematic biases, review the successes,
and identify further points for development.

Advanced representation learning techniques require reliable and general
evaluation methods. Recently, several algorithms based on the common idea of
geometric and topological analysis of a manifold approximated from the learned
data representations have been proposed. In this work, we introduce Delaunay
Component Analysis (DCA) - an evaluation algorithm which approximates the data
manifold using a more suitable neighbourhood graph called Delaunay graph. This
provides a reliable manifold estimation even for challenging geometric
arrangements of representations such as clusters with varying shape and density
as well as outliers, which is where existing methods often fail. Furthermore,
we exploit the nature of Delaunay graphs and introduce a framework for
assessing the quality of individual novel data representations. We
experimentally validate the proposed DCA method on representations obtained
from neural networks trained with contrastive objective, supervised and
generative models, and demonstrate various use cases of our extended single
point evaluation framework.

The evaluation of explanation methods is a research topic that has not yet
been explored deeply, however, since explainability is supposed to strengthen
trust in artificial intelligence, it is necessary to systematically review and
compare explanation methods in order to confirm their correctness. Until now,
no tool exists that exhaustively and speedily allows researchers to
quantitatively evaluate explanations of neural network predictions. To increase
transparency and reproducibility in the field, we therefore built Quantus - a
comprehensive, open-source toolkit in Python that includes a growing,
well-organised collection of evaluation metrics and tutorials for evaluating
explainable methods. The toolkit has been thoroughly tested and is available
under open source license on PyPi (or on
https://github.com/understandable-machine-intelligence-lab/quantus/).

Temperature field reconstruction is essential for analyzing satellite heat
reliability. As a representative machine learning model, the deep convolutional
neural network (DCNN) is a powerful tool for reconstructing the satellite
temperature field. However, DCNN needs a lot of labeled data to learn its
parameters, which is contrary to the fact that actual satellite engineering can
only acquire noisy unlabeled data. To solve the above problem, this paper
proposes an unsupervised method, i.e., the physics-informed deep Monte Carlo
quantile regression method, for reconstructing temperature field and
quantifying the aleatoric uncertainty caused by data noise. For one thing, the
proposed method combines a deep convolutional neural network with the known
physics knowledge to reconstruct an accurate temperature field using only
monitoring point temperatures. For another thing, the proposed method can
quantify the aleatoric uncertainty by the Monte Carlo quantile regression.
Based on the reconstructed temperature field and the quantified aleatoric
uncertainty, this paper models an interval multilevel Bayesian Network to
analyze satellite heat reliability. Two case studies are used to validate the

A common explanation for the failure of deep networks to generalize
out-of-distribution is that they fail to recover the "correct" features. We
challenge this notion with a simple experiment which suggests that ERM already
learns sufficient features and that the current bottleneck is not feature
learning, but robust regression. Our findings also imply that given a small
amount of data from the target distribution, retraining only the last linear
layer will give excellent performance. We therefore argue that devising simpler
methods for learning predictors on existing features is a promising direction
for future research. Towards this end, we introduce Domain-Adjusted Regression
(DARE), a convex objective for learning a linear predictor that is provably
robust under a new model of distribution shift. Rather than learning one
function, DARE performs a domain-specific adjustment to unify the domains in a
canonical latent space and learns to predict in this space. Under a natural
model, we prove that the DARE solution is the minimax-optimal predictor for a
constrained set of test distributions. Further, we provide the first
finite-environment convergence guarantee to the minimax risk, improving over
existing analyses which only yield minimax predictors after an environment
threshold. Evaluated on finetuned features, we find that DARE compares
favorably to prior methods, consistently achieving equal or better performance.

Due to its geometric properties, hyperbolic space can support high-fidelity
embeddings of tree- and graph-structured data. As a result, various hyperbolic
networks have been developed which outperform Euclidean networks on many tasks:
e.g. hyperbolic graph convolutional networks (GCN) can outperform vanilla GCN
on some graph learning tasks. However, most existing hyperbolic networks are
complicated, computationally expensive, and numerically unstable -- and they
cannot scale to large graphs due to these shortcomings. With more and more
hyperbolic networks proposed, it is becoming less and less clear what key
component is necessary to make the model behave. In this paper, we propose
HyLa, a simple and minimal approach to using hyperbolic space in networks: HyLa
maps once from a hyperbolic-space embedding to Euclidean space via the
eigenfunctions of the Laplacian operator in the hyperbolic space. We evaluate
HyLa on graph learning tasks including node classification and text
classification, where HyLa can be used together with any graph neural networks.
When used with a linear model, HyLa shows significant improvements over
hyperbolic networks and other baselines.

Human activity understanding is of widespread interest in artificial
intelligence and spans diverse applications like health care and behavior
analysis. Although there have been advances with deep learning, it remains
challenging. The object recognition-like solutions usually try to map pixels to
semantics directly, but activity patterns are much different from object
patterns, thus hindering another success. In this work, we propose a novel
paradigm to reformulate this task in two-stage: first mapping pixels to an
intermediate space spanned by atomic activity primitives, then programming
detected primitives with interpretable logic rules to infer semantics. To
afford a representative primitive space, we build a knowledge base including
26+ M primitive labels and logic rules from human priors or automatic
discovering. Our framework, Human Activity Knowledge Engine (HAKE), exhibits
superior generalization ability and performance upon canonical methods on
challenging benchmarks. Code and data are available at http://hake-mvig.cn/.

This paper introduces the NWPU Team's entry to the ICASSP 2022 AEC Challenge.
We take a hybrid approach that cascades a linear AEC with a neural post-filter.
The former is used to deal with the linear echo components while the latter
suppresses the residual non-linear echo components. We use gated convolutional
F-T-LSTM neural network (GFTNN) as the backbone and shape the post-filter by a
multi-task learning (MTL) framework, where a voice activity detection (VAD)
module is adopted as an auxiliary task along with echo suppression, with the
aim to avoid over suppression that may cause speech distortion. Moreover, we
adopt an echo-aware loss function, where the mean square error (MSE) loss can
be optimized particularly for every time-frequency bin (TF-bin) according to
the signal-to-echo ratio (SER), leading to further suppression on the echo.
Extensive ablation study shows that the time delay estimation (TDE) module in
neural post-filter leads to better perceptual quality, and an adaptive filter
with better convergence will bring consistent performance gain for the
post-filter. Besides, we find that using the linear echo as the input of our
neural post-filter is a better choice than using the reference signal directly.
In the ICASSP 2022 AEC-Challenge, our approach has ranked the 1st place on word
accuracy (WAcc) (0.817) and the 3rd place on both mean opinion score (MOS)
(4.502) and the final score (0.864).

Model identifiability is a desirable property in the context of unsupervised
representation learning. In absence thereof, different models may be
observationally indistinguishable while yielding representations that are
nontrivially related to one another, thus making the recovery of a ground truth
generative model fundamentally impossible, as often shown through suitably
constructed counterexamples. In this note, we discuss one such construction,
illustrating a potential failure case of an identifiability result presented in
"Desiderata for Representation Learning: A Causal Perspective" by Wang & Jordan
(2021). The construction is based on the theory of nonlinear independent
component analysis. We comment on implications of this and other
counterexamples for identifiable representation learning.

Methods for teaching motion skills to robots focus on training for a single
skill at a time. Robots capable of learning from demonstration can considerably
benefit from the added ability to learn new movement skills without forgetting
what was learned in the past. To this end, we propose an approach for continual
learning from demonstration using hypernetworks and neural ordinary
differential equation solvers. We empirically demonstrate the effectiveness of
this approach in remembering long sequences of trajectory learning tasks
without the need to store any data from past demonstrations. Our results show
that hypernetworks outperform other state-of-the-art continual learning
approaches for learning from demonstration. In our experiments, we use the
popular LASA benchmark, and two new datasets of kinesthetic demonstrations
collected with a real robot that we introduce in this paper called the
HelloWorld and RoboTasks datasets. We evaluate our approach on a physical robot
and demonstrate its effectiveness in learning realistic robotic tasks involving
changing positions as well as orientations. We report both trajectory error
metrics and continual learning metrics, and we propose two new continual
learning metrics. Our code, along with the newly collected datasets, is
available at https://github.com/sayantanauddy/clfd.

Power systems are prone to a variety of events (e.g. line trips and
generation loss) and real-time identification of such events is crucial in
terms of situational awareness, reliability, and security. Using measurements
from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose
to identify events by extracting features based on modal dynamics. We combine
such traditional physics-based feature extraction methods with machine learning
to distinguish different event types. Including all measurement channels at
each PMU allows exploiting diverse features but also requires learning
classification models over a high-dimensional space. To address this issue,
various feature selection methods are implemented to choose the best subset of
features. Using the obtained subset of features, we investigate the performance
of two well-known classification models, namely, logistic regression (LR) and
support vector machines (SVM) to identify generation loss and line trip events
in two datasets. The first dataset is obtained from simulated generation loss
and line trip events in the Texas 2000-bus synthetic grid. The second is a
proprietary dataset with labeled events obtained from a large utility in the
USA involving measurements from nearly 500 PMUs. Our results indicate that the
proposed framework is promising for identifying the two types of events.

SARSA, a classical on-policy control algorithm for reinforcement learning, is
known to chatter when combined with linear function approximation: SARSA does
not diverge but oscillates in a bounded region. However, little is know about
how fast SARSA converges to that region and how large the region is. In this
paper, we make progress towards solving this open problem by showing the
convergence rate of projected SARSA to a bounded region. Importantly, the
region is much smaller than the ball used for projection provided that the the
magnitude of the reward is not too large. Our analysis applies to expected
SARSA as well as SARSA($\lambda$). Existing works regarding the convergence of
linear SARSA to a fixed point all require the Lipschitz constant of SARSA's
policy improvement operator to be sufficiently small; our analysis instead
applies to arbitrary Lipschitz constants and thus characterizes the behavior of
linear SARSA for a new regime.

Although robust learning and local differential privacy are both widely
studied fields of research, combining the two settings is just starting to be
explored. We consider the problem of estimating a discrete distribution in
total variation from $n$ contaminated data batches under a local differential
privacy constraint. A fraction $1-\epsilon$ of the batches contain $k$ i.i.d.
samples drawn from a discrete distribution $p$ over $d$ elements. To protect
the users' privacy, each of the samples is privatized using an $\alpha$-locally
differentially private mechanism. The remaining $\epsilon n $ batches are an
adversarial contamination. The minimax rate of estimation under contamination
alone, with no privacy, is known to be $\epsilon/\sqrt{k}+\sqrt{d/kn}$, up to a
$\sqrt{\log(1/\epsilon)}$ factor. Under the privacy constraint alone, the
minimax rate of estimation is $\sqrt{d^2/\alpha^2 kn}$. We show that combining
the two constraints leads to a minimax estimation rate of
$\epsilon\sqrt{d/\alpha^2 k}+\sqrt{d^2/\alpha^2 kn}$ up to a
$\sqrt{\log(1/\epsilon)}$ factor, larger than the sum of the two separate
rates. We provide a polynomial-time algorithm achieving this bound, as well as
a matching information theoretic lower bound.

Breast cancer is the most common type of cancer worldwide. A robotic system
performing autonomous breast palpation can make a significant impact on the
related health sector worldwide. However, robot programming for breast
palpating with different geometries is very complex and unsolved. Robot
learning from demonstrations (LfD) reduces the programming time and cost.
However, the available LfD are lacking the modelling of the manipulation
path/trajectory as an explicit function of the visual sensory information. This
paper presents a novel approach to manipulation path/trajectory planning called
deep Movement Primitives that successfully generates the movements of a
manipulator to reach a breast phantom and perform the palpation. We show the
effectiveness of our approach by a series of real-robot experiments of reaching
and palpating a breast phantom. The experimental results indicate our approach
outperforms the state-of-the-art method.

Deep neural networks are a powerful tool for the characterization of quantum
states.
  Existing networks are typically trained with experimental data gathered from
the specific quantum state that needs to be characterized.
  But is it possible to train a neural network offline and to make predictions
about quantum states other than the ones used for the training?
  Here we introduce a model of network that can be trained with classically
simulated data from a fiducial set of states and measurements, and can later be
used to characterize quantum states that share structural similarities with the
states in the fiducial set. With little guidance of quantum physics, the
network builds its own data-driven representation of quantum states, and then
uses it to predict the outcome statistics of quantum measurements that have not
been performed yet.
  The state representation produced by the network can also be used for tasks
beyond the prediction of outcome statistics, including clustering of quantum
states and identification of different phases of matter.
  Our network model provides a flexible approach that can be applied to online
learning scenarios, where predictions must be generated as soon as experimental
data become available, and to blind learning scenarios where the learner has
only access to an encrypted description of the quantum hardware.

Identifying meaningful relationships between the price movements of financial
assets is a challenging but important problem in a variety of financial
applications. However with recent research, particularly those using machine
learning and deep learning techniques, focused mostly on price forecasting, the
literature investigating the modelling of asset correlations has lagged
somewhat. To address this, inspired by recent successes in natural language
processing, we propose a neural model for training stock embeddings, which
harnesses the dynamics of historical returns data in order to learn the nuanced
relationships that exist between financial assets. We describe our approach in
detail and discuss a number of ways that it can be used in the financial
domain. Furthermore, we present the evaluation results to demonstrate the
utility of this approach, compared to several important benchmarks, in two
real-world financial analytics tasks.

Automatic molecule generation plays an important role on drug discovery and
has received a great deal of attention in recent years thanks to deep learning
successful use. Graph-based neural network represents state of the art methods
on automatic molecule generation. However, it is still challenging to generate
molecule with desired properties, which is a core task in drug discovery. In
this paper, we focus on this task and propose a Controllable Junction Tree
Variational Autoencoder (C JTVAE), adding an extractor module into VAE
framework to describe some properties of molecule. Our method is able to
generate similar molecular with desired property given an input molecule.
Experimental results is encouraging.

The triplet loss with semi-hard negatives has become the de facto choice for
image-caption retrieval (ICR) methods that are optimized from scratch. Recent
progress in metric learning has given rise to new loss functions that
outperform the triplet loss on tasks such as image retrieval and representation
learning. We ask whether these findings generalize to the setting of ICR by
comparing three loss functions on two ICR methods. We answer this question
negatively: the triplet loss with semi-hard negative mining still outperforms
newly introduced loss functions from metric learning on the ICR task. To gain a
better understanding of these outcomes, we introduce an analysis method to
compare loss functions by counting how many samples contribute to the gradient
w.r.t. the query representation during optimization. We find that loss
functions that result in lower evaluation scores on the ICR task, in general,
take too many (non-informative) samples into account when computing a gradient
w.r.t. the query representation, which results in sub-optimal performance. The
triplet loss with semi-hard negatives is shown to outperform the other loss
functions, as it only takes one (hard) negative into account when computing the
gradient.

Continual learning can enable neural networks to evolve by learning new tasks
sequentially in task-changing scenarios. However, two general and related
challenges should be overcome in further research before we apply this
technique to real-world applications. Firstly, newly collected novelties from
the data stream in applications could contain anomalies that are meaningless
for continual learning. Instead of viewing them as a new task for updating, we
have to filter out such anomalies to reduce the disturbance of extremely
high-entropy data for the progression of convergence. Secondly, fewer efforts
have been put into research regarding the explainability of continual learning,
which leads to a lack of transparency and credibility of the updated neural
networks. Elaborated explanations about the process and result of continual
learning can help experts in judgment and making decisions. Therefore, we
propose the conceptual design of an explainability module with experts in the
loop based on techniques, such as dimension reduction, visualization, and
evaluation strategies. This work aims to overcome the mentioned challenges by
sufficiently explaining and visualizing the identified anomalies and the
updated neural network. With the help of this module, experts can be more
confident in decision-making regarding anomaly filtering, dynamic adjustment of
hyperparameters, data backup, etc.

The objective of Aspect Based Sentiment Analysis is to capture the sentiment
of reviewers associated with different aspects. However, complexity of the
review sentences, presence of double negation and specific usage of words found
in different domains make it difficult to predict the sentiment accurately and
overall a challenging natural language understanding task. While recurrent
neural network, attention mechanism and more recently, graph attention based
models are prevalent, in this paper we propose graph Fourier transform based
network with features created in the spectral domain. While this approach has
found considerable success in the forecasting domain, it has not been explored
earlier for any natural language processing task. The method relies on creating
and learning an underlying graph from the raw data and thereby using the
adjacency matrix to shift to the graph Fourier domain. Subsequently, Fourier
transform is used to switch to the frequency (spectral) domain where new
features are created. These series of transformation proved to be extremely
efficient in learning the right representation as we have found that our model
achieves the best result on both the SemEval-2014 datasets, i.e., "Laptop" and
"Restaurants" domain. Our proposed model also found competitive results on the
two other recently proposed datasets from the e-commerce domain.

Dermatological diagnosis automation is essential in addressing the high
prevalence of skin diseases and critical shortage of dermatologists. Despite
approaching expert-level diagnosis performance, convolutional neural network
(ConvNet) adoption in clinical practice is impeded by their limited
explainability, and by subjective, expensive explainability validations. We
introduce DermX and DermX+, an end-to-end framework for explainable automated
dermatological diagnosis. DermX is a clinically-inspired explainable
dermatological diagnosis ConvNet, trained using DermXDB, a 554 image dataset
annotated by eight dermatologists with diagnoses, supporting explanations, and
explanation attention maps. DermX+ extends DermX with guided attention training
for explanation attention maps. Both methods achieve near-expert diagnosis
performance, with DermX, DermX+, and dermatologist F1 scores of 0.79, 0.79, and
0.87, respectively. We assess the explanation performance in terms of
identification and localization by comparing model-selected with
dermatologist-selected explanations, and gradient-weighted class-activation
maps with dermatologist explanation maps, respectively. DermX obtained an
identification F1 score of 0.77, while DermX+ obtained 0.79. The localization
F1 score is 0.39 for DermX and 0.35 for DermX+. These results show that
explainability does not necessarily come at the expense of predictive power, as
our high-performance models provide expert-inspired explanations for their
diagnoses without lowering their diagnosis performance.

This paper describes the work of the Data Science for Digital Health (DS4DH)
group at the TREC Health Misinformation Track 2021. The TREC Health
Misinformation track focused on the development of retrieval methods that
provide relevant, correct and credible information for health related searches
on the Web. In our methodology, we used a two-step ranking approach that
includes i) a standard retrieval phase, based on BM25 model, and ii) a
re-ranking phase, with a pipeline of models focused on the usefulness,
supportiveness and credibility dimensions of the retrieved documents. To
estimate the usefulness, we classified the initial rank list using pre-trained
language models based on the transformers architecture fine-tuned on the MS
MARCO corpus. To assess the supportiveness, we utilized BERT-based models
fine-tuned on scientific and Wikipedia corpora. Finally, to evaluate the
credibility of the documents, we employed a random forest model trained on the
Microsoft Credibility dataset combined with a list of credible sites. The
resulting ranked lists were then combined using the Reciprocal Rank Fusion
algorithm to obtain the final list of useful, supporting and credible
documents. Our approach achieved competitive results, being top-2 in the
compatibility measurement for the automatic runs. Our findings suggest that
integrating automatic ranking models created for each information quality
dimension with transfer learning can increase the effectiveness of
health-related information retrieval.

Presented here is a method for automatic punctuation restoration in Swedish
using a BERT model. The method is based on KB-BERT, a publicly available,
neural network language model pre-trained on a Swedish corpus by National
Library of Sweden. This model has then been fine-tuned for this specific task
using a corpus of government texts. With a lower-case and unpunctuated Swedish
text as input, the model is supposed to return a grammatically correct
punctuated copy of the text as output. A successful solution to this problem
brings benefits for an array of NLP domains, such as speech-to-text and
automated text. Only the punctuation marks period, comma and question marks
were considered for the project, due to a lack of data for more rare marks such
as semicolon. Additionally, some marks are somewhat interchangeable with the
more common, such as exclamation points and periods. Thus, the data set had all
exclamation points replaced with periods. The fine-tuned Swedish BERT model,
dubbed prestoBERT, achieved an overall F1-score of 78.9. The proposed model
scored similarly to international counterparts, with Hungarian and Chinese
models obtaining F1-scores of 82.2 and 75.6 respectively. As further
comparison, a human evaluation case study was carried out. The human test group
achieved an overall F1-score of 81.7, but scored substantially worse than
prestoBERT on both period and comma. Inspecting output sentences from the model
and humans show satisfactory results, despite the difference in F1-score. The
disconnect seems to stem from an unnecessary focus on replicating the exact
same punctuation used in the test set, rather than providing any of the number
of correct interpretations. If the loss function could be rewritten to reward
all grammatically correct outputs, rather than only the one original example,
the performance could improve significantly for both prestoBERT and the human
group.

In recent years, deep metric learning and its probabilistic extensions
claimed state-of-the-art results in the face verification task. Despite
improvements in face verification, probabilistic methods received little
attention in the research community and practical applications. In this paper,
we, for the first time, perform an in-depth analysis of known probabilistic
methods in verification and retrieval tasks. We study different design choices
and propose a simple extension, achieving new state-of-the-art results among
probabilistic methods. Finally, we study confidence prediction and show that it
correlates with data quality, but contains little information about prediction
error probability. We thus provide a new confidence evaluation benchmark and
establish a baseline for future confidence prediction research. PyTorch
implementation is publicly released.

Vision-Language Pre-training (VLP) models have shown remarkable performance
on various downstream tasks. Their success heavily relies on the scale of
pre-trained cross-modal datasets. However, the lack of large-scale datasets and
benchmarks in Chinese hinders the development of Chinese VLP models and broader
multilingual applications. In this work, we release a large-scale Chinese
cross-modal dataset named Wukong, which contains 100 million Chinese image-text
pairs collected from the web. Wukong aims to benchmark different multi-modal
pre-training methods to facilitate the VLP research and community development.
Furthermore, we release a group of models pre-trained with various image
encoders (ViT-B/ViT-L/SwinT) and also apply advanced pre-training techniques
into VLP such as locked-image text tuning, token-wise similarity in contrastive
learning, and reduced-token interaction. Extensive experiments and a
benchmarking of different downstream tasks including a new largest
human-verified image-text test dataset are also provided. Experiments show that
Wukong can serve as a promising Chinese pre-training dataset and benchmark for
different cross-modal learning methods. For the zero-shot image classification
task on 10 datasets, $Wukong_{ViT-L}$ achieves an average accuracy of 73.03%.
For the image-text retrieval task, it achieves a mean recall of 71.6% on
AIC-ICC which is 12.9% higher than WenLan 2.0. Also, our Wukong models are
benchmarked on downstream tasks with other variants on multiple datasets, e.g.,
Flickr8K-CN, Flickr-30K-CN, COCO-CN, et al. More information can be referred
to: https://wukong-dataset.github.io/wukong-dataset/.

It is highly desirable that speech enhancement algorithms can achieve good
performance while keeping low latency for many applications, such as digital
hearing aids, acoustically transparent hearing devices, and public address
systems. To improve the performance of traditional low-latency speech
enhancement algorithms, a deep filter-bank equalizer (FBE) framework was
proposed, which integrated a deep learning-based subband noise reduction
network with a deep learning-based shortened digital filter mapping network. In
the first network, a deep learning model was trained with a controllable small
frame shift to satisfy the low-latency demand, i.e., $\le$ 4 ms, so as to
obtain (complex) subband gains, which could be regarded as an adaptive digital
filter in each frame. In the second network, to reduce the latency, this
adaptive digital filter was implicitly shortened by a deep learning-based
framework, and was then applied to noisy speech to reconstruct the enhanced
speech without the overlap-add method. Experimental results on the WSJ0-SI84
corpus indicated that the proposed deep FBE with only 4-ms latency achieved
much better performance than traditional low-latency speech enhancement
algorithms in terms of the indices such as PESQ, STOI, and the amount of noise
reduction.

Li-Ion Solid-State Electrolytes (Li-SSEs) are a promising solution that
resolves the critical issues of conventional Li-Ion Batteries (LIBs) such as
poor ionic conductivity, interfacial instability, and dendrites growth. In this
study, a platform consisting of a high-throughput screening and a
machine-learning surrogate model for discovering superionic Li-SSEs among
20,237 Li-containing materials is developed. For the training database, the
ionic conductivity of Na SuperIonic CONductor (NASICON) and Li SuperIonic
CONductor (LISICON) type SSEs are obtained from the previous literature. Then,
the chemical descriptor (CD) and additional structural properties are used as
machine-readable features. Li-SSE candidates are selected through the screening
criteria, and the prediction on the ionic conductivity of those is followed.
Then, to reduce uncertainty in the surrogate model, the ensemble method by
considering the best-performing two models is employed, whose mean prediction
accuracy is 0.843 and 0.829, respectively. Furthermore, first-principles
calculations are conducted for confirming the ionic conductivity of the strong
candidates. Finally, six potential superionic Li-SSEs that have not previously
been investigated are proposed. We believe that the constructed platform can
accelerate the search for Li-SSEs with high ionic conductivity at minimum cost.

The ultimate goal of various fields is to directly generate molecules with
desired properties, such as finding water-soluble molecules in drug development
and finding molecules suitable for organic light-emitting diode (OLED) or
photosensitizers in the field of development of new organic materials. In this
respect, this study proposes a molecular graph generative model based on the
autoencoder for de novo design. The performance of molecular graph conditional
variational autoencoder (MGCVAE) for generating molecules having specific
desired properties is investigated by comparing it to molecular graph
variational autoencoder (MGVAE). Furthermore, multi-objective optimization for
MGCVAE was applied to satisfy two selected properties simultaneously. In this
study, two physical properties -- logP and molar refractivity -- were used as
optimization targets for the purpose of designing de novo molecules, especially
in drug discovery. As a result, it was confirmed that among generated
molecules, 25.89% optimized molecules were generated in MGCVAE compared to
0.66% in MGVAE. Hence, it demonstrates that MGCVAE effectively produced
drug-like molecules with two target properties. The results of this study
suggest that these graph-based data-driven models are one of the effective
methods of designing new molecules that fulfill various physical properties,
such as drug discovery.

Multi-task learning leverages structural similarities between multiple tasks
to learn despite very few samples. Motivated by the recent success of neural
networks applied to data-scarce tasks, we consider a linear low-dimensional
shared representation model. Despite an extensive literature, existing
theoretical results either guarantee weak estimation rates or require a large
number of samples per task. This work provides the first estimation error bound
for the trace norm regularized estimator when the number of samples per task is
small. The advantages of trace norm regularization for learning data-scarce
tasks extend to meta-learning and are confirmed empirically on synthetic
datasets.

On hypothesis of self-scaling co-flows in tapered rectanglar PMMA
micro-channels for producing mono-dispersed liquid cells, universal scalings
through all liquid detaching regimes are found under a self-similarity frame.
Pan-dripping and Pan-jetting regimes are calculated clearly to border at the
Weber number approximately $1$ and the capillary number approximately $0.28$ by
machine learning classification. The sizes, as well as detaching frequencies,
of liquid cells in different flow regimes behave at the same manner and submit
to the same law, and such highly consistent behaviors breaking through physical
barriers among flow regimes have never been reported.

Battery discharge capacity forecasting is critically essential for the
applications of lithium-ion batteries. The capacity degeneration can be treated
as the memory of the initial battery state of charge from the data point of
view. The streaming sensor data collected by battery management systems (BMS)
reflect the usable battery capacity degradation rates under various operational
working conditions. The battery capacity in different cycles can be measured
with the temporal patterns extracted from the streaming sensor data based on
the attention mechanism. The attention-based similarity regarding the first
cycle can describe the battery capacity degradation in the following cycles.
The deep degradation network (DDN) is developed with the attention mechanism to
measure similarity and predict battery capacity. The DDN model can extract the
degeneration-related temporal patterns from the streaming sensor data and
perform the battery capacity prediction efficiently online in real-time. Based
on the MIT-Stanford open-access battery aging dataset, the root-mean-square
error of the capacity estimation is 1.3 mAh. The mean absolute percentage error
of the proposed DDN model is 0.06{\%}. The DDN model also performance well in
the Oxford Battery Degradation Dataset with dynamic load profiles. Therefore,
the high accuracy and strong robustness of the proposed algorithm are verified.

Cyber-Physical Systems (CPS) have gained popularity due to the increased
requirements on their uninterrupted connectivity and process automation. Due to
their connectivity over the network including intranet and internet, dependence
on sensitive data, heterogeneous nature, and large-scale deployment, they are
highly vulnerable to cyber-attacks. Cyber-attacks are performed by creating
anomalies in the normal operation of the systems with a goal either to disrupt
the operation or destroy the system completely. The study proposed here focuses
on detecting those anomalies which could be the cause of cyber-attacks. This is
achieved by deriving the rules that govern the physical behavior of a process
within a plant. These rules are called Invariants. We have proposed a
Data-Centric approach (DaC) to generate such invariants. The entire study was
conducted using the operational data of a functional smart power grid which is
also a living lab.

The success of multi-head self-attentions (MSAs) for computer vision is now
indisputable. However, little is known about how MSAs work. We present
fundamental explanations to help better understand the nature of MSAs. In
particular, we demonstrate the following properties of MSAs and Vision
Transformers (ViTs): (1) MSAs improve not only accuracy but also generalization
by flattening the loss landscapes. Such improvement is primarily attributable
to their data specificity, not long-range dependency. On the other hand, ViTs
suffer from non-convex losses. Large datasets and loss landscape smoothing
methods alleviate this problem; (2) MSAs and Convs exhibit opposite behaviors.
For example, MSAs are low-pass filters, but Convs are high-pass filters.
Therefore, MSAs and Convs are complementary; (3) Multi-stage neural networks
behave like a series connection of small individual models. In addition, MSAs
at the end of a stage play a key role in prediction. Based on these insights,
we propose AlterNet, a model in which Conv blocks at the end of a stage are
replaced with MSA blocks. AlterNet outperforms CNNs not only in large data
regimes but also in small data regimes. The code is available at
https://github.com/xxxnell/how-do-vits-work.

Including local automatic gain control (AGC) circuitry into a silicon cochlea
design has been challenging because of transistor mismatch and model
complexity. To address this, we present an alternative system-level algorithm
that implements channel-specific AGC in a silicon spiking cochlea by measuring
the output spike activity of individual channels. The bandpass filter gain of a
channel is adapted dynamically to the input amplitude so that the average
output spike rate stays within a defined range. Because this AGC mechanism only
needs counting and adding operations, it can be implemented at low hardware
cost in a future design. We evaluate the impact of the local AGC algorithm on a
classification task where the input signal varies over 32 dB input range. Two
classifier types receiving cochlea spike features were tested on a speech
versus noise classification task. The logistic regression classifier achieves
an average of 6% improvement and 40.8% relative improvement in accuracy when
the AGC is enabled. The deep neural network classifier shows a similar
improvement for the AGC case and achieves a higher mean accuracy of 96%
compared to the best accuracy of 91% from the logistic regression classifier.

News recommendation is critical for personalized news distribution. Federated
news recommendation enables collaborative model learning from many clients
without sharing their raw data. It is promising for privacy-preserving news
recommendation. However, the security of federated news recommendation is still
unclear. In this paper, we study this problem by proposing an untargeted attack
called UA-FedRec. By exploiting the prior knowledge of news recommendation and
federated learning, UA-FedRec can effectively degrade the model performance
with a small percentage of malicious clients. First, the effectiveness of news
recommendation highly depends on user modeling and news modeling. We design a
news similarity perturbation method to make representations of similar news
farther and those of dissimilar news closer to interrupt news modeling, and
propose a user model perturbation method to make malicious user updates in
opposite directions of benign updates to interrupt user modeling. Second,
updates from different clients are typically aggregated by weighted-averaging
based on their sample sizes. We propose a quantity perturbation method to
enlarge sample sizes of malicious clients in a reasonable range to amplify the
impact of malicious updates. Extensive experiments on two real-world datasets
show that UA-FedRec can effectively degrade the accuracy of existing federated
news recommendation methods, even when defense is applied. Our study reveals a
critical security issue in existing federated news recommendation systems and
calls for research efforts to address the issue.

We study the problem of $K$-armed dueling bandit for both stochastic and
adversarial environments, where the goal of the learner is to aggregate
information through relative preferences of pair of decisions points queried in
an online sequential manner. We first propose a novel reduction from any
(general) dueling bandits to multi-armed bandits and despite the simplicity, it
allows us to improve many existing results in dueling bandits. In particular,
\emph{we give the first best-of-both world result for the dueling bandits
regret minimization problem} -- a unified framework that is guaranteed to
perform optimally for both stochastic and adversarial preferences
simultaneously. Moreover, our algorithm is also the first to achieve an optimal
$O(\sum_{i = 1}^K \frac{\log T}{\Delta_i})$ regret bound against the
Condorcet-winner benchmark, which scales optimally both in terms of the
arm-size $K$ and the instance-specific suboptimality gaps $\{\Delta_i\}_{i =
1}^K$. This resolves the long-standing problem of designing an instancewise
gap-dependent order optimal regret algorithm for dueling bandits (with matching
lower bounds up to small constant factors). We further justify the robustness
of our proposed algorithm by proving its optimal regret rate under
adversarially corrupted preferences -- this outperforms the existing
state-of-the-art corrupted dueling results by a large margin. In summary, we
believe our reduction idea will find a broader scope in solving a diverse class
of dueling bandits setting, which are otherwise studied separately from
multi-armed bandits with often more complex solutions and worse guarantees. The
efficacy of our proposed algorithms is empirically corroborated against the
existing dueling bandit methods.

Code completion is an essential feature of IDEs, yet current autocompleters
are restricted to either grammar-based or NLP-based single token completions.
Both approaches have significant drawbacks: grammar-based autocompletion is
restricted in dynamically-typed language environments, whereas NLP-based
autocompleters struggle to understand the semantics of the programming language
and the developer's code context.
  In this work, we present CodeFill, a language model for autocompletion that
combines learned structure and naming information. Using a parallel Transformer
architecture and multi-task learning, CodeFill consumes sequences of source
code token names and their equivalent AST token types. Uniquely, CodeFill is
trained both for single-token and multi-token (statement) prediction, which
enables it to learn long-range dependencies among grammatical and naming
elements. We train CodeFill on two datasets, consisting of 29M and 425M lines
of code, respectively. To make the evaluation more realistic, we develop a
method to automatically infer points in the source code at which completion
matters. We compare CodeFill against four baselines and two state-of-the-art
models, GPT-C and TravTrans+.CodeFill surpasses all baselines in single token
prediction (MRR: 70.9% vs. 66.2% and 67.8%) and outperforms the state of the
art for multi-token prediction (ROUGE-L: 63.7% vs. 52.4% and 59.2%, for n=4
tokens). We publicly release our source code and datasets.

We study the problem of extracting accurate correspondences for point cloud
registration. Recent keypoint-free methods bypass the detection of repeatable
keypoints which is difficult in low-overlap scenarios, showing great potential
in registration. They seek correspondences over downsampled superpoints, which
are then propagated to dense points. Superpoints are matched based on whether
their neighboring patches overlap. Such sparse and loose matching requires
contextual features capturing the geometric structure of the point clouds. We
propose Geometric Transformer to learn geometric feature for robust superpoint
matching. It encodes pair-wise distances and triplet-wise angles, making it
robust in low-overlap cases and invariant to rigid transformation. The
simplistic design attains surprisingly high matching accuracy such that no
RANSAC is required in the estimation of alignment transformation, leading to
$100$ times acceleration. Our method improves the inlier ratio by $17{\sim}30$
percentage points and the registration recall by over $7$ points on the
challenging 3DLoMatch benchmark. Our code and models are available at
\url{https://github.com/qinzheng93/GeoTransformer}.

Unsupervised domain adaption (UDA) aims to adapt models learned from a
well-annotated source domain to a target domain, where only unlabeled samples
are given. Current UDA approaches learn domain-invariant features by aligning
source and target feature spaces. Such alignments are imposed by constraints
such as statistical discrepancy minimization or adversarial training. However,
these constraints could lead to the distortion of semantic feature structures
and loss of class discriminability. In this paper, we introduce a novel prompt
learning paradigm for UDA, named Domain Adaptation via Prompt Learning (DAPL).
In contrast to prior works, our approach makes use of pre-trained
vision-language models and optimizes only very few parameters. The main idea is
to embed domain information into prompts, a form of representations generated
from natural language, which is then used to perform classification. This
domain information is shared only by images from the same domain, thereby
dynamically adapting the classifier according to each domain. By adopting this
paradigm, we show that our model not only outperforms previous methods on
several cross-domain benchmarks but also is very efficient to train and easy to

The past few years have witnessed the significant advances of speech
synthesis and voice conversion technologies. However, such technologies can
undermine the robustness of broadly implemented biometric identification models
and can be harnessed by in-the-wild attackers for illegal uses. The ASVspoof
challenge mainly focuses on synthesized audios by advanced speech synthesis and
voice conversion models, and replay attacks. Recently, the first Audio Deep
Synthesis Detection challenge (ADD 2022) extends the attack scenarios into more
aspects. Also ADD 2022 is the first challenge to propose the partially fake
audio detection task. Such brand new attacks are dangerous and how to tackle
such attacks remains an open question. Thus, we propose a novel framework by
introducing the question-answering (fake span discovery) strategy with the
self-attention mechanism to detect partially fake audios. The proposed fake
span detection module tasks the anti-spoofing model to predict the start and
end positions of the fake clip within the partially fake audio, address the
model's attention into discovering the fake spans rather than other shortcuts
with less generalization, and finally equips the model with the discrimination
capacity between real and partially fake audios. Our submission ranked second
in the partially fake audio detection track of ADD 2022.

Byzantine state-machine replication (SMR) ensures the consistency of
replicated state in the presence of malicious replicas and lies at the heart of
the modern blockchain technology. Byzantine SMR protocols often guarantee
safety under all circumstances and liveness only under synchrony. However,
guaranteeing liveness even under this assumption is nontrivial. So far we have
lacked systematic ways of incorporating liveness mechanisms into Byzantine SMR
protocols, which often led to subtle bugs. To close this gap, we introduce a
modular framework to facilitate the design of provably live and efficient
Byzantine SMR protocols. Our framework relies on a view abstraction generated
by a special SMR synchronizer primitive to drive the agreement on command
ordering. We present a simple formal specification of an SMR synchronizer and
its bounded-space implementation under partial synchrony. We also apply our
specification to prove liveness and analyze the latency of three Byzantine SMR
protocols via a uniform methodology. In particular, one of these results yields
what we believe is the first rigorous liveness proof for the algorithmic core
of the seminal PBFT protocol.

Large datasets underlying much of current machine learning raise serious
issues concerning inappropriate content such as offensive, insulting,
threatening, or might otherwise cause anxiety. This calls for increased dataset
documentation, e.g., using datasheets. They, among other topics, encourage to
reflect on the composition of the datasets. So far, this documentation,
however, is done manually and therefore can be tedious and error-prone,
especially for large image datasets. Here we ask the arguably "circular"
question of whether a machine can help us reflect on inappropriate content,
answering Question 16 in Datasheets. To this end, we propose to use the
information stored in pre-trained transformer models to assist us in the
documentation process. Specifically, prompt-tuning based on a dataset of
socio-moral values steers CLIP to identify potentially inappropriate content,
therefore reducing human labor. We then document the inappropriate images found
using word clouds, based on captions generated using a vision-language model.
The documentations of two popular, large-scale computer vision datasets --
ImageNet and OpenImages -- produced this way suggest that machines can indeed
help dataset creators to answer Question 16 on inappropriate image content.

Task and motion planning (TAMP) algorithms have been developed to help robots
plan behaviors in discrete and continuous spaces. Robots face complex
real-world scenarios, where it is hardly possible to model all objects or their
physical properties for robot planning (e.g., in kitchens or shopping centers).
In this paper, we define a new object-centric TAMP problem, where the TAMP
robot does not know object properties (e.g., size and weight of blocks). We
then introduce Task-Motion Object-Centric planning ({\bf TMOC}), a grounded
TAMP algorithm that learns to ground objects and their physical properties with
a physics engine. TMOC is particularly useful for those tasks that involve
dynamic complex robot-multi-object interactions that can hardly be modeled
beforehand. We have demonstrated and evaluated TMOC in simulation and using a
real robot. Results show that TMOC outperforms competitive baselines from the
literature in cumulative utility.

Convolutional neural networks have become a popular research in the field of
finger vein recognition because of their powerful image feature representation.
However, most researchers focus on improving the performance of the network by
increasing the CNN depth and width, which often requires high computational
effort. Moreover, we can notice that not only the importance of pixels in
different channels is different, but also the importance of pixels in different
positions of the same channel is different. To reduce the computational effort
and to take into account the different importance of pixels, we propose a
lightweight convolutional neural network with a convolutional block attention
module (CBAM) for finger vein recognition, which can achieve a more accurate
capture of visual structures through an attention mechanism. First, image
sequences are fed into a lightweight convolutional neural network we designed
to improve visual features. Afterwards, it learns to assign feature weights in
an adaptive manner with the help of a convolutional block attention module. The
experiments are carried out on two publicly available databases and the results
demonstrate that the proposed method achieves a stable, highly accurate, and
robust performance in multimodal finger recognition.

Learning scientific document representations can be substantially improved
through contrastive learning objectives, where the challenge lies in creating
positive and negative training samples that encode the desired similarity
semantics. Prior work relies on discrete citation relations to generate
contrast samples. However, discrete citations enforce a hard cut-off to
similarity. This is counter-intuitive to similarity-based learning, and ignores
that scientific papers can be very similar despite lacking a direct citation -
a core problem of finding related research. Instead, we use controlled nearest
neighbor sampling over citation graph embeddings for contrastive learning. This
control allows us to learn continuous similarity, to sample hard-to-learn
negatives and positives, and also to avoid collisions between negative and
positive samples by controlling the sampling margin between them. The resulting
method SciNCL outperforms the state-of-the-art on the SciDocs benchmark.
Furthermore, we demonstrate that it can train (or tune) models
sample-efficiently, and that it can be combined with recent training-efficient
methods. Perhaps surprisingly, even training a general-domain language model
this way outperforms baselines pretrained in-domain.

We argue that a form of the valuable information provided by the auxiliary
information is its implied data clustering information. For instance,
considering hashtags as auxiliary information, we can hypothesize that an
Instagram image will be semantically more similar with the same hashtags. With
this intuition, we present a two-stage weakly-supervised contrastive learning
approach. The first stage is to cluster data according to its auxiliary
information. The second stage is to learn similar representations within the
same cluster and dissimilar representations for data from different clusters.
Our empirical experiments suggest the following three contributions. First,
compared to conventional self-supervised representations, the
auxiliary-information-infused representations bring the performance closer to
the supervised representations, which use direct downstream labels as
supervision signals. Second, our approach performs the best in most cases, when
comparing our approach with other baseline representation learning methods that
also leverage auxiliary data information. Third, we show that our approach also
works well with unsupervised constructed clusters (e.g., no auxiliary
information), resulting in a strong unsupervised representation learning
approach.

Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technology
for future wireless communications, enabling programmable radio propagation
environments. However, the passive reflecting feature of RISs induces notable
challenges on channel estimation, making coherent symbol detection a
challenging task. In this paper, we consider the uplink of RIS-aided multi-user
Multiple-Input Multiple-Output (MIMO) systems and propose a Machine Learning
(ML) approach to jointly design the multi-antenna receiver and configure the
RIS reflection coefficients, which does not require explicit full knowledge of
the channel input-output relationship. Our approach devises a ML-based
receiver, while the configurations of the RIS reflection patterns affecting the
underlying propagation channel are treated as hyperparameters. Based on this
system design formulation, we propose a Bayesian ML framework for optimizing
the RIS hyperparameters, according to which the transmitted pilots are directly
used to jointly tune the RIS and the multi-antenna receiver. Our simulation
results demonstrate the capability of the proposed approach to provide reliable
communications in non-linear channel conditions corrupted by Gaussian noise.

Ensemble methods have been widely used to improve the performance of machine
learning methods in terms of generalization, while they are hard to use in deep
learning systems, as training an ensemble of deep neural networks (DNNs) incurs
an extremely higher computational overhead of model training. Recently,
advanced techniques such as fast geometric ensembling (FGE) and snapshot
ensemble have been proposed. These methods can train the model ensembles in the
same time as a single model, thus getting around the hurdle of training time.
However, their memory overhead for test-time inference remains much higher than
single model based methods. Here we propose a parsimonious FGE (PFGE) that
employs a lightweight ensemble of higher-performing DNNs, generated by
successively-performed stochastic weight averaging procedures. Experimental
results across different modern DNN architectures on widely used image datasets
CIFAR-{10,100} and Imagenet, demonstrate that PFGE is 5x memory efficient than
prior art methods, yet without compromise in generalization. Our code is
available at https://github.com/ZJLAB-AMMI/PFGE.

We consider a special case of bandit problems, named batched bandits, in
which an agent observes batches of responses over a certain time period. Unlike
previous work, we consider a more practically relevant batch-centric scenario
of batch learning. That is to say, we provide a policy-agnostic regret analysis
and demonstrate upper and lower bounds for the regret of a candidate policy.
Our main theoretical results show that the impact of batch learning is a
multiplicative factor of batch size relative to the regret of online behavior.
Primarily, we study two settings of the stochastic linear bandits: bandits with
finitely and infinitely many arms. While the regret bounds are the same for
both settings, the former setting results hold under milder assumptions. Also,
we provide a more robust result for the 2-armed bandit problem as an important
insight. Finally, we demonstrate the consistency of theoretical results by
conducting empirical experiments and reflect on optimal batch size choice.

Transfer learning and meta-learning offer some of the most promising avenues
to unlock the scalability of healthcare and consumer technologies driven by
biosignal data. This is because current methods cannot generalise well across
human subjects' data and handle learning from different heterogeneously
collected data sets, thus limiting the scale of training data. On the other
side, developments in transfer learning would benefit significantly from a
real-world benchmark with immediate practical application. Therefore, we pick
electroencephalography (EEG) as an exemplar for what makes biosignal machine
learning hard. We design two transfer learning challenges around diagnostics
and Brain-Computer-Interfacing (BCI), that have to be solved in the face of low
signal-to-noise ratios, major variability among subjects, differences in the
data recording sessions and techniques, and even between the specific BCI tasks
recorded in the dataset. Task 1 is centred on the field of medical diagnostics,
addressing automatic sleep stage annotation across subjects. Task 2 is centred
on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across
both subjects and data sets. The BEETL competition with its over 30 competing
teams and its 3 winning entries brought attention to the potential of deep
transfer learning and combinations of set theory and conventional machine
learning techniques to overcome the challenges. The results set a new
state-of-the-art for the real-world BEETL benchmark.

Keyword extraction is the task of retrieving words that are essential to the
content of a given document. Researchers proposed various approaches to tackle
this problem. At the top-most level, approaches are divided into ones that
require training - supervised and ones that do not - unsupervised. In this
study, we are interested in settings, where for a language under investigation,
no training data is available. More specifically, we explore whether pretrained
multilingual language models can be employed for zero-shot cross-lingual
keyword extraction on low-resource languages with limited or no available
labeled training data and whether they outperform state-of-the-art unsupervised
keyword extractors. The comparison is conducted on six news article datasets
covering two high-resource languages, English and Russian, and four
low-resource languages, Croatian, Estonian, Latvian, and Slovenian. We find
that the pretrained models fine-tuned on a multilingual corpus covering
languages that do not appear in the test set (i.e. in a zero-shot setting),
consistently outscore unsupervised models in all six languages.

In this work, a model based on the Physics - Informed Neural Networks (PINNs)
for solving elastic deformation of heterogeneous solids and associated
Uncertainty Quantification (UQ) is presented. For the present study, the PINNs
framework - Modulus developed by Nvidia is utilized, wherein we implement a
module for mechanics of heterogeneous solids. We use PINNs to approximate
momentum balance by assuming isotropic linear elastic constitutive behavior
against a loss function. Along with governing equations, the associated initial
/ boundary conditions also softly participate in the loss function. Solids
where the heterogeneity manifests as voids (low elastic modulus regions) and
fibers (high elastic modulus regions) in a matrix are analyzed, and the results
are validated against solutions obtained from a commercial Finite Element (FE)
analysis package. The present study also reveals that PINNs can capture the
stress jumps precisely at the material interfaces. Additionally, the present
study explores the advantages associated with the surrogate features in PINNs
via the variation in geometry and material properties. The presented UQ studies
suggest that the mean and standard deviation of the PINNs solution are in good
agreement with Monte Carlo FE results. The effective Young's modulus predicted
by PINNs for single representative void and single fiber composites compare
very well against the ones predicted by FE, which establishes the PINNs
formulation as an efficient homogenization tool.

Social distancing in public spaces has become an essential aspect in helping
to reduce the impact of the COVID-19 pandemic. Exploiting recent advances in
machine learning, there have been many studies in the literature implementing
social distancing via object detection through the use of surveillance cameras
in public spaces. However, to date, there has been no study of social distance
measurement on public transport. The public transport setting has some unique
challenges, including some low-resolution images and camera locations that can
lead to the partial occlusion of passengers, which make it challenging to
perform accurate detection. Thus, in this paper, we investigate the challenges
of performing accurate social distance measurement on public transportation. We
benchmark several state-of-the-art object detection algorithms using real-world
footage taken from the London Underground and bus network. The work highlights
the complexity of performing social distancing measurement on images from
current public transportation onboard cameras. Further, exploiting domain
knowledge of expected passenger behaviour, we attempt to improve the quality of
the detections using various strategies and show improvement over using vanilla
object detection alone.

We develop a new continuous-time stochastic gradient descent method for
optimizing over the stationary distribution of stochastic differential equation
(SDE) models. The algorithm continuously updates the SDE model's parameters
using an estimate for the gradient of the stationary distribution. The gradient
estimate is simultaneously updated, asymptotically converging to the direction
of steepest descent. We rigorously prove convergence of our online algorithm
for linear SDE models and present numerical results for nonlinear examples. The
proof requires analysis of the fluctuations of the parameter evolution around
the direction of steepest descent. Bounds on the fluctuations are challenging
to obtain due to the online nature of the algorithm (e.g., the stationary
distribution will continuously change as the parameters change). We prove
bounds for the solutions of a new class of Poisson partial differential
equations, which are then used to analyze the parameter fluctuations in the

Drug development is an expensive and time-consuming process where thousands
of chemical compounds are being tested in order to find those possessing
drug-like properties while being safe and effective. One of key parts of the
early drug discovery process has become virtual drug screening -- a method used
to narrow down search for potential drugs by running computer simulations of
drug-target interactions. As these methods are known to demand huge amounts of
computational power to get accurate results, prediction models based on machine
learning techniques became a popular solution requiring less computational
power as well as offering the ability to generate novel chemical structures for
further research. Deep learning is to stay in drug discovery but has a long way
to go. Only in the past few years with increases in computing power have
researchers really started to embrace the potential of neural networks in
various stages of the drug discovery process. While prediction methods promise
great perspective in the future development of drug discovery they open new
questions and challenges that still have to be solved.

Most security proofs of quantum key distribution (QKD) disregard the effect
of information leakage from the users' devices, and, thus, do not protect
against Trojan-horse attacks (THAs). In a THA, the eavesdropper injects strong
light into the QKD apparatuses, and then analyzes the back-reflected light to
learn information about their internal setting choices. Only a few recent works
consider this security threat, but predict a rather poor performance of QKD
unless the devices are strongly isolated from the channel. Here, we derive
finite-key security bounds for decoy-state-based QKD schemes in the presence of
THAs, which significantly outperform previous analyses. Our results constitute
an important step forward to closing the existing gap between theory and
practice in QKD.

Video streaming usage has seen a significant rise as entertainment,
education, and business increasingly rely on online video. Optimizing video
compression has the potential to increase access and quality of content to
users, and reduce energy use and costs overall. In this paper, we present an
application of the MuZero algorithm to the challenge of video compression.
Specifically, we target the problem of learning a rate control policy to select
the quantization parameters (QP) in the encoding process of libvpx, an open
source VP9 video compression library widely used by popular video-on-demand
(VOD) services. We treat this as a sequential decision making problem to
maximize the video quality with an episodic constraint imposed by the target
bitrate. Notably, we introduce a novel self-competition based reward mechanism
to solve constrained RL with variable constraint satisfaction difficulty, which
is challenging for existing constrained RL methods. We demonstrate that the
MuZero-based rate control achieves an average 6.28% reduction in size of the
compressed videos for the same delivered video quality level (measured as PSNR
BD-rate) compared to libvpx's two-pass VBR rate control policy, while having
better constraint satisfaction behavior.

While rule-based attribution methods have proven useful for providing local
explanations for Deep Neural Networks, explaining modern and more varied
network architectures yields new challenges in generating trustworthy
explanations, since the established rule sets might not be sufficient or
applicable to novel network structures. As an elegant solution to the above
issue, network canonization has recently been introduced. This procedure
leverages the implementation-dependency of rule-based attributions and
restructures a model into a functionally identical equivalent of alternative
design to which established attribution rules can be applied. However, the idea
of canonization and its usefulness have so far only been explored
qualitatively. In this work, we quantitatively verify the beneficial effects of
network canonization to rule-based attributions on VGG-16 and ResNet18 models
with BatchNorm layers and thus extend the current best practices for obtaining
reliable neural network explanations.

Hard interaction learning between source sequences and their next targets is
challenging, which exists in a myriad of sequential prediction tasks. During
the training process, most existing methods focus on explicitly hard
interactions caused by wrong responses. However, a model might conduct correct
responses by capturing a subset of learnable patterns, which results in
implicitly hard interactions with some unlearned patterns. As such, its
generalization performance is weakened. The problem gets more serious in
sequential prediction due to the interference of substantial similar candidate
targets.
  To this end, we propose a Hardness Aware Interaction Learning framework
(HAIL) that mainly consists of two base sequential learning networks and mutual
exclusivity distillation (MED). The base networks are initialized differently
to learn distinctive view patterns, thus gaining different training
experiences. The experiences in the form of the unlikelihood of correct
responses are drawn from each other by MED, which provides mutual exclusivity
knowledge to figure out implicitly hard interactions. Moreover, we deduce that
the unlikelihood essentially introduces additional gradients to push the
pattern learning of correct responses. Our framework can be easily extended to
more peer base networks. Evaluation is conducted on four datasets covering
cyber and physical spaces. The experimental results demonstrate that our
framework outperforms several state-of-the-art methods in terms of top-k based
metrics.

Mutual Information (MI) has been widely used as a loss regularizer for
training neural networks. This has been particularly effective when learn
disentangled or compressed representations of high dimensional data. However,
differential entropy (DE), another fundamental measure of information, has not
found widespread use in neural network training. Although DE offers a
potentially wider range of applications than MI, off-the-shelf DE estimators
are either non differentiable, computationally intractable or fail to adapt to
changes in the underlying distribution. These drawbacks prevent them from being
used as regularizers in neural networks training. To address shortcomings in
previously proposed estimators for DE, here we introduce KNIFE, a fully
parameterized, differentiable kernel-based estimator of DE. The flexibility of
our approach also allows us to construct KNIFE-based estimators for conditional
(on either discrete or continuous variables) DE, as well as MI. We empirically
validate our method on high-dimensional synthetic data and further apply it to
guide the training of neural networks for real-world tasks. Our experiments on
a large variety of tasks, including visual domain adaptation, textual fair
classification, and textual fine-tuning demonstrate the effectiveness of
KNIFE-based estimation. Code can be found at
https://github.com/g-pichler/knife.

In this paper, we present a real-world application of online learning with
expert advice to the field of Space Operations, testing our theory on real-life
data coming from the Copernicus Sentinel-6 satellite. We show that in
Spacecraft Memory Dump Optimization, a lightweight Follow-The-Leader algorithm
leads to an increase in performance of over $60\%$ when compared to traditional

Scenario-based testing is a promising approach to solve the challenge of
proving the safe behavior of vehicles equipped with automated driving systems
(ADS). Since an infinite number of concrete scenarios can theoretically occur
in real-world road traffic, the extraction of relevant scenarios that are
sensitive regarding the safety-related behavior of ADS-equipped vehicles is a
key aspect for the successful verification and validation of these systems.
Therefore, this paper provides a method for extracting multimodal urban traffic
scenarios from naturalistic road traffic data in an unsupervised manner for
minimizing the amount of (potentially biased) prior expert knowledge needed.
Rather than an (expensive) rule-based assignment by extracting concrete
scenarios into predefined functional scenarios, the presented method deploys an
unsupervised machine learning pipeline. It includes principal feature analysis,
feature extraction with so-called scenario grids, dimensionality reduction by
principal component analysis, scenario clustering as well as cluster
validation. The approach allows exploring the unknown natures of the data and
interpreting them as scenarios that experts could not have anticipated. The
method is demonstrated and evaluated for naturalistic road traffic data at
urban intersections from the inD and the Silicon Valley dataset. The findings
encourage the use of this type of data as well as unsupervised machine learning
approaches as important pillar for a systematic construction of a relevant
scenario database with sufficient coverage for testing ADS.

As the final stage of the multi-stage recommender system (MRS), re-ranking
directly affects user experience and satisfaction by rearranging the input
ranking lists, and thereby plays a critical role in MRS. With the advances in
deep learning, neural re-ranking has become a trending topic and been widely
applied in industrial applications. This review aims at integrating re-ranking
algorithms into a broader picture, and paving ways for more comprehensive
solutions for future research. For this purpose, we first present a taxonomy of
current methods on neural re-ranking. Then we give a description of these
methods along with the historic development according to their objectives. The
network structure, personalization, and complexity are also discussed and
compared. Next, we provide benchmarks of the major neural re-ranking models and
quantitatively analyze their re-ranking performance. Finally, the review
concludes with a discussion on future prospects of this field. A list of papers
discussed in this review, the benchmark datasets, our re-ranking library
LibRerank, and detailed parameter settings are publicly available at
https://github.com/LibRerank-Community/LibRerank.

Segmentation and spatial alignment of ultrasound (US) imaging data acquired
in the in first trimester are crucial for monitoring human embryonic growth and
development throughout this crucial period of life. Current approaches are
either manual or semi-automatic and are therefore very time-consuming and prone
to errors. To automate these tasks, we propose a multi-atlas framework for
automatic segmentation and spatial alignment of the embryo using deep learning
with minimal supervision. Our framework learns to register the embryo to an
atlas, which consists of the US images acquired at a range of gestational age
(GA), segmented and spatially aligned to a predefined standard orientation.
From this, we can derive the segmentation of the embryo and put the embryo in
standard orientation. US images acquired at 8+0 till 12+6 weeks GA were used
and eight subjects were selected as atlas. We evaluated different fusion
strategies to incorporate multiple atlases: 1) training the framework using
atlas images from a single subject, 2) training the framework with data of all
available atlases and 3) ensembling of the frameworks trained per subject. To
evaluate the performance, we calculated the Dice score over the test set. We
found that training the framework using all available atlases outperformed
ensembling and gave similar results compared to the best of all frameworks
trained on a single subject. Furthermore, we found that selecting images from
the four atlases closest in GA out of all available atlases, regardless of the
individual quality, gave the best results with a median Dice score of 0.72. We
conclude that our framework can accurately segment and spatially align the
embryo in first trimester 3D US images and is robust for the variation in
quality that existed in the available atlases. Our code is publicly available
at: https://github.com/wapbastiaansen/multi-atlas-seg-reg.

For the temperature field reconstruction (TFR), a complex image-to-image
regression problem, the convolutional neural network (CNN) is a powerful
surrogate model due to the convolutional layer's good image feature extraction
ability. However, a lot of labeled data is needed to train CNN, and the common
CNN can not quantify the aleatoric uncertainty caused by data noise. In actual
engineering, the noiseless and labeled training data is hardly obtained for the
TFR. To solve these two problems, this paper proposes a deep Monte Carlo
quantile regression (Deep MC-QR) method for reconstructing the temperature
field and quantifying aleatoric uncertainty caused by data noise. On the one
hand, the Deep MC-QR method uses physical knowledge to guide the training of
CNN. Thereby, the Deep MC-QR method can reconstruct an accurate TFR surrogate
model without any labeled training data. On the other hand, the Deep MC-QR
method constructs a quantile level image for each input in each training epoch.
Then, the trained CNN model can quantify aleatoric uncertainty by quantile
level image sampling during the prediction stage. Finally, the effectiveness of
the proposed Deep MC-QR method is validated by many experiments, and the
influence of data noise on TFR is analyzed.

In this paper, we study statistical inference on the similarity/distance
between two time-series under uncertain environment by considering a
statistical hypothesis test on the distance obtained from Dynamic Time Warping
(DTW) algorithm. The sampling distribution of the DTW distance is too
complicated to derive because it is obtained based on the solution of a
complicated algorithm. To circumvent this difficulty, we propose to employ a
conditional sampling distribution for the inference, which enables us to derive
an exact (non-asymptotic) inference method on the DTW distance. Besides, we
also develop a novel computational method to compute the conditional sampling
distribution. To our knowledge, this is the first method that can provide valid
$p$-value to quantify the statistical significance of the DTW distance, which
is helpful for high-stake decision making. We evaluate the performance of the
proposed inference method on both synthetic and real-world datasets.

Continual learning needs to overcome catastrophic forgetting of the past.
Memory replay of representative old training samples has been shown as an
effective solution, and achieves the state-of-the-art (SOTA) performance.
However, existing work is mainly built on a small memory buffer containing a
few original data, which cannot fully characterize the old data distribution.
In this work, we propose memory replay with data compression (MRDC) to reduce
the storage cost of old training samples and thus increase their amount that
can be stored in the memory buffer. Observing that the trade-off between the
quality and quantity of compressed data is highly nontrivial for the efficacy
of memory replay, we propose a novel method based on determinantal point
processes (DPPs) to efficiently determine an appropriate compression quality
for currently-arrived training samples. In this way, using a naive data
compression algorithm with a properly selected quality can largely boost recent
strong baselines by saving more compressed data in a limited storage space. We
extensively validate this across several benchmarks of class-incremental
learning and in a realistic scenario of object detection for autonomous
driving.

Increased levels of tumor infiltrating lymphocytes (TILs) in cancer tissue
indicate favourable outcomes in many types of cancer. Manual quantification of
immune cells is inaccurate and time consuming for pathologists. Our aim is to
leverage a computational solution to automatically quantify TILs in whole slide
images (WSIs) of standard diagnostic haematoxylin and eosin stained sections
(H&E slides) from lung cancer patients. Our approach is to transfer an open
source machine learning method for segmentation and classification of nuclei in
H&E slides trained on public data to TIL quantification without manual labeling
of our data. Our results show that additional augmentation improves model
transferability when training on few samples/limited tissue types. Models
trained with sufficient samples/tissue types do not benefit from our additional
augmentation policy. Further, the resulting TIL quantification correlates to
patient prognosis and compares favorably to the current state-of-the-art method
for immune cell detection in non-small lung cancer (current standard CD8 cells
in DAB stained TMAs HR 0.34 95% CI 0.17-0.68 vs TILs in HE WSIs: HoVer-Net
PanNuke Aug Model HR 0.30 95% CI 0.15-0.60, HoVer-Net MoNuSAC Aug model HR 0.27
95% CI 0.14-0.53). Moreover, we implemented a cloud based system to train,
deploy and visually inspect machine learning based annotation for H&E slides.
Our pragmatic approach bridges the gap between machine learning research,
translational clinical research and clinical implementation. However,
validation in prospective studies is needed to assert that the method works in
a clinical setting.

Medication recommendation targets to provide a proper set of medicines
according to patients' diagnoses, which is a critical task in clinics.
Currently, the recommendation is manually conducted by doctors. However, for
complicated cases, like patients with multiple diseases at the same time, it's
difficult to propose a considerate recommendation even for experienced doctors.
This urges the emergence of automatic medication recommendation which can help
treat the diagnosed diseases without causing harmful drug-drug interactions.Due
to the clinical value, medication recommendation has attracted growing research
interests.Existing works mainly formulate medication recommendation as a
multi-label classification task to predict the set of medicines. In this paper,
we propose the Conditional Generation Net (COGNet) which introduces a novel
copy-or-predict mechanism to generate the set of medicines. Given a patient,
the proposed model first retrieves his or her historical diagnoses and
medication recommendations and mines their relationship with current diagnoses.
Then in predicting each medicine, the proposed model decides whether to copy a
medicine from previous recommendations or to predict a new one. This process is
quite similar to the decision process of human doctors. We validate the
proposed model on the public MIMIC data set, and the experimental results show
that the proposed model can outperform state-of-the-art approaches.

Graph neural networks (GNNs) have been widely applied to numerous fields. A
recent work which combines layered structure and residual connection proposes
an improved deep architecture to extend CAmouflage-REsistant GNN (CARE-GNN) to
deep models named as Residual Layered CARE-GNN (RLC-GNN), which forms
self-correcting and incremental learning mechanism, and achieves significant
performance improvements on fraud detection task. However, we spot three issues
of RLC-GNN, which are the usage of neighboring information reaching limitation,
the training difficulty which is inherent problem to deep models and lack of
comprehensive consideration about node features and external patterns. In this
work, we propose three approaches to solve those three problems respectively.
First, we suggest conducting similarity measure via cosine distance to take
both local features and external patterns into consideration. Then, we combine
the similarity measure module and the idea of adjacency-wise normalization with
node-wise and batch-wise normalization and then propound partial neighborhood
normalization methods to overcome the training difficulty while mitigating the
impact of too much noise caused by high-density of graph. Finally, we put
forward intermediate information supplement to solve the information
limitation. Experiments are conducted on Yelp and Amazon datasets. And the
results show that our proposed methods effectively solve the three problems.
After applying the three methods, we achieve 4.81%, 6.62% and 6.81%
improvements in the metrics of recall, AUC and Macro-F1 respectively on the
Yelp dataset. And we obtain 1.65% and 0.29% improvements in recall and AUC
respectively on the Amazon datasets.

Recurrent neural networks such as Long Short-Term Memories (LSTMs) learn
temporal dependencies by keeping an internal state, making them ideal for
time-series problems such as speech recognition. However, the output-to-input
feedback creates distinctive memory bandwidth and scalability challenges in
designing accelerators for RNNs. We present Muntaniala, an RNN accelerator
architecture for LSTM inference with a silicon-measured energy-efficiency of
3.25$TOP/s/W$ and performance of 30.53$GOP/s$ in UMC 65 $nm$ technology. The
scalable design of Muntaniala allows running large RNN models by combining
multiple tiles in a systolic array. We keep all parameters stationary on every
die in the array, drastically reducing the I/O communication to only loading
new features and sharing partial results with other dies. For quantifying the
overall system power, including I/O power, we built Vau da Muntanialas, to the
best of our knowledge, the first demonstration of a systolic multi-chip-on-PCB
array of RNN accelerator. Our multi-die prototype performs LSTM inference with
192 hidden states in 330$\mu s$ with a total system power of 9.0$mW$ at 10$MHz$
consuming 2.95$\mu J$. Targeting the 8/16-bit quantization implemented in
Muntaniala, we show a phoneme error rate (PER) drop of approximately 3% with
respect to floating-point (FP) on a 3L-384NH-123NI LSTM network on the TIMIT
dataset.

Logs provide first-hand information for engineers to diagnose failures in
large-scale online service systems. Log parsing, which transforms
semi-structured raw log messages into structured data, is a prerequisite of
automated log analysis such as log-based anomaly detection and diagnosis.
Almost all existing log parsers follow the general idea of extracting the
common part as templates and the dynamic part as parameters. However, these log
parsing methods, often neglect the semantic meaning of log messages.
Furthermore, high diversity among various log sources also poses an obstacle in
the generalization of log parsing across different systems. In this paper, we
propose UniParser to capture the common logging behaviours from heterogeneous
log data. UniParser utilizes a Token Encoder module and a Context Encoder
module to learn the patterns from the log token and its neighbouring context. A
Context Similarity module is specially designed to model the commonalities of
learned patterns. We have performed extensive experiments on 16 public log
datasets and our results show that UniParser outperperforms state-of-the-art
log parsers by a large margin.

Single image deraining is an important and challenging task for some
downstream artificial intelligence applications such as video surveillance and
self-driving systems. Most of the existing deep-learning-based methods
constrain the network to generate derained images but few of them explore
features from intermediate layers, different levels, and different modules
which are beneficial for rain streaks removal. In this paper, we propose a
high-order collaborative network with multi-scale compact constraints and a
bidirectional scale-content similarity mining module to exploit features from
deep networks externally and internally for rain streaks removal. Externally,
we design a deraining framework with three sub-networks trained in a
collaborative manner, where the bottom network transmits intermediate features
to the middle network which also receives shallower rainy features from the top
network and sends back features to the bottom network. Internally, we enforce
multi-scale compact constraints on the intermediate layers of deep networks to
learn useful features via a Laplacian pyramid. Further, we develop a
bidirectional scale-content similarity mining module to explore features at
different scales in a down-to-up and up-to-down manner. To improve the model
performance on real-world images, we propose an online-update learning
approach, which uses real-world rainy images to fine-tune the network and
update the deraining results in a self-supervised manner. Extensive experiments
demonstrate that our proposed method performs favorably against eleven
state-of-the-art methods on five public synthetic datasets and one real-world
dataset. The source code will be available at
\url{https://supercong94.wixsite.com/supercong94}.

Recurrent Neural Networks (RNNs) are a key technology for applications such
as automatic speech recognition or machine translation. Unlike conventional
feed-forward DNNs, RNNs remember past information to improve the accuracy of
future predictions and, therefore, they are very effective for sequence
processing problems. For each application run, recurrent layers are executed
many times for processing a potentially large sequence of inputs (words,
images, audio frames, etc.). In this paper, we observe that the output of a
neuron exhibits small changes in consecutive invocations.~We exploit this
property to build a neuron-level fuzzy memoization scheme, which dynamically
caches each neuron's output and reuses it whenever it is predicted that the
current output will be similar to a previously computed result, avoiding in
this way the output computations.
  The main challenge in this scheme is determining whether the new neuron's
output for the current input in the sequence will be similar to a recently
computed result. To this end, we extend the recurrent layer with a much simpler
Bitwise Neural Network (BNN), and show that the BNN and RNN outputs are highly
correlated: if two BNN outputs are very similar, the corresponding outputs in
the original RNN layer are likely to exhibit negligible changes. The BNN
provides a low-cost and effective mechanism for deciding when fuzzy memoization
can be applied with a small impact on accuracy. We evaluate our memoization
scheme on top of a state-of-the-art accelerator for RNNs, for a variety of
different neural networks from multiple application domains. We show that our
technique avoids more than 26.7\% of computations, resulting in 21\% energy
savings and 1.4x speedup on average.

Satisfying safety constraints almost surely (or with probability one) can be
critical for the deployment of Reinforcement Learning (RL) in real-life
applications. For example, plane landing and take-off should ideally occur with
probability one. We address the problem by introducing Safety Augmented (Saute)
Markov Decision Processes (MDPs), where the safety constraints are eliminated
by augmenting them into the state-space and reshaping the objective. We show
that Saute MDP satisfies the Bellman equation and moves us closer to solving
Safe RL with constraints satisfied almost surely. We argue that Saute MDP
allows viewing the Safe RL problem from a different perspective enabling new
features. For instance, our approach has a plug-and-play nature, i.e., any RL
algorithm can be "Sauteed". Additionally, state augmentation allows for policy
generalization across safety constraints. We finally show that Saute RL
algorithms can outperform their state-of-the-art counterparts when constraint
satisfaction is of high importance.

We consider a context-dependent Reinforcement Learning (RL) setting, which is
characterized by: a) an unknown finite number of not directly observable
contexts; b) abrupt (discontinuous) context changes occurring during an
episode; and c) Markovian context evolution. We argue that this challenging
case is often met in applications and we tackle it using a Bayesian approach
and variational inference. We adapt a sticky Hierarchical Dirichlet Process
(HDP) prior for model learning, which is arguably best-suited for Markov
process modeling. We then derive a context distillation procedure, which
identifies and removes spurious contexts in an unsupervised fashion. We argue
that the combination of these two components allows to infer the number of
contexts from data thus dealing with the context cardinality assumption. We
then find the representation of the optimal policy enabling efficient policy
learning using off-the-shelf RL algorithms. Finally, we demonstrate empirically
(using gym environments cart-pole swing-up, drone, intersection) that our
approach succeeds where state-of-the-art methods of other frameworks fail and
elaborate on the reasons for such failures.

18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) imaging
usually needs a full-dose radioactive tracer to obtain satisfactory diagnostic
results, which raises concerns about the potential health risks of radiation
exposure, especially for pediatric patients. Reconstructing the low-dose PET
(L-PET) images to the high-quality full-dose PET (F-PET) ones is an effective
way that both reduces the radiation exposure and remains diagnostic accuracy.
In this paper, we propose a resource-efficient deep learning framework for
L-PET reconstruction and analysis, referred to as transGAN-SDAM, to generate
F-PET from corresponding L-PET, and quantify the standard uptake value ratios
(SUVRs) of these generated F-PET at whole brain. The transGAN-SDAM consists of
two modules: a transformer-encoded Generative Adversarial Network (transGAN)
and a Spatial Deformable Aggregation Module (SDAM). The transGAN generates
higher quality F-PET images, and then the SDAM integrates the spatial
information of a sequence of generated F-PET slices to synthesize whole-brain
F-PET images. Experimental results demonstrate the superiority and rationality

In the sequential decision making setting, an agent aims to achieve
systematic generalization over a large, possibly infinite, set of environments.
Such environments are modeled as discrete Markov decision processes with both
states and actions represented through a feature vector. The underlying
structure of the environments allows the transition dynamics to be factored
into two components: one that is environment-specific and another one that is
shared. Consider a set of environments that share the laws of motion as an
illustrative example. In this setting, the agent can take a finite amount of
reward-free interactions from a subset of these environments. The agent then
must be able to approximately solve any planning task defined over any
environment in the original set, relying on the above interactions only. Can we
design a provably efficient algorithm that achieves this ambitious goal of
systematic generalization? In this paper, we give a partially positive answer
to this question. First, we provide the first tractable formulation of
systematic generalization by employing a causal viewpoint. Then, under specific
structural assumptions, we provide a simple learning algorithm that allows us
to guarantee any desired planning error up to an unavoidable sub-optimality
term, while showcasing a polynomial sample complexity.

E-commerce platforms generate vast amounts of customer behavior data, such as
clicks and purchases, from millions of unique users every day. However,
effectively using this data for behavior understanding tasks is challenging
because there are usually not enough labels to learn from all users in a
supervised manner. This paper extends the BERT model to e-commerce user data
for pre-training representations in a self-supervised manner. By viewing user
actions in sequences as analogous to words in sentences, we extend the existing
BERT model to user behavior data. Further, our model adopts a unified structure
to simultaneously learn from long-term and short-term user behavior, as well as
user attributes. We propose methods for the tokenization of different types of
user behavior sequences, the generation of input representation vectors, and a
novel pretext task to enable the pre-trained model to learn from its own input,
eliminating the need for labeled training data. Extensive experiments
demonstrate that the learned representations result in significant improvements
when transferred to three different real-world tasks, particularly compared to
task-specific modeling and multi-task representation learning

Past work has shown that large language models are susceptible to privacy
attacks, where adversaries generate sequences from a trained model and detect
which sequences are memorized from the training set. In this work, we show that
the success of these attacks is largely due to duplication in commonly used
web-scraped training sets. We first show that the rate at which language models
regenerate training sequences is superlinearly related to a sequence's count in
the training set. For instance, a sequence that is present 10 times in the
training data is on average generated ~1000 times more often than a sequence
that is present only once. We next show that existing methods for detecting
memorized sequences have near-chance accuracy on non-duplicated training
sequences. Finally, we find that after applying methods to deduplicate training
data, language models are considerably more secure against these types of
privacy attacks. Taken together, our results motivate an increased focus on
deduplication in privacy-sensitive applications and a reevaluation of the
practicality of existing privacy attacks.

Neural compression is the application of neural networks and other machine
learning methods to data compression. While machine learning deals with many
concepts closely related to compression, entering the field of neural
compression can be difficult due to its reliance on information theory,
perceptual metrics, and other knowledge specific to the field. This
introduction hopes to fill in the necessary background by reviewing basic
coding topics such as entropy coding and rate-distortion theory, related
machine learning ideas such as bits-back coding and perceptual metrics, and
providing a guide through the representative works in the literature so far.

Model-based methods for recommender systems have been studied extensively for
years. Modern recommender systems usually resort to 1) representation learning
models which define user-item preference as the distance between their
embedding representations, and 2) embedding-based Approximate Nearest Neighbor
(ANN) search to tackle the efficiency problem introduced by large-scale corpus.
While providing efficient retrieval, the embedding-based retrieval pattern also
limits the model capacity since the form of user-item preference measure is
restricted to the distance between their embedding representations. However,
for other more precise user-item preference measures, e.g., preference scores
directly derived from a deep neural network, they are computationally
intractable because of the lack of an efficient retrieval method, and an
exhaustive search for all user-item pairs is impractical. In this paper, we
propose a novel method to extend ANN search to arbitrary matching functions,
e.g., a deep neural network. Our main idea is to perform a greedy walk with a
matching function in a similarity graph constructed from all items. To solve
the problem that the similarity measures of graph construction and user-item
matching function are heterogeneous, we propose a pluggable adversarial
training task to ensure the graph search with arbitrary matching function can
achieve fairly high precision. Experimental results in both open source and
industry datasets demonstrate the effectiveness of our method. The proposed
method has been fully deployed in the Taobao display advertising platform and
brings a considerable advertising revenue increase. We also summarize our
detailed experiences in deployment in this paper.

Modern neural networks often have great expressive power and can be trained
to overfit the training data, while still achieving a good test performance.
This phenomenon is referred to as "benign overfitting". Recently, there emerges
a line of works studying "benign overfitting" from the theoretical perspective.
However, they are limited to linear models or kernel/random feature models, and
there is still a lack of theoretical understanding about when and how benign
overfitting occurs in neural networks. In this paper, we study the benign
overfitting phenomenon in training a two-layer convolutional neural network
(CNN). We show that when the signal-to-noise ratio satisfies a certain
condition, a two-layer CNN trained by gradient descent can achieve arbitrarily
small training and test loss. On the other hand, when this condition does not
hold, overfitting becomes harmful and the obtained CNN can only achieve a
constant level test loss. These together demonstrate a sharp phase transition
between benign overfitting and harmful overfitting, driven by the
signal-to-noise ratio. To the best of our knowledge, this is the first work
that precisely characterizes the conditions under which benign overfitting can
occur in training convolutional neural networks.

Speaker diarization has been investigated extensively as an important central
task for meeting analysis. Recent trend shows that integration of end-to-end
neural (EEND)-and clustering-based diarization is a promising approach to
handle realistic conversational data containing overlapped speech with an
arbitrarily large number of speakers, and achieved state-of-the-art results on
various tasks. However, the approaches proposed so far have not realized {\it
tight} integration yet, because the clustering employed therein was not optimal
in any sense for clustering the speaker embeddings estimated by the EEND
module. To address this problem, this paper introduces a {\it trainable}
clustering algorithm into the integration framework, by deep-unfolding a
non-parametric Bayesian model called the infinite Gaussian mixture model
(iGMM). Specifically, the speaker embeddings are optimized during training such
that it better fits iGMM clustering, based on a novel clustering loss based on
Adjusted Rand Index (ARI). Experimental results based on CALLHOME data show
that the proposed approach outperforms the conventional approach in terms of
diarization error rate (DER), especially by substantially reducing speaker
confusion errors, that indeed reflects the effectiveness of the proposed iGMM
integration.

Understanding the performance of machine learning models across diverse data
distributions is critically important for reliable applications. Motivated by
this, there is a growing focus on curating benchmark datasets that capture
distribution shifts. While valuable, the existing benchmarks are limited in
that many of them only contain a small number of shifts and they lack
systematic annotation about what is different across different shifts. We
present MetaShift--a collection of 12,868 sets of natural images across 410
classes--to address this challenge. We leverage the natural heterogeneity of
Visual Genome and its annotations to construct MetaShift. The key construction
idea is to cluster images using its metadata, which provides context for each
image (e.g. "cats with cars" or "cats in bathroom") that represent distinct
data distributions. MetaShift has two important benefits: first, it contains
orders of magnitude more natural data shifts than previously available. Second,
it provides explicit explanations of what is unique about each of its data sets
and a distance score that measures the amount of distribution shift between any
two of its data sets. We demonstrate the utility of MetaShift in benchmarking
several recent proposals for training models to be robust to data shifts. We
find that the simple empirical risk minimization performs the best when shifts
are moderate and no method had a systematic advantage for large shifts. We also
show how MetaShift can help to visualize conflicts between data subsets during

Source code summarization aims at generating concise and clear natural
language descriptions for programming languages. Well-written code summaries
are beneficial for programmers to participate in the software development and
maintenance process. To learn the semantic representations of source code,
recent efforts focus on incorporating the syntax structure of code into neural
networks such as Transformer. Such Transformer-based approaches can better
capture the long-range dependencies than other neural networks including
Recurrent Neural Networks (RNNs), however, most of them do not consider the
structural relative correlations between tokens, e.g., relative positions in
Abstract Syntax Trees (ASTs), which is beneficial for code semantics learning.
To model the structural dependency, we propose a Structural Relative Position
guided Transformer, named SCRIPT. SCRIPT first obtains the structural relative
positions between tokens via parsing the ASTs of source code, and then passes
them into two types of Transformer encoders. One Transformer directly adjusts
the input according to the structural relative distance; and the other
Transformer encodes the structural relative positions during computing the
self-attention scores. Finally, we stack these two types of Transformer
encoders to learn representations of source code. Experimental results show
that the proposed SCRIPT outperforms the state-of-the-art methods by at least
1.6%, 1.4% and 2.8% with respect to BLEU, ROUGE-L and METEOR on benchmark
datasets, respectively. We further show that how the proposed SCRIPT captures
the structural relative dependencies.

Shape deformation of targets in SAR image due to random orientation and
partial information loss caused by occlusion of the radar signal, is an
essential challenge in SAR ship detection. In this paper, we propose a data
augmentation method to train a deep network that is robust to partial
information loss within the targets. Taking advantage of ground-truth
annotations for bounding box and instance segmentation mask, we present a
simple and effective pipeline to simulate information loss on targets in
instance-level, while preserving contextual information. Furthermore, we adopt
deformable convolutional network to adaptively extract shape-invariant deep
features from geometrically translated targets. By learning sampling offset to
the grid of standard convolution, the network can robustly extract the features
from targets with shape variations for SAR ship detection. Experiments on the
HRSID dataset including comparisons with other deep networks and augmentation
methods, as well as ablation study, demonstrate the effectiveness of our

Deploying deep learning (DL) on mobile devices has been a notable trend in
recent years. To support fast inference of on-device DL, DL libraries play a
critical role as algorithms and hardware do. Unfortunately, no prior work ever
dives deep into the ecosystem of modern DL libs and provides quantitative
results on their performance. In this paper, we first build a comprehensive
benchmark that includes 6 representative DL libs and 15 diversified DL models.
We then perform extensive experiments on 10 mobile devices, which help reveal a
complete landscape of the current mobile DL libs ecosystem. For example, we
find that the best-performing DL lib is severely fragmented across different
models and hardware, and the gap between those DL libs can be rather huge. In
fact, the impacts of DL libs can overwhelm the optimizations from algorithms or
hardware, e.g., model quantization and GPU/DSP-based heterogeneous computing.
Finally, atop the observations, we summarize practical implications to
different roles in the DL lib ecosystem.

Affective brain-computer interfaces based on electroencephalography (EEG) is
an important branch in the field of affective computing. However, individual
differences and noisy labels seriously limit the effectiveness and
generalizability of EEG-based emotion recognition models. In this paper, we
propose a novel transfer learning framework with Prototypical Representation
based Pairwise Learning (PR-PL) to learn discriminative and generalized
prototypical representations for emotion revealing across individuals and
formulate emotion recognition as pairwise learning for alleviating the reliance
on precise label information. Extensive experiments are conducted on two
benchmark databases under four cross-validation evaluation protocols
(cross-subject cross-session, cross-subject within-session, within-subject
cross-session, and within-subject within-session). The experimental results
demonstrate the superiority of the proposed PR-PL against the state-of-the-arts
under all four evaluation protocols, which shows the effectiveness and
generalizability of PR-PL in dealing with the ambiguity of EEG responses in
affective studies. The source code is available at
https://github.com/KAZABANA/PR-PL.

Multimodal learning has been proven to be an effective method to improve
speech enhancement (SE) performance, especially in challenging situations such
as low signal-to-noise ratios, speech noise, or unseen noise types. In previous
studies, several types of auxiliary data have been used to construct multimodal
SE systems, such as lip images, electropalatography, or electromagnetic
midsagittal articulography. In this paper, we propose a novel EMGSE framework
for multimodal SE, which integrates audio and facial electromyography (EMG)
signals. Facial EMG is a biological signal containing articulatory movement
information, which can be measured in a non-invasive way. Experimental results
show that the proposed EMGSE system can achieve better performance than the
audio-only SE system. The benefits of fusing EMG signals with acoustic signals
for SE are notable under challenging circumstances. Furthermore, this study
reveals that cheek EMG is sufficient for SE.

With the advancement of deep learning techniques, an increasing number of
methods have been proposed for optic disc and cup (OD/OC) segmentation from the
fundus images. Clinically, OD/OC segmentation is often annotated by multiple
clinical experts to mitigate the personal bias. However, it is hard to train
the automated deep learning models on multiple labels. A common practice to
tackle the issue is majority vote, e.g., taking the average of multiple labels.
However such a strategy ignores the different expertness of medical experts.
Motivated by the observation that OD/OC segmentation is often used for the
glaucoma diagnosis clinically, in this paper, we propose a novel strategy to
fuse the multi-rater OD/OC segmentation labels via the glaucoma diagnosis
performance. Specifically, we assess the expertness of each rater through an
attentive glaucoma diagnosis network. For each rater, its contribution for the
diagnosis will be reflected as an expertness map. To ensure the expertness maps
are general for different glaucoma diagnosis models, we further propose an
Expertness Generator (ExpG) to eliminate the high-frequency components in the
optimization process. Based on the obtained expertness maps, the multi-rater
labels can be fused as a single ground-truth which we dubbed as Diagnosis First
Ground-truth (DiagFirstGT). Experimental results show that by using DiagFirstGT
as ground-truth, OD/OC segmentation networks will predict the masks with
superior glaucoma diagnosis performance.

Training convolutional neural networks (CNNs) with back-propagation (BP) is
time-consuming and resource-intensive particularly in view of the need to visit
the dataset multiple times. In contrast, analytic learning attempts to obtain
the weights in one epoch. However, existing attempts to analytic learning
considered only the multilayer perceptron (MLP). In this article, we propose an
analytic convolutional neural network learning (ACnnL). Theoretically we show
that ACnnL builds a closed-form solution similar to its MLP counterpart, but
differs in their regularization constraints. Consequently, we are able to
answer to a certain extent why CNNs usually generalize better than MLPs from
the implicit regularization point of view. The ACnnL is validated by conducting
classification tasks on several benchmark datasets. It is encouraging that the
ACnnL trains CNNs in a significantly fast manner with reasonably close
prediction accuracies to those using BP. Moreover, our experiments disclose a
unique advantage of ACnnL under the small-sample scenario when training data
are scarce or expensive.

For weakly supervised anomaly detection, most existing work is limited to the
problem of inadequate video representation due to the inability of modeling
long-term contextual information. To solve this, we propose a novel weakly
supervised adaptive graph convolutional network (WAGCN) to model the complex
contextual relationship among video segments. By which, we fully consider the
influence of other video segments on the current one when generating the
anomaly probability score for each segment. Firstly, we combine the temporal
consistency as well as feature similarity of video segments to construct a
global graph, which makes full use of the association information among
spatial-temporal features of anomalous events in videos. Secondly, we propose a
graph learning layer in order to break the limitation of setting topology
manually, which can extract graph adjacency matrix based on data adaptively and
effectively. Extensive experiments on two public datasets (i.e., UCF-Crime
dataset and ShanghaiTech dataset) demonstrate the effectiveness of our approach
which achieves state-of-the-art performance.

Real world recommendation systems influence a constantly growing set of
domains. With deep networks, that now drive such systems, recommendations have
been more relevant to the user's interests and tasks. However, they may not
always be reproducible even if produced by the same system for the same user,
recommendation sequence, request, or query. This problem received almost no
attention in academic publications, but is, in fact, very realistic and
critical in real production systems. We consider reproducibility of real large
scale deep models, whose predictions determine such recommendations. We
demonstrate that the celebrated Rectified Linear Unit (ReLU) activation, used
in deep models, can be a major contributor to irreproducibility. We propose the
use of smooth activations to improve recommendation reproducibility. We
describe a novel family of smooth activations; Smooth ReLU (SmeLU), designed to
improve reproducibility with mathematical simplicity, with potentially cheaper
implementation. SmeLU is a member of a wider family of smooth activations.
While other techniques that improve reproducibility in real systems usually
come at accuracy costs, smooth activations not only improve reproducibility,
but can even give accuracy gains. We report metrics from real systems in which
we were able to productionalize SmeLU with substantial reproducibility gains
and better accuracy-reproducibility trade-offs. These include
click-through-rate (CTR) prediction systems, content, and application
recommendation systems.

Few-shot learning allows machines to classify novel classes using only a few
labeled samples. Recently, few-shot segmentation aiming at semantic
segmentation on low sample data has also seen great interest. In this paper, we
propose a learnable module that can be placed on top of existing segmentation
networks for performing few-shot segmentation. This module, called the
task-adaptive feature transformer (TAFT), linearly transforms task-specific
high-level features to a set of task agnostic features well-suited to
conducting few-shot segmentation. The task-conditioned feature transformation
allows an effective utilization of the semantic information in novel classes to
generate tight segmentation masks. We also propose a semantic enrichment (SE)
module that utilizes a pixel-wise attention module for high-level feature and
an auxiliary loss from an auxiliary segmentation network conducting the
semantic segmentation for all training classes. Experiments on PASCAL-$5^i$ and
COCO-$20^i$ datasets confirm that the added modules successfully extend the
capability of existing segmentators to yield highly competitive few-shot
segmentation performances.

Predicting stochastic spreading processes on complex networks is critical in
epidemic control, opinion propagation, and viral marketing. We focus on the
problem of inferring the time-dependent marginal probabilities of states for
each node which collectively quantifies the spreading results. Dynamic Message
Passing (DMP) has been developed as an efficient inference algorithm for
several spreading models, and it is asymptotically exact on locally tree-like
networks. However, DMP can struggle in diffusion networks with lots of local
loops. We address this limitation by using Graph Neural Networks (GNN) to learn
the dependency amongst messages implicitly. Specifically, we propose a hybrid
model in which the GNN module runs jointly with DMP equations. The GNN module
refines the aggregated messages in DMP iterations by learning from simulation
data. We demonstrate numerically that after training, our model's inference
accuracy substantially outperforms DMP in conditions of various network
structure and dynamics parameters. Moreover, compared to pure data-driven
models, the proposed hybrid model has a better generalization ability for
out-of-training cases, profiting from the explicitly utilized dynamics priors
in the hybrid model. A PyTorch implementation of our model is at
https://github.com/FeiGSSS/NEDMP.

Emerging Autonomous Vehicles (AV) breed great potentials to exploit
data-driven techniques for adaptive and personalized Human-Vehicle
Interactions. However, the lack of high-quality and rich data supports limits
the opportunities to explore the design space of data-driven techniques, and
validate the effectiveness of concrete mechanisms. Our goal is to initialize
the efforts to deliver the building block for exploring data-driven
Human-Vehicle Interaction designs. To this end, we present BROOK dataset, a
multi-modal dataset with facial video records. We first brief our rationales to
build BROOK dataset. Then, we elaborate how to build the current version of
BROOK dataset via a year-long study, and give an overview of the dataset. Next,
we present three example studies using BROOK to justify the applicability of
BROOK dataset. We also identify key learning lessons from building BROOK
dataset, and discuss about how BROOK dataset can foster an extensive amount of
follow-up studies.

As easy-to-use deep learning libraries such as Tensorflow and Pytorch are
popular, it has become convenient to develop machine learning models. Due to
privacy issues with centralized machine learning, recently, federated learning
in the distributed computing framework is attracting attention. The central
server does not collect sensitive and personal data from clients in federated
learning, but it only aggregates the model parameters. Though federated
learning helps protect privacy, it is difficult for machine learning developers
to share the models that they could utilize for different-domain applications.
In this paper, we propose a federated learning model sharing service named
Federated Learning Hub (FLHub). Users can upload, download, and contribute the
model developed by other developers similarly to GitHub. We demonstrate that a
forked model can finish training faster than the existing model and that
learning progressed more quickly for each federated round.

representation learning. Recently, the data augmentation based contrastive
learning method has been extended from images to graphs. However, most prior
works are directly adapted from the models designed for images. Unlike the data
augmentation on images, the data augmentation on graphs is far less intuitive
and much harder to provide high-quality contrastive samples, which are the key
to the performance of contrastive learning models. This leaves much space for
improvement over the existing graph contrastive learning frameworks. In this
work, by introducing an adversarial graph view and an information regularizer,
we propose a simple but effective method, Adversarial Graph Contrastive
Learning (ARIEL), to extract informative contrastive samples within a
reasonable constraint. It consistently outperforms the current graph
contrastive learning methods in the node classification task over various
real-world datasets and further improves the robustness of graph contrastive
learning.

Modern deep neural networks (DNNs) are vulnerable to adversarial attacks and
adversarial training has been shown to be a promising method for improving the
adversarial robustness of DNNs. Pruning methods have been considered in
adversarial context to reduce model capacity and improve adversarial robustness
simultaneously in training. Existing adversarial pruning methods generally
mimic the classical pruning methods for natural training, which follow the
three-stage 'training-pruning-fine-tuning' pipelines. We observe that such
pruning methods do not necessarily preserve the dynamics of dense networks,
making it potentially hard to be fine-tuned to compensate the accuracy
degradation in pruning. Based on recent works of \textit{Neural Tangent Kernel}
(NTK), we systematically study the dynamics of adversarial training and prove
the existence of trainable sparse sub-network at initialization which can be
trained to be adversarial robust from scratch. This theoretically verifies the
\textit{lottery ticket hypothesis} in adversarial context and we refer such
sub-network structure as \textit{Adversarial Winning Ticket} (AWT). We also
show empirical evidences that AWT preserves the dynamics of adversarial
training and achieve equal performance as dense adversarial training.

In the field of domain adaptation, a trade-off exists between the model
performance and the number of target domain annotations. Active learning,
maximizing model performance with few informative labeled data, comes in handy
for such a scenario. In this work, we present D2ADA, a general active domain
adaptation framework for semantic segmentation. To adapt the model to the
target domain with minimum queried labels, we propose acquiring labels of the
samples with high probability density in the target domain yet with low
probability density in the source domain, complementary to the existing source
domain labeled data. To further facilitate labeling efficiency, we design a
dynamic scheduling policy to adjust the labeling budgets between domain
exploration and model uncertainty over time. Extensive experiments show that
our method outperforms existing active learning and domain adaptation baselines
on two benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than
5% target domain annotations, our method reaches comparable results with that
of full supervision. Our code is publicly available at
https://github.com/tsunghan-wu/D2ADA.

Low rank matrix approximation is a popular topic in machine learning. In this
paper, we propose a new algorithm for this topic by minimizing the
least-squares estimation over the Riemannian manifold of fixed-rank matrices.
The algorithm is an adaptation of classical gradient descent within the
framework of optimization on manifolds. In particular, we reformulate an
unconstrained optimization problem on a low-rank manifold into a differential
dynamic system. We develop a splitting numerical integration method by applying
a splitting integration scheme to the dynamic system. We conduct the
convergence analysis of our splitting numerical integration algorithm. It can
be guaranteed that the error between the recovered matrix and true result is
monotonically decreasing in the Frobenius norm. Moreover, our splitting
numerical integration can be adapted into matrix completion scenarios.
Experimental results show that our approach has good scalability for
large-scale problems with satisfactory accuracy

Research in cognitive science has provided extensive evidence of human
cognitive ability in performing physical reasoning of objects from noisy
perceptual inputs. Such a cognitive ability is commonly known as intuitive
physics. With advancements in deep learning, there is an increasing interest in
building intelligent systems that are capable of performing physical reasoning
from a given scene for the purpose of building better AI systems. As a result,
many contemporary approaches in modelling intuitive physics for machine
cognition have been inspired by literature from cognitive science. Despite the
wide range of work in physical reasoning for machine cognition, there is a
scarcity of reviews that organize and group these deep learning approaches.
Especially at the intersection of intuitive physics and artificial
intelligence, there is a need to make sense of the diverse range of ideas and
approaches. Therefore, this paper presents a comprehensive survey of recent
advances and techniques in intuitive physics-inspired deep learning approaches
for physical reasoning. The survey will first categorize existing deep learning
approaches into three facets of physical reasoning before organizing them into
three general technical approaches and propose six categorical tasks of the
field. Finally, we highlight the challenges of the current field and present
some future research directions.

Bayesian experimental design (BED) has been used as a method for conducting
efficient experiments based on Bayesian inference. The existing methods,
however, mostly focus on maximizing the expected information gain (EIG); the
cost of experiments and sample efficiency are often not taken into account. In
order to address this issue and enhance practical applicability of BED, we
provide a new approach Sequential Experimental Design via Reinforcement
Learning to construct BED in a sequential manner by applying reinforcement
learning in this paper. Here, reinforcement learning is a branch of machine
learning in which an agent learns a policy to maximize its reward by
interacting with the environment. The characteristics of interacting with the
environment are similar to the sequential experiment, and reinforcement
learning is indeed a method that excels at sequential decision making.
  By proposing a new real-world-oriented experimental environment, our approach
aims to maximize the EIG while keeping the cost of experiments and sample
efficiency in mind simultaneously. We conduct numerical experiments for three
different examples. It is confirmed that our method outperforms the existing
methods in various indices such as the EIG and sampling efficiency, indicating
that our proposed method and experimental environment can make a significant
contribution to application of BED to the real world.

Recent advances in computer architecture and networking opened the
opportunity for parallelizing the clustering algorithms. This
divide-and-conquer strategy often results in better results to centralized
clustering with a much-improved time performance. This paper reviews key
parallel clustering and provides insight into their strategy. The review brings
together disparate attempts in parallel clustering to provide a comprehensive
account of advances in this emerging field

The code intelligence (CI) models are often black-box and do not offer any
insights on the input features that they learn for making correct predictions.
This opacity may lead to distrust in their prediction and hamper their wider
adoption in safety-critical applications. In recent, the program reduction
technique is widely being used to identify key input features in order to
explain the prediction of CI models. The approach removes irrelevant parts from
an input program and keeps the minimal snippets that a CI model needs to
maintain its prediction. However, the state-of-the-art approaches mainly use a
syntax-unaware program reduction technique that does not follow the syntax of
programs, which adds significant overhead to the reduction of input programs
and explainability of models. In this paper, we apply a syntax-guided program
reduction technique that follows the syntax of input programs during reduction.
Our experiments on multiple models across different types of input programs
show that the syntax-guided program reduction technique significantly
outperforms the syntax-unaware program reduction technique in reducing the size
of input programs. Extracting key input features from reduced programs reveals
that the syntax-guided reduced programs contain more label-specific key input
features and are more vulnerable to adversarial transformation when renaming
the key tokens in programs. These label-specific key input features may help to
understand the reasoning of models' prediction from different perspectives and
increase the trustworthiness to correct classification given by CI models.

Alleviating the delayed feedback problem is of crucial importance for the
conversion rate(CVR) prediction in online advertising. Previous delayed
feedback modeling methods using an observation window to balance the trade-off
between waiting for accurate labels and consuming fresh feedback. Moreover, to
estimate CVR upon the freshly observed but biased distribution with fake
negatives, the importance sampling is widely used to reduce the distribution
bias. While effective, we argue that previous approaches falsely treat fake
negative samples as real negative during the importance weighting and have not
fully utilized the observed positive samples, leading to suboptimal
  In this work, we propose a new method, DElayed Feedback modeling with
UnbiaSed Estimation, (DEFUSE), which aim to respectively correct the importance
weights of the immediate positive, the fake negative, the real negative, and
the delay positive samples at finer granularity. Specifically, we propose a
two-step optimization approach that first infers the probability of fake
negatives among observed negatives before applying importance sampling. To
fully exploit the ground-truth immediate positives from the observed
distribution, we further develop a bi-distribution modeling framework to
jointly model the unbiased immediate positives and the biased delay
conversions. Experimental results on both public and our industrial datasets
validate the superiority of DEFUSE. Codes are available at
https://github.com/ychen216/DEFUSE.git.

Visual place recognition (VPR) is a key issue for robotics and autonomous
systems. For the trade-off between time and performance, most of methods use
the coarse-to-fine hierarchical architecture, which consists of retrieving
top-N candidates using global features, and re-ranking top-N with local
features. However, since the two types of features are usually processed
independently, re-ranking may harm global retrieval, termed re-ranking
confusion. Moreover, re-ranking is limited by global retrieval. In this paper,
we propose a tightly coupled learning (TCL) strategy to train triplet models.
Different from original triplet learning (OTL) strategy, it combines global and
local descriptors for joint optimization. In addition, a bidirectional search
dynamic time warping (BS-DTW) algorithm is also proposed to mine locally
spatial information tailored to VPR in re-ranking. The experimental results on
public benchmarks show that the models using TCL outperform the models using
OTL, and TCL can be used as a general strategy to improve performance for
weakly supervised ranking tasks. Further, our lightweight unified model is
better than several state-of-the-art methods and has over an order of magnitude
of computational efficiency to meet the real-time requirements of robots.

Differentially private data release receives rising attention in machine
learning community. Recently, an algorithm called DPMix is proposed to release
high-dimensional data after a random mixup of degree $m$ with differential
privacy. However, limited theoretical justifications are given about the "sweet
spot $m$" phenomenon, and directly applying DPMix to image data suffers from
severe loss of utility. In this paper, we revisit random mixup with recent
progress on differential privacy. In theory, equipped with Gaussian
Differential Privacy with Poisson subsampling, a tight closed form analysis is
presented that enables a quantitative characterization of optimal mixup $m^*$
based on linear regression models. In practice, mixup of features, extracted by
handcraft or pre-trained neural networks such as self-supervised learning
without labels, is adopted to significantly boost the performance with privacy
protection. We name it as Differentially Private Feature Mixup (DPFMix).
Experiments on MNIST, CIFAR10/100 are conducted to demonstrate its remarkable
utility improvement and protection against attacks.

Contrastive learning (CL), a self-supervised learning approach, can
effectively learn visual representations from unlabeled data. However, CL
requires learning on vast quantities of diverse data to achieve good
performance, without which the performance of CL will greatly degrade. To
tackle this problem, we propose a framework with two approaches to improve the
data efficiency of CL training by generating beneficial samples and joint
learning. The first approach generates hard samples for the main model. The
generator is jointly learned with the main model to dynamically customize hard
samples based on the training state of the main model. With the progressively
growing knowledge of the main model, the generated samples also become harder
to constantly encourage the main model to learn better representations.
Besides, a pair of data generators are proposed to generate similar but
distinct samples as positive pairs. In joint learning, the hardness of a
positive pair is progressively increased by decreasing their similarity. In
this way, the main model learns to cluster hard positives by pulling the
representations of similar yet distinct samples together, by which the
representations of similar samples are well-clustered and better
representations can be learned. Comprehensive experiments show superior
accuracy and data efficiency of the proposed methods over the state-of-the-art
on multiple datasets. For example, about 5% accuracy improvement on
ImageNet-100 and CIFAR-10, and more than 6% accuracy improvement on CIFAR-100
are achieved for linear classification. Besides, up to 2x data efficiency for
linear classification and up to 5x data efficiency for transfer learning are
achieved.

The Population-based HIV Impact Assessment (PHIA) is an ongoing project that
conducts nationally representative HIV-focused surveys for measuring national
and regional progress toward UNAIDS' 90-90-90 targets, the primary strategy to
end the HIV epidemic. We believe the PHIA survey offers a unique opportunity to
better understand the key factors that drive the HIV epidemics in the most
affected countries in sub-Saharan Africa. In this article, we propose a novel
causal structural learning algorithm to discover important covariates and
potential causal pathways for 90-90-90 targets. Existing constrained-based
causal structural learning algorithms are quite aggressive in edge removal. The
proposed algorithm preserves more information about important features and
potential causal pathways. It is applied to the Malawi PHIA (MPHIA) data set
and leads to interesting results. For example, it discovers age and condom
usage to be important for female HIV awareness; the number of sexual partners
to be important for male HIV awareness; and knowing the travel time to HIV care
facilities leads to a higher chance of being treated for both females and
males. We further compare and validate the proposed algorithm using BIC and
using Monte Carlo simulations, and show that the proposed algorithm achieves
improvement in true positive rates in important feature discovery over existing

Min-max optimization problems arise in several key machine learning setups,
including adversarial learning and generative modeling. In their general form,
in absence of convexity/concavity assumptions, finding pure equilibria of the
underlying two-player zero-sum game is computationally hard [Daskalakis et al.,
2021]. In this work we focus instead in finding mixed equilibria, and consider
the associated lifted problem in the space of probability measures. By adding
entropic regularization, our main result establishes global convergence towards
the global equilibrium by using simultaneous gradient ascent-descent with
respect to the Wasserstein metric -- a dynamics that admits efficient particle
discretization in high-dimensions, as opposed to entropic mirror descent. We
complement this positive result with a related entropy-regularized loss which
is not bilinear but still convex-concave in the Wasserstein geometry, and for
which simultaneous dynamics do not converge yet timescale separation does.
Taken together, these results showcase the benign geometry of bilinear games in
the space of measures, enabling particle dynamics with global qualitative
convergence guarantees.

Quantization of deep neural networks (DNN) has been proven effective for
compressing and accelerating DNN models. Data-free quantization (DFQ) is a
promising approach without the original datasets under privacy-sensitive and
confidential scenarios. However, current DFQ solutions degrade accuracy, need
synthetic data to calibrate networks, and are time-consuming and costly. This
paper proposes an on-the-fly DFQ framework with sub-second quantization time,
called SQuant, which can quantize networks on inference-only devices with low
computation and memory requirements. With the theoretical analysis of the
second-order information of DNN task loss, we decompose and approximate the
Hessian-based optimization objective into three diagonal sub-items, which have
different areas corresponding to three dimensions of weight tensor:
element-wise, kernel-wise, and output channel-wise. Then, we progressively
compose sub-items and propose a novel data-free optimization objective in the
discrete domain, minimizing Constrained Absolute Sum of Error (or CASE in
short), which surprisingly does not need any dataset and is even not aware of
network architecture. We also design an efficient algorithm without
back-propagation to further reduce the computation complexity of the objective
solver. Finally, without fine-tuning and synthetic datasets, SQuant accelerates
the data-free quantization process to a sub-second level with >30% accuracy
improvement over the existing data-free post-training quantization works, with
the evaluated models under 4-bit quantization. We have open-sourced the SQuant
framework at https://github.com/clevercool/SQuant.

This paper proposes a class of neural ordinary differential equations
parametrized by provably input-to-state stable continuous-time recurrent neural
networks. The model dynamics are defined by construction to be input-to-state
stable (ISS) with respect to an ISS-Lyapunov function that is learned jointly
with the dynamics. We use the proposed method to learn cheap-to-simulate
behavioral models for electronic circuits that can accurately reproduce the
behavior of various digital and analog circuits when simulated by a commercial
circuit simulator, even when interconnected with circuit components not
encountered during training. We also demonstrate the feasibility of learning
ISS-preserving perturbations to the dynamics for modeling degradation effects
due to circuit aging.

Deployment efficiency is an important criterion for many real-world
applications of reinforcement learning (RL). Despite the community's increasing
interest, there lacks a formal theoretical formulation for the problem. In this
paper, we propose such a formulation for deployment-efficient RL (DE-RL) from
an "optimization with constraints" perspective: we are interested in exploring
an MDP and obtaining a near-optimal policy within minimal \emph{deployment
complexity}, whereas in each deployment the policy can sample a large batch of
data. Using finite-horizon linear MDPs as a concrete structural model, we
reveal the fundamental limit in achieving deployment efficiency by establishing
information-theoretic lower bounds, and provide algorithms that achieve the
optimal deployment efficiency. Moreover, our formulation for DE-RL is flexible
and can serve as a building block for other practically relevant settings; we
give "Safe DE-RL" and "Sample-Efficient DE-RL" as two examples, which may be
worth future investigation.

Deep learning models have been deployed in an increasing number of edge and
mobile devices to provide healthcare. These models rely on training with a
tremendous amount of labeled data to achieve high accuracy. However, for
medical applications such as dermatological disease diagnosis, the private data
collected by mobile dermatology assistants exist on distributed mobile devices
of patients, and each device only has a limited amount of data. Directly
learning from limited data greatly deteriorates the performance of learned
models. Federated learning (FL) can train models by using data distributed on
devices while keeping the data local for privacy. Existing works on FL assume
all the data have ground-truth labels. However, medical data often comes
without any accompanying labels since labeling requires expertise and results
in prohibitively high labor costs. The recently developed self-supervised
learning approach, contrastive learning (CL), can leverage the unlabeled data
to pre-train a model, after which the model is fine-tuned on limited labeled
data for dermatological disease diagnosis. However, simply combining CL with FL
as federated contrastive learning (FCL) will result in ineffective learning
since CL requires diverse data for learning but each device only has limited
data. In this work, we propose an on-device FCL framework for dermatological
disease diagnosis with limited labels. Features are shared in the FCL
pre-training process to provide diverse and accurate contrastive information.
After that, the pre-trained model is fine-tuned with local labeled data
independently on each device or collaboratively with supervised federated
learning on all devices. Experiments on dermatological disease datasets show
that the proposed framework effectively improves the recall and precision of
dermatological disease diagnosis compared with state-of-the-art methods.

Convolutions are one of the most relevant operations in artificial
intelligence (AI) systems. High computational complexity scaling poses
significant challenges, especially in fast-responding network-edge AI
applications. Fortunately, the convolution theorem can be executed on-the-fly
in the optical domain via a joint transform correlator (JTC) offering to
fundamentally reduce the computational complexity. Nonetheless, the iterative
two-step process of a classical JTC renders them unpractical. Here we introduce
a novel implementation of an optical convolution-processor capable of near-zero
latency by utilizing all-optical nonlinearity inside a JTC, thus minimizing
electronic signal or conversion delay. Fundamentally we show how this nonlinear
auto-correlator enables reducing the high $O(n^4)$ scaling complexity of
processing two-dimensional data to only $O(n^2)$. Moreover, this optical JTC
processes millions of channels in time-parallel, ideal for large-matrix machine
learning tasks. Exemplary utilizing the nonlinear process of four-wave mixing,
we show light processing performing a full convolution that is temporally
limited only by geometric features of the lens and the nonlinear material's
response time. We further discuss that the all-optical nonlinearity exhibits
gain in excess of $>10^{3}$ when enhanced by slow-light effects such as
epsilon-near-zero. Such novel implementation for a machine learning accelerator
featuring low-latency and non-iterative massive data parallelism enabled by
fundamental reduced complexity scaling bears significant promise for
network-edge, and cloud AI systems.

Cooperative trajectory planning methods for automated vehicles can solve
traffic scenarios that require a high degree of cooperation between traffic
participants. However, for cooperative systems to integrate into human-centered
traffic, the automated systems must behave human-like so that humans can
anticipate the system's decisions. While Reinforcement Learning has made
remarkable progress in solving the decision-making part, it is non-trivial to
parameterize a reward model that yields predictable actions. This work employs
feature-based Maximum Entropy Inverse Reinforcement Learning combined with
Monte Carlo Tree Search to learn reward models that maximize the likelihood of
recorded multi-agent cooperative expert trajectories. The evaluation
demonstrates that the approach can recover a reasonable reward model that
mimics the expert and performs similarly to a manually tuned baseline reward
model.

We develop the first fast spectral algorithm to decompose a random
third-order tensor over $\mathbb{R}^d$ of rank up to
$O(d^{3/2}/\text{polylog}(d))$. Our algorithm only involves simple linear
algebra operations and can recover all components in time $O(d^{6.05})$ under
the current matrix multiplication time.
  Prior to this work, comparable guarantees could only be achieved via
sum-of-squares [Ma, Shi, Steurer 2016]. In contrast, fast algorithms [Hopkins,
Schramm, Shi, Steurer 2016] could only decompose tensors of rank at most
$O(d^{4/3}/\text{polylog}(d))$.
  Our algorithmic result rests on two key ingredients. A clean lifting of the
third-order tensor to a sixth-order tensor, which can be expressed in the
language of tensor networks. A careful decomposition of the tensor network into
a sequence of rectangular matrix multiplications, which allows us to have a
fast implementation of the algorithm.

The evolution of the future beyond-5G/6G networks towards a service-aware
network is based on network slicing technology. With network slicing,
communication service providers seek to meet all the requirements imposed by
the verticals, including ultra-reliable low-latency communication (URLLC)
services. In addition, the open radio access network (O-RAN) architecture paves
the way for flexible sharing of network resources by introducing more
programmability into the RAN. RAN slicing is an essential part of end-to-end
network slicing since it ensures efficient sharing of communication and
computation resources. However, due to the stringent requirements of URLLC
services and the dynamics of the RAN environment, RAN slicing is challenging.
In this article, we propose a two-level RAN slicing approach based on the O-RAN
architecture to allocate the communication and computation RAN resources among
URLLC end-devices. For each RAN slicing level, we model the resource slicing
problem as a single-agent Markov decision process and design a deep
reinforcement learning algorithm to solve it. Simulation results demonstrate
the efficiency of the proposed approach in meeting the desired quality of
service requirements.

We present the surprising result that randomly initialized neural networks
are good feature extractors in expectation. These random features correspond to
finite-sample realizations of what we call Neural Network Prior Kernel (NNPK),
which is inherently infinite-dimensional. We conduct ablations across multiple
architectures of varying sizes as well as initializations and activation
functions. Our analysis suggests that certain structures that manifest in a
trained model are already present at initialization. Therefore, NNPK may
provide further insight into why neural networks are so effective in learning
such structures.

Radio access network (RAN) slicing is a key technology that enables 5G
network to support heterogeneous requirements of generic services, namely
ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband
(eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to
optimize the performance of URLLC and eMBB services. In a large time-scale, an
SDN controller allocates radio resources to gNodeBs according to the
requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB
allocates its available resources to its end-users and requests, if needed,
additional resources from adjacent gNodeBs. We formulate this problem as a
non-linear binary program and prove its NP-hardness. Next, for each time-scale,
we model the problem as a Markov decision process (MDP), where the large-time
scale is modeled as a single agent MDP whereas the shorter time-scale is
modeled as a multi-agent MDP. We leverage the exponential-weight algorithm for
exploration and exploitation (EXP3) to solve the single-agent MDP of the large
time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the
multi-agent MDP of the short time-scale resource allocation. Extensive
simulations show that our approach is efficient under different network
parameters configuration and it outperforms recent benchmark solutions.

We propose a time-varying graph signal recovery method that estimates the
true graph signal from an observation corrupted by missing values, outliers of
unknown positions, and some random noise. Furthermore, we assume the underlying
graph to be time-varying like the signals, which we integrate into our
formulation for better performance. Conventional studies on time-varying graph
signal recovery have been focusing on online estimation and graph learning
under the assumption that entire dynamic graphs are unavailable. However, there
are many practical situations where the underlying graphs can be observed or
easily generated by simple algorithms like the k-nearest neighbor, especially
when targeting physical sensing data, where the graphs can be defined to
represent spatial correlations. To address such cases, in this paper, we tackle
a dynamic graph Laplacian-based recovery problem on given dynamic graphs. To
solve this, we formulate the recovery problem as a constrained convex
optimization problem to estimate both the time-varying graph signal and the
sparsely modeled outliers simultaneously. We integrate the graph dynamics into
the formulation by exploiting the given dynamic graph. In such a manner, we
succeed in separating the different types of corruption and achieving
state-of-the-art recovery performance in both synthetic and real-world problem
settings. We also conduct an extensive study to compare the contribution of
vertex and temporal domain regularization on the recovery performance.

Although deep learning-based computer-aided diagnosis systems have recently
achieved expert-level performance, developing a robust deep learning model
requires large, high-quality data with manual annotation, which is expensive to
obtain. This situation poses the problem that the chest x-rays collected
annually in hospitals cannot be used due to the lack of manual labeling by
experts, especially in deprived areas. To address this, here we present a novel
deep learning framework that uses knowledge distillation through
self-supervised learning and self-training, which shows that the performance of
the original model trained with a small number of labels can be gradually
improved with more unlabeled data. Experimental results show that the proposed
framework maintains impressive robustness against a real-world environment and
has general applicability to several diagnostic tasks such as tuberculosis,
pneumothorax, and COVID-19. Notably, we demonstrated that our model performs
even better than those trained with the same amount of labeled data. The
proposed framework has a great potential for medical imaging, where plenty of
data is accumulated every year, but ground truth annotations are expensive to
obtain.

Will people eventually learn the value of an asset through observable
information? This paper studies observational learning in a market with
competitive prices. Comparing a market with public signals and a market with
private signals in a sequential trading model, we find that Pairwise
Informativeness (PI) is the sufficient and necessary learning condition for a
market with public signals; and Avery and Zemsky Condition (AZC) is the
sufficient and necessary learning condition for a market with private signals.
Moreover, when the number of states is 2 or 3, PI and AZC are equivalent. And
when the number of states is greater than 3, PI and Monotonic Likelihood Ratio
Property (MLRP) together imply asymptotic learning in the private signal case.

This paper presents a comprehensive review of the design of experiments used
in the surrogate models. In particular, this study demonstrates the necessity
of the design of experiment schemes for the Physics-Informed Neural Network
(PINN), which belongs to the supervised learning class. Many complex partial
differential equations (PDEs) do not have any analytical solution; only
numerical methods are used to solve the equations, which is computationally
expensive. In recent decades, PINN has gained popularity as a replacement for
numerical methods to reduce the computational budget. PINN uses physical
information in the form of differential equations to enhance the performance of
the neural networks. Though it works efficiently, the choice of the design of
experiment scheme is important as the accuracy of the predicted responses using
PINN depends on the training data. In this study, five different PDEs are used
for numerical purposes, i.e., viscous Burger's equation, Shr\"{o}dinger
equation, heat equation, Allen-Cahn equation, and Korteweg-de Vries equation. A
comparative study is performed to establish the necessity of the selection of a
DoE scheme. It is seen that the Hammersley sampling-based PINN performs better
than other DoE sample strategies.

Advances in distributed machine learning can empower future communications
and networking. The emergence of federated learning (FL) has provided an
efficient framework for distributed machine learning, which, however, still
faces many security challenges. Among them, model poisoning attacks have a
significant impact on the security and performance of FL. Given that there have
been many studies focusing on defending against model poisoning attacks, it is
necessary to survey the existing work and provide insights to inspire future
research. In this paper, we first classify defense mechanisms for model
poisoning attacks into two categories: evaluation methods for local model
updates and aggregation methods for the global model. Then, we analyze some of
the existing defense strategies in detail. We also discuss some potential
challenges and future research directions. To the best of our knowledge, we are
the first to survey defense methods for model poisoning attacks in FL.

This paper presents a novel data augmentation technique for text-to-speech
(TTS), that allows to generate new (text, audio) training examples without
requiring any additional data. Our goal is to increase diversity of text
conditionings available during training. This helps to reduce overfitting,
especially in low-resource settings. Our method relies on substituting text and
audio fragments in a way that preserves syntactical correctness. We take
additional measures to ensure that synthesized speech does not contain
artifacts caused by combining inconsistent audio samples. The perceptual
evaluations show that our method improves speech quality over a number of
datasets, speakers, and TTS architectures. We also demonstrate that it greatly
improves robustness of attention-based TTS models.

In this paper we present SA-CNN, a hierarchical and lightweight
self-attention based encoding and decoding architecture for representation
learning of point cloud data. The proposed SA-CNN introduces convolution and
transposed convolution stacks to capture and generate contextual information
among unordered 3D points. Following conventional hierarchical pipeline, the
encoding process extracts feature in local-to-global manner, while the decoding
process generates feature and point cloud in coarse-to-fine, multi-resolution
stages. We demonstrate that SA-CNN is capable of a wide range of applications,
namely classification, part segmentation, reconstruction, shape retrieval, and
unsupervised classification. While achieving the state-of-the-art or comparable
performance in the benchmarks, SA-CNN maintains its model complexity several
order of magnitude lower than the others. In term of qualitative results, we
visualize the multi-stage point cloud reconstructions and latent walks on rigid
objects as well as deformable non-rigid human and robot models.

The task of audio-visual sound source localization has been well studied
under constrained scenes, where the audio recordings are clean. However, in
real-world scenarios, audios are usually contaminated by off-screen sound and
background noise. They will interfere with the procedure of identifying desired
sources and building visual-sound connections, making previous studies
non-applicable. In this work, we propose the Interference Eraser (IEr)
framework, which tackles the problem of audio-visual sound source localization
in the wild. The key idea is to eliminate the interference by redefining and
carving discriminative audio representations. Specifically, we observe that the
previous practice of learning only a single audio representation is
insufficient due to the additive nature of audio signals. We thus extend the
audio representation with our Audio-Instance-Identifier module, which clearly
distinguishes sounding instances when audio signals of different volumes are
unevenly mixed. Then we erase the influence of the audible but off-screen
sounds and the silent but visible objects by a Cross-modal Referrer module with
cross-modality distillation. Quantitative and qualitative evaluations
demonstrate that our proposed framework achieves superior results on sound
localization tasks, especially under real-world scenarios. Code is available at
https://github.com/alvinliu0/Visual-Sound-Localization-in-the-Wild.

The recent mean field game (MFG) formalism has enabled the application of
inverse reinforcement learning (IRL) methods in large-scale multi-agent
systems, with the goal of inferring reward signals that can explain
demonstrated behaviours of large populations. The existing IRL methods for MFGs
are built upon reducing an MFG to a Markov decision process (MDP) defined on
the collective behaviours and average rewards of the population. However, this
paper reveals that the reduction from MFG to MDP holds only for the fully
cooperative setting. This limitation invalidates existing IRL methods on MFGs
with non-cooperative environments. To measure more general behaviours in large
populations, we study the use of individual behaviours to infer ground-truth
reward functions for MFGs. We propose Mean Field IRL (MFIRL), the first
dedicated IRL framework for MFGs that can handle both cooperative and
non-cooperative environments. Based on this theoretically justified framework,
we develop a practical algorithm effective for MFGs with unknown dynamics. We
evaluate MFIRL on both cooperative and mixed cooperative-competitive scenarios
with many agents. Results demonstrate that MFIRL excels in reward recovery,
sample efficiency and robustness in the face of changing dynamics.

Many applications, such as system identification, classification of time
series, direct and inverse problems in partial differential equations, and
uncertainty quantification lead to the question of approximation of a
non-linear operator between metric spaces $\mathfrak{X}$ and $\mathfrak{Y}$. We
study the problem of determining the degree of approximation of a such
operators on a compact subset $K_\mathfrak{X}\subset \mathfrak{X}$ using a
finite amount of information. If $\mathcal{F}: K_\mathfrak{X}\to
K_\mathfrak{Y}$, a well established strategy to approximate $\mathcal{F}(F)$
for some $F\in K_\mathfrak{X}$ is to encode $F$ (respectively,
$\mathcal{F}(F)$) in terms of a finite number $d$ (repectively $m$) of real
numbers. Together with appropriate reconstruction algorithms (decoders), the
problem reduces to the approximation of $m$ functions on a compact subset of a
high dimensional Euclidean space $\mathbb{R}^d$, equivalently, the unit sphere
$\mathbb{S}^d$ embedded in $\mathbb{R}^{d+1}$. The problem is challenging
because $d$, $m$, as well as the complexity of the approximation on
$\mathbb{S}^d$ are all large, and it is necessary to estimate the accuracy
keeping track of the inter-dependence of all the approximations involved. In
this paper, we establish constructive methods to do this efficiently; i.e.,
with the constants involved in the estimates on the approximation on
$\\mathbb{S}^d$ being $\mathcal{O}(d^{1/6})$. We study different smoothness
classes for the operators, and also propose a method for approximation of
$\mathcal{F}(F)$ using only information in a small neighborhood of $F$,
resulting in an effective reduction in the number of parameters involved. To
further mitigate the problem of large number of parameters, we propose
prefabricated networks, resulting in a substantially smaller number of
effective parameters.

This article proposes Convolutional Neural Network-based Auto Encoder
(CNN-AE) to predict location-dependent rate and coverage probability of a
network from its topology. We train the CNN utilising BS location data of
India, Brazil, Germany, and the USA and compare its performance with stochastic
geometry (SG) based analytical models. In comparison to the best-fitted
SG-based model, CNN-AE improves the coverage and rate prediction errors by a
margin of as large as $40\%$ and $25\%$ respectively. As an application, we
propose a low complexity, provably convergent algorithm that, using trained
CNN-AE, can compute locations of new BSs that need to be deployed in a network
in order to satisfy pre-defined spatially heterogeneous performance goals.

Neural scaling laws define a predictable relationship between a model's
parameter count and its performance after training in the form of a power law.
However, most research to date has not explicitly investigated whether scaling
laws can be used to accelerate model development. In this work, we perform such
an empirical investigation across a wide range of language understanding tasks,
starting from models with as few as 10K parameters, and evaluate downstream
performance across 9 language understanding tasks. We find that scaling laws
emerge at finetuning time in some NLP tasks, and that they can also be
exploited for debugging convergence when training large models. Moreover, for
tasks where scaling laws exist, they can be used to predict the performance of
larger models, which enables effective model selection. However, revealing
scaling laws requires careful hyperparameter tuning and multiple runs for the
purpose of uncertainty estimation, which incurs additional overhead, partially
offsetting the computational benefits.

We study the proximal sampler of Lee, Shen, and Tian (2021) and obtain new
convergence guarantees under weaker assumptions than strong log-concavity:
namely, our results hold for (1) weakly log-concave targets, and (2) targets
satisfying isoperimetric assumptions which allow for non-log-concavity. We
demonstrate our results by obtaining new state-of-the-art sampling guarantees
for several classes of target distributions. We also strengthen the connection
between the proximal sampler and the proximal method in optimization by
interpreting the proximal sampler as an entropically regularized Wasserstein
proximal method, and the proximal point method as the limit of the proximal
sampler with vanishing noise.

We study the problem of reinforcement learning (RL) with low (policy)
switching cost - a problem well-motivated by real-life RL applications in which
deployments of new policies are costly and the number of policy updates must be
low. In this paper, we propose a new algorithm based on stage-wise exploration
and adaptive policy elimination that achieves a regret of
$\widetilde{O}(\sqrt{H^4S^2AT})$ while requiring a switching cost of $O(HSA
\log\log T)$. This is an exponential improvement over the best-known switching
cost $O(H^2SA\log T)$ among existing methods with
$\widetilde{O}(\mathrm{poly}(H,S,A)\sqrt{T})$ regret. In the above, $S,A$
denotes the number of states and actions in an $H$-horizon episodic Markov
Decision Process model with unknown transitions, and $T$ is the number of
steps. As a byproduct of our new techniques, we also derive a reward-free
exploration algorithm with a switching cost of $O(HSA)$. Furthermore, we prove
a pair of information-theoretical lower bounds which say that (1) Any no-regret
algorithm must have a switching cost of $\Omega(HSA)$; (2) Any
$\widetilde{O}(\sqrt{T})$ regret algorithm must incur a switching cost of
$\Omega(HSA\log\log T)$. Both our algorithms are thus optimal in their
switching costs.

Using data from cardiovascular surgery patients with long and highly variable
post-surgical lengths of stay (LOS), we develop a model to reduce recovery unit
congestion. We estimate LOS using a variety of machine learning models,
schedule procedures with a variety of online optimization models, and estimate
performance with simulation. The machine learning models achieved only modest
LOS prediction accuracy, despite access to a very rich set of patient
characteristics. Compared to the current paper-based system used in the
hospital, most optimization models failed to reduce congestion without
increasing wait times for surgery. A conservative stochastic optimization with
sufficient sampling to capture the long tail of the LOS distribution
outperformed the current manual process. These results highlight the perils of
using oversimplified distributional models of patient length of stay for
scheduling procedures and the importance of using stochastic optimization
well-suited to dealing with long-tailed behavior.

The backdoor or Trojan attack is a severe threat to deep neural networks
(DNNs). Researchers find that DNNs trained on benign data and settings can also
learn backdoor behaviors, which is known as the natural backdoor. Existing
works on anti-backdoor learning are based on weak observations that the
backdoor and benign behaviors can differentiate during training. An adaptive
attack with slow poisoning can bypass such defenses. Moreover, these methods
cannot defend natural backdoors. We found the fundamental differences between
backdoor-related neurons and benign neurons: backdoor-related neurons form a
hyperplane as the classification surface across input domains of all affected
labels. By further analyzing the training process and model architectures, we
found that piece-wise linear functions cause this hyperplane surface. In this
paper, we design a novel training method that forces the training to avoid
generating such hyperplanes and thus remove the injected backdoors. Our
extensive experiments on five datasets against five state-of-the-art attacks
and also benign training show that our method can outperform existing
state-of-the-art defenses. On average, the ASR (attack success rate) of the
models trained with NONE is 54.83 times lower than undefended models under
standard poisoning backdoor attack and 1.75 times lower under the natural
backdoor attack. Our code is available at
https://github.com/RU-System-Software-and-Security/NONE.

Recently, saddle point problems have received much attention due to their
powerful modeling capability for a lot of problems from diverse domains.
Applications of these problems occur in many applied areas, such as robust
optimization, distributed optimization, game theory, and many applications in
machine learning such as empirical risk minimization and generative adversarial
networks training. Therefore, many researchers have actively worked on
developing numerical methods for solving saddle point problems in many
different settings. This paper is devoted to developing a numerical method for
solving saddle point problems in the non-convex uniformly-concave setting. We
study a general class of saddle point problems with composite structure and
H\"older-continuous higher-order derivatives. To solve the problem under
consideration, we propose an approach in which we reduce the problem to a
combination of two auxiliary optimization problems separately for each group of
variables, outer minimization problem w.r.t. primal variables, and inner
maximization problem w.r.t the dual variables. For solving the outer
minimization problem, we use the \textit{Adaptive Gradient Method}, which is
applicable for non-convex problems and also works with an inexact oracle that
is generated by approximately solving the inner problem. For solving the inner
maximization problem, we use the \textit{Restarted Unified Acceleration
Framework}, which is a framework that unifies the high-order acceleration
methods for minimizing a convex function that has H\"older-continuous
higher-order derivatives. Separate complexity bounds are provided for the
number of calls to the first-order oracles for the outer minimization problem
and higher-order oracles for the inner maximization problem. Moreover, the
complexity of the whole proposed approach is then estimated.

We consider the broadcast problem in synchronous radio broadcast models with
collision detection. One node of the network is given a message that must be
learned by all nodes in the network. We provide a deterministic algorithm that
works on the beeping model, which is a restricted version of the radio
broadcast model with collision detection. This algorithm improves on the round
complexity of previous algorithms. We prove an exactly matching lower bound in
the radio broadcast model with collision detection. This shows that the extra
power provided by the radio broadcast model with collision detection does not
help improve the round complexity.

Predictive risk scores are increasingly used to guide clinical or other
interventions in complex settings, particularly healthcare. Directly updating a
risk score used to guide interventions leads to biased risk estimates. We
propose updating using a `holdout set' -- a subset of the population that does
not receive risk-score-guided interventions -- to prevent this. Since samples
in the holdout set do not benefit from risk predictions, its size must trade
off performance of the updated risk score whilst minimising the number of held
out samples. We prove that this approach outperforms simple alternatives, and
by defining a general loss function describe conditions under which an optimal
holdout size (OHS) can be readily identified. We introduce parametric and
semi-parametric algorithms for OHS estimation and demonstrate their use on a
recent risk score for pre-eclampsia. Based on these results, we argue that a
holdout set is a safe, viable and easily implemented means to safely update
predictive risk scores.

Machine learning and computer vision techniques have influenced many fields
including the biomedical one. The aim of this paper is to investigate the
important concept of schedulers in manipulating the learning rate (LR), for the
liver segmentation task, throughout the training process, focusing on the newly
devised OneCycleLR against the ReduceLRonPlateau. A dataset, published in 2018
and produced by the Medical Segmentation Decathlon Challenge organizers, called
Task 8 Hepatic Vessel (MSDC-T8) has been used for testing and validation. The
reported results that have the same number of maximum epochs (75), and are the
average of 5-fold cross-validation, indicate that ReduceLRonPlateau converges
faster while maintaining a similar or even better loss score on the validation
set when compared to OneCycleLR. The epoch at which the peak LR occurs perhaps
should be made early for the OneCycleLR such that the super-convergence feature
can be observed. Moreover, the overall results outperform the state-of-the-art
results from the researchers who published the liver masks for this dataset. To
conclude, both schedulers are suitable for medical segmentation challenges,
especially the MSDC-T8 dataset, and can be used confidently in rapidly
converging the validation loss with a minimal number of epochs.

The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing
threat to humans worldwide, creating a health crisis that infected millions of
lives, as well as devastating the global economy. Deep learning (DL) techniques
have proved helpful in analysis and delineation of infectious regions in
radiological images in a timely manner. This paper makes an in-depth survey of
DL techniques and draws a taxonomy based on diagnostic strategies and learning
approaches. DL techniques are systematically categorized into classification,
segmentation, and multi-stage approaches for COVID-19 diagnosis at image and
region level analysis. Each category includes pre-trained and custom-made
Convolutional Neural Network architectures for detecting COVID-19 infection in
radiographic imaging modalities; X-Ray, and Computer Tomography (CT).
Furthermore, a discussion is made on challenges in developing diagnostic
techniques such as cross-platform interoperability and examining imaging
modality. Similarly, a review of the various methodologies and performance
measures used in these techniques is also presented. This survey provides an
insight into the promising areas of research in DL for analyzing radiographic
images, and further accelerates the research in designing customized DL based
diagnostic tools for effectively dealing with new variants of COVID-19 and
emerging challenges.

Individual user profiles and interaction histories play a significant role in
providing customized experiences in real-world applications such as chatbots,
social media, retail, and education. Adaptive user representation learning by
utilizing user personalized information has become increasingly challenging due
to ever-growing history data. In this work, we propose an incremental user
embedding modeling approach, in which embeddings of user's recent interaction
histories are dynamically integrated into the accumulated history vectors via a
transformer encoder. This modeling paradigm allows us to create generalized
user representations in a consecutive manner and also alleviate the challenges
of data management. We demonstrate the effectiveness of this approach by
applying it to a personalized multi-class classification task based on the
Reddit dataset, and achieve 9% and 30% relative improvement on prediction
accuracy over a baseline system for two experiment settings through appropriate
comment history encoding and task modeling.

In cone-beam X-ray transmission imaging, due to the divergence of X-rays,
imaged structures with different depths have different magnification factors on
an X-ray detector, which results in perspective deformation. Perspective
deformation causes difficulty in direct, accurate geometric assessments of
anatomical structures. In this work, to reduce perspective deformation in X-ray
images acquired from regular cone-beam computed tomography (CBCT) systems, we
investigate on learning perspective deformation, i.e., converting perspective
projections into orthogonal projections. Directly converting a single
perspective projection image into an orthogonal projection image is extremely
challenging due to the lack of depth information. Therefore, we propose to
utilize one additional perspective projection, a complementary (180-degree) or
orthogonal (90-degree) view, to provide a certain degree of depth information.
Furthermore, learning perspective deformation in different spatial domains is
investigated. Our proposed method is evaluated on numerical spherical bead
phantoms as well as patients' chest and head X-ray data. The experiments on
numerical bead phantom data demonstrate that learning perspective deformation
in polar coordinates has significant advantages over learning in Cartesian
coordinates, as root-mean-square error (RMSE) decreases from 5.31 to 1.40,
while learning in log-polar coordinates has no further considerable improvement
(RMSE = 1.85). In addition, using a complementary view (RMSE = 1.40) is better
than an orthogonal view (RMSE = 3.87). The experiments on patients' chest and
head data demonstrate that learning perspective deformation using dual
complementary views is also applicable in anatomical X-ray data, allowing
accurate cardiothoracic ratio measurements in chest X-ray images and
cephalometric analysis in synthetic cephalograms from cone-beam X-ray
projections.

We present EXE-GAN, a novel exemplar-guided facial inpainting framework using
generative adversarial networks. Our approach can not only preserve the quality
of the input facial image but also complete the image with exemplar-like facial
attributes. We achieve this by simultaneously leveraging the global style of
the input image, the stochastic style generated from the random latent code,
and the exemplar style of exemplar image. We introduce a novel attribute
similarity metric to encourage networks to learn the style of facial attributes
from the exemplar in a self-supervised way. To guarantee the natural transition
across the boundaries of inpainted regions, we introduce a novel spatial
variant gradient backpropagation technique to adjust the loss gradients based
on the spatial location. Extensive evaluations and practical applications on
public CelebA-HQ and FFHQ datasets validate the superiority of EXE-GAN in terms
of the visual quality in facial inpainting.

Most approaches for goal recognition rely on specifications of the possible
dynamics of the actor in the environment when pursuing a goal. These
specifications suffer from two key issues. First, encoding these dynamics
requires careful design by a domain expert, which is often not robust to noise
at recognition time. Second, existing approaches often need costly real-time
computations to reason about the likelihood of each potential goal. In this
paper, we develop a framework that combines model-free reinforcement learning
and goal recognition to alleviate the need for careful, manual domain design,
and the need for costly online executions. This framework consists of two main
stages: Offline learning of policies or utility functions for each potential
goal, and online inference. We provide a first instance of this framework using
tabular Q-learning for the learning stage, as well as three measures that can
be used to perform the inference stage. The resulting instantiation achieves
state-of-the-art performance against goal recognizers on standard evaluation
domains and superior performance in noisy environments.

The characterization of dynamical processes in living systems provides
important clues for their mechanistic interpretation and link to biological
functions. Thanks to recent advances in microscopy techniques, it is now
possible to routinely record the motion of cells, organelles, and individual
molecules at multiple spatiotemporal scales in physiological conditions.
However, the automated analysis of dynamics occurring in crowded and complex
environments still lags behind the acquisition of microscopic image sequences.
Here, we present a framework based on geometric deep learning that achieves the
accurate estimation of dynamical properties in various biologically-relevant
scenarios. This deep-learning approach relies on a graph neural network
enhanced by attention-based components. By processing object features with
geometric priors, the network is capable of performing multiple tasks, from
linking coordinates into trajectories to inferring local and global dynamic
properties. We demonstrate the flexibility and reliability of this approach by
applying it to real and simulated data corresponding to a broad range of
biological experiments.

In this paper, we investigate a sequential power allocation problem over fast
varying channels, aiming to minimize the expected sum power while guaranteeing
the transmission success probability. In particular, a reinforcement learning
framework is constructed with appropriate reward design so that the optimal
policy maximizes the Lagrangian of the primal problem, where the maximizer of
the Lagrangian is shown to have several good properties. For the model-based
case, a fast converging algorithm is proposed to find the optimal Lagrange
multiplier and thus the corresponding optimal policy. For the model-free case,
we develop a three-stage strategy, composed in order of online sampling,
offline learning, and online operation, where a backward Q-learning with full
exploitation of sampled channel realizations is designed to accelerate the
learning process. According to our simulation, the proposed reinforcement
learning framework can solve the primal optimization problem from the dual
perspective. Moreover, the model-free strategy achieves a performance close to
that of the optimal model-based algorithm.

Automatic segmentation of glioma and its subregions is of great significance
for diagnosis, treatment and monitoring of disease. In this paper, an
augmentation method, called TensorMixup, was proposed and applied to the three
dimensional U-Net architecture for brain tumor segmentation. The main ideas
included that first, two image patches with size of 128 in three dimensions
were selected according to glioma information of ground truth labels from the
magnetic resonance imaging data of any two patients with the same modality.
Next, a tensor in which all elements were independently sampled from Beta
distribution was used to mix the image patches. Then the tensor was mapped to a
matrix which was used to mix the one-hot encoded labels of the above image
patches. Therefore, a new image and its one-hot encoded label were synthesized.
Finally, the new data was used to train the model which could be used to
segment glioma. The experimental results show that the mean accuracy of Dice
scores are 91.32%, 85.67%, and 82.20% respectively on the whole tumor, tumor
core, and enhancing tumor segmentation, which proves that the proposed
TensorMixup is feasible and effective for brain tumor segmentation.

Determining whether saddle points exist or are approximable for
nonconvex-nonconcave problems is usually intractable. We take a step towards
understanding certain nonconvex-nonconcave minimax problems that do remain
tractable. Specifically, we study minimax problems cast in geodesic metric
spaces, which provide a vast generalization of the usual convex-concave saddle
point problems. The first main result of the paper is a geodesic metric space
version of Sion's minimax theorem; we believe our proof is novel and
transparent, as it relies on Helly's theorem only. In our second main result,
we specialize to geodesically complete Riemannian manifolds: we devise and
analyze the complexity of first-order methods for smooth minimax problems.

Automating the recognition of outcomes reported in clinical trials using
machine learning has a huge potential of speeding up access to evidence
necessary in healthcare decision-making. Prior research has however
acknowledged inadequate training corpora as a challenge for the Outcome
detection (OD) task. Additionally, several contextualized representations like
BERT and ELMO have achieved unparalleled success in detecting various diseases,
genes, proteins, and chemicals, however, the same cannot be emphatically stated
for outcomes, because these models have been relatively under-tested and
studied for the OD task. We introduce "EBM-COMET", a dataset in which 300
PubMed abstracts are expertly annotated for clinical outcomes. Unlike prior
related datasets that use arbitrary outcome classifications, we use labels from
a taxonomy recently published to standardize outcome classifications. To
extract outcomes, we fine-tune a variety of pre-trained contextualized
representations, additionally, we use frozen contextualized and
context-independent representations in our custom neural model augmented with
clinically informed Part-Of-Speech embeddings and a cost-sensitive loss
function. We adopt strict evaluation for the trained models by rewarding them
for correctly identifying full outcome phrases rather than words within the
entities i.e. given an outcome "systolic blood pressure", the models are
rewarded a classification score only when they predict all 3 words in sequence,
otherwise, they are not rewarded. We observe our best model (BioBERT) achieve
81.5\% F1, 81.3\% sensitivity and 98.0\% specificity. We reach a consensus on
which contextualized representations are best suited for detecting outcomes
from clinical-trial abstracts. Furthermore, our best model outperforms scores
published on the original EBM-NLP dataset leader-board scores.

Learning to Rank (LETOR) algorithms are usually trained on annotated corpora
where a single relevance label is assigned to each available document-topic
pair. Within the Cranfield framework, relevance labels result from merging
either multiple expertly curated or crowdsourced human assessments. In this
paper, we explore how to train LETOR models with relevance judgments
distributions (either real or synthetically generated) assigned to
document-topic pairs instead of single-valued relevance labels. We propose five
new probabilistic loss functions to deal with the higher expressive power
provided by relevance judgments distributions and show how they can be applied
both to neural and GBM architectures. Moreover, we show how training a LETOR
model on a sampled version of the relevance judgments from certain probability
distributions can improve its performance when relying either on traditional or
probabilistic loss functions. Finally, we validate our hypothesis on real-world
crowdsourced relevance judgments distributions. Overall, we observe that
relying on relevance judgments distributions to train different LETOR models
can boost their performance and even outperform strong baselines such as
LambdaMART on several test collections.

Encrypted traffic classification requires discriminative and robust traffic
representation captured from content-invisible and imbalanced traffic data for
accurate classification, which is challenging but indispensable to achieve
network security and network management. The major limitation of existing
solutions is that they highly rely on the deep features, which are overly
dependent on data size and hard to generalize on unseen data. How to leverage
the open-domain unlabeled traffic data to learn representation with strong
generalization ability remains a key challenge. In this paper,we propose a new
traffic representation model called Encrypted Traffic Bidirectional Encoder
Representations from Transformer (ET-BERT), which pre-trains deep
contextualized datagram-level representation from large-scale unlabeled data.
The pre-trained model can be fine-tuned on a small number of task-specific
labeled data and achieves state-of-the-art performance across five encrypted
traffic classification tasks, remarkably pushing the F1 of ISCX-Tor to 99.2%
(4.4% absolute improvement), ISCX-VPN-Service to 98.9% (5.2% absolute
improvement), Cross-Platform (Android) to 92.5% (5.4% absolute improvement),
CSTNET-TLS 1.3 to 97.4% (10.0% absolute improvement). Notably, we provide
explanation of the empirically powerful pre-training model by analyzing the
randomness of ciphers. It gives us insights in understanding the boundary of
classification ability over encrypted traffic. The code is available at:
https://github.com/linwhitehat/ET-BERT.

In a convolution neural network, a composition of linear scalar product,
non-linear activation function and maximum pooling computations are intensively
invoked. As such, to design and implement privacy-preserving, high efficiency
machine learning mechanisms, one highly demands a practical crypto tool for
secure arithmetic computations. SPDZ, an interesting framework of secure
multi-party computations is a promising technique deployed for industry-scale
machine learning development if one is able to generate Beaver (multiplication)
triple offline efficiently. This paper studies secure yet efficient Beaver
triple generators leveraging privacy-preserving scalar product protocols which
in turn can be constructed from additive-only homomorphic encryptions(AHEs).
Different from the state-of-the-art solutions, where a party first splits her
private input into a shared vector and then invokes an AHE to compute scalar
product of the shared vectors managed by individual MPC server, we formalize
Beaver triple generators in the context of 2-party shared scalar product
protocol and then dispense the generated shares to MPC servers. As such, the
protocol presented in this paper can be viewed as a dual construction of the
state-of-the-art AHE based solutions. Furthermore, instead of applying the
Paillier encryption as a basis of our previous constructions or inheriting from
somewhat homomorphic encryptions, we propose an alternative construction of AHE
from polynomial ring learning with error (RLWE) which results in an efficient
implementation of Beaver triple generators.

We introduce the group-equivariant autoencoder (GE-autoencoder) -- a novel
deep neural network method that locates phase boundaries in the Ising
universality class by determining which symmetries of the Hamiltonian are
broken at each temperature. The encoder network of the GE-autoencoder models
the order parameter observable associated with the phase transition. The
parameters of the GE-autoencoder are constrained such that the encoder is
invariant to the subgroup of symmetries that never break; this results in a
dramatic reduction in the number of free parameters such that the
GE-autoencoder size is independent of the system size. The loss function of the
GE-autoencoder includes regularization terms that enforce equivariance to the
remaining quotient group of symmetries. We test the GE-autoencoder method on
the 2D classical ferromagnetic and antiferromagnetic Ising models, finding that
the GE-autoencoder (1) accurately determines which symmetries are broken at
each temperature, and (2) estimates the critical temperature with greater
accuracy and time-efficiency than a symmetry-agnostic autoencoder, once
finite-size scaling analysis is taken into account.

Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in
real-world systems, since it enables offline evaluation of new policies using
only historic log data. Unfortunately, when the number of actions is large,
existing OPE estimators -- most of which are based on inverse propensity score
weighting -- degrade severely and can suffer from extreme bias and variance.
This foils the use of OPE in many applications from recommender systems to
language models. To overcome this issue, we propose a new OPE estimator that
leverages marginalized importance weights when action embeddings provide
structure in the action space. We characterize the bias, variance, and mean
squared error of the proposed estimator and analyze the conditions under which
the action embedding provides statistical benefits over conventional
estimators. In addition to the theoretical analysis, we find that the empirical
performance improvement can be substantial, enabling reliable OPE even when
existing estimators collapse due to a large number of actions.

The development of the back-propagation algorithm represents a landmark in
neural networks. We provide an approach that conducts the back-propagation
again to reverse the traditional back-propagation process to optimize the input
loss at the input end of a neural network for better effects without extra
costs during the inference time. Then we further analyzed its principles and
advantages and disadvantages, reformulated the weight initialization strategy
for our method. And experiments on MNIST, CIFAR10, and CIFAR100 convinced our
approaches could adapt to a larger range of learning rate and learn better than
vanilla back-propagation.

Recently network embedding has gained increasing attention due to its
advantages in facilitating network computation tasks such as link prediction,
node classification and node clustering. The objective of network embedding is
to represent network nodes in a low-dimensional vector space while retaining as
much information as possible from the original network including structural,
relational, and semantic information. However, asymmetric nature of directed
networks poses many challenges as how to best preserve edge directions in the
embedding process. Here, we propose a novel deep asymmetric attributed network
embedding model based on convolutional graph neural network, called AAGCN. The
main idea is to maximally preserve the asymmetric proximity and asymmetric
similarity of directed attributed networks. AAGCN introduces two neighbourhood
feature aggregation schemes to separately aggregate the features of a node with
the features of its in- and out- neighbours. Then, it learns two embedding
vectors for each node, one source embedding vector and one target embedding
vector. The final representations are the results of concatenating source and
target embedding vectors. We test the performance of AAGCN on three real-world
networks for network reconstruction, link prediction, node classification and
visualization tasks. The experimental results show the superiority of AAGCN
against state-of-the-art embedding methods.

Lighting prediction from a single image is becoming increasingly important in
many vision and augmented reality (AR) applications in which shading and shadow
consistency between virtual and real objects should be guaranteed. However,
this is a notoriously ill-posed problem, especially for indoor scenarios,
because of the complexity of indoor luminaires and the limited information
involved in 2D images. In this paper, we propose a graph learning-based
framework for indoor lighting estimation. At its core is a new lighting model
(dubbed DSGLight) based on depth-augmented Spherical Gaussians (SG) and a Graph
Convolutional Network (GCN) that infers the new lighting representation from a
single LDR image of limited field-of-view. Our lighting model builds 128 evenly
distributed SGs over the indoor panorama, where each SG encoding the lighting
and the depth around that node. The proposed GCN then learns the mapping from
the input image to DSGLight. Compared with existing lighting models, our
DSGLight encodes both direct lighting and indirect environmental lighting more
faithfully and compactly. It also makes network training and inference more
stable. The estimated depth distribution enables temporally stable shading and
shadows under spatially-varying lighting. Through thorough experiments, we show
that our method obviously outperforms existing methods both qualitatively and

We study the generalized load-balancing (GLB) problem, where we are given $n$
jobs, each of which needs to be assigned to one of $m$ unrelated machines with
processing times $\{p_{ij}\}$. Under a job assignment $\sigma$, the load of
each machine $i$ is $\psi_i(\mathbf{p}_{i}[\sigma])$ where
$\psi_i:\mathbb{R}^n\rightarrow\mathbb{R}_{\geq0}$ is a symmetric monotone norm
and $\mathbf{p}_{i}[\sigma]$ is the $n$-dimensional vector $\{p_{ij}\cdot
\mathbf{1}[\sigma(j)=i]\}_{j\in [n]}$. Our goal is to minimize the generalized
makespan $\phi(\mathsf{load}(\sigma))$, where
$\phi:\mathbb{R}^m\rightarrow\mathbb{R}_{\geq0}$ is another symmetric monotone
norm and $\mathsf{load}(\sigma)$ is the $m$-dimensional machine load vector.
This problem significantly generalizes many classic optimization problems,
e.g., makespan minimization, set cover, minimum-norm load-balancing, etc.
  We obtain a polynomial time randomized algorithm that achieves an
approximation factor of $O(\log n)$, matching the lower bound of set cover up
to constant factor. We achieve this by rounding a novel configuration LP
relaxation with exponential number of variables. To approximately solve the
configuration LP, we design an approximate separation oracle for its dual
program. In particular, the separation oracle can be reduced to the norm
minimization with a linear constraint (NormLin) problem and we devise a
polynomial time approximation scheme (PTAS) for it, which may be of independent
interest.

Graph Neural Networks (GNNs) have achieved remarkable success by extending
traditional convolution to learning on non-Euclidean data. The key to the GNNs
is adopting the neural message-passing paradigm with two stages: aggregation
and update. The current design of GNNs considers the topology information in
the aggregation stage. However, in the updating stage, all nodes share the same
updating function. The identical updating function treats each node embedding
as i.i.d. random variables and thus ignores the implicit relationships between
neighborhoods, which limits the capacity of the GNNs. The updating function is
usually implemented with a linear transformation followed by a non-linear
activation function. To make the updating function topology-aware, we inject
the topological information into the non-linear activation function and propose
Graph-adaptive Rectified Linear Unit (GReLU), which is a new parametric
activation function incorporating the neighborhood information in a novel and
efficient way. The parameters of GReLU are obtained from a hyperfunction based
on both node features and the corresponding adjacent matrix. To reduce the risk
of overfitting and the computational cost, we decompose the hyperfunction as
two independent components for nodes and features respectively. We conduct
comprehensive experiments to show that our plug-and-play GReLU method is
efficient and effective given different GNN backbones and various downstream
tasks.

We consider the question introduced by \cite{Mason2020} of identifying all
the $\varepsilon$-optimal arms in a finite stochastic multi-armed bandit with
Gaussian rewards. We give two lower bounds on the sample complexity of any
algorithm solving the problem with a confidence at least $1-\delta$. The first,
unimprovable in the asymptotic regime, motivates the design of a Track-and-Stop
strategy whose average sample complexity is asymptotically optimal when the
risk $\delta$ goes to zero. Notably, we provide an efficient numerical method
to solve the convex max-min program that appears in the lower bound. Our method
is based on a complete characterization of the alternative bandit instances
that the optimal sampling strategy needs to rule out, thus making our bound
tighter than the one provided by \cite{Mason2020}. The second lower bound deals
with the regime of high and moderate values of the risk $\delta$, and
characterizes the behavior of any algorithm in the initial phase. It emphasizes
the linear dependency of the sample complexity in the number of arms. Finally,
we report on numerical simulations demonstrating our algorithm's advantage over
state-of-the-art methods, even for moderate risks.

Traditional portfolio management methods can incorporate specific investor
preferences but rely on accurate forecasts of asset returns and covariances.
Reinforcement learning (RL) methods do not rely on these explicit forecasts and
are better suited for multi-stage decision processes. To address limitations of
the evaluated research, experiments were conducted on three markets in
different economies with different overall trends. By incorporating specific
investor preferences into our RL models' reward functions, a more comprehensive
comparison could be made to traditional methods in risk-return space.
Transaction costs were also modelled more realistically by including nonlinear
changes introduced by market volatility and trading volume. The results of this
study suggest that there can be an advantage to using RL methods compared to
traditional convex mean-variance optimisation methods under certain market
conditions. Our RL models could significantly outperform traditional
single-period optimisation (SPO) and multi-period optimisation (MPO) models in
upward trending markets, but only up to specific risk limits. In sideways
trending markets, the performance of SPO and MPO models can be closely matched
by our RL models for the majority of the excess risk range tested. The specific
market conditions under which these models could outperform each other
highlight the importance of a more comprehensive comparison of Pareto optimal
frontiers in risk-return space. These frontiers give investors a more granular
view of which models might provide better performance for their specific risk
tolerance or return targets.

The importance of programming education has lead to dedicated educational
programming environments, where users visually arrange block-based programming
constructs that typically control graphical, interactive game-like programs.
The Scratch programming environment is particularly popular, with more than 70
million registered users at the time of this writing. While the block-based
nature of Scratch helps learners by preventing syntactical mistakes, there
nevertheless remains a need to provide feedback and support in order to
implement desired functionality. To support individual learning and classroom
settings, this feedback and support should ideally be provided in an automated
fashion, which requires tests to enable dynamic program analysis. The Whisker
framework enables automated testing of Scratch programs, but creating these
automated tests for Scratch programs is challenging. In this paper, we
therefore investigate how to automatically generate Whisker tests. This raises
important challenges: First, game-like programs are typically randomised,
leading to flaky tests. Second, Scratch programs usually consist of animations
and interactions with long delays, inhibiting the application of classical test
generation approaches. Evaluation on common programming exercises, a random
sample of 1000 Scratch user programs, and the 1000 most popular Scratch
programs demonstrates that our approach enables Whisker to reliably accelerate
test executions, and even though many Scratch programs are small and easy to
cover, there are many unique challenges for which advanced search-based test
generation using many-objective algorithms is needed in order to achieve high
coverage.

Learners are often introduced to programming via dedicated languages such as
Scratch, where block-based commands are assembled visually in order to control
the interactions of graphical sprites. Automated testing of such programs is an
important prerequisite for supporting debugging, providing hints, or assessing
learning outcomes. However, writing tests for Scratch programs can be
challenging: The game-like and randomised nature of typical Scratch programs
makes it difficult to identify specific timed input sequences used to control
the programs. Furthermore, precise test assertions to check the resulting
program states are incompatible with the fundamental principle of creative
freedom in programming in Scratch, where correct program behaviour may be
implemented with deviations in the graphical appearance or timing of the
program. The event-driven and actor-oriented nature of Scratch programs,
however, makes them a natural fit for describing program behaviour using finite
state machines. In this paper, we introduce a model-based testing approach by
extending Whisker, an automated testing framework for Scratch programs. The
model-based extension describes expected program behaviour in terms of state
machines, which makes it feasible to check the abstract behaviour of a program
independent of exact timing and pixel-precise graphical details, and to
automatically derive test inputs testing even challenging programs. A video
demonstrating model-based testing with Whisker is available at the following
URL: https://youtu.be/edgCNbGSGEY

Compared with traditional task-irrelevant downsampling methods, task-oriented
neural networks have shown improved performance in point cloud downsampling
range. Recently, Transformer family of networks has shown a more powerful
learning capacity in visual tasks. However, Transformer-based architectures
potentially consume too many resources which are usually worthless for low
overhead task networks in downsampling range. This paper proposes a novel
light-weight Transformer network (LighTN) for task-oriented point cloud
downsampling, as an end-to-end and plug-and-play solution. In LighTN, a
single-head self-correlation module is presented to extract refined global
contextual features, where three projection matrices are simultaneously
eliminated to save resource overhead, and the output of symmetric matrix
satisfies the permutation invariant. Then, we design a novel downsampling loss
function to guide LighTN focuses on critical point cloud regions with more
uniform distribution and prominent points coverage. Furthermore, We introduce a
feed-forward network scaling mechanism to enhance the learnable capacity of
LighTN according to the expand-reduce strategy. The result of extensive
experiments on classification and registration tasks demonstrates LighTN can
achieve state-of-the-art performance with limited resource overhead.

Transformers based on the attention mechanism have achieved impressive
success in various areas. However, the attention mechanism has a quadratic
complexity, significantly impeding Transformers from dealing with numerous
tokens and scaling up to bigger models. Previous methods mainly utilize the
similarity decomposition and the associativity of matrix multiplication to
devise linear-time attention mechanisms. They avoid degeneration of attention
to a trivial distribution by reintroducing inductive biases such as the
locality, thereby at the expense of model generality and expressiveness. In
this paper, we linearize Transformers free from specific inductive biases based
on the flow network theory. We cast attention as the information flow
aggregated from the sources (values) to the sinks (results) through the learned
flow capacities (attentions). Within this framework, we apply the property of
flow conservation into attention and propose the Flow-Attention mechanism of
linear complexity. By respectively conserving the incoming flow of sinks for
source competition and the outgoing flow of sources for sink allocation,
Flow-Attention inherently generates informative attentions without using
specific inductive biases. Empowered by the Flow-Attention, Flowformer yields
strong performance in linear time for wide areas, including long sequence, time
series, vision, natural language, and reinforcement learning. The code and
settings are available at this repository: https://github.com/thuml/Flowformer.

Predicting the number of infections in the anti-epidemic process is extremely
beneficial to the government in developing anti-epidemic strategies, especially
in fine-grained geographic units. Previous works focus on low spatial
resolution prediction, e.g., county-level, and preprocess data to the same
geographic level, which loses some useful information. In this paper, we
propose a fine-grained population mobility data-based model (FGC-COVID)
utilizing data of two geographic levels for community-level COVID-19
prediction. We use the population mobility data between Census Block Groups
(CBGs), which is a finer-grained geographic level than community, to build the
graph and capture the dependencies between CBGs using graph neural networks
(GNNs). To mine as finer-grained patterns as possible for prediction, a spatial
weighted aggregation module is introduced to aggregate the embeddings of CBGs
to community level based on their geographic affiliation and spatial
autocorrelation. Extensive experiments on 300 days LA city COVID-19 data
indicate our model outperforms existing forecasting models on community-level
COVID-19 prediction.

Recent anomaly detection algorithms have shown powerful performance by
adopting frame predicting autoencoders. However, these methods face two
challenging circumstances. First, they are likely to be trained to be
excessively powerful, generating even abnormal frames well, which leads to
failure in detecting anomalies. Second, they are distracted by the large number
of objects captured in both foreground and background. To solve these problems,
we propose a novel superpixel-based video data transformation technique named
Random Superpixel Erasing on Moving Objects (RandomSEMO) and Moving Object Loss
(MOLoss), built on top of a simple lightweight autoencoder. RandomSEMO is
applied to the moving object regions by randomly erasing their superpixels. It
enforces the network to pay attention to the foreground objects and learn the
normal features more effectively, rather than simply predicting the future
frame. Moreover, MOLoss urges the model to focus on learning normal objects
captured within RandomSEMO by amplifying the loss on the pixels near the moving
objects. The experimental results show that our model outperforms
state-of-the-arts on three benchmarks.

Autonomous modeling of artificial swarms is necessary because manual creation
is a time intensive and complicated procedure which makes it impractical. An
autonomous approach employing deep reinforcement learning is presented in this
study for swarm navigation. In this approach, complex 3D environments with
static and dynamic obstacles and resistive forces (like linear drag, angular
drag, and gravity) are modeled to track multiple dynamic targets. Moreover,
reward functions for robust swarm formation and target tracking are devised for
learning complex swarm behaviors. Since the number of agents is not fixed and
has only the partial observance of the environment, swarm formation and
navigation become challenging. In this regard, the proposed strategy consists
of three main phases to tackle the aforementioned challenges: 1) A methodology
for dynamic swarm management, 2) Avoiding obstacles, Finding the shortest path
towards the targets, 3) Tracking the targets and Island modeling. The dynamic
swarm management phase translates basic sensory input to high level commands to
enhance swarm navigation and decentralized setup while maintaining the swarms
size fluctuations. While, in the island modeling, the swarm can split into
individual subswarms according to the number of targets, conversely, these
subswarms may join to form a single huge swarm, giving the swarm ability to
track multiple targets. Customized state of the art policy based deep
reinforcement learning algorithms are employed to achieve significant results.
The promising results show that our proposed strategy enhances swarm navigation
and can track multiple static and dynamic targets in complex dynamic

Project ATHENA aims to develop an application to address information
overload, primarily focused on Recommendation Systems (RSs) with the
personalization and user experience design of a modern system. Two machine
learning (ML) algorithms were used: (1) TF-IDF for Content-based filtering
(CBF); (2) Classification with Matrix Factorization- Singular Value
Decomposition(SVD) applied with Collaborative filtering (CF) and mean
(normalization) for prediction accuracy of the CF. Data sampling in academic
Research and Development of Philippine Council for Agriculture, Aquatic, and
Natural Resources Research and Development (PCAARRD) e-Library and Project
SARAI publications plus simulated data used as training sets to generate a
recommendation of items that uses the three RS filtering (CF, CBF, and
personalized version of item recommendations). Series of Testing and TAM
performed and discussed. Findings allow users to engage in online information
and quickly evaluate retrieved items produced by the application.
Compatibility-testing (CoT) shows the application is compatible with all major
browsers and mobile-friendly. Performance-testing (PT) recommended v-parameter
specs and TAM evaluations results indicate strongly associated with overall
positive feedback, thoroughly enough to address the information-overload
problem as the core of the paper. A modular architecture presented addressing
the information overload, primarily focused on RSs with the personalization and
design of modern systems. Developers utilized Two ML algorithms and prototyped
a simplified version of the architecture. Series of testing (CoT and PT) and
evaluations with TAM were performed and discussed. Project ATHENA added a UX
feature design of a modern system.

Prior work has successfully incorporated optimization layers as the last
layer in neural networks for various problems, thereby allowing joint learning
and planning in one neural network forward pass. In this work, we identify a
weakness in such a set-up where inputs to the optimization layer lead to
undefined output of the neural network. Such undefined decision outputs can
lead to possible catastrophic outcomes in critical real time applications. We
show that an adversary can cause such failures by forcing rank deficiency on
the matrix fed to the optimization layer which results in the optimization
failing to produce a solution. We provide a defense for the failure cases by
controlling the condition number of the input matrix. We study the problem in
the settings of synthetic data, Jigsaw Sudoku, and in speed planning for
autonomous driving, building on top of prior frameworks in end-to-end learning
and optimization. We show that our proposed defense effectively prevents the
framework from failing with undefined output. Finally, we surface a number of
edge cases which lead to serious bugs in popular equation and optimization
solvers which can be abused as well.

Learning discriminative node representations benefits various downstream
tasks in graph analysis such as community detection and node classification.
Existing graph representation learning methods (e.g., based on random walk and
contrastive learning) are limited to maximizing the local similarity of
connected nodes. Such pair-wise learning schemes could fail to capture the
global distribution of representations, since it has no explicit constraints on
the global geometric properties of representation space. To this end, we
propose Geometric Graph Representation Learning (G2R) to learn node
representations in an unsupervised manner via maximizing rate reduction. In
this way, G2R maps nodes in distinct groups (implicitly stored in the adjacency
matrix) into different subspaces, while each subspace is compact and different
subspaces are dispersedly distributed. G2R adopts a graph neural network as the
encoder and maximizes the rate reduction with the adjacency matrix.
Furthermore, we theoretically and empirically demonstrate that rate reduction
maximization is equivalent to maximizing the principal angles between different
subspaces. Experiments on real-world datasets show that G2R outperforms various
baselines on node classification and community detection tasks.

Recent advances in generative adversarial networks have shown that it is
possible to generate high-resolution and hyperrealistic images. However, the
images produced by GANs are only as fair and representative as the datasets on
which they are trained. In this paper, we propose a method for directly
modifying a pre-trained StyleGAN2 model that can be used to generate a balanced
set of images with respect to one (e.g., eyeglasses) or more attributes (e.g.,
gender and eyeglasses). Our method takes advantage of the style space of the
StyleGAN2 model to perform disentangled control of the target attributes to be
debiased. Our method does not require training additional models and directly
debiases the GAN model, paving the way for its use in various downstream
applications. Our experiments show that our method successfully debiases the
GAN model within a few minutes without compromising the quality of the
generated images. To promote fair generative models, we share the code and
debiased models at http://catlab-team.github.io/fairstyle.

Policy constraint methods to offline reinforcement learning (RL) typically
utilize parameterization or regularization that constrains the policy to
perform actions within the support set of the behavior policy. The elaborative
designs of parameterization methods usually intrude into the policy networks,
which may bring extra inference cost and cannot take full advantage of
well-established online methods. Regularization methods reduce the divergence
between the learned policy and the behavior policy, which may mismatch the
inherent density-based definition of support set thereby failing to avoid the
out-of-distribution actions effectively. This paper presents Supported Policy
OpTimization (SPOT), which is directly derived from the theoretical
formalization of the density-based support constraint. SPOT adopts a VAE-based
density estimator to explicitly model the support set of behavior policy and
presents a simple but effective density-based regularization term, which can be
plugged non-intrusively into off-the-shelf off-policy RL algorithms. SPOT
achieves the state-of-the-art performance on standard benchmarks for offline
RL. Benefiting from the pluggable design, offline pretrained models from SPOT
can also be applied to perform online fine-tuning seamlessly.

We propose an efficient numerical method for computing natural gradient
descent directions with respect to a generic metric in the state space. Our
technique relies on representing the natural gradient direction as a solution
to a standard least-squares problem. Hence, instead of calculating, storing, or
inverting the information matrix directly, we apply efficient methods from
numerical linear algebra to solve this least-squares problem. We treat both
scenarios where the derivative of the state variable with respect to the
parameter is either explicitly known or implicitly given through constraints.
We apply the QR decomposition to solve the least-squares problem in the former
case and utilize the adjoint-state method to compute the natural gradient
descent direction in the latter case. As a result, we can reliably compute
several natural gradient descents, including the Wasserstein natural gradient,
for a large-scale parameter space with thousands of dimensions, which was
believed to be out of reach. Finally, our numerical results shed light on the
qualitative differences among the standard gradient descent method and various
natural gradient descent methods based on different metric spaces in
large-scale nonconvex optimization problems.

We study norm-based uniform convergence bounds for neural networks, aiming at
a tight understanding of how these are affected by the architecture and type of
norm constraint, for the simple class of scalar-valued one-hidden-layer
networks, and inputs bounded in Euclidean norm. We begin by proving that in
general, controlling the spectral norm of the hidden layer weight matrix is
insufficient to get uniform convergence guarantees (independent of the network
width), while a stronger Frobenius norm control is sufficient, extending and
improving on previous work. Motivated by the proof constructions, we identify
and analyze two important settings where (perhaps surprisingly) a mere spectral
norm control turns out to be sufficient: First, when the network's activation
functions are sufficiently smooth (with the result extending to deeper
networks); and second, for certain types of convolutional networks. In the
latter setting, we study how the sample complexity is additionally affected by
parameters such as the amount of overlap between patches and the overall number
of patches.

While natural gradients have been widely studied from both theoretical and
empirical perspectives, we argue that some fundamental theoretical issues
regarding the existence of gradients in infinite dimensional function spaces
remain underexplored. We address these issues by providing a geometric
perspective and mathematical framework for studying natural gradient that is
more complete and rigorous than existing studies. Our results also establish
new connections between natural gradients and RKHS theory, and specifically to
the Neural Tangent Kernel (NTK). Based on our theoretical framework, we derive
a new family of natural gradients induced by Sobolev metrics and develop
computational techniques for efficient approximation in practice. Preliminary
experimental results reveal the potential of this new natural gradient variant.

Vital node identification is the problem of finding nodes of highest
importance in complex networks. This problem has crucial applications in
various contexts such as viral marketing or controlling the propagation of
virus or rumours in real-world networks. Existing approaches for vital node
identification mainly focus on capturing the importance of a node through a
mathematical expression which directly relates structural properties of the
node to its vitality. Although these heuristic approaches have achieved good
performance in practice, they have weak adaptability, and their performance is
limited to specific settings and certain dynamics. Inspired by the power of
machine learning models for efficiently capturing different types of patterns
and relations, we propose a machine learning-based, data driven approach for
vital node identification. The main idea is to train the model with a small
portion of the graph, say 0.5% of the nodes, and do the prediction on the rest
of the nodes. The ground-truth vitality for the train data is computed by
simulating the SIR diffusion method starting from the train nodes. We use
collective feature engineering where each node in the network is represented by
incorporating elements of its connectivity, degree and extended coreness.
Several machine learning models are trained on the node representations, but
the best results are achieved by a Support Vector Regression machine with RBF
kernel. The empirical results confirms that the proposed model outperforms
state-of-the-art models on a selection of datasets, while it also shows more
adaptability to changes in the dynamics parameters.

Recent advances in deep learning have led to substantial improvements in
deepfake generation, resulting in fake media with a more realistic appearance.
Although deepfake media have potential application in a wide range of areas and
are drawing much attention from both the academic and industrial communities,
it also leads to serious social and criminal concerns. This chapter explores
the evolution of and challenges in deepfake generation and detection. It also
discusses possible ways to improve the robustness of deepfake detection for a
wide variety of media (e.g., in-the-wild images and videos). Finally, it
suggests a focus for future fake media research.

Using solar power in the process industry can reduce greenhouse gas emissions
and make the production process more sustainable. However, the intermittent
nature of solar power renders its usage challenging. Building a model to
predict photovoltaic (PV) power generation allows decision-makers to hedge
energy shortages and further design proper operations. The solar power output
is time-series data dependent on many factors, such as irradiance and weather.
A machine learning framework for 1-hour ahead solar power prediction is
developed in this paper based on the historical data. Our method extends the
input dataset into higher dimensional Chebyshev polynomial space. Then, a
feature selection scheme is developed with constrained linear regression to
construct the predictor for different weather types. Several tests show that
the proposed approach yields lower mean squared error than classical machine
learning methods, such as support vector machine (SVM), random forest (RF), and
gradient boosting decision tree (GBDT).

We present Persian Question Answering Dataset (PQuAD), a crowdsourced reading
comprehension dataset on Persian Wikipedia articles. It includes 80,000
questions along with their answers, with 25% of the questions being
adversarially unanswerable. We examine various properties of the dataset to
show the diversity and the level of its difficulty as an MRC benchmark. By
releasing this dataset, we aim to ease research on Persian reading
comprehension and development of Persian question answering systems. Our
experiments on different state-of-the-art pre-trained contextualized language
models show 74.8% Exact Match (EM) and 87.6% F1-score that can be used as the
baseline results for further research on Persian QA.

In recent years, monitoring hate speech and offensive language on social
media platforms has become paramount due to its widespread usage among all age
groups, races, and ethnicities. Consequently, there have been substantial
research efforts towards automated detection of such content using Natural
Language Processing (NLP). While successfully filtering textual data, no
research has focused on detecting hateful content in multimedia data. With
increased ease of data storage and the exponential growth of social media
platforms, multimedia content proliferates the internet as much as text data.
Nevertheless, it escapes the automatic filtering systems. Hate speech and
offensiveness can be detected in multimedia primarily via three modalities,
i.e., visual, acoustic, and verbal. Our preliminary study concluded that the
most essential features in classifying hate speech would be the speaker's
emotional state and its influence on the spoken words, therefore limiting our
current research to these modalities. This paper proposes the first multimodal
deep learning framework to combine the auditory features representing emotion
and the semantic features to detect hateful content. Our results demonstrate
that incorporating emotional attributes leads to significant improvement over
text-based models in detecting hateful multimedia content. This paper also
presents a new Hate Speech Detection Video Dataset (HSDVD) collected for the
purpose of multimodal learning as no such dataset exists today.

Embedding based retrieval (EBR) is a fundamental building block in many web
applications. However, EBR in sponsored search is distinguished from other
generic scenarios and technically challenging due to the need of serving
multiple retrieval purposes: firstly, it has to retrieve high-relevance ads,
which may exactly serve user's search intent; secondly, it needs to retrieve
high-CTR ads so as to maximize the overall user clicks. In this paper, we
present a novel representation learning framework Uni-Retriever developed for
Bing Search, which unifies two different training modes knowledge distillation
and contrastive learning to realize both required objectives. On one hand, the
capability of making high-relevance retrieval is established by distilling
knowledge from the ``relevance teacher model''. On the other hand, the
capability of making high-CTR retrieval is optimized by learning to
discriminate user's clicked ads from the entire corpus. The two training modes
are jointly performed as a multi-objective learning process, such that the ads
of high relevance and CTR can be favored by the generated embeddings. Besides
the learning strategy, we also elaborate our solution for EBR serving pipeline
built upon the substantially optimized DiskANN, where massive-scale EBR can be
performed with competitive time and memory efficiency, and accomplished in
high-quality. We make comprehensive offline and online experiments to evaluate
the proposed techniques, whose findings may provide useful insights for the
future development of EBR systems. Uni-Retriever has been mainstreamed as the
major retrieval path in Bing's production thanks to the notable improvements on
the representation and EBR serving quality.

Neural networks have been successfully employed in various domain such as
classification, regression and clustering, etc. Generally, the back propagation
(BP) based iterative approaches are used to train the neural networks, however,
it results in the issues of local minima, sensitivity to learning rate and slow
convergence. To overcome these issues, randomization based neural networks such
as random vector functional link (RVFL) network have been proposed. RVFL model
has several characteristics such as fast training speed, simple architecture,
and universal approximation capability, that make it a viable randomized neural
network. This article presents the comprehensive review of the development of
RVFL model, which can serve as the extensive summary for the beginners as well
as practitioners. We discuss the shallow RVFL, ensemble RVFL, deep RVFL and
ensemble deep RVFL models. The variations, improvements and applications of
RVFL models are discussed in detail. Moreover, we discuss the different
hyperparameter optimization techniques followed in the literature to improve
the generalization performance of the RVFL model. Finally, we give potential
future research directions/opportunities that can inspire the researchers to
improve the RVFL architecture further.

Generalizing knowledge beyond source domains is a crucial prerequisite for
many biomedical applications such as drug design and molecular property
prediction. To meet this challenge, researchers have used optimal transport
(OT) to perform representation alignment between the source and target domains.
Yet existing OT algorithms are mainly designed for classification tasks.
Accordingly, we consider regression tasks in the unsupervised and
semi-supervised settings in this paper. To exploit continuous labels, we
propose novel metrics to measure domain distances and introduce a posterior
variance regularizer on the transport plan. Further, while computationally
appealing, OT suffers from ambiguous decision boundaries and biased local data
distributions brought by the mini-batch training. To address those issues, we
propose to couple OT with metric learning to yield more robust boundaries and
reduce bias. Specifically, we present a dynamic hierarchical triplet loss to
describe the global data distribution, where the cluster centroids are
progressively adjusted among consecutive iterations. We evaluate our method on
both unsupervised and semi-supervised learning tasks in biochemistry.
Experiments show the proposed method significantly outperforms state-of-the-art
baselines across various benchmark datasets of small molecules and material
crystals.

Despite its benefits for children's skill development and parent-child
bonding, many parents do not often engage in interactive storytelling by having
story-related dialogues with their child due to limited availability or
challenges in coming up with appropriate questions. While recent advances made
AI generation of questions from stories possible, the fully-automated approach
excludes parent involvement, disregards educational goals, and underoptimizes
for child engagement. Informed by need-finding interviews and participatory
design (PD) results, we developed StoryBuddy, an AI-enabled system for parents
to create interactive storytelling experiences. StoryBuddy's design highlighted
the need for accommodating dynamic user needs between the desire for parent
involvement and parent-child bonding and the goal of minimizing parent
intervention when busy. The PD revealed varied assessment and educational goals
of parents, which StoryBuddy addressed by supporting configuring question types
and tracking child progress. A user study validated StoryBuddy's usability and
suggested design insights for future parent-AI collaboration systems.

The current methods for learning representations with auto-encoders almost
exclusively employ vectors as the latent representations. In this work, we
propose to employ a tensor product structure for this purpose. This way, the
obtained representations are naturally disentangled. In contrast to the
conventional variations methods, which are targeted toward normally distributed
features, the latent space in our representation is distributed uniformly over
a set of unit circles. We argue that the torus structure of the latent space
captures the generative factors effectively. We employ recent tools for
measuring unsupervised disentanglement, and in an extensive set of experiments
demonstrate the advantage of our method in terms of disentanglement,
completeness, and informativeness. The code for our proposed method is
available at https://github.com/rotmanmi/Unsupervised-Disentanglement-Torus.

Recently, graph collaborative filtering methods have been proposed as an
effective recommendation approach, which can capture users' preference over
items by modeling the user-item interaction graphs. In order to reduce the
influence of data sparsity, contrastive learning is adopted in graph
collaborative filtering for enhancing the performance. However, these methods
typically construct the contrastive pairs by random sampling, which neglect the
neighboring relations among users (or items) and fail to fully exploit the
potential of contrastive learning for recommendation. To tackle the above
issue, we propose a novel contrastive learning approach, named
Neighborhood-enriched Contrastive Learning, named NCL, which explicitly
incorporates the potential neighbors into contrastive pairs. Specifically, we
introduce the neighbors of a user (or an item) from graph structure and
semantic space respectively. For the structural neighbors on the interaction
graph, we develop a novel structure-contrastive objective that regards users
(or items) and their structural neighbors as positive contrastive pairs. In
implementation, the representations of users (or items) and neighbors
correspond to the outputs of different GNN layers. Furthermore, to excavate the
potential neighbor relation in semantic space, we assume that users with
similar representations are within the semantic neighborhood, and incorporate
these semantic neighbors into the prototype-contrastive objective. The proposed
NCL can be optimized with EM algorithm and generalized to apply to graph
collaborative filtering methods. Extensive experiments on five public datasets
demonstrate the effectiveness of the proposed NCL, notably with 26% and 17%
performance gain over a competitive graph collaborative filtering base model on
the Yelp and Amazon-book datasets respectively. Our code is available at:
https://github.com/RUCAIBox/NCL.

One central challenge in understanding phonon thermal transport is a lack of
experimental tools to investigate mode-based transport information. Although
recent advances in computation lead to mode-based information, it is hindered
by unknown defects in bulk region and at interfaces. Here we present a
framework that can reveal microscopic phonon transport information in
heterostructures, integrating state-of-the-art ultrafast electron diffraction
(UED) with advanced scientific machine learning. Taking advantage of the dual
temporal and reciprocal-space resolution in UED, we are able to reliably
recover the frequency-dependent interfacial transmittance with possible
extension to frequency-dependent relaxation times of the heterostructure. This
enables a direct reconstruction of real-space, real-time, frequency-resolved
phonon dynamics across an interface. Our work provides a new pathway to
experimentally probe phonon transport mechanisms with unprecedented details.

This paper investigates the parameter space of machine learning (ML)
algorithms in aggravating or mitigating fairness bugs. Data-driven software is
increasingly applied in social-critical applications where ensuring fairness is
of paramount importance. The existing approaches focus on addressing fairness
bugs by either modifying the input dataset or modifying the learning
algorithms. On the other hand, the selection of hyperparameters, which provide
finer controls of ML algorithms, may enable a less intrusive approach to
influence the fairness. Can hyperparameters amplify or suppress discrimination
present in the input dataset? How can we help programmers in detecting,
understanding, and exploiting the role of hyperparameters to improve the
fairness?
  We design three search-based software testing algorithms to uncover the
precision-fairness frontier of the hyperparameter space. We complement these
algorithms with statistical debugging to explain the role of these parameters
in improving fairness. We implement the proposed approaches in the tool
Parfait-ML (PARameter FAIrness Testing for ML Libraries) and show its
effectiveness and utility over five mature ML algorithms as used in six
social-critical applications. In these applications, our approach successfully
identified hyperparameters that significantly improve (vis-a-vis the
state-of-the-art techniques) the fairness without sacrificing precision.
Surprisingly, for some algorithms (e.g., random forest), our approach showed
that certain configuration of hyperparameters (e.g., restricting the search
space of attributes) can amplify biases across applications. Upon further
investigation, we found intuitive explanations of these phenomena, and the
results corroborate similar observations from the literature.

Knowledge sharing and model personalization are essential components to
tackle the non-IID challenge in federated learning (FL). Most existing FL
methods focus on two extremes: 1) to learn a shared model to serve all clients
with non-IID data, and 2) to learn personalized models for each client, namely
personalized FL. There is a trade-off solution, namely clustered FL or
cluster-wise personalized FL, which aims to cluster similar clients into one
cluster, and then learn a shared model for all clients within a cluster. This
paper is to revisit the research of clustered FL by formulating them into a
bi-level optimization framework that could unify existing methods. We propose a
new theoretical analysis framework to prove the convergence by considering the
clusterability among clients. In addition, we embody this framework in an
algorithm, named Weighted Clustered Federated Learning (WeCFL). Empirical
analysis verifies the theoretical results and demonstrates the effectiveness of
the proposed WeCFL under the proposed cluster-wise non-IID settings.

Seagrass meadows play an essential role in blue carbon and aquatic ecosystem
services. However, methods for the flourishing of seagrass are still being
explored. Here, data from 49 public coastal surveys on the distribution of
seagrass and seaweed around the onshore aquaculture facilities are revalidated,
and an exceptional area where the seagrass Zostera marina thrives was found.
The bacterial population there showed an apparent decrease in the pathogen
candidates belonging to the order Flavobacteriales. Moreover, structure
equation modeling and a linear non-Gaussian acyclic model based on the machine
learning data estimated an optimal symbiotic bacterial group candidate for
seagrass growth as follows: the Lachnospiraceae and Ruminococcaceae families as
gut-inhabitant bacteria, Rhodobacteraceae as photosynthetic bacteria, and
Desulfobulbaceae as cable bacteria modulating oxygen or nitrate reduction and
oxidation of sulfide. These observations confer a novel perspective on the
seagrass symbiotic bacterial structures critical for blue carbon and
low-pathogenic marine ecosystems in aquaculture.

Learning symbolic music representations, especially disentangled
representations with probabilistic interpretations, has been shown to benefit
both music understanding and generation. However, most models are only
applicable to short-term music, while learning long-term music representations
remains a challenging task. We have seen several studies attempting to learn
hierarchical representations directly in an end-to-end manner, but these models
have not been able to achieve the desired results and the training process is
not stable. In this paper, we propose a novel approach to learn long-term
symbolic music representations through contextual constraints. First, we use
contrastive learning to pre-train a long-term representation by constraining
its difference from the short-term representation (extracted by an
off-the-shelf model). Then, we fine-tune the long-term representation by a
hierarchical prediction model such that a good long-term representation (e.g.,
an 8-bar representation) can reconstruct the corresponding short-term ones
(e.g., the 2-bar representations within the 8-bar range). Experiments show that
our method stabilizes the training and the fine-tuning steps. In addition, the
designed contextual constraints benefit both reconstruction and
disentanglement, significantly outperforming the baselines.

Open-set domain adaptation (OSDA) has gained considerable attention in many
visual recognition tasks. However, most existing OSDA approaches are limited
due to three main reasons, including: (1) the lack of essential theoretical
analysis of generalization bound, (2) the reliance on the coexistence of source
and target data during adaptation, and (3) failing to accurately estimate the
uncertainty of model predictions. We propose a Progressive Graph Learning (PGL)
framework that decomposes the target hypothesis space into the shared and
unknown subspaces, and then progressively pseudo-labels the most confident
known samples from the target domain for hypothesis adaptation. Moreover, we
tackle a more realistic source-free open-set domain adaptation (SF-OSDA)
setting that makes no assumption about the coexistence of source and target
domains, and introduce a balanced pseudo-labeling (BP-L) strategy in a
two-stage framework, namely SF-PGL. Different from PGL that applies a
class-agnostic constant threshold for all target samples for pseudo-labeling,
the SF-PGL model uniformly selects the most confident target instances from
each category at a fixed ratio. The confidence thresholds in each class are
regarded as the 'uncertainty' of learning the semantic information, which are
then used to weigh the classification loss in the adaptation step. We conducted
unsupervised and semi-supervised OSDA and SF-OSDA experiments on the benchmark
image classification and action recognition datasets. Additionally, we find
that balanced pseudo-labeling plays a significant role in improving
calibration, which makes the trained model less prone to over-confident or
under-confident predictions on the target data. Source code is available at
https://github.com/Luoyadan/SF-PGL.

With the field of rigid-body robotics having matured in the last fifty years,
routing, planning, and manipulation of deformable objects have recently emerged
as a more untouched research area in many fields ranging from surgical robotics
to industrial assembly and construction. Routing approaches for deformable
objects which rely on learned implicit spatial representations (e.g.,
Learning-from-Demonstration methods) make them vulnerable to changes in the
environment and the specific setup. On the other hand, algorithms that entirely
separate the spatial representation of the deformable object from the routing
and manipulation, often using a representation approach independent of
planning, result in slow planning in high dimensional space.
  This paper proposes a novel approach to routing deformable one-dimensional
objects (e.g., wires, cables, ropes, sutures, threads). This approach utilizes
a compact representation for the object, allowing efficient and fast online
routing. The spatial representation is based on the geometrical decomposition
of the space into convex subspaces, resulting in a discrete coding of the
deformable object configuration as a sequence. With such a configuration, the
routing problem can be solved using a fast dynamic programming sequence
matching method that calculates the next routing move. The proposed method
couples the routing and efficient configuration for improved planning time. Our
simulation and real experiments show the method correctly computing the next
manipulation action in sub-millisecond time and accomplishing various routing
and manipulation tasks.

The modelling of multiphase flow in a pipe presents a significant challenge
for high-resolution computational fluid dynamics (CFD) models due to the high
aspect ratio (length over diameter) of the domain. In subsea applications, the
pipe length can be several hundreds of kilometres versus a pipe diameter of
just a few inches. In this paper, we present a new AI-based non-intrusive
reduced-order model within a domain decomposition framework (AI-DDNIROM) which
is capable of making predictions for domains significantly larger than the
domain used in training. This is achieved by using domain decomposition;
dimensionality reduction; training a neural network to make predictions for a
single subdomain; and by using an iteration-by-subdomain technique to converge
the solution over the whole domain. To find the low-dimensional space, we
explore several types of autoencoder networks, known for their ability to
compress information accurately and compactly. The performance of the
autoencoders is assessed on two advection-dominated problems: flow past a
cylinder and slug flow in a pipe. To make predictions in time, we exploit an
adversarial network which aims to learn the distribution of the training data,
in addition to learning the mapping between particular inputs and outputs. This
type of network has shown the potential to produce realistic outputs. The whole
framework is applied to multiphase slug flow in a horizontal pipe for which an
AI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 m
with an aspect ratio of 13:1, and tested by simulating the flow for a pipe of
length 98 m with an aspect ratio of almost 130:1. Statistics of the flows
obtained from the CFD simulations are compared to those of the AI-DDNIROM
predictions to demonstrate the success of our approach.

The task of ultra-fine entity typing (UFET) seeks to predict diverse and
free-form words or phrases that describe the appropriate types of entities
mentioned in sentences. A key challenge for this task lies in the large amount
of types and the scarcity of annotated data per type. Existing systems
formulate the task as a multi-way classification problem and train directly or
distantly supervised classifiers. This causes two issues: (i) the classifiers
do not capture the type semantics since types are often converted into indices;
(ii) systems developed in this way are limited to predicting within a
pre-defined type set, and often fall short of generalizing to types that are
rarely seen or unseen in training. This work presents LITE, a new approach that
formulates entity typing as a natural language inference (NLI) problem, making
use of (i) the indirect supervision from NLI to infer type information
meaningfully represented as textual hypotheses and alleviate the data scarcity
issue, as well as (ii) a learning-to-rank objective to avoid the pre-defining
of a type set. Experiments show that, with limited training data, LITE obtains
state-of-the-art performance on the UFET task. In addition, LITE demonstrates
its strong generalizability, by not only yielding best results on other
fine-grained entity typing benchmarks, more importantly, a pre-trained LITE
system works well on new data containing unseen types.

Evolving Neural Networks (NNs) has recently seen an increasing interest as an
alternative path that might be more successful. It has many advantages compared
to other approaches, such as learning the architecture of the NNs. However, the
extremely large search space and the existence of many complex interacting
parts still represent a major obstacle. Many criteria were recently
investigated to help guide the algorithm and to cut down the large search
space. Recently there has been growing research bringing insights from network
science to improve the design of NNs. In this paper, we investigate evolving
NNs architectures that have one of the most fundamental characteristics of
real-world networks, namely the optimal balance between connections cost and
information flow. The performance of different metrics that represent this
balance is evaluated and the improvement in the accuracy of putting more
selection pressure toward this balance is demonstrated on three datasets.

Neural population activity relating to behaviour is assumed to be inherently
low-dimensional despite the observed high dimensionality of data recorded using
multi-electrode arrays. Therefore, predicting behaviour from neural population
recordings has been shown to be most effective when using latent variable
models. Over time however, the activity of single neurons can drift, and
different neurons will be recorded due to movement of implanted neural probes.
This means that a decoder trained to predict behaviour on one day performs
worse when tested on a different day. On the other hand, evidence suggests that
the latent dynamics underlying behaviour may be stable even over months and
years. Based on this idea, we introduce a model capable of inferring
behaviourally relevant latent dynamics from previously unseen data recorded
from the same animal, without any need for decoder recalibration. We show that
unsupervised domain adaptation combined with a sequential variational
autoencoder, trained on several sessions, can achieve good generalisation to
unseen data and correctly predict behaviour where conventional methods fail.
Our results further support the hypothesis that behaviour-related neural
dynamics are low-dimensional and stable over time, and will enable more
effective and flexible use of brain computer interface technologies.

It is common practice to discretize continuous defect counts into defective
and non-defective classes and use them as a target variable when building
defect classifiers (discretized classifiers). However, this discretization of
continuous defect counts leads to information loss that might affect the
performance and interpretation of defect classifiers. Another possible approach
to build defect classifiers is through the use of regression models then
discretizing the predicted defect counts into defective and non-defective
classes (regression-based classifiers).
  In this paper, we compare the performance and interpretation of defect
classifiers that are built using both approaches (i.e., discretized classifiers
and regression-based classifiers) across six commonly used machine learning
classifiers (i.e., linear/logistic regression, random forest, KNN, SVM, CART,
and neural networks) and 17 datasets. We find that: i) Random forest based
classifiers outperform other classifiers (best AUC) for both classifier
building approaches; ii) In contrast to common practice, building a defect
classifier using discretized defect counts (i.e., discretized classifiers) does
not always lead to better performance.
  Hence we suggest that future defect classification studies should consider
building regression-based classifiers (in particular when the defective ratio
of the modeled dataset is low). Moreover, we suggest that both approaches for
building defect classifiers should be explored, so the best-performing
classifier can be used when determining the most influential features.

An emerging computational paradigm, named federated edge learning (FEL),
enables intelligent computing at the network edge with the feature of
preserving data privacy for edge devices. Given their constrained resources, it
becomes a great challenge to achieve high execution performance for FEL. Most
of the state-of-the-arts concentrate on enhancing FEL from the perspective of
system operation procedures, taking few precautions during the composition step
of the FEL system. Though a few recent studies recognize the importance of FEL
formation and propose server-centric device selection schemes, the impact of
data sizes is largely overlooked. In this paper, we take advantage of game
theory to depict the decision dilemma among edge devices regarding whether to
participate in FEL or not given their heterogeneous sizes of local datasets.
For realizing both the individual and global optimization, the server is
employed to solve the participation dilemma, which requires accurate
information collection for devices' local datasets. Hence, we utilize mechanism
design to enable truthful information solicitation. With the help of correlated
equilibrium, we derive a decision making strategy for devices from the global
perspective, which can achieve the long-term stability and efficacy of FEL. For
scalability consideration, we optimize the computational complexity of the
basic solution to the polynomial level. Lastly, extensive experiments based on
both real and synthetic data are conducted to evaluate our proposed mechanisms,
with experimental results demonstrating the performance advantages.

Dual-based proportional-integral (PI) controllers are often employed in
practice to solve online allocation problems with global constraints, such as
budget pacing in online advertising. However, controllers are used in a
heuristic fashion and come with no provable guarantees on their performance.
This paper provides the first regret bounds on the performance of dual-based PI
controllers for online allocation problems. We do so by first establishing a
fundamental connection between dual-based PI controllers and a new first-order
algorithm for online convex optimization, which, in a special case, recovers
online mirror descent with momentum. We prove the proposed first-order
algorithm attains low regret for general online convex optimization problems
with adversarial inputs. We leverage this new result to give the first regret
bound for dual-based PI controllers for online allocation problems. As a
byproduct of our proofs, we provide the first regret bound for online mirror
descent for non-smooth convex optimization, which might be of independent
interest.

We consider the problem of combining and learning over a set of adversarial
bandit algorithms with the goal of adaptively tracking the best one on the fly.
The CORRAL algorithm of Agarwal et al. (2017) and its variants (Foster et al.,
2020a) achieve this goal with a regret overhead of order
$\widetilde{O}(\sqrt{MT})$ where $M$ is the number of base algorithms and $T$
is the time horizon. The polynomial dependence on $M$, however, prevents one
from applying these algorithms to many applications where $M$ is poly$(T)$ or
even larger. Motivated by this issue, we propose a new recipe to corral a
larger band of bandit algorithms whose regret overhead has only
\emph{logarithmic} dependence on $M$ as long as some conditions are satisfied.
As the main example, we apply our recipe to the problem of adversarial linear
bandits over a $d$-dimensional $\ell_p$ unit-ball for $p \in (1,2]$. By
corralling a large set of $T$ base algorithms, each starting at a different
time step, our final algorithm achieves the first optimal switching regret
$\widetilde{O}(\sqrt{d S T})$ when competing against a sequence of comparators
with $S$ switches (for some known $S$). We further extend our results to linear
bandits over a smooth and strongly convex domain as well as unconstrained
linear bandits.

We consider the problem of adversarial bandit convex optimization, that is,
online learning over a sequence of arbitrary convex loss functions with only
one function evaluation for each of them. While all previous works assume known
and homogeneous curvature on these loss functions, we study a heterogeneous
setting where each function has its own curvature that is only revealed after
the learner makes a decision. We develop an efficient algorithm that is able to
adapt to the curvature on the fly. Specifically, our algorithm not only
recovers or \emph{even improves} existing results for several homogeneous
settings, but also leads to surprising results for some heterogeneous settings
-- for example, while Hazan and Levy (2014) showed that
$\widetilde{O}(d^{3/2}\sqrt{T})$ regret is achievable for a sequence of $T$
smooth and strongly convex $d$-dimensional functions, our algorithm reveals
that the same is achievable even if $T^{3/4}$ of them are not strongly convex,
and sometimes even if a constant fraction of them are not strongly convex. Our
approach is inspired by the framework of Bartlett et al. (2007) who studied a
similar heterogeneous setting but with stronger gradient feedback. Extending
their framework to the bandit feedback setting requires novel ideas such as
lifting the feasible domain and using a logarithmically homogeneous
self-concordant barrier regularizer.

Issue tracking systems are used in the software industry for the facilitation
of maintenance activities that keep the software robust and up to date with
ever-changing industry requirements. Usually, users report issues that can be
categorized into different labels such as bug reports, enhancement requests,
and questions related to the software. Most of the issue tracking systems make
the labelling of these issue reports optional for the issue submitter, which
leads to a large number of unlabeled issue reports. In this paper, we present a
state-of-the-art method to classify the issue reports into their respective
categories i.e. bug, enhancement, and question. This is a challenging task
because of the common use of informal language in the issue reports. Existing
studies use traditional natural language processing approaches adopting
key-word based features, which fail to incorporate the contextual relationship
between words and therefore result in a high rate of false positives and false
negatives. Moreover, previous works utilize a uni-label approach to classify
the issue reports however, in reality, an issue-submitter can tag one issue
report with more than one label at a time. This paper presents our approach to
classify the issue reports in a multi-label setting. We use an off-the-shelf
neural network called RoBERTa and fine-tune it to classify the issue reports.
We validate our approach on issue reports belonging to numerous industrial
projects from GitHub. We were able to achieve promising F-1 scores of 81%, 74%,
and 80% for bug reports, enhancements, and questions, respectively. We also
develop an industry tool called Automatic Issue Classifier (AIC), which
automatically assigns labels to newly reported issues on GitHub repositories

Since the birth of Bitcoin in 2009, cryptocurrencies have emerged to become a
global phenomenon and an important decentralized financial asset. Due to this
decentralization, the value of these digital currencies against fiat currencies
is highly volatile over time. Therefore, forecasting the crypto-fiat currency
exchange rate is an extremely challenging task. For reliable forecasting, this
paper proposes a multimodal AdaBoost-LSTM ensemble approach that employs all
modalities which derive price fluctuation such as social media sentiments,
search volumes, blockchain information, and trading data. To better support
investment decision making, the approach forecasts also the fluctuation
distribution. The conducted extensive experiments demonstrated the
effectiveness of relying on multimodalities instead of only trading data.
Further experiments demonstrate the outperformance of the proposed approach
compared to existing tools and methods with a 19.29% improvement.

Researchers usually discretize a continuous dependent variable into two
target classes by introducing an artificial discretization threshold (e.g.,
median). However, such discretization may introduce noise (i.e., discretization
noise) due to ambiguous class loyalty of data points that are close to the
artificial threshold. Previous studies do not provide a clear directive on the
impact of discretization noise on the classifiers and how to handle such noise.
In this paper, we propose a framework to help researchers and practitioners
systematically estimate the impact of discretization noise on classifiers in
terms of its impact on various performance measures and the interpretation of
classifiers. Through a case study of 7 software engineering datasets, we find
that: 1) discretization noise affects the different performance measures of a
classifier differently for different datasets; 2) Though the interpretation of
the classifiers are impacted by the discretization noise on the whole, the top
3 most important features are not affected by the discretization noise.
Therefore, we suggest that practitioners and researchers use our framework to
understand the impact of discretization noise on the performance of their built
classifiers and estimate the exact amount of discretization noise to be
discarded from the dataset to avoid the negative impact of such noise.

We consider a seller faced with buyers which have the ability to delay their
decision, which we call patience. Each buyer's type is composed of value and
patience, and it is sampled i.i.d. from a distribution. The seller, using
posted prices, would like to maximize her revenue from selling to the buyer. In
this paper, we formalize this setting and characterize the resulting
Stackelberg equilibrium, where the seller first commits to her strategy, and
then the buyers best respond. Following this, we show how to compute both the
optimal pure and mixed strategies. We then consider a learning setting, where
the seller does not have access to the distribution over buyer's types. Our
main results are the following. We derive a sample complexity bound for the
learning of an approximate optimal pure strategy, by computing the
fat-shattering dimension of this setting. Moreover, we provide a general sample
complexity bound for the approximate optimal mixed strategy. We also consider
an online setting and derive a vanishing regret bound with respect to both the
optimal pure strategy and the optimal mixed strategy.

Accurate quantification of cerebral blood flow (CBF) is essential for the
diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid
stenosis, aneurysms, and stroke. Positron emission tomography (PET) is
currently regarded as the gold standard for the measurement of CBF in the human
brain. PET imaging, however, is not widely available because of its prohibitive
costs, use of ionizing radiation, and logistical challenges, which require a
co-localized cyclotron to deliver the 2 min half-life Oxygen-15 radioisotope.
Magnetic resonance imaging (MRI), in contrast, is more readily available and
does not involve ionizing radiation. In this study, we propose a multi-task
learning framework for brain MRI-to-PET translation and disease diagnosis. The
proposed framework comprises two prime networks: (1) an attention-based 3D
encoder-decoder convolutional neural network (CNN) that synthesizes
high-quality PET CBF maps from multi-contrast MRI images, and (2) a multi-scale
3D CNN that identifies the brain disease corresponding to the input MRI images.
Our multi-task framework yields promising results on the task of MRI-to-PET
translation, achieving an average structural similarity index (SSIM) of 0.94
and peak signal-to-noise ratio (PSNR) of 38dB on a cohort of 120 subjects. In
addition, we show that integrating multiple MRI modalities can improve the
clinical diagnosis of brain diseases.

Despite the successful implementations of physics-informed neural networks in
different scientific domains, it has been shown that for complex nonlinear
systems, achieving an accurate model requires extensive hyperparameter tuning,
network architecture design, and costly and exhaustive training processes. To
avoid such obstacles and make the training of physics-informed models less
precarious, in this paper, a data-driven multi-fidelity physics-informed
framework is proposed based on transfer learning principles. The framework
incorporates the knowledge from low-fidelity auxiliary systems and limited
labeled data from target actual system to significantly improve the performance
of conventional physics-informed models. While minimizing the efforts of
designing a complex task-specific network for the problem at hand, the proposed
settings guide the physics-informed model towards a fast and efficient
convergence to a global optimum. An adaptive weighting method is utilized to
further enhance the optimization of the model composite loss function during
the training process. A data-driven strategy is also introduced for maintaining
high performance in subdomains with significant divergence between low- and
high-fidelity behaviours. The heat transfer of composite materials undergoing a
cure cycle is investigated as a case study to demonstrate the proposed
framework's performance compared to conventional physics-informed models.

As an emerging paradigm in scientific machine learning, neural operators aim
to learn operators, via neural networks, that map between infinite-dimensional
function spaces. Several neural operators have been recently developed.
However, all the existing neural operators are only designed to learn operators
defined on a single Banach space, i.e., the input of the operator is a single
function. Here, for the first time, we study the operator regression via neural
networks for multiple-input operators defined on the product of Banach spaces.
We first prove a universal approximation theorem of continuous multiple-input
operators. We also provide detailed theoretical analysis including the
approximation error, which provides a guidance of the design of the network
architecture. Based on our theory and a low-rank approximation, we propose a
novel neural operator, MIONet, to learn multiple-input operators. MIONet
consists of several branch nets for encoding the input functions and a trunk
net for encoding the domain of the output function. We demonstrate that MIONet
can learn solution operators involving systems governed by ordinary and partial
differential equations. In our computational examples, we also show that we can
endow MIONet with prior knowledge of the underlying system, such as linearity
and periodicity, to further improve the accuracy.

We introduce and study the online Bayesian recommendation problem for a
platform, who can observe a utility-relevant state of a product, repeatedly
interacting with a population of myopic users through an online recommendation
mechanism. This paradigm is common in a wide range of scenarios in the current
Internet economy. For each user with her own private preference and belief, the
platform commits to a recommendation strategy to utilize his information
advantage on the product state to persuade the self-interested user to follow
the recommendation. The platform does not know user's preferences and beliefs,
and has to use an adaptive recommendation strategy to persuade with gradually
learning user's preferences and beliefs in the process.
  We aim to design online learning policies with no Stackelberg regret for the
platform, i.e., against the optimum policy in hindsight under the assumption
that users will correspondingly adapt their behaviors to the benchmark policy.
Our first result is an online policy that achieves double logarithm regret
dependence on the number of rounds. We then present a hardness result showing
that no adaptive online policy can achieve regret with better dependency on the
number of rounds. Finally, by formulating the platform's problem as optimizing
a linear program with membership oracle access, we present our second online
policy that achieves regret with polynomial dependence on the number of states
but logarithm dependence on the number of rounds.

Due to the high costs associated with finetuning large language models,
various recent works propose to adapt them to specific tasks without any
parameter updates through in-context learning. Unfortunately, for in-context
learning there is currently no way to leverage unlabeled data, which is often
much easier to obtain in large quantities than labeled examples. In this work,
we therefore investigate ways to make use of unlabeled examples to improve the
zero-shot performance of pretrained language models without any finetuning: We
introduce Semantic-Oriented Unlabeled Priming (SOUP), a method that classifies
examples by retrieving semantically similar unlabeled examples, assigning
labels to them in a zero-shot fashion, and then using them for in-context
learning. We also propose bag-of-contexts priming, a new priming strategy that
is more suitable for our setting and enables the usage of more examples than
fit into the context window.

Reservoir computing is a machine learning algorithm that excels at predicting
the evolution of time series, in particular, dynamical systems. Moreover, it
has also shown superb performance at solving partial differential equations. In
this work, we adapt this methodology to integrate the time-dependent
Schr\"odinger equation, propagating an initial wavefunction in time. Since such
wavefunctions are complex-valued high-dimensional arrays the reservoir
computing formalism needs to be extended to cope with complex-valued data.
Furthermore, we propose a multi-step learning strategy that avoids overfitting
the training data. We illustrate the performance of our adapted reservoir
computing method by application to four standard problems in molecular
vibrational dynamics.

With the increasing demands on e-commerce platforms, numerous user action
history is emerging. Those enriched action records are vital to understand
users' interests and intents. Recently, prior works for user behavior
prediction mainly focus on the interactions with product-side information.
However, the interactions with search queries, which usually act as a bridge
between users and products, are still under investigated. In this paper, we
explore a new problem named temporal event forecasting, a generalized user
behavior prediction task in a unified query product evolutionary graph, to
embrace both query and product recommendation in a temporal manner. To fulfill
this setting, there involves two challenges: (1) the action data for most users
is scarce; (2) user preferences are dynamically evolving and shifting over
time. To tackle those issues, we propose a novel Retrieval-Enhanced Temporal
Event (RETE) forecasting framework. Unlike existing methods that enhance user
representations via roughly absorbing information from connected entities in
the whole graph, RETE efficiently and dynamically retrieves relevant entities
centrally on each user as high-quality subgraphs, preventing the noise
propagation from the densely evolutionary graph structures that incorporate
abundant search queries. And meanwhile, RETE autoregressively accumulates
retrieval-enhanced user representations from each time step, to capture
evolutionary patterns for joint query and product prediction. Empirically,
extensive experiments on both the public benchmark and four real-world
industrial datasets demonstrate the effectiveness of the proposed RETE method.

There are many examples of cases where access to improved models of human
behavior and cognition has allowed creation of robots which can better interact
with humans, and not least in road vehicle automation this is a rapidly growing
area of research. Human-robot interaction (HRI) therefore provides an important
applied setting for human behavior modeling - but given the vast complexity of
human behavior, how complete and accurate do these models need to be? Here, we
outline some possible ways of thinking about this problem, starting from the
suggestion that modelers need to keep the right end goal in sight: A successful
human-robot interaction, in terms of safety, performance, and human
satisfaction. Efforts toward model completeness and accuracy should be focused
on those aspects of human behavior to which interaction success is most
sensitive. We emphasise that identifying which those aspects are is a difficult
scientific objective in its own right, distinct for each given HRI context. We
propose and exemplify an approach to formulating a priori hypotheses on this
matter, in cases where robots are to be involved in interactions which
currently take place between humans, such as in automated driving. Our
perspective also highlights some possible risks of overreliance on
machine-learned models of human behavior in HRI, and how to mitigate against
those risks.

The graph-based invariant set (GIS) algorithm is a promising set-based
technique for computing the largest (with respect to inclusion) control
invariant set of general discrete-time nonlinear dynamical systems. However,
like other invariant set algorithms for nonlinear systems, the GIS algorithm
may require a lot of resources when computing the control invariant set. This
limits its applicability to higher dimensional systems. In this work, we
present an improved and efficient implementation of the GIS algorithm for
general discrete-time controlled nonlinear systems. We first identify the
bottlenecks through extensive analysis, and then provide remedial procedures to
improve the implementation of the GIS algorithm. Specifically, we developed an
adaptive subdivision scheme using a supervised machine learning-based algorithm
to reduce the cell growth rate and parallelize the graph construction step. We
extensively demonstrate the performance of the improved GIS algorithm using a
numerical example and compare the result to that of the standard GIS algorithm.
The results show that the adaptive subdivision and the parallelization improved
the speed of the algorithm by about 8x and 3x respectively, that of the
standard GIS algorithm.

Cancer is one of the most common and fatal diseases in the world. Breast
cancer affects one in every eight women and one in every eight hundred men.
Hence, our prime target should be early detection of cancer because the early
detection of cancer can be helpful to cure cancer effectively. Therefore, we
propose a saliency detection system with the help of advanced deep learning
techniques, such that the machine will be taught to emulate actions of
pathologists for localization of diagnostically pertinent regions. We study
identification of five diagnostic categories of breast cancer by training a CNN
(VGG16, ResNet architecture). We have used BreakHis dataset to train our model.
We focus on both detection and classification of cancerous regions in
histopathology images. The diagnostically relevant regions are salient. The
detection system will be available as an open source web application which can
be used by pathologists and medical institutions.

Catering to the proliferation of Internet of Things devices and distributed
machine learning at the edge, we propose an energy harvesting federated
learning (EHFL) framework in this paper. The introduction of EH implies that a
client's availability to participate in any FL round cannot be guaranteed,
which complicates the theoretical analysis. We derive novel convergence bounds
that capture the impact of time-varying device availabilities due to the random
EH characteristics of the participating clients, for both parallel and local
stochastic gradient descent (SGD) with non-convex loss functions. The results
suggest that having a uniform client scheduling that maximizes the minimum
number of clients throughout the FL process is desirable, which is further
corroborated by the numerical experiments using a real-world FL task and a
state-of-the-art EH scheduler.

The performance of supervised deep learning methods for medical image
segmentation is often limited by the scarcity of labeled data. As a promising
research direction, semi-supervised learning addresses this dilemma by
leveraging unlabeled data information to assist the learning process. In this
paper, a novel geometry-aware semi-supervised learning framework is proposed
for medical image segmentation, which is a consistency-based method.
Considering that the hard-to-segment regions are mainly located around the
object boundary, we introduce an auxiliary prediction task to learn the global
geometric information. Based on the geometric constraint, the ambiguous
boundary regions are emphasized through an exponentially weighted strategy for
the model training to better exploit both labeled and unlabeled data. In
addition, a dual-view network is designed to perform segmentation from
different perspectives and reduce the prediction uncertainty. The proposed
method is evaluated on the public left atrium benchmark dataset and improves
fully supervised method by 8.7% in Dice with 10% labeled images, while 4.3%
with 20% labeled images. Meanwhile, our framework outperforms six
state-of-the-art semi-supervised segmentation methods.

Despite impressive capabilities and outstanding performance, deep neural
network(DNN) has captured increasing public concern for its security problem,
due to frequent occurrence of erroneous behaviors. Therefore, it is necessary
to conduct systematically testing before its deployment to real-world
applications. Existing testing methods have provided fine-grained criteria
based on neuron coverage and reached high exploratory degree of testing. But
there is still a gap between the neuron coverage and model's robustness
evaluation. To bridge the gap, we observed that neurons which change the
activation value dramatically due to minor perturbation are prone to trigger
incorrect corner cases. Motivated by it, we propose neuron sensitivity and
develop a novel white-box testing framework for DNN, donated as DeepSensor. The
number of sensitive neurons is maximized by particle swarm optimization, thus
diverse corner cases could be triggered and neuron coverage be further improved
when compared with baselines. Besides, considerable robustness enhancement can
be reached when adopting testing examples based on neuron sensitivity for
retraining. Extensive experiments implemented on scalable datasets and models
can well demonstrate the testing effectiveness and robustness improvement of
DeepSensor.

Graph neural networks (GNN) have emerged as a powerful tool for fraud
detection tasks, where fraudulent nodes are identified by aggregating neighbor
information via different relations. To get around such detection, crafty
fraudsters resort to camouflage via connecting to legitimate users (i.e.,
relation camouflage) or providing seemingly legitimate feedbacks (i.e., feature
camouflage). A wide-spread solution reinforces the GNN aggregation process with
neighbor selectors according to original node features. This method may carry
limitations when identifying fraudsters not only with the relation camouflage,
but with the feature camouflage making them hard to distinguish from their
legitimate neighbors. In this paper, we propose a Hierarchical Attention-based
Graph Neural Network (HA-GNN) for fraud detection, which incorporates weighted
adjacency matrices across different relations against camouflage. This is
motivated in the Relational Density Theory and is exploited for forming a
hierarchical attention-based graph neural network. Specifically, we design a
relation attention module to reflect the tie strength between two nodes, while
a neighborhood attention module to capture the long-range structural affinity
associated with the graph. We generate node embeddings by aggregating
information from local/long-range structures and original node features.
Experiments on three real-world datasets demonstrate the effectiveness of our
model over the state-of-the-arts.

The fast-spreading information over the internet is essential to support the
rapid supply of numerous public utility services and entertainment to users.
Social networks and online media paved the way for modern,
timely-communication-fashion and convenient access to all types of information.
However, it also provides new chances for ill use of the massive amount of
available data, such as spreading fake content to manipulate public opinion.
Detection of counterfeit content has raised attention in the last few years for
the advances in deepfake generation. The rapid growth of machine learning
techniques, particularly deep learning, can predict fake content in several
application domains, including fake image and video manipulation. This paper
presents a comprehensive review of recent studies for deepfake content
detection using deep learning-based approaches. We aim to broaden the
state-of-the-art research by systematically reviewing the different categories
of fake content detection. Furthermore, we report the advantages and drawbacks
of the examined works and future directions towards the issues and shortcomings
still unsolved on deepfake detection.

The proliferation of deep learning applications in several areas has led to
the rapid adoption of such solutions from an ever-growing number of
institutions and companies. These entities' deep neural network (DNN) models
are often trained on proprietary data. They require powerful computational
resources, with the resulting DNN models being incorporated in the company's
work pipeline or provided as a service. Being trained on proprietary
information, these models provide a competitive edge for the owner company. At
the same time, these models can be attractive to competitors (or malicious
entities), which can employ state-of-the-art security attacks to obtain and use
these models for their benefit. As these attacks are hard to prevent, it
becomes imperative to have mechanisms that enable an affected entity to verify
the ownership of its DNN with high confidence. This paper presents TATTOOED, a
robust and efficient DNN watermarking technique based on spread-spectrum
channel coding. TATTOOED has a negligible effect on the performance of the DNN
model and is robust against several state-of-the-art mechanisms used to remove
watermarks from DNNs. Our results show that TATTOOED is robust to such removal
techniques even in extreme scenarios. For example, if the removal techniques
such as fine-tuning and parameter pruning change as much as 99% of the model
parameters, the TATTOOED watermark is still present in full in the DNN model
and ensures ownership verification.

Recently, deep learning techniques have been extensively studied for
pansharpening, which aims to generate a high resolution multispectral (HRMS)
image by fusing a low resolution multispectral (LRMS) image with a high
resolution panchromatic (PAN) image. However, existing deep learning-based
pansharpening methods directly learn the mapping from LRMS and PAN to HRMS.
These network architectures always lack sufficient interpretability, which
limits further performance improvements. To alleviate this issue, we propose a
novel deep network for pansharpening by combining the model-based methodology
with the deep learning method. Firstly, we build an observation model for
pansharpening using the convolutional sparse coding (CSC) technique and design
a proximal gradient algorithm to solve this model. Secondly, we unfold the
iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning
the proximal operators using convolutional neural networks. Finally, all the
learnable modules can be automatically learned in an end-to-end manner.
Experimental results on some benchmark datasets show that our network performs
better than other advanced methods both quantitatively and qualitatively.

We review the recent programme of using machine-learning to explore the
landscape of mathematical problems. With this paradigm as a model for human
intuition - complementary to and in contrast with the more formalistic approach
of automated theorem proving - we highlight some experiments on how AI helps
with conjecture formulation, pattern recognition and computation.

Cooperative perception of connected vehicles comes to the rescue when the
field of view restricts stand-alone intelligence. While raw-level cooperative
perception preserves most information to guarantee accuracy, it is demanding in
communication bandwidth and computation power. Therefore, it is important to
schedule the most beneficial vehicle to share its sensor in terms of
supplementary view and stable network connection. In this paper, we present a
model of raw-level cooperative perception and formulate the energy minimization
problem of sensor sharing scheduling as a variant of the Multi-Armed Bandit
(MAB) problem. Specifically, volatility of the neighboring vehicles,
heterogeneity of V2X channels, and the time-varying traffic context are taken
into consideration. Then we propose an online learning-based algorithm with
logarithmic performance loss, achieving a decent trade-off between exploration
and exploitation. Simulation results under different scenarios indicate that
the proposed algorithm quickly learns to schedule the optimal cooperative
vehicle and saves more energy as compared to baseline algorithms.

Recently, various Deep Neural Network (DNN) models have been proposed for
environments like embedded systems with stringent energy constraints. The
fundamental problem of determining the robustness of a DNN with respect to its
energy consumption (energy robustness) is relatively unexplored compared to
accuracy-based robustness. This work investigates the energy robustness of
Adaptive Neural Networks (AdNNs), a type of energy-saving DNNs proposed for
many energy-sensitive domains and have recently gained traction. We propose
EREBA, the first black-box testing method for determining the energy robustness
of an AdNN. EREBA explores and infers the relationship between inputs and the
energy consumption of AdNNs to generate energy surging samples. Extensive
implementation and evaluation using three state-of-the-art AdNNs demonstrate
that test inputs generated by EREBA could degrade the performance of the system
substantially. The test inputs generated by EREBA can increase the energy
consumption of AdNNs by 2,000% compared to the original inputs. Our results
also show that test inputs generated via EREBA are valuable in detecting energy
surging inputs.

In recent centralized nonconvex distributed learning and federated learning,
local methods are one of the promising approaches to reduce communication time.
However, existing work has mainly focused on studying first-order optimality
guarantees. On the other side, second-order optimality guaranteed algorithms,
i.e., algorithms escaping saddle points, have been extensively studied in the
non-distributed optimization literature. In this paper, we study a new local
algorithm called Bias-Variance Reduced Local Perturbed SGD (BVR-L-PSGD), that
combines the existing bias-variance reduced gradient estimator with parameter
perturbation to find second-order optimal points in centralized nonconvex
distributed optimization. BVR-L-PSGD enjoys second-order optimality with nearly
the same communication complexity as the best known one of BVR-L-SGD to find
first-order optimality. Particularly, the communication complexity is better
than non-local methods when the local datasets heterogeneity is smaller than
the smoothness of the local loss. In an extreme case, the communication
complexity approaches to $\widetilde \Theta(1)$ when the local datasets
heterogeneity goes to zero. Numerical results validate our theoretical
findings.

User preference modeling is a vital yet challenging problem in personalized
product search. In recent years, latent space based methods have achieved
state-of-the-art performance by jointly learning semantic representations of
products, users, and text tokens. However, existing methods are limited in
their ability to model user preferences. They typically represent users by the
products they visited in a short span of time using attentive models and lack
the ability to exploit relational information such as user-product interactions
or item co-occurrence relations. In this work, we propose to address the
limitations of prior arts by exploring local and global user behavior patterns
on a user successive behavior graph, which is constructed by utilizing
short-term actions of all users. To capture implicit user preference signals
and collaborative patterns, we use an efficient jumping graph convolution to
explore high-order relations to enrich product representations for user
preference modeling. Our approach can be seamlessly integrated with existing
latent space based methods and be potentially applied in any product retrieval
method that uses purchase history to model user preferences. Extensive
experiments on eight Amazon benchmarks demonstrate the effectiveness and
potential of our approach. The source code is available at
\url{https://github.com/floatSDSDS/SBG}.

The manipulation of latent space has recently become an interesting topic in
the field of generative models. Recent research shows that latent directions
can be used to manipulate images towards certain attributes. However,
controlling the generation process of 3D generative models remains a challenge.
In this work, we propose a novel 3D manipulation method that can manipulate
both the shape and texture of the model using text or image-based prompts such
as 'a young face' or 'a surprised face'. We leverage the power of Contrastive
Language-Image Pre-training (CLIP) model and a pre-trained 3D GAN model
designed to generate face avatars, and create a fully differentiable rendering
pipeline to manipulate meshes. More specifically, our method takes an input
latent code and modifies it such that the target attribute specified by a text
or image prompt is present or enhanced, while leaving other attributes largely
unaffected. Our method requires only 5 minutes per manipulation, and we
demonstrate the effectiveness of our approach with extensive results and
comparisons.

When a clinician refers a patient for an imaging exam, they include the
reason (e.g. relevant patient history, suspected disease) in the scan request;
this appears as the indication field in the radiology report. The
interpretation and reporting of the image are substantially influenced by this
request text, steering the radiologist to focus on particular aspects of the
image. We use the indication field to drive better image classification, by
taking a transformer network which is unimodally pre-trained on text (BERT) and
fine-tuning it for multimodal classification of a dual image-text input. We
evaluate the method on the MIMIC-CXR dataset, and present ablation studies to
investigate the effect of the indication field on the classification
performance. The experimental results show our approach achieves 87.8 average
micro AUROC, outperforming the state-of-the-art methods for unimodal (84.4) and
multimodal (86.0) classification. Our code is available at
https://github.com/jacenkow/mmbt.

Breast cancer is one of the leading causes of female mortality in the world.
This can be reduced when diagnoses are performed at the early stages of
progression. Further, the efficiency of the process can be significantly
improved with computer aided diagnosis. Deep learning based approaches have
been successfully applied to achieve this. One of the limiting factors for
training deep networks in a supervised manner is the dependency on large
amounts of expert annotated data. In reality, large amounts of unlabelled data
and only small amounts of expert annotated data are available. In such
scenarios, transfer learning approaches and self-supervised learning (SSL)
based approaches can be leveraged. In this study, we propose a novel
self-supervision pretext task to train a convolutional neural network (CNN) and
extract domain specific features. This method was compared with deep features
extracted using pre-trained CNNs such as DenseNet-121 and ResNet-50 trained on
ImageNet. Additionally, two types of patch-combination methods were introduced
and compared with majority voting. The methods were validated on the BACH
microscopy images dataset. Results indicated that the best performance of 99%
sensitivity was achieved for the deep features extracted using ResNet50 with
concatenation of patch-level embedding. Preliminary results of SSL to extract
domain specific features indicated that with just 15% of unlabelled data a high
sensitivity of 94% can be achieved for a four class classification of

This study aims to predict the pollution level that threatens the Marilao
River, located in the province of Bulacan, Philippines. The inhabitants of this
area are now being exposed to pollution. Contamination of this waterway comes
from both formal and informal industries, such as a used lead-acid battery,
open dumpsites metal refining, and other toxic metals. Using various water
quality parameters like Dissolved Oxygen (DO), Potential of Hydrogen (pH),
Biochemical Oxygen Demand (BOD) and Total Suspended Solids (TSS) were the basis
for predicting the pollution level. This study used the Data Mining technique
based on the sample data collected from January of 2013 to November of 2017.
These were used as a training data and test results to predict the river
condition with its corresponding pollution level classification indicated with
the used of colors such as Green for Normal, Yellow for Average, Orange for
Polluted and Red for Highly Polluted. The model got an accuracy of 91.75% with
a Kappa value of 0.8115, interpreted as Strong in terms of the level of
agreement.

One of the major open problems in machine learning is to characterize
generalization in the overparameterized regime, where most traditional
generalization bounds become inconsistent (Nagarajan and Kolter, 2019). In many
scenarios, their failure can be attributed to obscuring the crucial interplay
between the training algorithm and the underlying data distribution. To address
this issue, we propose a concept named compatibility, which quantitatively
characterizes generalization in a both data-relevant and algorithm-relevant
manner. By considering the entire training trajectory and focusing on
early-stopping iterates, compatibility exploits the data and the algorithm
information and is therefore a more suitable notion for generalization. We
validate this by theoretically studying compatibility under the setting of
solving overparameterized linear regression with gradient descent.
Specifically, we perform a data-dependent trajectory analysis and derive a
sufficient condition for compatibility in such a setting. Our theoretical
results demonstrate that in the sense of compatibility, generalization holds
with significantly weaker restrictions on the problem instance than the
previous last iterate analysis.

Advanced adversarial attacks such as membership inference and model
memorization can make federated learning (FL) vulnerable and potentially leak
sensitive private data. Local differentially private (LDP) approaches are
gaining more popularity due to stronger privacy notions and native support for
data distribution compared to other differentially private (DP) solutions.
However, DP approaches assume that the FL server (that aggregates the models)
is honest (run the FL protocol honestly) or semi-honest (run the FL protocol
honestly while also trying to learn as much information as possible). These
assumptions make such approaches unrealistic and unreliable for real-world
settings. Besides, in real-world industrial environments (e.g., healthcare),
the distributed entities (e.g., hospitals) are already composed of locally
running machine learning models (this setting is also referred to as the
cross-silo setting). Existing approaches do not provide a scalable mechanism
for privacy-preserving FL to be utilized under such settings, potentially with
untrusted parties. This paper proposes a new local differentially private FL
(named LDPFL) protocol for industrial settings. LDPFL can run in industrial
settings with untrusted entities while enforcing stronger privacy guarantees
than existing approaches. LDPFL shows high FL model performance (up to 98%)
under small privacy budgets (e.g., epsilon = 0.5) in comparison to existing
methods.

Questions in causality, control, and reinforcement learning go beyond the
classical machine learning task of prediction under i.i.d. observations.
Instead, these fields consider the problem of learning how to actively perturb
a system to achieve a certain effect on a response variable. Arguably, they
have complementary views on the problem: In control, one usually aims to first
identify the system by excitation strategies to then apply model-based design
techniques to control the system. In (non-model-based) reinforcement learning,
one directly optimizes a reward. In causality, one focus is on identifiability
of causal structure. We believe that combining the different views might create
synergies and this competition is meant as a first step toward such synergies.
The participants had access to observational and (offline) interventional data
generated by dynamical systems. Track CHEM considers an open-loop problem in
which a single impulse at the beginning of the dynamics can be set, while Track
ROBO considers a closed-loop problem in which control variables can be set at
each time step. The goal in both tracks is to infer controls that drive the
system to a desired state. Code is open-sourced (
https://github.com/LearningByDoingCompetition/learningbydoing-comp ) to
reproduce the winning solutions of the competition and to facilitate trying out
new methods on the competition tasks.

Survival analysis (SA) is an active field of research that is concerned with
time-to-event outcomes and is prevalent in many domains, particularly
biomedical applications. Despite its importance, SA remains challenging due to
small-scale data sets and complex outcome distributions, concealed by
truncation and censoring processes. The piecewise exponential additive mixed
model (PAMM) is a model class addressing many of these challenges, yet PAMMs
are not applicable in high-dimensional feature settings or in the case of
unstructured or multimodal data. We unify existing approaches by proposing
DeepPAMM, a versatile deep learning framework that is well-founded from a
statistical point of view, yet with enough flexibility for modeling complex
hazard structures. We illustrate that DeepPAMM is competitive with other
machine learning approaches with respect to predictive performance while
maintaining interpretability through benchmark experiments and an extended case
study.

Source code authorship attribution is an important problem often encountered
in applications such as software forensics, bug fixing, and software quality
analysis. Recent studies show that current source code authorship attribution
methods can be compromised by attackers exploiting adversarial examples and
coding style manipulation. This calls for robust solutions to the problem of
code authorship attribution. In this paper, we initiate the study on making
Deep Learning (DL)-based code authorship attribution robust. We propose an
innovative framework called Robust coding style Patterns Generation (RoPGen),
which essentially learns authors' unique coding style patterns that are hard
for attackers to manipulate or imitate. The key idea is to combine data
augmentation and gradient augmentation at the adversarial training phase. This
effectively increases the diversity of training examples, generates meaningful
perturbations to gradients of deep neural networks, and learns diversified
representations of coding styles. We evaluate the effectiveness of RoPGen using
four datasets of programs written in C, C++, and Java. Experimental results
show that RoPGen can significantly improve the robustness of DL-based code
authorship attribution, by respectively reducing 22.8% and 41.0% of the success
rate of targeted and untargeted attacks on average.

Convolutional Neural Networks (CNNs) are artificial learning systems
typically based on two operations: convolution, which implements feature
extraction through filtering, and pooling, which implements dimensionality
reduction. The impact of pooling in the classification performance of the CNNs
has been highlighted in several previous works, and a variety of alternative
pooling operators have been proposed. However, only a few of them tackle with
the uncertainty that is naturally propagated from the input layer to the
feature maps of the hidden layers through convolutions. In this paper we
present a novel pooling operation based on (type-1) fuzzy sets to cope with the
local imprecision of the feature maps, and we investigate its performance in
the context of image classification. Fuzzy pooling is performed by
fuzzification, aggregation and defuzzification of feature map neighborhoods. It
is used for the construction of a fuzzy pooling layer that can be applied as a
drop-in replacement of the current, crisp, pooling layers of CNN architectures.
Several experiments using publicly available datasets show that the proposed
approach can enhance the classification performance of a CNN. A comparative
evaluation shows that it outperforms state-of-the-art pooling approaches.

The Artificial Intelligence industry regularly develops applications that
mostly rely on Knowledge Bases, a data repository about specific, or general,
domains, usually represented in a graph shape. Similar to other databases, they
face two main challenges: information ingestion and information retrieval. We
approach these challenges by jointly learning graph extraction from text and
text generation from graphs. The proposed solution, a T5 architecture, is
trained in a multi-task semi-supervised environment, with our collected
non-parallel data, following a cycle training regime. Experiments on WebNLG
dataset show that our approach surpasses unsupervised state-of-the-art results
in text-to-graph and graph-to-text. More relevantly, our framework is more
consistent across seen and unseen domains than supervised models. The resulting
model can be easily trained in any new domain with non-parallel data, by simply
adding text and graphs about it, in our cycle framework.

Abstract object properties and their relations are deeply rooted in human
common sense, allowing people to predict the dynamics of the world even in
situations that are novel but governed by familiar laws of physics. Standard
machine learning models in model-based reinforcement learning are inadequate to
generalize in this way. Inspired by the classic framework of noisy
indeterministic deictic (NID) rules, we introduce here Neural NID, a method
that learns abstract object properties and relations between objects with a
suitably regularized graph neural network. We validate the greater
generalization capability of Neural NID on simple benchmarks specifically
designed to assess the transition dynamics learned by the model.

Music performance synthesis aims to synthesize a musical score into a natural
performance. In this paper, we borrow recent advances in text-to-speech
synthesis and present the Deep Performer -- a novel system for score-to-audio
music performance synthesis. Unlike speech, music often contains polyphony and
long notes. Hence, we propose two new techniques for handling polyphonic inputs
and providing a fine-grained conditioning in a transformer encoder-decoder
model. To train our proposed system, we present a new violin dataset consisting
of paired recordings and scores along with estimated alignments between them.
We show that our proposed model can synthesize music with clear polyphony and
harmonic structures. In a listening test, we achieve competitive quality
against the baseline model, a conditional generative audio model, in terms of
pitch accuracy, timbre and noise level. Moreover, our proposed model
significantly outperforms the baseline on an existing piano dataset in overall
quality.

In point cloud compression, sufficient contexts are significant for modeling
the point cloud distribution. However, the contexts gathered by the previous
voxel-based methods decrease when handling sparse point clouds. To address this
problem, we propose a multiple-contexts deep learning framework called
OctAttention employing the octree structure, a memory-efficient representation
for point clouds. Our approach encodes octree symbol sequences in a lossless
way by gathering the information of sibling and ancestor nodes. Expressly, we
first represent point clouds with octree to reduce spatial redundancy, which is
robust for point clouds with different resolutions. We then design a
conditional entropy model with a large receptive field that models the sibling
and ancestor contexts to exploit the strong dependency among the neighboring
nodes and employ an attention mechanism to emphasize the correlated nodes in
the context. Furthermore, we introduce a mask operation during training and
testing to make a trade-off between encoding time and performance. Compared to
the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate
gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset
(e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based
baseline. The code is available at https://github.com/zb12138/OctAttention.

We present an end-to-end Reinforcement Learning(RL) framework for robotic
manipulation tasks, using a robust and efficient keypoints representation. The
proposed method learns keypoints from camera images as the state
representation, through a self-supervised autoencoder architecture. The
keypoints encode the geometric information, as well as the relationship of the
tool and target in a compact representation to ensure efficient and robust
learning. After keypoints learning, the RL step then learns the robot motion
from the extracted keypoints state representation. The keypoints and RL
learning processes are entirely done in the simulated environment. We
demonstrate the effectiveness of the proposed method on robotic manipulation
tasks including grasping and pushing, in different scenarios. We also
investigate the generalization capability of the trained model. In addition to
the robust keypoints representation, we further apply domain randomization and
adversarial training examples to achieve zero-shot sim-to-real transfer in
real-world robotic manipulation tasks.

Cybersecurity educators have widely introduced hackathons to facilitate
practical knowledge gaining in cybersecurity education. Introducing such events
into cybersecurity courses can provide valuable learning experiences for
students. The nature of the hackathon format encourages a learning-by-doing
approach, and the hackathon outcomes can serve as evidence for students
knowledge, capability and learning gains. Prior work on hackathons in education
mainly focused on collocated hackathon events in the traditional classroom
setting. These hackathon events often took place as a one-off event at the end
of the course. However, one-off hackathon events at the end of a course might
not be sufficient to improve learning. Instead, we focus on analyzing the
integration of a series of online hackathon events into an online cybersecurity
course and explore how this integration can address online education issues by
encouraging collaboration and developing a practical understanding of the
delivered course by solving real-world challenges. We evaluate interventions to
foster learning and analyze its effect on collaboration and learning gains for
students in the course. Our findings indicate that students attribute learning
benefits to the introduced interventions that supported teamwork and
collaboration, maintained student participation and interest in the course, and
encouraged learning-by-doing.

The global optimization literature places large emphasis on reducing
intractable optimization problems into more tractable structured optimization
forms. In order to achieve this goal, many existing methods are restricted to
optimization over explicit constraints and objectives that use a subset of
possible mathematical primitives. These are limiting in real-world contexts
where more general explicit and black box constraints appear. Leveraging the
dramatic speed improvements in mixed-integer optimization (MIO) and recent
research in machine learning, we propose a new method to learn MIO-compatible
approximations of global optimization problems using optimal decision trees
with hyperplanes (OCT-Hs). This constraint learning approach only requires a
bounded variable domain, and can address both explicit and inexplicit
constraints. We solve the MIO approximation efficiently to find a near-optimal,
near-feasible solution to the global optimization problem. We further improve
the solution using a series of projected gradient descent iterations. We test
the method on a number of numerical benchmarks from the literature as well as
real-world design problems, demonstrating its promise in finding global optima

Development of new algorithms in the area of machine learning, especially
clustering, comparative studies of such algorithms as well as testing according
to software engineering principles requires availability of labeled data sets.
While standard benchmarks are made available, a broader range of such data sets
is necessary in order to avoid the problem of overfitting. In this context,
theoretical works on axiomatization of clustering algorithms, especially axioms
on clustering preserving transformations are quite a cheap way to produce
labeled data sets from existing ones. However, the frequently cited axiomatic
system of Kleinberg:2002, as we show in this paper, is not applicable for
finite dimensional Euclidean spaces, in which many algorithms like $k$-means,
operate. In particular, the so-called outer-consistency axiom fails upon making
small changes in datapoint positions and inner-consistency axiom is valid only
for identity transformation in general settings.
  Hence we propose an alternative axiomatic system, in which Kleinberg's inner
consistency axiom is replaced by a centric consistency axiom and outer
consistency axiom is replaced by motion consistency axiom. We demonstrate that
the new system is satisfiable for a hierarchical version of $k$-means with
auto-adjusted $k$, hence it is not contradictory. Additionally, as $k$-means
creates convex clusters only, we demonstrate that it is possible to create a
version detecting concave clusters and still the axiomatic system can be
satisfied. The practical application area of such an axiomatic system may be
the generation of new labeled test data from existent ones for clustering
algorithm testing. %We propose the gravitational consistency as a replacement
which does not have this deficiency.

1-bit gradient compression and local steps are two representative techniques
that enable drastic communication reduction in distributed SGD. Their benefits,
however, remain an open question on Adam-based large model pre-training (e.g.
BERT and GPT). In this paper, we demonstrate the non-linearity in Adam causes
slow convergence even when 1-bit compression or local steps are individually
applied. To alleviate this limitation, we propose 0/1 Adam that linearizes each
Adam step via approximating its optimizer states using their stale estimates
and linear correlation. 0/1 Adam performs an Adam-like step to preserve the
adaptivity, while its linearity allows utilizing 1-bit compression and local
steps simultaneously for wall-clock time speed up. We provide convergence
guarantee for 0/1 Adam on smooth non-convex objectives. On various large-scale
benchmarks such as BERT-Base, BERT-Large, GPT-2 pre-training and ImageNet, we
demonstrate on up to 128 GPUs that 0/1 Adam is able to reduce up to 87% of data
volume, 54% of communication rounds, and achieve up to 2$\times$ higher
training throughput and end-to-end training time reduction compared to the
state-of-the-art baseline 1-bit Adam; while enjoying the same statistical
convergence speed and end task model accuracy on GLUE dataset and ImageNet
validation set.

Understanding deep learning models is important for EEG-based brain-computer
interface (BCI), since it not only can boost trust of end users but also
potentially shed light on reasons that cause a model to fail. However, deep
learning interpretability has not yet raised wide attention in this field. It
remains unknown how reliably existing interpretation techniques can be used and
to which extent they can reflect the model decisions. In order to fill this
research gap, we conduct the first quantitative evaluation and explore the best
practice of interpreting deep learning models designed for EEG-based BCI. We
design metrics and test seven well-known interpretation techniques on benchmark
deep learning models. Results show that methods of GradientInput, DeepLIFT,
integrated gradient, and layer-wise relevance propagation (LRP) have similar
and better performance than saliency map, deconvolution and guided
backpropagation methods for interpreting the model decisions. In addition, we
propose a set of processing steps that allow the interpretation results to be
visualized in an understandable and trusted way. Finally, we illustrate with
samples on how deep learning interpretability can benefit the domain of
EEG-based BCI. Our work presents a promising direction of introducing deep
learning interpretability to EEG-based BCI.

Imitation learning (IL) is a popular paradigm for training policies in
robotic systems when specifying the reward function is difficult. However,
despite the success of IL algorithms, they impose the somewhat unrealistic
requirement that the expert demonstrations must come from the same domain in
which a new imitator policy is to be learned. We consider a practical setting,
where (i) state-only expert demonstrations from the real (deployment)
environment are given to the learner, (ii) the imitation learner policy is
trained in a simulation (training) environment whose transition dynamics is
slightly different from the real environment, and (iii) the learner does not
have any access to the real environment during the training phase beyond the
batch of demonstrations given. Most of the current IL methods, such as
generative adversarial imitation learning and its state-only variants, fail to
imitate the optimal expert behavior under the above setting. By leveraging
insights from the Robust reinforcement learning (RL) literature and building on
recent adversarial imitation approaches, we propose a robust IL algorithm to
learn policies that can effectively transfer to the real environment without
fine-tuning. Furthermore, we empirically demonstrate on continuous-control
benchmarks that our method outperforms the state-of-the-art state-only IL
method in terms of the zero-shot transfer performance in the real environment
and robust performance under different testing conditions.

The student peer-group is one of the most important influences on student
development. Group work is essential for creating positive learning
experiences, especially in remote-learning where student interactions are more
challenging. While the benefits of study groups are established, students from
underrepresented communities often face challenges in finding social support
for their education when compared with those from majority groups. We present a
system for flexible and inclusive study group formation that can scale to
thousands of students.
  Our focus is on long-term study groups that persist throughout the semester
and beyond. Students are periodically provided opportunities to obtain a new
study group if they feel their current group is not a good fit. In contrast to
prior work that generates single-use groups, our process enables continuous
refinement of groups for each student, which in turn informs our algorithm for
future iterations.
  We trialed our approach in a 1000+ student introductory Electrical
Engineering and Computer Science course that was conducted entirely online
during the COVID-19 pandemic. We found that of all students matched to study
groups through our algorithm, a large majority felt comfortable asking
questions (78%) and sharing ideas (74%) with their group. Students from
underrepresented backgrounds were more likely to request software-matching for
study groups when compared with students from majority groups. However,
underrepresented students that we did match into study groups had group
experiences that were just as successful as students from' majority groups.
Students in engaged, regularly participating study groups had more positive
results across all other indicators of the study group experience, and certain
positive group experiences were associated with higher exam scores overall.

Self-supervised learning establishes a new paradigm of learning
representations with much fewer or even no label annotations. Recently there
has been remarkable progress on large-scale contrastive learning models which
require substantial computing resources, yet such models are not practically
optimal for small-scale tasks. To fill the gap, we aim to study contrastive
learning on the wearable-based activity recognition task. Specifically, we
conduct an in-depth study of contrastive learning from both algorithmic-level
and task-level perspectives. For algorithmic-level analysis, we decompose
contrastive models into several key components and conduct rigorous
experimental evaluations to better understand the efficacy and rationale behind
contrastive learning. More importantly, for task-level analysis, we show that
the wearable-based signals bring unique challenges and opportunities to
existing contrastive models, which cannot be readily solved by existing
algorithms. Our thorough empirical studies suggest important practices and shed
light on future research challenges. In the meantime, this paper presents an
open-source PyTorch library \texttt{CL-HAR}, which can serve as a practical
tool for researchers. The library is highly modularized and easy to use, which
opens up avenues for exploring novel contrastive models quickly in the future.

The distributions of real-life data streams are usually nonstationary, where
one exciting setting is that a stream can be decomposed into several offline
intervals with a fixed time horizon but different distributions and an
out-of-distribution online interval. We call such data multi-distributional
data streams, on which learning an on-the-fly expert for unseen samples with a
desirable generalization is demanding yet highly challenging owing to the
multi-distributional streaming nature, particularly when initially limited data
is available for the online interval. To address these challenges, this work
introduces a novel optimization method named coupling online-offline learning
(CO$_2$) with theoretical guarantees about the knowledge transfer, the regret,
and the generalization error. CO$_2$ extracts knowledge by training an offline
expert for each offline interval and update an online expert by an
off-the-shelf online optimization method in the online interval. CO$_2$ outputs
a hypothesis for each sample by adaptively coupling both the offline experts
and the underlying online expert through an expert-tracking strategy to adapt
to the dynamic environment. To study the generalization performance of the
output hypothesis, we propose a general theory to analyze its excess risk bound
related to the loss function properties, the hypothesis class, the data
distribution, and the regret.

Given their ability to effectively learn non-linear mappings and perform fast
inference, deep neural networks (NNs) have been proposed as a viable
alternative to traditional simulation-driven approaches for solving
high-dimensional eigenvalue equations (HDEs), which are the foundation for many
scientific applications. Unfortunately, for the learned models in these
scientific applications to achieve generalization, a large, diverse, and
preferably annotated dataset is typically needed and is computationally
expensive to obtain. Furthermore, the learned models tend to be memory- and
compute-intensive primarily due to the size of the output layer. While
generalization, especially extrapolation, with scarce data has been attempted
by imposing physical constraints in the form of physics loss, the problem of
model scalability has remained.
  In this paper, we alleviate the compute bottleneck in the output layer by
using physics knowledge to decompose the complex regression task of predicting
the high-dimensional eigenvectors into multiple simpler sub-tasks, each of
which are learned by a simple "expert" network. We call the resulting
architecture of specialized experts Physics-Guided Mixture-of-Experts (PG-MoE).
We demonstrate the efficacy of such physics-guided problem decomposition for
the case of the Schr\"{o}dinger's Equation in Quantum Mechanics. Our proposed
PG-MoE model predicts the ground-state solution, i.e., the eigenvector that
corresponds to the smallest possible eigenvalue. The model is 150x smaller than
the network trained to learn the complex task while being competitive in
generalization. To improve the generalization of the PG-MoE, we also employ a
physics-guided loss function based on variational energy, which by quantum
mechanics principles is minimized iff the output is the ground-state solution.

Wav2Vec2.0 is a state-of-the-art model which learns speech representations
through unlabeled speech data, aka, self supervised learning. The pretrained
model is then fine tuned on small amounts of labeled data to use it for
speech-to-text and machine translation tasks. Wav2Vec 2.0 is a transformative
solution for low resource languages as it is mainly developed using unlabeled
audio data. Getting large amounts of labeled data is resource intensive and
especially challenging to do for low resource languages such as Swahilli,
Tatar, etc. Furthermore, Wav2Vec2.0 word-error-rate(WER) matches or surpasses
the very recent supervised learning algorithms while using 100x less labeled
data. Given its importance and enormous potential in enabling speech based
tasks on world's 7000 languages, it is key to evaluate the accuracy, latency
and efficiency of this model on low resource and low power edge devices and
investigate the feasibility of using it in such devices for private, secure and
reliable speech based tasks. On-device speech tasks preclude sending audio data
to the server hence inherently providing privacy, reduced latency and enhanced
reliability. In this paper, Wav2Vec2.0 model's accuracy and latency has been
evaluated on Raspberry Pi along with the KenLM language model for speech
recognition tasks. How to tune certain parameters to achieve desired level of
WER rate and latency while meeting the CPU, memory and energy budgets of the
product has been discussed.

With the High Luminosity LHC coming online in the near future, event
generators will need to provide very large event samples to match the
experimental precision. Currently, the estimated cost to generate these events
exceeds the computing budget of the LHC experiments. To address these issues,
the computing efficiency of event generators need to be improved. Many
different approaches are being taken to achieve this goal. I will cover the
ongoing work on implementing event generators on the GPUs, machine learning the
matrix element, machine learning the phase space, and minimizing the number of
negative weight events.

We tackle the problem of image inpainting in the remote sensing domain.
Remote sensing images possess high resolution and geographical variations, that
render the conventional inpainting methods less effective. This further entails
the requirement of models with high complexity to sufficiently capture the
spectral, spatial and textural nuances within an image, emerging from its high
spatial variability. To this end, we propose a novel inpainting method that
individually focuses on each aspect of an image such as edges, colour and
texture using a task specific GAN. Moreover, each individual GAN also
incorporates the attention mechanism that explicitly extracts the spectral and
spatial features. To ensure consistent gradient flow, the model uses residual
learning paradigm, thus simultaneously working with high and low level
features. We evaluate our model, alongwith previous state of the art models, on
the two well known remote sensing datasets, Open Cities AI and Earth on Canvas,
and achieve competitive performance.

Deep Learning (DL) models are widely used in machine learning due to their
performance and ability to deal with large datasets while producing high
accuracy and performance metrics. The size of such datasets and the complexity
of DL models cause such models to be complex, consuming large amount of
resources and time to train. Many recent libraries and applications are
introduced to deal with DL complexity and efficiency issues. In this paper, we
evaluated one example, Microsoft DeepSpeed library through classification
tasks. DeepSpeed public sources reported classification performance metrics on
the LeNet architecture. We extended this through evaluating the library on
several modern neural network architectures, including convolutional neural
networks (CNNs) and Vision Transformer (ViT). Results indicated that DeepSpeed,
while can make improvements in some of those cases, it has no or negative
impact on others.

In many practical applications of AI, an AI model is used as a decision aid
for human users. The AI provides advice that a human (sometimes) incorporates
into their decision-making process. The AI advice is often presented with some
measure of "confidence" that the human can use to calibrate how much they
depend on or trust the advice. In this paper, we present an initial exploration
that suggests showing AI models as more confident than they actually are, even
when the original AI is well-calibrated, can improve human-AI performance
(measured as the accuracy and confidence of the human's final prediction after
seeing the AI advice). We first train a model to predict human incorporation of
AI advice using data from thousands of human-AI interactions. This enables us
to explicitly estimate how to transform the AI's prediction confidence, making
the AI uncalibrated, in order to improve the final human prediction. We
empirically validate our results across four different tasks--dealing with
images, text and tabular data--involving hundreds of human participants. We
further support our findings with simulation analysis. Our findings suggest the
importance of jointly optimizing the human-AI system as opposed to the standard
paradigm of optimizing the AI model alone.

Automatic static analysis tools (ASATs), such as Findbugs, have a high false
alarm rate. The large number of false alarms produced poses a barrier to
adoption. Researchers have proposed the use of machine learning to prune false
alarms and present only actionable warnings to developers. The state-of-the-art
study has identified a set of "Golden Features" based on metrics computed over
the characteristics and history of the file, code, and warning. Recent studies
show that machine learning using these features is extremely effective and that
they achieve almost perfect performance.
  We perform a detailed analysis to better understand the strong performance of
the "Golden Features". We found that several studies used an experimental
procedure that results in data leakage and data duplication, which are subtle
issues with significant implications. Firstly, the ground-truth labels have
leaked into features that measure the proportion of actionable warnings in a
given context. Secondly, many warnings in the testing dataset appear in the
training dataset. Next, we demonstrate limitations in the warning oracle that
determines the ground-truth labels, a heuristic comparing warnings in a given
revision to a reference revision in the future. We show the choice of reference
revision influences the warning distribution. Moreover, the heuristic produces
labels that do not agree with human oracles. Hence, the strong performance of
these techniques previously seen is overoptimistic of their true performance if
adopted in practice. Our results convey several lessons and provide guidelines
for evaluating false alarm detectors.

Semi-supervised learning (SSL) is the branch of machine learning that aims to
improve learning performance by leveraging unlabeled data when labels are
insufficient. Recently, SSL with deep models has proven to be successful on
standard benchmark tasks. However, they are still vulnerable to various
robustness threats in real-world applications as these benchmarks provide
perfect unlabeled data, while in realistic scenarios, unlabeled data could be
corrupted. Many researchers have pointed out that after exploiting corrupted
unlabeled data, SSL suffers severe performance degradation problems. Thus,
there is an urgent need to develop SSL algorithms that could work robustly with
corrupted unlabeled data. To fully understand robust SSL, we conduct a survey
study. We first clarify a formal definition of robust SSL from the perspective
of machine learning. Then, we classify the robustness threats into three
categories: i) distribution corruption, i.e., unlabeled data distribution is
mismatched with labeled data; ii) feature corruption, i.e., the features of
unlabeled examples are adversarially attacked; and iii) label corruption, i.e.,
the label distribution of unlabeled data is imbalanced. Under this unified
taxonomy, we provide a thorough review and discussion of recent works that
focus on these issues. Finally, we propose possible promising directions within
robust SSL to provide insights for future research.

Motivated by their recent advances, deep learning techniques have been widely
applied to low-light image enhancement (LIE) problem. Among which, Retinex
theory based ones, mostly following a decomposition-adjustment pipeline, have
taken an important place due to its physical interpretation and promising
performance. However, current investigations on Retinex based deep learning are
still not sufficient, ignoring many useful experiences from traditional
methods. Besides, the adjustment step is either performed with simple image
processing techniques, or by complicated networks, both of which are
unsatisfactory in practice. To address these issues, we propose a new deep
learning framework for the LIE problem. The proposed framework contains a
decomposition network inspired by algorithm unrolling, and adjustment networks
considering both global brightness and local brightness sensitivity. By virtue
of algorithm unrolling, both implicit priors learned from data and explicit
priors borrowed from traditional methods can be embedded in the network,
facilitate to better decomposition. Meanwhile, the consideration of global and
local brightness can guide designing simple yet effective network modules for
adjustment. Besides, to avoid manually parameter tuning, we also propose a
self-supervised fine-tuning strategy, which can always guarantee a promising
performance. Experiments on a series of typical LIE datasets demonstrated the
effectiveness of the proposed method, both quantitatively and visually, as
compared with existing methods.

Adaptive optimization methods have become the default solvers for many
machine learning tasks. Unfortunately, the benefits of adaptivity may degrade
when training with differential privacy, as the noise added to ensure privacy
reduces the effectiveness of the adaptive preconditioner. To this end, we
propose AdaDPS, a general framework that uses non-sensitive side information to
precondition the gradients, allowing the effective use of adaptive methods in
private settings. We formally show AdaDPS reduces the amount of noise needed to
achieve similar privacy guarantees, thereby improving optimization performance.
Empirically, we leverage simple and readily available side information to
explore the performance of AdaDPS in practice, comparing to strong baselines in
both centralized and federated settings. Our results show that AdaDPS improves
accuracy by 7.7% (absolute) on average -- yielding state-of-the-art
privacy-utility trade-offs on large-scale text and image benchmarks.

As an essential attribute of organic compounds, polarity has a profound
influence on many molecular properties such as solubility and phase transition
temperature. Thin layer chromatography (TLC) represents a commonly used
technique for polarity measurement. However, current TLC analysis presents
several problems, including the need for a large number of attempts to obtain
suitable conditions, as well as irreproducibility due to non-standardization.
Herein, we describe an automated experiment system for TLC analysis. This
system is designed to conduct TLC analysis automatically, facilitating
high-throughput experimentation by collecting large experimental data under
standardized conditions. Using these datasets, machine learning (ML) methods
are employed to construct surrogate models correlating organic compounds'
structures and their polarity using retardation factor (Rf). The trained ML
models are able to predict the Rf value curve of organic compounds with high
accuracy. Furthermore, the constitutive relationship between the compound and
its polarity can also be discovered through these modeling methods, and the
underlying mechanism is rationalized through adsorption theories. The trained
ML models not only reduce the need for empirical optimization currently
required for TLC analysis, but also provide general guidelines for the
selection of conditions, making TLC an easily accessible tool for the broad

Human brain is continuously inundated with the multisensory information and
their complex interactions coming from the outside world at any given moment.
Such information is automatically analyzed by binding or segregating in our
brain. While this task might seem effortless for human brains, it is extremely
challenging to build a machine that can perform similar tasks since complex
interactions cannot be dealt with single type of integration but requires more
sophisticated approaches. In this paper, we propose a new model to address the
multisensory integration problem with individual event-specific layers in a
multi-task learning scheme. Unlike previous works where single type of fusion
is used, we design event-specific layers to deal with different audio-visual
relationship tasks, enabling different ways of audio-visual formation.
Experimental results show that our event-specific layers can discover unique
properties of the audio-visual relationships in the videos. Moreover, although
our network is formulated with single labels, it can output additional true
multi-labels to represent the given videos. We demonstrate that our proposed
framework also exposes the modality bias of the video data category-wise and
dataset-wise manner in popular benchmark datasets.

Stochastic approximation algorithms are iterative procedures which are used
to approximate a target value in an environment where the target is unknown and
direct observations are corrupted by noise. These algorithms are useful, for
instance, for root-finding and function minimization when the target function
or model is not directly known. Originally introduced in a 1951 paper by
Robbins and Monro, the field of Stochastic approximation has grown enormously
and has come to influence application domains from adaptive signal processing
to artificial intelligence. As an example, the Stochastic Gradient Descent
algorithm which is ubiquitous in various subdomains of Machine Learning is
based on stochastic approximation theory. In this paper, we give a formal proof
(in the Coq proof assistant) of a general convergence theorem due to Aryeh
Dvoretzky, which implies the convergence of important classical methods such as
the Robbins-Monro and the Kiefer-Wolfowitz algorithms. In the process, we build
a comprehensive Coq library of measure-theoretic probability theory and

In this paper, we propose "Confident AI" as a means to designing Artificial
Intelligence (AI) and Machine Learning (ML) systems with both algorithm and
user confidence in model predictions and reported results. The 4 basic tenets
of Confident AI are Repeatability, Believability, Sufficiency, and
Adaptability. Each of the tenets is used to explore fundamental issues in
current AI/ML systems and together provide an overall approach to Confident AI.

Open-set recognition and adversarial defense study two key aspects of deep
learning that are vital for real-world deployment. The objective of open-set
recognition is to identify samples from open-set classes during testing, while
adversarial defense aims to robustify the network against images perturbed by
imperceptible adversarial noise. This paper demonstrates that open-set
recognition systems are vulnerable to adversarial samples. Furthermore, this
paper shows that adversarial defense mechanisms trained on known classes are
unable to generalize well to open-set samples. Motivated by these observations,
we emphasize the necessity of an Open-Set Adversarial Defense (OSAD) mechanism.
This paper proposes an Open-Set Defense Network with Clean-Adversarial Mutual
Learning (OSDN-CAML) as a solution to the OSAD problem. The proposed network
designs an encoder with dual-attentive feature-denoising layers coupled with a
classifier to learn a noise-free latent feature representation, which
adaptively removes adversarial noise guided by channel and spatial-wise
attentive filters. Several techniques are exploited to learn a noise-free and
informative latent feature space with the aim of improving the performance of
adversarial defense and open-set recognition. First, we incorporate a decoder
to ensure that clean images can be well reconstructed from the obtained latent
features. Then, self-supervision is used to ensure that the latent features are
informative enough to carry out an auxiliary task. Finally, to exploit more
complementary knowledge from clean image classification to facilitate feature
denoising and search for a more generalized local minimum for open-set
recognition, we further propose clean-adversarial mutual learning, where a peer
network (classifying clean images) is further introduced to mutually learn with
the classifier (classifying adversarial images).

We develop a theoretical model to study strategic interactions between
adaptive learning algorithms. Applying continuous-time techniques, we uncover
the mechanism responsible for collusion between Artificial Intelligence
algorithms documented by recent experimental evidence. We show that spontaneous
coupling between the algorithms' estimates leads to periodic coordination on
actions that are more profitable than static Nash equilibria. We provide a
sufficient condition under which this coupling is guaranteed to disappear, and
algorithms learn to play undominated strategies. We apply our results to
interpret and complement experimental findings in the literature and to the
design of learning-robust strategy-proof mechanisms. We show that ex-post
feedback provision guarantees robustness to the presence of learning agents. We
fully characterize the optimal learning-robust mechanisms: they are menu

Recently, template-based (TB) and template-free (TF) molecule graph learning
methods have shown promising results to retrosynthesis. TB methods are more
accurate using pre-encoded reaction templates, and TF methods are more scalable
by decomposing retrosynthesis into subproblems, i.e., center identification and
synthon completion. To combine both advantages of TB and TF, we suggest
breaking a full-template into several semi-templates and embedding them into
the two-step TF framework. Since many semi-templates are reduplicative, the
template redundancy can be reduced while the essential chemical knowledge is
still preserved to facilitate synthon completion. We call our method SemiRetro,
introduce a new GNN layer (DRGAT) to enhance center identification, and propose
a novel self-correcting module to improve semi-template classification.
Experimental results show that SemiRetro significantly outperforms both
existing TB and TF methods. In scalability, SemiRetro covers 98.9\% data using
150 semi-templates, while previous template-based GLN requires 11,647 templates
to cover 93.3\% data. In top-1 accuracy, SemiRetro exceeds template-free G2G
4.8\% (class known) and 6.0\% (class unknown). Besides, SemiRetro has better
training efficiency than existing methods.

Most event extraction methods have traditionally relied on an annotated set
of event types. However, creating event ontologies and annotating supervised
training data are expensive and time-consuming. Previous work has proposed
semi-supervised approaches which leverage seen (annotated) types to learn how
to automatically discover new event types. State-of-the-art methods, both
semi-supervised or fully unsupervised, use a form of reconstruction loss on
specific tokens in a context. In contrast, we present a novel approach to
semi-supervised new event type induction using a masked contrastive loss, which
learns similarities between event mentions by enforcing an attention mechanism
over the data minibatch. We further disentangle the discovered clusters by
approximating the underlying manifolds in the data, which allows us to increase
normalized mutual information and Fowlkes-Mallows scores by over 20% absolute.
Building on these clustering results, we extend our approach to two new tasks:
predicting the type name of the discovered clusters and linking them to
FrameNet frames.

Object recognition from images means to automatically find object(s) of
interest and to return their category and location information. Benefiting from
research on deep learning, like convolutional neural networks~(CNNs) and
generative adversarial networks, the performance in this field has been
improved significantly, especially when training and test data are drawn from
similar distributions. However, mismatching distributions, i.e., domain shifts,
lead to a significant performance drop. In this paper, we build
domain-invariant detectors by learning domain classifiers via adversarial
training. Based on the previous works that align image and instance level
features, we mitigate the domain shift further by introducing a domain
adaptation component at the region level within Faster \mbox{R-CNN}. We embed a
domain classification network in the region proposal network~(RPN) using
adversarial learning. The RPN can now generate accurate region proposals in
different domains by effectively aligning the features between them. To
mitigate the unstable convergence during the adversarial learning, we introduce
a balanced domain classifier as well as a network learning rate adjustment
strategy. We conduct comprehensive experiments using four standard datasets.
The results demonstrate the effectiveness and robustness of our object
detection approach in domain shift scenarios.

As deep reinforcement learning (RL) showcases its strengths in networking and
systems, its pitfalls also come to the public's attention--when trained to
handle a wide range of network workloads and previously unseen deployment
environments, RL policies often manifest suboptimal performance and poor
  To tackle these problems, we present Genet, a new training framework for
learning better RL-based network adaptation algorithms. Genet is built on the
concept of curriculum learning, which has proved effective against similar
issues in other domains where RL is extensively employed. At a high level,
curriculum learning gradually presents more difficult environments to the
training, rather than choosing them randomly, so that the current RL model can
make meaningful progress in training. However, applying curriculum learning in
networking is challenging because it remains unknown how to measure the
"difficulty" of a network environment.
  Instead of relying on handcrafted heuristics to determine the environment's
difficulty level, our insight is to utilize traditional rule-based (non-RL)
baselines: If the current RL model performs significantly worse in a network
environment than the baselines, then the model's potential to improve when
further trained in this environment is substantial. Therefore, Genet
automatically searches for the environments where the current model falls
significantly behind a traditional baseline scheme and iteratively promotes
these environments as the training progresses. Through evaluating Genet on
three use cases--adaptive video streaming, congestion control, and load
balancing, we show that Genet produces RL policies which outperform both
regularly trained RL policies and traditional baselines in each context, not
only under synthetic workloads but also in real environments.

Momentive offers solutions in market research, customer experience, and
enterprise feedback. The technology is gleaned from the billions of real
responses to questions asked on the platform. However, people may create biased
questions. A double-barreled question (DBQ) is a common type of biased question
that asks two aspects in one question. For example, "Do you agree with the
statement: The food is yummy, and the service is great.". This DBQ confuses
survey respondents because there are two parts in a question. DBQs impact both
the survey respondents and the survey owners. Momentive aims to detect DBQs and
recommend survey creators to make a change towards gathering high quality
unbiased survey data. Previous research work has suggested detecting DBQs by
checking the existence of grammatical conjunction. While this is a simple
rule-based approach, this method is error-prone because conjunctions can also
exist in properly constructed questions. We present an end-to-end machine
learning approach for DBQ classification in this work. We handled this
imbalanced data using active learning, and compared state-of-the-art embedding
algorithms to transform text data into vectors. Furthermore, we proposed a
model interpretation technique propagating the vector-level SHAP values to a
SHAP value for each word in the questions. We concluded that the word2vec
subword embedding with maximum pooling is the optimal word embedding
representation in terms of precision and running time in the offline
experiments using the survey data at Momentive. The A/B test and production
metrics indicate that this model brings a positive change to the business. To
the best of our knowledge, this is the first machine learning framework for DBQ
detection, and it successfully differentiates Momentive from the competitors.
We hope our work sheds light on machine learning approaches for bias question

Large-scale multi-label text classification (LMTC) aims to associate a
document with its relevant labels from a large candidate set. Most existing
LMTC approaches rely on massive human-annotated training data, which are often
costly to obtain and suffer from a long-tailed label distribution (i.e., many
labels occur only a few times in the training set). In this paper, we study
LMTC under the zero-shot setting, which does not require any annotated
documents with labels and only relies on label surface names and descriptions.
To train a classifier that calculates the similarity score between a document
and a label, we propose a novel metadata-induced contrastive learning (MICoL)
method. Different from previous text-based contrastive learning techniques,
MICoL exploits document metadata (e.g., authors, venues, and references of
research papers), which are widely available on the Web, to derive similar
document-document pairs. Experimental results on two large-scale datasets show
that: (1) MICoL significantly outperforms strong zero-shot text classification
and contrastive learning baselines; (2) MICoL is on par with the
state-of-the-art supervised metadata-aware LMTC method trained on 10K-200K
labeled documents; and (3) MICoL tends to predict more infrequent labels than
supervised methods, thus alleviates the deteriorated performance on long-tailed
labels.

This paper presents an approach to detect out-of-context (OOC) objects in an
image. Given an image with a set of objects, our goal is to determine if an
object is inconsistent with the scene context and detect the OOC object with a
bounding box. In this work, we consider commonly explored contextual relations
such as co-occurrence relations, the relative size of an object with respect to
other objects, and the position of the object in the scene. We posit that
contextual cues are useful to determine object labels for in-context objects
and inconsistent context cues are detrimental to determining object labels for
out-of-context objects. To realize this hypothesis, we propose a graph
contextual reasoning network (GCRN) to detect OOC objects. GCRN consists of two
separate graphs to predict object labels based on the contextual cues in the
image: 1) a representation graph to learn object features based on the
neighboring objects and 2) a context graph to explicitly capture contextual
cues from the neighboring objects. GCRN explicitly captures the contextual cues
to improve the detection of in-context objects and identify objects that
violate contextual relations. In order to evaluate our approach, we create a
large-scale dataset by adding OOC object instances to the COCO images. We also
evaluate on recent OCD benchmark. Our results show that GCRN outperforms
competitive baselines in detecting OOC objects and correctly detecting
in-context objects.

Image recognition is an essential task in several mobile applications. For
instance, a smartphone can process a landmark photo to gather more information
about its location. If the device does not have enough computational resources
available, it offloads the processing task to a cloud infrastructure. Although
this approach solves resource shortages, it introduces a communication delay.
Image-recognition caches on the Internet's edge can mitigate this problem.
These caches run on servers close to mobile devices and stores information
about previously recognized images. If the server receives a request with a
photo stored in its cache, it replies to the device, avoiding cloud offloading.
The main challenge for this cache is to verify if the received image matches a
stored one. Furthermore, for outdoor photos, it is difficult to compare them if
one was taken in the daytime and the other at nighttime. In that case, the
cache might wrongly infer that they refer to different places, offloading the
processing to the cloud. This work shows that a well-known generative
adversarial network, called ToDayGAN, can solve this problem by generating
daytime images using nighttime ones. We can thus use this translation to
populate a cache with synthetic photos that can help image matching. We show
that our solution reduces cloud offloading and, therefore, the application's
latency.

Benign overfitting, the phenomenon where interpolating models generalize well
in the presence of noisy data, was first observed in neural network models
trained with gradient descent. To better understand this empirical observation,
we consider the generalization error of two-layer neural networks trained to
interpolation by gradient descent on the logistic loss following random
initialization. We assume the data comes from well-separated class-conditional
log-concave distributions and allow for a constant fraction of the training
labels to be corrupted by an adversary. We show that in this setting, neural
networks exhibit benign overfitting: they can be driven to zero training error,
perfectly fitting any noisy training labels, and simultaneously achieve minimax
optimal test error. In contrast to previous work on benign overfitting that
require linear or kernel-based predictors, our analysis holds in a setting
where both the model and learning dynamics are fundamentally nonlinear.

Compute, data, and algorithmic advances are the three fundamental factors
that guide the progress of modern Machine Learning (ML). In this paper we study
trends in the most readily quantified factor - compute. We show that before
2010 training compute grew in line with Moore's law, doubling roughly every 20
months. Since the advent of Deep Learning in the early 2010s, the scaling of
training compute has accelerated, doubling approximately every 6 months. In
late 2015, a new trend emerged as firms developed large-scale ML models with 10
to 100-fold larger requirements in training compute. Based on these
observations we split the history of compute in ML into three eras: the Pre
Deep Learning Era, the Deep Learning Era and the Large-Scale Era. Overall, our
work highlights the fast-growing compute requirements for training advanced ML
systems.

We propose a learning paradigm for numerical approximation of differential
invariants of planar curves. Deep neural-networks' (DNNs) universal
approximation properties are utilized to estimate geometric measures. The
proposed framework is shown to be a preferable alternative to axiomatic
constructions. Specifically, we show that DNNs can learn to overcome
instabilities and sampling artifacts and produce numerically-stable signatures
for curves subject to a given group of transformations in the plane. We compare
the proposed schemes to alternative state-of-the-art axiomatic constructions of
group invariant arc-lengths and curvatures.

We present an oracle-efficient algorithm for boosting the adversarial
robustness of barely robust learners. Barely robust learning algorithms learn
predictors that are adversarially robust only on a small fraction $\beta \ll 1$
of the data distribution. Our proposed notion of barely robust learning
requires robustness with respect to a "larger" perturbation set; which we show
is necessary for strongly robust learning, and that weaker relaxations are not
sufficient for strongly robust learning. Our results reveal a qualitative and
quantitative equivalence between two seemingly unrelated problems: strongly
robust learning and barely robust learning.

Truncation is widely used in generative models for improving the quality of
the generated samples, at the expense of reducing their diversity. We propose
to leverage the StyleGAN generative architecture to devise a new truncation
technique, based on a decomposition of the latent space into clusters, enabling
customized truncation to be performed at multiple semantic levels. We do so by
learning to re-generate W-space, the extended intermediate latent space of
StyleGAN, using a learnable mixture of Gaussians, while simultaneously training
a classifier to identify, for each latent vector, the cluster that it belongs
to. The resulting truncation scheme is more faithful to the original
untruncated samples and allows a better trade-off between quality and
diversity. We compare our method to other truncation approaches for StyleGAN,

Vulnerability of various machine learning methods to adversarial examples has
been recently explored in the literature. Power systems which use these
vulnerable methods face a huge threat against adversarial examples. To this
end, we first propose a signal-specific method and a universal signal-agnostic
method to attack power systems using generated adversarial examples. Black-box
attacks based on transferable characteristics and the above two methods are
also proposed and evaluated. We then adopt adversarial training to defend
systems against adversarial attacks. Experimental analyses demonstrate that our
signal-specific attack method provides less perturbation compared to the FGSM
(Fast Gradient Sign Method), and our signal-agnostic attack method can generate
perturbations fooling most natural signals with high probability. What's more,
the attack method based on the universal signal-agnostic algorithm has a higher
transfer rate of black-box attacks than the attack method based on the
signal-specific algorithm. In addition, the results show that the proposed
adversarial training improves robustness of power systems to adversarial
examples.

Surrogate modeling has become a valuable technique for black-box optimization
tasks with expensive evaluation of the objective function. In this paper, we
investigate the relationship between the predictive accuracy of surrogate
models and features of the black-box function landscape. We also study
properties of features for landscape analysis in the context of different
transformations and ways of selecting the input data. We perform the landscape
analysis of a large set of data generated using runs of a surrogate-assisted
version of the Covariance Matrix Adaptation Evolution Strategy on the noiseless
part of the Comparing Continuous Optimisers benchmark function testbed.

Even though most of its energy generation comes from renewable sources,
Brazil is one of the largest emitters of greenhouse gases in the world, due to
intense farming and deforestation of biomes such as the Amazon Rainforest,
whose preservation is essential for compliance with the Paris Agreement. Still,
regardless of lobbies or prevailing political orientation, all government legal
actions are published daily in the Brazilian Federal Official Gazette (BFOG, or
"Di\'ario Oficial da Uni\~ao" in Portuguese). However, with hundreds of decrees
issued every day by the authorities, it is absolutely burdensome to manually
analyze all these processes and find out which ones can pose serious
environmental hazards. In this paper, we present a strategy to compose
automated techniques and domain expert knowledge to process all the data from
the BFOG. We also provide the Government Actions Tracker, a highly curated
dataset, in Portuguese, annotated by domain experts, on federal government acts
about the Brazilian environmental policies. Finally, we build and compared four
different NLP models on the classfication task in this dataset. Our best model
achieved a F1-score of $0.714 \pm 0.031$. In the future, this system should
serve to scale up the high-quality tracking of all oficial documents with a
minimum of human supervision and contribute to increasing society's awareness
of government actions.

Geomagnetic disturbance forecasting is based on the identification of solar
wind structures and accurate determination of their magnetic field orientation.
For nowcasting activities, this is currently a tedious and manual process.
Focusing on the main driver of geomagnetic disturbances, the twisted internal
magnetic field of interplanetary coronal mass ejections (ICMEs), we explore a
convolutional neural network's (CNN) ability to predict the embedded magnetic
flux rope's orientation once it has been identified from in situ solar wind
observations. Our work uses CNNs trained with magnetic field vectors from
analytical flux rope data. The simulated flux ropes span many possible
spacecraft trajectories and flux rope orientations. We train CNNs first with
full duration flux ropes and then again with partial duration flux ropes. The
former provides us with a baseline of how well CNNs can predict flux rope
orientation while the latter provides insights into real-time forecasting by
exploring how accuracy is affected by percentage of flux rope observed. The
process of casting the physics problem as a machine learning problem is
discussed as well as the impacts of different factors on prediction accuracy
such as flux rope fluctuations and different neural network topologies.
Finally, results from evaluating the trained network against observed ICMEs
from Wind during 1995-2015 are presented.

In this paper, we consider the hypothesis testing of correlation between two
$m$-uniform hypergraphs on $n$ unlabelled nodes. Under the null hypothesis, the
hypergraphs are independent, while under the alternative hypothesis, the
hyperdges have the same marginal distributions as in the null hypothesis but
are correlated after some unknown node permutation. We focus on two scenarios:
the hypergraphs are generated from the Gaussian-Wigner model and the dense
Erd\"{o}s-R\'{e}nyi model. We derive the sharp information-theoretic testing
threshold. Above the threshold, there exists a powerful test to distinguish the
alternative hypothesis from the null hypothesis. Below the threshold, the
alternative hypothesis and the null hypothesis are not distinguishable. The
threshold involves $m$ and decreases as $m$ gets larger. This indicates testing
correlation of hypergraphs ($m\geq3$) becomes easier than testing correlation
of graphs ($m=2$)

Here we research the univariate quantitative approximation, ordinary and
fractional, of Banach space valued continuous functions on a compact interval
or all the real line by quasi-interpolation Banach space valued neural network
operators. These approximations are derived by establishing Jackson type
inequalities involving the modulus of continuity of the engaged function or its
Banach space valued high order derivative of fractional derivatives. Our
operators are defined by using a density function generated by an algebraic
sigmoid function. The approximations are pointwise and of the uniform norm. The
related Banach space valued feed-forward neural networks are with one hidden
layer.

Since the discovery of the Higgs boson, testing the many possible extensions
to the Standard Model has become a key challenge in particle physics. This
paper discusses a new method for predicting the compatibility of new physics
theories with existing experimental data from particle colliders. Using machine
learning, the technique obtained comparable results to previous methods (>90%
precision and recall) with only a fraction of their computing resources (<10%).
This makes it possible to test models that were impossible to probe before, and
allows for large-scale testing of new physics theories.

There are several reasons to extend the presentation of Navier-Stokes
equations to multicomponent systems. Many technological applications are based
on physical phenomena that are present neither in pure elements nor in binary
mixtures. Whereas Fourier's law must already be generalized in binaries, it is
only with more than two components that Fick's law breaks down in its simple
form. The emergence of dissipative phenomena affects also the inertial
confinement fusion configurations, designed as prototypes for the future fusion
nuclear plants hopefully replacing the fission ones.
  This important topic can be described in much simpler terms than in many
textbooks since the publication of the formalism put forward recently by Snider
in \textit{Phys. Rev. E} \textbf{82}, 051201 (2010). In a very natural way, it
replaces the linearly dependent atomic fractions by the independent set of
partial densities. Then, the Chapman-Enskog procedure is hardly more
complicated for multicomponent mixtures than for pure elements. Moreover, the
recent proposal of a convergent kinetic equation by Baalrud and Daligault in
\textit{Phys. Plasmas} \textbf{26}, 082106 (2019) demonstrates that Boltzmann
equation with the potential of mean force is a far better choice in situations
close to equilibrium, as described by the Navier-Stokes equations, than Landau
or Lenard-Balescu equations.
  In our comprehensive presentation, we emphasize the physical arguments behind
Chapman-Enskog derivation and keep the mathematics as simple as possible. This
excludes as a technical non-essential aspect the solution of the linearized
Boltzmann equation through an expansion in Hermite polynomials. We discuss the
link with the second principle of Thermodynamics of entropy increase, and what
can be learned from this exposition.

Tensor accelerators have gained popularity because they provide a cheap and
efficient solution for speeding up computational-expensive tasks in Deep
Learning and, more recently, in other Scientific Computing applications.
However, since their features are specifically designed for tensor algebra
(typically dense matrix-product), it is commonly assumed that they are not
suitable for applications with sparse data. To challenge this viewpoint, we
discuss methods and present solutions for accelerating sparse matrix
multiplication on such architectures. In particular, we present a 1-dimensional
blocking algorithm with theoretical guarantees on the density, which builds
dense blocks from arbitrary sparse matrices. Experimental results show that,
even for unstructured and highly-sparse matrices, our block-based solution
which exploits Nvidia Tensor Cores is faster than its sparse counterpart. We
observed significant speed-ups of up to two orders of magnitude on real-world
sparse matrices.

We present a novel computational approach for extracting weak signals, whose
exact location and width may be unknown, from complex background distributions
with an arbitrary functional form. We focus on datasets that can be naturally
presented as binned integer counts, demonstrating our approach on the CERN open
dataset from the ATLAS collaboration at the Large Hadron Collider, which
contains the Higgs boson signature. Our approach is based on Gaussian Process
(GP) regression - a powerful and flexible machine learning technique that
allowed us to model the background without specifying its functional form
explicitly, and to separate the background and signal contributions in a robust
and reproducible manner. Unlike functional fits, our GP-regression-based
approach does not need to be constantly updated as more data becomes available.
We discuss how to select the GP kernel type, considering trade-offs between
kernel complexity and its ability to capture the features of the background
distribution. We show that our GP framework can be used to detect the Higgs
boson resonance in the data with more statistical significance than a
polynomial fit specifically tailored to the dataset. Finally, we use Markov
Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of
the extracted Higgs signature.

We study the potential of deep learning to resolve the combinatorial problem
in SUSY-like events with two invisible particles at the LHC. As a concrete
example, we focus on dileptonic $t \bar t$ events, where the combinatorial
problem becomes an issue of binary classification: pairing the correct lepton
with each $b$ quark coming from the decays of the tops. We investigate the
performance of a number of machine learning algorithms, including
attention-based networks, which have been used for a similar problem in the
fully-hadronic channel of $t\bar t$ production; and the Lorentz Boost Network,
which is motivated by physics principles. We then consider the general case
when the underlying mass spectrum is unknown, and hence no kinematic endpoint
information is available. Compared against existing methods based on kinematic
variables, we demonstrate that the efficiency for selecting the correct pairing
is greatly improved by utilizing deep learning techniques.

We propose a new technique for classifying 5d Superconformal Field Theories
arising from brane webs in Type IIB String Theory, using technology from
Machine Learning to identify different webs giving rise to the same theory. We
concentrate on webs with three external legs, for which the problem is
analogous to that of classifying sets of 7-branes. Training a Siamese Neural
Network to determine equivalence between any two brane webs shows an improved
performance when webs are considered equivalent under a weaker set of
conditions. Thus, Machine Learning teaches us that the conjectured
classification of 7-brane sets is not complete, which we confirm with explicit
examples.

Quantum simulation has emerged as a valuable arena for demonstrating and
understanding the capabilities of near-term quantum computers. Quantum
annealing has been used successfully in simulating a range of open quantum
systems, both at equilibrium and out of equilibrium. However, in all previous
experiments, annealing has been too slow to simulate a closed quantum system
coherently, due to the onset of thermal effects from the environment. Here we
demonstrate coherent evolution through a quantum phase transition in the
paradigmatic setting of the 1D transverse-field Ising chain, using up to 2000
superconducting flux qubits in a programmable quantum annealer. In large
systems we observe the quantum Kibble-Zurek mechanism with theoretically
predicted kink statistics, as well as characteristic positive kink-kink
correlations, independent of system temperature. In small chains, excitation
statistics validate the picture of a Landau-Zener transition at a minimum gap.
In both cases, results are in quantitative agreement with analytical solutions
to the closed-system quantum model. For slower anneals we observe
anti-Kibble-Zurek scaling in a crossover to the open quantum regime. These
experiments demonstrate that large-scale quantum annealers can be operated
coherently, paving the way to exploiting coherent dynamics in quantum
optimization, machine learning, and simulation tasks.

We propose a metric -- Projection Norm -- to predict a model's performance on
out-of-distribution (OOD) data without access to ground truth labels.
Projection Norm first uses model predictions to pseudo-label test samples and
then trains a new model on the pseudo-labels. The more the new model's
parameters differ from an in-distribution model, the greater the predicted OOD
error. Empirically, our approach outperforms existing methods on both image and
text classification tasks and across different network architectures.
Theoretically, we connect our approach to a bound on the test error for
overparameterized linear models. Furthermore, we find that Projection Norm is
the only approach that achieves non-trivial detection performance on
adversarial examples. Our code is available at
https://github.com/yaodongyu/ProjNorm.

Internet of things (IoT) devices, such as smart meters, smart speakers and
activity monitors, have become highly popular thanks to the services they
offer. However, in addition to their many benefits, they raise privacy concerns
since they share fine-grained time-series user data with untrusted third
parties. In this work, we consider a user releasing her data containing
personal information in return of a service from an honest-but-curious service
provider (SP). We model user's personal information as two correlated random
variables (r.v.'s), one of them, called the secret variable, is to be kept
private, while the other, called the useful variable, is to be disclosed for
utility. We consider active sequential data release, where at each time step
the user chooses from among a finite set of release mechanisms, each revealing
some information about the user's personal information, i.e., the true values
of the r.v.'s, albeit with different statistics. The user manages data release
in an online fashion such that the maximum amount of information is revealed
about the latent useful variable as quickly as possible, while the confidence
for the sensitive variable is kept below a predefined level. For privacy
measure, we consider both the probability of correctly detecting the true value
of the secret and the mutual information (MI) between the secret and the
released data. We formulate both problems as partially observable Markov
decision processes (POMDPs), and numerically solve them by advantage
actor-critic (A2C) deep reinforcement learning (DRL). We evaluate the
privacy-utility trade-off (PUT) of the proposed policies on both the synthetic
data and smoking activity dataset, and show their validity by testing the
activity detection accuracy of the SP modeled by a long short-term memory
(LSTM) neural network.

Robots need object-level scene understanding to manipulate objects while
reasoning about contact, support, and occlusion among objects. Given a pile of
objects, object recognition and reconstruction can identify the boundary of
object instances, giving important cues as to how the objects form and support
the pile. In this work, we present a system, SafePicking, that integrates
object-level mapping and learning-based motion planning to generate a motion
that safely extracts occluded target objects from a pile. Planning is done by
learning a deep Q-network that receives observations of predicted poses and a
depth-based heightmap to output a motion trajectory, trained to maximize a
safety metric reward. Our results show that the observation fusion of poses and
depth-sensing gives both better performance and robustness to the model. We
evaluate our methods using the YCB objects in both simulation and the real
world, achieving safe object extraction from piles.

Diffusion models have emerged as an expressive family of generative models
rivaling GANs in sample quality and autoregressive models in likelihood scores.
Standard diffusion models typically require hundreds of forward passes through
the model to generate a single high-fidelity sample. We introduce
Differentiable Diffusion Sampler Search (DDSS): a method that optimizes fast
samplers for any pre-trained diffusion model by differentiating through sample
quality scores. We also present Generalized Gaussian Diffusion Models (GGDM), a
family of flexible non-Markovian samplers for diffusion models. We show that
optimizing the degrees of freedom of GGDM samplers by maximizing sample quality
scores via gradient descent leads to improved sample quality. Our optimization
procedure backpropagates through the sampling process using the
reparametrization trick and gradient rematerialization. DDSS achieves strong
results on unconditional image generation across various datasets (e.g., FID
scores on LSUN church 128x128 of 11.6 with only 10 inference steps, and 4.82
with 20 steps, compared to 51.1 and 14.9 with strongest DDPM/DDIM baselines).
Our method is compatible with any pre-trained diffusion model without
fine-tuning or re-training required.

This paper represents the first effort to explore an automated architecture
search for hyperdimensional computing (HDC), a type of brain-inspired neural
network. Currently, HDC design is largely carried out in an
application-specific ad-hoc manner, which significantly limits its application.
Furthermore, the approach leads to inferior accuracy and efficiency, which
suggests that HDC cannot perform competitively against deep neural networks.
Herein, we present a thorough study to formulate an HDC architecture search
space. On top of the search space, we apply reinforcement-learning to
automatically explore the HDC architectures. The searched HDC architectures
show competitive performance on case studies involving a drug discovery dataset
and a language recognition task. On the Clintox dataset, which tries to learn
features from developed drugs that passed/failed clinical trials for toxicity
reasons, the searched HDC architecture obtains the state-of-the-art ROC-AUC
scores, which are 0.80% higher than the manually designed HDC and 9.75% higher
than conventional neural networks. Similar results are achieved on the language
recognition task, with 1.27% higher performance than conventional methods.

Machine learning systems perform well on pattern matching tasks, but their
ability to perform algorithmic or logical reasoning is not well understood. One
important reasoning capability is algorithmic extrapolation, in which models
trained only on small/simple reasoning problems can synthesize complex
strategies for large/complex problems at test time. Algorithmic extrapolation
can be achieved through recurrent systems, which can be iterated many times to
solve difficult reasoning problems. We observe that this approach fails to
scale to highly complex problems because behavior degenerates when many
iterations are applied -- an issue we refer to as "overthinking." We propose a
recall architecture that keeps an explicit copy of the problem instance in
memory so that it cannot be forgotten. We also employ a progressive training
routine that prevents the model from learning behaviors that are specific to
iteration number and instead pushes it to learn behaviors that can be repeated
indefinitely. These innovations prevent the overthinking problem, and enable
recurrent systems to solve extremely hard extrapolation tasks.

Parkinson's disease (PD) is a common and complex neurodegenerative disorder
with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it
is challenging to classify early stages 1 and 2 and detect brain function
alterations. Functional magnetic resonance imaging (fMRI) is a promising tool
in revealing functional connectivity (FC) differences and developing biomarkers
in PD. Some machine learning approaches like support vector machine and
logistic regression have been successfully applied in the early diagnosis of PD
using fMRI data, which outperform classifiers based on manually selected
morphological features. However, the early-stage characterization in FC changes
has not been fully investigated. Given the complexity and non-linearity of fMRI
data, we propose the use of a long short-term memory (LSTM) network to
characterize the early stages of PD. The study included 84 subjects (56 in
stage 2 and 28 in stage 1) from the Parkinson's Progression Markers Initiative
(PPMI), the largest available public PD dataset. Under a repeated 10-fold
stratified cross-validation, the LSTM model reached an accuracy of 71.63%,
13.52% higher than the best traditional machine learning method, indicating
significantly better robustness and accuracy compared with other machine
learning classifiers. We used the learned LSTM model weights to select the top
brain regions that contributed to model prediction and performed FC analyses to
characterize functional changes with disease stage and motor impairment to gain
better insight into the brain mechanisms of PD.

This paper presents the design and results of the "PEg TRAnsfert Workflow
recognition" (PETRAW) challenge whose objective was to develop surgical
workflow recognition methods based on one or several modalities, among video,
kinematic, and segmentation data, in order to study their added value. The
PETRAW challenge provided a data set of 150 peg transfer sequences performed on
a virtual simulator. This data set was composed of videos, kinematics, semantic
segmentation, and workflow annotations which described the sequences at three
different granularity levels: phase, step, and activity. Five tasks were
proposed to the participants: three of them were related to the recognition of
all granularities with one of the available modalities, while the others
addressed the recognition with a combination of modalities. Average
application-dependent balanced accuracy (AD-Accuracy) was used as evaluation
metric to take unbalanced classes into account and because it is more
clinically relevant than a frame-by-frame score. Seven teams participated in at
least one task and four of them in all tasks. Best results are obtained with
the use of the video and the kinematics data with an AD-Accuracy between 93%
and 90% for the four teams who participated in all tasks. The improvement
between video/kinematic-based methods and the uni-modality ones was significant
for all of the teams. However, the difference in testing execution time between
the video/kinematic-based and the kinematic-based methods has to be taken into
consideration. Is it relevant to spend 20 to 200 times more computing time for
less than 3% of improvement? The PETRAW data set is publicly available at
www.synapse.org/PETRAW to encourage further research in surgical workflow

Sim2real transfer is primarily concerned with transferring policies trained
in simulation to potentially noisy real world environments. A common problem
associated with sim2real transfer is estimating the real-world environmental
parameters to ground the simulated environment to. Although existing methods
such as Domain Randomisation (DR) can produce robust policies by sampling from
a distribution of parameters during training, there is no established method
for identifying the parameters of the corresponding distribution for a given
real-world setting. In this work, we propose Uncertainty-aware policy search
(UncAPS), where we use Universal Policy Network (UPN) to store
simulation-trained task-specific policies across the full range of
environmental parameters and then subsequently employ robust Bayesian
optimisation to craft robust policies for the given environment by combining
relevant UPN policies in a DR like fashion. Such policy-driven grounding is
expected to be more efficient as it estimates only task-relevant sets of
parameters. Further, we also account for the estimation uncertainties in the
search process to produce policies that are robust against both aleatoric and
epistemic uncertainties. We empirically evaluate our approach in a range of
noisy, continuous control environments, and show its improved performance
compared to competing baselines.

In this paper, we propose GT-GDA, a distributed optimization method to solve
saddle point problems of the form: $\min_{\mathbf{x}} \max_{\mathbf{y}}
\{F(\mathbf{x},\mathbf{y}) :=G(\mathbf{x}) + \langle \mathbf{y}, \overline{P}
\mathbf{x} \rangle - H(\mathbf{y})\}$, where the functions $G(\cdot)$,
$H(\cdot)$, and the the coupling matrix $\overline{P}$ are distributed over a
strongly connected network of nodes. GT-GDA is a first-order method that uses
gradient tracking to eliminate the dissimilarity caused by heterogeneous data
distribution among the nodes. In the most general form, GT-GDA includes a
consensus over the local coupling matrices to achieve the optimal (unique)
saddle point, however, at the expense of increased communication. To avoid
this, we propose a more efficient variant GT-GDA-Lite that does not incur the
additional communication and analyze its convergence in various scenarios. We
show that GT-GDA converges linearly to the unique saddle point solution when
$G(\cdot)$ is smooth and convex, $H(\cdot)$ is smooth and strongly convex, and
the global coupling matrix $\overline{P}$ has full column rank. We further
characterize the regime under which GT-GDA exhibits a network
topology-independent convergence behavior. We next show the linear convergence
of GT-GDA to an error around the unique saddle point, which goes to zero when
the coupling cost ${\langle \mathbf y, \overline{P} \mathbf x \rangle}$ is
common to all nodes, or when $G(\cdot)$ and $H(\cdot)$ are quadratic. Numerical
experiments illustrate the convergence properties and importance of GT-GDA and
GT-GDA-Lite for several applications.

Representation learning that leverages large-scale labelled datasets, is
central to recent progress in machine learning. Access to task relevant labels
at scale is often scarce or expensive, motivating the need to learn from
unlabelled datasets with self-supervised learning (SSL). Such large unlabelled
datasets (with data augmentations) often provide a good coverage of the
underlying input distribution. However evaluating the representations learned
by SSL algorithms still requires task-specific labelled samples in the training
pipeline. Additionally, the generalization of task-specific encoding is often
sensitive to potential distribution shift. Inspired by recent advances in
theoretical machine learning and vision neuroscience, we observe that the
eigenspectrum of the empirical feature covariance matrix often follows a power
law. For visual representations, we estimate the coefficient of the power law,
$\alpha$, across three key attributes which influence representation learning:
learning objective (supervised, SimCLR, Barlow Twins and BYOL), network
architecture (VGG, ResNet and Vision Transformer), and tasks (object and scene
recognition). We observe that under mild conditions, proximity of $\alpha$ to
1, is strongly correlated to the downstream generalization performance.
Furthermore, $\alpha \approx 1$ is a strong indicator of robustness to label
noise during fine-tuning. Notably, $\alpha$ is computable from the
representations without knowledge of any labels, thereby offering a framework
to evaluate the quality of representations in unlabelled datasets.

Adapting an agent's behaviour to new environments has been one of the primary
focus areas of physics based reinforcement learning. Although recent approaches
such as universal policy networks partially address this issue by enabling the
storage of multiple policies trained in simulation on a wide range of
dynamic/latent factors, efficiently identifying the most appropriate policy for
a given environment remains a challenge. In this work, we propose a Gaussian
Process-based prior learned in simulation, that captures the likely performance
of a policy when transferred to a previously unseen environment. We integrate
this prior with a Bayesian Optimisation-based policy search process to improve
the efficiency of identifying the most appropriate policy from the universal
policy network. We empirically evaluate our approach in a range of continuous
and discrete control environments, and show that it outperforms other competing

There is a growing interest in the distributed optimization framework that
goes under the name of Federated Learning (FL). In particular, much attention
is being turned to FL scenarios where the network is strongly heterogeneous in
terms of communication resources (e.g., bandwidth) and data distribution. In
these cases, communication between local machines (agents) and the central
server (Master) is a main consideration. In this work, we present SHED, an
original communication-constrained Newton-type (NT) algorithm designed to
accelerate FL in such heterogeneous scenarios. SHED is by design robust to non
i.i.d. data distributions, handles heterogeneity of agents' communication
resources (CRs), only requires sporadic Hessian computations, and achieves
super-linear convergence. This is possible thanks to an incremental strategy,
based on eigendecomposition of the local Hessian matrices, which exploits
(possibly) outdated second-order information. The proposed solution is
thoroughly validated on real datasets by assessing (i) the number of
communication rounds required for convergence, (ii) the overall amount of data
transmitted and (iii) the number of local Hessian computations. For all these
metrics, the proposed approach shows superior performance against state-of-the
art techniques like GIANT and FedNL.

The theory of reinforcement learning currently suffers from a mismatch
between its empirical performance and the theoretical characterization of its
performance, with consequences for, e.g., the understanding of sample
efficiency, safety, and robustness. The linear quadratic regulator with unknown
dynamics is a fundamental reinforcement learning setting with significant
structure in its dynamics and cost function, yet even in this setting there is
a gap between the best known regret lower-bound of $\Omega_p(\sqrt{T})$ and the
best known upper-bound of $O_p(\sqrt{T}\,\text{polylog}(T))$. The contribution
of this paper is to close that gap by establishing a novel regret upper-bound
of $O_p(\sqrt{T})$. Our proof is constructive in that it analyzes the regret of
a concrete algorithm, and simultaneously establishes an estimation error bound
on the dynamics of $O_p(T^{-1/4})$ which is also the first to match the rate of
a known lower-bound. The two keys to our improved proof technique are (1) a
more precise upper- and lower-bound on the system Gram matrix and (2) a
self-bounding argument for the expected estimation error of the optimal

Linear layers in neural networks (NNs) trained by gradient descent can be
expressed as a key-value memory system which stores all training datapoints and
the initial weights, and produces outputs using unnormalised dot attention over
the entire training experience. While this has been technically known since the
1960s, no prior work has effectively studied the operations of NNs in such a
form, presumably due to prohibitive time and space complexities and impractical
model sizes, all of them growing linearly with the number of training patterns
which may get very large. However, this dual formulation offers a possibility
of directly visualising how an NN makes use of training patterns at test time,
by examining the corresponding attention weights. We conduct experiments on
small scale supervised image classification tasks in single-task, multi-task,
and continual learning settings, as well as language modelling, and discuss
potentials and limits of this view for better understanding and interpreting
how NNs exploit training patterns. Our code is public.

Suppose we are given two datasets: a labeled dataset and unlabeled dataset
which also has additional auxiliary features not present in the first dataset.
What is the most principled way to use these datasets together to construct a
predictor?
  The answer should depend upon whether these datasets are generated by the
same or different distributions over their mutual feature sets, and how similar
the test distribution will be to either of those distributions. In many
applications, the two datasets will likely follow different distributions, but
both may be close to the test distribution. We introduce the problem of
building a predictor which minimizes the maximum loss over all probability
distributions over the original features, auxiliary features, and binary
labels, whose Wasserstein distance is $r_1$ away from the empirical
distribution over the labeled dataset and $r_2$ away from that of the unlabeled
dataset. This can be thought of as a generalization of distributionally robust
optimization (DRO), which allows for two data sources, one of which is
unlabeled and may contain auxiliary features.

Anomaly detection has been an active research area with a wide range of
potential applications. Key challenges for anomaly detection in the AI era with
big data include lack of prior knowledge of potential anomaly types, highly
complex and noisy background in input data, scarce abnormal samples, and
imbalanced training dataset. In this work, we propose a meta-learning framework
for anomaly detection to deal with these issues. Within this framework, we
incorporate the idea of generative adversarial networks (GANs) with appropriate
choices of loss functions including structural similarity index measure (SSIM).
Experiments with limited labeled data for high-speed rail inspection
demonstrate that our meta-learning framework is sharp and robust in identifying
anomalies. Our framework has been deployed in five high-speed railways of China
since 2021: it has reduced more than 99.7% workload and saved 96.7% inspection
time.

We study convergence rates of AdaGrad-Norm as an exemplar of adaptive
stochastic gradient methods (SGD), where the step sizes change based on
observed stochastic gradients, for minimizing non-convex, smooth objectives.
Despite their popularity, the analysis of adaptive SGD lags behind that of non
adaptive methods in this setting. Specifically, all prior works rely on some
subset of the following assumptions: (i) uniformly-bounded gradient norms, (ii)
uniformly-bounded stochastic gradient variance (or even noise support), (iii)
conditional independence between the step size and stochastic gradient. In this
work, we show that AdaGrad-Norm exhibits an order optimal convergence rate of
$\mathcal{O}\left(\frac{\mathrm{poly}\log(T)}{\sqrt{T}}\right)$ after $T$
iterations under the same assumptions as optimally-tuned non adaptive SGD
(unbounded gradient norms and affine noise variance scaling), and crucially,
without needing any tuning parameters. We thus establish that adaptive gradient
methods exhibit order-optimal convergence in much broader regimes than
previously understood.

We present a catalog of 97 uniformly-vetted candidates for quadruple star
systems. The candidates were identified in TESS Full Frame Image data from
Sectors 1 through 42 through a combination of machine learning techniques and
visual examination, with major contributions from a dedicated group of citizen
scientists. All targets exhibit two sets of eclipses with two different
periods, both of which pass photocenter tests confirming that the eclipses are
on-target. This catalog outlines the statistical properties of the sample,
nearly doubles the number of known multiply-eclipsing quadruple systems, and
provides the basis for detailed future studies of individual systems. Several
important discoveries have already resulted from this effort, including the
first sextuply-eclipsing sextuple stellar system and the first transiting
circumbinary planet detected from one sector of TESS data.

The upheaval brought by the arrival of the COVID-19 pandemic has continued to
bring fresh challenges over the past two years. During this COVID-19 pandemic,
there has been a need for rapid identification of infected patients and
specific delineation of infection areas in computed tomography (CT) images.
Although deep supervised learning methods have been established quickly, the
scarcity of both image-level and pixel-level labels as well as the lack of
explainable transparency still hinder the applicability of AI. Can we identify
infected patients and delineate the infections with extreme minimal
supervision? Semi-supervised learning has demonstrated promising performance
under limited labelled data and sufficient unlabelled data. Inspired by
semi-supervised learning, we propose a model-agnostic calibrated
pseudo-labelling strategy and apply it under a consistency regularization
framework to generate explainable identification and delineation results. We
demonstrate the effectiveness of our model with the combination of limited
labelled data and sufficient unlabelled data or weakly-labelled data. Extensive
experiments have shown that our model can efficiently utilize limited labelled
data and provide explainable classification and segmentation results for
decision-making in clinical routine. The code is available at
https://github.com/ayanglab/XAI COVID-19.

The weight matrix (WM) of a neural network (NN) is its program. The programs
of many traditional NNs are learned through gradient descent in some error
function, then remain fixed. The WM of a self-referential NN, however, can keep
rapidly modifying all of itself during runtime. In principle, such NNs can
meta-learn to learn, and meta-meta-learn to meta-learn to learn, and so on, in
the sense of recursive self-improvement. While NN architectures potentially
capable of implementing such behaviour have been proposed since the '90s, there
have been few if any practical studies. Here we revisit such NNs, building upon
recent successes of fast weight programmers and closely related linear
Transformers. We propose a scalable self-referential WM (SRWM) that learns to
use outer products and the delta update rule to modify itself. We evaluate our
SRWM in supervised few-shot learning and in multi-task reinforcement learning
with procedurally generated game environments. Our experiments demonstrate both
practical applicability and competitive performance of the proposed SRWM. Our
code is public.

Gaussian Graphical Models (GGMs) are widely used for exploratory data
analysis in various fields such as genomics, ecology, psychometry. In a
high-dimensional setting, when the number of variables exceeds the number of
observations by several orders of magnitude, the estimation of GGM is a
difficult and unstable optimization problem. Clustering of variables or
variable selection is often performed prior to GGM estimation. We propose a new
method allowing to simultaneously infer a hierarchical clustering structure and
the graphs describing the structure of independence at each level of the
hierarchy. This method is based on solving a convex optimization problem
combining a graphical lasso penalty with a fused type lasso penalty. Results on
real and synthetic data are presented.

In this work isogeometric mortaring is used for the simulation of a six pole
permanent magnet synchronous machine. Isogeometric mortaring is especially well
suited for the efficient computation of rotating electric machines as it allows
for an exact geometry representation for arbitrary rotation angles without the
need of remeshing. The appropriate B-spline spaces needed for the solution of
Maxwell's equations and the corresponding mortar spaces are introduced. Unlike
in classical finite element methods their construction is straightforward in
the isogeometric case. The torque in the machine is computed using two
different methods, i.e., Arkkio's method and by using the Lagrange multipliers
from the mortaring.

This work addresses a version of the two-armed Bernoulli bandit problem where
the sum of the means of the arms is one (the symmetric two-armed Bernoulli
bandit). In a regime where the gap between these means goes to zero and the
number of prediction periods approaches infinity, we obtain the leading order
terms of the minmax optimal regret and pseudoregret for this problem by
associating each of them with a solution of a linear parabolic partial
differential equation. Our results improve upon the previously known results;
specifically, we explicitly compute these leading order terms in three
different scaling regimes for the gap. Additionally, we obtain new
non-asymptotic bounds for any given time horizon.

The inverse problem of supervised reconstruction of depth-variable
(time-dependent) parameters in a neural ordinary differential equation (NODE)
is considered, that means finding the weights of a residual network with time
continuous layers. The NODE is treated as an isolated entity describing the
full network as opposed to earlier research, which embedded it between pre- and
post-appended layers trained by conventional methods. The proposed parameter
reconstruction is done for a general first order differential equation by
minimizing a cost functional covering a variety of loss functions and penalty
terms. A nonlinear conjugate gradient method (NCG) is derived for the
minimization. Mathematical properties are stated for the differential equation
and the cost functional. The adjoint problem needed is derived together with a
sensitivity problem. The sensitivity problem can estimate changes in the
network output under perturbation of the trained parameters. To preserve
smoothness during the iterations the Sobolev gradient is calculated and
incorporated. As a proof-of-concept, numerical results are included for a NODE
and two synthetic datasets, and compared with standard gradient approaches (not
based on NODEs). The results show that the proposed method works well for deep
learning with infinite numbers of layers, and has built-in stability and
smoothness.

Attacks on deep learning models are often difficult to identify and therefore
are difficult to protect against. This problem is exacerbated by the use of
public datasets that typically are not manually inspected before use. In this
paper, we offer a solution to this vulnerability by using, during testing,
random perturbations such as spelling correction if necessary, substitution by
random synonym, or simply dropping the word. These perturbations are applied to
random words in random sentences to defend NLP models against adversarial
attacks. Our Random Perturbations Defense and Increased Randomness Defense
methods are successful in returning attacked models to similar accuracy of
models before attacks. The original accuracy of the model used in this work is
80% for sentiment classification. After undergoing attacks, the accuracy drops
to accuracy between 0% and 44%. After applying our defense methods, the
accuracy of the model is returned to the original accuracy within statistical
significance.

Current deep learning (DL) based approaches to speech intelligibility
enhancement in noisy environments are often trained to minimise the feature
distance between noise-free speech and enhanced speech signals. Despite
improving the speech quality, such approaches do not deliver required levels of
speech intelligibility in everyday noisy environments .
Intelligibility-oriented (I-O) loss functions have recently been developed to
train DL approaches for robust speech enhancement. Here, we formulate, for the
first time, a novel canonical correlation based I-O loss function to more
effectively train DL algorithms. Specifically, we present a
canonical-correlation based short-time objective intelligibility (CC-STOI) cost
function to train a fully convolutional neural network (FCN) model. We carry
out comparative simulation experiments to show that our CC-STOI based speech
enhancement framework outperforms state-of-the-art DL models trained with
conventional distance-based and STOI-based loss functions, using objective and
subjective evaluation measures for case of both unseen speakers and noises.
Ongoing future work is evaluating the proposed approach for design of robust
hearing-assistive technology.

The complexity of real-world geophysical systems is often compounded by the
fact that the observed measurements depend on hidden variables. These latent
variables include unresolved small scales and/or rapidly evolving processes,
partially observed couplings, or forcings in coupled systems. This is the case
in ocean-atmosphere dynamics, for which unknown interior dynamics can affect
surface observations. The identification of computationally-relevant
representations of such partially-observed and highly nonlinear systems is thus
challenging and often limited to short-term forecast applications. Here, we
investigate the physics-constrained learning of implicit dynamical embeddings,
leveraging neural ordinary differential equation (NODE) representations. A key
objective is to constrain their boundedness, which promotes the generalization
of the learned dynamics to arbitrary initial condition. The proposed
architecture is implemented within a deep learning framework, and its relevance
is demonstrated with respect to state-of-the-art schemes for different
case-studies representative of geophysical dynamics.

Visual Place Recognition (VPR) in areas with similar scenes such as urban or
indoor scenarios is a major challenge. Existing VPR methods using global
descriptors have difficulty capturing local specific regions (LSR) in the scene
and are therefore prone to localization confusion in such scenarios. As a
result, finding the LSR that are critical for location recognition becomes key.
To address this challenge, we introduced Patch-NetVLAD+, which was inspired by
patch-based VPR researches. Our method proposed a fine-tuning strategy with
triplet loss to make NetVLAD suitable for extracting patch-level descriptors.
Moreover, unlike existing methods that treat all patches in an image equally,
our method extracts patches of LSR, which present less frequently throughout
the dataset, and makes them play an important role in VPR by assigning proper
weights to them. Experiments on Pittsburgh30k and Tokyo247 datasets show that
our approach achieved up to 6.35\% performance improvement than existing
patch-based methods.

Recently Shah et al., 2020 pointed out the pitfalls of the simplicity bias -
the tendency of gradient-based algorithms to learn simple models - which
include the model's high sensitivity to small input perturbations, as well as
sub-optimal margins. In particular, while Stochastic Gradient Descent yields
max-margin boundary on linear models, such guarantee does not extend to
non-linear models. To mitigate the simplicity bias, we consider
uncertainty-driven perturbations (UDP) of the training data points, obtained
iteratively by following the direction that maximizes the model's estimated
uncertainty. The uncertainty estimate does not rely on the input's label and it
is highest at the decision boundary, and - unlike loss-driven perturbations -
it allows for using a larger range of values for the perturbation magnitude.
Furthermore, as real-world datasets have non-isotropic distances between data
points of different classes, the above property is particularly appealing for
increasing the margin of the decision boundary, which in turn improves the
model's generalization. We show that UDP is guaranteed to achieve the maximum
margin decision boundary on linear models and that it notably increases it on
challenging simulated datasets. For nonlinear models, we show empirically that
UDP reduces the simplicity bias and learns more exhaustive features.
Interestingly, it also achieves competitive loss-based robustness and
generalization trade-off on several datasets.

Introduction: Sleep staging is an essential component in the diagnosis of
sleep disorders and management of sleep health. It is traditionally measured in
a clinical setting and requires a labor-intensive labeling process. We
hypothesize that it is possible to perform robust 4-class sleep staging using
the raw photoplethysmography (PPG) time series and modern advances in deep
learning (DL). Methods: We used two publicly available sleep databases that
included raw PPG recordings, totalling 2,374 patients and 23,055 hours. We
developed SleepPPG-Net, a DL model for 4-class sleep staging from the raw PPG
time series. SleepPPG-Net was trained end-to-end and consists of a residual
convolutional network for automatic feature extraction and a temporal
convolutional network to capture long-range contextual information. We
benchmarked the performance of SleepPPG-Net against models based on the
best-reported state-of-the-art (SOTA) algorithms. Results: When benchmarked on
a held-out test set, SleepPPG-Net obtained a median Cohen's Kappa ($\kappa$)
score of 0.75 against 0.69 for the best SOTA approach. SleepPPG-Net showed good
generalization performance to an external database, obtaining a $\kappa$ score
of 0.74 after transfer learning. Perspective: Overall, SleepPPG-Net provides
new SOTA performance. In addition, performance is high enough to open the path
to the development of wearables that meet the requirements for usage in
clinical applications such as the diagnosis and monitoring of obstructive sleep
apnea.

We present the optical data links for the ATLAS liquid argon calorimeter. The
current status of the vertical cavity surface emitting laser failures, the
up-to-date results in searching for the failure cause, experiences gained in
the searching process, possible backup plans for the optical transmitters and
the lessons learned are also discussed.

This work aims at generating captions for soccer videos using deep learning.
In this context, this paper introduces a dataset, model, and triple-level
evaluation. The dataset consists of 22k caption-clip pairs and three visual
features (images, optical flow, inpainting) for ~500 hours of \emph{SoccerNet}
videos. The model is divided into three parts: a transformer learns language,
ConvNets learn vision, and a fusion of linguistic and visual features generates
captions. The paper suggests evaluating generated captions at three levels:
syntax (the commonly used evaluation metrics such as BLEU-score and CIDEr),
meaning (the quality of descriptions for a domain expert), and corpus (the
diversity of generated captions). The paper shows that the diversity of
generated captions has improved (from 0.07 reaching 0.18) with
semantics-related losses that prioritize selected words. Semantics-related
losses and the utilization of more visual features (optical flow, inpainting)
improved the normalized captioning score by 28\%. The web page of this work:
https://sites.google.com/view/soccercaptioning}{https://sites.google.com/view/soccercaptioning

Speaker verification systems have been widely used in smart phones and
Internet of things devices to identify legitimate users. In recent work, it has
been shown that adversarial attacks, such as FAKEBOB, can work effectively
against speaker verification systems. The goal of this paper is to design a
detector that can distinguish an original audio from an audio contaminated by
adversarial attacks. Specifically, our designed detector, called MEH-FEST,
calculates the minimum energy in high frequencies from the short-time Fourier
transform of an audio and uses it as a detection metric. Through both analysis
and experiments, we show that our proposed detector is easy to implement, fast
to process an input audio, and effective in determining whether an audio is
corrupted by FAKEBOB attacks. The experimental results indicate that the
detector is extremely effective: with near zero false positive and false
negative rates for detecting FAKEBOB attacks in Gaussian mixture model (GMM)
and i-vector speaker verification systems. Moreover, adaptive adversarial
attacks against our proposed detector and their countermeasures are discussed
and studied, showing the game between attackers and defenders.

We propose a new framework to reconstruct a stochastic process
$\left\{\mathbb{P}_{t}: t \in[0, T]\right\}$ using only samples from its
marginal distributions, observed at start and end times $0$ and $T$. This
reconstruction is useful to infer population dynamics, a crucial challenge,
e.g., when modeling the time-evolution of cell populations from single-cell
sequencing data. Our general framework encompasses the more specific
Schr\"odinger bridge (SB) problem, where $\mathbb{P}_{t}$ represents the
evolution of a thermodynamic system at almost equilibrium. Estimating such
bridges is notoriously difficult, motivating our proposal for a novel adaptive
scheme called the GSBflow. Our goal is to rely on Gaussian approximations of
the data to provide the reference stochastic process needed to estimate SB. To
that end, we solve the \acs{SB} problem with Gaussian marginals, for which we
provide, as a central contribution, a closed-form solution and
SDE-representation. We use these formulas to define the reference process used
to estimate more complex SBs, and show that this does indeed help with its
numerical solution. We obtain notable improvements when reconstructing both
synthetic processes and single-cell genomics experiments.

With increasing amounts of music being digitally transferred from production
to distribution, automatic means of determining media quality are needed.
Protection mechanisms in digital audio processing tools have not eliminated the
need of production entities located downstream the distribution chain to assess
audio quality and detect defects inserted further upstream. Such analysis often
relies on the received audio and scarce meta-data alone. Deliberate use of
artefacts such as clicks in popular music as well as more recent defects
stemming from corruption in modern audio encodings call for data-centric and
context sensitive solutions for detection. We present a convolutional network
architecture following end-to-end encoder decoder configuration to develop
detectors for two exemplary audio defects. A click detector is trained and
compared to a traditional signal processing method, with a discussion on
context sensitivity. Additional post-processing is used for data augmentation
and workflow simulation. The ability of our models to capture variance is
explored in a detector for artefacts from decompression of corrupted MP3
compressed audio. For both tasks we describe the synthetic generation of
artefacts for controlled detector training and evaluation. We evaluate our
detectors on the large open-source Free Music Archive (FMA) and genre-specific
datasets.

In the long term, reinforcement learning (RL) is considered by many AI
theorists to be the most promising path to artificial general intelligence.
This places RL practitioners in a position to design systems that have never
existed before and lack prior documentation in law and policy. Public agencies
could intervene on complex dynamics that were previously too opaque to
deliberate about, and long-held policy ambitions would finally be made
tractable. In this whitepaper we illustrate this potential and how it might be
technically enacted in the domains of energy infrastructure, social media
recommender systems, and transportation. Alongside these unprecedented
interventions come new forms of risk that exacerbate the harms already
generated by standard machine learning tools. We correspondingly present a new
typology of risks arising from RL design choices, falling under four
categories: scoping the horizon, defining rewards, pruning information, and
training multiple agents. Rather than allowing RL systems to unilaterally
reshape human domains, policymakers need new mechanisms for the rule of reason,
foreseeability, and interoperability that match the risks these systems pose.
We argue that criteria for these choices may be drawn from emerging subfields
within antitrust, tort, and administrative law. It will then be possible for
courts, federal and state agencies, and non-governmental organizations to play
more active roles in RL specification and evaluation. Building on the "model
cards" and "datasheets" frameworks proposed by Mitchell et al. and Gebru et
al., we argue the need for Reward Reports for AI systems. Reward Reports are
living documents for proposed RL deployments that demarcate design choices.

This paper proposes a new data-driven method for predicting water temperature
in stream networks with reservoirs. The water flows released from reservoirs
greatly affect the water temperature of downstream river segments. However, the
information of released water flow is often not available for many reservoirs,
which makes it difficult for data-driven models to capture the impact to
downstream river segments. In this paper, we first build a state-aware graph
model to represent the interactions amongst streams and reservoirs, and then
propose a parallel learning structure to extract the reservoir release
information and use it to improve the prediction. In particular, for reservoirs
with no available release information, we mimic the water managers' release
decision process through a pseudo-prospective learning method, which infers the
release information from anticipated water temperature dynamics. For reservoirs
with the release information, we leverage a physics-based model to simulate the
water release temperature and transfer such information to guide the learning
process for other reservoirs. The evaluation for the Delaware River Basin shows
that the proposed method brings over 10\% accuracy improvement over existing
data-driven models for stream temperature prediction when the release data is
not available for any reservoirs. The performance is further improved after we
incorporate the release data and physical simulations for a subset of
reservoirs.

Few-shot relation classification (RC) is one of the critical problems in
machine learning. Current research merely focuses on the set-ups that both
training and testing are from the same domain. However, in practice, this
assumption is not always guaranteed. In this study, we present a novel model
that takes into consideration the afore-mentioned cross-domain situation. Not
like previous models, we only use the source domain data to train the
prototypical networks and test the model on target domain data. A meta-based
adversarial training framework (MBATF) is proposed to fine-tune the trained
networks for adapting to data from the target domain. Empirical studies confirm

Modern production data processing and machine learning pipelines on the cloud
are critical components for many cloud-based companies. These pipelines are
typically composed of complex workflows represented by directed acyclic graphs
(DAGs). Cloud environments are attractive to these workflows due to the wide
range of choice with heterogeneous instances and prices that can provide the
flexibility for different cost-performance needs. However, this flexibility
also leads to the complexity of selecting the right resource configuration
(e.g., instance type, resource demands) for each task in the DAG, while
simultaneously scheduling the tasks with the selected resources to reach the
optimal end-to-end performance and cost. These two decisions are often
codependent resulting in an NP-hard scheduling optimization bottleneck.
Existing solutions only focus solely on either problem and ignore the co-effect
on the end-to-end optimum. We propose AGORA, a scheduler that considers both
task-level resource allocation and execution for DAG workflows as a whole in
heterogeneous cloud environments. AGORA first (1) studies the characteristics
of the tasks from prior runs and gives predictions on resource configurations,
and (2) automatically finds the best configuration with its corresponding
schedules for the entire workflow with a cost-performance objective. We
evaluate AGORA in a heterogeneous Amazon Web Services (AWS) cloud environment
with multi-tenant workflows served by Airflow and demonstrate a performance
improvement up to 45% and cost reduction up to 77% compared to state-of-the-art
schedulers. In addition, we apply AGORA to a real-world production trace from
Alibaba and show cost reduction of 65% and DAG completion time reduction of
57%.

Domain Adaptation methodologies have shown to effectively generalize from a
labeled source domain to a label scarce target domain. Previous research has
either focused on unlabeled domain adaptation without any target supervision or
semi-supervised domain adaptation with few labeled target examples per class.
On the other hand Positive-Unlabeled (PU-) Learning has attracted increasing
interest in the weakly supervised learning literature since in quite some real
world applications positive labels are much easier to obtain than negative
ones. In this work we are the first to introduce the challenge of
Positive-Unlabeled Domain Adaptation where we aim to generalise from a fully
labeled source domain to a target domain where only positive and unlabeled data
is available. We present a novel two-step learning approach to this problem by
firstly identifying reliable positive and negative pseudo-labels in the target
domain guided by source domain labels and a positive-unlabeled risk estimator.
This enables us to use a standard classifier on the target domain in a second
step. We validate our approach by running experiments on benchmark datasets for
visual object recognition. Furthermore we propose real world examples for our
setting and validate our superior performance on parking occupancy data.

Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex
decision-making problems through the synergy of Monte-Carlo planning and
Reinforcement Learning (RL). The highly combinatorial nature of the problems
commonly addressed by MCTS requires the use of efficient exploration strategies
for navigating the planning tree and quickly convergent value backup methods.
These crucial problems are particularly evident in recent advances that combine
MCTS with deep neural networks for function approximation. In this work, we
propose two methods for improving the convergence rate and exploration based on
a newly introduced backup operator and entropy regularization. We provide
strong theoretical guarantees to bound convergence rate, approximation error,
and regret of our methods. Moreover, we introduce a mathematical framework
based on the use of the $\alpha$-divergence for backup and exploration in MCTS.
We show that this theoretical formulation unifies different approaches,
including our newly introduced ones, under the same mathematical framework,
allowing to obtain different methods by simply changing the value of $\alpha$.
In practice, our unified perspective offers a flexible way to balance between
exploration and exploitation by tuning the single $\alpha$ parameter according
to the problem at hand. We validate our methods through a rigorous empirical
study from basic toy problems to the complex Atari games, and including both
MDP and POMDP problems.

Catastrophic forgetting (CF) happens whenever a neural network overwrites
past knowledge while being trained on new tasks. Common techniques to handle CF
include regularization of the weights (using, e.g., their importance on past
tasks), and rehearsal strategies, where the network is constantly re-trained on
past data. Generative models have also been applied for the latter, in order to
have endless sources of data. In this paper, we propose a novel method that
combines the strengths of regularization and generative-based rehearsal
approaches. Our generative model consists of a normalizing flow (NF), a
probabilistic and invertible neural network, trained on the internal embeddings
of the network. By keeping a single NF throughout the training process, we show
that our memory overhead remains constant. In addition, exploiting the
invertibility of the NF, we propose a simple approach to regularize the
network's embeddings with respect to past tasks. We show that our method
performs favorably with espect to state-of-the-art approaches in the
literature, with bounded computational power and memory overheads.

Detecting deepfakes remains an open problem. Current detection methods fail
against an adversary who adds imperceptible adversarial perturbations to the
deepfake to evade detection. We propose Disjoint Deepfake Detection (D3), a
deepfake detector designed to improve adversarial robustness beyond de facto
solutions such as adversarial training. D3 uses an ensemble of models over
disjoint subsets of the frequency spectrum to significantly improve robustness.
Our key insight is to leverage a redundancy in the frequency domain and apply a
saliency partitioning technique to disjointly distribute frequency components
across multiple models. We formally prove that these disjoint ensembles lead to
a reduction in the dimensionality of the input subspace where adversarial
deepfakes lie. We then empirically validate the D3 method against white-box
attacks and black-box attacks and find that D3 significantly outperforms
existing state-of-the-art defenses applied to deepfake detection.

We consider the problem of estimating the topology of multiple networks from
nodal observations, where these networks are assumed to be drawn from the same
(unknown) random graph model. We adopt a graphon as our random graph model,
which is a nonparametric model from which graphs of potentially different sizes
can be drawn. The versatility of graphons allows us to tackle the joint
inference problem even for the cases where the graphs to be recovered contain
different number of nodes and lack precise alignment across the graphs. Our
solution is based on combining a maximum likelihood penalty with graphon
estimation schemes and can be used to augment existing network inference
methods. We validate our proposed approach by comparing its performance against
competing methods in synthetic and real-world datasets.

Convolutional neural networks (CNNs) have achieved great success in skin
lesion classification. A balanced dataset is required to train a good model.
However, due to the appearance of different skin lesions in practice, severe or
even deadliest skin lesion types (e.g., melanoma) naturally have quite small
amount represented in a dataset. In that, classification performance
degradation occurs widely, it is significantly important to have CNNs that work
well on class imbalanced skin lesion image dataset. In this paper, we propose
SuperCon, a two-stage training strategy to overcome the class imbalance problem
on skin lesion classification. It contains two stages: (i) representation
training that tries to learn a feature representation that closely aligned
among intra-classes and distantly apart from inter-classes, and (ii) classifier
fine-tuning that aims to learn a classifier that correctly predict the label
based on the learnt representations. In the experimental evaluation, extensive
comparisons have been made among our approach and other existing approaches on
skin lesion benchmark datasets. The results show that our two-stage training
strategy effectively addresses the class imbalance classification problem, and
significantly improves existing works in terms of F1-score and AUC score,
resulting in state-of-the-art performance.

The recent progresses in Machine Learning opened the door to actual
applications of learning algorithms but also to new research directions both in
the field of Machine Learning directly and, at the edges with other
disciplines. The case that interests us is the interface with physics, and more
specifically Statistical Physics. In this short lecture, I will try to present
first a brief introduction to Machine Learning from the angle of neural
networks. After explaining quickly some fundamental models and global aspects
of the training procedure, I will discuss into more detail two examples
illustrate what can be done from the Statistical Physics perspective.

In this paper, an in-depth analysis of Escherichia coli seawater measurements
during the bathing season in the city of Rijeka, Croatia was conducted.
Submerged sources of groundwater were observed at several measurement locations
which could be the cause for increased E. coli values. This specificity of
karst terrain is usually not considered during the monitoring process, thus a
novel measurement methodology is proposed. A cascade machine learning model is
used to predict coastal water quality based on meteorological data, which
improves the level of accuracy due to data imbalance resulting from rare
occurrences of measurements with reduced water quality. Currently, the cascade
model is employed as a filter method, where measurements not classified as
excellent quality need to be further analyzed. However, with improvements
proposed in the paper, the cascade model could be ultimately used as a
standalone method.

Multimodal hearing aids (HAs) aim to deliver more intelligible audio in noisy
environments by contextually sensing and processing data in the form of not
only audio but also visual information (e.g. lip reading). Machine learning
techniques can play a pivotal role for the contextually processing of
multimodal data. However, since the computational power of HA devices is low,
therefore this data must be processed either on the edge or cloud which, in
turn, poses privacy concerns for sensitive user data. Existing literature
proposes several techniques for data encryption but their computational
complexity is a major bottleneck to meet strict latency requirements for
development of future multi-modal hearing aids. To overcome this problem, this
paper proposes a novel real-time audio/visual data encryption scheme based on
chaos-based encryption using the Tangent-Delay Ellipse Reflecting Cavity-Map
System (TD-ERCS) map and Non-linear Chaotic (NCA) Algorithm. The results
achieved against different security parameters, including Correlation
Coefficient, Unified Averaged Changed Intensity (UACI), Key Sensitivity
Analysis, Number of Changing Pixel Rate (NPCR), Mean-Square Error (MSE), Peak
Signal to Noise Ratio (PSNR), Entropy test, and Chi-test, indicate that the
newly proposed scheme is more lightweight due to its lower execution time as
compared to existing schemes and more secure due to increased key-space against
modern brute-force attacks.

We present a novel approach to evaluate the performance of interpretability
methods for time series classification, and propose a new strategy to assess
the similarity between domain experts and machine data interpretation. The
novel approach leverages a new family of synthetic datasets and introduces new
interpretability evaluation metrics. The approach addresses several common
issues encountered in the literature, and clearly depicts how well an
interpretability method is capturing neural network's data usage, providing a
systematic interpretability evaluation framework. The new methodology
highlights the superiority of Shapley Value Sampling and Integrated Gradients
for interpretability in time-series classification tasks.

Variational inference (VI) is a technique to approximate difficult to compute
posteriors by optimization. In contrast to MCMC, VI scales to many
observations. In the case of complex posteriors, however, state-of-the-art VI
approaches often yield unsatisfactory posterior approximations. This paper
presents Bernstein flow variational inference (BF-VI), a robust and easy-to-use
method, flexible enough to approximate complex multivariate posteriors. BF-VI
combines ideas from normalizing flows and Bernstein polynomial-based
transformation models. In benchmark experiments, we compare BF-VI solutions
with exact posteriors, MCMC solutions, and state-of-the-art VI methods
including normalizing flow based VI. We show for low-dimensional models that
BF-VI accurately approximates the true posterior; in higher-dimensional models,
BF-VI outperforms other VI methods. Further, we develop with BF-VI a Bayesian
model for the semi-structured Melanoma challenge data, combining a CNN model
part for image data with an interpretable model part for tabular data, and
demonstrate for the first time how the use of VI in semi-structured models.

It is well known that the training of the neural network can be viewed as a
mean field optimization problem. In this paper we are inspired by the
fictitious play, a classical algorithm in the game theory for learning the Nash
equilibria, and propose a new algorithm, different from the conventional
gradient-descent ones, to solve the mean field optimization. We rigorously
prove its (exponential) convergence, and show some simple numerical examples.

In this paper, we tackle the computational efficiency of kernelized UCB
algorithms in contextual bandits. While standard methods require a O(CT^3)
complexity where T is the horizon and the constant C is related to optimizing
the UCB rule, we propose an efficient contextual algorithm for large-scale
problems. Specifically, our method relies on incremental Nystrom approximations
of the joint kernel embedding of contexts and actions. This allows us to
achieve a complexity of O(CTm^2) where m is the number of Nystrom points. To
recover the same regret as the standard kernelized UCB algorithm, m needs to be
of order of the effective dimension of the problem, which is at most
O(\sqrt(T)) and nearly constant in some cases.

We propose an automatic framework for toll collection, consisting of three
steps: vehicle type recognition, license plate localization, and reading.
However, each of the three steps becomes non-trivial due to image variations
caused by several factors. The traditional vehicle decorations on the front
cause variations among vehicles of the same type. These decorations make
license plate localization and recognition difficult due to severe background
clutter and partial occlusions. Likewise, on most vehicles, specifically
trucks, the position of the license plate is not consistent. Lastly, for
license plate reading, the variations are induced by non-uniform font styles,
sizes, and partially occluded letters and numbers. Our proposed framework takes
advantage of both data availability and performance evaluation of the backbone
deep learning architectures. We gather a novel dataset, \emph{Diverse Vehicle
and License Plates Dataset (DVLPD)}, consisting of 10k images belonging to six
vehicle types. Each image is then manually annotated for vehicle type, license
plate, and its characters and digits. For each of the three tasks, we evaluate
You Only Look Once (YOLO)v2, YOLOv3, YOLOv4, and FasterRCNN. For real-time
implementation on a Raspberry Pi, we evaluate the lighter versions of YOLO
named Tiny YOLOv3 and Tiny YOLOv4. The best Mean Average Precision (mAP@0.5) of
98.8% for vehicle type recognition, 98.5% for license plate detection, and
98.3% for license plate reading is achieved by YOLOv4, while its lighter
version, i.e., Tiny YOLOv4 obtained a mAP of 97.1%, 97.4%, and 93.7% on vehicle
type recognition, license plate detection, and license plate reading,
respectively. The dataset and the training codes are available at
https://github.com/usama-x930/VT-LPR

A sequence of works in unconstrained online convex optimisation have
investigated the possibility of adapting simultaneously to the norm $U$ of the
comparator and the maximum norm $G$ of the gradients. In full generality,
matching upper and lower bounds are known which show that this comes at the
unavoidable cost of an additive $G U^3$, which is not needed when either $G$ or
$U$ is known in advance. Surprisingly, recent results by Kempka et al. (2019)
show that no such price for adaptivity is needed in the specific case of
$1$-Lipschitz losses like the hinge loss. We follow up on this observation by
showing that there is in fact never a price to pay for adaptivity if we
specialise to any of the other common supervised online learning losses: our
results cover log loss, (linear and non-parametric) logistic regression, square
loss prediction, and (linear and non-parametric) least-squares regression. We
also fill in several gaps in the literature by providing matching lower bounds
with an explicit dependence on $U$. In all cases we obtain scale-free
algorithms, which are suitably invariant under rescaling of the data. Our
general goal is to establish achievable rates without concern for computational
efficiency, but for linear logistic regression we also provide an adaptive
method that is as efficient as the recent non-adaptive algorithm by Agarwal et
al. (2021).

Classic image inpainting is a restoration method that reconstructs missing
image parts. However, a carefully selected mask of known pixels that yield a
high quality inpainting can also act as a sparse image representation. This
challenging spatial optimisation problem is essential for practical
applications such as compression. So far, it has been almost exclusively
addressed by model-based approaches. First attempts with neural networks seem
promising, but are tailored towards specific inpainting operators or require
postprocessing. To address this issue, we propose the first generative
adversarial network for spatial inpainting data optimisation. In contrast to
previous approaches, it allows joint training of an inpainting generator and a
corresponding mask optimisation network. With a Wasserstein distance, we ensure
that our inpainting results accurately reflect the statistics of natural
images. This yields significant improvements in visual quality and speed over
conventional stochastic models and also outperforms current spatial
optimisation networks.

In this paper, we study the long-time convergence and uniform strong
propagation of chaos for a class of nonlinear Markov chains for Markov chain
Monte Carlo (MCMC). Our technique is quite simple, making use of recent
contraction estimates for linear Markov kernels and basic techniques from
Markov theory and analysis. Moreover, the same proof strategy applies to both
the long-time convergence and propagation of chaos. We also show, via some
experiments, that these nonlinear MCMC techniques are viable for use in
real-world high-dimensional inference such as Bayesian neural networks.

Measures of similarity (or dissimilarity) are a key ingredient to many
machine learning algorithms. We introduce DID, a pairwise dissimilarity measure
applicable to a wide range of data spaces, which leverages the data's internal
structure to be invariant to diffeomorphisms. We prove that DID enjoys
properties which make it relevant for theoretical study and practical use. By
representing each datum as a function, DID is defined as the solution to an
optimization problem in a Reproducing Kernel Hilbert Space and can be expressed
in closed-form. In practice, it can be efficiently approximated via Nystr\"om
sampling. Empirical experiments support the merits of DID.

Modern deep neural networks can easily overfit to biased training data
containing corrupted labels or class imbalance. Sample re-weighting methods are
popularly used to alleviate this data bias issue. Most current methods,
however, require to manually pre-specify the weighting schemes as well as their
additional hyper-parameters relying on the characteristics of the investigated
problem and training data. This makes them fairly hard to be generally applied
in practical scenarios, due to their significant complexities and inter-class
variations of data bias situations. To address this issue, we propose a
meta-model capable of adaptively learning an explicit weighting scheme directly
from data. Specifically, by seeing each training class as a separate learning
task, our method aims to extract an explicit weighting function with sample
loss and task/class feature as input, and sample weight as output, expecting to
impose adaptively varying weighting schemes to different sample classes based
on their own intrinsic bias characteristics. Synthetic and real data
experiments substantiate the capability of our method on achieving proper
weighting schemes in various data bias cases, like the class imbalance,
feature-independent and dependent label noise scenarios, and more complicated
bias scenarios beyond conventional cases. Besides, the task-transferability of
the learned weighting scheme is also substantiated, by readily deploying the
weighting function learned on relatively smaller-scale CIFAR-10 dataset on much
larger-scale full WebVision dataset. A performance gain can be readily achieved
compared with previous SOAT ones without additional hyper-parameter tuning and
meta gradient descent step. The general availability of our method for multiple
robust deep learning issues, including partial-label learning, semi-supervised
learning and selective classification, has also been validated.

This paper studies the consistency and statistical inference of simulated
Ising models in the high dimensional background. Our estimators are based on
the Markov chain Monte Carlo maximum likelihood estimation (MCMC-MLE) method
penalized by the Elastic-net. Under mild conditions that ensure a specific
convergence rate of MCMC method, the $\ell_{1}$ consistency of
Elastic-net-penalized MCMC-MLE is proved. We further propose a decorrelated
score test based on the decorrelated score function and prove the asymptotic
normality of the score function without the influence of many nuisance
parameters under the assumption that accelerates the convergence of the MCMC
method. The one-step estimator for a single parameter of interest is purposed
by linearizing the decorrelated score function to solve its root, as well as
its normality and confidence interval for the true value, therefore, be
established. Finally, we use different algorithms to control the false
discovery rate (FDR) via traditional p-values and novel e-values.

Recent work has shown that offline reinforcement learning (RL) can be
formulated as a sequence modeling problem (Chen et al., 2021; Janner et al.,
2021) and solved via approaches similar to large-scale language modeling.
However, any practical instantiation of RL also involves an online component,
where policies pretrained on passive offline datasets are finetuned via
taskspecific interactions with the environment. We propose Online Decision
Transformers (ODT), an RL algorithm based on sequence modeling that blends
offline pretraining with online finetuning in a unified framework. Our
framework uses sequence-level entropy regularizers in conjunction with
autoregressive modeling objectives for sample-efficient exploration and
finetuning. Empirically, we show that ODT is competitive with the
state-of-the-art in absolute performance on the D4RL benchmark but shows much
more significant gains during the finetuning procedure.

Over the last few years, the Shapley value, a solution concept from
cooperative game theory, has found numerous applications in machine learning.
In this paper, we first discuss fundamental concepts of cooperative game theory
and axiomatic properties of the Shapley value. Then we give an overview of the
most important applications of the Shapley value in machine learning: feature
selection, explainability, multi-agent reinforcement learning, ensemble
pruning, and data valuation. We examine the most crucial limitations of the
Shapley value and point out directions for future research.

Production-level workflows for producing convincing 3D dynamic human faces
have long relied on an assortment of labor-intensive tools for geometry and
texture generation, motion capture and rigging, and expression synthesis.
Recent neural approaches automate individual components but the corresponding
latent representations cannot provide artists with explicit controls as in
conventional tools. In this paper, we present a new learning-based,
video-driven approach for generating dynamic facial geometries with
high-quality physically-based assets. For data collection, we construct a
hybrid multiview-photometric capture stage, coupling with ultra-fast video
cameras to obtain raw 3D facial assets. We then set out to model the facial
expression, geometry and physically-based textures using separate VAEs where we
impose a global MLP based expression mapping across the latent spaces of
respective networks, to preserve characteristics across respective attributes.
We also model the delta information as wrinkle maps for the physically-based
textures, achieving high-quality 4K dynamic textures. We demonstrate our
approach in high-fidelity performer-specific facial capture and cross-identity
facial motion retargeting. In addition, our multi-VAE-based neural asset, along
with the fast adaptation schemes, can also be deployed to handle in-the-wild
videos. Besides, we motivate the utility of our explicit facial disentangling
strategy by providing various promising physically-based editing results with
high realism. Comprehensive experiments show that our technique provides higher
accuracy and visual fidelity than previous video-driven facial reconstruction
and animation methods.

The instability of power generation from national grids has led industries
(e.g., telecommunication) to rely on plant generators to run their businesses.
However, these secondary generators create additional challenges such as fuel
leakages in and out of the system and perturbations in the fuel level gauges.
Consequently, telecommunication operators have been involved in a constant need
for fuel to supply diesel generators. With the increase in fuel prices due to
socio-economic factors, excessive fuel consumption and fuel pilferage become a
problem, and this affects the smooth run of the network companies. In this
work, we compared four machine learning algorithms (i.e. Gradient Boosting,
Random Forest, Neural Network, and Lasso) to predict the amount of fuel
consumed by a power generation plant. After evaluating the predictive accuracy
of these models, the Gradient Boosting model out-perform the other three
regressor models with the highest Nash efficiency value of 99.1%.

We consider measurement disturbance tradeoffs in quantum machine learning
protocols which seek to learn about quantum data. We study the simplest example
of a binary classification task, in the unsupervised regime. Specifically, we
investigate how a classification of two qubits, that can each be in one of two
unknown states, affects our ability to perform a subsequent classification on
three qubits when a third is added. Surprisingly, we find a range of strategies
in which a non-trivial first classification does not affect the success rate of
the second classification. There is, however, a non-trivial measurement
disturbance tradeoff between the success rate of the first and second
classifications, and we fully characterise this tradeoff analytically.

One of the first steps during the investigation of geological objects is the
interwell correlation. It provides information on the structure of the objects
under study, as it comprises the framework for constructing geological models
and assessing hydrocarbon reserves. Today, the detailed interwell correlation
relies on manual analysis of well-logging data. Thus, it is time-consuming and
of a subjective nature. The essence of the interwell correlation constitutes an
assessment of the similarities between geological profiles. There were many
attempts to automate the process of interwell correlation by means of
rule-based approaches, classic machine learning approaches, and deep learning
approaches in the past. However, most approaches are of limited usage and
inherent subjectivity of experts. We propose a novel framework to solve the
geological profile similarity estimation based on a deep learning model. Our
similarity model takes well-logging data as input and provides the similarity
of wells as output. The developed framework enables (1) extracting patterns and
essential characteristics of geological profiles within the wells and (2) model
training following the unsupervised paradigm without the need for manual
analysis and interpretation of well-logging data. For model testing, we used
two open datasets originating in New Zealand and Norway. Our data-based
similarity models provide high performance: the accuracy of our model is
$0.926$ compared to $0.787$ for baselines based on the popular gradient
boosting approach. With them, an oil\&gas practitioner can improve interwell
correlation quality and reduce operation time.

We propose new change of measure inequalities based on $f$-divergences (of
which the Kullback-Leibler divergence is a particular case). Our strategy
relies on combining the Legendre transform of $f$-divergences and the
Young-Fenchel inequality. By exploiting these new change of measure
inequalities, we derive new PAC-Bayesian generalisation bounds with a
complexity involving $f$-divergences, and holding in mostly unchartered
settings (such as heavy-tailed losses). We instantiate our results for the most
popular $f$-divergences.

Differential privacy (DP) has been recently introduced to linear contextual
bandits to formally address the privacy concerns in its associated personalized
services to participating users (e.g., recommendations). Prior work largely
focus on two trust models of DP: the central model, where a central server is
responsible for protecting users sensitive data, and the (stronger) local
model, where information needs to be protected directly on user side. However,
there remains a fundamental gap in the utility achieved by learning algorithms
under these two privacy models, e.g., $\tilde{O}(\sqrt{T})$ regret in the
central model as compared to $\tilde{O}(T^{3/4})$ regret in the local model, if
all users are unique within a learning horizon $T$. In this work, we aim to
achieve a stronger model of trust than the central model, while suffering a
smaller regret than the local model by considering recently popular shuffle
model of privacy. We propose a general algorithmic framework for linear
contextual bandits under the shuffle trust model, where there exists a trusted
shuffler in between users and the central server, that randomly permutes a
batch of users data before sending those to the server. We then instantiate
this framework with two specific shuffle protocols: one relying on privacy
amplification of local mechanisms, and another incorporating a protocol for
summing vectors and matrices of bounded norms. We prove that both these
instantiations lead to regret guarantees that significantly improve on that of
the local model, and can potentially be of the order $\tilde{O}(T^{3/5})$ if
all users are unique. We also verify this regret behavior with simulations on
synthetic data. Finally, under the practical scenario of non-unique users, we
show that the regret of our shuffle private algorithm scale as
$\tilde{O}(T^{2/3})$, which matches that the central model could achieve in
this case.

We establish new generalisation bounds for multiclass classification by
abstracting to a more general setting of discretised error types. Extending the
PAC-Bayes theory, we are hence able to provide fine-grained bounds on
performance for multiclass classification, as well as applications to other
learning problems including discretisation of regression losses. Tractable
training objectives are derived from the bounds. The bounds are uniform over
all weightings of the discretised error types and thus can be used to bound
weightings not foreseen at training, including the full confusion matrix in the
multiclass classification case.

Designing materials or structures that can achieve asymmetric shape-shifting
in response to symmetrically switching stimuli is a promising approach to
enhance the locomotion performance of soft actuators/robots. Inspired by the
geometry of slender leaves of many plants, we find that the thin-walled beam
with a U-shaped cross section exhibits asymmetric deformation behaviors under
bending with opposite orientations. Although this novel mechanical property has
been long noticed and utilized in some applications, its mechanism is still
unclear so far. In this study, we attribute this asymmetric bending behavior of
thin-walled U-shaped beams to the buckling of sidewalls caused by the
bending-induced compressive effect. Based on the Euler-Bernoulli beam theory
and Kirchhoff-Love thin plate theory, a simple but efficient model is
established to derive the critical moment for the sidewall buckling in a
semi-analytical form. Finite element analysis simulations and experiments are
employed to validate the theoretical foundations of our findings. The results
of our work not only shed light on the mechanics underlying the asymmetric
bending behavior of thin-walled U-shaped beams, but also open up new avenues
for the structure design of high-performance soft actuators/robots and other
novel devices.

Deep learning techniques have recently shown promise in the field of anomaly
detection, providing a flexible and effective method of modelling systems in
comparison to traditional statistical modelling and signal processing-based
methods. However, there are a few well publicised issues Neural Networks (NN)s
face such as generalisation ability, requiring large volumes of labelled data
to be able to train effectively and understanding spatial context in data. This
paper introduces a novel NN architecture which hybridises the
Long-Short-Term-Memory (LSTM) and Capsule Networks into a single network in a
branched input Autoencoder architecture for use on multivariate time series
data. The proposed method uses an unsupervised learning technique to overcome
the issues with finding large volumes of labelled training data. Experimental
results show that without hyperparameter optimisation, using Capsules
significantly reduces overfitting and improves the training efficiency.
Additionally, results also show that the branched input models can learn
multivariate data more consistently with or without Capsules in comparison to
the non-branched input models. The proposed model architecture was also tested
on an open-source benchmark, where it achieved state-of-the-art performance in
outlier detection, and overall performs best over the metrics tested in
comparison to current state-of-the art methods.

Traffic classification, i.e. the identification of the type of applications
flowing in a network, is a strategic task for numerous activities (e.g.,
intrusion detection, routing). This task faces some critical challenges that
current deep learning approaches do not address. The design of current
approaches do not take into consideration the fact that networking hardware
(e.g., routers) often runs with limited computational resources. Further, they
do not meet the need for faithful explainability highlighted by regulatory
bodies. Finally, these traffic classifiers are evaluated on small datasets
which fail to reflect the diversity of applications in real-world settings.
  Therefore, this paper introduces a Lightweight, Efficient and
eXplainable-by-design convolutional neural network (LEXNet) for Internet
traffic classification, which relies on a new residual block (for lightweight
and efficiency purposes) and prototype layer (for explainability). Based on a
commercial-grade dataset, our evaluation shows that LEXNet succeeds to maintain
the same accuracy as the best performing state-of-the-art neural network, while
providing the additional features previously mentioned. Moreover, we illustrate
the explainability feature of our approach, which stems from the communication
of detected application prototypes to the end-user, and we highlight the
faithfulness of LEXNet explanations through a comparison with post hoc methods.

Artificial neural networks (ANN) are inspired by human learning. However,
unlike human education, classical ANN does not use a curriculum. Curriculum
Learning (CL) refers to the process of ANN training in which examples are used
in a meaningful order. When using CL, training begins with a subset of the
dataset and new samples are added throughout the training, or training begins
with the entire dataset and the number of samples used is reduced. With these
changes in training dataset size, better results can be obtained with
curriculum, anti-curriculum, or random-curriculum methods than the vanilla
method. However, a generally efficient CL method for various architectures and
data sets is not found. In this paper, we propose cyclical curriculum learning
(CCL), in which the data size used during training changes cyclically rather
than simply increasing or decreasing. Instead of using only the vanilla method
or only the curriculum method, using both methods cyclically like in CCL
provides more successful results. We tested the method on 18 different data
sets and 15 architectures in image and text classification tasks and obtained
more successful results than no-CL and existing CL methods. We also have shown
theoretically that it is less erroneous to apply CL and vanilla cyclically
instead of using only CL or only vanilla method. The code of Cyclical
Curriculum is available at
https://github.com/CyclicalCurriculum/Cyclical-Curriculum.

Anomaly detection from graph data is an important data mining task in many
applications such as social networks, finance, and e-commerce. Existing efforts
in graph anomaly detection typically only consider the information in a single
scale (view), thus inevitably limiting their capability in capturing anomalous
patterns in complex graph data. To address this limitation, we propose a novel
framework, graph ANomaly dEtection framework with Multi-scale cONtrastive
lEarning (ANEMONE in short). By using a graph neural network as a backbone to
encode the information from multiple graph scales (views), we learn better
representation for nodes in a graph. In maximizing the agreements between
instances at both the patch and context levels concurrently, we estimate the
anomaly score of each node with a statistical anomaly estimator according to
the degree of agreement from multiple perspectives. To further exploit a
handful of ground-truth anomalies (few-shot anomalies) that may be collected in
real-life applications, we further propose an extended algorithm, ANEMONE-FS,
to integrate valuable information in our method. We conduct extensive
experiments under purely unsupervised settings and few-shot anomaly detection
settings, and we demonstrate that the proposed method ANEMONE and its variant
ANEMONE-FS consistently outperform state-of-the-art algorithms on six benchmark
datasets.

Recently, Deep Learning-based methods for inverse tone-mapping standard
dynamic range (SDR) images to obtain high dynamic range (HDR) images have
become very popular. These methods manage to fill over-exposed areas
convincingly both in terms of details and dynamic range. Typically, these
methods, to be effective, need to learn from large datasets and to transfer
this knowledge to the network weights. In this work, we tackle this problem
from a completely different perspective. What can we learn from a single SDR
video? With the presented zero-shot approach, we show that, in many cases, a
single SDR video is sufficient to be able to generate an HDR video of the same
quality or better than other state-of-the-art methods.

Natural language reflects our private lives and identities, making its
privacy concerns as broad as those of real life. Language models lack the
ability to understand the context and sensitivity of text, and tend to memorize
phrases present in their training sets. An adversary can exploit this tendency
to extract training data. Depending on the nature of the content and the
context in which this data was collected, this could violate expectations of
privacy. Thus there is a growing interest in techniques for training language
models that preserve privacy. In this paper, we discuss the mismatch between
the narrow assumptions made by popular data protection techniques (data
sanitization and differential privacy), and the broadness of natural language
and of privacy as a social norm. We argue that existing protection methods
cannot guarantee a generic and meaningful notion of privacy for language
models. We conclude that language models should be trained on text data which
was explicitly produced for public use.

In electricity markets, retailers or brokers want to maximize profits by
allocating tariff profiles to end consumers. One of the objectives of such
demand response management is to incentivize the consumers to adjust their
consumption so that the overall electricity procurement in the wholesale
markets is minimized, e.g. it is desirable that consumers consume less during
peak hours when cost of procurement for brokers from wholesale markets are
high. We consider a greedy solution to maximize the overall profit for brokers
by optimal tariff profile allocation. This in-turn requires forecasting
electricity consumption for each user for all tariff profiles. This forecasting
problem is challenging compared to standard forecasting problems due to
following reasons: i. the number of possible combinations of hourly tariffs is
high and retailers may not have considered all combinations in the past
resulting in a biased set of tariff profiles tried in the past, ii. the
profiles allocated in the past to each user is typically based on certain
policy. These reasons violate the standard i.i.d. assumptions, as there is a
need to evaluate new tariff profiles on existing customers and historical data
is biased by the policies used in the past for tariff allocation. In this work,
we consider several scenarios for forecasting and optimization under these
conditions. We leverage the underlying structure of how consumers respond to
variable tariff rates by comparing tariffs across hours and shifting loads, and
propose suitable inductive biases in the design of deep neural network based
architectures for forecasting under such scenarios. More specifically, we
leverage attention mechanisms and permutation equivariant networks that allow
desirable processing of tariff profiles to learn tariff representations that
are insensitive to the biases in the data and still representative of the task.

Accurate traffic prediction is a key ingredient to enable traffic management
like rerouting cars to reduce road congestion or regulating traffic via dynamic
speed limits to maintain a steady flow. A way to represent traffic data is in
the form of temporally changing heatmaps visualizing attributes of traffic,
such as speed and volume. In recent works, U-Net models have shown SOTA
performance on traffic forecasting from heatmaps. We propose to combine the
U-Net architecture with graph layers which improves spatial generalization to
unseen road networks compared to a Vanilla U-Net. In particular, we specialize
existing graph operations to be sensitive to geographical topology and
generalize pooling and upsampling operations to be applicable to graphs.

Understanding implicit bias of gradient descent for generalization capability
of ReLU networks has been an important research topic in machine learning
research. Unfortunately, even for a single ReLU neuron trained with the square
loss, it was recently shown impossible to characterize the implicit
regularization in terms of a norm of model parameters (Vardi & Shamir, 2021).
In order to close the gap toward understanding intriguing generalization
behavior of ReLU networks, here we examine the gradient flow dynamics in the
parameter space when training single-neuron ReLU networks. Specifically, we
discover an implicit bias in terms of support vectors, which plays a key role
in why and how ReLU networks generalize well. Moreover, we analyze gradient
flows with respect to the magnitude of the norm of initialization, and show
that the norm of the learned weight strictly increases through the gradient
flow. Lastly, we prove the global convergence of single ReLU neuron for $d = 2$
case.

Text spotting end-to-end methods have recently gained attention in the
literature due to the benefits of jointly optimizing the text detection and
recognition components. Existing methods usually have a distinct separation
between the detection and recognition branches, requiring exact annotations for
the two tasks. We introduce TextTranSpotter (TTS), a transformer-based approach
for text spotting and the first text spotting framework which may be trained
with both fully- and weakly-supervised settings. By learning a single latent
representation per word detection, and using a novel loss function based on the
Hungarian loss, our method alleviates the need for expensive localization
annotations. Trained with only text transcription annotations on real data, our
weakly-supervised method achieves competitive performance with previous
state-of-the-art fully-supervised methods. When trained in a fully-supervised
manner, TextTranSpotter shows state-of-the-art results on multiple benchmarks.

Imitation learning is employed to learn sensorimotor coordination for
steering angle prediction in an end-to-end fashion requires expert
demonstrations. These expert demonstrations are paired with environmental
perception and vehicle control data. The conventional frame-based RGB camera is
the most common exteroceptive sensor modality used to acquire the environmental
perception data. The frame-based RGB camera has produced promising results when
used as a single modality in learning end-to-end lateral control. However, the
conventional frame-based RGB camera has limited operability in illumination
variation conditions and is affected by the motion blur. The event camera
provides complementary information to the frame-based RGB camera. This work
explores the fusion of frame-based RGB and event data for learning end-to-end
lateral control by predicting steering angle. In addition, how the
representation from event data fuse with frame-based RGB data helps to predict
the lateral control robustly for the autonomous vehicle. To this end, we
propose DRFuser, a novel convolutional encoder-decoder architecture for
learning end-to-end lateral control. The encoder module is branched between the
frame-based RGB data and event data along with the self-attention layers.
Moreover, this study has also contributed to our own collected dataset
comprised of event, frame-based RGB, and vehicle control data. The efficacy of
the proposed method is experimentally evaluated on our collected dataset, Davis
Driving dataset (DDD), and Carla Eventscape dataset. The experimental results
illustrate that the proposed method DRFuser outperforms the state-of-the-art in
terms of root-mean-square error (RMSE) and mean absolute error (MAE) used as

Decentralized sparsity learning has attracted a significant amount of
attention recently due to its rapidly growing applications. To obtain the
robust and sparse estimators, a natural idea is to adopt the non-smooth median
loss combined with a $\ell_1$ sparsity regularizer. However, most of the
existing methods suffer from slow convergence performance caused by the {\em
double} non-smooth objective. To accelerate the computation, in this paper, we
proposed a decentralized surrogate median regression (deSMR) method for
efficiently solving the decentralized sparsity learning problem. We show that
our proposed algorithm enjoys a linear convergence rate with a simple
implementation. We also investigate the statistical guarantee, and it shows
that our proposed estimator achieves a near-oracle convergence rate without any
restriction on the number of network nodes. Moreover, we establish the
theoretical results for sparse support recovery. Thorough numerical experiments
and real data study are provided to demonstrate the effectiveness of our
method.

Multi-task learning by robots poses the challenge of the domain knowledge:
complexity of tasks, complexity of the actions required, relationship between
tasks for transfer learning. We demonstrate that this domain knowledge can be
learned to address the challenges in life-long learning. Specifically, the
hierarchy between tasks of various complexities is key to infer a curriculum
from simple to composite tasks. We propose a framework for robots to learn
sequences of actions of unbounded complexity in order to achieve multiple
control tasks of various complexity. Our hierarchical reinforcement learning
framework, named SGIM-SAHT, offers a new direction of research, and tries to
unify partial implementations on robot arms and mobile robots. We outline our
contributions to enable robots to map multiple control tasks to sequences of
actions: representations of task dependencies, an intrinsically motivated
exploration to learn task hierarchies, and active imitation learning. While
learning the hierarchy of tasks, it infers its curriculum by deciding which
tasks to explore first, how to transfer knowledge, and when, how and whom to
imitate.

The Wasserstein distance is a distance between two probability distributions
and has recently gained increasing popularity in statistics and machine
learning, owing to its attractive properties. One important approach to
extending this distance is using low-dimensional projections of distributions
to avoid a high computational cost and the curse of dimensionality in empirical
estimation, such as the sliced Wasserstein or max-sliced Wasserstein distances.
Despite their practical success in machine learning tasks, the availability of
statistical inferences for projection-based Wasserstein distances is limited
owing to the lack of distributional limit results. In this paper, we consider
distances defined by integrating or maximizing Wasserstein distances between
low-dimensional projections of two probability distributions. Then we derive
limit distributions regarding these distances when the two distributions are
supported on finite points. We also propose a bootstrap procedure to estimate
quantiles of limit distributions from data. This facilitates asymptotically
exact interval estimation and hypothesis testing for these distances. Our
theoretical results are based on the arguments of Sommerfeld and Munk (2018)
for deriving distributional limits regarding the original Wasserstein distance
on finite spaces and the theory of sensitivity analysis in nonlinear
programming. Finally, we conduct numerical experiments to illustrate the
theoretical results and demonstrate the applicability of our inferential
methods to real data analysis.

The Reynolds Averaged Navier Stokes (RANS) models are the most common form of
model in turbulence simulations. They are used to calculate Reynolds stress
tensor and give robust results for engineering flows. But RANS model
predictions have large error and uncertainty. In past, there has been some work
towards using data-driven methods to increase their accuracy. In this work we
outline a machine learning approach to aid the use of the Eigenspace
Perturbation Method to predict the uncertainty in the turbulence model
prediction. We use a trained neural network to predict the discrepancy in the
shape of the RANS predicted Reynolds stress ellipsoid. We apply the model to a
number of turbulent flows and demonstrate how the approach correctly identifies
the regions in which modeling errors occur when compared to direct numerical
simulation (DNS), large eddy simulation (LES) or experimental results from
previous works.

One critical component in lossy deep image compression is the entropy model,
which predicts the probability distribution of the quantized latent
representation in the encoding and decoding modules. Previous works build
entropy models upon convolutional neural networks which are inefficient in
capturing global dependencies. In this work, we propose a novel
transformer-based entropy model, termed Entroformer, to capture long-range
dependencies in probability distribution estimation effectively and
efficiently. Different from vision transformers in image classification, the
Entroformer is highly optimized for image compression, including a top-k
self-attention and a diamond relative position encoding. Meanwhile, we further
expand this architecture with a parallel bidirectional context model to speed
up the decoding process. The experiments show that the Entroformer achieves
state-of-the-art performance on image compression while being time-efficient.

Targeted for real world scenarios, online continual learning aims to learn
new tasks from sequentially available data under the condition that each data
is observed only once by the learner. Though recent works have made remarkable
achievements by storing part of learned task data as exemplars for knowledge
replay, the performance is greatly relied on the size of stored exemplars while
the storage consumption is a significant constraint in continual learning. In
addition, storing exemplars may not always be feasible for certain applications
due to privacy concerns. In this work, we propose a novel exemplar-free method
by leveraging nearest-class-mean (NCM) classifier where the class mean is
estimated during training phase on all data seen so far through online mean
update criteria. We focus on image classification task and conduct extensive
experiments on benchmark datasets including CIFAR-100 and Food-1k. The results
demonstrate that our method without using any exemplar outperforms
state-of-the-art exemplar-based approaches with large margins under standard
protocol (20 exemplars per class) and is able to achieve competitive
performance even with larger exemplar size (100 exemplars per class).

PGD-based and FGSM-based are two popular adversarial training (AT) approaches
for obtaining adversarially robust models. Compared with PGD-based AT,
FGSM-based one is significantly faster but fails with catastrophic overfitting
(CO). For mitigating CO in such Fast AT, there are two popular existing
strategies: random start (RandStart) and Gradient Alignment (GradAlign). The
former works only for a relatively small perturbation 8/255 with the l_\infty
constraint, and GradAlign improves it by extending the perturbation size to
16/255 (with the l_\infty constraint) but at the cost of being 3 to 4 times
slower. How to avoid CO in Fast AT for a large perturbation size but without
increasing the computation overhead remains as an unsolved issue, for which our
work provides a frustratingly simple (yet effective) solution. Specifically,
our solution lies in just noise augmentation (NoiseAug) which is a non-trivial
byproduct of simplifying GradAlign. By simplifying GradAlign we have two
findings: (i) aligning logit instead of gradient in GradAlign requires half the
training time but achieves higher performance than GradAlign; (ii) the
alignment operation can also be removed by only keeping noise augmentation
(NoiseAug). Simplified from GradAlign, our NoiseAug has a surprising
resemblance with RandStart except that we inject noise on the image instead of
perturbation. To understand why injecting noise to input prevents CO, we verify
that this is caused not by data augmentation effect (inject noise on image) but
by improved local linearity. We provide an intuitive explanation for why
NoiseAug improves local linearity without explicit regularization. Extensive
results demonstrate that our NoiseAug achieves SOTA results in FGSM AT. The
code will be released after accepted.

The major problem of fitting a higher order Markov model is the exponentially
growing number of parameters. The most popular approach is to use a Variable
Length Markov Chain (VLMC), which determines relevant contexts (recent pasts)
of variable orders and form a context tree. A more general approach is called
Sparse Markov Model (SMM), where all possible histories of order $m$ form a
partition so that the transition probability vectors are identical for the
histories belonging to a particular group. We develop an elegant method of
fitting SMM using convex clustering, which involves regularization. The
regularization parameter is selected using BIC criterion. Theoretical results
demonstrate the model selection consistency of our method for large sample
size. Extensive simulation studies under different set-up have been presented
to measure the performance of our method. We apply this method to classify
genome sequences, obtained from individuals affected by different viruses.

Convolution is one of the fundamental operations of deep neural networks with
demanding matrix computation. In a graphic processing unit (GPU), Tensor Core
is a specialized matrix processing hardware equipped with reduced-precision
matrix-multiply-accumulate (MMA) instructions to increase throughput. However,
it is challenging to achieve optimal performance since the best scheduling of
MMA instructions varies for different convolution sizes. In particular,
reduced-precision MMA requires many elements grouped as a matrix operand,
seriously limiting data reuse and imposing packing and layout overhead on the
schedule. This work proposes an automatic scheduling method of
reduced-precision MMA for convolution operation. In this method, we devise a
search space that explores the thread tile and warp sizes to increase the data
reuse despite a large matrix operand of reduced-precision MMA. The search space
also includes options of register-level packing and layout optimization to
lesson overhead of handling reduced-precision data. Finally, we propose a
search algorithm to find the best schedule by learning from the distinctive
candidates. This reduced-precision MMA optimization method is evaluated on
convolution operations of popular neural networks to demonstrate substantial
speedup on Tensor Core compared to the state of the arts with shortened search
time.

In this paper, we propose a locomotion training framework where a control
policy and a state estimator are trained concurrently. The framework consists
of a policy network which outputs the desired joint positions and a state
estimation network which outputs estimates of the robot's states such as the
base linear velocity, foot height, and contact probability. We exploit a fast
simulation environment to train the networks and the trained networks are
transferred to the real robot. The trained policy and state estimator are
capable of traversing diverse terrains such as a hill, slippery plate, and
bumpy road. We also demonstrate that the learned policy can run at up to 3.75
m/s on normal flat ground and 3.54 m/s on a slippery plate with the coefficient
of friction of 0.22.

We present a novel physics-informed deep learning framework for solving
steady-state incompressible flow on multiple sets of irregular geometries by
incorporating two main elements: using a point-cloud based neural network to
capture geometric features of computational domains, and using the mean squared
residuals of the governing partial differential equations, boundary conditions,
and sparse observations as the loss function of the network to capture the
physics. While the solution of the continuity and Navier-Stokes equations is a
function of the geometry of the computational domain, current versions of
physics-informed neural networks have no mechanism to express this functionally
in their outputs, and thus are restricted to obtain the solutions only for one
computational domain with each training procedure. Using the proposed
framework, three new facilities become available. First, the governing
equations are solvable on a set of computational domains containing irregular
geometries with high variations with respect to each other but requiring
training only once. Second, after training the introduced framework on the set,
it is now able to predict the solutions on domains with unseen geometries from
seen and unseen categories as well. The former and the latter both lead to
savings in computational costs. Finally, all the advantages of the point-cloud
based neural network for irregular geometries, already used for supervised
learning, are transferred to the proposed physics-informed framework. The
effectiveness of our framework is shown through the method of manufactured
solutions and thermally-driven flow for forward and inverse problems.

The continuous increase in the use of social media and the visual content on
the internet have accelerated the research in computer vision field in general
and the image captioning task in specific. The process of generating a caption
that best describes an image is a useful task for various applications such as
it can be used in image indexing and as a hearing aid for the visually
impaired. In recent years, the image captioning task has witnessed remarkable
advances regarding both datasets and architectures, and as a result, the
captioning quality has reached an astounding performance. However, the majority
of these advances especially in datasets are targeted for English, which left
other languages such as Arabic lagging behind. Although Arabic language, being
spoken by more than 450 million people and being the most growing language on
the internet, lacks the fundamental pillars it needs to advance its image
captioning research, such as benchmarks or unified datasets. This works is an
attempt to expedite the synergy in this task by providing unified datasets and
benchmarks, while also exploring methods and techniques that could enhance the
performance of Arabic image captioning. The use of multi-task learning is
explored, alongside exploring various word representations and different
features. The results showed that the use of multi-task learning and
pre-trained word embeddings noticeably enhanced the quality of image
captioning, however the presented results shows that Arabic captioning still
lags behind when compared to the English language. The used dataset and code
are available at this link.

Malware classifiers are subject to training-time exploitation due to the need
to regularly retrain using samples collected from the wild. Recent work has
demonstrated the feasibility of backdoor attacks against malware classifiers,
and yet the stealthiness of such attacks is not well understood. In this paper,
we investigate this phenomenon under the clean-label setting (i.e., attackers
do not have complete control over the training or labeling process).
Empirically, we show that existing backdoor attacks in malware classifiers are
still detectable by recent defenses such as MNTD. To improve stealthiness, we
propose a new attack, Jigsaw Puzzle (JP), based on the key observation that
malware authors have little to no incentive to protect any other authors'
malware but their own. As such, Jigsaw Puzzle learns a trigger to complement
the latent patterns of the malware author's samples, and activates the backdoor
only when the trigger and the latent pattern are pieced together in a sample.
We further focus on realizable triggers in the problem space (e.g., software
code) using bytecode gadgets broadly harvested from benign software. Our
evaluation confirms that Jigsaw Puzzle is effective as a backdoor, remains
stealthy against state-of-the-art defenses, and is a threat in realistic
settings that depart from reasoning about feature-space only attacks. We
conclude by exploring promising approaches to improve backdoor defenses.

Artificial intelligence and machine learning have been integrated into all
aspects of our lives and the privacy of personal data has attracted more and
more attention. Since the generation of the model needs to extract the
effective information of the training data, the model has the risk of leaking
the privacy of the training data. Membership inference attacks can measure the
model leakage of source data to a certain degree. In this paper, we design a
privacy-preserving generative framework against membership inference attacks,
through the information extraction and data generation capabilities of the
generative model variational autoencoder (VAE) to generate synthetic data that
meets the needs of differential privacy. Instead of adding noise to the model
output or tampering with the training process of the target model, we directly
process the original data. We first map the source data to the latent space
through the VAE model to get the latent code, then perform noise process
satisfying metric privacy on the latent code, and finally use the VAE model to
reconstruct the synthetic data. Our experimental evaluation demonstrates that
the machine learning model trained with newly generated synthetic data can
effectively resist membership inference attacks and still maintain high
utility.

We propose a unified data-driven reduced order model (ROM) that bridges the
performance gap between linear and nonlinear manifold approaches. Deep learning
ROM (DL-ROM) using deep-convolutional autoencoders (DC-AE) has been shown to
capture nonlinear solution manifolds but fails to perform adequately when
linear subspace approaches such as proper orthogonal decomposition (POD) would
be optimal. Besides, most DL-ROM models rely on convolutional layers, which
might limit its application to only a structured mesh. The proposed framework
in this study relies on the combination of an autoencoder (AE) and Barlow Twins
(BT) self-supervised learning, where BT maximizes the information content of
the embedding with the latent space through a joint embedding architecture.
Through a series of benchmark problems of natural convection in porous media,
BT-AE performs better than the previous DL-ROM framework by providing
comparable results to POD-based approaches for problems where the solution lies
within a linear subspace as well as DL-ROM autoencoder-based techniques where
the solution lies on a nonlinear manifold; consequently, bridges the gap
between linear and nonlinear reduced manifolds. Furthermore, this BT-AE
framework can operate on unstructured meshes, which provides flexibility in its
application to standard numerical solvers, on-site measurements, experimental
data, or a combination of these sources.

Conditional contrastive learning frameworks consider the conditional sampling
procedure that constructs positive or negative data pairs conditioned on
specific variables. Fair contrastive learning constructs negative pairs, for
example, from the same gender (conditioning on sensitive information), which in
turn reduces undesirable information from the learned representations; weakly
supervised contrastive learning constructs positive pairs with similar
annotative attributes (conditioning on auxiliary information), which in turn
are incorporated into the representations. Although conditional contrastive
learning enables many applications, the conditional sampling procedure can be
challenging if we cannot obtain sufficient data pairs for some values of the
conditioning variable. This paper presents Conditional Contrastive Learning
with Kernel (CCL-K) that converts existing conditional contrastive objectives
into alternative forms that mitigate the insufficient data problem. Instead of
sampling data according to the value of the conditioning variable, CCL-K uses
the Kernel Conditional Embedding Operator that samples data from all available
data and assigns weights to each sampled data given the kernel similarity
between the values of the conditioning variable. We conduct experiments using
weakly supervised, fair, and hard negatives contrastive learning, showing CCL-K
outperforms state-of-the-art baselines.

Sentiment Analysis typically refers to using natural language processing,
text analysis and computational linguistics to extract affect and emotion based
information from text data. Our work explores how we can effectively use deep
neural networks in transfer learning and joint dual input learning settings to
effectively classify sentiments and detect hate speech in Hindi and Bengali
data. We start by training Word2Vec word embeddings for Hindi \textbf{HASOC
dataset} and Bengali hate speech and then train LSTM and subsequently, employ
parameter sharing based transfer learning to Bengali sentiment classifiers by
reusing and fine-tuning the trained weights of Hindi classifiers with both
classifier being used as baseline in our study. Finally, we use BiLSTM with
self attention in joint dual input learning setting where we train a single
neural network on Hindi and Bengali dataset simultaneously using their
respective embeddings.

Real data are rarely pure. Hence the past half-century has seen great
interest in robust estimation algorithms that perform well even when part of
the data is corrupt. However, their vast majority approach optimal accuracy
only when given a tight upper bound on the fraction of corrupt data. Such
bounds are not available in practice, resulting in weak guarantees and often
poor performance. This brief note abstracts the complex and pervasive
robustness problem into a simple geometric puzzle. It then applies the puzzle's
solution to derive a universal meta technique that converts any robust
estimation algorithm requiring a tight corruption-level upper bound to achieve
its optimal accuracy into one achieving essentially the same accuracy without
using any upper bounds.

Recent research that applies Transformer-based architectures to image
captioning has resulted in state-of-the-art image captioning performance,
capitalising on the success of Transformers on natural language tasks.
Unfortunately, though these models work well, one major flaw is their large
model sizes. To this end, we present three parameter reduction methods for
image captioning Transformers: Radix Encoding, cross-layer parameter sharing,
and attention parameter sharing. By combining these methods, our proposed ACORT
models have 3.7x to 21.6x fewer parameters than the baseline model without
compromising test performance. Results on the MS-COCO dataset demonstrate that
our ACORT models are competitive against baselines and SOTA approaches, with
CIDEr score >=126. Finally, we present qualitative results and ablation studies
to demonstrate the efficacy of the proposed changes further. Code and
pre-trained models are publicly available at
https://github.com/jiahuei/sparse-image-captioning.

We consider a batch active learning scenario where the learner adaptively
issues batches of points to a labeling oracle. Sampling labels in batches is
highly desirable in practice due to the smaller number of interactive rounds
with the labeling oracle (often human beings). However, batch active learning
typically pays the price of a reduced adaptivity, leading to suboptimal
results. In this paper we propose a solution which requires a careful trade off
between the informativeness of the queried points and their diversity. We
theoretically investigate batch active learning in the practically relevant
scenario where the unlabeled pool of data is available beforehand ({\em
pool-based} active learning). We analyze a novel stage-wise greedy algorithm
and show that, as a function of the label complexity, the excess risk of this
algorithm matches the known minimax rates in standard statistical learning
settings. Our results also exhibit a mild dependence on the batch size. These
are the first theoretical results that employ careful trade offs between
informativeness and diversity to rigorously quantify the statistical
performance of batch active learning in the pool-based scenario.

A recent emerging trend in the literature on learning in games has been
concerned with providing faster learning dynamics for correlated and coarse
correlated equilibria in normal-form games. Much less is known about the
significantly more challenging setting of extensive-form games, which can
capture both sequential and simultaneous moves, as well as imperfect
information. In this paper we establish faster no-regret learning dynamics for
\textit{extensive-form correlated equilibria (EFCE)} in multiplayer general-sum
imperfect-information extensive-form games. When all players follow our
accelerated dynamics, the correlated distribution of play is an
$O(T^{-3/4})$-approximate EFCE, where the $O(\cdot)$ notation suppresses
parameters polynomial in the description of the game. This significantly
improves over the best prior rate of $O(T^{-1/2})$. To achieve this, we develop
a framework for performing accelerated \emph{Phi-regret minimization} via
predictions. One of our key technical contributions -- that enables us to
employ our generic template -- is to characterize the stability of fixed points
associated with \emph{trigger deviation functions} through a refined
perturbation analysis of a structured Markov chain. Furthermore, for the
simpler solution concept of extensive-form \emph{coarse} correlated equilibrium
(EFCCE) we give a new succinct closed-form characterization of the associated
fixed points, bypassing the expensive computation of stationary distributions
required for EFCE. Our results place EFCCE closer to \emph{normal-form coarse
correlated equilibria} in terms of the per-iteration complexity, although the
former prescribes a much more compelling notion of correlation. Finally,
experiments conducted on standard benchmarks corroborate our theoretical
findings.

Reinforcement learning with function approximation has recently achieved
tremendous results in applications with large state spaces. This empirical
success has motivated a growing body of theoretical work proposing necessary
and sufficient conditions under which efficient reinforcement learning is
possible. From this line of work, a remarkably simple minimal sufficient
condition has emerged for sample efficient reinforcement learning: MDPs with
optimal value function $V^*$ and $Q^*$ linear in some known low-dimensional
features. In this setting, recent works have designed sample efficient
algorithms which require a number of samples polynomial in the feature
dimension and independent of the size of state space. They however leave
finding computationally efficient algorithms as future work and this is
considered a major open problem in the community.
  In this work, we make progress on this open problem by presenting the first
computational lower bound for RL with linear function approximation: unless
NP=RP, no randomized polynomial time algorithm exists for deterministic
transition MDPs with a constant number of actions and linear optimal value
functions. To prove this, we show a reduction from Unique-Sat, where we convert
a CNF formula into an MDP with deterministic transitions, constant number of
actions and low dimensional linear optimal value functions. This result also
exhibits the first computational-statistical gap in reinforcement learning with
linear function approximation, as the underlying statistical problem is
information-theoretically solvable with a polynomial number of queries, but no
computationally efficient algorithm exists unless NP=RP. Finally, we also prove
a quasi-polynomial time lower bound under the Randomized Exponential Time
Hypothesis.

Despite recent success in using the invariance principle for
out-of-distribution (OOD) generalization on Euclidean data (e.g., images),
studies on graph data are still limited. Different from images, the complex
nature of graphs poses unique challenges to adopting the invariance principle.
In particular, distribution shifts on graphs can appear in a variety of forms
such as attributes and structures, making it difficult to identify the
invariance. Moreover, domain or environment partitions, which are often
required by OOD methods on Euclidean data, could be highly expensive to obtain
for graphs. To bridge this gap, we propose a new framework, called Causality
Inspired Invariant Graph LeArning (CIGA), to capture the invariance of graphs
for guaranteed OOD generalization under various distribution shifts.
Specifically, we characterize potential distribution shifts on graphs with
causal models, concluding that OOD generalization on graphs is achievable when
models focus only on subgraphs containing the most information about the causes
of labels. Accordingly, we propose an information-theoretic objective to
extract the desired subgraphs that maximally preserve the invariant intra-class
information. Learning with these subgraphs is immune to distribution shifts.
Extensive experiments on 16 synthetic or real-world datasets, including a
challenging setting -- DrugOOD, from AI-aided drug discovery, validate the
superior OOD performance of CIGA.

In this paper, we consider learning scenarios where the learned model is
evaluated under an unknown test distribution which potentially differs from the
training distribution (i.e. distribution shift). The learner has access to a
family of weight functions such that the test distribution is a reweighting of
the training distribution under one of these functions, a setting typically
studied under the name of Distributionally Robust Optimization (DRO). We
consider the problem of deriving regret bounds in the classical learning theory
setting, and require that the resulting regret bounds hold uniformly for all
potential test distributions. We show that the DRO formulation does not
guarantee uniformly small regret under distribution shift. We instead propose
an alternative method called Minimax Regret Optimization (MRO), and show that
under suitable conditions this method achieves uniformly low regret across all
test distributions. We also adapt our technique to have stronger guarantees
when the test distributions are heterogeneous in their similarity to the
training data. Given the widespead optimization of worst case risks in current
approaches to robust machine learning, we believe that MRO can be a strong
alternative to address distribution shift scenarios.

This paper introduces a simple yet effective data-centric approach for the
task of improving persona-conditioned dialogue agents. Prior model-centric
approaches unquestioningly depend on the raw crowdsourced benchmark datasets
such as Persona-Chat. In contrast, we aim to fix annotation artifacts in
benchmarking, which is orthogonally applicable to any dialogue model.
Specifically, we augment relevant personas to improve dialogue dataset/agent,
by leveraging the primal-dual structure of the two tasks, predicting dialogue
responses and personas based on each other. Experiments on Persona-Chat show
that our approach outperforms pre-trained LMs by an 11.7 point gain in terms of
accuracy.

Labeling training data has become one of the major roadblocks to using
machine learning. Among various weak supervision paradigms, programmatic weak
supervision (PWS) has achieved remarkable success in easing the manual labeling
bottleneck by programmatically synthesizing training labels from multiple
potentially noisy supervision sources. This paper presents a comprehensive
survey of recent advances in PWS. In particular, we give a brief introduction
of the PWS learning paradigm, and review representative approaches for each
component within PWS's learning workflow. In addition, we discuss complementary
learning paradigms for tackling limited labeled data scenarios and how these
related approaches can be used in conjunction with PWS. Finally, we identify
several critical challenges that remain under-explored in the area to hopefully
inspire future research directions in the field.

Over the recent years, reinforcement learning (RL) starts to show promising
results in tackling combinatorial optimization (CO) problems, in particular
when coupled with curriculum learning to facilitate training. Despite emerging
empirical evidence, theoretical study on why RL helps is still at its early
stage. This paper presents the first systematic study on policy optimization
methods for online CO problems. We show that online CO problems can be
naturally formulated as latent Markov Decision Processes (LMDPs), and prove
convergence bounds on natural policy gradient (NPG) for solving LMDPs.
Furthermore, our theory explains the benefit of curriculum learning: it can
find a strong sampling policy and reduce the distribution shift, a critical
quantity that governs the convergence rate in our theorem. For a canonical
online CO problem, Secretary Problem, we formally prove that distribution shift
is reduced exponentially with curriculum learning even if the curriculum is
randomly generated. Our theory also shows we can simplify the curriculum
learning scheme used in prior work from multi-step to single-step. Lastly, we
provide extensive experiments on Secretary Problem and Online Knapsack to
verify our findings.

Statistical modeling and inference problems with sample sizes substantially
smaller than the number of available covariates are challenging. Chakraborty et
al. (2012) did a full hierarchical Bayesian analysis of nonlinear regression in
such situations using relevance vector machines based on reproducing kernel
Hilbert space (RKHS). But they did not provide any theoretical properties
associated with their procedure. The present paper revisits their problem,
introduces a new class of global-local priors different from theirs, and
provides results on posterior consistency as well as posterior contraction
rates

We study the problem of learning an adversarially robust predictor to test
time attacks in the semi-supervised PAC model. We address the question of how
many labeled and unlabeled examples are required to ensure learning. We show
that having enough unlabeled data (the size of a labeled sample that a
fully-supervised method would require), the labeled sample complexity can be
arbitrarily smaller compared to previous works, and is sharply characterized by
a different complexity measure. We prove nearly matching upper and lower bounds
on this sample complexity. This shows that there is a significant benefit in
semi-supervised robust learning even in the worst-case distribution-free model,
and establishes a gap between the supervised and semi-supervised label
complexities which is known not to hold in standard non-robust PAC learning.

Analyzing air pollution data is challenging as there are various analysis
focuses from different aspects: feature (what), space (where), and time (when).
As in most geospatial analysis problems, besides high-dimensional features, the
temporal and spatial dependencies of air pollution induce the complexity of
performing analysis. Machine learning methods, such as dimensionality
reduction, can extract and summarize important information of the data to lift
the burden of understanding such a complicated environment. In this paper, we
present a methodology that utilizes multiple machine learning methods to
uniformly explore these aspects. With this methodology, we develop a visual
analytic system that supports a flexible analysis workflow, allowing domain
experts to freely explore different aspects based on their analysis needs. We
demonstrate the capability of our system and analysis workflow supporting a
variety of analysis tasks with multiple use cases.

This work proposes a minimal computational model for learning a structured
memory of multiple object classes in an incremental setting. Our approach is
based on establishing a closed-loop transcription between multiple classes and
their corresponding subspaces, known as a linear discriminative representation,
in a low-dimensional feature space. Our method is both simpler and more
efficient than existing approaches to incremental learning, in terms of model
size, storage, and computation: it requires only a single, fixed-capacity
autoencoding network with a feature space that is used for both discriminative
and generative purposes. All network parameters are optimized simultaneously
without architectural manipulations, by solving a constrained minimax game
between the encoding and decoding maps over a single rate reduction-based
objective. Experimental results show that our method can effectively alleviate
catastrophic forgetting, achieving significantly better performance than prior
work for both generative and discriminative purposes.

Network architectures and learning principles are key in forming complex
functions in artificial neural networks (ANNs) and spiking neural networks
(SNNs). SNNs are considered the new-generation artificial networks by
paradigms. In this paper, we propose a Motif-topology and Reward-learning
improved SNN (MR-SNN) for efficient multi-sensory integration. MR-SNN contains
13 types of 3-node Motif topologies which are first extracted from independent
single-sensory learning paradigms and then integrated for multi-sensory
classification. The experimental results showed higher accuracy and stronger
robustness of the proposed MR-SNN than other conventional SNNs without using
Motifs. Furthermore, the proposed reward learning paradigm was biologically
plausible and can better explain the cognitive McGurk effect caused by
incongruent visual and auditory sensory signals.

Unmanned aerial vehicles (UAVs) have gained wide research interests due to
their technological advancement and high mobility. The UAVs are equipped with
increasingly advanced capabilities to run computationally intensive
applications enabled by machine learning techniques. However, because of both
energy and computation constraints, the UAVs face issues hovering in the sky
while performing computation due to weather uncertainty. To overcome the
computation constraints, the UAVs can partially or fully offload their
computation tasks to the edge servers. In ordinary computation offloading
operations, the UAVs can retrieve the result from the returned output.
Nevertheless, if the UAVs are unable to retrieve the entire result from the
edge servers, i.e., straggling edge servers, this operation will fail. In this
paper, we propose a coded distributed computing approach for computation
offloading to mitigate straggling edge servers. The UAVs can retrieve the
returned result when the number of returned copies is greater than or equal to
the recovery threshold. There is a shortfall if the returned copies are less
than the recovery threshold. To minimize the cost of the network, energy
consumption by the UAVs, and prevent over and under subscription of the
resources, we devise a two-phase Stochastic Coded Offloading Scheme (SCOS). In
the first phase, the appropriate UAVs are allocated to the charging stations
amid weather uncertainty. In the second phase, we use the $z$-stage Stochastic
Integer Programming (SIP) to optimize the number of computation subtasks
offloaded and computed locally, while taking into account the computation
shortfall and demand uncertainty. By using a real dataset, the simulation
results show that our proposed scheme is fully dynamic, and minimizes the cost
of the network and UAV energy consumption amid stochastic uncertainties.

Q-learning is widely used algorithm in reinforcement learning community.
Under the lookup table setting, its convergence is well established. However,
its behavior is known to be unstable with the linear function approximation
case. This paper develops a new Q-learning algorithm that converges when linear
function approximation is used. We prove that simply adding an appropriate
regularization term ensures convergence of the algorithm. We prove its
stability using a recent analysis tool based on switching system models.
Moreover, we experimentally show that it converges in environments where
Q-learning with linear function approximation has known to diverge. We also
provide an error bound on the solution where the algorithm converges.

Events across a timeline are a common data representation, seen in different
temporal modalities. Individual atomic events can occur in a certain temporal
ordering to compose higher level composite events. Examples of a composite
event are a patient's medical symptom or a baseball player hitting a home run,
caused distinct temporal orderings of patient vitals and player movements
respectively. Such salient composite events are provided as labels in temporal
datasets and most works optimize models to predict these composite event labels
directly. We focus on uncovering the underlying atomic events and their
relations that lead to the composite events within a noisy temporal data
setting. We propose Neural Temporal Logic Programming (Neural TLP) which first
learns implicit temporal relations between atomic events and then lifts logic
rules for composite events, given only the composite events labels for
supervision. This is done through efficiently searching through the
combinatorial space of all temporal logic rules in an end-to-end differentiable
manner. We evaluate our method on video and healthcare datasets where it
outperforms the baseline methods for rule discovery.

When people receive advice while making difficult decisions, they often make
better decisions in the moment and also increase their knowledge in the
process. However, such incidental learning can only occur when people
cognitively engage with the information they receive and process this
information thoughtfully. How do people process the information and advice they
receive from AI, and do they engage with it deeply enough to enable learning?
To answer these questions, we conducted three experiments in which individuals
were asked to make nutritional decisions and received simulated AI
recommendations and explanations. In the first experiment, we found that when
people were presented with both a recommendation and an explanation before
making their choice, they made better decisions than they did when they
received no such help, but they did not learn. In the second experiment,
participants first made their own choice, and only then saw a recommendation
and an explanation from AI; this condition also resulted in improved decisions,
but no learning. However, in our third experiment, participants were presented
with just an AI explanation but no recommendation and had to arrive at their
own decision. This condition led to both more accurate decisions and learning
gains. We hypothesize that learning gains in this condition were due to deeper
engagement with explanations needed to arrive at the decisions. This work
provides some of the most direct evidence to date that it may not be sufficient
to include explanations together with AI-generated recommendation to ensure
that people engage carefully with the AI-provided information. This work also
presents one technique that enables incidental learning and, by implication,
can help people process AI recommendations and explanations more carefully.

We propose PARSE, a novel semi-supervised architecture for learning strong
EEG representations for emotion recognition. To reduce the potential
distribution mismatch between the large amounts of unlabeled data and the
limited amount of labeled data, PARSE uses pairwise representation alignment.
First, our model performs data augmentation followed by label guessing for
large amounts of original and augmented unlabeled data. This is then followed
by sharpening of the guessed labels and convex combinations of the unlabeled
and labeled data. Finally, representation alignment and emotion classification
are performed. To rigorously test our model, we compare PARSE to several
state-of-the-art semi-supervised approaches which we implement and adapt for
EEG learning. We perform these experiments on four public EEG-based emotion
recognition datasets, SEED, SEED-IV, SEED-V and AMIGOS (valence and arousal).
The experiments show that our proposed framework achieves the overall best
results with varying amounts of limited labeled samples in SEED, SEED-IV and
AMIGOS (valence), while approaching the overall best result (reaching the
second-best) in SEED-V and AMIGOS (arousal). The analysis shows that our
pairwise representation alignment considerably improves the performance by
reducing the distribution alignment between unlabeled and labeled data,
especially when only 1 sample per class is labeled.

State-of-the-art sign language generation frameworks lack expressivity and
naturalness which is the result of only focusing manual signs, neglecting the
affective, grammatical and semantic functions of facial expressions. The
purpose of this work is to augment semantic representation of sign language
through grounding facial expressions. We study the effect of modeling the
relationship between text, gloss, and facial expressions on the performance of
the sign generation systems. In particular, we propose a Dual Encoder
Transformer able to generate manual signs as well as facial expressions by
capturing the similarities and differences found in text and sign gloss
annotation. We take into consideration the role of facial muscle activity to
express intensities of manual signs by being the first to employ facial action
units in sign language generation. We perform a series of experiments showing
that our proposed model improves the quality of automatically generated sign
language.

Knee pain is undoubtedly the most common musculoskeletal symptom that impairs
quality of life, confines mobility and functionality across all ages. Knee pain
is clinically evaluated by routine radiographs, where the widespread adoption
of radiographic images and their availability at low cost, make them the
principle component in the assessment of knee pain and knee pathologies, such
as arthritis, trauma, and sport injuries. However, interpretation of the knee
radiographs is still highly subjective, and overlapping structures within the
radiographs and the large volume of images needing to be analyzed on a daily
basis, make interpretation challenging for both naive and experienced
practitioners. There is thus a need to implement an artificial intelligence
strategy to objectively and automatically interpret knee radiographs,
facilitating triage of abnormal radiographs in a timely fashion. The current
work proposes an accurate and effective pipeline for autonomous detection,
localization, and classification of knee joint area in plain radiographs
combining the You Only Look Once (YOLO v3) deep convolutional neural network
with a large and fully-annotated knee radiographs dataset. The present work is
expected to stimulate more interest from the deep learning computer vision
community to this pragmatic and clinical application.

In this research, we develop a framework to analyze the interaction between
the economy and the Covid-19 pandemic using an extension of SIR epidemic model.
At the outset, we assume there are two health related investments including
general medical expenditures and the other for a direct investment for
controlling the pandemic. We incorporate the learning dynamics associated with
the management of the virus into our model. Given that the labor force in a
society depends on the state of the epidemic, we allow birth, death, and
vaccination to occur in our model and assume labor force consists of the
susceptible, vaccinated, and recovered individuals. We also assume parameters
in our epidemic compartmental model depend on investment amount for directly
controlling the epidemic, the health stock of individual representative agents
in the society, and the knowledge or learning about the epidemic in the
community. By controlling consumption, the general medical expenditure, and the
direct investment of funds for controlling the epidemic, we optimize the
utility realized by the representative individuals because of consumption. This
problem is nontrivial since the disease dynamics results in a non-convex
optimization problem.

We characterise the problem of abstraction in the context of deep
reinforcement learning. Various well established approaches to analogical
reasoning and associative memory might be brought to bear on this issue, but
they present difficulties because of the need for end-to-end differentiability.
We review developments in AI and machine learning that could facilitate their
adoption.

While vaccines are crucial to end the COVID-19 pandemic, public confidence in
vaccine safety has always been vulnerable. Many statistical methods have been
applied to VAERS (Vaccine Adverse Event Reporting System) database to study the
safety of COVID-19 vaccines. However, all these methods ignored the adverse
event (AE) ontology. AEs are naturally related; for example, events of
retching, dysphagia, and reflux are all related to an abnormal digestive
system. Explicitly bringing AE relationships into the model can aid in the
detection of true AE signals amid the noise while reducing false positives. We
propose a Bayesian graphical model to estimate all AEs while incorporating the
AE ontology simultaneously. We proposed strategies to construct conjugate forms
leading to an efficient Gibbs sampler. Built upon the posterior distributions,
we proposed a negative control approach to mitigate reporting bias and an
enrichment approach to detect AE groups of concern. The proposed methods were
evaluated using simulation studies and were further illustrated on studying the
safety of COVID-19 vaccines. The proposed methods were implemented in R package
\textit{BGrass} and source code are available at
https://github.com/BangyaoZhao/BGrass.

Although deep neural networks have been immensely successful, there is no
comprehensive theoretical understanding of how they work or are structured. As
a result, deep networks are often seen as black boxes with unclear
interpretations and reliability. Understanding the performance of deep neural
networks is one of the greatest scientific challenges. This work aims to apply
principles and techniques from information theory to deep learning models to
increase our theoretical understanding and design better algorithms. We first
describe our information-theoretic approach to deep learning. Then, we propose
using the Information Bottleneck (IB) theory to explain deep learning systems.
The novel paradigm for analyzing networks sheds light on their layered
structure, generalization abilities, and learning dynamics. We later discuss
one of the most challenging problems of applying the IB to deep neural networks
- estimating mutual information. Recent theoretical developments, such as the
neural tangent kernel (NTK) framework, are used to investigate generalization
signals. In our study, we obtained tractable computations of many
information-theoretic quantities and their bounds for infinite ensembles of
infinitely wide neural networks. With these derivations, we can determine how
compression, generalization, and sample size pertain to the network and how
they are related. At the end, we present the dual Information Bottleneck
(dualIB). This new information-theoretic framework resolves some of the IB's
shortcomings by merely switching terms in the distortion function. The dualIB
can account for known data features and use them to make better predictions
over unseen examples. An analytical framework reveals the underlying structure
and optimal representations, and a variational framework using deep neural
network optimization validates the results.

There is great potential to apply machine learning in the area of numerical
lattice quantum field theory, but full exploitation of that potential will
require new strategies. In this white paper for the Snowmass community planning
process, we discuss the unique requirements of machine learning for lattice
quantum field theory research and outline what is needed to enable exploration
and deployment of this approach in the future.

Magnetic resonance imaging (MRI) provides high spatial resolution and
excellent soft-tissue contrast without using harmful ionising radiation.
Dynamic MRI is an essential tool for interventions to visualise movements or
changes of the target organ. However, such MRI acquisition with high temporal
resolution suffers from limited spatial resolution - also known as the
spatio-temporal trade-off of dynamic MRI. Several approaches, including deep
learning based super-resolution approaches, have been proposed to mitigate this
trade-off. Nevertheless, such an approach typically aims to super-resolve each
time-point separately, treating them as individual volumes. This research
addresses the problem by creating a deep learning model which attempts to learn
both spatial and temporal relationships. A modified 3D UNet model, DDoS-UNet,
is proposed - which takes the low-resolution volume of the current time-point
along with a prior image volume. Initially, the network is supplied with a
static high-resolution planning scan as the prior image along with the
low-resolution input to super-resolve the first time-point. Then it continues
step-wise by using the super-resolved time-points as the prior image while
super-resolving the subsequent time-points. The model performance was tested
with 3D dynamic data that was undersampled to different in-plane levels. The
proposed network achieved an average SSIM value of 0.951$\pm$0.017 while
reconstructing the lowest resolution data (i.e. only 4\% of the k-space
acquired) - which could result in a theoretical acceleration factor of 25. The
proposed approach can be used to reduce the required scan-time while achieving
high spatial resolution.

The dominant line of work in domain adaptation has focused on learning
invariant representations using domain-adversarial training. In this paper, we
interpret this approach from a game theoretical perspective. Defining optimal
solutions in domain-adversarial training as a local Nash equilibrium, we show
that gradient descent in domain-adversarial training can violate the asymptotic
convergence guarantees of the optimizer, oftentimes hindering the transfer
performance. Our analysis leads us to replace gradient descent with high-order
ODE solvers (i.e., Runge-Kutta), for which we derive asymptotic convergence
guarantees. This family of optimizers is significantly more stable and allows
more aggressive learning rates, leading to high performance gains when used as
a drop-in replacement over standard optimizers. Our experiments show that in
conjunction with state-of-the-art domain-adversarial methods, we achieve up to
3.5% improvement with less than of half training iterations. Our optimizers are
easy to implement, free of additional parameters, and can be plugged into any
domain-adversarial framework.

In this work, an AI-Driven (autonomous) model representation of the La Rance
tidal barrage was developed using novel parametrisation and Deep Reinforcement
Learning (DRL) techniques. Our model results were validated with experimental
measurements, yielding the first Tidal Range Structure (TRS) model validated
against a constructed tidal barrage and made available to academics. In order
to proper model La Rance, parametrisation methodologies were developed for
simulating (i) turbines (in pumping and power generation modes), (ii)
transition ramp functions (for opening and closing hydraulic structures) and
(iii) equivalent lagoon wetted area. Furthermore, an updated DRL method was
implemented for optimising the operation of the hydraulic structures that
compose La Rance. The achieved objective of this work was to verify the
capabilities of an AI-Driven TRS model to appropriately predict (i) turbine
power and (ii) lagoon water level variations. In addition, the observed
operational strategy and yearly energy output of our AI-Driven model appeared
to be comparable with those reported for the La Rance tidal barrage. The
outcomes of this work (developed methodologies and DRL implementations) are
generalisable and can be applied to other TRS projects. Furthermore, this work
provided insights which allow for more realistic simulation of TRS operation,
enabled through our AI-Driven model.

Curriculum Learning is the presentation of samples to the machine learning
model in a meaningful order instead of a random order. The main challenge of
Curriculum Learning is determining how to rank these samples. The ranking of
the samples is expressed by the scoring function. In this study, scoring
functions were compared using data set features, using the model to be trained,
and using another model and their ensemble versions. Experiments were performed
for 4 images and 4 text datasets. No significant differences were found between
scoring functions for text datasets, but significant improvements were obtained
in scoring functions created using transfer learning compared to classical
model training and other scoring functions for image datasets. It shows that
different new scoring functions are waiting to be found for text classification
tasks.

To avoid treating neural networks as highly complex black boxes, the deep
learning research community has tried to build interpretable models allowing
humans to understand the decisions taken by the model. Unfortunately, the focus
is mostly on manipulating only the very high-level features associated with the
last layers. In this work, we look at neural network architectures for
classification in a more general way and introduce an algorithm which defines
before the training the paths of the network through which the per-class
information flows. We show that using our algorithm we can extract a lighter
single-purpose binary classifier for a particular class by removing the
parameters that do not participate in the predefined information path of that
class, which is approximately 60% of the total parameters. Notably, leveraging
coding theory to design the information paths enables us to use intermediate
network layers for making early predictions without having to evaluate the full
network. We demonstrate that a slightly modified ResNeXt model, trained with
our algorithm, can achieve higher classification accuracy on CIFAR-10/100 and
ImageNet than the original ResNeXt, while having all the aforementioned

We study the complexity of closure operators, with applications to machine
learning and decision theory. In machine learning, closure operators emerge
naturally in data classification and clustering. In decision theory, they can
model equivalence of choice menus, and therefore situations with a preference
for flexibility. Our contribution is to formulate a notion of complexity of
closure operators, which translate into the complexity of a classifier in ML,
or of a utility function in decision theory.

In 1996, Accountability in a Computerized Society [95] issued a clarion call
concerning the erosion of accountability in society due to the ubiquitous
delegation of consequential functions to computerized systems. Nissenbaum [95]
described four barriers to accountability that computerization presented, which
we revisit in relation to the ascendance of data-driven algorithmic
systems--i.e., machine learning or artificial intelligence--to uncover new
challenges for accountability that these systems present. Nissenbaum's original
paper grounded discussion of the barriers in moral philosophy; we bring this
analysis together with recent scholarship on relational accountability
frameworks and discuss how the barriers present difficulties for instantiating
a unified moral, relational framework in practice for data-driven algorithmic
systems. We conclude by discussing ways of weakening the barriers in order to
do so.

An increasing trend in the use of neural networks in control systems is being
observed. The aim of this paper is to reveal that the straightforward
application of learning neural network feedforward controllers with closed-loop
data may introduce parameter inconsistency that degrades control performance,
and to provide a solution. The proposed method employs instrumental variables
to ensure consistent parameter estimates. A nonlinear system example reveals
that the developed instrumental variable neural network (IVNN) approach
asymptotically recovers the optimal solution, while pre-existing approaches are
shown to lead to inconsistent estimates.

Background subtraction is a significant task in computer vision and an
essential step for many real world applications. One of the challenges for
background subtraction methods is dynamic background, which constitute
stochastic movements in some parts of the background. In this paper, we have
proposed a new background subtraction method, called DBSGen, which uses two
generative neural networks, one for dynamic motion removal and another for
background generation. At the end, the foreground moving objects are obtained
by a pixel-wise distance threshold based on a dynamic entropy map. The proposed
method has a unified framework that can be optimized in an end-to-end and
unsupervised fashion. The performance of the method is evaluated over dynamic
background sequences and it outperforms most of state-of-the-art methods. Our
code is publicly available at https://github.com/FatemeBahri/DBSGen.

In this paper, we study the interaction between pedestrians and vehicles and
propose a novel neural network structure called the Pedestrian-Vehicle
Interaction (PVI) extractor for learning the pedestrian-vehicle interaction. We
implement the proposed PVI extractor on both sequential approaches (long
short-term memory (LSTM) models) and non-sequential approaches (convolutional
models). We use the Waymo Open Dataset that contains real-world urban traffic
scenes with both pedestrian and vehicle annotations. For the LSTM-based models,
our proposed model is compared with Social-LSTM and Social-GAN, and using our
proposed PVI extractor reduces the average displacement error (ADE) and the
final displacement error (FDE) by 7.46% and 5.24%, respectively. For the
convolutional-based models, our proposed model is compared with Social-STGCNN
and Social-IWSTCNN, and using our proposed PVI extractor reduces the ADE and
FDE by 2.10% and 1.27%, respectively. The results show that the
pedestrian-vehicle interaction influences pedestrian behavior, and the models
using the proposed PVI extractor can capture the interaction between
pedestrians and vehicles, and thereby outperform the compared methods.

World models for environments with many objects face a combinatorial
explosion of states: as the number of objects increases, the number of possible
arrangements grows exponentially. In this paper, we learn to generalize over
robotic pick-and-place tasks using object-factored world models, which combat
the combinatorial explosion by ensuring that predictions are equivariant to
permutations of objects. Previous object-factored models were limited either by
their inability to model actions, or by their inability to plan for complex
manipulation tasks. We build on recent contrastive methods for training
object-factored world models, which we extend to model continuous robot actions
and to accurately predict the physics of robotic pick-and-place. To do so, we
use a residual stack of graph neural networks that receive action information
at multiple levels in both their node and edge neural networks. Crucially, our
learned model can make predictions about tasks not represented in the training
data. That is, we demonstrate successful zero-shot generalization to novel
tasks, with only a minor decrease in model performance. Moreover, we show that
an ensemble of our models can be used to plan for tasks involving up to 12 pick
and place actions using heuristic search. We also demonstrate transfer to a
physical robot.

Several services for people with visual disabilities have emerged recently
due to achievements in Assistive Technologies and Artificial Intelligence
areas. Despite the growth in assistive systems availability, there is a lack of
services that support specific tasks, such as understanding the image context
presented in online content, e.g., webinars. Image captioning techniques and
their variants are limited as Assistive Technologies as they do not match the
needs of visually impaired people when generating specific descriptions. We
propose an approach for generating context of webinar images combining a dense
captioning technique with a set of filters, to fit the captions in our domain,
and a language model for the abstractive summary task. The results demonstrated
that we can produce descriptions with higher interpretability and focused on
the relevant information for that group of people by combining image analysis
methods and neural language models.

We report on the exact treatment of a random-matrix representation of bond
percolation model on a square lattice in two dimensions with occupation
probability $p$. The percolation problem is mapped onto a random complex matrix
composed of two random real-valued matrices of elements $+1$ and $-1$ with
probability $p$ and $1-p$, respectively. We find that the onset of percolation
transition can be detected by the emergence of power-law divergences due to the
coalescence of the first two extreme eigenvalues in the thermodynamic limit. We
develop a universal finite-size scaling law that fully characterizes the
scaling behavior of the extreme eigenvalue's fluctuation in terms of a set of
universal scaling exponents and amplitudes. We make use of the relative entropy
as an index of the disparity between two distributions of the first and
second-largest extreme eigenvalues, to show that its minimum underlies the
scaling framework. Our study may provide an inroad for developing new methods
and algorithms with diverse applications in machine learning, complex systems,
and statistical physics.

The high accuracy of detector simulation is crucial for modern particle
physics experiments. However, this accuracy comes with a high computational
cost, which will be exacerbated by the large datasets and complex detector
upgrades associated with next-generation facilities such as the High Luminosity
LHC. We explore the viability of regression-based machine learning (ML)
approaches using convolutional neural networks (CNNs) to "denoise" faster,
lower-quality detector simulations, augmenting them to produce a higher-quality
final result with a reduced computational burden. The denoising CNN works in
concert with classical detector simulation software rather than replacing it
entirely, increasing its reliability compared to other ML approaches to
simulation. We obtain promising results from a prototype based on photon
showers in the CMS electromagnetic calorimeter. Future directions are also

Large-scale machine learning systems often involve data distributed across a
collection of users. Federated learning algorithms leverage this structure by
communicating model updates to a central server, rather than entire datasets.
In this paper, we study stochastic optimization algorithms for a personalized
federated learning setting involving local and global models subject to
user-level (joint) differential privacy. While learning a private global model
induces a cost of privacy, local learning is perfectly private. We provide
generalization guarantees showing that coordinating local learning with private
centralized learning yields a generically useful and improved tradeoff between
accuracy and privacy. We illustrate our theoretical results with experiments on

Product ranking is a crucial component for many e-commerce services. One of
the major challenges in product search is the vocabulary mismatch between query
and products, which may be a larger vocabulary gap problem compared to other
information retrieval domains. While there is a growing collection of neural
learning to match methods aimed specifically at overcoming this issue, they do
not leverage the recent advances of large language models for product search.
On the other hand, product ranking often deals with multiple types of
engagement signals such as clicks, add-to-cart, and purchases, while most of
the existing works are focused on optimizing one single metric such as
click-through rate, which may suffer from data sparsity. In this work, we
propose a novel end-to-end multi-task learning framework for product ranking
with BERT to address the above challenges. The proposed model utilizes
domain-specific BERT with fine-tuning to bridge the vocabulary gap and employs
multi-task learning to optimize multiple objectives simultaneously, which
yields a general end-to-end learning framework for product search. We conduct a
set of comprehensive experiments on a real-world e-commerce dataset and
demonstrate significant improvement of the proposed approach over the
state-of-the-art baseline methods.

Children learn to build a visual representation of the world from
unsupervised exploration and we hypothesize that a key part of this learning
ability is the use of self-generated navigational information as a similarity
label to drive a learning objective for self-supervised learning. The goal of
this work is to exploit navigational information in a visual environment to
provide performance in training that exceeds the state-of-the-art
self-supervised training. Here, we show that using spatial and temporal
information in the pretraining stage of contrastive learning can improve the
performance of downstream classification relative to conventional contrastive
learning approaches that use instance discrimination to discriminate between
two alterations of the same image or two different images. We designed a
pipeline to generate egocentric-vision images from a photorealistic ray-tracing
environment (ThreeDWorld) and record relevant navigational information for each
image. Modifying the Momentum Contrast (MoCo) model, we introduced spatial and
temporal information to evaluate the similarity of two views in the pretraining
stage instead of instance discrimination. This work reveals the effectiveness
and efficiency of contextual information for improving representation learning.
The work informs our understanding of the means by which children might learn
to see the world without external supervision.

We hypothesize that due to the greedy nature of learning in multi-modal deep
neural networks, these models tend to rely on just one modality while
under-fitting the other modalities. Such behavior is counter-intuitive and
hurts the models' generalization, as we observe empirically. To estimate the
model's dependence on each modality, we compute the gain on the accuracy when
the model has access to it in addition to another modality. We refer to this
gain as the conditional utilization rate. In the experiments, we consistently
observe an imbalance in conditional utilization rates between modalities,
across multiple tasks and architectures. Since conditional utilization rate
cannot be computed efficiently during training, we introduce a proxy for it
based on the pace at which the model learns from each modality, which we refer
to as the conditional learning speed. We propose an algorithm to balance the
conditional learning speeds between modalities during training and demonstrate
that it indeed addresses the issue of greedy learning. The proposed algorithm
improves the model's generalization on three datasets: Colored MNIST,
ModelNet40, and NVIDIA Dynamic Hand Gesture.

The problem of human trust in artificial intelligence is one of the most
fundamental problems in applied machine learning. Our processes for evaluating
AI trustworthiness have substantial ramifications for ML's impact on science,
health, and humanity, yet confusion surrounds foundational concepts. What does
it mean to trust an AI, and how do humans assess AI trustworthiness? What are
the mechanisms for building trustworthy AI? And what is the role of
interpretable ML in trust? Here, we draw from statistical learning theory and
sociological lenses on human-automation trust to motivate an AI-as-tool
framework, which distinguishes human-AI trust from human-AI-human trust.
Evaluating an AI's contractual trustworthiness involves predicting future model
behavior using behavior certificates (BCs) that aggregate behavioral evidence
from diverse sources including empirical out-of-distribution and out-of-task
evaluation and theoretical proofs linking model architecture to behavior. We
clarify the role of interpretability in trust with a ladder of model access.
Interpretability (level 3) is not necessary or even sufficient for trust, while
the ability to run a black-box model at-will (level 2) is necessary and
sufficient. While interpretability can offer benefits for trust, it can also
incur costs. We clarify ways interpretability can contribute to trust, while
questioning the perceived centrality of interpretability to trust in popular
discourse. How can we empower people with tools to evaluate trust? Instead of
trying to understand how a model works, we argue for understanding how a model
behaves. Instead of opening up black boxes, we should create more behavior
certificates that are more correct, relevant, and understandable. We discuss
how to build trusted and trustworthy AI responsibly.

Salt water is ubiquitous, playing crucial roles in geological and
physiological processes. Despite centuries of investigations, whether or not
water's structure is drastically changed by dissolved ions is still debated.
Based on density functional theory, we employ machine learning based molecular
dynamics to model sodium chloride, potassium chloride, and sodium bromide
solutions at different concentrations. The resulting reciprocal-space structure
factors agree quantitatively with neutron diffraction data. Here we provide
clear evidence that the ions in salt water do not distort the structure of
water in the same way as neat water responds to elevated pressure. Rather, the
computed structural changes are restricted to the ionic first solvation shells
intruding into the hydrogen bond network, beyond which the oxygen
radial-distribution function does not undergo major change relative to neat
water. Our findings suggest that the widely cited pressure-like effect on the
solvent in Hofmeister series ionic solutions should be carefully revisited.

Systems that analyse faces have seen significant improvements in recent years
and are today used in numerous application scenarios. However, these systems
have been found to be negatively affected by facial alterations such as
tattoos. To better understand and mitigate the effect of facial tattoos in
facial analysis systems, large datasets of images of individuals with and
without tattoos are needed. To this end, we propose a generator for
automatically adding realistic tattoos to facial images. Moreover, we
demonstrate the feasibility of the generation by using a deep learning-based
model for removing tattoos from face images. The experimental results show that
it is possible to remove facial tattoos from real images without degrading the
quality of the image. Additionally, we show that it is possible to improve face
recognition accuracy by using the proposed deep learning-based tattoo removal
before extracting and comparing facial features.

Online selection of optimal waveforms for target tracking with active sensors
has long been a problem of interest. Many conventional solutions utilize an
estimation-theoretic interpretation, in which a waveform-specific
Cram\'{e}r-Rao lower bound on measurement error is used to select the optimal
waveform for each tracking step. However, this approach is only valid in the
high SNR regime, and requires a rather restrictive set of assumptions regarding
the target motion and measurement models. Further, due to computational
concerns, many traditional approaches are limited to near-term, or myopic,
optimization, even though radar scenes exhibit strong temporal correlation.
More recently, reinforcement learning has been proposed for waveform selection,
in which the problem is framed as a Markov decision process (MDP), allowing for
long-term planning. However, a major limitation of reinforcement learning is
that the memory length of the underlying Markov process is often unknown for
realistic target and channel dynamics, and a more general framework is
desirable. This work develops a universal sequential waveform selection scheme
which asymptotically achieves Bellman optimality in any radar scene which can
be modeled as a $U^{\text{th}}$ order Markov process for a finite, but unknown,
integer $U$. Our approach is based on well-established tools from the field of
universal source coding, where a stationary source is parsed into variable
length phrases in order to build a context-tree, which is used as a
probabalistic model for the scene's behavior. We show that an algorithm based
on a multi-alphabet version of the Context-Tree Weighting (CTW) method can be
used to optimally solve a broad class of waveform-agile tracking problems while
making minimal assumptions about the environment's behavior.

We consider the one-bit quantizer that minimizes the mean squared error for a
source living in a real Hilbert space. The optimal quantizer is a projection
followed by a thresholding operation, and we provide methods for identifying
the optimal direction along which to project. As an application of our methods,
we characterize the optimal one-bit quantizer for a continuous-time random
process that exhibits low-dimensional structure. We numerically show that this
optimal quantizer is found by a neural-network-based compressor trained via

We analyzed the unexposed to the sky outFOV region of the MOS2 detector on
board XMM-Newton covering 15 years of data amounting to 255 Ms. We show
convincing evidence that the origin of the unfocused background in XMM-Newton
is due to energetic protons, electrons and hard X-ray photons. Galactic Cosmic
Rays are the main contributors as shown by the tight correlation (2.6% of total
scatter) with 1 GeV protons data of the SOHO EPHIN detector. Tight correlations
are found with a proxy of the Chandra background rate, revealing the common
source of background for detectors in similar orbits, and with the data of the
EPIC Radiation Monitor (ERM), only when excluding Solar Energetic Particles
events (SEPs). The entrance to the outer electron belts is associated to a
sudden increase in the outFOV MOS2 rate and a spectral change. These facts
support the fact that MeV electrons can generate an unfocused background
signal. The correlation between MOS2 outFOV data and the SOHO EPHIN data
reveals a term constant in time and isotropic similar to the one found in the
study of the pn data. The most plausible origin of this component is hard
unfocused X-ray photons of the Cosmic X-ray Background (CXB) Compton-scattering
in the detector as supported by the strength of the signal in the two detectors
with different thicknesses. Based on this physical understanding a particle
radiation monitor on board ATHENA has been proposed and it is currently under
study. It will be able to track different species with the necessary accuracy
and precision to guarantee the challenging requirement of 2% reproducibility of
the background.

Our universe is homogeneous and isotropic, and its perturbations obey
translation and rotation symmetry. In this work we develop Translation and
Rotation Equivariant Normalizing Flow (TRENF), a generative Normalizing Flow
(NF) model which explicitly incorporates these symmetries, defining the data
likelihood via a sequence of Fourier space-based convolutions and pixel-wise
nonlinear transforms. TRENF gives direct access to the high dimensional data
likelihood p(x|y) as a function of the labels y, such as cosmological
parameters. In contrast to traditional analyses based on summary statistics,
the NF approach has no loss of information since it preserves the full
dimensionality of the data. On Gaussian random fields, the TRENF likelihood
agrees well with the analytical expression and saturates the Fisher information
content in the labels y. On nonlinear cosmological overdensity fields from
N-body simulations, TRENF leads to significant improvements in constraining
power over the standard power spectrum summary statistic. TRENF is also a
generative model of the data, and we show that TRENF samples agree well with
the N-body simulations it trained on, and that the inverse mapping of the data
agrees well with a Gaussian white noise both visually and on various summary
statistics: when this is perfectly achieved the resulting p(x|y) likelihood
analysis becomes optimal. Finally, we develop a generalization of this model
that can handle effects that break the symmetry of the data, such as the survey
mask, which enables likelihood analysis on data without periodic boundaries.

We present Block-NeRF, a variant of Neural Radiance Fields that can represent
large-scale environments. Specifically, we demonstrate that when scaling NeRF
to render city-scale scenes spanning multiple blocks, it is vital to decompose
the scene into individually trained NeRFs. This decomposition decouples
rendering time from scene size, enables rendering to scale to arbitrarily large
environments, and allows per-block updates of the environment. We adopt several
architectural changes to make NeRF robust to data captured over months under
different environmental conditions. We add appearance embeddings, learned pose
refinement, and controllable exposure to each individual NeRF, and introduce a
procedure for aligning appearance between adjacent NeRFs so that they can be
seamlessly combined. We build a grid of Block-NeRFs from 2.8 million images to
create the largest neural scene representation to date, capable of rendering an
entire neighborhood of San Francisco.

We introduce unique class of cyclic quantum thermal machines (QTMs) which can
maximize their performance at the finite value of cycle duration $\tau$ where
they are most irreversible. These QTMs are based on single-stroke thermodynamic
cycles realized by the non-equilibrium steady state (NESS) of the so-called
Periodically Refreshed Baths (PReB) process. We find that such QTMs can
interpolate between standard collisional QTMs, which consider repeated
interactions with single-site environments, and autonomous QTMs operated by
simultaneous coupling to multiple macroscopic baths. We discuss the physical
realization of such processes and show that their implementation requires a
finite number of copies of the baths. Interestingly, maximizing performance by
operating in the most irreversible point as a function of $\tau$ comes at the
cost of increasing the complexity of realizing such a regime, the latter
quantified by the increase in the number of copies of baths required. We
demonstrate this physics considering a simple example. We also introduce an
elegant description of the PReB process for Gaussian systems in terms of a
discrete-time Lyapunov equation. Further, our analysis also reveals interesting
connections with Zeno and anti-Zeno effects.

Image-to-image regression is an important learning task, used frequently in
biological imaging. Current algorithms, however, do not generally offer
statistical guarantees that protect against a model's mistakes and
hallucinations. To address this, we develop uncertainty quantification
techniques with rigorous statistical guarantees for image-to-image regression
problems. In particular, we show how to derive uncertainty intervals around
each pixel that are guaranteed to contain the true value with a user-specified
confidence probability. Our methods work in conjunction with any base machine
learning model, such as a neural network, and endow it with formal mathematical
guarantees -- regardless of the true unknown data distribution or choice of
model. Furthermore, they are simple to implement and computationally
inexpensive. We evaluate our procedure on three image-to-image regression
tasks: quantitative phase microscopy, accelerated magnetic resonance imaging,
and super-resolution transmission electron microscopy of a Drosophila
melanogaster brain.

We analyze the storage and recall of factual associations in autoregressive
transformer language models, finding evidence that these associations
correspond to localized, directly-editable computations. We first develop a
causal intervention for identifying neuron activations that are decisive in a
model's factual predictions. This reveals a distinct set of steps in
middle-layer feed-forward modules that mediate factual predictions while
processing subject tokens. To test our hypothesis that these computations
correspond to factual association recall, we modify feed-forward weights to
update specific factual associations using Rank-One Model Editing (ROME). We
find that ROME is effective on a standard zero-shot relation extraction (zsRE)
model-editing task, comparable to existing methods. To perform a more sensitive
evaluation, we also evaluate ROME on a new dataset of counterfactual
assertions, on which it simultaneously maintains both specificity and
generalization, whereas other methods sacrifice one or another. Our results
confirm an important role for mid-layer feed-forward modules in storing factual
associations and suggest that direct manipulation of computational mechanisms
may be a feasible approach for model editing. The code, dataset,
visualizations, and an interactive demo notebook are available at
https://rome.baulab.info/

We give superpolynomial statistical query (SQ) lower bounds for learning
two-hidden-layer ReLU networks with respect to Gaussian inputs in the standard
(noise-free) model. No general SQ lower bounds were known for learning ReLU
networks of any depth in this setting: previous SQ lower bounds held only for
adversarial noise models (agnostic learning) or restricted models such as
correlational SQ.
  Prior work hinted at the impossibility of our result: Vempala and Wilmes
showed that general SQ lower bounds cannot apply to any real-valued family of
functions that satisfies a simple non-degeneracy condition.
  To circumvent their result, we refine a lifting procedure due to Daniely and
Vardi that reduces Boolean PAC learning problems to Gaussian ones. We show how
to extend their technique to other learning models and, in many well-studied
cases, obtain a more efficient reduction. As such, we also prove new
cryptographic hardness results for PAC learning two-hidden-layer ReLU networks,
as well as new lower bounds for learning constant-depth ReLU networks from
label queries.

Moderators and automated methods enforce bans on malicious users who engage
in disruptive behavior. However, malicious users can easily create a new
account to evade such bans. Previous research has focused on other forms of
online deception, like the simultaneous operation of multiple accounts by the
same entities (sockpuppetry), impersonation of other individuals, and studying
the effects of de-platforming individuals and communities. Here we conduct the
first data-driven study of ban evasion, i.e., the act of circumventing bans on
an online platform, leading to temporally disjoint operation of accounts by the
same user.
  We curate a novel dataset of 8,551 ban evasion pairs (parent, child)
identified on Wikipedia and contrast their behavior with benign users and
non-evading malicious users. We find that evasion child accounts demonstrate
similarities with respect to their banned parent accounts on several behavioral
axes - from similarity in usernames and edited pages to similarity in content
added to the platform and its psycholinguistic attributes. We reveal key
behavioral attributes of accounts that are likely to evade bans. Based on the
insights from the analyses, we train logistic regression classifiers to detect
and predict ban evasion at three different points in the ban evasion lifecycle.
Results demonstrate the effectiveness of our methods in predicting future
evaders (AUC = 0.78), early detection of ban evasion (AUC = 0.85), and matching
child accounts with parent accounts (MRR = 0.97). Our work can aid moderators
by reducing their workload and identifying evasion pairs faster and more
efficiently than current manual and heuristic-based approaches. Dataset is
available $\href{https://github.com/srijankr/ban_evasion}{\text{here}}$.

Speech enhancement is a critical component of many user-oriented audio
applications, yet current systems still suffer from distorted and unnatural
outputs. While generative models have shown strong potential in speech
synthesis, they are still lagging behind in speech enhancement. This work
leverages recent advances in diffusion probabilistic models, and proposes a
novel speech enhancement algorithm that incorporates characteristics of the
observed noisy speech signal into the diffusion and reverse processes. More
specifically, we propose a generalized formulation of the diffusion
probabilistic model named conditional diffusion probabilistic model that, in
its reverse process, can adapt to non-Gaussian real noises in the estimated
speech signal. In our experiments, we demonstrate strong performance of the
proposed approach compared to representative generative models, and investigate
the generalization capability of our models to other datasets with noise
characteristics unseen during training.

Topological materials present unconventional electronic properties that make
them attractive for both basic science and next-generation technological
applications. The majority of currently-known topological materials have been
discovered using methods that involve symmetry-based analysis of the quantum
wavefunction. Here we use machine learning to develop a simple-to-use heuristic
chemical rule that diagnoses with a high accuracy whether a material is
topological using only its chemical formula. This heuristic rule is based on a
notion that we term topogivity, a machine-learned numerical value for each
element that loosely captures its tendency to form topological materials. We
next implement a high-throughput strategy for discovering topological materials
based on the heuristic topogivity-rule prediction followed by ab initio
validation. This way, we discover new topological materials that are not
diagnosable using symmetry indicators, including several that may be promising
for experimental observation.

A biological neural network in the cortex forms a neural field. Neurons in
the field have their own receptive fields, and connection weights between two
neurons are random but highly correlated when they are in close proximity in
receptive fields. In this paper, we investigate such neural fields in a
multilayer architecture to investigate the supervised learning of the fields.
We empirically compare the performances of our field model with those of
randomly connected deep networks. The behavior of a randomly connected network
is investigated on the basis of the key idea of the neural tangent kernel
regime, a recent development in the machine learning theory of
over-parameterized networks; for most randomly connected neural networks, it is
shown that global minima always exist in their small neighborhoods. We
numerically show that this claim also holds for our neural fields. In more
detail, our model has two structures: i) each neuron in a field has a
continuously distributed receptive field, and ii) the initial connection
weights are random but not independent, having correlations when the positions
of neurons are close in each layer. We show that such a multilayer neural field
is more robust than conventional models when input patterns are deformed by
noise disturbances. Moreover, its generalization ability can be slightly
superior to that of conventional models.

We study the multi-task learning problem that aims to simultaneously analyze
multiple datasets collected from different sources and learn one model for each
of them. We propose a family of adaptive methods that automatically utilize
possible similarities among those tasks while carefully handling their
differences. We derive sharp statistical guarantees for the methods and prove
their robustness against outlier tasks. Numerical experiments on synthetic and
real datasets demonstrate the efficacy of our new methods.

A principal hires an agent to acquire a distribution over unverifiable
posteriors. An agent's optimal learning and truthful disclosure completely
specify the relative incentives the principal must provide. When the agent is
risk neutral, and a) has a sufficiently high outside option, or b) can face
sufficiently large penalties, the principal can attain the first-best
outcome--even if the agent may exit the relationship after learning. We also
explore in detail the general problem of cheaply implementing distributions
over posteriors with limited liability constraints and a risk-averse agent.

The amount of training-data is one of the key factors which determines the
generalization capacity of learning algorithms. Intuitively, one expects the
error rate to decrease as the amount of training-data increases. Perhaps
surprisingly, natural attempts to formalize this intuition give rise to
interesting and challenging mathematical questions. For example, in their
classical book on pattern recognition, Devroye, Gyorfi, and Lugosi (1996) ask
whether there exists a {monotone} Bayes-consistent algorithm. This question
remained open for over 25 years, until recently Pestov (2021) resolved it for
binary classification, using an intricate construction of a monotone
Bayes-consistent algorithm.
  We derive a general result in multiclass classification, showing that every
learning algorithm A can be transformed to a monotone one with similar
performance. Further, the transformation is efficient and only uses a black-box
oracle access to A. This demonstrates that one can provably avoid non-monotonic
behaviour without compromising performance, thus answering questions asked by
Devroye et al (1996), Viering, Mey, and Loog (2019), Viering and Loog (2021),
and by Mhammedi (2021).
  Our transformation readily implies monotone learners in a variety of
contexts: for example it extends Pestov's result to classification tasks with
an arbitrary number of labels. This is in contrast with Pestov's work which is
tailored to binary classification.
  In addition, we provide uniform bounds on the error of the monotone
algorithm. This makes our transformation applicable in distribution-free
settings. For example, in PAC learning it implies that every learnable class
admits a monotone PAC learner. This resolves questions by Viering, Mey, and
Loog (2019); Viering and Loog (2021); Mhammedi (2021).

We study the benign overfitting theory in the prediction of the conditional
average treatment effect (CATE), with linear regression models. As the
development of machine learning for causal inference, a wide range of
large-scale models for causality are gaining attention. One problem is that
suspicions have been raised that the large-scale models are prone to
overfitting to observations with sample selection, hence the large models may
not be suitable for causal prediction. In this study, to resolve the
suspicious, we investigate on the validity of causal inference methods for
overparameterized models, by applying the recent theory of benign overfitting
(Bartlett et al., 2020). Specifically, we consider samples whose distribution
switches depending on an assignment rule, and study the prediction of CATE with
linear models whose dimension diverges to infinity. We focus on two methods:
the T-learner, which based on a difference between separately constructed
estimators with each treatment group, and the inverse probability weight
(IPW)-learner, which solves another regression problem approximated by a
propensity score. In both methods, the estimator consists of interpolators that
fit the samples perfectly. As a result, we show that the T-learner fails to
achieve the consistency except the random assignment, while the IPW-learner
converges the risk to zero if the propensity score is known. This difference
stems from that the T-learner is unable to preserve eigenspaces of the
covariances, which is necessary for benign overfitting in the overparameterized
setting. Our result provides new insights into the usage of causal inference
methods in the overparameterizated setting, in particular, doubly robust

A popular paradigm in robotic learning is to train a policy from scratch for
every new robot. This is not only inefficient but also often impractical for
complex robots. In this work, we consider the problem of transferring a policy
across two different robots with significantly different parameters such as
kinematics and morphology. Existing approaches that train a new policy by
matching the action or state transition distribution, including imitation
learning methods, fail due to optimal action and/or state distribution being
mismatched in different robots. In this paper, we propose a novel method named
$REvolveR$ of using continuous evolutionary models for robotic policy transfer
implemented in a physics simulator. We interpolate between the source robot and
the target robot by finding a continuous evolutionary change of robot
parameters. An expert policy on the source robot is transferred through
training on a sequence of intermediate robots that gradually evolve into the
target robot. Experiments on a physics simulator show that the proposed
continuous evolutionary model can effectively transfer the policy across robots
and achieve superior sample efficiency on new robots. The proposed method is
especially advantageous in sparse reward settings where exploration can be
significantly reduced. Code is released at https://github.com/xingyul/revolver.

In this paper, we introduce ChemicalX, a PyTorch-based deep learning library
designed for providing a range of state of the art models to solve the drug
pair scoring task. The primary objective of the library is to make deep drug
pair scoring models accessible to machine learning researchers and
practitioners in a streamlined framework.The design of ChemicalX reuses
existing high level model training utilities, geometric deep learning, and deep
chemistry layers from the PyTorch ecosystem. Our system provides neural network
layers, custom pair scoring architectures, data loaders, and batch iterators
for end users. We showcase these features with example code snippets and case
studies to highlight the characteristics of ChemicalX. A range of experiments
on real world drug-drug interaction, polypharmacy side effect, and combination
synergy prediction tasks demonstrate that the models available in ChemicalX are
effective at solving the pair scoring task. Finally, we show that ChemicalX
could be used to train and score machine learning models on large drug pair
datasets with hundreds of thousands of compounds on commodity hardware.

Neural network quantization is a promising compression technique to reduce
memory footprint and save energy consumption, potentially leading to real-time
inference. However, there is a performance gap between quantized and
full-precision models. To reduce it, existing quantization approaches require
high-precision INT32 or full-precision multiplication during inference for
scaling or dequantization. This introduces a noticeable cost in terms of
memory, speed, and required energy. To tackle these issues, we present F8Net, a
novel quantization framework consisting of only fixed-point 8-bit
multiplication. To derive our method, we first discuss the advantages of
fixed-point multiplication with different formats of fixed-point numbers and
study the statistical behavior of the associated fixed-point numbers. Second,
based on the statistical and algorithmic analysis, we apply different
fixed-point formats for weights and activations of different layers. We
introduce a novel algorithm to automatically determine the right format for
each layer during training. Third, we analyze a previous quantization algorithm
-- parameterized clipping activation (PACT) -- and reformulate it using
fixed-point arithmetic. Finally, we unify the recently proposed method for
quantization fine-tuning and our fixed-point approach to show the potential of
our method. We verify F8Net on ImageNet for MobileNet V1/V2 and ResNet18/50.
Our approach achieves comparable and better performance, when compared not only
to existing quantization techniques with INT32 multiplication or floating-point
arithmetic, but also to the full-precision counterparts, achieving

We introduce an efficient parallelization scheme to implement pixel-by-pixel
nanophotonic optimization using a Green's function based formalism. The crucial
insight in our proposal is the reframing of the optimization algorithm as a
large-scale data processing pipeline, which allows for the efficient
distribution of computational tasks across thousands of workers. We demonstrate
the utility of our implementation by exercising it to optimize a high numerical
aperture focusing metalens at problem sizes that would otherwise be far out of
reach for the Green's function based method. Finally, we highlight the
connection to powerful ideas from reinforcement learning as a natural corollary
of reinterpreting the nanophotonic inverse design problem as a graph traversal
enabled by the pixel-by-pixel optimization paradigm.

The ability of Deep Neural Networks to approximate highly complex functions
is key to their success. This benefit, however, comes at the expense of a large
model size, which challenges its deployment in resource-constrained
environments. Pruning is an effective technique used to limit this issue, but
often comes at the cost of reduced accuracy and adversarial robustness. This
paper addresses these shortcomings and introduces Deadwooding, a novel global
pruning technique that exploits a Lagrangian Dual method to encourage model
sparsity while retaining accuracy and ensuring robustness. The resulting model
is shown to significantly outperform the state-of-the-art studies in measures
of robustness and accuracy.

Modern software is incredibly complex. A typical application may comprise
hundreds or thousands of reusable components. Automated package managers can
help to maintain a consistent set of dependency versions, but ultimately the
solvers in these systems rely on constraints generated by humans. At scale,
small errors add up, and it becomes increasingly difficult to find
high-fidelity configurations. We cannot test all configurations, because the
space is combinatorial, so exhaustive exploration is infeasible.
  In this paper, we present Reliabuild, an auto-tuning framework that
efficiently explores the build configuration space and learns which package
versions are likely to result in a successful configuration. We implement two
models in Reliabuild to rank the different configurations and use adaptive
sampling to select good configurations with fewer samples. We demonstrate
Reliabuild's effectiveness by evaluating 31,186 build configurations of 61
packages from the Extreme-scale Scientific Software Stack(E4S). Reliabuild
selects good configurations efficiently. For example, Reliabuild selects 3X the
number of good configurations in comparison to random sampling for several
packages including Abyss, Bolt, libnrm, OpenMPI. Our framework is also able to
select all the high-fidelity builds in half the number of samples required by
random sampling for packages such as Chai, OpenMPI, py-petsc4py, and slepc. We
further use the model to learn statistics about the compatibility of different
packages, which will enable package solvers to better select high-fidelity
build configurations automatically.

For the task of sampling from a density $\pi \propto \exp(-V)$ on
$\mathbb{R}^d$, where $V$ is possibly non-convex but $L$-gradient Lipschitz, we
prove that averaged Langevin Monte Carlo outputs a sample with
$\varepsilon$-relative Fisher information after $O( L^2 d^2/\varepsilon^2)$
iterations. This is the sampling analogue of complexity bounds for finding an
$\varepsilon$-approximate first-order stationary points in non-convex
optimization and therefore constitutes a first step towards the general theory
of non-log-concave sampling. We discuss numerous extensions and applications of
our result; in particular, it yields a new state-of-the-art guarantee for
sampling from distributions which satisfy a Poincar\'e inequality.

ASR systems designed for native English (L1) usually underperform on
non-native English (L2). To address this performance gap, \textbf{(i)} we
extend our previous work to investigate fine-tuning of a pre-trained wav2vec
2.0 model \cite{baevski2020wav2vec,xu2021self} under a rich set of L1 and L2
training conditions. We further \textbf{(ii)} incorporate language model
decoding in the ASR system, along with the fine-tuning method. Quantifying
gains acquired from each of these two approaches separately and an error
analysis allows us to identify different sources of improvement within our
models. We find that while the large self-trained wav2vec 2.0 may be
internalizing sufficient decoding knowledge for clean L1 speech
\cite{xu2021self}, this does not hold for L2 speech and accounts for the
utility of employing language model decoding on L2 data.

Verification of neural networks is currently a hot topic in automated theorem
proving. Progress has been rapid and there are now a wide range of tools
available that can verify properties of networks with hundreds of thousands of
nodes. In theory this opens the door to the verification of larger control
systems that make use of neural network components. However, although work has
managed to incorporate the results of these verifiers to prove larger
properties of individual systems, there is currently no general methodology for
bridging the gap between verifiers and interactive theorem provers (ITPs).
  In this paper we present Vehicle, our solution to this problem. Vehicle is
equipped with an expressive domain specific language for stating neural network
specifications which can be compiled to both verifiers and ITPs. It overcomes
previous issues with maintainability and scalability in similar ITP
formalisations by using a standard ONNX file as the single canonical
representation of the network. We demonstrate its utility by using it to
connect the neural network verifier Marabou to Agda and then formally verifying
that a car steered by a neural network never leaves the road, even in the face
of an unpredictable cross wind and imperfect sensors. The network has over
20,000 nodes, and therefore this proof represents an improvement of 3 orders of
magnitude over prior proofs about neural network enhanced systems in ITPs.

The 2030 Challenge is aimed at making all new buildings and major renovations
carbon neutral by 2030. One of the potential solutions to meet this challenge
is through innovative sustainable design strategies. For developing such
strategies it is important to understand how the various building factors
contribute to energy usage of a building, right at design time. The growth of
artificial intelligence (AI) in recent years provides an unprecedented
opportunity to advance sustainable design by learning complex relationships
between building factors from available data. However, rich training datasets
are needed for AI-based solutions to achieve good prediction accuracy.
Unfortunately, obtaining training datasets are time consuming and expensive in
many real-world applications. Motivated by these reasons, we address the
problem of accurately predicting the energy usage of new or unknown building
types, i.e., those building types that do not have any training data. We
propose a novel approach based on zero-shot learning (ZSL) to solve this
problem. Our approach uses side information from building energy modeling
experts to predict the closest building types for a given new/unknown building
type. We then obtain the predicted energy usage for the k-closest building
types using the models learned during training and combine the predicted values
using a weighted averaging function. We evaluated our approach on a dataset
containing five building types generated using BuildSimHub, a popular platform
for building energy modeling. Our approach achieved better average accuracy
than a regression model (based on XGBoost) trained on the entire dataset of
known building types.

For soft continuum arms, visual servoing is a popular control strategy that
relies on visual feedback to close the control loop. However, robust visual
servoing is challenging as it requires reliable feature extraction from the
image, accurate control models and sensors to perceive the shape of the arm,
both of which can be hard to implement in a soft robot. This letter circumvents
these challenges by presenting a deep neural network-based method to perform
smooth and robust 3D positioning tasks on a soft arm by visual servoing using a
camera mounted at the distal end of the arm. A convolutional neural network is
trained to predict the actuations required to achieve the desired pose in a
structured environment. Integrated and modular approaches for estimating the
actuations from the image are proposed and are experimentally compared. A
proportional control law is implemented to reduce the error between the desired
and current image as seen by the camera. The model together with the
proportional feedback control makes the described approach robust to several
variations such as new targets, lighting, loads, and diminution of the soft
arm. Furthermore, the model lends itself to be transferred to a new environment
with minimal effort.

Biomechanical and clinical gait research observes muscles and tendons in
limbs to study their functions and behaviour. Therefore, movements of distinct
anatomical landmarks, such as muscle-tendon junctions, are frequently measured.
We propose a reliable and time efficient machine-learning approach to track
these junctions in ultrasound videos and support clinical biomechanists in gait
analysis. In order to facilitate this process, a method based on deep-learning
was introduced. We gathered an extensive dataset, covering 3 functional
movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3
different ultrasound systems, and providing a total of 66864 annotated
ultrasound images in our network training. Furthermore, we used data collected
across independent laboratories and curated by researchers with varying levels
of experience. For the evaluation of our method a diverse test-set was selected
that is independently verified by four specialists. We show that our model
achieves similar performance scores to the four human specialists in
identifying the muscle-tendon junction position. Our method provides
time-efficient tracking of muscle-tendon junctions, with prediction times of up
to 0.078 seconds per frame (approx. 100 times faster than manual labeling). All
our codes, trained models and test-set were made publicly available and our
model is provided as a free-to-use online service on https://deepmtj.org/.

We develop a class of mixed-integer formulations for disjunctive constraints
intermediate to the big-M and convex hull formulations in terms of relaxation
strength. The main idea is to capture the best of both the big-M and convex
hull formulations: a computationally light formulation with a tight relaxation.
The "$P$-split" formulations are based on a lifted transformation that splits
convex additively separable constraints into $P$ partitions and forms the
convex hull of the linearized and partitioned disjunction. We analyze the
continuous relaxation of the $P$-split formulations and show that, under
certain assumptions, the formulations form a hierarchy starting from a big-M
equivalent and converging to the convex hull. The goal of the $P$-split
formulations is to form a strong approximation of the convex hull through a
computationally simpler formulation. We computationally compare the $P$-split
formulations against big-M and convex hull formulations on 320 test instances.
The test problems include K-means clustering, P_ball problems, and optimization
over trained ReLU neural networks. The computational results show promising
potential of the $P$-split formulations. For many of the test problems,
$P$-split formulations are solved with a similar number of explored nodes as
the convex hull formulation, while reducing the solution time by an order of
magnitude and outperforming big-M both in time and number of explored nodes.

Deep Reinforcement Learning (RL) has considerably advanced over the past
decade. At the same time, state-of-the-art RL algorithms require a large
computational budget in terms of training time to converge. Recent work has
started to approach this problem through the lens of quantum computing, which
promises theoretical speed-ups for several traditionally hard tasks. In this
work, we examine a class of hybrid quantum-classical RL algorithms that we
collectively refer to as variational quantum deep Q-networks (VQ-DQN). We show
that VQ-DQN approaches are subject to instabilities that cause the learned
policy to diverge, study the extent to which this afflicts reproduciblity of
established results based on classical simulation, and perform systematic
experiments to identify potential explanations for the observed instabilities.
Additionally, and in contrast to most existing work on quantum reinforcement
learning, we execute RL algorithms on an actual quantum processing unit (an IBM
Quantum Device) and investigate differences in behaviour between simulated and
physical quantum systems that suffer from implementation deficiencies. Our
experiments show that, contrary to opposite claims in the literature, it cannot
be conclusively decided if known quantum approaches, even if simulated without
physical imperfections, can provide an advantage as compared to classical
approaches. Finally, we provide a robust, universal and well-tested
implementation of VQ-DQN as a reproducible testbed for future experiments.

We consider the fixed-budget best-arm identification problem with Normal
reward distributions. In this problem, the forecaster is given $K$ arms (or
treatments) and $T$ time steps. The forecaster attempts to find the best arm,
defined by the largest mean, via an adaptive experiment conducted using an
algorithm. The algorithm's performance is measured by the simple regret, that
is, the quality of the estimated best arm. The frequentist simple regret can be
exponentially small to $T$, whereas the Bayesian simple regret is polynomially
small to $T$. This paper demonstrates that Bayes optimal algorithm, which
minimizes the Bayesian simple regret, does not produce an exponential simple
regret for some parameters, a finding that contrasts with the many results
indicating the asymptotic equivalence of Bayesian and frequentist algorithms in
the context of fixed sampling regimes. While the Bayes optimal algorithm is
described in terms of a recursive equation that is virtually impossible to
compute exactly, we establish the foundations for further analysis by
introducing a key quantity that we call the expected Bellman improvement.

Neural networks are known to use spurious correlations such as background
information for classification. While prior work has looked at spurious
correlations that are widespread in the training data, in this work, we
investigate how sensitive neural networks are to rare spurious correlations,
which may be harder to detect and correct, and may lead to privacy leaks. We
introduce spurious patterns correlated with a fixed class to a few training
examples and find that it takes only a handful of such examples for the network
to learn the correlation. Furthermore, these rare spurious correlations also
impact accuracy and privacy. We empirically and theoretically analyze different
factors involved in rare spurious correlations and propose mitigation methods
accordingly. Specifically, we observe that $\ell_2$ regularization and adding
Gaussian noise to inputs can reduce the undesirable effects. Code available at
https://github.com/yangarbiter/rare-spurious-correlation.

Emotion recognition in children can help the early identification of, and
intervention on, psychological complications that arise in stressful situations
such as cancer treatment. Though deep learning models are increasingly being
adopted, data scarcity is often an issue in pediatric medicine, including for
facial emotion recognition in children. In this paper, we study the application
of data augmentation-based contrastive learning to overcome data scarcity in
facial emotion recognition for children. We explore the idea of ignoring
generational gaps, by adding abundantly available adult data to pediatric data,
to learn better representations. We investigate different ways by which adult
facial expression images can be used alongside those of children. In
particular, we propose to explicitly incorporate within each mini-batch adult
images as augmentations for children's. Out of $84$ combinations of learning
approaches and training set sizes, we find that supervised contrastive learning
with the proposed training scheme performs best, reaching a test accuracy that
typically surpasses the one of the second-best approach by 2% to 3%. Our
results indicate that adult data can be considered to be a meaningful
augmentation of pediatric data for the recognition of emotional facial
expression in children, and open up the possibility for other applications of
contrastive learning to improve pediatric care by complementing data of
children with that of adults.

Deep neural networks have been extremely successful as highly accurate wave
function ans\"atze for variational Monte Carlo calculations of molecular ground
states. We present an extension of one such ansatz, FermiNet, to calculations
of the ground states of periodic Hamiltonians, and study the homogeneous
electron gas. FermiNet calculations of the ground-state energies of small
electron gas systems are in excellent agreement with previous initiator full
configuration interaction quantum Monte Carlo and diffusion Monte Carlo
calculations. We investigate the spin-polarized homogeneous electron gas and
demonstrate that the same neural network architecture is capable of accurately
representing both the delocalized Fermi liquid state and the localized Wigner
crystal state. The network is given no \emph{a priori} knowledge that a phase
transition exists, but converges on the translationally invariant ground state
at high density and spontaneously breaks the symmetry to produce the
crystalline ground state at low density.

We consider a remote contextual multi-armed bandit (CMAB) problem, in which
the decision-maker observes the context and the reward, but must communicate
the actions to be taken by the agents over a rate-limited communication
channel. This can model, for example, a personalized ad placement application,
where the content owner observes the individual visitors to its website, and
hence has the context information, but must convey the ads that must be shown
to each visitor to a separate entity that manages the marketing content. In
this remote CMAB (R-CMAB) problem, the constraint on the communication rate
between the decision-maker and the agents imposes a trade-off between the
number of bits sent per agent and the acquired average reward. We are
particularly interested in characterizing the rate required to achieve
sub-linear regret. Consequently, this can be considered as a policy compression
problem, where the distortion metric is induced by the learning objectives. We
first study the fundamental information theoretic limits of this problem by
letting the number of agents go to infinity, and study the regret achieved when
Thompson sampling strategy is adopted. In particular, we identify two distinct
rate regions resulting in linear and sub-linear regret behavior, respectively.
Then, we provide upper bounds on the achievable regret when the decision-maker
can reliably transmit the policy without distortion.

Recent studies revealed that convolutional neural networks do not generalize
well to small image transformations, e.g. rotations by a few degrees or
translations of a few pixels. To improve the robustness to such
transformations, we propose to introduce data augmentation at intermediate
layers of the neural architecture, in addition to the common data augmentation
applied on the input images. By introducing small perturbations to activation
maps (features) at various levels, we develop the capacity of the neural
network to cope with such transformations. We conduct experiments on three
image classification benchmarks (Tiny ImageNet, Caltech-256 and Food-101),
considering two different convolutional architectures (ResNet-18 and
DenseNet-121). When compared with two state-of-the-art stabilization methods,
the empirical results show that our approach consistently attains the best
trade-off between accuracy and mean flip rate.

We consider the structure learning problem with all node variables having the
same error variance, an assumption known to ensure the identifiability of the
causal directed acyclic graph (DAG). We propose an empirical Bayes formulation
of the problem that yields a non-decomposable posterior score for DAG models.
To facilitate efficient posterior computation, we approximate the posterior
probability of each ordering by that of a "best" DAG model, which naturally
leads to an order-based Markov chain Monte Carlo (MCMC) algorithm. Strong
selection consistency for our model is proved under mild high-dimensional
conditions, and the mixing behavior of our sampler is theoretically
investigated. Further, we propose a new iterative top-down algorithm, which
quickly yields an approximate solution to the structure learning problem and
can be used to initialize the MCMC sampler. We demonstrate that our method
outperforms other state-of-the-art algorithms under various simulation
settings, and conclude the paper with a single-cell real-data study
illustrating practical advantages of the proposed method.

Neural metrics have achieved impressive correlation with human judgements in
the evaluation of machine translation systems, but before we can safely
optimise towards such metrics, we should be aware of (and ideally eliminate)
biases toward bad translations that receive high scores. Our experiments show
that sample-based Minimum Bayes Risk decoding can be used to explore and
quantify such weaknesses. When applying this strategy to COMET for en-de and
de-en, we find that COMET models are not sensitive enough to discrepancies in
numbers and named entities. We further show that these biases are hard to fully
remove by simply training on additional synthetic data and release our code and
data for facilitating further experiments.

The information retrieval community has recently witnessed a revolution due
to large pretrained transformer models. Another key ingredient for this
revolution was the MS MARCO dataset, whose scale and diversity has enabled
zero-shot transfer learning to various tasks. However, not all IR tasks and
domains can benefit from one single dataset equally. Extensive research in
various NLP tasks has shown that using domain-specific training data, as
opposed to a general-purpose one, improves the performance of neural models. In
this work, we harness the few-shot capabilities of large pretrained language
models as synthetic data generators for IR tasks. We show that models finetuned
solely on our unsupervised dataset outperform strong baselines such as BM25 as
well as recently proposed self-supervised dense retrieval methods. Furthermore,
retrievers finetuned on both supervised and our synthetic data achieve better
zero-shot transfer than models finetuned only on supervised data. Code, models,
and data are available at https://github.com/zetaalphavector/inpars .

Vertical federated learning (VFL) aims to train models from cross-silo data
with different feature spaces stored on different platforms. Existing VFL
methods usually assume all data on each platform can be used for model
training. However, due to the intrinsic privacy risks of federated learning,
the total amount of involved data may be constrained. In addition, existing VFL
studies usually assume only one platform has task labels and can benefit from
the collaboration, making it difficult to attract other platforms to join in
the collaborative learning. In this paper, we study the platform collaboration
problem in VFL under privacy constraint. We propose to incent different
platforms through a reciprocal collaboration, where all platforms can exploit
multi-platform information in the VFL framework to benefit their own tasks.
With limited privacy budgets, each platform needs to wisely allocate its data
quotas for collaboration with other platforms. Thereby, they naturally form a
multi-party game. There are two core problems in this game, i.e., how to
appraise other platforms' data value to compute game rewards and how to
optimize policies to solve the game. To evaluate the contributions of other
platforms' data, each platform offers a small amount of "deposit" data to
participate in the VFL. We propose a performance estimation method to predict
the expected model performance when involving different amount combinations of
inter-platform data. To solve the game, we propose a platform negotiation
method that simulates the bargaining among platforms and locally optimizes
their policies via gradient descent. Extensive experiments on two real-world
datasets show that our approach can effectively facilitate the collaborative
exploitation of multi-platform data in VFL under privacy restrictions.

In this paper, we will show that the quantization in layer's input is more
important than parameters' quantization for loss function. And the algorithm
which is based on the layer's input quantization error is better than
hessian-based mixed precision layout algorithm.

It can largely benefit the reinforcement learning process of each agent if
multiple agents perform their separate reinforcement learning tasks
cooperatively. Different from multi-agent reinforcement learning where multiple
agents are in a common environment and should learn to cooperate or compete
with each other, in this case each agent has its separate environment and only
communicate with others to share knowledge without any cooperative or
competitive behaviour as a learning outcome. In fact, this learning scenario is
not well understood yet and not well formulated. As the first effort, we
propose group-agent reinforcement learning as a formulation of this scenario
and the third type of reinforcement learning problem with respect to
single-agent and multi-agent reinforcement learning. We then propose the first
distributed reinforcement learning framework called DDAL (Decentralised
Distributed Asynchronous Learning) designed for group-agent reinforcement
learning. We show through experiments that DDAL achieved desirable performance
with very stable training and has good scalability.

Network slicing (NwS) is one of the main technologies in the fifth-generation
of mobile communication and beyond (5G+). One of the important challenges in
the NwS is information uncertainty which mainly involves demand and channel
state information (CSI). Demand uncertainty is divided into three types: number
of users requests, amount of bandwidth, and requested virtual network functions
workloads. Moreover, the CSI uncertainty is modeled by three methods:
worst-case, probabilistic, and hybrid. In this paper, our goal is to maximize
the utility of the infrastructure provider by exploiting deep reinforcement
learning algorithms in end-to-end NwS resource allocation under demand and CSI
uncertainties. The proposed formulation is a nonconvex mixed-integer non-linear
programming problem. To perform robust resource allocation in problems that
involve uncertainty, we need a history of previous information. To this end, we
use a recurrent deterministic policy gradient (RDPG) algorithm, a recurrent and
memory-based approach in deep reinforcement learning. Then, we compare the RDPG
method in different scenarios with soft actor-critic (SAC), deep deterministic
policy gradient (DDPG), distributed, and greedy algorithms. The simulation
results show that the SAC method is better than the DDPG, distributed, and
greedy methods, respectively. Moreover, the RDPG method out performs the SAC
approach on average by 70%.

In the quest for autonomous agents learning open-ended repertoires of skills,
most works take a Piagetian perspective: learning trajectories are the results
of interactions between developmental agents and their physical environment.
The Vygotskian perspective, on the other hand, emphasizes the centrality of the
socio-cultural environment: higher cognitive functions emerge from
transmissions of socio-cultural processes internalized by the agent. This paper
argues that both perspectives could be coupled within the learning of autotelic
agents to foster their skill acquisition. To this end, we make two
contributions: 1) a novel social interaction protocol called Help Me Explore
(HME), where autotelic agents can benefit from both individual and socially
guided exploration. In social episodes, a social partner suggests goals at the
frontier of the learning agent knowledge. In autotelic episodes, agents can
either learn to master their own discovered goals or autonomously rehearse
failed social goals; 2) GANGSTR, a graph-based autotelic agent for manipulation
domains capable of decomposing goals into sequences of intermediate sub-goals.
We show that when learning within HME, GANGSTR overcomes its individual
learning limits by mastering the most complex configurations (e.g. stacks of 5
blocks) with only few social interventions.

Computational cytology is a critical, rapid-developing, yet challenging topic
in the field of medical image computing which analyzes the digitized cytology
image by computer-aided technologies for cancer screening. Recently, an
increasing number of deep learning (DL) algorithms have made significant
progress in medical image analysis, leading to the boosting publications of
cytological studies. To investigate the advanced methods and comprehensive
applications, we survey more than 120 publications of DL-based cytology image
analysis in this article. We first introduce various deep learning methods,
including fully supervised, weakly supervised, unsupervised, and transfer
learning. Then, we systematically summarize the public datasets, evaluation
metrics, versatile cytology image analysis applications including
classification, detection, segmentation, and other related tasks. Finally, we
discuss current challenges and potential research directions of computational
cytology.

In this paper, we consider the imperfection within machine learning-based 2D
object detection and its impact on safety. We address a special sub-type of
performance limitations: the prediction bounding box cannot be perfectly
aligned with the ground truth, but the computed Intersection-over-Union metric
is always larger than a given threshold. Under such type of performance
limitation, we formally prove the minimum required bounding box enlargement
factor to cover the ground truth. We then demonstrate that the factor can be
mathematically adjusted to a smaller value, provided that the motion planner
takes a fixed-length buffer in making its decisions. Finally, observing the
difference between an empirically measured enlargement factor and our formally
derived worst-case enlargement factor offers an interesting connection between
the quantitative evidence (demonstrated by statistics) and the qualitative
evidence (demonstrated by worst-case analysis).

In this work, we propose using an ensemble Kalman method to learn a nonlinear
eddy viscosity model, represented as a tensor basis neural network, from
velocity data. Data-driven turbulence models have emerged as a promising
alternative to traditional models for providing closure mapping from the mean
velocities to Reynolds stresses. Most data-driven models in this category need
full-field Reynolds stress data for training, which not only places stringent
demand on the data generation but also makes the trained model ill-conditioned
and lacks robustness. This difficulty can be alleviated by incorporating the
Reynolds-averaged Navier-Stokes (RANS) solver in the training process. However,
this would necessitate developing adjoint solvers of the RANS model, which
requires extra effort in code development and maintenance. Given this
difficulty, we present an ensemble Kalman method with an adaptive step size to
train a neural network-based turbulence model by using indirect observation
data. To our knowledge, this is the first such attempt in turbulence modelling.
The ensemble method is first verified on the flow in a square duct, where it
correctly learns the underlying turbulence models from velocity data. Then, the
generalizability of the learned model is evaluated on a family of separated
flows over periodic hills. It is demonstrated that the turbulence model learned
in one flow can predict flows in similar configurations with varying slopes.

We study iterative methods based on Krylov subspaces for low-rank
approximation under any Schatten-$p$ norm. Here, given access to a matrix $A$
through matrix-vector products, an accuracy parameter $\epsilon$, and a target
rank $k$, the goal is to find a rank-$k$ matrix $Z$ with orthonormal columns
such that $\| A(I -ZZ^\top)\|_{S_p} \leq (1+\epsilon)\min_{U^\top U = I_k}
\|A(I - U U^\top)\|_{S_p}$, where $\|M\|_{S_p}$ denotes the $\ell_p$ norm of
the the singular values of $M$. For the special cases of $p=2$ (Frobenius norm)
and $p = \infty$ (Spectral norm), Musco and Musco (NeurIPS 2015) obtained an
algorithm based on Krylov methods that uses $\tilde{O}(k/\sqrt{\epsilon})$
matrix-vector products, improving on the na\"ive $\tilde{O}(k/\epsilon)$
dependence obtainable by the power method, where $\tilde{O}$ suppresses
poly$(\log(dk/\epsilon))$ factors.
  Our main result is an algorithm that uses only
$\tilde{O}(kp^{1/6}/\epsilon^{1/3})$ matrix-vector products, and works for all
$p \geq 1$. For $p = 2$ our bound improves the previous
$\tilde{O}(k/\epsilon^{1/2})$ bound to $\tilde{O}(k/\epsilon^{1/3})$. Since the
Schatten-$p$ and Schatten-$\infty$ norms are the same up to a $(1+
\epsilon)$-factor when $p \geq (\log d)/\epsilon$, our bound recovers the
result of Musco and Musco for $p = \infty$. Further, we prove a matrix-vector
query lower bound of $\Omega(1/\epsilon^{1/3})$ for any fixed constant $p \geq
1$, showing that surprisingly $\tilde{\Theta}(1/\epsilon^{1/3})$ is the optimal
complexity for constant~$k$.
  To obtain our results, we introduce several new techniques, including
optimizing over multiple Krylov subspaces simultaneously, and pinching
inequalities for partitioned operators. Our lower bound for $p \in [1,2]$ uses
the Araki-Lieb-Thirring trace inequality, whereas for $p>2$, we appeal to a
norm-compression inequality for aligned partitioned operators.

In this study, a real-time dispatching algorithm based on reinforcement
learning is proposed and for the first time, is deployed in large scale.
Current dispatching methods in ridehailing platforms are dominantly based on
myopic or rule-based non-myopic approaches. Reinforcement learning enables
dispatching policies that are informed of historical data and able to employ
the learned information to optimize returns of expected future trajectories.
Previous studies in this field yielded promising results, yet have left room
for further improvements in terms of performance gain, self-dependency,
transferability, and scalable deployment mechanisms. The present study proposes
a standalone RL-based dispatching solution that is equipped with multiple
mechanisms to ensure robust and efficient on-policy learning and inference
while being adaptable for full-scale deployment. A new form of value updating
based on temporal difference is proposed that is more adapted to the inherent
uncertainty of the problem. For the driver-order assignment, a customized
utility function is proposed that when tuned based on the statistics of the
market, results in remarkable performance improvement and interpretability. In
addition, for reducing the risk of cancellation after drivers' assignment, an
adaptive graph pruning strategy based on the multi-arm bandit problem is
introduced. The method is evaluated using offline simulation with real data and
yields notable performance improvement. In addition, the algorithm is deployed
online in multiple cities under DiDi's operation for A/B testing and is
launched in one of the major international markets as the primary mode of
dispatch. The deployed algorithm shows over 1.3% improvement in total driver
income from A/B testing. In addition, by causal inference analysis, as much as
5.3% improvement in major performance metrics is detected after full-scale

In a first part, we present a mathematical analysis of a general methodology
of a probabilistic learning inference that allows for estimating a posterior
probability model for a stochastic boundary value problem from a prior
probability model. The given targets are statistical moments for which the
underlying realizations are not available. Under these conditions, the
Kullback-Leibler divergence minimum principle is used for estimating the
posterior probability measure. A statistical surrogate model of the implicit
mapping, which represents the constraints, is introduced. The MCMC generator
and the necessary numerical elements are given to facilitate the implementation
of the methodology in a parallel computing framework. In a second part, an
application is presented to illustrate the proposed theory and is also, as
such, a contribution to the three-dimensional stochastic homogenization of
heterogeneous linear elastic media in the case of a non-separation of the
microscale and macroscale. For the construction of the posterior probability
measure by using the probabilistic learning inference, in addition to the
constraints defined by given statistical moments of the random effective
elasticity tensor, the second-order moment of the random normalized residue of
the stochastic partial differential equation has been added as a constraint.
This constraint guarantees that the algorithm seeks to bring the statistical
moments closer to their targets while preserving a small residue.

Large bandwidth at mm-wave is crucial for 5G and beyond but the high path
loss (PL) requires highly accurate PL prediction for network planning and
optimization. Statistical models with slope-intercept fit fall short in
capturing large variations seen in urban canyons, whereas ray-tracing, capable
of characterizing site-specific features, faces challenges in describing
foliage and street clutter and associated reflection/diffraction ray
calculation. Machine learning (ML) is promising but faces three key challenges
in PL prediction: 1) insufficient measurement data; 2) lack of extrapolation to
new streets; 3) overwhelmingly complex features/models. We propose an ML-based
urban canyon PL prediction model based on extensive 28 GHz measurements from
Manhattan where street clutters are modeled via a LiDAR point cloud dataset and
buildings by a mesh-grid building dataset. We extract expert knowledge-driven
street clutter features from the point cloud and aggressively compress
3D-building information using convolutional-autoencoder. Using a new
street-by-street training and testing procedure to improve generalizability,
the proposed model using both clutter and building features achieves a
prediction error (RMSE) of $4.8 \pm 1.1$ dB compared to $10.6 \pm 4.4$ dB and
$6.5 \pm 2.0$ dB for 3GPP LOS and slope-intercept prediction, respectively,
where the standard deviation indicates street-by-street variation. By only
using four most influential clutter features, RMSE of $5.5\pm 1.1$ dB is
achieved.

Multi-armed bandit problems provide a framework to identify the optimal
intervention over a sequence of repeated experiments. Without additional
assumptions, minimax optimal performance (measured by cumulative regret) is
well-understood. With access to additional observed variables that d-separate
the intervention from the outcome (i.e., they are a d-separator), recent
"causal bandit" algorithms provably incur less regret. However, in practice it
is desirable to be agnostic to whether observed variables are a d-separator.
Ideally, an algorithm should be adaptive; that is, perform nearly as well as an
algorithm with oracle knowledge of the presence or absence of a d-separator. In
this work, we formalize and study this notion of adaptivity, and provide a
novel algorithm that simultaneously achieves (a) optimal regret when a
d-separator is observed, improving on classical minimax algorithms, and (b)
significantly smaller regret than recent causal bandit algorithms when the
observed variables are not a d-separator. Crucially, our algorithm does not
require any oracle knowledge of whether a d-separator is observed. We also
generalize this adaptivity to other conditions, such as the front-door

Since proposed in the 70s, the Non-Equilibrium Green Function (NEGF) method
has been recognized as a standard approach to quantum transport simulations.
Although it achieves superiority in simulation accuracy, the tremendous
computational cost makes it unbearable for high-throughput simulation tasks
such as sensitivity analysis, inverse design, etc. In this work, we propose
AD-NEGF, to our best knowledge the first end-to-end differentiable NEGF model
for quantum transport simulations. We implement the entire numerical process in
PyTorch, and design customized backward pass with implicit layer techniques,
which provides gradient information at an affordable cost while guaranteeing
the correctness of the forward simulation. The proposed model is validated with
applications in calculating differential physical quantities, empirical
parameter fitting, and doping optimization, which demonstrates its capacity to
accelerate the material design process by conducting gradient-based parameter

We consider the well-studied problem of learning intersections of halfspaces
under the Gaussian distribution in the challenging \emph{agnostic learning}
model. Recent work of Diakonikolas et al. (2021) shows that any Statistical
Query (SQ) algorithm for agnostically learning the class of intersections of
$k$ halfspaces over $\mathbb{R}^n$ to constant excess error either must make
queries of tolerance at most $n^{-\tilde{\Omega}(\sqrt{\log k})}$ or must make
$2^{n^{\Omega(1)}}$ queries. We strengthen this result by improving the
tolerance requirement to $n^{-\tilde{\Omega}(\log k)}$. This lower bound is
essentially best possible since an SQ algorithm of Klivans et al. (2008)
agnostically learns this class to any constant excess error using $n^{O(\log
k)}$ queries of tolerance $n^{-O(\log k)}$. We prove two variants of our lower
bound, each of which combines ingredients from Diakonikolas et al. (2021) with
(an extension of) a different earlier approach for agnostic SQ lower bounds for
the Boolean setting due to Dachman-Soled et al. (2014). Our approach also
yields lower bounds for agnostically SQ learning the class of "convex subspace
juntas" (studied by Vempala, 2010) and the class of sets with bounded Gaussian
surface area; all of these lower bounds are nearly optimal since they
essentially match known upper bounds from Klivans et al. (2008).

Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive
Random Access Memories (RRAMs) based circuits for low power signal processing.
Their inherent computational sparsity naturally results in energy efficiency
benefits. The main challenge implementing robust SNNs is the intrinsic
variability (heterogeneity) of both analog CMOS circuits and RRAM technology.
In this work, we assessed the performance and variability of RRAM-based
neuromorphic circuits that were designed and fabricated using a 130\,nm
technology node. Based on these results, we propose a Neuromorphic Hardware
Calibrated (NHC) SNN, where the learning circuits are calibrated on the
measured data. We show that by taking into account the measured heterogeneity
characteristics in the off-chip learning phase, the NHC SNN self-corrects its
hardware non-idealities and learns to solve benchmark tasks with high accuracy.
This work demonstrates how to cope with the heterogeneity of neurons and
synapses for increasing classification accuracy in temporal tasks.

We introduce a data-driven anomaly detection framework using a manufacturing
dataset collected from a factory assembly line. Given heterogeneous time series
data consisting of operation cycle signals and sensor signals, we aim at
discovering abnormal events. Motivated by our empirical findings that
conventional single-stage benchmark approaches may not exhibit satisfactory
performance under our challenging circumstances, we propose a two-stage deep
anomaly detection (TDAD) framework in which two different unsupervised learning
models are adopted depending on types of signals. In Stage I, we select anomaly
candidates by using a model trained by operation cycle signals; in Stage II, we
finally detect abnormal events out of the candidates by using another model,
which is suitable for taking advantage of temporal continuity, trained by
sensor signals. A distinguishable feature of our framework is that operation
cycle signals are exploited first to find likely anomalous points, whereas
sensor signals are leveraged to filter out unlikely anomalous points afterward.
Our experiments comprehensively demonstrate the superiority over single-stage
benchmark approaches, the model-agnostic property, and the robustness to
difficult situations.

We present backpropagation clipping, a novel variant of differentially
private stochastic gradient descent (DP-SGD) for privacy-preserving deep
learning. Our approach clips each trainable layer's inputs (during the forward
pass) and its upstream gradients (during the backward pass) to ensure bounded
global sensitivity for the layer's gradient; this combination replaces the
gradient clipping step in existing DP-SGD variants. Our approach is simple to
implement in existing deep learning frameworks. The results of our empirical
evaluation demonstrate that backpropagation clipping provides higher accuracy
at lower values for the privacy parameter $\epsilon$ compared to previous work.
We achieve 98.7% accuracy for MNIST with $\epsilon = 0.07$ and 74% accuracy for
CIFAR-10 with $\epsilon = 3.64$.

We address the problem of cross-speaker style transfer for text-to-speech
(TTS) using data augmentation via voice conversion. We assume to have a corpus
of neutral non-expressive data from a target speaker and supporting
conversational expressive data from different speakers. Our goal is to build a
TTS system that is expressive, while retaining the target speaker's identity.
The proposed approach relies on voice conversion to first generate high-quality
data from the set of supporting expressive speakers. The voice converted data
is then pooled with natural data from the target speaker and used to train a
single-speaker multi-style TTS system. We provide evidence that this approach
is efficient, flexible, and scalable. The method is evaluated using one or more
supporting speakers, as well as a variable amount of supporting data. We
further provide evidence that this approach allows some controllability of
speaking style, when using multiple supporting speakers. We conclude by scaling
our proposed technology to a set of 14 speakers across 7 languages. Results
indicate that our technology consistently improves synthetic samples in terms
of style similarity, while retaining the target speaker's identity.

We study the issue of separating hadronic jets that contain bottom quarks
($b$-jets) from jets featuring light partons only. We develop a novel approach
to $b$-tagging that exploits the application of QCD-inspired jet substructure
observables such as one-dimensional jet angularities and the two-dimensional
primary Lund plane. We demonstrate that these observables can be used as inputs
to modern machine-learning algorithms to efficiently separate $b$-jets from
light ones. In order to test our tagging procedure, we consider simulated
events where a $Z$ boson is produced is association with jets and show that
using jet angularities as an input for a deep neural network, as well as using
images obtained from the primary Lund jet plane as input to a convolutional
neural network, one can achieve tagging accuracy comparable with the accuracy
of conventional track-based taggers. We argue that the complementary usage of
the track-based taggers together with the ones based upon QCD-inspired
observables could improve $b$-tagging accuracy.

A large number of current machine learning methods rely upon deep neural
networks. Yet, viewing neural networks as nonlinear dynamical systems, it
becomes quickly apparent that mathematically rigorously establishing certain
patterns generated by the nodes in the network is extremely difficult. Indeed,
it is well-understood in the nonlinear dynamics of complex systems that, even
in low-dimensional models, analytical techniques rooted in pencil-and-paper
approaches reach their limits quickly. In this work, we propose a completely
different perspective via the paradigm of rigorous numerical methods of
nonlinear dynamics. The idea is to use computer-assisted proofs to validate
mathematically the existence of nonlinear patterns in neural networks. As a
case study, we consider a class of recurrent neural networks, where we prove
via computer assistance the existence of several hundred Hopf bifurcation
points, their non-degeneracy, and hence also the existence of several hundred
periodic orbits. Our paradigm has the capability to rigorously verify complex
nonlinear behaviour of neural networks, which provides a first step to explain
the full abilities, as well as potential sensitivities, of machine learning
methods via computer-assisted proofs.

With the development of new sensors and monitoring devices, more sources of
data become available to be used as inputs for machine learning models. These
can on the one hand help to improve the accuracy of a model. On the other hand
however, combining these new inputs with historical data remains a challenge
that has not yet been studied in enough detail. In this work, we propose a
transfer-learning algorithm that combines the new and the historical data, that
is especially beneficial when the new data is scarce. We focus the approach on
the linear regression case, which allows us to conduct a rigorous theoretical
study on the benefits of the approach. We show that our approach is robust
against negative transfer-learning, and we confirm this result empirically with
real and simulated data.

While the class of Polynomial Nets demonstrates comparable performance to
neural networks (NN), it currently has neither theoretical generalization
characterization nor robustness guarantees. To this end, we derive new
complexity bounds for the set of Coupled CP-Decomposition (CCP) and Nested
Coupled CP-decomposition (NCP) models of Polynomial Nets in terms of the
$\ell_\infty$-operator-norm and the $\ell_2$-operator norm. In addition, we
derive bounds on the Lipschitz constant for both models to establish a
theoretical certificate for their robustness. The theoretical results enable us
to propose a principled regularization scheme that we also evaluate
experimentally in six datasets and show that it improves the accuracy as well
as the robustness of the models to adversarial perturbations. We showcase how
this regularization can be combined with adversarial training, resulting in

Learning data representations under uncertainty is an important task that
emerges in numerous machine learning applications. However, uncertainty
quantification (UQ) techniques are computationally intensive and become
prohibitively expensive for high-dimensional data. In this paper, we present a
novel surrogate model for representation learning and uncertainty
quantification, which aims to deal with data of moderate to high dimensions.
The proposed model combines a neural network approach for dimensionality
reduction of the (potentially high-dimensional) data, with a surrogate model
method for learning the data distribution. We first employ a variational
autoencoder (VAE) to learn a low-dimensional representation of the data
distribution. We then propose to harness polynomial chaos expansion (PCE)
formulation to map this distribution to the output target. The coefficients of
PCE are learned from the distribution representation of the training data using
a maximum mean discrepancy (MMD) approach. Our model enables us to (a) learn a
representation of the data, (b) estimate uncertainty in the high-dimensional
data system, and (c) match high order moments of the output distribution;
without any prior statistical assumptions on the data. Numerical experimental
results are presented to illustrate the performance of the proposed method.

In this work, we propose Regularization-by-Equivariance (REV), a novel
structure-adaptive regularization scheme for solving imaging inverse problems
under incomplete measurements. This regularization scheme utilizes the
equivariant structure in the physics of the measurements -- which is prevalent
in many inverse problems such as tomographic image reconstruction -- to
mitigate the ill-poseness of the inverse problem. Our proposed scheme can be
applied in a plug-and-play manner alongside with any classic first-order
optimization algorithm such as the accelerated gradient descent/FISTA for
simplicity and fast convergence. The numerical experiments in sparse-view X-ray
CT image reconstruction tasks demonstrate the effectiveness of our approach.

Although deep neural networks are typically computationally expensive to use,
technological advances in both the design of hardware platforms and of neural
network architectures, have made it possible to use powerful models on edge
devices. To enable widespread adoption of edge based machine learning, we
introduce a set of open-source tools that make it easy to deploy, update and
monitor machine learning models on a wide variety of edge devices. Our tools
bring the concept of containerization to the TinyML world. We propose to
package ML and application logic as containers called Runes to deploy onto edge
devices. The containerization allows us to target a fragmented
Internet-of-Things (IoT) ecosystem by providing a common platform for Runes to
run across devices.

Many popular algorithmic fairness measures depend on the joint distribution
of predictions, outcomes, and a sensitive feature like race or gender. These
measures are sensitive to distribution shift: a predictor which is trained to
satisfy one of these fairness definitions may become unfair if the distribution
changes. In performative prediction settings, however, predictors are precisely
intended to induce distribution shift. For example, in many applications in
criminal justice, healthcare, and consumer finance, the purpose of building a
predictor is to reduce the rate of adverse outcomes such as recidivism,
hospitalization, or default on a loan. We formalize the effect of such
predictors as a type of concept shift-a particular variety of distribution
shift-and show both theoretically and via simulated examples how this causes
predictors which are fair when they are trained to become unfair when they are
deployed. We further show how many of these issues can be avoided by using
fairness definitions that depend on counterfactual rather than observable
outcomes.

To adopt convolutional neural networks (CNN) for a range of
resource-constrained targets, it is necessary to compress the CNN models by
performing quantization, whereby precision representation is converted to a
lower bit representation. To overcome problems such as sensitivity of the
training dataset, high computational requirements, and large time consumption,
post-training quantization methods that do not require retraining have been
proposed. In addition, to compensate for the accuracy drop without retraining,
previous studies on post-training quantization have proposed several
complementary methods: calibration, schemes, clipping, granularity, and
mixed-precision. To generate a quantized model with minimal error, it is
necessary to study all possible combinations of the methods because each of
them is complementary and the CNN models have different characteristics.
However, an exhaustive or a heuristic search is either too time-consuming or
suboptimal. To overcome this challenge, we propose an auto-tuner known as
Quantune, which builds a gradient tree boosting model to accelerate the search
for the configurations of quantization and reduce the quantization error. We
evaluate and compare Quantune with the random, grid, and genetic algorithms.
The experimental results show that Quantune reduces the search time for
quantization by approximately 36.5x with an accuracy loss of 0.07 ~ 0.65%
across six CNN models, including the fragile ones (MobileNet, SqueezeNet, and
ShuffleNet). To support multiple targets and adopt continuously evolving
quantization works, Quantune is implemented on a full-fledged compiler for deep
learning as an open-sourced project.

We study computable PAC (CPAC) learning as introduced by Agarwal et al.
(2020). First, we consider the main open question of finding characterizations
of proper and improper CPAC learning. We give a characterization of a closely
related notion of strong CPAC learning, and provide a negative answer to the
COLT open problem posed by Agarwal et al. (2021) whether all decidably
representable VC classes are improperly CPAC learnable. Second, we consider
undecidability of (computable) PAC learnability. We give a simple general
argument to exhibit such ndecidability, and initiate a study of the
arithmetical complexity of learnability. We briefly discuss the relation to the
undecidability result of Ben-David et al. (2019), that motivated the work of
Agarwal et al.

The Omicron variant of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) has rapidly replaced the Delta variant as a dominating SARS-CoV-2
variant because of natural selection, which favors the variant with higher
infectivity and stronger vaccine breakthrough ability. Omicron has three
lineages or subvariants, BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3
(B.1.1.529.3). Among them, BA.1 is the currently prevailing subvariant. BA.2
shares 32 mutations with BA.1 but has 28 distinct ones. BA.3 shares most of its
mutations with BA.1 and BA.2 except for one. BA.2 is found to be able to
alarmingly reinfect patients originally infected by Omicron BA.1. An important
question is whether BA.2 or BA.3 will become a new dominating "variant of
concern". Currently, no experimental data has been reported about BA.2 and
BA.3. We construct a novel algebraic topology-based deep learning model trained
with tens of thousands of mutational and deep mutational data to systematically
evaluate BA.2's and BA.3's infectivity, vaccine breakthrough capability, and
antibody resistance. Our comparative analysis of all main variants namely,
Alpha, Beta, Gamma, Delta, Lambda, Mu, BA.1, BA.2, and BA.3, unveils that BA.2
is about 1.5 and 4.2 times as contagious as BA.1 and Delta, respectively. It is
also 30% and 17-fold more capable than BA.1 and Delta, respectively, to escape
current vaccines. Therefore, we project that Omicron BA.2 is on its path to
becoming the next dominating variant. We forecast that like Omicron BA.1, BA.2
will also seriously compromise most existing mAbs, except for sotrovimab
developed by GlaxoSmithKline.

In the last decade, research on artificial intelligence has seen rapid growth
with deep learning models, especially in the field of medical image
segmentation. Various studies demonstrated that these models have powerful
prediction capabilities and achieved similar results as clinicians. However,
recent studies revealed that the evaluation in image segmentation studies lacks
reliable model performance assessment and showed statistical bias by incorrect
metric implementation or usage. Thus, this work provides an overview and
interpretation guide on the following metrics for medical image segmentation
evaluation in binary as well as multi-class problems: Dice similarity
coefficient, Jaccard, Sensitivity, Specificity, Rand index, ROC curves, Cohen's
Kappa, and Hausdorff distance. As a summary, we propose a guideline for
standardized medical image segmentation evaluation to improve evaluation
quality, reproducibility, and comparability in the research field.

Bias-scalable analog computing is attractive for implementing machine
learning (ML) processors with distinct power-performance specifications. For
example, ML implementations for server workloads are focused on computational
throughput and faster training, whereas ML implementations for edge devices are
focused on energy-efficient inference. In this paper, we demonstrate the
implementation of bias-scalable analog computing circuits using a
generalization of the Margin Propagation (MP) principle called shape-based
analog computing (S-AC). The resulting S-AC core integrates several near-memory
compute elements, which include: (a) non-linear activation functions; (b)
inner-product compute circuits; and (c) a mixed-signal compressive memory.
Using measured results from prototypes fabricated in a 180nm CMOS process, we
demonstrate that the performance of computing modules remains robust to
transistor biasing and variations in temperature. In this paper, we also
demonstrate bias-scalability for a simple ML regression task.

In this paper, we study the resource allocation problem for an intelligent
reflecting surface (IRS)-assisted OFDM system. The system sum rate maximization
framework is formulated by jointly optimizing subcarrier allocation, base
station transmit beamforming and IRS phase shift. Considering the continuous
and discrete hybrid action space characteristics of the optimization variables,
we propose an efficient resource allocation algorithm combining multiple deep Q
networks (MDQN) and deep deterministic policy-gradient (DDPG) to deal with this
issue. In our algorithm, MDQN are employed to solve the problem of large
discrete action space, while DDPG is introduced to tackle the continuous action
allocation. Compared with the traditional approaches, our proposed MDQN-DDPG
based algorithm has the advantage of continuous behavior improvement through
learning from the environment. Simulation results demonstrate superior
performance of our design in terms of system sum rate compared with the

Short-term passenger flow prediction is an important but challenging task for
better managing urban rail transit (URT) systems. Some emerging deep learning
models provide good insights to improve short-term prediction accuracy.
However, there exist many complex spatiotemporal dependencies in URT systems.
Most previous methods only consider the absolute error between ground truth and
predictions as the optimization objective, which fails to account for spatial
and temporal constraints on the predictions. Furthermore, a large number of
existing prediction models introduce complex neural network layers to improve
accuracy while ignoring their training efficiency and memory occupancy,
decreasing the chances to be applied to the real world. To overcome these
limitations, we propose a novel deep learning-based spatiotemporal graph
generative adversarial network (STG-GAN) model with higher prediction accuracy,
higher efficiency, and lower memory occupancy to predict short-term passenger
flows of the URT network. Our model consists of two major parts, which are
optimized in an adversarial learning manner: (1) a generator network including
gated temporal conventional networks (TCN) and weight sharing graph convolution
networks (GCN) to capture structural spatiotemporal dependencies and generate
predictions with a relatively small computational burden; (2) a discriminator
network including a spatial discriminator and a temporal discriminator to
enhance the spatial and temporal constraints of the predictions. The STG-GAN is
evaluated on two large-scale real-world datasets from Beijing Subway. A
comparison with those of several state-of-the-art models illustrates its
superiority and robustness. This study can provide critical experience in
conducting short-term passenger flow predictions, especially from the
perspective of real-world applications.

Deep learning methods have gained popularity in high energy physics for fast
modeling of particle showers in detectors. Detailed simulation frameworks such
as the gold standard Geant4 are computationally intensive, and current deep
generative architectures work on discretized, lower resolution versions of the
detailed simulation. The development of models that work at higher spatial
resolutions is currently hindered by the complexity of the full simulation
data, and by the lack of simpler, more interpretable benchmarks. Our
contribution is SUPA, the SUrrogate PArticle propagation simulator, an
algorithm and software package for generating data by simulating simplified
particle propagation, scattering and shower development in matter. The
generation is extremely fast and easy to use compared to Geant4, but still
exhibits the key characteristics and challenges of the detailed simulation. We
support this claim experimentally by showing that performance of generative
models on data from our simulator reflects the performance on a dataset
generated with Geant4. The proposed simulator generates thousands of particle
showers per second on a desktop machine, a speed up of up to 6 orders of
magnitudes over Geant4, and stores detailed geometric information about the
shower propagation. SUPA provides much greater flexibility for setting initial
conditions and defining multiple benchmarks for the development of models.
Moreover, interpreting particle showers as point clouds creates a connection to
geometric machine learning and provides challenging and fundamentally new
datasets for the field.
  The code for SUPA is available at https://github.com/itsdaniele/SUPA.

Evolutionary computation has been shown to be a highly effective method for
training neural networks, particularly when employed at scale on CPU clusters.
Recent work have also showcased their effectiveness on hardware accelerators,
such as GPUs, but so far such demonstrations are tailored for very specific
tasks, limiting applicability to other domains. We present EvoJAX, a scalable,
general purpose, hardware-accelerated neuroevolution toolkit. Building on top
of the JAX library, our toolkit enables neuroevolution algorithms to work with
neural networks running in parallel across multiple TPU/GPUs. EvoJAX achieves
very high performance by implementing the evolution algorithm, neural network
and task all in NumPy, which is compiled just-in-time to run on accelerators.
We provide extensible examples of EvoJAX for a wide range of tasks, including
supervised learning, reinforcement learning and generative art. Since EvoJAX
can find solutions to most of these tasks within minutes on a single
accelerator, compared to hours or days when using CPUs, our toolkit can
significantly shorten the iteration cycle of evolutionary computation
experiments. EvoJAX is available at https://github.com/google/evojax

Estimating the probability with which a conditional branch instruction is
taken is an important analysis that enables many optimizations in modern
compilers. When using Profile Guided Optimizations (PGO), compilers are able to
make a good estimation of the branch probabilities. In the absence of profile
information, compilers resort to using heuristics for this purpose. In this
work, we propose learning branch probabilities from a large corpus of data
obtained from datacenter workloads.
  Using metrics including Root Mean Squared Error, Mean Absolute Error and
cross-entropy, we show that the machine learning model improves branch
probability estimation by 18-50% in comparison to compiler heuristics. This
translates to performance improvement of up to 8.1% on 24 out of a suite of 40
benchmarks with a 1% geomean improvement on the suite. This also results in
greater than 1.2% performance improvement in an important search application.

Data driven modeling based approaches have recently gained a lot of attention
in many challenging meteorological applications including weather element
forecasting. This paper introduces a novel data-driven predictive model based
on TransUNet for precipitation nowcasting task. The TransUNet model which
combines the Transformer and U-Net models has been previously successfully
applied in medical segmentation tasks. Here, TransUNet is used as a core model
and is further equipped with Convolutional Block Attention Modules (CBAM) and
Depthwise-separable Convolution (DSC). The proposed Attention Augmented
TransUNet (AA-TransUNet) model is evaluated on two distinct datasets: the Dutch
precipitation map dataset and the French cloud cover dataset. The obtained
results show that the proposed model outperforms other examined models on both
tested datasets. Furthermore, the uncertainty analysis of the proposed
AA-TransUNet is provided to give additional insights on its predictions.

Deep Neural Networks (DNNs) are widely used in many applications domains.
However, they require a vast amount of computations and memory accesses to
deliver outstanding accuracy. In this paper, we propose a scheme to predict
whether the output of each ReLu activated neuron will be a zero or a positive
number in order to skip the computation of those neurons that will likely
output a zero. Our predictor, named Mixture-of-Rookies, combines two
inexpensive components. The first one exploits the high linear correlation
between binarized (1-bit) and full-precision (8-bit) dot products, whereas the
second component clusters together neurons that tend to output zero at the same
time. We propose a novel clustering scheme based on the analysis of angles, as
the sign of the dot product of two vectors depends on the cosine of the angle
between them. We implement our hybrid zero output predictor on top of a
state-of-the-art DNN accelerator. Experimental results show that our scheme
introduces a small area overhead of 5.3% while achieving a speedup of 1.2x and
reducing energy consumption by 16.5% on average for a set of diverse DNNs.

Automatic Music Transcription, which consists in transforming an audio
recording of a musical performance into symbolic format, remains a difficult
Music Information Retrieval task. In this work, which focuses on piano
transcription, we propose a semi-supervised approach using low-rank matrix
factorization techniques, in particular Convolutive Nonnegative Matrix
Factorization. In the semi-supervised setting, only a single recording of each
individual notes is required. We show on the MAPS dataset that the proposed
semi-supervised CNMF method performs better than state-of-the-art low-rank
factorization techniques and a little worse than supervised deep learning
state-of-the-art methods, while however suffering from generalization issues.

Since the celebrated works of Russo and Zou (2016,2019) and Xu and Raginsky
(2017), it has been well known that the generalization error of supervised
learning algorithms can be bounded in terms of the mutual information between
their input and the output, given that the loss of any fixed hypothesis has a
subgaussian tail. In this work, we generalize this result beyond the standard
choice of Shannon's mutual information to measure the dependence between the
input and the output. Our main result shows that it is indeed possible to
replace the mutual information by any strongly convex function of the joint
input-output distribution, with the subgaussianity condition on the losses
replaced by a bound on an appropriately chosen norm capturing the geometry of
the dependence measure. This allows us to derive a range of generalization
bounds that are either entirely new or strengthen previously known ones.
Examples include bounds stated in terms of $p$-norm divergences and the
Wasserstein-2 distance, which are respectively applicable for heavy-tailed loss
distributions and highly smooth loss functions. Our analysis is entirely based
on elementary tools from convex analysis by tracking the growth of a potential
function associated with the dependence measure and the loss function.

Music Structure Analysis (MSA) consists in segmenting a music piece in
several distinct sections. We approach MSA within a compression framework,
under the hypothesis that the structure is more easily revealed by a simplified
representation of the original content of the song. More specifically, under
the hypothesis that MSA is correlated with similarities occurring at the bar
scale, this article introduces the use of linear and non-linear compression
schemes on barwise audio signals. Compressed representations capture the most
salient components of the different bars in the song and are then used to infer
the song structure using a dynamic programming algorithm. This work explores
both low-rank approximation models such as Principal Component Analysis or
Nonnegative Matrix Factorization and "piece-specific" Auto-Encoding Neural
Networks, with the objective to learn latent representations specific to a
given song. Such approaches do not rely on supervision nor annotations, which
are well-known to be tedious to collect and possibly ambiguous in MSA
description. In our experiments, several unsupervised compression schemes
achieve a level of performance comparable to that of state-of-the-art
supervised methods (for 3s tolerance) on the RWC-Pop dataset, showcasing the
importance of the barwise compression processing for MSA.

In this work, we present a new approach for fast tracking on multiwire
proportional chambers with neural networks. The tracking networks are developed
and adapted for the first-level trigger at hadron collider experiments. We use
Monte Carlo samples generated by Geant4 with a custom muon chamber, which
resembles part of the thin gap chambers from the ATLAS experiment, for training
and performance evaluations. The chamber has a total of seven gas gaps, where
the first and last gas gaps are displaced by ~1.5 m. Each gas gap has 50
channels with a size of 18-20 mm. Two neural network models are developed and
presented: a convolutional neural network and a neural network optimized for
the detector configuration of this study. In the latter network, a convolution
layer is provided for each of three groups formed from 2-3 gas gaps of the
chamber, and the outputs are fed into multilayer perceptrons in sequence. Both
networks are transformed into hardware description language and implemented in
Virtex UltraScale+ FPGA. The angular resolution is 2 mrad, which is comparable
to the maximum resolution of the detector estimated by the minimum chi2 method.
The latency achieved by the implemented firmware is less than 100 ns, and the
throughput rate is 160 MHz.

Federated learning (FL) is a feasible technique to learn personalized
recommendation models from decentralized user data. Unfortunately, federated
recommender systems are vulnerable to poisoning attacks by malicious clients.
Existing recommender system poisoning methods mainly focus on promoting the
recommendation chances of target items due to financial incentives. In fact, in
real-world scenarios, the attacker may also attempt to degrade the overall
performance of recommender systems. However, existing general FL poisoning
methods for degrading model performance are either ineffective or not concealed
in poisoning federated recommender systems. In this paper, we propose a simple
yet effective and covert poisoning attack method on federated recommendation,
named FedAttack. Its core idea is using globally hardest samples to subvert
model training. More specifically, the malicious clients first infer user
embeddings based on local user profiles. Next, they choose the candidate items
that are most relevant to the user embeddings as hardest negative samples, and
find the candidates farthest from the user embeddings as hardest positive
samples. The model gradients inferred from these poisoned samples are then
uploaded to the server for aggregation and model update. Since the behaviors of
malicious clients are somewhat similar to users with diverse interests, they
cannot be effectively distinguished from normal clients by the server.
Extensive experiments on two benchmark datasets show that FedAttack can
effectively degrade the performance of various federated recommender systems,
meanwhile cannot be effectively detected nor defended by many existing methods.

A good personalized product search (PPS) system should not only focus on
retrieving relevant products, but also consider user personalized preference.
Recent work on PPS mainly adopts the representation learning paradigm, e.g.,
learning representations for each entity (including user, product and query)
from historical user behaviors (aka. user-product-query interactions). However,
we argue that existing methods do not sufficiently exploit the crucial
collaborative signal, which is latent in historical interactions to reveal the
affinity between the entities. Collaborative signal is quite helpful for
generating high-quality representation, exploiting which would benefit the
representation learning of one node from its connected nodes.
  To tackle this limitation, in this work, we propose a new model IHGNN for
personalized product search. IHGNN resorts to a hypergraph constructed from the
historical user-product-query interactions, which could completely preserve
ternary relations and express collaborative signal based on the topological
structure. On this basis, we develop a specific interactive hypergraph neural
network to explicitly encode the structure information (i.e., collaborative
signal) into the embedding process. It collects the information from the
hypergraph neighbors and explicitly models neighbor feature interaction to
enhance the representation of the target entity. Extensive experiments on three
real-world datasets validate the superiority of our proposal over the

Off-Policy Evaluation (OPE) serves as one of the cornerstones in
Reinforcement Learning (RL). Fitted Q Evaluation (FQE) with various function
approximators, especially deep neural networks, has gained practical success.
While statistical analysis has proved FQE to be minimax-optimal with tabular,
linear and several nonparametric function families, its practical performance
with more general function approximator is less theoretically understood. We
focus on FQE with general differentiable function approximators, making our
theory applicable to neural function approximations. We approach this problem
using the Z-estimation theory and establish the following results: The FQE
estimation error is asymptotically normal with explicit variance determined
jointly by the tangent space of the function class at the ground truth, the
reward structure, and the distribution shift due to off-policy learning; The
finite-sample FQE error bound is dominated by the same variance term, and it
can also be bounded by function class-dependent divergence, which measures how
the off-policy distribution shift intertwines with the function approximator.
In addition, we study bootstrapping FQE estimators for error distribution
inference and estimating confidence intervals, accompanied by a Cramer-Rao
lower bound that matches our upper bounds. The Z-estimation analysis provides a
generalizable theoretical framework for studying off-policy estimation in RL
and provides sharp statistical theory for FQE with differentiable function
approximators.

Performance of convolutional neural networks (CNNs) in image analysis tasks
is often marred in the presence of acquisition-related distribution shifts
between training and test images. Recently, it has been proposed to tackle this
problem by fine-tuning trained CNNs for each test image. Such
test-time-adaptation (TTA) is a promising and practical strategy for improving
robustness to distribution shifts as it requires neither data sharing between
institutions nor annotating additional data. Previous TTA methods use a helper
model to increase similarity between outputs and/or features extracted from a
test image with those of the training images. Such helpers, which are typically
modeled using CNNs, can be task-specific and themselves vulnerable to
distribution shifts in their inputs. To overcome these problems, we propose to
carry out TTA by matching the feature distributions of test and training
images, as modelled by a field-of-experts (FoE) prior. FoEs model complicated
probability distributions as products of many simpler expert distributions. We
use 1D marginal distributions of a trained task CNN's features as experts in
the FoE model. Further, we compute principal components of patches of the task
CNN's features, and consider the distributions of PCA loadings as additional
experts. We validate the method on 5 MRI segmentation tasks (healthy tissues in
4 anatomical regions and lesions in 1 one anatomy), using data from 17 clinics,
and on a MRI registration task, using data from 3 clinics. We find that the
proposed FoE-based TTA is generically applicable in multiple tasks, and
outperforms all previous TTA methods for lesion segmentation. For healthy
tissue segmentation, the proposed method outperforms other task-agnostic
methods, but a previous TTA method which is specifically designed for
segmentation performs the best for most of the tested datasets. Our code is

Classifying the state of the atmosphere into a finite number of large-scale
circulation regimes is a popular way of investigating teleconnections, the
predictability of severe weather events, and climate change. Here, we
investigate a supervised machine learning approach based on deformable
convolutional neural networks (deCNNs) and transfer learning to forecast the
North Atlantic-European weather regimes during extended boreal winter for 1 to
15 days into the future. We apply state-of-the-art interpretation techniques
from the machine learning literature to attribute particular regions of
interest or potential teleconnections relevant for any given weather cluster
prediction or regime transition. We demonstrate superior forecasting
performance relative to several classical meteorological benchmarks, as well as
logistic regression and random forests. Due to its wider field of view, we also
observe deCNN achieving considerably better performance than regular
convolutional neural networks at lead times beyond 5-6 days. Finally, we find
transfer learning to be of paramount importance, similar to previous
data-driven atmospheric forecasting studies.

In recent years, deep learning techniques have been introduced into the field
of trajectory optimization to improve convergence and speed. Training such
models requires large trajectory datasets. However, the convergence of low
thrust (LT) optimizations is unpredictable before the optimization process
ends. For randomly initialized low thrust transfer data generation, most of the
computation power will be wasted on optimizing infeasible low thrust transfers,
which leads to an inefficient data generation process. This work proposes a
deep neural network (DNN) classifier to accurately identify feasible LT
transfer prior to the optimization process. The DNN-classifier achieves an
overall accuracy of 97.9%, which has the best performance among the tested
algorithms. The accurate low-thrust trajectory feasibility identification can
avoid optimization on undesired samples, so that the majority of the optimized
samples are LT trajectories that converge. This technique enables efficient
dataset generation for different mission scenarios with different spacecraft

In modern data analysis, sparse model selection becomes inevitable once the
number of predictors variables is very high. It is well-known that model
selection procedures like the Lasso or Boosting tend to overfit on real data.
The celebrated Stability Selection overcomes these weaknesses by aggregating
models, based on subsamples of the training data, followed by choosing a stable
predictor set which is usually much sparser than the predictor sets from the
raw models. The standard Stability Selection is based on a global criterion,
namely the per-family error rate, while additionally requiring expert knowledge
to suitably configure the hyperparameters. Since model selection depends on the
loss function, i.e., predictor sets selected w.r.t. some particular loss
function differ from those selected w.r.t. some other loss function, we propose
a Stability Selection variant which respects the chosen loss function via an
additional validation step based on out-of-sample validation data, optionally
enhanced with an exhaustive search strategy. Our Stability Selection variants
are widely applicable and user-friendly. Moreover, our Stability Selection
variants can avoid the issue of severe underfitting which affects the original
Stability Selection for noisy high-dimensional data, so our priority is not to
avoid false positives at all costs but to result in a sparse stable model with
which one can make predictions. Experiments where we consider both regression
and binary classification and where we use Boosting as model selection
algorithm reveal a significant precision improvement compared to raw Boosting
models while not suffering from any of the mentioned issues of the original
Stability Selection.

Experimental measurements have reported ultra-fast and radius-dependent water
transport in carbon nanotubes which are absent in boron nitride nanotubes.
Despite considerable effort, the origin of this contrasting (and fascinating)
behaviour is not understood. Here, with the aid of machine learning-based
molecular dynamics simulations that deliver first-principles accuracy, we
investigate water transport in single-wall carbon and boron nitride nanotubes.
Our simulations reveal a large, radius-dependent hydrodynamic slippage on both
materials with water experiencing indeed a $\approx 5$ times lower friction on
carbon surfaces compared to boron nitride. Analysis of the diffusion mechanisms
across the two materials reveals that the fast water transport on carbon is
governed by facile oxygen motion, whereas the higher friction on boron nitride
arises from specific hydrogen-nitrogen interactions. This work not only
delivers a clear reference of unprecedented accuracy for water flow in
single-wall nanotubes, but also provides detailed mechanistic insight into its
radius and material dependence for future technological application.

We propose the first accurate digitization and color reconstruction process
for historical lenticular film that is robust to artifacts. Lenticular films
emerged in the 1920s and were one of the first technologies that permitted to
capture full color information in motion. The technology leverages an RGB
filter and cylindrical lenticules embossed on the film surface to encode the
color in the horizontal spatial dimension of the image. To project the pictures
the encoding process was reversed using an appropriate analog device. In this
work, we introduce an automated, fully digital pipeline to process the scan of
lenticular films and colorize the image. Our method merges deep learning with a
model-based approach in order to maximize the performance while making sure
that the reconstructed colored images truthfully match the encoded color
information. Our model employs different strategies to achieve an effective
color reconstruction, in particular (i) we use data augmentation to create a
robust lenticule segmentation network, (ii) we fit the lenticules raster
prediction to obtain a precise vectorial lenticule localization, and (iii) we
train a colorization network that predicts interpolation coefficients in order
to obtain a truthful colorization. We validate the proposed method on a
lenticular film dataset and compare it to other approaches. Since no colored
groundtruth is available as reference, we conduct a user study to validate our
method in a subjective manner. The results of the study show that the proposed
method is largely preferred with respect to other existing and baseline
methods.

[Context] The COVID-19 pandemic has had a disruptive impact on how people
work and collaborate across all global economic sectors, including the software
business. While remote working is not new for software engineers, forced
Work-from-home situations to come with both constraints, limitations, and
opportunities for individuals, software teams and software companies. As the
"new normal" for working might be based on the current state of Work From Home
(WFH), it is useful to understand what has happened and learn from that.
[Objective] The goal of this study is to gain insights on how their WFH
environment impacts software projects and software companies. We are also
interested in understanding if the impact differs between software startups and
established companies. [Method] We conducted a global-scale, cross-sectional
survey during spring and summer 2021. Our results are based on quantitative and
qualitative analysis of 297 valid responses. [Results] We observed a mixed
perception of the impact of WFH on software project management, resilience, and
innovation. Certain patterns on WFH, control and coordination mechanisms and
collaborative tools are observed globally. We find that team, agility and
leadership are the three most important factors for achieving resilience during
the pandemic. Although startups do not perceive the impact of WFH differently,
there is a difference between engineers who work in a small team context and
those who work in a large team context. [Conclusion] The result suggests a
contingency approach in studying and improving WFH practices and environment in
the future software industry.

In chaotic dynamical systems such as the weather, prediction errors grow
faster in some situations than in others. Real-time knowledge about the error
growth could enable strategies to adjust the modelling and forecasting
infrastructure on-the-fly to increase accuracy and/or reduce computation time.
For example one could change the spatio-temporal resolution of the numerical
model, locally increase the data availability, etc. Local Lyapunov exponents
are known indicators of the rate at which very small prediction errors grow
over a finite time interval. However, their computation is very expensive: it
requires maintaining and evolving a tangent linear model, orthogonalisation
algorithms and storing large matrices.
  In this feasibility study, we investigate the accuracy of supervised machine
learning in estimating the imminent local Lyapunov exponents, from input of
current and recent time steps of the system trajectory, as an alternative to
the classical method. We test four popular supervised learning algorithms:
regression trees, multilayer perceptrons, convolutional neural networks and
long short-term memory networks. Experiments are conducted on two
low-dimensional chaotic systems of ordinary differential equations, the Lorenz
63 and the R\"ossler models. We find that on average the machine learning
algorithms predict the stable local Lyapunov exponent accurately, the unstable
exponent reasonably accurately, and the neutral exponent only somewhat
accurately. We show that greater prediction accuracy is associated with local
homogeneity of the local Lyapunov exponents on the system attractor.
Importantly, the situations in which (forecast) errors grow fastest are not
necessarily the same as those where it is more difficult to predict local
Lyapunov exponents with machine learning.

The importance of explainability in AI has become a pressing concern, for
which several explainable AI (XAI) approaches have been recently proposed.
However, most of the available XAI techniques are post-hoc methods, which
however may be only partially reliable, as they do not reflect exactly the
state of the original models. Thus, a more direct way for achieving XAI is
through interpretable (also called glass-box) models. These models have been
shown to obtain comparable (and, in some cases, better) performance with
respect to black-boxes models in various tasks such as classification and
reinforcement learning. However, they struggle when working with raw data,
especially when the input dimensionality increases and the raw inputs alone do
not give valuable insights on the decision-making process. Here, we propose to
use end-to-end pipelines composed of multiple interpretable models co-optimized
by means of evolutionary algorithms, that allows us to decompose the
decision-making process into two parts: computing high-level features from raw
data, and reasoning on the extracted high-level features. We test our approach
in reinforcement learning environments from the Atari benchmark, where we
obtain comparable results (with respect to black-box approaches) in settings
without stochastic frame-skipping, while performance degrades in frame-skipping
settings.

Using convolutional neural networks for 360images can induce sub-optimal
performance due to distortions entailed by a planar projection. The distortion
gets deteriorated when a rotation is applied to the 360image. Thus, many
researches based on convolutions attempt to reduce the distortions to learn
accurate representation. In contrast, we leverage the transformer architecture
to solve image classification problems for 360images. Using the proposed
transformer for 360images has two advantages. First, our method does not
require the erroneous planar projection process by sampling pixels from the
sphere surface. Second, our sampling method based on regular polyhedrons makes
low rotation equivariance errors, because specific rotations can be reduced to
permutations of faces. In experiments, we validate our network on two aspects,
as follows. First, we show that using a transformer with highly uniform
sampling methods can help reduce the distortion. Second, we demonstrate that
the transformer architecture can achieve rotation equivariance on specific
rotations. We compare our method to other state-of-the-art algorithms using the
SPH-MNIST, SPH-CIFAR, and SUN360 datasets and show that our method is
competitive with other methods.

The performance of graph representation learning is affected by the quality
of graph input. While existing research usually pursues a globally smoothed
graph embedding, we believe the rarely observed anomalies are as well harmful
to an accurate prediction. This work establishes a graph learning scheme that
automatically detects (locally) corrupted feature attributes and recovers
robust embedding for prediction tasks. The detection operation leverages a
graph autoencoder, which does not make any assumptions about the distribution
of the local corruptions. It pinpoints the positions of the anomalous node
attributes in an unbiased mask matrix, where robust estimations are recovered
with sparsity promoting regularizer. The optimizer approaches a new embedding
that is sparse in the framelet domain and conditionally close to input
observations. Extensive experiments are provided to validate our proposed model
can recover a robust graph representation from black-box poisoning and achieve
excellent performance.

Contrastive learning is a method of learning visual representations by
training Deep Neural Networks (DNNs) to increase the similarity between
representations of positive pairs (transformations of the same image) and
reduce the similarity between representations of negative pairs
(transformations of different images). Here we explore Energy-Based Contrastive
Learning (EBCLR) that leverages the power of generative learning by combining
contrastive learning with Energy-Based Models (EBMs). EBCLR can be
theoretically interpreted as learning the joint distribution of positive pairs,
and it shows promising results on small and medium-scale datasets such as
MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100. Specifically, we find EBCLR
demonstrates from X4 up to X20 acceleration compared to SimCLR and MoCo v2 in
terms of training epochs. Furthermore, in contrast to SimCLR, we observe EBCLR
achieves nearly the same performance with 254 negative pairs (batch size 128)
and 30 negative pairs (batch size 16) per positive pair, demonstrating the
robustness of EBCLR to small numbers of negative pairs. Hence, EBCLR provides a
novel avenue for improving contrastive learning methods that usually require
large datasets with a significant number of negative pairs per iteration to
achieve reasonable performance on downstream tasks. Code:
https://github.com/1202kbs/EBCLR

We analyzed the invariant mass spectrum of near-threshold exotic states for
one-channel candidates with a deep neural network. It can extract the
scattering length and effective range, which would shed light on the nature of
given states, from the experimental mass spectrum. As an application, the mass
spectrum of the $X(3872)$ and the $T_{cc}^+$ are studied. The obtained
scattering lengths, effective ranges, and most relevant thresholds are
consistent with those from fitting to the experimental data. The advantage of
the neural network is that it is more stable than the fitting, especially for
low-statistic data. The network, which provides another way to analyze the
experimental data, can also be applied to other one-channel near-threshold
exotic candidates.

Current spatiotemporal deep learning approaches to Magnetic Resonance
Fingerprinting (MRF) build artefact-removal models customised to a particular
k-space subsampling pattern which is used for fast (compressed) acquisition.
This may not be useful when the acquisition process is unknown during training
of the deep learning model and/or changes during testing time. This paper
proposes an iterative deep learning plug-and-play reconstruction approach to
MRF which is adaptive to the forward acquisition process. Spatiotemporal image
priors are learned by an image denoiser i.e. a Convolutional Neural Network
(CNN), trained to remove generic white gaussian noise (not a particular
subsampling artefact) from data. This CNN denoiser is then used as a
data-driven shrinkage operator within the iterative reconstruction algorithm.
This algorithm with the same denoiser model is then tested on two simulated
acquisition processes with distinct subsampling patterns. The results show
consistent de-aliasing performance against both acquisition schemes and
accurate mapping of tissues' quantitative bio-properties. Software available:
https://github.com/ketanfatania/QMRI-PnP-Recon-POC

Understanding the functional principles of information processing in deep
neural networks continues to be a challenge, in particular for networks with
trained and thus non-random weights. To address this issue, we study the
mapping between probability distributions implemented by a deep feed-forward
network. We characterize this mapping as an iterated transformation of
distributions, where the non-linearity in each layer transfers information
between different orders of correlation functions. This allows us to identify
essential statistics in the data, as well as different information
representations that can be used by neural networks. Applied to an XOR task and
to MNIST, we show that correlations up to second order predominantly capture
the information processing in the internal layers, while the input layer also
extracts higher-order correlations from the data. This analysis provides a
quantitative and explainable perspective on classification.

This paper evaluates a wide range of audio-based deep learning frameworks
applied to the breathing, cough, and speech sounds for detecting COVID-19. In
general, the audio recording inputs are transformed into low-level spectrogram
features, then they are fed into pre-trained deep learning models to extract
high-level embedding features. Next, the dimension of these high-level
embedding features are reduced before finetuning using Light Gradient Boosting
Machine (LightGBM) as a back-end classification. Our experiments on the Second
DiCOVA Challenge achieved the highest Area Under the Curve (AUC), F1 score,
sensitivity score, and specificity score of 89.03%, 64.41%, 63.33%, and 95.13%,
respectively. Based on these scores, our method outperforms the
state-of-the-art systems, and improves the challenge baseline by 4.33%, 6.00%
and 8.33% in terms of AUC, F1 score and sensitivity score, respectively.

Statistical analysis is increasingly confronted with complex data from
general metric spaces, such as symmetric positive definite matrix-valued data
and probability distribution functions. [47] and [17] establish a general
paradigm of Fr\'echet regression with complex metric space valued responses and
Euclidean predictors. However, their proposed local Fr\'echet regression
approach involves nonparametric kernel smoothing and suffers from the curse of
dimensionality. To address this issue, we in this paper propose a novel random
forests weighted local Fr\'echet regression paradigm. The main mechanism of our
approach relies on the adaptive kernels generated by random forests. Our first
method utilizes these weights as the local average to solve the Fr\'echet mean,
while the second method performs local linear Fr\'echet regression, making both
methods locally adaptive. Our proposals significantly improve existing
Fr\'echet regression methods. Based on the theory of infinite order U-processes
and infinite order Mmn-estimator, we establish the consistency, rate of
convergence, and asymptotic normality for our proposed random forests weighted
Fr\'echet regression estimator, which covers the current large sample theory of
random forests with Euclidean responses as a special case. Numerical studies
show the superiority of our proposed two methods for Fr\'echet regression with
several commonly encountered types of responses such as probability
distribution functions, symmetric positive definite matrices, and sphere data.
The practical merits of our proposals are also demonstrated through the
application to the human mortality distribution data.

We present our submission for the configuration task of the Machine Learning
for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition. The
configuration task is to predict a good configuration of the open-source solver
SCIP to solve a mixed integer linear program (MILP) efficiently. We pose this
task as a supervised learning problem: First, we compile a large dataset of the
solver performance for various configurations and all provided MILP instances.
Second, we use this data to train a graph neural network that learns to predict
a good configuration for a specific instance. The submission was tested on the
three problem benchmarks of the competition and improved solver performance
over the default by 12% and 35% and 8% across the hidden test instances. We
ranked 3rd out of 15 on the global leaderboard and won the student leaderboard.
We make our code publicly available at
\url{https://github.com/RomeoV/ml4co-competition} .

Knowledge graph embedding (KGE) models learn the representation of entities
and relations in knowledge graphs. Distance-based methods show promising
performance on link prediction task, which predicts the result by the distance
between two entity representations. However, most of these methods represent
the head entity and tail entity separately, which limits the model capacity. We
propose a novel distance-based method named InterHT that allows the head and
tail entities to interact better and get better entity representation.
Experimental results show that our proposed method achieves the best results on
ogbl-wikikg2 dataset.

Vector Quantized-Variational AutoEncoders (VQ-VAE) are generative models
based on discrete latent representations of the data, where inputs are mapped
to a finite set of learned embeddings.To generate new samples, an
autoregressive prior distribution over the discrete states must be trained
separately. This prior is generally very complex and leads to slow generation.
In this work, we propose a new model to train the prior and the encoder/decoder
networks simultaneously. We build a diffusion bridge between a continuous coded
vector and a non-informative prior distribution. The latent discrete states are
then given as random functions of these continuous vectors. We show that our
model is competitive with the autoregressive prior on the mini-Imagenet and
CIFAR dataset and is efficient in both optimization and sampling. Our framework
also extends the standard VQ-VAE and enables end-to-end training.

Cross Domain Recommendation (CDR) has been popularly studied to alleviate the
cold-start and data sparsity problem commonly existed in recommender systems.
CDR models can improve the recommendation performance of a target domain by
leveraging the data of other source domains. However, most existing CDR models
assume information can directly 'transfer across the bridge', ignoring the
privacy issues. To solve the privacy concern in CDR, in this paper, we propose
a novel two stage based privacy-preserving CDR framework (PriCDR). In the first
stage, we propose two methods, i.e., Johnson-Lindenstrauss Transform (JLT)
based and Sparse-awareJLT (SJLT) based, to publish the rating matrix of the
source domain using differential privacy. We theoretically analyze the privacy
and utility of our proposed differential privacy based rating publishing
methods. In the second stage, we propose a novel heterogeneous CDR model
(HeteroCDR), which uses deep auto-encoder and deep neural network to model the
published source rating matrix and target rating matrix respectively. To this
end, PriCDR can not only protect the data privacy of the source domain, but
also alleviate the data sparsity of the source domain. We conduct experiments
on two benchmark datasets and the results demonstrate the effectiveness of our
proposed PriCDR and HeteroCDR.

Satellite imagery is now widely used in the defense sector for monitoring
locations of interest. Although the increasing amount of data enables pattern
identification and therefore prediction, carrying this task manually is hardly
feasible. We hereby propose a cased-based reasoning approach for automatic
prediction of rare events on strategic sites. This method allows direct
incorporation of expert knowledge, and is adapted to irregular time series and
small-size datasets. Experiments are carried out on two use-cases using real
satellite images: the prediction of submarines arrivals and departures from a
naval base, and the forecasting of imminent rocket launches on two space bases.
The proposed method significantly outperforms a random selection of reference
cases on these challenging applications, showing its strong potential.

The earth observation industry provides satellite imagery with high spatial
resolution and short revisit time. To allow efficient operational employment of
these images, automating certain tasks has become necessary. In the defense
domain, aircraft detection on satellite imagery is a valuable tool for
analysts. Obtaining high performance detectors on such a task can only be
achieved by leveraging deep learning and thus us-ing a large amount of labeled
data. To obtain labels of a high enough quality, the knowledge of military
experts is needed.We propose a hybrid clustering active learning method to
select the most relevant data to label, thus limiting the amount of data
required and further improving the performances. It combines diversity- and
uncertainty-based active learning selection methods. For aircraft detection by
segmentation, we show that this method can provide better or competitive
results compared to other active learning methods.

Taxonomies are fundamental to many real-world applications in various
domains, serving as structural representations of knowledge. To deal with the
increasing volume of new concepts needed to be organized as taxonomies,
researchers turn to automatically completion of an existing taxonomy with new
concepts. In this paper, we propose TaxoEnrich, a new taxonomy completion
framework, which effectively leverages both semantic features and structural
information in the existing taxonomy and offers a better representation of
candidate position to boost the performance of taxonomy completion.
Specifically, TaxoEnrich consists of four components: (1)
taxonomy-contextualized embedding which incorporates both semantic meanings of
concept and taxonomic relations based on powerful pretrained language models;
(2) a taxonomy-aware sequential encoder which learns candidate position
representations by encoding the structural information of taxonomy; (3) a
query-aware sibling encoder which adaptively aggregates candidate siblings to
augment candidate position representations based on their importance to the
query-position matching; (4) a query-position matching model which extends
existing work with our new candidate position representations. Extensive
experiments on four large real-world datasets from different domains show that
\TaxoEnrich achieves the best performance among all evaluation metrics and
outperforms previous state-of-the-art methods by a large margin.

We present PVSeRF, a learning framework that reconstructs neural radiance
fields from single-view RGB images, for novel view synthesis. Previous
solutions, such as pixelNeRF, rely only on pixel-aligned features and suffer
from feature ambiguity issues. As a result, they struggle with the
disentanglement of geometry and appearance, leading to implausible geometries
and blurry results. To address this challenge, we propose to incorporate
explicit geometry reasoning and combine it with pixel-aligned features for
radiance field prediction. Specifically, in addition to pixel-aligned features,
we further constrain the radiance field learning to be conditioned on i)
voxel-aligned features learned from a coarse volumetric grid and ii) fine
surface-aligned features extracted from a regressed point cloud. We show that
the introduction of such geometry-aware features helps to achieve a better
disentanglement between appearance and geometry, i.e. recovering more accurate
geometries and synthesizing higher quality images of novel views. Extensive
experiments against state-of-the-art methods on ShapeNet benchmarks demonstrate
the superiority of our approach for single-image novel view synthesis.

Radio Frequency (RF) breakdowns are one of the most prevalent limiting
factors in RF cavities for particle accelerators. During a breakdown, field
enhancement associated with small deformations on the cavity surface results in
electrical arcs. Such arcs lead to beam aborts, reduce machine availability and
can cause irreparable damage on the RF cavity surface. In this paper, we
propose a machine learning strategy to discover breakdown precursors in CERN's
Compact Linear Collider (CLIC) accelerating structures. By interpreting the
parameters of the learned models with explainable Artificial Intelligence (AI),
we reverse-engineer physical properties for deriving fast, reliable, and simple
rule based models. Based on 6 months of historical data and dedicated
experiments, our models show fractions of data with high influence on the
occurrence of breakdowns. Specifically, it is shown that in many cases a rise
of the vacuum pressure is observed before a breakdown is detected with the
current interlock sensors.

Deep imitation learning is a promising approach that does not require
hard-coded control rules in autonomous robot manipulation. The current
applications of deep imitation learning to robot manipulation have been limited
to reactive control based on the states at the current time step. However,
future robots will also be required to solve tasks utilizing their memory
obtained by experience in complicated environments (e.g., when the robot is
asked to find a previously used object on a shelf). In such a situation, simple
deep imitation learning may fail because of distractions caused by complicated
environments. We propose that gaze prediction from sequential visual input
enables the robot to perform a manipulation task that requires memory. The
proposed algorithm uses a Transformer-based self-attention architecture for the
gaze estimation based on sequential data to implement memory. The proposed
method was evaluated with a real robot multi-object manipulation task that
requires memory of the previous states.

In this paper, we analyze zero-shot taxonomy learning methods which are based
on distilling knowledge from language models via prompting and sentence
scoring. We show that, despite their simplicity, these methods outperform some
supervised strategies and are competitive with the current state-of-the-art
under adequate conditions. We also show that statistical and linguistic
properties of prompts dictate downstream performance.

Two central problems in Stochastic Optimization are Min Sum Set Cover and
Pandora's Box. In Pandora's Box, we are presented with $n$ boxes, each
containing an unknown value and the goal is to open the boxes in some order to
minimize the sum of the search cost and the smallest value found. Given a
distribution of value vectors, we are asked to identify a near-optimal search
order. Min Sum Set Cover corresponds to the case where values are either 0 or
infinity. In this work, we study the case where the value vectors are not drawn
from a distribution but are presented to a learner in an online fashion. We
present a computationally efficient algorithm that is constant-competitive
against the cost of the optimal search order. We extend our results to a bandit
setting where only the values of the boxes opened are revealed to the learner
after every round. We also generalize our results to other commonly studied
variants of Pandora's Box and Min Sum Set Cover that involve selecting more
than a single value subject to a matroid constraint.

Value function decomposition is becoming a popular rule of thumb for scaling
up multi-agent reinforcement learning (MARL) in cooperative games. For such a
decomposition rule to hold, the assumption of the individual-global max (IGM)
principle must be made; that is, the local maxima on the decomposed value
function per every agent must amount to the global maximum on the joint value
function. This principle, however, does not have to hold in general. As a
result, the applicability of value decomposition algorithms is concealed and
their corresponding convergence properties remain unknown. In this paper, we
make the first effort to answer these questions. Specifically, we introduce the
set of cooperative games in which the value decomposition methods find their
validity, which is referred as decomposable games. In decomposable games, we
theoretically prove that applying the multi-agent fitted Q-Iteration algorithm
(MA-FQI) will lead to an optimal Q-function. In non-decomposable games, the
estimated Q-function by MA-FQI can still converge to the optimum under the
circumstance that the Q-function needs projecting into the decomposable
function space at each iteration. In both settings, we consider value function
representations by practical deep neural networks and derive their
corresponding convergence rates. To summarize, our results, for the first time,
offer theoretical insights for MARL practitioners in terms of when value
decomposition algorithms converge and why they perform well.

We study a novel setting in offline reinforcement learning (RL) where a
number of distributed machines jointly cooperate to solve the problem but only
one single round of communication is allowed and there is a budget constraint
on the total number of information (in terms of bits) that each machine can
send out. For value function prediction in contextual bandits, and both
episodic and non-episodic MDPs, we establish information-theoretic lower bounds
on the minimax risk for distributed statistical estimators; this reveals the
minimum amount of communication required by any offline RL algorithms.
Specifically, for contextual bandits, we show that the number of bits must
scale at least as $\Omega(AC)$ to match the centralised minimax optimal rate,
where $A$ is the number of actions and $C$ is the context dimension; meanwhile,
we reach similar results in the MDP settings. Furthermore, we develop learning
algorithms based on least-squares estimates and Monte-Carlo return estimates
and provide a sharp analysis showing that they can achieve optimal risk up to
logarithmic factors. Additionally, we also show that temporal difference is
unable to efficiently utilise information from all available devices under the
single-round communication setting due to the initial bias of this method. To
our best knowledge, this paper presents the first minimax lower bounds for
distributed offline RL problems.

Subspace clustering is a classical technique that has been widely used for
human motion segmentation and other related tasks. However, existing
segmentation methods often cluster data without guidance from prior knowledge,
resulting in unsatisfactory segmentation results. To this end, we propose a
novel Consistency and Diversity induced human Motion Segmentation (CDMS)
algorithm. Specifically, our model factorizes the source and target data into
distinct multi-layer feature spaces, in which transfer subspace learning is
conducted on different layers to capture multi-level information. A
multi-mutual consistency learning strategy is carried out to reduce the domain
gap between the source and target data. In this way, the domain-specific
knowledge and domain-invariant properties can be explored simultaneously.
Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion
(HSIC) is introduced to ensure the diversity of multi-level subspace
representations, which enables the complementarity of multi-level
representations to be explored to boost the transfer learning performance.
Moreover, to preserve the temporal correlations, an enhanced graph regularizer
is imposed on the learned representation coefficients and the multi-level
representations of the source data. The proposed model can be efficiently
solved using the Alternating Direction Method of Multipliers (ADMM) algorithm.
Extensive experimental results on public human motion datasets demonstrate the
effectiveness of our method against several state-of-the-art approaches.

Federated learning (FL) trains a global model across a number of
decentralized users, each with a local dataset. Compared to traditional
centralized learning, FL does not require direct access to local datasets and
thus aims to mitigate data privacy concerns. However, data privacy leakage in
FL still exists due to inference attacks, including membership inference,
property inference, and data inversion. In this work, we propose a new type of
privacy inference attack, coined Preference Profiling Attack (PPA), that
accurately profiles the private preferences of a local user, e.g., most liked
(disliked) items from the client's online shopping and most common expressions
from the user's selfies. In general, PPA can profile top-k (i.e., k = 1, 2, 3
and k = 1 in particular) preferences contingent on the local client (user)'s
characteristics. Our key insight is that the gradient variation of a local
user's model has a distinguishable sensitivity to the sample proportion of a
given class, especially the majority (minority) class. By observing a user
model's gradient sensitivity to a class, PPA can profile the sample proportion
of the class in the user's local dataset, and thus the user's preference of the
class is exposed. The inherent statistical heterogeneity of FL further
facilitates PPA. We have extensively evaluated the PPA's effectiveness using
four datasets (MNIST, CIFAR10, RAF-DB and Products-10K). Our results show that
PPA achieves 90% and 98% top-1 attack accuracy to the MNIST and CIFAR10,
respectively. More importantly, in real-world commercial scenarios of shopping
(i.e., Products-10K) and social network (i.e., RAF-DB), PPA gains a top-1
attack accuracy of 78% in the former case to infer the most ordered items
(i.e., as a commercial competitor), and 88% in the latter case to infer a
victim user's most often facial expressions, e.g., disgusted.

Methods that extract policy primitives from offline demonstrations using deep
generative models have shown promise at accelerating reinforcement learning(RL)
for new tasks. Intuitively, these methods should also help to trainsafeRLagents
because they enforce useful skills. However, we identify these techniques are
not well equipped for safe policy learning because they ignore negative
experiences(e.g., unsafe or unsuccessful), focusing only on positive
experiences, which harms their ability to generalize to new tasks safely.
Rather, we model the latentsafetycontextusing principled contrastive training
on an offline dataset of demonstrations from many tasks, including both
negative and positive experiences. Using this late variable, our RL framework,
SAFEty skill pRiors (SAFER) extracts task-specific safe primitive skills to
safely and successfully generalize to new tasks. In the inference stage,
policies trained with SAFER learn to compose safe skills into successful
policies. We theoretically characterize why SAFER can enforce safe policy
learning and demonstrate its effectiveness on several complex safety-critical
robotic grasping tasks inspired by the game Operation, in which
SAFERoutperforms state-of-the-art primitive learning methods in success and
safety.

In this survey paper, we overview major deep learning methods used in Natural
Language Processing (NLP) and source code over the last 35 years. Next, we
present a survey of the applications of Artificial Intelligence (AI) for source
code, also known as Code Intelligence (CI) and Programming Language Processing
(PLP). We survey over 287 publications and present a software-engineering
centered taxonomy for CI placing each of the works into one category describing
how it best assists the software development cycle. Then, we overview the field
of conversational assistants and their applications in software engineering and
education. Lastly, we highlight research opportunities at the intersection of
AI for code and conversational assistants and provide future directions for
researching conversational assistants with CI capabilities.

A well-known challenge associated with the multi-label classification problem
is modelling dependencies between labels. Most attempts at modelling label
dependencies focus on co-occurrences, ignoring the valuable information that
can be extracted by detecting label subsets that rarely occur together. For
example, consider customer product reviews; a product probably would not
simultaneously be tagged by both "recommended" (i.e., reviewer is happy and
recommends the product) and "urgent" (i.e., the review suggests immediate
action to remedy an unsatisfactory experience). Aside from the consideration of
positive and negative dependencies, the direction of a relationship should also
be considered. For a multi-label image classification problem, the "ship" and
"sea" labels have an obvious dependency, but the presence of the former implies
the latter much more strongly than the other way around. These examples
motivate the modelling of multiple types of bi-directional relationships
between labels. In this paper, we propose a novel method, entitled
Multi-relation Message Passing (MrMP), for the multi-label classification
problem. Experiments on benchmark multi-label text classification datasets show
that the MrMP module yields similar or superior performance compared to
state-of-the-art methods. The approach imposes only minor additional
computational and memory overheads.

We introduce the notion of heterogeneous calibration that applies a post-hoc
model-agnostic transformation to model outputs for improving AUC performance on
binary classification tasks. We consider overconfident models, whose
performance is significantly better on training vs test data and give intuition
onto why they might under-utilize moderately effective simple patterns in the
data. We refer to these simple patterns as heterogeneous partitions of the
feature space and show theoretically that perfectly calibrating each partition
separately optimizes AUC. This gives a general paradigm of heterogeneous
calibration as a post-hoc procedure by which heterogeneous partitions of the
feature space are identified through tree-based algorithms and post-hoc
calibration techniques are applied to each partition to improve AUC. While the
theoretical optimality of this framework holds for any model, we focus on deep
neural networks (DNNs) and test the simplest instantiation of this paradigm on
a variety of open-source datasets. Experiments demonstrate the effectiveness of
this framework and the future potential for applying higher-performing
partitioning schemes along with more effective calibration techniques.

Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian
NNs, dramatically outperform other learned dynamics models by leveraging strong
inductive biases. These models, however, are challenging to apply to many real
world systems, such as those that don't conserve energy or contain contacts, a
common setting for robotics and reinforcement learning. In this paper, we
examine the inductive biases that make physics-inspired models successful in
practice. We show that, contrary to conventional wisdom, the improved
generalization of HNNs is the result of modeling acceleration directly and
avoiding artificial complexity from the coordinate system, rather than
symplectic structure or energy conservation. We show that by relaxing the
inductive biases of these models, we can match or exceed performance on
energy-conserving systems while dramatically improving performance on
practical, non-conservative systems. We extend this approach to constructing
transition models for common Mujoco environments, showing that our model can
appropriately balance inductive biases with the flexibility required for
model-based control.

This paper devises, implements and benchmarks a novel shape retrieval method
that can accurately match individual labelled point clusters (instances) of
existing industrial facilities with their respective CAD models. It employs a
combination of image and point cloud deep learning networks to classify and
match instances to their geometrically similar CAD model. It extends our
previous research on geometric digital twin generation from point cloud data,
which currently is a tedious, manual process. Experiments with our joint
network reveal that it can reliably retrieve CAD models at 85.2\% accuracy. The
proposed research is a fundamental framework to enable the geometric Digital
Twin (gDT) pipeline and incorporate the real geometric configuration into the
Digital Twin.

Many real-world optimisation problems are defined over both categorical and
continuous variables, yet efficient optimisation methods such asBayesian
Optimisation (BO) are not designed tohandle such mixed-variable search spaces.
Recent approaches to this problem cast the selection of the categorical
variables as a bandit problem, operating independently alongside a BO component
which optimises the continuous variables. In this paper, we adopt a holistic
view and aim to consolidate optimisation of the categorical and continuous
sub-spaces under a single acquisition metric. We derive candidates from the
ExpectedImprovement criterion, which we call value proposals, and use these
proposals to make selections on both the categorical and continuous components
of the input. We show that this unified approach significantly outperforms
existing mixed-variable optimisation approaches across several mixed-variable
black-box optimisation tasks.

Generating molecules with desired biological activities has attracted growing
attention in drug discovery. Previous molecular generation models are designed
as chemocentric methods that hardly consider the drug-target interaction,
limiting their practical applications. In this paper, we aim to generate
molecular drugs in a target-aware manner that bridges biological activity and
molecular design. To solve this problem, we compile a benchmark dataset from
several publicly available datasets and build baselines in a unified framework.
Building on the recent advantages of flow-based molecular generation models, we
propose SiamFlow, which forces the flow to fit the distribution of target
sequence embeddings in latent space. Specifically, we employ an alignment loss
and a uniform loss to bring target sequence embeddings and drug graph
embeddings into agreements while avoiding collapse. Furthermore, we formulate
the alignment into a one-to-many problem by learning spaces of target sequence
embeddings. Experiments quantitatively show that our proposed method learns
meaningful representations in the latent space toward the target-aware
molecular graph generation and provides an alternative approach to bridge
biology and chemistry in drug discovery.

One critical challenge of time-series modeling is how to learn and quickly
correct the model under unknown distribution shifts. In this work, we propose a
principled framework, called LiLY, to first recover time-delayed latent causal
variables and identify their relations from measured temporal data under
different distribution shifts. The correction step is then formulated as
learning the low-dimensional change factors with a few samples from the new
environment, leveraging the identified causal structure. Specifically, the
framework factorizes unknown distribution shifts into transition distribution
changes caused by fixed dynamics and time-varying latent causal relations, and
by global changes in observation. We establish the identifiability theories of
nonparametric latent causal dynamics from their nonlinear mixtures under fixed
dynamics and under changes. Through experiments, we show that time-delayed
latent causal influences are reliably identified from observed variables under
different distribution changes. By exploiting this modular representation of
changes, we can efficiently learn to correct the model under unknown
distribution shifts with only a few samples.

When training samples are scarce, the semantic embedding technique, ie,
describing class labels with attributes, provides a condition to generate
visual features for unseen objects by transferring the knowledge from seen
objects. However, semantic descriptions are usually obtained in an external
paradigm, such as manual annotation, resulting in weak consistency between
descriptions and visual features. In this paper, we refine the coarse-grained
semantic description for any-shot learning tasks, ie, zero-shot learning (ZSL),
generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new
model, namely, the semantic refinement Wasserstein generative adversarial
network (SRWGAN) model, is designed with the proposed multihead representation
and hierarchical alignment techniques. Unlike conventional methods, semantic
refinement is performed with the aim of identifying a bias-eliminated condition
for disjoint-class feature generation and is applicable in both inductive and
transductive settings. We extensively evaluate model performance on six
benchmark datasets and observe state-of-the-art results for any-shot learning;
eg, we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset
and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the
standard GZSL setting. Various visualizations are also provided to show the
bias-eliminated generation of SRWGAN. Our code is available.

Prompt-based learning, with its capability to tackle zero-shot and few-shot
NLP tasks, has gained much attention in community. The main idea is to bridge
the gap between NLP downstream tasks and language modeling (LM), by mapping
these tasks into natural language prompts, which are then filled by pre-trained
language models (PLMs). However, for prompt learning, there are still two
salient gaps between NLP tasks and pretraining. First, prompt information is
not necessarily sufficiently present during LM pretraining. Second,
task-specific data are not necessarily well represented during pretraining. We
address these two issues by proposing AdaPrompt, adaptively retrieving external
data for continual pretraining of PLMs by making use of both task and prompt
characteristics. In addition, we make use of knowledge in Natural Language
Inference models for deriving adaptive verbalizers. Experimental results on
five NLP benchmarks show that AdaPrompt can improve over standard PLMs in
few-shot settings. In addition, in zero-shot settings, our method outperforms
standard prompt-based methods by up to 26.35\% relative error reduction.

Ranking by pairwise comparisons has shown improved reliability over ordinal
classification. However, as the annotations of pairwise comparisons scale
quadratically, this becomes less practical when the dataset is large. We
propose a method for reducing the number of pairwise comparisons required to
rank by a quantitative metric, demonstrating the effectiveness of the approach
in ranking medical images by image quality in this proof of concept study.
Using the medical image annotation software that we developed, we actively
subsample pairwise comparisons using a sorting algorithm with a human rater in
the loop. We find that this method substantially reduces the number of
comparisons required for a full ordinal ranking without compromising
inter-rater reliability when compared to pairwise comparisons without sorting.

Graph neural networks (GNNs) have been a hot spot of recent research and are
widely utilized in diverse applications. However, with the use of huger data
and deeper models, an urgent demand is unsurprisingly made to accelerate GNNs
for more efficient execution. In this paper, we provide a comprehensive survey
on acceleration methods for GNNs from an algorithmic perspective. We first
present a new taxonomy to classify existing acceleration methods into five
categories. Based on the classification, we systematically discuss these
methods and highlight their correlations. Next, we provide comparisons from
aspects of the efficiency and characteristics of these methods. Finally, we
suggest some promising prospects for future research.

Disentangled representation learning offers useful properties such as
dimension reduction and interpretability, which are essential to modern deep
learning approaches. Although deep learning techniques have been widely applied
to spatio-temporal data mining, there has been little attention to further
disentangle the latent features and understanding their contribution to the
model performance, particularly their mutual information and correlation across
features. In this study, we adopt two state-of-the-art disentangled
representation learning methods and apply them to three large-scale public
spatio-temporal datasets. To evaluate their performance, we propose an internal
evaluation metric focusing on the degree of correlations among latent variables
of the learned representations and the prediction performance of the downstream
tasks. Empirical results show that our modified method can learn disentangled
representations that achieve the same level of performance as existing
state-of-the-art ST deep learning methods in a spatio-temporal sequence
forecasting problem. Additionally, we find that our methods can be used to
discover real-world spatial-temporal semantics to describe the variables in the
learned representation.

We introduce L0Learn: an open-source package for sparse regression and
classification using L0 regularization. L0Learn implements scalable,
approximate algorithms, based on coordinate descent and local combinatorial
optimization. The package is built using C++ and has a user-friendly R
interface. Our experiments indicate that L0Learn can scale to problems with
millions of features, achieving competitive run times with state-of-the-art
sparse learning packages. L0Learn is available on both CRAN and GitHub.

Weakly-Supervised Semantic Segmentation (WSSS) methods with image-level
labels generally train a classification network to generate the Class
Activation Maps (CAMs) as the initial coarse segmentation labels. However,
current WSSS methods still perform far from satisfactorily because their
adopted CAMs 1) typically focus on partial discriminative object regions and 2)
usually contain useless background regions. These two problems are attributed
to the sole image-level supervision and aggregation of global information when
training the classification networks. In this work, we propose the visual words
learning module and hybrid pooling approach, and incorporate them in the
classification network to mitigate the above problems. In the visual words
learning module, we counter the first problem by enforcing the classification
network to learn fine-grained visual word labels so that more object extents
could be discovered. Specifically, the visual words are learned with a
codebook, which could be updated via two proposed strategies, i.e.
learning-based strategy and memory-bank strategy. The second drawback of CAMs
is alleviated with the proposed hybrid pooling, which incorporates the global
average and local discriminative information to simultaneously ensure object
completeness and reduce background regions. We evaluated our methods on PASCAL
VOC 2012 and MS COCO 2014 datasets. Without any extra saliency prior, our
method achieved 70.6% and 70.7% mIoU on the $val$ and $test$ set of PASCAL VOC
dataset, respectively, and 36.2% mIoU on the $val$ set of MS COCO dataset,
which significantly surpassed the performance of state-of-the-art WSSS methods.

MRI-guidance techniques that dynamically adapt radiation beams to follow
tumor motion in real-time will lead to more accurate cancer treatments and
reduced collateral healthy tissue damage. The gold-standard for reconstruction
of undersampled MR data is compressed sensing (CS) which is computationally
slow and limits the rate that images can be available for real-time adaptation.
Here, we demonstrate the use of automated transform by manifold approximation
(AUTOMAP), a generalized framework that maps raw MR signal to the target image
domain, to rapidly reconstruct images from undersampled radial k-space data.
The AUTOMAP neural network was trained to reconstruct images from a
golden-angle radial acquisition, a benchmark for motion-sensitive imaging, on
lung cancer patient data and generic images from ImageNet. Model training was
subsequently augmented with motion-encoded k-space data derived from videos in
the YouTube-8M dataset to encourage motion robust reconstruction. We find that
AUTOMAP-reconstructed radial k-space has equivalent accuracy to CS but with
much shorter processing times after initial fine-tuning on retrospectively
acquired lung cancer patient data. Validation of motion-trained models with a
virtual dynamic lung tumor phantom showed that the generalized motion
properties learned from YouTube lead to improved target tracking accuracy. Our
work shows that AUTOMAP can achieve real-time, accurate reconstruction of
radial data. These findings imply that neural-network-based reconstruction is
potentially superior to existing approaches for real-time image guidance

Hyperdimensional computing (HDC) is an emerging learning paradigm that
computes with high dimensional binary vectors. It is attractive because of its
energy efficiency and low latency, especially on emerging hardware -- but HDC
suffers from low model accuracy, with little theoretical understanding of what
limits its performance. We propose a new theoretical analysis of the limits of
HDC via a consideration of what similarity matrices can be "expressed" by
binary vectors, and we show how the limits of HDC can be approached using
random Fourier features (RFF). We extend our analysis to the more general class
of vector symbolic architectures (VSA), which compute with high-dimensional
vectors (hypervectors) that are not necessarily binary. We propose a new class
of VSAs, finite group VSAs, which surpass the limits of HDC. Using
representation theory, we characterize which similarity matrices can be
"expressed" by finite group VSA hypervectors, and we show how these VSAs can be
constructed. Experimental results show that our RFF method and group VSA can
both outperform the state-of-the-art HDC model by up to 7.6\% while maintaining
hardware efficiency.

When a patient is admitted to the intensive care unit (ICU) after a traumatic
brain injury (TBI), an early prognosis is essential for baseline risk
adjustment and shared decision making. TBI outcomes are commonly categorised by
the Glasgow Outcome Scale-Extended (GOSE) into 8, ordered levels of functional
recovery at 6 months after injury. Existing ICU prognostic models predict
binary outcomes at a certain threshold of GOSE (e.g., prediction of survival
[GOSE>1] or functional independence [GOSE>4]). We aimed to develop ordinal
prediction models that concurrently predict probabilities of each GOSE score.
From a prospective cohort (n=1,550, 65 centres) in the ICU stratum of the
Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI)
patient dataset, we extracted all clinical information within 24 hours of ICU
admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of
2 design elements on ordinal model performance: (1) the baseline predictor set,
ranging from a concise set of 10 validated predictors to a token-embedded
representation of all possible predictors, and (2) the modelling strategy, from
ordinal logistic regression to multinomial deep learning. With repeated k-fold
cross-validation, we found that expanding the baseline predictor set
significantly improved ordinal prediction performance while increasing
analytical complexity did not. Half of these gains could be achieved with the
addition of 8 high-impact predictors (2 demographic variables, 4 protein
biomarkers, and 2 severity assessments) to the concise set. At best, ordinal
models achieved 0.76 (95% CI: 0.74-0.77) ordinal discrimination ability
(ordinal c-index) and 57% (95% CI: 54%-60%) explanation of ordinal variation in
6-month GOSE (Somers' D). Our results motivate the search for informative
predictors for higher GOSE and the development of ordinal dynamic prediction
models.

The need for function estimation in label-limited settings is common in the
natural sciences. At the same time, prior knowledge of function values is often
available in these domains. For example, data-free biophysics-based models can
be informative on protein properties, while quantum-based computations can be
informative on small molecule properties. How can we coherently leverage such
prior knowledge to help improve a neural network model that is quite accurate
in some regions of input space -- typically near the training data -- but
wildly wrong in other regions? Bayesian neural networks (BNN) enable the user
to specify prior information only on the neural network weights, not directly
on the function values. Moreover, there is in general no clear mapping between
these. Herein, we tackle this problem by developing an approach to augment BNNs
with prior information on the function values themselves. Our probabilistic
approach yields predictions that rely more heavily on the prior information
when the epistemic uncertainty is large, and more heavily on the neural network
when the epistemic uncertainty is small.

Brain-inspired hyperdimensional computing (HDC) is continuously gaining
remarkable attention. It is a promising alternative to traditional
machine-learning approaches due to its ability to learn from little data,
lightweight implementation, and resiliency against errors. However, HDC is
overwhelmingly data-centric similar to traditional machine-learning algorithms.
In-memory computing is rapidly emerging to overcome the von Neumann bottleneck
by eliminating data movements between compute and storage units. In this work,
we investigate and model the impact of imprecise in-memory computing hardware
on the inference accuracy of HDC. Our modeling is based on 14nm FinFET
technology fully calibrated with Intel measurement data. We accurately model,
for the first time, the voltage-dependent error probability in SRAM-based and
FeFET-based in-memory computing. Thanks to HDC's resiliency against errors, the
complexity of the underlying hardware can be reduced, providing large energy
savings of up to 6x. Experimental results for SRAM reveal that
variability-induced errors have a probability of up to 39 percent. Despite such
a high error probability, the inference accuracy is only marginally impacted.
This opens doors to explore new tradeoffs. We also demonstrate that the
resiliency against errors is application-dependent. In addition, we investigate
the robustness of HDC against errors when the underlying in-memory hardware is
realized using emerging non-volatile FeFET devices instead of mature CMOS-based
SRAMs. We demonstrate that inference accuracy does remain high despite the
larger error probability, while large area and power savings can be obtained.
All in all, HW/SW co-design is the key for efficient yet reliable in-memory
hyperdimensional computing for both conventional CMOS technology and upcoming
emerging technologies.

The Robust Artificial Intelligence System Assurance (RAISA) workshop will
focus on research, development and application of robust artificial
intelligence (AI) and machine learning (ML) systems. Rather than studying
robustness with respect to particular ML algorithms, our approach will be to
explore robustness assurance at the system architecture level, during both
development and deployment, and within the human-machine teaming context. While
the research community is converging on robust solutions for individual AI
models in specific scenarios, the problem of evaluating and assuring the
robustness of an AI system across its entire life cycle is much more complex.
Moreover, the operational context in which AI systems are deployed necessitates
consideration of robustness and its relation to principles of fairness,
privacy, and explainability.

In a Stackelberg game, a leader commits to a randomized strategy, and a
follower chooses their best strategy in response. We consider an extension of a
standard Stackelberg game, called a discrete-time dynamic Stackelberg game,
that has an underlying state space that affects the leader's rewards and
available strategies and evolves in a Markovian manner depending on both the
leader and follower's selected strategies. Although standard Stackelberg games
have been utilized to improve scheduling in security domains, their deployment
is often limited by requiring complete information of the follower's utility
function. In contrast, we consider scenarios where the follower's utility
function is unknown to the leader; however, it can be linearly parameterized.
Our objective then is to provide an algorithm that prescribes a randomized
strategy to the leader at each step of the game based on observations of how
the follower responded in previous steps. We design a no-regret learning
algorithm that, with high probability, achieves a regret bound (when compared
to the best policy in hindsight) which is sublinear in the number of time
steps; the degree of sublinearity depends on the number of features
representing the follower's utility function. The regret of the proposed
learning algorithm is independent of the size of the state space and polynomial
in the rest of the parameters of the game. We show that the proposed learning
algorithm outperforms existing model-free reinforcement learning approaches.

This work finds the analytical expression of the global minima of a deep
linear network with weight decay and stochastic neurons, a fundamental model
for understanding the landscape of neural networks. Our result implies that
zero is a special point in deep neural network architecture. We show that
weight decay strongly interacts with the model architecture and can create bad
minima at zero in a network with more than $1$ hidden layer, qualitatively
different from a network with only $1$ hidden layer. Practically, our result
implies that common deep learning initialization methods are insufficient to
ease the optimization of neural networks in general.

Speech translation models are unable to directly process long audios, like
TED talks, which have to be split into shorter segments. Speech translation
datasets provide manual segmentations of the audios, which are not available in
real-world scenarios, and existing segmentation methods usually significantly
reduce translation quality at inference time. To bridge the gap between the
manual segmentation of training and the automatic one at inference, we propose
Supervised Hybrid Audio Segmentation (SHAS), a method that can effectively
learn the optimal segmentation from any manually segmented speech corpus.
First, we train a classifier to identify the included frames in a segmentation,
using speech representations from a pre-trained wav2vec 2.0. The optimal
splitting points are then found by a probabilistic Divide-and-Conquer algorithm
that progressively splits at the frame of lowest probability until all segments
are below a pre-specified length. Experiments on MuST-C and mTEDx show that the
translation of the segments produced by our method approaches the quality of
the manual segmentation on 5 language pairs. Namely, SHAS retains 95-98% of the
manual segmentation's BLEU score, compared to the 87-93% of the best existing
methods. Our method is additionally generalizable to different domains and
achieves high zero-shot performance in unseen languages.

Neural networks struggle in continual learning settings from catastrophic
forgetting: when trials are blocked, new learning can overwrite the learning
from previous blocks. Humans learn effectively in these settings, in some cases
even showing an advantage of blocking, suggesting the brain contains mechanisms
to overcome this problem. Here, we build on previous work and show that neural
networks equipped with a mechanism for cognitive control do not exhibit
catastrophic forgetting when trials are blocked. We further show an advantage
of blocking over interleaving when there is a bias for active maintenance in
the control signal, implying a tradeoff between maintenance and the strength of
control. Analyses of map-like representations learned by the networks provided
additional insights into these mechanisms. Our work highlights the potential of
cognitive control to aid continual learning in neural networks, and offers an
explanation for the advantage of blocking that has been observed in humans.

The use of remote sensing in humanitarian crisis response missions is
well-established and has proven relevant repeatedly. One of the problems is
obtaining gold annotations as it is costly and time consuming which makes it
almost impossible to fine-tune models to new regions affected by the crisis.
Where time is critical, resources are limited and environment is constantly
changing, models has to evolve and provide flexible ways to adapt to a new
situation. The question that we want to answer is if prioritization of samples
provide better results in fine-tuning vs other classical sampling methods under
annotated data scarcity? We propose a method to guide data collection during
fine-tuning, based on estimated model and sample properties, like predicted IOU
score. We propose two formulas for calculating sample priority. Our approach
blends techniques from interpretability, representation learning and active
learning. We have applied our method to a deep learning model for semantic
segmentation, U-Net, in a remote sensing application of building detection -
one of the core use cases of remote sensing in humanitarian applications.
Preliminary results shows utility in prioritization of samples for tuning
semantic segmentation models under scarcity of data condition.

Concept discovery is one of the open problems in the interpretability
literature that is important for bridging the gap between non-deep learning
experts and model end-users. Among current formulations, concepts defines them
by as a direction in a learned representation space. This definition makes it
possible to evaluate whether a particular concept significantly influences
classification decisions for classes of interest. However, finding relevant
concepts is tedious, as representation spaces are high-dimensional and hard to
navigate. Current approaches include hand-crafting concept datasets and then
converting them to latent space directions; alternatively, the process can be
automated by clustering the latent space. In this study, we offer another two
approaches to guide user discovery of meaningful concepts, one based on
multiple hypothesis testing, and another on interactive visualization. We
explore the potential value and limitations of these approaches through
simulation experiments and an demo visual interface to real data. Overall, we
find that these techniques offer a promising strategy for discovering relevant
concepts in settings where users do not have predefined descriptions of them,
but without completely automating the process.

Contactless monitoring using thermal imaging has become increasingly proposed
to monitor patient deterioration in hospital, most recently to detect fevers
and infections during the COVID-19 pandemic. In this letter, we propose a novel
method to estimate patient motion and observe clinical workload using a similar
technical setup but combined with open source object detection algorithms
(YOLOv4) and optical flow. Patient motion estimation was used to approximate
patient agitation and sedation, while worker motion was used as a surrogate for
caregiver workload. Performance was illustrated by comparing over 32000 frames
from videos of patients recorded in an Intensive Care Unit, to clinical
agitation scores recorded by clinical workers.

Simulator-based models are models for which the likelihood is intractable but
simulation of synthetic data is possible. They are often used to describe
complex real-world phenomena, and as such can often be misspecified in
practice. Unfortunately, existing Bayesian approaches for simulators are known
to perform poorly in those cases. In this paper, we propose a novel algorithm
based on the posterior bootstrap and maximum mean discrepancy estimators. This
leads to a highly-parallelisable Bayesian inference algorithm with strong
robustness properties. This is demonstrated through an in-depth theoretical
study which includes generalisation bounds and proofs of frequentist
consistency and robustness of our posterior. The approach is then assessed on a
range of examples including a g-and-k distribution and a toggle-switch model.

Machine reading comprehension (MRC) of text data is one important task in
Natural Language Understanding. It is a complex NLP problem with a lot of
ongoing research fueled by the release of the Stanford Question Answering
Dataset (SQuAD) and Conversational Question Answering (CoQA). It is considered
to be an effort to teach computers how to "understand" a text, and then to be
able to answer questions about it using deep learning. However, until now
large-scale training on private text data and knowledge sharing has been
missing for this NLP task. Hence, we present FedQAS, a privacy-preserving
machine reading system capable of leveraging large-scale private data without
the need to pool those datasets in a central location. The proposed approach
combines transformer models and federated learning technologies. The system is
developed using the FEDn framework and deployed as a proof-of-concept alliance
initiative. FedQAS is flexible, language-agnostic, and allows intuitive
participation and execution of local model training. In addition, we present
the architecture and implementation of the system, as well as provide a
reference evaluation based on the SQUAD dataset, to showcase how it overcomes
data privacy issues and enables knowledge sharing between alliance members in a
Federated learning setting.

The lottery ticket hypothesis (LTH) has shown that dense models contain
highly sparse subnetworks (i.e., winning tickets) that can be trained in
isolation to match full accuracy. Despite many exciting efforts being made,
there is one "commonsense" rarely challenged: a winning ticket is found by
iterative magnitude pruning (IMP) and hence the resultant pruned subnetworks
have only unstructured sparsity. That gap limits the appeal of winning tickets
in practice, since the highly irregular sparse patterns are challenging to
accelerate on hardware. Meanwhile, directly substituting structured pruning for
unstructured pruning in IMP damages performance more severely and is usually
unable to locate winning tickets. In this paper, we demonstrate the first
positive result that a structurally sparse winning ticket can be effectively
found in general. The core idea is to append "post-processing techniques" after
each round of (unstructured) IMP, to enforce the formation of structural
sparsity. Specifically, we first "re-fill" pruned elements back in some
channels deemed to be important, and then "re-group" non-zero elements to
create flexible group-wise structural patterns. Both our identified channel-
and group-wise structural subnetworks win the lottery, with substantial
inference speedups readily supported by existing hardware. Extensive
experiments, conducted on diverse datasets across multiple network backbones,
consistently validate our proposal, showing that the hardware acceleration
roadblock of LTH is now removed. Specifically, the structural winning tickets
obtain up to {64.93%, 64.84%, 60.23%} running time savings at {36%~80%, 74%,
58%} sparsity on {CIFAR, Tiny-ImageNet, ImageNet}, while maintaining comparable
accuracy. Code is at https://github.com/VITA-Group/Structure-LTH.

Motivated by robust dynamic resource allocation in operations research, we
study the \textit{Online Learning to Transport} (OLT) problem where the
decision variable is a probability measure, an infinite-dimensional object. We
draw connections between online learning, optimal transport, and partial
differential equations through an insight called the minimal selection
principle, originally studied in the Wasserstein gradient flow setting by
\citet{Ambrosio_2005}. This allows us to extend the standard online learning
framework to the infinite-dimensional setting seamlessly. Based on our
framework, we derive a novel method called the \textit{minimal selection or
exploration (MSoE) algorithm} to solve OLT problems using mean-field
approximation and discretization techniques. In the displacement convex
setting, the main theoretical message underpinning our approach is that
minimizing transport cost over time (via the minimal selection principle)
ensures optimal cumulative regret upper bounds. On the algorithmic side, our
MSoE algorithm applies beyond the displacement convex setting, making the
mathematical theory of optimal transport practically relevant to non-convex
settings common in dynamic resource allocation.

We present a novel graph neural network (GNN) approach for cell tracking in
high-throughput microscopy videos. By modeling the entire time-lapse sequence
as a direct graph where cell instances are represented by its nodes and their
associations by its edges, we extract the entire set of cell trajectories by
looking for the maximal paths in the graph. This is accomplished by several key
contributions incorporated into an end-to-end deep learning framework. We
exploit a deep metric learning algorithm to extract cell feature vectors that
distinguish between instances of different biological cells and assemble same
cell instances. We introduce a new GNN block type which enables a mutual update
of node and edge feature vectors, thus facilitating the underlying message
passing process. The message passing concept, whose extent is determined by the
number of GNN blocks, is of fundamental importance as it enables the `flow' of
information between nodes and edges much behind their neighbors in consecutive
frames. Finally, we solve an edge classification problem and use the identified
active edges to construct the cells' tracks and lineage trees. We demonstrate
the strengths of the proposed cell tracking approach by applying it to 2D and
3D datasets of different cell types, imaging setups, and experimental
conditions. We show that our framework outperforms current state-of-the-art
methods on most of the evaluated datasets. The code is available at our
repository: https://github.com/talbenha/cell-tracker-gnn.

Similarity judgments provide a well-established method for accessing mental
representations, with applications in psychology, neuroscience and machine
learning. However, collecting similarity judgments can be prohibitively
expensive for naturalistic datasets as the number of comparisons grows
quadratically in the number of stimuli. One way to tackle this problem is to
construct approximation procedures that rely on more accessible proxies for
predicting similarity. Here we leverage recent advances in language models and
online recruitment, proposing an efficient domain-general procedure for
predicting human similarity judgments based on text descriptions. Intuitively,
similar stimuli are likely to evoke similar descriptions, allowing us to use
description similarity to predict pairwise similarity judgments. Crucially, the
number of descriptions required grows only linearly with the number of stimuli,
drastically reducing the amount of data required. We test this procedure on six
datasets of naturalistic images and show that our models outperform previous
approaches based on visual information.

We present new efficient \textit{projection-free} algorithms for online
convex optimization (OCO), where by projection-free we refer to algorithms that
avoid computing orthogonal projections onto the feasible set, and instead relay
on different and potentially much more efficient oracles. While most
state-of-the-art projection-free algorithms are based on the
\textit{follow-the-leader} framework, our algorithms are fundamentally
different and are based on the \textit{online gradient descent} algorithm with
a novel and efficient approach to computing so-called \textit{infeasible
projections}. As a consequence, we obtain the first projection-free algorithms
which naturally yield \textit{adaptive regret} guarantees, i.e., regret bounds
that hold w.r.t. any sub-interval of the sequence. Concretely, when assuming
the availability of a linear optimization oracle (LOO) for the feasible set, on
a sequence of length $T$, our algorithms guarantee $O(T^{3/4})$ adaptive regret
and $O(T^{3/4})$ adaptive expected regret, for the full-information and bandit
settings, respectively, using only $O(T)$ calls to the LOO. These bounds match
the current state-of-the-art regret bounds for LOO-based projection-free OCO,
which are \textit{not adaptive}. We also consider a new natural setting in
which the feasible set is accessible through a separation oracle. We present
algorithms which, using overall $O(T)$ calls to the separation oracle,
guarantee $O(\sqrt{T})$ adaptive regret and $O(T^{3/4})$ adaptive expected
regret for the full-information and bandit settings, respectively.

Network data are commonly collected in a variety of applications,
representing either directly measured or statistically inferred connections
between features of interest. In an increasing number of domains, these
networks are collected over time, such as interactions between users of a
social media platform on different days, or across multiple subjects, such as
in multi-subject studies of brain connectivity. When analyzing multiple large
networks, dimensionality reduction techniques are often used to embed networks
in a more tractable low-dimensional space. To this end, we develop a framework
for principal components analysis (PCA) on collections of networks via a
specialized tensor decomposition we term Semi-Symmetric Tensor PCA or SS-TPCA.
We derive computationally efficient algorithms for computing our proposed
SS-TPCA decomposition and establish statistical efficiency of our approach
under a standard low-rank signal plus noise model. Remarkably, we show that
SS-TPCA achieves the same estimation accuracy as classical matrix PCA, with
error proportional to the square root of the number of vertices in the network
and not the number of edges as might be expected. Our framework inherits many
of the strengths of classical PCA and is suitable for a wide range of
unsupervised learning tasks, including identifying principal networks,
isolating meaningful changepoints or outlying observations, and for
characterizing the "variability network" of the most varying edges. Finally, we
demonstrate the effectiveness of our proposal on simulated data and on an
example from empirical legal studies. The techniques used to establish our main
consistency results are surprisingly straightforward and may find use in a
variety of other network analysis problems.

In hybrid human-machine deferral frameworks, a classifier can defer uncertain
cases to human decision-makers (who are often themselves fallible). Prior work
on simultaneous training of such classifier and deferral models has typically
assumed access to an oracle during training to obtain true class labels for
training samples, but in practice there often is no such oracle. In contrast,
we consider a "closed" decision-making pipeline in which the same fallible
human decision-makers used in deferral also provide training labels. How can
imperfect and biased human expert labels be used to train a fair and accurate
deferral framework? Our key insight is that by exploiting weak prior
information, we can match experts to input examples to ensure fairness and
accuracy of the resulting deferral framework, even when imperfect and biased
experts are used in place of ground truth labels. The efficacy of our approach
is shown both by theoretical analysis and by evaluation on two tasks.

Multi-layer perceptrons (MLP) have proven to be effective scene encoders when
combined with higher-dimensional projections of the input, commonly referred to
as \textit{positional encoding}. However, scenes with a wide frequency spectrum
remain a challenge: choosing high frequencies for positional encoding
introduces noise in low structure areas, while low frequencies result in poor
fitting of detailed regions. To address this, we propose a progressive
positional encoding, exposing a hierarchical MLP structure to incremental sets
of frequency encodings. Our model accurately reconstructs scenes with wide
frequency bands and learns a scene representation at progressive level of
detail \textit{without explicit per-level supervision}. The architecture is
modular: each level encodes a continuous implicit representation that can be
leveraged separately for its respective resolution, meaning a smaller network
for coarser reconstructions. Experiments on several 2D and 3D datasets show
improvements in reconstruction accuracy, representational capacity and training
speed compared to baselines.

We consider $Q$-learning with knowledge transfer, using samples from a target
reinforcement learning (RL) task as well as source samples from different but
related RL tasks. We propose transfer learning algorithms for both batch and
online $Q$-learning with offline source studies. The proposed transferred
$Q$-learning algorithm contains a novel re-targeting step that enables vertical
information-cascading along multiple steps in an RL task, besides the usual
horizontal information-gathering as transfer learning (TL) for supervised
learning. We establish the first theoretical justifications of TL in RL tasks
by showing a faster rate of convergence of the $Q$ function estimation in the
offline RL transfer, and a lower regret bound in the offline-to-online RL
transfer under certain similarity assumptions. Empirical evidences from both
synthetic and real datasets are presented to back up the proposed algorithm and
our theoretical results.

In this paper we introduce an active learning method for symbolic regression
using StackGP. The approach begins with a small number of data points for
StackGP to model. To improve the model the system incrementally adds a data
point such that the new point maximizes prediction uncertainty as measured by
the model ensemble. Symbolic regression is re-run with the larger data set.
This cycle continues until the system satisfies a termination criterion. We use
the Feynman AI benchmark set of equations to examine the ability of our method
to find appropriate models using fewer data points. The approach was found to
successfully rediscover 72 of the 100 Feynman equations using as few data
points as possible, and without use of domain expertise or data translation.

In attribute-based access control, users with certain verified attributes
will gain access to some particular data. Concerning with privacy of the users'
attributes, we study the problem of distributed attribute-based private access
control (DAPAC) with multiple authorities, where each authority will learn and
verify only one of the attributes.
  To investigate its fundamental limits, we introduce an information theoretic
DAPAC framework, with $N \in \mathbb{N}$, $N\geq 2$, replicated non-colluding
servers (authorities) and some users. Each user has an attribute vector
$\mathbf{v^*}=(v_1^*, ..., v_N^*)$ of dimension $N$ and is eligible to retrieve
a message $W^{\mathbf{v}^*}$, available in all servers. Each server $n\in [N]$
is able to only observe and verify the $n$'th attribute of a user. In response,
it sends a function of its data to the user. The system must satisfy the
following conditions: (1) Correctness: the user with attribute vector
$\mathbf{v^*}$ is able to retrieve his intended message $W^{\mathbf{v}^*}$ from
the servers' response, (2) Data Secrecy: the user will not learn anything about
the other messages, (3) Attribute Privacy: each Server~$n$ learns nothing
beyond attribute $n$ of the user. The capacity of the DAPAC is defined as the
ratio of the file size and the aggregated size of the responses, maximized over
all feasible schemes. We obtain a lower bound on the capacity of this problem
by proposing an achievable algorithm with rate $\frac{1}{2K}$, where $K$ is the
size of the alphabet of each attribute.

Much of modern learning theory has been split between two regimes: the
classical offline setting, where data arrive independently, and the online
setting, where data arrive adversarially. While the former model is often both
computationally and statistically tractable, the latter requires no
distributional assumptions. In an attempt to achieve the best of both worlds,
previous work proposed the smooth online setting where each sample is drawn
from an adversarially chosen distribution, which is smooth, i.e., it has a
bounded density with respect to a fixed dominating measure. We provide tight
bounds on the minimax regret of learning a nonparametric function class, with
nearly optimal dependence on both the horizon and smoothness parameters.
Furthermore, we provide the first oracle-efficient, no-regret algorithms in
this setting. In particular, we propose an oracle-efficient improper algorithm
whose regret achieves optimal dependence on the horizon and a proper algorithm
requiring only a single oracle call per round whose regret has the optimal
horizon dependence in the classification setting and is sublinear in general.
Both algorithms have exponentially worse dependence on the smoothness parameter
of the adversary than the minimax rate. We then prove a lower bound on the
oracle complexity of any proper learning algorithm, which matches the
oracle-efficient upper bounds up to a polynomial factor, thus demonstrating the
existence of a statistical-computational gap in smooth online learning.
Finally, we apply our results to the contextual bandit setting to show that if
a function class is learnable in the classical setting, then there is an
oracle-efficient, no-regret algorithm for contextual bandits in the case that
contexts arrive in a smooth manner.

In this paper, we consider the problem of non-linear dimensionality reduction
under uncertainty, both from a theoretical and algorithmic perspectives. Since
real-world data usually contain measurements with uncertainties and artifacts,
the input space in the proposed framework consists of probability distributions
to model the uncertainties associated with each sample. We propose a new
dimensionality reduction framework, called NGEU, which leverages uncertainty
information and directly extends several traditional approaches, e.g., KPCA,
MDA/KMFA, to receive as inputs the probability distributions instead of the
original data. We show that the proposed NGEU formulation exhibits a global
closed-form solution, and we analyze, based on the Rademacher complexity, how
the underlying uncertainties theoretically affect the generalization ability of
the framework. Empirical results on different datasets show the effectiveness
of the proposed framework.

Skills or low-level policies in reinforcement learning are temporally
extended actions that can speed up learning and enable complex behaviours.
Recent work in offline reinforcement learning and imitation learning has
proposed several techniques for skill discovery from a set of expert
trajectories. While these methods are promising, the number K of skills to
discover is always a fixed hyperparameter, which requires either prior
knowledge about the environment or an additional parameter search to tune it.
We first propose a method for offline learning of options (a particular skill
framework) exploiting advances in variational inference and continuous
relaxations. We then highlight an unexplored connection between Bayesian
nonparametrics and offline skill discovery, and show how to obtain a
nonparametric version of our model. This version is tractable thanks to a
carefully structured approximate posterior with a dynamically-changing number
of options, removing the need to specify K. We also show how our nonparametric
extension can be applied in other skill frameworks, and empirically demonstrate
that our method can outperform state-of-the-art offline skill learning
algorithms across a variety of environments. Our code is available at
https://github.com/layer6ai-labs/BNPO .

Being able to learn from small amounts of data is a key characteristic of
human intelligence, but exactly {\em how} small? In this paper, we introduce a
novel experimental paradigm that allows us to examine classification in an
extremely data-scarce setting, asking whether humans can learn more categories
than they have exemplars (i.e., can humans do "less-than-one shot" learning?).
An experiment conducted using this paradigm reveals that people are capable of
learning in such settings, and provides several insights into underlying
mechanisms. First, people can accurately infer and represent high-dimensional
feature spaces from very little data. Second, having inferred the relevant
spaces, people use a form of prototype-based categorization (as opposed to
exemplar-based) to make categorical inferences. Finally, systematic,
machine-learnable patterns in responses indicate that people may have efficient
inductive biases for dealing with this class of data-scarce problems.

We design accelerated algorithms with improved rates for several fundamental
classes of optimization problems. Our algorithms all build upon techniques
related to the analysis of primal-dual extragradient methods via relative
Lipschitzness proposed recently by [CST21].
  (1) Separable minimax optimization. We study separable minimax optimization
problems $\min_x \max_y f(x) - g(y) + h(x, y)$, where $f$ and $g$ have
smoothness and strong convexity parameters $(L^x, \mu^x)$, $(L^y, \mu^y)$, and
$h$ is convex-concave with a $(\Lambda^{xx}, \Lambda^{xy},
\Lambda^{yy})$-blockwise operator norm bounded Hessian. We provide an algorithm
with gradient query complexity $\tilde{O}\left(\sqrt{\frac{L^{x}}{\mu^{x}}} +
\sqrt{\frac{L^{y}}{\mu^{y}}} + \frac{\Lambda^{xx}}{\mu^{x}} +
\frac{\Lambda^{xy}}{\sqrt{\mu^{x}\mu^{y}}} +
\frac{\Lambda^{yy}}{\mu^{y}}\right)$. Notably, for convex-concave minimax
problems with bilinear coupling (e.g.\ quadratics), where $\Lambda^{xx} =
\Lambda^{yy} = 0$, our rate matches a lower bound of [ZHZ19].
  (2) Finite sum optimization. We study finite sum optimization problems
$\min_x \frac{1}{n}\sum_{i\in[n]} f_i(x)$, where each $f_i$ is $L_i$-smooth and
the overall problem is $\mu$-strongly convex. We provide an algorithm with
gradient query complexity $\tilde{O}\left(n + \sum_{i\in[n]}
\sqrt{\frac{L_i}{n\mu}} \right)$. Notably, when the smoothness bounds
$\{L_i\}_{i\in[n]}$ are non-uniform, our rate improves upon accelerated SVRG
[LMH15, FGKS15] and Katyusha [All17] by up to a $\sqrt{n}$ factor.
  (3) Minimax finite sums. We generalize our algorithms for minimax and finite
sum optimization to solve a natural family of minimax finite sum optimization
problems at an accelerated rate, encapsulating both above results up to a
logarithmic factor.

In this work we present point-level region contrast, a self-supervised
pre-training approach for the task of object detection. This approach is
motivated by the two key factors in detection: localization and recognition.
While accurate localization favors models that operate at the pixel- or
point-level, correct recognition typically relies on a more holistic,
region-level view of objects. Incorporating this perspective in pre-training,
our approach performs contrastive learning by directly sampling individual
point pairs from different regions. Compared to an aggregated representation
per region, our approach is more robust to the change in input region quality,
and further enables us to implicitly improve initial region assignments via
online knowledge distillation during training. Both advantages are important
when dealing with imperfect regions encountered in the unsupervised setting.
Experiments show point-level region contrast improves on state-of-the-art
pre-training methods for object detection and segmentation across multiple
tasks and datasets, and we provide extensive ablation studies and
visualizations to aid understanding. Code will be made available.

Sample-efficiency guarantees for offline reinforcement learning (RL) often
rely on strong assumptions on both the function classes (e.g.,
Bellman-completeness) and the data coverage (e.g., all-policy concentrability).
Despite the recent efforts on relaxing these assumptions, existing works are
only able to relax one of the two factors, leaving the strong assumption on the
other factor intact. As an important open problem, can we achieve
sample-efficient offline RL with weak assumptions on both factors?
  In this paper we answer the question in the positive. We analyze a simple
algorithm based on the primal-dual formulation of MDPs, where the dual
variables (discounted occupancy) are modeled using a density-ratio function
against offline data. With proper regularization, we show that the algorithm
enjoys polynomial sample complexity, under only realizability and single-policy
concentrability. We also provide alternative analyses based on different
assumptions to shed light on the nature of primal-dual algorithms for offline
RL.

In this paper, we present a local geometric analysis to interpret how deep
feedforward neural networks extract low-dimensional features from
high-dimensional data. Our study shows that, in a local geometric region, the
optimal weight in one layer of the neural network and the optimal feature
generated by the previous layer comprise a low-rank approximation of a matrix
that is determined by the Bayes action of this layer. This result holds (i) for
analyzing both the output layer and the hidden layers of the neural network,
and (ii) for neuron activation functions with non-vanishing gradients. We use
two supervised learning problems to illustrate our results: neural network
based maximum likelihood classification (i.e., softmax regression) and neural
network based minimum mean square estimation. Experimental validation of these
theoretical results will be conducted in our future work.

Autoencoders are a type of unsupervised neural networks, which can be used to
solve various tasks, e.g., dimensionality reduction, image compression, and
image denoising. An AE has two goals: (i) compress the original input to a
low-dimensional space at the bottleneck of the network topology using an
encoder, (ii) reconstruct the input from the representation at the bottleneck
using a decoder. Both encoder and decoder are optimized jointly by minimizing a
distortion-based loss which implicitly forces the model to keep only those
variations of input data that are required to reconstruct the and to reduce
redundancies. In this paper, we propose a scheme to explicitly penalize feature
redundancies in the bottleneck representation. To this end, we propose an
additional loss term, based on the pair-wise correlation of the neurons, which
complements the standard reconstruction loss forcing the encoder to learn a
more diverse and richer representation of the input. We tested our approach
across different tasks: dimensionality reduction using three different dataset,
image compression using the MNIST dataset, and image denoising using fashion
MNIST. The experimental results show that the proposed loss leads consistently
to superior performance compared to the standard AE loss.

In scoring systems used to measure the endoscopic activity of ulcerative
colitis, such as Mayo endoscopic score or Ulcerative Colitis Endoscopic Index
Severity, levels increase with severity of the disease activity. Such relative
ranking among the scores makes it an ordinal regression problem. On the other
hand, most studies use categorical cross-entropy loss function to train deep
learning models, which is not optimal for the ordinal regression problem. In
this study, we propose a novel loss function, class distance weighted
cross-entropy (CDW-CE), that respects the order of the classes and takes the
distance of the classes into account in calculation of the cost. Experimental
evaluations show that models trained with CDW-CE outperform the models trained
with conventional categorical cross-entropy and other commonly used loss
functions which are designed for the ordinal regression problems. In addition,
the class activation maps of models trained with CDW-CE loss are more
class-discriminative and they are found to be more reasonable by the domain
experts.

A major challenge in real-world reinforcement learning (RL) is the sparsity
of reward feedback. Often, what is available is an intuitive but sparse reward
function that only indicates whether the task is completed partially or fully.
However, the lack of carefully designed, fine grain feedback implies that most
existing RL algorithms fail to learn an acceptable policy in a reasonable time
frame. This is because of the large number of exploration actions that the
policy has to perform before it gets any useful feedback that it can learn
from. In this work, we address this challenging problem by developing an
algorithm that exploits the offline demonstration data generated by a
sub-optimal behavior policy for faster and efficient online RL in such sparse
reward settings. The proposed algorithm, which we call the Learning Online with
Guidance Offline (LOGO) algorithm, merges a policy improvement step with an
additional policy guidance step by using the offline demonstration data. The
key idea is that by obtaining guidance from - not imitating - the offline data,
LOGO orients its policy in the manner of the sub-optimal policy, while yet
being able to learn beyond and approach optimality. We provide a theoretical
analysis of our algorithm, and provide a lower bound on the performance
improvement in each learning episode. We also extend our algorithm to the even
more challenging incomplete observation setting, where the demonstration data
contains only a censored version of the true state observation. We demonstrate
the superior performance of our algorithm over state-of-the-art approaches on a
number of benchmark environments with sparse rewards and censored state.
Further, we demonstrate the value of our approach via implementing LOGO on a
mobile robot for trajectory tracking and obstacle avoidance, where it shows

Techniques of machine learning for automatic text categorization are applied
and adapted for the problem of inventory catalog data attribution, with
different approaches explored and optimal solution addressing the tradeoff
between accuracy and performance is selected.

In the absence of governing equations, dimensional analysis is a robust
technique for extracting insights and finding symmetries in physical systems.
Given measurement variables and parameters, the Buckingham Pi theorem provides
a procedure for finding a set of dimensionless groups that spans the solution
space, although this set is not unique. We propose an automated approach using
the symmetric and self-similar structure of available measurement data to
discover the dimensionless groups that best collapse this data to a lower
dimensional space according to an optimal fit. We develop three data-driven
techniques that use the Buckingham Pi theorem as a constraint: (i) a
constrained optimization problem with a non-parametric input-output fitting
function, (ii) a deep learning algorithm (BuckiNet) that projects the input
parameter space to a lower dimension in the first layer, and (iii) a technique
based on sparse identification of nonlinear dynamics (SINDy) to discover
dimensionless equations whose coefficients parameterize the dynamics. We
explore the accuracy, robustness and computational complexity of these methods
as applied to three example problems: a bead on a rotating hoop, a laminar
boundary layer, and Rayleigh-B\'enard convection.

Variational Autoencoders (VAEs) have recently been highly successful at
imputing and acquiring heterogeneous missing data. However, within this
specific application domain, existing VAE methods are restricted by using only
one layer of latent variables and strictly Gaussian posterior approximations.
To address these limitations, we present HH-VAEM, a Hierarchical VAE model for
mixed-type incomplete data that uses Hamiltonian Monte Carlo with automatic
hyper-parameter tuning for improved approximate inference. Our experiments show
that HH-VAEM outperforms existing baselines in the tasks of missing data
imputation and supervised learning with missing features. Finally, we also
present a sampling-based approach for efficiently computing the information
gain when missing features are to be acquired with HH-VAEM. Our experiments
show that this sampling-based approach is superior to alternatives based on
Gaussian approximations.

We initiate a formal study of reproducibility in optimization. We define a
quantitative measure of reproducibility of optimization procedures in the face
of noisy or error-prone operations such as inexact or stochastic gradient
computations or inexact initialization. We then analyze several convex
optimization settings of interest such as smooth, non-smooth, and
strongly-convex objective functions and establish tight bounds on the limits of
reproducibility in each setting. Our analysis reveals a fundamental trade-off
between computation and reproducibility: more computation is necessary (and
sufficient) for better reproducibility.

Neural image compression have reached or out-performed traditional methods
(such as JPEG, BPG, WebP). However,their sophisticated network structures with
cascaded convolution layers bring heavy computational burden for practical
deployment. In this paper, we explore the structural sparsity in neural image
compression network to obtain real-time acceleration without any specialized
hardware design or algorithm. We propose a simple plug-in adaptive binary
channel masking(ABCM) to judge the importance of each convolution channel and
introduce sparsity during training. During inference, the unimportant channels
are pruned to obtain slimmer network and less computation. We implement our
method into three neural image compression networks with different entropy
models to verify its effectiveness and generalization, the experiment results
show that up to 7x computation reduction and 3x acceleration can be achieved
with negligible performance drop.

We consider the regret minimization task in a dueling bandits problem with
context information. In every round of the sequential decision problem, the
learner makes a context-dependent selection of two choice alternatives (arms)
to be compared with each other and receives feedback in the form of noisy
preference information. We assume that the feedback process is determined by a
linear stochastic transitivity model with contextualized utilities (CoLST), and
the learner's task is to include the best arm (with highest latent
context-dependent utility) in the duel. We propose a computationally efficient
algorithm, $\texttt{CoLSTIM}$, which makes its choice based on imitating the
feedback process using perturbed context-dependent utility estimates of the
underlying CoLST model. If each arm is associated with a $d$-dimensional
feature vector, we show that $\texttt{CoLSTIM}$ achieves a regret of order
$\tilde O( \sqrt{dT})$ after $T$ learning rounds. Additionally, we also
establish the optimality of $\texttt{CoLSTIM}$ by showing a lower bound for the
weak regret that refines the existing average regret analysis. Our experiments
demonstrate its superiority over state-of-art algorithms for special cases of
CoLST models.

This paper is concerned with the stability analysis of the recurrent neural
networks (RNNs) by means of the integral quadratic constraint (IQC) framework.
The rectified linear unit (ReLU) is typically employed as the activation
function of the RNN, and the ReLU has specific nonnegativity properties
regarding its input and output signals. Therefore, it is effective if we can
derive IQC-based stability conditions with multipliers taking care of such
nonnegativity properties. However, such nonnegativity (linear) properties are
hardly captured by the existing multipliers defined on the positive
semidefinite cone. To get around this difficulty, we loosen the standard
positive semidefinite cone to the copositive cone, and employ copositive
multipliers to capture the nonnegativity properties. We show that, within the
framework of the IQC, we can employ copositive multipliers (or their inner
approximation) together with existing multipliers such as Zames-Falb
multipliers and polytopic bounding multipliers, and this directly enables us to
ensure that the introduction of the copositive multipliers leads to better (no
more conservative) results. We finally illustrate the effectiveness of the
IQC-based stability conditions with the copositive multipliers by numerical
examples.

Linear systems occur throughout engineering and the sciences, most notably as
differential equations. In many cases the forcing function for the system is
unknown, and interest lies in using noisy observations of the system to infer
the forcing, as well as other unknown parameters. In differential equations,
the forcing function is an unknown function of the independent variables
(typically time and space), and can be modelled as a Gaussian process (GP). In
this paper we show how the adjoint of a linear system can be used to
efficiently infer forcing functions modelled as GPs, using a truncated basis
expansion of the GP kernel. We show how exact conjugate Bayesian inference for
the truncated GP can be achieved, in many cases with substantially lower
computation than would be required using MCMC methods. We demonstrate the
approach on systems of both ordinary and partial differential equations, and
show that the basis expansion approach approximates well the true forcing with
a modest number of basis vectors. Finally, we show how to infer point estimates
for the non-linear model parameters, such as the kernel length-scales, using
Bayesian optimisation.

Pixel-level analysis of blood images plays a pivotal role in diagnosing
blood-related diseases, especially Anaemia. These analyses mainly rely on an
accurate diagnosis of morphological deformities like shape, size, and precise
pixel counting. In traditional segmentation approaches, instance or
object-based approaches have been adopted that are not feasible for pixel-level
analysis. The convolutional neural network (CNN) model required a large dataset
with detailed pixel-level information for the semantic segmentation of red
blood cells in the deep learning domain. In current research work, we address
these problems by proposing a multi-level deep convolutional encoder-decoder
network along with two state-of-the-art healthy and Anaemic-RBC datasets. The
proposed multi-level CNN model preserved pixel-level semantic information
extracted in one layer and then passed to the next layer to choose relevant
features. This phenomenon helps to precise pixel-level counting of healthy and
anaemic-RBC elements along with morphological analysis. For experimental
purposes, we proposed two state-of-the-art RBC datasets, i.e., Healthy-RBCs and
Anaemic-RBCs dataset. Each dataset contains 1000 images, ground truth masks,
relevant, complete blood count (CBC), and morphology reports for performance
evaluation. The proposed model results were evaluated using crossmatch analysis
with ground truth mask by finding IoU, individual training, validation, testing
accuracies, and global accuracies using a 05-fold training procedure. This
model got training, validation, and testing accuracies as 0.9856, 0.9760, and
0.9720 on the Healthy-RBC dataset and 0.9736, 0.9696, and 0.9591 on an
Anaemic-RBC dataset. The IoU and BFScore of the proposed model were 0.9311,
0.9138, and 0.9032, 0.8978 on healthy and anaemic datasets, respectively.

Topic models have been the prominent tools for automatic topic discovery from
text corpora. Despite their effectiveness, topic models suffer from several
limitations including the inability of modeling word ordering information in
documents, the difficulty of incorporating external linguistic knowledge, and
the lack of both accurate and efficient inference methods for approximating the
intractable posterior. Recently, pretrained language models (PLMs) have brought
astonishing performance improvements to a wide variety of tasks due to their
superior representations of text. Interestingly, there have not been standard
approaches to deploy PLMs for topic discovery as better alternatives to topic
models. In this paper, we begin by analyzing the challenges of using PLM
representations for topic discovery, and then propose a joint latent space
learning and clustering framework built upon PLM embeddings. In the latent
space, topic-word and document-topic distributions are jointly modeled so that
the discovered topics can be interpreted by coherent and distinctive terms and
meanwhile serve as meaningful summaries of the documents. Our model effectively
leverages the strong representation power and superb linguistic features
brought by PLMs for topic discovery, and is conceptually simpler than topic
models. On two benchmark datasets in different domains, our model generates
significantly more coherent and diverse topics than strong topic models, and
offers better topic-wise document representations, based on both automatic and
human evaluations.

In this paper we present the high-level functionalities of a
quantum-classical machine learning software, whose purpose is to learn the main
features (the fingerprint) of quantum noise sources affecting a quantum device,
as a quantum computer. Specifically, the software architecture is designed to
classify successfully (more than 99% of accuracy) the noise fingerprints in
different quantum devices with similar technical specifications, or distinct
time-dependences of a noise fingerprint in single quantum machines.

Cellular sheaves equip graphs with a "geometrical" structure by assigning
vector spaces and linear maps to nodes and edges. Graph Neural Networks (GNNs)
implicitly assume a graph with a trivial underlying sheaf. This choice is
reflected in the structure of the graph Laplacian operator, the properties of
the associated diffusion equation, and the characteristics of the convolutional
models that discretise this equation. In this paper, we use cellular sheaf
theory to show that the underlying geometry of the graph is deeply linked with
the performance of GNNs in heterophilic settings and their oversmoothing
behaviour. By considering a hierarchy of increasingly general sheaves, we study
how the ability of the sheaf diffusion process to achieve linear separation of
the classes in the infinite time limit expands. At the same time, we prove that
when the sheaf is non-trivial, discretised parametric diffusion processes have
greater control than GNNs over their asymptotic behaviour. On the practical
side, we study how sheaves can be learned from data. The resulting sheaf
diffusion models have many desirable properties that address the limitations of
classical graph diffusion equations (and corresponding GNN models) and obtain
competitive results in heterophilic settings. Overall, our work provides new
connections between GNNs and algebraic topology and would be of interest to
both fields.

This work provides a complete framework for the simulation, co-optimization,
and sim-to-real transfer of the design and control of soft legged robots. The
compliance of soft robots provides a form of "mechanical intelligence" -- the
ability to passively exhibit behaviors that would otherwise be difficult to
program. Exploiting this capacity requires careful consideration of the
coupling between mechanical design and control. Co-optimization provides a
promising means to generate sophisticated soft robots by reasoning over this
coupling. However, the complex nature of soft robot dynamics makes it difficult
to provide a simulation environment that is both sufficiently accurate to allow
for sim-to-real transfer, while also being fast enough for contemporary
co-optimization algorithms. In this work, we show that finite element
simulation combined with recent model order reduction techniques provide both
the efficiency and the accuracy required to successfully learn effective soft
robot design-control pairs that transfer to reality. We propose a reinforcement
learning-based framework for co-optimization and demonstrate successful
optimization, construction, and zero-shot sim-to-real transfer of several soft
crawling robots. Our learned robot outperforms an expert-designed crawling
robot, showing that our approach can generate novel, high-performing designs
even in well-understood domains.

Recently, Machine Learning (ML), Artificial Intelligence (AI), and
Convolutional Neural Network (CNN) have made huge progress with broad
applications, where their systems have deep learning structures and a large
number of hyperparameters that determine the quality and performance of the
CNNs and AI systems. These systems may have multi-objective ML and AI
performance needs. There is a key requirement to find the optimal
hyperparameters and structures for multi-objective robust optimal CNN systems.
This paper proposes a generalized Taguchi approach to effectively determine the
optimal hyperparameters and structure for the multi-objective robust optimal
CNN systems via their objective performance vector norm. The proposed approach
and methods are applied to a CNN classification system with the original ResNet
for CIFAR-10 dataset as a demonstration and validation, which shows the
proposed methods are highly effective to achieve an optimal accuracy rate of
the original ResNet on CIFAR-10.

In the precision medicine era, there is a growing need for precision
radiotherapy where the planned radiation dose needs to be optimally determined
by considering a myriad of patient-specific information in order to ensure
treatment efficacy. Existing artificial-intelligence (AI) methods can recommend
radiation dose prescriptions within the scope of this available information.
However, treating physicians may not fully entrust the AI's recommended
prescriptions due to known limitations or when the AI recommendation may go
beyond physicians' current knowledge. This paper lays out a systematic method
to integrate expert human knowledge with AI recommendations for optimizing
clinical decision making. Towards this goal, Gaussian process (GP) models are
integrated with deep neural networks (DNNs) to quantify the uncertainty of the
treatment outcomes given by physicians and AI recommendations, respectively,
which are further used as a guideline to educate clinical physicians and
improve AI models performance. The proposed method is demonstrated in a
comprehensive dataset where patient-specific information and treatment outcomes
are prospectively collected during radiotherapy of $67$ non-small cell lung
cancer patients and retrospectively analyzed.

A large number of neural network models of associative memory have been
proposed in the literature. These include the classical Hopfield networks
(HNs), sparse distributed memories (SDMs), and more recently the modern
continuous Hopfield networks (MCHNs), which possesses close links with
self-attention in machine learning. In this paper, we propose a general
framework for understanding the operation of such memory networks as a sequence
of three operations: similarity, separation, and projection. We derive all
these memory models as instances of our general framework with differing
similarity and separation functions. We extend the mathematical framework of
Krotov et al (2020) to express general associative memory models using neural
network dynamics with only second-order interactions between neurons, and
derive a general energy function that is a Lyapunov function of the dynamics.
Finally, using our framework, we empirically investigate the capacity of using
different similarity functions for these associative memory models, beyond the
dot product similarity measure, and demonstrate empirically that Euclidean or
Manhattan distance similarity metrics perform substantially better in practice
on many tasks, enabling a more robust retrieval and higher memory capacity than

Constructing surrogate models for uncertainty quantification (UQ) on complex
partial differential equations (PDEs) having inherently high-dimensional
$\mathcal{O}(10^{\ge 2})$ stochastic inputs (e.g., forcing terms, boundary
conditions, initial conditions) poses tremendous challenges. The curse of
dimensionality can be addressed with suitable unsupervised learning techniques
used as a pre-processing tool to encode inputs onto lower-dimensional subspaces
while retaining its structural information and meaningful properties. In this
work, we review and investigate thirteen dimension reduction methods including
linear and nonlinear, spectral, blind source separation, convex and non-convex
methods and utilize the resulting embeddings to construct a mapping to
quantities of interest via polynomial chaos expansions (PCE). We refer to the
general proposed approach as manifold PCE (m-PCE), where manifold corresponds
to the latent space resulting from any of the studied dimension reduction
methods. To investigate the capabilities and limitations of these methods we
conduct numerical tests for three physics-based systems (treated as
black-boxes) having high-dimensional stochastic inputs of varying complexity
modeled as both Gaussian and non-Gaussian random fields to investigate the
effect of the intrinsic dimensionality of input data. We demonstrate both the
advantages and limitations of the unsupervised learning methods and we conclude
that a suitable m-PCE model provides a cost-effective approach compared to
alternative algorithms proposed in the literature, including recently proposed
expensive deep neural network-based surrogates and can be readily applied for
high-dimensional UQ in stochastic PDEs.

Sports officials around the world are facing incredible challenges due to the
unfair means of practices performed by the athletes to improve their
performance in the game. It includes the intake of hormonal based drugs or
transfusion of blood to increase their strength and the result of their
training. However, the current direct test of detection of these cases includes
the laboratory-based method, which is limited because of the cost factors,
availability of medical experts, etc. This leads us to seek for indirect tests.
With the growing interest of Artificial Intelligence in healthcare, it is
important to propose an algorithm based on blood parameters to improve decision
making. In this paper, we proposed a statistical and machine learning-based
approach to identify the presence of doping substance rhEPO in blood samples.

The method of Common Spatial Patterns (CSP) is widely used for feature
extraction of electroencephalography (EEG) data, such as in motor imagery
brain-computer interface (BCI) systems. It is a data-driven method estimating a
set of spatial filters so that the power of the filtered EEG signal is
maximized for one motor imagery class and minimized for the other. This method,
however, is prone to overfitting and is known to suffer from poor
generalization especially with limited calibration data. Additionally, due to
the high heterogeneity in brain data and the non-stationarity of brain
activity, CSP is usually trained for each user separately resulting in long
calibration sessions or frequent re-calibrations that are tiring for the user.
In this work, we propose a novel algorithm called Spectrally Adaptive Common
Spatial Patterns (SACSP) that improves CSP by learning a temporal/spectral
filter for each spatial filter so that the spatial filters are concentrated on
the most relevant temporal frequencies for each user. We show the efficacy of
SACSP in providing better generalizability and higher classification accuracy
from calibration to online control compared to existing methods. Furthermore,
we show that SACSP provides neurophysiologically relevant information about the
temporal frequencies of the filtered signals. Our results highlight the
differences in the motor imagery signal among BCI users as well as spectral
differences in the signals generated for each class, and show the importance of
learning robust user-specific features in a data-driven manner.

Pretrained language models (PLMs) have demonstrated remarkable performance in
various natural language processing tasks: Unidirectional PLMs (e.g., GPT) are
well known for their superior text generation capabilities; bidirectional PLMs
(e.g., BERT) have been the prominent choice for natural language understanding
(NLU) tasks. While both types of models have achieved promising few-shot
learning performance, their potential for zero-shot learning has been
underexplored. In this paper, we present a simple approach that uses both types
of PLMs for fully zero-shot learning of NLU tasks without requiring any
task-specific data: A unidirectional PLM generates class-conditioned texts
guided by prompts, which are used as the training data for fine-tuning a
bidirectional PLM. With quality training data selected based on the generation
probability and regularization techniques (label smoothing and temporal
ensembling) applied to the fine-tuning stage for better generalization and
stability, our approach demonstrates strong performance across seven
classification tasks of the GLUE benchmark (e.g., 72.3/73.8 on MNLI-m/mm and
92.8 on SST-2), significantly outperforming zero-shot prompting methods and
achieving even comparable results to strong few-shot approaches using 32
training samples per class.

Emerging Metaverse applications demand reliable, accurate, and photorealistic
reproductions of human hands to perform sophisticated operations as if in the
physical world. While real human hand represents one of the most intricate
coordination between bones, muscle, tendon, and skin, state-of-the-art
techniques unanimously focus on modeling only the skeleton of the hand. In this
paper, we present NIMBLE, a novel parametric hand model that includes the
missing key components, bringing 3D hand model to a new level of realism. We
first annotate muscles, bones and skins on the recent Magnetic Resonance
Imaging hand (MRI-Hand) dataset and then register a volumetric template hand
onto individual poses and subjects within the dataset. NIMBLE consists of 20
bones as triangular meshes, 7 muscle groups as tetrahedral meshes, and a skin
mesh. Via iterative shape registration and parameter learning, it further
produces shape blend shapes, pose blend shapes, and a joint regressor. We
demonstrate applying NIMBLE to modeling, rendering, and visual inference tasks.
By enforcing the inner bones and muscles to match anatomic and kinematic rules,
NIMBLE can animate 3D hands to new poses at unprecedented realism. To model the
appearance of skin, we further construct a photometric HandStage to acquire
high-quality textures and normal maps to model wrinkles and palm print.
Finally, NIMBLE also benefits learning-based hand pose and shape estimation by
either synthesizing rich data or acting directly as a differentiable layer in
the inference network.

Diffeomorphic deformable multi-modal image registration is a challenging task
which aims to bring images acquired by different modalities to the same
coordinate space and at the same time to preserve the topology and the
invertibility of the transformation. Recent research has focused on leveraging
deep learning approaches for this task as these have been shown to achieve
competitive registration accuracy while being computationally more efficient
than traditional iterative registration methods. In this work, we propose a
simple yet effective unsupervised deep learning-based {\em multi-modal} image
registration approach that benefits from auxiliary information coming from the
gradient magnitude of the image, i.e. the image edges, during the training. The
intuition behind this is that image locations with a strong gradient are
assumed to denote a transition of tissues, which are locations of high
information value able to act as a geometry constraint. The task is similar to
using segmentation maps to drive the training, but the edge maps are easier and
faster to acquire and do not require annotations. We evaluate our approach in
the context of registering multi-modal (T1w to T2w) magnetic resonance (MR)
brain images of different subjects using three different loss functions that
are said to assist multi-modal registration, showing that in all cases the
auxiliary information leads to better results without compromising the runtime.

Recent works have investigated the sample complexity necessary for fair
machine learning. The most advanced of such sample complexity bounds are
developed by analyzing multicalibration uniform convergence for a given
predictor class. We present a framework which yields multicalibration error
uniform convergence bounds by reparametrizing sample complexities for Empirical
Risk Minimization (ERM) learning. From this framework, we demonstrate that
multicalibration error exhibits dependence on the classifier architecture as
well as the underlying data distribution. We perform an experimental evaluation
to investigate the behavior of multicalibration error for different families of
classifiers. We compare the results of this evaluation to multicalibration
error concentration bounds. Our investigation provides additional perspective
on both algorithmic fairness and multicalibration error convergence bounds.
Given the prevalence of ERM sample complexity bounds, our proposed framework
enables machine learning practitioners to easily understand the convergence
behavior of multicalibration error for a myriad of classifier architectures.

This paper proposes a novel multimodal self-supervised architecture for
energy-efficient audio-visual (AV) speech enhancement that integrates Graph
Neural Networks with canonical correlation analysis (CCA-GNN). The proposed
approach lays its foundations on a state-of-the-art CCA-GNN that learns
representative embeddings by maximizing the correlation between pairs of
augmented views of the same input while decorrelating disconnected features.
The key idea of the conventional CCA-GNN involves discarding
augmentation-variant information and preserving augmentation-invariant
information while preventing capturing of redundant information. Our proposed
AV CCA-GNN model deals with multimodal representation learning context.
Specifically, our model improves contextual AV speech processing by maximizing
canonical correlation from augmented views of the same channel and canonical
correlation from audio and visual embeddings. In addition, it proposes a
positional node encoding that considers a prior-frame sequence distance instead
of a feature-space representation when computing the node's nearest neighbors,
introducing temporal information in the embeddings through the neighborhood's
connectivity. Experiments conducted on the benchmark ChiME3 dataset show that
our proposed prior frame-based AV CCA-GNN ensures better feature learning in
the temporal context, leading to more energy-efficient speech reconstruction
than state-of-the-art CCA-GNN and multilayer perceptron.

Feature selection of high-dimensional labeled data with limited observations
is critical for making powerful predictive modeling accessible, scalable, and
interpretable for domain experts. Spectroscopy data, which records the
interaction between matter and electromagnetic radiation, particularly holds a
lot of information in a single sample. Since acquiring such high-dimensional
data is a complex task, it is crucial to exploit the best analytical tools to
extract necessary information. In this paper, we investigate the most commonly
used feature selection techniques and introduce applying recent explainable AI
techniques to interpret the prediction outcomes of high-dimensional and limited
spectral data. Interpretation of the prediction outcome is beneficial for the
domain experts as it ensures the transparency and faithfulness of the ML models
to the domain knowledge. Due to the instrument resolution limitations,
pinpointing important regions of the spectroscopy data creates a pathway to
optimize the data collection process through the miniaturization of the
spectrometer device. Reducing the device size and power and therefore cost is a
requirement for the real-world deployment of such a sensor-to-prediction system
as a whole. We specifically design three different scenarios to ensure that the
evaluation of ML models is robust for the real-time practice of the developed
methodologies and to uncover the hidden effect of noise sources on the final
outcome.

New concepts for next-generation wireless systems are being developed. It is
expected that these 6G and beyond systems will incorporate more than only
communication, but also sensing, positioning, (deep) edge computing, and other
services. The discussed measurement facility and approach, named Techtile, is
an open, both in design and operation, and unique testbed to evaluate these
newly envisioned systems. Techtile is a multi-functional and versatile testbed,
providing fine-grained distributed resources for new communication, positioning
and sensing technologies. The facility enables experimental research on
hyper-connected interactive environments and validation of new algorithms and
topologies. The backbone connects 140~resource units equipped with edge
computing devices, software-defined radios, sensors, and LED sources. By doing
so, different network topologies and local-versus-central computing can be
assessed. The introduced diversity of i) the technologies (e.g., RF, acoustics
and light), ii) the distributed resources and iii) the interconnectivity allows
exploring more degrees and new types of diversity, which can be investigated in
this testbed.

Fairness has been taken as a critical metric on machine learning models. Many
works studying how to obtain fairness for different tasks emerge. This paper
considers obtaining fairness for link prediction tasks, which can be measured
by dyadic fairness. We aim to propose a pre-processing methodology to obtain
dyadic fairness through data repairing and optimal transport. To obtain dyadic
fairness with satisfying flexibility and unambiguity requirements, we transform
the dyadic repairing to the conditional distribution alignment problem based on
optimal transport and obtain theoretical results on the connection between the
proposed alignment and dyadic fairness. The optimal transport-based dyadic
fairness algorithm is proposed for graph link prediction. Our proposed
algorithm shows superior results on obtaining fairness compared with the other
pre-processing methods on two benchmark graph datasets.

Video quality assessment is a challenging problem having a critical
significance in the context of medical imaging. For instance, in laparoscopic
surgery, the acquired video data suffers from different kinds of distortion
that not only hinder surgery performance but also affect the execution of
subsequent tasks in surgical navigation and robotic surgeries. For this reason,
we propose in this paper neural network-based approaches for distortion
classification as well as quality prediction. More precisely, a Residual
Network (ResNet) based approach is firstly developed for simultaneous ranking
and classification task. Then, this architecture is extended to make it
appropriate for the quality prediction task by using an additional Fully
Connected Neural Network (FCNN). To train the overall architecture (ResNet and
FCNN models), transfer learning and end-to-end learning approaches are
investigated. Experimental results, carried out on a new laparoscopic video
quality database, have shown the efficiency of the proposed methods compared to
recent conventional and deep learning based approaches.

We propose an input sparsity time sampling algorithm that can spectrally
approximate the Gram matrix corresponding to the $q$-fold column-wise tensor
product of $q$ matrices using a nearly optimal number of samples, improving
upon all previously known methods by poly$(q)$ factors. Furthermore, for the
important special case of the $q$-fold self-tensoring of a dataset, which is
the feature matrix of the degree-$q$ polynomial kernel, the leading term of our
method's runtime is proportional to the size of the input dataset and has no
dependence on $q$. Previous techniques either incur poly$(q)$ slowdowns in
their runtime or remove the dependence on $q$ at the expense of having
sub-optimal target dimension, and depend quadratically on the number of
data-points in their runtime. Our sampling technique relies on a collection of
$q$ partially correlated random projections which can be simultaneously applied
to a dataset $X$ in total time that only depends on the size of $X$, and at the
same time their $q$-fold Kronecker product acts as a near-isometry for any
fixed vector in the column span of $X^{\otimes q}$. We also show that our
sampling methods generalize to other classes of kernels beyond polynomial, such
as Gaussian and Neural Tangent kernels.

Machine-learning based recommender systems(RSs) has become an effective means
to help people automatically discover their interests. Existing models often
represent the rich information for recommendation, such as items, users, and
contexts, as embedding vectors and leverage them to predict users' feedback. In
the view of causal analysis, the associations between these embedding vectors
and users' feedback are a mixture of the causal part that describes why an item
is preferred by a user, and the non-causal part that merely reflects the
statistical dependencies between users and items, for example, the exposure
mechanism, public opinions, display position, etc. However, existing RSs mostly
ignored the striking differences between the causal parts and non-causal parts
when using these embedding vectors. In this paper, we propose a model-agnostic
framework named IV4Rec that can effectively decompose the embedding vectors
into these two parts, hence enhancing recommendation results. Specifically, we
jointly consider users' behaviors in search scenarios and recommendation
scenarios. Adopting the concepts in causal analysis, we embed users' search
behaviors as instrumental variables (IVs), to help decompose original embedding
vectors in recommendation, i.e., treatments. IV4Rec then combines the two parts
through deep neural networks and uses the combined results for recommendation.
IV4Rec is model-agnostic and can be applied to a number of existing RSs such as
DIN and NRHUB. Experimental results on both public and proprietary industrial
datasets demonstrate that IV4Rec consistently enhances RSs and outperforms a
framework that jointly considers search and recommendation.

The no-free-lunch theorems promote a skeptical conclusion that all possible
machine learning algorithms equally lack justification. But how could this
leave room for a learning theory, that shows that some algorithms are better
than others? Drawing parallels to the philosophy of induction, we point out
that the no-free-lunch results presuppose a conception of learning algorithms
as purely data-driven. On this conception, every algorithm must have an
inherent inductive bias, that wants justification. We argue that many standard
learning algorithms should rather be understood as model-dependent: in each
application they also require for input a model, representing a bias. Generic
algorithms themselves, they can be given a model-relative justification.

Learning rate schedules are ubiquitously used to speed up and improve
optimisation. Many different policies have been introduced on an empirical
basis, and theoretical analyses have been developed for convex settings.
However, in many realistic problems the loss-landscape is high-dimensional and
non convex -- a case for which results are scarce. In this paper we present a
first analytical study of the role of learning rate scheduling in this setting,
focusing on Langevin optimization with a learning rate decaying as
$\eta(t)=t^{-\beta}$. We begin by considering models where the loss is a
Gaussian random function on the $N$-dimensional sphere ($N\rightarrow \infty$),
featuring an extensive number of critical points. We find that to speed up
optimization without getting stuck in saddles, one must choose a decay rate
$\beta<1$, contrary to convex setups where $\beta=1$ is generally optimal. We
then add to the problem a signal to be recovered. In this setting, the dynamics
decompose into two phases: an \emph{exploration} phase where the dynamics
navigates through rough parts of the landscape, followed by a
\emph{convergence} phase where the signal is detected and the dynamics enter a
convex basin. In this case, it is optimal to keep a large learning rate during
the exploration phase to escape the non-convex region as quickly as possible,
then use the convex criterion $\beta=1$ to converge rapidly to the solution.
Finally, we demonstrate that our conclusions hold in a common regression task
involving neural networks.

Precision phenomenological studies of high-multiplicity scattering processes
at collider experiments present a substantial theoretical challenge and are
vitally important ingredients in experimental measurements. Machine learning
technology has the potential to dramatically optimise simulations for
complicated final states. We investigate the use of neural networks to
approximate matrix elements, studying the case of loop-induced diphoton
production through gluon fusion. We train neural network models on one-loop
amplitudes from the NJet C++ library and interface them with the Sherpa Monte
Carlo event generator to provide the matrix element within a realistic hadronic
collider simulation. Computing some standard observables with the models and
comparing to conventional techniques, we find excellent agreement in the
distributions and a reduced total simulation time by a factor of thirty.

As AI-based systems increasingly impact many areas of our lives, auditing
these systems for fairness is an increasingly high-stakes problem. Traditional
group fairness metrics can miss discrimination against individuals and are
difficult to apply after deployment. Counterfactual fairness describes an
individualized notion of fairness but is even more challenging to evaluate
after deployment. We present prediction sensitivity, an approach for continual
audit of counterfactual fairness in deployed classifiers. Prediction
sensitivity helps answer the question: would this prediction have been
different, if this individual had belonged to a different demographic group --
for every prediction made by the deployed model. Prediction sensitivity can
leverage correlations between protected status and other features and does not
require protected status information at prediction time. Our empirical results
demonstrate that prediction sensitivity is effective for detecting violations
of counterfactual fairness.

While Reinforcement Learning (RL) has made great strides towards solving
increasingly complicated problems, many algorithms are still brittle to even
slight changes in environments. Contextual Reinforcement Learning (cRL)
provides a theoretical framework to model such changes in a principled manner,
thereby enabling flexible, precise and interpretable task specification and
generation. Thus, cRL is an important formalization for studying generalization
in RL. In this work, we reason about solving cRL in theory and practice. We
show that theoretically optimal behavior in contextual Markov Decision
Processes requires explicit context information. In addition, we empirically
explore context-based task generation, utilizing context information in
training and propose cGate, our state-modulating policy architecture. To this
end, we introduce the first benchmark library designed for generalization based
on cRL extensions of popular benchmarks, CARL. In short: Context matters!

We apply object detection techniques based on deep convolutional blocks to
end-to-end jet identification and reconstruction tasks encountered at the CERN
Large Hadron Collider (LHC). Collision events produced at the LHC and
represented as an image composed of calorimeter and tracker cells are given as
an input to a Single Shot Detection network. The algorithm, named PFJet-SSD
performs simultaneous localization, classification and regression tasks to
cluster jets and reconstruct their features. This all-in-one single
feed-forward pass gives advantages in terms of execution time and an improved
accuracy w.r.t. traditional rule-based methods. A further gain is obtained from
network slimming, homogeneous quantization, and optimized runtime for meeting
memory and latency constraints of a typical real-time processing environment.
We experiment with 8-bit and ternary quantization, benchmarking their accuracy
and inference latency against a single-precision floating-point. We show that
the ternary network closely matches the performance of its full-precision
equivalent and outperforms the state-of-the-art rule-based algorithm. Finally,
we report the inference latency on different hardware platforms and discuss
future applications.

A novel strategy to automated classification is introduced which exploits a
fully trained dynamical system to steer items belonging to different categories
toward distinct asymptotic attractors. These latter are incorporated into the
model by taking advantage of the spectral decomposition of the operator that
rules the linear evolution across the processing network. Non-linear terms act
for a transient and allow to disentangle the data supplied as initial condition
to the discrete dynamical system, shaping the boundaries of different
attractors. The network can be equipped with several memory kernels which can
be sequentially activated for serial datasets handling. Our novel approach to
classification, that we here term Recurrent Spectral Network (RSN), is
successfully challenged against a simple test-bed model, created for
illustrative purposes, as well as a standard dataset for image processing
training.

Safe control of constrained linear systems under both epistemic and aleatory
uncertainties is considered. The aleatory uncertainty characterizes random
noises and is modeled by a probability distribution function (PDF) and the
epistemic uncertainty characterizes the lack of knowledge on the system
dynamics. Data-based probabilistic safe controllers are designed for the cases
where the noise PDF is 1) zero-mean Gaussian with a known covariance, 2)
zero-mean Gaussian with an uncertain covariance, and 3) zero-mean non-Gaussian
with an unknown distribution. Easy-to-check model-based conditions for
guaranteeing probabilistic safety are provided for the first case by
introducing probabilistic contractive sets. These results are then extended to
the second and third cases by leveraging distributionally-robust probabilistic
safe control and conditional value-at-risk (CVaR) based probabilistic safe
control, respectively. Data-based implementations of these probabilistic safe
controllers are then considered. It is shown that data-richness requirements
for directly learning a safe controller is considerably weaker than
data-richness requirements for model-based safe control approaches that
undertake a model identification. Moreover, an upper bound on the minimal risk
level, under which the existence of a safe controller is guaranteed, is learned
using collected data. A simulation example is provided to show the
effectiveness of the proposed approach.

Predicting the motion of surrounding vehicles is essential for autonomous
vehicles, as it governs their own motion plan. Current state-of-the-art vehicle
prediction models heavily rely on map information. In reality, however, this
information is not always available. We therefore propose CRAT-Pred, a
multi-modal and non-rasterization-based trajectory prediction model,
specifically designed to effectively model social interactions between
vehicles, without relying on map information. CRAT-Pred applies a graph
convolution method originating from the field of material science to vehicle
prediction, allowing to efficiently leverage edge features, and combines it
with multi-head self-attention. Compared to other map-free approaches, the
model achieves state-of-the-art performance with a significantly lower number
of model parameters. In addition to that, we quantitatively show that the
self-attention mechanism is able to learn social interactions between vehicles,
with the weights representing a measurable interaction score. The source code
is publicly available.

We consider the combinatorial bandits problem with semi-bandit feedback under
finite sampling budget constraints, in which the learner can carry out its
action only for a limited number of times specified by an overall budget. The
action is to choose a set of arms, whereupon feedback for each arm in the
chosen set is received. Unlike existing works, we study this problem in a
non-stochastic setting with subset-dependent feedback, i.e., the semi-bandit
feedback received could be generated by an oblivious adversary and also might
depend on the chosen set of arms. In addition, we consider a general feedback
scenario covering both the numerical-based as well as preference-based case and
introduce a sound theoretical framework for this setting guaranteeing sensible
notions of optimal arms, which a learner seeks to find. We suggest a generic
algorithm suitable to cover the full spectrum of conceivable arm elimination
strategies from aggressive to conservative. Theoretical questions about the
sufficient and necessary budget of the algorithm to find the best arm are
answered and complemented by deriving lower bounds for any learning algorithm
for this problem scenario.

In this brief, we show that sequentially learning new information presented
to a continual (incremental) learning model introduces new security risks: an
intelligent adversary can introduce small amount of misinformation to the model
during training to cause deliberate forgetting of a specific task or class at
test time, thus creating "false memory" about that task. We demonstrate such an
adversary's ability to assume control of the model by injecting "backdoor"
attack samples to commonly used generative replay and regularization based
continual learning approaches using continual learning benchmark variants of
MNIST, as well as the more challenging SVHN and CIFAR 10 datasets. Perhaps most
damaging, we show this vulnerability to be very acute and exceptionally
effective: the backdoor pattern in our attack model can be imperceptible to
human eye, can be provided at any point in time, can be added into the training
data of even a single possibly unrelated task and can be achieved with as few
as just 1\% of total training dataset of a single task.

Solving goal-conditioned tasks with sparse rewards using self-supervised
learning is promising because of its simplicity and stability over current
reinforcement learning (RL) algorithms. A recent work, called Goal-Conditioned
Supervised Learning (GCSL), provides a new learning framework by iteratively
relabeling and imitating self-generated experiences. In this paper, we revisit
the theoretical property of GCSL -- optimizing a lower bound of the goal
reaching objective, and extend GCSL as a novel offline goal-conditioned RL
algorithm. The proposed method is named Weighted GCSL (WGCSL), in which we
introduce an advanced compound weight consisting of three parts (1) discounted
weight for goal relabeling, (2) goal-conditioned exponential advantage weight,
and (3) best-advantage weight. Theoretically, WGCSL is proved to optimize an
equivalent lower bound of the goal-conditioned RL objective and generates
monotonically improved policies via an iterated scheme. The monotonic property
holds for any behavior policies, and therefore WGCSL can be applied to both
online and offline settings. To evaluate algorithms in the offline
goal-conditioned RL setting, we provide a benchmark including a range of point
and simulated robot domains. Experiments in the introduced benchmark
demonstrate that WGCSL can consistently outperform GCSL and existing
state-of-the-art offline methods in the fully offline goal-conditioned setting.

This article proposes an architecture, which allows the prediction of
intention by internally simulating perceptual states represented by action
pattern vectors. To this end, associative self-organising neural networks
(A-SOM) is utilised to build a hierarchical cognitive architecture for
recognition and simulation of the skeleton based human actions. The abilities
of the proposed architecture in recognising and predicting actions is evaluated
in experiments using three different datasets of 3D actions. Based on the
experiments of this article, applying internally simulated perceptual states
represented by action pattern vectors improves the performance of the
recognition task in all experiments. Furthermore, internal simulation of
perception addresses the problem of having limited access to the sensory input,
and also the future prediction of the consecutive perceptual sequences. The
performance of the system is compared and discussed with similar architecture
using self-organizing neural networks (SOM).

The field of automatic music composition has seen great progress in recent
years, specifically with the invention of transformer-based architectures. When
using any deep learning model which considers music as a sequence of events
with multiple complex dependencies, the selection of a proper data
representation is crucial. In this paper, we tackle the task of conditional
drums generation using a novel data encoding scheme inspired by the Compound
Word representation, a tokenization process of sequential data. Therefore, we
present a sequence-to-sequence architecture where a Bidirectional Long
short-term memory (BiLSTM) Encoder receives information about the conditioning
parameters (i.e., accompanying tracks and musical attributes), while a
Transformer-based Decoder with relative global attention produces the generated
drum sequences. We conducted experiments to thoroughly compare the
effectiveness of our method to several baselines. Quantitative evaluation shows
that our model is able to generate drums sequences that have similar
statistical distributions and characteristics to the training corpus. These
features include syncopation, compression ratio, and symmetry among others. We
also verified, through a listening test, that generated drum sequences sound
pleasant, natural and coherent while they "groove" with the given
accompaniment.

A common approach for modeling the environment of an autonomous vehicle are
dynamic occupancy grid maps, in which the surrounding is divided into cells,
each containing the occupancy and velocity state of its location. Despite the
advantage of modeling arbitrary shaped objects, the used algorithms rely on
hand-designed inverse sensor models and semantic information is missing.
Therefore, we introduce a multi-task recurrent neural network to predict grid
maps providing occupancies, velocity estimates, semantic information and the
driveable area. During training, our network architecture, which is a
combination of convolutional and recurrent layers, processes sequences of raw
lidar data, that is represented as bird's eye view images with several height
channels. The multi-task network is trained in an end-to-end fashion to predict
occupancy grid maps without the usual preprocessing steps consisting of
removing ground points and applying an inverse sensor model. In our
evaluations, we show that our learned inverse sensor model is able to overcome
some limitations of a geometric inverse sensor model in terms of representing
object shapes and modeling freespace. Moreover, we report a better runtime
performance and more accurate semantic predictions for our end-to-end approach,
compared to our network relying on measurement grid maps as input data.

We consider stochastic optimization problems where data is drawn from a
Markov chain. Existing methods for this setting crucially rely on knowing the
mixing time of the chain, which in real-world applications is usually unknown.
We propose the first optimization method that does not require the knowledge of
the mixing time, yet obtains the optimal asymptotic convergence rate when
applied to convex problems. We further show that our approach can be extended
to: (i) finding stationary points in non-convex optimization with Markovian
data, and (ii) obtaining better dependence on the mixing time in temporal
difference (TD) learning; in both cases, our method is completely oblivious to
the mixing time. Our method relies on a novel combination of multi-level Monte
Carlo (MLMC) gradient estimation together with an adaptive learning method.

In cooperative multi-agent systems, agents jointly take actions and receive a
team reward instead of individual rewards. In the absence of individual reward
signals, credit assignment mechanisms are usually introduced to discriminate
the contributions of different agents so as to achieve effective cooperation.
Recently, the value decomposition paradigm has been widely adopted to realize
credit assignment, and QMIX has become the state-of-the-art solution. In this
paper, we revisit QMIX from two aspects. First, we propose a new perspective on
credit assignment measurement and empirically show that QMIX suffers limited
discriminability on the assignment of credits to agents. Second, we propose a
gradient entropy regularization with QMIX to realize a discriminative credit
assignment, thereby improving the overall performance. The experiments
demonstrate that our approach can comparatively improve learning efficiency and
achieve better performance.

This paper provides some first steps in developing empirical process theory
for functions taking values in a vector space. Our main results provide bounds
on the entropy of classes of smooth functions taking values in a Hilbert space,
by leveraging theory from differential calculus of vector-valued functions and
fractal dimension theory of metric spaces. We demonstrate how these entropy
bounds can be used to show the uniform law of large numbers and asymptotic
equicontinuity of the function classes, and also apply it to statistical
learning theory in which the output space is a Hilbert space. We conclude with
a discussion on the extension of Rademacher complexities to vector-valued
function classes.

Gradient-based learning algorithms have an implicit simplicity bias which in
effect can limit the diversity of predictors being sampled by the learning
procedure. This behavior can hinder the transferability of trained models by
(i) favoring the learning of simpler but spurious features -- present in the
training data but absent from the test data -- and (ii) by only leveraging a
small subset of predictive features. Such an effect is especially magnified
when the test distribution does not exactly match the train distribution --
referred to as the Out of Distribution (OOD) generalization problem. However,
given only the training data, it is not always possible to apriori assess if a
given feature is spurious or transferable. Instead, we advocate for learning an
ensemble of models which capture a diverse set of predictive features. Towards
this, we propose a new algorithm D-BAT (Diversity-By-disAgreement Training),
which enforces agreement among the models on the training data, but
disagreement on the OOD data. We show how D-BAT naturally emerges from the
notion of generalized discrepancy, as well as demonstrate in multiple
experiments how the proposed method can mitigate shortcut-learning, enhance
uncertainty and OOD detection, as well as improve transferability.

Machine learning is being widely adapted in industrial applications owing to
the capabilities of commercially available hardware and rapidly advancing
research. Volkswagen Financial Services (VWFS), as a market leader in vehicle
leasing services, aims to leverage existing proprietary data and the latest
research to enhance existing and derive new business processes. The
collaboration between Information Systems and Machine Learning Lab (ISMLL) and
VWFS serves to realize this goal. In this paper, we propose methods in the
fields of recommender systems, object detection, and forecasting that enable
data-driven decisions for the vehicle life-cycle at VWFS.

The underwater acoustic signals separation is a key technique for the
underwater communications. The existing methods are mostly model-based, and
could not accurately characterise the practical underwater acoustic
communication environment. They are only suitable for binary signal separation,
but cannot handle multivariate signal separation. On the other hand, the
recurrent neural network (RNN) shows powerful capability in extracting the
features of the temporal sequences. Inspired by this, in this paper, we present
a data-driven approach for underwater acoustic signals separation using deep
learning technology. We use the Bi-directional Long Short-Term Memory (Bi-LSTM)
to explore the features of Time-Frequency (T-F) mask, and propose a T-F mask
aware Bi-LSTM for signal separation. Taking advantage of the sparseness of the
T-F image, the designed Bi-LSTM network is able to extract the discriminative
features for separation, which further improves the separation performance. In
particular, this method breaks through the limitations of the existing methods,
not only achieves good results in multivariate separation, but also effectively
separates signals when mixed with 40dB Gaussian noise signals. The experimental
results show that this method can achieve a $97\%$ guarantee ratio (PSR), and
the average similarity coefficient of the multivariate signal separation is
stable above 0.8 under high noise conditions.

There remains an open question about the usefulness and the interpretation of
Machine learning (MLE) approaches for discrimination of spatial patterns of
brain images between samples or activation states. In the last few decades,
these approaches have limited their operation to feature extraction and linear
classification tasks for between-group inference. In this context, statistical
inference is assessed by randomly permuting image labels or by the use of
random effect models that consider between-subject variability. These
multivariate MLE-based statistical pipelines, whilst potentially more effective
for detecting activations than hypotheses-driven methods, have lost their
mathematical elegance, ease of interpretation, and spatial localization of the
ubiquitous General linear Model (GLM). Recently, the estimation of the
conventional GLM has been demonstrated to be connected to an univariate
classification task when the design matrix is expressed as a binary indicator
matrix. In this paper we explore the complete connection between the univariate
GLM and MLE \emph{regressions}. To this purpose we derive a refined statistical
test with the GLM based on the parameters obtained by a linear Support Vector
Regression (SVR) in the \emph{inverse} problem (SVR-iGLM). Subsequently, random
field theory (RFT) is employed for assessing statistical significance following
a conventional GLM benchmark. Experimental results demonstrate how parameter
estimations derived from each model (mainly GLM and SVR) result in different
experimental design estimates that are significantly related to the predefined
functional task. Moreover, using real data from a multisite initiative the
proposed MLE-based inference demonstrates statistical power and the control of
false positives, outperforming the regular GLM.

Bayesian Neural Networks (BNNs) offer a mathematically grounded framework to
quantify the uncertainty of model predictions but come with a prohibitive
computation cost for both training and inference. In this work, we show a novel
network architecture search (NAS) that optimizes BNNs for both accuracy and
uncertainty while having a reduced inference latency. Different from canonical
NAS that optimizes solely for in-distribution likelihood, the proposed scheme
searches for the uncertainty performance using both in- and out-of-distribution
data. Our method is able to search for the correct placement of Bayesian
layer(s) in a network. In our experiments, the searched models show comparable
uncertainty quantification ability and accuracy compared to the
state-of-the-art (deep ensemble). In addition, the searched models use only a
fraction of the runtime compared to many popular BNN baselines, reducing the
inference runtime cost by $2.98 \times$ and $2.92 \times$ respectively on the
CIFAR10 dataset when compared to MCDropout and deep ensemble.

The optimality and sensitivity of the empirical risk minimization problem
with relative entropy regularization (ERM-RER) are investigated for the case in
which the reference is a sigma-finite measure instead of a probability measure.
This generalization allows for a larger degree of flexibility in the
incorporation of prior knowledge over the set of models. In this setting, the
interplay of the regularization parameter, the reference measure, the risk
function, and the empirical risk induced by the solution of the ERM-RER problem
is characterized. This characterization yields necessary and sufficient
conditions for the existence of a regularization parameter that achieves an
arbitrarily small empirical risk with arbitrarily high probability. The
sensitivity of the expected empirical risk to deviations from the solution of
the ERM-RER problem is studied. The sensitivity is then used to provide upper
and lower bounds on the expected empirical risk. Moreover, it is shown that the
expectation of the sensitivity is upper bounded, up to a constant factor, by
the square root of the lautum information between the models and the datasets.

As an important task for the management of bike sharing systems, accurate
forecast of travel demand could facilitate dispatch and relocation of bicycles
to improve user satisfaction. In recent years, many deep learning algorithms
have been introduced to improve bicycle usage forecast. A typical practice is
to integrate convolutional (CNN) and recurrent neural network (RNN) to capture
spatial-temporal dependency in historical travel demand. For typical CNN, the
convolution operation is conducted through a kernel that moves across a
"matrix-format" city to extract features over spatially adjacent urban areas.
This practice assumes that areas close to each other could provide useful
information that improves prediction accuracy. However, bicycle usage in
neighboring areas might not always be similar, given spatial variations in
built environment characteristics and travel behavior that affect cycling
activities. Yet, areas that are far apart can be relatively more similar in
temporal usage patterns. To utilize the hidden linkage among these distant
urban areas, the study proposes an irregular convolutional Long-Short Term
Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast.
The model modifies traditional CNN with irregular convolutional architecture to
extract dependency among "semantic neighbors". The proposed model is evaluated
with a set of benchmark models in five study sites, which include one dockless
bike sharing system in Singapore, and four station-based systems in Chicago,
Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms
other benchmark models in the five cities. The model also achieves superior
performance in areas with varying levels of bicycle usage and during peak
periods. The findings suggest that "thinking beyond spatial neighbors" can
further improve short-term travel demand prediction of urban bike sharing
systems.

Dynamic movement primitives (DMPs) are a flexible trajectory learning scheme
widely used in motion generation of robotic systems. However, existing
DMP-based methods mainly focus on simple go-to-goal tasks. Motivated to handle
tasks beyond point-to-point motion planning, this work presents temporal logic
guided optimization of motion primitives, namely PIBB-TL algorithm, for complex
manipulation tasks with user preferences. In particular, weighted truncated
linear temporal logic (wTLTL) is incorporated in the PIBB-TL algorithm, which
not only enables the encoding of complex tasks that involve a sequence of
logically organized action plans with user preferences, but also provides a
convenient and efficient means to design the cost function. The black-box
optimization is then adapted to identify optimal shape parameters of DMPs to
enable motion planning of robotic systems. The effectiveness of the PIBB-TL
algorithm is demonstrated via simulation and experime

A central problem in business concerns the optimal allocation of limited
resources to a set of available tasks, where the payoff of these tasks is
inherently uncertain. In credit card fraud detection, for instance, a bank can
only assign a small subset of transactions to their fraud investigations team.
Typically, such problems are solved using a classification framework, where the
focus is on predicting task outcomes given a set of characteristics. Resources
are then allocated to the tasks that are predicted to be the most likely to
succeed. However, we argue that using classification to address task
uncertainty is inherently suboptimal as it does not take into account the
available capacity. Therefore, we first frame the problem as a type of
assignment problem. Then, we present a novel solution using learning to rank by
directly optimizing the assignment's expected profit given limited, stochastic
capacity. This is achieved by optimizing a specific instance of the net
discounted cumulative gain, a commonly used class of metrics in learning to
rank. Empirically, we demonstrate that our new method achieves higher expected
profit and expected precision compared to a classification approach for a wide
variety of application areas and data sets. This illustrates the benefit of an
integrated approach and of explicitly considering the available resources when
learning a predictive model.

In recent years, symbolic regression has been of wide interest to provide an
interpretable symbolic representation of potentially large data relationships.
Initially circled to genetic algorithms, symbolic regression methods now
include a variety of Deep Learning based alternatives. However, these methods
still do not generalize well to real-world data, mainly because they hardly
include domain knowledge nor consider physical relationships between variables
such as known equations and units. Regarding these issues, we propose a
Reinforcement-Based Grammar-Guided Symbolic Regression (RBG2-SR) method that
constrains the representational space with domain-knowledge using context-free
grammar as reinforcement action space. We detail a Partially-Observable Markov
Decision Process (POMDP) modeling of the problem and benchmark our approach
against state-of-the-art methods. We also analyze the POMDP state definition
and propose a physical equation search use case on which we compare our
approach to grammar-based and non-grammarbased symbolic regression methods. The
experiment results show that our method is competitive against other
state-of-the-art methods on the benchmarks and offers the best error-complexity
trade-off, highlighting the interest of using a grammar-based method in a
real-world scenario.

This paper introduces AIVC, an end-to-end neural video codec. It is based on
two conditional autoencoders MNet and CNet, for motion compensation and coding.
AIVC learns to compress videos using any coding configurations through a single
end-to-end rate-distortion optimization. Furthermore, it offers performance
competitive with the recent video coder HEVC under several established test
conditions. A comprehensive ablation study is performed to evaluate the
benefits of the different modules composing AIVC. The implementation is made
available at https://orange-opensource.github.io/AIVC/.

Abstract: Bionic learning with fused sensing, memory and processing functions
outperforms artificial neural networks running on silicon chips in terms of
efficiency and footprint. However, digital hardware implementation of bionic
learning suffers from device heterogeneity in sensors and processing cores,
which incurs large hardware, energy and time overheads. Here, we present a
universal solution to simultaneously perform multi-modal sensing, memory and
processing using organic electrochemical transistors with designed architecture
and tailored channel morphology, selective ion injection into the
crystalline/amorphous regions. The resultant device work as either a volatile
receptor that shows multi-modal sensing, or a non-volatile synapse that
features record-high 10-bit analog states, low switching stochasticity and good
retention without the integration of any extra devices. Homogeneous integration
of such devices enables bionic learning functions such as conditioned reflex
and real-time cardiac disease diagnose via reservoir computing, illustrating
the promise for future smart edge health informatics.

In domain adaptation, when there is a large distance between the source and
target domains, the prediction performance will degrade. Gradual domain
adaptation is one of the solutions to such an issue, assuming that we have
access to intermediate domains, which shift gradually from the source to the
target domain. In previous works, it was assumed that the number of samples in
the intermediate domains was sufficiently large; hence, self-training was
possible without the need for labeled data. If the number of accessible
intermediate domains is restricted, the distances between domains become large,
and self-training will fail. Practically, the cost of samples in intermediate
domains will vary, and it is natural to consider that the closer an
intermediate domain is to the target domain, the higher the cost of obtaining
samples from the intermediate domain is. To solve the trade-off between cost
and accuracy, we propose a framework that combines multifidelity and active
domain adaptation. The effectiveness of the proposed method is evaluated by
experiments with real-world datasets.

Confronted with the spatial heterogeneity of real estate market, some
traditional research utilized Geographically Weighted Regression (GWR) to
estimate the house price. However, its kernel function is non-linear, elusive,
and complex to opt bandwidth, the predictive power could also be improved.
Consequently, a novel technique, Geographical Neural Network Weighted
Regression (GNNWR), has been applied to improve the accuracy of real estate
appraisal with the help of neural networks. Based on Shenzhen house price
dataset, this work conspicuously captures the weight distribution of different
variants at Shenzhen real estate market, which GWR is difficult to materialize.
Moreover, we focus on the performance of GNNWR, verify its robustness and
superiority, refine the experiment process with 10-fold cross-validation,
extend its application area from natural to socioeconomic geospatial data. It's
a practical and trenchant way to assess house price, and we demonstrate the
effectiveness of GNNWR on a complex socioeconomic dataset.

Analysis and prediction of real-world complex systems of nonlinear dynamics
relies largely on surrogate models. Reservoir computers (RC) have proven useful
in replicating the climate of chaotic dynamics. The quality of surrogate models
based on RCs is crucially dependent on judiciously determined optimal
implementation that involves selecting optimal reservoir topology and
hyperparameters. By systematically applying Bayesian hyperparameter
optimization and using ensembles of reservoirs of various topology we show that
the topology of linked reservoirs has no significance in forecasting dynamics
of the chaotic Lorenz system. By simulations we show that simple reservoirs of
unconnected nodes outperform reservoirs of linked reservoirs as surrogate
models for the Lorenz system in different regimes. We give a derivation for why
reservoirs of unconnected nodes have the maximum entropy and hence are optimal.
We conclude that the performance of an RC is based on mere functional
transformation, not in its dynamical properties as has been generally presumed.
Hence, RC could be improved by including information on dynamics more strongly
in the model.

Strategic classification studies learning in settings where self-interested
users can strategically modify their features to obtain favorable predictive
outcomes. A key working assumption, however, is that "favorable" always means
"positive"; this may be appropriate in some applications (e.g., loan approval),
but reduces to a fairly narrow view of what user interests can be. In this work
we argue for a broader perspective on what accounts for strategic user
behavior, and propose and study a flexible model of generalized strategic
classification. Our generalized model subsumes most current models but includes
other novel settings; among these, we identify and target one intriguing
sub-class of problems in which the interests of users and the system are
aligned. This setting reveals a surprising fact: that standard max-margin
losses are ill-suited for strategic inputs. Returning to our fully generalized
model, we propose a novel max-margin framework for strategic learning that is
practical and effective, and which we analyze theoretically. We conclude with a
set of experiments that empirically demonstrate the utility of our approach.

Large pre-trained language models drastically changed the natural language
processing(NLP) landscape. Nowadays, they represent the go-to framework to
tackle diverse NLP tasks, even with a limited number of annotations. However,
using those models in production, either in the cloud or at the edge, remains a
challenge due to the memory footprint and/or inference costs. As an
alternative, recent work on efficient NLP has shown that small weight-efficient
models can reach competitive performance at a fraction of the costs. Here, we
introduce pNLP-Mixer, an embbedding-free model based on the MLP-Mixer
architecture that achieves high weight-efficiency thanks to a novel
linguistically informed projection layer. We evaluate our model on two
multi-lingual semantic parsing datasets, MTOP and multiATIS. On MTOP our
pNLP-Mixer almost matches the performance of mBERT, which has 38 times more
parameters, and outperforms the state-of-the-art of tiny models (pQRNN) with 3
times fewer parameters. On a long-sequence classification task (Hyperpartisan)
our pNLP-Mixer without pretraining outperforms RoBERTa, which has 100 times
more parameters, demonstrating the potential of this architecture.

Most machine learning classifiers only concern classification accuracy, while
certain applications (such as medical diagnosis, meteorological forecasting,
and computation advertising) require the model to predict the true probability,
known as a calibrated estimate. In previous work, researchers have developed
several calibration methods to post-process the outputs of a predictor to
obtain calibrated values, such as binning and scaling methods. Compared with
scaling, binning methods are shown to have distribution-free theoretical
guarantees, which motivates us to prefer binning methods for calibration.
However, we notice that existing binning methods have several drawbacks: (a)
the binning scheme only considers the original prediction values, thus limiting
the calibration performance; and (b) the binning approach is non-individual,
mapping multiple samples in a bin to the same value, and thus is not suitable
for order-sensitive applications. In this paper, we propose a feature-aware
binning framework, called Multiple Boosting Calibration Trees (MBCT), along
with a multi-view calibration loss to tackle the above issues. Our MBCT
optimizes the binning scheme by the tree structures of features, and adopts a
linear function in a tree node to achieve individual calibration. Our MBCT is
non-monotonic, and has the potential to improve order accuracy, due to its
learnable binning scheme and the individual calibration. We conduct
comprehensive experiments on three datasets in different fields. Results show
that our method outperforms all competing models in terms of both calibration
error and order accuracy. We also conduct simulation experiments, justifying
that the proposed multi-view calibration loss is a better metric in modeling
calibration error.

Despite a great deal of research, it is still unclear why neural networks are
so susceptible to adversarial examples. In this work, we identify natural
settings where depth-$2$ ReLU networks trained with gradient flow are provably
non-robust (susceptible to small adversarial $\ell_2$-perturbations), even when
robust networks that classify the training dataset correctly exist. Perhaps
surprisingly, we show that the well-known implicit bias towards margin
maximization induces bias towards non-robust networks, by proving that every
network which satisfies the KKT conditions of the max-margin problem is
non-robust.

Deep reinforcement learning has proven remarkably useful in training agents
from unstructured data. However, the opacity of the produced agents makes it
difficult to ensure that they adhere to various requirements posed by human
engineers. In this work-in-progress report, we propose a technique for
enhancing the reinforcement learning training process (specifically, its reward
calculation), in a way that allows human engineers to directly contribute their
expert knowledge, making the agent under training more likely to comply with
various relevant constraints. Moreover, our proposed approach allows
formulating these constraints using advanced model engineering techniques, such
as scenario-based modeling. This mix of black-box learning-based tools with
classical modeling approaches could produce systems that are effective and
efficient, but are also more transparent and maintainable. We evaluated our
technique using a case-study from the domain of internet congestion control,
obtaining promising results.

With the prevalence of live broadcast business nowadays, a new type of
recommendation service, called live broadcast recommendation, is widely used in
many mobile e-commerce Apps. Different from classical item recommendation, live
broadcast recommendation is to automatically recommend user anchors instead of
items considering the interactions among triple-objects (i.e., users, anchors,
items) rather than binary interactions between users and items. Existing
methods based on binary objects, ranging from early matrix factorization to
recently emerged deep learning, obtain objects' embeddings by mapping from
pre-existing features. Directly applying these techniques would lead to limited
performance, as they are failing to encode collaborative signals among
triple-objects. In this paper, we propose a novel TWo-side Interactive NetworkS
(TWINS) for live broadcast recommendation. In order to fully use both static
and dynamic information on user and anchor sides, we combine a product-based
neural network with a recurrent neural network to learn the embedding of each
object. In addition, instead of directly measuring the similarity, TWINS
effectively injects the collaborative effects into the embedding process in an
explicit manner by modeling interactive patterns between the user's browsing
history and the anchor's broadcast history in both item and anchor aspects.
Furthermore, we design a novel co-retrieval technique to select key items among
massive historic records efficiently. Offline experiments on real large-scale
data show the superior performance of the proposed TWINS, compared to
representative methods; and further results of online experiments on Diantao
App show that TWINS gains average performance improvement of around 8% on ACTR
metric, 3% on UCTR metric, 3.5% on UCVR metric.

Imitation Learning from observation describes policy learning in a similar
way to human learning. An agent's policy is trained by observing an expert
performing a task. While many state-only imitation learning approaches are
based on adversarial imitation learning, one main drawback is that adversarial
training is often unstable and lacks a reliable convergence estimator. If the
true environment reward is unknown and cannot be used to select the
best-performing model, this can result in bad real-world policy performance. We
propose a non-adversarial learning-from-observations approach, together with an
interpretable convergence and performance metric.
  Our training objective minimizes the Kulback-Leibler divergence (KLD) between
the policy and expert state transition trajectories which can be optimized in a
non-adversarial fashion. Such methods demonstrate improved robustness when
learned density models guide the optimization. We further improve the sample
efficiency by rewriting the KLD minimization as the Soft Actor Critic objective
based on a modified reward using additional density models that estimate the
environment's forward and backward dynamics. Finally, we evaluate the
effectiveness of our approach on well-known continuous control environments and
show state-of-the-art performance while having a reliable performance estimator
compared to several recent learning-from-observation methods.

Cross-modal hashing still has some challenges needed to address: (1) most
existing CMH methods take graphs as input to model data distribution. These
methods omit to consider the correlation of graph structure among multiple
modalities; (2) most existing CMH methods ignores considering the fusion
affinity among multi-modalities data; (3) most existing CMH methods relax the
discrete constraints to solve the optimization objective, significantly
degrading the retrieval performance. To solve the above limitations, we propose
a novel Anchor Graph Structure Fusion Hashing (AGSFH). AGSFH constructs the
anchor graph structure fusion matrix from different anchor graphs of multiple
modalities with the Hadamard product, which can fully exploit the geometric
property of underlying data structure. Based on the anchor graph structure
fusion matrix, AGSFH attempts to directly learn an intrinsic anchor graph,
where the structure of the intrinsic anchor graph is adaptively tuned so that
the number of components of the intrinsic graph is exactly equal to the number
of clusters. Besides, AGSFH preserves the anchor fusion affinity into the
common binary Hamming space. Furthermore, a discrete optimization framework is
designed to learn the unified binary codes. Extensive experimental results on
three public social datasets demonstrate the superiority of AGSFH.

5G is the fifth generation wireless network, with a set of characteristics,
e.g., high bandwidth and data rates. The scenarios of using 5G include enhanced
Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and
ultra-Reliable and Low-Latency Communications (uRLLC). 5G is expected to
support a wide variety of applications. We conducted a systematic mapping study
that covers the literature published between Jan 2012 and Dec 2019 regarding
using 5G in smart cities. The scenarios, architecture, technologies,
challenges, and lessons learned of using 5G in smart cities are summarized and
further analyzed based on 32 selected studies, and the results are that: (1)
The studies are distributed over 27 publication venues. 17 studies report
results based on academic studies and 13 studies use demonstration or toy
examples. Only 2 studies report using 5G in smart cities based on industrial
studies. 16 studies include assumptions of 5G network design or smart city
scenarios. (2) The most discussed smart city scenario is transportation,
followed by public safety, healthcare, city tourism, entertainment, and
education. (3) 28 studies propose and/or discuss the architecture of 5G-enabled
smart cities, containing smart city architecture (treating 5G as a component),
5G network architecture in smart cities, and business architecture of using 5G
in smart cities. (4) The most mentioned 5G-related technologies are radio
access technologies, network slicing, and edge computing. (5) Challenges are
mainly about complex context, challenging requirements, and network development
of using 5G in smart cities. (6) Most of the lessons learned identified are
benefits regarding 5G itself or the proposed 5G-related methods in smart
cities. This work provides a reflection of the past eight years of the state of
the art on using 5G in smart cities, which can benefit both researchers and

The distributed nature and privacy-preserving characteristics of federated
learning make it prone to the threat of poisoning attacks, especially backdoor
attacks, where the adversary implants backdoors to misguide the model on
certain attacker-chosen sub-tasks. In this paper, we present a novel method
ARIBA to accurately and robustly identify backdoor attacks in federated
learning. By empirical study, we observe that backdoor attacks are discernible
by the filters of CNN layers. Based on this finding, we employ unsupervised
anomaly detection to evaluate the pre-processed filters and calculate an
anomaly score for each client. We then identify the most suspicious clients
according to their anomaly scores. Extensive experiments are conducted, which
show that our method ARIBA can effectively and robustly defend against multiple
state-of-the-art attacks without degrading model performance.

Recently, federated learning (FL) has emerged as a promising distributed
machine learning (ML) technology, owing to the advancing computational and
sensing capacities of end-user devices, however with the increasing concerns on
users' privacy. As a special architecture in FL, vertical FL (VFL) is capable
of constructing a hyper ML model by embracing sub-models from different
clients. These sub-models are trained locally by vertically partitioned data
with distinct attributes. Therefore, the design of VFL is fundamentally
different from that of conventional FL, raising new and unique research issues.
In this paper, we aim to discuss key challenges in VFL with effective
solutions, and conduct experiments on real-life datasets to shed light on these
issues. Specifically, we first propose a general framework on VFL, and
highlight the key differences between VFL and conventional FL. Then, we discuss
research challenges rooted in VFL systems under four aspects, i.e., security
and privacy risks, expensive computation and communication costs, possible
structural damage caused by model splitting, and system heterogeneity.
Afterwards, we develop solutions to addressing the aforementioned challenges,
and conduct extensive experiments to showcase the effectiveness of our proposed

The task of Outside Knowledge Visual Question Answering (OKVQA) requires an
automatic system to answer natural language questions about pictures and images
using external knowledge. We observe that many visual questions, which contain
deictic referential phrases referring to entities in the image, can be
rewritten as "non-grounded" questions and can be answered by existing
text-based question answering systems. This allows for the reuse of existing
text-based Open Domain Question Answering (QA) Systems for visual question
answering. In this work, we propose a potentially data-efficient approach that
reuses existing systems for (a) image analysis, (b) question rewriting, and (c)
text-based question answering to answer such visual questions. Given an image
and a question pertaining to that image (a visual question), we first extract
the entities present in the image using pre-trained object and scene
classifiers. Using these detected entities, the visual questions can be
rewritten so as to be answerable by open domain QA systems. We explore two
rewriting strategies: (1) an unsupervised method using BERT for masking and
rewriting, and (2) a weakly supervised approach that combines adaptive
rewriting and reinforcement learning techniques to use the implicit feedback
from the QA system. We test our strategies on the publicly available OKVQA
dataset and obtain a competitive performance with state-of-the-art models while
using only 10% of the training data.

Sparse tensors arise in problems in science, engineering, machine learning,
and data analytics. Programs that operate on such tensors can exploit sparsity
to reduce storage requirements and computational time. Developing and
maintaining sparse software by hand, however, is a complex and error-prone
task. Therefore, we propose treating sparsity as a property of tensors, not a
tedious implementation task, and letting a sparse compiler generate sparse code
automatically from a sparsity-agnostic definition of the computation. This
paper discusses integrating this idea into MLIR.

Cardiovascular disease is one of the most challenging diseases in middle-aged
and older people, which causes high mortality. Coronary artery disease (CAD) is
known as a common cardiovascular disease. A standard clinical tool for
diagnosing CAD is angiography. The main challenges are dangerous side effects
and high angiography costs. Today, the development of artificial
intelligence-based methods is a valuable achievement for diagnosing disease.
Hence, in this paper, artificial intelligence methods such as neural network
(NN), deep neural network (DNN), and Fuzzy C-Means clustering combined with
deep neural network (FCM-DNN) are developed for diagnosing CAD on a cardiac
magnetic resonance imaging (CMRI) dataset. The original dataset is used in two
different approaches. First, the labeled dataset is applied to the NN and DNN
to create the NN and DNN models. Second, the labels are removed, and the
unlabeled dataset is clustered via the FCM method, and then, the clustered
dataset is fed to the DNN to create the FCM-DNN model. By utilizing the second
clustering and modeling, the training process is improved, and consequently,
the accuracy is increased. As a result, the proposed FCM-DNN model achieves the
best performance with a 99.91% accuracy specifying 10 clusters, i.e., 5
clusters for healthy subjects and 5 clusters for sick subjects, through the
10-fold cross-validation technique compared to the NN and DNN models reaching
the accuracies of 92.18% and 99.63%, respectively. To the best of our
knowledge, no study has been conducted for CAD diagnosis on the CMRI dataset
using artificial intelligence methods. The results confirm that the proposed
FCM-DNN model can be helpful for scientific and research centers.

With the emergence of Artificial Intelligence (AI), new attention has been
given to implement AI algorithms on resource constrained tiny devices to expand
the application domain of IoT. Multimodal Learning has recently become very
popular with the classification task due to its impressive performance for both
image and audio event classification. This paper presents TinyM$^2$Net -- a
flexible system algorithm co-designed multimodal learning framework for
resource constrained tiny devices. The framework was designed to be evaluated
on two different case-studies: COVID-19 detection from multimodal audio
recordings and battle field object detection from multimodal images and audios.
In order to compress the model to implement on tiny devices, substantial
network architecture optimization and mixed precision quantization were
performed (mixed 8-bit and 4-bit). TinyM$^2$Net shows that even a tiny
multimodal learning model can improve the classification performance than that
of any unimodal frameworks. The most compressed TinyM$^2$Net achieves 88.4%
COVID-19 detection accuracy (14.5% improvement from unimodal base model) and
96.8% battle field object detection accuracy (3.9% improvement from unimodal
base model). Finally, we test our TinyM$^2$Net models on a Raspberry Pi 4 to
see how they perform when deployed to a resource constrained tiny device.

When using recurrent neural networks (RNNs) it is common practice to apply
trained models to sequences longer than those seen in training. This
"extrapolating" usage deviates from the traditional statistical learning setup
where guarantees are provided under the assumption that train and test
distributions are identical. Here we set out to understand when RNNs can
extrapolate, focusing on a simple case where the data generating distribution
is memoryless. We first show that even with infinite training data, there exist
RNN models that interpolate perfectly (i.e., they fit the training data) yet
extrapolate poorly to longer sequences. We then show that if gradient descent
is used for training, learning will converge to perfect extrapolation under
certain assumptions on initialization. Our results complement recent studies on
the implicit bias of gradient descent, showing that it plays a key role in
extrapolation when learning temporal prediction models.

Software-intensive systems produce logs for troubleshooting purposes.
Recently, many deep learning models have been proposed to automatically detect
system anomalies based on log data. These models typically claim very high
detection accuracy. For example, most models report an F-measure greater than
0.9 on the commonly-used HDFS dataset. To achieve a profound understanding of
how far we are from solving the problem of log-based anomaly detection, in this
paper, we conduct an in-depth analysis of five state-of-the-art deep
learning-based models for detecting system anomalies on four public log
datasets. Our experiments focus on several aspects of model evaluation,
including training data selection, data grouping, class distribution, data
noise, and early detection ability. Our results point out that all these
aspects have significant impact on the evaluation, and that all the studied
models do not always work well. The problem of log-based anomaly detection has
not been solved yet. Based on our findings, we also suggest possible future
work.

The Image Difference Captioning (IDC) task aims to describe the visual
differences between two similar images with natural language. The major
challenges of this task lie in two aspects: 1) fine-grained visual differences
that require learning stronger vision and language association and 2) high-cost
of manual annotations that leads to limited supervised data. To address these
challenges, we propose a new modeling framework following the
pre-training-finetuning paradigm. Specifically, we design three self-supervised
tasks and contrastive learning strategies to align visual differences and text
descriptions at a fine-grained level. Moreover, we propose a data expansion
strategy to utilize extra cross-task supervision information, such as data for
fine-grained image classification, to alleviate the limitation of available
supervised IDC data. Extensive experiments on two IDC benchmark datasets,
CLEVR-Change and Birds-to-Words, demonstrate the effectiveness of the proposed
modeling framework. The codes and models will be released at
https://github.com/yaolinli/IDC.

Mobile and edge computing devices for always-on audio classification require
energy-efficient neural network architectures. We present a neural architecture
search (NAS) that optimizes accuracy, energy efficiency and memory usage. The
search is run on Vizier, a black-box optimization service. We present a search
strategy that uses both Bayesian and regularized evolutionary search with
particle swarms, and employs early-stopping to reduce the computational burden.
The search returns architectures for a sound-event classification dataset based
upon AudioSet with similar accuracy to MobileNetV1/V2 implementations but with
an order of magnitude less energy per inference and a much smaller memory
footprint.

We propose a projection-free conditional gradient-type algorithm for smooth
stochastic multi-level composition optimization, where the objective function
is a nested composition of $T$ functions and the constraint set is a closed
convex set. Our algorithm assumes access to noisy evaluations of the functions
and their gradients, through a stochastic first-order oracle satisfying certain
standard unbiasedness and second moment assumptions. We show that the number of
calls to the stochastic first-order oracle and the linear-minimization oracle
required by the proposed algorithm, to obtain an $\epsilon$-stationary
solution, are of order $\mathcal{O}_T(\epsilon^{-2})$ and
$\mathcal{O}_T(\epsilon^{-3})$ respectively, where $\mathcal{O}_T$ hides
constants in $T$. Notably, the dependence of these complexity bounds on
$\epsilon$ and $T$ are separate in the sense that changing one does not impact
the dependence of the bounds on the other. Moreover, our algorithm is
parameter-free and does not require any (increasing) order of mini-batches to
converge unlike the common practice in the analysis of stochastic conditional
gradient-type algorithms.

The vast majority of convergence rates analysis for stochastic gradient
methods in the literature focus on convergence in expectation, whereas
trajectory-wise almost sure convergence is clearly important to ensure that any
instantiation of the stochastic algorithms would converge with probability one.
Here we provide a unified almost sure convergence rates analysis for stochastic
gradient descent (SGD), stochastic heavy-ball (SHB), and stochastic Nesterov's
accelerated gradient (SNAG) methods. We show, for the first time, that the
almost sure convergence rates obtained for these stochastic gradient methods on
strongly convex functions, are arbitrarily close to their optimal convergence
rates possible. For non-convex objective functions, we not only show that a
weighted average of the squared gradient norms converges to zero almost surely,
but also the last iterates of the algorithms. We further provide last-iterate
almost sure convergence rates analysis for stochastic gradient methods on
weakly convex smooth functions, in contrast with most existing results in the
literature that only provide convergence in expectation for a weighted average
of the iterates.

This paper considers the problem of online clustering with bandit feedback. A
set of arms (or items) can be partitioned into various groups that are unknown.
Within each group, the observations associated to each of the arms follow the
same distribution with the same mean vector. At each time step, the agent
queries or pulls an arm and obtains an independent observation from the
distribution it is associated to. Subsequent pulls depend on previous ones as
well as the previously obtained samples. The agent's task is to uncover the
underlying partition of the arms with the least number of arm pulls and with a
probability of error not exceeding a prescribed constant $\delta$. The problem
proposed finds numerous applications from clustering of variants of viruses to
online market segmentation. We present an instance-dependent
information-theoretic lower bound on the expected sample complexity for this
task, and design a computationally efficient and asymptotically optimal
algorithm, namely Bandit Online Clustering (BOC). The algorithm includes a
novel stopping rule for adaptive sequential testing that circumvents the need
to exactly solve any NP-hard weighted clustering problem as its subroutines. We
show through extensive simulations on synthetic and real-world datasets that
BOC's performance matches the lower bound asymptotically, and significantly
outperforms a non-adaptive baseline algorithm.

Deep neural networks are powerful tools for representation learning, but can
easily overfit to noisy labels which are prevalent in many real-world
scenarios. Generally, noisy supervision could stem from variation among
labelers, label corruption by adversaries, etc. To combat such label noises,
one popular line of approach is to apply customized weights to the training
instances, so that the corrupted examples contribute less to the model
learning. However, such learning mechanisms potentially erase important
information about the data distribution and therefore yield suboptimal results.
To leverage useful information from the corrupted instances, an alternative is
the bootstrapping loss, which reconstructs new training targets on-the-fly by
incorporating the network's own predictions (i.e., pseudo-labels).
  In this paper, we propose a more generic learnable loss objective which
enables a joint reweighting of instances and labels at once. Specifically, our
method dynamically adjusts the per-sample importance weight between the real
observed labels and pseudo-labels, where the weights are efficiently determined
in a meta process. Compared to the previous instance reweighting methods, our
approach concurrently conducts implicit relabeling, and thereby yield
substantial improvements with almost no extra cost. Extensive experimental
results demonstrated the strengths of our approach over existing methods on
multiple natural and medical image benchmark datasets, including CIFAR-10,
CIFAR-100, ISIC2019 and Clothing 1M. The code is publicly available at
https://github.com/yuyinzhou/L2B.

Open compound domain adaptation (OCDA) has emerged as a practical adaptation
setting which considers a single labeled source domain against a compound of
multi-modal unlabeled target data in order to generalize better on novel unseen
domains. We hypothesize that an improved disentanglement of domain-related and
task-related factors of dense intermediate layer features can greatly aid OCDA.
Prior-arts attempt this indirectly by employing adversarial domain
discriminators on the spatial CNN output. However, we find that latent features
derived from the Fourier-based amplitude spectrum of deep CNN features hold a
more tractable mapping with domain discrimination. Motivated by this, we
propose a novel feature space Amplitude Spectrum Transformation (AST). During
adaptation, we employ the AST auto-encoder for two purposes. First, carefully
mined source-target instance pairs undergo a simulation of cross-domain feature
stylization (AST-Sim) at a particular layer by altering the AST-latent. Second,
AST operating at a later layer is tasked to normalize (AST-Norm) the domain
content by fixing its latent to a mean prototype. Our simplified adaptation
technique is not only clustering-free but also free from complex adversarial
alignment. We achieve leading performance against the prior arts on the OCDA
scene segmentation benchmarks.

In package-handling facilities, boxes of varying sizes are used to ship
products. Improperly sized boxes with box dimensions much larger than the
product dimensions create wastage and unduly increase the shipping costs. Since
it is infeasible to make unique, tailor-made boxes for each of the $N$
products, the fundamental question that confronts e-commerce companies is: How
many $K << N$ cuboidal boxes need to manufactured and what should be their
dimensions? In this paper, we propose a solution for the single-count shipment
containing one product per box in two steps: (i) reduce it to a clustering
problem in the $3$ dimensional space of length, width and height where each
cluster corresponds to the group of products that will be shipped in a
particular size variant, and (ii) present an efficient forward-backward
decision tree based clustering method with low computational complexity on $N$
and $K$ to obtain these $K$ clusters and corresponding box dimensions. Our
algorithm has multiple constituent parts, each specifically designed to achieve
a high-quality clustering solution. As our method generates clusters in an
incremental fashion without discarding the present solution, adding or deleting
a size variant is as simple as stopping the backward pass early or executing it
for one more iteration. We tested the efficacy of our approach by simulating
actual single-count shipments that were transported during a month by Amazon
using the proposed box dimensions. Even by just modifying the existing box
dimensions and not adding a new size variant, we achieved a reduction of
$4.4\%$ in the shipment volume, contributing to the decrease in non-utilized,
air volume space by $2.2\%$. The reduction in shipment volume and air volume
improved significantly to $10.3\%$ and $6.1\%$ when we introduced $4$
additional boxes.

Recent studies show that deep learning models achieve good performance on
medical imaging tasks such as diagnosis prediction. Among the models,
multimodality has been an emerging trend, integrating different forms of data
such as chest X-ray (CXR) images and electronic medical records (EMRs).
However, most existing methods incorporate them in a model-free manner, which
lacks theoretical support and ignores the intrinsic relations between different
data sources. To address this problem, we propose a knowledge-driven and
data-driven framework for lung disease diagnosis. By incorporating domain
knowledge, machine learning models can reduce the dependence on labeled data
and improve interpretability. We formulate diagnosis rules according to
authoritative clinical medicine guidelines and learn the weights of rules from
text data. Finally, a multimodal fusion consisting of text and image data is
designed to infer the marginal probability of lung disease. We conduct
experiments on a real-world dataset collected from a hospital. The results show
that the proposed method outperforms the state-of-the-art multimodal baselines
in terms of accuracy and interpretability.

Learning-augmented algorithms -- in which, traditional algorithms are
augmented with machine-learned predictions -- have emerged as a framework to go
beyond worst-case analysis. The overarching goal is to design algorithms that
perform near-optimally when the predictions are accurate yet retain certain
worst-case guarantees irrespective of the accuracy of the predictions. This
framework has been successfully applied to online problems such as caching
where the predictions can be used to alleviate uncertainties.
  In this paper we introduce and study the setting in which the
learning-augmented algorithm can utilize the predictions parsimoniously. We
consider the caching problem -- which has been extensively studied in the
learning-augmented setting -- and show that one can achieve quantitatively
similar results but only using a sublinear number of predictions.

Hypothesis testing for small-sample scenarios is a practically important
problem. In this paper, we investigate the robust hypothesis testing problem in
a data-driven manner, where we seek the worst-case detector over distributional
uncertainty sets centered around the empirical distribution from samples using
Sinkhorn distance. Compared with the Wasserstein robust test, the corresponding
least favorable distributions are supported beyond the training samples, which
provides a more flexible detector. Various numerical experiments are conducted
on both synthetic and real datasets to validate the competitive performances of
our proposed method.

We scale perceived distances of the core-set algorithm by a factor of
uncertainty and search for low-confidence configurations, finding significant
improvements in sample efficiency across CIFAR10/100 and SVHN image
classification, especially in larger acquisition sizes. We show the necessity
of our modifications and explain how the improvement is due to a probabilistic
quadratic speed-up in the convergence of core-set loss, under assumptions about
the relationship of model uncertainty and misclassification.

Personalized pricing is a business strategy to charge different prices to
individual consumers based on their characteristics and behaviors. It has
become common practice in many industries nowadays due to the availability of a
growing amount of high granular consumer data. The discriminatory nature of
personalized pricing has triggered heated debates among policymakers and
academics on how to design regulation policies to balance market efficiency and
equity. In this paper, we propose two sound policy instruments, i.e., capping
the range of the personalized prices or their ratios. We investigate the
optimal pricing strategy of a profit-maximizing monopoly under both regulatory
constraints and the impact of imposing them on consumer surplus, producer
surplus, and social welfare. We theoretically prove that both proposed
constraints can help balance consumer surplus and producer surplus at the
expense of total surplus for common demand distributions, such as uniform,
logistic, and exponential distributions. Experiments on both simulation and
real-world datasets demonstrate the correctness of these theoretical results.
Our findings and insights shed light on regulatory policy design for the
increasingly monopolized business in the digital era.

Recently, occluded person re-identification(Re-ID) remains a challenging task
that people are frequently obscured by other people or obstacles, especially in
a crowd massing situation. In this paper, we propose a self-supervised deep
learning method to improve the location performance for human parts through
occluded person Re-ID. Unlike previous works, we find that motion information
derived from the photos of various human postures can help identify major human
body components. Firstly, a motion-aware transformer encoder-decoder
architecture is designed to obtain keypoints heatmaps and part-segmentation
maps. Secondly, an affine transformation module is utilized to acquire motion
information from the keypoint detection branch. Then the motion information
will support the segmentation branch to achieve refined human part segmentation
maps, and effectively divide the human body into reasonable groups. Finally,
several cases demonstrate the efficiency of the proposed model in
distinguishing different representative parts of the human body, which can
avoid the background and occlusion disturbs. Our method consistently achieves
state-of-the-art results on several popular datasets, including occluded,
partial, and holistic.

In this paper, we propose a simple and general framework for self-supervised
point cloud representation learning. Human beings understand the 3D world by
extracting two levels of information and establishing the relationship between
them. One is the global shape of an object, and the other is the local
structures of it. However, few existing studies in point cloud representation
learning explored how to learn both global shapes and local-to-global
relationships without a specified network architecture. Inspired by how human
beings understand the world, we utilize knowledge distillation to learn both
global shape information and the relationship between global shape and local
structures. At the same time, we combine contrastive learning with knowledge
distillation to make the teacher network be better updated. Our method achieves
the state-of-the-art performance on linear classification and multiple other
downstream tasks. Especially, we develop a variant of ViT for 3D point cloud
feature extraction, which also achieves comparable results with existing
backbones when combined with our framework, and visualization of the attention
maps show that our model does understand the point cloud by combining the
global shape information and multiple local structural information, which is
consistent with the inspiration of our representation learning method. Our code
will be released soon.

In presenting an irrigation detection methodology that leverages multiscale
satellite imagery of vegetation abundance, this paper introduces a process to
supplement limited ground-collected labels and ensure classifier applicability
in an area of interest. Spatiotemporal analysis of MODIS 250m Enhanced
Vegetation Index (EVI) timeseries characterizes native vegetation phenologies
at regional scale to provide the basis for a continuous phenology map that
guides supplementary label collection over irrigated and non-irrigated
agriculture. Subsequently, validated dry season greening and senescence cycles
observed in 10m Sentinel-2 imagery are used to train a suite of classifiers for
automated detection of potential smallholder irrigation. Strategies to improve
model robustness are demonstrated, including a method of data augmentation that
randomly shifts training samples; and an assessment of classifier types that
produce the best performance in withheld target regions. The methodology is
applied to detect smallholder irrigation in two states in the Ethiopian
highlands, Tigray and Amhara. Results show that a transformer-based neural
network architecture allows for the most robust prediction performance in
withheld regions, followed closely by a CatBoost random forest model. Over
withheld ground-collection survey labels, the transformer-based model achieves
96.7% accuracy over non-irrigated samples and 95.9% accuracy over irrigated
samples. Over a larger set of samples independently collected via the
introduced method of label supplementation, non-irrigated and irrigated labels
are predicted with 98.3% and 95.5% accuracy, respectively. The detection model
is then deployed over Tigray and Amhara, revealing crop rotation patterns and
year-over-year irrigated area change. Predictions suggest that irrigated area
in these two states has decreased by approximately 40% from 2020 to 2021.

t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data
visualization method in classical machine learning. It maps the data from the
high-dimensional space into a low-dimensional space, especially a
two-dimensional plane, while maintaining the relationship, or similarities,
between the surrounding points. In t-SNE, the initial position of the
low-dimensional data is randomly determined, and the visualization is achieved
by moving the low-dimensional data to minimize a cost function. Its variant
called parametric t-SNE uses neural networks for this mapping. In this paper,
we propose to use quantum neural networks for parametric t-SNE to reflect the
characteristics of high-dimensional quantum data on low-dimensional data. We
use fidelity-based metrics instead of Euclidean distance in calculating
high-dimensional data similarity. We visualize both classical (Iris dataset)
and quantum (time-depending Hamiltonian dynamics) data for classification
tasks. Since this method allows us to represent a quantum dataset in a higher
dimensional Hilbert space by a quantum dataset in a lower dimension while
keeping their similarity, the proposed method can also be used to compress
quantum data for further quantum machine learning.

Existing image recognition techniques based on convolutional neural networks
(CNNs) basically assume that the training and test datasets are sampled from
i.i.d distributions. However, this assumption is easily broken in the real
world because of the distribution shift that occurs when the co-occurrence
relations between objects and backgrounds in input images change. Under this
type of distribution shift, CNNs learn to focus on features that are not
task-relevant, such as backgrounds from the training data, and degrade their
accuracy on the test data. To tackle this problem, we propose relevant feature
focusing (ReFF). ReFF detects task-relevant features and regularizes CNNs via
explanation outputs (e.g., Grad-CAM). Since ReFF is composed of post-hoc
explanation modules, it can be easily applied to off-the-shelf CNNs.
Furthermore, ReFF requires no additional inference cost at test time because it
is only used for regularization while training. We demonstrate that CNNs
trained with ReFF focus on features relevant to the target task and that ReFF
improves the test-time accuracy.

Accident grouping is a crucial step in identifying accident-prone locations.
Among the different accident grouping modes, clustering methods present
excellent performance for discovering different distributions of accidents in
space. This work introduces the Affinity Propagation Clustering (APC) approach
for grouping traffic accidents based on criteria of similarity and
dissimilarity between distributions of data points in space. The APC provides
more realistic representations of the distribution of events from similarity
matrices between instances. The results showed that when representative data
samples obtain, the preference parameter of similarity provides the necessary
performance to calibrate the model and generate clusters according to the
desired characteristics. In addition, the study demonstrates that the
preference parameter as a continuous parameter facilitates the calibration and
control of the model's convergence, allowing the discovery of clustering
patterns with less effort and greater control of the results

Demand response (DR), as one of the important energy resources in the
future's grid, provides the services of peak shaving, enhancing the efficiency
of renewable energy utilization with a short response period, and low cost.
Various categories of DR are established, e.g. automated DR, incentive DR,
emergency DR, and demand bidding. However, with the practical issue of the
unawareness of residential and commercial consumers' utility models, the
researches about demand bidding aggregator involved in the electricity market
are just at the beginning stage. For this issue, the bidding price and bidding
quantity are two required decision variables while considering the
uncertainties due to the market and participants. In this paper, we determine
the bidding and purchasing strategy simultaneously employing the smart meter
data and functions. A two-agent deep deterministic policy gradient method is
developed to optimize the decisions through learning historical bidding
experiences. The online learning further utilizes the daily newest bidding
experience attained to ensure trend tracing and self-adaptation. Two
environment simulators are adopted for testifying the robustness of the model.
The results prove that when facing diverse situations the proposed model can
earn the optimal profit via off/online learning the bidding rules and robustly
making the proper bid.

Information loss in numerical physics simulations can arise from various
sources when solving discretized partial differential equations. In particular,
errors related to numerical precision ("sub-precision errors") can accumulate
in the quantities of interest when the simulations are performed using
low-precision 16-bit floating-point arithmetic compared to an equivalent 64-bit
simulation. On the other hand, low-precision computation is less resource
intensive than high-precision computation. Several machine learning techniques
proposed recently have been successful in correcting errors due to coarse
spatial discretization. In this work, we extend these techniques to improve CFD
simulations performed with low numerical precision. We quantify the
precision-related errors accumulated in a Kolmogorov forced turbulence test
case. Subsequently, we employ a Convolutional Neural Network together with a
fully differentiable numerical solver performing 16-bit arithmetic to learn a
tightly-coupled ML-CFD hybrid solver. Compared to the 16-bit solver, we
demonstrate the efficacy of the hybrid solver towards improving various metrics
pertaining to the statistical and pointwise accuracy of the simulation.

Connected Autonomous Vehicles will make autonomous intersection management a
reality replacing traditional traffic signal control. Autonomous intersection
management requires time and speed adjustment of vehicles arriving at an
intersection for collision-free passing through the intersection. Due to its
computational complexity, this problem has been studied only when vehicle
arrival times towards the vicinity of the intersection are known beforehand,
which limits the applicability of these solutions for real-time deployment. To
solve the real-time autonomous traffic intersection management problem, we
propose a reinforcement learning (RL) based multiagent architecture and a novel
RL algorithm coined multi-discount Q-learning. In multi-discount Q-learning, we
introduce a simple yet effective way to solve a Markov Decision Process by
preserving both short-term and long-term goals, which is crucial for
collision-free speed control. Our empirical results show that our RL-based
multiagent solution can achieve near-optimal performance efficiently when
minimizing the travel time through an intersection.

It is known that when the statistical models are singular, i.e., the Fisher
information matrix at the true parameter is degenerate, the fixed step-size
gradient descent algorithm takes polynomial number of steps in terms of the
sample size $n$ to converge to a final statistical radius around the true
parameter, which can be unsatisfactory for the application. To further improve
that computational complexity, we consider the utilization of the second-order
information in the design of optimization algorithms. Specifically, we study
the normalized gradient descent (NormGD) algorithm for solving parameter
estimation in parametric statistical models, which is a variant of gradient
descent algorithm whose step size is scaled by the maximum eigenvalue of the
Hessian matrix of the empirical loss function of statistical models. When the
population loss function, i.e., the limit of the empirical loss function when
$n$ goes to infinity, is homogeneous in all directions, we demonstrate that the
NormGD iterates reach a final statistical radius around the true parameter
after a logarithmic number of iterations in terms of $n$. Therefore, for fixed
dimension $d$, the NormGD algorithm achieves the optimal overall computational
complexity $\mathcal{O}(n)$ to reach the final statistical radius. This
computational complexity is cheaper than that of the fixed step-size gradient
descent algorithm, which is of the order $\mathcal{O}(n^{\tau})$ for some $\tau
> 1$, to reach the same statistical radius. We illustrate our general theory
under two statistical models: generalized linear models and mixture models, and
experimental results support our prediction with general theory.

We use machine learning techniques to investigate whether it is possible to
replicate the behavior of bank managers who assess the risk of commercial loans
made by a large commercial US bank. Even though a typical bank already relies
on an algorithmic scorecard process to evaluate risk, bank managers are given
significant latitude in adjusting the risk score in order to account for other
holistic factors based on their intuition and experience. We show that it is
possible to find machine learning algorithms that can replicate the behavior of
the bank managers. The input to the algorithms consists of a combination of
standard financials and soft information available to bank managers as part of
the typical loan review process. We also document the presence of significant
heterogeneity in the adjustment process that can be traced to differences
across managers and industries. Our results highlight the effectiveness of
machine learning based analytic approaches to banking and the potential
challenges to high-skill jobs in the financial sector.

Fault diagnosis plays an essential role in reducing the maintenance costs of
rotating machinery manufacturing systems. In many real applications of fault
detection and diagnosis, data tend to be imbalanced, meaning that the number of
samples for some fault classes is much less than the normal data samples. At
the same time, in an industrial condition, accelerometers encounter high levels
of disruptive signals and the collected samples turn out to be heavily noisy.
As a consequence, many traditional Fault Detection and Diagnosis (FDD)
frameworks get poor classification performances when dealing with real-world
circumstances. Three main solutions have been proposed in the literature to
cope with this problem: (1) the implementation of generative algorithms to
increase the amount of under-represented input samples, (2) the employment of a
classifier being powerful to learn from imbalanced and noisy data, (3) the
development of an efficient data pre-processing including feature extraction
and data augmentation. This paper proposes a hybrid framework which uses the
three aforementioned components to achieve an effective signal-based FDD system
for imbalanced conditions. Specifically, it first extracts the fault features,
using Fourier and wavelet transforms to make full use of the signals. Then, it
employs Wasserstein Generative Adversarial Networks (WGAN) to generate
synthetic samples to populate the rare fault class and enhance the training
set. Moreover, to achieve a higher performance a novel combination of
Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning
Machine (WELM) is proposed. To verify the effectiveness of the developed
framework, different datasets settings on different imbalance severities and
noise degrees were used. The comparative results demonstrate that in different
scenarios GAN-CLSTM-ELM outperforms the other state-of-the-art FDD frameworks.

Identifying personalized interventions for an individual is an important
task. Recent work has shown that interventions that do not consider the
demographic background of individual consumers can, in fact, produce the
reverse effect, strengthening opposition to electric vehicles. In this work, we
focus on methods for personalizing interventions based on an individual's
demographics to shift the preferences of consumers to be more positive towards
Battery Electric Vehicles (BEVs). One of the constraints in building models to
suggest interventions for shifting preferences is that each intervention can
influence the effectiveness of later interventions. This, in turn, requires
many subjects to evaluate effectiveness of each possible intervention. To
address this, we propose to identify personalized factors influencing BEV
adoption, such as barriers and motivators. We present a method for predicting
these factors and show that the performance is better than always predicting
the most frequent factors. We then present a Reinforcement Learning (RL) model
that learns the most effective interventions, and compare the number of
subjects required for each approach.

The fundamental challenge of drawing causal inference is that counterfactual
outcomes are not fully observed for any unit. Furthermore, in observational
studies, treatment assignment is likely to be confounded. Many statistical
methods have emerged for causal inference under unconfoundedness conditions
given pre-treatment covariates, including propensity score-based methods,
prognostic score-based methods, and doubly robust methods. Unfortunately for
applied researchers, there is no `one-size-fits-all' causal method that can
perform optimally universally. In practice, causal methods are primarily
evaluated quantitatively on handcrafted simulated data. Such data-generative
procedures can be of limited value because they are typically stylized models
of reality. They are simplified for tractability and lack the complexities of
real-world data. For applied researchers, it is critical to understand how well
a method performs for the data at hand. Our work introduces a deep generative
model-based framework, Credence, to validate causal inference methods. The
framework's novelty stems from its ability to generate synthetic data anchored
at the empirical distribution for the observed sample, and therefore virtually
indistinguishable from the latter. The approach allows the user to specify
ground truth for the form and magnitude of causal effects and confounding bias
as functions of covariates. Thus simulated data sets are used to evaluate the
potential performance of various causal estimation methods when applied to data
similar to the observed sample. We demonstrate Credence's ability to accurately
assess the relative performance of causal estimation techniques in an extensive
simulation study and two real-world data applications from Lalonde and Project
STAR studies.

The recently proposed identifiable variational autoencoder (iVAE) framework
provides a promising approach for learning latent independent components (ICs).
iVAEs use auxiliary covariates to build an identifiable generation structure
from covariates to ICs to observations, and the posterior network approximates
ICs given observations and covariates. Though the identifiability is appealing,
we show that iVAEs could have local minimum solution where observations and the
approximated ICs are independent given covariates.-a phenomenon we referred to
as the posterior collapse problem of iVAEs. To overcome this problem, we
develop a new approach, covariate-informed iVAE (CI-iVAE) by considering a
mixture of encoder and posterior distributions in the objective function. In
doing so, the objective function prevents the posterior collapse, resulting
latent representations that contain more information of the observations.
Furthermore, CI-iVAEs extend the original iVAE objective function to a larger
class and finds the optimal one among them, thus having tighter evidence lower
bounds than the original iVAE. Experiments on simulation datasets, EMNIST,
Fashion-MNIST, and a large-scale brain imaging dataset demonstrate the
effectiveness of our new method.

Generative transformers have experienced rapid popularity growth in the
computer vision community in synthesizing high-fidelity and high-resolution
images. The best generative transformer models so far, however, still treat an
image naively as a sequence of tokens, and decode an image sequentially
following the raster scan ordering (i.e. line-by-line). We find this strategy
neither optimal nor efficient. This paper proposes a novel image synthesis
paradigm using a bidirectional transformer decoder, which we term MaskGIT.
During training, MaskGIT learns to predict randomly masked tokens by attending
to tokens in all directions. At inference time, the model begins with
generating all tokens of an image simultaneously, and then refines the image
iteratively conditioned on the previous generation. Our experiments demonstrate
that MaskGIT significantly outperforms the state-of-the-art transformer model
on the ImageNet dataset, and accelerates autoregressive decoding by up to 64x.
Besides, we illustrate that MaskGIT can be easily extended to various image
editing tasks, such as inpainting, extrapolation, and image manipulation.

We present a data-driven algorithm for efficiently computing stochastic
control policies for general joint chance constrained optimal control problems.
Our approach leverages the theory of kernel distribution embeddings, which
allows representing expectation operators as inner products in a reproducing
kernel Hilbert space. This framework enables approximately reformulating the
original problem using a dataset of observed trajectories from the system
without imposing prior assumptions on the parameterization of the system
dynamics or the structure of the uncertainty. By optimizing over a finite
subset of stochastic open-loop control trajectories, we relax the original
problem to a linear program over the control parameters that can be efficiently
solved using standard convex optimization techniques. We demonstrate our
proposed approach in simulation on a system with nonlinear non-Markovian
dynamics navigating in a cluttered environment.

Programming is a powerful and ubiquitous problem-solving tool. Developing
systems that can assist programmers or even generate programs independently
could make programming more productive and accessible, yet so far incorporating
innovations in AI has proven challenging. Recent large-scale language models
have demonstrated an impressive ability to generate code, and are now able to
complete simple programming tasks. However, these models still perform poorly
when evaluated on more complex, unseen problems that require problem-solving
skills beyond simply translating instructions into code. For example,
competitive programming problems which require an understanding of algorithms
and complex natural language remain extremely challenging. To address this gap,
we introduce AlphaCode, a system for code generation that can create novel
solutions to these problems that require deeper reasoning. In simulated
evaluations on recent programming competitions on the Codeforces platform,
AlphaCode achieved on average a ranking of top 54.3% in competitions with more
than 5,000 participants. We found that three key components were critical to
achieve good and reliable performance: (1) an extensive and clean competitive
programming dataset for training and evaluation, (2) large and
efficient-to-sample transformer-based architectures, and (3) large-scale model
sampling to explore the search space, followed by filtering based on program
behavior to a small set of submissions.

Despite recent advances in achieving fair representations and predictions
through regularization, adversarial debiasing, and contrastive learning in
graph neural networks (GNNs), the working mechanism (i.e., message passing)
behind GNNs inducing unfairness issue remains unknown. In this work, we
theoretically and experimentally demonstrate that representative aggregation in
message-passing schemes accumulates bias in node representation due to topology
bias induced by graph topology. Thus, a \textsf{F}air \textsf{M}essage
\textsf{P}assing (FMP) scheme is proposed to aggregate useful information from
neighbors but minimize the effect of topology bias in a unified framework
considering graph smoothness and fairness objectives. The proposed FMP is
effective, transparent, and compatible with back-propagation training. An
acceleration approach on gradient calculation is also adopted to improve
algorithm efficiency. Experiments on node classification tasks demonstrate that
the proposed FMP outperforms the state-of-the-art baselines in effectively and
efficiently mitigating bias on three real-world datasets.

Deep neural networks need a big amount of training data, while in the real
world there is a scarcity of data available for training purposes. To resolve
this issue unsupervised methods are used for training with limited data. In
this report, we describe the unsupervised semantic feature learning approach
for recognition of the geometric transformation applied to the input data. The
basic concept of our approach is that if someone is unaware of the objects in
the images, he/she would not be able to quantitatively predict the geometric
transformation that was applied to them. This self supervised scheme is based
on pretext task and the downstream task. The pretext classification task to
quantify the geometric transformations should force the CNN to learn high-level
salient features of objects useful for image classification. In our baseline
model, we define image rotations by multiples of 90 degrees. The CNN trained on
this pretext task will be used for the classification of images in the CIFAR-10
dataset as a downstream task. we run the baseline method using various models,
including ResNet, DenseNet, VGG-16, and NIN with a varied number of rotations
in feature extracting and fine-tuning settings. In extension of this baseline
model we experiment with transformations other than rotation in pretext task.
We compare performance of selected models in various settings with different
transformations applied to images,various data augmentation techniques as well
as using different optimizers. This series of different type of experiments
will help us demonstrate the recognition accuracy of our self-supervised model
when applied to a downstream task of classification.

We analyze a large corpus of police incident narrative documents in
understanding the spatial distribution of the topics. The motivation for doing
this is that police narratives in each incident report contains very
fine-grained information that is richer than the category that is manually
assigned by the police. Our approach is to split the corpus into topics using
two different unsupervised machine learning algorithms - Latent Dirichlet
Allocation and Non-negative Matrix Factorization. We validate the performance
of each learned topic model using model coherence. Then, using a k-nearest
neighbors density ratio estimation (kNN-DRE) approach that we propose, we
estimate the spatial density ratio per topic and use this for data discovery
and analysis of each topic, allowing for insights into the described incidents
at scale. We provide a qualitative assessment of each topic and highlight some
key benefits for using our kNN-DRE model for estimating spatial trends.

Multi-institutional efforts can facilitate training of deep MRI
reconstruction models, albeit privacy risks arise during cross-site sharing of
imaging data. Federated learning (FL) has recently been introduced to address
privacy concerns by enabling distributed training without transfer of imaging
data. Existing FL methods for MRI reconstruction employ conditional models to
map from undersampled to fully-sampled acquisitions via explicit knowledge of
the imaging operator. Since conditional models generalize poorly across
different acceleration rates or sampling densities, imaging operators must be
fixed between training and testing, and they are typically matched across
sites. To improve generalization and flexibility in multi-institutional
collaborations, here we introduce a novel method for MRI reconstruction based
on Federated learning of Generative IMage Priors (FedGIMP). FedGIMP leverages a
two-stage approach: cross-site learning of a generative MRI prior, and
subject-specific injection of the imaging operator. The global MRI prior is
learned via an unconditional adversarial model that synthesizes high-quality MR
images based on latent variables. Specificity in the prior is preserved via a
mapper subnetwork that produces site-specific latents. During inference, the
prior is combined with subject-specific imaging operators to enable
reconstruction, and further adapted to individual test samples by minimizing
data-consistency loss. Comprehensive experiments on multi-institutional
datasets clearly demonstrate enhanced generalization performance of FedGIMP
against site-specific and federated methods based on conditional models, as
well as traditional reconstruction methods.

Pre-trained language models (LMs) are shown to easily generate toxic
language. In this work, we systematically explore domain-adaptive training to
reduce the toxicity of language models. We conduct this study on three
dimensions: training corpus, model size, and parameter efficiency. For the
training corpus, we propose to leverage the generative power of LMs and
generate nontoxic datasets for domain-adaptive training, which mitigates the
exposure bias and is shown to be more data-efficient than using a curated
pre-training corpus. We demonstrate that the self-generation method
consistently outperforms the existing baselines across various model sizes on
both automatic and human evaluations, even when it uses a 1/3 smaller training
corpus. We then comprehensively study detoxifying LMs with parameter sizes
ranging from 126M up to 530B (3x larger than GPT-3), a scale that has never
been studied before. We find that i) large LMs have similar toxicity levels as
smaller ones given the same pre-training corpus, and ii) large LMs require more
endeavor to detoxify. We also explore parameter-efficient training methods for
detoxification. We demonstrate that adding and training adapter-only layers in
LMs not only saves a lot of parameters but also achieves a better trade-off
between toxicity and perplexity than whole model adaptation for the large-scale
models.

enhancement in noisy environments are generally trained to minimise the
distance between clean and enhanced speech features. These often result in
improved speech quality however they suffer from a lack of generalisation and
may not deliver the required speech intelligibility in everyday noisy
situations. In an attempt to address these challenges, researchers have
explored intelligibility-oriented (I-O) loss functions to train DL approaches
for robust speech enhancement (SE). In this paper, we formulate a novel
canonical correlation-based I-O loss function to more effectively train DL
algorithms. Specifically, we present a fully convolutional SE model that uses a
modified canonical-correlation based short-time objective intelligibility
(CC-STOI) metric as a training cost function. To the best of our knowledge,
this is the first work that exploits the integration of canonical correlation
in an I-O based loss function for SE. Comparative experimental results
demonstrate that our proposed CC-STOI based SE framework outperforms DL models
trained with conventional STOI and distance-based loss functions, in terms of
both standard objective and subjective evaluation measures when dealing with
unseen speakers and noises.

We introduce a new approach for speech pre-training named SPIRAL which works
by learning denoising representation of perturbed data in a teacher-student
framework. Specifically, given a speech utterance, we first feed the utterance
to a teacher network to obtain corresponding representation. Then the same
utterance is perturbed and fed to a student network. The student network is
trained to output representation resembling that of the teacher. At the same
time, the teacher network is updated as moving average of student's weights
over training steps. In order to prevent representation collapse, we apply an
in-utterance contrastive loss as pre-training objective and impose position
randomization on the input to the teacher. SPIRAL achieves competitive or
better results compared to state-of-the-art speech pre-training method wav2vec
2.0, with significant reduction of training cost (80% for BASE model, 65% for
LARGE model). Furthermore, we address the problem of noise-robustness that is
critical to real-world speech applications. We propose multi-condition
pre-training by perturbing the student's input with various types of additive
noise. We demonstrate that multi-condition pre-trained SPIRAL models are more
robust to noisy speech (9.0% - 13.3% relative word error rate reduction on real
noisy test data), compared to applying multi-condition training solely in the
fine-tuning stage. Source code is available at
https://github.com/huawei-noah/Speech-Backbones/tree/main/SPIRAL.

This report proposes state-of-the-art research in the field of Computer
Assisted Language Learning (CALL). Mispronunciation detection is one of the
core components of Computer Assisted Pronunciation Training (CAPT) systems
which is a subset of CALL. Studies on automated pronunciation error detection
began in the 1990s, but the development of fullfledged CAPTs has only
accelerated in the last decade due to an increase in computing power and
availability of mobile devices for recording speech required for pronunciation
analysis. Detecting Pronunciation errors is a hard problem to solve as there is
no formal definition of correct and incorrect pronunciation. As a result,
typically prosodic and phoneme errors such as phoneme substitution, insertion,
and deletion are detected. Also, it has been agreed upon that learning
pronunciation should focus on speaker intelligibility rather than sounding like
an L1 English speaker. Initially, methods were developed on posterior
likelihood called Good of Pronunciation using Gaussian Mixture Model-Hidden
Markov Model and Deep Neural Network-Hidden Markov Model approaches. These are
complex systems to implement when compared with the recently proposed ASR based
End-to-End mispronunciations detection systems. The purpose of this research is
to create End-to-End (E2E) models using Connectionist Temporal Classification
(CTC) and Attention-based sequence decoder. Recently, E2E models have shown
considerable improvement in mispronunciation detection accuracy. This research
will draw comparison amongst baseline models CNN-RNN-CTC, CNN-RNN-CTC with
character sequence-based attention decoder, and CNN-RNN-CTC with phoneme-based
decoder systems. This study will help us in deciding a better approach towards
developing an efficient mispronunciation detection system.

We study the simultaneous actuator selection and controller design problem
for linear quadratic regulation over a finite horizon, when the system matrices
are unknown a priori. We propose an online actuator selection algorithm to
solve the problem which specifies both a set of actuators to be utilized and
the control policy corresponding to the set of selected actuators.
Specifically, our algorithm is a model based learning algorithm which maintains
an estimate of the system matrices using the system trajectories. The algorithm
then leverages an algorithm for the multiarmed bandit problem to determine the
set of actuators under an actuator selection budget constraint and also
identifies the corresponding control policy that minimizes a quadratic cost
based on the estimated system matrices. We show that the proposed online
actuator selection algorithm yields a sublinear regret.

Applications of neural networks on edge systems have proliferated in recent
years but the ever-increasing model size makes neural networks not able to
deploy on resource-constrained microcontrollers efficiently. We propose
bit-serial weight pools, an end-to-end framework that includes network
compression and acceleration of arbitrary sub-byte precision. The framework can
achieve up to 8x compression compared to 8-bit networks by sharing a pool of
weights across the entire network. We further propose a bit-serial lookup based
software implementation that allows runtime-bitwidth tradeoff and is able to
achieve more than 2.8x speedup and 7.5x storage compression compared to 8-bit
weight pool networks, with less than 1% accuracy drop.

It is well known that Drug Design is often a costly process both in terms of
time and economic effort. While good Quantitative Structure-Activity
Relationship models (QSAR) can help predicting molecular properties without the
need to synthesize them, it is still required to come up with new molecules to
be tested. This is mostly done in lack of tools to determine which
modifications are more promising or which aspects of a molecule are more
influential for the final activity/property. Here we present an automatic
process which involves Graph Convolutional Network models and input-attribution
methods to generate new molecules. We also explore the problems of
over-optimization and applicability, recognizing them as two important aspects
in the practical use of such automatic tools.

Since the introduction of artificial intelligence in medicinal chemistry, the
necessity has emerged to analyse how molecular property variation is modulated
by either single atoms or chemical groups. In this paper, we propose to train
graph-to-graph neural network using semi-supervised learning for attributing
structure-property relationships. As initial case studies we apply the method
to solubility and molecular acidity while checking its consistency in
comparison with known experimental chemical data. As final goal, our approach
could represent a valuable tool to deal with problems such as activity cliffs,
lead optimization and de-novo drug design.

The next generation of telescopes such as the SKA and the Rubin Observatory
will produce enormous data sets, requiring automated anomaly detection to
enable scientific discovery. Here, we present an overview and friendly user
guide to the Astronomaly framework for active anomaly detection in astronomical
data. Astronomaly uses active learning to combine the raw processing power of
machine learning with the intuition and experience of a human user, enabling
personalised recommendations of interesting anomalies. It makes use of a Python
backend to perform data processing, feature extraction and machine learning to
detect anomalous objects; and a JavaScript frontend to allow interaction with
the data, labelling of interesting anomalous and active learning. Astronomaly
is designed to be modular, extendable and run on almost any type of
astronomical data. In this paper, we detail the structure of the Astronomaly
code and provide guidelines for basic usage.

The possibility for humans to interact with physical or virtual systems using
gestures has been vastly explored by researchers and designers in the last
twenty years to provide new and intuitive interaction modalities.
Unfortunately, the literature about gestural interaction is not homogeneous,
and it is characterised by a lack of shared terminology. This leads to
fragmented results and makes it difficult for research activities to build on
top of state-of-the-art results and approaches. The analysis in this paper aims
at creating a common conceptual design framework to enforce development efforts
in gesture-based human-machine interaction. The main contributions of the paper
can be summarised as follows: (i) we provide a broad definition for the notion
of functional gesture in human-machine interaction, (ii) we design a flexible
and expandable gesture taxonomy, and (iii) we put forward a detailed problem
statement for gesture-based human-machine interaction. Finally, to support our
main contribution, the paper presents, and analyses 83 most pertinent articles
classified on the basis of our taxonomy and problem statement.

The performance of a zero-shot sketch-based image retrieval (ZS-SBIR) task is
primarily affected by two challenges. The substantial domain gap between image
and sketch features needs to be bridged, while at the same time the side
information has to be chosen tactfully. Existing literature has shown that
varying the semantic side information greatly affects the performance of
ZS-SBIR. To this end, we propose a novel graph transformer based zero-shot
sketch-based image retrieval (GTZSR) framework for solving ZS-SBIR tasks which
uses a novel graph transformer to preserve the topology of the classes in the
semantic space and propagates the context-graph of the classes within the
embedding features of the visual space. To bridge the domain gap between the
visual features, we propose minimizing the Wasserstein distance between images
and sketches in a learned domain-shared space. We also propose a novel
compatibility loss that further aligns the two visual domains by bridging the
domain gap of one class with respect to the domain gap of all other classes in
the training set. Experimental results obtained on the extended Sketchy,
TU-Berlin, and QuickDraw datasets exhibit sharp improvements over the existing
state-of-the-art methods in both ZS-SBIR and generalized ZS-SBIR.

Facial images disclose many hidden personal traits such as age, gender, race,
health, emotion, and psychology. Understanding these traits will help to
classify the people in different attributes. In this paper, we have presented a
novel method for classifying images using a pretrained transformer model. We
apply the pretrained transformer for the binary classification of facial images
in criminal and non-criminal classes. The pretrained transformer of GPT-2 is
trained to generate text and then fine-tuned to classify facial images. During
the finetuning process with images, most of the layers of GT-2 are frozen
during backpropagation and the model is frozen pretrained transformer (FPT).
The FPT acts as a universal image classifier, and this paper shows the
application of FPT on facial images. We also use our FPT on encrypted images
for classification. Our FPT shows high accuracy on both raw facial images and
encrypted images. We hypothesize the meta-learning capacity FPT gained because
of its large size and trained on a large size with theory and experiments. The
GPT-2 trained to generate a single word token at a time, through the
autoregressive process, forced to heavy-tail distribution. Then the FPT uses
the heavy-tail property as its meta-learning capacity for classifying images.
Our work shows one way to avoid bias during the machine classification of
images.The FPT encodes worldly knowledge because of the pretraining of one
text, which it uses during the classification. The statistical error of
classification is reduced because of the added context gained from the text.Our
paper shows the ethical dimension of using encrypted data for
classification.Criminal images are sensitive to share across the boundary but
encrypted largely evades ethical concern.FPT showing good classification
accuracy on encrypted images shows promise for further research on
privacy-preserving machine learning.

With the rapid expansion of urban areas and the increasingly use of
electricity, the need for locating buried cables is becoming urgent. In this
paper, a noval method to locate underground cables based on Ground Penetrating
Radar (GPR) and Gaussian-process regression is proposed. Firstly, the
coordinate system of the detected area is conducted, and the input and output
of locating buried cables are determined. The GPR is moved along the
established parallel detection lines, and the hyperbolic signatures generated
by buried cables are identified and fitted, thus the positions and depths of
some points on the cable could be derived. On the basis of the established
coordinate system and the derived points on the cable, the clustering method
and cable fitting algorithm based on Gaussian-process regression are proposed
to find the most likely locations of the underground cables. Furthermore, the
confidence intervals of the cable's locations are also obtained. Both the
position and depth noises are taken into account in our method, ensuring the
robustness and feasibility in different environments and equipments.
Experiments on real-world datasets are conducted, and the obtained results
demonstrate the effectiveness of the proposed method.

We propose using a pre-trained segmentation model to perform diagnostic
classification in order to achieve better generalization and interpretability,
terming the technique reverse-transfer learning. We present an architecture to
convert segmentation models to classification models. We compare and contrast
dense vs sparse segmentation labeling and study its impact on diagnostic
classification. We compare the performance of U-Net trained with dense and
sparse labels to segment A-lines, B-lines, and Pleural lines on a custom
dataset of lung ultrasound scans from 4 patients. Our experiments show that
dense labels help reduce false positive detection. We study the classification
capability of the dense and sparse trained U-Net and contrast it with a
non-pretrained U-Net, to detect and differentiate COVID-19 and Pneumonia on a
large ultrasound dataset of about 40k curvilinear and linear probe images. Our
segmentation-based models perform better classification when using pretrained
segmentation weights, with the dense-label pretrained U-Net performing the
best.

Humans communicate non-verbally by sharing physical rhythms, such as nodding
and gestures, to involve each other. This sharing of physicality creates a
sense of unity and makes humans feel involved with others. In this paper, we
developed a new body motion generation system based on the free-energy
principle (FEP), which not only responds passively but also prompts human
actions. The proposed system consists of two modules, the sampling module, and
the motion selection module. We conducted a subjective experiment to evaluate
the "feeling of interacting with the agent" of the FEP based behavior. The
results suggested that FEP based behaviors show more "feeling of interacting
with the agent". Furthermore, we confirmed that the agent's gestures elicited
subject gestures. This result not only reinforces the impression of feeling
interaction but could also realization of agents that encourage people to
change their behavior.

The classic studies of causal emergence have revealed that in some Markovian
dynamical systems, far stronger causal connections can be found on the
higher-level descriptions than the lower-level of the same systems if we
coarse-grain the system states in an appropriate way. However, identifying this
emergent causality from the data is still a hard problem that has not been
solved because the correct coarse-graining strategy can not be found easily.
This paper proposes a general machine learning framework called Neural
Information Squeezer to automatically extract the effective coarse-graining
strategy and the macro-state dynamics, as well as identify causal emergence
directly from the time series data. By decomposing a coarse-graining operation
into two processes: information conversion and information dropping out, we can
not only exactly control the width of the information channel, but also can
derive some important properties analytically including the exact expression of
the effective information of a macro-dynamics. We also show how our framework
can extract the dynamics on different levels and identify causal emergence from
the data on several exampled systems.

Deep learning-based image fusion approaches have obtained wide attention in
recent years, achieving promising performance in terms of visual perception.
However, the fusion module in the current deep learning-based methods suffers
from two limitations, \textit{i.e.}, manually designed fusion function, and
input-independent network learning. In this paper, we propose an unsupervised
adaptive image fusion method to address the above issues. We propose a feature
mutual mapping fusion module and dual-branch multi-scale autoencoder. More
specifically, we construct a global map to measure the connections of pixels
between the input source images. % The found mapping relationship guides the
image fusion. Besides, we design a dual-branch multi-scale network through
sampling transformation to extract discriminative image features. We further
enrich feature representations of different scales through feature aggregation
in the decoding process. Finally, we propose a modified loss function to train
the network with efficient convergence property. Through sufficient training on
infrared and visible image data sets, our method also shows excellent
generalized performance in multi-focus and medical image fusion. Our method
achieves superior performance in both visual perception and objective
evaluation. Experiments prove that the performance of our proposed method on a
variety of image fusion tasks surpasses other state-of-the-art methods, proving
the effectiveness and versatility of our approach.

The end-to-end image fusion framework has achieved promising performance,
with dedicated convolutional networks aggregating the multi-modal local
appearance. However, long-range dependencies are directly neglected in existing
CNN fusion approaches, impeding balancing the entire image-level perception for
complex scenario fusion. In this paper, therefore, we propose an infrared and
visible image fusion algorithm based on a lightweight transformer module and
adversarial learning. Inspired by the global interaction power, we use the
transformer technique to learn the effective global fusion relations. In
particular, shallow features extracted by CNN are interacted in the proposed
transformer fusion module to refine the fusion relationship within the spatial
scope and across channels simultaneously. Besides, adversarial learning is
designed in the training process to improve the output discrimination via
imposing competitive consistency from the inputs, reflecting the specific
characteristics in infrared and visible images. The experimental performance
demonstrates the effectiveness of the proposed modules, with superior
improvement against the state-of-the-art, generalising a novel paradigm via
transformer and adversarial learning in the fusion task.

We design and analyze VA-LUCB, a parameter-free algorithm, for identifying
the best arm under the fixed-confidence setup and under a stringent constraint
that the variance of the chosen arm is strictly smaller than a given threshold.
An upper bound on VA-LUCB's sample complexity is shown to be characterized by a
fundamental variance-aware hardness quantity $H_{VA}$. By proving a lower
bound, we show that sample complexity of VA-LUCB is optimal up to a factor
logarithmic in $H_{VA}$. Extensive experiments corroborate the dependence of
the sample complexity on the various terms in $H_{VA}$. By comparing VA-LUCB's
empirical performance to a close competitor RiskAverse-UCB-BAI by David et al.
(2018), our experiments suggest that VA-LUCB has the lowest sample complexity
for this class of risk-constrained best arm identification problems, especially
for the riskiest instances.

Offline Signature Verification (OSV) is a fundamental biometric task across
various forensic, commercial and legal applications. The underlying task at
hand is to carefully model fine-grained features of the signatures to
distinguish between genuine and forged ones, which differ only in minute
deformities. This makes OSV more challenging compared to other verification
problems. In this work, we propose a two-stage deep learning framework that
leverages self-supervised representation learning as well as metric learning
for writer-independent OSV. First, we train an image reconstruction network
using an encoder-decoder architecture that is augmented by a 2D spatial
attention mechanism using signature image patches. Next, the trained encoder
backbone is fine-tuned with a projector head using a supervised metric learning
framework, whose objective is to optimize a novel dual triplet loss by sampling
negative samples from both within the same writer class as well as from other
writers. The intuition behind this is to ensure that a signature sample lies
closer to its positive counterpart compared to negative samples from both
intra-writer and cross-writer sets. This results in robust discriminative
learning of the embedding space. To the best of our knowledge, this is the
first work of using self-supervised learning frameworks for OSV. The proposed
two-stage framework has been evaluated on two publicly available offline
signature datasets and compared with various state-of-the-art methods. It is
noted that the proposed method provided promising results outperforming several
existing pieces of work. The code is publicly available at:
https://github.com/soumitri2001/SURDS-SSL-OSV

The most common use of data visualization is to minimize the complexity for
proper understanding. A graph is one of the most commonly used representations
for understanding relational data. It produces a simplified representation of
data that is challenging to comprehend if kept in a textual format. In this
study, we propose a methodology to utilize the relational properties of source
code in the form of a graph to identify Just-in-Time (JIT) bug prediction in
software systems during different revisions of software evolution and
maintenance. We presented a method to convert the source codes of commit
patches to equivalent graph representations and named it Source Code Graph
(SCG). To understand and compare multiple source code graphs, we extracted
several structural properties of these graphs, such as the density, number of
cycles, nodes, edges, etc. We then utilized the attribute values of those SCGs
to visualize and detect buggy software commits. We process more than 246K
software commits from 12 subject systems in this investigation. Our
investigation on these 12 open-source software projects written in C++ and Java
programming languages shows that if we combine the features from SCG with
conventional features used in similar studies, we will get the increased
performance of Machine Learning (ML) based buggy commit detection models. We
also find the increase of F1~Scores in predicting buggy and non-buggy commits
statistically significant using the Wilcoxon Signed Rank Test. Since SCG-based
feature values represent the style or structural properties of source code
updates or changes in the software system, it suggests the importance of
careful maintenance of source code style or structure for keeping a software
system bug-free.

Autonomous vehicles are the culmination of advances in many areas such as
sensor technologies, artificial intelligence (AI), networking, and more. This
paper will introduce the reader to the technologies that build autonomous
vehicles. It will focus on open-source tools and libraries for autonomous
vehicle development, making it cheaper and easier for developers and
researchers to participate in the field. The topics covered are as follows.
First, we will discuss the sensors used in autonomous vehicles and summarize
their performance in different environments, costs, and unique features. Then
we will cover Simultaneous Localization and Mapping (SLAM) and algorithms for
each modality. Third, we will review popular open-source driving simulators, a
cost-effective way to train machine learning models and test vehicle software
performance. We will then highlight embedded operating systems and the security
and development considerations when choosing one. After that, we will discuss
Vehicle-to-Vehicle (V2V) and Internet-of-Vehicle (IoV) communication, which are
areas that fuse networking technologies with autonomous vehicles to extend
their functionality. We will then review the five levels of vehicle automation,
commercial and open-source Advanced Driving Assistance Systems, and their
features. Finally, we will touch on the major manufacturing and software
companies involved in the field, their investments, and their partnerships.
These topics will give the reader an understanding of the industry, its
technologies, active research, and the tools available for developers to build
autonomous vehicles.

Although theoretical properties such as expressive power and over-smoothing
of graph neural networks (GNN) have been extensively studied recently, its
convergence property is a relatively new direction. In this paper, we
investigate the convergence of one powerful GNN, Invariant Graph Network (IGN)
over graphs sampled from graphons.
  We first prove the stability of linear layers for general $k$-IGN (of order
$k$) based on a novel interpretation of linear equivariant layers. Building
upon this result, we prove the convergence of $k$-IGN under the model of
\citet{ruiz2020graphon}, where we access the edge weight but the convergence
error is measured for graphon inputs.
  Under the more natural (and more challenging) setting of
\citet{keriven2020convergence} where one can only access 0-1 adjacency matrix
sampled according to edge probability, we first show a negative result that the
convergence of any IGN is not possible. We then obtain the convergence of a
subset of IGNs, denoted as IGN-small, after the edge probability estimation. We
show that IGN-small still contains function class rich enough that can
approximate spectral GNNs arbitrarily well. Lastly, we perform experiments on
various graphon models to verify our statements.

Periodic double auctions (PDA) have applications in many areas such as in
e-commerce, intra-day equity markets, and day-ahead energy markets in
smart-grids. While the trades accomplished using PDAs are worth trillions of
dollars, finding a reliable bidding strategy in such auctions is still a
challenge as it requires the consideration of future auctions. A participating
buyer in a PDA has to design its bidding strategy by planning for current and
future auctions. Many equilibrium-based bidding strategies proposed are complex
to use in real-time. In the current exposition, we propose a scale-based
bidding strategy for buyers participating in PDA. We first present an
equilibrium analysis for single-buyer single-seller multi-unit single-shot
k-Double auctions. Specifically, we analyze the situation when a seller and a
buyer trade two identical units of quantity in a double auction where both the
buyer and the seller deploy a simple, scale-based bidding strategy. The
equilibrium analysis becomes intractable as the number of participants
increases. To be useful in more complex settings such as wholesale markets in
smart-grids, we model equilibrium bidding strategy as a learning problem. We
develop a deep deterministic policy gradient (DDPG) based learning strategy,
DDPGBBS, for a participating agent in PDAs to suggest an action at any auction
instance. DDPGBBS, which empirically follows the obtained theoretical
equilibrium, is easily extendable when the number of buyers/sellers increases.
We take Power Trading Agent Competition's (PowerTAC) wholesale market PDA as a
testbed to evaluate our novel bidding strategy. We benchmark our DDPG based
strategy against several baselines and state-of-the-art bidding strategies of
the PowerTAC wholesale market PDA and demonstrate the efficacy of DDPGBBS
against several benchmarked strategies.

Climate change in India is one of the most alarming problems faced by our
community. Due to adverse and sudden changes in climate in past few years,
mankind is at threat. Various impacts of climate change include extreme heat,
changing rainfall patterns, droughts, groundwater, glacier melt, sea-level
rise, and many more. Machine Learning can be used to analyze and predict the
graph of change using previous data and thus design a model which in the future
can furthermore be used to catalyze impactful work of climate change and take
steps in the direction to help India fight against the upcoming climate
changes. In this paper, we have analyzed 17 climate change parameters about
India. We have applied linear regression, exponential regression, and
polynomial regression to the parameters and evaluated the results. Using the
designed model, we will predict these parameters for the years 2025,2030, 2035.
These predicted values will thus help our community to prevent and take actions
against the adverse and hazardous effects on mankind. We have designed and
created this model which provides accurate results regarding all 17 parameters.
The predicted values will therefore help India to be well equipped against
climate change. This data when made available to the people of India will help
create awareness among them and will help us save our country from the
haphazard effects of climate change.

We present a novel paradigm for modeling certain types of dynamic simulation
in real-time with the aid of neural networks. In order to significantly reduce
the requirements on data (especially time-dependent data), as well as decrease
generalization error, our approach utilizes a data-driven neural network only
to capture quasistatic information (instead of dynamic or time-dependent
information). Subsequently, we augment our quasistatic neural network (QNN)
inference with a (real-time) dynamic simulation layer. Our key insight is that
the dynamic modes lost when using a QNN approximation can be captured with a
quite simple (and decoupled) zero-restlength spring model, which can be
integrated analytically (as opposed to numerically) and thus has no time-step
stability restrictions. Additionally, we demonstrate that the spring
constitutive parameters can be robustly learned from a surprisingly small
amount of dynamic simulation data. Although we illustrate the efficacy of our
approach by considering soft-tissue dynamics on animated human bodies, the
paradigm is extensible to many different simulation frameworks.

In structured prediction, the goal is to jointly predict many output
variables that together encode a structured object -- a path in a graph, an
entity-relation triple, or an ordering of objects. Such a large output space
makes learning hard and requires vast amounts of labeled data. Different
approaches leverage alternate sources of supervision. One approach -- entropy
regularization -- posits that decision boundaries should lie in low-probability
regions. It extracts supervision from unlabeled examples, but remains agnostic
to the structure of the output space. Conversely, neuro-symbolic approaches
exploit the knowledge that not every prediction corresponds to a valid
structure in the output space. Yet, they does not further restrict the learned
output distribution. This paper introduces a framework that unifies both
approaches. We propose a loss, neuro-symbolic entropy regularization, that
encourages the model to confidently predict a valid object. It is obtained by
restricting entropy regularization to the distribution over only valid
structures. This loss is efficiently computed when the output constraint is
expressed as a tractable logic circuit. Moreover, it seamlessly integrates with
other neuro-symbolic losses that eliminate invalid predictions. We demonstrate
the efficacy of our approach on a series of semi-supervised and
fully-supervised structured-prediction experiments, where we find that it leads
to models whose predictions are more accurate and more likely to be valid.

Power estimation is the basis of many hardware optimization strategies.
However, it is still challenging to offer accurate power estimation at an early
stage such as high-level synthesis (HLS). In this paper, we propose PowerGear,
a graph-learning-assisted power estimation approach for FPGA HLS, which
features high accuracy, efficiency and transferability. PowerGear comprises two
main components: a graph construction flow and a customized graph neural
network (GNN) model. Specifically, in the graph construction flow, we introduce
buffer insertion, datapath merging, graph trimming and feature annotation
techniques to transform HLS designs into graph-structured data, which encode
both intra-operation micro-architectures and inter-operation interconnects
annotated with switching activities. Furthermore, we propose a novel
power-aware heterogeneous edge-centric GNN model which effectively learns
heterogeneous edge semantics and structural properties of the constructed
graphs via edge-centric neighborhood aggregation, and fits the formulation of
dynamic power. Compared with on-board measurement, PowerGear estimates total
and dynamic power for new HLS designs with errors of 3.60% and 8.81%,
respectively, which outperforms the prior arts in research and the commercial
product Vivado. In addition, PowerGear demonstrates a speedup of 4x over Vivado
power estimator. Finally, we present a case study in which PowerGear is
exploited to facilitate design space exploration for FPGA HLS, leading to a
performance gain of up to 11.2%, compared with methods using state-of-the-art
predictive models.

As two important textual modalities in electronic health records (EHR), both
structured data (clinical codes) and unstructured data (clinical narratives)
have recently been increasingly applied to the healthcare domain. Most existing
EHR-oriented studies, however, either focus on a particular modality or
integrate data from different modalities in a straightforward manner, which
usually treats structured and unstructured data as two independent sources of
information about patient admission and ignore the intrinsic interactions
between them. In fact, the two modalities are documented during the same
encounter where structured data inform the documentation of unstructured data
and vice versa. In this paper, we proposed a Medical Multimodal Pre-trained
Language Model, named MedM-PLM, to learn enhanced EHR representations over
structured and unstructured data and explore the interaction of two modalities.
In MedM-PLM, two Transformer-based neural network components are firstly
adopted to learn representative characteristics from each modality. A
cross-modal module is then introduced to model their interactions. We
pre-trained MedM-PLM on the MIMIC-III dataset and verified the effectiveness of
the model on three downstream clinical tasks, i.e., medication recommendation,
30-day readmission prediction and ICD coding. Extensive experiments demonstrate
the power of MedM-PLM compared with state-of-the-art methods. Further analyses
and visualizations show the robustness of our model, which could potentially
provide more comprehensive interpretations for clinical decision-making.

Experimentally [1-38] and computationally [39-50] validated machine learning
(ML) articles are sorted based on the size of the training data: 1-100,
101-10000, and 10000+ in a comprehensive set summarizing legacy and recent
advances in the field. The review emphasizes the interrelated fields of
synthesis, characterization, and prediction. Size range 1-100 consists mostly
of Bayesian optimization (BO) articles, whereas 101-10000 consists mostly of
support vector machine (SVM) articles. The articles often use combinations of
ML, feature selection (FS), adaptive design (AD), high-throughput (HiTp)
techniques, and domain knowledge to enhance predictive performance and/or model
interpretability. Grouping cross-validation (G-CV) techniques curb overly
optimistic extrapolative predictive performance. Smaller datasets relying on AD
are typically able to identify new materials with desired properties but do so
in a constrained design space. In larger datasets, the low-hanging fruit of
materials optimization is typically already discovered, and the models are
generally less successful at extrapolating to new materials, especially when
the model training data favors a particular type of material. The large
increase of ML materials science articles that perform experimental or
computational validation on the predicted results demonstrates the
interpenetration of materials informatics with the materials science discipline
and an accelerating materials discovery for real-world applications.

People detection in top-view, fish-eye images is challenging as people in
fish-eye images often appear in arbitrary directions and are distorted
differently. Due to this unique radial geometry, axis-aligned people detectors
often work poorly on fish-eye frames. Recent works account for this variability
by modifying existing anchor-based detectors or relying on complex
pre/post-processing. Anchor-based methods spread a set of pre-defined bounding
boxes on the input image, most of which are invalid. In addition to being
inefficient, this approach could lead to a significant imbalance between the
positive and negative anchor boxes. In this work, we propose ARPD, a
single-stage anchor-free fully convolutional network to detect arbitrarily
rotated people in fish-eye images. Our network uses keypoint estimation to find
the center point of each object and regress the object's other properties
directly. To capture the various orientation of people in fish-eye cameras, in
addition to the center and size, ARPD also predicts the angle of each bounding
box. We also propose a periodic loss function that accounts for angle
periodicity and relieves the difficulty of learning small-angle oscillations.
Experimental results show that our method competes favorably with
state-of-the-art algorithms while running significantly faster.

General real-time runway occupancy time prediction modelling for multiple
airports is a current research gap. An attempt to generalize a real-time
prediction model for Arrival Runway Occupancy Time (AROT) is presented in this
paper by substituting categorical features by their numerical equivalences.
Three days of data, collected from Saab Sensis' Aerobahn system at three US
airports, has been used for this work. Three tree-based machine learning
algorithms: Decision Tree, Random Forest and Gradient Boosting are used to
assess the generalizability of the model using numerical equivalent features.
We have shown that the model trained on numerical equivalent features not only
have performances at least on par with models trained on categorical features
but also can make predictions on unseen data from other airports.

Neonatal respiratory distress is a common condition that if left untreated,
can lead to short- and long-term complications. This paper investigates the
usage of digital stethoscope recorded chest sounds taken within 1min
post-delivery, to enable early detection and prediction of neonatal respiratory
distress. Fifty-one term newborns were included in this study, 9 of whom
developed respiratory distress. For each newborn, 1min anterior and posterior
recordings were taken. These recordings were pre-processed to remove noisy
segments and obtain high-quality heart and lung sounds. The random
undersampling boosting (RUSBoost) classifier was then trained on a variety of
features, such as power and vital sign features extracted from the heart and
lung sounds. The RUSBoost algorithm produced specificity, sensitivity, and
accuracy results of 85.0%, 66.7% and 81.8%, respectively.

From the point of view of the human brain, continual learning can perform
various tasks without mutual interference. An effective way to reduce mutual
interference can be found in sparsity and selectivity of neurons. According to
Aljundi et al. and Hadsell et al., imposing sparsity at the representational
level is advantageous for continual learning because sparse neuronal
activations encourage less overlap between parameters, resulting in less
interference. Similarly, highly selective neural networks are likely to induce
less interference since particular response in neurons will reduce the chance
of overlap with other parameters. Considering that the human brain performs
continual learning over the lifespan, finding conditions where sparsity and
selectivity naturally arises may provide insight for understanding how the
brain functions. This paper investigates various conditions that naturally
increase sparsity and selectivity in a neural network. This paper tested
different optimizers with Hoyer's sparsity metric and CCMAS selectivity metric
in MNIST classification task. It is essential to note that investigations on
the natural occurrence of sparsity and selectivity concerning various
conditions have not been acknowledged in any sector of neuroscience nor machine
learning until this day. This paper found that particular conditions increase
sparsity and selectivity such as applying a large learning rate and lowering a
batch size. In addition to the relationship between the condition, sparsity,
and selectivity, the following will be discussed based on empirical analysis:
1. The relationship between sparsity and selectivity and 2. The relationship
between test accuracy, sparsity, and selectivity.

Bangla Handwritten Digit recognition is a significant step forward in the
development of Bangla OCR. However, intricate shape, structural likeness and
distinctive composition style of Bangla digits makes it relatively challenging
to distinguish. Thus, in this paper, we benchmarked four rigorous classifiers
to recognize Bangla Handwritten Digit: K-Nearest Neighbor (KNN), Support Vector
Machine (SVM), Random Forest (RF), and Gradient-Boosted Decision Trees (GBDT)
based on three handcrafted feature extraction techniques: Histogram of Oriented
Gradients (HOG), Local Binary Pattern (LBP), and Gabor filter on four publicly
available Bangla handwriting digits datasets: NumtaDB, CMARTdb, Ekush and BDRW.
Here, handcrafted feature extraction methods are used to extract features from
the dataset image, which are then utilized to train machine learning
classifiers to identify Bangla handwritten digits. We further fine-tuned the
hyperparameters of the classification algorithms in order to acquire the finest
Bangla handwritten digits recognition performance from these algorithms, and
among all the models we employed, the HOG features combined with SVM model
(HOG+SVM) attained the best performance metrics across all datasets. The
recognition accuracy of the HOG+SVM method on the NumtaDB, CMARTdb, Ekush and
BDRW datasets reached 93.32%, 98.08%, 95.68% and 89.68%, respectively as well
as we compared the model performance with recent state-of-art methods.

We propose RecShard, a fine-grained embedding table (EMB) partitioning and
placement technique for deep learning recommendation models (DLRMs). RecShard
is designed based on two key observations. First, not all EMBs are equal, nor
all rows within an EMB are equal in terms of access patterns. EMBs exhibit
distinct memory characteristics, providing performance optimization
opportunities for intelligent EMB partitioning and placement across a tiered
memory hierarchy. Second, in modern DLRMs, EMBs function as hash tables. As a
result, EMBs display interesting phenomena, such as the birthday paradox,
leaving EMBs severely under-utilized. RecShard determines an optimal EMB
sharding strategy for a set of EMBs based on training data distributions and
model characteristics, along with the bandwidth characteristics of the
underlying tiered memory hierarchy. In doing so, RecShard achieves over 6 times
higher EMB training throughput on average for capacity constrained DLRMs. The
throughput increase comes from improved EMB load balance by over 12 times and
from the reduced access to the slower memory by over 87 times.

Federated learning (FL) has attracted much attention as a privacy-preserving
distributed machine learning framework, where many clients collaboratively
train a machine learning model by exchanging model updates with a parameter
server instead of sharing their raw data. Nevertheless, FL training suffers
from slow convergence and unstable performance due to stragglers caused by the
heterogeneous computational resources of clients and fluctuating communication
rates. This paper proposes a coded FL framework to mitigate the straggler
issue, namely stochastic coded federated learning (SCFL). In this framework,
each client generates a privacy-preserving coded dataset by adding additive
noise to the random linear combination of its local data. The server collects
the coded datasets from all the clients to construct a composite dataset, which
helps to compensate for the straggling effect. In the training process, the
server as well as clients perform mini-batch stochastic gradient descent (SGD),
and the server adds a make-up term in model aggregation to obtain unbiased
gradient estimates. We characterize the privacy guarantee by the mutual
information differential privacy (MI-DP) and analyze the convergence
performance in federated learning. Besides, we demonstrate a
privacy-performance tradeoff of the proposed SCFL method by analyzing the
influence of the privacy constraint on the convergence rate. Finally, numerical
experiments corroborate our analysis and show the benefits of SCFL in achieving
fast convergence while preserving data privacy.

Understanding natural symmetries is key to making sense of our complex and
ever-changing world. Recent work has shown that neural networks can learn such
symmetries directly from data using Hamiltonian Neural Networks (HNNs). But
HNNs struggle when trained on datasets where energy is not conserved. In this
paper, we ask whether it is possible to identify and decompose conservative and
dissipative dynamics simultaneously. We propose Dissipative Hamiltonian Neural
Networks (D-HNNs), which parameterize both a Hamiltonian and a Rayleigh
dissipation function. Taken together, they represent an implicit Helmholtz
decomposition which can separate dissipative effects such as friction from
symmetries such as conservation of energy. We train our model to decompose a
damped mass-spring system into its friction and inertial terms and then show
that this decomposition can be used to predict dynamics for unseen friction
coefficients. Then we apply our model to real world data including a large,
noisy ocean current dataset where decomposing the velocity field yields useful
scientific insights.

Super-resolution as an ill-posed problem has many high-resolution candidates
for a low-resolution input. However, the popular $\ell_1$ loss used to best fit
the given HR image fails to consider this fundamental property of
non-uniqueness in image restoration. In this work, we fix the missing piece in
$\ell_1$ loss by formulating super-resolution with neural networks as a
probabilistic model. It shows that $\ell_1$ loss is equivalent to a degraded
likelihood function that removes the randomness from the learning process. By
introducing a data-adaptive random variable, we present a new objective
function that aims at minimizing the expectation of the reconstruction error
over all plausible solutions. The experimental results show consistent
improvements on mainstream architectures, with no extra parameter or computing
cost at inference time.

Cardiovascular disease has become one of the most significant threats
endangering human life and health. Recently, Electrocardiogram (ECG) monitoring
has been transformed into remote cardiac monitoring by Holter surveillance.
However, the widely used Holter can bring a great deal of discomfort and
inconvenience to the individuals who carry them. We developed a new wireless
ECG patch in this work and applied a deep learning framework based on the
Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) models.
However, we find that the models using the existing techniques are not able to
differentiate two main heartbeat types (Supraventricular premature beat and
Atrial fibrillation) in our newly obtained dataset, resulting in low accuracy
of 58.0 %. We proposed a semi-supervised method to process the badly labelled
data samples with using the confidence-level-based training. The experiment
results conclude that the proposed method can approach an average accuracy of
90.2 %, i.e., 5.4 % higher than the accuracy of conventional ECG classification
methods.

In this paper, we focus on a new method of data augmentation to solve the
data imbalance problem within imbalanced ECG datasets to improve the robustness
and accuracy of heart disease detection. By using Optimal Transport, we augment
the ECG disease data from normal ECG beats to balance the data among different
categories. We build a Multi-Feature Transformer (MF-Transformer) as our
classification model, where different features are extracted from both time and
frequency domains to diagnose various heart conditions. Learning from 12-lead
ECG signals, our model is able to distinguish five categories of cardiac
conditions. Our results demonstrate 1) the classification models' ability to
make competitive predictions on five ECG categories; 2) improvements in
accuracy and robustness reflecting the effectiveness of our data augmentation
method.

The ability to learn reward functions plays an important role in enabling the
deployment of intelligent agents in the real world. However, comparing reward
functions, for example as a means of evaluating reward learning methods,
presents a challenge. Reward functions are typically compared by considering
the behavior of optimized policies, but this approach conflates deficiencies in
the reward function with those of the policy search algorithm used to optimize
it. To address this challenge, Gleave et al. (2020) propose the
Equivalent-Policy Invariant Comparison (EPIC) distance. EPIC avoids policy
optimization, but in doing so requires computing reward values at transitions
that may be impossible under the system dynamics. This is problematic for
learned reward functions because it entails evaluating them outside of their
training distribution, resulting in inaccurate reward values that we show can
render EPIC ineffective at comparing rewards. To address this problem, we
propose the Dynamics-Aware Reward Distance (DARD), a new reward pseudometric.
DARD uses an approximate transition model of the environment to transform
reward functions into a form that allows for comparisons that are invariant to
reward shaping while only evaluating reward functions on transitions close to
their training distribution. Experiments in simulated physical domains
demonstrate that DARD enables reliable reward comparisons without policy
optimization and is significantly more predictive than baseline methods of
downstream policy performance when dealing with learned reward functions.

Automatic detection of polyps is challenging because different polyps vary
greatly, while the changes between polyps and their analogues are small. The
state-of-the-art methods are based on convolutional neural networks (CNNs).
However, they may fail due to lack of training data, resulting in high rates of
missed detection and false positives (FPs). In order to solve these problems,
our method combines the two-dimensional (2-D) CNN-based real-time object
detector network with spatiotemporal information. Firstly, we use a 2-D
detector network to detect static images and frames, and based on the detector
network, we propose two feature enhancement modules-the FP Relearning Module
(FPRM) to make the detector network learning more about the features of FPs for
higher precision, and the Image Style Transfer Module (ISTM) to enhance the
features of polyps for sensitivity improvement. In video detection, we
integrate spatiotemporal information, which uses Structural Similarity (SSIM)
to measure the similarity between video frames. Finally, we propose the
Inter-frame Similarity Correlation Unit (ISCU) to combine the results obtained
by the detector network and frame similarity to make the final decision. We
verify our method on both private databases and publicly available databases.
Experimental results show that these modules and units provide a performance
improvement compared with the baseline method. Comparison with the
state-of-the-art methods shows that the proposed method outperforms the
existing ones which can meet real-time constraints. It's demonstrated that our
method provides a performance improvement in sensitivity, precision and
specificity, and has great potential to be applied in clinical colonoscopy.

Frame interpolation is an essential video processing technique that adjusts
the temporal resolution of an image sequence. While deep learning has brought
great improvements to the area of video frame interpolation, techniques that
make use of neural networks can typically not easily be deployed in practical
applications like a video editor since they are either computationally too
demanding or fail at high resolutions. In contrast, we propose a deep learning
approach that solely relies on splatting to synthesize interpolated frames.
This splatting-based synthesis for video frame interpolation is not only much
faster than similar approaches, especially for multi-frame interpolation, but
can also yield new state-of-the-art results at high resolutions.

Spectral Barron spaces have received considerable interest recently as it is
the natural function space for approximation theory of two-layer neural
networks with a dimension-free convergence rate. In this paper we study the
regularity of solutions to the whole-space static Schr\"odinger equation in
spectral Barron spaces. We prove that if the source of the equation lies in the
spectral Barron space $\mathcal{B}^s(\mathbb{R}^d)$ and the potential function
admitting a non-negative lower bound decomposes as a positive constant plus a
function in $\mathcal{B}^s(\mathbb{R}^d)$, then the solution lies in the
spectral Barron space $\mathcal{B}^{s+2}(\mathbb{R}^d)$.

With the success of offline reinforcement learning (RL), offline trained RL
policies have the potential to be further improved when deployed online. A
smooth transfer of the policy matters in safe real-world deployment. Besides,
fast adaptation of the policy plays a vital role in practical online
performance improvement. To tackle these challenges, we propose a simple yet
efficient algorithm, Model-based Offline-to-Online Reinforcement learning
(MOORe), which employs a prioritized sampling scheme that can dynamically
adjust the offline and online data for smooth and efficient online adaptation
of the policy. We provide a theoretical foundation for our algorithms design.
Experiment results on the D4RL benchmark show that our algorithm smoothly
transfers from offline to online stages while enabling sample-efficient online
adaption, and also significantly outperforms existing methods.

Link prediction aims to infer the link existence between pairs of nodes in
networks/graphs. Despite their wide application, the success of traditional
link prediction algorithms is hindered by three major challenges -- link
sparsity, node attribute noise and dynamic changes -- that are faced by many
real-world networks. To address these challenges, we propose a Contextualized
Self-Supervised Learning (CSSL) framework that fully exploits structural
context prediction for link prediction. The proposed CSSL framework learns a
link encoder to infer the link existence probability from paired node
embeddings, which are constructed via a transformation on node attributes. To
generate informative node embeddings for link prediction, structural context
prediction is leveraged as a self-supervised learning task to boost the link
prediction performance. Two types of structural context are investigated, i.e.,
context nodes collected from random walks vs. context subgraphs. The CSSL
framework can be trained in an end-to-end manner, with the learning of model
parameters supervised by both the link prediction and self-supervised learning
tasks. The proposed CSSL is a generic and flexible framework in the sense that
it can handle both attributed and non-attributed networks, and operate under
both transductive and inductive link prediction settings. Extensive experiments
and ablation studies on seven real-world benchmark networks demonstrate the
superior performance of the proposed self-supervision based link prediction
algorithm over state-of-the-art baselines, on different types of networks under
both transductive and inductive settings. The proposed CSSL also yields
competitive performance in terms of its robustness to node attribute noise and
scalability over large-scale networks.

In recent years, large-scale data collection efforts have prioritized the
amount of data collected in order to improve the modeling capabilities of large
language models. This prioritization, however, has resulted in concerns with
respect to the rights of data subjects represented in data collections,
particularly when considering the difficulty in interrogating these collections
due to insufficient documentation and tools for analysis. Mindful of these
pitfalls, we present our methodology for a documentation-first, human-centered
data collection project as part of the BigScience initiative. We identified a
geographically diverse set of target language groups (Arabic, Basque, Chinese,
Catalan, English, French, Indic languages, Indonesian, Niger-Congo languages,
Portuguese, Spanish, and Vietnamese, as well as programming languages) for
which to collect metadata on potential data sources. To structure this effort,
we developed our online catalogue as a supporting tool for gathering metadata
through organized public hackathons. We present our development process;
analyses of the resulting resource metadata, including distributions over
languages, regions, and resource types; and our lessons learned in this
endeavor.

Entity alignment aims at integrating heterogeneous knowledge from different
knowledge graphs. Recent studies employ embedding-based methods by first
learning the representation of Knowledge Graphs and then performing entity
alignment via measuring the similarity between entity embeddings. However, they
failed to make good use of the relation semantic information due to the
trade-off problem caused by the different objectives of learning knowledge
embedding and neighborhood consensus. To address this problem, we propose
Relational Knowledge Distillation for Entity Alignment (RKDEA), a Graph
Convolutional Network (GCN) based model equipped with knowledge distillation
for entity alignment. We adopt GCN-based models to learn the representation of
entities by considering the graph structure and incorporating the relation
semantic information into GCN via knowledge distillation. Then, we introduce a
novel adaptive mechanism to transfer relational knowledge so as to jointly
learn entity embedding and neighborhood consensus. Experimental results on
several benchmarking datasets demonstrate the effectiveness of our proposed
model.

With recently successful applications of deep learning in computer vision and
general signal processing, deep learning has shown many unique advantages in
medical signal processing. However, data labelling quality has become one of
the most significant issues for AI applications, especially when it requires
domain knowledge (e.g. medical image labelling). In addition, there might be
noisy labels in practical datasets, which might impair the training process of
neural networks. In this work, we propose a semi-supervised algorithm for
training data samples with noisy labels by performing selected Positive
Learning (PL) and Negative Learning (NL). To verify the effectiveness of the
proposed scheme, we designed a portable ECG patch -- iRealCare -- and applied
the algorithm on a real-life dataset. Our experimental results show that we can
achieve an accuracy of 91.0 %, which is 6.2 % higher than a normal training
process with ResNet. There are 65 patients in our dataset and we randomly
picked 2 patients to perform validation.

Recently, there has been a surge of significant interest on application of
Deep Learning (DL) models to autonomously perform hand gesture recognition
using surface Electromyogram (sEMG) signals. DL models are, however, mainly
designed to be applied on sparse sEMG signals. Furthermore, due to their
complex structure, typically, we are faced with memory constraints; require
large training times and a large number of training samples, and; there is the
need to resort to data augmentation and/or transfer learning. In this paper,
for the first time (to the best of our knowledge), we investigate and design a
Vision Transformer (ViT) based architecture to perform hand gesture recognition
from High Density (HD-sEMG) signals. Intuitively speaking, we capitalize on the
recent breakthrough role of the transformer architecture in tackling different
complex problems together with its potential for employing more input
parallelization via its attention mechanism. The proposed Vision
Transformer-based Hand Gesture Recognition (ViT-HGR) framework can overcome the
aforementioned training time problems and can accurately classify a large
number of hand gestures from scratch without any need for data augmentation
and/or transfer learning. The efficiency of the proposed ViT-HGR framework is
evaluated using a recently-released HD-sEMG dataset consisting of 65 isometric
hand gestures. Our experiments with 64-sample (31.25 ms) window size yield
average test accuracy of 84.62 +/- 3.07%, where only 78, 210 number of
parameters is utilized. The compact structure of the proposed ViT-based ViT-HGR
framework (i.e., having significantly reduced number of trainable parameters)
shows great potentials for its practical application for prosthetic control.

With the recent increase in the number of underwater activities, having
effective underwater communication systems has become increasingly important.
Underwater acoustic communication has been widely used but greatly impaired due
to the complicated nature of the underwater environment. In a bid to better
understand the underwater acoustic channel so as to help in the design and
improvement of underwater communication systems, attempts have been made to
model the underwater acoustic channel using mathematical equations and
approximations under some assumptions. In this paper, we explore the capability
of machine learning and deep learning methods to learn and accurately model the
underwater acoustic channel using real underwater data collected from a water
tank with disturbance and from lake Tahoe. Specifically, Deep Neural Network
(DNN) and Long Short Term Memory (LSTM) are applied to model the underwater
acoustic channel. Experimental results show that these models are able to model
the underwater acoustic communication channel well and that deep learning
models, especially LSTM are better models in terms of mean absolute percentage
error.

Targeted training-set attacks inject malicious instances into the training
set to cause a trained model to mislabel one or more specific test instances.
This work proposes the task of target identification, which determines whether
a specific test instance is the target of a training-set attack. Target
identification can be combined with adversarial-instance identification to find
(and remove) the attack instances, mitigating the attack with minimal impact on
other predictions. Rather than focusing on a single attack method or data
modality, we build on influence estimation, which quantifies each training
instance's contribution to a model's prediction. We show that existing
influence estimators' poor practical performance often derives from their
over-reliance on training instances and iterations with large losses. Our
renormalized influence estimators fix this weakness; they far outperform the
original estimators at identifying influential groups of training examples in
both adversarial and non-adversarial settings, even finding up to 100% of
adversarial training instances with no clean-data false positives. Target
identification then simplifies to detecting test instances with anomalous
influence values. We demonstrate our method's effectiveness on backdoor and
poisoning attacks across various data domains, including text, vision, and
speech, as well as against a gray-box, adaptive attacker that specifically
optimizes the adversarial instances to evade our method. Our source code is
available at https://github.com/ZaydH/target_identification.

We study the problem of learning, from observational data, fair and
interpretable policies that effectively match heterogeneous individuals to
scarce resources of different types. We model this problem as a multi-class
multi-server queuing system where both individuals and resources arrive
stochastically over time. Each individual, upon arrival, is assigned to a queue
where they wait to be matched to a resource. The resources are assigned in a
first come first served (FCFS) fashion according to an eligibility structure
that encodes the resource types that serve each queue. We propose a methodology
based on techniques in modern causal inference to construct the individual
queues as well as learn the matching outcomes and provide a mixed-integer
optimization (MIO) formulation to optimize the eligibility structure. The MIO
problem maximizes policy outcome subject to wait time and fairness constraints.
It is very flexible, allowing for additional linear domain constraints. We
conduct extensive analyses using synthetic and real-world data. In particular,
we evaluate our framework using data from the U.S. Homeless Management
Information System (HMIS). We obtain wait times as low as an FCFS policy while
improving the rate of exit from homelessness for underserved or vulnerable
groups (7% higher for the Black individuals and 15% higher for those below 17
years old) and overall.

As facial recognition systems are deployed more widely, scholars and
activists have studied their biases and harms. Audits are commonly used to
accomplish this and compare the algorithmic facial recognition systems'
performance against datasets with various metadata labels about the subjects of
the images. Seminal works have found discrepancies in performance by gender
expression, age, perceived race, skin type, etc. These studies and audits often
examine algorithms which fall into two categories: academic models or
commercial models. We present a detailed comparison between academic and
commercial face detection systems, specifically examining robustness to noise.
We find that state-of-the-art academic face detection models exhibit
demographic disparities in their noise robustness, specifically by having
statistically significant decreased performance on older individuals and those
who present their gender in a masculine manner. When we compare the size of
these disparities to that of commercial models, we conclude that commercial
models - in contrast to their relatively larger development budget and
industry-level fairness commitments - are always as biased or more biased than
an academic model.

We present a numerical study of training a self-propelling agent to migrate
in the unsteady flow environment. We control the agent to utilize the
background flow structure by adopting the reinforcement learning algorithm to
minimize energy consumption. We considered the agent migrating in two types of
flows: one is simple periodical double-gyre flow as a proof-of-concept example,
while the other is complex turbulent Rayleigh-B\'enard convection as a paradigm
for migrating in the convective atmosphere or the ocean. The results show that
the smart agent in both flows can learn to migrate from one position to another
while utilizing background flow currents as much as possible to minimize the
energy consumption, which is evident by comparing the smart agent with a naive
agent that moves straight from the origin to the destination. In addition, we
found that compared to the double-gyre flow, the flow field in the turbulent
Rayleigh-B\'enard convection exhibits more substantial fluctuations, and the
training agent is more likely to explore different migration strategies; thus,
the training process is more difficult to converge. Nevertheless, we can still
identify an energy-efficient trajectory that corresponds to the strategy with
the highest reward received by the agent. These results have important
implications for many migration problems such as unmanned aerial vehicles
flying in a turbulent convective environment, where planning energy-efficient
trajectories are often involved.

It is well-documented how artificial intelligence can have (and already is
having) a big impact on chemical engineering. But classical machine learning
approaches may be weak for many chemical engineering applications. This review
discusses how challenging data characteristics arise in chemical engineering
applications. We identify four characteristics of data arising in chemical
engineering applications that make applying classical artificial intelligence
approaches difficult: (1) high variance, low volume data, (2) low variance,
high volume data, (3) noisy/corrupt/missing data, and (4) restricted data with
physics-based limitations. For each of these four data characteristics, we
discuss applications where these data characteristics arise and show how
current chemical engineering research is extending the fields of data science
and machine learning to incorporate these challenges. Finally, we identify
several challenges for future research.

Recent years have witnessed an increasing trend toward solving point cloud
registration problems with various deep learning-based algorithms. Compared to
supervised/semi-supervised registration methods, unsupervised methods require
no human annotations. However, unsupervised methods mainly depend on the global
descriptors, which ignore the high-level representations of local geometries.
In this paper, we propose a self-supervised registration scheme with a novel
Deep Versatile Descriptors (DVD), jointly considering global representations
and local representations. The DVD is motivated by a key observation that the
local distinctive geometric structures of the point cloud by two subset points
can be employed to enhance the representation ability of the feature extraction
module. Furthermore, we utilize two additional tasks (reconstruction and normal
estimation) to enhance the transformation awareness of the proposed DVDs.
Lastly, we conduct extensive experiments on synthetic and real-world datasets,
demonstrating that our method achieves state-of-the-art performance against
competing methods over a wide range of experimental settings.

Multi-access edge computing (MEC) is viewed as an integral part of future
wireless networks to support new applications with stringent service
reliability and latency requirements. However, guaranteeing ultra-reliable and
low-latency MEC (URLL MEC) is very challenging due to uncertainties of wireless
links, limited communications and computing resources, as well as dynamic
network traffic. Enabling URLL MEC mandates taking into account the statistics
of the end-to-end (E2E) latency and reliability across the wireless and edge
computing systems. In this paper, a novel framework is proposed to optimize the
reliability of MEC networks by considering the distribution of E2E service
delay, encompassing over-the-air transmission and edge computing latency. The
proposed framework builds on correlated variational autoencoders (VAEs) to
estimate the full distribution of the E2E service delay. Using this result, a
new optimization problem based on risk theory is formulated to maximize the
network reliability by minimizing the Conditional Value at Risk (CVaR) as a
risk measure of the E2E service delay. To solve this problem, a new algorithm
is developed to efficiently allocate users' processing tasks to edge computing
servers across the MEC network, while considering the statistics of the E2E
service delay learned by VAEs. The simulation results show that the proposed
scheme outperforms several baselines that do not account for the risk analyses
or statistics of the E2E service delay.

State-of-the-art approaches to ObjectGoal navigation rely on reinforcement
learning and typically require significant computational resources and time for
learning. We propose Potential functions for ObjectGoal Navigation with
Interaction-free learning (PONI), a modular approach that disentangles the
skills of `where to look?' for an object and `how to navigate to (x, y)?'. Our
key insight is that `where to look?' can be treated purely as a perception
problem, and learned without environment interactions. To address this, we
propose a network that predicts two complementary potential functions
conditioned on a semantic map and uses them to decide where to look for an
unseen object. We train the potential function network using supervised
learning on a passive dataset of top-down semantic maps, and integrate it into
a modular framework to perform ObjectGoal navigation. Experiments on Gibson and
Matterport3D demonstrate that our method achieves the state-of-the-art for
ObjectGoal navigation while incurring up to 1,600x less computational cost for
training. Code and pre-trained models are available:
https://vision.cs.utexas.edu/projects/poni/

Deep learning has contributed to major advances in the prediction of protein
structure from sequence, a fundamental problem in structural bioinformatics.
With predictions now approaching the accuracy of crystallographic resolution in
some cases, and with accelerators like GPUs and TPUs making inference using
large models rapid, fast genome-level structure prediction becomes an obvious
aim. Leadership-class computing resources can be used to perform genome-scale
protein structure prediction using state-of-the-art deep learning models,
providing a wealth of new data for systems biology applications. Here we
describe our efforts to efficiently deploy the AlphaFold2 program, for
full-proteome structure prediction, at scale on the Oak Ridge Leadership
Computing Facility's resources, including the Summit supercomputer. We
performed inference to produce the predicted structures for 35,634 protein
sequences, corresponding to three prokaryotic proteomes and one plant proteome,
using under 4,000 total Summit node hours, equivalent to using the majority of
the supercomputer for one hour. We also designed an optimized structure
refinement that reduced the time for the relaxation stage of the AlphaFold
pipeline by over 10X for longer sequences. We demonstrate the types of analyses
that can be performed on proteome-scale collections of sequences, including a
search for novel quaternary structures and implications for functional
annotation.

The Large Magellanic Cloud (LMC) is the most luminous satellite galaxy of the
Milky Way and owing to its companion, the Small Magellanic Cloud (SMC),
represents an excellent laboratory to study the interaction of dwarf galaxies.
The aim of this study is to investigate the kinematics of the outer regions of
the LMC by using stellar proper motions to understand the impact of
interactions, e.g. with the SMC about 250 Myr ago. {We calculate proper motions
using multi-epoch $K_\mathrm{s}$-band images from the VISTA survey of the
Magellanic Clouds system (VMC). Observations span a time baseline of 2$-$5 yr.
We combine the VMC data with data from the Gaia early Data Release 3 and
introduce a new method to distinguish between Magellanic and Milky Way stars
based on a machine learning algorithm. This new technique enables a larger and
cleaner sample selection of fainter sources as it reaches below the red clump
of the LMC. We investigate the impact of the SMC on the rotational field of the
LMC and find hints of stripped SMC debris. The south east region of the LMC
shows a slow rotational speed compared to the overall rotation. $N$-body
simulations suggest that this could be caused by a fraction of stripped SMC
stars, located in that particular region, that move opposite to the expected
rotation.

Subgraph matching is a fundamental problem in various fields that use graph
structured data. Subgraph matching algorithms enumerate all isomorphic
embeddings of a query graph q in a data graph G. An important branch of
matching algorithms exploit the backtracking search approach which recursively
extends intermediate results following a matching order of query vertices. It
has been shown that the matching order plays a critical role in time efficiency
of these backtracking based subgraph matching algorithms. In recent years, many
advanced techniques for query vertex ordering (i.e., matching order generation)
have been proposed to reduce the unpromising intermediate results according to
the preset heuristic rules. In this paper, for the first time we apply the
Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to
generate the high-quality matching order for subgraph matching algorithms.
Instead of using the fixed heuristics to generate the matching order, our model
could capture and make full use of the graph information, and thus determine
the query vertex order with the adaptive learning-based rule that could
significantly reduces the number of redundant enumerations. With the help of
the reinforcement learning framework, our model is able to consider the
long-term benefits rather than only consider the local information at current
ordering step.Extensive experiments on six real-life data graphs demonstrate
that our proposed matching order generation technique could reduce up to two
orders of magnitude of query processing time compared to the state-of-the-art

We propose a novel statistical inference paradigm for zero-inflated multiway
count data that dispenses with the need to distinguish between true and false
zero counts. Our approach ignores all zero entries and applies zero-truncated
Poisson regression on the positive counts. Inference is accomplished via tensor
completion that imposes low-rank structure on the Poisson parameter space. Our
main result shows that an $N$-way rank-$R$ parametric tensor
$\boldsymbol{\mathscr{M}}\in(0,\infty)^{I\times \cdots\times I}$ generating
Poisson observations can be accurately estimated from approximately
$IR^2\log_2^2(I)$ non-zero counts for a nonnegative canonical polyadic
decomposition. Several numerical experiments are presented demonstrating that
our zero-truncated paradigm is comparable to the ideal scenario where the
locations of false zero counts are known a priori.

Text embeddings are useful features in many applications such as semantic
search and computing text similarity. Previous work typically trains models
customized for different use cases, varying in dataset choice, training
objective and model architecture. In this work, we show that contrastive
pre-training on unsupervised data at scale leads to high quality vector
representations of text and code. The same unsupervised text embeddings that
achieve new state-of-the-art results in linear-probe classification also
display impressive semantic search capabilities and sometimes even perform
competitively with fine-tuned models. On linear-probe classification accuracy
averaging over 7 tasks, our best unsupervised model achieves a relative
improvement of 4% and 1.8% over previous best unsupervised and supervised text
embedding models respectively. The same text embeddings when evaluated on
large-scale semantic search attains a relative improvement of 23.4%, 14.7%, and
10.6% over previous best unsupervised methods on MSMARCO, Natural Questions and
TriviaQA benchmarks, respectively. Similarly to text embeddings, we train code
embedding models on (text, code) pairs, obtaining a 20.8% relative improvement
over prior best work on code search.

Existing Domain Adaptation (DA) algorithms train target models to classify
all samples in the target domain, but it fails to recognize the possibility
that, within the target domain, some samples are closer to the source domain
and thus should be classified by source domain models. In this paper, we
develop a novel unsupervised DA algorithm, the Enforced Transfer, which employs
an out-of-distribution detection algorithm to decide which model (i.e., source
domain or target domain) to apply on the testing instance, i.e.,
divide-and-conquer. Instead of choosing the models at the instance-level, we
make the choice of models at the layers of deep models. On three types of DA
tasks, we outperform the state-of-the-art algorithms.

Given a union of non-linear manifolds, non-linear subspace clustering or
manifold clustering aims to cluster data points based on manifold structures
and also learn to parameterize each manifold as a linear subspace in a feature
space. Deep neural networks have the potential to achieve this goal under
highly non-linear settings given their large capacity and flexibility. We argue
that achieving manifold clustering with neural networks requires two essential
ingredients: a domain-specific constraint that ensures the identification of
the manifolds, and a learning algorithm for embedding each manifold to a linear
subspace in the feature space. This work shows that many constraints can be
implemented by data augmentation. For subspace feature learning, Maximum Coding
Rate Reduction (MCR$^2$) objective can be used. Putting them together yields
{\em Neural Manifold Clustering and Embedding} (NMCE), a novel method for
general purpose manifold clustering, which significantly outperforms
autoencoder-based deep subspace clustering. Further, on more challenging
natural image datasets, NMCE can also outperform other algorithms specifically
designed for clustering. Qualitatively, we demonstrate that NMCE learns a
meaningful and interpretable feature space. As the formulation of NMCE is
closely related to several important Self-supervised learning (SSL) methods, we
believe this work can help us build a deeper understanding on SSL

The vast majority of existing datasets for Named Entity Recognition (NER) are
built primarily on news, research papers and Wikipedia with a few exceptions,
created from historical and literary texts. What is more, English is the main
source for data for further labelling. This paper aims to fill in multiple gaps
by creating a novel dataset "Razmecheno", gathered from the diary texts of the
project "Prozhito" in Russian. Our dataset is of interest for multiple research
lines: literary studies of diary texts, transfer learning from other domains,
low-resource or cross-lingual named entity recognition. Razmecheno comprises
1331 sentences and 14119 tokens, sampled from diaries, written during the
Perestroika. The annotation schema consists of five commonly used entity tags:
person, characteristics, location, organisation, and facility. The labelling is
carried out on the crowdsourcing platfrom Yandex.Toloka in two stages. First,
workers selected sentences, which contain an entity of particular type. Second,
they marked up entity spans. As a result 1113 entities were obtained. Empirical
evaluation of Razmecheno is carried out with off-the-shelf NER tools and by
fine-tuning pre-trained contextualized encoders. We release the annotated
dataset for open access.

HC4 is a new suite of test collections for ad hoc Cross-Language Information
Retrieval (CLIR), with Common Crawl News documents in Chinese, Persian, and
Russian, topics in English and in the document languages, and graded relevance
judgments. New test collections are needed because existing CLIR test
collections built using pooling of traditional CLIR runs have systematic gaps
in their relevance judgments when used to evaluate neural CLIR methods. The HC4
collections contain 60 topics and about half a million documents for each of
Chinese and Persian, and 54 topics and five million documents for Russian.
Active learning was used to determine which documents to annotate after being
seeded using interactive search and judgment. Documents were judged on a
three-grade relevance scale. This paper describes the design and construction
of the new test collections and provides baseline results for demonstrating
their utility for evaluating systems.

We present a method for solving linear and nonlinear PDEs based on the
variable projection (VarPro) framework and artificial neural networks (ANN).
For linear PDEs, enforcing the boundary/initial value problem on the
collocation points leads to a separable nonlinear least squares problem about
the network coefficients. We reformulate this problem by the VarPro approach to
eliminate the linear output-layer coefficients, leading to a reduced problem
about the hidden-layer coefficients only. The reduced problem is solved first
by the nonlinear least squares method to determine the hidden-layer
coefficients, and then the output-layer coefficients are computed by the linear
least squares method. For nonlinear PDEs, enforcing the boundary/initial value
problem on the collocation points leads to a nonlinear least squares problem
that is not separable, which precludes the VarPro strategy for such problems.
To enable the VarPro approach for nonlinear PDEs, we first linearize the
problem with a Newton iteration, using a particular form of linearization. The
linearized system is solved by the VarPro framework together with ANNs. Upon
convergence of the Newton iteration, the network coefficients provide the
representation of the solution field to the original nonlinear problem. We
present ample numerical examples with linear and nonlinear PDEs to demonstrate
the performance of the method herein. For smooth field solutions, the errors of
the current method decrease exponentially as the number of collocation points
or the number of output-layer coefficients increases. We compare the current
method with the ELM method from a previous work. Under identical conditions and
network configurations, the current method exhibits an accuracy significantly
superior to the ELM method.

The role of turbulence in setting boundary plasma conditions is presently a
key uncertainty in projecting to fusion energy reactors. To robustly diagnose
edge turbulence, we develop and demonstrate a technique to translate brightness
measurements of HeI line radiation into local plasma fluctuations via a novel
integrated deep learning framework that combines neutral transport physics and
collisional radiative theory for the $3^3 D - 2^3 P$ transition in atomic
helium. The tenets for experimental validity are reviewed, illustrating that
this turbulence analysis for ionized gases is transferable to both magnetized
and unmagnetized environments with arbitrary geometries. Based upon fast camera
data on the Alcator C-Mod tokamak, we present the first 2-dimensional
time-dependent experimental measurements of the turbulent electron density,
electron temperature, and neutral density revealing shadowing effects in a
fusion plasma using a single spectral line.

Continuous monitoring of blood pressure (BP)can help individuals manage their
chronic diseases such as hypertension, requiring non-invasive measurement
methods in free-living conditions. Recent approaches fuse Photoplethysmograph
(PPG) and electrocardiographic (ECG) signals using different machine and deep
learning approaches to non-invasively estimate BP; however, they fail to
reconstruct the complete signal, leading to less accurate models. In this
paper, we propose a cycle generative adversarial network (CycleGAN) based
approach to extract a BP signal known as ambulatory blood pressure (ABP) from a
clean PPG signal. Our approach uses a cycle generative adversarial network that
extends theGAN architecture for domain translation, and outperforms
state-of-the-art approaches by up to 2x in BP estimation.

Moving away from repetitive tasks, robots nowadays demand versatile skills
that adapt to different situations. Task-parameterized learning improves the
generalization of motion policies by encoding relevant contextual information
in the task parameters, hence enabling flexible task executions. However,
training such a policy often requires collecting multiple demonstrations in
different situations. To comprehensively create different situations is
non-trivial thus renders the method less applicable to real-world problems.
Therefore, training with fewer demonstrations/situations is desirable. This
paper presents a novel concept to augment the original training dataset with
synthetic data for policy improvements, thus allows learning task-parameterized
skills with few demonstrations.

At present, a major challenge for the application of automatic driving
technology is the accurate prediction of vehicle trajectory. With the vigorous
development of computer technology and the emergence of convolution depth
neural network, the accuracy of prediction results has been improved. But, the
depth, width of the network and image resolution are still important reasons
that restrict the accuracy of the model and the prediction results. The main
innovation of this paper is the combination of RESNET network and efficient net
network, which not only greatly increases the network depth, but also
comprehensively changes the choice of network width and image resolution, so as
to make the model performance better, but also save computing resources as much
as possible. The experimental results also show that our proposed model obtains
the optimal prediction results. Specifically, the loss value of our method is
separately 4 less and 2.1 less than that of resnet and efficientnet method.

Computer aided diagnosis (CAD) increases diagnosis efficiency, helping
doctors providing a quick and confident diagnosis, it has played an important
role in the treatment of COVID19. In our task, we solve the problem about
abnormality detection and classification. The dataset provided by Kaggle
platform and we choose YOLOv5 as our model. We introduce some methods on
objective detection in the related work section, the objection detection can be
divided into two streams: onestage and two stage. The representational model
are Faster RCNN and YOLO series. Then we describe the YOLOv5 model in the
detail. Compared Experiments and results are shown in section IV. We choose
mean average precision (mAP) as our experiments' metrics, and the higher (mean)
mAP is, the better result the model will gain. mAP@0.5 of our YOLOv5s is 0.623
which is 0.157 and 0.101 higher than Faster RCNN and EfficientDet respectively.

High-resolution optical satellite sensors, combined with dense stereo
algorithms, have made it possible to reconstruct 3D city models from space.
However, these models are, in practice, rather noisy and tend to miss small
geometric features that are clearly visible in the images. We argue that one
reason for the limited quality may be a too early, heuristic reduction of the
triangulated 3D point cloud to an explicit height field or surface mesh. To
make full use of the point cloud and the underlying images, we introduce
ImpliCity, a neural representation of the 3D scene as an implicit, continuous
occupancy field, driven by learned embeddings of the point cloud and a stereo
pair of ortho-photos. We show that this representation enables the extraction
of high-quality DSMs: with image resolution 0.5$\,$m, ImpliCity reaches a
median height error of $\approx\,$0.7$\,$m and outperforms competing methods,
especially w.r.t. building reconstruction, featuring intricate roof details,
smooth surfaces, and straight, regular outlines.

Expectation Maximization (EM) is the standard method to learn Gaussian
mixtures. Yet its classic, centralized form is often infeasible, due to privacy
concerns and computational and communication bottlenecks. Prior work dealt with
data distributed by examples, horizontal partitioning, but we lack a
counterpart for data scattered by features, an increasingly common scheme (e.g.
user profiling with data from multiple entities). To fill this gap, we provide
an EM-based algorithm to fit Gaussian mixtures to Vertically Partitioned data
(VP-EM). In federated learning setups, our algorithm matches the centralized EM
fitting of Gaussian mixtures constrained to a subspace. In arbitrary
communication graphs, consensus averaging allows VP-EM to run on large
peer-to-peer networks as an EM approximation. This mismatch comes from
consensus error only, which vanishes exponentially fast with the number of
consensus rounds. We demonstrate VP-EM on various topologies for both synthetic
and real data, evaluating its approximation of centralized EM and seeing that
it outperforms the available benchmark.

Browser fingerprinting aims to identify users or their devices, through
scripts that execute in the users' browser and collect information on software
or hardware characteristics. It is used to track users or as an additional
means of identification to improve security. In this paper, we report on a new
technique that can significantly extend the tracking time of fingerprint-based
tracking methods. Our technique, which we call DrawnApart, is a new GPU
fingerprinting technique that identifies a device based on the unique
properties of its GPU stack. Specifically, we show that variations in speed
among the multiple execution units that comprise a GPU can serve as a reliable
and robust device signature, which can be collected using unprivileged
JavaScript. We investigate the accuracy of DrawnApart under two scenarios. In
the first scenario, our controlled experiments confirm that the technique is
effective in distinguishing devices with similar hardware and software
configurations, even when they are considered identical by current
state-of-the-art fingerprinting algorithms. In the second scenario, we
integrate a one-shot learning version of our technique into a state-of-the-art
browser fingerprint tracking algorithm. We verify our technique through a
large-scale experiment involving data collected from over 2,500 crowd-sourced
devices over a period of several months and show it provides a boost of up to
67% to the median tracking duration, compared to the state-of-the-art method.
DrawnApart makes two contributions to the state of the art in browser
fingerprinting. On the conceptual front, it is the first work that explores the
manufacturing differences between identical GPUs and the first to exploit these
differences in a privacy context. On the practical front, it demonstrates a
robust technique for distinguishing between machines with identical hardware
and software configurations.

Architectural Education faces limitations due to its tactile approach to
learning in classrooms with only 2-D and 3-D tools. At a higher level, virtual
reality provides a potential for delivering more information to individuals
undergoing design learning. This paper investigates a hypothesis establishing
grounds towards a new research in Building Information Modeling (BIM) and
Virtual Reality (VR). The hypothesis is projected to determine best practices
for content creation and tactile object virtual interaction, which potentially
can improve learning in architectural & construction education with a less
costly approach and ease of access to well-known buildings. We explored this
hypothesis in a step-by-step game design demonstration in VR, by showcasing the
exploration of the Farnsworth House and reproducing assemblage of the same with
different game levels of difficulty which correspond with varying BIM levels of
development (LODs). The game design prototype equally provides an entry way and
learning style for users with or without a formal architectural or construction
education seeking to understand design tectonics within diverse or
cross-disciplinary study cases. This paper shows that developing geometric
abstract concepts of design pedagogy, using varying LODs for game content and
levels, while utilizing newly developed features such as snap-to-grid,
snap-to-position and snap-to-angle to improve user engagement during assemblage
may provide deeper learning objectives for architectural precedent study.

Efficiency is essential to support responsiveness w.r.t. ever-growing
datasets, especially for Deep Learning (DL) systems. DL frameworks have
traditionally embraced deferred execution-style DL code that supports symbolic,
graph-based Deep Neural Network (DNN) computation. While scalable, such
development tends to produce DL code that is error-prone, non-intuitive, and
difficult to debug. Consequently, more natural, less error-prone imperative DL
frameworks encouraging eager execution have emerged but at the expense of
run-time performance. While hybrid approaches aim for the "best of both
worlds," the challenges in applying them in the real world are largely unknown.
We conduct a data-driven analysis of challenges -- and resultant bugs --
involved in writing reliable yet performant imperative DL code by studying 250
open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually
examined code patches and bug reports, respectively. The results indicate that
hybridization: (i) is prone to API misuse, (ii) can result in performance
degradation -- the opposite of its intention, and (iii) has limited application
due to execution mode incompatibility. We put forth several recommendations,
best practices, and anti-patterns for effectively hybridizing imperative DL
code, potentially benefiting DL practitioners, API designers, tool developers,
and educators.

The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identified
in mid-December 2019 in the Hubei province of Wuhan, China and by now has
spread throughout the planet with more than 75.5 million confirmed cases and
more than 1.67 million deaths. With limited number of COVID-19 test kits
available in medical facilities, it is important to develop and implement an
automatic detection system as an alternative diagnosis option for COVID-19
detection that can used on a commercial scale. Chest X-ray is the first imaging
technique that plays an important role in the diagnosis of COVID-19 disease.
Computer vision and deep learning techniques can help in determining COVID-19
virus with Chest X-ray Images. Due to the high availability of large-scale
annotated image datasets, great success has been achieved using convolutional
neural network for image analysis and classification. In this research, we have
proposed a deep convolutional neural network trained on five open access
datasets with binary output: Normal and Covid. The performance of the model is
compared with four pre-trained convolutional neural network-based models
(COVID-Net, ResNet18, ResNet and MobileNet-V2) and it has been seen that the
proposed model provides better accuracy on the validation set as compared to
the other four pre-trained models. This research work provides promising
results which can be further improvise and implement on a commercial scale.

The development of powerful natural language models have increased the
ability to learn meaningful representations of protein sequences. In addition,
advances in high-throughput mutagenesis, directed evolution, and
next-generation sequencing have allowed for the accumulation of large amounts
of labeled fitness data. Leveraging these two trends, we introduce Regularized
Latent Space Optimization (ReLSO), a deep transformer-based autoencoder which
features a highly structured latent space that is trained to jointly generate
sequences as well as predict fitness. Through regularized prediction heads,
ReLSO introduces a powerful protein sequence encoder and novel approach for
efficient fitness landscape traversal. Using ReLSO, we explicitly model the
sequence-function landscape of large labeled datasets and generate new
molecules by optimizing within the latent space using gradient-based methods.
We evaluate this approach on several publicly-available protein datasets,
including variant sets of anti-ranibizumab and GFP. We observe a greater
sequence optimization efficiency (increase in fitness per optimization step) by
ReLSO compared to other approaches, where ReLSO more robustly generates
high-fitness sequences. Furthermore, the attention-based relationships learned
by the jointly-trained ReLSO models provides a potential avenue towards
sequence-level fitness attribution information.

In multichannel signal processing with distributed sensors, choosing the
optimal subset of observed sensor signals to be exploited is crucial in order
to maximize algorithmic performance and reduce computational load, ideally both
at the same time. In the acoustic domain, signal cross-correlation is a natural
choice to quantify the usefulness of microphone signals, i. e., microphone
utility, for array processing, but its estimation requires that the uncoded
signals are synchronized and transmitted between nodes. In resource-constrained
environments like acoustic sensor networks, low data transmission rates often
make transmission of all observed signals to the centralized location
infeasible, thus discouraging direct estimation of signal cross-correlation.
Instead, we employ characteristic features of the recorded signals to estimate
the usefulness of individual microphone signals. In this contribution, we
provide a comprehensive analysis of model-based microphone utility estimation
approaches that use signal features and, as an alternative, also propose
machine learning-based estimation methods that identify optimal sensor signal
utility features. The performance of both approaches is validated
experimentally using both simulated and recorded acoustic data, comprising a
variety of realistic and practically relevant acoustic scenarios including
moving and static sources.

Several applications have a community structure where the nodes of the same
community share similar attributes. Anomaly or outlier detection in networks is
a relevant and widely studied research topic with applications in various
domains. Despite a significant amount of anomaly detection frameworks, there is
a dearth on the literature of methods that consider both attributed graphs and
the community structure of the networks. This paper proposes a community-based
anomaly detection algorithm using a spectral graph-based filter that includes
the network community structure into the Laplacian matrix adopted as the basis
for the Fourier transform. In addition, the choice of the cutoff frequency of
the filter considers the number of communities found. In computational
experiments, the proposed strategy, called SpecF, showed an outstanding
performance in successfully identifying even discrete anomalies. SpecF is
better than a baseline disregarding the community structure, especially for
networks with a higher community overlapping. Additionally, we present a case
study to validate the proposed method to study the dissemination of COVID-19 in
the different districts of S\~ao Jos\'e dos Campos, Brazil.

Research on machine learning for channel estimation, especially neural
network solutions for wireless communications, is attracting significant
current interest. This is because conventional methods cannot meet the present
demands of the high speed communication. In the paper, we deploy a general
residual convolutional neural network to achieve channel estimation for the
orthogonal frequency-division multiplexing (OFDM) signals in a downlink
scenario. Our method also deploys a simple interpolation layer to replace the
transposed convolutional layer used in other networks to reduce the computation
cost. The proposed method is more easily adapted to different pilot patterns
and packet sizes. Compared with other deep learning methods for channel
estimation, our results for 3GPP channel models suggest improved mean squared
error performance for our approach.

Emotion recognition in smart eyewear devices is highly valuable but
challenging. One key limitation of previous works is that the
expression-related information like facial or eye images is considered as the
only emotional evidence. However, emotional status is not isolated; it is
tightly associated with people's visual perceptions, especially those
sentimental ones. However, little work has examined such associations to better
illustrate the cause of different emotions. In this paper, we study the
emotionship analysis problem in eyewear systems, an ambitious task that
requires not only classifying the user's emotions but also semantically
understanding the potential cause of such emotions. To this end, we devise
EMOShip, a deep-learning-based eyewear system that can automatically detect the
wearer's emotional status and simultaneously analyze its associations with
semantic-level visual perceptions. Experimental studies with 20 participants
demonstrate that, thanks to the emotionship awareness, EMOShip not only
achieves superior emotion recognition accuracy over existing methods (80.2% vs.
69.4%), but also provides a valuable understanding of the cause of emotions.
Pilot studies with 20 participants further motivate the potential use of
EMOShip to empower emotion-aware applications, such as emotionship
self-reflection and emotionship life-logging.

The increasing use of machine learning in high-stakes domains -- where
people's livelihoods are impacted -- creates an urgent need for interpretable
and fair algorithms. In these settings it is also critical for such algorithms
to be accurate. With these needs in mind, we propose a mixed integer
optimization (MIO) framework for learning optimal classification trees of fixed
depth that can be conveniently augmented with arbitrary domain specific
fairness constraints. We benchmark our method against the state-of-the-art
approach for building fair trees on popular datasets; given a fixed
discrimination threshold, our approach improves out-of-sample (OOS) accuracy by
2.3 percentage points on average and obtains a higher OOS accuracy on 88.9% of
the experiments. We also incorporate various algorithmic fairness notions into
our method, showcasing its versatile modeling power that allows decision makers
to fine-tune the trade-off between accuracy and fairness.

We introduce a similarity function on formulae of signal temporal logic
(STL). It comes in the form of a kernel function, well known in machine
learning as a conceptually and computationally efficient tool. The
corresponding kernel trick allows us to circumvent the complicated process of
feature extraction, i.e. the (typically manual) effort to identify the decisive
properties of formulae so that learning can be applied. We demonstrate this
consequence and its advantages on the task of predicting (quantitative)
satisfaction of STL formulae on stochastic processes: Using our kernel and the
kernel trick, we learn (i) computationally efficiently (ii) a practically
precise predictor of satisfaction, (iii) avoiding the difficult task of finding
a way to explicitly turn formulae into vectors of numbers in a sensible way. We
back the high precision we have achieved in the experiments by a theoretically
sound PAC guarantee, ensuring our procedure efficiently delivers a
close-to-optimal predictor.

Recently, increasing efforts are put into learning continual representations
for symbolic knowledge bases (KBs). However, these approaches either only embed
the data-level knowledge (ABox) or suffer from inherent limitations when
dealing with concept-level knowledge (TBox), i.e., they cannot faithfully model
the logical structure present in the KBs. We present BoxEL, a geometric KB
embedding approach that allows for better capturing the logical structure
(i.e., ABox and TBox axioms) in the description logic EL++. BoxEL models
concepts in a KB as axis-parallel boxes that are suitable for modeling concept
intersection, entities as points inside boxes, and relations between
concepts/entities as affine transformations. We show theoretical guarantees
(soundness) of BoxEL for preserving logical structure. Namely, the learned
model of BoxEL embedding with loss 0 is a (logical) model of the KB.
Experimental results on (plausible) subsumption reasonings and a real-world
application for protein-protein prediction show that BoxEL outperforms
traditional knowledge graph embedding methods as well as state-of-the-art EL++
embedding approaches.

Federated Learning (FL) has emerged as a result of data ownership and privacy
concerns to prevent data from being shared between multiple parties included in
a training procedure. Although issues, such as privacy, have gained significant
attention in this domain, not much attention has been given to satisfying
statistical fairness measures in the FL setting. With this goal in mind, we
conduct studies to show that FL is able to satisfy different fairness metrics
under different data regimes consisting of different types of clients. More
specifically, uncooperative or adversarial clients might contaminate the global
FL model by injecting biased or poisoned models due to existing biases in their
training datasets. Those biases might be a result of imbalanced training set
(Zhang and Zhou 2019), historical biases (Mehrabi et al. 2021a), or poisoned
data-points from data poisoning attacks against fairness (Mehrabi et al. 2021b;
Solans, Biggio, and Castillo 2020). Thus, we propose a new FL framework that is
able to satisfy multiple objectives including various statistical fairness
metrics. Through experimentation, we then show the effectiveness of this method
comparing it with various baselines, its ability in satisfying different
objectives collectively and individually, and its ability in identifying
uncooperative or adversarial clients and down-weighing their effect

Neural circuits exhibit complex activity patterns, both spontaneously and
evoked by external stimuli. Information encoding and learning in neural
circuits depend on how well time-varying stimuli can control spontaneous
network activity. We show that in firing-rate networks in the balanced state,
external control of recurrent dynamics, i.e., the suppression of
internally-generated chaotic variability, strongly depends on correlations in
the input. A unique feature of balanced networks is that, because common
external input is dynamically canceled by recurrent feedback, it is far easier
to suppress chaos with independent inputs into each neuron than through common
input. To study this phenomenon we develop a non-stationary dynamic mean-field
theory that determines how the activity statistics and largest Lyapunov
exponent depend on frequency and amplitude of the input, recurrent coupling
strength, and network size, for both common and independent input. We also show
that uncorrelated inputs facilitate learning in balanced networks.

Thanks to the power of representation learning, neural contextual bandit
algorithms demonstrate remarkable performance improvement against their
classical counterparts. But because their exploration has to be performed in
the entire neural network parameter space to obtain nearly optimal regret, the
resulting computational cost is prohibitively high. We perturb the rewards when
updating the neural network to eliminate the need of explicit exploration and
the corresponding computational overhead. We prove that a
$\tilde{O}(\tilde{d}\sqrt{T})$ regret upper bound is still achievable under
standard regularity conditions, where $T$ is the number of rounds of
interactions and $\tilde{d}$ is the effective dimension of a neural tangent
kernel matrix. Extensive comparisons with several benchmark contextual bandit
algorithms, including two recent neural contextual bandit models, demonstrate
the effectiveness and computational efficiency of our proposed neural bandit

In this paper, we propose an ordered time series classification framework
that is robust against missing classes in the training data, i.e., during
testing we can prescribe classes that are missing during training. This
framework relies on two main components: (1) our newly proposed
ordinal-quadruplet loss, which forces the model to learn latent representation
while preserving the ordinal relation among labels, (2) testing procedure,
which utilizes the property of latent representation (order preservation). We
conduct experiments based on real world multivariate time series data and show
the significant improvement in the prediction of missing labels even with 40%
of the classes are missing from training. Compared with the well-known triplet
loss optimization augmented with interpolation for missing information, in some
cases, we nearly double the accuracy.

In this study, we investigated the orientation model of Broad Absorption Line
(BAL) quasars using a sample of sources that are common in Sloan Digital Sky
Survey (SDSS) Data Release (DR)-16 quasar catalog and Very Large Array
(VLA)-Faint Images of the Radio Sky at Twenty Centimeters (FIRST) survey. Using
the radio cut-out images from the FIRST survey, we first designed a deep
learning model using convolutional neural networks (CNN) to classify the quasar
radio morphologies into the core-only, young jet, single lobe, or triples.
These radio morphologies are further sub-classified into core-dominated and
lobe-dominated sources. The CNN models can classify the sources with a high
precision of >98% for all the morphological sub-classes. The average BAL
fraction in the resolved core, core-dominated, and lobe-dominated quasars are
consistent with the BAL fraction inferred from radio and infra-red surveys. We
also present the distribution of BAL quasars as a function of quasar
orientation by using the radio core-dominance as an orientation indicator. A
similar analysis is performed for HiBALs, LoBALs, and FeLoBALs. All the radio
morphological sub-classes and BAL sub-classes show an increase in BAL fraction
at high orientation angles of the jets with respect to the line of sight. Our
analysis suggests that BAL quasars are more likely to be found in viewing
angles close to the equatorial plane of the quasar. However, a pure orientation
model is inadequate, and a combination of orientation and evolution is probably
the best way to explain the complete BAL phenomena.

Recently, variational autoencoder (VAE), a deep representation learning (DRL)
model, has been used to perform speech enhancement (SE). However, to the best
of our knowledge, current VAE-based SE methods only apply VAE to the model
speech signal, while noise is modeled using the traditional non-negative matrix
factorization (NMF) model. One of the most important reasons for using NMF is
that these VAE-based methods cannot disentangle the speech and noise latent
variables from the observed signal. Based on Bayesian theory, this paper
derives a novel variational lower bound for VAE, which ensures that VAE can be
trained in supervision, and can disentangle speech and noise latent variables
from the observed signal. This means that the proposed method can apply the VAE
to model both speech and noise signals, which is totally different from the
previous VAE-based SE works. More specifically, the proposed DRL method can
learn to impose speech and noise signal priors to different sets of latent
variables for SE. The experimental results show that the proposed method can
not only disentangle speech and noise latent variables from the observed signal
but also obtain a higher scale-invariant signal-to-distortion ratio and speech
quality score than the similar deep neural network-based (DNN) SE method.

The task of predicting stochastic behaviors of road agents in diverse
environments is a challenging problem for autonomous driving. To best
understand scene contexts and produce diverse possible future states of the
road agents adaptively in different environments, a prediction model should be
probabilistic, multi-modal, context-driven, and general. We present
Conditionalizing Variational AutoEncoders via Hypernetworks (CVAE-H); a
conditional VAE that extensively leverages hypernetwork and performs generative
tasks for high-dimensional problems like the prediction task. We first evaluate
CVAE-H on simple generative experiments to show that CVAE-H is probabilistic,
multi-modal, context-driven, and general. Then, we demonstrate that the
proposed model effectively solves a self-driving prediction problem by
producing accurate predictions of road agents in various environments.

In image generation, generative models can be evaluated naturally by visually
inspecting model outputs. However, this is not always the case for graph
generative models (GGMs), making their evaluation challenging. Currently, the
standard process for evaluating GGMs suffers from three critical limitations:
i) it does not produce a single score which makes model selection challenging,
ii) in many cases it fails to consider underlying edge and node features, and
iii) it is prohibitively slow to perform. In this work, we mitigate these
issues by searching for scalar, domain-agnostic, and scalable metrics for
evaluating and ranking GGMs. To this end, we study existing GGM metrics and
neural-network-based metrics emerging from generative models of images that use
embeddings extracted from a task-specific network. Motivated by the power of
certain Graph Neural Networks (GNNs) to extract meaningful graph
representations without any training, we introduce several metrics based on the
features extracted by an untrained random GNN. We design experiments to
thoroughly test metrics on their ability to measure the diversity and fidelity
of generated graphs, as well as their sample and computational efficiency.
Depending on the quantity of samples, we recommend one of two random-GNN-based
metrics that we show to be more expressive than pre-existing metrics. While we
focus on applying these metrics to GGM evaluation, in practice this enables the
ability to easily compute the dissimilarity between any two sets of graphs
regardless of domain. Our code is released at:
https://github.com/uoguelph-mlrg/GGM-metrics.

The aim of this study is to propose an alternative and hybrid solution method
for diagnosing the disease from histopathology images taken from animals with
paratuberculosis and intact intestine. In detail, the hybrid method is based on
using both image processing and deep learning for better results. Reliable
disease detection from histo-pathology images is known as an open problem in
medical image processing and alternative solutions need to be developed. In
this context, 520 histopathology images were collected in a joint study with
Burdur Mehmet Akif Ersoy University, Faculty of Veterinary Medicine, and
Department of Pathology. Manually detecting and interpreting these images
requires expertise and a lot of processing time. For this reason,
veterinarians, especially newly recruited physicians, have a great need for
imaging and computer vision systems in the development of detection and
treatment methods for this disease. The proposed solution method in this study
is to use the CLAHE method and image processing together. After this
preprocessing, the diagnosis is made by classifying a convolutional neural
network sup-ported by the VGG-16 architecture. This method uses completely
original dataset images. Two types of systems were applied for the evaluation
parameters. While the F1 Score was 93% in the method classified without data
preprocessing, it was 98% in the method that was preprocessed with the CLAHE
method.

Noise in pre-fault-tolerant quantum computers can result in biased estimates
of physical observables. Accurate bias-free estimates can be obtained using
probabilistic error cancellation (PEC), which is an error-mitigation technique
that effectively inverts well-characterized noise channels. Learning correlated
noise channels in large quantum circuits, however, has been a major challenge
and has severely hampered experimental realizations. Our work presents a
practical protocol for learning and inverting a sparse noise model that is able
to capture correlated noise and scales to large quantum devices. These advances
allow us to demonstrate PEC on a superconducting quantum processor with
crosstalk errors, thereby providing an important milestone in opening the way
to quantum computing with noise-free observables at larger circuit volumes.

Both the design and control of a robot play equally important roles in its
task performance. However, while optimal control is well studied in the machine
learning and robotics community, less attention is placed on finding the
optimal robot design. This is mainly because co-optimizing design and control
in robotics is characterized as a challenging problem, and more importantly, a
comprehensive evaluation benchmark for co-optimization does not exist. In this
paper, we propose Evolution Gym, the first large-scale benchmark for
co-optimizing the design and control of soft robots. In our benchmark, each
robot is composed of different types of voxels (e.g., soft, rigid, actuators),
resulting in a modular and expressive robot design space. Our benchmark
environments span a wide range of tasks, including locomotion on various types
of terrains and manipulation. Furthermore, we develop several robot
co-evolution algorithms by combining state-of-the-art design optimization
methods and deep reinforcement learning techniques. Evaluating the algorithms
on our benchmark platform, we observe robots exhibiting increasingly complex
behaviors as evolution progresses, with the best evolved designs solving many
of our proposed tasks. Additionally, even though robot designs are evolved
autonomously from scratch without prior knowledge, they often grow to resemble
existing natural creatures while outperforming hand-designed robots.
Nevertheless, all tested algorithms fail to find robots that succeed in our
hardest environments. This suggests that more advanced algorithms are required
to explore the high-dimensional design space and evolve increasingly
intelligent robots -- an area of research in which we hope Evolution Gym will
accelerate progress. Our website with code, environments, documentation, and
tutorials is available at http://evogym.csail.mit.edu.

Recent years have witnessed an emerging paradigm shift toward embodied
artificial intelligence, in which an agent must learn to solve challenging
tasks by interacting with its environment. There are several challenges in
solving embodied multimodal tasks, including long-horizon planning,
vision-and-language grounding, and efficient exploration. We focus on a
critical bottleneck, namely the performance of planning and navigation. To
tackle this challenge, we propose a Neural SLAM approach that, for the first
time, utilizes several modalities for exploration, predicts an affordance-aware
semantic map, and plans over it at the same time. This significantly improves
exploration efficiency, leads to robust long-horizon planning, and enables
effective vision-and-language grounding. With the proposed Affordance-aware
Multimodal Neural SLAM (AMSLAM) approach, we obtain more than 40% improvement
over prior published work on the ALFRED benchmark and set a new
state-of-the-art generalization performance at a success rate of 23.48% on the
test unseen scenes.

Modern control systems routinely employ wireless networks to exchange
information between spatially distributed plants, actuators and sensors. With
wireless networks defined by random, rapidly changing transmission conditions
that challenge assumptions commonly held in the design of control systems,
proper allocation of communication resources is essential to achieve reliable
operation. Designing resource allocation policies, however, is challenging,
motivating recent works to successfully exploit deep learning and deep
reinforcement learning techniques to design resource allocation and scheduling
policies for wireless control systems (WCSs). As the number of learnable
parameters in a neural network grows with the size of the input signal, deep
reinforcement learning may fail to scale, limiting the immediate generalization
of such scheduling and resource allocation policies to large-scale systems. The
interference and fading patterns among plants and controllers in the network,
however, induce a time-varying graph that can be used to construct policy
representations based on graph neural networks (GNNs), with the number of
learnable parameters now independent of the number of plants in the network. We
further establish in the context of WCSs that, due to inherent invariance to
graph permutations, the GNN is able to model scalable and transferable resource
allocation policies, which are subsequently trained with primal-dual
reinforcement learning. Numerical experiments show that the proposed graph
reinforcement learning approach yields policies that not only outperform
baseline solutions and deep reinforcement learning based policies in
large-scale systems, but that can also be transferred across networks of
varying size.

It remains challenging to deploy existing risk-averse approaches to
real-world applications. The reasons are multi-fold, including the lack of
global optimality guarantee and the necessity of learning from long-term
consecutive trajectories. Long-term consecutive trajectories are prone to
involving visiting hazardous states, which is a major concern in the
risk-averse setting. This paper proposes Short-Term VOlatility-controlled
Policy Search (STOPS), a novel algorithm that solves risk-averse problems by
learning from short-term trajectories instead of long-term trajectories.
Short-term trajectories are more flexible to generate, and can avoid the danger
of hazardous state visitations. By using an actor-critic scheme with an
overparameterized two-layer neural network, our algorithm finds a globally
optimal policy at a sublinear rate with proximal policy optimization and
natural policy gradient, with effectiveness comparable to the state-of-the-art
convergence rate of risk-neutral policy-search methods. The algorithm is
evaluated on challenging Mujoco robot simulation tasks under the mean-variance
evaluation metric. Both theoretical analysis and experimental results
demonstrate a state-of-the-art level of STOPS' performance among existing
risk-averse policy search methods.

Predicting the emergence of links in large evolving networks is a difficult
task with many practical applications. Recently, the Science4cast competition
has illustrated this challenge presenting a network of 64.000 AI concepts and
asking the participants to predict which topics are going to be researched
together in the future. In this paper, we present a solution to this problem
based on a new family of deep learning approaches, namely Graph Neural
Networks. The results of the challenge show that our solution is competitive
even if we had to impose severe restrictions to obtain a computationally
efficient and parsimonious model: ignoring the intrinsic dynamics of the graph
and using only a small subset of the nodes surrounding a target link.
Preliminary experiments presented in this paper suggest the model is learning
two related, but different patterns: the absorption of a node by a sub-graph
and union of more dense sub-graphs. The model seems to excel at recognizing the
first type of pattern.

To overcome inherent hardware limitations of hyperspectral imaging systems
with respect to their spatial resolution, fusion-based hyperspectral image
(HSI) super-resolution is attracting increasing attention. This technique aims
to fuse a low-resolution (LR) HSI and a conventional high-resolution (HR) RGB
image in order to obtain an HR HSI. Recently, deep learning architectures have
been used to address the HSI super-resolution problem and have achieved
remarkable performance. However, they ignore the degradation model even though
this model has a clear physical interpretation and may contribute to improve
the performance. We address this problem by proposing a method that, on the one
hand, makes use of the linear degradation model in the data-fidelity term of
the objective function and, on the other hand, utilizes the output of a
convolutional neural network for designing a deep prior regularizer in spectral
and spatial gradient domains. Experiments show the performance improvement
achieved with this strategy.

Person re-identification (Re-ID) has achieved great success in the supervised
scenario. However, it is difficult to directly transfer the supervised model to
arbitrary unseen domains due to the model overfitting to the seen source
domains. In this paper, we aim to tackle the generalizable multi-source person
Re-ID task (i.e., there are multiple available source domains, and the testing
domain is unseen during training) from the data augmentation perspective, thus
we put forward a novel method, termed MixNorm, which consists of domain-aware
mix-normalization (DMN) and domain-ware center regularization (DCR). Different
from the conventional data augmentation, the proposed domain-aware
mix-normalization to enhance the diversity of features during training from the
normalization view of the neural network, which can effectively alleviate the
model overfitting to the source domains, so as to boost the generalization
capability of the model in the unseen domain. To better learn the
domain-invariant model, we further develop the domain-aware center
regularization to better map the produced diverse features into the same space.
Extensive experiments on multiple benchmark datasets validate the effectiveness
of the proposed method and show that the proposed method can outperform the
state-of-the-art methods. Besides, further analysis also reveals the

Random access (RA) schemes are a topic of high interest in machine-type
communication (MTC). In RA protocols, backoff techniques such as exponential
backoff (EB) are used to stabilize the system to avoid low throughput and
excessive delays. However, these backoff techniques show varying performance
for different underlying assumptions and analytical models. Therefore, finding
a better transmission policy for slotted ALOHA RA is still a challenge. In this
paper, we show the potential of deep reinforcement learning (DRL) for RA. We
learn a transmission policy that balances between throughput and fairness. The
proposed algorithm learns transmission probabilities using previous action and
binary feedback signal, and it is adaptive to different traffic arrival rates.
Moreover, we propose average age of packet (AoP) as a metric to measure
fairness among users. Our results show that the proposed policy outperforms the
baseline EB transmission schemes in terms of throughput and fairness.

A new strategy was developed to investigate the effect of volatile fatty
acids (VFAs) on the efficiency of biogas production with a focus on improving
bio-H$_2$. The inoculum used, anaerobic granular sludge obtained from a UASB
reactor treating poultry slaughterhouse wastewater, was pretreated with five
different pretreatments. The relationship between VFAs and biogas compounds was
studied as time-dependent components. In time-dependent processes with small
sample size data, regression models may not be good enough at estimating
responses. Therefore, a deep learning neural network (DNN) model was developed
to estimate the biogas compounds based on the VFAs. The accuracy of this model
to predict the biogas compounds was higher than that of multivariate regression
models. Further, it could predict the effect of time changes on biogas
compounds. Analysis showed that all the pretreatments were able to increase the
ratio of butyric acid / acetic acid successfully, decrease propionic acid
drastically, and increase the efficiency of bio-H$_2$ production. As
discovered, butyric acid had the greatest effect on bio-H$_2$, and propionic
acid had the greatest effect on CH$_4$ production. The best amounts of the VFAs
were determined using an optimization method, integrated DNN and desirability
analysis, dynamically retrained based on digestion time. Accordingly, optimal
ranges of acetic, propionic, and butyric acids were 823.2 - 1534.3, 36.3 -
47.4, and 1522 - 1822 mg/L, respectively, determined for digestion time of
25.23 - 123.63 h. These values resulted in the production of bio-H$_2$, N$_2$,
CO$_2$, and CH$_4$ in ranges of 6.4 - 26.2, 12.2 - 43.2, 5 - 25.3, and 0 - 1.4
mmol/L, respectively. The optimum ranges of VFAs are relatively wide ranges and
practically can be used in biogas plants.

We consider in this work Edge Computing (EC) in a multi-tenant environment:
the resource owner, i.e., the Network Operator (NO), virtualizes the resources
and lets third party Service Providers (SPs - tenants) run their services,
which can be diverse and with heterogeneous requirements. Due to
confidentiality guarantees, the NO cannot observe the nature of the traffic of
SPs, which is encrypted. This makes resource allocation decisions challenging,
since they must be taken based solely on observed monitoring information. We
focus on one specific resource, i.e., cache space, deployed in some edge node,
e.g., a base station. We study the decision of the NO about how to partition
cache among several SPs in order to minimize the upstream traffic. Our goal is
to optimize cache allocation using purely data-driven, model-free Reinforcement
Learning (RL). Differently from most applications of RL, in which the decision
policy is learned offline on a simulator, we assume no previous knowledge is
available to build such a simulator. We thus apply RL in an \emph{online}
fashion, i.e., the policy is learned by directly perturbing the actual system
and monitoring how its performance changes. Since perturbations generate
spurious traffic, we also limit them. We show in simulation that our method
rapidly converges toward the theoretical optimum, we study its fairness, its
sensitivity to several scenario characteristics and compare it with a method
from the state-of-the-art.

In recent years, the monarch butterfly's iconic migration patterns have come
under threat from a number of factors, from climate change to pesticide use. To
track trends in their populations, scientists as well as citizen scientists
must identify individuals accurately. This is uniquely key for the study of
monarch butterflies because there exist other species of butterfly, such as
viceroy butterflies, that are "look-alikes" (coined by the Convention on
International Trade in Endangered Species of Wild Fauna and Flora), having
similar phenotypes. To tackle this problem and to aid in more efficient
identification, we present MonarchNet, the first comprehensive dataset
consisting of butterfly imagery for monarchs and five look-alike species. We
train a baseline deep-learning classification model to serve as a tool for
differentiating monarch butterflies and its various look-alikes. We seek to
contribute to the study of biodiversity and butterfly ecology by providing a
novel method for computational classification of these particular butterfly
species. The ultimate aim is to help scientists track monarch butterfly
population and migration trends in the most precise and efficient manner
possible.

Devising augmentations for graph contrastive learning is challenging due to
their irregular structure, drastic distribution shifts, and nonequivalent
feature spaces across datasets. We introduce LG2AR, Learning Graph
Augmentations to Learn Graph Representations, which is an end-to-end automatic
graph augmentation framework that helps encoders learn generalizable
representations on both node and graph levels. LG2AR consists of a
probabilistic policy that learns a distribution over augmentations and a set of
probabilistic augmentation heads that learn distributions over augmentation
parameters. We show that LG2AR achieves state-of-the-art results on 18 out of
20 graph-level and node-level benchmarks compared to previous unsupervised
models under both linear and semi-supervised evaluation protocols. The source
code will be released here: https://github.com/kavehhassani/lg2ar

Advances in machine learning (ML) techniques have enabled the development of
interatomic potentials that promise both the accuracy of first principles
methods and the low-cost, linear scaling, and parallel efficiency of empirical
potentials. Despite rapid progress in the last few years, ML-based potentials
often struggle to achieve transferability, that is, to provide consistent
accuracy across configurations that significantly differ from those used to
train the model. In order to truly realize the promise of ML-based interatomic
potentials, it is therefore imperative to develop systematic and scalable
approaches for the generation of diverse training sets that ensure broad
coverage of the space of atomic environments. This work explores a
diverse-by-construction approach that leverages the optimization of the entropy
of atomic descriptors to create a very large ($>2\cdot10^{5}$ configurations,
$>7\cdot10^{6}$ atomic environments) training set for tungsten in an automated
manner, i.e., without any human intervention. This dataset is used to train
polynomial as well as multiple neural network potentials with different
architectures. For comparison, a corresponding family of potentials were also
trained on an expert-curated dataset for tungsten. The models trained to
entropy-optimized data exhibited vastly superior transferability compared to
the expert-curated models. Furthermore, while the models trained with heavy
user input (i.e., domain expertise) yield the lowest errors when tested on
similar configurations, out-sample predictions are dramatically more robust
when the models are trained on a deliberately diverse set of training data.
Herein we demonstrate the development of both accurate and transferable ML
potentials using automated and data-driven approaches for generating large and
diverse training sets.

Current deep learning approaches for multimodal fusion rely on bottom-up
fusion of high and mid-level latent modality representations (late/mid fusion)
or low level sensory inputs (early fusion). Models of human perception
highlight the importance of top-down fusion, where high-level representations
affect the way sensory inputs are perceived, i.e. cognition affects perception.
These top-down interactions are not captured in current deep learning models.
In this work we propose a neural architecture that captures top-down
cross-modal interactions, using a feedback mechanism in the forward pass during
network training. The proposed mechanism extracts high-level representations
for each modality and uses these representations to mask the sensory inputs,
allowing the model to perform top-down feature masking. We apply the proposed
model for multimodal sentiment recognition on CMU-MOSEI. Our method shows
consistent improvements over the well established MulT and over our strong late
fusion baseline, achieving state-of-the-art results.

We give tight statistical query (SQ) lower bounds for learnining halfspaces
in the presence of Massart noise. In particular, suppose that all labels are
corrupted with probability at most $\eta$. We show that for arbitrary $\eta \in
[0,1/2]$ every SQ algorithm achieving misclassification error better than
$\eta$ requires queries of superpolynomial accuracy or at least a
superpolynomial number of queries. Further, this continues to hold even if the
information-theoretically optimal error $\mathrm{OPT}$ is as small as
$\exp\left(-\log^c(d)\right)$, where $d$ is the dimension and $0 < c < 1$ is an
arbitrary absolute constant, and an overwhelming fraction of examples are
noiseless. Our lower bound matches known polynomial time algorithms, which are
also implementable in the SQ framework. Previously, such lower bounds only
ruled out algorithms achieving error $\mathrm{OPT} + \epsilon$ or error better
than $\Omega(\eta)$ or, if $\eta$ is close to $1/2$, error $\eta - o_\eta(1)$,
where the term $o_\eta(1)$ is constant in $d$ but going to 0 for $\eta$
approaching $1/2$.
  As a consequence, we also show that achieving misclassification error better
than $1/2$ in the $(A,\alpha)$-Tsybakov model is SQ-hard for $A$ constant and
$\alpha$ bounded away from 1.

Bayesian neural networks have successfully designed and optimized a robust
neural network model in many application problems, including uncertainty
quantification. However, with its recent success, information-theoretic
understanding about the Bayesian neural network is still at an early stage.
Mutual information is an example of an uncertainty measure in a Bayesian neural
network to quantify epistemic uncertainty. Still, no analytic formula is known
to describe it, one of the fundamental information measures to understand the
Bayesian deep learning framework. In this paper, we derive the analytical
formula of the mutual information between model parameters and the predictive
output by leveraging the notion of the point process entropy. Then, as an
application, we discuss the parameter estimation of the Dirichlet distribution
and show its practical application in the active learning uncertainty measures
by demonstrating that our analytical formula can improve the performance of
active learning further in practice.

Improving sample-efficiency and safety are crucial challenges when deploying
reinforcement learning in high-stakes real world applications. We propose
LAMBDA, a novel model-based approach for policy optimization in safety critical
tasks modeled via constrained Markov decision processes. Our approach utilizes
Bayesian world models, and harnesses the resulting uncertainty to maximize
optimistic upper bounds on the task objective, as well as pessimistic upper
bounds on the safety constraints. We demonstrate LAMBDA's state of the art
performance on the Safety-Gym benchmark suite in terms of sample efficiency and
constraint violation.

Natural disasters ravage the world's cities, valleys, and shores on a regular
basis. Deploying precise and efficient computational mechanisms for assessing
infrastructure damage is essential to channel resources and minimize the loss
of life. Using a dataset that includes labeled pre- and post- disaster
satellite imagery, we take a machine learning-based remote sensing approach and
train multiple convolutional neural networks (CNNs) to assess building damage
on a per-building basis. We present a novel methodology of interpretable deep
learning that seeks to explicitly investigate the most useful modalities of
information in the training data to create an accurate classification model. We
also investigate which loss functions best optimize these models. Our findings
include that ordinal-cross entropy loss is the most optimal criterion for
optimization to use and that including the type of disaster that caused the
damage in combination with pre- and post-disaster training data most accurately
predicts the level of damage caused. Further, we make progress in the
qualitative representation of which parts of the images that the model is using
to predict damage levels, through gradient-weighted class activation mapping
(Grad-CAM). Our research seeks to computationally contribute to aiding in this
ongoing and growing humanitarian crisis, heightened by anthropogenic climate
change.

Most real-world optimization problems have multiple objectives. A system
designer needs to find a policy that trades off these objectives to reach a
desired operating point. This problem has been studied extensively in the
setting of known objective functions. We consider a more practical but
challenging setting of unknown objective functions. In industry, this problem
is mostly approached with online A/B testing, which is often costly and
inefficient. As an alternative, we propose interactive multi-objective
off-policy optimization (IMO$^3$). The key idea in our approach is to interact
with a system designer using policies evaluated in an off-policy fashion to
uncover which policy maximizes her unknown utility function. We theoretically
show that IMO$^3$ identifies a near-optimal policy with high probability,
depending on the amount of feedback from the designer and training data for
off-policy estimation. We demonstrate its effectiveness empirically on multiple
multi-objective optimization problems.

Although convolutional networks have been the dominant architecture for
vision tasks for many years, recent experiments have shown that
Transformer-based models, most notably the Vision Transformer (ViT), may exceed
their performance in some settings. However, due to the quadratic runtime of
the self-attention layers in Transformers, ViTs require the use of patch
embeddings, which group together small regions of the image into single input
features, in order to be applied to larger image sizes. This raises a question:
Is the performance of ViTs due to the inherently-more-powerful Transformer
architecture, or is it at least partly due to using patches as the input
representation? In this paper, we present some evidence for the latter:
specifically, we propose the ConvMixer, an extremely simple model that is
similar in spirit to the ViT and the even-more-basic MLP-Mixer in that it
operates directly on patches as input, separates the mixing of spatial and
channel dimensions, and maintains equal size and resolution throughout the
network. In contrast, however, the ConvMixer uses only standard convolutions to
achieve the mixing steps. Despite its simplicity, we show that the ConvMixer
outperforms the ViT, MLP-Mixer, and some of their variants for similar
parameter counts and data set sizes, in addition to outperforming classical
vision models such as the ResNet. Our code is available at
https://github.com/locuslab/convmixer.

The goals of this paper are twofold: (1) to present a new method that is able
to find linear laws governing the time evolution of Markov chains and (2) to
apply this method for anomaly detection in Bitcoin prices. To accomplish these
goals, first, the linear laws of Markov chains are derived by using the time
embedding of their (categorical) autocorrelation function. Then, a binary
series is generated from the first difference of Bitcoin exchange rate (against
the United States Dollar). Finally, the minimum number of parameters describing
the linear laws of this series is identified through stepped time windows.
Based on the results, linear laws typically became more complex (containing an
additional third parameter that indicates hidden Markov property) in two
periods: before the crash of cryptocurrency markets inducted by the COVID-19
pandemic (12 March 2020), and before the record-breaking surge in the price of
Bitcoin (Q4 2020 - Q1 2021). In addition, the locally high values of this third
parameter are often related to short-term price peaks, which suggests price

The introduction of online marketplace platforms has led to the advent of new
forms of flexible, on-demand (or 'gig') work. Yet, most prior research
concerning the experience of gig workers examines delivery or crowdsourcing
platforms, while the experience of the large numbers of workers who undertake
educational labour in the form of tutoring gigs remains understudied. To
address this, we use a computational grounded theory approach to analyse
tutors' discussions on Reddit. This approach consists of three phases including
data exploration, modelling and human-centred interpretation. We use both
validation and human evaluation to increase the trustworthiness and reliability
of the computational methods. This paper is a work in progress and reports on
the first of the three phases of this approach.

Neural architecture search (NAS) has gained immense popularity owing to its
ability to automate neural architecture design. A number of training-free
metrics are recently proposed to realize NAS without training, hence making NAS
more scalable. Despite their competitive empirical performances, a unified
theoretical understanding of these training-free metrics is lacking. As a
consequence, (a) the relationships among these metrics are unclear, (b) there
is no theoretical interpretation for their empirical performances, and (c)
there may exist untapped potential in existing training-free NAS, which
probably can be unveiled through a unified theoretical understanding. To this
end, this paper presents a unified theoretical analysis of gradient-based
training-free NAS, which allows us to (a) theoretically study their
relationships, (b) theoretically guarantee their generalization performances,
and (c) exploit our unified theoretical understanding to develop a novel
framework named hybrid NAS (HNAS) which consistently boosts training-free NAS
in a principled way. Remarkably, HNAS can enjoy the advantages of both
training-free (i.e., the superior search efficiency) and training-based (i.e.,
the remarkable search effectiveness) NAS, which we have demonstrated through
extensive experiments.

This paper proposes a new two-step procedure for sparse-view tomographic
image reconstruction. It is called RISING, since it combines an early-stopped
Rapid Iterative Solver with a subsequent Iteration Network-based Gaining step.
So far, regularized iterative methods have widely been used for X-ray computed
tomography image reconstruction from low-sampled data, since they converge to a
sparse solution in a suitable domain, as upheld by the Compressed Sensing
theory. Unfortunately, their use is practically limited by their high
computational cost which imposes to perform only a few iterations in the
available time for clinical exams. Data-driven methods, using neural networks
to post-process a coarse and noisy image obtained from geometrical algorithms,
have been recently studied and appreciated for both their computational speed
and accurate reconstructions. However, there is no evidence, neither
theoretically nor numerically, that neural networks based algorithms solve the
mathematical inverse problem modeling the tomographic reconstruction process.
In our two-step approach, the first phase executes very few iterations of a
regularized model-based algorithm whereas the second step completes the missing
iterations by means of a neural network. The resulting hybrid deep-variational
framework preserves the convergence properties of the iterative method and, at
the same time, it exploits the computational speed and flexibility of a
data-driven approach. Experiments performed on a simulated and a real data set
confirm the numerical and visual accuracy of the reconstructed RISING images in
short computational times.

Attack vectors that compromise machine learning pipelines in the physical
world have been demonstrated in recent research, from perturbations to
architectural components. Building on this work, we illustrate the
self-obfuscation attack: attackers target a pre-processing model in the system,
and poison the training set of generative models to obfuscate a specific class
during inference. Our contribution is to describe, implement and evaluate a
generalized attack, in the hope of raising awareness regarding the challenge of
architectural robustness within the machine learning community.

Standard model-free reinforcement learning algorithms optimize a policy that
generates the action to be taken in the current time step in order to maximize
expected future return. While flexible, it faces difficulties arising from the
inefficient exploration due to its single step nature. In this work, we present
Generative Planning method (GPM), which can generate actions not only for the
current step, but also for a number of future steps (thus termed as generative
planning). This brings several benefits to GPM. Firstly, since GPM is trained
by maximizing value, the plans generated from it can be regarded as intentional
action sequences for reaching high value regions. GPM can therefore leverage
its generated multi-step plans for temporally coordinated exploration towards
high value regions, which is potentially more effective than a sequence of
actions generated by perturbing each action at single step level, whose
consistent movement decays exponentially with the number of exploration steps.
Secondly, starting from a crude initial plan generator, GPM can refine it to be
adaptive to the task, which, in return, benefits future explorations. This is
potentially more effective than commonly used action-repeat strategy, which is
non-adaptive in its form of plans. Additionally, since the multi-step plan can
be interpreted as the intent of the agent from now to a span of time period
into the future, it offers a more informative and intuitive signal for
interpretation. Experiments are conducted on several benchmark environments and
the results demonstrated its effectiveness compared with several baseline
methods.

Learning the embeddings for urban regions from human mobility data can reveal
the functionality of regions, and then enables the correlated but distinct
tasks such as crime prediction. Human mobility data contains rich but abundant
information, which yields to the comprehensive region embeddings for cross
domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to
enable the cross domain prediction tasks. First, we integrate the graphs with
spatio-temporal similarity as mobility patterns through a mobility graph fusion
module. Then, in the mobility pattern joint learning module, we design the
multi-level cross-attention mechanism to learn the comprehensive embeddings
from multiple mobility patterns based on intra-pattern and inter-pattern
messages. Finally, we conduct extensive experiments on real-world urban
datasets. Experimental results demonstrate that the proposed MGFN outperforms
the state-of-the-art methods by up to 12.35% improvement.

Wearable and unobtrusive monitoring and prediction of epileptic seizures has
the potential to significantly increase the life quality of patients, but is
still an unreached goal due to challenges of real-time detection and wearable
devices design. Hyperdimensional (HD) computing has evolved in recent years as
a new promising machine learning approach, especially when talking about
wearable applications. But in the case of epilepsy detection, standard HD
computing is not performing at the level of other state-of-the-art algorithms.
This could be due to the inherent complexity of the seizures and their
signatures in different biosignals, such as the electroencephalogram (EEG), the
highly personalized nature, and the disbalance of seizure and non-seizure
instances. In the literature, different strategies for improved learning of HD
computing have been proposed, such as iterative (multi-pass) learning,
multi-centroid learning and learning with sample weight ("OnlineHD"). Yet, most
of them have not been tested on the challenging task of epileptic seizure
detection, and it stays unclear whether they can increase the HD computing
performance to the level of the current state-of-the-art algorithms, such as
random forests. Thus, in this paper, we implement different learning strategies
and assess their performance on an individual basis, or in combination,
regarding detection performance and memory and computational requirements.
Results show that the best-performing algorithm, which is a combination of
multi-centroid and multi-pass, can indeed reach the performance of the random
forest model on a highly unbalanced dataset imitating a real-life epileptic
seizure detection application.

Automated Machine Learning (AutoML) has been used successfully in settings
where the learning task is assumed to be static. In many real-world scenarios,
however, the data distribution will evolve over time, and it is yet to be shown
whether AutoML techniques can effectively design online pipelines in dynamic
environments. This study aims to automate pipeline design for online learning
while continuously adapting to data drift. For this purpose, we design an
adaptive Online Automated Machine Learning (OAML) system, searching the
complete pipeline configuration space of online learners, including
preprocessing algorithms and ensembling techniques. This system combines the
inherent adaptation capabilities of online learners with the fast automated
pipeline (re)optimization capabilities of AutoML. Focusing on optimization
techniques that can adapt to evolving objectives, we evaluate asynchronous
genetic programming and asynchronous successive halving to optimize these
pipelines continually. We experiment on real and artificial data streams with
varying types of concept drift to test the performance and adaptation
capabilities of the proposed system. The results confirm the utility of OAML
over popular online learning algorithms and underscore the benefits of
continuous pipeline redesign in the presence of data drift.

Being able to predict the failure of materials based on structural
information is a fundamental issue with enormous practical and industrial
relevance for the monitoring of devices and components. Thanks to recent
advances in deep learning, accurate failure predictions are becoming possible
even for strongly disordered solids, but the sheer number of parameters used in
the process renders a physical interpretation of the results impossible. Here
we address this issue and use machine learning methods to predict the failure
of simulated two dimensional silica glasses from their initial undeformed
structure. We then exploit Gradient-weighted Class Activation Mapping
(Grad-CAM) to build attention maps associated with the predictions, and we
demonstrate that these maps are amenable to physical interpretation in terms of
topological defects and local potential energies. We show that our predictions
can be transferred to samples with different shape or size than those used in
training, as well as to experimental images. Our strategy illustrates how
artificial neural networks trained with numerical simulation results can
provide interpretable predictions of the behavior of experimentally measured

Learning-based pre-simulation (i.e., layout-to-fabrication) models have been
proposed to predict the fabrication-induced shape deformation from an IC layout
to its fabricated circuit. Such models are usually driven by pairwise learning,
involving a training set of layout patterns and their reference shape images
after fabrication. However, it is expensive and time-consuming to collect the
reference shape images of all layout clips for model training and updating. To
address the problem, we propose a deep learning-based layout novelty detection
scheme to identify novel (unseen) layout patterns, which cannot be well
predicted by a pre-trained pre-simulation model. We devise a global-local
novelty scoring mechanism to assess the potential novelty of a layout by
exploiting two subnetworks: an autoencoder and a pretrained pre-simulation
model. The former characterizes the global structural dissimilarity between a
given layout and training samples, whereas the latter extracts a latent code
representing the fabrication-induced local deformation. By integrating the
global dissimilarity with the local deformation boosted by a self-attention
mechanism, our model can accurately detect novelties without the ground-truth
circuit shapes of test samples. Based on the detected novelties, we further
propose two active-learning strategies to sample a reduced amount of
representative layouts most worthy to be fabricated for acquiring their
ground-truth circuit shapes. Experimental results demonstrate i) our method's
effectiveness in layout novelty detection, and ii) our active-learning
strategies' ability in selecting representative novel layouts for keeping a
learning-based pre-simulation model updated.

As automatic speaker verification (ASV) systems are vulnerable to spoofing
attacks, they are typically used in conjunction with spoofing countermeasure
(CM) systems to improve security. For example, the CM can first determine
whether the input is human speech, then the ASV can determine whether this
speech matches the speaker's identity. The performance of such a tandem system
can be measured with a tandem detection cost function (t-DCF). However, ASV and
CM systems are usually trained separately, using different metrics and data,
which does not optimize their combined performance. In this work, we propose to
optimize the tandem system directly by creating a differentiable version of
t-DCF and employing techniques from reinforcement learning. The results
indicate that these approaches offer better outcomes than finetuning, with our
method providing a 20% relative improvement in the t-DCF in the ASVSpoof19
dataset in a constrained setting.

Knowledge and expertise in the real-world can be disjointedly owned. To solve
a complex question, collaboration among experts is often called for. In this
paper, we propose CollabQA, a novel QA task in which several expert agents
coordinated by a moderator work together to answer questions that cannot be
answered with any single agent alone. We make a synthetic dataset of a large
knowledge graph that can be distributed to experts. We define the process to
form a complex question from ground truth reasoning path, neural network agent
models that can learn to solve the task, and evaluation metrics to check the
performance. We show that the problem can be challenging without introducing
prior of the collaboration structure, unless experts are perfect and uniform.
Based on this experience, we elaborate extensions needed to approach
collaboration tasks in real-world settings.

In this paper we address the task of visual place recognition (VPR), where
the goal is to retrieve the correct GPS coordinates of a given query image
against a huge geotagged gallery. While recent works have shown that building
descriptors incorporating semantic and appearance information is beneficial,
current state-of-the-art methods opt for a top down definition of the
significant semantic content. Here we present the first VPR algorithm that
learns robust global embeddings from both visual appearance and semantic
content of the data, with the segmentation process being dynamically guided by
the recognition of places through a multi-scale attention module. Experiments
on various scenarios validate this new approach and demonstrate its performance
against state-of-the-art methods. Finally, we propose the first synthetic-world
dataset suited for both place recognition and segmentation tasks.

Few-shot learning aims at leveraging knowledge learned by one or more deep
learning models, in order to obtain good classification performance on new
problems, where only a few labeled samples per class are available. Recent
years have seen a fair number of works in the field, introducing methods with
numerous ingredients. A frequent problem, though, is the use of suboptimally
trained models to extract knowledge, leading to interrogations on whether
proposed approaches bring gains compared to using better initial models without
the introduced ingredients. In this work, we propose a simple methodology, that
reaches or even beats state of the art performance on multiple standardized
benchmarks of the field, while adding almost no hyperparameters or parameters
to those used for training the initial deep learning models on the generic
dataset. This methodology offers a new baseline on which to propose (and fairly
compare) new techniques or adapt existing ones.

Graph Convolutional Networks (GCN) is a pioneering model for graph-based
semi-supervised learning. However, GCN does not perform well on
sparsely-labeled graphs. Its two-layer version cannot effectively propagate the
label information to the whole graph structure (i.e., the under-smoothing
problem) while its deep version over-smoothens and is hard to train (i.e., the
over-smoothing problem). To solve these two issues, we propose a new graph
neural network called GND-Nets (for Graph Neural Diffusion Networks) that
exploits the local and global neighborhood information of a vertex in a single
layer. Exploiting the shallow network mitigates the over-smoothing problem
while exploiting the local and global neighborhood information mitigates the
under-smoothing problem. The utilization of the local and global neighborhood
information of a vertex is achieved by a new graph diffusion method called
neural diffusions, which integrate neural networks into the conventional linear
and nonlinear graph diffusions. The adoption of neural networks makes neural
diffusions adaptable to different datasets. Extensive experiments on various
sparsely-labeled graphs verify the effectiveness and efficiency of GND-Nets
compared to state-of-the-art approaches.

Question Generation (QG), as a challenging Natural Language Processing task,
aims at generating questions based on given answers and context. Existing QG
methods mainly focus on building or training models for specific QG datasets.
These works are subject to two major limitations: (1) They are dedicated to
specific QG formats (e.g., answer-extraction or multi-choice QG), therefore, if
we want to address a new format of QG, a re-design of the QG model is required.
(2) Optimal performance is only achieved on the dataset they were just trained
on. As a result, we have to train and keep various QG models for different QG
datasets, which is resource-intensive and ungeneralizable.
  To solve the problems, we propose a model named Unified-QG based on lifelong
learning techniques, which can continually learn QG tasks across different
datasets and formats. Specifically, we first build a format-convert encoding to
transform different kinds of QG formats into a unified representation. Then, a
method named \emph{STRIDER} (\emph{S}imilari\emph{T}y \emph{R}egular\emph{I}zed
\emph{D}ifficult \emph{E}xample \emph{R}eplay) is built to alleviate
catastrophic forgetting in continual QG learning. Extensive experiments were
conducted on $8$ QG datasets across $4$ QG formats (answer-extraction,
answer-abstraction, multi-choice, and boolean QG) to demonstrate the
effectiveness of our approach. Experimental results demonstrate that our
Unified-QG can effectively and continually adapt to QG tasks when datasets and
formats vary. In addition, we verify the ability of a single trained Unified-QG
model in improving $8$ Question Answering (QA) systems' performance through
generating synthetic QA data.

Image translation across domains for unpaired datasets has gained interest
and great improvement lately. In medical imaging, there are multiple imaging
modalities, with very different characteristics. Our goal is to use
cross-modality adaptation between CT and MRI whole cardiac scans for semantic
segmentation. We present a segmentation network using synthesised cardiac
volumes for extremely limited datasets. Our solution is based on a 3D
cross-modality generative adversarial network to share information between
modalities and generate synthesized data using unpaired datasets. Our network
utilizes semantic segmentation to improve generator shape consistency, thus
creating more realistic synthesised volumes to be used when re-training the
segmentation network. We show that improved segmentation can be achieved on
small datasets when using spatial augmentations to improve a generative
adversarial network. These augmentations improve the generator capabilities,
thus enhancing the performance of the Segmentor. Using only 16 CT and 16 MRI
cardiovascular volumes, improved results are shown over other segmentation
methods while using the suggested architecture.

The semantically disentangled latent subspace in GAN provides rich
interpretable controls in image generation. This paper includes two
contributions on semantic latent subspace analysis in the scenario of face
generation using StyleGAN2. First, we propose a novel approach to disentangle
latent subspace semantics by exploiting existing face analysis models, e.g.,
face parsers and face landmark detectors. These models provide the flexibility
to construct various criterions with very concrete and interpretable semantic
meanings (e.g., change face shape or change skin color) to restrict latent
subspace disentanglement. Rich latent space controls unknown previously can be
discovered using the constructed criterions. Second, we propose a new
perspective to explain the behavior of a CNN classifier by generating
counterfactuals in the interpretable latent subspaces we discovered. This
explanation helps reveal whether the classifier learns semantics as intended.
Experiments on various disentanglement criterions demonstrate the effectiveness
of our approach. We believe this approach contributes to both areas of image
manipulation and counterfactual explainability of CNNs. The code is available
at \url{https://github.com/prclibo/ice}.

Accurate forecasting of multivariate time series is an extensively studied
subject in finance, transportation, and computer science. Fully mining the
correlation and causation between the variables in a multivariate time series
exhibits noticeable results in improving the performance of a time series
model. Recently, some models have explored the dependencies between variables
through end-to-end graph structure learning without the need for predefined
graphs. However, current models do not incorporate the trade-off between
efficiency and flexibility and lack the guidance of domain knowledge in the
design of graph structure learning algorithms. This paper alleviates the above
issues by proposing Balanced Graph Structure Learning for Forecasting (BGSLF),
a novel deep learning model that joins graph structure learning and
forecasting. Technically, BGSLF leverages the spatial information into
convolutional operations and extracts temporal dynamics using the diffusion
convolutional recurrent network. The proposed framework balance the trade-off
between efficiency and flexibility by introducing Multi-Graph Generation
Network (MGN) and Graph Selection Module. In addition, a method named Smooth
Sparse Unit (SSU) is designed to sparse the learned graph structures, which
conforms to the sparse spatial correlations in the real world. Extensive
experiments on four real-world datasets demonstrate that our model achieves
state-of-the-art performances with minor trainable parameters. Code will be

Decision-making AI agents are often faced with two important challenges: the
depth of the planning horizon, and the branching factor due to having many
choices. Hierarchical reinforcement learning methods aim to solve the first
problem, by providing shortcuts that skip over multiple time steps. To cope
with the breadth, it is desirable to restrict the agent's attention at each
step to a reasonable number of possible choices. The concept of affordances
(Gibson, 1977) suggests that only certain actions are feasible in certain
states. In this work, we model "affordances" through an attention mechanism
that limits the available choices of temporally extended options. We present an
online, model-free algorithm to learn affordances that can be used to further
learn subgoal options. We investigate the role of hard versus soft attention in
training data collection, abstract value learning in long-horizon tasks, and
handling a growing number of choices. We identify and empirically illustrate
the settings in which the paradox of choice arises, i.e. when having fewer but
more meaningful choices improves the learning speed and performance of a
reinforcement learning agent.

Adversarial examples (AEs) pose severe threats to the applications of deep
neural networks (DNNs) to safety-critical domains, e.g., autonomous driving.
While there has been a vast body of AE defense solutions, to the best of our
knowledge, they all suffer from some weaknesses, e.g., defending against only a
subset of AEs or causing a relatively high accuracy loss for legitimate inputs.
Moreover, most existing solutions cannot defend against adaptive attacks,
wherein attackers are knowledgeable about the defense mechanisms and craft AEs
accordingly. In this paper, we propose a novel AE detection framework based on
the very nature of AEs, i.e., their semantic information is inconsistent with
the discriminative features extracted by the target DNN model. To be specific,
the proposed solution, namely ContraNet, models such contradiction by first
taking both the input and the inference result to a generator to obtain a
synthetic output and then comparing it against the original input. For
legitimate inputs that are correctly inferred, the synthetic output tries to
reconstruct the input. On the contrary, for AEs, instead of reconstructing the
input, the synthetic output would be created to conform to the wrong label
whenever possible. Consequently, by measuring the distance between the input
and the synthetic output with metric learning, we can differentiate AEs from
legitimate inputs. We perform comprehensive evaluations under various AE attack
scenarios, and experimental results show that ContraNet outperforms existing
solutions by a large margin, especially under adaptive attacks. Moreover, our
analysis shows that successful AEs that can bypass ContraNet tend to have
much-weakened adversarial semantics. We have also shown that ContraNet can be
easily combined with adversarial training techniques to achieve further
improved AE defense capabilities.

Realistic fine-grained multi-agent simulation of real-world complex systems
is crucial for many downstream tasks such as reinforcement learning. Recent
work has used generative models (GANs in particular) for providing
high-fidelity simulation of real-world systems. However, such generative models
are often monolithic and miss out on modeling the interaction in multi-agent
systems. In this work, we take a first step towards building multiple
interacting generative models (GANs) that reflects the interaction in real
world. We build and analyze a hierarchical set-up where a higher-level GAN is
conditioned on the output of multiple lower-level GANs. We present a technique
of using feedback from the higher-level GAN to improve performance of
lower-level GANs. We mathematically characterize the conditions under which our
technique is impactful, including understanding the transfer learning nature of
our set-up. We present three distinct experiments on synthetic data, time
series data, and image domain, revealing the wide applicability of our

AI-aided drug discovery (AIDD) is gaining increasing popularity due to its
promise of making the search for new pharmaceuticals quicker, cheaper and more
efficient. In spite of its extensive use in many fields, such as ADMET
prediction, virtual screening, protein folding and generative chemistry, little
has been explored in terms of the out-of-distribution (OOD) learning problem
with \emph{noise}, which is inevitable in real world AIDD applications.
  In this work, we present DrugOOD, a systematic OOD dataset curator and
benchmark for AI-aided drug discovery, which comes with an open-source Python
package that fully automates the data curation and OOD benchmarking processes.
We focus on one of the most crucial problems in AIDD: drug target binding
affinity prediction, which involves both macromolecule (protein target) and
small-molecule (drug compound). In contrast to only providing fixed datasets,
DrugOOD offers automated dataset curator with user-friendly customization
scripts, rich domain annotations aligned with biochemistry knowledge, realistic
noise annotations and rigorous benchmarking of state-of-the-art OOD algorithms.
Since the molecular data is often modeled as irregular graphs using graph
neural network (GNN) backbones, DrugOOD also serves as a valuable testbed for
\emph{graph OOD learning} problems. Extensive empirical studies have shown a
significant performance gap between in-distribution and out-of-distribution
experiments, which highlights the need to develop better schemes that can allow
for OOD generalization under noise for AIDD.

This work investigates the use of smooth neural networks for modeling dynamic
variations of implicit surfaces under partial differential equations (PDE). For
this purpose, it extends the representation of neural implicit surfaces to the
space-time $\mathbb{R}^3\times \mathbb{R}$, which opens up mechanisms for
\textbf{continuous} geometric transformations. Examples include evolving an
initial condition surface towards general vector fields, smoothing and
sharpening using the mean curvature equation, and interpolations of initial
conditions regularized by specific differential equations.
  The network training considers two constraints. A data term is responsible
for fitting the PDE's initial condition to the corresponding time instant,
usually $\mathbb{R}^3 \times \{0\}$. Then, a PDE term forces the network to
approximate a solution of the underlying equation, \textbf{without any
supervision}. The network can also be initialized based on previously trained
initial conditions resulting in faster convergence when compared with the
standard approach.

Hierarchical reinforcement learning (HRL) proposes to solve difficult tasks
by performing decision-making and control at successively higher levels of
temporal abstraction. However, off-policy HRL often suffers from the problem of
non-stationary high-level policy since the low-level policy is constantly
changing. In this paper, we propose a novel HRL approach for mitigating the
non-stationarity by adversarially enforcing the high-level policy to generate
subgoals compatible with the current instantiation of the low-level policy. In
practice, the adversarial learning is implemented by training a simple
discriminator network concurrently with the high-level policy which determines
the compatibility level of subgoals. Experiments with state-of-the-art
algorithms show that our approach improves both HRL learning efficiency and
overall performance in various challenging continuous control tasks.

Transcribing struck-through, handwritten words, for example for the purpose
of genetic criticism, can pose a challenge to both humans and machines, due to
the obstructive properties of the superimposed strokes. This paper investigates
the use of paired image to image translation approaches to remove strikethrough
strokes from handwritten words. Four different neural network architectures are
examined, ranging from a few simple convolutional layers to deeper ones,
employing Dense blocks. Experimental results, obtained from one synthetic and
one genuine paired strikethrough dataset, confirm that the proposed paired
models outperform the CycleGAN-based state of the art, while using less than a
sixth of the trainable parameters.

All-quantum signal processing techniques are at the core of the successful
advancement of most information-based quantum technologies. This paper develops
coherent and comprehensive methodologies and mathematical models to describe
Fourier optical signal processing in full quantum terms for any input quantum
state of light. We begin this paper by introducing a spatially two-dimensional
quantum state of a photon, associated with its wavefront and expressible as a
two-dimensional creation operator. Then, by breaking down the Fourier optical
processing apparatus into its key components, we strive to acquire the quantum
unitary transformation or the input/output quantum relation of the
two-dimensional creation operators. Subsequently, we take advantage of the
above results to develop and obtain the quantum analogous of a few essential
Fourier optical apparatus, such as quantum convolution via a 4f-processing
system and a quantum 4f-processing system with periodic pupils. Moreover, due
to the importance and widespread use of optical pulse shaping in various
optical communications and optical sciences fields, we also present an
analogous system in full quantum terms, namely quantum pulse shaping with an
8f-processing system. Finally, we apply our results to two extreme examples of
the quantum state of light. One is based on a coherent (Glauber) state and the
other on a single-photon number (Fock) state for each of the above optical
systems. We believe the schemes and mathematical models developed in this paper
can impact many areas of quantum optical signal processing, quantum holography,
quantum communications, quantum radars and multiple-input/multiple-output
antennas, and many more applications in quantum computations and quantum

Searching for bindings of geometric parameters in task and motion planning
(TAMP) is a finite-horizon stochastic planning problem with high-dimensional
decision spaces. A robot manipulator can only move in a subspace of its whole
range that is subjected to many geometric constraints. A TAMP solver usually
takes many explorations before finding a feasible binding set for each task. It
is favorable to learn those constraints once and then transfer them over
different tasks within the same workspace. We address this problem by
representing constraint knowledge with transferable primitives and using
Bayesian optimization (BO) based on these primitives to guide binding search in
further tasks. Via semantic and geometric backtracking in TAMP, we construct
constraint primitives to encode the geometric constraints respectively in a
reusable form. Then we devise a BO approach to efficiently utilize the
accumulated constraints for guiding node expansion of an MCTS-based binding
planner. We further compose a transfer mechanism to enable free knowledge flow
between TAMP tasks. Results indicate that our approach reduces the expensive
exploration calls in binding search by 43.60to 71.69 when compared to the
baseline unguided planner.

In the design phase of an electrical machine, finite element (FE) simulation
are commonly used to numerically optimize the performance. The output of the
magneto-static FE simulation characterizes the electromagnetic behavior of the
electrical machine. It usually includes intermediate measures such as nonlinear
iron losses, electromagnetic torque, and flux values at each operating point to
compute the key performance indicators (KPIs). We present a data-driven deep
learning approach that replaces the computationally heavy FE calculations by a
deep neural network (DNN). The DNN is trained by a large volume of stored FE
data in a supervised manner. During the learning process, the network response
(intermediate measures) is fed as input to a physics-based post-processing to
estimate characteristic maps and KPIs. Results indicate that the predictions of
intermediate measures and the subsequent computations of KPIs are close to the
ground truth for new machine designs. We show that this hybrid approach yields
flexibility in the simulation process. Finally, the proposed hybrid approach is
quantitatively compared to existing deep neural network-based direct prediction
approach of KPIs.

Supervised deep learning approaches to underdetermined audio source
separation achieve state-of-the-art performance but require a dataset of
mixtures along with their corresponding isolated source signals. Such datasets
can be extremely costly to obtain for musical mixtures. This raises a need for
unsupervised methods. We propose a novel unsupervised model-based deep learning
approach to musical source separation. Each source is modelled with a
differentiable parametric source-filter model. A neural network is trained to
reconstruct the observed mixture as a sum of the sources by estimating the
source models' parameters given their fundamental frequencies. At test time,
soft masks are obtained from the synthesized source signals. The experimental
evaluation on a vocal ensemble separation task shows that the proposed method
outperforms learning-free methods based on nonnegative matrix factorization and
a supervised deep learning baseline. Integrating domain knowledge in the form
of source models into a data-driven method leads to high data efficiency: the
proposed approach achieves good separation quality even when trained on less
than three minutes of audio. This work makes powerful deep learning based
separation usable in scenarios where training data with ground truth is
expensive or nonexistent.

Reinforcement learning is increasingly finding success across domains where
the problem can be represented as a Markov decision process. Evolutionary
computation algorithms have also proven successful in this domain, exhibiting
similar performance to the generally more complex reinforcement learning.
Whilst there exist many open-source reinforcement learning and evolutionary
computation libraries, no publicly available library combines the two
approaches for enhanced comparison, cooperation, or visualization. To this end,
we have created Pearl (https://github.com/LondonNode/Pearl), an open source
Python library designed to allow researchers to rapidly and conveniently
perform optimized reinforcement learning, evolutionary computation and
combinations of the two. The key features within Pearl include: modular and
expandable components, opinionated module settings, Tensorboard integration,
custom callbacks and comprehensive visualizations.

The punctuation restoration task aims to correctly punctuate the output
transcriptions of automatic speech recognition systems. Previous punctuation
models, either using text only or demanding the corresponding audio, tend to be
constrained by real scenes, where unpunctuated sentences are a mixture of those
with and without audio. This paper proposes a unified multimodal punctuation
restoration framework, named UniPunc, to punctuate the mixed sentences with a
single model. UniPunc jointly represents audio and non-audio samples in a
shared latent space, based on which the model learns a hybrid representation
and punctuates both kinds of samples. We validate the effectiveness of the
UniPunc on real-world datasets, which outperforms various strong baselines
(e.g. BERT, MuSe) by at least 0.8 overall F1 scores, making a new
state-of-the-art. Extensive experiments show that UniPunc's design is a
pervasive solution: by grafting onto previous models, UniPunc enables them to
punctuate on the mixed corpus. Our code is available at
github.com/Yaoming95/UniPunc

Lung cancer is the leading cause of cancer death worldwide and a good
prognosis depends on early diagnosis. Unfortunately, screening programs for the
early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk
groups being located in rural areas far from medical facilities. Reaching these
populations would require a scaled approach that combines mobility, low cost,
speed, accuracy, and privacy. We can resolve these issues by combining the
chest X-ray imaging mode with a federated deep-learning approach, provided that
the federated model is trained on homogenous data to ensure that no single data
source can adversely bias the model at any point in time. In this study we show
that an image pre-processing pipeline that homogenizes and debiases chest X-ray
images can improve both internal classification and external generalization,
paving the way for a low-cost and accessible deep learning-based clinical
system for lung cancer screening. An evolutionary pruning mechanism is used to
train a nodule detection deep learning model on the most informative images
from a publicly available lung nodule X-ray dataset. Histogram equalization is
used to remove systematic differences in image brightness and contrast. Model
training is performed using all combinations of lung field segmentation, close
cropping, and rib suppression operators. We show that this pre-processing
pipeline results in deep learning models that successfully generalize an
independent lung nodule dataset using ablation studies to assess the
contribution of each operator in this pipeline. In stripping chest X-ray images
of known confounding variables by lung field segmentation, along with
suppression of signal noise from the bone structure we can train a highly
accurate deep learning lung nodule detection algorithm with outstanding
generalization accuracy of 89% to nodule samples in unseen data.

Learning optimal control policies directly on physical systems is challenging
since even a single failure can lead to costly hardware damage. Most existing
model-free learning methods that guarantee safety, i.e., no failures, during
exploration are limited to local optima. A notable exception is the GoSafe
algorithm, which, unfortunately, cannot handle high-dimensional systems and
hence cannot be applied to most real-world dynamical systems. This work
proposes GoSafeOpt as the first algorithm that can safely discover globally
optimal policies for high-dimensional systems while giving safety and
optimality guarantees. We demonstrate the superiority of GoSafeOpt over
competing model-free safe learning methods on a robot arm that would be
prohibitive for GoSafe.

We present a method for reconstructing accurate and consistent 3D hands from
a monocular video. We observe that detected 2D hand keypoints and the image
texture provide important cues about the geometry and texture of the 3D hand,
which can reduce or even eliminate the requirement on 3D hand annotation. Thus
we propose ${\rm {S}^{2}HAND}$, a self-supervised 3D hand reconstruction model,
that can jointly estimate pose, shape, texture, and the camera viewpoint from a
single RGB input through the supervision of easily accessible 2D detected
keypoints. We leverage the continuous hand motion information contained in the
unlabeled video data and propose ${\rm {S}^{2}HAND(V)}$, which uses a set of
weights shared ${\rm {S}^{2}HAND}$ to process each frame and exploits
additional motion, texture, and shape consistency constrains to promote more
accurate hand poses and more consistent shapes and textures. Experiments on
benchmark datasets demonstrate that our self-supervised approach produces
comparable hand reconstruction performance compared with the recent
full-supervised methods in single-frame as input setup, and notably improves
the reconstruction accuracy and consistency when using video training data.

This article reports nonintuitive characteristic of a splashing drop on a
solid surface discovered through extracting image features using a feedforward
neural network (FNN). Ethanol of area-equivalent radius about 1.29 mm was
dropped from impact heights ranging from 4 cm to 60 cm (splashing threshold 20
cm) and impacted on a hydrophilic surface. The images captured when half of the
drop impacted the surface were labeled according to their outcome, splashing or
nonsplashing, and were used to train an FNN. A classification accuracy higher
than 96% was achieved. To extract the image features identified by the FNN for
classification, the weight matrix of the trained FNN for identifying splashing
drops was visualized. Remarkably, the visualization showed that the trained FNN
identified the contour height of the main body of the impacting drop as an
important characteristic differentiating between splashing and nonsplashing
drops, which has not been reported in previous studies. This feature was found
throughout the impact, even when one and three-quarters of the drop impacted
the surface. To confirm the importance of this image feature, the FNN was
retrained to classify using only the main body without checking for the
presence of ejected secondary droplets. The accuracy was still higher than 82%,
confirming that the contour height is an important feature distinguishing
splashing from nonsplashing drops. Several aspects of drop impact are analyzed
and discussed with the aim of identifying the possible mechanism underlying the
difference in contour height between splashing and nonsplashing drops.

Backdoor injection attack is an emerging threat to the security of neural
networks, however, there still exist limited effective defense methods against
the attack. In this paper, we propose BAERASE, a novel method that can erase
the backdoor injected into the victim model through machine unlearning.
Specifically, BAERASE mainly implements backdoor defense in two key steps.
First, trigger pattern recovery is conducted to extract the trigger patterns
infected by the victim model. Here, the trigger pattern recovery problem is
equivalent to the one of extracting an unknown noise distribution from the
victim model, which can be easily resolved by the entropy maximization based
generative model. Subsequently, BAERASE leverages these recovered trigger
patterns to reverse the backdoor injection procedure and induce the victim
model to erase the polluted memories through a newly designed gradient ascent
based machine unlearning method. Compared with the previous machine unlearning
solutions, the proposed approach gets rid of the reliance on the full access to
training data for retraining and shows higher effectiveness on backdoor erasing
than existing fine-tuning or pruning methods. Moreover, experiments show that
BAERASE can averagely lower the attack success rates of three kinds of
state-of-the-art backdoor attacks by 99\% on four benchmark datasets.

This paper takes a parallel learning approach for robust and transparent AI.
A deep neural network is trained in parallel on multiple tasks, where each task
is trained only on a subset of the network resources. Each subset consists of
network segments, that can be combined and shared across specific tasks. Tasks
can share resources with other tasks, while having independent task-related
network resources. Therefore, the trained network can share similar
representations across various tasks, while also enabling independent
task-related representations. The above allows for some crucial outcomes. (1)
The parallel nature of our approach negates the issue of catastrophic
forgetting. (2) The sharing of segments uses network resources more
efficiently. (3) We show that the network does indeed use learned knowledge
from some tasks in other tasks, through shared representations. (4) Through
examination of individual task-related and shared representations, the model
offers transparency in the network and in the relationships across tasks in a
multi-task setting. Evaluation of the proposed approach against complex
competing approaches such as Continual Learning, Neural Architecture Search,
and Multi-task learning shows that it is capable of learning robust
representations. This is the first effort to train a DL model on multiple tasks
in parallel. Our code is available at https://github.com/MahsaPaknezhad/PaRT

Federated learning (FL), as an emerging edge artificial intelligence
paradigm, enables many edge devices to collaboratively train a global model
without sharing their private data. To enhance the training efficiency of FL,
various algorithms have been proposed, ranging from first-order to second-order
methods. However, these algorithms cannot be applied in scenarios where the
gradient information is not available, e.g., federated black-box attack and
federated hyperparameter tuning. To address this issue, in this paper we
propose a derivative-free federated zeroth-order optimization (FedZO) algorithm
featured by performing multiple local updates based on stochastic gradient
estimators in each communication round and enabling partial device
participation. Under non-convex settings, we derive the convergence performance
of the FedZO algorithm on non-independent and identically distributed data and
characterize the impact of the numbers of local iterates and participating edge
devices on the convergence. To enable communication-efficient FedZO over
wireless networks, we further propose an over-the-air computation (AirComp)
assisted FedZO algorithm. With an appropriate transceiver design, we show that
the convergence of AirComp-assisted FedZO can still be preserved under certain
signal-to-noise ratio conditions. Simulation results demonstrate the
effectiveness of the FedZO algorithm and validate the theoretical observations.

Traditionally, a communication waveform is designed by experts based on
communication theory and their experiences on a case-by-case basis, which is
usually laborious and time-consuming. In this paper, we investigate the
waveform design from a novel perspective and propose a new waveform design
paradigm with the knowledge graph (KG)-based intelligent recommendation system.
The proposed paradigm aims to improve the design efficiency by structural
characterization and representations of existing waveforms and intelligently
utilizing the knowledge learned from them. To achieve this goal, we first build
a communication waveform knowledge graph (CWKG) with a first-order neighbor
node, for which both structured semantic knowledge and numerical parameters of
a waveform are integrated by representation learning. Based on the developed
CWKG, we further propose an intelligent communication waveform recommendation
system (CWRS) to generate waveform candidates. In the CWRS, an improved
involution1D operator, which is channel-agnostic and space-specific, is
introduced according to the characteristics of KG-based waveform representation
for feature extraction, and the multi-head self-attention is adopted to weigh
the influence of various components for feature fusion. Meanwhile, multilayer
perceptron-based collaborative filtering is used to evaluate the matching
degree between the requirement and the waveform candidate. Simulation results
show that the proposed CWKG-based CWRS can automatically recommend waveform
candidates with high reliability.

Named entity recognition (NER) is an essential task in natural language
processing, but the internal mechanism of most NER models is a black box for
users. In some high-stake decision-making areas, improving the interpretability
of an NER method is crucial but challenging. In this paper, based on the
existing Deterministic Talmudic Public announcement logic (TPK) model, we
propose a novel binary tree model (called BTPK) and apply it to two widely used
Bi-RNNs to obtain BTPK-based interpretable ones. Then, we design a
counterfactual verification module to verify the BTPK-based learning method.
Experimental results on three public datasets show that the BTPK-based learning
outperform two classical Bi-RNNs with self-attention, especially on small,
simple data and relatively large, complex data. Moreover, the counterfactual
verification demonstrates that the explanations provided by the BTPK-based
learning method are reasonable and accurate in NER tasks. Besides, the logical
reasoning based on BTPK shows how Bi-RNNs handle NER tasks, with different
distance of public announcements on long and complex sequences.

Intravascular ultrasound (IVUS) offers a unique perspective in the treatment
of vascular diseases by creating a sequence of ultrasound-slices acquired from
within the vessel. However, unlike conventional hand-held ultrasound, the thin
catheter only provides room for a small number of physical channels for signal
transfer from a transducer-array at the tip. For continued improvement of image
quality and frame rate, we present the use of deep reinforcement learning to
deal with the current physical information bottleneck. Valuable inspiration has
come from the field of magnetic resonance imaging (MRI), where learned
acquisition schemes have brought significant acceleration in image acquisition
at competing image quality. To efficiently accelerate IVUS imaging, we propose
a framework that utilizes deep reinforcement learning for an optimal adaptive
acquisition policy on a per-frame basis enabled by actor-critic methods and
Gumbel top-$K$ sampling.

The article explores new ways of written language aided by AI technologies,
like GPT-2 and GPT-3. The question that is stated in the paper is not about
whether these novel technologies will eventually replace authored books, but
how to relate to and contextualize such publications and what kind of new
tools, processes, and ideas are behind them. For that purpose, a new concept of
synthetic books is introduced in the article. It stands for the publications
created by deploying AI technology, more precisely autoregressive language
models that are able to generate human-like text. Supported by the case
studies, the value and reasoning of the synthetic books are discussed. The
paper emphasizes that artistic quality is an issue when it comes to
AI-generated content. The article introduces projects that demonstrate an
interactive input by an artist and/or audience combined with the
deep-learning-based language models. In the end, the paper focuses on
understanding the neural aesthetics of written language in the art context.

This paper presents the first implementation of autonomous robotic
auscultation of heart and lung sounds. To select auscultation locations that
generate high-quality sounds, a Bayesian Optimization (BO) formulation
leverages visual anatomical cues to predict where high-quality sounds might be
located, while using auditory feedback to adapt to patient-specific anatomical
qualities. Sound quality is estimated online using machine learning models
trained on a database of heart and lung stethoscope recordings. Experiments on
4 human subjects show that our system autonomously captures heart and lung
sounds of similar quality compared to tele-operation by a human trained in
clinical auscultation. Surprisingly, one of the subjects exhibited a previously
unknown cardiac pathology that was first identified using our robot, which
demonstrates the potential utility of autonomous robotic auscultation for
health screening.

The discovery of drug-target interactions (DTIs) is a very promising area of
research with great potential. The accurate identification of reliable
interactions among drugs and proteins via computational methods, which
typically leverage heterogeneous information retrieved from diverse data
sources, can boost the development of effective pharmaceuticals. Although
random walk and matrix factorization techniques are widely used in DTI
prediction, they have several limitations. Random walk-based embedding
generation is usually conducted in an unsupervised manner, while the linear
similarity combination in matrix factorization distorts individual insights
offered by different views. To tackle these issues, we take a multi-layered
network approach to handle diverse drug and target similarities, and propose a
novel optimization framework, called Multiple similarity DeepWalk-based Matrix
Factorization (MDMF), for DTI prediction. The framework unifies embedding
generation and interaction prediction, learning vector representations of drugs
and targets that not only retain higher-order proximity across all hyper-layers
and layer-specific local invariance, but also approximate the interactions with
their inner product. Furthermore, we develop an ensemble method (MDMF2A) that
integrates two instantiations of the MDMF model, optimizing the area under the
precision-recall curve (AUPR) and the area under the receiver operating
characteristic curve (AUC) respectively. The empirical study on real-world DTI
datasets shows that our method achieves statistically significant improvement
over current state-of-the-art approaches in four different settings. Moreover,
the validation of highly ranked non-interacting pairs also demonstrates the
potential of MDMF2A to discover novel DTIs.

The development of a reliable subgrid-scale (SGS) model for large-eddy
simulation (LES) is of great importance for many scientific and engineering
applications. Recently, deep learning approaches have been tested for this
purpose using high-fidelity data such as direct numerical simulation (DNS) in a
supervised learning process. However, such data are generally not available in
practice. Deep reinforcement learning (DRL) using only limited target
statistics can be an alternative algorithm in which the training and testing of
the model are conducted in the same LES environment. The DRL of turbulence
modeling remains challenging owing to its chaotic nature, high dimensionality
of the action space, and large computational cost. In the present study, we
propose a physics-constrained DRL framework that can develop a deep neural
network (DNN)-based SGS model for the LES of turbulent channel flow. The DRL
models that produce the SGS stress were trained based on the local gradient of
the filtered velocities. The developed SGS model automatically satisfies the
reflectional invariance and wall boundary conditions without an extra training
process so that DRL can quickly find the optimal policy. Furthermore, direct
accumulation of reward, spatially and temporally correlated exploration, and
the pre-training process are applied for the efficient and effective learning.
In various environments, our DRL could discover SGS models that produce the
viscous and Reynolds stress statistics perfectly consistent with the filtered
DNS. By comparing various statistics obtained by the trained models and
conventional SGS models, we present a possible interpretation of better
performance of the DRL model.

We propose data and knowledge-driven approaches for multilingual training of
the automated speech recognition (ASR) system for a target language by pooling
speech data from multiple source languages. Exploiting the acoustic
similarities between Indian languages, we implement two approaches. In
phone/senone mapping, deep neural network (DNN) learns to map senones or phones
from one language to the others, and the transcriptions of the source languages
are modified such that they can be used along with the target language data to
train and fine-tune the target language ASR system. In the other approach, we
model the acoustic information for all the languages simultaneously by training
a multitask DNN (MTDNN) to predict the senones of each language in different
output layers. The cross-entropy loss and the weight update procedure are
modified such that only the shared layers and the output layer responsible for
predicting the senone classes of a language are updated during training, if the
feature vector belongs to that particular language. In the low-resource setting
(LRS), 40 hours of transcribed data each for Tamil, Telugu and Gujarati
languages are used for training. The DNN based senone mapping technique gives
relative improvements in word error rates (WER) of 9.66%, 7.2% and 15.21% over
the baseline system for Tamil, Gujarati and Telugu languages, respectively. In
medium-resourced setting (MRS), 160, 275 and 135 hours of data for Tamil,
Kannada and Hindi languages are used, where, the same technique gives better
relative improvements of 13.94%, 10.28% and 27.24% for Tamil, Kannada and
Hindi, respectively. The MTDNN with senone mapping based training in LRS, gives
higher relative WER improvements of 15.0%, 17.54% and 16.06%, respectively for
Tamil, Gujarati and Telugu, whereas in MRS, we see improvements of 21.24%
21.05% and 30.17% for Tamil, Kannada and Hindi languages, respectively.

Recommender systems can automatically recommend users with items that they
probably like. The goal of them is to model the user-item interaction by
effectively representing the users and items. Existing methods have primarily
learned the user's preferences and item's features with vectorized embeddings,
and modeled the user's general preferences to items by the interaction of them.
In fact, users have their specific preferences to item attributes and different
preferences are usually related. Therefore, exploring the fine-grained
preferences as well as modeling the relationships among user's different
preferences could improve the recommendation performance. Toward this end, we
propose a dual preference distribution learning framework (DUPLE), which aims
to jointly learn a general preference distribution and a specific preference
distribution for a given user, where the former corresponds to the user's
general preference to items and the latter refers to the user's specific
preference to item attributes. Notably, the mean vector of each Gaussian
distribution can capture the user's preferences, and the covariance matrix can
learn their relationship. Moreover, we can summarize a preferred attribute
profile for each user, depicting his/her preferred item attributes. We then can
provide the explanation for each recommended item by checking the overlap
between its attributes and the user's preferred attribute profile. Extensive
quantitative and qualitative experiments on six public datasets demonstrate the
effectiveness and explainability of the DUPLE method.

Automated speaker recognition uses data processing to identify speakers by
their voice. Today, automated speaker recognition is deployed on billions of
smart devices and in services such as call centres. Despite their wide-scale
deployment and known sources of bias in related domains like face recognition
and natural language processing, bias in automated speaker recognition has not
been studied systematically. We present an in-depth empirical and analytical
study of bias in the machine learning development workflow of speaker
verification, a voice biometric and core task in automated speaker recognition.
Drawing on an established framework for understanding sources of harm in
machine learning, we show that bias exists at every development stage in the
well-known VoxCeleb Speaker Recognition Challenge, including data generation,
model building, and implementation. Most affected are female speakers and
non-US nationalities, who experience significant performance degradation.
Leveraging the insights from our findings, we make practical recommendations
for mitigating bias in automated speaker recognition, and outline future

Recent years have seen an increased interest in the application of methods
and techniques commonly associated with machine learning and artificial
intelligence to spatial statistics. Here, in a celebration of the ten-year
anniversary of the journal Spatial Statistics, we bring together normalizing
flows, commonly used for density function estimation in machine learning, and
spherical point processes, a topic of particular interest to the journal's
readership, to present a new approach for modeling non-homogeneous Poisson
process intensity functions on the sphere. The central idea of this framework
is to build, and estimate, a flexible bijective map that transforms the
underlying intensity function of interest on the sphere into a simpler,
reference, intensity function, also on the sphere. Map estimation can be done
efficiently using automatic differentiation and stochastic gradient descent,
and uncertainty quantification can be done straightforwardly via nonparametric
bootstrap. We investigate the viability of the proposed method in a simulation
study, and illustrate its use in a proof-of-concept study where we model the
intensity of cyclone events in the North Pacific Ocean. Our experiments reveal
that normalizing flows present a flexible and straightforward way to model
intensity functions on spheres, but that their potential to yield a good fit
depends on the architecture of the bijective map, which can be difficult to
establish in practice.

IoT devices generating enormous data and state-of-the-art machine learning
techniques together will revolutionize cyber-physical systems. In many diverse
fields, from autonomous driving to augmented reality, distributed IoT devices
compute specific target functions without simple forms like obstacle detection,
object recognition, etc. Traditional cloud-based methods that focus on
transferring data to a central location either for training or inference place
enormous strain on network resources. To address this, we develop, to the best
of our knowledge, the first machine learning framework for distributed
functional compression over both the Gaussian Multiple Access Channel (GMAC)
and orthogonal AWGN channels. Due to the Kolmogorov-Arnold representation
theorem, our machine learning framework can, by design, compute any arbitrary
function for the desired functional compression task in IoT. Importantly the
raw sensory data are never transferred to a central node for training or
inference, thus reducing communication. For these algorithms, we provide
theoretical convergence guarantees and upper bounds on communication. Our
simulations show that the learned encoders and decoders for functional
compression perform significantly better than traditional approaches, are
robust to channel condition changes and sensor outages. Compared to the
cloud-based scenario, our algorithms reduce channel use by two orders of

End-to-end neural TTS has shown improved performance in speech style
transfer. However, the improvement is still limited by the available training
data in both target styles and speakers. Additionally, degenerated performance
is observed when the trained TTS tries to transfer the speech to a target style
from a new speaker with an unknown, arbitrary style. In this paper, we propose
a new approach to seen and unseen style transfer training on disjoint,
multi-style datasets, i.e., datasets of different styles are recorded, one
individual style by one speaker in multiple utterances. An inverse
autoregressive flow (IAF) technique is first introduced to improve the
variational inference for learning an expressive style representation. A
speaker encoder network is then developed for learning a discriminative speaker
embedding, which is jointly trained with the rest neural TTS modules. The
proposed approach of seen and unseen style transfer is effectively trained with
six specifically-designed objectives: reconstruction loss, adversarial loss,
style distortion loss, cycle consistency loss, style classification loss, and
speaker classification loss. Experiments demonstrate, both objectively and
subjectively, the effectiveness of the proposed approach for seen and unseen
style transfer tasks. The performance of our approach is superior to and more
robust than those of four other reference systems of prior art.

Recent works on speech spoofing countermeasures still lack generalization
ability to unseen spoofing attacks. This is one of the key issues of ASVspoof
challenges especially with the rapid development of diverse and high-quality
spoofing algorithms. In this work, we address the generalizability of spoofing
detection by proposing prototypical loss under the meta-learning paradigm to
mimic the unseen test scenario during training. Prototypical loss with
metric-learning objectives can learn the embedding space directly and emerges
as a strong alternative to prevailing classification loss functions. We propose
an anti-spoofing system based on squeeze-excitation Residual network
(SE-ResNet) architecture with prototypical loss. We demonstrate that the
proposed single system without any data augmentation can achieve competitive
performance to the recent best anti-spoofing systems on ASVspoof 2019 logical
access (LA) task. Furthermore, the proposed system with data augmentation
outperforms the ASVspoof 2021 challenge best baseline both in the progress and
evaluation phase of the LA task. On ASVspoof 2019 and 2021 evaluation set LA
scenario, we attain a relative 68.4% and 3.6% improvement in min-tDCF compared
to the challenge best baselines, respectively.

Multi-agent path planning (MAPP) in continuous spaces is a challenging
problem with significant practical importance. One promising approach is to
first construct graphs approximating the spaces, called roadmaps, and then
apply multi-agent pathfinding (MAPF) algorithms to derive a set of
conflict-free paths. While conventional studies have utilized roadmap
construction methods developed for single-agent planning, it remains largely
unexplored how we can construct roadmaps that work effectively for multiple
agents. To this end, we propose a novel concept of roadmaps called cooperative
timed roadmaps (CTRMs). CTRMs enable each agent to focus on its important
locations around potential solution paths in a way that considers the behavior
of other agents to avoid inter-agent collisions (i.e., "cooperative"), while
being augmented in the time direction to make it easy to derive a "timed"
solution path. To construct CTRMs, we developed a machine-learning approach
that learns a generative model from a collection of relevant problem instances
and plausible solutions and then uses the learned model to sample the vertices
of CTRMs for new, previously unseen problem instances. Our empirical evaluation
revealed that the use of CTRMs significantly reduced the planning effort with
acceptable overheads while maintaining a success rate and solution quality
comparable to conventional roadmap construction approaches.

The hierarchical and recursive expressive capability of rooted trees is
applicable to represent statistical models in various areas, such as data
compression, image processing, and machine learning. On the other hand, such
hierarchical expressive capability causes a problem in tree selection to avoid
overfitting. One unified approach to solve this is a Bayesian approach, on
which the rooted tree is regarded as a random variable and a direct loss
function can be assumed on the selected model or the predicted value for a new
data point. However, all the previous studies on this approach are based on the
probability distribution on full trees, to the best of our knowledge. In this
paper, we propose a generalized probability distribution for any rooted trees
in which only the maximum number of child nodes and the maximum depth are
fixed. Furthermore, we derive recursive methods to evaluate the characteristics
of the probability distribution without any approximations.

Data heterogeneity is an intrinsic property of recommender systems, making
models trained over the global data on the cloud, which is the mainstream in
industry, non-optimal to each individual user's local data distribution. To
deal with data heterogeneity, model personalization with on-device learning is
a potential solution. However, on-device training using a user's small size of
local samples will incur severe overfitting and undermine the model's
generalization ability. In this work, we propose a new device-cloud
collaborative learning framework, called CoDA, to break the dilemmas of purely
cloud-based learning and on-device learning. The key principle of CoDA is to
retrieve similar samples from the cloud's global pool to augment each user's
local dataset to train the recommendation model. Specifically, after a
coarse-grained sample matching on the cloud, a personalized sample classifier
is further trained on each device for a fine-grained sample filtering, which
can learn the boundary between the local data distribution and the outside data
distribution. We also build an end-to-end pipeline to support the flows of
data, model, computation, and control between the cloud and each device. We
have deployed CoDA in a recommendation scenario of Mobile Taobao. Online A/B
testing results show the remarkable performance improvement of CoDA over both
cloud-based learning without model personalization and on-device training
without data augmentation. Overhead testing on a real device demonstrates the
computation, storage, and communication efficiency of the on-device tasks in
CoDA.

We propose the homotopic policy mirror descent (HPMD) method for solving
discounted, infinite horizon MDPs with finite state and action space, and study
its convergence properties. We report several findings that seem to be new in
the literature of policy gradient methods: (1) HPMD exhibits global linear
convergence of the value optimality gap, and local superlinear convergence of
both the policy and optimality gap with order $\gamma^{-2}$. The superlinear
convergence takes effect after no more than $\mathcal{O}(\log(1/\Delta^*))$
number of iterations, where $\Delta^*$ is defined via a gap quantity associated
with the optimal state-action value function; (2) HPMD also exhibits
last-iterate convergence of the policy, with the limiting policy corresponding
exactly to the optimal policy with the maximal entropy for every state. No
regularization is added to the optimization objective and hence the second
observation arises solely as an algorithmic property of the homotopic policy
gradient method; (3) The last-iterate convergence of HPMD holds for a much
broader class of decomposable distance-generating functions, including the
$p$-th power of $\ell_p$-norm and the negative Tsallis entropy. As a byproduct
of the analysis, we also discover the finite-time exact convergence of HPMD
with these divergences, and show that HPMD continues converging to the limiting
policy even if the current policy is already optimal; (4) For the stochastic
HPMD method, we further demonstrate that a better than
$\tilde{\mathcal{O}}(|\mathcal{S}| |\mathcal{A}| / \epsilon^2)$ sample
complexity for small optimality gap $\epsilon$ holds with high probability,
when assuming a generative model for policy evaluation.

It is a challenging task to learn discriminative representation from images
and videos, due to large local redundancy and complex global dependency in
these visual data. Convolution neural networks (CNNs) and vision transformers
(ViTs) have been two dominant frameworks in the past few years. Though CNNs can
efficiently decrease local redundancy by convolution within a small
neighborhood, the limited receptive field makes it hard to capture global
dependency. Alternatively, ViTs can effectively capture long-range dependency
via self-attention, while blind similarity comparisons among all the tokens
lead to high redundancy. To resolve these problems, we propose a novel Unified
transFormer (UniFormer), which can seamlessly integrate the merits of
convolution and self-attention in a concise transformer format. Different from
the typical transformer blocks, the relation aggregators in our UniFormer block
are equipped with local and global token affinity respectively in shallow and
deep layers, allowing to tackle both redundancy and dependency for efficient
and effective representation learning. Finally, we flexibly stack our UniFormer
blocks into a new powerful backbone, and adopt it for various vision tasks from
image to video domain, from classification to dense prediction. Without any
extra training data, our UniFormer achieves 86.3 top-1 accuracy on ImageNet-1K
classification. With only ImageNet-1K pre-training, it can simply achieve
state-of-the-art performance in a broad range of downstream tasks, e.g., it
obtains 82.9/84.8 top-1 accuracy on Kinetics-400/600, 60.9/71.2 top-1 accuracy
on Something-Something V1/V2 video classification tasks, 53.8 box AP and 46.4
mask AP on COCO object detection task, 50.8 mIoU on ADE20K semantic
segmentation task, and 77.4 AP on COCO pose estimation task. Code is available
at https://github.com/Sense-X/UniFormer.

There has been a steadily growing interest in development of novel methods to
learn a representation of a given input data and subsequently using them for
several downstream tasks. The field of natural language processing has seen a
significant improvement in different tasks by incorporating pre-trained
embeddings into their pipelines. Recently, these methods have been applied to
programming languages with a view to improve developer productivity. In this
paper, we present an unsupervised learning approach to encode old mainframe
languages into a fixed dimensional vector space. We use COBOL as our motivating
example and create a corpus and demonstrate the efficacy of our approach in a
code-retrieval task on our corpus.

Model compression methods can reduce model complexity on the premise of
maintaining acceptable performance, and thus promote the application of deep
neural networks under resource constrained environments. Despite their great
success, the selection of suitable compression methods and design of details of
the compression scheme are difficult, requiring lots of domain knowledge as
support, which is not friendly to non-expert users. To make more users easily
access to the model compression scheme that best meet their needs, in this
paper, we propose AutoMC, an effective automatic tool for model compression.
AutoMC builds the domain knowledge on model compression to deeply understand
the characteristics and advantages of each compression method under different
settings. In addition, it presents a progressive search strategy to efficiently
explore pareto optimal compression scheme according to the learned prior
knowledge combined with the historical evaluation information. Extensive
experimental results show that AutoMC can provide satisfying compression
schemes within short time, demonstrating the effectiveness of AutoMC.

Federated Learning (FL) is designed to protect the data privacy of each
client during the training process by transmitting only models instead of the
original data. However, the trained model may memorize certain information
about the training data. With the recent legislation on right to be forgotten,
it is crucially essential for the FL model to possess the ability to forget
what it has learned from each client. We propose a novel federated unlearning
method to eliminate a client's contribution by subtracting the accumulated
historical updates from the model and leveraging the knowledge distillation
method to restore the model's performance without using any data from the
clients. This method does not have any restrictions on the type of neural
networks and does not rely on clients' participation, so it is practical and
efficient in the FL system. We further introduce backdoor attacks in the
training process to help evaluate the unlearning effect. Experiments on three
canonical datasets demonstrate the effectiveness and efficiency of our method.

With the recent prevalence of remote education, academic assessments are
often conducted online, leading to further concerns surrounding assessment
misconducts. This paper investigates the potentials of online assessment
misconduct (e-cheating) and proposes practical countermeasures against them.
The mechanism for detecting the practices of online cheating is presented in
the form of an e-cheating intelligent agent, comprising of an internet protocol
(IP) detector and a behavioural monitor. The IP detector is an auxiliary
detector which assigns randomised and unique assessment sets as an early
procedure to reduce potential misconducts. The behavioural monitor scans for
irregularities in assessment responses from the candidates, further reducing
any misconduct attempts. This is highlighted through the proposal of the
DenseLSTM using a deep learning approach. Additionally, a new PT Behavioural
Database is presented and made publicly available. Experiments conducted on
this dataset confirm the effectiveness of the DenseLSTM, resulting in
classification accuracies of up to 90.7%.

We initiate the study of active learning polynomial threshold functions
(PTFs). While traditional lower bounds imply that even univariate quadratics
cannot be non-trivially actively learned, we show that allowing the learner
basic access to the derivatives of the underlying classifier circumvents this
issue and leads to a computationally efficient algorithm for active learning
degree-$d$ univariate PTFs in $\tilde{O}(d^3\log(1/\varepsilon\delta))$
queries. We also provide near-optimal algorithms and analyses for active
learning PTFs in several average case settings. Finally, we prove that access
to derivatives is insufficient for active learning multivariate PTFs, even
those of just two variables.

The price of carbon emission rights play a crucial role in carbon trading
markets. Therefore, accurate prediction of the price is critical. Taking the
Shanghai pilot market as an example, this paper attempted to design a carbon
emission purchasing strategy for enterprises, and establish a carbon emission
price prediction model to help them reduce the purchasing cost. To make
predictions more precise, we built a hybrid deep learning model by embedding
Generalized Autoregressive Conditional Heteroskedastic (GARCH) into the Gate
Recurrent Unit (GRU) model, and compared the performance with those of other
models. Then, based on the Iceberg Order Theory and the predicted price, we
proposed the purchasing strategy of carbon emission rights. As a result, the
prediction errors of the GARCH-GRU model with a 5-day sliding time window were
the minimum values of all six models. And in the simulation, the purchasing
strategy based on the GARCH-GRU model was executed with the least cost as well.
The carbon emission purchasing strategy constructed by the hybrid deep learning
method can accurately send out timing signals, and help enterprises reduce the
purchasing cost of carbon emission permits.

Deep-learning based methods have shown their advantages in audio coding over
traditional ones but limited attention has been paid on real-time
communications (RTC). This paper proposes the TFNet, an end-to-end neural
speech codec with low latency for RTC. It takes an encoder-temporal
filtering-decoder paradigm that has seldom been investigated in audio coding.
An interleaved structure is proposed for temporal filtering to capture both
short-term and long-term temporal dependencies. Furthermore, with end-to-end
optimization, the TFNet is jointly optimized with speech enhancement and packet
loss concealment, yielding a one-for-all network for three tasks. Both
subjective and objective results demonstrate the efficiency of the proposed
TFNet.

Dysarthric speech recognition is a challenging task due to acoustic
variability and limited amount of available data. Diverse conditions of
dysarthric speakers account for the acoustic variability, which make the
variability difficult to be modeled precisely. This paper presents a
variational auto-encoder based variability encoder (VAEVE) to explicitly encode
such variability for dysarthric speech. The VAEVE makes use of both phoneme
information and low-dimensional latent variable to reconstruct the input
acoustic features, thereby the latent variable is forced to encode the
phoneme-independent variability. Stochastic gradient variational Bayes
algorithm is applied to model the distribution for generating variability
encodings, which are further used as auxiliary features for DNN acoustic
modeling. Experiment results conducted on the UASpeech corpus show that the
VAEVE based variability encodings have complementary effect to the learning
hidden unit contributions (LHUC) speaker adaptation. The systems using
variability encodings consistently outperform the comparable baseline systems
without using them, and" obtain absolute word error rate (WER) reduction by up
to 2.2% on dysarthric speech with "Very lowintelligibility level, and up to 2%
on the "Mixed" type of dysarthric speech with diverse or uncertain conditions.

Continuous-variable quantum key distribution (CV QKD) with discrete
modulation has attracted increasing attention due to its experimental
simplicity, lower-cost implementation and compatibility with classical optical
communication. Correspondingly, some novel numerical methods have been proposed
to analyze the security of these protocols against collective attacks, which
promotes key rates over one hundred kilometers of fiber distance. However,
numerical methods are limited by their calculation time and resource
consumption, for which they cannot play more roles on mobile platforms in
quantum networks. To improve this issue, a neural network model predicting key
rates in nearly real time has been proposed previously. Here, we go further and
show a neural network model combined with Bayesian optimization. This model
automatically designs the best architecture of neural network computing key
rates in real time. We demonstrate our model with two variants of CV QKD
protocols with quaternary modulation. The results show high reliability with
secure probability as high as $99.15\%-99.59\%$, considerable tightness and
high efficiency with speedup of approximately $10^7$ in both cases. This
inspiring model enables the real-time computation of unstructured quantum key
distribution protocols' key rate more automatically and efficiently, which has
met the growing needs of implementing QKD protocols on moving platforms.

This paper studies the approximation capacity of ReLU neural networks with
norm constraint on the weights. We prove upper and lower bounds on the
approximation error of these networks for smooth function classes. The lower
bound is derived through the Rademacher complexity of neural networks, which
may be of independent interest. We apply these approximation bounds to analyze
the convergence of regression using norm constrained neural networks and
distribution estimation by GANs. In particular, we obtain convergence rates for
over-parameterized neural networks. It is also shown that GANs can achieve
optimal rate of learning probability distributions, when the discriminator is a
properly chosen norm constrained neural network.

This paper aims to interpret the mechanism of feedforward ReLU networks by
exploring their solutions for piecewise linear functions, through the deduction
from basic rules. The constructed solution should be universal enough to
explain some network architectures of engineering; in order for that, several
ways are provided to enhance the solution universality. Some of the
consequences of our theories include: Under affine-geometry background, the
solutions of both three-layer networks and deep-layer networks are given,
particularly for those architectures applied in practice, such as multilayer
feedforward neural networks and decoders; We give clear and intuitive
interpretations of each component of network architectures; The
parameter-sharing mechanism for multi-outputs is investigated; We provide an
explanation of overparameterization solutions in terms of affine transforms;
Under our framework, an advantage of deep layers compared to shallower ones is
natural to be obtained. Some intermediate results are the basic knowledge for
the modeling or understanding of neural networks, such as the classification of
data embedded in a higher-dimensional space, the generalization of affine
transforms, the probabilistic model of matrix ranks, and the concept of
distinguishable data sets.

Background: Recently, a high number of daily positive COVID-19 cases have
been reported in regions with relatively high vaccination rates; hence, booster
vaccination has become necessary. In addition, infections caused by the
different variants and correlated factors have not been discussed in depth.
With large variabilities and different co-factors, it is difficult to use
conventional mathematical models to forecast the incidence of COVID-19.
  Methods: Machine learning based on long short-term memory was applied to
forecasting the time series of new daily positive cases (DPC), serious cases,
hospitalized cases, and deaths. Data acquired from regions with high rates of
vaccination, such as Israel, were blended with the current data of other
regions in Japan to factor in the potential effects of vaccination. The
protection provided by symptomatic infection was also considered in terms of
the population effectiveness of vaccination as well as the waning protection
and ratio and infectivity of viral variants. To represent changes in public
behavior, public mobility and interactions through social media were also
included in the analysis.
  Findings: Comparing the observed and estimated new DPC in Tel Aviv, Israel,
the parameters characterizing vaccination effectiveness and the waning
protection from infection were well estimated; the vaccination effectiveness of
the second dose after 5 months and the third dose after two weeks from
infection by the delta variant were 0.24 and 0.95, respectively. Using the
extracted parameters regarding vaccination effectiveness, new cases in three
prefectures of Japan were replicated.

The document layout analysis (DLA) aims to decompose document images into
high-level semantic areas (i.e., figures, tables, texts, and background).
Creating a DLA framework with strong generalization capabilities is a challenge
due to document objects are diversity in layout, size, aspect ratio, texture,
etc. Many researchers devoted this challenge by synthesizing data to build
large training sets. However, the synthetic training data has different styles
and erratic quality. Besides, there is a large gap between the source data and
the target data. In this paper, we propose an unsupervised cross-domain DLA
framework based on document style guidance. We integrated the document quality
assessment and the document cross-domain analysis into a unified framework. Our
framework is composed of three components, Document Layout Generator (GLD),
Document Elements Decorator(GED), and Document Style Discriminator(DSD). The
GLD is used to document layout generates, the GED is used to document layout
elements fill, and the DSD is used to document quality assessment and
cross-domain guidance. First, we apply GLD to predict the positions of the
generated document. Then, we design a novel algorithm based on aesthetic
guidance to fill the document positions. Finally, we use contrastive learning
to evaluate the quality assessment of the document. Besides, we design a new
strategy to change the document quality assessment component into a document
cross-domain style guide component. Our framework is an unsupervised document
layout analysis framework. We have proved through numerous experiments that our
proposed method has achieved remarkable performance.

Magnetic resonance imaging (MRI) is a widely used non-radiative and
non-invasive method for clinical interrogation of organ structures and
metabolism, with an inherently long scanning time. Methods by k-space
undersampling and deep learning based reconstruction have been popularised to
accelerate the scanning process. This work focuses on investigating how
powerful transformers are for fast MRI by exploiting and comparing different
novel network architectures. In particular, a generative adversarial network
(GAN) based Swin transformer (ST-GAN) was introduced for the fast MRI
reconstruction. To further preserve the edge and texture information, edge
enhanced GAN based Swin transformer (EES-GAN) and texture enhanced GAN based
Swin transformer (TES-GAN) were also developed, where a dual-discriminator GAN
structure was applied. We compared our proposed GAN based transformers,
standalone Swin transformer and other convolutional neural networks based GAN
model in terms of the evaluation metrics PSNR, SSIM and FID. We showed that
transformers work well for the MRI reconstruction from different undersampling
conditions. The utilisation of GAN's adversarial structure improves the quality
of images reconstructed when undersampled for 30% or higher. The code is
publicly available at https://github.com/ayanglab/SwinGANMR.

Many real-world networks are inherently decentralized. For example, in social
networks, each user maintains a local view of a social graph, such as a list of
friends and her profile. It is typical to collect these local views of social
graphs and conduct graph learning tasks. However, learning over graphs can
raise privacy concerns as these local views often contain sensitive
  In this paper, we seek to ensure private graph learning on a decentralized
network graph. Towards this objective, we propose {\em Solitude}, a new
privacy-preserving learning framework based on graph neural networks (GNNs),
with formal privacy guarantees based on edge local differential privacy. The
crux of {\em Solitude} is a set of new delicate mechanisms that can calibrate
the introduced noise in the decentralized graph collected from the users. The
principle behind the calibration is the intrinsic properties shared by many
real-world graphs, such as sparsity. Unlike existing work on locally private
GNNs, our new framework can simultaneously protect node feature privacy and
edge privacy, and can seamlessly incorporate with any GNN with privacy-utility
guarantees. Extensive experiments on benchmarking datasets show that {\em
Solitude} can retain the generalization capability of the learned GNN while
preserving the users' data privacy under given privacy budgets.

Correct performance assessment is crucial for evaluating modern artificial
intelligence algorithms in medicine like deep-learning based medical image
segmentation models. However, there is no universal metric library in Python
for standardized and reproducible evaluation. Thus, we propose our open-source
publicly available Python package MISeval: a metric library for Medical Image
Segmentation Evaluation. The implemented metrics can be intuitively used and
easily integrated into any performance assessment pipeline. The package
utilizes modern CI/CD strategies to ensure functionality and stability. MISeval
is available from PyPI (miseval) and GitHub:
https://github.com/frankkramer-lab/miseval.

We developed an integrated recurrent neural network and nonlinear regression
spatio-temporal model for vector-borne disease evolution. We take into account
climate data and seasonality as external factors that correlate with disease
transmitting insects (e.g. flies), also spill-over infections from neighboring
regions surrounding a region of interest. The climate data is encoded to the
model through a quadratic embedding scheme motivated by recommendation systems.
The neighboring regions' influence is modeled by a long short-term memory
neural network. The integrated model is trained by stochastic gradient descent
and tested on leish-maniasis data in Sri Lanka from 2013-2018 where infection
outbreaks occurred. Our model outperformed ARIMA models across a number of
regions with high infections, and an associated ablation study renders support
to our modeling hypothesis and ideas.

We study the problem of semi-supervised learning with Graph Neural Networks
(GNNs) in an active learning setup. We propose GraphPart, a novel
partition-based active learning approach for GNNs. GraphPart first splits the
graph into disjoint partitions and then selects representative nodes within
each partition to query. The proposed method is motivated by a novel analysis
of the classification error under realistic smoothness assumptions over the
graph and the node features. Extensive experiments on multiple benchmark
datasets demonstrate that the proposed method outperforms existing active
learning methods for GNNs under a wide range of annotation budget constraints.
In addition, the proposed method does not introduce additional hyperparameters,
which is crucial for model training, especially in the active learning setting
where a labeled validation set may not be available.

This work proposes an attention-based sequence-to-sequence model for
handwritten word recognition and explores transfer learning for data-efficient
training of HTR systems. To overcome training data scarcity, this work
leverages models pre-trained on scene text images as a starting point towards
tailoring the handwriting recognition models. ResNet feature extraction and
bidirectional LSTM-based sequence modeling stages together form an encoder. The
prediction stage consists of a decoder and a content-based attention mechanism.
The effectiveness of the proposed end-to-end HTR system has been empirically
evaluated on a novel multi-writer dataset Imgur5K and the IAM dataset. The
experimental results evaluate the performance of the HTR framework, further
supported by an in-depth analysis of the error cases. Source code and
pre-trained models are available at https://github.com/dmitrijsk/AttentionHTR.

This paper provides a comprehensive study of Federated Learning (FL) with an
emphasis on components, challenges, applications and FL environment. FL can be
applicable in multiple fields and domains in real-life models. in the medical
system, the privacy of patients records and their medical condition is critical
data, therefore collaborative learning or federated learning comes into the
picture. On other hand build an intelligent system assist the medical staff
without sharing the data lead into the FL concept and one of the applications
that are used is a brain tumor diagnosis intelligent system based on AI methods
that can efficiently work in a collaborative environment.this paper will
introduce some of the applications and related work in the medical field and
work under the FL concept then summarize them to introduce the main limitations
of their work.

Continual learning (CL) is under-explored in the video domain. The few
existing works contain splits with imbalanced class distributions over the
tasks, or study the problem in unsuitable datasets. We introduce vCLIMB, a
novel video continual learning benchmark. vCLIMB is a standardized test-bed to
analyze catastrophic forgetting of deep models in video continual learning. In
contrast to previous work, we focus on class incremental continual learning
with models trained on a sequence of disjoint tasks, and distribute the number
of classes uniformly across the tasks. We perform in-depth evaluations of
existing CL methods in vCLIMB, and observe two unique challenges in video data.
The selection of instances to store in episodic memory is performed at the
frame level. Second, untrimmed training data influences the effectiveness of
frame sampling strategies. We address these two challenges by proposing a
temporal consistency regularization that can be applied on top of memory-based
continual learning methods. Our approach significantly improves the baseline,
by up to 24% on the untrimmed continual learning task.

Language models built using semi-supervised machine learning on large corpora
of natural language have very quickly enveloped the fields of natural language
generation and understanding. In this paper we apply a zero-shot approach
independently developed by a number of researchers now gaining recognition as a
significant alternative to fine-tuning for evaluation on common sense tasks. A
language model with relatively few parameters and training steps compared to a
more recent language model (T5) can outperform it on a recent large data set
(TimeDial), while displaying robustness in its performance across a similar
class of language tasks. Surprisingly, this result is achieved by using a
hyperparameter-free zero-shot method with the smaller model, compared to
fine-tuning to the larger model. We argue that robustness of the smaller model
ought to be understood in terms of compositionality, in a sense that we draw
from recent literature on a class of similar models. We identify a practical
cost for our method and model: high GPU-time for natural language evaluation.
The zero-shot measurement technique that produces remarkable stability, both
for ALBERT and other BERT variants, is an application of pseudo-log-likelihoods
to masked language models for the relative measurement of probability for
substitution alternatives in forced choice language tasks such as the Winograd
Schema Challenge, Winogrande, and others. One contribution of this paper is to
bring together a number of similar, but independent strands of research. We
produce some absolute state-of-the-art results for common sense reasoning in
binary choice tasks, performing better than any published result in the
literature, including fine-tuned efforts. We show a remarkable consistency of
the model's performance under adversarial settings, which we argue is best
explained by the model's compositionality of representations.

Magnetic Resonance Fingerprinting (MRF) has emerged as a promising
quantitative MR imaging approach. Deep learning methods have been proposed for
MRF and demonstrated improved performance over classical compressed sensing
algorithms. However many of these end-to-end models are physics-free, while
consistency of the predictions with respect to the physical forward model is
crucial for reliably solving inverse problems. To address this, recently [1]
proposed a proximal gradient descent framework that directly incorporates the
forward acquisition and Bloch dynamic models within an unrolled learning
mechanism. However, [1] only evaluated the unrolled model on synthetic data
using Cartesian sampling trajectories. In this paper, as a complementary to
[1], we investigate other choices of encoders to build the proximal neural
network, and evaluate the deep unrolling algorithm on real accelerated MRF
scans with non-Cartesian k-space sampling trajectories.

Much progress has been made in the supervised learning of 3D reconstruction
of rigid objects from multi-view images or a video. However, it is more
challenging to reconstruct severely deformed objects from a single-view RGB
image in an unsupervised manner. Although training-based methods, such as
specific category-level training, have been shown to successfully reconstruct
rigid objects and slightly deformed objects like birds from a single-view
image, they cannot effectively handle severely deformed objects and neither can
be applied to some downstream tasks in the real world due to the inconsistent
semantic meaning of vertices, which are crucial in defining the adopted 3D
templates of objects to be reconstructed. In this work, we introduce a
template-based method to infer 3D shapes from a single-view image and apply the
reconstructed mesh to a downstream task, i.e., absolute length measurement.
Without using 3D ground truth, our method faithfully reconstructs 3D meshes and
achieves state-of-the-art accuracy in a length measurement task on a severely
deformed fish dataset.

Accurate identification of synergistic treatment combinations and their
underlying biological mechanisms is critical across many disease domains,
especially cancer. In translational oncology research, preclinical systems such
as patient-derived xenografts (PDX) have emerged as a unique study design
evaluating multiple treatments administered to samples from the same human
tumor implanted into genetically identical mice. In this paper, we propose a
novel Bayesian probabilistic tree-based framework for PDX data to investigate
the hierarchical relationships between treatments by inferring treatment
cluster trees, referred to as treatment trees (Rx-tree). The framework
motivates a new metric of mechanistic similarity between two or more treatments
accounting for inherent uncertainty in tree estimation; treatments with a high
estimated similarity have potentially high mechanistic synergy. Building upon
Dirichlet Diffusion Trees, we derive a closed-form marginal likelihood encoding
the tree structure, which facilitates computationally efficient posterior
inference via a new two-stage algorithm. Simulation studies demonstrate
superior performance of the proposed method in recovering the tree structure
and treatment similarities. Our analyses of a recently collated PDX dataset
produce treatment similarity estimates that show a high degree of concordance
with known biological mechanisms across treatments in five different cancers.
More importantly, we uncover new and potentially effective combination
therapies that confer synergistic regulation of specific downstream biological
pathways for future clinical investigations. Our accompanying code, data, and
shiny application for visualization of results are available at:
https://github.com/bayesrx/RxTree.

Increasing use of machine learning (ML) technologies in privacy-sensitive
domains such as medical diagnoses, lifestyle predictions, and business
decisions highlights the need to better understand if these ML technologies are
introducing leakage of sensitive and proprietary training data. In this paper,
we focus on model inversion attacks where the adversary knows non-sensitive
attributes about records in the training data and aims to infer the value of a
sensitive attribute unknown to the adversary, using only black-box access to
the target classification model. We first devise a novel confidence score-based
model inversion attribute inference attack that significantly outperforms the
state-of-the-art. We then introduce a label-only model inversion attack that
relies only on the model's predicted labels but still matches our confidence
score-based attack in terms of attack effectiveness. We also extend our attacks
to the scenario where some of the other (non-sensitive) attributes of a target
record are unknown to the adversary. We evaluate our attacks on two types of
machine learning models, decision tree and deep neural network, trained on
three real datasets. Moreover, we empirically demonstrate the disparate
vulnerability of model inversion attacks, i.e., specific groups in the training
dataset (grouped by gender, race, etc.) could be more vulnerable to model
inversion attacks.

In the past two decades we have seen the popularity of neural networks
increase in conjunction with their classification accuracy. Parallel to this,
we have also witnessed how fragile the very same prediction models are: tiny
perturbations to the inputs can cause misclassification errors throughout
entire datasets. In this paper, we consider perturbations bounded by the
$\ell_0$--norm, which have been shown as effective attacks in the domains of
image-recognition, natural language processing, and malware-detection. To this
end, we propose a novel defense method that consists of "truncation" and
"adversarial training". We then theoretically study the Gaussian mixture
setting and prove the asymptotic optimality of our proposed classifier.
Motivated by the insights we obtain, we extend these components to neural
network classifiers. We conduct numerical experiments in the domain of computer
vision using the MNIST and CIFAR datasets, demonstrating significant
improvement for the robust classification error of neural networks.

The freeform architectural modeling process often involves two important
stages: concept design and digital modeling. In the first stage, architects
usually sketch the overall 3D shape and the panel layout on a physical or
digital paper briefly. In the second stage, a digital 3D model is created using
the sketch as a reference. The digital model needs to incorporate geometric
requirements for its components, such as the planarity of panels due to
consideration of construction costs, which can make the modeling process more
challenging. In this work, we present a novel sketch-based system to bridge the
concept design and digital modeling of freeform roof-like shapes represented as
planar quadrilateral (PQ) meshes. Our system allows the user to sketch the
surface boundary and contour lines under axonometric projection and supports
the sketching of occluded regions. In addition, the user can sketch feature
lines to provide directional guidance to the PQ mesh layout. Given the 2D
sketch input, we propose a deep neural network to infer in real-time the
underlying surface shape along with a dense conjugate direction field, both of
which are used to extract the final PQ mesh. To train and validate our network,
we generate a large synthetic dataset that mimics architect sketching of
freeform quadrilateral patches. The effectiveness and usability of our system
are demonstrated with quantitative and qualitative evaluation as well as user
studies.

To determine causal relationships between two variables, approaches based on
Functional Causal Models (FCMs) have been proposed by properly restricting
model classes; however, the performance is sensitive to the model assumptions,
which makes it difficult to use. In this paper, we provide a novel
dynamical-system view of FCMs and propose a new framework for identifying
causal direction in the bivariate case. We first show the connection between
FCMs and optimal transport, and then study optimal transport under the
constraints of FCMs. Furthermore, by exploiting the dynamical interpretation of
optimal transport under the FCM constraints, we determine the corresponding
underlying dynamical process of the static cause-effect pair data. It provides
a new dimension for describing static causal discovery tasks while enjoying
more freedom for modeling the quantitative causal influences. In particular, we
show that Additive Noise Models (ANMs) correspond to volume-preserving
pressureless flows. Consequently, based on their velocity field divergence, we
introduce a criterion for determining causal direction. With this criterion, we
propose a novel optimal transport-based algorithm for ANMs which is robust to
the choice of models and extend it to post-nonlinear models. Our method
demonstrated state-of-the-art results on both synthetic and causal discovery

A critical step in the fight against COVID-19, which continues to have a
catastrophic impact on peoples lives, is the effective screening of patients
presented in the clinics with severe COVID-19 symptoms. Chest radiography is
one of the promising screening approaches. Many studies reported detecting
COVID-19 in chest X-rays accurately using deep learning. A serious limitation
of many published approaches is insufficient attention paid to explaining
decisions made by deep learning models. Using explainable artificial
intelligence methods, we demonstrate that model decisions may rely on
confounding factors rather than medical pathology. After an analysis of
potential confounding factors found on chest X-ray images, we propose a novel
method to minimise their negative impact. We show that our proposed method is
more robust than previous attempts to counter confounding factors such as ECG
leads in chest X-rays that often influence model classification decisions. In
addition to being robust, our method achieves results comparable to the
state-of-the-art. The source code and pre-trained weights are publicly
available at (https://github.com/tomek1911/POTHER).

Can connectome-derived constraints inform computation? In this paper we
investigate the contribution of a fruit fly connectome's topology on the
performance of an Echo State Network (ESN) -- a subset of Reservoir Computing
which is state of the art in chaotic time series prediction. Specifically, we
replace the reservoir layer of a classical ESN -- normally a fixed, random
graph represented as a 2-d matrix -- with a particular (female) fruit fly
connectome-derived connectivity matrix. We refer to this experimental class of
models (with connectome-derived reservoirs) as "Fruit Fly ESNs" (FFESNs). We
train and validate the FFESN on a chaotic time series prediction task; here we
consider four sets of trials with different training input sizes (small, large)
and train-validate splits (two variants). We compare the validation performance
(Mean-Squared Error) of all of the best FFESN models to a class of control
model ESNs (simply referred to as "ESNs"). Overall, for all four sets of trials
we find that the FFESN either significantly outperforms (and has lower variance
than) the ESN; or simply has lower variance than the ESN.

Synthetic Aperture Radar (SAR) images are usually degraded by a
multiplicative noise known as speckle which makes processing and interpretation
of SAR images difficult. In this paper, we introduce a transformer-based
network for SAR image despeckling. The proposed despeckling network comprises
of a transformer-based encoder which allows the network to learn global
dependencies between different image regions - aiding in better despeckling.
The network is trained end-to-end with synthetically generated speckled images
using a composite loss function. Experiments show that the proposed method
achieves significant improvements over traditional and convolutional neural
network-based despeckling methods on both synthetic and real SAR images.

This paper offers a comprehensive survey of recent developments in 3D object
detection covering the full pipeline from input data, over data representation
and feature extraction to the actual detection modules. We include basic
concepts, focus our survey on a broad spectrum of different approaches arising
in the last ten years and propose a systematization which offers a practical
framework to compare those approaches on the methods level.

This paper tackles a multi-agent bandit setting where $M$ agents cooperate
together to solve the same instance of a $K$-armed stochastic bandit problem.
The agents are \textit{heterogeneous}: each agent has limited access to a local
subset of arms and the agents are asynchronous with different gaps between
decision-making rounds. The goal for each agent is to find its optimal local
arm, and agents can cooperate by sharing their observations with others. While
cooperation between agents improves the performance of learning, it comes with
an additional complexity of communication between agents. For this
heterogeneous multi-agent setting, we propose two learning algorithms, \ucbo
and \AAE. We prove that both algorithms achieve order-optimal regret, which is
$O\left(\sum_{i:\tilde{\Delta}_i>0} \log T/\tilde{\Delta}_i\right)$, where
$\tilde{\Delta}_i$ is the minimum suboptimality gap between the reward mean of
arm $i$ and any local optimal arm. In addition, a careful selection of the
valuable information for cooperation, \AAE achieves a low communication
complexity of $O(\log T)$. Last, numerical experiments verify the efficiency of
both algorithms.

Out of distribution (OOD) detection is a crucial part of making machine
learning systems robust. The ImageNet-O dataset is an important tool in testing
the robustness of ImageNet trained deep neural networks that are widely used
across a variety of systems and applications. We aim to perform a comparative
analysis of OOD detection methods on ImageNet-O, a first of its kind dataset
with a label distribution different than that of ImageNet, that has been
created to aid research in OOD detection for ImageNet models. As this dataset
is fairly new, we aim to provide a comprehensive benchmarking of some of the
current state of the art OOD detection methods on this novel dataset. This
benchmarking covers a variety of model architectures, settings where we haves
prior access to the OOD data versus when we don't, predictive score based
approaches, deep generative approaches to OOD detection, and more.

We review recent work in machine learning aspects of conformal field theory
and Lie algebra representation theory using neural networks.

By trade we usually mean the exchange of goods between states and countries.
International trade acts as a barometer of the economic prosperity index and
every country is overly dependent on resources, so international trade is
essential. Trade is significant to the global health crisis, saving lives and
livelihoods. By collecting the dataset called "Effects of COVID19 on trade"
from the state website NZ Tatauranga Aotearoa, we have developed a sustainable
prediction process on the effects of COVID-19 in world trade using a deep
learning model. In the research, we have given a 180-day trade forecast where
the ups and downs of daily imports and exports have been accurately predicted
in the Covid-19 period. In order to fulfill this prediction, we have taken data
from 1st January 2015 to 30th May 2021 for all countries, all commodities, and
all transport systems and have recovered what the world trade situation will be
in the next 180 days during the Covid-19 period. The deep learning method has
received equal attention from both investors and researchers in the field of
in-depth observation. This study predicts global trade using the Long-Short
Term Memory. Time series analysis can be useful to see how a given asset,
security, or economy changes over time. Time series analysis plays an important
role in past analysis to get different predictions of the future and it can be
observed that some factors affect a particular variable from period to period.
Through the time series it is possible to observe how various economic changes
or trade effects change over time. By reviewing these changes, one can be aware
of the steps to be taken in the future and a country can be more careful in
terms of imports and exports accordingly. From our time series analysis, it can
be said that the LSTM model has given a very gracious thought of the future
world import and export situation in terms of trade.

The recent works proposing transformer-based models for graphs have proven
the inadequacy of Vanilla Transformer for graph representation learning. To
understand this inadequacy, there is a need to investigate if spectral analysis
of the transformer will reveal insights into its expressive power. Similar
studies already established that spectral analysis of Graph neural networks
(GNNs) provides extra perspectives on their expressiveness. In this work, we
systematically study and establish the link between the spatial and spectral
domain in the realm of the transformer. We further provide a theoretical
analysis and prove that the spatial attention mechanism in the transformer
cannot effectively capture the desired frequency response, thus, inherently
limiting its expressiveness in spectral space. Therefore, we propose FeTA, a
framework that aims to perform attention over the entire graph spectrum (i.e.,
actual frequency components of the graphs) analogous to the attention in
spatial space. Empirical results suggest that FeTA provides homogeneous
performance gain against vanilla transformer across all tasks on standard
benchmarks and can easily be extended to GNN-based models with low-pass
characteristics (e.g., GAT).

Applying AI power to predict syntheses of novel materials requires
high-quality, large-scale datasets. Extraction of synthesis information from
scientific publications is still challenging, especially for extracting
synthesis actions, because of the lack of a comprehensive labeled dataset using
a solid, robust, and well-established ontology for describing synthesis
procedures. In this work, we propose the first Unified Language of Synthesis
Actions (ULSA) for describing ceramics synthesis procedures. We created a
dataset of 3,040 synthesis procedures annotated by domain experts according to
the proposed ULSA scheme. To demonstrate the capabilities of ULSA, we built a
neural network-based model to map arbitrary ceramics synthesis paragraphs into
ULSA and used it to construct synthesis flowcharts for synthesis procedures.
Analysis for the flowcharts showed that (a) ULSA covers essential vocabulary
used by researchers when describing synthesis procedures and (b) it can capture
important features of synthesis protocols. This work is an important step
towards creating a synthesis ontology and a solid foundation for autonomous
robotic synthesis.

Recently, there has been a surge in research in multimodal machine
translation (MMT), where additional modalities such as images are used to
improve translation quality of textual systems. A particular use for such
multimodal systems is the task of simultaneous machine translation, where
visual context has been shown to complement the partial information provided by
the source sentence, especially in the early phases of translation. In this
paper, we propose the first Transformer-based simultaneous MMT architecture,
which has not been previously explored in the field. Additionally, we extend
this model with an auxiliary supervision signal that guides its visual
attention mechanism using labelled phrase-region alignments. We perform
comprehensive experiments on three language directions and conduct thorough
quantitative and qualitative analyses using both automatic metrics and manual
inspection. Our results show that (i) supervised visual attention consistently
improves the translation quality of the MMT models, and (ii) fine-tuning the
MMT with supervision loss enabled leads to better performance than training the
MMT from scratch. Compared to the state-of-the-art, our proposed model achieves
improvements of up to 2.3 BLEU and 3.5 METEOR points.

A number of approaches have dealt with statistical assessment of
self-similarity, and many of those are based on multiscale concepts. Most rely
on certain distributional assumptions which are usually violated by real data
traces, often characterized by large temporal or spatial mean level shifts,
missing values or extreme observations. A novel, robust approach based on
Theil-type weighted regression is proposed for estimating self-similarity in
two-dimensional data (images). The method is compared to two traditional
estimation techniques that use wavelet decompositions; ordinary least squares
(OLS) and Abry-Veitch bias correcting estimator (AV). As an application, the
suitability of the self-similarity estimate resulting from the the robust
approach is illustrated as a predictive feature in the classification of
digitized mammogram images as cancerous or non-cancerous. The diagnostic
employed here is based on the properties of image backgrounds, which is
typically an unused modality in breast cancer screening. Classification results
show nearly 68% accuracy, varying slightly with the choice of wavelet basis,
and the range of multiresolution levels used.

Reconstruction of CT images from a limited set of projections through an
object is important in several applications ranging from medical imaging to
industrial settings. As the number of available projections decreases,
traditional reconstruction techniques such as the FDK algorithm and model-based
iterative reconstruction methods perform poorly. Recently, data-driven methods
such as deep learning-based reconstruction have garnered a lot of attention in
applications because they yield better performance when enough training data is
available. However, even these methods have their limitations when there is a
scarcity of available training data. This work focuses on image reconstruction
in such settings, i.e., when both the number of available CT projections and
the training data is extremely limited. We adopt a sequential reconstruction
approach over several stages using an adversarially trained shallow network for
'destreaking' followed by a data-consistency update in each stage. To deal with
the challenge of limited data, we use image subvolumes to train our method, and
patch aggregation during testing. To deal with the computational challenge of
learning on 3D datasets for 3D reconstruction, we use a hybrid 3D-to-2D mapping
network for the 'destreaking' part. Comparisons to other methods over several
test examples indicate that the proposed method has much potential, when both
the number of projections and available training data are highly limited.

Capturing high-resolution magnetic resonance (MR) images is a time consuming
process, which makes it unsuitable for medical emergencies and pediatric
patients. Low-resolution MR imaging, by contrast, is faster than its
high-resolution counterpart, but it compromises on fine details necessary for a
more precise diagnosis. Super-resolution (SR), when applied to low-resolution
MR images, can help increase their utility by synthetically generating
high-resolution images with little additional time. In this paper, we present a
SR technique for MR images that is based on generative adversarial networks
(GANs), which have proven to be quite useful in generating sharp-looking
details in SR. We introduce a conditional GAN with perceptual loss, which is
conditioned upon the input low-resolution image, which improves the performance
for isotropic and anisotropic MRI super-resolution.

Anatomical movements of the human body can change the channel state
information (CSI) of wireless signals in an indoor environment. These changes
in the CSI signals can be used for human activity recognition (HAR), which is a
predominant and unique approach due to preserving privacy and flexibility of
capturing motions in non-line-of-sight environments. Existing models for HAR
generally have a high computational complexity, contain very large number of
trainable parameters, and require extensive computational resources. This issue
is particularly important for implementation of these solutions on devices with
limited resources, such as edge devices. In this paper, we propose a
lightweight human activity recognition (LiteHAR) approach which, unlike the
state-of-the-art deep learning models, does not require extensive training of
large number of parameters. This approach uses randomly initialized convolution
kernels for feature extraction from CSI signals without training the kernels.
The extracted features are then classified using Ridge regression classifier,
which has a linear computational complexity and is very fast. LiteHAR is
evaluated on a public benchmark dataset and the results show its high
classification performance in comparison with the complex deep learning models
with a much lower computational complexity.

Softmax-based losses have achieved state-of-the-art performances on various
tasks such as face recognition and re-identification. However, these methods
highly relied on clean datasets with global labels, which limits their usage in
many real-world applications. An important reason is that merging and
organizing datasets from various temporal and spatial scenarios is usually not
realistic, as noisy labels can be introduced and exponential-increasing
resources are required. To address this issue, we propose a novel
mining-during-training strategy called Basket-based Softmax (BBS) as well as
its parallel version to effectively train models on multiple datasets in an
end-to-end fashion. Specifically, for each training sample, we simultaneously
adopt similarity scores as the clue to mining negative classes from other
datasets, and dynamically add them to assist the learning of discriminative
features. Experimentally, we demonstrate the efficiency and superiority of the
BBS on the tasks of face recognition and re-identification, with both simulated
and real-world datasets.

Spiking Neural Networks (SNNs) have gained huge attention as a potential
energy-efficient alternative to conventional Artificial Neural Networks (ANNs)
due to their inherent high-sparsity activation. However, most prior SNN methods
use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide
sub-optimal performance for temporal sequence processing of binary information
in SNNs. To address this, in this paper, we introduce a novel Neural
Architecture Search (NAS) approach for finding better SNN architectures.
Inspired by recent NAS approaches that find the optimal architecture from
activation patterns at initialization, we select the architecture that can
represent diverse spike activation patterns across different data samples
without training. Moreover, to further leverage the temporal information among
the spikes, we search for feed forward connections as well as backward
connections (i.e., temporal feedback connections) between layers.
Interestingly, SNASNet found by our search algorithm achieves higher
performance with backward connections, demonstrating the importance of
designing SNN architecture for suitably using temporal information. We conduct
extensive experiments on three image recognition benchmarks where we show that
SNASNet achieves state-of-the-art performance with significantly lower
timesteps (5 timesteps). Code is available at Github.

This is a detailed analysis and comparison of the ACT-R and Soar cognitive
architectures, including their overall structure, their representations of
agent data and metadata, and their associated processing. It focuses on working
memory, procedural memory, and long-term declarative memory. I emphasize the
commonalities, which are many, but also highlight the differences. I identify
the processes and distinct classes of information used by these architectures,
including agent data, metadata, and meta-process data, and explore the roles
that metadata play in decision making, memory retrievals, and learning.

In digital cameras, we find a major limitation: the image and video form
inherited from a film camera obstructs it from capturing the rapidly changing
photonic world. Here, we present vidar, a bit sequence array where each bit
represents whether the accumulation of photons has reached a threshold, to
record and reconstruct the scene radiance at any moment. By employing only
consumer-level CMOS sensors and integrated circuits, we have developed a vidar
camera that is 1,000x faster than conventional cameras. By treating vidar as
spike trains in biological vision, we have further developed a spiking neural
network-based machine vision system that combines the speed of the machine and
the mechanism of biological vision, achieving high-speed object detection and
tracking 1,000x faster than human vision. We demonstrate the utility of the
vidar camera and the super vision system in an assistant referee and target
pointing system. Our study is expected to fundamentally revolutionize the image
and video concepts and related industries, including photography, movies, and
visual media, and to unseal a new spiking neural network-enabled speed-free
machine vision era.

Visual object tracking with the visible (RGB) and thermal infrared (TIR)
electromagnetic waves, shorted in RGBT tracking, recently draws increasing
attention in the tracking community. Considering the rapid development of deep
learning, a survey for the recent deep neural network based RGBT trackers is
presented in this paper. Firstly, we give brief introduction for the RGBT
trackers concluded into this category. Then, a comparison among the existing
RGBT trackers on several challenging benchmarks is given statistically.
Specifically, MDNet and Siamese architectures are the two mainstream frameworks
in the RGBT community, especially the former. Trackers based on MDNet achieve
higher performance while Siamese-based trackers satisfy the real-time
requirement. In summary, since the large-scale dataset LasHeR is published, the
integration of end-to-end framework, e.g., Siamese and Transformer, should be
further considered to fulfil the real-time as well as more robust performance.
Furthermore, the mathematical meaning should be more considered during
designing the network. This survey can be treated as a look-up-table for
researchers who are concerned about RGBT tracking.

We focus on Maximum Inner Product Search (MIPS), which is an essential
problem in many machine learning communities. Given a query, MIPS finds the
most similar items with the maximum inner products. Methods for Nearest
Neighbor Search (NNS) which is usually defined on metric space don't exhibit
the satisfactory performance for MIPS problem since inner product is a
non-metric function. However, inner products exhibit many good properties
compared with metric functions, such as avoiding vanishing and exploding
gradients. As a result, inner product is widely used in many recommendation
systems, which makes efficient Maximum Inner Product Search a key for speeding
up many recommendation systems.
  Graph based methods for NNS problem show the superiorities compared with
other class methods. Each data point of the database is mapped to a node of the
proximity graph. Nearest neighbor search in the database can be converted to
route on the proximity graph to find the nearest neighbor for the query. This
technique can be used to solve MIPS problem. Instead of searching the nearest
neighbor for the query, we search the item with maximum inner product with
query on the proximity graph. In this paper, we propose a reinforcement model
to train an agent to search on the proximity graph automatically for MIPS
problem if we lack the ground truths of training queries. If we know the ground
truths of some training queries, our model can also utilize these ground truths
by imitation learning to improve the agent's search ability. By experiments, we
can see that our proposed mode which combines reinforcement learning with
imitation learning shows the superiorities over the state-of-the-art methods

Quickshift is a popular algorithm for image segmentation, used as a
preprocessing step in many applications. Unfortunately, it is quite challenging
to understand the hyperparameters' influence on the number and shape of
superpixels produced by the method. In this paper, we study theoretically a
slightly modified version of the quickshift algorithm, with a particular
emphasis on homogeneous image patches with i.i.d. pixel noise and sharp
boundaries between such patches. Leveraging this analysis, we derive a simple
heuristic to scale quickshift hyperparameters with respect to the image size,
which we check empirically.

Self-supervised protein language models have proved their effectiveness in
learning the proteins representations. With the increasing computational power,
current protein language models pre-trained with millions of diverse sequences
can advance the parameter scale from million-level to billion-level and achieve
remarkable improvement. However, those prevailing approaches rarely consider
incorporating knowledge graphs (KGs), which can provide rich structured
knowledge facts for better protein representations. We argue that informative
biology knowledge in KGs can enhance protein representation with external
knowledge. In this work, we propose OntoProtein, the first general framework
that makes use of structure in GO (Gene Ontology) into protein pre-training
models. We construct a novel large-scale knowledge graph that consists of GO
and its related proteins, and gene annotation texts or protein sequences
describe all nodes in the graph. We propose novel contrastive learning with
knowledge-aware negative sampling to jointly optimize the knowledge graph and
protein embedding during pre-training. Experimental results show that
OntoProtein can surpass state-of-the-art methods with pre-trained protein
language models in TAPE benchmark and yield better performance compared with
baselines in protein-protein interaction and protein function prediction. Code
and datasets are available in https://github.com/zjunlp/OntoProtein.

The task of automatic text summarization has gained a lot of traction due to
the recent advancements in machine learning techniques. However, evaluating the
quality of a generated summary remains to be an open problem. The literature
has widely adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as
the standard evaluation metric for summarization. However, ROUGE has some
long-established limitations; a major one being its dependence on the
availability of good quality reference summary. In this work, we propose the
metric WIDAR which in addition to utilizing the reference summary uses also the
input document in order to evaluate the quality of the generated summary. The
proposed metric is versatile, since it is designed to adapt the evaluation
score according to the quality of the reference summary. The proposed metric
correlates better than ROUGE by 26%, 76%, 82%, and 15%, respectively, in
coherence, consistency, fluency, and relevance on human judgement scores
provided in the SummEval dataset. The proposed metric is able to obtain
comparable results with other state-of-the-art metrics while requiring a
relatively short computational time.

According to the World Health Organisation (WHO), 235 million people suffer
from respiratory illnesses and four million people die annually due to air
pollution. Regular lung health monitoring can lead to prognoses about
deteriorating lung health conditions. This paper presents our system SpiroMask
that retrofits a microphone in consumer-grade masks (N95 and cloth masks) for
continuous lung health monitoring. We evaluate our approach on 48 participants
(including 14 with lung health issues) and find that we can estimate parameters
such as lung volume and respiration rate within the approved error range by the
American Thoracic Society (ATS). Further, we show that our approach is robust
to sensor placement inside the mask.

The set-union knapsack problem (SUKP) is a constrained composed optimization
problem. It is more difficulty for solving because values and weights depend on
items and elements respectively. In this paper, we present two self-adjusting
optimization algorithms for approximating SUKP from items and elements
perspective respectively. By analyzing the dynamic characters in the SUKP, we
design two types of self-adjusting repair and optimization operators that are
based on the different loading process. We use the novel
teaching-learning-based optimization algorithm (TLBO) to design a general
discrete framework (DTLBO) suitable for these two types of operators. In
addition, we introduce elite opposite search and natural selection mechanism
into DTLBO to furtherly improve the performance of the algorithm from the
perspective of population. Finally, we performed experimental comparisons on
benchmark sets to verify the effectiveness of the proposed algorithm. The
experimental results show that the item-based self-adjusting optimization
algorithm I-DTLBO is outstanding, and the algorithm is superior to the other
swarm intelligence methods for solving SUKP. IDTLBO algorithm reaches the upper
boundary of the current swarm intelligence algorithms for solving SUKP in 10
instances, and gotten new upper boundary in 15 instances. The algorithm E-DTLBO
based on element loading only perform slightly better on small and middle data
sets, but worse on large-scale instances. It shows that element-based design is
not suitable for solving SUKP.

The feature learning methods based on convolutional neural network (CNN) have
successfully produced tremendous achievements in image classification tasks.
However, the inherent noise and some other factors may weaken the effectiveness
of the convolutional feature statistics. In this paper, we investigate Discrete
Wavelet Transform (DWT) in the frequency domain and design a new
Wavelet-Attention (WA) block to only implement attention in the high-frequency
domain. Based on this, we propose a Wavelet-Attention convolutional neural
network (WA-CNN) for image classification. Specifically, WA-CNN decomposes the
feature maps into low-frequency and high-frequency components for storing the
structures of the basic objects, as well as the detailed information and noise,
respectively. Then, the WA block is leveraged to capture the detailed
information in the high-frequency domain with different attention factors but
reserves the basic object structures in the low-frequency domain. Experimental
results on CIFAR-10 and CIFAR-100 datasets show that our proposed WA-CNN
achieves significant improvements in classification accuracy compared to other
related networks. Specifically, based on MobileNetV2 backbones, WA-CNN achieves
1.26% Top-1 accuracy improvement on the CIFAR-10 benchmark and 1.54% Top-1
accuracy improvement on the CIFAR-100 benchmark.

This is a tutorial and survey paper on metric learning. Algorithms are
divided into spectral, probabilistic, and deep metric learning. We first start
with the definition of distance metric, Mahalanobis distance, and generalized
Mahalanobis distance. In spectral methods, we start with methods using scatters
of data, including the first spectral metric learning, relevant methods to
Fisher discriminant analysis, Relevant Component Analysis (RCA), Discriminant
Component Analysis (DCA), and the Fisher-HSIC method. Then, large-margin metric
learning, imbalanced metric learning, locally linear metric adaptation, and
adversarial metric learning are covered. We also explain several kernel
spectral methods for metric learning in the feature space. We also introduce
geometric metric learning methods on the Riemannian manifolds. In probabilistic
methods, we start with collapsing classes in both input and feature spaces and
then explain the neighborhood component analysis methods, Bayesian metric
learning, information theoretic methods, and empirical risk minimization in
metric learning. In deep learning methods, we first introduce reconstruction
autoencoders and supervised loss functions for metric learning. Then, Siamese
networks and its various loss functions, triplet mining, and triplet sampling
are explained. Deep discriminant analysis methods, based on Fisher discriminant
analysis, are also reviewed. Finally, we introduce multi-modal deep metric
learning, geometric metric learning by neural networks, and few-shot metric
learning.

We introduce a neural implicit framework that exploits the differentiable
properties of neural networks and the discrete geometry of point-sampled
surfaces to approximate them as the level sets of neural implicit functions.
  To train a neural implicit function, we propose a loss functional that
approximates a signed distance function, and allows terms with high-order
derivatives, such as the alignment between the principal directions of
curvature, to learn more geometric details. During training, we consider a
non-uniform sampling strategy based on the curvatures of the point-sampled
surface to prioritize points with more geometric details. This sampling implies
faster learning while preserving geometric accuracy when compared with previous
  We also use the analytical derivatives of a neural implicit function to
estimate the differential measures of the underlying point-sampled surface.

Text style transfer is a challenging text generation problem, which aims at
altering the style of a given sentence to a target one while keeping its
content unchanged. Since there is a natural scarcity of parallel datasets,
recent works mainly focus on solving the problem in an unsupervised manner.
However, previous gradient-based works generally suffer from the deficiencies
as follows, namely: (1) Content migration. Previous approaches lack explicit
modeling of content invariance and are thus susceptible to content shift
between the original sentence and the transferred one. (2) Style
misclassification. A natural drawback of the gradient-guided approaches is that
the inference process is homogeneous with a line of adversarial attack, making
latent optimization easily becomes an attack to the classifier due to
misclassification. This leads to difficulties in achieving high transfer
accuracy. To address the problems, we propose a novel gradient-guided model
through a contrastive paradigm for text style transfer, to explicitly gather
similar semantic sentences, and to design a siamese-structure based style
classifier for alleviating such two issues, respectively. Experiments on two
datasets show the effectiveness of our proposed approach, as compared to the

Conventional subspace learning approaches based on image gradient
orientations only employ the first-order gradient information. However, recent
researches on human vision system (HVS) uncover that the neural image is a
landscape or a surface whose geometric properties can be captured through the
second order gradient information. The second order image gradient orientations
(SOIGO) can mitigate the adverse effect of noises in face images. To reduce the
redundancy of SOIGO, we propose compact SOIGO (CSOIGO) by applying linear
complex principal component analysis (PCA) in SOIGO. Combined with
collaborative representation based classification (CRC) algorithm, the
classification performance of CSOIGO is further enhanced. CSOIGO is evaluated
under real-world disguise, synthesized occlusion and mixed variations.
Experimental results indicate that the proposed method is superior to its
competing approaches with few training samples, and even outperforms some
prevailing deep neural network based approaches. The source code of CSOIGO is
available at https://github.com/yinhefeng/SOIGO.

Transient stability prediction is critically essential to the fast online
assessment and maintaining the stable operation in power systems. The wide
deployment of phasor measurement units (PMUs) promotes the development of
data-driven approaches for transient stability assessment. This paper proposes
the temporal and topological embedding deep neural network (TTEDNN) model to
forecast transient stability with the early transient dynamics. The TTEDNN
model can accurately and efficiently predict the transient stability by
extracting the temporal and topological features from the time-series data of
the early transient dynamics. The grid-informed adjacency matrix is used to
incorporate the power grid structural and electrical parameter information. The
transient dynamics simulation environments under the single-node and
multiple-node perturbations are used to test the performance of the TTEDNN
model for the IEEE 39-bus and IEEE 118-bus power systems. The results show that
the TTEDNN model has the best and most robust prediction performance.
Furthermore, the TTEDNN model also demonstrates the transfer capability to
predict the transient stability in the more complicated transient dynamics
simulation environments.

In model extraction attacks, adversaries can steal a machine learning model
exposed via a public API by repeatedly querying it and adjusting their own
model based on obtained predictions. To prevent model stealing, existing
defenses focus on detecting malicious queries, truncating, or distorting
outputs, thus necessarily introducing a tradeoff between robustness and model
utility for legitimate users. Instead, we propose to impede model extraction by
requiring users to complete a proof-of-work before they can read the model's
predictions. This deters attackers by greatly increasing (even up to 100x) the
computational effort needed to leverage query access for model extraction.
Since we calibrate the effort required to complete the proof-of-work to each
query, this only introduces a slight overhead for regular users (up to 2x). To
achieve this, our calibration applies tools from differential privacy to
measure the information revealed by a query. Our method requires no
modification of the victim model and can be applied by machine learning
practitioners to guard their publicly exposed models against being easily
stolen.

In the realm of image processing and computer vision (CV), machine learning
(ML) architectures are widely applied. Convolutional neural networks (CNNs)
solve a wide range of image processing issues and can solve image compression
problem. Compression of images is necessary due to bandwidth and memory
constraints. Helpful, redundant, and irrelevant information are three different
forms of information found in images. This paper aims to survey recent
techniques utilizing mostly lossy image compression using ML architectures
including different auto-encoders (AEs) such as convolutional auto-encoders
(CAEs), variational auto-encoders (VAEs), and AEs with hyper-prior models,
recurrent neural networks (RNNs), CNNs, generative adversarial networks (GANs),
principal component analysis (PCA) and fuzzy means clustering. We divide all of
the algorithms into several groups based on architecture. We cover still image
compression in this survey. Various discoveries for the researchers are
emphasized and possible future directions for researchers. The open research
problems such as out of memory (OOM), striped region distortion (SRD),
aliasing, and compatibility of the frameworks with central processing unit
(CPU) and graphics processing unit (GPU) simultaneously are explained. The
majority of the publications in the compression domain surveyed are from the
previous five years and use a variety of approaches.

The generalization capacity of various machine learning models exhibits
different phenomena in the under- and over-parameterized regimes. In this
paper, we focus on regression models such as feature regression and kernel
regression and analyze a generalized weighted least-squares optimization method
for computational learning and inversion with noisy data. The highlight of the
proposed framework is that we allow weighting in both the parameter space and
the data space. The weighting scheme encodes both a priori knowledge on the
object to be learned and a strategy to weight the contribution of different
data points in the loss function. Here, we characterize the impact of the
weighting scheme on the generalization error of the learning method, where we
derive explicit generalization errors for the random Fourier feature model in
both the under- and over-parameterized regimes. For more general feature maps,
error bounds are provided based on the singular values of the feature matrix.
We demonstrate that appropriate weighting from prior knowledge can improve the
generalization capability of the learned model.

Establishing the correspondence between two images is an important research
direction of computer vision. When estimating the relationship between two
images, it is often disturbed by outliers. In this paper, we propose a
convolutional neural network that can filter the noise of outliers. It can
output the probability that the pair of feature points is an inlier and regress
the essential matrix representing the relative pose of the camera. The outliers
are mainly caused by the noise introduced by the previous processing. The
outliers rejection can be treated as a problem of noise elimination, and the
soft threshold function has a very good effect on noise reduction. Therefore,
we designed an adaptive denoising module based on soft threshold function to
remove noise components in the outliers, to reduce the probability that the
outlier is predicted to be an inlier. Experimental results on the YFCC100M
dataset show that our method exceeds the state-of-the-art in relative pose

Imperative programming allows users to implement their deep neural networks
(DNNs) easily and has become an essential part of recent deep learning (DL)
frameworks. Recently, several systems have been proposed to combine the
usability of imperative programming with the optimized performance of symbolic
graph execution. Such systems convert imperative Python DL programs to
optimized symbolic graphs and execute them. However, they cannot fully support
the usability of imperative programming. For example, if an imperative DL
program contains a Python feature with no corresponding symbolic representation
(e.g., third-party library calls or unsupported dynamic control flows) they
fail to execute the program. To overcome this limitation, we propose Terra, an
imperative-symbolic co-execution system that can handle any imperative DL
programs while achieving the optimized performance of symbolic graph execution.
To achieve this, Terra builds a symbolic graph by decoupling DL operations from
Python features. Then, Terra conducts the imperative execution to support all
Python features, while delegating the decoupled operations to the symbolic
execution. We evaluated the performance improvement and coverage of Terra with
ten imperative DL programs for several DNN architectures. The results show that
Terra can speed up the execution of all ten imperative DL programs, whereas
AutoGraph, one of the state-of-the-art systems, fails to execute five of them.

While dropout is known to be a successful regularization technique, insights
into the mechanisms that lead to this success are still lacking. We introduce
the concept of \emph{weight expansion}, an increase in the signed volume of a
parallelotope spanned by the column or row vectors of the weight covariance
matrix, and show that weight expansion is an effective means of increasing the
generalization in a PAC-Bayesian setting. We provide a theoretical argument
that dropout leads to weight expansion and extensive empirical support for the
correlation between dropout and weight expansion. To support our hypothesis
that weight expansion can be regarded as an \emph{indicator} of the enhanced
generalization capability endowed by dropout, and not just as a mere
by-product, we have studied other methods that achieve weight expansion (resp.\
contraction), and found that they generally lead to an increased (resp.\
decreased) generalization ability. This suggests that dropout is an attractive
regularizer, because it is a computationally cheap method for obtaining weight
expansion. This insight justifies the role of dropout as a regularizer, while
paving the way for identifying regularizers that promise improved
generalization through weight expansion.

One-shot learning has become an important research topic in the last decade
with many real-world applications. The goal of one-shot learning is to classify
unlabeled instances when there is only one labeled example per class.
Conventional problem setting of one-shot learning mainly focuses on the data
that is already in feature space (such as images). However, the data instances
in real-world applications are often more complex and feature vectors may not
be available. In this paper, we study the problem of one-shot learning on
attributed sequences, where each instance is composed of a set of attributes
(e.g., user profile) and a sequence of categorical items (e.g., clickstream).
This problem is important for a variety of real-world applications ranging from
fraud prevention to network intrusion detection. This problem is more
challenging than conventional one-shot learning since there are dependencies
between attributes and sequences. We design a deep learning framework OLAS to
tackle this problem. The proposed OLAS utilizes a twin network to generalize
the features from pairwise attributed sequence examples. Empirical results on
real-world datasets demonstrate the proposed OLAS can outperform the
state-of-the-art methods under a rich variety of parameter settings.

Unmanned Aerial Vehicle (UAV) localization capability is critical in a Global
Navigation Satellite System (GNSS) denial environment. The aim of this paper is
to investigate the problem of locating the UAV itself through a purely visual
approach. This task mainly refers to: matching the corresponding geo-tagged
satellite images through the images acquired by the camera when the UAV does
not acquire GNSS signals, where the satellite images are the bridge between the
UAV images and the location information. However, the sampling points of
previous cross-view datasets based on UAVs are discrete in spatial distribution
and the inter-class relationships are not established. In the actual process of
UAV-localization, the inter-class feature similarity of the proximity position
distribution should be small due to the continuity of UAV movement in space. In
view of this, this paper has reformulated an intensive dataset for UAV
positioning tasks, which is named DenseUAV, aiming to solve the problems caused
by spatial distance and scale transformation in practical application
scenarios, so as to achieve high-precision UAV-localization in GNSS denial
environment. In addition, a new continuum-type evaluation metric named SDM is
designed to evaluate the accuracy of model matching by exploiting the continuum
of UAVs in space. Specifically, with the ideas of siamese networks and metric
learning, a transformer-based baseline was constructed to enhance the capture
of spatially subtle features. Ultimately, a neighbor-search post-processing
strategy was proposed to solve the problem of large distance localisation bias.

Recent research in feature learning has been extended to sequence data, where
each instance consists of a sequence of heterogeneous items with a variable
length. However, in many real-world applications, the data exists in the form
of attributed sequences, which is composed of a set of fixed-size attributes
and variable-length sequences with dependencies between them. In the attributed
sequence context, feature learning remains challenging due to the dependencies
between sequences and their associated attributes. In this dissertation, we
focus on analyzing and building deep learning models for four new problems on
attributed sequences. Our extensive experiments on real-world datasets
demonstrate that the proposed solutions significantly improve the performance
of each task over the state-of-the-art methods on attributed sequences.

A key challenge for machine intelligence is to learn new visual concepts
without forgetting the previously acquired knowledge. Continual learning is
aimed towards addressing this challenge. However, there is a gap between
existing supervised continual learning and human-like intelligence, where human
is able to learn from both labeled and unlabeled data. How unlabeled data
affects learning and catastrophic forgetting in the continual learning process
remains unknown. To explore these issues, we formulate a new semi-supervised
continual learning method, which can be generically applied to existing
continual learning models. Specifically, a novel gradient learner learns from
labeled data to predict gradients on unlabeled data. Hence, the unlabeled data
could fit into the supervised continual learning method. Different from
conventional semi-supervised settings, we do not hypothesize that the
underlying classes, which are associated to the unlabeled data, are known to
the learning process. In other words, the unlabeled data could be very distinct
from the labeled data. We evaluate the proposed method on mainstream continual
learning, adversarial continual learning, and semi-supervised learning tasks.
The proposed method achieves state-of-the-art performance on classification
accuracy and backward transfer in the continual learning setting while
achieving desired performance on classification accuracy in the semi-supervised
learning setting. This implies that the unlabeled images can enhance the
generalizability of continual learning models on the predictive ability on
unseen data and significantly alleviate catastrophic forgetting. The code is
available at \url{https://github.com/luoyan407/grad_prediction.git}.

We consider the task of learning causal structures from data stored on
multiple machines, and propose a novel structure learning method called
distributed annealing on regularized likelihood score (DARLS) to solve this
problem. We model causal structures by a directed acyclic graph that is
parameterized with generalized linear models, so that our method is applicable
to various types of data. To obtain a high-scoring causal graph, DARLS
simulates an annealing process to search over the space of topological sorts,
where the optimal graphical structure compatible with a sort is found by a
distributed optimization method. This distributed optimization relies on
multiple rounds of communication between local and central machines to estimate
the optimal structure. We establish its convergence to a global optimizer of
the overall score that is computed on all data across local machines. To the
best of our knowledge, DARLS is the first distributed method for learning
causal graphs with such theoretical guarantees. Through extensive simulation
studies, DARLS has shown competing performance against existing methods on
distributed data, and achieved comparable structure learning accuracy and
test-data likelihood with competing methods applied to pooled data across all
local machines. In a real-world application for modeling protein-DNA binding
networks with distributed ChIP-Sequencing data, DARLS also exhibits higher
predictive power than other methods, demonstrating a great advantage in
estimating causal networks from distributed data.

The learning process of deep learning methods usually updates the model's
parameters in multiple iterations. Each iteration can be viewed as the
first-order approximation of Taylor's series expansion. The remainder, which
consists of higher-order terms, is usually ignored in the learning process for
simplicity. This learning scheme empowers various multimedia based
applications, such as image retrieval, recommendation system, and video search.
Generally, multimedia data (e.g., images) are semantics-rich and
high-dimensional, hence the remainders of approximations are possibly non-zero.
In this work, we consider the remainder to be informative and study how it
affects the learning process. To this end, we propose a new learning approach,
namely gradient adjustment learning (GAL), to leverage the knowledge learned
from the past training iterations to adjust vanilla gradients, such that the
remainders are minimized and the approximations are improved. The proposed GAL
is model- and optimizer-agnostic, and is easy to adapt to the standard learning
framework. It is evaluated on three tasks, i.e., image classification, object
detection, and regression, with state-of-the-art models and optimizers. The
experiments show that the proposed GAL consistently enhances the evaluated
models, whereas the ablation studies validate various aspects of the proposed
GAL. The code is available at
\url{https://github.com/luoyan407/gradient_adjustment.git}.

Global pooling is one of the most significant operations in many machine
learning models and tasks, whose implementation, however, is often empirical in
practice. In this study, we develop a novel and solid global pooling framework
through the lens of optimal transport. We demonstrate that most existing global
pooling methods are equivalent to solving some specializations of an unbalanced
optimal transport (UOT) problem. Making the parameters of the UOT problem
learnable, we unify various global pooling methods in the same framework, and
accordingly, propose a generalized global pooling layer called UOT-Pooling
(UOTP) for neural networks. Besides implementing the UOTP layer based on the
classic Sinkhorn-scaling algorithm, we design a new model architecture based on
the Bregman ADMM algorithm, which has better numerical stability and can
reproduce existing pooling layers more effectively. We test our UOTP layers in
several application scenarios, including multi-instance learning, graph
classification, and image classification. Our UOTP layers can either imitate
conventional global pooling layers or learn some new pooling mechanisms leading
to better performance.

Graph neural networks (GNNs) process large-scale graphs consisting of a
hundred billion edges. In contrast to traditional deep learning, unique
behaviors of the emerging GNNs are engaged with a large set of graphs and
embedding data on storage, which exhibits complex and irregular preprocessing.
  We propose a novel deep learning framework on large graphs, HolisticGNN, that
provides an easy-to-use, near-storage inference infrastructure for fast,
energy-efficient GNN processing. To achieve the best end-to-end latency and
high energy efficiency, HolisticGNN allows users to implement various GNN
algorithms and directly executes them where the actual data exist in a holistic
manner. It also enables RPC over PCIe such that the users can simply program
GNNs through a graph semantic library without any knowledge of the underlying
hardware or storage configurations.
  We fabricate HolisticGNN's hardware RTL and implement its software on an
FPGA-based computational SSD (CSSD). Our empirical evaluations show that the
inference time of HolisticGNN outperforms GNN inference services using
high-performance modern GPUs by 7.1x while reducing energy consumption by
33.2x, on average.

This paper proposes a new approach for privacy-preserving and verifiable
convolutional neural network (CNN) testing, enabling a CNN model developer to
convince a user of the truthful CNN performance over non-public data from
multiple testers, while respecting model privacy. To balance the security and
efficiency issues, three new efforts are done by appropriately integrating
homomorphic encryption (HE) and zero-knowledge succinct non-interactive
argument of knowledge (zk-SNARK) primitives with the CNN testing. First, a CNN
model to be tested is strategically partitioned into a private part kept
locally by the model developer, and a public part outsourced to an outside
server. Then, the private part runs over HE-protected test data sent by a
tester and transmits its outputs to the public part for accomplishing
subsequent computations of the CNN testing. Second, the correctness of the
above CNN testing is enforced by generating zk-SNARK based proofs, with an
emphasis on optimizing proving overhead for two-dimensional (2-D) convolution
operations, since the operations dominate the performance bottleneck during
generating proofs. We specifically present a new quadratic matrix programs
(QMPs)-based arithmetic circuit with a single multiplication gate for
expressing 2-D convolution operations between multiple filters and inputs in a
batch manner. Third, we aggregate multiple proofs with respect to a same CNN
model but different testers' test data (i.e., different statements) into one
proof, and ensure that the validity of the aggregated proof implies the
validity of the original multiple proofs. Lastly, our experimental results
demonstrate that our QMPs-based zk-SNARK performs nearly 13.9$\times$faster
than the existing QAPs-based zk-SNARK in proving time, and 17.6$\times$faster
in Setup time, for high-dimension matrix multiplication.

We introduce a pattern mining framework that operates on semi-structured
datasets and exploits the dichotomy between outcomes. Our approach takes
advantage of constraint reasoning to find sequential patterns that occur
frequently and exhibit desired properties. This allows the creation of novel
pattern embeddings that are useful for knowledge extraction and predictive
modeling. Finally, we present an application on customer intent prediction from
digital clickstream data. Overall, we show that pattern embeddings play an
integrator role between semi-structured data and machine learning models,
improve the performance of the downstream task and retain interpretability.

As a substantial amount of multivariate time series data is being produced by
the complex systems in Smart Manufacturing, improved anomaly detection
frameworks are needed to reduce the operational risks and the monitoring burden
placed on the system operators. However, building such frameworks is
challenging, as a sufficiently large amount of defective training data is often
not available and frameworks are required to capture both the temporal and
contextual dependencies across different time steps while being robust to
noise. In this paper, we propose an unsupervised Attention-based Convolutional
Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding
(ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time
series. The framework starts by pre-processing and enriching the data, before
constructing feature images to characterize the system statuses across
different time steps by capturing the inter-correlations between pairs of time
series. Afterwards, the constructed feature images are fed into an
attention-based ConvLSTM autoencoder, which aims to encode the constructed
feature images and capture the temporal behavior, followed by decoding the
compressed knowledge representation to reconstruct the feature images input.
The reconstruction errors are then computed and subjected to a
statistical-based, dynamic thresholding mechanism to detect and diagnose the
anomalies. Evaluation results conducted on real-life manufacturing data
demonstrate the performance strengths of the proposed approach over
state-of-the-art methods under different experimental settings.

Early action prediction aims to recognize human actions from only a part of
action execution, which is an important video analysis task for many practical
applications. Most prior works treat partial or full videos as a whole, which
neglects the semantic consistencies among partial videos of various progress
levels due to their large intra-class variances. In contrast, we partition
original partial or full videos to form a series of new partial videos and mine
the Action Semantic Consistent Knowledge (ASCK) among these new partial videos
evolving in arbitrary progress levels. Moreover, a novel Action Semantic
Consistent learning network (ASCNet) under the teacher-student framework is
proposed for early action prediction. Specifically, we treat partial videos as
nodes and their action semantic consistencies as edges. Then we build a
bi-directional fully connected graph for the teacher network and a
single-directional fully connected graph for the student network to model ASCK
among partial videos. The MSE and MMD losses are incorporated as our
distillation loss to further transfer the ASCK from the teacher to the student
network. Extensive experiments and ablative studies have been conducted,
demonstrating the effectiveness of modeling ASCK for early action prediction.
With the proposed ASCNet, we have achieved state-of-the-art performance on two
benchmarks. The code will be released if the paper is accepted.

This paper aims for the task of text-to-video retrieval, where given a query
in the form of a natural-language sentence, it is asked to retrieve videos
which are semantically relevant to the given query, from a great number of
unlabeled videos. The success of this task depends on cross-modal
representation learning that projects both videos and sentences into common
spaces for semantic similarity computation. In this work, we concentrate on
video representation learning, an essential component for text-to-video
retrieval. Inspired by the reading strategy of humans, we propose a
Reading-strategy Inspired Visual Representation Learning (RIVRL) to represent
videos, which consists of two branches: a previewing branch and an
intensive-reading branch. The previewing branch is designed to briefly capture
the overview information of videos, while the intensive-reading branch is
designed to obtain more in-depth information. Moreover, the intensive-reading
branch is aware of the video overview captured by the previewing branch. Such
holistic information is found to be useful for the intensive-reading branch to
extract more fine-grained features. Extensive experiments on three datasets are
conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and
VATEX. Moreover, on MSR-VTT, our model using two video features shows
comparable performance to the state-of-the-art using seven video features and
even outperforms models pre-trained on the large-scale HowTo100M dataset.

In this paper, we focus on X-ray images of paintings with concealed
sub-surface designs (e.g., deriving from reuse of the painting support or
revision of a composition by the artist), which include contributions from both
the surface painting and the concealed features. In particular, we propose a
self-supervised deep learning-based image separation approach that can be
applied to the X-ray images from such paintings to separate them into two
hypothetical X-ray images. One of these reconstructed images is related to the
X-ray image of the concealed painting, while the second one contains only
information related to the X-ray of the visible painting. The proposed
separation network consists of two components: the analysis and the synthesis
sub-networks. The analysis sub-network is based on learned coupled iterative
shrinkage thresholding algorithms (LCISTA) designed using algorithm unrolling
techniques, and the synthesis sub-network consists of several linear mappings.
The learning algorithm operates in a totally self-supervised fashion without
requiring a sample set that contains both the mixed X-ray images and the
separated ones. The proposed method is demonstrated on a real painting with
concealed content, Do\~na Isabel de Porcel by Francisco de Goya, to show its

Physical computing infrastructure, data gathering, and algorithms have
recently had significant advances to extract information from images and
videos. The growth has been especially outstanding in image captioning and
video captioning. However, most of the advancements in video captioning still
take place in short videos. In this research, we caption longer videos only by
using the keyframes, which are a small subset of the total video frames.
Instead of processing thousands of frames, only a few frames are processed
depending on the number of keyframes. There is a trade-off between the
computation of many frames and the speed of the captioning process. The
approach in this research is to allow the user to specify the trade-off between
execution time and accuracy. In addition, we argue that linking images, videos,
and natural language offers many practical benefits and immediate practical
applications. From the modeling perspective, instead of designing and staging
explicit algorithms to process videos and generate captions in complex
processing pipelines, our contribution lies in designing hybrid deep learning
architectures to apply in long videos by captioning video keyframes. We
consider the technology and the methodology that we have developed as steps
toward the applications discussed in this research.

Generative Adversarial Networks (GANs) are machine learning methods that are
used in many important and novel applications. For example, in imaging science,
GANs are effectively utilized in generating image datasets, photographs of
human faces, image and video captioning, image-to-image translation,
text-to-image translation, video prediction, and 3D object generation to name a
few. In this paper, we discuss how GANs can be used to create an artificial
world. More specifically, we discuss how GANs help to describe an image
utilizing image/video captioning methods and how to translate the image to a
new image using image-to-image translation frameworks in a theme we desire. We
articulate how GANs impact creating a customized world.

Automated hiring systems are among the fastest-developing of all high-stakes
AI systems. Among these are algorithmic personality tests that use insights
from psychometric testing, and promise to surface personality traits indicative
of future success based on job seekers' resumes or social media profiles. We
interrogate the validity of such systems using stability of the outputs they
produce, noting that reliability is a necessary, but not a sufficient,
condition for validity. Our approach is to (a) develop a methodology for an
external audit of stability of predictions made by algorithmic personality
tests, and (b) instantiate this methodology in an audit of two systems,
Humantic AI and Crystal. Crucially, rather than challenging or affirming the
assumptions made in psychometric testing -- that personality is a meaningful
and measurable construct, and that personality traits are indicative of future
success on the job -- we frame our methodology around testing the underlying
assumptions made by the vendors of the algorithmic personality tests
  Our main contribution is the development of a socio-technical framework for
auditing the stability of algorithmic systems. This contribution is
supplemented with an open-source software library that implements the technical
components of the audit, and can be used to conduct similar stability audits of
algorithmic systems. We instantiate our framework with the audit of two
real-world personality prediction systems, namely Humantic AI and Crystal. The
application of our audit framework demonstrates that both these systems show
substantial instability with respect to key facets of measurement, and hence
cannot be considered valid testing instruments.

The inclusion of cognitive processes, such as perception, learning and
memory, are inevitable in mechanistic animal movement modelling. Cognition is
the unique feature that distinguishes animal movement from mere particle
movement in chemistry or physics. Hence, it is essential to incorporate such
knowledge-based processes into animal movement models. We summarize popular
deterministic mathematical models derived from first principles and their
rigorous analyses. Mathematical rules of thumb will be provided to judge the
model rationality. We will briefly present available mathematical techniques
and introduce useful measures of success to compare and contrast the possible
outcomes. Throughout the review, we propose future mathematical challenges and
development in the form of open questions. This topic is timely and
cutting-edge in applied mathematics with many intriguing directions to be
explored.

Recently Reinforcement Learning (RL) has been applied as an anti-adversarial
remedy in wireless communication networks. However, studying the RL-based
approaches from the adversary's perspective has received little attention.
Additionally, RL-based approaches in an anti-adversary or adversarial paradigm
mostly consider single-channel communication (either channel selection or
single channel power control), while multi-channel communication is more common
in practice. In this paper, we propose a multi-agent adversary system (MAAS)
for modeling and analyzing adversaries in a wireless communication scenario by
careful design of the reward function under realistic communication scenarios.
In particular, by modeling the adversaries as learning agents, we show that the
proposed MAAS is able to successfully choose the transmitted channel(s) and
their respective allocated power(s) without any prior knowledge of the sender
strategy. Compared to the single-agent adversary (SAA), multi-agents in MAAS
can achieve significant reduction in signal-to-noise ratio (SINR) under the
same power constraints and partial observability, while providing improved
stability and a more efficient learning process. Moreover, through empirical
studies we show that the results in simulation are close to the ones in
communication in reality, a conclusion that is pivotal to the validity of
performance of agents evaluated in simulations.

In conversational question answering, systems must correctly interpret the
interconnected interactions and generate knowledgeable answers, which may
require the retrieval of relevant information from a background repository.
Recent approaches to this problem leverage neural language models, although
different alternatives can be considered in terms of modules for (a)
representing user questions in context, (b) retrieving the relevant background
information, and (c) generating the answer. This work presents a conversational
question answering system designed specifically for the Search-Oriented
Conversational AI (SCAI) shared task, and reports on a detailed analysis of its
question rewriting module. In particular, we considered different variations of
the question rewriting module to evaluate the influence on the subsequent
components, and performed a careful analysis of the results obtained with the
best system configuration. Our system achieved the best performance in the
shared task and our analysis emphasizes the importance of the conversation
context representation for the overall system performance.

We propose GLassoformer, a novel and efficient transformer architecture
leveraging group Lasso regularization to reduce the number of queries of the
standard self-attention mechanism. Due to the sparsified queries, GLassoformer
is more computationally efficient than the standard transformers. On the power
grid post-fault voltage prediction task, GLassoformer shows remarkably better
prediction than many existing benchmark algorithms in terms of accuracy and

Even though convolutional neural networks can classify objects in images very
accurately, it is well known that the attention of the network may not always
be on the semantically important regions of the scene. It has been observed
that networks often learn background textures which are not relevant to the
object of interest. In turn this makes the networks susceptible to variations
and changes in the background which negatively affect their performance. We
propose a new two-step training procedure called split training to reduce this
bias in CNNs on both Infrared imagery and RGB data. Our split training
procedure has two steps: using MSE loss first train the layers of the network
on images with background to match the activations of the same network when it
is trained using images without background; then with these layers frozen,
train the rest of the network with cross-entropy loss to classify the objects.
Our training method outperforms the traditional training procedure in both a
simple CNN architecture, and deep CNNs like VGG and Densenet which use lots of
hardware resources, and learns to mimic human vision which focuses more on
shape and structure than background with higher accuracy.

Neural networks have shown great promises in planning, control, and general
decision making for learning-enabled cyber-physical systems (LE-CPSs),
especially in improving performance under complex scenarios. However, it is
very challenging to formally analyze the behavior of neural network based
planners for ensuring system safety, which significantly impedes their
applications in safety-critical domains such as autonomous driving. In this
work, we propose a hierarchical neural network based planner that analyzes the
underlying physical scenarios of the system and learns a system-level behavior
planning scheme with multiple scenario-specific motion-planning strategies. We
then develop an efficient verification method that incorporates
overapproximation of the system state reachable set and novel partition and
union techniques for formally ensuring system safety under our physics-aware
planner. With theoretical analysis, we show that considering the different
physical scenarios and building a hierarchical planner based on such analysis
may improve system safety and verifiability. We also empirically demonstrate
the effectiveness of our approach and its advantage over other baselines in
practical case studies of unprotected left turn and highway merging, two common
challenging safety-critical tasks in autonomous driving.

It is critical to obtain high resolution features with long range dependency
for dense prediction tasks such as semantic segmentation. To generate
high-resolution output of size $H\times W$ from a low-resolution feature map of
size $h\times w$ ($hw\ll HW$), a naive dense transformer incurs an intractable
complexity of $\mathcal{O}(hwHW)$, limiting its application on high-resolution
dense prediction. We propose a Dual-Flattening Transformer (DFlatFormer) to
enable high-resolution output by reducing complexity to $\mathcal{O}(hw(H+W))$
that is multiple orders of magnitude smaller than the naive dense transformer.
Decomposed queries are presented to retrieve row and column attentions
tractably through separate transformers, and their outputs are combined to form
a dense feature map at high resolution. To this end, the input sequence fed
from an encoder is row-wise and column-wise flattened to align with decomposed
queries by preserving their row and column structures, respectively. Row and
column transformers also interact with each other to capture their mutual
attentions with the spatial crossings between rows and columns. We also propose
to perform attentions through efficient grouping and pooling to further reduce
the model complexity. Extensive experiments on ADE20K and Cityscapes datasets
demonstrate the superiority of the proposed dual-flattening transformer
architecture with higher mIoUs.

The quality of learning generally improves with the scale and diversity of
data. Companies and institutions can therefore benefit from building models
over shared data. Many cloud and blockchain platforms, as well as government
initiatives, are interested in providing this type of service.
  These cooperative efforts face a challenge, which we call ``exclusivity
attacks''. A firm can share distorted data, so that it learns the best model
fit, but is also able to mislead others. We study protocols for long-term
interactions and their vulnerability to these attacks, in particular for
regression and clustering tasks. We conclude that the choice of protocol, as
well as the number of Sybil identities an attacker may control, is material to

Eye movements have long been studied as a window into the attentional
mechanisms of the human brain and made accessible as novelty style
human-machine interfaces. However, not everything that we gaze upon, is
something we want to interact with; this is known as the Midas Touch problem
for gaze interfaces. To overcome the Midas Touch problem, present interfaces
tend not to rely on natural gaze cues, but rather use dwell time or gaze
gestures. Here we present an entirely data-driven approach to decode human
intention for object manipulation tasks based solely on natural gaze cues. We
run data collection experiments where 16 participants are given manipulation
and inspection tasks to be performed on various objects on a table in front of
them. The subjects' eye movements are recorded using wearable eye-trackers
allowing the participants to freely move their head and gaze upon the scene. We
use our Semantic Fovea, a convolutional neural network model to obtain the
objects in the scene and their relation to gaze traces at every frame. We then
evaluate the data and examine several ways to model the classification task for
intention prediction. Our evaluation shows that intention prediction is not a
naive result of the data, but rather relies on non-linear temporal processing
of gaze cues. We model the task as a time series classification problem and
design a bidirectional Long-Short-Term-Memory (LSTM) network architecture to
decode intentions. Our results show that we can decode human intention of
motion purely from natural gaze cues and object relative position, with
$91.9\%$ accuracy. Our work demonstrates the feasibility of natural gaze as a
Zero-UI interface for human-machine interaction, i.e., users will only need to
act naturally, and do not need to interact with the interface itself or deviate
from their natural eye movement patterns.

We propose a series of data-centric heuristics for improving the performance
of machine learning systems when applied to problems in quantum information
science. In particular, we consider how systematic engineering of training sets
can significantly enhance the accuracy of pre-trained neural networks used for
quantum state reconstruction without altering the underlying architecture. We
find that it is not always optimal to engineer training sets to exactly match
the expected distribution of a target scenario, and instead, performance can be
further improved by biasing the training set to be slightly more mixed than the
target. This is due to the heterogeneity in the number of free variables
required to describe states of different purity, and as a result, overall
accuracy of the network improves when training sets of a fixed size focus on
states with the least constrained free variables. For further clarity, we also
include a "toy model" demonstration of how spurious correlations can
inadvertently enter synthetic data sets used for training, how the performance
of systems trained with these correlations can degrade dramatically, and how
the inclusion of even relatively few counterexamples can effectively remedy
such problems.

X-ray computed tomographic infrastructures are medical imaging modalities
that rely on the acquisition of rays crossing examined objects while measuring
their intensity decrease. Physical measurements are post-processed by
mathematical reconstruction algorithms that may offer weaker or top-notch
consistency guarantees on the computed volumetric field. Superior results are
provided on the account of an abundance of low-noise measurements being
supplied. Nonetheless, such a scanning process would expose the examined body
to an undesirably large-intensity and long-lasting ionising radiation, imposing
severe health risks. One main objective of the ongoing research is the
reduction of the number of projections while keeping the quality performance
stable. Due to the under-sampling, the noise occurring inherently because of
photon-electron interactions is now supplemented by reconstruction artifacts.
Recently, deep learning methods, especially fully convolutional networks have
been extensively investigated and proven to be efficient in filtering such
deviations. In this report algorithms are presented that take as input a slice
of a low-quality reconstruction of the volume in question and aim to map it to
the reconstruction that is considered ideal, the ground truth. Above that, the
first system comprises two additional elements: firstly, it ensures the
consistency with the measured sinogram, secondly it adheres to constraints
proposed in classical compressive sampling theory. The second one, inspired by
classical ways of solving the inverse problem of reconstruction, takes an
iterative approach to regularise the hypothesis in the direction of the correct
result.

Death by suicide is the seventh leading death cause worldwide. The recent
advancement in Artificial Intelligence (AI), specifically AI applications in
image and voice processing, has created a promising opportunity to
revolutionize suicide risk assessment. Subsequently, we have witnessed
fast-growing literature of research that applies AI to extract audiovisual
non-verbal cues for mental illness assessment. However, the majority of the
recent works focus on depression, despite the evident difference between
depression symptoms and suicidal behavior and non-verbal cues. This paper
reviews recent works that study suicide ideation and suicide behavior detection
through audiovisual feature analysis, mainly suicidal voice/speech acoustic
features analysis and suicidal visual cues. Automatic suicide assessment is a
promising research direction that is still in the early stages. Accordingly,
there is a lack of large datasets that can be used to train machine learning
and deep learning models proven to be effective in other, similar tasks.

Controllable text generation concerns two fundamental tasks of wide
applications, namely generating text of given attributes (i.e.,
attribute-conditional generation), and minimally editing existing text to
possess desired attributes (i.e., text attribute transfer). Extensive prior
work has largely studied the two problems separately, and developed different
conditional models which, however, are prone to producing biased text (e.g.,
various gender stereotypes). This paper proposes to formulate controllable text
generation from a principled causal perspective which models the two tasks with
a unified framework. A direct advantage of the causal formulation is the use of
rich causality tools to mitigate generation biases and improve control. We
treat the two tasks as interventional and counterfactual causal inference based
on a structural causal model, respectively. We then apply the framework to the
challenging practical setting where confounding factors (that induce spurious
correlations) are observable only on a small fraction of data. Experiments show
significant superiority of the causal approach over previous conditional models
for improved control accuracy and reduced bias.

Biosensors are an essential tool for medical diagnostics, environmental
monitoring and food safety. Typically, biosensors are designed to detect
specific analytes through functionalization with the appropriate capture
agents. However, the use of capture agents limits the number of analytes that
can be simultaneously detected and reduces the robustness of the biosensor. In
this work, we report a versatile, capture agent free biosensor platform based
on an array of porous silicon (PSi) thin films, which has the potential to
robustly detect a wide variety of analytes based on their physical and chemical
properties in the nanoscale porous media. The ability of this system to
reproducibly classify, quantify, and discriminate three proteins is
demonstrated to concentrations down to at least 0.02g/L (between 300nM and
450nM) by utilizing PSi array elements with a unique combination of pore size
and buffer pH, employing linear discriminant analysis for dimensionality
reduction, and using support vector machines as a classifier. This approach
represents a significant step towards a low cost, simple and robust biosensor
platform that is able to detect a vast range of biomolecules.

Graph-based next-step prediction models have recently been very successful in
modeling complex high-dimensional physical systems on irregular meshes.
However, due to their short temporal attention span, these models suffer from
error accumulation and drift. In this paper, we propose a new method that
captures long-term dependencies through a transformer-style temporal attention
model. We introduce an encoder-decoder structure to summarize features and
create a compact mesh representation of the system state, to allow the temporal
model to operate on a low-dimensional mesh representations in a memory
efficient manner. Our method outperforms a competitive GNN baseline on several
complex fluid dynamics prediction tasks, from sonic shocks to vascular flow. We
demonstrate stable rollouts without the need for training noise and show
perfectly phase-stable predictions even for very long sequences. More broadly,
we believe our approach paves the way to bringing the benefits of
attention-based sequence models to solving high-dimensional complex physics
tasks.

Natural policy gradient methods are popular reinforcement learning methods
that improve the stability of policy gradient methods by utilizing second-order
approximations to precondition the gradient with the inverse of the
Fisher-information matrix. However, to the best of the authors' knowledge,
there has not been a study that has investigated the effects of different
second-order approximations in a comprehensive and systematic manner. To
address this, five different second-order approximations were studied and
compared across multiple key metrics including performance, stability, sample
efficiency, and computation time. Furthermore, hyperparameters which aren't
typically acknowledged in the literature are studied including the effect of
different batch sizes and optimizing the critic network with the natural
gradient. Experimental results show that on average, improved second-order
approximations achieve the best performance and that using properly tuned
hyperparameters can lead to large improvements in performance and sample
efficiency ranging up to +181%. We also make the code in this study available
at https://github.com/gebob19/natural-policy-gradient-reinforcement-learning.

Weather Forecasting is an attractive challengeable task due to its influence
on human life and complexity in atmospheric motion. Supported by massive
historical observed time series data, the task is suitable for data-driven
approaches, especially deep neural networks. Recently, the Graph Neural
Networks (GNNs) based methods have achieved excellent performance for
spatio-temporal forecasting. However, the canonical GNNs-based methods only
individually model the local graph of meteorological variables per station or
the global graph of whole stations, lacking information interaction between
meteorological variables in different stations. In this paper, we propose a
novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model
cross-regional spatio-temporal correlations among meteorological variables in
multiple stations. An adaptive graph learning layer and spatial graph
convolution are employed to construct self-learning graph and study hidden
dependency among nodes of variable-level and station-level graph. For capturing
temporal pattern, the dilated inception as the backbone of gate temporal
convolution is designed to model long and various meteorological trends.
Moreover, a dynamic interaction learning is proposed to build bidirectional
information passing in hierarchical graph. Experimental results on three
real-world meteorological datasets demonstrate the superior performance of
HiSTGNN beyond 7 baselines and it reduces the errors by 4.2% to 11.6%
especially compared to state-of-the-art weather forecasting method.

The face mask is an essential sanitaryware in daily lives growing during the
pandemic period and is a big threat to current face recognition systems. The
masks destroy a lot of details in a large area of face, and it makes it
difficult to recognize them even for humans. The evaluation report shows the
difficulty well when recognizing masked faces. Rapid development and
breakthrough of deep learning in the recent past have witnessed most promising
results from face recognition algorithms. But they fail to perform far from
satisfactory levels in the unconstrained environment during the challenges such
as varying lighting conditions, low resolution, facial expressions, pose
variation and occlusions. Facial occlusions are considered one of the most
intractable problems. Especially when the occlusion occupies a large region of
the face because it destroys lots of official features.

The framework of quantitative equational logic has been successfully applied
to reason about algebras whose carriers are metric spaces and operations are
nonexpansive. We extend this framework in two orthogonal directions: algebras
endowed with generalised metric space structures, and operations being
nonexpansive up to a lifting. We apply our results to the algebraic
axiomatisation of the {\L}ukaszyk--Karmowski distance on probability
distributions, which has recently found application in the field of
representation learning on Markov processes.

Graph representation learning has demonstrated improved performance in tasks
such as link prediction and node classification across a range of domains.
Research has shown that many natural graphs can be organized in hierarchical
communities, leading to approaches that use these communities to improve the
quality of node representations. However, these approaches do not take
advantage of the learned representations to also improve the quality of the
discovered communities and establish an iterative and joint optimization of
representation learning and community discovery. In this work, we present Mazi,
an algorithm that jointly learns the hierarchical community structure and the
node representations of the graph in an unsupervised fashion. To account for
the structure in the node representations, Mazi generates node representations
at each level of the hierarchy, and utilizes them to influence the node
representations of the original graph. Further, the communities at each level
are discovered by simultaneously maximizing the modularity metric and
minimizing the distance between the representations of a node and its
community. Using multi-label node classification and link prediction tasks, we
evaluate our method on a variety of synthetic and real-world graphs and
demonstrate that Mazi outperforms other hierarchical and non-hierarchical
methods.

Robust subspace recovery (RSR) is a fundamental problem in robust
representation learning. Here we focus on a recently proposed RSR method termed
Dual Principal Component Pursuit (DPCP) approach, which aims to recover a basis
of the orthogonal complement of the subspace and is amenable to handling
subspaces of high relative dimension. Prior work has shown that DPCP can
provably recover the correct subspace in the presence of outliers, as long as
the true dimension of the subspace is known. We show that DPCP can provably
solve RSR problems in the {\it unknown} subspace dimension regime, as long as
orthogonality constraints -- adopted in previous DPCP formulations -- are
relaxed and random initialization is used instead of spectral one. Namely, we
propose a very simple algorithm based on running multiple instances of a
projected sub-gradient descent method (PSGM), with each problem instance
seeking to find one vector in the null space of the subspace. We theoretically
prove that under mild conditions this approach will succeed with high
probability. In particular, we show that 1) all of the problem instances will
converge to a vector in the nullspace of the subspace and 2) the ensemble of
problem instance solutions will be sufficiently diverse to fully span the
nullspace of the subspace thus also revealing its true unknown codimension. We
provide empirical results that corroborate our theoretical results and showcase
the remarkable implicit rank regularization behavior of PSGM algorithm that
allows us to perform RSR without being aware of the subspace dimension.

Morphological attributes from histopathological images and molecular profiles
from genomic data are important information to drive diagnosis, prognosis, and
therapy of cancers. By integrating these heterogeneous but complementary data,
many multi-modal methods are proposed to study the complex mechanisms of
cancers, and most of them achieve comparable or better results from previous
single-modal methods. However, these multi-modal methods are restricted to a
single task (e.g., survival analysis or grade classification), and thus neglect
the correlation between different tasks. In this study, we present a
multi-modal fusion framework based on multi-task correlation learning
(MultiCoFusion) for survival analysis and cancer grade classification, which
combines the power of multiple modalities and multiple tasks. Specifically, a
pre-trained ResNet-152 and a sparse graph convolutional network (SGCN) are used
to learn the representations of histopathological images and mRNA expression
data respectively. Then these representations are fused by a fully connected
neural network (FCNN), which is also a multi-task shared network. Finally, the
results of survival analysis and cancer grade classification output
simultaneously. The framework is trained by an alternate scheme. We
systematically evaluate our framework using glioma datasets from The Cancer
Genome Atlas (TCGA). Results demonstrate that MultiCoFusion learns better
representations than traditional feature extraction methods. With the help of
multi-task alternating learning, even simple multi-modal concatenation can
achieve better performance than other deep learning and traditional methods.
Multi-task learning can improve the performance of multiple tasks not just one
of them, and it is effective in both single-modal and multi-modal data.

Location based services, already popular with end users, are now inevitably
becoming part of new wireless infrastructures and emerging business processes.
The increasingly popular Deep Learning (DL) artificial intelligence methods
perform very well in wireless fingerprinting localization based on extensive
indoor radio measurement data. However, with the increasing complexity these
methods become computationally very intensive and energy hungry, both for their
training and subsequent operation. Considering only mobile users, estimated to
exceed 7.4billion by the end of 2025, and assuming that the networks serving
these users will need to perform only one localization per user per hour on
average, the machine learning models used for the calculation would need to
perform 65*10^12 predictions per year. Add to this equation tens of billions of
other connected devices and applications that rely heavily on more frequent
location updates, and it becomes apparent that localization will contribute
significantly to carbon emissions unless more energy-efficient models are
developed and used. This motivated our work on a new DL-based architecture for
indoor localization that is more energy efficient compared to related
state-of-the-art approaches while showing only marginal performance
degradation. A detailed performance evaluation shows that the proposed model
producesonly 58 % of the carbon footprint while maintaining 98.7 % of the
overall performance compared to state of the art model external to our group.
Additionally, we elaborate on a methodology to calculate the complexity of the
DL model and thus the CO2 footprint during its training and operation.

This paper proposes to study neural networks through neuronal correlation, a
statistical measure of correlated neuronal activity on the penultimate layer.
We show that neuronal correlation can be efficiently estimated via weight
matrix, can be effectively enforced through layer structure, and is a strong
indicator of generalisation ability of the network. More importantly, we show
that neuronal correlation significantly impacts on the accuracy of entropy
estimation in high-dimensional hidden spaces. While previous estimation methods
may be subject to significant inaccuracy due to implicit assumption on neuronal
independence, we present a novel computational method to have an efficient and
authentic computation of entropy, by taking into consideration the neuronal
correlation. In doing so, we install neuronal correlation as a central concept
of neural network.

With rising uncertainty in the real world, online Reinforcement Learning (RL)
has been receiving increasing attention due to its fast learning capability and
improving data efficiency. However, online RL often suffers from complex Value
Function Approximation (VFA) and catastrophic interference, creating difficulty
for the deep neural network to be applied to an online RL algorithm in a fully
online setting. Therefore, a simpler and more adaptive approach is introduced
to evaluate value function with the kernel-based model. Sparse representations
are superior at handling interference, indicating that competitive sparse
representations should be learnable, non-prior, non-truncated and explicit when
compared with current sparse representation methods. Moreover, in learning
sparse representations, attention mechanisms are utilized to represent the
degree of sparsification, and a smooth attentive function is introduced into
the kernel-based VFA. In this paper, we propose an Online Attentive
Kernel-Based Temporal Difference (OAKTD) algorithm using two-timescale
optimization and provide convergence analysis of our proposed algorithm.
Experimental evaluations showed that OAKTD outperformed several Online
Kernel-based Temporal Difference (OKTD) learning algorithms in addition to the
Temporal Difference (TD) learning algorithm with Tile Coding on public Mountain
Car, Acrobot, CartPole and Puddle World tasks.

Facial expressions are a form of non-verbal communication that humans perform
seamlessly for meaningful transfer of information. Most of the literature
addresses the facial expression recognition aspect however, with the advent of
Generative Models, it has become possible to explore the affect space in
addition to mere classification of a set of expressions. In this article, we
propose a generative model architecture which robustly generates a set of
facial expressions for multiple character identities and explores the
possibilities of generating complex expressions by combining the simple ones.

Magnesium (Mg) alloys have shown great prospects as both structural and
biomedical materials, while poor corrosion resistance limits their further
application. In this work, to avoid the time-consuming and laborious experiment
trial, a high-throughput computational strategy based on first-principles
calculations is designed for screening corrosion-resistant binary Mg alloy with
intermetallics, from both the thermodynamic and kinetic perspectives. The
stable binary Mg intermetallics with low equilibrium potential difference with
respect to the Mg matrix are firstly identified. Then, the hydrogen adsorption
energies on the surfaces of these Mg intermetallics are calculated, and the
corrosion exchange current density is further calculated by a hydrogen
evolution reaction (HER) kinetic model. Several intermetallics, e.g. Y3Mg, Y2Mg
and La5Mg, are identified to be promising intermetallics which might
effectively hinder the cathodic HER. Furthermore, machine learning (ML) models
are developed to predict Mg intermetallics with proper hydrogen adsorption
energy employing work function (W_f) and weighted first ionization energy
(WFIE). The generalization of the ML models is tested on five new binary Mg
intermetallics with the average root mean square error (RMSE) of 0.11 eV. This
study not only predicts some promising binary Mg intermetallics which may
suppress the galvanic corrosion, but also provides a high-throughput screening
strategy and ML models for the design of corrosion-resistant alloy, which can
be extended to ternary Mg alloys or other alloy systems.

Clustering is one of the most common tasks of Machine Learning. In this paper
we examine how ideas from topology can be used to improve clustering

Counterfactual explanations (CEs) are a powerful means for understanding how
decisions made by algorithms can be changed. Researchers have proposed a number
of desiderata that CEs should meet to be practically useful, such as requiring
minimal effort to enact, or complying with causal models. We consider a further
aspect to improve the usability of CEs: robustness to adverse perturbations,
which may naturally happen due to unfortunate circumstances. Since CEs
typically prescribe a sparse form of intervention (i.e., only a subset of the
features should be changed), we study the effect of addressing robustness
separately for the features that are recommended to be changed and those that
are not. Our definitions are workable in that they can be incorporated as
penalty terms in the loss functions that are used for discovering CEs. To
experiment with robustness, we create and release code where five data sets
(commonly used in the field of fair and explainable machine learning) have been
enriched with feature-specific annotations that can be used to sample
meaningful perturbations. Our experiments show that CEs are often not robust
and, if adverse perturbations take place (even if not worst-case), the
intervention they prescribe may require a much larger cost than anticipated, or
even become impossible. However, accounting for robustness in the search
process, which can be done rather easily, allows discovering robust CEs
systematically. Robust CEs make additional intervention to contrast
perturbations much less costly than non-robust CEs. We also find that
robustness is easier to achieve for the features to change, posing an important
point of consideration for the choice of what counterfactual explanation is
best for the user. Our code is available at:
https://github.com/marcovirgolin/robust-counterfactuals.

Federated learning makes it possible for all parties with data isolation to
train the model collaboratively and efficiently while satisfying privacy
protection. To obtain a high-quality model, an incentive mechanism is necessary
to motivate more high-quality workers with data and computing power. The
existing incentive mechanisms are applied in offline scenarios, where the task
publisher collects all bids and selects workers before the task. However, it is
practical that different workers arrive online in different orders before or
during the task. Therefore, we propose a reverse auction-based online incentive
mechanism for horizontal federated learning with budget constraint. Workers
submit bids when they arrive online. The task publisher with a limited budget
leverages the information of the arrived workers to decide on whether to select
the new worker. Theoretical analysis proves that our mechanism satisfies budget
feasibility, computational efficiency, individual rationality, consumer
sovereignty, time truthfulness, and cost truthfulness with a sufficient budget.
The experimental results show that our online mechanism is efficient and can
obtain high-quality models.

Stochastic gradient descent ascent (SGDA) and its variants have been the
workhorse for solving minimax problems. However, in contrast to the
well-studied stochastic gradient descent (SGD) with differential privacy (DP)
constraints, there is little work on understanding the generalization (utility)
of SGDA with DP constraints. In this paper, we use the algorithmic stability
approach to establish the generalization (utility) of DP-SGDA in different
settings. In particular, for the convex-concave setting, we prove that the
DP-SGDA can achieve an optimal utility rate in terms of the weak primal-dual
population risk in both smooth and non-smooth cases. To our best knowledge,
this is the first-ever-known result for DP-SGDA in the non-smooth case. We
further provide its utility analysis in the nonconvex-strongly-concave setting
which is the first-ever-known result in terms of the primal population risk.
The convergence and generalization results for this nonconvex setting are new
even in the non-private setting. Finally, numerical experiments are conducted
to demonstrate the effectiveness of DP-SGDA for both convex and nonconvex
cases.

Several performance measures can be used for evaluating classification
results: accuracy, F-measure, and many others. Can we say that some of them are
better than others, or, ideally, choose one measure that is best in all
situations? To answer this question, we conduct a systematic analysis of
classification performance measures: we formally define a list of desirable
properties and theoretically analyze which measures satisfy which properties.
We also prove an impossibility theorem: some desirable properties cannot be
simultaneously satisfied. Finally, we propose a new family of measures
satisfying all desirable properties except one. This family includes the
Matthews Correlation Coefficient and a so-called Symmetric Balanced Accuracy
that was not previously used in classification literature. We believe that our
systematic approach gives an important tool to practitioners for adequately
evaluating classification results.

Automatic classification of diabetic retinopathy from retinal images has been
widely studied using deep neural networks with impressive results. However,
there is a clinical need for estimation of the uncertainty in the
classifications, a shortcoming of modern neural networks. Recently, approximate
Bayesian deep learning methods have been proposed for the task but the studies
have only considered the binary referable/non-referable diabetic retinopathy
classification applied to benchmark datasets. We present novel results by
systematically investigating a clinical dataset and a clinically relevant
5-class classification scheme, in addition to benchmark datasets and the binary
classification scheme. Moreover, we derive a connection between uncertainty
measures and classifier risk, from which we develop a new uncertainty measure.
We observe that the previously proposed entropy-based uncertainty measure
generalizes to the clinical dataset on the binary classification scheme but not
on the 5-class scheme, whereas our new uncertainty measure generalizes to the
latter case.

In computer vision, data shift has proven to be a major barrier for safe and
robust deep learning applications. In medical applications, histopathological
images are often associated with data shift and they are hardly available. It
is important to understand to what extent a model can be made robust against
data shift using all available data. Here, we first show that domain
adversarial methods can be very deleterious if they are wrongly used. We then
use domain adversarial methods to transfer data shift invariance from one
dataset to another dataset with different semantics and show that domain
adversarial methods are efficient inter-semantically with similar performance
than intra-semantical domain adversarial methods.

Structural matrix-variate observations routinely arise in diverse fields such
as multi-layer network analysis and brain image clustering. While data of this
type have been extensively investigated with fruitful outcomes being delivered,
the fundamental questions like its statistical optimality and computational
limit are largely under-explored. In this paper, we propose a low-rank Gaussian
mixture model (LrMM) assuming each matrix-valued observation has a planted
low-rank structure. Minimax lower bounds for estimating the underlying low-rank
matrix are established allowing a whole range of sample sizes and signal
strength. Under a minimal condition on signal strength, referred to as the
information-theoretical limit or statistical limit, we prove the minimax
optimality of a maximum likelihood estimator which, in general, is
computationally infeasible. If the signal is stronger than a certain threshold,
called the computational limit, we design a computationally fast estimator
based on spectral aggregation and demonstrate its minimax optimality. Moreover,
when the signal strength is smaller than the computational limit, we provide
evidences based on the low-degree likelihood ratio framework to claim that no
polynomial-time algorithm can consistently recover the underlying low-rank
matrix. Our results reveal multiple phase transitions in the minimax error
rates and the statistical-to-computational gap. Numerical experiments confirm
our theoretical findings. We further showcase the merit of our spectral
aggregation method on the worldwide food trading dataset.

Various neural network-based approaches have been proposed for more robust
and accurate voice activity detection (VAD). Manual design of such neural
architectures is an error-prone and time-consuming process, which prompted the
development of neural architecture search (NAS) that automatically design and
optimize network architectures. While NAS has been successfully applied to
improve performance in a variety of tasks, it has not yet been exploited in the
VAD domain. In this paper, we present the first work that utilizes NAS
approaches on the VAD task. To effectively search architectures for the VAD
task, we propose a modified macro structure and a new search space with a much
broader range of operations that includes attention operations. The results
show that the network structures found by the propose NAS framework outperform
previous manually designed state-of-the-art VAD models in various noise-added
and real-world-recorded datasets. We also show that the architectures searched
on a particular dataset achieve improved generalization performance on unseen
audio datasets. Our code and models are available at
https://github.com/daniel03c1/NAS_VAD.

Existing image-based rendering methods usually adopt depth-based image
warping operation to synthesize novel views. In this paper, we reason the
essential limitations of the traditional warping operation to be the limited
neighborhood and only distance-based interpolation weights. To this end, we
propose content-aware warping, which adaptively learns the interpolation
weights for pixels of a relatively large neighborhood from their contextual
information via a lightweight neural network. Based on this learnable warping
module, we propose a new end-to-end learning-based framework for novel view
synthesis from a set of input source views, in which two additional modules,
namely confidence-based blending and feature-assistant spatial refinement, are
naturally proposed to handle the occlusion issue and capture the spatial
correlation among pixels of the synthesized view, respectively. Besides, we
also propose a weight-smoothness loss term to regularize the network.
Experimental results on light field datasets with wide baselines and multi-view
datasets show that the proposed method significantly outperforms
state-of-the-art methods both quantitatively and visually. The source code will
be publicly available at https://github.com/MantangGuo/CW4VS.

The goal of Knowledge Tracing (KT) is to estimate how well students have
mastered a concept based on their historical learning of related exercises. The
benefit of knowledge tracing is that students' learning plans can be better
organised and adjusted, and interventions can be made when necessary. With the
recent rise of deep learning, Deep Knowledge Tracing (DKT) has utilised
Recurrent Neural Networks (RNNs) to accomplish this task with some success.
Other works have attempted to introduce Graph Neural Networks (GNNs) and
redefine the task accordingly to achieve significant improvements. However,
these efforts suffer from at least one of the following drawbacks: 1) they pay
too much attention to details of the nodes rather than to high-level semantic
information; 2) they struggle to effectively establish spatial associations and
complex structures of the nodes; and 3) they represent either concepts or
exercises only, without integrating them. Inspired by recent advances in
self-supervised learning, we propose a Bi-Graph Contrastive Learning based
Knowledge Tracing (Bi-CLKT) to address these limitations. Specifically, we
design a two-layer contrastive learning scheme based on an
"exercise-to-exercise" (E2E) relational subgraph. It involves node-level
contrastive learning of subgraphs to obtain discriminative representations of
exercises, and graph-level contrastive learning to obtain discriminative
representations of concepts. Moreover, we designed a joint contrastive loss to
obtain better representations and hence better prediction performance. Also, we
explored two different variants, using RNN and memory-augmented neural networks
as the prediction layer for comparison to obtain better representations of
exercises and concepts respectively. Extensive experiments on four real-world
datasets show that the proposed Bi-CLKT and its variants outperform other

Language data and models demonstrate various types of bias, be it ethnic,
religious, gender, or socioeconomic. AI/NLP models, when trained on the
racially biased dataset, AI/NLP models instigate poor model explainability,
influence user experience during decision making and thus further magnifies
societal biases, raising profound ethical implications for society. The
motivation of the study is to investigate how AI systems imbibe bias from data
and produce unexplainable discriminatory outcomes and influence an individual's
articulateness of system outcome due to the presence of racial bias features in
datasets. The design of the experiment involves studying the counterfactual
impact of racial bias features present in language datasets and its associated
effect on the model outcome. A mixed research methodology is adopted to
investigate the cross implication of biased model outcome on user experience,
effect on decision-making through controlled lab experimentation. The findings
provide foundation support for correlating the implication of carry-over an
artificial intelligence model solving NLP task due to biased concept presented
in the dataset. Further, the research outcomes justify the negative influence
on users' persuasiveness that leads to alter the decision-making quotient of an
individual when trying to rely on the model outcome to act. The paper bridges
the gap across the harm caused in establishing poor customer trustworthiness
due to an inequitable system design and provides strong support for
researchers, policymakers, and data scientists to build responsible AI
frameworks within organizations.

This is a review on blind image deblurring. First, we formulate the blind
image deblurring problem and explain why it is challenging. Next, we bring some
psychological and cognitive studies on the way our human vision system deblurs.
Then, relying on several previous reviews, we discuss the topic of metrics and
datasets, which is non-trivial to blind deblurring. Finally, we introduce some
typical optimization-based methods and learning-based methods.

Artificial intelligence (AI)-driven zero-touch massive network slicing is
envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy
would be extended to the final consumer in the form of advanced digital
use-cases. In this paper, we propose a novel model-free deep reinforcement
learning (DRL) framework, called collaborative statistical Actor-Critic (CS-AC)
that enables a scalable and farsighted slice performance management in a
6G-like RAN scenario that is built upon mobile edge computing (MEC) and massive
multiple-input multiple-output (mMIMO). In this intent, the proposed CS-AC
targets the optimization of the latency cost under a long-term statistical
service-level agreement (SLA). In particular, we consider the Q-th delay
percentile SLA metric and enforce some slice-specific preset constraints on it.
Moreover, to implement distributed learners, we propose a developed variant of
soft Actor-Critic (SAC) with less hyperparameter sensitivity. Finally, we
present numerical results to showcase the gain of the adopted approach on our
built OpenAI-based network slicing environment and verify the performance in
terms of latency, SLA Q-th percentile, and time efficiency. To the best of our
knowledge, this is the first work that studies the feasibility of an AI-driven
approach for massive network slicing under statistical SLA.

Bone Marrow Transplant, a gradational rescue for a wide range of disorders
emanating from the bone marrow, is an efficacious surgical treatment. Several
risk factors, such as post-transplant illnesses, new malignancies, and even
organ damage, can impair long-term survival. Therefore, technologies like
Machine Learning are deployed for investigating the survival prediction of BMT
receivers along with the influences that limit their resilience. In this study,
an efficient survival classification model is presented in a comprehensive
manner, incorporating the Chi-squared feature selection method to address the
dimensionality problem and Hyper Parameter Optimization (HPO) to increase
accuracy. A synthetic dataset is generated by imputing the missing values,
transforming the data using dummy variable encoding, and compressing the
dataset from 59 features to the 11 most correlated features using Chi-squared
feature selection. The dataset was split into train and test sets at a ratio of
80:20, and the hyperparameters were optimized using Grid Search
Cross-Validation. Several supervised ML methods were trained in this regard,
like Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors,
Gradient Boosting Classifier, Ada Boost, and XG Boost. The simulations have
been performed for both the default and optimized hyperparameters by using the
original and reduced synthetic dataset. After ranking the features using the
Chi-squared test, it was observed that the top 11 features with HPO, resulted
in the same accuracy of prediction (94.73%) as the entire dataset with default
parameters. Moreover, this approach requires less time and resources for
predicting the survivability of children undergoing BMT. Hence, the proposed
approach may aid in the development of a computer-aided diagnostic system with
satisfactory accuracy and minimal computation time by utilizing medical data
records.

To harness the full potential of beyond 5G (B5G) communication systems,
zero-touch network slicing (NS) is viewed as a promising fully-automated
management and orchestration (MANO) system. This paper proposes a novel
knowledge plane (KP)-based MANO framework that accommodates and exploits recent
NS technologies and is termed KB5G. Specifically, we deliberate on algorithmic
innovation and artificial intelligence (AI) in KB5G. We invoke a continuous
model-free deep reinforcement learning (DRL) method to minimize energy
consumption and virtual network function (VNF) instantiation cost. We present a
novel Actor-Critic-based NS approach to stabilize learning called, twin-delayed
double-Q soft Actor-Critic (TDSAC) method. The TDSAC enables central unit (CU)
to learn continuously to accumulate the knowledge learned in the past to
minimize future NS costs. Finally, we present numerical results to showcase the
gain of the adopted approach and verify the performance in terms of energy
consumption, CPU utilization, and time efficiency.

Partial label learning (PLL) is an important problem that allows each
training example to be labeled with a coarse candidate set, which well suits
many real-world data annotation scenarios with label ambiguity. Despite the
promise, the performance of PLL often lags behind the supervised counterpart.
In this work, we bridge the gap by addressing two key research challenges in
PLL -- representation learning and label disambiguation -- in one coherent
framework. Specifically, our proposed framework PiCO consists of a contrastive
learning module along with a novel class prototype-based label disambiguation
algorithm. PiCO produces closely aligned representations for examples from the
same classes and facilitates label disambiguation. Theoretically, we show that
these two components are mutually beneficial, and can be rigorously justified
from an expectation-maximization (EM) algorithm perspective. Extensive
experiments demonstrate that PiCO significantly outperforms the current
state-of-the-art approaches in PLL and even achieves comparable results to
fully supervised learning. Code and data available:
https://github.com/hbzju/PiCO.

Popular node embedding methods such as DeepWalk follow the paradigm of
performing random walks on the graph, and then requiring each node to be
proximate to those appearing along with it. Though proved to be successful in
various tasks, this paradigm reduces a graph with topology to a set of
sequential sentences, thus omitting global information. To produce global-aware
node embeddings, we propose GlobalWalk, a biased random walk strategy that
favors nodes with similar semantics. Empirical evidence suggests GlobalWalk can
generally enhance global awareness of the generated embeddings.

Deep learning methods are notoriously data-hungry, which requires a large
number of labeled samples. Unfortunately, the large amount of interactive
sample labeling efforts has dramatically hindered the application of deep
learning methods, especially for 3D modeling tasks, which require heterogeneous
samples. To alleviate the work of data annotation for learned 3D modeling of
fa\c{c}ades, this paper proposed a semi-supervised adversarial recognition
strategy embedded in inverse procedural modeling. Beginning with textured LOD-2
(Level-of-Details) models, we use the classical convolutional neural networks
to recognize the types and estimate the parameters of windows from image
patches. The window types and parameters are then assembled into procedural
grammar. A simple procedural engine is built inside an existing 3D modeling
software, producing fine-grained window geometries. To obtain a useful model
from a few labeled samples, we leverage the generative adversarial network to
train the feature extractor in a semi-supervised manner. The adversarial
training strategy can also exploit unlabeled data to make the training phase
more stable. Experiments using publicly available fa\c{c}ade image datasets
reveal that the proposed training strategy can obtain about 10% improvement in
classification accuracy and 50% improvement in parameter estimation under the
same network structure. In addition, performance gains are more pronounced when
testing against unseen data featuring different fa\c{c}ade styles.

In recent years, deep learning has achieved significant success in the
Chinese word segmentation (CWS) task. Most of these methods improve the
performance of CWS by leveraging external information, e.g., words, sub-words,
syntax. However, existing approaches fail to effectively integrate the
multi-level linguistic information and also ignore the structural feature of
the external information. Therefore, in this paper, we proposed a framework to
improve CWS, named HGNSeg. It exploits multi-level external information
sufficiently with the pre-trained language model and heterogeneous graph neural
network. The experimental results on six benchmark datasets (e.g., Bakeoff
2005, Bakeoff 2008) validate that our approach can effectively improve the
performance of Chinese word segmentation. Importantly, in cross-domain
scenarios, our method also shows a strong ability to alleviate the
out-of-vocabulary (OOV) problem.

Thick, Charge Coupled Devices (CCDs) have recently been explored for applied
physics, such as nuclear explosion monitoring, and dark matter detection
purposes. When run in fully-depleted mode, these devices are sensitive
detectors for energy depositions by a variety of primary particles. In this
study we are interested in applying the Deep Learning (DL) technique known as
panoptic segmentation to simulated CCD images to identify, attribute and
measure energy depositions from radioisotopes of interest. We simulate CCD
exposures of a chosen radioxenon isotope, $^{135}$Xe, and overlay a simulated
cosmic muon background appropriate for a surface-lab. We show that with this DL
technique we can reproduce the beta spectrum to good accuracy, while suffering
expected confusion with same-topology gammas and conversion electrons and
identifying cosmic muons less than optimally.

Multipath transport protocols enable the concurrent use of different network
paths, benefiting a fast and reliable data transmission. The scheduler of a
multipath transport protocol determines how to distribute data packets over
different paths. Existing multipath schedulers either conform to predefined
policies or to online trained policies. The adoption of millimeter wave
(mmWave) paths in 5th Generation (5G) networks and Wireless Local Area Networks
(WLANs) introduces time-varying network conditions, under which the existing
schedulers struggle to achieve fast and accurate adaptation. In this paper, we
propose FALCON, a learning-based multipath scheduler that can adapt fast and
accurately to time-varying network conditions. FALCON builds on the idea of
meta-learning where offline learning is used to create a set of meta-models
that represent coarse-grained network conditions, and online learning is used
to bootstrap a specific model for the current fine-grained network conditions
towards deriving the scheduling policy to deal with such conditions. Using
trace-driven emulation experiments, we demonstrate FALCON outperforms the best
state-of-the-art scheduler by up to 19.3% and 23.6% in static and mobile
networks, respectively. Furthermore, we show FALCON is quite flexible to work
with different types of applications such as bulk transfer and web services.
Moreover, we observe FALCON has a much faster adaptation time compared to all
the other learning-based schedulers, reaching almost an 8-fold speedup compared
to the best of them. Finally, we have validated the emulation results in
real-world settings illustrating that FALCON adapts well to the dynamicity of
real networks, consistently outperforming all other schedulers.

Shelves are common in homes, warehouses, and commercial settings due to their
storage efficiency. However, this efficiency comes at the cost of reduced
visibility and accessibility. When looking from a side (lateral) view of a
shelf, most objects will be fully occluded, resulting in a constrained
lateral-access mechanical search problem. To address this problem, we
introduce: (1) a novel bluction tool, which combines a thin pushing blade and
suction cup gripper, (2) an improved LAX-RAY simulation pipeline and perception
model that combines ray-casting with 2D Minkowski sums to efficiently generate
target occupancy distributions, and (3) a novel SLAX-RAY search policy, which
optimally reduces target object distribution support area using the bluction
tool. Experimental data from 2000 simulated shelf trials and 18 trials with a
physical Fetch robot equipped with the bluction tool suggest that using suction
grasping actions improves the success rate over the highest performing
push-only policy by 26% in simulation and 67% in physical environments.

Hot subdwarf star is a particular type of star that is crucial for studying
binary evolution and atmospheric diffusion processes. In recent years,
identifying Hot subdwarfs by machine learning methods has become a hot topic,
but there are still limitations in automation and accuracy. In this paper, we
proposed a robust identification method based on the convolutional neural
network (CNN). We first constructed the dataset using the spectral data of
LAMOS DR7-V1. We then constructed a hybrid recognition model including an
8-class classification model and a binary classification model. The model
achieved an accuracy of 96.17% on the testing set. To further validate the
accuracy of the model, we selected 835 Hot subdwarfs that were not involved in
the training process from the identified LAMOST catalog (2428, including
repeated observations) as the validation set. An accuracy of 96.05% was
achieved. On this basis, we used the model to filter and classify all
10,640,255 spectra of LAMOST DR7-V1, and obtained a catalog of 2393 Hot
subdwarf candidates, of which 2067 have been confirmed. We found 25 new Hot
subdwarfs among the remaining candidates by manual validation. The overall
accuracy of the model is 87.42%. Overall, the model presented in this study can
effectively identify specific spectra with robust results and high accuracy,
and can be further applied to the classification of large-scale spectra and the
search of specific targets.

Quantitative phase imaging (QPI) is a label-free computational imaging
technique that provides optical path length information of specimens. In modern
implementations, the quantitative phase image of an object is reconstructed
digitally through numerical methods running in a computer, often using
iterative algorithms. Here, we demonstrate a diffractive QPI network that can
synthesize the quantitative phase image of an object by converting the input
phase information of a scene into intensity variations at the output plane. A
diffractive QPI network is a specialized all-optical processor designed to
perform a quantitative phase-to-intensity transformation through passive
diffractive surfaces that are spatially engineered using deep learning and
image data. Forming a compact, all-optical network that axially extends only
~200-300 times the illumination wavelength, this framework can replace
traditional QPI systems and related digital computational burden with a set of
passive transmissive layers. All-optical diffractive QPI networks can
potentially enable power-efficient, high frame-rate and compact phase imaging
systems that might be useful for various applications, including, e.g., on-chip
microscopy and sensing.

We present an error-neural-modeling-based strategy for approximating
two-dimensional curvature in the level-set method. Our main contribution is a
redesigned hybrid solver [Larios-C\'ardenas and Gibou, J. Comput. Phys. (May
2022), 10.1016/j.jcp.2022.111291] that relies on numerical schemes to enable
machine-learning operations on demand. In particular, our routine features
double predicting to harness curvature symmetry invariance in favor of
precision and stability. The core of this solver is a multilayer perceptron
trained on circular- and sinusoidal-interface samples. Its role is to quantify
the error in numerical curvature approximations and emit corrected estimates
for select grid vertices along the free boundary. These corrections arise in
response to preprocessed context level-set, curvature, and gradient data. To
promote neural capacity, we have adopted sample negative-curvature
normalization, reorientation, and reflection-based augmentation. In the same
manner, our system incorporates dimensionality reduction, well-balancedness,
and regularization to minimize outlying effects. Our training approach is
likewise scalable across mesh sizes. For this purpose, we have introduced
dimensionless parametrization and probabilistic subsampling during data
production. Together, all these elements have improved the accuracy and
efficiency of curvature calculations around under-resolved regions. In most
experiments, our strategy has outperformed the numerical baseline at twice the
number of redistancing steps while requiring only a fraction of the cost.

Collaborative representation-based classification (CRC) has demonstrated
remarkable progress in the past few years because of its closed-form analytical
solutions. However, the existing CRC methods are incapable of processing the
nonlinear variational information directly. Recent advances illustrate that how
to effectively model these nonlinear variational information and learn
invariant representations is an open challenge in the community of computer
vision and pattern recognition To this end, we try to design a new algorithm to
handle this problem. Firstly, the second-order statistic, i.e., covariance
matrix is applied to model the original image sets. Due to the space formed by
a set of nonsingular covariance matrices is a well-known Symmetric Positive
Definite (SPD) manifold, generalising the Euclidean collaborative
representation to the SPD manifold is not an easy task. Then, we devise two
strategies to cope with this issue. One attempts to embed the SPD
manifold-valued data representations into an associated tangent space via the
matrix logarithm map. Another is to embed them into a Reproducing Kernel
Hilbert Space (RKHS) by utilizing the Riemannian kernel function. After these
two treatments, CRC is applicable to the SPD manifold-valued features. The
evaluations on four banchmarking datasets justify its effectiveness.

This work studies the problem of few-shot object counting, which counts the
number of exemplar objects (i.e., described by one or several support images)
occurring in the query image. The major challenge lies in that the target
objects can be densely packed in the query image, making it hard to recognize
every single one. To tackle the obstacle, we propose a novel learning block,
equipped with a similarity comparison module and a feature enhancement module.
Concretely, given a support image and a query image, we first derive a score
map by comparing their projected features at every spatial position. The score
maps regarding all support images are collected together and normalized across
both the exemplar dimension and the spatial dimensions, producing a reliable
similarity map. We then enhance the query feature with the support features by
employing the developed point-wise similarities as the weighting coefficients.
Such a design encourages the model to inspect the query image by focusing more
on the regions akin to the support images, leading to much clearer boundaries
between different objects. Extensive experiments on various benchmarks and
training setups suggest that we surpass the state-of-the-art methods by a
sufficiently large margin. For instance, on a recent large-scale FSC-147
dataset, we surpass the state-of-the-art method by improving the mean absolute
error from 22.08 to 14.32 (35%$\uparrow$). Code has been released in
https://github.com/zhiyuanyou/SAFECount.

It is a challenging problem to detect and recognize targets on complex
large-scene Synthetic Aperture Radar (SAR) images. Recently developed deep
learning algorithms can automatically learn the intrinsic features of SAR
images, but still have much room for improvement on large-scene SAR images with
limited data. In this paper, based on learning representations and multi-scale
features of SAR images, we propose an efficient and robust deep learning based
target detection method. Especially, by leveraging the effectiveness of
adversarial autoencoder (AAE) which influences the distribution of the
investigated data explicitly, the raw SAR dataset is augmented into an enhanced
version with a large quantity and diversity. Besides, an auto-labeling scheme
is proposed to improve labeling efficiency. Finally, with jointly training
small target chips and large-scene images, an integrated YOLO network combining
non-maximum suppression on sub-images is used to realize multiple targets
detection of high resolution images. The numerical experimental results on the
MSTAR dataset show that our method can realize target detection and recognition
on large-scene images accurately and efficiently. The superior anti-noise
performance is also confirmed by experiments.

Image super-resolution technology is the process of obtaining high-resolution
images from one or more low-resolution images. With the development of deep
learning, image super-resolution technology based on deep learning method is
emerging. This paper reviews the research progress of the application of depth
learning method in the field of image super-resolution, introduces this kind of
super-resolution work from several aspects, and looks forward to the further
application of depth learning method in the field of image super-resolution. By
collecting and counting the relevant literature on the application of depth
learning in the field of image super-resolution, we preliminarily summarizes
the application results of depth learning method in the field of image
super-resolution, and reports the latest progress of image super-resolution
technology based on depth learning method.

Adversarial risk quantifies the performance of classifiers on adversarially
perturbed data. Numerous definitions of adversarial risk -- not all
mathematically rigorous and differing subtly in the details -- have appeared in
the literature. In this paper, we revisit these definitions, make them
rigorous, and critically examine their similarities and differences. Our
technical tools derive from optimal transport, robust statistics, functional
analysis, and game theory. Our contributions include the following:
generalizing Strassen's theorem to the unbalanced optimal transport setting
with applications to adversarial classification with unequal priors; showing an
equivalence between adversarial robustness and robust hypothesis testing with
$\infty$-Wasserstein uncertainty sets; proving the existence of a pure Nash
equilibrium in the two-player game between the adversary and the algorithm; and
characterizing adversarial risk by the minimum Bayes error between a pair of
distributions belonging to the $\infty$-Wasserstein uncertainty sets. Our
results generalize and deepen recently discovered connections between optimal
transport and adversarial robustness and reveal new connections to Choquet
capacities and game theory.

Multi-modality images have been widely used and provide comprehensive
information for medical image analysis. However, acquiring all modalities among
all institutes is costly and often impossible in clinical settings. To leverage
more comprehensive multi-modality information, we propose a privacy secured
decentralized multi-modality adaptive learning architecture named ModalityBank.
Our method could learn a set of effective domain-specific modulation parameters
plugged into a common domain-agnostic network. We demonstrate by switching
different sets of configurations, the generator could output high-quality
images for a specific modality. Our method could also complete the missing
modalities across all data centers, thus could be used for modality completion
purposes. The downstream task trained from the synthesized multi-modality
samples could achieve higher performance than learning from one real data
center and achieve close-to-real performance compare with all real images.

Utilizing multi-modal neuroimaging data has been proved to be effective to
investigate human cognitive activities and certain pathologies. However, it is
not practical to obtain the full set of paired neuroimaging data centrally
since the collection faces several constraints, e.g., high examination cost,
long acquisition time, and image corruption. In addition, these data are
dispersed into different medical institutions and thus cannot be aggregated for
centralized training considering the privacy issues. There is a clear need to
launch a federated learning and facilitate the integration of the dispersed
data from different institutions. In this paper, we propose a new benchmark for
federated domain translation on unsupervised brain image synthesis (termed as
FedMed-GAN) to bridge the gap between federated learning and medical GAN.
FedMed-GAN mitigates the mode collapse without sacrificing the performance of
generators, and is widely applied to different proportions of unpaired and
paired data with variation adaptation property. We treat the gradient penalties
by federally averaging algorithm and then leveraging differential privacy
gradient descent to regularize the training dynamics. A comprehensive
evaluation is provided for comparing FedMed-GAN and other centralized methods,
which shows the new state-of-the-art performance by our FedMed-GAN. Our code
has been released on the website: https://github.com/M-3LAB/FedMed-GAN

Self-supervision has shown outstanding results for natural language
processing, and more recently, for image recognition. Simultaneously, vision
transformers and its variants have emerged as a promising and scalable
alternative to convolutions on various computer vision tasks. In this paper, we
are the first to question if self-supervised vision transformers (SSL-ViTs) can
be adapted to two important computer vision tasks in the low-label, high-data
regime: few-shot image classification and zero-shot image retrieval. The
motivation is to reduce the number of manual annotations required to train a
visual embedder, and to produce generalizable and semantically meaningful
embeddings. For few-shot image classification we train SSL-ViTs without any
supervision, on external data, and use this trained embedder to adapt quickly
to novel classes with limited number of labels. For zero-shot image retrieval,
we use SSL-ViTs pre-trained on a large dataset without any labels and fine-tune
them with several metric learning objectives. Our self-supervised attention
representations outperforms the state-of-the-art on several public benchmarks
for both tasks, namely miniImageNet and CUB200 for few-shot image
classification by up-to 6%-10%, and Stanford Online Products, Cars196 and
CUB200 for zero-shot image retrieval by up-to 4%-11%. Code is available at
\url{https://github.com/AutoVision-cloud/SSL-ViT-lowlabel-highdata}.

Visual object tracking with RGB and thermal infrared (TIR) spectra available,
shorted in RGBT tracking, is a novel and challenging research topic which draws
increasing attention nowadays. In this paper, we propose an RGBT tracker which
takes spatio-temporal clues into account for robust appearance model learning,
and simultaneously, constructs an adaptive fusion sub-network for cross-modal
interactions. Unlike most existing RGBT trackers that implement object tracking
tasks with only spatial information included, temporal information is further
considered in this method. Specifically, different from traditional Siamese
trackers, which only obtain one search image during the process of picking up
template-search image pairs, an extra search sample adjacent to the original
one is selected to predict the temporal transformation, resulting in improved
robustness of tracking performance.As for multi-modal tracking, constrained to
the limited RGBT datasets, the adaptive fusion sub-network is appended to our
method at the decision level to reflect the complementary characteristics
contained in two modalities. To design a thermal infrared assisted RGB tracker,
the outputs of the classification head from the TIR modality are taken into
consideration before the residual connection from the RGB modality. Extensive
experimental results on three challenging datasets, i.e. VOT-RGBT2019, GTOT and
RGBT210, verify the effectiveness of our method. Code will be shared at
\textcolor{blue}{\emph{https://github.com/Zhangyong-Tang/TAAT}}.

Evaporation studies of water using classical molecular dynamics simulations
are largely limited due to their high computational expense. This study
addresses that issue by developing coarse-grained molecular dynamics models
based on Morse potential. Models are optimized based on multi-temperature and
at room temperature using machine learning techniques like genetic algorithm,
Nelder-Mead algorithm, and Strength Pareto Evolutionary Algorithm. The
multi-temperature based model named as Morse-D is found to be more accurate
than single temperature model in representing the water properties at higher
temperatures. Using this Morse-D water model, evaporation from hydrophilic
nanopores with pore diameter varying from 2 nm to 5 nm is studied. Our results
show that the critical diameter to initiate continuous evaporation at nanopores
lies between 3 nm and 4 nm. A maximum heat flux of 21.3 kW/cm2 is observed for
a pore diameter of 4.5 nm and a maximum mass flow rate of 16.2 ng/s for a pore
diameter of 5 nm. The observed heat flux is an order of magnitude times larger
than the currently reported values from experiments in the literature for
water, which indicates that we need to focus on nanoscale evaporation to
enhance the critical heat flux.

Next generation Low Gain Avalanche Diodes (LGAD) produced by Hamamatsu
photonics (HPK) and Fondazione Bruno Kessler (FBK) were tested before and after
irradiation with ~1MeV neutrons at the JSI facility in Ljubljana. Sensors were
irradiated to a maximum 1-MeV equivalent fluence of 2.5E15 Neq/cm2. The sensors
analysed in this paper are an improvement after the lessons learned from
previous FBK and HPK productions that were already reported in precedent
papers. The gain layer of HPK sensors was fine-tuned to optimize the
performance before and after irradiation. FBK sensors instead combined the
benefit of Carbon infusion and deep gain layer to further the radiation
hardness of the sensors and reduced the bulk thickness to enhance the timing
resolution. The sensor performance was measured in charge collection studies
using \b{eta}-particles from a 90Sr source and in capacitance-voltage scans
(C-V) to determine the bias to deplete the gain layer. The collected charge and
the timing resolution were measured as a function of bias voltage at -30C.
Finally a correlation is shown between the bias voltage to deplete the gain
layer and the bias voltage needed to reach a certain amount of gain in the
sensor. HPK sensors showed a better performance before irradiation while
maintaining the radiation hardness of the previous production. FBK sensors
showed exceptional radiation hardness allowing a collected charge up to 10 fC
and a time resolution of 40 ps at the maximum fluence.

Deploying complex deep learning models on edge devices is challenging because
they have substantial compute and memory resource requirements, whereas edge
devices' resource budget is limited. To solve this problem, extensive pruning
techniques have been proposed for compressing networks. Recent advances based
on the Lottery Ticket Hypothesis (LTH) show that iterative model pruning tends
to produce smaller and more accurate models. However, LTH research focuses on
unstructured pruning, which is hardware-inefficient and difficult to accelerate
on hardware platforms.
  In this paper, we investigate iterative pruning in the context of structured
pruning because structurally pruned models map well on commodity hardware. We
find that directly applying a structured weight-based pruning technique
iteratively, called iterative L1-norm based pruning (ILP), does not produce
accurate pruned models. To solve this problem, we propose two activation-based
pruning methods, Iterative Activation-based Pruning (IAP) and Adaptive
Iterative Activation-based Pruning (AIAP). We observe that, with only 1%
accuracy loss, IAP and AIAP achieve 7.75X and 15.88$X compression on LeNet-5,
and 1.25X and 1.71X compression on ResNet-50, whereas ILP achieves 4.77X and
1.13X, respectively.

Geometric deep learning has made great strides towards generalizing the
design of structure-aware neural networks from traditional domains to
non-Euclidean ones, giving rise to graph neural networks (GNN) that can be
applied to graph-structured data arising in, e.g., social networks,
biochemistry, and material science. Graph convolutional networks (GCNs) in
particular, inspired by their Euclidean counterparts, have been successful in
processing graph data by extracting structure-aware features. However, current
GNN models are often constrained by various phenomena that limit their
expressive power and ability to generalize to more complex graph datasets. Most
models essentially rely on low-pass filtering of graph signals via local
averaging operations, leading to oversmoothing. Moreover, to avoid severe
oversmoothing, most popular GCN-style networks tend to be shallow, with narrow
receptive fields, leading to underreaching. Here, we propose a hybrid GNN
framework that combines traditional GCN filters with band-pass filters defined
via geometric scattering. We further introduce an attention framework that
allows the model to locally attend over combined information from different
filters at the node level. Our theoretical results establish the complementary
benefits of the scattering filters to leverage structural information from the
graph, while our experiments show the benefits of our method on various

Deep learning models have introduced various intelligent applications to edge
devices, such as image classification, speech recognition, and augmented
reality. There is an increasing need of training such models on the devices in
order to deliver personalized, responsive, and private learning. To address
this need, this paper presents a new solution for deploying and training
state-of-the-art models on the resource-constrained devices. First, the paper
proposes a novel filter-pruning-based model compression method to create
lightweight trainable models from large models trained in the cloud, without
much loss of accuracy. Second, it proposes a novel knowledge transfer method to
enable the on-device model to update incrementally in real time or near real
time using incremental learning on new data and enable the on-device model to
learn the unseen categories with the help of the in-cloud model in an
unsupervised fashion. The results show that 1) our model compression method can
remove up to 99.36% parameters of WRN-28-10, while preserving a Top-1 accuracy
of over 90% on CIFAR-10; 2) our knowledge transfer method enables the
compressed models to achieve more than 90% accuracy on CIFAR-10 and retain good
accuracy on old categories; 3) it allows the compressed models to converge
within real time (three to six minutes) on the edge for incremental learning
tasks; 4) it enables the model to classify unseen categories of data (78.92%
Top-1 accuracy) that it is never trained with.

Wav2vec2.0 is a popular self-supervised pre-training framework for learning
speech representations in the context of automatic speech recognition (ASR). It
was shown that wav2vec2.0 has a good robustness against the domain shift, while
the noise robustness is still unclear. In this work, we therefore first analyze
the noise robustness of wav2vec2.0 via experiments. We observe that wav2vec2.0
pre-trained on noisy data can obtain good representations and thus improve the
ASR performance on the noisy test set, which however brings a performance
degradation on the clean test set. To avoid this issue, in this work we propose
an enhanced wav2vec2.0 model. Specifically, the noisy speech and the
corresponding clean version are fed into the same feature encoder, where the
clean speech provides training targets for the model. Experimental results
reveal that the proposed method can not only improve the ASR performance on the
noisy test set which surpasses the original wav2vec2.0, but also ensure a tiny
performance decrease on the clean test set. In addition, the effectiveness of
the proposed method is demonstrated under different types of noise conditions.

Recent advances in theoretical Deep Learning have introduced geometric
properties that occur during training, past the Interpolation Threshold --
where the training error reaches zero. We inquire into the phenomena coined
Neural Collapse in the intermediate layers of the networks, and emphasize the
innerworkings of Nearest Class-Center Mismatch inside the deepnet. We further
show that these processes occur both in vision and language model
architectures. Lastly, we propose a Stochastic Variability-Simplification Loss
(SVSL) that encourages better geometrical features in intermediate layers, and
improves both train metrics and generalization.

How to obtain hierarchical representations with an increasing level of
abstraction becomes one of the key issues of learning with deep neural
networks. A variety of RNN models have recently been proposed to incorporate
both explicit and implicit hierarchical information in modeling languages in
the literature. In this paper, we propose a novel approach called the latent
indicator layer to identify and learn implicit hierarchical information (e.g.,
phrases), and further develop an EM algorithm to handle the latent indicator
layer in training. The latent indicator layer further simplifies a text's
hierarchical structure, which allows us to seamlessly integrate different
levels of attention mechanisms into the structure. We called the resulting
architecture as the EM-HRNN model. Furthermore, we develop two bootstrap
strategies to effectively and efficiently train the EM-HRNN model on long text
documents. Simulation studies and real data applications demonstrate that the
EM-HRNN model with bootstrap training outperforms other RNN-based models in
document classification tasks. The performance of the EM-HRNN model is
comparable to a Transformer-based method called Bert-base, though the former is
much smaller model and does not require pre-training.

The building sector accounts for almost 40 percent of the global energy
consumption. This reveals a great opportunity to exploit renewable energy
resources in buildings to achieve the climate target. In this context, this
paper offers a building energy system embracing a heat pump, a thermal energy
storage system along with grid-connected photovoltaic thermal (PVT) collectors
to supply both electric and thermal energy demands of the building with minimum
operating cost. To this end, the paper develops a stochastic model predictive
control (MPC) strategy to optimally determine the set-point of the whole
building energy system while accounting for the uncertainties associated with
the PVT energy generation. This system enables the building to 1-shift its
electric demand from high-peak to off-peak hours and 2- sell electricity to the
grid to make energy arbitrage.

Pruning is a promising approach to compress complex deep learning models in
order to deploy them on resource-constrained edge devices. However, many
existing pruning solutions are based on unstructured pruning, which yield
models that cannot efficiently run on commodity hardware, and require users to
manually explore and tune the pruning process, which is time consuming and
often leads to sub-optimal results. To address these limitations, this paper
presents an adaptive, activation-based, structured pruning approach to
automatically and efficiently generate small, accurate, and hardware-efficient
models that meet user requirements. First, it proposes iterative structured
pruning using activation-based attention feature maps to effectively identify
and prune unimportant filters. Then, it proposes adaptive pruning policies for
automatically meeting the pruning objectives of accuracy-critical,
memory-constrained, and latency-sensitive tasks. A comprehensive evaluation
shows that the proposed method can substantially outperform the
state-of-the-art structured pruning works on CIFAR-10 and ImageNet datasets.
For example, on ResNet-56 with CIFAR-10, without any accuracy drop, our method
achieves the largest parameter reduction (79.11%), outperforming the related
works by 22.81% to 66.07%, and the largest FLOPs reduction (70.13%),
outperforming the related works by 14.13% to 26.53%.

We have developed a machine learning-based interatomic potential (MLIP) for
the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high entropy alloys
(HEAs). MLIPs enabled accurate high throughput calculations of elastic and
mechanical properties of various non-equimolar R4 and R5 alloys, which are
otherwise very time consuming calculations when performed using density
functional theory (DFT). We demonstrate that the MLIP predicted properties
compare well with the DFT results on various test cases and are consistent with
the available experimental data. The MLIPs are also utilized for high
throughput optimization of non equimolar R4 candidates by guided iterative
tuning of R4 compositions to discover candidate materials with promising
hardness-ductility combinations. We also used this approach to study the effect
of Ti concentration on the elastic and mechanical properties of R4, by
statistically averaging the properties of over 100 random structures. MLIP
predicted hardness and bulk modulus of equimolar R4 and R5 HEAs are validated
using experimentally measured Vickers hardness and modulus. This approach opens
a new avenue for employing MLIPs for HEA candidate optimization.

We study the framework of universal dynamic regret minimization with strongly
convex losses. We answer an open problem in Baby and Wang 2021 by showing that
in a proper learning setup, Strongly Adaptive algorithms can achieve the near
optimal dynamic regret of $\tilde O(d^{1/3} n^{1/3}\text{TV}[u_{1:n}]^{2/3}
\vee d)$ against any comparator sequence $u_1,\ldots,u_n$ simultaneously, where
$n$ is the time horizon and $\text{TV}[u_{1:n}]$ is the Total Variation of
comparator. These results are facilitated by exploiting a number of new
structures imposed by the KKT conditions that were not considered in Baby and
Wang 2021 which also lead to other improvements over their results such as: (a)
handling non-smooth losses and (b) improving the dimension dependence on
regret. Further, we also derive near optimal dynamic regret rates for the
special case of proper online learning with exp-concave losses and an
$L_\infty$ constrained decision set.

We resolve an open problem of Hanneke on the subject of universally
consistent online learning with non-i.i.d. processes and unbounded losses. The
notion of an optimistically universal learning rule was defined by Hanneke in
an effort to study learning theory under minimal assumptions. A given learning
rule is said to be optimistically universal if it achieves a low long-run
average loss whenever the data generating process makes this goal achievable by
some learning rule. Hanneke posed as an open problem whether, for every
unbounded loss, the family of processes admitting universal learning are
precisely those having a finite number of distinct values almost surely. In
this paper, we completely resolve this problem, showing that this is indeed the
case. As a consequence, this also offers a dramatically simpler formulation of
an optimistically universal learning rule for any unbounded loss: namely, the
simple memorization rule already suffices. Our proof relies on constructing
random measurable partitions of the instance space and could be of independent
interest for solving other open questions. We extend the results to the
non-realizable setting thereby providing an optimistically universal Bayes
consistent learning rule.

Many real-world problems are compositional - solving them requires completing
interdependent sub-tasks, either in series or in parallel, that can be
represented as a dependency graph. Deep reinforcement learning (RL) agents
often struggle to learn such complex tasks due to the long time horizons and
sparse rewards. To address this problem, we present Compositional Design of
Environments (CoDE), which trains a Generator agent to automatically build a
series of compositional tasks tailored to the RL agent's current skill level.
This automatic curriculum not only enables the agent to learn more complex
tasks than it could have otherwise, but also selects tasks where the agent's
performance is weak, enhancing its robustness and ability to generalize
zero-shot to unseen tasks at test-time. We analyze why current environment
generation techniques are insufficient for the problem of generating
compositional tasks, and propose a new algorithm that addresses these issues.
Our results assess learning and generalization across multiple compositional
tasks, including the real-world problem of learning to navigate and interact
with web pages. We learn to generate environments composed of multiple pages or
rooms, and train RL agents capable of completing wide-range of complex tasks in
those environments. We contribute two new benchmark frameworks for generating
compositional tasks, compositional MiniGrid and gMiniWoB for web
navigation.CoDE yields 4x higher success rate than the strongest baseline, and
demonstrates strong performance of real websites learned on 3500 primitive
tasks.

Many multi-genic systemic diseases such as neurological disorders,
inflammatory diseases, and the majority of cancers do not have effective
treatments yet. Reinforcement learning powered systems pharmacology is a
potentially effective approach to design personalized therapies for untreatable
complex diseases. In this survey, state-of-the-art reinforcement learning
methods and their latest applications to drug design are reviewed. The
challenges on harnessing reinforcement learning for systems pharmacology and
personalized medicine are discussed. Potential solutions to overcome the
challenges are proposed. In spite of successful application of advanced
reinforcement learning techniques to target-based drug discovery, new
reinforcement learning strategies are needed to address systems
pharmacology-oriented personalized de novo drug design.

The gap in representations between image and video makes Image-to-Video
Re-identification (I2V Re-ID) challenging, and recent works formulate this
problem as a knowledge distillation (KD) process. In this paper, we propose a
mutual discriminative knowledge distillation framework to transfer a
video-based richer representation to an image based representation more
effectively. Specifically, we propose the triplet contrast loss (TCL), a novel
loss designed for KD. During the KD process, the TCL loss transfers the local
structure, exploits the higher order information, and mitigates the
misalignment of the heterogeneous output of teacher and student networks.
Compared with other losses for KD, the proposed TCL loss selectively transfers
the local discriminative features from teacher to student, making it effective
in the ReID. Besides the TCL loss, we adopt mutual learning to regularize both
the teacher and student networks training. Extensive experiments demonstrate
the effectiveness of our method on the MARS, DukeMTMC-VideoReID and VeRi-776

Quantum Machine Learning (QML) is an emerging research area advocating the
use of quantum computing for advancement in machine learning. Since the
discovery of the capability of Parametrized Variational Quantum Circuits (VQC)
to replace Artificial Neural Networks, they have been widely adopted to
different tasks in Quantum Machine Learning. However, despite their potential
to outperform neural networks, VQCs are limited to small scale applications
given the challenges in scalability of quantum circuits. To address this
shortcoming, we propose an algorithm that compresses the quantum state within
the circuit using a tensor ring representation. Using the input qubit state in
the tensor ring representation, single qubit gates maintain the tensor ring
representation. However, the same is not true for two qubit gates in general,
where an approximation is used to have the output as a tensor ring
representation. Using this approximation, the storage and computational time
increases linearly in the number of qubits and number of layers, as compared to
the exponential increase with exact simulation algorithms. This approximation
is used to implement the tensor ring VQC. The training of the parameters of
tensor ring VQC is performed using a gradient descent based algorithm, where
efficient approaches for backpropagation are used. The proposed approach is
evaluated on two datasets: Iris and MNIST for the classification task to show
the improved accuracy using more number of qubits. We achieve a test accuracy
of 83.33\% on Iris dataset and a maximum of 99.30\% and 76.31\% on binary and
ternary classification of MNIST dataset using various circuit architectures.
The results from the IRIS dataset outperform the results on VQC implemented on
Qiskit, and being scalable, demonstrates the potential for VQCs to be used for
large scale Quantum Machine Learning applications.

Conventional magneto-static finite element analysis of electrical machine
design is time-consuming and computationally expensive. Since each machine
topology has a distinct set of parameters, design optimization is commonly
performed independently. This paper presents a novel method for predicting Key
Performance Indicators (KPIs) of differently parameterized electrical machine
topologies at the same time by mapping a high dimensional integrated design
parameters in a lower dimensional latent space using a variational autoencoder.
After training, via a latent space, the decoder and multi-layer neural network
will function as meta-models for sampling new designs and predicting associated
KPIs, respectively. This enables parameter-based concurrent multi-topology

Determining the type of kidney stones allows urologists to prescribe a
treatment to avoid recurrence of renal lithiasis. An automated in-vivo
image-based classification method would be an important step towards an
immediate identification of the kidney stone type required as a first phase of
the diagnosis. In the literature it was shown on ex-vivo data (i.e., in very
controlled scene and image acquisition conditions) that an automated kidney
stone classification is indeed feasible. This pilot study compares the kidney
stone recognition performances of six shallow machine learning methods and
three deep-learning architectures which were tested with in-vivo images of the
four most frequent urinary calculi types acquired with an endoscope during
standard ureteroscopies. This contribution details the database construction
and the design of the tested kidney stones classifiers. Even if the best
results were obtained by the Inception v3 architecture (weighted precision,
recall and F1-score of 0.97, 0.98 and 0.97, respectively), it is also shown
that choosing an appropriate colour space and texture features allows a shallow
machine learning method to approach closely the performances of the most
promising deep-learning methods (the XGBoost classifier led to weighted
precision, recall and F1-score values of 0.96). This paper is the first one
that explores the most discriminant features to be extracted from images
acquired during ureteroscopies.

Normalizing flows are a class of deep generative models that provide a
promising route to sample lattice field theories more efficiently than
conventional Monte Carlo simulations. In this work we show that the theoretical
framework of stochastic normalizing flows, in which neural-network layers are
combined with Monte Carlo updates, is the same that underlies
out-of-equilibrium simulations based on Jarzynski's equality, which have been
recently deployed to compute free-energy differences in lattice gauge theories.
We lay out a strategy to optimize the efficiency of this extended class of
generative models and present examples of applications.

Answering complex questions about textual narratives requires reasoning over
both stated context and the world knowledge that underlies it. However,
pretrained language models (LM), the foundation of most modern QA systems, do
not robustly represent latent relationships between concepts, which is
necessary for reasoning. While knowledge graphs (KG) are often used to augment
LMs with structured representations of world knowledge, it remains an open
question how to effectively fuse and reason over the KG representations and the
language context, which provides situational constraints and nuances. In this
work, we propose GreaseLM, a new model that fuses encoded representations from
pretrained LMs and graph neural networks over multiple layers of modality
interaction operations. Information from both modalities propagates to the
other, allowing language context representations to be grounded by structured
world knowledge, and allowing linguistic nuances (e.g., negation, hedging) in
the context to inform the graph representations of knowledge. Our results on
three benchmarks in the commonsense reasoning (i.e., CommonsenseQA, OpenbookQA)
and medical question answering (i.e., MedQA-USMLE) domains demonstrate that
GreaseLM can more reliably answer questions that require reasoning over both
situational constraints and structured knowledge, even outperforming models 8x
larger.

We review recent efforts to machine learn relations between knot invariants.
Because these knot invariants have meaning in physics, we explore aspects of
Chern-Simons theory and higher dimensional gauge theories. The goal of this
work is to translate numerical experiments with Big Data to new analytic
results.

Beta coefficients for linear regression models represent the ideal form of an
interpretable feature effect. However, for non-linear models and especially
generalized linear models, the estimated coefficients cannot be interpreted as
a direct feature effect on the predicted outcome. Hence, marginal effects are
typically used as approximations for feature effects, either in the shape of
derivatives of the prediction function or forward differences in prediction due
to a change in a feature value. While marginal effects are commonly used in
many scientific fields, they have not yet been adopted as a model-agnostic
interpretation method for machine learning models. This may stem from their
inflexibility as a univariate feature effect and their inability to deal with
the non-linearities found in black box models. We introduce a new class of
marginal effects termed forward marginal effects. We argue to abandon
derivatives in favor of better-interpretable forward differences. Furthermore,
we generalize marginal effects based on forward differences to multivariate
changes in feature values. To account for the non-linearity of prediction
functions, we introduce a non-linearity measure for marginal effects. We argue
against summarizing feature effects of a non-linear prediction function in a
single metric such as the average marginal effect. Instead, we propose to
partition the feature space to compute conditional average marginal effects on
feature subspaces, which serve as conditional feature effect estimates.

Predictions of nuclear properties far from measured data are inherently
imprecise because of uncertainties in our knowledge of nuclear forces and in
our treatment of quantum many-body effects in strongly-interacting systems.
While the model bias can be directly calculated when experimental data is
available, only an estimate can be made in the absence of such measurements.
Current approaches to compute the estimated bias quickly lose predictive power
when input variables such as proton or neutron number are extrapolated,
resulting in uncontrolled uncertainties in applications such as nucleosynthesis
simulations. In this letter, we present a novel technique to identify the input
variables of machine learning algorithms that can provide robust estimates of
model bias. Our process is based on selecting input variables, or features,
based on their probability distribution functions across the entire nuclear
chart. We illustrate our approach on the problem of quantifying the model bias
in nuclear binding energies calculated with Density Functional Theory (DFT). We
show that feature selection can systematically improve theoretical predictions
without increasing uncertainties.

We develop a new measure of the exploration/exploitation trade-off in
infinite-horizon reinforcement learning problems called the occupancy
information ratio (OIR), which is comprised of a ratio between the
infinite-horizon average cost of a policy and the entropy of its long-term
state occupancy measure. The OIR ensures that no matter how many trajectories
an RL agent traverses or how well it learns to minimize cost, it maintains a
healthy skepticism about its environment, in that it defines an optimal policy
which induces a high-entropy occupancy measure. Different from earlier
information ratio notions, OIR is amenable to direct policy search over
parameterized families, and exhibits hidden quasiconcavity through invocation
of the perspective transformation. This feature ensures that under appropriate
policy parameterizations, the OIR optimization problem has no spurious
stationary points, despite the overall problem's nonconvexity. We develop for
the first time policy gradient and actor-critic algorithms for OIR optimization
based upon a new entropy gradient theorem, and establish both asymptotic and
non-asymptotic convergence results with global optimality guarantees. In
experiments, these methodologies outperform several deep RL baselines in
problems with sparse rewards, where many trajectories may be uninformative and
skepticism about the environment is crucial to success.

Doppelg\"angers (or lookalikes) usually yield an increased probability of
false matches in a facial recognition system, as opposed to random face image
pairs selected for non-mated comparison trials. In this work, we assess the
impact of doppelg\"angers on the HDA Doppelg\"anger and Disguised Faces in The
Wild databases using a state-of-the-art face recognition system. It is found
that doppelg\"anger image pairs yield very high similarity scores resulting in
a significant increase of false match rates. Further, we propose a
doppelg\"anger detection method which distinguishes doppelg\"angers from mated
comparison trials by analysing differences in deep representations obtained
from face image pairs. The proposed detection system employs a machine
learning-based classifier, which is trained with generated doppelg\"anger image
pairs utilising face morphing techniques. Experimental evaluations conducted on
the HDA Doppelg\"anger and Look-Alike Face databases reveal a detection equal
error rate of approximately 2.7% for the task of separating mated
authentication attempts from doppelg\"angers.

Data accesses between on- and off-chip memories account for a large fraction
of overall energy consumption during inference with deep learning networks. We
present APack, a simple and effective, lossless, off-chip memory compression
technique for fixed-point quantized models. APack reduces data widths by
exploiting the non-uniform value distribution in deep learning applications.
APack can be used to increase the effective memory capacity, to reduce off-chip
traffic, and/or to achieve the desired performance/energy targets while using
smaller off-chip memories. APack builds upon arithmetic coding, encoding each
value as an arithmetically coded variable length prefix, plus an offset. To
maximize compression ratio a heuristic software algorithm partitions the value
space into groups each sharing a common prefix. APack exploits memory access
parallelism by using several, pipelined encoder/decoder units in parallel and
keeps up with the high data bandwidth demands of deep learning. APack can be
used with any machine learning accelerator. In the demonstrated configuration,
APack is placed just before the off-chip memory controller so that he rest of
the on-chip memory and compute units thus see the original data stream. We
implemented the APack compressor and decompressor in Verilog and in a 65nm tech
node demonstrating its performance and energy efficiency. Indicatively, APack
reduces data footprint of weights and activations to 60% and 48% respectively
on average over a wide set of 8-bit quantized models. It naturally adapts and
compresses models that use even more aggressive quantization methods. When
integrated with a Tensorcore-based accelerator, APack boosts the speedup and
energy efficiency to 1.44X and 1.37X respectively.

The spread of hate speech and hateful imagery on the Web is a significant
problem that needs to be mitigated to improve our Web experience. This work
contributes to research efforts to detect and understand hateful content on the
Web by undertaking a multimodal analysis of Antisemitism and Islamophobia on
4chan's /pol/ using OpenAI's CLIP. This large pre-trained model uses the
Contrastive Learning paradigm. We devise a methodology to identify a set of
Antisemitic and Islamophobic hateful textual phrases using Google's Perspective
API and manual annotations. Then, we use OpenAI's CLIP to identify images that
are highly similar to our Antisemitic/Islamophobic textual phrases. By running
our methodology on a dataset that includes 66M posts and 5.8M images shared on
4chan's /pol/ for 18 months, we detect 173K posts containing 21K
Antisemitic/Islamophobic images and 246K posts that include 420 hateful
phrases. Among other things, we find that we can use OpenAI's CLIP model to
detect hateful content with an accuracy score of 0.81 (F1 score = 0.54). By
comparing CLIP with two baselines proposed by the literature, we find that CLIP
outperforms them, in terms of accuracy, precision, and F1 score, in detecting
Antisemitic/Islamophobic images. Also, we find that Antisemitic/Islamophobic
imagery is shared in a similar number of posts on 4chan's /pol/ compared to
Antisemitic/Islamophobic textual phrases, highlighting the need to design more
tools for detecting hateful imagery. Finally, we make available (upon request)
a dataset of 246K posts containing 420 Antisemitic/Islamophobic phrases and 21K
likely Antisemitic/Islamophobic images (automatically detected by CLIP) that
can assist researchers in further understanding Antisemitism and Islamophobia.

Graph neural networks are powerful architectures for structured datasets.
However, current methods struggle to represent long-range dependencies. Scaling
the depth or width of GNNs is insufficient to broaden receptive fields as
larger GNNs encounter optimization instabilities such as vanishing gradients
and representation oversmoothing, while pooling-based approaches have yet to
become as universally useful as in computer vision. In this work, we propose
the use of Transformer-based self-attention to learn long-range pairwise
relationships, with a novel "readout" mechanism to obtain a global graph
embedding. Inspired by recent computer vision results that find
position-invariant attention performant in learning long-range relationships,
our method, which we call GraphTrans, applies a permutation-invariant
Transformer module after a standard GNN module. This simple architecture leads
to state-of-the-art results on several graph classification tasks,
outperforming methods that explicitly encode graph structure. Our results
suggest that purely-learning-based approaches without graph structure may be
suitable for learning high-level, long-range relationships on graphs. Code for
GraphTrans is available at https://github.com/ucbrise/graphtrans.

Explainability of graph neural networks (GNNs) aims to answer "Why the GNN
made a certain prediction?", which is crucial to interpret the model
prediction. The feature attribution framework distributes a GNN's prediction to
its input features (e.g., edges), identifying an influential subgraph as the
explanation. When evaluating the explanation (i.e., subgraph importance), a
standard way is to audit the model prediction based on the subgraph solely.
However, we argue that a distribution shift exists between the full graph and
the subgraph, causing the out-of-distribution problem. Furthermore, with an
in-depth causal analysis, we find the OOD effect acts as the confounder, which
brings spurious associations between the subgraph importance and model
prediction, making the evaluation less reliable. In this work, we propose
Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an
explanatory subgraph on the model prediction. While the distribution shift is
generally intractable, we employ the front-door adjustment and introduce a
surrogate variable of the subgraphs. Specifically, we devise a generative model
to generate the plausible surrogates that conform to the data distribution,
thus approaching the unbiased estimation of subgraph importance. Empirical
results demonstrate the effectiveness of DSE in terms of explanation fidelity.

Nowadays, manufacturing sectors harness the power of machine learning and
data science algorithms to make predictions for the optimization of mechanical
and microstructure properties of fabricated mechanical components. The
application of these algorithms reduces the experimental cost beside leads to
reduce the time of experiments. The present research work is based on the
prediction of penetration depth using Supervised Machine Learning algorithms
such as Support Vector Machines (SVM), Random Forest Algorithm, and Robust
Regression algorithm. A Friction Stir Spot Welding (FSSW) was used to join two
elements of AA1230 aluminum alloys. The dataset consists of three input
parameters: Rotational Speed (rpm), Dwelling Time (seconds), and Axial Load
(KN), on which the machine learning models were trained and tested. It observed
that the Robust Regression machine learning algorithm outperformed the rest of
the algorithms by resulting in the coefficient of determination of 0.96. The
research work also highlights the application of image processing techniques to
find the geometrical features of the weld formation.

The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation)
includes state-of-the-art machine learning methods for exploratory and
predictive analysis of satellite imagery as well as repository of AI-ready
Earth Observation (EO) datasets. It can be easily applied for a variety of
Earth Observation tasks, such as land use and cover classification, crop type
prediction, localization of specific objects (semantic segmentation), etc. The
main goal of AiTLAS is to facilitate better usability and adoption of novel AI
methods (and models) by EO experts, while offering easy access and standardized
format of EO datasets to AI experts which further allows benchmarking of
various existing and novel AI methods tailored for EO data.

Proposed as a solution to mitigate the privacy implications related to the
adoption of deep learning, Federated Learning (FL) enables large numbers of
participants to successfully train deep neural networks without having to
reveal the actual private training data. To date, a substantial amount of
research has investigated the security and privacy properties of FL, resulting
in a plethora of innovative attack and defense strategies. This paper
thoroughly investigates the communication capabilities of an FL scheme. In
particular, we show that a party involved in the FL learning process can use FL
as a covert communication medium to send an arbitrary message. We introduce
FedComm, a novel multi-system covert-communication technique that enables
robust sharing and transfer of targeted payloads within the FL framework. Our
extensive theoretical and empirical evaluations show that FedComm provides a
stealthy communication channel, with minimal disruptions to the training
process. Our experiments show that FedComm successfully delivers 100% of a
payload in the order of kilobits before the FL procedure converges. Our
evaluation also shows that FedComm is independent of the application domain and
the neural network architecture used by the underlying FL scheme.

Electroencephalogram (EEG) is an important diagnostic test that physicians
use to record brain activity and detect seizures by monitoring the signals.
There have been several attempts to detect seizures and abnormalities in EEG
signals with modern deep learning models to reduce the clinical burden.
However, they cannot be fairly compared against each other as they were tested
in distinct experimental settings. Also, some of them are not trained in
real-time seizure detection tasks, making it hard for on-device applications.
Therefore in this work, for the first time, we extensively compare multiple
state-of-the-art models and signal feature extractors in a real-time seizure
detection framework suitable for real-world application, using various
evaluation metrics including a new one we propose to evaluate more practical
aspects of seizure detection models. Our code is available at
https://github.com/AITRICS/EEG_real_time_seizure_detection.

Medical image segmentation has been widely recognized as a pivot procedure
for clinical diagnosis, analysis, and treatment planning. However, the
laborious and expensive annotation process lags down the speed of further
advances. Contrastive learning-based weight pre-training provides an
alternative by leveraging unlabeled data to learn a good representation. In
this paper, we investigate how contrastive learning benefits the general
supervised medical segmentation tasks. To this end, patch-dragsaw contrastive
regularization (PDCR) is proposed to perform patch-level tugging and repulsing
with the extent controlled by a continuous affinity score. And a new structure
dubbed uncertainty-aware feature selection block (UAFS) is designed to perform
the feature selection process, which can handle the learning target shift
caused by minority features with high uncertainty. By plugging the proposed 2
modules into the existing segmentation architecture, we achieve
state-of-the-art results across 8 public datasets from 6 domains. Newly
designed modules further decrease the amount of training data to a quarter
while achieving comparable, if not better, performances. From this perspective,
we take the opposite direction of the original self/un-supervised contrastive
learning by further excavating information contained within the label.

With the help of special neuromorphic hardware, spiking neural networks
(SNNs) are expected to realize artificial intelligence with less energy
consumption. It provides a promising energy-efficient way for realistic control
tasks by combing SNNs and deep reinforcement learning (RL). There are only a
few existing SNN-based RL methods at present. Most of them either lack
generalization ability or employ Artificial Neural Networks (ANNs) to estimate
value function in training. The former needs to tune numerous hyper-parameters
for each scenario, and the latter limits the application of different types of
RL algorithm and ignores the large energy consumption in training. To develop a
robust spike-based RL method, we draw inspiration from non-spiking interneurons
found in insects and propose the deep spiking Q-network (DSQN), using the
membrane voltage of non-spiking neurons as the representation of Q-value, which
can directly learn robust policies from high-dimensional sensory inputs using
end-to-end RL. Experiments conducted on 17 Atari games demonstrate the
effectiveness of DSQN by outperforming the ANN-based deep Q-network (DQN) in
most games. Moreover, the experimental results show superior learning stability
and robustness to adversarial attacks of DSQN.

Defining and accurately measuring generalization in generative models remains
an ongoing challenge and a topic of active research within the machine learning
community. This is in contrast to discriminative models, where there is a clear
definition of generalization, i.e., the model's classification accuracy when
faced with unseen data. In this work, we construct a simple and unambiguous
approach to evaluate the generalization capabilities of generative models.
Using the sample-based generalization metrics proposed here, any generative
model, from state-of-the-art classical generative models such as GANs to
quantum models such as Quantum Circuit Born Machines, can be evaluated on the
same ground on a concrete well-defined framework. In contrast to other
sample-based metrics for probing generalization, we leverage constrained
optimization problems (e.g., cardinality constrained problems) and use these
discrete datasets to define specific metrics capable of unambiguously measuring
the quality of the samples and the model's generalization capabilities for
generating data beyond the training set but still within the valid solution
space. Additionally, our metrics can diagnose trainability issues such as mode
collapse and overfitting, as we illustrate when comparing GANs to
quantum-inspired models built out of tensor networks. Our simulation results
show that our quantum-inspired models have up to a $68 \times$ enhancement in
generating unseen unique and valid samples compared to GANs, and a ratio of
61:2 for generating samples with better quality than those observed in the
training set. We foresee these metrics as valuable tools for rigorously
defining practical quantum advantage in the domain of generative modeling.

Object detection is a challenging and popular computer vision problem. The
problem is even more challenging in aerial images due to significant variation
in scale and viewpoint in a diverse set of object categories. Recently, deep
learning-based object detection approaches have been actively explored for the
problem of object detection in aerial images. In this work, we investigate the
impact of Faster R-CNN for aerial object detection and explore numerous
strategies to improve its performance for aerial images. We conduct extensive
experiments on the challenging iSAID dataset. The resulting adapted Faster
R-CNN obtains a significant mAP gain of 4.96% over its vanilla baseline
counterpart on the iSAID validation set, demonstrating the impact of different
strategies investigated in this work.

We consider the problem of finding tuned regularized parameter estimators for
linear models. We start by showing that three known optimal linear estimators
belong to a wider class of estimators that can be formulated as a solution to a
weighted and constrained minimization problem. The optimal weights, however,
are typically unknown in many applications. This begs the question, how should
we choose the weights using only the data? We propose using the covariance
fitting SPICE-methodology to obtain data-adaptive weights and show that the
resulting class of estimators yields tuned versions of known regularized
estimators - such as ridge regression, LASSO, and regularized least absolute
deviation. These theoretical results unify several important estimators under a
common umbrella. The resulting tuned estimators are also shown to be
practically relevant by means of a number of numerical examples.

The advancement in the field of data science especially in machine learning
along with vast databases of variable star projects like the Optical
Gravitational Lensing Experiment (OGLE) encourages researchers to analyse as
well as classify light curves of different variable stars automatically with
efficiency. In the present work, we have demonstrated the relative performances
of principal component analysis (PCA) and independent component analysis (ICA)
applying to huge databases of OGLE variable star light curves after obtaining
1000 magnitudes between phase 0 to 1 with step length 0.001 for each light
curves in identifying resonances for fundamental mode (FU) and first overtone
(FO) Cepheids and in the classification of variable stars for Large Magellanic
Cloud (LMC), Small Magellanic Cloud (SMC) as well as Milky Way (MW). We have
seen that the performance of ICA is better for finding resonances for Cepheid
variables as well as for accurately classifying large data sets of light curves
than PCA. Using K-means clustering algorithm (CA) with respect to independent
components (ICs), we have plotted period-luminosity diagrams and
colour-magnitude diagrams separately for LMC, SMC and MW and found that ICA
along with K-means CA is a very robust tool for classification as well as
future prediction on the nature of light curves of variable stars.

Recent studies have shown that multi-modeling methods can provide new
insights into the analysis of brain components that are not possible when each
modality is acquired separately. The joint representations of different
modalities is a robust model to analyze simultaneously acquired
electroencephalography and functional magnetic resonance imaging (EEG-fMRI).
Advances in precision instruments have given us the ability to observe the
spatiotemporal neural dynamics of the human brain through non-invasive
neuroimaging techniques such as EEG & fMRI. Nonlinear fusion methods of streams
can extract effective brain components in different dimensions of temporal and
spatial. Graph-based analyzes, which have many similarities to brain structure,
can overcome the complexities of brain mapping analysis. Throughout, we outline
the correlations of several different media in time shifts from one source with
graph-based and deep learning methods. Determining overlaps can provide a new
perspective for diagnosing functional changes in neuroplasticity studies.

The article presents a new multi-label comprehensive image dataset from
flexible endoscopy, colonoscopy and capsule endoscopy, named ERS. The
collection has been labeled according to the full medical specification of
'Minimum Standard Terminology 3.0' (MST 3.0), describing all possible findings
in the gastrointestinal tract (104 possible labels), extended with an
additional 19 labels useful in common machine learning applications.
  The dataset contains around 6000 precisely and 115,000 approximately labeled
frames from endoscopy videos, 3600 precise and 22,600 approximate segmentation
masks, and 1.23 million unlabeled frames from flexible and capsule endoscopy
videos. The labeled data cover almost entirely the MST 3.0 standard. The data
came from 1520 videos of 1135 patients.
  Additionally, this paper proposes and describes four exemplary experiments in
gastrointestinal image classification task performed using the created dataset.
The obtained results indicate the high usefulness and flexibility of the
dataset in training and testing machine learning algorithms in the field of
endoscopic data analysis.

Many studies in the field of education analytics have identified student
grade point averages (GPA) as an important indicator and predictor of students'
final academic outcomes (graduate or halt). And while semester-to-semester
fluctuations in GPA are considered normal, significant changes in academic
performance may warrant more thorough investigation and consideration,
particularly with regards to final academic outcomes. However, such an approach
is challenging due to the difficulties of representing complex academic
trajectories over an academic career. In this study, we apply a Hidden Markov
Model (HMM) to provide a standard and intuitive classification over students'
academic-performance levels, which leads to a compact representation of
academic-performance trajectories. Next, we explore the relationship between
different academic-performance trajectories and their correspondence to final
academic success. Based on student transcript data from University of Central
Florida, our proposed HMM is trained using sequences of students' course grades
for each semester. Through the HMM, our analysis follows the expected finding
that higher academic performance levels correlate with lower halt rates.
However, in this paper, we identify that there exist many scenarios in which
both improving or worsening academic-performance trajectories actually
correlate to higher graduation rates. This counter-intuitive finding is made
possible through the proposed and developed HMM model.

It is well-known that most users do not read privacy policies, but almost all
users tick the box to agree with them. In this paper, we analyze the 25-year
history of privacy policies using methods from transparency research, machine
learning, and natural language processing. Specifically, we collect a
large-scale longitudinal corpus of privacy policies from 1996 to 2021 and
analyze the length and readability of privacy policies as well as their content
in terms of the data practices they describe, the rights they grant to users,
and the rights they reserve for their organizations. We pay particular
attention to changes in response to recent privacy regulations such as the GDPR
and CCPA. Our results show that policies are getting longer and harder to read,
especially after new regulations take effect, and we find a range of concerning
data practices. Our results allow us to speculate why privacy policies are
rarely read and propose changes that would make privacy policies serve their
readers instead of their writers.

We study meta-learning in Markov Decision Processes (MDP) with linear
transition models in the undiscounted episodic setting. Under a task sharedness
metric based on model proximity we study task families characterized by a
distribution over models specified by a bias term and a variance component. We
then propose BUC-MatrixRL, a version of the UC-Matrix RL algorithm, and show it
can meaningfully leverage a set of sampled training tasks to quickly solve a
test task sampled from the same task distribution by learning an estimator of
the bias parameter of the task distribution. The analysis leverages and extends
results in the learning to learn linear regression and linear bandit setting to
the more general case of MDP's with linear transition models. We prove that
compared to learning the tasks in isolation, BUC-Matrix RL provides significant
improvements in the transfer regret for high bias low variance task

Minute-long Gravitational Wave (GW) transients are events lasting from few to
hundreds of seconds. In opposition to compact binary mergers, their GW signals
cover a wide range of poorly understood astrophysical phenomena such as
accretion disk instabilities and magnetar flares. The lack of accurate and
rapidly generated gravitational-wave emission models prevents the use of
matched filtering methods. Such events are thus probed through the
template-free excess-power method, consisting in searching for a local excess
of power in the time-frequency space correlated between detectors. The problem
can be viewed as a search for high-value clustered pixels within an image,
which has been generally tackled by deep learning algorithms such as
Convolutional Neural Networks (CNNs). In this work, we use a CNN as a anomaly
detection tool for the long-duration searches. We show that it can reach a
pixel-wise detection despite trained with minimal assumptions, while being able
to retrieve both astrophysical signals and noise transients originating from
instrumental coupling within the detectors. We also note that our neural
network can extrapolate and connect partially disjoint signal tracks in the
time-frequency plane.

Traditional information retrieval (IR) ranking models process the full text
of documents. Newer models based on Transformers, however, would incur a high
computational cost when processing long texts, so typically use only snippets
from the document instead. The model's input based on a document's URL, title,
and snippet (UTS) is akin to the summaries that appear on a search engine
results page (SERP) to help searchers decide which result to click. This raises
questions about when such summaries are sufficient for relevance estimation by
the ranking model or the human assessor, and whether humans and machines
benefit from the document's full text in similar ways. To answer these
questions, we study human and neural model based relevance assessments on 12k
query-documents sampled from Bing's search logs. We compare changes in the
relevance assessments when only the document summaries and when the full text
is also exposed to assessors, studying a range of query and document
properties, e.g., query type, snippet length. Our findings show that the full
text is beneficial for humans and a BERT model for similar query and document
types, e.g., tail, long queries. A closer look, however, reveals that humans
and machines respond to the additional input in very different ways. Adding the
full text can also hurt the ranker's performance, e.g., for navigational
queries.

The term personality may be expressed in terms of the individual differences
in characteristics pattern of thinking, feeling, and behavior. This work
presents several machine learning techniques including Naive Bayes, Support
Vector Machines, and Recurrent Neural Networks to predict people personality
from text based on Myers-Briggs Type Indicator (MBTI). Furthermore, this
project applies CRISP-DM, which stands for Cross-Industry Standard Process for
Data Mining, to guide the learning process. Since, CRISP-DM is kind of
iterative development, we have adopted it with agile methodology, which is a
rapid iterative software development method, in order to reduce the development
cycle to be minimal.

Dot product kernels, such as polynomial and exponential (softmax) kernels,
are among the most widely used kernels in machine learning, as they enable
modeling the interactions between input features, which is crucial in
applications like computer vision, natural language processing, and recommender
systems. We make several novel contributions for improving the efficiency of
random feature approximations for dot product kernels, to make these kernels
more useful in large scale learning. First, we present a generalization of
existing random feature approximations for polynomial kernels, such as
Rademacher and Gaussian sketches and TensorSRHT, using complex-valued random
features. We show empirically that the use of complex features can
significantly reduce the variances of these approximations. Second, we provide
a theoretical analysis for understanding the factors affecting the efficiency
of various random feature approximations, by deriving closed-form expressions
for their variances. These variance formulas elucidate conditions under which
certain approximations (e.g., TensorSRHT) achieve lower variances than others
(e.g., Rademacher sketches), and conditions under which the use of complex
features leads to lower variances than real features. Third, by using these
variance formulas, which can be evaluated in practice, we develop a data-driven
optimization approach to improve random feature approximations for general dot
product kernels, which is also applicable to the Gaussian kernel. We describe
the improvements brought by these contributions with extensive experiments on a
variety of tasks and datasets.

The vast majority of the work on adaptive data analysis focuses on the case
where the samples in the dataset are independent. Several approaches and tools
have been successfully applied in this context, such as differential privacy,
max-information, compression arguments, and more. The situation is far less
well-understood without the independence assumption.
  We embark on a systematic study of the possibilities of adaptive data
analysis with correlated observations. First, we show that, in some cases,
differential privacy guarantees generalization even when there are dependencies
within the sample, which we quantify using a notion we call Gibbs-dependence.
We complement this result with a tight negative example. Second, we show that
the connection between transcript-compression and adaptive data analysis can be
extended to the non-iid setting.

Contrastive learning has achieved remarkable success in representation
learning via self-supervision in unsupervised settings. However, effectively
adapting contrastive learning to supervised learning tasks remains as a
challenge in practice. In this work, we introduce a dual contrastive learning
(DualCL) framework that simultaneously learns the features of input samples and
the parameters of classifiers in the same space. Specifically, DualCL regards
the parameters of the classifiers as augmented samples associating to different
labels and then exploits the contrastive learning between the input samples and
the augmented samples. Empirical studies on five benchmark text classification
datasets and their low-resource version demonstrate the improvement in
classification accuracy and confirm the capability of learning discriminative
representations of DualCL.

In metric learning, the goal is to learn an embedding so that data points
with the same class are close to each other and data points with different
classes are far apart. We propose a distance-ratio-based (DR) formulation for
metric learning. Like softmax-based formulation for metric learning, it models
$p(y=c|x')$, which is a probability that a query point $x'$ belongs to a class
$c$. The DR formulation has two useful properties. First, the corresponding
loss is not affected by scale changes of an embedding. Second, it outputs the
optimal (maximum or minimum) classification confidence scores on representing
points for classes. To demonstrate the effectiveness of our formulation, we
conduct few-shot classification experiments using softmax-based and DR
formulations on CUB and mini-ImageNet datasets. The results show that DR
formulation generally enables faster and more stable metric learning than the
softmax-based formulation. As a result, using DR formulation achieves improved
or comparable generalization performances.

Language has a profound impact on our thoughts, perceptions, and conceptions
of gender roles. Gender-inclusive language is, therefore, a key tool to promote
social inclusion and contribute to achieving gender equality. Consequently,
detecting and mitigating gender bias in texts is instrumental in halting its
propagation and societal implications. However, there is a lack of gender bias
datasets and lexicons for automating the detection of gender bias using
supervised and unsupervised machine learning (ML) and natural language
processing (NLP) techniques. Therefore, the main contribution of this work is
to publicly provide labeled datasets and exhaustive lexicons by collecting,
annotating, and augmenting relevant sentences to facilitate the detection of
gender bias in English text. Towards this end, we present an updated version of
our previously proposed taxonomy by re-formalizing its structure, adding a new
bias type, and mapping each bias subtype to an appropriate detection
methodology. The released datasets and lexicons span multiple bias subtypes
including: Generic He, Generic She, Explicit Marking of Sex, and Gendered
Neologisms. We leveraged the use of word embedding models to further augment
the collected lexicons. The underlying motivation of our work is to enable the
technical community to combat gender bias in text and halt its propagation
using ML and NLP techniques.

The recent developments in the machine learning domain have enabled the
development of complex multivariate probabilistic forecasting models.
Therefore, it is pivotal to have a precise evaluation method to gauge the
performance and predictability power of these complex methods. To do so,
several evaluation metrics have been proposed in the past (such as Energy
Score, Dawid-Sebastiani score, variogram score), however, they cannot reliably
measure the performance of a probabilistic forecaster. Recently, CRPS-sum has
gained a lot of prominence as a reliable metric for multivariate probabilistic
forecasting. This paper presents a systematic evaluation of CRPS-sum to
understand its discrimination ability. We show that the statistical properties
of target data affect the discrimination ability of CRPS-Sum. Furthermore, we
highlight that CRPS-Sum calculation overlooks the performance of the model on
each dimension. These flaws can lead us to an incorrect assessment of model
performance. Finally, with experiments on the real-world dataset, we
demonstrate that the shortcomings of CRPS-Sum provide a misleading indication
of the probabilistic forecasting performance method. We show that it is easily
possible to have a better CRPS-Sum for a dummy model, which looks like random
noise, in comparison to the state-of-the-art method.

Recently, pretrained language models (PLMs) have had exceptional success in
language generation. To leverage the rich knowledge encoded by PLMs, a simple
yet powerful paradigm is to use prompts in the form of either discrete tokens
or continuous embeddings. In existing studies, these prompting methods are
typically independent of the inputs, lacking sufficient consideration of input
semantics. To address this issue, we propose a novel continuous prompting
approach, called context-tuning, to fine-tuning PLMs for natural language
generation. Firstly, the prompts are derived based on the input text to elicit
useful knowledge from PLMs for generation. We refer to such prompts as
contextualized prompts. Secondly, we use continuous inverse prompting to
improve the process of natural language generation by modeling an inverse
generation process from output to input, making the generated text more
relevant to the inputs. Furthermore, we utilize a lightweight context-tuning
method that fine-tunes only 0.12% of the parameters while maintaining good
performance. Our code is publicly available at
https://github.com/RUCAIBox/Context-Tuning.

Candlestick pattern is one of the most fundamental and valuable graphical
tools in financial trading that supports traders observing the current market
conditions to make the proper decision. This task has a long history and, most
of the time, human experts. Recently, efforts have been made to automatically
classify these patterns with the deep learning models. The GAF-CNN model is a
well-suited way to imitate how human traders capture the candlestick pattern by
integrating spatial features visually. However, with the great potential of the
GAF encoding, this classification task can be extended to a more complicated
object detection level. This work presents an innovative integration of modern
object detection techniques and GAF time-series encoding on candlestick pattern
tasks. We make crucial modifications to the representative yet straightforward
YOLO version 1 model based on our time-series encoding method and the property
of such data type. Powered by the deep neural networks and the unique
architectural design, the proposed model performs pretty well in candlestick
classification and location recognition. The results show tremendous potential
in applying modern object detection techniques on time-series tasks in a
real-time manner.

Advanced radio-frequency pulse design used in magnetic resonance imaging has
recently been demonstrated with deep learning of (convolutional) neural
networks and reinforcement learning. For two-dimensionally selective
radio-frequency pulses, the (convolutional) neural network pulse prediction
time (few milliseconds) was in comparison more than three orders of magnitude
faster than the conventional optimal control computation. The network pulses
were from the supervised training capable of compensating scan-subject
dependent inhomogeneities of B0 and B+1 fields. Unfortunately, the network
presented with a non-negligible percentage of pulse amplitude overshoots in the
test subset, despite the optimal control pulses used in training were fully
constrained. Here, we have extended the convolutional neural network with a
custom-made clipping layer that completely eliminates the risk of pulse
amplitude overshoots, while preserving the ability to compensate the
inhomogeneous field conditions.

Computing the matrix square root or its inverse in a differentiable manner is
important in a variety of computer vision tasks. Previous methods either adopt
the Singular Value Decomposition (SVD) to explicitly factorize the matrix or
use the Newton-Schulz iteration (NS iteration) to derive the approximate
solution. However, both methods are not computationally efficient enough in
either the forward pass or in the backward pass. In this paper, we propose two
more efficient variants to compute the differentiable matrix square root. For
the forward propagation, one method is to use Matrix Taylor Polynomial (MTP),
and the other method is to use Matrix Pad\'e Approximants (MPA). The backward
gradient is computed by iteratively solving the continuous-time Lyapunov
equation using the matrix sign function. Both methods yield considerable
speed-up compared with the SVD or the Newton-Schulz iteration. Experimental
results on the de-correlated batch normalization and second-order vision
transformer demonstrate that our methods can also achieve competitive and even
slightly better performances. The code is available at
\href{https://github.com/KingJamesSong/FastDifferentiableMatSqrt}{https://github.com/KingJamesSong/FastDifferentiableMatSqrt}.

Deep learning has shown great promise in the domain of medical image
analysis. Medical professionals and healthcare providers have been adopting the
technology to speed up and enhance their work. These systems use deep neural
networks (DNN) which are vulnerable to adversarial samples; images with
imperceivable changes that can alter the model's prediction. Researchers have
proposed defences which either make a DNN more robust or detect the adversarial
samples before they do harm. However, none of these works consider an informed
attacker which can adapt to the defence mechanism. We show that an informed
attacker can evade five of the current state of the art defences while
successfully fooling the victim's deep learning model, rendering these defences
useless. We then suggest better alternatives for securing healthcare DNNs from
such attacks: (1) harden the system's security and (2) use digital signatures.

This paper presents a transfer learning approach which enables fast and
efficient adaptation of Recurrent Neural Network (RNN) models of dynamical
systems. A nominal RNN model is first identified using available measurements.
The system dynamics are then assumed to change, leading to an unacceptable
degradation of the nominal model performance on the perturbed system. To cope
with the mismatch, the model is augmented with an additive correction term
trained on fresh data from the new dynamic regime. The correction term is
learned through a Jacobian Feature Regression (JFR) method defined in terms of
the features spanned by the model's Jacobian with respect to its nominal
parameters. A non-parametric view of the approach is also proposed, which
extends recent work on Gaussian Process (GP) with Neural Tangent Kernel
(NTK-GP) to the RNN case (RNTK-GP). This can be more efficient for very large
networks or when only few data points are available. Implementation aspects for
fast and efficient computation of the correction term, as well as the initial
state estimation for the RNN model are described. Numerical examples show the
effectiveness of the proposed methodology in presence of significant system
variations.

We propose Unity Smoothing (US) for handling inconsistencies between a
Bayesian network model and new unseen observations. We show that prediction
accuracy, using the junction tree algorithm with US is comparable to that of
Laplace smoothing. Moreover, in applications were sparsity of the data
structures is utilized, US outperforms Laplace smoothing in terms of memory
usage. Furthermore, we detail how to avoid redundant calculations that must
otherwise be performed during the message passing scheme in the junction tree
algorithm which we refer to as Unity Propagation (UP). Experimental results
shows that it is always faster to exploit UP on top of the
Lauritzen-Spigelhalter message passing scheme for the junction tree algorithm.

Generalizing the medical image segmentation algorithms to unseen domains is
an important research topic for computer-aided diagnosis and surgery. Most
existing methods require a fully labeled dataset in each source domain.
Although some researchers developed a semi-supervised domain generalized
method, it still requires the domain labels. This paper presents a novel
confidence-aware cross pseudo supervision algorithm for semi-supervised domain
generalized medical image segmentation. The main goal is to enhance the pseudo
label quality for unlabeled images from unknown distributions. To achieve it,
we perform the Fourier transformation to learn low-level statistic information
across domains and augment the images to incorporate cross-domain information.
With these augmentations as perturbations, we feed the input to a
confidence-aware cross pseudo supervision network to measure the variance of
pseudo labels and regularize the network to learn with more confident pseudo
labels. Our method sets new records on public datasets, i.e., M&Ms and SCGM.
Notably, without using domain labels, our method surpasses the prior art that
even uses domain labels by 11.67% on Dice on M&Ms dataset with 2% labeled data.
Code is available at https://github.com/XMed-Lab/EPL_SemiDG.

To fit sparse linear associations, a LASSO sparsity inducing penalty with a
single hyperparameter provably allows to recover the important features
(needles) with high probability in certain regimes even if the sample size is
smaller than the dimension of the input vector (haystack). More recently
learners known as artificial neural networks (ANN) have shown great successes
in many machine learning tasks, in particular fitting nonlinear associations.
Small learning rate, stochastic gradient descent algorithm and large training
set help to cope with the explosion in the number of parameters present in deep
neural networks. Yet few ANN learners have been developed and studied to find
needles in nonlinear haystacks. Driven by a single hyperparameter, our ANN
learner, like for sparse linear associations, exhibits a phase transition in
the probability of retrieving the needles, which we do not observe with other
ANN learners. To select our penalty parameter, we generalize the universal
threshold of Donoho and Johnstone (1994) which is a better rule than the
conservative (too many false detections) and expensive cross-validation. In the
spirit of simulated annealing, we propose a warm-start sparsity inducing
algorithm to solve the high-dimensional, non-convex and non-differentiable
optimization problem. We perform precise Monte Carlo simulations to show the

Class Activation Mapping (CAM) has been widely adopted to generate saliency
maps which provides visual explanations for deep neural networks (DNNs). The
saliency maps are conventionally generated by fusing the channels of the target
feature map using a weighted average scheme. It is a weak model for the
inter-channel relation, in the sense that it only models the relation among
channels in a contrastive way (i.e., channels that play key roles in the
prediction are given higher weights for them to stand out in the fusion). The
collaborative relation, which makes the channels work together to provide cross
reference, has been ignored. Furthermore, the model has neglected the
intra-channel relation thoroughly.In this paper, we address this problem by
introducing Conceptor learning into CAM generation. Conceptor leaning has been
originally proposed to model the patterns of state changes in recurrent neural
networks (RNNs). By relaxing the dependency of Conceptor learning to RNNs, we
make Conceptor-CAM not only generalizable to more DNN architectures but also
able to learn both the inter- and intra-channel relations for better saliency
map generation. Moreover, we have enabled the use of Boolean operations to
combine the positive and pseudo-negative evidences, which has made the CAM
inference more robust and comprehensive. The effectiveness of Conceptor-CAM has
been validated with both formal verifications and experiments on the dataset of
the largest scale in literature. The experimental results show that
Conceptor-CAM is compatible with and can bring significant improvement to all
well recognized CAM-based methods, and has outperformed the state-of-the-art
methods by 43.14%~72.79% (88.39%~168.15%) on ILSVRC2012 in Average Increase
(Drop), 15.42%~42.55% (47.09%~372.09%) on VOC, and 17.43%~31.32%
(47.54%~206.45%) on COCO, respectively.

The layout optimization of the heat conduction is essential during design in
engineering, especially for thermal sensible products. When the optimization
algorithm iteratively evaluates different loading cases, the traditional
numerical simulation methods used usually lead to a substantial computational
cost. To effectively reduce the computational effort, data-driven approaches
are used to train a surrogate model as a mapping between the prescribed
external loads and various geometry. However, the existing model are trained by
data-driven methods which requires intensive training samples that from
numerical simulations and not really effectively solve the problem. Choosing
the steady heat conduction problems as examples, this paper proposes a
Physics-driven Convolutional Neural Networks (PD-CNN) method to infer the
physical field solutions for random varied loading cases. After that, the
Particle Swarm Optimization (PSO) algorithm is used to optimize the sizes and
the positions of the hole masks in the prescribed design domain, and the
average temperature value of the entire heat conduction field is minimized, and
the goal of minimizing heat transfer is achieved. Compared with the existing
data-driven approaches, the proposed PD-CNN optimization framework not only
predict field solutions that are highly consistent with conventional simulation
results, but also generate the solution space with without any pre-obtained

3D reconstruction of depth and motion from monocular video in dynamic
environments is a highly ill-posed problem due to scale ambiguities when
projecting to the 2D image domain. In this work, we investigate the performance
of the current State-of-the-Art (SotA) deep multi-view systems in such
environments. We find that current supervised methods work surprisingly well
despite not modelling individual object motions, but make systematic errors due
to a lack of dense ground truth data. To detect such errors during usage, we
extend the cost volume based Deep Video to Depth (DeepV2D) framework
\cite{teed2018deepv2d} with a learned uncertainty. Our Deep Video to certain
Depth (DeepV2cD) model allows i) to perform en par or better with current SotA
and ii) achieve a better uncertainty measure than the naive Shannon entropy.
Our experiments show that a simple filter strategy based on the uncertainty can
significantly reduce systematic errors. This results in cleaner reconstructions
both on static and dynamic parts of the scene.

Quantum machine learning has emerged as a potential practical application of
near-term quantum devices. In this work, we study a two-layer hybrid
classical-quantum classifier in which a first layer of quantum stochastic
neurons implementing generalized linear models (QGLMs) is followed by a second
classical combining layer. The input to the first, hidden, layer is obtained
via amplitude encoding in order to leverage the exponential size of the fan-in
of the quantum neurons in the number of qubits per neuron. To facilitate
implementation of the QGLMs, all weights and activations are binary. While the
state of the art on training strategies for this class of models is limited to
exhaustive search and single-neuron perceptron-like bit-flip strategies, this
letter introduces a stochastic variational optimization approach that enables
the joint training of quantum and classical layers via stochastic gradient
descent. Experiments show the advantages of the approach for a variety of
activation functions implemented by QGLM neurons.

The second edition of the "VIPriors: Visual Inductive Priors for
Data-Efficient Deep Learning" challenges featured five data-impaired
challenges, where models are trained from scratch on a reduced number of
training samples for various key computer vision tasks. To encourage new and
creative ideas on incorporating relevant inductive biases to improve the data
efficiency of deep learning models, we prohibited the use of pre-trained
checkpoints and other transfer learning techniques. The provided baselines are
outperformed by a large margin in all five challenges, mainly thanks to
extensive data augmentation policies, model ensembling, and data efficient
network architectures.

We present a principled data-driven strategy for learning deterministic
hydrodynamic models directly from stochastic non-equilibrium active particle
trajectories. We apply our method to learning a hydrodynamic model for the
propagating density lanes observed in self-propelled particle systems and to
learning a continuum description of cell dynamics in epithelial tissues. We
also infer from stochastic particle trajectories the latent phoretic fields
driving chemotaxis. This demonstrates that statistical learning theory combined
with physical priors can enable discovery of multi-scale models of
non-equilibrium stochastic processes characteristic of collective movement in
living systems.

Deep learning models have been shown to be vulnerable to recent backdoor
attacks. A backdoored model behaves normally for inputs containing no
attacker-secretly-chosen trigger and maliciously for inputs with the trigger.
To date, backdoor attacks and countermeasures mainly focus on image
classification tasks. And most of them are implemented in the digital world
with digital triggers. Besides the classification tasks, object detection
systems are also considered as one of the basic foundations of computer vision
tasks. However, there is no investigation and understanding of the backdoor
vulnerability of the object detector, even in the digital world with digital
triggers. For the first time, this work demonstrates that existing object
detectors are inherently susceptible to physical backdoor attacks. We use a
natural T-shirt bought from a market as a trigger to enable the cloaking
effect--the person bounding-box disappears in front of the object detector. We
show that such a backdoor can be implanted from two exploitable attack
scenarios into the object detector, which is outsourced or fine-tuned through a
pretrained model. We have extensively evaluated three popular object detection
algorithms: anchor-based Yolo-V3, Yolo-V4, and anchor-free CenterNet. Building
upon 19 videos shot in real-world scenes, we confirm that the backdoor attack
is robust against various factors: movement, distance, angle, non-rigid
deformation, and lighting. Specifically, the attack success rate (ASR) in most
videos is 100% or close to it, while the clean data accuracy of the backdoored
model is the same as its clean counterpart. The latter implies that it is
infeasible to detect the backdoor behavior merely through a validation set. The
averaged ASR still remains sufficiently high to be 78% in the transfer learning
attack scenarios evaluated on CenterNet. See the demo video on
https://youtu.be/Q3HOF4OobbY.

Localizing keypoints of an object is a basic visual problem. However,
supervised learning of a keypoint localization network often requires a large
amount of data, which is expensive and time-consuming to obtain. To remedy
this, there is an ever-growing interest in semi-supervised learning (SSL),
which leverages a small set of labeled data along with a large set of unlabeled
data. Among these SSL approaches, pseudo-labeling (PL) is one of the most
popular. PL approaches apply pseudo-labels to unlabeled data, and then train
the model with a combination of the labeled and pseudo-labeled data
iteratively. The key to the success of PL is the selection of high-quality
pseudo-labeled samples. Previous works mostly select training samples by
manually setting a single confidence threshold. We propose to automatically
select reliable pseudo-labeled samples with a series of dynamic thresholds,
which constitutes a learning curriculum. Extensive experiments on six keypoint
localization benchmark datasets demonstrate that the proposed approach
significantly outperforms the previous state-of-the-art SSL approaches.

In this paper, we place deep Q-learning into a control-oriented perspective
and study its learning dynamics with well-established techniques from robust
control. We formulate an uncertain linear time-invariant model by means of the
neural tangent kernel to describe learning. We show the instability of learning
and analyze the agent's behavior in frequency-domain. Then, we ensure
convergence via robust controllers acting as dynamical rewards in the loss
function. We synthesize three controllers: state-feedback gain scheduling H2,
dynamic Hinf, and constant gain Hinf controllers. Setting up the learning agent
with a control-oriented tuning methodology is more transparent and has
well-established literature compared to the heuristics in reinforcement
learning. In addition, our approach does not use a target network and
randomized replay memory. The role of the target network is overtaken by the
control input, which also exploits the temporal dependency of samples (opposed
to a randomized memory buffer). Numerical simulations in different OpenAI Gym
environments suggest that the Hinf controlled learning performs slightly better
than Double deep Q-learning.

Modern neural networks require long training to reach decent performance on
massive datasets. One common approach to speed up training is model
parallelization, where large neural networks are split across multiple devices.
However, different device placements of the same neural network lead to
different training times. Most of the existing device placement solutions treat
the problem as sequential decision-making by traversing neural network graphs
and assigning their neurons to different devices. This work studies the impact
of graph traversal order on device placement. In particular, we empirically
study how different graph traversal order leads to different device placement,
which in turn affects the training execution time. Our experiment results show
that the best graph traversal order depends on the type of neural networks and
their computation graphs features. In this work, we also provide
recommendations on choosing graph traversal order in device placement for
various neural network families to improve the training time in model
parallelization.

Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a
taxonomic backbone that allows the arrangement and structuring of various
concepts in accordance with the hypo-hypernym ("class-subclass") relationship.
With the rapid growth of lexical resources for specific domains, the problem of
automatic extension of the existing knowledge bases with new words is becoming
more and more widespread. In this paper, we address the problem of taxonomy
enrichment which aims at adding new words to the existing taxonomy.
  We present a new method that allows achieving high results on this task with
little effort. It uses the resources which exist for the majority of languages,
making the method universal. We extend our method by incorporating deep
representations of graph structures like node2vec, Poincar\'e embeddings, GCN
etc. that have recently demonstrated promising results on various NLP tasks.
Furthermore, combining these representations with word embeddings allows us to
beat the state of the art.
  We conduct a comprehensive study of the existing approaches to taxonomy
enrichment based on word and graph vector representations and their fusion
approaches. We also explore the ways of using deep learning architectures to
extend the taxonomic backbones of knowledge graphs. We create a number of
datasets for taxonomy extension for English and Russian. We achieve
state-of-the-art results across different datasets and provide an in-depth
error analysis of mistakes.

The increased complexity of state-of-the-art reinforcement learning (RL)
algorithms have resulted in an opacity that inhibits explainability and
understanding. This has led to the development of several post-hoc
explainability methods that aim to extract information from learned policies
thus aiding explainability. These methods rely on empirical observations of the
policy and thus aim to generalize a characterization of agents' behaviour. In
this study, we have instead developed a method to imbue a characteristic
behaviour into agents' policies through regularization of their objective
functions. Our method guides the agents' behaviour during learning which
results in an intrinsic characterization; it connects the learning process with
model explanation. We provide a formal argument and empirical evidence for the
viability of our method. In future work, we intend to employ it to develop
agents that optimize individual financial customers' investment portfolios
based on their spending personalities.

Basis function learning is the stepping stone towards effective
three-dimensional (3D) sound speed field (SSF) inversion for various acoustic
signal processing tasks, including ocean acoustic tomography, underwater target
localization/tracking, and underwater communications. Classical basis functions
include the empirical orthogonal functions (EOFs), Fourier basis functions, and
their combinations. The unsupervised machine learning method, e.g., the K-SVD
algorithm, has recently tapped into the basis function design, showing better
representation performance than the EOFs. However, existing methods do not
consider basis function learning approaches that treat 3D SSF data as a
third-order tensor, and thus cannot fully utilize the 3D
interactions/correlations therein. To circumvent such a drawback, basis
function learning is linked to tensor decomposition in this paper, which is the
primary drive for recent multi-dimensional data mining. In particular, a
tensor-based basis function learning framework is proposed, which can include
the classical basis functions (using EOFs and/or Fourier basis functions) as
its special cases. This provides a unified tensor perspective for understanding
and representing 3D SSFs. Numerical results using the South China Sea 3D SSF
data have demonstrated the excellent performance of the tensor-based basis

Current research on deep learning for medical image segmentation exposes
their limitations in learning either global semantic information or local
contextual information. To tackle these issues, a novel network named
SegTransVAE is proposed in this paper. SegTransVAE is built upon
encoder-decoder architecture, exploiting transformer with the variational
autoencoder (VAE) branch to the network to reconstruct the input images jointly
with segmentation. To the best of our knowledge, this is the first method
combining the success of CNN, transformer, and VAE. Evaluation on various
recently introduced datasets shows that SegTransVAE outperforms previous
methods in Dice Score and $95\%$-Haudorff Distance while having comparable
inference time to a simple CNN-based architecture network. The source code is
available at: https://github.com/itruonghai/SegTransVAE.

Knowledge graphs (KGs) have become a valuable asset for many AI applications.
Although some KGs contain plenty of facts, they are widely acknowledged as
incomplete. To address this issue, many KG completion methods are proposed.
Among them, open KG completion methods leverage the Web to find missing facts.
However, noisy data collected from diverse sources may damage the completion
accuracy. In this paper, we propose a new trustworthy method that exploits
facts for a KG based on multi-sourced noisy data and existing facts in the KG.
Specifically, we introduce a graph neural network with a holistic scoring
function to judge the plausibility of facts with various value types. We design
value alignment networks to resolve the heterogeneity between values and map
them to entities even outside the KG. Furthermore, we present a truth inference
model that incorporates data source qualities into the fact scoring function,
and design a semi-supervised learning way to infer the truths from
heterogeneous values. We conduct extensive experiments to compare our method
with the state-of-the-arts. The results show that our method achieves superior
accuracy not only in completing missing facts but also in discovering new
facts.

The AlphaFold computer program predicted protein structures for the whole
human genome, which has been considered as a remarkable breakthrough both in
artificial intelligence (AI) application and structural biology. Despite the
varying confidence level, these predicted structures still could significantly
contribute to structure-based drug design of novel targets, especially the ones
with no or limited structural information. In this work, we successfully
applied AlphaFold in our end-to-end AI-powered drug discovery engines
constituted of a biocomputational platform PandaOmics and a generative
chemistry platform Chemistry42, to identify a first-in-class hit molecule of a
novel target without an experimental structure starting from target selection
towards hit identification in a cost- and time-efficient manner. PandaOmics
provided the targets of interest and Chemistry42 generated the molecules based
on the AlphaFold predicted structure, and the selected molecules were
synthesized and tested in biological assays. Through this approach, we
identified a small molecule hit compound for CDK20 with a Kd value of 8.9 +/-
1.6 uM (n = 4) within 30 days from target selection and after only synthesizing
7 compounds. Based on the available data, the second round of AI-powered
compound generation was conducted and through which, a more potent hit
molecule, ISM042-2 048, was discovered with a Kd value of 210.0 +/- 42.4 nM (n
= 2), within 30 days and after synthesizing 6 compounds from the discovery of
the first hit ISM042-2-001. To the best of our knowledge, this is the first
reported small molecule targeting CDK20 and more importantly, this work is the
first demonstration of AlphaFold application in the hit identification process
in early drug discovery.

Based on the double pendulum and Lagrange equation, the moving particles are
captured by a binocular three-dimensional capture camera. Two trajectory models
of Astrojax and the relationship between trajectory empirical formula and
parameters are established. Through research, the calculated trajectory of this
formula and related parameters fit well with the actual measured trajectory,
and can accurately predict and change the trajectory of the model. The
equipment and materials required in the experiment are simple and easy to
obtain, and the experimental theme is relatively interesting and novel, which
can be applied as an extended experiment in college physics experiment course,
so that students can understand the motion characteristics of the double
pendulum and learn physics from life. The designing experiment can not only
improve students' interest in learning, but also broaden their knowledge and
cultivate their practical ability.

Human activity recognition has grown in popularity with its increase of
applications within daily lifestyles and medical environments. The goal of
having efficient and reliable human activity recognition brings benefits such
as accessible use and better allocation of resources; especially in the medical
industry. Activity recognition and classification can be obtained using many
sophisticated data recording setups, but there is also a need in observing how
performance varies among models that are strictly limited to using sensor data
from easily accessible devices: smartphones and smartwatches. This paper
presents the findings of different models that are limited to train using such
sensors. The models are trained using either the k-Nearest Neighbor, Support
Vector Machine, or Random Forest classifier algorithms. Performance and
evaluations are done by comparing various model performances using different
combinations of mobile sensors and how they affect recognitive performances of
models. Results show promise for models trained strictly using limited sensor
data collected from only smartphones and smartwatches coupled with traditional
machine learning concepts and algorithms.

In recent years the amount of secure information being stored on mobile
devices has grown exponentially. However, current security schemas for mobile
devices such as physiological biometrics and passwords are not secure enough to
protect this information. Behavioral biometrics have been heavily researched as
a possible solution to this security deficiency for mobile devices. This study
aims to contribute to this innovative research by evaluating the performance of
a multimodal behavioral biometric based user authentication scheme using touch
dynamics and phone movement. This study uses a fusion of two popular publicly
available datasets the Hand Movement Orientation and Grasp dataset and the
BioIdent dataset. This study evaluates our model performance using three common
machine learning algorithms which are Random Forest Support Vector Machine and
K-Nearest Neighbor reaching accuracy rates as high as 82% with each algorithm
performing respectively for all success metrics reported.

We develop a Causal-Deep Neural Network (CDNN) model trained in two stages to
infer causal impact estimates at an individual unit level. Using only the
pre-treatment features in stage 1 in the absence of any treatment information,
we learn an encoding for the covariates that best represents the outcome. In
the $2^{nd}$ stage we further seek to predict the unexplained outcome from
stage 1, by introducing the treatment indicator variables alongside the encoded
covariates. We prove that even without explicitly computing the treatment
residual, our method still satisfies the desirable local Neyman orthogonality,
making it robust to small perturbations in the nuisance parameters.
Furthermore, by establishing connections with the representation learning
approaches, we create a framework from which multiple variants of our algorithm
can be derived. We perform initial experiments on the publicly available data
sets to compare these variants and get guidance in selecting the best variant
of our CDNN method. On evaluating CDNN against the state-of-the-art approaches
on three benchmarking datasets, we observe that CDNN is highly competitive and
often yields the most accurate individual treatment effect estimates. We
highlight the strong merits of CDNN in terms of its extensibility to multiple
use cases.

Recent studies have shown that GNNs are vulnerable to adversarial attack.
Thus, many approaches are proposed to improve the robustness of GNNs against
adversarial attacks. Nevertheless, most of these methods measure the model
robustness based on label information and thus become infeasible when labels
information is not available. Therefore, this paper focuses on robust
unsupervised graph representation learning. In particular, to quantify the
robustness of GNNs without label information, we propose a robustness measure,
named graph representation robustness (GRR), to evaluate the mutual information
between adversarially perturbed node representations and the original graph.
There are mainly two challenges to estimate GRR: 1) mutual information
estimation upon adversarially attacked graphs; 2) high complexity of
adversarial attack to perturb node features and graph structure jointly in the
training procedure. To tackle these problems, we further propose an effective
mutual information estimator with subgraph-level summary and an efficient
adversarial training strategy with only feature perturbations. Moreover, we
theoretically establish a connection between our proposed GRR measure and the
robustness of downstream classifiers, which reveals that GRR can provide a
lower bound to the adversarial risk of downstream classifiers. Extensive
experiments over several benchmarks demonstrate the effectiveness and
superiority of our proposed method.

The landscape of adversarial attacks against text classifiers continues to
grow, with new attacks developed every year and many of them available in
standard toolkits, such as TextAttack and OpenAttack. In response, there is a
growing body of work on robust learning, which reduces vulnerability to these
attacks, though sometimes at a high cost in compute time or accuracy. In this
paper, we take an alternate approach -- we attempt to understand the attacker
by analyzing adversarial text to determine which methods were used to create
it. Our first contribution is an extensive dataset for attack detection and
labeling: 1.5~million attack instances, generated by twelve adversarial attacks
targeting three classifiers trained on six source datasets for sentiment
analysis and abuse detection in English. As our second contribution, we use
this dataset to develop and benchmark a number of classifiers for attack
identification -- determining if a given text has been adversarially
manipulated and by which attack. As a third contribution, we demonstrate the
effectiveness of three classes of features for these tasks: text properties,
capturing content and presentation of text; language model properties,
determining which tokens are more or less probable throughout the input; and
target model properties, representing how the text classifier is influenced by
the attack, including internal node activations. Overall, this represents a
first step towards forensics for adversarial attacks against text classifiers.

Recently, hyperbolic space has risen as a promising alternative for
semi-supervised graph representation learning. Many efforts have been made to
design hyperbolic versions of neural network operations. However, the inspiring
geometric properties of this unique geometry have not been fully explored yet.
The potency of graph models powered by the hyperbolic space is still largely
underestimated. Besides, the rich information carried by abundant unlabelled
samples is also not well utilized. Inspired by the recently active and emerging
self-supervised learning, in this study, we attempt to enhance the
representation power of hyperbolic graph models by drawing upon the advantages
of contrastive learning. More specifically, we put forward a novel Hyperbolic
Graph Contrastive Learning (HGCL) framework which learns node representations
through multiple hyperbolic spaces to implicitly capture the hierarchical
structure shared between different views. Then, we design a hyperbolic position
consistency (HPC) constraint based on hyperbolic distance and the homophily
assumption to make contrastive learning fit into hyperbolic space. Experimental
results on multiple real-world datasets demonstrate the superiority of the
proposed HGCL as it consistently outperforms competing methods by considerable
margins for the node classification task.

Federated Learning (FL) provides privacy preservation by allowing the model
training at edge devices without the need of sending the data from edge to a
centralized server. FL has distributed the implementation of ML. Another
variant of FL which is well suited for the Internet of Things (IoT) is known as
Collaborated Federated Learning (CFL), which does not require an edge device to
have a direct link to the model aggregator. Instead, the devices can connect to
the central model aggregator via other devices using them as relays. Although,
FL and CFL protect the privacy of edge devices but raises security challenges
for a centralized server that performs model aggregation. The centralized
server is prone to malfunction, backdoor attacks, model corruption, adversarial
attacks and external attacks. Moreover, edge device to centralized server data
exchange is not required in FL and CFL, but model parameters are sent from the
model aggregator (global model) to edge devices (local model), which is still
prone to cyber-attacks. These security and privacy concerns can be potentially
addressed by Blockchain technology. The blockchain is a decentralized and
consensus-based chain where devices can share consensus ledgers with increased
reliability and security, thus significantly reducing the cyberattacks on an
exchange of information. In this work, we will investigate the efficacy of
blockchain-based decentralized exchange of model parameters and relevant
information among edge devices and from a centralized server to edge devices.
Moreover, we will be conducting the feasibility analysis for blockchain-based
CFL models for different application scenarios like the internet of vehicles,
and the internet of things. The proposed study aims to improve the security,
reliability and privacy preservation by the use of blockchain-powered CFL.

In the past ten years, the computing power of machine vision (MV) has been
continuously improved, and image analysis algorithms have developed rapidly. At
the same time, histopathological slices can be stored as digital images.
Therefore, MV algorithms can provide doctors with diagnostic references. In
particular, the continuous improvement of deep learning algorithms has further
improved the accuracy of MV in disease detection and diagnosis. This paper
reviews the applications of image processing technology based on MV in lymphoma
histopathological images in recent years, including segmentation,
classification and detection. Finally, the current methods are analyzed, some
more potential methods are proposed, and further prospects are made.

Node representation learning has demonstrated its efficacy for various
applications on graphs, which leads to increasing attention towards the area.
However, fairness is a largely under-explored territory within the field, which
may lead to biased results towards underrepresented groups in ensuing tasks. To
this end, this work theoretically explains the sources of bias in node
representations obtained via Graph Neural Networks (GNNs). Our analysis reveals
that both nodal features and graph structure lead to bias in the obtained
representations. Building upon the analysis, fairness-aware data augmentation
frameworks on nodal features and graph structure are developed to reduce the
intrinsic bias. Our analysis and proposed schemes can be readily employed to
enhance the fairness of various GNN-based learning mechanisms. Extensive
experiments on node classification and link prediction are carried out over
real networks in the context of graph contrastive learning. Comparison with
multiple benchmarks demonstrates that the proposed augmentation strategies can
improve fairness in terms of statistical parity and equal opportunity, while
providing comparable utility to state-of-the-art contrastive methods.

Fast forecasting of reservoir pressure distribution in geologic carbon
storage (GCS) by assimilating monitoring data is a challenging problem. Due to
high drilling cost, GCS projects usually have spatially sparse measurements
from wells, leading to high uncertainties in reservoir pressure prediction. To
address this challenge, we propose to use low-cost Interferometric
Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir
pressure build up. We develop a deep learning-accelerated workflow to
assimilate surface displacement maps interpreted from InSAR and to forecast
dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data
Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D)
geologic properties and predicts reservoir pressure with quantified
uncertainties. We use a synthetic commercial-scale GCS model with bimodally
distributed permeability and porosity to demonstrate the efficacy of the
workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal
fields. The computational efficiency of the workflow is boosted by two residual
U-Net based surrogate models for surface displacement and reservoir pressure
predictions, respectively. The workflow can complete data assimilation and
reservoir pressure forecasting in half an hour on a personal computer.

Recently, large pre-trained models have significantly improved the
performance of various Natural LanguageProcessing (NLP) tasks but they are
expensive to serve due to long serving latency and large memory usage. To
compress these models, knowledge distillation has attracted an increasing
amount of interest as one of the most effective methods for model compression.
However, existing distillation methods have not yet addressed the unique
challenges of model serving in datacenters, such as handling fast evolving
models, considering serving performance, and optimizing for multiple
objectives. To solve these problems, we propose AutoDistill, an end-to-end
model distillation framework integrating model architecture exploration and
multi-objective optimization for building hardware-efficient NLP pre-trained
models. We use Bayesian Optimization to conduct multi-objective Neural
Architecture Search for selecting student model architectures. The proposed
search comprehensively considers both prediction accuracy and serving latency
on target hardware. The experiments on TPUv4i show the finding of seven model
architectures with better pre-trained accuracy (up to 3.2% higher) and lower
inference latency (up to 1.44x faster) than MobileBERT. By running downstream
NLP tasks in the GLUE benchmark, the model distilled for pre-training by
AutoDistill with 28.5M parameters achieves an 81.69 average score, which is
higher than BERT_BASE, DistillBERT, TinyBERT, NAS-BERT, and MobileBERT. The
most compact model found by AutoDistill contains only 20.6M parameters but
still outperform BERT_BASE(109M), DistillBERT(67M), TinyBERT(67M), and
MobileBERT(25.3M) regarding the average GLUE score. By evaluating on SQuAD, a
model found by AutoDistill achieves an 88.4% F1 score with 22.8M parameters,
which reduces parameters by more than 62% while maintaining higher accuracy
than DistillBERT, TinyBERT, and NAS-BERT.

Various algorithms for reinforcement learning (RL) exhibit dramatic variation
in their convergence rates as a function of problem structure. Such
problem-dependent behavior is not captured by worst-case analyses and has
accordingly inspired a growing effort in obtaining instance-dependent
guarantees and deriving instance-optimal algorithms for RL problems. This
research has been carried out, however, primarily within the confines of
theory, providing guarantees that explain \textit{ex post} the performance
differences observed. A natural next step is to convert these theoretical
guarantees into guidelines that are useful in practice. We address the problem
of obtaining sharp instance-dependent confidence regions for the policy
evaluation problem and the optimal value estimation problem of an MDP, given
access to an instance-optimal algorithm. As a consequence, we propose a
data-dependent stopping rule for instance-optimal algorithms. The proposed
stopping rule adapts to the instance-specific difficulty of the problem and
allows for early termination for problems with favorable structure.

The increasing scale of general-purpose Pre-trained Language Models (PLMs)
necessitates the study of more efficient adaptation across different downstream
tasks. In this paper, we establish a Black-box Discrete Prompt Learning (BDPL)
to resonate with pragmatic interactions between the cloud infrastructure and
edge devices. Particularly, instead of fine-tuning the model in the cloud, we
adapt PLMs by prompt learning, which efficiently optimizes only a few
parameters of the discrete prompts. Moreover, we consider the scenario that we
do not have access to the parameters and gradients of the pre-trained models,
except for its outputs given inputs. This black-box setting secures the cloud
infrastructure from potential attack and misuse to cause a single-point
failure, which is preferable to the white-box counterpart by current
infrastructures. Under this black-box constraint, we apply a variance-reduced
policy gradient algorithm to estimate the gradients of parameters in the
categorical distribution of each discrete prompt. In light of our method, the
user devices can efficiently tune their tasks by querying the PLMs bounded by a
range of API calls. Our experiments on RoBERTa and GPT-3 demonstrate that the
proposed algorithm achieves significant improvement on eight benchmarks in a
cloud-device collaboration manner. Finally, we conduct in-depth case studies to
comprehensively analyze our method in terms of various data sizes, prompt
lengths, training budgets, optimization objectives, prompt transferability, and
explanations of the learned prompts. Our code will be available at
https://github.com/shizhediao/Black-Box-Prompt-Learning.

Multivariate time-series have become abundant in recent years, as many
data-acquisition systems record information through multiple sensors
simultaneously. In this paper, we assume the variables pertain to some geometry
and present an operator-based approach for spatiotemporal analysis. Our
approach combines three components that are often considered separately: (i)
manifold learning for building operators representing the geometry of the
variables, (ii) Riemannian geometry of symmetric positive-definite matrices for
multiscale composition of operators corresponding to different time samples,
and (iii) spectral analysis of the composite operators for extracting different
dynamic modes. We propose a method that is analogous to the classical wavelet
analysis, which we term Riemannian multi-resolution analysis (RMRA). We provide
some theoretical results on the spectral analysis of the composite operators,
and we demonstrate the proposed method on simulations and on real data.

Balancing the data before training a classifier is a popular technique to
address the challenges of imbalanced binary classification in tabular data.
Balancing is commonly achieved by duplication of minority samples or by
generation of synthetic minority samples. While it is well known that balancing
affects each classifier differently, most prior empirical studies did not
include strong state-of-the-art (SOTA) classifiers as baselines. In this work,
we are interested in understanding whether balancing is beneficial,
particularly in the context of SOTA classifiers. Thus, we conduct extensive
experiments considering three SOTA classifiers along the weaker learners used
in previous investigations. Additionally, we carefully discern proper metrics,
consistent and non-consistent algorithms and hyper-parameter selection methods
and show that these have a significant impact on prediction quality and on the
effectiveness of balancing. Our results support the known utility of balancing
for weak classifiers. However, we find that balancing does not improve
prediction performance for the strong ones. We further identify several other
scenarios for which balancing is effective and observe that prior studies
demonstrated the utility of balancing by focusing on these settings.

User preference music transfer (UPMT) is a new problem in music style
transfer that can be applied to many scenarios but remains understudied.

We present a two-step hybrid reinforcement learning (RL) policy that is
designed to generate interpretable and robust hierarchical policies on the RL
problem with graph-based input. Unlike prior deep reinforcement learning
policies parameterized by an end-to-end black-box graph neural network, our
approach disentangles the decision-making process into two steps. The first
step is a simplified classification problem that maps the graph input to an
action group where all actions share a similar semantic meaning. The second
step implements a sophisticated rule-miner that conducts explicit one-hop
reasoning over the graph and identifies decisive edges in the graph input
without the necessity of heavy domain knowledge. This two-step hybrid policy
presents human-friendly interpretations and achieves better performance in
terms of generalization and robustness. Extensive experimental studies on four
levels of complex text-based games have demonstrated the superiority of the
proposed method compared to the state-of-the-art.

The COVID-19 pandemic has been a scourge upon humanity, claiming the lives of
more than 5.1 million people worldwide; the global economy contracted by 3.5%
in 2020. This paper presents a COVID-19 calculator, synthesizing existing
published calculators and data points, to measure the positive U.S.
socio-economic impact of a COVID-19 AI/ML pre-screening solution (algorithm &
application).

We study the problem of estimating the fixed point of a contractive operator
defined on a separable Banach space. Focusing on a stochastic query model that
provides noisy evaluations of the operator, we analyze a variance-reduced
stochastic approximation scheme, and establish non-asymptotic bounds for both
the operator defect and the estimation error, measured in an arbitrary
semi-norm. In contrast to worst-case guarantees, our bounds are
instance-dependent, and achieve the local asymptotic minimax risk
non-asymptotically. For linear operators, contractivity can be relaxed to
multi-step contractivity, so that the theory can be applied to problems like
average reward policy evaluation problem in reinforcement learning. We
illustrate the theory via applications to stochastic shortest path problems,
two-player zero-sum Markov games, as well as policy evaluation and $Q$-learning
for tabular Markov decision processes.

Low-rank inductive matrix completion (IMC) is currently widely used in IoT
data completion, recommendation systems, and so on, as the side information in
IMC has demonstrated great potential in reducing sample point remains a major
obstacle for the convergence of the nonconvex solutions to IMC. What's more,
carefully choosing the initial solution alone does not usually help remove the
saddle points. To address this problem, we propose a stocastic variance
reduction gradient-based algorithm called LRSVRG-IMC. LRSVRG-IMC can escape
from the saddle points under various low-rank and sparse conditions with a
properly chosen initial input. We also prove that LRSVVRG-IMC achieves both a
linear convergence rate and a near-optimal sample complexity. The superiority
and applicability of LRSVRG-IMC are verified via experiments on synthetic
datasets.

Self-training, a semi-supervised learning algorithm, leverages a large amount
of unlabeled data to improve learning when the labeled data are limited.
Despite empirical successes, its theoretical characterization remains elusive.
To the best of our knowledge, this work establishes the first theoretical
analysis for the known iterative self-training paradigm and proves the benefits
of unlabeled data in both training convergence and generalization ability. To
make our theoretical analysis feasible, we focus on the case of
one-hidden-layer neural networks. However, theoretical understanding of
iterative self-training is non-trivial even for a shallow neural network. One
of the key challenges is that existing neural network landscape analysis built
upon supervised learning no longer holds in the (semi-supervised) self-training
paradigm. We address this challenge and prove that iterative self-training
converges linearly with both convergence rate and generalization accuracy
improved in the order of $1/\sqrt{M}$, where $M$ is the number of unlabeled
samples. Experiments from shallow neural networks to deep neural networks are
also provided to justify the correctness of our established theoretical
insights on self-training.

This letter studies a vertical federated edge learning (FEEL) system for
collaborative objects/human motion recognition by exploiting the distributed
integrated sensing and communication (ISAC). In this system, distributed edge
devices first send wireless signals to sense targeted objects/human, and then
exchange intermediate computed vectors (instead of raw sensing data) for
collaborative recognition while preserving data privacy. To boost the spectrum
and hardware utilization efficiency for FEEL, we exploit ISAC for both target
sensing and data exchange, by employing dedicated frequency-modulated
continuous-wave (FMCW) signals at each edge device. Under this setup, we
propose a vertical FEEL framework for realizing the recognition based on the
collected multi-view wireless sensing data. In this framework, each edge device
owns an individual local L-model to transform its sensing data into an
intermediate vector with relatively low dimensions, which is then transmitted
to a coordinating edge device for final output via a common downstream S-model.
By considering a human motion recognition task, experimental results show that
our vertical FEEL based approach achieves recognition accuracy up to 98\% with
an improvement up to 8\% compared to the benchmarks, including on-device
training and horizontal FEEL.

We study sparse linear regression over a network of agents, modeled as an
undirected graph and no server node. The estimation of the $s$-sparse parameter
is formulated as a constrained LASSO problem wherein each agent owns a subset
of the $N$ total observations. We analyze the convergence rate and statistical
guarantees of a distributed projected gradient tracking-based algorithm under
high-dimensional scaling, allowing the ambient dimension $d$ to grow with (and
possibly exceed) the sample size $N$. Our theory shows that, under standard
notions of restricted strong convexity and smoothness of the loss functions,
suitable conditions on the network connectivity and algorithm tuning, the
distributed algorithm converges globally at a {\it linear} rate to an estimate
that is within the centralized {\it statistical precision} of the model,
$O(s\log d/N)$. When $s\log d/N=o(1)$, a condition necessary for statistical
consistency, an $\varepsilon$-optimal solution is attained after
$\mathcal{O}(\kappa \log (1/\varepsilon))$ gradient computations and $O
(\kappa/(1-\rho) \log (1/\varepsilon))$ communication rounds, where $\kappa$ is
the restricted condition number of the loss function and $\rho$ measures the
network connectivity. The computation cost matches that of the centralized
projected gradient algorithm despite having data distributed; whereas the
communication rounds reduce as the network connectivity improves. Overall, our
study reveals interesting connections between statistical efficiency, network
connectivity \& topology, and convergence rate in high dimensions.

Inference is crucial in modern astronomical research, where hidden
astrophysical features and patterns are often estimated from indirect and noisy
measurements. Inferring the posterior of hidden features, conditioned on the
observed measurements, is essential for understanding the uncertainty of
results and downstream scientific interpretations. Traditional approaches for
posterior estimation include sampling-based methods and variational inference.
However, sampling-based methods are typically slow for high-dimensional inverse
problems, while variational inference often lacks estimation accuracy. In this
paper, we propose alpha-DPI, a deep learning framework that first learns an
approximate posterior using alpha-divergence variational inference paired with
a generative neural network, and then produces more accurate posterior samples
through importance re-weighting of the network samples. It inherits strengths
from both sampling and variational inference methods: it is fast, accurate, and
scalable to high-dimensional problems. We apply our approach to two high-impact
astronomical inference problems using real data: exoplanet astrometry and black
hole feature extraction.

Deep reinforcement learning (DRL) has attracted much attention as an approach
to solve sequential decision making problems without mathematical models of
systems or environments. In general, a constraint may be imposed on a decision
making. In this study, we consider the optimal decision making problems with
constraints to complete temporal high-level tasks in the continuous
state-action domain. We describe the constraints using signal temporal logic
(STL), which is useful for time sensitive control tasks since it can specify
continuous signals within a bounded time interval. To deal with the STL
constraints, we introduce an extended constrained Markov decision process
(CMDP), which is called a $\tau$-CMDP. We formulate the STL constrained optimal
decision making problem as the $\tau$-CMDP and propose a two-phase constrained
DRL algorithm using the Lagrangian relaxation method. Through simulations, we
also demonstrate the learning performance of the proposed algorithm.

Advances in Federated Learning and an abundance of user data have enabled
rich collaborative learning between multiple clients, without sharing user
data. This is done via a central server that aggregates learning in the form of
weight updates. However, this comes at the cost of repeated expensive
communication between the clients and the server, and concerns about
compromised user privacy. The inversion of gradients into the data that
generated them is termed data leakage. Encryption techniques can be used to
counter this leakage, but at added expense. To address these challenges of
communication efficiency and privacy, we propose TOFU, a novel algorithm which
generates proxy data that encodes the weight updates for each client in its
gradients. Instead of weight updates, this proxy data is now shared. Since
input data is far lower in dimensional complexity than weights, this encoding
allows us to send much lesser data per communication round. Additionally, the
proxy data resembles noise, and even perfect reconstruction from data leakage
attacks would invert the decoded gradients into unrecognizable noise, enhancing
privacy. We show that TOFU enables learning with less than 1% and 7% accuracy
drops on MNIST and on CIFAR-10 datasets, respectively. This drop can be
recovered via a few rounds of expensive encrypted gradient exchange. This
enables us to learn to near-full accuracy in a federated setup, while being 4x
and 6.6x more communication efficient than the standard Federated Averaging
algorithm on MNIST and CIFAR-10, respectively.

Value-function (VF) approximation is a central problem in Reinforcement
Learning (RL). Classical non-parametric VF estimation suffers from the curse of
dimensionality. As a result, parsimonious parametric models have been adopted
to approximate VFs in high-dimensional spaces, with most efforts being focused
on linear and neural-network-based approaches. Differently, this paper puts
forth a a parsimonious non-parametric approach, where we use stochastic
low-rank algorithms to estimate the VF matrix in an online and model-free
fashion. Furthermore, as VFs tend to be multi-dimensional, we propose replacing
the classical VF matrix representation with a tensor (multi-way array)
representation and, then, use the PARAFAC decomposition to design an online
model-free tensor low-rank algorithm. Different versions of the algorithms are
proposed, their complexity is analyzed, and their performance is assessed
numerically using standardized RL environments.

Raman spectroscopy provides a vibrational profile of the molecules and thus
can be used to uniquely identify different kind of materials. This sort of
fingerprinting molecules has thus led to widespread application of Raman
spectrum in various fields like medical dignostics, forensics, mineralogy,
bacteriology and virology etc. Despite the recent rise in Raman spectra data
volume, there has not been any significant effort in developing generalized
machine learning methods for Raman spectra analysis. We examine, experiment and
evaluate existing methods and conjecture that neither current sequential models
nor traditional machine learning models are satisfactorily sufficient to
analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt
to mix the best of both worlds and propose a novel network architecture
RamanNet. RamanNet is immune to invariance property in CNN and at the same time
better than traditional machine learning models for the inclusion of sparse
connectivity. Our experiments on 4 public datasets demonstrate superior
performance over the much complex state-of-the-art methods and thus RamanNet
has the potential to become the defacto standard in Raman spectra data analysis

Information sharing is key in building team cognition and enables
coordination and cooperation. High-performing human teams also benefit from
acting strategically with hierarchical levels of iterated communication and
rationalizability, meaning a human agent can reason about the actions of their
teammates in their decision-making. Yet, the majority of prior work in
Multi-Agent Reinforcement Learning (MARL) does not support iterated
rationalizability and only encourage inter-agent communication, resulting in a
suboptimal equilibrium cooperation strategy. In this work, we show that
reformulating an agent's policy to be conditional on the policies of its
neighboring teammates inherently maximizes Mutual Information (MI) lower-bound
when optimizing under Policy Gradient (PG). Building on the idea of
decision-making under bounded rationality and cognitive hierarchy theory, we
show that our modified PG approach not only maximizes local agent rewards but
also implicitly reasons about MI between agents without the need for any
explicit ad-hoc regularization terms. Our approach, InfoPG, outperforms
baselines in learning emergent collaborative behaviors and sets the
state-of-the-art in decentralized cooperative MARL tasks. Our experiments
validate the utility of InfoPG by achieving higher sample efficiency and
significantly larger cumulative reward in several complex cooperative
multi-agent domains.

In astronomical surveys, such as the Zwicky Transient Facility (ZTF),
supernovae (SNe) are relatively uncommon objects compared to other classes of
variable events. Along with this scarcity, the processing of multi-band
light-curves is a challenging task due to the highly irregular cadence, long
time gaps, missing-values, low number of observations, etc. These issues are
particularly detrimental for the analysis of transient events with SN-like
light-curves. In this work, we offer three main contributions. First, based on
temporal modulation and attention mechanisms, we propose a Deep Attention model
called TimeModAttn to classify multi-band light-curves of different SN types,
avoiding photometric or hand-crafted feature computations, missing-values
assumptions, and explicit imputation and interpolation methods. Second, we
propose a model for the synthetic generation of SN multi-band light-curves
based on the Supernova Parametric Model (SPM). This allows us to increase the
number of samples and the diversity of the cadence. The TimeModAttn model is
first pre-trained using synthetic light-curves in a semi-supervised learning
scheme. Then, a fine-tuning process is performed for domain adaptation. The
proposed TimeModAttn model outperformed a Random Forest classifier, increasing
the balanced-$F_1$score from $\approx.525$ to $\approx.596$. The TimeModAttn
model also outperformed other Deep Learning models, based on Recurrent Neural
Networks (RNNs), in two scenarios: late-classification and
early-classification. Finally, we conduct interpretability experiments. High
attention scores are obtained for observations earlier than and close to the SN
brightness peaks, which are supported by an early and highly expressive learned
temporal modulation.

Graph representation learning (also called graph embeddings) is a popular
technique for incorporating network structure into machine learning models.
Unsupervised graph embedding methods aim to capture graph structure by learning
a low-dimensional vector representation (the embedding) for each node. Despite
the widespread use of these embeddings for a variety of downstream transductive
machine learning tasks, there is little principled analysis of the
effectiveness of this approach for common tasks. In this work, we provide an
empirical and theoretical analysis for the performance of a class of embeddings
on the common task of pairwise community labeling. This is a binary variant of
the classic community detection problem, which seeks to build a classifier to
determine whether a pair of vertices participate in a community. In line with
our goal of foundational understanding, we focus on a popular class of
unsupervised embedding techniques that learn low rank factorizations of a
vertex proximity matrix (this class includes methods like GraRep, DeepWalk,
node2vec, NetMF). We perform detailed empirical analysis for community labeling
over a variety of real and synthetic graphs with ground truth. In all cases we
studied, the models trained from embedding features perform poorly on community
labeling. In constrast, a simple logistic model with classic graph structural
features handily outperforms the embedding models. For a more principled
understanding, we provide a theoretical analysis for the (in)effectiveness of
these embeddings in capturing the community structure. We formally prove that
popular low-dimensional factorization methods either cannot produce community
structure, or can only produce ``unstable" communities. These communities are
inherently unstable under small perturbations.

Deep-unfolding neural networks (NNs) have received great attention since they
achieve satisfactory performance with relatively low complexity. Typically,
these deep-unfolding NNs are restricted to a fixed-depth for all inputs.
However, the optimal number of layers required for convergence changes with
different inputs. In this paper, we first develop a framework of deep
deterministic policy gradient (DDPG)-driven deep-unfolding with adaptive depth
for different inputs, where the trainable parameters of deep-unfolding NN are
learned by DDPG, rather than updated by the stochastic gradient descent
algorithm directly. Specifically, the optimization variables, trainable
parameters, and architecture of deep-unfolding NN are designed as the state,
action, and state transition of DDPG, respectively. Then, this framework is
employed to deal with the channel estimation problem in massive multiple-input
multiple-output systems. Specifically, first of all we formulate the channel
estimation problem with an off-grid basis and develop a sparse Bayesian
learning (SBL)-based algorithm to solve it. Secondly, the SBL-based algorithm
is unfolded into a layer-wise structure with a set of introduced trainable
parameters. Thirdly, the proposed DDPG-driven deep-unfolding framework is
employed to solve this channel estimation problem based on the unfolded
structure of the SBL-based algorithm. To realize adaptive depth, we design the
halting score to indicate when to stop, which is a function of the channel
reconstruction error. Furthermore, the proposed framework is extended to
realize the adaptive depth of the general deep neural networks (DNNs).
Simulation results show that the proposed algorithm outperforms the
conventional optimization algorithms and DNNs with fixed depth with much
reduced number of layers.

Graph neural networks (GNNs) have recently exploded in popularity thanks to
their broad applicability to ubiquitous graph-related problems such as quantum
chemistry, drug discovery, and high energy physics. However, meeting demand for
novel GNN models and fast inference simultaneously is challenging because of
the gap between the difficulty in developing efficient FPGA accelerators and
the rapid pace of creation of new GNN models. Prior art focuses on the
acceleration of specific classes of GNNs but lacks the generality to work
across existing models or to extend to new and emerging GNN models. In this
work, we propose a generic GNN acceleration framework using High-Level
Synthesis (HLS), named GenGNN, with two-fold goals. First, we aim to deliver
ultra-fast GNN inference without any graph pre-processing for real-time
requirements. Second, we aim to support a diverse set of GNN models with the
extensibility to flexibly adapt to new models. The framework features an
optimized message-passing structure applicable to all models, combined with a
rich library of model-specific components. We verify our implementation
on-board on the Xilinx Alveo U50 FPGA and observe a speed-up of up to 25x
against CPU (6226R) baseline and 13x against GPU (A6000) baseline. Our HLS code
will be open-source on GitHub upon acceptance.

Backdoor attacks (BAs) are an emerging threat to deep neural network
classifiers. A victim classifier will predict to an attacker-desired target
class whenever a test sample is embedded with the same backdoor pattern (BP)
that was used to poison the classifier's training set. Detecting whether a
classifier is backdoor attacked is not easy in practice, especially when the
defender is, e.g., a downstream user without access to the classifier's
training set. This challenge is addressed here by a reverse-engineering defense
(RED), which has been shown to yield state-of-the-art performance in several
domains. However, existing REDs are not applicable when there are only {\it two
classes} or when {\it multiple attacks} are present. These scenarios are first
studied in the current paper, under the practical constraints that the defender
neither has access to the classifier's training set nor to supervision from
clean reference classifiers trained for the same domain. We propose a detection
framework based on BP reverse-engineering and a novel {\it expected
transferability} (ET) statistic. We show that our ET statistic is effective
{\it using the same detection threshold}, irrespective of the classification
domain, the attack configuration, and the BP reverse-engineering algorithm that
is used. The excellent performance of our method is demonstrated on six
benchmark datasets. Notably, our detection framework is also applicable to
multi-class scenarios with multiple attacks. Code is available at
https://github.com/zhenxianglance/2ClassBADetection.

In this case study, we describe the design and assembly of a cyber security
testbed at Oak Ridge National Laboratory in Oak Ridge, TN, USA. The range is
designed to provide agile reconfigurations to facilitate a wide variety of
experiments for evaluations of cyber security tools -- particularly those
involving AI/ML. In particular, the testbed provides realistic test
environments while permitting control and programmatic observations/data
collection during the experiments. We have designed in the ability to repeat
the evaluations, so additional tools can be evaluated and compared at a later
time. The system is one that can be scaled up or down for experiment sizes. At
the time of the conference we will have completed two full-scale, national,
government challenges on this range. These challenges are evaluating the
performance and operating costs for AI/ML-based cyber security tools for
application into large, government-sized networks. These evaluations will be
described as examples providing motivation and context for various design
decisions and adaptations we have made. The first challenge measured end-point
security tools against 100K file samples (benignware and malware) chosen across
a range of file types. The second is an evaluation of network intrusion
detection systems efficacy in identifying multi-step adversarial campaigns --
involving reconnaissance, penetration and exploitations, lateral movement, etc.
-- with varying levels of covertness in a high-volume business network. The
scale of each of these challenges requires automation systems to repeat, or
simultaneously mirror identical the experiments for each ML tool under test.
Providing an array of easy-to-difficult malicious activity for sussing out the
true abilities of the AI/ML tools has been a particularly interesting and
challenging aspect of designing and executing these challenge events.

Robot systems are increasingly integrating into numerous avenues of modern
life. From cleaning houses to providing guidance and emotional support, robots
now work directly with humans. Due to their far-reaching applications and
progressively complex architecture, they are being targeted by adversarial
attacks such as sensor-actuator attacks, data spoofing, malware, and network
intrusion. Therefore, security for robotic systems has become crucial. In this
paper, we address the underserved area of malware detection in robotic
software. Since robots work in close proximity to humans, often with direct
interactions, malware could have life-threatening impacts. Hence, we propose
the RoboMal framework of static malware detection on binary executables to
detect malware before it gets a chance to execute. Additionally, we address the
great paucity of data in this space by providing the RoboMal dataset comprising
controller executables of a small-scale autonomous car. The performance of the
framework is compared against widely used supervised learning models: GRU, CNN,
and ANN. Notably, the LSTM-based RoboMal model outperforms the other models
with an accuracy of 85% and precision of 87% in 10-fold cross-validation, hence
proving the effectiveness of the proposed framework.

The advent of transformer-based models such as BERT has led to the rise of
neural ranking models. These models have improved the effectiveness of
retrieval systems well beyond that of lexical term matching models such as
BM25. While monolingual retrieval tasks have benefited from large-scale
training collections such as MS MARCO and advances in neural architectures,
cross-language retrieval tasks have fallen behind these advancements. This
paper introduces ColBERT-X, a generalization of the ColBERT
multi-representation dense retrieval model that uses the XLM-RoBERTa (XLM-R)
encoder to support cross-language information retrieval (CLIR). ColBERT-X can
be trained in two ways. In zero-shot training, the system is trained on the
English MS MARCO collection, relying on the XLM-R encoder for cross-language
mappings. In translate-train, the system is trained on the MS MARCO English
queries coupled with machine translations of the associated MS MARCO passages.
Results on ad hoc document ranking tasks in several languages demonstrate
substantial and statistically significant improvements of these trained dense
retrieval models over traditional lexical CLIR baselines.

To interact with humans, artificial intelligence (AI) systems must understand
our social world. Within this world norms play an important role in motivating
and guiding agents. However, very few computational theories for learning
social norms have been proposed. There also exists a long history of debate on
the distinction between what is normal (is) and what is normative (ought). Many
have argued that being capable of learning both concepts and recognizing the
difference is necessary for all social agents. This paper introduces and
demonstrates a computational approach to learning norms from natural language
text that accounts for both what is normal and what is normative. It provides a
foundation for everyday people to train AI systems about social norms.

We investigate the use of Android permissions as the vehicle to allow for
quick and effective differentiation between benign and malware apps. To this
end, we extract all Android permissions, eliminating those that have zero
impact, and apply two feature ranking algorithms namely Chi-Square test and
Fisher's Exact test to rank and additionally filter them, resulting in a
comparatively small set of relevant permissions. Then we use Decision Tree,
Support Vector Machine, and Random Forest Classifier algorithms to detect
malware apps. Our analysis indicates that this approach can result in better
accuracy and F-score value than other reported approaches. In particular, when
random forest is used as the classifier with the combination of Fisher's Exact
test, we achieve 99.34\% in accuracy and 92.17\% in F-score with the false
positive rate of 0.56\% for the dataset in question, with results improving to
99.82\% in accuracy and 95.28\% in F-score with the false positive rate as low
as 0.05\% when only malware from three most popular malware families are

An increasing number of large-scale multi-modal research initiatives has been
conducted in the typically developing population, as well as in psychiatric
cohorts. Missing data is a common problem in such datasets due to the
difficulty of assessing multiple measures on a large number of participants.
The consequences of missing data accumulate when researchers aim to explore
relationships between multiple measures. Here we aim to evaluate different
imputation strategies to fill in missing values in clinical data from a large
(total N=764) and deeply characterised (i.e. range of clinical and cognitive
instruments administered) sample of N=453 autistic individuals and N=311
control individuals recruited as part of the EU-AIMS Longitudinal European
Autism Project (LEAP) consortium. In particular we consider a total of 160
clinical measures divided in 15 overlapping subsets of participants. We use two
simple but common univariate strategies, mean and median imputation, as well as
a Round Robin regression approach involving four independent multivariate
regression models including a linear model, Bayesian Ridge regression, as well
as several non-linear models, Decision Trees, Extra Trees and K-Neighbours
regression. We evaluate the models using the traditional mean square error
towards removed available data, and consider in addition the KL divergence
between the observed and the imputed distributions. We show that all of the
multivariate approaches tested provide a substantial improvement compared to
typical univariate approaches. Further, our analyses reveal that across all 15
data-subsets tested, an Extra Trees regression approach provided the best
global results. This allows the selection of a unique model to impute missing
data for the LEAP project and deliver a fixed set of imputed clinical data to
be used by researchers working with the LEAP dataset in the future.

We present first empirical results from our ongoing investigation of
distribution shifts in image data used for various computer vision tasks.
Instead of analyzing the original training and test data, we propose to study
shifts in the learned weights of trained models. In this work, we focus on the
properties of the distributions of dominantly used 3x3 convolution filter
kernels. We collected and publicly provide a data set with over half a billion
filters from hundreds of trained CNNs, using a wide range of data sets,
architectures, and vision tasks. Our analysis shows interesting distribution
shifts (or the lack thereof) between trained filters along different axes of
meta-parameters, like data type, task, architecture, or layer depth. We argue,
that the observed properties are a valuable source for further investigation
into a better understanding of the impact of shifts in the input data to the
generalization abilities of CNN models and novel methods for more robust
transfer-learning in this domain. Data available at:
https://github.com/paulgavrikov/CNN-Filter-DB/.

Many datasets represent a combination of different ways of looking at the
same data that lead to different generalizations. For example, a corpus with
examples generated by different people may be mixtures of many perspectives and
can be viewed with different perspectives by others. It isnt always possible to
represent the viewpoints by a clean separation, in advance, of examples
representing each viewpoint and train a separate model for each viewpoint. We
introduce lensing, a mixed initiative technique to extract lenses or mappings
between machine learned representations and perspectives of human experts, and
to generate lensed models that afford multiple perspectives of the same
dataset. We apply lensing for two classes of latent variable models: a mixed
membership model, a matrix factorization model in the context of two mental
health applications, and we capture and imbue the perspectives of clinical
psychologists into these models. Our work shows the benefits of the machine
learning practitioner formally incorporating the perspective of a knowledgeable
domain expert into their models rather than estimating unlensed models
themselves in isolation.

Standard Federated Learning (FL) techniques are limited to clients with
identical network architectures. This restricts potential use-cases like
cross-platform training or inter-organizational collaboration when both data
privacy and architectural proprietary are required. We propose a new FL
framework that accommodates heterogeneous client architecture by adopting a
graph hypernetwork for parameter sharing. A property of the graph hyper network
is that it can adapt to various computational graphs, thereby allowing
meaningful parameter sharing across models. Unlike existing solutions, our
framework does not limit the clients to share the same architecture type, makes
no use of external data and does not require clients to disclose their model
architecture. Compared with distillation-based and non-graph hypernetwork
baselines, our method performs notably better on standard benchmarks. We
additionally show encouraging generalization performance to unseen

Despite the stride made by machine learning (ML) based performance modeling,
two major concerns that may impede production-ready ML applications in EDA are
stringent accuracy requirements and generalization capability. To this end, we
propose hybrid graph neural network (GNN) based approaches towards highly
accurate quality-of-result (QoR) estimations with great generalization
capability, specifically targeting logic synthesis optimization. The key idea
is to simultaneously leverage spatio-temporal information from hardware designs
and logic synthesis flows to forecast performance (i.e., delay/area) of various
synthesis flows on different designs. The structural characteristics inside
hardware designs are distilled and represented by GNNs; the temporal knowledge
(i.e., relative ordering of logic transformations) in synthesis flows can be
imposed on hardware designs by combining a virtually added supernode or a
sequence processing model with conventional GNN models. Evaluation on 3.3
million data points shows that the testing mean absolute percentage error
(MAPE) on designs seen and unseen during training are no more than 1.2% and
3.1%, respectively, which are 7-15X lower than existing studies.

The word embedding association test (WEAT) is an important method for
measuring linguistic biases against social groups such as ethnic minorities in
large text corpora. It does so by comparing the semantic relatedness of words
prototypical of the groups (e.g., names unique to those groups) and attribute
words (e.g., 'pleasant' and 'unpleasant' words). We show that anti-black WEAT
estimates from geo-tagged social media data at the level of metropolitan
statistical areas strongly correlate with several measures of racial
animus--even when controlling for sociodemographic covariates. However, we also
show that every one of these correlations is explained by a third variable: the
frequency of Black names in the underlying corpora relative to White names.
This occurs because word embeddings tend to group positive (negative) words and
frequent (rare) words together in the estimated semantic space. As the
frequency of Black names on social media is strongly correlated with Black
Americans' prevalence in the population, this results in spurious anti-Black
WEAT estimates wherever few Black Americans live. This suggests that research
using the WEAT to measure bias should consider term frequency, and also
demonstrates the potential consequences of using black-box models like word
embeddings to study human cognition and behavior.

In this paper, we create EMIR, the first-ever Music Information Retrieval
dataset for Ethiopian music. EMIR is freely available for research purposes and
contains 600 sample recordings of Orthodox Tewahedo chants, traditional Azmari
songs and contemporary Ethiopian secular music. Each sample is classified by
five expert judges into one of four well-known Ethiopian Kinits, Tizita, Bati,
Ambassel and Anchihoye. Each Kinit uses its own pentatonic scale and also has
its own stylistic characteristics. Thus, Kinit classification needs to combine
scale identification with genre recognition. After describing the dataset, we
present the Ethio Kinits Model (EKM), based on VGG, for classifying the EMIR
clips. In Experiment 1, we investigated whether Filterbank, Mel-spectrogram,
Chroma, or Mel-frequency Cepstral coefficient (MFCC) features work best for
Kinit classification using EKM. MFCC was found to be superior and was therefore
adopted for Experiment 2, where the performance of EKM models using MFCC was
compared using three different audio sample lengths. 3s length gave the best
results. In Experiment 3, EKM and four existing models were compared on the
EMIR dataset: AlexNet, ResNet50, VGG16 and LSTM. EKM was found to have the best
accuracy (95.00%) as well as the fastest training time. We hope this work will
encourage others to explore Ethiopian music and to experiment with other models
for Kinit classification.

Prescribing optimal operation based on the condition of the system and,
thereby, potentially prolonging the remaining useful lifetime has a large
potential for actively managing the availability, maintenance and costs of
complex systems. Reinforcement learning (RL) algorithms are particularly
suitable for this type of problems given their learning capabilities. A special
case of a prescriptive operation is the power allocation task, which can be
considered as a sequential allocation problem, where the action space is
bounded by a simplex constraint. A general continuous action-space solution of
such sequential allocation problems has still remained an open research
question for RL algorithms. In continuous action-space, the standard Gaussian
policy applied in reinforcement learning does not support simplex constraints,
while the Gaussian-softmax policy introduces a bias during training. In this
work, we propose the Dirichlet policy for continuous allocation tasks and
analyze the bias and variance of its policy gradients. We demonstrate that the
Dirichlet policy is bias-free and provides significantly faster convergence,
better performance and better hyperparameters robustness over the
Gaussian-softmax policy. Moreover, we demonstrate the applicability of the
proposed algorithm on a prescriptive operation case, where we propose the
Dirichlet power allocation policy and evaluate the performance on a case study
of a set of multiple lithium-ion (Li-I) battery systems. The experimental
results show the potential to prescribe optimal operation, improve the
efficiency and sustainability of multi-power source systems.

While neural networks have advanced the frontiers in many machine learning
applications, they often come at a high computational cost. Reducing the power
and latency of neural network inference is vital to integrating modern networks
into edge devices with strict power and compute requirements. Neural network
quantization is one of the most effective ways of achieving these savings, but
the additional noise it induces can lead to accuracy degradation. In this white
paper, we present an overview of neural network quantization using AI Model
Efficiency Toolkit (AIMET). AIMET is a library of state-of-the-art quantization
and compression algorithms designed to ease the effort required for model
optimization and thus drive the broader AI ecosystem towards low latency and
energy-efficient inference. AIMET provides users with the ability to simulate
as well as optimize PyTorch and TensorFlow models. Specifically for
quantization, AIMET includes various post-training quantization (PTQ, cf.
chapter 4) and quantization-aware training (QAT, cf. chapter 5) techniques that
guarantee near floating-point accuracy for 8-bit fixed-point inference. We
provide a practical guide to quantization via AIMET by covering PTQ and QAT
workflows, code examples and practical tips that enable users to efficiently
and effectively quantize models using AIMET and reap the benefits of low-bit
integer inference.

Context: Identifying potential vulnerable code is important to improve the
security of our software systems. However, the manual detection of software
vulnerabilities requires expert knowledge and is time-consuming, and must be
supported by automated techniques. Objective: Such automated vulnerability
detection techniques should achieve a high accuracy, point developers directly
to the vulnerable code fragments, scale to real-world software, generalize
across the boundaries of a specific software project, and require no or only
moderate setup or configuration effort. Method: In this article, we present
VUDENC (Vulnerability Detection with Deep Learning on a Natural Codebase), a
deep learning-based vulnerability detection tool that automatically learns
features of vulnerable code from a large and real-world Python codebase. VUDENC
applies a word2vec model to identify semantically similar code tokens and to
provide a vector representation. A network of long-short-term memory cells
(LSTM) is then used to classify vulnerable code token sequences at a
fine-grained level, highlight the specific areas in the source code that are
likely to contain vulnerabilities, and provide confidence levels for its
predictions. Results: To evaluate VUDENC, we used 1,009 vulnerability-fixing
commits from different GitHub repositories that contain seven different types
of vulnerabilities (SQL injection, XSS, Command injection, XSRF, Remote code
execution, Path disclosure, Open redirect) for training. In the experimental
evaluation, VUDENC achieves a recall of 78%-87%, a precision of 82%-96%, and an
F1 score of 80%-90%. VUDENC's code, the datasets for the vulnerabilities, and
the Python corpus for the word2vec model are available for reproduction.
Conclusions: Our experimental results suggest...

In recent years, domain randomization has gained a lot of traction as a
method for sim-to-real transfer of reinforcement learning policies in robotic
manipulation; however, finding optimal randomization distributions can be
difficult. In this paper, we introduce DROPO, a novel method for estimating
domain randomization distributions for safe sim-to-real transfer. Unlike prior
work, DROPO only requires a limited, precollected offline dataset of
trajectories, and explicitly models parameter uncertainty to match real data.
We demonstrate that DROPO is capable of recovering dynamic parameter
distributions in simulation and finding a distribution capable of compensating
for an unmodelled phenomenon. We also evaluate the method in two zero-shot
sim-to-real transfer scenarios, showing successful domain transfer and improved
performance over prior methods.

We introduce the notion of a reproducible algorithm in the context of
learning. A reproducible learning algorithm is resilient to variations in its
samples -- with high probability, it returns the exact same output when run on
two samples from the same underlying distribution. We begin by unpacking the
definition, clarifying how randomness is instrumental in balancing accuracy and
reproducibility. We initiate a theory of reproducible algorithms, showing how
reproducibility implies desirable properties such as data reuse and efficient
testability. Despite the exceedingly strong demand of reproducibility, there
are efficient reproducible algorithms for several fundamental problems in
statistics and learning. First, we show that any statistical query algorithm
can be made reproducible with a modest increase in sample complexity, and we
use this to construct reproducible algorithms for finding approximate
heavy-hitters and medians. Using these ideas, we give the first reproducible
algorithm for learning halfspaces via a reproducible weak learner and a
reproducible boosting algorithm. Finally, we initiate the study of lower bounds
and inherent tradeoffs for reproducible algorithms, giving nearly tight sample
complexity upper and lower bounds for reproducible versus nonreproducible SQ

Multidimensional data analysis has become increasingly important in many
fields, mainly due to current vast data availability and the increasing demand
to extract knowledge from it. In most applications, the role of the final user
is crucial to build proper machine learning models and to explain the patterns
found in data. In this paper, we present an open unified approach for
generating, evaluating, and applying regression models in high-dimensional data
sets within a user-guided process. The approach is based on exposing a broad
correlation panorama for attributes, by which the user can select relevant
attributes to build and evaluate prediction models for one or more contexts. We
name the approach UCReg (User-Centered Regression). We demonstrate
effectiveness and efficiency of UCReg through the application of our framework
to the analysis of Covid-19 and other synthetic and real health records data.

Recently, deep learning has achieved remarkable successes in medical image
analysis. Although deep neural networks generate clinically important
predictions, they have inherent uncertainty. Such uncertainty is a major
barrier to report these predictions with confidence. In this paper, we propose
a novel yet simple Bayesian inference approach called SoftDropConnect (SDC) to
quantify the network uncertainty in medical imaging tasks with gliomas
segmentation and metastases classification as initial examples. Our key idea is
that during training and testing SDC modulates network parameters continuously
so as to allow affected information processing channels still in operation,
instead of disabling them as Dropout or DropConnet does. When compared with
three popular Bayesian inference methods including Bayes By Backprop, Dropout,
and DropConnect, our SDC method (SDC-W after optimization) outperforms the
three competing methods with a substantial margin. Quantitatively, our proposed
method generates substantial improvements in prediction accuracy (by 3.4%,
2.5%, and 6.7% respectively for whole tumor segmentation in terms of dice
score; and by 11.7%, 3.9%, and 8.7% respectively for brain metastases
classification) and greatly reduced epistemic and aleatoric uncertainties. Our
approach promises to deliver better diagnostic performance and make medical AI
imaging more explainable and trustworthy.

A determinantal point process (DPP) on a collection of $M$ items is a model,
parameterized by a symmetric kernel matrix, that assigns a probability to every
subset of those items. Recent work shows that removing the kernel symmetry
constraint, yielding nonsymmetric DPPs (NDPPs), can lead to significant
predictive performance gains for machine learning applications. However,
existing work leaves open the question of scalable NDPP sampling. There is only
one known DPP sampling algorithm, based on Cholesky decomposition, that can
directly apply to NDPPs as well. Unfortunately, its runtime is cubic in $M$,
and thus does not scale to large item collections. In this work, we first note
that this algorithm can be transformed into a linear-time one for kernels with
low-rank structure. Furthermore, we develop a scalable sublinear-time rejection
sampling algorithm by constructing a novel proposal distribution. Additionally,
we show that imposing certain structural constraints on the NDPP kernel enables
us to bound the rejection rate in a way that depends only on the kernel rank.
In our experiments we compare the speed of all of these samplers for a variety
of real-world tasks.

Modern computer systems are highly configurable, with the total variability
space sometimes larger than the number of atoms in the universe. Understanding
and reasoning about the performance behavior of highly configurable systems,
over a vast and variable space, is challenging. State-of-the-art methods for
performance modeling and analyses rely on predictive machine learning models,
therefore, they become (i) unreliable in unseen environments (e.g., different
hardware, workloads), and (ii) may produce incorrect explanations. To tackle
this, we propose a new method, called Unicorn, which (i) captures intricate
interactions between configuration options across the software-hardware stack
and (ii) describes how such interactions can impact performance variations via
causal inference. We evaluated Unicorn on six highly configurable systems,
including three on-device machine learning systems, a video encoder, a database
management system, and a data analytics pipeline. The experimental results
indicate that Unicorn outperforms state-of-the-art performance debugging and
optimization methods in finding effective repairs for performance faults and
finding configurations with near-optimal performance. Further, unlike the
existing methods, the learned causal performance models reliably predict
performance for new environments.

A range of video modeling tasks, from optical flow to multiple object
tracking, share the same fundamental challenge: establishing space-time
correspondence. Yet, approaches that dominate each space differ. We take a step
towards bridging this gap by extending the recent contrastive random walk
formulation to much denser, pixel-level space-time graphs. The main
contribution is introducing hierarchy into the search problem by computing the
transition matrix between two frames in a coarse-to-fine manner, forming a
multiscale contrastive random walk when extended in time. This establishes a
unified technique for self-supervised learning of optical flow, keypoint
tracking, and video object segmentation. Experiments demonstrate that, for each
of these tasks, the unified model achieves performance competitive with strong
self-supervised approaches specific to that task. Project webpage:
https://jasonbian97.github.io/flowwalk

Prior work has studied different visual modalities in isolation and developed
separate architectures for recognition of images, videos, and 3D data. Instead,
in this paper, we propose a single model which excels at classifying images,
videos, and single-view 3D data using exactly the same model parameters. Our
'Omnivore' model leverages the flexibility of transformer-based architectures
and is trained jointly on classification tasks from different modalities.
Omnivore is simple to train, uses off-the-shelf standard datasets, and performs
at-par or better than modality-specific models of the same size. A single
Omnivore model obtains 86.0% on ImageNet, 84.1% on Kinetics, and 67.1% on SUN
RGB-D. After finetuning, our models outperform prior work on a variety of
vision tasks and generalize across modalities. Omnivore's shared visual
representation naturally enables cross-modal recognition without access to
correspondences between modalities. We hope our results motivate researchers to
model visual modalities together.

NFT or Non-Fungible Token is a token that certifies a digital asset to be
unique. A wide range of assets including, digital art, music, tweets, memes,
are being sold as NFTs. NFT-related content has been widely shared on social
media sites such as Twitter. We aim to understand the dominant factors that
influence NFT asset valuation. Towards this objective, we create a
first-of-its-kind dataset linking Twitter and OpenSea (the largest NFT
marketplace) to capture social media profiles and linked NFT assets. Our
dataset contains 245,159 tweets posted by 17,155 unique users, directly linking
62,997 NFT assets on OpenSea worth 19 Million USD. We have made the dataset
public. We analyze the growth of NFTs, characterize the Twitter users promoting
NFT assets, and gauge the impact of Twitter features on the virality of an NFT.
Further, we investigate the effectiveness of different social media and NFT
platform features by experimenting with multiple machine learning and deep
learning models to predict an asset's value. Our results show that social media
features improve the accuracy by 6% over baseline models that use only NFT
platform features. Among social media features, count of user membership lists,
number of likes and retweets are important features.

Model pre-training is a cornerstone of modern visual recognition systems.
Although fully supervised pre-training on datasets like ImageNet is still the
de-facto standard, recent studies suggest that large-scale weakly supervised
pre-training can outperform fully supervised approaches. This paper revisits
weakly-supervised pre-training of models using hashtag supervision with modern
versions of residual networks and the largest-ever dataset of images and
corresponding hashtags. We study the performance of the resulting models in
various transfer-learning settings including zero-shot transfer. We also
compare our models with those obtained via large-scale self-supervised
learning. We find our weakly-supervised models to be very competitive across
all settings, and find they substantially outperform their self-supervised
counterparts. We also include an investigation into whether our models learned
potentially troubling associations or stereotypes. Overall, our results provide
a compelling argument for the use of weakly supervised learning in the
development of visual recognition systems. Our models, Supervised Weakly
through hashtAGs (SWAG), are available publicly.

Nonparametric random coefficient (RC)-density estimation has mostly been
considered in the marginal density case under strict independence of RCs and
covariates. This paper deals with the estimation of RC-densities conditional on
a (large-dimensional) set of control variables using machine learning
techniques. The conditional RC-density allows to disentangle observable from
unobservable heterogeneity in partial effects of continuous treatments adding
to a growing literature on heterogeneous effect estimation using machine
learning. %It is also informative of the conditional potential outcome
distribution. This paper proposes a two-stage sieve estimation procedure. First
a closed-form sieve approximation of the conditional RC density is derived
where each sieve coefficient can be expressed as conditional expectation
function varying with controls. Second, sieve coefficients are estimated with
generic machine learning procedures and under appropriate sample splitting
rules. The $L_2$-convergence rate of the conditional RC-density estimator is
derived. The rate is slower by a factor then typical rates of mean regression
machine learning estimators which is due to the ill-posedness of the RC density
estimation problem. The performance and applicability of the estimator is
illustrated using random forest algorithms over a range of Monte Carlo
simulations and with real data from the SOEP-IS. Here behavioral heterogeneity
in an economic experiment on portfolio choice is studied. The method reveals
two types of behavior in the population, one type complying with economic
theory and one not. The assignment to types appears largely based on
unobservables not available in the data.

This work presents a physics-informed neural network based framework to model
the strain-rate and temperature dependence of the deformation fields
(displacement, stress, plastic strain) in elastic-viscoplastic solids. A
detailed discussion on the construction of the physics-based loss criterion
along with a brief outline on ways to avoid unbalanced back-propagated
gradients during training is also presented. We also present a simple strategy
with no added computational complexity for choosing scalar weights that balance
the interplay between different terms in the composite loss. Moreover, we also
highlight a fundamental challenge involving selection of appropriate model
outputs so that the mechanical problem can be faithfully solved using neural
networks. Finally, the effectiveness of the proposed framework is demonstrated
by studying two test problems modeling the elastic-viscoplastic deformation in
solids at different strain-rates and temperatures, respectively.

The ability of Generative Adversarial Networks to encode rich semantics
within their latent space has been widely adopted for facial image editing.
However, replicating their success with videos has proven challenging. Sets of
high-quality facial videos are lacking, and working with videos introduces a
fundamental barrier to overcome - temporal coherency. We propose that this
barrier is largely artificial. The source video is already temporally coherent,
and deviations from this state arise in part due to careless treatment of
individual components in the editing pipeline. We leverage the natural
alignment of StyleGAN and the tendency of neural networks to learn low
frequency functions, and demonstrate that they provide a strongly consistent
prior. We draw on these insights and propose a framework for semantic editing
of faces in videos, demonstrating significant improvements over the current
state-of-the-art. Our method produces meaningful face manipulations, maintains
a higher degree of temporal consistency, and can be applied to challenging,
high quality, talking head videos which current methods struggle with.

About 2.5 billion years ago, microbes learned to harness plentiful solar
energy to reduce CO$_2$ with H$_2$O, extracting energy and producing O$_2$ as
waste. O$_2$ production from this metabolic process was so vigorous that it
saturated its photochemical sinks, permitting it to reach "runaway" conditions
and rapidly accumulate in the atmosphere despite its reactivity. Here we argue
that O$_2$ may not be unique: diverse gases produced by life may experience a
"runaway" effect similar to O$_2$. This runaway occurs because the ability of
an atmosphere to photochemically cleanse itself of trace gases is generally
finite. If produced at rates exceeding this finite limit, even reactive gases
can rapidly accumulate to high concentrations and become potentially
detectable. Planets orbiting smaller, cooler stars, such as the M dwarfs that
are the prime targets for the James Webb Space Telescope (JWST), are especially
favorable for runaway due to their lower UV emission compared to higher-mass
stars. As an illustrative case study, we show that on a habitable exoplanet
with an H$_2$-N$_2$ atmosphere and net surface production of NH$_3$ orbiting an
M dwarf (the "Cold Haber World" scenario), the reactive biogenic gas NH$_3$ can
enter runaway, whereupon an increase in the surface production flux of one
order of magnitude can increase NH$_3$ concentrations by three orders of
magnitude and render it detectable by JWST in just two transits. Our work on
this and other gases suggests that diverse signs of life on exoplanets may be
readily detectable at biochemically plausible production rates.

Artificial intelligence (AI), especially deep learning, requires vast amounts
of data for training, testing, and validation. Collecting these data and the
corresponding annotations requires the implementation of imaging biobanks that
provide access to these data in a standardized way. This requires careful
design and implementation based on the current standards and guidelines and
complying with the current legal restrictions. However, the realization of
proper imaging data collections is not sufficient to train, validate and deploy
AI as resource demands are high and require a careful hybrid implementation of
AI pipelines both on-premise and in the cloud. This chapter aims to help the
reader when technical considerations have to be made about the AI environment
by providing a technical background of different concepts and implementation
aspects involved in data storage, cloud usage, and AI pipelines.

Safety is a critical component of autonomous systems and remains a challenge
for learning-based policies to be utilized in the real world. In particular,
policies learned using reinforcement learning often fail to generalize to novel
environments due to unsafe behavior. In this paper, we propose
Sim-to-Lab-to-Real to bridge the reality gap with a probabilistically
guaranteed safety-aware policy distribution. To improve safety, we apply a dual
policy setup where a performance policy is trained using the cumulative task
reward and a backup (safety) policy is trained by solving the Safety Bellman
Equation based on Hamilton-Jacobi (HJ) reachability analysis. In Sim-to-Lab
transfer, we apply a supervisory control scheme to shield unsafe actions during
exploration; in Lab-to-Real transfer, we leverage the Probably Approximately
Correct (PAC)-Bayes framework to provide lower bounds on the expected
performance and safety of policies in unseen environments. Additionally,
inheriting from the HJ reachability analysis, the bound accounts for the
expectation over the worst-case safety in each environment. We empirically
study the proposed framework for ego-vision navigation in two types of indoor
environments with varying degrees of photorealism. We also demonstrate strong
generalization performance through hardware experiments in real indoor spaces
with a quadrupedal robot. See
https://sites.google.com/princeton.edu/sim-to-lab-to-real for supplementary
material.

Preparing future physics teachers for the demanding nature of their
profession is an important and complex endeavor. Teacher education systems must
provide a structure for the coherent professional development of prospective
teachers. Worldwide, physics teacher education is organized in different ways,
but have to face similar challenges, like the relation between academic studies
and practical preparation. To meet these challenges, it is worth taking look at
different teacher education systems. In this chapter, we compare physics
teacher education in two countries, representing two different educational
traditions: Germany and the USA. Comparing different aspects of physics teacher
education (standards, organization and institutionalization, content of teacher
education, quality assurance), we describe both systems in their current state
and why they are organized in the way they are. In doing so, we identify
surprising commonalities but also different opportunities for both systems to
learn from each other.

We analyze the oracle complexity of sampling from polynomially decaying
heavy-tailed target densities based on running the Unadjusted Langevin
Algorithm on certain transformed versions of the target density. The specific
class of closed-form transformation maps that we construct are shown to be
diffeomorphisms, and are particularly suited for developing efficient
diffusion-based samplers. We characterize the precise class of heavy-tailed
densities for which polynomial-order oracle complexities (in dimension and
inverse target accuracy) could be obtained, and provide illustrative examples.
We highlight the relationship between our assumptions and functional
inequalities (super and weak Poincar\'e inequalities) based on non-local
Dirichlet forms defined via fractional Laplacian operators, used to
characterize the heavy-tailed equilibrium densities of certain stable-driven
stochastic differential equations.

Machine learning (ML) based models have greatly enhanced the traditional
materials discovery and design pipeline. Specifically, in recent years,
surrogate ML models for material property prediction have demonstrated success
in predicting discrete scalar-valued target properties to within reasonable
accuracy of their DFT-computed values. However, accurate prediction of spectral
targets such as the electron Density of States (DOS) poses a much more
challenging problem due to the complexity of the target, and the limited amount
of available training data. In this study, we present an extension of the
recently developed Atomistic Line Graph Neural Network (ALIGNN) to accurately
predict DOS of a large set of material unit cell structures, trained to the
publicly available JARVIS-DFT dataset. Furthermore, we evaluate two methods of
representation of the target quantity - a direct discretized spectrum, and a
compressed low-dimensional representation obtained using an autoencoder.
Through this work, we demonstrate the utility of graph-based featurization and
modeling methods in the prediction of complex targets that depend on both
chemistry and directional characteristics of material structures.

Conjoint analysis is a popular experimental design used to measure
multidimensional preferences. Researchers examine how varying a factor of
interest, while controlling for other relevant factors, influences
decision-making. Currently, there exist two methodological approaches to
analyzing data from a conjoint experiment. The first focuses on estimating the
average marginal effects of each factor while averaging over the other factors.
Although this allows for straightforward design-based estimation, the results
critically depend on the distribution of other factors and how interaction
effects are aggregated. An alternative model-based approach can compute various
quantities of interest, but requires researchers to correctly specify the
model, a challenging task for conjoint analysis with many factors and possible
interactions. In addition, a commonly used logistic regression has poor
statistical properties even with a moderate number of factors when
incorporating interactions. We propose a new hypothesis testing approach based
on the conditional randomization test to answer the most fundamental question
of conjoint analysis: Does a factor of interest matter in any way given the
other factors? Our methodology is solely based on the randomization of factors,
and hence is free from assumptions. Yet, it allows researchers to use any test
statistic, including those based on complex machine learning algorithms. As a
result, we are able to combine the strengths of the existing design-based and
model-based approaches. We illustrate the proposed methodology through conjoint
analysis of immigration preferences and political candidate evaluation. We also
extend the proposed approach to test for regularity assumptions commonly used
in conjoint analysis. An open-source software package is available for
implementing the proposed methodology.

Currently, fixed static sensing is a primary way to monitor environmental
data like air quality in cities. However, to obtain a dense spatial coverage, a
large number of static monitors are required, thereby making it a costly
option. Dense spatiotemporal coverage can be achieved using only a fraction of
static sensors by deploying them on the moving vehicles, known as the drive by
sensing paradigm. The redundancy present in the air quality data can be
exploited by processing the sparsely sampled data to impute the remaining
unobserved data points using the matrix completion techniques. However, the
accuracy of imputation is dependent on the extent to which the moving sensors
capture the inherent structure of the air quality matrix. Therefore, the
challenge is to pick those set of paths (using vehicles) that perform
representative sampling in space and time. Most works in the literature for
vehicle subset selection focus on maximizing the spatiotemporal coverage by
maximizing the number of samples for different locations and time stamps which
is not an effective representative sampling strategy. We present regressive
facility location-based drive by sensing, an efficient vehicle selection
framework that incorporates the smoothness in neighboring locations and
autoregressive time correlation while selecting the optimal set of vehicles for
effective spatiotemporal sampling. We show that the proposed drive by sensing
problem is submodular, thereby lending itself to a greedy algorithm but with
performance guarantees. We evaluate our framework on selecting a subset from
the fleet of public transport in Delhi, India. We illustrate that the proposed
method samples the representative spatiotemporal data against the baseline
methods, reducing the extrapolation error on the simulated air quality data.
Our method, therefore, has the potential to provide cost effective dense air
quality maps.

parameters are analyzed for ${\sim} 8,000$ metal-poor stars ([Fe/H] $\leq
-0.8$) compiled from the RAdial Velocity Experiment (RAVE) Data Release 6.
Selected as metal-poor candidates based on broadband photometry, RAVE collected
moderate-resolution ($R \sim 7,500$) spectra in the region of the Ca triplet
for these stars. About $20\%$ of the stars in this sample also have
medium-resolution ($1,200 \lesssim R \lesssim 2,000$) validation spectra
obtained over a four-year campaign from $2014$ to $2017$ with a variety of
telescopes. We match the candidate stars to photometric metallicity
determinations from the Huang et al. recalibration of the Sky Mapper Southern
Survey Data Release 2. We obtain dynamical clusters of these stars from the
orbital energy and cylindrical actions using the \HDBSCAN ~unsupervised
learning algorithm. We identify $179$ Dynamically Tagged Groups (DTGs) with
between $5$ and $35$ members; $67$ DTGs have at least $10$ member stars. Milky
Way (MW) substructures such as Gaia-Sausage-Enceladus, the Metal-Weak Thick
Disk, the Splashed Disk, Thamnos, the Helmi Stream, and LMS-1 (Wukong) are
identified. Associations with MW globular clusters are determined for $10$
DTGs; no recognized MW dwarf galaxies were associated with any of our DTGs.
Previously identified dynamical groups are also associated with our DTGs, with
emphasis placed on their structural determination and possible new
identifications. We identify chemically peculiar stars as members of several
DTGs; we find $22$ DTGs that are associated with \textit{r}-process-enhanced
stars. Carbon-enhanced metal-poor (CEMP) stars are identified among the targets
with available spectroscopy, and we assign these to morphological groups
following the approach given by Yoon et al.

Group or cluster structure on explanatory variables in machine learning
problems is a very general phenomenon, which has attracted broad interest from
practitioners and theoreticians alike. In this work we contribute an approach
to learning under such group structure, that does not require prior information
on the group identities. Our paradigm is motivated by the Laplacian geometry of
an underlying network with a related community structure, and proceeds by
directly incorporating this into a penalty that is effectively computed via a
heat flow-based local network dynamics. In fact, we demonstrate a procedure to
construct such a network based on the available data. Notably, we dispense with
computationally intensive pre-processing involving clustering of variables,
spectral or otherwise. Our technique is underpinned by rigorous theorems that
guarantee its effective performance and provide bounds on its sample
complexity. In particular, in a wide range of settings, it provably suffices to
run the heat flow dynamics for time that is only logarithmic in the problem
dimensions. We explore in detail the interfaces of our approach with key
statistical physics models in network science, such as the Gaussian Free Field
and the Stochastic Block Model. We validate our approach by successful
applications to real-world data from a wide array of application domains,
including computer science, genetics, climatology and economics. Our work
raises the possibility of applying similar diffusion-based techniques to
classical learning tasks, exploiting the interplay between geometric, dynamical
and stochastic structures underlying the data.

The expense of acquiring labels in large-scale statistical machine learning
makes partially and weakly-labeled data attractive, though it is not always
apparent how to leverage such data for model fitting or validation. We present
a methodology to bridge the gap between partial supervision and validation,
developing a conformal prediction framework to provide valid predictive
confidence sets -- sets that cover a true label with a prescribed probability,
independent of the underlying distribution -- using weakly labeled data. To do
so, we introduce a (necessary) new notion of coverage and predictive validity,
then develop several application scenarios, providing efficient algorithms for
classification and several large-scale structured prediction problems. We
corroborate the hypothesis that the new coverage definition allows for tighter
and more informative (but valid) confidence sets through several experiments.

In this paper, we reveal that metric learning would suffer from serious
inseparable problem if without informative sample mining. Since the inseparable
samples are often mixed with hard samples, current informative sample mining
strategies used to deal with inseparable problem may bring up some
side-effects, such as instability of objective function, etc. To alleviate this
problem, we propose a novel distance metric learning algorithm, named adaptive
neighborhood metric learning (ANML). In ANML, we design two thresholds to
adaptively identify the inseparable similar and dissimilar samples in the
training procedure, thus inseparable sample removing and metric parameter
learning are implemented in the same procedure. Due to the non-continuity of
the proposed ANML, we develop an ingenious function, named \emph{log-exp mean
function} to construct a continuous formulation to surrogate it, which can be
efficiently solved by the gradient descent method. Similar to Triplet loss,
ANML can be used to learn both the linear and deep embeddings. By analyzing the
proposed method, we find it has some interesting properties. For example, when
ANML is used to learn the linear embedding, current famous metric learning
algorithms such as the large margin nearest neighbor (LMNN) and neighbourhood
components analysis (NCA) are the special cases of the proposed ANML by setting
the parameters different values. When it is used to learn deep features, the
state-of-the-art deep metric learning algorithms such as Triplet loss, Lifted
structure loss, and Multi-similarity loss become the special cases of ANML.
Furthermore, the \emph{log-exp mean function} proposed in our method gives a
new perspective to review the deep metric learning methods such as Prox-NCA and
N-pairs loss. At last, promising experimental results demonstrate the

Acceleration and momentum are the de facto standard in modern applications of
machine learning and optimization, yet the bulk of the work on implicit
regularization focuses instead on unaccelerated methods. In this paper, we
study the statistical risk of the iterates generated by Nesterov's accelerated
gradient method and Polyak's heavy ball method, when applied to least squares
regression, drawing several connections to explicit penalization. We carry out
our analyses in continuous-time, allowing us to make sharper statements than in
prior work, and revealing complex interactions between early stopping,
stability, and the curvature of the loss function.

Source code summarization is the task of generating a high-level natural
language description for a segment of programming language code. Current neural
models for the task differ in their architecture and the aspects of code they
consider. In this paper, we show that three SOTA models for code summarization
work well on largely disjoint subsets of a large code-base. This
complementarity motivates model combination: We propose three meta-models that
select the best candidate summary for a given code segment. The two neural
models improve significantly over the performance of the best individual model,
obtaining an improvement of 2.1 BLEU points on a dataset of code segments where
at least one of the individual models obtains a non-zero BLEU.

Federated learning is an increasingly popular paradigm that enables a large
number of entities to collaboratively learn better models. In this work, we
study minimax group fairness in federated learning scenarios where different
participating entities may only have access to a subset of the population
groups during the training phase. We formally analyze how our proposed group
fairness objective differs from existing federated learning fairness criteria
that impose similar performance across participants instead of demographic
groups. We provide an optimization algorithm -- FedMinMax -- for solving the
proposed problem that provably enjoys the performance guarantees of centralized
learning algorithms. We experimentally compare the proposed approach against
other state-of-the-art methods in terms of group fairness in various federated
learning setups, showing that our approach exhibits competitive or superior

Psychological curiosity plays a significant role in human intelligence to
enhance learning through exploration and information acquisition. In the
Artificial Intelligence (AI) community, artificial curiosity provides a natural
intrinsic motivation for efficient learning as inspired by human cognitive
development; meanwhile, it can bridge the existing gap between AI research and
practical application scenarios, such as overfitting, poor generalization,
limited training samples, high computational cost, etc. As a result,
curiosity-driven learning (CDL) has become increasingly popular, where agents
are self-motivated to learn novel knowledge. In this paper, we first present a
comprehensive review on the psychological study of curiosity and summarize a
unified framework for quantifying curiosity as well as its arousal mechanism.
Based on the psychological principle, we further survey the literature of
existing CDL methods in the fields of Reinforcement Learning, Recommendation,
and Classification, where both advantages and disadvantages as well as future
work are discussed. As a result, this work provides fruitful insights for
future CDL research and yield possible directions for further improvement.

Goal-conditioned reinforcement learning (GCRL), related to a set of complex
RL problems, trains an agent to achieve different goals under particular
scenarios. Compared to the standard RL solutions that learn a policy solely
depending on the states or observations, GCRL additionally requires the agent
to make decisions according to different goals. In this survey, we provide a
comprehensive overview of the challenges and algorithms for GCRL. Firstly, we
answer what the basic problems are studied in this field. Then, we explain how
goals are represented and present how existing solutions are designed from
different points of view. Finally, we make the conclusion and discuss potential
future prospects that recent researches focus on.

In this paper, we introduce a new deep learning framework called DIVA-DAF. We
have developed this framework to support our research on historical document
image analysis tasks and to develop techniques to reduce the need for
manually-labeled ground truth. We want to apply self-supervised learning
techniques and use different kinds of training data. Our new framework aids us
in performing rapid prototyping and reproducible experiments. We present a
first semantic segmentation experiment on DIVA-HisDB using our framework,
achieving state-of-the-art results. The DIVA-DAF framework is open-source, and
we encourage other research groups to use it for their experiments.

We propose Echo State Networks (ESNs) to predict the statistics of extreme
events in a turbulent flow. We train the ESNs on small datasets that lack
information about the extreme events. We asses whether the networks are able to
extrapolate from the small imperfect datasets and predict the heavy-tail
statistics that describe the events. We find that the networks correctly
predict the events and improve the statistics of the system with respect to the
training data in almost all cases analysed. This opens up new possibilities for
the statistical prediction of extreme events in turbulence.

In multivariate time series systems, it has been observed that certain groups
of variables partially lead the evolution of the system, while other variables
follow this evolution with a time delay; the result is a lead-lag structure
amongst the time series variables. In this paper, we propose a method for the
detection of lead-lag clusters of time series in multivariate systems. We
demonstrate that the web of pairwise lead-lag relationships between time series
can be helpfully construed as a directed network, for which there exist
suitable algorithms for the detection of pairs of lead-lag clusters with high
pairwise imbalance. Within our framework, we consider a number of choices for
the pairwise lead-lag metric and directed network clustering components. Our
framework is validated on both a synthetic generative model for multivariate
lead-lag time series systems and daily real-world US equity prices data. We
showcase that our method is able to detect statistically significant lead-lag
clusters in the US equity market. We study the nature of these clusters in the
context of the empirical finance literature on lead-lag relations and
demonstrate how these can be used for the construction of predictive financial
signals.

With deep learning gaining attention from the research community for
prediction and control of real physical systems, learning important
representations is becoming now more than ever mandatory. It is of extreme
importance that deep learning representations are coherent with physics. When
learning from discrete data this can be guaranteed by including some sort of
prior into the learning, however, not all discretization priors preserve
important structures from the physics. In this paper, we introduce Symplectic
Momentum Neural Networks (SyMo) as models from a discrete formulation of
mechanics for non-separable mechanical systems. The combination of such
formulation leads SyMos to be constrained towards preserving important
geometric structures such as momentum and a symplectic form and learn from
limited data. Furthermore, it allows to learn dynamics only from the poses as
training data. We extend SyMos to include variational integrators within the
learning framework by developing an implicit root-find layer which leads to
End-to-End Symplectic Momentum Neural Networks (E2E-SyMo). Through experimental
results, using the pendulum and cartpole, we show that such combination not
only allows these models to learn from limited data but also provides the
models with the capability of preserving the symplectic form and show better
long-term behaviour.

The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in
artificial intelligence (AI) systems so that they are capable of learning (and
improving) continuously, leveraging data on one task to improve performance on
another, and doing so in a computationally sustainable way. Performers on this
program developed systems capable of performing a diverse range of functions,
including autonomous driving, real-time strategy, and drone simulation. These
systems featured a diverse range of characteristics (e.g., task structure,
lifetime duration), and an immediate challenge faced by the program's testing
and evaluation team was measuring system performance across these different
settings. This document, developed in close collaboration with DARPA and the
program performers, outlines a formalism for constructing and characterizing
the performance of agents performing lifelong learning scenarios.

We study the problem of few-shot graph classification across domains with
nonequivalent feature spaces by introducing three new cross-domain benchmarks
constructed from publicly available datasets. We also propose an
attention-based graph encoder that uses three congruent views of graphs, one
contextual and two topological views, to learn representations of task-specific
information for fast adaptation, and task-agnostic information for knowledge
transfer. We run exhaustive experiments to evaluate the performance of
contrastive and meta-learning strategies. We show that when coupled with
metric-based meta-learning frameworks, the proposed encoder achieves the best
average meta-test classification accuracy across all benchmarks. The source
code and data will be released here: https://github.com/kavehhassani/metagrl

Recent video and language pretraining frameworks lack the ability to generate
sentences. We present Multimodal Video Generative Pretraining (MV-GPT), a new
pretraining framework for learning from unlabelled videos which can be
effectively used for generative tasks such as multimodal video captioning.
Unlike recent video-language pretraining frameworks, our framework trains both
a multimodal video encoder and a sentence decoder jointly. To overcome the lack
of captions in unlabelled videos, we leverage the future utterance as an
additional text source and propose a bidirectional generation objective -- we
generate future utterances given the present mulitmodal context, and also the
present utterance given future observations. With this objective, we train an
encoder-decoder model end-to-end to generate a caption from raw pixels and
transcribed speech directly. Our model achieves state-of-the-art performance
for multimodal video captioning on four standard benchmarks, as well as for
other video understanding tasks such as VideoQA, video retrieval and action

Precise location of faults for large distance power transmission networks is
essential for faster repair and restoration process. High Voltage direct
current (HVdc) networks using modular multi-level converter (MMC) technology
has found its prominence for interconnected multi-terminal networks. This
allows for large distance bulk power transmission at lower costs. However, they
cope with the challenge of dc faults. Fast and efficient methods to isolate the
network under dc faults have been widely studied and investigated. After
successful isolation, it is essential to precisely locate the fault. The
post-fault voltage and current signatures are a function of multiple factors
and thus accurately locating faults on a multi-terminal network is challenging.
In this paper, we discuss a novel data-driven ensemble learning based approach
for accurate fault location. Here we utilize the eXtreme Gradient Boosting
(XGB) method for accurate fault location. The sensitivity of the proposed
algorithm to measurement noise, fault location, resistance and current limiting
inductance are performed on a radial three-terminal MTdc network designed in
Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including
dc (EMTdc).

Assessing the effects of a policy based on observational data from a
different policy is a common problem across several high-stake decision-making
domains, and several off-policy evaluation (OPE) techniques have been proposed.
However, these methods largely formulate OPE as a problem disassociated from
the process used to generate the data (i.e. structural assumptions in the form
of a causal graph). We argue that explicitly highlighting this association has
important implications on our understanding of the fundamental limits of OPE.
First, this implies that current formulation of OPE corresponds to a narrow set
of tasks, i.e. a specific causal estimand which is focused on prospective
evaluation of policies over populations or sub-populations. Second, we
demonstrate how this association motivates natural desiderata to consider a
general set of causal estimands, particularly extending the role of OPE for
counterfactual off-policy evaluation at the level of individuals of the
population. A precise description of the causal estimand highlights which OPE
estimands are identifiable from observational data under the stated generative
assumptions. For those OPE estimands that are not identifiable, the causal
perspective further highlights where more experimental data is necessary, and
highlights situations where human expertise can aid identification and
estimation. Furthermore, many formalisms of OPE overlook the role of
uncertainty entirely in the estimation process.We demonstrate how specifically
characterising the causal estimand highlights the different sources of
uncertainty and when human expertise can naturally manage this uncertainty. We
discuss each of these aspects as actionable desiderata for future OPE research
at scale and in-line with practical utility.

This paper describes OpenIPDM software for modelling the deterioration
process of infrastructures using network-scale visual inspection data. In
addition to the deterioration state estimates, OpenIPDM provides functions for
quantifying the effect of interventions, estimating the service life of an
intervention, and generating synthetic data for verification purposes. Each of
the aforementioned functions are accessible by an interactive graphical user
interface. OpenIPDM is designed based on the research work done on a network of
bridges in Quebec province, so that the concepts presented in the software have
been validated for applications in a real-world context. In addition, this
software provides foundations for future developments in the subject area of
modelling the deterioration as well as intervention planning.

The outcome of a collective decision-making process, such as crowdsourcing,
often relies on the procedure through which the perspectives of its individual
members are aggregated. Popular aggregation methods, such as the majority rule,
often fail to produce the optimal result, especially in high-complexity tasks.
Methods that rely on meta-cognitive information, such as confidence-based
methods and the Surprisingly Popular Option, had shown an improvement in
various tasks. However, there is still a significant number of cases with no
optimal solution. Our aim is to exploit meta-cognitive information and to learn
from it, for the purpose of enhancing the ability of the group to produce a
correct answer. Specifically, we propose two different feature-representation
approaches: (1) Response-Centered feature Representation (RCR), which focuses
on the characteristics of the individual response instances, and (2)
Answer-Centered feature Representation (ACR), which focuses on the
characteristics of each of the potential answers. Using these two
feature-representation approaches, we train Machine-Learning (ML) models, for
the purpose of predicting the correctness of a response and of an answer. The
trained models are used as the basis of an ML-based aggregation methodology
that, contrary to other ML-based techniques, has the advantage of being a
"one-shot" technique, independent from the crowd-specific composition and
personal record, and adaptive to various types of situations. To evaluate our
methodology, we collected 2490 responses for different tasks, which we used for
feature engineering and for the training of ML models. We tested our
feature-representation approaches through the performance of our proposed
ML-based aggregation methods. The results show an increase of 20% to 35% in the
success rate, compared to the use of standard rule-based aggregation methods.

Machine Learning (ML) projects incur novel challenges in their development
and productionisation over traditional software applications, though
established principles and best practices in ensuring the project's software
quality still apply. While using static analysis to catch code smells has been
shown to improve software quality attributes, it is only a small piece of the
software quality puzzle, especially in the case of ML projects given their
additional challenges and lower degree of Software Engineering (SE) experience
in the data scientists that develop them. We introduce the novel concept of
project smells which consider deficits in project management as a more holistic
perspective on software quality in ML projects. An open-source static analysis
tool mllint was also implemented to help detect and mitigate these. Our
research evaluates this novel concept of project smells in the industrial
context of ING, a global bank and large software- and data-intensive
organisation. We also investigate the perceived importance of these project
smells for proof-of-concept versus production-ready ML projects, as well as the
perceived obstructions and benefits to using static analysis tools such as
mllint. Our findings indicate a need for context-aware static analysis tools,
that fit the needs of the project at its current stage of development, while
requiring minimal configuration effort from the user.

Covering option discovery has been developed to improve the exploration of
reinforcement learning in single-agent scenarios with sparse reward signals,
through connecting the most distant states in the embedding space provided by
the Fiedler vector of the state transition graph. However, these option
discovery methods cannot be directly extended to multi-agent scenarios, since
the joint state space grows exponentially with the number of agents in the
system. Thus, existing researches on adopting options in multi-agent scenarios
still rely on single-agent option discovery and fail to directly discover the
joint options that can improve the connectivity of the joint state space of
agents. In this paper, we show that it is indeed possible to directly compute
multi-agent options with collaborative exploratory behaviors among the agents,
while still enjoying the ease of decomposition. Our key idea is to approximate
the joint state space as a Kronecker graph -- the Kronecker product of
individual agents' state transition graphs, based on which we can directly
estimate the Fiedler vector of the joint state space using the Laplacian
spectrum of individual agents' transition graphs. This decomposition enables us
to efficiently construct multi-agent joint options by encouraging agents to
connect the sub-goal joint states which are corresponding to the minimum or
maximum values of the estimated joint Fiedler vector. The evaluation based on
multi-agent collaborative tasks shows that the proposed algorithm can
successfully identify multi-agent options, and significantly outperforms prior
works using single-agent options or no options, in terms of both faster
exploration and higher cumulative rewards.

Semidefinite programming (SDP) is a powerful tool for tackling a wide range
of computationally hard problems such as clustering. Despite the high accuracy,
semidefinite programs are often too slow in practice with poor scalability on
large (or even moderate) datasets. In this paper, we introduce a linear time
complexity algorithm for approximating an SDP relaxed $K$-means clustering. The
proposed sketch-and-lift (SL) approach solves an SDP on a subsampled dataset
and then propagates the solution to all data points by a nearest-centroid
rounding procedure. It is shown that the SL approach enjoys a similar exact
recovery threshold as the $K$-means SDP on the full dataset, which is known to
be information-theoretically tight under the Gaussian mixture model. The SL
method can be made adaptive with enhanced theoretic properties when the cluster
sizes are unbalanced. Our simulation experiments demonstrate that the
statistical accuracy of the proposed method outperforms state-of-the-art fast
clustering algorithms without sacrificing too much computational efficiency,
and is comparable to the original $K$-means SDP with substantially reduced
runtime.

Performance forecasting is an age-old problem in economics and finance.
Recently, developments in machine learning and neural networks have given rise
to non-linear time series models that provide modern and promising alternatives
to traditional methods of analysis. In this paper, we present an ensemble of
independent and parallel long short-term memory (LSTM) neural networks for the
prediction of stock price movement. LSTMs have been shown to be especially
suited for time series data due to their ability to incorporate past
information, while neural network ensembles have been found to reduce
variability in results and improve generalization. A binary classification
problem based on the median of returns is used, and the ensemble's forecast
depends on a threshold value, which is the minimum number of LSTMs required to
agree upon the result. The model is applied to the constituents of the smaller,
less efficient Stockholm OMX30 instead of other major market indices such as
the DJIA and S&P500 commonly found in literature. With a straightforward
trading strategy, comparisons with a randomly chosen portfolio and a portfolio
containing all the stocks in the index show that the portfolio resulting from
the LSTM ensemble provides better average daily returns and higher cumulative
returns over time. Moreover, the LSTM portfolio also exhibits less volatility,
leading to higher risk-return ratios.

Contrastive learning has become a popular technique to pre-train image
encoders, which could be used to build various downstream classification models
in an efficient way. This process requires a large amount of data and
computation resources. Hence, the pre-trained encoders are an important
intellectual property that needs to be carefully protected. It is challenging
to migrate existing watermarking techniques from the classification tasks to
the contrastive learning scenario, as the owner of the encoder lacks the
knowledge of the downstream tasks which will be developed from the encoder in
the future. We propose the \textit{first} watermarking methodology for the
pre-trained encoders. We introduce a task-agnostic loss function to effectively
embed into the encoder a backdoor as the watermark. This backdoor can still
exist in any downstream models transferred from the encoder. Extensive
evaluations over different contrastive learning algorithms, datasets, and
downstream tasks indicate our watermarks exhibit high effectiveness and
robustness against different adversarial operations.

Self-supervised learning has not been fully explored for point cloud
analysis. Current frameworks are mainly based on point cloud reconstruction.
Given only 3D coordinates, such approaches tend to learn local geometric
structures and contours, while failing in understanding high level semantic
content. Consequently, they achieve unsatisfactory performance in downstream
tasks such as classification, segmentation, etc. To fill this gap, we propose a
generic Contour-Perturbed Reconstruction Network (CP-Net), which can
effectively guide self-supervised reconstruction to learn semantic content in
the point cloud, and thus promote discriminative power of point cloud
representation. First, we introduce a concise contour-perturbed augmentation
module for point cloud reconstruction. With guidance of geometry disentangling,
we divide point cloud into contour and content components. Subsequently, we
perturb the contour components and preserve the content components on the point
cloud. As a result, self supervisor can effectively focus on semantic content,
by reconstructing the original point cloud from such perturbed one. Second, we
use this perturbed reconstruction as an assistant branch, to guide the learning
of basic reconstruction branch via a distinct dual-branch consistency loss. In
this case, our CP-Net not only captures structural contour but also learn
semantic content for discriminative downstream tasks. Finally, we perform
extensive experiments on a number of point cloud benchmarks. Part segmentation
results demonstrate that our CP-Net (81.5% of mIoU) outperforms the previous
self-supervised models, and narrows the gap with the fully-supervised methods.
For classification, we get a competitive result with the fully-supervised
methods on ModelNet40 (92.5% accuracy) and ScanObjectNN (87.9% accuracy). The
codes and models will be released afterwards.

The success of pre-trained contextualized representations has prompted
researchers to analyze them for the presence of linguistic information. Indeed,
it is natural to assume that these pre-trained representations do encode some
level of linguistic knowledge as they have brought about large empirical
improvements on a wide variety of NLP tasks, which suggests they are learning
true linguistic generalization. In this work, we focus on intrinsic probing, an
analysis technique where the goal is not only to identify whether a
representation encodes a linguistic attribute, but also to pinpoint where this
attribute is encoded. We propose a novel latent-variable formulation for
constructing intrinsic probes and derive a tractable variational approximation
to the log-likelihood. Our results show that our model is versatile and yields
tighter mutual information estimates than two intrinsic probes previously
proposed in the literature. Finally, we find empirical evidence that
pre-trained representations develop a cross-lingually entangled notion of
morphosyntax.

The cosmic 21-cm line of hydrogen is expected to be measured in detail by the
next generation of radio telescopes. The enormous dataset from future 21-cm
surveys will revolutionize our understanding of early cosmic times. We present
a machine learning approach that uses emulation in order to uncover the
astrophysics in the epoch of reionization and cosmic dawn. Using a
seven-parameter astrophysical model that covers a very wide range of possible
21-cm signals, over the redshift range $6$ to $30$ and wavenumber range $0.05 \
\rm{Mpc}^{-1}$ to $1 \ \rm{Mpc}^{-1}$ we emulate the 21-cm power spectrum with
a typical accuracy of $10 - 20\%$. As a realistic example, we train an emulator
using the 21-cm power spectrum with an optimistic model for observational noise
as expected for the Square Kilometre Array (SKA). Fitting to mock SKA data
results in a typical measurement accuracy of $5\%$ in the optical depth to the
CMB, $30\%$ in the star-formation efficiency of galactic halos, and a factor of
$3.5$ in the X-ray efficiency of galactic halos; the latter two parameters are
currently uncertain by orders of magnitude. In addition to standard
astrophysical models, we also consider two exotic possibilities of strong
excess radio backgrounds at high redshifts. We use a neural network to identify
the type of radio background present in the 21-cm power spectrum, with an
accuracy of $87\%$ for mock SKA data.

The rapid development of multimedia and communication technology has resulted
in an urgent need for high-quality video streaming. However, robust video
streaming under fluctuating network conditions and heterogeneous client
computing capabilities remains a challenge. In this paper, we consider an
enhancement-enabled video streaming network under a time-varying wireless
network and limited computation capacity. "Enhancement" means that the client
can improve the quality of the downloaded video segments via image processing
modules. We aim to design a joint bitrate adaptation and client-side
enhancement algorithm toward maximizing the quality of experience (QoE). We
formulate the problem as a Markov decision process (MDP) and propose a deep
reinforcement learning (DRL)-based framework, named ENAVS. As video streaming
quality is mainly affected by video compression, we demonstrate that the video
enhancement algorithm outperforms the super-resolution algorithm in terms of
signal-to-noise ratio and frames per second, suggesting a better solution for
client processing in video streaming. Ultimately, we implement ENAVS and
demonstrate extensive testbed results under real-world bandwidth traces and
videos. The simulation shows that ENAVS is capable of delivering 5%-14% more
QoE under the same bandwidth and computing power conditions as conventional ABR
streaming.

Existing textual adversarial attacks usually utilize the gradient or
prediction confidence to generate adversarial examples, making it hard to be
deployed in real-world applications. To this end, we consider a rarely
investigated but more rigorous setting, namely hard-label attack, in which the
attacker can only access the prediction label. In particular, we find we can
learn the importance of different words via the change on prediction label
caused by word substitutions on the adversarial examples. Based on this
observation, we propose a novel adversarial attack, termed Text Hard-label
attacker (TextHacker). TextHacker randomly perturbs lots of words to craft an
adversarial example. Then, TextHacker adopts a hybrid local search algorithm
with the estimation of word importance from the attack history to minimize the
adversarial perturbation. Extensive evaluations for text classification and
textual entailment show that TextHacker significantly outperforms existing
hard-label attacks regarding the attack performance as well as adversary
quality.

Deep learning methods have been increasingly adopted to study jets in
particle physics. Since symmetry-preserving behavior has been shown to be an
important factor for improving the performance of deep learning in many
applications, Lorentz group equivariance - a fundamental spacetime symmetry for
elementary particles - has recently been incorporated into a deep learning
model for jet tagging. However, the design is computationally costly due to the
analytic construction of high-order tensors. In this article, we introduce
LorentzNet, a new symmetry-preserving deep learning model for jet tagging. The
message passing of LorentzNet relies on an efficient Minkowski dot product
attention. Experiments on two representative jet tagging benchmarks show that
LorentzNet achieves the best tagging performance and improves significantly
over existing state-of-the-art algorithms. The preservation of Lorentz symmetry
also greatly improves the efficiency and generalization power of the model,
allowing LorentzNet to reach highly competitive performance when trained on
only a few thousand jets. Code and models are available at
\url{https://github.com/sdogsq/LorentzNet-release}.

The widespread adoption of electronic health records (EHRs) and subsequent
increased availability of longitudinal healthcare data has led to significant
advances in our understanding of health and disease with direct and immediate
impact on the development of new diagnostics and therapeutic treatment options.
However, access to EHRs is often restricted due to their perceived sensitive
nature and associated legal concerns, and the cohorts therein typically are
those seen at a specific hospital or network of hospitals and therefore not
representative of the wider population of patients. Here, we present HealthGen,
a new approach for the conditional generation of synthetic EHRs that maintains
an accurate representation of real patient characteristics, temporal
information and missingness patterns. We demonstrate experimentally that
HealthGen generates synthetic cohorts that are significantly more faithful to
real patient EHRs than the current state-of-the-art, and that augmenting real
data sets with conditionally generated cohorts of underrepresented
subpopulations of patients can significantly enhance the generalisability of
models derived from these data sets to different patient populations. Synthetic
conditionally generated EHRs could help increase the accessibility of
longitudinal healthcare data sets and improve the generalisability of
inferences made from these data sets to underrepresented populations.

Robotic pick-and-place has been researched for a long time to cope with
uncertainty of novel objects and changeable environments. Past works mainly
focus on learning-based methods to achieve high precision. However, they have
difficulty being generalized for the limitation of specified training models.
To break through this drawback of learning-based approaches, we introduce a new
perspective of similarity matching between novel objects and a known database
based on category-association to achieve pick-and-place tasks with high
accuracy and stabilization. We calculate the category name similarity using
word embedding to quantify the semantic similarity between the categories of
known models and the target real-world objects. With a similar model identified
by a similarity prediction function, we preplan a series of robust grasps and
imitate them to plan new grasps on the real-world target object. We also
propose a distance-based method to infer the in-hand posture of objects and
adjust small rotations to achieve stable placements under uncertainty. Through
a real-world robotic pick-and-place experiment with a dozen of in-category and
out-of-category novel objects, our method achieved an average success rate of
90.6% and 75.9% respectively, validating the capacity of generalization to
diverse objects.

The rising popularity of explainable artificial intelligence (XAI) to
understand high-performing black boxes, also raised the question of how to
evaluate explanations of machine learning (ML) models. While interpretability
and explainability are often presented as a subjectively validated binary
property, we consider it a multi-faceted concept. We identify 12 conceptual
properties, such as Compactness and Correctness, that should be evaluated for
comprehensively assessing the quality of an explanation. Our so-called Co-12
properties serve as categorization scheme for systematically reviewing the
evaluation practice of more than 300 papers published in the last 7 years at
major AI and ML conferences that introduce an XAI method. We find that 1 in 3
papers evaluate exclusively with anecdotal evidence, and 1 in 5 papers evaluate
with users. We also contribute to the call for objective, quantifiable
evaluation methods by presenting an extensive overview of quantitative XAI
evaluation methods. This systematic collection of evaluation methods provides
researchers and practitioners with concrete tools to thoroughly validate,
benchmark and compare new and existing XAI methods. This also opens up
opportunities to include quantitative metrics as optimization criteria during
model training in order to optimize for accuracy and interpretability

The Cognitive Ledger Project is an effort to develop a modular system for
turning users' personal data into structured information and machine learning
models based on a blockchain-based infrastructure. In this work-in-progress
paper, we propose a cognitive architecture for cognitive digital twins. The
suggested design embraces a cognitive blockchain (Cognitive ledger) at its
core. The architecture includes several modules that turn users' activities in
the digital environment into reusable knowledge objects and artificial
intelligence that one day can work together to form the cognitive digital twin
of users.

A real-time motion training system for skydiving is proposed. Aerial
maneuvers are performed by changing the body posture and thus deflecting the
surrounding airflow. The natural learning process is extremely slow due to
unfamiliar free-fall dynamics, stress induced blocking of kinesthetic feedback,
and complexity of the required movements. The key idea is to augment the
learner with an automatic control system that would be able to perform the
trained activity if it had direct access to the learner's body as an actuator.
The aiding system will supply the following visual cues to the learner: 1.
Feedback of the current body posture; 2. The body posture that would bring the
body to perform the desired maneuver; 3. Prediction of the future inertial
position and orientation if the body retains its present posture. The system
will enable novices to maintain stability in free-fall and perceive the
unfamiliar environmental dynamics, thus accelerating the initial stages of
skill acquisition. This paper presents results of a Proof-of-Concept
experiment, whereby humans controlled a virtual skydiver free-falling in a
computer simulation, by the means of their bodies. This task was impossible
without the aiding system, enabling all participants to complete the task at
the first attempt.

Exploiting image patches instead of whole images have proved to be a powerful
approach to tackle various problems in image processing. Recently, Wasserstein
patch priors (WPP), which are based on the comparison of the patch
distributions of the unknown image and a reference image, were successfully
used as data-driven regularizers in the variational formulation of
superresolution. However, for each input image, this approach requires the
solution of a non-convex minimization problem which is computationally costly.
In this paper, we propose to learn two kinds of neural networks in an
unsupervised way based on WPP loss functions. First, we show how convolutional
neural networks (CNNs) can be incorporated. Once the network, called WPPNet, is
learned, it can very efficiently applied to any input image. Second, we
incorporate conditional normalizing flows to provide a tool for uncertainty
quantification. Numerical examples demonstrate the very good performance of
WPPNets for superresolution in various image classes even if the forward
operator is known only approximately.

Physical sketches are created by learning programs to control a drawing
robot. A differentiable rasteriser is used to optimise sets of drawing strokes
to match an input image, using deep networks to provide an encoding for which
we can compute a loss. The optimised drawing primitives can then be translated
into G-code commands which command a robot to draw the image using drawing
instruments such as pens and pencils on a physical support medium.

Parametric 3D models have formed a fundamental role in modeling deformable
objects, such as human bodies, faces, and hands; however, the construction of
such parametric models requires significant manual intervention and domain
expertise. Recently, neural implicit 3D representations have shown great
expressibility in capturing 3D shape geometry. We observe that deformable
object motion is often semantically structured, and thus propose to learn
Structured-implicit PArametric Models (SPAMs) as a deformable object
representation that structurally decomposes non-rigid object motion into
part-based disentangled representations of shape and pose, with each being
represented by deep implicit functions. This enables a structured
characterization of object movement, with part decomposition characterizing a
lower-dimensional space in which we can establish coarse motion correspondence.
In particular, we can leverage the part decompositions at test time to fit to
new depth sequences of unobserved shapes, by establishing part correspondences
between the input observation and our learned part spaces; this guides a robust
joint optimization between the shape and pose of all parts, even under dramatic
motion sequences. Experiments demonstrate that our part-aware shape and pose
understanding lead to state-of-the-art performance in reconstruction and
tracking of depth sequences of complex deforming object motion. We plan to
release models to the public at https://pablopalafox.github.io/spams.

Structure prediction has become a key task of the modern atomistic sciences,
and depends on the rapid and reliable computation of the energy landscape.
First principles density functional based calculations are highly reliable,
faithfully describing the entire energy landscape. They are, however,
computationally intensive and slow compared to interatomic potentials. Great
progress has been made in the development of machine learning, or data derived,
potentials, which promise to describe the entire energy landscape at first
principles quality. However, compared to first principles approaches, their
preparation can be time consuming and delay searching. Ab initio random
structure searching (AIRSS) is a straightforward and powerful approach to
structure prediction, based on the stochastic generation of sensible initial
structures, and their repeated local optimisation. Here, a scheme, compatible
with AIRSS, for the rapid construction of disposable, or ephemeral, data
derived potentials (EDDPs) is described. These potentials are constructed using
a homogeneous, separable manybody environment vector, and iterative neural
network fits, sparsely combined through non-negative least squares. The
approach is first tested on methane, boron nitride, elemental boron and urea.
In the case of boron, an EDDP generated using data from small unit cells is
used to rediscover the complex $\gamma$-boron structure without recourse to
symmetry or fragments. Finally, an EDDP generated for silane (SiH$_4$) at 500
GPa enables the discovery of an extremely complex, dense, structure which
significantly modifies silane's high pressure phase diagram. This has
implications for the theoretical exploration for high temperature
superconductivity in the dense hydrides, which have so far largely depended on
searches in smaller unit cells.

Medical professionals evaluating alternative treatment plans for a patient
often encounter time varying confounders, or covariates that affect both the
future treatment assignment and the patient outcome. The recently proposed
Counterfactual Recurrent Network (CRN) accounts for time varying confounders by
using adversarial training to balance recurrent historical representations of
patient data. However, this work assumes that all time varying covariates are
confounding and thus attempts to balance the full state representation. Given
that the actual subset of covariates that may in fact be confounding is in
general unknown, recent work on counterfactual evaluation in the static,
non-temporal setting has suggested that disentangling the covariate
representation into separate factors, where each either influence treatment
selection, patient outcome or both can help isolate selection bias and restrict
balancing efforts to factors that influence outcome, allowing the remaining
factors which predict treatment without needlessly being balanced.

Federated learning is a machine learning paradigm that emerges as a solution
to the privacy-preservation demands in artificial intelligence. As machine
learning, federated learning is threatened by adversarial attacks against the
integrity of the learning model and the privacy of data via a distributed
approach to tackle local and global learning. This weak point is exacerbated by
the inaccessibility of data in federated learning, which makes harder the
protection against adversarial attacks and evidences the need to furtherance
the research on defence methods to make federated learning a real solution for
safeguarding data privacy. In this paper, we present an extensive review of the
threats of federated learning, as well as as their corresponding
countermeasures, attacks versus defences. This survey provides a taxonomy of
adversarial attacks and a taxonomy of defence methods that depict a general
picture of this vulnerability of federated learning and how to overcome it.
Likewise, we expound guidelines for selecting the most adequate defence method
according to the category of the adversarial attack. Besides, we carry out an
extensive experimental study from which we draw further conclusions about the
behaviour of attacks and defences and the guidelines for selecting the most
adequate defence method according to the category of the adversarial attack.
This study is finished leading to meditated learned lessons and challenges.

Due to the availability of large-scale multi-modal data (e.g., satellite
images acquired by different sensors, text sentences, etc) archives, the
development of cross-modal retrieval systems that can search and retrieve
semantically relevant data across different modalities based on a query in any
modality has attracted great attention in RS. In this paper, we focus our
attention on cross-modal text-image retrieval, where queries from one modality
(e.g., text) can be matched to archive entries from another (e.g., image). Most
of the existing cross-modal text-image retrieval systems require a high number
of labeled training samples and also do not allow fast and memory-efficient
retrieval due to their intrinsic characteristics. These issues limit the
applicability of the existing cross-modal retrieval systems for large-scale
applications in RS. To address this problem, in this paper we introduce a novel
deep unsupervised cross-modal contrastive hashing (DUCH) method for RS
text-image retrieval. The proposed DUCH is made up of two main modules: 1)
feature extraction module (which extracts deep representations of the
text-image modalities); and 2) hashing module (which learns to generate
cross-modal binary hash codes from the extracted representations). Within the
hashing module, we introduce a novel multi-objective loss function including:
i) contrastive objectives that enable similarity preservation in both intra-
and inter-modal similarities; ii) an adversarial objective that is enforced
across two modalities for cross-modal representation consistency; iii)
binarization objectives for generating representative hash codes. Experimental
results show that the proposed DUCH outperforms state-of-the-art unsupervised
cross-modal hashing methods on two multi-modal (image and text) benchmark
archives in RS. Our code is publicly available at
https://git.tu-berlin.de/rsim/duch.

In cross-lingual speech synthesis, the speech in various languages can be
synthesized for a monoglot speaker. Normally, only the data of monoglot
speakers are available for model training, thus the speaker similarity is
relatively low between the synthesized cross-lingual speech and the native
language recordings. Based on the multilingual transformer text-to-speech
model, this paper studies a multi-task learning framework to improve the
cross-lingual speaker similarity. To further improve the speaker similarity,
joint training with a speaker classifier is proposed. Here, a scheme similar to
parallel scheduled sampling is proposed to train the transformer model
efficiently to avoid breaking the parallel training mechanism when introducing
joint training. By using multi-task learning and speaker classifier joint
training, in subjective and objective evaluations, the cross-lingual speaker
similarity can be consistently improved for both the seen and unseen speakers
in the training set.

A machine thinking model is proposed in this report based on recent advances
of computer vision and the recent results of neuroscience devoted to brain
understanding. We deliver the result of machine thinking in the form of
sentences of natural-language or drawn sketches either informative or
decisional. This result is obtained from a reasoning performed on new acquired
data and memorized data.

Legged robots that can operate autonomously in remote and hazardous
environments will greatly increase opportunities for exploration into
under-explored areas. Exteroceptive perception is crucial for fast and
energy-efficient locomotion: perceiving the terrain before making contact with
it enables planning and adaptation of the gait ahead of time to maintain speed
and stability. However, utilizing exteroceptive perception robustly for
locomotion has remained a grand challenge in robotics. Snow, vegetation, and
water visually appear as obstacles on which the robot cannot step~-- or are
missing altogether due to high reflectance. Additionally, depth perception can
degrade due to difficult lighting, dust, fog, reflective or transparent
surfaces, sensor occlusion, and more. For this reason, the most robust and
general solutions to legged locomotion to date rely solely on proprioception.
This severely limits locomotion speed, because the robot has to physically feel
out the terrain before adapting its gait accordingly. Here we present a robust
and general solution to integrating exteroceptive and proprioceptive perception
for legged locomotion. We leverage an attention-based recurrent encoder that
integrates proprioceptive and exteroceptive input. The encoder is trained
end-to-end and learns to seamlessly combine the different perception modalities
without resorting to heuristics. The result is a legged locomotion controller
with high robustness and speed. The controller was tested in a variety of
challenging natural and urban environments over multiple seasons and completed
an hour-long hike in the Alps in the time recommended for human hikers.

Autonomous driving has been at the forefront of public interest, and a
pivotal debate to widespread concerns is safety in the transportation system.
Deep reinforcement learning (DRL) has been applied to autonomous driving to
provide solutions for obstacle avoidance. However, in a road traffic junction
scenario, the vehicle typically receives partial observations from the
transportation environment, while DRL needs to rely on long-term rewards to
train a reliable model by maximising the cumulative rewards, which may take the
risk when exploring new actions and returning either a positive reward or a
penalty in the case of collisions. Although safety concerns are usually
considered in the design of a reward function, they are not fully considered as
the critical metric to directly evaluate the effectiveness of DRL algorithms in
autonomous driving. In this study, we evaluated the safety performance of three
baseline DRL models (DQN, A2C, and PPO) and proposed a self-awareness module
from an attention mechanism for DRL to improve the safety evaluation for an
anomalous vehicle in a complex road traffic junction environment, such as
intersection and roundabout scenarios, based on four metrics: collision rate,
success rate, freezing rate, and total reward. Our two experimental results in
the training and testing phases revealed the baseline DRL with poor safety
performance, while our proposed self-awareness attention-DQN can significantly
improve the safety performance in intersection and roundabout scenarios.

The ability to discover behaviours from past experience and transfer them to
new tasks is a hallmark of intelligent agents acting sample-efficiently in the
real world. Equipping embodied reinforcement learners with the same ability may
be crucial for their successful deployment in robotics. While hierarchical and
KL-regularized RL individually hold promise here, arguably a hybrid approach
could combine their respective benefits. Key to these fields is the use of
information asymmetry to bias which skills are learnt. While asymmetric choice
has a large influence on transferability, prior works have explored a narrow
range of asymmetries, primarily motivated by intuition. In this paper, we
theoretically and empirically show the crucial trade-off, controlled by
information asymmetry, between the expressivity and transferability of skills
across sequential tasks. Given this insight, we provide a principled approach
towards choosing asymmetry and apply our approach to a complex, robotic block
stacking domain, unsolvable by baselines, demonstrating the effectiveness of
hierarchical KL-regularized RL, coupled with correct asymmetric choice, for
sample-efficient transfer learning.

Our objective of this project is to make the extension based on the technique
mentioned in the paper "Safety-Aware Apprenticeship Learning" to improve the
utility and the efficiency of the existing Reinforcement Learning model from a
Single-Agent Learning framework to a Multi-Agent Learning framework. Our
contributions to the project are presented in the following bullet points: 1.
Regarding the fact that we will add an extension to the Inverse Reinforcement
Learning model from a Single-Agent scenario to a Multi-Agentscenario. Our first
contribution to this project is considering the case of extracting safe reward
functions from expert behaviors in a Multi-Agent scenario instead of being from
the Single-Agent scenario. 2. Our second contribution is extending the
Single-Agent Learning Framework to a Multi-Agent Learning framework and
designing a novel Learning Framework based on the extension in the end. 3. Our
final contribution to this project is evaluating empirically the performance of
my extension to the Single-Agent Inverse Reinforcement Learning framework.

Parametric and non-parametric machine learning potentials have emerged
recently as a way to improve the accuracy of bio-molecular simulations. Here,
we present NNP/MM, an hybrid method integrating neural network potentials
(NNPs) and molecular mechanics (MM). It allows to simulate a part of molecular
system with NNP, while the rest is simulated with MM for efficiency. The method
is currently available in ACEMD using OpenMM plugins to optimize the
performance of NNPs. The achieved performance is slower but comparable to the
state-of-the-art GPU-accelerated MM simulations. We validated NNP/MM by
performing MD simulations of four protein-ligand complexes, where NNP is used
for the intra-molecular interactions of a lignad and MM for the rest
interactions. This shows that NNP can already replace MM for small molecules in
protein-ligand simulations. The combined sampling of each complex is 1
microsecond, which are the longest simulations of NNP/MM ever reported.
Finally, we have made the setup of the NNP/MM simulations simple and
user-friendly.

The concept of median/consensus has been widely investigated in order to
provide a statistical summary of ranking data, i.e. realizations of a random
permutation $\Sigma$ of a finite set, $\{1,\; \ldots,\; n\}$ with $n\geq 1$
say. As it sheds light onto only one aspect of $\Sigma$'s distribution $P$, it
may neglect other informative features. It is the purpose of this paper to
define analogs of quantiles, ranks and statistical procedures based on such
quantities for the analysis of ranking data by means of a metric-based notion
of depth function on the symmetric group. Overcoming the absence of vector
space structure on $\mathfrak{S}_n$, the latter defines a center-outward
ordering of the permutations in the support of $P$ and extends the classic
metric-based formulation of consensus ranking (medians corresponding then to
the deepest permutations). The axiomatic properties that ranking depths should
ideally possess are listed, while computational and generalization issues are
studied at length. Beyond the theoretical analysis carried out, the relevance
of the novel concepts and methods introduced for a wide variety of statistical
tasks are also supported by numerous numerical experiments.

Agents should avoid unsafe behaviour during both training and deployment.
This typically requires a simulator and a procedural specification of unsafe
behaviour. Unfortunately, a simulator is not always available, and procedurally
specifying constraints can be difficult or impossible for many real-world
tasks. A recently introduced technique, ReQueST, aims to solve this problem by
learning a neural simulator of the environment from safe human trajectories,
then using the learned simulator to efficiently learn a reward model from human
feedback. However, it is yet unknown whether this approach is feasible in
complex 3D environments with feedback obtained from real humans - whether
sufficient pixel-based neural simulator quality can be achieved, and whether
the human data requirements are viable in terms of both quantity and quality.
In this paper we answer this question in the affirmative, using ReQueST to
train an agent to perform a 3D first-person object collection task using data
entirely from human contractors. We show that the resulting agent exhibits an
order of magnitude reduction in unsafe behaviour compared to standard

Understanding the patterns of misclassified ImageNet images is particularly
important, as it could guide us to design deep neural networks (DNN) that
generalize better. However, the richness of ImageNet imposes difficulties for
researchers to visually find any useful patterns of misclassification. Here, to
help find these patterns, we propose "Superclassing ImageNet dataset". It is a
subset of ImageNet which consists of 10 superclasses, each containing 7-116
related subclasses (e.g., 52 bird types, 116 dog types). By training neural
networks on this dataset, we found that: (i) Misclassifications are rarely
across superclasses, but mainly among subclasses within a superclass. (ii)
Ensemble networks trained each only on subclasses of a given superclass perform
better than the same network trained on all subclasses of all superclasses.
Hence, we propose a two-stage Super-Sub framework, and demonstrate that: (i)
The framework improves overall classification performance by 3.3%, by first
inferring a superclass using a generalist superclass-level network, and then
using a specialized network for final subclass-level classification. (ii)
Although the total parameter storage cost increases to a factor N+1 for N
superclasses compared to using a single network, with finetuning, delta and
quantization aware training techniques this can be reduced to 0.2N+1. Another
advantage of this efficient implementation is that the memory cost on the GPU
during inference is equivalent to using only one network. The reason is we
initiate each subclass-level network through addition of small parameter
variations (deltas) to the superclass-level network. (iii) Finally, our
framework promises to be more scalable and generalizable than the common
alternative of simply scaling up a vanilla network in size, since very large
networks often suffer from overfitting and gradient vanishing.

Human physical motion activity identification has many potential applications
in various fields, such as medical diagnosis, military sensing, sports
analysis, and human-computer security interaction. With the recent advances in
smartphones and wearable technologies, it has become common for such devices to
have embedded motion sensors that are able to sense even small body movements.
This study collected human activity data from 60 participants across two
different days for a total of six activities recorded by gyroscope and
accelerometer sensors in a modern smartphone. The paper investigates to what
extent different activities can be identified by utilising machine learning
algorithms using approaches such as majority algorithmic voting. More analyses
are also provided that reveal which time and frequency domain based features
were best able to identify individuals motion activity types. Overall, the
proposed approach achieved a classification accuracy of 98 percent in
identifying four different activities: walking, walking upstairs, walking
downstairs, and sitting while the subject is calm and doing a typical
desk-based activity.

For centuries nursing has been known as a job that requires complex manual
operations, that cannot be automated or replaced by any machinery. All the
devices and techniques have been invented only to support, but never fully
replace, a person with knowledge and expert intuition. With the rise of
Artificial Intelligence and continuously increasing digital data flow in
healthcare, new tools have arrived to improve patient care and reduce the
labour-intensive work conditions of a nurse.
  This cross-disciplinary review aims to build a bridge over the gap between
computer science and nursing. It outlines and classifies the methods for
machine learning and data processing in patient care before and after the
operation. It comprises of Process-, Patient-, Operator-, Feedback-, and
Technology-centric classifications. The presented classifications are based on
the technical aspects of patient case.

We address the problem of predicting the zero temperature dynamical stability
(DS) of a periodic crystal without computing its full phonon band structure.
Using data for two-dimensional (2D) crystals, we first present statistical
evidence that DS can be inferred with good reliability from the phonon
frequencies at the center and boundary of the Brillouin zone (BZ). This
analysis represents a validation of the DS test employed by the Computational
2D Materials Database (C2DB). For 137 dynamically unstable 2D crystals, we
displace the atoms along an unstable mode and relax the structure. This
procedure yields a dynamically stable crystal in 49 cases. The elementary
properties of these new structures are characterised using the C2DB workflow,
and it is found that their properties can differ significantly from those of
the original unstable crystals, e.g. band gaps are opened by 0.3 eV on average.
All the crystal structures and properties are available in the C2DB. Finally,
we train a classification model on the DS data for 3295 2D materials in the
C2DB using a representation encoding the electronic structure of the crystal.
We obtain an excellent receiver operating characteristic (ROC) curve with an
area under the curve (AUC) of 0.89, showing that the classification model can
drastically reduce computational efforts in high-throughput studies.

Empirical observation of high dimensional phenomena, such as the double
descent behaviour, has attracted a lot of interest in understanding classical
techniques such as kernel methods, and their implications to explain
generalization properties of neural networks. Many recent works analyze such
models in a certain high-dimensional regime where the covariates are
independent and the number of samples and the number of covariates grow at a
fixed ratio (i.e. proportional asymptotics). In this work we show that for a
large class of kernels, including the neural tangent kernel of fully connected
networks, kernel methods can only perform as well as linear models in this
regime. More surprisingly, when the data is generated by a kernel model where
the relationship between input and the response could be very nonlinear, we
show that linear models are in fact optimal, i.e. linear models achieve the
minimum risk among all models, linear or nonlinear. These results suggest that
more complex models for the data other than independent features are needed for
high-dimensional analysis.

Value-based reinforcement-learning algorithms have shown strong performances
in games, robotics, and other real-world applications. The most popular
sample-based method is $Q$-Learning. It subsequently performs updates by
adjusting the current $Q$-estimate towards the observed reward and the maximum
of the $Q$-estimates of the next state. The procedure introduces maximization
bias with approaches like Double $Q$-Learning. We frame the bias problem
statistically and consider it an instance of estimating the maximum expected
value (MEV) of a set of random variables. We propose the $T$-Estimator (TE)
based on two-sample testing for the mean, that flexibly interpolates between
over- and underestimation by adjusting the significance level of the underlying
hypothesis tests. A generalization, termed $K$-Estimator (KE), obeys the same
bias and variance bounds as the TE while relying on a nearly arbitrary kernel
function. We introduce modifications of $Q$-Learning and the Bootstrapped Deep
$Q$-Network (BDQN) using the TE and the KE. Furthermore, we propose an adaptive
variant of the TE-based BDQN that dynamically adjusts the significance level to
minimize the absolute estimation bias. All proposed estimators and algorithms
are thoroughly tested and validated on diverse tasks and environments,
illustrating the bias control and performance potential of the TE and KE.

In the present study, we propose novel sequence-to-sequence pre-training
objectives for low-resource machine translation (NMT): Japanese-specific
sequence to sequence (JASS) for language pairs involving Japanese as the source
or target language, and English-specific sequence to sequence (ENSS) for
language pairs involving English. JASS focuses on masking and reordering
Japanese linguistic units known as bunsetsu, whereas ENSS is proposed based on
phrase structure masking and reordering tasks. Experiments on ASPEC
Japanese--English & Japanese--Chinese, Wikipedia Japanese--Chinese, News
English--Korean corpora demonstrate that JASS and ENSS outperform MASS and
other existing language-agnostic pre-training methods by up to +2.9 BLEU points
for the Japanese--English tasks, up to +7.0 BLEU points for the
Japanese--Chinese tasks and up to +1.3 BLEU points for English--Korean tasks.
Empirical analysis, which focuses on the relationship between individual parts
in JASS and ENSS, reveals the complementary nature of the subtasks of JASS and
ENSS. Adequacy evaluation using LASER, human evaluation, and case studies
reveals that our proposed methods significantly outperform pre-training methods
without injected linguistic knowledge and they have a larger positive impact on
the adequacy as compared to the fluency. We release codes here:
https://github.com/Mao-KU/JASS/tree/master/linguistically-driven-pretraining.

We propose an empirical likelihood ratio test for nonparametric model
selection, where the competing models may be nested, nonnested, overlapping,
misspecified, or correctly specified. It compares the squared prediction errors
of models based on the cross-validation and allows for heteroscedasticity of
the errors of models. We develop its asymptotic distributions for comparing
additive models and varying-coefficient models and extend it to test
significance of variables in additive models with massive data. The method is
applicable to model selection among supervised learning models. To facilitate
implementation of the test, we provide a fast calculation procedure.
Simulations show that the proposed tests work well and have favorable finite
sample performance over some existing approaches. The methodology is validated
on an empirical application.

Existing multimodal machine translation (MMT) datasets consist of images and
video captions or general subtitles, which rarely contain linguistic ambiguity,
making visual information not so effective to generate appropriate
translations. We introduce VISA, a new dataset that consists of 40k
Japanese-English parallel sentence pairs and corresponding video clips with the
following key features: (1) the parallel sentences are subtitles from movies
and TV episodes; (2) the source subtitles are ambiguous, which means they have
multiple possible translations with different meanings; (3) we divide the
dataset into Polysemy and Omission according to the cause of ambiguity. We show
that VISA is challenging for the latest MMT system, and we hope that the
dataset can facilitate MMT research. The VISA dataset is available at:
https://github.com/ku-nlp/VISA.

We propose a new deep learning-based method for estimating the occupancy of
vegetation strata from airborne 3D LiDAR point clouds. Our model predicts
rasterized occupancy maps for three vegetation strata corresponding to lower,
medium, and higher cover. Our weakly-supervised training scheme allows our
network to only be supervised with vegetation occupancy values aggregated over
cylindrical plots containing thousands of points. Such ground truth is easier
to produce than pixel-wise or point-wise annotations. Our method outperforms
handcrafted and deep learning baselines in terms of precision by up to 30%,
while simultaneously providing visual and interpretable predictions. We provide
an open-source implementation along with a dataset of 199 agricultural plots to
train and evaluate weakly supervised occupancy regression algorithms.

Vision transformers (ViTs) have demonstrated great potential in various
visual tasks, but suffer from expensive computational and memory cost problems
when deployed on resource-constrained devices. In this paper, we introduce a
ternary vision transformer (TerViT) to ternarize the weights in ViTs, which are
challenged by the large loss surface gap between real-valued and ternary
parameters. To address the issue, we introduce a progressive training scheme by
first training 8-bit transformers and then TerViT, and achieve a better
optimization than conventional methods. Furthermore, we introduce channel-wise
ternarization, by partitioning each matrix to different channels, each of which
is with an unique distribution and ternarization interval. We apply our methods
to popular DeiT and Swin backbones, and extensive results show that we can
achieve competitive performance. For example, TerViT can quantize Swin-S to
13.1MB model size while achieving above 79% Top-1 accuracy on ImageNet dataset.

Classical Visual Servoing (VS) rely on handcrafted visual features, which
limit their generalizability. Recently, a number of approaches, some based on
Deep Neural Networks, have been proposed to overcome this limitation by
comparing directly the entire target and current camera images. However, by
getting rid of the visual features altogether, those approaches require the
target and current images to be essentially similar, which precludes the
generalization to unknown, cluttered, scenes. Here we propose to perform VS
based on visual features as in classical VS approaches but, contrary to the
latter, we leverage recent breakthroughs in Deep Learning to automatically
extract and match the visual features. By doing so, our approach enjoys the
advantages from both worlds: (i) because our approach is based on visual
features, it is able to steer the robot towards the object of interest even in
presence of significant distraction in the background; (ii) because the
features are automatically extracted and matched, our approach can easily and
automatically generalize to unseen objects and scenes. In addition, we propose
to use a render engine to synthesize the target image, which offers a further
level of generalization. We demonstrate these advantages in a robotic grasping
task, where the robot is able to steer, with high accuracy, towards the object
to grasp, based simply on an image of the object rendered from the camera view
corresponding to the desired robot grasping pose.

Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh
interest in generative modelling. They immediately achieved state-of-the-art in
image synthesis, image-to-image translation, text-to-image generation, image
inpainting and have been used in sciences ranging from medicine to high-energy
particle physics. Despite their popularity and ability to learn arbitrary
distributions, GAN have not been widely applied in recommender systems (RS).
Moreover, only few of the techniques that have introduced GAN in RS have
employed them directly as a collaborative filtering (CF) model. In this work we
propose a new GAN-based approach that learns user and item latent factors in a
matrix factorization setting for the generic top-N recommendation problem.
Following the vector-wise GAN training approach for RS introduced by CFGAN, we
identify 2 unique issues when utilizing GAN for CF. We propose solutions for
both of them by using an autoencoder as discriminator and incorporating an
additional loss function for the generator. We evaluate our model, GANMF,
through well-known datasets in the RS community and show improvements over
traditional CF approaches and GAN-based models. Through an ablation study on
the components of GANMF we aim to understand the effects of our architectural
choices. Finally, we provide a qualitative evaluation of the matrix
factorization performance of GANMF.

Deep learning is applied to many complex tasks in the field of wireless
communication, such as modulation recognition of spectrum waveforms, because of
its convenience and efficiency. This leads to the problem of a malicious third
party using a deep learning model to easily recognize the modulation format of
the transmitted waveform. Some existing works address this problem directly
using the concept of adversarial examples in the image domain without fully
considering the characteristics of the waveform transmission in the physical
world. Therefore, we propose a low-intercept waveform~(LIW) generation method
that can reduce the probability of the modulation being recognized by a third
party without affecting the reliable communication of the friendly party. Our
LIW exhibits significant low-interception performance even in the physical
hardware experiment, decreasing the accuracy of the state of the art model to
approximately $15\%$ with small perturbations.

Biophysical signals such as Electrocardiogram (ECG) and Photoplethysmogram
(PPG) are key to the sensing of vital parameters for wellbeing. Coincidentally,
ECG and PPG are signals, which provide a "different window" into the same
phenomena, namely the cardiac cycle. While they are used separately, there are
no studies regarding the exact correction of the different ECG and PPG events.
Such correlation would be helpful in many fronts such as sensor fusion for
improved accuracy using cheaper sensors and attack detection and mitigation
methods using multiple signals to enhance the robustness, for example.
Considering this, we present the first approach in formally establishing the
key relationships between ECG and PPG signals. We combine formal run-time
monitoring with statistical analysis and regression analysis for our results.

Due to extensive spread of fake news on social and news media it became an
emerging research topic now a days that gained attention. In the news media and
social media the information is spread highspeed but without accuracy and hence
detection mechanism should be able to predict news fast enough to tackle the
dissemination of fake news. It has the potential for negative impacts on
individuals and society. Therefore, detecting fake news on social media is
important and also a technically challenging problem these days. We knew that
Machine learning is helpful for building Artificial intelligence systems based
on tacit knowledge because it can help us to solve complex problems due to real
word data. On the other side we knew that Knowledge engineering is helpful for
representing experts knowledge which people aware of that knowledge. Due to
this we proposed that integration of Machine learning and knowledge engineering
can be helpful in detection of fake news. In this paper we present what is fake
news, importance of fake news, overall impact of fake news on different areas,
different ways to detect fake news on social media, existing detections
algorithms that can help us to overcome the issue, similar application areas
and at the end we proposed combination of data driven and engineered knowledge
to combat fake news. We studied and compared three different modules text
classifiers, stance detection applications and fact checking existing
techniques that can help to detect fake news. Furthermore, we investigated the
impact of fake news on society. Experimental evaluation of publically available
datasets and our proposed fake news detection combination can serve better in
detection of fake news.

Adaptation to out-of-distribution data is a meta-challenge for all
statistical learning algorithms that strongly rely on the i.i.d. assumption. It
leads to unavoidable labor costs and confidence crises in realistic
applications. For that, domain generalization aims at mining domain-irrelevant
knowledge from multiple source domains that can generalize to unseen target
domains. In this paper, by leveraging the frequency domain of an image, we
uniquely work with two key observations: (i) the high-frequency information of
an image depicts object edge structure, which preserves high-level semantic
information of the object is naturally consistent across different domains, and
(ii) the low-frequency component retains object smooth structure, while this
information is susceptible to domain shifts. Motivated by the above
observations, we introduce (i) an encoder-decoder structure to disentangle
high- and low-frequency feature of an image, (ii) an information interaction
mechanism to ensure the helpful knowledge from both two parts can cooperate
effectively, and (iii) a novel data augmentation technique that works on the
frequency domain to encourage the robustness of frequency-wise feature
disentangling. The proposed method obtains state-of-the-art performance on
three widely used domain generalization benchmarks (Digit-DG, Office-Home, and
PACS).

In this paper, we study the sharpness of a deep learning (DL) loss landscape
around local minima in order to reveal systematic mechanisms underlying the
generalization abilities of DL models. Our analysis is performed across varying
network and optimizer hyper-parameters, and involves a rich family of different
sharpness measures. We compare these measures and show that the low-pass
filter-based measure exhibits the highest correlation with the generalization
abilities of DL models, has high robustness to both data and label noise, and
furthermore can track the double descent behavior for neural networks. We next
derive the optimization algorithm, relying on the low-pass filter (LPF), that
actively searches the flat regions in the DL optimization landscape using
SGD-like procedure. The update of the proposed algorithm, that we call LPF-SGD,
is determined by the gradient of the convolution of the filter kernel with the
loss function and can be efficiently computed using MC sampling. We empirically
show that our algorithm achieves superior generalization performance compared
to the common DL training strategies. On the theoretical front, we prove that
LPF-SGD converges to a better optimal point with smaller generalization error
than SGD.

In online advertising, conventional post-click conversion rate (CVR)
estimation models are trained using clicked samples. However, during online
serving the models need to estimate for all impression ads, leading to the
sample selection bias (SSB) issue. Intuitively, providing reliable supervision
signals for unclicked ads is a feasible way to alleviate the SSB issue. This
paper proposes an uncertainty-regularized knowledge distillation (UKD)
framework to debias CVR estimation via distilling knowledge from unclicked ads.
A teacher model learns click-adaptive representations and produces
pseudo-conversion labels on unclicked ads as supervision signals. Then a
student model is trained on both clicked and unclicked ads with knowledge
distillation, performing uncertainty modeling to alleviate the inherent noise
in pseudo-labels. Experiments on billion-scale datasets show that UKD
outperforms previous debiasing methods. Online results verify that UKD achieves
significant improvements.

We propose a novel deep neural network (DNN) based approximation architecture
to learn estimates of measurements. We detail an algorithm that enables
training of the DNN. The DNN estimator only uses measurements, if and when they
are received over a communication network. The measurements are communicated
over a network as packets, at a rate unknown to the estimator. Packets may
suffer drops and need retransmission. They may suffer waiting delays as they
traverse a network path.
  Works on estimation often assume knowledge of the dynamic model of the
measured system, which may not be available in practice. The DNN estimator
doesn't assume knowledge of the dynamic system model or the communication
network. It doesn't require a history of measurements, often used by other
works.
  The DNN estimator results in significantly smaller average estimation error
than the commonly used Time-varying Kalman Filter and the Unscented Kalman
Filter, in simulations of linear and nonlinear dynamic systems. The DNN need
not be trained separately for different communications network settings. It is
robust to errors in estimation of network delays that occur due to imperfect
time synchronization between the measurement source and the estimator. Last but
not the least, our simulations shed light on the rate of updates that result in
low estimation error.

Recent artificial intelligence-based methods have shown great promise in the
use of neural networks for real-time sensing and detection of transmission line
faults and estimation of their locations. The expansion of power systems
including transmission lines with various lengths have made a fault detection,
classification, and location estimation process more challenging. Transmission
line datasets are stream data which are continuously collected by various
sensors and hence, require generalized and fast fault diagnosis approaches.
Newly collected datasets including voltages and currents might not have enough
and accurate labels (fault and no fault) that are useful to train neural
networks. In this paper, a novel transfer learning framework based on a
pre-trained LeNet-5 convolutional neural network is proposed. This method is
able to diagnose faults for different transmission line lengths and impedances
by transferring the knowledge from a source convolutional neural network to
predict a dissimilar target dataset. By transferring this knowledge, faults
from various transmission lines, without having enough labels, can be diagnosed
faster and more efficiently compared to the existing methods. To prove the
feasibility and effectiveness of this methodology, seven different datasets
that include various lengths of transmission lines are used. The robustness of
the proposed methodology against generator voltage fluctuation, variation in
fault distance, fault inception angle, fault resistance, and phase difference
between the two generators are well shown, thus proving its practical values in
the fault diagnosis of transmission lines.

Travel Time Estimation (TTE) is indispensable in intelligent transportation
system (ITS). It is significant to achieve the fine-grained Trajectory-based
Travel Time Estimation (TTTE) for multi-city scenarios, namely to accurately
estimate travel time of the given trajectory for multiple city scenarios.
However, it faces great challenges due to complex factors including dynamic
temporal dependencies and fine-grained spatial dependencies. To tackle these
challenges, we propose a meta learning based framework, MetaTTE, to
continuously provide accurate travel time estimation over time by leveraging
well-designed deep neural network model called DED, which consists of Data
preprocessing module and Encoder-Decoder network module. By introducing meta
learning techniques, the generalization ability of MetaTTE is enhanced using
small amount of examples, which opens up new opportunities to increase the
potential of achieving consistent performance on TTTE when traffic conditions
and road networks change over time in the future. The DED model adopts an
encoder-decoder network to capture fine-grained spatial and temporal
representations. Extensive experiments on two real-world datasets are conducted
to confirm that our MetaTTE outperforms six state-of-art baselines, and improve
29.35% and 25.93% accuracy than the best baseline on Chengdu and Porto

Although multifarious variants of convolutional neural networks (CNNs) have
proved successful in cardiac index quantification, they seem vulnerable to mild
input perturbations, e.g., spatial transformations, image distortions, and
adversarial attacks. Such brittleness erodes our trust in CNN-based automated
diagnosis of various cardiovascular diseases. In this work, we describe a
simple and effective method to learn robust CNNs for left ventricle (LV)
quantification, including cavity and myocardium areas, directional dimensions,
and regional wall thicknesses. The key to the success of our approach is the
use of the biologically-inspired steerable pyramid transform (SPT) as fixed
front-end processing, which brings three computational advantages to LV
quantification. First, the basis functions of SPT match the anatomical
structure of the LV as well as the geometric characteristics of the estimated
indices. Second, SPT enables sharing a CNN across different orientations as a
form of parameter regularization, and explicitly captures the scale variations
of the LV in a natural way. Third, the residual highpass subband can be
conveniently discarded to further encourage robust feature learning. A concise
and effective metric, named Robustness Ratio, is proposed to evaluate the
robustness under various input perturbations. Extensive experiments on 145
cardiac sequences show that our SPT-augmented method performs favorably against
state-of-the-art algorithms in terms of prediction accuracy, but is
significantly more robust under input perturbations.

Predicting forced displacement is an important undertaking of many
humanitarian aid agencies, which must anticipate flows in advance in order to
provide vulnerable refugees and Internally Displaced Persons (IDPs) with
shelter, food, and medical care. While there is a growing interest in using
machine learning to better anticipate future arrivals, there is little
standardized knowledge on how to predict refugee and IDP flows in practice.
Researchers and humanitarian officers are confronted with the need to make
decisions about how to structure their datasets and how to fit their problem to
predictive analytics approaches, and they must choose from a variety of
modeling options. Most of the time, these decisions are made without an
understanding of the full range of options that could be considered, and using
methodologies that have primarily been applied in different contexts - and with
different goals - as opportunistic references. In this work, we attempt to
facilitate a more comprehensive understanding of this emerging field of
research by providing a systematic model-agnostic framework, adapted to the use
of big data sources, for structuring the prediction problem. As we do so, we
highlight existing work on predicting refugee and IDP flows. We also draw on
our own experience building models to predict forced displacement in Somalia,
in order to illustrate the choices facing modelers and point to open research
questions that may be used to guide future work.

Mechanics of materials is a classic course of engineering presenting the
fundamentals of strain and stress analysis to junior undergraduate students in
several engineering majors. So far, material deformation and strain have been
only analyzed using theoretical and numerical approaches, and they have been
experimentally validated by expensive machines and tools. This paper presents a
novel approach for strain and deformation analysis by using quadcopters. We
propose to treat quadcopters as finite number of particles of a deformable body
and apply the principles of continuum mechanics to illustrate the concept of
axial and shear deformation by using quadcopter hardware in a $3$-D motion
space. The outcome of this work can have significant impact on undergraduate
education by filling the gap between in-class learning and hardware realization
and experiments, where we introduce new roles for drones as "teachers"
providing a great opportunity for practicing theoretical concepts of mechanics
in a fruitful and understandable way.

Understanding a plant's root system architecture (RSA) is crucial for a
variety of plant science problem domains including sustainability and climate
adaptation. Minirhizotron (MR) technology is a widely-used approach for
phenotyping RSA non-destructively by capturing root imagery over time.
Precisely segmenting roots from the soil in MR imagery is a critical step in
studying RSA features. In this paper, we introduce a large-scale dataset of
plant root images captured by MR technology. In total, there are over 72K RGB
root images across six different species including cotton, papaya, peanut,
sesame, sunflower, and switchgrass in the dataset. The images span a variety of
conditions including varied root age, root structures, soil types, and depths
under the soil surface. All of the images have been annotated with weak
image-level labels indicating whether each image contains roots or not. The
image-level labels can be used to support weakly supervised learning in plant
root segmentation tasks. In addition, 63K images have been manually annotated
to generate pixel-level binary masks indicating whether each pixel corresponds
to root or not. These pixel-level binary masks can be used as ground truth for
supervised learning in semantic segmentation tasks. By introducing this
dataset, we aim to facilitate the automatic segmentation of roots and the
research of RSA with deep learning and other image analysis algorithms.

A common class of problems in remote sensing is scene classification, a
fundamentally important task for natural hazards identification, geographic
image retrieval, and environment monitoring. Recent developments in this field
rely label-dependent supervised learning techniques which is antithetical to
the 35 petabytes of unlabelled satellite imagery in NASA GIBS. To solve this
problem, we establish CELESTIAL-a self-supervised learning pipeline for
effectively leveraging sparsely-labeled satellite imagery. This pipeline
successfully adapts SimCLR, an algorithm that first learns image
representations on unlabelled data and then fine-tunes this knowledge on the
provided labels. Our results show CELESTIAL requires only a third of the labels
that the supervised method needs to attain the same accuracy on an experimental
dataset. The first unsupervised tier can enable applications such as reverse
image search for NASA Worldview (i.e. searching similar atmospheric phenomenon
over years of unlabelled data with minimal samples) and the second supervised
tier can lower the necessity of expensive data annotation significantly. In the
future, we hope we can generalize the CELESTIAL pipeline to other data types,
algorithms, and applications.

In this work, we predict the sentiment of restaurant reviews based on a
subset of the Yelp Open Dataset. We utilize the meta features and text
available in the dataset and evaluate several machine learning and
state-of-the-art deep learning approaches for the prediction task. Through
several qualitative experiments, we show the success of the deep models with
attention mechanism in learning a balanced model for reviews across different
restaurants. Finally, we propose a novel Multi-tasked joint BERT model that
improves the overall classification performance.

We establish a high-dimensional statistical learning framework for
individualized asset allocation. Our proposed methodology addresses
continuous-action decision-making with a large number of characteristics. We
develop a discretization approach to model the effect of continuous actions and
allow the discretization frequency to be large and diverge with the number of
observations. The value function of continuous-action is estimated using
penalized regression with our proposed generalized penalties that are imposed
on linear transformations of the model coefficients. We show that our proposed
Discretization and Regression with generalized fOlded concaVe penalty on Effect
discontinuity (DROVE) approach enjoys desirable theoretical properties and
allows for statistical inference of the optimal value associated with optimal
decision-making. Empirically, the proposed framework is exercised with the
Health and Retirement Study data in finding individualized optimal asset
allocation. The results show that our individualized optimal strategy improves
the population financial well-being.

Self-supervised video representation learning has been shown to effectively
improve downstream tasks such as video retrieval and action recognition. In
this paper, we present the Cascade Positive Retrieval (CPR) that successively
mines positive examples w.r.t. the query for contrastive learning in a cascade
of stages. Specifically, CPR exploits multiple views of a query example in
different modalities, where an alternative view may help find another positive
example dissimilar in the query view. We explore the effects of possible CPR
configurations in ablations including the number of mining stages, the top
similar example selection ratio in each stage, and progressive training with an
incremental number of the final Top-k selection. The overall mining quality is
measured to reflect the recall across training set classes. CPR reaches a
median class mining recall of 83.3%, outperforming previous work by 5.5%.
Implementation-wise, CPR is complementary to pretext tasks and can be easily
applied to previous work. In the evaluation of pretraining on UCF101, CPR
consistently improves existing work and even achieves state-of-the-art R@1 of
56.7% and 24.4% in video retrieval as well as 83.8% and 54.8% in action
recognition on UCF101 and HMDB51. The code is available at
https://github.com/necla-ml/CPR.

Because of its wide application, critical nodes identification has become an
important research topic at the micro level of network science. Influence
maximization is one of the main problems in critical nodes mining and is
usually handled with heuristics. In this paper, a deep graph learning framework
IMGNN is proposed and the corresponding training sample generation scheme is
designed. The framework takes centralities of nodes in a network as input and
the probability that nodes in the optimal initial spreaders as output. By
training on a large number of small synthetic networks, IMGNN is more efficient
than human-based heuristics in minimizing the size of initial spreaders under
the fixed infection scale. The experimental results on one synthetic and five
real networks show that, compared with traditional non-iterative node ranking
algorithms, IMGNN has the smallest proportion of initial spreaders under
different infection probabilities when the final infection scale is fixed. And
the reordered version of IMGNN outperforms all the latest critical nodes mining

Graph contrastive learning is the state-of-the-art unsupervised graph
representation learning framework and has shown comparable performance with
supervised approaches. However, evaluating whether the graph contrastive
learning is robust to adversarial attacks is still an open problem because most
existing graph adversarial attacks are supervised models, which means they
heavily rely on labels and can only be used to evaluate the graph contrastive
learning in a specific scenario. For unsupervised graph representation methods
such as graph contrastive learning, it is difficult to acquire labels in
real-world scenarios, making traditional supervised graph attack methods
difficult to be applied to test their robustness. In this paper, we propose a
novel unsupervised gradient-based adversarial attack that does not rely on
labels for graph contrastive learning. We compute the gradients of the
adjacency matrices of the two views and flip the edges with gradient ascent to
maximize the contrastive loss. In this way, we can fully use multiple views
generated by the graph contrastive learning models and pick the most
informative edges without knowing their labels, and therefore can promisingly
support our model adapted to more kinds of downstream tasks. Extensive
experiments show that our attack outperforms unsupervised baseline attacks and
has comparable performance with supervised attacks in multiple downstream tasks
including node classification and link prediction. We further show that our
attack can be transferred to other graph representation models as well.

Using the pre-trained language models to understand source codes has
attracted increasing attention from financial institutions owing to the great
potential to uncover financial risks. However, there are several challenges in
applying these language models to solve programming language-related problems
directly. For instance, the shift of domain knowledge between natural language
(NL) and programming language (PL) requires understanding the semantic and
syntactic information from the data from different perspectives. To this end,
we propose the AstBERT model, a pre-trained PL model aiming to better
understand the financial codes using the abstract syntax tree (AST).
Specifically, we collect a sheer number of source codes (both Java and Python)
from the Alipay code repository and incorporate both syntactic and semantic
code knowledge into our model through the help of code parsers, in which AST
information of the source codes can be interpreted and integrated. We evaluate
the performance of the proposed model on three tasks, including code question
answering, code clone detection and code refinement. Experiment results show
that our AstBERT achieves promising performance on three different downstream
tasks.

Mobile Crowdsensing has become main stream paradigm for researchers to
collect behavioral data from citizens in large scales. This valuable data can
be leveraged to create centralized repositories that can be used to train
advanced Artificial Intelligent (AI) models for various services that benefit
society in all aspects. Although decades of research has explored the viability
of Mobile Crowdsensing in terms of incentives and many attempts have been made
to reduce the participation barriers, the overshadowing privacy concerns
regarding sharing personal data still remain. Recently a new pathway has
emerged to enable to shift MCS paradigm towards a more privacy-preserving
collaborative learning, namely Federated Learning. In this paper, we posit a
first of its kind framework for this emerging paradigm. We demonstrate the
functionalities of our framework through a case study of diversifying two
vision algorithms through to learn the representation of ordinary sidewalk
obstacles as part of enhancing visually impaired navigation.

High definition (HD) map needs to be updated frequently to capture road
changes, which is constrained by limited specialized collection vehicles. To
maintain an up-to-date map, we explore crowdsourcing data from connected
vehicles. Updating the map collaboratively is, however, challenging under
constrained transmission and computation resources in dynamic networks. In this
paper, we propose EdgeMap, a crowdsourcing HD map to minimize the usage of
network resources while maintaining the latency requirements. We design a DATE
algorithm to adaptively offload vehicular data on a small time scale and
reserve network resources on a large time scale, by leveraging the multi-agent
deep reinforcement learning and Gaussian process regression. We evaluate the
performance of EdgeMap with extensive network simulations in a time-driven
end-to-end simulator. The results show that EdgeMap reduces more than 30%
resource usage as compared to state-of-the-art solutions.

Security Orchestration, Automation, and Response (SOAR) platforms integrate
and orchestrate a wide variety of security tools to accelerate the operational
activities of Security Operation Center (SOC). Integration of security tools in
a SOAR platform is mostly done manually using APIs, plugins, and scripts. SOC
teams need to navigate through API calls of different security tools to find a
suitable API to define or update an incident response action. Analyzing various
types of API documentation with diverse API format and presentation structure
involves significant challenges such as data availability, data heterogeneity,
and semantic variation for automatic identification of security tool APIs
specific to a particular task. Given these challenges can have negative impact
on SOC team's ability to handle security incident effectively and efficiently,
we consider it important to devise suitable automated support solutions to
address these challenges. We propose a novel learning-based framework for
automated security tool API Recommendation for security Orchestration,
automation, and response, APIRO. To mitigate data availability constraint,
APIRO enriches security tool API description by applying a wide variety of data
augmentation techniques. To learn data heterogeneity of the security tools and
semantic variation in API descriptions, APIRO consists of an API-specific word
embedding model and a Convolutional Neural Network (CNN) model that are used
for prediction of top 3 relevant APIs for a task. We experimentally demonstrate
the effectiveness of APIRO in recommending APIs for different tasks using 3
security tools and 36 augmentation techniques. Our experimental results
demonstrate the feasibility of APIRO for achieving 91.9% Top-1 Accuracy.

We consider the challenge of finding a deterministic policy for a Markov
decision process that uniformly (in all states) maximizes one reward subject to
a probabilistic constraint over a different reward. Existing solutions do not
fully address our precise problem definition, which nevertheless arises
naturally in the context of safety-critical robotic systems. This class of
problem is known to be hard, but the combined requirements of determinism and
uniform optimality can create learning instability. In this work, after
describing and motivating our problem with a simple example, we present a
suitable constrained reinforcement learning algorithm that prevents learning
instability, using recursive constraints. Our proposed approach admits an
approximative form that improves efficiency and is conservative w.r.t. the
constraint.

Smart home IoT systems and devices are susceptible to attacks and
malfunctions. As a result, users' concerns about their security and safety
issues arise along with the prevalence of smart home deployments. In a smart
home, various anomalies (such as fire or flooding) could happen, due to cyber
attacks, device malfunctions, or human mistakes. These concerns motivate
researchers to propose various anomaly detection approaches. Existing works on
smart home anomaly detection focus on checking the sequence of IoT devices'
events but leave out the temporal information of events. This limitation
prevents them to detect anomalies that cause delay rather than
missing/injecting events. To fill this gap, in this paper, we propose a novel
anomaly detection method that takes the inter-event intervals into
consideration. We propose an innovative metric to quantify the temporal
similarity between two event sequences. We design a mechanism to learn the
temporal patterns of event sequences of common daily activities. Delay-caused
anomalies are detected by comparing the sequence with the learned patterns. We
collect device events from a real-world testbed for training and testing. The
experiment results show that our proposed method achieves accuracies of 93%,
88%, 89% for three daily activities.

For active impulsive noise control, a filtered-x recursive least $p$-power
(FxRLP) algorithm is proposed by minimizing the weighted summation of the
$p$-power of the \emph{a posteriori} errors. Since the characteristic of the
target noise is investigated, the FxRLP algorithm achieves good performance and
robustness. To obtain a better performance, we develop a filtered-x logarithmic
recursive least $p$-power (FxlogRLP) algorithm which integrates the $p$-order
moment with the logarithmic-order moment. Simulation results demonstrate that
the FxlogRLP algorithm is superior to the existing algorithms in terms of
convergence rate and noise reduction.

Graph Neural Networks (GNNs) have achieved state-of-the-art results for
semi-supervised node classification on graphs. Nevertheless, the challenge of
how to effectively learn GNNs with very few labels is still under-explored. As
one of the prevalent semi-supervised methods, pseudo-labeling has been proposed
to explicitly address the label scarcity problem. It aims to augment the
training set with pseudo-labeled unlabeled nodes with high confidence so as to
re-train a supervised model in a self-training cycle. However, the existing
pseudo-labeling approaches often suffer from two major drawbacks. First, they
tend to conservatively expand the label set by selecting only high-confidence
unlabeled nodes without assessing their informativeness. Unfortunately, those
high-confidence nodes often convey overlapping information with given labels,
leading to minor improvements for model re-training. Second, these methods
incorporate pseudo-labels to the same loss function with genuine labels,
ignoring their distinct contributions to the classification task. In this
paper, we propose a novel informative pseudo-labeling framework, called
InfoGNN, to facilitate learning of GNNs with extremely few labels. Our key idea
is to pseudo label the most informative nodes that can maximally represent the
local neighborhoods via mutual information maximization. To mitigate the
potential label noise and class-imbalance problem arising from pseudo labeling,
we also carefully devise a generalized cross entropy loss with a class-balanced
regularization to incorporate generated pseudo labels into model re-training.
Extensive experiments on six real-world graph datasets demonstrate that our
proposed approach significantly outperforms state-of-the-art baselines and
strong self-supervised methods on graphs.

Due to the discarding of downlink channel state information (CSI) amplitude
and the employing of iteration reconstruction algorithms, 1-bit compressed
sensing (CS)-based superimposed CSI feedback is challenged by low recovery
accuracy and large processing delay. To overcome these drawbacks, this letter
proposes a fusion learning scheme by exploiting the bi-directional channel
reciprocity. Specifically, a simplified version of the conventional downlink
CSI reconstruction is utilized to extract the initial feature of downlink CSI,
and a single hidden layer-based amplitude-learning network (AMPL-NET) is
designed to learn the auxiliary feature of the downlink CSI amplitude. Then,
based on the extracted and learned amplitude features, a simple but effective
amplitude-fusion network (AMPF-NET) is developed to perform the amplitude
fusion of downlink CSI and thus improves the reconstruction accuracy for 1-bit
CS-based superimposed CSI feedback while reducing the processing delay.
Simulation results show the effectiveness of the proposed feedback scheme and
the robustness against parameter variations.

The emergence and dynamics of filamentary structures associated with
edge-localized modes (ELMs) inside tokamak plasmas during high-confinement mode
is regularly studied using Electron Cyclotron Emission Imaging (ECEI)
diagnostic systems. ECEI allows inference of electron temperature variations,
often across a poloidal cross-section. Previously, detailed analyses of
filamentary dynamics and classification of the precursors to ELM crashes have
been done manually. We present a machine-learning-based model, capable of
automatically identifying the position, spatial extent, and amplitude of ELM
filaments. The model is a deep convolutional neural network that has been
trained and optimized on an extensive set of manually labeled ECEI data from
the KSTAR tokamak. Once trained, the model achieves a 93.7% precision and
allows to robustly identify plasma filaments in unseen ECEI data. The trained
model is used to characterize ELM filament dynamics in a single H-mode plasma
shot. We identify quasi-periodic oscillations of the filaments' size, total
heat content, and radial velocity. The detailed dynamics of these quantities
appear strongly correlated with each other and appear qualitatively different
during the pre-crash and ELM crash phases.

Jet fires are relatively small and have the least severe effects among the
diverse fire accidents that can occur in industrial plants; however, they are
usually involved in a process known as the domino effect, that leads to more
severe events, such as explosions or the initiation of another fire, making the
analysis of such fires an important part of risk analysis. This research work
explores the application of deep learning models in an alternative approach
that uses the semantic segmentation of jet fires flames to extract main
geometrical attributes, relevant for fire risk assessments. A comparison is
made between traditional image processing methods and some state-of-the-art
deep learning models. It is found that the best approach is a deep learning
architecture known as UNet, along with its two improvements, Attention UNet and
UNet++. The models are then used to segment a group of vertical jet flames of
varying pipe outlet diameters to extract their main geometrical
characteristics. Attention UNet obtained the best general performance in the
approximation of both height and area of the flames, while also showing a
statistically significant difference between it and UNet++. UNet obtained the
best overall performance for the approximation of the lift-off distances;
however, there is not enough data to prove a statistically significant
difference between Attention UNet and UNet++. The only instance where UNet++
outperformed the other models, was while obtaining the lift-off distances of
the jet flames with 0.01275 m pipe outlet diameter. In general, the explored
models show good agreement between the experimental and predicted values for
relatively large turbulent propane jet flames, released in sonic and subsonic
regimes; thus, making these radiation zones segmentation models, a suitable
approach for different jet flame risk management scenarios.

Egocentric 3D human pose estimation with a single fisheye camera has drawn a
significant amount of attention recently. However, existing methods struggle
with pose estimation from in-the-wild images, because they can only be trained
on synthetic data due to the unavailability of large-scale in-the-wild
egocentric datasets. Furthermore, these methods easily fail when the body parts
are occluded by or interacting with the surrounding scene. To address the
shortage of in-the-wild data, we collect a large-scale in-the-wild egocentric
dataset called Egocentric Poses in the Wild (EgoPW). This dataset is captured
by a head-mounted fisheye camera and an auxiliary external camera, which
provides an additional observation of the human body from a third-person
perspective during training. We present a new egocentric pose estimation
method, which can be trained on the new dataset with weak external supervision.
Specifically, we first generate pseudo labels for the EgoPW dataset with a
spatio-temporal optimization method by incorporating the external-view
supervision. The pseudo labels are then used to train an egocentric pose
estimation network. To facilitate the network training, we propose a novel
learning strategy to supervise the egocentric features with the high-quality
features extracted by a pretrained external-view pose estimation model. The
experiments show that our method predicts accurate 3D poses from a single
in-the-wild egocentric image and outperforms the state-of-the-art methods both

Despite recent advances in appearance-based gaze estimation techniques, the
need for training data that covers the target head pose and gaze distribution
remains a crucial challenge for practical deployment. This work examines a
novel approach for synthesizing gaze estimation training data based on
monocular 3D face reconstruction. Unlike prior works using multi-view
reconstruction, photo-realistic CG models, or generative neural networks, our
approach can manipulate and extend the head pose range of existing training
data without any additional requirements. We introduce a projective matching
procedure to align the reconstructed 3D facial mesh with the camera coordinate
system and synthesize face images with accurate gaze labels. We also propose a
mask-guided gaze estimation model and data augmentation strategies to further
improve the estimation accuracy by taking advantage of synthetic training data.
Experiments using multiple public datasets show that our approach significantly
improves the estimation performance on challenging cross-dataset settings with
non-overlapping gaze distributions.

A growing number of service providers are exploring methods to improve server
utilization, reduce power consumption, and reduce total cost of ownership by
co-scheduling high-priority latency-critical workloads with best-effort
workloads. This practice requires strict resource allocation between workloads
to reduce resource contention and maintain Quality of Service (QoS) guarantees.
Prior resource allocation works have been shown to improve server utilization
under ideal circumstances, yet often compromise QoS guarantees or fail to find
valid resource allocations in more dynamic operating environments. Further,
prior works are fundamentally reliant upon QoS measurements that can, in
practice, exhibit significant transient fluctuations, thus stable control
behavior cannot be reliably achieved. In this paper, we propose a novel
framework for dynamic resource allocation based on proactive QoS prediction.
These predictions help guide a reinforcement-learning-based resource controller
towards optimal resource allocations while avoiding transient QoS violations
due to fluctuating workload demands. Evaluation shows that the proposed method
incurs 4.3x fewer QoS violations, reduces severity of QoS violations by 3.7x,
improves best-effort workload performance, and improves overall power
efficiency compared with prior work.

Federated learning (FL) is a useful tool in distributed machine learning that
utilizes users' local datasets in a privacy-preserving manner. When deploying
FL in a constrained wireless environment; however, training models in a
time-efficient manner can be a challenging task due to intermittent
connectivity of devices, heterogeneous connection quality, and non-i.i.d. data.
In this paper, we provide a novel convergence analysis of non-convex loss
functions using FL on both i.i.d. and non-i.i.d. datasets with arbitrary device
selection probabilities for each round. Then, using the derived convergence
bound, we use stochastic optimization to develop a new client selection and
power allocation algorithm that minimizes a function of the convergence bound
and the average communication time under a transmit power constraint. We find
an analytical solution to the minimization problem. One key feature of the
algorithm is that knowledge of the channel statistics is not required and only
the instantaneous channel state information needs to be known. Using the
FEMNIST and CIFAR-10 datasets, we show through simulations that the
communication time can be significantly decreased using our algorithm, compared
to uniformly random participation.

Language models (LMs) show state of the art performance for common sense (CS)
question answering, but whether this ability implies a human-level mastery of
CS remains an open question. Understanding the limitations and strengths of LMs
can help researchers improve these models, potentially by developing novel ways
of integrating external CS knowledge. We devise a series of tests and
measurements to systematically quantify their performance on different aspects
of CS. We propose the use of cloze testing combined with word embeddings to
measure the LM's robustness and confidence. Our results show than although
language models tend to achieve human-like accuracy, their confidence is
subpar. Future work can leverage this information to build more complex
systems, such as an ensemble of symbolic and distributed knowledge.

The American Sign Language Linguistic Research Project (ASLLRP) provides
Internet access to high-quality ASL video data, generally including front and
side views and a close-up of the face. The manual and non-manual components of
the signing have been linguistically annotated using SignStream(R). The
recently expanded video corpora can be browsed and searched through the Data
Access Interface (DAI 2) we have designed; it is possible to carry out complex
searches. The data from our corpora can also be downloaded; annotations are
available in an XML export format. We have also developed the ASLLRP Sign Bank,
which contains almost 6,000 sign entries for lexical signs, with distinct
English-based glosses, with a total of 41,830 examples of lexical signs (in
addition to about 300 gestures, over 1,000 fingerspelled signs, and 475
classifier examples). The Sign Bank is likewise accessible and searchable on
the Internet; it can also be accessed from within SignStream(R) (software to
facilitate linguistic annotation and analysis of visual language data) to make
annotations more accurate and efficient. Here we describe the available
resources. These data have been used for many types of research in linguistics
and in computer-based sign language recognition from video; examples of such
research are provided in the latter part of this article.

There exists a distribution discrepancy between training and testing, in the
way images are fed to modern CNNs. Recent work tried to bridge this gap either
by fine-tuning or re-training the network at different resolutions. However
re-training a network is rarely cheap and not always viable. To this end, we
propose a simple solution to address the train-test distributional shift and
enhance the performance of pre-trained models -- which commonly ship as a
package with deep learning platforms \eg, PyTorch. Specifically, we demonstrate
that running inference on the center crop of an image is not always the best as
important discriminatory information may be cropped-off. Instead we propose to
combine results for multiple random crops for a test image. This not only
matches the train time augmentation but also provides the full coverage of the
input image. We explore combining representation of random crops through
averaging at different levels \ie, deep feature level, logit level, and softmax
level. We demonstrate that, for various families of modern deep networks, such
averaging results in better validation accuracy compared to using a single
central crop per image. The softmax averaging results in the best performance
for various pre-trained networks without requiring any re-training or
fine-tuning whatsoever. On modern GPUs with batch processing, the paper's
approach to inference of pre-trained networks, is essentially free as all
images in a batch can all be processed at once.

Unconstrained Online Linear Optimization (OLO) is a practical problem setting
to study the training of machine learning models. Existing works proposed a
number of potential-based algorithms, but in general the design of these
potential functions relies heavily on guessing. To streamline this workflow, we
present a framework that generates new potential functions by solving a Partial
Differential Equation (PDE). Specifically, when losses are 1-Lipschitz, our
framework produces a novel algorithm with anytime regret bound
$C\sqrt{T}+||u||\sqrt{2T}[\sqrt{\log(1+||u||/C)}+2]$, where $C$ is a
user-specified constant and $u$ is any comparator unknown and unbounded a
priori. Such a bound attains an optimal loss-regret trade-off without the
impractical doubling trick. Moreover, a matching lower bound shows that the
leading order term, including the constant multiplier $\sqrt{2}$, is tight. To
our knowledge, the proposed algorithm is the first to achieve such
optimalities.

The prediction of valence from speech is an important, but challenging
problem. The externalization of valence in speech has speaker-dependent cues,
which contribute to performances that are often significantly lower than the
prediction of other emotional attributes such as arousal and dominance. A
practical approach to improve valence prediction from speech is to adapt the
models to the target speakers in the test set. Adapting a speech emotion
recognition (SER) system to a particular speaker is a hard problem, especially
with deep neural networks (DNNs), since it requires optimizing millions of
parameters. This study proposes an unsupervised approach to address this
problem by searching for speakers in the train set with similar acoustic
patterns as the speaker in the test set. Speech samples from the selected
speakers are used to create the adaptation set. This approach leverages
transfer learning using pre-trained models, which are adapted with these speech
samples. We propose three alternative adaptation strategies: unique speaker,
oversampling and weighting approaches. These methods differ on the use of the
adaptation set in the personalization of the valence models. The results
demonstrate that a valence prediction model can be efficiently personalized
with these unsupervised approaches, leading to relative improvements as high as
13.52%.

Missing values in covariates due to censoring by signal interference or lack
of sensitivity in the measuring devices are common in industrial problems. We
propose a full Bayesian solution to the prediction problem with an efficient
Markov Chain Monte Carlo (MCMC) algorithm that updates all the censored
covariate values jointly in a random scan Gibbs sampler. We show that the joint
updating of missing covariate values can be at least two orders of magnitude
more efficient than univariate updating. This increased efficiency is shown to
be crucial for quickly learning the missing covariate values and their
uncertainty in a real-time decision making context, in particular when there is
substantial correlation in the posterior for the missing values. The approach
is evaluated on simulated data and on data from the telecom sector. Our results
show that the proposed Bayesian imputation gives substantially more accurate
predictions than na\"ive imputation, and that the use of auxiliary variables in
the imputation gives additional predictive power.

State-of-the-art Graph Neural Networks (GNNs) have limited scalability with
respect to the graph and model sizes. On large graphs, increasing the model
depth often means exponential expansion of the scope (i.e., receptive field).
Beyond just a few layers, two fundamental challenges emerge: 1. degraded
expressivity due to oversmoothing, and 2. expensive computation due to
neighborhood explosion. We propose a design principle to decouple the depth and
scope of GNNs -- to generate representation of a target entity (i.e., a node or
an edge), we first extract a localized subgraph as the bounded-size scope, and
then apply a GNN of arbitrary depth on top of the subgraph. A properly
extracted subgraph consists of a small number of critical neighbors, while
excluding irrelevant ones. The GNN, no matter how deep it is, smooths the local
neighborhood into informative representation rather than oversmoothing the
global graph into "white noise". Theoretically, decoupling improves the GNN
expressive power from the perspectives of graph signal processing (GCN),
function approximation (GraphSAGE) and topological learning (GIN). Empirically,
on seven graphs (with up to 110M nodes) and six backbone GNN architectures, our
design achieves significant accuracy improvement with orders of magnitude
reduction in computation and hardware cost.

Deep learning models have shown great potential for image-based diagnosis
assisting clinical decision making. At the same time, an increasing number of
reports raise concerns about the potential risk that machine learning could
amplify existing health disparities due to human biases that are embedded in
the training data. It is of great importance to carefully investigate the
extent to which biases may be reproduced or even amplified if we wish to build
fair artificial intelligence systems. Seyyed-Kalantari et al. advance this
conversation by analysing the performance of a disease classifier across
population subgroups. They raise performance disparities related to
underdiagnosis as a point of concern; we identify areas from this analysis
which we believe deserve additional attention. Specifically, we wish to
highlight some theoretical and practical difficulties associated with assessing
model fairness through testing on data drawn from the same biased distribution
as the training data, especially when the sources and amount of biases are
unknown.

With the fast-growing number of classification models being produced every
day, numerous model interpretation and comparison solutions have also been
introduced. For example, LIME and SHAP can interpret what input features
contribute more to a classifier's output predictions. Different numerical
metrics (e.g., accuracy) can be used to easily compare two classifiers.
However, few works can interpret the contribution of a data feature to a
classifier in comparison with its contribution to another classifier. This
comparative interpretation can help to disclose the fundamental difference
between two classifiers, select classifiers in different feature conditions,
and better ensemble two classifiers. To accomplish it, we propose a
learning-from-disagreement (LFD) framework to visually compare two
classification models. Specifically, LFD identifies data instances with
disagreed predictions from two compared classifiers and trains a discriminator
to learn from the disagreed instances. As the two classifiers' training
features may not be available, we train the discriminator through a set of
meta-features proposed based on certain hypotheses of the classifiers to probe
their behaviors. Interpreting the trained discriminator with the SHAP values of
different meta-features, we provide actionable insights into the compared
classifiers. Also, we introduce multiple metrics to profile the importance of
meta-features from different perspectives. With these metrics, one can easily
identify meta-features with the most complementary behaviors in two
classifiers, and use them to better ensemble the classifiers. We focus on
binary classification models in the financial services and advertising industry
to demonstrate the efficacy of our proposed framework and visualizations.

Accurate pressure drop estimation in forced boiling phenomena is important
during the thermal analysis and the geometric design of cryogenic heat
exchangers. However, current methods to predict the pressure drop have one of
two problems: lack of accuracy or generalization to different situations. In
this work, we present the correlated-informed neural networks (CoINN), a new
paradigm in applying the artificial neural network (ANN) technique combined
with a successful pressure drop correlation as a mapping tool to predict the
pressure drop of zeotropic mixtures in micro-channels. The proposed approach is
inspired by Transfer Learning, highly used in deep learning problems with
reduced datasets. Our method improves the ANN performance by transferring the
knowledge of the Sun & Mishima correlation for the pressure drop to the ANN.
The correlation having physical and phenomenological implications for the
pressure drop in micro-channels considerably improves the performance and
generalization capabilities of the ANN. The final architecture consists of
three inputs: the mixture vapor quality, the micro-channel inner diameter, and
the available pressure drop correlation. The results show the benefits gained
using the correlated-informed approach predicting experimental data used for
training and a posterior test with a mean relative error (mre) of 6%, lower
than the Sun & Mishima correlation of 13%. Additionally, this approach can be
extended to other mixtures and experimental settings, a missing feature in
other approaches for mapping correlations using ANNs for heat transfer

Taking advantage of both vehicle-to-everything (V2X) communication and
automated driving technology, connected and automated vehicles are quickly
becoming one of the transformative solutions to many transportation problems.
However, in a mixed traffic environment at signalized intersections, it is
still a challenging task to improve overall throughput and energy efficiency
considering the complexity and uncertainty in the traffic system. In this
study, we proposed a hybrid reinforcement learning (HRL) framework which
combines the rule-based strategy and the deep reinforcement learning (deep RL)
to support connected eco-driving at signalized intersections in mixed traffic.
Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I)
communications to achieve higher mobility and energy efficiency in mixed
connected traffic. The HRL framework has three components: a rule-based driving
manager that operates the collaboration between the rule-based policies and the
RL policy; a multi-stream neural network that extracts the hidden features of
vision and V2I information; and a deep RL-based policy network that generate
both longitudinal and lateral eco-driving actions. In order to evaluate our
approach, we developed a Unity-based simulator and designed a mixed-traffic
intersection scenario. Moreover, several baselines were implemented to compare
with our new design, and numerical experiments were conducted to test the
performance of the HRL model. The experiments show that our HRL method can
reduce energy consumption by 12.70% and save 11.75% travel time when compared
with a state-of-the-art model-based Eco-Driving approach.

Measurements of the cosmological parameter $S_8$ provided by cosmic microwave
background and large scale structure data reveal some tension between them,
suggesting that the clustering features of matter in these early and late
cosmological tracers could be different. In this work, we use a supervised
learning method designed to solve Bayesian approach to regression, known as
Gaussian Processes regression, to quantify the cosmic evolution of $S_8$ up to
$z \sim 1.5$. For this, we propose a novel approach to find firstly the
evolution of the function $\sigma_8(z)$, then we find the function $S_8(z)$. As
a sub-product we obtain a minimal cosmological model-dependent $\sigma_8(z=0)$
and $S_8(z=0)$ estimates. We select independent data measurements of the growth
rate $f(z)$ and of $[f\sigma_8](z)$ according to criteria of non-correlated
data, then we perform the Gaussian reconstruction of these data sets to obtain
the cosmic evolution of $\sigma_8(z)$, $S_8(z)$, and the growth index
$\gamma(z)$. Our statistical analyses show that $S_8(z)$ is compatible with
Planck $\Lambda$CDM cosmology; when evaluated at the present time we find
$\sigma_8(z=0) = 0.766 \pm 0.116$ and $S_8(z=0) = 0.732 \pm 0.115$. Applying
our methodology to the growth index, we find $\gamma(z=0) = 0.465 \pm 0.140$.
Moreover, we compare our results with others recently obtained in the
literature. In none of these functions, i.e. $\sigma_8(z)$, $S_8(z)$, and
$\gamma(z)$, do we find significant deviations from the standard cosmology

The latest video coding standard, Versatile Video Coding (VVC), achieves
almost twice coding efficiency compared to its predecessor, the High Efficiency
Video Coding (HEVC). However, achieving this efficiency (for intra coding)
requires 31x computational complexity compared to HEVC, making it challenging
for low power and real-time applications. This paper, proposes a novel machine
learning approach that jointly and separately employs two modalities of
features, to simplify the intra coding decision. First a set of features are
extracted that use the existing DCT core of VVC, to assess the texture
characteristics, and forms the first modality of data. This produces high
quality features with almost no overhead. The distribution of intra modes at
the neighboring blocks is also used to form the second modality of data, which
provides statistical information about the frame. Second, a two-step feature
reduction method is designed that reduces the size of feature set, such that a
lightweight model with a limited number of parameters can be used to learn the
intra mode decision task. Third, three separate training strategies are
proposed (1) an offline training strategy using the first (single) modality of
data, (2) an online training strategy that uses the second (single) modality,
and (3) a mixed online-offline strategy that uses bimodal learning. Finally, a
low-complexity encoding algorithms is proposed based on the proposed learning
strategies. Extensive experimental results show that the proposed methods can
reduce up to 24% of encoding time, with a negligible loss of coding efficiency.
Moreover, it is demonstrated how a bimodal learning strategy can boost the
performance of learning. Lastly, the proposed method has a very low
computational overhead (0.2%), and uses existing components of a VVC encoder,
which makes it much more practical compared to competing solutions.

We devise a performance model for GPU training of Deep Learning
Recommendation Models (DLRM), whose GPU utilization is low compared to other
well-optimized CV and NLP models. We show that both the device active time (the
sum of kernel runtimes) but also the device idle time are important components
of the overall device time. We therefore tackle them separately by (1) flexibly
adopting heuristic-based and ML-based kernel performance models for operators
that dominate the device active time, and (2) categorizing operator overheads
into five types to determine quantitatively their contribution to the device
active time. Combining these two parts, we propose a critical-path-based
algorithm to predict the per-batch training time of DLRM by traversing its
execution graph. We achieve less than 10% geometric mean average error (GMAE)
in all kernel performance modeling, and 4.61% and 7.96% geomean errors for GPU
active time and overall E2E per-batch training time prediction with overheads
from individual workloads, respectively. A slight increase of 2.19% incurred in
E2E prediction error with shared overheads across workloads suggests the
feasibility of using shared overheads in large-scale prediction. We show that
our general performance model not only achieves low prediction error on DLRM,
which has highly customized configurations and is dominated by multiple factors
but also yields comparable accuracy on other compute-bound ML models targeted
by most previous methods. Using this performance model and graph-level data and
task dependency analysis, we show our system can provide more general
model-system co-design than previous methods.

We present an analysis of the quenching of star formation in galaxies,
bulges, and disks throughout the bulk of cosmic history, from $z=2-0$. We
utilise observations from the SDSS and MaNGA at low redshifts. We complement
these data with observations from CANDELS at high redshifts. Additionally, we
compare the observations to detailed predictions from the LGalaxies
semi-analytic model. To analyse the data, we developed a machine learning
approach utilising a Random Forest classifier. We first demonstrate that this
technique is extremely effective at extracting causal insight from highly
complex and inter-correlated model data, before applying it to various
observational surveys. Our primary observational results are as follows: At all
redshifts studied in this work, we find bulge mass to be the most predictive
parameter of quenching, out of the photometric parameter set (incorporating
bulge mass, disk mass, total stellar mass, and $B/T$ structure). Moreover, we
also find bulge mass to be the most predictive parameter of quenching in both
bulge and disk structures, treated separately. Hence, intrinsic galaxy
quenching must be due to a stable mechanism operating over cosmic time, and the
same quenching mechanism must be effective in both bulge and disk regions.
Despite the success of bulge mass in predicting quenching, we find that central
velocity dispersion is even more predictive (when available in spectroscopic
data sets). In comparison to the LGalaxies model, we find that all of these
observational results may be consistently explained through quenching via
preventative `radio-mode' active galactic nucleus (AGN) feedback. Furthermore,
many alternative quenching mechanisms (including virial shocks, supernova
feedback, and morphological stabilisation) are found to be inconsistent with
our observational results and those from the literature.

Progress in 3D object understanding has relied on manually canonicalized
shape datasets that contain instances with consistent position and orientation
(3D pose). This has made it hard to generalize these methods to in-the-wild
shapes, eg., from internet model collections or depth sensors. ConDor is a
self-supervised method that learns to Canonicalize the 3D orientation and
position for full and partial 3D point clouds. We build on top of Tensor Field
Networks (TFNs), a class of permutation- and rotation-equivariant, and
translation-invariant 3D networks. During inference, our method takes an unseen
full or partial 3D point cloud at an arbitrary pose and outputs an equivariant
canonical pose. During training, this network uses self-supervision losses to
learn the canonical pose from an un-canonicalized collection of full and
partial 3D point clouds. ConDor can also learn to consistently co-segment
object parts without any supervision. Extensive quantitative results on four
new metrics show that our approach outperforms existing methods while enabling
new applications such as operation on depth images and annotation transfer.

Animating high-fidelity video portrait with speech audio is crucial for
virtual reality and digital entertainment. While most previous studies rely on
accurate explicit structural information, recent works explore the implicit
scene representation of Neural Radiance Fields (NeRF) for realistic generation.
In order to capture the inconsistent motions as well as the semantic difference
between human head and torso, some work models them via two individual sets of
NeRF, leading to unnatural results. In this work, we propose Semantic-aware
Speaking Portrait NeRF (SSP-NeRF), which creates delicate audio-driven
portraits using one unified set of NeRF. The proposed model can handle the
detailed local facial semantics and the global head-torso relationship through
two semantic-aware modules. Specifically, we first propose a Semantic-Aware
Dynamic Ray Sampling module with an additional parsing branch that facilitates
audio-driven volume rendering. Moreover, to enable portrait rendering in one
unified neural radiance field, a Torso Deformation module is designed to
stabilize the large-scale non-rigid torso motions. Extensive evaluations
demonstrate that our proposed approach renders more realistic video portraits
compared to previous methods. Project page:
https://alvinliu0.github.io/projects/SSP-NeRF

The Orbital Angular Momentum (OAM) of light has been at the center of several
classical and quantum applications for imaging, information processing and
communication. However, the complex structure inherent in OAM states makes
their detection and classification nontrivial in many circumstances. Most of
the current detection schemes are based on models of the OAM states built upon
the use of Laguerre-Gauss modes. However, this may not in general be sufficient
to capture full information on the generated states. In this paper, we go
beyond the Laguerre-Gauss assumption, and employ Hypergeometric-Gaussian modes
as the basis states of a refined model that can be used -- in certain scenarios
-- to better tailor OAM detection techniques. We show that enhanced
performances in OAM detection are obtained for holographic projection via
spatial light modulators in combination with single-mode fibers, and for
classification techniques based on a machine learning approach. Furthermore, a
three-fold enhancement in the single-mode fiber coupling efficiency is obtained
for the holographic technique, when using the Hypergeometric-Gaussian model
with respect to the Laguerre-Gauss one. This improvement provides a significant
boost in the overall efficiency of OAM-encoded single-photon detection systems.
Given that most of the experimental works using OAM states are effectively
based on the generation of Hypergeometric-Gauss modes, our findings thus
represent a relevant addition to experimental toolboxes for OAM-based protocols
in quantum communication, cryptography and simulation.

The human face conveys a significant amount of information. Through facial
expressions, the face is able to communicate numerous sentiments without the
need for verbalisation. Visual emotion recognition has been extensively
studied. Recently several end-to-end trained deep neural networks have been
proposed for this task. However, such models often lack generalisation ability
across datasets. In this paper, we propose the Deep Facial Expression Vector
ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual
feature extractor general enough to be applied to any other facial emotion
recognition task or dataset. DeepFEVER outperforms state-of-the-art results on
the AffectNet and Google Facial Expression Comparison datasets. DeepFEVER's
extracted features also generalise extremely well to other datasets -- even
those unseen during training -- namely, the Real-World Affective Faces (RAF)
dataset.

Learning to solve precision-based manipulation tasks from visual feedback
using Reinforcement Learning (RL) could drastically reduce the engineering
efforts required by traditional robot systems. However, performing fine-grained
motor control from visual inputs alone is challenging, especially with a static
third-person camera as often used in previous work. We propose a setting for
robotic manipulation in which the agent receives visual feedback from both a
third-person camera and an egocentric camera mounted on the robot's wrist.
While the third-person camera is static, the egocentric camera enables the
robot to actively control its vision to aid in precise manipulation. To fuse
visual information from both cameras effectively, we additionally propose to
use Transformers with a cross-view attention mechanism that models spatial
attention from one view to another (and vice-versa), and use the learned
features as input to an RL policy. Our method improves learning over strong
single-view and multi-view baselines, and successfully transfers to a set of
challenging manipulation tasks on a real robot with uncalibrated cameras, no
access to state information, and a high degree of task variability. In a hammer
manipulation task, our method succeeds in 75% of trials versus 38% and 13% for
multi-view and single-view baselines, respectively.

We present a full forward-modeled $w$CDM analysis of the KiDS-1000 weak
lensing maps using graph-convolutional neural networks (GCNN). Utilizing the
$\texttt{CosmoGrid}$, a novel massive simulation suite spanning six different
cosmological parameters, we generate almost one million tomographic mock
surveys on the sphere. Due to the large data set size and survey area, we
perform a spherical analysis while limiting our map resolution to
$\texttt{HEALPix}$ $n_\mathrm{side}=512$. We marginalize over systematics such
as photometric redshift errors, multiplicative calibration and additive shear
bias. Furthermore, we use a map-level implementation of the non-linear
intrinsic alignment model along with a novel treatment of baryonic feedback to
incorporate additional astrophysical nuisance parameters. We also perform a
spherical power spectrum analysis for comparison. The constraints of the
cosmological parameters are generated using a likelihood free inference method
called Gaussian Process Approximate Bayesian Computation (GPABC). Finally, we
check that our pipeline is robust against choices of the simulation parameters.
We find constraints on the degeneracy parameter of $S_8 \equiv
\sigma_8\sqrt{\Omega_M/0.3} = 0.78^{+0.06}_{-0.06}$ for our power spectrum
analysis and $S_8 = 0.79^{+0.05}_{-0.05}$ for our GCNN analysis, improving the
former by 16%. This is consistent with earlier analyses of the 2-point
function, albeit slightly higher. Baryonic corrections generally broaden the
constraints on the degeneracy parameter by about 10%. These results offer great
prospects for full machine learning based analyses of on-going and future weak
lensing surveys.

The future of innovation processes is anticipated to be more data-driven and
empowered by the ubiquitous digitalization, increasing data accessibility and
rapid advances in machine learning, artificial intelligence, and computing
technologies. While the data-driven innovation (DDI) paradigm is emerging, it
has yet been formally defined and theorized and often confused with several
other data-related phenomena. This paper defines and crystalizes "data-driven
innovation" as a formal innovation process paradigm, dissects its value
creation, and distinguishes it from data-driven optimization (DDO), data-based
innovation (DBI), and the traditional innovation processes that purely rely on
human intelligence. With real-world examples and theoretical framing, I
elucidate what DDI entails and how it addresses uncertainty and enhance
creativity in the innovation process and present a process-based taxonomy of
different data-driven innovation approaches. On this basis, I recommend the
strategies and actions for innovators, companies, R&D organizations, and
governments to enact data-driven innovation.

Neural networks (NNs) are currently changing the computational paradigm on
how to combine data with mathematical laws in physics and engineering in a
profound way, tackling challenging inverse and ill-posed problems not solvable
with traditional methods. However, quantifying errors and uncertainties in
NN-based inference is more complicated than in traditional methods. This is
because in addition to aleatoric uncertainty associated with noisy data, there
is also uncertainty due to limited data, but also due to NN hyperparameters,
overparametrization, optimization and sampling errors as well as model
misspecification. Although there are some recent works on uncertainty
quantification (UQ) in NNs, there is no systematic investigation of suitable
methods towards quantifying the total uncertainty effectively and efficiently
even for function approximation, and there is even less work on solving partial
differential equations and learning operator mappings between
infinite-dimensional function spaces using NNs. In this work, we present a
comprehensive framework that includes uncertainty modeling, new and existing
solution methods, as well as evaluation metrics and post-hoc improvement
approaches. To demonstrate the applicability and reliability of our framework,
we present an extensive comparative study in which various methods are tested
on prototype problems, including problems with mixed input-output data, and
stochastic problems in high dimensions. In the Appendix, we include a
comprehensive description of all the UQ methods employed, which we will make
available as open-source library of all codes included in this framework.

Shared spectrum systems facilitate spectrum allocation to unlicensed users
without harming the licensed users; they offer great promise in optimizing
spectrum utility, but their management (in particular, efficient spectrum
allocation to unlicensed users) is challenging. A significant shortcoming of
current allocation methods is that they are either done very conservatively to
ensure correctness, or are based on imperfect propagation models and/or
spectrum sensing with poor spatial granularity. This leads to poor spectrum
utilization, the fundamental objective of shared spectrum systems.
  To allocate spectrum near-optimally to secondary users in general scenarios,
we fundamentally need to have knowledge of the signal path-loss function. In
practice, however, even the best known path-loss models have unsatisfactory
accuracy, and conducting extensive surveys to gather path-loss values is
infeasible. To circumvent this challenge, we propose to learn the spectrum
allocation function directly using supervised learning techniques. We
particularly address the scenarios when the primary users' information may not
be available; for such settings, we make use of a crowdsourced sensing
architecture and use the spectrum sensor readings as features. We develop an
efficient CNN-based approach (called DeepAlloc) and address various challenges
that arise in its application to the learning the spectrum allocation function.
Via extensive large-scale simulation and a small testbed, we demonstrate the
effectiveness of our developed techniques; in particular, we observe that our
approach improves the accuracy of standard learning techniques and prior work
by up to 60%.

The Corona KH-4 reconnaissance satellite missions from 1962-1972 acquired
panoramic stereo imagery with high spatial resolution of 1.8-7.5 m. The
potential of 800,000+ declassified Corona images has not been leveraged due to
the complexities arising from handling of panoramic imaging geometry, film
distortions and limited availability of the metadata required for
georeferencing of the Corona imagery. This paper presents Corona Stereo
Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic
imagery. CoSP utlizes a deep learning based feature matcher SuperGlue to
automatically match features point between Corona KH-4 images and recent
satellite imagery to generate Ground Control Points (GCPs). To model the
imaging geometry and the scanning motion of the panoramic KH-4 cameras, a
rigorous camera model consisting of modified collinearity equations with time
dependent exterior orientation parameters is employed. The results show that
using the entire frame of the Corona image, bundle adjustment using
well-distributed GCPs results in an average standard deviation (SD) of less
than 2 pixels. The distortion pattern of image residuals of GCPs and y-parallax
in epipolar resampled images suggest that film distortions due to long term
storage as likely cause of systematic deviations. Compared to the SRTM DEM, the
Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation
(NMAD) of elevation differences of ~4 m over an area of approx. 4000 $km^2$. We
show that the proposed pipeline can be applied to sequence of complex scenes
involving high relief and glacierized terrain and that the resulting DEMs can
be used to compute long term glacier elevation changes over large areas.

We propose a novel \textit{capsule} based deep encoder-decoder model for
surrogate modeling and uncertainty quantification of systems in mechanics from
sparse data. The proposed framework is developed by adapting Capsule Network
(CapsNet) architecture into image-to-image regression encoder-decoder network.
Specifically, the aim is to exploit the benefits of CapsNet over convolution
neural network (CNN) $-$ retaining pose and position information related to an
entity to name a few. The performance of proposed approach is illustrated by
solving an elliptic stochastic partial differential equation (SPDE), which also
governs systems in mechanics such as steady heat conduction, ground water flow
or other diffusion processes, based uncertainty quantification problem with an
input dimensionality of $1024$. However, the problem definition does not the
restrict the random diffusion field to a particular covariance structure, and
the more strenuous task of response prediction for an arbitrary diffusion field
is solved. The obtained results from performance evaluation indicate that the
proposed approach is accurate, efficient, and robust.

We study the problem of {\sl certification}: given queries to a function $f :
\{0,1\}^n \to \{0,1\}$ with certificate complexity $\le k$ and an input
$x^\star$, output a size-$k$ certificate for $f$'s value on $x^\star$. This
abstractly models a central problem in explainable machine learning, where we
think of $f$ as a blackbox model that we seek to explain the predictions of.
  For monotone functions, a classic local search algorithm of Angluin
accomplishes this task with $n$ queries, which we show is optimal for local
search algorithms. Our main result is a new algorithm for certifying monotone
functions with $O(k^8 \log n)$ queries, which comes close to matching the
information-theoretic lower bound of $\Omega(k \log n)$. The design and
analysis of our algorithm are based on a new connection to threshold phenomena
in monotone functions.
  We further prove exponential-in-$k$ lower bounds when $f$ is non-monotone,
and when $f$ is monotone but the algorithm is only given random examples of
$f$. These lower bounds show that assumptions on the structure of $f$ and query
access to it are both necessary for the polynomial dependence on $k$ that we
achieve.

In Part I of this work, we have proposed a general framework of decentralized
stochastic quasi-Newton methods, which converge linearly to the optimal
solution under the assumption that the local Hessian inverse approximations
have bounded positive eigenvalues. In Part II, we specify two fully
decentralized stochastic quasi-Newton methods, damped regularized
limited-memory DFP (Davidon-Fletcher-Powell) and damped limited-memory BFGS
(Broyden-Fletcher-Goldfarb-Shanno), to locally construct such Hessian inverse
approximations without extra sampling or communication. Both of the methods use
a fixed moving window of $M$ past local gradient approximations and local
decision variables to adaptively construct positive definite Hessian inverse
approximations with bounded eigenvalues, satisfying the assumption in Part I
for the linear convergence. For the proposed damped regularized limited-memory
DFP, a regularization term is added to improve the performance. For the
proposed damped limited-memory BFGS, a two-loop recursion is applied, leading
to low storage and computation complexity. Numerical experiments demonstrate
that the proposed quasi-Newton methods are much faster than the existing
decentralized stochastic first-order algorithms.

Federated learning enables multiple data owners to jointly train a machine
learning model without revealing their private datasets. However, a malicious
aggregation server might use the model parameters to derive sensitive
information about the training dataset used. To address such leakage,
differential privacy and cryptographic techniques have been investigated in
prior work, but these often result in large communication overheads or impact
model performance. To mitigate this centralization of power, we propose SCOTCH,
a decentralized m-party secure-computation framework for federated aggregation
that deploys MPC primitives, such as secret sharing. Our protocol is simple,
efficient, and provides strict privacy guarantees against curious aggregators
or colluding data-owners with minimal communication overheads compared to other
existing state-of-the-art privacy-preserving federated learning frameworks. We
evaluate our framework by performing extensive experiments on multiple datasets
with promising results. SCOTCH can train the standard MLP NN with the training
dataset split amongst 3 participating users and 3 aggregating servers with
96.57% accuracy on MNIST, and 98.40% accuracy on the Extended MNIST (digits)
dataset, while providing various optimizations.

During the COVID-19 pandemic, health-related misinformation and harmful
content shared online had a significant adverse effect on society. To mitigate
this adverse effect, mainstream social media platforms employed soft moderation
interventions (i.e., warning labels) on potentially harmful posts. Despite the
recent popularity of these moderation interventions, we lack empirical analyses
aiming to uncover how these warning labels are used in the wild, particularly
during challenging times like the COVID-19 pandemic. In this work, we analyze
the use of warning labels on TikTok, focusing on COVID-19 videos. First, we
construct a set of 26 COVID-19 related hashtags, then we collect 41K videos
that include those hashtags in their description. Second, we perform a
quantitative analysis on the entire dataset to understand the use of warning
labels on TikTok. Then, we perform an in-depth qualitative study, using
thematic analysis, on 222 COVID-19 related videos to assess the content and the
connection between the content and the warning labels. Our analysis shows that
TikTok broadly applies warning labels on TikTok videos, likely based on
hashtags included in the description. More worrying is the addition of COVID-19
warning labels on videos where their actual content is not related to COVID-19
(23% of the cases in a sample of 143 English videos that are not related to
COVID-19). Finally, our qualitative analysis on a sample of 222 videos shows
that 7.7% of the videos share misinformation/harmful content and do not include
warning labels, 37.3% share benign information and include warning labels, and
that 35% of the videos that share misinformation/harmful content (and need a
warning label) are made for fun. Our study demonstrates the need to develop
more accurate and precise soft moderation systems, especially on a platform
like TikTok that is extremely popular among people of younger age.

One of the potential solutions for model interpretation is to train a
surrogate model: a more transparent model that approximates the behavior of the
model to be explained. Typically, classification rules or decision trees are
used due to the intelligibility of their logic-based expressions. However,
decision trees can grow too deep and rule sets can become too large to
approximate a complex model. Unlike paths on a decision tree that must share
ancestor nodes (conditions), rules are more flexible. However, the unstructured
visual representation of rules makes it hard to make inferences across rules.
To address these issues, we present a workflow that includes novel algorithmic
and interactive solutions. First, we present Hierarchical Surrogate Rules
(HSR), an algorithm that generates hierarchical rules based on user-defined
parameters. We also contribute SuRE, a visual analytics (VA) system that
integrates HSR and interactive surrogate rule visualizations. Particularly, we
present a novel feature-aligned tree to overcome the shortcomings of existing
rule visualizations. We evaluate the algorithm in terms of parameter
sensitivity, time performance, and comparison with surrogate decision trees and
find that it scales reasonably well and outperforms decision trees in many
respects. We also evaluate the visualization and the VA system by a usability
study with 24 volunteers and an observational study with 7 domain experts. Our
investigation shows that the participants can use feature-aligned trees to
perform non-trivial tasks with very high accuracy. We also discuss many
interesting observations that can be useful for future research on designing
effective rule-based VA systems.

Behavioural cloning has been extensively used to train agents and is
recognized as a fast and solid approach to teach general behaviours based on
expert trajectories. Such method follows the supervised learning paradigm and
it strongly depends on the distribution of the data. In our paper, we show how
combining behavioural cloning with human-in-the-loop training solves some of
its flaws and provides an agent task-specific corrections to overcome tricky
situations while speeding up the training time and lowering the required
resources. To do this, we introduce a novel approach that allows an expert to
take control of the agent at any moment during a simulation and provide optimal
solutions to its problematic situations. Our experiments show that this
approach leads to better policies both in terms of quantitative evaluation and
in human-likeliness.

Evaluation the prediction of Efficiency index by DVH parameter for SRS
treatment plans using Supervised Machine learning and the performance of
predictive model algorithms of RapidMiner GO in the parameter prediction are
investigated. Dose volume histogram (DVH) based Efficiency index was calculated
for 100 clinical SRS plans generated by Leksell Gamma plan, and the results
were compared to predicted values produced by machine learning toolbox of
RapidMiner Go, algorithms are namely, Generalized linear model (GLR), Decision
Tree Model, Support Vector Machine (SVM), Gradient Boosted Trees (GBT), Random
Forest (RF) and Deep learning Model (DL). Root mean square error (RMSE),
Average absolute error, Absolute relative error, squared correlation and model
building time were determined to evaluate the performance of each algorithm.
The GLR algorithm model had square correlation of 0.974 with the smallest RMSE
of 0.01, relatively high prediction speed, and fast model building time with
2.812 s, according to the results. The RMSE values for all models were between
0.01 upto 0.021, all algorithms performed well. The RMSE of the Gradient
Boosted Tree, Random Forest, and Decision Tree regression algorithms was found
to be greater than 0.01, suggesting that they are not appropriate for
predicting EI in this analysis. RapidMiner GO machine learning models can be
used to predict DVH parameters like EI in SRS treatment planning QA. To
effectively evaluate the parameter, it is necessary to choose a suitable
machine learning algorithm.

Brain computing interfaces (BCI) are used in a plethora of
safety/privacy-critical applications, ranging from healthcare to smart
communication and control. Wearable BCI setups typically involve a head-mounted
sensor connected to a mobile device, combined with ML-based data processing.
Consequently, they are susceptible to a multiplicity of attacks across the
hardware, software, and networking stacks used that can leak users' brainwave
data or at worst relinquish control of BCI-assisted devices to remote
attackers. In this paper, we: (i) analyse the whole-system security and privacy
threats to existing wearable BCI products from an operating system and
adversarial machine learning perspective; and (ii) introduce Argus, the first
information flow control system for wearable BCI applications that mitigates
these attacks. Argus' domain-specific design leads to a lightweight
implementation on Linux ARM platforms suitable for existing BCI use-cases. Our
proof of concept attacks on real-world BCI devices (Muse, NeuroSky, and
OpenBCI) led us to discover more than 300 vulnerabilities across the stacks of
six major attack vectors. Our evaluation shows Argus is highly effective in
tracking sensitive dataflows and restricting these attacks with an acceptable
memory and performance overhead (<15%).

Most existing Graph Neural Networks (GNNs) are proposed without considering
the selection bias in data, i.e., the inconsistent distribution between the
training set with test set. In reality, the test data is not even available
during the training process, making selection bias agnostic. Training GNNs with
biased selected nodes leads to significant parameter estimation bias and
greatly impacts the generalization ability on test nodes. In this paper, we
first present an experimental investigation, which clearly shows that the
selection bias drastically hinders the generalization ability of GNNs, and
theoretically prove that the selection bias will cause the biased estimation on
GNN parameters. Then to remove the bias in GNN estimation, we propose a novel
Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation
regularizer. The differentiated decorrelation regularizer estimates a sample
weight for each labeled node such that the spurious correlation of learned
embeddings could be eliminated. We analyze the regularizer in causal view and
it motivates us to differentiate the weights of the variables based on their
contribution on the confounding bias. Then, these sample weights are used for
reweighting GNNs to eliminate the estimation bias, thus help to improve the
stability of prediction on unknown test nodes. Comprehensive experiments are
conducted on several challenging graph datasets with two kinds of label
selection biases. The results well verify that our proposed model outperforms
the state-of-the-art methods and DGNN is a flexible framework to enhance
existing GNNs.

In this paper, we focus on the problem of modeling dynamic geo-human
interactions in streams for online POI recommendations. Specifically, we
formulate the in-stream geo-human interaction modeling problem into a novel
deep interactive reinforcement learning framework, where an agent is a
recommender and an action is a next POI to visit. We uniquely model the
reinforcement learning environment as a joint and connected composition of
users and geospatial contexts (POIs, POI categories, functional zones). An
event that a user visits a POI in stream updates the states of both users and
geospatial contexts; the agent perceives the updated environment state to make
online recommendations. Specifically, we model a mixed-user event stream by
unifying all users, visits, and geospatial contexts as a dynamic knowledge
graph stream, in order to model human-human, geo-human, geo-geo interactions.
We design an exit mechanism to address the expired information challenge,
devise a meta-path method to address the recommendation candidate generation
challenge, and develop a new deep policy network structure to address the
varying action space challenge, and, finally, propose an effective adversarial
training method for optimization. Finally, we present extensive experiments to
demonstrate the enhanced performance of our method.

Object detection is a computer vision task that has become an integral part
of many consumer applications today such as surveillance and security systems,
mobile text recognition, and diagnosing diseases from MRI/CT scans. Object
detection is also one of the critical components to support autonomous driving.
Autonomous vehicles rely on the perception of their surroundings to ensure safe
and robust driving performance. This perception system uses object detection
algorithms to accurately determine objects such as pedestrians, vehicles,
traffic signs, and barriers in the vehicle's vicinity. Deep learning-based
object detectors play a vital role in finding and localizing these objects in
real-time. This article discusses the state-of-the-art in object detectors and
open challenges for their integration into autonomous vehicles.

Autonomous vehicles are on the horizon and will be transforming
transportation safety and comfort. These vehicles will be connected to various
external systems and utilize advanced embedded systems to perceive their
environment and make intelligent decisions. However, this increased
connectivity makes these vehicles vulnerable to various cyber-attacks that can
have catastrophic effects. Attacks on automotive systems are already on the
rise in today's vehicles and are expected to become more commonplace in future
autonomous vehicles. Thus, there is a need to strengthen cybersecurity in
future autonomous vehicles. In this article, we discuss major automotive
cyber-attacks over the past decade and present state-of-the-art solutions that
leverage artificial intelligence (AI). We propose a roadmap towards building
secure autonomous vehicles and highlight key open challenges that need to be

Policy space response oracles (PSRO) is a multi-agent reinforcement learning
algorithm that has achieved state-of-the-art performance in very large
two-player zero-sum games. PSRO is based on the tabular double oracle (DO)
method, an algorithm that is guaranteed to converge to a Nash equilibrium, but
may increase exploitability from one iteration to the next. We propose anytime
double oracle (ADO), a tabular double oracle algorithm for 2-player zero-sum
games that is guaranteed to converge to a Nash equilibrium while decreasing
exploitability from one iteration to the next. Unlike DO, in which the
restricted distribution is based on the restricted game formed by each player's
strategy sets, ADO finds the restricted distribution for each player that
minimizes its exploitability against any policy in the full, unrestricted game.
We also propose a method of finding this restricted distribution via a
no-regret algorithm updated against best responses, called RM-BR DO. Finally,
we propose anytime PSRO (APSRO), a version of ADO that calculates best
responses via reinforcement learning. In experiments on Leduc poker and random
normal form games, we show that our methods achieve far lower exploitability
than DO and PSRO and decrease exploitability monotonically.

In this work, we investigate stochastic quasi-Newton methods for minimizing a
finite sum of cost functions over a decentralized network. In Part I, we
develop a general algorithmic framework that incorporates stochastic
quasi-Newton approximations with variance reduction so as to achieve fast
convergence. At each time each node constructs a local, inexact quasi-Newton
direction that asymptotically approaches the global, exact one. To be specific,
(i) A local gradient approximation is constructed by using dynamic average
consensus to track the average of variance-reduced local stochastic gradients
over the entire network; (ii) A local Hessian inverse approximation is assumed
to be positive definite with bounded eigenvalues, and how to construct it to
satisfy these assumptions will be given in Part II. Compared to the existing
decentralized stochastic first-order methods, the proposed general framework
introduces the second-order curvature information without incurring extra
sampling or communication. With a fixed step size, we establish the conditions
under which the proposed general framework linearly converges to an exact

We prove complexity bounds for the primal-dual algorithm with random
extrapolation and coordinate descent (PURE-CD), which has been shown to obtain
good practical performance for solving convex-concave min-max problems with
bilinear coupling. Our complexity bounds either match or improve the best-known
results in the literature for both dense and sparse
(strongly)-convex-(strongly)-concave problems.

Multimodal data arise in various applications where information about the
same phenomenon is acquired from multiple sensors and across different imaging
modalities. Learning from multimodal data is of great interest in machine
learning and statistics research as this offers the possibility of capturing
complementary information among modalities. Multimodal modeling helps to
explain the interdependence between heterogeneous data sources, discovers new
insights that may not be available from a single modality, and improves
decision-making. Recently, coupled matrix-tensor factorization has been
introduced for multimodal data fusion to jointly estimate latent factors and
identify complex interdependence among the latent factors. However, most of the
prior work on coupled matrix-tensor factors focuses on unsupervised learning
and there is little work on supervised learning using the jointly estimated
latent factors. This paper considers the multimodal tensor data classification
problem. A Coupled Support Tensor Machine (C-STM) built upon the latent factors
jointly estimated from the Advanced Coupled Matrix Tensor Factorization (ACMTF)
is proposed. C-STM combines individual and shared latent factors with multiple
kernels and estimates a maximal-margin classifier for coupled matrix tensor
data. The classification risk of C-STM is shown to converge to the optimal
Bayes risk, making it a statistically consistent rule. C-STM is validated
through simulation studies as well as a simultaneous EEG-fMRI analysis. The
empirical evidence shows that C-STM can utilize information from multiple
sources and provide a better classification performance than traditional
single-mode classifiers.

Listwise ranking losses have been widely studied in recommender systems.
However, new paradigms of content consumption present new challenges for
ranking methods. In this work we contribute an analysis of learning to rank for
personalized mobile push notifications and discuss the unique challenges this
presents compared to traditional ranking problems. To address these challenges,
we introduce a novel ranking loss based on weighting the pairwise loss between
candidates by the expected regret incurred for misordering the pair. We
demonstrate that the proposed method can outperform prior methods both in a
simulated environment and in a production experiment on a major social network.

Billions of distributed, heterogeneous and resource constrained smart
consumer devices deploy on-device machine learning (ML) to deliver private,
fast and offline inference on personal data. On-device ML systems are highly
context dependent, and sensitive to user, usage, hardware and environmental
attributes. Despite this sensitivity and the propensity towards bias in ML,
bias in on-device ML has not been studied. This paper studies the propagation
of bias through design choices in on-device ML development workflows. We
position reliability bias, which arises from disparate device failures across
demographic groups, as a source of unfairness in on-device ML settings and
quantify metrics to evaluate it. We then identify complex and interacting
technical design choices in the on-device ML workflow that can lead to
disparate performance across user groups, and thus reliability bias. Finally,
we show with an empirical case study that seemingly innocuous design choices
such as the data sample rate, pre-processing parameters used to construct input
features and pruning hyperparameters propagate reliability bias through an
audio keyword spotting development workflow. We leverage our insights to
suggest strategies for developers to develop fairer on-device ML.

A typical approach to programming is to first code the main execution
scenario, and then focus on filling out alternative behaviors and corner cases.
But, almost always, there exist unusual conditions that trigger atypical
behaviors, which are hard to predict in program specifications, and are thus
often not coded. In this paper, we consider the problem of detecting and
recommending such missing behaviors, a task that we call code sophistication.
Previous research on coding assistants usually focuses on recommending code
fragments based on specifications of the intended behavior. In contrast, code
sophistication happens in the absence of a specification, aiming to help
developers complete the logic of their programs with missing and unspecified
behaviors. We outline the research challenges to this problem and present early
results showing how program logic can be completed by leveraging code structure
and information about the usage of input parameters.

This textbook covers principles behind main modern deep reinforcement
learning algorithms that achieved breakthrough results in many domains from
game AI to robotics. All required theory is explained with proofs using unified
notation and emphasize on the differences between different types of algorithms
and the reasons why they are constructed the way they are.

How much does a CEO's personality impact the performance of their company?
Management theory posits a great influence, but it is difficult to show
empirically -- there is a lack of publicly available self-reported personality
data of top managers. Instead, we propose a text-based personality regressor
using crowd-sourced Myers--Briggs Type Indicator (MBTI) assessments. The
ratings have a high internal and external validity and can be predicted with
moderate to strong correlations for three out of four dimensions. Providing
evidence for the upper echelons theory, we demonstrate that the predicted CEO
personalities have explanatory power of financial risk.

Creating computer vision datasets requires careful planning and lots of time
and effort. In robotics research, we often have to use standardized objects,
such as the YCB object set, for tasks such as object tracking, pose estimation,
grasping and manipulation, as there are datasets and pre-learned methods
available for these objects. This limits the impact of our research since
learning-based computer vision methods can only be used in scenarios that are
supported by existing datasets.
  In this work, we present a full object keypoint tracking toolkit,
encompassing the entire process from data collection, labeling, model learning
and evaluation. We present a semi-automatic way of collecting and labeling
datasets using a wrist mounted camera on a standard robotic arm. Using our
toolkit and method, we are able to obtain a working 3D object keypoint detector
and go through the whole process of data collection, annotation and learning in
just a couple hours of active time.

This paper considers a multiblock nonsmooth nonconvex optimization problem
with nonlinear coupling constraints. By developing the idea of using the
information zone and adaptive regime proposed in [J. Bolte, S. Sabach and M.
Teboulle, Nonconvex Lagrangian-based optimization: Monitoring schemes and
global convergence, Mathematics of Operations Research, 43: 1210--1232, 2018],
we propose a multiblock alternating direction method of multipliers for solving
this problem. We specify the update of the primal variables by employing a
majorization minimization procedure in each block update. An independent
convergence analysis is conducted to prove the subsequential and global
convergence of the generated sequence to a critical point of the augmented
Lagrangian. We also establish iteration complexity and provide preliminary
numerical results for the proposed algorithm.

In this paper we propose a robotic system for Irrigation Water Management
(IWM) in a structured robotic greenhouse environment. A commercially available
robotic manipulator is equipped with an RGB-D camera and a soil moisture
sensor. The two are used to automate the procedure known as "feel and
appearance method", which is a way of monitoring soil moisture to determine
when to irrigate and how much water to apply. We develop a compliant force
control framework that enables the robot to insert the soil moisture sensor in
the sensitive plant root zone of the soil, without harming the plant. RGB-D
camera is used to roughly estimate the soil surface, in order to plan the soil
sampling approach. Used together with the developed adaptive force control
algorithm, the camera enables the robot to sample the soil without knowing the
exact soil stiffness a priori. Finally, we postulate a deep learning based
approach to utilize the camera to visually assess the soil health and moisture
content.

Machine learning potentials (MLPs) for atomistic simulations have an enormous
prospective impact on materials modeling, offering orders of magnitude speedup
over density functional theory (DFT) calculations without appreciably
sacrificing accuracy in the prediction of material properties. However, the
generation of large datasets needed for training MLPs is daunting. Herein, we
show that MLP-based material property predictions converge faster with respect
to precision for Brillouin zone integrations than DFT-based property
predictions. We demonstrate that this phenomenon is robust across material
properties for different metallic systems. Further, we provide statistical
error metrics to accurately determine a priori the precision level required of
DFT training datasets for MLPs to ensure accelerated convergence of material
property predictions, thus significantly reducing the computational expense of
MLP development.

In biomedical image analysis, the applicability of deep learning methods is
directly impacted by the quantity of image data available. This is due to deep
learning models requiring large image datasets to provide high-level
performance. Generative Adversarial Networks (GANs) have been widely utilized
to address data limitations through the generation of synthetic biomedical
images. GANs consist of two models. The generator, a model that learns how to
produce synthetic images based on the feedback it receives. The discriminator,
a model that classifies an image as synthetic or real and provides feedback to
the generator. Throughout the training process, a GAN can experience several
technical challenges that impede the generation of suitable synthetic imagery.
First, the mode collapse problem whereby the generator either produces an
identical image or produces a uniform image from distinct input features.
Second, the non-convergence problem whereby the gradient descent optimizer
fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem
whereby unstable training behavior occurs due to the discriminator achieving
optimal classification performance resulting in no meaningful feedback being
provided to the generator. These problems result in the production of synthetic
imagery that is blurry, unrealistic, and less diverse. To date, there has been
no survey article outlining the impact of these technical challenges in the
context of the biomedical imagery domain. This work presents a review and
taxonomy based on solutions to the training problems of GANs in the biomedical
imaging domain. This survey highlights important challenges and outlines future
research directions about the training of GANs in the domain of biomedical
imagery.

Although deep neural networks (DNN) have achieved state-of-the-art
performance in various fields, some unexpected errors are often found in the
neural network, which is very dangerous for some tasks requiring high
reliability and high security. The non-transparency and unexplainably of
Convolutional Neural Networks (CNN) still limit its application in many fields,
such as medical care and finance. Despite current studies that have been
committed to visualizing the decision process of DNN, most of these methods
focus on the low level and do not take into account the prior knowledge of
medicine. In this work, we propose an interpretable framework based on key
medical concepts, enabling CNN to explain from the perspective of doctors'
cognition. We propose an interpretable automatic recognition framework for the
ultrasonic standard plane, which uses a concept-based graph convolutional
neural network to construct the relationships between key medical concepts, to
obtain an interpretation consistent with a doctor's cognition. Extensive
experiments have empirically shown that our model can meaningfully explain the
decision of the classifier and provide quantitative support.

Cartoons and animation domain videos have very different characteristics
compared to real-life images and videos. In addition, this domain carries a
large variability in styles. Current computer vision and deep-learning
solutions often fail on animated content because they were trained on natural
images. In this paper we present a method to refine a semantic representation
suitable for specific animated content. We first train a neural network on a
large-scale set of animation videos and use the mapping to deep features as an
embedding space. Next, we use self-supervision to refine the representation for
any specific animation style by gathering many examples of animated characters
in this style, using a multi-object tracking. These examples are used to define
triplets for contrastive loss training. The refined semantic space allows
better clustering of animated characters even when they have diverse
manifestations. Using this space we can build dictionaries of characters in an
animation videos, and define specialized classifiers for specific stylistic
content (e.g., characters in a specific animation series) with very little user
effort. These classifiers are the basis for automatically labeling characters
in animation videos. We present results on a collection of characters in a
variety of animation styles.

In this article, we explore the shallow heuristics used by transformer-based
pre-trained language models (PLMs) that are fine-tuned for natural language
inference (NLI). To do so, we construct or own dataset based on syllogistic,
and we evaluate a number of models' performance on our dataset. We find
evidence that the models rely heavily on certain shallow heuristics, picking up
on symmetries and asymmetries between premise and hypothesis. We suggest that
the lack of generalization observable in our study, which is becoming a topic
of lively debate in the field, means that the PLMs are currently not learning
NLI, but rather spurious heuristics.

Policy makers often make decisions based on parameters such as GDP,
unemployment rate, industrial output, etc. The primary methods to obtain or
even estimate such information are resource intensive and time consuming. In
order to make timely and well-informed decisions, it is imperative to be able
to come up with proxies for these parameters which can be sampled quickly and
efficiently, especially during disruptive events, like the COVID-19 pandemic.
Recently, there has been a lot of focus on using remote sensing data for this
purpose. The data has become cheaper to collect compared to surveys, and can be
available in real time. In this work, we present Regional GDP NightLight
(ReGNL), a neural network based model which is trained on a custom dataset of
historical nightlights and GDP data along with the geographical coordinates of
a place, and estimates the GDP of the place, given the other parameters. Taking
the case of 50 US states, we find that ReGNL is disruption-agnostic and is able
to predict the GDP for both normal years (2019) and for years with a disruptive
event (2020). ReGNL outperforms timeseries ARIMA methods for prediction, even
during the pandemic. Following from our findings, we make a case for building
infrastructures to collect and make available granular data, especially in
resource-poor geographies, so that these can be leveraged for policy making
during disruptive events.

In this paper, we introduce a deep multi-view stereo (MVS) system that
jointly predicts depths, surface normals and per-view confidence maps. The key
to our approach is a novel solver that iteratively solves for per-view depth
map and normal map by optimizing an energy potential based on the locally
planar assumption. Specifically, the algorithm updates depth map by propagating
from neighboring pixels with slanted planes, and updates normal map with local
probabilistic plane fitting. Both two steps are monitored by a customized
confidence map. This solver is not only effective as a post-processing tool for
plane-based depth refinement and completion, but also differentiable such that
it can be efficiently integrated into deep learning pipelines. Our multi-view
stereo system employs multiple optimization steps of the solver over the
initial prediction of depths and surface normals. The whole system can be
trained end-to-end, decoupling the challenging problem of matching pixels
within poorly textured regions from the cost-volume based neural network.
Experimental results on ScanNet and RGB-D Scenes V2 demonstrate
state-of-the-art performance of the proposed deep MVS system on multi-view
depth estimation, with our proposed solver consistently improving the depth
quality over both conventional and deep learning based MVS pipelines. Code is
available at https://github.com/thuzhaowang/idn-solver.

Communication overhead is one of the major obstacles to train large deep
learning models at scale. Gradient sparsification is a promising technique to
reduce the communication volume. However, it is very challenging to obtain real
performance improvement because of (1) the difficulty of achieving an scalable
and efficient sparse allreduce algorithm and (2) the sparsification overhead.
This paper proposes O$k$-Top$k$, a scheme for distributed training with sparse
gradients. O$k$-Top$k$ integrates a novel sparse allreduce algorithm (less than
6$k$ communication volume which is asymptotically optimal) with the
decentralized parallel Stochastic Gradient Descent (SGD) optimizer, and its
convergence is proved. To reduce the sparsification overhead, O$k$-Top$k$
efficiently selects the top-$k$ gradient values according to an estimated
threshold. Evaluations are conducted on the Piz Daint supercomputer with neural
network models from different deep learning domains. Empirical results show
that O$k$-Top$k$ achieves similar model accuracy to dense allreduce. Compared
with the optimized dense and the state-of-the-art sparse allreduces,
O$k$-Top$k$ is more scalable and significantly improves training throughput
(e.g., 3.29x-12.95x improvement for BERT on 256 GPUs).

Nowadays, yoga has become a part of life for many people. Exercises and
sports technological assistance is implemented in yoga pose identification. In
this work, a self-assistance based yoga posture identification technique is
developed, which helps users to perform Yoga with the correction feature in
Real-time. The work also presents Yoga-hand mudra (hand gestures)
identification. The YOGI dataset has been developed which include 10 Yoga
postures with around 400-900 images of each pose and also contain 5 mudras for
identification of mudras postures. It contains around 500 images of each mudra.
The feature has been extracted by making a skeleton on the body for yoga poses
and hand for mudra poses. Two different algorithms have been used for creating
a skeleton one for yoga poses and the second for hand mudras. Angles of the
joints have been extracted as a features for different machine learning and
deep learning models. among all the models XGBoost with RandomSearch CV is most
accurate and gives 99.2\% accuracy. The complete design framework is described
in the present paper.

Many data-driven applications in material science have been made possible
because of recent breakthroughs in artificial intelligence(AI). The use of AI
in material engineering is becoming more viable as the number of material data
such as X-Ray diffraction, various spectroscopy, and microscope data grows. In
this work, we have reported a material search engine that uses the interatomic
space (d value) from X-ray diffraction to provide material information. We have
investigated various techniques for predicting prospective material using X-ray
diffraction data. We used the Random Forest, Naive Bayes (Gaussian), and Neural
Network algorithms to achieve this. These algorithms have an average accuracy
of 88.50\%, 100.0\%, and 88.89\%, respectively. Finally, we combined all these
techniques into an ensemble approach to make the prediction more generic. This
ensemble method has a ~100\% accuracy rate. Furthermore, we are designing a
graph neural network (GNN)-based architecture to improve interpretability and
accuracy. Thus, we want to solve the computational and time complexity of
traditional dictionary-based and metadata-based material search engines and to
provide a more generic prediction.

21cm tomography opens a window to directly study astrophysics and fundamental
physics of early epochs in our Universe's history, the Epoch of Reionisation
(EoR) and Cosmic Dawn (CD). Summary statistics such as the power spectrum omit
information encoded in this signal due to its highly non-Gaussian nature. Here
we adopt a network-based approach for direct inference of CD and EoR
astrophysics jointly with fundamental physics from 21cm tomography. We showcase
a warm dark matter (WDM) universe, where dark matter density parameter
$\Omega_\mathrm{m}$ and WDM mass $m_\mathrm{WDM}$ strongly influence both CD
and EoR. Reflecting the three-dimensional nature of 21cm light-cones, we
present a new, albeit simple, 3D convolutional neural network for efficient
parameter recovery at moderate training cost. On simulations we observe
high-fidelity parameter recovery for CD and EoR astrophysics ($R^2>0.78-0.99$),
together with DM density $\Omega_\mathrm{m}$ ($R^2>0.97$) and WDM mass
($R^2>0.61$, significantly better for $m_\mathrm{WDM}<3-4\,$keV). For realistic
mock observed light-cones that include noise and foreground levels expected for
the Square Kilometre Array, we note that in an optimistic foreground scenario
parameter recovery is unaffected, while for moderate, less optimistic
foreground levels (occupying the so-called wedge) the recovery of the WDM mass
deteriorates, while other parameters remain robust against increased foreground
levels at $R^2>0.9$. We further test the robustness of our network-based
inference against modelling uncertainties and systematics by transfer learning
between bare simulations and mock observations; we find robust recovery of
specific X-ray luminosity and ionising efficiency, while DM density and WDM
mass come with increased bias and scatter.

Although Raman spectroscopy is widely used for the investigation of
biomedical samples and has a high potential for use in clinical applications,
it is not common in clinical routines. One of the factors that obstruct the
integration of Raman spectroscopic tools into clinical routines is the
complexity of the data processing workflow. Software tools that simplify
spectroscopic data handling may facilitate such integration by familiarizing
clinical experts with the advantages of Raman spectroscopy.
  Here, RAMANMETRIX is introduced as a user-friendly software with an intuitive
web-based graphical user interface (GUI) that incorporates a complete workflow
for chemometric analysis of Raman spectra, from raw data pretreatment to a
robust validation of machine learning models. The software can be used both for
model training and for the application of the pretrained models onto new data
sets. Users have full control of the parameters during model training, but the
testing data flow is frozen and does not require additional user input.
RAMANMETRIX is available in two versions: as standalone software and web
application. Due to the modern software architecture, the computational backend
part can be executed separately from the GUI and accessed through an
application programming interface (API) for applying a preconstructed model to
the measured data. This opens up possibilities for using the software as a data
processing backend for the measurement devices in real-time.
  The models preconstructed by more experienced users can be exported and
reused for easy one-click data preprocessing and prediction, which requires
minimal interaction between the user and the software. The results of such
prediction and graphical outputs of the different data processing steps can be
exported and saved.

Anti-counterfeiting QR codes are widely used in people's work and life,
especially in product packaging. However, the anti-counterfeiting QR code has
the risk of being copied and forged in the circulation process. In reality,
copying is usually based on genuine anti-counterfeiting QR codes, but the
brands and models of copiers are diverse, and it is extremely difficult to
determine which individual copier the forged anti-counterfeiting code come
from. In response to the above problems, this paper proposes a method for copy
forgery identification of anti-counterfeiting QR code based on deep learning.
We first analyze the production principle of anti-counterfeiting QR code, and
convert the identification of copy forgery to device category forensics, and
then a Dual-Branch Multi-Scale Feature Fusion network is proposed. During the
design of the network, we conducted a detailed analysis of the data
preprocessing layer, single-branch design, etc., combined with experiments, the
specific structure of the dual-branch multi-scale feature fusion network is
determined. The experimental results show that the proposed method has achieved
a high accuracy of copy forgery identification, which exceeds the current
series of methods in the field of image forensics.

We propose and unify classes of different models for information propagation
over graphs. In a first class, propagation is modeled as a wave which emanates
from a set of known nodes at an initial time, to all other unknown nodes at
later times with an ordering determined by the arrival time of the information
wave front. A second class of models is based on the notion of a travel time
along paths between nodes. The time of information propagation from an initial
known set of nodes to a node is defined as the minimum of a generalized travel
time over subsets of all admissible paths. A final class is given by imposing a
local equation of an eikonal form at each unknown node, with boundary
conditions at the known nodes. The solution value of the local equation at a
node is coupled to those of neighbouring nodes with lower values. We provide
precise formulations of the model classes and prove equivalences between them.
Motivated by the connection between first arrival time model and the eikonal
equation in the continuum setting, we derive mean field limits for graphs based
on uniform grids in Euclidean space under grid refinement. For a specific
parameter setting, we demonstrate that the solution on the grid approximates
the Euclidean distance, and illustrate the use of front propagation on graphs
to semi-supervised learning.

Supervised deep learning has shown state-of-the-art performance for medical
image segmentation across different applications, including histopathology and
cancer research; however, the manual annotation of such data is extremely
laborious. In this work, we explore the use of superpixel approaches to compute
a pre-segmentation of HER2 stained images for breast cancer diagnosis that
facilitates faster manual annotation and correction in a second step. Four
methods are compared: Standard Simple Linear Iterative Clustering (SLIC) as a
baseline, a domain adapted SLIC, and superpixels based on feature embeddings of
a pretrained ResNet-50 and a denoising autoencoder. To tackle oversegmentation,
we propose to hierarchically merge superpixels, based on their content in the
respective feature space. When evaluating the approaches on fully manually
annotated images, we observe that the autoencoder-based superpixels achieve a
23% increase in boundary F1 score compared to the baseline SLIC superpixels.
Furthermore, the boundary F1 score increases by 73% when hierarchical
clustering is applied on the adapted SLIC and the autoencoder-based
superpixels. These evaluations show encouraging first results for a
pre-segmentation for efficient manual refinement without the need for an
initial set of annotated training data.

Recommender systems have been investigated for many years, with the aim of
generating the most accurate recommendations possible. However, available data
about new users is often insufficient, leading to inaccurate recommendations;
an issue that is known as the cold-start problem. A solution can be active
learning. Active learning strategies proactively select items and ask users to
rate these. This way, detailed user preferences can be acquired and as a
result, more accurate recommendations can be offered to the user. In this
study, we compare five active learning algorithms, combined with three
different predictor algorithms, which are used to estimate to what extent the
user would like the item that is asked to rate. In addition, two modes are
tested for selecting the items: batch mode (all items at once), and sequential
mode (the items one by one). Evaluation of the recommender in terms of rating
prediction, decision support, and the ranking of items, showed that sequential
mode produces the most accurate recommendations for dense data sets.
Differences between the active learning algorithms are small. For most active
learners, the best predictor turned out to be FunkSVD in combination with
sequential mode.

In this paper, we present a passive method to detect face presentation attack
a.k.a face liveness detection using an ensemble deep learning technique. Face
liveness detection is one of the key steps involved in user identity
verification of customers during the online onboarding/transaction processes.
During identity verification, an unauthenticated user tries to bypass the
verification system by several means, for example, they can capture a user
photo from social media and do an imposter attack using printouts of users
faces or using a digital photo from a mobile device and even create a more
sophisticated attack like video replay attack. We have tried to understand the
different methods of attack and created an in-house large-scale dataset
covering all the kinds of attacks to train a robust deep learning model. We
propose an ensemble method where multiple features of the face and background
regions are learned to predict whether the user is a bonafide or an attacker.

Using rough path techniques, we provide a priori estimates for the output of
Deep Residual Neural Networks in terms of both the input data and the (trained)
network weights. As trained network weights are typically very rough when seen
as functions of the layer, we propose to derive stability bounds in terms of
the total $p$-variation of trained weights for any $p\in[1,3]$. Unlike the
$C^1$-theory underlying the neural ODE literature, our estimates remain bounded
even in the limiting case of weights behaving like Brownian motions, as
suggested in [arXiv:2105.12245]. Mathematically, we interpret residual neural
network as solutions to (rough) difference equations, and analyse them based on
recent results of discrete time signatures and rough path theory.

Learned iterative reconstruction algorithms for inverse problems offer the
flexibility to combine analytical knowledge about the problem with modules
learned from data. This way, they achieve high reconstruction performance while
ensuring consistency with the measured data. In computed tomography, extending
such approaches from 2D fan-beam to 3D cone-beam data is challenging due to the
prohibitively high GPU memory that would be needed to train such models. This
paper proposes to use neural ordinary differential equations to solve the
reconstruction problem in a residual formulation via numerical integration. For
training, there is no need to backpropagate through several unrolled network
blocks nor through the internals of the solver. Instead, the gradients are
obtained very memory-efficiently in the neural ODE setting allowing for
training on a single consumer graphics card. The method is able to reduce the
root mean squared error by over 30% compared to the best performing classical
iterative reconstruction algorithm and produces high quality cone-beam
reconstructions even in a sparse view scenario.

Metasurfaces, a two-dimensional (2D) form of metamaterials constituted by
planar meta-atoms, exhibit exotic abilities to freely tailor electromagnetic
(EM) waves. Over the past decade, tunable metasurfaces have come to the
frontier in the field of nanophotonics, with tremendous effort focused on
developing and integrating various active materials into metasurfaces. As a
result, tunable/reconfigurable metasurfaces with multi-functionalities
triggered by various external stimuli have been successfully demonstrated,
openings a new avenue to dynamically manipulate and control EM waves for
photonic applications in demand. In this review, we first brief the progress of
tunable metasurfaces development in the last decade and highlight
representative works from the perspectives of active materials development,
design methodologies and application-driven exploration. Then, we elaborate on
the active tuning mechanisms and relevant active materials. Next, we discuss
recent achievements in theory as well as machine learning (ML) assisted design
methodologies to sustain the development of this field. After that, we
summarize and describe typical application areas of the tunable metasurfaces.
We conclude this review by analyzing existing challenges and presenting our
perspectives on future directions and opportunities in this vibrant and
fast-developing field.

Full characterization of the spectral behavior of generative models based on
neural networks remains an open issue. Recent research has focused heavily on
generative adversarial networks and the high-frequency discrepancies between
real and generated images. The current solution to avoid this is to either
replace transposed convolutions with bilinear up-sampling or add a spectral
regularization term in the generator. It is well known that Variational
Autoencoders (VAEs) also suffer from these issues. In this work, we propose a
simple 2D Fourier transform-based spectral regularization loss for the VAE and
show that it can achieve results equal to, or better than, the current
state-of-the-art in frequency-aware losses for generative models. In addition,
we experiment with altering the up-sampling procedure in the generator network
and investigate how it influences the spectral performance of the model. We
include experiments on synthetic and real data sets to demonstrate our results.

Magnetic resonance imaging (MRI) is a widely used medical imaging modality.
However, due to the limitations in hardware, scan time, and throughput, it is
often clinically challenging to obtain high-quality MR images. In this article,
we propose a method of using artificial intelligence to expand the channel to
achieve the goal of generating the virtual coils. The main characteristic of
our work is utilizing dummy variable technology to expand/extrapolate the
receive coils in both image and k-space domains. The high-dimensional
information formed by channel expansion is used as the prior information to
improve the reconstruction effect of parallel imaging. Two main components are
incorporated into the network design, namely variable augmentation technology
and sum of squares (SOS) objective function. Variable augmentation provides the
network with more high-dimensional prior information, which is helpful for the
network to extract the deep feature information of the data. The SOS objective
function is employed to solve the deficiency of k-space data training while
speeding up convergence. Experimental results demonstrated its great potentials
in super-resolution of MR images and accelerated parallel imaging

Bayesian Active Learning (BAL) is an efficient framework for learning the
parameters of a model, in which input stimuli are selected to maximize the
mutual information between the observations and the unknown parameters.
However, its applicability to real experiments is limited by two main
drawbacks. Firstly, it requires performing high-dimensional integrations and
optimizations in real time: current methods are either too time consuming, or
only applicable to specific models. Secondly, it only optimizes for the next
stimulus input (an approach referred to as a myopic design), disregarding all
future observations in the experiment. To address these issues, we propose an
Efficient Sampling-Based Bayesian Active Learning (ESB-BAL) framework, which is
efficient enough to be used in real-time biological experiments and to go
beyond myopic approaches. We apply our method to the problem of estimating the
parameters of a chemical synapse from its postsynaptic responses evoked by
presynaptic action potentials. Synaptic parameter inference is a challenging
application of BAL, as computation needs to be faster than the smallest
inter-stimulation interval, which can be on the order of a few milliseconds.
Using synthetic data and synaptic whole-cell patch clamp recordings in
cerebellar brain slices, we show that our method has the potential to
significantly improve the precision of model-based inferences, thereby paving
the way towards more systematic and automated experimental designs.

Feature preference in Convolutional Neural Network (CNN) image classifiers is
integral to their decision making process, and while the topic has been well
studied, it is still not understood at a fundamental level. We test a range of
task relevant feature attributes (including shape, texture, and color) with
varying degrees of signal and noise in highly controlled CNN image
classification experiments using synthetic datasets to determine feature
preferences. We find that CNNs will prefer features with stronger signal
strength and lower noise irrespective of whether the feature is texture, shape,
or color. This provides guidance for a predictive model for task relevant
feature preferences, demonstrates pathways for bias in machine models that can
be avoided with careful controls on experimental setup, and suggests that
comparisons between how humans and machines prefer task relevant features in
vision classification tasks should be revisited. Code to reproduce experiments
in this paper can be found at
\url{https://github.com/mwolff31/signal_preference}.

This paper presents a new Android malware detection method based on Graph
Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call
graphs (FCGs) consist of a set of program functions and their inter-procedural
calls. Thus, this paper proposes a GNN-based method for Android malware
detection by capturing meaningful intra-procedural call path patterns. In
addition, a Jumping-Knowledge technique is applied to minimize the effect of
the over-smoothing problem, which is common in GNNs. The proposed method has
been extensively evaluated using two benchmark datasets. The results
demonstrate the superiority of our approach compared to state-of-the-art
approaches in terms of key classification metrics, which demonstrates the
potential of GNNs in Android malware detection and classification.

In the process of Systematic Literature Review, citation screening is
estimated to be one of the most time-consuming steps. Multiple approaches to
automate it using various machine learning techniques have been proposed. The
first research papers that apply deep neural networks to this problem were
published in the last two years. In this work, we conduct a replicability study
of the first two deep learning papers for citation screening and evaluate their
performance on 23 publicly available datasets. While we succeeded in
replicating the results of one of the papers, we were unable to replicate the
results of the other. We summarise the challenges involved in the replication,
including difficulties in obtaining the datasets to match the experimental
setup of the original papers and problems with executing the original source
code. Motivated by this experience, we subsequently present a simpler model
based on averaging word embeddings that outperforms one of the models on 18 out
of 23 datasets and is, on average, 72 times faster than the second replicated
approach. Finally, we measure the training time and the invariance of the
models when exposed to a variety of input features and random initialisations,
demonstrating differences in the robustness of these approaches.

We introduce CM3, a family of causally masked generative models trained over
a large corpus of structured multi-modal documents that can contain both text
and image tokens. Our new causally masked approach generates tokens left to
right while also masking out a small number of long token spans that are
generated at the end of the string, instead of their original positions. The
casual masking object provides a type of hybrid of the more common causal and
masked language models, by enabling full generative modeling while also
providing bidirectional context when generating the masked spans. We train
causally masked language-image models on large-scale web and Wikipedia
articles, where each document contains all of the text, hypertext markup,
hyperlinks, and image tokens (from a VQVAE-GAN), provided in the order they
appear in the original HTML source (before masking). The resulting CM3 models
can generate rich structured, multi-modal outputs while conditioning on
arbitrary masked document contexts, and thereby implicitly learn a wide range
of text, image, and cross modal tasks. They can be prompted to recover, in a
zero-shot fashion, the functionality of models such as DALL-E, GENRE, and HTLM.
We set the new state-of-the-art in zero-shot summarization, entity linking, and
entity disambiguation while maintaining competitive performance in the
fine-tuning setting. We can generate images unconditionally, conditioned on
text (like DALL-E) and do captioning all in a zero-shot setting with a single
model.

As various mobile devices and location-based services are increasingly
developed in different smart city scenarios and applications, many unexpected
privacy leakages have arisen due to geolocated data collection and sharing.
While these geolocated data could provide a rich understanding of human
mobility patterns and address various societal research questions, privacy
concerns for users' sensitive information have limited their utilization. In
this paper, we design and implement a novel LSTM-based adversarial mechanism
with representation learning to attain a privacy-preserving feature
representation of the original geolocated data (mobility data) for a sharing
purpose. We quantify the utility-privacy trade-off of mobility datasets in
terms of trajectory reconstruction risk, user re-identification risk, and
mobility predictability. Our proposed architecture reports a Pareto Frontier
analysis that enables the user to assess this trade-off as a function of
Lagrangian loss weight parameters. The extensive comparison results on four
representative mobility datasets demonstrate the superiority of our proposed
architecture and the efficiency of the proposed privacy-preserving features
extractor. Our results show that by exploring Pareto optimal setting, we can
simultaneously increase both privacy (45%) and utility (32%).

Unsupervised representation learning techniques have been developing rapidly
to make full use of unlabeled images. They encode images into rich features
that are oblivious to downstream tasks. Behind its revolutionary representation
power, the requirements for dedicated model designs and a massive amount of
computation resources expose image encoders to the risks of potential model
stealing attacks -- a cheap way to mimic the well-trained encoder performance
while circumventing the demanding requirements. Yet conventional attacks only
target supervised classifiers given their predicted labels and/or posteriors,
which leaves the vulnerability of unsupervised encoders unexplored. In this
paper, we first instantiate the conventional stealing attacks against encoders
and demonstrate their severer vulnerability compared with downstream
classifiers. To better leverage the rich representation of encoders, we further
propose Cont-Steal, a contrastive-learning-based attack, and validate its
improved stealing effectiveness in various experiment settings. As a takeaway,
we appeal to our community's attention to the intellectual property protection
of representation learning techniques, especially to the defenses against
encoder stealing attacks like ours.

Predicting the stock market trend has always been challenging since its
movement is affected by many factors. Here, we approach the future trend
prediction problem as a machine learning classification problem by creating
tomorrow_trend feature as our label to be predicted. Different features are
given to help the machine learning model predict the label of a given day;
whether it is an uptrend or downtrend, those features are technical indicators
generated from the stock's price history. In addition, as financial news plays
a vital role in changing the investor's behavior, the overall sentiment score
on a given day is created from all news released on that day and added to the
model as another feature. Three different machine learning models are tested in
Spark (big-data computing platform), Logistic Regression, Random Forest, and
Gradient Boosting Machine. Random Forest was the best performing model with a
63.58% test accuracy.

Mechatronic systems have increasingly stringent performance requirements for
motion control, leading to a situation where many factors, such as
position-dependency, cannot be neglected in feedforward control. The aim of
this paper is to compensate for position-dependent effects by modeling
feedforward parameters as a function of position. A framework to model and
identify feedforward parameters as a continuous function of position is
developed by combining Gaussian processes and feedforward parameter learning
techniques. The framework results in a fully data-driven approach, which can be
readily implemented for industrial control applications. The framework is
experimentally validated and shows a significant performance increase on a
commercial wire bonder.

This letter focuses on integrating rate-splitting multiple-access (RSMA) with
time-division-duplex Cell-free Massive MIMO (multiple-input multiple-output)
for massive machine-type communications. Due to the large number of devices,
their sporadic access behaviour and limited coherence interval, we assume a
random access strategy with all active devices utilizing the same pilot for
uplink channel estimation. This gives rise to a highly pilot-contaminated
scenario, which inevitably deteriorates channel estimates. Motivated by the
robustness of RSMA towards imperfect channel state information, we propose a
novel RSMA-assisted downlink transmission framework for cell-free massive MIMO.
On the basis of the downlink achievable spectral efficiency of the common and
private streams, we devise a heuristic common precoder design and propose a
novel max-min power control method for the proposed RSMA-assisted scheme.
Numerical results show that RSMA effectively mitigates the effect of pilot
contamination in the downlink and achieves a significant performance gain over
a conventional cell-free massive MIMO network.

Fake news detection research is still in the early stage as this is a
relatively new phenomenon in the interest raised by society. Machine learning
helps to solve complex problems and to build AI systems nowadays and especially
in those cases where we have tacit knowledge or the knowledge that is not
known. We used machine learning algorithms and for identification of fake news;
we applied three classifiers; Passive Aggressive, Na\"ive Bayes, and Support
Vector Machine. Simple classification is not completely correct in fake news
detection because classification methods are not specialized for fake news.
With the integration of machine learning and text-based processing, we can
detect fake news and build classifiers that can classify the news data. Text
classification mainly focuses on extracting various features of text and after
that incorporating those features into classification. The big challenge in
this area is the lack of an efficient way to differentiate between fake and
non-fake due to the unavailability of corpora. We applied three different
machine learning classifiers on two publicly available datasets. Experimental
analysis based on the existing dataset indicates a very encouraging and
improved performance.

In recent years, neural signed distance function (SDF) has become one of the
most effective representation methods for 3D models. By learning continuous
SDFs in 3D space, neural networks can predict the distance from a given query
space point to its closest object surface,whose positive and negative signs
denote inside and outside of the object, respectively. Training a specific
network for each 3D model, which individually embeds its shape, can realize
compressed representation of objects by storing fewer network (and possibly
latent) parameters. Consequently, reconstruction through network inference and
surface recovery can be achieved. In this paper, we propose an SDF prediction
network using explicit key spheres as input. Key spheres are extracted from the
internal space of objects, whose centers either have relatively larger SDF
values (sphere radii), or are located at essential positions. By inputting the
spatial information of multiple spheres which imply different local shapes, the
proposed method can significantly improve the reconstruction accuracy with a
negligible storage cost. Compared to previous works, our method achieves the
high-fidelity and high-compression 3D object coding and reconstruction.
Experiments conducted on three datasets verify the superior performance of our
method.

Within the past two decades, Augmented Reality (AR) applications have
received increased attention. Augmented Reality is now widely used in the
education sector at level K to 12. AR is expected to be generally adopted in
two to three years in higher education and four to five years in K to 12.
Applying AR technology in the education sector especially in STEM subjects, can
result in having a smart campus. In adopting a SMART Campus strategy, education
practitioners must address many intrinsic issues in science, technology,
engineering, and mathematics (STEM) research. For example, in physics, there
are expensive or insufficient laboratory systems, system faults, and difficulty
simulating other experimental circumstances. In technology, many schools do not
have enough computers. In engineering, there are only a few instructors who are
knowledgeable in computer aided design (CAD). In mathematics, few teachers
incorporate technology into their lessons often because they believe it is
still better to teach through the traditional methods. Hence, In this paper we
discuss how AR is being used now in different learning areas in STEM to open
new doors to researchers and teachers as they transition their schools into
SMART campuses with the use of AR apps. Aligned with this, a suggested
framework for school administrators and policymakers is proposed based on a
review of the positive benefits of different AR apps.

Unlike the classical linear model, nonlinear generative models have been
addressed sparsely in the literature of statistical learning. This work aims to
bringing attention to these models and their secrecy potential. To this end, we
invoke the replica method to derive the asymptotic normalized cross entropy in
an inverse probability problem whose generative model is described by a
Gaussian random field with a generic covariance function. Our derivations
further demonstrate the asymptotic statistical decoupling of the Bayesian
estimator and specify the decoupled setting for a given nonlinear model.
  The replica solution depicts that strictly nonlinear models establish an
all-or-nothing phase transition: There exists a critical load at which the
optimal Bayesian inference changes from perfect to an uncorrelated learning.
Based on this finding, we design a new secure coding scheme which achieves the
secrecy capacity of the wiretap channel. This interesting result implies that
strictly nonlinear generative models are perfectly secured without any secure
coding. We justify this latter statement through the analysis of an
illustrative model for perfectly secure and reliable inference.

The current multiple sclerosis (MS) diagnostic criteria lack specificity, and
this may lead to misdiagnosis, which remains an issue in present-day clinical
practice. In addition, conventional biomarkers only moderately correlate with
MS disease progression. Recently, advanced MS lesional imaging biomarkers such
as cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim
lesions (PRL), visible in specialized magnetic resonance imaging (MRI)
sequences, have shown higher specificity in differential diagnosis. Moreover,
studies have shown that CL and PRL are potential prognostic biomarkers, the
former correlating with cognitive impairments and the latter with early
disability progression. As machine learning-based methods have achieved
extraordinary performance in the assessment of conventional imaging biomarkers,
such as white matter lesion segmentation, several automated or semi-automated
methods have been proposed for CL, CVS, and PRL as well. In the present review,
we first introduce these advanced MS imaging biomarkers and their imaging
methods. Subsequently, we describe the corresponding machine learning-based
methods that were used to tackle these clinical questions, putting them into
context with respect to the challenges they are still facing, including
non-standardized MRI protocols, limited datasets, and moderate inter-rater
variability. We conclude by presenting the current limitations that prevent
their broader deployment and suggesting future research directions.

Labeling a large set of data is expensive. Active learning aims to tackle
this problem by asking to annotate only the most informative data from the
unlabeled set. We propose a novel active learning approach that utilizes
self-supervised pretext tasks and a unique data sampler to select data that are
both difficult and representative. We discover that the loss of a simple
self-supervised pretext task, such as rotation prediction, is closely
correlated to the downstream task loss. Before the active learning iterations,
the pretext task learner is trained on the unlabeled set, and the unlabeled
data are sorted and split into batches by their pretext task losses. In each
active learning iteration, the main task model is used to sample the most
uncertain data in a batch to be annotated. We evaluate our method on various
image classification and segmentation benchmarks and achieve compelling
performances on CIFAR10, Caltech-101, ImageNet, and Cityscapes. We further show
that our method performs well on imbalanced datasets, and can be an effective
solution to the cold-start problem where active learning performance is
affected by the randomly sampled initial labeled set.

Communication enables agents to cooperate to achieve their goals. Learning
when to communicate, i.e. sparse communication, is particularly important where
bandwidth is limited, in situations where agents interact with humans, in
partially observable scenarios where agents must convey information unavailable
to others, and in non-cooperative scenarios where agents may hide information
to gain a competitive advantage. Recent work in learning sparse communication,
however, suffers from high variance training where, the price of decreasing
communication is a decrease in reward, particularly in cooperative tasks.
Sparse communications are necessary to match agent communication to limited
human bandwidth. Humans additionally communicate via discrete linguistic
tokens, previously shown to decrease task performance when compared to
continuous communication vectors. This research addresses the above issues by
limiting the loss in reward of decreasing communication and eliminating the
penalty for discretization. In this work, we successfully constrain training
using a learned gate to regulate when to communicate while using discrete
prototypes that reflect what to communicate for cooperative tasks with partial
observability. We provide two types of "Enforcers" for hard and soft budget
constraints and present results of communication under different budgets. We
show that our method satisfies constraints while yielding the same performance
as comparable, unconstrained methods.

Image fusion is a technique to integrate information from multiple source
images with complementary information to improve the richness of a single
image. Due to insufficient task-specific training data and corresponding ground
truth, most existing end-to-end image fusion methods easily fall into
overfitting or tedious parameter optimization processes. Two-stage methods
avoid the need of large amount of task-specific training data by training
encoder-decoder network on large natural image datasets and utilizing the
extracted features for fusion, but the domain gap between natural images and
different fusion tasks results in limited performance. In this study, we design
a novel encoder-decoder based image fusion framework and propose a
destruction-reconstruction based self-supervised training scheme to encourage
the network to learn task-specific features. Specifically, we propose three
destruction-reconstruction self-supervised auxiliary tasks for multi-modal
image fusion, multi-exposure image fusion and multi-focus image fusion based on
pixel intensity non-linear transformation, brightness transformation and noise
transformation, respectively. In order to encourage different fusion tasks to
promote each other and increase the generalizability of the trained network, we
integrate the three self-supervised auxiliary tasks by randomly choosing one of
them to destroy a natural image in model training. In addition, we design a new
encoder that combines CNN and Transformer for feature extraction, so that the
trained model can exploit both local and global information. Extensive
experiments on multi-modal image fusion, multi-exposure image fusion and
multi-focus image fusion tasks demonstrate that our proposed method achieves
the state-of-the-art performance in both subjective and objective evaluations.
The code will be publicly available soon.

The Bidirectional Encoder Representations from Transformers (BERT) is
currently one of the most important and state-of-the-art models for natural
language. However, it has also been shown that for domain-specific tasks it is
helpful to pretrain BERT on a domain-specific corpus. In this paper, we present
TourBERT, a pretrained language model for tourism. We describe how TourBERT was
developed and evaluated. The evaluations show that TourBERT is outperforming
BERT in all tourism-specific tasks.

We consider infinite-horizon discounted Markov decision problems with finite
state and action spaces and study the convergence rates of the projected policy
gradient method and a general class of policy mirror descent methods, all with
direct parametrization in the policy space. First, we develop a theory of weak
gradient-mapping dominance and use it to prove sharper sublinear convergence
rate of the projected policy gradient method. Then we show that with
geometrically increasing step sizes, a general class of policy mirror descent
methods, including the natural policy gradient method and a projected Q-descent
method, all enjoy a linear rate of convergence without relying on entropy or
other strongly convex regularization. Finally, we also analyze the convergence
rate of an inexact policy mirror descent method and estimate its sample
complexity under a simple generative model.

The provision of communication services via portable and mobile devices, such
as aerial base stations, is a crucial concept to be realized in 5G/6G networks.
Conventionally, IoT/edge devices need to transmit the data directly to the base
station for training the model using machine learning techniques. The data
transmission introduces privacy issues that might lead to security concerns and
monetary losses. Recently, Federated learning was proposed to partially solve
privacy issues via model-sharing with base station. However, the centralized
nature of federated learning only allow the devices within the vicinity of base
stations to share the trained models. Furthermore, the long-range communication
compels the devices to increase transmission power, which raises the energy
efficiency concerns. In this work, we propose distributed federated learning
(DBFL) framework that overcomes the connectivity and energy efficiency issues
for distant devices. The DBFL framework is compatible with mobile edge
computing architecture that connects the devices in a distributed manner using
clustering protocols. Experimental results show that the framework increases
the classification performance by 7.4\% in comparison to conventional federated
learning while reducing the energy consumption.

Automatic code generation from natural language descriptions can be highly
beneficial during the process of software development. In this work, we propose
GAP-Gen, an automatic code generation method guided by Python syntactic
constraints and semantic constraints. We first introduce Python syntactic
constraints in the form of Syntax-Flow, which is a simplified version of
Abstract Syntax Tree (AST) reducing the size and high complexity of Abstract
Syntax Tree but maintaining the crucial syn-tactic information of Python code.
In addition to Syntax-Flow, we introduce Variable-Flow which abstracts variable
and function names consistently throughout the code. In our work, rather than
pre-training, we focus on modifying the fine-tuning process which reduces
computational requirements but retains high generation performance on automatic
Python code generation task. GAP-Gen fine-tunes the transformer-based language
models T5 and CodeT5 using the Code-to-Docstring datasets CodeSearchNet,
CodeSearchNet AdvTest, and Code-Docstring-Corpus from EdinburghNLP. Our
experiments show that GAP-Gen achieves better results on automatic Python code
generation task than previous works

Arabic is a Semitic language which is widely spoken with many dialects. Given
the success of pre-trained language models, many transformer models trained on
Arabic and its dialects have surfaced. While these models have been compared
with respect to downstream NLP tasks, no evaluation has been carried out to
directly compare the internal representations. We probe how linguistic
information is encoded in Arabic pretrained models, trained on different
varieties of Arabic language. We perform a layer and neuron analysis on the
models using three intrinsic tasks: two morphological tagging tasks based on
MSA (modern standard Arabic) and dialectal POS-tagging and a dialectal
identification task. Our analysis enlightens interesting findings such as: i)
word morphology is learned at the lower and middle layers ii) dialectal
identification necessitate more knowledge and hence preserved even in the final
layers, iii) despite a large overlap in their vocabulary, the MSA-based models
fail to capture the nuances of Arabic dialects, iv) we found that neurons in
embedding layers are polysemous in nature, while the neurons in middle layers
are exclusive to specific properties.

A large number of coils are able to provide enhanced signal-to-noise ratio
and improve imaging performance in parallel imaging. Nevertheless, the
increasing growth of coil number simultaneously aggravates the drawbacks of
data storage and reconstruction speed, especially in some iterative
reconstructions. Coil compression addresses these issues by generating fewer
virtual coils. In this work, a novel variable augmentation network for
invertible coil compression termed VAN-ICC is presented. It utilizes inherent
reversibility of normalizing flow-based models for high-precision compression
and invertible recovery. By employing the variable augmentation technology to
image/k-space variables from multi-coils, VAN-ICC trains invertible networks by
finding an invertible and bijective function, which can map the original data
to the compressed counterpart and vice versa. Experiments conducted on both
fully-sampled and under-sampled data verified the effectiveness and flexibility
of VAN-ICC. Quantitative and qualitative comparisons with traditional non-deep
learning-based approaches demonstrated that VAN-ICC can carry much higher
compression effects. Additionally, its performance is not susceptible to
different number of virtual coils.

We study the bilinearly coupled minimax problem: $\min_{x} \max_{y} f(x) +
y^\top A x - h(y)$, where $f$ and $h$ are both strongly convex smooth functions
and admit first-order gradient oracles. Surprisingly, no known first-order
algorithms have hitherto achieved the lower complexity bound of
$\Omega((\sqrt{\frac{L_x}{\mu_x}} + \frac{\|A\|}{\sqrt{\mu_x \mu_y}} +
\sqrt{\frac{L_y}{\mu_y}}) \log(\frac1{\varepsilon}))$ for solving this problem
up to an $\varepsilon$ primal-dual gap in the general parameter regime, where
$L_x, L_y,\mu_x,\mu_y$ are the corresponding smoothness and strongly convexity
  We close this gap by devising the first optimal algorithm, the Lifted
Primal-Dual (LPD) method. Our method lifts the objective into an extended form
that allows both the smooth terms and the bilinear term to be handled optimally
and seamlessly with the same primal-dual framework. Besides optimality, our
method yields a desirably simple single-loop algorithm that uses only one
gradient oracle call per iteration. Moreover, when $f$ is just convex, the same
algorithm applied to a smoothed objective achieves the nearly optimal iteration
complexity. We also provide a direct single-loop algorithm, using the LPD
method, that achieves the iteration complexity of
$O(\sqrt{\frac{L_x}{\varepsilon}} + \frac{\|A\|}{\sqrt{\mu_y \varepsilon}} +
\sqrt{\frac{L_y}{\varepsilon}})$. Numerical experiments on quadratic minimax
problems and policy evaluation problems further demonstrate the fast
convergence of our algorithm in practice.

Existing deep learning-based video super-resolution (SR) methods usually
depend on the supervised learning approach, where the training data is usually
generated by the blurring operation with known or predefined kernels (e.g.,
Bicubic kernel) followed by a decimation operation. However, this does not hold
for real applications as the degradation process is complex and cannot be
approximated by these idea cases well. Moreover, obtaining high-resolution (HR)
videos and the corresponding low-resolution (LR) ones in real-world scenarios
is difficult. To overcome these problems, we propose a self-supervised learning
method to solve the blind video SR problem, which simultaneously estimates blur
kernels and HR videos from the LR videos. As directly using LR videos as
supervision usually leads to trivial solutions, we develop a simple and
effective method to generate auxiliary paired data from original LR videos
according to the image formation of video SR, so that the networks can be
better constrained by the generated paired data for both blur kernel estimation
and latent HR video restoration. In addition, we introduce an optical flow
estimation module to exploit the information from adjacent frames for HR video
restoration. Experiments show that our method performs favorably against
state-of-the-art ones on benchmarks and real-world videos.

Binary-source code matching plays an important role in many security and
software engineering related tasks such as malware detection, reverse
engineering and vulnerability assessment. Currently, several approaches have
been proposed for binary-source code matching by jointly learning the
embeddings of binary code and source code in a common vector space. Despite
much effort, existing approaches target on matching the binary code and source
code written in a single programming language. However, in practice, software
applications are often written in different programming languages to cater for
different requirements and computing platforms. Matching binary and source code
across programming languages introduces additional challenges when maintaining
multi-language and multi-platform applications. To this end, this paper
formulates the problem of cross-language binary-source code matching, and
develops a new dataset for this new problem. We present a novel approach XLIR,
which is a Transformer-based neural network by learning the intermediate
representations for both binary and source code. To validate the effectiveness
of XLIR, comprehensive experiments are conducted on two tasks of cross-language
binary-source code matching, and cross-language source-source code matching, on
top of our curated dataset. Experimental results and analysis show that our
proposed XLIR with intermediate representations significantly outperforms other
state-of-the-art models in both of the two tasks.

We propose a direct, regression-based approach to 2D human pose estimation
from single images. We formulate the problem as a sequence prediction task,
which we solve using a Transformer network. This network directly learns a
regression mapping from images to the keypoint coordinates, without resorting
to intermediate representations such as heatmaps. This approach avoids much of
the complexity associated with heatmap-based approaches. To overcome the
feature misalignment issues of previous regression-based methods, we propose an
attention mechanism that adaptively attends to the features that are most
relevant to the target keypoints, considerably improving the accuracy.
Importantly, our framework is end-to-end differentiable, and naturally learns
to exploit the dependencies between keypoints. Experiments on MS-COCO and MPII,
two predominant pose-estimation datasets, demonstrate that our method
significantly improves upon the state-of-the-art in regression-based pose
estimation. More notably, ours is the first regression-based approach to
perform favorably compared to the best heatmap-based pose estimation methods.

We assess the accuracy of a smartphone camera simulation. The simulation is
an end-to-end analysis that begins with a physical description of a high
dynamic range 3D scene and includes a specification of the optics and the image
sensor. The simulation is compared to measurements of a physical version of the
scene. The image system simulation accurately matched measurements of optical
blur, depth of field, spectral quantum efficiency, scene inter-reflections, and
sensor noise. The results support the use of image systems simulation methods
for soft prototyping cameras and for producing synthetic data in machine

A successful deep learning model is dependent on extensive training data and
processing power and time (known as training costs). There exist many tasks
without enough number of labeled data to train a deep learning model. Further,
the demand is rising for running deep learning models on edge devices with
limited processing capacity and training time. Deep transfer learning (DTL)
methods are the answer to tackle such limitations, e.g., fine-tuning a
pre-trained model on a massive semi-related dataset proved to be a simple and
effective method for many problems. DTLs handle limited target data concerns as
well as drastically reduce the training costs. In this paper, the definition
and taxonomy of deep transfer learning is reviewed. Then we focus on the
sub-category of network-based DTLs since it is the most common types of DTLs
that have been applied to various applications in the last decade.

Unsupervised graph representation learning has emerged as a powerful tool to
address real-world problems and achieves huge success in the graph learning
domain. Graph contrastive learning is one of the unsupervised graph
representation learning methods, which recently attracts attention from
researchers and has achieved state-of-the-art performances on various tasks.
The key to the success of graph contrastive learning is to construct proper
contrasting pairs to acquire the underlying structural semantics of the graph.
However, this key part is not fully explored currently, most of the ways
generating contrasting pairs focus on augmenting or perturbating graph
structures to obtain different views of the input graph. But such strategies
could degrade the performances via adding noise into the graph, which may
narrow down the field of the applications of graph contrastive learning. In
this paper, we propose a novel graph contrastive learning method, namely
\textbf{D}ual \textbf{S}pace \textbf{G}raph \textbf{C}ontrastive (DSGC)
Learning, to conduct graph contrastive learning among views generated in
different spaces including the hyperbolic space and the Euclidean space. Since
both spaces have their own advantages to represent graph data in the embedding
spaces, we hope to utilize graph contrastive learning to bridge the spaces and
leverage advantages from both sides. The comparison experiment results show
that DSGC achieves competitive or better performances among all the datasets.
In addition, we conduct extensive experiments to analyze the impact of
different graph encoders on DSGC, giving insights about how to better leverage
the advantages of contrastive learning between different spaces.

Image-based anomaly detection systems are of vital importance in various
manufacturing applications. The resolution and acquisition rate of such systems
is increasing significantly in recent years under the fast development of image
sensing technology. This enables the detection of tiny defects in real-time.
However, such a high resolution and acquisition rate of image data not only
slows down the speed of image processing algorithms but also increases data
storage and transmission cost. To tackle this problem, we propose a fast and
data-efficient method with theoretical performance guarantee that is suitable
for sparse anomaly detection in images with a smooth background (smooth plus
sparse signal). The proposed method, named Compressed Smooth Sparse
Decomposition (CSSD), is a one-step method that unifies the compressive image
acquisition and decomposition-based image processing techniques. To further
enhance its performance in a high-dimensional scenario, a Kronecker Compressed
Smooth Sparse Decomposition (KronCSSD) method is proposed. Compared to
traditional smooth and sparse decomposition algorithms, significant
transmission cost reduction and computational speed boost can be achieved with
negligible performance loss. Simulation examples and several case studies in
various applications illustrate the effectiveness of the proposed framework.

Traditionally, distributed machine learning takes the guise of (i) different
nodes training the same model (as in federated learning), or (ii) one model
being split among multiple nodes (as in distributed stochastic gradient
descent). In this work, we highlight how fog- and IoT-based scenarios often
require combining both approaches, and we present a framework for flexible
parallel learning (FPL), achieving both data and model parallelism. Further, we
investigate how different ways of distributing and parallelizing learning tasks
across the participating nodes result in different computation, communication,
and energy costs. Our experiments, carried out using state-of-the-art
deep-network architectures and large-scale datasets, confirm that FPL allows
for an excellent trade-off among computational (hence energy) cost,
communication overhead, and learning performance.

We consider the problem of multiway clustering in the presence of unknown
degree heterogeneity. Such data problems arise commonly in applications such as
recommendation system, neuroimaging, community detection, and hypergraph
partitions in social networks. The allowance of degree heterogeneity provides
great flexibility in clustering models, but the extra complexity poses
significant challenges in both statistics and computation. Here, we develop a
degree-corrected tensor block model with estimation accuracy guarantees. We
present the phase transition of clustering performance based on the notion of
angle separability, and we characterize three signal-to-noise regimes
corresponding to different statistical-computational behaviors. In particular,
we demonstrate that an intrinsic statistical-to-computational gap emerges only
for tensors of order three or greater. Further, we develop an efficient
polynomial-time algorithm that provably achieves exact clustering under mild
signal conditions. The efficacy of our procedure is demonstrated through two
data applications, one on human brain connectome project, and another on Peru
Legislation network dataset.

Causal discovery for purely observational, categorical data is a
long-standing challenging problem. Unlike continuous data, the vast majority of
existing methods for categorical data focus on inferring the Markov equivalence
class only, which leaves the direction of some causal relationships
undetermined. This paper proposes an identifiable ordinal causal discovery
method that exploits the ordinal information contained in many real-world
applications to uniquely identify the causal structure. The proposed method is
applicable beyond ordinal data via data discretization. Through real-world and
synthetic experiments, we demonstrate that the proposed ordinal causal
discovery method combined with simple score-and-search algorithms has favorable
and robust performance compared to state-of-the-art alternative methods in both
ordinal categorical and non-categorical data. An accompanied R package OCD is
freely available at https://web.stat.tamu.edu/~yni/files/OCD_0.1.0.tar.gz.

Understanding deep learning is increasingly emergent as it penetrates more
and more into industry and science. In recent years, a research line from
Fourier analysis sheds lights into this magical "black box" by showing a
Frequency Principle (F-Principle or spectral bias) of the training behavior of
deep neural networks (DNNs) -- DNNs often fit functions from low to high
frequency during the training. The F-Principle is first demonstrated by
one-dimensional synthetic data followed by the verification in high-dimensional
real datasets. A series of works subsequently enhance the validity of the
F-Principle. This low-frequency implicit bias reveals the strength of neural
network in learning low-frequency functions as well as its deficiency in
learning high-frequency functions. Such understanding inspires the design of
DNN-based algorithms in practical problems, explains experimental phenomena
emerging in various scenarios, and further advances the study of deep learning
from the frequency perspective. Although incomplete, we provide an overview of
F-Principle and propose some open problems for future research.

Feature learning is a widely used method employed for large-scale face
recognition. Recently, large-margin softmax loss methods have demonstrated
significant enhancements on deep face recognition. These methods propose fixed
positive margins in order to enforce intra-class compactness and inter-class
diversity. However, the majority of the proposed methods do not consider the
class imbalance issue, which is a major challenge in practice for developing
deep face recognition models. We hypothesize that it significantly affects the
generalization ability of the deep face models. Inspired by this observation,
we introduce a novel adaptive strategy, called KappaFace, to modulate the
relative importance based on class difficultness and imbalance. With the
support of the von Mises-Fisher distribution, our proposed KappaFace loss can
intensify the margin's magnitude for hard learning or low concentration classes
while relaxing it for counter classes. Experiments conducted on popular facial
benchmarks demonstrate that our proposed method achieves superior performance
to the state-of-the-art.

For model piracy forensics, previous model fingerprinting schemes are
commonly based on adversarial examples constructed for the owner's model as the
\textit{fingerprint}, and verify whether a suspect model is indeed pirated from
the original model by matching the behavioral pattern on the fingerprint
examples between one another. However, these methods heavily rely on the
characteristics of classification tasks which inhibits their application to
more general scenarios. To address this issue, we present MetaV, the first
task-agnostic model fingerprinting framework which enables fingerprinting on a
much wider range of DNNs independent from the downstream learning task, and
exhibits strong robustness against a variety of ownership obfuscation
techniques. Specifically, we generalize previous schemes into two critical
design components in MetaV: the \textit{adaptive fingerprint} and the
\textit{meta-verifier}, which are jointly optimized such that the meta-verifier
learns to determine whether a suspect model is stolen based on the concatenated
outputs of the suspect model on the adaptive fingerprint. As a key of being
task-agnostic, the full process makes no assumption on the model internals in
the ensemble only if they have the same input and output dimensions. Spanning
classification, regression and generative modeling, extensive experimental
results validate the substantially improved performance of MetaV over the
state-of-the-art fingerprinting schemes and demonstrate the enhanced generality
of MetaV for providing task-agnostic fingerprinting. For example, on
fingerprinting ResNet-18 trained for skin cancer diagnosis, MetaV achieves
simultaneously $100\%$ true positives and $100\%$ true negatives on a diverse
test set of $70$ suspect models, achieving an about $220\%$ relative
improvement in ARUC in comparison to the optimal baseline.

Since frequent severe droughts are lengthening the dry season in the Amazon
Rainforest, it is important to detect wildfires promptly and forecast possible
spread for effective suppression response. Current wildfire detection models
are not versatile enough for the low-technology conditions of South American
hot spots. This deep learning study first trains a Fully Convolutional Neural
Network on Landsat 8 images of Ecuador and the Galapagos, using Green and
Short-wave Infrared bands to predict pixel-level binary fire masks. This model
achieves a 0.962 validation F2 score and a 0.932 F2 score on test data from
Guyana and Suriname. Afterward, image segmentation is conducted on the Cirrus
band using K-Means Clustering to simplify continuous pixel values into three
discrete classes representing differing degrees of cirrus cloud contamination.
Three additional Convolutional Neural Networks are trained to conduct a
sensitivity analysis measuring the effect of simplified features on model
accuracy and train time. The Experimental model trained on the segmented cirrus
images provides a statistically significant decrease in train time compared to
the Control model trained on raw cirrus images, without compromising binary
accuracy. This proof of concept reveals that feature engineering can improve
the performance of wildfire detection models by lowering computational expense.

Data is the fuel of data science and machine learning techniques for smart
grid applications, similar to many other fields. However, the availability of
data can be an issue due to privacy concerns, data size, data quality, and so
on. To this end, in this paper, we propose a Variational AutoEncoder Generative
Adversarial Network (VAE-GAN) as a smart grid data generative model which is
capable of learning various types of data distributions and generating
plausible samples from the same distribution without performing any prior
analysis on the data before the training phase.We compared the Kullback-Leibler
(KL) divergence, maximum mean discrepancy (MMD), and Wasserstein distance
between the synthetic data (electrical load and PV production) distribution
generated by the proposed model, vanilla GAN network, and the real data
distribution, to evaluate the performance of our model. Furthermore, we used
five key statistical parameters to describe the smart grid data distribution
and compared them between synthetic data generated by both models and real
data. Experiments indicate that the proposed synthetic data generative model
outperforms the vanilla GAN network. The distribution of VAE-GAN synthetic data
is the most comparable to that of real data.

Recently, the concept of open radio access network (O-RAN) has been proposed,
which aims to adopt intelligence and openness in the next generation radio
access networks (RAN). It provides standardized interfaces and the ability to
host network applications from third-party vendors by x-applications (xAPPs),
which enables higher flexibility for network management. However, this may lead
to conflicts in network function implementations, especially when these
functions are implemented by different vendors. In this paper, we aim to
mitigate the conflicts between xAPPs for near-real-time (near-RT) radio
intelligent controller (RIC) of O-RAN. In particular, we propose a team
learning algorithm to enhance the performance of the network by increasing
cooperation between xAPPs. We compare the team learning approach with
independent deep Q-learning where network functions individually optimize
resources. Our simulations show that team learning has better network
performance under various user mobility and traffic loads. With 6 Mbps traffic
load and 20 m/s user movement speed, team learning achieves 8% higher
throughput and 64.8% lower PDR.

Online learning is an important technical means for sketching massive
real-time and high-speed data. Although this direction has attracted intensive
attention, most of the literature in this area ignore the following three
issues: (1) they think little of the underlying abstract hierarchical latent
information existing in examples, even if extracting these abstract
hierarchical latent representations is useful to better predict the class
labels of examples; (2) the idea of preassigned model on unseen datapoints is
not suitable for modeling streaming data with evolving probability
distribution. This challenge is referred as model flexibility. And so, with
this in minds, the online deep learning model we need to design should have a
variable underlying structure; (3) moreover, it is of utmost importance to
fusion these abstract hierarchical latent representations to achieve better
classification performance, and we should give different weights to different
levels of implicit representation information when dealing with the data
streaming where the data distribution changes. To address these issues, we
propose a two-phase Online Deep Learning based on Auto-Encoder (ODLAE). Based
on auto-encoder, considering reconstruction loss, we extract abstract
hierarchical latent representations of instances; Based on predictive loss, we
devise two fusion strategies: the output-level fusion strategy, which is
obtained by fusing the classification results of encoder each hidden layer; and
feature-level fusion strategy, which is leveraged self-attention mechanism to
fusion every hidden layer output. Finally, in order to improve the robustness
of the algorithm, we also try to utilize the denoising auto-encoder to yield
hierarchical latent representations. Experimental results on different datasets
are presented to verify the validity of our proposed algorithm (ODLAE)
outperforms several baselines.

Neural code models have introduced significant improvements over many
software analysis tasks like type inference, vulnerability detection, etc.
Despite the good performance of such models under the common intra-project
independent and identically distributed (IID) training and validation setting,
we observe that they usually fail to generalize to real-world inter-project
out-of-distribution (OOD) setting. In this work, we show that such phenomenon
is caused by model heavily relying on project-specific, ungeneralizable tokens
like self-defined variable and function names for downstream prediction, and we
formulate it as the project-specific bias learning behavior. We propose a
measurement to interpret such behavior, termed as Cond-Idf, which combines
co-occurrence probability and inverse document frequency to measure the level
of relatedness of token with label and its project-specificness. The
approximation indicates that without proper regularization with prior
knowledge, model tends to leverage spurious statistical cues for prediction.
Equipped with these observations, we propose a bias mitigation mechanism Batch
Partition Regularization (BPR) that regularizes model to infer based on proper
behavior by leveraging latent logic relations among samples. Experimental
results on two deep code benchmarks indicate that BPR can improve both
inter-project OOD generalization and adversarial robustness while not
sacrificing accuracy on IID data.

National Institute of Standard & Technology (NIST) is currently running a
multi-year-long standardization procedure to select quantum-safe or
post-quantum cryptographic schemes to be used in the future. Saber is the only
LWR based algorithm to be in the final of Round 3. This work presents a Saber
ASIC which provides 1.37X power-efficient, 1.75x lower area, and 4x less memory
implementation w.r.t. other SoA PQC ASIC. The energy-hungry multiplier block is
1.5x energyefficient than SoA.

Research on both natural intelligence (NI) and artificial intelligence (AI)
generally assumes that the future resembles the past: intelligent agents or
systems (what we call 'intelligence') observe and act on the world, then use
this experience to act on future experiences of the same kind. We call this
'retrospective learning'. For example, an intelligence may see a set of
pictures of objects, along with their names, and learn to name them. A
retrospective learning intelligence would merely be able to name more pictures
of the same objects. We argue that this is not what true intelligence is about.
In many real world problems, both NIs and AIs will have to learn for an
uncertain future. Both must update their internal models to be useful for
future tasks, such as naming fundamentally new objects and using these objects
effectively in a new context or to achieve previously unencountered goals. This
ability to learn for the future we call 'prospective learning'. We articulate
four relevant factors that jointly define prospective learning. Continual
learning enables intelligences to remember those aspects of the past which it
believes will be most useful in the future. Prospective constraints (including
biases and priors) facilitate the intelligence finding general solutions that
will be applicable to future problems. Curiosity motivates taking actions that
inform future decision making, including in previously unmet situations. Causal
estimation enables learning the structure of relations that guide choosing
actions for specific outcomes, even when the specific action-outcome
contingencies have never been observed before. We argue that a paradigm shift
from retrospective to prospective learning will enable the communities that
study intelligence to unite and overcome existing bottlenecks to more
effectively explain, augment, and engineer intelligences.

In this paper, we study the significance of the pleura and adipose tissue in
lung ultrasound AI analysis. We highlight their more prominent appearance when
using high-frequency linear (HFL) instead of curvilinear ultrasound probes,
showing HFL reveals better pleura detail. We compare the diagnostic utility of
the pleura and adipose tissue using an HFL ultrasound probe. Masking the
adipose tissue during training and inference (while retaining the pleural line
and Merlin's space artifacts such as A-lines and B-lines) improved the AI
model's diagnostic accuracy.

Recent work on contrastive losses for learning joint embeddings over
multimodal data has been successful at downstream tasks such as retrieval and
classification. On the other hand, work on joint representation learning for 3D
shapes and text has thus far mostly focused on improving embeddings through
modeling of complex attention between representations , or multi-task learning
. We show that with large batch contrastive learning we achieve SoTA on
text-shape retrieval without complex attention mechanisms or losses. Prior work
in 3D and text representations has also focused on bimodal representation
learning using either voxels or multi-view images with text. To this end, we
propose a trimodal learning scheme to achieve even higher performance and
better representations for all modalities.

We present a simple and effective pretraining strategy {D}en{o}ising
{T}raining DoT for neural machine translation. Specifically, we update the
model parameters with source- and target-side denoising tasks at the early
stage and then tune the model normally. Notably, our approach does not increase
any parameters or training steps, requiring the parallel data merely.
Experiments show that DoT consistently improves the neural machine translation
performance across 12 bilingual and 16 multilingual directions (data size
ranges from 80K to 20M). In addition, we show that DoT can complement existing
data manipulation strategies, i.e. curriculum learning, knowledge distillation,
data diversification, bidirectional training, and back-translation.
Encouragingly, we found that DoT outperforms costly pretrained model mBART in
high-resource settings. Analyses show DoT is a novel in-domain cross-lingual
pretraining strategy and could offer further improvements with task-relevant
self-supervisions.

The emergence of diverse network applications demands more flexible and
responsive resource allocation for networks. Network slicing is a key enabling
technology that provides each network service with a tailored set of network
resources to satisfy specific service requirements. The focus of this paper is
the network slicing of access networks realized by Passive Optical Networks
(PONs). This paper proposes a learning-based Dynamic Bandwidth Allocation (DBA)
algorithm for PON access networks, considering slice-awareness,
demand-responsiveness, and allocation fairness. Our online convex
optimization-based algorithm learns the implicit traffic trend over time and
determines the most robust window allocation that reduces the average latency.
Our simulation results indicate that the proposed algorithm reduces the average
latency by prioritizing delay-sensitive and heavily-loaded ONUs while
guaranteeing a minimal window allocation to all ONUs.

Behavioral Indicators of Compromise are associated with various automated
methods used to extract the sample behavior by observing the system function
calls performed in a virtual execution environment. Thus, every sample is
described by a set of BICs triggered by the sample behavior in the sandbox
environment. Here we discuss a Machine Learning approach to the classification
of the sandbox samples as MALICIOUS or BENIGN, based on the list of triggered
BICs. Besides the more traditional methods like Logistic Regression and Naive
Bayes Classification we also discuss a different approach inspired by the
statistical Monte Carlo methods. The numerical results are illustrated using
ThreatGRID and ReversingLabs data.

With the onset of the COVID-19 pandemic, ultrasound has emerged as an
effective tool for bedside monitoring of patients. Due to this, a large amount
of lung ultrasound scans have been made available which can be used for AI
based diagnosis and analysis. Several AI-based patient severity scoring models
have been proposed that rely on scoring the appearance of the ultrasound scans.
AI models are trained using ultrasound-appearance severity scores that are
manually labeled based on standardized visual features. We address the
challenge of labeling every ultrasound frame in the video clips. Our
contrastive learning method treats the video clip severity labels as noisy weak
severity labels for individual frames, thus requiring only video-level labels.
We show that it performs better than the conventional cross-entropy loss based
training. We combine frame severity predictions to come up with video severity
predictions and show that the frame based model achieves comparable performance
to a video based TSM model, on a large dataset combining public and private
sources.

I give a short introduction to data ethics. My focal audience is
mathematicians, but I hope that my discussion will also be useful to others. I
am not an expert about data ethics, and my article is only a starting point. I
encourage readers to examine the resources that I discuss and to continue to
reflect carefully on data ethics and on the societal implications of data and
data analysis throughout their lives.

We introduce a tensor-based model of shared representation for meta-learning
from a diverse set of tasks. Prior works on learning linear representations for
meta-learning assume that there is a common shared representation across
different tasks, and do not consider the additional task-specific observable
side information. In this work, we model the meta-parameter through an
order-$3$ tensor, which can adapt to the observed task features of the task. We
propose two methods to estimate the underlying tensor. The first method solves
a tensor regression problem and works under natural assumptions on the data
generating process. The second method uses the method of moments under
additional distributional assumptions and has an improved sample complexity in
terms of the number of tasks.
  We also focus on the meta-test phase, and consider estimating task-specific
parameters on a new task. Substituting the estimated tensor from the first step
allows us estimating the task-specific parameters with very few samples of the
new task, thereby showing the benefits of learning tensor representations for
meta-learning. Finally, through simulation and several real-world datasets, we
evaluate our methods and show that it improves over previous linear models of
shared representations for meta-learning.

We demonstrate how incorporating physics constraints into convolutional
neural networks (CNNs) enables learning subgrid-scale (SGS) closures for stable
and accurate large-eddy simulations (LES) in the small-data regime (i.e., when
the availability of high-quality training data is limited). Using several
setups of forced 2D turbulence as the testbeds, we examine the {\it a priori}
and {\it a posteriori} performance of three methods for incorporating physics:
1) data augmentation (DA), 2) CNN with group convolutions (GCNN), and 3) loss
functions that enforce a global enstrophy-transfer conservation (EnsCon). While
the data-driven closures from physics-agnostic CNNs trained in the big-data
regime are accurate and stable, and outperform dynamic Smagorinsky (DSMAG)
closures, their performance substantially deteriorate when these CNNs are
trained with 40x fewer samples (the small-data regime). We show that CNN with
DA and GCNN address this issue and each produce accurate and stable data-driven
closures in the small-data regime. Despite its simplicity, DA, which adds
appropriately rotated samples to the training set, performs as well or in some
cases even better than GCNN, which uses a sophisticated equivariance-preserving
architecture. EnsCon, which combines structural modeling with aspect of
functional modeling, also produces accurate and stable closures in the
small-data regime. Overall, GCNN+EnCon, which combines these two physics
constraints, shows the best {\it a posteriori} performance in this regime.
These results illustrate the power of physics-constrained learning in the
small-data regime for accurate and stable LES.

Well-labeled datasets of chest radiographs (CXRs) are difficult to acquire
due to the high cost of annotation. Thus, it is desirable to learn a robust and
transferable representation in an unsupervised manner to benefit tasks that
lack labeled data. Unlike natural images, medical images have their own domain
prior; e.g., we observe that many pulmonary diseases, such as the COVID-19,
manifest as changes in the lung tissue texture rather than the anatomical
structure. Therefore, we hypothesize that studying only the texture without the
influence of structure variations would be advantageous for downstream
prognostic and predictive modeling tasks. In this paper, we propose a
generative framework, the Lung Swapping Autoencoder (LSAE), that learns
factorized representations of a CXR to disentangle the texture factor from the
structure factor. Specifically, by adversarial training, the LSAE is optimized
to generate a hybrid image that preserves the lung shape in one image but
inherits the lung texture of another. To demonstrate the effectiveness of the
disentangled texture representation, we evaluate the texture encoder $Enc^t$ in
LSAE on ChestX-ray14 (N=112,120), and our own multi-institutional COVID-19
outcome prediction dataset, COVOC (N=340 (Subset-1) + 53 (Subset-2)). On both
datasets, we reach or surpass the state-of-the-art by finetuning $Enc^t$ in
LSAE that is 77% smaller than a baseline Inception v3. Additionally, in
semi-and-self supervised settings with a similar model budget, $Enc^t$ in LSAE
is also competitive with the state-of-the-art MoCo. By "re-mixing" the texture
and shape factors, we generate meaningful hybrid images that can augment the
training set. This data augmentation method can further improve COVOC
prediction performance. The improvement is consistent even when we directly
evaluate the Subset-1 trained model on Subset-2 without any fine-tuning.

Heterogeneous catalysts possess complex surface and bulk structures,
relatively poor intrinsic contrast, and often a sparse distribution of the
catalytic nanoparticles (NPs), posing a significant challenge for image
segmentation, including the current state-of-the-art deep learning methods. To
tackle this problem, we apply a deep learning-based approach for the
multi-class semantic segmentation of a $\gamma$-Alumina/Pt catalytic material
in a class imbalance situation. Specifically, we used the weighted focal loss
as a loss function and attached it to the U-Net's fully convolutional network
architecture. We assessed the accuracy of our results using Dice similarity
coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on
the overlap between the ground-truth and predicted segmentations. Our adopted
U-Net model with the weighted focal loss function achieved an average DSC score
of 0.96 $\pm$ 0.003 in the $\gamma$-Alumina support material and 0.84 $\pm$
0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap
error of less than 2 nm at the 90th percentile of HD for $\gamma$-Alumina and
Pt NPs segmentations. The complex surface morphology of the $\gamma$-Alumina
and its relation to the Pt NPs were visualized in 3D by the deep
learning-assisted automatic segmentation of a large data set of high-angle
annular dark-field (HAADF) scanning transmission electron microscopy (STEM)
tomography reconstructions.

We implement a divide-and-concur iterative projection approach to
context-free grammar inference. Unlike most state-of-the-art models of natural
language processing, our method requires a relatively small number of discrete
parameters, making the inferred grammar directly interpretable -- one can read
off from a solution how to construct grammatically valid sentences. Another
advantage of our approach is the ability to infer meaningful grammatical rules
from just a few sentences, compared to the hundreds of gigabytes of training
data many other models employ. We demonstrate several ways of applying our
approach: classifying words and inferring a grammar from scratch, taking an
existing grammar and refining its categories and rules, and taking an existing
grammar and expanding its lexicon as it encounters new words in new data.

Modern single-cell flow and mass cytometry technologies measure the
expression of several proteins of the individual cells within a blood or tissue
sample. Each profiled biological sample is thus represented by a set of
hundreds of thousands of multidimensional cell feature vectors, which incurs a
high computational cost to predict each biological sample's associated
phenotype with machine learning models. Such a large set cardinality also
limits the interpretability of machine learning models due to the difficulty in
tracking how each individual cell influences the ultimate prediction. We
propose using Kernel Mean Embedding to encode the cellular landscape of each
profiled biological sample. Although our foremost goal is to make a more
transparent model, we find that our method achieves comparable or better
accuracies than the state-of-the-art gating-free methods through a simple
linear classifier. As a result, our model contains few parameters but still
performs similarly to deep learning models with millions of parameters. In
contrast with deep learning approaches, the linearity and sub-selection step of
our model makes it easy to interpret classification results. Analysis further
shows that our method admits rich biological interpretability for linking
cellular heterogeneity to clinical phenotype.

With the continuous rise of the COVID-19 cases worldwide, it is imperative to
ensure that all those vulnerable countries lacking vaccine resources can
receive sufficient support to contain the risks. COVAX is such an initiative
operated by the WHO to supply vaccines to the most needed countries. One
critical problem faced by the COVAX is how to distribute the limited amount of
vaccines to these countries in the most efficient and equitable manner. This
paper aims to address this challenge by first proposing a data-driven risk
assessment and prediction model and then developing a decision-making framework
to support the strategic vaccine distribution. The machine learning-based risk
prediction model characterizes how the risk is influenced by the underlying
essential factors, e.g., the vaccination level among the population in each
COVAX country. This predictive model is then leveraged to design the optimal
vaccine distribution strategy that simultaneously minimizes the resulting risks
while maximizing the vaccination coverage in these countries targeted by COVAX.
Finally, we corroborate the proposed framework using case studies with

Federated Learning (FL) is a setting where multiple parties with distributed
data collaborate in training a joint Machine Learning (ML) model while keeping
all data local at the parties. Federated clustering is an area of research
within FL that is concerned with grouping together data that is globally
similar while keeping all data local. We describe how this area of research can
be of interest in itself, or how it helps addressing issues like
non-independently-identically-distributed (i.i.d.) data in supervised FL
frameworks. The focus of this work, however, is an extension of the federated
fuzzy $c$-means algorithm to the FL setting (FFCM) as a contribution towards
federated clustering. We propose two methods to calculate global cluster
centers and evaluate their behaviour through challenging numerical experiments.
We observe that one of the methods is able to identify good global clusters
even in challenging scenarios, but also acknowledge that many challenges remain
open.

Real-time object pose estimation is necessary for many robot manipulation
algorithms. However, state-of-the-art methods for object pose estimation are
trained for a specific set of objects; these methods thus need to be retrained
to estimate the pose of each new object, often requiring tens of GPU-days of
training for optimal performance. In this paper, we propose the OSSID
framework, leveraging a slow zero-shot pose estimator to self-supervise the
training of a fast detection algorithm. This fast detector can then be used to
filter the input to the pose estimator, drastically improving its inference
speed. We show that this self-supervised training exceeds the performance of
existing zero-shot detection methods on two widely used object pose estimation
and detection datasets, without requiring any human annotations. Further, we
show that the resulting method for pose estimation has a significantly faster
inference speed, due to the ability to filter out large parts of the image.
Thus, our method for self-supervised online learning of a detector (trained
using pseudo-labels from a slow pose estimator) leads to accurate pose
estimation at real-time speeds, without requiring human annotations.
Supplementary materials and code can be found at
https://georgegu1997.github.io/OSSID/

Principal-Agent (PA) problems describe a broad class of economic
relationships characterized by misaligned incentives and asymmetric
information. The Principal's problem is to find optimal incentives given the
available information, e.g., a manager setting optimal wages for its employees.
Whereas the Principal is often assumed rational, comparatively little is known
about solutions when the Principal is boundedly rational, especially in the
sequential setting, with multiple Agents, and with multiple information
channels. Here, we develop RIRL, a deep reinforcement learning framework that
solves such complex PA problems with a rationally inattentive Principal. Such a
Principal incurs a cost for paying attention to information, which can model
forms of bounded rationality. We use RIRL to analyze rich economic phenomena in
manager-employee relationships. In the single-step setting, 1) RIRL yields
wages that are consistent with theoretical predictions; and 2) non-zero
attention costs lead to simpler but less profitable wage structures, and
increased Agent welfare. In a sequential setting with multiple Agents, RIRL
shows opposing consequences of the Principal's inattention to different
information channels: 1) inattention to Agents' outputs closes wage gaps based
on ability differences; and 2) inattention to Agents' efforts induces a social
dilemma dynamic in which Agents work harder, but essentially for free.
Moreover, RIRL reveals non-trivial relationships between the Principal's
inattention and Agent types, e.g., if Agents are prone to sub-optimal effort
choices, payment schedules are more sensitive to the Principal's attention
cost. As such, RIRL can reveal novel economic relationships and enables
progress towards understanding the effects of bounded rationality in dynamic
settings.

Deep Reinforcement Learning (DRL) solutions are becoming pervasive at the
edge of the network as they enable autonomous decision-making in a dynamic
environment. However, to be able to adapt to the ever-changing environment, the
DRL solution implemented on an embedded device has to continue to occasionally
take exploratory actions even after initial convergence. In other words, the
device has to occasionally take random actions and update the value function,
i.e., re-train the Artificial Neural Network (ANN), to ensure its performance
remains optimal. Unfortunately, embedded devices often lack processing power
and energy required to train the ANN. The energy aspect is particularly
challenging when the edge device is powered only by a means of Energy
Harvesting (EH). To overcome this problem, we propose a two-part algorithm in
which the DRL process is trained at the sink. Then the weights of the fully
trained underlying ANN are periodically transferred to the EH-powered embedded
device taking actions. Using an EH-powered sensor, real-world measurements
dataset, and optimizing for Age of Information (AoI) metric, we demonstrate
that such a DRL solution can operate without any degradation in the
performance, with only a few ANN updates per day.

We revisit the method of mixture technique, also known as the Laplace method,
to study the concentration phenomenon in generic exponential families.
Combining the properties of Bregman divergence associated with log-partition
function of the family with the method of mixtures for super-martingales, we
establish a generic bound controlling the Bregman divergence between the
parameter of the family and a finite sample estimate of the parameter. Our
bound is time-uniform and makes appear a quantity extending the classical
information gain to exponential families, which we call the Bregman information
gain. For the practitioner, we instantiate this novel bound to several
classical families, e.g., Gaussian, Bernoulli, Exponential, Weibull, Pareto,
Poisson and Chi-square yielding explicit forms of the confidence sets and the
Bregman information gain. We further numerically compare the resulting
confidence bounds to state-of-the-art alternatives for time-uniform
concentration and show that this novel method yields competitive results.
Finally, we highlight the benefit of our concentration bounds on some
illustrative applications.

Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy
Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma.
Systematic constraints on these properties must combine measurements from
different collision systems and methodically account for experimental and
theoretical uncertainties. Such studies require a vast number of costly
numerical simulations. While computationally inexpensive surrogate models
("emulators") can be used to efficiently approximate the predictions of heavy
ion simulations across a broad range of model parameters, training a reliable
emulator remains a computationally expensive task. We use transfer learning to
map the parameter dependencies of one model emulator onto another, leveraging
similarities between different simulations of heavy ion collisions. By limiting
the need for large numbers of simulations to only one of the emulators, this
technique reduces the numerical cost of comprehensive uncertainty
quantification when studying multiple collision systems and exploring different
models.

We study the global convergence of policy gradient for infinite-horizon,
continuous state and action space, and entropy-regularized Markov decision
processes (MDPs). We consider a softmax policy with (one-hidden layer) neural
network approximation in a mean-field regime. Additional entropic
regularization in the associated mean-field probability measure is added, and
the corresponding gradient flow is studied in the 2-Wasserstein metric. We show
that the objective function is increasing along the gradient flow. Further, we
prove that if the regularization in terms of the mean-field measure is
sufficient, the gradient flow converges exponentially fast to the unique
stationary solution, which is the unique maximizer of the regularized MDP
objective. Lastly, we study the sensitivity of the value function along the
gradient flow with respect to regularization parameters and the initial
condition. Our results rely on the careful analysis of the non-linear
Fokker-Planck-Kolmogorov equation and extend the pioneering work of Mei et al.
2020 and Agarwal et al. 2020, which quantify the global convergence rate of
policy gradient for entropy-regularized MDPs in the tabular setting.

Inferring dependencies between complex biological traits while accounting for
evolutionary relationships between specimens is of great scientific interest
yet remains infeasible when trait and specimen counts grow large. The
state-of-the-art approach uses a phylogenetic multivariate probit model to
accommodate binary and continuous traits via a latent variable framework, and
utilizes an efficient bouncy particle sampler (BPS) to tackle the computational
bottleneck -- integrating many latent variables from a high-dimensional
truncated normal distribution. This approach breaks down as the number of
specimens grows and fails to reliably characterize conditional dependencies
between traits. Here, we propose an inference pipeline for phylogenetic probit
models that greatly outperforms BPS. The novelty lies in 1) a combination of
the recent Zigzag Hamiltonian Monte Carlo (Zigzag-HMC) with linear-time
gradient evaluations and 2) a joint sampling scheme for highly correlated
latent variables and correlation matrix elements. In an application exploring
HIV-1 evolution from 535 viruses, the inference requires joint sampling from an
11,235-dimensional truncated normal and a 24-dimensional covariance matrix. Our
method yields a 5-fold speedup compared to BPS and makes it possible to learn
partial correlations between candidate viral mutations and virulence.
Computational speedup now enables us to tackle even larger problems: we study
the evolution of influenza H1N1 glycosylations on around 900 viruses. For
broader applicability, we extend the phylogenetic probit model to incorporate
categorical traits, and demonstrate its use to study Aquilegia flower and
pollinator co-evolution.

Submodular functions are at the core of many machine learning and data mining
tasks. The underlying submodular functions for many of these tasks are
decomposable, i.e., they are sum of several simple submodular functions. In
many data intensive applications, however, the number of underlying submodular
functions in the original function is so large that we need prohibitively large
amount of time to process it and/or it does not even fit in the main memory. To
overcome this issue, we introduce the notion of sparsification for decomposable
submodular functions whose objective is to obtain an accurate approximation of
the original function that is a (weighted) sum of only a few submodular
functions. Our main result is a polynomial-time randomized sparsification
algorithm such that the expected number of functions used in the output is
independent of the number of underlying submodular functions in the original
function. We also study the effectiveness of our algorithm under various
constraints such as matroid and cardinality constraints. We complement our
theoretical analysis with an empirical study of the performance of our

Fictional languages have become increasingly popular over the recent years
appearing in novels, movies, TV shows, comics, and video games. While some of
these fictional languages have a complete vocabulary, most do not. We propose a
deep learning solution to the problem. Using style transfer and machine
translation tools, we generate new words for a given target fictional language,
while maintaining the style of its creator, hence extending this language
vocabulary.

Currently, quantum reservoir computing is one of the most promising and
experimentally accessible techniques for hybrid, quantum-classical machine
learning. However, its applications are limited due to practical restrictions
on the size of quantum systems and the influence of noise. Here we propose a
novel approach to connect two quantum reservoirs in a network to overcome these
issues and enhance their computing performance. To transfer information between
quantum reservoirs, we perform optimized two-qubit non-selective measurements.
We suggest a general heuristic optimization strategy based on tensor network
language and matrix representation of two-qubit quantum channels specified for
quantum reservoir computing. In addition, we introduce a single qubit
purification channel and its optimization for further enhancement of quantum
reservoir computing. We also demonstrate that the optimized channels applied to
a seven-qubit network can efficiently transfer information between its parts
with the resulting performance comparable to a network up to twenty-five qubits
connected via a classical information link.

Safety exploration can be regarded as a constrained Markov decision problem
where the expected long-term cost is constrained. Previous off-policy
algorithms convert the constrained optimization problem into the corresponding
unconstrained dual problem by introducing the Lagrangian relaxation technique.
However, the cost function of the above algorithms provides inaccurate
estimations and causes the instability of the Lagrange multiplier learning. In
this paper, we present a novel off-policy reinforcement learning algorithm
called Conservative Distributional Maximum a Posteriori Policy Optimization
(CDMPO). At first, to accurately judge whether the current situation satisfies
the constraints, CDMPO adapts distributional reinforcement learning method to
estimate the Q-function and C-function. Then, CDMPO uses a conservative value
function loss to reduce the number of violations of constraints during the
exploration process. In addition, we utilize Weighted Average Proportional
Integral Derivative (WAPID) to update the Lagrange multiplier stably. Empirical
results show that the proposed method has fewer violations of constraints in
the early exploration process. The final test results also illustrate that our
method has better risk control.

Efficient anomaly detection and diagnosis in multivariate time-series data is
of great importance for modern industrial applications. However, building a
system that is able to quickly and accurately pinpoint anomalous observations
is a challenging problem. This is due to the lack of anomaly labels, high data
volatility and the demands of ultra-low inference times in modern applications.
Despite the recent developments of deep learning approaches for anomaly
detection, only a few of them can address all of these challenges. In this
paper, we propose TranAD, a deep transformer network based anomaly detection
and diagnosis model which uses attention-based sequence encoders to swiftly
perform inference with the knowledge of the broader temporal trends in the
data. TranAD uses focus score-based self-conditioning to enable robust
multi-modal feature extraction and adversarial training to gain stability.
Additionally, model-agnostic meta learning (MAML) allows us to train the model
using limited data. Extensive empirical studies on six publicly available
datasets demonstrate that TranAD can outperform state-of-the-art baseline
methods in detection and diagnosis performance with data and time-efficient
training. Specifically, TranAD increases F1 scores by up to 17%, reducing
training times by up to 99% compared to the baselines.

We present a new methodology that is able to yield a simultaneous
determination of the Parton Distribution Functions (PDFs) of the proton
alongside any set of parameters that determine the theory predictions; whether
within the Standard Model (SM) or beyond it. The SIMUnet methodology is based
on an extension of the NNPDF4.0 neural network architecture, which allows the
addition of an extra layer to simultaneously determine PDFs alongside an
arbitrary number of such parameters. We illustrate its capabilities by
simultaneously fitting PDFs with a subset of Wilson coefficients within the
Standard Model Effective Field Theory framework and show how the methodology
extends naturally to larger subsets of Wilson coefficients and to other SM
precision parameters, such as the strong coupling constant or the heavy quark
masses.

Can world knowledge learned by large language models (LLMs) be used to act in
interactive environments? In this paper, we investigate the possibility of
grounding high-level tasks, expressed in natural language (e.g. "make
breakfast"), to a chosen set of actionable steps (e.g. "open fridge"). While
prior work focused on learning from explicit step-by-step examples of how to
act, we surprisingly find that if pre-trained LMs are large enough and prompted
appropriately, they can effectively decompose high-level tasks into mid-level
plans without any further training. However, the plans produced naively by LLMs
often cannot map precisely to admissible actions. We propose a procedure that
conditions on existing demonstrations and semantically translates the plans to
admissible actions. Our evaluation in the recent VirtualHome environment shows
that the resulting method substantially improves executability over the LLM
baseline. The conducted human evaluation reveals a trade-off between
executability and correctness but shows a promising sign towards extracting
actionable knowledge from language models. Website at
https://huangwl18.github.io/language-planner

Arguably the most fundamental question in the theory of generative
adversarial networks (GANs) is to understand to what extent GANs can actually
learn the underlying distribution. Theoretical and empirical evidence suggests
local optimality of the empirical training objective is insufficient. Yet, it
does not rule out the possibility that achieving a true population minimax
optimal solution might imply distribution learning.
  In this paper, we show that standard cryptographic assumptions imply that
this stronger condition is still insufficient. Namely, we show that if local
pseudorandom generators (PRGs) exist, then for a large family of natural
continuous target distributions, there are ReLU network generators of constant
depth and polynomial size which take Gaussian random seeds so that (i) the
output is far in Wasserstein distance from the target distribution, but (ii) no
polynomially large Lipschitz discriminator ReLU network can detect this. This
implies that even achieving a population minimax optimal solution to the
Wasserstein GAN objective is likely insufficient for distribution learning in
the usual statistical sense. Our techniques reveal a deep connection between
GANs and PRGs, which we believe will lead to further insights into the
computational landscape of GANs.

Algorithms that aid human tasks, such as recommendation systems, are
ubiquitous. They appear in everything from social media to streaming videos to
online shopping. However, the feedback loop between people and algorithms is
poorly understood and can amplify cognitive and social biases (algorithmic
confounding), leading to unexpected outcomes. In this work, we explore
algorithmic confounding in collaborative filtering-based recommendation
algorithms through teacher-student learning simulations. Namely, a student
collaborative filtering-based model, trained on simulated choices, is used by
the recommendation algorithm to recommend items to agents. Agents might choose
some of these items, according to an underlying teacher model, with new choices
then fed back into the student model as new training data (approximating online
machine learning). These simulations demonstrate how algorithmic confounding
produces erroneous recommendations which in turn lead to instability, i.e.,
wide variations in an item's popularity between each simulation realization. We
use the simulations to demonstrate a novel approach to training collaborative
filtering models that can create more stable and accurate recommendations. Our
methodology is general enough that it can be extended to other socio-technical
systems in order to better quantify and improve the stability of algorithms.
These results highlight the need to account for emergent behaviors from
interactions between people and algorithms.

We propose a method that learns to camouflage 3D objects within scenes. Given
an object's shape and a distribution of viewpoints from which it will be seen,
we estimate a texture that will make it difficult to detect. Successfully
solving this task requires a model that can accurately reproduce textures from
the scene, while simultaneously dealing with the highly conflicting constraints
imposed by each viewpoint. We address these challenges with a model based on
texture fields and adversarial learning. Our model learns to camouflage a
variety of object shapes from randomly sampled locations and viewpoints within
the input scene, and is the first to address the problem of hiding complex
object shapes. Using a human visual search study, we find that our estimated
textures conceal objects significantly better than previous methods. Project
site: https://rrrrrguo.github.io/ganmouflage/

Open source machine learning (ML) libraries allow developers to integrate
advanced ML functionality into their own applications. However, popular ML
libraries, such as TensorFlow, are not available natively in all programming
languages and software package ecosystems. Hence, developers who wish to use an
ML library which is not available in their programming language or ecosystem of
choice, may need to resort to using a so-called binding library. Binding
libraries provide support across programming languages and package ecosystems
for a source library. For example, the Keras .NET binding provides support for
the Keras library in the NuGet (.NET) ecosystem even though the Keras library
was written in Python. In this paper, we conduct an in-depth study of 155
cross-ecosystem bindings and their development for 36 popular open source ML
libraries. Our study shows that for most popular ML libraries, only one package
ecosystem is officially supported (usually PyPI). Cross-ecosystem support,
which is available for 25% of the studied ML libraries, is usually provided
through community-maintained bindings, e.g., 73% of the bindings in the npm
ecosystem are community-maintained. Our study shows that the vast majority of
the studied bindings cover only a small portion of the source library releases,
and the delay for receiving support for a source library release is large.

When we can not assume a large amount of annotated data , active learning is
a good strategy. It consists in learning a model on a small amount of annotated
data (annotation budget) and in choosing the best set of points to annotate in
order to improve the previous model and gain in generalization. In deep
learning, active learning is usually implemented as an iterative process in
which successive deep models are updated via fine tuning, but it still poses
some issues. First, the initial batch of annotated images has to be
sufficiently large to train a deep model. Such an assumption is strong,
especially when the total annotation budget is reduced. We tackle this issue by
using an approach inspired by transfer learning. A pre-trained model is used as
a feature extractor and only shallow classifiers are learned during the active
iterations. The second issue is the effectiveness of probability or feature
estimates of early models for AL task. Samples are generally selected for
annotation using acquisition functions based only on the last learned model. We
introduce a novel acquisition function which exploits the iterative nature of
AL process to select samples in a more robust fashion. Samples for which there
is a maximum shift towards uncertainty between the last two learned models
predictions are favored. A diversification step is added to select samples from
different regions of the classification space and thus introduces a
representativeness component in our approach. Evaluation is done against
competitive methods with three balanced and imbalanced datasets and outperforms
them.

We leverage representation learning and the inductive bias in
neural-net-based Standard Model jet classification tasks, to detect non-QCD
signal jets. In establishing the framework for classification-based anomaly
detection in jet physics, we demonstrate that, with a \emph{well-calibrated}
and \emph{powerful enough feature extractor}, a well-trained
\emph{mass-decorrelated} supervised Standard Model neural jet classifier can
serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD
background. Imposing \emph{data-augmented} mass-invariance (and thus decoupling
the dominant factor) not only facilitates background estimation, but also
induces more substructure-aware representation learning. We are able to reach
excellent tagging efficiencies for all the test signals considered. In the best
case, we reach a background rejection rate of 51 and a significance improvement
factor of 3.6 at 50 \% signal acceptance, with the jet mass decorrelated. This
study indicates that supervised Standard Model jet classifiers have great
potential in general new physics searches.

Accurate long-term trajectory prediction in complex scenes, where multiple
agents (e.g., pedestrians or vehicles) interact with each other and the
environment while attempting to accomplish diverse and often unknown goals, is
a challenging stochastic forecasting problem. In this work, we propose MUSE, a
new probabilistic modeling framework based on a cascade of Conditional VAEs,
which tackles the long-term, uncertain trajectory prediction task using a
coarse-to-fine multi-factor forecasting architecture. In its Macro stage, the
model learns a joint pixel-space representation of two key factors, the
underlying environment and the agent movements, to predict the long and
short-term motion goals. Conditioned on them, the Micro stage learns a
fine-grained spatio-temporal representation for the prediction of individual
agent trajectories. The VAE backbones across the two stages make it possible to
naturally account for the joint uncertainty at both levels of granularity. As a
result, MUSE offers diverse and simultaneously more accurate predictions
compared to the current state-of-the-art. We demonstrate these assertions
through a comprehensive set of experiments on nuScenes and SDD benchmarks as
well as PFSD, a new synthetic dataset, which challenges the forecasting ability
of models on complex agent-environment interaction scenarios.

In this work, we propose a computationally efficient algorithm for the
problem of global optimization in univariate loss functions. For the
performance evaluation, we study the cumulative regret of the algorithm instead
of the simple regret between our best query and the optimal value of the
objective function. Although our approach has similar regret results with the
traditional lower-bounding algorithms such as the Piyavskii-Shubert method for
the Lipschitz continuous or Lipschitz smooth functions, it has a major
computational cost advantage. In Piyavskii-Shubert method, for certain types of
functions, the query points may be hard to determine (as they are solutions to
additional optimization problems). However, this issue is circumvented in our
binary sampling approach, where the sampling set is predetermined irrespective
of the function characteristics. For a search space of $[0,1]$, our approach
has at most $L\log (3T)$ and $2.25H$ regret for $L$-Lipschitz continuous and
$H$-Lipschitz smooth functions respectively. We also analytically extend our
results for a broader class of functions that covers more complex regularity

Highly excited eigenstates of quantum many-body systems are typically
featureless thermal states. Some systems, however, possess a small number of
special, low-entanglement eigenstates known as quantum scars. We introduce a
quantum-inspired machine learning platform based on a Quantum Variational
Autoencoder (QVAE) that detects families of scar states in spectra of many-body
systems. Unlike a classical autoencoder, QVAE performs a parametrized unitary
operation, allowing us to compress a single eigenstate into a smaller number of
qubits. We demonstrate that the autoencoder trained on a scar state is able to
detect the whole family of scar states sharing common features with the input
state. We identify families of quantum many-body scars in the PXP model beyond
the $\mathbb{Z}_2$ and $\mathbb{Z}_3$ families and find dynamically decoupled
subspaces in the Hilbert space of disordered, interacting spin ladder model.
The possibility of an automatic detection of subspaces of scar states opens new
pathways in studies of models with a weak breakdown of ergodicity and
fragmented Hilbert spaces.

In online marketing, the advertisers' goal is usually a tradeoff between
achieving high volumes and high profitability. The companies' business units
customarily address this tradeoff by maximizing the volumes while guaranteeing
a lower bound to the Return On Investment (ROI). This paper investigates
combinatorial bandit algorithms for the bid optimization of advertising
campaigns subject to uncertain budget and ROI constraints. We study the nature
of both the optimization and learning problems. In particular, when focusing on
the optimization problem without uncertainty, we show that it is inapproximable
within any factor unless P=NP, and we provide a pseudo-polynomial-time
algorithm that achieves an optimal solution. When considering uncertainty, we
prove that no online learning algorithm can violate the (ROI or budget)
constraints during the learning process a sublinear number of times while
guaranteeing a sublinear pseudo-regret. Thus, we provide an algorithm, namely
GCB, guaranteeing sublinear regret at the cost of a potentially linear number
of constraints violations. We also design its safe version, namely GCB_{safe},
guaranteeing w.h.p. a constant upper bound on the number of constraints
violations at the cost of a linear pseudo-regret. More interestingly, we
provide an algorithm, namely GCB_{safe}(\psi,\phi), guaranteeing both sublinear
pseudo-regret and safety w.h.p. at the cost of accepting tolerances \psi and
\phi in the satisfaction of the ROI and budget constraints, respectively. This
algorithm actually mitigates the risks due to the constraints violations
without precluding the convergence to the optimal solution. Finally, we
experimentally compare our algorithms in terms of
pseudo-regret/constraint-violation tradeoff in settings generated from
real-world data, showing the importance of adopting safety constraints in
practice and the effectiveness of our algorithms.

Graph neural networks (GNN) are very popular methods in machine learning and
have been applied very successfully to the prediction of the properties of
molecules and materials. First-order GNNs are well known to be incomplete,
i.e., there exist graphs that are distinct but appear identical when seen
through the lens of the GNN. More complicated schemes have thus been designed
to increase their resolving power. Applications to molecules (and more
generally, point clouds), however, add a geometric dimension to the problem.
The most straightforward and prevalent approach to construct graph
representation for molecules regards atoms as vertices in a graph and draws a
bond between each pair of atoms within a chosen cutoff. Bonds can be decorated
with the distance between atoms, and the resulting "distance graph NNs" (dGNN)
have empirically demonstrated excellent resolving power and are widely used in
chemical ML, with all known indistinguishable configurations being resolved in
the fully-connected limit, which is equivalent to infinite or sufficiently
large cutoff. Here we present a counterexample that proves that dGNNs are not
complete even for the restricted case of fully-connected graphs induced by 3D
atom clouds. We construct pairs of distinct point clouds whose associated
graphs are, for any cutoff radius, equivalent based on a first-order
Weisfeiler-Lehman test. This class of degenerate structures includes
chemically-plausible configurations, both for isolated structures and for
infinite structures that are periodic in 1, 2, and 3 dimensions. The existence
of indistinguishable configurations sets an ultimate limit to the expressive
power of some of the well-established GNN architectures for atomistic machine
learning. Models that explicitly use angular or directional information in the
description of atomic environments can resolve this class of degeneracies.

X-ray phase-contrast imaging has become indispensable for visualizing samples
with low absorption contrast. In this regard, speckle-based techniques have
shown significant advantages in spatial resolution, phase sensitivity, and
implementation flexibility compared with traditional methods. However, their
computational cost has hindered their wider adoption. By exploiting the power
of deep learning, we developed a new speckle-based phase-contrast imaging
neural network (SPINNet) that boosts the phase retrieval speed by at least two
orders of magnitude compared to existing methods. To achieve this performance,
we combined SPINNet with a novel coded-mask-based technique, an enhanced
version of the speckle-based method. Using this scheme, we demonstrate a
simultaneous reconstruction of absorption and phase images on the order of 100
ms, where a traditional correlation-based analysis would take several minutes
even with a cluster. In addition to significant improvement in speed, our
experimental results show that the imaging resolution and phase retrieval
quality of SPINNet outperform existing single-shot speckle-based methods.
Furthermore, we successfully demonstrate its application in 3D X-ray
phase-contrast tomography. Our result shows that SPINNet could enable many
applications requiring high-resolution and fast data acquisition and
processing, such as in-situ and in-operando 2D and 3D phase-contrast imaging
and real-time at-wavelength metrology and wavefront sensing.

Being able to provide counterfactual interventions - sequences of actions we
would have had to take for a desirable outcome to happen - is essential to
explain how to change an unfavourable decision by a black-box machine learning
model (e.g., being denied a loan request). Existing solutions have mainly
focused on generating feasible interventions without providing explanations on
their rationale. Moreover, they need to solve a separate optimization problem
for each user. In this paper, we take a different approach and learn a program
that outputs a sequence of explainable counterfactual actions given a user
description and a causal graph. We leverage program synthesis techniques,
reinforcement learning coupled with Monte Carlo Tree Search for efficient
exploration, and rule learning to extract explanations for each recommended
action. An experimental evaluation on synthetic and real-world datasets shows
how our approach generates effective interventions by making orders of
magnitude fewer queries to the black-box classifier with respect to existing
solutions, with the additional benefit of complementing them with interpretable

One of the most pressing challenges for the detection of face-manipulated
videos is generalising to forgery methods not seen during training while
remaining effective under common corruptions such as compression. In this
paper, we examine whether we can tackle this issue by harnessing videos of real
talking faces, which contain rich information on natural facial appearance and
behaviour and are readily available in large quantities online. Our method,
termed RealForensics, consists of two stages. First, we exploit the natural
correspondence between the visual and auditory modalities in real videos to
learn, in a self-supervised cross-modal manner, temporally dense video
representations that capture factors such as facial movements, expression, and
identity. Second, we use these learned representations as targets to be
predicted by our forgery detector along with the usual binary forgery
classification task; this encourages it to base its real/fake decision on said
factors. We show that our method achieves state-of-the-art performance on
cross-manipulation generalisation and robustness experiments, and examine the
factors that contribute to its performance. Our results suggest that leveraging
natural and unlabelled videos is a promising direction for the development of
more robust face forgery detectors.

A fundamental challenge in Bayesian inference is efficient representation of
a target distribution. Many non-parametric approaches do so by sampling a large
number of points using variants of Markov Chain Monte Carlo (MCMC). We propose
an MCMC variant that retains only those posterior samples which exceed a KSD
threshold, which we call KSD Thinning. We establish the convergence and
complexity tradeoffs for several settings of KSD Thinning as a function of the
KSD threshold parameter, sample size, and other problem parameters. Finally, we
provide experimental comparisons against other online nonparametric Bayesian
methods that generate low-complexity posterior representations, and observe
superior consistency/complexity tradeoffs. Code is available at
github.com/colehawkins/KSD-Thinning.

Recently, prompt learning has become a new paradigm to utilize pre-trained
language models (PLMs) and achieves promising results in downstream tasks with
a negligible increase of parameters. The current usage of discrete and
continuous prompts assumes that the prompt is fixed for a specific task and all
samples in the task share the same prompt. However, a task may contain quite
diverse samples in which some are easy and others are difficult, and diverse
prompts are desirable. In this paper, we propose an instance-aware prompt
learning method that learns a different prompt for each instance. Specifically,
we suppose that each learnable prompt token has a different contribution to
different instances, and we learn the contribution by calculating the relevance
score between an instance and each prompt token. The contribution weighted
prompt would be instance aware. We apply our method to both unidirectional and
bidirectional PLMs on both language understanding and generation tasks.
Extensive experiments demonstrate that our method obtains considerable
improvements compared to strong baselines. Especially, our method achieves the
state-of-the-art on the SuperGLUE few-shot learning benchmark.

In this paper we present a novel approach for lane detection and segmentation
using generative models. Traditionally discriminative models have been employed
to classify pixels semantically on a road. We model the probability
distribution of lanes and road symbols by training a generative adversarial
network. Based on the learned probability distribution, context-aware lanes and
road signs are generated for a given image which are further quantized for
nearest class label. Proposed method has been tested on BDD100K and Baidu
ApolloScape datasets and performs better than state of the art and exhibits
robustness to adverse conditions by generating lanes in faded out and occluded

Rapid growth of the biomedical literature has led to many advances in the
biomedical text mining field. Among the vast amount of information, biomedical
article abstracts are the easily accessible sources. However, the number of the
structured abstracts, describing the rhetorical sections with one of
Background, Objective, Method, Result and Conclusion categories is still not
considerable. Exploration of valuable information in the biomedical abstracts
can be expedited with the improvements in the sequential sentence
classification task. Deep learning based models has great performance/potential
in achieving significant results in this task. However, they can often be
overly complex and overfit to specific data. In this project, we study a
state-of-the-art deep learning model, which we called SSN-4 model here. We
investigate different components of the SSN-4 model to study the trade-off
between the performance and complexity. We explore how well this model
generalizes to a new data set beyond Randomized Controlled Trials (RCT)
dataset. We address the question that whether word embeddings can be adjusted
to the task to improve the performance. Furthermore, we develop a second model
that addresses the confusion pairs in the first model. Results show that SSN-4
model does not appear to generalize well beyond RCT dataset.

Lesions or organ boundaries visible through medical imaging data are often
ambiguous, thus resulting in significant variations in multi-reader
delineations, i.e., the source of aleatoric uncertainty. In particular,
quantifying the inter-observer variability of manual annotations with Magnetic
Resonance (MR) Imaging data plays a crucial role in establishing a reference
standard for various diagnosis and treatment tasks. Most segmentation methods,
however, simply model a mapping from an image to its single segmentation map
and do not take the disagreement of annotators into consideration. In order to
account for inter-observer variability, without sacrificing accuracy, we
propose a novel variational inference framework to model the distribution of
plausible segmentation maps, given a specific MR image, which explicitly
represents the multi-reader variability. Specifically, we resort to a latent
vector to encode the multi-reader variability and counteract the inherent
information loss in the imaging data. Then, we apply a variational autoencoder
network and optimize its evidence lower bound (ELBO) to efficiently approximate
the distribution of the segmentation map, given an MR image. Experimental
results, carried out with the QUBIQ brain growth MRI segmentation datasets with
seven annotators, demonstrate the effectiveness of our approach.

Prediction over event sequences is critical for many real-world applications
in Information Retrieval and Natural Language Processing. Future Event
Generation (FEG) is a challenging task in event sequence prediction because it
requires not only fluent text generation but also commonsense reasoning to
maintain the logical coherence of the entire event story. In this paper, we
propose a novel explainable FEG framework, Coep. It highlights and integrates
two types of event knowledge, sequential knowledge of direct event-event
relations and inferential knowledge that reflects the intermediate character
psychology between events, such as intents, causes, reactions, which
intrinsically pushes the story forward. To alleviate the knowledge forgetting
issue, we design two modules, Im and Gm, for each type of knowledge, which are
combined via prompt tuning. First, Im focuses on understanding inferential
knowledge to generate commonsense explanations and provide a soft prompt vector
for Gm. We also design a contrastive discriminator for better generalization
ability. Second, Gm generates future events by modeling direct sequential
knowledge with the guidance of Im. Automatic and human evaluation demonstrate
that our approach can generate more coherent, specific, and logical future
events.

Traditionally, the performance of multi-agent deep reinforcement learning
algorithms are demonstrated and validated in gaming environments where we often
have a fixed number of agents. In many industrial applications, the number of
available agents can change at any given day and even when the number of agents
is known ahead of time, it is common for an agent to break during the operation
and become unavailable for a period of time. In this paper, we propose a new
deep reinforcement learning algorithm for multi-agent collaborative tasks with
a variable number of agents. We demonstrate the application of our algorithm
using a fleet management simulator developed by Hitachi to generate realistic
scenarios in a production site.

Graph neural networks are designed to learn functions on graphs. Typically,
the relevant target functions are invariant with respect to actions by
permutations. Therefore the design of some graph neural network architectures
has been inspired by graph-isomorphism algorithms. The classical
Weisfeiler-Lehman algorithm (WL) -- a graph-isomorphism test based on color
refinement -- became relevant to the study of graph neural networks. The WL
test can be generalized to a hierarchy of higher-order tests, known as $k$-WL.
This hierarchy has been used to characterize the expressive power of graph
neural networks, and to inspire the design of graph neural network
architectures. A few variants of the WL hierarchy appear in the literature. The
goal of this short note is pedagogical and practical: We explain the
differences between the WL and folklore-WL formulations, with pointers to
existing discussions in the literature. We illuminate the differences between
the formulations by visualizing an example.

Reward learning enables robots to learn adaptable behaviors from human input.
Traditional methods model the reward as a linear function of hand-crafted
features, but that requires specifying all the relevant features a priori,
which is impossible for real-world tasks. To get around this issue, recent deep
Inverse Reinforcement Learning (IRL) methods learn rewards directly from the
raw state but this is challenging because the robot has to implicitly learn the
features that are important and how to combine them, simultaneously. Instead,
we propose a divide and conquer approach: focus human input specifically on
learning the features separately, and only then learn how to combine them into
a reward. We introduce a novel type of human input for teaching features and an
algorithm that utilizes it to learn complex features from the raw state space.
The robot can then learn how to combine them into a reward using
demonstrations, corrections, or other reward learning frameworks. We
demonstrate our method in settings where all features have to be learned from
scratch, as well as where some of the features are known. By first focusing
human input specifically on the feature(s), our method decreases sample
complexity and improves generalization of the learned reward over a deepIRL
baseline. We show this in experiments with a physical 7DOF robot manipulator,
as well as in a user study conducted in a simulated environment.

We provide new insights regarding the finding that Medicaid increased
emergency department (ED) use from the Oregon experiment. We find meaningful
heterogeneous impacts of Medicaid on ED use using causal machine learning
methods. The treatment effect distribution is widely dispersed, and the average
effect is not representative of most individualized treatment effects. A small
group - about 14% of participants - in the right tail of the distribution
drives the overall effect. We identify priority groups with economically
significant increases in ED usage based on demographics and prior utilization.
Intensive margin effects are an important driver of increases in ED
utilization.

Autonomous exploration of subterranean environments constitutes a major
frontier for robotic systems as underground settings present key challenges
that can render robot autonomy hard to achieve. This has motivated the DARPA
Subterranean Challenge, where teams of robots search for objects of interest in
various underground environments. In response, the CERBERUS system-of-systems
is presented as a unified strategy towards subterranean exploration using
legged and flying robots. As primary robots, ANYmal quadruped systems are
deployed considering their endurance and potential to traverse challenging
terrain. For aerial robots, both conventional and collision-tolerant
multirotors are utilized to explore spaces too narrow or otherwise unreachable
by ground systems. Anticipating degraded sensing conditions, a complementary
multi-modal sensor fusion approach utilizing camera, LiDAR, and inertial data
for resilient robot pose estimation is proposed. Individual robot pose
estimates are refined by a centralized multi-robot map optimization approach to
improve the reported location accuracy of detected objects of interest in the
DARPA-defined coordinate frame. Furthermore, a unified exploration path
planning policy is presented to facilitate the autonomous operation of both
legged and aerial robots in complex underground networks. Finally, to enable
communication between the robots and the base station, CERBERUS utilizes a
ground rover with a high-gain antenna and an optical fiber connection to the
base station, alongside breadcrumbing of wireless nodes by our legged robots.
We report results from the CERBERUS system-of-systems deployment at the DARPA
Subterranean Challenge Tunnel and Urban Circuits, along with the current
limitations and the lessons learned for the benefit of the community.

This paper explores previously unknown backdoor risks in HyperNet-based
personalized federated learning (HyperNetFL) through poisoning attacks. Based
upon that, we propose a novel model transferring attack (called HNTROJ), i.e.,
the first of its kind, to transfer a local backdoor infected model to all
legitimate and personalized local models, which are generated by the HyperNetFL
model, through consistent and effective malicious local gradients computed
across all compromised clients in the whole training process. As a result,
HNTROJ reduces the number of compromised clients needed to successfully launch
the attack without any observable signs of sudden shifts or degradation
regarding model utility on legitimate data samples making our attack stealthy.
To defend against HNTROJ, we adapted several backdoor-resistant FL training
algorithms into HyperNetFL. An extensive experiment that is carried out using
several benchmark datasets shows that HNTROJ significantly outperforms data
poisoning and model replacement attacks and bypasses robust training

Image reconstruction based on indirect, noisy, or incomplete data remains an
important yet challenging task. While methods such as compressive sensing have
demonstrated high-resolution image recovery in various settings, there remain
issues of robustness due to parameter tuning. Moreover, since the recovery is
limited to a point estimate, it is impossible to quantify the uncertainty,
which is often desirable. Due to these inherent limitations, a sparse Bayesian
learning approach is sometimes adopted to recover a posterior distribution of
the unknown. Sparse Bayesian learning assumes that some linear transformation
of the unknown is sparse. However, most of the methods developed are tailored
to specific problems, with particular forward models and priors. Here, we
present a generalized approach to sparse Bayesian learning. It has the
advantage that it can be used for various types of data acquisitions and prior
information. Some preliminary results on image reconstruction/recovery indicate
its potential use for denoising, deblurring, and magnetic resonance imaging.

Despite their popularity, randomized controlled trials (RCTs) are not always
available for the purposes of advertising measurement. Non-experimental data is
thus required. However, Facebook and other ad platforms use complex and
evolving processes to select ads for users. Therefore, successful
non-experimental approaches need to "undo" this selection. We analyze 663
large-scale experiments at Facebook to investigate whether this is possible
with the data typically logged at large ad platforms. With access to over 5,000
user-level features, these data are richer than what most advertisers or their
measurement partners can access. We investigate how accurately two
non-experimental methods -- double/debiased machine learning (DML) and
stratified propensity score matching (SPSM) -- can recover the experimental
effects. Although DML performs better than SPSM, neither method performs well,
even using flexible deep learning models to implement the propensity and
outcome models. The median RCT lifts are 29%, 18%, and 5% for the upper,
middle, and lower funnel outcomes, respectively. Using DML (SPSM), the median
lift by funnel is 83% (173%), 58% (176%), and 24% (64%), respectively,
indicating significant relative measurement errors. We further characterize the
circumstances under which each method performs comparatively better. Overall,
despite having access to large-scale experiments and rich user-level data, we
are unable to reliably estimate an ad campaign's causal effect.

Motivated by the wide adoption of reinforcement learning (RL) in real-world
personalized services, where users' sensitive and private information needs to
be protected, we study regret minimization in finite-horizon Markov decision
processes (MDPs) under the constraints of differential privacy (DP). Compared
to existing private RL algorithms that work only on tabular finite-state,
finite-actions MDPs, we take the first step towards privacy-preserving learning
in MDPs with large state and action spaces. Specifically, we consider MDPs with
linear function approximation (in particular linear mixture MDPs) under the
notion of joint differential privacy (JDP), where the RL agent is responsible
for protecting users' sensitive data. We design two private RL algorithms that
are based on value iteration and policy optimization, respectively, and show
that they enjoy sub-linear regret performance while guaranteeing privacy
protection. Moreover, the regret bounds are independent of the number of
states, and scale at most logarithmically with the number of actions, making
the algorithms suitable for privacy protection in nowadays large-scale
personalized services. Our results are achieved via a general procedure for
learning in linear mixture MDPs under changing regularizers, which not only
generalizes previous results for non-private learning, but also serves as a
building block for general private reinforcement learning.

Dynamic scheduling is an important problem in applications from queuing to
wireless networks. It addresses how to choose an item among multiple scheduling
items in each timestep to achieve a long-term goal. Conventional approaches for
dynamic scheduling find the optimal policy for a given specific system so that
the policy from these approaches is usable only for the corresponding system
characteristics. Hence, it is hard to use such approaches for a practical
system in which system characteristics dynamically change. This paper proposes
a novel policy structure for MDP-based dynamic scheduling, a descriptive
policy, which has a system-agnostic capability to adapt to unseen system
characteristics for an identical task (dynamic scheduling). To this end, the
descriptive policy learns a system-agnostic scheduling principle--in a
nutshell, "which condition of items should have a higher priority in
scheduling". The scheduling principle can be applied to any system so that the
descriptive policy learned in one system can be used for another system.
Experiments with simple explanatory and realistic application scenarios
demonstrate that it enables system-agnostic meta-learning with very little
performance degradation compared with the system-specific conventional
policies.

Current AI systems lack several important human capabilities, such as
adaptability, generalizability, self-control, consistency, common sense, and
causal reasoning. We believe that existing cognitive theories of human decision
making, such as the thinking fast and slow theory, can provide insights on how
to advance AI systems towards some of these capabilities. In this paper, we
propose a general architecture that is based on fast/slow solvers and a
metacognitive component. We then present experimental results on the behavior
of an instance of this architecture, for AI systems that make decisions about
navigating in a constrained environment. We show how combining the fast and
slow decision modalities allows the system to evolve over time and gradually
pass from slow to fast thinking with enough experience, and that this greatly
helps in decision quality, resource consumption, and efficiency.

The analysis of causation is a challenging task that can be approached in
various ways. With the increasing use of machine learning based models in
computational socioeconomics, explaining these models while taking causal
connections into account is a necessity. In this work, we advocate the use of
an explanatory framework from cooperative game theory augmented with $do$
calculus, namely causal Shapley values. Using causal Shapley values, we analyze
socioeconomic disparities that have a causal link to the spread of COVID-19 in
the USA. We study several phases of the disease spread to show how the causal
connections change over time. We perform a causal analysis using random effects
models and discuss the correspondence between the two methods to verify our
results. We show the distinct advantages a non-linear machine learning models
have over linear models when performing a multivariate analysis, especially
since the machine learning models can map out non-linear correlations in the
data. In addition, the causal Shapley values allow for including the causal
structure in the variable importance computed for the machine learning model.

Weakly supervised semantic segmentation (WSSS) has gained significant
popularity since it relies only on weak labels such as image level annotations
rather than pixel level annotations required by supervised semantic
segmentation (SSS) methods. Despite drastically reduced annotation costs,
typical feature representations learned from WSSS are only representative of
some salient parts of objects and less reliable compared to SSS due to the weak
guidance during training. In this paper, we propose a novel Multi-Strategy
Contrastive Learning (MuSCLe) framework to obtain enhanced feature
representations and improve WSSS performance by exploiting similarity and
dissimilarity of contrastive sample pairs at image, region, pixel and object
boundary levels. Extensive experiments demonstrate the effectiveness of our
method and show that MuSCLe outperforms the current state-of-the-art on the
widely used PASCAL VOC 2012 dataset.

Learning informative representations from image-based observations is of
fundamental concern in deep Reinforcement Learning (RL). However,
data-inefficiency remains a significant barrier to this objective. To overcome
this obstacle, we propose to accelerate state representation learning by
enforcing view-consistency on the dynamics. Firstly, we introduce a formalism
of Multi-view Markov Decision Process (MMDP) that incorporates multiple views
of the state. Following the structure of MMDP, our method, View-Consistent
Dynamics (VCD), learns state representations by training a view-consistent
dynamics model in the latent space, where views are generated by applying data
augmentation to states. Empirical evaluation on DeepMind Control Suite and
Atari-100k demonstrates VCD to be the SoTA data-efficient algorithm on visual

Cervical cancer is the fourth most common cancer in women worldwide. The
availability of a robust automated cervical image classification system can
augment the clinical care provider's limitation in traditional visual
inspection with acetic acid (VIA). However, there are a wide variety of
cervical inspection objectives which impact the labeling criteria for
criteria-specific prediction model development. Moreover, due to the lack of
confirmatory test results and inter-rater labeling variation, many images are
left unlabeled.
  Motivated by these challenges, we propose a self-supervised learning (SSL)
based approach to produce a pre-trained cervix model from unlabeled cervical
images. The developed model is further fine-tuned to produce criteria-specific
classification models with the available labeled images. We demonstrate the
effectiveness of the proposed approach using two cervical image datasets. Both
datasets are partially labeled and labeling criteria are different. The
experimental results show that the SSL-based initialization improves
classification performance (Accuracy: 2.5% min) and the inclusion of images
from both datasets during SSL further improves the performance (Accuracy: 1.5%
min). Further, considering data-sharing restrictions, we experimented with the
effectiveness of Federated SSL and find that it can improve performance over
the SSL model developed with just its images. This justifies the importance of
SSL-based cervix model development. We believe that the present research shows
a novel direction in developing criteria-specific custom deep models for
cervical image classification by combining images from different sources
unlabeled and/or labeled with varying criteria, and addressing image access

There has been a significant progress in detecting out-of-distribution (OOD)
inputs in neural networks recently, primarily due to the use of large models
pretrained on large datasets, and an emerging use of multi-modality. We show a
severe adversarial vulnerability of even the strongest current OOD detection
techniques. With a small, targeted perturbation to the input pixels, we can
change the image assignment from an in-distribution to an out-distribution, and
vice versa, easily. In particular, we demonstrate severe adversarial
vulnerability on the challenging near OOD CIFAR-100 vs CIFAR-10 task, as well
as on the far OOD CIFAR-100 vs SVHN. We study the adversarial robustness of
several post-processing techniques, including the simple baseline of Maximum of
Softmax Probabilities (MSP), the Mahalanobis distance, and the newly proposed
\textit{Relative} Mahalanobis distance. By comparing the loss of OOD detection
performance at various perturbation strengths, we demonstrate the beneficial
effect of using ensembles of OOD detectors, and the use of the
\textit{Relative} Mahalanobis distance over other post-processing methods. In
addition, we show that even strong zero-shot OOD detection using CLIP and
multi-modality suffers from a severe lack of adversarial robustness as well.
https://github.com/stanislavfort/adversaries_to_OOD_detection

This paper introduces a method for the detection of knock occurrences in an
internal combustion engine (ICE) using a 1D convolutional neural network
trained on in-cylinder pressure data. The model architecture was based on
considerations regarding the expected frequency characteristics of knocking
combustion. To aid the feature extraction, all cycles were reduced to 60{\deg}
CA long windows, with no further processing applied to the pressure traces. The
neural networks were trained exclusively on in-cylinder pressure traces from
multiple conditions and labels provided by human experts. The best-performing
model architecture achieves an accuracy of above 92% on all test sets in a
tenfold cross-validation when distinguishing between knocking and non-knocking
cycles. In a multi-class problem where each cycle was labeled by the number of
experts who rated it as knocking, 78% of cycles were labeled perfectly, while
90% of cycles were classified at most one class from ground truth. They thus
considerably outperform the broadly applied MAPO (Maximum Amplitude of Pressure
Oscillation) detection method, as well as other references reconstructed from
previous works. Our analysis indicates that the neural network learned
physically meaningful features connected to engine-characteristic resonance
frequencies, thus verifying the intended theory-guided data science approach.
Deeper performance investigation further shows remarkable generalization
ability to unseen operating points. In addition, the model proved to classify
knocking cycles in unseen engines with increased accuracy of 89% after adapting
to their features via training on a small number of exclusively non-knocking
cycles. The algorithm takes below 1 ms (on CPU) to classify individual cycles,
effectively making it suitable for real-time engine control.

We develop a category-theoretic criterion for determining the equivalence of
causal models having different but homomorphic directed acyclic graphs over
discrete variables. Following Jacobs et al. (2019), we define a causal model as
a probabilistic interpretation of a causal string diagram, i.e., a functor from
the ``syntactic'' category $\textsf{Syn}_G$ of graph $G$ to the category
$\textsf{Stoch}$ of finite sets and stochastic matrices. The equivalence of
causal models is then defined in terms of a natural transformation or
isomorphism between two such functors, which we call a $\Phi$-abstraction and
$\Phi$-equivalence, respectively. It is shown that when one model is a
$\Phi$-abstraction of another, the intervention calculus of the former can be
consistently translated into that of the latter. We also identify the condition
under which a model accommodates a $\Phi$-abstraction, when transformations are
deterministic.

The authors investigate how teaching art and astronomy together has the
potential to inspire new art forms, enhance scientific public outreach, and
promote art and science education. The authors teach an astro-animation class
at the Maryland Institute College of Art in partnership with NASA scientists.
The animations explore science in creative ways. Astrophysicists, educators,
students, and the general public were surveyed to evaluate the experiences, and
benefits from this project. The responses were very positive - the program is
an effective way to stimulate art students to learn science, share an artist's
viewpoint beyond the classroom, and engage with the public.

Deep neural networks are typically trained in a single shot for a specific
task and data distribution, but in real world settings both the task and the
domain of application can change. The problem becomes even more challenging in
dense predictive tasks, such as semantic segmentation, and furthermore most
approaches tackle the two problems separately. In this paper we introduce the
novel task of coarse-to-fine learning of semantic segmentation architectures in
presence of domain shift. We consider subsequent learning stages progressively
refining the task at the semantic level; i.e., the finer set of semantic labels
at each learning step is hierarchically derived from the coarser set of the
previous step. We propose a new approach (CCDA) to tackle this scenario. First,
we employ the maximum squares loss to align source and target domains and, at
the same time, to balance the gradients between well-classified and harder
samples. Second, we introduce a novel coarse-to-fine knowledge distillation
constraint to transfer network capabilities acquired on a coarser set of labels
to a set of finer labels. Finally, we design a coarse-to-fine weight
initialization rule to spread the importance from each coarse class to the
respective finer classes. To evaluate our approach, we design two benchmarks
where source knowledge is extracted from the GTA5 dataset and it is transferred
to either the Cityscapes or the IDD datasets, and we show how it outperforms
the main competitors.

Capturing structural similarity has been a hot topic in the field of network
embedding recently due to its great help in understanding the node functions
and behaviors. However, existing works have paid very much attention to
learning structures on homogeneous networks while the related study on
heterogeneous networks is still a void. In this paper, we try to take the first
step for representation learning on heterostructures, which is very challenging
due to their highly diverse combinations of node types and underlying
structures. To effectively distinguish diverse heterostructures, we firstly
propose a theoretically guaranteed technique called heterogeneous anonymous
walk (HAW) and its variant coarse HAW (CHAW). Then, we devise the heterogeneous
anonymous walk embedding (HAWE) and its variant coarse HAWE in a data-driven
manner to circumvent using an extremely large number of possible walks and
train embeddings by predicting occurring walks in the neighborhood of each
node. Finally, we design and apply extensive and illustrative experiments on
synthetic and real-world networks to build a benchmark on heterostructure
learning and evaluate the effectiveness of our methods. The results demonstrate
our methods achieve outstanding performance compared with both homogeneous and
heterogeneous classic methods, and can be applied on large-scale networks.

Control Systems, particularly closed-loop control systems (CLCS), are
frequently used in production machines, vehicles, and robots nowadays. CLCS are
needed to actively align actual values of a process to a given reference or set
values in real-time with a very high precession. Yet, artificial intelligence
(AI) is not used to model, design, optimize, and tune CLCS. This paper will
highlight potential AI-empowered and -based control system designs and
designing procedures, gathering new opportunities and research direction in the
field of control system engineering. Therefore, this paper illustrates which
building blocks within the standard block diagram of CLCS can be replaced by
AI, i.e., artificial neuronal networks (ANN). Having processes with real-time
contains and functional safety in mind, it is discussed if AI-based controller
blocks can cope with these demands. By concluding the paper, the pros and cons
of AI-empowered as well as -based CLCS designs are discussed, and possible
research directions for introducing AI in the domain of control system
engineering are given.

The maritime industry is experiencing a technological revolution that affects
shipbuilding, operation of both seagoing and inland vessels, cargo management,
and working practices in harbors. This ongoing transformation is driven by the
ambition to make the ecosystem more sustainable and cost-efficient.
Digitalization and automation help achieve these goals by transforming shipping
and cruising into a much more cost- and energy-efficient, and decarbonized
industry segment. The key enablers in these processes are always-available
connectivity and content delivery services, which can not only aid shipping
companies in improving their operational efficiency and reducing carbon
emissions but also contribute to enhanced crew welfare and passenger
experience. Due to recent advancements in integrating high-capacity and
ultra-reliable terrestrial and non-terrestrial networking technologies,
ubiquitous maritime connectivity is becoming a reality. To cope with the
increased complexity of managing these integrated systems, this article
advocates the use of artificial intelligence and machine learning-based
approaches to meet the service requirements and energy efficiency targets in
various maritime communications scenarios.

The popularisation of acquisition devices capable of capturing volumetric
information such as LiDAR scans and depth cameras has lead to an increased
interest in point clouds as an imaging modality. Due to the high amount of data
needed for their representation, efficient compression solutions are needed to
enable practical applications. Among the many techniques that have been
proposed in the last years, learning-based methods are receiving large
attention due to their high performance and potential for improvement. Such
algorithms depend on large and diverse training sets to achieve good
compression performance. ShapeNet is a large-scale dataset composed of CAD
models with texture and constitute and effective option for training such
compression methods. This dataset is entirely composed of meshes, which must go
through a sampling process in order to obtain point clouds with geometry and
texture information. Although many existing software libraries are able to
sample geometry from meshes through simple functions, obtaining an output point
cloud with geometry and color of the external faces of the mesh models is not a
straightforward process for the ShapeNet dataset. The main difficulty
associated with this dataset is that its models are often defined with
duplicated faces sharing the same vertices, but with different color values.
This document describes a script for sampling the meshes from ShapeNet that
circumvent this issue by excluding the internal faces of the mesh models prior
to the sampling. The script can be accessed from the following link:
https://github.com/mmspg/mesh-sampling.

Predicting the future motion of surrounding road users is a crucial and
challenging task for autonomous driving (AD) and various advanced
driver-assistance systems (ADAS). Planning a safe future trajectory heavily
depends on understanding the traffic scene and anticipating its dynamics. The
challenges do not only lie in understanding the complex driving scenarios but
also the numerous possible interactions among road users and environments,
which are practically not feasible for explicit modeling. In this work, we
tackle the above challenges by jointly learning and predicting the motion of
all road users in a scene, using a novel convolutional neural network (CNN) and
recurrent neural network (RNN) based architecture. Moreover, by exploiting
grid-based input and output data structures, the computational cost is
independent of the number of road users and multi-modal predictions become
inherent properties of our proposed method. Evaluation on the nuScenes dataset
shows that our approach reaches state-of-the-art results in the prediction

The ability of snapshot compressive imaging (SCI) systems to efficiently
capture high-dimensional (HD) data has led to an inverse problem, which
consists of recovering the HD signal from the compressed and noisy measurement.
While reconstruction algorithms grow fast to solve it with the recent advances
of deep learning, the fundamental issue of accurate and stable recovery
remains. To this end, we propose deep equilibrium models (DEQ) for video SCI,
fusing data-driven regularization and stable convergence in a theoretically
sound manner. Each equilibrium model implicitly learns a nonexpansive operator
and analytically computes the fixed point, thus enabling unlimited iterative
steps and infinite network depth with only a constant memory requirement in
training and testing. Specifically, we demonstrate how DEQ can be applied to
two existing models for video SCI reconstruction: recurrent neural networks
(RNN) and Plug-and-Play (PnP) algorithms. On a variety of datasets and real
data, both quantitative and qualitative evaluations of our results demonstrate
the effectiveness and stability of our proposed method. The code and models
will be released to the public.

We propose a multitask pretraining approach ZeroPrompt for zero-shot
generalization, focusing on task scaling and zero-shot prompting. While
previous models are trained on only a few dozen tasks, we scale to 1,000 tasks
for the first time using real-world data. This leads to a crucial discovery
that task scaling can be an efficient alternative to model scaling; i.e., the
model size has little impact on performance with an extremely large number of
tasks. Our results show that task scaling can substantially improve training
efficiency by 30 times in FLOPs. Moreover, we present a prompting method that
incorporates a genetic algorithm to automatically search for the best prompt
for unseen tasks, along with a few other improvements. Empirically, ZeroPrompt
substantially improves both the efficiency and the performance of zero-shot
learning across a variety of academic and production datasets.

Strontium titanate (SrTiO3) is regarded as an essential material for oxide
electronics. One of its many remarkable features is subtle structural phase
transition, driven by antiferrodistortive lattice mode, from a high-temperature
cubic phase to a low-temperature tetragonal phase. Classical molecular dynamics
(MD) simulation is an efficient technique to reveal atomistic features of phase
transition, but its application is often limited by the accuracy of empirical
interatomic potentials. Here, we develop an accurate deep potential (DP) model
of SrTiO3 based on a machine learning method using data from first-principles
density functional theory (DFT) calculations. The DP model has DFT-level
accuracy, capable of performing efficient MD simulations and accurate property
predictions. Using the DP model, we investigate the temperature-driven
cubic-to-tetragonal phase transition and construct the in-plane biaxial
strain-temperature phase diagram of SrTiO3. The simulations demonstrate that
strain-induced ferroelectric phase is characterized by two order parameters,
ferroelectric distortion and antiferrodistortion, and the ferroelectric phase
transition has both displacive and order-disorder characters. This works lays
the foundation for the development of accurate DP models of other complex
perovskite materials.

Histopathological image analysis is the gold standard to diagnose cancer.
Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer
cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of
carcinoma, diagnosed by microscopic study of biopsy slides. However, manual
microscopic evaluation is a subjective and time-consuming process. Many
researchers have reported methods to automate carcinoma detection and
classification. The increasing use of artificial intelligence (AI) in the
automation of carcinoma diagnosis also reveals a significant rise in the use of
deep network models. In this systematic literature review, we present a
comprehensive review of the state-of-the-art approaches reported in carcinoma
diagnosis using histopathological images. Studies are selected from well-known
databases with strict inclusion/exclusion criteria. We have categorized the
articles and recapitulated their methods based on specific organs of carcinoma
origin. Further, we have summarized pertinent literature on AI methods,
highlighted critical challenges and limitations, and provided insights on
future research direction in automated carcinoma diagnosis. Out of 101 articles
selected, most of the studies experimented on private datasets with varied
image sizes, obtaining accuracy between 63% and 100%. Overall, this review
highlights the need for a generalized AI-based carcinoma diagnostic system.
Additionally, it is desirable to have accountable approaches to extract
microscopic features from images of multiple magnifications that should mimic
pathologists' evaluations.

Given the three dimensional complexity of a video signal, training a robust
and diverse GAN based video generative model is onerous due to large
stochasticity involved in data space. Learning disentangled representations of
the data help to improve robustness and provide control in the sampling
process. For video generation, there is a recent progress in this area by
considering motion and appearance as orthogonal information and designing
architectures that efficiently disentangle them. These approaches rely on
handcrafting architectures that impose structural priors on the generator to
decompose appearance and motion codes in the latent space. Inspired from the
recent advancements in the autoencoder based image generation, we present AVLAE
(Adversarial Video Latent AutoEncoder) which is a two stream latent autoencoder
where the video distribution is learned by adversarial training. In particular,
we propose to autoencode the motion and appearance latent vectors of the video
generator in the adversarial setting. We demonstrate that our approach learns
to disentangle motion and appearance codes even without the explicit structural
composition in the generator. Several experiments with qualitative and
quantitative results demonstrate the effectiveness of our method.

Click-through rate(CTR) prediction is a core task in cost-per-click(CPC)
advertising systems and has been studied extensively by machine learning
practitioners. While many existing methods have been successfully deployed in
practice, most of them are built upon i.i.d.(independent and identically
distributed) assumption, ignoring that the click data used for training and
inference is collected through time and is intrinsically non-stationary and
drifting. This mismatch will inevitably lead to sub-optimal performance. To
address this problem, we formulate CTR prediction as a continual learning task
and propose COLF, a hybrid COntinual Learning Framework for CTR prediction,
which has a memory-based modular architecture that is designed to adapt, learn
and give predictions continuously when faced with non-stationary drifting click
data streams. Married with a memory population method that explicitly controls
the discrepancy between memory and target data, COLF is able to gain positive
knowledge from its historical experience and makes improved CTR predictions.
Empirical evaluations on click log collected from a major shopping app in China
demonstrate our method's superiority over existing methods. Additionally, we
have deployed our method online and observed significant CTR and revenue
improvement, which further demonstrates our method's efficacy.

The prevalence and perniciousness of fake news has been a critical issue on
in turn. In this paper, we focus on the evidence-based fake news detection,
where several evidences are utilized to probe the veracity of news (i.e., a
claim). Most previous methods first employ sequential models to embed the
semantic information and then capture the claim-evidence interaction based on
different attention mechanisms. Despite their effectiveness, they still suffer
from two main weaknesses. Firstly, due to the inherent drawbacks of sequential
models, they fail to integrate the relevant information that is scattered far
apart in evidences for veracity checking. Secondly, they neglect much redundant
information contained in evidences that may be useless or even harmful. To
solve these problems, we propose a unified Graph-based sEmantic sTructure
mining framework, namely GET in short. Specifically, different from the
existing work that treats claims and evidences as sequences, we model them as
graph-structured data and capture the long-distance semantic dependency among
dispersed relevant snippets via neighborhood propagation. After obtaining
contextual semantic information, our model reduces information redundancy by
performing graph structure learning. Finally, the fine-grained semantic
representations are fed into the downstream claim-evidence interaction module
for predictions. Comprehensive experiments have demonstrated the superiority of
GET over the state-of-the-arts.

Virtual network functions (VNFs) have been widely deployed in mobile edge
computing (MEC) to flexibly and efficiently serve end users running
resource-intensive applications, which can be further serialized to form
service function chains (SFCs), providing customized networking services. To
ensure the availability of SFCs, it turns out to be effective to place
redundant SFC backups at the edge for quickly recovering from any failures. The
existing research largely overlooks the influences of SFC popularity, backup
completeness and failure rate on the optimal deployment of SFC backups on edge
servers. In this paper, we comprehensively consider from the perspectives of
both the end users and edge system to backup SFCs for providing popular
services with the lowest latency. To overcome the challenges resulted from
unknown SFC popularity and failure rate, as well as the known system parameter
constraints, we take advantage of the online bandit learning technique to cope
with the uncertainty issue. Combining the Prim-inspired method with the greedy
strategy, we propose a Real-Time Selection and Deployment(RTSD) algorithm.
Extensive simulation experiments are conducted to demonstrate the superiority
of our proposed algorithms.

This work is motivated by personalized digital twins based on observations
and physical models for treatment and prevention of Hypertension. The models
commonly used are simplification of the real process and the aim is to make
inference about physically interpretable parameters. To account for model
discrepancy we propose to set up the estimation problem in a Bayesian
calibration framework. This naturally solves the inverse problem accounting for
and quantifying the uncertainty in the model formulation, in the parameter
estimates and predictions. We focus on the inverse problem, i.e. to estimate
the physical parameters given observations. The models we consider are the two
and three parameters Windkessel models (WK2 and WK3). These models simulate the
blood pressure waveform given the blood inflow and a set of physically
interpretable calibration parameters. The third parameter in WK3 function as a
tuning parameter. The WK2 model offers physical interpretable parameters and
therefore we adopt it as a computer model choice in a Bayesian calibration
formulation. In a synthetic simulation study, we simulate noisy data from the
WK3 model. We estimate the model parameters using conventional methods, i.e.
least squares optimization and through the Bayesian calibration framework. It
is demonstrated that our formulation can reconstruct the blood pressure
waveform of the complex model, but most importantly can learn the parameters
according to known mathematical connections between the two models. We also
successfully apply this formulation to a real case study, where data was
obtained from a pilot randomized controlled trial study. Our approach is
successful for both the simulation study and the real cases.

Temperature field inversion of heat-source systems (TFI-HSS) with limited
observations is essential to monitor the system health. Although some methods
such as interpolation have been proposed to solve TFI-HSS, those existing
methods ignore correlations between data constraints and physics constraints,
causing the low precision. In this work, we develop a physics-informed neural
network-based temperature field inversion (PINN-TFI) method to solve the
TFI-HSS task and a coefficient matrix condition number based position selection
of observations (CMCN-PSO) method to select optima positions of noise
observations. For the TFI-HSS task, the PINN-TFI method encodes constrain terms
into the loss function, thus the task is transformed into an optimization
problem of minimizing the loss function. In addition, we have found that noise
observations significantly affect reconstruction performances of the PINN-TFI
method. To alleviate the effect of noise observations, the CMCN-PSO method is
proposed to find optimal positions, where the condition number of observations
is used to evaluate positions. The results demonstrate that the PINN-TFI method
can significantly improve prediction precisions and the CMCN-PSO method can
find good positions to acquire a more robust temperature field.

Generative Adversarial Networks (GAN) are cutting-edge algorithms for
generating new data samples based on the learned data distribution. However,
its performance comes at a significant cost in terms of computation and memory
requirements. In this paper, we proposed an HW/SW co-design approach for
training quantized deconvolution GAN (QDCGAN) implemented on FPGA using a
scalable streaming dataflow architecture capable of achieving higher throughput
versus resource utilization trade-off. The developed accelerator is based on an
efficient deconvolution engine that offers high parallelism with respect to
scaling factors for GAN-based edge computing. Furthermore, various precisions,
datasets, and network scalability were analyzed for low-power inference on
resource-constrained platforms. Lastly, an end-to-end open-source framework is
provided for training, implementation, state-space exploration, and scaling the
inference using Vivado high-level synthesis for Xilinx SoC-FPGAs, and a
comparison testbed with Jetson Nano.

We train Transformer-based neural machine translation models for
Hungarian-English and English-Hungarian using the Hunglish2 corpus. Our best
models achieve a BLEU score of 40.0 on HungarianEnglish and 33.4 on
English-Hungarian. Furthermore, we present results on an ongoing work about
syntax-based augmentation for neural machine translation. Both our code and

Development of new drugs is an expensive and time-consuming process. Due to
the world-wide SARS-CoV-2 outbreak, it is essential that new drugs for
SARS-CoV-2 are developed as soon as possible. Drug repurposing techniques can
reduce the time span needed to develop new drugs by probing the list of
existing FDA-approved drugs and their properties to reuse them for combating
the new disease. We propose a novel architecture DeepGLSTM, which is a Graph
Convolutional network and LSTM based method that predicts binding affinity
values between the FDA-approved drugs and the viral proteins of SARS-CoV-2. Our
proposed model has been trained on Davis, KIBA (Kinase Inhibitor Bioactivity),
DTC (Drug Target Commons), Metz, ToxCast and STITCH datasets. We use our novel
architecture to predict a Combined Score (calculated using Davis and KIBA
score) of 2,304 FDA-approved drugs against 5 viral proteins. On the basis of
the Combined Score, we prepare a list of the top-18 drugs with the highest
binding affinity for 5 viral proteins present in SARS-CoV-2. Subsequently, this
list may be used for the creation of new useful drugs.

For research in audiovisual interview archives often it is not only of
interest what is said but also how. Sentiment analysis and emotion recognition
can help capture, categorize and make these different facets searchable. In
particular, for oral history archives, such indexing technologies can be of
great interest. These technologies can help understand the role of emotions in
historical remembering. However, humans often perceive sentiments and emotions
ambiguously and subjectively. Moreover, oral history interviews have
multi-layered levels of complex, sometimes contradictory, sometimes very subtle
facets of emotions. Therefore, the question arises of the chance machines and
humans have capturing and assigning these into predefined categories. This
paper investigates the ambiguity in human perception of emotions and sentiment
in German oral history interviews and the impact on machine learning systems.
Our experiments reveal substantial differences in human perception for
different emotions. Furthermore, we report from ongoing machine learning
experiments with different modalities. We show that the human perceptual
ambiguity and other challenges, such as class imbalance and lack of training
data, currently limit the opportunities of these technologies for oral history
archives. Nonetheless, our work uncovers promising observations and
possibilities for further research.

Different from what happens for most types of software systems, testing video
games has largely remained a manual activity performed by human testers. This
is mostly due to the continuous and intelligent user interaction video games
require. Recently, reinforcement learning (RL) has been exploited to partially
automate functional testing. RL enables training smart agents that can even
achieve super-human performance in playing games, thus being suitable to
explore them looking for bugs. We investigate the possibility of using RL for
load testing video games. Indeed, the goal of game testing is not only to
identify functional bugs, but also to examine the game's performance, such as
its ability to avoid lags and keep a minimum number of frames per second (FPS)
when high-demanding 3D scenes are shown on screen. We define a methodology
employing RL to train an agent able to play the game as a human while also
trying to identify areas of the game resulting in a drop of FPS. We demonstrate
the feasibility of our approach on three games. Two of them are used as
proof-of-concept, by injecting artificial performance bugs. The third one is an
open-source 3D game that we load test using the trained agent showing its
potential to identify areas of the game resulting in lower FPS.

Reinforcement learning policies are often represented by neural networks, but
programmatic policies are preferred in some cases because they are more
interpretable, amenable to formal verification, or generalize better. While
efficient algorithms for learning neural policies exist, learning programmatic
policies is challenging. Combining imitation-projection and dataset aggregation
with a local search heuristic, we present a simple and direct approach to
extracting a programmatic policy from a pretrained neural policy. After
examining our local search heuristic on a programming by example problem, we
demonstrate our programmatic policy extraction method on a pendulum swing-up
problem. Both when trained using a hand crafted expert policy and a learned
neural policy, our method discovers simple and interpretable policies that
perform almost as well as the original.

Recently, self-supervised vision transformers have attracted unprecedented
attention for their impressive representation learning ability. However, the
dominant method, contrastive learning, mainly relies on an instance
discrimination pretext task, which learns a global understanding of the image.
This paper incorporates local feature learning into self-supervised vision
transformers via Reconstructive Pre-training (RePre). Our RePre extends
contrastive frameworks by adding a branch for reconstructing raw image pixels
in parallel with the existing contrastive objective. RePre is equipped with a
lightweight convolution-based decoder that fuses the multi-hierarchy features
from the transformer encoder. The multi-hierarchy features provide rich
supervisions from low to high semantic information, which are crucial for our
RePre. Our RePre brings decent improvements on various contrastive frameworks
with different vision transformer architectures. Transfer performance in
downstream tasks outperforms supervised pre-training and state-of-the-art
(SOTA) self-supervised counterparts.

In this paper, we propose a deep learning based numerical scheme for strongly
coupled FBSDEs, stemming from stochastic control. It is a modification of the
deep BSDE method in which the initial value to the backward equation is not a
free parameter, and with a new loss function being the weighted sum of the cost
of the control problem, and a variance term which coincides with the mean
squared error in the terminal condition. We show by a numerical example that a
direct extension of the classical deep BSDE method to FBSDEs, fails for a
simple linear-quadratic control problem, and motivate why the new method works.
Under regularity and boundedness assumptions on the exact controls of time
continuous and time discrete control problems, we provide an error analysis for
our method. We show empirically that the method converges for three different
problems, one being the one that failed for a direct extension of the deep BSDE
method.

Code review is a practice widely adopted in open source and industrial
projects. Given the non-negligible cost of such a process, researchers started
investigating the possibility of automating specific code review tasks. We
recently proposed Deep Learning (DL) models targeting the automation of two
tasks: the first model takes as input a code submitted for review and
implements in it changes likely to be recommended by a reviewer; the second
takes as input the submitted code and a reviewer comment posted in natural
language and automatically implements the change required by the reviewer.
While the preliminary results we achieved are encouraging, both models had been
tested in rather simple code review scenarios, substantially simplifying the
targeted problem. This was also due to the choices we made when designing both
the technique and the experiments. In this paper, we build on top of that work
by demonstrating that a pre-trained Text-To-Text Transfer Transformer (T5)
model can outperform previous DL models for automating code review tasks. Also,
we conducted our experiments on a larger and more realistic (and challenging)
dataset of code review activities.

End-to-end task-oriented dialog systems often suffer from out-of-distribution
(OOD) inputs after being deployed in dynamic, changing, and open environments.
In this work, we propose SL-Agent, a self-learning framework that combines
supervised learning, reinforcement learning, and machine teaching for building
end-to-end dialog systems in a more realistic changing environment setting.
SL-Agent consists of a dialog model and a pre-trained reward model to judge the
quality of a system response. SL-Agent enables dialog agents to automatically
adapt to environments with user behavior changes by learning from human-bot
interactions via reinforcement learning, with the incorporated pre-trained
reward model. We validate SL-Agent in four different dialog domains.
Experimental results show the effectiveness of SL-Agent for automatically
adapting to changing environments using both automatic and human evaluations.
Furthermore, experiments on a challenging domain extension setting demonstrate
that SL-Agent can effectively adapt to new tasks using limited human
corrections provided via machine teaching. We will release code, data, and
pre-trained models for further research.

Agile hardware development requires fast and accurate circuit quality
evaluation from early design stages. Existing work of high-level synthesis
(HLS) performance prediction usually needs extensive feature engineering after
the synthesis process. To expedite circuit evaluation from as earlier design
stage as possible, we propose a rapid and accurate performance modeling,
exploiting the representation power of graph neural networks (GNNs) by
representing C/C++ programs as graphs. The contribution of this work is
three-fold. First, we build a standard benchmark containing 40k C synthesizable
programs, which includes both synthetic programs and three sets of real-world
HLS benchmarks. Each program is implemented on FPGA to generate ground-truth
performance metrics. Second, we formally formulate the HLS performance
prediction problem on graphs, and propose multiple modeling strategies with
GNNs that leverage different trade-offs between prediction timeliness
(early/late prediction) and accuracy. Third, we further propose a novel
hierarchical GNN that does not sacrifice timeliness but largely improves
prediction accuracy, significantly outperforming HLS tools. We apply extensive
evaluations for both synthetic and unseen real-case programs; our proposed
predictor largely outperforms HLS by up to 40X and excels existing predictors
by 2X to 5X in terms of resource usage and timing prediction.

Landslides are notoriously difficult to predict because numerous spatially
and temporally varying factors contribute to slope stability. Artificial neural
networks (ANN) have been shown to improve prediction accuracy. However,
traditional ANNs are uninterpretable, complex black box models. This makes it
difficult to extract mechanistic information about landslide controls in the
modeled region or trust the outcome in this high-stakes application. Herein we
present the first application of an interpretable additive neural network to
landslide susceptibility modeling. We introduce a new additive ANN optimization
framework, as well as new dataset division and outcome interpretation
techniques uniquely suitable for modeling applications with spatially dependent
data structures such as landslide susceptibility. We refer to our approach
which features full interpretability, high accuracy, high generalizability and
low model complexity as superposable neural network (SNN) optimization. We
validate our approach by training models to assess landslide susceptibility in
three different regions of the easternmost Himalaya that are highly susceptible
to landslides. The interpretable neural network models generated by the SNN
outperform physically-based stability and statistical models and achieve
similar performance to state-of the-art deep neural networks while offering
insight regarding the relative importance of landslide control factors. The SNN
models found the product of slope and precipitation and hillslope aspect to be
important primary contributors to high landslide susceptibility in the studied
regions. These identified controls suggest that strong slope-climate couplings,
along with microclimates, play dominant roles in landslide occurrences of the
easternmost Himalaya.

My project tackles the question of whether Ray can be used to quickly train
autonomous vehicles using a simulator (Carla), and whether a platform robust
enough for further research purposes can be built around it. Ray is an
open-source framework that enables distributed machine learning applications.
Distributed computing is a technique which parallelizes computational tasks,
such as training a model, among many machines. Ray abstracts away the complex
coordination of these machines, making it rapidly scalable. Carla is a vehicle
simulator that generates data used to train a model. The bulk of the project
was writing the training logic that Ray would use to train my distributed
model. Imitation learning is the best fit for autonomous vehicles. Imitation
learning is an alternative to reinforcement learning and it works by trying to
learn the optimal policy by imitating an expert (usually a human) given a set
of demonstrations. A key deliverable for the project was showcasing my trained
agent in a few benchmark tests, such as navigating a complex turn through
traffic. Beyond that, the broader ambition was to develop a research platform
where others could quickly train and run experiments on huge amounts of Carla
vehicle data. Thus, my end product is not a single model, but a large-scale,
open-source research platform (RayCarla) for autonomous vehicle researchers to
utilize.

The ever-growing demand and complexity of machine learning are putting
pressure on hyper-parameter tuning systems: while the evaluation cost of models
continues to increase, the scalability of state-of-the-arts starts to become a
crucial bottleneck. In this paper, inspired by our experience when deploying
hyper-parameter tuning in a real-world application in production and the
limitations of existing systems, we propose Hyper-Tune, an efficient and robust
distributed hyper-parameter tuning framework. Compared with existing systems,
Hyper-Tune highlights multiple system optimizations, including (1) automatic
resource allocation, (2) asynchronous scheduling, and (3) multi-fidelity
optimizer. We conduct extensive evaluations on benchmark datasets and a
large-scale real-world dataset in production. Empirically, with the aid of
these optimizations, Hyper-Tune outperforms competitive hyper-parameter tuning
systems on a wide range of scenarios, including XGBoost, CNN, RNN, and some
architectural hyper-parameters for neural networks. Compared with the
state-of-the-art BOHB and A-BOHB, Hyper-Tune achieves up to 11.2x and 5.1x
speedups, respectively.

We report on a model built to predict links in a complex network of
scientific concepts, in the context of the Science4Cast 2021 competition. We
show that the network heavily favours linking nodes of high degree, indicating
that new scientific connections are primarily made between popular concepts,
which constitutes the main feature of our model. Besides this notion of
popularity, we use a measure of similarity between nodes quantified by a
normalized count of their common neighbours to improve the model. Finally, we
show that the model can be further improved by considering a time-weighted
adjacency matrix with both older and newer links having higher impact in the
predictions, representing rooted concepts and state of the art research,

In this paper we introduce a new approach to discrete-time semi-Markov
decision processes based on the sojourn time process. Different
characterizations of discrete-time semi-Markov processes are exploited and
decision processes are constructed by their means. With this new approach, the
agent is allowed to consider different actions depending also on the sojourn
time of the process in the current state. A numerical method based on
$Q$-learning algorithms for finite horizon reinforcement learning and
stochastic recursive relations is investigated. Finally, we consider two toy
examples: one in which the reward depends on the sojourn-time, according to the
gambler's fallacy; the other in which the environment is semi-Markov even if
the reward function does not depend on the sojourn time. These are used to
carry on some numerical evaluations on the previously presented $Q$-learning
algorithm and on a different naive method based on deep reinforcement learning.

License plate recognition systems have a very important role in many
applications such as toll management, parking control, and traffic management.
In this paper, a framework of deep convolutional neural networks is proposed
for Iranian license plate recognition. The first CNN is the YOLOv3 network that
detects the Iranian license plate in the input image while the second CNN is a
Faster R-CNN that recognizes and classifies the characters in the detected
license plate. A dataset of Iranian license plates consisting of
ill-conditioned images also developed in this paper. The YOLOv3 network
achieved 99.6% mAP, 98.26% recall, 98.08% accuracy, and average detection speed
is only 23ms. Also, the Faster R-CNN network trained and tested on the
developed dataset and achieved 98.97% recall, 99.9% precision, and 98.8%
accuracy. The proposed system can recognize the license plate in challenging
situations like unwanted data on the license plate. Comparing this system with
other Iranian license plate recognition systems shows that it is Faster, more
accurate and also this system can work in an open environment.

Online multi-object tracking (MOT) is a longstanding task for computer vision
and intelligent vehicle platform. At present, the main paradigm is
tracking-by-detection, and the main difficulty of this paradigm is how to
associate current candidate detections with historical tracklets. However, in
the MOT scenarios, each historical tracklet is composed of an object sequence,
while each candidate detection is just a flat image, which lacks temporal
features of the object sequence. The feature difference between current
candidate detections and historical tracklets makes the object association much
harder. Therefore, we propose a Spatial-Temporal Mutual Representation Learning
(STURE) approach which learns spatial-temporal representations between current
candidate detections and historical sequences in a mutual representation space.
For historical trackelets, the detection learning network is forced to match
the representations of sequence learning network in a mutual representation
space. The proposed approach is capable of extracting more distinguishing
detection and sequence representations by using various designed losses in
object association. As a result, spatial-temporal feature is learned mutually
to reinforce the current detection features, and the feature difference can be
relieved. To prove the robustness of the STURE, it is applied to the public MOT
challenge benchmarks and performs well compared with various state-of-the-art
online MOT trackers based on identity-preserving metrics.

Random forests are a widely used machine learning algorithm, but their
computational efficiency is undermined when applied to large-scale datasets
with numerous instances and useless features. Herein, we propose a
nonparametric feature selection algorithm that incorporates random forests and
deep neural networks, and its theoretical properties are also investigated
under regularity conditions. Using different synthetic models and a real-world
example, we demonstrate the advantage of the proposed algorithm over other
alternatives in terms of identifying useful features, avoiding useless ones,
and the computation efficiency. Although the algorithm is proposed using
standard random forests, it can be widely adapted to other machine learning
algorithms, as long as features can be sorted accordingly.

Recommender systems provide essential web services by learning users'
personal preferences from collected data. However, in many cases, systems also
need to forget some training data. From the perspective of privacy, several
privacy regulations have recently been proposed, requiring systems to eliminate
any impact of the data whose owner requests to forget. From the perspective of
utility, if a system's utility is damaged by some bad data, the system needs to
forget these data to regain utility. From the perspective of usability, users
can delete noise and incorrect entries so that a system can provide more useful
recommendations. While unlearning is very important, it has not been
well-considered in existing recommender systems. Although there are some
researches have studied the problem of machine unlearning in the domains of
image and text data, existing methods can not been directly applied to
recommendation as they are unable to consider the collaborative information.
  In this paper, we propose RecEraser, a general and efficient machine
unlearning framework tailored to recommendation task. The main idea of
RecEraser is to partition the training set into multiple shards and train a
constituent model for each shard. Specifically, to keep the collaborative
information of the data, we first design three novel data partition algorithms
to divide training data into balanced groups based on their similarity. Then,
considering that different shard models do not uniformly contribute to the
final prediction, we further propose an adaptive aggregation method to improve
the global model utility. Experimental results on three public benchmarks show
that RecEraser can not only achieve efficient unlearning, but also outperform
the state-of-the-art unlearning methods in terms of model utility. The source
code can be found at https://github.com/chenchongthu/Recommendation-Unlearning

Advertisers play an essential role in many e-commerce platforms like Taobao
and Amazon. Fulfilling their marketing needs and supporting their business
growth is critical to the long-term prosperity of platform economies. However,
compared with extensive studies on user modeling such as click-through rate
predictions, much less attention has been drawn to advertisers, especially in
terms of understanding their diverse demands and performance. Different from
user modeling, advertiser modeling generally involves many kinds of tasks (e.g.
predictions of advertisers' expenditure, active-rate, or total impressions of
promoted products). In addition, major e-commerce platforms often provide
multiple marketing scenarios (e.g. Sponsored Search, Display Ads, Live
Streaming Ads) while advertisers' behavior tend to be dispersed among many of
them. This raises the necessity of multi-task and multi-scenario consideration
in comprehensive advertiser modeling, which faces the following challenges:
First, one model per scenario or per task simply doesn't scale; Second, it is
particularly hard to model new or minor scenarios with limited data samples;
Third, inter-scenario correlations are complicated, and may vary given
different tasks. To tackle these challenges, we propose a multi-scenario
multi-task meta learning approach (M2M) which simultaneously predicts multiple
tasks in multiple advertising scenarios.

A common misconception among blockchain users is that pseudonymity guarantees
privacy. The reality is almost the opposite. Every transaction one makes is
recorded on a public ledger and reveals information about one's identity.
Mixers, such as Tornado Cash, were developed to preserve privacy through
"mixing" transactions with those of others in an anonymity pool, making it
harder to link deposits and withdrawals from the pool. Unfortunately, it is
still possible to reveal information about those in the anonymity pool if users
are not careful. We introduce Tutela, an application built on expert heuristics
to report the true anonymity of an Ethereum address. In particular, Tutela has
three functionalities: first, it clusters together Ethereum addresses based on
interaction history such that for an Ethereum address, we can identify other
addresses likely owned by the same entity; second, it shows Ethereum users
their potentially compromised transactions; third, Tutela computes the true
size of the anonymity pool of each Tornado Cash mixer by excluding potentially
compromised transactions. A public implementation of Tutela can be found at
https://github.com/TutelaLabs/tutela-app. To use Tutela, visit
https://www.tutela.xyz.

When using machine learning (ML) techniques, users typically need to choose a
plethora of algorithm-specific parameters, referred to as hyperparameters. In
this paper, we compare the performance of two algorithms, particle swarm
optimisation (PSO) and Bayesian optimisation (BO), for the autonomous
determination of these hyperparameters in applications to different ML tasks
typical for the field of high energy physics (HEP). Our evaluation of the
performance includes a comparison of the capability of the PSO and BO
algorithms to make efficient use of the highly parallel computing resources
that are characteristic of contemporary HEP experiments.

In collaborative outlier detection, multiple participants exchange their
local detectors trained on decentralized devices without exchanging their own
data. A key problem of collaborative outlier detection is efficiently
aggregating multiple local detectors to form a global detector without
breaching the privacy of participants' data and degrading the detection
accuracy. We study locality-sensitive hashing-based ensemble methods to detect
collaborative outliers since they are mergeable and compatible with
differentially private mechanisms. Our proposed LSH iTables is simple and
outperforms recent ensemble competitors on centralized and decentralized
scenarios over many real-world data sets.

This paper describes a feature extraction and gaze estimation software, named
Pistol that can be used with Pupil Invisible projects and other eye trackers in
the future. In offline mode, our software extracts multiple features from the
eye including, the pupil and iris ellipse, eye aperture, pupil vector, iris
vector, eye movement types from pupil and iris velocities, marker detection,
marker distance, 2D gaze estimation for the pupil center, iris center, pupil
vector, and iris vector using Levenberg Marquart fitting and neural networks.
The gaze signal is computed in 2D for each eye and each feature separately and
for both eyes in 3D also for each feature separately. We hope this software
helps other researchers to extract state-of-the-art features for their research
out of their recordings.

Human infants acquire their verbal lexicon from minimal prior knowledge of
language based on the statistical properties of phonological distributions and
the co-occurrence of other sensory stimuli. In this study, we propose a novel
fully unsupervised learning method discovering speech units by utilizing
phonological information as a distributional cue and object information as a
co-occurrence cue. The proposed method can not only (1) acquire words and
phonemes from speech signals using unsupervised learning, but can also (2)
utilize object information based on multiple modalities (i.e., vision, tactile,
and auditory) simultaneously. The proposed method is based on the Nonparametric
Bayesian Double Articulation Analyzer (NPB-DAA) discovering phonemes and words
from phonological features, and Multimodal Latent Dirichlet Allocation (MLDA)
categorizing multimodal information obtained from objects. In the experiment,
the proposed method showed higher word discovery performance than the baseline
methods. In particular, words that expressed the characteristics of the object
(i.e., words corresponding to nouns and adjectives) were segmented accurately.
Furthermore, we examined how learning performance is affected by differences in
the importance of linguistic information. When the weight of the word modality
was increased, the performance was further improved compared to the fixed

Human emotions involve basic and compound facial expressions. However,
current research on facial expression recognition (FER) mainly focuses on basic
expressions, and thus fails to address the diversity of human emotions in
practical scenarios. Meanwhile, existing work on compound FER relies heavily on
abundant labeled compound expression training data, which are often laboriously
collected under the professional instruction of psychology. In this paper, we
study compound FER in the cross-domain few-shot learning setting, where only a
few images of novel classes from the target domain are required as a reference.
In particular, we aim to identify unseen compound expressions with the model
trained on easily accessible basic expression datasets. To alleviate the
problem of limited base classes in our FER task, we propose a novel Emotion
Guided Similarity Network (EGS-Net), consisting of an emotion branch and a
similarity branch, based on a two-stage learning framework. Specifically, in
the first stage, the similarity branch is jointly trained with the emotion
branch in a multi-task fashion. With the regularization of the emotion branch,
we prevent the similarity branch from overfitting to sampled base classes that
are highly overlapped across different episodes. In the second stage, the
emotion branch and the similarity branch play a "two-student game" to
alternately learn from each other, thereby further improving the inference
ability of the similarity branch on unseen compound expressions. Experimental
results on both in-the-lab and in-the-wild compound expression datasets
demonstrate the superiority of our proposed method against several

We develop a new numerical framework, employing physics-informed neural
networks, to find a smooth self-similar solution for the Boussinesq equations.
The solution in addition corresponds to an asymptotic self-similar profile for
the 3-dimensional Euler equations in the presence of a cylindrical boundary. In
particular, the solution represents a precise description of the Luo-Hou
blow-up scenario [G. Luo, T. Hou, Proc. Natl. Acad. Sci. 111(36): 12968-12973,
2014] for 3-dimensional Euler. To the best of the authors' knowledge, the
solution is the first truly multi-dimensional smooth backwards self-similar
profile found for an equation from fluid mechanics. The new numerical framework
is shown to be both robust and readily adaptable to other equations.

Disease risk prediction has attracted increasing attention in the field of
modern healthcare, especially with the latest advances in artificial
intelligence (AI). Electronic health records (EHRs), which contain
heterogeneous patient information, are widely used in disease risk prediction
tasks. One challenge of applying AI models for risk prediction lies in
generating interpretable evidence to support the prediction results while
retaining the prediction ability. In order to address this problem, we propose
the method of jointly embedding words and labels whereby attention modules
learn the weights of words from medical notes according to their relevance to
the names of risk prediction labels. This approach boosts interpretability by
employing an attention mechanism and including the names of prediction tasks in
the model. However, its application is only limited to the handling of textual
inputs such as medical notes. In this paper, we propose a label dependent
attention model LDAM to 1) improve the interpretability by exploiting
Clinical-BERT (a biomedical language model pre-trained on a large clinical
corpus) to encode biomedically meaningful features and labels jointly; 2)
extend the idea of joint embedding to the processing of time-series data, and
develop a multi-modal learning framework for integrating heterogeneous
information from medical notes and time-series health status indicators. To
demonstrate our method, we apply LDAM to the MIMIC-III dataset to predict
different disease risks. We evaluate our method both quantitatively and
qualitatively. Specifically, the predictive power of LDAM will be shown, and
case studies will be carried out to illustrate its interpretability.

In an aerial hybrid massive multiple-input multiple-output (MIMO) and
orthogonal frequency division multiplexing (OFDM) system, how to design a
spectral-efficient broadband multi-user hybrid beamforming with a limited pilot
and feedback overhead is challenging. To this end, by modeling the key
transmission modules as an end-to-end (E2E) neural network, this paper proposes
a data-driven deep learning (DL)-based unified hybrid beamforming framework for
both the time division duplex (TDD) and frequency division duplex (FDD) systems
with implicit channel state information (CSI). For TDD systems, the proposed
DL-based approach jointly models the uplink pilot combining and downlink hybrid
beamforming modules as an E2E neural network. While for FDD systems, we jointly
model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid
beamforming modules as an E2E neural network. Different from conventional
approaches separately processing different modules, the proposed solution
simultaneously optimizes all modules with the sum rate as the optimization
object. Therefore, by perceiving the inherent property of air-to-ground massive
MIMO-OFDM channel samples, the DL-based E2E neural network can establish the
mapping function from the channel to the beamformer, so that the explicit
channel reconstruction can be avoided with reduced pilot and feedback overhead.
Besides, practical low-resolution phase shifters (PSs) introduce the
quantization constraint, leading to the intractable gradient backpropagation
when training the neural network. To mitigate the performance loss caused by
the phase quantization error, we adopt the transfer learning strategy to
further fine-tune the E2E neural network based on a pre-trained network that
assumes the ideal infinite-resolution PSs. Numerical results show that our
DL-based schemes have considerable advantages over state-of-the-art schemes.

Text classification algorithms investigate the intricate relationships
between words or phrases and attempt to deduce the document's interpretation.
In the last few years, these algorithms have progressed tremendously.
Transformer architecture and sentence encoders have proven to give superior
results on natural language processing tasks. But a major limitation of these
architectures is their applicability for text no longer than a few hundred
words. In this paper, we explore hierarchical transfer learning approaches for
long document classification. We employ pre-trained Universal Sentence Encoder
(USE) and Bidirectional Encoder Representations from Transformers (BERT) in a
hierarchical setup to capture better representations efficiently. Our proposed
models are conceptually simple where we divide the input data into chunks and
then pass this through base models of BERT and USE. Then output representation
for each chunk is then propagated through a shallow neural network comprising
of LSTMs or CNNs for classifying the text data. These extensions are evaluated
on 6 benchmark datasets. We show that USE + CNN/LSTM performs better than its
stand-alone baseline. Whereas the BERT + CNN/LSTM performs on par with its
stand-alone counterpart. However, the hierarchical BERT models are still
desirable as it avoids the quadratic complexity of the attention mechanism in
BERT. Along with the hierarchical approaches, this work also provides a
comparison of different deep learning algorithms like USE, BERT, HAN,
Longformer, and BigBird for long document classification. The Longformer
approach consistently performs well on most of the datasets.

Modern machine learning tools such as deep neural networks (DNNs) are playing
a revolutionary role in many fields such as natural language processing,
computer vision, and the internet of things. Once they are trained, deep
learning models can be deployed on edge computers to perform classification and
prediction on real-time data for these applications. Particularly for large
models, the limited computational and memory resources on a single edge device
can become the throughput bottleneck for an inference pipeline. To increase
throughput and decrease per-device compute load, we present DEFER (Distributed
Edge inFERence), a framework for distributed edge inference, which partitions
deep neural networks into layers that can be spread across multiple compute
nodes. The architecture consists of a single "dispatcher" node to distribute
DNN partitions and inference data to respective compute nodes. The compute
nodes are connected in a series pattern where each node's computed result is
relayed to the subsequent node. The result is then returned to the Dispatcher.
We quantify the throughput, energy consumption, network payload, and overhead
for our framework under realistic network conditions using the CORE network
emulator. We find that for the ResNet50 model, the inference throughput of
DEFER with 8 compute nodes is 53% higher and per node energy consumption is 63%
lower than single device inference. We further reduce network communication
demands and energy consumption using the ZFP serialization and LZ4 compression
algorithms. We have implemented DEFER in Python using the TensorFlow and Keras
ML libraries, and have released DEFER as an open-source framework to benefit

We propose r-ssGPFA, an unsupervised online anomaly detection model for uni-
and multivariate time series building on the efficient state space formulation
of Gaussian processes. For high-dimensional time series, we propose an
extension of Gaussian process factor analysis to identify the common latent
processes of the time series, allowing us to detect anomalies efficiently in an
interpretable manner. We gain explainability while speeding up computations by
imposing an orthogonality constraint on the mapping from the latent to the
observed. Our model's robustness is improved by using a simple heuristic to
skip Kalman updates when encountering anomalous observations. We investigate
the behaviour of our model on synthetic data and show on standard benchmark
datasets that our method is competitive with state-of-the-art methods while
being computationally cheaper.

Knowledge of the changing traffic is critical in risk management. Customs
offices worldwide have traditionally relied on local resources to accumulate
knowledge and detect tax fraud. This naturally poses countries with weak
infrastructure to become tax havens of potentially illicit trades. The current
paper proposes DAS, a memory bank platform to facilitate knowledge sharing
across multi-national customs administrations to support each other. We propose
a domain adaptation method to share transferable knowledge of frauds as
prototypes while safeguarding the local trade information. Data encompassing
over 8 million import declarations have been used to test the feasibility of
this new system, which shows that participating countries may benefit up to
2-11 times in fraud detection with the help of shared knowledge. We discuss
implications for substantial tax revenue potential and strengthened policy
against illicit trades.

Existing active learning studies typically work in the closed-set setting by
assuming that all data examples to be labeled are drawn from known classes.
However, in real annotation tasks, the unlabeled data usually contains a large
amount of examples from unknown classes, resulting in the failure of most
active learning methods. To tackle this open-set annotation (OSA) problem, we
propose a new active learning framework called LfOSA, which boosts the
classification performance with an effective sampling strategy to precisely
detect examples from known classes for annotation. The LfOSA framework
introduces an auxiliary network to model the per-example max activation value
(MAV) distribution with a Gaussian Mixture Model, which can dynamically select
the examples with highest probability from known classes in the unlabeled set.
Moreover, by reducing the temperature $T$ of the loss function, the detection
model will be further optimized by exploiting both known and unknown
supervision. The experimental results show that the proposed method can
significantly improve the selection quality of known classes, and achieve
higher classification accuracy with lower annotation cost than state-of-the-art
active learning methods. To the best of our knowledge, this is the first work
of active learning for open-set annotation.

Diacritics restoration has become a ubiquitous task in the
Latin-alphabet-based English-dominated Internet language environment. In this
paper, we describe a small footprint 1D dilated convolution-based approach
which operates on a character-level. We find that solutions based on 1D dilated
convolutional neural networks are competitive alternatives to models based on
recursive neural networks or linguistic modeling for the task of diacritics
restoration. Our solution surpasses the performance of similarly sized models
and is also competitive with larger models. A special feature of our solution
is that it even runs locally in a web browser. We also provide a working
example of this browser-based implementation. Our model is evaluated on
different corpora, with emphasis on the Hungarian language. We performed
comparative measurements about the generalization power of the model in
relation to three Hungarian corpora. We also analyzed the errors to understand
the limitation of corpus-based self-supervised training.

Most recently, machine learning has been used to study the dynamics of
integrable Hamiltonian systems and the chaotic 3-body problem. In this work, we
consider an intermediate case of regular motion in a non-integrable system: the
behaviour of objects in the 2:3 mean motion resonance with Neptune. We show
that, given initial data from a short 6250 yr numerical integration, the
best-trained artificial neural network (ANN) can predict the trajectories of
the 2:3 resonators over the subsequent 18750 yr evolution, covering a full
libration cycle over the combined time period. By comparing our ANN's
prediction of the resonant angle to the outcome of numerical integrations, the
former can predict the resonant angle with an accuracy as small as of a few
degrees only, while it has the advantage of considerably saving computational
time. More specifically, the trained ANN can effectively measure the resonant
amplitudes of the 2:3 resonators, and thus provides a fast approach that can
identify the resonant candidates. This may be helpful in classifying a huge
population of KBOs to be discovered in future surveys.

This paper presents a new concept formation approach that supports the
ability to incrementally learn and predict labels for visual images. This work
integrates the idea of convolutional image processing, from computer vision
research, with a concept formation approach that is based on psychological
studies of how humans incrementally form and use concepts. We experimentally
evaluate this new approach by applying it to an incremental variation of the
MNIST digit recognition task. We compare its performance to Cobweb, a concept
formation approach that does not support convolutional processing, as well as
two convolutional neural networks that vary in the complexity of their
convolutional processing. This work represents a first step towards unifying
modern computer vision ideas with classical concept formation research.

Existing task-oriented chatbots heavily rely on spoken language understanding
(SLU) systems to determine a user's utterance's intent and other key
information for fulfilling specific tasks. In real-life applications, it is
crucial to occasionally induce novel dialog intents from the conversation logs
to improve the user experience. In this paper, we propose the Density-based
Deep Clustering Ensemble (DDCE) method for dialog intent induction. Compared to
existing K-means based methods, our proposed method is more effective in
dealing with real-life scenarios where a large number of outliers exist. To
maximize data utilization, we jointly optimize texts' representations and the
hyperparameters of the clustering algorithm. In addition, we design an
outlier-aware clustering ensemble framework to handle the overfitting issue.
Experimental results over seven datasets show that our proposed method
significantly outperforms other state-of-the-art baselines.

Hate speech is a major issue in social networks due to the high volume of
data generated daily. Recent works demonstrate the usefulness of machine
learning (ML) in dealing with the nuances required to distinguish between
hateful posts from just sarcasm or offensive language. Many ML solutions for
hate speech detection have been proposed by either changing how features are
extracted from the text or the classification algorithm employed. However, most
works consider only one type of feature extraction and classification
algorithm. This work argues that a combination of multiple feature extraction
techniques and different classification models is needed. We propose a
framework to analyze the relationship between multiple feature extraction and
classification techniques to understand how they complement each other. The
framework is used to select a subset of complementary techniques to compose a
robust multiple classifiers system (MCS) for hate speech detection. The
experimental study considering four hate speech classification datasets
demonstrates that the proposed framework is a promising methodology for
analyzing and designing high-performing MCS for this task. MCS system obtained
using the proposed framework significantly outperforms the combination of all
models and the homogeneous and heterogeneous selection heuristics,
demonstrating the importance of having a proper selection scheme. Source code,
figures, and dataset splits can be found in the GitHub repository:
https://github.com/Menelau/Hate-Speech-MCS.

The release note is an essential software artifact of open-source software
that documents crucial information about changes, such as new features and bug
fixes. With the help of release notes, both developers and users could have a
general understanding of the latest version without browsing the source code.
However, it is a daunting and time-consuming job for developers to produce
release notes. Although prior studies have provided some automatic approaches,
they generate release notes mainly by extracting information from code changes.
This will result in language-specific and not being general enough to be
applicable. Therefore, helping developers produce release notes effectively
remains an unsolved challenge. To address the problem, we first conduct a
manual study on the release notes of 900 GitHub projects, which reveals that
more than 54% of projects produce their release notes with pull requests. Based
on the empirical finding, we propose a deep learning based approach named
DeepRelease (Deep learning based Release notes generator) to generate release
notes according to pull requests. The process of release notes generation in
DeepRelease includes the change entries generation and the change category
(i.e., new features or bug fixes) generation, which are formulated as a text
summarization task and a multi-class classification problem, respectively.
Since DeepRelease fully employs text information from pull requests to
summarize changes and identify the change category, it is language-agnostic and
can be used for projects in any language. We build a dataset with over 46K
release notes and evaluate DeepRelease on the dataset. The experimental results
indicate that DeepRelease outperforms four baselines and can generate release
notes similar to those manually written ones in a fraction of the time.

Spatiotemporal time series nowcasting should preserve temporal and spatial
dynamics in the sense that generated new sequences from models respect the
covariance relationship from history. Conventional feature extractors are built
with deep convolutional neural networks (CNN). However, CNN models have limits
to image-like applications where data can be formed with high-dimensional
arrays. In contrast, applications in social networks, road traffic, physics,
and chemical property prediction where data features can be organized with
nodes and edges of graphs. Transformer architecture is an emerging method for
predictive models, bringing high accuracy and efficiency due to attention
mechanism design. This paper proposes a spatiotemporal model, namely GTrans,
that transforms data features into graph embeddings and predicts temporal
dynamics with a transformer model. According to our experiments, we demonstrate
that GTrans can model spatial and temporal dynamics and nowcasts extreme events
for datasets. Furthermore, in all the experiments, GTrans can achieve the
highest F1 and F2 scores in binary-class prediction tests than the baseline
models.

Microstructures are attracting academic and industrial interests with the
rapid development of additive manufacturing. The numerical homogenization
method has been well studied for analyzing mechanical behaviors of
microstructures; however, it is too time-consuming to be applied to online
computing or applications requiring high-frequency calling, e.g., topology
optimization. Data-driven homogenization methods emerge as a more efficient
choice but limit the microstructures into a cubic shape, which are infeasible
to the periodic microstructures with a more general shape, e.g.,
parallelepiped. This paper introduces a fine-designed 3D convolutional neural
network (CNN) for fast homogenization of parallel-shaped microstructures, named
PH-Net. Superior to existing data-driven methods, PH-Net predicts the local
displacements of microstructures under specified macroscope strains instead of
direct homogeneous material, motivating us to present a label-free loss
function based on minimal potential energy. For dataset construction, we
introduce a shape-material transformation and voxel-material tensor to encode
microstructure type,base material and boundary shape together as the input of
PH-Net, such that it is CNN-friendly and enhances PH-Net on generalization in
terms of microstructure type, base material, and boundary shape. PH-Net
predicts homogenized properties with hundreds of acceleration compared to the
numerical homogenization method and even supports online computing. Moreover,
it does not require a labeled dataset and thus is much faster than current deep
learning methods in training processing. Benefiting from predicting local
displacement, PH-Net provides both homogeneous material properties and
microscopic mechanical properties, e.g., strain and stress distribution, yield
strength, etc. We design a group of physical experiments and verify the
prediction accuracy of PH-Net.

Recently, recommender system (RS) based on causal inference has gained much
attention in the industrial community, as well as the states of the art
performance in many prediction and debiasing tasks. Nevertheless, a unified
causal analysis framework has not been established yet. Many causal-based
prediction and debiasing studies rarely discuss the causal interpretation of
various biases and the rationality of the corresponding causal assumptions. In
this paper, we first provide a formal causal analysis framework to survey and
unify the existing causal-inspired recommendation methods, which can
accommodate different scenarios in RS. Then we propose a new taxonomy and give
formal causal definitions of various biases in RS from the perspective of
violating the assumptions adopted in causal analysis. Finally, we formalize
many debiasing and prediction tasks in RS, and summarize the statistical and
machine learning-based causal estimation methods, expecting to provide new
research opportunities and perspectives to the causal RS community.

As the problems to be optimized with deep learning become more practical,
their datasets inevitably contain a variety of noise, such as mislabeling and
substitution by estimated inputs/outputs, which would have negative impacts on
the optimization results. As a safety net, it is a natural idea to improve a
stochastic gradient descent (SGD) optimizer, which updates the network
parameters as the final process of learning, to be more robust to noise. The
related work revealed that the first momentum utilized in the Adam-like SGD
optimizers can be modified based on the noise-robust student's t-distribution,
resulting in inheriting the robustness to noise. In this paper, we propose
AdaTerm, which derives not only the first momentum but also all the involved
statistics based on the student's t-distribution. If the computed gradients
seem to probably be aberrant, AdaTerm is expected to exclude the computed
gradients for updates, and reinforce the robustness for the next updates;
otherwise, it updates the network parameters normally, and can relax the
robustness for the next updates. With this noise-adaptive behavior, the
excellent learning performance of AdaTerm was confirmed via typical
optimization problems with several cases where the noise ratio would be
different.

Hypervolume contribution is an important concept in evolutionary
multi-objective optimization (EMO). It involves in hypervolume-based EMO
algorithms and hypervolume subset selection algorithms. Its main drawback is
that it is computationally expensive in high-dimensional spaces, which limits
its applicability to many-objective optimization. Recently, an R2 indicator
variant (i.e., $R_2^{\text{HVC}}$ indicator) is proposed to approximate the
hypervolume contribution. The $R_2^{\text{HVC}}$ indicator uses line segments
along a number of direction vectors for hypervolume contribution approximation.
It has been shown that different direction vector sets lead to different
approximation quality. In this paper, we propose \textit{Learning to
Approximate (LtA)}, a direction vector set generation method for the
$R_2^{\text{HVC}}$ indicator. The direction vector set is automatically learned
from training data. The learned direction vector set can then be used in the
$R_2^{\text{HVC}}$ indicator to improve its approximation quality. The
usefulness of the proposed LtA method is examined by comparing it with other
commonly-used direction vector set generation methods for the
$R_2^{\text{HVC}}$ indicator. Experimental results suggest the superiority of
LtA over the other methods for generating high quality direction vector sets.

The impact of device and circuit-level effects in mixed-signal Resistive
Random Access Memory (RRAM) accelerators typically manifest as performance
degradation of Deep Learning (DL) algorithms, but the degree of impact varies
based on algorithmic features. These include network architecture, capacity,
weight distribution, and the type of inter-layer connections. Techniques are
continuously emerging to efficiently train sparse neural networks, which may
have activation sparsity, quantization, and memristive noise. In this paper, we
present an extended Design Space Exploration (DSE) methodology to quantify the
benefits and limitations of dense and sparse mapping schemes for a variety of
network architectures. While sparsity of connectivity promotes less power
consumption and is often optimized for extracting localized features, its
performance on tiled RRAM arrays may be more susceptible to noise due to
under-parameterization, when compared to dense mapping schemes. Moreover, we
present a case study quantifying and formalizing the trade-offs of typical
non-idealities introduced into 1-Transistor-1-Resistor (1T1R) tiled memristive
architectures and the size of modular crossbar tiles using the CIFAR-10
dataset.

We show that the task of synthesizing human motion conditioned on a set of
key frames can be solved more accurately and effectively if a deep learning
based interpolator operates in the delta mode using the spherical linear
interpolator as a baseline. We empirically demonstrate the strength of our
approach on publicly available datasets achieving state-of-the-art performance.
We further generalize these results by showing that the $\Delta$-regime is
viable with respect to the reference of the last known frame (also known as the
zero-velocity model). This supports the more general conclusion that operating
in the reference frame local to input frames is more accurate and robust than
in the global (world) reference frame advocated in previous work. Our code is
publicly available at https://github.com/boreshkinai/delta-interpolator.

Hybrid private inference (PI) protocol, which synergistically utilizes both
multi-party computation (MPC) and homomorphic encryption, is one of the most
prominent techniques for PI. However, even the state-of-the-art PI protocols
are bottlenecked by the non-linear layers, especially the activation functions.
Although a standard non-linear activation function can generate higher model
accuracy, it must be processed via a costly garbled-circuit MPC primitive. A
polynomial activation can be processed via Beaver's multiplication triples MPC
primitive but has been incurring severe accuracy drops so far.
  In this paper, we propose an accuracy preserving low-degree polynomial
activation function (AESPA) that exploits the Hermite expansion of the ReLU and
basis-wise normalization. We apply AESPA to popular ML models, such as VGGNet,
ResNet, and pre-activation ResNet, to show an inference accuracy comparable to
those of the standard models with ReLU activation, achieving superior accuracy
over prior low-degree polynomial studies. When applied to the all-RELU baseline
on the state-of-the-art Delphi PI protocol, AESPA shows up to 42.1x and 28.3x
lower online latency and communication cost.

Referring expression grounding is an important and challenging task in
computer vision. To avoid the laborious annotation in conventional referring
grounding, unpaired referring grounding is introduced, where the training data
only contains a number of images and queries without correspondences. The few
existing solutions to unpaired referring grounding are still preliminary, due
to the challenges of learning image-text matching and lack of the top-down
guidance with unpaired data. In this paper, we propose a novel bidirectional
cross-modal matching (BiCM) framework to address these challenges.
Particularly, we design a query-aware attention map (QAM) module that
introduces top-down perspective via generating query-specific visual attention
maps. A cross-modal object matching (COM) module is further introduced, which
exploits the recently emerged image-text matching pretrained model, CLIP, to
predict the target objects from a bottom-up perspective. The top-down and
bottom-up predictions are then integrated via a similarity funsion (SF) module.
We also propose a knowledge adaptation matching (KAM) module that leverages
unpaired training data to adapt pretrained knowledge to the target dataset and
task. Experiments show that our framework outperforms previous works by 6.55%
and 9.94% on two popular grounding datasets.

Attackers demonstrated the use of remote access to the in-vehicle network of
connected vehicles to launch cyber-attacks and remotely take control of these
vehicles. Machine-learning-based Intrusion Detection Systems (IDSs) techniques
have been proposed for the detection of such attacks. The evaluation of some of
these IDS demonstrated their efficacy in terms of accuracy in detecting message
injections but was performed offline, which limits the confidence in their use
for real-time protection scenarios. This paper evaluates four architecture
designs for real-time IDS for connected vehicles using Controller Area Network
(CAN) datasets collected from a moving vehicle under malicious speed reading
message injections. The evaluation shows that a real-time IDS for a connected
vehicle designed as two processes, a process for CAN Bus monitoring and another
one for anomaly detection engine is reliable (no loss of messages) and could be
used for real-time resilience mechanisms as a response to cyber-attacks.

We investigate how neural networks (NNs) understand physics using 1D quantum
mechanics. After training an NN to accurately predict energy eigenvalues from
potentials, we used it to confirm the NN's understanding of physics from four
different aspects. The trained NN could predict energy eigenvalues of different
kinds of potentials than the ones learned, predict the probability distribution
of the existence of particles not used during training, reproduce untrained
physical phenomena, and predict the energy eigenvalues of potentials with an
unknown matter effect. These results show that NNs can learn physical laws from
experimental data, predict the results of experiments under conditions
different from those used for training, and predict physical quantities of
types not provided during training. Because NNs understand physics in a
different way than humans, they will be a powerful tool for advancing physics
by complementing the human way of understanding.

The complex physics involved in atmospheric turbulence makes it very
difficult for ground-based astronomy to build accurate scintillation models and
develop efficient methodologies to remove this highly structured noise from
valuable astronomical observations. We argue that a Deep Learning approach can
bring a significant advance to treat this problem because of deep neural
networks' inherent ability to abstract non-linear patterns over a broad scale
range. We propose an architecture composed of long-short term memory cells and
an incremental training strategy inspired by transfer and curriculum learning.
We develop a scintillation model and employ an empirical method to generate a
vast catalog of atmospheric noise realizations and train the network with
representative data. We face two complexity axes: the signal-to-noise ratio
(SNR) and the degree of structure in the noise. Hence, we train our recurrent
network to recognize simulated astrophysical point-like sources embedded in
three structured noise levels, with a raw-data SNR ranging from 3 to 0.1. We
find that a slow and repetitive increase in complexity is crucial during
training to obtain a robust and stable learning rate that can transfer
information through different data contexts. We probe our recurrent model with
synthetic observational data, designing alongside a calibration methodology for
flux measurements. Furthermore, we implement a traditional matched filtering
(MF) to compare its performance with our neural network, finding that our final
trained network can successfully clean structured noise and significantly
enhance the SNR compared to raw data and in a more robust way than traditional
MF.

Existing online learning to rank (OL2R) solutions are limited to linear
models, which are incompetent to capture possible non-linear relations between
queries and documents. In this work, to unleash the power of representation
learning in OL2R, we propose to directly learn a neural ranking model from
users' implicit feedback (e.g., clicks) collected on the fly. We focus on
RankNet and LambdaRank, due to their great empirical success and wide adoption
in offline settings, and control the notorious explore-exploit trade-off based
on the convergence analysis of neural networks using neural tangent kernel.
Specifically, in each round of result serving, exploration is only performed on
document pairs where the predicted rank order between the two documents is
uncertain; otherwise, the ranker's predicted order will be followed in result
ranking. We prove that under standard assumptions our OL2R solution achieves a
gap-dependent upper regret bound of $O(\log^2(T))$, in which the regret is
defined on the total number of mis-ordered pairs over $T$ rounds. Comparisons
against an extensive set of state-of-the-art OL2R baselines on two public
learning to rank benchmark datasets demonstrate the effectiveness of the
proposed solution.

Chatbots are intelligent conversational computer systems designed to mimic
human conversation to enable automated online guidance and support. The
increased benefits of chatbots led to their wide adoption by many industries in
order to provide virtual assistance to customers. Chatbots utilise methods and
algorithms from two Artificial Intelligence domains: Natural Language
Processing and Machine Learning. However, there are many challenges and
limitations in their application. In this survey we review recent advances on
chatbots, where Artificial Intelligence and Natural Language processing are
used. We highlight the main challenges and limitations of current work and make
recommendations for future research investigation.

We prove that Riemannian contraction in a supervised learning setting implies
generalization. Specifically, we show that if an optimizer is contracting in
some Riemannian metric with rate $\lambda > 0$, it is uniformly algorithmically
stable with rate $\mathcal{O}(1/\lambda n)$, where $n$ is the number of
labelled examples in the training set. The results hold for stochastic and
deterministic optimization, in both continuous and discrete-time, for convex
and non-convex loss surfaces. The associated generalization bounds reduce to
well-known results in the particular case of gradient descent over convex or
strongly convex loss surfaces. They can be shown to be optimal in certain
linear settings, such as kernel ridge regression under gradient flow.

Machine learning research typically starts with a fixed data set created
early in the process. The focus of the experiments is finding a model and
training procedure that result in the best possible performance in terms of
some selected evaluation metric. This paper explores how changes in a data set
influence the measured performance of a model. Using three publicly available
data sets from the legal domain, we investigate how changes to their size, the
train/test splits, and the human labelling accuracy impact the performance of a
trained deep learning classifier. We assess the overall performance (weighted
average) as well as the per-class performance. The observed effects are
surprisingly pronounced, especially when the per-class performance is
considered. We investigate how "semantic homogeneity" of a class, i.e., the
proximity of sentences in a semantic embedding space, influences the difficulty
of its classification. The presented results have far reaching implications for
efforts related to data collection and curation in the field of AI & Law. The
results also indicate that enhancements to a data set could be considered,
alongside the advancement of the ML models, as an additional path for
increasing classification performance on various tasks in AI & Law. Finally, we
discuss the need for an established methodology to assess the potential effects
of data set properties.

Community resilience in the face of natural hazards relies on a community's
potential to bounce back. A failure to integrate equity into resilience
considerations results in unequal recovery and disproportionate impacts on
vulnerable populations, which has long been a concern in the United States.
This research investigated aspects of equity related to community resilience in
the aftermath of Winter Storm Uri in Texas which led to extended power outages
for more than 4 million households. County level outage and recovery data was
analyzed to explore potential significant links between various county
attributes and their share of the outages during the recovery and restoration
phases. Next, satellite imagery was used to examine data at a much higher
geographical resolution focusing on census tracts in the city of Houston. The
goal was to use computer vision to extract the extent of outages within census
tracts and investigate their linkages to census tracts attributes. Results from
various statistical procedures revealed statistically significant negative
associations between counties' percentage of non-Hispanic whites and median
household income with the ratio of outages. Additionally, at census tract
level, variables including percentages of linguistically isolated population
and public transport users exhibited positive associations with the group of
census tracts that were affected by the outage as detected by computer vision
analysis. Informed by these results, engineering solutions such as the
applicability of grid modernization technologies, together with distributed and
renewable energy resources, when controlled for the region's topographical
characteristics, are proposed to enhance equitable power grid resiliency in the
face of natural hazards.

We introduce OmniPrint, a synthetic data generator of isolated printed
characters, geared toward machine learning research. It draws inspiration from
famous datasets such as MNIST, SVHN and Omniglot, but offers the capability of
generating a wide variety of printed characters from various languages, fonts
and styles, with customized distortions. We include 935 fonts from 27 scripts
and many types of distortions. As a proof of concept, we show various use
cases, including an example of meta-learning dataset designed for the upcoming
MetaDL NeurIPS 2021 competition. OmniPrint is available at
https://github.com/SunHaozhe/OmniPrint.

Although autonomous vehicles (AVs) are expected to revolutionize
transportation, robust perception across a wide range of driving contexts
remains a significant challenge. Techniques to fuse sensor data from camera,
radar, and lidar sensors have been proposed to improve AV perception. However,
existing methods are insufficiently robust in difficult driving contexts (e.g.,
bad weather, low light, sensor obstruction) due to rigidity in their fusion
implementations. These methods fall into two broad categories: (i) early
fusion, which fails when sensor data is noisy or obscured, and (ii) late
fusion, which cannot leverage features from multiple sensors and thus produces
worse estimates. To address these limitations, we propose HydraFusion: a
selective sensor fusion framework that learns to identify the current driving
context and fuses the best combination of sensors to maximize robustness
without compromising efficiency. HydraFusion is the first approach to propose
dynamically adjusting between early fusion, late fusion, and combinations
in-between, thus varying both how and when fusion is applied. We show that, on
average, HydraFusion outperforms early and late fusion approaches by 13.66% and
14.54%, respectively, without increasing computational complexity or energy
consumption on the industry-standard Nvidia Drive PX2 AV hardware platform. We
also propose and evaluate both static and deep-learning-based context
identification strategies. Our open-source code and model implementation are
available at https://github.com/AICPS/hydrafusion.

Image classification is widely used to build predictive models for breast
cancer diagnosis. Most existing approaches overwhelmingly rely on deep
convolutional networks to build such diagnosis pipelines. These model
architectures, although remarkable in performance, are black-box systems that
provide minimal insight into the inner logic behind their predictions. This is
a major drawback as the explainability of prediction is vital for applications
such as cancer diagnosis. In this paper, we address this issue by proposing an
explainable machine learning pipeline for breast cancer diagnosis based on
ultrasound images. We extract first- and second-order texture features of the
ultrasound images and use them to build a probabilistic ensemble of decision
tree classifiers. Each decision tree learns to classify the input ultrasound
image by learning a set of robust decision thresholds for texture features of
the image. The decision path of the model predictions can then be interpreted
by decomposing the learned decision trees. Our results show that our proposed
framework achieves high predictive performance while being explainable.

The need for raw large raw corpora has dramatically increased in recent years
with the introduction of transfer learning and semi-supervised learning methods
to Natural Language Processing. And while there have been some recent attempts
to manually curate the amount of data necessary to train large language models,
the main way to obtain this data is still through automatic web crawling. In
this paper we take the existing multilingual web corpus OSCAR and its pipeline
Ungoliant that extracts and classifies data from Common Crawl at the line
level, and propose a set of improvements and automatic annotations in order to
produce a new document-oriented version of OSCAR that could prove more suitable
to pre-train large generative language models as well as hopefully other
applications in Natural Language Processing and Digital Humanities.

Existing methods in inexact unlearning are evaluated by measuring
indistinguishability from models retrained after removing the deletion set. We
argue that achieving indistinguishability is unnecessary and its practical
relaxations are insufficient. We formulate the goal of unlearning as forgetting
all information specific to the deletion set while maintaining high utility and
resource efficiency.
  We introduce a novel test for forgetting called Interclass Confusion (IC).
Despite being a black-box test, IC can investigate whether information from the
deletion set was erased until the early layers of the network. We analyze two
aspects of forgetting: (i) memorization and (ii) property generalization. We
empirically show that two simple unlearning methods, exact-unlearning and
catastrophic-forgetting the final k layers of a network, outperforms prior
unlearning methods when scaled to large deletion sets. Overall, we believe our
formulation and the IC test will guide the design of better unlearning

Social media has become an indispensable channel for political communication.
However, the political discourse is increasingly characterized by hate speech,
which affects not only the reputation of individual politicians but also the
functioning of society at large. In this work, we empirically analyze how the
amount of hate speech in replies to posts from politicians on Twitter depends
on personal characteristics, such as their party affiliation, gender, and
ethnicity. For this purpose, we employ Twitter's Historical API to collect
every tweet posted by members of the 117th U.S. Congress for an observation
period of more than six months. Additionally, we gather replies for each tweet
and use machine learning to predict the amount of hate speech they embed.
Subsequently, we implement hierarchical regression models to analyze whether
politicians with certain characteristics receive more hate speech. We find that
tweets are particularly likely to receive hate speech in replies if they are
authored by (i) persons of color from the Democratic party, (ii) white
Republicans, and (iii) women. Furthermore, our analysis reveals that more
negative sentiment (in the source tweet) is associated with more hate speech
(in replies). However, the association varies across parties: negative
sentiment attracts more hate speech for Democrats (vs. Republicans).
Altogether, our empirical findings imply significant differences in how
politicians are treated on social media depending on their party affiliation,
gender, and ethnicity.

Object detection is increasingly used onboard Unmanned Aerial Vehicles (UAV)
for various applications; however, the machine learning (ML) models for
UAV-based detection are often validated using data curated for tasks unrelated
to the UAV application. This is a concern because training neural networks on
large-scale benchmarks have shown excellent capability in generic object
detection tasks, yet conventional training approaches can lead to large
inference errors for UAV-based images. Such errors arise due to differences in
imaging conditions between images from UAVs and images in training. To overcome
this problem, we characterize boundary conditions of ML models, beyond which
the models exhibit rapid degradation in detection accuracy. Our work is focused
on understanding the impact of different UAV-based imaging conditions on
detection performance by using synthetic data generated using a game engine.
Properties of the game engine are exploited to populate the synthetic datasets
with realistic and annotated images. Specifically, it enables the fine control
of various parameters, such as camera position, view angle, illumination
conditions, and object pose. Using the synthetic datasets, we analyze detection
accuracy in different imaging conditions as a function of the above parameters.
We use three well-known neural network models with different model complexity
in our work. In our experiment, we observe and quantify the following: 1) how
detection accuracy drops as the camera moves toward the nadir-view region; 2)
how detection accuracy varies depending on different object poses, and 3) the
degree to which the robustness of the models changes as illumination conditions
vary.

In this paper, we present a deep learning technique for data-driven
predictions of wave propagation in a fluid medium. The technique relies on an
attention-based convolutional recurrent autoencoder network (AB-CRAN). To
construct a low-dimensional representation of wave propagation data, we employ
a denoising-based convolutional autoencoder. The AB-CRAN architecture with
attention-based long short-term memory cells forms our deep neural network
model for the time marching of the low-dimensional features. We assess the
proposed AB-CRAN framework against the standard recurrent neural network for
the low-dimensional learning of wave propagation. To demonstrate the
effectiveness of the AB-CRAN model, we consider three benchmark problems,
namely, one-dimensional linear convection, the nonlinear viscous Burgers
equation, and the two-dimensional Saint-Venant shallow water system. Using the
spatial-temporal datasets from the benchmark problems, our novel AB-CRAN
architecture accurately captures the wave amplitude and preserves the wave
characteristics of the solution for long time horizons. The attention-based
sequence-to-sequence network increases the time-horizon of prediction compared
to the standard recurrent neural network with long short-term memory cells. The
denoising autoencoder further reduces the mean squared error of prediction and
improves the generalization capability in the parameter space.

The transformer architectures with attention mechanisms have obtained success
in Nature Language Processing (NLP), and Vision Transformers (ViTs) have
recently extended the application domains to various vision tasks. While
achieving high performance, ViTs suffer from large model size and high
computation complexity that hinders the deployment of them on edge devices. To
achieve high throughput on hardware and preserve the model accuracy
simultaneously, we propose VAQF, a framework that builds inference accelerators
on FPGA platforms for quantized ViTs with binary weights and low-precision
activations. Given the model structure and the desired frame rate, VAQF will
automatically output the required quantization precision for activations as
well as the optimized parameter settings of the accelerator that fulfill the
hardware requirements. The implementations are developed with Vivado High-Level
Synthesis (HLS) on the Xilinx ZCU102 FPGA board, and the evaluation results
with the DeiT-base model indicate that a frame rate requirement of 24 frames
per second (FPS) is satisfied with 8-bit activation quantization, and a target
of 30 FPS is met with 6-bit activation quantization. To the best of our
knowledge, this is the first time quantization has been incorporated into ViT
acceleration on FPGAs with the help of a fully automatic framework to guide the
quantization strategy on the software side and the accelerator implementations
on the hardware side given the target frame rate. Very small compilation time
cost is incurred compared with quantization training, and the generated
accelerators show the capability of achieving real-time execution for
state-of-the-art ViT models on FPGAs.

In image denoising problems, the increasing density of available images makes
an exhaustive visual inspection impossible and therefore automated methods
based on machine-learning must be deployed for this purpose. This is
particulary the case in seismic signal processing. Engineers/geophysicists have
to deal with millions of seismic time series. Finding the sub-surface
properties useful for the oil industry may take up to a year and is very costly
in terms of computing/human resources. In particular, the data must go through
different steps of noise attenuation. Each denoise step is then ideally
followed by a quality control (QC) stage performed by means of human expertise.
To learn a quality control classifier in a supervised manner, labeled training
data must be available, but collecting the labels from human experts is
extremely time-consuming. We therefore propose a novel active learning
methodology to sequentially select the most relevant data, which are then given
back to a human expert for labeling. Beyond the application in geophysics, the
technique we promote in this paper, based on estimates of the local error and
its uncertainty, is generic. Its performance is supported by strong empirical
evidence, as illustrated by the numerical experiments presented in this
article, where it is compared to alternative active learning strategies both on
synthetic and real seismic datasets.

Emotion prediction is a key emerging research area that focuses on
identifying and forecasting the emotional state of a human from multiple
modalities. Among other data sources, physiological data can serve as an
indicator for emotions with an added advantage that it cannot be
masked/tampered by the individual and can be easily collected. This paper
surveys multiple machine learning methods that deploy smartphone and
physiological data to predict emotions in real-time, using self-reported
ecological momentary assessments (EMA) scores as ground-truth. Comparing
regression, long short-term memory (LSTM) networks, convolutional neural
networks (CNN), reinforcement online learning (ROL), and deep belief networks
(DBN), we showcase the variability of machine learning methods employed to
achieve accurate emotion prediction. We compare the state-of-the-art methods
and highlight that experimental performance is still not very good. The
performance can be improved in future works by considering the following
issues: improving scalability and generalizability, synchronizing multimodal
data, optimizing EMA sampling, integrating adaptability with sequence
prediction, collecting unbiased data, and leveraging sophisticated feature
engineering techniques.

We introduce a new approach for decoupling trends (drift) and changepoints
(shifts) in time series. Our locally adaptive model-based approach for robustly
decoupling combines Bayesian trend filtering and machine learning based
regularization. An over-parameterized Bayesian dynamic linear model (DLM) is
first applied to characterize drift. Then a weighted penalized likelihood
estimator is paired with the estimated DLM posterior distribution to identify
shifts. We show how Bayesian DLMs specified with so-called shrinkage priors can
provide smooth estimates of underlying trends in the presence of complex noise
components. However, their inability to shrink exactly to zero inhibits direct
changepoint detection. In contrast, penalized likelihood methods are highly
effective in locating changepoints. However, they require data with simple
patterns in both signal and noise. The proposed decoupling approach combines
the strengths of both, i.e.\ the flexibility of Bayesian DLMs with the hard
thresholding property of penalized likelihood estimators, to provide
changepoint analysis in complex, modern settings. The proposed framework is
outlier robust and can identify a variety of changes, including in mean and
slope. It is also easily extended for analysis of parameter shifts in
time-varying parameter models like dynamic regressions. We illustrate the
flexibility and contrast the performance and robustness of our approach with
several alternative methods across a wide range of simulations and application
examples.

The Granular Instrumental Variables (GIV) methodology exploits panels with
factor error structures to construct instruments to estimate structural time
series models with endogeneity even after controlling for latent factors. We
extend the GIV methodology in several dimensions. First, we extend the
identification procedure to a large $N$ and large $T$ framework, which depends
on the asymptotic Herfindahl index of the size distribution of $N$
cross-sectional units. Second, we treat both the factors and loadings as
unknown and show that the sampling error in the estimated instrument and
factors is negligible when considering the limiting distribution of the
structural parameters. Third, we show that the sampling error in the
high-dimensional precision matrix is negligible in our estimation algorithm.
Fourth, we overidentify the structural parameters with additional constructed
instruments, which leads to efficiency gains. Monte Carlo evidence is presented
to support our asymptotic theory and application to the global crude oil market
leads to new results.

The automation of condition monitoring and workpiece inspection plays an
essential role in maintaining high quality as well as high throughput of the
manufacturing process. To this end, the recent rise of developments in machine
learning has lead to vast improvements in the area of autonomous process
supervision. However, the more complex and powerful these models become, the
less transparent and explainable they generally are as well. One of the main
challenges is the monitoring of live deployments of these machine learning
systems and raising alerts when encountering events that might impact model
performance. In particular, supervised classifiers are typically build under
the assumption of stationarity in the underlying data distribution. For
example, a visual inspection system trained on a set of material surface
defects generally does not adapt or even recognize gradual changes in the data
distribution - an issue known as "data drift" - such as the emergence of new
types of surface defects. This, in turn, may lead to detrimental
mispredictions, e.g. samples from new defect classes being classified as
non-defective. To this end, it is desirable to provide real-time tracking of a
classifier's performance to inform about the putative onset of additional error
classes and the necessity for manual intervention with respect to classifier
re-training. Here, we propose an unsupervised framework that acts on top of a
supervised classification system, thereby harnessing its internal deep feature
representations as a proxy to track changes in the data distribution during
deployment and, hence, to anticipate classifier performance degradation.

Federated learning involves training statistical models over remote devices
such as mobile phones while keeping data localized. Training in heterogeneous
and potentially massive networks introduces opportunities for
privacy-preserving data analysis and diversifying these models to become more
inclusive of the population. Federated learning can be viewed as a unique
opportunity to bring fairness and parity to many existing models by enabling
model training to happen on a diverse set of participants and on data that is
generated regularly and dynamically. In this paper, we discuss the current
metrics and approaches that are available to measure and evaluate fairness in
the context of spatial-temporal models. We propose how these metrics and
approaches can be re-defined to address the challenges that are faced in the
federated learning setting.

Automated symmetry detection is still a difficult task in 2021. However, it
has applications in computer vision, and it also plays an important part in
understanding art. This paper focuses on aiding the latter by comparing
different state-of-the-art automated symmetry detection algorithms. For one of
such algorithms aimed at reflectional symmetries, we propose post-processing
improvements to find localised symmetries in images, improve the selection of
detected symmetries and identify another symmetry type (rotational). In order
to detect rotational symmetries, we contribute a machine learning model which
detects rotational symmetries based on provided reflection symmetry axis pairs.
We demonstrate and analyze the performance of the extended algorithm to detect
localised symmetries and the machine learning model to classify rotational
symmetries.

Deep learning (DL) systems can make our life much easier, and thus are
gaining more and more attention from both academia and industry. Meanwhile,
bugs in DL systems can be disastrous, and can even threaten human lives in
safety-critical applications. To date, a huge body of research efforts have
been dedicated to testing DL models. However, interestingly, there is still
limited work for testing the underlying DL libraries, which are the foundation
for building, optimizing, and running DL models. One potential reason is that
test generation for the underlying DL libraries can be rather challenging since
their public APIs are mainly exposed in Python, making it even hard to
automatically determine the API input parameter types due to dynamic typing. In
this paper, we propose FreeFuzz, the first approach to fuzzing DL libraries via
mining from open source. More specifically, FreeFuzz obtains code/models from
three different sources: 1) code snippets from the library documentation, 2)
library developer tests, and 3) DL models in the wild. Then, FreeFuzz
automatically runs all the collected code/models with instrumentation to trace
the dynamic information for each covered API, including the types and values of
each parameter during invocation, and shapes of input/output tensors. Lastly,
FreeFuzz will leverage the traced dynamic information to perform fuzz testing
for each covered API. The extensive study of FreeFuzz on PyTorch and
TensorFlow, two of the most popular DL libraries, shows that FreeFuzz is able
to automatically trace valid dynamic information for fuzzing 1158 popular APIs,
9X more than state-of-the-art LEMON with 3.5X lower overhead than LEMON. To
date, FreeFuzz has detected 49 bugs for PyTorch and TensorFlow (with 38 already
confirmed by developers as previously unknown).

Modern analysis on parton distribution functions (PDFs) requires calculations
of the log-likelihood functions from thousands of experimental data points, and
scans of multi-dimensional parameter space with tens of degrees of freedom. In
conventional analysis the Hessian approximation has been widely used for the
estimation of the PDF uncertainties.The Lagrange Multiplier (LM) scan while
being a more faithful method is less used due to computational limitations, and
is the main focus of this study. We propose to use Neural Networks (NNs) and
machine learning techniques to model the profile of the log-likelihood
functions or cross sections for multi-dimensional parameter space in order to
overcome those limitations which work beyond the quadratic approximations and
meanwhile ensures efficient scans of the full parameter space. We demonstrate
the efficiency of the new approach in the framework of the CT18 global analysis
of PDFs by constructing NNs for various target functions, and performing LM
scans on PDFs and cross sections at hadron colliders. We further study the
impact of the NOMAD dimuon data on constraining PDFs with the new approach, and
find enhanced strange-quark distributions and reduced PDF uncertainties.
Moreover, we show how the approach can be used to constrain new physics beyond
the Standard Model (BSM) by a joint fit of both PDFs and Wilson coefficients of
operators in the SM effective field theory.

Class-conditioning offers a direct means to control a Generative Adversarial
Network (GAN) based on a discrete input variable. While necessary in many
applications, the additional information provided by the class labels could
even be expected to benefit the training of the GAN itself. On the contrary, we
observe that class-conditioning causes mode collapse in limited data settings,
where unconditional learning leads to satisfactory generative ability.
Motivated by this observation, we propose a training strategy for
class-conditional GANs (cGANs) that effectively prevents the observed
mode-collapse by leveraging unconditional learning. Our training strategy
starts with an unconditional GAN and gradually injects the class conditioning
into the generator and the objective function. The proposed method for training
cGANs with limited data results not only in stable training but also in
generating high-quality images, thanks to the early-stage exploitation of the
shared information across classes. We analyze the observed mode collapse
problem in comprehensive experiments on four datasets. Our approach
demonstrates outstanding results compared with state-of-the-art methods and
established baselines. The code is available at
https://github.com/mshahbazi72/transitional-cGAN

In this paper we introduce PerPaDa, a Persian paraphrase dataset that is
collected from users' input in a plagiarism detection system. As an implicit
crowdsourcing experience, we have gathered a large collection of original and
paraphrased sentences from Hamtajoo; a Persian plagiarism detection system, in
which users try to conceal cases of text re-use in their documents by
paraphrasing and re-submitting manuscripts for analysis. The compiled dataset
contains 2446 instances of paraphrasing. In order to improve the overall
quality of the collected data, some heuristics have been used to exclude
sentences that don't meet the proposed criteria. The introduced corpus is much
larger than the available datasets for the task of paraphrase identification in
Persian. Moreover, there is less bias in the data compared to the similar
datasets, since the users did not try some fixed predefined rules in order to
generate similar texts to their original inputs.

The efficacy of zero-shot sketch-based image retrieval (ZS-SBIR) models is
governed by two challenges. The immense distributions-gap between the sketches
and the images requires a proper domain alignment. Moreover, the fine-grained
nature of the task and the high intra-class variance of many categories
necessitates a class-wise discriminative mapping among the sketch, image, and
the semantic spaces. Under this premise, we propose BDA-SketRet, a novel
ZS-SBIR framework performing a bi-level domain adaptation for aligning the
spatial and semantic features of the visual data pairs progressively. In order
to highlight the shared features and reduce the effects of any sketch or
image-specific artifacts, we propose a novel symmetric loss function based on
the notion of information bottleneck for aligning the semantic features while a
cross-entropy-based adversarial loss is introduced to align the spatial feature
maps. Finally, our CNN-based model confirms the discriminativeness of the
shared latent space through a novel topology-preserving semantic projection
network. Experimental results on the extended Sketchy, TU-Berlin, and QuickDraw
datasets exhibit sharp improvements over the literature.

Hybrid organic-inorganic perovskites have drawn significant interest for
applications in optoelectronics over the last few years. Despite rapid progress
in understanding the photophysics of perovskite, there remains a need for
improved understanding of the effect of microstructure on perovskite
photophysical processes. Here, we combine unsupervised machine learning and
cathodoluminescence microscopy of a prototypical hybrid perovskite film to
decode photophysical processes that are otherwise lost with conventional
Gaussian image processing. Hyperspectral maps are decoded with non-negative
matrix factorization, revealing components relating to primary band-edge
emission, photon recycling, and defect emission. A blind-spectral non-negative
matrix factorization procedure provides additional understanding of changes in
an intermediate perovskite phase under electron beam exposure and illustrates
how traditional Gaussian techniques may hide relevant emission features that
are critical to the development of environmentally robust perovskite devices

It can be difficult to autonomously produce driver behavior so that it
appears natural to other traffic participants. Through Inverse Reinforcement
Learning (IRL), we can automate this process by learning the underlying reward
function from human demonstrations. We propose a new IRL algorithm that learns
a goal-conditioned spatiotemporal reward function. The resulting costmap is
used by Model Predictive Controllers (MPCs) to perform a task without any
hand-designing or hand-tuning of the cost function. We evaluate our proposed
Goal-conditioned SpatioTemporal Zeroing Maximum Entropy Deep IRL (GSTZ)-MEDIRL
framework together with MPC in the CARLA simulator for autonomous driving, lane
keeping, and lane changing tasks in a challenging dense traffic highway
scenario. Our proposed methods show higher success rates compared to other
baseline methods including behavior cloning, state-of-the-art RL policies, and
MPC with a learning-based behavior prediction model.

We introduce a neural network architecture that logarithmically reduces the
number of self-rehearsal steps in the generative rehearsal of continually
learned models. In continual learning (CL), training samples come in subsequent
tasks, and the trained model can access only a single task at a time. To replay
previous samples, contemporary CL methods bootstrap generative models and train
them recursively with a combination of current and regenerated past data. This
recurrence leads to superfluous computations as the same past samples are
regenerated after each task, and the reconstruction quality successively
degrades. In this work, we address these limitations and propose a new
generative rehearsal architecture that requires at most logarithmic number of
retraining for each sample. Our approach leverages allocation of past data in
a~set of generative models such that most of them do not require retraining
after a~task. The experimental evaluation of our logarithmic continual learning
approach shows the superiority of our method with respect to the
state-of-the-art generative rehearsal methods.

We study the non-stationary stochastic multi-armed bandit problem, where the
reward statistics of each arm may change several times during the course of
learning. The performance of a learning algorithm is evaluated in terms of
their dynamic regret, which is defined as the difference between the expected
cumulative reward of an agent choosing the optimal arm in every time step and
the cumulative reward of the learning algorithm. One way to measure the
hardness of such environments is to consider how many times the identity of the
optimal arm can change. We propose a method that achieves, in $K$-armed bandit
problems, a near-optimal $\widetilde O(\sqrt{K N(S+1)})$ dynamic regret, where
$N$ is the time horizon of the problem and $S$ is the number of times the
identity of the optimal arm changes, without prior knowledge of $S$. Previous
works for this problem obtain regret bounds that scale with the number of
changes (or the amount of change) in the reward functions, which can be much
larger, or assume prior knowledge of $S$ to achieve similar bounds.

Regression in supervised learning often requires the enforcement of
constraints to ensure that the trained models are consistent with the
underlying structures of the input and output data. This paper presents an
iterative procedure to perform regression under arbitrary constraints. It is
achieved by alternating between a learning step and a constraint enforcement
step, to which an affine extension function is incorporated. We show this leads
to a contraction mapping under mild assumptions, from which the convergence is
guaranteed analytically. The presented proof of convergence in regression with
constraints is the unique contribution of this paper. Furthermore, numerical
experiments illustrate improvements in the trained model in terms of the
quality of regression, the satisfaction of constraints, and also the stability
in training, when compared to other existing algorithms.

This study aims to explore the associations between near-crash events and
road geometry and trip features by investigating a naturalistic driving dataset
and a corresponding roadway inventory dataset using an association rule mining
method.

Rule-based decision models are attractive due to their interpretability.
However, existing rule induction methods often result in long and consequently
less interpretable rule models. This problem can often be attributed to the
lack of appropriately expressive vocabulary, i.e., relevant predicates used as
literals in the decision model. Most existing rule induction algorithms presume
pre-defined literals, naturally decoupling the definition of the literals from
the rule learning phase. In contrast, we propose the Relational Rule Network
(R2N), a neural architecture that learns literals that represent a linear
relationship among numerical input features along with the rules that use them.
This approach opens the door to increasing the expressiveness of induced
decision models by coupling literal learning directly with rule learning in an
end-to-end differentiable fashion. On benchmark tasks, we show that these
learned literals are simple enough to retain interpretability, yet improve
prediction accuracy and provide sets of rules that are more concise compared to
state-of-the-art rule induction algorithms.

Compressing deep networks is essential to expand their range of applications
to constrained settings. The need for compression however often arises long
after the model was trained, when the original data might no longer be
available. On the other hand, unlabeled data, not necessarily related to the
target task, is usually plentiful, especially in image classification tasks. In
this work, we propose a scheme to leverage such samples to distill the
knowledge learned by a large teacher network to a smaller student. The proposed
technique relies on (i) preferentially sampling datapoints that appear related,
and (ii) taking better advantage of the learning signal. We show that the
former speeds up the student's convergence, while the latter boosts its
performance, achieving performances closed to what can be expected with the
original data.

Diffusion probabilistic models (DPMs) represent a class of powerful
generative models. Despite their success, the inference of DPMs is expensive
since it generally needs to iterate over thousands of timesteps. A key problem
in the inference is to estimate the variance in each timestep of the reverse
process. In this work, we present a surprising result that both the optimal
reverse variance and the corresponding optimal KL divergence of a DPM have
analytic forms w.r.t. its score function. Building upon it, we propose
Analytic-DPM, a training-free inference framework that estimates the analytic
forms of the variance and KL divergence using the Monte Carlo method and a
pretrained score-based model. Further, to correct the potential bias caused by
the score-based model, we derive both lower and upper bounds of the optimal
variance and clip the estimate for a better result. Empirically, our
analytic-DPM improves the log-likelihood of various DPMs, produces high-quality
samples, and meanwhile enjoys a 20x to 80x speed up.

We propose a general method for growing neural network with shared parameter
by matching trained network to new input. By leveraging Hoeffding's inequality,
we provide a theoretical base for improving performance by adding subnetwork to
existing network. With the theoretical base of adding new subnetwork, we
implement a matching method to apply trained subnetwork of existing network to
new input. Our method has shown the ability to improve performance with higher
parameter efficiency. It can also be applied to trans-task case and realize
transfer learning by changing the combination of subnetworks without training
on new task.

The paper describes the open Russian medical language understanding benchmark
covering several task types (classification, question answering, natural
language inference, named entity recognition) on a number of novel text sets.
Given the sensitive nature of the data in healthcare, such a benchmark
partially closes the problem of Russian medical dataset absence. We prepare the
unified format labeling, data split, and evaluation metrics for new tasks. The
remaining tasks are from existing datasets with a few modifications. A
single-number metric expresses a model's ability to cope with the benchmark.
Moreover, we implement several baseline models, from simple ones to neural
networks with transformer architecture, and release the code. Expectedly, the
more advanced models yield better performance, but even a simple model is
enough for a decent result in some tasks. Furthermore, for all tasks, we
provide a human evaluation. Interestingly the models outperform humans in the
large-scale classification tasks. However, the advantage of natural
intelligence remains in the tasks requiring more knowledge and reasoning.

Object detection through either RGB images or the LiDAR point clouds has been
extensively explored in autonomous driving. However, it remains challenging to
make these two data sources complementary and beneficial to each other. In this
paper, we propose \textit{AutoAlign}, an automatic feature fusion strategy for
3D object detection. Instead of establishing deterministic correspondence with
camera projection matrix, we model the mapping relationship between the image
and point clouds with a learnable alignment map. This map enables our model to
automate the alignment of non-homogenous features in a dynamic and data-driven
manner. Specifically, a cross-attention feature alignment module is devised to
adaptively aggregate \textit{pixel-level} image features for each voxel. To
enhance the semantic consistency during feature alignment, we also design a
self-supervised cross-modal feature interaction module, through which the model
can learn feature aggregation with \textit{instance-level} feature guidance.
Extensive experimental results show that our approach can lead to 2.3 mAP and
7.0 mAP improvements on the KITTI and nuScenes datasets, respectively. Notably,
our best model reaches 70.9 NDS on the nuScenes testing leaderboard, achieving
competitive performance among various state-of-the-arts.

Stock market forecasting is a lucrative field of interest with promising
profits but not without its difficulties and for some people could be even
causes of failure. Financial markets by their nature are complex, non-linear
and chaotic, which implies that accurately predicting the prices of assets that
are part of it becomes very complicated. In this paper we propose a stock
trading system having as main core the feed-forward deep neural networks (DNN)
to predict the price for the next 30 days of open market, of the shares issued
by Abercrombie & Fitch Co. (ANF) in the stock market of the New York Stock
Exchange (NYSE).
  The system we have elaborated calculates the most effective technical
indicator, applying it to the predictions computed by the DNNs, for generating
trades. The results showed an increase in values such as Expectancy Ratio of
2.112% of profitable trades with Sharpe, Sortino, and Calmar Ratios of 2.194,
3.340, and 12.403 respectively. As a verification, we adopted a backtracking
simulation module in our system, which maps trades to actual test data
consisting of the last 30 days of open market on the ANF asset. Overall, the
results were promising bringing a total profit factor of 3.2% in just one month
from a very modest budget of $100. This was possible because the system reduced
the number of trades by choosing the most effective and efficient trades,
saving on commissions and slippage costs.

Supervised classification techniques use training samples to learn a
classification rule with small expected 0-1 loss (error probability).
Conventional methods enable tractable learning and provide out-of-sample
generalization by using surrogate losses instead of the 0-1 loss and
considering specific families of rules (hypothesis classes). This paper
presents minimax risk classifiers (MRCs) that minimize the worst-case 0-1 loss
over general classification rules and provide tight performance guarantees at
learning. We show that MRCs are strongly universally consistent using feature
mappings given by characteristic kernels. The paper also proposes efficient
optimization techniques for MRC learning and shows that the methods presented
can provide accurate classification together with tight performance guarantees

To accumulate knowledge and improve its policy of behaviour, a reinforcement
learning agent can learn `off-policy' about policies that differ from the
policy used to generate its experience. This is important to learn
counterfactuals, or because the experience was generated out of its own
control. However, off-policy learning is non-trivial, and standard
reinforcement-learning algorithms can be unstable and divergent.
  In this paper we discuss a novel family of off-policy prediction algorithms
which are convergent by construction. The idea is to first learn on-policy
about the data-generating behaviour, and then bootstrap an off-policy value
estimate on this on-policy estimate, thereby constructing a value estimate that
is partially off-policy. This process can be repeated to build a chain of value
functions, each time bootstrapping a new estimate on the previous estimate in
the chain. Each step in the chain is stable and hence the complete algorithm is
guaranteed to be stable. Under mild conditions this comes arbitrarily close to
the off-policy TD solution when we increase the length of the chain. Hence it
can compute the solution even in cases where off-policy TD diverges.
  We prove that the proposed scheme is convergent and corresponds to an
iterative decomposition of the inverse key matrix. Furthermore it can be
interpreted as estimating a novel objective -- that we call a `k-step
expedition' -- of following the target policy for finitely many steps before
continuing indefinitely with the behaviour policy. Empirically we evaluate the
idea on challenging MDPs such as Baird's counter example and observe favourable
results.

Machine learning (ML) applications have automated numerous real-life tasks,
improving both private and public life. However, the black-box nature of many
state-of-the-art models poses the challenge of model verification; how can one
be sure that the algorithm bases its decisions on the proper criteria, or that
it does not discriminate against certain minority groups? In this paper we
propose a way to generate diverse counterfactual explanations from multilinear
models, a broad class which includes Random Forests, as well as Bayesian
Networks.

We introduce a computational efficient data-driven framework suitable for
quantifying the uncertainty in physical parameters and model formulation of
computer models, represented by differential equations. We construct
physics-informed priors, which are multi-output GP priors that encode the
model's structure in the covariance function. We extend this into a fully
Bayesian framework that quantifies the uncertainty of physical parameters and
model predictions. Since physical models often are imperfect descriptions of
the real process, we allow the model to deviate from the observed data by
considering a discrepancy function. To obtain the posterior distributions, we
use Hamiltonian Monte Carlo sampling. We demonstrate our approach in a
simulation study with hemodynamical models, which are time-dependent
differential equations. Data are simulated from a more complex model than our
modelling choice, and the aim is to learn physical parameters according to
known mathematical connections. To demonstrate the flexibility of our approach,
an example using the Heat equation, a space-time dependent differential
equation where we consider a case of a biased data-acquisition process is also
included. Finally, we fit the hemodynamic model using real data obtained in a
medical trial.

There is growing interest in the detection and characterization of
gravitational waves from postmerger oscillations of binary neutron stars. These
signals contain information about the nature of the remnant and the
high-density and out-of-equilibrium physics of the postmerger processes, which
would complement any electromagnetic signal. However, the construction of
binary neutron star postmerger waveforms is much more complicated than for
binary black holes: (i) there are theoretical uncertainties in the neutron-star
equation of state and other aspects of the high-density physics, (ii) numerical
simulations are expensive and available ones only cover a small fraction of the
parameter space with limited numerical accuracy, and (iii) it is unclear how to
parametrize the theoretical uncertainties and interpolate across parameter
space. In this work, we describe the use of a machine-learning method called a
conditional variational autoencoder (CVAE) to construct postmerger models for
hyper/massive neutron star remnant signals based on numerical-relativity
simulations. The CVAE provides a probabilistic model, which encodes
uncertainties in the training data within a set of latent parameters. We
estimate that training such a model will ultimately require $\sim 10^4$
waveforms. However, using synthetic training waveforms as a proof-of-principle,
we show that the CVAE can be used as an accurate generative model and that it
encodes the equation of state in a useful latent representation.

Expressive synthetic speech is essential for many human-computer interaction
and audio broadcast scenarios, and thus synthesizing expressive speech has
attracted much attention in recent years. Previous methods performed the
expressive speech synthesis either with explicit labels or with a fixed-length
style embedding extracted from reference audio, both of which can only learn an
average style and thus ignores the multi-scale nature of speech prosody. In
this paper, we propose MsEmoTTS, a multi-scale emotional speech synthesis
framework, to model the emotion from different levels. Specifically, the
proposed method is a typical attention-based sequence-to-sequence model and
with proposed three modules, including global-level emotion presenting module
(GM), utterance-level emotion presenting module (UM), and local-level emotion
presenting module (LM), to model the global emotion category, utterance-level
emotion variation, and syllable-level emotion strength, respectively. In
addition to modeling the emotion from different levels, the proposed method
also allows us to synthesize emotional speech in different ways, i.e.,
transferring the emotion from reference audio, predicting the emotion from
input text, and controlling the emotion strength manually. Extensive
experiments conducted on a Chinese emotional speech corpus demonstrate that the
proposed method outperforms the compared reference audio-based and text-based
emotional speech synthesis methods on the emotion transfer speech synthesis and
text-based emotion prediction speech synthesis respectively. Besides, the
experiments also show that the proposed method can control the emotion
expressions flexibly. Detailed analysis shows the effectiveness of each module
and the good design of the proposed method.

Remote sensing (RS) images are usually stored in compressed format to reduce
the storage size of the archives. Thus, existing content-based image retrieval
(CBIR) systems in RS require decoding images before applying CBIR (which is
computationally demanding in the case of large-scale CBIR problems). To address
this problem, in this paper, we present a joint framework that simultaneously
learns RS image compression and indexing. Thus, it eliminates the need for
decoding RS images before applying CBIR. The proposed framework is made up of
two modules. The first module compresses RS images based on an auto-encoder
architecture. The second module produces hash codes with a high discrimination
capability by employing soft pairwise, bit-balancing and classification loss
functions. We also introduce a two stage learning strategy with gradient
manipulation techniques to obtain image representations that are compatible
with both RS image indexing and compression. Experimental results show the
efficacy of the proposed framework when compared to widely used approaches in
RS. The code of the proposed framework is available at
https://git.tu-berlin.de/rsim/RS-JCIF.

The application of Deep Neural Networks (DNNs) to a broad variety of tasks
demands methods for coping with the complex and opaque nature of these
architectures. When a gold standard is available, performance assessment treats
the DNN as a black box and computes standard metrics based on the comparison of
the predictions with the ground truth. A deeper understanding of performances
requires going beyond such evaluation metrics to diagnose the model behavior
and the prediction errors. This goal can be pursued in two complementary ways.
On one side, model interpretation techniques "open the box" and assess the
relationship between the input, the inner layers and the output, so as to
identify the architecture modules most likely to cause the performance loss. On
the other hand, black-box error diagnosis techniques study the correlation
between the model response and some properties of the input not used for
training, so as to identify the features of the inputs that make the model
fail. Both approaches give hints on how to improve the architecture and/or the
training process. This paper focuses on the application of DNNs to Computer
Vision (CV) tasks and presents a survey of the tools that support the black-box
performance diagnosis paradigm. It illustrates the features and gaps of the
current proposals, discusses the relevant research directions and provides a
brief overview of the diagnosis tools in sectors other than CV.

Motivated by applications in single-cell biology and metagenomics, we
consider matrix reordering based on the noisy disordered matrix model. We first
establish the fundamental statistical limit for the matrix reordering problem
in a decision-theoretic framework and show that a constrained least square
estimator is rate-optimal. Given the computational hardness of the optimal
procedure, we analyze a popular polynomial-time algorithm, spectral seriation,
and show that it is suboptimal. We then propose a novel polynomial-time
adaptive sorting algorithm with guaranteed improvement on the performance. The
superiority of the adaptive sorting algorithm over the existing methods is
demonstrated in simulation studies and in the analysis of two real single-cell
RNA sequencing datasets.

Signed link prediction in graphs is an important problem that has
applications in diverse domains. It is a binary classification problem that
predicts whether an edge between a pair of nodes is positive or negative.
Existing approaches for link prediction in unsigned networks cannot be directly
applied for signed link prediction due to their inherent differences. Further,
additional structural constraints, like, the structural balance property of the
signed networks must be considered for signed link prediction. Recent signed
link prediction approaches generate node representations using either
generative models or discriminative models. Inspired by the recent success of
Generative Adversarial Network (GAN) based models which comprises of a
discriminator and generator in several applications, we propose a Generative
Adversarial Network (GAN) based model for signed networks, SigGAN. It considers
the requirements of signed networks, such as, integration of information from
negative edges, high imbalance in number of positive and negative edges and
structural balance theory. Comparing the performance with state of the art
techniques on several real-world datasets validates the effectiveness of
SigGAN.

In this paper, we develop a general theoretical framework for constructing
Haar-type tight framelets on any compact set with a hierarchical partition. In
particular, we construct a novel area-regular hierarchical partition on the
2-sphere and establish its corresponding spherical Haar tight framelets with
directionality. We conclude by evaluating and illustrating the effectiveness of
our area-regular spherical Haar tight framelets in several denoising
experiments. Furthermore, we propose a convolutional neural network (CNN) model
for spherical signal denoising which employs the fast framelet decomposition
and reconstruction algorithms. Experiment results show that our proposed CNN
model outperforms threshold methods, and processes strong generalization and
robustness properties.

The Henle's fiber layer (HFL) in the retina carries valuable information on
the macular condition of an eye. However, in the common practice, this layer is
not separately segmented but rather included in the outer nuclear layer since
it is difficult to perceive HFL contours on standard optical coherence
tomography (OCT) imaging. Due to its variable reflectivity under an imaging
beam, delineating the HFL contours necessitates directional OCT, which requires
additional imaging. This paper addresses this issue by introducing a
shape-preserving network, FourierNet, that achieves HFL segmentation in
standard OCT scans with the target performance obtained when directional OCT
scans are used. FourierNet is a new cascaded network design that puts forward
the idea of benefiting the shape prior of HFL in the network training. This
design proposes to represent the shape prior by extracting Fourier descriptors
on the HFL contours and defining an additional regression task of learning
these descriptors. It then formulates HFL segmentation as concurrent learning
of regression and classification tasks, in which Fourier descriptors are
estimated from an input image to encode the shape prior and used together with
the input image to construct the HFL segmentation map. Our experiments on 1470
images of 30 OCT scans reveal that quantifying the HFL shape with Fourier
descriptors and concurrently learning them with the main task of HFL
segmentation lead to better results. This indicates the effectiveness of
designing a shape-preserving network to improve HFL segmentation by reducing
the need to perform directional OCT imaging.

Most of the machine learning models have associated hyper-parameters along
with their parameters. While the algorithm gives the solution for parameters,
its utility for model performance is highly dependent on the choice of
hyperparameters. For a robust performance of a model, it is necessary to find
out the right hyper-parameter combination. Hyper-parameter optimization (HPO)
is a systematic process that helps in finding the right values for them. The
conventional methods for this purpose are grid search and random search and
both methods create issues in industrial-scale applications. Hence a set of
strategies have been recently proposed based on Bayesian optimization and
evolutionary algorithm principles that help in runtime issues in a production
environment and robust performance. In this paper, we compare the performance
of four python libraries, namely Optuna, Hyper-opt, Optunity, and sequential
model-based algorithm configuration (SMAC) that has been proposed for
hyper-parameter optimization. The performance of these tools is tested using
two benchmarks. The first one is to solve a combined algorithm selection and
hyper-parameter optimization (CASH) problem The second one is the NeurIPS
black-box optimization challenge in which a multilayer perception (MLP)
architecture has to be chosen from a set of related architecture constraints
and hyper-parameters. The benchmarking is done with six real-world datasets.
From the experiments, we found that Optuna has better performance for CASH
problem and HyperOpt for MLP problem.

Developing innovative informatics approaches aimed to enhance fetal
monitoring is a burgeoning field of study in reproductive medicine. Several
reviews have been conducted regarding Artificial intelligence (AI) techniques
to improve pregnancy outcomes. They are limited by focusing on specific data
such as mother's care during pregnancy. This systematic survey aims to explore
how artificial intelligence (AI) can assist with fetal growth monitoring via
Ultrasound (US) image. We used eight medical and computer science bibliographic
databases, including PubMed, Embase, PsycINFO, ScienceDirect, IEEE explore, ACM
Library, Google Scholar, and the Web of Science. We retrieved studies published
between 2010 to 2021. Data extracted from studies were synthesized using a
narrative approach. Out of 1269 retrieved studies, we included 107 distinct
studies from queries that were relevant to the topic in the survey. We found
that 2D ultrasound images were more popular (n=88) than 3D and 4D ultrasound
images (n=19). Classification is the most used method (n=42), followed by
segmentation (n=31), classification integrated with segmentation (n=16) and
other miscellaneous such as object-detection, regression and reinforcement
learning (n=18). The most common areas within the pregnancy domain were the
fetus head (n=43), then fetus body (n=31), fetus heart (n=13), fetus abdomen
(n=10), and lastly the fetus face (n=10). In the most recent studies, deep
learning techniques were primarily used (n=81), followed by machine learning
(n=16), artificial neural network (n=7), and reinforcement learning (n=2). AI
techniques played a crucial role in predicting fetal diseases and identifying
fetus anatomy structures during pregnancy. More research is required to
validate this technology from a physician's perspective, such as pilot studies
and randomized controlled trials on AI and its applications in a hospital
setting.

Modern face recognition systems (FRS) still fall short when the subjects are
wearing facial masks, a common theme in the age of respiratory pandemics. An
intuitive partial remedy is to add a mask detector to flag any masked faces so
that the FRS can act accordingly for those low-confidence masked faces. In this
work, we set out to investigate the potential vulnerability of such FRS
equipped with a mask detector, on large-scale masked faces, which might trigger
a serious risk, e.g., letting a suspect evade the FRS where both facial
identity and mask are undetected. As existing face recognizers and mask
detectors have high performance in their respective tasks, it is significantly
challenging to simultaneously fool them and preserve the transferability of the
attack. We formulate the new task as the generation of realistic &
adversarial-faced mask and make three main contributions: First, we study the
naive Delanunay-based masking method (DM) to simulate the process of wearing a
faced mask that is cropped from a template image, which reveals the main
challenges of this new task. Second, we further equip the DM with the
adversarial noise attack and propose the adversarial noise Delaunay-based
masking method (AdvNoise-DM) that can fool the face recognition and mask
detection effectively but make the face less natural. Third, we propose the
adversarial filtering Delaunay-based masking method denoted as MF2M by
employing the adversarial filtering for AdvNoise-DM and obtain more natural
faces. With the above efforts, the final version not only leads to significant
performance deterioration of the state-of-the-art (SOTA) deep learning-based
FRS, but also remains undetected by the SOTA facial mask detector, thus
successfully fooling both systems at the same time.

Deep representation learning has gained significant momentum in advancing
text-dependent speaker verification (TD-SV) systems. When designing deep neural
networks (DNN) for extracting bottleneck features, key considerations include
training targets, activation functions, and loss functions. In this paper, we
systematically study the impact of these choices on the performance of TD-SV.
For training targets, we consider speaker identity, time-contrastive learning
(TCL) and auto-regressive prediction coding with the first being supervised and
the last two being self-supervised. Furthermore, we study a range of loss
functions when speaker identity is used as the training target. With regard to
activation functions, we study the widely used sigmoid function, rectified
linear unit (ReLU), and Gaussian error linear unit (GELU). We experimentally
show that GELU is able to reduce the error rates of TD-SV significantly
compared to sigmoid, irrespective of training target. Among the three training
targets, TCL performs the best. Among the various loss functions, cross
entropy, joint-softmax and focal loss functions outperform the others. Finally,
score-level fusion of different systems is also able to reduce the error rates.
Experiments are conducted on the RedDots 2016 challenge database for TD-SV
using short utterances. For the speaker classifications, the well-known
Gaussian mixture model-universal background model (GMM-UBM) and i-vector
techniques are used.

Several techniques have been proposed to address the problem of recognizing
activities of daily living from signals. Deep learning techniques applied to
inertial signals have proven to be effective, achieving significant
classification accuracy. Recently, research in human activity recognition (HAR)
models has been almost totally model-centric. It has been proven that the
number of training samples and their quality are critical for obtaining deep
learning models that both perform well independently of their architecture, and
that are more robust to intraclass variability and interclass similarity.
Unfortunately, publicly available datasets do not always contain hight quality
data and a sufficiently large and diverse number of samples (e.g., number of
subjects, type of activity performed, and duration of trials). Furthermore,
datasets are heterogeneous among them and therefore cannot be trivially
combined to obtain a larger set. The final aim of our work is the definition
and implementation of a platform that integrates datasets of inertial signals
in order to make available to the scientific community large datasets of
homogeneous signals, enriched, when possible, with context information (e.g.,
characteristics of the subjects and device position). The main focus of our
platform is to emphasise data quality, which is essential for training
efficient models.

Multiplayer Online Battle Arena (MOBA) games such as Dota 2 attract hundreds
of thousands of players every year. Despite the large player base, it is still
important to attract new players to prevent the community of a game from
becoming inactive. Entering MOBA games is, however, often demanding, requiring
the player to learn numerous skills at once. An important factor of success is
buying the correct items which forms a complex task depending on various
in-game factors such as already purchased items, the team composition, or
available resources. A recommendation system can support players by reducing
the mental effort required to choose a suitable item, helping, e.g., newer
players or players returning to the game after a longer break, to focus on
other aspects of the game. Since Sequential Item Recommendation (SIR) has
proven to be effective in various domains (e.g. e-commerce, movie
recommendation or playlist continuation), we explore the applicability of
well-known SIR models in the context of purchase recommendations in Dota 2. To
facilitate this research, we collect, analyze and publish Dota-350k, a new
large dataset based on recent Dota 2 matches. We find that SIR models can be
employed effectively for item recommendation in Dota 2. Our results show that
models that consider the order of purchases are the most effective. In contrast
to other domains, we find RNN-based models to outperform the more recent
Transformer-based architectures on Dota-350k.

Generative models often incur the catastrophic forgetting problem when they
are used to sequentially learning multiple tasks, i.e., lifelong generative
learning. Although there are some endeavors to tackle this problem, they suffer
from high time-consumptions or error accumulation. In this work, we develop an
efficient and effective lifelong generative model based on variational
autoencoder (VAE). Unlike the generative adversarial network, VAE enjoys high
efficiency in the training process, providing natural benefits with few
resources. We deduce a lifelong generative model by expending the intrinsic
reconstruction character of VAE to the historical knowledge retention. Further,
we devise a feedback strategy about the reconstructed data to alleviate the
error accumulation. Experiments on the lifelong generating tasks of MNIST,
FashionMNIST, and SVHN verified the efficacy of our approach, where the results
were comparable to SOTA.

In recent years, semantic segmentation has taken benefit from various works
in computer vision. Inspired by the very versatile CycleGAN architecture, we
combine semantic segmentation with the concept of cycle consistency to enable a
multitask training protocol. However, learning is largely prevented by the
so-called steganography effect, which expresses itself as watermarks in the
latent segmentation domain, making image reconstruction a too easy task. To
combat this, we propose a noise injection, based either on quantization noise
or on Gaussian noise addition to avoid this disadvantageous information flow in
the cycle architecture. We find that noise injection significantly reduces the
generation of watermarks and thus allows the recognition of highly relevant
classes such as "traffic signs", which are hardly detected by the ERFNet
baseline. We report mIoU and PSNR results on the Cityscapes dataset for
semantic segmentation and image reconstruction, respectively. The proposed
methodology allows to achieve an mIoU improvement on the Cityscapes validation
set of 5.7% absolute over the same CycleGAN without noise injection, and still
an absolute 4.9% over the ERFNet non-cyclic baseline.

Online advertising is a major source of income for many online companies. One
common approach is to sell online advertisements via waterfall auctions,
through which a publisher makes sequential price offers to ad networks. The
publisher controls the order and prices of the waterfall in an attempt to
maximize his revenue. In this work, we propose a methodology to learn a
waterfall strategy from historical data by wisely searching in the space of
possible waterfalls and selecting the one leading to the highest revenues. The
contribution of this work is twofold; First, we propose a novel method to
estimate the valuation distribution of each user, with respect to each ad
network. Second, we utilize the valuation matrix to score our candidate
waterfalls as part of a procedure that iteratively searches in local
neighborhoods. Our framework guarantees that the waterfall revenue improves
between iterations ultimately converging into a local optimum. Real-world
demonstrations are provided to show that the proposed method improves the total
revenue of real-world waterfalls, as compared to manual expert optimization.
Finally, the code and the data are available here.

To assess fetal health during pregnancy, doctors use the gestational age (GA)
calculation based on the Crown Rump Length (CRL) measurement in order to check
for fetal size and growth trajectory. However, GA estimation based on CRL,
requires proper positioning of calipers on the fetal crown and rump view, which
is not always an easy plane to find, especially for an inexperienced
sonographer. Finding a slightly oblique view from the true CRL view could lead
to a different CRL value and therefore incorrect estimation of GA. This study
presents an AI-based method for a quality assessment of the CRL view by
verifying 7 clinical scoring criteria that are used to verify the correctness
of the acquired plane. We show how our proposed solution achieves high accuracy
on the majority of the scoring criteria when compared to an expert. We also
show that if such scoring system is used, it helps identify poorly acquired
images accurately and hence may help sonographers acquire better images which
could potentially lead to a better assessment of conditions such as
Intrauterine Growth Restriction (IUGR).

How resources are deployed to secure critical targets in networks can be
modelled by Network Security Games (NSGs). While recent advances in deep
learning (DL) provide a powerful approach to dealing with large-scale NSGs, DL
methods such as NSG-NFSP suffer from the problem of data inefficiency.
Furthermore, due to centralized control, they cannot scale to scenarios with a
large number of resources. In this paper, we propose a novel DL-based method,
NSGZero, to learn a non-exploitable policy in NSGs. NSGZero improves data
efficiency by performing planning with neural Monte Carlo Tree Search (MCTS).
Our main contributions are threefold. First, we design deep neural networks
(DNNs) to perform neural MCTS in NSGs. Second, we enable neural MCTS with
decentralized control, making NSGZero applicable to NSGs with many resources.
Third, we provide an efficient learning paradigm, to achieve joint training of
the DNNs in NSGZero. Compared to state-of-the-art algorithms, our method
achieves significantly better data efficiency and scalability.

We address general-shaped clustering problems under very weak parametric
assumptions with a two-step hybrid robust clustering algorithm based on trimmed
k-means and hierarchical agglomeration. The algorithm has low computational
complexity and effectively identifies the clusters also in presence of data
contamination. We also present natural generalizations of the approach as well
as an adaptive procedure to estimate the amount of contamination in a
data-driven fashion. Our proposal outperforms state-of-the-art robust,
model-based methods in our numerical simulations and real-world applications
related to color quantization for image analysis, human mobility patterns based
on GPS data, biomedical images of diabetic retinopathy, and functional data
across weather stations.

The proposal of perceptual loss solves the problem that per-pixel difference
loss function causes the reconstructed image to be overly-smooth, which
acquires a significant progress in the field of single image super-resolution
reconstruction. Furthermore, the generative adversarial networks (GAN) is
applied to the super-resolution field, which effectively improves the visual
quality of the reconstructed image. However, under the condtion of high
upscaling factors, the excessive abnormal reasoning of the network produces
some distorted structures, so that there is a certain deviation between the
reconstructed image and the ground-truth image. In order to fundamentally
improve the quality of reconstructed images, this paper proposes a effective
method called Dual Perceptual Loss (DP Loss), which is used to replace the
original perceptual loss to solve the problem of single image super-resolution
reconstruction. Due to the complementary property between the VGG features and
the ResNet features, the proposed DP Loss considers the advantages of learning
two features simultaneously, which significantly improves the reconstruction
effect of images. The qualitative and quantitative analysis on benchmark
datasets demonstrates the superiority of our proposed method over
state-of-the-art super-resolution methods.

Brushing is an everyday interaction in 2D scatterplots, which allows users to
select and filter data points within a continuous, enclosed region and conduct
further analysis on the points. However, such conventional brushing cannot be
directly applied to Multidimensional Projections (MDP), as they hardly escape
from False and Missing Neighbors distortions that make the relative positions
of the points unreliable. To alleviate this problem, we introduce
Distortion-aware brushing, a novel brushing technique for MDP. While users
perform brushing, Distortion-aware brushing resolves distortions around
currently brushed points by dynamically relocating points in the projection;
the points whose data are close to the brushed data in the multidimensional
(MD) space go near the corresponding brushed points in the projection, and the
opposites move away. Hence, users can overcome distortions and readily extract
out clustered data in the MD space using the technique. We demonstrate the
effectiveness and applicability of Distortion-aware brushing through usage
scenarios with two datasets. Finally, by conducting user studies with 30
participants, we verified that Distortion-aware brushing significantly
outperforms previous brushing techniques in precisely separating clusters in
the MD space, and works robustly regardless of the types or the amount of
distortions in MDP.

Detecting whether examples belong to a given in-distribution or are
Out-Of-Distribution (OOD) requires identifying features specific to the
in-distribution. In the absence of labels, these features can be learned by
self-supervised techniques under the generic assumption that the most abstract
features are those which are statistically most over-represented in comparison
to other distributions from the same domain. In this work, we show that
self-distillation of the in-distribution training set together with contrasting
against negative examples derived from shifting transformation of auxiliary
data strongly improves OOD detection. We find that this improvement depends on
how the negative samples are generated. In particular, we observe that by
leveraging negative samples, which keep the statistics of low-level features
while changing the high-level semantics, higher average detection performance
is obtained. Furthermore, good negative sampling strategies can be identified
from the sensitivity of the OOD detection score. The efficiency of our approach
is demonstrated across a diverse range of OOD detection problems, setting new
benchmarks for unsupervised OOD detection in the visual domain.

Blind face restoration is to recover a high-quality face image from unknown
degradations. As face image contains abundant contextual information, we
propose a method, RestoreFormer, which explores fully-spatial attentions to
model contextual information and surpasses existing works that use local
operators. RestoreFormer has several benefits compared to prior arts. First,
unlike the conventional multi-head self-attention in previous Vision
Transformers (ViTs), RestoreFormer incorporates a multi-head cross-attention
layer to learn fully-spatial interactions between corrupted queries and
high-quality key-value pairs. Second, the key-value pairs in ResotreFormer are
sampled from a reconstruction-oriented high-quality dictionary, whose elements
are rich in high-quality facial features specifically aimed for face
reconstruction, leading to superior restoration results. Third, RestoreFormer
outperforms advanced state-of-the-art methods on one synthetic dataset and
three real-world datasets, as well as produces images with better visual
quality.

In recent years, graph neural networks (GNNs) have emerged as a successful
tool in a variety of graph-related applications. However, the performance of
GNNs can be deteriorated when noisy connections occur in the original graph
structures; besides, the dependence on explicit structures prevents GNNs from
being applied to general unstructured scenarios. To address these issues,
recently emerged deep graph structure learning (GSL) methods propose to jointly
optimize the graph structure along with GNN under the supervision of a node
classification task. Nonetheless, these methods focus on a supervised learning
scenario, which leads to several problems, i.e., the reliance on labels, the
bias of edge distribution, and the limitation on application tasks. In this
paper, we propose a more practical GSL paradigm, unsupervised graph structure
learning, where the learned graph topology is optimized by data itself without
any external guidance (i.e., labels). To solve the unsupervised GSL problem, we
propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME
for abbreviation) with the aid of self-supervised contrastive learning.
Specifically, we generate a learning target from the original data as an
"anchor graph", and use a contrastive loss to maximize the agreement between
the anchor graph and the learned graph. To provide persistent guidance, we
design a novel bootstrapping mechanism that upgrades the anchor graph with
learned structures during model learning. We also design a series of graph
learners and post-processing schemes to model the structures to learn.
Extensive experiments on eight benchmark datasets demonstrate the significant
effectiveness of our proposed SUBLIME and high quality of the optimized graphs.

The ability to adapt medical image segmentation networks for a novel class
such as an unseen anatomical or pathological structure, when only a few
labelled examples of this class are available from local healthcare providers,
is sought-after. This potentially addresses two widely recognised limitations
in deploying modern deep learning models to clinical practice,
expertise-and-labour-intensive labelling and cross-institution generalisation.
This work presents the first 3D few-shot interclass segmentation network for
medical images, using a labelled multi-institution dataset from prostate cancer
patients with eight regions of interest. We propose an image alignment module
registering the predicted segmentation of both query and support data, in a
standard prototypical learning algorithm, to a reference atlas space. The
built-in registration mechanism can effectively utilise the prior knowledge of
consistent anatomy between subjects, regardless whether they are from the same
institution or not. Experimental results demonstrated that the proposed
registration-assisted prototypical learning significantly improved segmentation
accuracy (p-values<0.01) on query data from a holdout institution, with varying
availability of support data from multiple institutions. We also report the
additional benefits of the proposed 3D networks with 75% fewer parameters and
an arguably simpler implementation, compared with existing 2D few-shot
approaches that segment 2D slices of volumetric medical images.

Monocular height estimation (MHE) from remote sensing imagery has high
potential in generating 3D city models efficiently for a quick response to
natural disasters. Most existing works pursue higher performance. However,
there is little research exploring the interpretability of MHE networks. In
this paper, we target at exploring how deep neural networks predict height from
a single monocular image. Towards a comprehensive understanding of MHE
networks, we propose to interpret them from multiple levels: 1) Neurons:
unit-level dissection. Exploring the semantic and height selectivity of the
learned internal deep representations; 2) Instances: object-level
interpretation. Studying the effects of different semantic classes, scales, and
spatial contexts on height estimation; 3) Attribution: pixel-level analysis.
Understanding which input pixels are important for the height estimation. Based
on the multi-level interpretation, a disentangled latent Transformer network is
proposed towards a more compact, reliable, and explainable deep model for
monocular height estimation. Furthermore, a novel unsupervised semantic
segmentation task based on height estimation is first introduced in this work.
Additionally, we also construct a new dataset for joint semantic segmentation
and height estimation. Our work provides novel insights for both understanding
and designing MHE models.

A probabilistic alternative to the Gower distance is proposed. The
probabilistic distance enables the realization of a generic deodata predictor.

We introduce a data-driven, model-agnostic technique for generating a
human-interpretable summary of the salient points of contrast within an
evolving dynamical system, such as the learning process of a control agent. It
involves the aggregation of transition data along both spatial and temporal
dimensions according to an information-theoretic divergence measure. A
practical algorithm is outlined for continuous state spaces, and deployed to
summarise the learning histories of deep reinforcement learning agents with the
aid of graphical and textual communication methods. We expect our method to be
complementary to existing techniques in the realm of agent interpretability.

The conversational agents is one of the most interested topics in computer
science field in the recent decade. Which can be composite from more than one
subject in this field, which you need to apply Natural Language Processing
Concepts and some Artificial Intelligence Techniques such as Deep Learning
methods to make decision about how should be the response. This paper is
dedicated to discuss the system architecture for the conversational agent and
explain each component in details.

A widely used paradigm to improve the generalization performance of
high-capacity neural models is through the addition of auxiliary unsupervised
tasks during supervised training. Tasks such as similarity matching and input
reconstruction have been shown to provide a beneficial regularizing effect by
guiding representation learning. Real data often has complex underlying
structures and may be composed of heterogeneous subpopulations that are not
learned well with current approaches. In this work, we design ExpertNet, which
uses novel training strategies to learn clustered latent representations and
leverage them by effectively combining cluster-specific classifiers. We
theoretically analyze the effect of clustering on its generalization gap, and
empirically show that clustered latent representations from ExpertNet lead to
disentangling the intrinsic structure and improvement in classification
performance. ExpertNet also meets an important real-world need where
classifiers need to be tailored for distinct subpopulations, such as in
clinical risk models. We demonstrate the superiority of ExpertNet over
state-of-the-art methods on 6 large clinical datasets, where our approach leads
to valuable insights on group-specific risks.

Neural network architectures have been extensively employed in the fair
representation learning setting, where the objective is to learn a new
representation for a given vector which is independent of sensitive
information. Various "representation debiasing" techniques have been proposed
in the literature. However, as neural networks are inherently opaque, these
methods are hard to comprehend, which limits their usefulness. We propose a new
framework for fair representation learning which is centered around the
learning of "correction vectors", which have the same dimensionality as the
given data vectors. The corrections are then simply summed up to the original
features, and can therefore be analyzed as an explicit penalty or bonus to each
feature. We show experimentally that a fair representation learning problem
constrained in such a way does not impact performance.

The issue of fairness in machine learning stems from the fact that historical
data often displays biases against specific groups of people which have been
underprivileged in the recent past, or still are. In this context, one of the
possible approaches is to employ fair representation learning algorithms which
are able to remove biases from data, making groups statistically
indistinguishable. In this paper, we instead develop a fair representation
learning algorithm which is able to map individuals belonging to different
groups in a single group. This is made possible by training a pair of
Normalizing Flow models and constraining them to not remove information about
the ground truth by training a ranking or classification model on top of them.
The overall, ``chained'' model is invertible and has a tractable Jacobian,
which allows to relate together the probability densities for different groups
and ``translate'' individuals from one group to another. We show experimentally
that our methodology is competitive with other fair representation learning
algorithms. Furthermore, our algorithm achieves stronger invariance w.r.t. the
sensitive attribute.

Computational pathology is a domain that aims to develop algorithms to
automatically analyze large digitized histopathology images, called whole slide
images (WSI). WSIs are produced scanning thin tissue samples that are stained
to make specific structures visible. They show stain colour heterogeneity due
to different preparation and scanning settings applied across medical centers.
Stain colour heterogeneity is a problem to train convolutional neural networks
(CNN), the state-of-the-art algorithms for most computational pathology tasks,
since CNNs usually underperform when tested on images including different stain
variations than those within data used to train the CNN. Despite several
methods that were developed, stain colour heterogeneity is still an unsolved
challenge that limits the development of CNNs that can generalize on data from
several medical centers. This paper aims to present a novel method to train
CNNs that better generalize on data including several colour variations. The
method, called H&E-adversarial CNN, exploits H&E matrix information to learn
stain-invariant features during the training. The method is evaluated on the
classification of colon and prostate histopathology images, involving eleven
heterogeneous datasets, and compared with five other techniques used to handle
stain colour heterogeneity. H&E-adversarial CNNs show an improvement in
performance compared to the other algorithms, demonstrating that it can help to
better deal with stain colour heterogeneous images.

With the rapid rise of neural architecture search, the ability to understand
its complexity from the perspective of a search algorithm is desirable.
Recently, Traor\'e et al. have proposed the framework of Fitness Landscape
Footprint to help describe and compare neural architecture search problems. It
attempts at describing why a search strategy might be successful, struggle or
fail on a target task. Our study leverages this methodology in the context of
searching across sensors, including sensor data fusion. In particular, we apply
the Fitness Landscape Footprint to the real-world image classification problem
of So2Sat LCZ42, in order to identify the most beneficial sensor to our neural
network hyper-parameter optimization problem. From the perspective of
distributions of fitness, our findings indicate a similar behaviour of the
search space for all sensors: the longer the training time, the larger the
overall fitness, and more flatness in the landscapes (less ruggedness and
deviation). Regarding sensors, the better the fitness they enable (Sentinel-2),
the better the search trajectories (smoother, higher persistence). Results also
indicate very similar search behaviour for sensors that can be decently fitted
by the search space (Sentinel-2 and fusion).

Human infants learn language while interacting with their environment in
which their caregivers may describe the objects and actions they perform.
Similar to human infants, artificial agents can learn language while
interacting with their environment. In this work, first, we present a neural
model that bidirectionally binds robot actions and their language descriptions
in a simple object manipulation scenario. Building on our previous Paired
Variational Autoencoders (PVAE) model, we demonstrate the superiority of the
variational autoencoder over standard autoencoders by experimenting with cubes
of different colours, and by enabling the production of alternative
vocabularies. Additional experiments show that the model's channel-separated
visual feature extraction module can cope with objects of different shapes.
Next, we introduce PVAE-BERT, which equips the model with a pretrained
large-scale language model, i.e., Bidirectional Encoder Representations from
Transformers (BERT), enabling the model to go beyond comprehending only the
predefined descriptions that the network has been trained on; the recognition
of action descriptions generalises to unconstrained natural language as the
model becomes capable of understanding unlimited variations of the same
descriptions. Our experiments suggest that using a pretrained language model as
the language encoder allows our approach to scale up for real-world scenarios
with instructions from human users.

We propose a general Variational Embedding Learning Framework (VELF) for
alleviating the severe cold-start problem in CTR prediction. VELF addresses the
cold start problem via alleviating over-fits caused by data-sparsity in two
ways: learning probabilistic embedding, and incorporating trainable and
regularized priors which utilize the rich side information of cold start users
and advertisements (Ads). The two techniques are naturally integrated into a
variational inference framework, forming an end-to-end training process.
Abundant empirical tests on benchmark datasets well demonstrate the advantages
of our proposed VELF. Besides, extended experiments confirmed that our
parameterized and regularized priors provide more generalization capability
than traditional fixed priors.

Kernel methods provide a principled approach to nonparametric learning. While
their basic implementations scale poorly to large problems, recent advances
showed that approximate solvers can efficiently handle massive datasets. A
shortcoming of these solutions is that hyperparameter tuning is not taken care
of, and left for the user to perform. Hyperparameters are crucial in practice
and the lack of automated tuning greatly hinders efficiency and usability. In
this paper, we work to fill in this gap focusing on kernel ridge regression
based on the Nystr\"om approximation. After reviewing and contrasting a number
of hyperparameter tuning strategies, we propose a complexity regularization
criterion based on a data dependent penalty, and discuss its efficient
optimization. Then, we proceed to a careful and extensive empirical evaluation
highlighting strengths and weaknesses of the different tuning strategies. Our
analysis shows the benefit of the proposed approach, that we hence incorporate
in a library for large scale kernel methods to derive adaptively tuned

Aspect-based sentiment analysis is of great importance and application
because of its ability to identify all aspects discussed in the text. However,
aspect-based sentiment analysis will be most effective when, in addition to
identifying all the aspects discussed in the text, it can also identify their
polarity. Most previous methods use the pipeline approach, that is, they first
identify the aspects and then identify the polarities. Such methods are
unsuitable for practical applications since they can lead to model errors.
Therefore, in this study, we propose a multi-task learning model based on
Convolutional Neural Networks (CNNs), which can simultaneously detect aspect
category and detect aspect category polarity. creating a model alone may not
provide the best predictions and lead to errors such as bias and high variance.
To reduce these errors and improve the efficiency of model predictions,
combining several models known as ensemble learning may provide better results.
Therefore, the main purpose of this article is to create a model based on an
ensemble of multi-task deep convolutional neural networks to enhance sentiment
analysis in Persian reviews. We evaluated the proposed method using a Persian
language dataset in the movie domain. Jacquard index and Hamming loss measures
were used to evaluate the performance of the developed models. The results
indicate that this new approach increases the efficiency of the sentiment
analysis model in the Persian language.

Cross-camera image data association is essential for many multi-camera
computer vision tasks, such as multi-camera pedestrian detection, multi-camera
multi-target tracking, 3D pose estimation, etc. This association task is
typically stated as a bipartite graph matching problem and often solved by
applying minimum-cost flow techniques, which may be computationally inefficient
with large data. Furthermore, cameras are usually treated by pairs, obtaining
local solutions, rather than finding a global solution at once. Other key issue
is that of the affinity measurement: the widespread usage of non-learnable
pre-defined distances, such as the Euclidean and Cosine ones. This paper
proposes an efficient approach for cross-cameras data-association focused on a
global solution, instead of processing cameras by pairs. To avoid the usage of
fixed distances, we leverage the connectivity of Graph Neural Networks,
previously unused in this scope, using a Message Passing Network to jointly
learn features and similarity. We validate the proposal for pedestrian
multi-view association, showing results over the EPFL multi-camera pedestrian
dataset. Our approach considerably outperforms the literature data association
techniques, without requiring to be trained in the same scenario in which it is
tested. Our code is available at
\url{http://www-vpu.eps.uam.es/publications/gnn_cca}.

In recent years, the data science community has pursued excellence and made
significant research efforts to develop advanced analytics, focusing on solving
technical problems at the expense of organizational and socio-technical
challenges. According to previous surveys on the state of data science project
management, there is a significant gap between technical and organizational
processes. In this article we present new empirical data from a survey to 237
data science professionals on the use of project management methodologies for
data science. We provide additional profiling of the survey respondents' roles
and their priorities when executing data science projects. Based on this survey
study, the main findings are: (1) Agile data science lifecycle is the most
widely used framework, but only 25% of the survey participants state to follow
a data science project methodology. (2) The most important success factors are
precisely describing stakeholders' needs, communicating the results to
end-users, and team collaboration and coordination. (3) Professionals who
adhere to a project methodology place greater emphasis on the project's
potential risks and pitfalls, version control, the deployment pipeline to
production, and data security and privacy.

Liquid metals play a central role in new generation liquid metal cooled
nuclear reactors, for which numerical investigations require the use of
appropriate thermal turbulence models for low Prandtl number fluids. Given the
limitations of traditional modelling approaches and the increasing availability
of high-fidelity data for this class of fluids, we propose a Machine Learning
strategy for the modelling of the turbulent heat flux. A comprehensive
algebraic mathematical structure is derived and physical constraints are
imposed to ensure attractive properties promoting applicability, robustness and
stability. The closure coefficients of the model are predicted by an Artificial
Neural Network (ANN) which is trained with DNS data at different Prandtl
numbers. The validity of the approach was verified through a priori and a
posteriori validation for two and three-dimensional liquid metal flows. The
model provides a complete vectorial representation of the turbulent heat flux
and the predictions fit the DNS data in a wide range of Prandtl numbers
(Pr=0.01-0.71). The comparison with other existing thermal models shows that
the methodology is very promising.

Parameterized max-affine (PMA) and parameterized log-sum-exp (PLSE) networks
are proposed for general decision-making problems. The proposed approximators
generalize existing convex approximators, namely, max-affine (MA) and
log-sum-exp (LSE) networks, by considering function arguments of condition and
decision variables and replacing the network parameters of MA and LSE networks
with continuous functions with respect to the condition variable. The universal
approximation theorem of PMA and PLSE is proven, which implies that PMA and
PLSE are shape-preserving universal approximators for parameterized convex
continuous functions. Practical guidelines for incorporating deep neural
networks within PMA and PLSE networks are provided. A numerical simulation is
performed to demonstrate the performance of the proposed approximators. The
simulation results support that PLSE outperforms other existing approximators
in terms of minimizer and optimal value errors with scalable and efficient
computation for high-dimensional cases.

A key step in quantum machine learning with classical inputs is the design of
an embedding circuit mapping inputs to a quantum state. This paper studies a
transfer learning setting in which classical-to-quantum embedding is carried
out by an arbitrary parametric quantum circuit that is pre-trained based on
data from a source task. At run time, the binary classifier is then optimized
based on data from the target task of interest. Using an information-theoretic
approach, we demonstrate that the average excess risk, or optimality gap, can
be bounded in terms of two R\'enyi mutual information terms between classical
input and quantum embedding under source and target tasks, as well as in terms
of a measure of similarity between the source and target tasks related to the
trace distance. The main theoretical results are validated on a simple binary
classification example.

Continual learning (CL) is widely regarded as crucial challenge for lifelong
AI. However, existing CL benchmarks, e.g. Permuted-MNIST and Split-CIFAR, make
use of artificial temporal variation and do not align with or generalize to the
real-world. In this paper, we introduce CLEAR, the first continual image
classification benchmark dataset with a natural temporal evolution of visual
concepts in the real world that spans a decade (2004-2014). We build CLEAR from
existing large-scale image collections (YFCC100M) through a novel and scalable
low-cost approach to visio-linguistic dataset curation. Our pipeline makes use
of pretrained vision-language models (e.g. CLIP) to interactively build labeled
datasets, which are further validated with crowd-sourcing to remove errors and
even inappropriate images (hidden in original YFCC100M). The major strength of
CLEAR over prior CL benchmarks is the smooth temporal evolution of visual
concepts with real-world imagery, including both high-quality labeled data
along with abundant unlabeled samples per time period for continual
semi-supervised learning. We find that a simple unsupervised pre-training step
can already boost state-of-the-art CL algorithms that only utilize
fully-supervised data. Our analysis also reveals that mainstream CL evaluation
protocols that train and test on iid data artificially inflate performance of
CL system. To address this, we propose novel "streaming" protocols for CL that
always test on the (near) future. Interestingly, streaming protocols (a) can
simplify dataset curation since today's testset can be repurposed for
tomorrow's trainset and (b) can produce more generalizable models with more
accurate estimates of performance since all labeled data from each time-period
is used for both training and testing (unlike classic iid train-test splits).

In this paper, a resource-efficient FPGA-based neural network regression
model is developed for potential applications in the future hardware muon
trigger system of the ATLAS experiment at the Large Hadron Collider (LHC).
Effective real-time selection of muon candidates is the cornerstone of the
ATLAS physics programme. With the planned upgrades, the entirely new FPGA-based
hardware muon trigger system will be installed in 2025-2026 that will process
full muon detector data within a 10 ${\mu}s$ latency window. The planned large
FPGA devices should have sufficient spare resources to allow deployment of
machine learning methods for improving identification of muon candidates and
searching for new exotic particles. Our model promises to improve the rejection
of the dominant source of background events in the central detector region,
which are due to muon candidates with low transverse momenta. This neural
network was implemented in the hardware description language using 65 digital
signal processors and about 10,000 lookup tables. The simulated network latency
and deadtime are 245 and 60 ns, respectively, when implemented in the FPGA
device using a 400 MHz clock frequency. These results are well within the
requirements of the future ATLAS muon trigger system, therefore opening a
possibility for deploying machine learning methods for data taking by the ATLAS
experiment at the High Luminosity LHC.

Vocabulary learning is vital to foreign language learning. Correct and
adequate feedback is essential to successful and satisfying vocabulary
training. However, many vocabulary and language evaluation systems perform on
simple rules and do not account for real-life user learning data. This work
introduces Multi-Language Vocabulary Evaluation Data Set (MuLVE), a data set
consisting of vocabulary cards and real-life user answers, labeled indicating
whether the user answer is correct or incorrect. The data source is user
learning data from the Phase6 vocabulary trainer. The data set contains
vocabulary questions in German and English, Spanish, and French as target
language and is available in four different variations regarding pre-processing
and deduplication. We experiment to fine-tune pre-trained BERT language models
on the downstream task of vocabulary evaluation with the proposed MuLVE data
set. The results provide outstanding results of > 95.5 accuracy and F2-score.
The data set is available on the European Language Grid.

Positive-unlabeled learning (PU learning) is known as a special case of
semi-supervised binary classification where only a fraction of positive
examples are labeled. The challenge is then to find the correct classifier
despite this lack of information. Recently, new methodologies have been
introduced to address the case where the probability of being labeled may
depend on the covariates. In this paper, we are interested in establishing risk
bounds for PU learning under this general assumption. In addition, we quantify
the impact of label noise on PU learning compared to standard classification
setting. Finally, we provide a lower bound on minimax risk proving that the
upper bound is almost optimal.

The number of railway service disruptions has been increasing owing to
intensification of natural disasters. In addition, abrupt changes in social
situations such as the COVID-19 pandemic require railway companies to modify
the traffic schedule frequently. Therefore, automatic support for optimal
scheduling is anticipated. In this study, an automatic railway scheduling
system is presented. The system leverages reinforcement learning and a dynamic
simulator that can simulate the railway traffic and passenger flow of a whole
line. The proposed system enables rapid generation of the traffic schedule of a
whole line because the optimization process is conducted in advance as the
training. The system is evaluated using an interruption scenario, and the
results demonstrate that the system can generate optimized schedules of the
whole line in a few minutes.

Gridworlds have been long-utilised in AI research, particularly in
reinforcement learning, as they provide simple yet scalable models for many
real-world applications such as robot navigation, emergent behaviour, and
operations research. We initiate a study of gridworlds using the mathematical
framework of reconfigurable systems and state complexes due to Abrams, Ghrist &
Peterson. State complexes represent all possible configurations of a system as
a single geometric space, thus making them conducive to study using geometric,
topological, or combinatorial methods. The main contribution of this work is a
modification to the original Abrams, Ghrist & Peterson setup which we introduce
to capture agent braiding and thereby more naturally represent the topology of
gridworlds. With this modification, the state complexes may exhibit geometric
defects (failure of Gromov's Link Condition). Serendipitously, we discover
these failures occur exactly where undesirable or dangerous states appear in
the gridworld. Our results therefore provide a novel method for seeking
guaranteed safety limitations in discrete task environments with single or
multiple agents, and offer useful safety information (in geometric and
topological forms) for incorporation in or analysis of machine learning
systems. More broadly, our work introduces tools from geometric group theory
and combinatorics to the AI community and demonstrates a proof-of-concept for
this geometric viewpoint of the task domain through the example of simple
gridworld environments.

Transformers in their common form are inherently limited to operate on whole
token sequences rather than on one token at a time. Consequently, their use
during online inference on time-series data entails considerable redundancy due
to the overlap in successive token sequences. In this work, we propose novel
formulations of the Scaled Dot-Product Attention, which enable Transformers to
perform efficient online token-by-token inference on a continual input stream.
Importantly, our modifications are purely to the order of computations, while
the outputs and learned weights are identical to those of the original
Transformer Encoder. We validate our Continual Transformer Encoder with
experiments on the THUMOS14, TVSeries and GTZAN datasets with remarkable
results: Our Continual one- and two-block architectures reduce the floating
point operations per prediction by up to 63x and 2.6x, respectively, while
retaining predictive performance.

Multiplexing multiple orbital angular momentum (OAM) modes of light has the
potential to increase data capacity in optical communication. However, the
distribution of such modes over long distances remains challenging. Free-space
transmission is strongly influenced by atmospheric turbulence and light
scattering, while the wave distortion induced by the mode dispersion in fibers
disables OAM demultiplexing in fiber-optic communications. Here, a
deep-learning-based approach is developed to recover the data from scattered
OAM channels without measuring any phase information. Over a 1-km-long standard
multimode fiber, the method is able to identify different OAM modes with an
accuracy of more than 99.9% in parallel demultiplexing of 24 scattered OAM
channels. To demonstrate the transmission quality, color images are encoded in
multiplexed twisted light and our method achieves decoding the transmitted data
with an error rate of 0.13%. Our work shows the artificial intelligence
algorithm could benefit the use of OAM multiplexing in commercial fiber
networks and high-performance optical communication in turbulent environments.

This study presents a policy optimisation framework for structured nonlinear
control of continuous-time (deterministic) dynamic systems. The proposed
approach prescribes a structure for the controller based on relevant scientific
knowledge (such as Lyapunov stability theory or domain experiences) while
considering the tunable elements inside the given structure as the point of
parametrisation with neural networks. To optimise a cost represented as a
function of the neural network weights, the proposed approach utilises the
continuous-time policy gradient method based on adjoint sensitivity analysis as
a means for correct and performant computation of cost gradient. This enables
combining the stability, robustness, and physical interpretability of an
analytically-derived structure for the feedback controller with the
representational flexibility and optimised resulting performance provided by
machine learning techniques. Such a hybrid paradigm for fixed-structure control
synthesis is particularly useful for optimising adaptive nonlinear controllers
to achieve improved performance in online operation, an area where the existing
theory prevails the design of structure while lacking clear analytical
understandings about tuning of the gains and the uncertainty model basis
functions that govern the performance characteristics. Numerical experiments on
aerospace applications illustrate the utility of the structured nonlinear
controller optimisation framework.

In this report we want to present our method and results for the Carotid
Artery Vessel Wall Segmentation Challenge. We propose an image-based pipeline
utilizing the U-Net architecture and location priors to solve the segmentation
problem at hand.

Many real-world scenarios involve a team of agents that have to coordinate
their policies to achieve a shared goal. Previous studies mainly focus on
decentralized control to maximize a common reward and barely consider the
coordination among control policies, which is critical in dynamic and
complicated environments. In this work, we propose factorizing the joint team
policy into a graph generator and graph-based coordinated policy to enable
coordinated behaviours among agents. The graph generator adopts an
encoder-decoder framework that outputs directed acyclic graphs (DAGs) to
capture the underlying dynamic decision structure. We also apply the
DAGness-constrained and DAG depth-constrained optimization in the graph
generator to balance efficiency and performance. The graph-based coordinated
policy exploits the generated decision structure. The graph generator and
coordinated policy are trained simultaneously to maximize the discounted
return. Empirical evaluations on Collaborative Gaussian Squeeze, Cooperative
Navigation, and Google Research Football demonstrate the superiority of the

We propose a new and strengthened Branch-and-Bound (BnB) algorithm for the
maximum common (connected) induced subgraph problem based on two new operators,
Long-Short Memory (LSM) and Leaf vertex Union Match (LUM). Given two graphs for
which we search for the maximum common (connected) induced subgraph, the first
operator of LSM maintains a score for the branching node using the short-term
reward of each vertex of the first graph and the long-term reward of each
vertex pair of the two graphs. In this way, the BnB process learns to reduce
the search tree size significantly and improve the algorithm performance. The
second operator of LUM further improves the performance by simultaneously
matching the leaf vertices connected to the current matched vertices, and
allows the algorithm to match multiple vertex pairs without affecting the
solution optimality. We incorporate the two operators into the state-of-the-art
BnB algorithm McSplit, and denote the resulting algorithm as McSplit+LL.
Experiments show that McSplit+LL outperforms McSplit+RL, a more recent variant
of McSplit using reinforcement learning that is superior than McSplit.

Cancer is one of the leading causes of death worldwide, and head and neck
(H&N) cancer is amongst the most prevalent types. Positron emission tomography
and computed tomography are used to detect, segment and quantify the tumor
region. Clinically, tumor segmentation is extensively time-consuming and prone
to error. Machine learning, and deep learning in particular, can assist to
automate this process, yielding results as accurate as the results of a
clinician. In this paper, we investigate a vision transformer-based method to
automatically delineate H&N tumor, and compare its results to leading
convolutional neural network (CNN)-based models. We use multi-modal data from
CT and PET scans to perform the segmentation task. We show that a solution with
a transformer-based model has the potential to achieve comparable results to
CNN-based ones. With cross validation, the model achieves a mean dice
similarity coefficient (DSC) of 0.736, mean precision of 0.766 and mean recall
of 0.766. This is only 0.021 less than the 2020 competition winning model
(cross validated in-house) in terms of the DSC score. On the testing set, the
model performs similarly, with DSC of 0.736, precision of 0.773, and recall of
0.760, which is only 0.023 lower in DSC than the 2020 competition winning
model. This work shows that cancer segmentation via transformer-based models is
a promising research area to further explore.

In clinical diagnosis, diagnostic images that are obtained from the scanning
devices serve as preliminary evidence for further investigation in the process
of delivering quality healthcare. However, often the medical image may contain
fault artifacts, introduced due to noise, blur and faulty equipment. The reason
for this may be the low-quality or older scanning devices, the test environment
or technicians lack of training etc; however, the net result is that the
process of fast and reliable diagnosis is hampered. Resolving these issues
automatically can have a significant positive impact in a hospital clinical
workflow, where often, there is no other way but to work with faulty/older
equipment or inadequately qualified radiology technicians. In this paper,
automated image quality improvement approaches for adapted and benchmarked for
the task of medical image super-resolution. During experimental evaluation on
standard open datasets, the observations showed that certain algorithms perform
better and show significant improvement in the diagnostic quality of medical
scans, thereby enabling better visualization for human diagnostic purposes.

Personality can be defined as the combination of behavior, emotion,
motivation, and thoughts that aim at describing various aspects of human
behavior based on a few stable and measurable characteristics. Considering the
fact that our personality has a remarkable influence in our daily life,
automatic recognition of a person's personality attributes can provide many
essential practical applications in various aspects of cognitive science. deep
learning based method for the task of personality recognition from text is
proposed in this paper. Among various deep neural networks, Convolutional
Neural Networks (CNN) have demonstrated profound efficiency in natural language
processing and especially personality detection. Owing to the fact that various
filter sizes in CNN may influence its performance, we decided to combine CNN
with AdaBoost, a classical ensemble algorithm, to consider the possibility of
using the contribution of various filter lengths and gasp their potential in
the final classification via combining various classifiers with respective
filter size using AdaBoost. Our proposed method was validated on the Essay
dataset by conducting a series of experiments and the empirical results
demonstrated the superiority of our proposed method compared to both machine
learning and deep learning methods for the task of personality recognition.

Consistency regularization on label predictions becomes a fundamental
technique in semi-supervised learning, but it still requires a large number of
training iterations for high performance. In this study, we analyze that the
consistency regularization restricts the propagation of labeling information
due to the exclusion of samples with unconfident pseudo-labels in the model
updates. Then, we propose contrastive regularization to improve both efficiency
and accuracy of the consistency regularization by well-clustered features of
unlabeled data. In specific, after strongly augmented samples are assigned to
clusters by their pseudo-labels, our contrastive regularization updates the
model so that the features with confident pseudo-labels aggregate the features
in the same cluster, while pushing away features in different clusters. As a
result, the information of confident pseudo-labels can be effectively
propagated into more unlabeled samples during training by the well-clustered
features. On benchmarks of semi-supervised learning tasks, our contrastive
regularization improves the previous consistency-based methods and achieves
state-of-the-art results, especially with fewer training iterations. Our method
also shows robust performance on open-set semi-supervised learning where
unlabeled data includes out-of-distribution samples.

The COVID-19 pandemic triggered a question of how to measure and evaluate
adequacy of the applied restrictions. Available studies propose various methods
mainly grouped to statistical and machine learning techniques. The current
paper joins this line of research by introducing a simple-yet-accurate linear
regression model which eliminates effects of weekly cycle, available daylight,
temperature, and wind from the electricity consumption data. The model is
validated using real data and enables the qualitative analysis of economical
impact.

We propose a joint channel estimation and signal detection approach for the
uplink non-orthogonal multiple access using unsupervised machine learning. We
apply the Gaussian mixture model to cluster the received signals, and
accordingly optimize the decision regions to enhance the symbol error rate
(SER). We show that, when the received powers of the users are sufficiently
different, the proposed clustering-based approach achieves an SER performance
on a par with that of the conventional maximum-likelihood detector with full
channel state information. However, unlike the proposed approach, the
maximum-likelihood detector requires the transmission of a large number of
pilot symbols to accurately estimate the channel. The accuracy of the utilized
clustering algorithm depends on the number of the data points available at the
receiver. Therefore, there exists a tradeoff between accuracy and block length.
We provide a comprehensive performance analysis of the proposed approach as
well as deriving a theoretical bound on its SER performance as a function of
the block length. Our simulation results corroborate the effectiveness of the
proposed approach and verify that the calculated theoretical bound can predict
the SER performance of the proposed approach well.

Federated learning allows multiple participants to conduct joint modeling
without disclosing their local data. Vertical federated learning (VFL) handles
the situation where participants share the same ID space and different feature
spaces. In most VFL frameworks, to protect the security and privacy of the
participants' local data, a third party is needed to generate homomorphic
encryption key pairs and perform decryption operations. In this way, the third
party is granted the right to decrypt information related to model parameters.
However, it isn't easy to find such a credible entity in the real world.
Existing methods for solving this problem are either communication-intensive or
unsuitable for multi-party scenarios. By combining secret sharing and
homomorphic encryption, we propose a novel VFL framework without a third party
called EFMVFL, which supports flexible expansion to multiple participants with
low communication overhead and is applicable to generalized linear models. We
give instantiations of our framework under logistic regression and Poisson
regression. Theoretical analysis and experiments show that our framework is
secure, more efficient, and easy to be extended to multiple participants.

Despite the success of deep learning in computer vision and natural language
processing, Gradient Boosted Decision Tree (GBDT) is yet one of the most
powerful tools for applications with tabular data such as e-commerce and
FinTech. However, applying GBDT to multi-task learning is still a challenge.
Unlike deep models that can jointly learn a shared latent representation across
multiple tasks, GBDT can hardly learn a shared tree structure. In this paper,
we propose Multi-task Gradient Boosting Machine (MT-GBM), a GBDT-based method
for multi-task learning. The MT-GBM can find the shared tree structures and
split branches according to multi-task losses. First, it assigns multiple
outputs to each leaf node. Next, it computes the gradient corresponding to each
output (task). Then, we also propose an algorithm to combine the gradients of
all tasks and update the tree. Finally, we apply MT-GBM to LightGBM.
Experiments show that our MT-GBM improves the performance of the main task
significantly, which means the proposed MT-GBM is efficient and effective.

Personalized decision-making, aiming to derive optimal individualized
treatment rules (ITRs) based on individual characteristics, has recently
attracted increasing attention in many fields, such as medicine, social
services, and economics. Current literature mainly focuses on estimating ITRs
from a single source population. In real-world applications, the distribution
of a target population can be different from that of the source population.
Therefore, ITRs learned by existing methods may not generalize well to the
target population. Due to privacy concerns and other practical issues,
individual-level data from the target population is often not available, which
makes ITR learning more challenging. We consider an ITR estimation problem
where the source and target populations may be heterogeneous, individual data
is available from the source population, and only the summary information of
covariates, such as moments, is accessible from the target population. We
develop a weighting framework that tailors an ITR for a given target population
by leveraging the available summary statistics. Specifically, we propose a
calibrated augmented inverse probability weighted estimator of the value
function for the target population and estimate an optimal ITR by maximizing
this estimator within a class of pre-specified ITRs. We show that the proposed
calibrated estimator is consistent and asymptotically normal even with flexible
semi/nonparametric models for nuisance function approximation, and the variance
of the value estimator can be consistently estimated. We demonstrate the
empirical performance of the proposed method using simulation studies and a
real application to an eICU dataset as the source sample and a MIMIC-III
dataset as the target sample.

Training deep neural networks (DNNs) is time-consuming. While most existing
solutions try to overlap/schedule computation and communication for efficient
training, this paper goes one step further by skipping computing and
communication through DNN layer freezing. Our key insight is that the training
progress of internal DNN layers differs significantly, and front layers often
become well-trained much earlier than deep layers. To explore this, we first
introduce the notion of training plasticity to quantify the training progress
of internal DNN layers. Then we design KGT, a knowledge-guided DNN training
system that employs semantic knowledge from a reference model to accurately
evaluate individual layers' training plasticity and safely freeze the converged
ones, saving their corresponding backward computation and communication. Our
reference model is generated on the fly using quantization techniques and runs
forward operations asynchronously on available CPUs to minimize the overhead.
In addition, KGT caches the intermediate outputs of the frozen layers with
prefetching to further skip the forward computation. Our implementation and
testbed experiments with popular vision and language models show that KGT
achieves 19%-43% training speedup w.r.t. the state-of-the-art without
sacrificing accuracy.

Self-supervised entity alignment (EA) aims to link equivalent entities across
different knowledge graphs (KGs) without seed alignments. The current SOTA
self-supervised EA method draws inspiration from contrastive learning,
originally designed in computer vision based on instance discrimination and
contrastive loss, and suffers from two shortcomings. Firstly, it puts
unidirectional emphasis on pushing sampled negative entities far away rather
than pulling positively aligned pairs close, as is done in the well-established
supervised EA. Secondly, KGs contain rich side information (e.g., entity
description), and how to effectively leverage those information has not been
adequately investigated in self-supervised EA. In this paper, we propose an
interactive contrastive learning model for self-supervised EA. The model
encodes not only structures and semantics of entities (including entity name,
entity description, and entity neighborhood), but also conducts cross-KG
contrastive learning by building pseudo-aligned entity pairs. Experimental
results show that our approach outperforms previous best self-supervised
results by a large margin (over 9% average improvement) and performs on par
with previous SOTA supervised counterparts, demonstrating the effectiveness of
the interactive contrastive learning for self-supervised EA.

Industrial alarm systems have recently progressed considerably in terms of
network complexity and the number of alarms. The increase in complexity and
number of alarms presents challenges in these systems that decrease system
efficiency and cause distrust of the operator, which might result in widespread
damages. One contributing factor in alarm inefficiency is the correlated
alarms. These alarms do not contain new information and only confuse the
operator. This paper tries to present a novel method for detecting correlated
alarms based on artificial intelligence methods to help the operator. The
proposed method is based on graph embedding and alarm clustering, resulting in
the detection of correlated alarms. To evaluate the proposed method, a case
study is conducted on the well-known Tennessee-Eastman process.

Conversational Recommendation Systems (CRSs) have recently started to
leverage pretrained language models (LM) such as BERT for their ability to
semantically interpret a wide range of preference statement variations.
However, pretrained LMs are well-known to be prone to intrinsic biases in their
training data, which may be exacerbated by biases embedded in domain-specific
language data(e.g., user reviews) used to fine-tune LMs for CRSs. We study a
recently introduced LM-driven recommendation backbone (termed LMRec) of a CRS
to investigate how unintended bias i.e., language variations such as name
references or indirect indicators of sexual orientation or location that should
not affect recommendations manifests in significantly shifted price and
category distributions of restaurant recommendations. The alarming results we
observe strongly indicate that LMRec has learned to reinforce harmful
stereotypes through its recommendations. For example, offhand mention of names
associated with the black community significantly lowers the price distribution
of recommended restaurants, while offhand mentions of common male-associated
names lead to an increase in recommended alcohol-serving establishments. These
and many related results presented in this work raise a red flag that advances
in the language handling capability of LM-drivenCRSs do not come without
significant challenges related to mitigating unintended bias in future deployed
CRS assistants with a potential reach of hundreds of millions of end-users.

Face detection in unrestricted conditions has been a trouble for years due to
various expressions, brightness, and coloration fringing. Recent studies show
that deep learning knowledge of strategies can acquire spectacular performance
inside the identification of different gadgets and patterns. This face
detection in unconstrained surroundings is difficult due to various poses,
illuminations, and occlusions. Figuring out someone with a picture has been
popularized through the mass media. However, it's miles less sturdy to
fingerprint or retina scanning. The latest research shows that deep mastering
techniques can gain mind-blowing performance on those two responsibilities. In
this paper, I recommend a deep cascaded multi-venture framework that exploits
the inherent correlation among them to boost up their performance. In
particular, my framework adopts a cascaded shape with 3 layers of cautiously
designed deep convolutional networks that expect face and landmark region in a
coarse-to-fine way. Besides, within the gaining knowledge of the procedure, I
propose a new online tough sample mining method that can enhance the
performance robotically without manual pattern choice.

It has been verified that the linear programming (LP) is able to formulate
many real-life optimization problems, which can obtain the optimum by resorting
to corresponding solvers such as OptVerse, Gurobi and CPLEX. In the past
decades, a serial of traditional operation research algorithms have been
proposed to obtain the optimum of a given LP in a fewer solving time. Recently,
there is a trend of using machine learning (ML) techniques to improve the
performance of above solvers. However, almost no previous work takes advantage
of ML techniques to improve the performance of solver from the front end, i.e.,
the modeling (or formulation). In this paper, we are the first to propose a
reinforcement learning-based reformulation method for LP to improve the
performance of solving process. Using an open-source solver COIN-OR LP (CLP) as
an environment, we implement the proposed method over two public research LP
datasets and one large-scale LP dataset collected from practical production
planning scenario. The evaluation results suggest that the proposed method can
effectively reduce both the solving iteration number ($25\%\downarrow$) and the
solving time ($15\%\downarrow$) over above datasets in average, compared to
directly solving the original LP instances.

Most combinatorial optimization problems can be formulated as mixed integer
linear programming (MILP), in which branch-and-bound (B\&B) is a general and
widely used method. Recently, learning to branch has become a hot research
topic in the intersection of machine learning and combinatorial optimization.
In this paper, we propose a novel reinforcement learning-based B\&B algorithm.
Similar to offline reinforcement learning, we initially train on the
demonstration data to accelerate learning massively. With the improvement of
the training effect, the agent starts to interact with the environment with its
learned policy gradually. It is critical to improve the performance of the
algorithm by determining the mixing ratio between demonstration and
self-generated data. Thus, we propose a prioritized storage mechanism to
control this ratio automatically. In order to improve the robustness of the
training process, a superior network is additionally introduced based on Double
DQN, which always serves as a Q-network with competitive performance. We
evaluate the performance of the proposed algorithm over three public research
benchmarks and compare it against strong baselines, including three classical
heuristics and one state-of-the-art imitation learning-based branching
algorithm. The results show that the proposed algorithm achieves the best
performance among compared algorithms and possesses the potential to improve
B\&B algorithm performance continuously.

Deep learning (DL) models have achieved great success in many application
domains. As such, many industrial companies such as Google and Facebook have
acknowledged the importance of multi-tenant DL services. Although the
multi-tenant service has been studied in conventional workloads, it is not been
deeply studied on deep learning service, especially on general-purpose
hardware. In this work, we systematically analyze the opportunities and
challenges of providing multi-tenant deep learning services on the
general-purpose CPU architecture from the aspects of scheduling granularity and
code generation. We propose an adaptive granularity scheduling scheme to both
guarantee resource usage efficiency and reduce the scheduling conflict rate. We
also propose an adaptive compilation strategy, by which we can dynamically and
intelligently pick a program with proper exclusive and shared resource usage to
reduce overall interference-induced performance loss. Compared to the existing
works, our design can serve more requests under the same QoS target in various
scenarios (e.g., +71%, +62%, +45% for light, medium, and heavy workloads,
respectively), and reduce the averaged query latency by 50%.

This article presents a reduced-order modeling methodology via deep
convolutional neural networks (CNNs) for shape optimization applications. The
CNN provides a nonlinear mapping between the shapes and their associated
attributes while conserving the equivariance of these attributes to the shape
translations. To implicitly represent complex shapes via a CNN-applicable
Cartesian structured grid, a level-set method is employed. The CNN-based
reduced-order model (ROM) is constructed in a completely data-driven manner
thus well suited for non-intrusive applications. We demonstrate our ROM-based
shape optimization framework on a gradient-based three-dimensional shape
optimization problem to minimize the induced drag of a wing in low-fidelity
potential flow. We show a good agreement between ROM-based optimal aerodynamic
coefficients and their counterparts obtained via a potential flow solver. The
predicted behavior of the optimized shape is consistent with theoretical
predictions. We also present the learning mechanism of the deep CNN model in a
physically interpretable manner. The CNN-ROM-based shape optimization algorithm
exhibits significant computational efficiency compared to the full-order
model-based online optimization applications. The proposed algorithm promises
to develop a tractable DL-ROM-driven framework for shape optimization and
adaptive morphing structures.

Multi-hop knowledge graph (KG) reasoning has been widely studied in recent
years to provide interpretable predictions on missing links with evidential
paths. Most previous works use reinforcement learning (RL) based methods that
learn to navigate the path towards the target entity. However, these methods
suffer from slow and poor convergence, and they may fail to infer a certain
path when there is a missing edge along the path. Here we present SQUIRE, the
first Sequence-to-sequence based multi-hop reasoning framework, which utilizes
an encoder-decoder Transformer structure to translate the query to a path. Our
framework brings about two benefits: (1) It can learn and predict in an
end-to-end fashion, which gives better and faster convergence; (2) Our
Transformer model does not rely on existing edges to generate the path, and has
the flexibility to complete missing edges along the path, especially in sparse
KGs. Experiments on standard and sparse KGs show that our approach yields
significant improvement over prior methods, while converging 4x-7x faster.

In recent years, the Edge Computing (EC) paradigm has emerged as an enabling
factor for developing technologies like the Internet of Things (IoT) and 5G
networks, bridging the gap between Cloud Computing services and end-users,
supporting low latency, mobility, and location awareness to delay-sensitive
applications. Most solutions in EC employ machine learning (ML) methods to
perform data classification and other information processing tasks on
continuous and evolving data streams. Usually, such solutions have to cope with
vast amounts of data that come as data streams while balancing energy
consumption, latency, and the predictive performance of the algorithms.
Ensemble methods achieve remarkable predictive performance when applied to
evolving data streams due to the combination of several models and the
possibility of selective resets. This work investigates strategies for
optimizing the performance (i.e., delay, throughput) and energy consumption of
bagging ensembles to classify data streams. The experimental evaluation
involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size
Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive
RandomForest, and Streaming Random Patches) applying five widely used machine
learning benchmark datasets with varied characteristics on three computer
platforms. Such strategies can significantly reduce energy consumption in 96%
of the experimental scenarios evaluated. Despite the trade-offs, it is possible
to balance them to avoid significant loss in predictive performance.

Analysis of X-ray Photon Correlation Spectroscopy (XPCS) data for
non-equilibrium dynamics often requires manual binning of age regions of an
intensity-intensity correlation function. This leads to a loss of temporal
resolution and accumulation of systematic error for the parameters quantifying
the dynamics, especially in cases with considerable noise. Moreover, the
experiments with high data collection rates create the need for automated
online analysis, where manual binning is not possible. Here, we integrate a
denoising autoencoder model into algorithms for analysis of non-equilibrium
two-time intensity-intensity correlation functions. The model can be applied to
an input of an arbitrary size. Noise reduction allows to extract the parameters
that characterize the sample dynamics with temporal resolution limited only by
frame rates. Not only does it improve the quantitative usage of the data, but
it also creates the potential for automating the analytical workflow. Various
approaches for uncertainty quantification and extension of the model for
anomalies detection are discussed.

Modern aircraft are designed with redundant control effectors to cater for
fault tolerance and maneuverability requirements. This leads to an
over-actuated aircraft which requires a control allocation scheme to distribute
the control commands among effectors. Traditionally, optimization based control
allocation schemes are used; however, for nonlinear allocation problems these
methods require large computational resources. In this work, a novel ANN based
nonlinear control allocation scheme is proposed. To start, a general nonlinear
control allocation problem is posed in a different perspective to seek a
function which maps desired moments to control effectors. Few important results
on stability and performance of nonlinear allocation schemes in general and
this ANN based allocation scheme, in particular, are presented. To demonstrate
the efficacy of the proposed scheme, it is compared with standard quadratic
programming based method for control allocation.

When cloud layers cover photovoltaic (PV) panels, the amount of power the
panels produce fluctuates rapidly. Therefore, to maintain enough energy on a
power grid to match demand, utilities companies rely on reserve power sources
that typically come from fossil fuels and therefore pollute the environment.
Accurate short-term PV power prediction enables operators to maximize the
amount of power obtained from PV panels and safely reduce the reserve energy
needed from fossil fuel sources. While several studies have developed machine
learning models to predict solar irradiance at specific PV generation
facilities, little work has been done to model short-term solar irradiance on a
global scale. Furthermore, models that have been developed are proprietary and
have architectures that are not publicly available or rely on computationally
demanding Numerical Weather Prediction (NWP) models. Here, we propose a
Convolutional Long Short-Term Memory Network model that treats solar nowcasting
as a next frame prediction problem, is more efficient than NWP models and has a
straightforward, reproducible architecture. Our models can predict solar
irradiance for entire North America for up to 3 hours in under 60 seconds on a
single machine without a GPU and has a RMSE of 120 W/m2 when evaluated on 2
months of data.

We study the off-policy evaluation (OPE) problem in an infinite-horizon
Markov decision process with continuous states and actions. We recast the
$Q$-function estimation into a special form of the nonparametric instrumental
variables (NPIV) estimation problem. We first show that under one mild
condition the NPIV formulation of $Q$-function estimation is well-posed in the
sense of $L^2$-measure of ill-posedness with respect to the data generating
distribution, bypassing a strong assumption on the discount factor $\gamma$
imposed in the recent literature for obtaining the $L^2$ convergence rates of
various $Q$-function estimators. Thanks to this new well-posed property, we
derive the first minimax lower bounds for the convergence rates of
nonparametric estimation of $Q$-function and its derivatives in both sup-norm
and $L^2$-norm, which are shown to be the same as those for the classical
nonparametric regression (Stone, 1982). We then propose a sieve two-stage least
squares estimator and establish its rate-optimality in both norms under some
mild conditions. Our general results on the well-posedness and the minimax
lower bounds are of independent interest to study not only other nonparametric
estimators for $Q$-function but also efficient estimation on the value of any
target policy in off-policy settings.

Recently, minimax optimization received renewed focus due to modern
applications in machine learning, robust optimization, and reinforcement
learning. The scale of these applications naturally leads to the use of
first-order methods. However, the nonconvexities and nonconcavities present in
these problems, prevents the application of typical Gradient Descent-Ascent,
which is known to diverge even in bilinear problems. Recently, it was shown
that the Proximal Point Method (PPM) converges linearly for a family of
nonconvex-nonconcave problems. In this paper, we study the convergence of a
damped version of Extra-Gradient Method (EGM) which avoids potentially costly
proximal computations, only relying on gradient evaluation. We show that EGM
converges linearly for smooth minimax optimization problem satisfying the same
nonconvex-nonconcave condition needed by PPM.

Deep learning-based models have been shown to improve the accuracy of
fingerprint recognition. While these algorithms show exceptional performance,
they require large-scale fingerprint datasets for training and evaluation. In
this work, we propose a novel fingerprint synthesis and reconstruction
framework based on the StyleGan2 architecture, to address the privacy issues
related to the acquisition of such large-scale datasets. We also derive a
computational approach to modify the attributes of the generated fingerprint
while preserving their identity. This allows synthesizing multiple different
fingerprint images per finger. In particular, we introduce the SynFing
synthetic fingerprints dataset consisting of 100K image pairs, each pair
corresponding to the same identity. The proposed framework was experimentally
shown to outperform contemporary state-of-the-art approaches for both
fingerprint synthesis and reconstruction. It significantly improved the realism
of the generated fingerprints, both visually and in terms of their ability to
spoof fingerprint-based verification systems. The code and fingerprints dataset
are publicly available: https://github.com/rafaelbou/fingerprint_generator.

Using a combination of theory, experiment, and simulation, we revisit the
dynamics of two coupled metronomes on a moving platform. Our experiments show
that the platform's motion is damped by a dry friction force of Coulomb type,
not the viscous linear friction force that has often been assumed in the past.
Prompted by this result, we develop a new mathematical model that builds on
previously introduced models, but departs from them in its treatment of the
friction on the platform. We analyze the model by a two-timescale analysis and
derive the slow-flow equations that determine its long-term dynamics. The
derivation of the slow flow is challenging, due to the stick-slip motion of the
platform in some parameter regimes. Simulations of the slow flow reveal various
kinds of long-term behavior including in-phase and antiphase synchronization of
identical metronomes, phase locking and phase drift of non-identical
metronomes, and metronome suppression and death. In these latter two states,
one or both of the metronomes come to swing at such low amplitude that they no
longer engage their escapement mechanisms. We find good agreement between our
theory, simulations, and experiments, but stress that our exploration is far
from exhaustive. Indeed, much still remains to be learned about the dynamics of
coupled metronomes, despite their simplicity and familiarity.

Wearable devices that integrate multiple sensors, processors, and
communication technologies have the potential to transform mobile health for
remote monitoring of health parameters. However, the small form factor of the
wearable devices limits the battery size and operating lifetime. As a result,
the devices require frequent recharging, which has limited their widespread
adoption. Energy harvesting has emerged as an effective method towards
sustainable operation of wearable devices. Unfortunately, energy harvesting
alone is not sufficient to fulfill the energy requirements of wearable devices.
This paper studies the novel problem of adaptive energy management towards the
goal of self-sustainable wearables by using harvested energy to supplement the
battery energy and to reduce manual recharging by users. To solve this problem,
we propose a principled algorithm referred as AdaEM. There are two key ideas
behind AdaEM. First, it uses machine learning (ML) methods to learn predictive
models of user activity and energy usage patterns. These models allow us to
estimate the potential of energy harvesting in a day as a function of the user
activities. Second, it reasons about the uncertainty in predictions and
estimations from the ML models to optimize the energy management decisions
using a dynamic robust optimization (DyRO) formulation. We propose a
light-weight solution for DyRO to meet the practical needs of deployment. We
validate the AdaEM approach on a wearable device prototype consisting of solar
and motion energy harvesting using real-world data of user activities.
Experiments show that AdaEM achieves solutions that are within 5% of the
optimal with less than 0.005% execution time and energy overhead.

We present our submission to the Extreme Value Analysis 2021 Data Challenge
in which teams were asked to accurately predict distributions of wildfire
frequency and size within spatio-temporal regions of missing data. For the
purpose of this competition we developed a variant of the powerful variational
autoencoder models dubbed the Conditional Missing data Importance-Weighted
Autoencoder (CMIWAE). Our deep latent variable generative model requires little
to no feature engineering and does not necessarily rely on the specifics of
scoring in the Data Challenge. It is fully trained on incomplete data, with the
single objective to maximize log-likelihood of the observed wildfire
information. We mitigate the effects of the relatively low number of training
samples by stochastic sampling from a variational latent variable distribution,
as well as by ensembling a set of CMIWAE models trained and validated on
different splits of the provided data. The presented approach is not
domain-specific and is amenable to application in other missing data recovery
tasks with tabular or image-like information conditioned on auxiliary

Quantitative UltraSound (QUS) aims to reveal information about the tissue
microstructure using backscattered echo signals from clinical scanners. Among
different QUS parameters, scatterer number density is an important property
that can affect estimation of other QUS parameters. Scatterer number density
can be classified into high or low scatterer densities. If there are more than
10 scatterers inside the resolution cell, the envelope data is considered as
Fully Developed Speckle (FDS) and otherwise, as Under Developed Speckle (UDS).
In conventional methods, the envelope data is divided into small overlapping
windows (a strategy here we refer to as patching), and statistical parameters
such as SNR and skewness are employed to classify each patch of envelope data.
However, these parameters are system dependent meaning that their distribution
can change by the imaging settings and patch size. Therefore, reference
phantoms which have known scatterer number density are imaged with the same
imaging settings to mitigate system dependency. In this paper, we aim to
segment regions of ultrasound data without any patching. A large dataset is
generated which has different shapes of scatterer number density and mean
scatterer amplitude using a fast simulation method. We employ a convolutional
neural network (CNN) for the segmentation task and investigate the effect of
domain shift when the network is tested on different datasets with different
imaging settings. Nakagami parametric image is employed for the multi-task
learning to improve the performance. Furthermore, inspired by the reference
phantom methods in QUS, A domain adaptation stage is proposed which requires
only two frames of data from FDS and UDS classes. We evaluate our method for
different experimental phantoms and in vivo data.

An overarching goal in machine learning is to build a generalizable model
with few samples. To this end, overparameterization has been the subject of
immense interest to explain the generalization ability of deep nets even when
the size of the dataset is smaller than that of the model. While the prior
literature focuses on the classical supervised setting, this paper aims to
demystify overparameterization for meta-learning. Here we have a sequence of
linear-regression tasks and we ask: (1) Given earlier tasks, what is the
optimal linear representation of features for a new downstream task? and (2)
How many samples do we need to build this representation? This work shows that
surprisingly, overparameterization arises as a natural answer to these
fundamental meta-learning questions. Specifically, for (1), we first show that
learning the optimal representation coincides with the problem of designing a
task-aware regularization to promote inductive bias. We leverage this inductive
bias to explain how the downstream task actually benefits from
overparameterization, in contrast to prior works on few-shot learning. For (2),
we develop a theory to explain how feature covariance can implicitly help
reduce the sample complexity well below the degrees of freedom and lead to
small estimation error. We then integrate these findings to obtain an overall
performance guarantee for our meta-learning algorithm. Numerical experiments on
real and synthetic data verify our insights on overparameterized meta-learning.

Bayesian methods to solve imaging inverse problems usually combine an
explicit data likelihood function with a prior distribution that explicitly
models expected properties of the solution. Many kinds of priors have been
explored in the literature, from simple ones expressing local properties to
more involved ones exploiting image redundancy at a non-local scale. In a
departure from explicit modelling, several recent works have proposed and
studied the use of implicit priors defined by an image denoising algorithm.
This approach, commonly known as Plug & Play (PnP) regularisation, can deliver
remarkably accurate results, particularly when combined with state-of-the-art
denoisers based on convolutional neural networks. However, the theoretical
analysis of PnP Bayesian models and algorithms is difficult and works on the
topic often rely on unrealistic assumptions on the properties of the image
denoiser. This papers studies maximum-a-posteriori (MAP) estimation for
Bayesian models with PnP priors. We first consider questions related to
existence, stability and well-posedness, and then present a convergence proof
for MAP computation by PnP stochastic gradient descent (PnP-SGD) under
realistic assumptions on the denoiser used. We report a range of imaging
experiments demonstrating PnP-SGD as well as comparisons with other PnP
schemes.

A key challenge in inventory management is to identify policies that
optimally replenish inventory from multiple suppliers. To solve such
optimization problems, inventory managers need to decide what quantities to
order from each supplier, given the on-hand inventory and outstanding orders,
so that the expected backlogging, holding, and sourcing costs are jointly
minimized. Inventory management problems have been studied extensively for over
60 years, and yet even basic dual sourcing problems, in which orders from an
expensive supplier arrive faster than orders from a regular supplier, remain
intractable in their general form. In addition, there is an emerging need to
develop proactive, scalable optimization algorithms that can adjust their
recommendations to dynamic demand shifts in a timely fashion. In this work, we
approach dual sourcing from a neural-network-based optimization lens and
incorporate inventory dynamics into the design of deep neural networks. We show
that the proposed inventory-dynamics-informed neural networks (IDINNs) are able
to learn near-optimal policies of commonly used instances within a few minutes
of CPU time on a regular personal computer. To demonstrate the versatility of
IDINNs, we also show that they can control inventory dynamics with empirical,
non-stationary demand distributions that are challenging to tackle effectively
using alternative, state-of-the-art approaches.Our work shows that high-quality
solutions of complex inventory management problems with non-stationary demand
can be obtained with deep neural-network optimization approaches that directly
account for inventory dynamics in their optimization process. As such, our
research opens up new ways of efficiently managing complex, high-dimensional
inventory dynamics.

Temporal information extraction plays a critical role in natural language
understanding. Previous systems have incorporated advanced neural language
models and have successfully enhanced the accuracy of temporal information
extraction tasks. However, these systems have two major shortcomings. First,
they fail to make use of the two-sided nature of temporal relations in
prediction. Second, they involve non-parallelizable pipelines in inference
process that bring little performance gain. To this end, we propose a novel
temporal information extraction model based on deep biaffine attention to
extract temporal relationships between events in unstructured text efficiently
and accurately. Our model is performant because we perform relation extraction
tasks directly instead of considering event annotation as a prerequisite of
relation extraction. Moreover, our architecture uses Multilayer Perceptrons
(MLP) with biaffine attention to predict arcs and relation labels separately,
improving relation detecting accuracy by exploiting the two-sided nature of
temporal relationships. We experimentally demonstrate that our model achieves
state-of-the-art performance in temporal relation extraction.

In natural auditory environments, acoustic signals originate from the
temporal superimposition of different sound sources. The problem of inferring
individual sources from ambiguous mixtures of sounds is known as blind source
decomposition. Experiments on humans have demonstrated that the auditory system
can identify sound sources as repeating patterns embedded in the acoustic
input. Source repetition produces temporal regularities that can be detected
and used for segregation. Specifically, listeners can identify sounds occurring
more than once across different mixtures, but not sounds heard only in a single
mixture. However, whether such a behaviour can be computationally modelled has
not yet been explored. Here, we propose a biologically inspired computational
model to perform blind source separation on sequences of mixtures of acoustic
stimuli. Our method relies on a somatodendritic neuron model trained with a
Hebbian-like learning rule which can detect spatio-temporal patterns recurring
in synaptic inputs. We show that the segregation capabilities of our model are
reminiscent of the features of human performance in a variety of experimental
settings involving synthesized sounds with naturalistic properties.
Furthermore, we extend the study to investigate the properties of segregation
on task settings not yet explored with human subjects, namely natural sounds
and images. Overall, our work suggests that somatodendritic neuron models offer
a promising neuro-inspired learning strategy to account for the characteristics
of the brain segregation capabilities as well as to make predictions on yet
untested experimental settings.

While deep learning approaches to information extraction have had many
successes, they can be difficult to augment or maintain as needs shift.
Rule-based methods, on the other hand, can be more easily modified. However,
crafting rules requires expertise in linguistics and the domain of interest,
making it infeasible for most users. Here we attempt to combine the advantages
of these two directions while mitigating their drawbacks. We adapt recent
advances from the adjacent field of program synthesis to information
extraction, synthesizing rules from provided examples. We use a
transformer-based architecture to guide an enumerative search, and show that
this reduces the number of steps that need to be explored before a rule is
found. Further, we show that without training the synthesis algorithm on the
specific domain, our synthesized rules achieve state-of-the-art performance on
the 1-shot scenario of a task that focuses on few-shot learning for relation
classification, and competitive performance in the 5-shot scenario.

This dissertation addresses visual scene understanding and enhances
segmentation performance and generalization, training efficiency of networks,
and holistic understanding. First, we investigate semantic segmentation in the
context of street scenes and train semantic segmentation networks on
combinations of various datasets. In Chapter 2 we design a framework of
hierarchical classifiers over a single convolutional backbone, and train it
end-to-end on a combination of pixel-labeled datasets, improving
generalizability and the number of recognizable semantic concepts. Chapter 3
focuses on enriching semantic segmentation with weak supervision and proposes a
weakly-supervised algorithm for training with bounding box-level and
image-level supervision instead of only with per-pixel supervision. The memory
and computational load challenges that arise from simultaneous training on
multiple datasets are addressed in Chapter 4. We propose two methodologies for
selecting informative and diverse samples from datasets with weak supervision
to reduce our networks' ecological footprint without sacrificing performance.
Motivated by memory and computation efficiency requirements, in Chapter 5, we
rethink simultaneous training on heterogeneous datasets and propose a universal
semantic segmentation framework. This framework achieves consistent increases
in performance metrics and semantic knowledgeability by exploiting various
scene understanding datasets. Chapter 6 introduces the novel task of part-aware
panoptic segmentation, which extends our reasoning towards holistic scene
understanding. This task combines scene and parts-level semantics with
instance-level object detection. In conclusion, our contributions span over
convolutional network architectures, weakly-supervised learning, part and
panoptic segmentation, paving the way towards a holistic, rich, and sustainable
visual scene understanding.

This paper presents a physical perspective on the human migration phenomenon
claiming that human social behaviors are somehow but not completely inspired by
nature. Human displacement or migration in the world is highly affected by
equivalent weighted wealth and multidimensional poverty distribution across
countries. In this work, it has been shown that the equivalent weighted wealth
as an important characteristic of a society can be introduced in a mathematical
language in terms of a country gross productivity factor, safety and social
factors, environmental and economic factors, political and healthy factors,
educational and cultural factors and so on using some weights, where these
weights can be determined using regression or machine learning techniques. By
inspiring from the natural phenomena such as heat conduction models; heat
conduction and heat convection models, a constitutive model for migration flow
rate in terms of the equivalent weighted wealth gradient descent has been given
where accompanying with other balance laws it can make possible to determine
wealth distribution and migration flow rate across the world. Some critical
conditions such as earthquakes, war, famine, insecurity, and more prevent the
natural transmission of wealth and lead to the intensification of
non-uniformity in wealth distribution. The increase in the equivalent weighted
wealth difference between countries leads to an enhanced legal migration rate
as well as a boosted illegal migration flow rate where it exceeds a threshold
value. The introduced nature-inspired model could significantly predict wealth
distribution as well as migration rate, which will have helped those who try to
have a world with a logical almost uniform equivalent weighted wealth
distribution and will have made the world a better place for all of the people.

Measuring machine creativity is one of the most fascinating challenges in
Artificial Intelligence. This paper explores the possibility of using
generative learning techniques for automatic assessment of creativity. The
proposed solution does not involve human judgement, it is modular and of
general applicability. We introduce a new measure, namely DeepCreativity, based
on Margaret Boden's definition of creativity as composed by value, novelty and
surprise. We evaluate our methodology (and related measure) considering a case
study, i.e., the generation of 19th century American poetry, showing its
effectiveness and expressiveness.

We propose and establish the asymptotic properties of FNETS, a methodology
for network estimation and forecasting of high-dimensional time series
exhibiting strong serial- and cross-sectional correlations. We operate under a
factor-adjusted vector autoregressive (VAR) model where, after controlling for
{\it common} dynamic factors accounting for pervasive leading, lagging or
contemporaneous co-movements of the variables, the remaining {\it
idiosyncratic} dynamic dependence between the variables is modelled by a sparse
VAR process. Network estimation of FNETS consists of three steps: (i)
factor-adjustment via dynamic principal component analysis, (ii) estimation of
the parameters of the latent VAR process by means of $\ell_1$-regularised
Yule-Walker estimators, and (iii) estimation of partial correlation and
long-run partial correlation matrices. In doing so, we learn three networks
underpinning the latent VAR process, namely a directed network representing the
Granger causal linkages between the variables, an undirected one embedding
their contemporaneous relationships and finally, an undirected network that
summarises both lead-lag and contemporaneous linkages. In addition, FNETS
provides a suite of methods for separately forecasting the factor-driven and
the VAR processes. Under general conditions permitting heavy tails and weak
factors, we derive the consistency of FNETS in both network estimation and
forecasting as the dimension of the panel and the sample size diverge.
Simulation studies and real data application confirm the good performance of
FNETS.

Variability in social app usage across regions results in a high skew of the
quantity and the quality of check-in data collected, which in turn is a
challenge for effective location recommender systems. In this paper, we present
Axolotl (Automated cross Location-network Transfer Learning), a novel method
aimed at transferring location preference models learned in a data-rich region
to significantly boost the quality of recommendations in a data-scarce region.
Axolotl predominantly deploys two channels for information transfer, (1) a
meta-learning based procedure learned using location recommendation as well as
social predictions, and (2) a lightweight unsupervised cluster-based transfer
across users and locations with similar preferences. Both of these work
together synergistically to achieve improved accuracy of recommendations in
data-scarce regions without any prerequisite of overlapping users and with
minimal fine-tuning. We build Axolotl on top of a twin graph-attention neural
network model used for capturing the user- and location-conditioned influences
in a user-mobility graph for each region. We conduct extensive experiments on
12 user mobility datasets across the U.S., Japan, and Germany, using 3 as
source regions and 9 of them (that have much sparsely recorded mobility data)
as target regions. Empirically, we show that Axolotl achieves up to 18% better
recommendation performance than the existing state-of-the-art methods across
all metrics.

The Open Radio Access Network (O-RAN) is a new, open, adaptive, and
intelligent RAN architecture. Motivated by the success of artificial
intelligence in other domains, O-RAN strives to leverage machine learning (ML)
to automatically and efficiently manage network resources in diverse use cases
such as traffic steering, quality of experience prediction, and anomaly
detection. Unfortunately, ML-based systems are not free of vulnerabilities;
specifically, they suffer from a special type of logical vulnerabilities that
stem from the inherent limitations of the learning algorithms. To exploit these
vulnerabilities, an adversary can utilize an attack technique referred to as
adversarial machine learning (AML). These special type of attacks has already
been demonstrated in recent researches. In this paper, we present a systematic
AML threat analysis for the O-RAN. We start by reviewing relevant ML use cases
and analyzing the different ML workflow deployment scenarios in O-RAN. Then, we
define the threat model, identifying potential adversaries, enumerating their
adversarial capabilities, and analyzing their main goals. Finally, we explore
the various AML threats in the O-RAN and review a large number of attacks that
can be performed to materialize these threats and demonstrate an AML attack on
a traffic steering model.

Physics-Infused Machine Learning (PIML) architectures aim at integrating
machine learning with computationally-efficient, low-fidelity (partial) physics
models, leading to improved generalizability, extrapolability, and robustness
to noise, compared to pure data-driven approximation models. Recently a new
PIML architecture was reported by the same authors, known as Opportunistic
Physics-mining Transfer Mapping Architecture or OPTMA, which transfers the
original inputs into latent features using a transfer neural network; the
partial physics model then uses the latent features to generate the final
output that is as close as possible to the high-fidelity output. While
gradient-free solvers and back-propagation with supervised learning was earlier
used to train OPTMA, that approach is computationally inefficient and
challenging to generalize across different problems or popular ML
implementations. This paper aims to alleviate these issues by infusing the
partial physics model inside the neural network, as described via tensors in
the popular ML framework, PyTorch. Such a description also naturally allows
auto-differentiation (AD) of the partial physics model, thereby enabling the
use of efficient back-propagation methods to train the transfer network. The
benefits of the upgraded OPTMA architecture with AD (OPTMA-Net) is demonstrated
by applying it to the problem of modeling the sound pressure field created by a
hovering unmanned aerial vehicle (UAV). Ground truth data for this problem was
obtained from an indoor UAV noise measurement setup. Here, the partial physics
model is based on the interference of acoustic pressure waves generated by an
arbitrary number of acoustic monopole sources. Case studies show that OPTMA-Net
provides generalization performance close to, and extrapolation performance
that is 4 times better than, those given by a pure data-driven model.

Glioblastoma is a common brain malignancy that tends to occur in older adults
and is almost always lethal. The effectiveness of chemotherapy, being the
standard treatment for most cancer types, can be improved if a particular
genetic sequence in the tumor known as MGMT promoter is methylated. However, to
identify the state of the MGMT promoter, the conventional approach is to
perform a biopsy for genetic analysis, which is time and effort consuming. A
couple of recent publications proposed a connection between the MGMT promoter
state and the MRI scans of the tumor and hence suggested the use of deep
learning models for this purpose. Therefore, in this work, we use one of the
most extensive datasets, BraTS 2021, to study the potency of employing deep
learning solutions, including 2D and 3D CNN models and vision transformers.
After conducting a thorough analysis of the models' performance, we concluded
that there seems to be no connection between the MRI scans and the state of the
MGMT promoter.

Uniswap, like other DEXs, has gained much attention this year because it is a
non-custodial and publicly verifiable exchange that allows users to trade
digital assets without trusted third parties. However, its simplicity and lack
of regulation also makes it easy to execute initial coin offering scams by
listing non-valuable tokens. This method of performing scams is known as rug
pull, a phenomenon that already existed in traditional finance but has become
more relevant in DeFi. Various projects such as [34,37] have contributed to
detecting rug pulls in EVM compatible chains. However, the first longitudinal
and academic step to detecting and characterizing scam tokens on Uniswap was
made in [44]. The authors collected all the transactions related to the Uniswap
V2 exchange and proposed a machine learning algorithm to label tokens as scams.
However, the algorithm is only valuable for detecting scams accurately after
they have been executed. This paper increases their data set by 20K tokens and
proposes a new methodology to label tokens as scams. After manually analyzing
the data, we devised a theoretical classification of different malicious
maneuvers in Uniswap protocol. We propose various machine-learning-based
algorithms with new relevant features related to the token propagation and
smart contract heuristics to detect potential rug pulls before they occur. In
general, the models proposed achieved similar results. The best model obtained
an accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in
distinguishing non-malicious tokens from scams prior to the malicious maneuver.

Accurate information about protein content in the organism is instrumental
for a better understanding of human biology and disease mechanisms. While the
presence of certain types of proteins can be life-threatening, the abundance of
others is an essential condition for an individual's overall well-being.
Protein microarray is a technology that enables the quantification of thousands
of proteins in hundreds of human samples in a parallel manner. In a series of
studies involving protein microarrays, we have explored and implemented various
data science methods for all-around analysing of these data. This analysis has
enabled the identification and characterisation of proteins targeted by the
autoimmune reaction in patients with the APS1 condition. We have also assessed
the utility of applying machine learning methods alongside statistical tests in
a study based on protein expression data to evaluate potential biomarkers for
endometriosis. The keystone of this work is a web-tool PAWER. PAWER implements
relevant computational methods, and provides a semi-automatic way to run the
analysis of protein microarray data online in a drag-and-drop and
click-and-play style. The source code of the tool is publicly available. The
work that laid the foundation of this thesis has been instrumental for a number
of subsequent studies of human disease and also inspired a contribution to
refining standards for validation of machine learning methods in biology.

Due to diverse architectures in deep neural networks (DNNs) with severe
overparameterization, regularization techniques are critical for finding
optimal solutions in the huge hypothesis space. In this paper, we propose an
effective regularization technique, called Neighborhood Region Smoothing (NRS).
NRS leverages the finding that models would benefit from converging to flat
minima, and tries to regularize the neighborhood region in weight space to
yield approximate outputs. Specifically, gap between outputs of models in the
neighborhood region is gauged by a defined metric based on Kullback-Leibler
divergence. This metric provides similar insights with the minimum description
length principle on interpreting flat minima. By minimizing both this
divergence and empirical loss, NRS could explicitly drive the optimizer towards
converging to flat minima. We confirm the effectiveness of NRS by performing
image classification tasks across a wide range of model architectures on
commonly-used datasets such as CIFAR and ImageNet, where generalization ability
could be universally improved. Also, we empirically show that the minima found
by NRS would have relatively smaller Hessian eigenvalues compared to the
conventional method, which is considered as the evidence of flat minima.

Debiased recommendation has recently attracted increasing attention from both
industry and academic communities. Traditional models mostly rely on the
inverse propensity score (IPS), which can be hard to estimate and may suffer
from the high variance issue. To alleviate these problems, in this paper, we
propose a novel debiased recommendation framework based on user feature
balancing. The general idea is to introduce a projection function to adjust
user feature distributions, such that the ideal unbiased learning objective can
be upper bounded by a solvable objective purely based on the offline dataset.
In the upper bound, the projected user distributions are expected to be equal
given different items. From the causal inference perspective, this requirement
aims to remove the causal relation from the user to the item, which enables us
to achieve unbiased recommendation, bypassing the computation of IPS. In order
to efficiently balance the user distributions upon each item pair, we propose
three strategies, including clipping, sampling and adversarial learning to
improve the training process. For more robust optimization, we deploy an
explicit model to capture the potential latent confounders in recommendation
systems. To the best of our knowledge, this paper is the first work on debiased
recommendation based on confounder balancing. In the experiments, we compare
our framework with many state-of-the-art methods based on synthetic,
semi-synthetic and real-world datasets. Extensive experiments demonstrate that
our model is effective in promoting the recommendation performance.

Elucidating the connection between the properties of galaxies and the
properties of their hosting haloes is a key element in galaxy formation. When
the spatial distribution of objects is also taken under consideration, it
becomes very relevant for cosmological measurements. In this paper, we use
machine learning techniques to analyse these intricate relations in the
IllustrisTNG300 magnetohydrodynamical simulation, predicting baryonic
properties from halo properties. We employ four different algorithms: extremely
randomized trees, K-nearest neighbours, light gradient boosting machine, and
neural networks, along with a unique and powerful combination of the results
from all four approaches. Overall, the different algorithms produce consistent
results in terms of predicting galaxy properties from a set of input halo
properties that include halo mass, concentration, spin, and halo overdensity.
For stellar mass, the Pearson correlation coefficient is 0.98, dropping down to
0.7-0.8 for specific star formation rate (sSFR), colour, and size. In addition,
we apply, for the first time in this context, an existing data augmentation
method, synthetic minority over-sampling technique for regression with Gaussian
noise (SMOGN), designed to alleviate the problem of imbalanced data sets,
showing that it improves the overall shape of the predicted distributions and
the scatter in the halo-galaxy relations. We also demonstrate that our
predictions are good enough to reproduce the power spectra of multiple galaxy
populations, defined in terms of stellar mass, sSFR, colour, and size with high
accuracy. Our results align with previous reports suggesting that certain
galaxy properties cannot be reproduced using halo features alone.

Quick and accurate diagnosis is of paramount importance to mitigate the
effects of COVID-19 infection, particularly for severe cases. Enormous effort
has been put towards developing deep learning methods to classify and detect
COVID-19 infections from chest radiography images. However, recently some
questions have been raised surrounding the clinical viability and effectiveness
of such methods. In this work, we investigate the impact of multi-task learning
(classification and segmentation) on the ability of CNNs to differentiate
between various appearances of COVID-19 infections in the lung. We also employ
self-supervised pre-training approaches, namely MoCo and inpainting-CXR, to
eliminate the dependence on expensive ground truth annotations for COVID-19
classification. Finally, we conduct a critical evaluation of the models to
assess their deploy-readiness and provide insights into the difficulties of
fine-grained COVID-19 multi-class classification from chest X-rays.

In recent years, tons of research has been conducted on Single Image
Super-Resolution (SISR). However, to the best of our knowledge, few of these
studies are mainly focused on compressed images. A problem such as complicated
compression artifacts hinders the advance of this study in spite of its high
practical values. To tackle this problem, we proposed CISRNet; a network that
employs a two-stage coarse-to-fine learning framework that is mainly optimized
for Compressed Image Super-Resolution Problem. Specifically, CISRNet consists
of two main subnetworks; the coarse and refinement network, where recursive and
residual learning is employed within these two networks respectively. Extensive
experiments show that with a careful design choice, CISRNet performs favorably
against competing Single-Image Super-Resolution methods in the Compressed Image
Super-Resolution tasks.

Most software companies have extensive test suites and re-run parts of them
continuously to ensure recent changes have no adverse effects. Since test
suites are costly to execute, industry needs methods for test case
prioritisation (TCP). Recently, TCP methods use machine learning (ML) to
exploit the information known about the system under test (SUT) and its test
cases. However, the value added by ML-based TCP methods should be critically
assessed with respect to the cost of collecting the information. This paper
analyses two decades of TCP research, and presents a taxonomy of 91 information
attributes that have been used. The attributes are classified with respect to
their information sources and the characteristics of their extraction process.
Based on this taxonomy, TCP methods validated with industrial data and those
applying ML are analysed in terms of information availability, attribute
combination and definition of data features suitable for ML. Relying on a high
number of information attributes, assuming easy access to SUT code and
simplified testing environments are identified as factors that might hamper
industrial applicability of ML-based TCP. The TePIA taxonomy provides a
reference framework to unify terminology and evaluate alternatives considering
the cost-benefit of the information attributes.

Contrastive learning has proven useful in many applications where access to
labelled data is limited. The lack of annotated data is particularly
problematic in medical image segmentation as it is difficult to have clinical
experts manually annotate large volumes of data such as cardiac structures in
ultrasound images of the heart. In this paper, We propose a self supervised
contrastive learning method to segment the left ventricle from echocardiography
when limited annotated images exist. Furthermore, we study the effect of
contrastive pretraining on two well-known segmentation networks, UNet and
DeepLabV3. Our results show that contrastive pretraining helps improve the
performance on left ventricle segmentation, particularly when annotated data is
scarce. We show how to achieve comparable results to state-of-the-art fully
supervised algorithms when we train our models in a self-supervised fashion
followed by fine-tuning on just 5\% of the data. We show that our solution
outperforms what is currently published on a large public dataset
(EchoNet-Dynamic) achieving a Dice score of 0.9252. We also compare the
performance of our solution on another smaller dataset (CAMUS) to demonstrate
the generalizability of our proposed solution. The code is available at
(https://github.com/BioMedIA-MBZUAI/contrastive-echo).

mage steganography is the process of hiding information which can be text,
image, or video inside a cover image. The advantage of steganography over
cryptography is that the intended secret message does not attract attention and
is thus more suitable for secret communication in a highly-surveillant
environment such as civil disobedience movements. Internet memes in social
media and messaging apps have become a popular culture worldwide, so this folk
custom is a good application scenario for image steganography. We try to
explore and adopt the steganography techniques on the Internet memes in this
work. We implement and improve the HiDDeN model by changing the Conv-BN-ReLU
blocks convolution layer with a multiscale autoencoder network so that the
neural network learns to embed message bits in higher-level feature space.
Compared to methods that convolve feature filters on the row-pixel domain, our
proposed MS-Hidden network learns to hide secrets in both low-level and
high-level image features. As a result, the proposed model significantly
reduces the bit-error rate to empirically 0% and the required network
parameters are much less than the HiDDeN model.

Sequential recommendation models the dynamics of a user's previous behaviors
in order to forecast the next item, and has drawn a lot of attention.
Transformer-based approaches, which embed items as vectors and use dot-product
self-attention to measure the relationship between items, demonstrate superior
capabilities among existing sequential methods. However, users' real-world
sequential behaviors are \textit{\textbf{uncertain}} rather than deterministic,
posing a significant challenge to present techniques. We further suggest that
dot-product-based approaches cannot fully capture \textit{\textbf{collaborative
transitivity}}, which can be derived in item-item transitions inside sequences
and is beneficial for cold start items. We further argue that BPR loss has no
constraint on positive and sampled negative items, which misleads the
optimization. We propose a novel \textbf{STO}chastic
\textbf{S}elf-\textbf{A}ttention~(STOSA) to overcome these issues. STOSA, in
particular, embeds each item as a stochastic Gaussian distribution, the
covariance of which encodes the uncertainty. We devise a novel Wasserstein
Self-Attention module to characterize item-item position-wise relationships in
sequences, which effectively incorporates uncertainty into model training.
Wasserstein attentions also enlighten the collaborative transitivity learning
as it satisfies triangle inequality. Moreover, we introduce a novel
regularization term to the ranking loss, which assures the dissimilarity
between positive and the negative items. Extensive experiments on five
real-world benchmark datasets demonstrate the superiority of the proposed model
over state-of-the-art baselines, especially on cold start items. The code is
available in \url{https://github.com/zfan20/STOSA}.

Deep learning for change detection is one of the current hot topics in the
field of remote sensing. However, most end-to-end networks are proposed for
supervised change detection, and unsupervised change detection models depend on
traditional pre-detection methods. Therefore, we proposed a fully convolutional
change detection framework with generative adversarial network, to conclude
unsupervised, weakly supervised, regional supervised, and fully supervised
change detection tasks into one framework. A basic Unet segmentor is used to
obtain change detection map, an image-to-image generator is implemented to
model the spectral and spatial variation between multi-temporal images, and a
discriminator for changed and unchanged is proposed for modeling the semantic
changes in weakly and regional supervised change detection task. The iterative
optimization of segmentor and generator can build an end-to-end network for
unsupervised change detection, the adversarial process between segmentor and
discriminator can provide the solutions for weakly and regional supervised
change detection, the segmentor itself can be trained for fully supervised
task. The experiments indicate the effectiveness of the propsed framework in
unsupervised, weakly supervised and regional supervised change detection. This
paper provides theorical definitions for unsupervised, weakly supervised and
regional supervised change detection tasks, and shows great potentials in
exploring end-to-end network for remote sensing change detection.

In this paper, we propose a deep state-action-reward-state-action (SARSA)
$\lambda$ learning approach for optimising the uplink resource allocation in
non-orthogonal multiple access (NOMA) aided ultra-reliable low-latency
communication (URLLC). To reduce the mean decoding error probability in
time-varying network environments, this work designs a reliable learning
algorithm for providing a long-term resource allocation, where the reward
feedback is based on the instantaneous network performance. With the aid of the
proposed algorithm, this paper addresses three main challenges of the reliable
resource sharing in NOMA-URLLC networks: 1) user clustering; 2) Instantaneous
feedback system; and 3) Optimal resource allocation. All of these designs
interact with the considered communication environment. Lastly, we compare the
performance of the proposed algorithm with conventional Q-learning and SARSA
Q-learning algorithms. The simulation outcomes show that: 1) Compared with the
traditional Q learning algorithms, the proposed solution is able to converges
within \myb{200} episodes for providing as low as $10^{-2}$ long-term mean
error; 2) NOMA assisted URLLC outperforms traditional OMA systems in terms of
decoding error probabilities; and 3) The proposed feedback system is efficient
for the long-term learning process.

Modern consumer electronic devices often provide intelligence services with
deep neural networks. We have started migrating the computing locations of
intelligence services from cloud servers (traditional AI systems) to the
corresponding devices (on-device AI systems). On-device AI systems generally
have the advantages of preserving privacy, removing network latency, and saving
cloud costs. With the emergent of on-device AI systems having relatively low
computing power, the inconsistent and varying hardware resources and
capabilities pose difficulties. Authors' affiliation has started applying a
stream pipeline framework, NNStreamer, for on-device AI systems, saving
developmental costs and hardware resources and improving performance. We want
to expand the types of devices and applications with on-device AI services
products of both the affiliation and second/third parties. We also want to make
each AI service atomic, re-deployable, and shared among connected devices of
arbitrary vendors; we now have yet another requirement introduced as it always
has been. The new requirement of "among-device AI" includes connectivity
between AI pipelines so that they may share computing resources and hardware
capabilities across a wide range of devices regardless of vendors and
manufacturers. We propose extensions of the stream pipeline framework,
NNStreamer, for on-device AI so that NNStreamer may provide among-device AI
capability. This work is a Linux Foundation (LF AI and Data) open source
project accepting contributions from the general public.

Motivation: Predicting the drug-target interaction is crucial for drug
discovery as well as drug repurposing. Machine learning is commonly used in
drug-target affinity (DTA) problem. However, machine learning model faces the
cold-start problem where the model performance drops when predicting the
interaction of a novel drug or target. Previous works try to solve the cold
start problem by learning the drug or target representation using unsupervised
learning. While the drug or target representation can be learned in an
unsupervised manner, it still lacks the interaction information, which is
critical in drug-target interaction. Results: To incorporate the interaction
information into the drug and protein interaction, we proposed using transfer
learning from chemical-chemical interaction (CCI) and protein-protein
interaction (PPI) task to drug-target interaction task. The representation
learned by CCI and PPI tasks can be transferred smoothly to the DTA task due to
the similar nature of the tasks. The result on the drug-target affinity
datasets shows that our proposed method has advantages compared to other
pretraining methods in the DTA task.

Measurement-based realtime control is an important strategy in quantum
information processing, which is applied in fields from qubit readout to error
corrections. However, the time cost of quantum measurement inevitably induces a
latency in the control process and limits its performance. Here we introduce
the deep learning approach to relax this restriction by predicting and
compensating the latency-induced control error. We experimentally implement
feedforward quantum control of a single-spin system of nitrogen-vacancy (NV)
center in diamond to protect the coherence of the electron spin. The new
approach enhances the decoherence time as well as the the spectrum resolution
of Ramsey interferometry about three times comparing with conventional scheme.
This enables resolving optically indistinguishable NV centers from their
magnetic spectrum with a frequency difference less than 20 kHz. A theoretical
model is proposed to explain the improvement, where we show that the
low-frequency magnetic noise is perfectly reduced. This scheme could be applied
in general measurement schemes and extended to other quantum control systems.

This paper studies weakly supervised domain adaptation(WSDA) problem, where
we only have access to the source domain with noisy labels, from which we need
to transfer useful information to the unlabeled target domain. Although there
have been a few studies on this problem, most of them only exploit
unidirectional relationships from the source domain to the target domain. In
this paper, we propose a universal paradigm called GearNet to exploit bilateral
relationships between the two domains. Specifically, we take the two domains as
different inputs to train two models alternately, and asymmetrical
Kullback-Leibler loss is used for selectively matching the predictions of the
two models in the same domain. This interactive learning schema enables
implicit label noise canceling and exploits correlations between the source and
target domains. Therefore, our GearNet has the great potential to boost the
performance of a wide range of existing WSDL methods. Comprehensive
experimental results show that the performance of existing methods can be
significantly improved by equipping with our GearNet.

Due to the COVID-19 as a pandemic, the government has forced the nationwide
shutdown of several activities, including educational activities. It has
resulted in gigantic migration of universities with education over the internet
serving as the educational platform. Hand-on-based learning becomes a new
challenge. This paper aims to investigate the effect of computer
simulation-based learning on student self-efficacy in an electric circuit
analysis course. For the 17 participants included in this study, the students
have overcome their existing achievements indicated by a long-term average
score. Computer simulation-based learning provides positive results on student
self-efficacy. Students also perceived a valuable learning experience.

Neural graphics primitives, parameterized by fully connected neural networks,
can be costly to train and evaluate. We reduce this cost with a versatile new
input encoding that permits the use of a smaller network without sacrificing
quality, thus significantly reducing the number of floating point and memory
access operations: a small neural network is augmented by a multiresolution
hash table of trainable feature vectors whose values are optimized through
stochastic gradient descent. The multiresolution structure allows the network
to disambiguate hash collisions, making for a simple architecture that is
trivial to parallelize on modern GPUs. We leverage this parallelism by
implementing the whole system using fully-fused CUDA kernels with a focus on
minimizing wasted bandwidth and compute operations. We achieve a combined
speedup of several orders of magnitude, enabling training of high-quality
neural graphics primitives in a matter of seconds, and rendering in tens of
milliseconds at a resolution of ${1920\!\times\!1080}$.

This study quantifies influence between media outlets by applying a novel
methodology that uses causal effect estimation on networks and transformer
language models. We demonstrate the obscured influence of state-controlled
outlets over other outlets, regardless of orientation, by analyzing a large
dataset of quotations from over 100 thousand articles published by the most
prominent European and Russian traditional media outlets, appearing between May
2018 and October 2019. The analysis maps out the network structure of influence
with news wire services serving as prominent bridges that connect outlets in
different geo-political spheres. Overall, this approach demonstrates
capabilities to identify and quantify the channels of influence in intermedia
agenda setting over specific topics.

Current answer sentence selection (AS2) applied in open-domain question
answering (ODQA) selects answers by ranking a large set of possible candidates,
i.e., sentences, extracted from the retrieved text. In this paper, we present
Passage-based Extracting Answer Sentence In-place (PEASI), a novel design for
AS2 optimized for Web-scale setting, that, instead, computes such answer
without processing each candidate individually. Specifically, we design a
Transformer-based framework that jointly (i) reranks passages retrieved for a
question and (ii) identifies a probable answer from the top passages in place.
We train PEASI in a multi-task learning framework that encourages feature
sharing between the components: passage reranker and passage-based answer
sentence extractor. To facilitate our development, we construct a new
Web-sourced large-scale QA dataset consisting of 800,000+ labeled
passages/sentences for 60,000+ questions. The experiments show that our
proposed design effectively outperforms the current state-of-the-art setting
for AS2, i.e., a point-wise model for ranking sentences independently, by 6.51%
in accuracy, from 48.86% to 55.37%. In addition, PEASI is exceptionally
efficient in computing answer sentences, requiring only ~20% inferences
compared to the standard setting, i.e., reranking all possible candidates. We
believe the release of PEASI, both the dataset and our proposed design, can
contribute to advancing the research and development in deploying question
answering services at Web scale.

Unsupervised sentence embedding aims to obtain the most appropriate embedding
for a sentence to reflect its semantic. Contrastive learning has been
attracting developing attention. For a sentence, current models utilize diverse
data augmentation methods to generate positive samples, while consider other
independent sentences as negative samples. Then they adopt InfoNCE loss to pull
the embeddings of positive pairs gathered, and push those of negative pairs
scattered. Although these models have made great progress on sentence
embedding, we argue that they may suffer from feature suppression. The models
fail to distinguish and decouple textual similarity and semantic similarity.
And they may overestimate the semantic similarity of any pairs with similar
textual regardless of the actual semantic difference between them. This is
because positive pairs in unsupervised contrastive learning come with similar
and even the same textual through data augmentation. To alleviate feature
suppression, we propose contrastive learning for unsupervised sentence
embedding with soft negative samples (SNCSE). Soft negative samples share
highly similar textual but have surely and apparently different semantic with
the original samples. Specifically, we take the negation of original sentences
as soft negative samples, and propose Bidirectional Margin Loss (BML) to
introduce them into traditional contrastive learning framework, which merely
involves positive and negative samples. Our experimental results show that
SNCSE can obtain state-of-the-art performance on semantic textual similarity
(STS) task with average Spearman's correlation coefficient of 78.97% on
BERTbase and 79.23% on RoBERTabase. Besides, we adopt rank-based error analysis
method to detect the weakness of SNCSE for future study.

Machine learning (ML) methods are used in most technical areas such as image
recognition, product recommendation, financial analysis, medical diagnosis, and
predictive maintenance. An important aspect of implementing ML methods involves
controlling the learning process for the ML method so as to maximize the
performance of the method under consideration. Hyperparameter tuning is the
process of selecting a suitable set of ML method parameters that control its
learning process. In this work, we demonstrate the use of discrete simulation
optimization methods such as ranking and selection (R&S) and random search for
identifying a hyperparameter set that maximizes the performance of a ML method.
Specifically, we use the KN R&S method and the stochastic ruler random search
method and one of its variations for this purpose. We also construct the
theoretical basis for applying the KN method, which determines the optimal
solution with a statistical guarantee via solution space enumeration. In
comparison, the stochastic ruler method asymptotically converges to global
optima and incurs smaller computational overheads. We demonstrate the
application of these methods to a wide variety of machine learning models,
including deep neural network models used for time series prediction and image
classification. We benchmark our application of these methods with
state-of-the-art hyperparameter optimization libraries such as $hyperopt$ and
$mango$. The KN method consistently outperforms $hyperopt$'s random search (RS)
and Tree of Parzen Estimators (TPE) methods. The stochastic ruler method
outperforms the $hyperopt$ RS method and offers statistically comparable
performance with respect to $hyperopt$'s TPE method and the $mango$ algorithm.

Mapping and localization are two essential tasks for mobile robots in
real-world applications. However, largescale and dynamic scenes challenge the
accuracy and robustness of most current mature solutions. This situation
becomes even worse when computational resources are limited. In this paper, we
present a novel lightweight object-level mapping and localization method with
high accuracy and robustness. Different from previous methods, our method does
not need a prior constructed precise geometric map, which greatly releases the
storage burden, especially for large-scale navigation. We use object-level
features with both semantic and geometric information to model landmarks in the
environment. Particularly, a learning topological primitive is first proposed
to efficiently obtain and organize the object-level landmarks. On the basis of
this, we use a robot-centric mapping framework to represent the environment as
a semantic topology graph and relax the burden of maintaining global
consistency at the same time. Besides, a hierarchical memory management
mechanism is introduced to improve the efficiency of online mapping with
limited computational resources. Based on the proposed map, the robust
localization is achieved by constructing a novel local semantic scene graph
descriptor, and performing multi-constraint graph matching to compare scene
similarity. Finally, we test our method on a low-cost embedded platform to
demonstrate its advantages. Experimental results on a large scale and
multi-session real-world environment show that the proposed method outperforms
the state of arts in terms of lightweight and robustness.

Knowledge graph completion (KGC) can predict missing links and is crucial for
real-world knowledge graphs, which widely suffer from incompleteness. KGC
methods assume a knowledge graph is static, but that may lead to inaccurate
prediction results because many facts in the knowledge graphs change over time.
Recently, emerging methods have shown improved predictive results by further
incorporating the timestamps of facts; namely, temporal knowledge graph
completion (TKGC). With this temporal information, TKGC methods can learn the
dynamic evolution of the knowledge graph that KGC methods fail to capture. In
this paper, for the first time, we summarize the recent advances in TKGC
research. First, we detail the background of TKGC, including the problem
definition, benchmark datasets, and evaluation metrics. Then, we summarize
existing TKGC methods based on how timestamps of facts are used to capture the
temporal dynamics. Finally, we conclude the paper and present future research
directions of TKGC.

Human existence is getting more sophisticated and better in many areas due to
remarkable advances in the fields of automation. Automated systems are favored
over manual ones in the current environment. Home Automation is becoming more
popular in this scenario, as people are drawn to the concept of a home
environment that can automatically satisfy users' requirements. The key
challenges in an intelligent home are intelligent decision making,
location-aware service, and compatibility for all users of different ages and
physical conditions. Existing solutions address just one or two of these
challenges, but smart home automation that is robust, intelligent,
location-aware, and predictive is needed to satisfy the user's demand. This
paper presents a location-aware intelligent RSSI-based home automation system
(IRHA) that uses Wi-Fi signals to detect the user's location and control the
appliances automatically. The fingerprinting method is used to map the Wi-Fi
signals for different rooms, and the machine learning method, such as Decision
Tree, is used to classify the signals for different rooms. The machine learning
models are then implemented in the ESP32 microcontroller board to classify the
rooms based on the real-time Wi-Fi signal, and then the result is sent to the
main control board through the ESP32 MAC communication protocol to control the
appliances automatically. The proposed method has achieved 97% accuracy in
classifying the users' location.

In this paper, we focus on the generation of time-series data using neural
networks. It is often the case that input time-series data have only one
realized (and usually irregularly sampled) path, which makes it difficult to
extract time-series characteristics, and its noise structure is more
complicated than i.i.d. type. Time series data, especially from hydrology,
telecommunications, economics, and finance, exhibit long-term memory also
called long-range dependency (LRD). The main purpose of this paper is to
artificially generate time series with the help of neural networks, making the
LRD of paths into account. We propose fSDE-Net: neural fractional Stochastic
Differential Equation Network. It generalizes the neural stochastic
differential equation model by using fractional Brownian motion with a Hurst
index larger than half, which exhibits the LRD property. We derive the solver
of fSDE-Net and theoretically analyze the existence and uniqueness of the
solution to fSDE-Net. Our experiments with artificial and real time-series data
demonstrate that the fSDE-Net model can replicate distributional properties
well.

Cross-domain recommendation (CDR) has been attracting increasing attention of
researchers for its ability to alleviate the data sparsity problem in
recommender systems. However, the existing single-target or dual-target CDR
methods often suffer from two drawbacks, the assumption of at least one rich
domain and the heavy dependence on domain-invariant preference, which are
impractical in real world where sparsity is ubiquitous and might degrade the
user preference learning. To overcome these issues, we propose a
Multi-Sparse-Domain Collaborative Recommendation (MSDCR) model for multi-target
cross-domain recommendation. Unlike traditional CDR methods, MSDCR treats the
multiple relevant domains as all sparse and can simultaneously improve the
recommendation performance in each domain. We propose a Multi-Domain Separation
Network (MDSN) and a Gated Aspect Preference Enhancement (GAPE) module for
MSDCR to enhance a user's domain-specific aspect preferences in a domain by
transferring the complementary aspect preferences in other domains, during
which the uniqueness of the domain-specific preference can be preserved through
the adversarial training offered by MDSN and the complementarity can be
adaptively determined by GAPE. Meanwhile, we propose a Multi-Domain Adaptation
Network (MDAN) for MSDCR to capture a user's domain-invariant aspect
preference. With the integration of the enhanced domain-specific aspect
preference and the domain-invariant aspect preference, MSDCR can reach a
comprehensive understanding of a user's preference in each sparse domain. At
last, the extensive experiments conducted on real datasets demonstrate the
remarkable superiority of MSDCR over the state-of-the-art single-domain
recommendation models and CDR models.

Two major challenges of 3D LiDAR Panoptic Segmentation (PS) are that point
clouds of an object are surface-aggregated and thus hard to model the
long-range dependency especially for large instances, and that objects are too
close to separate each other. Recent literature addresses these problems by
time-consuming grouping processes such as dual-clustering, mean-shift offsets,
etc., or by bird-eye-view (BEV) dense centroid representation that downplays
geometry. However, the long-range geometry relationship has not been
sufficiently modeled by local feature learning from the above methods. To this
end, we present SCAN, a novel sparse cross-scale attention network to first
align multi-scale sparse features with global voxel-encoded attention to
capture the long-range relationship of instance context, which can boost the
regression accuracy of the over-segmented large objects. For the
surface-aggregated points, SCAN adopts a novel sparse class-agnostic
representation of instance centroids, which can not only maintain the sparsity
of aligned features to solve the under-segmentation on small objects, but also
reduce the computation amount of the network through sparse convolution. Our
method outperforms previous methods by a large margin in the SemanticKITTI
dataset for the challenging 3D PS task, achieving 1st place with a real-time

Traditional robust recommendation methods view atypical user-item
interactions as noise and aim to reduce their impact with some kind of noise
filtering technique, which often suffers from two challenges. First, in real
world, atypical interactions may signal users' temporary interest different
from their general preference. Therefore, simply filtering out the atypical
interactions as noise may be inappropriate and degrade the personalization of
recommendations. Second, it is hard to acquire the temporary interest since
there are no explicit supervision signals to indicate whether an interaction is
atypical or not. To address this challenges, we propose a novel model called
Temporary Interest Aware Recommendation (TIARec), which can distinguish
atypical interactions from normal ones without supervision and capture the
temporary interest as well as the general preference of users. Particularly, we
propose a reinforcement learning framework containing a recommender agent and
an auxiliary classifier agent, which are jointly trained with the objective of
maximizing the cumulative return of the recommendations made by the recommender
agent. During the joint training process, the classifier agent can judge
whether the interaction with an item recommended by the recommender agent is
atypical, and the knowledge about learning temporary interest from atypical
interactions can be transferred to the recommender agent, which makes the
recommender agent able to alone make recommendations that balance the general
preference and temporary interest of users. At last, the experiments conducted
on real world datasets verify the effectiveness of TIARec.

Structured knowledge grounding (SKG) leverages structured knowledge to
complete user requests, such as semantic parsing over databases and question
answering over knowledge bases. Since the inputs and outputs of SKG tasks are
heterogeneous, they have been studied separately by different communities,
which limits systematic and compatible research on SKG. In this paper, we
overcome this limitation by proposing the UnifiedSKG framework, which unifies
21 SKG tasks into a text-to-text format, aiming to promote systematic SKG
research, instead of being exclusive to a single task, domain, or dataset. We
use UnifiedSKG to benchmark T5 with different sizes and show that T5, with
simple modifications when necessary, achieves state-of-the-art performance on
almost all of the 21 tasks. We further demonstrate that multi-task
prefix-tuning improves the performance on most tasks, largely improving the
overall performance. UnifiedSKG also facilitates the investigation of zero-shot
and few-shot learning, and we show that T0, GPT-3, and Codex struggle in
zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a
series of controlled experiments on structured knowledge encoding variants
across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is
open-sourced at https://github.com/hkunlp/unifiedskg.

Patients are often encouraged to make use of wearable devices for remote
collection and monitoring of health data. This adoption of wearables results in
a significant increase in the volume of data collected and transmitted. The
battery life of the devices is then quickly diminished due to the high
processing requirements of the devices. Given the importance attached to
medical data, it is imperative that all transmitted data adhere to strict
integrity and availability requirements. Reducing the volume of healthcare data
for network transmission may improve sensor battery life without compromising
accuracy. There is a trade-off between efficiency and accuracy which can be
controlled by adjusting the sampling and transmission rates. This paper
demonstrates that machine learning can be used to analyse complex health data
metrics such as the accuracy and efficiency of data transmission to overcome
the trade-off problem. The study uses time series nonlinear autoregressive
neural network algorithms to enhance both data metrics by taking fewer samples
to transmit. The algorithms were tested with a standard heart rate dataset to
compare their accuracy and efficiency. The result showed that the
Levenbery-Marquardt algorithm was the best performer with an efficiency of 3.33
and accuracy of 79.17%, which is similar to other algorithms accuracy but
demonstrates improved efficiency. This proves that machine learning can improve
without sacrificing a metric over the other compared to the existing methods
with high efficiency.

Writer identification has practical applications for forgery detection and
forensic science. Most models based on deep neural networks extract features
from character image or sub-regions in character image, which ignoring features
contained in page-region image. Our proposed global regular network (GRN) pays
attention to these features. GRN network consists of two branches: one branch
takes page handwriting as input to extract global features, and the other takes
word handwriting as input to extract local features. Global features and local
features merge in a global residual way to form overall features of the
handwriting. The proposed GRN has two attributions: one is adding a branch to
extract features contained in page; the other is using residual attention
network to extract local feature. Experiments demonstrate the effectiveness of
both strategies. On CVL dataset, our models achieve impressive 99.98% top-1
accuracy and 100% top-5 accuracy with shorter training time and fewer network
parameters, which exceeded the state-of-the-art structure. The experiment shows
the powerful ability of the network in the field of writer identification. The
source code is available at https://github.com/wangshiyu001/GRN.

We study the subject of universal online learning with non-i.i.d. processes
for bounded losses. The notion of an universally consistent learning was
defined by Hanneke in an effort to study learning theory under minimal
assumptions, where the objective is to obtain low long-run average loss for any
target function. We are interested in characterizing processes for which
learning is possible and whether there exist learning rules guaranteed to be
universally consistent given the only assumption that such learning is
possible. The case of unbounded losses is very restrictive, since the learnable
processes almost surely visit a finite number of points and as a result, simple
memorization is optimistically universal. We focus on the bounded setting and
give a complete characterization of the processes admitting strong and weak
universal learning. We further show that k-nearest neighbor algorithm (kNN) is
not optimistically universal and present a novel variant of 1NN which is
optimistically universal for general input and value spaces in both strong and
weak setting. This closes all COLT 2021 open problems posed by Hanneke on
universal online learning.

Understanding political polarization on social platforms is important as
public opinions may become increasingly extreme when they are circulated in
homogeneous communities, thus potentially causing damage in the real world.
Automatically detecting the political ideology of social media users can help
better understand political polarization. However, it is challenging due to the
scarcity of ideology labels, complexity of multimodal contents, and cost of
time-consuming data collection process. In this study, we adopt a heterogeneous
graph neural network to jointly model user characteristics, multimodal post
contents as well as user-item relations in a bipartite graph to learn a
comprehensive and effective user embedding without requiring ideology labels.
We apply our framework to online discussions about economy and public health
topics. The learned embeddings are then used to detect political ideology and
understand political polarization. Our framework outperforms the unimodal,
early/late fusion baselines, and homogeneous GNN frameworks by a margin of at
least 9% absolute gain in the area under the receiver operating characteristic
on two social media datasets. More importantly, our work does not require a
time-consuming data collection process, which allows faster detection and in
turn allows the policy makers to conduct analysis and design policies in time
to respond to crises. We also show that our framework learns meaningful user
embeddings and can help better understand political polarization. Notable
differences in user descriptions, topics, images, and levels of retweet/quote
activities are observed. Our framework for decoding user-content interaction
shows wide applicability in understanding political polarization. Furthermore,
it can be extended to user-item bipartite information networks for other
applications such as content and product recommendation.

We propose GradTail, an algorithm that uses gradients to improve model
performance on the fly in the face of long-tailed training data distributions.
Unlike conventional long-tail classifiers which operate on converged - and
possibly overfit - models, we demonstrate that an approach based on gradient
dot product agreement can isolate long-tailed data early on during model
training and improve performance by dynamically picking higher sample weights
for that data. We show that such upweighting leads to model improvements for
both classification and regression models, the latter of which are relatively
unexplored in the long-tail literature, and that the long-tail examples found
by gradient alignment are consistent with our semantic expectations.

Digital solutions have substantially contributed to the growth and
dissemination of education. The distance education modality has been presented
as an opportunity for worldwide students in many types of courses. However,
projects of digital educational platforms require different expertise including
knowledge areas such as pedagogy, psychology, computing, and digital
technologies associated with education that allow the correct development and
application of these solutions. To support the evolution of such solutions with
satisfactory quality indicators, this research presents a model focused on
quality of online educational solutions grounded in an approach aimed to
continuous process improvement. The model considers of three maturity levels
and six common entities that address the specific practices for planning and
developing digital educational solutions, targeting quality standards that
satisfy their users, such as students, teachers, tutors, and other people
involved in development and use of these kinds of educational solutions.

Understanding the joint impact of vaccination and non-pharmaceutical
interventions on COVID-19 development is important for making public health
decisions that control the pandemic. Recently, we created a method in
forecasting the daily number of confirmed cases of infectious diseases by
combining a mechanistic ordinary differential equation (ODE) model for
infectious classes and a generalized boosting machine learning model (GBM) for
predicting how public health policies and mobility data affect the transmission
rate in the ODE model [WWR+]. In this paper, we extend the method to the
post-vaccination period, accordingly obtain a retrospective forecast of
COVID-19 daily confirmed cases in the US, and identify the relative influence
of the policies used as the predictor variables. In particular, our ODE model
contains both partially and fully vaccinated compartments and accounts for the
breakthrough cases, that is, vaccinated individuals can still get infected. Our
results indicate that the inclusion of data on non-pharmaceutical interventions
can significantly improve the accuracy of the predictions. With the use of
policy data, the model predicts the number of daily infected cases up to 35
days in the future, with an average mean absolute percentage error of 34%,
which is further improved to 21% if combined with human mobility data.
Moreover, similar to the pre-vaccination study, the most influential predictor
variable remains the policy of restrictions on gatherings. The modeling
approach used in this work can help policymakers design control measures as
variant strains threaten public health in the future.

To characterize the location (mean, median) of a set of graphs, one needs a
notion of centrality that is adapted to metric spaces, since graph sets are not
Euclidean spaces. A standard approach is to consider the Frechet mean. In this
work, we equip a set of graphs with the pseudometric defined by the norm
between the eigenvalues of their respective adjacency matrix. Unlike the edit
distance, this pseudometric reveals structural changes at multiple scales, and
is well adapted to studying various statistical problems for graph-valued data.
We describe an algorithm to compute an approximation to the sample Frechet mean
of a set of undirected unweighted graphs with a fixed size using this
pseudometric.

The goal of hate speech detection is to filter negative online content aiming
at certain groups of people. Due to the easy accessibility of social media
platforms it is crucial to protect everyone which requires building hate speech
detection systems for a wide range of languages. However, the available labeled
hate speech datasets are limited making it problematic to build systems for
many languages. In this paper we focus on cross-lingual transfer learning to
support hate speech detection in low-resource languages. We leverage
cross-lingual word embeddings to train our neural network systems on the source
language and apply it to the target language, which lacks labeled examples, and
show that good performance can be achieved. We then incorporate unlabeled
target language data for further model improvements by bootstrapping labels
using an ensemble of different model architectures. Furthermore, we investigate
the issue of label imbalance of hate speech datasets, since the high ratio of
non-hate examples compared to hate examples often leads to low model
performance. We test simple data undersampling and oversampling techniques and
show their effectiveness.

In this paper, we propose a novel network named Vision Transformer for
Biomedical Image Segmentation (ViTBIS). Our network splits the input feature
maps into three parts with $1\times 1$, $3\times 3$ and $5\times 5$
convolutions in both encoder and decoder. Concat operator is used to merge the
features before being fed to three consecutive transformer blocks with
attention mechanism embedded inside it. Skip connections are used to connect
encoder and decoder transformer blocks. Similarly, transformer blocks and multi
scale architecture is used in decoder before being linearly projected to
produce the output segmentation map. We test the performance of our network
using Synapse multi-organ segmentation dataset, Automated cardiac diagnosis
challenge dataset, Brain tumour MRI segmentation dataset and Spleen CT
segmentation dataset. Without bells and whistles, our network outperforms most
of the previous state of the art CNN and transformer based models using Dice
score and the Hausdorff distance as the evaluation metrics.

As an important algorithm in deep reinforcement learning, advantage actor
critic (A2C) has been widely succeeded in both discrete and continuous control
tasks with raw pixel inputs, but its sample efficiency still needs to improve
more. In traditional reinforcement learning, actor-critic algorithms generally
use the recursive least squares (RLS) technology to update the parameter of
linear function approximators for accelerating their convergence speed.
However, A2C algorithms seldom use this technology to train deep neural
networks (DNNs) for improving their sample efficiency. In this paper, we
propose two novel RLS-based A2C algorithms and investigate their performance.
Both proposed algorithms, called RLSSA2C and RLSNA2C, use the RLS method to
train the critic network and the hidden layers of the actor network. The main
difference between them is at the policy learning step. RLSSA2C uses an
ordinary first-order gradient descent algorithm and the standard policy
gradient to learn the policy parameter. RLSNA2C uses the Kronecker-factored
approximation, the RLS method and the natural policy gradient to learn the
compatible parameter and the policy parameter. In addition, we analyze the
complexity and convergence of both algorithms, and present three tricks for
further improving their convergence speed. Finally, we demonstrate the
effectiveness of both algorithms on 40 games in the Atari 2600 environment and
11 tasks in the MuJoCo environment. From the experimental results, it is shown
that our both algorithms have better sample efficiency than the vanilla A2C on
most games or tasks, and have higher computational efficiency than other two

This study offers an interdisciplinary approach to movement technique
analysis, designed to deal with intensive interaction between an environment
and a trainee. The free-fall stage of skydiving is investigated, when aerial
surrounding airflow. The natural learning process of body flight is hard and
protracted since the required movements are not similar to our daily movement
repertoire, and often counter-intuitive. The proposed method can provide a
valuable insight into the subject's learning process and may be used by coaches
to identify potentially successful technique changes. The main novelty is that
instead of comparing directly the trainee's movements to a template or a
movement pattern, extracted from a top-rated athlete in the field, we offer an
independent way of technique analysis. We incorporate design tools of automatic
control theory to link the trainee's movement patterns to specific performance
characteristics. This makes it possible to suggest technique modifications that
provide the desired performance improvement, taking into account the individual
body parameters and constraints. Representing the performed maneuvers in terms
of a dynamic response of closed-loop control system offers an unconventional
insight into the motor equivalence problem. The method is demonstrated on three
case studies of aerial rotation of skilled, less-skilled, and elite skydivers.

We propose a Multi-level Second-order (MlSo) few-shot learning network for
supervised or unsupervised few-shot image classification and few-shot action
recognition. We leverage so-called power-normalized second-order base learner
streams combined with features that express multiple levels of visual
abstraction, and we use self-supervised discriminating mechanisms. As
Second-order Pooling (SoP) is popular in image recognition, we employ its basic
element-wise variant in our pipeline. The goal of multi-level feature design is
to extract feature representations at different layer-wise levels of CNN,
realizing several levels of visual abstraction to achieve robust few-shot
learning. As SoP can handle convolutional feature maps of varying spatial
sizes, we also introduce image inputs at multiple spatial scales into MlSo. To
exploit the discriminative information from multi-level and multi-scale
features, we develop a Feature Matching (FM) module that reweights their
respective branches. We also introduce a self-supervised step, which is a
discriminator of the spatial level and the scale of abstraction. Our pipeline
is trained in an end-to-end manner. With a simple architecture, we demonstrate
respectable results on standard datasets such as Omniglot, mini-ImageNet,
tiered-ImageNet, Open MIC, fine-grained datasets such as CUB Birds, Stanford
Dogs and Cars, and action recognition datasets such as HMDB51, UCF101, and
mini-MIT.

This paper tackles the problem of zero-shot sign language recognition
(ZSSLR), where the goal is to leverage models learned over the seen sign
classes to recognize the instances of unseen sign classes. In this context,
readily available textual sign descriptions and attributes collected from sign
language dictionaries are utilized as semantic class representations for
knowledge transfer. For this novel problem setup, we introduce three benchmark
datasets with their accompanying textual and attribute descriptions to analyze
the problem in detail. Our proposed approach builds spatiotemporal models of
body and hand regions. By leveraging the descriptive text and attribute
embeddings along with these visual representations within a zero-shot learning
framework, we show that textual and attribute based class definitions can
provide effective knowledge for the recognition of previously unseen sign
classes. We additionally introduce techniques to analyze the influence of
binary attributes in correct and incorrect zero-shot predictions. We anticipate
that the introduced approaches and the accompanying datasets will provide a
basis for further exploration of zero-shot learning in sign language

The reason of significantly higher transmissibility of SARS Covid (2019
CoV-2) compared to SARS Covid (2003 CoV) and MERS Covid (2012 MERS) can be
attributed to mutations reported in structural proteins, and the role played by
non-structural proteins (nsps) and accessory proteins (ORFs) for viral
replication, assembly, and shedding. Envelope protein E is one of the four
structural proteins of minimum length. Recent studies have confirmed critical
role played by the envelope protein in the viral life cycle including assembly
of virion exported from infected cell for its transmission. However, the
determinants of the highly complex viral - host interactions of envelope
protein, particularly with host Golgi complex, have not been adequately
characterized. CoV-2 and CoV Envelope proteins of length 75 and 76 amino acids
differ in four amino acid locations. The additional amino acid Gly (G) at
location 70 makes CoV length 76. The amino acid pair EG at location 69-70 of
CoV in place of amino acid R in location 69 of CoV-2, has been identified as a
major determining factor in the current investigation. This paper concentrates
on the design of computational model to compare the structure/function of wild
and mutants of CoV-2 with wild and mutants of CoV in the functionally important
region of the protein chain pair. We hypothesize that differences of CAML model
parameter of CoV-2 and CoV characterize the deviation in structure and function
of envelope proteins in respect of interaction of virus with host Golgi
complex; and this difference gets reflected in the difference of their
transmissibility. The hypothesis has been validated from single point
mutational study on- (i) human HBB beta-globin hemoglobin protein associated
with sickle cell anemia, (ii) mutants of envelope protein of Covid-2 infected
patients reported in recent publications.

Current state of the art acoustic models can easily comprise more than 100
million parameters. This growing complexity demands larger training datasets to
maintain a decent generalization of the final decision function. An ideal
dataset is not necessarily large in size, but large with respect to the amount
of unique speakers, utilized hardware and varying recording conditions. This
enables a machine learning model to explore as much of the domain-specific
input space as possible during parameter estimation. This work introduces
Common Phone, a gender-balanced, multilingual corpus recorded from more than
11.000 contributors via Mozilla's Common Voice project. It comprises around 116
hours of speech enriched with automatically generated phonetic segmentation. A
Wav2Vec 2.0 acoustic model was trained with the Common Phone to perform
phonetic symbol recognition and validate the quality of the generated phonetic
annotation. The architecture achieved a PER of 18.1 % on the entire test set,
computed with all 101 unique phonetic symbols, showing slight differences
between the individual languages. We conclude that Common Phone provides
sufficient variability and reliable phonetic annotation to help bridging the
gap between research and application of acoustic models.

Deep Reinforcement Learning solutions have been applied to different control
problems with outperforming and promising results. In this research work we
have applied Proximal Policy Optimization, Soft Actor-Critic and Generative
Adversarial Imitation Learning to strategy design problem of three
cryptocurrency markets. Our input data includes price data and technical
indicators. We have implemented a Gym environment based on cryptocurrency
markets to be used with the algorithms. Our test results on unseen data shows a
great potential for this approach in helping investors with an expert system to
exploit the market and gain profit. Our highest gain for an unseen 66 day span
is 4850 US dollars per 10000 US dollars investment. We also discuss on how a
specific hyperparameter in the environment design can be used to adjust risk in
the generated strategies.

Capsule network is a recent new deep network architecture that has been
applied successfully for medical image segmentation tasks. This work extends
capsule networks for volumetric medical image segmentation with self-supervised
learning. To improve on the problem of weight initialization compared to
previous capsule networks, we leverage self-supervised learning for capsule
networks pre-training, where our pretext-task is optimized by
self-reconstruction. Our capsule network, SS-3DCapsNet, has a UNet-based
architecture with a 3D Capsule encoder and 3D CNNs decoder. Our experiments on
multiple datasets including iSeg-2017, Hippocampus, and Cardiac demonstrate
that our 3D capsule network with self-supervised pre-training considerably
outperforms previous capsule networks and 3D-UNets.

We formulate a mathematical setup for computational neural networks using
noncommutative algebras and near-rings, in motivation of quantum automata. We
study the moduli space of the corresponding framed quiver representations, and
find moduli of Euclidean and non-compact types in light of uniformization.

Since the average treatment effect (ATE) measures the change in social
welfare, even if positive, there is a risk of negative effect on, say, some 10%
of the population. Assessing such risk is difficult, however, because any one
individual treatment effect (ITE) is never observed, so the 10% worst-affected
cannot be identified, while distributional treatment effects only compare the
first deciles within each treatment group, which does not correspond to any
10%-subpopulation. In this paper we consider how to nonetheless assess this
important risk measure, formalized as the conditional value at risk (CVaR) of
the ITE-distribution. We leverage the availability of pre-treatment covariates
and characterize the tightest-possible upper and lower bounds on ITE-CVaR given
by the covariate-conditional average treatment effect (CATE) function. We then
proceed to study how to estimate these bounds efficiently from data and
construct confidence intervals. This is challenging even in randomized
experiments as it requires understanding the distribution of the unknown CATE
function, which can be very complex if we use rich covariates so as to best
control for heterogeneity. We develop a debiasing method that overcomes this
and prove it enjoys favorable statistical properties even when CATE and other
nuisances are estimated by black-box machine learning or even inconsistently.
Studying a hypothetical change to French job-search counseling services, our
bounds and inference demonstrate a small social benefit entails a negative
impact on a substantial subpopulation.

Probabilistic models often use neural networks to control their predictive
uncertainty. However, when making out-of-distribution (OOD)} predictions, the
often-uncontrollable extrapolation properties of neural networks yield poor
uncertainty predictions. Such models then don't know what they don't know,
which directly limits their robustness w.r.t unexpected inputs. To counter
this, we propose to explicitly train the uncertainty predictor where we are not
given data to make it reliable. As one cannot train without data, we provide
mechanisms for generating pseudo-inputs in informative low-density regions of
the input space, and show how to leverage these in a practical Bayesian
framework that casts a prior distribution over the model uncertainty. With a
holistic evaluation, we demonstrate that this yields robust and interpretable
predictions of uncertainty while retaining state-of-the-art performance on
diverse tasks such as regression and generative modelling

Pre-trained encoders are general-purpose feature extractors that can be used
for many downstream tasks. Recent progress in self-supervised learning can
pre-train highly effective encoders using a large volume of unlabeled data,
leading to the emerging encoder as a service (EaaS). A pre-trained encoder may
be deemed confidential because its training requires lots of data and
computation resources as well as its public release may facilitate misuse of
AI, e.g., for deepfakes generation. In this paper, we propose the first attack
called StolenEncoder to steal pre-trained image encoders. We evaluate
StolenEncoder on multiple target encoders pre-trained by ourselves and three
real-world target encoders including the ImageNet encoder pre-trained by
Google, CLIP encoder pre-trained by OpenAI, and Clarifai's General Embedding
encoder deployed as a paid EaaS. Our results show that our stolen encoders have
similar functionality with the target encoders. In particular, the downstream
classifiers built upon a target encoder and a stolen one have similar accuracy.
Moreover, stealing a target encoder using StolenEncoder requires much less data
and computation resources than pre-training it from scratch. We also explore
three defenses that perturb feature vectors produced by a target encoder. Our
results show these defenses are not enough to mitigate StolenEncoder.

Domain Adaptation (DA) aims to leverage the knowledge learned from a source
domain with ample labeled data to a target domain with unlabeled data only.
Most existing studies on DA contribute to learning domain-invariant feature
representations for both domains by minimizing the domain gap based on
convolution-based neural networks. Recently, vision transformers significantly
improved performance in multiple vision tasks. Built on vision transformers, in
this paper we propose a Bidirectional Cross-Attention Transformer (BCAT) for DA
with the aim to improve the performance. In the proposed BCAT, the attention
mechanism can extract implicit source and target mixup feature representations
to narrow the domain discrepancy. Specifically, in BCAT, we design a
weight-sharing quadruple-branch transformer with a bidirectional
cross-attention mechanism to learn domain-invariant feature representations.
Extensive experiments demonstrate that the proposed BCAT model achieves
superior performance on four benchmark datasets over existing state-of-the-art
DA methods that are based on convolutions or transformers.

The mortality rate for pedestrians using wheelchairs was 36% higher than the
overall population pedestrian mortality rate. However, there is no data to
clarify the pedestrians' categories in both fatal and nonfatal accidents, since
police reports often do not keep a record of whether a victim was using a
wheelchair or has a disability. Currently, real-time detection of vulnerable
road users using advanced traffic sensors installed at the infrastructure side
has a great potential to significantly improve traffic safety at the
intersection. In this research, we develop a systematic framework with a
combination of machine learning and deep learning models to distinguish
disabled people from normal walk pedestrians and predict the time needed to
reach the next side of the intersection. The proposed framework shows high
performance both at vulnerable user classification and arrival time prediction
accuracy.

Semantic segmentation methods can not directly identify abnormal objects in
images. Anomaly Segmentation algorithm from this realistic setting can
distinguish between in-distribution objects and Out-Of-Distribution (OOD)
objects and output the anomaly probability for pixels. In this paper, a
Prototype Guided Anomaly segmentation Network (PGAN) is proposed to extract
semantic prototypes for in-distribution training data from limited annotated
images. In the model, prototypes are used to model the hierarchical category
semantic information and distinguish OOD pixels. The proposed PGAN model
includes a semantic segmentation network and a prototype extraction network.
Similarity measures are adopted to optimize the prototypes. The learned
semantic prototypes are used as category semantics to compare the similarity
with features extracted from test images and then to generate semantic
segmentation prediction. The proposed prototype extraction network can also be
integrated into most semantic segmentation networks and recognize OOD pixels.
On the StreetHazards dataset, the proposed PGAN model produced mIoU of 53.4%
for anomaly segmentation. The experimental results demonstrate PGAN may achieve
the SOTA performance in the anomaly segmentation tasks.

Many large-scale production networks include thousands types of final
products and tens to hundreds thousands types of raw materials and intermediate
products. These networks face complicated inventory management decisions, which
are often too complicated for inventory models and too large for simulation
models. In this paper, by combing efficient computational tools of recurrent
neural networks (RNN) and the structural information of production networks, we
propose a RNN inspired simulation approach that may be thousands times faster
than existing simulation approach and is capable of solving large-scale
inventory optimization problems in a reasonable amount of time.

The success of deep learning algorithms generally depends on large-scale
data, while humans appear to have inherent ability of knowledge transfer, by
recognizing and applying relevant knowledge from previous learning experiences
when encountering and solving unseen tasks. Such an ability to acquire and
reuse knowledge is known as transferability in deep learning. It has formed the
long-term quest towards making deep learning as data-efficient as human
learning, and has been motivating fruitful design of more powerful deep
learning algorithms. We present this survey to connect different isolated areas
in deep learning with their relation to transferability, and to provide a
unified and complete view to investigating transferability through the whole
lifecycle of deep learning. The survey elaborates the fundamental goals and
challenges in parallel with the core principles and methods, covering recent
cornerstones in deep architectures, pre-training, task adaptation and domain
adaptation. This highlights unanswered questions on the appropriate objectives
for learning transferable knowledge and for adapting the knowledge to new tasks
and domains, avoiding catastrophic forgetting and negative transfer. Finally,
we implement a benchmark and an open-source library, enabling a fair evaluation
of deep learning methods in terms of transferability.

Building efficient architecture in neural speech processing is paramount to
success in keyword spotting deployment. However, it is very challenging for
lightweight models to achieve noise robustness with concise neural operations.
In a real-world application, the user environment is typically noisy and may
also contain reverberations. We proposed a novel feature interactive
convolutional model with merely 100K parameters to tackle this under the noisy
far-field condition. The interactive unit is proposed in place of the attention
module that promotes the flow of information with more efficient computations.
Moreover, curriculum-based multi-condition training is adopted to attain better
noise robustness. Our model achieves 98.2% top-1 accuracy on Google Speech
Command V2-12 and is competitive against large transformer models under the
designed noise condition.

Recommendation system has been a widely studied task both in academia and
industry. Previous works mainly focus on homogeneous recommendation and little
progress has been made for heterogeneous recommender systems. However,
heterogeneous recommendations, e.g., recommending different types of items
including products, videos, celebrity shopping notes, among many others, are
dominant nowadays. State-of-the-art methods are incapable of leveraging
attributes from different types of items and thus suffer from data sparsity
problems. And it is indeed quite challenging to represent items with different
feature spaces jointly. To tackle this problem, we propose a kernel-based
neural network, namely deep unified representation (or DURation) for
heterogeneous recommendation, to jointly model unified representations of
heterogeneous items while preserving their original feature space topology
structures. Theoretically, we prove the representation ability of the proposed
model. Besides, we conduct extensive experiments on real-world datasets.
Experimental results demonstrate that with the unified representation, our
model achieves remarkable improvement (e.g., 4.1% ~ 34.9% lift by AUC score and
3.7% lift by online CTR) over existing state-of-the-art models.

Limited urban parking space combined with urbanization has necessitated the
development of smart parking systems that can communicate the availability of
parking slots to the end users. Towards this, various deep learning based
solutions using convolutional neural networks have been proposed for parking
space occupation detection. Though these approaches are robust to partial
obstructions and lighting conditions, their performance is found to degrade in
the presence of haze conditions. Looking in this direction, this paper
investigates the use of dehazing networks that improves the performance of
parking space occupancy classifier under hazy conditions. Additionally,
training procedures are proposed for dehazing networks to maximize the
performance of the system on both hazy and non-hazy conditions. The proposed
system is deployable as part of existing smart parking systems where limited
number of cameras are used to monitor hundreds of parking spaces. To validate
our approach, we have developed a custom hazy parking system dataset from
real-world task-driven test set of RESIDE-\b{eta} dataset. The proposed
approach is tested against existing state-of-the-art parking space detectors on
CNRPark-EXT and hazy parking system datasets. Experimental results indicate
that there is a significant accuracy improvement of the proposed approach on
the hazy parking system dataset.

Chemical reactions on metal surfaces are important in various processes such
as heterogeneous catalysis and nanostructure growth. At moderate or lower
temperatures, these reactions generally follow the minimum energy path and
temperature effects can be reasonably described by a harmonic oscillator model.
At a high temperature approaching the melting point of the substrate, general
behaviors of surface reactions remain elusive. In this study, by taking
hydrocarbon species adsorbed on Cu(111) as a model system and performing
extensive molecular dynamics simulations powered by machine learning
potentials, we identify several important high-temperature effects, including
local chemical environment, surface atom mobility, and substrate thermal
expansion. They affect different aspects of a high-temperature surface reaction
in different ways. These results deepen our understanding of high-temperature
reactions.

Data and Science has stood out in the generation of results, whether in the
projects of the scientific domain or business domain. CERN Project, Scientific
Institutes, companies like Walmart, Google, Apple, among others, need data to
present their results and make predictions in the competitive data world. Data
and Science are words that together culminated in a globally recognized term
called Data Science. Data Science is in its initial phase, possibly being part
of formal sciences and also being presented as part of applied sciences,
capable of generating value and supporting decision making. Data Science
considers science and, consequently, the scientific method to promote decision
making through data intelligence. In many cases, the application of the method
(or part of it) is considered in Data Science projects in scientific domain
(social sciences, bioinformatics, geospatial projects) or business domain
(finance, logistic, retail), among others. In this sense, this article
addresses the perspectives of Data Science as a multidisciplinary area,
considering science and the scientific method, and its formal structure which
integrate Statistics, Computer Science, and Business Science, also taking into
account Artificial Intelligence, emphasizing Machine Learning, among others.
The article also deals with the perspective of applied Data Science, since Data
Science is used for generating value through scientific and business projects.
Data Science persona is also discussed in the article, concerning the education
of Data Science professionals and its corresponding profiles, since its
projection changes the field of data in the world.

With the development of modern nanoscience and nanotechnology, Bi1-xSbx can
be synthesized into different nanoscale and nanostructured forms, including
thin films, nanowires, nanotubes, nanoribbons, and many others. However, due to
the strong correlation between electrons and holes at the L-point in the
Brillouin zone, the direct band evolves in an anomalous manner under the
quantum confinement when nanostructured. Due to the alloying and the low
symmetry, predicting the L-point direct bandgap in a nanomaterial using either
ab initio calculations or kp perturbations can be computationally costive or
inaccurate. We here try to solve this problem using the machine learning
methods, including the support vector regression, the regression tree, the
Gaussian process regression, and the artificial neural network. A
goodness-of-fit of ~0.99 can be achieved for Bi1-xSbx thin films and nanowires.

Despite the rapid progress of automatic speech recognition (ASR) technologies
in the past few decades, recognition of disordered speech remains a highly
challenging task to date. Disordered speech presents a wide spectrum of
challenges to current data intensive deep neural networks (DNNs) based ASR
technologies that predominantly target normal speech. This paper presents
recent research efforts at the Chinese University of Hong Kong (CUHK) to
improve the performance of disordered speech recognition systems on the largest
publicly available UASpeech dysarthric speech corpus. A set of novel modelling
techniques including neural architectural search, data augmentation using
spectra-temporal perturbation, model based speaker adaptation and cross-domain
generation of visual features within an audio-visual speech recognition (AVSR)
system framework were employed to address the above challenges. The combination
of these techniques produced the lowest published word error rate (WER) of
25.21% on the UASpeech test set 16 dysarthric speakers, and an overall WER
reduction of 5.4% absolute (17.6% relative) over the CUHK 2018 dysarthric
speech recognition system featuring a 6-way DNN system combination and cross
adaptation of out-of-domain normal speech data trained systems. Bayesian model
adaptation further allows rapid adaptation to individual dysarthric speakers to
be performed using as little as 3.06 seconds of speech. The efficacy of these
techniques were further demonstrated on a CUDYS Cantonese dysarthric speech
recognition task.

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a
key technology for security in smart city environments. This paper creates a
case where the UAVs with CCTV-cameras fly over the city area for flexible and
reliable surveillance services. UAVs should be deployed to cover a large area
while minimize overlapping and shadow areas for a reliable surveillance system.
However, the operation of UAVs is subject to high uncertainty, necessitating
autonomous recovery systems. This work develops a multi-agent deep
reinforcement learning-based management scheme for reliable industry
surveillance in smart city applications. The core idea this paper employs is
autonomously replenishing the UAV's deficient network requirements with
communications. Via intensive simulations, our proposed algorithm outperforms
the state-of-the-art algorithms in terms of surveillance coverage, user support
capability, and computational costs.

Emerging Internet-of-things (IoT) applications are driving deployment of
neural networks (NNs) on heavily constrained low-cost hardware (HW) platforms,
where accuracy is typically limited by memory capacity. To address this TinyML
challenge, new HW platforms like neural processing units (NPUs) have support
for model compression, which exploits aggressive network quantization and
unstructured pruning optimizations. The combination of NPUs with HW compression
and compressible models allows more expressive models in the same memory
  However, adding optimizations for compressibility on top of conventional NN
architecture choices expands the design space across which we must make
balanced trade-offs. This work bridges the gap between NPU HW capability and NN
model design, by proposing a neural architecture search (NAS) algorithm to
efficiently search a large design space, including: network depth, operator
type, layer width, bitwidth, sparsity, and more. Building on differentiable NAS
(DNAS) with several key improvements, we demonstrate Unified DNAS for
Compressible models (UDC) on CIFAR100, ImageNet, and DIV2K super resolution
tasks. On ImageNet, we find Pareto dominant compressible models, which are 1.9x
smaller or 5.76% more accurate.

Sunspot groups observed in white-light appear as complex structures. Analysis
of these structures is usually based on simple morphological descriptors which
capture only generic properties and miss information about fine details. We
present a machine learning approach to introduce a complete yet compact
description of sunspot groups. The idea is to map sunspot group images into an
appropriate lower-dimensional (latent) space. We apply a combination of
Variational Autoencoder and Principal Component Analysis to obtain a set of 285
latent descriptors. We demonstrate that the standard descriptors are embedded
into the latent ones. Thus, latent features can be considered as an extended
description of sunspot groups and, in our opinion, can expand the possibilities
for the research on sunspot groups. In particular, we demonstrate an
application for estimation of the sunspot group complexity. The proposed
parametrization model is generic and can be applied to investigation of other
traces of solar activity observed in various spectrum lines. Key components of
this work, which are the parametrization model, dataset of sunspot groups and
latent vectors, are available in the public GitHub repository
github.com/observethesun/sunspot_groups and can be used to reproduce the
results and for further research.

The development of estimation and control theories for quantum systems is a
fundamental task for practical quantum technology. This vision article presents
a brief introduction to challenging problems and potential opportunities in the
emerging areas of quantum estimation, control and learning. The topics cover
quantum state estimation, quantum parameter identification, quantum filtering,
quantum open-loop control, quantum feedback control, machine learning for
estimation and control of quantum systems, and quantum machine learning.

Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various
human emotions from heterogeneous visual, audio and text modalities. Previous
methods mainly focus on projecting multiple modalities into a common latent
space and learning an identical representation for all labels, which neglects
the diversity of each modality and fails to capture richer semantic information
for each label from different perspectives. Besides, associated relationships
of modalities and labels have not been fully exploited. In this paper, we
propose versaTile multi-modAl learning for multI-labeL emOtion Recognition
(TAILOR), aiming to refine multi-modal representations and enhance
discriminative capacity of each label. Specifically, we design an adversarial
multi-modal refinement module to sufficiently explore the commonality among
different modalities and strengthen the diversity of each modality. To further
exploit label-modal dependence, we devise a BERT-like cross-modal encoder to
gradually fuse private and common modality representations in a granularity
descent way, as well as a label-guided decoder to adaptively generate a
tailored representation for each label with the guidance of label semantics. In
addition, we conduct experiments on the benchmark MMER dataset CMU-MOSEI in
both aligned and unaligned settings, which demonstrate the superiority of
TAILOR over the state-of-the-arts. Code is available at
https://github.com/kniter1/TAILOR.

Model-free and model-based reinforcement learning are two ends of a spectrum.
Learning a good policy without a dynamic model can be prohibitively expensive.
Learning the dynamic model of a system can reduce the cost of learning the
policy, but it can also introduce bias if it is not accurate. We propose a
middle ground where instead of the transition model, the sensitivity of the
trajectories with respect to the perturbation of the parameters is learned.
This allows us to predict the local behavior of the physical system around a
set of nominal policies without knowing the actual model. We assay our method
on a custom-built physical robot in extensive experiments and show the
feasibility of the approach in practice. We investigate potential challenges
when applying our method to physical systems and propose solutions to each of
them.

Over recent decades have witnessed considerable progress in whether
multi-task learning or multi-view learning, but the situation that consider
both learning scenes simultaneously has received not too much attention. How to
utilize multiple views latent representation of each single task to improve
each learning task performance is a challenge problem. Based on this, we
proposed a novel semi-supervised algorithm, termed as Multi-Task Multi-View
learning based on Common and Special Features (MTMVCSF). In general,
multi-views are the different aspects of an object and every view includes the
underlying common or special information of this object. As a consequence, we
will mine multiple views jointly latent factor of each learning task which
consists of each view special feature and the common feature of all views. By
this way, the original multi-task multi-view data has degenerated into
multi-task data, and exploring the correlations among multiple tasks enables to
make an improvement on the performance of learning algorithm. Another obvious
advantage of this approach is that we get latent representation of the set of
unlabeled instances by the constraint of regression task with labeled
instances. The performance of classification and semi-supervised clustering
task in these latent representations perform obviously better than it in raw
data. Furthermore, an anti-noise multi-task multi-view algorithm called
AN-MTMVCSF is proposed, which has a strong adaptability to noise labels. The
effectiveness of these algorithms is proved by a series of well-designed
experiments on both real world and synthetic data.

Large information sizes in samples and features can be encoded to speed up
the learning of statistical models based on linear algebra and remove unwanted
signals. Encoding information can reduce both sample and feature dimension to a
smaller representational set. Here two examples are shown on linear mixed
models and mixture models speeding up the run time for parameter estimation by
a factor defined by the user's choice on dimension reduction (can be linear,
quadratic or beyond based on dimension specification).

Recently, unsupervised person re-identification (Re-ID) has received
increasing research attention due to its potential for label-free applications.
A promising way to address unsupervised Re-ID is clustering-based, which
generates pseudo labels by clustering and uses the pseudo labels to train a
Re-ID model iteratively. However, most clustering-based methods take each
cluster as a pseudo identity class, neglecting the intra-cluster variance
mainly caused by the change of cameras. To address this issue, we propose to
split each single cluster into multiple proxies according to camera views. The
camera-aware proxies explicitly capture local structures within clusters, by
which the intra-ID variance and inter-ID similarity can be better tackled.
Assisted with the camera-aware proxies, we design two proxy-level contrastive
learning losses that are, respectively, based on offline and online association
results. The offline association directly associates proxies according to the
clustering and splitting results, while the online strategy dynamically
associates proxies in terms of up-to-date features to reduce the noise caused
by the delayed update of pseudo labels. The combination of two losses enables
us to train a desirable Re-ID model. Extensive experiments on three person
Re-ID datasets and one vehicle Re-ID dataset show that our proposed approach
demonstrates competitive performance with state-of-the-art methods. Code will
be available at: https://github.com/Terminator8758/O2CAP.

Social networks are frequently polluted by rumors, which can be detected by
advanced models such as graph neural networks. However, the models are
vulnerable to attacks and understanding the vulnerabilities is critical to
rumor detection in practice. To discover subtle vulnerabilities, we design a
powerful attacking algorithm to camouflage rumors in social networks based on
reinforcement learning that can interact with and attack any black-box
detectors. The environment has exponentially large state spaces, high-order
graph dependencies, and delayed noisy rewards, making the state-of-the-art
end-to-end approaches difficult to learn features as large learning costs and
expressive limitation of graph deep models. Instead, we design domain-specific
features to avoid learning features and produce interpretable attack policies.
To further speed up policy optimization, we devise: (i) a credit assignment
method that decomposes delayed rewards to atomic attacking actions proportional
to the their camouflage effects on target rumors; (ii) a time-dependent control
variate to reduce reward variance due to large graphs and many attacking steps,
supported by the reward variance analysis and a Bayesian analysis of the
prediction distribution. On three real world datasets of rumor detection tasks,
we demonstrate: (i) the effectiveness of the learned attacking policy compared
to rule-based attacks and current end-to-end approaches; (ii) the usefulness of
the proposed credit assignment strategy and variance reduction components;
(iii) the interpretability of the policy when generating strong attacks via the
case study.

Sometimes unexpected, novel, unconceivable events enter our lives. The
cause-effect mappings that usually guide our behaviour are destroyed. Surprised
and shocked by possibilities that we had never imagined, we are unable to make
any decision beyond mere routine. Among them there are decisions, such as
making investments, that are essential for the long-term survival of businesses
as well as the economy at large. We submit that the standard machinery of
utility maximization does not apply, but we propose measures inspired by
scenario planning and graph analysis, pointing to solutions being explored in

Camera, and associated with its objects within the field of view,
localization could benefit many computer vision fields, such as autonomous
driving, robot navigation, and augmented reality (AR). In this survey, we first
introduce specific application areas and the evaluation metrics for camera
localization pose according to different sub-tasks (learning-based 2D-2D task,
feature-based 2D-3D task, and 3D-3D task). Then, we review common methods for
structure-based camera pose estimation approaches, absolute pose regression and
relative pose regression approaches by critically modelling the methods to
inspire further improvements in their algorithms such as loss functions, neural
network structures. Furthermore, we summarise what are the popular datasets
used for camera localization and compare the quantitative and qualitative
results of these methods with detailed performance metrics. Finally, we discuss
future research possibilities and applications.

In this paper, we first introduce batch normalization to the edRVFL network.
This re-normalization method can help the network avoid divergence of the
hidden features. Then we propose novel variants of Ensemble Deep Random Vector
Functional Link (edRVFL). Weighted edRVFL (WedRVFL) uses weighting methods to
give training samples different weights in different layers according to how
the samples were classified confidently in the previous layer thereby
increasing the ensemble's diversity and accuracy. Furthermore, a pruning-based
edRVFL (PedRVFL) has also been proposed. We prune some inferior neurons based
on their importance for classification before generating the next hidden layer.
Through this method, we ensure that the randomly generated inferior features
will not propagate to deeper layers. Subsequently, the combination of weighting
and pruning, called Weighting and Pruning based Ensemble Deep Random Vector
Functional Link Network (WPedRVFL), is proposed. We compare their performances
with other state-of-the-art deep feedforward neural networks (FNNs) on 24
tabular UCI classification datasets. The experimental results illustrate the
superior performance of our proposed methods.

Deep learning methods minimise the empirical risk using loss functions such
as the cross entropy loss. When minimising the empirical risk, the
generalisation of the learnt function still depends on the performance on the
training data, the Vapnik-Chervonenkis(VC)-dimension of the function and the
number of training examples. Neural networks have a large number of parameters,
which correlates with their VC-dimension that is typically large but not
infinite, and typically a large number of training instances are needed to
effectively train them.
  In this work, we explore how to optimize feature mappings using neural
network with the intention to reduce the effective VC-dimension of the
hyperplane found in the space generated by the mapping. An interpretation of
the results of this study is that it is possible to define a loss that controls
the VC-dimension of the separating hyperplane. We evaluate this approach and
observe that the performance when using this method improves when the size of
the training set is small.

ML is being deployed in complex, real-world scenarios where errors have
impactful consequences. In these systems, thorough testing of the ML pipelines
is critical. A key component in ML deployment pipelines is the curation of
labeled training data. Common practice in the ML literature assumes that labels
are the ground truth. However, in our experience in a large autonomous vehicle
development center, we have found that vendors can often provide erroneous
labels, which can lead to downstream safety risks in trained models.
  To address these issues, we propose a new abstraction, learned observation
assertions, and implement it in a system called Fixy. Fixy leverages existing
organizational resources, such as existing (possibly noisy) labeled datasets or
previously trained ML models, to learn a probabilistic model for finding errors
in human- or model-generated labels. Given user-provided features and these
existing resources, Fixy learns feature distributions that specify likely and
unlikely values (e.g., that a speed of 30mph is likely but 300mph is unlikely).
It then uses these feature distributions to score labels for potential errors.
We show that FIxy can automatically rank potential errors in real datasets with
up to 2$\times$ higher precision compared to recent work on model assertions
and standard techniques such as uncertainty sampling.

NFTs (Non-Fungible Tokens) are blockchain-based cryptographic tokens to
represent ownership of unique content such as images, videos, or 3D objects.
Despite NFTs' increasing popularity and skyrocketing trading prices, little is
known about people's perceptions of and experiences with NFTs. In this work, we
focus on NFT creators and present results of an exploratory qualitative study
in which we interviewed 15 NFT creators from nine different countries. Our
participants had nuanced feelings about NFTs and their communities. We found
that most of our participants were enthusiastic about the underlying
technologies and how they empower individuals to express their creativity and
pursue new business models of content creation. Our participants also gave
kudos to the NFT communities that have supported them to learn, collaborate,
and grow in their NFT endeavors. However, these positivities were juxtaposed by
their accounts of the many challenges that they encountered and thorny issues
that the NFT ecosystem is grappling with around ownership of digital content,
low-quality NFTs, scams, possible money laundering, and regulations. We discuss
how the built-in properties (e.g., decentralization) of blockchains and NFTs
might have contributed to some of these issues. We present design implications
on how to improve the NFT ecosystem (e.g., making NFTs even more accessible to
newcomers and the broader population).

Symbolic Music Emotion Recognition(SMER) is to predict music emotion from
symbolic data, such as MIDI and MusicXML. Previous work mainly focused on
learning better representation via (mask) language model pre-training but
ignored the intrinsic structure of the music, which is extremely important to
the emotional expression of music. In this paper, we present a simple
multi-task framework for SMER, which incorporates the emotion recognition task
with other emotion-related auxiliary tasks derived from the intrinsic structure
of the music. The results show that our multi-task framework can be adapted to
different models. Moreover, the labels of auxiliary tasks are easy to be
obtained, which means our multi-task methods do not require manually annotated
labels other than emotion. Conducting on two publicly available datasets
(EMOPIA and VGMIDI), the experiments show that our methods perform better in
SMER task. Specifically, accuracy has been increased by 4.17 absolute point to
67.58 in EMOPIA dataset, and 1.97 absolute point to 55.85 in VGMIDI dataset.
Ablation studies also show the effectiveness of multi-task methods designed in

Collecting dialogue state labels, slots and values, for learning dialogue
state tracking (DST) models can be costly, especially with the wide application
of dialogue systems in new-rising domains. In this paper, we focus on how to
learn a DST model efficiently with limited labeled data. We design a prompt
learning framework for few-shot DST, which consists of two main components:
value-based prompt and inverse prompt mechanism. This framework aims to utilize
the language understanding and generation ability of pre-trained language
models (PLM). First, we design value-based prompt functions to probe the
DST-related knowledge from PLM, which do not rely on the known ontology of
slots. Further, an inverse prompt mechanism is utilized to self-check the
"prompted" knowledge and help the PLM understand the essence of DST task
further. Experiments show that our model can generate unseen slots and
outperforms existing state-of-the-art few-shot methods. It indicates that
DST-related knowledge can be probed from PLM and utilized to address
low-resource DST efficiently with the help of prompt learning.

Contemporary transfer learning-based methods to alleviate the data
insufficiency in change detection (CD) are mainly based on ImageNet
pre-training. Self-supervised learning (SSL) has recently been introduced to
remote sensing (RS) for learning in-domain representations. Here, we propose a
semantic decoupled representation learning for RS image CD. Typically, the
object of interest (e.g., building) is relatively small compared to the vast
background. Different from existing methods expressing an image into one
representation vector that may be dominated by irrelevant land-covers, we
disentangle representations of different semantic regions by leveraging the
semantic mask. We additionally force the model to distinguish different
semantic representations, which benefits the recognition of objects of interest
in the downstream CD task. We construct a dataset of bitemporal images with
semantic masks in an effortless manner for pre-training. Experiments on two CD
datasets show our model outperforms ImageNet pre-training, in-domain supervised
pre-training, and several recent SSL methods.

Learning from different data views by exploring the underlying complementary
information among them can endow the representation with stronger expressive
ability. However, high-dimensional features tend to contain noise, and
furthermore, the quality of data usually varies for different samples (even for
different views), i.e., one view may be informative for one sample but not the
case for another. Therefore, it is quite challenging to integrate multi-view
noisy data under unsupervised setting. Traditional multi-view methods either
simply treat each view with equal importance or tune the weights of different
views to fixed values, which are insufficient to capture the dynamic noise in
multi-view data. In this work, we devise a novel unsupervised multi-view
learning approach, termed as Dynamic Uncertainty-Aware Networks (DUA-Nets).
Guided by the uncertainty of data estimated from the generation perspective,
intrinsic information from multiple views is integrated to obtain noise-free
representations. Under the help of uncertainty, DUA-Nets weigh each view of
individual sample according to data quality so that the high-quality samples
(or views) can be fully exploited while the effects from the noisy samples (or
views) will be alleviated. Our model achieves superior performance in extensive
experiments and shows the robustness to noisy data.

Deep learning (DL) has proven to be an effective machine learning and
computer vision technique. DL-based image segmentation, object recognition and
classification will aid many in-situ Mars rover tasks such as path planning and
artifact recognition/extraction. However, most of the Deep Neural Network (DNN)
architectures are so complex that they are considered a 'black box'. In this
paper, we used integrated gradients to describe the attributions of each neuron
to the output classes. It provides a set of explainability tools (ET) that
opens the black box of a DNN so that the individual contribution of neurons to
category classification can be ranked and visualized. The neurons in each dense
layer are mapped and ranked by measuring expected contribution of a neuron to a
class vote given a true image label. The importance of neurons is prioritized
according to their correct or incorrect contribution to the output classes and
suppression or bolstering of incorrect classes, weighted by the size of each
class. ET provides an interface to prune the network to enhance high-rank
neurons and remove low-performing neurons. ET technology will make DNNs smaller
and more efficient for implementation in small embedded systems. It also leads
to more explainable and testable DNNs that can make systems easier for
Validation \& Verification. The goal of ET technology is to enable the adoption
of DL in future in-situ planetary exploration missions.

Randomized controlled trials (RCTs) are considered as the gold standard for
testing causal hypotheses in the clinical domain. However, the investigation of
prognostic variables of patient outcome in a hypothesized cause-effect route is
not feasible using standard statistical methods. Here, we propose a new
automated causal inference method (AutoCI) built upon the invariant causal
prediction (ICP) framework for the causal re-interpretation of clinical trial
data. Compared to existing methods, we show that the proposed AutoCI allows to
efficiently determine the causal variables with a clear differentiation on two
real-world RCTs of endometrial cancer patients with mature outcome and
extensive clinicopathological and molecular data. This is achieved via
suppressing the causal probability of non-causal variables by a wide margin. In
ablation studies, we further demonstrate that the assignment of causal
probabilities by AutoCI remain consistent in the presence of confounders. In
conclusion, these results confirm the robustness and feasibility of AutoCI for
future applications in real-world clinical analysis.

Remote sensing image retrieval (RSIR), aiming at searching for a set of
similar items to a given query image, is a very important task in remote
sensing applications. Deep hashing learning as the current mainstream method
has achieved satisfactory retrieval performance. On one hand, various deep
neural networks are used to extract semantic features of remote sensing images.
On the other hand, the hashing techniques are subsequently adopted to map the
high-dimensional deep features to the low-dimensional binary codes. This kind
of methods attempts to learn one hash function for both the query and database
samples in a symmetric way. However, with the number of database samples
increasing, it is typically time-consuming to generate the hash codes of
large-scale database images. In this paper, we propose a novel deep hashing
method, named asymmetric hash code learning (AHCL), for RSIR. The proposed AHCL
generates the hash codes of query and database images in an asymmetric way. In
more detail, the hash codes of query images are obtained by binarizing the
output of the network, while the hash codes of database images are directly
learned by solving the designed objective function. In addition, we combine the
semantic information of each image and the similarity information of pairs of
images as supervised information to train a deep hashing network, which
improves the representation ability of deep features and hash codes. The
experimental results on three public datasets demonstrate that the proposed
method outperforms symmetric methods in terms of retrieval accuracy and
efficiency. The source code is available at
https://github.com/weiweisong415/Demo AHCL for TGRS2022.

Message-passing neural networks (MPNN) have shown extremely high efficiency
and accuracy in predicting the physical properties of molecules and crystals,
and are expected to become the next-generation material simulation tool after
the density functional theory (DFT). However, there is currently a lack of a
general MPNN framework for directly predicting the tensor properties of the
crystals. In this work, a general framework for the prediction of tensor
properties was proposed: the tensor property of a crystal can be decomposed
into the average of the tensor contributions of all the atoms in the crystal,
and the tensor contribution of each atom can be expanded as the sum of the
tensor projections in the directions of the edges connecting the atoms. On this
basis, the edge-based expansions of force vectors, Born effective charges
(BECs), dielectric (DL) and piezoelectric (PZ) tensors were proposed. These
expansions are rotationally equivariant, while the coefficients in these tensor
expansions are rotationally invariant scalars which are similar to physical
quantities such as formation energy and band gap. The advantage of this tensor
prediction framework is that it does not require the network itself to be
equivariant. Therefore, in this work, we directly designed the edge-based
tensor prediction graph neural network (ETGNN) model on the basis of the
invariant graph neural network to predict tensors. The validity and high
precision of this tensor prediction framework were shown by the tests of ETGNN
on the extended systems, random perturbed structures and JARVIS-DFT datasets.
This tensor prediction framework is general for nearly all the GNNs and can
achieve higher accuracy with more advanced GNNs in the future.

Longitudinal properties of electron bunches are critical for the performance
of a wide range of scientific facilities. In a free-electron laser, for
example, the existing diagnostics only provide very limited longitudinal
information of the electron bunch during online tuning and optimization. We
leverage the power of artificial intelligence to build a neural network model
using experimental data, in order to bring the destructive longitudinal phase
space (LPS) diagnostics online virtually and improve the existing current
profile online diagnostics which uses a coherent transition radiation (CTR)
spectrometer. The model can also serve as a digital twin of the real machine on
which algorithms can be tested efficiently and effectively. We demonstrate at
the FLASH facility that the encoder-decoder model with more than one decoder
can make highly accurate predictions of megapixel LPS images and coherent
transition radiation spectra concurrently for electron bunches in a bunch train
with broad ranges of LPS shapes and peak currents, which are obtained by
scanning all the major control knobs for LPS manipulation. Furthermore, we
propose a way to significantly improve the CTR spectrometer online measurement
by combining the predicted and measured spectra. Our work showcases how to
combine virtual and real diagnostics in order to provide heterogeneous and
reliable mixed diagnostics for scientific facilities.

The potential energy formulation and deep learning are merged to solve
partial differential equations governing the deformation in hyperelastic and
viscoelastic materials. The presented deep energy method (DEM) is
self-contained and meshfree. It can accurately capture the three-dimensional
(3D) mechanical response without requiring any time-consuming training data
generation by classical numerical methods such as the finite element method.
Once the model is appropriately trained, the response can be attained almost
instantly at any point in the physical domain, given its spatial coordinates.
Therefore, the deep energy method is potentially a promising standalone method
for solving partial differential equations describing the mechanical
deformation of materials or structural systems and other physical phenomena.

Client selection strategies are widely adopted to handle the
communication-efficient problem in recent studies of Federated Learning (FL).
However, due to the large variance of the selected subset's update, prior
selection approaches with a limited sampling ratio cannot perform well on
convergence and accuracy in heterogeneous FL. To address this problem, in this
paper, we propose a novel stratified client selection scheme to reduce the
variance for the pursuit of better convergence and higher accuracy.
Specifically, to mitigate the impact of heterogeneity, we develop
stratification based on clients' local data distribution to derive approximate
homogeneous strata for better selection in each stratum. Concentrating on a
limited sampling ratio scenario, we next present an optimized sample size
allocation scheme by considering the diversity of stratum's variability, with
the promise of further variance reduction. Theoretically, we elaborate the
explicit relation among different selection schemes with regard to variance,
under heterogeneous settings, we demonstrate the effectiveness of our selection
scheme. Experimental results confirm that our approach not only allows for
better performance relative to state-of-the-art methods but also is compatible
with prevalent FL algorithms.

RGB-D data is essential for solving many problems in computer vision.
Hundreds of public RGB-D datasets containing various scenes, such as indoor,
outdoor, aerial, driving, and medical, have been proposed. These datasets are
useful for different applications and are fundamental for addressing classic
computer vision tasks, such as monocular depth estimation. This paper reviewed
and categorized image datasets that include depth information. We gathered 203
datasets that contain accessible data and grouped them into three categories:
scene/objects, body, and medical. We also provided an overview of the different
types of sensors, depth applications, and we examined trends and future
directions of the usage and creation of datasets containing depth data, and how
they can be applied to investigate the development of generalizable machine
learning models in the monocular depth estimation field.

The travel time data collected from widespread traffic monitoring sensors
necessitate big data analytic tools for querying, visualization, and
identifying meaningful traffic patterns. This paper utilizes a large-scale
travel time dataset from Caltrans Performance Measurement System (PeMS) system
that is an overflow for traditional data processing and modeling tools. To
overcome the challenges of the massive amount of data, the big data analytic
engines Apache Spark and Apache MXNet are applied for data wrangling and
modeling. Seasonality and autocorrelation were performed to explore and
visualize the trend of time-varying data. Inspired by the success of the
hierarchical architecture for many Artificial Intelligent (AI) tasks, we
consolidate the cell and hidden states passed from low-level to the high-level
LSTM with an attention pooling similar to how the human perception system
operates. The designed hierarchical LSTM model can consider the dependencies at
different time scales to capture the spatial-temporal correlations of
network-level travel time. Another self-attention module is then devised to
connect LSTM extracted features to the fully connected layers, predicting
travel time for all corridors instead of a single link/route. The comparison
results show that the Hierarchical LSTM with Attention (HierLSTMat) model gives
the best prediction results at 30-minute and 45-min horizons and can
successfully forecast unusual congestion. The efficiency gained from big data
analytic tools was evaluated by comparing them with popular data science and
deep learning frameworks.

Most fair machine learning methods either highly rely on the sensitive
information of the training samples or require a large modification on the
target models, which hinders their practical application. To address this
issue, we propose a two-stage training algorithm named FAIRIF. It minimizes the
loss over the reweighted data set (second stage) where the sample weights are
computed to balance the model performance across different demographic groups
(first stage). FAIRIF can be applied on a wide range of models trained by
stochastic gradient descent without changing the model, while only requiring
group annotations on a small validation set to compute sample weights.
Theoretically, we show that, in the classification setting, three notions of
disparity among different groups can be mitigated by training with the weights.
Experiments on synthetic data sets demonstrate that FAIRIF yields models with
better fairness-utility trade-offs against various types of bias; and on
real-world data sets, we show the effectiveness and scalability of FAIRIF.
Moreover, as evidenced by the experiments with pretrained models, FAIRIF is
able to alleviate the unfairness issue of pretrained models without hurting
their performance.

In this paper, we present a new policy gradient (PG) methods, namely the
block policy mirror descent (BPMD) method for solving a class of regularized
reinforcement learning (RL) problems with (strongly)-convex regularizers.
Compared to the traditional PG methods with a batch update rule, which visits
and updates the policy for every state, BPMD method has cheap per-iteration
computation via a partial update rule that performs the policy update on a
sampled state. Despite the nonconvex nature of the problem and a partial update
rule, we provide a unified analysis for several sampling schemes, and show that
BPMD achieves fast linear convergence to the global optimality. In particular,
uniform sampling leads to comparable worst-case total computational complexity
as batch PG methods. A necessary and sufficient condition for convergence with
on-policy sampling is also identified. With a hybrid sampling scheme, we
further show that BPMD enjoys potential instance-dependent acceleration,
leading to improved dependence on the state space and consequently
outperforming batch PG methods. We then extend BPMD methods to the stochastic
setting, by utilizing stochastic first-order information constructed from
samples. With a generative model, $\tilde{\mathcal{O}}(\left\lvert
\mathcal{S}\right\rvert \left\lvert \mathcal{A}\right\rvert /\epsilon)$ (resp.
$\tilde{\mathcal{O}}(\left\lvert \mathcal{S}\right\rvert \left\lvert
\mathcal{A} \right\rvert /\epsilon^2)$) sample complexities are established for
the strongly-convex (resp. non-strongly-convex) regularizers, where $\epsilon$
denotes the target accuracy. To the best of our knowledge, this is the first
time that block coordinate descent methods have been developed and analyzed for
policy optimization in reinforcement learning, which provides a new perspective
on solving large-scale RL problems.

Machine learning has recently been applied and deployed at several light
source facilities in the domain of Accelerator Physics. We introduce an
approach based on machine learning to produce a fast-executing model that
predicts the polarization and energy of the radiated light produced at an
insertion device. This paper demonstrates how a machine learning model can be
trained on simulated data and later calibrated to a smaller, limited measured
data set, a technique referred to as transfer learning. This result will enable
users to efficiently determine the insertion device settings for achieving
arbitrary beam characteristics.

Simulation modeling of robots, objects, and environments is the backbone for
all model-based control and learning. It is leveraged broadly across dynamic
programming and model-predictive control, as well as data generation for
imitation, transfer, and reinforcement learning. In addition to fidelity, key
features of models in these control and learning contexts are speed, stability,
and native differentiability. However, many popular simulation platforms for
robotics today lack at least one of the features above. More recently,
position-based dynamics (PBD) has become a very popular simulation tool for
modeling complex scenes of rigid and non-rigid object interactions, due to its
speed and stability, and is starting to gain significant interest in robotics
for its potential use in model-based control and learning. Thus, in this paper,
we present a mathematical formulation for coupling position-based dynamics
(PBD) simulation and optimal robot design, model-based motion control and
system identification. Our framework breaks down PBD definitions and
derivations for various types of joint-based articulated rigid bodies. We
present a back-propagation method with automatic differentiation, which can
integrate both positional and angular geometric constraints. Our framework can
critically provide the native gradient information and perform gradient-based
optimization tasks. We also propose articulated joint model representations and
simulation workflow for our differentiable framework. We demonstrate the
capability of the framework in efficient optimal robot design, accurate
trajectory torque estimation and supporting spring stiffness estimation, where
we achieve minor errors. We also implement impedance control in real robots to
demonstrate the potential of our differentiable framework in human-in-the-loop

Achieving efficient execution of machine learning models has attracted
significant attention recently. To generate tensor programs efficiently, a key
component of DNN compilers is the cost model that can predict the performance
of each configuration on specific devices. However, due to the rapid emergence
of hardware platforms, it is increasingly labor-intensive to train
domain-specific predictors for every new platform. Besides, current design of
cost models cannot provide transferable features between different hardware
accelerators efficiently and effectively. In this paper, we propose Moses, a
simple and efficient design based on the lottery ticket hypothesis, which fully
takes advantage of the features transferable to the target device via domain
adaptation. Compared with state-of-the-art approaches, Moses achieves up to
1.53X efficiency gain in the search stage and 1.41X inference speedup on
challenging DNN benchmarks.

Recently, deep learning-based algorithms are widely adopted due to the
advantage of being able to establish anomaly detection models without or with
minimal domain knowledge of the task. Instead, to train the artificial neural
network more stable, it should be better to define the appropriate neural
network structure or the loss function. For the training anomaly detection
model, the mean squared error (MSE) function is adopted widely. On the other
hand, the novel loss function, logarithmic mean squared error (LMSE), is
proposed in this paper to train the neural network more stable. This study
covers a variety of comparisons from mathematical comparisons, visualization in
the differential domain for backpropagation, loss convergence in the training
process, and anomaly detection performance. In an overall view, LMSE is
superior to the existing MSE function in terms of strongness of loss
convergence, anomaly detection performance. The LMSE function is expected to be
applicable for training not only the anomaly detection model but also the
general generative neural network.

Infrared tip-tilt sensors (IR TTSs) have been deployed on three different
laser guide star adaptive optics (AO) systems on three different telescopes.
These IR TTS benefit from the high-order loop PSF sharpening in the near
infrared, hence they provide a low tip-tilt residual and a good sky coverage.
Nevertheless, these IR TTS are challenging and their use in AO is limited. In
this paper, we outline existing IR TTS, provide on-sky performance results and
describe our experience using IR TTS and along with plans for the near future.
The second part of the paper deals with unresolved challenges for IR TTS. These
include algorithms and loop stability in the low Strehl regime, using IR TTSs
to measure higher-order modes and guiding on multiple guide stars with
different magnitudes.

The domain adaptation (DA) problem on symmetric positive definite (SPD)
manifolds has raised interest in the machine learning community because of the
growing potential for the SPD-matrix representations across many cross-domain
applicable scenarios. However, due to the different underlying space, the
previous experience and solution to the DA problem cannot benefit this new
scenario directly. This study addresses a specific DA problem: the marginal and
conditional distributions differ in the source and target domains on SPD
manifolds. We then formalize this problem from an optimal transport perspective
and derive an optimal transport framework on SPD manifolds for supervised
learning. In addition, we propose a computational scheme under the optimal
transport framework, Deep Optimal Transport (DOT), for general computation,
using the generalized joint distribution adaptation approach and the existing
Riemannian-based network architectures on SPD manifolds. DOT is applied to the
real-world scenario and becomes a specific EEG-BCI classifier against the
cross-session motor-imagery classification from the calibration phase to the
feedback phase. In the experiments, DOT exhibits a marked improvement in the
average accuracy in two highly non-stationary cross-session scenarios in the
EEG-BCI classification, respectively, indicating the proposed methodology's
validity.

The Science4cast 2021 competition focuses on predicting future edges in an
evolving semantic network, where each vertex represents an artificial
intelligence concept, and an edge between a pair of vertices denotes that the
two concepts have been investigated together in a scientific paper. In this
paper, we describe our solution to this competition. We present two distinct
approaches: a tree-based gradient boosting approach and a deep learning
approach, and demonstrate that both approaches achieve competitive performance.
Our final solution, which is based on a blend of the two approaches, achieved
the 1st place among all the participating teams. The source code for this paper
is available at https://github.com/YichaoLu/Science4cast2021.

Food reviews and recommendations have always been important for online food
service websites. However, reviewing and recommending food is not simple as it
is likely to be overwhelmed by disparate contexts and meanings. In this paper,
we use different deep learning approaches to address the problems of sentiment
analysis, automatic review tag generation, and retrieval of food reviews. We
propose to develop a web-based food review system at Nanyang Technological
University (NTU) named NTU Food Hunter, which incorporates different deep
learning approaches that help users with food selection. First, we implement
the BERT and LSTM deep learning models into the system for sentiment analysis
of food reviews. Then, we develop a Part-of-Speech (POS) algorithm to
automatically identify and extract adjective-noun pairs from the review content
for review tag generation based on POS tagging and dependency parsing. Finally,
we also train a RankNet model for the re-ranking of the retrieval results to
improve the accuracy in our Solr-based food reviews search system. The
experimental results show that our proposed deep learning approaches are
promising for the applications of real-world problems.

Action recognition is a key algorithmic part of emerging on-the-edge smart
video surveillance and security systems. Skeleton-based action recognition is
an attractive approach which, instead of using RGB pixel data, relies on human
pose information to classify appropriate actions. However, existing algorithms
often assume ideal conditions that are not representative of real-world
limitations, such as noisy input, latency requirements, and edge resource
  To address the limitations of existing approaches, this paper presents
Real-World Graph Convolution Networks (RW-GCNs), an architecture-level solution
for meeting the domain constraints of Real World Skeleton-based Action
Recognition. Inspired by the presence of feedback connections in the human
visual cortex, RW-GCNs leverage attentive feedback augmentation on existing
near state-of-the-art (SotA) Spatial-Temporal Graph Convolution Networks
(ST-GCNs). The ST-GCNs' design choices are derived from information
theory-centric principles to address both the spatial and temporal noise
typically encountered in end-to-end real-time and on-the-edge smart video
systems. Our results demonstrate RW-GCNs' ability to serve these applications
by achieving a new SotA accuracy on the NTU-RGB-D-120 dataset at 94.1%, and
achieving 32X less latency than baseline ST-GCN applications while still
achieving 90.4% accuracy on the Northwestern UCLA dataset in the presence of
spatial keypoint noise. RW-GCNs further show system scalability by running on
the 10X cost effective NVIDIA Jetson Nano (as opposed to NVIDIA Xavier NX),
while still maintaining a respectful range of throughput (15.6 to 5.5 Actions
per Second) on the resource constrained device. The code is available here:
https://github.com/TeCSAR-UNCC/RW-GCN.

In the paper a method for automatic classification of signals received by EKB
and MAGW ISTP SB RAS coherent scatter radars (8-20MHz operating frequency)
during 2021 is described. The method is suitable for automatic physical
interpretation of the resulting classification of the experimental data in
realtime. We called this algorithm Wrapped Classifier with Dummy Teacher. The
method is trained on unlabeled dataset and is based on training optimal
physics-based classification using clusterization results. The approach is
close to optimal embedding search, where the embedding is interpreted as a
vector of probabilities for soft classification. The approach allows to find
optimal classification algorithm, based on physically interpretable parameters
of the received data, both obtained during physics-based numerical simulation
and measured experimentally. Dummy Teacher clusterer used for labeling
unlabeled dataset is gaussian mixture clustering algorithm. For algorithm
functioning we extended the parameters obtained by the radar with additional
parameters, calculated during simulation of radiowave propagation using
ray-tracing and IRI-2012 and IGRF models for ionosphere and Earth's magnetic
field correspondingly. For clustering by Dummy Teacher we use the whole dataset
of available parameters (measured and simulated ones). For classification by
Wrapped Classifier we use only well physically interpreted parameters. As a
result we trained the classification network and found 11 well-interpretable
classes from physical point of view in the available data. Five other found
classes are not interpretable from physical point of view, demonstrating the
importance of taking into account radiowave propagation for correct

We present the results from combining machine learning with the profile
likelihood fit procedure, using data from the Large Underground Xenon (LUX)
dark matter experiment. This approach demonstrates reduction in computation
time by a factor of 30 when compared with the previous approach, without loss
of performance on real data. We establish its flexibility to capture non-linear
correlations between variables (such as smearing in light and charge signals
due to position variation) by achieving equal performance using pulse areas
with and without position-corrections applied. Its efficiency and scalability
furthermore enables searching for dark matter using additional variables
without significant computational burden. We demonstrate this by including a
light signal pulse shape variable alongside more traditional inputs such as
light and charge signal strengths. This technique can be exploited by future
dark matter experiments to make use of additional information, reduce
computational resources needed for signal searches and simulations, and make
inclusion of physical nuisance parameters in fits tractable.

The objective of image manipulation detection is to identify and locate the
manipulated regions in the images. Recent approaches mostly adopt the
sophisticated Convolutional Neural Networks (CNNs) to capture the tampering
artifacts left in the images to locate the manipulated regions. However, these
approaches ignore the feature correlations, i.e., feature inconsistencies,
between manipulated regions and non-manipulated regions, leading to inferior
detection performance. To address this issue, we propose a hierarchical Graph
Convolutional Network (HGCN-Net), which consists of two parallel branches: the
backbone network branch and the hierarchical graph representation learning
(HGRL) branch for image manipulation detection. Specifically, the feature maps
of a given image are extracted by the backbone network branch, and then the
feature correlations within the feature maps are modeled as a set of
fully-connected graphs for learning the hierarchical graph representation by
the HGRL branch. The learned hierarchical graph representation can sufficiently
capture the feature correlations across different scales, and thus it provides
high discriminability for distinguishing manipulated and non-manipulated
regions. Extensive experiments on four public datasets demonstrate that the
proposed HGCN-Net not only provides promising detection accuracy, but also
achieves strong robustness under a variety of common image attacks in the task
of image manipulation detection, compared to the state-of-the-arts.

Contrastive language-image pretraining (CLIP) links vision and language
modalities into a unified embedding space, yielding the tremendous potential
for vision-language (VL) tasks. While early concurrent works have begun to
study this potential on a subset of tasks, important questions remain: 1) What
is the benefit of CLIP on unstudied VL tasks? 2) Does CLIP provide benefit in
low-shot or domain-shifted scenarios? 3) Can CLIP improve existing approaches
without impacting inference or pretraining complexity? In this work, we seek to
answer these questions through two key contributions. First, we introduce an
evaluation protocol that includes Visual Commonsense Reasoning (VCR), Visual
Entailment (SNLI-VE), and Visual Question Answering (VQA), across a variety of
data availability constraints and conditions of domain shift. Second, we
propose an approach, named CLIP Targeted Distillation (CLIP-TD), to
intelligently distill knowledge from CLIP into existing architectures using a
dynamically weighted objective applied to adaptively selected tokens per
instance. Experiments demonstrate that our proposed CLIP-TD leads to
exceptional gains in the low-shot (up to 51.9%) and domain-shifted (up to
71.3%) conditions of VCR, while simultaneously improving performance under
standard fully-supervised conditions (up to 2%), achieving state-of-art
performance on VCR compared to other single models that are pretrained with
image-text data only. On SNLI-VE, CLIP-TD produces significant gains in
low-shot conditions (up to 6.6%) as well as fully supervised (up to 3%). On
VQA, CLIP-TD provides improvement in low-shot (up to 9%), and in
fully-supervised (up to 1.3%). Finally, CLIP-TD outperforms concurrent works
utilizing CLIP for finetuning, as well as baseline naive distillation
approaches. Code will be made available.

IEEE 802.11ax uses the concept of primary and secondary channels, leading to
the Dynamic Bandwidth Channel Access (DBCA) mechanism. By applying DBCA, a
wireless station can select a wider channel bandwidth, such as 40/80/160 MHz,
by applying the channel bonding feature. However, during channel bonding,
inappropriate bandwidth selection can cause collisions. Therefore, to avoid
collisions, a well-developed media access control (MAC) protocol is crucial to
effectively utilize the channel bonding mechanism. In this paper, we address a
collision scenario, called Outside Warning Range Problem (OWRP), that may occur
during DBCA when a wireless station interferes with another wireless station
after channel bonding is performed. Therefore, we propose a MAC layer
mechanism, Intelligent Bonding Avoiding Collision (IBAC), that adapts the
channel bonding level in DBCA in order to avoid the OWRP. We first design a
theoretical model based on Markov chains for DBCA while avoiding the OWRP.
Based on this model, we design a Thompson sampling based Bayesian approach to
select the best possible channel bonding level intelligently. We analyze the
performance of the IBAC through simulations where it is observed that,
comparing to other competing mechanisms, the proposed approach can enhance the
network performance significantly while avoiding the OWRP.

Unpaired video-to-video translation aims to translate videos between a source
and a target domain without the need of paired training data, making it more
feasible for real applications. Unfortunately, the translated videos generally
suffer from temporal and semantic inconsistency. To address this, many existing
works adopt spatiotemporal consistency constraints incorporating temporal
information based on motion estimation. However, the inaccuracies in the
estimation of motion deteriorate the quality of the guidance towards
spatiotemporal consistency, which leads to unstable translation. In this work,
we propose a novel paradigm that regularizes the spatiotemporal consistency by
synthesizing motions in input videos with the generated optical flow instead of
estimating them. Therefore, the synthetic motion can be applied in the
regularization paradigm to keep motions consistent across domains without the
risk of errors in motion estimation. Thereafter, we utilize our unsupervised
recycle and unsupervised spatial loss, guided by the pseudo-supervision
provided by the synthetic optical flow, to accurately enforce spatiotemporal
consistency in both domains. Experiments show that our method is versatile in
various scenarios and achieves state-of-the-art performance in generating
temporally and semantically consistent videos. Code is available at:
https://github.com/wangkaihong/Unsup_Recycle_GAN/.

In recent years, classical epidemic models, which assume stationary behavior
of individuals, have been extended to include an adaptive heterogeneous
response of the population to the current state of the epidemic. However, it is
widely accepted that human behavior can exhibit history-dependence as a
consequence of learned experiences. This history-dependence is similar to
hysteresis effects that have been well-studied in control theory. To illustrate
the importance of history-dependence for epidemic theory, we study dynamics of
a variant of the SIRS model where individuals exhibit lazy-switch responses to
prevalence dynamics. The resulting model, which includes the Preisach
hysteresis operator, possesses a continuum of endemic equilibrium states
characterized by different proportions of susceptible, infected and recovered
populations. We discuss stability properties of the endemic equilibrium set and
relate them to the degree of heterogeneity of the adaptive response. Our
results support the argument that public health responses during the emergence
of a new disease can have long-term consequences for subsequent management
efforts. The main mathematical contribution of this work is a method of global
stability analysis, which uses a family of Lyapunov functions corresponding to
different branches of the hysteresis operator.

Shared Automated Vehicles (SAVs) Fleets companies are starting pilot projects
nationwide. In 2020 in Fairfax Virginia it was announced the first Shared
Autonomous Vehicle Fleet pilot project in Virginia. SAVs promise to improve
quality of life. However, SAVs will also induce some negative externalities by
generating excessive vehicle miles traveled (VMT), which leads to more
congestions, energy consumption, and emissions. The excessive VMT are primarily
generated via empty relocation process. Reinforcement Learning based algorithms
are being researched as a possible solution to solve some of these problems:
most notably minimizing waiting time for riders. But no research using
Reinforcement Learning has been made about reducing parking space cost nor
reducing empty cruising time. This study explores different
\textbf{Reinforcement Learning approaches and then decide the best approach to
help minimize the rider waiting time, parking cost, and empty travel

Neural ordinary differential equations (NODEs) -- parametrizations of
differential equations using neural networks -- have shown tremendous promise
in learning models of unknown continuous-time dynamical systems from data.
However, every forward evaluation of a NODE requires numerical integration of
the neural network used to capture the system dynamics, making their training
prohibitively expensive. Existing works rely on off-the-shelf adaptive
step-size numerical integration schemes, which often require an excessive
number of evaluations of the underlying dynamics network to obtain sufficient
accuracy for training. By contrast, we accelerate the evaluation and the
training of NODEs by proposing a data-driven approach to their numerical
integration. The proposed Taylor-Lagrange NODEs (TL-NODEs) use a fixed-order
Taylor expansion for numerical integration, while also learning to estimate the
expansion's approximation error. As a result, the proposed approach achieves
the same accuracy as adaptive step-size schemes while employing only low-order
Taylor expansions, thus greatly reducing the computational cost necessary to
integrate the NODE. A suite of numerical experiments, including modeling
dynamical systems, image classification, and density estimation, demonstrate
that TL-NODEs can be trained more than an order of magnitude faster than
state-of-the-art approaches, without any loss in performance.

Network intelligence is a discipline that builds on the capabilities of
network systems to act intelligently by the usage of network resources for
delivering high-quality services in a changing environment. Wide area network
intelligence is a class of network intelligence in wide area network which
covers the core and the edge of Internet. In this paper, we propose a system
based on machine learning for wide area network intelligence. The whole system
consists of a core machine for pre-training and many terminal machines to
accomplish faster responses. Each machine is one of dual-hemisphere models
which are made of left and right hemispheres. The left hemisphere is used to
improve latency by terminal response and the right hemisphere is used to
improve communication by data generation. In an application on multimedia
service, the proposed model is superior to the latest deep feed forward neural
network in the data center with respect to the accuracy, latency and
communication. Evaluation shows scalable improvement with regard to the number
of terminal machines. Evaluation also shows the cost of improvement is longer
learning time.

In this paper, we propose a novel architecture for a deep learning system,
named k-degree layer-wise network, to realize efficient geo-distributed
computing between Cloud and Internet of Things (IoT). The geo-distributed
computing extends Cloud to the geographical verge of the network in the
neighbor of IoT. The basic ideas of the proposal include a k-degree constraint
and a layer-wise constraint. The k-degree constraint is defined such that the
degree of each vertex on the h-th layer is exactly k(h) to extend the existing
deep belief networks and control the communication cost. The layer-wise
constraint is defined such that the layer-wise degrees are monotonically
decreasing in positive direction to gradually reduce the dimension of data. We
prove the k-degree layer-wise network is sparse, while a typical deep neural
network is dense. In an evaluation on the M-distributed MNIST database, the
proposal is superior to a state-of-the-art model in terms of communication cost
and learning time with scalability.

Incorporating geometric transformations that reflect the relative position
changes between an observer and an object into computer vision and deep
learning models has attracted much attention in recent years. However, the
existing proposals mainly focus on the affine transformation that is
insufficient to reflect such geometric position changes. Furthermore, current
solutions often apply a neural network module to learn a single transformation
matrix, which not only ignores the importance of multi-view analysis but also
includes extra training parameters from the module apart from the
transformation matrix parameters that increase the model complexity. In this
paper, a perspective transformation layer is proposed in the context of deep
learning. The proposed layer can learn homography, therefore reflecting the
geometric positions between observers and objects. In addition, by directly
training its transformation matrices, a single proposed layer can learn an
adjustable number of multiple viewpoints without considering module parameters.
The experiments and evaluations confirm the superiority of the proposed layer.

Fluid antenna system promises to obtain enormous diversity in the small space
of a mobile device by switching the position of the radiating element to the
most desirable position from a large number of prescribed locations of the
given space. Previous researches have revealed the promising performance of
fluid antenna systems if the position with the maximum received signal-to-noise
ratio (SNR) is chosen. However, selecting the best position, referred to as
port selection, requires a huge number of SNR observations from the ports and
may prove to be infeasible. This letter tackles this problem by devising a
number of fast port selection algorithms utilizing a combination of machine
learning methods and analytical approximation when the system observes only a
few ports. Simulation results illustrate that with only 10% of the ports
observed, more than an order of magnitude reduction in the outage probability
can be achieved. Even in the extreme cases where only one port is observed,
considerable performance improvements are possible using the proposed

Diffusion tensor imaging (DTI) is a widely used method for studying brain
white matter development and degeneration. However, standard DTI estimation
methods depend on a large number of high-quality measurements. This would
require long scan times and can be particularly difficult to achieve with
certain patient populations such as neonates. Here, we propose a method that
can accurately estimate the diffusion tensor from only six diffusion-weighted
measurements. Our method achieves this by learning to exploit the relationships
between the diffusion signals and tensors in neighboring voxels. Our model is
based on transformer networks, which represent the state of the art in modeling
the relationship between signals in a sequence. In particular, our model
consists of two such networks. The first network estimates the diffusion tensor
based on the diffusion signals in a neighborhood of voxels. The second network
provides more accurate tensor estimations by learning the relationships between
the diffusion signals as well as the tensors estimated by the first network in
neighboring voxels. Our experiments with three datasets show that our proposed
method achieves highly accurate estimations of the diffusion tensor and is
significantly superior to three competing methods. Estimations produced by our
method with six diffusion-weighted measurements are comparable with those of
standard estimation methods with 30-88 diffusion-weighted measurements. Hence,
our method promises shorter scan times and more reliable assessment of brain
white matter, particularly in non-cooperative patients such as neonates and
infants.

While Active Learning (AL) techniques are explored in Neural Machine
Translation (NMT), only a few works focus on tackling low annotation budgets
where a limited number of sentences can get translated. Such situations are
especially challenging and can occur for endangered languages with few human
annotators or having cost constraints to label large amounts of data. Although
AL is shown to be helpful with large budgets, it is not enough to build
high-quality translation systems in these low-resource conditions. In this
work, we propose a cost-effective training procedure to increase the
performance of NMT models utilizing a small number of annotated sentences and
dictionary entries. Our method leverages monolingual data with self-supervised
objectives and a small-scale, inexpensive dictionary for additional supervision
to initialize the NMT model before applying AL. We show that improving the
model using a combination of these knowledge sources is essential to exploit AL
strategies and increase gains in low-resource conditions. We also present a
novel AL strategy inspired by domain adaptation for NMT and show that it is
effective for low budgets. We propose a new hybrid data-driven approach, which
samples sentences that are diverse from the labelled data and also most similar
to unlabelled data. Finally, we show that initializing the NMT model and
further using our AL strategy can achieve gains of up to $13$ BLEU compared to
conventional AL methods.

Symbolic representations are a useful tool for the dimension reduction of
temporal data, allowing for the efficient storage of and information retrieval
from time series. They can also enhance the training of machine learning
algorithms on time series data through noise reduction and reduced sensitivity
to hyperparameters. The adaptive Brownian bridge-based aggregation (ABBA)
method is one such effective and robust symbolic representation, demonstrated
to accurately capture important trends and shapes in time series. However, in
its current form the method struggles to process very large time series. Here
we present a new variant of the ABBA method, called fABBA. This variant
utilizes a new aggregation approach tailored to the piecewise representation of
time series. By replacing the k-means clustering used in ABBA with a
sorting-based aggregation technique, and thereby avoiding repeated
sum-of-squares error computations, the computational complexity is
significantly reduced. In contrast to the original method, the new approach
does not require the number of time series symbols to be specified in advance.
Through extensive tests we demonstrate that the new method significantly
outperforms ABBA with a considerable reduction in runtime while also
outperforming the popular SAX and 1d-SAX representations in terms of
reconstruction accuracy. We further demonstrate that fABBA can compress other
data types such as images.

In this paper, we study the "stability" of machine learning (ML) models
within the context of larger, complex NLP systems with continuous training data
updates. For this study, we propose a methodology for the assessment of model
stability (which we refer to as jitter under various experimental conditions.
We find that model design choices, including network architecture and input
representation, have a critical impact on stability through experiments on four
text classification tasks and two sequence labeling tasks. In classification
tasks, non-RNN-based models are observed to be more stable than RNN-based ones,
while the encoder-decoder model is less stable in sequence labeling tasks.
Moreover, input representations based on pre-trained fastText embeddings
contribute to more stability than other choices. We also show that two learning
strategies -- ensemble models and incremental training -- have a significant
influence on stability. We recommend ML model designers account for trade-offs
in accuracy and jitter when making modeling choices.

Recently, there has been an increased interest in the application of machine
learning (ML) techniques to a variety of problems in condensed matter physics.
In this regard, of particular significance is the characterization of simple
and complex phases of matter. Here, we use a ML approach to construct the full
phase diagram of a well known spin model combining ferromagnetic exchange and
Dzyaloshinskii-Moriya (DM) interactions where topological phases emerge. At low
temperatures, the system is tuned from a spiral phase to a skyrmion crystal by
a magnetic field. However, thermal fluctuations induce two types of
intermediate phases, bimerons and skyrmion gas, which are not as easily
determined as spirals or skyrmion crystals. We resort to large scale Monte
Carlo simulations to obtain low temperature spin configurations, and train a
convolutional neural network (CNN), taking only snapshots at specific values of
the DM couplings, to classify between the different phases, focusing on the
intermediate and intricate topological textures. We then apply the CNN to
higher temperature configurations and to other DM values, to construct a
detailed magnetic field-temperature phase diagram, achieving outstanding
results. We discuss the importance of including the disordered paramagnetic
phases in order to get the phase boundaries, and finally, we compare our
approach with other ML algorithms.

We introduce Active Predictive Coding Networks (APCNs), a new class of neural
networks that solve a major problem posed by Hinton and others in the fields of
artificial intelligence and brain modeling: how can neural networks learn
intrinsic reference frames for objects and parse visual scenes into part-whole
hierarchies by dynamically allocating nodes in a parse tree? APCNs address this
problem by using a novel combination of ideas: (1) hypernetworks are used for
dynamically generating recurrent neural networks that predict parts and their
locations within intrinsic reference frames conditioned on higher object-level
embedding vectors, and (2) reinforcement learning is used in conjunction with
backpropagation for end-to-end learning of model parameters. The APCN
architecture lends itself naturally to multi-level hierarchical learning and is
closely related to predictive coding models of cortical function. Using the
MNIST, Fashion-MNIST and Omniglot datasets, we demonstrate that APCNs can (a)
learn to parse images into part-whole hierarchies, (b) learn compositional
representations, and (c) transfer their knowledge to unseen classes of objects.
With their ability to dynamically generate parse trees with part locations for
objects, APCNs offer a new framework for explainable AI that leverages advances
in deep learning while retaining interpretability and compositionality.

The video action segmentation task is regularly explored under weaker forms
of supervision, such as transcript supervision, where a list of actions is
easier to obtain than dense frame-wise labels. In this formulation, the task
presents various challenges for sequence modeling approaches due to the
emphasis on action transition points, long sequence lengths, and frame
contextualization, making the task well-posed for transformers. Given
developments enabling transformers to scale linearly, we demonstrate through
our architecture how they can be applied to improve action alignment accuracy
over the equivalent RNN-based models with the attention mechanism focusing
around salient action transition regions. Additionally, given the recent focus
on inference-time transcript selection, we propose a supplemental transcript
embedding approach to select transcripts more quickly at inference-time.
Furthermore, we subsequently demonstrate how this approach can also improve the
overall segmentation performance. Finally, we evaluate our proposed methods
across the benchmark datasets to better understand the applicability of
transformers and the importance of transcript selection on this video-driven
weakly-supervised task.

The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial in
drug safety assessment. In this study, we discuss machine learning approaches
in the prediction of drug-induced TdP risks using preclinical data.
Specifically, the random forest model was trained on the dataset generated by
the rabbit ventricular wedge assay. The model prediction performance was
measured on 28 drugs from the Comprehensive In Vitro Proarrhythmia Assay
initiative. Leave-one-drug-out cross-validation provided an unbiased estimation
of model performance. Stratified bootstrap revealed the uncertainty in the
asymptotic model prediction. Our study validated the utility of machine
learning approaches in predicting drug-induced TdP risks from preclinical data.
Our methods can be extended to other preclinical protocols and serve as a
supplementary evaluation in drug safety assessment.

We report the development of a combined machine-learning and high-throughput
density functional theory (DFT) framework to accelerate the search for new
ferroelectric materials. The framework can predict potential ferroelectric
compounds using only elemental composition as input. A series of
machine-learning algorithms initially predict the possible stable and
insulating stoichiometries with a polar crystal structure, necessary for
ferroelectricity, within a given chemical composition space. A classification
model then predicts the point groups of these stoichiometries. A subsequent
series of high-throughput DFT calculations finds the lowest energy crystal
structure within the point group. As a final step, non-polar parent structures
are identified using group theory considerations, and the values of the
spontaneous polarization are calculated using DFT. By predicting the crystal
structures and the polarization values, this method provides a powerful tool to
explore new ferroelectric materials beyond those in existing databases.

Many of the causal discovery methods rely on the faithfulness assumption to
guarantee asymptotic correctness. However, the assumption can be approximately
violated in many ways, leading to sub-optimal solutions. Although there is a
line of research in Bayesian network structure learning that focuses on
weakening the assumption, such as exact search methods with well-defined score
functions, they do not scale well to large graphs. In this work, we introduce
several strategies to improve the scalability of exact score-based methods in
the linear Gaussian setting. In particular, we develop a super-structure
estimation method based on the support of inverse covariance matrix which
requires assumptions that are strictly weaker than faithfulness, and apply it
to restrict the search space of exact search. We also propose a local search
strategy that performs exact search on the local clusters formed by each
variable and its neighbors within two hops in the super-structure. Numerical
experiments validate the efficacy of the proposed procedure, and demonstrate
that it scales up to hundreds of nodes with a high accuracy.

Machine learning techniques are now well established in experimental particle
physics, allowing detector data to be analysed in new and unique ways. The
identification of signals in particle observatories is an essential data
processing task that can potentially be improved using such methods. This paper
aims at exploring the benefits that a dedicated machine learning approach might
provide to the classification of signals in dual-phase noble gas time
projection chambers. A full methodology is presented, from exploratory data
analysis using Gaussian mixture models and feature importance ranking to the
construction of dedicated predictive models based on standard implementations
of neural networks and random forests, validated using unlabelled simulated
data from the LZ experiment as a proxy to real data. The global classification
accuracy of the predictive models developed in this work is estimated to be
>99.0%, which is an improvement over conventional algorithms tested with the
same data. The results from the clustering analysis were also used to identify
anomalies in the data caused by miscalculated signal properties, showing that
this methodology can also be used for data monitoring.

A typical information extraction pipeline consists of token- or span-level
classification models coupled with a series of pre- and post-processing
scripts. In a production pipeline, requirements often change, with classes
being added and removed, which leads to nontrivial modifications to the source
code and the possible introduction of bugs. In this work, we evaluate
sequence-to-sequence models as an alternative to token-level classification
methods for information extraction of legal and registration documents. We
finetune models that jointly extract the information and generate the output
already in a structured format. Post-processing steps are learned during
training, thus eliminating the need for rule-based methods and simplifying the
pipeline. Furthermore, we propose a novel method to align the output with the
input text, thus facilitating system inspection and auditing. Our experiments
on four real-world datasets show that the proposed method is an alternative to
classical pipelines.

Predicting contextualised engagement in videos is a long-standing problem
that has been popularly attempted by exploiting the number of views or the
associated likes using different computational methods. The recent decade has
seen a boom in online learning resources, and during the pandemic, there has
been an exponential rise of online teaching videos without much quality
control. The quality of the content could be improved if the creators could get
constructive feedback on their content. Employing an army of domain expert
volunteers to provide feedback on the videos might not scale. As a result,
there has been a steep rise in developing computational methods to predict a
user engagement score that is indicative of some form of possible user
engagement, i.e., to what level a user would tend to engage with the content. A
drawback in current methods is that they model various features separately, in
a cascaded approach, that is prone to error propagation. Besides, most of them
do not provide crucial explanations on how the creator could improve their
content. In this paper, we have proposed a new unified model, CLUE for the
educational domain, which learns from the features extracted from freely
available public online teaching videos and provides explainable feedback on
the video along with a user engagement score. Given the complexity of the task,
our unified framework employs different pre-trained models working together as
an ensemble of classifiers. Our model exploits various multi-modal features to
model the complexity of language, context agnostic information, textual emotion
of the delivered content, animation, speaker's pitch and speech emotions. Under
a transfer learning setup, the overall model, in the unified space, is
fine-tuned for downstream applications.

Limited amount of labelled training data are a common problem in medical
imaging. This makes it difficult to train a well-generalised model and
therefore often leads to failure in unknown domains. Hippocampus segmentation
from magnetic resonance imaging (MRI) scans is critical for the diagnosis and
treatment of neuropsychatric disorders. Domain differences in contrast or shape
can significantly affect segmentation. We address this issue by disentangling a
T1-weighted MRI image into its content and domain. This separation enables us
to perform a domain transfer and thus convert data from new sources into the
training domain. This step thus simplifies the segmentation problem, resulting
in higher quality segmentations. We achieve the disentanglement with the
proposed novel methodology 'Content Domain Disentanglement GAN', and we propose
to retrain the UNet on the transformed outputs to deal with GAN-specific
artefacts. With these changes, we are able to improve performance on unseen
domains by 6-13% and outperform state-of-the-art domain transfer methods.

The success of machine learning (ML) in materials property prediction depends
heavily on how the materials are represented for learning. Two dominant
families of material descriptors exist, one that encodes crystal structure in
the representation and the other that only uses stoichiometric information with
the hope of discovering new materials. Graph neural networks (GNNs) in
particular have excelled in predicting material properties within chemical
accuracy. However, current GNNs are limited to only one of the above two
avenues owing to the little overlap between respective material
representations. Here, we introduce a new concept of formula graph which
unifies stoichiometry-only and structure-based material descriptors. We further
develop a self-attention integrated GNN that assimilates a formula graph and
show that the proposed architecture produces material embeddings transferable
between the two domains. Our model can outperform some previously proposed
structure-agnostic models and their structure-based counterparts while
exhibiting better sample efficiency and faster convergence. Finally, the model
is applied in a challenging exemplar to predict the complex dielectric function
of materials and nominate new substances that potentially exhibit
epsilon-near-zero phenomena.

Responsible Artificial Intelligence (AI) - the practice of developing,
evaluating, and maintaining accurate AI systems that also exhibit essential
properties such as robustness and explainability - represents a multifaceted
challenge that often stretches standard machine learning tooling, frameworks,
and testing methods beyond their limits. In this paper, we present two new
software libraries - hydra-zen and the rAI-toolbox - that address critical
needs for responsible AI engineering. hydra-zen dramatically simplifies the
process of making complex AI applications configurable, and their behaviors
reproducible. The rAI-toolbox is designed to enable methods for evaluating and
enhancing the robustness of AI-models in a way that is scalable and that
composes naturally with other popular ML frameworks. We describe the design
principles and methodologies that make these tools effective, including the use
of property-based testing to bolster the reliability of the tools themselves.
Finally, we demonstrate the composability and flexibility of the tools by
showing how various use cases from adversarial robustness and explainable AI
can be concisely implemented with familiar APIs.

We present a custom implementation of a 2D Convolutional Neural Network (CNN)
as a viable application for real-time data selection in high-resolution and
high-rate particle imaging detectors, making use of hardware acceleration in
high-end Field Programmable Gate Arrays (FPGAs). To meet FPGA resource
constraints, a two-layer CNN is optimized for accuracy and latency with
KerasTuner, and network \textit{quantization} is further used to minimize the
computing resource utilization of the network. We use "High Level Synthesis for
Machine Learning" (\textit{hls4ml}) tools to test CNN deployment on a Xilinx
UltraScale+ FPGA, which is a proposed FPGA technology for the front-end readout
system of the future Deep Underground Neutrino Experiment (DUNE) far detector.
We evaluate network accuracy and estimate latency and hardware resource usage,
and comment on the feasibility of applying CNNs for real-time data selection
within the proposed DUNE data acquisition system.

Experimentally engineering high-dimensional quantum states is a crucial task
for several quantum information protocols. However, a high degree of precision
in the characterization of experimental noisy apparatus is required to apply
existing quantum state engineering protocols. This is often lacking in
practical scenarios, affecting the quality of the engineered states. Here, we
implement experimentally an automated adaptive optimization protocol to
engineer photonic Orbital Angular Momentum (OAM) states. The protocol, given a
target output state, performs an online estimation of the quality of the
currently produced states, relying on output measurement statistics, and
determines how to tune the experimental parameters to optimize the state
generation. To achieve this, the algorithm needs not be imbued with a
description of the generation apparatus itself. Rather, it operates in a fully
black-box scenario, making the scheme applicable in a wide variety of
circumstances. The handles controlled by the algorithm are the rotation angles
of a series of waveplates and can be used to probabilistically generate
arbitrary four-dimensional OAM states. We showcase our scheme on different
target states both in classical and quantum regimes, and prove its robustness
to external perturbations on the control parameters. This approach represents a
powerful tool for automated optimizations of noisy experimental tasks for
quantum information protocols and technologies.

We present a simple yet novel time series imputation technique with the goal
of constructing an irregular time series that is uniform across every sample in
a data set. Specifically, we fix a grid defined by the midpoints of
non-overlapping bins (dubbed "slices") of observation times and ensure that
each sample has values for all of the features at that given time. This allows
one to both impute fully missing observations to allow uniform time series
classification across the entire data and, in special cases, to impute
individually missing features. To do so, we slightly generalize the well-known
class imbalance algorithm SMOTE \cite{smote} to allow component wise nearest
neighbor interpolation that preserves correlations when there are no missing
features. We visualize the method in the simplified setting of 2-dimensional
uncoupled harmonic oscillators. Next, we use tSMOTE to train an Encoder/Decoder
long-short term memory (LSTM) model with Logistic Regression for predicting and
classifying distinct trajectories of different 2D oscillators. After
illustrating the the utility of tSMOTE in this context, we use the same
architecture to train a clinical model for COVID-19 disease severity on an
imputed data set. Our experiments show an improvement over standard mean and
median imputation techniques by allowing a wider class of patient trajectories
to be recognized by the model, as well as improvement over aggregated

Modern privacy regulations grant citizens the right to be forgotten by
products, services and companies. In case of machine learning (ML)
applications, this necessitates deletion of data not only from storage archives
but also from ML models. Due to an increasing need for regulatory compliance
required for ML applications, machine unlearning is becoming an emerging
research problem. The right to be forgotten requests come in the form of
removal of a certain set or class of data from the already trained ML model.
Practical considerations preclude retraining of the model from scratch minus
the deleted data. The few existing studies use either the whole training data,
or a subset of training data, or some metadata stored during training to update
the model weights for unlearning. However, strict regulatory compliance
requires time-bound deletion of data. Thus, in many cases, no data related to
the training process or training samples may be accessible even for the
unlearning purpose. We therefore ask the question: is it possible to achieve
unlearning with zero training samples? In this paper, we introduce the novel
problem of zero-shot machine unlearning that caters for the extreme but
practical scenario where zero original data samples are available for use. We
then propose two novel solutions for zero-shot machine unlearning based on (a)
error minimizing-maximizing noise and (b) gated knowledge transfer. These
methods remove the information of the forget data from the model while
maintaining the model efficacy on the retain data. The zero-shot approach
offers good protection against the model inversion attacks and membership
inference attacks. We introduce a new evaluation metric, Anamnesis Index (AIN)
to effectively measure the quality of the unlearning method. The experiments
show promising results for unlearning in deep learning models on benchmark
vision data-sets.

Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of
the neural network itself. PINNs are nowadays used to solve PDEs, fractional
equations, integral-differential equations, and stochastic PDEs. This novel
methodology has arisen as a multi-task learning framework in which a NN must
fit observed data while reducing a PDE residual. This article provides a
comprehensive review of the literature on PINNs: while the primary goal of the
study was to characterize these networks and their related advantages and
disadvantages. The review also attempts to incorporate publications on a
broader range of collocation-based physics informed neural networks, which
stars form the vanilla PINN, as well as many other variants, such as
physics-constrained neural networks (PCNN), variational hp-VPINN, and
conservative PINN (CPINN). The study indicates that most research has focused
on customizing the PINN through different activation functions, gradient
optimization techniques, neural network structures, and loss function
structures. Despite the wide range of applications for which PINNs have been
used, by demonstrating their ability to be more feasible in some contexts than
classical numerical techniques like Finite Element Method (FEM), advancements
are still possible, most notably theoretical issues that remain unresolved.

How do neural network image classifiers respond to simpler and simpler
inputs? And what do such responses reveal about the learning process? To answer
these questions, we need a clear measure of input simplicity (or inversely,
complexity), an optimization objective that correlates with simplification, and
a framework to incorporate such objective into training and inference. Lastly
we need a variety of testbeds to experiment and evaluate the impact of such
simplification on learning. In this work, we measure simplicity with the
encoding bit size given by a pretrained generative model, and minimize the bit
size to simplify inputs in training and inference. We investigate the effect of
such simplification in several scenarios: conventional training, dataset
condensation and post-hoc explanations. In all settings, inputs are simplified
along with the original classification task, and we investigate the trade-off
between input simplicity and task performance. For images with injected
distractors, such simplification naturally removes superfluous information. For
dataset condensation, we find that inputs can be simplified with almost no
accuracy degradation. When used in post-hoc explanation, our learning-based
simplification approach offers a valuable new tool to explore the basis of
network decisions.

Swimming microrobots are increasingly developed with complex materials and
dynamic shapes and are expected to operate in complex environments in which the
system dynamics are difficult to model and positional control of the microrobot
is not straightforward to achieve. Deep reinforcement learning is a promising
method of autonomously developing robust controllers for creating smart
microrobots, which can adapt their behavior to operate in uncharacterized
environments without the need to model the system dynamics. Here, we report the
development of a smart helical magnetic hydrogel microrobot that used the soft
actor critic reinforcement learning algorithm to autonomously derive a control
policy which allowed the microrobot to swim through an uncharacterized
biomimetic fluidic environment under control of a time varying magnetic field
generated from a three-axis array of electromagnets. The reinforcement learning
agent learned successful control policies with fewer than 100,000 training
steps, demonstrating sample efficiency for fast learning. We also demonstrate
that we can fine tune the control policies learned by the reinforcement
learning agent by fitting mathematical functions to the learned policy's action
distribution via regression. Deep reinforcement learning applied to microrobot
control is likely to significantly expand the capabilities of the next
generation of microrobots.

Machine learning methods are becoming increasingly important for the
development of materials science. In spite of this, the use of image analysis
in the development of these systems is still recent and underexplored,
especially in materials often studied via optical imaging techniques such as
liquid crystals. Here we apply the recently proposed method of ordinal networks
to map optical textures obtained from experimental samples of liquid crystals
into complex networks and use this representation jointly with a simple
statistical learning algorithm to investigate different physical properties of
these materials. Our research demonstrates that ordinal networks formed by only
24 nodes encode crucial information about liquid crystal properties, thus
allowing us to train simple machine learning models capable of identifying and
classifying mesophase transitions, distinguishing among different doping
concentrations used to induce chiral mesophases, and predicting sample
temperatures with outstanding accuracy. The precision and scalability of our
approach indicate it can be used to probe properties of different materials in
situations involving large-scale datasets or real-time monitoring systems.

As the training of giant dense models hits the boundary on the availability
and capability of the hardware resources today, Mixture-of-Experts (MoE) models
become one of the most promising model architectures due to their significant
training cost reduction compared to a quality-equivalent dense model. Its
training cost saving is demonstrated from encoder-decoder models (prior works)
to a 5x saving for auto-aggressive language models (this work along with
parallel explorations). However, due to the much larger model size and unique
architecture, how to provide fast MoE model inference remains challenging and
unsolved, limiting its practical usage. To tackle this, we present
DeepSpeed-MoE, an end-to-end MoE training and inference solution as part of the
DeepSpeed library, including novel MoE architecture designs and model
compression techniques that reduce MoE model size by up to 3.7x, and a highly
optimized inference system that provides 7.3x better latency and cost compared
to existing MoE inference solutions. DeepSpeed-MoE offers an unprecedented
scale and efficiency to serve massive MoE models with up to 4.5x faster and 9x
cheaper inference compared to quality-equivalent dense models. We hope our
innovations and systems help open a promising path to new directions in the
large model landscape, a shift from dense to sparse MoE models, where training
and deploying higher-quality models with fewer resources becomes more widely
possible.

Auto-scheduling for tensor programs is a process where a search algorithm
automatically explores candidate schedules (program transformations) for a
given program on a target hardware platform to improve its performance. However
this can be a very time consuming process depending on the complexity of the
tensor program and the capacity of the target device, with often many thousands
of program variants being explored. To address this, in this paper we introduce
the idea of transfer-tuning, a novel approach to identify and reuse
auto-schedules between tensor programs. We demonstrate this concept using Deep
Neural Networks (DNNs), taking sets of auto-schedules from pre-tuned DNNs and
using them to reduce the inference time of a new DNN. We compare
transfer-tuning against the state-of-the-art Ansor auto-scheduler, defining the
maximum possible speedup for a given DNN model as what Ansor achieves using its
recommended full tuning time. On a server-class CPU and across 11 widely used
DNN models, we observe that transfer-tuning achieves up to $88.41\%$ ($49.13\%$
on average) of this maximum speedup, while Ansor requires $6.5\times$ more
search time on average to match it. We also evaluate transfer-tuning on a
constrained edge CPU and observe that the differences in search time are
exacerbated, with Ansor requiring $10.8\times$ more time on average to match
transfer-tuning's speedup, which further demonstrates its value. Our code is
available at https://www.github.com/gicLAB/transfer-tuning

Variance-based global sensitivity analysis (GSA) can provide a wealth of
information when applied to complex models. A well-known Achilles' heel of this
approach is its computational cost which often renders it unfeasible in
practice. An appealing alternative is to analyze instead the sensitivity of a
surrogate model with the goal of lowering computational costs while maintaining
sufficient accuracy. Should a surrogate be "simple" enough to be amenable to
the analytical calculations of its Sobol' indices, the cost of GSA is
essentially reduced to the construction of the surrogate. We propose a new
class of sparse weight Extreme Learning Machines (SW-ELMs) which, when
considered as surrogates in the context of GSA, admit analytical formulas for
their Sobol' indices and, unlike the standard ELMs, yield accurate
approximations of these indices. The effectiveness of this approach is
illustrated through both traditional benchmarks in the field and on a chemical
reaction network.

In this paper we address the challenging problem of domain adaptation in
LiDAR semantic segmentation. We consider the setting where we have a
fully-labeled data set from source domain and a target domain with a few
labeled and many unlabeled examples. We propose a domain adaption framework
that mitigates the issue of domain shift and produces appealing performance on
the target domain. To this end, we develop a GAN-based image-to-image
translation engine that has generators with alternating connections, and couple
it with a state-of-the-art LiDAR semantic segmentation network. Our framework
is hybrid in nature in the sense that our model learning is composed of
self-supervision, semi-supervision and unsupervised learning. Extensive
experiments on benchmark LiDAR semantic segmentation data sets demonstrate that
our method achieves superior performance in comparison to strong baselines and
prior arts.

This paper is a corrigendum and addendum to the previously published article:
'How Populist are Parties? Measuring Degrees of Populism in Party Manifestos
Using Supervised Machine Learning' (Political Analysis, 1-17.
doi:10.1017/pan.2021.29). These corrigendum and addendum were prepared to
correct errors in data labelling and show some extra insights not included in
the previously published paper. Here, we report these corrections and point to
some additional conclusions by focusing on the effects of the label reshuffling
per parties and years and presenting new figures wherever appropriate. We show
that although the simplified labelling method proposed in the
previously-published article can induce biases in the correlations with expert
scores, random labelling reduces correlations significantly. We show that this
is also true for correlations based on a manually-coded data set. These
modifications are based on other evidence and results reported in detail in a
future publication.

Recent technological developments and advances in Artificial Intelligence
(AI) have enabled sophisticated capabilities to be a part of Digital Twin (DT),
virtually making it possible to introduce automation into all aspects of work
processes. Given these possibilities that DT can offer, practitioners are
facing increasingly difficult decisions regarding what capabilities to select
while deploying a DT in practice. The lack of research in this field has not
helped either. It has resulted in the rebranding and reuse of emerging
technological capabilities like prediction, simulation, AI, and Machine
Learning (ML) as necessary constituents of DT. Inappropriate selection of
capabilities in a DT can result in missed opportunities, strategic
misalignments, inflated expectations, and risk of it being rejected as just
hype by the practitioners. To alleviate this challenge, this paper proposes the
digitalization framework, designed and developed by following a Design Science
Research (DSR) methodology over a period of 18 months. The framework can help
practitioners select an appropriate level of sophistication in a DT by weighing
the pros and cons for each level, deciding evaluation criteria for the digital
twin system, and assessing the implications of the selected DT on the
organizational processes and strategies, and value creation. Three real-life
case studies illustrate the application and usefulness of the framework.

Context. Several methods have been proposed to build 3D extinction maps of
the Milky Way (MW), most often based on Bayesian approaches. Although some
studies employed machine learning (ML) methods in part of their procedure, or
to specific targets, no 3D extinction map of a large volume of the MW solely
based on a Neural Network method has been reported so far. Aims. We aim to
apply deep learning as a solution to build 3D extinction maps of the MW.
Methods. We built a convolutional neural network (CNN) using the CIANNA
framework, and trained it with synthetic 2MASS data. We used the Besan\c{c}on
Galaxy model to generate mock star catalogs, and 1D Gaussian random fields to
simulate the extinction profiles. From these data we computed color-magnitude
diagrams (CMDs) to train the network, using the corresponding extinction
profiles as targets. A forward pass with observed 2MASS CMDs provided
extinction profile estimates for a grid of lines of sight. Results. We trained
our network with data simulating lines of sight in the area of the Carina
spiral arm tangent and obtained a 3D extinction map for a large sector in this
region ($l = 257 - 303$ deg, $|b| \le 5$ deg), with distance and angular
resolutions of $100$ pc and $30$ arcmin, respectively, and reaching up to $\sim
10$ kpc. Although each sightline is computed independently in the forward
phase, the so-called fingers-of-God artifacts are weaker than in many other 3D
extinction maps. We found that our CNN was efficient in taking advantage of
redundancy across lines of sight, enabling us to train it with only 9
sightlines simultaneously to build the whole map. Conclusions. We found deep
learning to be a reliable approach to produce 3D extinction maps from large
surveys. With this methodology, we expect to easily combine heterogeneous
surveys without cross-matching, and therefore to exploit several surveys in a
complementary fashion.

Stock price prediction is a challenging task and a lot of propositions exist
in the literature in this area. Portfolio construction is a process of choosing
a group of stocks and investing in them optimally to maximize the return while
minimizing the risk. Since the time when Markowitz proposed the Modern
Portfolio Theory, several advancements have happened in the area of building
efficient portfolios. An investor can get the best benefit out of the stock
market if the investor invests in an efficient portfolio and could take the buy
or sell decision in advance, by estimating the future asset value of the
portfolio with a high level of precision. In this project, we have built an
efficient portfolio and to predict the future asset value by means of
individual stock price prediction of the stocks in the portfolio. As part of
building an efficient portfolio we have studied multiple portfolio optimization
methods beginning with the Modern Portfolio theory. We have built the minimum
variance portfolio and optimal risk portfolio for all the five chosen sectors
by using past daily stock prices over the past five years as the training data,
and have also conducted back testing to check the performance of the portfolio.
A comparative study of minimum variance portfolio and optimal risk portfolio
with equal weight portfolio is done by backtesting.

In this work we present a rigorous application of the Expectation
Maximization algorithm to determine the marginal distributions and the
dependence structure in a Gaussian copula model with missing data. We further
show how to circumvent a priori assumptions on the marginals with
semiparametric modelling. The joint distribution learned through this algorithm
is considerably closer to the underlying distribution than existing methods.

Disordered speech recognition is a highly challenging task. The underlying
neuro-motor conditions of people with speech disorders, often compounded with
co-occurring physical disabilities, lead to the difficulty in collecting large
quantities of speech required for system development. This paper investigates a
set of data augmentation techniques for disordered speech recognition,
including vocal tract length perturbation (VTLP), tempo perturbation and speed
perturbation. Both normal and disordered speech were exploited in the
augmentation process. Variability among impaired speakers in both the original
and augmented data was modeled using learning hidden unit contributions (LHUC)
based speaker adaptive training. The final speaker adapted system constructed
using the UASpeech corpus and the best augmentation approach based on speed
perturbation produced up to 2.92% absolute (9.3% relative) word error rate
(WER) reduction over the baseline system without data augmentation, and gave an
overall WER of 26.37% on the test set containing 16 dysarthric speakers.

Weak lensing mass-mapping is a useful tool to access the full distribution of
dark matter on the sky, but because of intrinsic galaxy ellipticies and finite
fields/missing data, the recovery of dark matter maps constitutes a challenging
ill-posed inverse problem. We introduce a novel methodology allowing for
efficient sampling of the high-dimensional Bayesian posterior of the weak
lensing mass-mapping problem, and relying on simulations for defining a fully
non-Gaussian prior. We aim to demonstrate the accuracy of the method on
simulations, and then proceed to applying it to the mass reconstruction of the
HST/ACS COSMOS field. The proposed methodology combines elements of Bayesian
statistics, analytic theory, and a recent class of Deep Generative Models based
on Neural Score Matching. This approach allows us to do the following: 1) Make
full use of analytic cosmological theory to constrain the 2pt statistics of the
solution. 2) Learn from cosmological simulations any differences between this
analytic prior and full simulations. 3) Obtain samples from the full Bayesian
posterior of the problem for robust Uncertainty Quantification. We demonstrate
the method on the $\kappa$TNG simulations and find that the posterior mean
significantly outperfoms previous methods (Kaiser-Squires, Wiener filter,
Sparsity priors) both on root-mean-square error and in terms of the Pearson
correlation. We further illustrate the interpretability of the recovered
posterior by establishing a close correlation between posterior convergence
values and SNR of clusters artificially introduced into a field. Finally, we
apply the method to the reconstruction of the HST/ACS COSMOS field and yield
the highest quality convergence map of this field to date.

Recent research has turned to Reinforcement Learning (RL) to solve
challenging decision problems, as an alternative to hand-tuned heuristics. RL
can learn good policies without the need for modeling the environment's
dynamics. Despite this promise, RL remains an impractical solution for many
real-world systems problems. A particularly challenging case occurs when the
environment changes over time, i.e. it exhibits non-stationarity. In this work,
we characterize the challenges introduced by non-stationarity and develop a
framework for addressing them to train RL agents in live systems. Such agents
must explore and learn new environments, without hurting the system's
performance, and remember them over time. To this end, our framework (1)
identifies different environments encountered by the live system, (2) explores
and trains a separate expert policy for each environment, and (3) employs
safeguards to protect the system's performance. We apply our framework to two
systems problems: straggler mitigation and adaptive video streaming, and
evaluate it against a variety of alternative approaches using real-world and
synthetic data. We show that each component of our framework is necessary to
cope with non-stationarity.

Automatic recognition of disordered speech remains a highly challenging task
to date. Sources of variability commonly found in normal speech including
accent, age or gender, when further compounded with the underlying causes of
speech impairment and varying severity levels, create large diversity among
speakers. To this end, speaker adaptation techniques play a vital role in
current speech recognition systems. Motivated by the spectro-temporal level
differences between disordered and normal speech that systematically manifest
in articulatory imprecision, decreased volume and clarity, slower speaking
rates and increased dysfluencies, novel spectro-temporal subspace basis
embedding deep features derived by SVD decomposition of speech spectrum are
proposed to facilitate both accurate speech intelligibility assessment and
auxiliary feature based speaker adaptation of state-of-the-art hybrid DNN and
end-to-end disordered speech recognition systems. Experiments conducted on the
UASpeech corpus suggest the proposed spectro-temporal deep feature adapted
systems consistently outperformed baseline i-Vector adaptation by up to 2.63%
absolute (8.6% relative) reduction in word error rate (WER) with or without
data augmentation. Learning hidden unit contribution (LHUC) based speaker
adaptation was further applied. The final speaker adapted system using the
proposed spectral basis embedding features gave an overall WER of 25.6% on the
UASpeech test set of 16 dysarthric speakers

Solar home systems (SHS), a cost-effective solution for rural communities far
from the grid in developing countries, are small solar panels and associated
equipment that provides power to a single household. A crucial resource for
targeting further investment of public and private resources, as well as
tracking the progress of universal electrification goals, is shared access to
high-quality data on individual SHS installations including information such as
location and power capacity. Though recent studies utilizing satellite imagery
and machine learning to detect solar panels have emerged, they struggle to
accurately locate many SHS due to limited image resolution (some small solar
panels only occupy several pixels in satellite imagery). In this work, we
explore the viability and cost-performance tradeoff of using automatic SHS
detection on unmanned aerial vehicle (UAV) imagery as an alternative to
satellite imagery. More specifically, we explore three questions: (i) what is
the detection performance of SHS using drone imagery; (ii) how expensive is the
drone data collection, compared to satellite imagery; and (iii) how well does
drone-based SHS detection perform in real-world scenarios. We collect and
publicly-release a dataset of high-resolution drone imagery encompassing SHS
imaged under real-world conditions and use this dataset and a dataset from
Rwanda to evaluate the capabilities of deep learning models to recognize SHS,
including those that are too small to be reliably recognized in satellite
imagery. The results suggest that UAV imagery may be a viable alternative to
identify very small SHS from perspectives of both detection accuracy and
financial costs of data collection. UAV-based data collection may be a
practical option for supporting electricity access planning strategies for
achieving sustainable development goals and for monitoring the progress towards
those goals.

Unsupervised image hashing, which maps images into binary codes without
supervision, is a compressor with a high compression rate. Hence, how to
preserving meaningful information of the original data is a critical problem.
Inspired by the large-scale vision pre-training model, known as ViT, which has
shown significant progress for learning visual representations, in this paper,
we propose a simple information-preserving compressor to finetune the ViT model
for the target unsupervised hashing task. Specifically, from pixels to
continuous features, we first propose a feature-preserving module, using the
corrupted image as input to reconstruct the original feature from the
pre-trained ViT model and the complete image, so that the feature extractor can
focus on preserving the meaningful information of original data. Secondly, from
continuous features to hash codes, we propose a hashing-preserving module,
which aims to keep the semantic information from the pre-trained ViT model by
using the proposed Kullback-Leibler divergence loss. Besides, the quantization
loss and the similarity loss are added to minimize the quantization error. Our
method is very simple and achieves a significantly higher degree of MAP on
three benchmark image datasets.

Graph Structure Learning (GSL) recently has attracted considerable attentions
in its capacity of optimizing graph structure as well as learning suitable
parameters of Graph Neural Networks (GNNs) simultaneously. Current GSL methods
mainly learn an optimal graph structure (final view) from single or multiple
information sources (basic views), however the theoretical guidance on what is
the optimal graph structure is still unexplored. In essence, an optimal graph
structure should only contain the information about tasks while compress
redundant noise as much as possible, which is defined as "minimal sufficient
structure", so as to maintain the accurancy and robustness. How to obtain such
structure in a principled way? In this paper, we theoretically prove that if we
optimize basic views and final view based on mutual information, and keep their
performance on labels simultaneously, the final view will be a minimal
sufficient structure. With this guidance, we propose a Compact GSL architecture
by MI compression, named CoGSL. Specifically, two basic views are extracted
from original graph as two inputs of the model, which are refinedly reestimated
by a view estimator. Then, we propose an adaptive technique to fuse estimated
views into the final view. Furthermore, we maintain the performance of
estimated views and the final view and reduce the mutual information of every
two views. To comprehensively evaluate the performance of CoGSL, we conduct
extensive experiments on several datasets under clean and attacked conditions,
which demonstrate the effectiveness and robustness of CoGSL.

Pre-trained Transformers are challenging human performances in many natural
language processing tasks. The gigantic datasets used for pre-training seem to
be the key for their success on existing tasks. In this paper, we explore how a
range of pre-trained natural language understanding models perform on truly
novel and unexplored data, provided by classification tasks over a DarkNet
corpus. Surprisingly, results show that syntactic and lexical neural networks
largely outperform pre-trained Transformers. This seems to suggest that
pre-trained Transformers have serious difficulties in adapting to radically
novel texts.

The isocitrate dehydrogenase (IDH) gene mutation status is an important
biomarker for glioma patients. The gold standard of IDH mutation detection
requires tumour tissue obtained via invasive approaches and is usually
expensive. Recent advancement in radiogenomics provides a non-invasive approach
for predicting IDH mutation based on MRI. Meanwhile, tumor geometrics encompass
crucial information for tumour phenotyping. Here we propose a collaborative
learning framework that learns both tumor images and tumor geometrics using
convolutional neural networks (CNN) and graph neural networks (GNN),
respectively. Our results show that the proposed model outperforms the baseline
model of 3D-DenseNet121. Further, the collaborative learning model achieves
better performance than either the CNN or the GNN alone. The model
interpretation shows that the CNN and GNN could identify common and unique
regions of interest for IDH mutation prediction. In conclusion, collaborating
image and geometric learners provides a novel approach for predicting genotype
and characterising glioma.

The advent of artificial intelligence technology paved the way of many
researches to be made within air combat sector. Academicians and many other
researchers did a research on a prominent research direction called autonomous
maneuver decision of UAV. Elaborative researches produced some outcomes, but
decisions that include Reinforcement Learning(RL) came out to be more
efficient. There have been many researches and experiments done to make an
agent reach its target in an optimal way, most prominent are Genetic
Algorithm(GA) , A star, RRT and other various optimization techniques have been
used. But Reinforcement Learning is the well known one for its success. In
DARPHA Alpha Dogfight Trials, reinforcement learning prevailed against a real
veteran F16 human pilot who was trained by Boeing. This successor model was
developed by Heron Systems. After this accomplishment, reinforcement learning
bring tremendous attention on itself. In this research we aimed our UAV which
has a dubin vehicle dynamic property to move to the target in two dimensional
space in an optimal path using Twin Delayed Deep Deterministic Policy Gradients
(TD3) and used in experience replay Hindsight Experience Replay(HER).We did
tests on two different environments and used simulations.

From learning assistance to companionship, social robots promise to enhance
many aspects of daily life. However, social robots have not seen widespread
adoption, in part because (1) they do not adapt their behavior to new users,
and (2) they do not provide sufficient privacy protections. Centralized
learning, whereby robots develop skills by gathering data on a server,
contributes to these limitations by preventing online learning of new
experiences and requiring storage of privacy-sensitive data. In this work, we
propose a decentralized learning alternative that improves the privacy and
personalization of social robots. We combine two machine learning approaches,
Federated Learning and Continual Learning, to capture interaction dynamics
distributed physically across robots and temporally across repeated robot
encounters. We define a set of criteria that should be balanced in
decentralized robot learning scenarios. We also develop a new algorithm --
Elastic Transfer -- that leverages importance-based regularization to preserve
relevant parameters across robots and interactions with multiple humans. We
show that decentralized learning is a viable alternative to centralized
learning in a proof-of-concept Socially-Aware Navigation domain, and
demonstrate how Elastic Transfer improves several of the proposed criteria.

Machine learning (ML) has shown great promise in optimizing various aspects
of the physical layer processing in wireless communication systems. In this
paper, we use ML to learn jointly the transmit waveform and the
frequency-domain receiver. In particular, we consider a scenario where the
transmitter power amplifier is operating in a nonlinear manner, and ML is used
to optimize the waveform to minimize the out-of-band emissions. The system also
learns a constellation shape that facilitates pilotless detection by the
simultaneously learned receiver. The simulation results show that such an
end-to-end optimized system can communicate data more accurately and with less
out-of-band emissions than conventional systems, thereby demonstrating the
potential of ML in optimizing the air interface. To the best of our knowledge,
there are no prior works considering the power amplifier induced emissions in
an end-to-end learned system. These findings pave the way towards an ML-native
air interface, which could be one of the building blocks of 6G.

A robotic system of multiple unmanned ground vehicles (UGVs) and unmanned
aerial vehicles (UAVs) has the potential for advancing autonomous object
geolocation performance. Much research has focused on algorithmic improvements
on individual components, such as navigation, motion planning, and perception.
In this paper, we present a UGV-UAV object detection and geolocation system,
which performs perception, navigation, and planning autonomously in real scale
in unstructured environment. We designed novel sensor pods equipped with
multispectral (visible, near-infrared, thermal), high resolution (181.6 Mega
Pixels), stereo (near-infrared pair), wide field of view (192 degree HFOV)
array. We developed a novel on-board software-hardware architecture to process
the high volume sensor data in real-time, and we built a custom AI subsystem
composed of detection, tracking, navigation, and planning for autonomous
objects geolocation in real-time.
  This research is the first real scale demonstration of such high speed data
processing capability. Our novel modular sensor pod can boost relevant computer
vision and machine learning research. Our novel hardware-software architecture
is a solid foundation for system-level and component-level research. Our system
is validated through data-driven offline tests as well as a series of field
tests in unstructured environments. We present quantitative results as well as
discussions on key robotic system level challenges which manifest when we build
and test the system. This system is the first step toward a UGV-UAV cooperative
reconnaissance system in the future.

With increasing urbanization, flooding is a major challenge for many cities
today. Based on forecast precipitation, topography, and pipe networks, flood
simulations can provide early warnings for areas and buildings at risk of
flooding. Basement windows, doors, and underground garage entrances are common
places where floodwater can flow into a building. Some buildings have been
prepared or designed considering the threat of flooding, but others have not.
Therefore, knowing the heights of these facade openings helps to identify
places that are more susceptible to water ingress. However, such data is not
yet readily available in most cities. Traditional surveying of the desired
targets may be used, but this is a very time-consuming and laborious process.
This research presents a new process for the extraction of windows and doors
from LiDAR mobile mapping data. Deep learning object detection models are
trained to identify these objects. Usually, this requires to provide large
amounts of manual annotations. In this paper, we mitigate this problem by
leveraging a rule-based method. In a first step, the rule-based method is used
to generate pseudo-labels. A semi-supervised learning strategy is then applied
with three different levels of supervision. The results show that using only
automatically generated pseudo-labels, the learning-based model outperforms the
rule-based approach by 14.6% in terms of F1-score. After five hours of human
supervision, it is possible to improve the model by another 6.2%. By comparing
the detected facade openings' heights with the predicted water levels from a
flood simulation model, a map can be produced which assigns per-building flood
risk levels. This information can be combined with flood forecasting to provide
a more targeted disaster prevention guide for the city's infrastructure and
residential buildings.

Graph contrastive learning attracts/disperses node representations for
similar/dissimilar node pairs under some notion of similarity. It may be
combined with a low-dimensional embedding of nodes to preserve intrinsic and
structural properties of a graph. In this paper, we extend the celebrated
Laplacian Eigenmaps with contrastive learning, and call them COntrastive
Laplacian EigenmapS (COLES). Starting from a GAN-inspired contrastive
formulation, we show that the Jensen-Shannon divergence underlying many
contrastive graph embedding models fails under disjoint positive and negative
distributions, which may naturally emerge during sampling in the contrastive
setting. In contrast, we demonstrate analytically that COLES essentially
minimizes a surrogate of Wasserstein distance, which is known to cope well
under disjoint distributions. Moreover, we show that the loss of COLES belongs
to the family of so-called block-contrastive losses, previously shown to be
superior compared to pair-wise losses typically used by contrastive methods. We
show on popular benchmarks/backbones that COLES offers favourable
accuracy/scalability compared to DeepWalk, GCN, Graph2Gauss, DGI and GRACE

Representation is a core issue in artificial intelligence. Humans use
discrete language to communicate and learn from each other, while machines use
continuous features (like vector, matrix, or tensor in deep neural networks) to
represent cognitive patterns. Discrete symbols are low-dimensional, decoupled,
and have strong reasoning ability, while continuous features are
high-dimensional, coupled, and have incredible abstracting capabilities. In
recent years, deep learning has developed the idea of continuous representation
to the extreme, using millions of parameters to achieve high accuracies.
Although this is reasonable from the statistical perspective, it has other
major problems like lacking interpretability, poor generalization, and is easy
to be attacked. Since both paradigms have strengths and weaknesses, a better
choice is to seek reconciliation. In this paper, we make an initial attempt
towards this direction. Specifically, we propose to combine symbolism and
connectionism principles by using neural networks to derive a discrete
representation. This process is highly similar to human language, which is a
natural combination of discrete symbols and neural systems, where the brain
processes continuous signals and represents intelligence via discrete language.
To mimic this functionality, we denote our approach as machine language. By
designing an interactive environment and task, we demonstrated that machines
could generate a spontaneous, flexible, and semantic language through
cooperation. Moreover, through experiments we show that discrete language
representation has several advantages compared with continuous feature
representation, from the aspects of interpretability, generalization, and

Video Snapshot compressive imaging (SCI) is a promising technique to capture
high-speed videos, which transforms the imaging speed from the detector to mask
modulating and only needs a single measurement to capture multiple frames. The
algorithm to reconstruct high-speed frames from the measurement plays a vital
role in SCI. In this paper, we consider the promising reconstruction algorithm
framework, namely plug-and-play (PnP), which is flexible to the encoding
process comparing with other deep learning networks. One drawback of existing
PnP algorithms is that they use a pre-trained denoising network as a plugged
prior while the training data of the network might be different from the task
in real applications. Towards this end, in this work, we propose the online PnP
algorithm which can adaptively update the network's parameters within the PnP
iteration; this makes the denoising network more applicable to the desired data
in the SCI reconstruction. Furthermore, for color video imaging, RGB frames
need to be recovered from Bayer pattern or named demosaicing in the camera
pipeline. To address this challenge, we design a two-stage reconstruction
framework to optimize these two coupled ill-posed problems and introduce a deep
demosaicing prior specifically for video demosaicing which does not have much
past works instead of using single image demosaicing networks. Extensive
results on both simulation and real datasets verify the superiority of our
adaptive deep PnP algorithm.

This work is a systematical analysis on the so-called hard class problem in
zero-shot learning (ZSL), that is, some unseen classes disproportionally affect
the ZSL performances than others, as well as how to remedy the problem by
detecting and exploiting hard classes. At first, we report our empirical
finding that the hard class problem is a ubiquitous phenomenon and persists
regardless of used specific methods in ZSL. Then, we find that high semantic
affinity among unseen classes is a plausible underlying cause of hardness and
design two metrics to detect hard classes. Finally, two frameworks are proposed
to remedy the problem by detecting and exploiting hard classes, one under
inductive setting, the other under transductive setting. The proposed
frameworks could accommodate most existing ZSL methods to further significantly
boost their performances with little efforts. Extensive experiments on three
popular benchmarks demonstrate the benefits by identifying and exploiting the
hard classes in ZSL.

Solving high-dimensional optimal control problems in real-time is an
important but challenging problem, with applications to multi-agent path
planning problems, which have drawn increased attention given the growing
popularity of drones in recent years. In this paper, we propose a novel neural
network method called SympOCnet that applies the Symplectic network to solve
high-dimensional optimal control problems with state constraints. We present
several numerical results on path planning problems in two-dimensional and
three-dimensional spaces. Specifically, we demonstrate that our SympOCnet can
solve a problem with more than 500 dimensions in 1.5 hours on a single GPU,
which shows the effectiveness and efficiency of SympOCnet. The proposed method
is scalable and has the potential to solve truly high-dimensional path planning
problems in real-time.

Enquiries concerning the underlying mechanisms and the emergent properties of
a biological brain have a long history of theoretical postulates and
experimental findings. Today, the scientific community tends to converge to a
single interpretation of the brain's cognitive underpinnings -- that it is a
Bayesian inference machine. This contemporary view has naturally been a strong
driving force in recent developments around computational and cognitive
neurosciences. Of particular interest is the brain's ability to process the
passage of time -- one of the fundamental dimensions of our experience. How can
we explain empirical data on human time perception using the Bayesian brain
hypothesis? Can we replicate human estimation biases using Bayesian models?
What insights can the agent-based machine learning models provide for the study
of this subject? In this chapter, we review some of the recent advancements in
the field of time perception and discuss the role of Bayesian processing in the
construction of temporal models.

Financial time-series forecasting is one of the most challenging domains in
the field of time-series analysis. This is mostly due to the highly
non-stationary and noisy nature of financial time-series data. With progressive
efforts of the community to design specialized neural networks incorporating
prior domain knowledge, many financial analysis and forecasting problems have
been successfully tackled. The temporal attention mechanism is a neural layer
design that recently gained popularity due to its ability to focus on important
temporal events. In this paper, we propose a neural layer based on the ideas of
temporal attention and multi-head attention to extend the capability of the
underlying neural network in focusing simultaneously on multiple temporal
instances. The effectiveness of our approach is validated using large-scale
limit-order book market data to forecast the direction of mid-price movements.
Our experiments show that the use of multi-head temporal attention modules
leads to enhanced prediction performances compared to baseline models.

This paper proposes a framework based on a causal model of safety upon which
effective safety assurance cases for ML-based applications can be built. In
doing so, we build upon established principles of safety engineering as well as
previous work on structuring assurance arguments for ML. The paper defines four
categories of safety case evidence and a structured analysis approach within
which these evidences can be effectively combined. Where appropriate, abstract
formalisations of these contributions are used to illustrate the causalities
they evaluate, their contributions to the safety argument and desirable
properties of the evidences. Based on the proposed framework, progress in this
area is re-evaluated and a set of future research directions proposed in order
for tangible progress in this field to be made.

The tools and technology that are currently used to analyze chemical compound
structures that identify polymer types in microplastics are not well-calibrated
for environmentally weathered microplastics. Microplastics that have been
degraded by environmental weathering factors can offer less analytic certainty
than samples of microplastics that have not been exposed to weathering
processes. Machine learning tools and techniques allow us to better calibrate
the research tools for certainty in microplastics analysis. In this paper, we
investigate whether the signatures (Raman shift values) are distinct enough
such that well studied machine learning (ML) algorithms can learn to identify
polymer types using a relatively small amount of labeled input data when the
samples have not been impacted by environmental degradation. Several ML models
were trained on a well-known repository, Spectral Libraries of Plastic
Particles (SLOPP), that contain Raman shift and intensity results for a range
of plastic particles, then tested on environmentally aged plastic particles
(SloPP-E) consisting of 22 polymer types. After extensive preprocessing and
augmentation, the trained random forest model was then tested on the SloPP-E
dataset resulting in an improvement in classification accuracy of 93.81% from
89%.

Offline reinforcement learning (RL) Algorithms are often designed with
environments such as MuJoCo in mind, in which the planning horizon is extremely
long and no noise exists. We compare model-free, model-based, as well as hybrid
offline RL approaches on various industrial benchmark (IB) datasets to test the
algorithms in settings closer to real world problems, including complex noise
and partially observable states. We find that on the IB, hybrid approaches face
severe difficulties and that simpler algorithms, such as rollout based
algorithms or model-free algorithms with simpler regularizers perform best on
the datasets.

In synaptic molecular communication (MC), the activation of postsynaptic
receptors by neurotransmitters (NTs) is governed by a stochastic
reaction-diffusion process. This randomness of synaptic MC contributes to the
randomness of the electrochemical downstream signal in the postsynaptic cell,
called postsynaptic membrane potential (PSP). Since the randomness of the PSP
is relevant for neural computation and learning, characterizing the statistics
of the PSP is critical. However, the statistical characterization of the
synaptic reaction-diffusion process is difficult because the reversible
bi-molecular reaction of NTs with receptors renders the system nonlinear.
Consequently, there is currently no model available which characterizes the
impact of the statistics of postsynaptic receptor activation on the PSP. In
this work, we propose a novel statistical model for the synaptic
reaction-diffusion process in terms of the chemical master equation (CME). We
further propose a novel numerical method which allows to compute the CME
efficiently and we use this method to characterize the statistics of the PSP.
Finally, we present results from stochastic particle-based computer simulations
which validate the proposed models. We show that the biophysical parameters
governing synaptic transmission shape the autocovariance of the receptor
activation and, ultimately, the statistics of the PSP. Our results suggest that
the processing of the synaptic signal by the postsynaptic cell effectively
mitigates synaptic noise while the statistical characteristics of the synaptic
signal are preserved. The results presented in this paper contribute to a
better understanding of the impact of the randomness of synaptic signal
transmission on neuronal information processing.

In this study, we present recent developments of models trained with the
RNN-T loss in ESPnet. It involves the use of various architectures such as
recently proposed Conformer, multi-task learning with different auxiliary
criteria and multiple decoding strategies, including our own proposition.
Through experiments and benchmarks, we show that our proposed systems can be
competitive against other state-of-art systems on well-known datasets such as
LibriSpeech and AISHELL-1. Additionally, we demonstrate that these models are
promising against other already implemented systems in ESPnet in regards to
both performance and decoding speed, enabling the possibility to have powerful
systems for a streaming task. With these additions, we hope to expand the
usefulness of the ESPnet toolkit for the research community and also give tools
for the ASR industry to deploy our systems in realistic and production

Recent advances on prompt-tuning cast few-shot classification tasks as a
masked language modeling problem. By wrapping input into a template and using a
verbalizer which constructs a mapping between label space and label word space,
prompt-tuning can achieve excellent results in zero-shot and few-shot
scenarios. However, typical prompt-tuning needs a manually designed verbalizer
which requires domain expertise and human efforts. And the insufficient label
space may introduce considerable bias into the results. In this paper, we focus
on eliciting knowledge from pretrained language models and propose a
prototypical prompt verbalizer for prompt-tuning. Labels are represented by
prototypical embeddings in the feature space rather than by discrete words. The
distances between the embedding at the masked position of input and
prototypical embeddings are used as classification criterion. For zero-shot
settings, knowledge is elicited from pretrained language models by a manually
designed template to form initial prototypical embeddings. For few-shot
settings, models are tuned to learn meaningful and interpretable prototypical
embeddings. Our method optimizes models by contrastive learning. Extensive
experimental results on several many-class text classification datasets with
low-resource settings demonstrate the effectiveness of our approach compared
with other verbalizer construction methods. Our implementation is available at
https://github.com/Ydongd/prototypical-prompt-verbalizer.

Integrated sensing and communication (ISAC) is a novel paradigm using
crowdsensing spectrum sensors to help with the management of spectrum scarcity.
However, well-known vulnerabilities of resource-constrained spectrum sensors
and the possibility of being manipulated by users with physical access
complicate their protection against spectrum sensing data falsification (SSDF)
attacks. Most recent literature suggests using behavioral fingerprinting and
Machine/Deep Learning (ML/DL) for improving similar cybersecurity issues.
Nevertheless, the applicability of these techniques in resource-constrained
devices, the impact of attacks affecting spectrum data integrity, and the
performance and scalability of models suitable for heterogeneous sensors types
are still open challenges. To improve limitations, this work presents seven
SSDF attacks affecting spectrum sensors and introduces CyberSpec, an
ML/DL-oriented framework using device behavioral fingerprinting to detect
anomalies produced by SSDF attacks affecting resource-constrained spectrum
sensors. CyberSpec has been implemented and validated in ElectroSense, a real
crowdsensing RF monitoring platform where several configurations of the
proposed SSDF attacks have been executed in different sensors. A pool of
experiments with different unsupervised ML/DL-based models has demonstrated the
suitability of CyberSpec detecting the previous attacks within an acceptable
timeframe.

Ad-hoc search calls for the selection of appropriate answers from a
massive-scale corpus. Nowadays, the embedding-based retrieval (EBR) becomes a
promising solution, where deep learning based document representation and ANN
search techniques are allied to handle this task. However, a major challenge is
that the ANN index can be too large to fit into memory, given the considerable
size of answer corpus. In this work, we tackle this problem with Bi-Granular
Document Representation, where the lightweight sparse embeddings are indexed
and standby in memory for coarse-grained candidate search, and the heavyweight
dense embeddings are hosted in disk for fine-grained post verification. For the
best of retrieval accuracy, a Progressive Optimization framework is designed.
The sparse embeddings are learned ahead for high-quality search of candidates.
Conditioned on the candidate distribution induced by the sparse embeddings, the
dense embeddings are continuously learned to optimize the discrimination of
ground-truth from the shortlisted candidates. Besides, two techniques: the
contrastive quantization and the locality-centric sampling are introduced for
the learning of sparse and dense embeddings, which substantially contribute to
their performances. Thanks to the above features, our method effectively
handles massive-scale EBR with strong advantages in accuracy: with up to +4.3%
recall gain on million-scale corpus, and up to +17.5% recall gain on
billion-scale corpus. Besides, Our method is applied to a major sponsored
search platform with substantial gains on revenue (+1.95%), Recall (+1.01%) and
CTR (+0.49%). Our code is available at https://github.com/microsoft/BiDR.

MicroRNAs play an indispensable role in numerous biological processes ranging
from organismic development to tumor progression.In oncology,these microRNAs
constitute a fundamental regulation role in the pathology of cancer that
provides the basis for probing into the influences on clinical features through
transcriptome data. Previous work focused on machine learning (ML) for
searching biomarkers in different cancer databases, but the functions of these
biomarkers are fully not clear. Taking lung cancer as a prototype case of
study. Through integrating clinical information into the transcripts expression
data, we systematically analyzed the effect of microRNA on diagnostic and
prognostic factors at deteriorative lung adenocarcinoma (LUAD). After dimension
reduction, unsupervised hierarchical clustering was used to find the diagnostic
factors which represent the unique expression patterns of microRNA at various
patient's stages. In addition, we developed a classification framework, Light
Gradient Boosting Machine (LightGBM) and SHAPley Additive explanation (SHAP)
algorithm, to screen out the prognostic factors. Enrichment analyses show that
the diagnostic and prognostic factors are not only enriched in cancer-related
athways, but also involved in many vital cellular signaling transduction and
immune responses. These key microRNAs also impact the survival risk of LUAD
patients at all (or a specific) stage(s) and some of them target some important
Transcription Factors (TF).The key finding is that five microRNAs
(hsa-mir-196b, hsa-mir-31, hsa-mir-891a, hsa-mir-34c, and hsa-mir-653) can then
serve as not only potential diagnostic factors but also prognostic tools in the
monitoring of lung cancer.

The study on the implicit regularization induced by gradient-based
optimization is a longstanding pursuit. In the present paper, we characterize
the implicit regularization of momentum gradient descent (MGD) with early
stopping by comparing with the explicit $\ell_2$-regularization (ridge). In
details, we study MGD in the continuous-time view, so-called momentum gradient
flow (MGF), and show that its tendency is closer to ridge than the gradient
descent (GD) [Ali et al., 2019] for least squares regression. Moreover, we
prove that, under the calibration $t=\sqrt{2/\lambda}$, where $t$ is the time
parameter in MGF and $\lambda$ is the tuning parameter in ridge regression, the
risk of MGF is no more than 1.54 times that of ridge. In particular, the
relative Bayes risk of MGF to ridge is between 1 and 1.035 under the optimal
tuning. The numerical experiments support our theoretical results strongly.

In the last decade, several studies have proposed the use of automated
techniques to estimate the effort of agile software development. In this paper
we perform a close replication and extension of a seminal work proposing the
use of Deep Learning for agile effort estimation (namely Deep-SE), which has
set the state-of-the-art since. Specifically, we replicate three of the
original research questions aiming at investigating the effectiveness of
Deep-SE for both within-project and cross-project effort estimation. We
benchmark Deep-SE against three baseline techniques (i.e., Random, Mean and
Median effort prediction) and a previously proposed method to estimate agile
software project development effort (dubbed TF/IDF-SE), as done in the original
study. To this end, we use both the data from the original study and a new
larger dataset of 31,960 issues, which we mined from 29 open-source projects.
Using more data allows us to strengthen our confidence in the results and
further mitigate the threat to the external validity of the study. We also
extend the original study by investigating two additional research questions.
One evaluates the accuracy of Deep-SE when the training set is augmented with
issues from all other projects available in the repository at the time of
estimation, and the other examines whether an expensive pre-training step used
by the original Deep-SE, has any beneficial effect on its accuracy and
convergence speed. The results of our replication show that Deep-SE outperforms
the Median baseline estimator and TF/IDF-SE in only very few cases with
statistical significance (8/42 and 9/32 cases, respectively), thus confounding
previous findings on the efficacy of Deep-SE. The two additional RQs revealed
that neither augmenting the training set nor pre-training Deep-SE play a role
in improving its accuracy and convergence speed. ...

Class imbalance can often degrade predictive performance of supervised
learning algorithms. Balanced classes can be obtained by oversampling exact
copies, with noise, or interpolation between nearest neighbours (as in
traditional SMOTE methods). Oversampling tabular data using augmentation, as is
typical in computer vision tasks, can be achieved with deep generative models.
Deep generative models are effective data synthesisers due to their ability to
capture complex underlying distributions. Synthetic data in healthcare can
enhance interoperability between healthcare providers by ensuring patient
privacy. Equipped with large synthetic datasets which do well to represent
small patient groups, machine learning in healthcare can address the current
challenges of bias and generalisability. This paper evaluates synthetic data
generators ability to synthesise patient electronic health records. We test the
utility of synthetic data for patient outcome classification, observing
increased predictive performance when augmenting imbalanced datasets with
synthetic data.

We construct several classes of neural networks with ReLU and BiSU (Binary
Step Unit) activations, which exactly emulate the lowest order Finite Element
(FE) spaces on regular, simplicial partitions of polygonal and polyhedral
domains $\Omega \subset \mathbb{R}^d$, $d=2,3$. For continuous, piecewise
linear (CPwL) functions, our constructions generalize previous results in that
arbitrary, regular simplicial partitions of $\Omega$ are admitted, also in
arbitrary dimension $d\geq 2$.
  Vector-valued elements emulated include the classical Raviart-Thomas and the
first family of N\'{e}d\'{e}lec edge elements on triangles and tetrahedra.
Neural Networks emulating these FE spaces are required in the correct
approximation of boundary value problems of electromagnetism in nonconvex
polyhedra $\Omega \subset \mathbb{R}^3$, thereby constituting an essential
ingredient in the application of e.g. the methodology of ``physics-informed
NNs'' or ``deep Ritz methods'' to electromagnetic field simulation via deep
learning techniques. They satisfy exact (De Rham) sequence properties, and also
spawn discrete boundary complexes on $\partial\Omega$ which satisfy exact
sequence properties for the surface divergence and curl operators
$\mathrm{div}_\Gamma$ and $\mathrm{curl}_\Gamma$, respectively, thereby
enabling ``neural boundary elements'' for computational electromagnetism.
  We indicate generalizations of our constructions to higher-order compatible
spaces and other, non-compatible classes of discretizations in particular the
Crouzeix-Raviart elements and Hybridized, Higher Order (HHO) methods.

In this paper, we evaluate the use of Reinforcement Learning (RL) to solve a
classic combinatorial optimization problem: the Capacitated Vehicle Routing
Problem (CVRP). We formalize this problem in the RL framework and compare two
of the most promising RL approaches with traditional solving techniques on a
set of benchmark instances. We measure the different approaches with the
quality of the solution returned and the time required to return it. We found
that despite not returning the best solution, the RL approach has many
advantages over traditional solvers. First, the versatility of the framework
allows the resolution of more complex combinatorial problems. Moreover, instead
of trying to solve a specific instance of the problem, the RL algorithm learns
the skills required to solve the problem. The trained policy can then quasi
instantly provide a solution to an unseen problem without having to solve it
from scratch. Finally, the use of trained models makes the RL solver by far the
fastest, and therefore make this approach more suited for commercial use where
the user experience is paramount. Techniques like Knowledge Transfer can also
be used to improve the training efficiency of the algorithm and help solve
bigger and more complex problems.

Measuring the redshift of active galactic nuclei (AGNs) requires the use of
time-consuming and expensive spectroscopic analysis. However, obtaining
redshift measurements of AGNs is crucial as it can enable AGN population
studies, provide insight into the star formation rate, the luminosity function,
and the density rate evolution. Hence, there is a requirement for alternative
redshift measurement techniques. In this project, we aim to use the Fermi
gamma-ray space telescope's 4LAC Data Release (DR2) catalog to train a machine
learning model capable of predicting the redshift reliably. In addition, this
project aims at improving and extending with the new 4LAC Catalog the
predictive capabilities of the machine learning (ML) methodology published in
Dainotti et al. (2021). Furthermore, we implement feature engineering to expand
the parameter space and a bias correction technique to our final results. This
study uses additional machine learning techniques inside the ensemble method,
the SuperLearner, previously used in Dainotti et al.(2021). Additionally, we
also test a novel ML model called Sorted L-One Penalized Estimation (SLOPE).
Using these methods we provide a catalog of estimated redshift values for those
AGNs that do not have a spectroscopic redshift measurement. These estimates can
serve as a redshift reference for the community to verify as updated Fermi
catalogs are released with more redshift measurements.

Brain tumors analysis is important in timely diagnosis and effective
treatment to cure patients. Tumor analysis is challenging because of tumor
morphology like size, location, texture, and heteromorphic appearance in the
medical images. In this regard, a novel two-phase deep learning-based framework
is proposed to detect and categorize brain tumors in magnetic resonance images
(MRIs). In the first phase, a novel deep boosted features and ensemble
classifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthy
individuals effectively. The deep boosted feature space is achieved through the
customized and well-performing deep convolutional neural networks (CNNs), and
consequently, fed into the ensemble of machine learning (ML) classifiers. While
in the second phase, a new hybrid features fusion-based brain tumor
classification approach is proposed, comprised of dynamic-static feature and ML
classifier to categorize different tumor types. The dynamic features are
extracted from the proposed BRAIN-RENet CNN, which carefully learns
heteromorphic and inconsistent behavior of various tumors, while the static
features are extracted using HOG. The effectiveness of the proposed two-phase
brain tumor analysis framework is validated on two standard benchmark datasets;
collected from Kaggle and Figshare containing different types of tumor,
including glioma, meningioma, pituitary, and normal images. Experimental
results proved that the proposed DBF-EC detection scheme outperforms and
achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score
(0.9945), MCC (0.9892), and AUC-PR (0.9990). While the classification scheme,
the joint employment of the deep features fusion of proposed BRAIN-RENet and
HOG features improves performance significantly in terms of recall (0.9913),
precision (0.9906), F1-Score (0.9909), and accuracy (99.20%) on diverse
datasets.

Artificial Intelligence (AI) is making a significant impact in multiple areas
like medical, military, industrial, domestic, law, arts as AI is capable to
perform several roles such as managing smart factories, driving autonomous
vehicles, creating accurate weather forecasts, detecting cancer and personal
assistants, etc. Software testing is the process of putting the software to
test for some abnormal behaviour of the software. Software testing is a
tedious, laborious and most time-consuming process. Automation tools have been
developed that help to automate some activities of the testing process to
enhance quality and timely delivery. Over time with the inclusion of continuous
integration and continuous delivery (CI/CD) pipeline, automation tools are
becoming less effective. The testing community is turning to AI to fill the gap
as AI is able to check the code for bugs and errors without any human
intervention and in a much faster way than humans. In this study, we aim to
recognize the impact of AI technologies on various software testing activities
or facets in the STLC. Further, the study aims to recognize and explain some of
the biggest challenges software testers face while applying AI to testing. The
paper also proposes some key contributions of AI in the future to the domain of
software testing.

Atrial Fibrillation (AF) is a heart's arrhythmia which, despite being often
asymptomatic, represents an important risk factor for stroke, therefore being
able to predict AF at the electrocardiogram exam, would be of great impact on
actively targeting patients at high risk. In the present work we use
Convolution Neural Networks to analyze ECG and predict Atrial Fibrillation
starting from realistic datasets, i.e. considering fewer ECG than other studies
and extending the maximal distance between ECG and AF diagnosis. We achieved
75.5% (0.75) AUC firstly increasing our dataset size by a shifting technique
and secondarily using the dilation parameter of the convolution neural network.
In addition we find that, contrarily to what is commonly used by clinicians
reporting AF at the exam, the most informative leads for the task of predicting
AF are D1 and avR. Similarly, we find that the most important frequencies to
check are in the range of 5-20 Hz. Finally, we develop a net able to manage at
the same time the electrocardiographic signal together with the electronic
health record, showing that integration between different sources of data is a
profitable path. In fact, the 2.8% gain of such net brings us to a 78.6% (std
0.77) AUC. In future works we will deepen both the integration of sources and
the reason why we claim avR is the most informative lead.

Multitask learning often helps improve the performance of related tasks as
these often have inter-dependence on each other and perform better when solved
in a joint framework. In this paper, we present a deep multitask learning
framework that jointly performs polarity and subjective detection. We propose
an attention-based multitask model for predicting polarity and subjectivity.
The input sentences are transformed into vectors using pre-trained BERT and
Glove embeddings, and the results depict that BERT embedding based model works
better than the Glove based model. We compare our approach with
state-of-the-art models in both subjective and polarity classification
single-task and multitask frameworks. The proposed approach reports baseline
performances for both polarity detection and subjectivity detection.

Static analysis of structures is a fundamental step for determining the
stability of structures. Both linear and non-linear static analyses consist of
the resolution of sparse linear systems obtained by the finite element method.
The development of fast and optimized solvers for sparse linear systems
appearing in structural engineering requires data to compare existing
approaches, tune algorithms or to evaluate new ideas. We introduce the Static
Analysis Dataset (StAnD) containing 303.000 static analysis problems obtained
applying realistic loads to simulated frame structures. Along with the dataset,
we publish a detailed benchmark comparison of the running time of existing
solvers both on CPU and GPU. We release the code used to generate the dataset
and benchmark existing solvers on Github. To the best of our knowledge, this is
the largest dataset for static analysis problems and it is the first public
dataset of sparse linear systems (containing both the matrix and a realistic
constant term).

We present a framework of Training Free Graph Matching (TFGM) to boost the
performance of Graph Neural Networks (GNNs) based graph matching, providing a
fast promising solution without training (training-free). TFGM provides four
widely applicable principles for designing training-free GNNs and is
generalizable to supervised, semi-supervised, and unsupervised graph matching.
The keys are to handcraft the matching priors, which used to be learned by
training, into GNN's architecture and discard the components inessential under
the training-free setting. Further analysis shows that TFGM is a linear
relaxation to the quadratic assignment formulation of graph matching and
generalizes TFGM to a broad set of GNNs. Extensive experiments show that GNNs
with TFGM achieve comparable (if not better) performances to their fully
trained counterparts, and demonstrate TFGM's superiority in the unsupervised
setting. Our code is available at
https://github.com/acharkq/Training-Free-Graph-Matching.

Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology
information (scar and edema) to diagnose myocardial infarction. However,
automatic pathology segmentation can be challenging due to the difficulty of
effectively exploring the underlying information from the multi-sequence CMR
data. This paper aims to tackle the scar and edema segmentation from
multi-sequence CMR with a novel auto-weighted supervision framework, where the
interactions among different supervised layers are explored under a
task-specific objective using reinforcement learning. Furthermore, we design a
coarse-to-fine framework to boost the small myocardial pathology region
segmentation with shape prior knowledge. The coarse segmentation model
identifies the left ventricle myocardial structure as a shape prior, while the
fine segmentation model integrates a pixel-wise attention strategy with an
auto-weighted supervision model to learn and extract salient pathological
structures from the multi-sequence CMR data. Extensive experimental results on
a publicly available dataset from Myocardial pathology segmentation combining
multi-sequence CMR (MyoPS 2020) demonstrate our method can achieve promising
performance compared with other state-of-the-art methods. Our method is
promising in advancing the myocardial pathology assessment on multi-sequence
CMR data. To motivate the community, we have made our code publicly available
via https://github.com/soleilssss/AWSnet/tree/master.

Tree-based ensembles such as the Random Forest are modern classics among
statistical learning methods. In particular, they are used for predicting
univariate responses. In case of multiple outputs the question arises whether
we separately fit univariate models or directly follow a multivariate approach.
For the latter, several possibilities exist that are, e.g. based on modified
splitting or stopping rules for multi-output regression. In this work we
compare these methods in extensive simulations to help in answering the primary
question when to use multivariate ensemble techniques.

We enhance the accuracy and generalization of univariate time series point
prediction by an explainable ensemble on the fly. We propose an Interpretable
Dynamic Ensemble Architecture (IDEA), in which interpretable base learners give
predictions independently with sparse communication as a group. The model is
composed of several sequentially stacked groups connected by group backcast
residuals and recurrent input competition. Ensemble driven by end-to-end
training both horizontally and vertically brings state-of-the-art (SOTA)
performances. Forecast accuracy improves by 2.6% over the best statistical
benchmark on the TOURISM dataset and 2% over the best deep learning benchmark
on the M4 dataset. The architecture enjoys several advantages, being applicable
to time series from various domains, explainable to users with specialized
modular structure and robust to changes in task distribution.

Tropical cyclones (TC) generally carry large amounts of water vapor and can
cause large-scale extreme rainfall. Passive microwave rainfall (PMR) estimation
of TC with high spatial and temporal resolution is crucial for disaster warning
of TC, but remains a challenging problem due to the low temporal resolution of
microwave sensors. This study attempts to solve this problem by directly
forecasting PMR from satellite infrared (IR) images of TC. We develop a
generative adversarial network (GAN) to convert IR images into PMR, and
establish the mapping relationship between TC cloud-top bright temperature and
PMR, the algorithm is named TCR-GAN. Meanwhile, a new dataset that is available
as a benchmark, Dataset of Tropical Cyclone IR-to-Rainfall Prediction (TCIRRP)
was established, which is expected to advance the development of artificial
intelligence in this direction. Experimental results show that the algorithm
can effectively extract key features from IR. The end-to-end deep learning
approach shows potential as a technique that can be applied globally and
provides a new perspective tropical cyclone precipitation prediction via
satellite, which is expected to provide important insights for real-time
visualization of TC rainfall globally in operations.

Re-ranking models refine item recommendation lists generated by the prior
global ranking model, which have demonstrated their effectiveness in improving
the recommendation quality. However, most existing re-ranking solutions only
learn from implicit feedback with a shared prediction model, which regrettably
ignore inter-item relationships under diverse user intentions. In this paper,
we propose a novel Intention-aware Re-ranking Model with Dynamic Transformer
Encoder (RAISE), aiming to perform user-specific prediction for each individual
user based on her intentions. Specifically, we first propose to mine latent
user intentions from text reviews with an intention discovering module (IDM).
By differentiating the importance of review information with a co-attention
network, the latent user intention can be explicitly modeled for each user-item
pair. We then introduce a dynamic transformer encoder (DTE) to capture
user-specific inter-item relationships among item candidates by seamlessly
accommodating the learned latent user intentions via IDM. As such, one can not
only achieve more personalized recommendations but also obtain corresponding
explanations by constructing RAISE upon existing recommendation engines.
Empirical study on four public datasets shows the superiority of our proposed
RAISE, with up to 13.95%, 9.60%, and 13.03% relative improvements evaluated by
Precision@5, MAP@5, and NDCG@5 respectively.

Humans can generalize from only a few examples and from little pre-training
on similar tasks. Yet, machine learning (ML) typically requires large data to
learn or pre-learn to transfer. Inspired by nativism, we directly model basic
human-innate priors in abstract visual tasks e.g., character/doodle
recognition. This yields a white-box model that learns general-appearance
similarity -- how any two images look in general -- by mimicking how humans
naturally "distort" an object at first sight. Using simply the nearest-neighbor
classifier on this similarity space, we achieve human-level character
recognition using only 1--10 examples per class and nothing else (no
pre-training). This differs from few-shot learning (FSL) using significant
pre-training. On standard benchmarks MNIST/EMNIST and the Omniglot challenge,
we outperform both neural-network-based and classical ML in the "tiny-data"
regime, including FSL pre-trained on large data. Our model enables unsupervised
learning too: by learning the non-Euclidean, general-appearance similarity
space in a k-means style, we can generate human-intuitive archetypes as cluster
``centroids''.

This paper shows that masked autoencoder with extrapolator (ExtraMAE) is a
scalable self-supervised model for time series generation. ExtraMAE randomly
masks some patches of the original time series and learns temporal dynamics by
recovering the masked patches. Our approach has two core designs. First,
ExtraMAE is self-supervised. Supervision allows ExtraMAE to effectively and
efficiently capture the temporal dynamics of the original time series. Second,
ExtraMAE proposes an extrapolator to disentangle two jobs of the decoder:
recovering latent representations and mapping them back into the feature space.
These unique designs enable ExtraMAE to consistently and significantly
outperform state-of-the-art (SoTA) benchmarks in time series generation. The
lightweight architecture also makes ExtraMAE fast and scalable. ExtraMAE shows
outstanding behavior in various downstream tasks such as time series
classification, prediction, and imputation. As a self-supervised generative
model, ExtraMAE allows explicit management of the synthetic data. We hope this
paper will usher in a new era of time series generation with self-supervised
models.

Entity relationship extraction envisions the automatic generation of semantic
data models from collections of text, by automatic recognition of entities, by
association of entities to form relationships, and by classifying these
instances to assign them to entity sets (or classes) and relationship sets (or
associations). As a first step in this direction, the Lokahi prototype can
extract entities based on the TF*IDF measure, and generate semantic
relationships based on document-level co-occurrence statistics, for example
with likelihood ratios and pointwise mutual information. This paper presents
results of an explorative, prototypical, qualitative and synthetic research,
summarizes insights from two research projects and, based on this, indicates an
outline for further research in the field of entity relationship extraction
from text.

Cyber Security is a critical topic for organizations with IT/OT networks as
they are always susceptible to attack, whether insider or outsider. Since the
cyber landscape is an ever-evolving scenario, one must keep upgrading its
security systems to enhance the security of the infrastructure. Tools like
Security Information and Event Management (SIEM), Endpoint Detection and
Response (EDR), Threat Intelligence Platform (TIP), Information Technology
Service Management (ITSM), along with other defensive techniques like Intrusion
Detection System (IDS), Intrusion Protection System (IPS), and many others
enhance the cyber security posture of the infrastructure. However, the proposed
protection mechanisms have their limitations, they are insufficient to ensure
security, and the attacker penetrates the network. Deception technology, along
with Honeypots, provides a false sense of vulnerability in the target systems
to the attackers. The attacker deceived reveals threat intel about their modus
operandi. We have developed a Security Orchestration, Automation, and Response
(SOAR) Engine that dynamically deploys custom honeypots inside the internal
network infrastructure based on the attacker's behavior. The architecture is
robust enough to support multiple VLANs connected to the system and used for
orchestration. The presence of botnet traffic and DDOS attacks on the honeypots
in the network is detected, along with a malware collection system. After being
exposed to live traffic for four days, our engine dynamically orchestrated the
honeypots 40 times, detected 7823 attacks, 965 DDOS attack packets, and three
malicious samples. While our experiments with static honeypots show an average
attacker engagement time of 102 seconds per instance, our SOAR Engine-based
dynamic honeypots engage attackers on average 3148 seconds.

Constructing benchmarks that test the abilities of modern natural language
understanding models is difficult - pre-trained language models exploit
artifacts in benchmarks to achieve human parity, but still fail on adversarial
examples and make errors that demonstrate a lack of common sense. In this work,
we propose gamification as a framework for data construction. The goal of
players in the game is to compose questions that mislead a rival AI while using
specific phrases for extra points. The game environment leads to enhanced user
engagement and simultaneously gives the game designer control over the
collected data, allowing us to collect high-quality data at scale. Using our
method we create CommonsenseQA 2.0, which includes 14,343 yes/no questions, and
demonstrate its difficulty for models that are orders-of-magnitude larger than
the AI used in the game itself. Our best baseline, the T5-based Unicorn with
11B parameters achieves an accuracy of 70.2%, substantially higher than GPT-3
(52.9%) in a few-shot inference setup. Both score well below human performance
which is at 94.1%.

The deep neural network suffers from many fundamental issues in machine
learning. For example, it often gets trapped into a local minimum in training,
and its prediction uncertainty is hard to be assessed. To address these issues,
we propose the so-called kernel-expanded stochastic neural network (K-StoNet)
model, which incorporates support vector regression (SVR) as the first hidden
layer and reformulates the neural network as a latent variable model. The
former maps the input vector into an infinite dimensional feature space via a
radial basis function (RBF) kernel, ensuring absence of local minima on its
training loss surface. The latter breaks the high-dimensional nonconvex neural
network training problem into a series of low-dimensional convex optimization
problems, and enables its prediction uncertainty easily assessed. The K-StoNet
can be easily trained using the imputation-regularized optimization (IRO)
algorithm. Compared to traditional deep neural networks, K-StoNet possesses a
theoretical guarantee to asymptotically converge to the global optimum and
enables the prediction uncertainty easily assessed. The performances of the new
model in training, prediction and uncertainty quantification are illustrated by
simulated and real data examples.

Temporal video grounding (TVG) aims to localize a target segment in a video
according to a given sentence query. Though respectable works have made decent
achievements in this task, they severely rely on abundant video-query paired
data, which is expensive and time-consuming to collect in real-world scenarios.
In this paper, we explore whether a video grounding model can be learned
without any paired annotations. To the best of our knowledge, this paper is the
first work trying to address TVG in an unsupervised setting. Considering there
is no paired supervision, we propose a novel Deep Semantic Clustering Network
(DSCNet) to leverage all semantic information from the whole query set to
compose the possible activity in each video for grounding. Specifically, we
first develop a language semantic mining module, which extracts implicit
semantic features from the whole query set. Then, these language semantic
features serve as the guidance to compose the activity in video via a
video-based semantic aggregation module. Finally, we utilize a foreground
attention branch to filter out the redundant background activities and refine
the grounding results. To validate the effectiveness of our DSCNet, we conduct
experiments on both ActivityNet Captions and Charades-STA datasets. The results
demonstrate that DSCNet achieves competitive performance, and even outperforms
most weakly-supervised approaches.

Graph Neural Networks (GNNs) have shown promising results in various tasks,
among which link prediction is an important one. GNN models usually follow a
node-centric message passing procedure that aggregates the neighborhood
information to the central node recursively. Following this paradigm, features
of nodes are passed through edges without caring about where the nodes are
located and which role they played. However, the neglected topological
information is shown to be valuable for link prediction tasks. In this paper,
we propose Structure Enhanced Graph neural network (SEG) for link prediction.
SEG introduces the path labeling method to capture surrounding topological
information of target nodes and then incorporates the structure into an
ordinary GNN model. By jointly training the structure encoder and deep GNN
model, SEG fuses topological structures and node features to take full
advantage of graph information. Experiments on the OGB link prediction datasets
demonstrate that SEG achieves state-of-the-art results among all three public
datasets.

Multi-block CCA constructs linear relationships explaining coherent
variations across multiple blocks of data. We view the multi-block CCA problem
as finding leading generalized eigenvectors and propose to solve it via a
proximal gradient descent algorithm with $\ell_1$ constraint for high
dimensional data. In particular, we use a decaying sequence of constraints over
proximal iterations, and show that the resulting estimate is rate-optimal under
suitable assumptions. Although several previous works have demonstrated such
optimality for the $\ell_0$ constrained problem using iterative approaches, the
same level of theoretical understanding for the $\ell_1$ constrained
formulation is still lacking. We also describe an easy-to-implement deflation
procedure to estimate multiple eigenvectors sequentially. We compare our
proposals to several existing methods whose implementations are available on R
CRAN, and the proposed methods show competitive performances in both
simulations and a real data example.

Federated learning (FL) is an emerging promising privacy-preserving machine
learning paradigm and has raised more and more attention from researchers and
developers. FL keeps users' private data on devices and exchanges the gradients
of local models to cooperatively train a shared Deep Learning (DL) model on
central custodians. However, the security and fault tolerance of FL have been
increasingly discussed, because its central custodian mechanism or star-shaped
architecture can be vulnerable to malicious attacks or software failures. To
address these problems, Swarm Learning (SL) introduces a permissioned
blockchain to securely onboard members and dynamically elect the leader, which
allows performing DL in an extremely decentralized manner. Compared with
tremendous attention to SL, there are few empirical studies on SL or
blockchain-based decentralized FL, which provide comprehensive knowledge of
best practices and precautions of deploying SL in real-world scenarios.
Therefore, we conduct the first comprehensive study of SL to date, to fill the
knowledge gap between SL deployment and developers, as far as we are concerned.
In this paper, we conduct various experiments on 3 public datasets of 5
research questions, present interesting findings, quantitatively analyze the
reasons behind these findings, and provide developers and researchers with
practical suggestions. The findings have evidenced that SL is supposed to be
suitable for most application scenarios, no matter whether the dataset is
balanced, polluted, or biased over irrelevant features.

A predictor, $f_A : X \to Y$, learned with data from a source domain (A)
might not be accurate on a target domain (B) when their distributions are
different. Domain adaptation aims to reduce the negative effects of this
distribution mismatch. Here, we analyze the case where $P_A(Y\ |\ X) \neq
P_B(Y\ |\ X)$, $P_A(X) \neq P_B(X)$ but $P_A(Y) = P_B(Y)$; where there are
affine transformations of $X$ that makes all distributions equivalent. We
propose an approach to project the source and target domains into a
lower-dimensional, common space, by (1) projecting the domains into the
eigenvectors of the empirical covariance matrices of each domain, then (2)
finding an orthogonal matrix that minimizes the maximum mean discrepancy
between the projections of both domains. For arbitrary affine transformations,
there is an inherent unidentifiability problem when performing unsupervised
domain adaptation that can be alleviated in the semi-supervised case. We show
the effectiveness of our approach in simulated data and in binary digit
classification tasks, obtaining improvements up to 48% accuracy when correcting
for the domain shift in the data.

A neural network with the widely-used ReLU activation has been shown to
partition the sample space into many convex polytopes for prediction. However,
the parameterized way a neural network and other machine learning models use to
partition the space has imperfections, \textit{e}.\textit{g}., the compromised
interpretability for complex models, the inflexibility in decision boundary
construction due to the generic character of the model, and the risk of being
trapped into shortcut solutions. In contrast, although the non-parameterized
models can adorably avoid or downplay these issues, they are usually
insufficiently powerful either due to over-simplification or the failure to
accommodate the manifold structures of data. In this context, we first propose
a new type of machine learning models referred to as Manifoldron that directly
derives decision boundaries from data and partitions the space via manifold
structure discovery. Then, we systematically analyze the key characteristics of
the Manifoldron such as manifold characterization capability and its link to
neural networks. The experimental results on 4 synthetic examples, 20 public
benchmark datasets, and 1 real-world application demonstrate that the proposed
Manifoldron performs competitively compared to the mainstream machine learning
models. We have shared our code in \url{https://github.com/wdayang/Manifoldron}
for free download and evaluation.

Self-supervised learning has drawn attention through its effectiveness in
learning in-domain representations with no ground-truth annotations; in
particular, it is shown that properly designed pretext tasks (e.g., contrastive
prediction task) bring significant performance gains for downstream tasks
(e.g., classification task). Inspired from this, we tackle video scene
segmentation, which is a task of temporally localizing scene boundaries in a
video, with a self-supervised learning framework where we mainly focus on
designing effective pretext tasks. In our framework, we discover a
pseudo-boundary from a sequence of shots by splitting it into two continuous,
non-overlapping sub-sequences and leverage the pseudo-boundary to facilitate
the pre-training. Based on this, we introduce three novel boundary-aware
pretext tasks: 1) Shot-Scene Matching (SSM), 2) Contextual Group Matching (CGM)
and 3) Pseudo-boundary Prediction (PP); SSM and CGM guide the model to maximize
intra-scene similarity and inter-scene discrimination while PP encourages the
model to identify transitional moments. Through comprehensive analysis, we
empirically show that pre-training and transferring contextual representation
are both critical to improving the video scene segmentation performance.
Lastly, we achieve the new state-of-the-art on the MovieNet-SSeg benchmark. The
code is available at https://github.com/kakaobrain/bassl.

Staircases are some of the most common building structures in urban
environments. Stair detection is an important task for various applications,
including the environmental perception of exoskeleton robots, humanoid robots,
and rescue robots and the navigation of visually impaired people. Most existing
stair detection algorithms have difficulty dealing with the diversity of stair
structure materials, extreme light and serious occlusion. Inspired by human
perception, we propose an end-to-end method based on deep learning.
Specifically, we treat the process of stair line detection as a multitask
involving coarse-grained semantic segmentation and object detection. The input
images are divided into cells, and a simple neural network is used to judge
whether each cell contains stair lines. For cells containing stair lines, the
locations of the stair lines relative to each cell are regressed. Extensive
experiments on our dataset show that our method can achieve high performance in
terms of both speed and accuracy. A lightweight version can even achieve 300+
frames per second with the same resolution. Our code and dataset will be soon
available at GitHub.

Statistical depths provide a fundamental generalization of quantiles and
medians to data in higher dimensions. This paper proposes a new type of
globally defined statistical depth, based upon control theory and eikonal
equations, which measures the smallest amount of probability density that has
to be passed through in a path to points outside the support of the
distribution: for example spatial infinity. This depth is easy to interpret and
compute, expressively captures multi-modal behavior, and extends naturally to
data that is non-Euclidean. We prove various properties of this depth, and
provide discussion of computational considerations. In particular, we
demonstrate that this notion of depth is robust under an aproximate
isometrically constrained adversarial model, a property which is not enjoyed by
the Tukey depth. Finally we give some illustrative examples in the context of
two-dimensional mixture models and MNIST.

Text Generation aims to produce plausible and readable text in a human
language from input data. The resurgence of deep learning has greatly advanced
this field, in particular, with the help of neural generation models based on
pre-trained language models (PLMs). Text generation based on PLMs is viewed as
a promising approach in both academia and industry. In this paper, we provide a
survey on the utilization of PLMs in text generation. We begin with introducing
three key aspects of applying PLMs to text generation: 1) how to encode the
input into representations preserving input semantics which can be fused into
PLMs; 2) how to design an effective PLM to serve as the generation model; and
3) how to effectively optimize PLMs given the reference text and to ensure that
the generated texts satisfy special text properties. Then, we show the major
challenges arisen in these aspects, as well as possible solutions for them. We
also include a summary of various useful resources and typical text generation
applications based on PLMs. Finally, we highlight the future research
directions which will further improve these PLMs for text generation. This
comprehensive survey is intended to help researchers interested in text
generation problems to learn the core concepts, the main techniques and the
latest developments in this area based on PLMs.

Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on
studying the diverse traits of plants related to the plants' growth. To be more
specific, by accurately measuring the plant's anatomical, ontogenetical,
physiological and biochemical properties, it allows identifying the crucial
factors of plants' growth in different environments. One commonly used approach
is to predict the plant's traits using hyperspectral reflectance (Yendrek et
al. 2017; Wang et al. 2021). However, the data distributions of the
hyperspectral reflectance data in plant phenotyping might vary in different
environments for different plants. That is, it would be computationally
expansive to learn the machine learning models separately for one plant in
different environments. To solve this problem, we focus on studying the
knowledge transferability of modern machine learning models in plant
phenotyping. More specifically, this work aims to answer the following
questions. (1) How is the performance of conventional machine learning models,
e.g., partial least squares regression (PLSR), Gaussian process regression
(GPR) and multi-layer perceptron (MLP), affected by the number of annotated
samples for plant phenotyping? (2) Whether could the neural network based
transfer learning models improve the performance of plant phenotyping? (3)
Could the neural network based transfer learning be improved by using
infinite-width hidden layers for plant phenotyping?

The task of finding the best developer to fix a bug is called bug triage.
Most of the existing approaches consider the bug triage task as a
classification problem, however, classification is not appropriate when the
sets of classes change over time (as developers often do in a project).
Furthermore, to the best of our knowledge, all the existing models use textual
sources of information, i.e., bug descriptions, which are not always available.
  In this work, we explore the applicability of existing solutions for the bug
triage problem when stack traces are used as the main data source of bug
reports. Additionally, we reformulate this task as a ranking problem and
propose new deep learning models to solve it. The models are based on a
bidirectional recurrent neural network with attention and on a convolutional
neural network, with the weights of the models optimized using a ranking loss
function. To improve the quality of ranking, we propose using additional
information from version control system annotations. Two approaches are
proposed for extracting features from annotations: manual and using an
additional neural network. To evaluate our models, we collected two datasets of
real-world stack traces. Our experiments show that the proposed models
outperform existing models adapted to handle stack traces. To facilitate
further research in this area, we publish the source code of our models and one
of the collected datasets.

Approximations of optimization problems arise in computational procedures and
sensitivity analysis. The resulting effect on solutions can be significant,
with even small approximations of components of a problem translating into
large errors in the solutions. We specify conditions under which approximations
are well behaved in the sense of minimizers, stationary points, and level-sets
and this leads to a framework of consistent approximations. The framework is
developed for a broad class of composite problems, which are neither convex nor
smooth. We demonstrate the framework using examples from stochastic
optimization, neural-network based machine learning, distributionally robust
optimization, penalty and augmented Lagrangian methods, interior-point methods,
homotopy methods, smoothing methods, extended nonlinear programming,
difference-of-convex programming, and multi-objective optimization. An enhanced
proximal method illustrates the algorithmic possibilities. A quantitative
analysis supplements the development by furnishing rates of convergence.

Artificial neural networks for simulated motor control and robotics often
adopt generic architectures like fully connected MLPs. While general, these
tabula rasa architectures rely on large amounts of experience to learn, are not
easily transferable to new bodies, and have internal dynamics that are
difficult to interpret. In nature, animals are born with highly structured
connectivity in their nervous systems shaped by evolution; this innate
circuitry acts synergistically with learning mechanisms to provide inductive
biases that enable most animals to function well soon after birth and improve
abilities efficiently. Convolutional networks inspired by visual circuitry have
encoded useful biases for vision. However, it is unknown the extent to which
ANN architectures inspired by neural circuitry can yield useful biases for
other domains. In this work, we ask what advantages biologically inspired
network architecture can provide in the context of motor control. Specifically,
we translate C. elegans circuits for locomotion into an ANN model controlling a
simulated Swimmer agent. On a locomotion task, our architecture achieves good
initial performance and asymptotic performance comparable with MLPs, while
dramatically improving data efficiency and requiring orders of magnitude fewer
parameters. Our architecture is more interpretable and transfers to new body
designs. An ablation analysis shows that principled excitation/inhibition is
crucial for learning, while weight initialization contributes to good initial
performance. Our work demonstrates several advantages of ANN architectures
inspired by systems neuroscience and suggests a path towards modeling more
complex behavior.

We present a novel method for controlling extrapolation in the prediction
profiler in the JMP software. The prediction profiler is a graphical tool for
exploring high dimensional prediction surfaces for statistical and machine
learning models. The profiler contains interactive cross-sectional views, or
profile traces, of the prediction surface of a model. Our method helps users
avoid exploring predictions that should be considered extrapolation. It also
performs optimization over a constrained factor region that avoids
extrapolation using a genetic algorithm. In simulations and real world
examples, we demonstrate how optimal factor settings without constraint in the
profiler are frequently extrapolated, and how extrapolation control helps avoid
these solutions with invalid factor settings that may not be useful to the
user.

Motivated by scenarios of information diffusion and advertising in social
media, we study an influence maximization problem in which little is assumed to
be known about the diffusion network or about the model that determines how
information may propagate. In such a highly uncertain environment, one can
focus on multi-round diffusion campaigns, with the objective to maximize the
number of distinct users that are influenced or activated, starting from a
known base of few influential nodes. During a campaign, spread seeds are
selected sequentially at consecutive rounds, and feedback is collected in the
form of the activated nodes at each round. A round's impact (reward) is then
quantified as the number of newly activated nodes. Overall, one must maximize
the campaign's total spread, as the sum of rounds' rewards. In this setting, an
explore-exploit approach could be used to learn the key underlying diffusion
parameters, while running the campaign. We describe and compare two methods of
contextual multi-armed bandits, with upper-confidence bounds on the remaining
potential of influencers, one using a generalized linear model and the
Good-Turing estimator for remaining potential (GLM-GT-UCB), and another one
that directly adapts the LinUCB algorithm to our setting (LogNorm-LinUCB). We
show that they outperform baseline methods using state-of-the-art ideas, on
synthetic and real-world data, while at the same time exhibiting different and
complementary behavior, depending on the scenarios in which they are deployed.

Recently several structured pruning techniques have been introduced for
energy-efficient implementation of Deep Neural Networks (DNNs) with lesser
number of crossbars. Although, these techniques have claimed to preserve the
accuracy of the sparse DNNs on crossbars, none have studied the impact of the
inexorable crossbar non-idealities on the actual performance of the pruned
networks. To this end, we perform a comprehensive study to show how highly
sparse DNNs, that result in significant crossbar-compression-rate, can lead to
severe accuracy losses compared to unpruned DNNs mapped onto non-ideal
crossbars. We perform experiments with multiple structured-pruning approaches
(such as, C/F pruning, XCS and XRS) on VGG11 and VGG16 DNNs with benchmark
datasets (CIFAR10 and CIFAR100). We propose two mitigation approaches -
Crossbar column rearrangement and Weight-Constrained-Training (WCT) - that can
be integrated with the crossbar-mapping of the sparse DNNs to minimize accuracy
losses incurred by the pruned models. These help in mitigating non-idealities
by increasing the proportion of low conductance synapses on crossbars, thereby
improving their computational accuracies.

Machine learning is increasingly used in the most diverse applications and
domains, whether in healthcare, to predict pathologies, or in the financial
sector to detect fraud. One of the linchpins for efficiency and accuracy in
machine learning is data utility. However, when it contains personal
information, full access may be restricted due to laws and regulations aiming
to protect individuals' privacy. Therefore, data owners must ensure that any
data shared guarantees such privacy. Removal or transformation of private
information (de-identification) are among the most common techniques.
Intuitively, one can anticipate that reducing detail or distorting information
would result in losses for model predictive performance. However, previous work
concerning classification tasks using de-identified data generally demonstrates
that predictive performance can be preserved in specific applications. In this
paper, we aim to evaluate the existence of a trade-off between data privacy and
predictive performance in classification tasks. We leverage a large set of
privacy-preserving techniques and learning algorithms to provide an assessment
of re-identification ability and the impact of transformed variants on
predictive performance. Unlike previous literature, we confirm that the higher
the level of privacy (lower re-identification risk), the higher the impact on
predictive performance, pointing towards clear evidence of a trade-off.

Wireless links using massive MIMO transceivers are vital for next generation
wireless communications networks networks. Precoding in Massive MIMO
transmission requires accurate downlink channel state information (CSI). Many
recent works have effectively applied deep learning (DL) to jointly train
UE-side compression networks for delay domain CSI and a BS-side decoding
scheme. Vitally, these works assume that the full delay domain CSI is available
at the UE, but in reality, the UE must estimate the delay domain based on a
limited number of frequency domain pilots. In this work, we propose a linear
pilot-to-delay (P2D) estimator that transforms sparse frequency pilots to the
truncated delay CSI. We show that the P2D estimator is accurate under frequency
downsampling, and we demonstrate that the P2D estimate can be effectively
utilized with existing autoencoder-based CSI estimation networks. In addition
to accounting for pilot-based estimates of downlink CSI, we apply unrolled
optimization networks to emulate iterative solutions to compressed sensing
(CS), and we demonstrate better estimation performance than prior
autoencoder-based DL networks. Finally, we investigate the efficacy of
trainable CS networks for in a differential encoding network for time-varying
CSI estimation, and we propose a new network, MarkovNet-ISTA-ENet, comprised of
both a CS network for initial CSI estimation and multiple autoencoders to
estimate the error terms. We demonstrate that this heterogeneous network has
better asymptotic performance than networks comprised of only one type of
network.

Automatic code summarization is beneficial to daily software development
since it could help reduce the requirement of manual writing. Currently,
artificial intelligence is undergoing a paradigm shift. The foundation models
pretrained on massive data and finetuned to downstream tasks surpass specially
customized models. This trend inspired us to consider reusing foundation models
instead of learning from scratch. Thereby, we propose a flexible and robust
approach for automatic code summarization, based on neural models. We assemble
available foundation models, such as CodeBERT and GPT-2, into a single neural
model named AdaMo. Moreover, we utilize Gaussian noise as the simulation of
contextual information to optimize the latent representation. Furthermore, we
introduce two adaptive schemes from the perspective of knowledge transfer,
namely continuous pretraining and intermediate finetuning, and design
intermediate stage tasks for general sequence-to-sequence learning. Finally, we
evaluate AdaMo against a benchmark dataset for code summarization, by comparing
it with state-of-the-art models.

In this paper, a straightforward enhancement learning algorithm based on
Separation Index (SI) concept is proposed for Convolutional Neural Networks
(CNNs). At first, the SI as a supervised complexity measure is explained its
usage in better learning of CNNs for classification problems illustrate. Then,
a learning strategy proposes through which the first layer of a CNN is
optimized by maximizing the SI, and the further layers are trained through the
backpropagation algorithm to learn further layers. In order to maximize the SI
at the first layer, A variant of ranking loss is optimized by using the quasi
least square error technique. Applying such a learning strategy to some known
CNNs and datasets, its enhancement impact in almost all cases is demonstrated.

Over the past several years, a slew of different methods to measure the
fairness of a machine learning model have been proposed. However, despite the
growing number of publications and implementations, there is still a critical
lack of literature that explains the interplay of fair machine learning with
the social sciences of philosophy, sociology, and law. We hope to remedy this
issue by accumulating and expounding upon the thoughts and discussions of fair
machine learning produced by both social and formal (specifically machine
learning and statistics) sciences in this field guide. Specifically, in
addition to giving the mathematical and algorithmic backgrounds of several
popular statistical and causal-based fair machine learning methods, we explain
the underlying philosophical and legal thoughts that support them. Further, we
explore several criticisms of the current approaches to fair machine learning
from sociological and philosophical viewpoints. It is our hope that this field
guide will help fair machine learning practitioners better understand how their
algorithms align with important humanistic values (such as fairness) and how we
can, as a field, design methods and metrics to better serve oppressed and
marginalized populaces.

The failure of the Euclidean norm to reliably distinguish between nearby and
distant points in high dimensional space is well-known. This phenomenon of
distance concentration manifests in a variety of data distributions, with iid
or correlated features, including centrally-distributed and clustered data.
Unsupervised learning based on Euclidean nearest-neighbors and more general
proximity-oriented data mining tasks like clustering, might therefore be
adversely affected by distance concentration for high-dimensional applications.
While considerable work has been done developing clustering algorithms with
reliable high-dimensional performance, the problem of cluster validation--of
determining the natural number of clusters in a dataset--has not been carefully
examined in high-dimensional problems. In this work we investigate how the
sensitivities of common Euclidean norm-based cluster validity indices scale
with dimension for a variety of synthetic data schemes, including
well-separated and noisy clusters, and find that the overwhelming majority of
indices have improved or stable sensitivity in high dimensions. The curse of
dimensionality is therefore dispelled for this class of fairly generic data
schemes.

The recently proposed Neural Local Lossless Compression (NeLLoC), which is
based on a local autoregressive model, has achieved state-of-the-art (SOTA)
out-of-distribution (OOD) generalization performance in the image compression
task. In addition to the encouragement of OOD generalization, the local model
also allows parallel inference in the decoding stage. In this paper, we propose
two parallelization schemes for local autoregressive models. We discuss the
practicalities of implementing the schemes and provide experimental evidence of
significant gains in compression runtime compared to the previous, non-parallel

This survey is focused on certain sequential decision-making problems that
involve optimizing over probability functions. We discuss the relevance of
these problems for learning and control. The survey is organized around a
framework that combines a problem formulation and a set of resolution methods.
The formulation consists of an infinite-dimensional optimization problem. The
methods come from approaches to search optimal solutions in the space of
probability functions. Through the lenses of this overarching framework we
revisit popular learning and control algorithms, showing that these naturally
arise from suitable variations on the formulation mixed with different
resolution methods. A running example, for which we make the code available,
complements the survey. Finally, a number of challenges arising from the survey
are also outlined.

Variational autoencoders are among the most popular methods for distilling
low-dimensional structure from high-dimensional data, making them increasingly
valuable as tools for data exploration and scientific discovery. However,
unlike typical machine learning problems in which a single model is trained
once on a single large dataset, scientific workflows privilege learned features
that are reproducible, portable across labs, and capable of incrementally
adding new data. Ideally, methods used by different research groups should
produce comparable results, even without sharing fully trained models or entire
data sets. Here, we address this challenge by introducing the Rosetta VAE
(R-VAE), a method of distilling previously learned representations and
retraining new models to reproduce and build on prior results. The R-VAE uses
post hoc clustering over the latent space of a fully-trained model to identify
a small number of Rosetta Points (input, latent pairs) to serve as anchors for
training future models. An adjustable hyperparameter, $\rho$, balances fidelity
to the previously learned latent space against accommodation of new data. We
demonstrate that the R-VAE reconstructs data as well as the VAE and
$\beta$-VAE, outperforms both methods in recovery of a target latent space in a
sequential training setting, and dramatically increases consistency of the
learned representation across training runs.

The communication revolution has perpetually reshaped the means through which
people send and receive information. Social media is an important pillar of
this revolution and has brought profound changes to various aspects of our
lives. However, the open environment and popularity of these platforms
inaugurate windows of opportunities for various cyber threats, thus social
networks have become a fertile venue for spammers and other illegitimate users
to execute their malicious activities. These activities include phishing hot
and trendy topics and posting a wide range of contents in many topics. Hence,
it is crucial to continuously introduce new techniques and approaches to detect
and stop this category of users. This paper proposes a novel and effective
approach to detect social spammers. An investigation into several attributes to
measure topic-dependent and topic-independent users' behaviours on Twitter is
carried out. The experiments of this study are undertaken on various machine
learning classifiers. The performance of these classifiers are compared and
their effectiveness is measured via a number of robust evaluation measures.
Further, the proposed approach is benchmarked against state-of-the-art social
spam and anomalous detection techniques. These experiments report the
effectiveness and utility of the proposed approach and embedded modules.

The development of John Aitchison's approach to compositional data analysis
is followed since his paper read to the Royal Statistical Society in 1982.
Aitchison's logratio approach, which was proposed to solve the problematic
aspects of working with data with a fixed sum constraint, is summarized and
reappraised. It is maintained that the principles on which this approach was
originally built, the main one being subcompositional coherence, are not
required to be satisfied exactly -- quasi-coherence is sufficient, that is near
enough to being coherent for all practical purposes. This opens up the field to
using simpler data transformations, such as power transformations, that permit
zero values in the data. The additional principle of exact isometry, which was
subsequently introduced and not in Aitchison's original conception, imposed the
use of isometric logratio transformations, but these are complicated and
problematic to interpret, involving ratios of geometric means. If this
principle is regarded as important in certain analytical contexts, for example
unsupervised learning, it can be relaxed by showing that regular pairwise
logratios, as well as the alternative quasi-coherent transformations, can also
be quasi-isometric, meaning they are close enough to exact isometry for all
practical purposes. It is concluded that the isometric and related logratio
transformations such as pivot logratios are not a prerequisite for good
practice, although many authors insist on their obligatory use. This conclusion
is fully supported here by case studies in geochemistry and in genomics, where
the good performance is demonstrated of pairwise logratios, as originally
proposed by Aitchison, or Box-Cox power transforms of the original compositions
where no zero replacements are necessary.

Next generation reservoir computing based on nonlinear vector autoregression
(NVAR) is applied to emulate simple dynamical system models and compared to
numerical integration schemes such as Euler and the $2^\text{nd}$ order
Runge-Kutta. It is shown that the NVAR emulator can be interpreted as a
data-driven method used to recover the numerical integration scheme that
produced the data. It is also shown that the approach can be extended to
produce high-order numerical schemes directly from data. The impacts of the
presence of noise and temporal sparsity in the training set is further examined
to gauge the potential use of this method for more realistic applications.

End-to-end (E2E) quality of experience (QoE) for 6G applications depends on
the synchronous allocation of networking and computing resources, also known as
slicing. However, the relationship between the resources and the E2E QoE
outcomes is typically stochastic and non-stationary. Existing works consider
known resource demands for slicing and formulate optimization problems for
slice reconfiguration. In this work, we create and manage slices by learning
the relationship between E2E QoE and resources. We develop a gradient-based
online slice reconfiguration algorithm (OSRA) to reconfigure and manage slices
in resource-constrained scenarios for radio access networks (RAN). We observe
that our methodology meets the QoE requirements with high accuracy compared to
existing approaches. It improves upon the existing approaches by approximately
98\% for bursty traffic variations. Our algorithm has fast convergence and
achieves low E2E delay violations for lower priority slices.

Emerging applications such as Augmented Reality, the Internet of Vehicles and
Remote Surgery require both computing and networking functions working in
harmony. The End-to-end (E2E) quality of experience (QoE) for these
applications depends on the synchronous allocation of networking and computing
resources. However, the relationship between the resources and the E2E QoE
outcomes is typically stochastic and non-linear. In order to make efficient
resource allocation decisions, it is essential to model these relationships.
This article presents a novel machine-learning based approach to learn these
relationships and concurrently orchestrate both resources for this purpose. The
machine learning models further help make robust allocation decisions regarding
stochastic variations and simplify robust optimization to a conventional
constrained optimization. When resources are insufficient to accommodate all
application requirements, our framework supports executing some of the
applications with minimal degradation (graceful degradation) of E2E QoE. We
also show how we can implement the learning and optimization methods in a
distributed fashion by the Software-Defined Network (SDN) and Kubernetes
technologies. Our results show that deep learning-based modelling achieves E2E
QoE with approximately 99.8\% accuracy, and our robust joint-optimization
technique allocates resources efficiently when compared to existing
differential services alternatives.

Rationales, snippets of extracted text that explain an inference, have
emerged as a popular framework for interpretable natural language processing
(NLP). Rationale models typically consist of two cooperating modules: a
selector and a classifier with the goal of maximizing the mutual information
(MMI) between the "selected" text and the document label. Despite their
promises, MMI-based methods often pick up on spurious text patterns and result
in models with nonsensical behaviors. In this work, we investigate whether
counterfactual data augmentation (CDA), without human assistance, can improve
the performance of the selector by lowering the mutual information between
spurious signals and the document label. Our counterfactuals are produced in an
unsupervised fashion using class-dependent generative models. From an
information theoretic lens, we derive properties of the unaugmented dataset for
which our CDA approach would succeed. The effectiveness of CDA is empirically
evaluated by comparing against several baselines including an improved
MMI-based rationale schema on two multi aspect datasets. Our results show that
CDA produces rationales that better capture the signal of interest.

A conversational information retrieval (CIR) system is an information
retrieval (IR) system with a conversational interface which allows users to
interact with the system to seek information via multi-turn conversations of
natural language, in spoken or written form. Recent progress in deep learning
has brought tremendous improvements in natural language processing (NLP) and
conversational AI, leading to a plethora of commercial conversational services
that allow naturally spoken and typed interaction, increasing the need for more
human-centric interactions in IR. As a result, we have witnessed a resurgent
interest in developing modern CIR systems in both research communities and
industry. This book surveys recent advances in CIR, focusing on neural
approaches that have been developed in the last few years. This book is based
on the authors' tutorial at SIGIR'2020 (Gao et al., 2020b), with IR and NLP
communities as the primary target audience. However, audiences with other
background, such as machine learning and human-computer interaction, will also
find it an accessible introduction to CIR. We hope that this book will prove a
valuable resource for students, researchers, and software developers. This
manuscript is a working draft. Comments are welcome.

Federated learning (FL) offers a decentralized learning environment so that a
group of clients can collaborate to train a global model at the server, while
keeping their training data confidential. This paper studies how to launch
over-the-air jamming attacks to disrupt the FL process when it is executed over
a wireless network. As a wireless example, FL is applied to learn how to
classify wireless signals collected by clients (spectrum sensors) at different
locations (such as in cooperative sensing). An adversary can jam the
transmissions for the local model updates from clients to the server (uplink
attack), or the transmissions for the global model updates the server to
clients (downlink attack), or both. Given a budget imposed on the number of
clients that can be attacked per FL round, clients for the (uplink/downlink)
attack are selected according to their local model accuracies that would be
expected without an attack or ranked via spectrum observations. This novel
attack is extended to general settings by accounting different processing
speeds and attack success probabilities for clients. Compared to benchmark
attack schemes, this attack approach degrades the FL performance significantly,
thereby revealing new vulnerabilities of FL to jamming attacks in wireless
networks.

Modern deep learning requires large-scale extensively labelled datasets for
training. Few-shot learning aims to alleviate this issue by learning
effectively from few labelled examples. In previously proposed few-shot visual
classifiers, it is assumed that the feature manifold, where classifier
decisions are made, has uncorrelated feature dimensions and uniform feature
variance. In this work, we focus on addressing the limitations arising from
this assumption by proposing a variance-sensitive class of models that operates
in a low-label regime. The first method, Simple CNAPS, employs a hierarchically
regularized Mahalanobis-distance based classifier combined with a state of the
art neural adaptive feature extractor to achieve strong performance on
Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks. We further extend
this approach to a transductive learning setting, proposing Transductive CNAPS.
This transductive method combines a soft k-means parameter refinement procedure
with a two-step task encoder to achieve improved test-time classification
accuracy using unlabelled data. Transductive CNAPS achieves state of the art
performance on Meta-Dataset. Finally, we explore the use of our methods (Simple
and Transductive) for "out of the box" continual and active learning. Extensive
experiments on large scale benchmarks illustrate robustness and versatility of
this, relatively speaking, simple class of models. All trained model
checkpoints and corresponding source codes have been made publicly available.

Successful deep learning models often involve training neural network
architectures that contain more parameters than the number of training samples.
Such overparametrized models have been extensively studied in recent years, and
the virtues of overparametrization have been established from both the
statistical perspective, via the double-descent phenomenon, and the
computational perspective via the structural properties of the optimization
landscape.
  Despite the remarkable success of deep learning architectures in the
overparametrized regime, it is also well known that these models are highly
vulnerable to small adversarial perturbations in their inputs. Even when
adversarially trained, their performance on perturbed inputs (robust
generalization) is considerably worse than their best attainable performance on
benign inputs (standard generalization). It is thus imperative to understand
how overparametrization fundamentally affects robustness.
  In this paper, we will provide a precise characterization of the role of
overparametrization on robustness by focusing on random features regression
models (two-layer neural networks with random first layer weights). We consider
a regime where the sample size, the input dimension and the number of
parameters grow in proportion to each other, and derive an asymptotically exact
formula for the robust generalization error when the model is adversarially
trained. Our developed theory reveals the nontrivial effect of
overparametrization on robustness and indicates that for adversarially trained
random features models, high overparametrization can hurt robust

We define a perturbatively calculable quantity--the on-shell
correlator--which furnishes a unified description of particle dynamics in
curved spacetime. Specializing to the case of flat and anti-de Sitter space,
on-shell correlators coincide precisely with on-shell scattering amplitudes and
boundary correlators, respectively. Remarkably, we find that symmetric
manifolds admit a generalization of on-shell kinematics in which the
corresponding momenta are literally the isometry generators of the spacetime
acting on the external kinematic data. These isometric momenta are
intrinsically non-commutative but exhibit on-shell conditions that are
identical to those of flat space, thus providing a common language for
computing and representing on-shell correlators which is agnostic about the
underlying geometry. Afterwards, we compute tree-level on-shell correlators for
biadjoint scalar (BAS) theory and the nonlinear sigma model (NLSM) and learn
that color-kinematics duality is manifested at the level of fields under a
mapping of the color algebra to the algebra of gauged isometries on the
spacetime manifold. Last but not least, we present a field theoretic derivation
of the fundamental BCJ relations for on-shell correlators following from the
existence of certain conserved currents in BAS theory and the NLSM.

I propose kernel ridge regression estimators for long term causal inference,
where a short term experimental data set containing randomized treatment and
short term surrogates is fused with a long term observational data set
containing short term surrogates and long term outcomes. I propose estimators
of treatment effects, dose responses, and counterfactual distributions with
closed form solutions in terms of kernel matrix operations. I allow covariates,
treatment, and surrogates to be discrete or continuous, and low, high, or
infinite dimensional. For long term treatment effects, I prove $\sqrt{n}$
consistency, Gaussian approximation, and semiparametric efficiency. For long
term dose responses, I prove uniform consistency with finite sample rates. For
long term counterfactual distributions, I prove convergence in distribution.

A central challenge in data-driven model discovery is the presence of hidden,
or latent, variables that are not directly measured but are dynamically
important. Takens' theorem provides conditions for when it is possible to
augment these partial measurements with time delayed information, resulting in
an attractor that is diffeomorphic to that of the original full-state system.
However, the coordinate transformation back to the original attractor is
typically unknown, and learning the dynamics in the embedding space has
remained an open challenge for decades. Here, we design a custom deep
autoencoder network to learn a coordinate transformation from the delay
embedded space into a new space where it is possible to represent the dynamics
in a sparse, closed form. We demonstrate this approach on the Lorenz,
R\"ossler, and Lotka-Volterra systems, learning dynamics from a single
measurement variable. As a challenging example, we learn a Lorenz analogue from
a single scalar variable extracted from a video of a chaotic waterwheel
experiment. The resulting modeling framework combines deep learning to uncover
effective coordinates and the sparse identification of nonlinear dynamics
(SINDy) for interpretable modeling. Thus, we show that it is possible to
simultaneously learn a closed-form model and the associated coordinate system
for partially observed dynamics.

Hyperparameter plays an essential role in the fitting of supervised machine
learning algorithms. However, it is computationally expensive to tune all the
tunable hyperparameters simultaneously especially for large data sets. In this
paper, we give a definition of hyperparameter importance that can be estimated
by subsampling procedures. According to the importance, hyperparameters can
then be tuned on the entire data set more efficiently. We show theoretically
that the proposed importance on subsets of data is consistent with the one on
the population data under weak conditions. Numerical experiments show that the
proposed importance is consistent and can save a lot of computational

The architecture and the parameters of neural networks are often optimized
independently, which requires costly retraining of the parameters whenever the
architecture is modified. In this work we instead focus on growing the
architecture without requiring costly retraining. We present a method that adds
new neurons during training without impacting what is already learned, while
improving the training dynamics. We achieve the latter by maximizing the
gradients of the new weights and find the optimal initialization efficiently by
means of the singular value decomposition (SVD). We call this technique
Gradient Maximizing Growth (GradMax) and demonstrate its effectiveness in
variety of vision tasks and architectures.

Learning-based edge detection has hereunto been strongly supervised with
pixel-wise annotations which are tedious to obtain manually. We study the
problem of self-training edge detection, leveraging the untapped wealth of
large-scale unlabeled image datasets. We design a self-supervised framework
with multi-layer regularization and self-teaching. In particular, we impose a
consistency regularization which enforces the outputs from each of the multiple
layers to be consistent for the input image and its perturbed counterpart. We
adopt L0-smoothing as the 'perturbation' to encourage edge prediction lying on
salient boundaries following the cluster assumption in self-supervised
learning. Meanwhile, the network is trained with multi-layer supervision by
pseudo labels which are initialized with Canny edges and then iteratively
refined by the network as the training proceeds. The regularization and
self-teaching together attain a good balance of precision and recall, leading
to a significant performance boost over supervised methods, with lightweight
refinement on the target dataset. Furthermore, our method demonstrates strong
cross-dataset generality. For example, it attains 4.8% improvement for ODS and
5.8% for OIS when tested on the unseen BIPED dataset, compared to the

We present SeamlessGAN, a method capable of automatically generating tileable
texture maps from a single input exemplar. In contrast to most existing
methods, focused solely on solving the synthesis problem, our work tackles both
problems, synthesis and tileability, simultaneously. Our key idea is to realize
that tiling a latent space within a generative network trained using
adversarial expansion techniques produces outputs with continuity at the seam
intersection that can be then be turned into tileable images by cropping the
central area. Since not every value of the latent space is valid to produce
high-quality outputs, we leverage the discriminator as a perceptual error
metric capable of identifying artifact-free textures during a sampling process.
Further, in contrast to previous work on deep texture synthesis, our model is
designed and optimized to work with multi-layered texture representations,
enabling textures composed of multiple maps such as albedo, normals, etc. We
extensively test our design choices for the network architecture, loss function
and sampling parameters. We show qualitatively and quantitatively that our
approach outperforms previous methods and works for textures of different
types.

Despite recent progress made by self-supervised methods in representation
learning with residual networks, they still underperform supervised learning on
the ImageNet classification benchmark, limiting their applicability in
performance-critical settings. Building on prior theoretical insights from
ReLIC [Mitrovic et al., 2021], we include additional inductive biases into
self-supervised learning. We propose a new self-supervised representation
learning method, ReLICv2, which combines an explicit invariance loss with a
contrastive objective over a varied set of appropriately constructed data views
to avoid learning spurious correlations and obtain more informative
representations. ReLICv2 achieves $77.1\%$ top-$1$ accuracy on ImageNet under
linear evaluation on a ResNet50, thus improving the previous state-of-the-art
by absolute $+1.5\%$; on larger ResNet models, ReLICv2 achieves up to $80.6\%$
outperforming previous self-supervised approaches with margins up to $+2.3\%$.
Most notably, ReLICv2 is the first unsupervised representation learning method
to consistently outperform the supervised baseline in a like-for-like
comparison over a range of ResNet architectures. Using ReLICv2, we also learn
more robust and transferable representations that generalize better
out-of-distribution than previous work, both on image classification and
semantic segmentation. Finally, we show that despite using ResNet encoders,
ReLICv2 is comparable to state-of-the-art self-supervised vision transformers.

The increasing automation in many areas of the Industry expressly demands to
design efficient machine-learning solutions for the detection of abnormal
events. With the ubiquitous deployment of sensors monitoring nearly
continuously the health of complex infrastructures, anomaly detection can now
rely on measurements sampled at a very high frequency, providing a very rich
representation of the phenomenon under surveillance. In order to exploit fully
the information thus collected, the observations cannot be treated as
multivariate data anymore and a functional analysis approach is required. It is
the purpose of this paper to investigate the performance of recent techniques
for anomaly detection in the functional setup on real datasets. After an
overview of the state-of-the-art and a visual-descriptive study, a variety of
anomaly detection methods are compared. While taxonomies of abnormalities (e.g.
shape, location) in the functional setup are documented in the literature,
assigning a specific type to the identified anomalies appears to be a
challenging task. Thus, strengths and weaknesses of the existing approaches are
benchmarked in view of these highlighted types in a simulation study. Anomaly
detection methods are next evaluated on two datasets, related to the monitoring
of helicopters in flight and to the spectrometry of construction materials
namely. The benchmark analysis is concluded by recommendation guidance for

The new generation of wireless technologies, fitness trackers, and devices
with embedded sensors can have a big impact on healthcare systems and quality
of life. Among the most crucial aspects to consider in these devices are the
accuracy of the data produced and power consumption. Many of the events that
can be monitored, while apparently simple, may not be easily detectable and
recognizable by devices equipped with embedded sensors, especially on devices
with low computing capabilities. It is well known that deep learning reduces
the study of features that contribute to the recognition of the different
target classes. In this work, we present a portable and battery-powered
microcontroller-based device applicable to a wobble board. Wobble boards are
low-cost equipment that can be used for sensorimotor training to avoid ankle
injuries or as part of the rehabilitation process after an injury. The exercise
recognition process was implemented through the use of cognitive techniques
based on deep learning. To reduce power consumption, we add an adaptivity layer
that dynamically manages the device's hardware and software configuration to
adapt it to the required operating mode at runtime. Our experimental results
show that adjusting the node configuration to the workload at runtime can save
up to 60% of the power consumed. On a custom dataset, our optimized and
quantized neural network achieves an accuracy value greater than 97% for
detecting some specific physical exercises on a wobble board.

Learning and synthesizing stabilizing controllers for unknown nonlinear
control systems is a challenging problem for real-world and industrial
applications. Koopman operator theory allows one to analyze nonlinear systems
through the lens of linear systems and nonlinear control systems through the
lens of bilinear control systems. The key idea of these methods lies in the
transformation of the coordinates of the nonlinear system into the Koopman
observables, which are coordinates that allow the representation of the
original system (control system) as a higher dimensional linear (bilinear
control) system. However, for nonlinear control systems, the bilinear control
model obtained by applying Koopman operator based learning methods is not
necessarily stabilizable. Simultaneous identification of stabilizable lifted
bilinear control systems as well as the associated Koopman observables is still
an open problem. In this paper, we propose a framework to construct these
stabilizable bilinear models and identify its associated observables from data
by simultaneously learning a bilinear Koopman embedding for the underlying
unknown control affine nonlinear system as well as a Control Lyapunov Function
(CLF) for the Koopman based bilinear model using a learner and falsifier. Our
proposed approach thereby provides provable guarantees of asymptotic stability
for the Koopman based representation of the unknown control affine nonlinear
control system as a bilinear system. Numerical simulations are provided to
validate the efficacy of our proposed class of stabilizing feedback controllers
for unknown control-affine nonlinear systems.

The analysis of data streams has received considerable attention over the
past few decades due to sensors, social media, etc. It aims to recognize
patterns in an unordered, infinite, and evolving stream of observations.
Clustering this type of data requires some restrictions in time and memory.
This paper introduces a new data stream clustering method (IMOC-Stream). This
method, unlike the other clustering algorithms, uses two different objective
functions to capture different aspects of the data. The goal of IMOC-Stream is
to: 1) reduce computation time by using idle times to apply genetic operations
and enhance the solution. 2) reduce memory allocation by introducing a new tree
synopsis. 3) find arbitrarily shaped clusters by using a multi-objective
framework. We conducted an experimental study with high dimensional stream
datasets and compared them to well-known stream clustering techniques. The
experiments show the ability of our method to partition the data stream in
arbitrarily shaped, compact, and well-separated clusters while optimizing the
time and memory. Our method also outperformed most of the stream algorithms in
terms of NMI and ARAND measures.

Vision-language (V+L) pretraining models have achieved great success in
supporting multimedia applications by understanding the alignments between
images and text. While existing vision-language pretraining models primarily
focus on understanding objects in images or entities in text, they often ignore
the alignment at the level of events and their argument structures. In this
work, we propose a contrastive learning framework to enforce vision-language
pretraining models to comprehend events and associated argument (participant)
roles. To achieve this, we take advantage of text information extraction
technologies to obtain event structural knowledge, and utilize multiple prompt
functions to contrast difficult negative descriptions by manipulating event
structures. We also design an event graph alignment loss based on optimal
transport to capture event argument structures. In addition, we collect a large
event-rich dataset (106,875 images) for pretraining, which provides a more
challenging image retrieval benchmark to assess the understanding of
complicated lengthy sentences. Experiments show that our zero-shot CLIP-Event
outperforms the state-of-the-art supervised model in argument extraction on
Multimedia Event Extraction, achieving more than 5% absolute F-score gain in
event extraction, as well as significant improvements on a variety of
downstream tasks under zero-shot settings.

Deep neural networks (DNNs) have demonstrated superior performance over
classical machine learning to support many features in safety-critical systems.
Although DNNs are now widely used in such systems (e.g., self driving cars),
there is limited progress regarding automated support for functional safety
analysis in DNN-based systems. For example, the identification of root causes
of errors, to enable both risk analysis and DNN retraining, remains an open
problem. In this paper, we propose SAFE, a black-box approach to automatically
characterize the root causes of DNN errors. SAFE relies on a transfer learning
model pre-trained on ImageNet to extract the features from error-inducing
images. It then applies a density-based clustering algorithm to detect
arbitrary shaped clusters of images modeling plausible causes of error. Last,
clusters are used to effectively retrain and improve the DNN. The black-box
nature of SAFE is motivated by our objective not to require changes or even
access to the DNN internals to facilitate adoption. Experimental results show
the superior ability of SAFE in identifying different root causes of DNN errors
based on case studies in the automotive domain. It also yields significant
improvements in DNN accuracy after retraining, while saving significant
execution time and memory when compared to alternatives.

The study examines the problem of using augmented and virtual reality in the
process of blended learning in general secondary education. The study analyzes
the meaning of the concept of "blended learning". The conceptual principles of
blended learning are considered. The definition of augmented and virtual
reality is given. The mixed reality is considered as a separate kind of notion.
Separate applications of virtual and augmented reality that can be used in the
process of blended learning are considered. As a result of the study, the
authors propose possible ways to use augmented reality in the educational
process. The model of using augmented and virtual reality in blended learning
in general secondary education institutions was designed. It consists of the
following blocks: goal; teacher's activity; forms of education; teaching
methods; teaching aids; organizational forms of education; pupil activity and
results. Based on the model, the methodology of using augmented and virtual
reality in blended learning in general secondary education was developed. The
methodology contains the following components: target component, content
component, technological component and resultant component. The methodology is
quite universal and can be used for any subject in general secondary education.
The types of lessons in which it is expedient to use augmented (AR) and virtual
reality(VR) are determined. Recommendations are given at which stage of the
lesson it is better to use AR and VR tools (depending on the type of lesson).

Knowledge in the human mind exhibits a dualistic vector/network nature.
Modelling words as vectors is key to natural language processing, whereas
networks of word associations can map the nature of semantic memory. We
reconcile these paradigms - fragmented across linguistics, psychology and
computer science - by introducing FEature-Rich MUltiplex LEXical (FERMULEX)
networks. This novel framework merges structural similarities in networks and
vector features of words, which can be combined or explored independently.
Similarities model heterogenous word associations across
semantic/syntactic/phonological aspects of knowledge. Words are enriched with
multi-dimensional feature embeddings including frequency, age of acquisition,
length and polysemy. These aspects enable unprecedented explorations of
cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model
normative language acquisition by 1000 toddlers between 18 and 30 months.
Similarities and embeddings capture word homophily via conformity, which
measures assortative mixing via distance and features. Conformity unearths a
language kernel of frequent/polysemous/short nouns and verbs key for basic
sentence production, supporting recent evidence of children's syntactic
constructs emerging at 30 months. This kernel is invisible to network
core-detection and feature-only clustering: It emerges from the dual
vector/network nature of words. Our quantitative analysis reveals two key
strategies in early word learning. Modelling word acquisition as random walks
on FERMULEX topology, we highlight non-uniform filling of communicative
developmental inventories (CDIs). Conformity-based walkers lead to accurate
(75%), precise (55%) and partially well-recalled (34%) predictions of early
word learning in CDIs, providing quantitative support to previous empirical
findings and developmental theories.

Many statistical machine approaches could ultimately highlight novel features
of the etiology of complex diseases by analyzing multi-omics data. However,
they are sensitive to some deviations in distribution when the observed samples
are potentially contaminated with adversarial corrupted outliers (e.g., a
fictional data distribution). Likewise, statistical advances lag in supporting
comprehensive data-driven analyses of complex multi-omics data integration. We
propose a novel non-linear M-estimator-based approach, "robust kernel machine
regression (RobKMR)," to improve the robustness of statistical machine
regression and the diversity of fictional data to examine the higher-order
composite effect of multi-omics datasets. We address a robust kernel-centered
Gram matrix to estimate the model parameters accurately. We also propose a
robust score test to assess the marginal and joint Hadamard product of features
from multi-omics data. We apply our proposed approach to a multi-omics dataset
of osteoporosis (OP) from Caucasian females. Experiments demonstrate that the
proposed approach effectively identifies the inter-related risk factors of OP.
With solid evidence (p-value = 0.00001), biological validations, network-based
analysis, causal inference, and drug repurposing, the selected three triplets
((DKK1, SMTN, DRGX), (MTND5, FASTKD2, CSMD3), (MTND5, COG3, CSMD3)) are
significant biomarkers and directly relate to BMD. Overall, the top three
selected genes (DKK1, MTND5, FASTKD2) and one gene (SIDT1 at p-value= 0.001)
significantly bond with four drugs- Tacrolimus, Ibandronate, Alendronate, and
Bazedoxifene out of 30 candidates for drug repurposing in OP. Further, the
proposed approach can be applied to any disease model where multi-omics
datasets are available.

Quantum machine learning is a fast emerging field that aims to tackle machine
learning using quantum algorithms and quantum computing. Due to the lack of
physical qubits and an effective means to map real-world data from Euclidean
space to Hilbert space, most of these methods focus on quantum analogies or
process simulations rather than devising concrete architectures based on
qubits. In this paper, we propose a novel hybrid quantum-classical algorithm
for graph-structured data, which we refer to as the Decompositional Quantum
Graph Neural Network (DQGNN). DQGNN implements the GNN theoretical framework
using the tensor product and unity matrices representation, which greatly
reduces the number of model parameters required. When controlled by a classical
computer, DQGNN can accommodate arbitrarily sized graphs by processing
substructures from the input graph using a modestly-sized quantum device. The
architecture is based on a novel mapping from real-world data to Hilbert space.
This mapping maintains the distance relations present in the data and reduces
information loss. Experimental results show that the proposed method
outperforms competitive state-of-the-art models with only 1.68\% parameters
compared to those models.

This paper presents flood prediction models for the state of Kerala in India
by analyzing the monthly rainfall data and applying machine learning algorithms
including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random
Forests, and Support Vector Machine. Although these models have shown high
accuracy prediction of the occurrence of flood in a particular year, they do
not quantitatively and qualitatively explain the prediction decision. This
paper shows how the background features are learned that contributed to the
prediction decision and further extended to explain the inner workings with the
development of explainable artificial intelligence modules. The obtained
results have confirmed the validity of the findings uncovered by the explainer
modules basing on the historical flood monthly rainfall data in Kerala.

Neyman-Scott processes (NSPs) are point process models that generate clusters
of points in time or space. They are natural models for a wide range of
phenomena, ranging from neural spike trains to document streams. The clustering
property is achieved via a doubly stochastic formulation: first, a set of
latent events is drawn from a Poisson process; then, each latent event
generates a set of observed data points according to another Poisson process.
This construction is similar to Bayesian nonparametric mixture models like the
Dirichlet process mixture model (DPMM) in that the number of latent events
(i.e. clusters) is a random variable, but the point process formulation makes
the NSP especially well suited to modeling spatiotemporal data. While many
specialized algorithms have been developed for DPMMs, comparatively fewer works
have focused on inference in NSPs. Here, we present novel connections between
NSPs and DPMMs, with the key link being a third class of Bayesian mixture
models called mixture of finite mixture models (MFMMs). Leveraging this
connection, we adapt the standard collapsed Gibbs sampling algorithm for DPMMs
to enable scalable Bayesian inference on NSP models. We demonstrate the
potential of Neyman-Scott processes on a variety of applications including
sequence detection in neural spike trains and event detection in document
streams.

Machine learning techniques typically applied to dementia forecasting lack in
their capabilities to jointly learn several tasks, handle time dependent
heterogeneous data and missing values. In this paper, we propose a framework
using the recently presented SSHIBA model for jointly learning different tasks
on longitudinal data with missing values. The method uses Bayesian variational
inference to impute missing values and combine information of several views.
This way, we can combine different data-views from different time-points in a
common latent space and learn the relations between each time-point while
simultaneously modelling and predicting several output variables. We apply this
model to predict together diagnosis, ventricle volume, and clinical scores in
dementia. The results demonstrate that SSHIBA is capable of learning a good
imputation of the missing values and outperforming the baselines while
simultaneously predicting three different tasks.

In the context of reinforcement learning we introduce the concept of
criticality of a state, which indicates the extent to which the choice of
action in that particular state influences the expected return. That is, a
state in which the choice of action is more likely to influence the final
outcome is considered as more critical than a state in which it is less likely
to influence the final outcome.
  We formulate a criticality-based varying step number algorithm (CVS) - a
flexible step number algorithm that utilizes the criticality function provided
by a human, or learned directly from the environment. We test it in three
different domains including the Atari Pong environment, Road-Tree environment,
and Shooter environment. We demonstrate that CVS is able to outperform popular
learning algorithms such as Deep Q-Learning and Monte Carlo.

It is commonly acknowledged that the availability of the huge amount of
(training) data is one of the most important factors for many recent advances
in Artificial Intelligence (AI). However, datasets are often designed for
specific tasks in narrow AI sub areas and there is no unified way to manage and
access them. This not only creates unnecessary overheads when training or
deploying Machine Learning models but also limits the understanding of the
data, which is very important for data-centric AI. In this paper, we present
our vision about a unified framework for different datasets so that they can be
integrated and queried easily, e.g., using standard query languages. We
demonstrate this in our ongoing work to create a framework for datasets in
Computer Vision and show its advantages in different scenarios. Our
demonstration is available at https://vision.semkg.org.

In this feasibility study, we have implemented a recently proposed partially
linear multiuser detection algorithm in reproducing kernel Hilbert spaces
(RKHSs) on a GPU-accelerated platform. Partially linear multiuser detection,
which combines the robustness of linear detection with the power of nonlinear
methods, has been proposed for a massive connectivity scenario with the
non-orthogonal multiple access (NOMA). This is a promising approach, but
detecting payloads within a received orthogonal frequency division multiplexing
(OFDM) radio frame requires the execution of a large number of inner product
operations, which are the main computational burden of the algorithm. Although
inner-product operations consist of simple kernel evaluations, their vast
number poses a challenge in ultra-low latency (ULL) applications, because the
time needed for computing the inner products might exceed the sub-millisecond
latency requirement. To address this problem, this study demonstrates the
acceleration of the inner-product operations through massive parallelization.
The result is a GPU-accelerated real-time OFDM receiver that enables
sub-millisecond latency detection to meet the requirements of 5th generation
(5G) and beyond ultra-reliable and low latency communications (URLLC) systems.
Moreover, the parallelization and acceleration techniques explored and
demonstrated in this study can be extended to many other signal processing
algorithms in Hilbert spaces, such as those based on projection onto convex
sets (POCS) and adaptive projected subgradient method (APSM) algorithms.
Experimental results and comparisons with the state-of-art confirm the
effectiveness of our techniques.

Unsupervised domain adaptation (UDA) between two significantly disparate
domains to learn high-level semantic alignment is a crucial yet challenging
task.~To this end, in this work, we propose exploiting low-level edge
information to facilitate the adaptation as a precursor task, which has a small
cross-domain gap, compared with semantic segmentation.~The precise contour then
provides spatial information to guide the semantic adaptation. More
specifically, we propose a multi-task framework to learn a contouring
adaptation network along with a semantic segmentation adaptation network, which
takes both magnetic resonance imaging (MRI) slice and its initial edge map as
input.~These two networks are jointly trained with source domain labels, and
the feature and edge map level adversarial learning is carried out for
cross-domain alignment. In addition, self-entropy minimization is incorporated
to further enhance segmentation performance. We evaluated our framework on the
BraTS2018 database for cross-modality segmentation of brain tumors, showing the
validity and superiority of our approach, compared with competing methods.

Since sparse neural networks usually contain many zero weights, these
unnecessary network connections can potentially be eliminated without degrading
network performance. Therefore, well-designed sparse neural networks have the
potential to significantly reduce FLOPs and computational resources. In this
work, we propose a new automatic pruning method - Sparse Connectivity Learning
(SCL). Specifically, a weight is re-parameterized as an element-wise
multiplication of a trainable weight variable and a binary mask. Thus, network
connectivity is fully described by the binary mask, which is modulated by a
unit step function. We theoretically prove the fundamental principle of using a
straight-through estimator (STE) for network pruning. This principle is that
the proxy gradients of STE should be positive, ensuring that mask variables
converge at their minima. After finding Leaky ReLU, Softplus, and Identity STEs
can satisfy this principle, we propose to adopt Identity STE in SCL for
discrete mask relaxation. We find that mask gradients of different features are
very unbalanced, hence, we propose to normalize mask gradients of each feature
to optimize mask variable training. In order to automatically train sparse
masks, we include the total number of network connections as a regularization
term in our objective function. As SCL does not require pruning criteria or
hyper-parameters defined by designers for network layers, the network is
explored in a larger hypothesis space to achieve optimized sparse connectivity
for the best performance. SCL overcomes the limitations of existing automatic
pruning methods. Experimental results demonstrate that SCL can automatically
learn and select important network connections for various baseline network
structures. Deep learning models trained by SCL outperform the SOTA
human-designed and automatic pruning methods in sparsity, accuracy, and FLOPs

Characterizing the patterns of errors that a system makes helps researchers
focus future development on increasing its accuracy and robustness. We propose
a novel form of "meta learning" that automatically learns interpretable rules
that characterize the types of errors that a system makes, and demonstrate
these rules' ability to help understand and improve two NLP systems. Our
approach works by collecting error cases on validation data, extracting
meta-features describing these samples, and finally learning rules that
characterize errors using these features. We apply our approach to VilBERT, for
Visual Question Answering, and RoBERTa, for Common Sense Question Answering.
Our system learns interpretable rules that provide insights into systemic
errors these systems make on the given tasks. Using these insights, we are also
able to "close the loop" and modestly improve performance of these systems.

The marine ecosystem is changing at an alarming rate, exhibiting biodiversity
loss and the migration of tropical species to temperate basins. Monitoring the
underwater environments and their inhabitants is of fundamental importance to
understand the evolution of these systems and implement safeguard policies.
However, assessing and tracking biodiversity is often a complex task,
especially in large and uncontrolled environments, such as the oceans. One of
the most popular and effective methods for monitoring marine biodiversity is
passive acoustics monitoring (PAM), which employs hydrophones to capture
underwater sound. Many aquatic animals produce sounds characteristic of their
own species; these signals travel efficiently underwater and can be detected
even at great distances. Furthermore, modern technologies are becoming more and
more convenient and precise, allowing for very accurate and careful data
acquisition. To date, audio captured with PAM devices is frequently manually
processed by marine biologists and interpreted with traditional signal
processing techniques for the detection of animal vocalizations. This is a
challenging task, as PAM recordings are often over long periods of time.
Moreover, one of the causes of biodiversity loss is sound pollution; in data
obtained from regions with loud anthropic noise, it is hard to separate the
artificial from the fish sound manually. Nowadays, machine learning and, in
particular, deep learning represents the state of the art for processing audio
signals. Specifically, sound separation networks are able to identify and
separate human voices and musical instruments. In this work, we show that the
same techniques can be successfully used to automatically extract fish
vocalizations in PAM recordings, opening up the possibility for biodiversity
monitoring at a large scale.

Upward lightning is much rarer than downward lightning and requires tall
($100+$~m) structures to initiate. It may be either triggered by other
lightning discharges or completely self-initiated. While conventional lightning
location systems reliably detect downward lightning, they miss a specific flash
type of upward lightning that consists only of a continuous current. Globally,
only few specially instrumented towers can detect this flash type. The
proliferation of wind turbines in combination with large damage from upward
lightning necessitates an improved understanding under which conditions the
self-initiated and the undetected subtype of upward lightning occur. To find
larger-scale meteorological conditions favorable for self-initiated and
undetectable upward lightning, this study uses a random forest machine learning
model. It combines direct measurements at the specially instrumented tower at
Gaisberg mountain in Austria with explanatory variables from larger-scale
atmospheric reanalysis data (ERA5). Atmospheric variables reliably explain
whether upward lightning is self-initiated by the tower or triggered by other
lightning discharges. The most important variable is the height of the
$-10~^\circ$C isotherm above the tall structure: the closer it is the higher is
the probability of self-initiated upward lightning. Two-meter temperature and
the amount of CAPE are also important. For the occurrence of upward lightning
undetectable by lightning location systems, this study finds a strong
relationship to the absence of lightning in the vicinity.

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of
retrieving a particular photo in a given query sketch. However, its widespread
applicability is limited by the fact that it is difficult to draw a complete
sketch for most people, and the drawing process often takes time. In this
study, we aim to retrieve the target photo with the least number of strokes
possible (incomplete sketch), named on-the-fly FG-SBIR (Bhunia et al. 2020),
which starts retrieving at each stroke as soon as the drawing begins. We
consider that there is a significant correlation among these incomplete
sketches in the sketch drawing episode of each photo. To learn more efficient
joint embedding space shared between the photo and its incomplete sketches, we
propose a multi-granularity association learning framework that further
optimizes the embedding space of all incomplete sketches. Specifically, based
on the integrity of the sketch, we can divide a complete sketch episode into
several stages, each of which corresponds to a simple linear mapping layer.
Moreover, our framework guides the vector space representation of the current
sketch to approximate that of its later sketches to realize the retrieval
performance of the sketch with fewer strokes to approach that of the sketch
with more strokes. In the experiments, we proposed more realistic challenges,
and our method achieved superior early retrieval efficiency over the
state-of-the-art methods and alternative baselines on two publicly available
fine-grained sketch retrieval datasets.

With the fast development of machine learning technologies, deep learning
models have been deployed in almost every aspect of everyday life. However, the
privacy and security of these models are threatened by adversarial attacks.
Among which black-box attack is closer to reality, where limited knowledge can
be acquired from the model. In this paper, we provided basic background
knowledge about adversarial attack and analyzed four black-box attack
algorithms: Bandits, NES, Square Attack and ZOsignSGD comprehensively. We also
explored the newly proposed Square Attack method with respect to square size,
hoping to improve its query efficiency.

Reinforcement Learning and recently Deep Reinforcement Learning are popular
methods for solving sequential decision making problems modeled as Markov
Decision Processes. RL modeling of a problem and selecting algorithms and
hyper-parameters require careful considerations as different configurations may
entail completely different performances. These considerations are mainly the
task of RL experts; however, RL is progressively becoming popular in other
fields where the researchers and system designers are not RL experts. Besides,
many modeling decisions, such as defining state and action space, size of
batches and frequency of batch updating, and number of timesteps are typically
made manually. For these reasons, automating different components of RL
framework is of great importance and it has attracted much attention in recent
years. Automated RL provides a framework in which different components of RL
including MDP modeling, algorithm selection and hyper-parameter optimization
are modeled and defined automatically. In this article, we explore the
literature and present recent work that can be used in automated RL. Moreover,
we discuss the challenges, open questions and research directions in AutoRL.

We develop a methodology to construct low-dimensional predictive models from
data sets representing essentially nonlinear (or non-linearizable) dynamical
systems with a hyperbolic linear part that are subject to external forcing with
finitely many frequencies. Our data-driven, sparse, nonlinear models are
obtained as extended normal forms of the reduced dynamics on low-dimensional,
attracting spectral submanifolds (SSMs) of the dynamical system. We illustrate
the power of data-driven SSM reduction on high-dimensional numerical data sets
and experimental measurements involving beam oscillations, vortex shedding and
sloshing in a water tank. We find that SSM reduction trained on unforced data
also predicts nonlinear response accurately under additional external forcing.

The recommendation system, relying on historical observational data to model
the complex relationships among the users and items, has achieved great success
in real-world applications. Selection bias is one of the most important issues
of the existing observational data based approaches, which is actually caused
by multiple types of unobserved exposure strategies (e.g. promotions and
holiday effects). Though various methods have been proposed to address this
problem, they are mainly relying on the implicit debiasing techniques but not
explicitly modeling the unobserved exposure strategies. By explicitly
Reconstructing Exposure STrategies (REST in short), we formalize the
recommendation problem as the counterfactual reasoning and propose the debiased
social recommendation method. In REST, we assume that the exposure of an item
is controlled by the latent exposure strategies, the user, and the item. Based
on the above generation process, we first provide the theoretical guarantee of
our method via identification analysis. Second, we employ a variational
auto-encoder to reconstruct the latent exposure strategies, with the help of
the social networks and the items. Third, we devise a counterfactual reasoning
based recommendation algorithm by leveraging the recovered exposure strategies.
Experiments on four real-world datasets, including three published datasets and
one private WeChat Official Account dataset, demonstrate significant
improvements over several state-of-the-art methods.

In order to develop complex relationships between their inputs and outputs,
deep neural networks train and adjust large number of parameters. To make these
networks work at high accuracy, vast amounts of data are needed. Sometimes,
however, the quantity of data needed is not present or obtainable for training.
Neuron-specific dropout (NSDropout) is a tool to address this problem.
NSDropout looks at both the training pass, and validation pass, of a layer in a
model. By comparing the average values produced by each neuron for each class
in a data set, the network is able to drop targeted units. The layer is able to
predict what features, or noise, the model is looking at during testing that
isn't present when looking at samples from validation. Unlike dropout, the
"thinned" networks cannot be "unthinned" for testing. Neuron-specific dropout
has proved to achieve similar, if not better, testing accuracy with far less
data than traditional methods including dropout and other regularization
methods. Experimentation has shown that neuron-specific dropout reduces the
chance of a network overfitting and reduces the need for large training samples
on supervised learning tasks in image recognition, all while producing
best-in-class results.

Industry 4.0 in health care has evolved drastically over the past century. In
fact, it is evolving every day, with new tools and strategies being developed
by physicians and researchers alike. Health care and technology have been
intertwined together with the advancement of cloud computing and big data. This
study aims to analyze the impact of industry 4.0 in health care systems. To do
so, a systematic literature review was carried out considering peer-reviewed
articles extracted from the two popular databases: Scopus and Web of Science
(WoS). PRISMA statement 2015 was used to include and exclude that data. At
first, a bibliometric analysis was carried out using 346 articles considering
the following factors: publication by year, journal, authors, countries,
institutions, authors' keywords, and citations. Finally, qualitative analysis
was carried out based on selected 32 articles considering the following
factors: a conceptual framework, schedule problems, security, COVID-19, digital
supply chain, and blockchain technology. Study finding suggests that during the
onset of COVID-19, health care and industry 4.0 has been merged and evolved
jointly, considering various crisis such as data security, resource allocation,
and data transparency. Industry 4.0 enables many technologies such as the
internet of things (IoT), blockchain, big data, cloud computing, machine
learning, deep learning, information, and communication technologies (ICT) to
track patients' records and helps reduce social transmission COVID-19 and so
on. The study findings will give future researchers and practitioners some
insights regarding the integration of health care and Industry 4.0.

In this paper, we focus on the two tasks of 3D shape abstraction and semantic
analysis. This is in contrast to current methods, which focus solely on either
3D shape abstraction or semantic analysis. In addition, previous methods have
had difficulty producing instance-level semantic results, which has limited
their application. We present a novel method for the joint estimation of a 3D
shape abstraction and semantic analysis. Our approach first generates a number
of 3D semantic candidate regions for a 3D shape; we then employ these
candidates to directly predict the semantic categories and refine the
parameters of the candidate regions simultaneously using a deep convolutional
neural network. Finally, we design an algorithm to fuse the predicted results
and obtain the final semantic abstraction, which is shown to be an improvement
over a standard non maximum suppression. Experimental results demonstrate that
our approach can produce state-of-the-art results. Moreover, we also find that
our results can be easily applied to instance-level semantic part segmentation
and shape matching.

Collecting labeled data for many important tasks in chemoinformatics is time
consuming and requires expensive experiments. In recent years, machine learning
has been used to learn rich representations of molecules using large scale
unlabeled molecular datasets and transfer the knowledge to solve the more
challenging tasks with limited datasets. Variational autoencoders are one of
the tools that have been proposed to perform the transfer for both chemical
property prediction and molecular generation tasks. In this work we propose a
simple method to improve chemical property prediction performance of machine
learning models by incorporating additional information on correlated molecular
descriptors in the representations learned by variational autoencoders. We
verify the method on three property prediction asks. We explore the impact of
the number of incorporated descriptors, correlation between the descriptors and
the target properties, sizes of the datasets etc. Finally, we show the relation
between the performance of property prediction models and the distance between
property prediction dataset and the larger unlabeled dataset in the
representation space.

In the SSLAD-Track 3B challenge on continual learning, we propose the method
of COntinual Learning with Transformer (COLT). We find that transformers suffer
less from catastrophic forgetting compared to convolutional neural network. The
major principle of our method is to equip the transformer based feature
extractor with old knowledge distillation and head expanding strategies to
compete catastrophic forgetting. In this report, we first introduce the overall
framework of continual learning for object detection. Then, we analyse the key
elements' effect on withstanding catastrophic forgetting in our solution. Our
method achieves 70.78 mAP on the SSLAD-Track 3B challenge test set.

Recent studies have significantly enhanced the performance of single-image
super-resolution (SR) using convolutional neural networks (CNNs). While there
can be many high-resolution (HR) solutions for a given input, most existing
CNN-based methods do not explore alternative solutions during the inference. A
typical approach to obtaining alternative SR results is to train multiple SR
models with different loss weightings and exploit the combination of these
models. Instead of using multiple models, we present a more efficient method to
train a single adjustable SR model on various combinations of losses by taking
advantage of multi-task learning. Specifically, we optimize an SR model with a
conditional objective during training, where the objective is a weighted sum of
multiple perceptual losses at different feature levels. The weights vary
according to given conditions, and the set of weights is defined as a style
controller. Also, we present an architecture appropriate for this training
scheme, which is the Residual-in-Residual Dense Block equipped with spatial
feature transformation layers. At the inference phase, our trained model can
generate locally different outputs conditioned on the style control map.
Extensive experiments show that the proposed SR model produces various
desirable reconstructions without artifacts and yields comparable quantitative
performance to state-of-the-art SR methods.

Graph problems such as traveling salesman problem, or finding minimal Steiner
trees are widely studied and used in data engineering and computer science.
Typically, in real-world applications, the features of the graph tend to change
over time, thus, finding a solution to the problem becomes challenging. The
dynamic version of many graph problems are the key for a plethora of real-world
problems in transportation, telecommunication, and social networks. In recent
years, using deep learning techniques to find heuristic solutions for NP-hard
graph combinatorial problems has gained much interest as these learned
heuristics can find near-optimal solutions efficiently. However, most of the
existing methods for learning heuristics focus on static graph problems. The
dynamic nature makes NP-hard graph problems much more challenging to learn, and
the existing methods fail to find reasonable solutions.
  In this paper, we propose a novel architecture named Graph Temporal Attention
with Reinforcement Learning (GTA-RL) to learn heuristic solutions for
graph-based dynamic combinatorial optimization problems. The GTA-RL
architecture consists of an encoder capable of embedding temporal features of a
combinatorial problem instance and a decoder capable of dynamically focusing on
the embedded features to find a solution to a given combinatorial problem
instance. We then extend our architecture to learn heuristics for the real-time
version of combinatorial optimization problems where all input features of a
problem are not known a prior, but rather learned in real-time. Our
experimental results against several state-of-the-art learning-based algorithms
and optimal solvers demonstrate that our approach outperforms the
state-of-the-art learning-based approaches in terms of effectiveness and
optimal solvers in terms of efficiency on dynamic and real-time graph
combinatorial optimization.

With the proliferation of AI-enabled software systems in smart manufacturing,
the role of such systems moves away from a reactive to a proactive role that
provides context-specific support to manufacturing operators. In the frame of
the EU funded Teaming.AI project, we identified the monitoring of teaming
aspects in human-AI collaboration, the runtime monitoring and validation of
ethical policies, and the support for experimentation with data and machine
learning algorithms as the most relevant challenges for human-AI teaming in
smart manufacturing. Based on these challenges, we developed a reference
software architecture based on knowledge graphs, tracking and scene analysis,
and components for relational machine learning with a particular focus on its
scalability. Our approach uses knowledge graphs to capture product- and process
specific knowledge in the manufacturing process and to utilize it for
relational machine learning. This allows for context-specific recommendations
for actions in the manufacturing process for the optimization of product
quality and the prevention of physical harm. The empirical validation of this
software architecture will be conducted in cooperation with three large-scale
companies in the automotive, energy systems, and precision machining domain. In
this paper we discuss the identified challenges for such a reference software
architecture, present its preliminary status, and sketch our further research
vision in this project.

The challenging field of scene text detection requires complex data
annotation, which is time-consuming and expensive. Techniques, such as weak
supervision, can reduce the amount of data needed. In this paper we propose a
weak supervision method for scene text detection, which makes use of
reinforcement learning (RL). The reward received by the RL agent is estimated
by a neural network, instead of being inferred from ground-truth labels. First,
we enhance an existing supervised RL approach to text detection with several
training optimizations, allowing us to close the performance gap to
regression-based algorithms. We then use our proposed system in a weakly- and
semi-supervised training on real-world data. Our results show that training in
a weakly supervised setting is feasible. However, we find that using our model
in a semi-supervised setting , e.g. when combining labeled synthetic data with
unannotated real-world data, produces the best results.

Dancing video retargeting aims to synthesize a video that transfers the dance
movements from a source video to a target person. Previous work need collect a
several-minute-long video of a target person with thousands of frames to train
a personalized model. However, the trained model can only generate videos of
the same person. To address the limitations, recent work tackled few-shot
dancing video retargeting, which learns to synthesize videos of unseen persons
by leveraging a few frames of them. In practice, given a few frames of a
person, these work simply regarded them as a batch of individual images without
temporal correlations, thus generating temporally incoherent dancing videos of
low visual quality. In this work, we model a few frames of a person as a series
of dancing moves, where each move contains two consecutive frames, to extract
the appearance patterns and the temporal dynamics of this person. We propose
MetaDance, which utilizes temporal-aware meta-learning to optimize the
initialization of a model through the synthesis of dancing moves, such that the
meta-trained model can be efficiently tuned towards enhanced visual quality and
strengthened temporal stability for unseen persons with a few frames. Extensive
evaluations show large superiority of our method.

Pre-training a model to learn transferable video-text representation for
retrieval has attracted a lot of attention in recent years. Previous dominant
works mainly adopt two separate encoders for efficient retrieval, but ignore
local associations between videos and texts. Another line of research uses a
joint encoder to interact video with texts, but results in low efficiency since
each text-video pair needs to be fed into the model. In this work, we enable
fine-grained video-text interactions while maintaining high efficiency for
retrieval via a novel pretext task, dubbed as Multiple Choice Questions (MCQ),
where a parametric module BridgeFormer is trained to answer the "questions"
constructed by the text features via resorting to the video features.
Specifically, we exploit the rich semantics of text (i.e., nouns and verbs) to
build questions, with which the video encoder can be trained to capture more
regional content and temporal dynamics. In the form of questions and answers,
the semantic associations between local video-text features can be properly
established. BridgeFormer is able to be removed for downstream retrieval,
rendering an efficient and flexible model with only two encoders. Our method
outperforms state-of-the-art methods on the popular text-to-video retrieval
task in five datasets with different experimental setups (i.e., zero-shot and
fine-tune), including HowTo100M (one million videos). We further conduct
zero-shot action recognition, which can be cast as video-to-text retrieval, and
our approach also significantly surpasses its counterparts. As an additional
benefit, our method achieves competitive results with much shorter pre-training
videos on single-modality downstream tasks, e.g., action recognition with
linear evaluation.

Given access to a machine learning model, can an adversary reconstruct the
model's training data? This work studies this question from the lens of a
powerful informed adversary who knows all the training data points except one.
By instantiating concrete attacks, we show it is feasible to reconstruct the
remaining data point in this stringent threat model. For convex models (e.g.
logistic regression), reconstruction attacks are simple and can be derived in
closed-form. For more general models (e.g. neural networks), we propose an
attack strategy based on training a reconstructor network that receives as
input the weights of the model under attack and produces as output the target
data point. We demonstrate the effectiveness of our attack on image classifiers
trained on MNIST and CIFAR-10, and systematically investigate which factors of
standard machine learning pipelines affect reconstruction success. Finally, we
theoretically investigate what amount of differential privacy suffices to
mitigate reconstruction attacks by informed adversaries. Our work provides an
effective reconstruction attack that model developers can use to assess
memorization of individual points in general settings beyond those considered
in previous works (e.g. generative language models or access to training
gradients); it shows that standard models have the capacity to store enough
information to enable high-fidelity reconstruction of training data points; and
it demonstrates that differential privacy can successfully mitigate such
attacks in a parameter regime where utility degradation is minimal.

Semantic networks, such as the knowledge graph, can represent the knowledge
leveraging the graph structure. Although the knowledge graph shows promising
values in natural language processing, it suffers from incompleteness. This
paper focuses on knowledge graph completion by predicting linkage between
entities, which is a fundamental yet critical task. Semantic matching is a
potential solution as it can deal with unseen entities, which the translational
distance based methods struggle with. However, to achieve competitive
performance as translational distance based methods, semantic matching based
methods require large-scale datasets for the training purpose, which are
typically unavailable in practical settings. Therefore, we employ the language
model and introduce a novel knowledge graph architecture named LP-BERT, which
contains two main stages: multi-task pre-training and knowledge graph
fine-tuning. In the pre-training phase, three tasks are taken to drive the
model to learn the relationship from triples by predicting either entities or
relations. While in the fine-tuning phase, inspired by contrastive learning, we
design a triple-style negative sampling in a batch, which greatly increases the
proportion of negative sampling while keeping the training time almost
unchanged. Furthermore, we propose a new data augmentation method utilizing the
inverse relationship of triples to improve the performance and robustness of
the model. To demonstrate the effectiveness of our method, we conduct extensive
experiments on three widely-used datasets, WN18RR, FB15k-237, and UMLS. The
experimental results demonstrate the superiority of our methods, and our
approach achieves state-of-the-art results on WN18RR and FB15k-237 datasets.
Significantly, Hits@10 indicator is improved by 5% from previous
state-of-the-art result on the WN18RR dataset while reaching 100% on the UMLS
dataset.

Logging is a practice widely adopted in several phases of the software
lifecycle. For example, during software development log statements allow
engineers to verify and debug the system by exposing fine-grained information
of the running software. While the benefits of logging are undisputed, taking
proper decisions about where to inject log statements, what information to log,
and at which log level (e.g., error, warning) is crucial for the logging
effectiveness. In this paper, we present LANCE (Log stAtemeNt reCommEnder), the
first approach supporting developers in all these decisions. LANCE features a
Text-To-Text-Transfer-Transformer (T5) model that has been trained on 6,894,456
Java methods. LANCE takes as input a Java method and injects in it a full log
statement, including a human-comprehensible logging message and properly
choosing the needed log level and the statement location. Our results show that
LANCE is able to (i) properly identify the location in the code where to inject
the statement in 65.9% of Java methods requiring it; (ii) selecting the proper
log level in 66.2% of cases; and (iii) generate a completely correct log
statement including a meaningful logging message in 15.2% of cases.

Manually annotating complex scene point cloud datasets is both costly and
error-prone. To reduce the reliance on labeled data, a new model called
SnapshotNet is proposed as a self-supervised feature learning approach, which
directly works on the unlabeled point cloud data of a complex 3D scene. The
SnapshotNet pipeline includes three stages. In the snapshot capturing stage,
snapshots, which are defined as local collections of points, are sampled from
the point cloud scene. A snapshot could be a view of a local 3D scan directly
captured from the real scene, or a virtual view of such from a large 3D point
cloud dataset. Snapshots could also be sampled at different sampling rates or
fields of view (FOVs), thus multi-FOV snapshots, to capture scale information
from the scene. In the feature learning stage, a new pre-text task called
multi-FOV contrasting is proposed to recognize whether two snapshots are from
the same object or not, within the same FOV or across different FOVs. Snapshots
go through two self-supervised learning steps: the contrastive learning step
with both part and scale contrasting, followed by a snapshot clustering step to
extract higher level semantic features. Then a weakly-supervised segmentation
stage is implemented by first training a standard SVM classifier on the learned
features with a small fraction of labeled snapshots. The trained SVM is used to
predict labels for input snapshots and predicted labels are converted into
point-wise label assignments for semantic segmentation of the entire scene
using a voting procedure. The experiments are conducted on the Semantic3D
dataset and the results have shown that the proposed method is capable of
learning effective features from snapshots of complex scene data without any
labels. Moreover, the proposed method has shown advantages when comparing to
the SOA method on weakly-supervised point cloud semantic segmentation.

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment
analysis. To better comprehend long complicated sentences and obtain accurate
aspect-specific information, linguistic and commonsense knowledge are generally
required in this task. However, most methods employ complicated and inefficient
approaches to incorporate external knowledge, e.g., directly searching the
graph nodes. Additionally, the complementarity between external knowledge and
linguistic information has not been thoroughly studied. To this end, we propose
a knowledge graph augmented network (KGAN), which aims to effectively
incorporate external knowledge with explicitly syntactic and contextual
information. In particular, KGAN captures the sentiment feature representations
from multiple different perspectives, i.e., context-, syntax- and
knowledge-based. First, KGAN learns the contextual and syntactic
representations in parallel to fully extract the semantic features. Then, KGAN
integrates the knowledge graphs into the embedding space, based on which the
aspect-specific knowledge representations are further obtained via an attention
mechanism. Last, we propose a hierarchical fusion module to complement these
multiview representations in a local-to-global manner. Extensive experiments on
three popular ABSA benchmarks demonstrate the effectiveness and robustness of
our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN
achieves a new record of state-of-the-art performance.

Significant advances in edge computing capabilities enable learning to occur
at geographically diverse locations. In general, the training data needed in
those learning tasks are not only heterogeneous but also not fully generated
locally. In this paper, we propose an experimental design network paradigm,
wherein learner nodes train possibly different Bayesian linear regression
models via consuming data streams generated by data source nodes over a
network. We formulate this problem as a social welfare optimization problem in
which the global objective is defined as the sum of experimental design
objectives of individual learners, and the decision variables are the data
transmission strategies subject to network constraints. We first show that,
assuming Poisson data streams, the global objective is a continuous
DR-submodular function. We then propose a Frank-Wolfe type algorithm that
outputs a solution within a 1-1/e factor from the optimal. Our algorithm
contains a novel gradient estimation component which is carefully designed
based on Poisson tail bounds and sampling. Finally, we complement our
theoretical findings through extensive experiments. Our numerical evaluation
shows that the proposed algorithm outperforms several baseline algorithms both
in maximizing the global objective and in the quality of the trained models.

Multivariate time series (MTS) forecasting plays an important role in the
automation and optimization of intelligent applications. It is a challenging
task, as we need to consider both complex intra-variable dependencies and
inter-variable dependencies. Existing works only learn temporal patterns with
the help of single inter-variable dependencies. However, there are multi-scale
temporal patterns in many real-world MTS. Single inter-variable dependencies
make the model prefer to learn one type of prominent and shared temporal
patterns. In this paper, we propose a multi-scale adaptive graph neural network
(MAGNN) to address the above issue. MAGNN exploits a multi-scale pyramid
network to preserve the underlying temporal dependencies at different time
scales. Since the inter-variable dependencies may be different under distinct
time scales, an adaptive graph learning module is designed to infer the
scale-specific inter-variable dependencies without pre-defined priors. Given
the multi-scale feature representations and scale-specific inter-variable
dependencies, a multi-scale temporal graph neural network is introduced to
jointly model intra-variable dependencies and inter-variable dependencies.
After that, we develop a scale-wise fusion module to effectively promote the
collaboration across different time scales, and automatically capture the
importance of contributed temporal patterns. Experiments on four real-world
datasets demonstrate that MAGNN outperforms the state-of-the-art methods across
various settings.

$k$-means clustering is a fundamental problem in various disciplines. This
problem is nonconvex, and standard algorithms are only guaranteed to find a
local optimum. Leveraging the structure of local solutions characterized in
[1], we propose a general algorithmic framework for escaping undesirable local
solutions and recovering the global solution (or the ground truth). This
framework consists of alternating between the following two steps iteratively:
(i) detect mis-specified clusters in a local solution and (ii) improve the
current local solution by non-local operations. We discuss implementation of
these steps, and elucidate how the proposed framework unifies variants of
$k$-means algorithm in literature from a geometric perspective. In addition, we
introduce two natural extensions of the proposed framework, where the initial
number of clusters is misspecified. We provide theoretical justification for
our approach, which is corroborated with extensive experiments.

In this paper, based on results of exact learning, test theory, and rough set
theory, we study arbitrary infinite families of concepts each of which consists
of an infinite set of elements and an infinite set of subsets of this set
called concepts. We consider the notion of a problem over a family of concepts
that is described by a finite number of elements: for a given concept, we
should recognize which of the elements under consideration belong to this
concept. As algorithms for problem solving, we consider decision trees of five
types: (i) using membership queries, (ii) using equivalence queries, (iii)
using both membership and equivalence queries, (iv) using proper equivalence
queries, and (v) using both membership and proper equivalence queries. As time
complexity, we study the depth of decision trees. In the worst case, with the
growth of the number of elements in the problem description, the minimum depth
of decision trees of the first type either grows as a logarithm or linearly,
and the minimum depth of decision trees of each of the other types either is
bounded from above by a constant or grows as a logarithm, or linearly. The
obtained results allow us to distinguish seven complexity classes of infinite
families of concepts.

Most conventional crowd counting methods utilize a fully-supervised learning
framework to learn a mapping between scene images and crowd density maps. Under
the circumstances of such fully-supervised training settings, a large quantity
of expensive and time-consuming pixel-level annotations are required to
generate density maps as the supervision. One way to reduce costly labeling is
to exploit self-structural information and inner-relations among unlabeled
images. Unlike the previous methods utilizing these relations and structural
information from the original image level, we explore such self-relations from
the latent feature spaces because it can extract more abundant relations and
structural information. Specifically, we propose S$^2$FPR which can extract
structural information and learn partial orders of coarse-to-fine pyramid
features in the latent space for better crowd counting with massive unlabeled
images. In addition, we collect a new unlabeled crowd counting dataset
(FUDAN-UCC) with 4,000 images in total for training. One by-product is that our
proposed S$^2$FPR method can leverage numerous partial orders in the latent
space among unlabeled images to strengthen the model representation capability
and reduce the estimation errors for the crowd counting task. Extensive
experiments on four benchmark datasets, i.e. the UCF-QNRF, the ShanghaiTech
PartA and PartB, and the UCF-CC-50, show the effectiveness of our method
compared with previous semi-supervised methods. The source code and dataset are
available at https://github.com/bridgeqiqi/S2FPR.

AI/Computing at scale is a difficult problem, especially in a health care
setting. We outline the requirements, planning and implementation choices as
well as the guiding principles that led to the implementation of our secure
research computing enclave, the Essen Medical Computing Platform (EMCP),
affiliated with a major German hospital. Compliance, data privacy and usability
were the immutable requirements of the system. We will discuss the features of
our computing enclave and we will provide our recipe for groups wishing to
adopt a similar setup.

Neuroevolution has recently been shown to be quite competitive in
reinforcement learning (RL) settings, and is able to alleviate some of the
drawbacks of gradient-based approaches. This paper will focus on applying
neuroevolution using a simple genetic algorithm (GA) to find the weights of a
neural network that produce optimally behaving agents. In addition, we present
two novel modifications that improve the data efficiency and speed of
convergence when compared to the initial implementation. The modifications are
evaluated on the FrozenLake environment provided by OpenAI gym and prove to be
significantly better than the baseline approach.

Convolutional neural networks (CNNs) have succeeded in many practical
applications. However, their high computation and storage requirements often
make them difficult to deploy on resource-constrained devices. In order to
tackle this issue, many pruning algorithms have been proposed for CNNs, but
most of them can't prune CNNs to a reasonable level. In this paper, we propose
a novel algorithm for training and pruning CNNs based on the recursive least
squares (RLS) optimization. After training a CNN for some epochs, our algorithm
combines inverse input autocorrelation matrices and weight matrices to evaluate
and prune unimportant input channels or nodes layer by layer. Then, our
algorithm will continue to train the pruned network, and won't do the next
pruning until the pruned network recovers the full performance of the old
network. Besides for CNNs, the proposed algorithm can be used for feedforward
neural networks (FNNs). Three experiments on MNIST, CIFAR-10 and SVHN datasets
show that our algorithm can achieve the more reasonable pruning and have higher
learning efficiency than other four popular pruning algorithms.

Various deep learning models have been developed to segment anatomical
structures from medical images, but they typically have poor performance when
tested on another target domain with different data distribution. Recently,
unsupervised domain adaptation methods have been proposed to alleviate this
so-called domain shift issue, but most of them are designed for scenarios with
relatively small domain shifts and are likely to fail when encountering a large
domain gap. In this paper, we propose DCDA, a novel cross-modality unsupervised
domain adaptation framework for tasks with large domain shifts, e.g.,
segmenting retinal vessels from OCTA and OCT images. DCDA mainly consists of a
disentangling representation style transfer (DRST) module and a collaborative
consistency learning (CCL) module. DRST decomposes images into content
components and style codes and performs style transfer and image
reconstruction. CCL contains two segmentation models, one for source domain and
the other for target domain. The two models use labeled data (together with the
corresponding transferred images) for supervised learning and perform
collaborative consistency learning on unlabeled data. Each model focuses on the
corresponding single domain and aims to yield an expertized domain-specific
segmentation model. Through extensive experiments on retinal vessel
segmentation, our framework achieves Dice scores close to target-trained oracle
both from OCTA to OCT and from OCT to OCTA, significantly outperforming other

Class imbalance occurs in many real-world applications, including image
classification, where the number of images in each class differs significantly.
With imbalanced data, the generative adversarial networks (GANs) leans to
majority class samples. The two recent methods, Balancing GAN (BAGAN) and
improved BAGAN (BAGAN-GP), are proposed as an augmentation tool to handle this
problem and restore the balance to the data. The former pre-trains the
autoencoder weights in an unsupervised manner. However, it is unstable when the
images from different categories have similar features. The latter is improved
based on BAGAN by facilitating supervised autoencoder training, but the
pre-training is biased towards the majority classes. In this work, we propose a
novel Conditional Variational Autoencoder with Balanced Pre-training for
Generative Adversarial Networks (CAPGAN) as an augmentation tool to generate
realistic synthetic images. In particular, we utilize a conditional
convolutional variational autoencoder with supervised and balanced pre-training
for the GAN initialization and training with gradient penalty. Our proposed
method presents a superior performance of other state-of-the-art methods on the
highly imbalanced version of MNIST, Fashion-MNIST, CIFAR-10, and two medical
imaging datasets. Our method can synthesize high-quality minority samples in
terms of Fr\'echet inception distance, structural similarity index measure and
perceptual quality.

Multistage sequential decision-making scenarios are commonly seen in the
healthcare diagnosis process. In this paper, an active learning-based method is
developed to actively collect only the necessary patient data in a sequential
manner. There are two novelties in the proposed method. First, unlike the
existing ordinal logistic regression model which only models a single stage, we
estimate the parameters for all stages together. Second, it is assumed that the
coefficients for common features in different stages are kept consistent. The
effectiveness of the proposed method is validated in both a simulation study
and a real case study. Compared with the baseline method where the data is
modeled individually and independently, the proposed method improves the
estimation efficiency by 62\%-1838\%. For both simulation and testing cohorts,
the proposed method is more effective, stable, interpretable, and
computationally efficient on parameter estimation. The proposed method can be
easily extended to a variety of scenarios where decision-making can be done
sequentially with only necessary information.

In this paper, we study representation learning for multi-task
decision-making in non-stationary environments. We consider the framework of
sequential linear bandits, where the agent performs a series of tasks drawn
from distinct sets associated with different environments. The embeddings of
tasks in each set share a low-dimensional feature extractor called
representation, and representations are different across sets. We propose an
online algorithm that facilitates efficient decision-making by learning and
transferring non-stationary representations in an adaptive fashion. We prove
that our algorithm significantly outperforms the existing ones that treat tasks
independently. We also conduct experiments using both synthetic and real data
to validate our theoretical insights and demonstrate the efficacy of our

Recently, there is growing attention on one-stage panoptic segmentation
methods which aim to segment instances and stuff jointly within a fully
convolutional pipeline efficiently. However, most of the existing works
directly feed the backbone features to various segmentation heads ignoring the
demands for semantic and instance segmentation are different: The former needs
semantic-level discriminative features, while the latter requires features to
be distinguishable across instances. To alleviate this, we propose to first
predict semantic-level and instance-level correlations among different
locations that are utilized to enhance the backbone features, and then feed the
improved discriminative features into the corresponding segmentation heads,
respectively. Specifically, we organize the correlations between a given
location and all locations as a continuous sequence and predict it as a whole.
Considering that such a sequence can be extremely complicated, we adopt
Discrete Fourier Transform (DFT), a tool that can approximate an arbitrary
sequence parameterized by amplitudes and phrases. For different tasks, we
generate these parameters from the backbone features in a fully convolutional
way which is optimized implicitly by corresponding tasks. As a result, these
accurate and consistent correlations contribute to producing plausible
discriminative features which meet the requirements of the complicated panoptic
segmentation task. To verify the effectiveness of our methods, we conduct
experiments on several challenging panoptic segmentation datasets and achieve
state-of-the-art performance on MS COCO with $45.1$\% PQ and ADE20k with
$32.6$\% PQ.

Deep learning-based computer-aided diagnosis has achieved unprecedented
performance in breast cancer detection. However, most approaches are
computationally intensive, which impedes their broader dissemination in
real-world applications. In this work, we propose an efficient and
light-weighted multitask learning architecture to classify and segment breast
tumors simultaneously. We incorporate a segmentation task into a tumor
classification network, which makes the backbone network learn representations
focused on tumor regions. Moreover, we propose a new numerically stable loss
function that easily controls the balance between the sensitivity and
specificity of cancer detection. The proposed approach is evaluated using a
breast ultrasound dataset with 1,511 images. The accuracy, sensitivity, and
specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
We validate the model using a virtual mobile device, and the average inference
time is 0.35 seconds per image.

Today's cyber-world is vastly multivariate. Metrics collected at extreme
varieties demand multivariate algorithms to properly detect anomalies. However,
forecast-based algorithms, as widely proven approaches, often perform
sub-optimally or inconsistently across datasets. A key common issue is they
strive to be one-size-fits-all but anomalies are distinctive in nature. We
propose a method that tailors to such distinction. Presenting FMUAD - a
Forecast-based, Multi-aspect, Unsupervised Anomaly Detection framework. FMUAD
explicitly and separately captures the signature traits of anomaly types -
spatial change, temporal change and correlation change - with independent
modules. The modules then jointly learn an optimal feature representation,
which is highly flexible and intuitive, unlike most other models in the
category. Extensive experiments show our FMUAD framework consistently
outperforms other state-of-the-art forecast-based anomaly detectors.

The development of powerful deep learning technologies has brought about some
negative effects to both society and individuals. One such issue is the
emergence of fake media. To tackle the issue, we have organized the Trusted
Media Challenge (TMC) to explore how Artificial Intelligence (AI) technologies
could be leveraged to combat fake media. To enable further research, we are
releasing the dataset that we had prepared from the TMC challenge, consisting
of 4,380 fake and 2,563 real videos, with various video and/or audio
manipulation methods employed to produce different types of fake media. All the
videos in the TMC dataset are accompanied with audios and have a minimum
resolution of 360p. The videos have various durations, background,
illumination, and may contain perturbations that mimic transmission errors and
compression. We have also carried out a user study to demonstrate the quality
of the TMC dataset and to compare the performance of humans and AI models. The
results showed that the TMC dataset can fool human participants in many cases,
and the winning AI models of the Trusted Media Challenge outperformed humans.
The TMC dataset is available for research purpose upon request via
tmc-dataset@aisingapore.org.

Moment methods are an important means of density estimation, but they are
generally strongly dependent on the choice of feasible functions, which
severely affects the performance. We propose a non-classical parameterization
for density estimation using the sample moments, which does not require the
choice of such functions. The parameterization is induced by the
Kullback-Leibler distance, and the solution of it, which is proved to exist and
be unique subject to simple prior that does not depend on data, can be obtained
by convex optimization. Simulation results show the performance of the proposed
estimator in estimating multi-modal densities which are mixtures of different
types of functions.

Cellular providers and data aggregating companies crowdsource celluar signal
strength measurements from user devices to generate signal maps, which can be
used to improve network performance. Recognizing that this data collection may
be at odds with growing awareness of privacy concerns, we consider obfuscating
such data before the data leaves the mobile device. The goal is to increase
privacy such that it is difficult to recover sensitive features from the
obfuscated data (e.g. user ids and user whereabouts), while still allowing
network providers to use the data for improving network services (i.e. create
accurate signal maps). To examine this privacy-utility tradeoff, we identify
privacy and utility metrics and threat models suited to signal strength
measurements. We then obfuscate the measurements using several preeminent
techniques, spanning differential privacy, generative adversarial privacy, and
information-theoretic privacy techniques, in order to benchmark a variety of
promising obfuscation approaches and provide guidance to real-world engineers
who are tasked to build signal maps that protect privacy without hurting
utility. Our evaluation results, based on multiple, diverse, real-world signal
map datasets, demonstrate the feasibility of concurrently achieving adequate
privacy and utility, with obfuscation strategies which use the structure and
intended use of datasets in their design, and target average-case, rather than
worst-case, guarantees.

The echo state network (ESN) is a special type of recurrent neural networks
for processing the time-series dataset. However, limited by the strong
correlation among sequential samples of the agent, ESN-based policy control
algorithms are difficult to use the recursive least squares (RLS) algorithm to
update the ESN's parameters. To solve this problem, we propose two novel policy
control algorithms, ESNRLS-Q and ESNRLS-Sarsa. Firstly, to reduce the
correlation of training samples, we use the leaky integrator ESN and the
mini-batch learning mode. Secondly, to make RLS suitable for training ESN in
mini-batch mode, we present a new mean-approximation method for updating the
RLS correlation matrix. Thirdly, to prevent ESN from over-fitting, we use the
L1 regularization technique. Lastly, to prevent the target state-action value
from overestimation, we employ the Mellowmax method. Simulation results show
that our algorithms have good convergence performance.

Motivation: Bacteriophages are viruses infecting bacteria. Being key players
in microbial communities, they can regulate the composition/function of
microbiome by infecting their bacterial hosts and mediating gene transfer.
Recently, metagenomic sequencing, which can sequence all genetic materials from
various microbiome, has become a popular means for new phage discovery.
However, accurate and comprehensive detection of phages from the metagenomic
data remains difficult. High diversity/abundance, and limited reference genomes
pose major challenges for recruiting phage fragments from metagenomic data.
Existing alignment-based or learning-based models have either low recall or
precision on metagenomic data. Results: In this work, we adopt the
state-of-the-art language model, Transformer, to conduct contextual embedding
for phage contigs. By constructing a protein-cluster vocabulary, we can feed
both the protein composition and the proteins' positions from each contig into
the Transformer. The Transformer can learn the protein organization and
associations using the self-attention mechanism and predicts the label for test
contigs. We rigorously tested our developed tool named PhaMer on multiple
datasets with increasing difficulty, including quality RefSeq genomes, short
contigs, simulated metagenomic data, mock metagenomic data, and the public
IMG/VR dataset. All the experimental results show that PhaMer outperforms the
state-of-the-art tools. In the real metagenomic data experiment, PhaMer
improves the F1-score of phage detection by 27\%.

Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to
the world since its outbreak. To fight against the disease, a series of
artificial intelligence (AI) techniques are developed and applied to real-world
scenarios such as safety monitoring, disease diagnosis, infection risk
assessment, lesion segmentation of COVID-19 CT scans,etc. The coronavirus
epidemics have forced people wear masks to counteract the transmission of
virus, which also brings difficulties to monitor large groups of people wearing
masks. In this paper, we primarily focus on the AI techniques of masked facial
detection and related datasets. We survey the recent advances, beginning with
the descriptions of masked facial detection datasets. Thirteen available
datasets are described and discussed in details. Then, the methods are roughly
categorized into two classes: conventional methods and neural network-based
methods. Conventional methods are usually trained by boosting algorithms with
hand-crafted features, which accounts for a small proportion. Neural
network-based methods are further classified as three parts according to the
number of processing stages. Representative algorithms are described in detail,
coupled with some typical techniques that are described briefly. Finally, we
summarize the recent benchmarking results, give the discussions on the
limitations of datasets and methods, and expand future research directions. To
our knowledge, this is the first survey about masked facial detection methods
and datasets. Hopefully our survey could provide some help to fight against
epidemics.

Crop field boundaries aid in mapping crop types, predicting yields, and
delivering field-scale analytics to farmers. Recent years have seen the
successful application of deep learning to delineating field boundaries in
industrial agricultural systems, but field boundary datasets remain missing in
smallholder systems due to (1) small fields that require high resolution
satellite imagery to delineate and (2) a lack of ground labels for model
training and validation. In this work, we combine transfer learning and weak
supervision to overcome these challenges, and we demonstrate the methods'
success in India where we efficiently generated 10,000 new field labels. Our
best model uses 1.5m resolution Airbus SPOT imagery as input, pre-trains a
state-of-the-art neural network on France field boundaries, and fine-tunes on
India labels to achieve a median Intersection over Union (IoU) of 0.86 in
India. If using 4.8m resolution PlanetScope imagery instead, the best model
achieves a median IoU of 0.72. Experiments also show that pre-training in
France reduces the number of India field labels needed to achieve a given
performance level by as much as $20\times$ when datasets are small. These
findings suggest our method is a scalable approach for delineating crop fields
in regions of the world that currently lack field boundary datasets. We
publicly release the 10,000 labels and delineation model to facilitate the
creation of field boundary maps and new methods by the community.

ML models are typically trained using large datasets of high quality.
However, training datasets often contain inconsistent or incomplete data. To
tackle this issue, one solution is to develop algorithms that can check whether
a prediction of a model is certifiably robust. Given a learning algorithm that
produces a classifier and given an example at test time, a classification
outcome is certifiably robust if it is predicted by every model trained across
all possible worlds (repairs) of the uncertain (inconsistent) dataset. This
notion of robustness falls naturally under the framework of certain answers. In
this paper, we study the complexity of certifying robustness for a simple but
widely deployed classification algorithm, $k$-Nearest Neighbors ($k$-NN). Our
main focus is on inconsistent datasets when the integrity constraints are
functional dependencies (FDs). For this setting, we establish a dichotomy in
the complexity of certifying robustness w.r.t. the set of FDs: the problem
either admits a polynomial time algorithm, or it is coNP-hard. Additionally, we
exhibit a similar dichotomy for the counting version of the problem, where the
goal is to count the number of possible worlds that predict a certain label. As
a byproduct of our study, we also establish the complexity of a problem related
to finding an optimal subset repair that may be of independent interest.

The prediction of microsatellite instability (MSI) and microsatellite
stability (MSS) is essential in predicting both the treatment response and
prognosis of gastrointestinal cancer. In clinical practice, a universal MSI
testing is recommended, but the accessibility of such a test is limited. Thus,
a more cost-efficient and broadly accessible tool is desired to cover the
traditionally untested patients. In the past few years, deep-learning-based
algorithms have been proposed to predict MSI directly from haematoxylin and
eosin (H&E)-stained whole-slide images (WSIs). Such algorithms can be
summarized as (1) patch-level MSI/MSS prediction, and (2) patient-level
aggregation. Compared with the advanced deep learning approaches that have been
employed for the first stage, only the na\"ive first-order statistics (e.g.,
averaging and counting) were employed in the second stage. In this paper, we
propose a simple yet broadly generalizable patient-level MSI aggregation (MAg)
method to effectively integrate the precious patch-level information. Briefly,
the entire probabilistic distribution in the first stage is modeled as
histogram-based features to be fused as the final outcome with machine learning
(e.g., SVM). The proposed MAg method can be easily used in a plug-and-play
manner, which has been evaluated upon five broadly used deep neural networks:
ResNet, MobileNetV2, EfficientNet, Dpn and ResNext. From the results, the
proposed MAg method consistently improves the accuracy of patient-level
aggregation for two publicly available datasets. It is our hope that the
proposed method could potentially leverage the low-cost H&E based MSI detection
method. The code of our work has been made publicly available at
https://github.com/Calvin-Pang/MAg.

We study the practical consequences of dataset sampling strategies on the
ranking performance of recommendation algorithms. Recommender systems are
generally trained and evaluated on samples of larger datasets. Samples are
often taken in a naive or ad-hoc fashion: e.g. by sampling a dataset randomly
or by selecting users or items with many interactions. As we demonstrate,
commonly-used data sampling schemes can have significant consequences on
algorithm performance. Following this observation, this paper makes three main
contributions: (1) characterizing the effect of sampling on algorithm
performance, in terms of algorithm and dataset characteristics (e.g. sparsity
characteristics, sequential dynamics, etc.); (2) designing SVP-CF, which is a
data-specific sampling strategy, that aims to preserve the relative performance
of models after sampling, and is especially suited to long-tailed interaction
data; and (3) developing an oracle, Data-Genie, which can suggest the sampling
scheme that is most likely to preserve model performance for a given dataset.
The main benefit of Data-Genie is that it will allow recommender system
practitioners to quickly prototype and compare various approaches, while
remaining confident that algorithm performance will be preserved, once the
algorithm is retrained and deployed on the complete data. Detailed experiments
show that using Data-Genie, we can discard upto 5x more data than any sampling
strategy with the same level of performance.

In recent days, with increased population and traffic on roadways, vehicle
collision is one of the leading causes of death worldwide. The automotive
industry is motivated on developing techniques to use sensors and advancements
in the field of computer vision to build collision detection and collision
prevention systems to assist drivers. In this article, a deep-learning-based
model comprising of ResNext architecture with SENet blocks is proposed. The
performance of the model is compared to popular deep learning models like
VGG16, VGG19, Resnet50, and stand-alone ResNext. The proposed model outperforms
the existing baseline models achieving a ROC-AUC of 0.91 using a significantly
less proportion of the GTACrash synthetic data for training, thus reducing the
computational overhead.

Aggregate time-series data like traffic flow and site occupancy repeatedly
sample statistics from a population across time. Such data can be profoundly
useful for understanding trends within a given population, but also pose a
significant privacy risk, potentially revealing e.g., who spends time where.
Producing a private version of a time-series satisfying the standard definition
of Differential Privacy (DP) is challenging due to the large influence a single
participant can have on the sequence: if an individual can contribute to each
time step, the amount of additive noise needed to satisfy privacy increases
linearly with the number of time steps sampled. As such, if a signal spans a
long duration or is oversampled, an excessive amount of noise must be added,
drowning out underlying trends. However, in many applications an individual
realistically cannot participate at every time step. When this is the case, we
observe that the influence of a single participant (sensitivity) can be reduced
by subsampling and/or filtering in time, while still meeting privacy
requirements. Using a novel analysis, we show this significant reduction in
sensitivity and propose a corresponding class of privacy mechanisms. We
demonstrate the utility benefits of these techniques empirically with
real-world and synthetic time-series data.

In this paper, we developed the solution of roadside LiDAR object detection
using a combination of two unsupervised learning algorithms. The 3D point
clouds are firstly converted into spherical coordinates and filled into the
elevation-azimuth matrix using a hash function. After that, the raw LiDAR data
were rearranged into new data structures to store the information of range,
azimuth, and intensity. Then, the Dynamic Mode Decomposition method is applied
to decompose the LiDAR data into low-rank backgrounds and sparse foregrounds
based on intensity channel pattern recognition. The Coarse Fine Triangle
Algorithm (CFTA) automatically finds the dividing value to separate the moving
targets from static background according to range information. After intensity
and range background subtraction, the foreground moving objects will be
detected using a density-based detector and encoded into the state-space model
for tracking. The output of the proposed solution includes vehicle trajectories
that can enable many mobility and safety applications. The method was validated
at both path and point levels and outperformed the state-of-the-art. In
contrast to the previous methods that process directly on the scattered and
discrete point clouds, the dynamic classification method can establish the less
sophisticated linear relationship of the 3D measurement data, which captures
the spatial-temporal structure that we often desire.

This paper presents a machine-learning-enhanced longitudinal scanline method
to extract vehicle trajectories from high-angle traffic cameras. The Dynamic
Mode Decomposition (DMD) method is applied to extract vehicle strands by
decomposing the Spatial-Temporal Map (STMap) into the sparse foreground and
low-rank background. A deep neural network named Res-UNet+ was designed for the
semantic segmentation task by adapting two prevalent deep learning
architectures. The Res-UNet+ neural networks significantly improve the
performance of the STMap-based vehicle detection, and the DMD model provides
many interesting insights for understanding the evolution of underlying
spatial-temporal structures preserved by STMap. The model outputs were compared
with the previous image processing model and mainstream semantic segmentation
deep neural networks. After a thorough evaluation, the model is proved to be
accurate and robust against many challenging factors. Last but not least, this
paper fundamentally addressed many quality issues found in NGSIM trajectory
data. The cleaned high-quality trajectory data are published to support future
theoretical and modeling research on traffic flow and microscopic vehicle
control. This method is a reliable solution for video-based trajectory
extraction and has wide applicability.

The world population is anticipated to increase by close to 2 billion by 2050
causing a rapid escalation of food demand. A recent projection shows that the
world is lagging behind accomplishing the "Zero Hunger" goal, in spite of some
advancements. Socio-economic and well being fallout will affect the food
security. Vulnerable groups of people will suffer malnutrition. To cater to the
needs of the increasing population, the agricultural industry needs to be
modernized, become smart, and automated. Traditional agriculture can be remade
to efficient, sustainable, eco-friendly smart agriculture by adopting existing
technologies. In this survey paper the authors present the applications,
technological trends, available datasets, networking options, and challenges in
smart agriculture. How Agro Cyber Physical Systems are built upon the
Internet-of-Agro-Things is discussed through various application fields.
Agriculture 4.0 is also discussed as a whole. We focus on the technologies,
such as Artificial Intelligence (AI) and Machine Learning (ML) which support
the automation, along with the Distributed Ledger Technology (DLT) which
provides data integrity and security. After an in-depth study of different
architectures, we also present a smart agriculture framework which relies on
the location of data processing. We have divided open research problems of
smart agriculture as future research work in two groups - from a technological
perspective and from a networking perspective. AI, ML, the blockchain as a DLT,
and Physical Unclonable Functions (PUF) based hardware security fall under the
technology group, whereas any network related attacks, fake data injection and
similar threats fall under the network research problem group.

This paper is concerned with the asymptotic distribution of the largest
eigenvalues for some nonlinear random matrix ensemble stemming from the study
of neural networks. More precisely we consider $M= \frac{1}{m} YY^\top$ with
$Y=f(WX)$ where $W$ and $X$ are random rectangular matrices with i.i.d.
centered entries. This models the data covariance matrix or the Conjugate
Kernel of a single layered random Feed-Forward Neural Network. The function $f$
is applied entrywise and can be seen as the activation function of the neural
network. We show that the largest eigenvalue has the same limit (in
probability) as that of some well-known linear random matrix ensembles. In
particular, we relate the asymptotic limit of the largest eigenvalue for the
nonlinear model to that of an information-plus-noise random matrix,
establishing a possible phase transition depending on the function $f$ and the
distribution of $W$ and $X$. This may be of interest for applications to

To better understand the theoretical behavior of large neural networks,
several works have analyzed the case where a network's width tends to infinity.
In this regime, the effect of random initialization and the process of training
a neural network can be formally expressed with analytical tools like Gaussian
processes and neural tangent kernels. In this paper, we review methods for
quantifying uncertainty in such infinite-width neural networks and compare
their relationship to Gaussian processes in the Bayesian inference framework.
We make use of several equivalence results along the way to obtain exact
closed-form solutions for predictive uncertainty.

We study the dynamics of a neural network in function space when optimizing
the mean squared error via gradient flow. We show that in the
underparameterized regime the network learns eigenfunctions of an integral
operator $T_{K^\infty}$ determined by the Neural Tangent Kernel (NTK) at rates
corresponding to their eigenvalues. For example, for uniformly distributed data
on the sphere $S^{d - 1}$ and rotation invariant weight distributions, the
eigenfunctions of $T_{K^\infty}$ are the spherical harmonics. Our results can
be understood as describing a spectral bias in the underparameterized regime.
The proofs use the concept of "Damped Deviations", where deviations of the NTK
matter less for eigendirections with large eigenvalues due to the occurence of
a damping factor. Aside from the underparameterized regime, the damped
deviations point-of-view can be used to track the dynamics of the empirical
risk in the overparameterized setting, allowing us to extend certain results in
the literature. We conclude that damped deviations offers a simple and unifying
perspective of the dynamics when optimizing the squared error.

The rapid development of Machine Learning (ML) has demonstrated superior
performance in many areas, such as computer vision, video and speech
recognition. It has now been increasingly leveraged in software systems to
automate the core tasks. However, how to securely develop the machine
learning-based modern software systems (MLBSS) remains a big challenge, for
which the insufficient consideration will largely limit its application in
safety-critical domains. One concern is that the present MLBSS development
tends to be rush, and the latent vulnerabilities and privacy issues exposed to
external users and attackers will be largely neglected and hard to be
identified. Additionally, machine learning-based software systems exhibit
different liabilities towards novel vulnerabilities at different development
stages from requirement analysis to system maintenance, due to its inherent
limitations from the model and data and the external adversary capabilities. In
this work, we consider that security for machine learning-based software
systems may arise by inherent system defects or external adversarial attacks,
and the secure development practices should be taken throughout the whole
lifecycle. While machine learning has become a new threat domain for existing
software engineering practices, there is no such review work covering the
topic. Overall, we present a holistic review regarding the security for MLBSS,
which covers a systematic understanding from a structure review of three
distinct aspects in terms of security threats. Moreover, it provides a thorough
state-of-the-practice for MLBSS secure development. Finally, we summarise the
literature for system security assurance, and motivate the future research
directions with open challenges. We anticipate this work provides sufficient
discussion and novel insights to incorporate system security engineering for
future exploration.

Partially Observable Markov Decision Processes (POMDPs) are a natural and
general model in reinforcement learning that take into account the agent's
uncertainty about its current state. In the literature on POMDPs, it is
customary to assume access to a planning oracle that computes an optimal policy
when the parameters are known, even though the problem is known to be
computationally hard. Almost all existing planning algorithms either run in
exponential time, lack provable performance guarantees, or require placing
strong assumptions on the transition dynamics under every possible policy. In
this work, we revisit the planning problem and ask: are there natural and
well-motivated assumptions that make planning easy?
  Our main result is a quasipolynomial-time algorithm for planning in
(one-step) observable POMDPs. Specifically, we assume that well-separated
distributions on states lead to well-separated distributions on observations,
and thus the observations are at least somewhat informative in each step.
Crucially, this assumption places no restrictions on the transition dynamics of
the POMDP; nevertheless, it implies that near-optimal policies admit
quasi-succinct descriptions, which is not true in general (under standard
hardness assumptions). Our analysis is based on new quantitative bounds for
filter stability -- i.e. the rate at which an optimal filter for the latent
state forgets its initialization. Furthermore, we prove matching hardness for
planning in observable POMDPs under the Exponential Time Hypothesis.

Most adversarial attack defense methods rely on obfuscating gradients. These
methods are successful in defending against gradient-based attacks; however,
they are easily circumvented by attacks which either do not use the gradient or
by attacks which approximate and use the corrected gradient. Defenses that do
not obfuscate gradients such as adversarial training exist, but these
approaches generally make assumptions about the attack such as its magnitude.
We propose a classification model that does not obfuscate gradients and is
robust by construction without assuming prior knowledge about the attack. Our
method casts classification as an optimization problem where we "invert" a
conditional generator trained on unperturbed, natural images to find the class
that generates the closest sample to the query image. We hypothesize that a
potential source of brittleness against adversarial attacks is the
high-to-low-dimensional nature of feed-forward classifiers which allows an
adversary to find small perturbations in the input space that lead to large
changes in the output space. On the other hand, a generative model is typically
a low-to-high-dimensional mapping. While the method is related to Defense-GAN,
the use of a conditional generative model and inversion in our model instead of
the feed-forward classifier is a critical difference. Unlike Defense-GAN, which
was shown to generate obfuscated gradients that are easily circumvented, we
show that our method does not obfuscate gradients. We demonstrate that our
model is extremely robust against black-box attacks and has improved robustness
against white-box attacks compared to naturally trained, feed-forward

Phonon Boltzmann transport equation (BTE) is a key tool for modeling
multiscale phonon transport, which is critical to the thermal management of
miniaturized integrated circuits, but assumptions about the system temperatures
(i.e., small temperature gradients) are usually made to ensure that it is
computationally tractable. To include the effects of large temperature
non-equilibrium, we demonstrate a data-free deep learning scheme,
physics-informed neural network (PINN), for solving stationary, mode-resolved
phonon BTE with arbitrary temperature gradients. This scheme uses the
temperature-dependent phonon relaxation times and learns the solutions in
parameterized spaces with both length scale and temperature gradient treated as
input variables. Numerical experiments suggest that the proposed PINN can
accurately predict phonon transport (from 1D to 3D) under arbitrary temperature
gradients. Moreover, the proposed scheme shows great promise in simulating
device-level phonon heat conduction efficiently and can be potentially used for
thermal design.

Surrogate neural network-based models have been lately trained and used in a
variety of science and engineering applications where the number of evaluations
of a target function is limited by execution time. In cell phone camera
systems, various errors, such as interferences at the lens-barrel and lens-lens
interfaces and axial, radial, and tilt misalignments, accumulate and alter
profile of the lenses in a stochastic manner which ultimately changes optical
focusing properties. Nonlinear finite element analysis of the stochastic
mechanical behavior of lenses due to the interference fits is used on
high-performance computing (HPC) to generate sufficient training and testing
data for subsequent deep learning. Once properly trained and validated, the
surrogate neural network model enabled accurate and almost instant evaluations
of millions of function evaluations providing the final lens profiles. This
computational model, enhanced by artificial intelligence, enabled us to
efficiently perform Monte-Carlo analysis for sensitivity and uncertainty
quantification of the final lens profile to various interferences. It can be
further coupled with an optical analysis to perform ray tracing and analyze the
focal properties of the lens module. Moreover, it can provide a valuable tool
for optimizing tolerance design and intelligent components matching for many
similar press-fit assembly processes.

We propose a decentralised "local2global"' approach to graph representation
learning, that one can a-priori use to scale any embedding technique. Our
local2global approach proceeds by first dividing the input graph into
overlapping subgraphs (or "patches") and training local representations for
each patch independently. In a second step, we combine the local
representations into a globally consistent representation by estimating the set
of rigid motions that best align the local representations using information
from the patch overlaps, via group synchronization. A key distinguishing
feature of local2global relative to existing work is that patches are trained
independently without the need for the often costly parameter synchronization
during distributed training. This allows local2global to scale to large-scale
industrial applications, where the input graph may not even fit into memory and
may be stored in a distributed manner. We apply local2global on data sets of
different sizes and show that our approach achieves a good trade-off between
scale and accuracy on edge reconstruction and semi-supervised classification.
We also consider the downstream task of anomaly detection and show how one can
use local2global to highlight anomalies in cybersecurity networks.

Methodologies for characterization of the lateral indentation of
silicon-germanium (SiGe) nanosheets using different non-destructive and in-line
compatible metrology techniques are presented and discussed. Gate-all-around
nanosheet device structures with a total of three sacrificial SiGe sheets were
fabricated and different etch process conditions used to induce indent depth
variations. Scatterometry with spectral interferometry and x-ray fluorescence
in conjunction with advanced interpretation and machine learning algorithms
were used to quantify the SiGe indentation. Solutions for two approaches,
average indent (represented by a single parameter) as well as sheet-specific
indent, are presented. Both scatterometry with spectral interferometry as well
as x-ray fluorescence measurements are suitable techniques to quantify the
average indent through a single parameter. Furthermore, machine learning
algorithms enable a fast solution path by combining x-ray fluorescence
difference data with scatterometry spectra, therefore avoiding the need for a
full optical model solution. A similar machine learning model approach can be
employed for sheet-specific indent monitoring; however, reference data from
cross-section transmission electron microscopy image analyses are required for
training. It was found that scatterometry with spectral interferometry spectra
and a traditional optical model in combination with advanced algorithms can
achieve a very good match to sheet-specific reference data.

Astronomical source deblending is the process of separating the contribution
of individual stars or galaxies (sources) to an image comprised of multiple,
possibly overlapping sources. Astronomical sources display a wide range of
sizes and brightnesses and may show substantial overlap in images. Astronomical
imaging data can further challenge off-the-shelf computer vision algorithms
owing to its high dynamic range, low signal-to-noise ratio, and unconventional
image format. These challenges make source deblending an open area of
astronomical research, and in this work, we introduce a new approach called
Partial-Attribution Instance Segmentation that enables source detection and
deblending in a manner tractable for deep learning models. We provide a novel
neural network implementation as a demonstration of the method.

Beam selection for millimeter-wave links in a vehicular scenario is a
challenging problem, as an exhaustive search among all candidate beam pairs
cannot be assuredly completed within short contact times. We solve this problem
via a novel expediting beam selection by leveraging multimodal data collected
from sensors like LiDAR, camera images, and GPS. We propose individual modality
and distributed fusion-based deep learning (F-DL) architectures that can
execute locally as well as at a mobile edge computing center (MEC), with a
study on associated tradeoffs. We also formulate and solve an optimization
problem that considers practical beam-searching, MEC processing and
sensor-to-MEC data delivery latency overheads for determining the output
dimensions of the above F-DL architectures. Results from extensive evaluations
conducted on publicly available synthetic and home-grown real-world datasets
reveal 95% and 96% improvement in beam selection speed over classical RF-only
beam sweeping, respectively. F-DL also outperforms the state-of-the-art
techniques by 20-22% in predicting top-10 best beam pairs.

An algorithm capable of processing NMR images was developed for analysis
using machine learning techniques to detect the presence of brain tumors.

We demonstrate a novel application of online transfer learning for a digital
assets trading agent. This agent uses a powerful feature space representation
in the form of an echo state network, the output of which is made available to
a direct, recurrent reinforcement learning agent. The agent learns to trade the
XBTUSD (Bitcoin versus US Dollars) perpetual swap derivatives contract on
BitMEX on an intraday basis. By learning from the multiple sources of impact on
the quadratic risk-adjusted utility that it seeks to maximise, the agent avoids
excessive over-trading, captures a funding profit, and can predict the market's
direction. Overall, our crypto agent realises a total return of 350\%, net of
transaction costs, over roughly five years, 71\% of which is down to funding
profit. The annualised information ratio that it achieves is 1.46.

Entity Matching is an essential part of all real-world systems that take in
structured and unstructured data coming from different sources. Typically no
common key is available for connecting records. Massive data cleaning and
integration processes require completion before any data analytics, or further
processing can be performed. Although record linkage is frequently regarded as
a somewhat tedious but necessary step, it reveals valuable insights, supports
data visualization, and guides further analytic approaches to the data. Here,
we focus on organization entity matching. We introduce CompanyName2Vec, a novel
algorithm to solve company entity matching (CEM) using a neural network model
to learn company name semantics from a job ad corpus, without relying on any
information on the matched company besides its name. Based on a real-world
data, we show that CompanyName2Vec outperforms other evaluated methods and
solves the CEM challenge with an average success rate of 89.3%.

The exponential growth of internet connected systems has generated numerous
challenges, such as spectrum shortage issues, which require efficient spectrum
sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to
different potential security and privacy issues, requiring protection
mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based
methods have frequently been proposed to address those issues. In this article,
we provide a comprehensive survey of the recent development of ML based SS
methods, the most critical security issues, and corresponding defense
mechanisms. In particular, we elaborate the state-of-the-art methodologies for
improving the performance of SS communication systems for various vital
aspects, including ML based cognitive radio networks (CRNs), ML based database
assisted SS networks, ML based LTE-U networks, ML based ambient backscatter
networks, and other ML based SS solutions. We also present security issues from
the physical layer and corresponding defending strategies based on ML
algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing
Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and
privacy issues. Finally, extensive discussions on open challenges for ML based
SS are also given. This comprehensive review is intended to provide the
foundation for and facilitate future studies on exploring the potential of
emerging ML for coping with increasingly complex SS and their security
problems.

It is a challenging task to learn rich and multi-scale spatiotemporal
semantics from high-dimensional videos, due to large local redundancy and
complex global dependency between video frames. The recent advances in this
research have been mainly driven by 3D convolutional neural networks and vision
transformers. Although 3D convolution can efficiently aggregate local context
to suppress local redundancy from a small 3D neighborhood, it lacks the
capability to capture global dependency because of the limited receptive field.
Alternatively, vision transformers can effectively capture long-range
dependency by self-attention mechanism, while having the limitation on reducing
local redundancy with blind similarity comparison among all the tokens in each
layer. Based on these observations, we propose a novel Unified transFormer
(UniFormer) which seamlessly integrates merits of 3D convolution and
spatiotemporal self-attention in a concise transformer format, and achieves a
preferable balance between computation and accuracy. Different from traditional
transformers, our relation aggregator can tackle both spatiotemporal redundancy
and dependency, by learning local and global token affinity respectively in
shallow and deep layers. We conduct extensive experiments on the popular video
benchmarks, e.g., Kinetics-400, Kinetics-600, and Something-Something V1&V2.
With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1
accuracy on Kinetics-400/Kinetics-600, while requiring 10x fewer GFLOPs than
other state-of-the-art methods. For Something-Something V1 and V2, our
UniFormer achieves new state-of-the-art performances of 60.9% and 71.2% top-1
accuracy respectively. Code is available at
https://github.com/Sense-X/UniFormer.

Graph neural networks (GNNs), as a group of powerful tools for representation
learning on irregular data, have manifested superiority in various downstream
tasks. With unstructured texts represented as concept maps, GNNs can be
exploited for tasks like document retrieval. Intrigued by how can GNNs help
document retrieval, we conduct an empirical study on a large-scale
multi-discipline dataset CORD-19. Results show that instead of the complex
structure-oriented GNNs such as GINs and GATs, our proposed semantics-oriented
graph functions achieve better and more stable performance based on the BM25
retrieved candidates. Our insights in this case study can serve as a guideline
for future work to develop effective GNNs with appropriate semantics-oriented
inductive biases for textual reasoning tasks like document retrieval and
classification. All code for this case study is available at
https://github.com/HennyJie/GNN-DocRetrieval.

In this short note, we reify the connection between work on the storage
capacity problem in wide two-layer treelike neural networks and the
rapidly-growing body of literature on kernel limits of wide neural networks.
Concretely, we observe that the "effective order parameter" studied in the
statistical mechanics literature is exactly equivalent to the infinite-width
Neural Network Gaussian Process Kernel. This correspondence connects the
expressivity and trainability of wide two-layer neural networks.

Modern online services continuously generate data at very fast rates. This
continuous flow of data encompasses content - e.g., posts, news, products,
comments -, but also user feedback - e.g., ratings, views, reads, clicks -,
together with context data - user device, spatial or temporal data, user task
or activity, weather. This can be overwhelming for systems and algorithms
designed to train in batches, given the continuous and potentially fast change
of content, context and user preferences or intents. Therefore, it is important
to investigate online methods able to transparently adapt to the inherent
dynamics of online services. Incremental models that learn from data streams
are gaining attention in the recommender systems community, given their natural
ability to deal with the continuous flows of data generated in dynamic, complex
environments. User modeling and personalization can particularly benefit from
algorithms capable of maintaining models incrementally and online.
  The objective of this workshop is to foster contributions and bring together
a growing community of researchers and practitioners interested in online,
adaptive approaches to user modeling, recommendation and personalization, and
their implications regarding multiple dimensions, such as evaluation,
reproducibility, privacy and explainability.

Configuring millimeter wave links following a conventional beam training
protocol, as the one proposed in the current cellular standard, introduces a
large communication overhead, specially relevant in vehicular systems, where
the channels are highly dynamic. In this paper, we propose the use of a passive
radar array to sense automotive radar transmissions coming from multiple
vehicles on the road, and a radar processing chain that provides information
about a reduced set of candidate beams for the links between the
road-infrastructure and each one of the vehicles. This prior information can be
later leveraged by the beam training protocol to significantly reduce overhead.
The radar processing chain estimates both the timing and chirp rates of the
radar signals, isolates the individual signals by filtering out interfering
radar chirps, and estimates the spatial covariance of each individual radar
transmission. Then, a deep network is used to translate features of these radar
spatial covariances into features of the communication spatial covariances, by
learning the intricate mapping between radar and communication channels, in
both line-of-sight and non-line-of-sight settings. The communication rates and
outage probabilities of this approach are compared against exhaustive search
and pure radar-aided beam training methods (without deep learning-based
mapping), and evaluated on multi-user channels simulated by ray tracing.
Results show that: (i) the proposed processing chain can reliably isolate the
spatial covariances for individual radars, and (ii) the radar-to-communications
translation strategy based on deep learning provides a significant improvement
over pure radar-aided methods in both LOS and NLOS channels.

We address the problem of production planning and distribution in
multi-echelon supply chains. We consider uncertain demands and lead times which
makes the problem stochastic and non-linear. A Markov Decision Process
formulation and a Non-linear Programming model are presented. As a sequential
decision-making problem, Deep Reinforcement Learning (RL) is a possible
solution approach. This type of technique has gained a lot of attention from
Artificial Intelligence and Optimization communities in recent years.
Considering the good results obtained with Deep RL approaches in different
areas there is a growing interest in applying them in problems from the
Operations Research field. We have used a Deep RL technique, namely Proximal
Policy Optimization (PPO2), to solve the problem considering uncertain, regular
and seasonal demands and constant or stochastic lead times. Experiments are
carried out in different scenarios to better assess the suitability of the
algorithm. An agent based on a linearized model is used as a baseline.
Experimental results indicate that PPO2 is a competitive and adequate tool for
this type of problem. PPO2 agent is better than baseline in all scenarios with
stochastic lead times (7.3-11.2%), regardless of whether demands are seasonal
or not. In scenarios with constant lead times, the PPO2 agent is better when
uncertain demands are non-seasonal (2.2-4.7%). The results show that the
greater the uncertainty of the scenario, the greater the viability of this type
of approach.

This paper considers multi-agent reinforcement learning (MARL) tasks where
agents receive a shared global reward at the end of an episode. The delayed
nature of this reward affects the ability of the agents to assess the quality
of their actions at intermediate time-steps. This paper focuses on developing
methods to learn a temporal redistribution of the episodic reward to obtain a
dense reward signal. Solving such MARL problems requires addressing two
challenges: identifying (1) relative importance of states along the length of
an episode (along time), and (2) relative importance of individual agents'
states at any single time-step (among agents). In this paper, we introduce
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent
Reinforcement Learning (AREL) to address these two challenges. AREL uses
attention mechanisms to characterize the influence of actions on state
transitions along trajectories (temporal attention), and how each agent is
affected by other agents at each time-step (agent attention). The redistributed
rewards predicted by AREL are dense, and can be integrated with any given MARL
algorithm. We evaluate AREL on challenging tasks from the Particle World
environment and the StarCraft Multi-Agent Challenge. AREL results in higher
rewards in Particle World, and improved win rates in StarCraft compared to
three state-of-the-art reward redistribution methods. Our code is available at
https://github.com/baicenxiao/AREL.

The most effective of the solutions against Covid-19 is the various vaccines
developed. Distrust of vaccines can hinder the rapid and effective use of this
remedy. One of the means of expressing the thoughts of society is social media.
Determining the time intervals during which anti-vaccination increases in
social media can help institutions determine the strategy to be used in
combating anti-vaccination. Recording and tracking every tweet entered with
human labor would be inefficient, so various automation solutions are needed.
In this study, The Bidirectional Encoder Representations from Transformers
(BERT) model, which is a deep learning-based natural language processing (NLP)
model, was used. In a dataset of 1506 tweets divided into four different
categories as news, irrelevant, anti-vaccine, and vaccine supporters, the model
was trained with a learning rate of 5e-6 for 25 epochs. To determine the
intervals in which anti-vaccine tweets are concentrated, the categories to
which 652840 tweets belong were determined by using the trained model. The
change of the determined categories overtime was visualized and the events that
could cause the change were determined. As a result of model training, in the
test dataset, the f-score of 0.81 and AUC values for different classes were
obtained as 0.99,0.91, 0.92, 0.92, respectively. In this model, unlike the
studies in the literature, an auxiliary system is designed that provides data
that institutions can use when determining their strategy by measuring and
visualizing the frequency of anti-vaccine tweets in a time interval, different
from detecting and censoring such tweets.

Finding low dimensional representation of data from long-timescale
trajectories of biomolecular processes such as protein-folding or
ligand-receptor binding is of fundamental importance and kinetic models such as
Markov modeling have proven useful in describing the kinetics of these systems.
Recently, an unsupervised machine learning technique called VAMPNet was
introduced to learn the low dimensional representation and linear dynamical
model in an end-to-end manner. VAMPNet is based on variational approach to
Markov processes (VAMP) and relies on neural networks to learn the
coarse-grained dynamics. In this contribution, we combine VAMPNet and graph
neural networks to generate an end-to-end framework to efficiently learn
high-level dynamics and metastable states from the long-timescale molecular
dynamics trajectories. This method bears the advantages of graph representation
learning and uses graph message passing operations to generate an embedding for
each datapoint which is used in the VAMPNet to generate a coarse-grained
representation. This type of molecular representation results in a higher
resolution and more interpretable Markov model than the standard VAMPNet
enabling a more detailed kinetic study of the biomolecular processes. Our
GraphVAMPNet approach is also enhanced with an attention mechanism to find the
important residues for classification into different metastable states.

When a student is asked to perform a given task, her subjective estimate of
the difficulty of that task has a strong influence on her performance. There
exists a rich literature on the impact of perceived task difficulty on
performance and motivation. Yet, there is another topic that is closely related
to the subject of the influence of perceived task difficulty that did not
receive any attention in previous research - the influence of revealing the
true difficulty of a task to the student. This paper investigates the impact of
revealing the task difficulty on the student's performance, motivation,
self-efficacy and subjective task value via an experiment in which workers are
asked to solve matchstick riddles. Furthermore, we discuss how the experiment
results might be relevant for AI-aided education. Specifically, we elaborate on
the question of how a student's learning experience might be improved by
supporting her with two types of AI systems: an AI system that predicts task
difficulty and an AI system that determines when task difficulty should be
revealed and when not.

Multi-view subspace clustering has conventionally focused on integrating
heterogeneous feature descriptions to capture higher-dimensional information.
One popular strategy is to generate a common subspace from different views and
then apply graph-based approaches to deal with clustering. However, the
performance of these methods is still subject to two limitations, namely the
multiple views fusion pattern and the connection between the fusion process and
clustering tasks. To address these problems, we propose a novel multi-view
subspace clustering framework via fine-grained graph learning, which can tell
the consistency of local structures between different views and integrate all
views more delicately than previous weight regularizations. Different from
other models in the literature, the point-level graph regularization and the
reformulation of spectral clustering are introduced to perform graphs fusion
and learn the shared cluster structure together. Extensive experiments on five
real-world datasets show that the proposed framework has comparable performance
to the SOTA algorithms.

Symbolic regression, i.e. predicting a function from the observation of its
values, is well-known to be a challenging task. In this paper, we train
Transformers to infer the function or recurrence relation underlying sequences
of integers or floats, a typical task in human IQ tests which has hardly been
tackled in the machine learning literature. We evaluate our integer model on a
subset of OEIS sequences, and show that it outperforms built-in Mathematica
functions for recurrence prediction. We also demonstrate that our float model
is able to yield informative approximations of out-of-vocabulary functions and
constants, e.g. $\operatorname{bessel0}(x)\approx
\frac{\sin(x)+\cos(x)}{\sqrt{\pi x}}$ and $1.644934\approx \pi^2/6$. An
interactive demonstration of our models is provided at
https://symbolicregression.metademolab.com.

Automatic segmentation of lung lesions associated with COVID-19 in CT images
requires large amount of annotated volumes. Annotations mandate expert
knowledge and are time-intensive to obtain through fully manual segmentation
methods. Additionally, lung lesions have large inter-patient variations, with
some pathologies having similar visual appearance as healthy lung tissues. This
poses a challenge when applying existing semi-automatic interactive
segmentation techniques for data labelling. To address these challenges, we
propose an efficient convolutional neural networks (CNNs) that can be learned
online while the annotator provides scribble-based interaction. To accelerate
learning from only the samples labelled through user-interactions, a
patch-based approach is used for training the network. Moreover, we use
weighted cross-entropy loss to address the class imbalance that may result from
user-interactions. During online inference, the learned network is applied to
the whole input volume using a fully convolutional approach. We compare our
proposed method with state-of-the-art using synthetic scribbles and show that
it outperforms existing methods on the task of annotating lung lesions
associated with COVID-19, achieving 16% higher Dice score while reducing
execution time by 3$\times$ and requiring 9000 lesser scribbles-based labelled
voxels. Due to the online learning aspect, our approach adapts quickly to user
input, resulting in high quality segmentation labels. Source code for ECONet is
available at: https://github.com/masadcv/ECONet-MONAILabel.

The past decade has seen a rapid adoption of Artificial Intelligence (AI),
specifically the deep learning networks, in Internet of Medical Things (IoMT)
ecosystem. However, it has been shown recently that the deep learning networks
can be exploited by adversarial attacks that not only make IoMT vulnerable to
the data theft but also to the manipulation of medical diagnosis. The existing
studies consider adding noise to the raw IoMT data or model parameters which
not only reduces the overall performance concerning medical inferences but also
is ineffective to the likes of deep leakage from gradients method. In this
work, we propose proximal gradient split learning (PSGL) method for defense
against the model inversion attacks. The proposed method intentionally attacks
the IoMT data when undergoing the deep neural network training process at
client side. We propose the use of proximal gradient method to recover gradient
maps and a decision-level fusion strategy to improve the recognition
performance. Extensive analysis show that the PGSL not only provides effective
defense mechanism against the model inversion attacks but also helps in
improving the recognition performance on publicly available datasets. We report
14.0$\%$, 17.9$\%$, and 36.9$\%$ gains in accuracy over reconstructed and
adversarial attacked images, respectively.

The emerging demand for advanced structural and biological materials calls
for novel modeling tools that can rapidly yield high-fidelity estimation on
materials properties in design cycles. Lattice spring model (LSM), a
coarse-grained particle spring network, has gained attention in recent years
for predicting the mechanical properties and giving insights into the fracture
mechanism with high reproducibility and generalizability. However, to simulate
the materials in sufficient detail for guaranteed numerical stability and
convergence, most of the time a large number of particles are needed, greatly
diminishing the potential for high-throughput computation and therewith data
generation for machine learning frameworks. Here, we implement CuLSM, a
GPU-accelerated CUDA C++ code realizing parallelism over the spring list
instead of the commonly used spatial decomposition, which requires intermittent
updates on the particle neighbor list. Along with the image-to-particle
conversion tool Img2Particle, our toolkit offers a fast and flexible platform
to characterize the elastic and fracture behaviors of materials, expediting the
design process between additive manufacturing and computer-aided design. With
the growing demand for new lightweight, adaptable, and multi-functional
materials and structures, such tailored and optimized modeling platform has
profound impacts, enabling faster exploration in design spaces, better quality
control for 3D printing by digital twin techniques, and larger data generation
pipelines for image-based generative machine learning models.

A key issue in federated learning over wireless channels is how to exchange a
large number of the model parameters via time-varying channels. Two types of
solutions based on digital and analog schemes are used typically. The
digital-based solution takes quantization and entropy coding for compression,
whereas transmissions via wireless channels may cause catastrophic errors owing
to the all-or-nothing behavior in entropy coding. The analog-based solutions
such as AirNet and AirComp use analog modulation for the parameter
transmissions. However, such an analog scheme often causes significant
distortion due to the source signal's large power without compression gain.
This paper proposes a novel hybrid digital-analog transmission-Federated
AirNet--for the model parameter transmissions in federated learning. The
Federated AirNet integrates low-rate digital coding and energy-compact analog
modulation. The digital coding offers the baseline of the model parameters and
compacts the source signal power. In addition, the residual parameters, which
are obtained from the original and encoded model parameters, are
analog-modulated to enhance the baseline according to the instantaneous
wireless channel quality. We show that the proposed Federated AirNet yields
better image classification accuracy compared with the digital-based and
analog-based solutions over a wide range of wireless channel signal-to-noise
ratios (SNRs).

The classical statistical learning theory implies that fitting too many
parameters leads to overfitting and poor performance. That modern deep neural
networks generalize well despite a large number of parameters contradicts this
finding and constitutes a major unsolved problem towards explaining the success
of deep learning. While previous work focuses on the implicit regularization
induced by stochastic gradient descent (SGD), we study here how the local
geometry of the energy landscape around local minima affects the statistical
properties of SGD with Gaussian gradient noise. We argue that under reasonable
assumptions, the local geometry forces SGD to stay close to a low dimensional
subspace and that this induces another form of implicit regularization and
results in tighter bounds on the generalization error for deep neural networks.
To derive generalization error bounds for neural networks, we first introduce a
notion of stagnation sets around the local minima and impose a local essential
convexity property of the population risk. Under these conditions, lower bounds
for SGD to remain in these stagnation sets are derived. If stagnation occurs,
we derive a bound on the generalization error of deep neural networks involving
the spectral norms of the weight matrices but not the number of network
parameters. Technically, our proofs are based on controlling the change of
parameter values in the SGD iterates and local uniform convergence of the
empirical loss functions based on the entropy of suitable neighborhoods around
local minima.

The paper deals with the distribution of singular values of the input-output
Jacobian of deep untrained neural networks in the limit of their infinite
width. The Jacobian is the product of random matrices where the independent
rectangular weight matrices alternate with diagonal matrices whose entries
depend on the corresponding column of the nearest neighbor weight matrix. The
problem was considered in \cite{Pe-Co:18} for the Gaussian weights and biases
and also for the weights that are Haar distributed orthogonal matrices and
Gaussian biases. Basing on a free probability argument, it was claimed that in
these cases the singular value distribution of the Jacobian in the limit of
infinite width (matrix size) coincides with that of the analog of the Jacobian
with special random but weight independent diagonal matrices, the case well
known in random matrix theory. The claim was rigorously proved in
\cite{Pa-Sl:21} for a quite general class of weights and biases with i.i.d.
(including Gaussian) entries by using a version of the techniques of random
matrix theory. In this paper we use another version of the techniques to
justify the claim for random Haar distributed weight matrices and Gaussian
biases.

In 2020, covid-19 virus had reached more than 200 countries. Till December
20th 2021, 221 nations in the world had collectively reported 275M confirmed
cases of covid-19 & total death toll of 5.37M. Many countries which include
United States, India, Brazil, United Kingdom, Russia etc were badly affected by
covid-19 pandemic due to the large population. The total confirmed cases
reported in this country are 51.7M, 34.7M, 22.2M, 11.3M, 10.2M respectively
till December 20, 2021. This pandemic can be controlled with the help of
precautionary steps by government & civilians of the country. The early
prediction of covid-19 cases helps to track the transmission dynamics & alert
the government to take the necessary precautions. Recurrent Deep learning
algorithms is a data driven model which plays a key role to capture the
patterns present in time series data. In many literatures, the Recurrent Neural
Network (RNN) based model are proposed for the efficient prediction of COVID-19
cases for different provinces. The study in the literature doesnt involve the
interpretation of the model behavior & robustness. In this study, The LSTM
model is proposed for the efficient prediction of active cases in each
provinces of India. The active cases dataset for each province in India is
taken from John Hopkins publicly available dataset for the duration from 10th
June, 2020 to 4th August, 2021. The proposed LSTM model is trained on one state
i.e., Maharashtra and tested for rest of the provinces in India. The concept of
Explainable AI is involved in this study for the better interpretation &
understanding of the model behavior. The proposed model is used to forecast the
active cases in India from 16th December, 2021 to 5th March, 2022. It is
notated that there will be a emergence of third wave on January, 2022 in India.

Parkinsons disease, PD, is a chronic condition that affects motor skills and
includes symptoms like tremors and rigidity. The current diagnostic procedure
uses patient assessments to evaluate symptoms and sometimes a magnetic
resonance imaging or MRI scan. However, symptom variations cause inaccurate
assessments, and the analysis of MRI scans requires experienced specialists.
This research proposes to accurately diagnose PD severity with deep learning by
combining symptoms data and MRI data from the Parkinsons Progression Markers
Initiative database. A new hybrid model architecture was implemented to fully
utilize both forms of clinical data, and models based on only symptoms and only
MRI scans were also developed. The symptoms based model integrates a fully
connected deep learning neural network, and the MRI scans and hybrid models
integrate transfer learning based convolutional neural networks. Instead of
performing only binary classification, all models diagnose patients into five
severity categories, with stage zero representing healthy patients and stages
four and five representing patients with PD. The symptoms only, MRI scans only,
and hybrid models achieved accuracies of 0.77, 0.68, and 0.94, respectively.
The hybrid model also had high precision and recall scores of 0.94 and 0.95.
Real clinical cases confirm the strong performance of the hybrid, where
patients were classified incorrectly with both other models but correctly by
the hybrid. It is also consistent across the five severity stages, indicating
accurate early detection. This is the first report to combine symptoms data and
MRI scans with a machine learning approach on such a large scale.

We present a fast and precise method to approximate the physics model of the
Karlsruhe Tritium Neutrino (KATRIN) experiment using a neural network. KATRIN
is designed to measure the effective electron anti-neutrino mass using the
kinematics of beta-decay with a sensitivity of 200 meV at 90% confidence level.
To achieve this goal, a highly accurate model prediction with relative errors
below the 1e-4-level is required. Using the regular numerical model for the
analysis of the final KATRIN dataset is computationally extremely costly or
requires approximations to decrease the computation time. Our solution to
reduce the computational requirements is to train a neural network to learn the
predicted beta-spectrum and its dependence on all relevant input parameters.
This results in a speed-up of the calculation by about three orders of
magnitude, while meeting the stringent accuracy requirements of KATRIN.

With the rapid development of big data and cloud computing, data management
has become increasingly challenging. Over the years, a number of frameworks for
data management and storage with various characteristics and features have
become available. Most of these are highly efficient, but ultimately create
data silos. It becomes difficult to move and work coherently with data as new
requirements emerge as no single framework can efficiently fulfill the data
management needs of diverse applications. A possible solution is to design
smart and efficient hierarchical (multi-tier) storage solutions. A hierarchical
storage system (HSS) is a meta solution that consists of different storage
frameworks organized as a jointly constructed large storage pool. It brings a
number of benefits including better utilization of the storage,
cost-efficiency, and use of different features provided by the underlying
storage frameworks. In order to maximize the gains of hierarchical storage
solutions, it is important that they include intelligent and autonomous
mechanisms for data management grounded in the features of the different
underlying frameworks. These decisions should be made according to the
characteristics of the dataset, tier status, and access patterns. These are
highly dynamic parameters and defining a policy based on the mentioned
parameters is a non-trivial task. This paper presents an open-source
hierarchical storage framework with a dynamic migration policy based on
reinforcement learning (RL). We present a mathematical model, a software
architecture, and an implementation based on both simulations and a live
cloud-based environment. We compare the proposed RL-based strategy to a
baseline of three rule-based policies, showing that the RL-based policy
achieves significantly higher efficiency and optimal data distribution in
different scenarios compared to the dynamic rule-based policies.

In this paper, based on results of exact learning and test theory, we study
arbitrary infinite binary information systems each of which consists of an
infinite set of elements and an infinite set of two-valued functions
(attributes) defined on the set of elements. We consider the notion of a
problem over information system, which is described by a finite number of
attributes: for a given element, we should recognize values of these
attributes. As algorithms for problem solving, we consider decision trees of
two types: (i) using only proper hypotheses (an analog of proper equivalence
queries from exact learning), and (ii) using both attributes and proper
hypotheses. As time complexity, we study the depth of decision trees. In the
worst case, with the growth of the number of attributes in the problem
description, the minimum depth of decision trees of both types either is
bounded from above by a constant or grows as a logarithm, or linearly. Based on
these results and results obtained earlier for attributes and arbitrary
hypotheses, we divide the set of all infinite binary information systems into
seven complexity classes.

The Cloud paradigm is at a critical point in which the existing
energy-efficiency techniques are reaching a plateau, while the computing
resources demand at Data Center facilities continues to increase exponentially.
The main challenge in achieving a global energy efficiency strategy based on
Artificial Intelligence is that we need massive amounts of data to feed the
algorithms. This paper proposes a time-series data augmentation methodology
based on synthetic scenario forecasting within the Data Center. For this
purpose, we will implement a powerful generative algorithm: Generative
Adversarial Networks (GANs). Specifically, our work combines the disciplines of
GAN-based data augmentation and scenario forecasting, filling the gap in the
generation of synthetic data in DCs. Furthermore, we propose a methodology to
increase the variability and heterogeneity of the generated data by introducing
on-demand anomalies without additional effort or expert knowledge. We also
suggest the use of Kullback-Leibler Divergence and Mean Squared Error as new
metrics in the validation of synthetic time series generation, as they provide
a better overall comparison of multivariate data distributions. We validate our
approach using real data collected in an operating Data Center, successfully
generating synthetic data helpful for prediction and optimization models. Our
research will help optimize the energy consumed in Data Centers, although the
proposed methodology can be employed in any similar time-series-like problem.

In this work we present a preliminary investigation of a novel algorithm
called Dyna-T. In reinforcement learning (RL) a planning agent has its own
representation of the environment as a model. To discover an optimal policy to
interact with the environment, the agent collects experience in a trial and
error fashion. Experience can be used for learning a better model or improve
directly the value function and policy. Typically separated, Dyna-Q is an
hybrid approach which, at each iteration, exploits the real experience to
update the model as well as the value function, while planning its action using
simulated data from its model. However, the planning process is computationally
expensive and strongly depends on the dimensionality of the state-action space.
We propose to build a Upper Confidence Tree (UCT) on the simulated experience
and search for the best action to be selected during the on-line learning
process. We prove the effectiveness of our proposed method on a set of
preliminary tests on three testbed environments from Open AI. In contrast to
Dyna-Q, Dyna-T outperforms state-of-the-art RL agents in the stochastic
environments by choosing a more robust action selection strategy.

Understanding the scene is key for autonomously navigating vehicles and the
ability to segment the surroundings online into moving and non-moving objects
is a central ingredient for this task. Often, deep learning-based methods are
used to perform moving object segmentation (MOS). The performance of these
networks, however, strongly depends on the diversity and amount of labeled
training data, information that may be costly to obtain. In this paper, we
propose an automatic data labeling pipeline for 3D LiDAR data to save the
extensive manual labeling effort and to improve the performance of existing
learning-based MOS systems by automatically generating labeled training data.
Our proposed approach achieves this by processing the data offline in batches.
It first exploits an occupancy-based dynamic object removal to detect possible
dynamic objects coarsely. Second, it extracts segments among the proposals and
tracks them using a Kalman filter. Based on the tracked trajectories, it labels
the actually moving objects such as driving cars and pedestrians as moving. In
contrast, the non-moving objects, e.g., parked cars, lamps, roads, or
buildings, are labeled as static. We show that this approach allows us to label
LiDAR data highly effectively and compare our results to those of other label
generation methods. We also train a deep neural network with our auto-generated
labels and achieve similar performance compared to the one trained with manual
labels on the same data, and an even better performance when using additional
datasets with labels generated by our approach. Furthermore, we evaluate our
method on multiple datasets using different sensors and our experiments
indicate that our method can generate labels in diverse environments.

With the recent availability and affordability of commercial depth sensors
and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets
have been publicized to facilitate research in 3D computer vision. However,
existing datasets either cover relatively small areas or have limited semantic
annotations. Fine-grained understanding of urban-scale 3D scenes is still in
its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV
photogrammetry point cloud dataset consisting of nearly three billion points
collected from three UK cities, covering 7.6 km^2. Each point in the dataset
has been labelled with fine-grained semantic annotations, resulting in a
dataset that is three times the size of the previous existing largest
photogrammetric point cloud dataset. In addition to the more commonly
encountered categories such as road and vegetation, urban-level categories
including rail, bridge, and river are also included in our dataset. Based on
this dataset, we further build a benchmark to evaluate the performance of
state-of-the-art segmentation algorithms. In particular, we provide a
comprehensive analysis and identify several key challenges limiting urban-scale
point cloud understanding. The dataset is available at
http://point-cloud-analysis.cs.ox.ac.uk.

Deep learning (DL) has indeed emerged as a powerful tool for rapidly and
accurately predicting materials properties from big data, such as the design of
current commercial Li-ion batteries. However, its practical utility for
multivalent metal-ion batteries (MIBs), the most promising future solution of
large-scale energy storage, is limited due to the scarce MIB data availability
and poor DL model interpretability. Here, we develop an interpretable DL model
as an effective and accurate method for learning electrode voltages of
multivalent MIBs (divalent magnesium, calcium, zinc, and trivalent aluminum) at
small dataset limits (150~500). Using the experimental results as validation,
our model is much more accurate than machine-learning models which usually are
better than DL in the small dataset regime. Besides the high accuracy, our
feature-engineering-free DL model is explainable, which automatically extracts
the atom covalent radius as the most important feature for the voltage learning
by visualizing vectors from the layers of the neural network. The presented
model potentially accelerates the design and optimization of multivalent MIB
materials with fewer data and less domain-knowledge restriction, and is
implemented into a publicly available online tool kit in
http://batteries.2dmatpedia.org/ for the battery community.

In this paper, we implement Neural Ordinary Differential Equations in a
Variational Autoencoder setting for generative time series modeling. An
object-oriented approach to the code was taken to allow for easier development
and research and all code used in the paper can be found here:
https://github.com/simonmoesorensen/neural-ode-project
  The results were initially recreated and the reconstructions compared to a
baseline Long-Short Term Memory AutoEncoder. The model was then extended with a
LSTM encoder and challenged by more complex data consisting of time series in
the form of spring oscillations. The model showed promise, and was able to
reconstruct true trajectories for all complexities of data with a smaller RMSE
than the baseline model. However, it was able to capture the dynamic behavior
of the time series for known data in the decoder but was not able to produce
extrapolations following the true trajectory very well for any of the
complexities of spring data. A final experiment was carried out where the model
was also presented with 68 days of solar power production data, and was able to
reconstruct just as well as the baseline, even when very little data is
  Finally, the models training time was compared to the baseline. It was found
that for small amounts of data the NODE method was significantly slower at
training than the baseline, while for larger amounts of data the NODE method
would be equal or faster at training.
  The paper is ended with a future work section which describes the many
natural extensions to the work presented in this paper, with examples being
investigating further the importance of input data, including extrapolation in
the baseline model or testing more specific model setups.

As the video streaming traffic in mobile networks is increasing, improving
the content delivery process becomes crucial, e.g., by utilizing edge computing
support. At an edge node, we can deploy adaptive bitrate (ABR) algorithms with
a better understanding of network behavior and access to radio and player
metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for
HTTP Adaptive Streaming with Machine Learning. ECAS-ML focuses on managing the
tradeoff among bitrate, segment switches, and stalls to achieve a higher
quality of experience (QoE). For that purpose, we use machine learning
techniques to analyze radio throughput traces and predict the best parameters
of our algorithm to achieve better performance. The results show that ECAS-ML
outperforms other client-based and edge-based ABR algorithms.

The field of cardiac electrophysiology tries to abstract, describe and
finally model the electrical characteristics of a heartbeat. With recent
advances in cardiac electrophysiology, models have become more powerful and
descriptive as ever. However, to advance to the field of inverse
electrophysiological modeling, i.e. creating models from electrical
measurements such as the ECG, the less investigated field of smoothness of the
simulated ECGs w.r.t. model parameters need to be further explored. The present
paper discusses smoothness in terms of the whole pipeline which describes how
from physiological parameters, we arrive at the simulated ECG. Employing such a
pipeline, we create a test-bench of a simplified idealized left ventricle model
and demonstrate the most important factors for efficient inverse modeling
through smooth cost functionals. Such knowledge will be important for designing
and creating inverse models in future optimization and machine learning
methods.

The Elo rating system is widely adopted to evaluate the skills of (chess)
game and sports players. Recently it has been also integrated into machine
learning algorithms in evaluating the performance of computerised AI agents.
However, an accurate estimation of the Elo rating (for the top players) often
requires many rounds of competitions, which can be expensive to carry out. In
this paper, to improve the sample efficiency of the Elo evaluation (for top
players), we propose an efficient online match scheduling algorithm.
Specifically, we identify and match the top players through a dueling bandits
framework and tailor the bandit algorithm to the gradient-based update of Elo.
We show that it reduces the per-step memory and time complexity to constant,
compared to the traditional likelihood maximization approaches requiring $O(t)$
time. Our algorithm has a regret guarantee of $\tilde{O}(\sqrt{T})$, sublinear
in the number of competition rounds and has been extended to the
multidimensional Elo ratings for handling intransitive games. We empirically
demonstrate that our method achieves superior convergence speed and time
efficiency on a variety of gaming tasks.

We consider fixed-budget best arm identification in two-armed bandit
problems. One of the longstanding open questions is a tight lower bound on the
probability of misidentifying the best arm and a strategy whose upper bound
matches the lower bound when the optimal target allocation ratio of arm draws
is unknown. We address this problem when the gap between the expected rewards
is small. First, we introduce a distribution-dependent lower bound. Then, we
propose the ``RS-AIPW'' strategy, which consists of the random sampling (RS)
rule using the estimated optimal target allocation ratio and the recommendation
rule using the augmented inverse probability weighting (AIPW) estimator. Our
proposed strategy is optimal in the sense that the upper bound achieves the
lower bound when the budget goes to infinity and the gap goes to zero. In the
course of the analysis, we present a novel large deviation bound for
martingales.

Applying standard machine learning approaches for classification can produce
unequal results across different demographic groups. When then used in
real-world settings, these inequities can have negative societal impacts. This
has motivated the development of various approaches to fair classification with
machine learning models in recent years. In this paper, we consider the problem
of modifying the predictions of a blackbox machine learning classifier in order
to achieve fairness in a multiclass setting. To accomplish this, we extend the
'post-processing' approach in Hardt et al. 2016, which focuses on fairness for
binary classification, to the setting of fair multiclass classification. We
explore when our approach produces both fair and accurate predictions through
systematic synthetic experiments and also evaluate discrimination-fairness
tradeoffs on several publicly available real-world application datasets. We
find that overall, our approach produces minor drops in accuracy and enforces
fairness when the number of individuals in the dataset is high relative to the
number of classes and protected groups.

Word embeddings, made widely popular in 2013 with the release of word2vec,
have become a mainstay of NLP engineering pipelines. Recently, with the release
of BERT, word embeddings have moved from the term-based embedding space to the
contextual embedding space -- each term is no longer represented by a single
low-dimensional vector but instead each term and \emph{its context} determine
the vector weights. BERT's setup and architecture have been shown to be general
enough to be applicable to many natural language tasks. Importantly for
Information Retrieval (IR), in contrast to prior deep learning solutions to IR
problems which required significant tuning of neural net architectures and
training regimes, "vanilla BERT" has been shown to outperform existing
retrieval algorithms by a wide margin, including on tasks and corpora that have
long resisted retrieval effectiveness gains over traditional IR baselines (such
as Robust04). In this paper, we employ the recently proposed axiomatic dataset
analysis technique -- that is, we create diagnostic datasets that each fulfil a
retrieval heuristic (both term matching and semantic-based) -- to explore what
BERT is able to learn. In contrast to our expectations, we find BERT, when
applied to a recently released large-scale web corpus with ad-hoc topics, to
\emph{not} adhere to any of the explored axioms. At the same time, BERT
outperforms the traditional query likelihood retrieval model by 40\%. This
means that the axiomatic approach to IR (and its extension of diagnostic
datasets created for retrieval heuristics) may in its current form not be
applicable to large-scale corpora. Additional -- different -- axioms are
needed.

Existing methods for explaining black box learning models often focus on
building local explanations of model behaviour for a particular data item. It
is possible to create global explanations for all data items, but these
explanations generally have low fidelity for complex black box models.
  We propose a new supervised manifold visualisation method, SLISEMAP, that
simultaneously finds local explanations for all data items and builds a
(typically) two-dimensional global visualisation of the black box model such
that data items with similar local explanations are projected nearby. We
provide a mathematical derivation of our problem and an open source
implementation implemented using the GPU-optimised PyTorch library.
  We compare SLISEMAP to multiple popular dimensionality reduction methods and
find that SLISEMAP is able to utilise labelled data to create embeddings with
consistent local white box models. We also compare SLISEMAP to other
model-agnostic local explanation methods and show that SLISEMAP provides
comparable explanations and that the visualisations can give a broader
understanding of black box regression and classification models.

Due to a high spatial angle resolution and low circuit cost of massive hybrid
analog and digital (HAD) multiple-input multiple-output (MIMO), it is viewed as
a key technology for future wireless networks. Combining a massive HAD-MIMO
with direction of arrinal (DOA) will provide a high-precision even
ultra-high-precision DOA measurement performance approaching the fully-digital
(FD) MIMO. However, phase ambiguity is a challenge issue for a massive HAD-MIMO
DOA estimation. In this paper, we review three aspects: detection, estimation,
and Cramer-Rao lower bound (CRLB) with low-resolution ADCs at receiver. First,
a multi-layer-neural-network (MLNN) detector is proposed to infer the existence
of passive emitters. Then, a two-layer HAD (TLHAD) MIMO structure is proposed
to eliminate phase ambiguity using only one-snapshot. Simulation results show
that the proposed MLNN detector is much better than both the existing
generalized likelihood ratio test (GRLT) and the ratio of maximum eigen-value
(Max-EV) to minimum eigen-value (R-MaxEV-MinEV) in terms of detection
probability. Additionally, the proposed TLHAD structure can achieve the
corresponding CRLB using single snapshot.

In practice, it is very demanding and sometimes impossible to collect
datasets of tagged data large enough to successfully train a machine learning
model, and one possible solution to this problem is transfer learning. This
study aims to assess how transferable are the features between different
domains of time series data and under which conditions. The effects of transfer
learning are observed in terms of predictive performance of the models and
their convergence rate during training. In our experiment, we use reduced data
sets of 1,500 and 9,000 data instances to mimic real world conditions. Using
the same scaled-down datasets, we trained two sets of machine learning models:
those that were trained with transfer learning and those that were trained from
scratch. Four machine learning models were used for the experiment. Transfer of
knowledge was performed within the same domain of application (seismology), as
well as between mutually different domains of application (seismology, speech,
medicine, finance). We observe the predictive performance of the models and the
convergence rate during the training. In order to confirm the validity of the
obtained results, we repeated the experiments seven times and applied
statistical tests to confirm the significance of the results. The general
conclusion of our study is that transfer learning is very likely to either
increase or not negatively affect the predictive performance of the model or
its convergence rate. The collected data is analysed in more details to
determine which source and target domains are compatible for transfer of
knowledge. We also analyse the effect of target dataset size and the selection
of model and its hyperparameters on the effects of transfer learning.

Controlling the manner in which a character moves in a real-time animation
system is a challenging task with useful applications. Existing style transfer
systems require access to a reference content motion clip, however, in
real-time systems the future motion content is unknown and liable to change
with user input. In this work we present a style modelling system that uses an
animation synthesis network to model motion content based on local motion
phases. An additional style modulation network uses feature-wise
transformations to modulate style in real-time. To evaluate our method, we
create and release a new style modelling dataset, 100STYLE, containing over 4
million frames of stylised locomotion data in 100 different styles that present
a number of challenges for existing systems. To model these styles, we extend
the local phase calculation with a contact-free formulation. In comparison to
other methods for real-time style modelling, we show our system is more robust
and efficient in its style representation while improving motion quality.

Self-supervised representation learning (SSL) on biomedical networks provides
new opportunities for drug discovery which is lack of available biological or
clinic phenotype. However, how to effectively combine multiple SSL models is
challenging and rarely explored. Therefore, we propose multi-task joint
strategies of self-supervised representation learning on biomedical networks
for drug discovery, named MSSL2drug. We design six basic SSL tasks that are
inspired by various modality features including structures, semantics, and
attributes in biomedical heterogeneous networks. In addition, fifteen
combinations of multiple tasks are evaluated by a graph attention-based
adversarial multi-task learning framework in two drug discovery scenarios. The
results suggest two important findings. (1) The combinations of multimodal
tasks achieve the best performance compared to other multi-task joint
strategies. (2) The joint training of local and global SSL tasks yields higher
performance than random task combinations. Therefore, we conjecture that the
multimodal and local-global combination strategies can be regarded as a
guideline for multi-task SSL to drug discovery.

There is a recent proliferation of research on the integration of machine
learning and optimization. One expansive area within this research stream is
predictive-model embedded optimization, which proposes the use of pre-trained
predictive models as surrogates for uncertain or highly complex objective
functions. In this setting, features of the predictive models become decision
variables in the optimization problem. Despite a recent surge in publications
in this area, only a few papers note the importance of incorporating trust
region considerations in this decision-making pipeline, i.e., enforcing
solutions to be similar to the data used to train the predictive models.
Without such constraints, the evaluation of the predictive model at solutions
obtained from optimization cannot be trusted and the practicality of the
solutions may be unreasonable. In this paper, we provide an overview of the
approaches appearing in the literature to construct a trust region, and propose
three alternative approaches. Our numerical evaluation highlights that
trust-region constraints learned through isolation forests, one of the newly
proposed approaches, outperform all previously suggested approaches, both in
terms of solution quality and computational time.

Ransomware has been an ongoing issue since the early 1990s. In recent times
ransomware has spread from traditional computational resources to
cyber-physical systems and industrial controls. We devised a series of
experiments in which virtual instances are infected with ransomware. We
instrumented the instances and collected resource utilization data across a
variety of metrics (CPU, Memory, Disk Utility). We design a change point
detection and learning method for identifying ransomware execution. Finally we
evaluate and demonstrate its ability to detect ransomware efficiently in a
timely manner when trained on a minimal set of samples. Our results represent a
step forward for defense, and we conclude with further remarks for the path
forward.

Glioblastoma Multiforme (GBM) is a malignant brain cancer forming around 48%
of al brain and Central Nervous System (CNS) cancers. It is estimated that
annually over 13,000 deaths occur in the US due to GBM, making it crucial to
have early diagnosis systems that can lead to predictable and effective
treatment. The most common treatment after GBM diagnosis is chemotherapy, which
works by sending rapidly dividing cells to apoptosis. However, this form of
treatment is not effective when the MGMT promoter sequence is methylated, and
instead leads to severe side effects decreasing patient survivability.
Therefore, it is important to be able to identify the MGMT promoter methylation
status through non-invasive magnetic resonance imaging (MRI) based machine
learning (ML) models. This is accomplished using the Brain Tumor Segmentation
(BraTS) 2021 dataset, which was recently used for an international Kaggle
competition. We developed four primary models - two radiomic models and two CNN
models - each solving the binary classification task with progressive
improvements. We built a novel ML model termed as the Intermediate State
Generator which was used to normalize the slice thicknesses of all MRI scans.
With further improvements, our best model was able to achieve performance
significantly ($p < 0.05$) better than the best performing Kaggle model with a
6% increase in average cross-validation accuracy. This improvement could
potentially lead to a more informed choice of chemotherapy as a treatment
option, prolonging lives of thousands of patients with GBM each year.

Critical periods are phases during which a toddler's brain develops in
spurts. To promote children's cognitive development, proper guidance is
critical in this stage. However, it is not clear whether such a critical period
also exists for the training of AI agents. Similar to human toddlers,
well-timed guidance and multimodal interactions might significantly enhance the
training efficiency of AI agents as well. To validate this hypothesis, we adapt
this notion of critical periods to learning in AI agents and investigate the
critical period in the virtual environment for AI agents. We formalize the
critical period and Toddler-guidance learning in the reinforcement learning
(RL) framework. Then, we built up a toddler-like environment with VECA toolkit
to mimic human toddlers' learning characteristics. We study three discrete
levels of mutual interaction: weak-mentor guidance (sparse reward), moderate
mentor guidance (helper-reward), and mentor demonstration (behavioral cloning).
We also introduce the EAVE dataset consisting of 30,000 real-world images to
fully reflect the toddler's viewpoint. We evaluate the impact of critical
periods on AI agents from two perspectives: how and when they are guided best
in both uni- and multimodal learning. Our experimental results show that both
uni- and multimodal agents with moderate mentor guidance and critical period on
1 million and 2 million training steps show a noticeable improvement. We
validate these results with transfer learning on the EAVE dataset and find the
performance advancement on the same critical period and the guidance.

Robust machine learning is an increasingly important topic that focuses on
developing models resilient to various forms of imperfect data. Due to the
pervasiveness of recommender systems in online technologies, researchers have
carried out several robustness studies focusing on data sparsity and profile
injection attacks. Instead, we propose a more holistic view of robustness for
recommender systems that encompasses multiple dimensions - robustness with
respect to sub-populations, transformations, distributional disparity, attack,
and data sparsity. While there are several libraries that allow users to
compare different recommender system models, there is no software library for
comprehensive robustness evaluation of recommender system models under
different scenarios. As our main contribution, we present a robustness
evaluation toolkit, Robustness Gym for RecSys (RGRecSys --
https://www.github.com/salesforce/RGRecSys), that allows us to quickly and
uniformly evaluate the robustness of recommender system models.

This work systematically investigates the adversarial robustness of deep
image denoisers (DIDs), i.e, how well DIDs can recover the ground truth from
noisy observations degraded by adversarial perturbations. Firstly, to evaluate
DIDs' robustness, we propose a novel adversarial attack, namely
Observation-based Zero-mean Attack ({\sc ObsAtk}), to craft adversarial
zero-mean perturbations on given noisy images. We find that existing DIDs are
vulnerable to the adversarial noise generated by {\sc ObsAtk}. Secondly, to
robustify DIDs, we propose an adversarial training strategy, hybrid adversarial
training ({\sc HAT}), that jointly trains DIDs with adversarial and
non-adversarial noisy data to ensure that the reconstruction quality is high
and the denoisers around non-adversarial data are locally smooth. The resultant
DIDs can effectively remove various types of synthetic and adversarial noise.
We also uncover that the robustness of DIDs benefits their generalization
capability on unseen real-world noise. Indeed, {\sc HAT}-trained DIDs can
recover high-quality clean images from real-world noise even without training
on real noisy data. Extensive experiments on benchmark datasets, including
Set68, PolyU, and SIDD, corroborate the effectiveness of {\sc ObsAtk} and {\sc
HAT}.

Computational modeling of the manufacturing process of Lithium-Ion Battery
(LIB) composite electrodes based on mechanistic approaches, allows predicting
the influence of manufacturing parameters on electrode properties. However,
ensuring that the calculated properties match well with experimental data, is
typically time and resources consuming In this work, we tackled this issue by
proposing a functional data-driven framework combining Functional Principal
Component Analysis and K-Nearest Neighbors algorithms. This aims first to
recover the early numerical values of a mechanistic electrode manufacturing
simulation to predict if the observable being calculated is prone to match or
not, \textit{i.e} screening step. In a second step it recovers additional
numerical values of the ongoing mechanistic simulation iterations to predict
the mechanistic simulation result, \textit{i.e} forecasting step. We
demonstrated this approach in context of LIB manufacturing through
non-equilibrium molecular dynamics (NEMD) simulations, aiming to capture the
rheological behavior of electrode slurries. We discuss in full details our
novel methodology and we report that the expected mechanistic simulation
results can be obtained 11 times faster with respect to running the complete
mechanistic simulation, while being accurate enough from an experimental point
of view, with a $F1_{score}$ equals to 0.90, and a $R^2_{score}$ equals to 0.96
for the learnings validation. This paves the way towards a powerful tool to
drastically reduce the utilization of computational resources while running
mechanistic simulations of battery manufacturing electrodes.

Measuring distances of cosmological sources such as galaxies, stars and
quasars plays an increasingly critical role in modern cosmology. Obtaining the
optical spectrum and consequently calculating the redshift as a distance
indicator could instantly classify these objects. As long as spectroscopic
observations are not available for many galaxies and the process of measuring
the redshift is time-consuming and infeasible for large samples, machine
learning (ML) approaches could be applied to determine the redshifts of
galaxies from different features including their photometric colors. In this
paper, by using the flux magnitudes from the Sloan Digital Sky Survey (SDSS)
catalog, we develop two ML regression algorithms (Decision Tree and Random
Forest) for estimating the redshifts taking color indices as input features. We
find that the Random Forest algorithm produces the optimum result for the
redshift prediction, and it will be further improved when the dataset is
limited to a subset with z $\le$ 2 giving the normalised standard deviation
$\overline{\Delta Z}_{\text {norm}}=0.005$ and the standard deviation
$\sigma_{\Delta z}=0.12$. This work shows a great potential of using the ML
approach to determine the photometric redshifts of distant sources.

In this paper, we propose a framework named OCSampler to explore a compact
yet effective video representation with one short clip for efficient video
recognition. Recent works prefer to formulate frame sampling as a sequential
decision task by selecting frames one by one according to their importance,
while we present a new paradigm of learning instance-specific video
condensation policies to select informative frames for representing the entire
video only in a single step. Our basic motivation is that the efficient video
recognition task lies in processing a whole sequence at once rather than
picking up frames sequentially. Accordingly, these policies are derived from a
light-weighted skim network together with a simple yet effective policy network
within one step. Moreover, we extend the proposed method with a frame number
budget, enabling the framework to produce correct predictions in high
confidence with as few frames as possible. Experiments on four benchmarks,
i.e., ActivityNet, Mini-Kinetics, FCVID, Mini-Sports1M, demonstrate the
effectiveness of our OCSampler over previous methods in terms of accuracy,
theoretical computational expense, actual inference speed. We also evaluate its
generalization power across different classifiers, sampled frames, and search
spaces. Especially, we achieve 76.9% mAP and 21.7 GFLOPs on ActivityNet with an
impressive throughput: 123.9 Videos/s on a single TITAN Xp GPU.

Brain Age (BA) estimation via Deep Learning has become a strong and reliable
bio-marker for brain health, but the black-box nature of Neural Networks does
not easily allow insight into the features of brain ageing.We trained a ResNet
model as a BA regressor on T1 structural MRI volumes from a small
cross-sectional cohort of 524 individuals. Using Layer-wise Relevance
Propagation (LRP) and DeepLIFT saliency mapping techniques, we analysed the
trained model to determine the most relevant structures for brain ageing for
the network, and compare these between the saliency mapping techniques. We show
the change in attribution of relevance to different brain regions through the
course of ageing. A tripartite pattern of relevance attribution to brain
regions emerges. Some regions increase in relevance with age (e.g. the right
Transverse Temporal Gyrus); some decrease in relevance with age (e.g. the right
Fourth Ventricle); and others are consistently relevant across ages. We also
examine the effect of the Brain Age Gap (BAG) on the distribution of relevance
within the brain volume. It is hoped that these findings will provide
clinically relevant region-wise trajectories for normal brain ageing, and a
baseline against which to compare brain ageing trajectories.

Recently, self-supervised learning of depth and ego-motion from thermal
images shows strong robustness and reliability under challenging scenarios.
However, the inherent thermal image properties such as weak contrast, blurry
edges, and noise hinder to generate effective self-supervision from thermal
images. Therefore, most research relies on additional self-supervision sources
such as well-lit RGB images, generative models, and Lidar information. In this
paper, we conduct an in-depth analysis of thermal image characteristics that
degenerates self-supervision from thermal images. Based on the analysis, we
propose an effective thermal image mapping method that significantly increases
image information, such as overall structure, contrast, and details, while
preserving temporal consistency. The proposed method shows outperformed depth
and pose results than previous state-of-the-art networks without leveraging
additional RGB guidance.

Alzheimer's disease (AD) is an irreversible neurode generative disease of the
brain.The disease may causes memory loss, difficulty communicating and
disorientation. For the diagnosis of Alzheimer's disease, a series of scales
are often needed to evaluate the diagnosis clinically, which not only increases
the workload of doctors, but also makes the results of diagnosis highly
subjective. Therefore, for Alzheimer's disease, imaging means to find early
diagnostic markers has become a top priority.
  In this paper, we propose a novel 3DMgNet architecture which is a unified
framework of multigrid and convolutional neural network to diagnose Alzheimer's
disease (AD). The model is trained using an open dataset (ADNI dataset) and
then test with a smaller dataset of ours. Finally, the model achieved 92.133%
accuracy for AD vs NC classification and significantly reduced the model

Mixup is a data-dependent regularization technique that consists in linearly
interpolating input samples and associated outputs. It has been shown to
improve accuracy when used to train on standard machine learning datasets.
However, authors have pointed out that Mixup can produce out-of-distribution
virtual samples and even contradictions in the augmented training set,
potentially resulting in adversarial effects. In this paper, we introduce Local
Mixup in which distant input samples are weighted down when computing the loss.
In constrained settings we demonstrate that Local Mixup can create a trade-off
between bias and variance, with the extreme cases reducing to vanilla training
and classical Mixup. Using standardized computer vision benchmarks , we also
show that Local Mixup can improve test accuracy.

We prove that the Ring Learning With Errors (RLWE) and the Polynomial
Learning With Errors (PLWE) problems over the cyclotomic field
$\mathbb{Q}(\zeta_n)$ are not equivalent. Precisely, we show that reducing one
problem to the other increases the noise by a factor that is more than
polynomial in $n$. We do so by providing a lower bound, holding for infinitely
many positive integers $n$, for the condition number of the Vandermonde matrix
of the $n$th cyclotomic polynomial.

In the practical application of restoring low-resolution gray-scale images,
we generally need to run three separate processes of image colorization,
super-resolution, and dows-sampling operation for the target device. However,
this pipeline is redundant and inefficient for the independent processes, and
some inner features could have been shared. Therefore, we present an efficient
paradigm to perform {S}imultaneously Image {C}olorization and
{S}uper-resolution (SCS) and propose an end-to-end SCSNet to achieve this goal.
The proposed method consists of two parts: colorization branch for learning
color information that employs the proposed plug-and-play \emph{Pyramid Valve
Cross Attention} (PVCAttn) module to aggregate feature maps between source and
reference images; and super-resolution branch for integrating color and texture
information to predict target images, which uses the designed \emph{Continuous
Pixel Mapping} (CPM) module to predict high-resolution images at continuous
magnification. Furthermore, our SCSNet supports both automatic and referential
modes that is more flexible for practical application. Abundant experiments
demonstrate the superiority of our method for generating authentic images over
state-of-the-art methods, e.g., averagely decreasing FID by 1.8$\downarrow$ and
5.1 $\downarrow$ compared with current best scores for automatic and
referential modes, respectively, while owning fewer parameters (more than
$\times$2$\downarrow$) and faster running speed (more than
$\times$3$\uparrow$).

This paper describes the evolution of our research from video analytics to a
global security system with focus on the video surveillance component. Indeed
video surveillance has evolved from a commodity security tool up to the most
efficient way of tracking perpetrators when terrorism hits our modern urban
centers. As number of cameras soars, one could expect the system to leverage
the huge amount of data carried through the video streams to provide fast
access to video evidences, actionable intelligence for monitoring real-time
events and enabling predictive capacities to assist operators in their
surveillance tasks. This research explores a hybrid platform for video
intelligence capture, automated data extraction, supervised Machine Learning
for intelligently assisted urban video surveillance; Extension to other
components of a global security system are discussed. Applying Knowledge
Management principles in this research helps with deep problem understanding
and facilitates the implementation of efficient information and experience
sharing decision support systems providing assistance to people on the field as
well as in operations centers. The originality of this work is also the
creation of "common" human-machine and machine to machine language and a
security ontology.

Distributed computing has become a common practice nowadays, where the recent
focus has been given to the usage of smart networking devices with in-network
computing capabilities. State-of-the-art switches with near-line rate computing
and aggregation capabilities enable acceleration and improved performance for
various modern applications like big data analytics and large-scale distributed
and federated machine learning.
  In this paper, we formulate and study the theoretical algorithmic foundations
of such approaches, and focus on how to deploy and use constrained in-network
computing capabilities within the data center. We focus our attention on
reducing the network congestion, i.e., the most congested link in the network,
while supporting the given workload(s). We present an efficient optimal
algorithm for tree-like network topologies and show that our solution provides
as much as an x13 improvement over common alternative approaches. In
particular, our results show that having merely a small fraction of network
devices that support in-network aggregation can significantly reduce the
network congestion, both for single and multiple workloads.

We introduce PyHHMM, an object-oriented open-source Python implementation of
Heterogeneous-Hidden Markov Models (HHMMs). In addition to HMM's basic core
functionalities, such as different initialization algorithms and classical
observations models, i.e., continuous and multinoulli, PyHHMM distinctively
emphasizes features not supported in similar available frameworks: a
heterogeneous observation model, missing data inference, different model order
selection criterias, and semi-supervised training. These characteristics result
in a feature-rich implementation for researchers working with sequential data.
PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages,
and is distributed under the Apache-2.0 License. PyHHMM's source code is
publicly available on Github (https://github.com/fmorenopino/HeterogeneousHMM)
to facilitate adoptions and future contributions. A detailed documentation
(https://pyhhmm.readthedocs.io/en/latest), which covers examples of use and
models' theoretical explanation, is available. The package can be installed
through the Python Package Index (PyPI), via 'pip install pyhhmm'.

Granular-ball computing is an efficient, robust, and scalable learning method
for granular computing. The basis of granular-ball computing is the
granular-ball generation method. This paper proposes a method for accelerating
the granular-ball generation using the division to replace $k$-means. It can
greatly improve the efficiency of granular-ball generation while ensuring the
accuracy similar to the existing method. Besides, a new adaptive method for the
granular-ball generation is proposed by considering granular-ball's overlap
eliminating and some other factors. This makes the granular-ball generation
process of parameter-free and completely adaptive in the true sense. In
addition, this paper first provides the mathematical models for the
granular-ball covering. The experimental results on some real data sets
demonstrate that the proposed two granular-ball generation methods have similar
accuracies with the existing method while adaptiveness or acceleration is
realized.

In real-world robotics applications, accurate models of robot dynamics are
critical for safe and stable control in rapidly changing operational
conditions. This motivates the use of machine learning techniques to
approximate robot dynamics and their disturbances over a training set of
state-control trajectories. This paper demonstrates that inductive biases
arising from physics laws can be used to improve the data efficiency and
accuracy of the approximated dynamics model. For example, the dynamics of many
robots, including ground, aerial, and underwater vehicles, are described using
their $SE(3)$ pose and satisfy conservation of energy principles. We design a
physically plausible model of the robot dynamics by imposing the structure of
Hamilton's equations of motion in the design of a neural ordinary differential
equation (ODE) network. The Hamiltonian structure guarantees satisfaction of
$SE(3)$ kinematic constraints and energy conservation by construction. It also
allows us to derive an energy-based adaptive controller that achieves
trajectory tracking while compensating for disturbances. Our learning-based
adaptive controller is verified on an under-actuated quadrotor robot.

We propose PromptBERT, a novel contrastive learning method for learning
better sentence representation. We firstly analyze the drawback of current
sentence embedding from original BERT and find that it is mainly due to the
static token embedding bias and ineffective BERT layers. Then we propose the
first prompt-based sentence embeddings method and discuss two prompt
representing methods and three prompt searching methods to make BERT achieve
better sentence embeddings. Moreover, we propose a novel unsupervised training
objective by the technology of template denoising, which substantially shortens
the performance gap between the supervised and unsupervised settings. Extensive
experiments show the effectiveness of our method. Compared to SimCSE,
PromptBert achieves 2.29 and 2.58 points of improvement based on BERT and
RoBERTa in the unsupervised setting.

Dementia related cognitive impairment (CI) is a neurodegenerative disorder,
affecting over 55 million people worldwide and growing rapidly at the rate of
one new case every 3 seconds. 75% cases go undiagnosed globally with up to 90%
in low-and-middle-income countries, leading to an estimated annual worldwide
cost of USD 1.3 trillion, forecasted to reach 2.8 trillion by 2030. With no
cure, a recurring failure of clinical trials, and a lack of early diagnosis,
the mortality rate is 100%. Information in electronic health records (EHR) can
provide vital clues for early detection of CI, but a manual review by experts
is tedious and error prone. Several computational methods have been proposed,
however, they lack an enhanced understanding of the linguistic context in
complex language structures of EHR. Therefore, I propose a novel and more
accurate framework, NeuraHealth, to identify patients who had no earlier
diagnosis. In NeuraHealth, using patient EHR from Mass General Brigham BioBank,
I fine-tuned a bi-directional attention-based deep learning natural language
processing model to classify sequences. The sequence predictions were used to
generate structured features as input for a patient level regularized logistic
regression model. This two-step framework creates high dimensionality,
outperforming all existing state-of-the-art computational methods as well as
clinical methods. Further, I integrate the models into a real-world product, a
web app, to create an automated EHR screening pipeline for scalable and
high-speed discovery of undetected CI in EHR, making early diagnosis viable in
medical facilities and in regions with scarce health services.

The modeling of optical wave propagation in optical fiber is a task of fast
and accurate solving the nonlinear Schr\"odinger equation (NLSE), and can
enable the optical system design, digital signal processing verification and
fast waveform calculation. Traditional waveform modeling of full-time and
full-frequency information is the split-step Fourier method (SSFM), which has
long been regarded as challenging in long-haul wavelength division multiplexing
(WDM) optical fiber communication systems because it is extremely
time-consuming. Here we propose a linear-nonlinear feature decoupling
distributed (FDD) waveform modeling scheme to model long-haul WDM fiber
channel, where the channel linear effects are modelled by the NLSE-derived
model-driven methods and the nonlinear effects are modelled by the data-driven
deep learning methods. Meanwhile, the proposed scheme only focuses on one-span
fiber distance fitting, and then recursively transmits the model to achieve the
required transmission distance. The proposed modeling scheme is demonstrated to
have high accuracy, high computing speeds, and robust generalization abilities
for different optical launch powers, modulation formats, channel numbers and
transmission distances. The total running time of FDD waveform modeling scheme
for 41-channel 1040-km fiber transmission is only 3 minutes versus more than 2
hours using SSFM for each input condition, which achieves a 98% reduction in
computing time. Considering the multi-round optimization by adjusting system
parameters, the complexity reduction is significant. The results represent a
remarkable improvement in nonlinear fiber modeling and open up novel
perspectives for solution of NLSE-like partial differential equations and
optical fiber physics problems.

Cloud applications use more than just server resources, they also require
networking resources. We propose a new technique to model network bandwidth
demand of networked cloud applications. Our technique, Gridiron, augments VM
workload traces from Azure cloud with network bandwidth requirements. The key
to the Gridiron technique is to derive inter-VM network bandwidth requirements
using Amdahl's second law. As a case study, we use Gridiron to generate
realistic traces with network bandwidth demands for a distributed machine
learning training application. Workloads generated with Gridiron allow
datacenter operators to estimate the network bandwidth demands of cloud
applications and enable more realistic cloud resource scheduler evaluation.

Knee osteoarthritis (OA) is the most common osteoarthritis and a leading
cause of disability. Cartilage defects are regarded as major manifestations of
knee OA, which are visible by magnetic resonance imaging (MRI). Thus early
detection and assessment for knee cartilage defects are important for
protecting patients from knee OA. In this way, many attempts have been made on
knee cartilage defect assessment by applying convolutional neural networks
(CNNs) to knee MRI. However, the physiologic characteristics of the cartilage
may hinder such efforts: the cartilage is a thin curved layer, implying that
only a small portion of voxels in knee MRI can contribute to the cartilage
defect assessment; heterogeneous scanning protocols further challenge the
feasibility of the CNNs in clinical practice; the CNN-based knee cartilage
evaluation results lack interpretability. To address these challenges, we model
the cartilages structure and appearance from knee MRI into a graph
representation, which is capable of handling highly diverse clinical data.
Then, guided by the cartilage graph representation, we design a non-Euclidean
deep learning network with the self-attention mechanism, to extract cartilage
features in the local and global, and to derive the final assessment with a
visualized result. Our comprehensive experiments show that the proposed method
yields superior performance in knee cartilage defect assessment, plus its
convenient 3D visualization for interpretability.

Given $n$ i.i.d. samples drawn from an unknown distribution $P$, when is it
possible to produce a larger set of $n+m$ samples which cannot be distinguished
from $n+m$ i.i.d. samples drawn from $P$? (Axelrod et al. 2019) formalized this
question as the sample amplification problem, and gave optimal amplification
procedures for discrete distributions and Gaussian location models. However,
these procedures and associated lower bounds are tailored to the specific
distribution classes, and a general statistical understanding of sample
amplification is still largely missing. In this work, we place the sample
amplification problem on a firm statistical foundation by deriving generally
applicable amplification procedures, lower bound techniques and connections to
existing statistical notions. Our techniques apply to a large class of
distributions including the exponential family, and establish a rigorous
connection between sample amplification and distribution learning.

Contrastive learning relies on an assumption that positive pairs contain
related views, e.g., patches of an image or co-occurring multimodal signals of
a video, that share certain underlying information about an instance. But what
if this assumption is violated? The literature suggests that contrastive
learning produces suboptimal representations in the presence of noisy views,
e.g., false positive pairs with no apparent shared information. In this work,
we propose a new contrastive loss function that is robust against noisy views.
We provide rigorous theoretical justifications by showing connections to robust
symmetric losses for noisy binary classification and by establishing a new
contrastive bound for mutual information maximization based on the Wasserstein
distance measure. The proposed loss is completely modality-agnostic and a
simple drop-in replacement for the InfoNCE loss, which makes it easy to apply
to existing contrastive frameworks. We show that our approach provides
consistent improvements over the state-of-the-art on image, video, and graph
contrastive learning benchmarks that exhibit a variety of real-world noise
patterns.

As a hybrid imaging technology, photoacoustic microscopy (PAM) imaging
suffers from noise due to the maximum permissible exposure of laser intensity,
attenuation of ultrasound in the tissue, and the inherent noise of the
transducer. De-noising is a post-processing method to reduce noise, and PAM
image quality can be recovered. However, previous de-noising techniques usually
heavily rely on mathematical priors as well as manually selected parameters,
resulting in unsatisfactory and slow de-noising performance for different noisy
images, which greatly hinders practical and clinical applications. In this
work, we propose a deep learning-based method to remove complex noise from PAM
images without mathematical priors and manual selection of settings for
different input images. An attention enhanced generative adversarial network is
used to extract image features and remove various noises. The proposed method
is demonstrated on both synthetic and real datasets, including phantom (leaf
veins) and in vivo (mouse ear blood vessels and zebrafish pigment) experiments.
The results show that compared with previous PAM de-noising methods, our method
exhibits good performance in recovering images qualitatively and
quantitatively. In addition, the de-noising speed of 0.016 s is achieved for an
image with $256\times256$ pixels. Our approach is effective and practical for
the de-noising of PAM images.

Wall-clock convergence time and communication load are key performance
metrics for the distributed implementation of stochastic gradient descent (SGD)
in parameter server settings. Communication-adaptive distributed Adam (CADA)
has been recently proposed as a way to reduce communication load via the
adaptive selection of workers. CADA is subject to performance degradation in
terms of wall-clock convergence time in the presence of stragglers. This paper
proposes a novel scheme named grouping-based CADA (G-CADA) that retains the
advantages of CADA in reducing the communication load, while increasing the
robustness to stragglers at the cost of additional storage at the workers.
G-CADA partitions the workers into groups of workers that are assigned the same
data shards. Groups are scheduled adaptively at each iteration, and the server
only waits for the fastest worker in each selected group. We provide analysis
and experimental results to elaborate the significant gains on the wall-clock
time, as well as communication load and computation load, of G-CADA over other

Terrorism is a major problem worldwide, causing thousands of fatalities and
billions of dollars in damage every year. Toward the end of better
understanding and mitigating these attacks, we present a set of machine
learning models that learn from localized news data in order to predict whether
a terrorist attack will occur on a given calendar date and in a given state.
The best model--a Random Forest that learns from a novel variable-length moving
average representation of the feature space--achieves area under the receiver
operating characteristic scores $> .667$ on four of the five states that were
impacted most by terrorism between 2015 and 2018. Our key findings include that
modeling terrorism as a set of independent events, rather than as a continuous
process, is a fruitful approach--especially when the events are sparse and
dissimilar. Additionally, our results highlight the need for localized models
that account for differences between locations. From a machine learning
perspective, we found that the Random Forest model outperformed several deep
models on our multimodal, noisy, and imbalanced data set, thus demonstrating
the efficacy of our novel feature representation method in such a context. We
also show that its predictions are relatively robust to time gaps between
attacks and observed characteristics of the attacks. Finally, we analyze
factors that limit model performance, which include a noisy feature space and
small amount of available data. These contributions provide an important
foundation for the use of machine learning in efforts against terrorism in the
United States and beyond.

Video understanding requires reasoning at multiple spatiotemporal resolutions
-- from short fine-grained motions to events taking place over longer
durations. Although transformer architectures have recently advanced the
state-of-the-art, they have not explicitly modelled different spatiotemporal
resolutions. To this end, we present Multiview Transformers for Video
Recognition (MTV). Our model consists of separate encoders to represent
different views of the input video with lateral connections to fuse information
across views. We present thorough ablation studies of our model and show that
MTV consistently performs better than single-view counterparts in terms of
accuracy and computational cost across a range of model sizes. Furthermore, we
achieve state-of-the-art results on six standard datasets, and improve even
further with large-scale pretraining. Code and checkpoints are available at:
https://github.com/google-research/scenic/tree/main/scenic/projects/mtv.

Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL) have
recently been integrated to take the advantage of the both methods for better
exploration and exploitation.The evolutionary part in these hybrid methods
maintains a population of policy networks.However, existing methods focus on
optimizing the parameters of policy network, which is usually high-dimensional
and tricky for EA.In this paper, we shift the target of evolution from
high-dimensional parameter space to low-dimensional action space.We propose
Evolutionary Action Selection-Twin Delayed Deep Deterministic Policy Gradient
(EAS-TD3), a novel hybrid method of EA and DRL.In EAS, we focus on optimizing
the action chosen by the policy network and attempt to obtain high-quality
actions to promote policy learning through an evolutionary algorithm. We
conduct several experiments on challenging continuous control tasks.The result
shows that EAS-TD3 shows superior performance over other state-of-art methods.

Recent work on audio-visual navigation targets a single static sound in
noise-free audio environments and struggles to generalize to unheard sounds. We
introduce the novel dynamic audio-visual navigation benchmark in which an
embodied AI agent must catch a moving sound source in an unmapped environment
in the presence of distractors and noisy sounds. We propose an end-to-end
reinforcement learning approach that relies on a multi-modal architecture that
fuses the spatial audio-visual information from a binaural audio signal and
spatial occupancy maps to encode the features needed to learn a robust
navigation policy for our new complex task settings. We demonstrate that our
approach outperforms the current state-of-the-art with better generalization to
unheard sounds and better robustness to noisy scenarios on the two challenging
3D scanned real-world datasets Replica and Matterport3D, for the static and
dynamic audio-visual navigation benchmarks. Our novel benchmark will be made
available at http://dav-nav.cs.uni-freiburg.de.

The exponential functional link network (EFLN) filter has attracted
tremendous interest due to its enhanced nonlinear modeling capability. However,
the computational complexity will dramatically increase with the dimension
growth of the EFLN-based filter. To improve the computational efficiency, we
propose a novel frequency domain exponential functional link network (FDEFLN)
filter in this paper. The idea is to organize the samples in blocks of expanded
input data, transform them from time domain to frequency domain, and thus
execute the filtering and adaptation procedures in frequency domain with the
overlap-save method. A FDEFLN-based nonlinear active noise control (NANC)
system has also been developed to form the frequency domain exponential
filtered-s least mean-square (FDEFsLMS) algorithm. Moreover, the stability,
steady-state performance and computational complexity of algorithms are
analyzed. Finally, several numerical experiments corroborate the proposed
FDEFLN-based algorithms in nonlinear system identification, acoustic echo
cancellation and NANC implementations, which demonstrate much better

Despite the widespread adoption of neural networks, their training dynamics
remain poorly understood. We show experimentally that as the size of the
dataset increases, a point forms where the magnitude of the gradient of the
loss becomes unbounded. Gradient descent rapidly brings the network close to
this singularity in parameter space, and further training takes place near it.
This singularity explains a variety of phenomena recently observed in the
Hessian of neural network loss functions, such as training on the edge of
stability and the concentration of the gradient in a top subspace. Once the
network approaches the singularity, the top subspace contributes little to
learning, even though it constitutes the majority of the gradient.

This work investigates the origins of cation ordering of double perovskites
using first-principles theory computations combined with machine learning (ML)
and causal relations. We have considered various oxidation states of A, A', B,
and B' from the family of transition metal ions to construct a diverse
compositional space. A conventional framework employing traditional ML
classification algorithms such as Random Forest (RF) coupled with appropriate
features including geometry-driven and key structural modes leads to highly
accurate prediction (~98%) of A-site cation ordering. We have evaluated the
accuracy of ML models by entailing analyses of decision paths, assignments of
probabilistic confidence bound, and finally introducing a direct non-Gaussian
acyclic structural equation model to investigate causality. Our study suggests
that the structural modes are the most important features for classifying
layered, columnar and rock-salt ordering. For clear layered ordering, the
charge difference between the A and A' is the most important feature which in
turn depends on the B, B' charge separation. Based on the outputs from ML
models, we have designed functional forms with these features to derive energy
differences forming clear layered ordering. The trilinear coupling between
tilt, rotation, and A-site antiferroelectric displacement in Landau free-energy
expansion becomes the necessary condition behind the formation of A-site cation
ordering.

The envisioned 6G networks are expected to support extremely high data rates,
low-latency, and radically new applications empowered by machine learning. The
futuristic 6G networks require a novel framework that can be used to operate,
manage, and optimize its underlying services such as ultra-reliable and
low-latency communication, and Internet of everything. In recent years,
artificial intelligence (AI) has demonstrated significant success in optimizing
and designing networks. The AI-enabled traffic orchestration can dynamically
organize different network architectures and slices to provide quality of
experience considering the dynamic nature of the wireless communication
network.
  In this paper, we propose a digital twin enabled network framework, empowered
by AI to cater the variability and complexity of envisioned 6G networks, to
provide smart resource management and intelligent service provisioning. Digital
twin paves a way for achieving optimizing 6G services by creating a virtual
representation of the 6G network along with its associated communication
technologies (e.g., intelligent reflecting surfaces, terahertz and millimeter
communication), computing systems (e.g., cloud computing and fog computing)
with its associated algorithms (e.g., optimization and machine learning). We
then discuss and review the existing AI-enabled traffic management and
orchestration techniques and highlight future research directions and potential
solutions in 6G networks.

Appropriately identifying and treating molecules and materials with
significant multi-reference (MR) character is crucial for achieving high data
fidelity in virtual high throughput screening (VHTS). Nevertheless, most VHTS
is carried out with approximate density functional theory (DFT) using a single
functional. Despite development of numerous MR diagnostics, the extent to which
a single value of such a diagnostic indicates MR effect on chemical property
prediction is not well established. We evaluate MR diagnostics of over 10,000
transition metal complexes (TMCs) and compare to those in organic molecules. We
reveal that only some MR diagnostics are transferable across these materials
spaces. By studying the influence of MR character on chemical properties (i.e.,
MR effect) that involves multiple potential energy surfaces (i.e., adiabatic
spin splitting, $\Delta E_\mathrm{H-L}$, and ionization potential, IP), we
observe that cancellation in MR effect outweighs accumulation. Differences in
MR character are more important than the total degree of MR character in
predicting MR effect in property prediction. Motivated by this observation, we
build transfer learning models to directly predict CCSD(T)-level adiabatic
$\Delta E_\mathrm{H-L}$ and IP from lower levels of theory. By combining these
models with uncertainty quantification and multi-level modeling, we introduce a
multi-pronged strategy that accelerates data acquisition by at least a factor
of three while achieving chemical accuracy (i.e., 1 kcal/mol) for robust VHTS.

Technical debt (TD) is a metaphor for code-related problems that arise as a
result of prioritizing speedy delivery over perfect code. Given that the
reduction of TDs can have long-term positive impact in the software engineering
life-cycle (SDLC), TDs are studied extensively in the literature. However, very
few of the existing research focused on the technical debts of R programming
language despite its popularity and usage. Recent research by Codabux et al.
[21] finds that R packages can have 10 diverse TD types analyzing peer-review
documentation. However, the findings are based on the manual analysis of a
small sample of R package review comments. In this paper, we develop a suite of
Machine Learning (ML) classifiers to detect the 10 TDs automatically. The best
performing classifier is based on the deep ML model BERT, which achieves
F1-scores of 0.71 - 0.91. We then apply the trained BERT models on all
available peer-review issue comments from two platforms, rOpenSci and
BioConductor (13.5K review comments coming from a total of 1297 R packages). We
conduct an empirical study on the prevalence and evolution of 10 TDs in the two
R platforms. We discovered documentation debt is the most prevalent among all
types of TD, and it is also expanding rapidly. We also find that R packages of
generic platform (i.e. rOpenSci) are more prone to TD compared to
domain-specific platform (i.e. BioConductor). Our empirical study findings can
guide future improvements opportunities in R package documentation. Our ML
models can be used to automatically monitor the prevalence and evolution of TDs
in R package documentation.

The Poisson multinomial distribution (PMD) describes the distribution of the
sum of $n$ independent but non-identically distributed random vectors, in which
each random vector is of length $m$ with 0/1 valued elements and only one of
its elements can take value 1 with a certain probability. Those probabilities
are different for the $m$ elements across the $n$ random vectors, and form an
$n \times m$ matrix with row sum equals to 1. We call this $n\times m$ matrix
the success probability matrix (SPM). Each SPM uniquely defines a PMD. The PMD
is useful in many areas such as, voting theory, ecological inference, and
machine learning. The distribution functions of PMD, however, are usually
difficult to compute. In this paper, we develop efficient methods to compute
the probability mass function (pmf) for the PMD using multivariate Fourier
transform, normal approximation, and simulations. We study the accuracy and
efficiency of those methods and give recommendations for which methods to use
under various scenarios. We also illustrate the use of the PMD via three
applications, namely, in voting probability calculation, aggregated data
inference, and uncertainty quantification in classification. We build an R
package that implements the proposed methods, and illustrate the package with
examples.

Mobile devices increasingly rely on object detection (OD) through deep neural
networks (DNNs) to perform critical tasks. Due to their high complexity, the
execution of these DNNs requires excessive time and energy. Low-complexity
object tracking (OT) can be used with OD, where the latter is periodically
applied to generate "fresh" references for tracking. However, the frames
processed with OD incur large delays, which may make the reference outdated and
degrade tracking quality. Herein, we propose to use edge computing in this
context, and establish parallel OT (at the mobile device) and OD (at the edge
server) processes that are resilient to large OD latency. We propose Katch-Up,
a novel tracking mechanism that improves the system resilience to excessive OD
delay. However, while Katch-Up significantly improves performance, it also
increases the computing load of the mobile device. Hence, we design SmartDet, a
low-complexity controller based on deep reinforcement learning (DRL) that
learns controlling the trade-off between resource utilization and OD
performance. SmartDet takes as input context-related information related to the
current video content and the current network conditions to optimize frequency
and type of OD offloading, as well as Katch-Up utilization. We extensively
evaluate SmartDet on a real-world testbed composed of a JetSon Nano as mobile
device and a GTX 980 Ti as edge server, connected through a Wi-Fi link.
Experimental results show that SmartDet achieves an optimal balance between
tracking performance - mean Average Recall (mAR) and resource usage. With
respect to a baseline with full Katch-Upusage and maximum channel usage, we
still increase mAR by 4% while using 50% less of the channel and 30% power
resources associated with Katch-Up. With respect to a fixed strategy using
minimal resources, we increase mAR by 20% while using Katch-Up on 1/3 of the
frames.

Real-world machine learning deployments are characterized by mismatches
between the source (training) and target (test) distributions that may cause
performance drops. In this work, we investigate methods for predicting the
target domain accuracy using only labeled source data and unlabeled target
data. We propose Average Thresholded Confidence (ATC), a practical method that
learns a threshold on the model's confidence, predicting accuracy as the
fraction of unlabeled examples for which model confidence exceeds that
threshold. ATC outperforms previous methods across several model architectures,
types of distribution shifts (e.g., due to synthetic corruptions, dataset
reproduction, or novel subpopulations), and datasets (Wilds, ImageNet, Breeds,
CIFAR, and MNIST). In our experiments, ATC estimates target performance
$2$-$4\times$ more accurately than prior methods. We also explore the
theoretical foundations of the problem, proving that, in general, identifying
the accuracy is just as hard as identifying the optimal predictor and thus, the
efficacy of any method rests upon (perhaps unstated) assumptions on the nature
of the shift. Finally, analyzing our method on some toy distributions, we
provide insights concerning when it works. Code is available at
https://github.com/saurabhgarg1996/ATC_code/.

We present and study a novel algorithm for the computation of 2-Wasserstein
population barycenters. The proposed method admits, at least, two relevant
interpretations: it can be seen as a stochastic gradient descent procedure in
the 2-Wasserstein space, and also as a manifestation of a Law of Large Numbers
in the same space. Building on stochastic gradient descent, the proposed
algorithm is online and computationally inexpensive. Furthermore, we show that
the noise in the method can be attenuated via the use of mini-batches, and also
analyse those cases where the iterations of the algorithm have a semi-explicit
form. The accompanying paper "Bayesian learning with Wasserstein barycenters"
develops a statistical application (and numerical implementation) of the
proposed method aiming at Bayesian learning.

The detection of hate speech online has become an important task, as
offensive language such as hurtful, obscene and insulting content can harm
marginalized people or groups. This paper presents TU Berlin team experiments
and results on the task 1A and 1B of the shared task on hate speech and
offensive content identification in Indo-European languages 2021. The success
of different Natural Language Processing models is evaluated for the respective
subtasks throughout the competition. We tested different models based on
recurrent neural networks in word and character levels and transfer learning
approaches based on Bert on the provided dataset by the competition. Among the
tested models that have been used for the experiments, the transfer
learning-based models achieved the best results in both subtasks.

This paper presents a benchmarking study of some of the state-of-the-art
reinforcement learning algorithms used for solving two simulated vision-based
robotics problems. The algorithms considered in this study include soft
actor-critic (SAC), proximal policy optimization (PPO), interpolated policy
gradients (IPG), and their variants with Hindsight Experience replay (HER). The
performances of these algorithms are compared against PyBullet's two simulation
environments known as KukaDiverseObjectEnv and RacecarZEDGymEnv respectively.
The state observations in these environments are available in the form of RGB
images and the action space is continuous, making them difficult to solve. A
number of strategies are suggested to provide intermediate hindsight goals
required for implementing HER algorithm on these problems which are essentially
single-goal environments. In addition, a number of feature extraction
architectures are proposed to incorporate spatial and temporal attention in the
learning process. Through rigorous simulation experiments, the improvement
achieved with these components are established. To the best of our knowledge,
such a benchmarking study is not available for the above two vision-based
robotics problems making it a novel contribution in the field.

The Layout Analysis (LA) stage is of vital importance to the correct
performance of an Optical Music Recognition (OMR) system. It identifies the
regions of interest, such as staves or lyrics, which must then be processed in
order to transcribe their content. Despite the existence of modern approaches
based on deep learning, an exhaustive study of LA in OMR has not yet been
carried out with regard to the precision of different models, their
generalization to different domains or, more importantly, their impact on
subsequent stages of the pipeline. This work focuses on filling this gap in
literature by means of an experimental study of different neural architectures,
music document types and evaluation scenarios. The need for training data has
also led to a proposal for a new semi-synthetic data generation technique that
enables the efficient applicability of LA approaches in real scenarios. Our
results show that: (i) the choice of the model and its performance are crucial
for the entire transcription process; (ii) the metrics commonly used to
evaluate the LA stage do not always correlate with the final performance of the
OMR system, and (iii) the proposed data-generation technique enables
state-of-the-art results to be achieved with a limited set of labeled data.

Motion tracking systems based on optical sensors typically often suffer from
issues, such as poor lighting conditions, occlusion, limited coverage, and may
raise privacy concerns. More recently, radio frequency (RF)-based approaches
using commercial WiFi devices have emerged which offer low-cost ubiquitous
sensing whilst preserving privacy. However, the output of an RF sensing system,
such as Range-Doppler spectrograms, cannot represent human motion intuitively
and usually requires further processing. In this study, MDPose, a novel
framework for human skeletal motion reconstruction based on WiFi micro-Doppler
signatures, is proposed. It provides an effective solution to track human
activities by reconstructing a skeleton model with 17 key points, which can
assist with the interpretation of conventional RF sensing outputs in a more
understandable way. Specifically, MDPose has various incremental stages to
gradually address a series of challenges: First, a denoising algorithm is
implemented to remove any unwanted noise that may affect the feature extraction
and enhance weak Doppler signatures. Secondly, the convolutional neural network
(CNN)-recurrent neural network (RNN) architecture is applied to learn
temporal-spatial dependency from clean micro-Doppler signatures and restore key
points' velocity information. Finally, a pose optimising mechanism is employed
to estimate the initial state of the skeleton and to limit the increase of
error. We have conducted comprehensive tests in a variety of environments using
numerous subjects with a single receiver radar system to demonstrate the
performance of MDPose, and report 29.4mm mean absolute error over all key
points positions, which outperforms state-of-the-art RF-based pose estimation
systems.

Money laundering is a profound global problem. Nonetheless, there is little
statistical and machine learning research on the topic. In this paper, we focus
on anti-money laundering in banks. To help organize existing research, we
propose a unifying terminology and provide a review of the literature. This is
structured around two central tasks: (i) client risk profiling and (ii)
suspicious behavior flagging. We find that client risk profiling is
characterized by diagnostics, i.e., efforts to find and explain risk factors.
Suspicious behavior flagging, on the other hand, is characterized by
non-disclosed features and hand-crafted risk indices. Finally, we discuss
directions for future research. One major challenge is a lack of public data
sets. This may, potentially, be addressed by synthetic data generation. Other
possible research directions include semi-supervised and deep learning,
interpretability, and fairness of the results.

In 1950, Alan Turing proposed an imitation game as the ultimate test of
whether a machine was intelligent: could a machine imitate a human so well that
its answers to questions indistinguishable from a human. Ever since, creating
intelligence that matches human intelligence has implicitly or explicitly been
the goal of thousands of researchers, engineers, and entrepreneurs. The
benefits of human-like artificial intelligence (HLAI) include soaring
productivity, increased leisure, and perhaps most profoundly, a better
understanding of our own minds.
  But not all types of AI are human-like. In fact, many of the most powerful
systems are very different from humans. So an excessive focus on developing and
deploying HLAI can lead us into a trap. As machines become better substitutes
for human labor, workers lose economic and political bargaining power and
become increasingly dependent on those who control the technology. In contrast,
when AI is focused on augmenting humans rather than mimicking them, then humans
retain the power to insist on a share of the value created. Furthermore,
augmentation creates new capabilities and new products and services, ultimately
generating far more value than merely human-like AI. While both types of AI can
be enormously beneficial, there are currently excess incentives for automation
rather than augmentation among technologists, business executives, and

Efficient model selection for identifying a suitable pre-trained neural
network to a downstream task is a fundamental yet challenging task in deep
learning. Current practice requires expensive computational costs in model
training for performance prediction. In this paper, we propose a novel
framework for neural network selection by analyzing the governing dynamics over
synaptic connections (edges) during training. Our framework is built on the
fact that back-propagation during neural network training is equivalent to the
dynamical evolution of synaptic connections. Therefore, a converged neural
network is associated with an equilibrium state of a networked system composed
of those edges. To this end, we construct a network mapping $\phi$, converting
a neural network $G_A$ to a directed line graph $G_B$ that is defined on those
edges in $G_A$. Next, we derive a neural capacitance metric $\beta_{\rm eff}$
as a predictive measure universally capturing the generalization capability of
$G_A$ on the downstream task using only a handful of early training results. We
carried out extensive experiments using 17 popular pre-trained ImageNet models
and five benchmark datasets, including CIFAR10, CIFAR100, SVHN, Fashion MNIST
and Birds, to evaluate the fine-tuning performance of our framework. Our neural
capacitance metric is shown to be a powerful indicator for model selection
based only on early training results and is more efficient than

The improvement of air-quality in urban areas is one of the main concerns of
public government bodies. This concern emerges from the evidence between the
air quality and the public health. Major efforts from government bodies in this
area include monitoring and forecasting systems, banning more pollutant motor
vehicles, and traffic limitations during the periods of low-quality air. In
this work, a proposal for dynamic prices in regulated parking services is
presented. The dynamic prices in parking service must discourage motor vehicles
parking when low-quality episodes are predicted. For this purpose, diverse deep
learning strategies are evaluated. They have in common the use of collective
air-quality measurements for forecasting labels about air quality in the city.
The proposal is evaluated by using economic parameters and deep learning
quality criteria at Madrid (Spain).

In this work we propose a HyperTransformer, a Transformer-based model for
supervised and semi-supervised few-shot learning that generates weights of a
convolutional neural network (CNN) directly from support samples. Since the
dependence of a small generated CNN model on a specific task is encoded by a
high-capacity Transformer model, we effectively decouple the complexity of the
large task space from the complexity of individual tasks. Our method is
particularly effective for small target CNN architectures where learning a
fixed universal task-independent embedding is not optimal and better
performance is attained when the information about the task can modulate all
model parameters. For larger models we discover that generating the last layer
alone allows us to produce competitive or better results than those obtained
with state-of-the-art methods while being end-to-end differentiable.

Tether-net launched from a chaser spacecraft provides a promising method to
capture and dispose of large space debris in orbit. This tether-net system is
subject to several sources of uncertainty in sensing and actuation that affect
the performance of its net launch and closing control. Earlier
reliability-based optimization approaches to design control actions however
remain challenging and computationally prohibitive to generalize over varying
launch scenarios and target (debris) state relative to the chaser. To search
for a general and reliable control policy, this paper presents a reinforcement
learning framework that integrates a proximal policy optimization (PPO2)
approach with net dynamics simulations. The latter allows evaluating the
episodes of net-based target capture, and estimate the capture quality index
that serves as the reward feedback to PPO2. Here, the learned policy is
designed to model the timing of the net closing action based on the state of
the moving net and the target, under any given launch scenario. A stochastic
state transition model is considered in order to incorporate synthetic
uncertainties in state estimation and launch actuation. Along with notable
reward improvement during training, the trained policy demonstrates capture
performance (over a wide range of launch/target scenarios) that is close to
that obtained with reliability-based optimization run over an individual
scenario.

Recent work has demonstrated the catastrophic effects of poor cardinality
estimates on query processing time. In particular, underestimating query
cardinality can result in overly optimistic query plans which take orders of
magnitude longer to complete than one generated with the true cardinality.
Cardinality bounding avoids this pitfall by computing a strict upper bound on
the query's output size using statistics about the database such as table sizes
and degrees, i.e. value frequencies. In this paper, we extend this line of work
by proving a novel bound called the Degree Sequence Bound which takes into
account the full degree sequences and the max tuple multiplicity. This bound
improves upon previous work incorporating degree constraints which focused on
the maximum degree rather than the degree sequence. Further, we describe how to
practically compute this bound using a learned approximation of the true degree

Resolving a conjecture of Littlestone and Warmuth, we show that any concept
class of VC-dimension $d$ has a sample compression scheme of size $d$.

Radio maps find numerous applications in wireless communications and mobile
robotics tasks, including resource allocation, interference coordination, and
mission planning. Although numerous techniques have been proposed to construct
radio maps from spatially distributed measurements, the locations of such
measurements are assumed predetermined beforehand. In contrast, this paper
proposes spectrum surveying, where a mobile robot such as an unmanned aerial
vehicle (UAV) collects measurements at a set of locations that are actively
selected to obtain high-quality map estimates in a short surveying time. This
is performed in two steps. First, two novel algorithms, a model-based online
Bayesian estimator and a data-driven deep learning algorithm, are devised for
updating a map estimate and an uncertainty metric that indicates the
informativeness of measurements at each possible location. These algorithms
offer complementary benefits and feature constant complexity per measurement.
Second, the uncertainty metric is used to plan the trajectory of the UAV to
gather measurements at the most informative locations. To overcome the
combinatorial complexity of this problem, a dynamic programming approach is
proposed to obtain lists of waypoints through areas of large uncertainty in
linear time. Numerical experiments conducted on a realistic dataset confirm
that the proposed scheme constructs accurate radio maps quickly.

To make 3D human avatars widely available, we must be able to generate a
variety of 3D virtual humans with varied identities and shapes in arbitrary
poses. This task is challenging due to the diversity of clothed body shapes,
their complex articulations, and the resulting rich, yet stochastic geometric
detail in clothing. Hence, current methods to represent 3D people do not
provide a full generative model of people in clothing. In this paper, we
propose a novel method that learns to generate detailed 3D shapes of people in
a variety of garments with corresponding skinning weights. Specifically, we
devise a multi-subject forward skinning module that is learned from only a few
posed, un-rigged scans per subject. To capture the stochastic nature of
high-frequency details in garments, we leverage an adversarial loss formulation
that encourages the model to capture the underlying statistics. We provide
empirical evidence that this leads to realistic generation of local details
such as wrinkles. We show that our model is able to generate natural human
avatars wearing diverse and detailed clothing. Furthermore, we show that our
method can be used on the task of fitting human models to raw scans,
outperforming the previous state-of-the-art.

Recent multi-task learning research argues against unitary scalarization,
where training simply minimizes the sum of the task losses. Several ad-hoc
multi-task optimization algorithms have instead been proposed, inspired by
various hypotheses about what makes multi-task settings difficult. The majority
of these optimizers require per-task gradients, and introduce significant
memory, runtime, and implementation overhead. We show that unitary
scalarization, coupled with standard regularization and stabilization
techniques from single-task learning, matches or improves upon the performance
of complex multi-task optimizers in popular supervised and reinforcement
learning settings. We then present an analysis suggesting that many specialized
multi-task optimizers can be partly interpreted as forms of regularization,
potentially explaining our surprising results. We believe our results call for
a critical reevaluation of recent research in the area.

The layout of a mobile screen is a critical data source for UI design
research and semantic understanding of the screen. However, UI layouts in
existing datasets are often noisy, have mismatches with their visual
representation, or consists of generic or app-specific types that are difficult
to analyze and model. In this paper, we propose the CLAY pipeline that uses a
deep learning approach for denoising UI layouts, allowing us to automatically
improve existing mobile UI layout datasets at scale. Our pipeline takes both
the screenshot and the raw UI layout, and annotates the raw layout by removing
incorrect nodes and assigning a semantically meaningful type to each node. To
experiment with our data-cleaning pipeline, we create the CLAY dataset of
59,555 human-annotated screen layouts, based on screenshots and raw layouts
from Rico, a public mobile UI corpus. Our deep models achieve high accuracy
with F1 scores of 82.7% for detecting layout objects that do not have a valid
visual representation and 85.9% for recognizing object types, which
significantly outperforms a heuristic baseline. Our work lays a foundation for
creating large-scale high quality UI layout datasets for data-driven mobile UI
research and reduces the need of manual labeling efforts that are prohibitively
expensive.

Quantum computing is the process of performing calculations using quantum
mechanics. This field studies the quantum behavior of certain subatomic
particles for subsequent use in performing calculations, as well as for
large-scale information processing. These capabilities can give quantum
computers an advantage in terms of computational time and cost over classical
computers. Nowadays, there are scientific challenges that are impossible to
perform by classical computation due to computational complexity or the time
the calculation would take, and quantum computation is one of the possible
answers. However, current quantum devices have not yet the necessary qubits and
are not fault-tolerant enough to achieve these goals. Nonetheless, there are
other fields like machine learning or chemistry where quantum computation could
be useful with current quantum devices. This manuscript aims to present a
Systematic Literature Review of the papers published between 2017 and 2021 to
identify, analyze and classify the different algorithms used in quantum machine
learning and their applications. Consequently, this study identified 52
articles that used quantum machine learning techniques and algorithms. The main
types of found algorithms are quantum implementations of classical machine
learning algorithms, such as support vector machines or the k-nearest neighbor
model, and classical deep learning algorithms, like quantum neural networks.
Many articles try to solve problems currently answered by classical machine
learning but using quantum devices and algorithms. Even though results are
promising, quantum machine learning is far from achieving its full potential.
An improvement in the quantum hardware is required since the existing quantum
computers lack enough quality, speed, and scale to allow quantum computing to
achieve its full potential.

Optimal control is a successful approach to generate motions for complex
robots, in particular for legged locomotion. However, these techniques are
often too slow to run in real time for model predictive control or one needs to
drastically simplify the dynamics model. In this work, we present a method to
learn to predict the gradient and hessian of the problem value function,
enabling fast resolution of the predictive control problem with a one-step
quadratic program. In addition, our method is able to satisfy constraints like
friction cones and unilateral constraints, which are important for high
dynamics locomotion tasks. We demonstrate the capability of our method in
simulation and on a real quadruped robot performing trotting and bounding
motions.

Data transformation (DT) is a process that transfers the original data into a
form which supports a particular classification algorithm and helps to analyze
the data for a special purpose. To improve the prediction performance we
investigated various data transform methods. This study is conducted in a
customer churn prediction (CCP) context in the telecommunication industry
(TCI), where customer attrition is a common phenomenon. We have proposed a
novel approach of combining data transformation methods with the machine
learning models for the CCP problem. We conducted our experiments on publicly
available TCI datasets and assessed the performance in terms of the widely used
evaluation measures (e.g. AUC, precision, recall, and F-measure). In this
study, we presented comprehensive comparisons to affirm the effect of the
transformation methods. The comparison results and statistical test proved that
most of the proposed data transformation based optimized models improve the
performance of CCP significantly. Overall, an efficient and optimized CCP model
for the telecommunication industry has been presented through this manuscript.

Common Spatial Patterns (CSP) is a feature extraction algorithm widely used
in Brain-Computer Interface (BCI) Systems for detecting Event-Related
Potentials (ERPs) in multi-channel magneto/electroencephalography (MEG/EEG)
time series data. In this article, we develop and apply a CSP algorithm to the
problem of identifying whether a given epoch of multi-detector Gravitational
Wave (GW) strains contains coalescenses. Paired with Signal Processing
techniques and a Logistic Regression classifier, we find that our pipeline is
correctly able to detect 76 out of 82 confident events from Gravitational Wave
Transient Catalog, using H1 and L1 strains, with a classification score of
$93.72 \pm 0.04\%$ using $10 \times 5$ cross validation. The false negative
events were: GW170817-v3, GW191219 163120-v1, GW200115 042309-v2, GW200210
092254-v1, GW200220 061928-v1, and GW200322 091133-v1.

The capability of current force fields to reproduce RNA structural dynamics
is limited. Several methods have been developed to take advantage of
experimental data in order to enforce agreement with experiments. We herein
extend an existing framework, which allows arbitrarily chosen force-field
correction terms to be fitted by quantification of the discrepancy between
observables back-calculated from simulation and corresponding experiments. We
apply a robust regularization protocol to avoid overfitting, and additionally
introduce and compare a number of different regularization strategies, namely
L1-, L2-, Kish Size-, Relative Kish Size- and Relative Entropy-penalties. The
training set includes a GACC tetramer as well as more challenging systems,
namely gcGAGAgc and gcUUCGgc RNA tetraloops. Specific intramolecular hydrogen
bonds in the AMBER RNA force field are corrected with automatically determined
parameters that we call gHBfix$_{opt}$. A validation involving a separate
simulation of a system present in the training set (gcUUCGgc) and new systems
not seen during training (CAAU and UUUU tetramers) displays improvements
regarding native population of the tetraloop as well as good agreement with
NMR-experiments for tetramers when using the new parameters. Then we simulate
folded RNAs (a kink-turn and L1 stalk rRNA) including hydrogen bond types not
sufficiently present in the training set. This allows a final modification of
the parameter set which is named gHBfix21 and is suggested to be applicable to
a wider range of RNA systems.

The goal of the state estimation (SE) algorithm is to estimate complex bus
voltages as state variables based on the available set of measurements in the
power system. Because phasor measurement units (PMUs) are increasingly being
used in transmission power systems, there is a need for a fast SE solver that
can take advantage of high sampling rates of PMUs. This paper proposes training
a graph neural network (GNN) to learn the estimates given the PMU voltage and
current measurements as inputs, with the intent of obtaining fast and accurate
predictions during the evaluation phase. GNN is trained using synthetic
datasets, created by randomly sampling sets of measurements in the power system
and labelling them with a solution obtained using a linear SE with PMUs solver.
The presented results display the accuracy of GNN predictions in various test
scenarios and tackle the sensitivity of the predictions to the missing input
data.

In the past few years, neural character animation has emerged and offered an
automatic method for animating virtual characters. Their motion is synthesized
by a neural network. Controlling this movement in real time with a user-defined
control signal is also an important task in video games for example. Solutions
based on fully-connected layers (MLPs) and Mixture-of-Experts (MoE) have given
impressive results in generating and controlling various movements with
close-range interactions between the environment and the virtual character.
However, a major shortcoming of fully-connected layers is their computational
and memory cost which may lead to sub-optimized solution. In this work, we
apply pruning algorithms to compress an MLP- MoE neural network in the context
of interactive character animation, which reduces its number of parameters and
accelerates its computation time with a trade-off between this acceleration and
the synthesized motion quality. This work demonstrates that, with the same
number of experts and parameters, the pruned model produces less motion
artifacts than the dense model and the learned high-level motion features are
similar for both

PEPit is a Python package aiming at simplifying the access to worst-case
analyses of a large family of first-order optimization methods possibly
involving gradient, projection, proximal, or linear optimization oracles, along
with their approximate, or Bregman variants.
  In short, PEPit is a package enabling computer-assisted worst-case analyses
of first-order optimization methods. The key underlying idea is to cast the
problem of performing a worst-case analysis, often referred to as a performance
estimation problem (PEP), as a semidefinite program (SDP) which can be solved
numerically. For doing that, the package users are only required to write
first-order methods nearly as they would have implemented them. The package
then takes care of the SDP modelling parts, and the worst-case analysis is
performed numerically via a standard solver.

In many real-world scenarios, we often deal with streaming data that is
sequentially collected over time. Due to the non-stationary nature of the
environment, the streaming data distribution may change in unpredictable ways,
which is known as concept drift. To handle concept drift, previous methods
first detect when/where the concept drift happens and then adapt models to fit
the distribution of the latest data. However, there are still many cases that
some underlying factors of environment evolution are predictable, making it
possible to model the future concept drift trend of the streaming data, while
such cases are not fully explored in previous work.
  In this paper, we propose a novel method DDG-DA, that can effectively
forecast the evolution of data distribution and improve the performance of
models. Specifically, we first train a predictor to estimate the future data
distribution, then leverage it to generate training samples, and finally train
models on the generated data. We conduct experiments on three real-world tasks
(forecasting on stock price trend, electricity load and solar irradiance) and
obtain significant improvement on multiple widely-used models.

Motion, as the most distinct phenomenon in a video to involve the changes
over time, has been unique and critical to the development of video
representation learning. In this paper, we ask the question: how important is
the motion particularly for self-supervised video representation learning. To
this end, we compose a duet of exploiting the motion for data augmentation and
feature learning in the regime of contrastive learning. Specifically, we
present a Motion-focused Contrastive Learning (MCL) method that regards such
duet as the foundation. On one hand, MCL capitalizes on optical flow of each
frame in a video to temporally and spatially sample the tubelets (i.e.,
sequences of associated frame patches across time) as data augmentations. On
the other hand, MCL further aligns gradient maps of the convolutional layers to
optical flow maps from spatial, temporal and spatio-temporal perspectives, in
order to ground motion information in feature learning. Extensive experiments
conducted on R(2+1)D backbone demonstrate the effectiveness of our MCL. On
UCF101, the linear classifier trained on the representations learnt by MCL
achieves 81.91% top-1 accuracy, outperforming ImageNet supervised pre-training
by 6.78%. On Kinetics-400, MCL achieves 66.62% top-1 accuracy under the linear
protocol. Code is available at
https://github.com/YihengZhang-CV/MCL-Motion-Focused-Contrastive-Learning.

Human actions are typically of combinatorial structures or patterns, i.e.,
subjects, objects, plus spatio-temporal interactions in between. Discovering
such structures is therefore a rewarding way to reason about the dynamics of
interactions and recognize the actions. In this paper, we introduce a new
design of sub-graphs to represent and encode the discriminative patterns of
each action in the videos. Specifically, we present MUlti-scale Sub-graph
LEarning (MUSLE) framework that novelly builds space-time graphs and clusters
the graphs into compact sub-graphs on each scale with respect to the number of
nodes. Technically, MUSLE produces 3D bounding boxes, i.e., tubelets, in each
video clip, as graph nodes and takes dense connectivity as graph edges between
tubelets. For each action category, we execute online clustering to decompose
the graph into sub-graphs on each scale through learning Gaussian Mixture Layer
and select the discriminative sub-graphs as action prototypes for recognition.
Extensive experiments are conducted on both Something-Something V1 & V2 and
Kinetics-400 datasets, and superior results are reported when comparing to
state-of-the-art methods. More remarkably, our MUSLE achieves to-date the best
reported accuracy of 65.0% on Something-Something V2 validation set.

Vision-language pre-training has been an emerging and fast-developing
research topic, which transfers multi-modal knowledge from rich-resource
pre-training task to limited-resource downstream tasks. Unlike existing works
that predominantly learn a single generic encoder, we present a pre-trainable
Universal Encoder-DEcoder Network (Uni-EDEN) to facilitate both vision-language
perception (e.g., visual question answering) and generation (e.g., image
captioning). Uni-EDEN is a two-stream Transformer based structure, consisting
of three modules: object and sentence encoders that separately learns the
representations of each modality, and sentence decoder that enables both
multi-modal reasoning and sentence generation via inter-modal interaction.
Considering that the linguistic representations of each image can span
different granularities in this hierarchy including, from simple to
comprehensive, individual label, a phrase, and a natural sentence, we pre-train
Uni-EDEN through multi-granular vision-language proxy tasks: Masked Object
Classification (MOC), Masked Region Phrase Generation (MRPG), Image-Sentence
Matching (ISM), and Masked Sentence Generation (MSG). In this way, Uni-EDEN is
endowed with the power of both multi-modal representation extraction and
language modeling. Extensive experiments demonstrate the compelling
generalizability of Uni-EDEN by fine-tuning it to four vision-language
perception and generation downstream tasks.

Video content is multifaceted, consisting of objects, scenes, interactions or
actions. The existing datasets mostly label only one of the facets for model
training, resulting in the video representation that biases to only one facet
depending on the training dataset. There is no study yet on how to learn a
video representation from multifaceted labels, and whether multifaceted
information is helpful for video representation learning. In this paper, we
propose a new learning framework, MUlti-Faceted Integration (MUFI), to
aggregate facets from different datasets for learning a representation that
could reflect the full spectrum of video content. Technically, MUFI formulates
the problem as visual-semantic embedding learning, which explicitly maps video
representation into a rich semantic embedding space, and jointly optimizes
video representation from two perspectives. One is to capitalize on the
intra-facet supervision between each video and its own label descriptions, and
the second predicts the "semantic representation" of each video from the facets
of other datasets as the inter-facet supervision. Extensive experiments
demonstrate that learning 3D CNN via our MUFI framework on a union of four
large-scale video datasets plus two image datasets leads to superior capability
of video representation. The pre-learnt 3D CNN with MUFI also shows clear
improvements over other approaches on several downstream video applications.
More remarkably, MUFI achieves 98.1%/80.9% on UCF101/HMDB51 for action
recognition and 101.5% in terms of CIDEr-D score on MSVD for video captioning.

Video is complex due to large variations in motion and rich content in
fine-grained visual details. Abstracting useful information from such
information-intensive media requires exhaustive computing resources. This paper
studies a two-step alternative that first condenses the video sequence to an
informative "frame" and then exploits off-the-shelf image recognition system on
the synthetic frame. A valid question is how to define "useful information" and
then distill it from a video sequence down to one synthetic frame. This paper
presents a novel Informative Frame Synthesis (IFS) architecture that
incorporates three objective tasks, i.e., appearance reconstruction, video
categorization, motion estimation, and two regularizers, i.e., adversarial
learning, color consistency. Each task equips the synthetic frame with one
ability, while each regularizer enhances its visual quality. With these, by
jointly learning the frame synthesis in an end-to-end manner, the generated
frame is expected to encapsulate the required spatio-temporal information
useful for video analysis. Extensive experiments are conducted on the
large-scale Kinetics dataset. When comparing to baseline methods that map video
sequence to a single image, IFS shows superior performance. More remarkably,
IFS consistently demonstrates evident improvements on image-based 2D networks
and clip-based 3D networks, and achieves comparable performance with the
state-of-the-art methods with less computational cost.

It is not trivial to optimally learn a 3D Convolutional Neural Networks (3D
ConvNets) due to high complexity and various options of the training scheme.
The most common hand-tuning process starts from learning 3D ConvNets using
short video clips and then is followed by learning long-term temporal
dependency using lengthy clips, while gradually decaying the learning rate from
high to low as training progresses. The fact that such process comes along with
several heuristic settings motivates the study to seek an optimal "path" to
automate the entire training. In this paper, we decompose the path into a
series of training "states" and specify the hyper-parameters, e.g., learning
rate and the length of input clips, in each state. The estimation of the knee
point on the performance-epoch curve triggers the transition from one state to
another. We perform dynamic programming over all the candidate states to plan
the optimal permutation of states, i.e., optimization path. Furthermore, we
devise a new 3D ConvNets with a unique design of dual-head classifier to
improve spatial and temporal discrimination. Extensive experiments on seven
public video recognition benchmarks demonstrate the advantages of our proposal.
With the optimization planning, our 3D ConvNets achieves superior results when
comparing to the state-of-the-art recognition methods. More remarkably, we
obtain the top-1 accuracy of 80.5% and 82.7% on Kinetics-400 and Kinetics-600
datasets, respectively. Source code is available at
https://github.com/ZhaofanQiu/Optimization-Planning-for-3D-ConvNets.

Split learning and differential privacy are technologies with growing
potential to help with privacy-compliant advanced analytics on distributed
datasets. Attacks against split learning are an important evaluation tool and
have been receiving increased research attention recently. This work's
contribution is applying a recent feature space hijacking attack (FSHA) to the
learning process of a split neural network enhanced with differential privacy
(DP), using a client-side off-the-shelf DP optimizer. The FSHA attack obtains
client's private data reconstruction with low error rates at arbitrarily set DP
epsilon levels. We also experiment with dimensionality reduction as a potential
attack risk mitigation and show that it might help to some extent. We discuss
the reasons why differential privacy is not an effective protection in this
setting and mention potential other risk mitigation methods.

Nowadays, people generate and share massive content on online platforms
(e.g., social networks, blogs). In 2021, the 1.9 billion daily active Facebook
users posted around 150 thousand photos every minute. Content moderators
constantly monitor these online platforms to prevent the spreading of
inappropriate content (e.g., hate speech, nudity images). Based on deep
learning (DL) advances, Automatic Content Moderators (ACM) help human
moderators handle high data volume. Despite their advantages, attackers can
exploit weaknesses of DL components (e.g., preprocessing, model) to affect
their performance. Therefore, an attacker can leverage such techniques to
spread inappropriate content by evading ACM.
  In this work, we propose CAPtcha Attack (CAPA), an adversarial technique that
allows users to spread inappropriate text online by evading ACM controls. CAPA,
by generating custom textual CAPTCHAs, exploits ACM's careless design
implementations and internal procedures vulnerabilities. We test our attack on
real-world ACM, and the results confirm the ferocity of our simple yet
effective attack, reaching up to a 100% evasion success in most cases. At the
same time, we demonstrate the difficulties in designing CAPA mitigations,
opening new challenges in CAPTCHAs research area.

Traffic forecasting models rely on data that needs to be sensed, processed,
and stored. This requires the deployment and maintenance of traffic sensing
infrastructure, often leading to unaffordable monetary costs. The lack of
sensed locations can be complemented with synthetic data simulations that
further lower the economical investment needed for traffic monitoring. One of
the most common data generative approaches consists of producing real-like
traffic patterns, according to data distributions from analogous roads. The
process of detecting roads with similar traffic is the key point of these
systems. However, without collecting data at the target location no flow
metrics can be employed for this similarity-based search. We present a method
to discover locations among those with available traffic data by inspecting
topological features. These features are extracted from domain-specific
knowledge as numerical representations (embeddings) to compare different
locations and eventually find roads with analogous daily traffic profiles based
on the similarity between embeddings. The performance of this novel selection
system is examined and compared to simpler traffic estimation approaches. After
finding a similar source of data, a generative method is used to synthesize
traffic profiles. Depending on the resemblance of the traffic behavior at the
sensed road, the generation method can be fed with data from one road only.
Several generation approaches are analyzed in terms of the precision of the
synthesized samples. Above all, this work intends to stimulate further research
efforts towards enhancing the quality of synthetic traffic samples and thereby,
reducing the need for sensing infrastructure.

Image quality is a nebulous concept with different meanings to different
people. To quantify image quality a relative difference is typically calculated
between a corrupted image and a ground truth image. But what metric should we
use for measuring this difference? Ideally, the metric should perform well for
both natural and scientific images. The structural similarity index (SSIM) is a
good measure for how humans perceive image similarities, but is not sensitive
to differences that are scientifically meaningful in microscopy. In electron
and super-resolution microscopy, the Fourier Ring Correlation (FRC) is often
used, but is little known outside of these fields. Here we show that the FRC
can equally well be applied to natural images, e.g. the Google Open Images
dataset. We then define a loss function based on the FRC, show that it is
analytically differentiable, and use it to train a U-net for denoising of
images. This FRC-based loss function allows the network to train faster and
achieve similar or better results than when using L1- or L2- based losses. We
also investigate the properties and limitations of neural network denoising
with the FRC analysis.

Third-party libraries with rich functionalities facilitate the fast
development of Node.js software, but also bring new security threats that
vulnerabilities could be introduced through dependencies. In particular, the
threats could be excessively amplified by transitive dependencies. Existing
research either considers direct dependencies or reasoning transitive
dependencies based on reachability analysis, which neglects the NPM-specific
dependency resolution rules, resulting in wrongly resolved dependencies.
Consequently, further fine-grained analysis, such as vulnerability propagation
and their evolution in dependencies, cannot be carried out precisely at a large
scale, as well as deriving ecosystem-wide solutions for vulnerabilities in
dependencies. To fill this gap, we propose a knowledge graph-based dependency
resolution, which resolves the dependency relations of dependencies as trees
(i.e., dependency trees), and investigates the security threats from
vulnerabilities in dependency trees at a large scale. We first construct a
complete dependency-vulnerability knowledge graph (DVGraph) that captures the
whole NPM ecosystem (over 10 million library versions and 60 million
well-resolved dependency relations). Based on it, we propose DTResolver to
statically and precisely resolve dependency trees, as well as transitive
vulnerability propagation paths, by considering the official dependency
resolution rules. Based on that, we carry out an ecosystem-wide empirical study
on vulnerability propagation and its evolution in dependency trees. Our study
unveils lots of useful findings, and we further discuss the lessons learned and
solutions for different stakeholders to mitigate the vulnerability impact in
NPM. For example, we implement a dependency tree based vulnerability
remediation method (DTReme) for NPM packages, and receive much better
performance than the official tool (npm audit fix).

In recent years, multi-modal transformers have shown significant progress in
Vision-Language tasks, such as Visual Question Answering (VQA), outperforming
previous architectures by a considerable margin. This improvement in VQA is
often attributed to the rich interactions between vision and language streams.
In this work, we investigate the efficacy of co-attention transformer layers in
helping the network focus on relevant regions while answering the question. We
generate visual attention maps using the question-conditioned image attention
scores in these co-attention layers. We evaluate the effect of the following
critical components on visual attention of a state-of-the-art VQA model: (i)
number of object region proposals, (ii) question part of speech (POS) tags,
(iii) question semantics, (iv) number of co-attention layers, and (v) answer
accuracy. We compare the neural network attention maps against human attention
maps both qualitatively and quantitatively. Our findings indicate that
co-attention transformer modules are crucial in attending to relevant regions
of the image given a question. Importantly, we observe that the semantic
meaning of the question is not what drives visual attention, but specific
keywords in the question do. Our work sheds light on the function and
interpretation of co-attention transformer layers, highlights gaps in current
networks, and can guide the development of future VQA models and networks that
simultaneously process visual and language streams.

Machine learning techniques are increasingly being used in fundamental
research to solve various challenging problems. Here we explore one such
technique to address an important problem in quantum communication scenario.
While transferring quantum information through a noisy quantum channel, the
utility of the channel is characterized by its quantum capacity. Quantum
channels, however, display an intriguing property called super-additivity of
coherent information. This makes the calculation of quantum capacity a hard
computational problem involving optimization over an exponentially increasing
search space. In this work, we first utilize a neural network ansatz to
represent quantum states and then apply an evolutionary optimization scheme to
address this problem. We find regions in the three-parameter space of qubit
Pauli channels where coherent information exhibits this super-additivity
feature. We characterised the quantum codes that achieves high coherent
information, finding several non-trivial quantum codes that outperforms the
repetition codes for some Pauli channels. For some Pauli channels, these codes
displays very high super-additivity of the order of 0.01, much higher than the
observed values in other well studied quantum channels. We further compared the
learning performance of the Neural Network ansatz with the raw ansatz to find
that in the three-shot case, the neural network ansatz outperforms the raw
representation in finding quantum codes of high coherent information. We also
compared the learning performance of the evolutionary algorithm with a simple
Particle Swarm Optimisation scheme and show empirical results indicating
comparable performance, suggesting that the Neural Network ansatz coupled with
the evolutionary scheme is indeed a promising approach to finding non-trivial
quantum codes of high coherent information.

The imbalanced classification problem turns out to be one of the important
and challenging problems in data mining and machine learning. The performances
of traditional classifiers will be severely affected by many data problems,
such as class imbalanced problem, class overlap and noise. The Tomek-Link
algorithm was only used to clean data when it was proposed. In recent years,
there have been reports of combining Tomek-Link algorithm with sampling
technique. The Tomek-Link sampling algorithm can effectively reduce the class
overlap on data, remove the majority instances that are difficult to
distinguish, and improve the algorithm classification accuracy. However, the
Tomek-Links under-sampling algorithm only considers the boundary instances that
are the nearest neighbors to each other globally and ignores the potential
local overlapping instances. When the number of minority instances is small,
the under-sampling effect is not satisfactory, and the performance improvement
of the classification model is not obvious. Therefore, on the basis of
Tomek-Link, a multi-granularity relabeled under-sampling algorithm (MGRU) is
proposed. This algorithm fully considers the local information of the data set
in the local granularity subspace, and detects the local potential overlapping
instances in the data set. Then, the overlapped majority instances are
eliminated according to the global relabeled index value, which effectively
expands the detection range of Tomek-Links. The simulation results show that
when we select the optimal global relabeled index value for under-sampling, the
classification accuracy and generalization performance of the proposed
under-sampling algorithm are significantly better than other baseline

Internet-delivered psychological treatments (IDPT) are seen as an effective
and scalable pathway to improving the accessibility of mental healthcare.
Within this context, treatment adherence is an especially pertinent challenge
to address due to the reduced interaction between healthcare professionals and
patients. In parallel, the increase in regulations surrounding the use of
personal data, such as the General Data Protection Regulation (GDPR), makes
data minimization a core consideration for real-world implementation of IDPTs.
Consequently, this work proposes a Self-Attention-based deep learning approach
to perform automatic adherence forecasting, while only relying on minimally
sensitive login/logout-timestamp data. This approach was tested on a dataset
containing 342 patients undergoing Guided Internet-delivered Cognitive
Behavioral Therapy (G-ICBT) treatment. Of these 342 patients, 101 (~30%) were
considered non-adherent (dropout) based on the adherence definition used in
this work (i.e. at least eight connections to the platform lasting more than a
minute over 56 days). The proposed model achieved over 70% average balanced
accuracy, after only 20 out of the 56 days (~1/3) of the treatment had elapsed.
This study demonstrates that automatic adherence forecasting for G-ICBT, is
achievable using only minimally sensitive data, thus facilitating the
implementation of such tools within real-world IDPT platforms.

In graph analysis, a classic task consists in computing similarity measures
between (groups of) nodes. In latent space random graphs, nodes are associated
to unknown latent variables. One may then seek to compute distances directly in
the latent space, using only the graph structure. In this paper, we show that
it is possible to consistently estimate entropic-regularized Optimal Transport
(OT) distances between groups of nodes in the latent space. We provide a
general stability result for entropic OT with respect to perturbations of the
cost matrix. We then apply it to several examples of random graphs, such as
graphons or $\epsilon$-graphs on manifolds. Along the way, we prove new
concentration results for the so-called Universal Singular Value Thresholding
estimator, and for the estimation of geodesic distances on a manifold.

Intelligent systems have the ability to improve their behaviour over time
taking observations, experiences or explicit feedback into account. Traditional
approaches separate the learning problem and make isolated use of techniques
from different field of machine learning such as reinforcement learning, active
learning, anomaly detection or transfer learning, for instance. In this
context, the fundamental reinforcement learning approaches come with several
drawbacks that hinder their application to real-world systems: trial-and-error,
purely reactive behaviour or isolated problem handling. The idea of this
article is to present a concept for alleviating these drawbacks by setting up a
research agenda towards what we call "active reinforcement learning" in
intelligent systems.

In this study, we propose a feature extraction framework based on contrastive
learning with adaptive positive and negative samples (CL-FEFA) that is suitable
for unsupervised, supervised, and semi-supervised single-view feature
extraction. CL-FEFA constructs adaptively the positive and negative samples
from the results of feature extraction, which makes it more appropriate and
accurate. Thereafter, the discriminative features are re extracted to according
to InfoNCE loss based on previous positive and negative samples, which will
make the intra-class samples more compact and the inter-class samples more
dispersed. At the same time, using the potential structure information of
subspace samples to dynamically construct positive and negative samples can
make our framework more robust to noisy data. Furthermore, CL-FEFA considers
the mutual information between positive samples, that is, similar samples in
potential structures, which provides theoretical support for its advantages in
feature extraction. The final numerical experiments prove that the proposed
framework has a strong advantage over the traditional feature extraction
methods and contrastive learning methods.

Repeated epileptic seizures impair around 65 million people worldwide and a
successful prediction of seizures could significantly help patients suffering
from refractory epilepsy. For two dogs with yearlong intracranial
electroencephalography (iEEG) recordings, we studied the influence of time
series nonstationarity on the performance of seizure prediction using in-house
developed machine learning algorithms. We observed a long-term evolution on the
scale of weeks or months in iEEG time series that may be represented as
switching between certain meta-states. To better predict impending seizures,
retraining of prediction algorithms is therefore necessary and the retraining
schedule should be adjusted to the change in meta-states. There is evidence
that the nature of seizure-free interictal clips also changes with the
transition between meta-states, accwhich has been shown relevant for seizure

The relationship between Facebook posts and the corresponding reaction
feature is an interesting subject to explore and understand. To achieve this
end, we test state-of-the-art Sinhala sentiment analysis models against a data
set containing a decade worth of Sinhala posts with millions of reactions. For
the purpose of establishing benchmarks and with the goal of identifying the
best model for Sinhala sentiment analysis, we also test, on the same data set
configuration, other deep learning models catered for sentiment analysis. In
this study we report that the 3 layer Bidirectional LSTM model achieves an F1
score of 84.58% for Sinhala sentiment analysis, surpassing the current
state-of-the-art model; Capsule B, which only manages to get an F1 score of
82.04%. Further, since all the deep learning models show F1 scores above 75% we
conclude that it is safe to claim that Facebook reactions are suitable to
predict the sentiment of a text.

The combination of Reinforcement Learning (RL) with deep learning has led to
a series of impressive feats, with many believing (deep) RL provides a path
towards generally capable agents. However, the success of RL agents is often
highly sensitive to design choices in the training process, which may require
tedious and error-prone manual tuning. This makes it challenging to use RL for
new problems, while also limits its full potential. In many other areas of
machine learning, AutoML has shown it is possible to automate such design
choices and has also yielded promising initial results when applied to RL.
However, Automated Reinforcement Learning (AutoRL) involves not only standard
applications of AutoML but also includes additional challenges unique to RL,
that naturally produce a different set of methods. As such, AutoRL has been
emerging as an important area of research in RL, providing promise in a variety
of applications from RNA design to playing games such as Go. Given the
diversity of methods and environments considered in RL, much of the research
has been conducted in distinct subfields, ranging from meta-learning to
evolution. In this survey we seek to unify the field of AutoRL, we provide a
common taxonomy, discuss each area in detail and pose open problems which would
be of interest to researchers going forward.

Active inference is an account of cognition and behavior in complex systems
which brings together action, perception, and learning under the theoretical
mantle of Bayesian inference. Active inference has seen growing applications in
academic research, especially in fields that seek to model human or animal
behavior. While in recent years, some of the code arising from the active
inference literature has been written in open source languages like Python and
Julia, to-date, the most popular software for simulating active inference
agents is the DEM toolbox of SPM, a MATLAB library originally developed for the
statistical analysis and modelling of neuroimaging data. Increasing interest in
active inference, manifested both in terms of sheer number as well as
diversifying applications across scientific disciplines, has thus created a
need for generic, widely-available, and user-friendly code for simulating
active inference in open-source scientific computing languages like Python. The
Python package we present here, pymdp (see
https://github.com/infer-actively/pymdp), represents a significant step in this
direction: namely, we provide the first open-source package for simulating
active inference with partially-observable Markov Decision Processes or POMDPs.
We review the package's structure and explain its advantages like modular
design and customizability, while providing in-text code blocks along the way
to demonstrate how it can be used to build and run active inference processes
with ease. We developed pymdp to increase the accessibility and exposure of the
active inference framework to researchers, engineers, and developers with
diverse disciplinary backgrounds. In the spirit of open-source software, we
also hope that it spurs new innovation, development, and collaboration in the
growing active inference community.

In this article, we will look at autoencoders. This article covers the
mathematics and the fundamental concepts of autoencoders. We will discuss what
they are, what the limitations are, the typical use cases, and we will look at
some examples. We will start with a general introduction to autoencoders, and
we will discuss the role of the activation function in the output layer and the
loss function. We will then discuss what the reconstruction error is. Finally,
we will look at typical applications as dimensionality reduction,
classification, denoising, and anomaly detection. This paper contains the notes
of a PhD-level lecture on autoencoders given in 2021.

Rank aggregation aims to combine the preference rankings of a number of
alternatives from different voters into a single consensus ranking. As a useful
model for a variety of practical applications, however, it is a computationally
challenging problem. In this paper, we propose an effective hybrid evolutionary
ranking algorithm to solve the rank aggregation problem with both complete and
partial rankings. The algorithm features a semantic crossover based on
concordant pairs and a late acceptance local search reinforced by an efficient
incremental evaluation technique. Experiments are conducted to assess the
algorithm, indicating a highly competitive performance on benchmark instances
compared with state-of-the-art algorithms. To demonstrate its practical
usefulness, the algorithm is applied to label ranking, which is an important
machine learning task.

Among the different modalities to assess emotion, electroencephalogram (EEG),
representing the electrical brain activity, achieved motivating results over
the last decade. Emotion estimation from EEG could help in the diagnosis or
rehabilitation of certain diseases. In this paper, we propose a dual model
considering two different representations of EEG feature maps: 1) a sequential
based representation of EEG band power, 2) an image-based representation of the
feature vectors. We also propose an innovative method to combine the
information based on a saliency analysis of the image-based model to promote
joint learning of both model parts. The model has been evaluated on four
publicly available datasets: SEED-IV, SEED, DEAP and MPED. The achieved results
outperform results from state-of-the-art approaches for three of the proposed
datasets with a lower standard deviation that reflects higher stability. For
sake of reproducibility, the codes and models proposed in this paper are
available at https://github.com/VDelv/Emotion-EEG.

The combination of a deep neural network (DNN) -based speech enhancement (SE)
front-end and an automatic speech recognition (ASR) back-end is a widely used
approach to implement overlapping speech recognition. However, the SE front-end
generates processing artifacts that can degrade the ASR performance. We
previously found that such performance degradation can occur even under fully
overlapping conditions, depending on the signal-to-interference ratio (SIR) and
signal-to-noise ratio (SNR). To mitigate the degradation, we introduced a
rule-based method to switch the ASR input between the enhanced and observed
signals, which showed promising results. However, the rule's optimality was
unclear because it was heuristically designed and based only on SIR and SNR
values. In this work, we propose a DNN-based switching method that directly
estimates whether ASR will perform better on the enhanced or observed signals.
We also introduce soft-switching that computes a weighted sum of the enhanced
and observed signals for ASR input, with weights given by the switching model's
output posteriors. The proposed learning-based switching showed performance
comparable to that of rule-based oracle switching. The soft-switching further
improved the ASR performance and achieved a relative character error rate
reduction of up to 23 % as compared with the conventional method.

Deep reinforcement learning produces robust locomotion policies for legged
robots over challenging terrains. To date, few studies have leveraged
model-based methods to combine these locomotion skills with the precise control
of manipulators. Here, we incorporate external dynamics plans into
learning-based locomotion policies for mobile manipulation. We train the base
policy by applying a random wrench sequence on the robot base in simulation and
adding the noisified wrench sequence prediction to the policy observations. The
policy then learns to counteract the partially-known future disturbance. The
random wrench sequences are replaced with the wrench prediction generated with
the dynamics plans from model predictive control to enable deployment. We show
zero-shot adaptation for manipulators unseen during training. On the hardware,
we demonstrate stable locomotion of legged robots with the prediction of the
external wrench.

Many applications like audio and image processing show that sparse
representations are a powerful and efficient signal modeling technique. Finding
an optimal dictionary that generates at the same time the sparsest
representations of data and the smallest approximation error is a hard problem
approached by dictionary learning (DL). We study how DL performs in detecting
abnormal samples in a dataset of signals. In this paper we use a particular DL
formulation that seeks uniform sparse representations model to detect the
underlying subspace of the majority of samples in a dataset, using a K-SVD-type
algorithm. Numerical simulations show that one can efficiently use this
resulted subspace to discriminate the anomalies over the regular data points.

Deep learning (DL) has proven its unprecedented success in diverse fields
such as computer vision, natural language processing, and speech recognition by
its strong representation ability and ease of computation. As we move forward
to a thoroughly intelligent society with 6G wireless networks, new applications
and use-cases have been emerging with stringent requirements for
next-generation wireless communications. Therefore, recent studies have focused
on the potential of DL approaches in satisfying these rigorous needs and
overcoming the deficiencies of existing model-based techniques. The main
objective of this article is to unveil the state-of-the-art advancements in the
field of DL-based physical layer (PHY) methods to pave the way for fascinating
applications of 6G. In particular, we have focused our attention on four
promising PHY concepts foreseen to dominate next-generation communications,
namely massive multiple-input multiple-output (MIMO) systems, sophisticated
multi-carrier (MC) waveform designs, reconfigurable intelligent surface
(RIS)-empowered communications, and PHY security. We examine up-to-date
developments in DL-based techniques, provide comparisons with state-of-the-art
methods, and introduce a comprehensive guide for future directions. We also
present an overview of the underlying concepts of DL, along with the
theoretical background of well-known DL techniques. Furthermore, this article
provides programming examples for a number of DL techniques and the
implementation of a DL-based MIMO by sharing user-friendly code snippets, which
might be useful for interested readers.

Existing learning methods for LiDAR-based applications use 3D points scanned
under a pre-determined beam configuration, e.g., the elevation angles of beams
are often evenly distributed. Those fixed configurations are task-agnostic, so
simply using them can lead to sub-optimal performance. In this work, we take a
new route to learn to optimize the LiDAR beam configuration for a given
application. Specifically, we propose a reinforcement learning-based
learning-to-optimize (RL-L2O) framework to automatically optimize the beam
configuration in an end-to-end manner for different LiDAR-based applications.
The optimization is guided by the final performance of the target task and thus
our method can be integrated easily with any LiDAR-based application as a
simple drop-in module. The method is especially useful when a low-resolution
(low-cost) LiDAR is needed, for instance, for system deployment at a massive
scale. We use our method to search for the beam configuration of a
low-resolution LiDAR for two important tasks: 3D object detection and
localization. Experiments show that the proposed RL-L2O method improves the
performance in both tasks significantly compared to the baseline methods. We
believe that a combination of our method with the recent advances of
programmable LiDARs can start a new research direction for LiDAR-based active
perception. The code is publicly available at
https://github.com/vniclas/lidar_beam_selection

Visible-infrared person re-identification (VI-ReID) has been challenging due
to the existence of large discrepancies between visible and infrared
modalities. Most pioneering approaches reduce intra-class variations and
inter-modality discrepancies by learning modality-shared and ID-related
features. However, an explicit modality-shared cue, i.e., body keypoints, has
not been fully exploited in VI-ReID. Additionally, existing feature learning
paradigms imposed constraints on either global features or partitioned feature
stripes, which neglect the prediction consistency of global and part features.
To address the above problems, we exploit Pose Estimation as an auxiliary
learning task to assist the VI-ReID task in an end-to-end framework. By jointly
training these two tasks in a mutually beneficial manner, our model learns
higher quality modality-shared and ID-related features. On top of it, the
learnings of global features and local features are seamlessly synchronized by
Hierarchical Feature Constraint (HFC), where the former supervises the latter
using the knowledge distillation strategy. Experimental results on two
benchmark VI-ReID datasets show that the proposed method consistently improves
state-of-the-art methods by significant margins. Specifically, our method
achieves nearly 20$\%$ mAP improvements against the state-of-the-art method on
the RegDB dataset. Our intriguing findings highlight the usage of auxiliary
task learning in VI-ReID.

This paper presents a new training dataset for automatic genre identification
GINCO, which is based on 1,125 crawled Slovenian web documents that consist of
650 thousand words. Each document was manually annotated for genre with a new
annotation schema that builds upon existing schemata, having primarily clarity
of labels and inter-annotator agreement in mind. The dataset consists of
various challenges related to web-based data, such as machine translated
content, encoding errors, multiple contents presented in one document etc.,
enabling evaluation of classifiers in realistic conditions. The initial machine
learning experiments on the dataset show that (1) pre-Transformer models are
drastically less able to model the phenomena, with macro F1 metrics ranging
around 0.22, while Transformer-based models achieve scores of around 0.58, and
(2) multilingual Transformer models work as well on the task as the monolingual
models that were previously proven to be superior to multilingual models on
standard NLP tasks.

We propose an adversarial learning method to tackle a Domain Adaptation (DA)
time series regression task (DANNTe). The regression aims at building a virtual
copy of a sensor installed on a gas turbine, to be used in place of the
physical sensor which can be missing in certain situations. Our DA approach is
to search for a domain-invariant representation of the features. The learner
has access to both a labelled source dataset and an unlabeled target dataset
(unsupervised DA) and is trained on both, exploiting the minmax game between a
task regressor and a domain classifier Neural Networks. Both models share the
same feature representation, learnt by a feature extractor. This work is based
on the results published by Ganin et al. arXiv:1505.07818; indeed, we present
an extension suitable to time series applications. We report a significant
improvement in regression performance, compared to the baseline model trained
on the source domain only.

Satisfaction measurement, which emerges in every sector today, is a very
important factor for many companies. In this study, it is aimed to reach the
highest accuracy rate with various machine learning algorithms by using the
data on Yemek Sepeti and variations of this data. The accuracy values of each
algorithm were calculated together with the various natural language processing
methods used. While calculating these accuracy values, the parameters of the
algorithms used were tried to be optimized. The models trained in this study on
labeled data can be used on unlabeled data and can give companies an idea in
measuring customer satisfaction. It was observed that 3 different natural
language processing methods applied resulted in approximately 5% accuracy
increase in most of the developed models.

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 posed
diverse challenges for unmanned aerial vehicles (UAVs). We present our four
tailored UAVs, specifically developed for individual aerial-robot tasks of
MBZIRC, including custom hardware- and software components.
  In Challenge 1, a target UAV is pursued using a high-efficiency, onboard
object detection pipeline to capture a ball from the target UAV. A second UAV
uses a similar detection method to find and pop balloons scattered throughout
the arena.
  For Challenge 2, we demonstrate a larger UAV capable of autonomous aerial
manipulation: Bricks are found and tracked from camera images. Subsequently,
they are approached, picked, transported, and placed on a wall.
  Finally, in Challenge 3, our UAV autonomously finds fires using LiDAR and
thermal cameras. It extinguishes the fires with an onboard fire extinguisher.
  While every robot features task-specific subsystems, all UAVs rely on a
standard software stack developed for this particular and future competitions.
We present our mostly open-source software solutions, including tools for
system configuration, monitoring, robust wireless communication, high-level
control, and agile trajectory generation. For solving the MBZIRC 2020 tasks, we
advanced the state of the art in multiple research areas like machine vision
and trajectory generation.
  We present our scientific contributions that constitute the foundation for
our algorithms and systems and analyze the results from the MBZIRC competition
2020 in Abu Dhabi, where our systems reached second place in the Grand
Challenge. Furthermore, we discuss lessons learned from our participation in
this complex robotic challenge.

During recent years, deep reinforcement learning (DRL) has made successful
incursions into complex decision-making applications such as robotics,
autonomous driving or video games. In the search for more sample-efficient
algorithms, a promising direction is to leverage as much external off-policy
data as possible. One staple of this data-driven approach is to learn from
expert demonstrations. In the past, multiple ideas have been proposed to make
good use of the demonstrations added to the replay buffer, such as pretraining
on demonstrations only or minimizing additional cost functions. We present a
new method, able to leverage demonstrations and episodes collected online in
any sparse-reward environment with any off-policy algorithm. Our method is
based on a reward bonus given to demonstrations and successful episodes,
encouraging expert imitation and self-imitation. First, we give a reward bonus
to the transitions coming from demonstrations to encourage the agent to match
the demonstrated behaviour. Then, upon collecting a successful episode, we
relabel its transitions with the same bonus before adding them to the replay
buffer, encouraging the agent to also match its previous successes. Our
experiments focus on manipulation robotics, specifically on three tasks for a 6
degrees-of-freedom robotic arm in simulation. We show that our method based on
reward relabeling improves the performance of the base algorithm (SAC and DDPG)
on these tasks, even in the absence of demonstrations. Furthermore, integrating
into our method two improvements from previous works allows our approach to
outperform all baselines.

International public health policies increasingly favor mandatory
immunization. If its short-term effects on vaccine coverage are well
documented, there has been little consideration to its effects on public
attitudes towards vaccines. In this paper, we examine Google searches related
to vaccines in five countries (Australia, France, Germany, Italy, Serbia) and
two American states (California) which experienced at least one vaccine mandate
extension in the past decade. We found that the effects of a new mandate
implementation heavily depends on the context in each specific country or
state. We also observed that there is little indication that the passing of new
or extended mandates attenuated public doubt towards vaccines.

Deep-learning-based NLP models are found to be vulnerable to word
substitution perturbations. Before they are widely adopted, the fundamental
issues of robustness need to be addressed. Along this line, we propose a formal
framework to evaluate word-level robustness. First, to study safe regions for a
model, we introduce robustness radius which is the boundary where the model can
resist any perturbation. As calculating the maximum robustness radius is
computationally hard, we estimate its upper and lower bound. We repurpose
attack methods as ways of seeking upper bound and design a pseudo-dynamic
programming algorithm for a tighter upper bound. Then verification method is
utilized for a lower bound. Further, for evaluating the robustness of regions
outside a safe radius, we reexamine robustness from another view:
quantification. A robustness metric with a rigorous statistical guarantee is
introduced to measure the quantification of adversarial examples, which
indicates the model's susceptibility to perturbations outside the safe radius.
The metric helps us figure out why state-of-the-art models like BERT can be
easily fooled by a few word substitutions, but generalize well in the presence
of real-world noises.

Atomistic simulations using accurate energy functions can provide
molecular-level insight into functional motions of molecules in the gas- and in
the condensed phase. Together with recently developed and currently pursued
efforts in integrating and combining this with machine learning techniques
provides a unique opportunity to bring such dynamics simulations closer to
reality. This perspective delineates the present status of the field from
efforts of others in the field and some of your own work and discusses open
questions and future prospects.

We consider flows of ordinary differential equations (ODEs) driven by path
differentiable vector fields. Path differentiable functions constitute a proper
subclass of Lipschitz functions which admit conservative gradients, a notion of
generalized derivative compatible with basic calculus rules. Our main result
states that such flows inherit the path differentiability property of the
driving vector field. We show indeed that forward propagation of derivatives
given by the sensitivity differential inclusions provide a conservative
Jacobian for the flow. This allows to propose a nonsmooth version of the
adjoint method, which can be applied to integral costs under an ODE constraint.
This result constitutes a theoretical ground to the application of small step
first order methods to solve a broad class of nonsmooth optimization problems
with parametrized ODE constraints. This is illustrated with the convergence of
small step first order methods based on the proposed nonsmooth adjoint.

Proximity detection is to determine whether an IoT receiver is within a
certain distance from a signal transmitter. Due to its low cost and high
popularity, Bluetooth low energy (BLE) has been used to detect proximity based
on the received signal strength indicator (RSSI). To address the fact that RSSI
can be markedly influenced by device carriage states, previous works have
incorporated RSSI with inertial measurement unit (IMU) using deep learning.
However, they have not sufficiently accounted for the impact of multipath.
Furthermore, due to the special setup, the IMU data collected in the training
process may be biased, which hampers the system's robustness and
generalizability. This issue has not been studied before. We propose PRID, an
IMU-assisted BLE proximity detection approach robust against RSSI fluctuation
and IMU data bias. PRID histogramizes RSSI to extract multipath features and
uses carriage state regularization to mitigate overfitting due to IMU data
bias. We further propose PRID-lite based on a binarized neural network to
substantially cut memory requirements for resource-constrained devices. We have
conducted extensive experiments under different multipath environments, data
bias levels, and a crowdsourced dataset. Our results show that PRID
significantly reduces false detection cases compared with the existing arts (by
over 50%). PRID-lite further reduces over 90% PRID model size and extends 60%
battery life, with a minor compromise in accuracy (7%).

Stock Movement Prediction (SMP) aims at predicting listed companies' stock
future price trend, which is a challenging task due to the volatile nature of
financial markets. Recent financial studies show that the momentum spillover
effect plays a significant role in stock fluctuation. However, previous studies
typically only learn the simple connection information among related companies,
which inevitably fail to model complex relations of listed companies in the
real financial market. To address this issue, we first construct a more
comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities
including listed companies and their associated executives, and
hybrid-relations including the explicit relations and implicit relations.
Afterward, we propose DanSmp, a novel Dual Attention Networks to learn the
momentum spillover signals based upon the constructed MKG for stock prediction.
The empirical experiments on our constructed datasets against nine SOTA
baselines demonstrate that the proposed DanSmp is capable of improving stock
prediction with the constructed MKG.

Embracing the deep learning techniques for representation learning in
clustering research has attracted broad attention in recent years, yielding a
newly developed clustering paradigm, viz. the deep clustering (DC). Typically,
the DC models capitalize on autoencoders to learn the intrinsic features which
facilitate the clustering process in consequence. Nowadays, a generative model
named variational autoencoder (VAE) has got wide acceptance in DC studies.
Nevertheless, the plain VAE is insufficient to perceive the comprehensive
latent features, leading to the deteriorative clustering performance. In this
paper, a novel DC method is proposed to address this issue. Specifically, the
generative adversarial network and VAE are coalesced into a new autoencoder
called fusion autoencoder (FAE) for discerning more discriminative
representation that benefits the downstream clustering task. Besides, the FAE
is implemented with the deep residual network architecture which further
enhances the representation learning ability. Finally, the latent space of the
FAE is transformed to an embedding space shaped by a deep dense neural network
for pulling away different clusters from each other and collapsing data points
within individual clusters. Experiment conducted on several image datasets
demonstrate the effectiveness of the proposed DC model against the baseline
methods.

Recent works explore learning graph representations in a self-supervised
manner. In graph contrastive learning, benchmark methods apply various graph
augmentation approaches. However, most of the augmentation methods are
non-learnable, which causes the issue of generating unbeneficial augmented
graphs. Such augmentation may degenerate the representation ability of graph
contrastive learning methods. Therefore, we motivate our method to generate
augmented graph by a learnable graph augmenter, called MEta Graph Augmentation
(MEGA). We then clarify that a "good" graph augmentation must have uniformity
at the instance-level and informativeness at the feature-level. To this end, we
propose a novel approach to learning a graph augmenter that can generate an
augmentation with uniformity and informativeness. The objective of the graph
augmenter is to promote our feature extraction network to learn a more
discriminative feature representation, which motivates us to propose a
meta-learning paradigm. Empirically, the experiments across multiple benchmark
datasets demonstrate that MEGA outperforms the state-of-the-art methods in
graph self-supervised learning tasks. Further experimental studies prove the
effectiveness of different terms of MEGA.

We consider a lifelong learning scenario in which a learner faces a
neverending and arbitrary stream of facts and has to decide which ones to
retain in its limited memory. We introduce a mathematical model based on the
online learning framework, in which the learner measures itself against a
collection of experts that are also memory-constrained and that reflect
different policies for what to remember. Interspersed with the stream of facts
are occasional questions, and on each of these the learner incurs a loss if it
has not remembered the corresponding fact. Its goal is to do almost as well as
the best expert in hindsight, while using roughly the same amount of memory. We
identify difficulties with using the multiplicative weights update algorithm in
this memory-constrained scenario, and design an alternative scheme whose regret
guarantees are close to the best possible.

With the rise of deep learning and intelligent vehicle, the smart assistant
has become an essential in-car component to facilitate driving and provide
extra functionalities. In-car smart assistants should be able to process
general as well as car-related commands and perform corresponding actions,
which eases driving and improves safety. However, there is a data scarcity
issue for low resource languages, hindering the development of research and
applications. In this paper, we introduce a new dataset, Cantonese In-car
Audio-Visual Speech Recognition (CI-AVSR), for in-car command recognition in
the Cantonese language with both video and audio data. It consists of 4,984
samples (8.3 hours) of 200 in-car commands recorded by 30 native Cantonese
speakers. Furthermore, we augment our dataset using common in-car background
noises to simulate real environments, producing a dataset 10 times larger than
the collected one. We provide detailed statistics of both the clean and the
augmented versions of our dataset. Moreover, we implement two multimodal
baselines to demonstrate the validity of CI-AVSR. Experiment results show that
leveraging the visual signal improves the overall performance of the model.
Although our best model can achieve a considerable quality on the clean test
set, the speech recognition quality on the noisy data is still inferior and
remains as an extremely challenging task for real in-car speech recognition
systems. The dataset and code will be released at
https://github.com/HLTCHKUST/CI-AVSR.

The unsupervised domain adaptive person re-identification (re-ID) task has
been a challenge because, unlike the general domain adaptive tasks, there is no
overlap between the classes of source and target domain data in the person
re-ID, which leads to a significant domain gap. State-of-the-art unsupervised
re-ID methods train the neural networks using a memory-based contrastive loss.
However, performing contrastive learning by treating each unlabeled instance as
a class will lead to the problem of class collision, and the updating intensity
is inconsistent due to the difference in the number of instances of different
categories when updating in the memory bank. To address such problems, we
propose Prototype Dictionary Learning for person re-ID which is able to utilize
both source domain data and target domain data by one training stage while
avoiding the problem of class collision and the problem of updating intensity
inconsistency by cluster-level prototype dictionary learning. In order to
reduce the interference of domain gap on the model, we propose a local-enhance
module to improve the domain adaptation of the model without increasing the
number of model parameters. Our experiments on two large datasets demonstrate
the effectiveness of the prototype dictionary learning. 71.5\% mAP is achieved
in the Market-to-Duke task, which is a 2.3\% improvement compared to the
state-of-the-art unsupervised domain adaptive re-ID methods. It achieves 83.9\%
mAP in the Duke-to-Market task, which improves by 4.4\% compared to the
state-of-the-art unsupervised adaptive re-ID methods.

This paper reports the results and post-challenge analyses of ChaLearn's
AutoDL challenge series, which helped sorting out a profusion of AutoML
solutions for Deep Learning (DL) that had been introduced in a variety of
settings, but lacked fair comparisons. All input data modalities (time series,
images, videos, text, tabular) were formatted as tensors and all tasks were
multi-label classification problems. Code submissions were executed on hidden
tasks, with limited time and computational resources, pushing solutions that
get results quickly. In this setting, DL methods dominated, though popular
Neural Architecture Search (NAS) was impractical. Solutions relied on
fine-tuned pre-trained networks, with architectures matching data modality.
Post-challenge tests did not reveal improvements beyond the imposed time limit.
While no component is particularly original or novel, a high level modular
organization emerged featuring a "meta-learner", "data ingestor", "model
selector", "model/learner", and "evaluator". This modularity enabled ablation
studies, which revealed the importance of (off-platform) meta-learning,
ensembling, and efficient data management. Experiments on heterogeneous module
combinations further confirm the (local) optimality of the winning solutions.
Our challenge legacy includes an ever-lasting benchmark
(http://autodl.chalearn.org), the open-sourced code of the winners, and a free
"AutoDL self-service".

Glaucoma is one of the ophthalmic diseases that may cause blindness, for
which early detection and treatment are very important. Fundus images and
optical coherence tomography (OCT) images are both widely-used modalities in
diagnosing glaucoma. However, existing glaucoma grading approaches mainly
utilize a single modality, ignoring the complementary information between
fundus and OCT. In this paper, we propose an efficient multi-modality
supervised contrastive learning framework, named COROLLA, for glaucoma grading.
Through layer segmentation as well as thickness calculation and projection,
retinal thickness maps are extracted from the original OCT volumes and used as
a replacing modality, resulting in more efficient calculations with less memory
usage. Given the high structure and distribution similarities across medical
image samples, we employ supervised contrastive learning to increase our
models' discriminative power with better convergence. Moreover, feature-level
fusion of paired fundus image and thickness map is conducted for enhanced
diagnosis accuracy. On the GAMMA dataset, our COROLLA framework achieves
overwhelming glaucoma grading performance compared to state-of-the-art methods.

Non-Local Attention (NLA) brings significant improvement for Single Image
Super-Resolution (SISR) by leveraging intrinsic feature correlation in natural
images. However, NLA gives noisy information large weights and consumes
quadratic computation resources with respect to the input size, limiting its
performance and application. In this paper, we propose a novel Efficient
Non-Local Contrastive Attention (ENLCA) to perform long-range visual modeling
and leverage more relevant non-local features. Specifically, ENLCA consists of
two parts, Efficient Non-Local Attention (ENLA) and Sparse Aggregation. ENLA
adopts the kernel method to approximate exponential function and obtains linear
computation complexity. For Sparse Aggregation, we multiply inputs by an
amplification factor to focus on informative features, yet the variance of
approximation increases exponentially. Therefore, contrastive learning is
applied to further separate relevant and irrelevant features. To demonstrate
the effectiveness of ENLCA, we build an architecture called Efficient Non-Local
Contrastive Network (ENLCN) by adding a few of our modules in a simple
backbone. Extensive experimental results show that ENLCN reaches superior
performance over state-of-the-art approaches on both quantitative and

This work aims to design a low complexity spoken command recognition (SCR)
system by considering different trade-offs between the number of model
parameters and classification accuracy. More specifically, we exploit a deep
hybrid architecture of a tensor-train (TT) network to build an end-to-end SRC
pipeline. Our command recognition system, namely CNN+(TT-DNN), is composed of
convolutional layers at the bottom for spectral feature extraction and TT
layers at the top for command classification. Compared with a traditional
end-to-end CNN baseline for SCR, our proposed CNN+(TT-DNN) model replaces fully
connected (FC) layers with TT ones and it can substantially reduce the number
of model parameters while maintaining the baseline performance of the CNN
model. We initialize the CNN+(TT-DNN) model in a randomized manner or based on
a well-trained CNN+DNN, and assess the CNN+(TT-DNN) models on the Google Speech
Command Dataset. Our experimental results show that the proposed CNN+(TT-DNN)
model attains a competitive accuracy of 96.31% with 4 times fewer model
parameters than the CNN model. Furthermore, the CNN+(TT-DNN) model can obtain a
97.2% accuracy when the number of parameters is increased.

Classification problems are common in Computer Vision. Despite this, there is
no dedicated work for the classification of beer bottles. As part of the
challenge of the master course Deep Learning, a dataset of 5207 beer bottle
images and brand labels was created. An image contains exactly one beer bottle.
In this paper we present a deep learning model which classifies pictures of
beer bottles in a two step approach. As the first step, a Faster-R-CNN detects
image sections relevant for classification independently of the brand. In the
second step, the relevant image sections are classified by a ResNet-18. The
image section with the highest confidence is returned as class label. We
propose a model, with which we surpass the classic one step transfer learning
approach and reached an accuracy of 99.86% during the challenge on the final
test dataset. We were able to achieve 100% accuracy after the challenge ended

Local Stochastic Gradient Descent (SGD) with periodic model averaging
(FedAvg) is a foundational algorithm in Federated Learning. The algorithm
independently runs SGD on multiple workers and periodically averages the model
across all the workers. When local SGD runs with many workers, however, the
periodic averaging causes a significant model discrepancy across the workers
making the global loss converge slowly. While recent advanced optimization
methods tackle the issue focused on non-IID settings, there still exists the
model discrepancy issue due to the underlying periodic model averaging. We
propose a partial model averaging framework that mitigates the model
discrepancy issue in Federated Learning. The partial averaging encourages the
local models to stay close to each other on parameter space, and it enables to
more effectively minimize the global loss. Given a fixed number of iterations
and a large number of workers (128), the partial averaging achieves up to 2.2%
higher validation accuracy than the periodic full averaging.

In recent years, the computational demands of deep learning applications have
necessitated the introduction of energy-efficient hardware accelerators.
Optical neural networks are a promising option; however, thus far they have
been largely limited by the lack of energy-efficient nonlinear optical
functions. Here, we experimentally demonstrate an all-optical Rectified Linear
Unit (ReLU), which is the most widely used nonlinear activation function for
deep learning, using a periodically-poled thin-film lithium niobate
nanophotonic waveguide and achieve ultra-low energies in the regime of
femtojoules per activation with near-instantaneous operation. Our results
provide a clear and practical path towards truly all-optical, energy-efficient
nanophotonic deep learning.

Object detection using aerial drone imagery has received a great deal of
attention in recent years. While visible light images are adequate for
detecting objects in most scenarios, thermal cameras can extend the
capabilities of object detection to night-time or occluded objects. As such,
RGB and Infrared (IR) fusion methods for object detection are useful and
important. One of the biggest challenges in applying deep learning methods to
RGB/IR object detection is the lack of available training data for drone IR
imagery, especially at night. In this paper, we develop several strategies for
creating synthetic IR images using the AIRSim simulation engine and CycleGAN.
Furthermore, we utilize an illumination-aware fusion framework to fuse RGB and
IR images for object detection on the ground. We characterize and test our
methods for both simulated and actual data. Our solution is implemented on an
NVIDIA Jetson Xavier running on an actual drone, requiring about 28
milliseconds of processing per RGB/IR image pair.

This paper proposes an adversarial learning based training approach for brain
tumor segmentation task. In this concept, the 3D segmentation network learns
from dual reciprocal adversarial learning approaches. To enhance the
generalization across the segmentation predictions and to make the segmentation
network robust, we adhere to the Virtual Adversarial Training approach by
generating more adversarial examples via adding some noise on original patient
data. By incorporating a critic that acts as a quantitative subjective referee,
the segmentation network learns from the uncertainty information associated
with segmentation results. We trained and evaluated network architecture on the
RSNA-ASNR-MICCAI BraTS 2021 dataset. Our performance on the online validation
dataset is as follows: Dice Similarity Score of 81.38%, 90.77% and 85.39%;
Hausdorff Distance (95\%) of 21.83 mm, 5.37 mm, 8.56 mm for the enhancing
tumor, whole tumor and tumor core, respectively. Similarly, our approach
achieved a Dice Similarity Score of 84.55%, 90.46% and 85.30%, as well as
Hausdorff Distance (95\%) of 13.48 mm, 6.32 mm and 16.98 mm on the final test
dataset. Overall, our proposed approach yielded better performance in
segmentation accuracy for each tumor sub-region. Our code implementation is
publicly available at https://github.com/himashi92/vizviva_brats_2021

Federated learning (FL) is a distributed machine learning paradigm where
enormous scattered clients (e.g. mobile devices or IoT devices) collaboratively
train a model under the orchestration of a central server (e.g. service
provider), while keeping the training data decentralized. Unfortunately, FL is
susceptible to a variety of attacks, including backdoor attack, which is made
substantially worse in the presence of malicious attackers. Most of algorithms
usually assume that the malicious at tackers no more than benign clients or the
data distribution is independent identically distribution (IID). However, no
one knows the number of malicious attackers and the data distribution is
usually non identically distribution (Non-IID). In this paper, we propose
RFLBAT which utilizes principal component analysis (PCA) technique and Kmeans
clustering algorithm to defend against backdoor attack. Our algorithm RFLBAT
does not bound the number of backdoored attackers and the data distribution,
and requires no auxiliary information outside of the learning process. We
conduct extensive experiments including a variety of backdoor attack types.
Experimental results demonstrate that RFLBAT outperforms the existing
state-of-the-art algorithms and is able to resist various backdoor attack
scenarios including different number of attackers (DNA), different Non-IID
scenarios (DNS), different number of clients (DNC) and distributed backdoor
attack (DBA).

In CRYPTO 2019, Gohr made a pioneering attempt and successfully applied deep
learning to the differential cryptanalysis against NSA block cipher SPECK32/64,
achieving higher accuracy than the pure differential distinguishers. By its
very nature, mining effective features in data plays a crucial role in
data-driven deep learning. In this paper, in addition to considering the
integrity of the information from the training data of the ciphertext pair,
domain knowledge about the structure of differential cryptanalysis is also
considered into the training process of deep learning to improve the
performance. Meanwhile, taking the performance of the differential-neural
distinguisher of SIMON32/64 as an entry point, we investigate the impact of
input difference on the performance of the hybrid distinguishers to choose the
proper input difference. Eventually, we improve the accuracy of the neural
distinguishers of SIMON32/64, SIMON64/128, SIMECK32/64, and SIMECK64/128. We
also obtain related-key differential-based neural distinguishers on
round-reduced versions of SIMON32/64, SIMON64/128, SIMECK32/64, and
SIMECK64/128 for the first time.

Radiology report generation aims to produce computer-aided diagnoses to
alleviate the workload of radiologists and has drawn increasing attention
recently. However, previous deep learning methods tend to neglect the mutual
influences between medical findings, which can be the bottleneck that limits
the quality of generated reports. In this work, we propose to mine and
represent the associations among medical findings in an informative knowledge
graph and incorporate this prior knowledge with radiology report generation to
help improve the quality of generated reports. Experiment results demonstrate
the superior performance of our proposed method on the IU X-ray dataset with a
ROUGE-L of 0.384$\pm$0.007 and CIDEr of 0.340$\pm$0.011. Compared with previous
works, our model achieves an average of 1.6% improvement (2.0% and 1.5%
improvements in CIDEr and ROUGE-L, respectively). The experiments suggest that
prior knowledge can bring performance gains to accurate radiology report
generation. We will make the code publicly available at
https://github.com/bionlplab/report_generation_amia2022.

In this article, we present an efficient deep learning method called coupled
deep neural networks (CDNNs) for coupled physical problems. Our method compiles
the interface conditions of the coupled PDEs into the networks properly and can
be served as an efficient alternative to the complex coupled problems. To
impose energy conservation constraints, the CDNNs utilize simple fully
connected layers and a custom loss function to perform the model training
process as well as the physical property of the exact solution. The approach
can be beneficial for the following reasons: Firstly, we sampled randomly and
only input spatial coordinates without being restricted by the nature of
samples. Secondly, our method is meshfree which makes it more efficient than
the traditional methods. Finally, our method is parallel and can solve multiple
variables independently at the same time. We give the theory to guarantee the
convergence of the loss function and the convergence of the neural networks to
the exact solution. Some numerical experiments are performed and discussed to
demonstrate the performance of the proposed method.

Today's programmers, especially data science practitioners, make heavy use of
data-processing libraries (APIs) such as PyTorch, Tensorflow, NumPy, Pandas,
and the like. Program synthesizers can provide significant coding assistance to
this community of users; however program synthesis also can be slow due to
enormous search spaces. In this work, we examine ways in which machine learning
can be used to accelerate enumerative program synthesis. We present a
deep-learning-based model to predict the sequence of API functions that would
be needed to go from a given input to a desired output, both being numeric
vectors. Our work is based on two insights. First, it is possible to learn,
based on a large number of input-output examples, to predict the likely API
function needed in a given situation. Second, and crucially, it is also
possible to learn to compose API functions into a sequence, given an input and
the desired final output, without explicitly knowing the intermediate values.
We show that we can speed up an enumerative program synthesizer by using
predictions from our model variants. These speedups significantly outperform
previous ways (e.g. DeepCoder) in which researchers have used ML models in
enumerative synthesis.

In this work, we explore the approximation capability of deep Rectified
Quadratic Unit neural networks for H\"older-regular functions, with respect to
the uniform norm. We find that theoretical approximation heavily depends on the
selected activation function in the neural network.

A long-standing goal of science is to accurately solve the Schr\"odinger
equation for large molecular systems. The poor scaling of current quantum
chemistry algorithms on classical computers imposes an effective limit of about
a few dozen atoms for which we can calculate molecular electronic structure. We
present a machine learning (ML) method to break through this scaling limit and
make quantum chemistry calculations of very large systems possible. We show
that Euclidean Neural Networks can be trained to predict the electron density
with high fidelity from limited data. Learning the electron density allows us
to train a machine learning model on small systems and make accurate
predictions on large ones. We show that this ML electron density model can
break through the quantum scaling limit and calculate the electron density of
systems of thousands of atoms with quantum accuracy.

Retrieving relevant documents from a corpus is typically based on the
semantic similarity between the document content and query text. The inclusion
of structural relationship between documents can benefit the retrieval
mechanism by addressing semantic gaps. However, incorporating these
relationships requires tractable mechanisms that balance structure with
semantics and take advantage of the prevalent pre-train/fine-tune paradigm. We
propose here a holistic approach to learning document representations by
integrating intra-document content with inter-document relations. Our deep
metric learning solution analyzes the complex neighborhood structure in the
relationship network to efficiently sample similar/dissimilar document pairs
and defines a novel quintuplet loss function that simultaneously encourages
document pairs that are semantically relevant to be closer and structurally
unrelated to be far apart in the representation space. Furthermore, the
separation margins between the documents are varied flexibly to encode the
heterogeneity in relationship strengths. The model is fully fine-tunable and
natively supports query projection during inference. We demonstrate that it
outperforms competing methods on multiple datasets for document retrieval
tasks.

Changepoints are abrupt variations in the underlying distribution of data.
Detecting changes in a data stream is an important problem with many
applications. In this paper, we are interested in changepoint detection
algorithms which operate in an online setting in the sense that both its
storage requirements and worst-case computational complexity per observation
are independent of the number of previous observations. We propose an online
changepoint detection algorithm for both univariate and multivariate data which
compares favorably with offline changepoint detection algorithms while also
operating in a strictly more constrained computational model. In addition, we
present a simple online hyperparameter auto tuning technique for these

In this paper, we contribute a multi-faceted study into Pavlovian signalling
-- a process by which learned, temporally extended predictions made by one
agent inform decision-making by another agent. Signalling is intimately
connected to time and timing. In service of generating and receiving signals,
humans and other animals are known to represent time, determine time since past
events, predict the time until a future stimulus, and both recognize and
generate patterns that unfold in time. We investigate how different temporal
processes impact coordination and signalling between learning agents by
introducing a partially observable decision-making domain we call the Frost
Hollow. In this domain, a prediction learning agent and a reinforcement
learning agent are coupled into a two-part decision-making system that works to
acquire sparse reward while avoiding time-conditional hazards. We evaluate two
domain variations: machine agents interacting in a seven-state linear walk, and
human-machine interaction in a virtual-reality environment. Our results
showcase the speed of learning for Pavlovian signalling, the impact that
different temporal representations do (and do not) have on agent-agent
coordination, and how temporal aliasing impacts agent-agent and human-agent
interactions differently. As a main contribution, we establish Pavlovian
signalling as a natural bridge between fixed signalling paradigms and fully
adaptive communication learning between two agents. We further show how to
computationally build this adaptive signalling process out of a fixed
signalling process, characterized by fast continual prediction learning and
minimal constraints on the nature of the agent receiving signals. Our results
therefore suggest an actionable, constructivist path towards communication
learning between reinforcement learning agents.

This paper aims to provide a novel design of a multiscale framelets
convolution for spectral graph neural networks. In the spectral paradigm,
spectral GNNs improve graph learning task performance via proposing various
spectral filters in spectral domain to capture both global and local graph
structure information. Although the existing spectral approaches show superior
performance in some graphs, they suffer from lack of flexibility and being
fragile when graph information are incomplete or perturbated. Our new framelets
convolution incorporates the filtering func-tions directly designed in the
spectral domain to overcome these limitations. The proposed convolution shows a
great flexibility in cutting-off spectral information and effectively mitigate
the negative effect of noisy graph signals. Besides, to exploit the
heterogeneity in real-world graph data, the heterogeneous graph neural network
with our new framelet convolution provides a solution for embedding the
intrinsic topological information of meta-path with a multi-level graph
analysis.Extensive experiments have been conducted on real-world heterogeneous
graphs and homogeneous graphs under settings with noisy node features and
superior performance results are achieved.

The field of artificial neural networks is expected to strongly benefit from
recent developments of quantum computers. In particular, quantum machine
learning, a class of quantum algorithms which exploit qubits for creating
trainable neural networks, will provide more power to solve problems such as
pattern recognition, clustering and machine learning in general. The building
block of feed-forward neural networks consists of one layer of neurons
connected to an output neuron that is activated according to an arbitrary
activation function. The corresponding learning algorithm goes under the name
of Rosenblatt perceptron. Quantum perceptrons with specific activation
functions are known, but a general method to realize arbitrary activation
functions on a quantum computer is still lacking. Here we fill this gap with a
quantum algorithm which is capable to approximate any analytic activation
functions to any given order of its power series. Unlike previous proposals
providing irreversible measurement--based and simplified activation functions,
here we show how to approximate any analytic function to any required accuracy
without the need to measure the states encoding the information. Thanks to the
generality of this construction, any feed-forward neural network may acquire
the universal approximation properties according to Hornik's theorem. Our
results recast the science of artificial neural networks in the architecture of
gate-model quantum computers.

Deep reinforcement learning is an increasingly popular technique for
synthesising policies to control an agent's interaction with its environment.
There is also growing interest in formally verifying that such policies are
correct and execute safely. Progress has been made in this area by building on
existing work for verification of deep neural networks and of continuous-state
dynamical systems. In this paper, we tackle the problem of verifying
probabilistic policies for deep reinforcement learning, which are used to, for
example, tackle adversarial environments, break symmetries and manage
trade-offs. We propose an abstraction approach, based on interval Markov
decision processes, that yields probabilistic guarantees on a policy's
execution, and present techniques to build and solve these models using
abstract interpretation, mixed-integer linear programming, entropy-based
refinement and probabilistic model checking. We implement our approach and
illustrate its effectiveness on a selection of reinforcement learning

In classic graph signal processing, given a real-valued graph signal, its
graph Fourier transform is typically defined as the series of inner products
between the signal and each eigenvector of the graph Laplacian. Unfortunately,
this definition is not mathematically valid in the cases of vector-valued graph
signals which however are typical operands in the state-of-the-art graph
learning modeling and analyses. Seeking a generalized transformation decoding
the magnitudes of eigencomponents from vector-valued signals is thus the main
objective of this paper. Several attempts are explored, and also it is found
that performing the transformation at hierarchical levels of adjacency help
profile the spectral characteristics of signals more insightfully. The proposed
methods are introduced as a new tool assisting on diagnosing and profiling
behaviors of graph learning models.

The use of deep learning for water extraction requires precise pixel-level
labels. However, it is very difficult to label high-resolution remote sensing
images at the pixel level. Therefore, we study how to utilize point labels to
extract water bodies and propose a novel method called the neighbor feature
aggregation network (NFANet). Compared with pixellevel labels, point labels are
much easier to obtain, but they will lose much information. In this paper, we
take advantage of the similarity between the adjacent pixels of a local
water-body, and propose a neighbor sampler to resample remote sensing images.
Then, the sampled images are sent to the network for feature aggregation. In
addition, we use an improved recursive training algorithm to further improve
the extraction accuracy, making the water boundary more natural. Furthermore,
our method utilizes neighboring features instead of global or local features to
learn more representative features. The experimental results show that the
proposed NFANet method not only outperforms other studied weakly supervised
approaches, but also obtains similar results as the state-of-the-art ones.

Graph Neural Networks (GNNs) have proven to excel in predictive modeling
tasks where the underlying data is a graph. However, as GNNs are extensively
used in human-centered applications, the issue of fairness has arisen. While
edge deletion is a common method used to promote fairness in GNNs, it fails to
consider when data is inherently missing fair connections. In this work we
consider the unexplored method of edge addition, accompanied by deletion, to
promote fairness. We propose two model-agnostic algorithms to perform edge
editing: a brute force approach and a continuous approximation approach,
FairEdit. FairEdit performs efficient edge editing by leveraging gradient
information of a fairness loss to find edges that improve fairness. We find
that FairEdit outperforms standard training for many data sets and GNN methods,
while performing comparably to many state-of-the-art methods, demonstrating
FairEdit's ability to improve fairness across many domains and models.

Currently, publicly available models for website classification do not offer
an embedding method and have limited support for languages beyond English. We
release a dataset of more than two million category-labeled websites in 92
languages collected from Curlie, the largest multilingual human-edited Web
directory. The dataset contains 14 website categories aligned across languages.
Alongside it, we introduce Homepage2Vec, a machine-learned pre-trained model
for classifying and embedding websites based on their homepage in a
language-agnostic way. Homepage2Vec, thanks to its feature set (textual
content, metadata tags, and visual attributes) and recent progress in natural
language representation, is language-independent by design and generates
embedding-based representations. We show that Homepage2Vec correctly classifies
websites with a macro-averaged F1-score of 0.90, with stable performance across
low- as well as high-resource languages. Feature analysis shows that a small
subset of efficiently computable features suffices to achieve high performance
even with limited computational resources. We make publicly available the
curated Curlie dataset aligned across languages, the pre-trained Homepage2Vec
model, and libraries

Analysis of longitudinal Electronic Health Record (EHR) data is an important
goal for precision medicine. Difficulty in applying Machine Learning (ML)
methods, either predictive or unsupervised, stems in part from the
heterogeneity and irregular sampling of EHR data. We present an unsupervised
probabilistic model that captures nonlinear relationships between variables
over continuous-time. This method works with arbitrary sampling patterns and
captures the joint probability distribution between variable measurements and
the time intervals between them. Inference algorithms are derived that can be
used to evaluate the likelihood of future using under a trained model. As an
example, we consider data from the United States Veterans Health Administration
(VHA) in the areas of diabetes and depression. Likelihood ratio maps are
produced showing the likelihood of risk for moderate-severe vs minimal
depression as measured by the Patient Health Questionnaire-9 (PHQ-9).

A major impediment to researchers working in the area of fingerprint
recognition is the lack of publicly available, large-scale, fingerprint
datasets. The publicly available datasets that do exist contain very few
identities and impressions per finger. This limits research on a number of
topics, including e.g., using deep networks to learn fixed length fingerprint
embeddings. Therefore, we propose PrintsGAN, a synthetic fingerprint generator
capable of generating unique fingerprints along with multiple impressions for a
given fingerprint. Using PrintsGAN, we synthesize a database of 525k
fingerprints (35K distinct fingers, each with 15 impressions). Next, we show
the utility of the PrintsGAN generated dataset by training a deep network to
extract a fixed-length embedding from a fingerprint. In particular, an
embedding model trained on our synthetic fingerprints and fine-tuned on a small
number of publicly available real fingerprints (25K prints from NIST SD302)
obtains a TAR of 87.03% @ FAR=0.01% on the NIST SD4 database (a boost from
TAR=73.37% when only trained on NIST SD302). Prevailing synthetic fingerprint
generation methods do not enable such performance gains due to i) lack of
realism or ii) inability to generate multiple impressions per finger. We plan
to release our database of synthetic fingerprints to the public.

Modern histopathological image analysis relies on the segmentation of cell
structures to derive quantitative metrics required in biomedical research and
clinical diagnostics. State-of-the-art deep learning approaches predominantly
apply convolutional layers in segmentation and are typically highly customized
for a specific experimental configuration; often unable to generalize to
unknown data. As the model capacity of classical convolutional layers is
limited by a finite set of learned kernels, our approach uses a graph
representation of the image and focuses on the node transitions in multiple
magnifications. We propose a novel architecture for semantic segmentation of
cell nuclei robust to differences in experimental configuration such as
staining and variation of cell types. The architecture is comprised of a novel
neuroplastic graph attention network based on residual graph attention layers
and concurrent optimization of the graph structure representing multiple
magnification levels of the histopathological image. The modification of graph
structure, which generates the node features by projection, is as important to
the architecture as the graph neural network itself. It determines the possible
message flow and critical properties to optimize attention, graph structure,
and node updates in a balanced magnification loss. In experimental evaluation,
our framework outperforms ensembles of state-of-the-art neural networks, with a
fraction of the neurons typically required, and sets new standards for the
segmentation of new nuclei datasets.

Learning invariant representations is an important requirement when training
machine learning models that are driven by spurious correlations in the
datasets. These spurious correlations, between input samples and the target
labels, wrongly direct the neural network predictions resulting in poor
performance on certain groups, especially the minority groups. Robust training
against these spurious correlations requires the knowledge of group membership
for every sample. Such a requirement is impractical in situations where the
data labeling efforts for minority or rare groups are significantly laborious
or where the individuals comprising the dataset choose to conceal sensitive
information. On the other hand, the presence of such data collection efforts
results in datasets that contain partially labeled group information. Recent
works have tackled the fully unsupervised scenario where no labels for groups
are available. Thus, we aim to fill the missing gap in the literature by
tackling a more realistic setting that can leverage partially available
sensitive or group information during training. First, we construct a
constraint set and derive a high probability bound for the group assignment to
belong to the set. Second, we propose an algorithm that optimizes for the
worst-off group assignments from the constraint set. Through experiments on
image and tabular datasets, we show improvements in the minority group's
performance while preserving overall aggregate accuracy across groups.

Fair machine learning aims to mitigate the biases of model predictions
against certain subpopulations regarding sensitive attributes such as race and
gender. Among the many existing fairness notions, counterfactual fairness
measures the model fairness from a causal perspective by comparing the
predictions of each individual from the original data and the counterfactuals.
In counterfactuals, the sensitive attribute values of this individual had been
modified. Recently, a few works extend counterfactual fairness to graph data,
but most of them neglect the following facts that can lead to biases: 1) the
sensitive attributes of each node's neighbors may causally affect the
prediction w.r.t. this node; 2) the sensitive attributes may causally affect
other features and the graph structure. To tackle these issues, in this paper,
we propose a novel fairness notion - graph counterfactual fairness, which
considers the biases led by the above facts. To learn node representations
towards graph counterfactual fairness, we propose a novel framework based on
counterfactual data augmentation. In this framework, we generate
counterfactuals corresponding to perturbations on each node's and their
neighbors' sensitive attributes. Then we enforce fairness by minimizing the
discrepancy between the representations learned from the original graph and the
counterfactuals for each node. Experiments on both synthetic and real-world
graphs show that our framework outperforms the state-of-the-art baselines in
graph counterfactual fairness, and also achieves comparable prediction

End-to-end (E2E) automatic speech recognition models like Recurrent Neural
Networks Transducer (RNN-T) are becoming a popular choice for streaming ASR
applications like voice assistants. While E2E models are very effective at
learning representation of the training data they are trained on, their
accuracy on unseen domains remains a challenging problem. Additionally, these
models require paired audio and text training data, are computationally
expensive and are difficult to adapt towards the fast evolving nature of
conversational speech. In this work, we explore a contextual biasing approach
using likelihood-ratio that leverages text data sources to adapt RNN-T model to
new domains and entities. We show that this method is effective in improving
rare words recognition, and results in a relative improvement of 10% in 1-best
word error rate (WER) and 10% in n-best Oracle WER (n=8) on multiple
out-of-domain datasets without any degradation on a general dataset. We also
show that complementing the contextual biasing adaptation with adaptation of a
second-pass rescoring model gives additive WER improvements.

The convolutional layer and loss function are two fundamental components in
deep learning. Because of the success of conventional deep learning kernels,
the less versatile Gabor kernels become less popular despite the fact that they
can provide abundant features at different frequencies, orientations, and
scales with much fewer parameters. For existing loss functions for multi-class
image segmentation, there is usually a tradeoff among accuracy, robustness to
hyperparameters, and manual weight selections for combining different losses.
Therefore, to gain the benefits of using Gabor kernels while keeping the
advantage of automatic feature generation in deep learning, we propose a fully
trainable Gabor-based convolutional layer where all Gabor parameters are
trainable through backpropagation. Furthermore, we propose a loss function
based on the Pearson's correlation coefficient, which is accurate, robust to
learning rates, and does not require manual weight selections. Experiments on
43 3D brain magnetic resonance images with 19 anatomical structures show that,
using the proposed loss function with a proper combination of conventional and
Gabor-based kernels, we can train a network with only 1.6 million parameters to
achieve an average Dice coefficient of 83%. This size is 44 times smaller than
the V-Net which has 71 million parameters. This paper demonstrates the
potentials of using learnable parametric kernels in deep learning for 3D

The NASA/IPAC Extragalactic Database (NED) is a comprehensive online service
that combines fundamental multi-wavelength information for known objects beyond
the Milky Way and provides value-added, derived quantities and tools to search
and access the data. The contents and relationships between measurements in the
database are continuously augmented and revised to stay current with
astrophysics literature and new sky surveys. The conventional process of
distilling and extracting data from the literature involves human experts to
review the journal articles and determine if an article is of extragalactic
nature, and if so, what types of data it contains. This is both labor intensive
and unsustainable, especially given the ever-increasing number of publications
each year. We present here a machine learning (ML) approach developed and
integrated into the NED production pipeline to help automate the classification
of journal article topics and their data content for inclusion into NED. We
show that this ML application can successfully reproduce the classifications of
a human expert to an accuracy of over 90% in a fraction of the time it takes a
human, allowing us to focus human expertise on tasks that are more difficult to
automate.

This work aims to address the long-established problem of learning
diversified representations. To this end, we combine information-theoretic
arguments with stochastic competition-based activations, namely Stochastic
Local Winner-Takes-All (LWTA) units. In this context, we ditch the conventional
deep architectures commonly used in Representation Learning, that rely on
non-linear activations; instead, we replace them with sets of locally and
stochastically competing linear units. In this setting, each network layer
yields sparse outputs, determined by the outcome of the competition between
units that are organized into blocks of competitors. We adopt stochastic
arguments for the competition mechanism, which perform posterior sampling to
determine the winner of each block. We further endow the considered networks
with the ability to infer the sub-part of the network that is essential for
modeling the data at hand; we impose appropriate stick-breaking priors to this
end. To further enrich the information of the emerging representations, we
resort to information-theoretic principles, namely the Information Competing
Process (ICP). Then, all the components are tied together under the stochastic
Variational Bayes framework for inference. We perform a thorough experimental
investigation for our approach using benchmark datasets on image
classification. As we experimentally show, the resulting networks yield
significant discriminative representation learning abilities. In addition, the
introduced paradigm allows for a principled investigation mechanism of the
emerging intermediate network representations.

Analyzing large-scale data from simulations of turbulent flows is memory
intensive, requiring significant resources. This major challenge highlights the
need for data compression techniques. In this study, we apply a
physics-informed Deep Learning technique based on vector quantization to
generate a discrete, low-dimensional representation of data from simulations of
three-dimensional turbulent flows. The deep learning framework is composed of
convolutional layers and incorporates physical constraints on the flow, such as
preserving incompressibility and global statistical characteristics of the
velocity gradients. The accuracy of the model is assessed using statistical,
comparison-based similarity and physics-based metrics. The training data set is
produced from Direct Numerical Simulation of an incompressible, statistically
stationary, isotropic turbulent flow. The performance of this lossy data
compression scheme is evaluated not only with unseen data from the stationary,
isotropic turbulent flow, but also with data from decaying isotropic
turbulence, and a Taylor-Green vortex flow. Defining the compression ratio (CR)
as the ratio of original data size to the compressed one, the results show that
our model based on vector quantization can offer CR $=85$ with a mean square
error (MSE) of $O(10^{-3})$, and predictions that faithfully reproduce the
statistics of the flow, except at the very smallest scales where there is some
loss. Compared to the recent study based on a conventional autoencoder where
compression is performed in a continuous space, our model improves the CR by
more than $30$ percent, and reduces the MSE by an order of magnitude. Our
compression model is an attractive solution for situations where fast, high
quality and low-overhead encoding and decoding of large data are required.

Effective space traffic management requires positive identification of
artificial satellites. Current methods for extracting object identification
from observed data require spatially resolved imagery which limits
identification to objects in low earth orbits. Most artificial satellites,
however, operate in geostationary orbits at distances which prohibit ground
based observatories from resolving spatial information. This paper demonstrates
an object identification solution leveraging modified residual convolutional
neural networks to map distance-invariant spectroscopic data to object
identity. We report classification accuracies exceeding 80% for a simulated
64-class satellite problem--even in the case of satellites undergoing constant,
random re-orientation. An astronomical observing campaign driven by these
results returned accuracies of 72% for a nine-class problem with an average of
100 examples per class, performing as expected from simulation. We demonstrate
the application of variational Bayesian inference by dropout, stochastic weight
averaging (SWA), and SWA-focused deep ensembling to measure classification
uncertainties--critical components in space traffic management where routine
decisions risk expensive space assets and carry geopolitical consequences.

The trend towards specialization of software and hardware - fuelled by the
end of Moore's law and the still accelerating interest in domain-specific
computing, such as machine learning - forces us to radically rethink our
compiler designs. The era of a universal compiler framework built around a
single one-size-fits-all intermediate representation (IR) is over. This
realization has sparked the creation of the MLIR compiler framework that
empowers compiler engineers to design and integrate IRs capturing specific
abstractions. MLIR provides a generic framework for SSA-based IRs, but it
doesn't help us to decide how we should design IRs that are easy to develop, to
work with and to combine into working compilers.
  To address the challenge of IR design, we advocate for a language-oriented
compiler design that understands IRs as formal programming languages and
enforces their correct use via an accompanying type system. We argue that
programming language techniques directly guide extensible IR designs and
provide a formal framework to reason about transforming between multiple IRs.
In this paper, we discuss the design of the Shine compiler that compiles the
high-level functional pattern-based data-parallel language RISE via a hybrid
functional-imperative intermediate language to C, OpenCL, and OpenMP.
  We compare our work directly with the closely related pattern-based Lift IR
and compiler. We demonstrate that our language-oriented compiler design results
in a more robust and predictable compiler that is extensible at various
abstraction levels. Our experimental evaluation shows that this compiler design
is able to generate high-performance GPU code.

Despite growing interest in probabilistic modeling approaches and
availability of learning tools, people with no or less statistical background
feel hesitant to use them. There is need for tools for communicating
probabilistic models to less experienced users more intuitively to help them
build, validate, use effectively or trust probabilistic models. Users'
comprehension of probabilistic models is vital in these cases and interactive
visualizations could enhance it. Although there are various studies evaluating
interactivity in Bayesian reasoning and available tools for visualizing the
sample-based distributions, we focus specifically on evaluating the effect of
interaction on users' comprehension of probabilistic models' structure. We
conducted a user study based on our Interactive Pair Plot for visualizing
models' distribution and conditioning the sample space graphically. Our results
suggest that improvements in the understanding of the interaction group are
most pronounced for more exotic structures, such as hierarchical models or
unfamiliar parameterizations in comparison to the static group. As the detail
of the inferred information increases, interaction does not lead to
considerably longer response times. Finally, interaction improves users'

Probabilistic models inform an increasingly broad range of business and
policy decisions ultimately made by people. Recent algorithmic, computational,
and software framework development progress facilitate the proliferation of
Bayesian probabilistic models, which characterise unobserved parameters by
their joint distribution instead of point estimates. While they can empower
decision makers to explore complex queries and to perform what-if-style
conditioning in theory, suitable visualisations and interactive tools are
needed to maximise users' comprehension and rational decision making under
uncertainty. In this paper, propose a protocol for quantitative evaluation of
Bayesian model visualisations and introduce a software framework implementing
this protocol to support standardisation in evaluation practice and facilitate
reproducibility. We illustrate the evaluation and analysis workflow on a user
study that explores whether making Boxplots and Hypothetical Outcome Plots
interactive can increase comprehension or rationality and conclude with design
guidelines for researchers looking to conduct similar studies in the future.

In tissue characterization and cancer diagnostics, multimodal imaging has
emerged as a powerful technique. Thanks to computational advances, large
datasets can be exploited to improve diagnosis and discover patterns in
pathologies. However, this requires efficient and scalable image retrieval
methods. Cross-modality image retrieval is particularly demanding, as images of
the same content captured in different modalities may display little common
information. We propose a content-based image retrieval system (CBIR) for
reverse (sub-)image search to retrieve microscopy images in one modality given
a corresponding image captured by a different modality, where images are not
aligned and share only few structures. We propose to combine deep learning to
generate representations which embed both modalities in a common space, with
classic, fast, and robust feature extractors (SIFT, SURF) to create a
bag-of-words model for efficient and reliable retrieval. Our
application-independent approach shows promising results on a publicly
available dataset of brightfield and second harmonic generation microscopy
images. We obtain 75.4% and 83.6% top-10 retrieval success for retrieval in one
or the other direction. Our proposed method significantly outperforms both
direct retrieval of the original multimodal (sub-)images, as well as their
corresponding generative adversarial network (GAN)-based image-to-image
translations. We establish that the proposed method performs better in
comparison with a recent sub-image retrieval toolkit, GAN-based image-to-image
translations, and learnt feature extractors for the downstream task of
cross-modal image retrieval. We highlight the shortcomings of the latter
methods and observe the importance of equivariance and invariance properties of
the learnt representations and feature extractors in the CBIR pipeline. Code
will be available at github.com/MIDA-group.

We present LSeg, a novel model for language-driven semantic image
segmentation. LSeg uses a text encoder to compute embeddings of descriptive
input labels (e.g., "grass" or "building") together with a transformer-based
image encoder that computes dense per-pixel embeddings of the input image. The
image encoder is trained with a contrastive objective to align pixel embeddings
to the text embedding of the corresponding semantic class. The text embeddings
provide a flexible label representation in which semantically similar labels
map to similar regions in the embedding space (e.g., "cat" and "furry"). This
allows LSeg to generalize to previously unseen categories at test time, without
retraining or even requiring a single additional training sample. We
demonstrate that our approach achieves highly competitive zero-shot performance
compared to existing zero- and few-shot semantic segmentation methods, and even
matches the accuracy of traditional segmentation algorithms when a fixed label
set is provided. Code and demo are available at
https://github.com/isl-org/lang-seg.

Reward hacking -- where RL agents exploit gaps in misspecified reward
functions -- has been widely observed, but not yet systematically studied. To
understand how reward hacking arises, we construct four RL environments with
misspecified rewards. We investigate reward hacking as a function of agent
capabilities: model capacity, action space resolution, observation space noise,
and training time. More capable agents often exploit reward misspecifications,
achieving higher proxy reward and lower true reward than less capable agents.
Moreover, we find instances of phase transitions: capability thresholds at
which the agent's behavior qualitatively shifts, leading to a sharp decrease in
the true reward. Such phase transitions pose challenges to monitoring the
safety of ML systems. To address this, we propose an anomaly detection task for
aberrant policies and offer several baseline detectors.

Human migration patterns influence the redistribution of population
characteristics over the geography and since such distributions are closely
related to social and economic outcomes, investigating the structure and
dynamics of internal migration plays a crucial role in understanding and
designing policies for such systems. We provide an in-depth investigation into
the structure and dynamics of the internal migration in Turkey from 2008 to
2020. We identify a set of classical migration laws and examine them via
various methods for signed network analysis, ego network analysis,
representation learning, temporal stability analysis, community detection, and
network visualization. The findings show that, in line with the classical
migration laws, most migration links are geographically bounded with several
exceptions involving cities with large economic activity, major migration flows
are countered with migration flows in the opposite direction, there are
well-defined migration routes, and the migration system is generally stable
over the investigated period. Apart from these general results, we also provide
unique and specific insights into Turkey. Overall, the novel toolset we employ
for the first time in the literature allows the investigation of selected
migration laws from a complex networks perspective and sheds light on future
migration research on different geographies.

Machine learning (ML) classification tasks can be carried out on a quantum
computer (QC) using Probabilistic Quantum Memory (PQM) and its extension,
Parameteric PQM (P-PQM) by calculating the Hamming distance between an input
pattern and a database of $r$ patterns containing $z$ features with $a$
distinct attributes.
  For accurate computations, the feature must be encoded using one-hot
encoding, which is memory-intensive for multi-attribute datasets with $a>2$. We
can easily represent multi-attribute data more compactly on a classical
computer by replacing one-hot encoding with label encoding. However, replacing
these encoding schemes on a QC is not straightforward as PQM and P-PQM operate
at the quantum bit level.
  We present an enhanced P-PQM, called EP-PQM, that allows label encoding of
data stored in a PQM data structure and reduces the circuit depth of the data
storage and retrieval procedures. We show implementations for an ideal QC and a
noisy intermediate-scale quantum (NISQ) device.
  Our complexity analysis shows that the EP-PQM approach requires $O\left(z
\log_2(a)\right)$ qubits as opposed to $O(za)$ qubits for P-PQM. EP-PQM also
requires fewer gates, reducing gate count from $O\left(rza\right)$ to
$O\left(rz\log_2(a)\right)$.
  For five datasets, we demonstrate that training an ML classification model
using EP-PQM requires 48% to 77% fewer qubits than P-PQM for datasets with
$a>2$. EP-PQM reduces circuit depth in the range of 60% to 96%, depending on
the dataset. The depth decreases further with a decomposed circuit, ranging
between 94% and 99%.
  EP-PQM requires less space; thus, it can train on and classify larger
datasets than previous PQM implementations on NISQ devices. Furthermore,
reducing the number of gates speeds up the classification and reduces the noise
associated with deep quantum circuits. Thus, EP-PQM brings us closer to
scalable ML on a NISQ device.

In this paper, we present a novel Bayesian online prediction algorithm for
the problem setting of ad hoc teamwork under partial observability (ATPO),
which enables on-the-fly collaboration with unknown teammates performing an
unknown task without needing a pre-coordination protocol. Unlike previous works
that assume a fully observable state of the environment, ATPO accommodates
partial observability, using the agent's observations to identify which task is
being performed by the teammates. Our approach assumes neither that the
teammate's actions are visible nor an environment reward signal. We evaluate
ATPO in three domains -- two modified versions of the Pursuit domain with
partial observability and the overcooked domain. Our results show that ATPO is
effective and robust in identifying the teammate's task from a large library of
possible tasks, efficient at solving it in near-optimal time, and scalable in
adapting to increasingly larger problem sizes.

NLP benchmarks have largely focused on short texts, such as sentences and
paragraphs, even though long texts comprise a considerable amount of natural
language in the wild. We introduce SCROLLS, a suite of tasks that require
reasoning over long texts. We examine existing long-text datasets, and handpick
ones where the text is naturally long, while prioritizing tasks that involve
synthesizing information across the input. SCROLLS contains summarization,
question answering, and natural language inference tasks, covering multiple
domains, including literature, science, business, and entertainment. Initial
baselines, including Longformer Encoder-Decoder, indicate that there is ample
room for improvement on SCROLLS. We make all datasets available in a unified
text-to-text format and host a live leaderboard to facilitate research on model
architecture and pretraining methods.

As the production of and reliance on datasets to produce automated
decision-making systems (ADS) increases, so does the need for processes for
evaluating and interrogating the underlying data. After launching the Dataset
Nutrition Label in 2018, the Data Nutrition Project has made significant
updates to the design and purpose of the Label, and is launching an updated
Label in late 2020, which is previewed in this paper. The new Label includes
context-specific Use Cases &Alerts presented through an updated design and user
interface targeted towards the data scientist profile. This paper discusses the
harm and bias from underlying training data that the Label is intended to
mitigate, the current state of the work including new datasets being labeled,
new and existing challenges, and further directions of the work, as well as
Figures previewing the new label.

Transfer-learning methods aim to improve performance in a data-scarce target
domain using a model pretrained on a data-rich source domain. A cost-efficient
strategy, linear probing, involves freezing the source model and training a new
classification head for the target domain. This strategy is outperformed by a
more costly but state-of-the-art method -- fine-tuning all parameters of the
source model to the target domain -- possibly because fine-tuning allows the
model to leverage useful information from intermediate layers which is
otherwise discarded by the later pretrained layers. We explore the hypothesis
that these intermediate layers might be directly exploited. We propose a
method, Head-to-Toe probing (Head2Toe), that selects features from all layers
of the source model to train a classification head for the target-domain. In
evaluations on the VTAB-1k, Head2Toe matches performance obtained with
fine-tuning on average while reducing training and storage cost hundred folds
or more, but critically, for out-of-distribution transfer, Head2Toe outperforms

Suppose that we have a regression problem with response variable Y in
$\mathbb{R}^d$ and predictor X in $\mathbb{R}^d$, for $d \geq 1$. In permuted
or unlinked regression we have access to separate unordered data on X and Y, as
opposed to data on (X,Y)-pairs in usual regression. So far in the literature
the case $d=1$ has received attention, see e.g., the recent papers by Rigollet
and Weed [Information & Inference, 8, 619--717] and Balabdaoui et al. [J. Mach.
Learn. Res., 22(172), 1--60]. In this paper, we consider the general
multivariate setting with $d \geq 1$. We show that the notion of cyclical
monotonicity of the regression function is sufficient for identification and
estimation in the permuted/unlinked regression model. We study permutation
recovery in the permuted regression setting and develop a computationally
efficient and easy-to-use algorithm for denoising based on the Kiefer-Wolfowitz
[Ann. Math. Statist., 27, 887--906] nonparametric maximum likelihood estimator
and techniques from the theory of optimal transport. We provide explicit upper
bounds on the associated mean squared denoising error for Gaussian noise. As in
previous work on the case $d = 1$, the permuted/unlinked setting involves slow
(logarithmic) rates of convergence rooting in the underlying deconvolution
problem. Numerical studies corroborate our theoretical analysis and show that
the proposed approach performs at least on par with the methods in the
aforementioned prior work in the case $d = 1$ while achieving substantial
reductions in terms of computational complexity.

We study what dataset assumption permits solving offline two-player zero-sum
Markov games. In stark contrast to the offline single-agent Markov decision
process, we show that the single strategy concentration assumption is
insufficient for learning the Nash equilibrium (NE) strategy in offline
two-player zero-sum Markov games. On the other hand, we propose a new
assumption named unilateral concentration and design a pessimism-type algorithm
that is provably efficient under this assumption. In addition, we show that the
unilateral concentration assumption is necessary for learning an NE strategy.
Furthermore, our algorithm can achieve minimax sample complexity without any
modification for two widely studied settings: dataset with uniform
concentration assumption and turn-based Markov games. Our work serves as an
important initial step towards understanding offline multi-agent reinforcement
learning.

Despite recent breakthroughs in Machine Learning for Natural Language
Processing, the Natural Language Inference (NLI) problems still constitute a
challenge. To this purpose we contribute a new dataset that focuses exclusively
on the factivity phenomenon; however, our task remains the same as other NLI
tasks, i.e. prediction of entailment, contradiction or neutral (ECN). The
dataset contains entirely natural language utterances in Polish and gathers
2,432 verb-complement pairs and 309 unique verbs. The dataset is based on the
National Corpus of Polish (NKJP) and is a representative sample in regards to
frequency of main verbs and other linguistic features (e.g. occurrence of
internal negation). We found that transformer BERT-based models working on
sentences obtained relatively good results ($\approx89\%$ F1 score). Even
though better results were achieved using linguistic features ($\approx91\%$ F1
score), this model requires more human labour (humans in the loop) because
features were prepared manually by expert linguists. BERT-based models
consuming only the input sentences show that they capture most of the
complexity of NLI/factivity. Complex cases in the phenomenon - e.g. cases with
entitlement (E) and non-factive verbs - remain an open issue for further
research.

Extremely large pre-trained language models (PTMs) such as GPT-3 are usually
released as a service. It allows users to design task-specific prompts to query
the PTMs through some black-box APIs. In such a scenario, which we call
Language-Model-as-a-Service (LMaaS), the gradients of PTMs are usually
unavailable. Can we optimize the task prompts by only accessing the model
inference APIs? This paper proposes the black-box tuning framework to optimize
the continuous prompt prepended to the input text via derivative-free
optimization. Instead of optimizing in the original high-dimensional prompt
space, which is intractable for traditional derivative-free optimization, we
perform optimization in a randomly generated subspace due to the low intrinsic
dimensionality of large PTMs. The experimental results show that the black-box
tuning with RoBERTa on a few labeled samples not only significantly outperforms
manual prompt and GPT-3's in-context learning, but also surpasses the
gradient-based counterparts, i.e., prompt tuning and full model tuning.

Private set intersection is an important problem with implications in many
areas, ranging from remote diagnostics to private contact discovery. In this
work, we consider the case of two-party PSI in the honest-but-curious setting.
We propose a protocol that solves the server-aided PSI problem using delegated
blind quantum computing. More specifically, the proposed protocol allows Alice
and Bob (who do not have any quantum computational resources or quantum memory)
to interact with Steve (who has a quantum computer) in order for Alice and Bob
to obtain set intersection such that privacy is preserved. In particular, Steve
learns nothing about the clients' input, output, or desired computation. The
proposed protocol is correct, secure and blind against a malicious server, and
characterized by a quantum communication complexity that is linear in the input
size.

Session-based recommendation (SBR) has drawn increasingly research attention
in recent years, due to its great practical value by only exploiting the
limited user behavior history in the current session. Existing methods
typically learn the session embedding at the item level, namely, aggregating
the embeddings of items with or without the attention weights assigned to
items. However, they ignore the fact that a user's intent on adopting an item
is driven by certain factors of the item (e.g., the leading actors of an
movie). In other words, they have not explored finer-granularity interests of
users at the factor level to generate the session embedding, leading to
sub-optimal performance. To address the problem, we propose a novel method
called Disentangled Graph Neural Network (Disen-GNN) to capture the session
purpose with the consideration of factor-level attention on each item.
Specifically, we first employ the disentangled learning technique to cast item
embeddings into the embedding of multiple factors, and then use the gated graph
neural network (GGNN) to learn the embedding factor-wisely based on the item
adjacent similarity matrix computed for each factor. Moreover, the distance
correlation is adopted to enhance the independence between each pair of
factors. After representing each item with independent factors, an attention
mechanism is designed to learn user intent to different factors of each item in
the session. The session embedding is then generated by aggregating the item
embeddings with attention weights of each item's factors. To this end, our
model takes user intents at the factor level into account to infer the user
purpose in a session. Extensive experiments on three benchmark datasets
demonstrate the superiority of our method over existing methods.

Statistical shape modeling is an essential tool for the quantitative analysis
of anatomical populations. Point distribution models (PDMs) represent the
anatomical surface via a dense set of correspondences, an intuitive and
easy-to-use shape representation for subsequent applications. These
correspondences are exhibited in two coordinate spaces: the local coordinates
describing the geometrical features of each individual anatomical surface and
the world coordinates representing the population-level statistical shape
information after removing global alignment differences across samples in the
given cohort. We propose a deep-learning-based framework that simultaneously
learns these two coordinate spaces directly from the volumetric images. The
proposed joint model serves a dual purpose; the world correspondences can
directly be used for shape analysis applications, circumventing the heavy
pre-processing and segmentation involved in traditional PDM models.
Additionally, the local correspondences can be used for anatomy segmentation.
We demonstrate the efficacy of this joint model for both shape modeling
applications on two datasets and its utility in inferring the anatomical
surface.

Semi-supervised learning has received attention from researchers, as it
allows one to exploit the structure of unlabeled data to achieve competitive
classification results with much fewer labels than supervised approaches. The
Local and Global Consistency (LGC) algorithm is one of the most well-known
graph-based semi-supervised (GSSL) classifiers. Notably, its solution can be
written as a linear combination of the known labels. The coefficients of this
linear combination depend on a parameter $\alpha$, determining the decay of the
reward over time when reaching labeled vertices in a random walk. In this work,
we discuss how removing the self-influence of a labeled instance may be
beneficial, and how it relates to leave-one-out error. Moreover, we propose to
minimize this leave-one-out loss with automatic differentiation. Within this
framework, we propose methods to estimate label reliability and diffusion rate.
Optimizing the diffusion rate is more efficiently accomplished with a spectral
representation. Results show that the label reliability approach competes with
robust L1-norm methods and that removing diagonal entries reduces the risk of
overfitting and leads to suitable criteria for parameter selection.

We present simple conditions for Bayesian consistency in the supremum metric.
The key to the technique is a triangle inequality which allows us to explicitly
use weak convergence, a consequence of the standard Kullback--Leibler support
condition for the prior. A further condition is to ensure that smoothed
versions of densities are not too far from the original density, thus dealing
with densities which could track the data too closely. A key result of the
paper is that we demonstrate supremum consistency using weaker conditions
compared to those currently used to secure $\mathbb{L}_1$ consistency.

Conversational Information Retrieval (CIR) is an emerging field of
Information Retrieval (IR) at the intersection of interactive IR and dialogue
systems for open domain information needs. In order to optimize these
interactions and enhance the user experience, it is necessary to improve IR
models by taking into account sequential heterogeneous user-system
interactions. Reinforcement learning has emerged as a paradigm particularly
suited to optimize sequential decision making in many domains and has recently
appeared in IR. However, training these systems by reinforcement learning on
users is not feasible. One solution is to train IR systems on user simulations
that model the behavior of real users. Our contribution is twofold: 1)reviewing
the literature on user modeling and user simulation for information access, and
2) discussing the different research perspectives for user simulations in the
context of CIR

Purpose: To evaluate an iterative learning approach for enhanced performance
of Robust Artificial-neural-networks for K-space Interpolation (RAKI), when
only a limited amount of training data (auto-calibration signals, ACS) are
available for accelerated standard 2D imaging. Methods: In a first step, the
RAKI model was optimized for the case of strongly limited training data amount.
In the iterative learning approach (termed iterative RAKI), the optimized RAKI
model is initially trained using original and augmented ACS obtained from a
linear parallel imaging reconstruction. Subsequently, the RAKI convolution
filters are refined iteratively using original and augmented ACS extracted from
the previous RAKI reconstruction. Evaluation was carried out on 200
retrospectively undersampled in-vivo datasets from the fastMRI neuro database
with different contrast settings. Results: For limited training data (18 and 22
ACS lines for R=4 and R=5, respectively), iterative RAKI outperforms standard
RAKI by reducing residual artefacts and yields strong noise suppression when
compared to standard parallel imaging, underlined by quantitative
reconstruction quality metrics. In combination with a phase constraint, further
reconstruction improvements can be achieved. Additionally, iterative RAKI shows
better performance than both GRAPPA and RAKI in case of pre-scan calibration
with varying contrast between training- and undersampled data. Conclusion: The
iterative learning approach with RAKI benefits from standard RAKIs well known
noise suppression feature but requires less original training data for the
accurate reconstruction of standard 2D images thereby improving net
acceleration.

Cardiovascular disease is a large worldwide healthcare issue; symptoms often
present suddenly with minimal warning. The electrocardiogram (ECG) is a fast,
simple and reliable method of evaluating the health of the heart, by measuring
electrical activity recorded through electrodes placed on the skin. ECGs often
need to be analyzed by a cardiologist, taking time which could be spent on
improving patient care and outcomes. Because of this, automatic ECG
classification systems using machine learning have been proposed, which can
learn complex interactions between ECG features and use this to detect
abnormalities. However, algorithms built for this purpose often fail to
generalize well to unseen data, reporting initially impressive results which
drop dramatically when applied to new environments. Additionally, machine
learning algorithms suffer a "black-box" issue, in which it is difficult to
determine how a decision has been made. This is vital for applications in
healthcare, as clinicians need to be able to verify the process of evaluation
in order to trust the algorithm. This paper proposes a method for visualizing
model decisions across each class in the MIT-BIH arrhythmia dataset, using
adapted saliency maps averaged across complete classes to determine what
patterns are being learned. We do this by building two algorithms based on
state-of-the-art models. This paper highlights how these maps can be used to
find problems in the model which could be affecting generalizability and model
performance. Comparing saliency maps across complete classes gives an overall
impression of confounding variables or other biases in the model, unlike what
would be highlighted when comparing saliency maps on an ECG-by-ECG basis.

We consider the task of reconstructing polytopes with fixed facet directions
from finitely many support function evaluations. We show that for a fixed
simplicial normal fan the least-squares estimate is given by a convex quadratic
program. We study the geometry of the solution set and give a combinatorial
characterization for the uniqueness of the reconstruction in this case. We
provide an algorithm that, under mild assumptions, converges to the unknown
input shape as the number of noisy support function evaluations increases. We
also discuss limitations of our results if the restriction on the normal fan is
removed.

A new generation of increasingly autonomous and self-learning embodied
systems is about to be developed. When deploying embodied systems into a
real-life context we face various engineering challenges, as it is crucial to
coordinate the behavior of embodied systems in a beneficial manner, ensure
their compatibility with our human-centered social values, and design
verifiably safe and reliable human-machine interaction. We are arguing that
traditional systems engineering is coming to a climacteric from embedded to
embodied systems, and with assuring the trustworthiness of dynamic federations
of situationally aware, intent-driven, explorative, ever-evolving, largely
non-predictable, and increasingly autonomous embodied systems in uncertain,
complex, and unpredictable real-world contexts. We are therefore identifying a
number of urgent systems challenges for trustworthy embodied systems, including
robust and human-centric AI, cognitive architectures, uncertainty
quantification, trustworthy self-integration, and continual analysis and
assurance.

Decisions made by various Artificial Intelligence (AI) systems greatly
influence our day-to-day lives. With the increasing use of AI systems, it
becomes crucial to know that they are fair, identify the underlying biases in
their decision-making, and create a standardized framework to ascertain their
fairness. In this paper, we propose a novel Fairness Score to measure the
fairness of a data-driven AI system and a Standard Operating Procedure (SOP)
for issuing Fairness Certification for such systems. Fairness Score and audit
process standardization will ensure quality, reduce ambiguity, enable
comparison and improve the trustworthiness of the AI systems. It will also
provide a framework to operationalise the concept of fairness and facilitate
the commercial deployment of such systems. Furthermore, a Fairness Certificate
issued by a designated third-party auditing agency following the standardized
process would boost the conviction of the organizations in the AI systems that
they intend to deploy. The Bias Index proposed in this paper also reveals
comparative bias amongst the various protected attributes within the dataset.
To substantiate the proposed framework, we iteratively train a model on biased
and unbiased data using multiple datasets and check that the Fairness Score and
the proposed process correctly identify the biases and judge the fairness.

Biometric recognition is a highly adopted technology to support different
kinds of applications, ranging from security and access control applications to
low enforcement applications. However, such systems raise serious privacy and
data protection concerns. Misuse of data, compromising the privacy of
individuals and/or authorized processing of data may be irreversible and could
have severe consequences on the individual's rights to privacy and data
protection. This is partly due to the lack of methods and guidance for the
integration of data protection and privacy by design in the system development
process. In this paper, we present an example of privacy and data protection
best practices to provide more guidance for data controllers and developers on
how to comply with the legal obligation for data protection. These privacy and
data protection best practices and considerations are based on the lessons
learned from the SMart mobILity at the European land borders (SMILE) project.

Learning problems commonly exhibit an interesting feedback mechanism wherein
the population data reacts to competing decision makers' actions. This paper
formulates a new game theoretic framework for this phenomenon, called
"multi-player performative prediction". We focus on two distinct solution
concepts, namely (i) performatively stable equilibria and (ii) Nash equilibria
of the game. The latter equilibria are arguably more informative, but can be
found efficiently only when the game is monotone. We show that under mild
assumptions, the performatively stable equilibria can be found efficiently by a
variety of algorithms, including repeated retraining and the repeated
(stochastic) gradient method. We then establish transparent sufficient
conditions for strong monotonicity of the game and use them to develop
algorithms for finding Nash equilibria. We investigate derivative free methods
and adaptive gradient algorithms wherein each player alternates between
learning a parametric description of their distribution and gradient steps on
the empirical risk. Synthetic and semi-synthetic numerical experiments
illustrate the results.

We have developed a graphic user interface (GUI), ExBrainable, dedicated to
convolutional neural networks (CNN) model training and visualization in
electroencephalography (EEG) decoding. Available functions include model
training, evaluation, and parameter visualization in terms of temporal and
spatial representations. We demonstrate these functions using a well-studied
public dataset of motor-imagery EEG and compare the results with existing
knowledge of neuroscience. The primary objective of ExBrainable is to provide a
fast, simplified, and user-friendly solution of EEG decoding for investigators
across disciplines to leverage cutting-edge methods in brain/neuroscience
research.

Studying the cosmological sources at their cosmological rest-frames is
crucial to track the cosmic history and properties of compact objects. In view
of the increasing data volume of existing and upcoming telescopes/detectors, we
here construct a 1--dimensional convolutional neural network (CNN) with a
residual neural network (ResNet) structure to estimate the redshift of quasars
in Sloan Digital Sky Survey IV (SDSS-IV) catalog from DR16 quasar-only (DR16Q)
of eBOSS on a broad range of signal-to-noise ratios, named \code{FNet}. Owing
to its $24$ convolutional layers and the ResNet structure with different kernel
sizes of $500$, $200$ and $15$, FNet is able to discover the "\textit{local}"
and "\textit{global}" patterns in the whole sample of spectra by a
self-learning procedure. It reaches the accuracy of 97.0$\%$ for the velocity
difference for redshift, $|\Delta\nu|< 6000~ \rm km/s$ and 98.0$\%$ for
$|\Delta\nu|< 12000~ \rm km/s$. While \code{QuasarNET}, which is a standard CNN
adopted in the SDSS routine and is constructed by 4 convolutional layers (no
ResNet structure), with kernel sizes of $10$, to measure the redshift via
identifying seven emission lines (\textit{local} patterns), fails in estimating
redshift of $\sim 1.3\%$ of visually inspected quasars in DR16Q catalog, and it
gives 97.8$\%$ for $|\Delta\nu|< 6000~ \rm km/s$ and 97.9$\%$ for $|\Delta\nu|<
12000~ \rm km/s$. Hence, FNet provides similar accuracy to \code{QuasarNET},
but it is applicable for a wider range of SDSS spectra, especially for those
missing the clear emission lines exploited by \code{QuasarNET}. These
properties of \code{FNet}, together with the fast predictive power of machine
learning, allow \code{FNet} to be a more accurate alternative for the pipeline
redshift estimator and can make it practical in the upcoming catalogs to reduce
the number of spectra to visually inspect.

We formulate an adaptive version of Kelly's horse model in which the gambler
learns from past race results using Bayesian inference. A known asymptotic
scaling for the difference between the growth rate of the gambler and the
optimal growth rate, known as the gambler'sregret, is recovered. We show how
this adaptive strategy is related to the universal portfolio strategy, and we
build improved adaptive strategies in which the gambler exploits information
contained in the bookmaker odds distribution to reduce his/her initial loss of
the capital during the learning phase.

Accurate temperature measurements are essential for the proper monitoring and
control of industrial furnaces. However, measurement uncertainty is a risk for
such a critical parameter. Certain instrumental and environmental errors must
be considered when using spectral-band radiation thermometry techniques, such
as the uncertainty in the emissivity of the target surface, reflected radiation
from surrounding objects, or atmospheric absorption and emission, to name a
few. Undesired contributions to measured radiation can be isolated using
measurement models, also known as error-correction models. This paper presents
a methodology for budgeting significant sources of error and uncertainty during
temperature measurements in a petrochemical furnace scenario. A continuous
monitoring system is also presented, aided by a deep-learning-based measurement
correction model, to allow domain experts to analyze the furnace's operation in
real-time. To validate the proposed system's functionality, a real-world
application case in a petrochemical plant is presented. The proposed solution
demonstrates the viability of precise industrial furnace monitoring, thereby
increasing operational security and improving the efficiency of such
energy-intensive systems.

BERT has revolutionized the NLP field by enabling transfer learning with
large language models that can capture complex textual patterns, reaching the
state-of-the-art for an expressive number of NLP applications. For text
classification tasks, BERT has already been extensively explored. However,
aspects like how to better cope with the different embeddings provided by the
BERT output layer and the usage of language-specific instead of multilingual
models are not well studied in the literature, especially for the Brazilian
Portuguese language. The purpose of this article is to conduct an extensive
experimental study regarding different strategies for aggregating the features
produced in the BERT output layer, with a focus on the sentiment analysis task.
The experiments include BERT models trained with Brazilian Portuguese corpora
and the multilingual version, contemplating multiple aggregation strategies and
open-source datasets with predefined training, validation, and test partitions
to facilitate the reproducibility of the results. BERT achieved the highest
ROC-AUC values for the majority of cases as compared to TF-IDF. Nonetheless,
TF-IDF represents a good trade-off between the predictive performance and

The recent trend of integrating multi-source Chest X-Ray datasets to improve
automated diagnostics raises concerns that models learn to exploit
source-specific correlations to improve performance by recognizing the source
domain of an image rather than the medical pathology. We hypothesize that this
effect is enforced by and leverages label-imbalance across the source domains,
i.e, prevalence of a disease corresponding to a source. Therefore, in this
work, we perform a thorough study of the effect of label-imbalance in
multi-source training for the task of pneumonia detection on the widely used
ChestX-ray14 and CheXpert datasets. The results highlight and stress the
importance of using more faithful and transparent self-explaining models for
automated diagnosis, thus enabling the inherent detection of spurious learning.
They further illustrate that this undesirable effect of learning spurious
correlations can be reduced considerably when ensuring label-balanced source
domain datasets.

This study proposes a method to develop neural models of the morphological
analyzer for Japanese Hiragana sentences using the Bi-LSTM CRF model.
Morphological analysis is a technique that divides text data into words and
assigns information such as parts of speech. This technique plays an essential
role in downstream applications in Japanese natural language processing systems
because the Japanese language does not have word delimiters between words.
Hiragana is a type of Japanese phonogramic characters, which is used for texts
for children or people who cannot read Chinese characters. Morphological
analysis of Hiragana sentences is more difficult than that of ordinary Japanese
sentences because there is less information for dividing. For morphological
analysis of Hiragana sentences, we demonstrated the effectiveness of
fine-tuning using a model based on ordinary Japanese text and examined the
influence of training data on texts of various genres.

In information retrieval (IR) systems, trends and users' interests may change
over time, altering either the distribution of requests or contents to be
recommended. Since neural ranking approaches heavily depend on the training
data, it is crucial to understand the transfer capacity of recent IR approaches
to address new domains in the long term. In this paper, we first propose a
dataset based upon the MSMarco corpus aiming at modeling a long stream of
topics as well as IR property-driven controlled settings. We then in-depth
analyze the ability of recent neural IR models while continually learning those
streams. Our empirical study highlights in which particular cases catastrophic
forgetting occurs (e.g., level of similarity between tasks, peculiarities on
text length, and ways of learning models) to provide future directions in terms
of model design.

Pawlak rough set and neighborhood rough set are the two most common rough set
theoretical models. Pawlak can use equivalence classes to represent knowledge,
but it cannot process continuous data; neighborhood rough sets can process
continuous data, but it loses the ability of using equivalence classes to
represent knowledge. To this end, this paper presents a granular-ball rough set
based on the granular-ball computing. The granular-ball rough set can
simultaneously represent Pawlak rough sets, and the neighborhood rough set, so
as to realize the unified representation of the two. This makes the
granular-ball rough set not only can deal with continuous data, but also can
use equivalence classes for knowledge representation. In addition, we propose
an implementation algorithms of granular-ball rough sets. The experimental
results on benchmark datasets demonstrate that, due to the combination of the
robustness and adaptability of the granular-ball computing, the learning
accuracy of the granular-ball rough set has been greatly improved compared with
the Pawlak rough set and the traditional neighborhood rough set. The
granular-ball rough set also outperforms nine popular or the state-of-the-art
feature selection methods.

The progress made in code modeling has been tremendous in recent years thanks
to the design of natural language processing learning approaches based on
state-of-the-art model architectures. Nevertheless, we believe that the current
state-of-the-art does not focus enough on the full potential that data may
bring to a learning process in software engineering. Our vision articulates on
the idea of leveraging multi-modal learning approaches to modeling the
programming world. In this paper, we investigate one of the underlying idea of
our vision whose objective based on concept graphs of identifiers aims at
leveraging high-level relationships between domain concepts manipulated through
particular language constructs. In particular, we propose to enhance an
existing pretrained language model of code by joint-learning it with a graph
neural network based on our concept graphs. We conducted a preliminary
evaluation that shows gain of effectiveness of the models for code search using
a simple joint-learning method and prompts us to further investigate our
research vision.

Due to the significant advancement of Natural Language Processing and
Computer Vision-based models, Visual Question Answering (VQA) systems are
becoming more intelligent and advanced. However, they are still error-prone
when dealing with relatively complex questions. Therefore, it is important to
understand the behaviour of the VQA models before adopting their results. In
this paper, we introduce an interpretability approach for VQA models by
generating counterfactual images. Specifically, the generated image is supposed
to have the minimal possible change to the original image and leads the VQA
model to give a different answer. In addition, our approach ensures that the
generated image is realistic. Since quantitative metrics cannot be employed to
evaluate the interpretability of the model, we carried out a user study to
assess different aspects of our approach. In addition to interpreting the
result of VQA models on single images, the obtained results and the discussion
provides an extensive explanation of VQA models' behaviour.

Heterogeneous graph neural networks can represent information of
heterogeneous graphs with excellent ability. Recently, self-supervised learning
manner is researched which learns the unique expression of a graph through a
contrastive learning method. In the absence of labels, this learning methods
show great potential. However, contrastive learning relies heavily on positive
and negative pairs, and generating high-quality pairs from heterogeneous graphs
is difficult. In this paper, in line with recent innovations in self-supervised
learning called BYOL or bootstrapping, we introduce a that can generate good
representations without generating large number of pairs. In addition, paying
attention to the fact that heterogeneous graphs can be viewed from two
perspectives, network schema and meta-path views, high-level expressions in the
graphs are captured and expressed. The proposed model showed state-of-the-art
performance than other methods in various real world datasets.

Emerging two terminal nanoscale memory devices, known as memristors, have
over the past decade demonstrated great potential for implementing energy
efficient neuro-inspired computing architectures. As a result, a wide-range of
technologies have been developed that in turn are described via distinct
empirical models. This diversity of technologies requires the establishment of
versatile tools that can enable designers to translate memristors' attributes
in novel neuro-inspired topologies. In this paper, we present NeuroPack, a
modular, algorithm level Python-based simulation platform that can support
studies of memristor neuro-inspired architectures for performing online
learning or offline classification. The NeuroPack environment is designed with
versatility being central, allowing the user to chose from a variety of neuron
models, learning rules and memristors models. Its hierarchical structure,
empowers NeuroPack to predict any memristor state changes and the corresponding
neural network behavior across a variety of design decisions and user
parameters options. The use of NeuroPack is demonstrated herein via an
application example of performing handwritten digit classification with the
MNIST dataset and an existing empirical model for metal-oxide memristors.

We present an open-source and extensible knowledge extraction toolkit DeepKE,
supporting complicated low-resource, document-level and multimodal scenarios in
the knowledge base population. DeepKE implements various information extraction
tasks, including named entity recognition, relation extraction and attribute
extraction. With a unified framework, DeepKE allows developers and researchers
to customize datasets and models to extract information from unstructured data
according to their requirements. Specifically, DeepKE not only provides various
functional modules and model implementation for different tasks and scenarios
but also organizes all components by consistent frameworks to maintain
sufficient modularity and extensibility. We release the source code at GitHub
in https://github.com/zjunlp/DeepKE with Google Colab tutorials and
comprehensive documents for beginners. Besides, we present an online system in
http://deepke.openkg.cn/EN/re_doc_show.html for real-time extraction of various
tasks, and a demo video.

Graph Neural Networks (GNNs) have become the state-of-the-art method for many
applications on graph structured data. GNNs are a model for graph
representation learning, which aims at learning to generate low dimensional
node embeddings that encapsulate structural and feature-related information.
GNNs are usually trained in an end-to-end fashion, leading to highly
specialized node embeddings. While this approach achieves great results in the
single-task setting, the generation of node embeddings that can be used to
perform multiple tasks (with performance comparable to single-task models) is
still an open problem. We propose the use of meta-learning to allow the
training of a GNN model capable of producing multi-task node embeddings. In
particular, we exploit the properties of optimization-based meta-learning to
learn GNNs that can produce general node representations by learning parameters
that can quickly (i.e. with a few steps of gradient descent) adapt to multiple
tasks. Our experiments show that the embeddings produced by a model trained
with our purposely designed meta-learning procedure can be used to perform
multiple tasks with comparable or, surprisingly, even higher performance than
both single-task and multi-task end-to-end models.

The problem of attacks on neural networks through input modification (i.e.,
adversarial examples) has attracted much attention recently. Being relatively
easy to generate and hard to detect, these attacks pose a security breach that
many suggested defenses try to mitigate. However, the evaluation of the effect
of attacks and defenses commonly relies on traditional classification metrics,
without adequate adaptation to adversarial scenarios. Most of these metrics are
accuracy-based, and therefore may have a limited scope and low distinctive
power. Other metrics do not consider the unique characteristics of neural
networks functionality, or measure the effect of the attacks indirectly (e.g.,
through the complexity of their generation). In this paper, we present two
metrics which are specifically designed to measure the effect of attacks, or
the recovery effect of defenses, on the output of neural networks in multiclass
classification tasks. Inspired by the normalized discounted cumulative gain and
the reciprocal rank metrics used in information retrieval literature, we treat
the neural network predictions as ranked lists of results. Using additional
information about the probability of the rank enabled us to define novel
metrics that are suited to the task at hand. We evaluate our metrics using
various attacks and defenses on a pretrained VGG19 model and the ImageNet
dataset. Compared to the common classification metrics, our proposed metrics
demonstrate superior informativeness and distinctiveness.

In general, biometry-based control systems may not rely on individual
expected behavior or cooperation to operate appropriately. Instead, such
systems should be aware of malicious procedures for unauthorized access
attempts. Some works available in the literature suggest addressing the problem
through gait recognition approaches. Such methods aim at identifying human
beings through intrinsic perceptible features, despite dressed clothes or
accessories. Although the issue denotes a relatively long-time challenge, most
of the techniques developed to handle the problem present several drawbacks
related to feature extraction and low classification rates, among other issues.
However, deep learning-based approaches recently emerged as a robust set of
tools to deal with virtually any image and computer-vision related problem,
providing paramount results for gait recognition as well. Therefore, this work
provides a surveyed compilation of recent works regarding biometric detection
through gait recognition with a focus on deep learning approaches, emphasizing
their benefits, and exposing their weaknesses. Besides, it also presents
categorized and characterized descriptions of the datasets, approaches, and
architectures employed to tackle associated constraints.

3D ultrasound (3DUS) becomes more interesting for target tracking in
radiation therapy due to its capability to provide volumetric images in
real-time without using ionizing radiation. It is potentially usable for
tracking without using fiducials. For this, a method for learning meaningful
representations would be useful to recognize anatomical structures in different
time frames in representation space (r-space). In this study, 3DUS patches are
reduced into a 128-dimensional r-space using conventional autoencoder,
variational autoencoder and sliced-wasserstein autoencoder. In the r-space, the
capability of separating different ultrasound patches as well as recognizing
similar patches is investigated and compared based on a dataset of liver
images. Two metrics to evaluate the tracking capability in the r-space are
proposed. It is shown that ultrasound patches with different anatomical
structures can be distinguished and sets of similar patches can be clustered in
r-space. The results indicate that the investigated autoencoders have different
levels of usability for target tracking in 3DUS.

Although modern automatic speech recognition (ASR) systems can achieve high
performance, they may produce errors that weaken readers' experience and do
harm to downstream tasks. To improve the accuracy and reliability of ASR
hypotheses, we propose a cross-modal post-processing system for speech
recognizers, which 1) fuses acoustic features and textual features from
different modalities, 2) joints a confidence estimator and an error corrector
in multi-task learning fashion and 3) unifies error correction and utterance
rejection modules. Compared with single-modal or single-task models, our
proposed system is proved to be more effective and efficient. Experiment result
shows that our post-processing system leads to more than 10% relative reduction
of character error rate (CER) for both single-speaker and multi-speaker speech
on our industrial ASR system, with about 1.7ms latency for each token, which
ensures that extra latency introduced by post-processing is acceptable in
streaming speech recognition.

Several image processing tasks, such as image classification and object
detection, have been significantly improved using Convolutional Neural Networks
(CNN). Like ResNet and EfficientNet, many architectures have achieved
outstanding results in at least one dataset by the time of their creation. A
critical factor in training concerns the network's regularization, which
prevents the structure from overfitting. This work analyzes several
regularization methods developed in the last few years, showing significant
improvements for different CNN models. The works are classified into three main
areas: the first one is called "data augmentation", where all the techniques
focus on performing changes in the input data. The second, named "internal
changes", which aims to describe procedures to modify the feature maps
generated by the neural network or the kernels. The last one, called "label",
concerns transforming the labels of a given input. This work presents two main
differences comparing to other available surveys about regularization: (i) the
first concerns the papers gathered in the manuscript, which are not older than
five years, and (ii) the second distinction is about reproducibility, i.e., all
works refered here have their code available in public repositories or they
have been directly implemented in some framework, such as TensorFlow or Torch.

Risk scores are widely used for clinical decision making and commonly
generated from logistic regression models. Machine-learning-based methods may
work well for identifying important predictors, but such 'black box' variable
selection limits interpretability, and variable importance evaluated from a
single model can be biased. We propose a robust and interpretable variable
selection approach using the recently developed Shapley variable importance
cloud (ShapleyVIC) that accounts for variability across models. Our approach
evaluates and visualizes overall variable contributions for in-depth inference
and transparent variable selection, and filters out non-significant
contributors to simplify model building steps. We derive an ensemble variable
ranking from variable contributions, which is easily integrated with an
automated and modularized risk score generator, AutoScore, for convenient
implementation. In a study of early death or unplanned readmission, ShapleyVIC
selected 6 of 41 candidate variables to create a well-performing model, which
had similar performance to a 16-variable model from machine-learning-based
ranking.

There is a knowledge gap regarding which types of failures robots undergo in
domestic settings and how these failures influence customer experience. We
classified 10,072 customer reviews of small utilitarian domestic robots on
Amazon by the robotic failures described in them, grouping failures into twelve
types and three categories (Technical, Interaction, and Service). We identified
sources and types of failures previously overlooked in the literature,
combining them into an updated failure taxonomy. We analyzed their frequencies
and relations to customer star ratings. Results indicate that for utilitarian
domestic robots, Technical failures were more detrimental to customer
experience than Interaction or Service failures. Issues with Task Completion
and Robustness & Resilience were commonly reported and had the most significant
negative impact. Future failure-prevention and response strategies should
address the technical ability of the robot to meet functional goals, operate
and maintain structural integrity over time. Usability and interaction design
were less detrimental to customer experience, indicating that customers may be
more forgiving of failures that impact these aspects for the robots and
practical uses examined. Further, we developed a Natural Language Processing
model capable of predicting whether a customer review contains content that
describes a failure and the type of failure it describes. With this knowledge,
designers and researchers of robotic systems can prioritize design and
development efforts towards essential issues.

Here, we use Machine Learning (ML) algorithms to update and improve the
efficiencies of fitting GARCH model parameters to empirical data. We employ an
Artificial Neural Network (ANN) to predict the parameters of these models. We
present a fitting algorithm for GARCH-normal(1,1) models to predict one of the
model's parameters, $\alpha_1$ and then use the analytical expressions for the
fourth order standardised moment, $\Gamma_4$ and the unconditional second order
moment, $\sigma^2$ to fit the other two parameters; $\beta_1$ and $\alpha_0$,
respectively. The speed of fitting of the parameters and quick implementation
of this approach allows for real time tracking of GARCH parameters. We further
show that different inputs to the ANN namely, higher order standardised moments
and the autocovariance of time series can be used for fitting model parameters
using the ANN, but not always with the same level of accuracy.

The filtering equations govern the evolution of the conditional distribution
of a signal process given partial, and possibly noisy, observations arriving
sequentially in time. Their numerical approximation plays a central role in
many real-life applications, including numerical weather prediction, finance
and engineering. One of the classical approaches to approximate the solution of
the filtering equations is to use a PDE inspired method, called the
splitting-up method, initiated by Gyongy, Krylov, LeGland, among other
contributors. This method, and other PDE based approaches, have particular
applicability for solving low-dimensional problems. In this work we combine
this method with a neural network representation. The new methodology is used
to produce an approximation of the unnormalised conditional distribution of the
signal process. We further develop a recursive normalisation procedure to
recover the normalised conditional distribution of the signal process. The new
scheme can be iterated over multiple time steps whilst keeping its asymptotic
unbiasedness property intact.
  We test the neural network approximations with numerical approximation
results for the Kalman and Benes filter.

With the proliferation of IoT devices, researchers have developed a variety
of IoT device identification methods with the assistance of machine learning.
Nevertheless, the security of these identification methods mostly depends on
collected training data. In this research, we propose a novel attack strategy
named IoTGAN to manipulate an IoT device's traffic such that it can evade
machine learning based IoT device identification. In the development of IoTGAN,
we have two major technical challenges: (i) How to obtain the discriminative
model in a black-box setting, and (ii) How to add perturbations to IoT traffic
through the manipulative model, so as to evade the identification while not
influencing the functionality of IoT devices. To address these challenges, a
neural network based substitute model is used to fit the target model in
black-box settings, it works as a discriminative model in IoTGAN. A
manipulative model is trained to add adversarial perturbations into the IoT
device's traffic to evade the substitute model. Experimental results show that
IoTGAN can successfully achieve the attack goals. We also develop efficient
countermeasures to protect machine learning based IoT device identification
from been undermined by IoTGAN.

Outcomes of data-driven AI models cannot be assumed to be always correct. To
estimate the uncertainty in these outcomes, the uncertainty wrapper framework
has been proposed, which considers uncertainties related to model fit, input
quality, and scope compliance. Uncertainty wrappers use a decision tree
approach to cluster input quality related uncertainties, assigning inputs
strictly to distinct uncertainty clusters. Hence, a slight variation in only
one feature may lead to a cluster assignment with a significantly different
uncertainty. Our objective is to replace this with an approach that mitigates
hard decision boundaries of these assignments while preserving
interpretability, runtime complexity, and prediction performance. Five
approaches were selected as candidates and integrated into the uncertainty
wrapper framework. For the evaluation based on the Brier score, datasets for a
pedestrian detection use case were generated using the CARLA simulator and
YOLOv3. All integrated approaches achieved a softening, i.e., smoothing, of
uncertainty estimation. Yet, compared to decision trees, they are not so easy
to interpret and have higher runtime complexity. Moreover, some components of
the Brier score impaired while others improved. Most promising regarding the
Brier score were random forests. In conclusion, softening hard decision tree
boundaries appears to be a trade-off decision.

We analyse and compare various empirical models of wall pressure spectra
beneath turbulent boundary layers and propose an alternative machine learning
approach using Artificial Neural Networks (ANN). The analysis and the training
of the ANN are performed on data from experiments and high-fidelity simulations
by various authors, covering a wide range of flow conditions. We present a
methodology to extract all the turbulent boundary layer parameters required by
these models, also considering flows experiencing strong adverse pressure
gradients. Moreover, the database is explored to unveil important dependencies
within the boundary layer parameters and to propose a possible set of features
from which the ANN should predict the wall pressure spectra. The results show
that the ANN outperforms traditional models in adverse pressure gradients, and
its predictive capabilities generalise better over the range of investigated
conditions. The analysis is completed with a deep ensemble approach for
quantifying the uncertainties in the model prediction and integrated gradient
analysis of the model sensitivity to its inputs. Uncertainties and
sensitivities allow for identifying the regions where new training data would
be most beneficial to the model's accuracy, thus opening the path towards a
self-calibrating modelling approach.

This paper contributes a method to design a novel navigation planner
exploiting a learning-based collision prediction network. The neural network is
tasked to predict the collision cost of each action sequence in a predefined
motion primitives library in the robot's velocity-steering angle space, given
only the current depth image and the estimated linear and angular velocities of
the robot. Furthermore, we account for the uncertainty of the robot's partial
state by utilizing the Unscented Transform and the uncertainty of the neural
network model by using Monte Carlo dropout. The uncertainty-aware collision
cost is then combined with the goal direction given by a global planner in
order to determine the best action sequence to execute in a receding horizon
manner. To demonstrate the method, we develop a resilient small flying robot
integrating lightweight sensing and computing resources. A set of simulation
and experimental studies, including a field deployment, in both cluttered and
perceptually-challenging environments is conducted to evaluate the quality of
the prediction network and the performance of the proposed planner.

In an autonomous driving system, perception - identification of features and
objects from the environment - is crucial. In autonomous racing, high speeds
and small margins demand rapid and accurate detection systems. During the race,
the weather can change abruptly, causing significant degradation in perception,
resulting in ineffective manoeuvres. In order to improve detection in adverse
weather, deep-learning-based models typically require extensive datasets
captured in such conditions - the collection of which is a tedious, laborious,
and costly process. However, recent developments in CycleGAN architectures
allow the synthesis of highly realistic scenes in multiple weather conditions.
To this end, we introduce an approach of using synthesised adverse condition
datasets in autonomous racing (generated using CycleGAN) to improve the
performance of four out of five state-of-the-art detectors by an average of
42.7 and 4.4 mAP percentage points in the presence of night-time conditions and
droplets, respectively. Furthermore, we present a comparative analysis of five
object detectors - identifying the optimal pairing of detector and training
data for use during autonomous racing in challenging conditions.

With the development of traffic prediction technology, spatiotemporal
prediction models have attracted more and more attention from academia
communities and industry. However, most existing researches focus on reducing
model's prediction error but ignore the error caused by the uneven distribution
of spatial events within a region. In this paper, we study a region
partitioning problem, namely optimal grid size selection problem (OGSS), which
aims to minimize the real error of spatiotemporal prediction models by
selecting the optimal grid size. In order to solve OGSS, we analyze the upper
bound of real error of spatiotemporal prediction models and minimize the real
error by minimizing its upper bound. Through in-depth analysis, we find that
the upper bound of real error will decrease then increase when the number of
model grids increase from 1 to the maximum allowed value. Then, we propose two
algorithms, namely Ternary Search and Iterative Method, to automatically find
the optimal grid size. Finally, the experiments verify that the error of
prediction has the same trend as its upper bound, and the change trend of the
upper bound of real error with respect to the increase of the number of model
grids will decrease then increase. Meanwhile, in a case study, by selecting the
optimal grid size, the order dispatching results of a state-of-the-art
prediction-based algorithm can be improved up to 13.6%, which shows the
effectiveness of our methods on tuning the region partition for spatiotemporal
prediction models.

Now a days, UAVs such as drones are greatly used for various purposes like
that of capturing and target detection from ariel imagery etc. Easy access of
these small ariel vehicles to public can cause serious security threats. For
instance, critical places may be monitored by spies blended in public using
drones. Study in hand proposes an improved and efficient Deep Learning based
autonomous system which can detect and track very small drones with great
precision. The proposed system consists of a custom deep learning model Tiny
YOLOv3, one of the flavors of very fast object detection model You Look Only
Once (YOLO) is built and used for detection. The object detection algorithm
will efficiently the detect the drones. The proposed architecture has shown
significantly better performance as compared to the previous YOLO version. The
improvement is observed in the terms of resource usage and time complexity. The
performance is measured using the metrics of recall and precision that are 93%
and 91% respectively.

Nearest neighbor search supports important applications in many domains, such
as database, machine learning, computer vision. Since the computational cost
for accurate search is too high, the community turned to the research of
approximate nearest neighbor search (ANNS). Among them, graph-based algorithm
is one of the most important branches. Research by Fu et al. shows that the
algorithms based on Monotonic Search Network (MSNET), such as NSG and NSSG,
have achieved the state-of-the-art search performance in efficiency. The MSNET
is dedicated to achieving monotonic search with minimal out-degree of nodes to
pursue high efficiency. However, the current MSNET designs did not optimize the
probability of the monotonic search, and the lower bound of the probability is
only 50%. If they fail in monotonic search stage, they have to suffer
tremendous backtracking cost to achieve the required accuracy. This will cause
performance problems in search efficiency. To address this problem, we propose
(r,p)-MSNET, which achieves guaranteed probability on monotonic search. Due to
the high building complexity of a strict (r,p)-MSNET, we propose TBSG, which is
an approximation with low complexity. Experiment conducted on four
million-scaled datasets show that TBSG outperforms existing state-of-the-art
graph-based algorithms in search efficiency. Our code has been released on
Github.

Magnetic resonance imaging (MRI) is an important non-invasive clinical tool
that can produce high-resolution and reproducible images. However, a long
scanning time is required for high-quality MR images, which leads to exhaustion
and discomfort of patients, inducing more artefacts due to voluntary movements
of the patients and involuntary physiological movements. To accelerate the
scanning process, methods by k-space undersampling and deep learning based
reconstruction have been popularised. This work introduced SwinMR, a novel Swin
transformer based method for fast MRI reconstruction. The whole network
consisted of an input module (IM), a feature extraction module (FEM) and an
output module (OM). The IM and OM were 2D convolutional layers and the FEM was
composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D
convolutional layers. The RSTB consisted of a series of Swin transformer layers
(STLs). The shifted windows multi-head self-attention (W-MSA/SW-MSA) of STL was
performed in shifted windows rather than the multi-head self-attention (MSA) of
the original transformer in the whole image space. A novel multi-channel loss
was proposed by using the sensitivity maps, which was proved to reserve more
textures and details. We performed a series of comparative studies and ablation
studies in the Calgary-Campinas public brain MR dataset and conducted a
downstream segmentation experiment in the Multi-modal Brain Tumour Segmentation
Challenge 2017 dataset. The results demonstrate our SwinMR achieved
high-quality reconstruction compared with other benchmark methods, and it shows
great robustness with different undersampling masks, under noise interruption
and on different datasets. The code is publicly available at
https://github.com/ayanglab/SwinMR.

With the increased penetration of wind energy into the power grid, it has
become increasingly important to be able to predict the expected power
production for larger wind farms. Deep learning (DL) models can learn complex
patterns in the data and have found wide success in predicting wake losses and
expected power production. This paper proposes a modular framework for
attention-based graph neural networks (GNN), where attention can be applied to
any desired component of a graph block. The results show that the model
significantly outperforms a multilayer perceptron (MLP) and a bidirectional
LSTM (BLSTM) model, while delivering performance on-par with a vanilla GNN
model. Moreover, we argue that the proposed graph attention architecture can
easily adapt to different applications by offering flexibility into the desired
attention operations to be used, which might depend on the specific
application. Through analysis of the attention weights, it was showed that
employing attention-based GNNs can provide insights into what the models learn.
In particular, the attention networks seemed to realise turbine dependencies
that aligned with some physical intuition about wake losses.

Landslides have been a regular occurrence and an alarming threat to human
life and property in the era of anthropogenic global warming. An early
prediction of landslide susceptibility using a data-driven approach is a demand
of time. In this study, we explored the eloquent features that best describe
landslide susceptibility with state-of-the-art machine learning methods. In our
study, we employed state-of-the-art machine learning algorithms including
XgBoost, LR, KNN, SVM, Adaboost for landslide susceptibility prediction. To
find the best hyperparameters of each individual classifier for optimized
performance, we have incorporated the Grid Search method, with 10 Fold
Cross-Validation. In this context, the optimized version of XgBoost
outperformed all other classifiers with a Cross-validation Weighted F1 score of
94.62%. Followed by this empirical evidence, we explored the XgBoost classifier
by incorporating TreeSHAP and identified eloquent features such as SLOPE,
ELEVATION, TWI that complement the performance of the XGBoost classifier mostly
and features such as LANDUSE, NDVI, SPI which has less effect on models
performance. According to the TreeSHAP explanation of features, we selected the
9 most significant landslide causal factors out of 15. Evidently, an optimized
version of XgBoost along with feature reduction by 40%, has outperformed all
other classifiers in terms of popular evaluation metrics with a
Cross-Validation Weighted F1 score of 95.01% on the training and AUC score of
97%.

Automated audio captioning aims to describe audio data with captions using
natural language. Existing methods often employ an encoder-decoder structure,
where the attention-based decoder (e.g., Transformer decoder) is widely used
and achieves state-of-the-art performance. Although this method effectively
captures global information within audio data via the self-attention mechanism,
it may ignore the event with short time duration, due to its limitation in
capturing local information in an audio signal, leading to inaccurate
prediction of captions. To address this issue, we propose a method using the
pretrained audio neural networks (PANNs) as the encoder and local information
assisted attention-free Transformer (LocalAFT) as the decoder. The novelty of
our method is in the proposal of the LocalAFT decoder, which allows local
information within an audio signal to be captured while retaining the global
information. This enables the events of different duration, including short
duration, to be captured for more precise caption generation. Experiments show
that our method outperforms the state-of-the-art methods in Task 6 of the DCASE
2021 Challenge with the standard attention-based decoder for caption

This paper presents an experiment of automatically scoring handwritten
descriptive answers in the trial tests for the new Japanese university entrance
examination, which were made for about 120,000 examinees in 2017 and 2018.
There are about 400,000 answers with more than 20 million characters. Although
all answers have been scored by human examiners, handwritten characters are not
labelled. We present our attempt to adapt deep neural network-based handwriting
recognizers trained on a labelled handwriting dataset into this unlabeled
answer set. Our proposed method combines different training strategies,
ensembles multiple recognizers, and uses a language model built from a large
general corpus to avoid overfitting into specific data. In our experiment, the
proposed method records character accuracy of over 97% using about 2,000
verified labelled answers that account for less than 0.5% of the dataset. Then,
the recognized answers are fed into a pre-trained automatic scoring system
based on the BERT model without correcting misrecognized characters and
providing rubric annotations. The automatic scoring system achieves from 0.84
to 0.98 of Quadratic Weighted Kappa (QWK). As QWK is over 0.8, it represents
acceptable similarity of scoring between the automatic scoring system and the
human examiners. These results are promising for further research on end-to-end
automatic scoring of descriptive answers.

Deep learning-based image retrieval techniques for the loop closure detection
demonstrate satisfactory performance. However, it is still challenging to
achieve high-level performance based on previously trained models in different
geographical regions. This paper addresses the problem of their deployment with
simultaneous localization and mapping (SLAM) systems in the new environment.
The general baseline approach uses additional information, such as GPS,
sequential keyframes tracking, and re-training the whole environment to enhance
the recall rate. We propose a novel approach for improving image retrieval
based on previously trained models. We present an intelligent method, MAQBOOL,
to amplify the power of pre-trained models for better image recall and its
application to real-time multiagent SLAM systems. We achieve comparable image
retrieval results at a low descriptor dimension (512-D), compared to the high
descriptor dimension (4096-D) of state-of-the-art methods. We use spatial
information to improve the recall rate in image retrieval on pre-trained
models.

Stethoscope-recorded chest sounds provide the opportunity for remote
cardio-respiratory health monitoring of neonates. However, reliable monitoring
requires high-quality heart and lung sounds. This paper presents novel
Non-negative Matrix Factorisation (NMF) and Non-negative Matrix
Co-Factorisation (NMCF) methods for neonatal chest sound separation. To assess
these methods and compare with existing single-source separation methods, an
artificial mixture dataset was generated comprising of heart, lung and noise
sounds. Signal-to-noise ratios were then calculated for these artificial
mixtures. These methods were also tested on real-world noisy neonatal chest
sounds and assessed based on vital sign estimation error and a signal quality
score of 1-5 developed in our previous works. Additionally, the computational
cost of all methods was assessed to determine the applicability for real-time
processing. Overall, both the proposed NMF and NMCF methods outperform the next
best existing method by 2.7dB to 11.6dB for the artificial dataset and 0.40 to
1.12 signal quality improvement for the real-world dataset. The median
processing time for the sound separation of a 10s recording was found to be
28.3s for NMCF and 342ms for NMF. Because of stable and robust performance, we
believe that our proposed methods are useful to denoise neonatal heart and lung
sound in a real-world environment. Codes for proposed and existing methods can
be found at: https://github.com/egrooby-monash/Heart-and-Lung-Sound-Separation.

Digital cameras transform sensor RAW readings into RGB images by means of
their Image Signal Processor (ISP). Computational photography tasks such as
image denoising and colour constancy are commonly performed in the RAW domain,
in part due to the inherent hardware design, but also due to the appealing
simplicity of noise statistics that result from the direct sensor readings.
Despite this, the availability of RAW images is limited in comparison with the
abundance and diversity of available RGB data. Recent approaches have attempted
to bridge this gap by estimating the RGB to RAW mapping: handcrafted
model-based methods that are interpretable and controllable usually require
manual parameter fine-tuning, while end-to-end learnable neural networks
require large amounts of training data, at times with complex training
procedures, and generally lack interpretability and parametric control. Towards
addressing these existing limitations, we present a novel hybrid model-based
and data-driven ISP that builds on canonical ISP operations and is both
learnable and interpretable. Our proposed invertible model, capable of
bidirectional mapping between RAW and RGB domains, employs end-to-end learning
of rich parameter representations, i.e. dictionaries, that are free from direct
parametric supervision and additionally enable simple and plausible data
augmentation. We evidence the value of our data generation process by extensive
experiments under both RAW image reconstruction and RAW image denoising tasks,
obtaining state-of-the-art performance in both. Additionally, we show that our
ISP can learn meaningful mappings from few data samples, and that denoising
models trained with our dictionary-based data augmentation are competitive
despite having only few or zero ground-truth labels.

We study the problem of Differentially Private Stochastic Convex Optimization
(DP-SCO) with heavy-tailed data. Specifically, we focus on the $\ell_1$-norm
linear regression in the $\epsilon$-DP model. While most of the previous work
focuses on the case where the loss function is Lipschitz, here we only need to
assume the variates has bounded moments. Firstly, we study the case where the
$\ell_2$ norm of data has bounded second order moment. We propose an algorithm
which is based on the exponential mechanism and show that it is possible to
achieve an upper bound of $\tilde{O}(\sqrt{\frac{d}{n\epsilon}})$ (with high
probability). Next, we relax the assumption to bounded $\theta$-th order moment
with some $\theta\in (1, 2)$ and show that it is possible to achieve an upper
bound of $\tilde{O}(({\frac{d}{n\epsilon}})^\frac{\theta-1}{\theta})$. Our
algorithms can also be extended to more relaxed cases where only each
coordinate of the data has bounded moments, and we can get an upper bound of
$\tilde{O}({\frac{d}{\sqrt{n\epsilon}}})$ and
$\tilde{O}({\frac{d}{({n\epsilon})^\frac{\theta-1}{\theta}}})$ in the second
and $\theta$-th moment case respectively.

Data imputation has been extensively explored to solve the missing data
problem. The dramatically increasing volume of incomplete data makes the
imputation models computationally infeasible in many real-life applications. In
this paper, we propose an effective scalable imputation system named SCIS to
significantly speed up the training of the differentiable generative
adversarial imputation models under accuracy-guarantees for large-scale
incomplete data. SCIS consists of two modules, differentiable imputation
modeling (DIM) and sample size estimation (SSE). DIM leverages a new masking
Sinkhorn divergence function to make an arbitrary generative adversarial
imputation model differentiable, while for such a differentiable imputation
model, SSE can estimate an appropriate sample size to ensure the user-specified
imputation accuracy of the final model. Extensive experiments upon several
real-life large-scale datasets demonstrate that, our proposed system can
accelerate the generative adversarial model training by 7.1x. Using around 7.6%
samples, SCIS yields competitive accuracy with the state-of-the-art imputation
methods in a much shorter computation time.

In this paper, we develop a multivariate regression model and a neural
network model to predict the Reynolds number (Re) and Nusselt number in
turbulent thermal convection. We compare their predictions with those of
earlier models of convection: Grossmann-Lohse~[Phys. Rev. Lett. \textbf{86},
3316 (2001)], revised Grossmann-Lohse~[Phys. Fluids \textbf{33}, 015113
(2021)], and Pandey-Verma [Phys. Rev. E \textbf{94}, 053106 (2016)] models. We
observe that although the predictions of all the models are quite close to each
other, the machine learning models developed in this work provide the best
match with the experimental and numerical results.

As a fundamental data format representing spatial information, depth map is
widely used in signal processing and computer vision fields. Massive amount of
high precision depth maps are produced with the rapid development of equipment
like laser scanner or LiDAR. Therefore, it is urgent to explore a new
compression method with better compression ratio for high precision depth maps.
Utilizing the wide spread deep learning environment, we propose an end-to-end
learning-based lossless compression method for high precision depth maps. The
whole process is comprised of two sub-processes, named pre-processing of depth
maps and deep lossless compression of processed depth maps. The deep lossless
compression network consists of two sub-networks, named lossy compression
network and lossless compression network. We leverage the concept of
pseudo-residual to guide the generation of distribution for residual and avoid
introducing context models. Our end-to-end lossless compression network
achieves competitive performance over engineered codecs and has low

Hierarchical multi-granularity classification (HMC) assigns hierarchical
multi-granularity labels to each object and focuses on encoding the label
hierarchy, e.g., ["Albatross", "Laysan Albatross"] from coarse-to-fine levels.
However, the definition of what is fine-grained is subjective, and the image
quality may affect the identification. Thus, samples could be observed at any
level of the hierarchy, e.g., ["Albatross"] or ["Albatross", "Laysan
Albatross"], and examples discerned at coarse categories are often neglected in
the conventional setting of HMC. In this paper, we study the HMC problem in
which objects are labeled at any level of the hierarchy. The essential designs
of the proposed method are derived from two motivations: (1) learning with
objects labeled at various levels should transfer hierarchical knowledge
between levels; (2) lower-level classes should inherit attributes related to
upper-level superclasses. The proposed combinatorial loss maximizes the
marginal probability of the observed ground truth label by aggregating
information from related labels defined in the tree hierarchy. If the observed
label is at the leaf level, the combinatorial loss further imposes the
multi-class cross-entropy loss to increase the weight of fine-grained
classification loss. Considering the hierarchical feature interaction, we
propose a hierarchical residual network (HRN), in which granularity-specific
features from parent levels acting as residual connections are added to
features of children levels. Experiments on three commonly used datasets
demonstrate the effectiveness of our approach compared to the state-of-the-art
HMC approaches and fine-grained visual classification (FGVC) methods exploiting
the label hierarchy.

This work aims to reproduce results from the CVPR 2020 paper by Gidaris et
al. Self-supervised learning (SSL) is used to learn feature representations of
an image using an unlabeled dataset. This work proposes to use bag-of-words
(BoW) deep feature descriptors as a self-supervised learning target to learn
robust, deep representations. BowNet is trained to reconstruct the histogram of
visual words (ie. the deep BoW descriptor) of a reference image when presented
a perturbed version of the image as input. Thus, this method aims to learn
perturbation-invariant and context-aware image features that can be useful for
few-shot tasks or supervised downstream tasks. In the paper, the author
describes BowNet as a network consisting of a convolutional feature extractor
$\Phi(\cdot)$ and a Dense-softmax layer $\Omega(\cdot)$ trained to predict BoW
features from images. After BoW training, the features of $\Phi$ are used in
downstream tasks. For this challenge we were trying to build and train a
network that could reproduce the CIFAR-100 accuracy improvements reported in
the original paper. However, we were unsuccessful in reproducing an accuracy
improvement comparable to what the authors mentioned. This could be for a
variety of factors and we believe that time constraints were the primary
bottleneck.

A major limitation of fuzzy or neuro-fuzzy systems is their failure to deal
with high-dimensional datasets. This happens primarily due to the use of
T-norm, particularly, product or minimum (or a softer version of it). Thus,
there are hardly any work dealing with datasets with dimensions more than
hundred or so. Here, we propose a neuro-fuzzy framework that can handle
datasets with dimensions even more than 7000! In this context, we propose an
adaptive softmin (Ada-softmin) which effectively overcomes the drawbacks of
``numeric underflow" and ``fake minimum" that arise for existing fuzzy systems
while dealing with high-dimensional problems. We call it an Adaptive
Takagi-Sugeno-Kang (AdaTSK) fuzzy system. We then equip the AdaTSK system to
perform feature selection and rule extraction in an integrated manner. In this
context, a novel gate function is introduced and embedded only in the
consequent parts, which can determine the useful features and rules, in two
successive phases of learning. Unlike conventional fuzzy rule bases, we design
an enhanced fuzzy rule base (En-FRB), which maintains adequate rules but does
not grow the number of rules exponentially with dimension that typically
happens for fuzzy neural networks. The integrated Feature Selection and Rule
Extraction AdaTSK (FSRE-AdaTSK) system consists of three sequential phases: (i)
feature selection, (ii) rule extraction, and (iii) fine tuning. The
effectiveness of the FSRE-AdaTSK is demonstrated on 19 datasets of which five
are in more than 2000 dimension including two with dimension greater than 7000.
This may be the first time fuzzy systems are realized for classification
involving more than 7000 input features.

Assessment of myocardial viability is essential in diagnosis and treatment
management of patients suffering from myocardial infarction, and classification
of pathology on myocardium is the key to this assessment. This work defines a
new task of medical image analysis, i.e., to perform myocardial pathology
segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR)
images, which was first proposed in the MyoPS challenge, in conjunction with
MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images,
allowing algorithms to combine the complementary information from the three CMR
sequences for pathology segmentation. In this article, we provide details of
the challenge, survey the works from fifteen participants and interpret their
methods according to five aspects, i.e., preprocessing, data augmentation,
learning strategy, model architecture and post-processing. In addition, we
analyze the results with respect to different factors, in order to examine the
key obstacles and explore potential of solutions, as well as to provide a
benchmark for future research. We conclude that while promising results have
been reported, the research is still in the early stage, and more in-depth
exploration is needed before a successful application to the clinics. Note that
MyoPS data and evaluation tool continue to be publicly available upon
registration via its homepage
(www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

Scene-text recognition is remarkably better in Latin languages than the
non-Latin languages due to several factors like multiple fonts, simplistic
vocabulary statistics, updated data generation tools, and writing systems. This
paper examines the possible reasons for low accuracy by comparing English
datasets with non-Latin languages. We compare various features like the size
(width and height) of the word images and word length statistics. Over the last
decade, generating synthetic datasets with powerful deep learning techniques
has tremendously improved scene-text recognition. Several controlled
experiments are performed on English, by varying the number of (i) fonts to
create the synthetic data and (ii) created word images. We discover that these
factors are critical for the scene-text recognition systems. The English
synthetic datasets utilize over 1400 fonts while Arabic and other non-Latin
datasets utilize less than 100 fonts for data generation. Since some of these
languages are a part of different regions, we garner additional fonts through a
region-based search to improve the scene-text recognition models in Arabic and
Devanagari. We improve the Word Recognition Rates (WRRs) on Arabic MLT-17 and
MLT-19 datasets by 24.54% and 2.32% compared to previous works or baselines. We
achieve WRR gains of 7.88% and 3.72% for IIIT-ILST and MLT-19 Devanagari
datasets.

There has been a surge of interest in developing robust estimators for models
with heavy-tailed and bounded variance data in statistics and machine learning,
while few works impose unbounded variance. This paper proposes two type of
robust estimators, the ridge log-truncated M-estimator and the elastic net
log-truncated M-estimator. The first estimator is applied to convex regressions
such as quantile regression and generalized linear models, while the other one
is applied to high dimensional non-convex learning problems such as regressions
via
  deep neural networks. Simulations and real data analysis demonstrate the
{robustness} of log-truncated estimations over standard estimations.

Scene text recognition in low-resource Indian languages is challenging
because of complexities like multiple scripts, fonts, text size, and
orientations. In this work, we investigate the power of transfer learning for
all the layers of deep scene text recognition networks from English to two
common Indian languages. We perform experiments on the conventional CRNN model
and STAR-Net to ensure generalisability. To study the effect of change in
different scripts, we initially run our experiments on synthetic word images
rendered using Unicode fonts. We show that the transfer of English models to
simple synthetic datasets of Indian languages is not practical. Instead, we
propose to apply transfer learning techniques among Indian languages due to
similarity in their n-gram distributions and visual features like the vowels
and conjunct characters. We then study the transfer learning among six Indian
languages with varying complexities in fonts and word length statistics. We
also demonstrate that the learned features of the models transferred from other
Indian languages are visually closer (and sometimes even better) to the
individual model features than those transferred from English. We finally set
new benchmarks for scene-text recognition on Hindi, Telugu, and Malayalam
datasets from IIIT-ILST and Bangla dataset from MLT-17 by achieving 6%, 5%, 2%,
and 23% gains in Word Recognition Rates (WRRs) compared to previous works. We
further improve the MLT-17 Bangla results by plugging in a novel correction
BiLSTM into our model. We additionally release a dataset of around 440 scene
images containing 500 Gujarati and 2535 Tamil words. WRRs improve over the
baselines by 8%, 4%, 5%, and 3% on the MLT-19 Hindi and Bangla datasets and the
Gujarati and Tamil datasets.

Significant progress has been made in automatic road extra-ction or
segmentation based on deep learning, but there are still margins to improve in
terms of the completeness and connectivity of the results. This is mainly due
to the challenges of large intra-class variances, ambiguous inter-class
distinctions, and occlusions from shadows, trees, and buildings. Therefore,
being able to perceive global context and model geometric information is
essential to further improve the accuracy of road segmentation. In this paper,
we design a novel dual-branch encoding block CoSwin which exploits the
capability of global context modeling of Swin Transformer and that of local
feature extraction of ResNet. Furthermore, we also propose a context-guided
filter block named CFilter, which can filter out context-independent noisy
features for better reconstructing of the details. We use CoSwin and CFilter in
a U-shaped network architecture. Experiments on Massachusetts and CHN6-CUG
datasets show that the proposed method outperforms other state-of-the-art
methods on the metrics of F1, IoU, and OA. Further analysis reveals that the
improvement in accuracy comes from better integrity and connectivity of
segmented roads.

Pedestrian detection is the cornerstone of many vision based applications,
starting from object tracking to video surveillance and more recently,
autonomous driving. With the rapid development of deep learning in object
detection, pedestrian detection has achieved very good performance in
traditional single-dataset training and evaluation setting. However, in this
study on generalizable pedestrian detectors, we show that, current pedestrian
detectors poorly handle even small domain shifts in cross-dataset evaluation.
We attribute the limited generalization to two main factors, the method and the
current sources of data. Regarding the method, we illustrate that biasness
present in the design choices (e.g anchor settings) of current pedestrian
detectors are the main contributing factor to the limited generalization. Most
modern pedestrian detectors are tailored towards target dataset, where they do
achieve high performance in traditional single training and testing pipeline,
but suffer a degrade in performance when evaluated through cross-dataset
evaluation. Consequently, a general object detector performs better in
cross-dataset evaluation compared with state of the art pedestrian detectors,
due to its generic design. As for the data, we show that the autonomous driving
benchmarks are monotonous in nature, that is, they are not diverse in scenarios
and dense in pedestrians. Therefore, benchmarks curated by crawling the web
(which contain diverse and dense scenarios), are an efficient source of
pre-training for providing a more robust representation. Accordingly, we
propose a progressive fine-tuning strategy which improves generalization. Code
and models can accessed at https://github.com/hasanirtiza/Pedestron.

Multi-tenant machine learning services have become emerging data-intensive
workloads in data centers with heavy usage of GPU resources. Due to the large
scale, many tuning parameters and heavy resource usage, it is usually
impractical to evaluate and benchmark those machine learning services on real
clusters. In this demonstration, we present AnalySIM, a cluster simulator that
allows efficient design explorations for multi-tenant machine learning
services. Specifically, by trace-driven cluster workload simulation, AnalySIM
can easily test and analyze various scheduling policies in a number of
performance metrics such as GPU resource utilization. AnalySIM simulates the
cluster computational resource based on both physical topology and logical
partition. The tool has been used in SenseTime to understand the impact of
different scheduling policies with the trace from a real production cluster of
over 1000 GPUs. We find that preemption and migration are able to significantly
reduce average job completion time and mitigate the resource fragmentation
problem.

Cultural items like songs have an important impact in creating and
reinforcing stereotypes, biases, and discrimination. But the actual nature of
such items is often less transparent. Take songs, for example. Are lyrics
biased against women? And how have any such biases changed over time? Natural
language processing of a quarter of a million songs over 50 years quantifies
misogyny. Women are less likely to be associated with desirable traits (i.e.,
competence), and while this bias has decreased, it persists. Ancillary analyses
further suggest that song lyrics may help drive shifts in societal stereotypes
towards women, and that lyrical shifts are driven by male artists (as female
artists were less biased to begin with). Overall, these results shed light on
cultural evolution, subtle measures of bias and discrimination, and how natural
language processing and machine learning can provide deeper insight into
stereotypes and cultural change.

In a neuron network, synapses update individually using local information,
allowing for entirely decentralized learning. In contrast, elements in an
artificial neural network (ANN) are typically updated simultaneously using a
central processor. Here we investigate the feasibility and effect of
asynchronous learning in a recently introduced decentralized, physics-driven
learning network. We show that desynchronizing the learning process does not
degrade performance for a variety of tasks in an idealized simulation. In
experiment, desynchronization actually improves performance by allowing the
system to better explore the discretized state space of solutions. We draw an
analogy between asynchronicity and mini-batching in stochastic gradient
descent, and show that they have similar effects on the learning process.
Desynchronizing the learning process establishes physics-driven learning
networks as truly fully distributed learning machines, promoting better
performance and scalability in deployment.

Federated learning often suffers from unstable and slow convergence due to
heterogeneous characteristics of participating clients. Such tendency is
aggravated when the client participation ratio is low since the information
collected from the clients at each round is prone to be more inconsistent. To
tackle the challenge, we propose a novel federated learning framework, which
improves the stability of the server-side aggregation step, which is achieved
by sending the clients an accelerated model estimated with the global gradient
to guide the local gradient updates. Our algorithm naturally aggregates and
conveys the global update information to participants with no additional
communication cost and does not require to store the past models in the
clients. We also regularize local update to further reduce the bias and improve
the stability of local updates. We perform comprehensive empirical studies on
real data under various settings and demonstrate the remarkable performance of
the proposed method in terms of accuracy and communication-efficiency compared
to the state-of-the-art methods, especially with low client participation
rates. Our code is available at https://github.com/ ninigapa0/FedAGM

Applying knowledge distillation to personalized cross-silo federated learning
can well alleviate the problem of user heterogeneity. This approach, however,
requires a proxy dataset, which is difficult to obtain in the real world.
Moreover, the global model based on parameter averaging will lead to the
leakage of user privacy. We introduce a distributed three-player GAN to
implement datafree co-distillation between clients. This technique mitigates
the user heterogeneity problem and better protects user privacy. We confirmed
that thefake samples generated by GAN can make federated distillation more
efficient and robust, and the co-distillation can achieve good performance for
individual clients on the basis of obtaining global knowledge. Our extensive
experiments on benchmark datasets demonstrate the superior generalization
performance of the proposed methods, compared with the state-of-the-art.

As the deep learning techniques have expanded to real-world recommendation
tasks, many deep neural network based Collaborative Filtering (CF) models have
been developed to project user-item interactions into latent feature space,
based on various neural architectures, such as multi-layer perceptron,
auto-encoder and graph neural networks. However, the majority of existing
collaborative filtering systems are not well designed to handle missing data.
Particularly, in order to inject the negative signals in the training phase,
these solutions largely rely on negative sampling from unobserved user-item
interactions and simply treating them as negative instances, which brings the
recommendation performance degradation. To address the issues, we develop a
Collaborative Reflection-Augmented Autoencoder Network (CRANet), that is
capable of exploring transferable knowledge from observed and unobserved
user-item interactions. The network architecture of CRANet is formed of an
integrative structure with a reflective receptor network and an information
fusion autoencoder module, which endows our recommendation framework with the
ability of encoding implicit user's pairwise preference on both interacted and
non-interacted items. Additionally, a parametric regularization-based
tied-weight scheme is designed to perform robust joint training of the
two-stage CRANet model. We finally experimentally validate CRANet on four
diverse benchmark datasets corresponding to two recommendation tasks, to show
that debiasing the negative signals of user-item interactions improves the
performance as compared to various state-of-the-art recommendation techniques.
Our source code is available at https://github.com/akaxlh/CRANet.

This study aims to identify chicken eggs fertility using the support vector
machine (SVM) classifier method. The classification basis used the first-order
statistical (FOS) parameters as feature extraction in the identification
process. This research was developed based on the process's identification
process, which is still manual (conventional). Although currently there are
many technologies in the identification process, they still need development.
Thus, this research is one of the developments in the field of image processing
technology. The sample data uses datasets from previous studies with a total of
100 egg images. The egg object in the image is a single object. From these
data, the classification of each fertile and infertile egg is 50 image data.
Chicken egg image data became input in image processing, with the initial
process is segmentation. This initial segmentation aims to get the cropped
image according to the object. The cropped image is repaired using image
preprocessing with grayscaling and image enhancement methods. This method
(image enhancement) used two combination methods: contrast limited adaptive
histogram equalization (CLAHE) and histogram equalization (HE). The improved
image becomes the input for feature extraction using the FOS method. The FOS
uses five parameters, namely mean, entropy, variance, skewness, and kurtosis.
The five parameters entered into the SVM classifier method to identify the
fertility of chicken eggs. The results of these experiments, the method
proposed in the identification process has a success percentage of 84.57%.
Thus, the implementation of this method can be used as a reference for future
research improvements. In addition, it may be possible to use a second-order
feature extraction method to improve its accuracy and improve supervised
learning for classification.

Existing distributed cooperative multi-agent reinforcement learning (MARL)
frameworks usually assume undirected coordination graphs and communication
graphs while estimating a global reward via consensus algorithms for policy
evaluation. Such a framework may induce expensive communication costs and
exhibit poor scalability due to requirement of global consensus. In this work,
we study MARLs with directed coordination graphs, and propose a distributed RL
algorithm where the local policy evaluations are based on local value
functions. The local value function of each agent is obtained by local
communication with its neighbors through a directed learning-induced
communication graph, without using any consensus algorithm. A zeroth-order
optimization (ZOO) approach based on parameter perturbation is employed to
achieve gradient estimation. By comparing with existing ZOO-based RL
algorithms, we show that our proposed distributed RL algorithm guarantees high
scalability. A distributed resource allocation example is shown to illustrate

The quasiparticle effective mass $m^\ast$ of interacting electrons is a
fundamental quantity in the Fermi liquid theory. However, the precise value of
the effective mass of uniform electron gas is still elusive after decades of
research. The newly developed neural canonical transformation approach
arXiv:2105.08644 offers a principled way to extract the effective mass of
electron gas by directly calculating the thermal entropy at low temperature.
The approach models a variational many-electron density matrix using two
generative neural networks: an autoregressive model for momentum occupation and
a normalizing flow for electron coordinates. Our calculation reveals a
suppression of effective mass in the two-dimensional spin-polarized electron
gas, which is more pronounced than previous reports in the low-density
strong-coupling region. This prediction calls for verification in
two-dimensional electron gas experiments.

An explainable AI (XAI) model aims to provide transparency (in the form of
justification, explanation, etc) for its predictions or actions made by it.
Recently, there has been a lot of focus on building XAI models, especially to
provide explanations for understanding and interpreting the predictions made by
deep learning models. At UCLA, we propose a generic framework to interact with
an XAI model in natural language.

In this work, we aim to consider the application of contrastive learning in
the scenario of the recommendation system adequately, making it more suitable
for recommendation task. We propose a learning paradigm called supervised
contrastive learning(SCL) to support the graph convolutional neural network.
Specifically, we will calculate the similarity between different nodes in user
side and item side respectively during data preprocessing, and then when
applying contrastive learning, not only will the augmented views be regarded as
the positive samples, but also a certain number of similar samples will be
regarded as the positive samples, which is different with SimCLR that treats
other samples in a batch as negative samples. We apply SCL on the most advanced
LightGCN. In addition, in order to consider the uncertainty of node
interaction, we also propose a new data augment method called node replication.
Empirical research and ablation study on Gowalla, Yelp2018, Amazon-Book
datasets prove the effectiveness of SCL and node replication, which improve the
accuracy of recommendations and robustness to interactive noise.

The attention mechanism is widely used in deep learning because of its
excellent performance in neural networks without introducing additional
information. However, in unsupervised person re-identification, the attention
module represented by multi-headed self-attention suffers from attention
spreading in the condition of non-ground truth. To solve this problem, we
design pixel-level attention module to provide constraints for multi-headed
self-attention. Meanwhile, for the trait that the identification targets of
person re-identification data are all pedestrians in the samples, we design
domain-level attention module to provide more comprehensive pedestrian
features. We combine head-level, pixel-level and domain-level attention to
propose multi-level attention block and validate its performance on for large
person re-identification datasets (Market-1501, DukeMTMC-reID and MSMT17 and
PersonX).

To protect sensitive data in training a Generative Adversarial Network (GAN),
the standard approach is to use differentially private (DP) stochastic gradient
descent method in which controlled noise is added to the gradients. The quality
of the output synthetic samples can be adversely affected and the training of
the network may not even converge in the presence of these noises. We propose
Differentially Private Model Inversion (DPMI) method where the private data is
first mapped to the latent space via a public generator, followed by a
lower-dimensional DP-GAN with better convergent properties. Experimental
results on standard datasets CIFAR10 and SVHN as well as on a facial landmark
dataset for Autism screening show that our approach outperforms the standard
DP-GAN method based on Inception Score, Fr\'echet Inception Distance, and
classification accuracy under the same privacy guarantee.

In cybersecurity courses, it is quite challenging to do hands-on activities
that involve many components of the Internet, such as bringing down the
Internet, attacking a blockchain, etc. To solve this problem, we have developed
an open-source Internet Emulator, which is a Python library, consisting of the
classes for each essential element of the Internet, including autonomous
system, network, host, router, BGP router, Internet exchange, etc. It also
includes the classes for a variety of services, including Web server, DNS,
Botnet, Darknet, Blockchain, and more are being developed. Using these classes,
users can construct a mini-Internet to emulate the real-world Internet.
Although it is small, it has all the essential elements of the real Internet.
The construction is compiled into Docker container files, and the emulation is
executed by Docker on a single machine, or on multiple cloud machines.
  With this Internet Emulator, we can develop a variety of hands-on activities
for cybersecurity courses, including BGP prefix hijacking, attacks on smart
contract, using Darknet to achieve anonymity, launching Botnet and ransomware
attacks, etc. While the emulator was initially developed for cybersecurity
courses, it can also be used for network courses, for students to learn how the
Internet technologies work, such as routing, BGP, IP Anycast, and DNS. Many
other interesting network technologies can also be deployed on the emulator,
such as content delivery network and software-defined network.

Learning-based Network Intrusion Detection Systems (NIDSs) are widely
deployed for defending various cyberattacks. Existing learning-based NIDS
mainly uses Neural Network (NN) as a classifier that relies on the quality and
quantity of cyberattack data. Such NN-based approaches are also hard to
interpret for improving efficiency and scalability. In this paper, we design a
new local-global computation paradigm, FEDFOREST, a novel learning-based NIDS
by combining the interpretable Gradient Boosting Decision Tree (GBDT) and
Federated Learning (FL) framework. Specifically, FEDFOREST is composed of
multiple clients that extract local cyberattack data features for the server to
train models and detect intrusions. A privacy-enhanced technology is also
proposed in FEDFOREST to further defeat the privacy of the FL systems.
Extensive experiments on 4 cyberattack datasets of different tasks demonstrate
that FEDFOREST is effective, efficient, interpretable, and extendable.
FEDFOREST ranks first in the collaborative learning and cybersecurity
competition 2021 for Chinese college students.

Emotional voice conversion (EVC) seeks to convert the emotional state of an
utterance while preserving the linguistic content and speaker identity. In EVC,
emotions are usually treated as discrete categories overlooking the fact that
speech also conveys emotions with various intensity levels that the listener
can perceive. In this paper, we aim to explicitly characterize and control the
intensity of emotion. We propose to disentangle the speaker style from
linguistic content and encode the speaker style into a style embedding in a
continuous space that forms the prototype of emotion embedding. We further
learn the actual emotion encoder from an emotion-labelled database and study
the use of relative attributes to represent fine-grained emotion intensity. To
ensure emotional intelligibility, we incorporate emotion classification loss
and emotion embedding similarity loss into the training of the EVC network. As
desired, the proposed network controls the fine-grained emotion intensity in
the output speech. Through both objective and subjective evaluations, we
validate the effectiveness of the proposed network for emotional expressiveness
and emotion intensity control.

Deep convolutional neural networks (DCNNs) have become the most common
solution for automatic image annotation due to their non-parametric nature,
good performance, and their accessibility through libraries such as TensorFlow.
Among other fields, DCNNs are also a common approach to the annotation of large
astronomical image databases acquired by digital sky surveys. One of the main
downsides of DCNNs is the complex non-intuitive rules that make DCNNs act as a
``black box", providing annotations in a manner that is unclear to the user.
Therefore, the user is often not able to know what information is used by the
DCNNs for the classification. Here we demonstrate that the training of a DCNN
is sensitive to the context of the training data such as the location of the
objects in the sky. We show that for basic classification of elliptical and
spiral galaxies, the sky location of the galaxies used for training affects the
behavior of the algorithm, and leads to a small but consistent and
statistically significant bias. That bias exhibits itself in the form of
cosmological-scale anisotropy in the distribution of basic galaxy morphology.
Therefore, while DCNNs are powerful tools for annotating images of extended
sources, the construction of training sets for galaxy morphology should take
into consideration more aspects than the visual appearance of the object. In
any case, catalogs created with deep neural networks that exhibit signs of
cosmological anisotropy should be interpreted with the possibility of
consistent bias.

Approximate Bayesian inference methods provide a powerful suite of tools for
finding approximations to intractable posterior distributions. However, machine
learning applications typically involve selecting actions, which -- in a
Bayesian setting -- depend on the posterior distribution only via its
contribution to expected utility. A growing body of work on loss-calibrated
approximate inference methods has therefore sought to develop posterior
approximations sensitive to the influence of the utility function. Here we
introduce loss-calibrated expectation propagation (Loss-EP), a loss-calibrated
variant of expectation propagation. This method resembles standard EP with an
additional factor that "tilts" the posterior towards higher-utility decisions.
We show applications to Gaussian process classification under binary utility
functions with asymmetric penalties on False Negative and False Positive
errors, and show how this asymmetry can have dramatic consequences on what
information is "useful" to capture in an approximation.

Multimodal sentiment analysis (MSA) is a fundamental complex research problem
due to the heterogeneity gap between different modalities and the ambiguity of
human emotional expression. Although there have been many successful attempts
to construct multimodal representations for MSA, there are still two challenges
to be addressed: 1) A more robust multimodal representation needs to be
constructed to bridge the heterogeneity gap and cope with the complex
multimodal interactions, and 2) the contextual dynamics must be modeled
effectively throughout the information flow. In this work, we propose a
multimodal representation model based on Mutual information Maximization and
Minimization and Identity Embedding (MMMIE). We combine mutual information
maximization between modal pairs, and mutual information minimization between
input data and corresponding features to mine the modal-invariant and
task-related information. Furthermore, Identity Embedding is proposed to prompt
the downstream network to perceive the contextual information. Experimental
results on two public datasets demonstrate the effectiveness of the proposed
model.

In this paper, a human-like driving framework is designed for autonomous
vehicles (AVs), which aims to make AVs better integrate into the transportation
ecology of human driving and eliminate the misunderstanding and incompatibility
of human drivers to autonomous driving. Based on the analysis of the real world
INTERACTION dataset, a driving aggressiveness estimation model is established
with the fuzzy inference approach. Then, a human-like driving model, which
integrates the brain emotional learning circuit model (BELCM) with the
two-point preview model, is designed. In the human-like lane-change
decision-making algorithm, the cost function is designed comprehensively
considering driving safety and travel efficiency. Based on the cost function
and multi-constraint, the dynamic game algorithm is applied to modelling the
interaction and decision making between AV and human driver. Additionally, to
guarantee the lane-change safety of AVs, an artificial potential field model is
built for collision risk assessment. Finally, the proposed algorithm is
evaluated through human-in-the-loop experiments on a driving simulator, and the
results demonstrated the feasibility and effectiveness of the proposed method.

We propose an information-theoretic bias measurement technique through a
causal interpretation of spurious correlation, which is effective to identify
the feature-level algorithmic bias by taking advantage of conditional mutual
information. Although several bias measurement methods have been proposed and
widely investigated to achieve algorithmic fairness in various tasks such as
face recognition, their accuracy- or logit-based metrics are susceptible to
leading to trivial prediction score adjustment rather than fundamental bias
reduction. Hence, we design a novel debiasing framework against the algorithmic
bias, which incorporates a bias regularization loss derived by the proposed
information-theoretic bias measurement approach. In addition, we present a
simple yet effective unsupervised debiasing technique based on stochastic label
noise, which does not require the explicit supervision of bias information. The
proposed bias measurement and debiasing approaches are validated in diverse
realistic scenarios through extensive experiments on multiple standard

The Internet of things (IoT) is a rapidly advancing area of technology that
has quickly become more widespread in recent years. With greater numbers of
everyday objects being connected to the Internet, many different innovations
have been presented to make our everyday lives more straightforward. Pattern
recognition is extremely prevalent in IoT devices because of the many
applications and benefits that can come from it. A multitude of studies has
been conducted with the intention of improving speed and accuracy, decreasing
complexity, and reducing the overall required processing power of pattern
recognition algorithms in IoT devices. After reviewing the applications of
different machine learning algorithms, results vary from case to case, but a
general conclusion can be drawn that the optimal machine learning-based pattern
recognition algorithms to be used with IoT devices are support vector machine,
k-nearest neighbor, and random forest.

We consider a platform's problem of collecting data from privacy sensitive
users to estimate an underlying parameter of interest. We formulate this
question as a Bayesian-optimal mechanism design problem, in which an individual
can share her (verifiable) data in exchange for a monetary reward or services,
but at the same time has a (private) heterogeneous privacy cost which we
quantify using differential privacy. We consider two popular differential
privacy settings for providing privacy guarantees for the users: central and
local. In both settings, we establish minimax lower bounds for the estimation
error and derive (near) optimal estimators for given heterogeneous privacy loss
levels for users. Building on this characterization, we pose the mechanism
design problem as the optimal selection of an estimator and payments that will
elicit truthful reporting of users' privacy sensitivities. Under a regularity
condition on the distribution of privacy sensitivities we develop efficient
algorithmic mechanisms to solve this problem in both privacy settings. Our
mechanism in the central setting can be implemented in time $\mathcal{O}(n \log
n)$ where $n$ is the number of users and our mechanism in the local setting
admits a Polynomial Time Approximation Scheme (PTAS).

Driven by the key challenges of cell therapy manufacturing, including high
complexity, high uncertainty, and very limited process observations, we propose
a hybrid model-based reinforcement learning (RL) to efficiently guide process
control. We first create a probabilistic knowledge graph (KG) hybrid model
characterizing the risk- and science-based understanding of biomanufacturing
process mechanisms and quantifying inherent stochasticity, e.g., batch-to-batch
variation. It can capture the key features, including nonlinear reactions,
nonstationary dynamics, and partially observed state. This hybrid model can
leverage existing mechanistic models and facilitate learning from heterogeneous
process data. A computational sampling approach is used to generate posterior
samples quantifying model uncertainty. Then, we introduce hybrid model-based
Bayesian RL, accounting for both inherent stochasticity and model uncertainty,
to guide optimal, robust, and interpretable dynamic decision making. Cell
therapy manufacturing examples are used to empirically demonstrate that the
proposed framework can outperform the classical deterministic mechanistic model
assisted process optimization.

It is well known that translations of songs and poems not only break rhythm
and rhyming patterns, but can also result in loss of semantic information. The
Bhagavad Gita is an ancient Hindu philosophical text originally written in
Sanskrit that features a conversation between Lord Krishna and Arjuna prior to
the Mahabharata war. The Bhagavad Gita is also one of the key sacred texts in
Hinduism and is known as the forefront of the Vedic corpus of Hinduism. In the
last two centuries, there has been a lot of interest in Hindu philosophy from
western scholars; hence, the Bhagavad Gita has been translated in a number of
languages. However, there is not much work that validates the quality of the
English translations. Recent progress of language models powered by deep
learning has enabled not only translations but a better understanding of
language and texts with semantic and sentiment analysis. Our work is motivated
by the recent progress of language models powered by deep learning methods. In
this paper, we present a framework that compares selected translations (from
Sanskrit to English) of the Bhagavad Gita using semantic and sentiment
analyses. We use hand-labelled sentiment dataset for tuning state-of-art deep
learning-based language model known as bidirectional encoder representations
from transformers (BERT). We provide sentiment and semantic analysis for
selected chapters and verses across translations. Our results show that
although the style and vocabulary in the respective translations vary widely,
the sentiment analysis and semantic similarity shows that the message conveyed
are mostly similar.

Results from randomized experiments (trials) can be severely distorted by
outcome misclassification, such as from measurement error or reporting bias in
binary outcomes. All existing approaches to outcome misclassification rely on
some data-generating (super-population) model and therefore may not be
applicable to randomized experiments without additional assumptions. We propose
a model-free and finite-population-exact framework for randomized experiments
subject to outcome misclassification. A central quantity in our framework is
"warning accuracy," defined as the threshold such that the causal conclusion
drawn from the measured outcomes may differ from that based on the true
outcomes if the outcome measurement accuracy did not surpass that threshold. We
show how learning the warning accuracy and related concepts can benefit a
randomized experiment subject to outcome misclassification. We show that the
warning accuracy can be computed efficiently (even for large datasets) by
adaptively reformulating an integer program with respect to the randomization
design. Our framework covers both Fisher's sharp null and Neyman's weak null,
works for a wide range of randomization designs, and can also be applied to
observational studies adopting randomization-based inference. We apply our
framework to a large randomized clinical trial for the prevention of prostate
cancer.

Achieving universal translation between all human language pairs is the
holy-grail of machine translation (MT) research. While recent progress in
massively multilingual MT is one step closer to reaching this goal, it is
becoming evident that extending a multilingual MT system simply by training on
more parallel data is unscalable, since the availability of labeled data for
low-resource and non-English-centric language pairs is forbiddingly limited. To
this end, we present a pragmatic approach towards building a multilingual MT
model that covers hundreds of languages, using a mixture of supervised and
self-supervised objectives, depending on the data availability for different
language pairs. We demonstrate that the synergy between these two training
paradigms enables the model to produce high-quality translations in the
zero-resource setting, even surpassing supervised translation quality for low-
and mid-resource languages. We conduct a wide array of experiments to
understand the effect of the degree of multilingual supervision, domain
mismatches and amounts of parallel and monolingual data on the quality of our
self-supervised multilingual models. To demonstrate the scalability of the
approach, we train models with over 200 languages and demonstrate high
performance on zero-resource translation on several previously under-studied
languages. We hope our findings will serve as a stepping stone towards enabling
translation for the next thousand languages.

Communication of dense information between humans and machines is relatively
low bandwidth. Many modern search and recommender systems operate as machine
learning black boxes, giving little insight as to how they represent
information or why they take certain actions. We present Projection, a
mixed-initiative interface that aims to increase the bandwidth of communication
between humans and machines throughout the research process. The interface
supports adding context to searches and visualizing information in multiple
dimensions with techniques such as hierarchical clustering and spatial
projections. Potential customers have shown interest in the application
integrating their research outlining and search processes, enabling them to
structure their searches in hierarchies, and helping them visualize related
spaces of knowledge.

Recent advances in unsupervised domain adaptation have shown that mitigating
the domain divergence by extracting the domain-invariant representation could
significantly improve the generalization of a model to an unlabeled data
domain. Nevertheless, the existing methods fail to effectively preserve the
representation that is private to the label-missing domain, which could
adversely affect the generalization. In this paper, we propose an approach to
preserve such representation so that the latent distribution of the unlabeled
domain could represent both the domain-invariant features and the individual
characteristics that are private to the unlabeled domain. In particular, we
demonstrate that maximizing the mutual information between the unlabeled domain
and its latent space while mitigating the domain divergence can achieve such
preservation. We also theoretically and empirically validate that preserving
the representation that is private to the unlabeled domain is important and of
necessity for the cross-domain generalization. Our approach outperforms
state-of-the-art methods on several public datasets.

In this study we propose a hybrid estimation of distribution algorithm (HEDA)
to solve the joint stratification and sample allocation problem. This is a
complex problem in which each the quality of each stratification from the set
of all possible stratifications is measured its optimal sample allocation. EDAs
are stochastic black-box optimization algorithms which can be used to estimate,
build and sample probability models in the search for an optimal
stratification. In this paper we enhance the exploitation properties of the EDA
by adding a simulated annealing algorithm to make it a hybrid EDA. Results of
empirical comparisons for atomic and continuous strata show that the HEDA
attains the bests results found so far when compared to benchmark tests on the
same data using a grouping genetic algorithm, simulated annealing algorithm or
hill-climbing algorithm. However, the execution times and total execution are,
in general, higher for the HEDA.

We develop a structural econometric model to capture the decision dynamics of
human evaluators on an online micro-lending platform, and estimate the model
parameters using a real-world dataset. We find two types of biases in gender,
preference-based bias and belief-based bias, are present in human evaluators'
decisions. Both types of biases are in favor of female applicants. Through
counterfactual simulations, we quantify the effect of gender bias on loan
granting outcomes and the welfare of the company and the borrowers. Our results
imply that both the existence of the preference-based bias and that of the
belief-based bias reduce the company's profits. When the preference-based bias
is removed, the company earns more profits. When the belief-based bias is
removed, the company's profits also increase. Both increases result from
raising the approval probability for borrowers, especially male borrowers, who
eventually pay back loans. For borrowers, the elimination of either bias
decreases the gender gap of the true positive rates in the credit risk
evaluation. We also train machine learning algorithms on both the real-world
data and the data from the counterfactual simulations. We compare the decisions
made by those algorithms to see how evaluators' biases are inherited by the
algorithms and reflected in machine-based decisions. We find that machine
learning algorithms can mitigate both the preference-based bias and the
belief-based bias.

We focus on Partial Differential Equation (PDE) based Data Assimilatio
problems (DA) solved by means of variational approaches and Kalman filter
algorithm. Recently, we presented a Domain Decomposition framework (we call it
DD-DA, for short) performing a decomposition of the whole physical domain along
space and time directions, and joining the idea of Schwarz' methods and
parallel in time approaches. For effective parallelization of DD-DA algorithms,
the computational load assigned to subdomains must be equally distributed.
Usually computational cost is proportional to the amount of data entities
assigned to partitions. Good quality partitioning also requires the volume of
communication during calculation to be kept at its minimum. In order to deal
with DD-DA problems where the observations are nonuniformly distributed and
general sparse, in the present work we employ a parallel load balancing
algorithm based on adaptive and dynamic defining of boundaries of DD -- which
is aimed to balance workload according to data location. We call it DyDD. As
the numerical model underlying DA problems arising from the so-called
discretize-then-optimize approach is the constrained least square model (CLS),
we will use CLS as a reference state estimation problem and we validate DyDD on
different scenarios.

The rapid emergence of airborne platforms and imaging sensors are enabling
new forms of aerial surveillance due to their unprecedented advantages in
scale, mobility, deployment and covert observation capabilities. This paper
provides a comprehensive overview of human-centric aerial surveillance tasks
from a computer vision and pattern recognition perspective. It aims to provide
readers with an in-depth systematic review and technical analysis of the
current state of aerial surveillance tasks using drones, UAVs and other
airborne platforms. The main object of interest is humans, where single or
multiple subjects are to be detected, identified, tracked, re-identified and
have their behavior analyzed. More specifically, for each of these four tasks,
we first discuss unique challenges in performing these tasks in an aerial
setting compared to a ground-based setting. We then review and analyze the
aerial datasets publicly available for each task, and delve deep into the
approaches in the aerial literature and investigate how they presently address
the aerial challenges. We conclude the paper with discussion on the missing
gaps and open research questions to inform future research avenues.

We formulate selecting the best optimizing system (SBOS) problems and provide
solutions for those problems. In an SBOS problem, a finite number of systems
are contenders. Inside each system, a continuous decision variable affects the
system's expected performance. An SBOS problem compares different systems based
on their expected performances under their own optimally chosen decision to
select the best, without advance knowledge of expected performances of the
systems nor the optimizing decision inside each system. We design
easy-to-implement algorithms that adaptively chooses a system and a choice of
decision to evaluate the noisy system performance, sequentially eliminates
inferior systems, and eventually recommends a system as the best after spending
a user-specified budget. The proposed algorithms integrate the stochastic
gradient descent method and the sequential elimination method to simultaneously
exploit the structure inside each system and make comparisons across systems.
For the proposed algorithms, we prove exponential rates of convergence to zero
for the probability of false selection, as the budget grows to infinity. We
conduct three numerical examples that represent three practical cases of SBOS
problems. Our proposed algorithms demonstrate consistent and stronger
performances in terms of the probability of false selection over benchmark
algorithms under a range of problem settings and sampling budgets.

Recent years have seen advances in generalization bounds for noisy stochastic
algorithms, especially stochastic gradient Langevin dynamics (SGLD) based on
stability (Mou et al., 2018; Li et al., 2020) and information theoretic
approaches (Xu and Raginsky, 2017; Negrea et al., 2019; Steinke and
Zakynthinou, 2020). In this paper, we unify and substantially generalize
stability based generalization bounds and make three technical contributions.
First, we bound the generalization error in terms of expected (not uniform)
stability which arguably leads to quantitatively sharper bounds. Second, as our
main contribution, we introduce Exponential Family Langevin Dynamics (EFLD), a
substantial generalization of SGLD, which includes noisy versions of Sign-SGD
and quantized SGD as special cases. We establish data-dependent expected
stability based generalization bounds for any EFLD algorithm with a O(1/n)
sample dependence and dependence on gradient discrepancy rather than the norm
of gradients, yielding significantly sharper bounds. Third, we establish
optimization guarantees for special cases of EFLD. Further, empirical results
on benchmarks illustrate that our bounds are non-vacuous, quantitatively
sharper than existing bounds, and behave correctly under noisy labels.

Imaging, scattering, and spectroscopy are fundamental in understanding and
discovering new functional materials. Contemporary innovations in automation
and experimental techniques have led to these measurements being performed much
faster and with higher resolution, thus producing vast amounts of data for
analysis. These innovations are particularly pronounced at user facilities and
synchrotron light sources. Machine learning (ML) methods are regularly
developed to process and interpret large datasets in real-time with
measurements. However, there remain conceptual barriers to entry for the
facility general user community, whom often lack expertise in ML, and technical
barriers for deploying ML models. Herein, we demonstrate a variety of
archetypal ML models for on-the-fly analysis at multiple beamlines at the
National Synchrotron Light Source II (NSLS-II). We describe these examples
instructively, with a focus on integrating the models into existing
experimental workflows, such that the reader can easily include their own ML
techniques into experiments at NSLS-II or facilities with a common
infrastructure. The framework presented here shows how with little effort,
diverse ML models operate in conjunction with feedback loops via integration
into the existing Bluesky Suite for experimental orchestration and data

In this paper, we evaluate various deep learning frameworks for detecting
respiratory anomalies from input audio recordings. To this end, we firstly
transform audio respiratory cycles collected from patients into spectrograms
where both temporal and spectral features are presented, referred to as the
front-end feature extraction. We then feed the spectrograms into back-end deep
learning networks for classifying these respiratory cycles into certain
categories. Finally, results from high-performed deep learning frameworks are
fused to obtain the best score. Our experiments on ICBHI benchmark dataset
achieve the highest ICBHI score of 57.3 from a late fusion of inception based
and transfer learning based deep learning frameworks, which outperforms the
state-of-the-art systems.

This paper proposes a segmentation method of infection regions in the lung
from CT volumes of COVID-19 patients. COVID-19 spread worldwide, causing many
infected patients and deaths. CT image-based diagnosis of COVID-19 can provide
quick and accurate diagnosis results. An automated segmentation method of
infection regions in the lung provides a quantitative criterion for diagnosis.
Previous methods employ whole 2D image or 3D volume-based processes. Infection
regions have a considerable variation in their sizes. Such processes easily
miss small infection regions. Patch-based process is effective for segmenting
small targets. However, selecting the appropriate patch size is difficult in
infection region segmentation. We utilize the scale uncertainty among various
receptive field sizes of a segmentation FCN to obtain infection regions. The
receptive field sizes can be defined as the patch size and the resolution of
volumes where patches are clipped from. This paper proposes an infection
segmentation network (ISNet) that performs patch-based segmentation and a scale
uncertainty-aware prediction aggregation method that refines the segmentation
result. We design ISNet to segment infection regions that have various
intensity values. ISNet has multiple encoding paths to process patch volumes
normalized by multiple intensity ranges. We collect prediction results
generated by ISNets having various receptive field sizes. Scale uncertainty
among the prediction results is extracted by the prediction aggregation method.
We use an aggregation FCN to generate a refined segmentation result considering
scale uncertainty among the predictions. In our experiments using 199 chest CT
volumes of COVID-19 cases, the prediction aggregation method improved the dice
similarity score from 47.6% to 62.1%.

This paper proposes an automated segmentation method of infection and normal
regions in the lung from CT volumes of COVID-19 patients. From December 2019,
novel coronavirus disease 2019 (COVID-19) spreads over the world and giving
significant impacts to our economic activities and daily lives. To diagnose the
large number of infected patients, diagnosis assistance by computers is needed.
Chest CT is effective for diagnosis of viral pneumonia including COVID-19. A
quantitative analysis method of condition of the lung from CT volumes by
computers is required for diagnosis assistance of COVID-19. This paper proposes
an automated segmentation method of infection and normal regions in the lung
from CT volumes using a COVID-19 segmentation fully convolutional network
(FCN). In diagnosis of lung diseases including COVID-19, analysis of conditions
of normal and infection regions in the lung is important. Our method recognizes
and segments lung normal and infection regions in CT volumes. To segment
infection regions that have various shapes and sizes, we introduced dense
pooling connections and dilated convolutions in our FCN. We applied the
proposed method to CT volumes of COVID-19 cases. From mild to severe cases of
COVID-19, the proposed method correctly segmented normal and infection regions
in the lung. Dice scores of normal and infection regions were 0.911 and 0.753,

The precise age estimation of child sexual abuse and exploitation (CSAE)
victims is one of the most significant digital forensic challenges.
Investigators often need to determine the age of victims by looking at images
and interpreting the sexual development stages and other human characteristics.
The main priority - safeguarding children -- is often negatively impacted by a
huge forensic backlog, cognitive bias and the immense psychological stress that
this work can entail. This paper evaluates existing facial image datasets and
proposes a new dataset tailored to the needs of similar digital forensic
research contributions. This small, diverse dataset of 0 to 20-year-old
individuals contains 245 images and is merged with 82 unique images from the
FG-NET dataset, thus achieving a total of 327 images with high image diversity
and low age range density. The new dataset is tested on the Deep EXpectation
(DEX) algorithm pre-trained on the IMDB-WIKI dataset. The overall results for
young adolescents aged 10 to 15 and older adolescents/adults aged 16 to 20 are
very encouraging -- achieving MAEs as low as 1.79, but also suggest that the
accuracy for children aged 0 to 10 needs further work. In order to determine
the efficacy of the prototype, valuable input of four digital forensic experts,
including two forensic investigators, has been taken into account to improve
age estimation results. Further research is required to extend datasets both
concerning image density and the equal distribution of factors such as gender
and racial diversity.

Over the last couple of years few-shot learning (FSL) has attracted great
attention towards minimizing the dependency on labeled training examples. An
inherent difficulty in FSL is the handling of ambiguities resulting from having
too few training samples per class. To tackle this fundamental challenge in
FSL, we aim to train meta-learner models that can leverage prior semantic
knowledge about novel classes to guide the classifier synthesis process. In
particular, we propose semantically-conditioned feature attention and sample
attention mechanisms that estimate the importance of representation dimensions
and training instances. We also study the problem of sample noise in FSL,
towards the utilization of meta-learners in more realistic and imperfect
settings. Our experimental results demonstrate the effectiveness of the
proposed semantic FSL model with and without sample noise.

Medication errors most commonly occur at the ordering or prescribing stage,
potentially leading to medical complications and poor health outcomes. While it
is possible to catch these errors using different techniques; the focus of this
work is on textual and contextual analysis of prescription information to
detect and prevent potential medication errors. In this paper, we demonstrate
how to use BERT-based contextual language models to detect anomalies in written
or spoken text based on a data set extracted from real-world medical data of
thousands of patient records. The proposed models are able to learn patterns of
text dependency and predict erroneous output based on contextual information
such as patient data. The experimental results yield accuracy up to 96.63% for
text input and up to 79.55% for speech input, which is satisfactory for most

We consider a network coding problem where the destination wants to recover
the sum of the signals (Gaussian random variables or random finite field
elements) at all the source nodes, but the sum must be kept secret from an
eavesdropper that can wiretap on a subset of edges. This setting arises
naturally in sensor networks and federated learning, where the secrecy of the
sum of the signals (e.g. weights, gradients) may be desired. While the case for
finite field can be solved, the case for Gaussian random variables is
surprisingly difficult. We give a simple conjecture on the necessary and
sufficient condition under which such secret computation is possible for the
Gaussian case, and prove the conjecture when the number of wiretapped edges is
at most 2.

A lot of prognostication methodologies have been formulated for early
detection of Polycystic Ovary Syndrome also known as PCOS using Machine
Learning. PCOS is a binary classification problem. Dimensionality Reduction
methods impact the performance of Machine Learning to a greater extent and
using a Supervised Dimensionality Reduction method can give us a new edge to
tackle this problem. In this paper we present Discriminant Analysis in
different dimensions with Linear and Quadratic form for binary classification
along with metrics. We were able to achieve good accuracy and less variation
with Discriminant Analysis as compared to many commonly used classification
algorithms with training accuracy reaching 97.37% and testing accuracy of
95.92% using Quadratic Discriminant Analysis. Paper also gives the analysis of
data with visualizations for deeper understanding of problem.

Solar shading design should be done for the desired Indoor Environmental
Quality (IEQ) in the early design stages. This field can be very challenging
and time-consuming also requires experts, sophisticated software, and a large
amount of money. The primary purpose of this research is to design a simple
tool to study various models of solar shadings and make decisions easier and
faster in the early stages. Database generation methods, artificial
intelligence, and optimization have been used to achieve this goal. This tool
includes two main parts of 1. predicting the performance of the user-selected
model along with proposing effective parameters and 2. proposing optimal
pre-prepared models to the user. In this regard, initially, a side-lit shoebox
model with variable parameters was modeled parametrically, and five common
solar shading models with their variables were applied to the space. For each
solar shadings and the state without shading, metrics related to daylight and
glare, view, and initial costs were simulated. The database generated in this
research includes 87912 alternatives and six calculated metrics introduced to
optimized machine learning models, including neural network, random Forrest,
support vector regression, and k nearest neighbor. According to the results,
the most accurate and fastest estimation model was Random Forrest, with an
r2_score of 0.967 to 1. Then, sensitivity analysis was performed to identify
the most influential parameters for each shading model and the state without
it. This analysis distinguished the most effective parameters, including window
orientation, WWR, room width, length, and shading depth. Finally, by optimizing
the estimation function of machine learning models with the NSGA II algorithm,
about 7300 optimal models were identified. The developed tool can evaluate
various design alternatives in less than a few seconds for each.

Identifying anomaly multimedia traffic in cyberspace is a big challenge in
distributed service systems, multiple generation networks and future internet
of everything. This letter explores meta-generalization for a multiparty
privacy learning model in graynet to improve the performance of anomaly
multimedia traffic identification. The multiparty privacy learning model in
graynet is a globally shared model that is partitioned, distributed and trained
by exchanging multiparty parameters updates with preserving private data. The
meta-generalization refers to discovering the inherent attributes of a learning
model to reduce its generalization error. In experiments, three
meta-generalization principles are tested as follows. The generalization error
of the multiparty privacy learning model in graynet is reduced by changing the
dimension of byte-level imbedding. Following that, the error is reduced by
adapting the depth for extracting packet-level features. Finally, the error is
reduced by adjusting the size of support set for preprocessing traffic-level
data. Experimental results demonstrate that the proposal outperforms the
state-of-the-art learning models for identifying anomaly multimedia traffic.

Data-Free Knowledge Distillation (KD) allows knowledge transfer from a
trained neural network (teacher) to a more compact one (student) in the absence
of original training data. Existing works use a validation set to monitor the
accuracy of the student over real data and report the highest performance
throughout the entire process. However, validation data may not be available at
distillation time either, making it infeasible to record the student snapshot
that achieved the peak accuracy. Therefore, a practical data-free KD method
should be robust and ideally provide monotonically increasing student accuracy
during distillation. This is challenging because the student experiences
knowledge degradation due to the distribution shift of the synthetic data. A
straightforward approach to overcome this issue is to store and rehearse the
generated samples periodically, which increases the memory footprint and
creates privacy concerns. We propose to model the distribution of the
previously observed synthetic samples with a generative network. In particular,
we design a Variational Autoencoder (VAE) with a training objective that is
customized to learn the synthetic data representations optimally. The student
is rehearsed by the generative pseudo replay technique, with samples produced
by the VAE. Hence knowledge degradation can be prevented without storing any
samples. Experiments on image classification benchmarks show that our method
optimizes the expected value of the distilled model accuracy while eliminating
the large memory overhead incurred by the sample-storing methods.

Self-supervised learning on point clouds has gained a lot of attention
recently, since it addresses the label-efficiency and domain-gap problems on
point cloud tasks. In this paper, we propose a novel self-supervised framework
to learn informative representations from partial point clouds. We leverage
partial point clouds scanned by LiDAR that contain both content and pose
attributes, and we show that disentangling such two factors from partial point
clouds enhances feature representation learning. To this end, our framework
consists of three main parts: 1) a completion network to capture holistic
semantics of point clouds; 2) a pose regression network to understand the
viewing angle where partial data is scanned from; 3) a partial reconstruction
network to encourage the model to learn content and pose features. To
demonstrate the robustness of the learnt feature representations, we conduct
several downstream tasks including classification, part segmentation, and
registration, with comparisons against state-of-the-art methods. Our method not
only outperforms existing self-supervised methods, but also shows a better
generalizability across synthetic and real-world datasets.

MeSH (Medical Subject Headings) is a large thesaurus created by the National
Library of Medicine and used for fine-grained indexing of publications in the
biomedical domain. In the context of the COVID-19 pandemic, MeSH descriptors
have emerged in relation to articles published on the corresponding topic.
Zero-shot classification is an adequate response for timely labeling of the
stream of papers with MeSH categories. In this work, we hypothesise that rich
semantic information available in MeSH has potential to improve BioBERT
representations and make them more suitable for zero-shot/few-shot tasks. We
frame the problem as determining if MeSH term definitions, concatenated with
paper abstracts are valid instances or not, and leverage multi-task learning to
induce the MeSH hierarchy in the representations thanks to a seq2seq task.
Results establish a baseline on the MedLine and LitCovid datasets, and probing
shows that the resulting representations convey the hierarchical relations
present in MeSH.

The detection of early signs of volcanic unrest preceding an eruption, in the
form of ground deformation in Interferometric Synthetic Aperture Radar (InSAR)
data is critical for assessing volcanic hazard. In this work we treat this as a
binary classification problem of InSAR images, and propose a novel deep
learning methodology that exploits a rich source of synthetically generated
interferograms to train quality classifiers that perform equally well in real
interferograms. The imbalanced nature of the problem, with orders of magnitude
fewer positive samples, coupled with the lack of a curated database with
labeled InSAR data, sets a challenging task for conventional deep learning
architectures. We propose a new framework for domain adaptation, in which we
learn class prototypes from synthetic data with vision transformers. We report
detection accuracy that amounts to the highest reported accuracy on a large
test set for volcanic unrest detection. Moreover, we built upon this knowledge
by learning a new, non-linear, projection between the learnt representations
and prototype space, using pseudo labels produced by our model from an
unlabeled real InSAR dataset. This leads to the new state of the art with 97.1%
accuracy on our test set. We demonstrate the robustness of our approach by
training a simple ResNet-18 Convolutional Neural Network on the unlabeled real
InSAR dataset with pseudo-labels generated from our top transformer-prototype
model. Our methodology provides a significant improvement in performance
without the need of manually labeling any sample, opening the road for further
exploitation of synthetic InSAR data in various remote sensing applications.

Spatial redundancy widely exists in visual recognition tasks, i.e.,
discriminative features in an image or video frame usually correspond to only a
subset of pixels, while the remaining regions are irrelevant to the task at
hand. Therefore, static models which process all the pixels with an equal
amount of computation result in considerable redundancy in terms of time and
space consumption. In this paper, we formulate the image recognition problem as
a sequential coarse-to-fine feature learning process, mimicking the human
visual system. Specifically, the proposed Glance and Focus Network (GFNet)
first extracts a quick global representation of the input image at a low
resolution scale, and then strategically attends to a series of salient (small)
regions to learn finer features. The sequential process naturally facilitates
adaptive inference at test time, as it can be terminated once the model is
sufficiently confident about its prediction, avoiding further redundant
computation. It is worth noting that the problem of locating discriminant
regions in our model is formulated as a reinforcement learning task, thus
requiring no additional manual annotations other than classification labels.
GFNet is general and flexible as it is compatible with any off-the-shelf
backbone models (such as MobileNets, EfficientNets and TSM), which can be
conveniently deployed as the feature extractor. Extensive experiments on a
variety of image classification and video recognition tasks and with various
backbone models demonstrate the remarkable efficiency of our method. For
example, it reduces the average latency of the highly efficient MobileNet-V3 on
an iPhone XS Max by 1.3x without sacrificing accuracy. Code and pre-trained
models are available at https://github.com/blackfeather-wang/GFNet-Pytorch.

Early detection of COVID-19 is an ongoing area of research that can help with
triage, monitoring and general health assessment of potential patients and may
reduce operational strain on hospitals that cope with the coronavirus pandemic.
Different machine learning techniques have been used in the literature to
detect coronavirus using routine clinical data (blood tests, and vital signs).
Data breaches and information leakage when using these models can bring
reputational damage and cause legal issues for hospitals. In spite of this,
protecting healthcare models against leakage of potentially sensitive
information is an understudied research area. In this work, we examine two
machine learning approaches, intended to predict a patient's COVID-19 status
using routinely collected and readily available clinical data. We employ
adversarial training to explore robust deep learning architectures that protect
attributes related to demographic information about the patients. The two
models we examine in this work are intended to preserve sensitive information
against adversarial attacks and information leakage. In a series of experiments
using datasets from the Oxford University Hospitals, Bedfordshire Hospitals NHS
Foundation Trust, University Hospitals Birmingham NHS Foundation Trust, and
Portsmouth Hospitals University NHS Trust we train and test two neural networks
that predict PCR test results using information from basic laboratory blood
tests, and vital signs performed on a patients' arrival to hospital. We assess
the level of privacy each one of the models can provide and show the efficacy
and robustness of our proposed architectures against a comparable baseline. One
of our main contributions is that we specifically target the development of
effective COVID-19 detection models with built-in mechanisms in order to
selectively protect sensitive attributes against adversarial attacks.

Predicting attributes in the landmark free facial images is itself a
challenging task which gets further complicated when the face gets occluded due
to the usage of masks. Smart access control gates which utilize identity
verification or the secure login to personal electronic gadgets may utilize
face as a biometric trait. Particularly, the Covid-19 pandemic increasingly
validates the essentiality of hygienic and contactless identity verification.
In such cases, the usage of masks become more inevitable and performing
attribute prediction helps in segregating the target vulnerable groups from
community spread or ensuring social distancing for them in a collaborative
environment. We create a masked face dataset by efficiently overlaying masks of
different shape, size and textures to effectively model variability generated
by wearing mask. This paper presents a deep Multi-Task Learning (MTL) approach
to jointly estimate various heterogeneous attributes from a single masked
facial image. Experimental results on benchmark face attribute UTKFace dataset
demonstrate that the proposed approach supersedes in performance to other
competing techniques. The source code is available at
https://github.com/ritikajha/Attribute-prediction-in-masked-facial-images-with-deep-multitask-learning

Speaker recognition systems are widely used in various applications to
identify a person by their voice; however, the high degree of variability in
speech signals makes this a challenging task. Dealing with emotional variations
is very difficult because emotions alter the voice characteristics of a person;
thus, the acoustic features differ from those used to train models in a neutral
environment. Therefore, speaker recognition models trained on neutral speech
fail to correctly identify speakers under emotional stress. Although
considerable advancements in speaker identification have been made using
convolutional neural networks (CNN), CNNs cannot exploit the spatial
association between low-level features. Inspired by the recent introduction of
capsule networks (CapsNets), which are based on deep learning to overcome the
inadequacy of CNNs in preserving the pose relationship between low-level
features with their pooling technique, this study investigates the performance
of using CapsNets in identifying speakers from emotional speech recordings. A
CapsNet-based speaker identification model is proposed and evaluated using
three distinct speech databases, i.e., the Emirati Speech Database, SUSAS
Dataset, and RAVDESS (open-access). The proposed model is also compared to
baseline systems. Experimental results demonstrate that the novel proposed
CapsNet model trains faster and provides better results over current
state-of-the-art schemes. The effect of the routing algorithm on speaker
identification performance was also studied by varying the number of
iterations, both with and without a decoder network.

It has been shown that natural language processing (NLP) models are
vulnerable to a kind of security threat called the Backdoor Attack, which
utilizes a `backdoor trigger' paradigm to mislead the models. The most
threatening backdoor attack is the stealthy backdoor, which defines the
triggers as text style or syntactic. Although they have achieved an incredible
high attack success rate (ASR), we find that the principal factor contributing
to their ASR is not the `backdoor trigger' paradigm. Thus the capacity of these
stealthy backdoor attacks is overestimated when categorized as backdoor
attacks. Therefore, to evaluate the real attack power of backdoor attacks, we
propose a new metric called attack successful rate difference (ASRD), which
measures the ASR difference between clean state and poison state models.
Besides, since the defenses against stealthy backdoor attacks are absent, we
propose Trigger Breaker, consisting of two too simple tricks that can defend
against stealthy backdoor attacks effectively. Experiments show that our method
achieves significantly better performance than state-of-the-art defense methods
against stealthy backdoor attacks.

Machine learning has long been considered as a black box for predicting
combustion chemical kinetics due to the extremely large number of parameters
and the lack of evaluation standards and reproducibility. The current work aims
to understand two basic questions regarding the deep neural network (DNN)
method: what data the DNN needs and how general the DNN method can be. Sampling
and preprocessing determine the DNN training dataset, further affect DNN
prediction ability. The current work proposes using Box-Cox transformation
(BCT) to preprocess the combustion data. In addition, this work compares
different sampling methods with or without preprocessing, including the Monte
Carlo method, manifold sampling, generative neural network method (cycle-GAN),
and newly-proposed multi-scale sampling. Our results reveal that the DNN
trained by the manifold data can capture the chemical kinetics in limited
configurations but cannot remain robust toward perturbation, which is
inevitable for the DNN coupled with the flow field. The Monte Carlo and
cycle-GAN samplings can cover a wider phase space but fail to capture
small-scale intermediate species, producing poor prediction results. A
three-hidden-layer DNN, based on the multi-scale method without specific flame
simulation data, allows predicting chemical kinetics in various scenarios and
being stable during the temporal evolutions. This single DNN is readily
implemented with several CFD codes and validated in various combustors,
including (1). zero-dimensional autoignition, (2). one-dimensional freely
propagating flame, (3). two-dimensional jet flame with triple-flame structure,
and (4). three-dimensional turbulent lifted flames. The results demonstrate the
satisfying accuracy and generalization ability of the pre-trained DNN. The
Fortran and Python versions of DNN and example code are attached in the
supplementary for reproducibility.

Face Recognition has proven to be one of the most successful technology and
has impacted heterogeneous domains. Deep learning has proven to be the most
successful at computer vision tasks because of its convolution-based
architecture. Since the advent of deep learning, face recognition technology
has had a substantial increase in its accuracy. In this paper, some of the most
impactful face recognition systems were surveyed. Firstly, the paper gives an
overview of a general face recognition system. Secondly, the survey covers
various network architectures and training losses that have had a substantial
impact. Finally, the paper talks about various databases that are used to
evaluate the capabilities of a face recognition system.

We consider the traffic assignment problem in nonatomic routing games where
the players' cost functions may be subject to random fluctuations (e.g.,
weather disturbances, perturbations in the underlying network, etc.). We tackle
this problem from the viewpoint of a control interface that makes routing
recommendations based solely on observed costs and without any further
knowledge of the system's governing dynamics -- such as the network's cost
functions, the distribution of any random events affecting the network, etc. In
this online setting, learning methods based on the popular exponential weights
algorithm converge to equilibrium at an $\mathcal{O}({1/\sqrt{T}})$ rate: this
rate is known to be order-optimal in stochastic networks, but it is otherwise
suboptimal in static networks. In the latter case, it is possible to achieve an
$\mathcal{O}({1/T^{2}})$ equilibrium convergence rate via the use of finely
tuned accelerated algorithms; on the other hand, these accelerated algorithms
fail to converge altogether in the presence of persistent randomness, so it is
not clear how to achieve the "best of both worlds" in terms of convergence
speed. Our paper seeks to fill this gap by proposing an adaptive routing
algortihm with the following desirable properties: $(i)$ it seamlessly
interpolates between the $\mathcal{O}({1/T^{2}})$ and
$\mathcal{O}({1/\sqrt{T}})$ rates for static and stochastic environments
respectively; $(ii)$ its convergence speed is polylogarithmic in the number of
paths in the network; ${(iii)}$ the method's per-iteration complexity and
memory requirements are both linear in the number of nodes and edges in the
network; and ${(iv)}$ it does not require any prior knowledge of the problem's

Deep convolutional neural networks (CNNs) are broadly considered to be
state-of-the-art generic end-to-end image classification systems. However, they
are known to underperform when training data are limited and thus require data
augmentation strategies that render the method computationally expensive and
not always effective. Rather than using a data augmentation strategy to encode
invariances as typically done in machine learning, here we propose to
mathematically augment a nearest subspace classification model in
sliced-Wasserstein space by exploiting certain mathematical properties of the
Radon Cumulative Distribution Transform (R-CDT), a recently introduced image
transform. We demonstrate that for a particular type of learning problem, our
mathematical solution has advantages over data augmentation with deep CNNs in
terms of classification accuracy and computational complexity, and is
particularly effective under a limited training data setting. The method is
simple, effective, computationally efficient, non-iterative, and requires no
parameters to be tuned. Python code implementing our method is available at
https://github.com/rohdelab/mathematical_augmentation. Our method is integrated
as a part of the software package PyTransKit, which is available at
https://github.com/rohdelab/PyTransKit.

Multi-view learning is a learning problem that utilizes the various
representations of an object to mine valuable knowledge and improve the
performance of learning algorithm, and one of the significant directions of
multi-view learning is sub-space learning. As we known, auto-encoder is a
method of deep learning, which can learn the latent feature of raw data by
reconstructing the input, and based on this, we propose a novel algorithm
called Auto-encoder based Co-training Multi-View Learning (ACMVL), which
utilizes both complementarity and consistency and finds a joint latent feature
representation of multiple views. The algorithm has two stages, the first is to
train auto-encoder of each view, and the second stage is to train a supervised
network. Interestingly, the two stages share the weights partly and assist each
other by co-training process. According to the experimental result, we can
learn a well performed latent feature representation, and auto-encoder of each
view has more powerful reconstruction ability than traditional auto-encoder.

Recently, the applications of deep neural network (DNN) have been very
prominent in many fields such as computer vision (CV) and natural language
processing (NLP) due to its superior feature extraction performance. However,
the high-dimension parameter model and large-scale mathematical calculation
restrict the execution efficiency, especially for Internet of Things (IoT)
devices. Different from the previous cloud/edge-only pattern that brings huge
pressure for uplink communication and device-only fashion that undertakes
unaffordable calculation strength, we highlight the collaborative computation
between the device and edge for DNN models, which can achieve a good balance
between the communication load and execution accuracy. Specifically, a
systematic on-demand co-inference framework is proposed to exploit the
multi-branch structure, in which the pre-trained Alexnet is right-sized through
\emph{early-exit} and partitioned at an intermediate DNN layer. The integer
quantization is enforced to further compress transmission bits. As a result, we
establish a new Deep Reinforcement Learning (DRL) optimizer-Soft Actor Critic
for discrete (SAC-d), which generates the \emph{exit point}, \emph{partition
point}, and \emph{compressing bits} by soft policy iterations. Based on the
latency and accuracy aware reward design, such an optimizer can well adapt to
the complex environment like dynamic wireless channel and arbitrary CPU
processing, and is capable of supporting the 5G URLLC. Real-world experiment on
Raspberry Pi 4 and PC shows the outperformance of the proposed solution.

Linear and Quadratic Discriminant Analysis are well-known classical methods
but can heavily suffer from non-Gaussian distributions and/or contaminated
datasets, mainly because of the underlying Gaussian assumption that is not
robust. To fill this gap, this paper presents a new robust discriminant
analysis where each data point is drawn by its own arbitrary Elliptically
Symmetrical (ES) distribution and its own arbitrary scale parameter. Such a
model allows for possibly very heterogeneous, independent but non-identically
distributed samples. After deriving a new decision rule, it is shown that
maximum-likelihood parameter estimation and classification are very simple,
fast and robust compared to state-of-the-art methods.

Learning dense point-wise semantics from unstructured 3D point clouds with
fewer labels, although a realistic problem, has been under-explored in
literature. While existing weakly supervised methods can effectively learn
semantics with only a small fraction of point-level annotations, we find that
the vanilla bounding box-level annotation is also informative for semantic
segmentation of large-scale 3D point clouds. In this paper, we introduce a
neural architecture, termed Box2Seg, to learn point-level semantics of 3D point
clouds with bounding box-level supervision. The key to our approach is to
generate accurate pseudo labels by exploring the geometric and topological
structure inside and outside each bounding box. Specifically, an
attention-based self-training (AST) technique and Point Class Activation
Mapping (PCAM) are utilized to estimate pseudo-labels. The network is further
trained and refined with pseudo labels. Experiments on two large-scale
benchmarks including S3DIS and ScanNet demonstrate the competitive performance
of the proposed method. In particular, the proposed network can be trained with
cheap, or even off-the-shelf bounding box-level annotations and subcloud-level
tags.

Nested simulation concerns estimating functionals of a conditional
expectation via simulation. In this paper, we propose a new method based on
kernel ridge regression to exploit the smoothness of the conditional
expectation as a function of the multidimensional conditioning variable.
Asymptotic analysis shows that the proposed method can effectively alleviate
the curse of dimensionality on the convergence rate as the simulation budget
increases, provided that the conditional expectation is sufficiently smooth.
The smoothness bridges the gap between the cubic root convergence rate (that
is, the optimal rate for the standard nested simulation) and the square root
convergence rate (that is, the canonical rate for the standard Monte Carlo
simulation). We demonstrate the performance of the proposed method via
numerical examples from portfolio risk management and input uncertainty
quantification.

Background and Objective High medicine diversity has always been a
significant challenge for prescription, causing confusion or doubt in
physicians' decision-making process. This paper aims to develop a medicine
recommender system called RecoMed to aid the physician in the prescription
process of hypertension by providing information about what medications have
been prescribed by other doctors and figuring out what other medicines can be
recommended in addition to the one in question. Methods There are two steps to
the developed method: First, association rule mining algorithms are employed to
find medicine association rules. The second step entails graph mining and
clustering to present an enriched recommendation via ATC code, which itself
comprises several steps. First, the initial graph is constructed from
historical prescription data. Then, data pruning is performed in the second
step, after which the medicines with a high repetition rate are removed at the
discretion of a general medical practitioner. Next, the medicines are matched
to a well-known medicine classification system called the ATC code to provide
an enriched recommendation. And finally, the DBSCAN and Louvain algorithms
cluster medicines in the final step. Results A list of recommended medicines is
provided as the system's output, and physicians can choose one or more of the
medicines based on the patient's clinical symptoms. Only the medicines of class
2, related to high blood pressure medications, are used to assess the system's
performance. The results obtained from this system have been reviewed and
confirmed by an expert in this field.

Full-waveform inversion (FWI), a popular technique that promises
high-resolution models, has helped in improving the salt definition in inverted
velocity models. The success of the inversion relies heavily on having prior
knowledge of the salt, and using advanced acquisition technology with long
offsets and low frequencies. Salt bodies are often constructed by recursively
picking the top and bottom of the salt from seismic images corresponding to
tomography models, combined with flooding techniques. The process is
time-consuming and highly prone to error, especially in picking the bottom of
the salt (BoS). Many studies suggest performing FWI with long offsets and low
frequencies after constructing the salt bodies to correct the miss-interpreted
boundaries. Here, we focus on detecting the BoS automatically by utilizing deep
learning tools. We specifically generate many random 1D models, containing or
free of salt bodies, and calculate the corresponding shot gathers. We then
apply FWI starting with salt flooded versions of those models, and the results
of the FWI become inputs to the neural network, whereas the corresponding true
1D models are the output. The network is trained in a regression manner to
detect the BoS and estimate the subsalt velocity. We analyze three scenarios in
creating the training datasets and test their performance on the 2D BP 2004
salt model. We show that when the network succeeds in estimating the subsalt
velocity, the requirement of low frequencies and long offsets are somewhat
mitigated. In general, this work allows us to merge the top-to-bottom approach
with FWI, save the BoS picking time, and empower FWI to converge in the absence
of low frequencies and long offsets in the data.

Most Gaze estimation research only works on a setup condition that a camera
perfectly captures eyes gaze. They have not literarily specified how to set up
a camera correctly for a given position of a person. In this paper, we carry
out a study on gaze estimation with a logical camera setup position. We further
bring our research in a practical application by using inexpensive edge devices
with a realistic scenario. That is, we first set up a shopping environment
where we want to grasp customers gazing behaviors. This setup needs an optimal
camera position in order to maintain estimation accuracy from existing gaze
estimation research. We then apply the state-of-the-art of few-shot learning
gaze estimation to reduce training sampling in the inference phase. In the
experiment, we perform our implemented research on NVIDIA Jetson TX2 and
achieve a reasonable speed, 12 FPS which is faster compared with our reference
work, without much degradation of gaze estimation accuracy. The source code is
released at https://github.com/linh-gist/GazeEstimationTX2.

To ensure the performance of online service systems, their status is closely
monitored with various software and system metrics. Performance anomalies
represent the performance degradation issues (e.g., slow response) of the
service systems. When performing anomaly detection over the metrics, existing
methods often lack the merit of interpretability, which is vital for engineers
and analysts to take remediation actions. Moreover, they are unable to
effectively accommodate the ever-changing services in an online fashion. To
address these limitations, in this paper, we propose ADSketch, an interpretable
and adaptive performance anomaly detection approach based on pattern sketching.
ADSketch achieves interpretability by identifying groups of anomalous metric
patterns, which represent particular types of performance issues. The
underlying issues can then be immediately recognized if similar patterns emerge
again. In addition, an adaptive learning algorithm is designed to embrace
unprecedented patterns induced by service updates or user behavior changes. The
proposed approach is evaluated with public data as well as industrial data
collected from a representative online service system in Huawei Cloud. The
experimental results show that ADSketch outperforms state-of-the-art approaches
by a significant margin, and demonstrate the effectiveness of the online
algorithm in new pattern discovery. Furthermore, our approach has been
successfully deployed in industrial practice.

This paper presents a novel and fast solver for the J2-perturbed Lambert
problem. The solver consists of an intelligent initial guess generator combined
with a differential correction procedure. The intelligent initial guess
generator is a deep neural network that is trained to correct the initial
velocity vector coming from the solution of the unperturbed Lambert problem.
The differential correction module takes the initial guess and uses a forward
shooting procedure to further update the initial velocity and exactly meet the
terminal conditions. Eight sample forms are analyzed and compared to find the
optimum form to train the neural network on the J2-perturbed Lambert problem.
The accuracy and performance of this novel approach will be demonstrated on a
representative test case: the solution of a multi-revolution J2-perturbed
Lambert problem in the Jupiter system. We will compare the performance of the
proposed approach against a classical standard shooting method and a
homotopy-based perturbed Lambert algorithm. It will be shown that, for a
comparable level of accuracy, the proposed method is significantly faster than
the other two.

Trajectory Prediction (TP) is an important research topic in computer vision
and robotics fields. Recently, many stochastic TP models have been proposed to
deal with this problem and have achieved better performance than the
traditional models with deterministic trajectory outputs. However, these
stochastic models can generate a number of future trajectories with different
qualities. They are lack of self-evaluation ability, that is, to examine the
rationality of their prediction results, thus failing to guide users to
identify high-quality ones from their candidate results. This hinders them from
playing their best in real applications. In this paper, we make up for this
defect and propose TPAD, a novel TP evaluation method based on the trajectory
Anomaly Detection (AD) technique. In TPAD, we firstly combine the Automated
Machine Learning (AutoML) technique and the experience in the AD and TP field
to automatically design an effective trajectory AD model. Then, we utilize the
learned trajectory AD model to examine the rationality of the predicted
trajectories, and screen out good TP results for users. Extensive experimental
results demonstrate that TPAD can effectively identify near-optimal prediction
results, improving stochastic TP models' practical application effect.

Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet
powerful way to diagnose heart disease. ECG studies using Machine Learning to
automatically detect abnormal heartbeats so far depend on large, manually
annotated datasets. While collecting vast amounts of unlabeled data can be
straightforward, the point-by-point annotation of abnormal heartbeats is
tedious and expensive. We explore the use of multiple weak supervision sources
to learn diagnostic models of abnormal heartbeats via human designed
heuristics, without using ground truth labels on individual data points. Our
work is among the first to define weak supervision sources directly on time
series data. Results show that with as few as six intuitive time series
heuristics, we are able to infer high quality probabilistic label estimates for
over 100,000 heartbeats with little human effort, and use the estimated labels
to train competitive classifiers evaluated on held out test data.

Causal discovery between collections of time-series data can help diagnose
causes of symptoms and hopefully prevent faults before they occur. However,
reliable causal discovery can be very challenging, especially when the data
acquisition rate varies (i.e., non-uniform data sampling), or in the presence
of missing data points (e.g., sparse data sampling). To address these issues,
we proposed a new system comprised of two parts, the first part fills missing
data with a Gaussian Process Regression, and the second part leverages an Echo
State Network, which is a type of reservoir computer (i.e., used for chaotic
system modeling) for Causal discovery. We evaluate the performance of our
proposed system against three other off-the-shelf causal discovery algorithms,
namely, structural expectation-maximization, sub-sampled linear auto-regression
absolute coefficients, and multivariate Granger Causality with vector
auto-regressive using the Tennessee Eastman chemical dataset; we report on
their corresponding Matthews Correlation Coefficient(MCC) and Receiver
Operating Characteristic curves (ROC) and show that the proposed system
outperforms existing algorithms, demonstrating the viability of our approach to
discover causal relationships in a complex system with missing entries.

Federated learning (FL) is a training technique that enables client devices
to jointly learn a shared model by aggregating locally-computed models without
exposing their raw data. While most of the existing work focuses on improving
the FL model accuracy, in this paper, we focus on the improving the training
efficiency, which is often a hurdle for adopting FL in real-world applications.
Specifically, we design an efficient FL framework which jointly optimizes model
accuracy, processing latency and communication efficiency, all of which are
primary design considerations for real implementation of FL. Inspired by the
recent success of Multi-Agent Reinforcement Learning (MARL) in solving complex
control problems, we present \textit{FedMarl}, an MARL-based FL framework which
performs efficient run-time client selection. Experiments show that FedMarl can
significantly improve model accuracy with much lower processing latency and

Numerical simulations of geophysical and atmospheric flows have to rely on
parameterizations of subgrid scale processes due to their limited spatial
resolution. Despite substantial progress in developing parameterization (or
closure) models for subgrid scale (SGS) processes using physical insights and
mathematical approximations, they remain imperfect and can lead to inaccurate
predictions. In recent years, machine learning has been successful in
extracting complex patterns from high-resolution spatio-temporal data, leading
to improved parameterization models, and ultimately better coarse grid
prediction. However, the inability to satisfy known physics and poor
generalization hinders the application of these models for real-world problems.
In this work, we propose a frame invariant closure approach to improve the
accuracy and generalizability of deep learning-based subgrid scale closure
models by embedding physical symmetries directly into the structure of the
neural network. Specifically, we utilized specialized layers within the
convolutional neural network in such a way that desired constraints are
theoretically guaranteed without the need for any regularization terms. We
demonstrate our framework for a two-dimensional decaying turbulence test case
mostly characterized by the forward enstrophy cascade. We show that our frame
invariant SGS model (i) accurately predicts the subgrid scale source term, (ii)
respects the physical symmetries such as translation, Galilean, and rotation
invariance, and (iii) is numerically stable when implemented in coarse-grid
simulation with generalization to different initial conditions and Reynolds
number. This work builds a bridge between extensive physics-based theories and
data-driven modeling paradigms, and thus represents a promising step towards
the development of physically consistent data-driven turbulence closure models.

Open-set recognition generalizes a classification task by classifying test
samples as one of the known classes from training or "unknown." As novel cancer
drug cocktails with improved treatment are continually discovered, predicting
cancer treatments can naturally be formulated in terms of an open-set
recognition problem. Drawbacks, due to modeling unknown samples during
training, arise from straightforward implementations of prior work in
healthcare open-set learning. Accordingly, we reframe the problem methodology
and apply a recent existing Gaussian mixture variational autoencoder model,
which achieves state-of-the-art results for image datasets, to breast cancer
patient data. Not only do we obtain more accurate and robust classification
results, with a 24.5% average F1 increase compared to a recent method, but we
also reexamine open-set recognition in terms of deployability to a clinical
setting.

Attention-based deep networks have been successfully applied on textual data
in the field of NLP. However, their application on protein sequences poses
additional challenges due to the weak semantics of the protein words, unlike
the plain text words. These unexplored challenges faced by the standard
attention technique include (i) vanishing attention score problem and (ii) high
variations in the attention distribution. In this regard, we introduce a novel
{\lambda}-scaled attention technique for fast and efficient modeling of the
protein sequences that addresses both the above problems. This is used to
develop the {\lambda}-scaled attention network and is evaluated for the task of
protein function prediction implemented at the protein sub-sequence level.
Experiments on the datasets for biological process (BP) and molecular function
(MF) showed significant improvements in the F1 score values for the proposed
{\lambda}-scaled attention technique over its counterpart approach based on the
standard attention technique (+2.01% for BP and +4.67% for MF) and
state-of-the-art ProtVecGen-Plus approach (+2.61% for BP and +4.20% for MF).
Further, fast convergence (converging in half the number of epochs) and
efficient learning (in terms of very low difference between the training and
validation losses) were also observed during the training process.

E-commerce product pages on the web often present product specification data
in structured tabular blocks. Extraction of these product attribute-value
specifications has benefited applications like product catalogue curation,
search, question answering, and others. However, across different Websites,
there is a wide variety of HTML elements (like <table>, <ul>, <div>, <span>,
<dl> etc.) typically used to render these blocks that makes their automatic
extraction a challenge. Most of the current research has focused on extracting
product specifications from tables and lists and, therefore, suffers from
recall when applied to a large-scale extraction setting. In this paper, we
present a product specification extraction approach that goes beyond tables or
lists and generalizes across the diverse HTML elements used for rendering
specification blocks. Using a combination of hand-coded features and deep
learned spatial and token features, we first identify the specification blocks
on a product page. We then extract the product attribute-value pairs from these
blocks following an approach inspired by wrapper induction. We created a
labeled dataset of product specifications extracted from 14,111 diverse
specification blocks taken from a range of different product websites. Our
experiments show the efficacy of our approach compared to the current
specification extraction models and support our claim about its application to
large-scale product specification extraction.

For decades, researchers on Vehicular Ad-hoc Networks (VANETs) and autonomous
vehicles presented various solutions for vehicular safety and autonomy,
respectively. Yet, the developed work in these two areas has been mostly
conducted in their own separate worlds, and barely affect one-another despite
the obvious relationships. In the coming years, the Internet of Vehicles (IoV),
encompassing sensing, communications, connectivity, processing, networking, and
computation is expected to bridge many technologies to offer value-added
information for the navigation of self-driving vehicles, to reduce vehicle on
board computation, and to deliver desired functionalities. Potentials for
bridging the gap between these two worlds and creating synergies of these two
technologies have recently started to attract significant attention of many
companies and government agencies. In this article, we first present a
comprehensive survey and an overview of the emerging key challenges related to
the two worlds of Vehicular Clouds (VCs) including communications, networking,
traffic modelling, medium access, VC Computing (VCC), VC collation strategies,
security issues, and autonomous driving (AD) including 3D environment learning
approaches and AD enabling deep-learning, computer vision and Artificial
Intelligence (AI) techniques. We then discuss the recent related work and
potential trends on merging these two worlds in order to enrich vehicle
cognition of its surroundings, and enable safer and more informed and
coordinated AD systems. Compared to other survey papers, this work offers more
detailed summaries of the most relevant VCs and ADs systems in the literature,
along with some key challenges and insights on how different technologies fit
together to deliver safety, autonomy and infotainment services.

We consider the general problem of online convex optimization with
time-varying additive constraints in the presence of predictions for the next
cost and constraint functions. A novel primal-dual algorithm is designed by
combining a Follow-The-Regularized-Leader iteration with prediction-adaptive
dynamic steps. The algorithm achieves $\mathcal O(T^{\frac{3-\beta}{4}})$
regret and $\mathcal O(T^{\frac{1+\beta}{2}})$ constraint violation bounds that
are tunable via parameter $\beta\!\in\![1/2,1)$ and have constant factors that
shrink with the predictions quality, achieving eventually $\mathcal O(1)$
regret for perfect predictions. Our work extends the FTRL framework for this
constrained OCO setting and outperforms the respective state-of-the-art
greedy-based solutions, without imposing conditions on the quality of
predictions, the cost functions or the geometry of constraints, beyond
convexity.

Sensor embedded glove systems have been reported to require careful, time
consuming and precise calibrations on a per user basis in order to obtain
consistent usable data. We have developed a low cost, flex sensor based smart
glove system that may be resilient to the common limitations of data gloves.
This system utilizes an Arduino based micro controller as well as a single flex
sensor on each finger. Feedback from the Arduinos analog to digital converter
can be used to infer objects dimensional properties, the reactions of each
individual finger will differ with respect to the size and shape of a grasped
object. In this work, we report its use in statistically differentiating
stationary objects of spherical and cylindrical shapes of varying radii
regardless of the variations introduced by gloves users. Using our sensor
embedded glove system, we explored the practicability of object classification
based on the tactile sensor responses from each finger of the smart glove. An
estimated standard error of the mean was calculated from each of the of five
fingers averaged flex sensor readings. Consistent with the literature, we found
that there is a systematic dependence between an objects shape, dimension and
the flex sensor readings. The sensor output from at least one finger, indicated
a non-overlapping confidence interval when comparing spherical and cylindrical
objects of the same radius. When sensing spheres and cylinders of varying
sizes, all five fingers had a categorically varying reaction to each shape. We
believe that our findings could be used in machine learning models for
real-time object identification.

UAV-based image retrieval in modern agriculture enables gathering large
amounts of spatially referenced crop image data. In large-scale experiments,
however, UAV images suffer from containing a multitudinous amount of crops in a
complex canopy architecture. Especially for the observation of temporal
effects, this complicates the recognition of individual plants over several
images and the extraction of relevant information tremendously. In this work,
we present a hands-on workflow for the automatized temporal and spatial
identification and individualization of crop images from UAVs abbreviated as
"cataloging" based on comprehensible computer vision methods. We evaluate the
workflow on two real-world datasets. One dataset is recorded for observation of
Cercospora leaf spot - a fungal disease - in sugar beet over an entire growing
cycle. The other one deals with harvest prediction of cauliflower plants. The
plant catalog is utilized for the extraction of single plant images seen over
multiple time points. This gathers large-scale spatio-temporal image dataset
that in turn can be applied to train further machine learning models including
various data layers. The presented approach improves analysis and
interpretation of UAV data in agriculture significantly. By validation with
some reference data, our method shows an accuracy that is similar to more
complex deep learning-based recognition techniques. Our workflow is able to
automatize plant cataloging and training image extraction, especially for large
datasets.

A new approach called ABRF (the attention-based random forest) and its
modifications for applying the attention mechanism to the random forest (RF)
for regression and classification are proposed. The main idea behind the
proposed ABRF models is to assign attention weights with trainable parameters
to decision trees in a specific way. The weights depend on the distance between
an instance, which falls into a corresponding leaf of a tree, and instances,
which fall in the same leaf. This idea stems from representation of the
Nadaraya-Watson kernel regression in the form of a RF. Three modifications of
the general approach are proposed. The first one is based on applying the
Huber's contamination model and on computing the attention weights by solving
quadratic or linear optimization problems. The second and the third
modifications use the gradient-based algorithms for computing trainable
parameters. Numerical experiments with various regression and classification
datasets illustrate the proposed method.

Active learning is an increasingly important branch of machine learning and a
powerful technique for natural language processing. The main advantage of
active learning is its potential to reduce the amount of labeled data needed to
learn high-performing models. A vital aspect of an effective active learning
algorithm is the determination of when to stop obtaining additional labeled
data. Several leading state-of-the-art stopping methods use a stop set to help
make this decision. However, there has been relatively less attention given to
the choice of stop set than to the stopping algorithms that are applied on the
stop set. Different choices of stop sets can lead to significant differences in
stopping method performance. We investigate the impact of different stop set
choices on different stopping methods. This paper shows the choice of the stop
set can have a significant impact on the performance of stopping methods and
the impact is different for stability-based methods from that on
confidence-based methods. Furthermore, the unbiased representative stop sets
suggested by original authors of methods work better than the systematically
biased stop sets used in recently published work, and stopping methods based on
stabilizing predictions have stronger performance than confidence-based
stopping methods when unbiased representative stop sets are used. We provide
the largest quantity of experimental results on the impact of stop sets to
date. The findings are important for helping to illuminate the impact of this
important aspect of stopping methods that has been under-considered in recently
published work and that can have a large practical impact on the performance of
stopping methods for important semantic computing applications such as
technology assisted review and text classification more broadly.

The clarity of microscopic images is vital in biology research and diagnosis.
When taking microscopy images at cell or molecule level, mechanical drift
occurs and could be difficult and expansive to counter. Such a problem could be
overcome by developing an end-to-end deep learning-based workflow capable of
predicting in focused microscopic images from out-of-focused counterparts. In
our model, we adopt a structure of multi-level U-net, each level connected
head-to-tail with corresponding convolution layers from each other. In contrast
to the conventional coarse-to-fine model, our model uses the knowledge
distilled from the coarse network transferred to the finer network. We evaluate
the performance of our model and found our method to be effective and has a
better performance by comparing the results with existing models.

The model-based reinforcement learning paradigm, which uses planning
algorithms and neural network models, has recently achieved unprecedented
results in diverse applications, leading to what is now known as deep
reinforcement learning. These agents are quite complex and involve multiple
components, factors that can create challenges for research. In this work, we
propose a new modular software architecture suited for these types of agents,
and a set of building blocks that can be easily reused and assembled to
construct new model-based reinforcement learning agents. These building blocks
include planning algorithms, policies, and loss functions.
  We illustrate the use of this architecture by combining several of these
building blocks to implement and test agents that are optimized to three
different test environments: Cartpole, Minigrid, and Tictactoe. One particular
planning algorithm, made available in our implementation and not previously
used in reinforcement learning, which we called averaged minimax, achieved good
results in the three tested environments.
  Experiments performed with this architecture have shown that the best
combination of planning algorithm, policy, and loss function is heavily problem
dependent. This result provides evidence that the proposed architecture, which
is modular and reusable, is useful for reinforcement learning researchers who
want to study new environments and techniques.

Federated learning (FL) provides a high efficient decentralized machine
learning framework, where the training data remains distributed at remote
clients in a network. Though FL enables a privacy-preserving mobile edge
computing framework using IoT devices, recent studies have shown that this
approach is susceptible to poisoning attacks from the side of remote clients.
To address the poisoning attacks on FL, we provide a \textit{two-phase} defense
algorithm called {Lo}cal {Ma}licious Facto{r} (LoMar). In phase I, LoMar scores
model updates from each remote client by measuring the relative distribution
over their neighbors using a kernel density estimation method. In phase II, an
optimal threshold is approximated to distinguish malicious and clean updates
from a statistical perspective. Comprehensive experiments on four real-world
datasets have been conducted, and the experimental results show that our
defense strategy can effectively protect the FL system. {Specifically, the
defense performance on Amazon dataset under a label-flipping attack indicates
that, compared with FG+Krum, LoMar increases the target label testing accuracy
from $96.0\%$ to $98.8\%$, and the overall averaged testing accuracy from
$90.1\%$ to $97.0\%$.

In this study, we propose a novel machine learning based algorithm to improve
the performance of beyond 5 generation (B5G) wireless communication system that
is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and
Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin
support vector machine (SVM) problem is used to provide an automatic modulation
classifier (AMC) and a signal power to noise and interference ratio (SINR)
estimator. The estimation results of AMC and SINR are used to reassign the
modulation type, codding rate, and transmit power through frames of eNode B
connections. The AMC success rate versus SINR, total power consuming, and sum
capacity are evaluated for OFDM-NOMA assisted 5G system. Results show
improvement of success rate compared of some published method. Furthermore, the
algorithm directly computes SINR after signal is detected by successive
interference cancellation (SIC) and before any signal decoding. Moreover,
because of the direct sense of physical channel, the presented algorithm can
discount occupied symbols (overhead signaling) for channel quality information
(CQI) in network communication signaling. The results also prove that the
proposed algorithm reduces the total power consumption and increases the sum
capacity through the eNode B connections. Simulation results in compare to
other algorithms show more successful AMC, efficient SINR estimator, easier
practical implantation, less overhead signaling, less power consumption, and
more capacity achievement.

Recent breakthroughs in high-resolution imaging of biomolecules in solution
with cryo-electron microscopy (cryo-EM) have unlocked new doors for the
reconstruction of molecular volumes, thereby promising further advances in
biology, chemistry, and pharmacological research. Recent next-generation volume
reconstruction algorithms that combine generative modeling with end-to-end
unsupervised deep learning techniques have shown promising preliminary results,
but still face considerable technical and theoretical hurdles when applied to
experimental cryo-EM images. In light of the proliferation of such methods, we
propose here a critical review of recent advances in the field of deep
generative modeling for cryo-EM volume reconstruction. The present review aims
to (i) unify and compare these new methods using a consistent statistical
framework, (ii) present them using a terminology familiar to machine learning
researchers and computational biologists with no specific background in
cryo-EM, and (iii) provide the necessary perspective on current advances to
highlight their relative strengths and weaknesses, along with outstanding
bottlenecks and avenues for improvements in the field. This review might also
raise the interest of computer vision practitioners, as it highlights
significant limits of deep generative models in low signal-to-noise regimes --
therefore emphasizing a need for new theoretical and methodological
developments.

Due to the nature of applications such as critical infrastructure and the
Internet of Things etc. side channel analysis attacks are becoming a serious
threat. Side channel analysis attacks take advantage from the fact that the
behavior of crypto implementations can be observed and provides hints that
simplify revealing keys. A new type of SCA are the so called horizontal SCAs.
Well known randomization based countermeasures are effective means against
vertical DPA attacks but they are not effective against horizontal DPA attacks.
In this paper we investigate how the formula used to implement the
multiplication of $GF(2^n)$-elements influences the results of horizontal DPA
attacks against a Montgomery kP implementation. We implemented 5 designs with
different partial multipliers, i.e. based on different multiplication formulae.
We used two different technologies, i.e. a 130 and a 250 nm technology, to
simulate power traces for our analysis. We show that the implemented
multiplication formula influences the success of horizontal attacks
significantly, but we also learned that its impact differs from technology to
technology. Our analysis also reveals that the use of different multiplication
formulae as the single countermeasure is not sufficient to protect
cryptographic designs against horizontal DPA attacks.

Standard deep learning algorithms are implemented using floating-point real
numbers. This presents an obstacle for implementing them on low-end devices
which may not have dedicated floating-point units (FPUs). As a result,
researchers in tinyML have considered machine learning algorithms that can
train and run a deep neural network (DNN) on a low-end device using integer
operations only. In this paper we propose PocketNN, a light and self-contained
proof-of-concept framework in pure C++ for the training and inference of DNNs
using only integers. Unlike other approaches, PocketNN directly operates on
integers without requiring any explicit quantization algorithms or customized
fixed-point formats. This was made possible by pocket activations, which are a
family of activation functions devised for integer-only DNNs, and an emerging
DNN training algorithm called direct feedback alignment (DFA). Unlike the
standard backpropagation (BP), DFA trains each layer independently, thus
avoiding integer overflow which is a key problem when using BP with
integer-only operations. We used PocketNN to train some DNNs on two well-known
datasets, MNIST and Fashion-MNIST. Our experiments show that the DNNs trained
with our PocketNN achieved 96.98% and 87.7% accuracies on MNIST and
Fashion-MNIST datasets, respectively. The accuracies are very close to the
equivalent DNNs trained using BP with floating-point real number operations,
such that accuracy degradations were just 1.02%p and 2.09%p, respectively.
Finally, our PocketNN has high compatibility and portability for low-end
devices as it is open source and implemented in pure C++ without any

Weakly supervised learning can help local feature methods to overcome the
obstacle of acquiring a large-scale dataset with densely labeled
correspondences. However, since weak supervision cannot distinguish the losses
caused by the detection and description steps, directly conducting weakly
supervised learning within a joint describe-then-detect pipeline suffers
limited performance. In this paper, we propose a decoupled describe-then-detect
pipeline tailored for weakly supervised local feature learning. Within our
pipeline, the detection step is decoupled from the description step and
postponed until discriminative and robust descriptors are learned. In addition,
we introduce a line-to-window search strategy to explicitly use the camera pose
information for better descriptor learning. Extensive experiments show that our
method, namely PoSFeat (Camera Pose Supervised Feature), outperforms previous
fully and weakly supervised methods and achieves state-of-the-art performance
on a wide range of downstream tasks.

Toxic contents in online product review are a common phenomenon. A content is
perceived to be toxic when it is rude, disrespectful, or unreasonable and make
individuals leave the discussion. Machine learning algorithms helps the sell
side community to identify such toxic patterns and eventually moderate such
inputs. Yet, the extant literature provides fewer information about the
sentiment of a prospective consumer on the perception of a product after being
exposed to such toxic review content. In this study, we collect a balanced data
set of review comments from 18 different players segregated into three
different sectors from google play-store. Then we calculate the sentence-level
sentiment and toxicity score of individual review content. Finally, we use
structural equation modelling to quantitatively study the influence of toxic
content on overall product sentiment. We observe that comment toxicity
negatively influences overall product sentiment but do not exhibit a mediating
effect over reviewer score to influence sector-wise relative rating.

Hilsa is the national fish of Bangladesh. Bangladesh is earning a lot of
foreign currency by exporting this fish. Unfortunately, in recent days, some
unscrupulous businessmen are selling fake Hilsa fishes to gain profit. The
Sardines and Sardinella are the most sold in the market as Hilsa. The
government agency of Bangladesh, namely Bangladesh Food Safety Authority said
that these fake Hilsa fish contain high levels of cadmium and lead which are
detrimental for humans. In this research, we have proposed a method that can
readily identify original Hilsa fish and fake Hilsa fish. Based on the research
available on online literature, we are the first to do research on identifying
original Hilsa fish. We have collected more than 16,000 images of original and
counterfeit Hilsa fish. To classify these images, we have used several deep
learning-based models. Then, the performance has been compared between them.
Among those models, DenseNet201 achieved the highest accuracy of 97.02%.

The task of temporal grounding aims to locate video moment in an untrimmed
video, with a given sentence query. This paper for the first time investigates
some superficial biases that are specific to the temporal grounding task, and
proposes a novel targeted solution. Most alarmingly, we observe that existing
temporal ground models heavily rely on some biases (e.g., high preference on
frequent concepts or certain temporal intervals) in the visual modal. This
leads to inferior performance when generalizing the model in cross-scenario
test setting. To this end, we propose a novel method called Debiased Temporal
Language Localizer (DebiasTLL) to prevent the model from naively memorizing the
biases and enforce it to ground the query sentence based on true inter-modal
relationship. Debias-TLL simultaneously trains two models. By our design, a
large discrepancy of these two models' predictions when judging a sample
reveals higher probability of being a biased sample. Harnessing the informative
discrepancy, we devise a data re-weighing scheme for mitigating the data
biases. We evaluate the proposed model in cross-scenario temporal grounding,
where the train / test data are heterogeneously sourced. Experiments show
large-margin superiority of the proposed method in comparison with

Distributed document representation is one of the basic problems in natural
language processing. Currently distributed document representation methods
mainly consider the context information of words or sentences. These methods do
not take into account the coherence of the document as a whole, e.g., a
relation between the paper title and abstract, headline and description, or
adjacent bodies in the document. The coherence shows whether a document is
meaningful, both logically and syntactically, especially in scientific
documents (papers or patents, etc.). In this paper, we propose a coupled text
pair embedding (CTPE) model to learn the representation of scientific
documents, which maintains the coherence of the document with coupled text
pairs formed by segmenting the document. First, we divide the document into two
parts (e.g., title and abstract, etc) which construct a coupled text pair.
Then, we adopt negative sampling to construct uncoupled text pairs whose two
parts are from different documents. Finally, we train the model to judge
whether the text pair is coupled or uncoupled and use the obtained embedding of
coupled text pairs as the embedding of documents. We perform experiments on
three datasets for one information retrieval task and two recommendation tasks.
The experimental results verify the effectiveness of the proposed CTPE model.

An increasing number of mobile applications rely on Machine Learning (ML)
routines for analyzing data. Executing such tasks at the user devices saves the
energy spent on transmitting and processing large data volumes at distant
cloud-deployed servers. However, due to memory and computing limitations, the
devices often cannot support the required resource-intensive routines and fail
to accurately execute the tasks. In this work, we address the problem of
edge-assisted analytics in resource-constrained systems by proposing and
evaluating a rigorous selective offloading framework. The devices execute their
tasks locally and outsource them to cloudlet servers only when they predict a
significant performance improvement. We consider the practical scenario where
the offloading gain and resource costs are time-varying; and propose an online
optimization algorithm that maximizes the service performance without requiring
to know this information. Our approach relies on an approximate dual
subgradient method combined with a primal-averaging scheme, and works under
minimal assumptions about the system stochasticity. We fully implement the
proposed algorithm in a wireless testbed and evaluate its performance using a
state-of-the-art image recognition application, finding significant performance
gains and cost savings.

Reconfigurable intelligent surface (RIS) is an emerging technology for future
wireless communication systems. In this work, we consider downlink spatial
multiplexing enabled by the RIS for weighted sum-rate (WSR) maximization. In
the literature, most solutions use alternating gradient-based optimization,
which has moderate performance, high complexity, and limited scalability. We
propose to apply a fully convolutional network (FCN) to solve this problem,
which was originally designed for semantic segmentation of images. The
rectangular shape of the RIS and the spatial correlation of channels with
adjacent RIS antennas due to the short distance between them encourage us to
apply it for the RIS configuration. We design a set of channel features that
includes both cascaded channels via the RIS and the direct channel. In the base
station (BS), the differentiable minimum mean squared error (MMSE) precoder is
used for pretraining and the weighted minimum mean squared error (WMMSE)
precoder is then applied for fine-tuning, which is nondifferentiable, more
complex, but achieves a better performance. Evaluation results show that the
proposed solution has higher performance and allows for a faster evaluation
than the baselines. Hence it scales better to a large number of antennas,
advancing the RIS one step closer to practical deployment.

Due to the wavelength-dependent light attenuation, refraction and scattering,
underwater images usually suffer from color distortion and blurred details.
However, due to the limited number of paired underwater images with undistorted
images as reference, training deep enhancement models for diverse degradation
types is quite difficult. To boost the performance of data-driven approaches,
it is essential to establish more effective learning mechanisms that mine
richer supervised information from limited training sample resources. In this
paper, we propose a novel underwater image enhancement network, called
SGUIE-Net, in which we introduce semantic information as high-level guidance
across different images that share common semantic regions. Accordingly, we
propose semantic region-wise enhancement module to perceive the degradation of
different semantic regions from multiple scales and feed it back to the global
attention features extracted from its original scale. This strategy helps to
achieve robust and visually pleasant enhancements to different semantic
objects, which should thanks to the guidance of semantic information for
differentiated enhancement. More importantly, for those degradation types that
are not common in the training sample distribution, the guidance connects them
with the already well-learned types according to their semantic relevance.
Extensive experiments on the publicly available datasets and our proposed
dataset demonstrated the impressive performance of SGUIE-Net. The code and
proposed dataset are available at: https://trentqq.github.io/SGUIE-Net.html

Humans ability to transfer knowledge through teaching is one of the essential
aspects for human intelligence. A human teacher can track the knowledge of
students to customize the teaching on students needs. With the rise of online
education platforms, there is a similar need for machines to track the
knowledge of students and tailor their learning experience. This is known as
the Knowledge Tracing (KT) problem in the literature. Effectively solving the
KT problem would unlock the potential of computer-aided education applications
such as intelligent tutoring systems, curriculum learning, and learning
materials' recommendation. Moreover, from a more general viewpoint, a student
may represent any kind of intelligent agents including both human and
artificial agents. Thus, the potential of KT can be extended to any machine
teaching application scenarios which seek for customizing the learning
experience for a student agent (i.e., a machine learning model). In this paper,
we provide a comprehensive and systematic review for the KT literature. We
cover a broad range of methods starting from the early attempts to the recent
state-of-the-art methods using deep learning, while highlighting the
theoretical aspects of models and the characteristics of benchmark datasets.
Besides these, we shed light on key modelling differences between closely
related methods and summarize them in an easy-to-understand format. Finally, we
discuss current research gaps in the KT literature and possible future research
and application directions.

Loss of Signal (LOS) represents a significant cost for operators of optical
networks. By studying large sets of real-world Performance Monitoring (PM) data
collected from six international optical networks, we find that it is possible
to forecast LOS events with good precision 1-7 days before they occur, albeit
at relatively low recall, with supervised machine learning (ML). Our study
covers twelve facility types, including 100G lines and ETH10G clients. We show
that the precision for a given network improves when training on multiple
networks simultaneously relative to training on an individual network.
Furthermore, we show that it is possible to forecast LOS from all facility
types and all networks with a single model, whereas fine-tuning for a
particular facility or network only brings modest improvements. Hence our ML
models remain effective for optical networks previously unknown to the model,
which makes them usable for commercial applications.

The mathematical forces at work behind Generative Adversarial Networks raise
challenging theoretical issues. Motivated by the important question of
characterizing the geometrical properties of the generated distributions, we
provide a thorough analysis of Wasserstein GANs (WGANs) in both the finite
sample and asymptotic regimes. We study the specific case where the latent
space is univariate and derive results valid regardless of the dimension of the
output space. We show in particular that for a fixed sample size, the optimal
WGANs are closely linked with connected paths minimizing the sum of the squared
Euclidean distances between the sample points. We also highlight the fact that
WGANs are able to approach (for the 1-Wasserstein distance) the target
distribution as the sample size tends to infinity, at a given convergence rate
and provided the family of generative Lipschitz functions grows appropriately.
We derive in passing new results on optimal transport theory in the
semi-discrete setting.

It is observed that high classification performance is achieved for one- and
two-dimensional signals by using deep learning methods. In this context, most
researchers have tried to classify hyperspectral images by using deep learning
methods and classification success over 90% has been achieved for these images.
Deep neural networks (DNN) actually consist of two parts: i) Convolutional
neural network (CNN) and ii) fully connected neural network (FCNN). While CNN
determines the features, FCNN is used in classification. In classification of
the hyperspectral images, it is observed that almost all of the researchers
used 2D or 3D convolution filters on the spatial data beside spectral data
(features). It is convenient to use convolution filters on images or time
signals. In hyperspectral images, each pixel is represented by a signature
vector which consists of individual features that are independent of each
other. Since the order of the features in the vector can be changed, it doesn't
make sense to use convolution filters on these features as on time signals. At
the same time, since the hyperspectral images do not have a textural structure,
there is no need to use spatial data besides spectral data. In this study,
hyperspectral images of Indian pines, Salinas, Pavia centre, Pavia university
and Botswana are classified by using only fully connected neural network and
the spectral data with one dimensional. An average accuracy of 97.5% is
achieved for the test sets of all hyperspectral images.

Machine learning algorithms have opened a breach in the fortress of the
prediction of high-dimensional chaotic systems. Their ability to find hidden
correlations in data can be exploited to perform model-free forecasting of
spatiotemporal chaos and extreme events. However, the extensive feature of
these evolutions constitutes a critical limitation for full-size forecasting
processes. Hence, the main challenge for forecasting relevant events is to
establish the set of pertinent information. Here, we identify precursors from
the transfer entropy of the system and a deep Long Short-Term Memory network to
forecast the complex dynamics of a system evolving in a high-dimensional
spatiotemporal chaotic regime. Performances of this triggerable model-free
prediction protocol based on the information flowing map are tested from
experimental data originating from a passive resonator operating in such a
complex nonlinear regime. We have been able to predict the occurrence of
extreme events up to 9 round trips after the detection of precursor, i.e., 3
times the horizon provided by Lyapunov exponents, with 92 % of true positive
predictions leading to 60 % of accuracy.

Clustering Text has been an important problem in the domain of Natural
Language Processing. While there are techniques to cluster text based on using
conventional clustering techniques on top of contextual or non-contextual
vector space representations, it still remains a prevalent area of research
possible to various improvements in performance and implementation of these
techniques. This paper discusses a novel technique to cluster text using
attention mechanisms. Attention Mechanisms have proven to be highly effective
in various NLP tasks in recent times. This paper extends the idea of attention
mechanism in clustering space and sheds some light on a whole new area of
research

In this paper, we propose a novel nonconvex approach to robust principal
component analysis for HSI denoising, which focuses on simultaneously
developing more accurate approximations to both rank and column-wise sparsity
for the low-rank and sparse components, respectively. In particular, the new
method adopts the log-determinant rank approximation and a novel
$\ell_{2,\log}$ norm, to restrict the local low-rank or column-wisely sparse
properties for the component matrices, respectively. For the
$\ell_{2,\log}$-regularized shrinkage problem, we develop an efficient,
closed-form solution, which is named $\ell_{2,\log}$-shrinkage operator. The
new regularization and the corresponding operator can be generally used in
other problems that require column-wise sparsity. Moreover, we impose the
spatial-spectral total variation regularization in the log-based nonconvex RPCA
model, which enhances the global piece-wise smoothness and spectral consistency
from the spatial and spectral views in the recovered HSI. Extensive experiments
on both simulated and real HSIs demonstrate the effectiveness of the proposed
method in denoising HSIs.

A deep understanding of defects is essential for the optimisation of
materials for solar energy conversion. This is particularly true for metal
oxide photo(electro)catalysts, which typically feature high concentrations of
charged point defects that are electronically active. In photovoltaic
materials, except for selected dopants, defects are considered detrimental and
should be eliminated to minimise charge recombination. However, photocatalysis
is a more complex process where defects can play an active role, for example,
by stabilising charge separation and mediating rate-limiting catalytic steps.
Here, we review the behaviour of electronic defects in metal oxides, paying
special attention to the principles underpinning the formation and function of
trapped charges in the form of polarons. We focus on how defects alter the
electronic structure, statically or transiently upon illumination, and discuss
the implications of such changes in light-driven catalytic reactions. Finally,
we consider the applicability of lessons learned from oxide defect chemistry to
new photocatalysts based on carbon nitrides, polymers and metal halide
perovskites.

Multi-view learning accomplishes the task objectives of classification by
leverag-ing the relationships between different views of the same object. Most
existing methods usually focus on consistency and complementarity between
multiple views. But not all of this information is useful for classification
tasks. Instead, it is the specific discriminating information that plays an
important role. Zhong Zhang et al. explore the discriminative and
non-discriminative information exist-ing in common and view-specific parts
among different views via joint non-negative matrix factorization. In this
paper, we improve this algorithm on this ba-sis by using the cross entropy loss
function to constrain the objective function better. At last, we implement
better classification effect than original on the same data sets and show its
superiority over many state-of-the-art algorithms.

Since convolutional neural networks (CNNs) have revolutionized the image
processing field, they have been widely applied in the audio context. A common
approach is to convert the one-dimensional audio signal time series to
two-dimensional images using a time-frequency decomposition method. Also it is
common to discard the phase information. In this paper, we propose to map
one-dimensional audio waveforms to two-dimensional images using space filling
curves (SFCs). These mappings do not compress the input signal, while
preserving its local structure. Moreover, the mappings benefit from progress
made in deep learning and the large collection of existing computer vision
networks. We test eight SFCs on two keyword spotting problems. We show that the
Z curve yields the best results due to its shift equivariance under convolution
operations. Additionally, the Z curve produces comparable results to the widely
used mel frequency cepstral coefficients across multiple CNNs.

Falling, especially in the elderly, is a critical issue to care for and
surveil. There have been many studies focusing on fall detection. However, from
our survey, there is still no research indicating the prior-fall activities,
which we believe that they have a strong correlation with the intensity of the
fall. The purpose of this research is to develop a fall alert system that also
identifies prior-fall activities. First, we want to find a suitable location to
attach a sensor to the body. We created multiple-spot on-body devices to
collect various activity data. We used that dataset to train 5 different
classification models. We selected the XGBoost classification model for
detecting a prior-fall activity and the chest location for use in fall
detection from a comparison of the detection accuracy. We then tested 3
existing fall detection threshold algorithms to detect fall and fall to their
knees first, and selected the 3-phase threshold algorithm of Chaitep and
Chawachat [3] in our system. From the experiment, we found that the fall
detection accuracy is 88.91%, the fall to their knees first detection accuracy
is 91.25%, and the average accuracy of detection of prior-fall activities is
86.25%. Although we use an activity dataset of young to middle-aged adults
(18-49 years), we are confident that this system can be developed to monitor
activities before the fall, especially in the elderly, so that caretakers can
better manage the situation.

Automated monitoring of dark web (DW) platforms on a large scale is the first
step toward developing proactive Cyber Threat Intelligence (CTI). While there
are efficient methods for collecting data from the surface web, large-scale
dark web data collection is often hindered by anti-crawling measures. In
particular, text-based CAPTCHA serves as the most prevalent and prohibiting
type of these measures in the dark web. Text-based CAPTCHA identifies and
blocks automated crawlers by forcing the user to enter a combination of
hard-to-recognize alphanumeric characters. In the dark web, CAPTCHA images are
meticulously designed with additional background noise and variable character
length to prevent automated CAPTCHA breaking. Existing automated CAPTCHA
breaking methods have difficulties in overcoming these dark web challenges. As
such, solving dark web text-based CAPTCHA has been relying heavily on human
involvement, which is labor-intensive and time-consuming. In this study, we
propose a novel framework for automated breaking of dark web CAPTCHA to
facilitate dark web data collection. This framework encompasses a novel
generative method to recognize dark web text-based CAPTCHA with noisy
background and variable character length. To eliminate the need for human
involvement, the proposed framework utilizes Generative Adversarial Network
(GAN) to counteract dark web background noise and leverages an enhanced
character segmentation algorithm to handle CAPTCHA images with variable
character length. Our proposed framework, DW-GAN, was systematically evaluated
on multiple dark web CAPTCHA testbeds. DW-GAN significantly outperformed the
state-of-the-art benchmark methods on all datasets, achieving over 94.4%
success rate on a carefully collected real-world dark web dataset...

The gap between simulation and the real-world restrains many machine learning
breakthroughs in computer vision and reinforcement learning from being
applicable in the real world. In this work, we tackle this gap for the specific
case of camera-based navigation, formulating it as following a visual cue in
the foreground with arbitrary backgrounds. The visual cue in the foreground can
often be simulated realistically, such as a line, gate or cone. The challenge
then lies in coping with the unknown backgrounds and integrating both. As such,
the goal is to train a visual agent on data captured in an empty simulated
environment except for this foreground cue and test this model directly in a
visually diverse real world. In order to bridge this big gap, we show it's
crucial to combine following techniques namely: Randomized augmentation of the
fore- and background, regularization with both deep supervision and triplet
loss and finally abstraction of the dynamics by using waypoints rather than
direct velocity commands. The various techniques are ablated in our
experimental results both qualitatively and quantitatively finally
demonstrating a successful transfer from simulation to the real world.

Automated medical coding, an essential task for healthcare operation and
delivery, makes unstructured data manageable by predicting medical codes from
clinical documents. Recent advances in deep learning models in natural language
processing have been widely applied to this task. However, it lacks a unified
view of the design of neural network architectures for medical coding. This
review proposes a unified framework to provide a general understanding of the
building blocks of medical coding models and summarizes recent advanced models
under the proposed framework. Our unified framework decomposes medical coding
into four main components, i.e., encoder modules for text feature extraction,
mechanisms for building deep encoder architectures, decoder modules for
transforming hidden representations into medical codes, and the usage of
auxiliary information. Finally, we discuss key research challenges and future

Developing scalable solutions for training Graph Neural Networks (GNNs) for
link prediction tasks is challenging due to the high data dependencies which
entail high computational cost and huge memory footprint. We propose a new
method for scaling training of knowledge graph embedding models for link
prediction to address these challenges. Towards this end, we propose the
following algorithmic strategies: self-sufficient partitions, constraint-based
negative sampling, and edge mini-batch training. Both, partitioning strategy
and constraint-based negative sampling, avoid cross partition data transfer
during training. In our experimental evaluation, we show that our scaling
solution for GNN-based knowledge graph embedding models achieves a 16x speed up
on benchmark datasets while maintaining a comparable model performance as
non-distributed methods on standard metrics.

Accurate estimation of DL CSI is required to achieve high spectrum and energy
efficiency in massive MIMO systems. Previous works have developed
learning-based CSI feedback framework within FDD systems for efficient CSI
encoding and recovery with demonstrated benefits. However, downlink pilots for
CSI estimation by receiving terminals may occupy excessively large number of
resource elements for massive number of antennas and compromise spectrum
efficiency. To overcome this problem, we propose a new learning-based feedback
architecture for efficient encoding of partial CSI feedback of interleaved
non-overlapped antenna subarrays by exploiting CSI temporal correlation. For
ease of encoding, we further design an IFFT approach to decouple partial CSI of
antenna subarrays and to preserve partial CSI sparsity. Our results show
superior performance in indoor/outdoor scenarios by the proposed model for CSI
recovery at significantly reduced computation power and storage needs.

Herein, minimization of time-averaged age-of-information (AoI) in an energy
harvesting (EH) source equipped remote sensing setting is considered. The EH
source opportunistically samples one or multiple processes over discrete time
instants, and sends the status updates to a sink node over a time-varying
wireless link. At any discrete time instant, the EH node decides whether to
ascertain the link quality using its stored energy (called probing) and then,
decides whether to sample a process and communicate the data based on the
channel probe outcome. The trade-off is between the freshness of information
available at the sink node and the available energy at the energy buffer of the
source node. To this end, infinite horizon Markov decision process (MDP) theory
is used to formulate the problem of minimization of time-averaged expected AoI
for a single energy harvesting source node. The following two scenarios are
considered: (i) energy arrival process and channel fading process are
independent and identically distributed (i.i.d.) across time, (ii) energy
arrival process and channel fading process are Markovian. In the i.i.d.
setting, after probing a channel, the optimal source node sampling policy is
shown to be a threshold policy involving the instantaneous age of the process,
the available energy in the buffer and the instantaneous channel quality as the
decision variables. Also, for unknown channel state and energy harvesting
characteristics, a variant of the Q-learning algorithm is proposed for the
two-stage action taken by the source, that seeks to learn the optimal status
update policy over time. For Markovian channel and Markovian energy arrival
processes, the problem is again formulated as an MDP, and a learning algorithm
is provided to handle unknown dynamics. Finally, numerical results are provided
to demonstrate the policy structures and performance trade-offs.

State-of-the-art automatic speech recognition (ASR) system development is
data and computation intensive. The optimal design of deep neural networks
(DNNs) for these systems often require expert knowledge and empirical
evaluation. In this paper, a range of neural architecture search (NAS)
techniques are used to automatically learn two types of hyper-parameters of
factored time delay neural networks (TDNN-Fs): i) the left and right splicing
context offsets; and ii) the dimensionality of the bottleneck linear projection
at each hidden layer. These techniques include the differentiable neural
architecture search (DARTS) method integrating architecture learning with
lattice-free MMI training; Gumbel-Softmax and pipelined DARTS methods reducing
the confusion over candidate architectures and improving the generalization of
architecture selection; and Penalized DARTS incorporating resource constraints
to balance the trade-off between performance and system complexity. Parameter
sharing among TDNN-F architectures allows an efficient search over up to 7^28
different systems. Statistically significant word error rate (WER) reductions
of up to 1.2% absolute and relative model size reduction of 31% were obtained
over a state-of-the-art 300-hour Switchboard corpus trained baseline LF-MMI
TDNN-F system featuring speed perturbation, i-Vector and learning hidden unit
contribution (LHUC) based speaker adaptation as well as RNNLM rescoring.
Performance contrasts on the same task against recent end-to-end systems
reported in the literature suggest the best NAS auto-configured system achieves
state-of-the-art WERs of 9.9% and 11.1% on the NIST Hub5' 00 and Rt03s test
sets respectively with up to 96% model size reduction. Further analysis using
Bayesian learning shows that ...

Long-tailed instance segmentation is a challenging task due to the extreme
imbalance of training samples among classes. It causes severe biases of the
head classes (with majority samples) against the tailed ones. This renders "how
to appropriately define and alleviate the bias" one of the most important
issues. Prior works mainly use label distribution or mean score information to
indicate a coarse-grained bias. In this paper, we explore to excavate the
confusion matrix, which carries the fine-grained misclassification details, to
relieve the pairwise biases, generalizing the coarse one. To this end, we
propose a novel Pairwise Class Balance (PCB) method, built upon a confusion
matrix which is updated during training to accumulate the ongoing prediction
preferences. PCB generates fightback soft labels for regularization during
training. Besides, an iterative learning paradigm is developed to support a
progressive and smooth regularization in such debiasing. PCB can be plugged and
played to any existing method as a complement. Experimental results on LVIS
demonstrate that our method achieves state-of-the-art performance without bells
and whistles. Superior results across various architectures show the
generalization ability. The code and trained models are available at
https://github.com/megvii-research/PCB.

In a modern power system, real-time data on power generation/consumption and
its relevant features are stored in various distributed parties, including
household meters, transformer stations and external organizations. To fully
exploit the underlying patterns of these distributed data for accurate power
prediction, federated learning is needed as a collaborative but
privacy-preserving training scheme. However, current federated learning
frameworks are polarized towards addressing either the horizontal or vertical
separation of data, and tend to overlook the case where both are present.
Furthermore, in mainstream horizontal federated learning frameworks, only
artificial neural networks are employed to learn the data patterns, which are
considered less accurate and interpretable compared to tree-based models on
tabular datasets. To this end, we propose a hybrid federated learning framework
based on XGBoost, for distributed power prediction from real-time external
features. In addition to introducing boosted trees to improve accuracy and
interpretability, we combine horizontal and vertical federated learning, to
address the scenario where features are scattered in local heterogeneous
parties and samples are scattered in various local districts. Moreover, we
design a dynamic task allocation scheme such that each party gets a fair share
of information, and the computing power of each party can be fully leveraged to
boost training efficiency. A follow-up case study is presented to justify the
necessity of adopting the proposed framework. The advantages of the proposed
framework in fairness, efficiency and accuracy performance are also confirmed.

Vertical collaborative learning system also known as vertical federated
learning (VFL) system has recently become prominent as a concept to process
data distributed across many individual sources without the need to centralize
it. Multiple participants collaboratively train models based on their local
data in a privacy-preserving manner. To date, VFL has become a de facto
solution to securely learn a model among organizations, allowing knowledge to
be shared without compromising privacy of any individual organizations.
  Despite the prosperous development of VFL systems, we find that certain
inputs of a participant, named adversarial dominating inputs (ADIs), can
dominate the joint inference towards the direction of the adversary's will and
force other (victim) participants to make negligible contributions, losing
rewards that are usually offered regarding the importance of their
contributions in collaborative learning scenarios.
  We conduct a systematic study on ADIs by first proving their existence in
typical VFL systems. We then propose gradient-based methods to synthesize ADIs
of various formats and exploit common VFL systems. We further launch greybox
fuzz testing, guided by the resiliency score of "victim" participants, to
perturb adversary-controlled inputs and systematically explore the VFL attack
surface in a privacy-preserving manner. We conduct an in-depth study on the
influence of critical parameters and settings in synthesizing ADIs. Our study
reveals new VFL attack opportunities, promoting the identification of unknown
threats before breaches and building more secure VFL systems.

Quantum computers are expected to surpass the computational capabilities of
classical computers during this decade and have transformative impact on
numerous industry sectors, particularly finance. In fact, finance is estimated
to be the first industry sector to benefit from quantum computing, not only in
the medium and long terms, but even in the short term. This survey paper
presents a comprehensive summary of the state of the art of quantum computing
for financial applications, with particular emphasis on stochastic modeling,
optimization, and machine learning, describing how these solutions, adapted to
work on a quantum computer, can potentially help to solve financial problems,
such as derivative pricing, risk modeling, portfolio optimization, natural
language processing, and fraud detection, more efficiently and accurately. We
also discuss the feasibility of these algorithms on near-term quantum computers
with various hardware implementations and demonstrate how they relate to a wide
range of use cases in finance. We hope this article will not only serve as a
reference for academic researchers and industry practitioners but also inspire
new ideas for future research.

Cross-Modal Retrieval (CMR) is an important research topic across multimodal
computing and information retrieval, which takes one type of data as the query
to retrieve relevant data of another type. It has been widely used in many
real-world applications. Recently, the vision-language pre-trained models
represented by CLIP demonstrate its superiority in learning the visual and
textual representations and gain impressive performance on various vision and
language related tasks. Although CLIP as well as the previous pre-trained
models have shown great performance improvement in the unsupervised CMR, the
performance and impact of these pre-trained models on the supervised CMR were
rarely explored due to the lack of common representation for the multimodal
class-level associations. In this paper, we take CLIP as the current
representative vision-language pre-trained model to conduct a comprehensive
empirical study. We evaluate its performance and impact on the supervised CMR,
and attempt to answer several key research questions. To this end, we first
propose a novel model CLIP4CMR (CLIP enhanced network for Cross-Modal
Retrieval) that employs the pre-trained CLIP as backbone network to perform the
supervised CMR. Then by means of the CLIP4CMR framework, we revisit the design
of different learning objectives in current CMR methods to provide new insights
on model design. Moreover, we investigate the most concerned aspects in
applying CMR, including the robustness to modality imbalance and sensitivity to
hyper-parameters, to provide new perspectives for practical applications.
Through extensive experiments, we show that CLIP4CMR achieves the SOTA results
with prominent improvements on the benchmark datasets, and can be used as a
fundamental framework to empirically study the key research issues of the
supervised CMR, with significant implications for model design and practical
considerations.

Instead of using current deep-learning segmentation models (like the UNet and
variants), we approach the segmentation problem using trained Convolutional
Neural Network (CNN) classifiers, which automatically extract important
features from images for classification. Those extracted features can be
visualized and formed into heatmaps using Gradient-weighted Class Activation
Mapping (Grad-CAM). This study tested whether the heatmaps could be used to
segment the classified targets. We also proposed an evaluation method for the
heatmaps; that is, to re-train the CNN classifier using images filtered by
heatmaps and examine its performance. We used the mean-Dice coefficient to
evaluate segmentation results. Results from our experiments show that heatmaps
can locate and segment partial tumor areas. But use of only the heatmaps from
CNN classifiers may not be an optimal approach for segmentation. We have
verified that the predictions of CNN classifiers mainly depend on tumor areas,
and dark regions in Grad-CAM's heatmaps also contribute to classification.

As we enter the UN Decade on Ecosystem Restoration, creating effective
incentive structures for forest and landscape restoration has never been more
critical. Policy analysis is necessary for policymakers to understand the
actors and rules involved in restoration in order to shift economic and
financial incentives to the right places. Classical policy analysis is
resource-intensive and complex, lacks comprehensive central information
sources, and is prone to overlapping jurisdictions. We propose a Knowledge
Management Framework based on Natural Language Processing (NLP) techniques that
would tackle these challenges and automate repetitive tasks, reducing the
policy analysis process from weeks to minutes. Our framework was designed in
collaboration with policy analysis experts and made to be platform-, language-
and policy-agnostic. In this paper, we describe the design of the NLP pipeline,
review the state-of-the-art methods for each of its components, and discuss the
challenges that rise when building a framework oriented towards policy
analysis.

In this paper, we follow Eftekhari's work to give a non-local convergence
analysis of deep linear networks. Specifically, we consider optimizing deep
linear networks which have a layer with one neuron under quadratic loss. We
describe the convergent point of trajectories with arbitrary starting point
under gradient flow, including the paths which converge to one of the saddle
points or the original point. We also show specific convergence rates of
trajectories that converge to the global minimizer by stages. To achieve these
results, this paper mainly extends the machinery in Eftekhari's work to
provably identify the rank-stable set and the global minimizer convergent set.
We also give specific examples to show the necessity of our definitions.
Crucially, as far as we know, our results appear to be the first to give a
non-local global analysis of linear neural networks from arbitrary initialized
points, rather than the lazy training regime which has dominated the literature
of neural networks, and restricted benign initialization in Eftekhari's work.
We also note that extending our results to general linear networks without one
hidden neuron assumption remains a challenging open problem.

AI and humans bring complementary skills to group deliberations. Modeling
this group decision making is especially challenging when the deliberations
include an element of risk and an exploration-exploitation process of
appraising the capabilities of the human and AI agents. To investigate this
question, we presented a sequence of intellective issues to a set of human
groups aided by imperfect AI agents. A group's goal was to appraise the
relative expertise of the group's members and its available AI agents, evaluate
the risks associated with different actions, and maximize the overall reward by
reaching consensus. We propose and empirically validate models of human-AI team
decision making under such uncertain circumstances, and show the value of
socio-cognitive constructs of prospect theory, influence dynamics, and Bayesian
learning in predicting the behavior of human-AI groups.

Modeling heterogeneity by extraction and exploitation of high-order
information from heterogeneous information networks (HINs) has been attracting
immense research attention in recent times. Such heterogeneous network
embedding (HNE) methods effectively harness the heterogeneity of small-scale
HINs. However, in the real world, the size of HINs grow exponentially with the
continuous introduction of new nodes and different types of links, making it a
billion-scale network. Learning node embeddings on such HINs creates a
performance bottleneck for existing HNE methods that are commonly centralized,
i.e., complete data and the model are both on a single machine. To address
large-scale HNE tasks with strong efficiency and effectiveness guarantee, we
present \textit{Decentralized Embedding Framework for Heterogeneous Information
Network} (DeHIN) in this paper. In DeHIN, we generate a distributed parallel
pipeline that utilizes hypergraphs in order to infuse parallelization into the
HNE task. DeHIN presents a context preserving partition mechanism that
innovatively formulates a large HIN as a hypergraph, whose hyperedges connect
semantically similar nodes. Our framework then adopts a decentralized strategy
to efficiently partition HINs by adopting a tree-like pipeline. Then, each
resulting subnetwork is assigned to a distributed worker, which employs the
deep information maximization theorem to locally learn node embeddings from the
partition it receives. We further devise a novel embedding alignment scheme to
precisely project independently learned node embeddings from all subnetworks
onto a common vector space, thus allowing for downstream tasks like link
prediction and node classification.

Machine Learning (ML) has garnered considerable attention from researchers
and practitioners as a new and adaptable tool for disease diagnosis. With the
advancement of ML and the proliferation of papers and research in this field, a
complete examination of Machine Learning-Based Disease Diagnosis (MLBDD) is
required. From a bibliometrics standpoint, this article comprehensively studies
MLBDD papers from 2012 to 2021. Consequently, with particular keywords, 1710
papers with associate information have been extracted from the Scopus and Web
of Science (WOS) database and integrated into the excel datasheet for further
analysis. First, we examine the publication structures based on yearly
publications and the most productive countries/regions, institutions, and
authors. Second, the co-citation networks of countries/regions, institutions,
authors, and articles are visualized using R-studio software. They are further
examined in terms of citation structure and the most influential ones. This
article gives an overview of MLBDD for researchers interested in the subject
and conducts a thorough and complete study of MLBDD for those interested in
conducting more research in this field.

Some real-world decision-making problems require making probabilistic
forecasts over multiple steps at once. However, methods for probabilistic
forecasting may fail to capture correlations in the underlying time-series that
exist over long time horizons as errors accumulate. One such application is
with resource scheduling under uncertainty in a grid environment, which
requires forecasting electricity demand that is inherently noisy, but often
cyclic. In this paper, we introduce the conditional approximate normalizing
flow (CANF) to make probabilistic multi-step time-series forecasts when
correlations are present over long time horizons. We first demonstrate our
method's efficacy on estimating the density of a toy distribution, finding that
CANF improves the KL divergence by one-third compared to that of a Gaussian
mixture model while still being amenable to explicit conditioning. We then use
a publicly available household electricity consumption dataset to showcase the
effectiveness of CANF on joint probabilistic multi-step forecasting. Empirical
results show that conditional approximate normalizing flows outperform other
methods in terms of multi-step forecast accuracy and lead to up to 10x better
scheduling decisions. Our implementation is available at
https://github.com/sisl/JointDemandForecasting.

Radiomics and deep learning have shown high popularity in automatic glioma
grading. Radiomics can extract hand-crafted features that quantitatively
describe the expert knowledge of glioma grades, and deep learning is powerful
in extracting a large number of high-throughput features that facilitate the
final classification. However, the performance of existing methods can still be
improved as their complementary strengths have not been sufficiently
investigated and integrated. Furthermore, lesion maps are usually needed for
the final prediction at the testing phase, which is very troublesome. In this
paper, we propose an expert knowledge-guided geometric representation learning
(ENROL) framework . Geometric manifolds of hand-crafted features and learned
features are constructed to mine the implicit relationship between deep
learning and radiomics, and therefore to dig mutual consent and essential
representation for the glioma grades. With a specially designed manifold
discrepancy measurement, the grading model can exploit the input image data and
expert knowledge more effectively in the training phase and get rid of the
requirement of lesion segmentation maps at the testing phase. The proposed
framework is flexible regarding deep learning architectures to be utilized.
Three different architectures have been evaluated and five models have been
compared, which show that our framework can always generate promising results.

This paper focuses on the Matrix Factorization based Clustering (MFC) method
which is one of the few closed form algorithms for the subspace clustering
problem. Despite being simple, closed-form, and computation-efficient, MFC can
outperform the other sophisticated subspace clustering methods in many
challenging scenarios. We reveal the connection between MFC and the Innovation
Pursuit (iPursuit) algorithm which was shown to be able to outperform the other
spectral clustering based methods with a notable margin especially when the
span of clusters are close. A novel theoretical study is presented which sheds
light on the key performance factors of both algorithms (MFC/iPursuit) and it
is shown that both algorithms can be robust to notable intersections between
the span of clusters. Importantly, in contrast to the theoretical guarantees of
other algorithms which emphasized on the distance between the subspaces as the
key performance factor and without making the innovation assumption, it is
shown that the performance of MFC/iPursuit mainly depends on the distance
between the innovative components of the clusters.

Community detection is a fundamental and important issue in network science,
but there are only a few community detection algorithms based on graph neural
networks, among which unsupervised algorithms are almost blank. By fusing the
high-order modularity information with network features, this paper proposes a
Variational Graph AutoEncoder Reconstruction based community detection VGAER
for the first time, and gives its non-probabilistic version. They do not need
any prior information. We have carefully designed corresponding input features,
decoder, and downstream tasks based on the community detection task and these
designs are concise, natural, and perform well (NMI values under our design are
improved by 59.1% - 565.9%). Based on a series of experiments with wide range
of datasets and advanced methods, VGAER has achieved superior performance and
shows strong competitiveness and potential with a simpler design. Finally, we
report the results of algorithm convergence analysis and t-SNE visualization,
which clearly depicted the stable performance and powerful network modularity
ability of VGAER. Our codes are available at https://github.com/qcydm/VGAER.

Structured distributions, i.e. distributions over combinatorial spaces, are
commonly used to learn latent probabilistic representations from observed data.
However, scaling these models is bottlenecked by the high computational and
memory complexity with respect to the size of the latent representations.
Common models such as Hidden Markov Models (HMMs) and Probabilistic
Context-Free Grammars (PCFGs) require time and space quadratic and cubic in the
number of hidden states respectively. This work demonstrates a simple approach
to reduce the computational and memory complexity of a large class of
structured models. We show that by viewing the central inference step as a
matrix-vector product and using a low-rank constraint, we can trade off model
expressivity and speed via the rank. Experiments with neural parameterized
structured models for language modeling, polyphonic music modeling,
unsupervised grammar induction, and video modeling show that our approach
matches the accuracy of standard models at large state spaces while providing
practical speedups.

In the tasks of image aesthetic quality evaluation, it is difficult to reach
both the high score area and low score area due to the normal distribution of
aesthetic datasets. To reduce the error in labeling and solve the problem of
normal data distribution, we propose a new aesthetic mixed dataset with
classification and regression called AMD-CR, and we train a meta reweighting
network to reweight the loss of training data differently. In addition, we
provide a training strategy acccording to different stages, based on pseudo
labels of the binary classification task, and then we use it for aesthetic
training acccording to different stages in classification and regression tasks.
In the construction of the network structure, we construct an aesthetic
adaptive block (AAB) structure that can adapt to any size of the input images.
Besides, we also use the efficient channel attention (ECA) to strengthen the
feature extracting ability of each task. The experimental result shows that our
method improves 0.1112 compared with the conventional methods in SROCC. The
method can also help to find best aesthetic path planning for unmanned aerial
vehicles (UAV) and vehicles.

Convolution has been the core operation of modern deep neural networks. It is
well-known that convolutions can be implemented in the Fourier Transform
domain. In this paper, we propose to use binary block Walsh-Hadamard transform
(WHT) instead of the Fourier transform. We use WHT-based binary layers to
replace some of the regular convolution layers in deep neural networks. We
utilize both one-dimensional (1-D) and two-dimensional (2-D) binary WHTs in
this paper. In both 1-D and 2-D layers, we compute the binary WHT of the input
feature map and denoise the WHT domain coefficients using a nonlinearity which
is obtained by combining soft-thresholding with the tanh function. After
denoising, we compute the inverse WHT. We use 1D-WHT to replace the $1\times 1$
convolutional layers, and 2D-WHT layers can replace the 3$\times$3 convolution
layers and Squeeze-and-Excite layers. 2D-WHT layers with trainable weights can
be also inserted before the Global Average Pooling (GAP) layers to assist the
dense layers. In this way, we can reduce the number of trainable parameters
significantly with a slight decrease in trainable parameters. In this paper, we
implement the WHT layers into MobileNet-V2, MobileNet-V3-Large, and ResNet to
reduce the number of parameters significantly with negligible accuracy loss.
Moreover, according to our speed test, the 2D-FWHT layer runs about 24 times as
fast as the regular $3\times 3$ convolution with 19.51\% less RAM usage in an
NVIDIA Jetson Nano experiment.

The cycle of baryons through the circumgalactic medium (CGM) is important to
understand in the context of galaxy formation and evolution. In this study we
forecast constraints on the feedback processes heating the CGM with current and
future Sunyaev-Zeldovich (SZ) observations. To constrain these processes, we
use a suite of cosmological simulations, the Cosmology and Astrophysics with
MachinE Learning Simulations (CAMELS), that varies four different feedback
parameters of two previously existing hydrodynamical simulations, IllustrisTNG
and SIMBA. We capture the dependencies of SZ radial profiles on these feedback
parameters with an emulator, calculate their derivatives, and forecast future
constraints on these feedback parameters from upcoming experiments. We find
that for a DESI-like (Dark Energy Spectroscopic Instrument) galaxy sample
observed by the Simons Observatory all four feedback parameters are able to be
constrained (some within the $10\%$ level), indicating that future observations
will be able to further restrict the parameter space for these sub-grid models.
Given the modeled galaxy sample and forecasted errors in this work, we find
that the inner SZ profiles contribute more to the constraining power than the
outer profiles. Finally, we find that, despite the wide range of AGN feedback
parameter variation in the CAMELS simulation suite, we cannot reproduce the tSZ
signal of galaxies selected by the Baryon Oscillation Spectroscopic Survey as
measured by the Atacama Cosmology Telescope.

BRAVO, the most widely tried method for risk-limiting election audits, cannot
accommodate sampling without replacement or stratified sampling, which can
improve efficiency and may be required by law. It applies only to
ballot-polling audits, which are less efficient than comparison audits. It
applies to plurality, majority, super-majority, proportional representation,
and ranked-choice voting contests, but not to many social choice functions for
which there are RLA methods, such as approval voting, STAR-voting, Borda count,
and general scoring rules. And while BRAVO has the smallest expected sample
size among sequentially valid ballot-polling-with-replacement methods when
reported vote shares are exactly right, it can require arbitrarily large
samples when the reported reported winner(s) really won but reported vote
shares are wrong. ALPHA is a simple generalization of BRAVO that (i) works for
sampling with and without replacement and Bernoulli sampling; (ii) increases
power for stratified audits by avoiding the need to use a $P$-value combining
function or to maximize $P$-values over nuisance parameters within strata, and
allowing adaptive sampling across strata; (iii) works not only for
ballot-polling but also for ballot-level comparison, batch-polling, and
batch-level comparison audits, sampling with or without replacement, uniformly
or with weights proportional to size; (iv) works for all social choice
functions covered by SHANGRLA; and (v) in situations where both ALPHA and BRAVO
apply, requires smaller samples than BRAVO when the reported vote shares are
wrong but the outcome is correct--five orders of magnitude in some examples.
ALPHA includes the family of betting martingale tests in RiLACS, with a
different betting strategy parametrized as an estimator of the population mean
and explicit flexibility to accommodate sampling weights and population bounds
that vary by draw.

Sepsis is a life-threatening medical emergency, which is a major cause of
death worldwide and the second highest cause of mortality in the United States.
Researching the optimal control treatment or intervention strategy on the
comprehensive sepsis system is key in reducing mortality. For this purpose,
first, this paper improves a complex nonlinear sepsis model proposed in our
previous work. Then, bifurcation analyses are conducted for each sepsis
subsystem to study the model behaviors under some system parameters. The
bifurcation analysis results also further indicate the necessity of control
treatment and intervention therapy. If the sepsis system is without adding any
control under some parameter and initial system value settings, the system will
perform persistent inflammation outcomes as time goes by. Therefore, we develop
our complex improved nonlinear sepsis model into a sepsis optimal control
model, and then use some effective biomarkers recommended in existing clinic
practices as optimization objective function to measure the development of
sepsis. Besides that, a Bayesian optimization algorithm by combining Recurrent
neural network (RNN-BO algorithm) is introduced to predict the optimal control
strategy for the studied sepsis optimal control system. The difference between
the RNN-BO algorithm from other optimization algorithms is that once given any
new initial system value setting (initial value is associated with the initial
conditions of patients), the RNN-BO algorithm is capable of quickly predicting
a corresponding time-series optimal control based on the historical optimal
control data for any new sepsis patient. To demonstrate the effectiveness and
efficiency of the RNN-BO algorithm on solving the optimal control solution on
the complex nonlinear sepsis system, some numerical simulations are implemented
by comparing with other optimization algorithms in this paper.

The paper quantifies the impact of race, poverty, politics, and age on
COVID-19 vaccination rates in counties in the continental US. Both, OLS
regression analysis and Random Forest machine learning algorithms are applied
to quantify factors for county-level vaccination hesitancy. The machine
learning model considers joint effects of variables (race/ethnicity,
partisanship, age, etc.) simultaneously to capture the unique combination of
these factors on the vaccination rate. By implementing a state-of-the-art
Artificial Intelligence Explanations (AIX) algorithm, it is possible to solve
the black box problem with machine learning models and provide answers to the
"how much" question for each measured impact factor in every county. For most
counties, a higher percentage vote for Republicans, a greater African American
population share, and a higher poverty rate lower the vaccination rate. While a
higher Asian population share increases the predicted vaccination rate. The
impact on the vaccination rate from the Hispanic population proportion is
positive in the OLS model, but only positive for counties with a high Hispanic
population (>65%) in the Random Forest model. Both the proportion of seniors
and the one for young people in a county have a significant impact in the OLS
model - positive and negative, respectively. In contrast, the impacts are
ambiguous in the Random Forest model. Because results vary between geographies
and since the AIX algorithm is able to quantify vaccine impacts individually
for each county, this research can be tailored to local communities. An
interactive online mapping dashboard that identifies impact factors for
individual U.S. counties is available at
https://www.cpp.edu/~clange/vacmap.html. It is apparent that the influence of
impact factors is not universally the same across different geographies.

Unmanned aerial vehicles (UAV) are used successfully in many application
areas such as military, security, monitoring, emergency aid, tourism,
agriculture, and forestry. This study aims to automatically count trees in
designated areas on the Siirt University campus from high-resolution images
obtained by UAV. Images obtained at 30 meters height with 20% overlap were
stitched offline at the ground station using Adobe Photoshop's photo merge
tool. The resulting image was denoised and smoothed by applying the 3x3 median
and mean filter, respectively. After generating the orthophoto map of the
aerial images captured by the UAV in certain regions, the bounding boxes of
different objects on these maps were labeled in the modalities of HSV (Hue
Saturation Value), RGB (Red Green Blue) and Gray. Training, validation, and
test datasets were generated and then have been evaluated for classification
success rates related to tree detection using various machine learning
algorithms. In the last step, a ground truth model was established by obtaining
the actual tree numbers, and then the prediction performance was calculated by
comparing the reference ground truth data with the proposed model. It is
considered that significant success has been achieved for tree count with an
average accuracy rate of 87% obtained using the MLP classifier in predetermined
regions.

Transit spectroscopy is a powerful tool to decode the chemical composition of
the atmospheres of extrasolar planets. In this paper we focus on unsupervised
techniques for analyzing spectral data from transiting exoplanets. We
demonstrate methods for i) cleaning and validating the data, ii) initial
exploratory data analysis based on summary statistics (estimates of location
and variability), iii) exploring and quantifying the existing correlations in
the data, iv) pre-processing and linearly transforming the data to its
principal components, v) dimensionality reduction and manifold learning, vi)
clustering and anomaly detection, vii) visualization and interpretation of the
data. To illustrate the proposed unsupervised methodology, we use a well-known
public benchmark data set of synthetic transit spectra. We show that there is a
high degree of correlation in the spectral data, which calls for appropriate
low-dimensional representations. We explore a number of different techniques
for such dimensionality reduction and identify several suitable options in
terms of summary statistics, principal components, etc. We uncover interesting
structures in the principal component basis, namely, well-defined branches
corresponding to different chemical regimes of the underlying atmospheres. We
demonstrate that those branches can be successfully recovered with a K-means
clustering algorithm in fully unsupervised fashion. We advocate for a
three-dimensional representation of the spectroscopic data in terms of the
first three principal components, in order to reveal the existing structure in
the data and quickly characterize the chemical class of a planet.

Although mission-critical applications require the use of deep neural
networks (DNNs), their continuous execution at mobile devices results in a
significant increase in energy consumption. While edge offloading can decrease
energy consumption, erratic patterns in channel quality, network and edge
server load can lead to severe disruption of the system's key operations. An
alternative approach, called split computing, generates compressed
representations within the model (called "bottlenecks"), to reduce bandwidth
usage and energy consumption. Prior work has proposed approaches that introduce
additional layers, to the detriment of energy consumption and latency. For this
reason, we propose a new framework called BottleFit, which, in addition to
targeted DNN architecture modifications, includes a novel training strategy to
achieve high accuracy even with strong compression rates. We apply BottleFit on
cutting-edge DNN models in image classification, and show that BottleFit
achieves 77.1% data compression with up to 0.6% accuracy loss on ImageNet
dataset, while state of the art such as SPINN loses up to 6% in accuracy. We
experimentally measure the power consumption and latency of an image
classification application running on an NVIDIA Jetson Nano board (GPU-based)
and a Raspberry PI board (GPU-less). We show that BottleFit decreases power
consumption and latency respectively by up to 49% and 89% with respect to
(w.r.t.) local computing and by 37% and 55% w.r.t. edge offloading. We also
compare BottleFit with state-of-the-art autoencoders-based approaches, and show
that (i) BottleFit reduces power consumption and execution time respectively by
up to 54% and 44% on the Jetson and 40% and 62% on Raspberry PI; (ii) the size
of the head model executed on the mobile device is 83 times smaller. We publish
the code repository for reproducibility of the results in this study.

We study the GAN conditioning problem, whose goal is to convert a pretrained
unconditional GAN into a conditional GAN using labeled data. We first identify
and analyze three approaches to this problem -- conditional GAN training from
scratch, fine-tuning, and input reprogramming. Our analysis reveals that when
the amount of labeled data is small, input reprogramming performs the best.
Motivated by real-world scenarios with scarce labeled data, we focus on the
input reprogramming approach and carefully analyze the existing algorithm.
After identifying a few critical issues of the previous input reprogramming
approach, we propose a new algorithm called InRep+. Our algorithm InRep+
addresses the existing issues with the novel uses of invertible neural networks
and Positive-Unlabeled (PU) learning. Via extensive experiments, we show that
InRep+ outperforms all existing methods, particularly when label information is
scarce, noisy, and/or imbalanced. For instance, for the task of conditioning a
CIFAR10 GAN with 1% labeled data, InRep+ achieves an average Intra-FID of
76.24, whereas the second-best method achieves 114.51.

The paper deals with Video Coding for Machines that is a new paradigm in
video coding related to consumption of decoded video by humans and machines.
For such tasks, joint transmission of compressed video and features is
considered. In this paper, we focus our considerations of features on SIFT
keypoints. They can be extracted from the decoded video with losses in number
of keypoints and their parameters as compared to the SIFT keypoints extracted
from the original video. Such losses are studied for HEVC and VVC as functions
of the quantization parameter and the bitrate. In the paper, we propose to
transmit the residual feature data together with the compressed video.
Therefore, even for strongly compressed video, the transmission of whole all
SIFT keypoint information is avoided.

This paper presents an approach for identification of vulnerable IoT
applications. The approach focuses on a category of vulnerabilities that leads
to sensitive information leakage which can be identified by using taint flow
analysis. Tainted flows vulnerability is very much impacted by the structure of
the program and the order of the statements in the code, designing an approach
to detect such vulnerability needs to take into consideration such information
in order to provide precise results. In this paper, we propose and develop an
approach, FlowsMiner, that mines features from the code related to program
structure such as control statements and methods, in addition to program's
statement order. FlowsMiner, generates features in the form of tainted flows.
We developed, Flows2Vec, a tool that transform the features recovered by
FlowsMiner into vectors, which are then used to aid the process of machine
learning by providing a flow's aware model building process. The resulting
model is capable of accurately classify applications as vulnerable if the
vulnerability is exhibited by changes in the order of statements in source
code. When compared to a base Bag of Words (BoW) approach, the experiments show
that the proposed approach has improved the AUC of the prediction models for
all algorithms and the best case for Corpus1 dataset is improved from 0.91 to
0.94 and for Corpus2 from 0.56 to 0.96

Vehicular Controller Area Networks (CANs) are susceptible to cyber attacks of
different levels of sophistication. Fabrication attacks are the easiest to
administer -- an adversary simply sends (extra) frames on a CAN -- but also the
easiest to detect because they disrupt frame frequency. To overcome time-based
detection methods, adversaries must administer masquerade attacks by sending
frames in lieu of (and therefore at the expected time of) benign frames but
with malicious payloads. Research efforts have proven that CAN attacks, and
masquerade attacks in particular, can affect vehicle functionality. Examples
include causing unintended acceleration, deactivation of vehicle's brakes, as
well as steering the vehicle. We hypothesize that masquerade attacks modify the
nuanced correlations of CAN signal time series and how they cluster together.
Therefore, changes in cluster assignments should indicate anomalous behavior.
We confirm this hypothesis by leveraging our previously developed capability
for reverse engineering CAN signals (i.e., CAN-D [Controller Area Network
Decoder]) and focus on advancing the state of the art for detecting masquerade
attacks by analyzing time series extracted from raw CAN frames. Specifically,
we demonstrate that masquerade attacks can be detected by computing time series
clustering similarity using hierarchical clustering on the vehicle's CAN
signals (time series) and comparing the clustering similarity across CAN
captures with and without attacks. We test our approach in a previously
collected CAN dataset with masquerade attacks (i.e., the ROAD dataset) and
develop a forensic tool as a proof of concept to demonstrate the potential of
the proposed approach for detecting CAN masquerade attacks.

A significant bottleneck in federated learning (FL) is the network
communication cost of sending model updates from client devices to the central
server. We present a comprehensive empirical study of the statistics of model
updates in FL, as well as the role and benefits of various compression
techniques. Motivated by these observations, we propose a novel method to
reduce the average communication cost, which is near-optimal in many use cases,
and outperforms Top-K, DRIVE, 3LC and QSGD on Stack Overflow next-word
prediction, a realistic and challenging FL benchmark. This is achieved by
examining the problem using rate-distortion theory, and proposing distortion as
a reliable proxy for model accuracy. Distortion can be more effectively used
for optimizing the trade-off between model performance and communication cost
across clients. We demonstrate empirically that in spite of the non-i.i.d.
nature of federated learning, the rate-distortion frontier is consistent across
datasets, optimizers, clients and training rounds.

Lifelong experiences and learned knowledge lead to shared expectations about
how common situations tend to unfold. Such knowledge of narrative event flow
enables people to weave together a story. However, comparable computational
tools to evaluate the flow of events in narratives are limited. We quantify the
differences between autobiographical and imagined stories by introducing
sequentiality, a measure of narrative flow of events, drawing probabilistic
inferences from a cutting-edge large language model (GPT-3). Sequentiality
captures the flow of a narrative by comparing the probability of a sentence
with and without its preceding story context. We applied our measure to study
thousands of diary-like stories, collected from crowdworkers about either a
recent remembered experience or an imagined story on the same topic. The
results show that imagined stories have higher sequentiality than
autobiographical stories and that the sequentiality of autobiographical stories
increases when the memories are retold several months later. In pursuit of
deeper understandings of how sequentiality measures the flow of narratives, we
explore proportions of major and minor events in story sentences, as annotated
by crowdworkers. We find that lower sequentiality is associated with higher
proportions of major events. The methods and results highlight opportunities to
use cutting-edge computational analyses, such as sequentiality, on large
corpora of matched imagined and autobiographical stories to investigate the
influences of memory and reasoning on language generation processes.

Knowledge graphs (KGs) are typically incomplete and we often wish to infer
new facts given the existing ones. This can be thought of as a binary
classification problem; we aim to predict if new facts are true or false.
Unfortunately, we generally only have positive examples (the known facts) but
we also need negative ones to train a classifier. To resolve this, it is usual
to generate negative examples using a negative sampling strategy. However, this
can produce false negatives which may reduce performance, is computationally
expensive, and does not produce calibrated classification probabilities. In
this paper, we propose a training procedure that obviates the need for negative
sampling by adding a novel regularization term to the loss function. Our
results for two relational embedding models (DistMult and SimplE) show the
merit of our proposal both in terms of performance and speed.

Federated learning is a popular technology for training machine learning
models on distributed data sources without sharing data. Vertical federated
learning or feature-based federated learning applies to the cases that
different data sources share the same sample ID space but differ in feature
space. To ensure the data owners' long-term engagement, it is critical to
objectively assess the contribution from each data source and recompense them
accordingly. The Shapley value (SV) is a provably fair contribution valuation
metric originated from cooperative game theory. However, computing the SV
requires extensively retraining the model on each subset of data sources, which
causes prohibitively high communication costs in federated learning. We propose
a contribution valuation metric called vertical federated Shapley value
(VerFedSV) based on SV. We show that VerFedSV not only satisfies many desirable
properties for fairness but is also efficient to compute, and can be adapted to
both synchronous and asynchronous vertical federated learning algorithms. Both
theoretical analysis and extensive experimental results verify the fairness,
efficiency, and adaptability of VerFedSV.

Image segmentation is an important task in the medical image field and many
convolutional neural networks (CNNs) based methods have been proposed, among
which U-Net and its variants show promising performance. In this paper, we
propose GP-module and GPU-Net based on U-Net, which can learn more diverse
features by introducing Ghost module and atrous spatial pyramid pooling (ASPP).
Our method achieves better performance with more than 4 times fewer parameters
and 2 times fewer FLOPs, which provides a new potential direction for future
research. Our plug-and-play module can also be applied to existing segmentation
methods to further improve their performance.

The COVID-19 pandemic has emphasized the need for a robust understanding of
epidemic models. Current models of epidemics are classified as either
mechanistic or non-mechanistic: mechanistic models make explicit assumptions on
the dynamics of disease, whereas non-mechanistic models make assumptions on the
form of observed time series. Here, we introduce a simple mixture-based model
which bridges the two approaches while retaining benefits of both. The model
represents time series of cases and fatalities as a mixture of Gaussian curves,
providing a flexible function class to learn from data compared to traditional
mechanistic models. Although the model is non-mechanistic, we show that it
arises as the natural outcome of a stochastic process based on a networked SIR
framework. This allows learned parameters to take on a more meaningful
interpretation compared to similar non-mechanistic models, and we validate the
interpretations using auxiliary mobility data collected during the COVID-19
pandemic. We provide a simple learning algorithm to identify model parameters
and establish theoretical results which show the model can be efficiently
learned from data. Empirically, we find the model to have low prediction error.
The model is available live at covidpredictions.mit.edu. Ultimately, this
allows us to systematically understand the impacts of interventions on
COVID-19, which is critical in developing data-driven solutions to controlling

This paper formulates a general cross validation framework for signal
denoising. The general framework is then applied to nonparametric regression
methods such as Trend Filtering and Dyadic CART. The resulting cross validated
versions are then shown to attain nearly the same rates of convergence as are
known for the optimally tuned analogues. There did not exist any previous
theoretical analyses of cross validated versions of Trend Filtering or Dyadic
CART. To illustrate the generality of the framework we also propose and study
cross validated versions of two fundamental estimators; lasso for high
dimensional linear regression and singular value thresholding for matrix
estimation. Our general framework is inspired by the ideas in Chatterjee and
Jafarov (2015) and is potentially applicable to a wide range of estimation
methods which use tuning parameters.

Automating information extraction from form-like documents at scale is a
pressing need due to its potential impact on automating business workflows
across many industries like financial services, insurance, and healthcare. The
key challenge is that form-like documents in these business workflows can be
laid out in virtually infinitely many ways; hence, a good solution to this
problem should generalize to documents with unseen layouts and languages. A
solution to this problem requires a holistic understanding of both the textual
segments and the visual cues within a document, which is non-trivial. While the
natural language processing and computer vision communities are starting to
tackle this problem, there has not been much focus on (1) data-efficiency, and
(2) ability to generalize across different document types and languages.
  In this paper, we show that when we have only a small number of labeled
documents for training (~50), a straightforward transfer learning approach from
a considerably structurally-different larger labeled corpus yields up to a 27
F1 point improvement over simply training on the small corpus in the target
domain. We improve on this with a simple multi-domain transfer learning
approach, that is currently in production use, and show that this yields up to
a further 8 F1 point improvement. We make the case that data efficiency is
critical to enable information extraction systems to scale to handle hundreds
of different document-types, and learning good representations is critical to
accomplishing this.

As humans, we navigate a multimodal world, building a holistic understanding
from all our senses. We introduce MERLOT Reserve, a model that represents
videos jointly over time -- through a new training objective that learns from
audio, subtitles, and video frames. Given a video, we replace snippets of text
and audio with a MASK token; the model learns by choosing the correct
masked-out snippet. Our objective learns faster than alternatives, and performs
well at scale: we pretrain on 20 million YouTube videos.
  Empirical results show that MERLOT Reserve learns strong multimodal
representations. When finetuned, it sets state-of-the-art on Visual Commonsense
Reasoning (VCR), TVQA, and Kinetics-600; outperforming prior work by 5%, 7%,
and 1.5% respectively. Ablations show that these tasks benefit from audio
pretraining -- even VCR, a QA task centered around images (without sound).
Moreover, our objective enables out-of-the-box prediction, revealing strong
multimodal commonsense understanding. In a fully zero-shot setting, our model
obtains competitive results on four video tasks, even outperforming supervised
approaches on the recently proposed Situated Reasoning (STAR) benchmark.
  We analyze why audio enables better vision-language representations,
suggesting significant opportunities for future research. We conclude by
discussing ethical and societal implications of multimodal pretraining.

Humans move their hands and bodies together to communicate and solve tasks.
Capturing and replicating such coordinated activity is critical for virtual
characters that behave realistically. Surprisingly, most methods treat the 3D
modeling and tracking of bodies and hands separately. Here we formulate a model
of hands and bodies interacting together and fit it to full-body 4D sequences.
When scanning or capturing the full body in 3D, hands are small and often
partially occluded, making their shape and pose hard to recover. To cope with
low-resolution, occlusion, and noise, we develop a new model called MANO (hand
Model with Articulated and Non-rigid defOrmations). MANO is learned from around
1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand
poses. The model is realistic, low-dimensional, captures non-rigid shape
changes with pose, is compatible with standard graphics packages, and can fit
any human hand. MANO provides a compact mapping from hand poses to pose blend
shape corrections and a linear manifold of pose synergies. We attach MANO to a
standard parameterized 3D body shape model (SMPL), resulting in a fully
articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting
complex, natural, activities of subjects captured with a 4D scanner. The
fitting is fully automatic and results in full body models that move naturally
with detailed hand motions and a realism not seen before in full body
performance capture. The models and data are freely available for research
purposes in our website (http://mano.is.tue.mpg.de).

In this paper, we consider a highly general image recognition setting
wherein, given a labelled and unlabelled set of images, the task is to
categorize all images in the unlabelled set. Here, the unlabelled images may
come from labelled classes or from novel ones. Existing recognition methods are
not able to deal with this setting, because they make several restrictive
assumptions, such as the unlabelled instances only coming from known - or
unknown - classes, and the number of unknown classes being known a-priori. We
address the more unconstrained setting, naming it 'Generalized Category
Discovery', and challenge all these assumptions. We first establish strong
baselines by taking state-of-the-art algorithms from novel category discovery
and adapting them for this task. Next, we propose the use of vision
transformers with contrastive representation learning for this open-world
setting. We then introduce a simple yet effective semi-supervised $k$-means
method to cluster the unlabelled data into seen and unseen classes
automatically, substantially outperforming the baselines. Finally, we also
propose a new approach to estimate the number of classes in the unlabelled
data. We thoroughly evaluate our approach on public datasets for generic object
classification and on fine-grained datasets, leveraging the recent Semantic
Shift Benchmark suite. Project page at
https://www.robots.ox.ac.uk/~vgg/research/gcd

Simultaneous segmentation and detection of liver tumors (hemangioma and
hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic
resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it
is still a challenging task due to: (1) the HCC information on NCMRI is
invisible or insufficient makes extraction of liver tumors feature difficult;
(2) diverse imaging characteristics in multi-modality NCMRI causes feature
fusion and selection difficult; (3) no specific information between hemangioma
and HCC on NCMRI cause liver tumors detection difficult. In this study, we
propose a united adversarial learning framework (UAL) for simultaneous liver
tumors segmentation and detection using multi-modality NCMRI. The UAL first
utilizes a multi-view aware encoder to extract multi-modality NCMRI information
for liver tumor segmentation and detection. In this encoder, a novel edge
dissimilarity feature pyramid module is designed to facilitate the
complementary multi-modality feature extraction. Second, the newly designed
fusion and selection channel is used to fuse the multi-modality feature and
make the decision of the feature selection. Then, the proposed mechanism of
coordinate sharing with padding integrates the multi-task of segmentation and
detection so that it enables multi-task to perform united adversarial learning
in one discriminator. Lastly, an innovative multi-phase radiomics guided
discriminator exploits the clear and specific tumor information to improve the
multi-task performance via the adversarial learning strategy. The UAL is
validated in corresponding multi-modality NCMRI (i.e. T1FS pre-contrast MRI,
T2FS MRI, and DWI) and three phases contrast-enhanced MRI of 255 clinical
subjects. The experiments show that UAL has great potential in the clinical
diagnosis of liver tumors.

Temporal abstraction in reinforcement learning is the ability of an agent to
learn and use high-level behaviors, called options. The option-critic
architecture provides a gradient-based end-to-end learning method to construct
options. We propose an attention-based extension to this framework, which
enables the agent to learn to focus different options on different aspects of
the observation space. We show that this leads to behaviorally diverse options
which are also capable of state abstraction, and prevents the degeneracy
problems of option domination and frequent option switching that occur in
option-critic, while achieving a similar sample complexity. We also demonstrate
the more efficient, interpretable, and reusable nature of the learned options
in comparison with option-critic, through different transfer learning tasks.
Experimental results in a relatively simple four-rooms environment and the more
complex ALE (Arcade Learning Environment) showcase the efficacy of our
approach.

The El Nino Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea
surface temperature (SST) over the tropical central and eastern Pacific Ocean
that influences interannual variability in regional hydrology across the world
through long-range dependence or teleconnections. Recent research has
demonstrated the value of Deep Learning (DL) methods for improving ENSO
prediction as well as Complex Networks (CN) for understanding teleconnections.
However, gaps in predictive understanding of ENSO-driven river flows include
the black box nature of DL, the use of simple ENSO indices to describe a
complex phenomenon and translating DL-based ENSO predictions to river flow
predictions. Here we show that eXplainable DL (XDL) methods, based on saliency
maps, can extract interpretable predictive information contained in global SST
and discover novel SST information regions and dependence structures relevant
for river flows which, in tandem with climate network constructions, enable
improved predictive understanding. Our results reveal additional information
content in global SST beyond ENSO indices, develop new understanding of how
SSTs influence river flows, and generate improved river flow predictions with
uncertainties. Observations, reanalysis data, and earth system model
simulations are used to demonstrate the value of the XDL-CN based methods for
future interannual and decadal scale climate projections.

This paper presents FogAdapt, a novel approach for domain adaptation of
semantic segmentation for dense foggy scenes. Although significant research has
been directed to reduce the domain shift in semantic segmentation, adaptation
to scenes with adverse weather conditions remains an open question. Large
variations in the visibility of the scene due to weather conditions, such as
fog, smog, and haze, exacerbate the domain shift, thus making unsupervised
adaptation in such scenarios challenging. We propose a self-entropy and
multi-scale information augmented self-supervised domain adaptation method
(FogAdapt) to minimize the domain shift in foggy scenes segmentation. Supported
by the empirical evidence that an increase in fog density results in high
self-entropy for segmentation probabilities, we introduce a self-entropy based
loss function to guide the adaptation method. Furthermore, inferences obtained
at different image scales are combined and weighted by the uncertainty to
generate scale-invariant pseudo-labels for the target domain. These
scale-invariant pseudo-labels are robust to visibility and scale variations. We
evaluate the proposed model on real clear-weather scenes to real foggy scenes
adaptation and synthetic non-foggy images to real foggy scenes adaptation
scenarios. Our experiments demonstrate that FogAdapt significantly outperforms
the current state-of-the-art in semantic segmentation of foggy images.
Specifically, by considering the standard settings compared to state-of-the-art
(SOTA) methods, FogAdapt gains 3.8% on Foggy Zurich, 6.0% on Foggy
Driving-dense, and 3.6% on Foggy Driving in mIoU when adapted from Cityscapes
to Foggy Zurich.

Federated learning is a form of distributed learning with the key challenge
being the non-identically distributed nature of the data in the participating
clients. In this paper, we extend federated learning to the setting where
multiple unrelated models are trained simultaneously. Specifically, every
client is able to train any one of M models at a time and the server maintains
a model for each of the M models which is typically a suitably averaged version
of the model computed by the clients. We propose multiple policies for
assigning learning tasks to clients over time. In the first policy, we extend
the widely studied FedAvg to multi-model learning by allotting models to
clients in an i.i.d. stochastic manner. In addition, we propose two new
policies for client selection in a multi-model federated setting which make
decisions based on current local losses for each client-model pair. We compare
the performance of the policies on tasks involving synthetic and real-world
data and characterize the performance of the proposed policies. The key
take-away from our work is that the proposed multi-model policies perform
better or at least as good as single model training using FedAvg.

Pulmonary diseases can cause severe respiratory problems, leading to sudden
death if not treated timely. Many researchers have utilized deep learning
systems to diagnose pulmonary disorders using chest X-rays (CXRs). However,
such systems require exhaustive training efforts on large-scale data to
effectively diagnose chest abnormalities. Furthermore, procuring such
large-scale data is often infeasible and impractical, especially for rare
diseases. With the recent advances in incremental learning, researchers have
periodically tuned deep neural networks to learn different classification tasks
with few training examples. Although, such systems can resist catastrophic
forgetting, they treat the knowledge representations independently of each
other, and this limits their classification performance. Also, to the best of
our knowledge, there is no incremental learning-driven image diagnostic
framework that is specifically designed to screen pulmonary disorders from the
CXRs. To address this, we present a novel framework that can learn to screen
different chest abnormalities incrementally. In addition to this, the proposed
framework is penalized through an incremental learning loss function that
infers Bayesian theory to recognize structural and semantic inter-dependencies
between incrementally learned knowledge representations to diagnose the
pulmonary diseases effectively, regardless of the scanner specifications. We
tested the proposed framework on five public CXR datasets containing different
chest abnormalities, where it outperformed various state-of-the-art system
through various metrics.

A critical challenge of training deep learning models in the Digital
Pathology (DP) domain is the high annotation cost by medical experts. One way
to tackle this issue is via transfer learning from the natural image domain
(NI), where the annotation cost is considerably cheaper. Cross-domain transfer
learning from NI to DP is shown to be successful via class labels. One
potential weakness of relying on class labels is the lack of spatial
information, which can be obtained from spatial labels such as full pixel-wise
segmentation labels and scribble labels. We demonstrate that scribble labels
from NI domain can boost the performance of DP models on two cancer
classification datasets (Patch Camelyon Breast Cancer and Colorectal Cancer
dataset). Furthermore, we show that models trained with scribble labels yield
the same performance boost as full pixel-wise segmentation labels despite being
significantly easier and faster to collect.

This article proposes a sparse computation-based method for optimizing neural
networks for reinforcement learning (RL) tasks. This method combines two ideas:
neural network pruning and taking into account input data correlations; it
makes it possible to update neuron states only when changes in them exceed a
certain threshold. It significantly reduces the number of multiplications when
running neural networks. We tested different RL tasks and achieved 20-150x
reduction in the number of multiplications. There were no substantial
performance losses; sometimes the performance even improved.

Humans race drones faster than neural networks trained for end-to-end
autonomous flight. This may be related to the ability of human pilots to select
task-relevant visual information effectively. This work investigates whether
neural networks capable of imitating human eye gaze behavior and attention can
improve neural network performance for the challenging task of vision-based
autonomous drone racing. We hypothesize that gaze-based attention prediction
can be an efficient mechanism for visual information selection and decision
making in a simulator-based drone racing task. We test this hypothesis using
eye gaze and flight trajectory data from 18 human drone pilots to train a
visual attention prediction model. We then use this visual attention prediction
model to train an end-to-end controller for vision-based autonomous drone
racing using imitation learning. We compare the drone racing performance of the
attention-prediction controller to those using raw image inputs and image-based
abstractions (i.e., feature tracks). Comparing success rates for completing a
challenging race track by autonomous flight, our results show that the
attention-prediction based controller (88% success rate) outperforms the
RGB-image (61% success rate) and feature-tracks (55% success rate) controller
baselines. Furthermore, visual attention-prediction and feature-track based
models showed better generalization performance than image-based models when
evaluated on hold-out reference trajectories. Our results demonstrate that
human visual attention prediction improves the performance of autonomous
vision-based drone racing agents and provides an essential step towards
vision-based, fast, and agile autonomous flight that eventually can reach and
even exceed human performances.

Screening cluttered and occluded contraband items from baggage X-ray scans is
a cumbersome task even for the expert security staff. This paper presents a
novel strategy that extends a conventional encoder-decoder architecture to
perform instance-aware segmentation and extract merged instances of contraband
items without using any additional sub-network or an object detector. The
encoder-decoder network first performs conventional semantic segmentation and
retrieves cluttered baggage items. The model then incrementally evolves during
training to recognize individual instances using significantly reduced training
batches. To avoid catastrophic forgetting, a novel objective function minimizes
the network loss in each iteration by retaining the previously acquired
knowledge while learning new class representations and resolving their complex
structural inter-dependencies through Bayesian inference. A thorough evaluation
of our framework on two publicly available X-ray datasets shows that it
outperforms state-of-the-art methods, especially within the challenging
cluttered scenarios, while achieving an optimal trade-off between detection
accuracy and efficiency.

Robots are often shipped insecure and in some cases fully unprotected. The
rationale behind is fourfold: first, defensive security mechanisms for robots
are still on their early stages, not covering the complete threat landscape.
Second, the inherent complexity of robotic systems makes their protection
costly, both technically and economically. Third, robot vendors do not
generally take responsibility in a timely manner, extending the zero-days
exposure window (time until mitigation of a zero-day) to several years on
average. Fourth, contrary to the common-sense expectations in 21st century and
similar to Ford in the 1920s with cars, most robot manufacturers oppose or
difficult robot repairs.
  The Robot Hacking Manual (RHM) is an introductory series about cybersecurity
for robots, with an attempt to provide comprehensive case studies and
step-by-step tutorials with the intent to raise awareness in the field and
highlight the importance of taking a security-first approach. The material
available here is also a personal learning attempt and it's disconnected from
any particular organization. Content is provided as is and by no means it's
encouraged or promoted the unauthorized tampering of robotic systems or related

Classification models are a fundamental component of physical-asset
management technologies such as structural health monitoring (SHM) systems and
digital twins. Previous work introduced risk-based active learning, an online
approach for the development of statistical classifiers that takes into account
the decision-support context in which they are applied. Decision-making is
considered by preferentially querying data labels according to expected value
of perfect information (EVPI). Although several benefits are gained by adopting
a risk-based active learning approach, including improved decision-making
performance, the algorithms suffer from issues relating to sampling bias as a
result of the guided querying process. This sampling bias ultimately manifests
as a decline in decision-making performance during the later stages of active
learning, which in turn corresponds to lost resource/utility.
  The current paper proposes two novel approaches to counteract the effects of
sampling bias: semi-supervised learning, and discriminative classification
models. These approaches are first visualised using a synthetic dataset, then
subsequently applied to an experimental case study, specifically, the Z24
Bridge dataset. The semi-supervised learning approach is shown to have variable
performance; with robustness to sampling bias dependent on the suitability of
the generative distributions selected for the model with respect to each
dataset. In contrast, the discriminative classifiers are shown to have
excellent robustness to the effects of sampling bias. Moreover, it was found
that the number of inspections made during a monitoring campaign, and therefore
resource expenditure, could be reduced with the careful selection of the
statistical classifiers used within a decision-supporting monitoring system.

Unsupervised clustering algorithm can effectively reduce the dimension of
high-dimensional unlabeled data, thus reducing the time and space complexity of
data processing. However, the traditional clustering algorithm needs to set the
upper bound of the number of categories in advance, and the deep learning
clustering algorithm will fall into the problem of local optimum. In order to
solve these problems, a probabilistic spatial clustering algorithm based on the
Self Discipline Learning(SDL) model is proposed. The algorithm is based on the
Gaussian probability distribution of the probability space distance between
vectors, and uses the probability scale and maximum probability value of the
probability space distance as the distance measurement judgment, and then
determines the category of each sample according to the distribution
characteristics of the data set itself. The algorithm is tested in Laboratory
for Intelligent and Safe Automobiles(LISA) traffic light data set, the accuracy
rate is 99.03%, the recall rate is 91%, and the effect is achieved.

Deep autoencoders are often extended with a supervised or adversarial loss to
learn latent representations with desirable properties, such as greater
predictivity of labels and outcomes or fairness with respects to a sensitive
variable. Despite the ubiquity of supervised and adversarial deep latent factor
models, these methods should demonstrate improvement over simpler linear
approaches to be preferred in practice. This necessitates a reproducible linear
analog that still adheres to an augmenting supervised or adversarial objective.
We address this methodological gap by presenting methods that augment the
principal component analysis (PCA) objective with either a supervised or an
adversarial objective and provide analytic and reproducible solutions. We
implement these methods in an open-source Python package, AugmentedPCA, that
can produce excellent real-world baselines. We demonstrate the utility of these
factor models on an open-source, RNA-seq cancer gene expression dataset,
showing that augmenting with a supervised objective results in improved
downstream classification performance, produces principal components with
greater class fidelity, and facilitates identification of genes aligned with
the principal axes of data variance with implications to development of
specific types of cancer.

Rankings and scores are two common data types used by judges to express
preferences and/or perceptions of quality in a collection of objects. Numerous
models exist to study data of each type separately, but no unified statistical
model captures both data types simultaneously without first performing data
conversion. We propose the Mallows-Binomial model to close this gap, which
combines a Mallows' $\phi$ ranking model with Binomial score models through
shared parameters that quantify object quality, a consensus ranking, and the
level of consensus between judges. We propose an efficient tree-search
algorithm to calculate the exact MLE of model parameters, study statistical
properties of the model both analytically and through simulation, and apply our
model to real data from an instance of grant panel review that collected both
scores and partial rankings. Furthermore, we demonstrate how model outputs can
be used to rank objects with confidence. The proposed model is shown to
sensibly combine information from both scores and rankings to quantify object
quality and measure consensus with appropriate levels of statistical

Column generation is an iterative method used to solve a variety of
optimization problems. It decomposes the problem into two parts: a master
problem, and one or more pricing problems (PP). The total computing time taken
by the method is divided between these two parts. In routing or scheduling
applications, the problems are mostly defined on a network, and the PP is
usually an NP-hard shortest path problem with resource constraints. In this
work, we propose a new heuristic pricing algorithm based on machine learning.
By taking advantage of the data collected during previous executions, the
objective is to reduce the size of the network and accelerate the PP, keeping
only the arcs that have a high chance to be part of the linear relaxation
solution. The method has been applied to two specific problems: the vehicle and
crew scheduling problem in public transit and the vehicle routing problem with
time windows. Reductions in computational time of up to 40% can be obtained.

We introduce a novel masked graph autoencoder (MGAE) framework to perform
effective learning on graph structure data. Taking insights from
self-supervised learning, we randomly mask a large proportion of edges and try
to reconstruct these missing edges during training. MGAE has two core designs.
First, we find that masking a high ratio of the input graph structure, e.g.,
$70\%$, yields a nontrivial and meaningful self-supervisory task that benefits
downstream applications. Second, we employ a graph neural network (GNN) as an
encoder to perform message propagation on the partially-masked graph. To
reconstruct the large number of masked edges, a tailored cross-correlation
decoder is proposed. It could capture the cross-correlation between the head
and tail nodes of anchor edge in multi-granularity. Coupling these two designs
enables MGAE to be trained efficiently and effectively. Extensive experiments
on multiple open datasets (Planetoid and OGB benchmarks) demonstrate that MGAE
generally performs better than state-of-the-art unsupervised learning
competitors on link prediction and node classification.

We introduce a novel backbone architecture to improve target-perception
ability of feature representation for tracking. Specifically, having observed
that de facto frameworks perform feature matching simply using the outputs from
backbone for target localization, there is no direct feedback from the matching
module to the backbone network, especially the shallow layers. More concretely,
only the matching module can directly access the target information (in the
reference frame), while the representation learning of candidate frame is blind
to the reference target. As a consequence, the accumulation effect of
target-irrelevant interference in the shallow stages may degrade the feature
quality of deeper layers. In this paper, we approach the problem from a
different angle by conducting multiple branch-wise interactions inside the
Siamese-like backbone networks (InBN). At the core of InBN is a general
interaction modeler (GIM) that injects the prior knowledge of reference image
to different stages of the backbone network, leading to better
target-perception and robust distractor-resistance of candidate feature
representation with negligible computation cost. The proposed GIM module and
InBN mechanism are general and applicable to different backbone types including
CNN and Transformer for improvements, as evidenced by our extensive experiments
on multiple benchmarks. In particular, the CNN version (based on SiamCAR)
improves the baseline with 3.2/6.9 absolute gains of SUC on LaSOT/TNL2K,
respectively. The Transformer version obtains SUC scores of 65.7/52.0 on
LaSOT/TNL2K, which are on par with recent state of the arts. Code and models

We compare different neural network architectures for Machine Learning (ML)
algorithms designed to identify the neutrino interaction vertex position in the
MINERvA detector. The architectures developed and optimized by hand are
compared with the architectures developed in an automated way using the package
"Multi-node Evolutionary Neural Networks for Deep Learning" (MENNDL), developed
at Oak Ridge National Laboratory (ORNL). The two architectures resulted in a
similar performance which suggests that the systematics associated with the
optimized network architecture are small. Furthermore, we find that while the
domain expert hand-tuned network was the best performer, the differences were
negligible and the auto-generated networks performed well. There is always a
trade-off between human, and computer resources for network optimization and
this work suggests that automated optimization, assuming resources are
available, provides a compelling way to save significant expert time.

Graph embedding techniques have led to significant progress in recent years.
However, present techniques are not effective enough to capture the patterns of
networks. This paper propose neighbor2vec, a neighbor-based sampling strategy
used algorithm to learn the neighborhood representations of node, a framework
to gather the structure information by feature propagation between the node and
its neighbors. We claim that neighbor2vec is a simple and effective approach to
enhancing the scalability as well as equality of graph embedding, and it breaks
the limits of the existing state-of-the-art unsupervised techniques. We conduct
experiments on several node classification and link prediction tasks for
networks such as ogbn-arxiv, ogbn-products, ogbn-proteins, ogbl-ppa,ogbl-collab
and ogbl-citation2. The result shows that Neighbor2vec's representations
provide an average accuracy scores up to 6.8 percent higher than competing
methods in node classification tasks and 3.0 percent higher in link prediction
tasks. The neighbor2vec's representations are able to outperform all baseline
methods and two classical GNN models in all six experiments.

Markerless motion capture has become an active field of research in computer
vision in recent years. Its extensive applications are known in a great variety
of fields, including computer animation, human motion analysis, biomedical
research, virtual reality, and sports science. Estimating human posture has
recently gained increasing attention in the computer vision community, but due
to the depth of uncertainty and the lack of the synthetic datasets, it is a
challenging task. Various approaches have recently been proposed to solve this
problem, many of which are based on deep learning. They are primarily focused
on improving the performance of existing benchmarks with significant advances,
especially 2D images. Based on powerful deep learning techniques and recently
collected real-world datasets, we explored a model that can predict the
skeleton of an animation based solely on 2D images. Frames generated from
different real-world datasets with synthesized poses using different body
shapes from simple to complex. The implementation process uses DeepLabCut on
its own dataset to perform many necessary steps, then use the input frames to
train the model. The output is an animated skeleton for human movement. The
composite dataset and other results are the "ground truth" of the deep model.

Systems that can efficiently search collections of sign language videos have
been highlighted as a useful application of sign language technology. However,
the problem of searching videos beyond individual keywords has received limited
attention in the literature. To address this gap, in this work we introduce the
task of sign language retrieval with free-form textual queries: given a written
query (e.g., a sentence) and a large collection of sign language videos, the
objective is to find the signing video in the collection that best matches the
written query. We propose to tackle this task by learning cross-modal
embeddings on the recently introduced large-scale How2Sign dataset of American
Sign Language (ASL). We identify that a key bottleneck in the performance of
the system is the quality of the sign video embedding which suffers from a
scarcity of labeled training data. We, therefore, propose SPOT-ALIGN, a
framework for interleaving iterative rounds of sign spotting and feature
alignment to expand the scope and scale of available training data. We validate
the effectiveness of SPOT-ALIGN for learning a robust sign video embedding
through improvements in both sign recognition and the proposed video retrieval
task.

Modern video summarization methods are based on deep neural networks that
require a large amount of annotated data for training. However, existing
datasets for video summarization are small-scale, easily leading to
over-fitting of the deep models. Considering that the annotation of large-scale
datasets is time-consuming, we propose a multimodal self-supervised learning
framework to obtain semantic representations of videos, which benefits the
video summarization task. Specifically, the self-supervised learning is
conducted by exploring the semantic consistency between the videos and text in
both coarse-grained and fine-grained fashions, as well as recovering masked
frames in the videos. The multimodal framework is trained on a newly-collected
dataset that consists of video-text pairs. Additionally, we introduce a
progressive video summarization method, where the important content in a video
is pinpointed progressively to generate better summaries. Extensive experiments
have proved the effectiveness and superiority of our method in rank correlation
coefficients and F-score.

Rule-based classifier, that extract a subset of induced rules to efficiently
learn/mine while preserving the discernibility information, plays a crucial
role in human-explainable artificial intelligence. However, in this era of big
data, rule induction on the whole datasets is computationally intensive. So
far, to the best of our knowledge, no known method focusing on accelerating
rule induction has been reported. This is first study to consider the
acceleration technique to reduce the scale of computation in rule induction. We
propose an accelerator for rule induction based on fuzzy rough theory; the
accelerator can avoid redundant computation and accelerate the building of a
rule classifier. First, a rule induction method based on consistence degree,
called Consistence-based Value Reduction (CVR), is proposed and used as basis
to accelerate. Second, we introduce a compacted search space termed Key Set,
which only contains the key instances required to update the induced rule, to
conduct value reduction. The monotonicity of Key Set ensures the feasibility of
our accelerator. Third, a rule-induction accelerator is designed based on Key
Set, and it is theoretically guaranteed to display the same results as the
unaccelerated version. Specifically, the rank preservation property of Key Set
ensures consistency between the rule induction achieved by the accelerator and
the unaccelerated method. Finally, extensive experiments demonstrate that the
proposed accelerator can perform remarkably faster than the unaccelerated
rule-based classifier methods, especially on datasets with numerous instances.

The rise of deep learning algorithms has led many researchers to withdraw
from using classic signal processing methods for sound generation. Deep
learning models have achieved expressive voice synthesis, realistic sound
textures, and musical notes from virtual instruments. However, the most
suitable deep learning architecture is still under investigation. The choice of
architecture is tightly coupled to the audio representations. A sound's
original waveform can be too dense and rich for deep learning models to deal
with efficiently - and complexity increases training time and computational
cost. Also, it does not represent sound in the manner in which it is perceived.
Therefore, in many cases, the raw audio has been transformed into a compressed
and more meaningful form using upsampling, feature-extraction, or even by
adopting a higher level illustration of the waveform. Furthermore, conditional
on the form chosen, additional conditioning representations, different model
architectures, and numerous metrics for evaluating the reconstructed sound have
been investigated. This paper provides an overview of audio representations
applied to sound synthesis using deep learning. Additionally, it presents the
most significant methods for developing and evaluating a sound synthesis
architecture using deep learning models, always depending on the audio

It's hard for neural MWP solvers to deal with tiny local variances. In MWP
task, some local changes conserve the original semantic while the others may
totally change the underlying logic. Currently, existing datasets for MWP task
contain limited samples which are key for neural models to learn to
disambiguate different kinds of local variances in questions and solve the
questions correctly. In this paper, we propose a set of novel data augmentation
approaches to supplement existing datasets with such data that are augmented
with different kinds of local variances, and help to improve the generalization
ability of current neural models. New samples are generated by knowledge guided
entity replacement, and logic guided problem reorganization. The augmentation
approaches are ensured to keep the consistency between the new data and their
labels. Experimental results have shown the necessity and the effectiveness of
our methods.

Sparse PCA is the optimization problem obtained from PCA by adding a sparsity
constraint on the principal components. Sparse PCA is NP-hard and hard to
approximate even in the single-component case. In this paper we settle the
computational complexity of sparse PCA with respect to the rank of the
covariance matrix. We show that, if the rank of the covariance matrix is a
fixed value, then there is an algorithm that solves sparse PCA to global
optimality, whose running time is polynomial in the number of features. We also
prove a similar result for the version of sparse PCA which requires the
principal components to have disjoint supports.

The application of machine learning (ML) techniques, especially neural
networks, has seen tremendous success at processing images and language. This
is because we often lack formal models to understand visual and audio input, so
here neural networks can unfold their abilities as they can model solely from
data. In the field of physics we typically have models that describe natural
processes reasonably well on a formal level. Nonetheless, in recent years, ML
has also proven useful in these realms, be it by speeding up numerical
simulations or by improving accuracy. One important and so far unsolved problem
in classical physics is understanding turbulent fluid motion. In this work we
construct a strongly simplified representation of turbulence by using the
Gledzer-Ohkitani-Yamada (GOY) shell model. With this system we intend to
investigate the potential of ML-supported and physics-constrained small-scale
turbulence modelling. Instead of standard supervised learning we propose an
approach that aims to reconstruct statistical properties of turbulence such as
the self-similar inertial-range scaling, where we could achieve encouraging
experimental results. Furthermore we discuss pitfalls when combining machine
learning with differential equations.

The synthesis of sound via deep learning methods has recently received much
attention. Some problems for deep learning approaches to sound synthesis relate
to the amount of data needed to specify an audio signal and the necessity of
preserving both the long and short time coherence of the synthesised signal.
Visual time-frequency representations such as the log-mel-spectrogram have
gained in popularity. The log-mel-spectrogram is a perceptually informed
representation of audio that greatly compresses the amount of information
required for the description of the sound. However, because of this
compression, this representation is not directly invertible. Both signal
processing and machine learning techniques have previously been applied to the
inversion of the log-mel-spectrogram but they both caused audible distortions
in the synthesized sounds due to issues of temporal and spectral coherence. In
this paper, we outline the application of a sinusoidal model to the inversion
of the log-mel-spectrogram for pitched musical instrument sounds outperforming
state-of-the-art deep learning methods. The approach could be later used as a
general decoding step from spectral to time intervals in neural applications.

Recent advances in deep learning have led to a paradigm shift in reversible
steganography. A fundamental pillar of reversible steganography is predictive
modelling which can be realised via deep neural networks. However, non-trivial
errors exist in inferences about some out-of-distribution and noisy data. In
view of this issue, we propose to consider uncertainty in predictive models
based upon a theoretical framework of Bayesian deep learning. Bayesian neural
networks can be regarded as self-aware machinery; that is, a machine that knows
its own limitations. To quantify uncertainty, we approximate the posterior
predictive distribution through Monte Carlo sampling with stochastic forward
passes. We further show that predictive uncertainty can be disentangled into
aleatoric and epistemic uncertainties and these quantities can be learnt in an
unsupervised manner. Experimental results demonstrate an improvement delivered
by Bayesian uncertainty analysis upon steganographic capacity-distortion

Data scientists and statisticians are often at odds when determining the best
approach, machine learning or statistical modeling, to solve an analytics
challenge. However, machine learning and statistical modeling are more cousins
than adversaries on different sides of an analysis battleground. Choosing
between the two approaches or in some cases using both is based on the problem
to be solved and outcomes required as well as the data available for use and
circumstances of the analysis. Machine learning and statistical modeling are
complementary, based on similar mathematical principles, but simply using
different tools in an overall analytics knowledge base. Determining the
predominant approach should be based on the problem to be solved as well as
empirical evidence, such as size and completeness of the data, number of
variables, assumptions or lack thereof, and expected outcomes such as
predictions or causality. Good analysts and data scientists should be well
versed in both techniques and their proper application, thereby using the right
tool for the right project to achieve the desired results.

High dynamic range (HDR) imaging is of fundamental importance in modern
digital photography pipelines and used to produce a high-quality photograph
with well exposed regions despite varying illumination across the image. This
is typically achieved by merging multiple low dynamic range (LDR) images taken
at different exposures. However, over-exposed regions and misalignment errors
due to poorly compensated motion result in artefacts such as ghosting. In this
paper, we present a new HDR imaging technique that specifically models
alignment and exposure uncertainties to produce high quality HDR results. We
introduce a strategy that learns to jointly align and assess the alignment and
exposure reliability using an HDR-aware, uncertainty-driven attention map that
robustly merges the frames into a single high quality HDR image. Further, we
introduce a progressive, multi-stage image fusion approach that can flexibly
merge any number of LDR images in a permutation-invariant manner. Experimental
results show our method can produce better quality HDR images with up to 1.1dB
PSNR improvement to the state-of-the-art, and subjective improvements in terms
of better detail, colours, and fewer artefacts.

Recently, significant progress has been made in learned image and video
compression. In particular the usage of Generative Adversarial Networks has
lead to impressive results in the low bit rate regime. However, the model size
remains an important issue in current state-of-the-art proposals and existing
solutions require significant computation effort on the decoding side. This
limits their usage in realistic scenarios and the extension to video
compression. In this paper, we demonstrate how to leverage knowledge
distillation to obtain equally capable image decoders at a fraction of the
original number of parameters. We investigate several aspects of our solution
including sequence specialization with side information for image coding.
Finally, we also show how to transfer the obtained benefits into the setting of
video compression. Overall, this allows us to reduce the model size by a factor
of 20 and to achieve 50% reduction in decoding time.

In business retention, churn prevention has always been a major concern. This
work contributes to this domain by formalizing the problem of churn prediction
in the context of online gambling as a binary classification task. We also
propose an algorithmic answer to this problem based on recurrent neural
network. This algorithm is tested with online gambling data that have the form
of time series, which can be efficiently processed by recurrent neural
networks. To evaluate the performances of the trained models, standard machine
learning metrics were used, such as accuracy, precision and recall. For this
problem in particular, the conducted experiments allowed to assess that the
choice of a specific architecture depends on the metric which is given the
greatest importance. Architectures using nBRC favour precision, those using
LSTM give better recall, while GRU-based architectures allow a higher accuracy
and balance two other metrics. Moreover, further experiments showed that using
only the more recent time-series histories to train the networks decreases the
quality of the results. We also study the performances of models learned at a
specific instant $t$, at other times $t^{\prime} > t$. The results show that
the performances of the models learned at time $t$ remain good at the following
instants $t^{\prime} > t$, suggesting that there is no need to refresh the
models at a high rate. However, the performances of the models were subject to
noticeable variance due to one-off events impacting the data.

We study how to exploit the notion of strategy templates to learn strategies
for multi-issue bilateral negotiation. Each strategy template consists of a set
of interpretable parameterized tactics that are used to decide an optimal
action at any time. We use deep reinforcement learning throughout an
actor-critic architecture to estimate the tactic parameter values for a
threshold utility, when to accept an offer and how to generate a new bid. This
contrasts with existing work that only estimates the threshold utility for
those tactics. We pre-train the strategy by supervision from the dataset
collected using "teacher strategies", thereby decreasing the exploration time
required for learning during negotiation. As a result, we build automated
agents for multi-issue negotiations that can adapt to different negotiation
domains without the need to be pre-programmed. We empirically show that our
work outperforms the state-of-the-art in terms of the individual as well as
social efficiency.

Metric $k$-center clustering is a fundamental unsupervised learning
primitive. Although widely used, this primitive is heavily affected by noise in
the data, so that a more sensible variant seeks for the best solution that
disregards a given number $z$ of points of the dataset, called outliers. We
provide efficient algorithms for this important variant in the streaming model
under the sliding window setting, where, at each time step, the dataset to be
clustered is the window $W$ of the most recent data items. Our algorithms
achieve $O(1)$ approximation and, remarkably, require a working memory linear
in $k+z$ and only logarithmic in $|W|$. As a by-product, we show how to
estimate the effective diameter of the window $W$, which is a measure of the
spread of the window points, disregarding a given fraction of noisy distances.
We also provide experimental evidence of the practical viability of our
theoretical results.

Using only global image-class labels, weakly-supervised learning methods,
such as class activation mapping, allow training CNNs to jointly classify an
image, and locate regions of interest associated with the predicted class.
However, without any guidance at the pixel level, such methods may yield
inaccurate regions. This problem is known to be more challenging with histology
images than with natural ones, since objects are less salient, structures have
more variations, and foreground and background regions have stronger
similarities. Therefore, computer vision methods for visual interpretation of
CNNs may not directly apply. In this paper, a simple yet efficient method based
on a composite loss is proposed to learn information from the fully negative
samples (i.e., samples without positive regions), and thereby reduce false
positives/negatives. Our new loss function contains two complementary terms:
the first exploits positive evidence collected from the CNN classifier, while
the second leverages the fully negative samples from training data. In
particular, a pre-trained CNN is equipped with a decoder that allows refining
the regions of interest. The CNN is exploited to collect both positive and
negative evidence at the pixel level to train the decoder. Our method called
NEGEV benefits from the fully negative samples that naturally occur in the
data, without any additional supervision signals beyond image-class labels.
Extensive experiments show that our proposed method can substantial outperform
related state-of-art methods on GlaS (public benchmark for colon cancer), and
Camelyon16 (patch-based benchmark for breast cancer using three different
backbones). Our results highlight the benefits of using both positive and
negative evidence, the first obtained from a classifier, and the other
naturally available in datasets.

Anomaly detection is the process of identifying abnormal instances or events
in data sets which deviate from the norm significantly. In this study, we
propose a signatures based machine learning algorithm to detect rare or
unexpected items in a given data set of time series type. We present
applications of signature or randomized signature as feature extractors for
anomaly detection algorithms; additionally we provide an easy, representation
theoretic justification for the construction of randomized signatures. Our
first application is based on synthetic data and aims at distinguishing between
real and fake trajectories of stock prices, which are indistinguishable by
visual inspection. We also show a real life application by using transaction
data from the cryptocurrency market. In this case, we are able to identify pump
and dump attempts organized on social networks with F1 scores up to 88% by
means of our unsupervised learning algorithm, thus achieving results that are
close to the state-of-the-art in the field based on supervised learning.

Crime prediction is crucial for public safety and resource optimization, yet
is very challenging due to two aspects: i) the dynamics of criminal patterns
across time and space, crime events are distributed unevenly on both spatial
and temporal domains; ii) time-evolving dependencies between different types of
crimes (e.g., Theft, Robbery, Assault, Damage) which reveal fine-grained
semantics of crimes. To tackle these challenges, we propose Spatial-Temporal
Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime
spatial-temporal patterns as well as the underlying category-wise crime
semantic relationships. In specific, to handle spatial-temporal dynamics under
the long-range and global context, we design a graph-structured message passing
architecture with the integration of the hypergraph learning paradigm. To
capture category-wise crime heterogeneous relations in a dynamic environment,
we introduce a multi-channel routing mechanism to learn the time-evolving
structural dependency across crime types. We conduct extensive experiments on
two real-world datasets, showing that our proposed ST-SHN framework can
significantly improve the prediction performance as compared to various
state-of-the-art baselines. The source code is available at:
https://github.com/akaxlh/ST-SHN.

Starting from the Kaya identity, we used a Neural ODE model to predict the
evolution of several indicators related to carbon emissions, on a
country-level: population, GDP per capita, energy intensity of GDP, carbon
intensity of energy. We compared the model with a baseline statistical model -
VAR - and obtained good performances. We conclude that this machine-learning
approach can be used to produce a wide range of results and give relevant
insight to policymakers

In this work, we analyze the noisy importance sampling (IS), i.e., IS working
with noisy evaluations of the target density. We present the general framework
and derive optimal proposal densities for noisy IS estimators. The optimal
proposals incorporate the information of the variance of the noisy
realizations, proposing points in regions where the noise power is higher. We
also compare the use of the optimal proposals with previous optimality
approaches considered in a noisy IS framework.

Recently, various view synthesis distortion estimation models have been
studied to better serve for 3-D video coding. However, they can hardly model
the relationship quantitatively among different levels of depth changes,
texture degeneration, and the view synthesis distortion (VSD), which is crucial
for rate-distortion optimization and rate allocation. In this paper, an
auto-weighted layer representation based view synthesis distortion estimation
model is developed. Firstly, the sub-VSD (S-VSD) is defined according to the
level of depth changes and their associated texture degeneration. After that, a
set of theoretical derivations demonstrate that the VSD can be approximately
decomposed into the S-VSDs multiplied by their associated weights. To obtain
the S-VSDs, a layer-based representation of S-VSD is developed, where all the
pixels with the same level of depth changes are represented with a layer to
enable efficient S-VSD calculation at the layer level. Meanwhile, a nonlinear
mapping function is learnt to accurately represent the relationship between the
VSD and S-VSDs, automatically providing weights for S-VSDs during the VSD
estimation. To learn such function, a dataset of VSD and its associated S-VSDs
are built. Experimental results show that the VSD can be accurately estimated
with the weights learnt by the nonlinear mapping function once its associated
S-VSDs are available. The proposed method outperforms the relevant
state-of-the-art methods in both accuracy and efficiency. The dataset and
source code of the proposed method will be available at
https://github.com/jianjin008/.

Automatic speech recognition (ASR) on low resource languages improves the
access of linguistic minorities to technological advantages provided by
artificial intelligence (AI). In this paper, we address the problem of data
scarcity for the Hong Kong Cantonese language by creating a new Cantonese
dataset. Our dataset, Multi-Domain Cantonese Corpus (MDCC), consists of 73.6
hours of clean read speech paired with transcripts, collected from Cantonese
audiobooks from Hong Kong. It comprises philosophy, politics, education,
culture, lifestyle and family domains, covering a wide range of topics. We also
review all existing Cantonese datasets and analyze them according to their
speech type, data source, total size and availability. We further conduct
experiments with Fairseq S2T Transformer, a state-of-the-art ASR model, on the
biggest existing dataset, Common Voice zh-HK, and our proposed MDCC, and the
results show the effectiveness of our dataset. In addition, we create a
powerful and robust Cantonese ASR model by applying multi-dataset learning on
MDCC and Common Voice zh-HK.

Federated learning trains models across devices with distributed data, while
protecting the privacy and obtaining a model similar to that of centralized ML.
A large number of workers with data and computing power are the foundation of
federal learning. However, the inevitable costs prevent self-interested workers
from serving for free. Moreover, due to data isolation, task publishers lack
effective methods to select, evaluate and pay reliable workers with
high-quality data. Therefore, we design an auction-based incentive mechanism
for horizontal federated learning with reputation and contribution measurement.
By designing a reasonable method of measuring contribution, we establish the
reputation of workers, which is easy to decline and difficult to improve.
Through reverse auctions, workers bid for tasks, and the task publisher selects
workers combining reputation and bid price. With the budget constraint, winning
workers are paid based on performance. We proved that our mechanism satisfies
the individual rationality of the honest worker, budget feasibility,
truthfulness, and computational efficiency.

Markov chains play a key role in a vast number of areas, including life
insurance mathematics. Standard actuarial quantities as the premium value can
be interpreted as compressed, lossy information about the underlying Markov
process. We introduce a method to reconstruct the underlying Markov chain given
collective information of a portfolio of contracts. Our neural architecture
explainably characterizes the process by explicitly providing one-step
transition probabilities. Further, we provide an intrinsic, economic model
validation to inspect the quality of the information decompression. Lastly, our
methodology is successfully tested for a realistic data set of German term life
insurance contracts.

The computational models for geophysical flows are computationally very
expensive to employ in multi-query tasks such as data assimilation, uncertainty
quantification, and hence surrogate models sought to alleviate the
computational burden associated with these full order models. Researchers have
started applying machine learning algorithms, particularly neural networks, to
build data-driven surrogate models for geophysical flows. The performance of
the neural network highly relies upon its architecture design and selection of
other hyperparameters. These neural networks are usually manually designed
through trial and error to maximize their performance. This often requires
domain knowledge of the neural network as well as the problems of interest.
This limitation can be addressed by using an evolutionary algorithm to
automatically design architecture and select optimal hyperparameters of the
neural network. In this paper, we apply the genetic algorithm to effectively
design the long short-term memory (LSTM) neural network to build the
non-intrusive reduced order model of the sea-surface temperature field.

The term robot generally refers to a machine that looks and works in a way
similar to a human. The modern industry is rapidly shifting from manual control
of systems to automation, in order to increase productivity and to deliver
quality products. Computer-based systems, though feasible for improving quality
and productivity, are inflexible to work with, and the cost of such systems is
significantly high. This led to the swift adoption of automated systems to
perform industrial tasks. One such task of industrial significance is of
picking and placing objects from one place to another. The implementation of
automation in pick and place tasks helps to improve efficiency of system and
also the performance. In this paper, we propose to demonstrate the designing
and working of an automated robotic arm with the Machine Learning approach. The
work uses Machine Learning approach for object identification detection and
traversal, which is adopted with Tensor flow package for better and accurate
results.

Knowledge tracing is the task of predicting a learner's future performance
based on the history of the learner's performance. Current knowledge tracing
models are built based on an extensive set of data that are collected from
multiple schools. However, it is impossible to pool learner's data from all
schools, due to data privacy and PDPA policies. Hence, this paper explores the
feasibility of building knowledge tracing models while preserving the privacy
of learners' data within their respective schools. This study is conducted
using part of the ASSISTment 2009 dataset, with data from multiple schools
being treated as separate tasks in a continual learning framework. The results
show that learning sequentially with the Self Attentive Knowledge Tracing
(SAKT) algorithm is able to achieve considerably similar performance to that of
pooling all the data together.

Most dialogue systems in real world rely on predefined intents and answers
for QA service, so discovering potential intents from large corpus previously
is really important for building such dialogue services. Considering that most
scenarios have few intents known already and most intents waiting to be
discovered, we focus on semi-supervised text clustering and try to make the
proposed method benefit from labeled samples for better overall clustering
performance. In this paper, we propose Deep Contrastive Semi-supervised
Clustering (DCSC), which aims to cluster text samples in a semi-supervised way
and provide grouped intents to operation staff. To make DCSC fully utilize the
limited known intents, we propose a two-stage training procedure for DCSC, in
which DCSC will be trained on both labeled samples and unlabeled samples, and
achieve better text representation and clustering performance. We conduct
experiments on two public datasets to compare our model with several popular
methods, and the results show DCSC achieve best performance across all datasets
and circumstances, indicating the effect of the improvements in our work.

Traffic signal control is an important problem in urban mobility with a
significant potential of economic and environmental impact. While there is a
growing interest in Reinforcement Learning (RL) for traffic signal control, the
work so far has focussed on learning through simulations which could lead to
inaccuracies due to simplifying assumptions. Instead, real experience data on
traffic is available and could be exploited at minimal costs. Recent progress
in {\em offline} or {\em batch} RL has enabled just that. Model-based offline
RL methods, in particular, have been shown to generalize from the experience
data much better than others.
  We build a model-based learning framework which infers a Markov Decision
Process (MDP) from a dataset collected using a cyclic traffic signal control
policy that is both commonplace and easy to gather. The MDP is built with
pessimistic costs to manage out-of-distribution scenarios using an adaptive
shaping of rewards which is shown to provide better regularization compared to
the prior related work in addition to being PAC-optimal. Our model is evaluated
on a complex signalized roundabout showing that it is possible to build highly
performant traffic control policies in a data efficient manner.

Modern deep reinforcement learning (RL) algorithms are motivated by either
the generalised policy iteration (GPI) or trust-region learning (TRL)
frameworks. However, algorithms that strictly respect these theoretical
frameworks have proven unscalable. Surprisingly, the only known scalable
algorithms violate the GPI/TRL assumptions, e.g. due to required regularisation
or other heuristics. The current explanation of their empirical success is
essentially "by analogy": they are deemed approximate adaptations of
theoretically sound methods. Unfortunately, studies have shown that in practice
these algorithms differ greatly from their conceptual ancestors. In contrast,
in this paper we introduce a novel theoretical framework, named Mirror
Learning, which provides theoretical guarantees to a large class of algorithms,
including TRPO and PPO. While the latter two exploit the flexibility of our
framework, GPI and TRL fit in merely as pathologically restrictive corner cases
thereof. This suggests that the empirical performance of state-of-the-art
methods is a direct consequence of their theoretical properties, rather than of
aforementioned approximate analogies. Mirror learning sets us free to boldly
explore novel, theoretically sound RL algorithms, a thus far uncharted
wonderland.

Automated generation and (user) authoring of the realistic virtual terrain is
most sought for by the multimedia applications like VR models and gaming. The
most common representation adopted for terrain is Digital Elevation Model
(DEM). Existing terrain authoring and modeling techniques have addressed some
of these and can be broadly categorized as: procedural modeling, simulation
method, and example-based methods. In this paper, we propose a novel realistic
terrain authoring framework powered by a combination of VAE and generative
conditional GAN model. Our framework is an example-based method that attempts
to overcome the limitations of existing methods by learning a latent space from
a real-world terrain dataset. This latent space allows us to generate multiple
variants of terrain from a single input as well as interpolate between terrains
while keeping the generated terrains close to real-world data distribution. We
also developed an interactive tool, that lets the user generate diverse
terrains with minimalist inputs. We perform thorough qualitative and
quantitative analysis and provide comparisons with other SOTA methods. We
intend to release our code/tool to the academic community.

Motion prediction is a classic problem in computer vision, which aims at
forecasting future motion given the observed pose sequence. Various deep
learning models have been proposed, achieving state-of-the-art performance on
motion prediction. However, existing methods typically focus on modeling
temporal dynamics in the pose space. Unfortunately, the complicated and high
dimensionality nature of human motion brings inherent challenges for dynamic
context capturing. Therefore, we move away from the conventional pose based
representation and present a novel approach employing a phase space trajectory
representation of individual joints. Moreover, current methods tend to only
consider the dependencies between physically connected joints. In this paper,
we introduce a novel convolutional neural model to effectively leverage
explicit prior knowledge of motion anatomy, and simultaneously capture both
spatial and temporal information of joint trajectory dynamics. We then propose
a global optimization module that learns the implicit relationships between
individual joint features.
  Empirically, our method is evaluated on large-scale 3D human motion benchmark
datasets (i.e., Human3.6M, CMU MoCap). These results demonstrate that our
method sets the new state-of-the-art on the benchmark datasets. Our code will
be available at https://github.com/Pose-Group/TEID.

Motivated by potential financial gain, companies may hire fraudster groups to
write fake reviews to either demote competitors or promote their own
businesses. Such groups are considerably more successful in misleading
customers, as people are more likely to be influenced by the opinion of a large
group. To detect such groups, a common model is to represent fraudster groups'
static networks, consequently overlooking the longitudinal behavior of a
reviewer thus the dynamics of co-review relations among reviewers in a group.
Hence, these approaches are incapable of excluding outlier reviewers, which are
fraudsters intentionally camouflaging themselves in a group and genuine
reviewers happen to co-review in fraudster groups. To address this issue, in
this work, we propose to first capitalize on the effectiveness of the HIN-RNN
in both reviewers' representation learning while capturing the collaboration
between reviewers, we first utilize the HIN-RNN to model the co-review
relations of reviewers in a group in a fixed time window of 28 days. We refer
to this as spatial relation learning representation to signify the
generalisability of this work to other networked scenarios. Then we use an RNN
on the spatial relations to predict the spatio-temporal relations of reviewers
in the group. In the third step, a Graph Convolution Network (GCN) refines the
reviewers' vector representations using these predicted relations. These
refined representations are then used to remove outlier reviewers. The average
of the remaining reviewers' representation is then fed to a simple fully
connected layer to predict if the group is a fraudster group or not. Exhaustive
experiments of the proposed approach showed a 5% (4%), 12% (5%), 12% (5%)
improvement over three of the most recent approaches on precision, recall, and
F1-value over the Yelp (Amazon) dataset, respectively.

Recent advances in machine learning and prevalence of digital medical images
have opened up an opportunity to address the challenging brain tumor
segmentation (BTS) task by using deep convolutional neural networks. However,
different from the RGB image data that are very widespread, the medical image
data used in brain tumor segmentation are relatively scarce in terms of the
data scale but contain the richer information in terms of the modality
property. To this end, this paper proposes a novel cross-modality deep feature
learning framework to segment brain tumors from the multi-modality MRI data.
The core idea is to mine rich patterns across the multi-modality data to make
up for the insufficient data scale. The proposed cross-modality deep feature
learning framework consists of two learning processes: the cross-modality
feature transition (CMFT) process and the cross-modality feature fusion (CMFF)
process, which aims at learning rich feature representations by transiting
knowledge across different modality data and fusing knowledge from different
modality data, respectively. Comprehensive experiments are conducted on the
BraTS benchmarks, which show that the proposed cross-modality deep feature
learning framework can effectively improve the brain tumor segmentation
performance when compared with the baseline methods and state-of-the-art
methods.

Mixup is a data augmentation method that generates new data points by mixing
a pair of input data. While mixup generally improves the prediction
performance, it sometimes degrades the performance. In this paper, we first
identify the main causes of this phenomenon by theoretically and empirically
analyzing the mixup algorithm. To resolve this, we propose GenLabel, a simple
yet effective relabeling algorithm designed for mixup. In particular, GenLabel
helps the mixup algorithm correctly label mixup samples by learning the
class-conditional data distribution using generative models. Via extensive
theoretical and empirical analysis, we show that mixup, when used together with
GenLabel, can effectively resolve the aforementioned phenomenon, improving the
generalization performance and the adversarial robustness.

Few-sample compression aims to compress a big redundant model into a small
compact one with only few samples. If we fine-tune models with these limited
few samples directly, models will be vulnerable to overfit and learn almost
nothing. Hence, previous methods optimize the compressed model layer-by-layer
and try to make every layer have the same outputs as the corresponding layer in
the teacher model, which is cumbersome. In this paper, we propose a new
framework named Mimicking then Replacing (MiR) for few-sample compression,
which firstly urges the pruned model to output the same features as the
teacher's in the penultimate layer, and then replaces teacher's layers before
penultimate with a well-tuned compact one. Unlike previous layer-wise
reconstruction methods, our MiR optimizes the entire network holistically,
which is not only simple and effective, but also unsupervised and general. MiR
outperforms previous methods with large margins. Codes will be available soon.

Comparing with lecturer marked assessments, peer assessment is a more
comprehensive learning process and many of the associated problems have
occurred. In this research work, we study the peer-assessment impact on group
learning activities in order to provide a complete and systematic review,
increase the practice and quality of the peer assessment process. Pilot studies
were conducted and took the form of surveys, focus group interviews, and
questionnaires. Prelimi-nary surveys were conducted with 582 students and 276
responses were received, giving a response rate of 47.4%. The results show 37%
student will choose individual work over group work if given the choice. In the
case study, 82.1% of the total of 28 students have en-joyed working in a group
using Facebook as communication tools. 89.3% of the students can demonstrate
their skills through group-working and most importantly, 82.1% of them agree
that peer assess-ment is an impartial method of assessment with the help of
Facebook as proof of self-contribution. Our suggestions to make group work a
pleasant experience are by identifying and taking action against the
freeloader, giving credit to the deserving students, educating students on how
to give constructive feedback and making the assessment pro-cess transparent to
all.

The emerging research field Search as Learning investigates how the Web
facilitates learning through modern information retrieval systems. SAL research
requires significant amounts of data that capture both search behavior of users
and their acquired knowledge in order to obtain conclusive insights or train
supervised machine learning models. However, the creation of such datasets is
costly and requires interdisciplinary efforts in order to design studies and
capture a wide range of features. In this paper, we address this issue and
introduce an extensive dataset based on a user study, in which $114$
participants were asked to learn about the formation of lightning and thunder.
Participants' knowledge states were measured before and after Web search
through multiple-choice questionnaires and essay-based free recall tasks. To
enable future research in SAL-related tasks we recorded a plethora of features
and person-related attributes. Besides the screen recordings, visited Web
pages, and detailed browsing histories, a large number of behavioral features
and resource features were monitored. We underline the usefulness of the
dataset by describing three, already published, use cases.

Machine learning methods such as deep neural networks (DNNs), despite their
success across different domains, are known to often generate incorrect
predictions with high confidence on inputs outside their training distribution.
The deployment of DNNs in safety-critical domains requires detection of
out-of-distribution (OOD) data so that DNNs can abstain from making predictions
on those. A number of methods have been recently developed for OOD detection,
but there is still room for improvement. We propose the new method iDECODe,
leveraging in-distribution equivariance for conformal OOD detection. It relies
on a novel base non-conformity measure and a new aggregation method, used in
the inductive conformal anomaly detection framework, thereby guaranteeing a
bounded false detection rate. We demonstrate the efficacy of iDECODe by
experiments on image and audio datasets, obtaining state-of-the-art results. We
also show that iDECODe can detect adversarial examples.

Learning objectives of recommender models remain largely unexplored. Most
methods routinely adopt either pointwise or pairwise loss to train the model
parameters, while rarely pay attention to softmax loss due to the high
computational cost. Sampled softmax loss emerges as an efficient substitute for
softmax loss. Its special case, InfoNCE loss, has been widely used in
self-supervised learning and exhibited remarkable performance for contrastive
learning. Nonetheless, limited studies use sampled softmax loss as the learning
objective to train the recommender. Worse still, none of them explore its
properties and answer "Does sampled softmax loss suit for item recommendation?"
and "What are the conceptual advantages of sampled softmax loss, as compared
with the prevalent losses?", to the best of our knowledge. In this work, we aim
to better understand sampled softmax loss for item recommendation.
Specifically, we first theoretically reveal three model-agnostic advantages:
(1) mitigating popularity bias, which is beneficial to long-tail
recommendation; (2) mining hard negative samples, which offers informative
gradients to optimize model parameters; and (3) maximizing the ranking metric,
which facilitates top-K performance. Moreover, we probe the model-specific
characteristics on the top of various recommenders. Experimental results
suggest that sampled softmax loss is more friendly to history and graph-based
recommenders (e.g., SVD++ and LightGCN), but performs poorly for ID-based
models (e.g., MF). We ascribe this to its shortcoming in learning
representation magnitude, making the combination with the models that are also
incapable of adjusting representation magnitude learn poor representations. In
contrast, the history- and graph-based models, which naturally adjust
representation magnitude according to node degree, are able to compensate for
the shortcoming of sampled softmax loss.

In time series data analysis, detecting change points on a real-time basis
(online) is of great interest in many areas, such as finance, environmental
monitoring, and medicine. One promising means to achieve this is the Bayesian
online change point detection (BOCPD) algorithm, which has been successfully
adopted in particular cases in which the time series of interest has a fixed
baseline. However, we have found that the algorithm struggles when the baseline
irreversibly shifts from its initial state. This is because with the original
BOCPD algorithm, the sensitivity with which a change point can be detected is
degraded if the data points are fluctuating at locations relatively far from
the original baseline. In this paper, we not only extend the original BOCPD
algorithm to be applicable to a time series whose baseline is constantly
shifting toward unknown values but also visualize why the proposed extension
works. To demonstrate the efficacy of the proposed algorithm compared to the
original one, we examine these algorithms on two real-world data sets and six
synthetic data sets.

Image restoration algorithms such as super resolution (SR) are indispensable
pre-processing modules for object detection in degraded images. However, most
of these algorithms assume the degradation is fixed and known a priori. When
the real degradation is unknown or differs from assumption, both the
pre-processing module and the consequent high-level task such as object
detection would fail. Here, we propose a novel framework, RestoreDet, to detect
objects in degraded low resolution images. RestoreDet utilizes the downsampling
degradation as a kind of transformation for self-supervised signals to explore
the equivariant representation against various resolutions and other
degradation conditions. Specifically, we learn this intrinsic visual structure
by encoding and decoding the degradation transformation from a pair of original
and randomly degraded images. The framework could further take the advantage of
advanced SR architectures with an arbitrary resolution restoring decoder to
reconstruct the original correspondence from the degraded input image. Both the
representation learning and object detection are optimized jointly in an
end-to-end training fashion. RestoreDet is a generic framework that could be
implemented on any mainstream object detection architectures. The extensive
experiment shows that our framework based on CenterNet has achieved superior
performance compared with existing methods when facing variant degradation
situations. Our code would be released soon.

This paper focuses on stochastic saddle point problems with
decision-dependent distributions. These are problems whose objective is the
expected value of a stochastic payoff function and whose data distribution
drifts in response to decision variables--a phenomenon represented by a
distributional map. A common approach to accommodating distributional shift is
to retrain optimal decisions once a new distribution is revealed, or repeated
retraining. We introduce the notion of equilibrium points, which are the fixed
points of this repeated retraining procedure, and provide sufficient conditions
for their existence and uniqueness. To find equilibrium points, we develop
deterministic and stochastic primal-dual algorithms and demonstrate their
convergence with constant step-size in the former and polynomial decay
step-size schedule in the latter. By modeling errors emerging from a stochastic
gradient estimator as sub-Weibull random variables, we provide error bounds in
expectation and in high probability that hold for each iteration. Without
additional knowledge of the distributional map, computing saddle points is
intractable. Thus we propose a condition on the distributional map--which we
call opposing mixture dominance--that ensures that the objective is
strongly-convex-strongly-concave. Finally, we demonstrate that derivative-free
algorithms with a single function evaluation are capable of approximating
saddle points

Effective human learning depends on a wide selection of educational materials
that align with the learner's current understanding of the topic. While the
Internet has revolutionized human learning or education, a substantial resource
accessibility barrier still exists. Namely, the excess of online information
can make it challenging to navigate and discover high-quality learning
materials. In this paper, we propose the educational resource discovery (ERD)
pipeline that automates web resource discovery for novel domains. The pipeline
consists of three main steps: data collection, feature extraction, and resource
classification. We start with a known source domain and conduct resource
discovery on two unseen target domains via transfer learning. We first collect
frequent queries from a set of seed documents and search on the web to obtain
candidate resources, such as lecture slides and introductory blog posts. Then
we introduce a novel pretrained information retrieval deep neural network
model, query-document masked language modeling (QD-MLM), to extract deep
features of these candidate resources. We apply a tree-based classifier to
decide whether the candidate is a positive learning resource. The pipeline
achieves F1 scores of 0.94 and 0.82 when evaluated on two similar but novel
target domains. Finally, we demonstrate how this pipeline can benefit an
application: leading paragraph generation for surveys. This is the first study
that considers various web resources for survey generation, to the best of our
knowledge. We also release a corpus of 39,728 manually labeled web resources
and 659 queries from NLP, Computer Vision (CV), and Statistics (STATS).

The similarity between objects is significant in a broad range of areas.
While similarity can be measured using off-the-shelf distance functions, they
may fail to capture the inherent meaning of similarity, which tends to depend
on the underlying data and task. Moreover, conventional distance functions
limit the space of similarity measures to be symmetric and do not directly
allow comparing objects from different spaces. We propose using quantum
networks (GQSim) for learning task-dependent (a)symmetric similarity between
data that need not have the same dimensionality. We analyze the properties of
such similarity function analytically (for a simple case) and numerically (for
a complex case) and showthat these similarity measures can extract salient
features of the data. We also demonstrate that the similarity measure derived
using this technique is $(\epsilon,\gamma,\tau)$-good, resulting in
theoretically guaranteed performance. Finally, we conclude by applying this
technique for three relevant applications - Classification, Graph Completion,
Generative modeling.

In this paper, we propose a new GPU implementation of the Katsevich algorithm
for helical CT reconstruction. Our implementation divides the sinograms and
reconstructs the CT images pitch by pitch. By utilizing the periodic properties
of the parameters of the Katsevich algorithm, our method only needs to
calculate these parameters once for all the pitches and so has lower GPU-memory
burdens and is very suitable for deep learning. By embedding our implementation
into the network, we propose an end-to-end deep network for the high pitch
helical CT reconstruction with sparse detectors. Since our network utilizes the
features extracted from both sinograms and CT images, it can simultaneously
reduce the streak artifacts caused by the sparsity of sinograms and preserve
fine details in the CT images. Experiments show that our network outperforms
the related methods both in subjective and objective evaluations.

Many previous studies aim to augment collaborative filtering with deep neural
network techniques, so as to achieve better recommendation performance.
However, most existing deep learning-based recommender systems are designed for
modeling singular type of user-item interaction behavior, which can hardly
distill the heterogeneous relations between user and item. In practical
recommendation scenarios, there exist multityped user behaviors, such as browse
and purchase. Due to the overlook of user's multi-behavioral patterns over
different items, existing recommendation methods are insufficient to capture
heterogeneous collaborative signals from user multi-behavior data. Inspired by
the strength of graph neural networks for structured data modeling, this work
proposes a Graph Neural Multi-Behavior Enhanced Recommendation (GNMR) framework
which explicitly models the dependencies between different types of user-item
interactions under a graph-based message passing architecture. GNMR devises a
relation aggregation network to model interaction heterogeneity, and
recursively performs embedding propagation between neighboring nodes over the
user-item interaction graph. Experiments on real-world recommendation datasets
show that our GNMR consistently outperforms state-of-the-art methods. The
source code is available at https://github.com/akaxlh/GNMR.

Multi-task learning is to improve the performance of the model by
transferring and exploiting common knowledge among tasks. Existing MTL works
mainly focus on the scenario where label sets among multiple tasks (MTs) are
usually the same, thus they can be utilized for learning across the tasks.
While almost rare works explore the scenario where each task only has a small
amount of training samples, and their label sets are just partially overlapped
or even not. Learning such MTs is more challenging because of less correlation
information available among these tasks. For this, we propose a framework to
learn these tasks by jointly leveraging both abundant information from a learnt
auxiliary big task with sufficiently many classes to cover those of all these
tasks and the information shared among those partially-overlapped tasks. In our
implementation of using the same neural network architecture of the learnt
auxiliary task to learn individual tasks, the key idea is to utilize available
label information to adaptively prune the hidden layer neurons of the auxiliary
network to construct corresponding network for each task, while accompanying a
joint learning across individual tasks. Our experimental results demonstrate
its effectiveness in comparison with the state-of-the-art approaches.

This paper tackles the problem of few-shot learning, which aims to learn new
visual concepts from a few examples. A common problem setting in few-shot
classification assumes random sampling strategy in acquiring data labels, which
is inefficient in practical applications. In this work, we introduce a new
budget-aware few-shot learning problem that not only aims to learn novel object
categories, but also needs to select informative examples to annotate in order
to achieve data efficiency.
  We develop a meta-learning strategy for our budget-aware few-shot learning
task, which jointly learns a novel data selection policy based on a Graph
Convolutional Network (GCN) and an example-based few-shot classifier. Our
selection policy computes a context-sensitive representation for each unlabeled
data by graph message passing, which is then used to predict an informativeness
score for sequential selection. We validate our method by extensive experiments
on the mini-ImageNet, tiered-ImageNet and Omniglot datasets. The results show
our few-shot learning strategy outperforms baselines by a sizable margin, which
demonstrates the efficacy of our method.

Adaptive approaches, allowing for more flexible trial design, have been
proposed for individually randomized trials to save time or reduce sample size.
However, adaptive designs for cluster-randomized trials in which groups of
participants rather than individuals are randomized to treatment arms are less
common. Motivated by a cluster-randomized trial designed to assess the
effectiveness of a machine-learning based clinical decision support system for
physicians treating patients with depression, two Bayesian adaptive designs for
cluster-randomized trials are proposed to allow for early stopping for efficacy
at pre-planned interim analyses. The difference between the two designs lies in
the way that participants are sequentially recruited. Given a maximum number of
clusters as well as maximum cluster size allowed in the trial, one design
sequentially recruits clusters with the given maximum cluster size, while the
other recruits all clusters at the beginning of the trial but sequentially
enrolls individual participants until the trial is stopped early for efficacy
or the final analysis has been reached. The design operating characteristics
are explored via simulations for a variety of scenarios and two outcome types
for the two designs. The simulation results show that for different outcomes
the design choice may be different. We make recommendations for designs of
Bayesian adaptive cluster-randomized trial based on the simulation results.

We are concerned with the problem of hyperparameter selection for the fitted
Q-evaluation (FQE). FQE is one of the state-of-the-art method for offline
policy evaluation (OPE), which is essential to the reinforcement learning
without environment simulators. However, like other OPE methods, FQE is not
hyperparameter-free itself and that undermines the utility in real-life
applications. We address this issue by proposing a framework of approximate
hyperparameter selection (AHS) for FQE, which defines a notion of optimality
(called selection criteria) in a quantitative and interpretable manner without
hyperparameters. We then derive four AHS methods each of which has different
characteristics such as distribution-mismatch tolerance and time complexity. We
also confirm in experiments that the error bound given by the theory matches
empirical observations.

Tensor optimization is crucial to massive machine learning and signal
processing tasks. In this paper, we consider tensor optimization with a convex
and well-conditioned objective function and reformulate it into a nonconvex
optimization using the Burer-Monteiro type parameterization. We analyze the
local convergence of applying vanilla gradient descent to the factored
formulation and establish a local regularity condition under mild assumptions.
We also provide a linear convergence analysis of the gradient descent algorithm
started in a neighborhood of the true tensor factors. Complementary to the
local analysis, this work also characterizes the global geometry of the best
rank-one tensor approximation problem and demonstrates that for orthogonally
decomposable tensors the problem has no spurious local minima and all saddle
points are strict except for the one at zero which is a third-order saddle
point.

Among various soft computing approaches for time series forecasting, Fuzzy
Cognitive Maps (FCM) have shown remarkable results as a tool to model and
analyze the dynamics of complex systems. FCM have similarities to recurrent
neural networks and can be classified as a neuro-fuzzy method. In other words,
FCMs are a mixture of fuzzy logic, neural network, and expert system aspects,
which act as a powerful tool for simulating and studying the dynamic behavior
of complex systems. The most interesting features are knowledge
interpretability, dynamic characteristics and learning capability. The goal of
this survey paper is mainly to present an overview on the most relevant and
recent FCM-based time series forecasting models proposed in the literature. In
addition, this article considers an introduction on the fundamentals of FCM
model and learning methodologies. Also, this survey provides some ideas for
future research to enhance the capabilities of FCM in order to cover some
challenges in the real-world experiments such as handling non-stationary data
and scalability issues. Moreover, equipping FCMs with fast learning algorithms
is one of the major concerns in this area.

An Important tool in the field topological data analysis is known as
persistent Homology (PH) which is used to encode abstract representation of the
homology of data at different resolutions in the form of persistence diagram
(PD). In this work we build more than one PD representation of a single image
based on a landmark selection method, known as local binary patterns, that
encode different types of local textures from images. We employed different PD
vectorizations using persistence landscapes, persistence images, persistence
binning (Betti Curve) and statistics. We tested the effectiveness of proposed
landmark based PH on two publicly available breast abnormality detection
datasets using mammogram scans. Sensitivity of landmark based PH obtained is
over 90% in both datasets for the detection of abnormal breast scans. Finally,
experimental results give new insights on using different types of PD
vectorizations which help in utilising PH in conjunction with machine learning

We develop the "generalized consistent weighted sampling" (GCWS) for hashing
the "powered-GMM" (pGMM) kernel (with a tuning parameter $p$). It turns out
that GCWS provides a numerically stable scheme for applying power
transformation on the original data, regardless of the magnitude of $p$ and the
data. The power transformation is often effective for boosting the performance,
in many cases considerably so. We feed the hashed data to neural networks on a
variety of public classification datasets and name our method ``GCWSNet''. Our
extensive experiments show that GCWSNet often improves the classification
accuracy. Furthermore, it is evident from the experiments that GCWSNet
converges substantially faster. In fact, GCWS often reaches a reasonable
accuracy with merely (less than) one epoch of the training process. This
property is much desired because many applications, such as advertisement
click-through rate (CTR) prediction models, or data streams (i.e., data seen
only once), often train just one epoch. Another beneficial side effect is that
the computations of the first layer of the neural networks become additions
instead of multiplications because the input data become binary (and highly
sparse).
  Empirical comparisons with (normalized) random Fourier features (NRFF) are
provided. We also propose to reduce the model size of GCWSNet by count-sketch
and develop the theory for analyzing the impact of using count-sketch on the
accuracy of GCWS. Our analysis shows that an ``8-bit'' strategy should work
well in that we can always apply an 8-bit count-sketch hashing on the output of
GCWS hashing without hurting the accuracy much. There are many other ways to
take advantage of GCWS when training deep neural networks. For example, one can
apply GCWS on the outputs of the last layer to boost the accuracy of trained
deep neural networks.

With increasing focus on augmented and virtual reality applications (XR)
comes the demand for algorithms that can lift objects from images and videos
into representations that are suitable for a wide variety of related 3D tasks.
Large-scale deployment of XR devices and applications means that we cannot
solely rely on supervised learning, as collecting and annotating data for the
unlimited variety of objects in the real world is infeasible. We present a
weakly supervised method that is able to decompose a single image of an object
into shape (depth and normals), material (albedo, reflectivity and shininess)
and global lighting parameters. For training, the method only relies on a rough
initial shape estimate of the training objects to bootstrap the learning
process. This shape supervision can come for example from a pretrained depth
network or - more generically - from a traditional structure-from-motion
pipeline. In our experiments, we show that the method can successfully
de-render 2D images into a decomposed 3D representation and generalizes to
unseen object categories. Since in-the-wild evaluation is difficult due to the
lack of ground truth data, we also introduce a photo-realistic synthetic test
set that allows for quantitative evaluation.

COVID-19 pandemic, is still unknown and is an important open question. There
are speculations that bats are a possible origin. Likewise, there are many
closely related (corona-) viruses, such as SARS, which was found to be
transmitted through civets. The study of the different hosts which can be
potential carriers and transmitters of deadly viruses to humans is crucial to
understanding, mitigating and preventing current and future pandemics. In
coronaviruses, the surface (S) protein, or spike protein, is an important part
of determining host specificity since it is the point of contact between the
virus and the host cell membrane. In this paper, we classify the hosts of over
five thousand coronaviruses from their spike protein sequences, segregating
them into clusters of distinct hosts among avians, bats, camels, swines, humans
and weasels, to name a few. We propose a feature embedding based on the
well-known position-weight matrix (PWM), which we call PWM2Vec, and use to
generate feature vectors from the spike protein sequences of these
coronaviruses. While our embedding is inspired by the success of PWMs in
biological applications such as determining protein function, or identifying
transcription factor binding sites, we are the first (to the best of our
knowledge) to use PWMs in the context of host classification from viral
sequences to generate a fixed-length feature vector representation. The results
on the real world data show that in using PWM2Vec, we are able to perform
comparably well as compared to baseline models. We also measure the importance
of different amino acids using information gain to show the amino acids which
are important for predicting the host of a given coronavirus.

We study the difficulties in learning that arise from robust and
differentially private optimization. We first study convergence of gradient
descent based adversarial training with differential privacy, taking a simple
binary classification task on linearly separable data as an illustrative
example. We compare the gap between adversarial and nominal risk in both
private and non-private settings, showing that the data dimensionality
dependent term introduced by private optimization compounds the difficulties of
learning a robust model. After this, we discuss what parts of adversarial
training and differential privacy hurt optimization, identifying that the size
of adversarial perturbation and clipping norm in differential privacy both
increase the curvature of the loss landscape, implying poorer generalization

State-of-the-art stereo matching networks trained only on synthetic data
often fail to generalize to more challenging real data domains. In this paper,
we attempt to unfold an important factor that hinders the networks from
generalizing across domains: through the lens of shortcut learning. We
demonstrate that the learning of feature representations in stereo matching
networks is heavily influenced by synthetic data artefacts (shortcut
attributes). To mitigate this issue, we propose an Information-Theoretic
Shortcut Avoidance~(ITSA) approach to automatically restrict shortcut-related
information from being encoded into the feature representations. As a result,
our proposed method learns robust and shortcut-invariant features by minimizing
the sensitivity of latent features to input variations. To avoid the
prohibitive computational cost of direct input sensitivity optimization, we
propose an effective yet feasible algorithm to achieve robustness. We show that
using this method, state-of-the-art stereo matching networks that are trained
purely on synthetic data can effectively generalize to challenging and
previously unseen real data scenarios. Importantly, the proposed method
enhances the robustness of the synthetic trained networks to the point that
they outperform their fine-tuned counterparts (on real data) for challenging
out-of-domain stereo datasets.

The field of artificial intelligence has significantly advanced over the past
decades, inspired by discoveries from the fields of biology and neuroscience.
The idea of this work is inspired by the process of self-organization of
cortical areas in the human brain from both afferent and lateral/internal
connections. In this work, we develop an original brain-inspired neural model
associating Self-Organizing Maps (SOM) and Hebbian learning in the Reentrant
SOM (ReSOM) model. The framework is applied to multimodal classification
problems. Compared to existing methods based on unsupervised learning with
post-labeling, the model enhances the state-of-the-art results. This work also
demonstrates the distributed and scalable nature of the model through both
simulation results and hardware execution on a dedicated FPGA-based platform
named SCALP (Self-configurable 3D Cellular Adaptive Platform). SCALP boards can
be interconnected in a modular way to support the structure of the neural
model. Such a unified software and hardware approach enables the processing to
be scaled and allows information from several modalities to be merged
dynamically. The deployment on hardware boards provides performance results of
parallel execution on several devices, with the communication between each
board through dedicated serial links. The proposed unified architecture,
composed of the ReSOM model and the SCALP hardware platform, demonstrates a
significant increase in accuracy thanks to multimodal association, and a good
trade-off between latency and power consumption compared to a centralized GPU

While designing sustainable and resilient urban built environment is
increasingly promoted around the world, significant data gaps have made
research on pressing sustainability issues challenging to carry out. Pavements
are known to have strong economic and environmental impacts; however, most
cities lack a spatial catalog of their surfaces due to the cost-prohibitive and
time-consuming nature of data collection. Recent advancements in computer
vision, together with the availability of street-level images, provide new
opportunities for cities to extract large-scale built environment data with
lower implementation costs and higher accuracy. In this paper, we propose
CitySurfaces, an active learning-based framework that leverages computer vision
techniques for classifying sidewalk materials using widely available
street-level images. We trained the framework on images from New York City and
Boston and the evaluation results show a 90.5% mIoU score. Furthermore, we
evaluated the framework using images from six different cities, demonstrating
that it can be applied to regions with distinct urban fabrics, even outside the
domain of the training data. CitySurfaces can provide researchers and city
agencies with a low-cost, accurate, and extensible method to collect sidewalk
material data which plays a critical role in addressing major sustainability
issues, including climate change and surface water management.

This paper aims to improve the path quality and computational efficiency of
kinodynamic planners used for vehicular systems. It proposes a learning
framework for identifying promising controls during the expansion process of
sampling-based motion planners for systems with dynamics. Offline, the learning
process is trained to return the highest-quality control that reaches a local
goal state (i.e., a waypoint) in the absence of obstacles from an input
difference vector between its current state and a local goal state. The data
generation scheme provides bounds on the target dispersion and uses state space
pruning to ensure high-quality controls. By focusing on the system's dynamics,
this process is data efficient and takes place once for a dynamical system, so
that it can be used for different environments with modular expansion
functions. This work integrates the proposed learning process with a) an
exploratory expansion function that generates waypoints with biased coverage
over the reachable space, and b) proposes an exploitative expansion function
for mobile robots, which generates waypoints using medial axis information.
This paper evaluates the learning process and the corresponding planners for a
first and second-order differential drive systems. The results show that the
proposed integration of learning and planning can produce better quality paths
than kinodynamic planning with random controls in fewer iterations and

Humans use causality and hypothetical retrospection in their daily
decision-making, planning, and understanding of life events. The human mind,
while retrospecting a given situation, think about questions such as "What was
the cause of the given situation?", "What would be the effect of my action?",
or "Which action led to this effect?". It develops a causal model of the world,
which learns with fewer data points, makes inferences, and contemplates
counterfactual scenarios. The unseen, unknown, scenarios are known as
counterfactuals. AI algorithms use a representation based on knowledge graphs
(KG) to represent the concepts of time, space, and facts. A KG is a graphical
data model which captures the semantic relationships between entities such as
events, objects, or concepts. The existing KGs represent causal relationships
extracted from texts based on linguistic patterns of noun phrases for causes
and effects as in ConceptNet and WordNet. The current causality representation
in KGs makes it challenging to support counterfactual reasoning. A richer
representation of causality in AI systems using a KG-based approach is needed
for better explainability, and support for intervention and counterfactuals
reasoning, leading to improved understanding of AI systems by humans. The
causality representation requires a higher representation framework to define
the context, the causal information, and the causal effects. The proposed
Causal Knowledge Graph (CausalKG) framework, leverages recent progress of
causality and KG towards explainability. CausalKG intends to address the lack
of a domain adaptable causal model and represent the complex causal relations
using the hyper-relational graph representation in the KG. We show that the
CausalKG's interventional and counterfactual reasoning can be used by the AI
system for the domain explainability.

Protein-protein interactions (PPIs) are critical to normal cellular function
and are related to many disease pathways. However, only 4% of PPIs are
annotated with PTMs in biological knowledge databases such as IntAct, mainly
performed through manual curation, which is neither time nor cost-effective. We
use the IntAct PPI database to create a distant supervised dataset annotated
with interacting protein pairs, their corresponding PTM type, and associated
abstracts from the PubMed database. We train an ensemble of BioBERT models -
dubbed PPI-BioBERT-x10 to improve confidence calibration. We extend the use of
ensemble average confidence approach with confidence variation to counteract
the effects of class imbalance to extract high confidence predictions. The
PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro
41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low
variation to identify high quality predictions, tuning the predictions for
precision, we retained 19% of the test predictions with 100% precision. We
evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6
million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter ~ 5700
(4584 unique) high confidence predictions. Of the 5700, human evaluation on a
small randomly sampled subset shows that the precision drops to 33.7% despite
confidence calibration and highlights the challenges of generalisability beyond
the test set even with confidence calibration. We circumvent the problem by
only including predictions associated with multiple papers, improving the
precision to 58.8%. In this work, we highlight the benefits and challenges of
deep learning-based text mining in practice, and the need for increased
emphasis on confidence calibration to facilitate human curation efforts.

Neural operators have recently become popular tools for designing solution
maps between function spaces in the form of neural networks. Differently from
classical scientific machine learning approaches that learn parameters of a
known partial differential equation (PDE) for a single instance of the input
parameters at a fixed resolution, neural operators approximate the solution map
of a family of PDEs. Despite their success, the uses of neural operators are so
far restricted to relatively shallow neural networks and confined to learning
hidden governing laws. In this work, we propose a novel nonlocal neural
operator, which we refer to as nonlocal kernel network (NKN), that is
resolution independent, characterized by deep neural networks, and capable of
handling a variety of tasks such as learning governing equations and
classifying images. Our NKN stems from the interpretation of the neural network
as a discrete nonlocal diffusion reaction equation that, in the limit of
infinite layers, is equivalent to a parabolic nonlocal equation, whose
stability is analyzed via nonlocal vector calculus. The resemblance with
integral forms of neural operators allows NKNs to capture long-range
dependencies in the feature space, while the continuous treatment of
node-to-node interactions makes NKNs resolution independent. The resemblance
with neural ODEs, reinterpreted in a nonlocal sense, and the stable network
dynamics between layers allow for generalization of NKN's optimal parameters
from shallow to deep networks. This fact enables the use of shallow-to-deep
initialization techniques. Our tests show that NKNs outperform baseline methods
in both learning governing equations and image classification tasks and
generalize well to different resolutions and depths.

Deep learning-based RF fingerprinting has recently been recognized as a
potential solution for enabling newly emerging wireless network applications,
such as spectrum access policy enforcement, automated network device
authentication, and unauthorized network access monitoring and control. Real,
comprehensive RF datasets are now needed more than ever to enable the study,
assessment, and validation of newly developed RF fingerprinting approaches. In
this paper, we present and release a large-scale RF fingerprinting dataset,
collected from 25 different LoRa-enabled IoT transmitting devices using USRP
B210 receivers. Our dataset consists of a large number of SigMF-compliant
binary files representing the I/Q time-domain samples and their corresponding
FFT-based files of LoRa transmissions. This dataset provides a comprehensive
set of essential experimental scenarios, considering both indoor and outdoor
environments and various network deployments and configurations, such as the
distance between the transmitters and the receiver, the configuration of the
considered LoRa modulation, the physical location of the conducted experiment,
and the receiver hardware used for training and testing the neural network
models.

High-multiplicity signatures at particle colliders can arise in Standard
Model processes and beyond. With such signatures, difficulties often arise from
the large dimensionality of the kinematic space. For final states containing a
single type of particle signature, this results in a combinatorial problem that
hides underlying kinematic information. We explore using a neural network that
includes a Lorentz Layer to extract high-dimensional correlations. We use the
case of squark decays in $R$-Parity-violating Supersymmetry as a benchmark,
comparing the performance to that of classical methods. With this approach, we
demonstrate significant improvement over traditional methods.

We develop a materials descriptor based on the electronic density of states
and investigate the similarity of materials based on it. As an application
example, we study the Computational 2D Materials Database that hosts thousands
of two-dimensional materials with their properties calculated by
density-functional theory. Combining our descriptor with a clustering
algorithm, we identify groups of materials with similar electronic structure.
We characterize these clusters in terms of their crystal structure, their
atomic composition, and the respective electronic configurations to rationalize
the found (dis)similarities.

We study reward design strategies for incentivizing a reinforcement learning
agent to adopt a policy from a set of admissible policies. The goal of the
reward designer is to modify the underlying reward function cost-efficiently
while ensuring that any approximately optimal deterministic policy under the
new reward function is admissible and performs well under the original reward
function. This problem can be viewed as a dual to the problem of optimal reward
poisoning attacks: instead of forcing an agent to adopt a specific policy, the
reward designer incentivizes an agent to avoid taking actions that are
inadmissible in certain states. Perhaps surprisingly, and in contrast to the
problem of optimal reward poisoning attacks, we first show that the reward
design problem for admissible policy teaching is computationally challenging,
and it is NP-hard to find an approximately optimal reward modification. We then
proceed by formulating a surrogate problem whose optimal solution approximates
the optimal solution to the reward design problem in our setting, but is more
amenable to optimization techniques and analysis. For this surrogate problem,
we present characterization results that provide bounds on the value of the
optimal solution. Finally, we design a local search algorithm to solve the
surrogate problem and showcase its utility using simulation-based experiments.

In this paper we propose to study generalization of neural networks on small
algorithmically generated datasets. In this setting, questions about data
efficiency, memorization, generalization, and speed of learning can be studied
in great detail. In some situations we show that neural networks learn through
a process of "grokking" a pattern in the data, improving generalization
performance from random chance level to perfect generalization, and that this
improvement in generalization can happen well past the point of overfitting. We
also study generalization as a function of dataset size and find that smaller
datasets require increasing amounts of optimization for generalization. We
argue that these datasets provide a fertile ground for studying a poorly
understood aspect of deep learning: generalization of overparametrized neural
networks beyond memorization of the finite training dataset.

Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel
and determining its reliability is critical for the success of advanced nuclear
technologies. However, TRISO failure probabilities are small and the associated
computational models are expensive. We used coupled active learning,
multifidelity modeling, and subset simulation to estimate the failure
probabilities of TRISO fuels using several 1D and 2D models. With multifidelity
modeling, we replaced expensive high-fidelity (HF) model evaluations with
information fusion from two low-fidelity (LF) models. For the 1D TRISO models,
we considered three multifidelity modeling strategies: only Kriging, Kriging LF
prediction plus Kriging correction, and deep neural network (DNN) LF prediction
plus Kriging correction. While the results across these multifidelity modeling
strategies compared satisfactorily, strategies employing information fusion
from two LF models consistently called the HF model least often. Next, for the
2D TRISO model, we considered two multifidelity modeling strategies: DNN LF
prediction plus Kriging correction (data-driven) and 1D TRISO LF prediction
plus Kriging correction (physics-based). The physics-based strategy, as
expected, consistently required the fewest calls to the HF model. However, the
data-driven strategy had a lower overall simulation time since the DNN
predictions are instantaneous, and the 1D TRISO model requires a non-negligible
simulation time.

Controlling antenna tilts in cellular networks is imperative to reach an
efficient trade-off between network coverage and capacity. In this paper, we
devise algorithms learning optimal tilt control policies from existing data (in
the so-called passive learning setting) or from data actively generated by the
algorithms (the active learning setting). We formalize the design of such
algorithms as a Best Policy Identification (BPI) problem in Contextual Linear
Multi-Arm Bandits (CL-MAB). An arm represents an antenna tilt update; the
context captures current network conditions; the reward corresponds to an
improvement of performance, mixing coverage and capacity; and the objective is
to identify, with a given level of confidence, an approximately optimal policy
(a function mapping the context to an arm with maximal reward). For CL-MAB in
both active and passive learning settings, we derive information-theoretical
lower bounds on the number of samples required by any algorithm returning an
approximately optimal policy with a given level of certainty, and devise
algorithms achieving these fundamental limits. We apply our algorithms to the
Remote Electrical Tilt (RET) optimization problem in cellular networks, and
show that they can produce optimal tilt update policy using much fewer data
samples than naive or existing rule-based learning algorithms.

Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted
digraph method consisting of nodes (concepts) and weights which represent the
dependencies among the concepts. Although FCMs have attained considerable
achievements in various time series prediction applications, designing an FCM
model with time-efficient training method is still an open challenge. Thus,
this paper introduces a novel univariate time series forecasting technique,
which is composed of a group of randomized high order FCM models labeled
R-HFCM. The novelty of the proposed R-HFCM model is relevant to merging the
concepts of FCM and Echo State Network (ESN) as an efficient and particular
family of Reservoir Computing (RC) models, where the least squares algorithm is
applied to train the model. From another perspective, the structure of R-HFCM
consists of the input layer, reservoir layer, and output layer in which only
the output layer is trainable while the weights of each sub-reservoir
components are selected randomly and keep constant during the training process.
As case studies, this model considers solar energy forecasting with public data
for Brazilian solar stations as well as Malaysia dataset, which includes hourly
electric load and temperature data of the power supply company of the city of
Johor in Malaysia. The experiment also includes the effect of the map size,
activation function, the presence of bias and the size of the reservoir on the
accuracy of R-HFCM method. The obtained results confirm the outperformance of
the proposed R-HFCM model in comparison to the other methods. This study
provides evidence that FCM can be a new way to implement a reservoir of
dynamics in time series modelling.

Local explainability methods -- those which seek to generate an explanation
for each prediction -- are becoming increasingly prevalent due to the need for
practitioners to rationalize their model outputs. However, comparing local
explainability methods is difficult since they each generate outputs in various
scales and dimensions. Furthermore, due to the stochastic nature of some
explainability methods, it is possible for different runs of a method to
produce contradictory explanations for a given observation. In this paper, we
propose a topology-based framework to extract a simplified representation from
a set of local explanations. We do so by first modeling the relationship
between the explanation space and the model predictions as a scalar function.
Then, we compute the topological skeleton of this function. This topological
skeleton acts as a signature for such functions, which we use to compare
different explanation methods. We demonstrate that our framework can not only
reliably identify differences between explainability techniques but also
provides stable representations. Then, we show how our framework can be used to
identify appropriate parameters for local explainability methods. Our framework
is simple, does not require complex optimizations, and can be broadly applied
to most local explanation methods. We believe the practicality and versatility
of our approach will help promote topology-based approaches as a tool for
understanding and comparing explanation methods.

Min-Nets are inspired by end-stopped cortical cells with units that output
the minimum of two learned filters. We insert such Min-units into
state-of-the-art deep networks, such as the popular ResNet and DenseNet, and
show that the resulting Min-Nets perform better on the Cifar-10 benchmark.
Moreover, we show that Min-Nets are more robust against JPEG compression
artifacts. We argue that the minimum operation is the simplest way of
implementing an AND operation on pairs of filters and that such AND operations
introduce a bias that is appropriate given the statistics of natural images.

A paper published in the CVPR 2019 conference outlines a new technique called
'kervolution' used in a new type of augmented convolutional neural network
(CNN) called a 'kervolutional neural network' (KNN). The paper asserts that
KNNs achieve faster convergence and higher accuracies than CNNs. This "mini
paper" will further examine the findings in the original paper and perform a
more in depth analysis of the KNN architecture. This will be done by analyzing
the impact of hyper parameters (specifically the learning rate) on KNNs versus
CNNs, experimenting with other types of kervolution operations not tested in
the original paper, a more rigourous statistical analysis of accuracies and
convergence times and additional theoretical analysis. The accompanying code is

Classification of long sequential data is an important Machine Learning task
and appears in many application scenarios. Recurrent Neural Networks,
Transformers, and Convolutional Neural Networks are three major techniques for
learning from sequential data. Among these methods, Temporal Convolutional
Networks (TCNs) which are scalable to very long sequences have achieved
remarkable progress in time series regression. However, the performance of TCNs
for sequence classification is not satisfactory because they use a skewed
connection protocol and output classes at the last position. Such asymmetry
restricts their performance for classification which depends on the whole
sequence. In this work, we propose a symmetric multi-scale architecture called
Circular Dilated Convolutional Neural Network (CDIL-CNN), where every position
has an equal chance to receive information from other positions at the previous
layers. Our model gives classification logits in all positions, and we can
apply a simple ensemble learning to achieve a better decision. We have tested
CDIL-CNN on various long sequential datasets. The experimental results show
that our method has superior performance over many state-of-the-art approaches.

This paper presents a deep learning assisted synthesis approach for direct
end-to-end generation of RF/mm-wave passive matching network with 3D EM
structures. Different from prior approaches that synthesize EM structures from
target circuit component values and target topologies, our proposed approach
achieves the direct synthesis of the passive network given the network topology
from desired performance values as input. We showcase the proposed synthesis
Neural Network (NN) model on an on-chip 1:1 transformer-based impedance
matching network. By leveraging parameter sharing, the synthesis NN model
successfully extracts relevant features from the input impedance and load
capacitors, and predict the transformer 3D EM geometry in a 45nm SOI process
that will match the standard 50$\Omega$ load to the target input impedance
while absorbing the two loading capacitors. As a proof-of-concept, several
example transformer geometries were synthesized, and verified in Ansys HFSS to
provide the desired input impedance.

We consider social learning in a changing world. Society can remain
responsive to state changes only if agents regularly act upon fresh
information, which limits the value of social learning. When the state is close
to persistent, a consensus whereby most agents choose the same action typically
emerges. The consensus action is not perfectly correlated with the state
though, because the society exhibits inertia following state changes. Phases of
inertia may be longer when signals are more precise, even if agents draw large
samples of past actions, as actions then become too correlated within samples,
thereby reducing informativeness and welfare.

The end of Dennard scaling and the slowing of Moore's Law has put the energy
use of datacenters on an unsustainable path. Datacenters are already a
significant fraction of worldwide electricity use, with application demand
scaling at a rapid rate. We argue that substantial reductions in the carbon
intensity of datacenter computing are possible with a software-centric
approach: by making energy and carbon visible to application developers on a
fine-grained basis, by modifying system APIs to make it possible to make
informed trade offs between performance and carbon emissions, and by raising
the level of application programming to allow for flexible use of more energy
efficient means of compute and storage. We also lay out a research agenda for
systems software to reduce the carbon footprint of datacenter computing.

The global world is crossing a pandemic situation where this is a
catastrophic outbreak of Respiratory Syndrome recognized as COVID-19. This is a
global threat all over the 212 countries that people every day meet with mighty
situations. On the contrary, thousands of infected people live rich in
mountains. Mental health is also affected by this worldwide coronavirus
situation. Due to this situation online sources made a communicative place that
common people shares their opinion in any agenda. Such as affected news related
positive and negative, financial issues, country and family crisis, lack of
import and export earning system etc. different kinds of circumstances are
recent trendy news in anywhere. Thus, vast amounts of text are produced within
moments therefore, in subcontinent areas the same as situation in other
countries and peoples opinion of text and situation also same but the language
is different. This article has proposed some specific inputs along with Bangla
text comments from individual sources which can assure the goal of illustration
that machine learning outcome capable of building an assistive system. Opinion
mining assistive system can be impactful in all language preferences possible.
To the best of our knowledge, the article predicted the Bangla input text on
COVID-19 issues proposed ML algorithms and deep learning models analysis also
check the future reachability with a comparative analysis. Comparative analysis
states a report on text prediction accuracy is 91% along with ML algorithms and
79% along with Deep Learning Models.

Autoencoders are the simplest neural network for unsupervised learning, and
thus an ideal framework for studying feature learning. While a detailed
understanding of the dynamics of linear autoencoders has recently been
obtained, the study of non-linear autoencoders has been hindered by the
technical difficulty of handling training data with non-trivial correlations -
a fundamental prerequisite for feature extraction. Here, we study the dynamics
of feature learning in non-linear, shallow autoencoders. We derive a set of
asymptotically exact equations that describe the generalisation dynamics of
autoencoders trained with stochastic gradient descent (SGD) in the limit of
high-dimensional inputs. These equations reveal that autoencoders learn the
leading principal components of their inputs sequentially. An analysis of the
long-time dynamics explains the failure of sigmoidal autoencoders to learn with
tied weights, and highlights the importance of training the bias in ReLU
autoencoders. Building on previous results for linear networks, we analyse a
modification of the vanilla SGD algorithm which allows learning of the exact
principal components. Finally, we show that our equations accurately describe
the generalisation dynamics of non-linear autoencoders on realistic datasets
such as CIFAR10.

With the effects of global climate change impacting the world, collective
efforts are needed to reduce greenhouse gas emissions. The energy sector is the
single largest contributor to climate change and many efforts are focused on
reducing dependence on carbon-emitting power plants and moving to renewable
energy sources, such as solar power. A comprehensive database of the location
of solar panels is important to assist analysts and policymakers in defining
strategies for further expansion of solar energy. In this paper we focus on
creating a world map of solar panels. We identify locations and total surface
area of solar panels within a given geographic area. We use deep learning
methods for automated detection of solar panel locations and their surface area
using aerial imagery. The framework, which consists of a two-branch model using
an image classifier in tandem with a semantic segmentation model, is trained on
our created dataset of satellite images. Our work provides an efficient and
scalable method for detecting solar panels, achieving an accuracy of 0.96 for
classification and an IoU score of 0.82 for segmentation performance.

The paper studies the linear model for Bitcoin price which includes
regression features based on Bitcoin currency statistics, mining processes,
Google search trends, Wikipedia pages visits. The pattern of deviation of
regression model prediction from real prices is simpler comparing to price time
series. It is assumed that this pattern can be predicted by an experienced
expert. In such a way, using the combination of the regression model and expert
correction, one can receive better results than with either regression model or
expert opinion only. It is shown that Bayesian approach makes it possible to
utilize the probabilistic approach using distributions with fat tails and take
into account the outliers in Bitcoin price time series.

A deep learning model gives an incredible result for image processing by
studying from the trained dataset. Spinach is a leaf vegetable that contains
vitamins and nutrients. In our research, a Deep learning method has been used
that can automatically identify spinach and this method has a dataset of a
total of five species of spinach that contains 3785 images. Four Convolutional
Neural Network (CNN) models were used to classify our spinach. These models
give more accurate results for image classification. Before applying these
models there is some preprocessing of the image data. For the preprocessing of
data, some methods need to happen. Those are RGB conversion, filtering, resize
& rescaling, and categorization. After applying these methods image data are
pre-processed and ready to be used in the classifier algorithms. The accuracy
of these classifiers is in between 98.68% - 99.79%. Among those models, VGG16
achieved the highest accuracy of 99.79%.

Traditional recommender systems aim to estimate a user's rating to an item
based on observed ratings from the population. As with all observational
studies, hidden confounders, which are factors that affect both item exposures
and user ratings, lead to a systematic bias in the estimation. Consequently, a
new trend in recommender system research is to negate the influence of
confounders from a causal perspective. Observing that confounders in
recommendations are usually shared among items and are therefore multi-cause
confounders, we model the recommendation as a multi-cause multi-outcome (MCMO)
inference problem. Specifically, to remedy confounding bias, we estimate
user-specific latent variables that render the item exposures independent
Bernoulli trials. The generative distribution is parameterized by a DNN with
factorized logistic likelihood and the intractable posteriors are estimated by
variational inference. Controlling these factors as substitute confounders,
under mild assumptions, can eliminate the bias incurred by multi-cause
confounders. Furthermore, we show that MCMO modeling may lead to high variance
due to scarce observations associated with the high-dimensional causal space.
Fortunately, we theoretically demonstrate that introducing user features as
pre-treatment variables can substantially improve sample efficiency and
alleviate overfitting. Empirical studies on simulated and real-world datasets
show that the proposed deep causal recommender shows more robustness to
unobserved confounders than state-of-the-art causal recommenders. Codes and
datasets are released at https://github.com/yaochenzhu/deep-deconf.

This work studies how the introduction of the entropic regularization term in
unbalanced Optimal Transport (OT) models may alter their homogeneity with
respect to the input measures. We observe that in common settings (including
balanced OT and unbalanced OT with Kullback-Leibler divergence to the
marginals), although the optimal transport cost itself is not homogeneous,
optimal transport plans and the so-called Sinkhorn divergences are indeed
homogeneous. However, homogeneity does not hold in more general Unbalanced
Regularized Optimal Transport (UROT) models, for instance those using the Total
Variation as divergence to the marginals. We propose to modify the entropic
regularization term to retrieve an UROT model that is homogeneous while
preserving most properties of the standard UROT model. We showcase the
importance of using our Homogeneous UROT (HUROT) model when it comes to
regularize Optimal Transport with Boundary, a transportation model involving a
spatially varying divergence to the marginals for which the standard
(inhomogeneous) UROT model would yield inappropriate behavior.

In this paper, we present a CNN-based fully unsupervised method for motion
segmentation from optical flow. We assume that the input optical flow can be
represented as a piecewise set of parametric motion models, typically, affine
or quadratic motion models. The core idea of our work is to leverage the
Expectation-Maximization (EM) framework in order to design in a well-founded
manner a loss function and a training procedure of our motion segmentation
neural network that does not require either ground-truth or manual annotation.
However, in contrast to the classical iterative EM, once the network is
trained, we can provide a segmentation for any unseen optical flow field in a
single inference step and without estimating any motion models. We investigate
different loss functions including robust ones and propose a novel efficient
data augmentation technique on the optical flow field, applicable to any
network taking optical flow as input. In addition, our method is able by design
to segment multiple motions. Our motion segmentation network was tested on four
benchmarks, DAVIS2016, SegTrackV2, FBMS59, and MoCA, and performed very well,
while being fast at test time.

Machine learning (ML) has often been applied to space weather (SW) problems
in recent years. SW originates from solar perturbations and is comprised of the
resulting complex variations they cause within the systems between the Sun and
Earth. These systems are tightly coupled and not well understood. This creates
a need for skillful models with knowledge about the confidence of their
predictions. One example of such a dynamical system is the thermosphere, the
neutral region of Earth's upper atmosphere. Our inability to forecast it has
severe repercussions in the context of satellite drag and collision avoidance
operations for objects in low Earth orbit. Even with (assumed) perfect driver
forecasts, our incomplete knowledge of the system results in often inaccurate
neutral mass density predictions. Continuing efforts are being made to improve
model accuracy, but density models rarely provide estimates of uncertainty. In
this work, we propose two techniques to develop nonlinear ML models to predict
thermospheric density while providing calibrated uncertainty estimates: Monte
Carlo (MC) dropout and direct prediction of the probability distribution, both
using the negative logarithm of predictive density (NLPD) loss function. We
show the performance for models trained on local and global datasets. This
shows that NLPD provides similar results for both techniques but the direct
probability method has a much lower computational cost. For the global model
regressed on the SET HASDM density database, we achieve errors of 11% on
independent test data with well-calibrated uncertainty estimates. Using an
in-situ CHAMP density dataset, both techniques provide test error on the order
of 13%. The CHAMP models (on independent data) are within 2% of perfect
calibration for all prediction intervals tested. This model can also be used to
obtain global predictions with uncertainties at a given epoch.

The article describes the use of deep Q-learning models in the problems of
sales time series analytics. In contrast to supervised machine learning which
is a kind of passive learning using historical data, Q-learning is a kind of
active learning with goal to maximize a reward by optimal sequence of actions.
Model free Q-learning approach for optimal pricing strategies and supply-demand
problems was considered in the work. The main idea of the study is to show that
using deep Q-learning approach in time series analytics, the sequence of
actions can be optimized by maximizing the reward function when the environment
for learning agent interaction can be modeled using the parametric model and in
the case of using the model which is based on the historical data. In the
pricing optimizing case study environment was modeled using sales dependence on
extras price and randomly simulated demand. In the pricing optimizing case
study, the environment was modeled using sales dependence on extra price and
randomly simulated demand. In the supply-demand case study, it was proposed to
use historical demand time series for environment modeling, agent states were
represented by promo actions, previous demand values and weekly seasonality
features. Obtained results show that using deep Q-learning, we can optimize the
decision making process for price optimization and supply-demand problems.
Environment modeling using parametric models and historical data can be used
for the cold start of learning agent. On the next steps, after the cold start,
the trained agent can be used in real business environment.

The article describes the approaches for forming different predictive
features of tweet data sets and using them in the predictive analysis for
decision-making support. The graph theory as well as frequent itemsets and
association rules theory is used for forming and retrieving different features
from these datasests. The use of these approaches makes it possible to reveal a
semantic structure in tweets related to a specified entity. It is shown that
quantitative characteristics of semantic frequent itemsets can be used in
predictive regression models with specified target variables.

This paper explores some variations of a hierarchical control framework that
has been recently proposed. The framework is dedicated to control a network of
interconnected subsystems such as the ones describing cryogenic processes or
power plants. Recent investigations showed that handling constraints and
nonlinearities might challenge the real-time feasibility of the approach. This
paper investigates and combine two successful directions, namely, the use of
truncated fast gradient and deep neural networks based controller modeling in
order to reduce the computation time of the most critical subsystem. It is also
shown that by doing so, the control updating period can be drastically reduced
and the closed-loop performances highly improved. The paper can therefore be
seen as a concrete implementation and validation of some key ideas in real-time
distributed NMPC design. All the concepts are validated using the realistic and
challenging example of real-life cryogenic refrigerator.

We study the selection of adjustment sets for estimating the interventional
mean under an individualized treatment rule. We assume a non-parametric causal
graphical model with, possibly, hidden variables and at least one adjustment
set comprised of observable variables. Moreover, we assume that observable
variables have positive costs associated with them. We define the cost of an
observable adjustment set as the sum of the costs of the variables that
comprise it. We show that in this setting there exist adjustment sets that are
minimum cost optimal, in the sense that they yield non-parametric estimators of
the interventional mean with the smallest asymptotic variance among those that
control for observable adjustment sets that have minimum cost. Our results are
based on the construction of a special flow network associated with the
original causal graph. We show that a minimum cost optimal adjustment set can
be found by computing a maximum flow on the network, and then finding the set
of vertices that are reachable from the source by augmenting paths. The
optimaladj Python package implements the algorithms introduced in this paper.

The paper describes the use of Bayesian regression for building time series
models and stacking different predictive models for time series. Using Bayesian
regression for time series modeling with nonlinear trend was analyzed. This
approach makes it possible to estimate an uncertainty of time series prediction
and calculate value at risk characteristics. A hierarchical model for time
series using Bayesian regression has been considered. In this approach, one set
of parameters is the same for all data samples, other parameters can be
different for different groups of data samples. Such an approach allows using
this model in the case of short historical data for specified time series, e.g.
in the case of new stores or new products in the sales prediction problem. In
the study of predictive models stacking, the models ARIMA, Neural Network,
Random Forest, Extra Tree were used for the prediction on the first level of
model ensemble. On the second level, time series predictions of these models on
the validation set were used for stacking by Bayesian regression. This approach
gives distributions for regression coefficients of these models. It makes it
possible to estimate the uncertainty contributed by each model to stacking
result. The information about these distributions allows us to select an
optimal set of stacking models, taking into account the domain knowledge. The
probabilistic approach for stacking predictive models allows us to make risk
assessment for the predictions that are important in a decision-making process.

In recent years, large pre-trained deep neural networks (DNNs) have
revolutionized the field of computer vision (CV). Although these DNNs have been
shown to be very well suited for general image recognition tasks, application
in industry is often precluded for three reasons: 1) large pre-trained DNNs are
built on hundreds of millions of parameters, making deployment on many devices
impossible, 2) the underlying dataset for pre-training consists of general
objects, while industrial cases often consist of very specific objects, such as
structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal
issues for companies. As a remedy, we study neural networks for CV that we
train from scratch. For this purpose, we use a real-world case from a solar
wafer manufacturer. We find that our neural networks achieve similar
performances as pre-trained DNNs, even though they consist of far fewer
parameters and do not rely on third-party datasets.

A deep learning-based model reduction (DeePMR) method for simplifying
chemical kinetics is proposed and validated using high-temperature
auto-ignitions, perfectly stirred reactors (PSR), and one-dimensional freely
propagating flames of n-heptane/air mixtures. The mechanism reduction is
modeled as an optimization problem on Boolean space, where a Boolean vector,
each entry corresponding to a species, represents a reduced mechanism. The
optimization goal is to minimize the reduced mechanism size given the error
tolerance of a group of pre-selected benchmark quantities. The key idea of the
DeePMR is to employ a deep neural network (DNN) to formulate the objective
function in the optimization problem. In order to explore high dimensional
Boolean space efficiently, an iterative DNN-assisted data sampling and DNN
training procedure are implemented. The results show that DNN-assistance
improves sampling efficiency significantly, selecting only $10^5$ samples out
of $10^{34}$ possible samples for DNN to achieve sufficient accuracy. The
results demonstrate the capability of the DNN to recognize key species and
reasonably predict reduced mechanism performance. The well-trained DNN
guarantees the optimal reduced mechanism by solving an inverse optimization
problem. By comparing ignition delay times, laminar flame speeds, temperatures
in PSRs, the resulting skeletal mechanism has fewer species (45 species) but
the same level of accuracy as the skeletal mechanism (56 species) obtained by
the Path Flux Analysis (PFA) method. In addition, the skeletal mechanism can be
further reduced to 28 species if only considering atmospheric,
near-stoichiometric conditions (equivalence ratio between 0.6 and 1.2). The
DeePMR provides an innovative way to perform model reduction and demonstrates
the great potential of data-driven methods in the combustion area.

The Internet of Behaviors (IoB) puts human behavior at the core of
engineering intelligent connected systems. IoB links the digital world to human
behavior to establish human-driven design, development, and adaptation
processes. This paper defines the novel concept by an IoB model based on a
collective effort interacting with software engineers, human-computer
interaction scientists, social scientists, and cognitive science communities.
The model for IoB is created based on an exploratory study that synthesizes
state-of-the-art analysis and experts interviews. The architecture of a real
industry 4.0 manufacturing infrastructure helps to explain the IoB model and
it's application. The conceptual model was used to successfully implement a
socio-technical infrastructure for a crowd monitoring and queue management
system for the Uffizi Galleries, Florence, Italy. The experiment, which started
in the fall of 2016 and was operational in the fall of 2018, used a data-driven
approach to feed the system with real-time sensory data. It also incorporated
prediction models on visitors' mobility behavior. The system's main objective
was to capture human behavior, model it, and build a mechanism that considers
changes, adapts in real-time, and continuously learns from repetitive
behaviors. In addition to the conceptual model and the real-life evaluation,
this paper provides recommendations from experts and gives future directions
for IoB to become a significant technological advancement in the coming few
years.

In this paper, we propose a novel approach to enhance the 3D body pose
estimation of a person computed from videos captured from a single wearable
camera. The key idea is to leverage high-level features linking first- and
third-views in a joint embedding space. To learn such embedding space we
introduce First2Third-Pose, a new paired synchronized dataset of nearly 2,000
videos depicting human activities captured from both first- and third-view
perspectives. We explicitly consider spatial- and motion-domain features,
combined using a semi-Siamese architecture trained in a self-supervised
fashion. Experimental results demonstrate that the joint multi-view embedded
space learned with our dataset is useful to extract discriminatory features
from arbitrary single-view egocentric videos, without needing domain adaptation
nor knowledge of camera parameters. We achieve significant improvement of
egocentric 3D body pose estimation performance on two unconstrained datasets,
over three supervised state-of-the-art approaches. Our dataset and code will be
available for research purposes.

While end-to-end neural machine translation (NMT) has achieved impressive
progress, noisy input usually leads models to become fragile and unstable.
Generating adversarial examples as the augmented data has been proved to be
useful to alleviate this problem. Existing methods for adversarial example
generation (AEG) are word-level or character-level, which ignore the ubiquitous
phrase structure. In this paper, we propose a Phrase-level Adversarial Example
Generation (PAEG) framework to enhance the robustness of the translation model.
Our method further improves the gradient-based word-level AEG method by
adopting a phrase-level substitution strategy. We verify our method on three
benchmarks, including LDC Chinese-English, IWSLT14 German-English, and WMT14
English-German tasks. Experimental results demonstrate that our approach
significantly improves translation performance and robustness to noise compared
to previous strong baselines.

Anomalous pattern detection aims to identify instances where deviation from
normalcy is evident, and is widely applicable across domains. Multiple
anomalous detection techniques have been proposed in the state of the art.
However, there is a common lack of a principled and scalable feature selection
method for efficient discovery. Existing feature selection techniques are often
conducted by optimizing the performance of prediction outcomes rather than its
systemic deviations from the expected. In this paper, we proposed a
sparsity-based automated feature selection (SAFS) framework, which encodes
systemic outcome deviations via the sparsity of feature-driven odds ratios.
SAFS is a model-agnostic approach with usability across different discovery
techniques. SAFS achieves more than $3\times$ reduction in computation time
while maintaining detection performance when validated on publicly available
critical care dataset. SAFS also results in a superior performance when
compared against multiple baselines for feature selection.

This work presents our approach to train a neural network to detect
hate-speech texts in Hindi and Bengali. We also explore how transfer learning
can be applied to learning these languages, given that they have the same
origin and thus, are similar to some extend. Even though the whole experiment
was conducted with low computational power, the obtained result is comparable
to the results of other, more expensive, models. Furthermore, since the
training data in use is relatively small and the two languages are almost
entirely unknown to us, this work can be generalized as an effort to demystify
lost or alien languages that no human is capable of understanding.

Deep Learning has impacted various fields especially in bio-medical
applications. Deep learning algorithms work well with both structured and
unstructured data. Especially, convolutional neural network work well with
signal-based data like EEG data. These types of data may or may not follow a
pattern in their data. Algorithms like CNN help feature engineering and
simplistic interpretation of the data. These algorithms are also better in
comparison to other algorithms when generalised to a data belonging to larger
data set.

The Resource Description Framework (RDF) is a framework for describing
metadata, such as attributes and relationships of resources on the Web. Machine
learning tasks for RDF graphs adopt three methods: (i) support vector machines
(SVMs) with RDF graph kernels, (ii) RDF graph embeddings, and (iii) relational
graph convolutional networks. In this paper, we propose a novel feature vector
(called a Skip vector) that represents some features of each resource in an RDF
graph by extracting various combinations of neighboring edges and nodes. In
order to make the Skip vector low-dimensional, we select important features for
classification tasks based on the information gain ratio of each feature. The
classification tasks can be performed by applying the low-dimensional Skip
vector of each resource to conventional machine learning algorithms, such as
SVMs, the k-nearest neighbors method, neural networks, random forests, and
AdaBoost. In our evaluation experiments with RDF data, such as Wikidata,
DBpedia, and YAGO, we compare our method with RDF graph kernels in an SVM. We
also compare our method with the two approaches: RDF graph embeddings such as
RDF2vec and relational graph convolutional networks on the AIFB, MUTAG, BGS,
and AM benchmarks.

Decentralized learning involves training machine learning models over remote
mobile devices, edge servers, or cloud servers while keeping data localized.
Even though many studies have shown the feasibility of preserving privacy,
enhancing training performance or introducing Byzantine resilience, but none of
them simultaneously considers all of them. Therefore we face the following
problem: \textit{how can we efficiently coordinate the decentralized learning
process while simultaneously maintaining learning security and data privacy?}
To address this issue, in this paper we propose SPDL, a blockchain-secured and
privacy-preserving decentralized learning scheme. SPDL integrates blockchain,
Byzantine Fault-Tolerant (BFT) consensus, BFT Gradients Aggregation Rule (GAR),
and differential privacy seamlessly into one system, ensuring efficient machine
learning while maintaining data privacy, Byzantine fault tolerance,
transparency, and traceability. To validate our scheme, we provide rigorous
analysis on convergence and regret in the presence of Byzantine nodes. We also
build a SPDL prototype and conduct extensive experiments to demonstrate that
SPDL is effective and efficient with strong security and privacy guarantees.

Logistic Bandits have recently undergone careful scrutiny by virtue of their
combined theoretical and practical relevance. This research effort delivered
statistically efficient algorithms, improving the regret of previous strategies
by exponentially large factors. Such algorithms are however strikingly costly
as they require $\Omega(t)$ operations at each round. On the other hand, a
different line of research focused on computational efficiency
($\mathcal{O}(1)$ per-round cost), but at the cost of letting go of the
aforementioned exponential improvements. Obtaining the best of both world is
unfortunately not a matter of marrying both approaches. Instead we introduce a
new learning procedure for Logistic Bandits. It yields confidence sets which
sufficient statistics can be easily maintained online without sacrificing
statistical tightness. Combined with efficient planning mechanisms we design
fast algorithms which regret performance still match the problem-dependent
lower-bound of Abeille et al. (2021). To the best of our knowledge, those are
the first Logistic Bandit algorithms that simultaneously enjoy statistical and

Deep learning-based person Re-IDentification (ReID) often requires a large
amount of training data to achieve good performance. Thus it appears that
collecting more training data from diverse environments tends to improve the
ReID performance. This paper re-examines this common belief and makes a somehow
surprising observation: using more samples, i.e., training with samples from
multiple datasets, does not necessarily lead to better performance by using the
popular ReID models. In some cases, training with more samples may even hurt
the performance of the evaluation is carried out in one of those datasets. We
postulate that this phenomenon is due to the incapability of the standard
network in adapting to diverse environments. To overcome this issue, we propose
an approach called Domain-Camera-Sample Dynamic network (DCSD) whose parameters
can be adaptive to various factors. Specifically, we consider the internal
domain-related factor that can be identified from the input features, and
external domain-related factors, such as domain information or camera
information. Our discovery is that training with such an adaptive model can
better benefit from more training samples. Experimental results show that our
DCSD can greatly boost the performance (up to 12.3%) while joint training in
multiple datasets.

Convolutional neural networks have gained vast popularity due to their
excellent performance in the fields of computer vision, image processing, and
others. Unfortunately, it is now well known that convolutional networks often
produce erroneous results - for example, minor perturbations of the inputs of
these networks can result in severe classification errors. Numerous
verification approaches have been proposed in recent years to prove the absence
of such errors, but these are typically geared for fully connected networks and
suffer from exacerbated scalability issues when applied to convolutional
networks. To address this gap, we present here the Cnn-Abs framework, which is
particularly aimed at the verification of convolutional networks. The core of
Cnn-Abs is an abstraction-refinement technique, which simplifies the
verification problem through the removal of convolutional connections in a way
that soundly creates an over-approximation of the original problem; and which
restores these connections if the resulting problem becomes too abstract.
Cnn-Abs is designed to use existing verification engines as a backend, and our
evaluation demonstrates that it can significantly boost the performance of a
state-of-the-art DNN verification engine, reducing runtime by 15.7% on average.

Although point-based networks are demonstrated to be accurate for 3D point
cloud modeling, they are still falling behind their voxel-based competitors in
3D detection. We observe that the prevailing set abstraction design for
down-sampling points may maintain too much unimportant background information
that can affect feature learning for detecting objects. To tackle this issue,
we propose a novel set abstraction method named Semantics-Augmented Set
Abstraction (SASA). Technically, we first add a binary segmentation module as
the side output to help identify foreground points. Based on the estimated
point-wise foreground scores, we then propose a semantics-guided point sampling
algorithm to help retain more important foreground points during down-sampling.
In practice, SASA shows to be effective in identifying valuable points related
to foreground objects and improving feature learning for point-based 3D
detection. Additionally, it is an easy-to-plug-in module and able to boost
various point-based detectors, including single-stage and two-stage ones.
Extensive experiments on the popular KITTI and nuScenes datasets validate the
superiority of SASA, lifting point-based detection models to reach comparable
performance to state-of-the-art voxel-based methods.

The problem of linear predictions has been extensively studied for the past
century under pretty generalized frameworks. Recent advances in the robust
statistics literature allow us to analyze robust versions of classical linear
models through the prism of Median of Means (MoM). Combining these approaches
in a piecemeal way might lead to ad-hoc procedures, and the restricted
theoretical conclusions that underpin each individual contribution may no
longer be valid. To meet these challenges coherently, in this study, we offer a
unified robust framework that includes a broad variety of linear prediction
problems on a Hilbert space, coupled with a generic class of loss functions.
Notably, we do not require any assumptions on the distribution of the outlying
data points ($\mathcal{O}$) nor the compactness of the support of the inlying
ones ($\mathcal{I}$). Under mild conditions on the dual norm, we show that for
misspecification level $\epsilon$, these estimators achieve an error rate of
$O(\max\left\{|\mathcal{O}|^{1/2}n^{-1/2}, |\mathcal{I}|^{1/2}n^{-1}
\right\}+\epsilon)$, matching the best-known rates in literature. This rate is
slightly slower than the classical rates of $O(n^{-1/2})$, indicating that we
need to pay a price in terms of error rates to obtain robust estimates.
Additionally, we show that this rate can be improved to achieve so-called "fast
rates" under additional assumptions.

E-learning is a widely used learning method, but with the development of
society, traditional E-learning method has exposed some shortcomings, such as
the boring way of teaching, so that it is difficult to increase the enthusiasm
of students and raise their attention in class. The application of serious
games in E-learning can make up for these shortcomings and effectively improve
the quality of teaching. When applying serious games to E-learning, there are
two main considerations: educational goals and game design. A successful
serious game should organically combine the two aspects and balance the
educational and entertaining nature of serious games. This paper mainly
discusses the role of serious games in E-learning, various elements of game
design, the classification of the educational goals of serious games and the
relationship between educational goals and game design. In addition, we try to
classify serious games and match educational goals with game types to provide
guidance and assistance in the design of serious games. This paper also
summarizes some shortcomings that serious games may have in the application of
E-learning.

Acquiring information on large areas on the earth's surface through satellite
cameras allows us to see much more than we can see while standing on the
ground. This assists us in detecting and monitoring the physical
characteristics of an area like land-use patterns, atmospheric conditions,
forest cover, and many unlisted aspects. The obtained images not only keep
track of continuous natural phenomena but are also crucial in tackling the
global challenge of severe deforestation. Among which Amazon basin accounts for
the largest share every year. Proper data analysis would help limit detrimental
effects on the ecosystem and biodiversity with a sustainable healthy
atmosphere. This report aims to label the satellite image chips of the Amazon
rainforest with atmospheric and various classes of land cover or land use
through different machine learning and superior deep learning models.
Evaluation is done based on the F2 metric, while for loss function, we have
both sigmoid cross-entropy as well as softmax cross-entropy. Images are fed
indirectly to the machine learning classifiers after only features are
extracted using pre-trained ImageNet architectures. Whereas for deep learning
models, ensembles of fine-tuned ImageNet pre-trained models are used via
transfer learning. Our best score was achieved so far with the F2 metric is
0.927.

The non-convexity of the artificial neural network (ANN) training landscape
brings inherent optimization difficulties. While the traditional
back-propagation stochastic gradient descent (SGD) algorithm and its variants
are effective in certain cases, they can become stuck at spurious local minima
and are sensitive to initializations and hyperparameters. Recent work has shown
that the training of an ANN with ReLU activations can be reformulated as a
convex program, bringing hope to globally optimizing interpretable ANNs.
However, naively solving the convex training formulation has an exponential
complexity, and even an approximation heuristic requires cubic time. In this
work, we characterize the quality of this approximation and develop two
efficient algorithms that train ANNs with global convergence guarantees. The
first algorithm is based on the alternating direction method of multiplier
(ADMM). It solves both the exact convex formulation and the approximate
counterpart. Linear global convergence is achieved, and the initial several
iterations often yield a solution with high prediction accuracy. When solving
the approximate formulation, the per-iteration time complexity is quadratic.
The second algorithm, based on the "sampled convex programs" theory, is simpler
to implement. It solves unconstrained convex formulations and converges to an
approximately globally optimal classifier. The non-convexity of the ANN
training landscape exacerbates when adversarial training is considered. We
apply the robust convex optimization theory to convex training and develop
convex formulations that train ANNs robust to adversarial inputs. Our analysis
explicitly focuses on one-hidden-layer fully connected ANNs, but can extend to
more sophisticated architectures.

Zero-Shot Learning (ZSL) aims to transfer classification capability from seen
to unseen classes. Recent methods have proved that generalization and
specialization are two essential abilities to achieve good performance in ZSL.
However, focusing on only one of the abilities may result in models that are
either too general with degraded classification ability or too specialized to
generalize to unseen classes. In this paper, we propose an end-to-end network,
termed as BGSNet, which equips and balances generalization and specialization
abilities at the instance and dataset level. Specifically, BGSNet consists of
two branches: the Generalization Network (GNet), which applies episodic
meta-learning to learn generalized knowledge, and the Balanced Specialization
Network (BSNet), which adopts multiple attentive extractors to extract
discriminative features and achieve instance-level balance. A novel
self-adjusted diversity loss is designed to optimize BSNet with redundancy
reduced and diversity boosted. We further propose a differentiable
dataset-level balance and update the weights in a linear annealing schedule to
simulate network pruning and thus obtain the optimal structure for BSNet with
dataset-level balance achieved. Experiments on four benchmark datasets
demonstrate our model's effectiveness. Sufficient component ablations prove the
necessity of integrating and balancing generalization and specialization

Although there are a couple of open-source language processing pipelines
available for Hungarian, none of them satisfies the requirements of today's NLP
applications. A language processing pipeline should consist of close to
state-of-the-art lemmatization, morphosyntactic analysis, entity recognition
and word embeddings. Industrial text processing applications have to satisfy
non-functional software quality requirements, what is more, frameworks
supporting multiple languages are more and more favored. This paper introduces
HuSpaCy, an industry-ready Hungarian language processing toolkit. The presented
tool provides components for the most important basic linguistic analysis
tasks. It is open-source and is available under a permissive license. Our
system is built upon spaCy's NLP components resulting in an easily usable, fast
yet accurate application. Experiments confirm that HuSpaCy has high accuracy
while maintaining resource-efficient prediction capabilities.

In this work, we study empirical risk minimization (ERM) within a federated
learning framework, where a central server minimizes an ERM objective function
using training data that is stored across $m$ clients. In this setting, the
Federated Averaging (FedAve) algorithm is the staple for determining
$\epsilon$-approximate solutions to the ERM problem. Similar to standard
optimization algorithms, the convergence analysis of FedAve only relies on
smoothness of the loss function in the optimization parameter. However, loss
functions are often very smooth in the training data too. To exploit this
additional smoothness, we propose the Federated Low Rank Gradient Descent
(FedLRGD) algorithm. Since smoothness in data induces an approximate low rank
structure on the loss function, our method first performs a few rounds of
communication between the server and clients to learn weights that the server
can use to approximate clients' gradients. Then, our method solves the ERM
problem at the server using inexact gradient descent. To show that FedLRGD can
have superior performance to FedAve, we present a notion of federated oracle
complexity as a counterpart to canonical oracle complexity. Under some
assumptions on the loss function, e.g., strong convexity in parameter,
$\eta$-H\"older smoothness in data, etc., we prove that the federated oracle
complexity of FedLRGD scales like $\phi m(p/\epsilon)^{\Theta(d/\eta)}$ and
that of FedAve scales like $\phi m(p/\epsilon)^{3/4}$ (neglecting sub-dominant
factors), where $\phi\gg 1$ is a "communication-to-computation ratio," $p$ is
the parameter dimension, and $d$ is the data dimension. Then, we show that when
$d$ is small and the loss function is sufficiently smooth in the data, FedLRGD
beats FedAve in federated oracle complexity. Finally, in the course of
analyzing FedLRGD, we also establish a result on low rank approximation of
latent variable models.

Given an aerial image, aerial scene parsing (ASP) targets to interpret the
semantic structure of the image content, e.g., by assigning a semantic label to
every pixel of the image. With the popularization of data-driven methods, the
past decades have witnessed promising progress on ASP by approaching the
problem with the schemes of tile-level scene classification or
segmentation-based image analysis, when using high-resolution aerial images.
However, the former scheme often produces results with tile-wise boundaries,
while the latter one needs to handle the complex modeling process from pixels
to semantics, which often requires large-scale and well-annotated image samples
with pixel-wise semantic labels. In this paper, we address these issues in ASP,
with perspectives from tile-level scene classification to pixel-wise semantic
labeling. Specifically, we first revisit aerial image interpretation by a
literature review. We then present a large-scale scene classification dataset
that contains one million aerial images termed Million-AID. With the presented
dataset, we also report benchmarking experiments using classical convolutional
neural networks (CNNs). Finally, we perform ASP by unifying the tile-level
scene classification and object-based image analysis to achieve pixel-wise
semantic labeling. Intensive experiments show that Million-AID is a challenging
yet useful dataset, which can serve as a benchmark for evaluating newly
developed algorithms. When transferring knowledge from Million-AID, fine-tuning
CNN models pretrained on Million-AID perform consistently better than those
pretrained ImageNet for aerial scene classification. Moreover, our designed
hierarchical multi-task learning method achieves the state-of-the-art
pixel-wise classification on the challenging GID, bridging the tile-level scene
classification toward pixel-wise semantic labeling for aerial image
interpretation.

Recent times are witnessing rapid development in machine learning algorithm
systems, especially in reinforcement learning, natural language processing,
computer and robot vision, image processing, speech, and emotional processing
and understanding. In tune with the increasing importance and relevance of
machine learning models, algorithms, and their applications, and with the
emergence of more innovative uses cases of deep learning and artificial
intelligence, the current volume presents a few innovative research works and
their applications in real world, such as stock trading, medical and healthcare
systems, and software automation. The chapters in the book illustrate how
machine learning and deep learning algorithms and models are designed,
optimized, and deployed. The volume will be useful for advanced graduate and
doctoral students, researchers, faculty members of universities, practicing
data scientists and data engineers, professionals, and consultants working on
the broad areas of machine learning, deep learning, and artificial

This paper proposes an efficient approach to learning disentangled
representations with causal mechanisms based on the difference of conditional
probabilities in original and new distributions. We approximate the difference
with models' generalization abilities so that it fits in the standard machine
learning framework and can be efficiently computed. In contrast to the
state-of-the-art approach, which relies on the learner's adaptation speed to
new distribution, the proposed approach only requires evaluating the model's
generalization ability. We provide a theoretical explanation for the advantage
of the proposed method, and our experiments show that the proposed technique is
1.9--11.0$\times$ more sample efficient and 9.4--32.4 times quicker than the
previous method on various tasks. The source code is available at
\url{https://github.com/yuanpeng16/EDCR}.

Knowledge of phase diagrams is essential for material design as it helps in
understanding microstructure evolution during processing. The determination of
phase diagrams is thus one of the central tasks in materials science. When
exploring new materials for which the phase diagram is unknown,
experimentalists often try to determine the key experiments that should be
performed by referencing known phase diagrams of similar systems. To enhance
this practical strategy, we attempted to estimate unknown phase diagrams based
on known phase diagrams using a machine learning-based classification approach.
As a proof of concept, we focused on predicting the number of coexisting phases
across the 800 K isothermal section of each of the 10 ternaries of the
Al-Cu-Mg-Si-Zn system from the other 9 sections. To increase the prediction
accuracy, we introduced new descriptors generated from the thermodynamic
properties of the elements and CALPHAD extrapolations from lower-order systems.
Using the random forest method, the presence of single-, two-, and three-phase
domains was predicted with an average accuracy of 84% across all 10 considered
sections with a standard deviation of 11%. The proposed approach represents a
promising tool for assisting the investigator in developing new materials and
determining phase equilibria efficiently.

Recent advances in unsupervised domain adaptation (UDA) techniques have
witnessed great success in cross-domain computer vision tasks, enhancing the
generalization ability of data-driven deep learning architectures by bridging
the domain distribution gaps. For the UDA-based cross-domain object detection
methods, the majority of them alleviate the domain bias by inducing the
domain-invariant feature generation via adversarial learning strategy. However,
their domain discriminators have limited classification ability due to the
unstable adversarial training process. Therefore, the extracted features
induced by them cannot be perfectly domain-invariant and still contain
domain-private factors, bringing obstacles to further alleviate the
cross-domain discrepancy. To tackle this issue, we design a Domain
Disentanglement Faster-RCNN (DDF) to eliminate the source-specific information
in the features for detection task learning. Our DDF method facilitates the
feature disentanglement at the global and local stages, with a Global Triplet
Disentanglement (GTD) module and an Instance Similarity Disentanglement (ISD)
module, respectively. By outperforming state-of-the-art methods on four
benchmark UDA object detection tasks, our DDF method is demonstrated to be
effective with wide applicability.

Augmented reality devices have the potential to enhance human perception and
enable other assistive functionalities in complex conversational environments.
Effectively capturing the audio-visual context necessary for understanding
these social interactions first requires detecting and localizing the voice
activities of the device wearer and the surrounding people. These tasks are
challenging due to their egocentric nature: the wearer's head motion may cause
motion blur, surrounding people may appear in difficult viewing angles, and
there may be occlusions, visual clutter, audio noise, and bad lighting. Under
these conditions, previous state-of-the-art active speaker detection methods do
not give satisfactory results. Instead, we tackle the problem from a new
setting using both video and multi-channel microphone array audio. We propose a
novel end-to-end deep learning approach that is able to give robust voice
activity detection and localization results. In contrast to previous methods,
our method localizes active speakers from all possible directions on the
sphere, even outside the camera's field of view, while simultaneously detecting
the device wearer's own voice activity. Our experiments show that the proposed
method gives superior results, can run in real time, and is robust against
noise and clutter.

We present Contrastive Neighborhood Alignment (CNA), a manifold learning
approach to maintain the topology of learned features whereby data points that
are mapped to nearby representations by the source (teacher) model are also
mapped to neighbors by the target (student) model. The target model aims to
mimic the local structure of the source representation space using a
contrastive loss. CNA is an unsupervised learning algorithm that does not
require ground-truth labels for the individual samples. CNA is illustrated in
three scenarios: manifold learning, where the model maintains the local
topology of the original data in a dimension-reduced space; model distillation,
where a small student model is trained to mimic a larger teacher; and legacy
model update, where an older model is replaced by a more powerful one.
Experiments show that CNA is able to capture the manifold in a high-dimensional
space and improves performance compared to the competing methods in their
domains.

Control certificates based on barrier functions have been a powerful tool to
generate probably safe control policies for dynamical systems. However,
existing methods based on barrier certificates are normally for white-box
systems with differentiable dynamics, which makes them inapplicable to many
practical applications where the system is a black-box and cannot be accurately
modeled. On the other side, model-free reinforcement learning (RL) methods for
black-box systems suffer from lack of safety guarantees and low sampling
efficiency. In this paper, we propose a novel method that can learn safe
control policies and barrier certificates for black-box dynamical systems,
without requiring for an accurate system model. Our method re-designs the loss
function to back-propagate gradient to the control policy even when the
black-box dynamical system is non-differentiable, and we show that the safety
certificates hold on the black-box system. Empirical results in simulation show
that our method can significantly improve the performance of the learned
policies by achieving nearly 100% safety and goal reaching rates using much
fewer training samples, compared to state-of-the-art black-box safe control
methods. Our learned agents can also generalize to unseen scenarios while
keeping the original performance. The source code can be found at
https://github.com/Zengyi-Qin/bcbf.

We present a bibliometric analysis of the research papers on quantum
computation and quantum algorithms published in 1985-2020. We identify three
distinct periods from the trend of the annual number of published papers, and
show the 20 top contributing countries in each period in terms of the number of
publications and the number of total citations. The bibliographic coupling
network of the publications in the latest period is characterized as a dense,
small-world network with a small diameter, which contains 14 large communities,
whose topics are the fabrication of qubits in various physical systems, studies
of the quantum algorithms, and other related topics. Quantum machine learning,
one of the emerging topics in quantum computation, is found to be the
fifth-largest independent community.

Assuming distributions are Gaussian often facilitates computations that are
otherwise intractable. We study the performance of an agent that attains a
bounded information ratio with respect to a bandit environment with a Gaussian
prior distribution and a Gaussian likelihood function when applied instead to a
Bernoulli bandit. Relative to an information-theoretic bound on the Bayesian
regret the agent would incur when interacting with the Gaussian bandit, we
bound the increase in regret when the agent interacts with the Bernoulli
bandit. If the Gaussian prior distribution and likelihood function are
sufficiently diffuse, this increase grows at a rate which is at most linear in
the square-root of the time horizon, and thus the per-timestep increase
vanishes. Our results formalize the folklore that so-called Bayesian agents
remain effective when instantiated with diffuse misspecified distributions.

Intracranial aneurysms are common nowadays and how to detect them
intelligently is of great significance in digital health. While most existing
deep learning research focused on medical images in a supervised way, we
introduce an unsupervised method for the detection of intracranial aneurysms
based on 3D point cloud data. In particular, our method consists of two stages:
unsupervised pre-training and downstream tasks. As for the former, the main
idea is to pair each point cloud with its jittered counterpart and maximise
their correspondence. Then we design a dual-branch contrastive network with an
encoder for each branch and a subsequent common projection head. As for the
latter, we design simple networks for supervised classification and
segmentation training. Experiments on the public dataset (IntrA) show that our
unsupervised method achieves comparable or even better performance than some
state-of-the-art supervised techniques, and it is most prominent in the
detection of aneurysmal vessels. Experiments on the ModelNet40 also show that
our method achieves the accuracy of 90.79\% which outperforms existing
state-of-the-art unsupervised models.

In multi-robot system (MRS) bounding overwatch, it is crucial to determine
which point to choose for overwatch at each step and whether the robots'
positions are trustworthy so that the overwatch can be performed effectively.
In this paper, we develop a Bayesian optimization based computational
trustworthiness model (CTM) for the MRS to select overwatch points. The CTM can
provide real-time trustworthiness evaluation for the MRS on the overwatch
points by referring to the robots' situational awareness information, such as
traversability and line of sight. The evaluation can quantify each robot's
trustworthiness in protecting its robot team members during the bounding
overwatch. The trustworthiness evaluation can generate a dynamic cost map for
each robot in the workspace and help obtain the most trustworthy bounding
overwatch path. Our proposed Bayesian based CTM and motion planning can reduce
the number of explorations for the workspace in data collection and improve the
CTM learning efficiency. It also enables the MRS to deal with the dynamic and
uncertain environments for the multi-robot bounding overwatch task. A robot
simulation is implemented in ROS Gazebo to demonstrate the effectiveness of the
proposed framework.

We suggest a simple practical method to combine the human and artificial
intelligence to both learn best investment practices of fund managers, and
provide recommendations to improve them. Our approach is based on a combination
of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns
the intent of fund managers as suggested by their trading history, and recovers
their implied reward function. At the second step, this reward function is used
by a direct RL algorithm to optimize asset allocation decisions. We show that
our method is able to improve over the performance of individual fund managers.

We introduce NeuralMLS, a space-based deformation technique, guided by a set
of displaced control points. We leverage the power of neural networks to inject
the underlying shape geometry into the deformation parameters. The goal of our
technique is to enable a realistic and intuitive shape deformation. Our method
is built upon moving least-squares (MLS), since it minimizes a weighted sum of
the given control point displacements. Traditionally, the influence of each
control point on every point in space (i.e., the weighting function) is defined
using inverse distance heuristics. In this work, we opt to learn the weighting
function, by training a neural network on the control points from a single
input shape, and exploit the innate smoothness of neural networks. Our
geometry-aware control point deformation is agnostic to the surface
representation and quality; it can be applied to point clouds or meshes,
including non-manifold and disconnected surface soups. We show that our
technique facilitates intuitive piecewise smooth deformations, which are well
suited for manufactured objects. We show the advantages of our approach
compared to existing surface and space-based deformation techniques, both

Need for the efficient processing of neural networks has given rise to the
development of hardware accelerators. The increased adoption of specialized
hardware has highlighted the need for more agile design flows for
hardware-software co-design and domain-specific optimizations. We present CFU
Playground, a full-stack open-source framework that enables rapid and iterative
design of machine learning (ML) accelerators for embedded ML systems. Our
toolchain integrates open-source software, open-source RTL generators, and
open-source FPGA tools for synthesis, place, and route. This full-stack
framework gives the users access to explore bespoke architectures that are
customized and co-optimized for embedded ML. The rapid,
deploy-profile-optimization feedback loop lets ML hardware and software
developers achieve significant returns out of a relatively small investment in
customization. Using CFU Playground's design loop, we show substantial speedups
between 55$\times$ and 75$\times$. The soft CPU coupled with the accelerator
opens up a new, rich design space between the two components that we explore in
an automated fashion using Vizier, a black-box optimization service.

3D scanning is a complex multistage process that generates a point cloud of
an object typically containing damaged parts due to occlusions, reflections,
shadows, scanner motion, specific properties of the object surface, imperfect
reconstruction algorithms, etc. Point cloud completion is specifically designed
to fill in the missing parts of the object and obtain its high-quality 3D
representation. The existing completion approaches perform well on the academic
datasets with a predefined set of object classes and very specific types of
defects; however, their performance drops significantly in the real-world
settings and degrades even further on previously unseen object classes.
  We propose a novel framework that performs well on symmetric objects, which
are ubiquitous in man-made environments. Unlike learning-based approaches, the
proposed framework does not require training data and is capable of completing
non-critical damages occurring in customer 3D scanning process using e.g.
Kinect, time-of-flight, or structured light scanners. With thorough
experiments, we demonstrate that the proposed framework achieves
state-of-the-art efficiency in point cloud completion of real-world customer
scans. We benchmark the framework performance on two types of datasets:
properly augmented existing academic dataset and the actual 3D scans of various
objects.

Continual learning in environments with shifting data distributions is a
challenging problem with several real-world applications. In this paper we
consider settings in which the data distribution(task) shifts abruptly and the
timing of these shifts are not known. Furthermore, we consider a
semi-supervised task-agnostic setting in which the learning algorithm has
access to both task-segmented and unsegmented data for offline training. We
propose a novel approach called mixture of Basismodels (MoB) for addressing
this problem setting. The core idea is to learn a small set of basis models and
to construct a dynamic, task-dependent mixture of the models to predict for the
current task. We also propose a new methodology to detect observations that are
out-of-distribution with respect to the existing basis models and to
instantiate new models as needed. We test our approach in multiple domains and
show that it attains better prediction error than existing methods in most
cases while using fewer models than other multiple model approaches. Moreover,
we analyze the latent task representations learned by MoB and show that similar
tasks tend to cluster in the latent space and that the latent representation
shifts at the task boundaries when tasks are dissimilar.

The existence of real-world adversarial examples (commonly in the form of
patches) poses a serious threat for the use of deep learning models in
safety-critical computer vision tasks such as visual perception in autonomous
driving. This paper presents an extensive evaluation of the robustness of
semantic segmentation models when attacked with different types of adversarial
patches, including digital, simulated, and physical ones. A novel loss function
is proposed to improve the capabilities of attackers in inducing a
misclassification of pixels. Also, a novel attack strategy is presented to
improve the Expectation Over Transformation method for placing a patch in the
scene. Finally, a state-of-the-art method for detecting adversarial patch is
first extended to cope with semantic segmentation models, then improved to
obtain real-time performance, and eventually evaluated in real-world scenarios.
Experimental results reveal that, even though the adversarial effect is visible
with both digital and real-world attacks, its impact is often spatially
confined to areas of the image around the patch. This opens to further
questions about the spatial robustness of real-time semantic segmentation
models.

For the Internet-of-unmanned aerial vehicles (UAVs) some challenges in
broadcasting and from new points of view are explored. In this paper, first, we
investigate a single broadcast transceiver. From a control of noisy-channel
viewpoint, we consider: (\textit{i}) Alice sends $\mathcal{X}$ to Bob as more
\textcolor{black}{efficient} as possible while she wishes Bob not to get access
to the private message $\mathcal{S}$ regarding the correlation between
$\mathcal{S}$ and $\mathcal{X}$ $-$ i.e., Alice purposefully sends a
\textit{turbulent-flow} of the information to Bob; and (\textit{ii}) where
$\big (\Theta_1;\Theta_2 \big)$ is the control-action-pair which actualise a
\textit{pursuit-Evasion}. We consider \textit{dissipativity} in our system due
to the memory effect relating to the previous states. We thus propose a
federated-learning based \textit{Blahut-Arimoto} algorithm while a 2-D
\textit{dissipativity}-theoretic continuous-Mean-Field-Game (MFG) is proposed
with regard to (w.r.t.) a joint probability-distribution-function (PDF) of the
population distribution $-$ relating to a continuous-control-law. We also
analyse what if Alice is owed to multiple Bobs in a multi-user scenario which
we apply a bankruptcy based $3-$level nested game for.

Modern computing systems have led cyber adversaries to create more
sophisticated malware than was previously available in the early days of
technology. Dated detection techniques such as Anti-Virus Software (AVS) based
on signature-based methods could no longer keep up with the demand that
computer systems required of them. The complexity of modern malware has led to
the development of contemporary detection techniques that use the machine
learning field and hardware to boost the detection rates of malicious software.
These new techniques use Hardware Performance Counters (HPCs) that form a
digital signature of sorts. After the models are fed training data, they can
reference these HPCs to classify zero-day malware samples. A problem emerges
when malware with no comparable HPC values comes into contact with these new
techniques. We provide an analysis of several machine learning and deep
learning models that run zero-day samples and evaluate the results from the
conversion of C++ algorithms to a hardware description language (HDL) used to
begin a hardware implementation. Our results present a lack of accuracy from
the models when running zero-day malware data as our highest detector, decision
tree, was only able to reach 91.2% accuracy and had an F1-Score of 91.5% in the
form of a decision tree. Next, through the Receiver Operating Curve (ROC) and
area-under-the-curve (AUC), we can also determine that the algorithms did not
present significant robustness as the largest AUC was only 0.819. In addition,
we viewed relatively high overhead for our ensemble learning algorithm while
also only having an 86.3% accuracy and 86% F1-Score. Finally, as an additional
task, we adapted the one rule algorithm to fit many rules to make malware
classification understandable to everyday users by allowing them to view the
regulations while maintaining relatively high accuracy.

This paper technically explores the secrecy rate $\Lambda$ and a maximisation
problem over the concave version of the secrecy outage probability (SOP) as
$\mathop{{\rm \mathbb{M}ax}}\limits_{\Delta } {\rm \; } \mathbb{P}\mathscr{r}
\big( \Lambda \ge \lambda \big) $. We do this from a generic viewpoint even
though we use a traditional Wyner's wiretap channel for our system model $-$
something that can be extended to every kind of secrecy modeling and analysis.
We consider a Riemannian mani-fold for it and we mathematically define a volume
for it as $\mathbb{V}\mathscr{ol}\big \lbrace \Lambda \big \rbrace$. Through
achieving a new bound for the Riemannian mani-fold and its volume, we
subsequently relate it to the number of eigen-values existing in the relative
probabilistic closure. We prove in-between some novel lemmas with the aid of
some useful inequalities such as the \textit{Finsler's} lemma, the generalised
\textit{Young's} inequality, the generalised \textit{Brunn-Minkowski}
inequality, the \textit{Talagrand's} concentration inequality. We additionally
propose a novel Markov decision process based reinforcement learning algorithm
in order to find the optimal policy in relation to the eigenvalue distributions
$-$ something that is extended to a possibilisitically semi-Markov decision
process for the case of periodic attacks.

We in this paper theoretically go over a rate-distortion based sparse
dictionary learning problem. We show that the Degrees-of-Freedom (DoF)
interested to be calculated $-$ satnding for the minimal set that guarantees
our rate-distortion trade-off $-$ are basically accessible through a
\textit{Langevin} equation. We indeed explore that the relative time evolution
of DoF, i.e., the transition jumps is the essential issue for a relaxation over
the relative optimisation problem. We subsequently prove the aforementioned
relaxation through the \textit{Graphon} principle w.r.t. a stochastic
\textit{Chordal Schramm-Loewner} evolution etc {via a minimisation over a
distortion between the relative realisation times of two given graphs
$\mathscr{G}_1$ and $\mathscr{G}_2$ as $ \mathop{{\rm \mathbb{M}in}}\limits_{
\mathscr{G}_1, \mathscr{G}_2} {\rm \; } \mathcal{D} \Big( t\big( \mathscr{G}_1
, \mathscr{G} \big) , t\big( \mathscr{G}_2 , \mathscr{G} \big) \Big)$}. We also
extend our scenario to the eavesdropping case. We finally prove the efficiency
of our proposed scheme via simulations.

Frame shift is a cross-linguistic phenomenon in translation which results in
corresponding pairs of linguistic material evoking different frames. The
ability to predict frame shifts enables automatic creation of multilingual
FrameNets through annotation projection. Here, we propose the Frame Shift
Prediction task and demonstrate that graph attention networks, combined with
auxiliary training, can learn cross-linguistic frame-to-frame correspondence
and predict frame shifts.

Estimating value functions is a core component of reinforcement learning
algorithms. Temporal difference (TD) learning algorithms use bootstrapping,
i.e. they update the value function toward a learning target using value
estimates at subsequent time-steps. Alternatively, the value function can be
updated toward a learning target constructed by separately predicting successor
features (SF)--a policy-dependent model--and linearly combining them with
instantaneous rewards. We focus on bootstrapping targets used when estimating
value functions, and propose a new backup target, the $\eta$-return mixture,
which implicitly combines value-predictive knowledge (used by TD methods) with
(successor) feature-predictive knowledge--with a parameter $\eta$ capturing how
much to rely on each. We illustrate that incorporating predictive knowledge
through an $\eta\gamma$-discounted SF model makes more efficient use of sampled
experience, compared to either extreme, i.e. bootstrapping entirely on the
value function estimate, or bootstrapping on the product of separately
estimated successor features and instantaneous reward models. We empirically
show this approach leads to faster policy evaluation and better control
performance, for tabular and nonlinear function approximations, indicating
scalability and generality.

Implicit neural networks have been successfully used for surface
reconstruction from point clouds. However, many of them face scalability issues
as they encode the isosurface function of a whole object or scene into a single
latent vector. To overcome this limitation, a few approaches infer latent
vectors on a coarse regular 3D grid or on 3D patches, and interpolate them to
answer occupancy queries. In doing so, they loose the direct connection with
the input points sampled on the surface of objects, and they attach information
uniformly in space rather than where it matters the most, i.e., near the
surface. Besides, relying on fixed patch sizes may require discretization
tuning. To address these issues, we propose to use point cloud convolutions and
compute latent vectors at each input point. We then perform a learning-based
interpolation on nearest neighbors using inferred weights. Experiments on both
object and scene datasets show that our approach significantly outperforms
other methods on most classical metrics, producing finer details and better
reconstructing thinner volumes. The code is available at
https://github.com/valeoai/POCO.

Multiple methods of finding the vertices belonging to a planted dense
subgraph in a random dense $G(n, p)$ graph have been proposed, with an emphasis
on planted cliques. Such methods can identify the planted subgraph in
polynomial time, but are all limited to several subgraph structures. Here, we
present PYGON, a graph neural network-based algorithm, which is insensitive to
the structure of the planted subgraph. This is the first algorithm that uses
advanced learning tools for recovering dense subgraphs. We show that PYGON can
recover cliques of sizes $\Theta\left(\sqrt{n}\right)$, where $n$ is the size
of the background graph, comparable with the state of the art. We also show
that the same algorithm can recover multiple other planted subgraphs of size
$\Theta\left(\sqrt{n}\right)$, in both directed and undirected graphs. We
suggest a conjecture that no polynomial time PAC-learning algorithm can detect
planted dense subgraphs with size smaller than $O\left(\sqrt{n}\right)$, even
if in principle one could find dense subgraphs of logarithmic size.

Zero-shot learning (ZSL) aims at recognizing classes for which no visual
sample is available at training time. To address this issue, one can rely on a
semantic description of each class. A typical ZSL model learns a mapping
between the visual samples of seen classes and the corresponding semantic
descriptions, in order to do the same on unseen classes at test time. State of
the art approaches rely on generative models that synthesize visual features
from the prototype of a class, such that a classifier can then be learned in a
supervised manner. However, these approaches are usually biased towards seen
classes whose visual instances are the only one that can be matched to a given
class prototype. We propose a regularization method that can be applied to any
conditional generative-based ZSL method, by leveraging only the semantic class
prototypes. It learns to synthesize discriminative features for possible
semantic description that are not available at training time, that is the
unseen ones. The approach is evaluated for ZSL and GZSL on four datasets
commonly used in the literature, either in inductive and transductive settings,
with results on-par or above state of the art approaches.

Deep learning is one of the most successful and far-reaching strategies used
in machine learning today. However, the scale and utility of neural networks is
still greatly limited by the current hardware used to train them. These
concerns have become increasingly pressing as conventional computers quickly
approach physical limitations that will slow performance improvements in years
to come. For these reasons, scientists have begun to explore alternative
computing platforms, like quantum computers, for training neural networks. In
recent years, variational quantum circuits have emerged as one of the most
successful approaches to quantum deep learning on noisy intermediate scale
quantum devices. We propose a hybrid quantum-classical neural network
architecture where each neuron is a variational quantum circuit. We empirically
analyze the performance of this hybrid neural network on a series of binary
classification data sets using a simulated universal quantum computer and a
state of the art universal quantum computer. On simulated hardware, we observe
that the hybrid neural network achieves roughly 10% higher classification
accuracy and 20% better minimization of cost than an individual variational
quantum circuit. On quantum hardware, we observe that each model only performs
well when the qubit and gate count is sufficiently small.

We present a machine learning system that can quantify fine art paintings
with a set of visual elements and principles of art. This formal analysis is
fundamental for understanding art, but developing such a system is challenging.
Paintings have high visual complexities, but it is also difficult to collect
enough training data with direct labels. To resolve these practical
limitations, we introduce a novel mechanism, called proxy learning, which
learns visual concepts in paintings though their general relation to styles.
This framework does not require any visual annotation, but only uses style
labels and a general relationship between visual concepts and style. In this
paper, we propose a novel proxy model and reformulate four pre-existing methods
in the context of proxy learning. Through quantitative and qualitative
comparison, we evaluate these methods and compare their effectiveness in
quantifying the artistic visual concepts, where the general relationship is
estimated by language models; GloVe or BERT. The language modeling is a
practical and scalable solution requiring no labeling, but it is inevitably
imperfect. We demonstrate how the new proxy model is robust to the
imperfection, while the other models are sensitively affected by it.

A key challenge in the study of multiagent cooperation is the need for
individual agents not only to cooperate effectively, but to decide with whom to
cooperate. This is particularly critical in situations when other agents have
hidden, possibly misaligned motivations and goals. Social deduction games offer
an avenue to study how individuals might learn to synthesize potentially
unreliable information about others, and elucidate their true motivations. In
this work, we present Hidden Agenda, a two-team social deduction game that
provides a 2D environment for studying learning agents in scenarios of unknown
team alignment. The environment admits a rich set of strategies for both teams.
Reinforcement learning agents trained in Hidden Agenda show that agents can
learn a variety of behaviors, including partnering and voting without need for
communication in natural language.

This work explores the reproducibility of CFGAN. CFGAN and its family of
models (TagRec, MTPR, and CRGAN) learn to generate personalized and
fake-but-realistic rankings of preferences for top-N recommendations by using
previous interactions. This work successfully replicates the results published
in the original paper and discusses the impact of certain differences between
the CFGAN framework and the model used in the original evaluation. The absence
of random noise and the use of real user profiles as condition vectors leaves
the generator prone to learn a degenerate solution in which the output vector
is identical to the input vector, therefore, behaving essentially as a simple
autoencoder. The work further expands the experimental analysis comparing CFGAN
against a selection of simple and well-known properly optimized baselines,
observing that CFGAN is not consistently competitive against them despite its
high computational cost. To ensure the reproducibility of these analyses, this
work describes the experimental methodology and publishes all datasets and
source code.

I introduce a dynamic model of learning and random meetings between a
long-lived agent with unknown ability and heterogenous projects with observable
types. There is incomplete yet symmetric information about the agent's ability.
She needs to accept the contacting projects and create success to learn her
type. Alternatively, lack of success during a match leads to a reputational
loss followed from Bayesian learning, in that the reputation is interpreted as
the posterior belief about the agent's ability. Developing a self-type learning
framework with endogenous outside option, I find the optimal matching strategy
of the agent, that determines what types of projects the agent with a certain
level of reputation will accept. Comparing with a perfect information
benchmark, I show learning incentives lead to larger matching sets in the
optimum.

We present CausalSim, a causal inference framework for unbiased trace-driven
simulation. Current trace-driven simulators assume that the interventions being
simulated (e.g., a new algorithm) would not affect the validity of the traces.
However, real-world traces are often biased by the choices of algorithms made
during trace collection, and hence replaying traces under an intervention may
lead to incorrect results. CausalSim addresses this challenge by learning a
causal model of the system dynamics and latent factors capturing the underlying
system conditions during trace collection. It learns these models using an
initial randomized control trial (RCT) under a fixed set of algorithms, and
then applies them to remove biases from trace data when simulating new
  Key to CausalSim is mapping unbiased trace-driven simulation to a tensor
completion problem with extremely sparse observations. By exploiting a basic
distributional invariance property present in RCT data, CausalSim enables a
novel tensor completion method despite the sparsity of observations. Our
extensive evaluation of CausalSim on both real and synthetic datasets,
including more than ten months of real data from the Puffer video streaming
system show it improves simulation accuracy, reducing errors by 53% and 61% on
average compared to expert-designed and supervised learning baselines.
Moreover, CausalSim provides markedly different insights about ABR algorithms
compared to the biased baseline simulator, which we validate with a real

Poor sitting habits have been identified as a risk factor to musculoskeletal
disorders and lower back pain especially on the elderly, disabled people, and
office workers. In the current computerized world, even while involved in
leisure or work activity, people tend to spend most of their days sitting at
computer desks. This can result in spinal pain and related problems. Therefore,
a means to remind people about their sitting habits and provide recommendations
to counterbalance, such as physical exercise, is important. Posture recognition
for seated postures have not received enough attention as most works focus on
standing postures. Wearable sensors, pressure or force sensors, videos and
images were used for posture recognition in the literature. The aim of this
study is to build Machine Learning models for classifying sitting posture of a
person by analyzing data collected from a chair platted with two 32 by 32
pressure sensors at its seat and backrest. Models were built using five
algorithms: Random Forest (RF), Gaussian Na\"ive Bayes, Logistic Regression,
Support Vector Machine and Deep Neural Network (DNN). All the models are
evaluated using KFold cross-validation technique. This paper presents
experiments conducted using the two separate datasets, controlled and
realistic, and discusses results achieved at classifying six sitting postures.
Average classification accuracies of 98% and 97% were achieved on the
controlled and realistic datasets, respectively.

Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge
from a labeled source domain to an unlabeled target domain. Traditionally,
subspace-based methods form an important class of solutions to this problem.
Despite their mathematical elegance and tractability, these methods are often
found to be ineffective at producing domain-invariant features with complex,
real-world datasets. Motivated by the recent advances in representation
learning with deep networks, this paper revisits the use of subspace alignment
for UDA and proposes a novel adaptation algorithm that consistently leads to
improved generalization. In contrast to existing adversarial training-based DA
methods, our approach isolates feature learning and distribution alignment
steps, and utilizes a primary-auxiliary optimization strategy to effectively
balance the objectives of domain invariance and model fidelity. While providing
a significant reduction in target data and computational requirements, our
subspace-based DA performs competitively and sometimes even outperforms
state-of-the-art approaches on several standard UDA benchmarks. Furthermore,
subspace alignment leads to intrinsically well-regularized models that
demonstrate strong generalization even in the challenging partial DA setting.
Finally, the design of our UDA framework inherently supports progressive
adaptation to new target domains at test-time, without requiring retraining of
the model from scratch. In summary, powered by powerful feature learners and an
effective optimization strategy, we establish subspace-based DA as a highly
effective approach for visual recognition.

In this work a machine learning-based Reduced Order Model (ROM) is developed
to investigate in a rapid and reliable way the hemodynamic patterns in a
patient-specific configuration of Coronary Artery Bypass Graft (CABG). The
computational domain is composed by the left branches of coronary arteries when
a stenosis of the Left Main Coronary Artery (LMCA) occurs. A reduced basis
space is extracted from a collection of Finite Volume (FV) solutions of the
incompressible Navier-Stokes equations by using the Proper Orthogonal
Decomposition (POD) algorithm. Artificial Neural Networks (ANNs) are employed
to compute the modal coefficients. Stenosis is introduced by morphing the
volume meshes with a Free Form Deformation (FFD) by means of a Non-Uniform
Rational Basis Spline (NURBS) volumetric parameterization.

The idea that memory behavior relies on a gradually-changing internal state
has a long history in mathematical psychology. This chapter traces this line of
thought from statistical learning theory in the 1950s, through distributed
memory models in the latter part of the 20th century and early part of the 21st
century through to modern models based on a scale-invariant temporal history.
We discuss the neural phenomena consistent with this form of representation and
sketch the kinds of cognitive models that can be constructed using it and
connections with formal models of various memory tasks.

Consider a panel data setting where repeated observations on individuals are
available. Often it is reasonable to assume that there exist groups of
individuals that share similar effects of observed characteristics, but the
grouping is typically unknown in advance. We propose a novel approach to
estimate such unobserved groupings for general panel data models. Our method
explicitly accounts for the uncertainty in individual parameter estimates and
remains computationally feasible with a large number of individuals and/or
repeated measurements on each individual. The developed ideas can be applied
even when individual-level data are not available and only parameter estimates
together with some quantification of uncertainty are given to the researcher.

Pre-trained language models (LMs) often struggle to reason logically or
generalize in a compositional fashion. Recent work suggests that incorporating
external entity knowledge can improve LMs' abilities to reason and generalize.
However, the effect of explicitly providing entity abstraction remains unclear,
especially with recent studies suggesting that pre-trained LMs already encode
some of that knowledge in their parameters. We study the utility of
incorporating entity type abstractions into pre-trained Transformers and test
these methods on four NLP tasks requiring different forms of logical reasoning:
(1) compositional language understanding with text-based relational reasoning
(CLUTRR), (2) abductive reasoning (ProofWriter), (3) multi-hop question
answering (HotpotQA), and (4) conversational question answering (CoQA). We
propose and empirically explore three ways to add such abstraction: (i) as
additional input embeddings, (ii) as a separate sequence to encode, and (iii)
as an auxiliary prediction task for the model. Overall, our analysis
demonstrates that models with abstract entity knowledge performs better than
without it. However, our experiments also show that the benefits strongly
depend on the technique used and the task at hand. The best abstraction aware
models achieved an overall accuracy of 88.8% and 91.8% compared to the baseline
model achieving 62.3% and 89.8% on CLUTRR and ProofWriter respectively. In
addition, abstraction-aware models showed improved compositional generalization
in both interpolation and extrapolation settings. However, for HotpotQA and
CoQA, we find that F1 scores improve by only 0.5% on average. Our results
suggest that the benefit of explicit abstraction is significant in formally
defined logical reasoning settings requiring many reasoning hops, but point to
the notion that it is less beneficial for NLP tasks having less formal logical

Automated scoring of free drawings or images as responses has yet to be
utilized in large-scale assessments of student achievement. In this study, we
propose artificial neural networks to classify these types of graphical
responses from a computer based international mathematics and science
assessment. We are comparing classification accuracy of convolutional and
feedforward approaches. Our results show that convolutional neural networks
(CNNs) outperform feedforward neural networks in both loss and accuracy. The
CNN models classified up to 97.71% of the image responses into the appropriate
scoring category, which is comparable to, if not more accurate, than typical
human raters. These findings were further strengthened by the observation that
the most accurate CNN models correctly classified some image responses that had
been incorrectly scored by the human raters. As an additional innovation, we
outline a method to select human rated responses for the training sample based
on an application of the expected response function derived from item response
theory. This paper argues that CNN-based automated scoring of image responses
is a highly accurate procedure that could potentially replace the workload and
cost of second human raters for large scale assessments, while improving the
validity and comparability of scoring complex constructed-response items.

Capsule networks, which incorporate the paradigms of connectionism and
symbolism, have brought fresh insights into artificial intelligence. The
capsule, as the building block of capsule networks, is a group of neurons
represented by a vector to encode different features of an entity. The
information is extracted hierarchically through capsule layers via routing
algorithms. Here, we introduce a quantum capsule network (dubbed QCapsNet)
together with an efficient quantum dynamic routing algorithm. To benchmark the
performance of the QCapsNet, we carry out extensive numerical simulations on
the classification of handwritten digits and symmetry-protected topological
phases, and show that the QCapsNet can achieve an enhanced accuracy and
outperform conventional quantum classifiers evidently. We further unpack the
output capsule state and find that a particular subspace may correspond to a
human-understandable feature of the input data, which indicates the potential
explainability of such networks. Our work reveals an intriguing prospect of
quantum capsule networks in quantum machine learning, which may provide a
valuable guide towards explainable quantum artificial intelligence.

Audio-based automatic speech recognition (ASR) degrades significantly in
noisy environments and is particularly vulnerable to interfering speech, as the
model cannot determine which speaker to transcribe. Audio-visual speech
recognition (AVSR) systems improve robustness by complementing the audio stream
with the visual information that is invariant to noise and helps the model
focus on the desired speaker. However, previous AVSR work focused solely on the
supervised learning setup; hence the progress was hindered by the amount of
labeled data available. In this work, we present a self-supervised AVSR
framework built upon Audio-Visual HuBERT (AV-HuBERT), a state-of-the-art
audio-visual speech representation learning model. On the largest available
AVSR benchmark dataset LRS3, our approach outperforms prior state-of-the-art by
~50% (28.0% vs. 14.1%) using less than 10% of labeled data (433hr vs. 30hr) in
the presence of babble noise, while reducing the WER of an audio-based model by
over 75% (25.8% vs. 5.8%) on average.

The Equation of State (EoS) of strongly interacting cold and hot ultra-dense
QCD matter remains a major challenge in the field of nuclear astrophysics. With
the advancements in measurements of neutron star masses, radii, and tidal
deformabilities, from electromagnetic and gravitational wave observations,
neutron stars play an important role in constraining the ultra-dense QCD matter
EoS. In this work, we present a novel method that exploits deep learning
techniques to reconstruct the neutron star EoS from mass-radius (M-R)
observations. We employ neural networks (NNs) to represent the EoS in a
model-independent way, within the range of $\sim$1-7 times the nuclear
saturation density. The unsupervised Automatic Differentiation (AD) framework
is implemented to optimize the EoS, so as to yield through TOV equations, an
M-R curve that best fits the observations. We demonstrate that this method
works by rebuilding the EoS on mock data, i.e., mass-radius pairs derived from
a randomly generated polytropic EoS. The reconstructed EoS fits the mock data
with reasonable accuracy, using just 11 mock M-R pairs observations, close to
the current number of actual observations.

We argue that current IR metrics, modeled on optimizing user experience,
measure too narrow a portion of the IR space. If IR systems are weak, these
metrics undersample or completely filter out the deeper documents that need
improvement. If IR systems are relatively strong, these metrics undersample
deeper relevant documents that could underpin even stronger IR systems, ones
that could present content from tens or hundreds of relevant documents in a
user-digestible hierarchy or text summary. We reanalyze over 70 TREC tracks
from the past 28 years, showing that roughly half undersample top ranked
documents and nearly all undersample tail documents. We show that in the 2020
Deep Learning tracks, neural systems were actually near-optimal at top-ranked
documents, compared to only modest gains over BM25 on tail documents. Our
analysis is based on a simple new systems-oriented metric, 'atomized search
length', which is capable of accurately and evenly measuring all relevant
documents at any depth.

Click-Through Rate (CTR) prediction is a crucial component in the online
advertising industry. In order to produce a personalized CTR prediction, an
industry-level CTR prediction model commonly takes a high-dimensional (e.g.,
100 or 1000 billions of features) sparse vector (that is encoded from query
keywords, user portraits, etc.) as input. As a result, the model requires
Terabyte scale parameters to embed the high-dimensional input. Hierarchical
distributed GPU parameter server has been proposed to enable GPU with limited
memory to train the massive network by leveraging CPU main memory and SSDs as
secondary storage. We identify two major challenges in the existing GPU
training framework for massive-scale ad models and propose a collection of
optimizations to tackle these challenges: (a) the GPU, CPU, SSD rapidly
communicate with each other during the training. The connections between GPUs
and CPUs are non-uniform due to the hardware topology. The data communication
route should be optimized according to the hardware topology; (b) GPUs in
different computing nodes frequently communicates to synchronize parameters. We
are required to optimize the communications so that the distributed system can
become scalable.
  In this paper, we propose a hardware-aware training workflow that couples the
hardware topology into the algorithm design. To reduce the extensive
communication between computing nodes, we introduce a $k$-step model merging
algorithm for the popular Adam optimizer and provide its convergence rate in
non-convex optimization. To the best of our knowledge, this is the first
application of $k$-step adaptive optimization method in industrial-level CTR
model training. The numerical results on real-world data confirm that the
optimized system design considerably reduces the training time of the massive
model, with essentially no loss in accuracy.

Entropy coding is the backbone data compression. Novel machine-learning based
compression methods often use a new entropy coder called Asymmetric Numeral
Systems (ANS) [Duda et al., 2015], which provides very close to optimal
bitrates and simplifies [Townsend et al., 2019] advanced compression techniques
such as bits-back coding. However, researchers with a background in machine
learning often struggle to understand how ANS works, which prevents them from
exploiting its full versatility. This paper is meant as an educational resource
to make ANS more approachable by presenting it from a new perspective of latent
variable models and the so-called bits-back trick. We guide the reader step by
step to a complete implementation of ANS in the Python programming language,
which we then generalize for more advanced use cases. We also present and
empirically evaluate an open-source library of various entropy coders designed
for both research and production use. Related teaching videos and problem sets
are available online.

Effective treatments that prevent or reduce drug relapse vulnerability should
be developed to relieve the high burden of drug addiction to society. This will
only be possible by enhancing the understanding of the molecular mechanisms
underlying the neurobiology of addiction. Recent experimental data have shown
that dendritic spines, small protrusions from the dendrites that receive input
from excitatory neurons, from spiny neurons in the nucleus accumbens exhibit
morphological changes during drug exposure and withdrawal. Moreover, these
changes relate to the characteristic drug-seeking behavior of addiction.
However, due to the complexity of the dendritic spines, we do not yet fully
understand the processes underlying their structural changes in response to
different inputs. We propose that biophysical models can enhance the current
understanding of these processes by incorporating different, and sometimes,
discrepant experimental data to identify the shared underlying mechanisms and
generate experimentally testable hypotheses. This review aims to give an
up-to-date report on biophysical models of dendritic spines, focusing on those
models that describe their shape changes, which are well-known to relate to
learning and memory. Moreover, it examines how these models can enhance our
understanding of the effect of the drugs and the synaptic changes during
disease progression.

Over the year, people have been using deep learning to tackle inversion
problems, and we see the framework has been applied to build relationship
between recording wavefield and velocity (Yang et al., 2016). Here we will
extend the work from 2 perspectives, one is deriving a more appropriate loss
function, as we now, pixel-2-pixel comparison might not be the best choice to
characterize image structure, and we will elaborate on how to construct cost
function to capture high level feature to enhance the model performance.
Another dimension is searching for the more appropriate neural architecture,
which is a subset of an even bigger picture, the automatic machine learning, or
AutoML. There are several famous networks, U-net, ResNet (He et al., 2016) and
DenseNet (Huang et al., 2017), and they achieve phenomenal results for certain
problems, yet it's hard to argue they are the best for inversion problems
without thoroughly searching within certain space. Here we will be showing our
architecture search results for inversion.

Video recordings of speech contain correlated audio and visual information,
providing a strong signal for speech representation learning from the speaker's
lip movements and the produced sound. We introduce Audio-Visual Hidden Unit
BERT (AV-HuBERT), a self-supervised representation learning framework for
audio-visual speech, which masks multi-stream video input and predicts
automatically discovered and iteratively refined multimodal hidden units.
AV-HuBERT learns powerful audio-visual speech representation benefiting both
lip-reading and automatic speech recognition. On the largest public lip-reading
benchmark LRS3 (433 hours), AV-HuBERT achieves 32.5% WER with only 30 hours of
labeled data, outperforming the former state-of-the-art approach (33.6%)
trained with a thousand times more transcribed video data (31K hours). The
lip-reading WER is further reduced to 26.9% when using all 433 hours of labeled
data from LRS3 and combined with self-training. Using our audio-visual
representation on the same benchmark for audio-only speech recognition leads to
a 40% relative WER reduction over the state-of-the-art performance (1.3% vs
2.3%). Our code and models are available at
https://github.com/facebookresearch/av_hubert

Evaluation of the resistance of implemented cryptographic algorithms against
SCA attacks, as well as detecting of SCA leakage sources at an early stage of
the design process, is important for an efficient re-design of the
implementation. Thus, effective SCA methods that do not depend on the key
processed in the cryptographic operations are beneficially and can be a part of
the efficient design methodology for implementing cryptographic approaches. In
this work we compare two different methods that are used to analyse power
traces of elliptic curve point multiplications. The first method the comparison
to the mean is a simple method based on statistical analysis. The second one is
K-means - the mostly used unsupervised machine learning algorithm for data
clustering. The results of our early work showed that the machine learning
algorithm was not superior to the simple approach. In this work we concentrate
on the comparison of the attack results using both analysis methods with the
goal to understand their benefits and drawbacks. Our results show that the
comparison to the mean works properly only if the scalar processed during the
attacked kP execution is balanced, i.e. if the number of '1' in the scalar k is
about as high as the number of '0'. In contrast to this, K-means is effective
also if the scalar is highly unbalanced. It is still effective even if the
scalar k contains only a very small number of '0' bits.

Side Channel Analysis attacks take advantage of the information leaked from
the implementations of cryptographic algorithms. In this paper we describe two
key revealing methods which are based on machine learning algorithms: K-means
and PCA. We performed the attacks against ECDSA implementations without any
prior knowledge about the key and achieved 100% accuracy for an implementation
without any countermeasures against horizontal attacks and 88.7% accuracy for
an implementation with bus address sequencing. In the scenario where the kP
operation inputs are controlled by the attacker (as during signature
verification), we achieved 98.3% accuracy for the implementation with
countermeasures.

Despite well-known limitations of Reynolds-averaged Navier-Stokes (RANS)
simulations, this methodology remains the most widely used tool for predicting
many turbulent flows, due to computational efficiency. Machine learning is a
promising approach to improve the accuracy of RANS simulations. One major area
of improvement is using machine learning models to represent the complex
relationship between the mean flow field gradients and the Reynolds stress
tensor. In the present work, modifications to improve the stability of previous
optimal eddy viscosity approaches for RANS simulations are presented and
evaluated. The optimal eddy viscosity is reformulated with a non-negativity
constraint, which promotes numerical stability. We demonstrate that the new
formulation of the optimal eddy viscosity improves the conditioning of the RANS
equations for a periodic hills test case. To demonstrate the suitability of
this proportional/orthogonal tensor decomposition for use in a physics-informed
data-driven turbulence closure, we use two neural networks (structured on this
specific tensor decomposition which is incorporated as an inductive bias into
the network design) to predict the newly reformulated linear and non-linear
parts of the Reynolds stress tensor. Injecting these network model predictions
for the Reynolds stresses into a RANS simulation improves predictions of the
velocity field, even when compared to a sophisticated (state of the art)
physics-based turbulence closure model. Finally, we apply SHAP (SHapley
Additive exPlanations) values to obtain insights from the learned
representation for the inner workings of the neural network used to predict the
optimal eddy viscosity from the input feature data.

Deep neural networks have proved hugely successful, achieving human-like
performance on a variety of tasks. However, they are also computationally
expensive, which has motivated the development of model compression techniques
which reduce the resource consumption associated with deep learning models.
Nevertheless, recent studies have suggested that model compression can have an
adverse effect on algorithmic fairness, amplifying existing biases in machine
learning models. With this project we aim to extend those studies to the
context of facial expression recognition. To do that, we set up a neural
network classifier to perform facial expression recognition and implement
several model compression techniques on top of it. We then run experiments on
two facial expression datasets, namely the Extended Cohn-Kanade Dataset (CK+DB)
and the Real-World Affective Faces Database (RAF-DB), to examine the individual
and combined effect that compression techniques have on the model size,
accuracy and fairness. Our experimental results show that: (i) Compression and
quantisation achieve significant reduction in model size with minimal impact on
overall accuracy for both CK+DB and RAF-DB; (ii) in terms of model accuracy,
the classifier trained and tested on RAF-DB seems more robust to compression
compared to the CK+ DB; (iii) for RAF-DB, the different compression strategies
do not seem to increase the gap in predictive performance across the sensitive
attributes of gender, race and age which is in contrast with the results on the
CK+DB, where compression seems to amplify existing biases for gender. We
analyse the results and discuss the potential reasons for our findings.

TryOnGAN is a recent virtual try-on approach, which generates highly
realistic images and outperforms most previous approaches. In this article, we
reproduce the TryOnGAN implementation and probe it along diverse angles: impact
of transfer learning, variants of conditioning image generation with poses and
properties of latent space interpolation. Some of these facets have never been
explored in literature earlier. We find that transfer helps training initially
but gains are lost as models train longer and pose conditioning via
concatenation performs better. The latent space self-disentangles the pose and
the style features and enables style transfer across poses. Our code and models
are available in open source.

Protecting a fingerprint database against attackers is very vital in order to
protect against false acceptance rate or false rejection rate. A key property
in distinguishing fingerprint images is by exploiting the characteristics of
these different types of fingerprint images. The aim of this paper is to
perform the classification of fingerprint images using the Ben-ford's law
divergence values and machine learning techniques. The usage of these
Ben-ford's law divergence values as features fed into the machine learning
techniques has proved to be very effective and efficient in the classification
of fingerprint images. The effectiveness of our proposed methodology was
demonstrated on five datasets, achieving very high classification "accuracies"
of 100% for the Decision Tree and CNN. However, the "Naive" Bayes, and Logistic
Regression achieved "accuracies" of 95.95%, and 90.54%, respectively. These
results showed that Ben-ford's law features and machine learning techniques
especially Decision Tree and CNN can be effectively applied for the
classification of fingerprint images.

A common approach to solving prediction tasks on large networks, such as node
classification or link prediction, begin by learning a Euclidean embedding of
the nodes of the network, from which traditional machine learning methods can
then be applied. This includes methods such as DeepWalk and node2vec, which
learn embeddings by optimizing stochastic losses formed over subsamples of the
graph at each iteration of stochastic gradient descent. In this paper, we study
the effects of adding an $\ell_2$ penalty of the embedding vectors to the
training loss of these types of methods. We prove that, under some
exchangeability assumptions on the graph, this asymptotically leads to learning
a graphon with a nuclear-norm-type penalty, and give guarantees for the
asymptotic distribution of the learned embedding vectors. In particular, the
exact form of the penalty depends on the choice of subsampling method used as
part of stochastic gradient descent. We also illustrate empirically that
concatenating node covariates to $\ell_2$ regularized node2vec embeddings leads
to comparable, when not superior, performance to methods which incorporate node
covariates and the network structure in a non-linear manner.

GPUs are widely used to accelerate the training of machine learning
workloads. As modern machine learning models become increasingly larger, they
require a longer time to train, leading to higher GPU energy consumption. This
paper presents GPOEO, an online GPU energy optimization framework for machine
learning training workloads. GPOEO dynamically determines the optimal energy
configuration by employing novel techniques for online measurement,
multi-objective prediction modeling, and search optimization. To characterize
the target workload behavior, GPOEO utilizes GPU performance counters. To
reduce the performance counter profiling overhead, it uses an analytical model
to detect the training iteration change and only collects performance counter
data when an iteration shift is detected. GPOEO employs multi-objective models
based on gradient boosting and a local search algorithm to find a trade-off
between execution time and energy consumption. We evaluate the GPOEO by
applying it to 71 machine learning workloads from two AI benchmark suites
running on an NVIDIA RTX3080Ti GPU. Compared with the NVIDIA default scheduling
strategy, GPOEO delivers a mean energy saving of 16.2% with a modest average
execution time increase of 5.1%.

We propose a new method for reconstructing controllable implicit 3D human
models from sparse multi-view RGB videos. Our method defines the neural scene
representation on the mesh surface points and signed distances from the surface
of a human body mesh. We identify an indistinguishability issue that arises
when a point in 3D space is mapped to its nearest surface point on a mesh for
learning surface-aligned neural scene representation. To address this issue, we
propose projecting a point onto a mesh surface using a barycentric
interpolation with modified vertex normals. Experiments with the ZJU-MoCap and
Human3.6M datasets show that our approach achieves a higher quality in a
novel-view and novel-pose synthesis than existing methods. We also demonstrate
that our method easily supports the control of body shape and clothes. Project
page: https://pfnet-research.github.io/surface-aligned-nerf/.

This paper presents local minimax regret lower bounds for adaptively
controlling linear-quadratic-Gaussian (LQG) systems. We consider smoothly
parametrized instances and provide an understanding of when logarithmic regret
is impossible which is both instance specific and flexible enough to take
problem structure into account. This understanding relies on two key notions:
That of local-uninformativeness; when the optimal policy does not provide
sufficient excitation for identification of the optimal policy, and yields a
degenerate Fisher information matrix; and that of
information-regret-boundedness, when the small eigenvalues of a
policy-dependent information matrix are boundable in terms of the regret of
that policy. Combined with a reduction to Bayesian estimation and application
of Van Trees' inequality, these two conditions are sufficient for proving
regret bounds on order of magnitude $\sqrt{T}$ in the time horizon, $T$. This
method yields lower bounds that exhibit tight dimensional dependencies and
scale naturally with control-theoretic problem constants. For instance, we are
able to prove that systems operating near marginal stability are fundamentally
hard to learn to control. We further show that large classes of systems satisfy
these conditions, among them any state-feedback system with both $A$- and
$B$-matrices unknown. Most importantly, we also establish that a nontrivial
class of partially observable systems, essentially those that are
over-actuated, satisfy these conditions, thus providing a $\sqrt{T}$ lower
bound also valid for partially observable systems. Finally, we turn to two
simple examples which demonstrate that our lower bound captures classical
control-theoretic intuition: our lower bounds diverge for systems operating
near marginal stability or with large filter gain -- these can be arbitrarily
hard to (learn to) control.

The Covid-19 pandemic has been one of the most devastating events in recent
history, claiming the lives of more than 5 million people worldwide. Even with
the worldwide distribution of vaccines, there is an apparent need for
affordable, reliable, and accessible screening techniques to serve parts of the
World that do not have access to Western medicine. Artificial Intelligence can
provide a solution utilizing cough sounds as a primary screening mode for
COVID-19 diagnosis. This paper presents multiple models that have achieved
relatively respectable performance on the largest evaluation dataset currently
presented in academic literature. Through investigation of a self-supervised
learning model (Area under the ROC curve, AUC = 0.807) and a convolutional
nerual network (CNN) model (AUC = 0.802), we observe the possibility of model
bias with limited datasets. Moreover, we observe that performance increases
with training data size, showing the need for the worldwide collection of data
to help combat the Covid-19 pandemic with non-traditional means.

What can we hope to learn about brains from the free energy principle? In
adopting the "primordial soup" physical model, Bruineberg et al. perpetuate the
unsupported notion that the free energy principle has a meaningful
physical--and neuronal--interpretation. We examine how minimization of free
energy arises in physical contexts, and what this can and cannot tell us about
brains.

In model-free deep reinforcement learning (RL) algorithms, using noisy value
estimates to supervise policy evaluation and optimization is detrimental to the
sample efficiency. As this noise is heteroscedastic, its effects can be
mitigated using uncertainty-based weights in the optimization process. Previous
methods rely on sampled ensembles, which do not capture all aspects of
uncertainty. We provide a systematic analysis of the sources of uncertainty in
the noisy supervision that occurs in RL, and introduce inverse-variance RL, a
Bayesian framework which combines probabilistic ensembles and Batch Inverse
Variance weighting. We propose a method whereby two complementary uncertainty
estimation methods account for both the Q-value and the environment
stochasticity to better mitigate the negative impacts of noisy supervision. Our
results show significant improvement in terms of sample efficiency on discrete
and continuous control tasks.

High-significance measurements of the monopole thermal Sunyaev-Zel'dovich CMB
spectral distortions have the potential to tightly constrain poorly understood
baryonic feedback processes. The sky-averaged Compton-y distortion and its
relativistic correction are measures of the total thermal energy in electrons
in the observable universe and their mean temperature. We use the CAMELS suite
of hydrodynamic simulations to explore possible constraints on parameters
describing the subgrid implementation of feedback from active galactic nuclei
and supernovae, assuming a PIXIE-like measurement. The small 25 Mpc/h CAMELS
boxes present challenges due to the significant cosmic variance. We utilize
machine learning to construct interpolators through the noisy simulation data.
Using the halo model, we translate the simulation halo mass functions into
correction factors to reduce cosmic variance where required. Our results depend
on the subgrid model. In the case of IllustrisTNG, we find that the
best-determined parameter combination can be measured to ~2% and corresponds to
a product of AGN and SN feedback. In the case of SIMBA, the tightest constraint
is ~0.2% on a ratio between AGN and SN feedback. A second orthogonal parameter
combination can be measured to ~8%. Our results demonstrate the significant
constraining power a measurement of the late-time spectral distortion monopoles
would have for baryonic feedback models.

We perform a systematic analysis of the quality of fit of the stochastic
block model (SBM) for 275 empirical networks spanning a wide range of domains
and orders of size magnitude. We employ posterior predictive model checking as
a criterion to assess the quality of fit, which involves comparing networks
generated by the inferred model with the empirical network, according to a set
of network descriptors. We observe that the SBM is capable of providing an
accurate description for the majority of networks considered, but falls short
of saturating all modeling requirements. In particular, networks possessing a
large diameter and slow-mixing random walks tend to be badly described by the
SBM. However, contrary to what is often assumed, networks with a high abundance
of triangles can be well described by the SBM in many cases. We demonstrate
that simple network descriptors can be used to evaluate whether or not the SBM
can provide a sufficiently accurate representation, potentially pointing to
possible model extensions that can systematically improve the expressiveness of
this class of models.

Stochastic majorization-minimization (SMM) is a class of stochastic
optimization algorithms, which consists of sampling i.i.d. data points from a
fixed data distribution and minimizing recursively defined averaged surrogates
of an objective function. The surrogates are required to be strongly convex,
and convergence rate analysis was missing, in contrast to the extensive
literature on stochastic gradient descent (SGD) methods. In this paper, we
extend SMM where surrogates are allowed to be weakly convex or block
multi-convex, and the averaged surrogates are approximately minimized with
proximal regularization or block-minimized within diminishing radii,
respectively. Our general framework gives wider applicability to SMM including
online CANDECOMP/PARAFAC (CP) dictionary learning and yields greater
computational efficiency especially when the problem dimension is large. We
show that the first-order optimality gap of the proposed algorithm decays at
the rate $O((\log n)^{1+\eps}/n^{1/2})$ for the empirical loss and $O((\log
n)^{1+\eps}/n^{1/4})$ for the expected loss, where $n$ denotes the number of
data samples processed. Under some additional assumption, the latter
convergence rate can be improved to $O((\log n)^{1+\eps}/n^{1/2})$. Our
analysis shows that SMM could be faster than SGD in optimizing for the
empirical loss and could match the same optimal rate as SGD for the expected
loss. As a corollary, our results provide first convergence rate bounds for
various online matrix and tensor decomposition algorithms under a general
Markovian data setting.

The complexity of browsers has steadily increased over the years, driven by
the continuous introduction and update of Web platform components, such as
novel Web APIs and security mechanisms. Their specifications are manually
reviewed by experts to identify potential security issues. However, this
process has proved to be error-prone due to the extensiveness of modern browser
specifications and the interplay between new and existing Web platform
components. To tackle this problem, we developed WebSpec, the first formal
security framework for the analysis of browser security mechanisms, which
enables both the automatic discovery of logical flaws and the development of
machine-checked security proofs. WebSpec, in particular, includes a
comprehensive semantic model of the browser in the Coq proof assistant, a
formalization in this model of ten Web security invariants, and a toolchain
turning the Coq model and the Web invariants into SMT-lib formulas to enable
model checking with the Z3 theorem prover. If a violation is found, the
toolchain automatically generates executable tests corresponding to the
discovered attack trace, which is validated across major browsers. We showcase
the effectiveness of WebSpec by discovering two new logical flaws caused by the
interaction of different browser mechanisms and by identifying three previously
discovered logical flaws in the current Web platform, as well as five in old
versions. Finally, we show how WebSpec can aid the verification of our proposed
changes to amend the reported inconsistencies affecting the current Web
platform.

Biomedical research is growing at such an exponential pace that scientists,
researchers, and practitioners are no more able to cope with the amount of
published literature in the domain. The knowledge presented in the literature
needs to be systematized in such a way that claims and hypotheses can be easily
found, accessed, and validated. Knowledge graphs can provide such a framework
for semantic knowledge representation from literature. However, in order to
build a knowledge graph, it is necessary to extract knowledge as relationships
between biomedical entities and normalize both entities and relationship types.
In this paper, we present and compare few rule-based and machine learning-based
(Naive Bayes, Random Forests as examples of traditional machine learning
methods and DistilBERT, PubMedBERT, T5 and SciFive-based models as examples of
modern deep learning transformers) methods for scalable relationship extraction
from biomedical literature, and for the integration into the knowledge graphs.
We examine how resilient are these various methods to unbalanced and fairly
small datasets. Our experiments show that transformer-based models handle well
both small (due to pre-training on a large dataset) and unbalanced datasets.
The best performing model was the PubMedBERT-based model fine-tuned on balanced
data, with a reported F1-score of 0.92. DistilBERT-based model followed with
F1-score of 0.89, performing faster and with lower resource requirements.
BERT-based models performed better then T5-based generative models.

On the long-established classification problems in general relativity we take
a novel perspective by adopting fruitful techniques from machine learning and
modern data-science. In particular, we model Petrov's classification of
spacetimes, and show that a feed-forward neural network can achieve high degree
of success. We also show how data visualization techniques with dimensionality
reduction can help analyze the underlying patterns in the structure of the
different types of spacetimes.

We report the detection of the kinetic Sunyaev-Zel'dovich (kSZ) effect in
galaxy clusters with a 4.9 sigma significance using the latest 217 GHz Planck
map from data release 4. For the detection, we stacked the Planck map at the
positions of 30,431 galaxy clusters from the Wen-Han-Liu (WHL) catalog. To
align the sign of the kSZ signals, the line-of-sight velocities of galaxy
clusters were estimated with a machine-learning approach, in which the relation
between the galaxy distribution around a cluster and its line-of-sight velocity
was trained through a convolutional neural network. To train our network, we
used the simulated galaxies and galaxy clusters in the Magneticum cosmological
hydrodynamic simulations. The trained model was applied to the large-scale
distribution of the Sloan Digital Sky Survey galaxies to derive the
line-of-sight velocities of the WHL galaxy clusters. Assuming a standard
beta-model for the intracluster medium, we obtained the gas mass fraction in
R500 to be fgas,500 = 0.09 +- 0.02 within the galaxy clusters with the average
mass of M500 ~ 1.0 x 10^14 Msun/h.

The study of time series has motivated many researchers, particularly on the
area of multivariate-analysis. The study of co-movements and dependency between
random variables leads us to develop metrics to describe existing connection
between assets. The most commonly used are correlation and causality. Despite
the growing literature, some connections remained still undetected. The
objective of this paper is to propose a new representation learning algorithm
capable to integrate synchronous and asynchronous relationships.

The segmentation of organs at risk (OAR) is a required precondition for the
cancer treatment with image guided radiation therapy. The automation of the
segmentation task is therefore of high clinical relevance. Deep Learning (DL)
based medical image segmentation is currently the most successful approach, but
suffers from the over-presence of the background class and the anatomically
given organ size difference, which is most severe in the head and neck (HAN)
area. To tackle the HAN area specific class imbalance problem we first optimize
the patch-size of the currently best performing general purpose segmentation
framework, the nnU-Net, based on the introduced class imbalance measurement,
and second, introduce the class adaptive Dice loss to further compensate for
the highly imbalanced setting. Both the patch-size and the loss function are
parameters with direct influence on the class imbalance and their optimization
leads to a 3\% increase of the Dice score and 22% reduction of the 95%
Hausdorff distance compared to the baseline, finally reaching $0.8\pm0.15$ and
$3.17\pm1.7$ mm for the segmentation of seven HAN organs using a single and
simple neural network. The patch-size optimization and the class adaptive Dice
loss are both simply integrable in current DL based segmentation approaches and
allow to increase the performance for class imbalanced segmentation tasks.

Recent years have witnessed growing interests in online incremental learning.
However, there are three major challenges in this area. The first major
difficulty is concept drift, that is, the probability distribution in the
streaming data would change as the data arrives. The second major difficulty is
catastrophic forgetting, that is, forgetting what we have learned before when
learning new knowledge. The last one we often ignore is the learning of the
latent representation. Only good latent representation can improve the
prediction accuracy of the model. Our research builds on this observation and
attempts to overcome these difficulties. To this end, we propose an Adaptive
Online Incremental Learning for evolving data streams (AOIL). We use
auto-encoder with the memory module, on the one hand, we obtained the latent
features of the input, on the other hand, according to the reconstruction loss
of the auto-encoder with memory module, we could successfully detect the
existence of concept drift and trigger the update mechanism, adjust the model
parameters in time. In addition, we divide features, which are derived from the
activation of the hidden layers, into two parts, which are used to extract the
common and private features respectively. By means of this approach, the model
could learn the private features of the new coming instances, but do not forget
what we have learned in the past (shared features), which reduces the
occurrence of catastrophic forgetting. At the same time, to get the fusion
feature vector we use the self-attention mechanism to effectively fuse the
extracted features, which further improved the latent representation learning.

Recently proposed spintronic devices use magnetic skyrmions as bits of
information. The reliable detection of those chiral magnetic objects is an
indispensable requirement. Yet, the high mobility of magnetic skyrmions leads
to their stochastic motion at finite temperatures, which hinders the precise
measurement of the topological numbers. Here, we demonstrate the successful
training of artificial neural networks to reconstruct the skyrmion number in
confined geometries from time-integrated, dimensionally reduced data. Our
results prove the possibility to recover the topological charge from a
time-averaged measurement and hence smeared dynamic skyrmion ensemble, which is
of immediate relevance to the interpretation of experimental results,
skyrmion-based computing, and memory concepts

In the multi-armed bandit framework, there are two formulations that are
commonly employed to handle time-varying reward distributions: adversarial
bandit and nonstationary bandit. Although their oracles, algorithms, and regret
analysis differ significantly, we provide a unified formulation in this paper
that smoothly bridges the two as special cases. The formulation uses an oracle
that takes the best-fixed arm within time windows. Depending on the window
size, it turns into the oracle in hindsight in the adversarial bandit and
dynamic oracle in the nonstationary bandit. We provide algorithms that attain
the optimal regret with the matching lower bound.

Advances in deep learning have enabled a wide range of promising
applications. However, these systems are vulnerable to Adversarial Machine
Learning (AML) attacks; adversarially crafted perturbations to their inputs
could cause them to misclassify. Several state-of-the-art adversarial attacks
have demonstrated that they can reliably fool classifiers making these attacks
a significant threat. Adversarial attack generation algorithms focus primarily
on creating successful examples while controlling the noise magnitude and
distribution to make detection more difficult. The underlying assumption of
these attacks is that the adversarial noise is generated offline, making their
execution time a secondary consideration. However, recently, just-in-time
adversarial attacks where an attacker opportunistically generates adversarial
examples on the fly have been shown to be possible. This paper introduces a new
problem: how do we generate adversarial noise under real-time constraints to
support such real-time adversarial attacks? Understanding this problem improves
our understanding of the threat these attacks pose to real-time systems and
provides security evaluation benchmarks for future defenses. Therefore, we
first conduct a run-time analysis of adversarial generation algorithms.
Universal attacks produce a general attack offline, with no online overhead,
and can be applied to any input; however, their success rate is limited because
of their generality. In contrast, online algorithms, which work on a specific
input, are computationally expensive, making them inappropriate for operation
under time constraints. Thus, we propose ROOM, a novel Real-time Online-Offline
attack construction Model where an offline component serves to warm up the
online algorithm, making it possible to generate highly successful attacks
under time constraints.

Sign Language is the dominant form of communication language used in the deaf
and hearing-impaired community. To make an easy and mutual communication
between the hearing-impaired and the hearing communities, building a robust
system capable of translating the spoken language into sign language and vice
versa is fundamental. To this end, sign language recognition and production are
two necessary parts for making such a two-way system. Sign language recognition
and production need to cope with some critical challenges. In this survey, we
review recent advances in Sign Language Production (SLP) and related areas
using deep learning. To have more realistic perspectives to sign language, we
present an introduction to the Deaf culture, Deaf centers, psychological
perspective of sign language, the main differences between spoken language and
sign language. Furthermore, we present the fundamental components of a
bi-directional sign language translation system, discussing the main challenges
in this area. Also, the backbone architectures and methods in SLP are briefly
introduced and the proposed taxonomy on SLP is presented. Finally, a general
framework for SLP and performance evaluation, and also a discussion on the
recent developments, advantages, and limitations in SLP, commenting on possible
lines for future research are presented.

Social bots have become an important component of online social media.
Deceptive bots, in particular, can manipulate online discussions of important
issues ranging from elections to public health, threatening the constructive
exchange of information. Their ubiquity makes them an interesting research
subject and requires researchers to properly handle them when conducting
studies using social media data. Therefore, it is important for researchers to
gain access to bot detection tools that are reliable and easy to use. This
paper aims to provide an introductory tutorial of Botometer, a public tool for
bot detection on Twitter, for readers who are new to this topic and may not be
familiar with programming and machine learning. We introduce how Botometer
works, the different ways users can access it, and present a case study as a
demonstration. Readers can use the case study code as a template for their own
research. We also discuss recommended practice for using Botometer.

Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular
Clusters (GCs) around galaxies are known to be the tracers of the merger events
that have been forming these galaxies. Therefore, identifying such systems
allows to study galaxies mass assembly, formation and evolution. However, in
the lack of spectroscopic information detecting UCDs/GCs using imaging data is
very uncertain. Here, we aim to train a machine learning model to separate
these objects from the foreground stars and background galaxies using the
multi-wavelength imaging data of the Fornax galaxy cluster in 6 filters, namely
u, g, r, i, J and Ks. The classes of objects are highly imbalanced which is
problematic for many automatic classification techniques. Hence, we employ
Synthetic Minority Over-sampling to handle the imbalance of the training data.
Then, we compare two classifiers, namely Localized Generalized Matrix Learning
Vector Quantization (LGMLVQ) and Random Forest (RF). Both methods are able to
identify UCDs/GCs with a precision and a recall of >93 percent and provide
relevances that reflect the importance of each feature dimension %(colors and
angular sizes) for the classification. Both methods detect angular sizes as
important markers for this classification problem. While it is astronomical
expectation that color indices of u-i and i-Ks are the most important colors,
our analysis shows that colors such as g-r are more informative, potentially
because of higher signal-to-noise ratio. Besides the excellent performance the
LGMLVQ method allows further interpretability by providing the feature
importance for each individual class, class-wise representative samples and the
possibility for non-linear visualization of the data as demonstrated in this
contribution. We conclude that employing machine learning techniques to
identify UCDs/GCs can lead to promising results.

Most previous learning-based graph matching algorithms solve the
\textit{quadratic assignment problem} (QAP) by dropping one or more of the
matching constraints and adopting a relaxed assignment solver to obtain
sub-optimal correspondences. Such relaxation may actually weaken the original
graph matching problem, and in turn hurt the matching performance. In this
paper we propose a deep learning-based graph matching framework that works for
the original QAP without compromising on the matching constraints. In
particular, we design an affinity-assignment prediction network to jointly
learn the pairwise affinity and estimate the node assignments, and we then
develop a differentiable solver inspired by the probabilistic perspective of
the pairwise affinities. Aiming to obtain better matching results, the
probabilistic solver refines the estimated assignments in an iterative manner
to impose both discrete and one-to-one matching constraints. The proposed
method is evaluated on three popularly tested benchmarks (Pascal VOC, Willow
Object and SPair-71k), and it outperforms all previous state-of-the-arts on all

Federated Learning (FL) trains a machine learning model on distributed
clients without exposing individual data. Unlike centralized training that is
usually based on carefully-organized data, FL deals with on-device data that
are often unfiltered and imbalanced. As a result, conventional FL training
protocol that treats all data equally leads to a waste of local computational
resources and slows down the global learning process. To this end, we propose
FedBalancer, a systematic FL framework that actively selects clients' training
samples. Our sample selection strategy prioritizes more "informative" data
while respecting privacy and computational capabilities of clients. To better
utilize the sample selection to speed up global training, we further introduce
an adaptive deadline control scheme that predicts the optimal deadline for each
round with varying client training data. Compared with existing FL algorithms
with deadline configuration methods, our evaluation on five datasets from three
different domains shows that FedBalancer improves the time-to-accuracy
performance by 1.20~4.48x while improving the model accuracy by 1.1~5.0%. We
also show that FedBalancer is readily applicable to other FL approaches by
demonstrating that FedBalancer improves the convergence speed and accuracy when
operating jointly with three different FL algorithms.

Annotating musical beats is a very long in tedious process. In order to
combat this problem, we present a new self-supervised learning pretext task for
beat tracking and downbeat estimation. This task makes use of Spleeter, an
audio source separation model, to separate a song's drums from the rest of its
signal. The first set of signals are used as positives, and by extension
negatives, for contrastive learning pre-training. The drum-less signals, on the
other hand, are used as anchors. When pre-training a fully-convolutional and
recurrent model using this pretext task, an onset function is learned. In some
cases, this function was found to be mapped to periodic elements in a song. We
found that pre-trained models outperformed randomly initialized models when a
beat tracking training set was extremely small (less than 10 examples). When
that was not the case, pre-training led to a learning speed-up that caused the
model to overfit to the training set. More generally, this work defines new
perspectives in the realm of musical self-supervised learning. It is notably
one of the first works to use audio source separation as a fundamental
component of self-supervision.

Differentiable solvers for the linear assignment problem (LAP) have attracted
much research attention in recent years, which are usually embedded into
learning frameworks as components. However, previous algorithms, with or
without learning strategies, usually suffer from the degradation of the
optimality with the increment of the problem size. In this paper, we propose a
learnable linear assignment solver based on deep graph networks. Specifically,
we first transform the cost matrix to a bipartite graph and convert the
assignment task to the problem of selecting reliable edges from the constructed
graph. Subsequently, a deep graph network is developed to aggregate and update
the features of nodes and edges. Finally, the network predicts a label for each
edge that indicates the assignment relationship. The experimental results on a
synthetic dataset reveal that our method outperforms state-of-the-art baselines
and achieves consistently high accuracy with the increment of the problem size.
Furthermore, we also embed the proposed solver, in comparison with
state-of-the-art baseline solvers, into a popular multi-object tracking (MOT)
framework to train the tracker in an end-to-end manner. The experimental
results on MOT benchmarks illustrate that the proposed LAP solver improves the
tracker by the largest margin.

In recent years, significant progress has been achieved in biphasic face
photo-sketch synthesis with the development of Generative Adversarial Network
(GAN). Biphasic face photo-sketch synthesis could be applied in wide-ranging
fields such as digital entertainment and law enforcement. However, generating
realistic photos and distinct sketches suffers from great challenges due to the
low quality of sketches and complex photo variations in the real scenes. To
this end, we propose a novel Semantic-Driven Generative Adversarial Network to
address the above issues, cooperating with the Graph Representation Learning.
Specifically, we inject class-wise semantic layouts into the generator to
provide style-based spatial supervision for synthesized face photos and
sketches. In addition, to improve the fidelity of the generated results, we
leverage the semantic layouts to construct two types of Representational Graphs
which indicate the intra-class semantic features and inter-class structural
features of the synthesized images. Furthermore, we design two types of
constraints based on the proposed Representational Graphs which facilitate the
preservation of the details in generated face photos and sketches. Moreover, to
further enhance the perceptual quality of synthesized images, we propose a
novel biphasic training strategy which is dedicated to refine the generated
results through Iterative Cycle Training. Extensive experiments are conducted
on CUFS and CUFSF datasets to demonstrate the prominent ability of our proposed
method which achieves the state-of-the-art performance.

The emergence of new nanoscale technologies has imposed significant
challenges to designing reliable electronic systems in radiation environments.
A few types of radiation like Total Ionizing Dose (TID) effects often cause
permanent damages on such nanoscale electronic devices, and current
state-of-the-art technologies to tackle TID make use of expensive
radiation-hardened devices. This paper focuses on a novel and different
approach: using machine learning algorithms on consumer electronic level Field
Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to
replace before they stop working. This condition has a research challenge to
anticipate when the board results in a total failure due to TID effects. We
observed internal measurements of the FPGA boards under gamma radiation and
used three different anomaly detection machine learning (ML) algorithms to
detect anomalies in the sensor measurements in a gamma-radiated environment.
The statistical results show a highly significant relationship between the
gamma radiation exposure levels and the board measurements. Moreover, our
anomaly detection results have shown that a One-Class Support Vector Machine
with Radial Basis Function Kernel has an average Recall score of 0.95. Also,
all anomalies can be detected before the boards stop working.

Even though rate-distortion optimization is a crucial part of traditional
image and video compression, not many approaches exist which transfer this
concept to end-to-end-trained image compression. Most frameworks contain static
compression and decompression models which are fixed after training, so
efficient rate-distortion optimization is not possible. In a previous work, we
proposed RDONet, which enables an RDO approach comparable to adaptive block
partitioning in HEVC. In this paper, we enhance the training by introducing
low-complexity estimations of the RDO result into the training. Additionally,
we propose fast and very fast RDO inference modes. With our novel training
method, we achieve average rate savings of 19.6% in MS-SSIM over the previous
RDONet model, which equals rate savings of 27.3% over a comparable conventional
deep image coder.

This paper presents how learning experience influences students' capability
to learn and their motivation for learning. Although each student is different,
standard instruction methods do not adapt to individuals. Adaptive learning
reverses this practice and attempts to improve the student experience. While
adaptive learning is well-established in programming, it is rarely used in
cybersecurity education. This paper is one of the first works investigating
adaptive learning in security training. First, we analyze the performance of 95
students in 12 training sessions to understand the limitations of the current
training practice. Less than half of the students completed the training
without displaying a solution, and only in two sessions, all students completed
all phases. Then, we simulate how students would proceed in one of the past
training sessions if it would offer more paths of various difficulty. Based on
this simulation, we propose a novel tutor model for adaptive training, which
considers students' proficiency before and during an ongoing training session.
The proficiency is assessed using a pre-training questionnaire and various
in-training metrics. Finally, we conduct a study with 24 students and new
training using the proposed tutor model and adaptive training format. The
results show that the adaptive training does not overwhelm students as the
original static training. Adaptive training enables students to enter several
alternative training phases with lower difficulty than the original training.
The proposed format is not restricted to a particular training. Therefore, it
can be applied to practicing any security topic or even in related fields, such
as networking or operating systems. Our study indicates that adaptive learning
is a promising approach for improving the student experience in security
education. We also highlight implications for educational practice.

This paper presents an inexpensive, high-precision, but at the same time,
easy-to-maintain PIEEG board to convert a RaspberryPI to a Brain-computer
interface. This shield allows measuring and processing eight real-time EEG
(Electroencephalography) signals. We used the most popular programming
languages - C, C++ and Python to read the signals, recorded by the device . The
process of reading EEG signals was demonstrated as completely and clearly as
possible. This device can be easily used for machine learning enthusiasts to
create projects for controlling robots and mechanical limbs using the power of
thought. We will post use cases on GitHub
(https://github.com/Ildaron/EEGwithRaspberryPI) for controlling a robotic
machine, unmanned aerial vehicle, and more just using the power of thought.

Learned Indexes are a novel approach to search in a sorted table. A model is
used to predict an interval in which to search into and a Binary Search routine
is used to finalize the search. They are quite effective. For the final stage,
usually, the lower_bound routine of the Standard C++ library is used, although
this is more of a natural choice rather than a requirement. However, recent
studies, that do not use Machine Learning predictions, indicate that other
implementations of Binary Search or variants, namely k-ary Search, are better
suited to take advantage of the features offered by modern computer
architectures. With the use of the Searching on Sorted Sets SOSD Learned
Indexing benchmarking software, we investigate how to choose a Search routine
for the final stage of searching in a Learned Index. Our results provide
indications that better choices than the lower_bound routine can be made. We
also highlight how such a choice may be dependent on the computer architecture
that is to be used. Overall, our findings provide new and much-needed
guidelines for the selection of the Search routine within the Learned Indexing

Recent years have seen the successful application of large pre-trained models
to code representation learning, resulting in substantial improvements on many
code-related downstream tasks. But there are issues surrounding their
application to SE tasks. First, the majority of the pre-trained models focus on
pre-training only the encoder of the Transformer. For generation tasks that are
addressed using models with the encoder-decoder architecture, however, there is
no reason why the decoder should be left out during pre-training. Second, many
existing pre-trained models, including state-of-the-art models such as
T5-learning, simply reuse the pre-training tasks designed for natural
languages. Moreover, to learn the natural language description of source code
needed eventually for code-related tasks such as code summarization, existing
pre-training tasks require a bilingual corpus composed of source code and the
associated natural language description, which severely limits the amount of
data for pre-training. To this end, we propose SPT-Code, a sequence-to-sequence
pre-trained model for source code. In order to pre-train SPT-Code in a
sequence-to-sequence manner and address the aforementioned weaknesses
associated with existing pre-training tasks, we introduce three pre-training
tasks that are specifically designed to enable SPT-Code to learn knowledge of
source code, the corresponding code structure, as well as a natural language
description of the code without relying on any bilingual corpus, and eventually
exploit these three sources of information when it is applied to downstream
tasks. Experimental results demonstrate that SPT-Code achieves state-of-the-art
performance on five code-related downstream tasks after fine-tuning.

Recent advances in counter-adversarial systems have garnered significant
research attention to inverse filtering from a Bayesian perspective. For
example, interest in estimating the adversary's Kalman filter tracked estimate
with the purpose of predicting the adversary's future steps has led to recent
formulations of inverse Kalman filter (I-KF). In this context of inverse
filtering, we address the key challenges of non-linear process dynamics and
unknown input to the forward filter by proposing an inverse extended Kalman
filter (I-EKF). The purpose of this paper and the companion paper (Part II) is
to develop the theory of I-EKF in detail. In this paper, we assume perfect
system model information and derive I-EKF with and without an unknown input
when both forward and inverse state-space models are non-linear. In the
process, I-KF-with-unknown-input is also obtained. We then provide theoretical
stability guarantees using both bounded non-linearity and unknown matrix
approaches. Numerical experiments validate our methods for various proposed
inverse filters using the recursive Cram\'{e}r-Rao lower bound as a benchmark.
In the companion paper (Part II), we further generalize these formulations to
highly non-linear models and propose reproducing kernel Hilbert space-based EKF
to handle incomplete system model information.

Online learning to rank (OLTR) aims to learn a ranker directly from implicit
feedback derived from users' interactions, such as clicks. Clicks however are a
biased signal: specifically, top-ranked documents are likely to attract more
clicks than documents down the ranking (position bias). In this paper, we
propose a novel learning algorithm for OLTR that uses reinforcement learning to
optimize rankers: Reinforcement Online Learning to Rank (ROLTR). In ROLTR, the
gradients of the ranker are estimated based on the rewards assigned to clicked
and unclicked documents. In order to de-bias the users' position bias contained
in the reward signals, we introduce unbiased reward shaping functions that
exploit inverse propensity scoring for clicked and unclicked documents. The
fact that our method can also model unclicked documents provides a further
advantage in that less users interactions are required to effectively train a
ranker, thus providing gains in efficiency. Empirical evaluation on standard
OLTR datasets shows that ROLTR achieves state-of-the-art performance, and
provides significantly better user experience than other OLTR approaches. To
facilitate the reproducibility of our experiments, we make all experiment code
available at https://github.com/ielab/OLTR.

Estimating the effects of interventions on patient outcome is one of the key
aspects of personalized medicine. Their inference is often challenged by the
fact that the training data comprises only the outcome for the administered
treatment, and not for alternative treatments (the so-called counterfactual
outcomes). Several methods were suggested for this scenario based on
observational data, i.e.~data where the intervention was not applied randomly,
for both continuous and binary outcome variables. However, patient outcome is
often recorded in terms of time-to-event data, comprising right-censored event
times if an event does not occur within the observation period. Albeit their
enormous importance, time-to-event data is rarely used for treatment
  We suggest an approach named BITES (Balanced Individual Treatment Effect for
Survival data), which combines a treatment-specific semi-parametric Cox loss
with a treatment-balanced deep neural network; i.e.~we regularize differences
between treated and non-treated patients using Integral Probability Metrics
(IPM). We show in simulation studies that this approach outperforms the state
of the art. Further, we demonstrate in an application to a cohort of breast
cancer patients that hormone treatment can be optimized based on six routine
parameters. We successfully validated this finding in an independent cohort.
BITES is provided as an easy-to-use python implementation.

Learning to solve combinatorial optimization problems, such as the vehicle
routing problem, offers great computational advantages over classical
operations research solvers and heuristics. The recently developed deep
reinforcement learning approaches either improve an initially given solution
iteratively or sequentially construct a set of individual tours. However, most
of the existing learning-based approaches are not able to work for a fixed
number of vehicles and thus bypass the complex assignment problem of the
customers onto an apriori given number of available vehicles. On the other
hand, this makes them less suitable for real applications, as many logistic
service providers rely on solutions provided for a specific bounded fleet size
and cannot accommodate short term changes to the number of vehicles. In
contrast we propose a powerful supervised deep learning framework that
constructs a complete tour plan from scratch while respecting an apriori fixed
number of available vehicles. In combination with an efficient post-processing
scheme, our supervised approach is not only much faster and easier to train but
also achieves competitive results that incorporate the practical aspect of
vehicle costs. In thorough controlled experiments we compare our method to
multiple state-of-the-art approaches where we demonstrate stable performance,
while utilizing less vehicles and shed some light on existent inconsistencies
in the experimentation protocols of the related work.

Formant tracking is investigated in this study by using trackers based on
dynamic programming (DP) and deep neural nets (DNNs). Using the DP approach,
six formant estimation methods were first compared. The six methods include
linear prediction (LP) algorithms, weighted LP algorithms and the recently
developed quasi-closed phase forward-backward (QCP-FB) method. QCP-FB gave the
best performance in the comparison. Therefore, a novel formant tracking
approach, which combines benefits of deep learning and signal processing based
on QCP-FB, was proposed. In this approach, the formants predicted by a
DNN-based tracker from a speech frame are refined using the peaks of the
all-pole spectrum computed by QCP-FB from the same frame. Results show that the
proposed DNN-based tracker performed better both in detection rate and
estimation error for the lowest three formants compared to reference formant
trackers. Compared to the popular Wavesurfer, for example, the proposed tracker
gave a reduction of 29%, 48% and 35% in the estimation error for the lowest
three formants, respectively.

Financial forecasting has been an important and active area of machine
learning research because of the challenges it presents and the potential
rewards that even minor improvements in prediction accuracy or forecasting may
entail. Traditionally, financial forecasting has heavily relied on quantitative
indicators and metrics derived from structured financial statements. Earnings
conference call data, including text and audio, is an important source of
unstructured data that has been used for various prediction tasks using deep
earning and related approaches. However, current deep learning-based methods
are limited in the way that they deal with numeric data; numbers are typically
treated as plain-text tokens without taking advantage of their underlying
numeric structure. This paper describes a numeric-oriented hierarchical
transformer model to predict stock returns, and financial risk using
multi-modal aligned earnings calls data by taking advantage of the different
categories of numbers (monetary, temporal, percentages etc.) and their
magnitude. We present the results of a comprehensive evaluation of NumHTML
against several state-of-the-art baselines using a real-world publicly
available dataset. The results indicate that NumHTML significantly outperforms
the current state-of-the-art across a variety of evaluation metrics and that it
has the potential to offer significant financial gains in a practical trading
context.

The adoption of machine learning techniques in next-generation networks has
increasingly attracted the attention of the research community. This is to
provide adaptive learning and decision-making approaches to meet the
requirements of different verticals, and to guarantee the appropriate
performance requirements in complex mobility scenarios. In this perspective,
the characterization of mobile service usage represents a funda-mental step. In
this vein, this paper highlights the new features and capabilities offered by
the "Network Slice Planner"(NSP) in its second version [12]. It also proposes a
method combining both supervised and unsupervised learning techniques to
analyze the behavior of a mass of mobile users in terms of service consumption.
We exploit the data provided by the NSP v2 to conduct our analysis.
Furthermore, we provide an evaluation of both the accuracy of the predictor and
the performance of the underlying MEC infrastructure.

Uncertainty quantification (UQ) in computational chemistry (CC) is still in
its infancy. Very few CC methods are designed to provide a confidence level on
their predictions, and most users still rely improperly on the mean absolute
error as an accuracy metric. The development of reliable uncertainty
quantification methods is essential, notably for computational chemistry to be
used confidently in industrial processes. A review of the CC-UQ literature
shows that there is no common standard procedure to report nor validate
prediction uncertainty. I consider here analysis tools using concepts
(calibration and sharpness) developed in meteorology and machine learning for
the validation of probabilistic forecasters. These tools are adapted to CC-UQ
and applied to datasets of prediction uncertainties provided by composite
methods, Bayesian Ensembles methods, machine learning and a posteriori
statistical methods.

Depth estimation is solved as a regression or classification problem in
existing learning-based multi-view stereo methods. Although these two
representations have recently demonstrated their excellent performance, they
still have apparent shortcomings, e.g., regression methods tend to overfit due
to the indirect learning cost volume, and classification methods cannot
directly infer the exact depth due to its discrete prediction. In this paper,
we propose a novel representation, termed Unification, to unify the advantages
of regression and classification. It can directly constrain the cost volume
like classification methods, but also realize the sub-pixel depth prediction
like regression methods. To excavate the potential of unification, we design a
new loss function named Unified Focal Loss, which is more uniform and
reasonable to combat the challenge of sample imbalance. Combining these two
unburdened modules, we present a coarse-to-fine framework, that we call
UniMVSNet. The results of ranking first on both DTU and Tanks and Temples
benchmarks verify that our model not only performs the best but also has the
best generalization ability.

In this work, we use the coherent WaveBurst (cWB) pipeline enhanced with
machine learning (ML) to search for binary black hole (BBH) mergers in the
Advanced LIGO-Virgo strain data from the third observing run (O3). We detect,
with equivalent or higher significance, all gravitational-wave (GW) events
previously reported by the standard cWB search for BBH mergers in the third GW
Transient Catalog (GWTC-3). The ML-enhanced cWB search identifies five
additional GW candidate events from the catalog that were previously missed by
the standard cWB search. Moreover, we identify three marginal candidate events
not listed in GWTC-3. For simulated events distributed uniformly in a fiducial
volume, we improve the detection efficiency with respect to the standard cWB
search by approximately $20\%$ for both stellar-mass and intermediate mass
black hole binary mergers, detected with a false-alarm rate less than
$1\,\mathrm{yr}^{-1}$. We show the robustness of the ML-enhanced search for
detection of generic BBH signals by reporting increased sensitivity to the
spin-precessing and eccentric BBH events as compared to the standard cWB
search. Furthermore, we compare the improvement of the ML-enhanced cWB search
for different detector networks.

Pseudo-labels are confident predictions made on unlabeled target data by a
classifier trained on labeled source data. They are widely used for adapting a
model to unlabeled data, e.g., in a semi-supervised learning setting.
  Our key insight is that pseudo-labels are naturally imbalanced due to
intrinsic data similarity, even when a model is trained on balanced source data
and evaluated on balanced target data. If we address this previously unknown
imbalanced classification problem arising from pseudo-labels instead of
ground-truth training labels, we could remove model biases towards false
majorities created by pseudo-labels.
  We propose a novel and effective debiased learning method with pseudo-labels,
based on counterfactual reasoning and adaptive margins: The former removes the
classifier response bias, whereas the latter adjusts the margin of each class
according to the imbalance of pseudo-labels. Validated by extensive
experimentation, our simple debiased learning delivers significant accuracy
gains over the state-of-the-art on ImageNet-1K: 26% for semi-supervised
learning with 0.2% annotations and 9% for zero-shot learning. Our code is
available at: https://github.com/frank-xwang/debiased-pseudo-labeling.

The exemplar-free class incremental learning requires classification models
to learn new class knowledge incrementally without retaining any old samples.
Recently, the framework based on parallel one-class classifiers (POC), which
trains a one-class classifier (OCC) independently for each category, has
attracted extensive attention, since it can naturally avoid catastrophic
forgetting. POC, however, suffers from weak discriminability and comparability
due to its independent training strategy for different OOCs. To meet this
challenge, we propose a new framework, named Discriminative and Comparable
One-class classifiers for Incremental Learning (DisCOIL). DisCOIL follows the
basic principle of POC, but it adopts variational auto-encoders (VAE) instead
of other well-established one-class classifiers (e.g. deep SVDD), because a
trained VAE can not only identify the probability of an input sample belonging
to a class but also generate pseudo samples of the class to assist in learning
new tasks. With this advantage, DisCOIL trains a new-class VAE in contrast with
the old-class VAEs, which forces the new-class VAE to reconstruct better for
new-class samples but worse for the old-class pseudo samples, thus enhancing
the comparability. Furthermore, DisCOIL introduces a hinge reconstruction loss
to ensure the discriminability. We evaluate our method extensively on MNIST,
CIFAR10, and Tiny-ImageNet. The experimental results show that DisCOIL achieves

Communication is defined as the act of sharing or exchanging information,
ideas or feelings. To establish communication between two people, both of them
are required to have knowledge and understanding of a common language. But in
the case of deaf and dumb people, the means of communication are different.
Deaf is the inability to hear and dumb is the inability to speak. They
communicate using sign language among themselves and with normal people but
normal people do not take seriously the importance of sign language. Not
everyone possesses the knowledge and understanding of sign language which makes
communication difficult between a normal person and a deaf and dumb person. To
overcome this barrier, one can build a model based on machine learning. A model
can be trained to recognize different gestures of sign language and translate
them into English. This will help a lot of people in communicating and
conversing with deaf and dumb people. The existing Indian Sing Language
Recognition systems are designed using machine learning algorithms with single
and double-handed gestures but they are not real-time. In this paper, we
propose a method to create an Indian Sign Language dataset using a webcam and
then using transfer learning, train a TensorFlow model to create a real-time
Sign Language Recognition system. The system achieves a good level of accuracy
even with a limited size dataset.

Binary memristive crossbars have gained huge attention as an energy-efficient
deep learning hardware accelerator. Nonetheless, they suffer from various
noises due to the analog nature of the crossbars. To overcome such limitations,
most previous works train weight parameters with noise data obtained from a
crossbar. These methods are, however, ineffective because it is difficult to
collect noise data in large-volume manufacturing environment where each
crossbar has a large device/circuit level variation. Moreover, we argue that
there is still room for improvement even though these methods somewhat improve
accuracy. This paper explores a new perspective on mitigating crossbar noise in
a more generalized way by manipulating input binary bit encoding rather than
training the weight of networks with respect to noise data. We first
mathematically show that the noise decreases as the number of binary bit
encoding pulses increases when representing the same amount of information. In
addition, we propose Gradient-based Bit Encoding Optimization (GBO) which
optimizes a different number of pulses at each layer, based on our in-depth
analysis that each layer has a different level of noise sensitivity. The
proposed heterogeneous layer-wise bit encoding scheme achieves high noise
robustness with low computational cost. Our experimental results on public
benchmark datasets show that GBO improves the classification accuracy by ~5-40%
in severe noise scenarios.

Artificial intelligence (AI) has become a part of everyday conversation and
our lives. It is considered as the new electricity that is revolutionizing the
world. AI is heavily invested in both industry and academy. However, there is
also a lot of hype in the current AI debate. AI based on so-called deep
learning has achieved impressive results in many problems, but its limits are
already visible. AI has been under research since the 1940s, and the industry
has seen many ups and downs due to over-expectations and related
disappointments that have followed.
  The purpose of this book is to give a realistic picture of AI, its history,
its potential and limitations. We believe that AI is a helper, not a ruler of
humans. We begin by describing what AI is and how it has evolved over the
decades. After fundamentals, we explain the importance of massive data for the
current mainstream of artificial intelligence. The most common representations
for AI, methods, and machine learning are covered. In addition, the main
application areas are introduced. Computer vision has been central to the
development of AI. The book provides a general introduction to computer vision,
and includes an exposure to the results and applications of our own research.
Emotions are central to human intelligence, but little use has been made in AI.
We present the basics of emotional intelligence and our own research on the
topic. We discuss super-intelligence that transcends human understanding,
explaining why such achievement seems impossible on the basis of present
knowledge,and how AI could be improved. Finally, a summary is made of the
current state of AI and what to do in the future. In the appendix, we look at
the development of AI education, especially from the perspective of contents at
our own university.

Spiking neural networks (SNNs) in neuromorphic systems are more energy
efficient compared to deep learning-based methods, but there is no clear
competitive learning algorithm for training such SNNs. Eligibility propagation
(e-prop) offers an efficient and biologically plausible way to train
competitive recurrent SNNs in low-power neuromorphic hardware. In this report,
previous performance of e-prop on a speech classification task is reproduced,
and the effects of including STDP-like behavior are analyzed. Including STDP to
the ALIF neuron model improves the classification performance, but this is not
the case for the Izhikevich e-prop neuron. Finally, it was found that e-prop
implemented in a single-layer recurrent SNN consistently outperforms a
multi-layer variant.

Cancers are complex adaptive diseases regulated by the nonlinear feedback
systems between genetic instabilities, environmental signals, cellular protein
flows, and gene regulatory networks. Understanding the cybernetics of cancer
requires the integration of information dynamics across multidimensional
spatiotemporal scales, including genetic, transcriptional, metabolic,
proteomic, epigenetic, and multi-cellular networks. However, the time-series
analysis of these complex networks remains vastly absent in cancer research.
With longitudinal screening and time-series analysis of cellular dynamics,
universally observed causal patterns pertaining to dynamical systems, may
self-organize in the signaling or gene expression state-space of cancer
triggering processes. A class of these patterns, strange attractors, may be
mathematical biomarkers of cancer progression. The emergence of intracellular
chaos and chaotic cell population dynamics remains a new paradigm in systems
oncology. As such, chaotic and complex dynamics are discussed as mathematical
hallmarks of cancer cell fate dynamics herein. Given the assumption that
time-resolved single-cell datasets are made available, a survey of
interdisciplinary tools and algorithms from complexity theory, are hereby
reviewed to investigate critical phenomena and chaotic dynamics in cancer
ecosystems. To conclude, the perspective cultivates an intuition for
computational systems oncology in terms of nonlinear dynamics, information
theory, inverse problems and complexity. We highlight the limitations we see in
the area of statistical machine learning but the opportunity at combining it
with the symbolic computational power offered by the mathematical tools
explored.

It is challenging to restore low-resolution (LR) images to super-resolution
(SR) images with correct and clear details. Existing deep learning works almost
neglect the inherent structural information of images, which acts as an
important role for visual perception of SR results. In this paper, we design a
hierarchical feature exploitation network to probe and preserve structural
information in a multi-scale feature fusion manner. First, we propose a cross
convolution upon traditional edge detectors to localize and represent edge
features. Then, cross convolution blocks (CCBs) are designed with feature
normalization and channel attention to consider the inherent correlations of
features. Finally, we leverage multi-scale feature fusion group (MFFG) to embed
the cross convolution blocks and develop the relations of structural features
in different scales hierarchically, invoking a lightweight structure-preserving
network named as Cross-SRN. Experimental results demonstrate the Cross-SRN
achieves competitive or superior restoration performances against the
state-of-the-art methods with accurate and clear structural details. Moreover,
we set a criterion to select images with rich structural textures. The proposed
Cross-SRN outperforms the state-of-the-art methods on the selected benchmark,
which demonstrates that our network has a significant advantage in preserving
edges.

This paper investigates the dynamics of competition among organizations with
unequal expertise. Multi-agent reinforcement learning has been used to simulate
and understand the impact of various incentive schemes designed to offset such
inequality. We design Touch-Mark, a game based on well-known
multi-agent-particle-environment, where two teams (weak, strong) with unequal
but changing skill levels compete against each other. For training such a game,
we propose a novel controller assisted multi-agent reinforcement learning
algorithm \our\, which empowers each agent with an ensemble of policies along
with a supervised controller that by selectively partitioning the sample space,
triggers intelligent role division among the teammates. Using C-MADDPG as an
underlying framework, we propose an incentive scheme for the weak team such
that the final rewards of both teams become the same. We find that in spite of
the incentive, the final reward of the weak team falls short of the strong
team. On inspecting, we realize that an overall incentive scheme for the weak
team does not incentivize the weaker agents within that team to learn and
improve. To offset this, we now specially incentivize the weaker player to
learn and as a result, observe that the weak team beyond an initial phase
performs at par with the stronger team. The final goal of the paper has been to
formulate a dynamic incentive scheme that continuously balances the reward of
the two teams. This is achieved by devising an incentive scheme enriched with
an RL agent which takes minimum information from the environment.

In recent years, deep learning has successfully been applied to automate a
wide variety of tasks in diagnostic histopathology. However, fast and reliable
localization of small-scale regions-of-interest (ROI) has remained a key
challenge, as discriminative morphologic features often occupy only a small
fraction of a gigapixel-scale whole-slide image (WSI). In this paper, we
propose a sparse WSI analysis method for the rapid identification of high-power
ROI for WSI-level classification. We develop an evaluation framework inspired
by the early classification literature, in order to quantify the tradeoff
between diagnostic performance and inference time for sparse analytic
approaches. We test our method on a common but time-consuming task in pathology
- that of diagnosing gastric intestinal metaplasia (GIM) on hematoxylin and
eosin (H&E)-stained slides from endoscopic biopsy specimens. GIM is a
well-known precursor lesion along the pathway to development of gastric cancer.
We performed a thorough evaluation of the performance and inference time of our
approach on a test set of GIM-positive and GIM-negative WSI, finding that our
method successfully detects GIM in all positive WSI, with a WSI-level
classification area under the receiver operating characteristic curve (AUC) of
0.98 and an average precision (AP) of 0.95. Furthermore, we show that our
method can attain these metrics in under one minute on a standard CPU. Our
results are applicable toward the goal of developing neural networks that can
easily be deployed in clinical settings to support pathologists in quickly
localizing and diagnosing small-scale morphologic features in WSI.

While advances in multi-agent learning have enabled the training of
increasingly complex agents, most existing techniques produce a final policy
that is not designed to adapt to a new partner's strategy. However, we would
like our AI agents to adjust their strategy based on the strategies of those
around them. In this work, we study the problem of conditional multi-agent
imitation learning, where we have access to joint trajectory demonstrations at
training time, and we must interact with and adapt to new partners at test
time. This setting is challenging because we must infer a new partner's
strategy and adapt our policy to that strategy, all without knowledge of the
environment reward or dynamics. We formalize this problem of conditional
multi-agent imitation learning, and propose a novel approach to address the
difficulties of scalability and data scarcity. Our key insight is that
variations across partners in multi-agent games are often highly structured,
and can be represented via a low-rank subspace. Leveraging tools from tensor
decomposition, our model learns a low-rank subspace over ego and partner agent
strategies, then infers and adapts to a new partner strategy by interpolating
in the subspace. We experiments with a mix of collaborative tasks, including
bandits, particle, and Hanabi environments. Additionally, we test our
conditional policies against real human partners in a user study on the
Overcooked game. Our model adapts better to new partners compared to baselines,
and robustly handles diverse settings ranging from discrete/continuous actions
and static/online evaluation with AI/human partners.

Image reconstruction of low-count positron emission tomography (PET) data is
challenging. Kernel methods address the challenge by incorporating image prior
information in the forward model of iterative PET image reconstruction. The
kernelized expectation-maximization (KEM) algorithm has been developed and
demonstrated to be effective and easy to implement. A common approach for a
further improvement of the kernel method would be adding an explicit
regularization, which however leads to a complex optimization problem. In this
paper, we propose an implicit regularization for the kernel method by using a
deep coefficient prior, which represents the kernel coefficient image in the
PET forward model using a convolutional neural-network. To solve the
maximum-likelihood neural network-based reconstruction problem, we apply the
principle of optimization transfer to derive a neural KEM algorithm. Each
iteration of the algorithm consists of two separate steps: a KEM step for image
update from the projection data and a deep-learning step in the image domain
for updating the kernel coefficient image using the neural network. This
optimization algorithm is guaranteed to monotonically increase the data
likelihood. The results from computer simulations and real patient data have
demonstrated that the neural KEM can outperform existing KEM and deep image
prior methods.

Query optimizers are a performance-critical component in every database
system. Due to their complexity, optimizers take experts months to write and
years to refine. In this work, we demonstrate for the first time that learning
to optimize queries without learning from an expert optimizer is both possible
and efficient. We present Balsa, a query optimizer built by deep reinforcement
learning. Balsa first learns basic knowledge from a simple,
environment-agnostic simulator, followed by safe learning in real execution. On
the Join Order Benchmark, Balsa matches the performance of two expert query
optimizers, both open-source and commercial, with two hours of learning, and
outperforms them by up to 2.8$\times$ in workload runtime after a few more
hours. Balsa thus opens the possibility of automatically learning to optimize
in future compute environments where expert-designed optimizers do not exist.

A single unmanned aerial vehicle (UAV) has limited computing resources and
battery capacity, making it difficult to handle computationally intensive tasks
such as the convolution operations in many deep learning applications.
UAV-based networked airborne computing (NAC) is a promising technique to
address this challenge. It allows UAVs within a range to share resources among
each other via UAV-to-UAV communication links and carry out
computation-intensive tasks in a collaborative manner. This paper investigates
the vector convolution problem over the NAC architecture. A novel dynamic coded
convolution strategy with privacy awareness is developed to address the unique
features of UAV-based NAC, including node heterogeneity, frequently changing
network typologies, time-varying communication and computation resources.
Simulation results show its high efficiency and resilience to uncertain
stragglers.

Encoder-decoder models have been widely used in RGBD semantic segmentation,
and most of them are designed via a two-stream network. In general, jointly
reasoning the color and geometric information from RGBD is beneficial for
semantic segmentation. However, most existing approaches fail to
comprehensively utilize multimodal information in both the encoder and decoder.
In this paper, we propose a novel attention-based dual supervised decoder for
RGBD semantic segmentation. In the encoder, we design a simple yet effective
attention-based multimodal fusion module to extract and fuse deeply multi-level
paired complementary information. To learn more robust deep representations and
rich multi-modal information, we introduce a dual-branch decoder to effectively
leverage the correlations and complementary cues of different tasks. Extensive
experiments on NYUDv2 and SUN-RGBD datasets demonstrate that our method
achieves superior performance against the state-of-the-art methods.

The difficulties in both data acquisition and annotation substantially
restrict the sample sizes of training datasets for 3D medical imaging
applications. As a result, constructing high-performance 3D convolutional
neural networks from scratch remains a difficult task in the absence of a
sufficient pre-training parameter. Previous efforts on 3D pre-training have
frequently relied on self-supervised approaches, which use either predictive or
contrastive learning on unlabeled data to build invariant 3D representations.
However, because of the unavailability of large-scale supervision information,
obtaining semantically invariant and discriminative representations from these
learning frameworks remains problematic. In this paper, we revisit an
innovative yet simple fully-supervised 3D network pre-training framework to
take advantage of semantic supervisions from large-scale 2D natural image
datasets. With a redesigned 3D network architecture, reformulated natural
images are used to address the problem of data scarcity and develop powerful 3D
representations. Comprehensive experiments on four benchmark datasets
demonstrate that the proposed pre-trained models can effectively accelerate
convergence while also improving accuracy for a variety of 3D medical imaging
tasks such as classification, segmentation and detection. In addition, as
compared to training from scratch, it can save up to 60% of annotation efforts.
On the NIH DeepLesion dataset, it likewise achieves state-of-the-art detection
performance, outperforming earlier self-supervised and fully-supervised
pre-training approaches, as well as methods that do training from scratch. To
facilitate further development of 3D medical models, our code and pre-trained
model weights are publicly available at https://github.com/urmagicsmine/CSPR.

Eye movement biometrics (EMB) is a relatively recent behavioral biometric
modality that may have the potential to become the primary authentication
method in virtual- and augmented-reality devices due to their emerging use of
eye-tracking sensors to enable foveated rendering techniques. However, existing
EMB models have yet to demonstrate levels of performance that would be
acceptable for real-world use. Deep learning approaches to EMB have largely
employed plain convolutional neural networks (CNNs), but there have been many
milestone improvements to convolutional architectures over the years including
residual networks (ResNets) and densely connected convolutional networks
(DenseNets). The present study employs a DenseNet architecture for end-to-end
EMB and compares the proposed model against the most relevant prior works. The
proposed technique not only outperforms the previous state of the art, but is
also the first to approach a level of authentication performance that would be
acceptable for real-world use.

Domain classification is the fundamental task in natural language
understanding (NLU), which often requires fast accommodation to new emerging
domains. This constraint makes it impossible to retrain all previous domains,
even if they are accessible to the new model. Most existing continual learning
approaches suffer from low accuracy and performance fluctuation, especially
when the distributions of old and new data are significantly different. In
fact, the key real-world problem is not the absence of old data, but the
inefficiency to retrain the model with the whole old dataset. Is it potential
to utilize some old data to yield high accuracy and maintain stable
performance, while at the same time, without introducing extra hyperparameters?
In this paper, we proposed a hyperparameter-free continual learning model for
text data that can stably produce high performance under various environments.
Specifically, we utilize Fisher information to select exemplars that can
"record" key information of the original model. Also, a novel scheme called
dynamical weight consolidation is proposed to enable hyperparameter-free
learning during the retrain process. Extensive experiments demonstrate that
baselines suffer from fluctuated performance and therefore useless in practice.
On the contrary, our proposed model CCFI significantly and consistently
outperforms the best state-of-the-art method by up to 20% in average accuracy,
and each component of CCFI contributes effectively to overall performance.

Deep learning methods can classify various unstructured data such as images,
language, and voice as input data. As the task of classifying anomalies becomes
more important in the real world, various methods exist for classifying using
deep learning with data collected in the real world. As the task of classifying
anomalies becomes more important in the real world, there are various methods
for classifying using deep learning with data collected in the real world.
Among the various methods, the representative approach is a method of
extracting and learning the main features based on a transition model from
pre-trained models, and a method of learning an autoencoderbased structure only
with normal data and classifying it as abnormal through a threshold value.
However, if the dataset is imbalanced, even the state-of-the-arts models do not
achieve good performance. This can be addressed by augmenting normal and
abnormal features in imbalanced data as features with strong distinction. We
use the features of the autoencoder to train latent vectors from low to high
dimensionality. We train normal and abnormal data as a feature that has a
strong distinction among the features of imbalanced data. We propose a latent
vector expansion autoencoder model that improves classification performance at
imbalanced data. The proposed method shows performance improvement compared to
the basic autoencoder using imbalanced anomaly dataset.

This paper assesses a new classification approach that examines
low-resolution images first, only moving to higher resolution images if the
classification from the initial pass does not have a high degree of confidence.
This multi-stage strategy for classification can be used with any classifier
and does not require additional training. The approach is tested on five common
datasets using four different classification approaches. It is found to be
effective for cases in which at least some fraction of cases can be correctly
classified using coarser data than are typically used. neural networks
performing digit recognition, for instance, the proposed approach reduces the
resource cost of classifying test cases by 60% to 85% with less than 5%
reduction in accuracy.

We study time-series classification (TSC), a fundamental task of time-series
data mining. Prior work has approached TSC from two major directions: (1)
similarity-based methods that classify time-series based on the nearest
neighbors, and (2) deep learning models that directly learn the representations
for classification in a data-driven manner. Motivated by the different working
mechanisms within these two research lines, we aim to connect them in such a
way as to jointly model time-series similarities and learn the representations.
This is a challenging task because it is unclear how we should efficiently
leverage similarity information. To tackle the challenge, we propose
Similarity-Aware Time-Series Classification (SimTSC), a conceptually simple and
general framework that models similarity information with graph neural networks
(GNNs). Specifically, we formulate TSC as a node classification problem in
graphs, where the nodes correspond to time-series, and the links correspond to
pair-wise similarities. We further design a graph construction strategy and a
batch training algorithm with negative sampling to improve training efficiency.
We instantiate SimTSC with ResNet as the backbone and Dynamic Time Warping
(DTW) as the similarity measure. Extensive experiments on the full UCR datasets
and several multivariate datasets demonstrate the effectiveness of
incorporating similarity information into deep learning models in both
supervised and semi-supervised settings. Our code is available at
https://github.com/daochenzha/SimTSC

Self-attention shows outstanding competence in capturing long-range
relationships while enhancing performance on vision tasks, such as image
classification and image captioning. However, the self-attention module highly
relies on the dot product multiplication and dimension alignment among
query-key-value features, which cause two problems: (1) The dot product
multiplication results in exhaustive and redundant computation. (2) Due to the
visual feature map often appearing as a multi-dimensional tensor, reshaping the
scale of the tensor feature to adapt to the dimension alignment might destroy
the internal structure of the tensor feature map. To address these problems,
this paper proposes a self-attention plug-in module with its variants, namely,
Synthesizing Tensor Transformations (STT), for directly processing image tensor
features. Without computing the dot-product multiplication among
query-key-value, the basic STT is composed of the tensor transformation to
learn the synthetic attention weight from visual information. The effectiveness
of STT series is validated on the image classification and image caption.
Experiments show that the proposed STT achieves competitive performance while
keeping robustness compared to self-attention based above vision tasks.

Federated learning (FL) is a widely adopted distributed learning paradigm in
practice, which intends to preserve users' data privacy while leveraging the
entire dataset of all participants for training. In FL, multiple models are
trained independently on the users and aggregated centrally to update a global
model in an iterative process. Although this approach is excellent at
preserving privacy by design, FL still tends to suffer from quality issues such
as attacks or byzantine faults. Some recent attempts have been made to address
such quality challenges on the robust aggregation techniques for FL. However,
the effectiveness of state-of-the-art (SOTA) robust FL techniques is still
unclear and lacks a comprehensive study. Therefore, to better understand the
current quality status and challenges of these SOTA FL techniques in the
presence of attacks and faults, in this paper, we perform a large-scale
empirical study to investigate the SOTA FL's quality from multiple angles of
attacks, simulated faults (via mutation operators), and aggregation (defense)
methods. In particular, we perform our study on two generic image datasets and
one real-world federated medical image dataset. We also systematically
investigate the effect of the distribution of attacks/faults over users and the
independent and identically distributed (IID) factors, per dataset, on the
robustness results. After a large-scale analysis with 496 configurations, we
find that most mutators on each individual user have a negligible effect on the
final model. Moreover, choosing the most robust FL aggregator depends on the
attacks and datasets. Finally, we illustrate that it is possible to achieve a
generic solution that works almost as well or even better than any single
aggregator on all attacks and configurations with a simple ensemble model of
aggregators.

Adverse drug reactions / events (ADR/ADE) have a major impact on patient
health and health care costs. Detecting ADR's as early as possible and sharing
them with regulators, pharma companies, and healthcare providers can prevent
morbidity and save many lives. While most ADR's are not reported via formal
channels, they are often documented in a variety of unstructured conversations
such as social media posts by patients, customer support call transcripts, or
CRM notes of meetings between healthcare providers and pharma sales reps. In
this paper, we propose a natural language processing (NLP) solution that
detects ADR's in such unstructured free-text conversations, which improves on
previous work in three ways. First, a new Named Entity Recognition (NER) model
obtains new state-of-the-art accuracy for ADR and Drug entity extraction on the
ADE, CADEC, and SMM4H benchmark datasets (91.75%, 78.76%, and 83.41% F1 scores
respectively). Second, two new Relation Extraction (RE) models are introduced -
one based on BioBERT while the other utilizing crafted features over a Fully
Connected Neural Network (FCNN) - are shown to perform on par with existing
state-of-the-art models, and outperform them when trained with a supplementary
clinician-annotated RE dataset. Third, a new text classification model, for
deciding if a conversation includes an ADR, obtains new state-of-the-art
accuracy on the CADEC dataset (86.69% F1 score). The complete solution is
implemented as a unified NLP pipeline in a production-grade library built on
top of Apache Spark, making it natively scalable and able to process millions
of batch or streaming records on commodity clusters.

Although deep neural networks have achieved great performance on
classification tasks, recent studies showed that well trained networks can be
fooled by adding subtle noises. This paper introduces a new approach to improve
neural network robustness by applying the recovery process on top of the
naturally trained classifier. In this approach, images will be intentionally
corrupted by some significant operator and then be recovered before passing
through the classifiers. SARGAN -- an extension on Generative Adversarial
Networks (GAN) is capable of denoising radar signals. This paper will show that
SARGAN can also recover corrupted images by removing the adversarial effects.
Our results show that this approach does improve the performance of naturally
trained networks.

Due to the fundamental limit to reducing power consumption of running deep
learning models on von-Neumann architecture, research on neuromorphic computing
systems based on low-power spiking neural networks using analog neurons is in
the spotlight. In order to integrate a large number of neurons, neurons need to
be designed to occupy a small area, but as technology scales down, analog
neurons are difficult to scale, and they suffer from reduced voltage
headroom/dynamic range and circuit nonlinearities. In light of this, this paper
first models the nonlinear behavior of existing current-mirror-based
voltage-domain neurons designed in a 28nm process, and show SNN inference
accuracy can be severely degraded by the effect of neuron's nonlinearity. Then,
to mitigate this problem, we propose a novel neuron, which processes incoming
spikes in the time domain and greatly improves the linearity, thereby improving
the inference accuracy compared to the existing voltage-domain neuron. Tested
on the MNIST dataset, the inference error rate of the proposed neuron differs
by less than 0.1% from that of the ideal neuron.

In recent years, self-supervised learning has had significant success in
applications involving computer vision and natural language processing. The
type of pretext task is important to this boost in performance. One common
pretext task is the measure of similarity and dissimilarity between pairs of
images. In this scenario, the two images that make up the negative pair are
visibly different to humans. However, in entomology, species are nearly
indistinguishable and thus hard to differentiate. In this study, we explored
the performance of a Siamese neural network using contrastive loss by learning
to push apart embeddings of bumblebee species pair that are dissimilar, and
pull together similar embeddings. Our experimental results show a 61% F1-score
on zero-shot instances, a performance showing 11% improvement on samples of
classes that share intersections with the training set.

Dynamic graph neural networks have been widely used in modeling and
representation learning of graph structure data. Current dynamic representation
learning focuses on either discrete learning which results in temporal
information loss or continuous learning that involves heavy computation. In
this work, we proposed a novel dynamic graph neural network, Sparse-Dyn. It
adaptively encodes temporal information into a sequence of patches with an
equal amount of temporal-topological structure. Therefore, while avoiding the
use of snapshots which causes information loss, it also achieves a finer time
granularity, which is close to what continuous networks could provide. In
addition, we also designed a lightweight module, Sparse Temporal Transformer,
to compute node representations through both structural neighborhoods and
temporal dynamics. Since the fully-connected attention conjunction is
simplified, the computation cost is far lower than the current
state-of-the-arts. Link prediction experiments are conducted on both continuous
and discrete graph datasets. Through comparing with several state-of-the-art
graph embedding baselines, the experimental results demonstrate that Sparse-Dyn
has a faster inference speed while having competitive performance.

Graph neural networks can be effectively applied to find solutions for many
real-world problems across widely diverse fields. The success of graph neural
networks is linked to the message-passing mechanism on the graph, however, the
message-aggregating behavior is still not entirely clear in most algorithms. To
improve functionality, we propose a new transparent network called Graph
Decipher to investigate the message-passing mechanism by prioritizing in two
main components: the graph structure and node attributes, at the graph,
feature, and global levels on a graph under the node classification task.
However, the computation burden now becomes the most significant issue because
the relevance of both graph structure and node attributes are computed on a
graph. In order to solve this issue, only relevant representative node
attributes are extracted by graph feature filters, allowing calculations to be
performed in a category-oriented manner. Experiments on seven datasets show
that Graph Decipher achieves state-of-the-art performance while imposing a
substantially lower computation burden under the node classification task.
Additionally, since our algorithm has the ability to explore the representative
node attributes by category, it is utilized to alleviate the imbalanced node
classification problem on multi-class graph datasets.

Machine learning can be enhanced through the integration of external
knowledge. This method, called knowledge informed machine learning, is also
applicable within the field of Prognostics and Health Management (PHM). In this
paper, the various methods of knowledge informed machine learning, from a PHM
context, are reviewed with the goal of helping the reader understand the
domain. In addition, a knowledge informed machine learning technique is
demonstrated, using the common IMS and PRONOSTIA bearing data sets, for
remaining useful life (RUL) prediction. Specifically, knowledge is garnered
from the field of reliability engineering which is represented through the
Weibull distribution. The knowledge is then integrated into a neural network
through a novel Weibull-based loss function. A thorough statistical analysis of
the Weibull-based loss function is conducted, demonstrating the effectiveness
of the method on the PRONOSTIA data set. However, the Weibull-based loss
function is less effective on the IMS data set. The results, shortcomings, and
benefits of the approach are discussed in length. Finally, all the code is
publicly available for the benefit of other researchers.

In studying solidification process by simulations on the atomic scale, the
modeling of crystal nucleation or amorphisation requires the construction of
interatomic interactions that are able to reproduce the properties of both the
solid and the liquid states. Taking into account rare nucleation events or
structural relaxation under deep undercooling conditions requires much larger
length scales and longer time scales than those achievable by \textit{ab
initio} molecular dynamics (AIMD). This problem is addressed by means of
classical MD simulations using a well established high dimensional neural
network potential trained on a relevant set of configurations generated by
AIMD. Our dataset contains various crystalline structures and liquid states at
different pressures, including their time fluctuations in a wide range of
temperatures considering only their energy labels. Applied to elemental
aluminium, the resulting potential is shown to be efficient to reproduce the
basic structural, dynamics and thermodynamic quantities in the liquid and
undercooled states without the need to include neither explicitly the forces
nor all kind of configurations in the training procedure. The early stage of
crystallization is further investigated on a much larger scale with one million
atoms, allowing us to unravel features of the homogeneous nucleation mechanisms
in the fcc phase at ambient pressure as well as in the bcc phase at high
pressure with unprecedented accuracy close to the \textit{ab initio} one. In
both case, a single step nucleation process is observed.

Learning novel classes from a very few labeled samples has attracted
increasing attention in machine learning areas. Recent research on either
meta-learning based or transfer-learning based paradigm demonstrates that
gaining information on a good feature space can be an effective solution to
achieve favorable performance on few-shot tasks. In this paper, we propose a
simple but effective paradigm that decouples the tasks of learning feature
representations and classifiers and only learns the feature embedding
architecture from base classes via the typical transfer-learning training
strategy. To maintain both the generalization ability across base and novel
classes and discrimination ability within each class, we propose a dual path
feature learning scheme that effectively combines structural similarity with
contrastive feature construction. In this way, both inner-class alignment and
inter-class uniformity can be well balanced, and result in improved
performance. Experiments on three popular benchmarks show that when
incorporated with a simple prototype based classifier, our method can still
achieve promising results for both standard and generalized few-shot problems
in either an inductive or transductive inference setting.

The volume of academic literature, such as academic conference papers and
journals, has increased rapidly worldwide, and research on metadata extraction
is ongoing. However, high-performing metadata extraction is still challenging
due to diverse layout formats according to journal publishers. To accommodate
the diversity of the layouts of academic journals, we propose a novel
LAyout-aware Metadata Extraction (LAME) framework equipped with the three
characteristics (e.g., design of an automatic layout analysis, construction of
a large meta-data training set, and construction of Layout-MetaBERT). We
designed an automatic layout analysis using PDFMiner. Based on the layout
analysis, a large volume of metadata-separated training data, including the
title, abstract, author name, author affiliated organization, and keywords,
were automatically extracted. Moreover, we constructed Layout-MetaBERT to
extract the metadata from academic journals with varying layout formats. The
experimental results with Layout-MetaBERT exhibited robust performance
(Macro-F1, 93.27%) in metadata extraction for unseen journals with different
layout formats.

We consider the problem of abnormality localization for clinical
applications. While deep learning has driven much recent progress in medical
imaging, many clinical challenges are not fully addressed, limiting its broader
usage. While recent methods report high diagnostic accuracies, physicians have
concerns trusting these algorithm results for diagnostic decision-making
purposes because of a general lack of algorithm decision reasoning and
interpretability. One potential way to address this problem is to further train
these models to localize abnormalities in addition to just classifying them.
However, doing this accurately will require a large amount of disease
localization annotations by clinical experts, a task that is prohibitively
expensive to accomplish for most applications. In this work, we take a step
towards addressing these issues by means of a new attention-driven weakly
supervised algorithm comprising a hierarchical attention mining framework that
unifies activation- and gradient-based visual attention in a holistic manner.
Our key algorithmic innovations include the design of explicit ordinal
attention constraints, enabling principled model training in a
weakly-supervised fashion, while also facilitating the generation of
visual-attention-driven model explanations by means of localization cues. On
two large-scale chest X-ray datasets (NIH ChestX-ray14 and CheXpert), we
demonstrate significant localization performance improvements over the current
state of the art while also achieving competitive classification performance.
Our code is available on https://github.com/oyxhust/HAM.

Relying on random matrix theory (RMT), this paper studies asymmetric
order-$d$ spiked tensor models with Gaussian noise. Using the variational
definition of the singular vectors and values of (Lim, 2005), we show that the
analysis of the considered model boils down to the analysis of an equivalent
spiked symmetric block-wise random matrix, that is constructed from
contractions of the studied tensor with the singular vectors associated to its
best rank-1 approximation. Our approach allows the exact characterization of
the almost sure asymptotic singular value and alignments of the corresponding
singular vectors with the true spike components, when $\frac{n_i}{\sum_{j=1}^d
n_j}\to c_i\in [0, 1]$ with $n_i$'s the tensor dimensions. In contrast to other
works that rely mostly on tools from statistical physics to study random
tensors, our results rely solely on classical RMT tools such as Stein's lemma.
Finally, classical RMT results concerning spiked random matrices are recovered
as a particular case.

With the popularity of smartphones, mobile applications (apps) have
penetrated the daily life of people. Although apps provide rich
functionalities, they also access a large amount of personal information
simultaneously. As a result, privacy concerns are raised. To understand what
personal information the apps collect, many solutions are presented to detect
privacy leaks in apps. Recently, the traffic monitoring-based privacy leak
detection method has shown promising performance and strong scalability.
However, it still has some shortcomings. Firstly, it suffers from detecting the
leakage of personal information with obfuscation. Secondly, it cannot discover
the privacy leaks of undefined type. Aiming at solving the above problems, a
new personal information detection method based on traffic monitoring is
proposed in this paper. In this paper, statistical features of personal
information are designed to depict the occurrence patterns of personal
information in the traffic, including local patterns and global patterns. Then
a detector is trained based on machine learning algorithms to discover
potential personal information with similar patterns. Since the statistical
features are independent of the value and type of personal information, the
trained detector is capable of identifying various types of privacy leaks and
obfuscated privacy leaks. As far as we know, this is the first work that
detects personal information based on statistical features. Finally, the
experimental results show that the proposed method could achieve better
performance than the state-of-the-art.

Geometric deep learning, i.e., designing neural networks to handle the
ubiquitous geometric data such as point clouds and graphs, have achieved great
successes in the last decade. One critical inductive bias is that the model can
maintain invariance towards various transformations such as translation,
rotation, and scaling. The existing graph neural network (GNN) approaches can
only maintain permutation-invariance, failing to guarantee invariance with
respect to other transformations. Besides GNNs, other works design
sophisticated transformation-invariant layers, which are computationally
expensive and difficult to be extended. To solve this problem, we revisit why
the existing neural networks cannot maintain transformation invariance when
handling geometric data. Our findings show that transformation-invariant and
distance-preserving initial representations are sufficient to achieve
transformation invariance rather than needing sophisticated neural layer
designs. Motivated by these findings, we propose Transformation Invariant
Neural Networks (TinvNN), a straightforward and general framework for geometric
data. Specifically, we realize transformation-invariant and distance-preserving
initial point representations by modifying multi-dimensional scaling before
feeding the representations into neural networks. We prove that TinvNN can
strictly guarantee transformation invariance, being general and flexible enough
to be combined with the existing neural networks. Extensive experimental
results on point cloud analysis and combinatorial optimization demonstrate the
effectiveness and general applicability of our proposed method. Based on the
experimental results, we advocate that TinvNN should be considered a new
starting point and an essential baseline for further studies of
transformation-invariant geometric deep learning.

During the inversion of discrete linear systems, noise in data can be
amplified and result in meaningless solutions. To combat this effect,
characteristics of solutions that are considered desirable are mathematically
implemented during inversion, which is a process called regularization. The
influence of provided prior information is controlled by non-negative
regularization parameter(s). There are a number of methods used to select
appropriate regularization parameters, as well as a number of methods used for
inversion. In this paper, we consider the unbiased risk estimator, generalized
cross validation, and the discrepancy principle as the means of selecting
regularization parameters. When multiple data sets describing the same physical
phenomena are available, the use of multiple regularization parameters can
enhance results. Here we demonstrate that it is possible to learn multiple
parameter regularization parameters using regularization parameter estimators
that are modified to handle multiple parameters and multiple data. The results
demonstrate that these modified methods, which do not require the use of true
data for learning regularization parameters, are effective and efficient, and
perform comparably to methods based on true data for learning the relevant

Seating location in the classroom can affect student engagement, attention
and academic performance by providing better visibility, improved movement, and
participation in discussions. Existing studies typically explore how
traditional seating arrangements (e.g. grouped tables or traditional rows)
influence students' perceived engagement, without considering group seating
behaviours under more flexible seating arrangements. Furthermore, survey-based
measures of student engagement are prone to subjectivity and various response
bias. Therefore, in this research, we investigate how individual and group-wise
classroom seating experiences affect student engagement using wearable
physiological sensors. We conducted a field study at a high school and
collected survey and wearable data from 23 students in 10 courses over four
weeks. We aim to answer the following research questions: 1. How does the
seating proximity between students relate to their perceived learning
engagement? 2. How do students' group seating behaviours relate to their
physiologically-based measures of engagement (i.e. physiological arousal and
physiological synchrony)? Experiment results indicate that the individual and
group-wise classroom seating experience is associated with perceived student
engagement and physiologically-based engagement measured from electrodermal
activity. We also find that students who sit close together are more likely to
have similar learning engagement and tend to have high physiological synchrony.
This research opens up opportunities to explore the implications of flexible
seating arrangements and has great potential to maximize student engagement by
suggesting intelligent seating choices in the future.

We generalize the "indirect learning" technique of Furst et. al., 1991 to
reduce from learning a concept class over a samplable distribution $\mu$ to
learning the same concept class over the uniform distribution. The reduction
succeeds when the sampler for $\mu$ is both contained in the target concept
class and efficiently invertible in the sense of Impagliazzo & Luby, 1989. We
give two applications.
  - We show that AC0[q] is learnable over any succinctly-described product
distribution. AC0[q] is the class of constant-depth Boolean circuits of
polynomial size with AND, OR, NOT, and counting modulo $q$ gates of unbounded
fanins. Our algorithm runs in randomized quasi-polynomial time and uses
membership queries.
  - If there is a strongly useful natural property in the sense of Razborov &
Rudich 1997 -- an efficient algorithm that can distinguish between random
strings and strings of non-trivial circuit complexity -- then general
polynomial-sized Boolean circuits are learnable over any efficiently samplable
distribution in randomized polynomial time, given membership queries to the
target function

We propose a new method for supervised learning with multiple sets of
features ("views"). The multiview problem is especially important in biology
and medicine, where "-omics" data such as genomics, proteomics and radiomics
are measured on a common set of samples. Cooperative learning combines the
usual squared error loss of predictions with an "agreement" penalty to
encourage the predictions from different data views to agree. By varying the
weight of the agreement penalty, we get a continuum of solutions that include
the well-known early and late fusion approaches. Cooperative learning chooses
the degree of agreement (or fusion) in an adaptive manner, using a validation
set or cross-validation to estimate test set prediction error. One version of
our fitting procedure is modular, where one can choose different fitting
mechanisms (e.g. lasso, random forests, boosting, neural networks) appropriate
for different data views. In the setting of cooperative regularized linear
regression, the method combines the lasso penalty with the agreement penalty,
yielding feature sparsity. The method can be especially powerful when the
different data views share some underlying relationship in their signals that
can be exploited to boost the signals. We show that cooperative learning
achieves higher predictive accuracy on simulated data and a real multiomics
example of labor onset prediction. Leveraging aligned signals and allowing
flexible fitting mechanisms for different modalities, cooperative learning
offers a powerful approach to multiomics data fusion.

To generalize the model trained in source domains to unseen target domains,
domain generalization (DG) has recently attracted lots of attention. Since
target domains can not be involved in training, overfitting source domains is
inevitable. As a popular regularization technique, the meta-learning training
scheme has shown its ability to resist overfitting. However, in the training
stage, current meta-learning-based methods utilize only one task along a single
optimization trajectory, which might produce a biased and noisy optimization
direction. Beyond the training stage, overfitting could also cause unstable
prediction in the test stage. In this paper, we propose a novel multi-view DG
framework to effectively reduce the overfitting in both the training and test
stage. Specifically, in the training stage, we develop a multi-view regularized
meta-learning algorithm that employs multiple optimization trajectories to
produce a suitable optimization direction for model updating. We also
theoretically show that the generalization bound could be reduced by increasing
the number of tasks in each trajectory. In the test stage, we utilize multiple
augmented images to yield a multi-view prediction to alleviate unstable
prediction, which significantly promotes model reliability. Extensive
experiments on three benchmark datasets validate that our method can find a
flat minimum to enhance generalization and outperform several state-of-the-art

Current fully-supervised facial landmark detection methods have progressed
rapidly and achieved remarkable performance. However, they still suffer when
coping with faces under large poses and heavy occlusions for inaccurate facial
shape constraints and insufficient labeled training samples. In this paper, we
propose a semi-supervised framework, i.e., a Self-Calibrated Pose Attention
Network (SCPAN) to achieve more robust and precise facial landmark detection in
challenging scenarios. To be specific, a Boundary-Aware Landmark Intensity
(BALI) field is proposed to model more effective facial shape constraints by
fusing boundary and landmark intensity field information. Moreover, a
Self-Calibrated Pose Attention (SCPA) model is designed to provide a
self-learned objective function that enforces intermediate supervision without
label information by introducing a self-calibrated mechanism and a pose
attention mask. We show that by integrating the BALI fields and SCPA model into
a novel self-calibrated pose attention network, more facial prior knowledge can
be learned and the detection accuracy and robustness of our method for faces
with large poses and heavy occlusions have been improved. The experimental
results obtained for challenging benchmark datasets demonstrate that our
approach outperforms state-of-the-art methods in the literature.

Machine learning is gaining growing momentum in various recent models for the
dynamic analysis of information flows in data communications networks. These
preliminary models often rely on off-the-shelf learning models to predict from
historical statistics while disregarding the physics governing the generating
behaviors of these flows. This paper instead introduces Flow Neural Network
(FlowNN) to improve the feature representation with learned physical bias. This
is implemented by an induction layer, working upon the embedding layer, to
impose the physics connected data correlations, and a self-supervised learning
strategy with stop-gradient to make the learned physics universal. For the
short-timescale network prediction tasks, FlowNN achieves 17% - 71% of loss
decrease than the state-of-the-art baselines on both synthetic and real-world
networking datasets, which shows the strength of this new approach. Code will

We study the problem of model selection in batch policy optimization: given a
fixed, partial-feedback dataset and $M$ model classes, learn a policy with
performance that is competitive with the policy derived from the best model
class. We formalize the problem in the contextual bandit setting with linear
model classes by identifying three sources of error that any model selection
algorithm should optimally trade-off in order to be competitive: (1)
approximation error, (2) statistical complexity, and (3) coverage. The first
two sources are common in model selection for supervised learning, where
optimally trading-off these properties is well-studied. In contrast, the third
source is unique to batch policy optimization and is due to dataset shift
inherent to the setting. We first show that no batch policy optimization
algorithm can achieve a guarantee addressing all three simultaneously,
revealing a stark contrast between difficulties in batch policy optimization
and the positive results available in supervised learning. Despite this
negative result, we show that relaxing any one of the three error sources
enables the design of algorithms achieving near-oracle inequalities for the
remaining two. We conclude with experiments demonstrating the efficacy of these

Malware has been one of the most damaging threats to computers that span
across multiple operating systems and various file formats. To defend against
ever-increasing and ever-evolving malware, tremendous efforts have been made to
propose a variety of malware detection that attempt to effectively and
efficiently detect malware so as to mitigate possible damages as early as
possible. Recent studies have shown that, on the one hand, existing ML and DL
techniques enable superior solutions in detecting newly emerging and previously
unseen malware. However, on the other hand, ML and DL models are inherently
vulnerable to adversarial attacks in the form of adversarial examples. In this
paper, we focus on malware with the file format of portable executable (PE) in
the family of Windows operating systems, namely Windows PE malware, as a
representative case to study the adversarial attack methods in such adversarial
settings. To be specific, we start by first outlining the general learning
framework of Windows PE malware detection based on ML/DL and subsequently
highlighting three unique challenges of performing adversarial attacks in the
context of Windows PE malware. Then, we conduct a comprehensive and systematic
review to categorize the state-of-the-art adversarial attacks against PE
malware detection, as well as corresponding defenses to increase the robustness
of Windows PE malware detection. Finally, we conclude the paper by first
presenting other related attacks against Windows PE malware detection beyond
the adversarial attacks and then shedding light on future research directions
and opportunities. In addition, a curated resource list of adversarial attacks
and defenses for Windows PE malware detection is also available at
https://github.com/ryderling/
adversarial-attacks-and-defenses-for-windows-pe-malware-detection.

While quantum computers are naturally well-suited to implementing linear
operations, it is less clear how to implement nonlinear operations on quantum
computers. However, nonlinear subroutines may prove key to a range of
applications of quantum computing from solving nonlinear equations to data
processing and quantum machine learning. Here we develop algorithms for
implementing nonlinear transformations of input quantum states. Our algorithms
are framed around the concept of a weighted state, a mathematical entity
describing the output of an operational procedure involving both quantum
circuits and classical post-processing.

We propose Selective Multiple Power Iterations (SMPI), a new algorithm to
address the important Tensor PCA problem that consists in recovering a spike
$\bf{v_0}^{\otimes k}$ corrupted by a Gaussian noise tensor $\bf{Z} \in
(\mathbb{R}^n)^{\otimes k}$ such that $\bf{T}=\sqrt{n} \beta \bf{v_0}^{\otimes
k} + \bf{Z}$ where $\beta$ is the signal-to-noise ratio (SNR). SMPI consists in
generating a polynomial number of random initializations, performing a
polynomial number of symmetrized tensor power iterations on each
initialization, then selecting the one that maximizes $\langle \bf{T},
\bf{v}^{\otimes k} \rangle$. Various numerical simulations for $k=3$ in the
conventionally considered range $n \leq 1000$ show that the experimental
performances of SMPI improve drastically upon existent algorithms and becomes
comparable to the theoretical optimal recovery. We show that these unexpected
performances are due to a powerful mechanism in which the noise plays a key
role for the signal recovery and that takes place at low $\beta$. Furthermore,
this mechanism results from five essential features of SMPI that distinguish it
from previous algorithms based on power iteration. These remarkable results may
have strong impact on both practical and theoretical applications of Tensor
PCA. (i) We provide a variant of this algorithm to tackle low-rank CP tensor
decomposition. These proposed algorithms also outperforms existent methods even
on real data which shows a huge potential impact for practical applications.
(ii) We present new theoretical insights on the behavior of SMPI and gradient
descent methods for the optimization in high-dimensional non-convex landscapes
that are present in various machine learning problems. (iii) We expect that
these results may help the discussion concerning the existence of the
conjectured statistical-algorithmic gap.

Partial-label learning is a kind of weakly-supervised learning with inexact
labels, where for each training example, we are given a set of candidate labels
instead of only one true label. Recently, various approaches on partial-label
learning have been proposed under different generation models of candidate
label sets. However, these methods require relatively strong distributional
assumptions on the generation models. When the assumptions do not hold, the
performance of the methods is not guaranteed theoretically. In this paper, we
propose the notion of properness on partial labels. We show that this proper
partial-label learning framework requires a weaker distributional assumption
and includes many previous partial-label learning settings as special cases. We
then derive a unified unbiased estimator of the classification risk. We prove
that our estimator is risk-consistent, and we also establish an estimation
error bound. Finally, we validate the effectiveness of our algorithm through

Batch Normalization is an essential component of all state-of-the-art neural
networks architectures. However, since it introduces many practical issues,
much recent research has been devoted to designing normalization-free
architectures. In this paper, we show that weights initialization is key to
train ResNet-like normalization-free networks. In particular, we propose a
slight modification to the summation operation of a block output to the
skip-connection branch, so that the whole network is correctly initialized. We
show that this modified architecture achieves competitive results on CIFAR-10,
CIFAR-100 and ImageNet without further regularization nor algorithmic
modifications.

Atrial fibrillation(termed as AF/Afib henceforth) is a discrete and often
rapid heart rhythm that can lead to clots near the heart. We can detect Afib by
ECG signal by the absence of p and inconsistent intervals between R waves as
shown in fig(1). Existing methods revolve around CNN that are used to detect
afib but most of them work with 12 point lead ECG data where in our case the
health gauge watch deals with single-point ECG data. Twelve-point lead ECG data
is more accurate than a single point. Furthermore, the health gauge watch data
is much noisier. Implementing a model to detect Afib for the watch is a test of
how the CNN is changed/modified to work with real life data

Convolutional neural networks are paramount in image and signal processing
including the relevant classification and training tasks alike and constitute
for the majority of machine learning compute demand today. With convolution
operations being computationally intensive, next generation hardware
accelerators need to offer parallelization and algorithmic-hardware
homomorphism. Fortunately, diffractive display optics is capable of
million-channel parallel data processing at low latency, however, thus far only
showed tens of Hertz slow single image and kernel capability, thereby
significantly underdelivering from its performance potential. Here, we
demonstrate an operation-parallelized high-throughput Fourier optic
convolutional neural network accelerator. For the first time simultaneously
processing of multiple kernels in Fourier domain enabled by optical diffraction
has been achieved alongside with already conventional in the field input
parallelism. Additionally, we show an about one hundred times system speed up
over existing optical diffraction-based processors and this demonstration
rivals performance of modern electronic solutions. Therefore, this system is
capable of processing large-scale matrices about ten times faster than state of
art electronic systems.

Reach-avoid optimal control problems, in which the system must reach certain
goal conditions while staying clear of unacceptable failure modes, are central
to safety and liveness assurance for autonomous robotic systems, but their
exact solutions are intractable for complex dynamics and environments. Recent
successes in reinforcement learning methods to approximately solve optimal
control problems with performance objectives make their application to
certification problems attractive; however, the Lagrange-type objective used in
reinforcement learning is not suitable to encode temporal logic requirements.
Recent work has shown promise in extending the reinforcement learning machinery
to safety-type problems, whose objective is not a sum, but a minimum (or
maximum) over time. In this work, we generalize the reinforcement learning
formulation to handle all optimal control problems in the reach-avoid category.
We derive a time-discounted reach-avoid Bellman backup with contraction mapping
properties and prove that the resulting reach-avoid Q-learning algorithm
converges under analogous conditions to the traditional Lagrange-type problem,
yielding an arbitrarily tight conservative approximation to the reach-avoid
set. We further demonstrate the use of this formulation with deep reinforcement
learning methods, retaining zero-violation guarantees by treating the
approximate solutions as untrusted oracles in a model-predictive supervisory
control framework. We evaluate our proposed framework on a range of nonlinear
systems, validating the results against analytic and numerical solutions, and
through Monte Carlo simulation in previously intractable problems. Our results
open the door to a range of learning-based methods for safe-and-live autonomous
behavior, with applications across robotics and automation. See
https://github.com/SafeRoboticsLab/safety_rl for code and supplementary
material.

Off-policy learning from multistep returns is crucial for sample-efficient
reinforcement learning, particularly in the experience replay setting now
commonly used with deep neural networks. Classically, off-policy estimation
bias is corrected in a per-decision manner: past temporal-difference errors are
re-weighted by the instantaneous Importance Sampling (IS) ratio (via
eligibility traces) after each action. Many important off-policy algorithms
such as Tree Backup and Retrace rely on this mechanism along with differing
protocols for truncating ("cutting") the ratios ("traces") to counteract the
excessive variance of the IS estimator. Unfortunately, cutting traces on a
per-decision basis is not necessarily efficient; once a trace has been cut
according to local information, the effect cannot be reversed later,
potentially resulting in the premature truncation of estimated returns and
slower learning. In the interest of motivating efficient off-policy algorithms,
we propose a multistep operator that permits arbitrary past-dependent traces.
We prove that our operator is convergent for policy evaluation, and for optimal
control when targeting greedy-in-the-limit policies. Our theorems establish the
first convergence guarantees for many existing algorithms including Truncated
IS, Non-Markov Retrace, and history-dependent TD($\lambda$). Our theoretical
results also provide guidance for the development of new algorithms that
jointly consider multiple past decisions for better credit assignment and
faster learning.

Audio or visual data analysis tasks usually have to deal with
high-dimensional and nonnegative signals. However, most data analysis methods
suffer from overfitting and numerical problems when data have more than a few
dimensions needing a dimensionality reduction preprocessing. Moreover,
interpretability about how and why filters work for audio or visual
applications is a desired property, especially when energy or spectral signals
are involved. In these cases, due to the nature of these signals, the
nonnegativity of the filter weights is a desired property to better understand
its working. Because of these two necessities, we propose different methods to
reduce the dimensionality of data while the nonnegativity and interpretability
of the solution are assured. In particular, we propose a generalized
methodology to design filter banks in a supervised way for applications dealing
with nonnegative data, and we explore different ways of solving the proposed
objective function consisting of a nonnegative version of the orthonormalized
partial least-squares method. We analyze the discriminative power of the
features obtained with the proposed methods for two different and widely
studied applications: texture and music genre classification. Furthermore, we
compare the filter banks achieved by our methods with other state-of-the-art
methods specifically designed for feature extraction.

Many real-life decisions involve both perceptual processes and weighing the
consequences of different actions. However, the neural mechanisms underlying
perceptual decisions have typically been examined separately from those
underlying economic decisions. Here, we trained rats to make choices informed
by both perceptual and value cues on a trial-by-trial basis. As in typical
perceptual tasks, subjects were rewarded for correctly categorizing a tone
relative to a learned threshold. To add an economic component, a light
indicated, on each trial, whether correct responses to one side gave higher
rewards than correct responses to the other side. As such, on trials with some
perceptual uncertainty, it could be worthwhile to choose the unlikely option,
if it had higher expected value. We found that, despite subjects sensitivity to
the frequency of the cue and the reward sizes, their behavior was not optimal:
subjects tended to shift their choices in a stimulus-independent way following
light flashes. Moreover, subjects tended to under-shift, which could be
interpreted as being over-confident in their perceptual beliefs or as being
risk-averse.

When mining large datasets in order to predict new data, limitations of the
principles behind statistical machine learning pose a serious challenge not
only to the Big Data deluge, but also to the traditional assumptions that data
generating processes are biased toward low algorithmic complexity. Even when
one assumes an underlying algorithmic-informational bias toward simplicity in
finite dataset generators, we show that fully automated, with or without access
to pseudo-random generators, computable learning algorithms, in particular
those of statistical nature used in current approaches to machine learning
(including deep learning), can always be deceived, naturally or artificially,
by sufficiently large datasets. In particular, we demonstrate that, for every
finite learning algorithm, there is a sufficiently large dataset size above
which the algorithmic probability of an unpredictable deceiver is an upper
bound (up to a multiplicative constant that only depends on the learning
algorithm) for the algorithmic probability of any other larger dataset. In
other words, very large and complex datasets are as likely to deceive learning
algorithms into a "simplicity bubble" as any other particular dataset. These
deceiving datasets guarantee that any prediction will diverge from the
high-algorithmic-complexity globally optimal solution while converging toward
the low-algorithmic-complexity locally optimal solution. We discuss the
framework and empirical conditions for circumventing this deceptive phenomenon,
moving away from statistical machine learning towards a stronger type of
machine learning based on, or motivated by, the intrinsic power of algorithmic
information theory and computability theory.

In an earlier study, we gathered perceptual evaluations of the audio, video,
and audiovisual quality for 360 audiovisual content. This paper investigates
perceived audiovisual quality prediction based on objective quality metrics and
subjective scores of 360 video and spatial audio content. Thirteen objective
video quality metrics and three objective audio quality metrics were evaluated
for five stimuli for each coding parameter. Four regression-based machine
learning models were trained and tested here, i.e., multiple linear regression,
decision tree, random forest, and support vector machine. Each model was
constructed using a combination of audio and video quality metrics and two
cross-validation methods (k-Fold and Leave-One-Out) were investigated and
produced 312 predictive models. The results indicate that the model based on
the evaluation of VMAF and AMBIQUAL is better than other combinations of
audio-video quality metric. In this study, support vector machine provides
higher performance using k-Fold (PCC = 0.909, SROCC = 0.914, and RMSE = 0.416).
These results can provide insights for the design of multimedia quality metrics
and the development of predictive models for audiovisual omnidirectional media.

Measures of Activity of Daily Living (ADL) are an important indicator of
overall health but difficult to measure in-clinic. Automated and accurate human
activity recognition (HAR) using wrist-worn accelerometers enables practical
and cost efficient remote monitoring of ADL. Key obstacles in developing high
quality HAR is the lack of large labeled datasets and the performance loss when
applying models trained on small curated datasets to the continuous stream of
heterogeneous data in real-life. In this work we design a self-supervised
learning paradigm to create a robust representation of accelerometer data that
can generalize across devices and subjects. We demonstrate that this
representation can separate activities of daily living and achieve strong HAR
accuracy (on multiple benchmark datasets) using very few labels. We also
propose a segmentation algorithm which can identify segments of salient
activity and boost HAR accuracy on continuous real-life data.

Functional magnetic resonance imaging (fMRI) is a notoriously noisy
measurement of brain activity because of the large variations between
individuals, signals marred by environmental differences during collection, and
spatiotemporal averaging required by the measurement resolution. In addition,
the data is extremely high dimensional, with the space of the activity
typically having much lower intrinsic dimension. In order to understand the
connection between stimuli of interest and brain activity, and analyze
differences and commonalities between subjects, it becomes important to learn a
meaningful embedding of the data that denoises, and reveals its intrinsic
structure. Specifically, we assume that while noise varies significantly
between individuals, true responses to stimuli will share common,
low-dimensional features between subjects which are jointly discoverable.
Similar approaches have been exploited previously but they have mainly used
linear methods such as PCA and shared response modeling (SRM). In contrast, we
propose a neural network called MRMD-AE (manifold-regularized multiple decoder,
autoencoder), that learns a common embedding from multiple subjects in an
experiment while retaining the ability to decode to individual raw fMRI
signals. We show that our learned common space represents an extensible
manifold (where new points not seen during training can be mapped), improves
the classification accuracy of stimulus features of unseen timepoints, as well
as improves cross-subject translation of fMRI signals. We believe this
framework can be used for many downstream applications such as guided
brain-computer interface (BCI) training in the future.

Laboratory courses are an important part of the undergraduate physics
curriculum. During physics labs, students can engage in authentic, hands-on
experimental practices, which can prepare them for graduate school, research
laboratories, and jobs in industry. Due to the COVID-19 pandemic in spring
2020, colleges and universities across the world rapidly transitioned to
teaching labs remotely. In this work, we report results from a survey of
physics lab instructors on how they adapted their courses in the transition to
emergency remote teaching. We found that the instructors who responded to the
survey faced numerous challenges when transitioning their classes to remote
instruction, particularly in providing students with a similar experience to
the in-person labs. In addition, we identified common themes in the
instructors' responses including changing learning goals of the courses to be
more concept-focused, reducing group work due to equity and technological
concerns, and using a variety simulation tools, as well as report on factors
that the instructors hoped to continue once they have returned to in-person
instruction.

Acquiring data to train deep learning-based object detectors on Unmanned
Aerial Vehicles (UAVs) is expensive, time-consuming and may even be prohibited
by law in specific environments. On the other hand, synthetic data is fast and
cheap to access. In this work, we explore the potential use of synthetic data
in object detection from UAVs across various application environments. For
that, we extend the open-source framework DeepGTAV to work for UAV scenarios.
We capture various large-scale high-resolution synthetic data sets in several
domains to demonstrate their use in real-world object detection from UAVs by
analyzing multiple training strategies across several models. Furthermore, we
analyze several different data generation and sampling parameters to provide
actionable engineering advice for further scientific research. The DeepGTAV
framework is available at https://git.io/Jyf5j.

The 2021 NeurIPS Machine Learning for Combinatorial Optimization (ML4CO)
competition was designed with the goal of improving state-of-the-art
combinatorial optimization solvers by replacing key heuristic components with
machine learning models. The competition's main scientific question was the
following: is machine learning a viable option for improving traditional
combinatorial optimization solvers on specific problem distributions, when
historical data is available? This was motivated by the fact that in many
practical scenarios, the data changes only slightly between the repetitions of
a combinatorial optimization problem, and this is an area where machine
learning models are particularly powerful at. This paper summarizes the
solution and lessons learned by the Huawei EI-OROAS team in the dual task of
the competition. The submission of our team achieved the second place in the
final ranking, with a very close distance to the first spot. In addition, our
solution was ranked first consistently for several weekly leaderboard updates
before the final evaluation. We provide insights gained from a large number of
experiments, and argue that a simple Graph Convolutional Neural Network (GCNNs)
can achieve state-of-the-art results if trained and tuned properly.

Multivariate Analysis (MVA) comprises a family of well-known methods for
feature extraction which exploit correlations among input variables
representing the data. One important property that is enjoyed by most such
methods is uncorrelation among the extracted features. Recently, regularized
versions of MVA methods have appeared in the literature, mainly with the goal
to gain interpretability of the solution. In these cases, the solutions can no
longer be obtained in a closed manner, and more complex optimization methods
that rely on the iteration of two steps are frequently used. This paper recurs
to an alternative approach to solve efficiently this iterative problem. The
main novelty of this approach lies in preserving several properties of the
original methods, most notably the uncorrelation of the extracted features.
Under this framework, we propose a novel method that takes advantage of the
l-21 norm to perform variable selection during the feature extraction process.
Experimental results over different problems corroborate the advantages of the
proposed formulation in comparison to state of the art formulations.

Adaptive filters are at the core of many signal processing applications,
ranging from acoustic noise supression to echo cancelation, array beamforming,
channel equalization, to more recent sensor network applications in
surveillance, target localization, and tracking. A trending approach in this
direction is to recur to in-network distributed processing in which individual
nodes implement adaptation rules and diffuse their estimation to the network.
  When the a priori knowledge about the filtering scenario is limited or
imprecise, selecting the most adequate filter structure and adjusting its
parameters becomes a challenging task, and erroneous choices can lead to
inadequate performance. To address this difficulty, one useful approach is to
rely on combinations of adaptive structures.
  The combination of adaptive filters exploits to some extent the same divide
and conquer principle that has also been successfully exploited by the
machine-learning community (e.g., in bagging or boosting). In particular, the
problem of combining the outputs of several learning algorithms (mixture of
experts) has been studied in the computational learning field under a different
perspective: rather than studying the expected performance of the mixture,
deterministic bounds are derived that apply to individual sequences and,
therefore, reflect worst-case scenarios. These bounds require assumptions
different from the ones typically used in adaptive filtering, which is the
emphasis of this overview article. We review the key ideas and principles
behind these combination schemes, with emphasis on design rules. We also
illustrate their performance with a variety of examples.

Unscheduled maintenance has contributed to longer downtime for vehicles and
increased costs for Logistic Readiness Squadrons (LRSs) in the Air Force. When
vehicles are in need of repair outside of their scheduled time, depending on
their priority level, the entire squadron's slated repair schedule is
transformed negatively. The repercussions of unscheduled maintenance are
specifically seen in the increase of man hours required to maintain vehicles
that should have been working well: this can include more man hours spent on
maintenance itself, waiting for parts to arrive, hours spent re-organizing the
repair schedule, and more. The dominant trend in the current maintenance system
at LRSs is that they do not have predictive maintenance infrastructure to
counteract the influx of unscheduled repairs they experience currently, and as
a result, their readiness and performance levels are lower than desired.
  We use data pulled from the Defense Property and Accountability System
(DPAS), that the LRSs currently use to store their vehicle maintenance
information. Using historical vehicle maintenance data we receive from DPAS, we
apply three different algorithms independently to construct an accurate
predictive system to optimize maintenance schedules at any given time. Through
the application of Logistics Regression, Random Forest, and Gradient Boosted
Trees algorithms, we found that a Logistic Regression algorithm, fitted to our
data, produced the most accurate results. Our findings indicate that not only
would continuing the use of Logistic Regression be prudent for our research
purposes, but that there is opportunity to further tune and optimize our
Logistic Regression model for higher accuracy.

The standard formulation of Reinforcement Learning lacks a practical way of
specifying what are admissible and forbidden behaviors. Most often,
practitioners go about the task of behavior specification by manually
engineering the reward function, a counter-intuitive process that requires
several iterations and is prone to reward hacking by the agent. In this work,
we argue that constrained RL, which has almost exclusively been used for safe
RL, also has the potential to significantly reduce the amount of work spent for
reward specification in applied RL projects. To this end, we propose to specify
behavioral preferences in the CMDP framework and to use Lagrangian methods to
automatically weigh each of these behavioral constraints. Specifically, we
investigate how CMDPs can be adapted to solve goal-based tasks while adhering
to several constraints simultaneously. We evaluate this framework on a set of
continuous control tasks relevant to the application of Reinforcement Learning
for NPC design in video games.

The modern digital world is increasingly becoming multimodal. Although
multimodal learning has recently revolutionized the state-of-the-art
performance in multimodal tasks, relatively little is known about the
robustness of multimodal learning in an adversarial setting. In this paper, we
introduce a comprehensive measurement of the adversarial robustness of
multimodal learning by focusing on the fusion of input modalities in multimodal
models, via a framework called MUROAN (MUltimodal RObustness ANalyzer). We
first present a unified view of multimodal models in MUROAN and identify the
fusion mechanism of multimodal models as a key vulnerability. We then introduce
a new type of multimodal adversarial attacks called decoupling attack in MUROAN
that aims to compromise multimodal models by decoupling their fused modalities.
We leverage the decoupling attack of MUROAN to measure several state-of-the-art
multimodal models and find that the multimodal fusion mechanism in all these
models is vulnerable to decoupling attacks. We especially demonstrate that, in
the worst case, the decoupling attack of MUROAN achieves an attack success rate
of 100% by decoupling just 1.16% of the input space. Finally, we show that
traditional adversarial training is insufficient to improve the robustness of
multimodal models with respect to decoupling attacks. We hope our findings
encourage researchers to pursue improving the robustness of multimodal
learning.

The goal of spatial-configuration-based classification is to build a
classifier to distinguish two classes (e.g., responder, non-responder) based on
the spatial arrangements (e.g., spatial interactions between different point
categories) given multi-category point data from two classes. This problem is
important for generating hypotheses in medical pathology towards discovering
new immunotherapies for cancer treatment as well as for other applications in
biomedical research and microbial ecology. This problem is challenging due to
an exponential number of category subsets which may vary in the strength of
spatial interactions. Most prior efforts on using human selected spatial
association measures may not be sufficient for capturing the relevant (e.g.,
surrounded by) spatial interactions which may be of biological significance. In
addition, the related deep neural networks are limited to category pairs and do
not explore larger subsets of point categories. To overcome these limitations,
we propose a Spatial-interaction Aware Multi-Category deep neural Network
(SAMCNet) architecture and contribute novel local reference frame
characterization and point pair prioritization layers for
spatial-configuration-based classification. Extensive experimental results on
multiple cancer datasets show that the proposed architecture provides higher
prediction accuracy over baseline methods.

Modern deep neural networks have achieved remarkable progress in medical
image segmentation tasks. However, it has recently been observed that they tend
to produce overconfident estimates, even in situations of high uncertainty,
leading to poorly calibrated and unreliable models. In this work we introduce
Maximum Entropy on Erroneous Predictions (MEEP), a training strategy for
segmentation networks which selectively penalizes overconfident predictions,
focusing only on misclassified pixels. In particular, we design a
regularization term that encourages high entropy posteriors for wrong
predictions, increasing the network uncertainty in complex scenarios. Our
method is agnostic to the neural architecture, does not increase model
complexity and can be coupled with multiple segmentation loss functions. We
benchmark the proposed strategy in two challenging medical image segmentation
tasks: white matter hyperintensity lesions in magnetic resonance images (MRI)
of the brain, and atrial segmentation in cardiac MRI. The experimental results
demonstrate that coupling MEEP with standard segmentation losses leads to
improvements not only in terms of model calibration, but also in segmentation
quality.

We introduce the problem of detecting a group of students from classroom
videos. The problem requires the detection of students from different angles
and the separation of the group from other groups in long videos (one to one
and a half hours). We use multiple image representations to solve the problem.
We use FM components to separate each group from background groups, AM-FM
components for detecting the back-of-the-head, and YOLO for face detection. We
use classroom videos from four different groups to validate our approach. Our
use of multiple representations is shown to be significantly more accurate than
the use of YOLO alone.

Safety-critical applications require controllers/policies that can guarantee
safety with high confidence. The control barrier function is a useful tool to
guarantee safety if we have access to the ground-truth system dynamics. In
practice, we have inaccurate knowledge of the system dynamics, which can lead
to unsafe behaviors due to unmodeled residual dynamics. Learning the residual
dynamics with deterministic machine learning models can prevent the unsafe
behavior but can fail when the predictions are imperfect. In this situation, a
probabilistic learning method that reasons about the uncertainty of its
predictions can help provide robust safety margins. In this work, we use a
Gaussian process to model the projection of the residual dynamics onto a
control barrier function. We propose a novel optimization procedure to generate
safe controls that can guarantee safety with high probability. The safety
filter is provided with the ability to reason about the uncertainty of the
predictions from the GP. We show the efficacy of this method through
experiments on Segway and Quadrotor simulations. Our proposed probabilistic
approach is able to reduce the number of safety violations significantly as
compared to the deterministic approach with a neural network.

The automatic diagnosis of heart diseases from the electrocardiogram (ECG)
signal is crucial in clinical decision-making. However, the use of
computer-based decision rules in clinical practice is still deficient, mainly
due to their complexity and a lack of medical interpretation. The objetive of
this research is to address these issues by providing valuable diagnostic rules
that can be easily implemented in clinical practice. In this research,
efficient diagnostic rules friendly in clinical practice are provided. In this
paper, interesting parameters obtained from the ECG signals analysis are
presented and two simple rules for automatic diagnosis of Bundle Branch Blocks
are defined using new markers derived from the so-called FMMecg delineator. The
main advantages of these markers are the good statistical properties and their
clear interpretation in clinically meaningful terms. High sensitivity and
specificity values have been obtained using the proposed rules with data from
more than 35000 patients from well known benchmarking databases. In particular,
to identify Complete Left Bundle Branch Blocks and differentiate this condition
from subjects without heart diseases, sensitivity and specificity values
ranging from 93% to 99% and from 96% to 99%, respectively. The new markers and
the automatic diagnosis are easily available at
https://fmmmodel.shinyapps.io/fmmEcg/, an app specifically developed for any
given ECG signal. The proposal is different from others in the literature and
it is compelling for three main reasons. On the one hand, the markers have a
concise electrophysiological interpretation. On the other hand, the diagnosis
rules have a very high accuracy. Finally, the markers can be provided by any
device that registers the ECG signal and the automatic diagnosis is made
straightforwardly, in contrast to the black-box and deep learning algorithms.

Variational inference is a powerful paradigm for approximate Bayesian
inference with a number of appealing properties, including support for model
learning and data subsampling. By contrast MCMC methods like Hamiltonian Monte
Carlo do not share these properties but remain attractive since, contrary to
parametric methods, MCMC is asymptotically unbiased. For these reasons
researchers have sought to combine the strengths of both classes of algorithms,
with recent approaches coming closer to realizing this vision in practice.
However, supporting data subsampling in these hybrid methods can be a
challenge, a shortcoming that we address by introducing a surrogate likelihood
that can be learned jointly with other variational parameters. We argue
theoretically that the resulting algorithm permits the user to make an
intuitive trade-off between inference fidelity and computational cost. In an
extensive empirical comparison we show that our method performs well in
practice and that it is well-suited for black-box inference in probabilistic
programming frameworks.

Injury analysis may be one of the most beneficial applications of deep
learning based human pose estimation. To facilitate further research on this
topic, we provide an injury specific 2D dataset for alpine skiing, covering in
total 533 images. We further propose a post processing routine, that combines
rotational information with a simple kinematic model. We could improve
detection results in fall situations by up to 21% regarding the PCK@0.2 metric.

Building AI agents can be costly. Consider a question answering agent such as
Jill Watson that automatically answers students' questions on the discussion
forums of online classes based on their syllabi and other course materials.
Training a Jill on the syllabus of a new online class can take a hundred hours
or more. Machine teaching - interactive teaching of an AI agent using synthetic
data sets - can reduce the training time because it combines the advantages of
knowledge-based AI, machine learning using large data sets, and interactive
human-in-loop training. We describe Agent Smith, an interactive machine
teaching agent that reduces the time taken to train a Jill for a new online
class by an order of magnitude.

Multi-Modal Self-Supervised Learning from videos has been shown to improve
model's performance on various downstream tasks. However, such Self-Supervised
pre-training requires large batch sizes and a large amount of computation
resources due to the noise present in the uncurated data. This is partly due to
the fact that the prevalent training scheme is trained on coarse-grained
setting, in which vectors representing the whole video clips or natural
language sentences are used for computing similarity. Such scheme makes
training noisy as part of the video clips can be totally not correlated with
the other-modality input such as text description. In this paper, we propose a
fine-grained multi-modal self-supervised training scheme that computes the
similarity between embeddings at finer-scale (such as individual feature map
embeddings and embeddings of phrases), and uses attention mechanisms to reduce
noisy pairs' weighting in the loss function. We show that with the proposed
pre-training scheme, we can train smaller models, with smaller batch-size and
much less computational resources to achieve downstream tasks performances
comparable to State-Of-The-Art, for tasks including action recognition and
text-image retrievals.

Multi-group agnostic learning is a formal learning criterion that is
concerned with the conditional risks of predictors within subgroups of a
population. The criterion addresses recent practical concerns such as subgroup
fairness and hidden stratification. This paper studies the structure of
solutions to the multi-group learning problem, and provides simple and
near-optimal algorithms for the learning problem.

We train an agent to compete in the game of Gardner minichess, a downsized
variation of chess played on a 5x5 board. We motivated and applied a SOTA
actor-critic method Proximal Policy Optimization with Generalized Advantage
Estimation. Our initial task centered around training the agent against a
random agent. Once we obtained reasonable performance, we then adopted a
version of iterative policy improvement adopted by AlphaGo to pit the agent
against increasingly stronger versions of itself, and evaluate the resulting
performance gain. The final agent achieves a near (.97) perfect win rate
against a random agent. We also explore the effects of pretraining the network
using a collection of positions obtained via self-play.

Most action recognition models today are highly parameterized, and evaluated
on datasets with appearance-wise distinct classes. It has also been shown that
2D Convolutional Neural Networks (CNNs) tend to be biased toward texture rather
than shape in still image recognition tasks, in contrast to humans. Taken
together, this raises suspicion that large video models partly learn spurious
spatial texture correlations rather than to track relevant shapes over time to
infer generalizable semantics from their movement. A natural way to avoid
parameter explosion when learning visual patterns over time is to make use of
recurrence. Biological vision consists of abundant recurrent circuitry, and is
superior to computer vision in terms of domain shift generalization. In this
article, we empirically study whether the choice of low-level temporal modeling
has consequences for texture bias and cross-domain robustness. In order to
enable a light-weight and systematic assessment of the ability to capture
temporal structure, not revealed from single frames, we provide the Temporal
Shape (TS) dataset, as well as modified domains of Diving48 allowing for the
investigation of spatial texture bias in video models. The combined results of
our experiments indicate that sound physical inductive bias such as recurrence
in temporal modeling may be advantageous when robustness to domain shift is
important for the task.

Adapting a definition given by Bjerkevik and Lesnick for multiparameter
persistence modules, we introduce an $\ell^p$-type extension of the
interleaving distance on merge trees. We show that our distance is a metric,
and that it upper-bounds the $p$-Wasserstein distance between the associated
barcodes. For each $p\in[1,\infty]$, we prove that this distance is stable with
respect to cellular sublevel filtrations and that it is the universal (i.e.,
largest) distance satisfying this stability property. In the $p=\infty$ case,
this gives a novel proof of universality for the interleaving distance on merge
trees.

The principled design and discovery of biologically- and physically-informed
models of neuronal dynamics has been advancing since the mid-twentieth century.
Recent developments in artificial intelligence (AI) have accelerated this
progress. This review article gives a high-level overview of the approaches
across different scales of organization and levels of abstraction. The studies
covered in this paper include fundamental models in computational neuroscience,
nonlinear dynamics, data-driven methods, as well as emergent practices. While
not all of these models span the intersection of neuroscience, AI, and system
dynamics, all of them do or can work in tandem as generative models, which, as
we argue, provide superior properties for the analysis of neuroscientific data.
We discuss the limitations and unique dynamical traits of brain data and the
complementary need for hypothesis- and data-driven modeling. By way of
conclusion, we present several hybrid generative models from recent literature
in scientific machine learning, which can be efficiently deployed to yield
interpretable models of neural dynamics.

We design an open-vocabulary image segmentation model to organize an image
into meaningful regions indicated by arbitrary texts. Recent works (CLIP and
ALIGN), despite attaining impressive open-vocabulary classification accuracy
with image-level caption labels, are unable to segment visual concepts with
pixels. We argue that these models miss an important step of visual grouping,
which organizes pixels into groups before learning visual-semantic alignments.
We propose OpenSeg to address the above issue while still making use of
scalable image-level supervision of captions. First, it learns to propose
segmentation masks for possible organizations. Then it learns visual-semantic
alignments by aligning each word in a caption to one or a few predicted masks.
We find the mask representations are the key to support learning image
segmentation from captions, making it possible to scale up the dataset and
vocabulary sizes. OpenSeg significantly outperforms the recent open-vocabulary
method of LSeg by +19.9 mIoU on PASCAL dataset, thanks to its scalability.

This paper studies online optimization from a high-level unified theoretical
perspective. We not only generalize both Optimistic-DA and Optimistic-MD in
normed vector space, but also unify their analysis methods for dynamic regret.
Regret bounds are the tightest possible due to the introduction of
$\phi$-convex. As instantiations, regret bounds of normalized exponentiated
subgradient and greedy/lazy projection are better than the currently known
optimal results. By replacing losses of online game with monotone operators,
and extending the definition of regret, namely regret$^n$, we extend online
convex optimization to online monotone optimization.

We present a generalized potential theory of nonequilibrium torques for the
Landau-Lifshitz equation. The general formulation of exchange forces in terms
of two potential energies allows for the implementation of accurate machine
learning models for adiabatic spin dynamics of out-of-equilibrium itinerant
magnetic systems. To demonstrate our approach, we develop a deep-learning
neural network that successfully learns the forces in a driven s-d model
computed from the nonequilibrium Green's function method. We show that the
Landau-Lifshitz dynamics simulations with forces predicted from the neural-net
model accurately reproduce the voltage-driven domain-wall propagation. Our work
opens a new avenue for multi-scale modeling of nonequilibrium dynamical
phenomena in itinerant magnets and spintronics based on machine-learning
models.

Clues to the identity of dark matter have remained surprisingly elusive,
given the scope of experimental programs aimed at its identification. While
terrestrial experiments may be able to nail down a model, an alternative, and
equally promising, method is to identify dark matter based on astrophysical or
cosmological signatures. A particularly sensitive approach is based on the
unique signature of dark matter substructure on galaxy-galaxy strong lensing
images. Machine learning applications have been explored in detail for
extracting just this signal. With limited availability of high quality strong
lensing data, these approaches have exclusively relied on simulations.
Naturally, due to the differences with the real instrumental data, machine
learning models trained on simulations are expected to lose accuracy when
applied to real data. This is where domain adaptation can serve as a crucial
bridge between simulations and real data applications. In this work, we
demonstrate the power of domain adaptation techniques applied to strong
gravitational lensing data with dark matter substructure. We show with
simulated data sets of varying complexity, that domain adaptation can
significantly mitigate the losses in the model performance. This technique can
help domain experts build and apply better machine learning models for
extracting useful information from strong gravitational lensing data expected
from the upcoming surveys.

Informal learning procedures have been changing extremely fast over the
recent decades not only due to the advent of online learning, but also due to
changes in what humans need to learn to meet their various life and career
goals. Consequently, online, educational platforms are expected to provide
personalized, up-to-date curricula to assist learners. Therefore, in this
paper, we propose an Artificial Intelligence (AI) and Crowdsourcing based
approach to create and update curricula for individual learners. We show the
design of this curriculum development system prototype, in which contributors
receive AI-based recommendations to be able to define and update high-level
learning goals, skills, and learning topics together with associated learning
content. This curriculum development system was also integrated into our
personalized online learning platform. To evaluate our prototype we compared
experts' opinion with our system's recommendations, and resulted in 89%, 79%,
and 93% F1-scores when recommending skills, learning topics, and educational
materials respectively. Also, we interviewed eight senior level experts from
educational institutions and career consulting organizations. Interviewees
agreed that our curriculum development method has high potential to support
authoring activities in dynamic, personalized learning environments.

In the last decade, the use of Machine Learning techniques in anomaly-based
intrusion detection systems has seen much success. However, recent studies have
shown that Machine learning in general and deep learning specifically are
vulnerable to adversarial attacks where the attacker attempts to fool models by
supplying deceptive input. Research in computer vision, where this
vulnerability was first discovered, has shown that adversarial images designed
to fool a specific model can deceive other machine learning models. In this
paper, we investigate the transferability of adversarial network traffic
against multiple machine learning-based intrusion detection systems.
Furthermore, we analyze the robustness of the ensemble intrusion detection
system, which is notorious for its better accuracy compared to a single model,
against the transferability of adversarial attacks. Finally, we examine Detect
& Reject as a defensive mechanism to limit the effect of the transferability
property of adversarial network traffic against machine learning-based
intrusion detection systems.

State-of-the-art deep learning approaches for skin lesion recognition often
require pretraining on larger and more varied datasets, to overcome the
generalization limitations derived from the reduced size of the skin lesion
imaging datasets. ImageNet is often used as the pretraining dataset, but its
transferring potential is hindered by the domain gap between the source dataset
and the target dermatoscopic scenario. In this work, we introduce a novel
pretraining approach that sequentially trains a series of Self-Supervised
Learning pretext tasks and only requires the unlabeled skin lesion imaging
data. We present a simple methodology to establish an ordering that defines a
pretext task curriculum. For the multi-class skin lesion classification
problem, and ISIC-2019 dataset, we provide experimental evidence showing that:
i) a model pretrained by a curriculum of pretext tasks outperforms models
pretrained by individual pretext tasks, and ii) a model pretrained by the
optimal pretext task curriculum outperforms a model pretrained on ImageNet. We
demonstrate that this performance gain is related to the fact that the
curriculum of pretext tasks better focuses the attention of the final model on
the skin lesion. Beyond performance improvement, this strategy allows for a
large reduction in the training time with respect to ImageNet pretraining,
which is especially advantageous for network architectures tailored for a
specific problem.

An unmanned aerial vehicle (UAV) network can serve as an aerial relay to
periodically receive packets from macro base stations (BSs). Severe packet loss
may happen especially when UAVs have bad wireless connections to a BS. In this
paper, a data exchange scheme is proposed utilizing unsupervised learning to
enable efficient lost packet retrieval through reliable wireless transmissions
between UAVs instead of through retransmissions of macro BSs with a longer
delay and higher overhead. With the proposed scheme, all UAVs are assigned to
multiple clusters and a UAV can only request its lost packets to UAVs in the
same cluster. By this way, UAVs in different clusters could carry out their
lost packets retrieval processes simultaneously to expedite data exchange. The
agglomerative hierarchical clustering, a type of unsupervised learning, is used
to conduct clustering, guaranteeing that UAVs clustered together could supply
and supplement each other's lost packets. To further enhance data exchange
efficiency, a data exchange mechanism is designed, where the priority of UAVs
performing data exchange is determined by the number of their lost packets or
the number of requested packets that they can provide. The introduced data
exchange mechanism can make each request-reply process maximally beneficial to
other UAVs' lost packet retrieval in the same cluster. A new random backoff
procedure based on the carrier sense multiple access with collision avoidance
(CSMA/CA) is designed to support the data exchange mechanism. Finally,
simulation studies are conducted to demonstrate the effectiveness and
superiority of our proposed data exchange scheme.

Artificial Intelligence (AI) is now entering every sub-field of science,
technology, engineering, arts, and management. Thanks to the hype and
availability of research funds, it is being adapted in many fields without much
thought. Computational Science and Engineering (CS&E) is one such sub-field. By
highlighting some critical questions around the issues and challenges in
adapting Machine Learning (ML) for CS&E, most of which are often overlooked in
journal papers, this contribution hopes to offer some insights into the
adaptation of ML for applications in CS\&E and related fields. This is a
general-purpose article written for a general audience and researchers new to
the fields of ML and/or CS\&E. This work focuses only on the forward problems
in computational science and engineering. Some basic equations and MATLAB code
are also provided to help the reader understand the basics.

The recent availability of electronic health records (EHRs) have provided
enormous opportunities to develop artificial intelligence (AI) algorithms.
However, patient privacy has become a major concern that limits data sharing
across hospital settings and subsequently hinders the advances in AI.
\textit{Synthetic data}, which benefits from the development and proliferation
of generative models, has served as a promising substitute for real patient EHR
data. However, the current generative models are limited as they only generate
\textit{single type} of clinical data, i.e., either continuous-valued or
discrete-valued. In this paper, we propose a generative adversarial network
(GAN) entitled EHR-M-GAN which synthesizes \textit{mixed-type} timeseries EHR
data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous,
and correlated temporal dynamics in patient trajectories. We have validated
EHR-M-GAN on three publicly-available intensive care unit databases with
records from a total of 141,488 unique patients, and performed privacy risk
evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority in
performance over state-of-the-art benchmarks for synthesizing clinical
timeseries with high fidelity. Notably, prediction models for outcomes of
intensive care performed significantly better when training data was augmented
with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in
developing AI algorithms in resource-limited settings, lowering the barrier for
data acquisition while preserving patient privacy.

This thesis introduces the wide multiple baseline stereo (WxBS) problem.
WxBS, a generalization of the standard wide baseline stereo problem, considers
the matching of images that simultaneously differ in more than one image
acquisition factor such as viewpoint, illumination, sensor type, or where
object appearance changes significantly, e.g., over time. A new dataset with
the ground truth, evaluation metric and baselines has been introduced.
  The thesis presents the following improvements of the WxBS pipeline. (i) A
loss function, called HardNeg, for learning a local image descriptor that
relies on hard negative mining within a mini-batch and on the maximization of
the distance between the closest positive and the closest negative patches.
(ii) The descriptor trained with the HardNeg loss, called HardNet, is compact
and shows state-of-the-art performance in standard matching, patch verification
and retrieval benchmarks. (iii) A method for learning the affine shape,
orientation, and potentially other parameters related to geometric and
appearance properties of local features. (iv) A tentative correspondences
generation strategy which generalizes the standard first to second closest
distance ratio is presented. The selection strategy, which shows performance
superior to the standard method, is applicable to either hard-engineered
descriptors like SIFT, LIOP, and MROGH or deeply learned like HardNet. (v) A
feedback loop is introduced for the two-view matching problem, resulting in
MODS -- matching with on-demand view synthesis -- algorithm. MODS is an
algorithm that handles a viewing angle difference even larger than the previous
state-of-the-art ASIFT algorithm, without a significant increase of
computational cost over "standard" wide and narrow baseline approaches. Last,
but not least, a comprehensive benchmark for local features and robust
estimation algorithms is introduced.

Correctly dealing with categorical data in a supervised learning context is
still a major issue. Furthermore, though some machine learning methods embody
builtin methods to deal with categorical features, it is unclear whether they
bring some improvements and how do they compare with usual categorical encoding
methods. In this paper, we describe several well-known categorical encoding
methods that are based on target statistics and weight of evidence. We apply
them on a large and real credit card fraud detection database. Then, we train
the encoded databases using state-of-the-art gradient boosting methods and
evaluate their performances. We show that categorical encoding methods
generally bring substantial improvements with respect to the absence of
encoding. The contribution of this work is twofold: (1) we compare many
state-of-the-art "lite" categorical encoding methods on a large scale database
and (2) we use a real credit card fraud detection database.

Medical image datasets can have large number of images representing patients
with different health conditions and various disease severity. When dealing
with raw unlabeled image datasets, the large number of samples often makes it
hard for experts and non-experts to understand the variety of images present in
a dataset. Supervised learning methods rely on labeled images which requires a
considerable effort by medical experts to first understand the communities of
images present in the data and then labeling the images. Here, we propose an
algorithm to facilitate the automatic identification of communities in medical
image datasets. We further demonstrate that such analysis can be insightful in
a supervised setting when the images are already labeled. Such insights are
useful because, health and disease severity can be considered a continuous
spectrum, and within each class, there usually are finer communities worthy of
investigation, especially when they have similarities to communities in other
classes. In our approach, we use wavelet decomposition of images in tandem with
spectral methods. We show that the eigenvalues of a graph Laplacian can reveal
the number of notable communities in an image dataset. Moreover, analyzing the
similarities may be used to infer a spectrum representing the severity of the
disease. In our experiments, we use a dataset of images labeled with different
conditions for COVID patients. We detect 25 communities in the dataset and then
observe that only 6 of those communities contain patients with pneumonia. We
also investigate the contents of a colorectal cancer histology dataset.

We present a solid state thermal machine based on quantum dots to generate
steady-state entanglement between distant spins. Unlike previous approaches our
system is controlled by experimentally feasible steady state currents
manipulated by dc voltages. By analyzing the Liouvillian eigenspectrum as a
function of the control parameters, we show that our device operates over a
large voltage region. As an extension, the proposed device also works as an
entanglement thermal machine under a temperature gradient that can even give
rise to entanglement at zero voltage bias. Finally, we highlight a
post-selection scheme based on currently feasible non-demolition measurement
techniques that can generate perfect Bell-pairs from the steady state output of
our thermal machine.

With artificial intelligence (AI) becoming relevant in various parts of
everyday life, other technologies are already widely influenced by the new way
of handling large amounts of data. Although widespread already, AI has had only
punctual influences on the cybersecurity field specifically. Many techniques
and technologies used by cybersecurity experts function through manual labor
and barely draw on automation, e.g., logs are often reviewed manually by system
admins for potentially malicious keywords. This work evaluates the use of a
special type of AI called generative adversarial networks (GANs) for log
generation. More precisely, three different generative adversarial networks,
SeqGAN, MaliGAN, and CoT, are reviewed in this research regarding their
performance, focusing on generating new logs as a means of deceiving system
admins for red teams. Although static generators for fake logs have been around
for a while, their produces are usually easy to reveal as such. Using AI as an
approach to this problem has not been widely researched. Identified challenges
consist of formatting, dates and times, and overall consistency. Summing up the
results, GANs seem not to be a good fit for generating fake logs. Their
capability to detect fake logs, however, might be of use in practical

This paper tackles the problem of semi-supervised learning when the set of
labeled samples is limited to a small number of images per class, typically
less than 10, problem that we refer to as barely-supervised learning. We
analyze in depth the behavior of a state-of-the-art semi-supervised method,
FixMatch, which relies on a weakly-augmented version of an image to obtain
supervision signal for a more strongly-augmented version. We show that it
frequently fails in barely-supervised scenarios, due to a lack of training
signal when no pseudo-label can be predicted with high confidence. We propose a
method to leverage self-supervised methods that provides training signal in the
absence of confident pseudo-labels. We then propose two methods to refine the
pseudo-label selection process which lead to further improvements. The first
one relies on a per-sample history of the model predictions, akin to a voting
scheme. The second iteratively updates class-dependent confidence thresholds to
better explore classes that are under-represented in the pseudo-labels. Our
experiments show that our approach performs significantly better on STL-10 in
the barely-supervised regime, e.g. with 4 or 8 labeled images per class.

Learnable keypoint detectors and descriptors are beginning to outperform
classical hand-crafted feature extraction methods. Recent studies on
self-supervised learning of visual representations have driven the increasing
performance of learnable models based on deep networks. By leveraging
traditional data augmentations and homography transformations, these networks
learn to detect corners under adverse conditions such as extreme illumination
changes. However, their generalization capabilities are limited to corner-like
features detected a priori by classical methods or synthetically generated
data.
  In this paper, we propose the Correspondence Network (CorrNet) that learns to
detect repeatable keypoints and to extract discriminative descriptions via
unsupervised contrastive learning under spatial constraints. Our experiments
show that CorrNet is not only able to detect low-level features such as
corners, but also high-level features that represent similar objects present in
a pair of input images through our proposed joint guided backpropagation of
their latent space. Our approach obtains competitive results under viewpoint
changes and achieves state-of-the-art performance under illumination changes.

Due to the advancement of Generative Adversarial Networks (GAN),
Autoencoders, and other AI technologies, it has been much easier to create fake
images such as "Deepfakes". More recent research has introduced few-shot
learning, which uses a small amount of training data to produce fake images and
videos more effectively. Therefore, the ease of generating manipulated images
and the difficulty of distinguishing those images can cause a serious threat to
our society, such as propagating fake information. However, detecting realistic
fake images generated by the latest AI technology is challenging due to the
reasons mentioned above. In this work, we propose Dual Attention Fake Detection
Fine-tuning Network (DA-FDFtNet) to detect the manipulated fake face images
from the real face data. Our DA-FDFtNet integrates the pre-trained model with
Fine-Tune Transformer, MBblockV3, and a channel attention module to improve the
performance and robustness across different types of fake images. In
particular, Fine-Tune Transformer consists of multiple numbers of an
image-based self-attention module and a down-sampling layer. The channel
attention module is also connected with the pre-trained model to capture the
fake images feature space. We experiment with our DA-FDFtNet with the
FaceForensics++ dataset and various GAN-generated datasets, and we show that
our approach outperforms the previous baseline models.

Research tasks related to human body analysis have been drawing a lot of
attention in computer vision area over the last few decades, considering its
potential benefits on our day-to-day life. Anthropometry is a field defining
physical measures of a human body size, form, and functional capacities.
Specifically, the accurate estimation of anthropometric body measurements from
visual human body data is one of the challenging problems, where the solution
would ease many different areas of applications, including ergonomics, garment
manufacturing, etc. This paper formulates a research in the field of deep
learning and neural networks, to tackle the challenge of body measurements
estimation from various types of visual input data (such as 2D images or 3D
point clouds). Also, we deal with the lack of real human data annotated with
ground truth body measurements required for training and evaluation, by
generating a synthetic dataset of various human body shapes and performing a
skeleton-driven annotation.

Federated Learning (FL) is a decentralized machine learning architecture,
which leverages a large number of remote devices to learn a joint model with
distributed training data. However, the system-heterogeneity is one major
challenge in a FL network to achieve robust distributed learning performance,
which comes from two aspects: i) device-heterogeneity due to the diverse
computational capacity among devices; ii) data-heterogeneity due to the
non-identically distributed data across the network. Prior studies addressing
the heterogeneous FL issue, e.g., FedProx, lack formalization and it remains an
open problem. This work first formalizes the system-heterogeneous FL problem
and proposes a new algorithm, called FedLGA, to address this problem by
bridging the divergence of local model updates via gradient approximation. To
achieve this, FedLGA provides an alternated Hessian estimation method, which
only requires extra linear complexity on the aggregator. Theoretically, we show
that with a device-heterogeneous ratio $\rho$, FedLGA achieves convergence
rates on non-i.i.d. distributed FL training data for the non-convex
optimization problems with $\mathcal{O} \left( \frac{(1+\rho)}{\sqrt{ENT}} +
\frac{1}{T} \right)$ and $\mathcal{O} \left( \frac{(1+\rho)\sqrt{E}}{\sqrt{TK}}
+ \frac{1}{T} \right)$ for full and partial device participation respectively,
where $E$ is the number of local learning epoch, $T$ is the number of total
communication round, $N$ is the total device number and $K$ is the number of
selected device in one communication round under partially participation
scheme. The results of comprehensive experiments on multiple datasets show that
FedLGA outperforms current FL methods against the system-heterogeneity.

This paper presents methods for improving automated essay scoring with
techniques that address the computational trade-offs of self-attention and
document length. To make Automated Essay Scoring (AES) more useful to
practitioners, researchers must overcome the challenges of data and label
availability, authentic and extended writing, domain scoring, prompt and source
variety, and transfer learning. This paper addresses these challenges using
neural network models by employing techniques that preserve essay length as an
important feature without increasing model training costs. It introduces
techniques for minimizing classification loss on ordinal labels using
multi-objective learning, capturing semantic information across the entire
essay using sentence embeddings to use transformer architecture across
arbitrarily long documents, the use of such models for transfer learning,
automated hyperparameter generation based on prompt-corpus metadata, and, most
importantly, the use of semantic information to provide meaningful insights
into student reading through analysis of passage-dependent writing resulting in
state-of-the-art results for various essay tasks.

Likelihood-free Bayesian inference algorithms are popular methods for
calibrating the parameters of complex, stochastic models, required when the
likelihood of the observed data is intractable. These algorithms
characteristically rely heavily on repeated model simulations. However,
whenever the computational cost of simulation is even moderately expensive, the
significant burden incurred by likelihood-free algorithms leaves them unviable
in many practical applications. The multifidelity approach has been introduced
(originally in the context of approximate Bayesian computation) to reduce the
simulation burden of likelihood-free inference without loss of accuracy, by
using the information provided by simulating computationally cheap, approximate
models in place of the model of interest. The first contribution of this work
is to demonstrate that multifidelity techniques can be applied in the general
likelihood-free Bayesian inference setting. Analytical results on the optimal
allocation of computational resources to simulations at different levels of
fidelity are derived, and subsequently implemented practically. We provide an
adaptive multifidelity likelihood-free inference algorithm that learns the
relationships between models at different fidelities and adapts resource
allocation accordingly, and demonstrate that this algorithm produces posterior
estimates with near-optimal efficiency.

Scanning Transmission Electron Microscopy (STEM) offers high-resolution
images that are used to quantify the nanoscale atomic structure and composition
of materials and biological specimens. In many cases, however, the resolution
is limited by the electron beam damage, since in traditional STEM, a focused
electron beam scans every location of the sample in a raster fashion. In this
paper, we propose a scanning method based on the theory of Compressive Sensing
(CS) and subsampling the electron probe locations using a line hop sampling
scheme that significantly reduces the electron beam damage. We experimentally
validate the feasibility of the proposed method by acquiring real CS-STEM data,
and recovering images using a Bayesian dictionary learning approach. We support
the proposed method by applying a series of masks to fully-sampled STEM data to
simulate the expectation of real CS-STEM. Finally, we perform the real data
experimental series using a constrained-dose budget to limit the impact of
electron dose upon the results, by ensuring that the total electron count
remains constant for each image.

Deep reinforcement learning is actively used for training autonomous and
adversarial car policies in a simulated driving environment. Due to the large
availability of various reinforcement learning algorithms and the lack of their
systematic comparison across different driving scenarios, we are unsure of
which ones are more effective for training and testing autonomous car software
in single-agent as well as multi-agent driving environments. A benchmarking
framework for the comparison of deep reinforcement learning in a vision-based
autonomous driving will open up the possibilities for training better
autonomous car driving policies. Furthermore, autonomous cars trained on deep
reinforcement learning-based algorithms are known for being vulnerable to
adversarial attacks. To guard against adversarial attacks, we can train
autonomous cars on adversarial driving policies. However, we lack the knowledge
of which deep reinforcement learning algorithms would act as good adversarial
agents able to effectively test autonomous cars. To address these challenges,
we provide an open and reusable benchmarking framework for systematic
evaluation and comparative analysis of deep reinforcement learning algorithms
for autonomous and adversarial driving in a single- and multi-agent
environment. Using the framework, we perform a comparative study of five
discrete and two continuous action space deep reinforcement learning
algorithms. We run the experiments in a vision-only high fidelity urban driving
simulated environments. The results indicate that only some of the deep
reinforcement learning algorithms perform consistently better across single and
multi-agent scenarios when trained in a multi-agent-only setting.

Continual learning denotes machine learning methods which can adapt to new
environments while retaining and reusing knowledge gained from past
experiences. Such methods address two issues encountered by models in
non-stationary environments: ungeneralisability to new data, and the
catastrophic forgetting of previous knowledge when retrained. This is a
pervasive problem in clinical settings where patient data exhibits covariate
shift not only between populations, but also continuously over time. However,
while continual learning methods have seen nascent success in the imaging
domain, they have been little applied to the multi-variate sequential data
characteristic of critical care patient recordings.
  Here we evaluate a variety of continual learning methods on longitudinal ICU
data in a series of representative healthcare scenarios. We find that while
several methods mitigate short-term forgetting, domain shift remains a
challenging problem over large series of tasks, with only replay based methods
achieving stable long-term performance.
  Code for reproducing all experiments can be found at
https://github.com/iacobo/continual

Autonomous cars are well known for being vulnerable to adversarial attacks
that can compromise the safety of the car and pose danger to other road users.
To effectively defend against adversaries, it is required to not only test
autonomous cars for finding driving errors, but to improve the robustness of
the cars to these errors. To this end, in this paper, we propose a two-step
methodology for autonomous cars that consists of (i) finding failure states in
autonomous cars by training the adversarial driving agent, and (ii) improving
the robustness of autonomous cars by retraining them with effective adversarial
inputs. Our methodology supports testing ACs in a multi-agent environment,
where we train and compare adversarial car policy on two custom reward
functions to test the driving control decision of autonomous cars. We run
experiments in a vision-based high fidelity urban driving simulated
environment. Our results show that adversarial testing can be used for finding
erroneous autonomous driving behavior, followed by adversarial training for
improving the robustness of deep reinforcement learning based autonomous
driving policies. We demonstrate that the autonomous cars retrained using the
effective adversarial inputs noticeably increase the performance of their
driving policies in terms of reduced collision and offroad steering errors.

Recent advances in deep learning, in particular enabled by hardware advances
and big data, have provided impressive results across a wide range of
computational problems such as computer vision, natural language, or
reinforcement learning. Many of these improvements are however constrained to
problems with large-scale curated data-sets which require a lot of human labor
to gather. Additionally, these models tend to generalize poorly under both
slight distributional shifts and low-data regimes. In recent years, emerging
fields such as meta-learning or self-supervised learning have been closing the
gap between proof-of-concept results and real-life applications of machine
learning by extending deep-learning to the semi-supervised and few-shot
domains. We follow this line of work and explore spatio-temporal structure in a
recently introduced image-to-image translation problem in order to: i)
formulate a novel multi-task few-shot image generation benchmark and ii)
explore data augmentations in contrastive pre-training for image translation
downstream tasks. We present several baselines for the few-shot problem and
discuss trade-offs between different approaches. Our code is available at
https://github.com/irugina/meta-image-translation.

We study a stochastic first order primal-dual method for solving
convex-concave saddle point problems over real reflexive Banach spaces using
Bregman divergences and relative smoothness assumptions, in which we allow for
stochastic error in the computation of gradient terms within the algorithm. We
show ergodic convergence in expectation of the Lagrangian optimality gap with a
rate of O(1/k) and that every almost sure weak cluster point of the ergodic
sequence is a saddle point in expectation under mild assumptions. Under
slightly stricter assumptions, we show almost sure weak convergence of the
pointwise iterates to a saddle point. Under a relative strong convexity
assumption on the objective functions and a total convexity assumption on the
entropies of the Bregman divergences, we establish almost sure strong
convergence of the pointwise iterates to a saddle point. Our framework is
general and does not need strong convexity of the entropies inducing the
Bregman divergences in the algorithm. Numerical applications are considered
including entropically regularized Wasserstein barycenter problems and
regularized inverse problems on the simplex.

We study the use of machine learning techniques to solve a fundamental
shortest path problem, which is also known as the single-source many targets
shortest path problem (SSMTSP). Given a directed graph with non-negative edge
weights, our goal is to compute a shortest path from a given source node to any
of several designated target nodes. Basically, our idea is to combine a machine
learning approach with an adapted version of Dijkstra's algorithm to solve this
problem: Based on the trace of Dijkstra's algorithm, we design a neural network
that predicts the shortest path distance after only a few iterations. The
prediction is then used to prune the search space explored by Dijkstra's
algorithm, which allows us to save a significant fraction of operations on the
underlying priority queue. Crucially, our approach always computes the exact
shortest path distances, even if the prediction is inaccurate, and never uses
more queue operations than the standard algorithm. In fact, we are able to
prove a lower bound on the number of queue operations saved by our new
algorithm, which depends on the accuracy of the prediction. Our bound applies
to arbitrary graphs as long as (some of) the edge weights are drawn at random.
Our experimental findings on random graphs confirm these bounds and show that
the actual savings are oftentimes significantly higher.

Machine Learning operations is unarguably a very important and also one of
the hottest topics in Artificial Intelligence lately. Being able to define very
clear hypotheses for actual real-life problems that can be addressed by machine
learning models, collecting and curating large amounts of data for model
training and validation followed by model architecture search and actual
optimization and finally presenting the results fits very well the scenario of
Data Science experiments. This approach however does not supply the needed
procedures and pipelines for the actual deployment of machine learning
capabilities in real production grade systems. Automating live configuration
mechanisms, on the fly adapting to live or offline data capture and
consumption, serving multiple models in parallel either on edge or cloud
architectures, addressing specific limitations of GPU memory or compute power,
post-processing inference or prediction results and serving those either as
APIs or with IoT based communication stacks in the same end-to-end pipeline are
the real challenges that we try to address in this particular paper. In this
paper we present a unified deployment pipeline and freedom-to-operate approach
that supports all above requirements while using basic cross-platform tensor
framework and script language engines.

Automatic few-shot font generation aims to solve a well-defined, real-world
problem because manual font designs are expensive and sensitive to the
expertise of designers. Existing methods learn to disentangle style and content
elements by developing a universal style representation for each font style.
However, this approach limits the model in representing diverse local styles,
because it is unsuitable for complicated letter systems, for example, Chinese,
whose characters consist of a varying number of components (often called
"radical") -- with a highly complex structure. In this paper, we propose a
novel font generation method that learns localized styles, namely
component-wise style representations, instead of universal styles. The proposed
style representations enable the synthesis of complex local details in text
designs. However, learning component-wise styles solely from a few reference
glyphs is infeasible when a target script has a large number of components, for
example, over 200 for Chinese. To reduce the number of required reference
glyphs, we represent component-wise styles by a product of component and style
factors, inspired by low-rank matrix factorization. Owing to the combination of
strong representation and a compact factorization strategy, our method shows
remarkably better few-shot font generation results (with only eight reference
glyphs) than other state-of-the-art methods. Moreover, strong locality
supervision, for example, location of each component, skeleton, or strokes, was
not utilized. The source code is available at https://github.com/clovaai/lffont
and https://github.com/clovaai/fewshot-font-generation.

The microscopic description of the local structure of water remains an open
challenge. Here, we adopt an agnostic approach to understanding water's
hydrogen bond network using data harvested from molecular dynamics simulations
of an empirical water model. A battery of state-of-the-art unsupervised
data-science techniques are used to characterize the free energy landscape of
water starting from encoding the water environment using local-atomic
descriptors, through dimensionality reduction and finally the use of advanced
clustering techniques. Analysis of the free energy at ambient conditions was
found to be consistent with a rough single basin and independent of the choice
of the water model. We find that the fluctuations of the water network occur in
a high-dimensional space which we characterize using a combination of both
atomic descriptors and chemical-intuition based coordinates. We demonstrate
that a combination of both types of variables are needed in order to adequately
capture the complexity of the fluctuations in the hydrogen bond network at
different length-scales both at room temperature and also close to the critical
point of water. Our results provide a general framework for examining
fluctuations in water under different conditions.

This paper presents an annotated portfolio of projects that seek to
understand and communicate the social and societal implications of blockchains,
distributed ledgers and smart contracts. These complex technologies rely on
human and technical factors to deliver cryptocurrencies, shared computation and
trustless protocols but have a secondary benefit in providing a moment to
re-think many aspects of society, and imagine alternative possibilities. The
projects use design and HCI methods to relate blockchains to a range of topics,
including global supply chains, delivery infrastructure, smart grids,
volunteering and charitable giving, through engaging publics, exploring ideas
and speculating on possible futures. Based on an extensive annotated portfolio
we draw out learning for the design of blockchain systems, broadening
participation and surfacing questions around imaginaries, social implications
and engagement with new technology. This paints a comprehensive picture of how
HCI and design can shape understandings of the future of complex technologies.

A well-known approach to describe the dynamics of an open quantum system is
to compute the master equation evolving the reduced density matrix of the
system. This approach plays an important role in describing excitation transfer
through photosynthetic light harvesting complexes (LHCs). The hierarchical
equations of motion (HEOM) was adapted by Ishizaki and Fleming (J. Chem. Phys.,
2009) to simulate open quantum dynamics in the biological regime. We generate a
set of time dependent observables that depict the coherent propagation of
electronic excitations through the LHCs by solving the HEOM. We solve the
inverse problem using classical machine learning (ML) models as this is a
computationally intractable problem. The objective here is to determine whether
a trained ML model can perform Hamiltonian tomography by using the time
dependence of the observables as inputs. We demonstrate the capability of
convolutional neural networks to tackle this research problem. The models
developed here can predict Hamiltonian parameters such as excited state
energies and inter-site couplings of a system up to 99.28\% accuracy and
mean-squared error as low as 0.65.

The rapid expansion of data worldwide invites the need for more distributed
solutions in order to apply machine learning on a much wider scale. The
resultant distributed learning systems can have various degrees of
centralization. In this work, we demonstrate our solution FLoBC for building a
generic decentralized federated learning system using blockchain technology,
accommodating any machine learning model that is compatible with gradient
descent optimization. We present our system design comprising the two
decentralized actors: trainer and validator, alongside our methodology for
ensuring reliable and efficient operation of said system. Finally, we utilize
FLoBC as an experimental sandbox to compare and contrast the effects of
trainer-to-validator ratio, reward-penalty policy, and model synchronization
schemes on the overall system performance, ultimately showing by example that a
decentralized federated learning system is indeed a feasible alternative to
more centralized architectures.

In this paper, we use The Quijote simulations in order to extract the
cosmological parameters through Bayesian Neural Networks. This kind of model
has a remarkable ability to estimate the associated uncertainty, which is one
of the ultimate goals in the precision cosmology era. We demonstrate the
advantages of BNNs for extracting more complex output distributions and
non-Gaussianities information from the simulations.

End to end learning is machine learning starting in raw data and predicting a
desired concept, with all steps done automatically. In software engineering
context, we see it as starting from the source code and predicting process
metrics. This framework can be used for predicting defects, code quality,
productivity and more. End-to-end improves over features based machine learning
by not requiring domain experts and being able to extract new knowledge. We
describe a dataset of 5M files from 15k projects constructed for this goal. The
dataset is constructed in a way that enables not only predicting concepts but
also investigating their causes.

Recommendation systems are perhaps one of the most important agents for
industry growth through the modern Internet world. Previous approaches on
recommendation systems include collaborative filtering and content based
filtering recommendation systems. These 2 methods are disjointed in nature and
require the continuous storage of user preferences for a better recommendation.
To provide better integration of the two processes, we propose a hybrid
recommendation system based on the integration of collaborative and
content-based content, taking into account the top critic consensus and movie
rating score. We would like to present a novel model that recommends movies
based on the combination of user preferences and critical consensus scores.

Brain metastases occur frequently in patients with metastatic cancer. Early
and accurate detection of brain metastases is very essential for treatment
planning and prognosis in radiation therapy. To improve brain metastasis
detection performance with deep learning, a custom detection loss called
volume-level sensitivity-specificity (VSS) is proposed, which rates individual
metastasis detection sensitivity and specificity in (sub-)volume levels. As
sensitivity and precision are always a trade-off in a metastasis level, either
a high sensitivity or a high precision can be achieved by adjusting the weights
in the VSS loss without decline in dice score coefficient for segmented
metastases. To reduce metastasis-like structures being detected as false
positive metastases, a temporal prior volume is proposed as an additional input
of DeepMedic. The modified network is called DeepMedic+ for distinction. Our
proposed VSS loss improves the sensitivity of brain metastasis detection for
DeepMedic, increasing the sensitivity from 85.3% to 97.5%. Alternatively, it
improves the precision from 69.1% to 98.7%. Comparing DeepMedic+ with DeepMedic
with the same VSS loss, 44.4% of the false positive metastases are reduced in
the high sensitivity model and the precision reaches 99.6% for the high
specificity model. The mean dice coefficient for all metastases is about 0.81.
With the ensemble of the high sensitivity and high specificity models, on
average only 1.5 false positive metastases per patient needs further check,
while the majority of true positive metastases are confirmed. The ensemble
learning is able to distinguish high confidence true positive metastases from
metastases candidates that require special expert review or further follow-up,
being particularly well-fit to the requirements of expert support in real
clinical practice.

Testing Machine Learning (ML) models and AI-Infused Applications (AIIAs), or
systems that contain ML models, is highly challenging. In addition to the
challenges of testing classical software, it is acceptable and expected that
statistical ML models sometimes output incorrect results. A major challenge is
to determine when the level of incorrectness, e.g., model accuracy or F1 score
for classifiers, is acceptable and when it is not. In addition to business
requirements that should provide a threshold, it is a best practice to require
any proposed ML solution to out-perform simple baseline models, such as a
decision tree.
  We have developed complexity measures, which quantify how difficult given
observations are to assign to their true class label; these measures can then
be used to automatically determine a baseline performance threshold. These
measures are superior to the best practice baseline in that, for a linear
computation cost, they also quantify each observation' classification
complexity in an explainable form, regardless of the classifier model used. Our
experiments with both numeric synthetic data and real natural language chatbot
data demonstrate that the complexity measures effectively highlight data
regions and observations that are likely to be misclassified.

Object Skeletonization is the process of extracting skeletal, line-like
representations of shapes. It provides a very useful tool for geometric shape
understanding and minimal shape representation. It also has a wide variety of
applications, most notably in anatomical research and activity detection.
Several mathematical algorithmic approaches have been developed to solve this
problem, and some of them have been proven quite robust. However, a lesser
amount of attention has been invested into deep learning solutions for it. In
this paper, we use a 2-stage variant of the famous U-Net architecture to split
the problem space into two sub-problems: shape minimization and corrective
skeleton thinning. Our model produces results that are visually much better
than the baseline SkelNetOn model. We propose a new metric, M-CCORR, based on
normalized correlation coefficients as an alternative to F1 for this challenge
as it solves the problem of class imbalance, managing to recognize skeleton
similarity without suffering from F1's over-sensitivity to pixel-shifts.

Mobile edge computing facilitates users to offload computation tasks to edge
servers for meeting their stringent delay requirements. Previous works mainly
explore task offloading when system-side information is given (e.g., server
processing speed, cellular data rate), or centralized offloading under system
uncertainty. But both generally fall short to handle task placement involving
many coexisting users in a dynamic and uncertain environment. In this paper, we
develop a multi-user offloading framework considering unknown yet stochastic
system-side information to enable a decentralized user-initiated service
placement. Specifically, we formulate the dynamic task placement as an online
multi-user multi-armed bandit process, and propose a decentralized epoch based
offloading (DEBO) to optimize user rewards which are subjected under network
delay. We show that DEBO can deduce the optimal user-server assignment, thereby
achieving a close-to-optimal service performance and tight O(log T) offloading
regret. Moreover, we generalize DEBO to various common scenarios such as
unknown reward gap, dynamic entering or leaving of clients, and fair reward
distribution, while further exploring when users' offloaded tasks require
heterogeneous computing resources. Particularly, we accomplish a sub-linear
regret for each of these instances. Real measurements based evaluations
corroborate the superiority of our offloading schemes over state-of-the-art
approaches in optimizing delay-sensitive rewards.

Flux inversion is the process by which sources and sinks of a gas are
identified from observations of gas mole fraction. The inversion often involves
running a Lagrangian particle dispersion model (LPDM) to generate sensitivities
between observations and fluxes over a spatial domain of interest. The LPDM
must be run backward in time for every gas measurement, and this can be
computationally prohibitive. To address this problem, here we develop a novel
spatio-temporal emulator for LPDM sensitivities that is built using a
convolutional variational autoencoder (CVAE). With the encoder segment of the
CVAE, we obtain approximate (variational) posterior distributions over latent
variables in a low-dimensional space. We then use a spatio-temporal Gaussian
process emulator on the low-dimensional space to emulate new variables at
prediction locations and time points. Emulated variables are then passed
through the decoder segment of the CVAE to yield emulated sensitivities. We
show that our CVAE-based emulator outperforms the more traditional emulator
built using empirical orthogonal functions and that it can be used with
different LPDMs. We conclude that our emulation-based approach can be used to
reliably reduce the computing time needed to generate LPDM outputs for use in
high-resolution flux inversions.

Efficient omission of symmetric solution candidates is essential for
combinatorial problem-solving. Most of the existing approaches are
instance-specific and focus on the automatic computation of Symmetry Breaking
Constraints (SBCs) for each given problem instance. However, the application of
such approaches to large-scale instances or advanced problem encodings might be
problematic since the computed SBCs are propositional and, therefore, can
neither be meaningfully interpreted nor transferred to other instances. As a
result, a time-consuming recomputation of SBCs must be done before every
invocation of a solver.
  To overcome these limitations, we introduce a new model-oriented approach for
Answer Set Programming that lifts the SBCs of small problem instances into a
set of interpretable first-order constraints using the Inductive Logic
Programming paradigm. Experiments demonstrate the ability of our framework to
learn general constraints from instance-specific SBCs for a collection of
combinatorial problems. The obtained results indicate that our approach
significantly outperforms a state-of-the-art instance-specific method as well
as the direct application of a solver.

Development of the new methods of surface water observation is crucial in the
perspective of increasingly frequent extreme hydrological events related to
global warming and increasing demand for water. Orthophotos and digital surface
models (DSMs) obtained using UAV photogrammetry can be used to determine the
Water Surface Elevation (WSE) of a river. However, this task is difficult due
to disturbances of the water surface on DSMs caused by limitations of
photogrammetric algorithms. In this study, machine learning was used to extract
a WSE value from disturbed photogrammetric data. A brand new dataset has been
prepared specifically for this purpose by hydrology and photogrammetry experts.
The new method is an important step toward automating water surface level
measurements with high spatial and temporal resolution. Such data can be used
to validate and calibrate of hydrological, hydraulic and hydrodynamic models
making hydrological forecasts more accurate, in particular predicting extreme
and dangerous events such as floods or droughts. For our knowledge this is the
first approach in which dataset was created for this purpose and deep learning
models were used for this task. Additionally, neuroevolution algorithm was set
to explore different architectures to find local optimal models and
non-gradient search was performed to fine-tune the model parameters. The
achieved results have better accuracy compared to manual methods of determining
WSE from photogrammetric DSMs.

We propose neural-symbolic integration for abstract concept explanation and
interactive learning. Neural-symbolic integration and explanation allow users
and domain-experts to learn about the data-driven decision making process of
large neural models. The models are queried using a symbolic logic language.
Interaction with the user then confirms or rejects a revision of the neural
model using logic-based constraints that can be distilled into the model
architecture. The approach is illustrated using the Logic Tensor Network
framework alongside Concept Activation Vectors and applied to a Convolutional
Neural Network.

We present a new deep-neural-network (DNN) based error correction code for
fading channels with output feedback, called deep SNR-robust feedback (DRF)
code. At the encoder, parity symbols are generated by a long short term memory
(LSTM) network based on the message as well as the past forward channel outputs
observed by the transmitter in a noisy fashion. The decoder uses a
bi-directional LSTM architecture along with a signal to noise ratio (SNR)-aware
attention NN to decode the message. The proposed code overcomes two major
shortcomings of the previously proposed DNN-based codes over channels with
passive output feedback: (i) the SNR-aware attention mechanism at the decoder
enables reliable application of the same trained NN over a wide range of SNR
values; (ii) curriculum training with batch-size scheduling is used to speed up
and stabilize training while improving the SNR-robustness of the resulting
code. We show that the DRF codes significantly outperform state-of-the-art in
terms of both the SNR-robustness and the error rate in additive white Gaussian
noise (AWGN) channel with feedback. In fading channels with perfect phase
compensation at the receiver, DRF codes learn to efficiently exploit knowledge
of the instantaneous fading amplitude (which is available to the encoder
through feedback) to reduce the overhead and complexity associated with channel
estimation at the decoder. Finally, we show the effectiveness of DRF codes in
multicast channels with feedback, where linear feedback codes are known to be
strictly suboptimal.

Image denoising is of vital importance in many imaging or computer vision
related areas. With the convolutional neural networks showing strong capability
in computer vision tasks, the performance of image denoising has also been
brought up by CNN based methods. Though CNN based image denoisers show
promising results on this task, most of the current CNN based methods try to
learn the mapping from noisy image to clean image directly, which lacks the
explicit exploration of prior knowledge of images and noises. Natural images
are observed to obey the reciprocal power law, implying the low-frequency band
of image tend to occupy most of the energy. Thus in the condition of AGWN
(additive gaussian white noise) deterioration, low-frequency band tend to
preserve a higher PSNR than high-frequency band. Considering the spatial
morphological consistency of different frequency bands, low-frequency band with
more fidelity can be used as a guidance to refine the more contaminated
high-frequency bands. Based on this thought, we proposed a novel network
architecture denoted as IGNet, in order to refine the frequency bands from low
to high in a progressive manner. Firstly, it decomposes the feature maps into
high- and low-frequency subbands using DWT (discrete wavelet transform)
iteratively, and then each low band features are used to refine the high band
features. Finally, the refined feature maps are processed by a decoder to
recover the clean result. With this design, more inter-frequency prior and
information are utilized, thus the model size can be lightened while still
perserves competitive results. Experiments on several public datasets show that
our model obtains competitive performance comparing with other state-of-the-art
methods yet with a lightweight structure.

The last advances in sequence modeling are mainly based on deep learning
approaches. The current state of the art involves the use of variations of the
standard LSTM architecture, combined with several tricks that improve the final
prediction rates of the trained neural networks. However, in some cases, these
adaptations might be too much tuned to the particular problems being addressed.
In this article, we show that a very simple idea, to add a direct connection
between the input and the output, skipping the recurrent module, leads to an
increase of the prediction accuracy in sequence modeling problems related to
natural language processing. Experiments carried out on different problems show
that the addition of this kind of connection to a recurrent network always
improves the results, regardless of the architecture and training-specific
details. When this idea is introduced into the models that lead the field, the
resulting networks achieve a new state-of-the-art perplexity in language
modeling problems.

Conversational recommendation system (CRS) is able to obtain fine-grained and
dynamic user preferences based on interactive dialogue. Previous CRS assumes
that the user has a clear target item. However, for many users who resort to
CRS, they might not have a clear idea about what they really like.
Specifically, the user may have a clear single preference for some attribute
types (e.g. color) of items, while for other attribute types, the user may have
multiple preferences or even no clear preferences, which leads to multiple
acceptable attribute instances (e.g. black and red) of one attribute type.
Therefore, the users could show their preferences over items under multiple
combinations of attribute instances rather than a single item with unique
combination of all attribute instances. As a result, we first propose a more
realistic CRS learning setting, namely Multi-Interest Multi-round
Conversational Recommendation, where users may have multiple interests in
attribute instance combinations and accept multiple items with partially
overlapped combinations of attribute instances. To effectively cope with the
new CRS learning setting, in this paper, we propose a novel learning framework
namely, Multi-Choice questions based Multi-Interest Policy Learning . In order
to obtain user preferences more efficiently, the agent generates multi-choice
questions rather than binary yes/no ones on specific attribute instance.
Besides, we propose a union set strategy to select candidate items instead of
existing intersection set strategy in order to overcome over-filtering items
during the conversation. Finally, we design a Multi-Interest Policy Learning
module, which utilizes captured multiple interests of the user to decide next
action, either asking attribute instances or recommending items. Extensive
experimental results on four datasets verify the superiority of our method for
the proposed setting.

Entropic Outlier Sparsification (EOS) is proposed as a robust computational
strategy for the detection of data anomalies in a broad class of learning
methods, including the unsupervised problems (like detection of non-Gaussian
outliers in mostly-Gaussian data) and in the supervised learning with
mislabeled data. EOS dwells on the derived analytic closed-form solution of the
(weighted) expected error minimization problem subject to the Shannon entropy
regularization. In contrast to common regularization strategies requiring
computational costs that scale polynomial with the data dimension, identified
closed-form solution is proven to impose additional iteration costs that depend
linearly on statistics size and are independent of data dimension. Obtained
analytic results also explain why the mixtures of spherically-symmetric
Gaussians - used heuristically in many popular data analysis algorithms -
represent an optimal choice for the non-parametric probability distributions
when working with squared Euclidean distances, combining expected error
minimality, maximal entropy/unbiasedness, and a linear cost scaling. The
performance of EOS is compared to a range of commonly-used tools on synthetic
problems and on partially-mislabeled supervised classification problems from
biomedicine.

Many recent problems in signal processing and machine learning such as
compressed sensing, image restoration, matrix/tensor recovery, and non-negative
matrix factorization can be cast as constrained optimization. Projected
gradient descent is a simple yet efficient method for solving such constrained
optimization problems. Local convergence analysis furthers our understanding of
its asymptotic behavior near the solution, offering sharper bounds on the
convergence rate compared to global convergence analysis. However, local
guarantees often appear scattered in problem-specific areas of machine learning
and signal processing. This manuscript presents a unified framework for the
local convergence analysis of projected gradient descent in the context of
constrained least squares. The proposed analysis offers insights into pivotal
local convergence properties such as the conditions for linear convergence, the
region of convergence, the exact asymptotic rate of convergence, and the bound
on the number of iterations needed to reach a certain level of accuracy. To
demonstrate the applicability of the proposed approach, we present a recipe for
the convergence analysis of projected gradient descent and demonstrate it via a
beginning-to-end application of the recipe on four fundamental problems,
namely, linear equality-constrained least squares, sparse recovery, least
squares with the unit norm constraint, and matrix completion.

In all areas of human knowledge, datasets are increasing in both size and
complexity, creating the need for richer statistical models. This trend is also
true for economic data, where high-dimensional and nonlinear/nonparametric
inference is the norm in several fields of applied econometric work. The
purpose of this paper is to introduce the reader to the world of Bayesian model
determination, by surveying modern shrinkage and variable selection algorithms
and methodologies. Bayesian inference is a natural probabilistic framework for
quantifying uncertainty and learning about model parameters, and this feature
is particularly important for inference in modern models of high dimensions and
increased complexity.
  We begin with a linear regression setting in order to introduce various
classes of priors that lead to shrinkage/sparse estimators of comparable value
to popular penalized likelihood estimators (e.g.\ ridge, lasso). We explore
various methods of exact and approximate inference, and discuss their pros and
cons. Finally, we explore how priors developed for the simple regression
setting can be extended in a straightforward way to various classes of
interesting econometric models. In particular, the following case-studies are
considered, that demonstrate application of Bayesian shrinkage and variable
selection strategies to popular econometric contexts: i) vector autoregressive
models; ii) factor models; iii) time-varying parameter regressions; iv)
confounder selection in treatment effects models; and v) quantile regression
models. A MATLAB package and an accompanying technical manual allow the reader
to replicate many of the algorithms described in this review.

Audiovisual scenes are pervasive in our daily life. It is commonplace for
humans to discriminatively localize different sounding objects but quite
challenging for machines to achieve class-aware sounding objects localization
without category annotations, i.e., localizing the sounding object and
recognizing its category. To address this problem, we propose a two-stage
step-by-step learning framework to localize and recognize sounding objects in
complex audiovisual scenarios using only the correspondence between audio and
vision. First, we propose to determine the sounding area via coarse-grained
audiovisual correspondence in the single source cases. Then visual features in
the sounding area are leveraged as candidate object representations to
establish a category-representation object dictionary for expressive visual
character extraction. We generate class-aware object localization maps in
cocktail-party scenarios and use audiovisual correspondence to suppress silent
areas by referring to this dictionary. Finally, we employ category-level
audiovisual consistency as the supervision to achieve fine-grained audio and
sounding object distribution alignment. Experiments on both realistic and
synthesized videos show that our model is superior in localizing and
recognizing objects as well as filtering out silent ones. We also transfer the
learned audiovisual network into the unsupervised object detection task,
obtaining reasonable performance.

Contrastive losses have long been a key ingredient of deep metric learning
and are now becoming more popular due to the success of self-supervised
learning. Recent research has shown the benefit of decomposing such losses into
two sub-losses which act in a complementary way when learning the
representation network: a positive term and an entropy term. Although the
overall loss is thus defined as a combination of two terms, the balance of
these two terms is often hidden behind implementation details and is largely
ignored and sub-optimal in practice. In this work, we approach the balance of
contrastive losses as a hyper-parameter optimization problem, and propose a
coordinate descent-based search method that efficiently find the
hyper-parameters that optimize evaluation performance. In the process, we
extend existing balance analyses to the contrastive margin loss, include batch
size in the balance, and explain how to aggregate loss elements from the batch
to maintain near-optimal performance over a larger range of batch sizes.
Extensive experiments with benchmarks from deep metric learning and
self-supervised learning show that optimal hyper-parameters are found faster
with our method than with other common search methods.

Implicit discourse relation recognition (IDRR) is a challenging but crucial
task in discourse analysis. Most existing methods train multiple models to
predict multi-level labels independently, while ignoring the dependence between
hierarchically structured labels. In this paper, we consider multi-level IDRR
as a conditional label sequence generation task and propose a Label
Dependence-aware Sequence Generation Model (LDSGM) for it. Specifically, we
first design a label attentive encoder to learn the global representation of an
input instance and its level-specific contexts, where the label dependence is
integrated to obtain better label embeddings. Then, we employ a label sequence
decoder to output the predicted labels in a top-down manner, where the
predicted higher-level labels are directly used to guide the label prediction
at the current level. We further develop a mutual learning enhanced training
method to exploit the label dependence in a bottomup direction, which is
captured by an auxiliary decoder introduced during training. Experimental
results on the PDTB dataset show that our model achieves the state-of-the-art
performance on multi-level IDRR. We will release our code at
https://github.com/nlpersECJTU/LDSGM.

This paper offers a comprehensive review of the research on Natural Language
Generation (NLG) over the past two decades, especially in relation to
data-to-text generation and text-to-text generation deep learning methods, as
well as new applications of NLG technology. This survey aims to (a) give the
latest synthesis of deep learning research on the NLG core tasks, as well as
the architectures adopted in the field; (b) detail meticulously and
comprehensively various NLG tasks and datasets, and draw attention to the
challenges in NLG evaluation, focusing on different evaluation methods and
their relationships; (c) highlight some future emphasis and relatively recent
research issues that arise due to the increasing synergy between NLG and other
artificial intelligence areas, such as computer vision, text and computational

Digraph Representation Learning (DRL) aims to learn representations for
directed homogeneous graphs (digraphs). Prior work in DRL is largely
constrained (e.g., limited to directed acyclic graphs), or has poor
generalizability across tasks (e.g., evaluated solely on one task). Most Graph
Neural Networks (GNNs) exhibit poor performance on digraphs due to the neglect
of modeling neighborhoods and preserving asymmetry. In this paper, we address
these notable challenges by leveraging hyperbolic collaborative learning from
multi-ordered and partitioned neighborhoods, and regularizers inspired by
socio-psychological factors. Our resulting formalism, Digraph Hyperbolic
Networks (D-HYPR) - albeit conceptually simple - generalizes to digraphs where
cycles and non-transitive relations are common, and is applicable to multiple
downstream tasks including node classification, link presence prediction, and
link property prediction. In order to assess the effectiveness of D-HYPR,
extensive evaluations were performed across 8 real-world digraph datasets
involving 21 prior techniques. D-HYPR statistically significantly outperforms
the current state of the art. We release our code at
https://github.com/hongluzhou/dhypr

Information-theoretic stealth attacks are data injection attacks that
minimize the amount of information acquired by the operator about the state
variables, while simultaneously limiting the Kullback-Leibler divergence
between the distribution of the measurements under attack and the distribution
under normal operation with the aim of controling the probability of detection.
For Gaussian distributed state variables, attack construction requires
knowledge of the second order statistics of the state variables, which is
estimated from a finite number of past realizations using a sample covariance
matrix. Within this framework, the attack performance is studied for the attack
construction with the sample covariance matrix. This results in an analysis of
the amount of data required to learn the covariance matrix of the state
variables used on the attack construction. The ergodic attack performance is
characterized using asymptotic random matrix theory tools, and the variance of
the attack performance is bounded. The ergodic performance and the variance
bounds are assessed with simulations on IEEE test systems.

We address planning and navigation in challenging 3D video games featuring
maps with disconnected regions reachable by agents using special actions. In
this setting, classical symbolic planners are not applicable or difficult to
adapt. We introduce a hybrid technique combining a low level policy trained
with reinforcement learning and a graph based high level classical planner. In
addition to providing human-interpretable paths, the approach improves the
generalization performance of an end-to-end approach in unseen maps, where it
achieves a 20% absolute increase in success rate over a recurrent end-to-end
agent on a point to point navigation task in yet unseen large-scale maps of
size 1km x 1km. In an in-depth experimental study, we quantify the limitations
of end-to-end Deep RL approaches in vast environments and we also introduce
"GameRLand3D", a new benchmark and soon to be released environment can generate
complex procedural 3D maps for navigation tasks.

This work proposes novel synchronous, asynchronous, and session-based designs
for energy-efficient massive multiple-input multiple-output networks to support
federated learning (FL). The synchronous design relies on strict
synchronization among users when executing each FL communication round, while
the asynchronous design allows more flexibility for users to save energy by
using lower computing frequencies. The session-based design splits the downlink
and uplink phases in each FL communication round into separate sessions. In
this design, we assign users such that one of the participating users in each
session finishes its transmission and does not join the next session. As such,
more power and degrees of freedom will be allocated to unfinished users,
leading to higher rates, lower transmission times, and hence, a higher energy
efficiency. In all three designs, we use zero-forcing processing for both
uplink and downlink, and develop algorithms that optimize user assignment, time
allocation, power, and computing frequencies to minimize the energy consumption
at the base station and users, while guaranteeing a predefined maximum
execution time of one FL communication round.

The temporal aspect of blockchain transactions enables us to study the
address's behavior and detect if it is involved in any illicit activity.
However, due to the concept of change addresses (used to thwart replay
attacks), temporal aspects are not directly applicable in the Bitcoin
blockchain. Several pre-processing steps should be performed before such
temporal aspects are utilized. We are motivated to study the Bitcoin
transaction network and use the temporal features such as burst,
attractiveness, and inter-event time along with several graph-based properties
such as the degree of node and clustering coefficient to validate the
applicability of already existing approaches known for other cryptocurrency
blockchains on the Bitcoin blockchain. We generate the temporal and
non-temporal feature set and train the Machine Learning (ML) algorithm over
different temporal granularities to validate the state-of-the-art methods. We
study the behavior of the addresses over different time granularities of the
dataset. We identify that after applying change-address clustering, in Bitcoin,
existing temporal features can be extracted and ML approaches can be applied. A
comparative analysis of results show that the behavior of addresses in Ethereum
and Bitcoin is similar with respect to in-degree, out-degree and inter-event
time. Further, we identify 3 suspects that showed malicious behavior across
different temporal granularities. These suspects are not marked as malicious in
Bitcoin.

Emotion recognition in conversation (ERC) aims to detect the emotion label
for each utterance. Motivated by recent studies which have proven that feeding
training examples in a meaningful order rather than considering them randomly
can boost the performance of models, we propose an ERC-oriented hybrid
curriculum learning framework. Our framework consists of two curricula: (1)
conversation-level curriculum (CC); and (2) utterance-level curriculum (UC). In
CC, we construct a difficulty measurer based on "emotion shift" frequency
within a conversation, then the conversations are scheduled in an "easy to
hard" schema according to the difficulty score returned by the difficulty
measurer. For UC, it is implemented from an emotion-similarity perspective,
which progressively strengthens the model's ability in identifying the
confusing emotions. With the proposed model-agnostic hybrid curriculum learning
strategy, we observe significant performance boosts over a wide range of
existing ERC models and we are able to achieve new state-of-the-art results on
four public ERC datasets.

Deep learning approaches have achieved highly accurate face recognition by
training the models with very large face image datasets. Unlike the
availability of large 2D face image datasets, there is a lack of large 3D face
datasets available to the public. Existing public 3D face datasets were usually
collected with few subjects, leading to the over-fitting problem. This paper
proposes two CNN models to improve the RGB-D face recognition task. The first
is a segmentation-aware depth estimation network, called DepthNet, which
estimates depth maps from RGB face images by including semantic segmentation
information for more accurate face region localization. The other is a novel
mask-guided RGB-D face recognition model that contains an RGB recognition
branch, a depth map recognition branch, and an auxiliary segmentation mask
branch with a spatial attention module. Our DepthNet is used to augment a large
2D face image dataset to a large RGB-D face dataset, which is used for training
an accurate RGB-D face recognition model. Furthermore, the proposed mask-guided
RGB-D face recognition model can fully exploit the depth map and segmentation
mask information and is more robust against pose variation than previous
methods. Our experimental results show that DepthNet can produce more reliable
depth maps from face images with the segmentation mask. Our mask-guided face
recognition model outperforms state-of-the-art methods on several public 3D
face datasets.

We study the problem of training a Reinforcement Learning (RL) agent that is
collaborative with humans without using any human data. Although such agents
can be obtained through self-play training, they can suffer significantly from
distributional shift when paired with unencountered partners, such as humans.
To mitigate this distributional shift, we propose Maximum Entropy
Population-based training (MEP). In MEP, agents in the population are trained
with our derived Population Entropy bonus to promote both pairwise diversity
between agents and individual diversity of agents themselves, and a common best
agent is trained by paring with agents in this diversified population via
prioritized sampling. The prioritization is dynamically adjusted based on the
training progress. We demonstrate the effectiveness of our method MEP, with
comparison to Self-Play PPO (SP), Population-Based Training (PBT), Trajectory
Diversity (TrajeDi), and Fictitious Co-Play (FCP) in the Overcooked game
environment, with partners being human proxy models and real humans. A
supplementary video showing experimental results is available at
https://youtu.be/Xh-FKD0AAKE.

Image regression tasks for medical applications, such as bone mineral density
(BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play
an important role in computer-aided disease assessment. Most deep regression
methods train the neural network with a single regression loss function like
MSE or L1 loss. In this paper, we propose the first contrastive learning
framework for deep image regression, namely AdaCon, which consists of a feature
learning branch via a novel adaptive-margin contrastive loss and a regression
prediction branch. Our method incorporates label distance relationships as part
of the learned feature representations, which allows for better performance in
downstream regression tasks. Moreover, it can be used as a plug-and-play module
to improve performance of existing regression methods. We demonstrate the
effectiveness of AdaCon on two medical image regression tasks, ie, bone mineral
density estimation from X-ray images and left-ventricular ejection fraction
prediction from echocardiogram videos. AdaCon leads to relative improvements of
3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction
methods, respectively.

Humans are able to learn to recognize new objects even from a few examples.
In contrast, training deep-learning-based object detectors requires huge
amounts of annotated data. To avoid the need to acquire and annotate these huge
amounts of data, few-shot object detection aims to learn from few object
instances of new categories in the target domain. In this survey, we provide an
overview of the state of the art in few-shot object detection. We categorize
approaches according to their training scheme and architectural layout. For
each type of approaches, we describe the general realization as well as
concepts to improve the performance on novel categories. Whenever appropriate,
we give short takeaways regarding these concepts in order to highlight the best
ideas. Eventually, we introduce commonly used datasets and their evaluation
protocols and analyze reported benchmark results. As a result, we emphasize
common challenges in evaluation and identify the most promising current trends
in this emerging field of few-shot object detection.

Decompilation transforms low-level program languages (PL) (e.g., binary code)
into high-level PLs (e.g., C/C++). It has been widely used when analysts
perform security analysis on software (systems) whose source code is
unavailable, such as vulnerability search and malware analysis. However,
current decompilation tools usually need lots of experts' efforts, even for
years, to generate the rules for decompilation, which also requires long-term
maintenance as the syntax of high-level PL or low-level PL changes. Also, an
ideal decompiler should concisely generate high-level PL with similar
functionality to the source low-level PL and semantic information (e.g.,
meaningful variable names), just like human-written code. Unfortunately,
existing manually-defined rule-based decompilation techniques only functionally
restore the low-level PL to a similar high-level PL and are still powerless to
recover semantic information. In this paper, we propose a novel neural
decompilation approach to translate low-level PL into accurate and
user-friendly high-level PL, effectively improving its readability and
understandability. Furthermore, we implement the proposed approaches called
SEAM. Evaluations on four real-world applications show that SEAM has an average
accuracy of 94.41%, which is much better than prior neural machine translation
(NMT) models. Finally, we evaluate the effectiveness of semantic information
recovery through a questionnaire survey, and the average accuracy is 92.64%,
which is comparable or superior to the state-of-the-art compilers.

Mixed communities of organisms are found in many environments (from the human
gut to marine ecosystems) and can have profound impact on human health and the
environment. Metagenomics studies the genomic material of such communities
through high-throughput sequencing that yields DNA subsequences for subsequent
analysis. A fundamental problem in the standard workflow, called binning, is to
discover clusters, of genomic subsequences, associated with the unknown
constituent organisms. Inherent noise in the subsequences, various biological
constraints that need to be imposed on them and the skewed cluster size
distribution exacerbate the difficulty of this unsupervised learning problem.
In this paper, we present a new formulation using a graph where the nodes are
subsequences and edges represent homophily information. In addition, we model
biological constraints providing heterophilous signal about nodes that cannot
be clustered together. We solve the binning problem by developing new
algorithms for (i) graph representation learning that preserves both homophily
relations and heterophily constraints (ii) constraint-based graph clustering
method that addresses the problems of skewed cluster size distribution.
Extensive experiments, on real and synthetic datasets, demonstrate that our
approach, called RepBin, outperforms a wide variety of competing methods. Our
constraint-based graph representation learning and clustering methods, that may
be useful in other domains as well, advance the state-of-the-art in both
metagenomics binning and graph representation learning.

Recently, many approaches tackle the Unsupervised Domain Adaptive person
re-identification (UDA re-ID) problem through pseudo-label-based contrastive
learning. During training, a uni-centroid representation is obtained by simply
averaging all the instance features from a cluster with the same pseudo label.
However, a cluster may contain images with different identities (label noises)
due to the imperfect clustering results, which makes the uni-centroid
representation inappropriate. In this paper, we present a novel Multi-Centroid
Memory (MCM) to adaptively capture different identity information within the
cluster. MCM can effectively alleviate the issue of label noises by selecting
proper positive/negative centroids for the query image. Moreover, we further
propose two strategies to improve the contrastive learning process. First, we
present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully
explore intradomain information by comparing samples only from the same domain.
Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant
and informative negative samples. We integrate MCM, DSCL, and SONI into a
unified framework named Multi-Centroid Representation Network (MCRN). Extensive
experiments demonstrate the superiority of MCRN over state-of-the-art
approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks.

We present squareplus, an activation function that resembles softplus, but
which can be computed using only algebraic operations: addition,
multiplication, and square-root. Because squareplus is ~6x faster to evaluate
than softplus on a CPU and does not require access to transcendental functions,
it may have practical value in resource-limited deep learning applications.

Motivated by recent progress in quantum technologies and in particular
quantum software, research and industrial communities have been trying to
discover new applications of quantum algorithms such as quantum optimization
and machine learning. Regardless of which hardware platform these novel
algorithms operate on, whether it is adiabatic or gate based, from theoretical
point of view, they are performing drastically better than their classical
counterparts. Two promising areas of quantum algorithms quantum machine
learning and quantum optimization. These are based on performing matrix
operations using quantum states and operation, in order to speed up data
analysis where quantum computing can efficiently work with high dimensional
vectors. Motivated by that, quantum inspired algorithms (e.g. for
recommendation systems and principal component analysis) are developed to cope
with high dimensionality using probabilistic techniques that are inspire from
quantum computing. In this paper we review recent progress in the area of
quantum inspired algorithms for low rank matrix approximation. We further
explore the possibility of using parametrized complexity for such algorithms to
refine practical complexity analysis. Finally, we conjecture that quantum
inspired algorithms that use low rank approximation and also sample and query
technique for input representations are Fixed Parameter Tractable (FPT).

We introduce a novel cost aggregation network, dubbed Volumetric Aggregation
with Transformers (VAT), to tackle the few-shot segmentation task by using both
convolutions and transformers to efficiently handle high dimensional
correlation maps between query and support. In specific, we propose our encoder
consisting of volume embedding module to not only transform the correlation
maps into more tractable size but also inject some convolutional inductive bias
and volumetric transformer module for the cost aggregation. Our encoder has a
pyramidal structure to let the coarser level aggregation to guide the finer
level and enforce to learn complementary matching scores. We then feed the
output into our affinity-aware decoder along with the projected feature maps
for guiding the segmentation process. Combining these components, we conduct
experiments to demonstrate the effectiveness of the proposed method, and our
method sets a new state-of-the-art for all the standard benchmarks in few-shot
segmentation task. Furthermore, we find that the proposed method attains
state-of-the-art performance even for the standard benchmarks in semantic
correspondence task although not specifically designed for this task. We also
provide an extensive ablation study to validate our architectural choices. The
trained weights and codes are available at: https://seokju-cho.github.io/VAT/.

Graph Neural Networks (GNNs) have shown great success in many applications
such as recommendation systems, molecular property prediction, traffic
prediction, etc. Recently, CPU-FPGA heterogeneous platforms have been used to
accelerate many applications by exploiting customizable data path and abundant
user-controllable on-chip memory resources of FPGAs. Yet, accelerating and
deploying GNN training on such platforms requires not only expertise in
hardware design but also substantial development efforts.
  We propose HP-GNN, a novel framework that generates high throughput GNN
training implementations on a given CPU-FPGA platform that can benefit both
application developers and machine learning researchers. HP-GNN takes GNN
training algorithms, GNN models as the inputs, and automatically performs
hardware mapping onto the target CPU-FPGA platform. HP-GNN consists of: (1)
data layout and internal representation that reduce the memory traffic and
random memory accesses; (2) optimized hardware templates that support various
GNN models; (3) a design space exploration engine for automatic hardware
mapping; (4) high-level application programming interfaces (APIs) that allows
users to specify GNN training with only a handful of lines of code. To evaluate
HP-GNN, we experiment with two well-known sampling-based GNN training
algorithms and two GNN models. For each training algorithm and model, HP-GNN
generates implementation on a state-of-the-art CPU-FPGA platform. Compared with
CPU-only and CPU-GPU platforms, experimental results show that the generated
implementations achieve $55.67\times$ and $2.17\times$ speedup on the average,
respectively. Compared with the state-of-the-art GNN training implementations,
HP-GNN achieves up to $4.45\times$ speedup.

I present a deep reinforcement learning (RL) solution to the mathematical
problem known as the Newsvendor model, which seeks to optimize profit given a
probabilistic demand distribution. To reflect a more realistic and complex
situation, the demand distribution can change for different days of the week,
thus changing the optimum behavior. I used a Twin-Delayed Deep Deterministic
Policy Gradient agent (written as completely original code) with both an actor
and critic network to solve this problem. The agent was able to learn optimal
behavior consistent with the analytical solution of the problem, and could
identify separate probability distributions for different days of the week and
behave accordingly.

Forming a molecular candidate set that contains a wide range of potentially
effective compounds is crucial to the success of drug discovery. While most
databases and machine-learning-based generation models aim to optimize
particular chemical properties, there is limited literature on how to properly
measure the coverage of the chemical space by those candidates included or
generated. This problem is challenging due to the lack of formal criteria to
select good measures of the chemical space. In this paper, we propose a novel
evaluation framework for measures of the chemical space based on two analyses:
an axiomatic analysis with two intuitive axioms that a good measure should
obey, and an empirical analysis on the correlation between a measure and a
proxy gold standard. Using this framework, we are able to identify a novel
chemical space coverage measure, #Circles, superior to existing measures both
analytically and empirically. We further evaluate how well the existing
databases and generation models cover the chemical space in terms of #Circles.
The results suggest that many generation models fail to explore a larger space
over existing databases, which leads to new opportunities for improving
generation models by encouraging exploration.

We consider the community detection problem in sparse random hypergraphs
under the non-uniform hypergraph stochastic block model (HSBM), a general model
of random networks with community structure and higher-order interactions. When
the random hypergraph has bounded expected degrees, we provide a spectral
algorithm that outputs a partition with at least a $\gamma$ fraction of the
vertices classified correctly, where $\gamma\in (0.5,1)$ depends on the
signal-to-noise ratio (SNR) of the model. When the SNR grows slowly as the
number of vertices goes to infinity, our algorithm achieves weak consistency,
which improves the previous results in Ghoshdastidar and Dukkipati (2017) for
non-uniform HSBMs.
  Our spectral algorithm consists of three major steps: (1) Hyperedge
selection: select hyperedges of certain sizes to provide the maximal
signal-to-noise ratio for the induced sub-hypergraph; (2) Spectral partition:
construct a regularized adjacency matrix and obtain an approximate partition
based on singular vectors; (3) Correction and merging: incorporate the
hyperedge information from adjacency tensors to upgrade the error rate
guarantee. The theoretical analysis of our algorithm relies on the
concentration and regularization of the adjacency matrix for sparse non-uniform
random hypergraphs, which can be of independent interest.

The Query Focused Text Summarization (QFTS) task aims at building systems
that generate the summary of the text document(s) based on the given query. A
key challenge in addressing this task is the lack of large labeled data for
training the summarization model. In this paper, we address this challenge by
exploring a series of domain adaptation techniques. Given the recent success of
pre-trained transformer models in a wide range of natural language processing
tasks, we utilize such models to generate abstractive summaries for the QFTS
task for both single-document and multi-document scenarios. For domain
adaptation, we apply a variety of techniques using pre-trained
transformer-based summarization models including transfer learning, weakly
supervised learning, and distant supervision. Extensive experiments on six
datasets show that our proposed approach is very effective in generating
abstractive summaries for the QFTS task while setting a new state-of-the-art
result in several datasets across a set of automatic and human evaluation
metrics.

Human space exploration beyond low Earth orbit will involve missions of
significant distance and duration. To effectively mitigate myriad space health
hazards, paradigm shifts in data and space health systems are necessary to
enable Earth-independence, rather than Earth-reliance. Promising developments
in the fields of artificial intelligence and machine learning for biology and
health can address these needs. We propose an appropriately autonomous and
intelligent Precision Space Health system that will monitor, aggregate, and
assess biomedical statuses; analyze and predict personalized adverse health
outcomes; adapt and respond to newly accumulated data; and provide preventive,
actionable, and timely insights to individual deep space crew members and
iterative decision support to their crew medical officer. Here we present a
summary of recommendations from a workshop organized by the National
Aeronautics and Space Administration, on future applications of artificial
intelligence in space biology and health. In the next decade, biomonitoring
technology, biomarker science, spacecraft hardware, intelligent software, and
streamlined data management must mature and be woven together into a Precision
Space Health system to enable humanity to thrive in deep space.

Space biology research aims to understand fundamental effects of spaceflight
on organisms, develop foundational knowledge to support deep space exploration,
and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem
of plants, crops, microbes, animals, and humans for sustained multi-planetary
life. To advance these aims, the field leverages experiments, platforms, data,
and model organisms from both spaceborne and ground-analog studies. As research
is extended beyond low Earth orbit, experiments and platforms must be maximally
autonomous, light, agile, and intelligent to expedite knowledge discovery. Here
we present a summary of recommendations from a workshop organized by the
National Aeronautics and Space Administration on artificial intelligence,
machine learning, and modeling applications which offer key solutions toward
these space biology challenges. In the next decade, the synthesis of artificial
intelligence into the field of space biology will deepen the biological
understanding of spaceflight effects, facilitate predictive modeling and
analytics, support maximally autonomous and reproducible experiments, and
efficiently manage spaceborne data and metadata, all with the goal to enable
life to thrive in deep space.

We introduce a mixture of heterogeneous experts framework called
\texttt{MECATS}, which simultaneously forecasts the values of a set of time
series that are related through an aggregation hierarchy. Different types of
forecasting models can be employed as individual experts so that the form of
each model can be tailored to the nature of the corresponding time series.
\texttt{MECATS} learns hierarchical relationships during the training stage to
help generalize better across all the time series being modeled and also
mitigates coherency issues that arise due to constraints imposed by the
hierarchy. We further build multiple quantile estimators on top of the point
forecasts. The resulting probabilistic forecasts are nearly coherent,
distribution-free, and independent of the choice of forecasting models. We
conduct a comprehensive evaluation on both point and probabilistic forecasts
and also formulate an extension for situations where change points exist in
sequential data. In general, our method is robust, adaptive to datasets with
different properties, and highly configurable and efficient for large-scale
forecasting pipelines.

The fine-tuning of pre-trained language models has a great success in many
NLP fields. Yet, it is strikingly vulnerable to adversarial examples, e.g.,
word substitution attacks using only synonyms can easily fool a BERT-based
sentiment analysis model. In this paper, we demonstrate that adversarial
training, the prevalent defense technique, does not directly fit a conventional
fine-tuning scenario, because it suffers severely from catastrophic forgetting:
failing to retain the generic and robust linguistic features that have already
been captured by the pre-trained model. In this light, we propose Robust
Informative Fine-Tuning (RIFT), a novel adversarial fine-tuning method from an
information-theoretical perspective. In particular, RIFT encourages an
objective model to retain the features learned from the pre-trained model
throughout the entire fine-tuning process, whereas a conventional one only uses
the pre-trained weights for initialization. Experimental results show that RIFT
consistently outperforms the state-of-the-arts on two popular NLP tasks:
sentiment analysis and natural language inference, under different attacks
across various pre-trained language models.

Reinforcement learning has recently shown promise in learning quality
solutions in many combinatorial optimization problems. In particular, the
attention-based encoder-decoder models show high effectiveness on various
routing problems, including the Traveling Salesman Problem (TSP).
Unfortunately, they perform poorly for the TSP with Drone (TSP-D), requiring
routing a heterogeneous fleet of vehicles in coordination -- a truck and a
drone. In TSP-D, the two vehicles are moving in tandem and may need to wait at
a node for the other vehicle to join. State-less attention-based decoder fails
to make such coordination between vehicles. We propose an attention
encoder-LSTM decoder hybrid model, in which the decoder's hidden state can
represent the sequence of actions made. We empirically demonstrate that such a
hybrid model improves upon a purely attention-based model for both solution
quality and computational efficiency. Our experiments on the min-max
Capacitated Vehicle Routing Problem (mmCVRP) also confirm that the hybrid model
is more suitable for coordinated routing of multiple vehicles than the
attention-based model.

An important issue in quadcopter control is that an accurate dynamic model of
the system is nonlinear, complex, and costly to obtain. This limits achievable
control performance in practice. Gaussian process (GP) based estimation is an
effective tool to learn unknown dynamics from input/output data. However,
conventional GP-based control methods often ignore the computational cost
associated with accumulating data during the operation of the system and how to
handle forgetting in continuous adaption. In this paper, we present a novel
Dual Gaussian Process (DGP) based model predictive control strategy that
improves the performance of a quadcopter during trajectory tracking. The
bio-inspired DGP structure is a combination of a long-term GP and a short-term
GP, where the long-term GP is used to keep the learned knowledge in memory and
the short-term GP is employed to rapidly compensate unknown dynamics during
online operation. Furthermore, a novel recursive online update strategy for the
short-term GP is proposed to successively improve the learnt model during
online operation in an efficient way. Numerical simulations are used to
demonstrate the effectiveness of the proposed strategy.

Alternating gradient-descent-ascent (AltGDA) is an optimization algorithm
that has been widely used for model training in various machine learning
applications, which aims to solve a nonconvex minimax optimization problem.
However, the existing studies show that it suffers from a high computation
complexity in nonconvex minimax optimization. In this paper, we develop a
single-loop and fast AltGDA-type algorithm that leverages proximal gradient
updates and momentum acceleration to solve regularized nonconvex minimax
optimization problems. By leveraging the momentum acceleration technique, we
prove that the algorithm converges to a critical point in nonconvex minimax
optimization and achieves a computation complexity in the order of
$\mathcal{O}(\kappa^{\frac{11}{6}}\epsilon^{-2})$, where $\epsilon$ is the
desired level of accuracy and $\kappa$ is the problem's condition number. {Such
a computation complexity improves the state-of-the-art complexities of
single-loop GDA and AltGDA algorithms (see the summary of comparison in
\Cref{table1})}. We demonstrate the effectiveness of our algorithm via an
experiment on adversarial deep learning.

Deep neural networks have a wide range of applications in solving various
real-world tasks and have achieved satisfactory results, in domains such as
computer vision, image classification, and natural language processing.
Meanwhile, the security and robustness of neural networks have become
imperative, as diverse researches have shown the vulnerable aspects of neural
networks. Case in point, in Natural language processing tasks, the neural
network may be fooled by an attentively modified text, which has a high
similarity to the original one. As per previous research, most of the studies
are focused on the image domain; Different from image adversarial attacks, the
text is represented in a discrete sequence, traditional image attack methods
are not applicable in the NLP field. In this paper, we propose a word-level NLP
sentiment classifier attack model, which includes a self-attention
mechanism-based word selection method and a greedy search algorithm for word
substitution. We experiment with our attack model by attacking GRU and 1D-CNN
victim models on IMDB datasets. Experimental results demonstrate that our model
achieves a higher attack success rate and more efficient than previous methods
due to the efficient word selection algorithms are employed and minimized the
word substitute number. Also, our model is transferable, which can be used in
the image domain with several modifications.

Renewable energy resources (RERs) have been increasingly integrated into
large-scale distributed power systems. Considering uncertainties and voltage
fluctuation issues introduced by RERs, in this paper, we propose a deep
reinforcement learning (DRL)-based strategy leveraging spatial-temporal (ST)
graphical information of power systems, to dynamically search for the optimal
operation, i.e., optimal power flow (OPF), of power systems with a high uptake
of RERs. Specifically, we formulate the OPF problem as a multi-objective
optimization problem considering generation cost, voltage fluctuation, and
transmission loss, and employ deep deterministic policy gradient (DDPG) to
learn an optimal allocation strategy for OPF. Moreover, given that the nodes in
power systems are self-correlated and interrelated in temporal and spatial
views, we develop a multi-grained attention-based spatial-temporal graph
convolution network (MG-ASTGCN) for extracting ST graphical correlations and
features, aiming to provide prior knowledge of power systems for its sequential
DDPG algorithm to more effectively solve OPF. We validate our algorithm on
modified IEEE 33, 69, and 118-bus radial distribution systems and demonstrate
that our algorithm outperforms other benchmark algorithms. Our experimental
results also reveal that our MG-ASTGCN can significantly accelerate DDPG's
training process and performance in solving OPF.

The golden ratio is usually shrouded in mystique and mystery, however,
showing its emergence from a familiar geometric setting makes it a more natural
phenomenon. In this work, we present a new theorem connecting the Tangent
Secant theorem to the golden ratio. Such a connection enhances the learning
process of the theorem, and it demystifies the golden ratio in physics and
other disciplines.

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs)
critically depends on the gas-solvent interfacial area (IA), making
maximization of IA a foundational challenge in CCS design. While the IA
associated with a particular CCS design can be estimated via a computational
fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with
numerous CCS designs is prohibitively costly. Fortunately, previous works such
as Deep Fluids (DF) (Kim et al., 2019) show that large simulation speedups are
achievable by replacing CFD simulators with neural network (NN) surrogates that
faithfully mimic the CFD simulation process. This raises the possibility of a
fast, accurate replacement for a CFD simulator and therefore efficient
approximation of the IAs required by CCS design optimization. Thus, here, we
build on the DF approach to develop surrogates that can successfully be applied
to our complex carbon-capture CFD simulations. Our optimized DF-style
surrogates produce large speedups (4000x) while obtaining IA relative errors as
low as 4% on unseen CCS configurations that lie within the range of training
configurations. This hints at the promise of NN surrogates for our CCS design
optimization problem. Nonetheless, DF has inherent limitations with respect to
CCS design (e.g., limited transferability of trained models to new CCS
packings). We conclude with ideas to address these challenges.

There is an increasing need to determine whether inputs are
out-of-distribution (\emph{OOD}) for safely deploying machine learning models
in the open world scenario. Typical neural classifiers are based on the closed
world assumption, where the training data and the test data are drawn
\emph{i.i.d.} from the same distribution, and as a result, give over-confident
predictions even faced with \emph{OOD} inputs. For tackling this problem,
previous studies either use real outliers for training or generate synthetic
\emph{OOD} data under strong assumptions, which are either costly or
intractable to generalize. In this paper, we propose boundary aware learning
(\textbf{BAL}), a novel framework that can learn the distribution of \emph{OOD}
features adaptively. The key idea of BAL is to generate \emph{OOD} features
from trivial to hard progressively with a generator, meanwhile, a discriminator
is trained for distinguishing these synthetic \emph{OOD} features and
in-distribution (\emph{ID}) features. Benefiting from the adversarial training
scheme, the discriminator can well separate \emph{ID} and \emph{OOD} features,
allowing more robust \emph{OOD} detection. The proposed BAL achieves
\emph{state-of-the-art} performance on classification benchmarks, reducing up
to 13.9\% FPR95 compared with previous methods.

Autonomous and semi-autonomous vehicles' perception algorithms can encounter
situations with erroneous object detection, such as misclassification of
objects on the road, which can lead to safety violations and potentially fatal
consequences. While there has been substantial work in the robustness of object
detection algorithms and online metric learning, there is little research on
benchmarking scoring metrics to determine any possible indicators of potential
misclassification. An emphasis is put on exploring the potential of taking
these scoring metrics online in order to allow the AV to make perception-based
decisions given real-time constraints. In this work, we explore which, if any,
metrics act as online indicators of when perception algorithms and object
detectors are failing. Our work provides insight on better design principles
and characteristics of online metrics to accurately evaluate the credibility of
object detectors. Our approach employs non-adversarial and realistic
perturbations to images, on which we evaluate various quantitative metrics. We
found that offline metrics can be designed to account for real-world
corruptions such as poor weather conditions and that the analysis of such
metrics can provide a segue into designing online metrics. This is a clear next
step as it can allow for error-free autonomous vehicle perception and safer
time-critical and safety-critical decision-making.

Deep encoders have been proven to be effective in improving neural machine
translation (NMT) systems, but it reaches the upper bound of translation
quality when the number of encoder layers exceeds 18. Worse still, deeper
networks consume a lot of memory, making it impossible to train efficiently. In
this paper, we present Symbiosis Networks, which include a full network as the
Symbiosis Main Network (M-Net) and another shared sub-network with the same
structure but less layers as the Symbiotic Sub Network (S-Net). We adopt
Symbiosis Networks on Transformer-deep (m-n) architecture and define a
particular regularization loss $\mathcal{L}_{\tau}$ between the M-Net and S-Net
in NMT. We apply joint-training on the Symbiosis Networks and aim to improve
the M-Net performance. Our proposed training strategy improves Transformer-deep
(12-6) by 0.61, 0.49 and 0.69 BLEU over the baselines under classic training on
WMT'14 EN->DE, DE->EN and EN->FR tasks. Furthermore, our Transformer-deep
(12-6) even outperforms classic Transformer-deep (18-6).

A style mapper applies some fixed style to its input images (so, for example,
taking faces to cartoons). This paper describes a simple procedure -- JoJoGAN
-- to learn a style mapper from a single example of the style. JoJoGAN uses a
GAN inversion procedure and StyleGAN's style-mixing property to produce a
substantial paired dataset from a single example style. The paired dataset is
then used to fine-tune a StyleGAN. An image can then be style mapped by
GAN-inversion followed by the fine-tuned StyleGAN. JoJoGAN needs just one
reference and as little as 30 seconds of training time. JoJoGAN can use extreme
style references (say, animal faces) successfully. Furthermore, one can control
what aspects of the style are used and how much of the style is applied.
Qualitative and quantitative evaluation show that JoJoGAN produces high quality
high resolution images that vastly outperform the current state-of-the-art.

Recently, non-autoregressive (NAT) models predict outputs in parallel,
achieving substantial improvements in generation speed compared to
autoregressive (AT) models. While performing worse on raw data, most NAT models
are trained as student models on distilled data generated by AT teacher models,
which is known as sequence-level Knowledge Distillation. An effective training
strategy to improve the performance of AT models is Self-Distillation Mixup
(SDM) Training, which pre-trains a model on raw data, generates distilled data
by the pre-trained model itself and finally re-trains a model on the
combination of raw data and distilled data. In this work, we aim to view SDM
for NAT models, but find directly adopting SDM to NAT models gains no
improvements in terms of translation quality. Through careful analysis, we
observe the invalidation is correlated to Modeling Diversity and Confirmation
Bias between the AT teacher model and the NAT student models. Based on these
findings, we propose an enhanced strategy named SDMRT by adding two stages to
classic SDM: one is Pre-Rerank on self-distilled data, the other is Fine-Tune
on Filtered teacher-distilled data. Our results outperform baselines by 0.6 to
1.2 BLEU on multiple NAT models. As another bonus, for Iterative Refinement NAT
models, our methods can outperform baselines within half iteration number,
which means 2X acceleration.

Artificial Intelligence (AI) development has encouraged many new research
areas, including AI-enabled Internet of Things (IoT) network. AI analytics and
intelligent paradigms greatly improve learning efficiency and accuracy.
Applying these learning paradigms to network scenarios provide technical
advantages of new networking solutions. In this paper, we propose an improved
approach for IoT security from data perspective. The network traffic of IoT
devices can be analyzed using AI techniques. The Adversary Learning (AdLIoTLog)
model is proposed using Recurrent Neural Network (RNN) with attention mechanism
on sequences of network events in the network traffic. We define network events
as a sequence of the time series packets of protocols captured in the log. We
have considered different packets TCP packets, UDP packets, and HTTP packets in
the network log to make the algorithm robust. The distributed IoT devices can
collaborate to cripple our world which is extending to Internet of
Intelligence. The time series packets are converted into structured data by
removing noise and adding timestamps. The resulting data set is trained by RNN
and can detect the node pairs collaborating with each other. We used the BLEU
score to evaluate the model performance. Our results show that the predicting
performance of the AdLIoTLog model trained by our method degrades by 3-4% in
the presence of attack in comparison to the scenario when the network is not
under attack. AdLIoTLog can detect adversaries because when adversaries are
present the model gets duped by the collaborative events and therefore predicts
the next event with a biased event rather than a benign event. We conclude that
AI can provision ubiquitous learning for the new generation of Internet of
Things.

Autoregressive (AR) and Non-autoregressive (NAR) models have their own
superiority on the performance and latency, combining them into one model may
take advantage of both. Current combination frameworks focus more on the
integration of multiple decoding paradigms with a unified generative model,
e.g. Masked Language Model. However, the generalization can be harmful to the
performance due to the gap between training objective and inference. In this
paper, we aim to close the gap by preserving the original objective of AR and
NAR under a unified framework. Specifically, we propose the Directional
Transformer (Diformer) by jointly modelling AR and NAR into three generation
directions (left-to-right, right-to-left and straight) with a newly introduced
direction variable, which works by controlling the prediction of each token to
have specific dependencies under that direction. The unification achieved by
direction successfully preserves the original dependency assumption used in AR
and NAR, retaining both generalization and performance. Experiments on 4 WMT
benchmarks demonstrate that Diformer outperforms current united-modelling works
with more than 1.5 BLEU points for both AR and NAR decoding, and is also
competitive to the state-of-the-art independent AR and NAR models.

Breast cancer is the most common cancer in the world and the most prevalent
cause of death among women worldwide. Nevertheless, it is also one of the most
treatable malignancies if detected early. In this paper, a deep convolutional
neural network-based algorithm is proposed to aid in accurately identifying
breast cancer from ultrasonic images. In this algorithm, several neural
networks are fused in a parallel architecture to perform the classification
process and the voting criteria are applied in the final classification
decision between the candidate object classes where the output of each neural
network is representing a single vote. Several experiments were conducted on
the breast ultrasound dataset consisting of 537 Benign, 360 malignant, and 133
normal images. These experiments show an optimistic result and a capability of
the proposed model to outperform many state-of-the-art algorithms on several
measures. Using k-fold cross-validation and a bagging classifier ensemble, we
achieved an accuracy of 99.5% and a sensitivity of 99.6%.

Graph Convolutional Networks (GCNs) suffer from performance degradation when
models go deeper. However, earlier works only attributed the performance
degradation to over-smoothing. In this paper, we conduct theoretical and
experimental analysis to explore the fundamental causes of performance
degradation in deep GCNs: over-smoothing and gradient vanishing have a mutually
reinforcing effect that causes the performance to deteriorate more quickly in
deep GCNs. On the other hand, existing anti-over-smoothing methods all perform
full convolutions up to the model depth. They could not well resist the
exponential convergence of over-smoothing due to model depth increasing. In
this work, we propose a simple yet effective plug-and-play module, SkipNode, to
overcome the performance degradation of deep GCNs. It samples graph nodes in
each convolutional layer to skip the convolution operation. In this way, both
over-smoothing and gradient vanishing can be effectively suppressed since (1)
not all nodes perform full convolutions up to the model depth and, (2) the
gradient can be directly passed back through ``skipped'' nodes. We provide both
theoretical analysis and empirical evaluation to demonstrate the efficacy of
SkipNode and its superiority over SOTA baselines.

Policy gradient (PG) estimators are ineffective in dealing with softmax
policies that are sub-optimally saturated, which refers to the situation when
the policy concentrates its probability mass on sub-optimal actions.
Sub-optimal policy saturation may arise from bad policy initialization or
sudden changes in the environment that occur after the policy has already
converged. Current softmax PG estimators require a large number of updates to
overcome policy saturation, which causes low sample efficiency and poor
adaptability to new situations. To mitigate this problem, we propose a novel PG
estimator for softmax policies that utilizes the bias in the critic estimate
and the noise present in the reward signal to escape the saturated regions of
the policy parameter space. Our theoretical analysis and experiments, conducted
on bandits and various reinforcement learning environments, show that this new
estimator is significantly more robust to policy saturation.

As a well-known optimization framework, the Alternating Direction Method of
Multipliers (ADMM) has achieved tremendous success in many classification and
regression applications. Recently, it has attracted the attention of deep
learning researchers and is considered to be a potential substitute to Gradient
Descent (GD). However, as an emerging domain, several challenges remain
unsolved, including 1) The lack of global convergence guarantees, 2) Slow
convergence towards solutions, and 3) Cubic time complexity with regard to
feature dimensions. In this paper, we propose a novel optimization framework to
solve a general neural network training problem via ADMM (dlADMM) to address
these challenges simultaneously. Specifically, the parameters in each layer are
updated backward and then forward so that parameter information in each layer
is exchanged efficiently. When the dlADMM is applied to specific architectures,
the time complexity of subproblems is reduced from cubic to quadratic via a
dedicated algorithm design utilizing quadratic approximations and backtracking
techniques. Last but not least, we provide the first proof of convergence to a
critical point sublinearly for an ADMM-type method (dlADMM) under mild
conditions. Experiments on seven benchmark datasets demonstrate the
convergence, efficiency, and effectiveness of our proposed dlADMM algorithm.

Herding is a deterministic algorithm used to generate data points that can be
regarded as random samples satisfying input moment conditions. The algorithm is
based on the complex behavior of a high-dimensional dynamical system and is
inspired by the maximum entropy principle of statistical inference. In this
paper, we propose an extension of the herding algorithm, called entropic
herding, which generates a sequence of distributions instead of points.
Entropic herding is derived as the optimization of the target function obtained
from the maximum entropy principle. Using the proposed entropic herding
algorithm as a framework, we discuss a closer connection between herding and
the maximum entropy principle. Specifically, we interpret the original herding
algorithm as a tractable version of entropic herding, the ideal output
distribution of which is mathematically represented. We further discuss how the
complex behavior of the herding algorithm contributes to optimization. We argue
that the proposed entropic herding algorithm extends the application of herding
to probabilistic modeling. In contrast to original herding, entropic herding
can generate a smooth distribution such that both efficient probability density
calculation and sample generation become possible. To demonstrate the viability
of these arguments in this study, numerical experiments were conducted,
including a comparison with other conventional methods, on both synthetic and
real data.

A practical eye authentication (EA) system targeted for edge devices needs to
perform authentication and be robust to presentation attacks, all while
remaining compute and latency efficient. However, existing eye-based frameworks
a) perform authentication and Presentation Attack Detection (PAD) independently
and b) involve significant pre-processing steps to extract the iris region.
Here, we introduce a joint framework for EA and PAD using periocular images.
While a deep Multitask Learning (MTL) network can perform both the tasks, MTL
suffers from the forgetting effect since the training datasets for EA and PAD
are disjoint. To overcome this, we propose Eye Authentication with PAD
(EyePAD), a distillation-based method that trains a single network for EA and
PAD while reducing the effect of forgetting. To further improve the EA
performance, we introduce a novel approach called EyePAD++ that includes
training an MTL network on both EA and PAD data, while distilling the
`versatility' of the EyePAD network through an additional distillation step.
Our proposed methods outperform the SOTA in PAD and obtain near-SOTA
performance in eye-to-eye verification, without any pre-processing. We also
demonstrate the efficacy of EyePAD and EyePAD++ in user-to-user verification
with PAD across network backbones and image quality.

A Bayesian Network is a directed acyclic graph (DAG) on a set of $n$ random
variables (identified with the vertices); a Bayesian Network Distribution (BND)
is a probability distribution on the rv's that is Markovian on the graph. A
finite mixture of such models is the projection on these variables of a BND on
the larger graph which has an additional "hidden" (or "latent") random variable
$U$, ranging in $\{1,\ldots,k\}$, and a directed edge from $U$ to every other
vertex.
  Models of this type are fundamental to research in Causal Inference, where
$U$ models a confounding effect. One extremely special case has been of
longstanding interest in the theory literature: the empty graph. Such a
distribution is simply a mixture of $k$ product distributions. A longstanding
problem has been, given the joint distribution of a mixture of $k$ product
distributions, to identify each of the product distributions, and their mixture
weights. Our results are:
  (1) We improve the sample complexity (and runtime) for identifying mixtures
of $k$ product distributions from $\exp(O(k^2))$ to $\exp(O(k \log k))$. This
is almost best possible in view of a known $\exp(\Omega(k))$ lower bound.
  (2) We give the first algorithm for the case of non-empty graphs. The
complexity for a graph of maximum degree $\Delta$ is $\exp(O(k(\Delta^2 + \log
k)))$.
  (The above complexities are approximate and suppress dependence on secondary
parameters.)

The physical characteristics and atmospheric chemical composition of newly
discovered exoplanets are often inferred from their transit spectra which are
obtained from complex numerical models of radiative transfer. Alternatively,
simple analytical expressions provide insightful physical intuition into the
relevant atmospheric processes. The deep learning revolution has opened the
door for deriving such analytical results directly with a computer algorithm
fitting to the data. As a proof of concept, we successfully demonstrate the use
of symbolic regression on synthetic data for the transit radii of generic hot
Jupiter exoplanets to derive a corresponding analytical formula. As a
preprocessing step, we use dimensional analysis to identify the relevant
dimensionless combinations of variables and reduce the number of independent
inputs, which improves the performance of the symbolic regression. The
dimensional analysis also allowed us to mathematically derive and properly
parametrize the most general family of degeneracies among the input atmospheric
parameters which affect the characterization of an exoplanet atmosphere through
transit spectroscopy.

graph learning model. However, it can be notoriously challenging to inference
GCNs over large graph datasets, limiting their application to large real-world
graphs and hindering the exploration of deeper and more sophisticated GCN
graphs. This is because real-world graphs can be extremely large and sparse.
Furthermore, the node degree of GCNs tends to follow the power-law distribution
and therefore have highly irregular adjacency matrices, resulting in
prohibitive inefficiencies in both data processing and movement and thus
substantially limiting the achievable GCN acceleration efficiency. To this end,
this paper proposes a GCN algorithm and accelerator Co-Design framework dubbed
GCoD which can largely alleviate the aforementioned GCN irregularity and boost
GCNs' inference efficiency. Specifically, on the algorithm level, GCoD
integrates a split and conquer GCN training strategy that polarizes the graphs
to be either denser or sparser in local neighborhoods without compromising the
model accuracy, resulting in graph adjacency matrices that (mostly) have merely
two levels of workload and enjoys largely enhanced regularity and thus ease of
acceleration. On the hardware level, we further develop a dedicated two-pronged
accelerator with a separated engine to process each of the aforementioned
denser and sparser workloads, further boosting the overall utilization and
acceleration efficiency. Extensive experiments and ablation studies validate
that our GCoD consistently reduces the number of off-chip accesses, leading to
speedups of 15286x, 294x, 7.8x, and 2.5x as compared to CPUs, GPUs, and
prior-art GCN accelerators including HyGCN and AWB-GCN, respectively, while
maintaining or even improving the task accuracy. Codes are available at
https://github.com/RICE-EIC/GCoD.

This paper addresses the problem of cross-dataset generalization of 3D human
pose estimation models. Testing a pre-trained 3D pose estimator on a new
dataset results in a major performance drop. Previous methods have mainly
addressed this problem by improving the diversity of the training data. We
argue that diversity alone is not sufficient and that the characteristics of
the training data need to be adapted to those of the new dataset such as camera
viewpoint, position, human actions, and body size. To this end, we propose
AdaptPose, an end-to-end framework that generates synthetic 3D human motions
from a source dataset and uses them to fine-tune a 3D pose estimator. AdaptPose
follows an adversarial training scheme. From a source 3D pose the generator
generates a sequence of 3D poses and a camera orientation that is used to
project the generated poses to a novel view. Without any 3D labels or camera
information AdaptPose successfully learns to create synthetic 3D poses from the
target dataset while only being trained on 2D poses. In experiments on the
Human3.6M, MPI-INF-3DHP, 3DPW, and Ski-Pose datasets our method outperforms
previous work in cross-dataset evaluations by 14% and previous semi-supervised
learning methods that use partial 3D annotations by 16%.

In the framework of physical reservoir computing (RC), machine learning
algorithms designed for digital computers are executed using analog
computer-like nonlinear physical systems that can provide energy-efficient
computational power for predicting time-dependent quantities that can be found
using nonlinear differential equations. We suggest a bubble-based RC (BRC)
system that combines the nonlinearity of an acoustic response of a cluster of
oscillating gas bubbles in water with a standard Echo State Network (ESN)
algorithm that is well-suited to forecast chaotic time series. We confirm the
plausibility of the BRC system by numerically demonstrating its ability to
forecast certain chaotic time series similarly to or even more accurately than
ESN.

Parameterized tight-binding models fit to first principles calculations can
provide an efficient and accurate quantum mechanical method for predicting
properties of molecules and solids. However, well-tested parameter sets are
generally only available for a limited number of atom combinations, making
routine use of this method difficult. Furthermore, most previous models
consider only simple two-body interactions, which limits accuracy. To tackle
these challenges, we develop a density functional theory database of nearly one
million materials, which we use to fit a universal set of tight-binding
parameters for 65 elements and their binary combinations. We include both
two-body and three-body effective interaction terms in our model, plus
self-consistent charge transfer, enabling our model to work for metallic,
covalent, and ionic bonds with the same parameter set. To ensure predictive
power, we adopt a learning framework where we repeatedly test the model on new
low energy crystal structures and then add them to the fitting dataset,
iterating until predictions improve. We distribute the materials database and
tools developed in this work publicly.

To address the lack of familiarity with nuclear history common among nuclear
engineers and physicists, we outline the design and deployment of a
special-topics course entitled "NE290: Nuclear History, Politics, and Futures"
across which we contextualize the importance of the field at its inception, in
current affairs, and in future endeavors. We begin by outlining the gaps in
secondary educational offerings for nuclear history and their importance in
consideration with nontechnical engineering guidelines. We then outline a
number ABET specifications as pedagogical goals for NE290 from which we derive
a list of target student learning objectives. Next, we outline the NE290
syllabus in terms of assignments and an overview of course content in the form
of a class timeline. We provide an extensive description of the materials and
teaching methodologies for the four units of NE290: 20th-Century Physics,
Physics in WWII, the Early Cold War, and the Late Cold War and Modern Era. We
detail the sequence of lectures across the course and historical timelines --
leading up to a showcasing of NE290 final projects which mirror in creativity
the novelty of course offering. Because NE290 was first offered during Spring
2021 during the COVID-19 pandemic, additional measures in the form of new tools
were used to augment the mandate of remote learning such as videoconferencing
technology to recruit geographically diverse guest lecturers, and used the MIRO
tool for virtual whiteboarding. Lastly, we provide an accounting of course
outcomes drawn from student feedback which -- in tandem with the complete
distribution of course material -- facilitates the integration of nuclear
history into the curriculum for the wider nuclear engineering and physics

We propose a novel method to enhance the performance of coordinate-MLPs by
learning instance-specific positional embeddings. End-to-end optimization of
positional embedding parameters along with network weights leads to poor
generalization performance. Instead, we develop a generic framework to learn
the positional embedding based on the classic graph-Laplacian regularization,
which can implicitly balance the trade-off between memorization and
generalization. This framework is then used to propose a novel positional
embedding scheme, where the hyperparameters are learned per coordinate (i.e,
instance) to deliver optimal performance. We show that the proposed embedding
achieves better performance with higher stability compared to the
well-established random Fourier features (RFF). Further, we demonstrate that
the proposed embedding scheme yields stable gradients, enabling seamless
integration into deep architectures as intermediate layers.

Active learning is of great interest for many practical applications,
especially in industry and the physical sciences, where there is a strong need
to minimize the number of costly experiments necessary to train predictive
models. However, there remain significant challenges for the adoption of active
learning methods in many practical applications. One important challenge is
that many methods assume a fixed model, where model hyperparameters are chosen
a priori. In practice, it is rarely true that a good model will be known in
advance. Existing methods for active learning with model selection typically
depend on a medium-sized labeling budget. In this work, we focus on the case of
having a very small labeling budget, on the order of a few dozen data points,
and develop a simple and fast method for practical active learning with model
selection. Our method is based on an underlying pool-based active learner for
binary classification using support vector classification with a radial basis
function kernel. First we show empirically that our method is able to find
hyperparameters that lead to the best performance compared to an oracle model
on less separable, difficult to classify datasets, and reasonable performance
on datasets that are more separable and easier to classify. Then, we
demonstrate that it is possible to refine our model selection method using a
weighted approach to trade-off between achieving optimal performance on
datasets that are easy to classify, versus datasets that are difficult to
classify, which can be tuned based on prior domain knowledge about the dataset.

The Jaccard index, also known as Intersection-over-Union (IoU), is one of the
most critical evaluation metrics in image semantic segmentation. However,
direct optimization of IoU score is very difficult because the learning
objective is neither differentiable nor decomposable. Although some algorithms
have been proposed to optimize its surrogates, there is no guarantee provided
for the generalization ability. In this paper, we propose a margin calibration
method, which can be directly used as a learning objective, for an improved
generalization of IoU over the data-distribution, underpinned by a rigid lower
bound. This scheme theoretically ensures a better segmentation performance in
terms of IoU score. We evaluated the effectiveness of the proposed margin
calibration method on seven image datasets, showing substantial improvements in
IoU score over other learning objectives using deep segmentation models.

In this paper, we propose a framework centering around a novel architecture
called the Event Decomposition Recomposition Network (EDRNet) to tackle the
Audio-Visual Event (AVE) localization problem in the supervised and weakly
supervised settings. AVEs in the real world exhibit common unravelling patterns
(termed as Event Progress Checkpoints (EPC)), which humans can perceive through
the cooperation of their auditory and visual senses. Unlike earlier methods
which attempt to recognize entire event sequences, the EDRNet models EPCs and
inter-EPC relationships using stacked temporal convolutions. Based on the
postulation that EPC representations are theoretically consistent for an event
category, we introduce the State Machine Based Video Fusion, a novel
augmentation technique that blends source videos using different EPC template
sequences. Additionally, we design a new loss function called the
Land-Shore-Sea loss to compactify continuous foreground and background
representations. Lastly, to alleviate the issue of confusing events during weak
supervision, we propose a prediction stabilization method called Bag to
Instance Label Correction. Experiments on the AVE dataset show that our
collective framework outperforms the state-of-the-art by a sizable margin.

ViTs are often too computationally expensive to be fitted onto real-world
resource-constrained devices, due to (1) their quadratically increased
complexity with the number of input tokens and (2) their overparameterized
self-attention heads and model depth. In parallel, different images are of
varied complexity and their different regions can contain various levels of
visual information, indicating that treating all regions/tokens equally in
terms of model complexity is unnecessary while such opportunities for trimming
down ViTs' complexity have not been fully explored. To this end, we propose a
Multi-grained Input-adaptive Vision Transformer framework dubbed MIA-Former
that can input-adaptively adjust the structure of ViTs at three
coarse-to-fine-grained granularities (i.e., model depth and the number of model
heads/tokens). In particular, our MIA-Former adopts a low-cost network trained
with a hybrid supervised and reinforcement training method to skip unnecessary
layers, heads, and tokens in an input adaptive manner, reducing the overall
computational cost. Furthermore, an interesting side effect of our MIA-Former
is that its resulting ViTs are naturally equipped with improved robustness
against adversarial attacks over their static counterparts, because
MIA-Former's multi-grained dynamic control improves the model diversity similar
to the effect of ensemble and thus increases the difficulty of adversarial
attacks against all its sub-models. Extensive experiments and ablation studies
validate that the proposed MIA-Former framework can effectively allocate
computation budgets adaptive to the difficulty of input images meanwhile
increase robustness, achieving state-of-the-art (SOTA) accuracy-efficiency
trade-offs, e.g., 20% computation savings with the same or even a higher
accuracy compared with SOTA dynamic transformer models.

Purpose: Automating tasks such as lung tumor localization and segmentation in
radiological images can free valuable time for radiologists and other clinical
personnel. Convolutional neural networks may be suited for such tasks, but
require substantial amounts of labeled data to train. Obtaining labeled data is
a challenge, especially in the medical domain. Methods: This paper investigates
the use of a teacher-student design to utilize datasets with different types of
supervision to train an automatic model performing pulmonary tumor segmentation
on computed tomography images. The framework consists of two models: the
student that performs end-to-end automatic tumor segmentation and the teacher
that supplies the student additional pseudo-annotated data during training.
Results: Using only a small proportion of semantically labeled data and a large
number of bounding box annotated data, we achieved competitive performance
using a teacher-student design. Models trained on larger amounts of semantic
annotations did not perform better than those trained on teacher-annotated
data. Conclusions: Our results demonstrate the potential of utilizing
teacher-student designs to reduce the annotation load, as less supervised
annotation schemes may be performed, without any real degradation in
segmentation accuracy.

Unlocking the design of Li-S batteries where no shuttle effects appears, and
thus their energy storage capacity does not diminish over time, would enable
the manufacturing of energy storage devices more performant than the current
Li-ion commercial ones. Computational screening of Li-polysulphide (LiPS)
adsorption on single-atom catalyst (SAC) substrates is of great aid to the
design of Li-S batteries which are robust against the LiPS shuttling from the
cathode to the anode and the electrolyte. To aid this process, we develop a
machine learning protocol to accelerate the systematic mapping of dominant
local minima found with DFT calculations, and, in turn, fast screen LiPS
adsorption properties on SACs. We first validate the approach by probing the
potential energy surface for Li-polysulphides adsorbed on graphene decorated
with a Fe-N$_4$-C SAC bound to four nitrogen atoms. We identify minima whose
binding energy is better or on par with the one previously reported in the
literature. We then move to analyse the adsorption trends on Zn-N$_4$-C SAC and
observe similar adsorption strength and behaviour with the Fe-N$_4$-C SAC,
highlighting the good predictive power of our protocol.

Ising machines are a promising non-von-Neumann computational concept for
neural network training and combinatorial optimization. However, while various
neural networks can be implemented with Ising machines, their inability to
perform fast statistical sampling makes them inefficient for training neural
networks compared to digital computers. Here, we introduce a universal concept
to achieve ultrafast statistical sampling with analog Ising machines by
injecting noise. With an opto-electronic Ising machine, we experimentally
demonstrate that this can be used for accurate sampling of Boltzmann
distributions and for unsupervised training of neural networks, with equal
accuracy as software-based training. Through simulations, we find that Ising
machines can perform statistical sampling orders-of-magnitudes faster than
software-based methods. This enables the use of Ising machines beyond
combinatorial optimization and makes them into efficient tools for machine
learning and other applications.

Applying reinforcement learning (RL) methods on robots typically involves
training a policy in simulation and deploying it on a robot in the real world.
Because of the model mismatch between the real world and the simulator, RL
agents deployed in this manner tend to perform suboptimally. To tackle this
problem, researchers have developed robust policy learning algorithms that rely
on synthetic noise disturbances. However, such methods do not guarantee
performance in the target environment. We propose a convex risk minimization
algorithm to estimate the model mismatch between the simulator and the target
domain using trajectory data from both environments. We show that this
estimator can be used along with the simulator to evaluate performance of an RL
agents in the target domain, effectively bridging the gap between these two
environments. We also show that the convergence rate of our estimator to be of
the order of ${n^{-1/4}}$, where $n$ is the number of training samples. In
simulation, we demonstrate how our method effectively approximates and
evaluates performance on Gridworld, Cartpole, and Reacher environments on a
range of policies. We also show that the our method is able to estimate
performance of a 7 DOF robotic arm using the simulator and remotely collected
data from the robot in the real world.

Recent advances in artificial intelligence applied to biomedical text are
opening exciting opportunities for improving pharmacovigilance activities
currently burdened by the ever growing volumes of real world data. To fully
realize these opportunities, existing regulatory guidance and industry best
practices should be taken into consideration in order to increase the overall
trustworthiness of the system and enable broader adoption. In this paper we
present a case study on how to operationalize existing guidance for validated
AI systems in pharmacovigilance focusing on the specific task of medical
literature monitoring (MLM) of adverse events from the scientific literature.
We describe an AI system designed with the goal of reducing effort in MLM
activities built in close collaboration with subject matter experts and
considering guidance for validated systems in pharmacovigilance and AI
transparency. In particular we make use of public disclosures as a useful risk
control measure to mitigate system misuse and earn user trust. In addition we
present experimental results showing the system can significantly remove
screening effort while maintaining high levels of recall (filtering 55% of
irrelevant articles on average, for a target recall of 0.99 on suspected
adverse articles) and provide a robust method for tuning the desired recall to
suit a particular risk profile.

As one of the most prevalent neurodegenerative disorders, Parkinson's disease
(PD) has a significant impact on the fine motor skills of patients. The complex
interplay of different articulators during speech production and realization of
required muscle tension become increasingly difficult, thus leading to a
dysarthric speech. Characteristic patterns such as vowel instability, slurred
pronunciation and slow speech can often be observed in the affected individuals
and were analyzed in previous studies to determine the presence and progression
of PD. In this work, we used a phonetic recognizer trained exclusively on
healthy speech data to investigate how PD affected the phonetic footprint of
patients. We rediscovered numerous patterns that had been described in previous
contributions although our system had never seen any pathological speech
previously. Furthermore, we could show that intermediate activations from the
neural network could serve as feature vectors encoding information related to
the disease state of individuals. We were also able to directly correlate the
expert-rated intelligibility of a speaker with the mean confidence of phonetic
predictions. Our results support the assumption that pathological data is not
necessarily required to train systems that are capable of analyzing PD speech.

Numerical stability is a crucial requirement of reliable scientific
computing. However, despite the pervasiveness of Python in data science,
analyzing large Python programs remains challenging due to the lack of scalable
numerical analysis tools available for this language. To fill this gap, we
developed PyTracer, a profiler to quantify numerical instability in Python
applications. PyTracer transparently instruments Python code to produce
numerical traces and visualize them interactively in a Plotly dashboard. We
designed PyTracer to be agnostic to numerical noise model, allowing for tool
evaluation through Monte-Carlo Arithmetic, random rounding, random data
perturbation, or structured noise for a particular application. We illustrate
PyTracer's capabilities by testing the numerical stability of key functions in
both SciPy and Scikit-learn, two dominant Python libraries for mathematical
modeling. Through these evaluations, we demonstrate PyTracer as a scalable,
automatic, and generic framework for numerical profiling in Python.

Missing data are present in most real world problems and need careful
handling to preserve the prediction accuracy and statistical consistency in the
downstream analysis. As the gold standard of handling missing data, multiple
imputation (MI) methods are proposed to account for the imputation uncertainty
and provide proper statistical inference.
  In this work, we propose Multiple Imputation via Generative Adversarial
Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple
imputation method, that can work under missing at random (MAR) mechanism with
theoretical support. MI-GAN leverages recent progress in conditional generative
adversarial neural works and shows strong performance matching existing
state-of-the-art imputation methods on high-dimensional datasets, in terms of
imputation error. In particular, MI-GAN significantly outperforms other
imputation methods in the sense of statistical inference and computational
speed.

Background. Absorbed radiation dose to the mandible is an important risk
factor in the development of mandibular osteoradionecrosis (ORN) in head and
neck cancer (HNC) patients treated with radiotherapy (RT). The prediction of
mandibular ORN may not only guide the RT treatment planning optimisation
process but also identify which patients would benefit from a closer follow-up
post-RT for an early diagnosis and intervention of ORN. Existing mandibular ORN
prediction models are based on dose-volume histogram (DVH) metrics that omit
the spatial localisation and dose gradient and direction information provided
by the clinical mandible radiation dose distribution maps. Methods. We propose
the use of a binary classification 3D DenseNet121 to extract the relevant
dosimetric information directly from the 3D mandible radiation dose
distribution maps and predict the incidence of ORN. We compare the results to a
Random Forest ensemble with DVH-based parameters. Results. The 3D DenseNet121
model was able to discriminate ORN vs. non-ORN cases with an average AUC of
0.71 (0.64-0.79), compared to 0.65 (0.57-0.73) for the RF model. Conclusion.
Obtaining the dosimetric information directly from the clinical radiation dose
distribution maps may enhance the performance and functionality of ORN normal
tissue complication probability (NTCP) models.

Human-performed annotation of sentences in legal documents is an important
prerequisite to many machine learning based systems supporting legal tasks.
Typically, the annotation is done sequentially, sentence by sentence, which is
often time consuming and, hence, expensive. In this paper, we introduce a
proof-of-concept system for annotating sentences "laterally." The approach is
based on the observation that sentences that are similar in meaning often have
the same label in terms of a particular type system. We use this observation in
allowing annotators to quickly view and annotate sentences that are
semantically similar to a given sentence, across an entire corpus of documents.
Here, we present the interface of the system and empirically evaluate the
approach. The experiments show that lateral annotation has the potential to
make the annotation process quicker and more consistent.

Error correcting codes are a fundamental component in modern day
communication systems, demanding extremely high throughput, ultra-reliability
and low latency. Recent approaches using machine learning (ML) models as the
decoders offer both improved performance and great adaptability to unknown
environments, where traditional decoders struggle. We introduce a general
framework to further boost the performance and applicability of ML models. We
propose to combine ML decoders with a competing discriminator network that
tries to distinguish between codewords and noisy words, and, hence, guides the
decoding models to recover transmitted codewords. Our framework is
game-theoretic, motivated by generative adversarial networks (GANs), with the
decoder and discriminator competing in a zero-sum game. The decoder learns to
simultaneously decode and generate codewords while the discriminator learns to
tell the differences between decoded outputs and codewords. Thus, the decoder
is able to decode noisy received signals into codewords, increasing the
probability of successful decoding. We show a strong connection of our
framework with the optimal maximum likelihood decoder by proving that this
decoder defines a Nash equilibrium point of our game. Hence, training to
equilibrium has a good possibility of achieving the optimal maximum likelihood
performance. Moreover, our framework does not require training labels, which
are typically unavailable during communications, and, thus, seemingly can be
trained online and adapt to channel dynamics. To demonstrate the performance of
our framework, we combine it with the very recent neural decoders and show
improved performance compared to the original models and traditional decoding
algorithms on various codes.

The growing trend of fledgling reinforcement learning systems making their
way into real-world applications has been accompanied by growing concerns for
their safety and robustness. In recent years, a variety of approaches have been
put forward to address the challenges of safety-aware reinforcement learning;
however, these methods often either require a handcrafted model of the
environment to be provided beforehand, or that the environment is relatively
simple and low-dimensional. We present a novel approach to safety-aware deep
reinforcement learning in high-dimensional environments called latent
shielding. Latent shielding leverages internal representations of the
environment learnt by model-based agents to "imagine" future trajectories and
avoid those deemed unsafe. We experimentally demonstrate that this approach
leads to improved adherence to formally-defined safety specifications.

Edge computing has revolutionized the world of mobile and wireless networks
world thanks to its flexible, secure, and performing characteristics. Lately,
we have witnessed the increasing use of it to make more performing the
deployment of machine learning (ML) techniques such as federated learning (FL).
FL was debuted to improve communication efficiency compared to conventional
distributed machine learning (ML). The original FL assumes a central
aggregation server to aggregate locally optimized parameters and might bring
reliability and latency issues. In this paper, we conduct an in-depth study of
strategies to replace this central server by a flying master that is
dynamically selected based on the current participants and/or available
resources at every FL round of optimization. Specifically, we compare different
metrics to select this flying master and assess consensus algorithms to perform
the selection. Our results demonstrate a significant reduction of runtime using
our flying master FL framework compared to the original FL from measurements
results conducted in our EdgeAI testbed and over real 5G networks using an
operational edge testbed.

As AI systems demonstrate increasingly strong predictive performance, their
adoption has grown in numerous domains. However, in high-stakes domains such as
criminal justice and healthcare, full automation is often not desirable due to
safety, ethical, and legal concerns, yet fully manual approaches can be
inaccurate and time consuming. As a result, there is growing interest in the
research community to augment human decision making with AI assistance. Besides
developing AI technologies for this purpose, the emerging field of human-AI
decision making must embrace empirical approaches to form a foundational
understanding of how humans interact and work with AI to make decisions. To
invite and help structure research efforts towards a science of understanding
and improving human-AI decision making, we survey recent literature of
empirical human-subject studies on this topic. We summarize the study design
choices made in over 100 papers in three important aspects: (1) decision tasks,
(2) AI models and AI assistance elements, and (3) evaluation metrics. For each
aspect, we summarize current trends, discuss gaps in current practices of the
field, and make a list of recommendations for future research. Our survey
highlights the need to develop common frameworks to account for the design and
research spaces of human-AI decision making, so that researchers can make
rigorous choices in study design, and the research community can build on each
other's work and produce generalizable scientific knowledge. We also hope this
survey will serve as a bridge for HCI and AI communities to work together to
mutually shape the empirical science and computational technologies for
human-AI decision making.

Standard contrastive learning approaches usually require a large number of
negatives for effective unsupervised learning and often exhibit slow
convergence. We suspect this behavior is due to the suboptimal selection of
negatives used for offering contrast to the positives. We counter this
difficulty by taking inspiration from support vector machines (SVMs) to present
max-margin contrastive learning (MMCL). Our approach selects negatives as the
sparse support vectors obtained via a quadratic optimization problem, and
contrastiveness is enforced by maximizing the decision margin. As SVM
optimization can be computationally demanding, especially in an end-to-end
setting, we present simplifications that alleviate the computational burden. We
validate our approach on standard vision benchmark datasets, demonstrating
better performance in unsupervised representation learning over
state-of-the-art, while having better empirical convergence properties.

We consider the problem of constructing bounds on the average treatment
effect (ATE) when unmeasured confounders exist but have bounded influence.
Specifically, we assume that omitted confounders could not change the odds of
treatment for any unit by more than a fixed factor. We derive the sharp partial
identification bounds implied by this assumption by leveraging distributionally
robust optimization, and we propose estimators of these bounds with several
novel robustness properties. The first is double sharpness: our estimators
consistently estimate the sharp ATE bounds when one of two nuisance parameters
is misspecified and achieve semiparametric efficiency when all nuisance
parameters are suitably consistent. The second is double validity: even when
most nuisance parameters are misspecified, our estimators still provide valid
but possibly conservative bounds for the ATE and our Wald confidence intervals
remain valid even when our estimators are not asymptotically normal. As a
result, our estimators provide a highly credible method for sensitivity
analysis of causal inferences.

In the context of multi-modality knowledge distillation research, the
existing methods was mainly focus on the problem of only learning teacher final
output. Thus, there are still deep differences between the teacher network and
the student network. It is necessary to force the student network to learn the
modality relationship information of the teacher network. To effectively
exploit transfering knowledge from teachers to students, a novel modality
relation distillation paradigm by modeling the relationship information among
different modality are adopted, that is learning the teacher modality-level
Gram Matrix.

Vision Transformers (ViT) serve as powerful vision models. Unlike
convolutional neural networks, which dominated vision research in previous
years, vision transformers enjoy the ability to capture long-range dependencies
in the data. Nonetheless, an integral part of any transformer architecture, the
self-attention mechanism, suffers from high latency and inefficient memory
utilization, making it less suitable for high-resolution input images. To
alleviate these shortcomings, hierarchical vision models locally employ
self-attention on non-interleaving windows. This relaxation reduces the
complexity to be linear in the input size; however, it limits the cross-window
interaction, hurting the model performance. In this paper, we propose a new
shift-invariant local attention layer, called query and attend (QnA), that
aggregates the input locally in an overlapping manner, much like convolutions.
The key idea behind QnA is to introduce learned queries, which allow fast and
efficient implementation. We verify the effectiveness of our layer by
incorporating it into a hierarchical vision transformer model. We show
improvements in speed and memory complexity while achieving comparable accuracy
with state-of-the-art models. Finally, our layer scales especially well with
window size, requiring up-to x10 less memory while being up-to x5 faster than
existing methods. The code is publicly available at
\url{https://github.com/moabarar/qna}.

Can we improve the modeling of urban land surface processes with machine
learning (ML)? A prior comparison of urban land surface models (ULSMs) found
that no single model is 'best' at predicting all common surface fluxes. Here,
we develop an urban neural network (UNN) trained on the mean predicted fluxes
from 22 ULSMs at one site. The UNN emulates the mean output of ULSMs
accurately. When compared to a reference ULSM (Town Energy Balance; TEB), the
UNN has greater accuracy relative to flux observations, less computational
cost, and requires fewer input parameters. When coupled to the Weather Research
Forecasting (WRF) model using TensorFlow bindings, WRF-UNN is stable and more
accurate than the reference WRF-TEB. Although the application is currently
constrained by the training data (1 site), we show a novel approach to improve
the modeling of surface fluxes by combining the strengths of several ULSMs into
one using ML.

We introduce a high resolution, 3D-consistent image and shape generation
technique which we call StyleSDF. Our method is trained on single-view RGB data
only, and stands on the shoulders of StyleGAN2 for image generation, while
solving two main challenges in 3D-aware GANs: 1) high-resolution,
view-consistent generation of the RGB images, and 2) detailed 3D shape. We
achieve this by merging a SDF-based 3D representation with a style-based 2D
generator. Our 3D implicit network renders low-resolution feature maps, from
which the style-based network generates view-consistent, 1024x1024 images.
Notably, our SDF-based 3D modeling defines detailed 3D surfaces, leading to
consistent volume rendering. Our method shows higher quality results compared
to state of the art in terms of visual and geometric quality.

In many real-world problems, first-order (FO) derivative evaluations are too
expensive or even inaccessible. For solving these problems, zeroth-order (ZO)
methods that only need function evaluations are often more efficient than FO
methods or sometimes the only options. In this paper, we propose a novel
zeroth-order inexact augmented Lagrangian method (ZO-iALM) to solve black-box
optimization problems, which involve a composite (i.e., smooth+nonsmooth)
objective and functional constraints. Under a certain regularity condition
(also assumed by several existing works on FO methods), the query complexity of
our ZO-iALM is $\tilde{O}(d\varepsilon^{-3})$ to find an $\varepsilon$-KKT
point for problems with a nonconvex objective and nonconvex constraints, and
$\tilde{O}(d\varepsilon^{-2.5})$ for nonconvex problems with convex
constraints, where $d$ is the variable dimension. This appears to be the first
work that develops an iALM-based ZO method for functional constrained
optimization and meanwhile achieves query complexity results matching the
best-known FO complexity results up to a factor of $d$. With an extensive
experimental study, we show the effectiveness of our method. The applications
of our method span from classical optimization problems to practical machine
learning examples such as resource allocation in sensor networks and
adversarial example generation.

Following the recent Internet of Things-induced trends on digitization in
general, industrial applications will further evolve as well. With a focus on
the domains of manufacturing and production, the Internet of Production pursues
the vision of a digitized, globally interconnected, yet secure environment by
establishing a distributed knowledge base. Background. As part of our
collaborative research of advancing the scope of industrial applications
through cybersecurity and privacy, we identified a set of common challenges and
pitfalls that surface in such applied interdisciplinary collaborations. Aim.
Our goal with this paper is to support researchers in the emerging field of
cybersecurity in industrial settings by formalizing our experiences as
reference for other research efforts, in industry and academia alike. Method.
Based on our experience, we derived a process cycle of performing such
interdisciplinary research, from the initial idea to the eventual dissemination
and paper writing. This presented methodology strives to successfully bootstrap
further research and to encourage further work in this emerging area. Results.
Apart from our newly proposed process cycle, we report on our experiences and
conduct a case study applying this methodology, raising awareness for
challenges in cybersecurity research for industrial applications. We further
detail the interplay between our process cycle and the data lifecycle in
applied research data management. Finally, we augment our discussion with an
industrial as well as an academic view on this research area and highlight that
both areas still have to overcome significant challenges to sustainably and
securely advance industrial applications. Conclusions. With our proposed
process cycle for interdisciplinary research in the intersection of
cybersecurity and industrial application, we provide a foundation for further
research.

By moving from massive antennas to antenna surfaces for software-defined
wireless systems, the reconfigurable intelligent surfaces (RISs) rely on arrays
of unit cells to control the scattering and reflection profiles of signals,
mitigating the propagation loss and multipath attenuation, and thereby
improving the coverage and spectral efficiency. In this paper, covert
communication is considered in the presence of the RIS. While there is an
ongoing transmission boosted by the RIS, both the intended receiver and an
eavesdropper individually try to detect this transmission using their own deep
neural network (DNN) classifiers. The RIS interaction vector is designed by
balancing two (potentially conflicting) objectives of focusing the transmitted
signal to the receiver and keeping the transmitted signal away from the
eavesdropper. To boost covert communications, adversarial perturbations are
added to signals at the transmitter to fool the eavesdropper's classifier while
keeping the effect on the receiver low. Results from different network
topologies show that adversarial perturbation and RIS interaction vector can be
jointly designed to effectively increase the signal detection accuracy at the
receiver while reducing the detection accuracy at the eavesdropper to enable
covert communications.

With the emergence of edge computing, the problem of offloading jobs between
an Edge Device (ED) and an Edge Server (ES) received significant attention in
the past. Motivated by the fact that an increasing number of applications are
using Machine Learning (ML) inference, we study the problem of offloading
inference jobs by considering the following novel aspects: 1) in contrast to a
typical computational job, the processing time of an inference job depends on
the size of the ML model, and 2) recently proposed Deep Neural Networks (DNNs)
for resource-constrained devices provide the choice of scaling the model size.
We formulate an assignment problem with the aim of maximizing the total
inference accuracy of n data samples available at the ED, subject to a time
constraint T on the makespan. We propose an approximation algorithm AMR2, and
prove that it results in a makespan at most 2T, and achieves a total accuracy
that is lower by a small constant from optimal total accuracy. As proof of
concept, we implemented AMR2 on a Raspberry Pi, equipped with MobileNet, and is
connected to a server equipped with ResNet, and studied the total accuracy and
makespan performance of AMR2 for image classification application.

In addition to the impressive predictive power of machine learning (ML)
models, more recently, explanation methods have emerged that enable an
interpretation of complex non-linear learning models such as deep neural
networks. Gaining a better understanding is especially important e.g. for
safety-critical ML applications or medical diagnostics etc. While such
Explainable AI (XAI) techniques have reached significant popularity for
classifiers, so far little attention has been devoted to XAI for regression
models (XAIR). In this review, we clarify the fundamental conceptual
differences of XAI for regression and classification tasks, establish novel
theoretical insights and analysis for XAIR, provide demonstrations of XAIR on
genuine practical regression problems, and finally discuss the challenges
remaining for the field.

Physical networks, such as biological neural networks, can learn desired
functions without a central processor, using local learning rules in space and
time to learn in a fully distributed manner. Learning approaches such as
equilibrium propagation, directed aging, and coupled learning similarly exploit
local rules to accomplish learning in physical networks such as mechanical,
flow, or electrical networks. In contrast to certain natural neural networks,
however, such approaches have so far been restricted to the quasistatic limit,
where they learn on time scales slow compared to their physical relaxation.
This quasistatic constraint slows down learning, limiting the use of these
methods as machine learning algorithms, and potentially restricting physical
networks that could be used as learning platforms. Here we explore learning in
an electrical resistor network that implements coupled learning, both in the
lab and on the computer, at rates that range from slow to far above the
quasistatic limit. We find that up to a critical threshold in the ratio of the
learning rate to the physical rate of relaxation, learning speeds up without
much change of behavior or error. Beyond the critical threshold, the error
exhibits oscillatory dynamics but the networks still learn successfully.

Interpretability of neural networks and their underlying theoretical
behaviour remain an open field of study even after the great success of their
practical applications, particularly with the emergence of deep learning. In
this work, NN2Poly is proposed: a theoretical approach to obtain an explicit
polynomial model that provides an accurate representation of an already trained
fully-connected feed-forward artificial neural network (a multilayer perceptron
or MLP). This approach extends a previous idea proposed in the literature,
which was limited to single hidden layer networks, to work with arbitrarily
deep MLPs in both regression and classification tasks. The objective of this
paper is to achieve this by using a Taylor expansion on the activation
function, at each layer, and then using several combinatorial properties to
calculate the coefficients of the desired polynomials. Discussion is presented
on the main computational challenges of this method, and the way to overcome
them by imposing certain constraints during the training phase. Finally,
simulation experiments as well as an application to a real data set are
presented to demonstrate the effectiveness of the proposed method.

Jitter and shimmer measurements have shown to be carriers of voice quality
and prosodic information which enhance the performance of tasks like speaker
recognition, diarization or automatic speech recognition (ASR). However, such
features have been seldom used in the context of neural-based ASR, where
spectral features often prevail. In this work, we study the effects of
incorporating voice quality and pitch features altogether and separately to a
Transformer-based ASR model, with the intuition that the attention mechanisms
might exploit latent prosodic traits. For doing so, we propose separated
convolutional front-ends for prosodic and spectral features, showing that this
architectural choice yields better results than simple concatenation of such
pitch and voice quality features to mel-spectrogram filterbanks. Furthermore,
we find mean Word Error Rate relative reductions of up to 5.6% with the
LibriSpeech benchmark. Such findings motivate further research on the
application of prosody knowledge for increasing the robustness of
Transformer-based ASR.

Contrastive pretraining techniques for text classification has been largely
studied in an unsupervised setting. However, oftentimes labeled data from
related tasks which share label semantics with current task is available. We
hypothesize that using this labeled data effectively can lead to better
generalization on current task. In this paper, we propose a novel way to
effectively utilize labeled data from related tasks with a graph based
supervised contrastive learning approach. We formulate a token-graph by
extrapolating the supervised information from examples to tokens. Our
formulation results in an embedding space where tokens with high/low
probability of belonging to same class are near/further-away from one another.
We also develop detailed theoretical insights which serve as a motivation for
our method. In our experiments with $13$ datasets, we show our method
outperforms pretraining schemes by $2.5\%$ and also example-level contrastive
learning based formulation by $1.8\%$ on average. In addition, we show
cross-domain effectiveness of our method in a zero-shot setting by $3.91\%$ on
average. Lastly, we also demonstrate our method can be used as a noisy teacher
in a knowledge distillation setting to significantly improve performance of
transformer based models in low labeled data regime by $4.57\%$ on average.

This work proposes a new type of classifier called Morphological Classifier
(MC). MCs aggregate concepts from mathematical morphology and supervised
learning. The outcomes of this aggregation are classifiers that may preserve
shape characteristics of classes, subject to the choice of a stopping criterion
and structuring element. MCs are fundamentally based on set theory, and their
classification model can be a mathematical set itself. Two types of
morphological classifiers are proposed in the current work, namely,
Morphological k-NN (MkNN) and Morphological Dilation Classifier (MDC), which
demonstrate the feasibility of the approach. This work provides evidence
regarding the advantages of MCs, e.g., very fast classification times as well
as competitive accuracy rates. The performance of MkNN and MDC was tested using
p -dimensional datasets. MCs tied or outperformed 14 well established
classifiers in 5 out of 8 datasets. In all occasions, the obtained accuracies
were higher than the average accuracy obtained with all classifiers. Moreover,
the proposed implementations utilize the power of the Graphics Processing Units
(GPUs) to speed up processing.

We present a new data-driven approach with physics-based priors to
scene-level normal estimation from a single polarization image. Existing shape
from polarization (SfP) works mainly focus on estimating the normal of a single
object rather than complex scenes in the wild. A key barrier to high-quality
scene-level SfP is the lack of real-world SfP data in complex scenes. Hence, we
contribute the first real-world scene-level SfP dataset with paired input
polarization images and ground-truth normal maps. Then we propose a
learning-based framework with a multi-head self-attention module and viewing
encoding, which is designed to handle increasing polarization ambiguities
caused by complex materials and non-orthographic projection in scene-level SfP.
Our trained model can be generalized to far-field outdoor scenes as the
relationship between polarized light and surface normals is not affected by
distance. Experimental results demonstrate that our approach significantly
outperforms existing SfP models on two datasets. Our dataset and source code
will be publicly available at https://github.com/ChenyangLEI/sfp-wild

This paper develops a data-driven approach to accurately predict the
restoration time of outages under different scales and factors. To achieve the
goal, the proposed method consists of three stages. First, given the
unprecedented amount of data collected by utilities, a sparse dictionary-based
ensemble spectral clustering (SDESC) method is proposed to decompose historical
outage datasets, which enjoys good computational efficiency and scalability.
Specifically, each outage sample is represented by a linear combination of a
small number of selected dictionary samples using a density-based method. Then,
the dictionary-based representation is utilized to perform the spectral
analysis to group the data samples with similar features into the same subsets.
In the second stage, a knowledge-transfer-added restoration time prediction
model is trained for each subset by combining weather information and
outage-related features. The transfer learning technology is introduced with
the aim of dealing with the underestimation problem caused by data imbalance in
different subsets, thus improving the model performance. Furthermore, to
connect unseen outages with the learned outage subsets, a t-distributed
stochastic neighbor embedding-based strategy is applied. The proposed method
fully builds on and is also tested on a large real-world outage dataset from a
utility provider with a time span of six consecutive years. The numerical
results validate that our method has high prediction accuracy while showing
good stability against real-world data limitations.

The synergistic combination of deep learning models and Earth observation
promises significant advances to support the sustainable development goals
(SDGs). New developments and a plethora of applications are already changing
the way humanity will face the living planet challenges. This paper reviews
current deep learning approaches for Earth observation data, along with their
application towards monitoring and achieving the SDGs most impacted by the
rapid development of deep learning in Earth observation. We systematically
review case studies to 1) achieve zero hunger, 2) sustainable cities, 3)
deliver tenure security, 4) mitigate and adapt to climate change, and 5)
preserve biodiversity. Important societal, economic and environmental
implications are concerned. Exciting times ahead are coming where algorithms
and Earth data can help in our endeavor to address the climate crisis and
support more sustainable development.

Object recognition for the most part has been approached as a one-hot problem
that treats classes to be discrete and unrelated. Each image region has to be
assigned to one member of a set of objects, including a background class,
disregarding any similarities in the object types. In this work, we compare the
error statistics of the class embeddings learned from a one-hot approach with
semantically structured embeddings from natural language processing or
knowledge graphs that are widely applied in open world object detection.
Extensive experimental results on multiple knowledge-embeddings as well as
distance metrics indicate that knowledge-based class representations result in
more semantically grounded misclassifications while performing on par compared
to one-hot methods on the challenging COCO and Cityscapes object detection
benchmarks. We generalize our findings to multiple object detection
architectures by proposing a knowledge-embedded design for keypoint-based and
transformer-based object detection architectures.

Deep learning has shown successful application in visual recognition and
certain artificial intelligence tasks. Deep learning is also considered as a
powerful tool with high flexibility to approximate functions. In the present
work, functions with desired properties are devised to approximate the
solutions of PDEs. Our approach is based on a posteriori error estimation in
which the adjoint problem is solved for the error localization to formulate an
error estimator within the framework of neural network. An efficient and easy
to implement algorithm is developed to obtain a posteriori error estimate for
multiple goal functionals by employing the dual-weighted residual approach,
which is followed by the computation of both primal and adjoint solutions using
the neural network. The present study shows that such a data-driven model based
learning has superior approximation of quantities of interest even with
relatively less training data. The novel algorithmic developments are
substantiated with numerical test examples. The advantages of using deep neural
network over the shallow neural network are demonstrated and the convergence
enhancing techniques are also presented

The classification of jets induced by quarks or gluons is important for New
Physics searches at high-energy colliders. However, available taggers usually
rely on modelling the data through Monte Carlo simulations, which could veil
intractable theoretical and systematical uncertainties. To significantly reduce
biases, we propose an unsupervised learning algorithm that, given a sample of
jets, can learn the SoftDrop Poissonian rates for quark- and gluon-initiated
jets and their fractions. We extract the Maximum Likelihood Estimates for the
mixture parameters and the posterior probability over them. We then construct a
quark-gluon tagger and estimate its accuracy in actual data to be in the
$0.65-0.7$ range, below supervised algorithms but nevertheless competitive. We
also show how relevant unsupervised metrics perform well, allowing for an
unsupervised hyperparameter selection. Further, we find that this result is not
affected by an angular smearing introduced to simulate detector effects for
central jets. The presented unsupervised learning algorithm is simple; its
result is interpretable and depends on very few assumptions.

This work proposes a distance that combines Minkowski and Chebyshev distances
and can be seen as an intermediary distance. This combination not only achieves
efficient run times in neighbourhood iteration tasks in Z^2, but also obtains
good accuracies when coupled with the k-Nearest Neighbours (k-NN) classifier.
The proposed distance is approximately 1.3 times faster than Manhattan distance
and 329.5 times faster than Euclidean distance in discrete neighbourhood
iterations. An accuracy analysis of the k-NN classifier using a total of 33
datasets from the UCI repository, 15 distances and values assigned to k that
vary from 1 to 200 is presented. In this experiment, the proposed distance
obtained accuracies that were better than the average more often than its
counterparts (in 26 cases out of 33), and also obtained the best accuracy more
frequently (in 9 out of 33 cases).

Optical devices lie at the heart of most of the technology we see around us.
When one actually wants to make such an optical device, one can predict its
optical behavior using computational simulations of Maxwell's equations. If one
then asks what the optimal design would be in order to obtain a certain optical
behavior, the only way to go further would be to try out all of the possible
designs and compute the electromagnetic spectrum they produce. When there are
many design parameters, this brute force approach quickly becomes too
computationally expensive. We therefore need other methods to create optimal
optical devices. An alternative to the brute force approach is inverse design.
In this paradigm, one starts from the desired optical response of a material
and then determines the design parameters that are needed to obtain this
optical response. There are many algorithms known in the literature that
implement this inverse design. Some of the best performing, recent approaches
are based on Deep Learning. The central idea is to train a neural network to
predict the optical response for given design parameters. Since neural networks
are completely differentiable, we can compute gradients of the response with
respect to the design parameters. We can use these gradients to update the
design parameters and get an optical response closer to the one we want. This
allows us to obtain an optimal design much faster compared to the brute force
approach. In my thesis, I use Deep Learning for the inverse design of the
Fabry-P\'erot resonator. This system can be described fully analytically and is
therefore ideal to study.

Rendering articulated objects while controlling their poses is critical to
applications such as virtual reality or animation for movies. Manipulating the
pose of an object, however, requires the understanding of its underlying
structure, that is, its joints and how they interact with each other.
Unfortunately, assuming the structure to be known, as existing methods do,
precludes the ability to work on new object categories. We propose to learn
both the appearance and the structure of previously unseen articulated objects
by observing them move from multiple views, with no joints annotation
supervision, or information about the structure. We observe that 3D points that
are static relative to one another should belong to the same part, and that
adjacent parts that move relative to each other must be connected by a joint.
To leverage this insight, we model the object parts in 3D as ellipsoids, which
allows us to identify joints. We combine this explicit representation with an
implicit one that compensates for the approximation introduced. We show that
our method works for different structures, from quadrupeds, to single-arm
robots, to humans.

We study the problem of estimating room layouts from a single panorama image.
Most former works have two stages: feature extraction and parametric model
fitting. Here we propose an end-to-end method that directly predicts parametric
layouts from an input panorama image. It exploits an implicit encoding
procedure that embeds parametric layouts into a latent space. Then learning a
mapping from images to this latent space makes end-to-end room layout
estimation possible. However end-to-end methods have several notorious
drawbacks despite many intriguing properties. A widely raised criticism is that
they are troubled with dataset bias and do not transfer to unfamiliar domains.
Our study echos this common belief. To this end, we propose to use semantic
boundary prediction maps as an intermediate domain. It brings significant
performance boost on four benchmarks (Structured3D, PanoContext, S3DIS, and
Matterport3D), notably in the zero-shot transfer setting. Code, data, and
models will be released.

Knowledge of forest biomass stocks and their development is important for
implementing effective climate change mitigation measures. It is needed for
studying the processes driving af-, re-, and deforestation and is a
prerequisite for carbon-accounting. Remote sensing using airborne LiDAR can be
used to measure vegetation biomass at large scale. We present deep learning
systems for predicting wood volume, above-ground biomass (AGB), and
subsequently carbon directly from 3D LiDAR point cloud data. We devise
different neural network architectures for point cloud regression and evaluate
them on remote sensing data of areas for which AGB estimates have been obtained
from field measurements in a national forest inventory. Our adaptation of
Minkowski convolutional neural networks for regression gave the best results.
The deep neural networks produced significantly more accurate wood volume, AGB,
and carbon estimates compared to state-of-the-art approaches operating on basic
statistics of the point clouds, and we expect this finding to have a strong
impact on LiDAR-based analyses of terrestrial ecosystem dynamics.

In this work we deal with parametric inverse problems, which consist in
recovering a finite number of parameters describing the structure of an unknown
object, from indirect measurements. State-of-the-art methods for approximating
a regularizing inverse operator by using a dataset of input-output pairs of the
forward model rely on deep learning techniques. In these approaches, a neural
network is trained to predict the value of the sought parameters directly from
the data. In this paper, we show that these methods provide suboptimal results
when the topology of the parameter space is strictly coarser than the Euclidean
one. To overcome this issue, we propose a two-step strategy for approximating a
regularizing inverse operator by means of a neural network, which works under
general topological conditions. First, we embed the parameters into a subspace
of a low-dimensional Euclidean space; second, we use a neural network to
approximate a homeomorphism between the subspace and the image of the parameter
space through the forward operator. The parameters are then retrieved by
applying the inverse of the embedding to the network predictions. The results
are shown for the problem of X-ray imaging of solar flares with data from the
Spectrometer/Telescope for Imaging X-rays. In this case, the parameter space is
a family of Moebius strips that collapse into a point. Our simulation studies
show that the use of a neural network for predicting the parameters directly
from the data yields systematic errors due to the non-Euclidean topology of the
parameter space. The proposed strategy overcomes the topological issues and
furnishes stable and accurate reconstructions.

Stroke rehabilitation seeks to increase neuroplasticity through the repeated
practice of functional motions, but may have minimal impact on recovery because
of insufficient repetitions. The optimal training content and quantity are
currently unknown because no practical tools exist to measure them. Here, we
present PrimSeq, a pipeline to classify and count functional motions trained in
stroke rehabilitation. Our approach integrates wearable sensors to capture
upper-body motion, a deep learning model to predict motion sequences, and an
algorithm to tally motions. The trained model accurately decomposes
rehabilitation activities into component functional motions, outperforming
competitive machine learning methods. PrimSeq furthermore quantifies these
motions at a fraction of the time and labor costs of human experts. We
demonstrate the capabilities of PrimSeq in previously unseen stroke patients
with a range of upper extremity motor impairment. We expect that these advances
will support the rigorous measurement required for quantitative dosing trials
in stroke rehabilitation.

Multispectral image fusion is a fundamental problem of image processing and
remote sensing. This problem is addressed by both classic and deep learning
approaches. This paper is focused on the classic solutions that can work in
real-time systems and introduces a new novel approach to this group of works.
The proposed method carries out multispectral image fusion based on the content
of the fused images. Furthermore, it relies on an analysis of the level of
information of segmented superpixels in the fused inputs. Specifically, the
proposed method addresses the task of visible color RGB to Near-Infrared (NIR)
fusion. The RGB image captures the color of the scene while the NIR channel
captures details and sees beyond haze and clouds. Since each channel senses
different information of the scene, their multispectral fusion is challenging
and interesting. Therefore, the proposed method is designed to produce a fusion
that contains the relevant content of each spectra. The experiments of this
manuscript show that the proposed method is visually informative with respect
to other classic fusion methods. Moreover, it can be run fastly on embedded
devices without heavy computation requirements.

We propose iSegFormer, a memory-efficient transformer that combines a Swin
transformer with a lightweight multilayer perceptron (MLP) decoder. With the
efficient Swin transformer blocks for hierarchical self-attention and the
simple MLP decoder for aggregating both local and global attention, iSegFormer
learns powerful representations while achieving high computational
efficiencies. Specifically, we apply iSegFormer to interactive 3D medical image

Satellite-based Communication systems are gaining renewed momentum in
Industry and Academia, thanks to innovative services introduced by leading tech
companies and the promising impact they can deliver towards the global
connectivity objective tackled by early 6G initiatives. On the one hand, the
emergence of new manufacturing processes and radio technologies promises to
reduce service costs while guaranteeing outstanding communication latency,
available bandwidth, flexibility, and coverage range. On the other hand,
cybersecurity techniques and solutions applied in SATCOM links should be
updated to reflect the substantial advancements in attacker capabilities
characterizing the last two decades. However, business urgency and
opportunities are leading operators towards challenging system trade-offs,
resulting in an increased attack surface and a general relaxation of the
available security services. In this paper, we tackle the cited problems and
present a comprehensive survey on the link-layer security threats, solutions,
and challenges faced when deploying and operating SATCOM systems.Specifically,
we classify the literature on security for SATCOM systems into two main
branches, i.e., physical-layer security and cryptography schemes.Then, we
further identify specific research domains for each of the identified branches,
focusing on dedicated security issues, including, e.g., physical-layer
confidentiality, anti-jamming schemes, anti-spoofing strategies, and
quantum-based key distribution schemes. For each of the above domains, we
highlight the most essential techniques, peculiarities, advantages,
disadvantages, lessons learned, and future directions.Finally, we also identify
emerging research topics whose additional investigation by Academia and
Industry could further attract researchers and investors, ultimately unleashing
the full potential behind ubiquitous satellite communications.

A mesh-free approach for modelling beam-wall interactions in particle
accelerators is proposed. The key idea of our method is to use a deep neural
network as a surrogate for the solution to a set of partial differential
equations involving the particle beam, and the surface impedance concept. The
proposed approach is applied to the coupling impedance of an accelerator vacuum
chamber with thin conductive coating, and also verified in comparison with the
existing analytical formula.

A promising approach to improve cloud parameterizations within climate models
and thus climate projections is to use deep learning in combination with
training data from storm-resolving model (SRM) simulations. The ICOsahedral
Non-hydrostatic (ICON) modeling framework permits simulations ranging from
numerical weather prediction to climate projections, making it an ideal target
to develop neural network (NN) based parameterizations for sub-grid scale
processes. Within the ICON framework, we train NN based cloud cover
parameterizations with coarse-grained data based on realistic regional and
global ICON SRM simulations. We set up three different types of NNs that differ
in the degree of vertical locality they assume for diagnosing cloud cover from
coarse-grained atmospheric state variables. The NNs accurately estimate
sub-grid scale cloud cover from coarse-grained data that has similar
geographical characteristics as their training data. Additionally, globally
trained NNs can reproduce sub-grid scale cloud cover of the regional SRM
simulation. Using the game-theory based interpretability library SHapley
Additive exPlanations, we identify an overemphasis on specific humidity and
cloud ice as the reason why our column-based NN cannot perfectly generalize
from the global to the regional coarse-grained SRM data. The interpretability
tool also helps visualize similarities and differences in feature importance
between regionally and globally trained column-based NNs, and reveals a local
relationship between their cloud cover predictions and the thermodynamic
environment. Our results show the potential of deep learning to derive accurate
yet interpretable cloud cover parameterizations from global SRMs, and suggest
that neighborhood-based models may be a good compromise between accuracy and

Inductive logic programming is a type of machine learning in which logic
programs are learned from examples. This learning typically occurs relative to
some background knowledge provided as a logic program. This dissertation
introduces bottom preprocessing, a method for generating initial constraints on
the programs an ILP system must consider. Bottom preprocessing applies ideas
from inverse entailment to modern ILP systems. Inverse entailment is an
influential early ILP approach introduced with Progol. This dissertation also
presents $\bot$-Popper, an implementation of bottom preprocessing for the
modern ILP system Popper. It is shown experimentally that bottom preprocessing
can reduce learning times of ILP systems on hard problems. This reduction can
be especially significant when the amount of background knowledge in the
problem is large.

Algorithmic recourse seeks to provide actionable recommendations for
individuals to overcome unfavorable classification outcomes from automated
decision-making systems. Recourse recommendations should ideally be robust to
reasonably small uncertainty in the features of the individual seeking
recourse. In this work, we formulate the adversarially robust recourse problem
and show that recourse methods that offer minimally costly recourse fail to be
robust. We then present methods for generating adversarially robust recourse
for linear and for differentiable classifiers. Finally, we show that
regularizing the decision-making classifier to behave locally linearly and to
rely more strongly on actionable features facilitates the existence of
adversarially robust recourse.

We propose a method to compress full-resolution video sequences with implicit
neural representations. Each frame is represented as a neural network that maps
coordinate positions to pixel values. We use a separate implicit network to
modulate the coordinate inputs, which enables efficient motion compensation
between frames. Together with a small residual network, this allows us to
efficiently compress P-frames relative to the previous frame. We further lower
the bitrate by storing the network weights with learned integer quantization.
Our method, which we call implicit pixel flow (IPF), offers several
simplifications over established neural video codecs: it does not require the
receiver to have access to a pretrained neural network, does not use expensive
interpolation-based warping operations, and does not require a separate
training dataset. We demonstrate the feasibility of neural implicit compression
on image and video data.

Gaze behaviour has been used as a way to gather cognitive information for a
number of years. In this paper, we discuss the use of gaze behaviour in solving
different tasks in natural language processing (NLP) without having to record
it at test time. This is because the collection of gaze behaviour is a costly
task, both in terms of time and money. Hence, in this paper, we focus on
research done to alleviate the need for recording gaze behaviour at run time.
We also mention different eye tracking corpora in multiple languages, which are
currently available and can be used in natural language processing. We conclude
our paper by discussing applications in a domain - education - and how learning
gaze behaviour can help in solving the tasks of complex word identification and
automatic essay grading.

E-commerce provides rich multimodal data that is barely leveraged in
practice. One aspect of this data is a category tree that is being used in
search and recommendation. However, in practice, during a user's session there
is often a mismatch between a textual and a visual representation of a given
category. Motivated by the problem, we introduce the task of category-to-image
retrieval in e-commerce and propose a model for the task, CLIP-ITA. The model
leverages information from multiple modalities (textual, visual, and attribute
modality) to create product representations. We explore how adding information
from multiple modalities (textual, visual, and attribute modality) impacts the
model's performance. In particular, we observe that CLIP-ITA significantly
outperforms a comparable model that leverages only the visual modality and a
comparable model that leverages the visual and attribute modality.

With their high energy efficiency, processing-in-memory (PIM) arrays are
increasingly used for convolutional neural network (CNN) inference. In
PIM-based CNN inference, the computational latency and energy are dependent on
how the CNN weights are mapped to the PIM array. A recent study proposed
shifted and duplicated kernel (SDK) mapping that reuses the input feature maps
with a unit of a parallel window, which is convolved with duplicated kernels to
obtain multiple output elements in parallel. However, the existing SDK-based
mapping algorithm does not always result in the minimum computing cycles
because it only maps a square-shaped parallel window with the entire channels.
In this paper, we introduce a novel mapping algorithm called variable-window
SDK (VW-SDK), which adaptively determines the shape of the parallel window that
leads to the minimum computing cycles for a given convolutional layer and PIM
array. By allowing rectangular-shaped windows with partial channels, VW-SDK
utilizes the PIM array more efficiently, thereby further reduces the number of
computing cycles. The simulation with a 512x512 PIM array and Resnet-18 shows
that VW-SDK improves the inference speed by 1.69x compared to the existing
SDK-based algorithm.

Fairness in decision-making has been a long-standing issue in our society.
Despite the increasing number of research activities on unfairness mitigation
in machine learning models, there is little research focusing on mitigating
unfairness in human decisions. Fairness in human decisions is as important as,
if not more important than, fairness in machine learning models since there are
processes where humans make the final decisions and machine learning models can
inherit bias from the human decisions they were trained on. As a result, this
work aims to detect unfairness in human decisions, the very first step of
solving the unfair human decision problem.
  This paper proposes to utilize the existing machine learning fairness
detection mechanisms to detect unfairness in human decisions. The rationale
behind this is, while it is difficult to directly test whether a human makes
unfair decisions, with current research on machine learning fairness, it is now
easy to test, on a large scale at a low cost, whether a machine learning model
is unfair. By synthesizing unfair labels on four general machine learning
fairness datasets and one image processing dataset, this paper shows that the
proposed approach is able to detect (1) whether or not unfair labels exist in
the training data and (2) the degree and direction of the unfairness. We
believe that this work demonstrates the potential of utilizing machine learning
fairness to detect human decision fairness. Following this work, research can
be conducted on (1) preventing future unfair decisions, (2) fixing prior unfair
decisions, and (3) training a fairer machine learning model.

In this paper, we propose a novel algorithm for energy-efficient, low-latency
dynamic mobile edge computing (MEC), in the context of beyond 5G networks
endowed with Reconfigurable Intelligent Surfaces (RISs). In our setting, new
computing requests are continuously generated by a set of devices and are
handled through a dynamic queueing system. Building on stochastic optimization
tools, we devise a dynamic learning algorithm that jointly optimizes the
allocation of radio resources (i.e., power, transmission rates, sleep mode and
duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity
parameters (i.e., phase shifts), while guaranteeing a target performance in
terms of average end-to-end (E2E) delay. The proposed strategy is dynamic,
since it performs a low-complexity optimization on a per-slot basis while
dealing with time-varying radio channels and task arrivals, whose statistics
are unknown. The presence and optimization of RISs helps boosting the
performance of dynamic MEC, thanks to the capability to shape and adapt the
wireless propagation environment. Numerical results assess the performance in
terms of service delay, learning, and adaptation capabilities of the proposed
strategy for RIS empowered MEC.

The development of trajectory-based operations and the rolling network
operations plan in European air traffic management network implies a move
towards more collaborative, strategic flight planning. This opens up the
possibility for inclusion of additional information in the collaborative
decision-making process. With that in mind, we define the indicator for the
economic risk of network elements (e.g., sectors or airports) as the expected
costs that the elements impose on airspace users due to Air Traffic Flow
Management (ATFM) regulations. The definition of the indicator is based on the
analysis of historical ATFM regulations data, that provides an indication of
the risk of accruing delay. This risk of delay is translated into a monetary
risk for the airspace users, creating the new metric of the economic risk of a
given airspace element. We then use some machine learning techniques to find
the parameters leading to this economic risk. The metric is accompanied by an
indication of the accuracy of the delay cost prediction model. Lastly, the
economic risk is transformed into a qualitative economic severity
classification. The economic risks and consequently economic severity can be
estimated for different temporal horizons and time periods providing an
indicator which can be used by Air Navigation Service Providers to identify
areas which might need the implementation of strategic measures (e.g.,
resectorisation or capacity provision change), and by Airspace Users to
consider operation of routes which use specific airspace regions.

Federated learning (FL) algorithms usually sample a fraction of clients in
each round (partial participation) when the number of participants is large and
the server's communication bandwidth is limited. Recent works on the
convergence analysis of FL have focused on unbiased client sampling, e.g.,
sampling uniformly at random, which suffers from slow wall-clock time for
convergence due to high degrees of system heterogeneity and statistical
heterogeneity. This paper aims to design an adaptive client sampling algorithm
that tackles both system and statistical heterogeneity to minimize the
wall-clock convergence time. We obtain a new tractable convergence bound for FL
algorithms with arbitrary client sampling probabilities. Based on the bound, we
analytically establish the relationship between the total learning time and
sampling probabilities, which results in a non-convex optimization problem for
training time minimization. We design an efficient algorithm for learning the
unknown parameters in the convergence bound and develop a low-complexity
algorithm to approximately solve the non-convex problem. Experimental results
from both hardware prototype and simulation demonstrate that our proposed
sampling scheme significantly reduces the convergence time compared to several
baseline sampling schemes. Notably, our scheme in hardware prototype spends 73%
less time than the uniform sampling baseline for reaching the same target loss.

In these proceedings we present lattice gauge equivariant convolutional
neural networks (L-CNNs) which are able to process data from lattice gauge
theory simulations while exactly preserving gauge symmetry. We review aspects
of the architecture and show how L-CNNs can represent a large class of gauge
invariant and equivariant functions on the lattice. We compare the performance
of L-CNNs and non-equivariant networks using a non-linear regression problem
and demonstrate how gauge invariance is broken for non-equivariant models.

Adversarial robustness is one of the most challenging problems in Deep
Learning and Computer Vision research. All the state-of-the-art techniques
require a time-consuming procedure that creates cleverly perturbed images. Due
to its cost, many solutions have been proposed to avoid Adversarial Training.
However, all these attempts proved ineffective as the attacker manages to
exploit spurious correlations among pixels to trigger brittle features
implicitly learned by the model. This paper first introduces a new image
filtering scheme called Image-Graph Extractor (IGE) that extracts the
fundamental nodes of an image and their connections through a graph structure.
By leveraging the IGE representation, we build a new defense method, Filtering
As a Defense, that does not allow the attacker to entangle pixels to create
malicious patterns. Moreover, we show that data augmentation with filtered
images effectively improves the model's robustness to data corruption. We
validate our techniques on CIFAR-10, CIFAR-100, and ImageNet.

It is vital to accurately estimate the value function in Deep Reinforcement
Learning (DRL) such that the agent could execute proper actions instead of
suboptimal ones. However, existing actor-critic methods suffer more or less
from underestimation bias or overestimation bias, which negatively affect their
performance. In this paper, we reveal a simple but effective principle: proper
value correction benefits bias alleviation, where we propose the
generalized-activated weighting operator that uses any non-decreasing function,
namely activation function, as weights for better value estimation.
Particularly, we integrate the generalized-activated weighting operator into
value estimation and introduce a novel algorithm, Generalized-activated Deep
Double Deterministic Policy Gradients (GD3). We theoretically show that GD3 is
capable of alleviating the potential estimation bias. We interestingly find
that simple activation functions lead to satisfying performance with no
additional tricks, and could contribute to faster convergence. Experimental
results on numerous challenging continuous control tasks show that GD3 with
task-specific activation outperforms the common baseline methods. We also
uncover a fact that fine-tuning the polynomial activation function achieves
superior results on most of the tasks.

We present a discretized design that expounds an algorithm recently
introduced in Gagliardi and Russo (2021) to synthesize control policies from
examples for constrained, possibly stochastic and nonlinear, systems. The
constraints do not need to be fulfilled in the possibly noisy example data,
which in turn might be collected from a system that is different from the one
under control. For this discretized design, we discuss a number of properties
and give a design pipeline. The design, which we term as discrete fully
probabilistic design, is benchmarked numerically on an example that involves
controlling an inverted pendulum with actuation constraints starting from data
collected from a physically different pendulum that does not satisfy the
system-specific actuation constraints.

In dialogue systems, utterances with similar semantics may have distinctive
emotions under different contexts. Therefore, modeling long-range contextual
emotional relationships with speaker dependency plays a crucial part in
dialogue emotion recognition. Meanwhile, distinguishing the different emotion
categories is non-trivial since they usually have semantically similar
sentiments. To this end, we adopt supervised contrastive learning to make
different emotions mutually exclusive to identify similar emotions better.
Meanwhile, we utilize an auxiliary response generation task to enhance the
model's ability of handling context information, thereby forcing the model to
recognize emotions with similar semantics in diverse contexts. To achieve these
objectives, we use the pre-trained encoder-decoder model BART as our backbone
model since it is very suitable for both understanding and generation tasks.
The experiments on four datasets demonstrate that our proposed model obtains
significantly more favorable results than the state-of-the-art model in
dialogue emotion recognition. The ablation study further demonstrates the
effectiveness of supervised contrastive loss and generative loss.

An effective implementation of enhanced sampling algorithms for molecular
dynamics simulations requires a priori knowledge of the approximate reaction
coordinate describing the relevant mechanisms in the system. Here we
demonstrate how the artificial intelligence based recent State Predictive
Information Bottleneck (SPIB) approach can learn such a reaction coordinate as
a deep neural network even from under-sampled trajectories. We demonstrate its
usefulness by achieving more than 40 magnitudes of acceleration in simulating
two test-piece biophysical systems through well-tempered metadynamics performed
by biasing along the SPIB learned reaction coordinate. These include left- to
right- handed chirality transitions in a synthetic protein (Aib)_9, and
permeation of a small, asymmetric molecule benzoic acid through a synthetic,
symmetric phospholipid bilayer. In addition to significantly accelerating the
dynamics and achieving back-and-forth movement between different metastable
states, the SPIB based reaction coordinate gives mechanistic insight into the
processes driving these two important problems.

Correlated time series (CTS) forecasting plays an essential role in many
cyber-physical systems, where multiple sensors emit time series that capture
interconnected processes. Solutions based on deep learning that deliver
state-of-the-art CTS forecasting performance employ a variety of
spatio-temporal (ST) blocks that are able to model temporal dependencies and
spatial correlations among time series. However, two challenges remain. First,
ST-blocks are designed manually, which is time consuming and costly. Second,
existing forecasting models simply stack the same ST-blocks multiple times,
which limits the model potential. To address these challenges, we propose
AutoCTS that is able to automatically identify highly competitive ST-blocks as
well as forecasting models with heterogeneous ST-blocks connected using diverse
topologies, as opposed to the same ST-blocks connected using simple stacking.
Specifically, we design both a micro and a macro search space to model possible
architectures of ST-blocks and the connections among heterogeneous ST-blocks,
and we provide a search strategy that is able to jointly explore the search
spaces to identify optimal forecasting models. Extensive experiments on eight
commonly used CTS forecasting benchmark datasets justify our design choices and
demonstrate that AutoCTS is capable of automatically discovering forecasting
models that outperform state-of-the-art human-designed models. This is an
extended version of ``AutoCTS: Automated Correlated Time Series Forecasting'',
to appear in PVLDB 2022.

In a Riemannian manifold, the Ricci flow is a partial differential equation
for evolving the metric to become more regular. We hope that topological
structures from such metrics may be used to assist in the tasks of machine
learning. However, this part of the work is still missing. In this paper, we
propose Ricci flow assisted Eucl2Hyp2Eucl neural networks that bridge this gap
between the Ricci flow and deep neural networks by mapping neural manifolds
from the Euclidean space to the dynamically stable Poincar\'e ball and then
back to the Euclidean space. As a result, we prove that, if initial metrics
have an $L^2$-norm perturbation which deviates from the Hyperbolic metric on
the Poincar\'e ball, the scaled Ricci-DeTurck flow of such metrics smoothly and
exponentially converges to the Hyperbolic metric. Specifically, the role of the
Ricci flow is to serve as naturally evolving to the stable Poincar\'e ball. For
such dynamically stable neural manifolds under the Ricci flow, the convergence
of neural networks embedded with such manifolds is not susceptible to
perturbations. And we show that Ricci flow assisted Eucl2Hyp2Eucl neural
networks outperform with their all Euclidean counterparts on image

In self-supervised learning, it is challenging to reduce the gap between the
enhancement performance on the estimated and target speech signals with existed
pre-tasks. In this paper, we propose a multi-task pre-training method to
improve the speech enhancement performance with self-supervised learning.
Within the pre-training autoencoder (PAE), only a limited set of clean speech
signals are required to learn their latent representations. Meanwhile, to solve
the limitation of single pre-task, the proposed masking module exploits the
dereverberated mask and estimated ratio mask to denoise the mixture as the
second pre-task. Different from the PAE, where the target speech signals are
estimated, the downstream task autoencoder (DAE) utilizes a large number of
unlabeled and unseen reverberant mixtures to generate the estimated mixtures.
The trained DAE is shared by the learned representations and masks.
Experimental results on a benchmark dataset demonstrate that the proposed
method outperforms the state-of-the-art approaches.

Federated learning (FL) over wireless communication channels, specifically,
over-the-air (OTA) model aggregation framework is considered. In OTA wireless
setups, the adverse channel effects can be alleviated by increasing the number
of receive antennas at the parameter server (PS), which performs model
aggregation. However, the performance of OTA FL is limited by the presence of
mobile users (MUs) located far away from the PS. In this paper, to mitigate
this limitation, we propose hierarchical over-the-air federated learning
(HOTAFL), which utilizes intermediary servers (IS) to form clusters near MUs.
We provide a convergence analysis for the proposed setup, and demonstrate
through theoretical and experimental results that local aggregation in each
cluster before global aggregation leads to a better performance and faster
convergence than OTA FL.

Conventional 3D human pose estimation relies on first detecting 2D body
keypoints and then solving the 2D to 3D correspondence problem.Despite the
promising results, this learning paradigm is highly dependent on the quality of
the 2D keypoint detector, which is inevitably fragile to occlusions and
out-of-image absences.In this paper,we propose a novel Pose Orientation Net
(PONet) that is able to robustly estimate 3D pose by learning orientations
only, hence bypassing the error-prone keypoint detector in the absence of image
evidence. For images with partially invisible limbs, PONet estimates the 3D
orientation of these limbs by taking advantage of the local image evidence to
recover the 3D pose.Moreover, PONet is competent to infer full 3D poses even
from images with completely invisible limbs, by exploiting the orientation
correlation between visible limbs to complement the estimated poses,further
improving the robustness of 3D pose estimation.We evaluate our method on
multiple datasets, including Human3.6M, MPII, MPI-INF-3DHP, and 3DPW. Our
method achieves results on par with state-of-the-art techniques in ideal
settings, yet significantly eliminates the dependency on keypoint detectors and
the corresponding computation burden. In highly challenging scenarios, such as
truncation and erasing, our method performs very robustly and yields much
superior results as compared to state of the art,demonstrating its potential
for real-world applications.

Recently, self-supervised learning (SSL) techniques have been introduced to
solve the monaural speech enhancement problem. Due to the lack of using clean
phase information, the enhancement performance is limited in most SSL methods.
Therefore, in this paper, we propose a phase-aware self-supervised learning
based monaural speech enhancement method. The latent representations of both
amplitude and phase are studied in two decoders of the foundation autoencoder
(FAE) with only a limited set of clean speech signals independently. Then, the
downstream autoencoder (DAE) learns a shared latent space between the clean
speech and mixture representations with a large number of unseen mixtures. A
complex-cycle-consistent (CCC) mechanism is proposed to minimize the
reconstruction loss between the amplitude and phase domains. Besides, it is
noticed that if the speech features are extracted as the multi-resolution
spectra, the desired information distributed in spectra of different scales can
be studied to further boost the performance. The NOISEX and DAPS corpora are
used to generate mixtures with different interferences to evaluate the efficacy
of the proposed method. It is highlighted that the clean speech and mixtures
fed in FAE and DAE are not paired. Both ablation and comparison experimental
results show that the proposed method clearly outperforms the state-of-the-art

Exploration-Exploitation (E{\&}E) algorithms are commonly adopted to deal
with the feedback-loop issue in large-scale online recommender systems. Most of
existing studies believe that high uncertainty can be a good indicator of
potential reward, and thus primarily focus on the estimation of model
uncertainty. We argue that such an approach overlooks the subsequent effect of
exploration on model training. From the perspective of online learning, the
adoption of an exploration strategy would also affect the collecting of
training data, which further influences model learning. To understand the
interaction between exploration and training, we design a Pseudo-Exploration
module that simulates the model updating process after a certain item is
explored and the corresponding feedback is received. We further show that such
a process is equivalent to adding an adversarial perturbation to the model
input, and thereby name our proposed approach as an the Adversarial Gradient
Driven Exploration (AGE). For production deployment, we propose a dynamic
gating unit to pre-determine the utility of an exploration. This enables us to
utilize the limited amount of resources for exploration, and avoid wasting
pageview resources on ineffective exploration. The effectiveness of AGE was
firstly examined through an extensive number of ablation studies on an academic
dataset. Meanwhile, AGE has also been deployed to one of the world-leading
display advertising platforms, and we observe significant improvements on
various top-line evaluation metrics.

With the growing number of Location-Based Social Networks, privacy preserving
location prediction has become a primary task for helping users discover new
points-of-interest (POIs). Traditional systems consider a centralized approach
that requires the transmission and collection of users' private data. In this
work, we present FedPOIRec, a privacy preserving federated learning approach
enhanced with features from users' social circles for top-$N$ POI
recommendations. First, the FedPOIRec framework is built on the principle that
local data never leave the owner's device, while the local updates are blindly
aggregated by a parameter server. Second, the local recommenders get
personalized by allowing users to exchange their learned parameters, enabling
knowledge transfer among friends. To this end, we propose a privacy preserving
protocol for integrating the preferences of a user's friends after the
federated computation, by exploiting the properties of the CKKS fully
homomorphic encryption scheme. To evaluate FedPOIRec, we apply our approach
into five real-world datasets using two recommendation models. Extensive
experiments demonstrate that FedPOIRec achieves comparable recommendation
quality to centralized approaches, while the social integration protocol incurs
low computation and communication overhead on the user side.

In the area of 3D shape analysis, the geometric properties of a shape have
long been studied. Instead of directly extracting representative features using
expert-designed descriptors or end-to-end deep neural networks, this paper is
dedicated to discovering distinctive information from the shape formation
process. Concretely, a spherical point cloud served as the template is
progressively deformed to fit the target shape in a coarse-to-fine manner.
During the shape formation process, several checkpoints are inserted to
facilitate recording and investigating the intermediate stages. For each stage,
the offset field is evaluated as a stage-aware description. The summation of
the offsets throughout the shape formation process can completely define the
target shape in terms of geometry. In this perspective, one can derive the
point-wise shape correspondence from the template inexpensively, which benefits
various graphic applications. In this paper, the Progressive Deformation-based
Auto-Encoder (PDAE) is proposed to learn the stage-aware description through a
coarse-to-fine shape fitting task. Experimental results show that the proposed
PDAE has the ability to reconstruct 3D shapes with high fidelity, and
consistent topology is preserved in the multi-stage deformation process.
Additional applications based on the stage-aware description are performed,
demonstrating its universality.

We propose a general framework for machine learning based optimization under
uncertainty. Our approach replaces the complex forward model by a surrogate,
e.g., a neural network, which is learned simultaneously in a one-shot sense
when solving the optimal control problem. Our approach relies on a
reformulation of the problem as a penalized empirical risk minimization problem
for which we provide a consistency analysis in terms of large data and
increasing penalty parameter. To solve the resulting problem, we suggest a
stochastic gradient method with adaptive control of the penalty parameter and
prove convergence under suitable assumptions on the surrogate model. Numerical
experiments illustrate the results for linear and nonlinear surrogate models.

Human motion understanding and prediction is an integral aspect in our
pursuit of machine intelligence and human-machine interaction systems. Current
methods typically pursue a kinematics modeling approach, relying heavily upon
prior anatomical knowledge and constraints. However, such an approach is hard
to generalize to different skeletal model representations, and also tends to be
inadequate in accounting for the dynamic range and complexity of motion, thus
hindering predictive accuracy. In this work, we propose a novel approach in
modeling the motion prediction problem based on stochastic differential
equations and path integrals. The motion profile of each skeletal joint is
formulated as a basic stochastic variable and modeled with the Langevin
equation. We develop a strategy of employing GANs to simulate path integrals
that amounts to optimizing over possible future paths. We conduct experiments
in two large benchmark datasets, Human 3.6M and CMU MoCap. It is highlighted
that our approach achieves a 12.48% accuracy improvement over current
state-of-the-art methods in average.

When learning cybersecurity, operating systems, or networking, students
perform practical tasks using a broad range of command-line tools. Collecting
and analyzing data about the command usage can reveal valuable insights into
how students progress and where they make mistakes. However, few learning
environments support recording and inspecting command-line inputs, and setting
up an efficient infrastructure for this purpose is challenging. To aid
engineering and computing educators, we share the design and implementation of
an open-source toolset for logging commands that students execute on Linux
machines. Compared to basic solutions, such as shell history files, the
toolset's added value is threefold. 1) Its configuration is automated so that
it can be easily used in classes on different topics. 2) It collects metadata
about the command execution, such as a timestamp, hostname, and IP address. 3)
Data are instantly forwarded to central storage in a unified, semi-structured
format. This enables automated processing, both in real-time and post hoc, to
enhance the instructors' understanding of student actions. The toolset works
independently of the teaching content, the training network's topology, or the
number of students working in parallel. We demonstrated the toolset's value in
two learning environments at four training sessions. Over two semesters, 50
students played educational cybersecurity games using a Linux command-line
interface. Each training session lasted approximately two hours, during which
we recorded 4439 shell commands. The semi-automated data analysis revealed
solution patterns, used tools, and misconceptions of students. Our insights
from creating the toolset and applying it in teaching practice are relevant for
instructors, researchers, and developers of learning environments. We provide
the software and data resulting from this work so that others can use them.

Soft Actor-Critic (SAC) is one of the state-of-the-art off-policy
reinforcement learning (RL) algorithms that is within the maximum entropy based
RL framework. SAC is demonstrated to perform very well in a list of continous
control tasks with good stability and robustness. SAC learns a stochastic
Gaussian policy that can maximize a trade-off between total expected reward and
the policy entropy. To update the policy, SAC minimizes the KL-Divergence
between the current policy density and the soft value function density.
Reparameterization trick is then used to obtain the approximate gradient of
this divergence. In this paper, we propose Soft Actor-Critic with Cross-Entropy
Policy Optimization (SAC-CEPO), which uses Cross-Entropy Method (CEM) to
optimize the policy network of SAC. The initial idea is to use CEM to
iteratively sample the closest distribution towards the soft value function
density and uses the resultant distribution as a target to update the policy
network. For the purpose of reducing the computational complexity, we also
introduce a decoupled policy structure that decouples the Gaussian policy into
one policy that learns the mean and one other policy that learns the deviation
such that only the mean policy is trained by CEM. We show that this decoupled
policy structure does converge to a optimal and we also demonstrate by
experiments that SAC-CEPO achieves competitive performance against the original
SAC.

Sparse prediction with categorical data is challenging even for a moderate
number of variables, because one parameter is roughly needed to encode one
category or level. The Group Lasso is a well known efficient algorithm for
selection continuous or categorical variables, but all estimates related to a
selected factor usually differ, so a fitted model may not be sparse. To make
the Group Lasso solution sparse, we propose to merge levels of the selected
factor, if a difference between its corresponding estimates is less than some
predetermined threshold. We prove that under weak conditions our algorithm,
called GLAMER for Group LAsso MERger, recovers the true, sparse linear or
logistic model even for the high-dimensional scenario, that is when a number of
parameters is greater than a learning sample size. To our knowledge, selection
consistency has been proven many times for different algorithms fitting sparse
models with categorical variables, but our result is the first for the
high-dimensional scenario. Numerical experiments show the satisfactory
performance of the GLAMER.

This report documents recent technical work on developing and validating
stochastic occupancy models in commercial buildings, performed by the Pacific
Northwest National Laboratory (PNNL) as part of the Sensor Impact Evaluation
and Verification project under the U.S. Department of Energy (DOE) Building
Technologies Office (BTO). In this report, we present our work on developing
and validating inhomogeneous semi-Markov chain models for generating sequences
of zone-level occupancy presence and occupancy counts in a commercial building.
Real datasets are used to learn and validate the generative occupancy models.
Relevant metrics such as normalized Jensen-Shannon distance (NJSD) are used to
demonstrate the ability of the models to express realistic occupancy behavioral
patterns.

We have developed a technique combining the accuracy of quantum Monte Carlo
in describing the electron correlation with the efficiency of a Machine
Learning Potential (MLP). We use kernel regression in combination with SOAP
(Smooth Overlap of Atomic Position) features, implemented here in a very
efficient way. The key ingredients are: i) a sparsification technique, based on
farthest point sampling, ensuring generality and transferability of our MLPs
and ii) the so called $\Delta$-learning, allowing a small training data set, a
fundamental property for highly accurate but computationally demanding
calculations, such as the ones based on quantum Monte Carlo. As the first
application we present a benchmark study of the liquid-liquid transition of
high-pressure hydrogen and show the quality of our MLP, by emphasizing the
importance of high accuracy for this very debated subject, where experiments
are difficult in the lab, and theory is still far from being conclusive.

The unmanned aerial vehicle (UAV) is one of the technological breakthroughs
that supports a variety of services, including communications. UAV will play a
critical role in enhancing the physical layer security of wireless networks.
This paper defines the problem of eavesdropping on the link between the ground
user and the UAV, which serves as an aerial base station (ABS). The
reinforcement learning algorithms Q-learning and deep Q-network (DQN) are
proposed for optimizing the position of the ABS and the transmission power to
enhance the data rate of the ground user. This increases the secrecy capacity
without the system knowing the location of the eavesdropper. Simulation results
show fast convergence and the highest secrecy capacity of the proposed DQN
compared to Q-learning and baseline approaches.

Analysing statistical properties of neural networks is a central topic in
statistics and machine learning. However, most results in the literature focus
on the properties of the neural network minimizing the training error. The goal
of this paper is to consider aggregated neural networks using a Gaussian prior.
The departure point of our approach is an arbitrary aggregate satisfying the
PAC-Bayesian inequality. The main contribution is a precise nonasymptotic
assessment of the estimation error appearing in the PAC-Bayes bound. We also
review available bounds on the error of approximating a function by a neural
network. Combining bounds on estimation and approximation errors, we establish
risk bounds that are sharp enough to lead to minimax rates of estimation over
Sobolev smoothness classes.

Depth maps captured with commodity sensors often require super-resolution to
be used in applications. In this work we study a super-resolution approach
based on a variational problem statement with Tikhonov regularization where the
regularizer is parametrized with a deep neural network. This approach was
previously applied successfully in photoacoustic tomography. We experimentally
show that its application to depth map super-resolution is difficult, and
provide suggestions about the reasons for that.

Compared to convolutional layers, fully-connected (FC) layers are better at
modeling the long-range dependencies but worse at capturing the local patterns,
hence usually less favored for image recognition. In this paper, we propose a
methodology, Locality Injection, to incorporate local priors into an FC layer
via merging the trained parameters of a parallel conv kernel into the FC
kernel. Locality Injection can be viewed as a novel Structural
Re-parameterization method since it equivalently converts the structures via
transforming the parameters. Based on that, we propose a multi-layer-perceptron
(MLP) block named RepMLP Block, which uses three FC layers to extract features,
and a novel architecture named RepMLPNet. The hierarchical design distinguishes
RepMLPNet from the other concurrently proposed vision MLPs. As it produces
feature maps of different levels, it qualifies as a backbone model for
downstream tasks like semantic segmentation. Our results reveal that 1)
Locality Injection is a general methodology for MLP models; 2) RepMLPNet has
favorable accuracy-efficiency trade-off compared to the other MLPs; 3)
RepMLPNet is the first MLP that seamlessly transfer to Cityscapes semantic
segmentation. The code and models are available at
https://github.com/DingXiaoH/RepMLP.

Suppose we observe a random vector $X$ from some distribution $P$ in a known
family with unknown parameters. We ask the following question: when is it
possible to split $X$ into two parts $f(X)$ and $g(X)$ such that neither part
is sufficient to reconstruct $X$ by itself, but both together can recover $X$
fully, and the joint distribution of $(f(X),g(X))$ is tractable? As one
example, if $X=(X_1,\dots,X_n)$ and $P$ is a product distribution, then for any
$m<n$, we can split the sample to define $f(X)=(X_1,\dots,X_m)$ and
$g(X)=(X_{m+1},\dots,X_n)$. Rasines and Young (2021) offers an alternative
route of accomplishing this task through randomization of $X$ with additive
Gaussian noise which enables post-selection inference in finite samples for
Gaussian distributed data and asymptotically for non-Gaussian additive models.
In this paper, we offer a more general methodology for achieving such a split
in finite samples by borrowing ideas from Bayesian inference to yield a
(frequentist) solution that can be viewed as a continuous analog of data
splitting. We call our method data fission, as an alternative to data
splitting, data carving and p-value masking. We exemplify the method on a few
prototypical applications, such as post-selection inference for trend filtering
and other regression problems.

When using machine learning techniques in decision-making processes, the
interpretability of the models is important. In the present paper, we adopted
the Shapley additive explanation (SHAP), which is based on fair profit
allocation among many stakeholders depending on their contribution, for
interpreting a gradient-boosting decision tree model using hospital data. For
better interpretability, we propose two novel techniques as follows: (1) a new
metric of feature importance using SHAP and (2) a technique termed feature
packing, which packs multiple similar features into one grouped feature to
allow an easier understanding of the model without reconstruction of the model.
We then compared the explanation results between the SHAP framework and
existing methods. In addition, we showed how the A/G ratio works as an
important prognostic factor for cerebral infarction using our hospital data and
proposed techniques.

Knowledge Graphs (KG) act as a great tool for holding distilled information
from large natural language text corpora. The problem of natural language
querying over knowledge graphs is essential for the human consumption of this
information. This problem is typically addressed by converting the natural
language query to a structured query and then firing the structured query on
the KG. Direct answering models over knowledge graphs in literature are very
few. The query conversion models and direct models both require specific
training data pertaining to the domain of the knowledge graph. In this work, we
convert the problem of natural language querying over knowledge graphs to an
inference problem over premise-hypothesis pairs. Using trained deep learning
models for the converted proxy inferencing problem, we provide the solution for
the original natural language querying problem. Our method achieves over 90%
accuracy on MetaQA dataset, beating the existing state-of-the-art. We also
propose a model for inferencing called Hierarchical Recurrent Path
Encoder(HRPE). The inferencing models can be fine-tuned to be used across
domains with less training data. Our approach does not require large
domain-specific training data for querying on new knowledge graphs from
different domains.

We consider the control of McKean-Vlasov dynamics (or mean-field control)
with probabilistic state constraints. We rely on a level-set approach which
provides a representation of the constrained problem in terms of an
unconstrained one with exact penalization and running maximum or integral cost.
The method is then extended to the common noise setting. Our work extends
(Bokanowski, Picarelli, and Zidani, SIAM J. Control Optim. 54.5 (2016), pp.
2568--2593) and (Bokanowski, Picarelli, and Zidani, Appl. Math. Optim. 71
(2015), pp. 125--163) to a mean-field setting. The reformulation as an
unconstrained problem is particularly suitable for the numerical resolution of
the problem, that is achieved from an extension of a machine learning algorithm
from (Carmona, Lauri{\`e}re, arXiv:1908.01613 to appear in Ann. Appl. Prob.,
2019). A first application concerns the storage of renewable electricity in the
presence of mean-field price impact and another one focuses on a mean-variance
portfolio selection problem with probabilistic constraints on the wealth. We
also illustrate our approach for a direct numerical resolution of the primal
Markowitz continuous-time problem without relying on duality.

Coflow is a recently proposed networking abstraction to help improve the
communication performance of data-parallel computing jobs. In multi-stage jobs,
each job consists of multiple coflows and is represented by a Directed Acyclic
Graph (DAG). Efficiently scheduling coflows is critical to improve the
data-parallel computing performance in data centers. Compared with hand-tuned
scheduling heuristics, existing work DeepWeave [1] utilizes Reinforcement
Learning (RL) framework to generate highly-efficient coflow scheduling policies
automatically. It employs a graph neural network (GNN) to encode the job
information in a set of embedding vectors, and feeds a flat embedding vector
containing the whole job information to the policy network. However, this
method has poor scalability as it is unable to cope with jobs represented by
DAGs of arbitrary sizes and shapes, which requires a large policy network for
processing a high-dimensional embedding vector that is difficult to train. In
this paper, we first utilize a directed acyclic graph neural network (DAGNN) to
process the input and propose a novel Pipelined-DAGNN, which can effectively
speed up the feature extraction process of the DAGNN. Next, we feed the
embedding sequence composed of schedulable coflows instead of a flat embedding
of all coflows to the policy network, and output a priority sequence, which
makes the size of the policy network depend on only the dimension of features
instead of the product of dimension and number of nodes in the job's
DAG.Furthermore, to improve the accuracy of the priority scheduling policy, we
incorporate the Self-Attention Mechanism into a deep RL model to capture the
interaction between different parts of the embedding sequence to make the
output priority scores relevant. Based on this model, we then develop a coflow
scheduling algorithm for online multi-stage jobs.

Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge from the
labeled source domain to the unlabeled target domain in the presence of dataset
shift. Most existing methods cannot address the domain alignment and class
discrimination well, which may distort the intrinsic data structure for
downstream tasks (e.g., classification). To this end, we propose a novel
geometry-aware model to learn the transferability and discriminability
simultaneously via nuclear norm optimization. We introduce the domain coherence
and class orthogonality for UDA from the perspective of subspace geometry. The
domain coherence will ensure the model has a larger capacity for learning
separable representations, and class orthogonality will minimize the
correlation between clusters to alleviate the misalignment. So, they are
consistent and can benefit from each other. Besides, we provide a theoretical
insight into the norm-based learning literature in UDA, which ensures the
interpretability of our model. We show that the norms of domains and clusters
are expected to be larger and smaller to enhance the transferability and
discriminability, respectively. Extensive experimental results on standard UDA
datasets demonstrate the effectiveness of our theory and model.

The utilisation of large and diverse datasets for machine learning (ML) at
scale is required to promote scientific insight into many meaningful problems.
However, due to data governance regulations such as GDPR as well as ethical
concerns, the aggregation of personal and sensitive data is problematic, which
prompted the development of alternative strategies such as distributed ML
(DML). Techniques such as Federated Learning (FL) allow the data owner to
maintain data governance and perform model training locally without having to
share their data. FL and related techniques are often described as
privacy-preserving. We explain why this term is not appropriate and outline the
risks associated with over-reliance on protocols that were not designed with
formal definitions of privacy in mind. We further provide recommendations and
examples on how such algorithms can be augmented to provide guarantees of
governance, security, privacy and verifiability for a general ML audience
without prior exposure to formal privacy techniques.

This paper presents an end-to-end instance segmentation framework, termed
SOIT, that Segments Objects with Instance-aware Transformers. Inspired by DETR
\cite{carion2020end}, our method views instance segmentation as a direct set
prediction problem and effectively removes the need for many hand-crafted
components like RoI cropping, one-to-many label assignment, and non-maximum
suppression (NMS). In SOIT, multiple queries are learned to directly reason a
set of object embeddings of semantic category, bounding-box location, and
pixel-wise mask in parallel under the global image context. The class and
bounding-box can be easily embedded by a fixed-length vector. The pixel-wise
mask, especially, is embedded by a group of parameters to construct a
lightweight instance-aware transformer. Afterward, a full-resolution mask is
produced by the instance-aware transformer without involving any RoI-based
operation. Overall, SOIT introduces a simple single-stage instance segmentation
framework that is both RoI- and NMS-free. Experimental results on the MS COCO
dataset demonstrate that SOIT outperforms state-of-the-art instance
segmentation approaches significantly. Moreover, the joint learning of multiple
tasks in a unified query embedding can also substantially improve the detection
performance. Code is available at \url{https://github.com/yuxiaodongHRI/SOIT}.

We present ANUBIS, a highly effective machine learning-based APT detection
system. Our design philosophy for ANUBIS involves two principal components.
Firstly, we intend ANUBIS to be effectively utilized by cyber-response teams.
Therefore, prediction explainability is one of the main focuses of ANUBIS
design. Secondly, ANUBIS uses system provenance graphs to capture causality and
thereby achieve high detection performance. At the core of the predictive
capability of ANUBIS, there is a Bayesian Neural Network that can tell how
confident it is in its predictions. We evaluate ANUBIS against a recent APT
dataset (DARPA OpTC) and show that ANUBIS can detect malicious activity akin to
APT campaigns with high accuracy. Moreover, ANUBIS learns about high-level
patterns that allow it to explain its predictions to threat analysts. The high
predictive performance with explainable attack story reconstruction makes
ANUBIS an effective tool to use for enterprise cyber defense.

In this work we present a systematic empirical study focused on the
suitability of the state-of-the-art multilingual encoders for cross-lingual
document and sentence retrieval tasks across a number of diverse language
pairs. We first treat these models as multilingual text encoders and benchmark
their performance in unsupervised ad-hoc sentence- and document-level CLIR. In
contrast to supervised language understanding, our results indicate that for
unsupervised document-level CLIR -- a setup with no relevance judgments for
IR-specific fine-tuning -- pretrained multilingual encoders on average fail to
significantly outperform earlier models based on CLWEs. For sentence-level
retrieval, we do obtain state-of-the-art performance: the peak scores, however,
are met by multilingual encoders that have been further specialized, in a
supervised fashion, for sentence understanding tasks, rather than using their
vanilla 'off-the-shelf' variants. Following these results, we introduce
localized relevance matching for document-level CLIR, where we independently
score a query against document sections. In the second part, we evaluate
multilingual encoders fine-tuned in a supervised fashion (i.e., we learn to
rank) on English relevance data in a series of zero-shot language and domain
transfer CLIR experiments. Our results show that supervised re-ranking rarely
improves the performance of multilingual transformers as unsupervised base
rankers. Finally, only with in-domain contrastive fine-tuning (i.e., same
domain, only language transfer), we manage to improve the ranking quality. We
uncover substantial empirical differences between cross-lingual retrieval
results and results of (zero-shot) cross-lingual transfer for monolingual
retrieval in target languages, which point to "monolingual overfitting" of
retrieval models trained on monolingual data.

In deep learning it is common to overparameterize neural networks, that is,
to use more parameters than training samples. Quite surprisingly training the
neural network via (stochastic) gradient descent leads to models that
generalize very well, while classical statistics would suggest overfitting. In
order to gain understanding of this implicit bias phenomenon we study the
special case of sparse recovery (compressed sensing) which is of interest on
its own. More precisely, in order to reconstruct a vector from underdetermined
linear measurements, we introduce a corresponding overparameterized square loss
functional, where the vector to be reconstructed is deeply factorized into
several vectors. We show that, if there exists an exact solution, vanilla
gradient flow for the overparameterized loss functional converges to a good
approximation of the solution of minimal $\ell_1$-norm. The latter is
well-known to promote sparse solutions. As a by-product, our results
significantly improve the sample complexity for compressed sensing via gradient
flow/descent on overparameterized models derived in previous works. The theory
accurately predicts the recovery rate in numerical experiments. Our proof
relies on a analyzing a certain Bregman divergence of the flow. This bypasses
the obstacles caused by non-convexity and should be of independent interest.

Container Orchestrator (CO) is a vital technology for managing clusters of
containers, which may form a virtualized infrastructure for developing and
operating software systems. Like any other software system, securing CO is
critical, but can be quite challenging task due to large number of configurable
options. Manual configuration is not only knowledge intensive and time
consuming, but also is error prone. For automating security configuration of
CO, we propose a novel Knowledge Graph based Security Configuration,
KGSecConfig, approach. Our solution leverages keyword and learning models to
systematically capture, link, and correlate heterogeneous and multi-vendor
configuration space in a unified structure for supporting automation of
security configuration of CO. We implement KGSecConfig on Kubernetes, Docker,
Azure, and VMWare to build secured configuration knowledge graph. Our
evaluation results show 0.98 and 0.94 accuracy for keyword and learning-based
secured configuration option and concept extraction, respectively. We also
demonstrate the utilization of the knowledge graph for automated
misconfiguration mitigation in a Kubernetes cluster. We assert that our
knowledge graph based approach can help in addressing several challenges, e.g.,
misconfiguration of security, associated with manually configuring the security
of CO.

In cryptocurrency-based permissionless blockchain networks, the decentralized
structure enables any user to join and operate across different regions. The
criminal entities exploit it by using cryptocurrency transactions on the
blockchain to facilitate activities such as money laundering, gambling, and
ransomware attacks. In recent times, different machine learning-based
techniques can detect such criminal elements based on blockchain transaction
data. However, there is no provision within the blockchain to deal with such
elements. We propose a reputation-based methodology for response to the users
detected carrying out the aforementioned illicit activities. We select Algorand
blockchain to implement our methodology by incorporating it within the
consensus protocol. The theoretical results obtained prove the restriction and
exclusion of criminal elements through block proposal rejection and attenuation
of the voting power as a validator for such entities. Further, we analyze the
efficacy of our method and show that it puts no additional strain on the
communication resources.

Synthetic data and simulators have the potential to markedly improve the
performance and robustness of recommendation systems. These approaches have
already had a beneficial impact in other machine-learning driven fields. We
identify and discuss a key trade-off between data fidelity and privacy in the
past work on synthetic data and simulators for recommendation systems. For the
important use case of predicting algorithm rankings on real data from synthetic
data, we provide motivation and current successes versus limitations. Finally
we outline a number of exciting future directions for recommendation systems
that we believe deserve further attention and work, including mixing real and
synthetic data, feedback in dataset generation, robust simulations, and
privacy-preserving methods.

Mining data streams poses a number of challenges, including the continuous
and non-stationary nature of data, the massive volume of information to be
processed and constraints put on the computational resources. While there is a
number of supervised solutions proposed for this problem in the literature,
most of them assume that access to the ground truth (in form of class labels)
is unlimited and such information can be instantly utilized when updating the
learning system. This is far from being realistic, as one must consider the
underlying cost of acquiring labels. Therefore, solutions that can reduce the
requirements for ground truth in streaming scenarios are required. In this
paper, we propose a novel framework for mining drifting data streams on a
budget, by combining information coming from active learning and self-labeling.
We introduce several strategies that can take advantage of both intelligent
instance selection and semi-supervised procedures, while taking into account
the potential presence of concept drift. Such a hybrid approach allows for
efficient exploration and exploitation of streaming data structures within
realistic labeling budgets. Since our framework works as a wrapper, it may be
applied with different learning algorithms. Experimental study, carried out on
a diverse set of real-world data streams with various types of concept drift,
proves the usefulness of the proposed strategies when dealing with highly
limited access to class labels. The presented hybrid approach is especially
feasible when one cannot increase a budget for labeling or replace an
inefficient classifier. We deliver a set of recommendations regarding areas of
applicability for our strategies.

Backpropagation is widely used for calculating gradients in deep neural
networks (DNNs). Applied often along with stochastic gradient descent (SGD) or
its variants, backpropagation is considered as a de-facto choice in a variety
of machine learning tasks including DNN training and adversarial
attack/defense. Recently, a linear variant of BP named LinBP was introduced for
generating more transferable adversarial examples for black-box adversarial
attacks, by Guo et al. Yet, it has not been theoretically studied and the
convergence analysis of such a method is lacking. This paper serves as a
complement and somewhat an extension to Guo et al.'s paper, by providing
theoretical analyses on LinBP in neural-network-involved learning tasks
including adversarial attack and model training. We demonstrate that, somewhat
surprisingly, LinBP can lead to faster convergence in these tasks in the same
hyper-parameter settings, compared to BP. We confirm our theoretical results
with extensive experiments.

We obtain a personal signature of a person's learning progress in a
self-neuromodulation task, guided by functional MRI (fMRI). The signature is
based on predicting the activity of the Amygdala in a second neurofeedback
session, given a similar fMRI-derived brain state in the first session. The
prediction is made by a deep neural network, which is trained on the entire
training cohort of patients. This signal, which is indicative of a person's
progress in performing the task of Amygdala modulation, is aggregated across
multiple prototypical brain states and then classified by a linear classifier
to various personal and clinical indications. The predictive power of the
obtained signature is stronger than previous approaches for obtaining a
personal signature from fMRI neurofeedback and provides an indication that a
person's learning pattern may be used as a diagnostic tool. Our code has been
made available, and data would be shared, subject to ethical approvals.

This paper develops a new approach to post-selection inference for screening
high-dimensional predictors of survival outcomes. Post-selection inference for
right-censored outcome data has been investigated in the literature, but much
remains to be done to make the methods both reliable and
computationally-scalable in high-dimensions. Machine learning tools are
commonly used to provide {\it predictions} of survival outcomes, but the
estimated effect of a selected predictor suffers from confirmation bias unless
the selection is taken into account. The new approach involves construction of
semi-parametrically efficient estimators of the linear association between the
predictors and the survival outcome, which are used to build a test statistic
for detecting the presence of an association between any of the predictors and
the outcome. Further, a stabilization technique reminiscent of bagging allows a
normal calibration for the resulting test statistic, which enables the
construction of confidence intervals for the maximal association between
predictors and the outcome and also greatly reduces computational cost.
Theoretical results show that this testing procedure is valid even when the
number of predictors grows superpolynomially with sample size, and our
simulations support that this asymptotic guarantee is indicative the
performance of the test at moderate sample sizes. The new approach is applied
to the problem of identifying patterns in viral gene expression associated with
the potency of an antiviral drug.

This paper studies the Random Utility Model (RUM) in a repeated stochastic
choice situation, in which the decision maker is imperfectly informed about the
payoffs of each available alternative. We develop a gradient-based learning
algorithm by embedding the RUM into an online decision problem. We show that a
large class of RUMs are Hannan consistent (\citet{Hahn1957}); that is, the
average difference between the expected payoffs generated by a RUM and that of
the best-fixed policy in hindsight goes to zero as the number of periods
increase. In addition, we show that our gradient-based algorithm is equivalent
to the Follow the Regularized Leader (FTRL) algorithm, which is widely used in
the machine learning literature to model learning in repeated stochastic choice
problems. Thus, we provide an economically grounded optimization framework to
the FTRL algorithm. Finally, we apply our framework to study recency bias,
no-regret learning in normal form games, and prediction markets.

This work focuses on the task of elderly activity recognition, which is a
challenging task due to the existence of individual actions and human-object
interactions in elderly activities. Thus, we attempt to effectively aggregate
the discriminative information of actions and interactions from both RGB videos
and skeleton sequences by attentively fusing multi-modal features. Recently,
some nonlinear multi-modal fusion approaches are proposed by utilizing
nonlinear attention mechanism that is extended from Squeeze-and-Excitation
Networks (SENet). Inspired by this, we propose a novel
Expansion-Squeeze-Excitation Fusion Network (ESE-FN) to effectively address the
problem of elderly activity recognition, which learns modal and channel-wise
Expansion-Squeeze-Excitation (ESE) attentions for attentively fusing the
multi-modal features in the modal and channel-wise ways. Furthermore, we design
a new Multi-modal Loss (ML) to keep the consistency between the single-modal
features and the fused multi-modal features by adding the penalty of difference
between the minimum prediction losses on single modalities and the prediction
loss on the fused modality. Finally, we conduct experiments on a largest-scale
elderly activity dataset, i.e., ETRI-Activity3D (including 110,000+ videos, and
50+ categories), to demonstrate that the proposed ESE-FN achieves the best
accuracy compared with the state-of-the-art methods. In addition, more
extensive experimental results show that the proposed ESE-FN is also comparable
to the other methods in terms of normal action recognition task.

Transparency in Machine Learning (ML), attempts to reveal the working
mechanisms of complex models. Transparent ML promises to advance human factors
engineering goals of human-centered AI in the target users. From a
human-centered design perspective, transparency is not a property of the ML
model but an affordance, i.e. a relationship between algorithm and user; as a
result, iterative prototyping and evaluation with users is critical to
attaining adequate solutions that afford transparency. However, following
human-centered design principles in healthcare and medical image analysis is
challenging due to the limited availability of and access to end users. To
investigate the state of transparent ML in medical image analysis, we conducted
a systematic review of the literature. Our review reveals multiple severe
shortcomings in the design and validation of transparent ML for medical image
analysis applications. We find that most studies to date approach transparency
as a property of the model itself, similar to task performance, without
considering end users during neither development nor evaluation. Additionally,
the lack of user research, and the sporadic validation of transparency claims
put contemporary research on transparent ML for medical image analysis at risk
of being incomprehensible to users, and thus, clinically irrelevant. To
alleviate these shortcomings in forthcoming research while acknowledging the
challenges of human-centered design in healthcare, we introduce the INTRPRT
guideline, a systematic design directive for transparent ML systems in medical
image analysis. The INTRPRT guideline suggests formative user research as the
first step of transparent model design to understand user needs and domain
requirements. Following this process produces evidence to support design
choices, and ultimately, increases the likelihood that the algorithms afford
transparency.

Concentrated Animal Feeding Operations (CAFOs) pose serious risks to air,
water, and public health, but have proven to be challenging to regulate. The
U.S. Government Accountability Office notes that a basic challenge is the lack
of comprehensive location information on CAFOs. We use the USDA's National
Agricultural Imagery Program (NAIP) 1m/pixel aerial imagery to detect poultry
CAFOs across the continental United States. We train convolutional neural
network (CNN) models to identify individual poultry barns and apply the best
performing model to over 42 TB of imagery to create the first national,
open-source dataset of poultry CAFOs. We validate the model predictions against
held-out validation set on poultry CAFO facility locations from 10 hand-labeled
counties in California and demonstrate that this approach has significant
potential to fill gaps in environmental monitoring.

The learned iterative shrinkage thresholding algorithm (LISTA) introduces
deep unfolding models with learnable thresholds in some shrinkage functions for
sparse coding. Drawing on some theoretical insights, we advocate an error-based
thresholding (EBT) mechanism for LISTA, which leverages a function of the
layer-wise reconstruction error to suggest an appropriate threshold value for
each observation on each layer. We show that the EBT mechanism well
disentangles the learnable parameters in the shrinkage functions from the
reconstruction errors, making them more adaptive to the various observations.
With rigorous theoretical analyses, we show that the proposed EBT can lead to a
faster convergence on the basis of LISTA and its variants, in addition to its
higher adaptivity. Extensive experimental results confirm our theoretical
analyses and verify the effectiveness of our methods.

The fast transmission rate of COVID-19 worldwide has made this virus the most
important challenge of year 2020. Many mitigation policies have been imposed by
the governments at different regional levels (country, state, county, and city)
to stop the spread of this virus. Quantifying the effect of such mitigation
strategies on the transmission and recovery rates, and predicting the rate of
new daily cases are two crucial tasks. In this paper, we propose a hybrid
modeling framework which not only accounts for such policies but also utilizes
the spatial and temporal information to characterize the pattern of COVID-19
progression. Specifically, a piecewise susceptible-infected-recovered (SIR)
model is developed while the dates at which the transmission/recover rates
change significantly are defined as "break points" in this model. A novel and
data-driven algorithm is designed to locate the break points using ideas from
fused lasso and thresholding. In order to enhance the forecasting power and to
describe additional temporal dependence among the daily number of cases, this
model is further coupled with spatial smoothing covariates and vector
auto-regressive (VAR) model. The proposed model is applied to several U.S.
states and counties, and the results confirm the effect of "stay-at-home
orders" and some states' early "re-openings" by detecting break points close to
such events. Further, the model provided satisfactory short-term forecasts of
the number of new daily cases at regional levels by utilizing the estimated
spatio-temporal covariance structures. They were also better or on par with
other proposed models in the literature, including flexible deep learning ones.
Finally, selected theoretical results and empirical performance of the proposed
methodology on synthetic data are reported which justify the good performance

Generalized few-shot semantic segmentation was introduced to move beyond only
evaluating few-shot segmentation models on novel classes to include testing
their ability to remember base classes. While the current state-of-the-art
approach is based on meta-learning, it performs poorly and saturates in
learning after observing only a few shots. We propose the first fine-tuning
solution, and demonstrate that it addresses the saturation problem while
achieving state-of-the-art results on two datasets, PASCAL-5i and COCO-20i. We
also show that it outperforms existing methods, whether fine-tuning multiple
final layers or only the final layer. Finally, we present a triplet loss
regularization that shows how to redistribute the balance of performance
between novel and base categories so that there is a smaller gap between them.

Weakly-supervised temporal action localization (WTAL) in untrimmed videos has
emerged as a practical but challenging task since only video-level labels are
available. Existing approaches typically leverage off-the-shelf segment-level
features, which suffer from spatial incompleteness and temporal incoherence,
thus limiting their performance. In this paper, we tackle this problem from a
new perspective by enhancing segment-level representations with a simple yet
effective graph convolutional network, namely action complement graph network
(ACGNet). It facilitates the current video segment to perceive spatial-temporal
dependencies from others that potentially convey complementary clues,
implicitly mitigating the negative effects caused by the two issues above. By
this means, the segment-level features are more discriminative and robust to
spatial-temporal variations, contributing to higher localization accuracies.
More importantly, the proposed ACGNet works as a universal module that can be
flexibly plugged into different WTAL frameworks, while maintaining the
end-to-end training fashion. Extensive experiments are conducted on the
THUMOS'14 and ActivityNet1.2 benchmarks, where the state-of-the-art results
clearly demonstrate the superiority of the proposed approach.

The growing number of Internet of Things (IoT) devices makes it imperative to
be aware of the real-world threats they face in terms of cybersecurity. While
honeypots have been historically used as decoy devices to help
researchers/organizations gain a better understanding of the dynamic of threats
on a network and their impact, IoT devices pose a unique challenge for this
purpose due to the variety of devices and their physical connections. In this
work, by observing real-world attackers' behavior in a low-interaction honeypot
ecosystem, we (1) presented a new approach to creating a multi-phased,
multi-faceted honeypot ecosystem, which gradually increases the sophistication
of honeypots' interactions with adversaries, (2) designed and developed a
low-interaction honeypot for cameras that allowed researchers to gain a deeper
understanding of what attackers are targeting, and (3) devised an innovative
data analytics method to identify the goals of adversaries. Our honeypots have
been active for over three years. We were able to collect increasingly
sophisticated attack data in each phase. Furthermore, our data analytics points
to the fact that the vast majority of attack activities captured in the
honeypots share significant similarity, and can be clustered and grouped to
better understand the goals, patterns, and trends of IoT attacks in the wild.

Motivation: We consider continuous-time Markov chains that describe the
stochastic evolution of a dynamical system by a transition-rate matrix $Q$
which depends on a parameter $\theta$. Computing the probability distribution
over states at time $t$ requires the matrix exponential $\exp(tQ)$, and
inferring $\theta$ from data requires its derivative
$\partial\exp\!(tQ)/\partial\theta$. Both are challenging to compute when the
state space and hence the size of $Q$ is huge. This can happen when the state
space consists of all combinations of the values of several interacting
discrete variables. Often it is even impossible to store $Q$. However, when $Q$
can be written as a sum of tensor products, computing $\exp(tQ)$ becomes
feasible by the uniformization method, which does not require explicit storage
of $Q$.
  Results: Here we provide an analogous algorithm for computing
$\partial\exp\!(tQ)/\partial\theta$, the differentiated uniformization method.
We demonstrate our algorithm for the stochastic SIR model of epidemic spread,
for which we show that $Q$ can be written as a sum of tensor products. We
estimate monthly infection and recovery rates during the first wave of the
COVID-19 pandemic in Austria and quantify their uncertainty in a full Bayesian
analysis.
  Availability: Implementation and data are available at
https://github.com/spang-lab/TenSIR.

A line search in gradient-based optimization algorithm solves the problem of
determining the optimal learning rate for a given gradient or search direction
in a single iteration. For most problems, this is determined by evaluating
different candidate learning rates to find the optimum, which can be expensive.
Recent work has provided an efficient way to perform a line search with the use
of the Shanks transformation of a Born series derived from the
Lippman-Schwinger formalism. In this paper we show that the cost for performing
such a line search can be further reduced with the use of the method of the
Schur complement domain decomposition, which can lead to a 10-fold total
speed-up resulting from the reduced number of iterations to convergence and
reduced wall-clock time per iteration.

In this paper, we propose a class of high-efficiency deep joint
source-channel coding methods that can closely adapt to the source distribution
under the nonlinear transform, it can be collected under the name nonlinear
transform source-channel coding (NTSCC). In the considered model, the
transmitter first learns a nonlinear analysis transform to map the source data
into latent space, then transmits the latent representation to the receiver via
deep joint source-channel coding. Our model incorporates the nonlinear
transform as a strong prior to effectively extract the source semantic features
and provide side information for source-channel coding. Unlike existing
conventional deep joint source-channel coding methods, the proposed NTSCC
essentially learns both the source latent representation and an entropy model
as the prior on the latent representation. Accordingly, novel adaptive rate
transmission and hyperprior-aided codec refinement mechanisms are developed to
upgrade deep joint source-channel coding. The whole system design is formulated
as an optimization problem whose goal is to minimize the end-to-end
transmission rate-distortion performance under established perceptual quality
metrics. Across test image sources with various resolutions, we find that the
proposed NTSCC transmission method generally outperforms both the analog
transmission using the standard deep joint source-channel coding and the
classical separation-based digital transmission. Notably, the proposed NTSCC
method can potentially support future semantic communications due to its
content-aware ability and perceptual optimization goal.

In order to perform unconditional video generation, we must learn the
distribution of the real-world videos. In an effort to synthesize high-quality
videos, various studies attempted to learn a mapping function between noise and
videos, including recent efforts to separate motion distribution and appearance
distribution. Previous methods, however, learn motion dynamics in discretized,
fixed-interval timesteps, which is contrary to the continuous nature of motion
of a physical body. In this paper, we propose a novel video generation approach
that learns separate distributions for motion and appearance, the former
modeled by neural ODE to learn natural motion dynamics. Specifically, we employ
a two-stage approach where the first stage converts a noise vector to a
sequence of keypoints in arbitrary frame rates, and the second stage
synthesizes videos based on the given keypoints sequence and the appearance
noise vector. Our model not only quantitatively outperforms recent baselines
for video generation, but also demonstrates versatile functionality such as
dynamic frame rate manipulation and motion transfer between two datasets, thus
opening new doors to diverse video generation applications.

In this paper, we present a crash frequency data augmentation method based on
Conditional Generative Adversarial Networks to improve crash frequency models.
The proposed method is evaluated by comparing the performance of Base SPFs
(developed using original data) and Augmented SPFs (developed using original
data plus synthesised data) in terms of hotspot identification performance,
model prediction accuracy, and dispersion parameter estimation accuracy. The
experiments are conducted using simulated and real-world crash data sets. The
results indicate that the synthesised crash data by CGAN have the same
distribution as the original data and the Augmented SPFs outperforms Base SPFs
in almost all aspects especially when the dispersion parameter is low.

In this study, we present a method to predict the Received signal strength
indication (RSSI) in an area of the base station. Traditional attenuated wave
propagation models are often time consuming as well as computationally complex,
depending on the unique factors of the medium. This study focuses on providing
a solution to predict signal quality using coordinate values of many points in
the considering area. We apply machine learning models such as linear
regression, Support Vector Machine (SVM) or Decision tree model, to directly
predict the RSSI of many points in the range of a base station without
computing the complex parameters of the attenuated propagation model. The
effectiveness of RSSI prediction was evaluated by mean square error (MSE) and
mean absolute error (MAE). The stage of training and testing machine learning
models in the research uses data that are the actual measurement results during
the research process.

Linear time-invariant systems are very popular models in system theory and
applications. A fundamental problem in system identification that remains
rather unaddressed in extant literature is to leverage commonalities amongst
related linear systems to estimate their transition matrices more accurately.
To address this problem, the current paper investigates methods for jointly
estimating the transition matrices of multiple systems. It is assumed that the
transition matrices are unknown linear functions of some unknown shared basis
matrices. We establish finite-time estimation error rates that fully reflect
the roles of trajectory lengths, dimension, and number of systems under
consideration. The presented results are fairly general and show the
significant gains that can be achieved by pooling data across systems in
comparison to learning each system individually. Further, they are shown to be
robust against model misspecifications. To obtain the results, we develop novel
techniques that are of interest for addressing similar joint-learning problems.
They include tightly bounding estimation errors in terms of the
eigen-structures of transition matrices, establishing sharp high probability
bounds for singular values of dependent random matrices, and capturing effects
of misspecified transition matrices as the systems evolve over time.

InfoMap is a popular approach for detecting densely connected "communities"
of nodes in networks. To detect such communities, it builds on the standard
type of Markov chain and ideas from information theory. Motivated by the
dynamics of disease spread on networks, whose nodes may have heterogeneous
disease-removal rates, we extend InfoMap to absorbing random walks. To do this,
we use absorption-scaled graphs, in which the edge weights are scaled according
to the absorption rates, along with Markov time sweeping. One of our extensions
of InfoMap converges to the standard version of InfoMap in the limit in which
the absorption rates approach $0$. We find that the community structure that
one detects using our extensions of InfoMap can differ markedly from the
community structure that one detects using methods that do not take
node-absorption rates into account. Additionally, we demonstrate that the
community structure that is induced by local dynamics can have important
implications for susceptible-infected-recovered (SIR) dynamics on ring-lattice
networks. For example, we find situations in which the outbreak duration is
maximized when a moderate number of nodes have large node-absorption rates. We
also use our extensions of InfoMap to study community structure in a
sexual-contact network. We consider the community structure that corresponds to
different absorption rates for homeless individuals in the network and the
associated impact on syphilis dynamics on the network. We observe that the
final outbreak size can be smaller when treatment rates are lower in the
homeless population than in other populations than when they are the same in
all populations.

Variational quantum algorithms (VQAs) are widely applied in the noisy
intermediate-scale quantum era and are expected to demonstrate quantum
advantage. However, training VQAs faces difficulties, one of which is the
so-called barren plateaus (BP) phenomenon, where gradients of cost functions
vanish exponentially with the number of qubits. In this paper, inspired by
transfer learning, where knowledge of pre-solved tasks could be further used in
a different but related work with training efficiency improved, we report a
parameter initialization method to mitigate BP. In the method, a small-sized
task is solved with a VQA. Then the ansatz and its optimum parameters are
transferred to tasks with larger sizes. Numerical simulations show that this
method could mitigate BP and improve training efficiency. A brief discussion on
how this method can work well is also provided. This work provides a reference
for mitigating BP, and therefore, VQAs could be applied to more practical
problems.

We propose a decision-making model for joint intentionality by interpreting
it as group-mindedness at the microlevel. We apply this model to give a formal
justification of the first part of the Interdependence Hypothesis due to
Tomasello et al. [Current Anthropology, 2012] which asserts that the emergence
of joint intentionality evolved due to the challenges of difficult
collaborative foraging practices among early humans, and that its evolution led
to robust collaboration and some form of altruism.
  In another application of the microlevel group-mindedness we consider the
problem of establishing cooperation in high-risk-of-defection strategic
conflicts and we show that the emergence of cooperation in such situations can
be explained in the context of cultural group selection as the result of
adaptive learning.

Improving generalization is a major challenge in audio classification due to
labeled data scarcity. Self-supervised learning (SSL) methods tackle this by
leveraging unlabeled data to learn useful features for downstream
classification tasks. In this work, we propose an augmented contrastive SSL
framework to learn invariant representations from unlabeled data. Our method
applies various perturbations to the unlabeled input data and utilizes
contrastive learning to learn representations robust to such perturbations.
Experimental results on the Audioset and DESED datasets show that our framework
significantly outperforms state-of-the-art SSL and supervised learning methods
on sound/event classification tasks.

Heating, ventilation, and air conditioning (HVAC) systems are well proved to
be capable to provide operating reserve for power systems. As a type of
large-capacity and energy-efficient HVAC system (up to 100 MW), district
cooling system (DCS) is emerging in modern cities and has huge potential to be
regulated as a flexible load. However, strategically controlling a DCS to
provide flexibility is challenging, because one DCS services multiple buildings
with complex thermal dynamics and uncertain cooling demands. Improper control
may lead to significant thermal discomfort and even deteriorate the power
system's operation security. To address the above issues, we propose a
model-free control strategy based on the deep reinforcement learning (DRL)
without the requirement of accurate system model and uncertainty distribution.
To avoid damaging "trial & error" actions that may violate the system's
operation security during the training process, we further propose a safe layer
combined to the DRL to guarantee the satisfaction of critical constraints,
forming a safe-DRL scheme. Moreover, after providing operating reserve, DCS
increases power and tries to recover all the buildings' temperature back to set
values, which may probably cause an instantaneous peak-power rebound and bring
a secondary impact on power systems. Therefore, we design a self-adaption
reward function within the proposed safe-DRL scheme to constrain the peak-power
effectively. Numerical studies based on a realistic DCS demonstrate the

The vigorous developments of Internet of Things make it possible to extend
its computing and storage capabilities to computing tasks in the aerial system
with collaboration of cloud and edge, especially for artificial intelligence
(AI) tasks based on deep learning (DL). Collecting a large amount of
image/video data, Unmanned aerial vehicles (UAVs) can only handover intelligent
analysis tasks to the back-end mobile edge computing (MEC) server due to their
limited storage and computing capabilities. How to efficiently transmit the
most correlated information for the AI model is a challenging topic. Inspired
by the task-oriented communication in recent years, we propose a new aerial
image transmission paradigm for the scene classification task. A lightweight
model is developed on the front-end UAV for semantic blocks transmission with
perception of images and channel conditions. In order to achieve the tradeoff
between transmission latency and classification accuracy, deep reinforcement
learning (DRL) is used to explore the semantic blocks which have the best
contribution to the back-end classifier under various channel conditions.
Experimental results show that the proposed method can significantly improve
classification accuracy compared to the fixed transmission strategy and
traditional content perception methods.

For any convex body $K \subseteq \mathbb R^n$, S. Bubeck and R. Eldan
introduced the entropic barrier on $K$ and showed that it is a $(1+o(1)) \,
n$-self-concordant barrier. In this note, we observe that the optimal bound of
$n$ on the self-concordance parameter holds as a consequence of the dimensional
Brascamp-Lieb inequality.

Engineering problems that are modeled using sophisticated mathematical
methods or are characterized by expensive-to-conduct tests or experiments, are
encumbered with limited budget or finite computational resources. Moreover,
practical scenarios in the industry, impose restrictions, based on logistics
and preference, on the manner in which the experiments can be conducted. For
example, material supply may enable only a handful of experiments in a
single-shot or in the case of computational models one may face significant
wait-time based on shared computational resources. In such scenarios, one
usually resorts to performing experiments in a manner that allows for
maximizing one's state-of-knowledge while satisfying the above mentioned
practical constraints. Sequential design of experiments (SDOE) is a popular
suite of methods, that has yielded promising results in recent years across
different engineering and practical problems. A common strategy, that leverages
Bayesian formalism is the Bayesian SDOE, which usually works best in the
one-step-ahead or myopic scenario of selecting a single experiment at each step
of a sequence of experiments. In this work, we aim to extend the SDOE strategy,
to query the experiment or computer code at a batch of inputs. To this end, we
leverage deep reinforcement learning (RL) based policy gradient methods, to
propose batches of queries that are selected taking into account entire budget
in hand. The algorithm retains the sequential nature, inherent in the SDOE,
while incorporating elements of reward based on task from the domain of deep
RL. A unique capability of the proposed methodology is its ability to be
applied to multiple tasks, for example optimization of a function, once its
trained. We demonstrate the performance of the proposed algorithm on a
synthetic problem, and a challenging high-dimensional engineering problem.

Multi-label image recognition is a fundamental yet practical task because
real-world images inherently possess multiple semantic labels. However, it is
difficult to collect large-scale multi-label annotations due to the complexity
of both the input images and output label spaces. To reduce the annotation
cost, we propose a structured semantic transfer (SST) framework that enables
training multi-label recognition models with partial labels, i.e., merely some
labels are known while other labels are missing (also called unknown labels)
per image. The framework consists of two complementary transfer modules that
explore within-image and cross-image semantic correlations to transfer
knowledge of known labels to generate pseudo labels for unknown labels.
Specifically, an intra-image semantic transfer module learns image-specific
label co-occurrence matrix and maps the known labels to complement unknown
labels based on this matrix. Meanwhile, a cross-image transfer module learns
category-specific feature similarities and helps complement unknown labels with
high similarities. Finally, both known and generated labels are used to train
the multi-label recognition models. Extensive experiments on the Microsoft
COCO, Visual Genome and Pascal VOC datasets show that the proposed SST
framework obtains superior performance over current state-of-the-art
algorithms. Codes are available at https://github.com/HCPLab-SYSU/HCP-MLR-PL.

Multi-touch attribution (MTA), aiming to estimate the contribution of each
advertisement touchpoint in conversion journeys, is essential for budget
allocation and automatically advertising. Existing methods first train a model
to predict the conversion probability of the advertisement journeys with
historical data and calculate the attribution of each touchpoint using
counterfactual predictions. An assumption of these works is the conversion
prediction model is unbiased, i.e., it can give accurate predictions on any
randomly assigned journey, including both the factual and counterfactual ones.
Nevertheless, this assumption does not always hold as the exposed
advertisements are recommended according to user preferences. This confounding
bias of users would lead to an out-of-distribution (OOD) problem in the
counterfactual prediction and cause concept drift in attribution. In this
paper, we define the causal MTA task and propose CausalMTA to eliminate the
influence of user preferences. It systemically eliminates the confounding bias
from both static and dynamic preferences to learn the conversion prediction
model using historical data. We also provide a theoretical analysis to prove
CausalMTA can learn an unbiased prediction model with sufficient data.
Extensive experiments on both public datasets and the impression data in an
e-commerce company show that CausalMTA not only achieves better prediction
performance than the state-of-the-art method but also generates meaningful
attribution credits across different advertising channels.

In today's era of digital misinformation, we are increasingly faced with new
threats posed by video falsification techniques. Such falsifications range from
cheapfakes (e.g., lookalikes or audio dubbing) to deepfakes (e.g.,
sophisticated AI media synthesis methods), which are becoming perceptually
indistinguishable from real videos. To tackle this challenge, we propose a
multi-modal semantic forensic approach to discover clues that go beyond
detecting discrepancies in visual quality, thereby handling both simpler
cheapfakes and visually persuasive deepfakes. In this work, our goal is to
verify that the purported person seen in the video is indeed themselves by
detecting anomalous correspondences between their facial movements and the
words they are saying. We leverage the idea of attribution to learn
person-specific biometric patterns that distinguish a given speaker from
others. We use interpretable Action Units (AUs) to capture a persons' face and
head movement as opposed to deep CNN visual features, and we are the first to
use word-conditioned facial motion analysis. Unlike existing person-specific
approaches, our method is also effective against attacks that focus on lip
manipulation. We further demonstrate our method's effectiveness on a range of
fakes not seen in training including those without video manipulation, that
were not addressed in prior work.

Policy optimization methods are one of the most widely used classes of
Reinforcement Learning (RL) algorithms. However, theoretical understanding of
these methods remains insufficient. Even in the episodic (time-inhomogeneous)
tabular setting, the state-of-the-art theoretical result of policy-based method
in \citet{shani2020optimistic} is only $\tilde{O}(\sqrt{S^2AH^4K})$ where $S$
is the number of states, $A$ is the number of actions, $H$ is the horizon, and
$K$ is the number of episodes, and there is a $\sqrt{SH}$ gap compared with the
information theoretic lower bound $\tilde{\Omega}(\sqrt{SAH^3K})$. To bridge
such a gap, we propose a novel algorithm Reference-based Policy Optimization
with Stable at Any Time guarantee (\algnameacro), which features the property
"Stable at Any Time". We prove that our algorithm achieves
$\tilde{O}(\sqrt{SAH^3K} + \sqrt{AH^4K})$ regret. When $S > H$, our algorithm
is minimax optimal when ignoring logarithmic factors. To our best knowledge,
RPO-SAT is the first computationally efficient, nearly minimax optimal
policy-based algorithm for tabular RL.

Architecture erosion (AEr) can adversely affect software development and has
received significant attention in the last decade. However, there is an absence
of a comprehensive understanding of the state of research about the reasons and
consequences of AEr, and the countermeasures to address AEr. This work aims at
systematically investigating, identifying, and analyzing the reasons,
consequences, and ways of detecting and handling AEr. With 73 studies included,
the main results are as follows: (1) AEr manifests not only through
architectural violations and structural issues but also causing problems in
software quality and during software evolution; (2) non-technical reasons that
cause AEr should receive the same attention as technical reasons, and
practitioners should raise awareness of the grave consequences of AEr, thereby
taking actions to tackle AEr-related issues; (3) a spectrum of approaches,
tools, and measures has been proposed and employed to detect and tackle AEr;
and (4) three categories of difficulties and five categories of lessons learned
on tackling AEr were identified. The results can provide researchers a
comprehensive understanding of AEr and help practitioners handle AEr and
improve the sustainability of their architecture. More empirical studies are
required to investigate the practices of detecting and addressing AEr in
industrial settings.

Deep neural networks (DNNs) have shown to provide superb performance in many
real life applications, but their large computation cost and storage
requirement have prevented them from being deployed to many edge and
internet-of-things (IoT) devices. Sparse deep neural networks, whose majority
weight parameters are zeros, can substantially reduce the computation
complexity and memory consumption of the models. In real-use scenarios, devices
may suffer from large fluctuations of the available computation and memory
resources under different environment, and the quality of service (QoS) is
difficult to maintain due to the long tail inferences with large latency.
Facing the real-life challenges, we propose to train a sparse model that
supports multiple sparse levels. That is, a hierarchical structure of weights
are satisfied such that the locations and the values of the non-zero parameters
of the more-sparse sub-model area subset of the less-sparse sub-model. In this
way, one can dynamically select the appropriate sparsity level during
inference, while the storage cost is capped by the least sparse sub-model. We
have verified our methodologies on a variety of DNN models and tasks, including
the ResNet-50, PointNet++, GNMT, and graph attention networks. We obtain sparse
sub-models with an average of 13.38% weights and 14.97% FLOPs, while the
accuracies are as good as their dense counterparts. More-sparse sub-models with
5.38% weights and 4.47% of FLOPs, which are subsets of the less-sparse ones,
can be obtained with only 3.25% relative accuracy loss.

Electro-optic modulators provide a key function in optical transceivers and
increasingly in photonic programmable Application Specific Integrated Circuits
(ASICs) for machine learning and signal processing. However, both foundry ready
silicon based modulators and conventional material based devices utilizing
Lithium niobate fall short in simultaneously providing high chip packaging
density and fast speed. Current driven ITO based modulators have the potential
to achieve both enabled by efficient light matter interactions. Here, we
introduce micrometer compact Mach Zehnder Interferometer (MZI) based modulators
capable of exceeding 100 GHz switching rates. Integrating ITO thin films atop a
photonic waveguide, spectrally broadband, and compact MZI phase shifter.
Remarkably, this allows integrating more than 3500 of these modulators within
the same chip area as only one single silicon MZI modulator. The modulator
design introduced here features a holistic photonic, electronic, and RF-based
optimization and includes an asymmetric MZI tuning step to optimize the
Extinction Ratio (ER) to Insertion Loss (IL) and dielectric thickness sweep to
balance the tradeoffs between ER and speed. Driven by CMOS compatible bias
voltage levels, this device is the first to address next generation modulator
demands for processors of the machine intelligence revolution, in addition to
the edge and cloud computing demands as well as optical transceivers alike.

DB-BERT is a database tuning tool that exploits information gained via
natural language analysis of manuals and other relevant text documents. It uses
text to identify database system parameters to tune as well as recommended
parameter values. DB-BERT applies large, pre-trained language models
(specifically, the BERT model) for text analysis. During an initial training
phase, it fine-tunes model weights in order to translate natural language hints
into recommended settings. At run time, DB-BERT learns to aggregate, adapt, and
prioritize hints to achieve optimal performance for a specific database system
and benchmark. Both phases are iterative and use reinforcement learning to
guide the selection of tuning settings to evaluate (penalizing settings that
the database system rejects while rewarding settings that improve performance).
In our experiments, we leverage hundreds of text documents about database
tuning as input for DB-BERT. We compare DB-BERT against various baselines,
considering different benchmarks (TPC-C and TPC-H), metrics (throughput and run
time), as well as database systems (Postgres and MySQL). In all cases, DB-BERT
finds the best parameter settings among all compared methods. The code of
DB-BERT is available online at https://itrummer.github.io/dbbert/.

Building software systems often requires knowledge and skills beyond what
developers already possess. In such cases, developers have to leverage
different sources of information to seek help. A growing number of researchers
and practitioners have started investigating what programming-related
information developers seek during software development. However, being a high
level and a type of the most important development-related information,
architectural information search activity is seldom explored. To fill this gap,
we conducted an industrial survey completed by 103 participants to understand
how developers search for architectural information to solve their
architectural problems in development. Our main findings are: (1) searching for
architectural information to learn about the pros and cons of certain
architectural solutions (e.g., patterns, tactics) and to make an architecture
decision among multiple choices are the most frequent purposes or tasks; (2)
developers find difficulties mostly in getting relevant architectural
information for addressing quality concerns and making design decisions among
multiple choices when seeking architectural information; (3) taking too much
time to go through architectural information retrieved from various sources and
feeling overwhelmed due to the dispersion and abundance of architectural
information in various sources are the top two major challenges developers face
when searching for architectural information. Our findings (1) provide
researchers with future directions, such as the design and development of
approaches and tools for searching architectural information from multiple
sources, and (2) can be used to provide guidelines for practitioners to refer
to when seeking architectural information and providing architectural
information that could be considered useful.

We propose a modular architecture for the lifelong learning of hierarchically
structured tasks. Specifically, we prove that our architecture is theoretically
able to learn tasks that can be solved by functions that are learnable given
access to functions for other, previously learned tasks as subroutines. We
empirically show that some tasks that we can learn in this way are not learned
by standard training methods in practice; indeed, prior work suggests that some
such tasks cannot be learned by any efficient method without the aid of the
simpler tasks. We also consider methods for identifying the tasks
automatically, without relying on explicitly given indicators.

Databases covering all individuals of a population are increasingly used for
research and decision-making. The massive size of such databases is often
mistaken as a guarantee for valid inferences. However, population data have
characteristics that make them challenging to use. Various assumptions on
population coverage and data quality are commonly made, including how such data
were captured and what types of processing have been applied to them.
Furthermore, the full potential of population data can often only be unlocked
when such data are linked to other databases. Record linkage often implies
subtle technical problems, which are easily missed. We discuss a diverse range
of misconceptions relevant for anybody capturing, processing, linking, or
analysing population data. Remarkably many of these misconceptions are due to
the social nature of data collections and are therefore missed by purely
technical accounts of data processing. Many of these misconceptions are also
not well documented in scientific publications. We conclude with a set of
recommendations for using population data.

Contact lenses have traditionally been used for vision correction
applications. Recent advances in microelectronics and nanofabrication on
flexible substrates have now enabled sensors, circuits and other essential
components to be integrated on a small contact lens platform. This has opened
up the possibility of using contact lenses for a range of human-machine
interaction applications including vision assistance, eye tracking, displays
and health care. In this article, we systematically review the range of smart
contact lens materials, device architectures and components that facilitate
this interaction for different applications. In fact, evidence from our
systematic review demonstrates that these lenses can be used to display
information, detect eye movements, restore vision and detect certain biomarkers
in tear fluid. Consequently, whereas previous state-of the-art reviews in
contact lenses focused exclusively on biosensing, our systematic review covers
a wider range of smart contact lens applications in HMI. Moreover, we present a
new method of classifying the literature on smart contact lenses according to
their six constituent building blocks, which are the sensing, energy
management, driver electronics, communications, substrate and the interfacing
modules. Based on recent developments in each of these categories, we speculate
the challenges and opportunities of smart contact lenses for human-machine
interaction. Moreover, we propose a novel self-powered smart contact lens
concept with integrated energy harvesters, sensors and communication modules to
enable autonomous operation. Our review is therefore a critical evaluation of
current data and is presented with the aim of guiding researchers to new
research directions in smart contact lenses.

Deep neural networks (DNNs) have been proven to be effective in solving many
real-life problems, but its high computation cost prohibits those models from
being deployed to edge devices. Pruning, as a method to introduce zeros to
model weights, has shown to be an effective method to provide good trade-offs
between model accuracy and computation efficiency, and is a widely-used method
to generate compressed models. However, the granularity of pruning makes
important trade-offs. At the same sparsity level, a coarse-grained structured
sparse pattern is more efficient on conventional hardware but results in worse
accuracy, while a fine-grained unstructured sparse pattern can achieve better
accuracy but is inefficient on existing hardware.
  On the other hand, some modern processors are equipped with fast on-chip
scratchpad memories and gather/scatter engines that perform indirect load and
store operations on such memories. In this work, we propose a set of novel
sparse patterns, named gather-scatter (GS) patterns, to utilize the scratchpad
memories and gather/scatter engines to speed up neural network inferences.
Correspondingly, we present a compact sparse format. The proposed set of sparse
patterns, along with a novel pruning methodology, address the load imbalance
issue and result in models with quality close to unstructured sparse models and
computation efficiency close to structured sparse models. Our experiments show
that GS patterns consistently make better trade-offs between accuracy and
computation efficiency compared to conventional structured sparse patterns. GS
patterns can reduce the runtime of the DNN components by two to three times at
the same accuracy levels. This is confirmed on three different deep learning
tasks and popular models, namely, GNMT for machine translation, ResNet50 for
image recognition, and Japser for acoustic speech recognition.

Automatically locating vulnerable statements in source code is crucial to
assure software security and alleviate developers' debugging efforts. This
becomes even more important in today's software ecosystem, where vulnerable
code can flow easily and unwittingly within and across software repositories
like GitHub. Across such millions of lines of code, traditional static and
dynamic approaches struggle to scale. Although existing machine-learning-based
approaches look promising in such a setting, most work detects vulnerable code
at a higher granularity -- at the method or file level. Thus, developers still
need to inspect a significant amount of code to locate the vulnerable
statement(s) that need to be fixed.
  This paper presents VELVET, a novel ensemble learning approach to locate
vulnerable statements. Our model combines graph-based and sequence-based neural
networks to successfully capture the local and global context of a program
graph and effectively understand code semantics and vulnerable patterns. To
study VELVET's effectiveness, we use an off-the-shelf synthetic dataset and a
recently published real-world dataset. In the static analysis setting, where
vulnerable functions are not detected in advance, VELVET achieves 4.5x better
performance than the baseline static analyzers on the real-world data. For the
isolated vulnerability localization task, where we assume the vulnerability of
a function is known while the specific vulnerable statement is unknown, we
compare VELVET with several neural networks that also attend to local and
global context of code. VELVET achieves 99.6% and 43.6% top-1 accuracy over
synthetic data and real-world data, respectively, outperforming the baseline
deep-learning models by 5.3-29.0%.

Many recent results in imperfect information games were only formulated for,
or evaluated on, poker and poker-like games such as liar's dice. We argue that
sequential Bayesian games constitute a natural class of games for generalizing
these results. In particular, this model allows for an elegant formulation of
the counterfactual regret minimization algorithm, called public-state CFR
(PS-CFR), which naturally lends itself to an efficient implementation.
Empirically, solving a poker subgame with 10^7 states by public-state CFR takes
3 minutes and 700 MB while a comparable version of vanilla CFR takes 5.5 hours
and 20 GB. Additionally, the public-state formulation of CFR opens up the
possibility for exploiting domain-specific assumptions, leading to a quadratic
reduction in asymptotic complexity (and a further empirical speedup) over
vanilla CFR in poker and other domains. Overall, this suggests that the ability
to represent poker as a sequential Bayesian game played a key role in the
success of CFR-based methods. Finally, we extend public-state CFR to general
extensive-form games, arguing that this extension enjoys some - but not all -
of the benefits of the version for sequential Bayesian games.

Shallow water equations are the foundation of most models for flooding and
river hydraulics analysis. These physics-based models are usually expensive and
slow to run, thus not suitable for real-time prediction or parameter inversion.
An attractive alternative is surrogate model. This work introduces an
efficient, accurate, and flexible surrogate model, NN-p2p, based on deep
learning and it can make point-to-point predictions on unstructured or
irregular meshes. The new method was evaluated and compared against existing
methods based on convolutional neural networks (CNNs), which can only make
image-to-image predictions on structured or regular meshes. In NN-p2p, the
input includes both spatial coordinates and boundary features that can describe
the geometry of hydraulic structures, such as bridge piers. All surrogate
models perform well in predicting flow around different types of piers in the
training domain. However, only NN-p2p works well when spatial extrapolation is
performed. The limitations of CNN-based methods are rooted in their
raster-image nature which cannot capture boundary geometry and flow features
exactly, which are of paramount importance to fluid dynamics. NN-p2p also has
good performance in predicting flow around piers unseen by the neural network.
The NN-p2p model also respects conservation laws more strictly. The application
of the proposed surrogate model was demonstrated by calculating the drag
coefficient $C_D$ for piers and a new linear relationship between $C_D$ and the
logarithmic transformation of pier's length/width ratio was discovered.

In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and
frequency synchronization using the first field of the preamble known as the
legacy short training field (L-STF). The L-STF occupies upto 40% of the
preamble length and takes upto 32 us of airtime. With the goal of reducing
communication overhead, we propose a modified waveform, where the preamble
length is reduced by eliminating the L-STF. To decode this modified waveform,
we propose a machine learning (ML)-based scheme called PRONTO that performs
coarse time and frequency estimations using other preamble fields, specifically
the legacy long training field (L-LTF). Our contributions are threefold: (i) We
present PRONTO featuring customized convolutional neural networks (CNNs) for
packet detection and coarse CFO estimation, along with data augmentation steps
for robust training. (ii) We propose a generalized decision flow that makes
PRONTO compatible with legacy waveforms that include the standard L-STF. (iii)
We validate the outcomes on an over-the-air WiFi dataset from a testbed of
software defined radios (SDRs). Our evaluations show that PRONTO can perform
packet detection with 100% accuracy, and coarse CFO estimation with errors as
small as 3%. We demonstrate that PRONTO provides upto 40% preamble length
reduction with no bit error rate (BER) degradation. Finally, we experimentally
show the speedup achieved by PRONTO through GPU parallelization over the
corresponding CPU-only implementations.

We study the problem of learning a Bayesian network (BN) of a set of
variables when structural side information about the system is available. It is
well known that learning the structure of a general BN is both computationally
and statistically challenging. However, often in many applications, side
information about the underlying structure can potentially reduce the learning
complexity. In this paper, we develop a recursive constraint-based algorithm
that efficiently incorporates such knowledge (i.e., side information) into the
learning process. In particular, we study two types of structural side
information about the underlying BN: (I) an upper bound on its clique number is
known, or (II) it is diamond-free. We provide theoretical guarantees for the
learning algorithms, including the worst-case number of tests required in each
scenario. As a consequence of our work, we show that bounded treewidth BNs can
be learned with polynomial complexity. Furthermore, we evaluate the performance
and the scalability of our algorithms in both synthetic and real-world
structures and show that they outperform the state-of-the-art structure

Lack of vivid sensory imagery has recently become an active subject of
research, under the name of aphantasia. Extremely vivid imagery, or
hyperphantasia, is at the other end of the spectrum of individual differences.
While most research has focused on visual imagery in this paper I argue that
from a neuropsychological perspective this phenomenon is much more widespread,
and should be categorized as imagery sensory agnosia. After over twenty years
of learning to play music phenomenology of auditory imagery agnosia is
described from the first-person perspective. Reflections on other forms of
imagery agnosia and deficits of autobiographical memories are presented and a
hypothesis about putative brain processes that can account for such phenomena
is discussed. Extreme individual differences in imagery and in autobiographical
memory have implications for many fields of study, from consciousness research
to education.

Recent Neural Architecture Search (NAS) solutions have produced impressive
results training super-networks and then deriving subnetworks, a.k.a. child
models that outperform expert-crafted models from a pre-defined search space.
Efficient and robust subnetworks can be selected for resource-constrained edge
devices, allowing them to perform well in the wild. However, constructing
super-networks for arbitrary architectures is still a challenge that often
prevents the adoption of these approaches. To address this challenge, we
present BootstrapNAS, a software framework for automatic generation of
super-networks for NAS. BootstrapNAS takes a pre-trained model from a popular
architecture, e.g., ResNet- 50, or from a valid custom design, and
automatically creates a super-network out of it, then uses state-of-the-art NAS
techniques to train the super-network, resulting in subnetworks that
significantly outperform the given pre-trained model. We demonstrate the
solution by generating super-networks from arbitrary model repositories and
make available the resulting super-networks for reproducibility of the results.

In this work, we establish heuristics and learning strategies for the
autonomous control of a dozer grading an uneven area studded with sand piles.
We formalize the problem as a Markov Decision Process, design a simulation
which demonstrates agent-environment interactions and finally compare our
simulator to a real dozer prototype. We use methods from reinforcement
learning, behavior cloning and contrastive learning to train a hybrid policy.
Our trained agent, AGPNet, reaches human-level performance and outperforms
current state-of-the-art machine learning methods for the autonomous grading
task. In addition, our agent is capable of generalizing from random scenarios
to unseen real world problems.

We apply machine learning to the problem of finding numerical Calabi-Yau
metrics. We extend previous work on learning approximate Ricci-flat metrics
calculated using Donaldson's algorithm to the much more accurate "optimal"
metrics of Headrick and Nassar. We show that machine learning is able to
predict the K\"ahler potential of a Calabi-Yau metric having seen only a small
sample of training data.

Generalized compositional zero-shot learning means to learn composed concepts
of attribute-object pairs in a zero-shot fashion, where a model is trained on a
set of seen concepts and tested on a combined set of seen and unseen concepts.
This task is very challenging because of not only the gap between seen and
unseen concepts but also the contextual dependency between attributes and
objects. This paper introduces a new approach, termed translational concept
embedding, to solve these two difficulties in a unified framework. It models
the effect of applying an attribute to an object as adding a translational
attribute feature to an object prototype. We explicitly take into account of
the contextual dependency between attributes and objects by generating
translational attribute features conditionally dependent on the object
prototypes. Furthermore, we design a ratio variance constraint loss to promote
the model's generalization ability on unseen concepts. It regularizes the
distances between concepts by utilizing knowledge from their pretrained word
embeddings. We evaluate the performance of our model under both the unbiased
and biased concept classification tasks, and show that our model is able to
achieve good balance in predicting unseen and seen concepts.

Infotaxis is a popular search algorithm designed to track a source of odor in
a turbulent environment using information provided by odor detections. To
exemplify its capabilities, the source-tracking task was framed as a partially
observable Markov decision process consisting in finding, as fast as possible,
a stationary target hidden in a 2D grid using stochastic partial observations
of the target location. Here we provide an extended review of infotaxis,
together with a toolkit for devising better strategies. We first characterize
the performance of infotaxis in domains from 1D to 4D. Our results show that,
while being suboptimal, infotaxis is reliable (the probability of not reaching
the source approaches zero), efficient (the mean search time scales as expected
for the optimal strategy), and safe (the tail of the distribution of search
times decays faster than any power law, though subexponentially). We then
present three possible ways of beating infotaxis, all inspired by methods used
in artificial intelligence: tree search, heuristic approximation of the value
function, and deep reinforcement learning. The latter is able to find, without
any prior human knowledge, the (near) optimal strategy. Altogether, our results
provide evidence that the margin of improvement of infotaxis toward the optimal
strategy gets smaller as the dimensionality increases.

In this work we study the ranking algorithm used by F\'ed\'eration
Internationale de Football Association (FIFA); we analyze the parameters it
currently uses, show the formal probabilistic model from which it can be
derived, and optimize the latter. In particular, analyzing the games since the
introduction of the algorithm in 2018, we conclude that the game's "importance"
(as defined by FIFA) used in the algorithm is counterproductive from the point
of view of the predictive capability of the algorithm. We also postulate the
algorithm to be rooted in the formal modelling principle, where the Davidson
model proposed in 1970 seems to be an excellent candidate, preserving the form
of the algorithm currently used. The results indicate that the predictive
capability of the algorithm is notably improved by using the home-field
advantage and the explicit model for the draws in the game. Moderate, but
notable improvement may be attained by introducing the weighting of the results
with the goal differential, which although not rooted in a formal modelling
principle, is compatible with the current algorithm and can be tuned to the
characteristics of the football competition.

Critical sectors of human society are progressing toward the adoption of
powerful artificial intelligence (AI) agents, which are trained individually on
behalf of self-interested principals but deployed in a shared environment.
Short of direct centralized regulation of AI, which is as difficult an issue as
regulation of human actions, one must design institutional mechanisms that
indirectly guide agents' behaviors to safeguard and improve social welfare in
the shared environment. Our paper focuses on one important class of such
mechanisms: the problem of adaptive incentive design, whereby a central planner
intervenes on the payoffs of an agent population via incentives in order to
optimize a system objective. To tackle this problem in high-dimensional
environments whose dynamics may be unknown or too complex to model, we propose
a model-free meta-gradient method to learn an adaptive incentive function in
the context of multi-agent reinforcement learning. Via the principle of online
cross-validation, the incentive designer explicitly accounts for its impact on
agents' learning and, through them, the impact on future social welfare.
Experiments on didactic benchmark problems show that the proposed method can
induce selfish agents to learn near-optimal cooperative behavior and
significantly outperform learning-oblivious baselines. When applied to a
complex simulated economy, the proposed method finds tax policies that achieve
better trade-off between economic productivity and equality than baselines, a
result that we interpret via a detailed behavioral analysis.

The CP tensor decomposition is used in applications such as machine learning
and signal processing to discover latent low-rank structure in multidimensional
data. Computing a CP decomposition via an alternating least squares (ALS)
method reduces the problem to several linear least squares problems. The
standard way to solve these linear least squares subproblems is to use the
normal equations, which inherit special tensor structure that can be exploited
for computational efficiency. However, the normal equations are sensitive to
numerical ill-conditioning, which can compromise the results of the
decomposition. In this paper, we develop versions of the CP-ALS algorithm using
the QR decomposition and the singular value decomposition (SVD), which are more
numerically stable than the normal equations, to solve the linear least squares
problems. Our algorithms utilize the tensor structure of the CP-ALS subproblems
efficiently, have the same complexity as the standard CP-ALS algorithm when the
rank is small, and are shown via examples to produce more stable results when
ill-conditioning is present. Our MATLAB implementation achieves the same
running time as the standard algorithm for small ranks, and we show that the
new methods can obtain lower approximation error and more reliably recover
low-rank signals from data with known ground truth.

Stochastic Gradient Descent (SGD) is the workhorse algorithm of deep learning
technology. At each step of the training phase, a mini batch of samples is
drawn from the training dataset and the weights of the neural network are
adjusted according to the performance on this specific subset of examples. The
mini-batch sampling procedure introduces a stochastic dynamics to the gradient
descent, with a non-trivial state-dependent noise. We characterize the
stochasticity of SGD and a recently-introduced variant, \emph{persistent} SGD,
in a prototypical neural network model. In the under-parametrized regime, where
the final training error is positive, the SGD dynamics reaches a stationary
state and we define an effective temperature from the fluctuation-dissipation
theorem, computed from dynamical mean-field theory. We use the effective
temperature to quantify the magnitude of the SGD noise as a function of the
problem parameters. In the over-parametrized regime, where the training error
vanishes, we measure the noise magnitude of SGD by computing the average
distance between two replicas of the system with the same initialization and
two different realizations of SGD noise. We find that the two noise measures
behave similarly as a function of the problem parameters. Moreover, we observe
that noisier algorithms lead to wider decision boundaries of the corresponding
constraint satisfaction problem.

Over the past decade, deep neural networks have proven to be adept in image
classification tasks, often surpassing humans in terms of accuracy. However,
standard neural networks often fail to understand the concept of hierarchical
structures and dependencies among different classes for vision related tasks.
Humans on the other hand, seem to intuitively learn categories conceptually,
progressively growing from understanding high-level concepts down to granular
levels of categories. One of the issues arising from the inability of neural
networks to encode such dependencies within its learned structure is that of
subpopulation shift -- where models are queried with novel unseen classes taken
from a shifted population of the training set categories. Since the neural
network treats each class as independent from all others, it struggles to
categorize shifting populations that are dependent at higher levels of the
hierarchy. In this work, we study the aforementioned problems through the lens
of a novel conditional supervised training framework. We tackle subpopulation
shift by a structured learning procedure that incorporates hierarchical
information conditionally through labels. Furthermore, we introduce a notion of
graphical distance to model the catastrophic effect of mispredictions. We show
that learning in this structured hierarchical manner results in networks that
are more robust against subpopulation shifts, with an improvement up to 3\% in
terms of accuracy and up to 11\% in terms of graphical distance over standard
models on subpopulation shift benchmarks.

Deep Neural Networks (DNNs) have been extensively used in many areas
including image processing, medical diagnostics, and autonomous driving.
However, DNNs can exhibit erroneous behaviours that may lead to critical
errors, especially when used in safety-critical systems. Inspired by testing
techniques for traditional software systems, researchers have proposed neuron
coverage criteria, as an analogy to source code coverage, to guide the testing
of DNN models. Despite very active research on DNN coverage, several recent
studies have questioned the usefulness of such criteria in guiding DNN testing.
Further, from a practical standpoint, these criteria are white-box as they
require access to the internals or training data of DNN models, which is in
many contexts not feasible or convenient. In this paper, we investigate
black-box input diversity metrics as an alternative to white-box coverage
criteria. To this end, we first select and adapt three diversity metrics and
study, in a controlled manner, their capacity to measure actual diversity in
input sets. We then analyse their statistical association with fault detection
using four datasets and five DNN models. We further compare diversity with
state-of-the-art white-box coverage criteria. Our experiments show that relying
on the diversity of image features embedded in test input sets is a more
reliable indicator than coverage criteria to effectively guide the testing of
DNNs. Indeed, we found that one of our selected black-box diversity metrics far
outperforms existing coverage criteria in terms of fault-revealing capability
and computational time. Results also confirm the suspicions that
state-of-the-art coverage metrics are not adequate to guide the construction of
test input sets to detect as many faults as possible with natural inputs.

We present the first one-shot personalized sketch segmentation method. We aim
to segment all sketches belonging to the same category provisioned with a
single sketch with a given part annotation while (i) preserving the parts
semantics embedded in the exemplar, and (ii) being robust to input style and
abstraction. We refer to this scenario as personalized. With that, we
importantly enable a much-desired personalization capability for downstream
fine-grained sketch analysis tasks. To train a robust segmentation module, we
deform the exemplar sketch to each of the available sketches of the same
category. Our method generalizes to sketches not observed during training. Our
central contribution is a sketch-specific hierarchical deformation network.
Given a multi-level sketch-strokes encoding obtained via a graph convolutional
network, our method estimates rigid-body transformation from the target to the
exemplar, on the upper level. Finer deformation from the exemplar to the
globally warped target sketch is further obtained through stroke-wise
deformations, on the lower level. Both levels of deformation are guided by mean
squared distances between the keypoints learned without supervision, ensuring
that the stroke semantics are preserved. We evaluate our method against the
state-of-the-art segmentation and perceptual grouping baselines re-purposed for
the one-shot setting and against two few-shot 3D shape segmentation methods. We
show that our method outperforms all the alternatives by more than $10\%$ on
average. Ablation studies further demonstrate that our method is robust to
personalization: changes in input part semantics and style differences.

Systemic lupus erythematosus (SLE) is a rare autoimmune disorder
characterized by an unpredictable course of flares and remission with diverse
manifestations. Lupus nephritis, one of the major disease manifestations of SLE
for organ damage and mortality, is a key component of lupus classification
criteria. Accurately identifying lupus nephritis in electronic health records
(EHRs) would therefore benefit large cohort observational studies and clinical
trials where characterization of the patient population is critical for
recruitment, study design, and analysis. Lupus nephritis can be recognized
through procedure codes and structured data, such as laboratory tests. However,
other critical information documenting lupus nephritis, such as histologic
reports from kidney biopsies and prior medical history narratives, require
sophisticated text processing to mine information from pathology reports and
clinical notes. In this study, we developed algorithms to identify lupus
nephritis with and without natural language processing (NLP) using EHR data. We
developed four algorithms: a rule-based algorithm using only structured data
(baseline algorithm) and three algorithms using different NLP models. The three
NLP models are based on regularized logistic regression and use different sets
of features including positive mention of concept unique identifiers (CUIs),
number of appearances of CUIs, and a mixture of three components respectively.
The baseline algorithm and the best performed NLP algorithm were external
validated on a dataset from Vanderbilt University Medical Center (VUMC). Our
best performing NLP model incorporating features from both structured data,
regular expression concepts, and mapped CUIs improved F measure in both the
NMEDW (0.41 vs 0.79) and VUMC (0.62 vs 0.96) datasets compared to the baseline
lupus nephritis algorithm.

Consider a mechanism designer who implements a choice rule through a dynamic
protocol, gradually learning agents' private information through yes-or-no
questions addressed to one agent at a time. A protocol is contextually private
for a choice rule if the designer learns only the subsets of private
information needed to determine the outcome. While the serial dictatorship and
the first-price auction have contextually private implementations, the
second-price auction does not, nor does any stable matching rule. When the
designer can additionally ask about the number of agents whose private
information satisfies a certain property, the second-price auction, the uniform
$k$th price auction, and the Walrasian double auction have contextually private
t\^atonnement implementations.

This paper considers the problem of learning temporal task specifications,
e.g. automata and temporal logic, from expert demonstrations. Task
specifications are a class of sparse memory augmented rewards with explicit
support for temporal and Boolean composition. Three features make learning
temporal task specifications difficult: (1) the (countably) infinite number of
tasks under consideration; (2) an a-priori ignorance of what memory is needed
to encode the task; and (3) the discrete solution space - typically addressed
by (brute force) enumeration. To overcome these hurdles, we propose
Demonstration Informed Specification Search (DISS): a family of algorithms
requiring only black box access to a maximum entropy planner and a task sampler
from labeled examples. DISS then works by alternating between conjecturing
labeled examples to make the provided demonstrations less surprising and
sampling tasks consistent with the conjectured labeled examples. We provide a
concrete implementation of DISS in the context of tasks described by
Deterministic Finite Automata, and show that DISS is able to efficiently
identify tasks from only one or two expert demonstrations.

Machine learning has recently emerged as a promising approach for studying
complex phenomena characterized by rich datasets. In particular, data-centric
approaches lend to the possibility of automatically discovering structures in
experimental datasets that manual inspection may miss. Here, we introduce an
interpretable unsupervised-supervised hybrid machine learning approach, the
hybrid-correlation convolutional neural network (Hybrid-CCNN), and apply it to
experimental data generated using a programmable quantum simulator based on
Rydberg atom arrays. Specifically, we apply Hybrid-CCNN to analyze new quantum
phases on square lattices with programmable interactions. The initial
unsupervised dimensionality reduction and clustering stage first reveals five
distinct quantum phase regions. In a second supervised stage, we refine these
phase boundaries and characterize each phase by training fully interpretable
CCNNs and extracting the relevant correlations for each phase. The
characteristic spatial weightings and snippets of correlations specifically
recognized in each phase capture quantum fluctuations in the striated phase and
identify two previously undetected phases, the rhombic and boundary-ordered
phases. These observations demonstrate that a combination of programmable
quantum simulators with machine learning can be used as a powerful tool for
detailed exploration of correlated quantum states of matter.

We find Mask2Former also achieves state-of-the-art performance on video
instance segmentation without modifying the architecture, the loss or even the
training pipeline. In this report, we show universal image segmentation
architectures trivially generalize to video segmentation by directly predicting
3D segmentation volumes. Specifically, Mask2Former sets a new state-of-the-art
of 60.4 AP on YouTubeVIS-2019 and 52.6 AP on YouTubeVIS-2021. We believe
Mask2Former is also capable of handling video semantic and panoptic
segmentation, given its versatility in image segmentation. We hope this will
make state-of-the-art video segmentation research more accessible and bring
more attention to designing universal image and video segmentation

Making generative models 3D-aware bridges the 2D image space and the 3D
physical world yet remains challenging. Recent attempts equip a Generative
Adversarial Network (GAN) with a Neural Radiance Field (NeRF), which maps 3D
coordinates to pixel values, as a 3D prior. However, the implicit function in
NeRF has a very local receptive field, making the generator hard to become
aware of the global structure. Meanwhile, NeRF is built on volume rendering
which can be too costly to produce high-resolution results, increasing the
optimization difficulty. To alleviate these two problems, we propose a novel
framework, termed as VolumeGAN, for high-fidelity 3D-aware image synthesis,
through explicitly learning a structural representation and a textural
representation. We first learn a feature volume to represent the underlying
structure, which is then converted to a feature field using a NeRF-like model.
The feature field is further accumulated into a 2D feature map as the textural
representation, followed by a neural renderer for appearance synthesis. Such a
design enables independent control of the shape and the appearance. Extensive
experiments on a wide range of datasets show that our approach achieves
sufficiently higher image quality and better 3D control than the previous
methods.

All physical laws are described as relationships between state variables that
give a complete and non-redundant description of the relevant system dynamics.
However, despite the prevalence of computing power and AI, the process of
identifying the hidden state variables themselves has resisted automation. Most
data-driven methods for modeling physical phenomena still assume that observed
data streams already correspond to relevant state variables. A key challenge is
to identify the possible sets of state variables from scratch, given only
high-dimensional observational data. Here we propose a new principle for
determining how many state variables an observed system is likely to have, and
what these variables might be, directly from video streams. We demonstrate the
effectiveness of this approach using video recordings of a variety of physical
dynamical systems, ranging from elastic double pendulums to fire flames.
Without any prior knowledge of the underlying physics, our algorithm discovers
the intrinsic dimension of the observed dynamics and identifies candidate sets
of state variables. We suggest that this approach could help catalyze the
understanding, prediction and control of increasingly complex systems. Project
website is at: https://www.cs.columbia.edu/~bchen/neural-state-variables

In this paper, we investigate the problem of system identification for
autonomous switched linear systems with complete state observations. We propose
switched least squares method for the identification for switched linear
systems, show that this method is strongly consistent, and derive
data-dependent and data-independent rates of convergence. In particular, our
data-dependent rate of convergence shows that, almost surely, the system
identification error is $\mathcal{O}\big(\sqrt{\log(T)/T} \big)$ where $T$ is
the time horizon. These results show that our method for switched linear
systems has the same rate of convergence as least squares method for
non-switched linear systems. We compare our results with those in the
literature. We present numerical examples to illustrate the performance of the
proposed system identification method.

Recent work has shown that supervised learning alone, without temporal
difference (TD) learning, can be remarkably effective for offline RL. When does
this hold true, and which algorithmic components are necessary? Through
extensive experiments, we boil supervised learning for offline RL down to its
essential elements. In every environment suite we consider, simply maximizing
likelihood with a two-layer feedforward MLP is competitive with
state-of-the-art results of substantially more complex methods based on TD
learning or sequence modeling with Transformers. Carefully choosing model
capacity (e.g., via regularization or architecture) and choosing which
information to condition on (e.g., goals or rewards) are critical for
performance. These insights serve as a field guide for practitioners doing
Reinforcement Learning via Supervised Learning (which we coin "RvS learning").
They also probe the limits of existing RvS methods, which are comparatively
weak on random data, and suggest a number of open problems.

We establish conditions for which graph Laplacians
$\Delta_{\lambda,\epsilon}$ on compact, boundaryless, smooth submanifolds
$\mathcal{M}$ of Euclidean space are semiclassical pseudodifferential operators
($\Psi$DOs): essentially, that the graph Laplacian's kernel bandwidth (*bias
term*) $\sqrt{\epsilon}$ decays faster than the semiclassical parameter $h$,
*i.e.*, $h \gg \sqrt{\epsilon}$ and we compute the symbol. Coupling this with
Egorov's theorem and coherent states $\psi_h$ localized at $(x_0, \xi_0) \in
T^*\mathcal{M}$, we show that with $U_{\lambda,\epsilon}^t := e^{i t
\sqrt{\Delta}_{\lambda,\epsilon}}$ spectrally defined, the (co-)geodesic flow
$\Gamma^t$ on $T^*\mathcal{M}$ is approximated by $\langle
U_{\lambda,\epsilon}^{-t} \operatorname{Op}_h(a) U_{\lambda,\epsilon}^t \psi_h,
\psi_h \rangle = a \circ \Gamma^t(x_0, \xi_0) + O(h)$. Then, we turn to the
discrete setting: for $\Delta_{\lambda,\epsilon,N}$ a normalized graph
Laplacian defined on a set of $N$ points $x_1, \ldots, x_N$ sampled *i.i.d.*
from a probability distribution with smooth density, we establish
Bernstein-type lower bounds on the probability that
$||U_{\lambda,\epsilon,N}^t[u] - U_{\lambda,\epsilon}^t[u]||_{L^{\infty}} \leq
\delta$ with $U_{\lambda,\epsilon,N}^t := e^{i t
\sqrt{\Delta}_{\lambda,\epsilon,N}}$. We apply this to coherent states to show
that the geodesic flow on $\mathcal{M}$ can be approximated by matrix dynamics
on the discrete sample set, namely that *with high probability*, $c_{t,N}^{-1}
\sum_{j=1}^N |U_{\lambda,\epsilon,N}^t[\psi_h](x_j)|^2 u(x_j) = u(x_t) + O(h)$
for $c_{t,N} := \sum_{j=1}^N |U_{\lambda,\epsilon,N}^t[\psi_h](x_j)|^2$ and
$x_t$ the projection of $\Gamma^t(x_0, \xi_0)$ onto $\mathcal{M}$.

We introduce an algorithm for computing geodesics on sampled manifolds that
relies on simulation of quantum dynamics on a graph embedding of the sampled
data. Our approach exploits classic results in semiclassical analysis and the
quantum-classical correspondence, and forms a basis for techniques to learn the
manifold from which a dataset is sampled, and subsequently for nonlinear
dimensionality reduction of high-dimensional datasets. We illustrate the new
algorithm with data sampled from model manifolds and also by a clustering
demonstration based on COVID-19 mobility data. Finally, our method reveals
interesting connections between the discretization provided by data sampling
and quantization.

Beyond their primary diagnostic purpose, radiology reports have been an
invaluable source of information in medical research. Given a corpus of
radiology reports, researchers are often interested in identifying a subset of
reports describing a particular medical finding. Because the space of medical
findings in radiology reports is vast and potentially unlimited, recent studies
proposed mapping free-text statements in radiology reports to semi-structured
strings of terms taken from a limited vocabulary. This paper aims to present an
approach for the automatic generation of semi-structured representations of
radiology reports. The approach consists of matching sentences from radiology
reports to manually created semi-structured representations, followed by
learning a sequence-to-sequence neural model that maps matched sentences to
their semi-structured representations. We evaluated the proposed approach on
the OpenI corpus of manually annotated chest x-ray radiology reports. The
results indicate that the proposed approach is superior to several baselines,
both in terms of (1) quantitative measures such as BLEU, ROUGE, and METEOR and
(2) qualitative judgment of a radiologist. The results also demonstrate that
the trained model produces reasonable semi-structured representations on an
out-of-sample corpus of chest x-ray radiology reports from a different medical
provider.

Diffusion models have recently been shown to generate high-quality synthetic
images, especially when paired with a guidance technique to trade off diversity
for fidelity. We explore diffusion models for the problem of text-conditional
image synthesis and compare two different guidance strategies: CLIP guidance
and classifier-free guidance. We find that the latter is preferred by human
evaluators for both photorealism and caption similarity, and often produces
photorealistic samples. Samples from a 3.5 billion parameter text-conditional
diffusion model using classifier-free guidance are favored by human evaluators
to those from DALL-E, even when the latter uses expensive CLIP reranking.
Additionally, we find that our models can be fine-tuned to perform image
inpainting, enabling powerful text-driven image editing. We train a smaller
model on a filtered dataset and release the code and weights at
https://github.com/openai/glide-text2im.

Introduction: Identifying neurobiological differences between patients
suffering from Major Depressive Disorder (MDD) and healthy individuals has been
a mainstay of clinical neuroscience for decades. However, recent meta- and
mega-analyses have raised concerns regarding the replicability and clinical
relevance of brain alterations in depression. Methods: Here, we systematically
investigate healthy controls and MDD patients across a comprehensive range of
modalities including structural magnetic resonance imaging (MRI), diffusion
tensor imaging, functional task-based and resting-state MRI under near-ideal
conditions. To this end, we quantify the upper bounds of univariate effect
sizes, predictive utility, and distributional dissimilarity in a fully
harmonized cohort of N=1,809 participants. We compare the results to an MDD
polygenic risk score (PRS) and environmental variables. Results: The upper
bound of the effect sizes range from partial eta squared = .004 to .017,
distributions overlap between 89% and 95%, with classification accuracies
ranging between 54% and 55% across neuroimaging modalities. This pattern
remains virtually unchanged when considering only acutely or chronically
depressed patients. Differences are comparable to those found for PRS, but
substantially smaller than for environmental variables. Discussion: We provide
a large-scale, multimodal analysis of univariate biological differences between
MDD patients and controls and show that even under near-ideal conditions and
for maximum biological differences, deviations are extremely small and
similarity dominates. We sketch an agenda for a new focus of future research in
biological psychiatry facilitating quantitative, theory-driven research, an
emphasis on computational psychiatry and multivariate machine learning
approaches, as well as the utilization of ecologically valid phenotyping.

In this paper, we propose to predict the physics parameters of real fabrics
and garments by learning their physics similarities between simulated fabrics
via a Physics Similarity Network (PhySNet). For this, we estimate wind speeds
generated by an electric fan and the area weight to predict bending stiffness
of simulated and real fabrics and garments. We found that PhySNet coupled with
a Bayesian optimiser can predict physics parameters and improve the
state-of-art by 34%for real fabrics and 68% for real garments.

Learning dynamical systems properties from data provides important insights
that help us understand such systems and mitigate undesired outcomes. In this
work, we propose a framework for learning spatio-temporal (ST) properties as
formal logic specifications from data. We introduce SVM-STL, an extension of
Signal Signal Temporal Logic (STL), capable of specifying spatial and temporal
properties of a wide range of dynamical systems that exhibit time-varying
spatial patterns. Our framework utilizes machine learning techniques to learn
SVM-STL specifications from system executions given by sequences of spatial
patterns. We present methods to deal with both labeled and unlabeled data. In
addition, given system requirements in the form of SVM-STL specifications, we
provide an approach for parameter synthesis to find parameters that maximize
the satisfaction of such specifications. Our learning framework and parameter
synthesis approach are showcased in an example of a reaction-diffusion system.

The Optically Segmented Single Volume Scatter Camera (OS-SVSC) aims to image
neutron sources for nuclear non-proliferation applications using the kinematic
reconstruction of elastic double-scatter events. We report on the design,
construction, and calibration of one module of a new prototype. The module
includes 16 EJ-204 organic plastic scintillating bars individually wrapped in
Teflon tape, each measuring 0.5 cm$\times$0.5 cm$\times$20 cm. The scintillator
array is coupled to two custom Silicon Photomultiplier (SiPM) boards consisting
of a 2$\times$8 array of SensL J-Series-60035 Silicon Photomultipliers, which
are read out by a custom 16 channel DRS-4 based digitizer board. The electrical
crosstalk between SiPMs within the electronics chain is measured as 0.76% $\pm$
0.11% among all 16 channels. We report the detector response of one module
including interaction position, time, and energy, using two different optical
coupling materials: EJ-560 silicone rubber optical coupling pads and EJ-550
optical coupling grease. We present results in terms of the overall mean and
standard deviation of the z-position reconstruction and interaction time
resolutions for all 16 bars in the module. We observed the z-position
resolution for gamma interactions in the 0.3 MeVee to 0.4 MeVee range to be
2.24 cm$\pm$1.10 cm and 1.45 cm$\pm$0.19 cm for silicone optical coupling pad
and optical grease, respectively. The observed interaction time resolution is
265 ps$\pm$29 ps and 235 ps$\pm$10 ps for silicone optical coupling pad and
optical grease, respectively.

We use neural radiance fields (NeRFs) to build interactive 3D environments
from large-scale visual captures spanning buildings or even multiple city
blocks collected primarily from drones. In contrast to single object scenes (on
which NeRFs are traditionally evaluated), our scale poses multiple challenges
including (1) the need to model thousands of images with varying lighting
conditions, each of which capture only a small subset of the scene, (2)
prohibitively large model capacities that make it infeasible to train on a
single GPU, and (3) significant challenges for fast rendering that would enable
interactive fly-throughs.
  To address these challenges, we begin by analyzing visibility statistics for
large-scale scenes, motivating a sparse network structure where parameters are
specialized to different regions of the scene. We introduce a simple geometric
clustering algorithm for data parallelism that partitions training images (or
rather pixels) into different NeRF submodules that can be trained in parallel.
  We evaluate our approach on existing datasets (Quad 6k and UrbanScene3D) as
well as against our own drone footage, improving training speed by 3x and PSNR
by 12%. We also evaluate recent NeRF fast renderers on top of Mega-NeRF and
introduce a novel method that exploits temporal coherence. Our technique
achieves a 40x speedup over conventional NeRF rendering while remaining within
0.8 db in PSNR quality, exceeding the fidelity of existing fast renderers.

Digital harms are widespread in the mobile ecosystem. As these devices gain
ever more prominence in our daily lives, so too increases the potential for
malicious attacks against individuals. The last line of defense against a range
of digital harms - including digital distraction, political polarisation
through hate speech, and children being exposed to damaging material - is the
user interface. This work introduces GreaseTerminator to enable researchers to
develop, deploy, and test interventions against these harms with end-users. We
demonstrate the ease of intervention development and deployment, as well as the
broad range of harms potentially covered with GreaseTerminator in five in-depth

Motivated by bridging the simulation to reality gap in the context of
safety-critical systems, we consider learning adversarially robust stability
certificates for unknown nonlinear dynamical systems. In line with approaches
from robust control, we consider additive and Lipschitz bounded adversaries
that perturb the system dynamics. We show that under suitable assumptions of
incremental stability on the underlying system, the statistical cost of
learning an adversarial stability certificate is equivalent, up to constant
factors, to that of learning a nominal stability certificate. Our results hinge
on novel bounds for the Rademacher complexity of the resulting adversarial loss
class, which may be of independent interest. To the best of our knowledge, this
is the first characterization of sample-complexity bounds when performing
adversarial learning over data generated by a dynamical system. We further
provide a practical algorithm for approximating the adversarial training
algorithm, and validate our findings on a damped pendulum example.

Social networks play a fundamental role in propagation of information and
news. Characterizing the content of the messages becomes vital for different
tasks, like breaking news detection, personalized message recommendation, fake
users detection, information flow characterization and others. However, Twitter
posts are short and often less coherent than other text documents, which makes
it challenging to apply text mining algorithms to these datasets efficiently.
Tweet-pooling (aggregating tweets into longer documents) has been shown to
improve automatic topic decomposition, but the performance achieved in this
task varies depending on the pooling method.
  In this paper, we propose a new pooling scheme for topic modeling in Twitter,
which groups tweets whose authors belong to the same community (group of users
who mainly interact with each other but not with other groups) on a user
interaction graph. We present a complete evaluation of this methodology, state
of the art schemes and previous pooling models in terms of the cluster quality,
document retrieval tasks performance and supervised machine learning
classification score. Results show that our Community polling method
outperformed other methods on the majority of metrics in two heterogeneous
datasets, while also reducing the running time. This is useful when dealing
with big amounts of noisy and short user-generated social media texts. Overall,
our findings contribute to an improved methodology for identifying the latent
topics in a Twitter dataset, without the need of modifying the basic machinery
of a topic decomposition model.

Mixture of Experts layers (MoEs) enable efficient scaling of language models
through conditional computation. This paper presents a detailed empirical study
of how autoregressive MoE language models scale in comparison with dense models
in a wide range of settings: in- and out-of-domain language modeling, zero- and
few-shot priming, and full-shot fine-tuning. With the exception of fine-tuning,
we find MoEs to be substantially more compute efficient. At more modest
training budgets, MoEs can match the performance of dense models using $\sim$4
times less compute. This gap narrows at scale, but our largest MoE model (1.1T
parameters) consistently outperforms a compute-equivalent dense model (6.7B
parameters). Overall, this performance gap varies greatly across tasks and
domains, suggesting that MoE and dense models generalize differently in ways
that are worthy of future study. We make our code and models publicly available
for research use.

Human Activity Recognition (HAR) using wearable devices such as smart watches
embedded with Inertial Measurement Unit (IMU) sensors has various applications
relevant to our daily life, such as workout tracking and health monitoring. In
this paper, we propose a novel attention-based approach to human activity
recognition using multiple IMU sensors worn at different body locations.
Firstly, a sensor-wise feature extraction module is designed to extract the
most discriminative features from individual sensors with Convolutional Neural
Networks (CNNs). Secondly, an attention-based fusion mechanism is developed to
learn the importance of sensors at different body locations and to generate an
attentive feature representation. Finally, an inter-sensor feature extraction
module is applied to learn the inter-sensor correlations, which are connected
to a classifier to output the predicted classes of activities. The proposed
approach is evaluated using five public datasets and it outperforms
state-of-the-art methods on a wide variety of activity categories.

Large-scale generative language models such as GPT-3 are competitive few-shot
learners. While these models are known to be able to jointly represent many
different languages, their training data is dominated by English, potentially
limiting their cross-lingual generalization. In this work, we train
multilingual generative language models on a corpus covering a diverse set of
languages, and study their few- and zero-shot learning capabilities in a wide
range of tasks. Our largest model with 7.5 billion parameters sets new state of
the art in few-shot learning in more than 20 representative languages,
outperforming GPT-3 of comparable size in multilingual commonsense reasoning
(with +7.4% absolute accuracy improvement in 0-shot settings and +9.4% in
4-shot settings) and natural language inference (+5.4% in each of 0-shot and
4-shot settings). On the FLORES-101 machine translation benchmark, our model
outperforms GPT-3 on 171 out of 182 directions with 32 training examples, while
surpassing the official supervised baseline in 45 directions. We conduct an
in-depth analysis of different multilingual prompting approaches, showing in
particular that strong few-shot learning performance across languages can be
achieved via cross-lingual transfer through both templates and demonstration
examples. Finally, we evaluate our models in social value tasks such as hate
speech detection in five languages and find it has limitations similar to
comparable sized GPT-3 models.

Traditional automated theorem provers for first-order logic depend on
speed-optimized search and many handcrafted heuristics that are designed to
work best over a wide range of domains. Machine learning approaches in
literature either depend on these traditional provers to bootstrap themselves
or fall short on reaching comparable performance. In this paper, we propose a
general incremental learning algorithm for training domain specific provers for
first-order logic without equality, based only on a basic given-clause
algorithm, but using a learned clause-scoring function. Clauses are represented
as graphs and presented to transformer networks with spectral features. To
address the sparsity and the initial lack of training data as well as the lack
of a natural curriculum, we adapt hindsight experience replay to theorem
proving, so as to be able to learn even when no proof can be found. We show
that provers trained this way can match and sometimes surpass state-of-the-art
traditional provers on the TPTP dataset in terms of both quantity and quality
of the proofs.

We consider Bayesian inverse problems wherein the unknown state is assumed to
be a function with discontinuous structure a priori. A class of prior
distributions based on the output of neural networks with heavy-tailed weights
is introduced, motivated by existing results concerning the infinite-width
limit of such networks. We show theoretically that samples from such priors
have desirable discontinuous-like properties even when the network width is
finite, making them appropriate for edge-preserving inversion. Numerically we
consider deconvolution problems defined on one- and two-dimensional spatial
domains to illustrate the effectiveness of these priors; MAP estimation,
dimension-robust MCMC sampling and ensemble-based approximations are utilized
to probe the posterior distribution. The accuracy of point estimates is shown
to exceed those obtained from non-heavy tailed priors, and uncertainty
estimates are shown to provide more useful qualitative information.

Semantic segmentation of 3D medical images is a challenging task due to the
high variability of the shape and pattern of objects (such as organs or
tumors). Given the recent success of deep learning in medical image
segmentation, Neural Architecture Search (NAS) has been introduced to find
high-performance 3D segmentation network architectures. However, because of the
massive computational requirements of 3D data and the discrete optimization
nature of architecture search, previous NAS methods require a long search time
or necessary continuous relaxation, and commonly lead to sub-optimal network
architectures. While one-shot NAS can potentially address these disadvantages,
its application in the segmentation domain has not been well studied in the
expansive multi-scale multi-path search space. To enable one-shot NAS for
medical image segmentation, our method, named HyperSegNAS, introduces a
HyperNet to assist super-net training by incorporating architecture topology
information. Such a HyperNet can be removed once the super-net is trained and
introduces no overhead during architecture search. We show that HyperSegNAS
yields better performing and more intuitive architectures compared to the
previous state-of-the-art (SOTA) segmentation networks; furthermore, it can
quickly and accurately find good architecture candidates under different
computing constraints. Our method is evaluated on public datasets from the
Medical Segmentation Decathlon (MSD) challenge, and achieves SOTA performances.

With their robustness to adverse weather conditions and ability to measure
speeds, radar sensors have been part of the automotive landscape for more than
two decades. Recent progress toward High Definition (HD) Imaging radar has
driven the angular resolution below the degree, thus approaching laser scanning
performance. However, the amount of data a HD radar delivers and the
computational cost to estimate the angular positions remain a challenge. In
this paper, we propose a novel HD radar sensing model, FFT-RadNet, that
eliminates the overhead of computing the range-azimuth-Doppler 3D tensor,
learning instead to recover angles from a range-Doppler spectrum. FFT-RadNet is
trained both to detect vehicles and to segment free driving space. On both
tasks, it competes with the most recent radar-based models while requiring less
compute and memory. Also, we collected and annotated 2-hour worth of raw data
from synchronized automotive-grade sensors (camera, laser, HD radar) in various
environments (city street, highway, countryside road). This unique dataset,
nick-named RADIal for "Radar, Lidar et al.", is available at
https://github.com/valeoai/RADIal.

In this thesis, we propose new theoretical frameworks for the analysis of
stochastic and distributed methods with error compensation and local updates.
Using these frameworks, we develop more than 20 new optimization methods,
including the first linearly converging Error-Compensated SGD and the first
linearly converging Local-SGD for arbitrarily heterogeneous local functions.
Moreover, the thesis contains several new distributed methods with unbiased
compression for distributed non-convex optimization problems. The derived
complexity results for these methods outperform the previous best-known results
for the considered problems. Finally, we propose a new scalable decentralized
fault-tolerant distributed method, and under reasonable assumptions, we derive
the iteration complexity bounds for this method that match the ones of
centralized Local-SGD.

A few models have tried to tackle the link prediction problem, also known as
knowledge graph completion, by embedding knowledge graphs in comparably lower
dimensions. However, the state-of-the-art results are attained at the cost of
considerably increasing the dimensionality of embeddings which causes
scalability issues in the case of huge knowledge bases. Transformers have been
successfully used recently as powerful encoders for knowledge graphs, but
available models still have scalability issues. To address this limitation, we
introduce a Transformer-based model to gain expressive low-dimensional
embeddings. We utilize a large number of self-attention heads as the key to
applying query-dependent projections to capture mutual information between
entities and relations. Empirical results on WN18RR and FB15k-237 as standard
link prediction benchmarks demonstrate that our model has favorably comparable
performance with the current state-of-the-art models. Notably, we yield our
promising results with a significant reduction of 66.9% in the dimensionality
of embeddings compared to the five best recent state-of-the-art competitors on
average.

Latte (for LATent Tensor Evaluation) is a Python library for evaluation of
latent-based generative models in the fields of disentanglement learning and
controllable generation. Latte is compatible with both PyTorch and
TensorFlow/Keras, and provides both functional and modular APIs that can be
easily extended to support other deep learning frameworks. Using NumPy-based
and framework-agnostic implementation, Latte ensures reproducible, consistent,
and deterministic metric calculations regardless of the deep learning framework
of choice.

This paper investigates a master unmanned aerial vehicle (MUAV)-powered
Internet of Things (IoT) network, in which we propose using a rechargeable
auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to
enhance the communication signals from the MUAV and also leverage the MUAV as a
recharging power source. Under the proposed model, we investigate the optimal
collaboration strategy of these energy-limited UAVs to maximize the accumulated
throughput of the IoT network. Depending on whether there is charging between
the two UAVs, two optimization problems are formulated. To solve them, two
multi-agent deep reinforcement learning (DRL) approaches are proposed, which
are centralized training multi-agent deep deterministic policy gradient
(CT-MADDPG) and multi-agent deep deterministic policy option critic (MADDPOC).
It is shown that the CT-MADDPG can greatly reduce the requirement on the
computing capability of the UAV hardware, and the proposed MADDPOC is able to
support low-level multi-agent cooperative learning in the continuous action
domains, which has great advantages over the existing option-based hierarchical
DRL that only support single-agent learning and discrete actions.

We present Turbo-Sim, a generalised autoencoder framework derived from
principles of information theory that can be used as a generative model. By
maximising the mutual information between the input and the output of both the
encoder and the decoder, we are able to rediscover the loss terms usually found
in adversarial autoencoders and generative adversarial networks, as well as
various more sophisticated related models. Our generalised framework makes
these models mathematically interpretable and allows for a diversity of new
ones by setting the weight of each loss term separately. The framework is also
independent of the intrinsic architecture of the encoder and the decoder thus
leaving a wide choice for the building blocks of the whole network. We apply
Turbo-Sim to a collider physics generation problem: the transformation of the
properties of several particles from a theory space, right after the collision,
to an observation space, right after the detection in an experiment.

Road networks are the core infrastructure for connected and autonomous
vehicles, but creating meaningful representations for machine learning
applications is a challenging task. In this work, we propose to integrate
remote sensing vision data into road network data for improved embeddings with
graph neural networks. We present a segmentation of road edges based on
spatio-temporal road and traffic characteristics, which allows to enrich the
attribute set of road networks with visual features of satellite imagery and
digital surface models. We show that both, the segmentation and the integration
of vision data can increase performance on a road type classification task, and
we achieve state-of-the-art performance on the OSM+DiDi Chuxing dataset on
Chengdu, China.

In this effort, we derive a formula for the integral representation of a
shallow neural network with the Rectified Power Unit activation function.
Mainly, our first result deals with the univariate case of representation
capability of RePU shallow networks. The multidimensional result in this paper
characterizes the set of functions that can be represented with bounded norm
and possibly unbounded width.

One of the challenges of supervised learning training is the need to procure
an substantial amount of tagged data. A well-known method of solving this
problem is to use synthetic data in a copy-paste fashion, so that we cut
objects and paste them onto relevant backgrounds. Pasting the objects naively
results in artifacts that cause models to give poor results on real data. We
present a new method for cleanly pasting objects on different backgrounds so
that the dataset created gives competitive performance on real data. The main
emphasis is on the treatment of the border of the pasted object using
inpainting. We show state-of-the-art results both on instance detection and
foreground segmentation

We study regret minimization in finite horizon tabular Markov decision
processes (MDPs) under the constraints of differential privacy (DP). This is
motivated by the widespread applications of reinforcement learning (RL) in
real-world sequential decision making problems, where protecting users'
sensitive and private information is becoming paramount. We consider two
variants of DP -- joint DP (JDP), where a centralized agent is responsible for
protecting users' sensitive data and local DP (LDP), where information needs to
be protected directly on the user side. We first propose two general frameworks
-- one for policy optimization and another for value iteration -- for designing
private, optimistic RL algorithms. We then instantiate these frameworks with
suitable privacy mechanisms to satisfy JDP and LDP requirements, and
simultaneously obtain sublinear regret guarantees. The regret bounds show that
under JDP, the cost of privacy is only a lower order additive term, while for a
stronger privacy protection under LDP, the cost suffered is multiplicative.
Finally, the regret bounds are obtained by a unified analysis, which, we
believe, can be extended beyond tabular MDPs.

In Bayesian Networks (BNs), the direction of edges is crucial for causal
reasoning and inference. However, Markov equivalence class considerations mean
it is not always possible to establish edge orientations, which is why many BN
structure learning algorithms cannot orientate all edges from purely
observational data. Moreover, latent confounders can lead to false positive
edges. Relatively few methods have been proposed to address these issues. In
this work, we present the hybrid mFGS-BS (majority rule and Fast Greedy
equivalence Search with Bayesian Scoring) algorithm for structure learning from
discrete data that involves an observational data set and one or more
interventional data sets. The algorithm assumes causal insufficiency in the
presence of latent variables and produces a Partial Ancestral Graph (PAG).
Structure learning relies on a hybrid approach and a novel Bayesian scoring
paradigm that calculates the posterior probability of each directed edge being
added to the learnt graph. Experimental results based on well-known networks of
up to 109 variables and 10k sample size show that mFGS-BS improves structure
learning accuracy relative to the state-of-the-art and it is computationally

Neural networks often make predictions relying on the spurious correlations
from the datasets rather than the intrinsic properties of the task of interest,
facing sharp degradation on out-of-distribution (OOD) test data. Existing
de-bias learning frameworks try to capture specific dataset bias by annotations
but they fail to handle complicated OOD scenarios. Others implicitly identify
the dataset bias by special design low capability biased models or losses, but
they degrade when the training and testing data are from the same distribution.
In this paper, we propose a General Greedy De-bias learning framework (GGD),
which greedily trains the biased models and the base model. The base model is
encouraged to focus on examples that are hard to solve with biased models, thus
remaining robust against spurious correlations in the test stage. GGD largely
improves models' OOD generalization ability on various tasks, but sometimes
over-estimates the bias level and degrades on the in-distribution test. We
further re-analyze the ensemble process of GGD and introduce the Curriculum
Regularization inspired by curriculum learning, which achieves a good trade-off
between in-distribution and out-of-distribution performance. Extensive
experiments on image classification, adversarial question answering, and visual
question answering demonstrate the effectiveness of our method. GGD can learn a
more robust base model under the settings of both task-specific biased models
with prior knowledge and self-ensemble biased model without prior knowledge.

We introduce PyChEst, a Python package which provides tools for the
simultaneous estimation of multiple changepoints in the distribution of
piece-wise stationary time series. The nonparametric algorithms implemented are
provably consistent in a general framework: when the samples are generated by
unknown piece-wise stationary processes. In this setting, samples may have
long-range dependencies of arbitrary form and the finite-dimensional marginals
of any (unknown) fixed size before and after the changepoints may be the same.
The strength of the algorithms included in the package is in their ability to
consistently detect the changes without imposing any assumptions beyond
stationarity on the underlying process distributions. We illustrate this
distinguishing feature by comparing the performance of the package against
state-of-the-art models designed for a setting where the samples are
independently and identically distributed.

Large-scale graph data in the real-world are often dynamic rather than
static. The data are changing with new nodes, edges, and even classes appearing
over time, such as in citation networks and research-and-development
collaboration networks. Graph neural networks (GNNs) have emerged as the
standard method for numerous tasks on graph-structured data. In this work, we
employ a two-step procedure to explore how GNNs can be incrementally adapted to
new unseen graph data. First, we analyze the verge between transductive and
inductive learning on standard benchmark datasets. After inductive pretraining,
we add unlabeled data to the graph and show that the models are stable. Then,
we explore the case of continually adding more and more labeled data, while
considering cases, where not all past instances are annotated with class
labels. Furthermore, we introduce new classes while the graph evolves and
explore methods that automatically detect instances from previously unseen
classes. In order to deal with evolving graphs in a principled way, we propose
a lifelong learning framework for graph data along with an evaluation protocol.
In this framework, we evaluate representative GNN architectures. We observe
that implicit knowledge within model parameters becomes more important when
explicit knowledge, i.e., data from past tasks, is limited. We find that in
open-world node classification, the data from surprisingly few past tasks are
sufficient to reach the performance reached by remembering data from all past
tasks. In the challenging task of unseen class detection, we find that using a
weighted cross-entropy loss is important for stability.

Certain aspects of the explainability of AI systems will be critically
discussed. This especially with focus on the feasibility of the task of making
every AI system explainable. Emphasis will be given to difficulties related to
the explainability of highly complex and efficient AI systems which deliver
decisions whose explanation defies classical logical schemes of cause and
effect. AI systems have provably delivered unintelligible solutions which in
retrospect were characterized as ingenious (for example move 37 of the game 2
of AlphaGo). It will be elaborated on arguments supporting the notion that if
AI-solutions were to be discarded in advance because of their not being
thoroughly comprehensible, a great deal of the potentiality of intelligent
systems would be wasted.

In recent years, a considerable research effort has shown the energy benefits
of implementing neural networks with memristors or other emerging memory
technologies. However, for extreme-edge applications with high uncertainty,
access to reduced amounts of data, and where explainable decisions are
required, neural networks may not provide an acceptable form of intelligence.
Bayesian reasoning can solve these concerns, but it is computationally
expensive and, unlike neural networks, does not translate naturally to
memristor-based architectures. In this work, we introduce, demonstrate
experimentally on a fully fabricated hybrid CMOS-memristor system, and analyze
a Bayesian machine designed for highly-energy efficient Bayesian reasoning. The
architecture of the machine is obtained by writing Bayes' law in a way making
its implementation natural by the principles of distributed memory and
stochastic computing, allowing the circuit to function using solely local
memory and minimal data movement. Measurements on a fabricated small-scale
Bayesian machine featuring 2,048 memristors and 30,080 transistors show the
viability of this approach and the possibility of overcoming the challenges
associated with its design: the inherent imperfections of memristors, as well
as the need to distribute very locally higher-than-nominal supply voltages. The
design of a scaled-up version of the machine shows its outstanding energy
efficiency on a real-life gesture recognition task: a gesture can be recognized
using 5,000 times less energy than using a microcontroller unit. The Bayesian
machine also features several desirable features, e.g., instant on/off
operation, compatibility with low supply voltages, and resilience to
single-event upsets. These results open the road for Bayesian reasoning as an
attractive way for energy-efficient, robust, and explainable intelligence at
the edge.

Hyperspectral image (HSI) super-resolution without additional auxiliary image
remains a constant challenge due to its high-dimensional spectral patterns,
where learning an effective spatial and spectral representation is a
fundamental issue. Recently, Implicit Neural Representations (INRs) are making
strides as a novel and effective representation, especially in the
reconstruction task. Therefore, in this work, we propose a novel HSI
reconstruction model based on INR which represents HSI by a continuous function
mapping a spatial coordinate to its corresponding spectral radiance values. In
particular, as a specific implementation of INR, the parameters of parametric
model are predicted by a hypernetwork that operates on feature extraction using
convolution network. It makes the continuous functions map the spatial
coordinates to pixel values in a content-aware manner. Moreover, periodic
spatial encoding are deeply integrated with the reconstruction procedure, which
makes our model capable of recovering more high frequency details. To verify
the efficacy of our model, we conduct experiments on three HSI datasets (CAVE,
NUS, and NTIRE2018). Experimental results show that the proposed model can
achieve competitive reconstruction performance in comparison with the
state-of-the-art methods. In addition, we provide an ablation study on the
effect of individual components of our model. We hope this paper could server
as a potent reference for future research.

Road maintenance planning is an integral part of road asset management. One
of the main challenges in Maintenance and Rehabilitation (M&R) practices is to
determine maintenance type and timing. This research proposes a framework using
Reinforcement Learning (RL) based on the Long Term Pavement Performance (LTPP)
database to determine the type and timing of M&R practices. A predictive DNN
model is first developed in the proposed algorithm, which serves as the
Environment for the RL algorithm. For the Policy estimation of the RL model,
both DQN and PPO models are developed. However, PPO has been selected in the
end due to better convergence and higher sample efficiency. Indicators used in
this study are International Roughness Index (IRI) and Rutting Depth (RD).
Initially, we considered Cracking Metric (CM) as the third indicator, but it
was then excluded due to the much fewer data compared to other indicators,
which resulted in lower accuracy of the results. Furthermore, in
cost-effectiveness calculation (reward), we considered both the economic and
environmental impacts of M&R treatments. Costs and environmental impacts have
been evaluated with paLATE 2.0 software. Our method is tested on a hypothetical
case study of a six-lane highway with 23 kilometers length located in Texas,
which has a warm and wet climate. The results propose a 20-year M&R plan in
which road condition remains in an excellent condition range. Because the early
state of the road is at a good level of service, there is no need for heavy
maintenance practices in the first years. Later, after heavy M&R actions, there
are several 1-2 years of no need for treatments. All of these show that the
proposed plan has a logical result. Decision-makers and transportation agencies
can use this scheme to conduct better maintenance practices that can prevent
budget waste and, at the same time, minimize the environmental impacts.

We discuss the use of machine learning techniques in effectively
nonparametric modelling of generalised parton distributions (GPDs) in view of
their future extraction from experimental data. Current parameterisations of
GPDs suffer from model dependency that lessens their impact on phenomenology
and brings unknown systematics to the estimation of quantities like Mellin
moments. The new strategy presented in this study allows to describe GPDs in a
way fulfilling theory-driven constraints, keeping model dependency to a
minimum. Getting a better grip on the control of systematic effects, our work
will help the GPD phenomenology to achieve its maturity in the precision era
commenced by the new generation of experiments.

The event-driven and sparse nature of communication between spiking neurons
in the brain holds great promise for flexible and energy-efficient AI. Recent
advances in learning algorithms have demonstrated that recurrent networks of
spiking neurons can be effectively trained to achieve competitive performance
compared to standard recurrent neural networks. Still, as these learning
algorithms use error-backpropagation through time (BPTT), they suffer from high
memory requirements, are slow to train, and are incompatible with online
learning. This limits the application of these learning algorithms to
relatively small networks and to limited temporal sequence lengths. Online
approximations to BPTT with lower computational and memory complexity have been
proposed (e-prop, OSTL), but in practice also suffer from memory limitations
and, as approximations, do not outperform standard BPTT training. Here, we show
how a recently developed alternative to BPTT, Forward Propagation Through Time
(FPTT) can be applied in spiking neural networks. Different from BPTT, FPTT
attempts to minimize an ongoing dynamically regularized risk on the loss. As a
result, FPTT can be computed in an online fashion and has fixed complexity with
respect to the sequence length. When combined with a novel dynamic spiking
neuron model, the Liquid-Time-Constant neuron, we show that SNNs trained with
FPTT outperform online BPTT approximations, and approach or exceed offline BPTT
accuracy on temporal classification tasks. This approach thus makes it feasible
to train SNNs in a memory-friendly online fashion on long sequences and scale
up SNNs to novel and complex neural architectures.

We introduce version 3 of NetKet, the machine learning toolbox for many-body
quantum physics. NetKet is built around neural-network quantum states and
provides efficient algorithms for their evaluation and optimization. This new
version is built on top of JAX, a differentiable programming and accelerated
linear algebra framework for the Python programming language. The most
significant new feature is the possibility to define arbitrary neural network
ans\"atze in pure Python code using the concise notation of machine-learning
frameworks, which allows for just-in-time compilation as well as the implicit
generation of gradients thanks to automatic differentiation. NetKet 3 also
comes with support for GPU and TPU accelerators, advanced support for discrete
symmetry groups, chunking to scale up to thousands of degrees of freedom,
drivers for quantum dynamics applications, and improved modularity, allowing
users to use only parts of the toolbox as a foundation for their own code.

In federated learning (FL), robust aggregation schemes have been developed to
protect against malicious clients. Many robust aggregation schemes rely on
certain numbers of benign clients being present in a quorum of workers. This
can be hard to guarantee when clients can join at will, or join based on
factors such as idle system status, and connected to power and WiFi. We tackle
the scenario of securing FL systems conducting adversarial training when a
quorum of workers could be completely malicious. We model an attacker who
poisons the model to insert a weakness into the adversarial training such that
the model displays apparent adversarial robustness, while the attacker can
exploit the inserted weakness to bypass the adversarial training and force the
model to misclassify adversarial examples. We use abstract interpretation
techniques to detect such stealthy attacks and block the corrupted model
updates. We show that this defence can preserve adversarial robustness even
against an adaptive attacker.

Objectives. Sustainable management of plant diseases is an open challenge
which has relevant economic and environmental impact. Optimal strategies rely
on human expertise for field scouting under favourable conditions to assess the
current presence and extent of disease symptoms. This labor-intensive task is
complicated by the large field area to be scouted, combined with the
millimeter-scale size of the early symptoms to be detected. In view of this,
image-based detection of early disease symptoms is an attractive approach to
automate this process, enabling a potential high throughput monitoring at
sustainable costs.
  Methods. Deep learning has been successfully applied in various domains to
obtain an automatic selection of the relevant image features by learning
filters via a training procedure. Deep learning has recently entered also the
domain of plant disease detection: following this idea, in this work we present
a deep learning approach to automatically recognize powdery mildew on cucumber
leaves. We focus on unsupervised deep learning techniques applied to
multispectral imaging data and we propose the use of autoencoder architectures
to investigate two strategies for disease detection: i) clusterization of
features in a compressed space; ii) anomaly detection.
  Results. The two proposed approaches have been assessed by quantitative
indices. The clusterization approach is not fully capable by itself to provide
accurate predictions but it does cater relevant information. Anomaly detection
has instead a significant potential of resolution which could be further
exploited as a prior for supervised architectures with a very limited number of
labeled samples.

Multiple medical institutions collaboratively training a model using
federated learning (FL) has become a promising solution for maximizing the
potential of data-driven models, yet the non-independent and identically
distributed (non-iid) data in medical images is still an outstanding challenge
in real-world practice. The feature heterogeneity caused by diverse scanners or
protocols introduces a drift in the learning process, in both local (client)
and global (server) optimizations, which harms the convergence as well as model
performance. Many previous works have attempted to address the non-iid issue by
tackling the drift locally or globally, but how to jointly solve the two
essentially coupled drifts is still unclear. In this work, we concentrate on
handling both local and global drifts and introduce a new harmonizing framework
called HarmoFL. First, we propose to mitigate the local update drift by
normalizing amplitudes of images transformed into the frequency domain to mimic
a unified imaging setting, in order to generate a harmonized feature space
across local clients. Second, based on harmonized features, we design a client
weight perturbation guiding each local model to reach a flat optimum, where a
neighborhood area of the local optimal solution has a uniformly low loss.
Without any extra communication cost, the perturbation assists the global model
to optimize towards a converged optimal solution by aggregating several local
flat optima. We have theoretically analyzed the proposed method and empirically
conducted extensive experiments on three medical image classification and
segmentation tasks, showing that HarmoFL outperforms a set of recent
state-of-the-art methods with promising convergence behavior. Code is available
at https://github.com/med-air/HarmoFL.

With the development of AIoT, data-driven attack detection methods for
cyber-physical systems (CPSs) have attracted lots of attention. However,
existing methods usually adopt tractable distributions to approximate data
distributions, which are not suitable for complex systems. Besides, the
correlation of the data in different channels does not attract sufficient
attention. To address these issues, we use energy-based generative models,
which are less restrictive on functional forms of the data distribution. In
addition, graph neural networks are used to explicitly model the correlation of
the data in different channels. In the end, we propose TFDPM, a general
framework for attack detection tasks in CPSs. It simultaneously extracts
temporal pattern and feature pattern given the historical data. Then extract
features are sent to a conditional diffusion probabilistic model. Predicted
values can be obtained with the conditional generative network and attacks are
detected based on the difference between predicted values and observed values.
In addition, to realize real-time detection, a conditional noise scheduling
network is proposed to accelerate the prediction process. Experimental results
show that TFDPM outperforms existing state-of-the-art attack detection methods.
The noise scheduling network increases the detection speed by three times.

Deep reinforcement learning algorithms can perform poorly in real-world tasks
due to the discrepancy between source and target environments. This discrepancy
is commonly viewed as the disturbance in transition dynamics. Many existing
algorithms learn robust policies by modeling the disturbance and applying it to
source environments during training, which usually requires prior knowledge
about the disturbance and control of simulators. However, these algorithms can
fail in scenarios where the disturbance from target environments is unknown or
is intractable to model in simulators. To tackle this problem, we propose a
novel model-free actor-critic algorithm -- namely, state-conservative policy
optimization (SCPO) -- to learn robust policies without modeling the
disturbance in advance. Specifically, SCPO reduces the disturbance in
transition dynamics to that in state space and then approximates it by a simple
gradient-based regularizer. The appealing features of SCPO include that it is
simple to implement and does not require additional knowledge about the
disturbance or specially designed simulators. Experiments in several robot
control tasks demonstrate that SCPO learns robust policies against the
disturbance in transition dynamics.

Currently, it is hard to reap the benefits of deep learning for Bayesian
methods, which allow the explicit specification of prior knowledge and
accurately capture model uncertainty. We present Prior-Data Fitted Networks
(PFNs). PFNs leverage large-scale machine learning techniques to approximate a
large set of posteriors. The only requirement for PFNs to work is the ability
to sample from a prior distribution over supervised learning tasks (or
functions). Our method restates the objective of posterior approximation as a
supervised classification problem with a set-valued input: it repeatedly draws
a task (or function) from the prior, draws a set of data points and their
labels from it, masks one of the labels and learns to make probabilistic
predictions for it based on the set-valued input of the rest of the data
points. Presented with a set of samples from a new supervised learning task as
input, PFNs make probabilistic predictions for arbitrary other data points in a
single forward propagation, having learned to approximate Bayesian inference.
We demonstrate that PFNs can near-perfectly mimic Gaussian processes and also
enable efficient Bayesian inference for intractable problems, with over
200-fold speedups in multiple setups compared to current methods. We obtain
strong results in very diverse areas such as Gaussian process regression,
Bayesian neural networks, classification for small tabular data sets, and
few-shot image classification, demonstrating the generality of PFNs. Code and
trained PFNs are released at
https://github.com/automl/TransformersCanDoBayesianInference.

What are the units of text that we want to model? From bytes to multi-word
expressions, text can be analyzed and generated at many granularities. Until
recently, most natural language processing (NLP) models operated over words,
treating those as discrete and atomic tokens, but starting with byte-pair
encoding (BPE), subword-based approaches have become dominant in many areas,
enabling small vocabularies while still allowing for fast inference. Is the end
of the road character-level model or byte-level processing? In this survey, we
connect several lines of work from the pre-neural and neural era, by showing
how hybrid approaches of words and characters as well as subword-based
approaches based on learned segmentation have been proposed and evaluated. We
conclude that there is and likely will never be a silver bullet singular
solution for all applications and that thinking seriously about tokenization
remains important for many applications.

Most of the previous works on data flow optimizations for Machine Learning
hardware accelerators try to find algorithmic re-factorization such as
loop-reordering and loop-tiling. However, the analysis and information they
provide are still at very high level and one must further map them onto
instructions that hardware can understand. This paper presents
"Dijkstra-Through-Time" (DTT), an ahead of time compute and memory
scheduling-mapping algorithm for deterministic workloads. It provides a simple
implementation and supports accelerators with complex NoC configurations, at
the expense of a long compilation process. This initial paper illustrates a
proof of concept implementation to merge scheduling and data cache coherence
mechanisms to get more optimized data flows.

Most image matching methods perform poorly when encountering large scale
changes in images. To solve this problem, firstly, we propose a
scale-difference-aware image matching method (SDAIM) that reduces image scale
differences before local feature extraction, via resizing both images of an
image pair according to an estimated scale ratio. Secondly, in order to
accurately estimate the scale ratio, we propose a
covisibility-attention-reinforced matching module (CVARM) and then design a
novel neural network, termed as Scale-Net, based on CVARM. The proposed CVARM
can lay more stress on covisible areas within the image pair and suppress the
distraction from those areas visible in only one image. Quantitative and
qualitative experiments confirm that the proposed Scale-Net has higher scale
ratio estimation accuracy and much better generalization ability compared with
all the existing scale ratio estimation methods. Further experiments on image
matching and relative pose estimation tasks demonstrate that our SDAIM and
Scale-Net are able to greatly boost the performance of representative local
features and state-of-the-art local feature matching methods.

We study the problem of learning association between face and voice, which is
gaining interest in the computer vision community lately. Prior works adopt
pairwise or triplet loss formulations to learn an embedding space amenable for
associated matching and verification tasks. Albeit showing some progress, such
loss formulations are, however, restrictive due to dependency on
distance-dependent margin parameter, poor run-time training complexity, and
reliance on carefully crafted negative mining procedures. In this work, we
hypothesize that enriched feature representation coupled with an effective yet
efficient supervision is necessary in realizing a discriminative joint
embedding space for improved face-voice association. To this end, we propose a
light-weight, plug-and-play mechanism that exploits the complementary cues in
both modalities to form enriched fused embeddings and clusters them based on
their identity labels via orthogonality constraints. We coin our proposed
mechanism as fusion and orthogonal projection (FOP) and instantiate in a
two-stream pipeline. The overall resulting framework is evaluated on a
large-scale VoxCeleb dataset with a multitude of tasks, including cross-modal
verification and matching. Results show that our method performs favourably
against the current state-of-the-art methods and our proposed supervision
formulation is more effective and efficient than the ones employed by the
contemporary methods.

We propose a new 3D spatial understanding task of 3D Question Answering
(3D-QA). In the 3D-QA task, models receive visual information from the entire
3D scene of the rich RGB-D indoor scan and answer the given textual questions
about the 3D scene. Unlike the 2D-question answering of VQA, the conventional
2D-QA models suffer from problems with spatial understanding of object
alignment and directions and fail the object identification from the textual
questions in 3D-QA. We propose a baseline model for 3D-QA, named ScanQA model,
where the model learns a fused descriptor from 3D object proposals and encoded
sentence embeddings. This learned descriptor correlates the language
expressions with the underlying geometric features of the 3D scan and
facilitates the regression of 3D bounding boxes to determine described objects
in textual questions and outputs correct answers. We collected human-edited
question-answer pairs with free-form answers that are grounded to 3D objects in
each 3D scene. Our new ScanQA dataset contains over 40K question-answer pairs
from the 800 indoor scenes drawn from the ScanNet dataset. To the best of our
knowledge, the proposed 3D-QA task is the first large-scale effort to perform
object-grounded question-answering in 3D environments.

In the current salient object detection network, the most popular method is
using U-shape structure. However, the massive number of parameters leads to
more consumption of computing and storage resources which are not feasible to
deploy on the limited memory device. Some others shallow layer network will not
maintain the same accuracy compared with U-shape structure and the deep network
structure with more parameters will not converge to a global minimum loss with
great speed. To overcome all of these disadvantages, we proposed a new deep
convolution network architecture with three contributions: (1) using smaller
convolution neural networks (CNNs) to compress the model in our improved
salient object features compression and reinforcement extraction module
(ISFCREM) to reduce parameters of the model. (2) introducing channel attention
mechanism in ISFCREM to weigh different channels for improving the ability of
feature representation. (3) applying a new optimizer to accumulate the
long-term gradient information during training to adaptively tune the learning
rate. The results demonstrate that the proposed method can compress the model
to 1/3 of the original size nearly without losing the accuracy and converging
faster and more smoothly on six widely used datasets of salient object
detection compared with the others models. Our code is published in
https://gitee.com/binzhangbinzhangbin/code-a-novel-attention-based-network-for-fast-salient-object-detection.git

In this paper we carry out a joint optimization of probabilistic (PS) and
geometric shaping (GS) for four-dimensional (4D) modulation formats in
long-haul coherent wavelength division multiplexed (WDM) optical fiber
communications using an auto-encoder framework. We propose a 4D 10 bits/symbol
constellation which we obtained via end-to-end deep learning over the
split-step Fourier model of the fiber channel. The constellation achieved 13.6%
reach increase at a data rate of approximately 400 Gbits/second in comparison
to the ubiquitously employed polarization multiplexed 32-QAM format at a
forward error correction overhead of 20%.

One prominent tactic used to keep malicious behavior from being detected
during dynamic test campaigns is logic bombs, where malicious operations are
triggered only when specific conditions are satisfied. Defusing logic bombs
remains an unsolved problem in the literature. In this work, we propose to
investigate Suspicious Hidden Sensitive Operations (SHSOs) as a step towards
triaging logic bombs. To that end, we develop a novel hybrid approach that
combines static analysis and anomaly detection techniques to uncover SHSOs,
which we predict as likely implementations of logic bombs. Concretely, Difuzer
identifies SHSO entry-points using an instrumentation engine and an
inter-procedural data-flow analysis. Then, it extracts trigger-specific
features to characterize SHSOs and leverages One-Class SVM to implement an
unsupervised learning model for detecting abnormal triggers.
  We evaluate our prototype and show that it yields a precision of 99.02% to
detect SHSOs among which 29.7% are logic bombs. Difuzer outperforms the
state-of-the-art in revealing more logic bombs while yielding less false
positives in about one order of magnitude less time. All our artifacts are
released to the community.

Real-world networks often come with side information that can help to improve
the performance of network analysis tasks such as clustering. Despite a large
number of empirical and theoretical studies conducted on network clustering
methods during the past decade, the added value of side information and the
methods used to incorporate it optimally in clustering algorithms are
relatively less understood. We propose a new iterative algorithm to cluster
networks with side information for nodes (in the form of covariates) and show
that our algorithm is optimal under the Contextual Symmetric Stochastic Block
Model. Our algorithm can be applied to general Contextual Stochastic Block
Models and avoids hyperparameter tuning in contrast to previously proposed
methods. We confirm our theoretical results on synthetic data experiments where
our algorithm significantly outperforms other methods, and show that it can
also be applied to signed graphs. Finally we demonstrate the practical interest
of our method on real data.

This paper proposes a safe reinforcement learning algorithm for generation
bidding decisions and unit maintenance scheduling in a competitive electricity
market environment. In this problem, each unit aims to find a bidding strategy
that maximizes its revenue while concurrently retaining its reliability by
scheduling preventive maintenance. The maintenance scheduling provides some
safety constraints which should be satisfied at all times. Satisfying the
critical safety and reliability constraints while the generation units have an
incomplete information of each others' bidding strategy is a challenging
problem. Bi-level optimization and reinforcement learning are state of the art
approaches for solving this type of problems. However, neither bi-level
optimization nor reinforcement learning can handle the challenges of incomplete
information and critical safety constraints. To tackle these challenges, we
propose the safe deep deterministic policy gradient reinforcement learning
algorithm which is based on a combination of reinforcement learning and a
predicted safety filter. The case study demonstrates that the proposed approach
can achieve a higher profit compared to other state of the art methods while
concurrently satisfying the system safety constraints.

Learning with noisy labels is an active research area for image
classification. However, the effect of noisy labels on image retrieval has been
less studied. In this work, we propose a noise-resistant method for image
retrieval named Teacher-based Selection of Interactions, T-SINT, which
identifies noisy interactions, ie. elements in the distance matrix, and selects
correct positive and negative interactions to be considered in the retrieval
loss by using a teacher-based training setup which contributes to the
stability. As a result, it consistently outperforms state-of-the-art methods on
high noise rates across benchmark datasets with synthetic noise and more
realistic noise.

Machine learning is used in medicine to support physicians in examination,
diagnosis, and predicting outcomes. One of the most dynamic area is the usage
of patient generated health data from intensive care units. The goal of this
paper is to demonstrate how we advance cross-patient ML model development by
combining the patient's demographics data with their physiological data. We
used a population of patients undergoing Carotid Enderarterectomy (CEA), where
we studied differences in model performance and explainability when trained for
all patients and one patient at a time. The results show that patients'
demographics has a large impact on the performance and explainability and thus
trustworthiness. We conclude that we can increase trust in ML models in a
cross-patient context, by careful selection of models and patients based on
their demographics and the surgical procedure.

We describe a workflow used to analyze the source code of the {\sc Android OS
kernel} and rate for a particular kind of bugginess that exposes a program to
hacking. The workflow represents a novel approach for components' vulnerability
rating. The approach is inspired by recent work on embedding source code
functions. The workflow combines deep learning with heuristics and machine
learning. Deep learning is used to embed function/method labels into a
Euclidean space. Because the corpus of Android kernel source code is rather
limited (containing approximately 2 million C/C++ functions \& Java methods), a
straightforward embedding is untenable. To overcome the challenge of the dearth
of data, it's necessary to go through an intermediate step of the
\textit{Byte-Pair Encoding}. Subsequently, we embed the tokens from which we
assemble an embedding of function/method labels. Long short-term memory
networks (LSTM) are used to embed tokens into vectors in $\mathbb{R}^d$ from
which we form a \textit{cosine matrix} consisting of the cosine between every
pair of vectors. The cosine matrix may be interpreted as a (combinatorial)
`weighted' graph whose vertices represent functions/methods and `weighted'
edges correspond to matrix entries. Features that include function vectors plus
those defined heuristically are used to score for risk of bugginess.

We report our experience of an extracurricular online intervention on
cryptography principles in 10th grade. This paper's first goal is to present
the learning path we designed, influenced by cryptography core ideas rather
than technical knowledge. We will detail how we used Snap! (a visual
programming language) to realize hands-on activities: programming playgrounds
to experiment with cryptosystems and their limits, and interactive support for
an unplugged activity on the Diffie-Hellman key exchange. The second goal is to
evaluate our intervention in terms of both student perceptions and learning of
core cryptography ideas. The students appreciated the course and felt that,
despite being remote, it was fun, interesting, and engaging. They said the
course helped them understand the role of cryptography, CS, and Math in society
and sparked their interest, especially in cryptography and CS. The third goal
is to discuss what worked well and areas of improvement. Pedagogically, remote
teaching caused high "instructor blindness" and prevented us from giving the
optimal amount of guidance during the exploration activities with Snap!
playgrounds, making them sometimes too challenging for total programming
novices. On the other hand, the "remote-unplugged" Diffie-Hellman worked well:
it embodies a coherent metaphor that engaged the students and made them grasp
this groundbreaking protocol. The students praised the activities as engaging,
even when challenging. The final assessment showed that the core cryptography
ideas were well understood.

We show that, contrary to popular assumptions, predictions from machine
learning potentials built upon high-dimensional atom-density representations
almost exclusively occur in regions of the representation space which lie
outside the convex hull defined by the training set points. We then propose a
perspective to rationalize the domain of robust extrapolation and accurate
prediction of atomistic machine learning potentials in terms of the probability
density induced by training points in the representation space

Automatic diagnosis has attracted increasing attention but remains
challenging due to multi-step reasoning. Recent works usually address it by
reinforcement learning methods. However, these methods show low efficiency and
require taskspecific reward functions. Considering the conversation between
doctor and patient allows doctors to probe for symptoms and make diagnoses, the
diagnosis process can be naturally seen as the generation of a sequence
including symptoms and diagnoses. Inspired by this, we reformulate automatic
diagnosis as a symptoms Sequence Generation (SG) task and propose a simple but
effective automatic Diagnosis model based on Transformer (Diaformer). We
firstly design the symptom attention framework to learn the generation of
symptom inquiry and the disease diagnosis. To alleviate the discrepancy between
sequential generation and disorder of implicit symptoms, we further design
three orderless training mechanisms. Experiments on three public datasets show
that our model outperforms baselines on disease diagnosis by 1%, 6% and 11.5%
with the highest training efficiency. Detailed analysis on symptom inquiry
prediction demonstrates that the potential of applying symptoms sequence
generation for automatic diagnosis.

The potential of reinforcement learning (RL) to deliver aligned and
performant agents is partially bottlenecked by the reward engineering problem.
One alternative to heuristic trial-and-error is preference-based RL (PbRL),
where a reward function is inferred from sparse human feedback. However, prior
PbRL methods lack interpretability of the learned reward structure, which
hampers the ability to assess robustness and alignment. We propose an online,
active preference learning algorithm that constructs reward functions with the
intrinsically interpretable, compositional structure of a tree. Using both
synthetic and human-provided feedback, we demonstrate sample-efficient learning
of tree-structured reward functions in several environments, then harness the
enhanced interpretability to explore and debug for alignment.

Traditional ways for handling missing values are not designed for the
clustering purpose and they rarely apply to the general case, though frequent
in practice, of Missing Not At Random (MNAR) values. This paper proposes to
embed MNAR data directly within model-based clustering algorithms. We introduce
a mixture model for different types of data (continuous, count, categorical and
mixed) to jointly model the data distribution and the MNAR mechanism. Eight
different MNAR models are proposed, which may depend on the underlying
(unknown) classes and/or the values of the missing variables themselves. We
prove the identifiability of the parameters of both the data distribution and
the mechanism, whatever the type of data and the mechanism, and propose an EM
or Stochastic EM algorithm to estimate them. The code is available on
\url{https://github.com/AudeSportisse/Clustering-MNAR}.
%\url{https://anonymous.4open.science/r/Clustering-MNAR-0201} We also prove
that MNAR models for which the missingness depends on the class membership have
the nice property that the statistical inference can be carried out on the data
matrix concatenated with the mask by considering a MAR mechanism instead.
Finally, we perform empirical evaluations for the proposed sub-models on
synthetic data and we illustrate the relevance of our method on a medical
register, the TraumaBase$^{\mbox{\normalsize{\textregistered}}}$ dataset.

Quantum state tomography is an essential tool for the characterization and
verification of quantum states. However, as it cannot be directly applied to
systems with more than a few qubits, efficient tomography of larger states on
mid-sized quantum devices remains an important challenge in quantum computing.
We develop a tomography approach that requires moderate computational and
quantum resources for the tomography of states that can be approximated by
Gibbs states of local Hamiltonians. The proposed method, Hamiltonian Learning
Tomography, uses a Hamiltonian learning algorithm to get a parametrized ansatz
for the Gibbs Hamiltonian, and optimizes it with respect to the results of
local measurements. We demonstrate the utility of this method with a high
fidelity reconstruction of the density matrix of 4 to 10 qubits in a Gibbs
state of the transverse-field Ising model, in numerical simulations as well as
in experiments on IBM Quantum superconducting devices accessed via the cloud.

This paper proposes a novel approach to object detection on drone imagery,
namely Multi-Proxy Detection Network with Unified Foreground Packing
(UFPMP-Det). To deal with the numerous instances of very small scales,
different from the common solution that divides the high-resolution input image
into quite a number of chips with low foreground ratios to perform detection on
them each, the Unified Foreground Packing (UFP) module is designed, where the
sub-regions given by a coarse detector are initially merged through clustering
to suppress background and the resulting ones are subsequently packed into a
mosaic for a single inference, thus significantly reducing overall time cost.
Furthermore, to address the more serious confusion between inter-class
similarities and intra-class variations of instances, which deteriorates
detection performance but is rarely discussed, the Multi-Proxy Detection
Network (MP-Det) is presented to model object distributions in a fine-grained
manner by employing multiple proxy learning, and the proxies are enforced to be
diverse by minimizing a Bag-of-Instance-Words (BoIW) guided optimal transport
loss. By such means, UFPMP-Det largely promotes both the detection accuracy and
efficiency. Extensive experiments are carried out on the widely used VisDrone
and UAVDT datasets, and UFPMP-Det reports new state-of-the-art scores at a much
higher speed, highlighting its advantages.

This paper proposes a simple yet efficient high-altitude wind nowcasting
pipeline. It processes efficiently a vast amount of live data recorded by
airplanes over the whole airspace and reconstructs the wind field with good
accuracy. It creates a unique context for each point in the dataset and then
extrapolates from it. As creating such context is computationally intensive,
this paper proposes a novel algorithm that reduces the time and memory cost by
efficiently fetching nearest neighbors in a data set whose elements are
organized along smooth trajectories that can be approximated with piece-wise
linear structures.
  We introduce an efficient and exact strategy implemented through algebraic
tensorial operations, which is well-suited to modern GPU-based computing
infrastructure. This method employs a scalable Euclidean metric and allows
masking data points along one dimension. When applied, this method is more
efficient than plain Euclidean k-NN and other well-known data selection methods
such as KDTrees and provides a several-fold speedup. We provide an
implementation in PyTorch and a novel data set to allow the replication of
empirical results.

Online product reviews are a valuable resource for product developers to
improve the design of their products. Yet, the potential value of customer
feedback to improve the sustainability performance of products is still to be
exploited. The present paper investigates and analyzes Amazon product reviews
to bring new light on the following question: ``What sustainable design
insights can be identified or interpreted from online product reviews?''. To do
so, the top 100 reviews, evenly distributed by star ratings, for three product
categories (laptop, printer, cable) are collected, manually annotated, analyzed
and interpreted. For each product category, the reviews of two similar products
(one with environmental certification and one standard version) are compared
and combined to come up with sustainable design solutions. In all, for the six
products considered, between 12% and 20% of the reviews mentioned directly or
indirectly aspects or attributes that could be exploited to improve the design
of these products from a sustainability perspective. Concrete examples of
sustainable design leads that could be elicited from product reviews are given
and discussed. As such, this contribution provides a baseline for future work
willing to automate this process to gain further insights from online product
reviews. Notably, the deployment of machine learning tools and the use of
natural language processing techniques to do so are discussed as promising
lines for future research.

A design is a collection of distinct points in a given set $X$, which is
assumed to be a compact subset of $R^d$, and the mesh-ratio of a design is the
ratio of its fill distance to its separation radius. The uniformity constant of
a sequence of nested designs is the smallest upper bound for the mesh-ratios of
the designs. We derive a lower bound on this uniformity constant and show that
a simple greedy construction achieves this lower bound. We then extend this
scheme to allow more flexibility in the design construction.

Measuring treatment effects in observational studies is challenging because
of confounding bias. Confounding occurs when a variable affects both the
treatment and the outcome. Traditional methods such as propensity score
matching estimate treatment effects by conditioning on the confounders. Recent
literature has presented new methods that use machine learning to predict the
counterfactuals in observational studies which then allow for estimating
treatment effects. These studies however, have been applied to real world data
where the true treatment effects have not been known. This study aimed to study
the effectiveness of this counterfactual prediction method by simulating two
main scenarios: with and without confounding. Each type also included linear
and non-linear relationships between input and output data. The key item in the
simulations was that we generated known true causal effects. Linear regression,
lasso regression and random forest models were used to predict the
counterfactuals and treatment effects. These were compared these with the true
treatment effect as well as a naive treatment effect. The results show that the
most important factor in whether this machine learning method performs well, is
the degree of non-linearity in the data. Surprisingly, for both non-confounding
\textit{and} confounding, the machine learning models all performed well on the
linear dataset. However, when non-linearity was introduced, the models
performed very poorly. Therefore under the conditions of this simulation study,
the machine learning method performs well under conditions of linearity, even
if confounding is present, but at this stage should not be trusted when
non-linearity is introduced.

In this work, we study a status update system with a source node sending
timely information to the destination through a channel with random delay. We
measure the timeliness of the information stored at the receiver via the Age of
Information (AoI), the time elapsed since the freshest sample stored at the
receiver is generated. The goal is to design a sampling strategy that minimizes
the total cost of the expected time average AoI and sampling cost in the
absence of transmission delay statistics. We reformulate the total cost
minimization problem as the optimization of a renewal-reward process, and
propose an online sampling strategy based on the Robbins-Monro algorithm.
Denote $K$ to be the number of samples we have taken. We show that, when the
transmission delay is bounded, the expected time average total cost obtained by
the proposed online algorithm converges to the minimum cost when $K$ goes to
infinity, and the optimality gap decays with rate $\mathcal{O}\left(\ln
K/K\right)$. Simulation results validate the performance of our proposed

Compared with contact detection techniques, pavement crack identification
with visual images via deep learning algorithms has the advantages of not being
limited by the material of object to be detected, fast speed and low cost. The
fundamental frameworks and typical model architectures of transfer learning
(TL), encoder-decoder (ED), generative adversarial networks (GAN), and their
common modules were first reviewed, and then the evolution of convolutional
neural network (CNN) backbone models and GAN models were summarized. The crack
classification, segmentation performance, and effect were tested on the
SDNET2018 and CFD public data sets. In the aspect of patch sample
classification, the fine-tuned TL models can be equivalent to or even slightly
better than the ED models in accuracy, and the predicting time is faster; In
the aspect of accurate crack location, both ED and GAN algorithms can achieve
pixel-level segmentation and is expected to be detected in real time on low
computing power platform. Furthermore, a weakly supervised learning framework
of combined TL-SSGAN and its performance enhancement measures are proposed,
which can maintain comparable crack identification performance with that of the
supervised learning, while greatly reducing the number of labeled samples
required.

In decentralized learning, a network of nodes cooperate to minimize an
overall objective function that is usually the finite-sum of their local
objectives, and incorporates a non-smooth regularization term for the better
generalization ability. Decentralized stochastic proximal gradient (DSPG)
method is commonly used to train this type of learning models, while the
convergence rate is retarded by the variance of stochastic gradients. In this
paper, we propose a novel algorithm, namely DPSVRG, to accelerate the
decentralized training by leveraging the variance reduction technique. The
basic idea is to introduce an estimator in each node, which tracks the local
full gradient periodically, to correct the stochastic gradient at each
iteration. By transforming our decentralized algorithm into a centralized
inexact proximal gradient algorithm with variance reduction, and controlling
the bounds of error sequences, we prove that DPSVRG converges at the rate of
$O(1/T)$ for general convex objectives plus a non-smooth term with $T$ as the
number of iterations, while DSPG converges at the rate $O(\frac{1}{\sqrt{T}})$.
Our experiments on different applications, network topologies and learning
models demonstrate that DPSVRG converges much faster than DSPG, and the loss
function of DPSVRG decreases smoothly along with the training epochs.

Learning from multimodal data is an important research topic in machine
learning, which has the potential to obtain better representations. In this
work, we propose a novel approach to generative modeling of multimodal data
based on generative adversarial networks. To learn a coherent multimodal
generative model, we show that it is necessary to align different encoder
distributions with the joint decoder distribution simultaneously. To this end,
we construct a specific form of the discriminator to enable our model to
utilize data efficiently, which can be trained constrastively. By taking
advantage of contrastive learning through factorizing the discriminator, we
train our model on unimodal data. We have conducted experiments on the
benchmark datasets, whose promising results show that our proposed approach
outperforms the-state-of-the-art methods on a variety of metrics. The source
code will be made publicly available.

Learning to optimize (L2O) has recently emerged as a promising approach to
solving optimization problems by exploiting the strong prediction power of
neural networks and offering lower runtime complexity than conventional
solvers. While L2O has been applied to various problems, a crucial yet
challenging class of problems -- robust combinatorial optimization in the form
of minimax optimization -- have largely remained under-explored. In addition to
the exponentially large decision space, a key challenge for robust
combinatorial optimization lies in the inner optimization problem, which is
typically non-convex and entangled with outer optimization. In this paper, we
study robust combinatorial optimization and propose a novel learning-based
optimizer, called LRCO (Learning for Robust Combinatorial Optimization), which
quickly outputs a robust solution in the presence of uncertain context. LRCO
leverages a pair of learning-based optimizers -- one for the minimizer and the
other for the maximizer -- that use their respective objective functions as
losses and can be trained without the need of labels for training problem
instances. To evaluate the performance of LRCO, we perform simulations for the
task offloading problem in vehicular edge computing. Our results highlight that
LRCO can greatly reduce the worst-case cost and improve robustness, while
having a very low runtime complexity.

Communication is one of the core components for cooperative multi-agent
reinforcement learning (MARL). The communication bandwidth, in many real
applications, is always subject to certain constraints. To improve
communication efficiency, in this article, we propose to simultaneously
optimize whom to communicate with and what to communicate for each agent in
MARL. By initiating the communication between agents with a directed complete
graph, we propose a novel communication model, named Communicative Graph
Information Bottleneck Network (CGIBNet), to simultaneously compress the graph
structure and the node information with the graph information bottleneck
principle. The graph structure compression is designed to cut the redundant
edges for determining whom to communicate with. The node information
compression aims to address the problem of what to communicate via learning
compact node representations. Moreover, CGIBNet is the first universal module
for bandwidth-constrained communication, which can be applied to various
training frameworks (i.e., policy-based and value-based MARL frameworks) and
communication modes (i.e., single-round and multi-round communication).
Extensive experiments are conducted in Traffic Control and StarCraft II
environments. The results indicate that our method can achieve better
performance in bandwidth-constrained settings compared with state-of-the-art
algorithms, especially for large-scale multi-agent tasks.

Graph representation learning is a fast-growing field where one of the main
objectives is to generate meaningful representations of graphs in
lower-dimensional spaces. The learned embeddings have been successfully applied
to perform various prediction tasks, such as link prediction, node
classification, clustering, and visualization. The collective effort of the
graph learning community has delivered hundreds of methods, but no single
method excels under all evaluation metrics such as prediction accuracy, running
time, scalability, etc. This survey aims to evaluate all major classes of graph
embedding methods by considering algorithmic variations, parameter selections,
scalability, hardware and software platforms, downstream ML tasks, and diverse
datasets. We organized graph embedding techniques using a taxonomy that
includes methods from manual feature engineering, matrix factorization, shallow
neural networks, and deep graph convolutional networks. We evaluated these
classes of algorithms for node classification, link prediction, clustering, and
visualization tasks using widely used benchmark graphs. We designed our
experiments on top of PyTorch Geometric and DGL libraries and run experiments
on different multicore CPU and GPU platforms. We rigorously scrutinize the
performance of embedding methods under various performance metrics and
summarize the results. Thus, this paper may serve as a comparative guide to
help users select methods that are most suitable for their tasks.

Hateful Meme Challenge proposed by Facebook AI has attracted contestants
around the world. The challenge focuses on detecting hateful speech in
multimodal memes. Various state-of-the-art deep learning models have been
applied to this problem and the performance on challenge's leaderboard has also
been constantly improved. In this paper, we enhance the hateful detection
framework, including utilizing Detectron for feature extraction, exploring
different setups of VisualBERT and UNITER models with different loss functions,
researching the association between the hateful memes and the sensitive text
features, and finally building ensemble method to boost model performance. The
AUROC of our fine-tuned VisualBERT, UNITER, and ensemble method achieves 0.765,
0.790, and 0.803 on the challenge's test set, respectively, which beats the
baseline models. Our code is available at
https://github.com/yatingtian/hateful-meme

Local-to-global learning approach plays an essential role in Bayesian network
(BN) structure learning. Existing local-to-global learning algorithms first
construct the skeleton of a DAG (directed acyclic graph) by learning the MB
(Markov blanket) or PC (parents and children) of each variable in a data set,
then orient edges in the skeleton. However, existing MB or PC learning methods
are often computationally expensive especially with a large-sized BN, resulting
in inefficient local-to-global learning algorithms. To tackle the problem, in
this paper, we develop an efficient local-to-global learning approach using
feature selection. Specifically, we first analyze the rationale of the
well-known Minimum-Redundancy and Maximum-Relevance (MRMR) feature selection
approach for learning a PC set of a variable. Based on the analysis, we propose
an efficient F2SL (feature selection-based structure learning) approach to
local-to-global BN structure learning. The F2SL approach first employs the MRMR
approach to learn a DAG skeleton, then orients edges in the skeleton. Employing
independence tests or score functions for orienting edges, we instantiate the
F2SL approach into two new algorithms, F2SL-c (using independence tests) and
F2SL-s (using score functions). Compared to the state-of-the-art
local-to-global BN learning algorithms, the experiments validated that the
proposed algorithms in this paper are more efficient and provide competitive
structure learning quality than the compared algorithms.

Due to the irregular shapes,various sizes and indistinguishable boundaries
between the normal and infected tissues, it is still a challenging task to
accurately segment the infected lesions of COVID-19 on CT images. In this
paper, a novel segmentation scheme is proposed for the infections of COVID-19
by enhancing supervised information and fusing multi-scale feature maps of
different levels based on the encoder-decoder architecture. To this end, a deep
collaborative supervision (Co-supervision) scheme is proposed to guide the
network learning the features of edges and semantics. More specifically, an
Edge Supervised Module (ESM) is firstly designed to highlight low-level
boundary features by incorporating the edge supervised information into the
initial stage of down-sampling. Meanwhile, an Auxiliary Semantic Supervised
Module (ASSM) is proposed to strengthen high-level semantic information by
integrating mask supervised information into the later stage. Then an Attention
Fusion Module (AFM) is developed to fuse multiple scale feature maps of
different levels by using an attention mechanism to reduce the semantic gaps
between high-level and low-level feature maps. Finally, the effectiveness of
the proposed scheme is demonstrated on four various COVID-19 CT datasets. The
results show that the proposed three modules are all promising. Based on the
baseline (ResUnet), using ESM, ASSM, or AFM alone can respectively increase
Dice metric by 1.12\%, 1.95\%,1.63\% in our dataset, while the integration by
incorporating three models together can rise 3.97\%. Compared with the existing
approaches in various datasets, the proposed method can obtain better
segmentation performance in some main metrics, and can achieve the best
generalization and comprehensive performance.

Human motion prediction is an important and challenging task in many computer
vision application domains. Recent work concentrates on utilizing the timing
processing ability of recurrent neural networks (RNNs) to achieve smooth and
reliable results in short-term prediction. However, as evidenced by previous
work, RNNs suffer from errors accumulation, leading to unreliable results. In
this paper, we propose a simple feed-forward deep neural network for motion
prediction, which takes into account temporal smoothness and spatial
dependencies between human body joints. We design a Multi-scale Spatio-temporal
graph convolutional networks (GCNs) to implicitly establish the Spatio-temporal
dependence in the process of human movement, where different scales fused
dynamically during training. The entire model is suitable for all actions and
follows a framework of encoder-decoder. The encoder consists of temporal GCNs
to capture motion features between frames and semi-autonomous learned spatial
GCNs to extract spatial structure among joint trajectories. The decoder uses
temporal convolution networks (TCNs) to maintain its extensive ability.
Extensive experiments show that our approach outperforms SOTA methods on the
datasets of Human3.6M and CMU Mocap while only requiring much lesser
parameters. Code will be available at https://github.com/yzg9353/DMSGCN.

Quantum computing has a superior advantage in tackling specific problems,
such as integer factorization and Simon's problem. For more general tasks in
machine learning, by applying variational quantum circuits, more and more
quantum algorithms have been proposed recently, especially in supervised
learning and unsupervised learning. However, little work has been done in
reinforcement learning, arguably more important and challenging. Previous work
in quantum reinforcement learning mainly focuses on discrete control tasks
where the action space is discrete. In this work, we develop a quantum
reinforcement learning algorithm based on soft actor-critic -- one of the
state-of-the-art methods for continuous control. Specifically, we use a hybrid
quantum-classical policy network consisting of a variational quantum circuit
and a classical artificial neural network. Tested in a standard reinforcement
learning benchmark, we show that this quantum version of soft actor-critic is
comparable with the original soft actor-critic, using much less adjustable
parameters. Furthermore, we analyze the effect of different hyper-parameters
and policy network architectures, pointing out the importance of architecture
design for quantum reinforcement learning.

In programming, learning code representations has a variety of applications,
including code classification, code search, comment generation, bug prediction,
and so on. Various representations of code in terms of tokens, syntax trees,
dependency graphs, code navigation paths, or a combination of their variants
have been proposed, however, existing vanilla learning techniques have a major
limitation in robustness, i.e., it is easy for the models to make incorrect
predictions when the inputs are altered in a subtle way. To enhance the
robustness, existing approaches focus on recognizing adversarial samples rather
than on the valid samples that fall outside a given distribution, which we
refer to as out-of-distribution (OOD) samples. Recognizing such OOD samples is
the novel problem investigated in this paper. To this end, we propose to first
augment the in=distribution datasets with out-of-distribution samples such
that, when trained together, they will enhance the model's robustness. We
propose the use of an energy-bounded learning objective function to assign a
higher score to in-distribution samples and a lower score to
out-of-distribution samples in order to incorporate such out-of-distribution
samples into the training process of source code models. In terms of OOD
detection and adversarial samples detection, our evaluation results demonstrate
a greater robustness for existing source code models to become more accurate at
recognizing OOD data while being more resistant to adversarial attacks at the
same time. Furthermore, the proposed energy-bounded score outperforms all
existing OOD detection scores by a large margin, including the softmax
confidence score, the Mahalanobis score, and ODIN.

The main target of retrosynthesis is to recursively decompose desired
molecules into available building blocks. Existing template-based
retrosynthesis methods follow a template selection stereotype and suffer from
the limited training templates, which prevents them from discovering novel
reactions. To overcome the limitation, we propose an innovative retrosynthesis
prediction framework that can compose novel templates beyond training
templates. So far as we know, this is the first method that can find novel
templates for retrosynthesis prediction. Besides, we propose an effective
reactant candidates scoring model that can capture atom-level transformation
information, and it helps our method outperform existing methods by a large
margin. Experimental results show that our method can produce novel templates
for 328 test reactions in the USPTO-50K dataset, including 21 test reactions
that are not covered by the training templates.

We examined the feasibility of generative adversarial networks (GANs) to
generate photo-realistic images from LiDAR point clouds. For this purpose, we
created a dataset of point cloud image pairs and trained the GAN to predict
photorealistic images from LiDAR point clouds containing reflectance and
distance information. Our models learned how to predict realistically looking
images from just point cloud data, even images with black cars. Black cars are
difficult to detect directly from point clouds because of their low level of
reflectivity. This approach might be used in the future to perform visual
object recognition on photorealistic images generated from LiDAR point clouds.
In addition to the conventional LiDAR system, a second system that generates
photorealistic images from LiDAR point clouds would run simultaneously for
visual object recognition in real-time. In this way, we might preserve the
supremacy of LiDAR and benefit from using photo-realistic images for visual
object recognition without the usage of any camera. In addition, this approach
could be used to colorize point clouds without the usage of any camera images.

Closed-loop irrigation can deliver a promising solution for precision
irrigation. The accurate soil moisture (state) estimation is critical in
implementing the closed-loop irrigation of agrohydrological systems. In
general, the agricultural fields are high dimensional systems. Due to the high
dimensionality for a large field, it is very challenging to solve an
optimizationbased advanced state estimator like moving horizon estimation
(MHE). This work addresses the aforementioned challenge and proposes a
systematic approach for state estimation of large agricultural fields. We use a
non-linear state-space model based on discretization of the cylindrical
coordinate version of Richards equation to describe the agro-hydrological
systems equipped with a central pivot irrigation system. We propose a
structure-preserving adaptive model reduction method using trajectory-based
unsupervised machine learning techniques. Furthermore, the adaptive MHE
algorithm is developed based on an adaptive reduced model. The proposed
algorithms are applied to a small simulated field to compare the performance of
adaptive MHE over original MHE. Finally, the proposed approach is applied to a
large-scale real agricultural field to test the effectiveness and superiority
to address the current challenges. Extensive simulations are carried out to
show the efficiency of the proposed approach.

Key to being able to accurately model the properties of realistic materials
is being able to predict their properties in the thermodynamic limit.
Nevertheless, because most many-body electronic structure methods scale as a
high order polynomial, or even exponentially, with system size, directly
simulating large systems in their thermodynamic limit rapidly becomes
computationally intractable. As a result, researchers typically estimate the
properties of large systems that approach the thermodynamic limit by
extrapolating the properties of smaller, computationally-accessible systems
based on relatively simple scaling expressions. In this work, we employ
Gaussian processes to more accurately and efficiently extrapolate many-body
simulations to their thermodynamic limit. We train our Gaussian processes on
Smooth Overlap of Atomic Positions (SOAP) descriptors to extrapolate the
energies of one-dimensional hydrogen chains obtained using two many-body
methods: Coupled Cluster theory and Auxiliary Field Quantum Monte Carlo
(AFQMC). In so doing, we show that Gaussian processes trained on relatively
short, 10-30-atom chains can predict the energies of hydrogen chains in their
thermodynamic limit with sub-milliHartree accuracy. Unlike standard scaling
expressions, our GPR-based approach is highly generalizable given
representative training data and is not dependent on systems' geometries or
dimensionality. This work highlights the potential for machine learning to
correct for the finite size effects that routinely complicate the
interpretation of finite size many-body simulations.

This paper provides both an introduction to and a detailed overview of the
principles and practice of classifier calibration. A well-calibrated classifier
correctly quantifies the level of uncertainty or confidence associated with its
instance-wise predictions. This is essential for critical applications, optimal
decision making, cost-sensitive classification, and for some types of context
change. Calibration research has a rich history which predates the birth of
machine learning as an academic field by decades. However, a recent increase in
the interest on calibration has led to new methods and the extension from
binary to the multiclass setting. The space of options and issues to consider
is large, and navigating it requires the right set of concepts and tools. We
provide both introductory material and up-to-date technical details of the main
concepts and methods, including proper scoring rules and other evaluation
metrics, visualisation approaches, a comprehensive account of post-hoc
calibration methods for binary and multiclass classification, and several
advanced topics.

Due to the constraints of the imaging device and high cost in operation time,
computer tomography (CT) scans are usually acquired with low intra-slice
resolution. Improving the intra-slice resolution is beneficial to the disease
diagnosis for both human experts and computer-aided systems. To this end, this
paper builds a novel medical slice synthesis to increase the between-slice
resolution. Considering that the ground-truth intermediate medical slices are
always absent in clinical practice, we introduce the incremental cross-view
mutual distillation strategy to accomplish this task in the self-supervised
learning manner. Specifically, we model this problem from three different
views: slice-wise interpolation from axial view and pixel-wise interpolation
from coronal and sagittal views. Under this circumstance, the models learned
from different views can distill valuable knowledge to guide the learning
processes of each other. We can repeat this process to make the models
synthesize intermediate slice data with increasing inter-slice resolution. To
demonstrate the effectiveness of the proposed approach, we conduct
comprehensive experiments on a large-scale CT dataset. Quantitative and
qualitative comparison results show that our method outperforms
state-of-the-art algorithms by clear margins.

Transliteration is a task of translating named entities from a language to
another, based on phonetic similarity. The task has embraced deep learning
approaches in recent years, yet, most ignore the phonetic features of the
involved languages. In this work, we incorporate phonetic information into
neural networks in two ways: we synthesize extra data using forward and
back-translation but in a phonetic manner; and we pre-train models on a
phonetic task before learning transliteration. Our experiments include three
language pairs and six directions, namely English to and from Chinese, Hebrew
and Thai. Results indicate that our proposed approach brings benefits to the
model and achieves better or similar performance when compared to state of the
art.

We propose a novel software service recommendation model to help users find
their suitable repositories in GitHub. Our model first designs a novel
context-induced repository graph embedding method to leverage rich contextual
information of repositories to alleviate the difficulties caused by the data
sparsity issue. It then leverages sequence information of user-repository
interactions for the first time in the software service recommendation field.
Specifically, a deep-learning based sequential recommendation technique is
adopted to capture the dynamics of user preferences. Comprehensive experiments
have been conducted on a large dataset collected from GitHub against a list of
existing methods. The results illustrate the superiority of our method in
various aspects.

Inspired by the multi-head attention (MHA) mechanism in natural language
processing, this letter proposes an iterative single-head attention (ISHA)
mechanism for multi-UAV path planning. The ISHA mechanism is run by a
communication helper collecting the state embeddings of UAVs and distributing
an attention score vector to each UAV. The attention scores computed by ISHA
identify how many interactions with other UAVs should be considered in each
UAV's control decision-making. Simulation results corroborate that the
ISHA-based communication and control framework achieves faster travel with
lower inter-UAV collision risks than an MHA-aided baseline, particularly under
limited communication resources.

We study the problem of guaranteeing low regret in repeated games against an
opponent with unknown membership in one of several classes. We add the
constraint that our algorithm is non-exploitable, in that the opponent lacks an
incentive to use an algorithm against which we cannot achieve rewards exceeding
some "fair" value. Our solution is an expert algorithm (LAFF) that searches
within a set of sub-algorithms that are optimal for each opponent class and
uses a punishment policy upon detecting evidence of exploitation by the
opponent. With benchmarks that depend on the opponent class, we show that LAFF
has sublinear regret uniformly over the possible opponents, except exploitative
ones, for which we guarantee that the opponent has linear regret. To our
knowledge, this work is the first to provide guarantees for both regret and
non-exploitability in multi-agent learning.

Federated edge learning (FEEL) has attracted much attention as a
privacy-preserving paradigm to effectively incorporate the distributed data at
the network edge for training deep learning models. Nevertheless, the limited
coverage of a single edge server results in an insufficient number of
participated client nodes, which may impair the learning performance. In this
paper, we investigate a novel framework of FEEL, namely semi-decentralized
federated edge learning (SD-FEEL), where multiple edge servers are employed to
collectively coordinate a large number of client nodes. By exploiting the
low-latency communication among edge servers for efficient model sharing,
SD-FEEL can incorporate more training data, while enjoying much lower latency
compared with conventional federated learning. We detail the training algorithm
for SD-FEEL with three main steps, including local model update, intra-cluster,
and inter-cluster model aggregations. The convergence of this algorithm is
proved on non-independent and identically distributed (non-IID) data, which
also helps to reveal the effects of key parameters on the training efficiency
and provides practical design guidelines. Meanwhile, the heterogeneity of edge
devices may cause the straggler effect and deteriorate the convergence speed of
SD-FEEL. To resolve this issue, we propose an asynchronous training algorithm
with a staleness-aware aggregation scheme for SD-FEEL, of which, the
convergence performance is also analyzed. The simulation results demonstrate
the effectiveness and efficiency of the proposed algorithms for SD-FEEL and
corroborate our analysis.

Most modern face completion approaches adopt an autoencoder or its variants
to restore missing regions in face images. Encoders are often utilized to learn
powerful representations that play an important role in meeting the challenges
of sophisticated learning tasks. Specifically, various kinds of masks are often
presented in face images in the wild, forming complex patterns, especially in
this hard period of COVID-19. It's difficult for encoders to capture such
powerful representations under this complex situation. To address this
challenge, we propose a self-supervised Siamese inference network to improve
the generalization and robustness of encoders. It can encode contextual
semantics from full-resolution images and obtain more discriminative
representations. To deal with geometric variations of face images, a dense
correspondence field is integrated into the network. We further propose a
multi-scale decoder with a novel dual attention fusion module (DAF), which can
combine the restored and known regions in an adaptive manner. This multi-scale
architecture is beneficial for the decoder to utilize discriminative
representations learned from encoders into images. Extensive experiments
clearly demonstrate that the proposed approach not only achieves more appealing
results compared with state-of-the-art methods but also improves the
performance of masked face recognition dramatically.

Accurate diagnostics of a skin lesion is a critical task in classification
dermoscopic images. In this research, we form a new type of image features,
called hybrid features, which has stronger discrimination ability than single
method features. This study involves a new technique where we inject the
handcrafted features or feature transfer into the fully connected layer of
Convolutional Neural Network (CNN) model during the training process. Based on
our literature review until now, no study has examined or investigated the
impact on classification performance by injecting the handcrafted features into
the CNN model during the training process. In addition, we also investigated
the impact of segmentation mask and its effect on the overall classification
performance. Our model achieves an 92.3% balanced multiclass accuracy, which is
6.8% better than the typical single method classifier architecture for deep
learning.

Tensor robust principal component analysis (TRPCA) is a fundamental model in
machine learning and computer vision. Recently, tensor train (TT) decomposition
has been verified effective to capture the global low-rank correlation for
tensor recovery tasks. However, due to the large-scale tensor data in
real-world applications, previous TRPCA models often suffer from high
computational complexity. In this letter, we propose an efficient TRPCA under
hybrid model of Tucker and TT. Specifically, in theory we reveal that TT
nuclear norm (TTNN) of the original big tensor can be equivalently converted to
that of a much smaller tensor via a Tucker compression format, thereby
significantly reducing the computational cost of singular value decomposition
(SVD). Numerical experiments on both synthetic and real-world tensor data
verify the superiority of the proposed model.

Energy dependence of specific heat extracted from temperature fluctuation of
Au + Au collisions at $\sqrt{s_{NN}}$ = 7.7 GeV to 200 GeV was investigated by
using a multiphase transport (AMPT) model. The results were compared with those
from other models and some differences at low $\sqrt{s_{NN}}$ were found. To
explain the above differences and describe the properties of the hot dense
matter at low $\sqrt{s_{NN}}$, a new quantity $C_v^{*}$ was derived for
describing specific heat in heavy-ion collisions. It was found that, by using
$C_v^{*}$ together with its high order moments (skewness and kurtosis), thermal
properties of the hot dense matter can be described and different thermal
properties with or without parton process can be clearly distinguished. The
proposed observable provides a way to learn the property of QCD matter % phase
transition %along with critical point in heavy-ion collisions.

Driver drowsiness detection using videos/images is one of the most essential
areas in today's time for driver safety. The development of deep learning
techniques, notably Convolutional Neural Networks (CNN), applied in computer
vision applications such as drowsiness detection, has shown promising results
due to the tremendous increase in technology in the recent few decades. Eyes
that are closed or blinking excessively, yawning, nodding, and occlusion are
all key aspects of drowsiness. In this work, we have applied four different
Convolutional Neural Network (CNN) techniques on the YawDD dataset to detect
and examine the extent of drowsiness depending on the yawning frequency with
specific pose and occlusion variation. Preliminary computational results show
that our proposed Ensemble Convolutional Neural Network (ECNN) outperformed the
traditional CNN-based approach by achieving an F1 score of 0.935, whereas the
other three CNN, such as CNN1, CNN2, and CNN3 approaches gained 0.92, 0.90, and
0.912 F1 scores, respectively.

Machine learning models are updated as new data is acquired or new
architectures are developed. These updates usually increase model performance,
but may introduce backward compatibility errors, where individual users or
groups of users see their performance on the updated model adversely affected.
This problem can also be present when training datasets do not accurately
reflect overall population demographics, with some groups having overall lower
participation in the data collection process, posing a significant fairness
concern. We analyze how ideas from distributional robustness and minimax
fairness can aid backward compatibility in this scenario, and propose two
methods to directly address this issue. Our theoretical analysis is backed by
experimental results on CIFAR-10, CelebA, and Waterbirds, three standard image
classification datasets. Code available at github.com/natalialmg/GroupBC

The scientific interest in studying high-energy transient phenomena in the
Universe has largely grown for the last decade. Now, multiple ground-based
survey projects have emerged to continuously monitor the optical (and
multi-messenger) transient sky at higher image cadences and cover always larger
portions of the sky every night. These novel approaches lead to a huge increase
of the global alert rates which need to be handled with care especially by
keeping the false alarms as low as possible. Therefore, the standard transient
detection pipelines previously designed for narrow field of view instruments
must now integrate more sophisticated tools to deal with the growing number and
diversity of alerts and false alarms. Deep machine learning algorithms have now
proven their efficiency in recognizing patterns in images. We explore this
method to provide a robust and flexible algorithm that could be included in any
kind of transient detection pipeline. We built a Convolutional Neural Network
(CNN) algorithm in order to perform a real/bogus classification task on
transient candidate cutouts (subtraction residuals) provided by different kinds
of optical telescopes. The training involved human-supervised labeling of the
cutouts, which had been split in two balanced data sets with \textit{True} and
\textit{False} point-like source candidates. We tested our CNN model on the
candidates produced by two different transient detection pipelines. We show
that our CNN algorithm can be successfully trained on a large diversity of
images having very different pixel scales. Tested on optical images from four
different telescopes and utilising two different transient detection pipelines,
our CNN model provides robust real/bogus classification performance accuracy
from 93% up to 98% of well classified candidates.

Keypoint detection plays an important role in a wide range of applications.
However, predicting keypoints of small objects such as human hands is a
challenging problem. Recent works fuse feature maps of deep Convolutional
Neural Networks (CNNs), either via multi-level feature integration or
multi-resolution aggregation. Despite achieving some success, the feature
fusion approaches increase the complexity and the opacity of CNNs. To address
this issue, we propose a novel CNN model named Multi-Scale Deep Supervision
Network (P-MSDSNet) that learns feature maps at different scales with deep
supervisions to produce attention maps for adaptive feature propagation from
layers to layers. P-MSDSNet has a multi-stage architecture which makes it
scalable while its deep supervision with spatial attention improves
transparency to the feature learning at each stage. We show that P-MSDSNet
outperforms the state-of-the-art approaches on benchmark datasets while
requiring fewer number of parameters. We also show the application of P-MSDSNet
to quantify finger tapping hand movements in a neuroscience study.

Online reviews enable consumers to engage with companies and provide
important feedback. Due to the complexity of the high-dimensional text, these
reviews are often simplified as a single numerical score, e.g., ratings or
sentiment scores. This work empirically examines the causal effects of
user-generated online reviews on a granular level: we consider multiple
aspects, e.g., the Food and Service of a restaurant. Understanding consumers'
opinions toward different aspects can help evaluate business performance in
detail and strategize business operations effectively. Specifically, we aim to
answer interventional questions such as What will the restaurant popularity be
if the quality w.r.t. its aspect Service is increased by 10%? The defining
challenge of causal inference with observational data is the presence of
"confounder", which might not be observed or measured, e.g., consumers'
preference to food type, rendering the estimated effects biased and
high-variance. To address this challenge, we have recourse to the multi-modal
proxies such as the consumer profile information and interactions between
consumers and businesses. We show how to effectively leverage the rich
information to identify and estimate causal effects of multiple aspects
embedded in online reviews. Empirical evaluations on synthetic and real-world
data corroborate the efficacy and shed light on the actionable insight of the

Blind deconvolution is an ill-posed problem arising in various fields ranging
from microscopy to astronomy. The ill-posed nature of the problem requires
adequate priors to arrive to a desirable solution. Recently, it has been shown
that deep learning architectures can serve as an image generation prior during
unsupervised blind deconvolution optimization, however often exhibiting a
performance fluctuation even on a single image. We propose to use
Wiener-deconvolution to guide the image generator during optimization by
providing it a sharpened version of the blurry image using an auxiliary kernel
estimate starting from a Gaussian. We observe that the high-frequency artifacts
of deconvolution are reproduced with a delay compared to low-frequency
features. In addition, the image generator reproduces low-frequency features of
the deconvolved image faster than that of a blurry image. We embed the
computational process in a constrained optimization framework and show that the
proposed method yields higher stability and performance across multiple
datasets. In addition, we provide the code.

Few Bayesian methods for analyzing high-dimensional sparse survival data
provide scalable variable selection, effect estimation and uncertainty
quantification. Such methods often either sacrifice uncertainty quantification
by computing maximum a posteriori estimates, or quantify the uncertainty at
high (unscalable) computational expense. We bridge this gap and develop an
interpretable and scalable Bayesian proportional hazards model for prediction
and variable selection, referred to as SVB. Our method, based on a mean-field
variational approximation, overcomes the high computational cost of MCMC whilst
retaining useful features, providing a posterior distribution for the
parameters and offering a natural mechanism for variable selection via
posterior inclusion probabilities. The performance of our proposed method is
assessed via extensive simulations and compared against other state-of-the-art
Bayesian variable selection methods, demonstrating comparable or better
performance. Finally, we demonstrate how the proposed method can be used for
variable selection on two transcriptomic datasets with censored survival
outcomes, and how the uncertainty quantification offered by our method can be
used to provide an interpretable assessment of patient risk.

We develop a probabilistic framework for analysing model-based reinforcement
learning in the episodic setting. We then apply it to study finite-time horizon
stochastic control problems with linear dynamics but unknown coefficients and
convex, but possibly irregular, objective function. Using probabilistic
representations, we study regularity of the associated cost functions and
establish precise estimates for the performance gap between applying optimal
feedback control derived from estimated and true model parameters. We identify
conditions under which this performance gap is quadratic, improving the linear
performance gap in recent work [X. Guo, A. Hu, and Y. Zhang, arXiv preprint,
arXiv:2104.09311, (2021)], which matches the results obtained for stochastic
linear-quadratic problems. Next, we propose a phase-based learning algorithm
for which we show how to optimise exploration-exploitation trade-off and
achieve sublinear regrets in high probability and expectation. When assumptions
needed for the quadratic performance gap hold, the algorithm achieves an order
$\mathcal{O}(\sqrt{N} \ln N)$ high probability regret, in the general case, and
an order $\mathcal{O}((\ln N)^2)$ expected regret, in self-exploration case,
over $N$ episodes, matching the best possible results from the literature. The
analysis requires novel concentration inequalities for correlated
continuous-time observations, which we derive.

This work details a highly efficient implementation of the 3D scale-invariant
feature transform (SIFT) algorithm, for the purpose of machine learning from
large sets of volumetric medical image data. The primary operations of the 3D
SIFT code are implemented on a graphics processing unit (GPU), including
convolution, sub-sampling, and 4D peak detection from scale-space pyramids. The
performance improvements are quantified in keypoint detection and
image-to-image matching experiments, using 3D MRI human brain volumes of
different people. Computationally efficient 3D keypoint descriptors are
proposed based on the Binary Robust Independent Elementary Feature (BRIEF)
code, including a novel descriptor we call Ranked Robust Independent Elementary
Features (RRIEF), and compared to the original 3D SIFT-Rank
method\citep{toews2013efficient}. The GPU implementation affords a speedup of
approximately 7X beyond an optimised CPU implementation, where computation time
is reduced from 1.4 seconds to 0.2 seconds for 3D volumes of size (145, 174,
145) voxels with approximately 3000 keypoints. Notable speedups include the
convolution operation (20X), 4D peak detection (3X), sub-sampling (3X), and
difference-of-Gaussian pyramid construction (2X). Efficient descriptors offer a
speedup of 2X and a memory savings of 6X compared to standard SIFT-Rank
descriptors, at a cost of reduced numbers of keypoint correspondences,
revealing a trade-off between computational efficiency and algorithmic
performance. The speedups gained by our implementation will allow for a more
efficient analysis on larger data sets. Our optimized GPU implementation of the
3D SIFT-Rank extractor is available at
https://github.com/CarluerJB/3D_SIFT_CUDA.

While semantic communications have shown the potential in the case of
single-modal single-users, its applications to the multi-user scenario remain
limited. In this paper, we investigate deep learning (DL) based multi-user
semantic communication systems for transmitting single-modal data and
multimodal data, respectively. We will adopt three intelligent tasks,
including, image retrieval, machine translation, and visual question answering
(VQA) as the transmission goal of semantic communication systems. We will then
propose a Transformer based unique framework to unify the structure of
transmitters for different tasks. For the single-modal multi-user system, we
will propose two Transformer based models, named, DeepSC-IR and DeepSC-MT, to
perform image retrieval and machine translation, respectively. In this case,
DeepSC-IR is trained to optimize the distance in embedding space between images
and DeepSC-MT is trained to minimize the semantic errors by recovering the
semantic meaning of sentences. For the multimodal multi-user system, we develop
a Transformer enabled model, named, DeepSC-VQA, for the VQA task by extracting
text-image information at the transmitters and fusing it at the receiver. In
particular, a novel layer-wise Transformer is designed to help fuse multimodal
data by adding connection between each of the encoder and decoder layers.
Numerical results will show that the proposed models are superior to
traditional communications in terms of the robustness to channels,
computational complexity, transmission delay, and the task-execution
performance at various task-specific metrics.

Deep learning (DL) inverse techniques have increased the speed of artificial
electromagnetic material (AEM) design and improved the quality of resulting
devices. Many DL inverse techniques have succeeded on a number of AEM design
tasks, but to compare, contrast, and evaluate assorted techniques it is
critical to clarify the underlying ill-posedness of inverse problems. Here we
review state-of-the-art approaches and present a comprehensive survey of deep
learning inverse methods and invertible and conditional invertible neural
networks to AEM design. We produce easily accessible and rapidly implementable
AEM design benchmarks, which offers a methodology to efficiently determine the
DL technique best suited to solving different design challenges. Our
methodology is guided by constraints on repeated simulation and an easily
integrated metric, which we propose expresses the relative ill-posedness of any
AEM design problem. We show that as the problem becomes increasingly ill-posed,
the neural adjoint with boundary loss (NA) generates better solutions faster,
regardless of simulation constraints. On simpler AEM design tasks, direct
neural networks (NN) fare better when simulations are limited, while geometries
predicted by mixture density networks (MDN) and conditional variational
auto-encoders (VAE) can improve with continued sampling and re-simulation.

In this paper, we present SSDNet, a novel deep learning approach for time
series forecasting. SSDNet combines the Transformer architecture with state
space models to provide probabilistic and interpretable forecasts, including
trend and seasonality components and previous time steps important for the
prediction. The Transformer architecture is used to learn the temporal patterns
and estimate the parameters of the state space model directly and efficiently,
without the need for Kalman filters. We comprehensively evaluate the
performance of SSDNet on five data sets, showing that SSDNet is an effective
method in terms of accuracy and speed, outperforming state-of-the-art deep
learning and statistical methods, and able to provide meaningful trend and
seasonality components.

Simulation is essential for developing quantum hardware and algorithms.
However, simulating quantum circuits on classical hardware is challenging due
to the exponential scaling of quantum state space. While factorized tensors can
greatly reduce this overhead, tensor network-based simulators are relatively
few and often lack crucial functionalities. To address this deficiency, we
created TensorLy-Quantum, a Python library for quantum circuit simulation that
adopts the PyTorch API. Our library leverages the optimized tensor methods of
the existing TensorLy ecosystem to represent, simulate, and manipulate
large-scale quantum circuits. Through compact tensor representations and
efficient operations, TensorLy-Quantum can scale to hundreds of qubits on a
single GPU and thousands of qubits on multiple GPUs. TensorLy-Quantum is
open-source and accessible at https://github.com/tensorly/quantum

Prediction, where observed data is used to quantify uncertainty about a
future observation, is a fundamental problem in statistics. Prediction sets
with coverage probability guarantees are a common solution, but these do not
provide probabilistic uncertainty quantification in the sense of assigning
beliefs to relevant assertions about the future observable. Alternatively, we
recommend the use of a {\em probabilistic predictor}, a data-dependent
(imprecise) probability distribution for the to-be-predicted observation given
the observed data. It is essential that the probabilistic predictor be reliable
or valid, and here we offer a notion of validity and explore its behavioral and
statistical implications. In particular, we show that valid probabilistic
predictors must be imprecise, that they avoid sure loss, and that they lead to
prediction procedures with desirable frequentist error rate control properties.
We provide a general construction of a provably valid probabilistic predictor,
which has close connections to the powerful conformal prediction machinery, and
we illustrate this construction in regression and classification applications.

Existing frameworks for probabilistic inference assume the quantity of
interest is the parameter of a posited statistical model. In machine learning
applications, however, often there is no statistical model/parameter; the
quantity of interest is a statistical functional, a feature of the underlying
distribution. Model-based methods can only handle such problems indirectly, via
marginalization from a model parameter to the real quantity of interest. Here
we develop a generalized inferential model (IM) framework for direct
probabilistic uncertainty quantification on the quantity of interest. In
particular, we construct a data-dependent, bootstrap-based possibility measure
for uncertainty quantification and inference. We then prove that this new
approach provides approximately valid inference in the sense that the
plausibility values assigned to hypotheses about the unknowns are
asymptotically well-calibrated in a frequentist sense. Among other things, this
implies that confidence regions for the underlying functional derived from our
proposed IM are approximately valid. The method is shown to perform well in key
examples, including quantile regression, and in a personalized medicine

The increasing CO2 level is a critical concern and suitable materials are
needed to capture such gases from the environment. While experimental and
conventional computational methods are useful in finding such materials, they
are usually slow and there is a need to expedite such processes. We use
Atomistic Line Graph Neural Network (ALIGNN) method to predict CO2 adsorption
in metal organic frameworks (MOF), which are known for their high functional
tunability. We train ALIGNN models for hypothetical MOF (hMOF) database with
137953 MOFs with grand canonical Monte Carlo (GCMC) based CO2 adsorption
isotherms. We develop high accuracy and fast models for pre-screening
applications. We apply the trained model on CoREMOF database and
computationally rank them for experimental synthesis. In addition to the CO2
adsorption isotherm, we also train models for electronic bandgaps, surface
area, void fraction, lowest cavity diameter, and pore limiting diameter, and
illustrate the strength and limitation of such graph neural network models. For
a few candidate MOFs we carry out GCMC calculations to evaluate the
deep-learning (DL) predictions.

Neural networks (NNs) are making a large impact both on research and
industry. Nevertheless, as NNs' accuracy increases, it is followed by an
expansion in their size, required number of compute operations and energy
consumption. Increase in resource consumption results in NNs' reduced adoption
rate and real-world deployment impracticality. Therefore, NNs need to be
compressed to make them available to a wider audience and at the same time
decrease their runtime costs. In this work, we approach this challenge from a
causal inference perspective, and we propose a scoring mechanism to facilitate
structured pruning of NNs. The approach is based on measuring mutual
information under a maximum entropy perturbation, sequentially propagated
through the NN. We demonstrate the method's performance on two datasets and
various NNs' sizes, and we show that our approach achieves competitive
performance under challenging conditions.

When one observes a sequence of variables $(x_1, y_1), ..., (x_n, y_n)$,
conformal prediction is a methodology that allows to estimate a confidence set
for $y_{n+1}$ given $x_{n+1}$ by merely assuming that the distribution of the
data is exchangeable. While appealing, the computation of such set turns out to
be infeasible in general, e.g. when the unknown variable $y_{n+1}$ is
continuous. In this paper, we combine conformal prediction techniques with
algorithmic stability bounds to derive a prediction set computable with a
single model fit. We perform some numerical experiments that illustrate the
tightness of our estimation when the sample size is sufficiently large.

We develop a family of parallel algorithms for the SpKAdd operation that adds
a collection of k sparse matrices. SpKAdd is a much needed operation in many
applications including distributed memory sparse matrix-matrix multiplication
(SpGEMM), streaming accumulations of graphs, and algorithmic sparsification of
the gradient updates in deep learning. While adding two sparse matrices is a
common operation in Matlab, Python, Intel MKL, and various GraphBLAS libraries,
these implementations do not perform well when adding a large collection of
sparse matrices. We develop a series of algorithms using tree merging, heap,
sparse accumulator, hash table, and sliding hash table data structures. Among
them, hash-based algorithms attain the theoretical lower bounds both on the
computational and I/O complexities and perform the best in practice. The
newly-developed hash SpKAdd makes the computation of a distributed-memory
SpGEMM algorithm at least 2x faster than that the previous state-of-the-art

We apply digitized Quantum Annealing (QA) and Quantum Approximate
Optimization Algorithm (QAOA) to a paradigmatic task of supervised learning in
artificial neural networks: the optimization of synaptic weights for the binary
perceptron. At variance with the usual QAOA applications to MaxCut, or to
quantum spin-chains ground state preparation, the classical Hamiltonian is
characterized by highly non-local multi-spin interactions. Yet, we provide
evidence for the existence of optimal smooth solutions for the QAOA parameters,
which are transferable among typical instances of the same problem, and we
prove numerically an enhanced performance of QAOA over traditional QA. We also
investigate on the role of the QAOA optimization landscape geometry in this
problem, showing that the detrimental effect of a gap-closing transition
encountered in QA is also negatively affecting the performance of our
implementation of QAOA.

Molecular communication (MC) implemented on Nano networks has extremely
attractive characteristics in terms of energy efficiency, dependability, and
robustness. Even though, the impact of incredibly slow molecule diffusion and
high variability environments remains unknown. Analysis and designs of
communication systems usually rely on developing mathematical models that
describe the communication channel. However, the underlying channel models are
unknown in some systems, such as MC systems, where chemical signals are used to
transfer information. In these cases, a new method to analyze and design is
needed. In this paper, we concentrate on one critical aspect of the MC system,
modelling MC received signal until time t , and demonstrate that using tools
from ML makes it promising to train detectors that can be executed well without
any information about the channel model. Machine learning (ML) is one of the
intelligent methodologies that has shown promising results in the domain. This
paper applies Azure Machine Learning (Azure ML) for flexible pavement
maintenance regressions problems and solutions. For prediction, four parameters
are used as inputs: the receiver radius, transmitter radius, distance between
receiver and transmitter, and diffusion coefficient, while the output is mAP
(mean average precision) of the received signal. Azure ML enables algorithms
that can learn from data and experiences and accomplish tasks without having to
be coded. In the established Azure ML, the regression algorithms such as, boost
decision tree regression, Bayesian linear regression, neural network, and
decision forest regression are selected. The best performance is chosen as an
optimality criterion. Finally, a comparison that shows the potential benefits
of Azure ML tool over programmed based tool (Python), used by developers on
local PCs, is demonstrated

We propose a novel neural rendering pipeline, Hybrid Volumetric-Textural
Rendering (HVTR), which synthesizes virtual human avatars from arbitrary poses
efficiently and at high quality. First, we learn to encode articulated human
motions on a dense UV manifold of the human body surface. To handle complicated
motions (e.g., self-occlusions), we then leverage the encoded information on
the UV manifold to construct a 3D volumetric representation based on a dynamic
pose-conditioned neural radiance field. While this allows us to represent 3D
geometry with changing topology, volumetric rendering is computationally heavy.
Hence we employ only a rough volumetric representation using a pose-conditioned
downsampled neural radiance field (PD-NeRF), which we can render efficiently at
low resolutions. In addition, we learn 2D textural features that are fused with
rendered volumetric features in image space. The key advantage of our approach
is that we can then convert the fused features into a high-resolution,
high-quality avatar by a fast GAN-based textural renderer. We demonstrate that
hybrid rendering enables HVTR to handle complicated motions, render
high-quality avatars under user-controlled poses/shapes and even loose
clothing, and most importantly, be efficient at inference time. Our
experimental results also demonstrate state-of-the-art quantitative results.

In this paper, we study the representation of the shape and pose of objects
using their keypoints. Therefore, we propose an end-to-end method that
simultaneously detects 2D keypoints from an image and lifts them to 3D. The
proposed method learns both 2D detection and 3D lifting only from 2D keypoints
annotations. In addition to being end-to-end from images to 3D keypoints, our
method also handles objects from multiple categories using a single neural
network. We use a Transformer-based architecture to detect the keypoints, as
well as to summarize the visual context of the image. This visual context
information is then used while lifting the keypoints to 3D, to allow
context-based reasoning for better performance. Our method can handle
occlusions as well as a wide variety of object classes. Our experiments on
three benchmarks demonstrate that our method performs better than the
state-of-the-art. Our source code will be made publicly available.

With AI systems becoming more powerful and pervasive, there is increasing
debate about keeping their actions aligned with the broader goals and needs of
humanity. This multi-disciplinary and multi-stakeholder debate must resolve
many issues, here we examine three of them. The first issue is to clarify what
demands stakeholders might usefully make on the designers of AI systems, useful
because the technology exists to implement them. We make this technical topic
more accessible by using the framing of cognitive architectures. The second
issue is to move beyond an analytical framing that treats useful intelligence
as being reward maximization only. To support this move, we define several AI
cognitive architectures that combine reward maximization with other technical
elements designed to improve alignment. The third issue is how stakeholders
should calibrate their interactions with modern machine learning researchers.
We consider how current fashions in machine learning create a narrative pull
that participants in technical and policy discussions should be aware of, so
that they can compensate for it. We identify several technically tractable but
currently unfashionable options for improving AI alignment.

This work aims to evaluate the ability that both probabilistic and
state-of-the-art vector space modeling (VSM) methods provide to well known
machine learning algorithms to identify social network documents to be
classified as aggressive, gender biased or communally charged. To this end, an
exploratory stage was performed first in order to find relevant settings to
test, i.e. by using training and development samples, we trained multiple
algorithms using multiple vector space modeling and probabilistic methods and
discarded the less informative configurations. These systems were submitted to
the competition of the ComMA@ICON'21 Workshop on Multilingual Gender Biased and
Communal Language Identification.

PURPOSE: This study aimed to develop a deep learning-based tool to detect and
localize lung nodules with chest radiographs(CXRs). We expected it to enhance
the efficiency of interpreting CXRs and reduce the possibilities of delayed
diagnosis of lung cancer.
  MATERIALS AND METHODS: We collected CXRs from NCKUH database and VBD, an
open-source medical image dataset, as our training and validation data. A
number of CXRs from the Ministry of Health and Welfare(MOHW) database served as
our test data. We built a segmentation model to identify lung areas from CXRs,
and sliced them into 16 patches. Physicians labeled the CXRs by clicking the
patches. These labeled patches were then used to train and fine-tune a deep
neural network(DNN) model, classifying the patches as positive or negative.
Finally, we test the DNN model with the lung patches of CXRs from MOHW.
  RESULTS: Our segmentation model identified the lung regions well from the
whole CXR. The Intersection over Union(IoU) between the ground truth and the
segmentation result was 0.9228. In addition, our DNN model achieved a
sensitivity of 0.81, specificity of 0.82, and AUROC of 0.869 in 98 of 125
cases. For the other 27 difficult cases, the sensitivity was 0.54, specificity
0.494, and AUROC 0.682. Overall, we obtained a sensitivity of 0.78, specificity
of 0.79, and AUROC 0.837.
  CONCLUSIONS: Our two-step workflow is comparable to state-of-the-art
algorithms in the sensitivity and specificity of localizing lung nodules from
CXRs. Notably, our workflow provides an efficient way for specialists to label
the data, which is valuable for relevant researches because of the relative
rarity of labeled medical image data.

With the rapid demand of data and computational resources in deep learning
systems, a growing number of algorithms to utilize collaborative machine
learning techniques, for example, federated learning, to train a shared deep
model across multiple participants. It could effectively take advantage of the
resources of each participant and obtain a more powerful learning system.
However, integrity and privacy threats in such systems have greatly obstructed
the applications of collaborative learning. And a large amount of works have
been proposed to maintain the model integrity and mitigate the privacy leakage
of training data during the training phase for different collaborative learning
systems. Compared with existing surveys that mainly focus on one specific
collaborative learning system, this survey aims to provide a systematic and
comprehensive review of security and privacy researches in collaborative
learning. Our survey first provides the system overview of collaborative
learning, followed by a brief introduction of integrity and privacy threats. In
an organized way, we then detail the existing integrity and privacy attacks as
well as their defenses. We also list some open problems in this area and
opensource the related papers on GitHub:
https://github.com/csl-cqu/awesome-secure-collebrative-learning-papers.

TAIGA is a hybrid observatory for gamma-ray astronomy at high energies in
range from 10 TeV to several EeV. It consists of instruments such as
TAIGA-IACT, TAIGA-HiSCORE, and others. TAIGA-HiSCORE, in particular, is an
array of wide-angle timing Cherenkov light stations. TAIGA-HiSCORE data enable
to reconstruct air shower characteristics, such as air shower energy, arrival
direction, and axis coordinates. In this report, we propose to consider the use
of convolution neural networks in task of air shower characteristics
determination. We use Convolutional Neural Networks (CNN) to analyze HiSCORE
events, treating them like images. For this, the times and amplitudes of events
recorded at HiSCORE stations are used. The work discusses a simple
convolutional neural network and its training. In addition, we present some
preliminary results on the determination of the parameters of air showers such
as the direction and position of the shower axis and the energy of the primary
particle and compare them with the results obtained by the traditional method.

The imaging Cherenkov telescopes TAIGA-IACT, located in the Tunka valley of
the republic Buryatia, accumulate a lot of data in a short period of time which
must be efficiently and quickly analyzed. One of the methods of such analysis
is the machine learning, which has proven its effectiveness in many
technological and scientific fields in recent years. The aim of the work is to
study the possibility of the machine learning application to solve the tasks
set for TAIGA-IACT: the identification of the primary particle of cosmic rays
and reconstruction their physical parameters. In the work the method of
Convolutional Neural Networks (CNN) was applied to process and analyze
Monte-Carlo events simulated with CORSIKA. Also various CNN architectures for
the processing were considered. It has been demonstrated that this method gives
good results in the determining the type of primary particles of Extensive Air
Shower (EAS) and the reconstruction of gamma-rays energy. The results are
significantly improved in the case of stereoscopic observations.

With the increasing popularity of convolutional neural networks (CNNs),
recent works on face-based age estimation employ these networks as the
backbone. However, state-of-the-art CNN-based methods treat each facial region
equally, thus entirely ignoring the importance of some facial patches that may
contain rich age-specific information. In this paper, we propose a face-based
age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF).
In ADPF, two separate CNNs are implemented, namely the AttentionNet and the
FusionNet. The AttentionNet dynamically locates and ranks age-specific patches
by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA)
mechanism. The FusionNet uses the discovered patches along with the facial
image to predict the age of the subject. Since the proposed RMHHA mechanism
ranks the discovered patches based on their importance, the length of the
learning path of each patch in the FusionNet is proportional to the amount of
information it carries (the longer, the more important). ADPF also introduces a
novel diversity loss to guide the training of the AttentionNet and reduce the
overlap among patches so that the diverse and important patches are discovered.
Through extensive experiments, we show that our proposed framework outperforms
state-of-the-art methods on several age estimation benchmark datasets.

Graph Convolutional Neural Networks (GCNs) are widely used for graph
analysis. Specifically, in medical applications, GCNs can be used for disease
prediction on a population graph, where graph nodes represent individuals and
edges represent individual similarities. However, GCNs rely on a vast amount of
data, which is challenging to collect for a single medical institution. In
addition, a critical challenge that most medical institutions continue to face
is addressing disease prediction in isolation with incomplete data information.
To address these issues, Federated Learning (FL) allows isolated local
institutions to collaboratively train a global model without data sharing. In
this work, we propose a framework, FedNI, to leverage network inpainting and
inter-institutional data via FL. Specifically, we first federatively train
missing node and edge predictor using a graph generative adversarial network
(GAN) to complete the missing information of local networks. Then we train a
global GCN node classifier across institutions using a federated graph learning
platform. The novel design enables us to build more accurate machine learning
models by leveraging federated learning and also graph learning approaches. We
demonstrate that our federated model outperforms local and baseline FL methods
with significant margins on two public neuroimaging datasets.

Despite the significant improvements that representation learning via
self-supervision has led to when learning from unlabeled data, no methods exist
that explain what influences the learned representation. We address this need
through our proposed approach, RELAX, which is the first approach for
attribution-based explanations of representations. Our approach can also model
the uncertainty in its explanations, which is essential to produce trustworthy
explanations. RELAX explains representations by measuring similarities in the
representation space between an input and masked out versions of itself,
providing intuitive explanations and significantly outperforming the
gradient-based baseline. We provide theoretical interpretations of RELAX and
conduct a novel analysis of feature extractors trained using supervised and
unsupervised learning, providing insights into different learning strategies.
Finally, we illustrate the usability of RELAX in multi-view clustering and
highlight that incorporating uncertainty can be essential for providing
low-complexity explanations, taking a crucial step towards explaining

A key assumption in supervised learning is that training and test data follow
the same probability distribution. However, this fundamental assumption is not
always satisfied in practice, e.g., due to changing environments, sample
selection bias, privacy concerns, or high labeling costs. Transfer learning
(TL) relaxes this assumption and allows us to learn under distribution shift.
Classical TL methods typically rely on importance-weighting -- a predictor is
trained based on the training losses weighted according to the importance
(i.e., the test-over-training density ratio). However, as real-world machine
learning tasks are becoming increasingly complex, high-dimensional, and
dynamical, novel approaches are explored to cope with such challenges recently.
In this article, after introducing the foundation of TL based on
importance-weighting, we review recent advances based on joint and dynamic
importance-predictor estimation. Furthermore, we introduce a method of causal
mechanism transfer that incorporates causal structure in TL. Finally, we
discuss future perspectives of TL research.

Knowledge of the road network topology is crucial for autonomous planning and
navigation. Yet, recovering such topology from a single image has only been
explored in part. Furthermore, it needs to refer to the ground plane, where
also the driving actions are taken. This paper aims at extracting the local
road network topology, directly in the bird's-eye-view (BEV), all in a complex
urban setting. The only input consists of a single onboard, forward looking
camera image. We represent the road topology using a set of directed lane
curves and their interactions, which are captured using their intersection
points. To better capture topology, we introduce the concept of \emph{minimal
cycles} and their covers. A minimal cycle is the smallest cycle formed by the
directed curve segments (between two intersections). The cover is a set of
curves whose segments are involved in forming a minimal cycle. We first show
that the covers suffice to uniquely represent the road topology. The covers are
then used to supervise deep neural networks, along with the lane curve
supervision. These learn to predict the road topology from a single input
image. The results on the NuScenes and Argoverse benchmarks are significantly
better than those obtained with baselines. Code:
https://github.com/ybarancan/TopologicalLaneGraph

The explosion of digital information and the growing involvement of people in
social networks led to enormous research activity to develop methods that can
extract meaningful information from interaction data. Commonly, interactions
are represented by edges in a network or a graph, which implicitly assumes that
the interactions are pairwise and static. However, real-world interactions
deviate from these assumptions: (i) interactions can be multi-way involving
more than two nodes or individuals (e.g., family relationships, protein
interactions), and (ii) interactions can change over a period of time (e.g.,
change of opinions and friendship status). While pairwise interactions have
been studied in a dynamic network setting and multi-way interactions have been
studied using hypergraphs in static networks, there exists no method that can
predict multi-way interactions or hyperedges in dynamic settings. Existing
related methods cannot answer temporal queries like what type of interaction
will occur next and when it will occur. This paper proposes a temporal point
process model for hyperedge prediction to address these problems. Our proposed
model uses dynamic representation techniques for nodes in a neural point
process framework to forecast hyperedges. We present several experimental
results and set benchmark results. As far as our knowledge, this is the first
work that uses the temporal point process to forecast hyperedges in dynamic
networks.

Quantization of the weights and activations is one of the main methods to
reduce the computational footprint of Deep Neural Networks (DNNs) training.
Current methods enable 4-bit quantization of the forward phase. However, this
constitutes only a third of the training process. Reducing the computational
footprint of the entire training process requires the quantization of the
neural gradients, i.e., the loss gradients with respect to the outputs of
intermediate neural layers. In this work, we examine the importance of having
unbiased quantization in quantized neural network training, where to maintain
it, and how. Based on this, we suggest a \textit{logarithmic unbiased
quantization} (LUQ) method to quantize all both the forward and backward phase
to 4-bit, achieving state-of-the-art results in 4-bit training without
overhead. For example, in ResNet50 on ImageNet, we achieved a degradation of
1.1\%. We further improve this to degradation of only 0.32\% after three epochs
of high precision fine-tunining, combined with a variance reduction method --
where both these methods add overhead comparable to previously suggested
methods.

Human beings can perceive a target sound type from a multi-source mixture
signal by the selective auditory attention, however, such functionality was
hardly ever explored in machine hearing. This paper addresses the target sound
detection (TSD) task, which aims to detect the target sound signal from a
mixture audio when a target sound's reference audio is given. We present a
novel target sound detection network (TSDNet) which consists of two main parts:
A conditional network which aims at generating a sound-discriminative
conditional embedding vector representing the target sound, and a detection
network which takes both the mixture audio and the conditional embedding vector
as inputs and produces the detection result of the target sound. These two
networks can be jointly optimized with a multi-task learning approach to
further improve the performance. In addition, we study both strong-supervised
and weakly-supervised strategies to train TSDNet and propose a data
augmentation method by mixing two samples. To facilitate this research, we
build a target sound detection dataset (\textit{i.e.} URBAN-TSD) based on
URBAN-SED and UrbanSound8K datasets, and experimental results indicate our
method could get the segment-based F scores of 76.3$\%$ and 56.8$\%$ on the
strongly-labelled and weakly-labelled data respectively.

As a representative evidential clustering algorithm, evidential c-means (ECM)
provides a deeper insight into the data by allowing an object to belong not
only to a single class, but also to any subset of a collection of classes,
which generalizes the hard, fuzzy, possibilistic, and rough partitions.
However, compared with other partition-based algorithms, ECM must estimate
numerous additional parameters, and thus insufficient or contaminated data will
have a greater influence on its clustering performance. To solve this problem,
in this study, a transfer learning-based ECM (TECM) algorithm is proposed by
introducing the strategy of transfer learning into the process of evidential
clustering. The TECM objective function is constructed by integrating the
knowledge learned from the source domain with the data in the target domain to
cluster the target data. Subsequently, an alternate optimization scheme is
developed to solve the constraint objective function of the TECM algorithm. The
proposed TECM algorithm is applicable to cases where the source and target
domains have the same or different numbers of clusters. A series of experiments
were conducted on both synthetic and real datasets, and the experimental
results demonstrated the effectiveness of the proposed TECM algorithm compared
to ECM and other representative multitask or transfer-clustering algorithms.

One of the significant problems of streaming data classification is the
occurrence of concept drift, consisting of the change of probabilistic
characteristics of the classification task. This phenomenon destabilizes the
performance of the classification model and seriously degrades its quality. An
appropriate strategy counteracting this phenomenon is required to adapt the
classifier to the changing probabilistic characteristics. One of the
significant problems in implementing such a solution is the access to data
labels. It is usually costly, so to minimize the expenses related to this
process, learning strategies based on semi-supervised learning are proposed,
e.g., employing active learning methods indicating which of the incoming
objects are valuable to be labeled for improving the classifier's performance.
This paper proposes a novel chunk-based method for non-stationary data streams
based on classifier ensemble learning and an active learning strategy
considering a limited budget that can be successfully applied to any data
stream classification algorithm. The proposed method has been evaluated through
computer experiments using both real and generated data streams. The results
confirm the high quality of the proposed algorithm over state-of-the-art
methods.

Part assembly is a typical but challenging task in robotics, where robots
assemble a set of individual parts into a complete shape. In this paper, we
develop a robotic assembly simulation environment for furniture assembly. We
formulate the part assembly task as a concrete reinforcement learning problem
and propose a pipeline for robots to learn to assemble a diverse set of chairs.
Experiments show that when testing with unseen chairs, our approach achieves a
success rate of 74.5% under the object-centric setting and 50.0% under the full
setting. We adopt an RRT-Connect algorithm as the baseline, which only achieves
a success rate of 18.8% after a significantly longer computation time.
Supplemental materials and videos are available on our project webpage.

The introduction of electronic trading platforms effectively changed the
organisation of traditional systemic trading from quote-driven markets into
order-driven markets. Its convenience led to an exponentially increasing amount
of financial data, which is however hard to use for the prediction of future
prices, due to the low signal-to-noise ratio and the non-stationarity of
financial time series. Simpler classification tasks -- where the goal is to
predict the directions of future price movement -- via supervised learning
algorithms, need sufficiently reliable labels to generalise well. Labelling
financial data is however less well defined than other domains: did the price
go up because of noise or because of signal? The existing labelling methods
have limited countermeasures against noise and limited effects in improving
learning algorithms. This work takes inspiration from image classification in
trading and success in self-supervised learning. We investigate the idea of
applying computer vision techniques to financial time-series to reduce the
noise exposure and hence generate correct labels. We look at the label
generation as the pretext task of a self-supervised learning approach and
compare the naive (and noisy) labels, commonly used in the literature, with the
labels generated by a denoising autoencoder for the same downstream
classification task. Our results show that our denoised labels improve the
performances of the downstream learning algorithm, for both small and large
datasets. We further show that the signals we obtain can be used to effectively
trade with binary strategies. We suggest that with proposed techniques,
self-supervised learning constitutes a powerful framework for generating
"better" financial labels that are useful for studying the underlying patterns
of the market.

Information field theory (IFT), the information theory for fields, is a
mathematical framework for signal reconstruction and non-parametric inverse
problems. Artificial intelligence (AI) and machine learning (ML) aim at
generating intelligent systems including such for perception, cognition, and
learning. This overlaps with IFT, which is designed to address perception,
reasoning, and inference tasks. Here, the relation between concepts and tools
in IFT and those in AI and ML research are discussed. In the context of IFT,
fields denote physical quantities that change continuously as a function of
space (and time) and information theory refers to Bayesian probabilistic logic
equipped with the associated entropic information measures. Reconstructing a
signal with IFT is a computational problem similar to training a generative
neural network (GNN) in ML. In this paper, the process of inference in IFT is
reformulated in terms of GNN training. In contrast to classical neural
networks, IFT based GNNs can operate without pre-training thanks to
incorporating expert knowledge into their architecture. Furthermore, the
cross-fertilization of variational inference methods used in IFT and ML are
discussed. These discussions suggests that IFT is well suited to address many
problems in AI and ML research and application.

Providing appropriate questions according to a student's knowledge level is
imperative in personalized learning. However, It requires a lot of manual
effort for teachers to understand students' knowledge status and provide
optimal questions accordingly. To address this problem, we introduce a question
scheduling model that can effectively boost student knowledge level using
Reinforcement Learning (RL). Our proposed method first evaluates students'
concept-level knowledge using knowledge tracing (KT) model. Given predicted
student knowledge, RL-based recommender predicts the benefits of each question.
With curriculum range restriction and duplicate penalty, the recommender
selects questions sequentially until it reaches the predefined number of
questions. In an experimental setting using a student simulator, which gives 20
questions per day for two weeks, questions recommended by the proposed method
increased average student knowledge level by 21.3%, superior to an
expert-designed schedule baseline with a 10% increase in student knowledge
levels.

Bug reports are common artefacts in software development. They serve as the
main channel for users to communicate to developers information about the
issues that they encounter when using released versions of software programs.
In the descriptions of issues, however, a user may, intentionally or not,
expose a vulnerability. In a typical maintenance scenario, such
security-relevant bug reports are prioritised by the development team when
preparing corrective patches. Nevertheless, when security relevance is not
immediately expressed (e.g., via a tag) or rapidly identified by triaging
teams, the open security-relevant bug report can become a critical leak of
sensitive information that attackers can leverage to perform zero-day attacks.
To support practitioners in triaging bug reports, the research community has
proposed a number of approaches for the detection of security-relevant bug
reports. In recent years, approaches in this respect based on machine learning
have been reported with promising performance. Our work focuses on such
approaches, and revisits their building blocks to provide a comprehensive view
on the current achievements. To that end, we built a large experimental dataset
and performed extensive experiments with variations in feature sets and
learning algorithms. Eventually, our study highlights different approach
configurations that yield best performing classifiers.

In this paper we evaluate the use of system identification methods to build a
thermal prediction model of heterogeneous SoC platforms that can be used to
quickly predict the temperature of different configurations without the need of
hardware. Specifically, we focus on modeling approaches that can predict the
temperature based on the clock frequency and the utilization percentage of each
core. We investigate three methods with respect to their prediction accuracy: a
linear state-space identification approach using polynomial regressors, a NARX
neural network approach and a recurrent neural network approach configured in
an FIR model structure. We evaluate the methods on an Odroid-XU4 board
featuring an Exynos 5422 SoC. The results show that the model based on
polynomial regressors significantly outperformed the other two models when
trained with 1 hour and 6 hours of data.

Recent developments in Quantum Machine Learning have seen the introduction of
several models to generalize the classical perceptron to the quantum regime.
The capabilities of these quantum models need to be determined precisely in
order to establish if a quantum advantage is achievable. Here we use a
statistical physics approach to compute the pattern capacity of a particular
model of quantum perceptron realized by means of a continuous variable quantum
system.

We investigate densely connected convolutional networks (DenseNets) and their
extension with domain adversarial training for noise robust speech recognition.
DenseNets are very deep, compact convolutional neural networks which have
demonstrated incredible improvements over the state-of-the-art results in
computer vision. Our experimental results reveal that DenseNets are more robust
against noise than other neural network based models such as deep feed forward
neural networks and convolutional neural networks. Moreover, domain adversarial
learning can further improve the robustness of DenseNets against both, known
and unknown noise conditions.

Many studies confirmed that a proper traffic state representation is more
important than complex algorithms for the classical traffic signal control
(TSC) problem. In this paper, we (1) present a novel, flexible and efficient
method, namely advanced max pressure (Advanced-MP), taking both running and
queuing vehicles into consideration to decide whether to change current signal
phase; (2) inventively design the traffic movement representation with the
efficient pressure and effective running vehicles from Advanced-MP, namely
advanced traffic state (ATS); and (3) develop a reinforcement learning (RL)
based algorithm template, called Advanced-XLight, by combining ATS with the
latest RL approaches, and generate two RL algorithms, namely "Advanced-MPLight"
and "Advanced-CoLight" from Advanced-XLight. Comprehensive experiments on
multiple real-world datasets show that: (1) the Advanced-MP outperforms
baseline methods, and it is also efficient and reliable for deployment; and (2)
Advanced-MPLight and Advanced-CoLight can achieve the state-of-the-art.

The synthesis of human grasping has numerous applications including AR/VR,
video games and robotics. While methods have been proposed to generate
realistic hand-object interaction for object grasping and manipulation, these
typically only consider interacting hand alone. Our goal is to synthesize
whole-body grasping motions. Starting from an arbitrary initial pose, we aim to
generate diverse and natural whole-body human motions to approach and grasp a
target object in 3D space. This task is challenging as it requires modeling
both whole-body dynamics and dexterous finger movements. To this end, we
propose SAGA (StochAstic whole-body Grasping with contAct), a framework which
consists of two key components: (a) Static whole-body grasping pose generation.
Specifically, we propose a multi-task generative model, to jointly learn static
whole-body grasping poses and human-object contacts. (b) Grasping motion
infilling. Given an initial pose and the generated whole-body grasping pose as
the start and end of the motion respectively, we design a novel contact-aware
generative motion infilling module to generate a diverse set of grasp-oriented
motions. We demonstrate the effectiveness of our method, which is a novel
generative framework to synthesize realistic and expressive whole-body motions
that approach and grasp randomly placed unseen objects. Code and models are
available at https://jiahaoplus.github.io/SAGA/saga.html.

The main idea of this paper is that if a model can recognize a person, of
course, it must be able to know the gender of that person, too. Therefore,
instead of defining a new model for gender classification, this paper uses
ArcFace features to determine gender, based on the facial features. A face
image is given to ArcFace and 512 features are obtained for the face. Then,
with the help of traditional machine learning models, gender is determined.
Discriminative methods such as Support Vector Machine (SVM), Linear
Discriminant, and Logistic Regression well demonstrate that the features
extracted from the ArcFace create a remarkable distinction between the gender
classes. Experiments on the Gender Classification Dataset show that SVM with
Gaussian kernel is able to classify gender with an accuracy of 96.4% using
ArcFace features.

Unsupervised person re-identification (ReID) is a challenging task without
data annotation to guide discriminative learning. Existing methods attempt to
solve this problem by clustering extracted embeddings to generate pseudo
labels. However, most methods ignore the intra-class gap caused by camera style
variance, and some methods are relatively complex and indirect although they
try to solve the negative impact of the camera style on feature distribution.
To solve this problem, we propose a camera-aware style separation and
contrastive learning method (CA-UReID), which directly separates camera styles
in the feature space with the designed camera-aware attention module. It can
explicitly divide the learnable feature into camera-specific and
camera-agnostic parts, reducing the influence of different cameras. Moreover,
to further narrow the gap across cameras, we design a camera-aware contrastive
center loss to learn more discriminative embedding for each identity. Extensive
experiments demonstrate the superiority of our method over the state-of-the-art
methods on the unsupervised person ReID task.

To support the modern machine-type communications, a crucial task during the
random access phase is device activity detection, which is to detect the active
devices from a large number of potential devices based on the received signal
at the access point. By utilizing the statistical properties of the channel,
state-of-the-art covariance based methods have been demonstrated to achieve
better activity detection performance than compressed sensing based methods.
However, covariance based methods require to solve a high dimensional nonconvex
optimization problem by updating the estimate of the activity status of each
device sequentially. Since the number of updates is proportional to the device
number, the computational complexity and delay make the iterative updates
difficult for real-time implementation especially when the device number scales
up. Inspired by the success of deep learning for real-time inference, this
paper proposes a learning based method with a customized heterogeneous
transformer architecture for device activity detection. By adopting an
attention mechanism in the architecture design, the proposed method is able to
extract the relevance between device pilots and received signal, is permutation
equivariant with respect to devices, and is scale adaptable to different
numbers of devices. Simulation results demonstrate that the proposed method
achieves better activity detection performance with much shorter computation
time than state-of-the-art covariance approach, and generalizes well to
different numbers of devices, BS-antennas, and different signal-to-noise
ratios.

News recommendation is an effective information dissemination solution in
modern society. While recent years have witnessed many promising news
recommendation models, they mostly capture the user-news interactions on the
document-level in a static manner. However, in real-world scenarios, the news
can be quite complex and diverse, blindly squeezing all the contents into an
embedding vector can be less effective in extracting information compatible
with the personalized preference of the users. In addition, user preferences in
the news recommendation scenario can be highly dynamic, and a tailored dynamic
mechanism should be designed for better recommendation performance. In this
paper, we propose a novel dynamic news recommender model. For better
understanding the news content, we leverage the attention mechanism to
represent the news from the sentence-, element- and document-levels,
respectively. For capturing users' dynamic preferences, the continuous time
information is seamlessly incorporated into the computing of the attention
weights. More specifically, we design a hierarchical attention network, where
the lower layer learns the importance of different sentences and elements, and
the upper layer captures the correlations between the previously interacted and
the target news. To comprehensively model the dynamic characters, we firstly
enhance the traditional attention mechanism by incorporating both absolute and
relative time information, and then we propose a dynamic negative sampling
method to optimize the users' implicit feedback. We conduct extensive
experiments based on three real-world datasets to demonstrate our model's
effectiveness. Our source code and pre-trained representations are available at
https://github.com/lshowway/D-HAN.

The Black-Scholes model, defined under the assumption of a perfect financial
market, theoretically creates a flawless hedging strategy allowing the trader
to evade risks in a portfolio of options. However, the concept of a "perfect
financial market," which requires zero transaction and continuous trading, is
challenging to meet in the real world. Despite such widely known limitations,
academics have failed to develop alternative models successful enough to be
long-established. In this paper, we explore the landscape of Deep Neural
Networks(DNN) based hedging systems by testing the hedging capacity of the
following neural architectures: Recurrent Neural Networks, Temporal
Convolutional Networks, Attention Networks, and Span Multi-Layer Perceptron
Networks. In addition, we attempt to achieve even more promising results by
combining traditional derivative hedging models with DNN based approaches.
Lastly, we construct \textbf{NNHedge}, a deep learning framework that provides
seamless pipelines for model development and assessment for the experiments.

We present a novel framework that brings the 3D motion retargeting task from
controlled environments to in-the-wild scenarios. In particular, our method is
capable of retargeting body motion from a character in a 2D monocular video to
a 3D character without using any motion capture system or 3D reconstruction
procedure. It is designed to leverage massive online videos for unsupervised
training, needless of 3D annotations or motion-body pairing information. The
proposed method is built upon two novel canonicalization operations, structure
canonicalization and view canonicalization. Trained with the canonicalization
operations and the derived regularizations, our method learns to factorize a
skeleton sequence into three independent semantic subspaces, i.e., motion,
structure, and view angle. The disentangled representation enables motion
retargeting from 2D to 3D with high precision. Our method achieves superior
performance on motion transfer benchmarks with large body variations and
challenging actions. Notably, the canonicalized skeleton sequence could serve
as a disentangled and interpretable representation of human motion that
benefits action analysis and motion retrieval.

Dataset shift is common in credit scoring scenarios, and the inconsistency
between the distribution of training data and the data that actually needs to
be predicted is likely to cause poor model performance. However, most of the
current studies do not take this into account, and they directly mix data from
different time periods when training the models. This brings about two
problems. Firstly, there is a risk of data leakage, i.e., using future data to
predict the past. This can result in inflated results in offline validation,
but unsatisfactory results in practical applications. Secondly, the
macroeconomic environment and risk control strategies are likely to be
different in different time periods, and the behavior patterns of borrowers may
also change. The model trained with past data may not be applicable to the
recent stage. Therefore, we propose a method based on adversarial validation to
alleviate the dataset shift problem in credit scoring scenarios. In this
method, partial training set samples with the closest distribution to the
predicted data are selected for cross-validation by adversarial validation to
ensure the generalization performance of the trained model on the predicted
samples. In addition, through a simple splicing method, samples in the training
data that are inconsistent with the test data distribution are also involved in
the training process of cross-validation, which makes full use of all the data
and further improves the model performance. To verify the effectiveness of the
proposed method, comparative experiments with several other data split methods
are conducted with the data provided by Lending Club. The experimental results
demonstrate the importance of dataset shift in the field of credit scoring and

Deep learning (DL) models have provided state-of-the-art performance in
various medical imaging benchmarking challenges, including the Brain Tumor
Segmentation (BraTS) challenges. However, the task of focal pathology
multi-compartment segmentation (e.g., tumor and lesion sub-regions) is
particularly challenging, and potential errors hinder translating DL models
into clinical workflows. Quantifying the reliability of DL model predictions in
the form of uncertainties could enable clinical review of the most uncertain
regions, thereby building trust and paving the way toward clinical translation.
Several uncertainty estimation methods have recently been introduced for DL
medical image segmentation tasks. Developing scores to evaluate and compare the
performance of uncertainty measures will assist the end-user in making more
informed decisions. In this study, we explore and evaluate a score developed
during the BraTS 2019 and BraTS 2020 task on uncertainty quantification
(QU-BraTS) and designed to assess and rank uncertainty estimates for brain
tumor multi-compartment segmentation. This score (1) rewards uncertainty
estimates that produce high confidence in correct assertions and those that
assign low confidence levels at incorrect assertions, and (2) penalizes
uncertainty measures that lead to a higher percentage of under-confident
correct assertions. We further benchmark the segmentation uncertainties
generated by 14 independent participating teams of QU-BraTS 2020, all of which
also participated in the main BraTS segmentation task. Overall, our findings
confirm the importance and complementary value that uncertainty estimates
provide to segmentation algorithms, highlighting the need for uncertainty
quantification in medical image analyses. Finally, in favor of transparency and
reproducibility, our evaluation code is made publicly available at:
https://github.com/RagMeh11/QU-BraTS.

Creative image animations are attractive in e-commerce applications, where
motion transfer is one of the import ways to generate animations from static
images. However, existing methods rarely transfer motion to objects other than
human body or human face, and even fewer apply motion transfer in practical
scenarios. In this work, we apply motion transfer on the Taobao product images
in real e-commerce scenario to generate creative animations, which are more
attractive than static images and they will bring more benefits. We animate the
Taobao products of dolls, copper running horses and toy dinosaurs based on
motion transfer method for demonstration.

With the AI of Things (AIoT) development, a huge amount of visual data, e.g.,
images and videos, are produced in our daily work and life. These visual data
are not only used for human viewing or understanding but also for machine
analysis or decision-making, e.g., intelligent surveillance, automated
vehicles, and many other smart city applications. To this end, a new image
codec paradigm for both human and machine uses is proposed in this work.
Firstly, the high-level instance segmentation map and the low-level signal
features are extracted with neural networks. Then, the instance segmentation
map is further represented as a profile with the proposed 16-bit gray-scale
representation. After that, both 16-bit gray-scale profile and signal features
are encoded with a lossless codec. Meanwhile, an image predictor is designed
and trained to achieve the general-quality image reconstruction with the 16-bit
gray-scale profile and signal features. Finally, the residual map between the
original image and the predicted one is compressed with a lossy codec, used for
high-quality image reconstruction. With such designs, on the one hand, we can
achieve scalable image compression to meet the requirements of different human
consumption; on the other hand, we can directly achieve several machine vision
tasks at the decoder side with the decoded 16-bit gray-scale profile, e.g.,
object classification, detection, and segmentation. Experimental results show
that the proposed codec achieves comparable results as most learning-based
codecs and outperforms the traditional codecs (e.g., BPG and JPEG2000) in terms
of PSNR and MS-SSIM for image reconstruction. At the same time, it outperforms
the existing codecs in terms of the mAP for object detection and segmentation.

Sequencing technologies are prone to errors, making error correction (EC)
necessary for downstream applications. EC tools need to be manually configured
for optimal performance. We find that the optimal parameters (e.g., k-mer size)
are both tool- and dataset-dependent. Moreover, evaluating the performance
(i.e., Alignment-rate or Gain) of a given tool usually relies on a reference
genome, but quality reference genomes are not always available. We introduce
Lerna for the automated configuration of k-mer-based EC tools. Lerna first
creates a language model (LM) of the uncorrected genomic reads; then,
calculates the perplexity metric to evaluate the corrected reads for different
parameter choices. Next, it finds the one that produces the highest alignment
rate without using a reference genome. The fundamental intuition of our
approach is that the perplexity metric is inversely correlated with the quality
of the assembly after error correction. Results: First, we show that the best
k-mer value can vary for different datasets, even for the same EC tool. Second,
we show the gains of our LM using its component attention-based transformers.
We show the model's estimation of the perplexity metric before and after error
correction. The lower the perplexity after correction, the better the k-mer
size. We also show that the alignment rate and assembly quality computed for
the corrected reads are strongly negatively correlated with the perplexity,
enabling the automated selection of k-mer values for better error correction,
and hence, improved assembly quality. Additionally, we show that our
attention-based models have significant runtime improvement for the entire
pipeline -- 18X faster than previous works, due to parallelizing the attention
mechanism and the use of JIT compilation for GPU inferencing.

Entity type prediction is an important problem in knowledge graph (KG)
research. A new KG entity type prediction method, named CORE (COmplex space
Regression and Embedding), is proposed in this work. The proposed CORE method
leverages the expressive power of two complex space embedding models; namely,
RotatE and ComplEx models. It embeds entities and types in two different
complex spaces using either RotatE or ComplEx. Then, we derive a complex
regression model to link these two spaces. Finally, a mechanism to optimize
embedding and regression parameters jointly is introduced. Experiments show
that CORE outperforms benchmarking methods on representative KG entity type
inference datasets. Strengths and weaknesses of various entity type prediction
methods are analyzed.

We propose LocFormer, a Transformer-based model for video grounding which
operates at a constant memory footprint regardless of the video length, i.e.
number of frames. LocFormer is designed for tasks where it is necessary to
process the entire long video and at its core lie two main contributions.
First, our model incorporates a new sampling technique that splits the input
feature sequence into a fixed number of sections and selects a single feature
per section using a stochastic approach, which allows us to obtain a feature
sample set that is representative of the video content for the task at hand
while keeping the memory footprint constant. Second, we propose a modular
design that separates functionality, enabling us to learn an inductive bias via
supervising the self-attention heads, while also effectively leveraging
pre-trained text and video encoders. We test our proposals on relevant
benchmark datasets for video grounding, showing that not only LocFormer can
achieve excellent results including state-of-the-art performance on YouCookII,
but also that our sampling technique is more effective than competing
counterparts and that it consistently improves the performance of prior work,
by up to 3.13\% in the mean temporal IoU, ultimately leading to a new
state-of-the-art performance on Charades-STA.

As emerging deep neural network (DNN) models continue to grow in size, using
large GPU clusters to train DNNs is becoming an essential requirement to
achieving acceptable training times. In this paper, we consider the case where
future increases in cluster size will cause the global batch size that can be
used to train models to reach a fundamental limit: beyond a certain point,
larger global batch sizes cause sample efficiency to degrade, increasing
overall time to accuracy. As a result, to achieve further improvements in
training performance, we must instead consider "strong scaling" strategies that
hold the global batch size constant and allocate smaller batches to each GPU.
Unfortunately, this makes it significantly more difficult to use cluster
resources efficiently. We present DeepPool, a system that addresses this
efficiency challenge through two key ideas. First, burst parallelism allocates
large numbers of GPUs to foreground jobs in bursts to exploit the unevenness in
parallelism across layers. Second, GPU multiplexing prioritizes throughput for
foreground training jobs, while packing in background training jobs to reclaim
underutilized GPU resources, thereby improving cluster-wide utilization.
Together, these two ideas enable DeepPool to deliver a 1.2 - 2.3x improvement
in total cluster throughput over standard data parallelism with a single task
when the cluster scale is large.

Graph-level anomaly detection (GAD) describes the problem of detecting graphs
that are abnormal in their structure and/or the features of their nodes, as
compared to other graphs. One of the challenges in GAD is to devise graph
representations that enable the detection of both locally- and
globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained
(node-level) or holistic (graph-level) properties, respectively. To tackle this
challenge we introduce a novel deep anomaly detection approach for GAD that
learns rich global and local normal pattern information by joint random
distillation of graph and node representations. The random distillation is
achieved by training one GNN to predict another GNN with randomly initialized
network weights. Extensive experiments on 16 real-world graph datasets from
diverse domains show that our model significantly outperforms seven
state-of-the-art models. Code and datasets are available at
https://git.io/GLocalKD.

Human action recognition has drawn a lot of attention in the recent years due
to the research and application significance. Most existing works on action
recognition focus on learning effective spatial-temporal features from videos,
but neglect the strong causal relationship among the precondition, action and
effect. Such relationships are also crucial to the accuracy of action
recognition. In this paper, we propose to model the causal relationships based
on the precondition and effect to improve the performance of action
recognition. Specifically, a Cycle-Reasoning model is proposed to capture the
causal relationships for action recognition. To this end, we annotate
precondition and effect for a large-scale action dataset. Experimental results
show that the proposed Cycle-Reasoning model can effectively reason about the
precondition and effect and can enhance action recognition performance.

With the advent of intelligent jammers, jamming attacks have become a more
severe threat to the performance of wireless systems. An intelligent jammer is
able to change its policy to minimize the probability of being traced by
legitimate nodes. Thus, an anti-jamming mechanism capable of constantly
adjusting to the jamming policy is required to combat such a jammer.
Remarkably, existing anti-jamming methods are not applicable here because they
mainly focus on mitigating jamming attacks with an invariant jamming policy,
and they rarely consider an intelligent jammer as an adversary. Therefore, in
this paper, to employ a jamming type recognition technique working alongside an
anti-jamming technique is proposed. The proposed recognition method employs a
recurrent neural network that takes the jammer's occupied channels as inputs
and outputs the jammer type. Under this scheme, the real-time jammer policy is
first identified, and, then, the most appropriate countermeasure is chosen.
Consequently, any changes to the jammer policy can be instantly detected with
the proposed recognition technique allowing for a rapid switch to a new
anti-jamming method fitted to the new jamming policy. To evaluate the
performance of the proposed recognition method, the accuracy of the detection
is derived as a function of the jammer policy switching time. Simulation
results show the detection accuracy for all the considered users numbers is
greater than 70% when the jammer switches its policy every 5 time slots and the
accuracy raises to 90% when the jammer policy switching time is 45.

This paper is about a deep learning approach for linear and nonlinear
filtering. The idea is to train a neural network with Monte Carlo samples
generated from a nominal dynamic model. Then the network weights are applied to
Monte Carlo samples from an actual dynamic model. A main focus of this paper is
on the deep filters with three major neural network architectures (DNN, CNN,
RNN). Our deep filter compares favorably to the traditional Kalman filter in
linear cases and outperform the extended Kalman filter in nonlinear cases. Then
a switching model with jumps is studied to show the adaptiveness and power of
our deep filtering. Among the three major NNs, the CNN outperform the others on
average. while the RNN does not seem to be suitable for the filtering problem.
One advantage of the deep filter is its robustness when the nominal model and
actual model differ. The other advantage of deep filtering is real data can be
used directly to train the deep neutral network. Therefore, model calibration
can be by-passed all together.

In this paper, we present list autoencoder (listAE) to mimic list decoding
used in classical coding theory. With listAE, the decoder network outputs a
list of decoded message word candidates. To train the listAE, a genie is
assumed to be available at the output of the decoder. A specific loss function
is proposed to optimize the performance of a genie-aided (GA) list decoding.
The listAE is a general framework and can be used with any AE architecture. We
propose a specific architecture, referred to as incremental-redundancy AE
(IR-AE), which decodes the received word on a sequence of component codes with
non-increasing rates. Then, the listAE is trained and evaluated with both IR-AE
and Turbo-AE. Finally, we employ cyclic redundancy check (CRC) codes to replace
the genie at the decoder output and obtain a CRC aided (CA) list decoder. Our
simulation results show that the IR-AE under CA list decoding demonstrates
meaningful coding gain over Turbo-AE and polar code at low block error rates
range.

Predictive coding is a unifying framework for understanding perception,
action and neocortical organization. In predictive coding, different areas of
the neocortex implement a hierarchical generative model of the world that is
learned from sensory inputs. Cortical circuits are hypothesized to perform
Bayesian inference based on this generative model. Specifically, the
Rao-Ballard hierarchical predictive coding model assumes that the top-down
feedback connections from higher to lower order cortical areas convey
predictions of lower-level activities. The bottom-up, feedforward connections
in turn convey the errors between top-down predictions and actual activities.
These errors are used to correct current estimates of the state of the world
and generate new predictions. Through the objective of minimizing prediction
errors, predictive coding provides a functional explanation for a wide range of
neural responses and many aspects of brain organization.

The performance of a distillation-based compressed network is governed by the
quality of distillation. The reason for the suboptimal distillation of a large
network (teacher) to a smaller network (student) is largely attributed to the
gap in the learning capacities of given teacher-student pair. While it is hard
to distill all the knowledge of a teacher, the quality of distillation can be
controlled to a large extent to achieve better performance. Our experiments
show that the quality of distillation is largely governed by the quality of
teacher's response, which in turn is heavily affected by the presence of
similarity information in its response. A well-trained large capacity teacher
loses similarity information between classes in the process of learning
fine-grained discriminative properties for classification. The absence of
similarity information causes the distillation process to be reduced from one
example-many class learning to one example-one class learning, thereby
throttling the flow of diverse knowledge from the teacher. With the implicit
assumption that only the instilled knowledge can be distilled, instead of
focusing only on the knowledge distilling process, we scrutinize the knowledge
inculcation process. We argue that for a given teacher-student pair, the
quality of distillation can be improved by finding the sweet spot between batch
size and number of epochs while training the teacher. We discuss the steps to
find this sweet spot for better distillation. We also propose the distillation
hypothesis to differentiate the behavior of the distillation process between
knowledge distillation and regularization effect. We conduct all our
experiments on three different datasets.

Blind image super-resolution(SR) is a long-standing task in CV that aims to
restore low-resolution images suffering from unknown and complex distortions.
Recent work has largely focused on adopting more complicated degradation models
to emulate real-world degradations. The resulting models have made
breakthroughs in perceptual loss and yield perceptually convincing results.
However, the limitation brought by current generative adversarial network
structures is still significant: treating pixels equally leads to the ignorance
of the image's structural features, and results in performance drawbacks such
as twisted lines and background over-sharpening or blurring. In this paper, we
present A-ESRGAN, a GAN model for blind SR tasks featuring an attention U-Net
based, multi-scale discriminator that can be seamlessly integrated with other
generators. To our knowledge, this is the first work to introduce attention
U-Net structure as the discriminator of GAN to solve blind SR problems. And the
paper also gives an interpretation for the mechanism behind multi-scale
attention U-Net that brings performance breakthrough to the model. Through
comparison experiments with prior works, our model presents state-of-the-art
level performance on the non-reference natural image quality evaluator metric.
And our ablation studies have shown that with our discriminator, the RRDB based
generator can leverage the structural features of an image in multiple scales,
and consequently yields more perceptually realistic high-resolution images
compared to prior works.

Conditional distribution is a fundamental quantity for describing the
relationship between a response and a predictor. We propose a Wasserstein
generative approach to learning a conditional distribution. The proposed
approach uses a conditional generator to transform a known distribution to the
target conditional distribution. The conditional generator is estimated by
matching a joint distribution involving the conditional generator and the
target joint distribution, using the Wasserstein distance as the discrepancy
measure for these joint distributions. We establish non-asymptotic error bound
of the conditional sampling distribution generated by the proposed method and
show that it is able to mitigate the curse of dimensionality, assuming that the
data distribution is supported on a lower-dimensional set. We conduct numerical
experiments to validate proposed method and illustrate its applications to
conditional sample generation, nonparametric conditional density estimation,
prediction uncertainty quantification, bivariate response data, image
reconstruction and image generation.

With the popularity of Android growing exponentially, the amount of malware
has significantly exploded. It is arguably one of the most viral problems on
mobile platforms. Recently, various approaches have been introduced to detect
Android malware, the majority of these are either based on the Manifest File
features or the structural information, such as control flow graph and API
calls. Among those methods, nearly all of them only consider the Java byte-code
as the target to detect malicious behaviors. However, Recent research and our
own statistics show that native payloads are commonly used in both benign and
malicious apps. Current state-of-the-art Android static analysis tools avoid
handling native method invocation. None of those tools have the capability to
capture the inter-language behaviors.
  In this work, we explore an ensemble mechanism, which presents how the
combination of byte-code and native-code analysis of Android applications can
be efficiently used to cope with the advanced sophistication of Android
malware. We, therefore, present a multi-layer approach that utilizes deep
learning, natural language processing (NLP), as well as graph embedding
techniques to handle the threats of Android malware, both from the Java
byte-code and native code. After that, we design an ensemble algorithm to get
the final result of malware detection system. To be specific, the first layer
of our detection approach operates on the byte-code of application and the
native code level, whereas the second layer focuses on the ensemble algorithm.
Large-scale experiments on 100,113 samples (35,113 malware and 65,000 benign)
show that only byte-code sub-system yields 99.8% accuracy and native-code
sub-system yields an accuracy of 96.6%, whereas the Android-COCO method attains
an accuracy of 99.86% which outperforms various related works.

Unplanned intensive care unit (ICU) readmission rate is an important metric
for evaluating the quality of hospital care. Efficient and accurate prediction
of ICU readmission risk can not only help prevent patients from inappropriate
discharge and potential dangers, but also reduce associated costs of
healthcare. In this paper, we propose a new method that uses medical text of
Electronic Health Records (EHRs) for prediction, which provides an alternative
perspective to previous studies that heavily depend on numerical and
time-series features of patients. More specifically, we extract discharge
summaries of patients from their EHRs, and represent them with multiview graphs
enhanced by an external knowledge graph. Graph convolutional networks are then
used for representation learning. Experimental results prove the effectiveness
of our method, yielding state-of-the-art performance for this task.

As CUDA programs become the de facto program among data parallel applications
such as high-performance computing or machine learning applications, running
CUDA on other platforms has been a compelling option. Although several efforts
have attempted to support CUDA on other than NVIDIA GPU devices, due to extra
steps in the translation, the support is always behind a few years from
supporting CUDA's latest features. The examples are DPC, Hipfy, where CUDA
source code have to be translated to their native supporting language and then
they are supported. In particular, the new CUDA programming model exposes the
warp concept in the programming language, which greatly changes the way the
CUDA code should be mapped to CPU programs. In this paper, hierarchical
collapsing that \emph{correctly} supports CUDA warp-level functions on CPUs is
proposed. Based on hierarchical collapsing, a framework, COX, is developed that
allows CUDA programs with the latest features to be executed efficiently on CPU
platforms. COX consists of a compiler IR transformation (new LLVM pass) and a
runtime system to execute the transformed programs on CPU devices. COX can
support the most recent CUDA features, and the application coverage is much
higher (90%) than for previous frameworks (68%) with comparable performance. We
also show that the warp-level functions in CUDA can be efficiently executed by
utilizing CPU SIMD (AVX) instructions.

Motor imagery brain--machine interfaces enable us to control machines by
merely thinking of performing a motor action. Practical use cases require a
wearable solution where the classification of the brain signals is done locally
near the sensor using machine learning models embedded on energy-efficient
microcontroller units, for assured privacy, user comfort, and long-term usage.
In this work, we provide practical insights on the accuracy-cost trade-off for
embedded BMI solutions. Our multispectral Riemannian classifier reaches 75.1%
accuracy on a 4-class MI task. The accuracy is further improved by tuning
different types of classifiers to each subject, achieving 76.4%. We further
scale down the model by quantizing it to mixed-precision representations with a
minimal accuracy loss of 1% and 1.4%, respectively, which is still up to 4.1%
more accurate than the state-of-the-art embedded convolutional neural network.
We implement the model on a low-power MCU within an energy budget of merely
198uJ and taking only 16.9ms per classification. Classifying samples
continuously, overlapping the 3.5s samples by 50% to avoid missing user inputs
allows for operation at just 85uW. Compared to related works in embedded
MI-BMIs, our solution sets the new state-of-the-art in terms of accuracy-energy
trade-off for near-sensor classification.

This paper studies continual learning (CL) for sentiment classification (SC).
In this setting, the CL system learns a sequence of SC tasks incrementally in a
neural network, where each task builds a classifier to classify the sentiment
of reviews of a particular product category or domain. Two natural questions
are: Can the system transfer the knowledge learned in the past from the
previous tasks to the new task to help it learn a better model for the new
task? And, can old models for previous tasks be improved in the process as
well? This paper proposes a novel technique called KAN to achieve these
objectives. KAN can markedly improve the SC accuracy of both the new task and
the old tasks via forward and backward knowledge transfer. The effectiveness of
KAN is demonstrated through extensive experiments.

Existing research on continual learning of a sequence of tasks focused on
dealing with catastrophic forgetting, where the tasks are assumed to be
dissimilar and have little shared knowledge. Some work has also been done to
transfer previously learned knowledge to the new task when the tasks are
similar and have shared knowledge. To the best of our knowledge, no technique
has been proposed to learn a sequence of mixed similar and dissimilar tasks
that can deal with forgetting and also transfer knowledge forward and backward.
This paper proposes such a technique to learn both types of tasks in the same
network. For dissimilar tasks, the algorithm focuses on dealing with
forgetting, and for similar tasks, the algorithm focuses on selectively
transferring the knowledge learned from some similar previous tasks to improve
the new task learning. Additionally, the algorithm automatically detects
whether a new task is similar to any previous tasks. Empirical evaluation using
sequences of mixed tasks demonstrates the effectiveness of the proposed model.

If a robotic agent wants to exploit symbolic planning techniques to achieve
some goal, it must be able to properly ground an abstract planning domain in
the environment in which it operates. However, if the environment is initially
unknown by the agent, the agent needs to explore it and discover the salient
aspects of the environment needed to reach its goals. Namely, the agent has to
discover: (i) the objects present in the environment, (ii) the properties of
these objects and their relations, and finally (iii) how abstract actions can
be successfully executed. The paper proposes a framework that aims to
accomplish the aforementioned perspective for an agent that perceives the
environment partially and subjectively, through real value sensors (e.g., GPS,
and on-board camera) and can operate in the environment through low level
actuators (e.g., move forward of 20 cm). We evaluate the proposed architecture
in photo-realistic simulated environments, where the sensors are RGB-D on-board
camera, GPS and compass, and low level actions include movements,
grasping/releasing objects, and manipulating objects. The agent is placed in an
unknown environment and asked to find objects of a certain type, place an
object on top of another, close or open an object of a certain type. We compare
our approach with the state of the art methods on object goal navigation based
on reinforcement learning, showing better performances.

Machine learning methods especially deep neural networks have achieved great
success but many of them often rely on a number of labeled samples for
training. In real-world applications, we often need to address sample shortage
due to e.g., dynamic contexts with emerging prediction targets and costly
sample annotation. Therefore, low-resource learning, which aims to learn robust
prediction models with limited training samples, is now being widely
investigated. Among all the low-resource learning studies, many prefer to
utilize some auxiliary information in the form of Knowledge Graph (KG), which
is becoming more and more popular for knowledge representation, to reduce the
reliance on labeled samples. In this survey, we very comprehensively reviewed
over 90 papers about KG-aware research for two major low-resource learning
settings -- zero-shot learning (ZSL) where new classes for prediction have
never appeared in training, and few-shot learning (FSL) where new classes for
prediction have only a small number of labeled samples that are available. We
first introduced the KGs used in ZSL and FSL studies as well as the existing
and potential KG construction solutions, and then systematically categorized
and summarized KG-aware ZSL and FSL methods, dividing them into different
paradigms such as the mapping-based, the data augmentation, the
propagation-based and the optimization-based. We next presented different
applications, including not only KG augmented prediction tasks in Computer
Vision and Natural Language Processing (e.g., image classification, visual
question answering and knowledge extraction), but also tasks for KG curation
(e.g., inductive KG completion), and some typical evaluation resources for each
task. We eventually discussed some challenges and future directions on aspects
such as new learning and reasoning paradigms, and the construction of high
quality KGs.

Federated learning is increasingly being explored in the field of medical
imaging to train deep learning models on large scale datasets distributed
across different data centers while preserving privacy by avoiding the need to
transfer sensitive patient information. In this manuscript, we explore
federated learning in a multi-domain, multi-task setting wherein different
participating nodes may contain datasets sourced from different domains and are
trained to solve different tasks. We evaluated cross-domain federated learning
for the tasks of object detection and segmentation across two different
experimental settings: multi-modal and multi-organ. The result from our
experiments on cross-domain federated learning framework were very encouraging
with an overlap similarity of 0.79 for organ localization and 0.65 for lesion
segmentation. Our results demonstrate the potential of federated learning in
developing multi-domain, multi-task deep learning models without sharing data
from different domains.

In neural networks, non-linearity is introduced by activation functions. One
commonly used activation function is Rectified Linear Unit (ReLU). ReLU has
been a popular choice as an activation but has flaws. State-of-the-art
functions like Swish and Mish are now gaining attention as a better choice as
they combat many flaws presented by other activation functions. CoLU is an
activation function similar to Swish and Mish in properties. It is defined as
f(x)=x/(1-xe^-(x+e^x)). It is smooth, continuously differentiable, unbounded
above, bounded below, non-saturating, and non-monotonic. Based on experiments
done with CoLU with different activation functions, it is observed that CoLU
usually performs better than other functions on deeper neural networks. While
training different neural networks on MNIST on an incrementally increasing
number of convolutional layers, CoLU retained the highest accuracy for more
layers. On a smaller network with 8 convolutional layers, CoLU had the highest
mean accuracy, closely followed by ReLU. On VGG-13 trained on Fashion-MNIST,
CoLU had a 4.20% higher accuracy than Mish and 3.31% higher accuracy than ReLU.
On ResNet-9 trained on Cifar-10, CoLU had 0.05% higher accuracy than Swish,
0.09% higher accuracy than Mish, and 0.29% higher accuracy than ReLU. It is
observed that activation functions may behave better than other activation
functions based on different factors including the number of layers, types of
layers, number of parameters, learning rate, optimizer, etc. Further research
can be done on these factors and activation functions for more optimal
activation functions and more knowledge on their behavior.

Accurate gravity field models are essential for safe proximity operations
around small bodies. State-of-the-art techniques use spherical harmonics or
high-fidelity polyhedron shape models. Unfortunately, these techniques can
become inaccurate near the surface of the small body or have high computational
costs, especially for binary or heterogeneous small bodies. New learning-based
techniques do not encode a predefined structure and are more versatile. In
exchange for versatility, learning-based techniques can be less robust outside
the training data domain. In deployment, the spacecraft trajectory is the
primary source of dynamics data. Therefore, the training data domain should
include spacecraft trajectories to accurately evaluate the learned model's
safety and robustness. We have developed a novel method for learning-based
gravity models that directly uses the spacecraft's past trajectories. We
further introduce a method to evaluate the safety and robustness of
learning-based techniques via comparing accuracy within and outside of the
training domain. We demonstrate this safety and robustness method for two
learning-based frameworks: Gaussian processes and neural networks. Along with
the detailed analysis provided, we empirically establish the need for
robustness verification of learned gravity models when used for proximity

This paper describes how domain knowledge of power system operators can be
integrated into reinforcement learning (RL) frameworks to effectively learn
agents that control the grid's topology to prevent thermal cascading. Typical
RL-based topology controllers fail to perform well due to the large
search/optimization space. Here, we propose an actor-critic-based agent to
address the problem's combinatorial nature and train the agent using the RL
environment developed by RTE, the French TSO. To address the challenge of the
large optimization space, a curriculum-based approach with reward tuning is
incorporated into the training procedure by modifying the environment using
network physics for enhanced agent learning. Further, a parallel training
approach on multiple scenarios is employed to avoid biasing the agent to a few
scenarios and make it robust to the natural variability in grid operations.
Without these modifications to the training procedure, the RL agent failed for
most test scenarios, illustrating the importance of properly integrating domain
knowledge of physical systems for real-world RL learning. The agent was tested
by RTE for the 2019 learning to run the power network challenge and was awarded
the 2nd place in accuracy and 1st place in speed. The developed code is
open-sourced for public use.

We present algorithms for estimating the forward reachable set of a dynamical
system using only a finite collection of independent and identically
distributed samples. The produced estimate is the sublevel set of a function
called an empirical inverse Christoffel function: empirical inverse Christoffel
functions are known to provide good approximations to the support of
probability distributions. In addition to reachability analysis, the same
approach can be applied to general problems of estimating the support of a
random variable, which has applications in data science towards detection of
novelties and outliers in data sets. In applications where safety is a concern,
having a guarantee of accuracy that holds on finite data sets is critical. In
this paper, we prove such bounds for our algorithms under the Probably
Approximately Correct (PAC) framework. In addition to applying classical
Vapnik-Chervonenkis (VC) dimension bound arguments, we apply the PAC-Bayes
theorem by leveraging a formal connection between kernelized empirical inverse
Christoffel functions and Gaussian process regression models. The bound based
on PAC-Bayes applies to a more general class of Christoffel functions than the
VC dimension argument, and achieves greater sample efficiency in experiments.

In recent years, algorithms and neural architectures based on the
Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism
problem, emerged as a powerful tool for machine learning with graphs and
relational data. Here, we give a comprehensive overview of the algorithm's use
in a machine learning setting, focusing on the supervised regime. We discuss
the theoretical background, show how to use it for supervised graph- and node
representation learning, discuss recent extensions, and outline the algorithm's
connection to (permutation-)equivariant neural architectures. Moreover, we give
an overview of current applications and future directions to stimulate further
research.

In several machine learning tasks for graph structured data, the graphs under
consideration may be composed of a varying number of nodes. Therefore, it is
necessary to design pooling methods that aggregate the graph representations of
varying size to representations of fixed size which can be used in downstream
tasks, such as graph classification. Existing graph pooling methods offer no
guarantee with regards to the similarity of a graph representation and its
pooled version. In this work, we address this limitation by proposing FlowPool,
a pooling method that optimally preserves the statistics of a graph
representation to its pooled counterpart by minimising their Wasserstein
distance. This is achieved by performing a Wasserstein gradient flow with
respect to the pooled graph representation. Our method relies on a versatile
implementation which can take into account the geometry of the representation
space through any ground cost and computes the gradient of the Wasserstein
distance with automatic differentiation. We propose the differentiation of the
Wasserstein flow layer using an implicit differentiation scheme. Therefore, our
pooling method is amenable to automatic differentiation and can be integrated
in end-to-end deep learning architectures. Further, FlowPool is invariant to
permutations and can therefore be combined with permutation equivariant feature
extraction layers in GNNs in order to obtain predictions that are independent
of the ordering of the nodes. Experimental results demonstrate that our method
leads to an increase in performance compared to existing pooling methods when
evaluated on graph classification.

As the technological advancement and capabilities of automated systems have
increased drastically, the usage of unmanned aerial vehicles for performing
human-dependent tasks without human indulgence has also spiked. Since unmanned
aerial vehicles are heavily dependent on Information and Communication
Technology, they are highly prone to cyber-attacks. With time more advanced and
new attacks are being developed and employed. However, the current Intrusion
detection system lacks detection and classification of new and unknown attacks.
Therefore, for having an autonomous and reliable operation of unmanned aerial
vehicles, more robust and automated cyber detection and protection schemes are
needed. To address this, we have proposed an autonomous self-incremental
learning architecture, capable of detecting known and unknown cyber-attacks on
its own without any human interference. In our approach, we have combined
signature-based detection along with anomaly detection in such a way that the
signature-based detector autonomously updates its attack classes with the help
of an anomaly detector. To achieve this, we have implemented an incremental
learning approach, updating our model to incorporate new classes without
forgetting the old ones. To validate the applicability and effectiveness of our
proposed architecture, we have implemented it in a trial scenario and then
compared it with the traditional offline learning approach. Moreover, our
anomaly-based detector has achieved a 100% detection rate for attacks.

The ubiquity of AI leads to situations where humans and AI work together,
creating the need for learning-to-defer algorithms that determine how to
partition tasks between AI and humans. We work to improve learning-to-defer
algorithms when paired with specific individuals by incorporating two
fine-tuning algorithms and testing their efficacy using both synthetic and
image datasets. We find that fine-tuning can pick up on simple human skill
patterns, but struggles with nuance, and we suggest future work that uses
robust semi-supervised to improve learning.

In the current era of the internet, where social media platforms are easily
accessible for everyone, people often have to deal with threats, identity
attacks, hate, and bullying due to their association with a cast, creed,
gender, religion, or even acceptance or rejection of a notion. Existing works
in hate speech detection primarily focus on individual comment classification
as a sequence labeling task and often fail to consider the context of the
conversation. The context of a conversation often plays a substantial role when
determining the author's intent and sentiment behind the tweet. This paper
describes the system proposed by team MIDAS-IIITD for HASOC 2021 subtask 2, one
of the first shared tasks focusing on detecting hate speech from Hindi-English
code-mixed conversations on Twitter. We approach this problem using neural
networks, leveraging the transformer's cross-lingual embeddings and further
finetuning them for low-resource hate-speech classification in transliterated
Hindi text. Our best performing system, a hard voting ensemble of Indic-BERT,
XLM-RoBERTa, and Multilingual BERT, achieved a macro F1 score of 0.7253,
placing us first on the overall leaderboard standings.

Purpose: (1) To develop a deep learning algorithm to identify major tissue
structures of the optic nerve head (ONH) in 3D optical coherence tomography
(OCT) scans; (2) to exploit such information to robustly differentiate among
healthy, optic disc drusen (ODD), and papilledema ONHs.
  It was a cross-sectional comparative study with confirmed ODD (105 eyes),
papilledema due to high intracranial pressure (51 eyes), and healthy controls
(100 eyes). 3D scans of the ONHs were acquired using OCT, then processed to
improve deep-tissue visibility. At first, a deep learning algorithm was
developed using 984 B-scans (from 130 eyes) in order to identify: major
neural/connective tissues, and ODD regions. The performance of our algorithm
was assessed using the Dice coefficient (DC). In a 2nd step, a classification
algorithm (random forest) was designed using 150 OCT volumes to perform 3-class
classifications (1: ODD, 2: papilledema, 3: healthy) strictly from their drusen
and prelamina swelling scores (derived from the segmentations). To assess
performance, we reported the area under the receiver operating characteristic
curves (AUCs) for each class.
  Our segmentation algorithm was able to isolate neural and connective tissues,
and ODD regions whenever present. This was confirmed by an average DC of
0.93$\pm$0.03 on the test set, corresponding to good performance.
Classification was achieved with high AUCs, i.e. 0.99$\pm$0.01 for the
detection of ODD, 0.99 $\pm$ 0.01 for the detection of papilledema, and
0.98$\pm$0.02 for the detection of healthy ONHs.
  Our AI approach accurately discriminated ODD from papilledema, using a single
OCT scan. Our classification performance was excellent, with the caveat that
validation in a much larger population is warranted. Our approach may have the
potential to establish OCT as the mainstay of diagnostic imaging in
neuro-ophthalmology.

Amongst a variety of approaches aimed at making the learning procedure of
neural networks more effective, the scientific community developed strategies
to order the examples according to their estimated complexity, to distil
knowledge from larger networks, or to exploit the principles behind adversarial
machine learning. A different idea has been recently proposed, named Friendly
Training, which consists in altering the input data by adding an automatically
estimated perturbation, with the goal of facilitating the learning process of a
neural classifier. The transformation progressively fades-out as long as
training proceeds, until it completely vanishes. In this work we revisit and
extend this idea, introducing a radically different and novel approach inspired
by the effectiveness of neural generators in the context of Adversarial Machine
Learning. We propose an auxiliary multi-layer network that is responsible of
altering the input data to make them easier to be handled by the classifier at
the current stage of the training procedure. The auxiliary network is trained
jointly with the neural classifier, thus intrinsically increasing the 'depth'
of the classifier, and it is expected to spot general regularities in the data
alteration process. The effect of the auxiliary network is progressively
reduced up to the end of training, when it is fully dropped and the classifier
is deployed for applications. We refer to this approach as Neural Friendly
Training. An extended experimental procedure involving several datasets and
different neural architectures shows that Neural Friendly Training overcomes
the originally proposed Friendly Training technique, improving the
generalization of the classifier, especially in the case of noisy data.

We review previous approaches to nowcasting earthquakes and introduce new
approaches based on deep learning using three distinct models based on
recurrent neural networks and transformers. We discuss different choices for
observables and measures presenting promising initial results for a region of
Southern California from 1950-2020. Earthquake activity is predicted as a
function of 0.1-degree spatial bins for time periods varying from two weeks to
four years. The overall quality is measured by the Nash Sutcliffe Efficiency
comparing the deviation of nowcast and observation with the variance over time
in each spatial region. The software is available as open-source together with
the preprocessed data from the USGS.

We consider the problem of probabilistic forecasting over categories with
graph structure, where the dynamics at a vertex depends on its local
connectivity structure. We present GOPHER, a method that combines the inductive
bias of graph neural networks with neural ODEs to capture the intrinsic local
continuous-time dynamics of our probabilistic forecasts. We study the benefits
of these two inductive biases by comparing against baseline models that help
disentangle the benefits of each. We find that capturing the graph structure is
crucial for accurate in-domain probabilistic predictions and more sample
efficient models. Surprisingly, our experiments demonstrate that the continuous
time evolution inductive bias brings little to no benefit despite reflecting
the true probability dynamics.

We explain how to use Kolmogorov Superposition Theorem (KST) to overcome the
curse of dimensionality in approximating multi-dimensional functions and
learning multi-dimensional data sets by using neural networks of two hidden
layers. That is, there is a class of functions called $K$-Lipschitz continuous
in the sense that the K-outer function $g$ of $f$ is Lipschitz continuous and
can be approximated by a ReLU network of two layers with $(2d+1)dn, dn$ widths
to have an approximation order $O(d^2/n)$. In addition, we show that
polynomials of high degree can be reproduced by using neural networks with
activation function $\sigma_\ell(t)=(t_+)^\ell$ for $\ell\ge 2$ with multiple
layers and appropriate widths. More layers of neural networks, the higher
degree polynomials can be reproduced. Furthermore, we explain how many layers,
weights, and neurons of neural networks are needed in order to reproduce high
degree polynomials based on $\sigma_\ell$. Finally, we present a mathematical
justification for image classification by using the convolutional neural
network algorithm.

As Covid-19 has been constantly getting mutated and in three or four months a
new variant gets introduced to us and it comes with more deadly problems. The
things that prevent us from getting Covid is getting vaccinated and wearing a
face mask. In this paper, we have implemented a new Face Mask Detection and
Person Recognition model named Insight face which is based on SoftMax loss
classification algorithm Arc Face loss and names it as RFMPI-DNN(Rapid Face
Detection and Peron Identification Model based on Deep Neural Networks) to
detect face mask and person identity rapidly as compared to other models
available. To compare our new model, we have used previous MobileNet_V2 model
and face recognition module for effective comparison on the basis of time. The
proposed model implemented in the system has outperformed the model compared in
this paper in every aspect

Offline estimation of the dynamical model of a Markov Decision Process (MDP)
is a non-trivial task that greatly depends on the data available to the
learning phase. Sometimes the dynamics of the model is invariant with respect
to some transformations of the current state and action. Recent works showed
that an expert-guided pipeline relying on Density Estimation methods as Deep
Neural Network based Normalizing Flows effectively detects this structure in
deterministic environments, both categorical and continuous-valued. The
acquired knowledge can be exploited to augment the original data set, leading
eventually to a reduction in the distributional shift between the true and the
learnt model. Such data augmentation technique can be exploited as a
preliminary process to be executed before the adoption of an Offline
Reinforcement Learning architecture, increasing its performance. In this work
we extend the paradigm to also tackle non deterministic MDPs, in particular 1)
we propose a detection threshold in categorical environments based on
statistical distances, and 2) we show that the former results lead to a
performance improvement when solving the learnt MDP and then applying the
optimal policy in the real environment.

Biomedical decision making involves multiple signal processing, either from
different sensors or from different channels. In both cases, information fusion
plays a significant role. A deep learning based electroencephalogram channels'
feature level fusion is carried out in this work for the electroencephalogram
cyclic alternating pattern A phase classification. Channel selection, fusion,
and classification procedures were optimized by two optimization algorithms,
namely, Genetic Algorithm and Particle Swarm Optimization. The developed
methodologies were evaluated by fusing the information from multiple
electroencephalogram channels for patients with nocturnal frontal lobe epilepsy
and patients without any neurological disorder, which was significantly more
challenging when compared to other state of the art works. Results showed that
both optimization algorithms selected a comparable structure with similar
feature level fusion, consisting of three electroencephalogram channels, which
is in line with the CAP protocol to ensure multiple channels' arousals for CAP
detection. Moreover, the two optimized models reached an area under the
receiver operating characteristic curve of 0.82, with average accuracy ranging
from 77% to 79%, a result which is in the upper range of the specialist
agreement. The proposed approach is still in the upper range of the best state
of the art works despite a difficult dataset, and has the advantage of
providing a fully automatic analysis without requiring any manual procedure.
Ultimately, the models revealed to be noise resistant and resilient to multiple
channel loss.

With the rapid development of information technology, online platforms (e.g.,
news portals and social media) generate enormous web information every moment.
Therefore, it is crucial to extract structured representations of events from
social streams. Generally, existing event extraction research utilizes pattern
matching, machine learning, or deep learning methods to perform event
extraction tasks. However, the performance of Chinese event extraction is not
as good as English due to the unique characteristics of the Chinese language.
In this paper, we propose an integrated framework to perform Chinese event
extraction. The proposed approach is a multiple channel input neural framework
that integrates semantic features and syntactic features. The semantic features
are captured by BERT architecture. The Part of Speech (POS) features and
Dependency Parsing (DP) features are captured by profiling embeddings and Graph
Convolutional Network (GCN), respectively. We also evaluate our model on a
real-world dataset. Experimental results show that the proposed method
outperforms the benchmark approaches significantly.

Registration of point clouds related by rigid transformations is one of the
fundamental problems in computer vision. However, a solution to the practical
scenario of aligning sparsely and differently sampled observations in the
presence of noise is still lacking. We approach registration in this scenario
with a fusion of the closed-form Universal Mani-fold Embedding (UME) method and
a deep neural network. The two are combined into a single unified framework,
named DeepUME, trained end-to-end and in an unsupervised manner. To
successfully provide a global solution in the presence of large
transformations, we employ an SO(3)-invariant coordinate system to learn both a
joint-resampling strategy of the point clouds and SO(3)-invariant features.
These features are then utilized by the geometric UME method for transformation
estimation. The parameters of DeepUME are optimized using a metric designed to
overcome an ambiguity problem emerging in the registration of symmetric shapes,
when noisy scenarios are considered. We show that our hybrid method outperforms
state-of-the-art registration methods in various scenarios, and generalizes
well to unseen data sets. Our code is publicly available.

Knowledge graphs (KGs), as structured representations of real world facts,
are intelligent databases incorporating human knowledge that can help machine
imitate the way of human problem solving. However, due to the nature of rapid
iteration as well as incompleteness of data, KGs are usually huge and there are
inevitably missing facts in KGs. Link prediction for knowledge graphs is the
task aiming to complete missing facts by reasoning based on the existing
knowledge. Two main streams of research are widely studied: one learns
low-dimensional embeddings for entities and relations that can capture latent
patterns, and the other gains good interpretability by mining logical rules.
Unfortunately, previous studies rarely pay attention to heterogeneous KGs. In
this paper, we propose DegreEmbed, a model that combines embedding-based
learning and logic rule mining for inferring on KGs. Specifically, we study the
problem of predicting missing links in heterogeneous KGs that involve entities
and relations of various types from the perspective of the degrees of nodes.
Experimentally, we demonstrate that our DegreEmbed model outperforms the
state-of-the-art methods on real world datasets. Meanwhile, the rules mined by
our model are of high quality and interpretability.

In order to address increasing demands of real-world applications, the
research for knowledge-intensive NLP (KI-NLP) should advance by capturing the
challenges of a truly open-domain environment: web-scale knowledge, lack of
structure, inconsistent quality and noise. To this end, we propose a new setup
for evaluating existing knowledge intensive tasks in which we generalize the
background corpus to a universal web snapshot. We investigate a slate of NLP
tasks which rely on knowledge - either factual or common sense, and ask systems
to use a subset of CCNet - the Sphere corpus - as a knowledge source. In
contrast to Wikipedia, otherwise a common background corpus in KI-NLP, Sphere
is orders of magnitude larger and better reflects the full diversity of
knowledge on the web. Despite potential gaps in coverage, challenges of scale,
lack of structure and lower quality, we find that retrieval from Sphere enables
a state of the art system to match and even outperform Wikipedia-based models
on several tasks. We also observe that while a dense index can outperform a
sparse BM25 baseline on Wikipedia, on Sphere this is not yet possible. To
facilitate further research and minimise the community's reliance on
proprietary, black-box search engines, we share our indices, evaluation metrics
and infrastructure.

Real-time registration of partially overlapping point clouds has emerging
applications in cooperative perception for autonomous vehicles and multi-agent
SLAM. The relative translation between point clouds in these applications is
higher than in traditional SLAM and odometry applications, which challenges the
identification of correspondences and a successful registration. In this paper,
we propose a novel registration method for partially overlapping point clouds
where correspondences are learned using an efficient point-wise feature
encoder, and refined using a graph-based attention network. This attention
network exploits geometrical relationships between key points to improve the
matching in point clouds with low overlap. At inference time, the relative pose
transformation is obtained by robustly fitting the correspondences through
sample consensus. The evaluation is performed on the KITTI dataset and a novel
synthetic dataset including low-overlapping point clouds with displacements of
up to 30m. The proposed method achieves on-par performance with
state-of-the-art methods on the KITTI dataset, and outperforms existing methods
for low overlapping point clouds. Additionally, the proposed method achieves
significantly faster inference times, as low as 410ms, between 5 and 35 times
faster than competing methods. Our code and dataset are available at
https://github.com/eduardohenriquearnold/fastreg.

Recent advances in neuroimaging along with algorithmic innovations in
statistical learning from network data offer a unique pathway to integrate
brain structure and function, and thus facilitate revealing some of the brain's
organizing principles at the system level. In this direction, we develop a
supervised graph representation learning framework to model the relationship
between brain structural connectivity (SC) and functional connectivity (FC) via
a graph encoder-decoder system, where the SC is used as input to predict
empirical FC. A trainable graph convolutional encoder captures direct and
indirect interactions between brain regions-of-interest that mimic actual
neural communications, as well as to integrate information from both the
structural network topology and nodal (i.e., region-specific) attributes. The
encoder learns node-level SC embeddings which are combined to generate (whole
brain) graph-level representations for reconstructing empirical FC networks.
The proposed end-to-end model utilizes a multi-objective loss function to
jointly reconstruct FC networks and learn discriminative graph representations
of the SC-to-FC mapping for downstream subject (i.e., graph-level)
classification. Comprehensive experiments demonstrate that the learnt
representations of said relationship capture valuable information from the
intrinsic properties of the subject's brain networks and lead to improved
accuracy in classifying a large population of heavy drinkers and non-drinkers
from the Human Connectome Project. Our work offers new insights on the
relationship between brain networks that support the promising prospect of
using graph representation learning to discover more about human brain activity
and function.

We present a novel 3D instance segmentation framework for Multi-View Stereo
(MVS) buildings in urban scenes. Unlike existing works focusing on semantic
segmentation of urban scenes, the emphasis of this work lies in detecting and
segmenting 3D building instances even if they are attached and embedded in a
large and imprecise 3D surface model. Multi-view RGB images are first enhanced
to RGBH images by adding a heightmap and are segmented to obtain all roof
instances using a fine-tuned 2D instance segmentation neural network. Instance
masks from different multi-view images are then clustered into global masks.
Our mask clustering accounts for spatial occlusion and overlapping, which can
eliminate segmentation ambiguities among multi-view images. Based on these
global masks, 3D roof instances are segmented out by mask back-projections and
extended to the entire building instances through a Markov random field
optimization. A new dataset that contains instance-level annotation for both 3D
urban scenes (roofs and buildings) and drone images (roofs) is provided. To the
best of our knowledge, it is the first outdoor dataset dedicated to 3D instance
segmentation with much more annotations of attached 3D buildings than existing
datasets. Quantitative evaluations and ablation studies have shown the
effectiveness of all major steps and the advantages of our multi-view framework
over the orthophoto-based method.

Rule-based fine-grained IP geolocation methods are hard to generalize in
computer networks which do not follow hypothetical rules. Recently, deep
learning methods, like multi-layer perceptron (MLP), are tried to increase
generalization capabilities. However, MLP is not so suitable for
graph-structured data like networks. MLP treats IP addresses as isolated
instances and ignores the connection information, which limits geolocation
accuracy. In this work, we research how to increase the generalization
capability with an emerging graph deep learning method - Graph Neural Network
(GNN). First, IP geolocation is re-formulated as an attributed graph node
regression problem. Then, we propose a GNN-based IP geolocation framework named
GNN-Geo. GNN-Geo consists of a preprocessor, an encoder, messaging passing (MP)
layers and a decoder. The preprocessor and encoder transform measurement data
into the initial node embeddings. MP layers refine the initial node embeddings
by modeling the connection information. The decoder maps the refined embeddings
to nodes' locations and relieves the convergence problem of GNN by considering
prior knowledge. The experiments in different real-world datasets show: the
proposed GNN-Geo outperforms the state-of-art rule-based and learning-based
baselines on all datasets w.r.t median error distance by 16% to 28%. This work
verifies the great potential of GNN for fine-grained IP geolocation.

Subgraph recognition aims at discovering a compressed substructure of a graph
that is most informative to the graph property. It can be formulated by
optimizing Graph Information Bottleneck (GIB) with a mutual information
estimator. However, GIB suffers from training instability and degenerated
results due to its intrinsic optimization process. To tackle these issues, we
reformulate the subgraph recognition problem into two steps: graph perturbation
and subgraph selection, leading to a novel Variational Graph Information
Bottleneck (VGIB) framework. VGIB first employs the noise injection to modulate
the information flow from the input graph to the perturbed graph. Then, the
perturbed graph is encouraged to be informative to the graph property. VGIB
further obtains the desired subgraph by filtering out the noise in the
perturbed graph. With the customized noise prior for each input, the VGIB
objective is endowed with a tractable variational upper bound, leading to a
superior empirical performance as well as theoretical properties. Extensive
experiments on graph interpretation, explainability of Graph Neural Networks,
and graph classification show that VGIB finds better subgraphs than existing
methods. Code is avaliable at https://github.com/Samyu0304/VGIB

Recently, barrier function-based safe reinforcement learning (RL) with the
actor-critic structure for continuous control tasks has received increasing
attention. It is still challenging to learn a near-optimal control policy with
safety and convergence guarantees. Also, few works have addressed the safe RL
algorithm design under time-varying safety constraints. This paper proposes a
model-based safe RL algorithm for optimal control of nonlinear systems with
time-varying state and control constraints. In the proposed approach, we
construct a novel barrier-based control policy structure that can guarantee
control safety. A multi-step policy evaluation mechanism is proposed to predict
the policy's safety risk under time-varying safety constraints and guide the
policy to update safely. Theoretical results on stability and robustness are
proven. Also, the convergence of the actor-critic learning algorithm is
analyzed. The performance of the proposed algorithm outperforms several
state-of-the-art RL algorithms in the simulated Safety Gym environment.
Furthermore, the approach is applied to the integrated path following and
collision avoidance problem for two real-world intelligent vehicles. A
differential-drive vehicle and an Ackermann-drive one are used to verify the
offline deployment performance and the online learning performance,
respectively. Our approach shows an impressive sim-to-real transfer capability
and a satisfactory online control performance in the experiment.

Machine learning (ML) in general and deep learning (DL) in particular has
become an extremely popular tool in several vision applications (like object
detection, super resolution, segmentation, object tracking etc.). Almost in
parallel, the issue of explainability in ML (i.e. the ability to
explain/elaborate the way a trained ML model arrived at its decision) in vision
has also received fairly significant attention from various quarters. However,
we argue that the current philosophy behind explainable ML suffers from certain
limitations, and the resulting explanations may not meaningfully uncover black
box ML models. To elaborate our assertion, we first raise a few fundamental
questions which have not been adequately discussed in the corresponding
literature. We also provide perspectives on how explainablity in ML can benefit
by relying on more rigorous principles in the related areas.

As Graph Neural Networks (GNNs) are widely adopted in digital pathology,
there is increasing attention to developing explanation models (explainers) of
GNNs for improved transparency in clinical decisions.
  Existing explainers discover an explanatory subgraph relevant to the
  However, such a subgraph is insufficient to reveal all the critical
biological substructures for the prediction because the prediction will remain
unchanged after removing that subgraph.
  Hence, an explanatory subgraph should be not only necessary for prediction,
but also sufficient to uncover the most predictive regions for the explanation.
  Such explanation requires a measurement of information transferred from
different input subgraphs to the predictive output, which we define as
information flow.
  In this work, we address these key challenges and propose IFEXPLAINER, which
generates a necessary and sufficient explanation for GNNs.
  To evaluate the information flow within GNN's prediction, we first propose a
novel notion of predictiveness, named $f$-information, which is directional and
incorporates the realistic capacity of the GNN model.
  Based on it, IFEXPLAINER generates the explanatory subgraph with maximal
information flow to the prediction.
  Meanwhile, it minimizes the information flow from the input to the predictive
result after removing the explanation.
  Thus, the produced explanation is necessarily important to the prediction and
sufficient to reveal the most crucial substructures.
  We evaluate IFEXPLAINER to interpret GNN's predictions on breast cancer
subtyping.
  Experimental results on the BRACS dataset show the superior performance of

Matrix approximations are a key element in large-scale algebraic machine
learning approaches. The recently proposed method MEKA (Si et al., 2014)
effectively employs two common assumptions in Hilbert spaces: the low-rank
property of an inner product matrix obtained from a shift-invariant kernel
function and a data compactness hypothesis by means of an inherent
block-cluster structure. In this work, we extend MEKA to be applicable not only
for shift-invariant kernels but also for non-stationary kernels like polynomial
kernels and an extreme learning kernel. We also address in detail how to handle
non-positive semi-definite kernel functions within MEKA, either caused by the
approximation itself or by the intentional use of general kernel functions. We
present a Lanczos-based estimation of a spectrum shift to develop a stable
positive semi-definite MEKA approximation, also usable in classical convex
optimization frameworks. Furthermore, we support our findings with theoretical
considerations and a variety of experiments on synthetic and real-world data.

Recently, model-driven deep learning unrolls a certain iterative algorithm of
a regularization model into a cascade network by replacing the first-order
information (i.e., (sub)gradient or proximal operator) of the regularizer with
a network module, which appears more explainable and predictable compared to
common data-driven networks. Conversely, in theory, there is not necessarily
such a functional regularizer whose first-order information matches the
replaced network module, which means the network output may not be covered by
the original regularization model. Moreover, up to now, there is also no theory
to guarantee the global convergence and robustness (regularity) of unrolled
networks under realistic assumptions. To bridge this gap, this paper propose to
present a safeguarded methodology on network unrolling. Specifically, focusing
on accelerated MRI, we unroll a zeroth-order algorithm, of which the network
module represents the regularizer itself, so that the network output can be
still covered by the regularization model. Furthermore, inspired by the ideal
of deep equilibrium models, before backpropagating, we carry out the unrolled
iterative network to converge to a fixed point to ensure the convergence. In
case the measurement data contains noise, we prove that the proposed network is
robust against noisy interference. Finally, numerical experiments show that the
proposed network consistently outperforms the state-of-the-art MRI
reconstruction methods including traditional regularization methods and other
deep learning methods.

Action prediction aims to infer the forthcoming human action with
partially-observed videos, which is a challenging task due to the limited
information underlying early observations. Existing methods mainly adopt a
reconstruction strategy to handle this task, expecting to learn a single
mapping function from partial observations to full videos to facilitate the
prediction process. In this study, we propose adversarial memory networks
(AMemNet) to generate the "full video" feature conditioning on a partial video
query from two new aspects. Firstly, a key-value structured memory generator is
designed to memorize different partial videos as key memories and dynamically
write full videos in value memories with gating mechanism and querying
attention. Secondly, we develop a class-aware discriminator to guide the memory
generator to deliver not only realistic but also discriminative full video
features upon adversarial training. The final prediction result of AMemNet is
given by late fusion over RGB and optical flow streams. Extensive experimental
results on two benchmark video datasets, UCF-101 and HMDB51, are provided to
demonstrate the effectiveness of the proposed AMemNet model over

Transformer based architectures have shown notable results on many down
streaming tasks including question answering. The availability of data, on the
other hand, impedes obtaining legitimate performance for low-resource
languages. In this paper, we investigate the applicability of pre-trained
multilingual models to improve the performance of question answering in
low-resource languages. We tested four combinations of language and task
adapters using multilingual transformer architectures on seven languages
similar to MLQA dataset. Additionally, we have also proposed zero-shot transfer
learning of low-resource question answering using language and task adapters.
We observed that stacking the language and the task adapters improves the
multilingual transformer models' performance significantly for low-resource

We consider the off-policy evaluation (OPE) problem in contextual bandits,
where the goal is to estimate the value of a target policy using the data
collected by a logging policy. Most popular approaches to the OPE are variants
of the doubly robust (DR) estimator obtained by combining a direct method (DM)
estimator and a correction term involving the inverse propensity score (IPS).
Existing algorithms primarily focus on strategies to reduce the variance of the
DR estimator arising from large IPS. We propose a new approach called the
Doubly Robust with Information borrowing and Context-based switching (DR-IC)
estimator that focuses on reducing both bias and variance. The DR-IC estimator
replaces the standard DM estimator with a parametric reward model that borrows
information from the 'closer' contexts through a correlation structure that
depends on the IPS. The DR-IC estimator also adaptively interpolates between
this modified DM estimator and a modified DR estimator based on a
context-specific switching rule. We give provable guarantees on the performance
of the DR-IC estimator. We also demonstrate the superior performance of the
DR-IC estimator compared to the state-of-the-art OPE algorithms on a number of
benchmark problems.

Developing Natural Language Processing resources for a low resource language
is a challenging but essential task. In this paper, we present a Morphological
Analyzer for Gujarati. We have used a Bi-Directional LSTM based approach to
perform morpheme boundary detection and grammatical feature tagging. We have
created a data set of Gujarati words with lemma and grammatical features. The
Bi-LSTM based model of Morph Analyzer discussed in the paper handles the
language morphology effectively without the knowledge of any hand-crafted
suffix rules. To the best of our knowledge, this is the first dataset and morph
analyzer model for the Gujarati language which performs both grammatical
feature tagging and morpheme boundary detection tasks.

We introduce two new no-regret algorithms for the stochastic shortest path
(SSP) problem with a linear MDP that significantly improve over the only
existing results of (Vial et al., 2021). Our first algorithm is computationally
efficient and achieves a regret bound
$\widetilde{O}\left(\sqrt{d^3B_{\star}^2T_{\star} K}\right)$, where $d$ is the
dimension of the feature space, $B_{\star}$ and $T_{\star}$ are upper bounds of
the expected costs and hitting time of the optimal policy respectively, and $K$
is the number of episodes. The same algorithm with a slight modification also
achieves logarithmic regret of order
$O\left(\frac{d^3B_{\star}^4}{c_{\min}^2\text{gap}_{\min}}\ln^5\frac{dB_{\star}
K}{c_{\min}} \right)$, where $\text{gap}_{\min}$ is the minimum sub-optimality
gap and $c_{\min}$ is the minimum cost over all state-action pairs. Our result
is obtained by developing a simpler and improved analysis for the
finite-horizon approximation of (Cohen et al., 2021) with a smaller
approximation error, which might be of independent interest. On the other hand,
using variance-aware confidence sets in a global optimization problem, our
second algorithm is computationally inefficient but achieves the first
"horizon-free" regret bound $\widetilde{O}(d^{3.5}B_{\star}\sqrt{K})$ with no
polynomial dependency on $T_{\star}$ or $1/c_{\min}$, almost matching the
$\Omega(dB_{\star}\sqrt{K})$ lower bound from (Min et al., 2021).

In this paper, we present a deep learning-based reduced-order model (DL-ROM)
for the stability prediction of unsteady 3D fluid-structure interaction
systems. The proposed DL-ROM has the format of a nonlinear state-space model
and employs a recurrent neural network with long short-term memory (LSTM). We
consider a canonical fluid-structure system of an elastically-mounted sphere
coupled with incompressible fluid flow in a state-space format. We develop a
nonlinear data-driven coupling for predicting unsteady forces and
vortex-induced vibration (VIV) lock-in of the freely vibrating sphere in a
transverse direction. We design an input-output relationship as a temporal
sequence of force and displacement datasets for a low-dimensional approximation
of the fluid-structure system. Based on the prior knowledge of the VIV lock-in
process, the input function contains a range of frequencies and amplitudes,
which enables an efficient DL-ROM without the need for a massive training
dataset for the low-dimensional modeling. Once trained, the network provides a
nonlinear mapping of input-output dynamics that can predict the coupled
fluid-structure dynamics for a longer horizon via the feedback process. By
integrating the LSTM network with the eigensystem realization algorithm (ERA),
we construct a data-driven state-space model for the reduced-order stability
analysis. We investigate the underlying mechanism and stability characteristics
of VIV via an eigenvalue selection process. To understand the frequency lock-in
mechanism, we study the eigenvalue trajectories for a range of the reduced
oscillation frequencies and the mass ratios. Consistent with the full-order
simulations, the frequency lock-in branches are accurately captured by the
combined LSTM-ERA procedure. The proposed DL-ROM aligns with the development of
physics-based digital twin of engineering systems involving fluid-structure

Active visual tracking of space non-cooperative object is significant for
future intelligent spacecraft to realise space debris removal, asteroid
exploration, autonomous rendezvous and docking. However, existing works often
consider this task into different subproblems (e.g. image preprocessing,
feature extraction and matching, position and pose estimation, control law
design) and optimize each module alone, which are trivial and sub-optimal. To
this end, we propose an end-to-end active visual tracking method based on DQN
algorithm, named as DRLAVT. It can guide the chasing spacecraft approach to
arbitrary space non-cooperative target merely relied on color or RGBD images,
which significantly outperforms position-based visual servoing baseline
algorithm that adopts state-of-the-art 2D monocular tracker, SiamRPN. Extensive
experiments implemented with diverse network architectures, different
perturbations and multiple targets demonstrate the advancement and robustness
of DRLAVT. In addition, We further prove our method indeed learnt the motion
patterns of target with deep reinforcement learning through hundreds of
trial-and-errors.

We present a new neighbor sampling method on temporal graphs. In a temporal
graph, predicting different nodes' time-varying properties can require the
receptive neighborhood of various temporal scales. In this work, we propose the
TNS (Time-aware Neighbor Sampling) method: TNS learns from temporal information
to provide an adaptive receptive neighborhood for every node at any time.
Learning how to sample neighbors is non-trivial, since the neighbor indices in
time order are discrete and not differentiable. To address this challenge, we
transform neighbor indices from discrete values to continuous ones by
interpolating the neighbors' messages. TNS can be flexibly incorporated into
popular temporal graph networks to improve their effectiveness without
increasing their time complexity. TNS can be trained in an end-to-end manner.
It needs no extra supervision and is automatically and implicitly guided to
sample the neighbors that are most beneficial for prediction. Empirical results
on multiple standard datasets show that TNS yields significant gains on edge

Building Information Modelling (BIM) software use scalable vector formats to
enable flexible designing of floor plans in the industry. Floor plans in the
architectural domain can come from many sources that may or may not be in
scalable vector format. The conversion of floor plan images to fully annotated
vector images is a process that can now be realized by computer vision. Novel
datasets in this field have been used to train Convolutional Neural Network
(CNN) architectures for object detection. Image enhancement through
Super-Resolution (SR) is also an established CNN based network in computer
vision that is used for converting low resolution images to high resolution
ones. This work focuses on creating a multi-component module that stacks a SR
model on a floor plan object detection model. The proposed stacked model shows
greater performance than the corresponding vanilla object detection model. For
the best case, the the inclusion of SR showed an improvement of 39.47% in
object detection over the vanilla network. Data and code are made publicly
available at https://github.com/rbg-research/Floor-Plan-Detection.

This article introduces a new data-driven approach that leverages a manifold
embedding generated by the invertible neural network to improve the robustness,
efficiency, and accuracy of the constitutive-law-free simulations with limited
data. We achieve this by training a deep neural network to globally map data
from the constitutive manifold onto a lower-dimensional Euclidean vector space.
As such, we establish the relation between the norm of the mapped Euclidean
vector space and the metric of the manifold and lead to a more physically
consistent notion of distance for the material data. This treatment in return
allows us to bypass the expensive combinatorial optimization, which may
significantly speed up the model-free simulations when data are abundant and of
high dimensions. Meanwhile, the learning of embedding also improves the
robustness of the algorithm when the data is sparse or distributed unevenly in
the parametric space. Numerical experiments are provided to demonstrate and
measure the performance of the manifold embedding technique under different
circumstances. Results obtained from the proposed method and those obtained via
the classical energy norms are compared.

Calorie and nutrition research has attained increased interest in recent
years. But, due to the complexity of the problem, literature in this area
focuses on a limited subset of ingredients or dish types and simple
convolutional neural networks or traditional machine learning. Simultaneously,
estimation of ingredient portions can help improve calorie estimation and meal
re-production from a given image. In this paper, given a single cooking image,
a pipeline for calorie estimation and meal re-production for different servings
of the meal is proposed. The pipeline contains two stages. In the first stage,
a set of ingredients associated with the meal in the given image are predicted.
In the second stage, given image features and ingredients, portions of the
ingredients and finally the total meal calorie are simultaneously estimated
using a deep transformer-based model. Portion estimation introduced in the
model helps improve calorie estimation and is also beneficial for meal
re-production in different serving sizes. To demonstrate the benefits of the
pipeline, the model can be used for meal kits generation. To evaluate the
pipeline, the large scale dataset Recipe1M is used. Prior to experiments, the
Recipe1M dataset is parsed and explicitly annotated with portions of
ingredients. Experiments show that using ingredients and their portions
significantly improves calorie estimation. Also, a visual interface is created
in which a user can interact with the pipeline to reach accurate calorie
estimations and generate a meal kit for cooking purposes.

Despite of achieving great success in real life, Deep Reinforcement Learning
(DRL) is still suffering from three critical issues, which are data efficiency,
lack of the interpretability and transferability. Recent research shows that
embedding symbolic knowledge into DRL is promising in addressing those
challenges. Inspired by this, we introduce a novel deep reinforcement learning
framework with symbolic options. This framework features a loop training
procedure, which enables guiding the improvement of policy by planning with
action models and symbolic options learned from interactive trajectories
automatically. The learned symbolic options alleviate the dense requirement of
expert domain knowledge and provide inherent interpretability of policies.
Moreover, the transferability and data efficiency can be further improved by
planning with the action models. To validate the effectiveness of this
framework, we conduct experiments on two domains, Montezuma's Revenge and
Office World, respectively. The results demonstrate the comparable performance,
improved data efficiency, interpretability and transferability.

Often, machine learning applications have to cope with dynamic environments
where data are collected in the form of continuous data streams with
potentially infinite length and transient behavior. Compared to traditional
(batch) data mining, stream processing algorithms have additional requirements
regarding computational resources and adaptability to data evolution. They must
process instances incrementally because the data's continuous flow prohibits
storing data for multiple passes. Ensemble learning achieved remarkable
predictive performance in this scenario. Implemented as a set of (several)
individual classifiers, ensembles are naturally amendable for task parallelism.
However, the incremental learning and dynamic data structures used to capture
the concept drift increase the cache misses and hinder the benefit of
parallelism. This paper proposes a mini-batching strategy that can improve
memory access locality and performance of several ensemble algorithms for
stream mining in multi-core environments. With the aid of a formal framework,
we demonstrate that mini-batching can significantly decrease the reuse distance
(and the number of cache misses). Experiments on six different state-of-the-art
ensemble algorithms applying four benchmark datasets with varied
characteristics show speedups of up to 5X on 8-core processors. These benefits
come at the expense of a small reduction in predictive performance.

In this paper, we propose the neural Born iteration method (NeuralBIM) for
solving 2D inverse scattering problems (ISPs) by drawing on the scheme of
physics-informed supervised residual learning (PhiSRL) to emulate the computing
process of the traditional Born iteration method (TBIM). NeuralBIM employs
independent convolutional neural networks (CNNs) to learn the alternate update
rules of two different candidate solutions with their corresponding residuals.
Two different schemes of NeuralBIMs are presented in this paper including
supervised and unsupervised learning schemes. With the data set generated by
method of moments (MoM), supervised NeuralBIMs are trained with the knowledge
of total fields and contrasts. Unsupervised NeuralBIM is guided by the
physics-embedded loss functions founding on the governing equations of ISPs,
which results in no requirements of total fields and contrasts for training.
Representative numerical results further validate the effectiveness and
competitiveness of both supervised and unsupervised NeuralBIMs.

Dynamic scene graph generation from a video is challenging due to the
temporal dynamics of the scene and the inherent temporal fluctuations of
predictions. We hypothesize that capturing long-term temporal dependencies is
the key to effective generation of dynamic scene graphs. We propose to learn
the long-term dependencies in a video by capturing the object-level consistency
and inter-object relationship dynamics over object-level long-term tracklets
using transformers. Experimental results demonstrate that our Dynamic Scene
Graph Detection Transformer (DSG-DETR) outperforms state-of-the-art methods by
a significant margin on the benchmark dataset Action Genome. Our ablation
studies validate the effectiveness of each component of the proposed approach.
The source code is available at https://github.com/Shengyu-Feng/DSG-DETR.

The integration of distributed renewable generation (DRG) in distribution
networks can be effectively promoted by scheduling flexible resources such as
heating, ventilation, and air conditioning (HVAC) loads. However, finding the
optimal scheduling for them is nontrivial because DRG outputs are highly
uncertain. To address this issue, this paper proposes a learning-based joint
chance-constrained approach to coordinate HVAC loads with DRG. Unlike
cutting-edge works adopting individual chance constraints to manage
uncertainties, this paper controls the violation probability of all critical
constraints with joint chance constraints (JCCs). This joint manner can
explicitly guarantee the operational security of the entire system based on
operators' preferences. To overcome the intractability of JCCs, we first prove
that JCCs can be safely approximated by robust constraints with proper
uncertainty sets. A famous machine learning algorithm, one-class support vector
clustering, is then introduced to construct a small enough polyhedron
uncertainty set for these robust constraints. A linear robust counterpart is
further developed based on the strong duality to ensure computational
efficiency. Numerical results based on various distributed uncertainties
confirm the advantages of the proposed method in optimality and feasibility.

Federated learning (FL) enables distribution of machine learning workloads
from the cloud to resource-limited edge devices. Unfortunately, current deep
networks remain not only too compute-heavy for inference and training on edge
devices, but also too large for communicating updates over
bandwidth-constrained networks. In this paper, we develop, implement, and
experimentally validate a novel FL framework termed Federated Dynamic Sparse
Training (FedDST) by which complex neural networks can be deployed and trained
with substantially improved efficiency in both on-device computation and
in-network communication. At the core of FedDST is a dynamic process that
extracts and trains sparse sub-networks from the target full network. With this
scheme, "two birds are killed with one stone:" instead of full models, each
client performs efficient training of its own sparse networks, and only sparse
networks are transmitted between devices and the cloud. Furthermore, our
results reveal that the dynamic sparsity during FL training more flexibly
accommodates local heterogeneity in FL agents than the fixed, shared sparse
masks. Moreover, dynamic sparsity naturally introduces an "in-time
self-ensembling effect" into the training dynamics and improves the FL
performance even over dense training. In a realistic and challenging non i.i.d.
FL setting, FedDST consistently outperforms competing algorithms in our
experiments: for instance, at any fixed upload data cap on non-iid CIFAR-10, it
gains an impressive accuracy advantage of 10% over FedAvgM when given the same
upload data cap; the accuracy gap remains 3% even when FedAvgM is given 2x the
upload data cap, further demonstrating efficacy of FedDST. Code is available
at: https://github.com/bibikar/feddst.

A direct proof of the Steiner-Lehmus theorem has eluded geometers for over
170 years. The challenge has been that a proof is only considered direct if it
does not rely on reductio ad absurdum. Thus, any proof that claims to be direct
must show, going back to the axioms, that all of the auxiliary theorems used
are also proved directly. In this paper, we give a proof of the Steiner-Lehmus
theorem that is guaranteed to be direct. The evidence for this claim is derived
from our methodology: we have formalized a constructive axiom set for Euclidean
plane geometry in a proof assistant that implements a constructive logic and
have built the proof of the Steiner-Lehmus theorem on this constructive
foundation.

We formally map the problem of sampling from an unknown distribution with a
density in $\mathbb{R}^d$ to the problem of learning and sampling a smoother
density in $\mathbb{R}^{Md}$ obtained by convolution with a fixed factorial
kernel: the new density is referred to as M-density and the kernel as
multimeasurement noise model (MNM). The M-density in $\mathbb{R}^{Md}$ is
smoother than the original density in $\mathbb{R}^d$, easier to learn and
sample from, yet for large $M$ the two problems are mathematically equivalent
since clean data can be estimated exactly given a multimeasurement noisy
observation using the Bayes estimator. To formulate the problem, we derive the
Bayes estimator for Poisson and Gaussian MNMs in closed form in terms of the
unnormalized M-density. This leads to a simple least-squares objective for
learning parametric energy and score functions. We present various
parametrization schemes of interest including one in which studying Gaussian
M-densities directly leads to multidenoising autoencoders--this is the first
theoretical connection made between denoising autoencoders and empirical Bayes
in the literature. Samples in $\mathbb{R}^d$ are obtained by walk-jump sampling
(Saremi & Hyvarinen, 2019) via underdamped Langevin MCMC (walk) to sample from
M-density and the multimeasurement Bayes estimation (jump). We study
permutation invariant Gaussian M-densities on MNIST, CIFAR-10, and FFHQ-256
datasets, and demonstrate the effectiveness of this framework for realizing
fast-mixing stable Markov chains in high dimensions.

Machine learning researchers have long noted a trade-off between
interpretability and prediction performance. On the one hand, traditional
models are often interpretable to humans but they cannot achieve high
prediction performances. At the opposite end of the spectrum, deep models can
achieve state-of-the-art performances in many tasks. However, deep models'
predictions are known to be uninterpretable to humans. In this paper we present
a framework that shortens the gap between the two aforementioned groups of
methods. Given an artificial neural network (ANN), our method finds a Gaussian
process (GP) whose predictions almost match those of the ANN. As GPs are highly
interpretable, we use the trained GP to explain the ANN's decisions. We use our
method to explain ANNs' decisions on may datasets. The explanations provide
intriguing insights about the ANNs' decisions. With the best of our knowledge,
our inference formulation for GPs is the first one in which an ANN and a
similarly behaving Gaussian process naturally appear. Furthermore, we examine
some of the known theoretical conditions under which an ANN is interpretable by
GPs. Some of those theoretical conditions are too restrictive for modern
architectures. However, we hypothesize that only a subset of those theoretical
conditions are sufficient. Finally, we implement our framework as a publicly
available tool called GPEX. Given any pytorch feed-forward module, GPEX allows
users to interpret any ANN subcomponent of the module effortlessly and without
having to be involved in the inference algorithm. GPEX is publicly available
online:www.github.com/Nilanjan-Ray/gpex

Novelty detection aims to automatically identify out-of-distribution (OOD)
data, without any prior knowledge of them. It is a critical step in data
monitoring, behavior analysis and other applications, helping enable continual
learning in the field. Conventional methods of OOD detection perform
multi-variate analysis on an ensemble of data or features, and usually resort
to the supervision with OOD data to improve the accuracy. In reality, such
supervision is impractical as one cannot anticipate the anomalous data. In this
paper, we propose a novel, self-supervised approach that does not rely on any
pre-defined OOD data: (1) The new method evaluates the Mahalanobis distance of
the gradients between the in-distribution and OOD data. (2) It is assisted by a
self-supervised binary classifier to guide the label selection to generate the
gradients, and maximize the Mahalanobis distance. In the evaluation with
multiple datasets, such as CIFAR-10, CIFAR-100, SVHN and TinyImageNet, the
proposed approach consistently outperforms state-of-the-art supervised and
unsupervised methods in the area under the receiver operating characteristic
(AUROC) and area under the precision-recall curve (AUPR) metrics. We further
demonstrate that this detector is able to accurately learn one OOD class in

Inspired by the extensive success of deep learning, graph neural networks
(GNNs) have been proposed to learn expressive node representations and
demonstrated promising performance in various graph learning tasks. However,
existing endeavors predominately focus on the conventional semi-supervised
setting where relatively abundant gold-labeled nodes are provided. While it is
often impractical due to the fact that data labeling is unbearably laborious
and requires intensive domain knowledge, especially when considering the
heterogeneity of graph-structured data. Under the few-shot semi-supervised
setting, the performance of most of the existing GNNs is inevitably undermined
by the overfitting and oversmoothing issues, largely owing to the shortage of
labeled data. In this paper, we propose a decoupled network architecture
equipped with a novel meta-learning algorithm to solve this problem. In
essence, our framework Meta-PN infers high-quality pseudo labels on unlabeled
nodes via a meta-learned label propagation strategy, which effectively augments
the scarce labeled data while enabling large receptive fields during training.
Extensive experiments demonstrate that our approach offers easy and substantial
performance gains compared to existing techniques on various benchmark
datasets.

Domain generalization (DG) methods aim to develop models that generalize to
settings where the test distribution is different from the training data. In
this paper, we focus on the challenging problem of multi-source zero shot DG
(MDG), where labeled training data from multiple source domains is available
but with no access to data from the target domain. A wide range of solutions
have been proposed for this problem, including the state-of-the-art
multi-domain ensembling approaches. Despite these advances, the na\"ive ERM
solution of pooling all source data together and training a single classifier
is surprisingly effective on standard benchmarks. In this paper, we hypothesize
that, it is important to elucidate the link between pre-specified domain labels
and MDG performance, in order to explain this behavior. More specifically, we
consider two popular classes of MDG algorithms -- distributional robust
optimization (DRO) and multi-domain ensembles, in order to demonstrate how
inferring custom domain groups can lead to consistent improvements over the
original domain labels that come with the dataset. To this end, we propose (i)
Group-DRO++, which incorporates an explicit clustering step to identify custom
domains in an existing DRO technique; and (ii) DReaME, which produces effective
multi-domain ensembles through implicit domain re-labeling with a novel
meta-optimization algorithm. Using empirical studies on multiple standard
benchmarks, we show that our variants consistently outperform ERM by
significant margins (1.5% - 9%), and produce state-of-the-art MDG performance.
Our code can be found at https://github.com/kowshikthopalli/DREAME

We provide a regularization framework for subject transfer learning in which
we seek to train an encoder and classifier to minimize classification loss,
subject to a penalty measuring independence between the latent representation
and the subject label. We introduce three notions of independence and
corresponding penalty terms using mutual information or divergence as a proxy
for independence. For each penalty term, we provide several concrete estimation
algorithms, using analytic methods as well as neural critic functions. We
provide a hands-off strategy for applying this diverse family of regularization
algorithms to a new dataset, which we call "AutoTransfer". We evaluate the
performance of these individual regularization strategies and our AutoTransfer
method on EEG, EMG, and ECoG datasets, showing that these approaches can
improve subject transfer learning for challenging real-world datasets.

Deep reinforcement learning has shown promising results on an abundance of
robotic tasks in simulation, including visual navigation and manipulation.
Prior work generally aims to build embodied agents that solve their assigned
tasks as quickly as possible, while largely ignoring the problems caused by
collision with objects during interaction. This lack of prioritization is
understandable: there is no inherent cost in breaking virtual objects. As a
result, "well-trained" agents frequently collide with objects before achieving
their primary goals, a behavior that would be catastrophic in the real world.
In this paper, we study the problem of training agents to complete the task of
visual mobile manipulation in the ManipulaTHOR environment while avoiding
unnecessary collision (disturbance) with objects. We formulate disturbance
avoidance as a penalty term in the reward function, but find that directly
training with such penalized rewards often results in agents being unable to
escape poor local optima. Instead, we propose a two-stage training curriculum
where an agent is first allowed to freely explore and build basic competencies
without penalization, after which a disturbance penalty is introduced to refine
the agent's behavior. Results on testing scenes show that our curriculum not
only avoids these poor local optima, but also leads to 10% absolute gains in
success rate without disturbance, compared to our state-of-the-art baselines.
Moreover, our curriculum is significantly more performant than a safe RL
algorithm that casts collision avoidance as a constraint. Finally, we propose a
novel disturbance-prediction auxiliary task that accelerates learning.

This paper studies distributed Bayesian learning in a setting encompassing a
central server and multiple workers by focusing on the problem of mitigating
the impact of stragglers. The standard one-shot, or embarrassingly parallel,
Bayesian learning protocol known as consensus Monte Carlo (CMC) is generalized
by proposing two straggler-resilient solutions based on grouping and coding.
Two main challenges in designing straggler-resilient algorithms for CMC are the
need to estimate the statistics of the workers' outputs across multiple shots,
and the joint non-linear post-processing of the outputs of the workers carried
out at the server. This is in stark contrast to other distributed settings like
gradient coding, which only require the per-shot sum of the workers' outputs.
The proposed methods, referred to as Group-based CMC (G-CMC) and Coded CMC
(C-CMC), leverage redundant computing at the workers in order to enable the
estimation of global posterior samples at the server based on partial outputs
from the workers. Simulation results show that C-CMC may outperform G-CMC for a
small number of workers, while G-CMC is generally preferable for a larger
number of workers.

New mobile platforms, connected seamlessly to the Internet via wireless
access have become increasingly more powerful and have found usage in a diverse
set of application areas, including the education sector. The educational
institutions are becoming more open to embracing new learning platforms, which
in turn has sparked the interest in developing new assessment frameworks. This
paper has used the framework of the Capability Maturity Model (CMM) to design a
model for M-learning within educational institutions. The framework has been
validated with studies cases from higher education institutions.

Using the data from loop detector sensors for near-real-time detection of
traffic incidents in highways is crucial to averting major traffic congestion.
While recent supervised machine learning methods offer solutions to incident
detection by leveraging human-labeled incident data, the false alarm rate is
often too high to be used in practice. Specifically, the inconsistency in the
human labeling of the incidents significantly affects the performance of
supervised learning models. To that end, we focus on a data-centric approach to
improve the accuracy and reduce the false alarm rate of traffic incident
detection on highways. We develop a weak supervised learning workflow to
generate high-quality training labels for the incident data without the ground
truth labels, and we use those generated labels in the supervised learning
setup for final detection. This approach comprises three stages. First, we
introduce a data preprocessing and curation pipeline that processes traffic
sensor data to generate high-quality training data through leveraging labeling
functions, which can be domain knowledge-related or simple heuristic rules.
Second, we evaluate the training data generated by weak supervision using three
supervised learning models -- random forest, k-nearest neighbors, and a support
vector machine ensemble -- and long short-term memory classifiers. The results
show that the accuracy of all of the models improves significantly after using
the training data generated by weak supervision. Third, we develop an online
real-time incident detection approach that leverages the model ensemble and the
uncertainty quantification while detecting incidents. Overall, we show that our
proposed weak supervised learning workflow achieves a high incident detection
rate (0.90) and low false alarm rate (0.08).

Few-shot object detection (FSOD) aims to detect never-seen objects using few
examples. This field sees recent improvement owing to the meta-learning
techniques by learning how to match between the query image and few-shot class
examples, such that the learned model can generalize to few-shot novel classes.
However, currently, most of the meta-learning-based methods perform pairwise
matching between query image regions (usually proposals) and novel classes
separately, therefore failing to take into account multiple relationships among
them. In this paper, we propose a novel FSOD model using heterogeneous graph
convolutional networks. Through efficient message passing among all the
proposal and class nodes with three different types of edges, we could obtain
context-aware proposal features and query-adaptive, multiclass-enhanced
prototype representations for each class, which could help promote the pairwise
matching and improve final FSOD accuracy. Extensive experimental results show
that our proposed model, denoted as QA-FewDet, outperforms the current
state-of-the-art approaches on the PASCAL VOC and MSCOCO FSOD benchmarks under
different shots and evaluation metrics.

Score-based model research in the last few years has produced state of the
art generative models by employing Gaussian denoising score-matching (DSM).
However, the Gaussian noise assumption has several high-dimensional
limitations, motivating a more concrete route toward even higher dimension PDF
estimation in future. We outline this limitation, before extending the theory
to a broader family of noising distributions -- namely, the generalised normal
distribution. To theoretically ground this, we relax a key assumption in
(denoising) score matching theory, demonstrating that distributions which are
differentiable almost everywhere permit the same objective simplification as
Gaussians. For noise vector norm distributions, we demonstrate favourable
concentration of measure in the high-dimensional spaces prevalent in deep
learning. In the process, we uncover a skewed noise vector norm distribution
and develop an iterative noise scaling algorithm to consistently initialise the
multiple levels of noise in annealed Langevin dynamics (LD). On the practical
side, our use of heavy-tailed DSM leads to improved score estimation,
controllable sampling convergence, and more balanced unconditional generative
performance for imbalanced datasets.

Deep Learning-based (DL) applications are becoming increasingly popular and
advancing at an unprecedented pace. While many research works are being
undertaken to enhance Deep Neural Networks (DNN) -- the centerpiece of DL
applications -- practical deployment challenges of these applications in the
Cloud and Edge systems, and their impact on the usability of the applications
have not been sufficiently investigated. In particular, the impact of deploying
different virtualization platforms, offered by the Cloud and Edge, on the
usability of DL applications (in terms of the End-to-End (E2E) inference time)
has remained an open question. Importantly, resource elasticity (by means of
scale-up), CPU pinning, and processor type (CPU vs GPU) configurations have
shown to be influential on the virtualization overhead. Accordingly, the goal
of this research is to study the impact of these potentially decisive
deployment options on the E2E performance, thus, usability of the DL
applications. To that end, we measure the impact of four popular execution
platforms (namely, bare-metal, virtual machine (VM), container, and container
in VM) on the E2E inference time of four types of DL applications, upon
changing processor configuration (scale-up, CPU pinning) and processor types.
This study reveals a set of interesting and sometimes counter-intuitive
findings that can be used as best practices by Cloud solution architects to
efficiently deploy DL applications in various systems. The notable finding is
that the solution architects must be aware of the DL application
characteristics, particularly, their pre- and post-processing requirements, to
be able to optimally choose and configure an execution platform, determine the
use of GPU, and decide the efficient scale-up range.

This work investigates the problem of unmanned aerial vehicles (UAVs)
identification using their radar crosssection (RCS) signature. The RCS of six
commercial UAVs are measured at 15 GHz and 25 GHz in an anechoic chamber, for
both vertical-vertical and horizontal-horizontal polarization. The RCS
signatures are used to train 15 different classification algorithms, each
belonging to one of three different categories: statistical learning (SL),
machine learning (ML), and deep learning (DL). The study shows that while the
classification accuracy of all the algorithms increases with the
signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the
SL and DL algorithms. For example, the classification tree ML achieves an
accuracy of 98.66% at 3 dB SNR using the 15 GHz VV-polarized RCS test data from
the UAVs. We investigate the classification accuracy using Monte Carlo analysis
with the aid of boxplots, confusion matrices, and classification plots. On
average, the accuracy of the classification tree ML model performed better than
the other algorithms, followed by the Peter Swerling statistical models and the
discriminant analysis ML model. In general, the classification accuracy of the
ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet,
Nasnet, and Resnet 101) considered in the study. Furthermore, the computational
time of each algorithm is analyzed. The study concludes that while the SL
algorithms achieved good classification accuracy, the computational time was
relatively long when compared to the ML and DL algorithms. Also, the study
shows that the classification tree achieved the fastest average classification
time of about 0.46 ms.

We introduce a novel approach to solve optimization problems on a boson
sampling device assisted by classical machine-learning techniques. By virtue of
the parity function, we map all measurement patterns, which label the basis
spanning an $M$-mode bosonic Hilbert space, to the Hilbert space of $M$ qubits.
As a result, the sampled probability function can be interpreted as a result of
sampling a multiqubit circuit. The method is presented on several instances of
a QUBO/Ising problem as well as portfolio optimization problems. Among many
demonstrated properties of the parity function is the ability to chart the
entire qubit Hilbert space no matter how shallow the initial bosonic circuits
is. In order to show this we link boson sampling circuits to a class of finite
Young's lattices (a special poset with the so-called Ferrers diagrams ordered
by inclusion), Boolean lattices and the properties of Dyck/staircase paths on
integer lattices. Our results and methods can be applied to a large variety of
photonic circuits, including the deep ones of essentially any geometry, but our
main focus is on shallow circuits as they are less affected by photon loss and
relatively easy to implement in the form of a time-bin interferometer.

Applied machine learning (ML) has rapidly spread throughout the physical
sciences; in fact, ML-based data analysis and experimental decision-making has
become commonplace. We suggest a shift in the conversation from proving that ML
can be used to evaluating how to equitably and effectively implement ML for
science.We advocate a shift from a "more data, more compute" mentality to a
model-oriented approach that prioritizes using machine learning to support the
ecosystem of computational models and experimental measurements.We also
recommend an open conversation about dataset bias to stabilize productive
research through careful model interrogation and deliberate exploitation of
known biases. Further, we encourage the community to develop ML methods that
connect experiments with theoretical models to increase scientific
understanding rather than incrementally optimizing materials. Moreover we
envision a future of radical materials innovations enabled by computational
creativity tools combined with online visualization and analysis tools that
support active outside-the-box thinking inside the scientific knowledge
feedback loop. Finally, as a community we must acknowledge ethical issues that
can arise from blindly following machine learning predictions and the issues of
social equity that will arise if data, code, and computational resources are
not readily available to all.

Cloud computing has become a major approach to help reproduce computational
experiments because it supports on-demand hardware and software resource
provisioning. Yet there are still two main difficulties in reproducing big data
applications in the cloud. The first is how to automate end-to-end execution of
analytics including environment provisioning, analytics pipeline description,
pipeline execution, and resource termination. The second is that an application
developed for one cloud is difficult to be reproduced in another cloud, a.k.a.
vendor lock-in problem. To tackle these problems, we leverage serverless
computing and containerization techniques for automated scalable execution and
reproducibility, and utilize the adapter design pattern to enable application
portability and reproducibility across different clouds. We propose and develop
an open-source toolkit that supports 1) fully automated end-to-end execution
and reproduction via a single command, 2) automated data and configuration
storage for each execution, 3) flexible client modes based on user preferences,
4) execution history query, and 5) simple reproduction of existing executions
in the same environment or a different environment. We did extensive
experiments on both AWS and Azure using four big data analytics applications
that run on virtual CPU/GPU clusters. The experiments show our toolkit can
achieve good execution performance, scalability, and efficient reproducibility
for cloud-based big data analytics.

Magnetic Resonance (MR) image reconstruction from highly undersampled
$k$-space data is critical in accelerated MR imaging (MRI) techniques. In
recent years, deep learning-based methods have shown great potential in this
task. This paper proposes a learned half-quadratic splitting algorithm for MR
image reconstruction and implements the algorithm in an unrolled deep learning
network architecture. We compare the performance of our proposed method on a
public cardiac MR dataset against DC-CNN and LPDNet, and our method outperforms
other methods in both quantitative results and qualitative results with fewer
model parameters and faster reconstruction speed. Finally, we enlarge our model
to achieve superior reconstruction quality, and the improvement is $1.76$ dB
and $2.74$ dB over LPDNet in peak signal-to-noise ratio on $5\times$ and
$10\times$ acceleration, respectively. Code for our method is publicly
available at https://github.com/hellopipu/HQS-Net.

Optimal transport (OT) formalizes the problem of finding an optimal coupling
between probability measures given a cost matrix. The inverse problem of
inferring the cost given a coupling is Inverse Optimal Transport (IOT). IOT is
less well understood than OT. We formalize and systematically analyze the
properties of IOT using tools from the study of entropy-regularized OT.
Theoretical contributions include characterization of the manifold of
cross-ratio equivalent costs, the implications of model priors, and derivation
of an MCMC sampler. Empirical contributions include visualizations of
cross-ratio equivalent effect on basic examples and simulations validating

Language-enabled AI systems can answer complex, multi-hop questions to high
accuracy, but supporting answers with evidence is a more challenging task which
is important for the transparency and trustworthiness to users. Prior work in
this area typically makes a trade-off between efficiency and accuracy;
state-of-the-art deep neural network systems are too cumbersome to be useful in
large-scale applications, while the fastest systems lack reliability. In this
work, we integrate fast syntactic methods with powerful semantic methods for
multi-hop explanation generation based on declarative facts. Our best system,
which learns a lightweight operation to simulate multi-hop reasoning over
pieces of evidence and fine-tunes language models to re-rank generated
explanation chains, outperforms a purely syntactic baseline from prior work by
up to 7% in gold explanation retrieval rate.

Node classification is a central task in relational learning, with the
current state-of-the-art hinging on two key principles: (i) predictions are
permutation-invariant to the ordering of a node's neighbors, and (ii)
predictions are a function of the node's $r$-hop neighborhood topology and
attributes, $r \geq 2$. Both graph neural networks and collective inference
methods (e.g., belief propagation) rely on information from up to $r$-hops
away. In this work, we study if the use of more powerful permutation-invariant
functions can sometimes avoid the need for classifiers to collect information
beyond $1$-hop. Towards this, we introduce a new architecture, the Set Twister,
which generalizes DeepSets (Zaheer et al., 2017), a simple and widely-used
permutation-invariant representation. Set Twister theoretically increases
expressiveness of DeepSets, allowing it to capture higher-order dependencies,
while keeping its simplicity and low computational cost. Empirically, we see
accuracy improvements of Set Twister over DeepSets as well as a variety of
graph neural networks and collective inference schemes in several tasks, while
showcasing its implementation simplicity and computational efficiency.

Modern high-dimensional methods often adopt the "bet on sparsity" principle,
while in supervised multivariate learning statisticians may face "dense"
problems with a large number of nonzero coefficients. This paper proposes a
novel clustered reduced-rank learning (CRL) framework that imposes two joint
matrix regularizations to automatically group the features in constructing
predictive factors. CRL is more interpretable than low-rank modeling and
relaxes the stringent sparsity assumption in variable selection. In this paper,
new information-theoretical limits are presented to reveal the intrinsic cost
of seeking for clusters, as well as the blessing from dimensionality in
multivariate learning. Moreover, an efficient optimization algorithm is
developed, which performs subspace learning and clustering with guaranteed
convergence. The obtained fixed-point estimators, though not necessarily
globally optimal, enjoy the desired statistical accuracy beyond the standard
likelihood setup under some regularity conditions. Moreover, a new kind of
information criterion, as well as its scale-free form, is proposed for cluster
and rank selection, and has a rigorous theoretical support without assuming an
infinite sample size. Extensive simulations and real-data experiments
demonstrate the statistical accuracy and interpretability of the proposed
method.

A wide variety of fairness metrics and eXplainable Artificial Intelligence
(XAI) approaches have been proposed in the literature to identify bias in
machine learning models that are used in critical real-life contexts. However,
merely reporting on a model's bias, or generating explanations using existing
XAI techniques is insufficient to locate and eventually mitigate sources of
bias. We introduce Gopher, a system that produces compact, interpretable and
causal explanations for bias or unexpected model behavior by identifying
coherent subsets of the training data that are root-causes for this behavior.
Specifically, we introduce the concept of causal responsibility that quantifies
the extent to which intervening on training data by removing or updating
subsets of it can resolve the bias. Building on this concept, we develop an
efficient approach for generating the top-k patterns that explain model bias
that utilizes techniques from the machine learning (ML) community to
approximate causal responsibility and uses pruning rules to manage the large
search space for patterns. Our experimental evaluation demonstrates the
effectiveness of Gopher in generating interpretable explanations for
identifying and debugging sources of bias.

To advance deep learning methodologies in the next decade, a theoretical
framework for reasoning about modern neural networks is needed. While efforts
are increasing toward demystifying why deep learning is so effective, a
comprehensive picture remains lacking, suggesting that a better theory is
possible. We argue that a future deep learning theory should inherit three
characteristics: a \textit{hierarchically} structured network architecture,
parameters \textit{iteratively} optimized using stochastic gradient-based
methods, and information from the data that evolves \textit{compressively}. As
an instantiation, we integrate these characteristics into a graphical model
called \textit{neurashed}. This model effectively explains some common
empirical patterns in deep learning. In particular, neurashed enables insights
into implicit regularization, information bottleneck, and local elasticity.
Finally, we discuss how neurashed can guide the development of deep learning
theories.

Path-Guiding algorithms for sampling scattering directions can drastically
decrease the variance of Monte Carlo estimators of Light Transport Equation,
but their usage was limited to offline rendering because of memory and
computational limitations. We introduce a new robust screen-space technique
that is based on online learning of parametric mixture models for guiding the
real-time path-tracing algorithm. It requires storing of 8 parameters for every
pixel, achieves a reduction of FLIP metric up to 4 times with 1 spp rendering.
Also, it consumes less than 1.5ms on RTX 2070 for 1080p and reduces
path-tracing timings by generating more coherent rays by about 5% on average.
Moreover, it leads to significant bias reduction and a lower level of
flickering of SVGF output.

Multiclass classification (MCC) is a fundamental machine learning problem
which aims to classify each instance into one of a predefined set of classes.
Given an instance, a classification model computes a score for each class, all
of which are then used to sort the classes. The performance of a classification
model is usually measured by Top-K Accuracy/Error (e.g., K=1 or 5). In this
paper, we do not aim to propose new neural representation learning models as
most recent works do, but to show that it is easy to boost MCC performance with
a novel formulation through the lens of ranking. In particular, by viewing MCC
as to rank classes for an instance, we first argue that ranking metrics, such
as Normalized Discounted Cumulative Gain (NDCG), can be more informative than
existing Top-K metrics. We further demonstrate that the dominant neural MCC
architecture can be formulated as a neural ranking framework with a specific
set of design choices. Based on such generalization, we show that it is
straightforward and intuitive to leverage techniques from the rich information
retrieval literature to improve the MCC performance out of the box. Extensive
empirical results on both text and image classification tasks with diverse
datasets and backbone models (e.g., BERT and ResNet for text and image
classification) show the value of our proposed framework.

In the art of video editing, sound is really half the story. A skilled video
editor overlays sounds, such as effects and ambients, over footage to add
character to an object or immerse the viewer within a space. However, through
formative interviews with professional video editors, we found that this
process can be extremely tedious and time-consuming. We introduce Soundify, a
system that matches sound effects to video. By leveraging labeled,
studio-quality sound effects libraries and extending CLIP, a neural network
with impressive zero-shot image classification capabilities, into a "zero-shot
detector", we are able to produce high-quality results without
resource-intensive correspondence learning or audio generation. We encourage
you to have a look at, or better yet, have a listen to the results at
https://chuanenlin.com/soundify.

Motivated by the computational limitations of simulating interactions of
particles in highly-granular detectors, there exists a concerted effort to
build fast and exact machine-learning-based shower simulators. This work
reports progress on two important fronts. First, the previously investigated
WGAN and BIB-AE generative models are improved and successful learning of
hadronic showers initiated by charged pions in a segment of the hadronic
calorimeter of the International Large Detector (ILD) is demonstrated for the
first time. Second, we consider how state-of-the-art reconstruction software
applied to generated shower energies affects the obtainable energy response and
resolution. While many challenges remain, these results constitute an important
milestone in using generative models in a realistic setting.

Semi-supervised action recognition is a challenging but important task due to
the high cost of data annotation. A common approach to this problem is to
assign unlabeled data with pseudo-labels, which are then used as additional
supervision in training. Typically in recent work, the pseudo-labels are
obtained by training a model on the labeled data, and then using confident
predictions from the model to teach itself. In this work, we propose a more
effective pseudo-labeling scheme, called Cross-Model Pseudo-Labeling (CMPL).
Concretely, we introduce a lightweight auxiliary network in addition to the
primary backbone, and ask them to predict pseudo-labels for each other. We
observe that, due to their different structural biases, these two models tend
to learn complementary representations from the same video clips. Each model
can thus benefit from its counterpart by utilizing cross-model predictions as
supervision. Experiments on different data partition protocols demonstrate the
significant improvement of our framework over existing alternatives. For
example, CMPL achieves $17.6\%$ and $25.1\%$ Top-1 accuracy on Kinetics-400 and
UCF-101 using only the RGB modality and $1\%$ labeled data, outperforming our
baseline model, FixMatch, by $9.0\%$ and $10.3\%$, respectively.

Classical light field rendering for novel view synthesis can accurately
reproduce view-dependent effects such as reflection, refraction, and
translucency, but requires a dense view sampling of the scene. Methods based on
geometric reconstruction need only sparse views, but cannot accurately model
non-Lambertian effects. We introduce a model that combines the strengths and
mitigates the limitations of these two directions. By operating on a
four-dimensional representation of the light field, our model learns to
represent view-dependent effects accurately. By enforcing geometric constraints
during training and inference, the scene geometry is implicitly learned from a
sparse set of views. Concretely, we introduce a two-stage transformer-based
model that first aggregates features along epipolar lines, then aggregates
features along reference views to produce the color of a target ray. Our model
outperforms the state-of-the-art on multiple forward-facing and 360{\deg}
datasets, with larger margins on scenes with severe view-dependent variations.

In this article we study fully-connected feedforward deep ReLU ANNs with an
arbitrarily large number of hidden layers and we prove convergence of the risk
of the GD optimization method with random initializations in the training of
such ANNs under the assumption that the unnormalized probability density
function of the probability distribution of the input data of the considered
supervised learning problem is piecewise polynomial, under the assumption that
the target function (describing the relationship between input data and the
output data) is piecewise polynomial, and under the assumption that the risk
function of the considered supervised learning problem admits at least one
regular global minimum. In addition, in the special situation of shallow ANNs
with just one hidden layer and one-dimensional input we also verify this
assumption by proving in the training of such shallow ANNs that for every
Lipschitz continuous target function there exists a global minimum in the risk
landscape. Finally, in the training of deep ANNs with ReLU activation we also
study solutions of gradient flow (GF) differential equations and we prove that
every non-divergent GF trajectory converges with a polynomial rate of
convergence to a critical point (in the sense of limiting Fr\'echet
subdifferentiability). Our mathematical convergence analysis builds up on ideas
from our previous article Eberle et al., on tools from real algebraic geometry
such as the concept of semi-algebraic functions and generalized
Kurdyka-Lojasiewicz inequalities, on tools from functional analysis such as the
Arzel\`a-Ascoli theorem, on tools from nonsmooth analysis such as the concept
of limiting Fr\'echet subgradients, as well as on the fact that the set of
realization functions of shallow ReLU ANNs with fixed architecture forms a
closed subset of the set of continuous functions revealed by Petersen et al.

In this paper we study the efficacy of combining machine-learning methods
with projection-based model reduction techniques for creating data-driven
surrogate models of computationally expensive, high-fidelity physics models.
Such surrogate models are essential for many-query applications e.g.,
engineering design optimization and parameter estimation, where it is necessary
to invoke the high-fidelity model sequentially, many times. Surrogate models
are usually constructed for individual scalar quantities. However there are
scenarios where a spatially varying field needs to be modeled as a function of
the model's input parameters. We develop a method to do so, using projections
to represent spatial variability while a machine-learned model captures the
dependence of the model's response on the inputs. The method is demonstrated on
modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in
a Mach 7.16 turbulent flow. The surrogate model is then used to perform
Bayesian estimation of freestream conditions and parameters of the SST (Shear
Stress Transport) turbulence model embedded in the high-fidelity
(Reynolds-Averaged Navier-Stokes) flow simulator, using wind-tunnel data. The
paper provides the first-ever Bayesian calibration of a turbulence model for
complex hypersonic turbulent flows. We find that the primary issues in
estimating the SST model parameters are the limited information content of the
heat flux and pressure measurements and the large model-form error encountered
in a certain part of the flow.

Reinforcement learning agents perform well when presented with inputs within
the distribution of those encountered during training. However, they are unable
to respond effectively when faced with novel, out-of-distribution events, until
they have undergone additional training. This paper presents an online,
data-driven, emergency-response method that aims to provide autonomous agents
the ability to react to unexpected situations that are very different from
those it has been trained or designed to address. In such situations, learned
policies cannot be expected to perform appropriately since the observations
obtained in these novel situations would fall outside the distribution of
inputs that the agent has been optimized to handle. The proposed approach
devises a customized response to the unforeseen situation sequentially, by
selecting actions that minimize the rate of increase of the reconstruction
error from a variational auto-encoder. This optimization is achieved online in
a data-efficient manner (on the order of 30 data-points) using a modified
Bayesian optimization procedure. We demonstrate the potential of this approach
in a simulated 3D car driving scenario, in which the agent devises a response
in under 2 seconds to avoid collisions with objects it has not seen during
training.

In attempts to "explain" predictions of machine learning models, researchers
have proposed hundreds of techniques for attributing predictions to features
that are deemed important. While these attributions are often claimed to hold
the potential to improve human "understanding" of the models, surprisingly
little work explicitly evaluates progress towards this aspiration. In this
paper, we conduct a crowdsourcing study, where participants interact with
deception detection models that have been trained to distinguish between
genuine and fake hotel reviews. They are challenged both to simulate the model
on fresh reviews, and to edit reviews with the goal of lowering the probability
of the originally predicted class. Successful manipulations would lead to an
adversarial example. During the training (but not the test) phase, input spans
are highlighted to communicate salience. Through our evaluation, we observe
that for a linear bag-of-words model, participants with access to the feature
coefficients during training are able to cause a larger reduction in model
confidence in the testing phase when compared to the no-explanation control.
For the BERT-based classifier, popular local explanations do not improve their
ability to reduce the model confidence over the no-explanation case.
Remarkably, when the explanation for the BERT model is given by the (global)
attributions of a linear model trained to imitate the BERT model, people can
effectively manipulate the model.

Urbanization has a strong impact on the health and wellbeing of populations
across the world. Predictive spatial modeling of urbanization therefore can be
a useful tool for effective public health planning. Many spatial urbanization
models have been developed using classic machine learning and numerical
modeling techniques. However, deep learning with its proven capacity to capture
complex spatiotemporal phenomena has not been applied to urbanization modeling.
Here we explore the capacity of deep spatial learning for the predictive
modeling of urbanization. We treat numerical geospatial data as images with
pixels and channels, and enrich the dataset by augmentation, in order to
leverage the high capacity of deep learning. Our resulting model can generate
end-to-end multi-variable urbanization predictions, and outperforms a
state-of-the-art classic machine learning urbanization model in preliminary

Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for
optimal decisions. The MCTS's popularity is based on its extraordinary results
in the challenging two-player based game Go, a game considered much harder than
Chess and that until very recently was considered infeasible for Artificial
Intelligence methods. The success of MCTS depends heavily on how the tree is
built and the selection process plays a fundamental role in this. One
particular selection mechanism that has proved to be reliable is based on the
Upper Confidence Bounds for Trees, commonly referred as UCT. The UCT attempts
to nicely balance exploration and exploitation by considering the values stored
in the statistical tree of the MCTS. However, some tuning of the MCTS UCT is
necessary for this to work well. In this work, we use Evolutionary Algorithms
(EAs) to evolve mathematical expressions with the goal to substitute the UCT
mathematical expression. We compare our proposed approach, called Evolution
Strategy in MCTS (ES-MCTS) against five variants of the MCTS UCT, three
variants of the star-minimax family of algorithms as well as a random
controller in the Game of Carcassonne. We also use a variant of our proposed
EA-based controller, dubbed ES partially integrated in MCTS. We show how the
ES-MCTS controller, is able to outperform all these 10 intelligent controllers,
including robust MCTS UCT controllers.

We consider the challenge of policy simplification and verification in the
context of policies learned through reinforcement learning (RL) in continuous
environments. In well-behaved settings, RL algorithms have convergence
guarantees in the limit. While these guarantees are valuable, they are
insufficient for safety-critical applications. Furthermore, they are lost when
applying advanced techniques such as deep-RL. To recover guarantees when
applying advanced RL algorithms to more complex environments with (i)
reachability, (ii) safety-constrained reachability, or (iii) discounted-reward
objectives, we build upon the DeepMDP framework introduced by Gelada et al. to
derive new bisimulation bounds between the unknown environment and a learned
discrete latent model of it. Our bisimulation bounds enable the application of
formal methods for Markov decision processes. Finally, we show how one can use
a policy obtained via state-of-the-art RL to efficiently train a variational
autoencoder that yields a discrete latent model with provably approximately
correct bisimulation guarantees. Additionally, we obtain a distilled version of
the policy for the latent model.

Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below
1.0 mm for improved structure definition and morphometry. Yet, only few,
time-intensive automated image analysis pipelines have been validated for
high-resolution (HiRes) settings. Efficient deep learning approaches, on the
other hand, rarely support more than one fixed resolution (usually 1.0 mm).
Furthermore, the lack of a standard submillimeter resolution as well as limited
availability of diverse HiRes data with sufficient coverage of scanner, age,
diseases, or genetic variance poses additional, unsolved challenges for
training HiRes networks. Incorporating resolution-independence into deep
learning-based segmentation, i.e., the ability to segment images at their
native resolution across a range of different voxel sizes, promises to overcome
these challenges, yet no such approach currently exists. We now fill this gap
by introducing a Voxelsize Independent Neural Network (VINN) for
resolution-independent segmentation tasks and present FastSurferVINN, which (i)
establishes and implements resolution-independence for deep learning as the
first method simultaneously supporting 0.7-1.0 mm whole brain segmentation,
(ii) significantly outperforms state-of-the-art methods across resolutions, and
(iii) mitigates the data imbalance problem present in HiRes datasets. Overall,
internal resolution-independence mutually benefits both HiRes and 1.0 mm MRI
segmentation. With our rigorously validated FastSurferVINN we distribute a
rapid tool for morphometric neuroimage analysis. The VINN architecture,
furthermore, represents an efficient resolution-independent segmentation method
for wider application

Conditional generation is a subclass of generative problems where the output
of the generation is conditioned by the attribute information. In this paper,
we present a stochastic contrastive conditional generative adversarial network
(InfoSCC-GAN) with an explorable latent space. The InfoSCC-GAN architecture is
based on an unsupervised contrastive encoder built on the InfoNCE paradigm, an
attribute classifier and an EigenGAN generator. We propose a novel training
method, based on generator regularization using external or internal attributes
every $n$-th iteration, using a pre-trained contrastive encoder and a
pre-trained classifier. The proposed InfoSCC-GAN is derived based on an
information-theoretic formulation of mutual information maximization between
input data and latent space representation as well as latent space and
generated data. Thus, we demonstrate a link between the training objective
functions and the above information-theoretic formulation. The experimental
results show that InfoSCC-GAN outperforms the "vanilla" EigenGAN in the image
generation on AFHQ and CelebA datasets. In addition, we investigate the impact
of discriminator architectures and loss functions by performing ablation
studies. Finally, we demonstrate that thanks to the EigenGAN generator, the
proposed framework enjoys a stochastic generation in contrast to vanilla
deterministic GANs yet with the independent training of encoder, classifier,
and generator in contrast to existing frameworks. Code, experimental results,
and demos are available online at https://github.com/vkinakh/InfoSCC-GAN.

After performing the morpho-kinematic analysis of the planetary nebula (PN)
PC 22, we now present its nebular and stellar analysis. The plasma
investigation relies on the novel use of a Monte Carlo analysis associated to
the PyNeb code for the uncertainty propagation. The innermost region of the
nebula shows electronic temperatures $T_{\rm e}$ $\approx$ 10,800~K using [NII]
and $\approx$ 13,000 K using [OIII] and electronic densities $n_{\rm e}$
$\approx$ 600~cm$^{-3}$. We also used for the first time a Machine Learning
Algorithm to calculate Ionisation Correction Factors (ICFs) specifically
adapted to PC 22. This has allowed us to have pioneer ICFs for (S$^{+}$ +
S$^{++}$)/O$^{++}$, Cl$^{++}$/O$^{++}$, and Ar$^{3+}$+Ar$^{4+}$, as well as a
possible new determination for the total abundance of neon. The study of the
stellar spectrum revealed the presence of broad emission lines consistent with
a Wolf-Rayet-type [WR] classification and more precisely a [WO1]-subtype based
on different qualitative and quantitative criteria. This classification is also
coherent with the high stellar temperature derived from the reproduction of the
ionization state of the gas with the Mexican Million Models database (3MdB) and
the best fit model obtained with the NLTE model atmosphere code PoWR. PC 22 is
therefore a new addition to the [WO1]-subtype PNe.

We study the problem of distinguishing $b$-jets stemming from the decay of a
colour singlet, such as the Higgs boson, from those originating from the
abundant QCD background. In particular, as a case study, we focus on associate
production of a vector boson and a Higgs boson decaying into a pair of
$b$-jets, which has been recently observed at the LHC. We consider the
combination of several theory-driven observables proposed in the literature,
together with Lund jet plane images, in order to design an original $Hbb$
tagger. The observables are combined by means of standard machine learning
algorithms, which are trained on events obtained with fast detector simulation
techniques. We find that the combination of high-level single-variable
observables with the Lund jet plane provides an excellent discrimination
performance. We also study the dependence of the tagger on the invariant mass
of the decaying particles, in order to assess the extension to a generic $Xbb$
tagger.

Neural implicit surfaces have become an important technique for multi-view 3D
reconstruction but their accuracy remains limited. In this paper, we argue that
this comes from the difficulty to learn and render high frequency textures with
neural networks. We thus propose to add to the standard neural rendering
optimization a direct photo-consistency term across the different views.
Intuitively, we optimize the implicit geometry so that it warps views on each
other in a consistent way. We demonstrate that two elements are key to the
success of such an approach: (i) warping entire patches, using the predicted
occupancy and normals of the 3D points along each ray, and measuring their
similarity with a robust structural similarity (SSIM); (ii) handling visibility
and occlusion in such a way that incorrect warps are not given too much
importance while encouraging a reconstruction as complete as possible. We
evaluate our approach, dubbed NeuralWarp, on the standard DTU and EPFL
benchmarks and show it outperforms state of the art unsupervised implicit
surfaces reconstructions by over 20% on both datasets.

Trajectories are fundamental in different skiing disciplines. Tools enabling
the analysis of such curves can enhance the training activity and enrich the
broadcasting contents. However, the solutions currently available are based on
geo-localized sensors and surface models. In this short paper, we propose a
video-based approach to reconstruct the sequence of points traversed by an
athlete during its performance. Our prototype is constituted by a pipeline of
deep learning-based algorithms to reconstruct the athlete's motion and to
visualize it according to the camera perspective. This is achieved for
different skiing disciplines in the wild without any camera calibration. We
tested our solution on broadcast and smartphone-captured videos of alpine
skiing and ski jumping professional competitions. The qualitative results
achieved show the potential of our solution.

The generation of discontinuous distributions is a difficult task for most
known frameworks such as generative autoencoders and generative adversarial
networks. Generative non-invertible models are unable to accurately generate
such distributions, require long training and often are subject to mode
collapse. Variational autoencoders (VAEs), which are based on the idea of
keeping the latent space to be Gaussian for the sake of a simple sampling,
allow an accurate reconstruction, while they experience significant limitations
at generation task. In this work, instead of trying to keep the latent space to
be Gaussian, we use a pre-trained contrastive encoder to obtain a clustered
latent space. Then, for each cluster, representing a unimodal submanifold, we
train a dedicated low complexity network to generate this submanifold from the
Gaussian distribution. The proposed framework is based on the
the input data and latent space representation. We derive a link between the
cost functions and the information-theoretic formulation. We apply our approach
to synthetic 2D distributions to demonstrate both reconstruction and generation
of discontinuous distributions using continuous stochastic networks.

Supervised deep learning-based methods yield accurate results for medical
image segmentation. However, they require large labeled datasets for this, and
obtaining them is a laborious task that requires clinical expertise.
Semi/self-supervised learning-based approaches address this limitation by
exploiting unlabeled data along with limited annotated data. Recent
self-supervised learning methods use contrastive loss to learn good global
level representations from unlabeled images and achieve high performance in
classification tasks on popular natural image datasets like ImageNet. In
pixel-level prediction tasks such as segmentation, it is crucial to also learn
good local level representations along with global representations to achieve
better accuracy. However, the impact of the existing local contrastive
loss-based methods remains limited for learning good local representations
because similar and dissimilar local regions are defined based on random
augmentations and spatial proximity; not based on the semantic label of local
regions due to lack of large-scale expert annotations in the
semi/self-supervised setting. In this paper, we propose a local contrastive
loss to learn good pixel level features useful for segmentation by exploiting
semantic label information obtained from pseudo-labels of unlabeled images
alongside limited annotated images. In particular, we define the proposed loss
to encourage similar representations for the pixels that have the same
pseudo-label/ label while being dissimilar to the representation of pixels with
different pseudo-label/label in the dataset. We perform pseudo-label based
self-training and train the network by jointly optimizing the proposed
contrastive loss on both labeled and unlabeled sets and segmentation loss on
only the limited labeled set. We evaluated on three public cardiac and prostate
datasets, and obtain high segmentation performance.

Predictive monitoring of business processes is a subfield of process mining
that aims to predict, among other things, the characteristics of the next event
or the sequence of next events. Although multiple approaches based on deep
learning have been proposed, mainly recurrent neural networks and convolutional
neural networks, none of them really exploit the structural information
available in process models. This paper proposes an approach based on graph
convolutional networks and recurrent neural networks that uses information
directly from the process model. An experimental evaluation on real-life event
logs shows that our approach is more consistent and outperforms the current

Segmentation of marine oil spills in Synthetic Aperture Radar (SAR) images is
a challenging task because of the complexity and irregularities in SAR images.
In this work, we aim to develop an effective segmentation method which
addresses marine oil spill identification in SAR images by investigating the
distribution representation of SAR images. To seek effective oil spill
segmentation, we revisit the SAR imaging mechanism in order to attain the
probability distribution representation of oil spill SAR images, in which the
characteristics of SAR images are properly modelled. We then exploit the
distribution representation to formulate the segmentation energy functional, by
which oil spill characteristics are incorporated to guide oil spill
segmentation. Moreover, the oil spill segmentation model contains the oil spill
contour regularisation term and the updated level set regularisation term which
enhance the representational power of the segmentation energy functional.
Benefiting from the synchronisation of SAR image representation and oil spill
segmentation, our proposed method establishes an effective oil spill
segmentation framework. Experimental evaluations demonstrate the effectiveness
of our proposed segmentation framework for different types of marine oil spill
SAR image segmentation.

We study algorithms for approximating pairwise similarity matrices that arise
in natural language processing. Generally, computing a similarity matrix for
$n$ data points requires $\Omega(n^2)$ similarity computations. This quadratic
scaling is a significant bottleneck, especially when similarities are computed
via expensive functions, e.g., via transformer models. Approximation methods
reduce this quadratic complexity, often by using a small subset of exactly
computed similarities to approximate the remainder of the complete pairwise
similarity matrix.
  Significant work focuses on the efficient approximation of positive
semidefinite (PSD) similarity matrices, which arise e.g., in kernel methods.
However, much less is understood about indefinite (non-PSD) similarity
matrices, which often arise in NLP. Motivated by the observation that many of
these matrices are still somewhat close to PSD, we introduce a generalization
of the popular Nystr\"{o}m method to the indefinite setting. Our algorithm can
be applied to any similarity matrix and runs in sublinear time in the size of
the matrix, producing a rank-$s$ approximation with just $O(ns)$ similarity
  We show that our method, along with a simple variant of CUR decomposition,
performs very well in approximating a variety of similarity matrices arising in
NLP tasks. We demonstrate high accuracy of the approximated similarity matrices
in the downstream tasks of document classification, sentence similarity, and
cross-document coreference.

Sparse lexical representation learning has demonstrated much progress in
improving passage retrieval effectiveness in recent models such as DeepImpact,
uniCOIL, and SPLADE. This paper describes a straightforward yet effective
approach for sparsifying lexical representations for passage retrieval,
building on SPLADE by introducing a top-$k$ masking scheme to control sparsity
and a self-learning method to coax masked representations to mimic unmasked
representations. A basic implementation of our model is competitive with more
sophisticated approaches and achieves a good balance between effectiveness and
efficiency. The simplicity of our methods opens the door for future
explorations in lexical representation learning for passage retrieval.

Modern machine learning systems have been applied successfully to a variety
of tasks in recent years but making such systems robust against adversarially
chosen modifications of input instances seems to be a much harder problem. It
is probably fair to say that no fully satisfying solution has been found up to
date and it is not clear if the standard formulation even allows for a
principled solution. Hence, rather than following the classical path of bounded
perturbations, we consider a model similar to the quantum PAC-learning model
introduced by Bshouty and Jackson [1995]. Our first key contribution shows that
in this model we can reduce adversarial robustness to the conjunction of two
classical learning theory problems, namely (Problem 1) the problem of finding
generative models and (Problem 2) the problem of devising classifiers that are
robust with respect to distributional shifts. Our second key contribution is
that the considered framework does not rely on specific (and hence also
somewhat arbitrary) threat models like $\ell_p$ bounded perturbations. Instead,
our reduction guarantees that in order to solve the adversarial robustness
problem in our model it suffices to consider a single distance notion, i.e. the
Hellinger distance. From the technical perspective our protocols are heavily
based on the recent advances on delegation of quantum computation, e.g. Mahadev
[2018].
  Although the considered model is quantum and therefore not immediately
applicable to ``real-world'' situations, one might hope that in the future
either one can find a way to embed ``real-world'' problems into a quantum
framework or that classical algorithms can be found that are capable of
mimicking their powerful quantum counterparts.

Data-driven synthesis planning with machine learning is a key step in the
design and discovery of novel inorganic compounds with desirable properties.
Inorganic materials synthesis is often guided by chemists' prior knowledge and
experience, built upon experimental trial-and-error that is both time and
resource consuming. Recent developments in natural language processing (NLP)
have enabled large-scale text mining of scientific literature, providing open
source databases of synthesis information of synthesized compounds, material
precursors, and reaction conditions (temperatures, times). In this work, we
employ a conditional variational autoencoder (CVAE) to predict suitable
inorganic reaction conditions for the crucial inorganic synthesis steps of
calcination and sintering. We find that the CVAE model is capable of learning
subtle differences in target material composition, precursor compound
identities, and choice of synthesis route (solid-state, sol-gel) that are
present in the inorganic synthesis space. Moreover, the CVAE can generalize
well to unseen chemical entities and shows promise for predicting reaction
conditions for previously unsynthesized compounds of interest.

Reinforcement learning (RL) provides a naturalistic framing for learning
through trial and error, which is appealing both because of its simplicity and
effectiveness and because of its resemblance to how humans and animals acquire
skills through experience. However, real-world embodied learning, such as that
performed by humans and animals, is situated in a continual, non-episodic
world, whereas common benchmark tasks in RL are episodic, with the environment
resetting between trials to provide the agent with multiple attempts. This
discrepancy presents a major challenge when attempting to take RL algorithms
developed for episodic simulated environments and run them on real-world
platforms, such as robots. In this paper, we aim to address this discrepancy by
laying out a framework for Autonomous Reinforcement Learning (ARL):
reinforcement learning where the agent not only learns through its own
experience, but also contends with lack of human supervision to reset between
trials. We introduce a simulated benchmark EARL around this framework,
containing a set of diverse and challenging simulated tasks reflective of the
hurdles introduced to learning when only a minimal reliance on extrinsic
intervention can be assumed. We show that standard approaches to episodic RL
and existing approaches struggle as interventions are minimized, underscoring
the need for developing new algorithms for reinforcement learning with a
greater focus on autonomy.

Machine learning (ML) methods have proved to be a very successful tool in
physical sciences, especially when applied to experimental data analysis.
Artificial intelligence is particularly good at recognizing patterns in high
dimensional data, where it usually outperforms humans. Here we applied a simple
ML tool called principal component analysis (PCA) to study data from muon
spectroscopy. The measured quantity from this experiment is an asymmetry
function, which holds the information about the average intrinsic magnetic
field of the sample. A change in the asymmetry function might indicate a phase
transition; however, these changes can be very subtle, and existing methods of
analyzing the data require knowledge about the specific physics of the
material. PCA is an unsupervised ML tool, which means that no assumption about
the input data is required, yet we found that it still can be successfully
applied to asymmetry curves, and the indications of phase transitions can be
recovered. The method was applied to a range of magnetic materials with
different underlying physics. We discovered that performing PCA on all those
materials simultaneously can have a positive effect on the clarity of phase
transition indicators and can also improve the detection of the most important
variations of asymmetry functions. For this joint PCA we introduce a simple way
to track the contributions from different materials for a more meaningful
analysis.

This work studies the task of glossification, of which the aim is to em
transcribe natural spoken language sentences for the Deaf (hard-of-hearing)
community to ordered sign language glosses. Previous sequence-to-sequence
language models trained with paired sentence-gloss data often fail to capture
the rich connections between the two distinct languages, leading to
unsatisfactory transcriptions. We observe that despite different grammars,
glosses effectively simplify sentences for the ease of deaf communication,
while sharing a large portion of vocabulary with sentences. This has motivated
us to implement glossification by executing a collection of editing actions,
e.g. word addition, deletion, and copying, called editing programs, on their
natural spoken language counterparts. Specifically, we design a new neural
agent that learns to synthesize and execute editing programs, conditioned on
sentence contexts and partial editing results. The agent is trained to imitate
minimal editing programs, while exploring more widely the program space via
policy gradients to optimize sequence-wise transcription quality. Results show
that our approach outperforms previous glossification models by a large margin.

An automated robotic system needs to be as robust as possible and fail-safe
in general while having relatively high precision and repeatability. Although
deep learning-based methods are becoming research standard on how to approach
3D scan and image processing tasks, the industry standard for processing this
data is still analytically-based. Our paper claims that analytical methods are
less robust and harder for testing, updating, and maintaining. This paper
focuses on a specific task of 6D pose estimation of a bin in 3D scans.
Therefore, we present a high-quality dataset composed of synthetic data and
real scans captured by a structured-light scanner with precise annotations.
Additionally, we propose two different methods for 6D bin pose estimation, an
analytical method as the industrial standard and a baseline data-driven method.
Both approaches are cross-evaluated, and our experiments show that augmenting
the training on real scans with synthetic data improves our proposed
data-driven neural model. This position paper is preliminary, as proposed
methods are trained and evaluated on a relatively small initial dataset which
we plan to extend in the future.

This work explores the effect of gender and linguistic-based vocal variations
on the accuracy of emotive expression classification. Emotive expressions are
considered from the perspective of spectral features in speech (Mel-frequency
Cepstral Coefficient, Melspectrogram, Spectral Contrast). Emotions are
considered from the perspective of Basic Emotion Theory. A convolutional neural
network is utilised to classify emotive expressions in emotive audio datasets
in English, German, and Italian. Vocal variations for spectral features
assessed by (i) a comparative analysis identifying suitable spectral features,
(ii) the classification performance for mono, multi and cross-lingual emotive
data and (iii) an empirical evaluation of a machine learning model to assess
the effects of gender and linguistic variation on classification accuracy. The
results showed that spectral features provide a potential avenue for increasing
emotive expression classification. Additionally, the accuracy of emotive
expression classification was high within mono and cross-lingual emotive data,
but poor in multi-lingual data. Similarly, there were differences in
classification accuracy between gender populations. These results demonstrate
the importance of accounting for population differences to enable accurate
speech emotion recognition.

Deep learning (DL) models are very effective on many computer vision problems
and increasingly used in critical applications. They are also inherently black
box. A number of methods exist to generate image-wise explanations that allow
practitioners to understand and verify model predictions for a given image.
Beyond that, it would be desirable to validate that a DL model
\textit{generally} works in a sensible way, i.e. consistent with domain
knowledge and not relying on undesirable data artefacts. For this purpose, the
model needs to be explained globally. In this work, we focus on image
modalities that are naturally aligned such that each pixel position represents
a similar relative position on the imaged object, as is common in medical
imaging. We propose the pixel-wise aggregation of image-wise explanations as a
simple method to obtain label-wise and overall global explanations. These can
then be used for model validation, knowledge discovery, and as an efficient way
to communicate qualitative conclusions drawn from inspecting image-wise
explanations. We further propose Progressive Erasing Plus Progressive
Restoration (PEPPR) as a method to quantitatively validate that these global
explanations are faithful to how the model makes its predictions. We then apply
these methods to ultra-widefield retinal images, a naturally aligned modality.
We find that the global explanations are consistent with domain knowledge and
faithfully reflect the model's workings.

Video-and-language pre-training has shown promising improvements on various
downstream tasks. Most previous methods capture cross-modal interactions with a
transformer-based multimodal encoder, not fully addressing the misalignment
between unimodal video and text features. Besides, learning fine-grained
visual-language alignment usually requires off-the-shelf object detectors to
provide object information, which is bottlenecked by the detector's limited
vocabulary and expensive computation cost.
  We propose Align and Prompt: an efficient and effective video-and-language
pre-training framework with better cross-modal alignment. First, we introduce a
video-text contrastive (VTC) loss to align unimodal video-text features at the
instance level, which eases the modeling of cross-modal interactions. Then, we
propose a new visually-grounded pre-training task, prompting entity modeling
(PEM), which aims to learn fine-grained region-entity alignment. To achieve
this, we first introduce an entity prompter module, which is trained with VTC
to produce the similarity between a video crop and text prompts instantiated
with entity names. The PEM task then asks the model to predict the entity
pseudo-labels (i.e~normalized similarity scores) for randomly-selected video
crops. The resulting pre-trained model achieves state-of-the-art performance on
both text-video retrieval and videoQA, outperforming prior work by a
substantial margin. Our code and pre-trained models are available at
https://github.com/salesforce/ALPRO.

We revisit watermarking techniques based on pre-trained deep networks, in the
light of self-supervised approaches. We present a way to embed both marks and
binary messages into their latent spaces, leveraging data augmentation at
marking time. Our method can operate at any resolution and creates watermarks
robust to a broad range of transformations (rotations, crops, JPEG, contrast,
etc). It significantly outperforms the previous zero-bit methods, and its
performance on multi-bit watermarking is on par with state-of-the-art
encoder-decoder architectures trained end-to-end for watermarking. The code is
available at github.com/facebookresearch/ssl_watermarking

There are much recent interests in solving noncovnex min-max optimization
problems due to its broad applications in many areas including machine
learning, networked resource allocations, and distributed optimization.
Perhaps, the most popular first-order method in solving min-max optimization is
the so-called simultaneous (or single-loop) gradient descent-ascent algorithm
due to its simplicity in implementation. However, theoretical guarantees on the
convergence of this algorithm is very sparse since it can diverge even in a
simple bilinear problem.
  In this paper, our focus is to characterize the finite-time performance (or
convergence rates) of the continuous-time variant of simultaneous gradient
descent-ascent algorithm. In particular, we derive the rates of convergence of
this method under a number of different conditions on the underlying objective
function, namely, two-sided Polyak-L ojasiewicz (PL), one-sided PL,
nonconvex-strongly concave, and strongly convex-nonconcave conditions. Our
convergence results improve the ones in prior works under the same conditions
of objective functions. The key idea in our analysis is to use the classic
singular perturbation theory and coupling Lyapunov functions to address the
time-scale difference and interactions between the gradient descent and ascent
dynamics. Our results on the behavior of continuous-time algorithm may be used
to enhance the convergence properties of its discrete-time counterpart.

Suicidal ideation detection from social media is an evolving research with
great challenges. Many of the people who have the tendency to suicide share
their thoughts and opinions through social media platforms. As part of many
researches it is observed that the publicly available posts from social media
contain valuable criteria to effectively detect individuals with suicidal
thoughts. The most difficult part to prevent suicide is to detect and
understand the complex risk factors and warning signs that may lead to suicide.
This can be achieved by identifying the sudden changes in a user behavior
automatically. Natural language processing techniques can be used to collect
behavioral and textual features from social media interactions and these
features can be passed to a specially designed framework to detect anomalies in
human interactions that are indicators of suicidal intentions. We can achieve
fast detection of suicidal ideation using deep learning and/or machine learning
based classification approaches. For such a purpose, we can employ the
combination of LSTM and CNN models to detect such emotions from posts of the
users. In order to improve the accuracy, some approaches like using more data
for training, using attention model to improve the efficiency of existing
models etc. could be done. This paper proposes a LSTM-Attention-CNN combined
model to analyze social media submissions to detect any underlying suicidal
intentions. During evaluations, the proposed model demonstrated an accuracy of
90.3 percent and an F1-score of 92.6 percent, which is greater than the

To date, live-cell imaging at the nanometer scale remains challenging. Even
though super-resolution microscopy methods have enabled visualization of
subcellular structures below the optical resolution limit, the spatial
resolution is still far from enough for the structural reconstruction of
biomolecules in vivo (i.e. ~24 nm thickness of microtubule fiber). In this
study, we proposed an A-net network and showed that the resolution of
cytoskeleton images captured by a confocal microscope can be significantly
improved by combining the A-net deep learning network with the DWDC algorithm
based on degradation model. Utilizing the DWDC algorithm to construct new
datasets and taking advantage of A-net neural network's features (i.e.,
considerably fewer layers), we successfully removed the noise and flocculent
structures, which originally interfere with the cellular structure in the raw
image, and improved the spatial resolution by 10 times using relatively small
dataset. We, therefore, conclude that the proposed algorithm that combines
A-net neural network with the DWDC method is a suitable and universal approach
for exacting structural details of biomolecules, cells and organs from
low-resolution images.

Document summarization provides an instrument for faster understanding the
collection of text documents and has several real-life applications. With the
growth of online text data, numerous summarization models have been proposed
recently. The Sequence-to-Sequence (Seq2Seq) based neural summarization model
is the most widely used in the summarization field due to its high performance.
This is because semantic information and structure information in the text is
adequately considered when encoding. However, the existing extractive
summarization models pay little attention to and use the central topic
information to assist the generation of summaries, which leads to models not
ensuring the generated summary under the primary topic. A lengthy document can
span several topics, and a single summary cannot do justice to all the topics.
Therefore, the key to generating a high-quality summary is determining the
central topic and building a summary based on it, especially for a long
document. We propose a topic-aware encoding for document summarization to deal
with this issue. This model effectively combines syntactic-level and
topic-level information to build a comprehensive sentence representation.
Specifically, a neural topic model is added in the neural-based sentence-level
representation learning to adequately consider the central topic information
for capturing the critical content in the original document. The experimental
results on three public datasets show that our model outperforms the

Designing new industrial materials with desired properties can be very
expensive and time consuming. The main difficulty is to generate compounds that
correspond to realistic materials. Indeed, the description of compounds as
vectors of components' proportions is characterized by discrete features and a
severe sparsity. Furthermore, traditional generative model validation processes
as visual verification, FID and Inception scores are tailored for images and
cannot then be used as such in this context. To tackle these issues, we develop
an original Binded-VAE model dedicated to the generation of discrete datasets
with high sparsity. We validate the model with novel metrics adapted to the
problem of compounds generation. We show on a real issue of rubber compound
design that the proposed approach outperforms the standard generative models
which opens new perspectives for material design optimization.

Modern approaches for fast retrieval of similar vectors on billion-scaled
datasets rely on compressed-domain approaches such as binary sketches or
product quantization. These methods minimize a certain loss, typically the mean
squared error or other objective functions tailored to the retrieval problem.
In this paper, we re-interpret popular methods such as binary hashing or
product quantizers as auto-encoders, and point out that they implicitly make
suboptimal assumptions on the form of the decoder. We design
backward-compatible decoders that improve the reconstruction of the vectors
from the same codes, which translates to a better performance in nearest
neighbor search. Our method significantly improves over binary hashing methods
or product quantization on popular benchmarks.

In spite of the new opportunities brought about by the Open RAN, advances in
ML-based network automation have been slow, mainly because of the
unavailability of large-scale datasets and experimental testing infrastructure.
This slows down the development and widespread adoption of Deep Reinforcement
Learning (DRL) agents on real networks, delaying progress in intelligent and
autonomous RAN control. In this paper, we address these challenges by proposing
practical solutions and software pipelines for the design, training, testing,
and experimental evaluation of DRL-based closed-loop control in the Open RAN.
We introduce ColO-RAN, the first publicly-available large-scale O-RAN testing
framework with software-defined radios-in-the-loop. Building on the scale and
computational capabilities of the Colosseum wireless network emulator, ColO-RAN
enables ML research at scale using O-RAN components, programmable base
stations, and a "wireless data factory". Specifically, we design and develop
three exemplary xApps for DRL-based control of RAN slicing, scheduling and
online model training, and evaluate their performance on a cellular network
with 7 softwarized base stations and 42 users. Finally, we showcase the
portability of ColO-RAN to different platforms by deploying it on Arena, an
indoor programmable testbed. Extensive results from our first-of-its-kind
large-scale evaluation highlight the benefits and challenges of DRL-based
adaptive control. They also provide insights on the development of wireless DRL
pipelines, from data analysis to the design of DRL agents, and on the tradeoffs
associated to training on a live RAN. ColO-RAN and the collected large-scale
dataset will be made publicly available to the research community.

An approach to resilient planning and control of autonomous vehicles in
multi-vehicle traffic scenarios is proposed. The proposed method is based on
model predictive control (MPC), where alternative predictions of the
surrounding traffic are determined automatically such that they are
intentionally adversarial to the ego vehicle. This provides robustness against
the inherent uncertainty in traffic predictions. To reduce conservatism, an
assumption that other agents are of no ill intent is formalized. Simulation
results from highway driving scenarios show that the proposed method in
real-time negotiates traffic situations out of scope for a nominal MPC approach
and performs favorably to state-of-the-art reinforcement-learning approaches
without requiring prior training. The results also show that the proposed
method performs effectively, with the ability to prune disturbance sequences
with a lower risk for the ego vehicle.

Digital arts have gained an unprecedented level of popularity with the
emergence of non-fungible tokens (NFTs). NFTs are cryptographic assets that are
stored on blockchain networks and represent a digital certificate of ownership
that cannot be forged. NFTs can be incorporated into a smart contract which
allows the owner to benefit from a future sale percentage. While digital art
producers can benefit immensely with NFTs, their production is time consuming.
Therefore, this paper explores the possibility of using generative adversarial
networks (GANs) for automatic generation of digital arts. GANs are deep
learning architectures that are widely and effectively used for synthesis of
audio, images, and video contents. However, their application to NFT arts have
been limited. In this paper, a GAN-based architecture is implemented and
evaluated for novel NFT-style digital arts generation. Results from the
qualitative case study indicate that the generated artworks are comparable to
the real samples in terms of being interesting and inspiring and they were
judged to be more innovative than real samples.

We describe a model for polarization in multi-agent systems based on Esteban
and Ray's standard family of polarization measures from economics. Agents
evolve by updating their beliefs (opinions) based on an underlying influence
graph, as in the standard DeGroot model for social learning, but under a
confirmation bias; i.e., a discounting of opinions of agents with dissimilar
views. We show that even under this bias polarization eventually vanishes
(converges to zero) if the influence graph is strongly-connected. If the
influence graph is a regular symmetric circulation, we determine the unique
belief value to which all agents converge. Our more insightful result
establishes that, under some natural assumptions, if polarization does not
eventually vanish then either there is a disconnected subgroup of agents, or
some agent influences others more than she is influenced. We also prove that
polarization does not necessarily vanish in weakly-connected graphs under
confirmation bias. Furthermore, we show how our model relates to the classic
DeGroot model for social learning. We illustrate our model with several
simulations of a running example about polarization over vaccines and of other
case studies. The theoretical results and simulations will provide insight into
the phenomenon of polarization.

We consider the conditional treatment effect for competing risks data in
observational studies. While it is described as a constant difference between
the hazard functions given the covariates, we do not assume specific functional
forms for the covariates. We derive the efficient score for the treatment
effect using modern semiparametric theory, as well as two doubly robust scores
with respect to 1) the assumed propensity score for treatment and the censoring
model, and 2) the outcome models for the competing risks. An important
asymptotic result regarding the estimators is rate double robustness, in
addition to the classical model double robustness. Rate double robustness
enables the use of machine learning and nonparametric methods in order to
estimate the nuisance parameters, while preserving the root-$n$ asymptotic
normality of the estimators for inferential purposes. We study the performance
of the estimators using simulation. The estimators are applied to the data from
a cohort of Japanese men in Hawaii followed since 1960s in order to study the
effect of mid-life drinking behavior on late life cognitive outcomes.

Compared with traditional face-to-face teaching, online learning is more
convenient. However, during online classes, it is more difficult for teachers
to observe all student reactions at the same time. Our system is designed to
help teachers to adjust the speed of their lessons by detecting student
reactions. In this study, we estimate student head pose, hand poses, and
expressions through the camera, all these poses will be used as criteria for
judging the student participation. After estimating, we proposed a method to
evaluate classroom participation based on student head pose, hand poses, and
facial expression recognition. The estimated result divides the class quality
into positive, neutral, and negative, then, under the help of the system,
teachers can rearrange the content of the class.

The great success of deep learning is mainly due to the large-scale network
architecture and the high-quality training data. However, it is still
challenging to deploy recent deep models on portable devices with limited
memory and imaging ability. Some existing works have engaged to compress the
model via knowledge distillation. Unfortunately, these methods cannot deal with
images with reduced image quality, such as the low-resolution (LR) images. To
this end, we make a pioneering effort to distill helpful knowledge from a heavy
network model learned from high-resolution (HR) images to a compact network
model that will handle LR images, thus advancing the current knowledge
distillation technique with the novel pixel distillation. To achieve this goal,
we propose a Teacher-Assistant-Student (TAS) framework, which disentangles
knowledge distillation into the model compression stage and the high resolution
representation transfer stage. By equipping a novel Feature Super Resolution
(FSR) module, our approach can learn lightweight network model that can achieve
similar accuracy as the heavy teacher model but with much fewer parameters,
faster inference speed, and lower-resolution inputs. Comprehensive experiments
on three widely-used benchmarks, \ie, CUB-200-2011, PASCAL VOC 2007, and
ImageNetSub, demonstrate the effectiveness of our approach.

Conventional video compression (VC) methods are based on motion compensated
transform coding, and the steps of motion estimation, mode and quantization
parameter selection, and entropy coding are optimized individually due to the
combinatorial nature of the end-to-end optimization problem. Learned VC allows
end-to-end rate-distortion (R-D) optimized training of nonlinear transform,
motion and entropy model simultaneously. Most works on learned VC consider
end-to-end optimization of a sequential video codec based on R-D loss averaged
over pairs of successive frames. It is well-known in conventional VC that
hierarchical, bi-directional coding outperforms sequential compression because
of its ability to use both past and future reference frames. This paper
proposes a learned hierarchical bi-directional video codec (LHBDC) that
combines the benefits of hierarchical motion-compensated prediction and
end-to-end optimization. Experimental results show that we achieve the best R-D
results that are reported for learned VC schemes to date in both PSNR and
MS-SSIM. Compared to conventional video codecs, the R-D performance of our
end-to-end optimized codec outperforms those of both x265 and SVT-HEVC encoders
("veryslow" preset) in PSNR and MS-SSIM as well as HM 16.23 reference software
in MS-SSIM. We present ablation studies showing performance gains due to
proposed novel tools such as learned masking, flow-field subsampling, and
temporal flow vector prediction. The models and instructions to reproduce our
results can be found in https://github.com/makinyilmaz/LHBDC/

Gaussian processes (GPs) are an important tool in machine learning and
statistics with applications ranging from social and natural science through
engineering. They constitute a powerful kernelized non-parametric method with
well-calibrated uncertainty estimates, however, off-the-shelf GP inference
procedures are limited to datasets with several thousand data points because of
their cubic computational complexity. For this reason, many sparse GPs
techniques have been developed over the past years. In this paper, we focus on
GP regression tasks and propose a new approach based on aggregating predictions
from several local and correlated experts. Thereby, the degree of correlation
between the experts can vary between independent up to fully correlated
experts. The individual predictions of the experts are aggregated taking into
account their correlation resulting in consistent uncertainty estimates. Our
method recovers independent Product of Experts, sparse GP and full GP in the
limiting cases. The presented framework can deal with a general kernel function
and multiple variables, and has a time and space complexity which is linear in
the number of experts and data samples, which makes our approach highly
scalable. We demonstrate superior performance, in a time vs. accuracy sense, of
our proposed method against state-of-the-art GP approximation methods for
synthetic as well as several real-world datasets with deterministic and
stochastic optimization.

We propose a simple and efficient image classification architecture based on
deep multiple instance learning, and apply it to the challenging task of caries
detection in dental radiographs. Technically, our approach contributes in two
ways: First, it outputs a heatmap of local patch classification probabilities
despite being trained with weak image-level labels. Second, it is amenable to
learning from segmentation labels to guide training. In contrast to existing
methods, the human user can faithfully interpret predictions and interact with
the model to decide which regions to attend to. Experiments are conducted on a
large clinical dataset of $\sim$38k bitewings ($\sim$316k teeth), where we
achieve competitive performance compared to various baselines. When guided by
an external caries segmentation model, a significant improvement in
classification and localization performance is observed.

Visual exploration is a task that seeks to visit all the navigable areas of
an environment as quickly as possible. The existing methods employ deep
reinforcement learning (RL) as the standard tool for the task. However, they
tend to be vulnerable to statistical shifts between the training and test data,
resulting in poor generalization over novel environments that are
out-of-distribution (OOD) from the training data. In this paper, we attempt to
improve the generalization ability by utilizing the inductive biases available
for the task. Employing the active neural SLAM (ANS) that learns exploration
policies with the advantage actor-critic (A2C) method as the base framework, we
first point out that the mappings represented by the actor and the critic
should satisfy specific symmetries. We then propose a network design for the
actor and the critic to inherently attain these symmetries. Specifically, we
use $G$-convolution instead of the standard convolution and insert the
semi-global polar pooling (SGPP) layer, which we newly design in this study, in
the last section of the critic network. Experimental results show that our
method increases area coverage by $8.1 m^2$ when trained on the Gibson dataset
and tested on the MP3D dataset, establishing the new state-of-the-art.

Computational Pathology (CPath) is an emerging field concerned with the study
of tissue pathology via computational algorithms for the processing and
analysis of digitized high-resolution images of tissue slides. Recent deep
learning based developments in CPath have successfully leveraged sheer volume
of raw pixel data in histology images for predicting target parameters in the
domains of diagnostics, prognostics, treatment sensitivity and patient
stratification -- heralding the promise of a new data-driven AI era for both
histopathology and oncology. With data serving as the fuel and AI as the
engine, CPath algorithms are poised to be ready for takeoff and eventual launch
into clinical and pharmaceutical orbits. In this paper, we discuss CPath
limitations and associated challenges to enable the readers distinguish hope
from hype and provide directions for future research to overcome some of the
major challenges faced by this budding field to enable its launch into the two
orbits.

We consider the problem of formally verifying almost-sure (a.s.) asymptotic
stability in discrete-time nonlinear stochastic control systems. While
verifying stability in deterministic control systems is extensively studied in
the literature, verifying stability in stochastic control systems is an open
problem. The few existing works on this topic either consider only specialized
forms of stochasticity or make restrictive assumptions on the system, rendering
them inapplicable to learning algorithms with neural network policies. In this
work, we present an approach for general nonlinear stochastic control problems
with two novel aspects: (a) instead of classical stochastic extensions of
Lyapunov functions, we use ranking supermartingales (RSMs) to certify
a.s.~asymptotic stability, and (b) we present a method for learning neural
network RSMs. We prove that our approach guarantees a.s.~asymptotic stability
of the system and provides the first method to obtain bounds on the
stabilization time, which stochastic Lyapunov functions do not. Finally, we
validate our approach experimentally on a set of nonlinear stochastic
reinforcement learning environments with neural network policies.

Difficulties in following speech due to loud background sounds are common in
broadcasting. Object-based audio, e.g., MPEG-H Audio solves this problem by
providing a user-adjustable speech level. While object-based audio is gaining
momentum, transitioning to it requires time and effort. Also, lots of content
exists, produced and archived outside the object-based workflows. To address
this, Fraunhofer IIS has developed a deep-learning solution called Dialog+,
capable of enabling speech level personalization also for content with only the
final audio tracks available. This paper reports on public field tests
evaluating Dialog+, conducted together with Westdeutscher Rundfunk (WDR) and
Bayerischer Rundfunk (BR), starting from September 2020. To our knowledge,
these are the first large-scale tests of this kind. As part of one of these, a
survey with more than 2,000 participants showed that 90% of the people above 60
years old have problems in understanding speech in TV "often" or "very often".
Overall, 83% of the participants liked the possibility to switch to Dialog+,
including those who do not normally struggle with speech intelligibility.
Dialog+ introduces a clear benefit for the audience, filling the gap between
object-based broadcasting and traditionally produced material.

Concrete is the standard construction material for buildings, bridges, and
roads. As safety plays a central role in the design, monitoring, and
maintenance of such constructions, it is important to understand the cracking
behavior of concrete. Computed tomography captures the microstructure of
building materials and allows to study crack initiation and propagation. Manual
segmentation of crack surfaces in large 3d images is not feasible. In this
paper, automatic crack segmentation methods for 3d images are reviewed and
compared. Classical image processing methods (edge detection filters, template
matching, minimal path and region growing algorithms) and learning methods
(convolutional neural networks, random forests) are considered and tested on
semi-synthetic 3d images. Their performance strongly depends on parameter
selection which should be adapted to the grayvalue distribution of the images
and the geometric properties of the concrete. In general, the learning methods
perform best, in particular for thin cracks and low grayvalue contrast.

We apply deep metric learning for the first time to the problem of
classifying planktic foraminifer shells on microscopic images. This species
recognition task is an important information source and scientific pillar for
reconstructing past climates. All foraminifer CNN recognition pipelines in the
literature produce black-box classifiers that lack visualization options for
human experts and cannot be applied to open-set problems. Here, we benchmark
metric learning against these pipelines, produce the first scientific
visualization of the phenotypic planktic foraminifer morphology space, and
demonstrate that metric learning can be used to cluster species unseen during
training. We show that metric learning outperforms all published CNN-based
state-of-the-art benchmarks in this domain. We evaluate our approach on the
34,640 expert-annotated images of the Endless Forams public library of 35
modern planktic foraminifera species. Our results on this data show leading 92%
accuracy (at 0.84 F1-score) in reproducing expert labels on withheld test data,
and 66.5% accuracy (at 0.70 F1-score) when clustering species never encountered
in training. We conclude that metric learning is highly effective for this
domain and serves as an important tool towards expert-in-the-loop automation of
microfossil identification. Keycode, network weights, and data splits are
published with this paper for full reproducibility.

Chinese word segmentation and part-of-speech tagging are necessary tasks in
terms of computational linguistics and application of natural language
processing. Many re-searchers still debate the demand for Chinese word
segmentation and part-of-speech tagging in the deep learning era. Nevertheless,
resolving ambiguities and detecting unknown words are challenging problems in
this field. Previous studies on joint Chinese word segmentation and
part-of-speech tagging mainly follow the character-based tagging model focusing
on modeling n-gram features. Unlike previous works, we propose a neural model
named SpanSegTag for joint Chinese word segmentation and part-of-speech tagging
following the span labeling in which the probability of each n-gram being the
word and the part-of-speech tag is the main problem. We use the biaffine
operation over the left and right boundary representations of consecutive
characters to model the n-grams. Our experiments show that our BERT-based model
SpanSegTag achieved competitive performances on the CTB5, CTB6, and UD, or
significant improvements on CTB7 and CTB9 benchmark datasets compared with the
current state-of-the-art method using BERT or ZEN encoders.

We consider an extension to the restless multi-armed bandit (RMAB) problem
with unknown arm dynamics, where an unknown exogenous global Markov process
governs the rewards distribution of each arm. Under each global state, the
rewards process of each arm evolves according to an unknown Markovian rule,
which is non-identical among different arms. At each time, a player chooses an
arm out of $N$ arms to play, and receives a random reward from a finite set of
reward states. The arms are restless, that is, their local state evolves
regardless of the player's actions. Motivated by recent studies on related RMAB
settings, the regret is defined as the reward loss with respect to a player
that knows the dynamics of the problem, and plays at each time $t$ the arm that
maximizes the expected immediate value. The objective is to develop an
arm-selection policy that minimizes the regret. To that end, we develop the
Learning under Exogenous Markov Process (LEMP) algorithm. We analyze LEMP
theoretically and establish a finite-sample bound on the regret. We show that
LEMP achieves a logarithmic regret order with time. We further analyze LEMP
numerically and present simulation results that support the theoretical
findings and demonstrate that LEMP significantly outperforms alternative

This work proposes a decentralized architecture, where individual agents aim
at solving a classification problem while observing streaming features of
different dimensions and arising from possibly different distributions. In the
context of social learning, several useful strategies have been developed,
which solve decision making problems through local cooperation across
distributed agents and allow them to learn from streaming data. However,
traditional social learning strategies rely on the fundamental assumption that
each agent has significant prior knowledge of the underlying distribution of
the observations. In this work we overcome this issue by introducing a machine
learning framework that exploits social interactions over a graph, leading to a
fully data-driven solution to the distributed classification problem. In the
proposed social machine learning (SML) strategy, two phases are present: in the
training phase, classifiers are independently trained to generate a belief over
a set of hypotheses using a finite number of training samples; in the
prediction phase, classifiers evaluate streaming unlabeled observations and
share their instantaneous beliefs with neighboring classifiers. We show that
the SML strategy enables the agents to learn consistently under this
highly-heterogeneous setting and allows the network to continue learning even
during the prediction phase when it is deciding on unlabeled samples. The
prediction decisions are used to continually improve performance thereafter in
a manner that is markedly different from most existing static classification
schemes where, following training, the decisions on unlabeled data are not
re-used to improve future performance.

Reinforcement learning (RL) is a central problem in artificial intelligence.
This problem consists of defining artificial agents that can learn optimal
behaviour by interacting with an environment -- where the optimal behaviour is
defined with respect to a reward signal that the agent seeks to maximize.
Reward machines (RMs) provide a structured, automata-based representation of a
reward function that enables an RL agent to decompose an RL problem into
structured subproblems that can be efficiently learned via off-policy learning.
Here we show that RMs can be learned from experience, instead of being
specified by the user, and that the resulting problem decomposition can be used
to effectively solve partially observable RL problems. We pose the task of
learning RMs as a discrete optimization problem where the objective is to find
an RM that decomposes the problem into a set of subproblems such that the
combination of their optimal memoryless policies is an optimal policy for the
original problem. We show the effectiveness of this approach on three partially
observable domains, where it significantly outperforms A3C, PPO, and ACER, and
discuss its advantages, limitations, and broader potential.

In this paper, we focus on exploiting neural networks for the analysis and
planning stage in self-adaptive architectures. The studied motivating cases in
the paper involve existing (legacy) self-adaptive architectures and their
adaptation logic, which has been specified by logical rules. We further assume
that there is a need to endow these systems with the ability to learn based on
examples of inputs and expected outputs. One simple option to address such a
need is to replace the reasoning based on logical rules with a neural network.
However, this step brings several problems that often create at least a
temporary regress. The reason is the logical rules typically represent a large
and tested body of domain knowledge, which may be lost if the logical rules are
replaced by a neural network. Further, the black-box nature of generic neural
networks obfuscates how the systems work inside and consequently introduces
more uncertainty. In this paper, we present a method that makes it possible to
endow an existing self-adaptive architectures with the ability to learn using
neural networks, while preserving domain knowledge existing in the logical
rules. We introduce a continuum between the existing rule-based system and a
system based on a generic neural network. We show how to navigate in this
continuum and create a neural network architecture that naturally embeds the
original logical rules and how to gradually scale the learning potential of the
network, thus controlling the uncertainty inherent to all soft computing
models. We showcase and evaluate the approach on representative excerpts from
two larger real-life use cases.

Bipolar disorder is a mental health disorder that causes mood swings that
range from depression to mania. Diagnosis of bipolar disorder is usually done
based on patient interviews, and reports obtained from the caregivers of the
patients. Subsequently, the diagnosis depends on the experience of the expert,
and it is possible to have confusions of the disorder with other mental
disorders. Automated processes in the diagnosis of bipolar disorder can help
providing quantitative indicators, and allow easier observations of the
patients for longer periods. Furthermore, the need for remote treatment and
diagnosis became especially important during the COVID-19 pandemic. In this
thesis, we create a multimodal decision system based on recordings of the
patient in acoustic, linguistic, and visual modalities. The system is trained
on the Bipolar Disorder corpus. Comprehensive analysis of unimodal and
multimodal systems, as well as various fusion techniques are performed. Besides
processing entire patient sessions using unimodal features, a task-level
investigation of the clips is studied. Using acoustic, linguistic, and visual
features in a multimodal fusion system, we achieved a 64.8% unweighted average
recall score, which improves the state-of-the-art performance achieved on this
dataset.

This paper addresses and solves some challenges in the adoption of machine
learning in insurance with the democratization of model deployment. The first
challenge is reducing the labelling effort (hence focusing on the data quality)
with the help of active learning, a feedback loop between the model inference
and an oracle: as in insurance the unlabeled data is usually abundant, active
learning can become a significant asset in reducing the labelling cost. For
that purpose, this paper sketches out various classical active learning
methodologies before studying their empirical impact on both synthetic and real
datasets. Another key challenge in insurance is the fairness issue in model
inferences. We will introduce and integrate a post-processing fairness for
multi-class tasks in this active learning framework to solve these two issues.
Finally numerical experiments on unfair datasets highlight that the proposed
setup presents a good compromise between model precision and fairness.

In complex tasks where the reward function is not straightforward and
consists of a set of objectives, multiple reinforcement learning (RL) policies
that perform task adequately, but employ different strategies can be trained by
adjusting the impact of individual objectives on reward function. Understanding
the differences in strategies between policies is necessary to enable users to
choose between offered policies, and can help developers understand different
behaviors that emerge from various reward functions and training
hyperparameters in RL systems. In this work we compare behavior of two policies
trained on the same task, but with different preferences in objectives. We
propose a method for distinguishing between differences in behavior that stem
from different abilities from those that are a consequence of opposing
preferences of two RL agents. Furthermore, we use only data on preference-based
differences in order to generate contrasting explanations about agents'
preferences. Finally, we test and evaluate our approach on an autonomous
driving task and compare the behavior of a safety-oriented policy and one that
prefers speed.

Current weakly supervised semantic segmentation (WSSS) frameworks usually
contain the separated mask-refinement model and the main semantic region mining
model. These approaches would contain redundant feature extraction backbones
and biased learning objectives, making them computational complex yet
sub-optimal to addressing the WSSS task. To solve this problem, this paper
establishes a compact learning framework that embeds the classification and
mask-refinement components into a unified deep model. With the shared feature
extraction backbone, our model is able to facilitate knowledge sharing between
the two components while preserving a low computational complexity. To
encourage high-quality knowledge interaction, we propose a novel alternative
self-dual teaching (ASDT) mechanism. Unlike the conventional distillation
strategy, the knowledge of the two teacher branches in our model is
alternatively distilled to the student branch by a Pulse Width Modulation
(PWM), which generates PW wave-like selection signal to guide the knowledge
distillation process. In this way, the student branch can help prevent the
model from falling into local minimum solutions caused by the imperfect
knowledge provided of either teacher branch. Comprehensive experiments on the
PASCAL VOC 2012 and COCO-Stuff 10K demonstrate the effectiveness of the
proposed alternative self-dual teaching mechanism as well as the new
state-of-the-art performance of our approach.

The Partially Observable Markov Decision Process (POMDP) is a powerful
framework for capturing decision-making problems that involve state and
transition uncertainty. However, most current POMDP planners cannot effectively
handle very high-dimensional observations they often encounter in the real
world (e.g. image observations in robotic domains). In this work, we propose
Visual Tree Search (VTS), a learning and planning procedure that combines
generative models learned offline with online model-based POMDP planning. VTS
bridges offline model training and online planning by utilizing a set of deep
generative observation models to predict and evaluate the likelihood of image
observations in a Monte Carlo tree search planner. We show that VTS is robust
to different observation noises and, since it utilizes online, model-based
planning, can adapt to different reward structures without the need to
re-train. This new approach outperforms a baseline state-of-the-art on-policy
planning algorithm while using significantly less offline training time.

In a previous work, we proposed a geometric framework to study a deep neural
network, seen as sequence of maps between manifolds, employing singular
Riemannian geometry. In this paper, we present an application of this
framework, proposing a way to build the class of equivalence of an input point:
such class is defined as the set of the points on the input manifold mapped to
the same output by the neural network. In other words, we build the preimage of
a point in the output manifold in the input space. In particular. we focus for
simplicity on the case of neural networks maps from n-dimensional real spaces
to (n - 1)-dimensional real spaces, we propose an algorithm allowing to build
the set of points lying on the same class of equivalence. This approach leads
to two main applications: the generation of new synthetic data and it may
provides some insights on how a classifier can be confused by small
perturbation on the input data (e.g. a penguin image classified as an image
containing a chihuahua). In addition, for neural networks from 2D to 1D real
spaces, we also discuss how to find the preimages of closed intervals of the
real line. We also present some numerical experiments with several neural
networks trained to perform non-linear regression tasks, including the case of
a binary classifier.

Deep Neural Networks are widely used for solving complex problems in several
scientific areas, such as speech recognition, machine translation, image
analysis. The strategies employed to investigate their theoretical properties
mainly rely on Euclidean geometry, but in the last years new approaches based
on Riemannian geometry have been developed. Motivated by some open problems, we
study a particular sequence of maps between manifolds, with the last manifold
of the sequence equipped with a Riemannian metric. We investigate the
structures induced trough pullbacks on the other manifolds of the sequence and
on some related quotients. In particular, we show that the pullbacks of the
final Riemannian metric to any manifolds of the sequence is a degenerate
Riemannian metric inducing a structure of pseudometric space, we show that the
Kolmogorov quotient of this pseudometric space yields a smooth manifold, which
is the base space of a particular vertical bundle. We investigate the
theoretical properties of the maps of such sequence, eventually we focus on the
case of maps between manifolds implementing neural networks of practical
interest and we present some applications of the geometric framework we
introduced in the first part of the paper.

Modeling spatial-temporal relations is imperative for recognizing human
actions, especially when a human is interacting with objects, while multiple
objects appear around the human differently over time. Most existing action
recognition models focus on learning overall visual cues of a scene but
disregard informative fine-grained features, which can be captured by learning
human-object relationships and interactions. In this paper, we learn
human-object relationships by exploiting the interaction of their local and
global contexts. We hence propose the Global-Local Interaction Distillation
Network (GLIDN), learning human and object interactions through space and time
via knowledge distillation for fine-grained scene understanding. GLIDN encodes
humans and objects into graph nodes and learns local and global relations via
graph attention network. The local context graphs learn the relation between
humans and objects at a frame level by capturing their co-occurrence at a
specific time step. The global relation graph is constructed based on the
video-level of human and object interactions, identifying their long-term
relations throughout a video sequence. More importantly, we investigate how
knowledge from these graphs can be distilled to their counterparts for
improving human-object interaction (HOI) recognition. We evaluate our model by
conducting comprehensive experiments on two datasets including Charades and
CAD-120 datasets. We have achieved better results than the baselines and
counterpart approaches.

Traditional computer vision models are trained to predict a fixed set of
predefined categories. Recently, natural language has been shown to be a
broader and richer source of supervision that provides finer descriptions to
visual concepts than supervised "gold" labels. Previous works, such as CLIP,
use InfoNCE loss to train a model to predict the pairing between images and
text captions. CLIP, however, is data hungry and requires more than 400M
image-text pairs for training. The inefficiency can be partially attributed to
the fact that the image-text pairs are noisy. To address this, we propose OTTER
(Optimal TransporT distillation for Efficient zero-shot Recognition), which
uses online entropic optimal transport to find a soft image-text match as
labels for contrastive learning. Based on pretrained image and text encoders,
models trained with OTTER achieve strong performance with only 3M image text
pairs. Compared with InfoNCE loss, label smoothing, and knowledge distillation,
OTTER consistently outperforms these baselines in zero shot evaluation on
Google Open Images (19,958 classes) and multi-labeled ImageNet 10K (10032
classes) from Tencent ML-Images. Over 42 evaluations on 7 different
dataset/architecture settings x 6 metrics, OTTER outperforms (32) or ties (2)
all baselines in 34 of them.

To enhance the nonlinearity of neural networks and increase their mapping
abilities between the inputs and response variables, activation functions play
a crucial role to model more complex relationships and patterns in the data. In
this work, a novel methodology is proposed to adaptively customize activation
functions only by adding very few parameters to the traditional activation
functions such as Sigmoid, Tanh, and ReLU. To verify the effectiveness of the
proposed methodology, some theoretical and experimental analysis on
accelerating the convergence and improving the performance is presented, and a
series of experiments are conducted based on various network models (such as
AlexNet, VGGNet, GoogLeNet, ResNet and DenseNet), and various datasets (such as
CIFAR10, CIFAR100, miniImageNet, PASCAL VOC and COCO) . To further verify the
validity and suitability in various optimization strategies and usage
scenarios, some comparison experiments are also implemented among different
optimization strategies (such as SGD, Momentum, AdaGrad, AdaDelta and ADAM) and
different recognition tasks like classification and detection. The results show
that the proposed methodology is very simple but with significant performance
in convergence speed, precision and generalization, and it can surpass other
popular methods like ReLU and adaptive functions like Swish in almost all
experiments in terms of overall performance.The code is publicly available at
https://github.com/HuHaigen/Adaptively-Customizing-Activation-Functions. The
package includes the proposed three adaptive activation functions for
reproducibility purposes.

In order to classify the indeterministic termination behavior of the open
source SAT solver CryptoMiniSat in multi-threading mode while processing hard
to solve boolean satisfiability problem instances, internal solver runtime
parameters have been collected and analyzed. A subset of these parameters has
been selected and employed as features vector to successfully create a machine
learning model for the binary classification of the solver's termination
behavior with any single new solving run of a not yet solved instance. The
model can be used for the early estimation of a solving attempt as belonging or
not belonging to the class of candidates with good chances for a fast
termination. In this context a combination of active profiles of runtime
characteristics appear to mirror the influence of the solver's momentary
heuristics on the immediate quality of the solver's resolution process. Because
runtime parameters of already the first two solving iterations are enough to
forecast termination of the attempt with good success scores, the results of
the present work deliver a promising basis which can be further developed in
order to enrich CryptoMiniSat or generally any modern SAT solver with AI

Privacy-preserving machine learning enables the training of models on
decentralized datasets without the need to reveal the data, both on horizontal
and vertically partitioned data. However, it relies on specialized techniques
and algorithms to perform the necessary computations. The privacy preserving
scalar product protocol, which enables the dot product of vectors without
revealing them, is one popular example for its versatility. Unfortunately, the
solutions currently proposed in the literature focus mainly on two-party
scenarios, even though scenarios with a higher number of data parties are
becoming more relevant. For example when performing analyses that require
counting the number of samples which fulfill certain criteria defined across
various sites, such as calculating the information gain at a node in a decision
tree. In this paper we propose a generalization of the protocol for an
arbitrary number of parties, based on an existing two-party method. Our
proposed solution relies on a recursive resolution of smaller scalar products.
After describing our proposed method, we discuss potential scalability issues.
Finally, we describe the privacy guarantees and identify any concerns, as well
as comparing the proposed method to the original solution in this aspect.

We present a federated learning framework that is designed to robustly
deliver good predictive performance across individual clients with
heterogeneous data. The proposed approach hinges upon a superquantile-based
learning objective that captures the tail statistics of the error distribution
over heterogeneous clients. We present a stochastic training algorithm which
interleaves differentially private client reweighting steps with federated
averaging steps. The proposed algorithm is supported with finite time
convergence guarantees that cover both convex and non-convex settings.
Experimental results on benchmark datasets for federated learning demonstrate
that our approach is competitive with classical ones in terms of average error
and outperforms them in terms of tail statistics of the error.

In this paper, we investigate the dynamics-aware adversarial attack problem
in deep neural networks. Most existing adversarial attack algorithms are
designed under a basic assumption -- the network architecture is fixed
throughout the attack process. However, this assumption does not hold for many
recently proposed networks, e.g. 3D sparse convolution network, which contains
input-dependent execution to improve computational efficiency. It results in a
serious issue of lagged gradient, making the learned attack at the current step
ineffective due to the architecture changes afterward. To address this issue,
we propose a Leaded Gradient Method (LGM) and show the significant effects of
the lagged gradient. More specifically, we re-formulate the gradients to be
aware of the potential dynamic changes of network architectures, so that the
learned attack better "leads" the next step than the dynamics-unaware methods
when network architecture changes dynamically. Extensive experiments on various
datasets show that our LGM achieves impressive performance on semantic
segmentation and classification. Compared with the dynamic-unaware methods, LGM
achieves about 20% lower mIoU averagely on the ScanNet and S3DIS datasets. LGM
also outperforms the recent point cloud attacks.

Adapting Automatic Speech Recognition (ASR) models to new domains results in
a deterioration of performance on the original domain(s), a phenomenon called
Catastrophic Forgetting (CF). Even monolingual ASR models cannot be extended to
new accents, dialects, topics, etc. without suffering from CF, making them
unable to be continually enhanced without storing all past data. Fortunately,
Continual Learning (CL) methods, which aim to enable continual adaptation while
overcoming CF, can be used. In this paper, we implement an extensive number of
CL methods for End-to-End ASR and test and compare their ability to extend a
monolingual Hybrid CTC-Transformer model across four new tasks. We find that
the best performing CL method closes the gap between the fine-tuned model
(lower bound) and the model trained jointly on all tasks (upper bound) by more
than 40%, while requiring access to only 0.6% of the original data.

We propose a novel zero-shot multi-frame image restoration method for
removing unwanted obstruction elements (such as rains, snow, and moire
patterns) that vary in successive frames. It has three stages: transformer
pre-training, zero-shot restoration, and hard patch refinement. Using the
pre-trained transformers, our model is able to tell the motion difference
between the true image information and the obstructing elements. For zero-shot
image restoration, we design a novel model, termed SiamTrans, which is
constructed by Siamese transformers, encoders, and decoders. Each transformer
has a temporal attention layer and several self-attention layers, to capture
both temporal and spatial information of multiple frames. Only pre-trained
(self-supervised) on the denoising task, SiamTrans is tested on three different
low-level vision tasks (deraining, demoireing, and desnowing). Compared with
related methods, ours achieves the best performances, even outperforming those
with supervised learning.

Knowledge Graphs (KGs) have shown great success in recommendation. This is
attributed to the rich attribute information contained in KG to improve item
and user representations as side information. However, existing knowledge-aware
methods leverage attribute information at a coarse-grained level both in item
and user side. In this paper, we proposed a novel attentive knowledge graph
attribute network(AKGAN) to learn item attributes and user interests via
attribute information in KG. Technically, AKGAN adopts a heterogeneous graph
neural network framework, which has a different design between the first layer
and the latter layer. With one attribute placed in the corresponding range of
element-wise positions, AKGAN employs a novel interest-aware attention network,
which releases the limitation that the sum of attention weight is 1, to model
the complexity and personality of user interests towards attributes.
Experimental results on three benchmark datasets show the effectiveness and
explainability of AKGAN.

Deep learning models have set benchmark results in various Natural Language
Processing tasks. However, these models require an enormous amount of training
data, which is infeasible in many practical problems. While various techniques
like domain adaptation, fewshot learning techniques address this problem, we
introduce a new technique of actively infusing external knowledge into learning
to solve low data regime problems. We propose a technique called ActKnow that
actively infuses knowledge from Knowledge Graphs (KG) based "on-demand" into
learning for Question Answering (QA). By infusing world knowledge from
Concept-Net, we show significant improvements on the ARC Challenge-set
benchmark over purely text-based transformer models like RoBERTa in the low
data regime. For example, by using only 20% training examples, we demonstrate a
4% improvement in the accuracy for both ARC-challenge and OpenBookQA,

This paper reviews the state of the art in visual privacy protection
techniques, with particular attention paid to techniques applicable to the
field of active and assisted living (AAL). A novel taxonomy with which
state-of-the-art visual privacy protection methods can be classified is
introduced. Perceptual obfuscation methods, a category in the taxonomy, is
highlighted. These are a category of visual privacy preservation techniques
particularly relevant when considering scenarios that come under video-based
AAL monitoring. Obfuscation against machine learning models is also explored. A
high-level classification scheme of the different levels of privacy by design
is connected to the proposed taxonomy of visual privacy preservation
techniques. Finally, we note open questions that exist in the field and
introduce the reader to some exciting avenues for future research in the area
of visual privacy.

We study a generic ensemble of deep belief networks which is parametrized by
the distribution of energy levels of the hidden states of each layer. We show
that, within a random energy approach, statistical dependence can propagate
from the visible to deep layers only if each layer is tuned close to the
critical point during learning. As a consequence, efficiently trained learning
machines are characterised by a broad distribution of energy levels. The
analysis of Deep Belief Networks and Restricted Boltzmann Machines on different
datasets confirms these conclusions.

To develop driving automation technologies for human, a human-centered
methodology should be adopted for ensured safety and satisfactory user
experience. Automated lane change decision in dense highway traffic is
challenging, especially when considering the personalized preferences of
different drivers. To fulfill human driver centered decision algorithm
development, we carry out driver-in-the-loop experiments on a
6-Degree-of-Freedom driving simulator. Based on the analysis of the lane change
data by drivers of three specific styles,personalization indicators are
selected to describe the driver preferences in lane change decision. Then a
deep reinforcement learning (RL) approach is applied to design human-like
agents for automated lane change decision, with refined reward and loss
functions to capture the driver preferences.The trained RL agents and benchmark
agents are tested in a two-lane highway driving scenario, and by comparing the
agents with the specific drivers at the same initial states of lane change, the
statistics show that the proposed algorithm can guarantee higher consistency of
lane change decision preferences. The driver personalization indicators and the
proposed RL-based lane change decision algorithm are promising to contribute in
automated lane change system developing.

Deep learning (DL) has shown great potential in digital pathology
applications. The robustness of a diagnostic DL-based solution is essential for
safe clinical deployment. In this work we evaluate if adding uncertainty
estimates for DL predictions in digital pathology could result in increased
value for the clinical applications, by boosting the general predictive
performance or by detecting mispredictions. We compare the effectiveness of
model-integrated methods (MC dropout and Deep ensembles) with a model-agnostic
approach (Test time augmentation, TTA). Moreover, four uncertainty metrics are
compared. Our experiments focus on two domain shift scenarios: a shift to a
different medical center and to an underrepresented subtype of cancer. Our
results show that uncertainty estimates increase reliability by reducing a
model's sensitivity to classification threshold selection as well as by
detecting between 70\% and 90\% of the mispredictions done by the model.
Overall, the deep ensembles method achieved the best performance closely
followed by TTA.

Skeleton sequences are widely used for action recognition task due to its
lightweight and compact characteristics. Recent graph convolutional network
(GCN) approaches have achieved great success for skeleton-based action
recognition since its grateful modeling ability of non-Euclidean data. GCN is
able to utilize the short-range joint dependencies while lack to directly model
the distant joints relations that are vital to distinguishing various actions.
Thus, many GCN approaches try to employ hierarchical mechanism to aggregate
wider-range neighborhood information. We propose a novel self-attention based
skeleton-anchor proposal (SAP) module to comprehensively model the internal
relations of a human body for motion feature learning. The proposed SAP module
aims to explore inherent relationship within human body using a triplet
representation via encoding high order angle information rather than the fixed
pair-wise bone connection used in the existing hierarchical GCN approaches. A
Self-attention based anchor selection method is designed in the proposed SAP
module for extracting the root point of encoding angular information. By
coupling proposed SAP module with popular spatial-temporal graph neural
networks, e.g. MSG3D, it achieves new state-of-the-art accuracy on challenging
benchmark datasets. Further ablation study have shown the effectiveness of our
proposed SAP module, which is able to obviously improve the performance of many
popular skeleton-based action recognition methods.

The distributed inference (DI) framework has gained traction as a technique
for real-time applications empowered by cutting-edge deep machine learning (ML)
on resource-constrained Internet of things (IoT) devices. In DI, computational
tasks are offloaded from the IoT device to the edge server via lossy IoT
networks. However, generally, there is a communication system-level trade-off
between communication latency and reliability; thus, to provide accurate DI
results, a reliable and high-latency communication system is required to be
adapted, which results in non-negligible end-to-end latency of the DI. This
motivated us to improve the trade-off between the communication latency and
accuracy by efforts on ML techniques. Specifically, we have proposed a
communication-oriented model tuning (COMtune), which aims to achieve highly
accurate DI with low-latency but unreliable communication links. In COMtune,
the key idea is to fine-tune the ML model by emulating the effect of unreliable
communication links through the application of the dropout technique. This
enables the DI system to obtain robustness against unreliable communication
links. Our ML experiments revealed that COMtune enables accurate predictions
with low latency and under lossy networks.

A common assumption exists according to which machine learning models improve
their performance when they have more data to learn from. In this study, the
authors wished to clarify the dilemma by performing an empirical experiment
utilizing novel vocational student data. The experiment compared different
machine learning algorithms while varying the number of data and feature
combinations available for training and testing the models. The experiment
revealed that the increase of data records or their sample frequency does not
immediately lead to significant increases in the model accuracies or
performance, however the variance of accuracies does diminish in the case of
ensemble models. Similar phenomenon was witnessed while increasing the number
of input features for the models. The study refutes the starting assumption and
continues to state that in this case the significance in data lies in the
quality of the data instead of the quantity of the data.

We connect the problem of semi-supervised clustering to constrained Markov
aggregation, i.e., the task of partitioning the state space of a Markov chain.
We achieve this connection by considering every data point in the dataset as an
element of the Markov chain's state space, by defining the transition
probabilities between states via similarities between corresponding data
points, and by incorporating semi-supervision information as hard constraints
in a Hartigan-style algorithm. The introduced Constrained Markov Clustering
(CoMaC) is an extension of a recent information-theoretic framework for
(unsupervised) Markov aggregation to the semi-supervised case. Instantiating
CoMaC for certain parameter settings further generalizes two previous
information-theoretic objectives for unsupervised clustering. Our results
indicate that CoMaC is competitive with the state-of-the-art.

The increasing number of product reviews posted online is a gold mine for
designers to know better about the products they develop, by capturing the
voice of customers, and to improve these products accordingly. In the meantime,
product design and development have an essential role in creating a more
sustainable future. With the recent advance of artificial intelligence
techniques in the field of natural language processing, this research aims to
develop an integrated machine learning solution to obtain sustainable design
insights from online product reviews automatically. In this paper, the
opportunities and challenges offered by existing frameworks - including Python
libraries, packages, as well as state-of-the-art algorithms like BERT - are
discussed, illustrated, and positioned along an ad hoc machine learning
process. This contribution discusses the opportunities to reach and the
challenges to address for building a machine learning pipeline, in order to get
insights from product reviews to design more sustainable products, including
the five following stages, from the identification of sustainability-related
reviews to the interpretation of sustainable design leads: data collection,
data formatting, model training, model evaluation, and model deployment.
Examples of sustainable design insights that can be produced out of product
review mining and processing are given. Finally, promising lines for future
research in the field are provided, including case studies putting in parallel
standard products with their sustainable alternatives, to compare the features
valued by customers and to generate in fine relevant sustainable design leads.

With the development of machine learning, a data-driven model has been widely
used in vibration signal fault diagnosis. Most data-driven machine learning
algorithms are built based on well-designed features, but feature extraction is
usually required to be completed in advance. In the deep learning era, feature
extraction and classifier learning are conducted simultaneously, which will
lead to an end-to-end learning system. This paper explores which one of the two
key factors, i.e., feature extraction and classification algorithm, is more
essential for a specific task of vibration signal diagnosis during a learning
system is generated. Feature extractions from vibration signal based on both
well-known Gaussian model and statistical characteristics are discussed,
respectively. And several classification algorithms are selected to
experimentally validate the comparative impact of both feature extraction and
classification algorithm on prediction performance.

Recent Transformer-based methods have achieved advanced performance in point
cloud registration by utilizing advantages of the Transformer in
order-invariance and modeling dependency to aggregate information. However,
they still suffer from indistinct feature extraction, sensitivity to noise, and
outliers. The reasons are: (1) the adoption of CNNs fails to model global
relations due to their local receptive fields, resulting in extracted features
susceptible to noise; (2) the shallow-wide architecture of Transformers and
lack of positional encoding lead to indistinct feature extraction due to
inefficient information interaction; (3) the omission of geometrical
compatibility leads to inaccurate classification between inliers and outliers.
To address above limitations, a novel full Transformer network for point cloud
registration is proposed, named the Deep Interaction Transformer (DIT), which
incorporates: (1) a Point Cloud Structure Extractor (PSE) to model global
relations and retrieve structural information with Transformer encoders; (2) a
deep-narrow Point Feature Transformer (PFT) to facilitate deep information
interaction across two point clouds with positional encoding, such that
Transformers can establish comprehensive associations and directly learn
relative position between points; (3) a Geometric Matching-based Correspondence
Confidence Evaluation (GMCCE) method to measure spatial consistency and
estimate inlier confidence by designing the triangulated descriptor. Extensive
experiments on clean, noisy, partially overlapping point cloud registration
demonstrate that our method outperforms state-of-the-art methods.

Deep learning based models have significantly improved the performance of
speech separation with input mixtures like the cocktail party. Prominent
methods (e.g., frequency-domain and time-domain speech separation) usually
build regression models to predict the ground-truth speech from the mixture,
using the masking-based design and the signal-level loss criterion (e.g., MSE
or SI-SNR). This study demonstrates, for the first time, that the
synthesis-based approach can also perform well on this problem, with great
flexibility and strong potential. Specifically, we propose a novel speech
separation/enhancement model based on the recognition of discrete symbols, and
convert the paradigm of the speech separation/enhancement related tasks from
regression to classification. By utilizing the synthesis model with the input
of discrete symbols, after the prediction of discrete symbol sequence, each
target speech could be re-synthesized. Evaluation results based on the
WSJ0-2mix and VCTK-noisy corpora in various settings show that our proposed
method can steadily synthesize the separated speech with high speech quality
and without any interference, which is difficult to avoid in regression-based
methods. In addition, with negligible loss of listening quality, the speaker
conversion of enhanced/separated speech could be easily realized through our
method.

The Evidential regression network (ENet) estimates a continuous target and
its predictive uncertainty without costly Bayesian model averaging. However, it
is possible that the target is inaccurately predicted due to the gradient
shrinkage problem of the original loss function of the ENet, the negative log
marginal likelihood (NLL) loss. In this paper, the objective is to improve the
prediction accuracy of the ENet while maintaining its efficient uncertainty
estimation by resolving the gradient shrinkage problem. A multi-task learning
(MTL) framework, referred to as MT-ENet, is proposed to accomplish this aim. In
the MTL, we define the Lipschitz modified mean squared error (MSE) loss
function as another loss and add it to the existing NLL loss. The Lipschitz
modified MSE loss is designed to mitigate the gradient conflict with the NLL
loss by dynamically adjusting its Lipschitz constant. By doing so, the
Lipschitz MSE loss does not disturb the uncertainty estimation of the NLL loss.
The MT-ENet enhances the predictive accuracy of the ENet without losing
uncertainty estimation capability on the synthetic dataset and real-world
benchmarks, including drug-target affinity (DTA) regression. Furthermore, the
MT-ENet shows remarkable calibration and out-of-distribution detection
capability on the DTA benchmarks.

Arrays of quantum dots (QDs) are a promising candidate system to realize
scalable, coupled qubits systems and serve as a fundamental building block for
quantum computers. In such semiconductor quantum systems, devices now have tens
of individual electrostatic and dynamical voltages that must be carefully set
to localize the system into the single-electron regime and to realize good
qubit operational performance. The mapping of requisite QD locations and
charges to gate voltages presents a challenging classical control problem. With
an increasing number of QD qubits, the relevant parameter space grows
sufficiently to make heuristic control unfeasible.In recent years, there has
been a considerable effort to automate device control that combines
script-based algorithms with machine learning (ML) techniques. In this
colloquium, we present a comprehensive overview of the recent progress in the
automation of QD device control, with a particular emphasis on silicon- and
GaAs-based QDs formed in two-dimensional electron gases. Combining
physics-based modeling with modern numerical optimization and ML has proven
quite effective in yielding efficient, scalable control. Further integration of
theoretical, computational, and experimental efforts with computer science and
ML holds tremendous potential in advancing semiconductor and other platforms
for quantum computing.

Audiograms are a particular type of line charts representing individuals'
hearing level at various frequencies. They are used by audiologists to diagnose
hearing loss, and further select and tune appropriate hearing aids for
customers. There have been several projects such as Autoaudio that aim to
accelerate this process through means of machine learning. But all existing
models at their best can only detect audiograms in images and classify them
into general categories. They are unable to extract hearing level information
from detected audiograms by interpreting the marks, axis, and lines. To address
this issue, we propose a Multi-stage Audiogram Interpretation Network (MAIN)
that directly reads hearing level data from photos of audiograms. We also
established Open Audiogram, an open dataset of audiogram images with
annotations of marks and axes on which we trained and evaluated our proposed
model. Experiments show that our model is feasible and reliable.

Self-supervised learning (SSL) opens up huge opportunities for medical image
analysis that is well known for its lack of annotations. However, aggregating
massive (unlabeled) 3D medical images like computerized tomography (CT) remains
challenging due to its high imaging cost and privacy restrictions. In this
paper, we advocate bringing a wealth of 2D images like chest X-rays as
compensation for the lack of 3D data, aiming to build a universal medical
self-supervised representation learning framework, called UniMiSS. The
following problem is how to break the dimensionality barrier, \ie, making it
possible to perform SSL with both 2D and 3D images? To achieve this, we design
a pyramid U-like medical Transformer (MiT). It is composed of the switchable
patch embedding (SPE) module and Transformers. The SPE module adaptively
switches to either 2D or 3D patch embedding, depending on the input dimension.
The embedded patches are converted into a sequence regardless of their original
dimensions. The Transformers model the long-term dependencies in a
sequence-to-sequence manner, thus enabling UniMiSS to learn representations
from both 2D and 3D images. With the MiT as the backbone, we perform the
UniMiSS in a self-distillation manner. We conduct expensive experiments on six
3D/2D medical image analysis tasks, including segmentation and classification.
The results show that the proposed UniMiSS achieves promising performance on
various downstream tasks, outperforming the ImageNet pre-training and other
advanced SSL counterparts substantially. Code is available at
\def\UrlFont{\rm\small\ttfamily}
\url{https://github.com/YtongXie/UniMiSS-code}.

In the setting of federated optimization, where a global model is aggregated
periodically, step asynchronism occurs when participants conduct model training
with fully utilizing their computational resources. It is well acknowledged
that step asynchronism leads to objective inconsistency under non-i.i.d. data,
which degrades the model accuracy. To address this issue, we propose a new
algorithm \texttt{FedaGrac}, which calibrates the local direction to a
predictive global orientation. Taking the advantage of estimated orientation,
we guarantee that the aggregated model does not excessively deviate from the
expected orientation while fully utilizing the local updates of faster nodes.
We theoretically prove that \texttt{FedaGrac} holds an improved order of
convergence rate than the state-of-the-art approaches and eliminates the
negative effect of step asynchronism. Empirical results show that our algorithm
accelerates the training and enhances the final accuracy.

The Quantum Fourier Transform (QFT) grants competitive advantages, especially
in resource usage and circuit approximation, for performing arithmetic
operations on quantum computers, and offers a potential route towards a
numerical quantum-computational paradigm. In this paper, we utilize efficient
techniques to implement QFT-based integer addition and multiplications. These
operations are fundamental to various quantum applications including Shor's
algorithm, weighted sum optimization problems in data processing and machine
learning, and quantum algorithms requiring inner products. We carry out
performance evaluations of these implementations based on IBM's superconducting
qubit architecture using different compatible noise models. We isolate the
sensitivity of the component quantum circuits on both one-/two-qubit gate error
rates, and the number of the arithmetic operands' superposed integer states. We
analyze performance, and identify the most effective approximation depths for
quantum add and quantum multiply within the given context. We observe
significant dependency of the optimal approximation depth on the degree of
machine noise and the number of superposed states in certain performance
regimes. Finally, we elaborate on the algorithmic challenges - relevant to
signed, unsigned, modular and non-modular versions - that could also be applied
to current implementations of QFT-based subtraction, division, exponentiation,
and their potential tensor extensions. We analyze performance trends in our
results and speculate on possible future development within this computational
paradigm.

We present a system for bottom-up cumulative learning of myriad concepts
corresponding to meaningful character strings, and their part-related and
prediction edges. The learning is self-supervised in that the concepts
discovered are used as predictors as well as targets of prediction. We devise
an objective for segmenting with the learned concepts, derived from comparing
to a baseline prediction system, that promotes making and using larger
concepts, which in turn allows for predicting larger spans of text, and we
describe a simple technique to promote exploration, i.e. trying out newly
generated concepts in the segmentation process. We motivate and explain a
layering of the concepts, to help separate the (conditional) distributions
learnt among concepts. The layering of the concepts roughly corresponds to a
part-whole concept hierarchy. With rudimentary segmentation and learning
algorithms, the system is promising in that it acquires many concepts (tens of
thousands in our small-scale experiments), and it learns to segment text well:
when fed with English text with spaces removed, starting at the character
level, much of what is learned respects word or phrase boundaries, and over
time the average number of "bad" splits within segmentations, i.e. splits
inside words, decreases as larger concepts are discovered and the system learns
when to use them during segmentation. We report on promising experiments when
the input text is converted to binary and the system begins with only two
concepts, "0" and "1". The system is transparent, in the sense that it is easy
to tell what the concepts learned correspond to, and which ones are active in a
segmentation, or how the system "sees" its input. We expect this framework to
be extensible and we discuss the current limitations and a number of directions
for enhancing the learning and inference capabilities.

Novel technologies and new materials are in high demand for future
energy-efficient electronic devices to overcome the fundamental limitations of
miniaturization of current silicon-based devices. Two-dimensional (2D)
materials show promising applications in the next generation devices because
they can be tailored on the specific property that a technology is based on,
and be compatible with other technologies, such as the silicon-based
(opto)electronics. Although the number of experimentally discovered 2D
materials is growing, the speed is very slow and only a few dozen 2D materials
have been synthesized or exfoliated since the discovery of graphene. Recently,
a novel computational technique, dubbed "high-throughput computational
materials design", becomes a burgeoning area of materials science, which is the
combination of the quantum-mechanical theory, materials genome, and database
construction with intelligent data mining. This new and powerful tool can
greatly accelerate the discovery, design and application of 2D materials by
creating database containing a large amount of 2D materials with calculated
fundamental properties, and then intelligently mining (via high-throughput
automation or machine learning) the database in the search of 2D materials with
the desired properties for particular applications, such as energy conversion,
electronics, spintronics, and optoelectronics.

The Invariant Risk Minimization (IRM) framework aims to learn invariant
features from a set of environments for solving the out-of-distribution (OOD)
generalization problem. The underlying assumption is that the causal components
of the data generating distributions remain constant across the environments or
alternately, the data "overlaps" across environments to find meaningful
invariant features. Consequently, when the "overlap" assumption does not hold,
the set of truly invariant features may not be sufficient for optimal
prediction performance. Such cases arise naturally in networked settings and
hierarchical data-generating models, wherein the IRM performance becomes
suboptimal. To mitigate this failure case, we argue for a partial invariance
framework. The key idea is to introduce flexibility into the IRM framework by
partitioning the environments based on hierarchical differences, while
enforcing invariance locally within the partitions. We motivate this framework
in classification settings with causal distribution shifts across environments.
Our results show the capability of the partial invariant risk minimization to
alleviate the trade-off between fairness and risk in certain settings.

As a popular geometric representation, point clouds have attracted much
attention in 3D vision, leading to many applications in autonomous driving and
robotics. One important yet unsolved issue for learning on point cloud is that
point clouds of the same object can have significant geometric variations if
generated using different procedures or captured using different sensors. These
inconsistencies induce domain gaps such that neural networks trained on one
domain may fail to generalize on others. A typical technique to reduce the
domain gap is to perform adversarial training so that point clouds in the
feature space can align. However, adversarial training is easy to fall into
degenerated local minima, resulting in negative adaptation gains. Here we
propose a simple yet effective method for unsupervised domain adaptation on
point clouds by employing a self-supervised task of learning geometry-aware
implicits, which plays two critical roles in one shot. First, the geometric
information in the point clouds is preserved through the implicit
representations for downstream tasks. More importantly, the domain-specific
variations can be effectively learned away in the implicit space. We also
propose an adaptive strategy to compute unsigned distance fields for arbitrary
point clouds due to the lack of shape models in practice. When combined with a
task loss, the proposed outperforms state-of-the-art unsupervised domain
adaptation methods that rely on adversarial domain alignment and more
complicated self-supervised tasks. Our method is evaluated on both PointDA-10
and GraspNet datasets. The code and trained models will be publicly available.

Signatures, one of the key concepts of rough path theory, have recently
gained prominence as a means to find appropriate feature sets in machine
learning systems. In this paper, in order to compute signatures directly from
discrete data without going through the transformation to continuous data, we
introduced a discretized version of signatures, called "flat discrete
signatures". We showed that the flat discrete signatures can represent the
quadratic variation that has a high relevance in financial applications. We
also introduced the concept of "discrete signatures" that is a generalization
of "flat discrete signatures". This concept is defined to reflect the fact that
data closer to the current time is more important than older data, and is
expected to be applied to time series analysis. As an application of discrete
signatures, we took up a stock market related problem and succeeded in
performing a good estimation with fewer data points than before.

Actuated by the growing attention to personal healthcare and the pandemic,
the popularity of E-health is proliferating. Nowadays, enhancement on medical
diagnosis via machine learning models has been highly effective in many aspects
of e-health analytics. Nevertheless, in the classic cloud-based/centralized
e-health paradigms, all the data will be centrally stored on the server to
facilitate model training, which inevitably incurs privacy concerns and high
time delay. Distributed solutions like Decentralized Stochastic Gradient
Descent (D-SGD) are proposed to provide safe and timely diagnostic results
based on personal devices. However, methods like D-SGD are subject to the
gradient vanishing issue and usually proceed slowly at the early training
stage, thereby impeding the effectiveness and efficiency of training. In
addition, existing methods are prone to learning models that are biased towards
users with dense data, compromising the fairness when providing E-health
analytics for minority groups. In this paper, we propose a Decentralized Block
Coordinate Descent (D-BCD) learning framework that can better optimize deep
neural network-based models distributed on decentralized devices for E-health
analytics. Benchmarking experiments on three real-world datasets illustrate the
effectiveness and practicality of our proposed D-BCD, where additional
simulation study showcases the strong applicability of D-BCD in real-life
E-health scenarios.

Knowledge base completion is formulated as a binary classification problem in
this work, where an XGBoost binary classifier is trained for each relation
using relevant links in knowledge graphs (KGs). The new method, named KGBoost,
adopts a modularized design and attempts to find hard negative samples so as to
train a powerful classifier for missing link prediction. We conduct experiments
on multiple benchmark datasets, and demonstrate that KGBoost outperforms
state-of-the-art methods across most datasets. Furthermore, as compared with
models trained by end-to-end optimization, KGBoost works well under the
low-dimensional setting so as to allow a smaller model size.

Trajectory similarity computation has drawn massive attention, as it is core
functionality in a wide range of applications such as ride-sharing, traffic
analysis, and social recommendation. Motivated by the recent success of deep
learning technologies, researchers start devoting efforts to learning-based
similarity analyses to overcome the limitations (i.e., high cost and poor
adaptability) of traditional methods. Specifically, deep trajectory similarity
computation aims to learn a distance function that can evaluate how similar two
trajectories are via neural networks. However, existing learning-based methods
focus on spatial similarity but ignore the time dimension of trajectories,
which is suboptimal for time-aware applications. Besides, they tend to
disregard the embedding of trajectories into road networks, restricting their
applicability in real scenarios. In this paper, we propose an effective
learning-based framework, called ST2Vec, to perform efficient spatially and
temporally aware trajectory similarity computation in road networks. Finally,
extensive experimental evaluation using three real trajectory data sets shows
that ST2Vec outperforms all the state-of-the-art approaches substantially.

The Graph Convolutional Network (GCN) has been successfully applied to many
graph-based applications. Training a large-scale GCN model, however, is still
challenging: Due to the node dependency and layer dependency of the GCN
architecture, a huge amount of computational time and memory is required in the
training process. In this paper, we propose a parallel and distributed GCN
training algorithm based on the Alternating Direction Method of Multipliers
(ADMM) to tackle the two challenges simultaneously. We first split GCN layers
into independent blocks to achieve layer parallelism. Furthermore, we reduce
node dependency by dividing the graph into several dense communities such that
each of them can be trained with an agent in parallel. Finally, we provide
solutions for all subproblems in the community-based ADMM algorithm.
Preliminary results demonstrate that our proposed community-based ADMM training
algorithm can lead to more than triple speedup while achieving the best
performance compared with state-of-the-art methods.

With the rise of self-drive cars and connected vehicles, cars are equipped
with various devices to assistant the drivers or support self-drive systems.
Undoubtedly, cars have become more intelligent as we can deploy more and more
devices and software on the cars. Accordingly, the security of assistant and
self-drive systems in the cars becomes a life-threatening issue as smart cars
can be invaded by malicious attacks that cause traffic accidents. Currently,
canonical machine learning and deep learning methods are extensively employed
in car hacking detection. However, machine learning and deep learning methods
can easily be overconfident and defeated by carefully designed adversarial
examples. Moreover, those methods cannot provide explanations for security
engineers for further analysis. In this work, we investigated Deep Bayesian
Learning models to detect and analyze car hacking behaviors. The Bayesian
learning methods can capture the uncertainty of the data and avoid
overconfident issues. Moreover, the Bayesian models can provide more
information to support the prediction results that can help security engineers
further identify the attacks. We have compared our model with deep learning
models and the results show the advantages of our proposed model. The code of
this work is publicly available

We fine-tune GPT-3 to answer long-form questions using a text-based
web-browsing environment, which allows the model to search and navigate the
web. By setting up the task so that it can be performed by humans, we are able
to train models on the task using imitation learning, and then optimize answer
quality with human feedback. To make human evaluation of factual accuracy
easier, models must collect references while browsing in support of their
answers. We train and evaluate our models on ELI5, a dataset of questions asked
by Reddit users. Our best model is obtained by fine-tuning GPT-3 using behavior
cloning, and then performing rejection sampling against a reward model trained
to predict human preferences. This model's answers are preferred by humans 56%
of the time to those of our human demonstrators, and 69% of the time to the
highest-voted answer from Reddit.

Pioneering dual-encoder pre-training works (e.g., CLIP and ALIGN) have
revealed the potential of aligning multi-modal representations with contrastive
learning. However, these works require a tremendous amount of data and
computational resources (e.g., billion-level web data and hundreds of GPUs),
which prevent researchers with limited resources from reproduction and further
exploration. To this end, we propose a stack of novel methods, which
significantly cut down the heavy resource dependency and allow us to conduct
dual-encoder multi-modal representation alignment with limited resources.
Besides, we provide a reproducible baseline of competitive results, namely
ZeroVL, with only 14M publicly accessible academic datasets and 8 V100 GPUs.
Additionally, we collect 100M web data for pre-training, and achieve comparable
or superior results than state-of-the-art methods, further proving the
effectiveness of our methods on large-scale data. We hope that this work will
provide useful data points and experience for future research in contrastive
vision-language pre-training. Code is available at
https://github.com/zerovl/ZeroVL.

We propose Point2Cyl, a supervised network transforming a raw 3D point cloud
to a set of extrusion cylinders. Reverse engineering from a raw geometry to a
CAD model is an essential task to enable manipulation of the 3D data in shape
editing software and thus expand their usages in many downstream applications.
Particularly, the form of CAD models having a sequence of extrusion cylinders
-- a 2D sketch plus an extrusion axis and range -- and their boolean
combinations is not only widely used in the CAD community/software but also has
great expressivity of shapes, compared to having limited types of primitives
(e.g., planes, spheres, and cylinders). In this work, we introduce a neural
network that solves the extrusion cylinder decomposition problem in a
geometry-grounded way by first learning underlying geometric proxies.
Precisely, our approach first predicts per-point segmentation, base/barrel
labels and normals, then estimates for the underlying extrusion parameters in
differentiable and closed-form formulations. Our experiments show that our
approach demonstrates the best performance on two recent CAD datasets, Fusion
Gallery and DeepCAD, and we further showcase our approach on reverse
engineering and editing.

Mobile Edge Computing (MEC) has been regarded as a promising paradigm to
reduce service latency for data processing in the Internet of Things, by
provisioning computing resources at the network edge. In this work, we jointly
optimize the task partitioning and computational power allocation for
computation offloading in a dynamic environment with multiple IoT devices and
multiple edge servers. We formulate the problem as a Markov decision process
with constrained hybrid action space, which cannot be well handled by existing
deep reinforcement learning (DRL) algorithms. Therefore, we develop a novel
Deep Reinforcement Learning called Dirichlet Deep Deterministic Policy Gradient
(D3PG), which is built on Deep Deterministic Policy Gradient (DDPG) to solve
the problem. The developed model can learn to solve multi-objective
optimization, including maximizing the number of tasks processed before
expiration and minimizing the energy cost and service latency.} More
importantly, D3PG can effectively deal with constrained distribution-continuous
hybrid action space, where the distribution variables are for the task
partitioning and offloading, while the continuous variables are for
computational frequency control. Moreover, the D3PG can address many similar
issues in MEC and general reinforcement learning problems. Extensive simulation
results show that the proposed D3PG outperforms the state-of-art methods.

This paper presents a novel collaborative generative modeling (CGM) framework
that incentivizes collaboration among self-interested parties to contribute
data to a pool for training a generative model (e.g., GAN), from which
synthetic data are drawn and distributed to the parties as rewards commensurate
to their contributions. Distributing synthetic data as rewards (instead of
trained models or money) offers task- and model-agnostic benefits for
downstream learning tasks and is less likely to violate data privacy
regulation. To realize the framework, we firstly propose a data valuation
function using maximum mean discrepancy (MMD) that values data based on its
quantity and quality in terms of its closeness to the true data distribution
and provide theoretical results guiding the kernel choice in our MMD-based data
valuation function. Then, we formulate the reward scheme as a linear
optimization problem that when solved, guarantees certain incentives such as
fairness in the CGM framework. We devise a weighted sampling algorithm for
generating synthetic data to be distributed to each party as reward such that
the value of its data and the synthetic data combined matches its assigned
reward value by the reward scheme. We empirically show using simulated and
real-world datasets that the parties' synthetic data rewards are commensurate
to their contributions.

In this paper, we construct a new Japanese speech corpus called
"JTubeSpeech." Although recent end-to-end learning requires large-size speech
corpora, open-sourced such corpora for languages other than English have not
yet been established. In this paper, we describe the construction of a corpus
from YouTube videos and subtitles for speech recognition and speaker
verification. Our method can automatically filter the videos and subtitles with
almost no language-dependent processes. We consistently employ Connectionist
Temporal Classification (CTC)-based techniques for automatic speech recognition
(ASR) and a speaker variation-based method for automatic speaker verification
(ASV). We build 1) a large-scale Japanese ASR benchmark with more than 1,300
hours of data and 2) 900 hours of data for Japanese ASV.

In the last decade Convolutional Neural Networks (CNNs) have defined the
state of the art for many low level image processing and restoration tasks such
as denoising, demosaicking, upscaling, or inpainting. However, on-device mobile
photography is still dominated by traditional image processing techniques, and
uses mostly simple machine learning techniques or limits the neural network
processing to producing low resolution masks. High computational and memory
requirements of CNNs, limited processing power and thermal constraints of
mobile devices, combined with large output image resolutions (typically 8--12
MPix) prevent their wider application. In this work, we introduce Procedural
Kernel Networks (PKNs), a family of machine learning models which generate
parameters of image filter kernels or other traditional algorithms. A
lightweight CNN processes the input image at a lower resolution, which yields a
significant speedup compared to other kernel-based machine learning methods and
allows for new applications. The architecture is learned end-to-end and is
especially well suited for a wide range of low-level image processing tasks,
where it improves the performance of many traditional algorithms. We also
describe how this framework unifies some previous work applying machine
learning for common image restoration tasks.

Clinical decision support tools rooted in machine learning and optimization
can provide significant value to healthcare providers, including through better
management of intensive care units. In particular, it is important that the
patient discharge task addresses the nuanced trade-off between decreasing a
patient's length of stay (and associated hospitalization costs) and the risk of
readmission or even death following the discharge decision. This work
introduces an end-to-end general framework for capturing this trade-off to
recommend optimal discharge timing decisions given a patient's electronic
health records. A data-driven approach is used to derive a parsimonious,
discrete state space representation that captures a patient's physiological
condition. Based on this model and a given cost function, an infinite-horizon
discounted Markov decision process is formulated and solved numerically to
compute an optimal discharge policy, whose value is assessed using off-policy
evaluation strategies. Extensive numerical experiments are performed to
validate the proposed framework using real-life intensive care unit patient
data.

Federated learning of causal estimands may greatly improve estimation
efficiency by leveraging data from multiple study sites, but robustness to
heterogeneity and model mis-specifications is vital for ensuring validity. We
develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate
heterogeneous data from multiple sites to provide treatment effect estimation
and inference for a flexibly specified target population of interest. FACE
accounts for site-level heterogeneity in the distribution of covariates through
density ratio weighting. To safely incorporate source sites and avoid negative
transfer, we introduce an adaptive weighting procedure via a penalized
regression, which achieves both consistency and optimal efficiency. Our
strategy is communication-efficient and privacy-preserving, allowing
participating sites to only share summary statistics once with other sites. We
conduct both theoretical and numerical evaluations of FACE, and apply it to
conduct a comparative effectiveness study of BNT162b2 (Pfizer) and mRNA-1273
(Moderna) vaccines on COVID-19 outcomes in U.S. veterans using electronic
health records from five VA regional sites. We show that compared to
traditional methods, FACE meaningfully increases the precision of treatment
effect estimates, with reductions in standard errors ranging from $26\%$ to
$67\%$.

Musical expression requires control of both what notes are played, and how
they are performed. Conventional audio synthesizers provide detailed expressive
controls, but at the cost of realism. Black-box neural audio synthesis and
concatenative samplers can produce realistic audio, but have few mechanisms for
control. In this work, we introduce MIDI-DDSP a hierarchical model of musical
instruments that enables both realistic neural audio synthesis and detailed
user control. Starting from interpretable Differentiable Digital Signal
Processing (DDSP) synthesis parameters, we infer musical notes and high-level
properties of their expressive performance (such as timbre, vibrato, dynamics,
and articulation). This creates a 3-level hierarchy (notes, performance,
synthesis) that affords individuals the option to intervene at each level, or
utilize trained priors (performance given notes, synthesis given performance)
for creative assistance. Through quantitative experiments and listening tests,
we demonstrate that this hierarchy can reconstruct high-fidelity audio,
accurately predict performance attributes for a note sequence, independently
manipulate the attributes of a given performance, and as a complete system,
generate realistic audio from a novel note sequence. By utilizing an
interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens
the door to assistive tools to empower individuals across a diverse range of
musical experience.

Discretization of continuous-time diffusion processes is a widely recognized
method for sampling. However, it seems to be a considerable restriction when
the potentials are often required to be smooth (gradient Lipschitz). This paper
studies the problem of sampling through Euler discretization, where the
potential function is assumed to be a mixture of weakly smooth distributions
and satisfies weakly dissipative. We establish the convergence in
Kullback-Leibler (KL) divergence with the number of iterations to reach
$\epsilon$-neighborhood of a target distribution in only polynomial dependence
on the dimension. We relax the degenerated convex at infinity conditions of
\citet{erdogdu2020convergence} and prove convergence guarantees under
Poincar\'{e} inequality or non-strongly convex outside the ball. In addition,
we also provide convergence in $L_{\beta}$-Wasserstein metric for the smoothing
potential.

We prove that Centered Kernel Alignment (CKA) based on a Gaussian RBF kernel
converges to linear CKA in the large-bandwidth limit. We show that convergence
onset is sensitive to the geometry of the feature representations, and that
representation eccentricity bounds the range of bandwidths for which Gaussian
CKA behaves nonlinearly.

We present a deep learning-based reduced order model (DL-ROM) for predicting
the fluid forces and unsteady vortex patterns. We consider flow past a sphere
to examine the accuracy of our DL-ROM predictions. The proposed methodology
relies on a three-dimensional convolutional recurrent autoencoder network (3D
CRAN) to extract the low-dimensional flow features from full-order snapshots.
The low-dimensional features are evolved in time using a long short-term
memory-based recurrent neural network and reconstructed back to the full-order
as flow voxels. These 3D voxels are introduced as static and uniform query
probes in the point cloud domain to reduce the unstructured mesh complexity
while providing convenience in 3D CRAN training. We analyze a novel procedure
to recover the interface description and the force quantities from the 3D flow
voxels. The 3D CRAN methodology is first applied to an external flow past a
static sphere at a single Reynolds number of Re = 300. We provide an assessment
of the computing requirements in terms of the memory usage, training costs, and
testing times associated with the 3D CRAN framework. Subsequently, variable
Re-based flow information is infused in one 3D CRAN to learn a complicated
symmetry-breaking flow regime (280 < Re < 460) for the flow past a sphere.
Effects of transfer learning are analyzed for training this complicated 3D flow
regime on a relatively smaller time series dataset. The 3D CRAN framework
learns the flow regime nearly 20 times faster than the parallel full-order
model and predicts unsteady flows with an excellent to good accuracy. Based on
the predicted flow fields, the network demonstrates an R2 accuracy of 98.58%
for drag and 76.43% for lift over the sphere in the chosen flow regime. The
proposed framework aligns with the development of a digital twin for 3D
unsteady flow field with variable Re effects.

Stability is one of the most fundamental requirements for systems synthesis.
In this paper, we address the stabilization problem for unknown linear systems
via policy gradient (PG) methods. We leverage a key feature of PG for Linear
Quadratic Regulator (LQR), i.e., it drives the policy away from the boundary of
the unstabilizing region along the descent direction, provided with an initial
policy with finite cost. To this end, we discount the LQR cost with a factor,
by adaptively increasing which gradient leads the policy to the stabilizing set
while maintaining a finite cost. Based on the Lyapunov theory, we design an
update rule for the discount factor which can be directly computed from data,
rendering our method purely model-free. Compared to recent work
\citep{perdomo2021stabilizing}, our algorithm allows the policy to be updated
only once for each discount factor. Moreover, the number of sampled
trajectories and simulation time for gradient descent is significantly reduced
to $\mathcal{O}(\log(1/\epsilon))$ for the desired accuracy $\epsilon$.
Finally, we conduct simulations on both small-scale and large-scale examples to
show the efficiency of our discount PG method.

There exist several data-driven approaches that enable us model time series
data including traditional regression-based modeling approaches (i.e., ARIMA).
Recently, deep learning techniques have been introduced and explored in the
context of time series analysis and prediction. A major research question to
ask is the performance of these many variations of deep learning techniques in
predicting time series data. This paper compares two prominent deep learning
modeling techniques. The Recurrent Neural Network (RNN)-based Long Short-Term
Memory (LSTM) and the convolutional Neural Network (CNN)-based Temporal
Convolutional Networks (TCN) are compared and their performance and training
time are reported. According to our experimental results, both modeling
techniques perform comparably having TCN-based models outperform LSTM slightly.
Moreover, the CNN-based TCN model builds a stable model faster than the
RNN-based LSTM models.

In recent years, person detection and human pose estimation have made great
strides, helped by large-scale labeled datasets. However, these datasets had no
guarantees or analysis of human activities, poses, or context diversity.
Additionally, privacy, legal, safety, and ethical concerns may limit the
ability to collect more human data. An emerging alternative to real-world data
that alleviates some of these issues is synthetic data. However, creation of
synthetic data generators is incredibly challenging and prevents researchers
from exploring their usefulness. Therefore, we release a human-centric
synthetic data generator PeopleSansPeople which contains simulation-ready 3D
human assets, a parameterized lighting and camera system, and generates 2D and
3D bounding box, instance and semantic segmentation, and COCO pose labels.
Using PeopleSansPeople, we performed benchmark synthetic data training using a
Detectron2 Keypoint R-CNN variant [1]. We found that pre-training a network
using synthetic data and fine-tuning on various sizes of real-world data
resulted in a keypoint AP increase of $+38.03$ ($44.43 \pm 0.17$ vs. $6.40$)
for few-shot transfer (limited subsets of COCO-person train [2]), and an
increase of $+1.47$ ($63.47 \pm 0.19$ vs. $62.00$) for abundant real data
regimes, outperforming models trained with the same real data alone. We also
found that our models outperformed those pre-trained with ImageNet with a
keypoint AP increase of $+22.53$ ($44.43 \pm 0.17$ vs. $21.90$) for few-shot
transfer and $+1.07$ ($63.47 \pm 0.19$ vs. $62.40$) for abundant real data
regimes. This freely-available data generator should enable a wide range of
research into the emerging field of simulation to real transfer learning in the
critical area of human-centric computer vision.

We introduce a family of deep-learning architectures for inter-sentence
relation extraction, i.e., relations where the participants are not necessarily
in the same sentence. We apply these architectures to an important use case in
the biomedical domain: assigning biological context to biochemical events. In
this work, biological context is defined as the type of biological system
within which the biochemical event is observed. The neural architectures encode
and aggregate multiple occurrences of the same candidate context mentions to
determine whether it is the correct context for a particular event mention. We
propose two broad types of architectures: the first type aggregates multiple
instances that correspond to the same candidate context with respect to event
mention before emitting a classification; the second type independently
classifies each instance and uses the results to vote for the final class, akin
to an ensemble approach. Our experiments show that the proposed neural
classifiers are competitive and some achieve better performance than previous
state of the art traditional machine learning methods without the need for
feature engineering. Our analysis shows that the neural methods particularly
improve precision compared to traditional machine learning classifiers and also
demonstrates how the difficulty of inter-sentence relation extraction increases
as the distance between the event and context mentions increase.

With the advent of Advanced Driver Assistance Systems (ADAS), there is an
increasing need to evaluate driver behavior while using such technology. In
this unique study, a forward collision warning (FCW) system using connected
vehicle technology, was introduced in a driving simulator environment, to
evaluate driver braking behavior and then the results are validated using data
from field tests. A total of 93 participants were recruited for this study, for
which a virtual network of South Baltimore was created. A one sample t-test was
conducted, and it was found that the mean reduction in speed of 15.07 mph post
FCW, is statistically significant. A random forest, machine learning algorithm
was found to be the best fit for ranking the most important variables in the
dataset by order of importance. Field data obtained from the University of
Michigan Transportation Research Institute (UMTRI), substantiated the FCW
findings from this driving simulator study.

Many state-of-the-art adversarial training methods leverage upper bounds of
the adversarial loss to provide security guarantees. Yet, these methods require
computations at each training step that can not be incorporated in the gradient
for backpropagation. We introduce a new, more principled approach to
adversarial training based on a closed form solution of an upper bound of the
adversarial loss, which can be effectively trained with backpropagation. This
bound is facilitated by state-of-the-art tools from robust optimization. We
derive two new methods with our approach. The first method (Approximated Robust
Upper Bound or aRUB) uses the first order approximation of the network as well
as basic tools from linear robust optimization to obtain an approximate upper
bound of the adversarial loss that can be easily implemented. The second method
(Robust Upper Bound or RUB), computes an exact upper bound of the adversarial
loss. Across a variety of tabular and vision data sets we demonstrate the
effectiveness of our more principled approach -- RUB is substantially more
robust than state-of-the-art methods for larger perturbations, while aRUB
matches the performance of state-of-the-art methods for small perturbations.
Also, both RUB and aRUB run faster than standard adversarial training (at the
expense of an increase in memory). All the code to reproduce the results can be
found at https://github.com/kimvc7/Robustness.

In many classification problems, we want a classifier that is robust to a
range of non-semantic transformations. For example, a human can identify a dog
in a picture regardless of the orientation and pose in which it appears. There
is substantial evidence that this kind of invariance can significantly improve
the accuracy and generalization of machine learning models. A common technique
to teach a model geometric invariances is to augment training data with
transformed inputs. However, which invariances are desired for a given
classification task is not always known. Determining an effective data
augmentation policy can require domain expertise or extensive data
pre-processing. Recent efforts like AutoAugment optimize over a parameterized
search space of data augmentation policies to automate the augmentation
process. While AutoAugment and similar methods achieve state-of-the-art
classification accuracy on several common datasets, they are limited to
learning one data augmentation policy. Often times different classes or
features call for different geometric invariances. We introduce Dynamic Network
Augmentation (DNA), which learns input-conditional augmentation policies.
Augmentation parameters in our model are outputs of a neural network and are
implicitly learned as the network weights are updated. Our model allows for
dynamic augmentation policies and performs well on data with geometric
transformations conditional on input features.

Knowledge graph (KG) alignment - the task of recognizing entities referring
to the same thing in different KGs - is recognized as one of the most important
operations in the field of KG construction and completion. However, existing
alignment techniques often assume that the input KGs are complete and
isomorphic, which is not true due to the real-world heterogeneity in the
domain, size, and sparsity. In this work, we address the problem of aligning
incomplete KGs with representation learning. Our KG embedding framework
exploits two feature channels: transitivity-based and proximity-based. The
former captures the consistency constraints between entities via translation
paths, while the latter captures the neighbourhood structure of KGs via
attention guided relation-aware graph neural network. The two feature channels
are jointly learned to exchange important features between the input KGs while
enforcing the output representations of the input KGs in the same embedding
space. Also, we develop a missing links detector that discovers and recovers
the missing links in the input KGs during the training process, which helps
mitigate the incompleteness issue and thus improve the compatibility of the
learned representations. The embeddings then are fused to generate the
alignment result, and the high-confidence matched node pairs are updated to the
pre-aligned supervision data to improve the embeddings gradually. Empirical
results show that our model is more accurate than the SOTA and is robust
against different levels of incompleteness.

The recently developed concept of spreadability, $\mathcal{S}(t)$, provides a
direct link between time-dependent diffusive transport and the microstructure
of two-phase media across length scales. We explicitly compute $\mathcal{S}(t)$
for well-known two-dimensional and three-dimensional model structures,
including nonhyperuniform fully penetrable spheres and equilibrium hard
spheres, as well as hyperuniform sphere packings derived from perfect glasses,
uniformly randomized lattices (URL), disordered stealthy point processes and
Bravais lattices. We further confirm that the small-, intermediate- and
long-time behaviors of $\mathcal{S}(t)$ sensitively capture the small-,
intermediate- and large-scale characteristics of the models. In instances in
which the spectral density $\tilde{\chi}_{_V}(\mathbf{k})$ has a power-law form
$B|\mathbf{k}|^\alpha$ in the limit $|\mathbf{k}|\rightarrow 0$, the long-time
spreadability provides a simple means to extract the value of the coefficients
$\alpha$ and $B$ that is robust against noise in
$\tilde{\chi}_{_V}(\mathbf{k})$ at small wavenumbers. Interestingly, the excess
spreadability $\mathcal{S}(\infty)-\mathcal{S}(t)$ for URL packings has nearly
exponential decay at small to intermediate $t$, but transforms to a power-law
decay at large $t$. Our study of the aforementioned models enables us to devise
an algorithm that accurately extracts large-scale behaviors from diffusion data
alone. Lessons learned from such analyses of our models are used to determine
accurately the large-scale structural characteristics of a sample Fontainebleau
sandstone, which we show is nonhyperuniform. Our study demonstrates the
practical applicability of the diffusion spreadability to extract crucial
microstructural information from real data across length scales and provides a
basis for the inverse design of materials with desirable time-dependent
diffusion properties.

In an effort to identify nearby and unusual cold objects in the solar
neighborhood, we searched for previously unidentified moving objects using
CatWISE2020 proper motion data combined with machine learning methods. We
paired the motion candidates with their counterparts in 2MASS, UHS, and VHS.
Then we searched for white dwarf, brown dwarf, and subdwarf outliers on the
resulting color-color diagrams. This resulted in the discovery of 16 new dwarfs
including two nearby M dwarfs (< 30 pc), a possible young L dwarf, a high
motion early T dwarf and 3 later T dwarfs. This research represents a step
forward in completing the census of the Sun's neighbors.

Image inpainting is an effective method to enhance distorted digital images.
Different inpainting methods use the information of neighboring pixels to
predict the value of missing pixels. Recently deep neural networks have been
used to learn structural and semantic details of images for inpainting
purposes. In this paper, we propose a network for image inpainting. This
network, similar to U-Net, extracts various features from images, leading to
better results. We improved the final results by replacing the damaged pixels
with the recovered pixels of the output images. Our experimental results show
that this method produces high-quality results compare to the traditional
methods.

This paper studies the robustness of estimated policy effects to changes in
the distribution of covariates. Robustness to covariate shifts is important,
for example, when evaluating the external validity of quasi-experimental
results, which are often used as a benchmark for evidence-based policy-making.
I propose a novel scalar robustness metric. This metric measures the magnitude
of the smallest covariate shift needed to invalidate a claim on the policy
effect (for example, $ATE \geq 0$) supported by the quasi-experimental
evidence. My metric links the heterogeneity of policy effects and robustness in
a flexible, nonparametric way and does not require functional form assumptions.
I cast the estimation of the robustness metric as a de-biased GMM problem. This
approach guarantees a parametric convergence rate for the robustness metric
while allowing for machine learning-based estimators of policy effect
heterogeneity (for example, lasso, random forest, boosting, neural nets). I
apply my procedure to the Oregon Health Insurance experiment. I study the
robustness of policy effects estimates of health-care utilization and financial
strain outcomes, relative to a shift in the distribution of context-specific
covariates. Such covariates are likely to differ across US states, making
quantification of robustness an important exercise for adoption of the
insurance policy in states other than Oregon. I find that the effect on
outpatient visits is the most robust among the metrics of health-care
utilization considered.

Deep learning (DL) has proven to be a highly effective approach for
developing models in diverse contexts, including visual perception, speech
recognition, and machine translation. However, the end-to-end process for
applying DL is not trivial. It requires grappling with problem formulation and
context understanding, data engineering, model development, deployment,
continuous monitoring and maintenance, and so on. Moreover, each of these steps
typically relies heavily on humans, in terms of both knowledge and
interactions, which impedes the further advancement and democratization of DL.
Consequently, in response to these issues, a new field has emerged over the
last few years: automated deep learning (AutoDL). This endeavor seeks to
minimize the need for human involvement and is best known for its achievements
in neural architecture search (NAS), a topic that has been the focus of several
surveys. That stated, NAS is not the be-all and end-all of AutoDL. Accordingly,
this review adopts an overarching perspective, examining research efforts into
automation across the entirety of an archetypal DL workflow. In so doing, this
work also proposes a comprehensive set of ten criteria by which to assess
existing work in both individual publications and broader research areas. These
criteria are: novelty, solution quality, efficiency, stability,
interpretability, reproducibility, engineering quality, scalability,
generalizability, and eco-friendliness. Thus, ultimately, this review provides
an evaluative overview of AutoDL in the early 2020s, identifying where future
opportunities for progress may exist.

We present an effective GNN-based knowledge graph embedding model, named WGE,
to capture entity- and relation-focused graph structures. In particular, given
the knowledge graph, WGE builds a single undirected entity-focused graph that
views entities as nodes. In addition, WGE also constructs another single
undirected graph from relation-focused constraints, which views entities and
relations as nodes. WGE then proposes a GNN-based architecture to better learn
vector representations of entities and relations from these two single entity-
and relation-focused graphs. WGE feeds the learned entity and relation
representations into a weighted score function to return the triple scores for
knowledge graph completion. Experimental results show that WGE outperforms
competitive baselines, obtaining state-of-the-art performances on seven
benchmark datasets for knowledge graph completion.

In the past, computer vision systems for digitized documents could rely on
systematically captured, high-quality scans. Today, transactions involving
digital documents are more likely to start as mobile phone photo uploads taken
by non-professionals. As such, computer vision for document automation must now
account for documents captured in natural scene contexts. An additional
challenge is that task objectives for document processing can be highly
use-case specific, which makes publicly-available datasets limited in their
utility, while manual data labeling is also costly and poorly translates
between use cases.
  To address these issues we created Sim2Real Docs - a framework for
synthesizing datasets and performing domain randomization of documents in
natural scenes. Sim2Real Docs enables programmatic 3D rendering of documents
using Blender, an open source tool for 3D modeling and ray-traced rendering. By
using rendering that simulates physical interactions of light, geometry,
camera, and background, we synthesize datasets of documents in a natural scene
context. Each render is paired with use-case specific ground truth data
specifying latent characteristics of interest, producing unlimited fit-for-task
training data. The role of machine learning models is then to solve the inverse
problem posed by the rendering pipeline. Such models can be further iterated
upon with real-world data by either fine tuning or making adjustments to domain
randomization parameters.

Existing neural networks for computer vision tasks are vulnerable to
adversarial attacks: adding imperceptible perturbations to the input images can
fool these methods to make a false prediction on an image that was correctly
predicted without the perturbation. Various defense methods have proposed
image-to-image mapping methods, either including these perturbations in the
training process or removing them in a preprocessing denoising step. In doing
so, existing methods often ignore that the natural RGB images in today's
datasets are not captured but, in fact, recovered from RAW color filter array
captures that are subject to various degradations in the capture. In this work,
we exploit this RAW data distribution as an empirical prior for adversarial
defense. Specifically, we proposed a model-agnostic adversarial defensive
method, which maps the input RGB images to Bayer RAW space and back to output
RGB using a learned camera image signal processing (ISP) pipeline to eliminate
potential adversarial patterns. The proposed method acts as an off-the-shelf
preprocessing module and, unlike model-specific adversarial training methods,
does not require adversarial images to train. As a result, the method
generalizes to unseen tasks without additional retraining. Experiments on
large-scale datasets (e.g., ImageNet, COCO) for different vision tasks (e.g.,
classification, semantic segmentation, object detection) validate that the
method significantly outperforms existing methods across task domains.

Inference of the marginal probability distribution is defined as the
calculation of the probability of a subset of the variables and is relevant for
handling missing data and hidden variables. While inference of the marginal
probability distribution is crucial for various problems in machine learning
and statistics, its exact computation is generally not feasible for categorical
variables in Bayesian networks due to the NP-hardness of this task. We develop
a divide-and-conquer approach using the graphical properties of Bayesian
networks to split the computation of the marginal probability distribution into
sub-calculations of lower dimensionality, thus reducing the overall
computational complexity. Exploiting this property, we present an efficient and
scalable algorithm for calculating the marginal probability distribution for
categorical variables. The novel method is compared against state-of-the-art
approximate inference methods in a benchmarking study, where it displays
superior performance. As an immediate application, we demonstrate how our
method can be used to classify incomplete data against Bayesian networks and
use this approach for identifying the cancer subtype of kidney cancer patient
samples.

We present a deep-learning based computing framework for fast-and-accurate CT
(DL-FACT) testing of COVID-19. Our CT-based DL framework was developed to
improve the testing speed and accuracy of COVID-19 (plus its variants) via a
DL-based approach for CT image enhancement and classification. The image
enhancement network is adapted from DDnet, short for DenseNet and Deconvolution
based network. To demonstrate its speed and accuracy, we evaluated DL-FACT
across several sources of COVID-19 CT images. Our results show that DL-FACT can
significantly shorten the turnaround time from days to minutes and improve the
COVID-19 testing accuracy up to 91%. DL-FACT could be used as a software tool
for medical professionals in diagnosing and monitoring COVID-19.

Sparse representations of images are useful in many computer vision
applications. Sparse coding with an $l_1$ penalty and a learned linear
dictionary requires regularization of the dictionary to prevent a collapse in
the $l_1$ norms of the codes. Typically, this regularization entails bounding
the Euclidean norms of the dictionary's elements. In this work, we propose a
novel sparse coding protocol which prevents a collapse in the codes without the
need to regularize the decoder. Our method regularizes the codes directly so
that each latent code component has variance greater than a fixed threshold
over a set of sparse representations for a given set of inputs. Furthermore, we
explore ways to effectively train sparse coding systems with multi-layer
decoders since they can model more complex relationships than linear
dictionaries. In our experiments with MNIST and natural image patches, we show
that decoders learned with our approach have interpretable features both in the
linear and multi-layer case. Moreover, we show that sparse autoencoders with
multi-layer decoders trained using our variance regularization method produce
higher quality reconstructions with sparser representations when compared to
autoencoders with linear dictionaries. Additionally, sparse representations
obtained with our variance regularization approach are useful in the downstream
tasks of denoising and classification in the low-data regime.

Variation in the local thermal history during the laser powder bed fusion
(LPBF) process in additive manufacturing (AM) can cause microporosity defects.
in-situ sensing has been proposed to monitor the AM process to minimize
defects, but the success requires establishing a quantitative relationship
between the sensing data and the porosity, which is especially challenging for
a large number of variables and computationally costly. In this work, we
develop machine learning (ML) models that can use in-situ thermographic data to
predict the microporosity of LPBF stainless steel materials. This work
considers two identified key features from the thermal histories: the time
above the apparent melting threshold (/tau) and the maximum radiance (T_{max}).
These features are computed, stored for each voxel in the built material, are
used as inputs. The binary state of each voxel, either defective or normal, is
the output. Different ML models are trained and tested for the binary
classification task. In addition to using the thermal features of each voxel to
predict its own state, the thermal features of neighboring voxels are also
included as inputs. This is shown to improve the prediction accuracy, which is
consistent with thermal transport physics around each voxel contributing to its
final state. Among the models trained, the F1 scores on test sets reach above
0.96 for random forests. Feature importance analysis based on the ML models
shows that T_{max}is more important to the voxel state than /tau. The analysis
also finds that the thermal history of the voxels above the present voxel is
more influential than those beneath it.

The empirical Bayes (EB) method based on parametric statistical models such
as the negative binomial (NB) has been widely used for ranking sites in road
network safety screening process. This paper is the continuation of the authors
previous research, where a novel non-parametric EB method for modelling crash
frequency data data based on Conditional Generative Adversarial Networks (CGAN)
was proposed and evaluated over several simulated crash data sets. Unlike
parametric approaches, there is no need for a pre-specified underlying
relationship between dependent and independent variables in the proposed
CGAN-EB and they are able to model any types of distributions. The proposed
methodology is now applied to a real-world data set collected for road segments
from 2012 to 2017 in Washington State. The performance of CGAN-EB in terms of
model fit, predictive performance and network screening outcomes is compared
with the conventional approach (NB-EB) as a benchmark. The results indicate
that the proposed CGAN-EB approach outperforms NB-EB in terms of prediction
power and hotspot identification tests.

Monitoring the health and vigor of grasslands is vital for informing
management decisions to optimize rotational grazing in agriculture
applications. To take advantage of forage resources and improve land
productivity, we require knowledge of pastureland growth patterns that is
simply unavailable at state of the art. In this paper, we propose to deploy a
team of robots to monitor the evolution of an unknown pastureland environment
to fulfill the above goal. To monitor such an environment, which usually
evolves slowly, we need to design a strategy for rapid assessment of the
environment over large areas at a low cost. Thus, we propose an integrated
pipeline comprising of data synthesis, deep neural network training and
prediction along with a multi-robot deployment algorithm that monitors
pasturelands intermittently. Specifically, using expert-informed agricultural
data coupled with novel data synthesis in ROS Gazebo, we first propose a new
neural network architecture to learn the spatiotemporal dynamics of the
environment. Such predictions help us to understand pastureland growth patterns
on large scales and make appropriate monitoring decisions for the future. Based
on our predictions, we then design an intermittent multi-robot deployment
policy for low-cost monitoring. Finally, we compare the proposed pipeline with
other methods, from data synthesis to prediction and planning, to corroborate
our pipeline's performance.

Few-shot classification tasks aim to classify images in query sets based on
only a few labeled examples in support sets. Most studies usually assume that
each image in a task has a single and unique class association. Under these
assumptions, these algorithms may not be able to identify the proper class
assignment when there is no exact matching between support and query classes.
For example, given a few images of lions, bikes, and apples to classify a
tiger. However, in a more general setting, we could consider the higher-level
concept, the large carnivores, to match the tiger to the lion for semantic
classification. Existing studies rarely considered this situation due to the
incompatibility of label-based supervision with complex conception
relationships. In this work, we advance the few-shot learning towards this more
challenging scenario, the semantic-based few-shot learning, and propose a
method to address the paradigm by capturing the inner semantic relationships
using interactive psychometric learning. The experiment results on the
CIFAR-100 dataset show the superiority of our method for the semantic-based
few-shot learning compared to the baseline.

A biosignal is a signal that can be continuously measured from human bodies,
such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based
on which, machine learning models have been developed with very promising
performance for automatic disease detection and health status monitoring.
However, dataset shift, i.e., data distribution of inference varies from the
distribution of the training, is not uncommon for real biosignal-based
applications. To improve the robustness, probabilistic models with uncertainty
quantification are adapted to capture how reliable a prediction is. Yet,
assessing the quality of the estimated uncertainty remains a challenge. In this
work, we propose a framework to evaluate the capability of the estimated
uncertainty in capturing different types of biosignal dataset shifts with
various degrees. In particular, we use three classification tasks based on
respiratory sounds and electrocardiography signals to benchmark five
representative uncertainty quantification methods. Extensive experiments show
that, although Ensemble and Bayesian models could provide relatively better
uncertainty estimations under dataset shifts, all tested models fail to meet
the promise in trustworthy prediction and model calibration. Our work paves the
way for a comprehensive evaluation for any newly developed biosignal

Convolutional networks are considered shift invariant, but it was
demonstrated that their response may vary according to the exact location of
the objects. In this paper we will demonstrate that most commonly investigated
datasets have a bias, where objects are over-represented at the center of the
image during training. This bias and the boundary condition of these networks
can have a significant effect on the performance of these architectures and
their accuracy drops significantly as an object approaches the boundary. We
will also demonstrate how this effect can be mitigated with data augmentation

Modern statistical applications often involve minimizing an objective
function that may be nonsmooth and/or nonconvex. This paper focuses on a broad
Bregman-surrogate algorithm framework including the local linear approximation,
mirror descent, iterative thresholding, DC programming and many others as
particular instances. The recharacterization via generalized Bregman functions
enables us to construct suitable error measures and establish global
convergence rates for nonconvex and nonsmooth objectives in possibly high
dimensions. For sparse learning problems with a composite objective, under some
regularity conditions, the obtained estimators as the surrogate's fixed points,
though not necessarily local minimizers, enjoy provable statistical guarantees,
and the sequence of iterates can be shown to approach the statistical truth
within the desired accuracy geometrically fast. The paper also studies how to
design adaptive momentum based accelerations without assuming convexity or
smoothness by carefully controlling stepsize and relaxation parameters.

Street-level imagery holds a significant potential to scale-up in-situ data
collection. This is enabled by combining the use of cheap high quality cameras
with recent advances in deep learning compute solutions to derive relevant
thematic information. We present a framework to collect and extract crop type
and phenological information from street level imagery using computer vision.
During the 2018 growing season, high definition pictures were captured with
side-looking action cameras in the Flevoland province of the Netherlands. Each
month from March to October, a fixed 200-km route was surveyed collecting one
picture per second resulting in a total of 400,000 geo-tagged pictures. At 220
specific parcel locations detailed on the spot crop phenology observations were
recorded for 17 crop types. Furthermore, the time span included specific
pre-emergence parcel stages, such as differently cultivated bare soil for
spring and summer crops as well as post-harvest cultivation practices, e.g.
green manuring and catch crops. Classification was done using TensorFlow with a
well-known image recognition model, based on transfer learning with
convolutional neural networks (MobileNet). A hypertuning methodology was
developed to obtain the best performing model among 160 models. This best model
was applied on an independent inference set discriminating crop type with a
Macro F1 score of 88.1% and main phenological stage at 86.9% at the parcel
level. Potential and caveats of the approach along with practical
considerations for implementation and improvement are discussed. The proposed
framework speeds up high quality in-situ data collection and suggests avenues
for massive data collection via automated classification using computer vision.

Additive feature explanations using Shapley values have become popular for
providing transparency into the relative importance of each feature to an
individual prediction of a machine learning model. While Shapley values provide
a unique additive feature attribution in cooperative game theory, the Shapley
values that can be generated for even a single machine learning model are far
from unique, with theoretical and implementational decisions affecting the
resulting attributions. Here, we consider the application of Shapley values for
explaining decision tree ensembles and present a novel approach to Shapley
value-based feature attribution that can be applied to random forests and
boosted decision trees. This new method provides attributions that accurately
reflect details of the model prediction algorithm for individual instances,
while being computationally competitive with one of the most widely used
current methods. We explain the theoretical differences between the standard
and novel approaches and compare their performance using synthetic and real
data.

In this paper we demonstrate that reservoir computing can be used to learn
the dynamics of the shallow-water equations. In particular, while most previous
applications of reservoir computing have required training on a particular
trajectory to further predict the evolution along that trajectory alone, we
show the capability of reservoir computing to predict trajectories of the
shallow-water equations with initial conditions not seen in the training
process. However, in this setting, we find that the performance of the network
deteriorates for initial conditions with ambient conditions (such as total
water height and average velocity) that are different from those in the
training dataset. To circumvent this deficiency, we introduce a transfer
learning approach wherein a small additional training step with the relevant
ambient conditions is used to improve the predictions.

This paper examines the approximation capabilities of coarsely quantized
neural networks -- those whose parameters are selected from a small set of
allowable values. We show that any smooth multivariate function can be
arbitrarily well approximated by an appropriate coarsely quantized neural
network and provide a quantitative approximation rate. For the quadratic
activation, this can be done with only a one-bit alphabet; for the ReLU
activation, we use a three-bit alphabet. The main theorems rely on important
properties of Bernstein polynomials. We prove new results on approximation of
functions with Bernstein polynomials, noise-shaping quantization on the
Bernstein basis, and implementation of the Bernstein polynomials by coarsely
quantized neural networks.

Knee osteoarthritis (OA) is a common degenerate joint disorder that affects a
large population of elderly people worldwide. Accurate radiographic assessment
of knee OA severity plays a critical role in chronic patient management.
Current clinically-adopted knee OA grading systems are observer subjective and
suffer from inter-rater disagreements. In this work, we propose a
computer-aided diagnosis approach to provide more accurate and consistent
assessments of both composite and fine-grained OA grades simultaneously. A
novel semi-supervised learning method is presented to exploit the underlying
coherence in the composite and fine-grained OA grades by learning from
unlabeled data. By representing the grade coherence using the log-probability
of a pre-trained Gaussian Mixture Model, we formulate an incoherence loss to
incorporate unlabeled data in training. The proposed method also describes a
keypoint-based pooling network, where deep image features are pooled from the
disease-targeted keypoints (extracted along the knee joint) to provide more
aligned and pathologically informative feature representations, for accurate OA
grade assessments. The proposed method is comprehensively evaluated on the
public Osteoarthritis Initiative (OAI) data, a multi-center ten-year
observational study on 4,796 subjects. Experimental results demonstrate that
our method leads to significant improvements over previous strong whole
image-based deep classification network baselines (like ResNet-50).

Lifelong machine learning or continual learning models attempt to learn
incrementally by accumulating knowledge across a sequence of tasks. Therefore,
these models learn better and faster. They are used in various intelligent
systems that have to interact with humans or any dynamic environment e.g.,
chatbots and self-driving cars. Memory-less approach is more often used with
deep neural networks that accommodates incoming information from tasks within
its architecture. It allows them to perform well on all the seen tasks. These
models suffer from semantic drift or the plasticity-stability dilemma. The
existing models use Minkowski distance measures to decide which nodes to
freeze, update or duplicate. These distance metrics do not provide better
separation of nodes as they are susceptible to high dimensional sparse vectors.
In our proposed approach, we use angular distance to evaluate the semantic
drift in individual nodes that provide better separation of nodes and thus
better balancing between stability and plasticity. The proposed approach
outperforms state-of-the art models by maintaining higher accuracy on standard
datasets.

Long Short-Term Memory (LSTM) and Transformers are two popular neural
architectures used for natural language processing tasks. Theoretical results
show that both are Turing-complete and can represent any context-free language
(CFL).In practice, it is often observed that Transformer models have better
representation power than LSTM. But the reason is barely understood. We study
such practical differences between LSTM and Transformer and propose an
explanation based on their latent space decomposition patterns. To achieve this
goal, we introduce an oracle training paradigm, which forces the decomposition
of the latent representation of LSTM and the Transformer and supervises with
the transitions of the Pushdown Automaton (PDA) of the corresponding CFL. With
the forced decomposition, we show that the performance upper bounds of LSTM and
Transformer in learning CFL are close: both of them can simulate a stack and
perform stack operation along with state transitions. However, the absence of
forced decomposition leads to the failure of LSTM models to capture the stack
and stack operations, while having a marginal impact on the Transformer model.
Lastly, we connect the experiment on the prototypical PDA to a real-world
parsing task to re-verify the conclusions

This paper presents a task of audio-visual scene classification (SC) where
input videos are classified into one of five real-life crowded scenes: 'Riot',
'Noise-Street', 'Firework-Event', 'Music-Event', and 'Sport-Atmosphere'. To
this end, we firstly collect an audio-visual dataset (videos) of these five
crowded contexts from Youtube (in-the-wild scenes). Then, a wide range of deep
learning frameworks are proposed to deploy either audio or visual input data
independently. Finally, results obtained from high-performed deep learning
frameworks are fused to achieve the best accuracy score. Our experimental
results indicate that audio and visual input factors independently contribute
to the SC task's performance. Significantly, an ensemble of deep learning
frameworks exploring either audio or visual input data can achieve the best
accuracy of 95.7%.

This paper presents a framework for how to incorporate prior sources of
information into the design of a sequential experiment. These sources can
include previous experiments, expert opinions, or the experimenter's own
introspection. We formalize this problem using a multi-prior Bayesian approach
that maps each source to a Bayesian model. These models are aggregated
according to their associated posterior probabilities. We evaluate a broad of
policy rules according to three criteria: whether the experimenter learns the
parameters of the payoff distributions, the probability that the experimenter
chooses the wrong treatment when deciding to stop the experiment, and the
average rewards. We show that our framework exhibits several nice finite sample
properties, including robustness to any source that is not externally valid.

Shared autonomy refers to approaches for enabling an autonomous agent to
collaborate with a human with the aim of improving human performance. However,
besides improving performance, it may often also be beneficial that the agent
concurrently accounts for preserving the user's experience or satisfaction of
collaboration. In order to address this additional goal, we examine approaches
for improving the user experience by constraining the number of interventions
by the autonomous agent. We propose two model-free reinforcement learning
methods that can account for both hard and soft constraints on the number of
interventions. We show that not only does our method outperform the existing
baseline, but also eliminates the need to manually tune a black-box
hyperparameter for controlling the level of assistance. We also provide an
in-depth analysis of intervention scenarios in order to further illuminate
system understanding.

This work presents a feasibility study of remote attention level estimation
based on eye blink frequency. We first propose an eye blink detection system
based on Convolutional Neural Networks (CNNs), very competitive with respect to
related works. Using this detector, we experimentally evaluate the relationship
between the eye blink rate and the attention level of students captured during
online sessions. The experimental framework is carried out using a public
multimodal database for eye blink detection and attention level estimation
called mEBAL, which comprises data from 38 students and multiples acquisition
sensors, in particular, i) an electroencephalogram (EEG) band which provides
the time signals coming from the student's cognitive information, and ii) RGB
and NIR cameras to capture the students face gestures. The results achieved
suggest an inverse correlation between the eye blink frequency and the
attention level. This relation is used in our proposed method called ALEBk for
estimating the attention level as the inverse of the eye blink frequency. Our
results open a new research line to introduce this technology for attention
level estimation on future e-learning platforms, among other applications of
this kind of behavioral biometrics based on face analysis.

Discovering what is learned by neural networks remains a challenge. In
self-supervised learning, classification is the most common task used to
evaluate how good a representation is. However, relying only on such downstream
task can limit our understanding of what information is retained in the
representation of a given input. In this work, we showcase the use of a
Representation Conditional Diffusion Model (RCDM) to visualize in data space
the representations learned by self-supervised models. The use of RCDM is
motivated by its ability to generate high-quality samples -- on par with
state-of-the-art generative models -- while ensuring that the representations
of those samples are faithful i.e. close to the one used for conditioning. By
using RCDM to analyze self-supervised models, we are able to clearly show
visually that i) SSL (backbone) representation are not invariant to the data
augmentations they were trained with -- thus debunking an often restated but
mistaken belief; ii) SSL post-projector embeddings appear indeed invariant to
these data augmentation, along with many other data symmetries; iii) SSL
representations appear more robust to small adversarial perturbation of their
inputs than representations trained in a supervised manner; and iv) that
SSL-trained representations exhibit an inherent structure that can be explored
thanks to RCDM visualization and enables image manipulation.

In the area of physical simulations, nearly all neural-network-based methods
directly predict future states from the input states. However, many traditional
simulation engines instead model the constraints of the system and select the
state which satisfies them. Here we present a framework for constraint-based
learned simulation, where a scalar constraint function is implemented as a
graph neural network, and future predictions are computed by solving the
optimization problem defined by the learned constraint. Our model achieves
comparable or better accuracy to top learned simulators on a variety of
challenging physical domains, and offers several unique advantages. We can
improve the simulation accuracy on a larger system by applying more solver
iterations at test time. We also can incorporate novel hand-designed
constraints at test time and simulate new dynamics which were not present in
the training data. Our constraint-based framework shows how key techniques from
traditional simulation and numerical methods can be leveraged as inductive
biases in machine learning simulators.

The increasing scale of neural networks and their growing application space
have produced demand for more energy- and memory-efficient
artificial-intelligence-specific hardware. Avenues to mitigate the main issue,
the von Neumann bottleneck, include in-memory and near-memory architectures, as
well as algorithmic approaches. Here we leverage the low-power and the
inherently binary operation of magnetic tunnel junctions (MTJs) to demonstrate
neural network hardware inference based on passive arrays of MTJs. In general,
transferring a trained network model to hardware for inference is confronted by
degradation in performance due to device-to-device variations, write errors,
parasitic resistance, and nonidealities in the substrate. To quantify the
effect of these hardware realities, we benchmark 300 unique weight matrix
solutions of a 2-layer perceptron to classify the Wine dataset for both
classification accuracy and write fidelity. Despite device imperfections, we
achieve software-equivalent accuracy of up to 95.3 % with proper tuning of
network parameters in 15 x 15 MTJ arrays having a range of device sizes. The
success of this tuning process shows that new metrics are needed to
characterize the performance and quality of networks reproduced in mixed signal
hardware.

The lifelong learning paradigm in machine learning is an attractive
alternative to the more prominent isolated learning scheme not only due to its
resemblance to biological learning, but also its potential to reduce energy
waste by obviating excessive model re-training. A key challenge to this
paradigm is the phenomenon of catastrophic forgetting. With the increasing
popularity and success of pre-trained models in machine learning, we pose the
question: What role does pre-training play in lifelong learning, specifically
with respect to catastrophic forgetting? We investigate existing methods in the
context of large, pre-trained models and evaluate their performance on a
variety of text and image classification tasks, including a large-scale study
using a novel dataset of 15 diverse NLP tasks. Across all settings, we observe
that generic pre-training implicitly alleviates the effects of catastrophic
forgetting when learning multiple tasks sequentially compared to randomly
initialized models. We then further investigate why pre-training alleviates
forgetting in this setting. We study this phenomenon by analyzing the loss
landscape, finding that pre-trained weights appear to ease forgetting by
leading to wider minima. Based on this insight, we propose jointly optimizing
for current task loss and loss basin sharpness in order to explicitly encourage
wider basins during sequential fine-tuning. We show that this optimization
approach leads to performance comparable to the state-of-the-art in
task-sequential continual learning across multiple settings, without retaining
a memory that scales in size with the number of tasks.

Discriminating quark and gluon jets is a long-standing topic in collider
phenomenology. In this paper, we address this question using the Lund jet plane
substructure technique introduced in recent years. We present two complementary
approaches: one where the quark/gluon likelihood ratio is computed
analytically, to single-logarithmic accuracy, in perturbative QCD, and one
where the Lund declusterings are used to train a neural network. For both
approaches, we either consider only the primary Lund plane or the full
clustering tree. The analytic and machine-learning discriminants are shown to
be equivalent on a toy event sample resumming exactly leading collinear single
logarithms, where the analytic calculation corresponds to the exact likelihood
ratio. On a full Monte Carlo event sample, both approaches show a good
discriminating power, with the machine-learning models usually being superior.
We carry on a study in the asymptotic limit of large logarithm, allowing us to
gain confidence that this superior performance comes from effects that are
subleading in our analytic approach. We then compare our approach to other
quark-gluon discriminants in the literature. Finally, we study the resilience
of our quark-gluon discriminants against the details of the event sample and
observe that the analytic and machine-learning approaches show similar
behaviour.

We present Masked Feature Prediction (MaskFeat) for self-supervised
pre-training of video models. Our approach first randomly masks out a portion
of the input sequence and then predicts the feature of the masked regions. We
study five different types of features and find Histograms of Oriented
Gradients (HOG), a hand-crafted feature descriptor, works particularly well in
terms of both performance and efficiency. We observe that the local contrast
normalization in HOG is essential for good results, which is in line with
earlier work using HOG for visual recognition. Our approach can learn abundant
visual knowledge and drive large-scale Transformer-based models. Without using
extra model weights or supervision, MaskFeat pre-trained on unlabeled videos
achieves unprecedented results of 86.7% with MViT-L on Kinetics-400, 88.3% on
Kinetics-600, 80.4% on Kinetics-700, 38.8 mAP on AVA, and 75.0% on SSv2.
MaskFeat further generalizes to image input, which can be interpreted as a
video with a single frame and obtains competitive results on ImageNet.

Existing state-of-the-art methods for Video Object Segmentation (VOS) learn
low-level pixel-to-pixel correspondences between frames to propagate object
masks across video. This requires a large amount of densely annotated video
data, which is costly to annotate, and largely redundant since frames within a
video are highly correlated. In light of this, we propose HODOR: a novel method
that tackles VOS by effectively leveraging annotated static images for
understanding object appearance and scene context. We encode object instances
and scene information from an image frame into robust high-level descriptors
which can then be used to re-segment those objects in different frames. As a
result, HODOR achieves state-of-the-art performance on the DAVIS and
YouTube-VOS benchmarks compared to existing methods trained without video
annotations. Without any architectural modification, HODOR can also learn from
video context around single annotated video frames by utilizing cyclic
consistency, whereas other methods rely on dense, temporally consistent
annotations. Source code is available at: https://github.com/Ali2500/HODOR

The advent of large-scale training has produced a cornucopia of powerful
visual recognition models. However, generative models, such as GANs, have
traditionally been trained from scratch in an unsupervised manner. Can the
collective "knowledge" from a large bank of pretrained vision models be
leveraged to improve GAN training? If so, with so many models to choose from,
which one(s) should be selected, and in what manner are they most effective? We
find that pretrained computer vision models can significantly improve
performance when used in an ensemble of discriminators. Notably, the particular
subset of selected models greatly affects performance. We propose an effective
selection mechanism, by probing the linear separability between real and fake
samples in pretrained model embeddings, choosing the most accurate model, and
progressively adding it to the discriminator ensemble. Interestingly, our
method can improve GAN training in both limited data and large-scale settings.
Given only 10k training samples, our FID on LSUN Cat matches the StyleGAN2
trained on 1.6M images. On the full dataset, our method improves FID by 1.5x to
2x on cat, church, and horse categories of LSUN.

Decoupling spatiotemporal representation refers to decomposing the spatial
and temporal features into dimension-independent factors. Although previous
RGB-D-based motion recognition methods have achieved promising performance
through the tightly coupled multi-modal spatiotemporal representation, they
still suffer from (i) optimization difficulty under small data setting due to
the tightly spatiotemporal-entangled modeling;(ii) information redundancy as it
usually contains lots of marginal information that is weakly relevant to
classification; and (iii) low interaction between multi-modal spatiotemporal
information caused by insufficient late fusion. To alleviate these drawbacks,
we propose to decouple and recouple spatiotemporal representation for
RGB-D-based motion recognition. Specifically, we disentangle the task of
learning spatiotemporal representation into 3 sub-tasks: (1) Learning
high-quality and dimension independent features through a decoupled spatial and
temporal modeling network. (2) Recoupling the decoupled representation to
establish stronger space-time dependency. (3) Introducing a Cross-modal
Adaptive Posterior Fusion (CAPF) mechanism to capture cross-modal
spatiotemporal information from RGB-D data. Seamless combination of these novel
designs forms a robust spatialtemporal representation and achieves better
performance than state-of-the-art methods on four public motion datasets. Our
code is available at https://github.com/damo-cv/MotionRGBD.

Current methods for learning realistic and animatable 3D clothed avatars need
either posed 3D scans or 2D images with carefully controlled user poses. In
contrast, our goal is to learn an avatar from only 2D images of people in
unconstrained poses. Given a set of images, our method estimates a detailed 3D
surface from each image and then combines these into an animatable avatar.
Implicit functions are well suited to the first task, as they can capture
details like hair and clothes. Current methods, however, are not robust to
varied human poses and often produce 3D surfaces with broken or disembodied
limbs, missing details, or non-human shapes. The problem is that these methods
use global feature encoders that are sensitive to global pose. To address this,
we propose ICON ("Implicit Clothed humans Obtained from Normals"), which,
instead, uses local features. ICON has two main modules, both of which exploit
the SMPL(-X) body model. First, ICON infers detailed clothed-human normals
(front/back) conditioned on the SMPL(-X) normals. Second, a visibility-aware
implicit surface regressor produces an iso-surface of a human occupancy field.
Importantly, at inference time, a feedback loop alternates between refining the
SMPL(-X) mesh using the inferred clothed normals and then refining the normals.
Given multiple reconstructed frames of a subject in varied poses, we use
SCANimate to produce an animatable avatar from them. Evaluation on the AGORA
and CAPE datasets shows that ICON outperforms the state of the art in
reconstruction, even with heavily limited training data. Additionally, it is
much more robust to out-of-distribution samples, e.g., in-the-wild poses/images
and out-of-frame cropping. ICON takes a step towards robust 3D clothed human
reconstruction from in-the-wild images. This enables creating avatars directly
from video with personalized and natural pose-dependent cloth deformation.

Object detection in high-resolution satellite imagery is emerging as a
scalable alternative to on-the-ground survey data collection in many
environmental and socioeconomic monitoring applications. However, performing
object detection over large geographies can still be prohibitively expensive
due to the high cost of purchasing imagery and compute. Inspired by traditional
survey data collection strategies, we propose an approach to estimate object
count statistics over large geographies through sampling. Given a cost budget,
our method selects a small number of representative areas by sampling from a
learnable proposal distribution. Using importance sampling, we are able to
accurately estimate object counts after processing only a small fraction of the
images compared to an exhaustive approach. We show empirically that the
proposed framework achieves strong performance on estimating the number of
buildings in the United States and Africa, cars in Kenya, brick kilns in
Bangladesh, and swimming pools in the U.S., while requiring as few as 0.01% of
satellite images compared to an exhaustive approach.

Interactive object understanding, or what we can do to objects and how is a
long-standing goal of computer vision. In this paper, we tackle this problem
through observation of human hands in in-the-wild egocentric videos. We
demonstrate that observation of what human hands interact with and how can
provide both the relevant data and the necessary supervision. Attending to
hands, readily localizes and stabilizes active objects for learning and reveals
places where interactions with objects occur. Analyzing the hands shows what we
can do to objects and how. We apply these basic principles on the EPIC-KITCHENS
dataset, and successfully learn state-sensitive features, and object
affordances (regions of interaction and afforded grasps), purely by observing
hands in egocentric videos.

Recently, information retrieval has seen the emergence of dense retrievers,
using neural networks, as an alternative to classical sparse methods based on
term-frequency. These models have obtained state-of-the-art results on datasets
and tasks where large training sets are available. However, they do not
transfer well to new applications with no training data, and are outperformed
by unsupervised term-frequency methods such as BM25. In this work, we explore
the limits of contrastive learning as a way to train unsupervised dense
retrievers and show that it leads to strong performance in various retrieval
settings. On the BEIR benchmark our unsupervised model outperforms BM25 on 11
out of 15 datasets for the Recall@100. When used as pre-training before
fine-tuning, either on a few thousands in-domain examples or on the large
MS~MARCO dataset, our contrastive model leads to improvements on the BEIR
benchmark. Finally, we evaluate our approach for multi-lingual retrieval, where
training data is even scarcer than for English, and show that our approach
leads to strong unsupervised performance. Our model also exhibits strong
cross-lingual transfer when fine-tuned on supervised English data only and
evaluated on low resources language such as Swahili. We show that our
unsupervised models can perform cross-lingual retrieval between different
scripts, such as retrieving English documents from Arabic queries, which would
not be possible with term matching methods.

Using a fully connected feedforward neural network we study topological
invariants of a class of Calabi--Yau manifolds constructed as hypersurfaces in
toric varieties associated with reflexive polytopes from the Kreuzer--Skarke
database. In particular, we find the existence of a simple expression for the
Euler number that can be learned in terms of limited data extracted from the
polytope and its dual.

Contrastive language-image pretraining (CLIP) using image-text pairs has
achieved impressive results on image classification in both zero-shot and
transfer learning settings. However, we show that directly applying such models
to recognize image regions for object detection leads to poor performance due
to a domain shift: CLIP was trained to match an image as a whole to a text
description, without capturing the fine-grained alignment between image regions
and text spans. To mitigate this issue, we propose a new method called
RegionCLIP that significantly extends CLIP to learn region-level visual
representations, thus enabling fine-grained alignment between image regions and
textual concepts. Our method leverages a CLIP model to match image regions with
template captions and then pretrains our model to align these region-text pairs
in the feature space. When transferring our pretrained model to the
open-vocabulary object detection tasks, our method significantly outperforms
the state of the art by 3.8 AP50 and 2.2 AP for novel categories on COCO and
LVIS datasets, respectively. Moreoever, the learned region representations
support zero-shot inference for object detection, showing promising results on
both COCO and LVIS datasets. Our code is available at
https://github.com/microsoft/RegionCLIP.

Non-Gaussian Component Analysis (NGCA) is the following distribution learning
problem: Given i.i.d. samples from a distribution on $\mathbb{R}^d$ that is
non-gaussian in a hidden direction $v$ and an independent standard Gaussian in
the orthogonal directions, the goal is to approximate the hidden direction $v$.
Prior work \cite{DKS17-sq} provided formal evidence for the existence of an
information-computation tradeoff for NGCA under appropriate moment-matching
conditions on the univariate non-gaussian distribution $A$. The latter result
does not apply when the distribution $A$ is discrete. A natural question is
whether information-computation tradeoffs persist in this setting. In this
paper, we answer this question in the negative by obtaining a sample and
computationally efficient algorithm for NGCA in the regime that $A$ is discrete
or nearly discrete, in a well-defined technical sense. The key tool leveraged
in our algorithm is the LLL method \cite{LLL82} for lattice basis reduction.

Multiagent reinforcement learning algorithms have not been widely adopted in
large scale environments with many agents as they often scale poorly with the
number of agents. Using mean field theory to aggregate agents has been proposed
as a solution to this problem. However, almost all previous methods in this
area make a strong assumption of a centralized system where all the agents in
the environment learn the same policy and are effectively indistinguishable
from each other. In this paper, we relax this assumption about
indistinguishable agents and propose a new mean field system known as
Decentralized Mean Field Games, where each agent can be quite different from
others. All agents learn independent policies in a decentralized fashion, based
on their local observations. We define a theoretical solution concept for this
system and provide a fixed point guarantee for a Q-learning based algorithm in
this system. A practical consequence of our approach is that we can address a
`chicken-and-egg' problem in empirical mean field reinforcement learning
algorithms. Further, we provide Q-learning and actor-critic algorithms that use
the decentralized mean field learning approach and give stronger performances
compared to common baselines in this area. In our setting, agents do not need
to be clones of each other and learn in a fully decentralized fashion. Hence,
for the first time, we show the application of mean field learning methods in
fully competitive environments, large-scale continuous action space
environments, and other environments with heterogeneous agents. Importantly, we
also apply the mean field method in a ride-sharing problem using a real-world
dataset. We propose a decentralized solution to this problem, which is more
practical than existing centralized training methods.

Undirected neural sequence models have achieved performance competitive with
the state-of-the-art directed sequence models that generate monotonically from
left to right in machine translation tasks. In this work, we train a policy
that learns the generation order for a pre-trained, undirected translation
model via reinforcement learning. We show that the translations decoded by our
learned orders achieve higher BLEU scores than the outputs decoded from left to
right or decoded by the learned order from Mansimov et al. (2019) on the WMT'14
German-English translation task. On examples with a maximum source and target
length of 30 from De-En, WMT'16 English-Romanian, and WMT'21 English-Chinese
translation tasks, our learned order outperforms all heuristic generation
orders on four out of six tasks. We next carefully analyze the learned order
patterns via qualitative and quantitative analysis. We show that our policy
generally follows an outer-to-inner order, predicting the left-most and
right-most positions first, and then moving toward the middle while skipping
less important words at the beginning. Furthermore, the policy usually predicts
positions for a single syntactic constituent structure in consecutive steps. We
believe our findings could provide more insights on the mechanism of undirected
generation models and encourage further research in this direction. Our code is
publicly available at https://github.com/jiangycTarheel/undirected-generation

We provide a new interpretation of Hessian locally linear embedding (HLLE),
revealing that it is essentially a variant way to implement the same idea of
locally linear embedding (LLE). Based on the new interpretation, a substantial
simplification can be made, in which the idea of "Hessian" is replaced by
rather arbitrary weights. Moreover, we show by numerical examples that HLLE may
produce projection-like results when the dimension of the target space is
larger than that of the data manifold, and hence one further modification
concerning the manifold dimension is suggested. Combining all the observations,
we finally achieve a new LLE-type method, which is called tangential LLE
(TLLE). It is simpler and more robust than HLLE.

We propose a thermodynamics-based learning strategy for non-equilibrium
evolution equations based on Onsager's variational principle, which allows to
write such PDEs in terms of two potentials: the free energy and the dissipation
potential. Specifically, these two potentials are learned from spatio-temporal
measurements of macroscopic observables via proposed neural network
architectures that strongly enforce the satisfaction of the second law of
thermodynamics. The method is applied to three distinct physical processes
aimed at highlighting the robustness and versatility of the proposed approach.
These include (i) the phase transformation of a coiled-coil protein,
characterized by a non-convex free-energy density; (ii) the one-dimensional
dynamic response of a three-dimensional viscoelastic solid, which leverages the
variational formulation as a tool for obtaining reduced order models; and (iii)
linear and nonlinear diffusion models, characterized by a lack of uniqueness of
the free energy and dissipation potentials. These illustrative examples
showcase the possibility of learning partial differential equations through
their variational action density (i.e., a function instead), by leveraging the
thermodynamic structure intrinsic to mechanical and multiphysics problems.

We present a visual localization system that learns to estimate camera poses
in the real world with the help of synthetic data. Despite significant progress
in recent years, most learning-based approaches to visual localization target
at a single domain and require a dense database of geo-tagged images to
function well. To mitigate the data scarcity issue and improve the scalability
of the neural localization models, we introduce TOPO-DataGen, a versatile
synthetic data generation tool that traverses smoothly between the real and
virtual world, hinged on the geographic camera viewpoint. New large-scale
sim-to-real benchmark datasets are proposed to showcase and evaluate the
utility of the said synthetic data. Our experiments reveal that synthetic data
generically enhances the neural network performance on real data. Furthermore,
we introduce CrossLoc, a cross-modal visual representation learning approach to
pose estimation that makes full use of the scene coordinate ground truth via
self-supervision. Without any extra data, CrossLoc significantly outperforms
the state-of-the-art methods and achieves substantially higher real-data sample
efficiency. Our code and datasets are all available at
https://crossloc.github.io/.

We introduce a novel framework for solving inverse problems using NeRF-style
generative models. We are interested in the problem of 3-D scene reconstruction
given a single 2-D image and known camera parameters. We show that naively
optimizing the latent space leads to artifacts and poor novel view rendering.
We attribute this problem to volume obstructions that are clear in the 3-D
geometry and become visible in the renderings of novel views. We propose a
novel radiance field regularization method to obtain better 3-D surfaces and
improved novel views given single view observations. Our method naturally
extends to general inverse problems including inpainting where one observes
only partially a single view. We experimentally evaluate our method, achieving
visual improvements and performance boosts over the baselines in a wide range
of tasks. Our method achieves $30-40\%$ MSE reduction and $15-25\%$ reduction
in LPIPS loss compared to the previous state of the art.

The human brain contextually exploits heterogeneous sensory information to
efficiently perform cognitive tasks including vision and hearing. For example,
during the cocktail party situation, the human auditory cortex contextually
integrates audio-visual (AV) cues in order to better perceive speech. Recent
studies have shown that AV speech enhancement (SE) models can significantly
improve speech quality and intelligibility in very low signal to noise ratio
(SNR) environments as compared to audio-only SE models. However, despite
significant research in the area of AV SE, development of real-time processing
models with low latency remains a formidable technical challenge. In this
paper, we present a novel framework for low latency speaker-independent AV SE
that can generalise on a range of visual and acoustic noises. In particular, a
generative adversarial networks (GAN) is proposed to address the practical
issue of visual imperfections in AV SE. In addition, we propose a deep neural
network based real-time AV SE model that takes into account the cleaned visual
speech output from GAN to deliver more robust SE. The proposed framework is
evaluated on synthetic and real noisy AV corpora using objective speech quality
and intelligibility metrics and subjective listing tests. Comparative
simulation results show that our real time AV SE framework outperforms
state-of-the-art SE approaches, including recent DNN based SE models.

Hierarchical clustering studies a recursive partition of a data set into
clusters of successively smaller size, and is a fundamental problem in data
analysis. In this work we study the cost function for hierarchical clustering
introduced by Dasgupta, and present two polynomial-time approximation
algorithms: Our first result is an $O(1)$-approximation algorithm for graphs of
high conductance. Our simple construction bypasses complicated recursive
routines of finding sparse cuts known in the literature. Our second and main
result is an $O(1)$-approximation algorithm for a wide family of graphs that
exhibit a well-defined structure of clusters. This result generalises the
previous state-of-the-art, which holds only for graphs generated from
stochastic models. The significance of our work is demonstrated by the
empirical analysis on both synthetic and real-world data sets, on which our
presented algorithm outperforms the previously proposed algorithm for graphs
with a well-defined cluster structure.

Investigating the reasoning abilities of transformer models, and discovering
new challenging tasks for them, has been a topic of much interest. Recent
studies have found these models to be surprisingly strong at performing
deductive reasoning over formal logical theories expressed in natural language.
A shortcoming of these studies, however, is that they do not take into account
that logical theories, when sampled uniformly at random, do not necessarily
lead to hard instances. We propose a new methodology for creating challenging
algorithmic reasoning datasets that focus on natural language satisfiability
(NLSat) problems. The key idea is to draw insights from empirical sampling of
hard propositional SAT problems and from complexity-theoretic studies of
language. This methodology allows us to distinguish easy from hard instances,
and to systematically increase the complexity of existing reasoning benchmarks
such as RuleTaker. We find that current transformers, given sufficient training
data, are surprisingly robust at solving the resulting NLSat problems of
substantially increased difficulty. They also exhibit some degree of
scale-invariance - the ability to generalize to problems of larger size and
scope. Our results, however, reveal important limitations too: a careful
sampling of training data is crucial for building models that generalize to
larger problems, and transformer models' limited scale-invariance suggests they
are far from learning robust deductive reasoning algorithms.

This dissertation demonstrates three new types of attacks against the KLJN
scheme. The first attack type is based on compromised RNGs. The first RNG
attacks are deterministic. First, Eve knows both noises. She can crack the bit
via Ohm's Law and one-bit powers within a fraction of the bit exchange period.
Second, Eve knows only Bob's noise, so she can learn Bob's resistance value via
Ohm's Law and Alice's resistance at the end of the bit exchange period. She can
also use a process of elimination. The second RNG attacks are statistical.
First, Eve has partial knowledge of Alice's and Bob's noises. She can crack the
bit by taking the highest cross-correlation between her noises and the measured
noise in the wire, and by taking the highest cross-correlation between her
noises and Alice's/Bob's noises. Second, Eve has partial knowledge of only
Alice's noise. She can still crack the bit, but after the bit exchange period.
The second attack type is based on thermodynamics. Previously, the KLJN scheme
required thermal equilibrium. However, Vadai, et al, in (Nature) Science
Reports shows a modified scheme, where there is a non-zero thermal noise, yet
the system resists all the known attacks. We utilize coincidence events between
the line current and voltage and show that there is non-zero information leak.
As soon as thermal equilibrium is restored, the system is perfectly secure
again. The final attack type is based on the nonlinearity of the noise
generators. We explore the effect of distortion at the second and third orders.
It is demonstrated that 1% distortion results in a significant information
leak. We also show that decreasing the effective temperature results in the
KLJN scheme approaching perfect security.

Solar radio flux along with geomagnetic indices are important indicators of
solar activity and its effects. Extreme solar events such as flares and
geomagnetic storms can negatively affect the space environment including
satellites in low-Earth orbit. Therefore, forecasting these space weather
indices is of great importance in space operations and science. In this study,
we propose a model based on long short-term memory neural networks to learn the
distribution of time series data with the capability to provide a simultaneous
multivariate 27-day forecast of the space weather indices using time series as
well as solar image data. We show a 30-40\% improvement of the root mean-square
error while including solar image data with time series data compared to using
time series data alone. Simple baselines such as a persistence and running
average forecasts are also compared with the trained deep neural network
models. We also quantify the uncertainty in our prediction using a model
ensemble.

Large-scale cyber-physical systems require that control policies are
distributed, that is, that they only rely on local real-time measurements and
communication with neighboring agents. Optimal Distributed Control (ODC)
problems are, however, highly intractable even in seemingly simple cases.
Recent work has thus proposed training Neural Network (NN) distributed
controllers. A main challenge of NN controllers is that they are not dependable
during and after training, that is, the closed-loop system may be unstable, and
the training may fail due to vanishing and exploding gradients. In this paper,
we address these issues for networks of nonlinear port-Hamiltonian (pH)
systems, whose modeling power ranges from energy systems to non-holonomic
vehicles and chemical reactions. Specifically, we embrace the compositional
properties of pH systems to characterize deep Hamiltonian control policies with
built-in closed-loop stability guarantees, irrespective of the interconnection
topology and the chosen NN parameters. Furthermore, our setup enables
leveraging recent results on well-behaved neural ODEs to prevent the phenomenon
of vanishing gradients by design. Numerical experiments corroborate the
dependability of the proposed architecture, while matching the performance of
general neural network policies.

Background and objectives. Domain shift is a generalisation problem of
machine learning models that occurs when the data distribution of the training
set is different to the data distribution encountered by the model when it is
deployed. This is common in the context of biomedical image segmentation due to
the variance of experimental conditions, equipment, and capturing settings. In
this work, we address this challenge by studying both neural style transfer
algorithms and unpaired image-to-image translation methods in the context of
the segmentation of tumour spheroids.
  Methods. We have illustrated the domain shift problem in the context of
spheroid segmentation with 4 deep learning segmentation models that achieved an
IoU over 97% when tested with images following the training distribution, but
whose performance decreased up to an 84\% when applied to images captured under
different conditions. In order to deal with this problem, we have explored 3
style transfer algorithms (NST, deep image analogy, and STROTSS), and 6
unpaired image-to-image translations algorithms (CycleGAN, DualGAN, ForkGAN,
GANILLA, CUT, and FastCUT). These algorithms have been integrated into a
high-level API that facilitates their application to other contexts where the
domain-shift problem occurs.
  Results. We have considerably improved the performance of the 4 segmentation
models when applied to images captured under different conditions by using both
style transfer and image-to-image translation algorithms. In particular, there
are 2 style transfer algorithms (NST and deep image analogy) and 1 unpaired
image-to-image translations algorithm (CycleGAN) that improve the IoU of the
models in a range from 0.24 to 76.07. Therefore, reaching a similar performance
to the one obtained with the models are applied to images following the
training distribution.

We present an automatic static analyzer PyTea that detects tensor-shape
errors in PyTorch code. The tensor-shape error is critical in the deep neural
net code; much of the training cost and intermediate results are to be lost
once a tensor shape mismatch occurs in the midst of the training phase. Given
the input PyTorch source, PyTea statically traces every possible execution
path, collects tensor shape constraints required by the tensor operation
sequence of the path, and decides if the constraints are unsatisfiable (hence a
shape error can occur). PyTea's scalability and precision hinges on the
characteristics of real-world PyTorch applications: the number of execution
paths after PyTea's conservative pruning rarely explodes and loops are simple
enough to be circumscribed by our symbolic abstraction. We tested PyTea against
the projects in the official PyTorch repository and some tensor-error code
questioned in the StackOverflow. PyTea successfully detects tensor shape errors
in these codes, each within a few seconds.

Learning the graphical structure of Bayesian networks is key to describing
data generating mechanisms in many complex applications but poses considerable
computational challenges. Observational data can only identify the equivalence
class of the directed acyclic graph underlying a Bayesian network model, and a
variety of methods exist to tackle the problem. Under certain assumptions, the
popular PC algorithm can consistently recover the correct equivalence class by
reverse-engineering the conditional independence (CI) relationships holding in
the variable distribution. Here, we propose the dual PC algorithm, a novel
scheme to carry out the CI tests within the PC algorithm by leveraging the
inverse relationship between covariance and precision matrices. By exploiting
block matrix inversions we can simultaneously perform tests on partial
correlations of complementary (or dual) conditioning sets. The multiple CI
tests of the dual PC algorithm proceed by first considering marginal and
full-order CI relationships and progressively moving to central-order ones.
Simulation studies show that the dual PC algorithm outperforms the classic PC
algorithm both in terms of run time and in recovering the underlying network
structure, even in the presence of deviations from Gaussianity.

The use of deep neural networks as function approximators has led to striking
progress for reinforcement learning algorithms and applications. Yet the
knowledge we have on decision boundary geometry and the loss landscape of
neural policies is still quite limited. In this paper we propose a framework to
investigate the decision boundary and loss landscape similarities across states
and across MDPs. We conduct experiments in various games from Arcade Learning
Environment, and discover that high sensitivity directions for neural policies
are correlated across MDPs. We argue that these high sensitivity directions
support the hypothesis that non-robust features are shared across training
environments of reinforcement learning agents. We believe our results reveal
fundamental properties of the environments used in deep reinforcement learning
training, and represent a tangible step towards building robust and reliable
deep reinforcement learning agents.

Modern computational neuroscience strives to develop complex network models
to explain dynamics and function of brains in health and disease. This process
goes hand in hand with advancements in the theory of neuronal networks and
increasing availability of detailed anatomical data on brain connectivity.
Large-scale models that study interactions between multiple brain areas with
intricate connectivity and investigate phenomena on long time scales such as
system-level learning require progress in simulation speed. The corresponding
development of state-of-the-art simulation engines relies on information
provided by benchmark simulations which assess the time-to-solution for
scientifically relevant, complementary network models using various
combinations of hardware and software revisions. However, maintaining
comparability of benchmark results is difficult due to a lack of standardized
specifications for measuring the scaling performance of simulators on
high-performance computing (HPC) systems. Motivated by the challenging
complexity of benchmarking, we define a generic workflow that decomposes the
endeavor into unique segments consisting of separate modules. As a reference
implementation for the conceptual workflow, we develop beNNch: an open-source
software framework for the configuration, execution, and analysis of benchmarks
for neuronal network simulations. The framework records benchmarking data and
metadata in a unified way to foster reproducibility. For illustration, we
measure the performance of various versions of the NEST simulator across
network models with different levels of complexity on a contemporary HPC
system, demonstrating how performance bottlenecks can be identified, ultimately
guiding the development toward more efficient simulation technology.

We have repurposed Google Tensor Processing Units (TPUs),
application-specific chips developed for machine learning, into large-scale
dense linear algebra supercomputers. The TPUs' fast inter-core interconnects
(ICI)s, physically two-dimensional network topology, and high-bandwidth memory
(HBM) permit distributed matrix multiplication algorithms to rapidly become
computationally bound. In this regime, the matrix-multiply units (MXU)s
dominate the runtime, yielding impressive scaling, performance, and raw size:
operating in float32 precision, a full 2048-core pod of third generation TPUs
can multiply two matrices with linear size $N= 220= 1 048 576$ in about 2
minutes. Via curated algorithms emphasizing large, single-core matrix
multiplications, other tasks in dense linear algebra can similarly scale. As
examples, we present (i) QR decomposition; (ii) resolution of linear systems;
and (iii) the computation of matrix functions by polynomial iteration,
demonstrated by the matrix polar factorization.

We propose a novel benchmark environment for Safe Reinforcement Learning
focusing on aquatic navigation. Aquatic navigation is an extremely challenging
task due to the non-stationary environment and the uncertainties of the robotic
platform, hence it is crucial to consider the safety aspect of the problem, by
analyzing the behavior of the trained network to avoid dangerous situations
(e.g., collisions). To this end, we consider a value-based and policy-gradient
Deep Reinforcement Learning (DRL) and we propose a crossover-based strategy
that combines gradient-based and gradient-free DRL to improve
sample-efficiency. Moreover, we propose a verification strategy based on
interval analysis that checks the behavior of the trained models over a set of
desired properties. Our results show that the crossover-based training
outperforms prior DRL approaches, while our verification allows us to quantify
the number of configurations that violate the behaviors that are described by
the properties. Crucially, this will serve as a benchmark for future research
in this domain of applications.

The existing publications demonstrate that the limit order book data is
useful in predicting short-term volatility in stock markets. Since stocks are
not independent, changes on one stock can also impact other related stocks. In
this paper, we are interested in forecasting short-term realized volatility in
a multivariate approach based on limit order book data and relational data. To
achieve this goal, we introduce Graph Transformer Network for Volatility
Forecasting. The model allows to combine limit order book features and an
unlimited number of temporal and cross-sectional relations from different
sources. Through experiments based on about 500 stocks from S&P 500 index, we
find a better performance for our model than for other benchmarks.

We study the problem of multi-robot mapless navigation in the popular
Centralized Training and Decentralized Execution (CTDE) paradigm. This problem
is challenging when each robot considers its path without explicitly sharing
observations with other robots and can lead to non-stationary issues in Deep
Reinforcement Learning (DRL). The typical CTDE algorithm factorizes the joint
action-value function into individual ones, to favor cooperation and achieve
decentralized execution. Such factorization involves constraints (e.g.,
monotonicity) that limit the emergence of novel behaviors in an individual as
each agent is trained starting from a joint action-value. In contrast, we
propose a novel architecture for CTDE that uses a centralized state-value
network to compute a joint state-value, which is used to inject global state
information in the value-based updates of the agents. Consequently, each model
computes its gradient update for the weights, considering the overall state of
the environment. Our idea follows the insights of Dueling Networks as a
separate estimation of the joint state-value has both the advantage of
improving sample efficiency, while providing each robot information whether the
global state is (or is not) valuable. Experiments in a robotic navigation task
with 2 4, and 8 robots, confirm the superior performance of our approach over
prior CTDE methods (e.g., VDN, QMIX).

This report presents deep learning and data augmentation techniques used by a
system entered into the Few-Shot Bioacoustic Event Detection for the DCASE2021
Challenge. The remit was to develop a few-shot learning system for animal
(mammal and bird) vocalisations. Participants were tasked with developing a
method that can extract information from five exemplar vocalisations, or shots,
of mammals or birds and detect and classify sounds in field recordings. In the
system described in this report, prototypical networks are used to learn a
metric space, from which classification is performed by computing the distance
of a query point to class prototypes, classifying based on shortest distance.
We describe the architecture of this network, feature extraction methods, and
data augmentation performed on the given dataset and compare our work to the
challenge's baseline networks.

Machines that can represent and describe environmental soundscapes have
practical potential, e.g., for audio tagging and captioning systems. Prevailing
learning paradigms have been relying on parallel audio-text data, which is,
however, scarcely available on the web. We propose VIP-ANT that induces
\textbf{A}udio-\textbf{T}ext alignment without using any parallel audio-text
data. Our key idea is to share the image modality between bi-modal image-text
representations and bi-modal image-audio representations; the image modality
functions as a pivot and connects audio and text in a tri-modal embedding space
implicitly.
  In a difficult zero-shot setting with no paired audio-text data, our model
demonstrates state-of-the-art zero-shot performance on the ESC50 and US8K audio
classification tasks, and even surpasses the supervised state of the art for
Clotho caption retrieval (with audio queries) by 2.2\% R@1. We further
investigate cases of minimal audio-text supervision, finding that, e.g., just a
few hundred supervised audio-text pairs increase the zero-shot audio
classification accuracy by 8\% on US8K. However, to match human parity on some
zero-shot tasks, our empirical scaling experiments suggest that we would need
about $2^{21} \approx 2M$ supervised audio-caption pairs. Our work opens up new
avenues for learning audio-text connections with little to no parallel
audio-text data.

In light of the finite nature of the wireless spectrum and the increasing
demand for spectrum use arising from recent technological breakthroughs in
wireless communication, the problem of interference continues to persist.
Despite recent advancements in resolving interference issues, interference
still presents a difficult challenge to effective usage of the spectrum. This
is partly due to the rise in the use of license-free and managed shared bands
for Wi-Fi, long term evolution (LTE) unlicensed (LTE-U), LTE licensed assisted
access (LAA), 5G NR, and other opportunistic spectrum access solutions. As a
result of this, the need for efficient spectrum usage schemes that are robust
against interference has never been more important. In the past, most solutions
to interference have addressed the problem by using avoidance techniques as
well as non-AI mitigation approaches (for example, adaptive filters). The key
downside to non-AI techniques is the need for domain expertise in the
extraction or exploitation of signal features such as cyclostationarity,
bandwidth and modulation of the interfering signals. More recently, researchers
have successfully explored AI/ML enabled physical (PHY) layer techniques,
especially deep learning which reduces or compensates for the interfering
signal instead of simply avoiding it. The underlying idea of ML based
approaches is to learn the interference or the interference characteristics
from the data, thereby sidelining the need for domain expertise in suppressing
the interference. In this paper, we review a wide range of techniques that have
used deep learning to suppress interference. We provide comparison and
guidelines for many different types of deep learning techniques in interference
suppression. In addition, we highlight challenges and potential future research
directions for the successful adoption of deep learning in interference
suppression.

A cyber-attack is a malicious attempt by experienced hackers to breach the
target information system. Usually, the cyber-attacks are characterized as
hybrid TTPs (Tactics, Techniques, and Procedures) and long-term adversarial
behaviors, making the traditional intrusion detection methods ineffective. Most
existing cyber-attack detection systems are implemented based on manually
designed rules by referring to domain knowledge (e.g., threat models, threat
intelligences). However, this process is lack of intelligence and
generalization ability. Aiming at this limitation, this paper proposes an
intelligent cyber-attack detection method based on provenance data. To
effective and efficient detect cyber-attacks from a huge number of system
events in the provenance data, we firstly model the provenance data by a
heterogeneous graph to capture the rich context information of each system
entities (e.g., process, file, socket, etc.), and learns a semantic vector
representation for each system entity. Then, we perform online cyber-attack
detection by sampling a small and compact local graph from the heterogeneous
graph, and classifying the key system entities as malicious or benign. We
conducted a series of experiments on two provenance datasets with real
cyber-attacks. The experiment results show that the proposed method outperforms
other learning based detection models, and has competitive performance against
state-of-the-art rule based cyber-attack detection systems.

Federated Learning is the most promising way to train robust Deep Learning
models for the segmentation of Covid-19-related findings in chest CTs. By
learning in a decentralized fashion, heterogeneous data can be leveraged from a
variety of sources and acquisition protocols whilst ensuring patient privacy.
It is, however, crucial to continuously monitor the performance of the model.
Yet when it comes to the segmentation of diffuse lung lesions, a quick visual
inspection is not enough to assess the quality, and thorough monitoring of all
network outputs by expert radiologists is not feasible. In this work, we
present an array of lightweight metrics that can be calculated locally in each
hospital and then aggregated for central monitoring of a federated system. Our
linear model detects over 70% of low-quality segmentations on an
out-of-distribution dataset and thus reliably signals a decline in model

End-to-end driving with a deep learning neural network (DNN) has become a
rapidly growing paradigm of autonomous driving in industry and academia. Yet
safety measures and interpretability still pose challenges to this paradigm. We
propose an end-to-end driving algorithm that integrates multi-task DNN, path
prediction, and control models in a pipeline of data flow from sensory devices
through these models to driving decisions. It provides quantitative measures to
evaluate the holistic, dynamic, and real-time performance of end-to-end driving
systems, and thus allows to quantify their safety and interpretability. The DNN
is a modified UNet, a well known encoder-decoder neural network of semantic
segmentation. It consists of one segmentation, one regression, and two
classification tasks for lane segmentation, path prediction, and vehicle
controls. We present three variants of the modified UNet architecture having
different complexities, compare them on different tasks in four static measures
for both single and multi-task (MT) architectures, and then identify the best
one by two additional dynamic measures in real-time simulation. We also propose
a learning- and model-based longitudinal controller using model predictive
control method. With the Stanley lateral controller, our results show that
MTUNet outperforms an earlier modified UNet in terms of curvature and lateral
offset estimation on curvy roads at normal speed, which has been tested in a
real car driving on real roads.

Model-based reinforcement learning algorithms, which aim to learn a model of
the environment to make decisions, are more sample efficient than their
model-free counterparts. The sample efficiency of model-based approaches relies
on whether the model can well approximate the environment. However, learning an
accurate model is challenging, especially in complex and noisy environments. To
tackle this problem, we propose the conservative model-based actor-critic
(CMBAC), a novel approach that achieves high sample efficiency without the
strong reliance on accurate learned models. Specifically, CMBAC learns multiple
estimates of the Q-value function from a set of inaccurate models and uses the
average of the bottom-k estimates -- a conservative estimate -- to optimize the
policy. An appealing feature of CMBAC is that the conservative estimates
effectively encourage the agent to avoid unreliable "promising actions" --
whose values are high in only a small fraction of the models. Experiments
demonstrate that CMBAC significantly outperforms state-of-the-art approaches in
terms of sample efficiency on several challenging tasks, and the proposed
method is more robust than previous methods in noisy environments.

Hearing loss is a major health problem and psychological burden in humans.
Mouse models offer a possibility to elucidate genes involved in the underlying
developmental and pathophysiological mechanisms of hearing impairment. To this
end, large-scale mouse phenotyping programs include auditory phenotyping of
single-gene knockout mouse lines. Using the auditory brainstem response (ABR)
procedure, the German Mouse Clinic and similar facilities worldwide have
produced large, uniform data sets of averaged ABR raw data of mutant and
wildtype mice. In the course of standard ABR analysis, hearing thresholds are
assessed visually by trained staff from series of signal curves of increasing
sound pressure level. This is time-consuming and prone to be biased by the
reader as well as the graphical display quality and scale. In an attempt to
reduce workload and improve quality and reproducibility, we developed and
compared two methods for automated hearing threshold identification from
averaged ABR raw data: a supervised approach involving two combined neural
networks trained on human-generated labels and a self-supervised approach,
which exploits the signal power spectrum and combines random forest sound level
estimation with a piece-wise curve fitting algorithm for threshold finding. We
show that both models work well, outperform human threshold detection, and are
suitable for fast, reliable, and unbiased hearing threshold detection and
quality control. In a high-throughput mouse phenotyping environment, both
methods perform well as part of an automated end-to-end screening pipeline to
detect candidate genes for hearing involvement. Code for both models as well as
data used for this work are freely available.

Personalized medicine is expected to maximize the intended drug effects and
minimize side effects by treating patients based on their genetic profiles.
Thus, it is important to generate drugs based on the genetic profiles of
diseases, especially in anticancer drug discovery. However, this is challenging
because the vast chemical space and variations in cancer properties require a
huge time resource to search for proper molecules. Therefore, an efficient and
fast search method considering genetic profiles is required for de novo
molecular design of anticancer drugs. Here, we propose a faster molecular
generative model with genetic algorithm and tree search for cancer samples
(FasterGTS). FasterGTS is constructed with a genetic algorithm and a Monte
Carlo tree search with three deep neural networks: supervised learning,
self-trained, and value networks, and it generates anticancer molecules based
on the genetic profiles of a cancer sample. When compared to other methods,
FasterGTS generated cancer sample-specific molecules with general chemical
properties required for cancer drugs within the limited numbers of samplings.
We expect that FasterGTS contributes to the anticancer drug generation.

How does neural connectivity in autistic children differ from neural
connectivity in healthy children or autistic youths? What patterns in global
trade networks are shared across classes of goods, and how do these patterns
change over time? Answering questions like these requires us to differentially
describe groups of graphs: Given a set of graphs and a partition of these
graphs into groups, discover what graphs in one group have in common, how they
systematically differ from graphs in other groups, and how multiple groups of
graphs are related. We refer to this task as graph group analysis, which seeks
to describe similarities and differences between graph groups by means of
statistically significant subgraphs. To perform graph group analysis, we
introduce Gragra, which uses maximum entropy modeling to identify a
non-redundant set of subgraphs with statistically significant associations to
one or more graph groups. Through an extensive set of experiments on a wide
range of synthetic and real-world graph groups, we confirm that Gragra works
well in practice.

Recurrent models have gained popularity in deep learning (DL) based video
super-resolution (VSR), due to their increased computational efficiency,
temporal receptive field and temporal consistency compared to sliding-window
based models. However, when inferring on long video sequences presenting low
motion (i.e. in which some parts of the scene barely move), recurrent models
diverge through recurrent processing, generating high frequency artifacts. To
the best of our knowledge, no study about VSR pointed out this instability
problem, which can be critical for some real-world applications. Video
surveillance is a typical example where such artifacts would occur, as both the
camera and the scene stay static for a long time.
  In this work, we expose instabilities of existing recurrent VSR networks on
long sequences with low motion. We demonstrate it on a new long sequence
dataset Quasi-Static Video Set, that we have created. Finally, we introduce a
new framework of recurrent VSR networks that is both stable and competitive,
based on Lipschitz stability theory. We propose a new recurrent VSR network,
coined Middle Recurrent Video Super-Resolution (MRVSR), based on this
framework. We empirically show its competitive performance on long sequences
with low motion.

Video Panoptic Segmentation (VPS) aims at assigning a class label to each
pixel, uniquely segmenting and identifying all object instances consistently
across all frames. Classic solutions usually decompose the VPS task into
several sub-tasks and utilize multiple surrogates (e.g. boxes and masks,
centres and offsets) to represent objects. However, this divide-and-conquer
strategy requires complex post-processing in both spatial and temporal domains
and is vulnerable to failures from surrogate tasks. In this paper, inspired by
object-centric learning which learns compact and robust object representations,
we present Slot-VPS, the first end-to-end framework for this task. We encode
all panoptic entities in a video, including both foreground instances and
background semantics, with a unified representation called panoptic slots. The
coherent spatio-temporal object's information is retrieved and encoded into the
panoptic slots by the proposed Video Panoptic Retriever, enabling it to
localize, segment, differentiate, and associate objects in a unified manner.
Finally, the output panoptic slots can be directly converted into the class,
mask, and object ID of panoptic objects in the video. We conduct extensive
ablation studies and demonstrate the effectiveness of our approach on two
benchmark datasets, Cityscapes-VPS (\textit{val} and test sets) and VIPER
(\textit{val} set), achieving new state-of-the-art performance of 63.7, 63.3
and 56.2 VPQ, respectively.

As manipulating images by copy-move, splicing and/or inpainting may lead to
misinterpretation of the visual content, detecting these sorts of manipulations
is crucial for media forensics. Given the variety of possible attacks on the
content, devising a generic method is nontrivial. Current deep learning based
methods are promising when training and test data are well aligned, but perform
poorly on independent tests. Moreover, due to the absence of authentic test
images, their image-level detection specificity is in doubt. The key question
is how to design and train a deep neural network capable of learning
generalizable features sensitive to manipulations in novel data, whilst
specific to prevent false alarms on the authentic. We propose multi-view
feature learning to jointly exploit tampering boundary artifacts and the noise
view of the input image. As both clues are meant to be semantic-agnostic, the
learned features are thus generalizable. For effectively learning from
authentic images, we train with multi-scale (pixel / edge / image) supervision.
We term the new network MVSS-Net and its enhanced version MVSS-Net++.
Experiments are conducted in both within-dataset and cross-dataset scenarios,
showing that MVSS-Net++ performs the best, and exhibits better robustness
against JPEG compression, Gaussian blur and screenshot based image
re-capturing.

Effective exploration continues to be a significant challenge that prevents
the deployment of reinforcement learning for many physical systems. This is
particularly true for systems with continuous and high-dimensional state and
action spaces, such as robotic manipulators. The challenge is accentuated in
the sparse rewards setting, where the low-level state information required for
the design of dense rewards is unavailable. Adversarial imitation learning
(AIL) can partially overcome this barrier by leveraging expert-generated
demonstrations of optimal behaviour and providing, essentially, a replacement
for dense reward information. Unfortunately, the availability of expert
demonstrations does not necessarily improve an agent's capability to explore
effectively and, as we empirically show, can lead to inefficient or stagnated
learning. We present Learning from Guided Play (LfGP), a framework in which we
leverage expert demonstrations of, in addition to a main task, multiple
auxiliary tasks. Subsequently, a hierarchical model is used to learn each task
reward and policy through a modified AIL procedure, in which exploration of all
tasks is enforced via a scheduler composing different tasks together. This
affords many benefits: learning efficiency is improved for main tasks with
challenging bottleneck transitions, expert data becomes reusable between tasks,
and transfer learning through the reuse of learned auxiliary task models
becomes possible. Our experimental results in a challenging multitask robotic
manipulation domain indicate that our method compares favourably to supervised
imitation learning and to a state-of-the-art AIL method. Code is available at
https://github.com/utiasSTARS/lfgp.

The generation of well-designed artwork is often quite time-consuming and
assumes a high degree of proficiency on part of the human painter. In order to
facilitate the human painting process, substantial research efforts have been
made on teaching machines how to "paint like a human", and then using the
trained agent as a painting assistant tool for human users. However, current
research in this direction is often reliant on a progressive grid-based
division strategy wherein the agent divides the overall image into successively
finer grids, and then proceeds to paint each of them in parallel. This
inevitably leads to artificial painting sequences which are not easily
intelligible to human users. To address this, we propose a novel painting
approach which learns to generate output canvases while exhibiting a more
human-like painting style. The proposed painting pipeline Intelli-Paint
consists of 1) a progressive layering strategy which allows the agent to first
paint a natural background scene representation before adding in each of the
foreground objects in a progressive fashion. 2) We also introduce a novel
sequential brushstroke guidance strategy which helps the painting agent to
shift its attention between different image regions in a semantic-aware manner.
3) Finally, we propose a brushstroke regularization strategy which allows for
~60-80% reduction in the total number of required brushstrokes without any
perceivable differences in the quality of the generated canvases. Through both
quantitative and qualitative results, we show that the resulting agents not
only show enhanced efficiency in output canvas generation but also exhibit a
more natural-looking painting style which would better assist human users
express their ideas through digital artwork.

In this paper, we propose self-supervised speaker representation learning
strategies, which comprise of a bootstrap equilibrium speaker representation
learning in the front-end and an uncertainty-aware probabilistic speaker
embedding training in the back-end. In the front-end stage, we learn the
speaker representations via the bootstrap training scheme with the uniformity
regularization term. In the back-end stage, the probabilistic speaker
embeddings are estimated by maximizing the mutual likelihood score between the
speech samples belonging to the same speaker, which provide not only speaker
representations but also data uncertainty. Experimental results show that the
proposed bootstrap equilibrium training strategy can effectively help learn the
speaker representations and outperforms the conventional methods based on
contrastive learning. Also, we demonstrate that the integrated two-stage
framework further improves the speaker verification performance on the
VoxCeleb1 test set in terms of EER and MinDCF.

Recently, intelligent scheduling approaches using surrogate models have been
proposed to efficiently allocate volatile tasks in heterogeneous fog
environments. Advances like deterministic surrogate models, deep neural
networks (DNN) and gradient-based optimization allow low energy consumption and
response times to be reached. However, deterministic surrogate models, which
estimate objective values for optimization, do not consider the uncertainties
in the distribution of the Quality of Service (QoS) objective function that can
lead to high Service Level Agreement (SLA) violation rates. Moreover, the
brittle nature of DNN training and prevent such models from reaching minimal
energy or response times. To overcome these difficulties, we present a novel
scheduler: GOSH i.e. Gradient Based Optimization using Second Order derivatives
and Heteroscedastic Deep Surrogate Models. GOSH uses a second-order gradient
based optimization approach to obtain better QoS and reduce the number of
iterations to converge to a scheduling decision, subsequently lowering the
scheduling time. Instead of a vanilla DNN, GOSH uses a Natural Parameter
Network to approximate objective scores. Further, a Lower Confidence Bound
optimization approach allows GOSH to find an optimal trade-off between greedy
minimization of the mean latency and uncertainty reduction by employing
error-based exploration. Thus, GOSH and its co-simulation based extension
GOSH*, can adapt quickly and reach better objective scores than baseline
methods. We show that GOSH* reaches better objective scores than GOSH, but it
is suitable only for high resource availability settings, whereas GOSH is apt
for limited resource settings. Real system experiments for both GOSH and GOSH*
show significant improvements against the state-of-the-art in terms of energy
consumption, response time and SLA violations by up to 18, 27 and 82 percent,

Neural autoregressive sequence models smear the probability among many
possible sequences including degenerate ones, such as empty or repetitive
sequences. In this work, we tackle one specific case where the model assigns a
high probability to unreasonably short sequences. We define the oversmoothing
rate to quantify this issue. After confirming the high degree of oversmoothing
in neural machine translation, we propose to explicitly minimize the
oversmoothing rate during training. We conduct a set of experiments to study
the effect of the proposed regularization on both model distribution and
decoding performance. We use a neural machine translation task as the testbed
and consider three different datasets of varying size. Our experiments reveal
three major findings. First, we can control the oversmoothing rate of the model
by tuning the strength of the regularization. Second, by enhancing the
oversmoothing loss contribution, the probability and the rank of <eos> token
decrease heavily at positions where it is not supposed to be. Third, the
proposed regularization impacts the outcome of beam search especially when a
large beam is used. The degradation of translation quality (measured in BLEU)
with a large beam significantly lessens with lower oversmoothing rate, but the
degradation compared to smaller beam sizes remains to exist. From these
observations, we conclude that the high degree of oversmoothing is the main
reason behind the degenerate case of overly probable short sequences in a
neural autoregressive model.

Spatio-temporal representation learning is critical for video self-supervised
representation. Recent approaches mainly use contrastive learning and pretext
tasks. However, these approaches learn representation by discriminating sampled
instances via feature similarity in the latent space while ignoring the
intermediate state of the learned representations, which limits the overall
performance. In this work, taking into account the degree of similarity of
sampled instances as the intermediate state, we propose a novel pretext task -
spatio-temporal overlap rate (STOR) prediction. It stems from the observation
that humans are capable of discriminating the overlap rates of videos in space
and time. This task encourages the model to discriminate the STOR of two
generated samples to learn the representations. Moreover, we employ a joint
optimization combining pretext tasks with contrastive learning to further
enhance the spatio-temporal representation learning. We also study the mutual
influence of each component in the proposed scheme. Extensive experiments
demonstrate that our proposed STOR task can favor both contrastive learning and
pretext tasks. The joint optimization scheme can significantly improve the
spatio-temporal representation in video understanding. The code is available at
https://github.com/Katou2/CSTP.

We present two novel federated learning (FL) schemes that mitigate the effect
of straggling devices by introducing redundancy on the devices' data across the
network. Compared to other schemes in the literature, which deal with
stragglers or device dropouts by ignoring their contribution, the proposed
schemes do not suffer from the client drift problem. The first scheme,
CodedPaddedFL, mitigates the effect of stragglers while retaining the privacy
level of conventional FL. It combines one-time padding for user data privacy
with gradient codes to yield straggler resiliency. The second scheme,
CodedSecAgg, provides straggler resiliency and robustness against model
inversion attacks and is based on Shamir's secret sharing. We apply
CodedPaddedFL and CodedSecAgg to a classification problem. For a scenario with
120 devices, CodedPaddedFL achieves a speed-up factor of 18 for an accuracy of
95% on the MNIST dataset compared to conventional FL. Furthermore, it yields
similar performance in terms of latency compared to a recently proposed scheme
by Prakash et al. without the shortcoming of additional leakage of private
data. CodedSecAgg outperforms the state-of-the-art secure aggregation scheme
LightSecAgg by a speed-up factor of 6.6-18.7 for the MNIST dataset for an
accuracy of 95%.

We focus on the task of creating a reinforcement learning agent that is
inherently explainable -- with the ability to produce immediate local
explanations by thinking out loud while performing a task and analyzing entire
trajectories post-hoc to produce causal explanations. This Hierarchically
Explainable Reinforcement Learning agent (HEX-RL), operates in Interactive
Fictions, text-based game environments in which an agent perceives and acts
upon the world using textual natural language. These games are usually
structured as puzzles or quests with long-term dependencies in which an agent
must complete a sequence of actions to succeed -- providing ideal environments
in which to test an agent's ability to explain its actions. Our agent is
designed to treat explainability as a first-class citizen, using an extracted
symbolic knowledge graph-based state representation coupled with a Hierarchical
Graph Attention mechanism that points to the facts in the internal graph
representation that most influenced the choice of actions. Experiments show
that this agent provides significantly improved explanations over strong
baselines, as rated by human participants generally unfamiliar with the
environment, while also matching state-of-the-art task performance.

Estimating depth information from endoscopic images is a prerequisite for a
wide set of AI-assisted technologies, such as accurate localization and
measurement of tumors, or identification of non-inspected areas. As the domain
specificity of colonoscopies -- deformable low-texture environments with
fluids, poor lighting conditions and abrupt sensor motions -- pose challenges
to multi-view 3D reconstructions, single-view depth learning stands out as a
promising line of research. Depth learning can be extended in a Bayesian
setting, which enables continual learning, improves decision making and can be
used to compute confidence intervals or quantify uncertainty for in-body
measurements. In this paper, we explore for the first time Bayesian deep
networks for single-view depth estimation in colonoscopies. Our specific
contribution is two-fold: 1) an exhaustive analysis of scalable Bayesian
networks for depth learning in different datasets, highlighting challenges and
conclusions regarding synthetic-to-real domain changes and supervised vs.
self-supervised methods; and 2) a novel teacher-student approach to deep depth
learning that takes into account the teacher uncertainty.

Graph Neural Networks (GNNs) have shown promising results on a broad spectrum
of applications. Most empirical studies of GNNs directly take the observed
graph as input, assuming the observed structure perfectly depicts the accurate
and complete relations between nodes. However, graphs in the real world are
inevitably noisy or incomplete, which could even exacerbate the quality of
graph representations. In this work, we propose a novel Variational Information
Bottleneck guided Graph Structure Learning framework, namely VIB-GSL, in the
perspective of information theory. VIB-GSL advances the Information Bottleneck
(IB) principle for graph structure learning, providing a more elegant and
universal framework for mining underlying task-relevant relations. VIB-GSL
learns an informative and compressive graph structure to distill the actionable
information for specific downstream tasks. VIB-GSL deduces a variational
approximation for irregular graph data to form a tractable IB objective
function, which facilitates training stability. Extensive experimental results
demonstrate that the superior effectiveness and robustness of VIB-GSL.

Most models tasked to ground referential utterances in 2D and 3D scenes learn
to select the referred object from a pool of object proposals provided by a
pre-trained detector. This is limiting because an utterance may refer to visual
entities at various levels of granularity, such as the chair, the leg of the
chair, or the tip of the front leg of the chair, which may be missed by the
detector. We propose a language grounding model that attends on the referential
utterance and on the object proposal pool computed from a pre-trained detector
to decode referenced objects with a detection head, without selecting them from
the pool. In this way, it is helped by powerful pre-trained object detectors
without being restricted by their misses. We call our model Bottom Up Top Down
DEtection TRansformers (BUTD-DETR) because it uses both language guidance (top
down) and objectness guidance (bottom-up) to ground referential utterances in
images and point clouds. Moreover, BUTD-DETR casts object detection as
referential grounding and uses object labels as language prompts to be grounded
in the visual scene, augmenting supervision for the referential grounding task
in this way. The proposed model sets a new state-of-the-art across popular 3D
language grounding benchmarks with significant performance gains over previous
3D approaches (12.6% on SR3D, 11.6% on NR3D and 6.3% on ScanRefer). When
applied in 2D images, it performs on par with the previous state of the art. We
ablate the design choices of our model and quantify their contribution to
performance. Our code and checkpoints can be found at the project website
https://butd-detr.github.io.

The Shapley value is a game-theoretic notion for wealth distribution that is
nowadays extensively used to explain complex data-intensive computation, for
instance, in network analysis or machine learning. Recent theoretical works
show that query evaluation over relational databases fits well in this
explanation paradigm. Yet, these works fall short of providing practical
solutions to the computational challenge inherent to the Shapley computation.
We present in this paper two practically effective solutions for computing
Shapley values in query answering. We start by establishing a tight theoretical
connection to the extensively studied problem of query evaluation over
probabilistic databases, which allows us to obtain a polynomial-time algorithm
for the class of queries for which probability computation is tractable. We
then propose a first practical solution for computing Shapley values that
adopts tools from probabilistic query evaluation. In particular, we capture the
dependence of query answers on input database facts using Boolean expressions
(data provenance), and then transform it, via Knowledge Compilation, into a
particular circuit form for which we devise an algorithm for computing the
Shapley values. Our second practical solution is a faster yet inexact approach
that transforms the provenance to a Conjunctive Normal Form and uses a
heuristic to compute the Shapley values. Our experiments on TPC-H and IMDB
demonstrate the practical effectiveness of our solutions.

3D-aware image generative modeling aims to generate 3D-consistent images with
explicitly controllable camera poses. Recent works have shown promising results
by training neural radiance field (NeRF) generators on unstructured 2D images,
but still can not generate highly-realistic images with fine details. A
critical reason is that the high memory and computation cost of volumetric
representation learning greatly restricts the number of point samples for
radiance integration during training. Deficient sampling not only limits the
expressive power of the generator to handle fine details but also impedes
effective GAN training due to the noise caused by unstable Monte Carlo
sampling. We propose a novel approach that regulates point sampling and
radiance field learning on 2D manifolds, embodied as a set of learned implicit
surfaces in the 3D volume. For each viewing ray, we calculate ray-surface
intersections and accumulate their radiance generated by the network. By
training and rendering such radiance manifolds, our generator can produce high
quality images with realistic fine details and strong visual 3D consistency.

Neural density estimators have proven remarkably powerful in performing
efficient simulation-based Bayesian inference in various research domains. In
particular, the BayesFlow framework uses a two-step approach to enable
amortized parameter estimation in settings where the likelihood function is
implicitly defined by a simulation program. But how faithful is such inference
when simulations are poor representations of reality? In this paper, we
conceptualize the types of model misspecification arising in simulation-based
inference and systematically investigate the performance of the BayesFlow
framework under these misspecifications. We propose an augmented optimization
objective which imposes a probabilistic structure on the latent data space and
utilize maximum mean discrepancy (MMD) to detect potentially catastrophic
misspecifications during inference undermining the validity of the obtained
results. We verify our detection criterion on a number of artificial and
realistic misspecifications, ranging from toy conjugate models to complex
models of decision making and disease outbreak dynamics applied to real data.
Further, we show that posterior inference errors increase as a function of the
distance between the true data-generating distribution and the typical set of
simulations in the latent summary space. Thus, we demonstrate the dual utility
of MMD as a method for detecting model misspecification and as a proxy for
verifying the faithfulness of amortized Bayesian inference.

Smart healthcare systems are gaining popularity with the rapid development of
intelligent sensors, the Internet of Things (IoT) applications and services,
and wireless communications. However, at the same time, several vulnerabilities
and adversarial attacks make it challenging for a safe and secure smart
healthcare system from a security point of view. Machine learning has been used
widely to develop suitable models to predict and mitigate attacks. Still, the
attacks could trick the machine learning models and misclassify outputs
generated by the model. As a result, it leads to incorrect decisions, for
example, false disease detection and wrong treatment plans for patients. In
this paper, we address the type of adversarial attacks and their impact on
smart healthcare systems. We propose a model to examine how adversarial attacks
impact machine learning classifiers. To test the model, we use a medical image
dataset. Our model can classify medical images with high accuracy. We then
attacked the model with a Fast Gradient Sign Method attack (FGSM) to cause the
model to predict the images and misclassify them inaccurately. Using transfer
learning, we train a VGG-19 model with the medical dataset and later implement
the FGSM to the Convolutional Neural Network (CNN) to examine the significant
impact it causes on the performance and accuracy of the machine learning model.
Our results demonstrate that the adversarial attack misclassifies the images,
causing the model's accuracy rate to drop from 88% to 11%.

When many labels are possible, choosing a single one can lead to low
precision. A common alternative, referred to as top-$K$ classification, is to
choose some number $K$ (commonly around 5) and to return the $K$ labels with
the highest scores. Unfortunately, for unambiguous cases, $K>1$ is too many
and, for very ambiguous cases, $K \leq 5$ (for example) can be too small. An
alternative sensible strategy is to use an adaptive approach in which the
number of labels returned varies as a function of the computed ambiguity, but
must average to some particular $K$ over all the samples. We denote this
alternative average-$K$ classification. This paper formally characterizes the
ambiguity profile when average-$K$ classification can achieve a lower error
rate than a fixed top-$K$ classification. Moreover, it provides natural
estimation procedures for both the fixed-size and the adaptive classifier and
proves their consistency. Finally, it reports experiments on real-world image
data sets revealing the benefit of average-$K$ classification over top-$K$ in
practice. Overall, when the ambiguity is known precisely, average-$K$ is never
worse than top-$K$, and, in our experiments, when it is estimated, this also
holds.

We apply deep learning (DL) on Magnetic resonance spectroscopy (MRS) data for
the task of brain tumor detection. Medical applications often suffer from data
scarcity and corruption by noise. Both of these problems are prominent in our
data set. Furthermore, a varying number of spectra are available for the
different patients. We address these issues by considering the task as a
multiple instance learning (MIL) problem. Specifically, we aggregate multiple
spectra from the same patient into a "bag" for classification and apply data
augmentation techniques. To achieve the permutation invariance during the
process of bagging, we proposed two approaches: (1) to apply min-, max-, and
average-pooling on the features of all samples in one bag and (2) to apply an
attention mechanism. We tested these two approaches on multiple neural network
architectures. We demonstrate that classification performance is significantly
improved when training on multiple instances rather than single spectra. We
propose a simple oversampling data augmentation method and show that it could
further improve the performance. Finally, we demonstrate that our proposed
model outperforms manual classification by neuroradiologists according to most
performance metrics.

This paper summarizes our submission to Task 2 of the second track of the
10th Dialog System Technology Challenge (DSTC10) "Knowledge-grounded
Task-oriented Dialogue Modeling on Spoken Conversations". Similar to the
previous year's iteration, the task consists of three subtasks: detecting
whether a turn is knowledge seeking, selecting the relevant knowledge document
and finally generating a grounded response. This year, the focus lies on
adapting the system to noisy ASR transcripts. We explore different approaches
to make the models more robust to this type of input and to adapt the generated
responses to the style of spoken conversations. For the latter, we get the best
results with a noisy channel model that additionally reduces the number of
short and generic responses. Our best system achieved the 1st rank in the
automatic and the 3rd rank in the human evaluation of the challenge.

Time series data of urban land cover is of great utility in analyzing urban
growth patterns, changes in distribution of impervious surface and vegetation
and resulting impacts on urban micro climate. While Landsat data is ideal for
such analysis due to the long time series of free imagery, traditional
per-pixel hard classification fails to yield full potential of the Landsat
data. This paper proposes a sub-pixel classification method that leverages the
temporal overlap of Landsat-5 TM and Resourcesat-1 LISS-IV sensors. We train a
convolutional neural network to predict fractional land cover maps from 30m
Landsat-5 TM data. The reference land cover fractions are estimated from a
hard-classified 5.8m LISS-IV image for Bengaluru from 2011. Further, we
demonstrate the generalizability and superior performance of the proposed model
using data for Mumbai from 2009 and comparing it to the results obtained using
a Random Forest classifier. For both Bengaluru (2011) and Mumbai (2009) data,
Mean Absolute Percentage Error of our CNN model is in the range of 7.2 to 11.3
for both built-up and vegetation fraction prediction at the 30m cell level.
Unlike most recent studies where validation is conducted using data for a
limited spatial extent, our model has been trained and validated using data for
the complete spatial extent of two mega cities for two different time periods.
Hence it can reliably generate 30m built-up and vegetation fraction maps from
Landsat-5 TM time series data to analyze long term urban growth patterns.

In digital pathology, many image analysis tasks are challenged by the need
for large and time-consuming manual data annotations to cope with various
sources of variability in the image domain. Unsupervised domain adaptation
based on image-to-image translation is gaining importance in this field by
addressing variabilities without the manual overhead. Here, we tackle the
variation of different histological stains by unsupervised stain-to-stain
translation to enable a stain-independent applicability of a deep learning
segmentation model. We use CycleGANs for stain-to-stain translation in kidney
histopathology, and propose two novel approaches to improve translational
effectivity. First, we integrate a prior segmentation network into the CycleGAN
for a self-supervised, application-oriented optimization of translation through
semantic guidance, and second, we incorporate extra channels to the translation
output to implicitly separate artificial meta-information otherwise encoded for
tackling underdetermined reconstructions. The latter showed partially superior
performances to the unmodified CycleGAN, but the former performed best in all
stains providing instance-level Dice scores ranging between 78% and 92% for
most kidney structures, such as glomeruli, tubules, and veins. However,
CycleGANs showed only limited performance in the translation of other
structures, e.g. arteries. Our study also found somewhat lower performance for
all structures in all stains when compared to segmentation in the original
stain. Our study suggests that with current unsupervised technologies, it seems
unlikely to produce generally applicable simulated stains.

Although deep learning has achieved impressive advances in transient
stability assessment of power systems, the insufficient and imbalanced samples
still trap the training effect of the data-driven methods. This paper proposes
a controllable sample generation framework based on Conditional Tabular
Generative Adversarial Network (CTGAN) to generate specified transient
stability samples. To fit the complex feature distribution of the transient
stability samples, the proposed framework firstly models the samples as tabular
data and uses Gaussian mixture models to normalize the tabular data. Then we
transform multiple conditions into a single conditional vector to enable
multi-conditional generation. Furthermore, this paper introduces three
evaluation metrics to verify the quality of generated samples based on the
proposed framework. Experimental results on the IEEE 39-bus system show that
the proposed framework effectively balances the transient stability samples and
significantly improves the performance of transient stability assessment
models.

Several methods for discovering interpretable directions in the latent space
of pre-trained GANs have been proposed. Latent semantics discovered by
unsupervised methods are relatively less disentangled than supervised methods
since they do not use pre-trained attribute classifiers. We propose Scale
Ranking Estimator (SRE), which is trained using self-supervision. SRE enhances
the disentanglement in directions obtained by existing unsupervised
disentanglement techniques. These directions are updated to preserve the
ordering of variation within each direction in latent space. Qualitative and
quantitative evaluation of the discovered directions demonstrates that our
proposed method significantly improves disentanglement in various datasets. We
also show that the learned SRE can be used to perform Attribute-based image
retrieval task without further training.

Unsupervised graph-level representation learning plays a crucial role in a
variety of tasks such as molecular property prediction and community analysis,
especially when data annotation is expensive. Currently, most of the
best-performing graph embedding methods are based on Infomax principle. The
performance of these methods highly depends on the selection of negative
samples and hurt the performance, if the samples were not carefully selected.
Inter-graph similarity-based methods also suffer if the selected set of graphs
for similarity matching is low in quality. To address this, we focus only on
utilizing the current input graph for embedding learning. We are motivated by
an observation from real-world graph generation processes where the graphs are
formed based on one or more global factors which are common to all elements of
the graph (e.g., topic of a discussion thread, solubility level of a molecule).
We hypothesize extracting these common factors could be highly beneficial.
Hence, this work proposes a new principle for unsupervised graph representation
learning: Graph-wise Common latent Factor EXtraction (GCFX). We further propose
a deep model for GCFX, deepGCFX, based on the idea of reversing the
above-mentioned graph generation process which could explicitly extract common
latent factors from an input graph and achieve improved results on downstream
tasks to the current state-of-the-art. Through extensive experiments and
analysis, we demonstrate that, while extracting common latent factors is
beneficial for graph-level tasks to alleviate distractions caused by local
variations of individual nodes or local neighbourhoods, it also benefits
node-level tasks by enabling long-range node dependencies, especially for
disassortative graphs.

Studying cell morphology changes in time is critical to understanding cell
migration mechanisms. In this work, we present a deep learning-based workflow
to segment cancer cells embedded in 3D collagen matrices and imaged with
phase-contrast microscopy. Our approach uses transfer learning and recurrent
convolutional long-short term memory units to exploit the temporal information
from the past and provide a consistent segmentation result. Lastly, we propose
a geometrical-characterization approach to studying cancer cell morphology. Our
approach provides stable results in time, and it is robust to the different
weight initialization or training data sampling. We introduce a new annotated
dataset for 2D cell segmentation and tracking, and an open-source
implementation to replicate the experiments or adapt them to new image
processing problems.

In hash-based image retrieval systems, degraded or transformed inputs usually
generate different codes from the original, deteriorating the retrieval
accuracy. To mitigate this issue, data augmentation can be applied during
training. However, even if augmented samples of an image are similar in real
feature space, the quantization can scatter them far away in Hamming space.
This results in representation discrepancies that can impede training and
degrade performance. In this work, we propose a novel self-distilled hashing
scheme to minimize the discrepancy while exploiting the potential of augmented
data. By transferring the hash knowledge of the weakly-transformed samples to
the strong ones, we make the hash code insensitive to various transformations.
We also introduce hash proxy-based similarity learning and binary cross
entropy-based quantization loss to provide fine quality hash codes. Ultimately,
we construct a deep hashing framework that not only improves the existing deep
hashing approaches, but also achieves the state-of-the-art retrieval results.
Extensive experiments are conducted and confirm the effectiveness of our work.

Conversational question answering aims to provide natural-language answers to
users in information-seeking conversations. Existing conversational QA
benchmarks compare models with pre-collected human-human conversations, using
ground-truth answers provided in conversational history. It remains unclear
whether we can rely on this static evaluation for model development and whether
current systems can well generalize to real-world human-machine conversations.
In this work, we conduct the first large-scale human evaluation of
state-of-the-art conversational QA systems, where human evaluators converse
with models and judge the correctness of their answers. We find that the
distribution of human machine conversations differs drastically from that of
human-human conversations, and there is a disagreement between human and
gold-history evaluation in terms of model ranking. We further investigate how
to improve automatic evaluations, and propose a question rewriting mechanism
based on predicted history, which better correlates with human judgments.
Finally, we analyze the impact of various modeling strategies and discuss
future directions towards building better conversational question answering
systems.

Machine learning models are increasingly used by businesses and organizations
around the world to automate tasks and decision-making. Trained on potentially
sensitive datasets, machine learning models have been shown to leak information
about individuals in the dataset as well as global dataset information. We here
take research in dataset property inference attacks one step further by
proposing a new attack against ML models: a dataset correlation inference
attack, where an attacker's goal is to infer the correlation between input
variables of a model. We first show that an attacker can exploit the spherical
parametrization of correlation matrices, to make an informed guess. This means
that using only the correlation between the input variables and the target
variable, an attacker can infer the correlation between two input variables
much better than a random guess baseline. We propose a second attack which
exploits the access to a machine learning model using shadow modeling to refine
the guess. Our attack uses Gaussian copula-based generative modeling to
generate synthetic datasets with a wide variety of correlations in order to
train a meta-model for the correlation inference task. We evaluate our attack
against Logistic Regression and Multi-layer perceptron models and show it to
outperform the model-less attack. Our results show that the accuracy of the
second, machine learning-based attack decreases with the number of variables
and converges towards the accuracy of the model-less attack. However,
correlations between input variables which are highly correlated with the
target variable are more vulnerable regardless of the number of variables. Our
work bridges the gap between what can be considered a global leakage about the
training dataset and individual-level leakages. When coupled with marginal
leakage attacks,it might also constitute a first step towards dataset

An extractive rationale explains a language model's (LM's) prediction on a
given task instance by highlighting the text inputs that most influenced the
prediction. Ideally, rationale extraction should be faithful (reflective of
LM's actual behavior) and plausible (convincing to humans), without
compromising the LM's (i.e., task model's) task performance. Although
attribution algorithms and select-predict pipelines are commonly used in
rationale extraction, they both rely on certain heuristics that hinder them
from satisfying all three desiderata. In light of this, we propose UNIREX, a
flexible learning framework which generalizes rationale extractor optimization
as follows: (1) specify architecture for a learned rationale extractor; (2)
select explainability objectives (i.e., faithfulness and plausibility
criteria); and (3) jointly the train task model and rationale extractor on the
task using selected objectives. UNIREX enables replacing prior works' heuristic
design choices with a generic learned rationale extractor in (1) and optimizing
it for all three desiderata in (2)-(3). To facilitate comparison between
methods w.r.t. multiple desiderata, we introduce the Normalized Relative Gain
(NRG) metric. Across five text classification datasets, our best UNIREX
configuration outperforms baselines by an average of 32.9% NRG. Plus, we find
that UNIREX-trained rationale extractors can even generalize to unseen datasets
and tasks.

Over-parameterized deep neural networks are able to achieve excellent
training accuracy while maintaining a small generalization error. It has also
been found that they are able to fit arbitrary labels, and this behaviour is
referred to as the phenomenon of memorization. In this work, we study the
phenomenon of memorization with turn-over dropout, an efficient method to
estimate influence and memorization, for data with true labels (real data) and
data with random labels (random data). Our main findings are: (i) For both real
data and random data, the optimization of easy examples (e.g., real data) and
difficult examples (e.g., random data) are conducted by the network
simultaneously, with easy ones at a higher speed; (ii) For real data, a correct
difficult example in the training dataset is more informative than an easy one.
By showing the existence of memorization on random data and real data, we
highlight the consistency between them regarding optimization and we emphasize
the implication of memorization during optimization.

The Mixup scheme suggests mixing a pair of samples to create an augmented
training sample and has gained considerable attention recently for improving
the generalizability of neural networks. A straightforward and widely used
extension of Mixup is to combine with regional dropout-like methods: removing
random patches from a sample and replacing it with the features from another
sample. Albeit their simplicity and effectiveness, these methods are prone to
create harmful samples due to their randomness. To address this issue, 'maximum
saliency' strategies were recently proposed: they select only the most
informative features to prevent such a phenomenon. However, they now suffer
from lack of sample diversification as they always deterministically select
regions with maximum saliency, injecting bias into the augmented data. In this
paper, we present, a novel, yet simple Mixup-variant that captures the best of
both worlds. Our idea is two-fold. By stochastically sampling the features and
'grafting' them onto another sample, our method effectively generates diverse
yet meaningful samples. Its second ingredient is to produce the label of the
grafted sample by mixing the labels in a saliency-calibrated fashion, which
rectifies supervision misguidance introduced by the random sampling procedure.
Our experiments under CIFAR, Tiny-ImageNet, and ImageNet datasets show that our
scheme outperforms the current state-of-the-art augmentation strategies not
only in terms of classification accuracy, but is also superior in coping under
stress conditions such as data corruption and object occlusion.

While pre-trained language model (PLM) fine-tuning has achieved strong
performance in many NLP tasks, the fine-tuning stage can be still demanding in
labeled data. Recent works have resorted to active fine-tuning to improve the
label efficiency of PLM fine-tuning, but none of them investigate the potential
of unlabeled data. We propose {\ours}, a new framework that leverages unlabeled
data to improve the label efficiency of active PLM fine-tuning. AcTune switches
between data annotation and model self-training based on uncertainty: it
selects high-uncertainty unlabeled samples for active annotation and
low-uncertainty ones for model self-training. Under this framework, we design
(1) a region-aware sampling strategy that reduces redundancy when actively
querying for annotations and (2) a momentum-based memory bank that dynamically
aggregates the model's pseudo labels to suppress label noise in self-training.
Experiments on 6 text classification datasets show that AcTune outperforms the
strongest active learning and self-training baselines and improves the label
efficiency of PLM fine-tuning by 56.2\% on average. Our implementation will be
available at \url{https://github.com/yueyu1030/actune}.

Traffic sign detection is a challenging task for the unmanned driving system,
especially for the detection of multi-scale targets and the real-time problem
of detection. In the traffic sign detection process, the scale of the targets
changes greatly, which will have a certain impact on the detection accuracy.
Feature pyramid is widely used to solve this problem but it might break the
feature consistency across different scales of traffic signs. Moreover, in
practical application, it is difficult for common methods to improve the
detection accuracy of multi-scale traffic signs while ensuring real-time
detection. In this paper, we propose an improved feature pyramid model, named
AF-FPN, which utilizes the adaptive attention module (AAM) and feature
enhancement module (FEM) to reduce the information loss in the process of
feature map generation and enhance the representation ability of the feature
pyramid. We replaced the original feature pyramid network in YOLOv5 with
AF-FPN, which improves the detection performance for multi-scale targets of the
YOLOv5 network under the premise of ensuring real-time detection. Furthermore,
a new automatic learning data augmentation method is proposed to enrich the
dataset and improve the robustness of the model to make it more suitable for
practical scenarios. Extensive experimental results on the Tsinghua-Tencent
100K (TT100K) dataset demonstrate the effectiveness and superiority of the
proposed method when compared with several state-of-the-art methods.

Recently, pioneer work finds that speech pre-trained models can solve
full-stack speech processing tasks, because the model utilizes bottom layers to
learn speaker-related information and top layers to encode content-related
information. Since the network capacity is limited, we believe the speech
recognition performance could be further improved if the model is dedicated to
audio content information learning. To this end, we propose Intermediate Layer
Supervision for Self-Supervised Learning (ILS-SSL), which forces the model to
concentrate on content information as much as possible by adding an additional
SSL loss on the intermediate layers. Experiments on LibriSpeech test-other set
show that our method outperforms HuBERT significantly, which achieves a
23.5%/11.6% relative word error rate reduction in the w/o language model
setting for base/large models. Detailed analysis shows the bottom layers of our
model have a better correlation with phonetic units, which is consistent with
our intuition and explains the success of our method for ASR.

Long-context question answering (QA) tasks require reasoning over a long
document or multiple documents. Addressing these tasks often benefits from
identifying a set of evidence spans (e.g., sentences), which provide supporting
evidence for answering the question. In this work, we propose a novel method
for equipping long-context QA models with an additional sequence-level
objective for better identification of the supporting evidence. We achieve this
via an additional contrastive supervision signal in finetuning, where the model
is encouraged to explicitly discriminate supporting evidence sentences from
negative ones by maximizing question-evidence similarity. The proposed
additional loss exhibits consistent improvements on three different strong
long-context transformer models, across two challenging question answering
benchmarks -- HotpotQA and QAsper.

Entity resolution is a widely studied problem with several proposals to match
records across relations. Matching textual content is a widespread task in many
applications, such as question answering and search. While recent methods
achieve promising results for these two tasks, there is no clear solution for
the more general problem of matching textual content and structured data. We
introduce a framework that supports this new task in an unsupervised setting
for any pair of corpora, being relational tables or text documents. Our method
builds a fine-grained graph over the content of the corpora and derives word
embeddings to represent the objects to match in a low dimensional space. The
learned representation enables effective and efficient matching at different
granularity, from relational tuples to text sentences and paragraphs. Our
flexible framework can exploit pre-trained resources, but it does not depends
on their existence and achieves better quality performance in matching content
when the vocabulary is domain specific. We also introduce optimizations in the
graph creation process with an "expand and compress" approach that first
identifies new valid relationships across elements, to improve matching, and
then prunes nodes and edges, to reduce the graph size. Experiments on real use
cases and public datasets show that our framework produces embeddings that
outperform word embeddings and fine-tuned language models both in results'
quality and in execution times.

Current auto-tuning frameworks struggle with tuning computer systems
configurations due to their large parameter space, complex interdependencies,
and high evaluation cost. Utilizing probabilistic models, Structured Bayesian
Optimization (SBO) has recently overcome these difficulties. SBO decomposes the
parameter space by utilizing contextual information provided by system experts
leading to fast convergence. However, the complexity of building probabilistic
models has hindered its wider adoption. We propose BoAnon, a SBO framework that
learns the system structure from its logs. BoAnon provides an API enabling
experts to encode knowledge of the system as performance models or components
dependency. BoAnon takes in the learned structure and transforms it into a
probabilistic graph model. Then it applies the expert-provided knowledge to the
graph to further contextualize the system behavior. BoAnon probabilistic graph
allows the optimizer to find efficient configurations faster than other
methods. We evaluate BoAnon via a hardware architecture search problem,
achieving an improvement in energy-latency objectives ranging from $5-7$
x-factors improvement over the default architecture. With its novel contextual
structure learning pipeline, BoAnon makes using SBO accessible for a wide range
of other computer systems such as databases and stream processors.

Deep neural networks are often overparameterized and may not easily achieve
model generalization. Adversarial training has shown effectiveness in improving
generalization by regularizing the change of loss on top of adversarially
chosen perturbations. The recently proposed sharpness-aware minimization (SAM)
algorithm conducts adversarial weight perturbation, encouraging the model to
converge to a flat minima. SAM finds a common adversarial weight perturbation
per-batch. Although per-instance adversarial weight perturbations are stronger
adversaries and they can potentially lead to better generalization performance,
their computational cost is very high and thus it is impossible to use
per-instance perturbations efficiently in SAM. In this paper, we tackle this
efficiency bottleneck and propose sharpness-aware minimization with dynamic
reweighting ({\delta}-SAM). Our theoretical analysis motivates that it is
possible to approach the stronger, per-instance adversarial weight
perturbations using reweighted per-batch weight perturbations. {\delta}-SAM
dynamically reweights perturbation within each batch according to the
theoretically principled weighting factors, serving as a good approximation to
per-instance perturbation. Experiments on various natural language
understanding tasks demonstrate the effectiveness of {\delta}-SAM.

Text clustering methods were traditionally incorporated into multi-document
summarization (MDS) as a means for coping with considerable information
repetition. Particularly, clusters were leveraged to indicate information
saliency as well as to avoid redundancy. Such prior methods focused on
clustering sentences, even though closely related sentences usually contain
also non-aligned parts. In this work, we revisit the clustering approach,
grouping together sub-sentential propositions, aiming at more precise
information alignment. Specifically, our method detects salient propositions,
clusters them into paraphrastic clusters, and generates a representative
sentence for each cluster via text fusion. Our summarization method improves
over the previous state-of-the-art MDS method in the DUC 2004 and TAC 2011
datasets, both in automatic ROUGE scores and human preference.

In 2019, the UK's Immigration and Asylum Chamber of the Upper Tribunal
dismissed an asylum appeal basing the decision on the output of a biometric
system, alongside other discrepancies. The fingerprints of the asylum seeker
were found in a biometric database which contradicted the appellant's account.
The Tribunal found this evidence unequivocal and denied the asylum claim.
Nowadays, the proliferation of biometric systems is shaping public debates
around its political, social and ethical implications. Yet whilst concerns
towards the racialised use of this technology for migration control have been
on the rise, investment in the biometrics industry and innovation is increasing
considerably. Moreover, fairness has also been recently adopted by biometrics
to mitigate bias and discrimination on biometrics. However, algorithmic
fairness cannot distribute justice in scenarios which are broken or intended
purpose is to discriminate, such as biometrics deployed at the border.
  In this paper, we offer a critical reading of recent debates about biometric
fairness and show its limitations drawing on research in fairness in machine
learning and critical border studies. Building on previous fairness
demonstrations, we prove that biometric fairness criteria are mathematically
mutually exclusive. Then, the paper moves on illustrating empirically that a
fair biometric system is not possible by reproducing experiments from previous
works. Finally, we discuss the politics of fairness in biometrics by situating
the debate at the border. We claim that bias and error rates have different
impact on citizens and asylum seekers. Fairness has overshadowed the elephant
in the room of biometrics, focusing on the demographic biases and ethical
discourses of algorithms rather than examine how these systems reproduce
historical and political injustices.

Neural image coding represents now the state-of-the-art image compression
approach. However, a lot of work is still to be done in the video domain. In
this work, we propose an end-to-end learned video codec that introduces several
architectural novelties as well as training novelties, revolving around the
concepts of adaptation and attention. Our codec is organized as an intra-frame
codec paired with an inter-frame codec. As one architectural novelty, we
propose to train the inter-frame codec model to adapt the motion estimation
process based on the resolution of the input video. A second architectural
novelty is a new neural block that combines concepts from split-attention based
neural networks and from DenseNets. Finally, we propose to overfit a set of
decoder-side multiplicative parameters at inference time. Through ablation
studies and comparisons to prior art, we show the benefits of our proposed
techniques in terms of coding gains. We compare our codec to VVC/H.266 and
RLVC, which represent the state-of-the-art traditional and end-to-end learned
codecs, respectively, and to the top performing end-to-end learned approach in
2021 CLIC competition, E2E_T_OL. Our codec clearly outperforms E2E_T_OL, and
compare favorably to VVC and RLVC in some settings.

Contrastive learning has been the dominant approach to training dense
retrieval models. In this work, we investigate the impact of ranking context -
an often overlooked aspect of learning dense retrieval models. In particular,
we examine the effect of its constituent parts: jointly scoring a large number
of negatives per query, using retrieved (query-specific) instead of random
negatives, and a fully list-wise loss. To incorporate these factors into
training, we introduce Contextual Document Embedding Reranking (CODER), a
highly efficient retrieval framework. When reranking, it incurs only a
negligible computational overhead on top of a first-stage method at run time
(delay per query in the order of milliseconds), allowing it to be easily
combined with any state-of-the-art dual encoder method. After fine-tuning
through CODER, which is a lightweight and fast process, models can also be used
as stand-alone retrievers. Evaluating CODER in a large set of experiments on
the MS~MARCO and TripClick collections, we show that the contextual reranking
of precomputed document embeddings leads to a significant improvement in
retrieval performance. This improvement becomes even more pronounced when more
relevance information per query is available, shown in the TripClick
collection, where we establish new state-of-the-art results by a large margin.

Graph neural networks (GNNs) and message passing neural networks (MPNNs) have
been proven to be expressive for subgraph structures in many applications. Some
applications in heterogeneous graphs require explicit edge modeling, such as
subgraph isomorphism counting and matching. However, existing message passing
mechanisms are not designed well in theory. In this paper, we start from a
particular edge-to-vertex transform and exploit the isomorphism property in the
edge-to-vertex dual graphs. We prove that searching isomorphisms on the
original graph is equivalent to searching on its dual graph. Based on this
observation, we propose dual message passing neural networks (DMPNNs) to
enhance the substructure representation learning in an asynchronous way for
subgraph isomorphism counting and matching as well as unsupervised node
classification. Extensive experiments demonstrate the robust performance of
DMPNNs by combining both node and edge representation learning in synthetic and
real heterogeneous graphs. Code is available at
https://github.com/HKUST-KnowComp/DualMessagePassing.

This paper presents the baseline method proposed for the Sports Video task
part of the MediaEval 2021 benchmark. This task proposes a stroke detection and
a stroke classification subtasks. This baseline addresses both subtasks. The
spatio-temporal CNN architecture and the training process of the model are
tailored according to the addressed subtask. The method has the purpose of
helping the participants to solve the task and is not meant to reach
stateof-the-art performance. Still, for the detection task, the baseline is
performing better than the other participants, which stresses the difficulty of
such a task.

This paper presents a table tennis stroke detection method from videos. The
method relies on a two-stream Convolutional Neural Network processing in
parallel the RGB Stream and its computed optical flow. The method has been
developed as part of the MediaEval 2021 benchmark for the Sport task. Our
contribution did not outperform the provided baseline on the test set but has
performed the best among the other participants with regard to the mAP metric.

Sports video analysis is a prevalent research topic due to the variety of
application areas, ranging from multimedia intelligent devices with
user-tailored digests up to analysis of athletes' performance. The Sports Video
task is part of the MediaEval 2021 benchmark. This task tackles fine-grained
action detection and classification from videos. The focus is on recordings of
table tennis games. Running since 2019, the task has offered a classification
challenge from untrimmed video recorded in natural conditions with known
temporal boundaries for each stroke. This year, the dataset is extended and
offers, in addition, a detection challenge from untrimmed videos without
annotations. This work aims at creating tools for sports coaches and players in
order to analyze sports performance. Movement analysis and player profiling may
be built upon such technology to enrich the training experience of athletes and
improve their performance.

We study the problem of distributed training of neural networks (NNs) on
devices with heterogeneous, limited, and time-varying availability of
computational resources. We present an adaptive, resource-aware, on-device
learning mechanism, DISTREAL, which is able to fully and efficiently utilize
the available resources on devices in a distributed manner, increasing the
convergence speed. This is achieved with a dropout mechanism that dynamically
adjusts the computational complexity of training an NN by randomly dropping
filters of convolutional layers of the model. Our main contribution is the
introduction of a design space exploration (DSE) technique, which finds
Pareto-optimal per-layer dropout vectors with respect to resource requirements
and convergence speed of the training. Applying this technique, each device is
able to dynamically select the dropout vector that fits its available resource
without requiring any assistance from the server. We implement our solution in
a federated learning (FL) system, where the availability of computational
resources varies both between devices and over time, and show through extensive
evaluation that we are able to significantly increase the convergence speed
over the state of the art without compromising on the final accuracy.

Adhesive joints are increasingly used in industry for a wide variety of
applications because of their favorable characteristics such as high
strength-to-weight ratio, design flexibility, limited stress concentrations,
planar force transfer, good damage tolerance and fatigue resistance. Finding
the optimal process parameters for an adhesive bonding process is challenging:
the optimization is inherently multi-objective (aiming to maximize break
strength while minimizing cost) and constrained (the process should not result
in any visual damage to the materials, and stress tests should not result in
failures that are adhesion-related). Real life physical experiments in the lab
are expensive to perform; traditional evolutionary approaches (such as genetic
algorithms) are then ill-suited to solve the problem, due to the prohibitive
amount of experiments required for evaluation. In this research, we
successfully applied specific machine learning techniques (Gaussian Process
Regression and Logistic Regression) to emulate the objective and constraint
functions based on a \emph{limited} amount of experimental data. The techniques
are embedded in a Bayesian optimization algorithm, which succeeds in detecting
Pareto-optimal process settings in a highly efficient way (i.e., requiring a
limited number of extra experiments).

Pattern discovery in multidimensional data sets has been the subject of
research for decades. There exists a wide spectrum of clustering algorithms
that can be used for this purpose. However, their practical applications share
a common post-clustering phase, which concerns expert-based interpretation and
analysis of the obtained results. We argue that this can be the bottleneck in
the process, especially in cases where domain knowledge exists prior to
clustering. Such a situation requires not only a proper analysis of
automatically discovered clusters but also conformance checking with existing
knowledge. In this work, we present Knowledge Augmented Clustering (KnAC). Its
main goal is to confront expert-based labelling with automated clustering for
the sake of updating and refining the former. Our solution is not restricted to
any existing clustering algorithm. Instead, KnAC can serve as an augmentation
of an arbitrary clustering algorithm, making the approach robust and a
model-agnostic improvement of any state-of-the-art clustering method. We
demonstrate the feasibility of our method on artificially, reproducible
examples and in a real life use case scenario. In both cases, we achieved
better results than classic clustering algorithms without augmentation.

The ubiquity of fluids in the physical world explains the need to accurately
simulate their dynamics for many scientific and engineering applications.
Traditionally, well established but resource intensive CFD solvers provide such
simulations. The recent years have seen a surge of deep learning surrogate
models substituting these solvers to alleviate the simulation process. Some
approaches to build data-driven surrogates mimic the solver iterative process.
They infer the next state of the fluid given its previous one. Others directly
infer the state from time input. Approaches also differ in their management of
the spatial information. Graph Neural Networks (GNN) can address the
specificity of the irregular meshes commonly used in CFD simulations. In this
article, we present our ongoing work to design a novel direct time GNN
architecture for irregular meshes. It consists of a succession of graphs of
increasing size connected by spline convolutions. We test our architecture on
the Von K{\'a}rm{\'a}n's vortex street benchmark. It achieves small
generalization errors while mitigating error accumulation along the trajectory.

The field of natural language processing (NLP) has recently seen a large
change towards using pre-trained language models for solving almost any task.
Despite showing great improvements in benchmark datasets for various tasks,
these models often perform sub-optimal in non-standard domains like the
clinical domain where a large gap between pre-training documents and target
documents is observed. In this paper, we aim at closing this gap with
domain-specific training of the language model and we investigate its effect on
a diverse set of downstream tasks and settings. We introduce the pre-trained
CLIN-X (Clinical XLM-R) language models and show how CLIN-X outperforms other
pre-trained transformer models by a large margin for ten clinical concept
extraction tasks from two languages. In addition, we demonstrate how the
transformer model can be further improved with our proposed task- and
language-agnostic model architecture based on ensembles over random splits and
cross-sentence context. Our studies in low-resource and transfer settings
reveal stable model performance despite a lack of annotated data with
improvements of up to 47 F1 points when only 250 labeled sentences are
available. Our results highlight the importance of specialized language models
as CLIN-X for concept extraction in non-standard domains, but also show that
our task-agnostic model architecture is robust across the tested tasks and
languages so that domain- or task-specific adaptations are not required.

Data augmentation has been an important ingredient for boosting performances
of learned models. Prior data augmentation methods for few-shot text
classification have led to great performance boosts. However, they have not
been designed to capture the intricate compositional structure of natural
language. As a result, they fail to generate samples with plausible and diverse
sentence structures. Motivated by this, we present the data Augmentation using
Lexicalized Probabilistic context-free grammars (ALP) that generates augmented
samples with diverse syntactic structures with plausible grammar. The
lexicalized PCFG parse trees consider both the constituents and dependencies to
produce a syntactic frame that maximizes a variety of word choices in a
syntactically preservable manner without specific domain experts. Experiments
on few-shot text classification tasks demonstrate that ALP enhances many
state-of-the-art classification methods. As a second contribution, we delve
into the train-val splitting methodologies when a data augmentation method
comes into play. We argue empirically that the traditional splitting of
training and validation sets is sub-optimal compared to our novel
augmentation-based splitting strategies that further expand the training split
with the same number of labeled data. Taken together, our contributions on the
data augmentation strategies yield a strong training recipe for few-shot text

Several recent works have been dedicated to unsupervised reinforcement
learning in a single environment, in which a policy is first pre-trained with
unsupervised interactions, and then fine-tuned towards the optimal policy for
several downstream supervised tasks defined over the same environment. Along
this line, we address the problem of unsupervised reinforcement learning in a
class of multiple environments, in which the policy is pre-trained with
interactions from the whole class, and then fine-tuned for several tasks in any
environment of the class. Notably, the problem is inherently multi-objective as
we can trade off the pre-training objective between environments in many ways.
In this work, we foster an exploration strategy that is sensitive to the most
adverse cases within the class. Hence, we cast the exploration problem as the
maximization of the mean of a critical percentile of the state visitation
entropy induced by the exploration strategy over the class of environments.
Then, we present a policy gradient algorithm, $\alpha$MEPOL, to optimize the
introduced objective through mediated interactions with the class. Finally, we
empirically demonstrate the ability of the algorithm in learning to explore
challenging classes of continuous environments and we show that reinforcement
learning greatly benefits from the pre-trained exploration strategy w.r.t.
learning from scratch.

Recent research has achieved impressive progress in the session-based
recommendation. However, information such as item knowledge and click time
interval, which could be potentially utilized to improve the performance,
remains largely unexploited. In this paper, we propose a framework called
Knowledge-enhanced Session-based Recommendation with Temporal Transformer
(KSTT) to incorporate such information when learning the item and session
embeddings. Specifically, a knowledge graph, which models contexts among items
within a session and their corresponding attributes, is proposed to obtain item
embeddings through graph representation learning. We introduce time interval
embedding to represent the time pattern between the item that needs to be
predicted and historical click, and use it to replace the position embedding in
the original transformer (called temporal transformer). The item embeddings in
a session are passed through the temporal transformer network to get the
session embedding, based on which the final recommendation is made. Extensive
experiments demonstrate that our model outperforms state-of-the-art baselines
on four benchmark datasets.

We describe a novel decision-making problem developed in response to the
demands of retail electronic commerce (e-commerce). While working with
logistics and retail industry business collaborators, we found that the cost of
delivery of products from the most opportune node in the supply chain (a
quantity called the cost-to-serve or CTS) is a key challenge. The large scale,
high stochasticity, and large geographical spread of e-commerce supply chains
make this setting ideal for a carefully designed data-driven decision-making
algorithm. In this preliminary work, we focus on the specific subproblem of
delivering multiple products in arbitrary quantities from any warehouse to
multiple customers in each time period. We compare the relative performance and
computational efficiency of several baselines, including heuristics and
mixed-integer linear programming. We show that a reinforcement learning based
algorithm is competitive with these policies, with the potential of efficient
scale-up in the real world.

Self-supervised learning on graphs has recently drawn a lot of attention due
to its independence from labels and its robustness in representation. Current
studies on this topic mainly use static information such as graph structures
but cannot well capture dynamic information such as timestamps of edges.
Realistic graphs are often dynamic, which means the interaction between nodes
occurs at a specific time. This paper proposes a self-supervised dynamic graph
representation learning framework (DySubC), which defines a temporal subgraph
contrastive learning task to simultaneously learn the structural and
evolutional features of a dynamic graph. Specifically, a novel temporal
subgraph sampling strategy is firstly proposed, which takes each node of the
dynamic graph as the central node and uses both neighborhood structures and
edge timestamps to sample the corresponding temporal subgraph. The subgraph
representation function is then designed according to the influence of
neighborhood nodes on the central node after encoding the nodes in each
subgraph. Finally, the structural and temporal contrastive loss are defined to
maximize the mutual information between node representation and temporal
subgraph representation. Experiments on five real-world datasets demonstrate
that (1) DySubC performs better than the related baselines including two graph
contrastive learning models and four dynamic graph representation learning
models in the downstream link prediction task, and (2) the use of temporal
information can not only sample more effective subgraphs, but also learn better
representation by temporal contrastive loss.

We propose a cross-modal attention distillation framework to train a
dual-encoder model for vision-language understanding tasks, such as visual
reasoning and visual question answering. Dual-encoder models have a faster
inference speed than fusion-encoder models and enable the pre-computation of
images and text during inference. However, the shallow interaction module used
in dual-encoder models is insufficient to handle complex vision-language
understanding tasks. In order to learn deep interactions of images and text, we
introduce cross-modal attention distillation, which uses the image-to-text and
text-to-image attention distributions of a fusion-encoder model to guide the
training of our dual-encoder model. In addition, we show that applying the
cross-modal attention distillation for both pre-training and fine-tuning stages
achieves further improvements. Experimental results demonstrate that the
distilled dual-encoder model achieves competitive performance for visual
reasoning, visual entailment and visual question answering tasks while enjoying
a much faster inference speed than fusion-encoder models. Our code and models
will be publicly available at https://github.com/kugwzk/Distilled-DualEncoder.

Automatic Speech Recognition (ASR) systems have found their use in numerous
industrial applications in very diverse domains creating a need to adapt to new
domains with small memory and deployment overhead. In this work, we introduce
domain-prompts, a methodology that involves training a small number of domain
embedding parameters to prime a Transformer-based Language Model (LM) to a
particular domain. Using this domain-adapted LM for rescoring ASR hypotheses
can achieve 7-13% WER reduction for a new domain with just 1000 unlabeled
textual domain-specific sentences. This improvement is comparable or even
better than fully fine-tuned models even though just 0.02% of the parameters of
the base LM are updated. Additionally, our method is deployment-friendly as the
learnt domain embeddings are prefixed to the input to the model rather than
changing the base model architecture. Therefore, our method is an ideal choice
for on-the-fly adaptation of LMs used in ASR systems to progressively scale it
to new domains.

Sequential recommendation based on multi-interest framework models the user's
recent interaction sequence into multiple different interest vectors, since a
single low-dimensional vector cannot fully represent the diversity of user
interests. However, most existing models only intercept users' recent
interaction behaviors as training data, discarding a large amount of historical
interaction sequences. This may raise two issues. On the one hand, data
reflecting multiple interests of users is missing; on the other hand, the
co-occurrence between items in historical user-item interactions is not fully
explored. To tackle the two issues, this paper proposes a novel sequential
recommendation model called "Global Interaction Aware Multi-Interest Framework
for Sequential Recommendation (GIMIRec)". Specifically, a global context
extraction module is firstly proposed without introducing any external
information, which calculates a weighted co-occurrence matrix based on the
constrained co-occurrence number of each item pair and their time interval from
the historical interaction sequences of all users and then obtains the global
context embedding of each item by using a simplified graph convolution.
Secondly, the time interval of each item pair in the recent interaction
sequence of each user is captured and combined with the global context item
embedding to get the personalized item embedding. Finally, a self-attention
based multi-interest framework is applied to learn the diverse interests of
users for sequential recommendation. Extensive experiments on the three
real-world datasets of Amazon-Books, Taobao-Buy and Amazon-Hybrid show that the
performance of GIMIRec on the Recall, NDCG and Hit Rate indicators is
significantly superior to that of the state-of-the-art methods. Moreover, the
proposed global context extraction module can be easily transplanted to most
sequential recommendation models.

Sales forecasting is the prerequisite for a lot of managerial decisions such
as production planning, material resource planning and budgeting in the supply
chain. Promotions are one of the most important business strategies that are
often used to boost sales. While promotions are attractive for generating
demand, it is often difficult to forecast demand in their presence. In the past
few decades, several quantitative models have been developed to forecast sales
including statistical and machine learning models. However, these methods may
not be adequate to account for all the internal and external factors that may
impact sales. As a result, qualitative models have been adopted along with
quantitative methods as consulting experts has been proven to improve forecast
accuracy by providing contextual information. Such models are being used
extensively to account for factors that can lead to a rapid change in sales,
such as during promotions. In this paper, we aim to use Bayesian Networks to
forecast promotional sales where a combination of factors such as price, type
of promotions, and product location impacts sales. We choose to develop a BN
model because BN models essentially have the capability to combine various
qualitative and quantitative factors with causal forms, making it an attractive
tool for sales forecasting during promotions. This can be used to adjust a
company's promotional strategy in the context of this case study. We gather
sales data for a particular product from a retailer that sells products in
Australia. We develop a Bayesian Network for this product and validate our
results by empirical analysis. This paper confirms that BNs can be effectively
used to forecast sales, especially during promotions. In the end, we provide
some research avenues for using BNs in forecasting sales.

In this paper, we study the problem of networked multi-agent reinforcement
learning (MARL), where a number of agents are deployed as a partially connected
network and each interacts only with nearby agents. Networked MARL requires all
agents to make decisions in a decentralized manner to optimize a global
objective with restricted communication between neighbors over the network.
Inspired by the fact that sharing plays a key role in human's learning of
cooperation, we propose LToS, a hierarchically decentralized MARL framework
that enables agents to learn to dynamically share reward with neighbors so as
to encourage agents to cooperate on the global objective through collectives.
For each agent, the high-level policy learns how to share reward with neighbors
to decompose the global objective, while the low-level policy learns to
optimize the local objective induced by the high-level policies in the
neighborhood. The two policies form a bi-level optimization and learn
alternately. We empirically demonstrate that LToS outperforms existing methods
in both social dilemma and networked MARL scenarios across scales.

We present a self-supervised pre-training approach for learning rich visual
language representations for both handwritten and printed historical document
transcription. After supervised fine-tuning of our pre-trained encoder
representations for low-resource document transcription on two languages, (1) a
heterogeneous set of handwritten Islamicate manuscript images and (2) early
modern English printed documents, we show a meaningful improvement in
recognition accuracy over the same supervised model trained from scratch with
as few as 30 line image transcriptions for training. Our masked language
model-style pre-training strategy, where the model is trained to be able to
identify the true masked visual representation from distractors sampled from
within the same line, encourages learning robust contextualized language
representations invariant to scribal writing style and printing noise present
across documents.

The accuracy of galaxy photometric redshift (photo-$z$) can significantly
affect the analysis of weak gravitational lensing measurements, especially for
future high-precision surveys. In this work, we try to extract photo-$z$
information from both galaxy flux and image data expected to be obtained by
China Space Station Telescope (CSST) using neural networks. We generate mock
galaxy images based on the observational images from the Advanced Camera for
Surveys of Hubble Space Telescope (HST-ACS) and COSMOS catalogs, considering
the CSST instrumental effects. Galaxy flux data are then measured directly from
these images by aperture photometry. The Multi-Layer Perceptron (MLP) and
Convolutional Neural Network (CNN) are constructed to predict photo-$z$ from
fluxes and images, respectively. We also propose to use an efficient hybrid
network, which combines MLP and CNN, by employing transfer learning techniques
to investigate the improvement of the result with both flux and image data
included. We find that the photo-$z$ accuracy and outlier fraction can achieve
$\sigma_{\rm NMAD} = 0.023$ and $\eta = 1.43\%$ for the MLP using flux data
only, and $\sigma_{\rm NMAD} = 0.025$ and $\eta = 1.21\%$ for the CNN using
image data only. The result can be further improved in high efficiency as
$\sigma_{\rm NMAD} = 0.020$ and $\eta = 0.90\%$ for the hybrid transfer
network. These approaches result in similar galaxy median and mean redshifts
~0.8 and 0.9, respectively, for the redshift range from 0 to 4. This indicates
that our networks can effectively and properly extract photo-$z$ information
from the CSST galaxy flux and image data.

Retrieval-augmented generation models have shown state-of-the-art performance
across many knowledge-intensive NLP tasks such as open question answering and
fact verification. These models are trained to generate the final output given
the retrieved passages, which can be irrelevant to the original query, leading
to learning spurious cues or answer memorization. This work introduces a method
to incorporate the evidentiality of passages -- whether a passage contains
correct evidence to support the output -- into training the generator. We
introduce a multi-task learning framework to jointly generate the final output
and predict the evidentiality of each passage, leveraging a new task-agnostic
method to obtain silver evidentiality labels for supervision. Our experiments
on five datasets across three knowledge-intensive tasks show that our new
evidentiality-guided generator significantly outperforms its direct counterpart
with the same-size model and advances the state of the art on FaVIQ-Ambig. We
attribute these improvements to both the auxiliary multi-task learning and
silver evidentiality mining techniques.

Domain generalizable (DG) person re-identification (ReID) aims to test across
unseen domains without access to the target domain data at training time, which
is a realistic but challenging problem. In contrast to methods assuming an
identical model for different domains, Mixture of Experts (MoE) exploits
multiple domain-specific networks for leveraging complementary information
between domains, obtaining impressive results. However, prior MoE-based DG ReID
methods suffer from a large model size with the increase of the number of
source domains, and most of them overlook the exploitation of domain-invariant
characteristics. To handle the two issues above, this paper presents a new
approach called Mimic Embedding via adapTive Aggregation (META) for DG person
ReID. To avoid the large model size, experts in META do not adopt a branch
network for each source domain but share all the parameters except for the
batch normalization layers. Besides multiple experts, META leverages Instance
Normalization (IN) and introduces it into a global branch to pursue invariant
features across domains. Meanwhile, META considers the relevance of an unseen
target sample and source domains via normalization statistics and develops an
aggregation module to adaptively integrate multiple experts for mimicking
unseen target domain. Benefiting from a proposed consistency loss and an
episodic training algorithm, META is expected to mimic embedding for a truly
unseen target domain. Extensive experiments verify that META surpasses
state-of-the-art DG person ReID methods by a large margin. Our code is
available at https://github.com/xbq1994/META.

Automatic dubbing (AD) is among the machine translation (MT) use cases where
translations should match a given length to allow for synchronicity between
source and target speech. For neural MT, generating translations of length
close to the source length (e.g. within +-10% in character count), while
preserving quality is a challenging task. Controlling MT output length comes at
a cost to translation quality, which is usually mitigated with a two step
approach of generating N-best hypotheses and then re-ranking based on length
and quality. This work introduces a self-learning approach that allows a
transformer model to directly learn to generate outputs that closely match the
source length, in short Isometric MT. In particular, our approach does not
require to generate multiple hypotheses nor any auxiliary ranking function. We
report results on four language pairs (English - French, Italian, German,
Spanish) with a publicly available benchmark. Automatic and manual evaluations
show that our method for Isometric MT outperforms more complex approaches
proposed in the literature.

Contrastive learning (CL) recently has spurred a fruitful line of research in
the field of recommendation, since its ability to extract self-supervised
signals from the raw data is well-aligned with recommender systems' needs for
tackling the data sparsity issue. A typical pipeline of CL-based recommendation
models is first augmenting the user-item bipartite graph with structure
perturbations, and then maximizing the node representation consistency between
different graph augmentations. Although this paradigm turns out to be
effective, what underlies the performance gains is still a mystery. In this
paper, we first experimentally disclose that, in CL-based recommendation
models, CL operates by learning more evenly distributed user/item
representations that can implicitly mitigate the popularity bias. Meanwhile, we
reveal that the graph augmentations, which were considered necessary, just play
a trivial role. Based on this finding, we propose a simple CL method which
discards the graph augmentations and instead adds uniform noises to the
embedding space for creating contrastive views. A comprehensive experimental
study on three benchmark datasets demonstrates that, though it appears
strikingly simple, the proposed method can smoothly adjust the uniformity of
learned representations and has distinct advantages over its graph
augmentation-based counterparts in terms of recommendation accuracy and
training efficiency. The code is released at https://github.com/Coder-Yu/QRec.

This work presents a novel physics-informed deep learning based
super-resolution framework to reconstruct high-resolution deformation fields
from low-resolution counterparts, obtained from coarse mesh simulations or
experiments. We leverage the governing equations and boundary conditions of the
physical system to train the model without using any high-resolution labeled
data. The proposed approach is applied to obtain the super-resolved deformation
fields from the low-resolution stress and displacement fields obtained by
running simulations on a coarse mesh for a body undergoing linear elastic
deformation. We demonstrate that the super-resolved fields match the accuracy
of an advanced numerical solver running at 400 times the coarse mesh
resolution, while simultaneously satisfying the governing laws. A brief
evaluation study comparing the performance of two deep learning based
super-resolution architectures is also presented.

Rolling bearings are one of the most widely used bearings in industrial
machines. Deterioration in the condition of rolling bearings can result in the
total failure of rotating machinery. AI-based methods are widely applied in the
diagnosis of rolling bearings. Hybrid NN-based methods have been shown to
achieve the best diagnosis results. Typically, raw data is generated from
accelerometers mounted on the machine housing. However, the diagnostic utility
of each signal is highly dependent on the location of the corresponding
accelerometer. This paper proposes a novel hybrid CNN-MLP model-based
diagnostic method which combines mixed input to perform rolling bearing
diagnostics. The method successfully detects and localizes bearing defects
using acceleration data from a shaft-mounted wireless acceleration sensor. The
experimental results show that the hybrid model is superior to the CNN and MLP
models operating separately, and can deliver a high detection accuracy of 99,6%
for the bearing faults compared to 98% for CNN and 81% for MLP models.

Noisy channel models have been especially effective in neural machine
translation (NMT). However, recent approaches like "beam search and rerank"
(BSR) incur significant computation overhead during inference, making
real-world application infeasible. We aim to study if it is possible to build
an amortized noisy channel NMT model such that when we do greedy decoding
during inference, the translation accuracy matches that of BSR in terms of
reward (based on the source-to-target log probability and the target-to-source
log probability) and quality (based on BLEU and BLEURT). We attempt three
approaches to train the new model: knowledge distillation, one-step-deviation
imitation learning, and Q learning. The first approach obtains the noisy
channel signal from a pseudo-corpus, and the latter two approaches aim to
optimize toward a noisy-channel MT reward directly. For all three approaches,
the generated translations fail to achieve rewards comparable to BSR, but the
translation quality approximated by BLEU and BLEURT is similar to the quality
of BSR-produced translations. Additionally, all three approaches speed up
inference by 1-2 orders of magnitude.

Large language models (LMs), while powerful, are not immune to mistakes, but
can be difficult to retrain. Our goal is for an LM to continue to improve after
deployment, without retraining, using feedback from the user. Our approach
pairs an LM with (i) a growing memory of cases where the user identified an
output error and provided general feedback on how to correct it (ii) a
corrector model, trained to translate this general feedback into specific edits
to repair the model output. Given a new, unseen input, our model can then use
feedback from similar, past cases to repair output errors that may occur. We
instantiate our approach using an existing, fixed model for script generation,
that takes a goal (e.g., "bake a cake") and generates a partially ordered
sequence of actions to achieve that goal, sometimes containing errors. Our
memory-enhanced system, FBNet, learns to apply user feedback to repair such
errors (up to 30 points improvement), while making a start at avoiding similar
past mistakes on new, unseen examples (up to 7 points improvement in a
controlled setting). This is a first step towards strengthening deployed
models, potentially broadening their utility. Our code and data is available at
https://github.com/allenai/interscript/.

In this paper we consider the problem of unsupervised anomaly segmentation in
medical images, which has attracted increasing attention in recent years due to
the expensive pixel-level annotations from experts and the existence of a large
amount of unannotated normal and abnormal image scans. We introduce a
segmentation network that utilizes adversarial learning to partition an image
into two cuts, with one of them falling into a reference distribution provided
by the user. This Adversarial-based Selective Cutting network (ASC-Net) bridges
the two domains of cluster-based deep segmentation and adversarial-based
anomaly/novelty detection algorithms. Our ASC-Net learns from normal and
abnormal medical scans to segment anomalies in medical scans without any masks
for supervision. We evaluate this unsupervised anomly segmentation model on
three public datasets, i.e., BraTS 2019 for brain tumor segmentation, LiTS for
liver lesion segmentation, and MS-SEG 2015 for brain lesion segmentation, and
also on a private dataset for brain tumor segmentation. Compared to existing
methods, our model demonstrates tremendous performance gains in unsupervised
anomaly segmentation tasks. Although there is still room to further improve
performance compared to supervised learning algorithms, the promising
experimental results and interesting observations shed light on building an
unsupervised learning algorithm for medical anomaly identification using
user-defined knowledge.

The mainstream paradigm behind continual learning has been to adapt the model
parameters to non-stationary data distributions, where catastrophic forgetting
is the central challenge. Typical methods rely on a rehearsal buffer or known
task identity at test time to retrieve learned knowledge and address
forgetting, while this work presents a new paradigm for continual learning that
aims to train a more succinct memory system without accessing task identity at
test time. Our method learns to dynamically prompt (L2P) a pre-trained model to
learn tasks sequentially under different task transitions. In our proposed
framework, prompts are small learnable parameters, which are maintained in a
memory space. The objective is to optimize prompts to instruct the model
prediction and explicitly manage task-invariant and task-specific knowledge
while maintaining model plasticity. We conduct comprehensive experiments under
popular image classification benchmarks with different challenging continual
learning settings, where L2P consistently outperforms prior state-of-the-art
methods. Surprisingly, L2P achieves competitive results against rehearsal-based
methods even without a rehearsal buffer and is directly applicable to
challenging task-agnostic continual learning. Source code is available at
https://github.com/google-research/l2p.

Explanation of an AI agent requires knowledge of its design and operation. An
open question is how to identify, access and use this design knowledge for
generating explanations. Many AI agents used in practice, such as intelligent
tutoring systems fielded in educational contexts, typically come with a User
Guide that explains what the agent does, how it works and how to use the agent.
However, few humans actually read the User Guide in detail. Instead, most users
seek answers to their questions on demand. In this paper, we describe a
question answering agent (AskJill) that uses the User Guide for an interactive
learning environment (VERA) to automatically answer questions and thereby
explains the domain, functioning, and operation of VERA. We present a
preliminary assessment of AskJill in VERA.

The eXtreme Multi-label text Classification (XMC) problem concerns finding
most relevant labels for an input text instance from a large label set.
However, the XMC setup faces two challenges: (1) it is not generalizable to
predict unseen labels in dynamic environments, and (2) it requires a large
amount of supervised (instance, label) pairs, which can be difficult to obtain
for emerging domains. Recently, the generalized zero-shot XMC (GZ-XMC) setup
has been studied and ZestXML is proposed accordingly to handle the unseen
labels, which still requires a large number of annotated (instance, label)
pairs. In this paper, we consider a more practical scenario called Extreme
Zero-Shot XMC (EZ-XMC), in which no supervision is needed and merely raw text
of instances and labels are accessible. Few-Shot XMC (FS-XMC), an extension to
EZ-XMC with limited supervision is also investigated. To learn the semantic
embeddings of instances and labels with raw text, we propose to pre-train
Transformer-based encoders with self-supervised contrastive losses.
Specifically, we develop a pre-training method MACLR, which thoroughly
leverages the raw text with techniques including Multi-scale Adaptive
Clustering, Label Regularization, and self-training with pseudo positive pairs.
Experimental results on four public EZ-XMC datasets demonstrate that MACLR
achieves superior performance compared to all other leading baseline methods,
in particular with approximately 5-10% improvement in precision and recall on
average. Moreover, we also show that our pre-trained encoder can be further
improved on FS-XMC when there are a limited number of ground-truth positive
pairs in training. By fine-tuning the encoder on such a few-shot subset, MACLR
still outperforms other extreme classifiers significantly.

Monumental advances in deep learning have led to unprecedented achievements
across various domains. While the performance of deep neural networks is
indubitable, the architectural design and interpretability of such models are
nontrivial. Research has been introduced to automate the design of neural
network architectures through neural architecture search (NAS). Recent progress
has made these methods more pragmatic by exploiting distributed computation and
novel optimization algorithms. However, there is little work in optimizing
architectures for interpretability. To this end, we propose a multi-objective
distributed NAS framework that optimizes for both task performance and
"introspectability," a surrogate metric for aspects of interpretability. We
leverage the non-dominated sorting genetic algorithm (NSGA-II) and explainable
AI (XAI) techniques to reward architectures that can be better comprehended by
domain experts. The framework is evaluated on several image classification
datasets. We demonstrate that jointly optimizing for task error and
introspectability leads to more disentangled and debuggable architectures that
perform within tolerable error.

X-ray micro-computed tomography (micro-CT) has been widely leveraged to
characterise pore-scale geometry in subsurface porous rock. Recent developments
in super resolution (SR) methods using deep learning allow the digital
enhancement of low resolution (LR) images over large spatial scales, creating
SR images comparable to the high resolution (HR) ground truth. This circumvents
traditional resolution and field-of-view trade-offs. An outstanding issue is
the use of paired (registered) LR and HR data, which is often required in the
training step of such methods but is difficult to obtain. In this work, we
rigorously compare two different state-of-the-art SR deep learning techniques,
using both paired and unpaired data, with like-for-like ground truth data. The
first approach requires paired images to train a convolutional neural network
(CNN) while the second approach uses unpaired images to train a generative
adversarial network (GAN). The two approaches are compared using a micro-CT
carbonate rock sample with complicated micro-porous textures. We implemented
various image based and numerical verifications and experimental validation to
quantitatively evaluate the physical accuracy and sensitivities of the two
methods. Our quantitative results show that unpaired GAN approach can
reconstruct super-resolution images as precise as paired CNN method, with
comparable training times and dataset requirement. This unlocks new
applications for micro-CT image enhancement using unpaired deep learning
methods; image registration is no longer needed during the data processing
stage. Decoupled images from data storage platforms can be exploited more
efficiently to train networks for SR digital rock applications. This opens up a
new pathway for various applications of multi-scale flow simulation in
heterogeneous porous media.

Zero-shot learning (ZSL) tackles the novel class recognition problem by
transferring semantic knowledge from seen classes to unseen ones. Existing
attention-based models have struggled to learn inferior region features in a
single image by solely using unidirectional attention, which ignore the
transferability and discriminative attribute localization of visual features.
In this paper, we propose a cross attribute-guided Transformer network, termed
TransZero++, to refine visual features and learn accurate attribute
localization for semantic-augmented visual embedding representations in ZSL.
TransZero++ consists of an attribute$\rightarrow$visual Transformer sub-net
(AVT) and a visual$\rightarrow$attribute Transformer sub-net (VAT).
Specifically, AVT first takes a feature augmentation encoder to alleviate the
cross-dataset problem, and improves the transferability of visual features by
reducing the entangled relative geometry relationships among region features.
Then, an attribute$\rightarrow$visual decoder is employed to localize the image
regions most relevant to each attribute in a given image for attribute-based
visual feature representations. Analogously, VAT uses the similar feature
augmentation encoder to refine the visual features, which are further applied
in visual$\rightarrow$attribute decoder to learn visual-based attribute
features. By further introducing semantical collaborative losses, the two
attribute-guided transformers teach each other to learn semantic-augmented
visual embeddings via semantical collaborative learning. Extensive experiments
show that TransZero++ achieves the new state-of-the-art results on three
challenging ZSL benchmarks. The codes are available at:
\url{https://github.com/shiming-chen/TransZero_pp}.

Structure from motion (SFM) and ground plane homography estimation are
critical to autonomous driving and other robotics applications. Recently, much
progress has been made in using deep neural networks for SFM and homography
estimation respectively. However, directly applying existing methods for ground
plane homography estimation may fail because the road is often a small part of
the scene. Besides, the performances of deep SFM approaches are still inferior
to traditional methods. In this paper, we propose a method that learns to solve
both problems in an end-to-end manner, improving performance on both. The
proposed networks consist of a Depth-CNN, a Pose-CNN and a Ground-CNN. The
Depth-CNN and Pose-CNN estimate dense depth map and ego-motion respectively,
solving SFM, while the Pose-CNN and Ground-CNN followed by a homography layer
solve the ground plane estimation problem. By enforcing coherency between SFM
and homography estimation results, the whole network can be trained end to end
using photometric loss and homography loss without any groundtruth except the
road segmentation provided by an off-the-shelf segmenter. Comprehensive
experiments are conducted on KITTI benchmark to demonstrate promising results
compared with various state-of-the-art approaches.

In-context learning is a recent paradigm in natural language understanding,
where a large pre-trained language model (LM) observes a test instance and a
few training examples as its input, and directly decodes the output without any
update to its parameters. However, performance has been shown to strongly
depend on the selected training examples (termed prompt). In this work, we
propose an efficient method for retrieving prompts for in-context learning
using annotated data and a LM. Given an input-output pair, we estimate the
probability of the output given the input and a candidate training example as
the prompt, and label training examples as positive or negative based on this
probability. We then train an efficient dense retriever from this data, which
is used to retrieve training examples as prompts at test time. We evaluate our
approach on three sequence-to-sequence tasks where language utterances are
mapped to meaning representations, and find that it substantially outperforms
prior work and multiple baselines across the board.

In this paper, we propose a recommendation framework named Cayley-Dickson
Recommender. We introduce Cayley-Dickson construction which uses a recursive
process to define hypercomplex algebras and their mathematical operations. We
also design a graph convolution operator to learn representations in the
hypercomplex space. To the best of our knowledge, it is the first time that
Cayley-Dickson construction and graph convolution techniques have been used in
hypercomplex recommendation. Compared with the state-of-the-art recommendation
methods, our method achieves superior performance on real-world datasets.

Newtonian machine learning (NML) is a wave-equation inversion method that
inverts single-dimensional latent space (LS) features of the seismic data for
retrieving the subsurface background velocity model. The single-dimensional LS
features mainly contain the kinematic information of the seismic data, which
are automatically extracted from the seismic signal by using an autoencoder
network. Because its LS feature dimension is too small to preserve the dynamic
information, such as the waveform variations, of the seismic data. Therefore
the NML inversion is not able to recover the high-wavenumber velocity details.
To mitigate this problem, we propose to invert multi-dimensional LS features,
which can fully represent the entire characters of the seismic data. We denote
this method as multi-dimensional Newtonian machine learning (MNML). In MNML, we
define a new multi-variable connective function that works together with the
multi-variable implicit function theorem to connect the velocity perturbations
to the multi-dimensional LS feature perturbations. Numerical tests show that
(1) the multi-dimensional LS features can preserve more data information than
the single-dimensional LS features; (2) a higher resolution velocity model can
be recovered by inverting the multi-dimensional LS features, and the inversion
quality is comparable to that of FWI; (3) the MNML method requires a much
smaller storage space than conventional FWI because only the low-dimensional
representations of the high-dimensional seismic data are needed to be stored.
The disadvantage of MNML is that it can more easily get stuck in local minima
compared to the NML method. So we suggest a multiscale inversion approach that
inverts for higher dimensional LS features as the iteration count increase.

Despite empirical successes of recurrent neural networks (RNNs) in natural
language processing (NLP), theoretical understanding of RNNs is still limited
due to intrinsically complex non-linear computations. We systematically analyze
RNNs' behaviors in a ubiquitous NLP task, the sentiment analysis of movie
reviews, via the mapping between a class of RNNs called recurrent arithmetic
circuits (RACs) and a matrix product state (MPS). Using the von-Neumann
entanglement entropy (EE) as a proxy for information propagation, we show that
single-layer RACs possess a maximum information propagation capacity, reflected
by the saturation of the EE. Enlarging the bond dimension beyond the EE
saturation threshold does not increase model prediction accuracies, so a
minimal model that best estimates the data statistics can be inferred. Although
the saturated EE is smaller than the maximum EE allowed by the area law, our
minimal model still achieves ~99% training accuracies in realistic sentiment
analysis data sets. Thus, low EE is not a warrant against the adoption of
single-layer RACs for NLP. Contrary to a common belief that long-range
information propagation is the main source of RNNs' successes, we show that
single-layer RACs harness high expressiveness from the subtle interplay between
the information propagation and the word vector embeddings. Our work sheds
light on the phenomenology of learning in RACs, and more generally on the
explainability of RNNs for NLP, using tools from many-body quantum physics.

Hybrid methods have been shown to outperform pure statistical and pure deep
learning methods at forecasting tasks and quantifying the associated
uncertainty with those forecasts (prediction intervals). One example is
Exponential Smoothing Recurrent Neural Network (ES-RNN), a hybrid between a
statistical forecasting model and a recurrent neural network variant. ES-RNN
achieves a 9.4\% improvement in absolute error in the Makridakis-4 Forecasting
Competition. This improvement and similar outperformance from other hybrid
models have primarily been demonstrated only on univariate datasets.
Difficulties with applying hybrid forecast methods to multivariate data include
($i$) the high computational cost involved in hyperparameter tuning for models
that are not parsimonious, ($ii$) challenges associated with auto-correlation
inherent in the data, as well as ($iii$) complex dependency (cross-correlation)
between the covariates that may be hard to capture. This paper presents
Multivariate Exponential Smoothing Long Short Term Memory (MES-LSTM), a
generalized multivariate extension to ES-RNN, that overcomes these challenges.
MES-LSTM utilizes a vectorized implementation. We test MES-LSTM on several
aggregated coronavirus disease of 2019 (COVID-19) morbidity datasets and find
our hybrid approach shows consistent, significant improvement over pure
statistical and deep learning methods at forecast accuracy and prediction
interval construction.

Physical measurements constitute a large portion of numbers in academic
papers, engineering reports, and web tables. Current benchmarks fall short of
properly evaluating numeracy of pretrained language models on measurements,
hindering research on developing new methods and applying them to numerical
tasks. To that end, we introduce a novel task, Masked Measurement Prediction
(MMP), where a model learns to reconstruct a number together with its
associated unit given masked text. MMP is useful for both training new
numerically informed models as well as evaluating numeracy of existing systems.
In order to address this task, we introduce a new Generative Masked Measurement
(GeMM) model that jointly learns to predict numbers along with their units. We
perform fine-grained analyses comparing our model with various ablations and
baselines. We use linear probing of traditional pretrained transformer models
(RoBERTa) to show that they significantly underperform jointly trained
number-unit models, highlighting the difficulty of this new task and the
benefits of our proposed pretraining approach. We hope this framework
accelerates the progress towards building more robust numerical reasoning
systems in the future.

Unscrupulous content creators on YouTube employ deceptive techniques such as
spam and clickbait to reach a broad audience and trick users into clicking on
their videos to increase their advertisement revenue. Clickbait detection on
YouTube requires an in depth examination and analysis of the intricate
relationship between the video content and video descriptors title and
thumbnail. However, the current solutions are mostly centred around the study
of video descriptors and other metadata such as likes, tags, comments, etc and
fail to utilize the video content, both video and audio. Therefore, we
introduce a novel model to detect clickbaits on YouTube that consider the
relationship between video content and title or thumbnail. The proposed model
consists of a stacking classifier framework composed of six base models (K
Nearest Neighbours, Support Vector Machine, XGBoost, Naive Bayes, Logistic
Regression, and Multilayer Perceptron) and a meta classifier. The developed
clickbait detection model achieved a high accuracy of 92.89% for the novel
BollyBAIT dataset and 95.38% for Misleading Video Dataset. Additionally, the
stated classifier does not use meta features or other statistics dependent on
user interaction with the video (the number of likes, followers, or comments)
for classification, and thus, can be used to detect potential clickbait videos
before they are uploaded, thereby preventing the nuisance of clickbaits
altogether and improving the users streaming experience.

Protecting privileged communications and data from inadvertent disclosure is
a paramount task in the US legal practice. Traditionally counsels rely on
keyword searching and manual review to identify privileged documents in cases.
As data volumes increase, this approach becomes less and less defensible in
costs. Machine learning methods have been used in identifying privilege
documents. Given the generalizable nature of privilege in legal cases, we
hypothesize that transfer learning can capitalize knowledge learned from
existing labeled data to identify privilege documents without requiring
labeling new training data. In this paper, we study both traditional machine
learning models and deep learning models based on BERT for privilege document
classification tasks in legal document review, and we examine the effectiveness
of transfer learning in privilege model on three real world datasets with
privilege labels. Our results show that BERT model outperforms the industry
standard logistic regression algorithm and transfer learning models can achieve
decent performance on datasets in same or close domains.

During the past decade breakthroughs in GPU hardware and deep neural networks
technologies have revolutionized the field of computer vision, making image
analytical potentials accessible to a range of real-world applications.
Technology Assisted Review (TAR) in electronic discovery though traditionally
has dominantly dealt with textual content, is witnessing a rising need to
incorporate multimedia content in the scope. We have developed innovative image
analytics applications for TAR in the past years, such as image classification,
image clustering, and object detection, etc. In this paper, we discuss the use
of image clustering applications to facilitate TAR based on our experiences in
serving clients. We describe our general workflow on leveraging image
clustering in tasks and use statistics from real projects to showcase the
effectiveness of using image clustering in TAR. We also summarize lessons
learned and best practices on using image clustering in TAR.

The size and frequency of wildland fires in the western United States have
dramatically increased in recent years. On high-fire-risk days, a small fire
ignition can rapidly grow and become out of control. Early detection of fire
ignitions from initial smoke can assist the response to such fires before they
become difficult to manage. Past deep learning approaches for wildfire smoke
detection have suffered from small or unreliable datasets that make it
difficult to extrapolate performance to real-world scenarios. In this work, we
present the Fire Ignition Library (FIgLib), a publicly available dataset of
nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras
deployed in Southern California. We also introduce SmokeyNet, a novel deep
learning architecture using spatiotemporal information from camera imagery for
real-time wildfire smoke detection. When trained on the FIgLib dataset,
SmokeyNet outperforms comparable baselines and rivals human performance. We
hope that the availability of the FIgLib dataset and the SmokeyNet architecture
will inspire further research into deep learning methods for wildfire smoke
detection, leading to automated notification systems that reduce the time to
wildfire response.

The advent of large pre-trained generative language models has provided a
common framework for AI story generation via sampling the model to create
sequences that continue the story. However, sampling alone is insufficient for
story generation. In particular, it is hard to direct a language model to
create stories to reach a specific goal event. We present two automated
techniques grounded in deep reinforcement learning and reward shaping to
control the plot of computer-generated stories. The first utilizes proximal
policy optimization to fine-tune an existing transformer-based language model
to generate text continuations but also be goal-seeking. The second extracts a
knowledge graph from the unfolding story, which is used by a policy network
with graph attention to select a candidate continuation generated by a language
model. We report on automated metrics pertaining to how often stories achieve a
given goal event as well as human participant rankings of coherence and overall
story quality compared to baselines and ablations.

We introduce a machine-in-the-loop pipeline that aims to address root causes
of unwanted bias in natural language based supervised machine learning tasks in
the education domain. Learning from the experiences of students is foundational
for education researchers, and academic administrators. 21st-century skills
learned from experience are becoming a core part of college and career
readiness as well as the hiring process in the new knowledge economy.
Minoritized students demonstrate these skills in their daily lives, but
documenting, assessing, and validating these skills is a huge problem for
educational institutions. As an equity focused online platform, LivedX
translates minoritized students' lived experiences into the 21st century
skills, issues micro-credentials, and creates personal 21st century skills
portfolio. To automate the micro credential mining from the natural language
texts received from the students' submitted essays, we employed a bag-of-word
model to construct a multi-output classifier. Despite our goal, our model
initially exacerbated disparate impact on minoritized students. We used a
machine-in-the-loop model development pipeline to address the problem and
refine the aforementioned model to ensure fairness in its prediction.

A hallmark of intelligence is the ability to autonomously learn new flexible,
cognitive behaviors. Here we define cognitive behaviors as those that require
working memory, that is, behaviors where the appropriate action depends not
just on immediate stimuli (as in simple reflexive stimulus-response
associations), but on memorized contextual information. Such cognitive,
memory-dependent behaviors are by definition meta-learning (``learning to
learn'') tasks. In typical meta-learning experiments, agents are trained with
an external, human-designed algorithm to acquire one specific cognitive task.
By contrast, animals are able to pick up new cognitive tasks (including tasks
never seen during evolution) automatically, from stimuli and rewards alone,
through the operation of their own evolved internal machinery. Can we harness
this process to generate artificial agents with such abilities? Here we evolve
neural networks, endowed with plastic connections and neuromodulation, over a
sizable set of simple meta-learning tasks based on a framework from
computational neuroscience. The resulting evolved networks can automatically
modify their own connectivity to acquire a novel simple cognitive task, never
seen during evolution, from stimuli and rewards alone, through the spontaneous
operation of their evolved neural organization and plasticity system. We
suggest that attending to the multiplicity of loops involved in natural
learning may provide useful insight into the emergence of intelligent behavior.

Answering complex questions about images is an ambitious goal for machine
intelligence, which requires a joint understanding of images, text, and
commonsense knowledge, as well as a strong reasoning ability. Recently,
multimodal Transformers have made great progress in the task of Visual
Commonsense Reasoning (VCR), by jointly understanding visual objects and text
tokens through layers of cross-modality attention. However, these approaches do
not utilize the rich structure of the scene and the interactions between
objects which are essential in answering complex commonsense questions. We
propose a Scene Graph Enhanced Image-Text Learning (SGEITL) framework to
incorporate visual scene graphs in commonsense reasoning. To exploit the scene
graph structure, at the model structure level, we propose a multihop graph
transformer for regularizing attention interaction among hops. As for
pre-training, a scene-graph-aware pre-training method is proposed to leverage
structure knowledge extracted in the visual scene graph. Moreover, we introduce
a method to train and generate domain-relevant visual scene graphs using
textual annotations in a weakly-supervised manner. Extensive experiments on VCR
and other tasks show a significant performance boost compared with the
state-of-the-art methods and prove the efficacy of each proposed component.

Cryptocurrency has attracted significant attention. Considering the number of
individuals investing in bitcoin, their motivations are comparatively less
clear than traditional investment decisions. As of December 2020, the market
has continuously increased in cryptocurrency. Especially, the spike of joke
Dogecoin shows the weirdness of the modern meme economy with the support of
Elon Musk, whom himself appointed as "Dogefather". In this paper, we analysis
the impact of tweets by Elon musk and present some statistical analyze with
event study.

A classification scheme of a scientific subject gives an overview of its body
of knowledge. It can also be used to facilitate access to research articles and
other materials related to the subject. For example, the ACM Computing
Classification System (CCS) is used in the ACM Digital Library search interface
and also for indexing computer science papers. We observed that a comprehensive
classification system like CCS or Mathematics Subject Classification (MSC) does
not exist for Computational Linguistics (CL) and Natural Language Processing
(NLP). We propose a classification scheme -- CLICKER for CL/NLP based on the
analysis of online lectures from 77 university courses on this subject. The
currently proposed taxonomy includes 334 topics and focuses on educational
aspects of CL/NLP; it is based primarily, but not exclusively, on lecture notes
from NLP courses. We discuss how such a taxonomy can help in various real-world
applications, including tutoring platforms, resource retrieval, resource
recommendation, prerequisite chain learning, and survey generation.

We present an efficient, parametric modeling framework for predictive
resource allocations, focusing on the amount of computational resources, that
can optimize for a range of price-performance objectives for data analytics in
serverless query processing settings. We discuss and evaluate in depth how our
system, AutoExecutor, can use this framework to automatically select
near-optimal executor and core counts for Spark SQL queries running on Azure
Synapse. Our techniques improve upon Spark's in-built, reactive, dynamic
executor allocation capabilities by substantially reducing the total executors
allocated and executor occupancy while running queries, thereby freeing up
executors that can potentially be used by other concurrent queries or in
reducing the overall cluster provisioning needs. In contrast with
post-execution analysis tools such as Sparklens, we predict resource
allocations for queries before executing them and can also account for changes
in input data sizes for predicting the desired allocations.

Modern cosmological data demand modern data analysis techniques. We introduce
BayOp, a new likelihood sampling and maximisation method which is based on the
Bayesian Optimisation algorithm and learns a function instead of randomly
sampling from it. We apply BayOp to analyse Planck data for traces of
inflationary features models with global periodic modulations of the primordial
power spectrum. While we do not find any new evidence for features, we
demonstrate that BayOp provides an extremely efficient way of sampling
likelihoods over low-to-moderate-dimensional parameter spaces, even for very
complex likelihood landscapes.

Non-autoregressive (NAR) machine translation has recently achieved
significant improvements, and now outperforms autoregressive (AR) models on
some benchmarks, providing an efficient alternative to AR inference. However,
while AR translation is often implemented using multilingual models that
benefit from transfer between languages and from improved serving efficiency,
multilingual NAR models remain relatively unexplored. Taking Connectionist
Temporal Classification (CTC) as an example NAR model and Imputer as a semi-NAR
model, we present a comprehensive empirical study of multilingual NAR. We test
its capabilities with respect to positive transfer between related languages
and negative transfer under capacity constraints. As NAR models require
distilled training sets, we carefully study the impact of bilingual versus
multilingual teachers. Finally, we fit a scaling law for multilingual NAR,
which quantifies its performance relative to the AR model as model scale

Motivation: Identifying drug-target interactions (DTIs) is a key step in drug
repositioning. In recent years, the accumulation of a large number of genomics
and pharmacology data has formed mass drug and target related heterogeneous
networks (HNs), which provides new opportunities of developing HN-based
computational models to accurately predict DTIs. The HN implies lots of useful
information about DTIs but also contains irrelevant data, and how to make the
best of heterogeneous networks remains a challenge. Results: In this paper, we
propose a heterogeneous graph automatic meta-path learning based DTI prediction
method (HampDTI). HampDTI automatically learns the important meta-paths between
drugs and targets from the HN, and generates meta-path graphs. For each
meta-path graph, the features learned from drug molecule graphs and target
protein sequences serve as the node attributes, and then a node-type specific
graph convolutional network (NSGCN) which efficiently considers node type
information (drugs or targets) is designed to learn embeddings of drugs and
targets. Finally, the embeddings from multiple meta-path graphs are combined to
predict novel DTIs. The experiments on benchmark datasets show that our
proposed HampDTI achieves superior performance compared with state-of-the-art
DTI prediction methods. More importantly, HampDTI identifies the important
meta-paths for DTI prediction, which could explain how drugs connect with
targets in HNs.

Artificial Intelligence (AI ) has been very successful in creating and
predicting music playlists for online users based on their data; data received
from users experience using the app such as searching the songs they like.
There are lots of current technological advancements in AI due to the
competition between music platform owners such as Spotify, Pandora, and more.
In this paper, 6 machine learning algorithms and their individual accuracy for
predicting whether a user will like a song are explored across 3 different
platforms including Weka, SKLearn, and Orange. The algorithms explored include
Logistic Regression, Naive Bayes, Sequential Minimal Optimization (SMO),
Multilayer Perceptron (Neural Network), Nearest Neighbor, and Random Forest.
With the analysis of the specific characteristics of each song provided by the
Spotify API [1], Random Forest is the most successful algorithm for predicting
whether a user will like a song with an accuracy of 84%. This is higher than
the accuracy of 82.72% found by Mungekar using the Random Forest technique and
slightly different characteristics of a song [2]. The characteristics in
Mungekars Random Forest algorithm focus more on the artist and popularity
rather than the sonic features of the songs. Removing the popularity aspect and
focusing purely on the sonic qualities improve the accuracy of recommendations.
Finally, this paper shows how song prediction can be accomplished without any
monetary investments, and thus, inspires an idea of what amazing results can be
accomplished with full financial research.

Multimodal summarization with multimodal output (MSMO) generates a summary
with both textual and visual content. Multimodal news report contains
heterogeneous contents, which makes MSMO nontrivial. Moreover, it is observed
that different modalities of data in the news report correlate hierarchically.
Traditional MSMO methods indistinguishably handle different modalities of data
by learning a representation for the whole data, which is not directly
adaptable to the heterogeneous contents and hierarchical correlation. In this
paper, we propose a hierarchical cross-modality semantic correlation learning
model (HCSCL) to learn the intra- and inter-modal correlation existing in the
multimodal data. HCSCL adopts a graph network to encode the intra-modal
correlation. Then, a hierarchical fusion framework is proposed to learn the
hierarchical correlation between text and images. Furthermore, we construct a
new dataset with relevant image annotation and image object label information
to provide the supervision information for the learning procedure. Extensive
experiments on the dataset show that HCSCL significantly outperforms the
baseline methods in automatic summarization metrics and fine-grained diversity
tests.

Transformer models have achieved promising results on natural language
processing (NLP) tasks including extractive question answering (QA). Common
Transformer encoders used in NLP tasks process the hidden states of all input
tokens in the context paragraph throughout all layers. However, different from
other tasks such as sequence classification, answering the raised question does
not necessarily need all the tokens in the context paragraph. Following this
motivation, we propose Block-skim, which learns to skim unnecessary context in
higher hidden layers to improve and accelerate the Transformer performance. The
key idea of Block-Skim is to identify the context that must be further
processed and those that could be safely discarded early on during inference.
Critically, we find that such information could be sufficiently derived from
the self-attention weights inside the Transformer model. We further prune the
hidden states corresponding to the unnecessary positions early in lower layers,
achieving significant inference-time speedup. To our surprise, we observe that
models pruned in this way outperform their full-size counterparts. Block-Skim
improves QA models' accuracy on different datasets and achieves 3 times speedup
on BERT-base model.

Compared to standard retrieval tasks, passage retrieval for conversational
question answering (CQA) poses new challenges in understanding the current user
question, as each question needs to be interpreted within the dialogue context.
Moreover, it can be expensive to re-train well-established retrievers such as
search engines that are originally developed for non-conversational queries. To
facilitate their use, we develop a query rewriting model CONQRR that rewrites a
conversational question in the context into a standalone question. It is
trained with a novel reward function to directly optimize towards retrieval
using reinforcement learning and can be adapted to any off-the-shelf retriever.
CONQRR achieves state-of-the-art results on a recent open-domain CQA dataset
containing conversations from three different sources, and is effective for two
different off-the-shelf retrievers. Our extensive analysis also shows the
robustness of CONQRR to out-of-domain dialogues as well as to zero query
rewriting supervision.

Information Security in the cyber world is a major cause for concern, with a
significant increase in the number of attack surfaces. Existing information on
vulnerabilities, attacks, controls, and advisories available on the web
provides an opportunity to represent knowledge and perform security analytics
to mitigate some of the concerns. Representing security knowledge in the form
of ontology facilitates anomaly detection, threat intelligence, reasoning and
relevance attribution of attacks, and many more. This necessitates dynamic and
automated enrichment of information security ontologies. However, existing
ontology enrichment algorithms based on natural language processing and ML
models have issues with contextual extraction of concepts in words, phrases,
and sentences. This motivates the need for sequential Deep Learning
architectures that traverse through dependency paths in text and extract
embedded vulnerabilities, threats, controls, products, and other
security-related concepts and instances from learned path representations. In
the proposed approach, Bidirectional LSTMs trained on a large DBpedia dataset
and Wikipedia corpus of 2.8 GB along with Universal Sentence Encoder is
deployed to enrich ISO 27001-based information security ontology. The model is
trained and tested on a high-performance computing (HPC) environment to handle
Wiki text dimensionality. The approach yielded a test accuracy of over 80% when
tested with knocked-out concepts from ontology and web page instances to
validate the robustness.

Learning to reject unknown samples (not present in the source classes) in the
target domain is fairly important for unsupervised domain adaptation (UDA).
There exist two typical UDA scenarios, i.e., open-set, and open-partial-set,
and the latter assumes that not all source classes appear in the target domain.
However, most prior methods are designed for one UDA scenario and always
perform badly on the other UDA scenario. Moreover, they also require the
labeled source data during adaptation, limiting their usability in data
privacy-sensitive applications. To address these issues, this paper proposes a
Universal Model ADaptation (UMAD) framework which handles both UDA scenarios
without access to the source data nor prior knowledge about the category shift
between domains. Specifically, we aim to learn a source model with an elegantly
designed two-head classifier and provide it to the target domain. During
adaptation, we develop an informative consistency score to help distinguish
unknown samples from known samples. To achieve bilateral adaptation in the
target domain, we further maximize localized mutual information to align known
samples with the source classifier and employ an entropic loss to push unknown
samples far away from the source classification boundary, respectively.
Experiments on open-set and open-partial-set UDA scenarios demonstrate that
UMAD, as a unified approach without access to source data, exhibits comparable,
if not superior, performance to state-of-the-art data-dependent methods.

Patient scheduling is a difficult task involving stochastic factors such as
the unknown arrival times of patients. Similarly, the scheduling of
radiotherapy for cancer treatments needs to handle patients with different
urgency levels when allocating resources. High priority patients may arrive at
any time, and there must be resources available to accommodate them. A common
solution is to reserve a flat percentage of treatment capacity for emergency
patients. However, this solution can result in overdue treatments for urgent
patients, a failure to fully exploit treatment capacity, and delayed treatments
for low-priority patients. This problem is especially severe in large and
crowded hospitals. In this paper, we propose a prediction-based approach for
online dynamic radiotherapy scheduling that dynamically adapts the present
scheduling decision based on each incoming patient and the current allocation
of resources. Our approach is based on a regression model trained to recognize
the links between patients' arrival patterns, and their ideal waiting time in
optimal offline solutions where all future arrivals are known in advance. When
our prediction-based approach is compared to flat-reservation policies, it does
a better job of preventing overdue treatments for emergency patients, while
also maintaining comparable waiting times for the other patients. We also
demonstrate how our proposed approach supports explainability and
interpretability in scheduling decisions using SHAP values.

We introduce the task of isochrony-aware machine translation which aims at
generating translations suitable for dubbing. Dubbing of a spoken sentence
requires transferring the content as well as the speech-pause structure of the
source into the target language to achieve audiovisual coherence. Practically,
this implies correctly projecting pauses from the source to the target and
ensuring that target speech segments have roughly the same duration of the
corresponding source speech segments. In this work, we propose implicit and
explicit modeling approaches to integrate isochrony information into neural
machine translation. Experiments on English-German/French language pairs with
automatic metrics show that the simplest of the considered approaches works
best. Results are confirmed by human evaluations of translations and dubbed
videos.

In this work, we explore how to train task-specific language models aimed
towards learning rich representation of keyphrases from text documents. We
experiment with different masking strategies for pre-training transformer
language models (LMs) in discriminative as well as generative settings. In the
discriminative setting, we introduce a new pre-training objective - Keyphrase
Boundary Infilling with Replacement (KBIR), showing large gains in performance
(upto 8.16 points in F1) over SOTA, when the LM pre-trained using KBIR is
fine-tuned for the task of keyphrase extraction. In the generative setting, we
introduce a new pre-training setup for BART - KeyBART, that reproduces the
keyphrases related to the input text in the CatSeq format, instead of the
denoised original input. This also led to gains in performance (upto 4.33
points in F1@M) over SOTA for keyphrase generation. Additionally, we also
fine-tune the pre-trained language models on named entity recognition (NER),
question answering (QA), relation extraction (RE), abstractive summarization
and achieve comparable performance with that of the SOTA, showing that learning
rich representation of keyphrases is indeed beneficial for many other
fundamental NLP tasks.

Graph neural networks (GNNs) have extended the success of deep neural
networks (DNNs) to non-Euclidean graph data, achieving ground-breaking
performance on various tasks such as node classification and graph property
prediction. Nonetheless, existing systems are inefficient to train large graphs
with billions of nodes and edges with GPUs. The main bottlenecks are the
process of preparing data for GPUs - subgraph sampling and feature retrieving.
This paper proposes BGL, a distributed GNN training system designed to address
the bottlenecks with a few key ideas. First, we propose a dynamic cache engine
to minimize feature retrieving traffic. By a co-design of caching policy and
the order of sampling, we find a sweet spot of low overhead and high cache hit
ratio. Second, we improve the graph partition algorithm to reduce
cross-partition communication during subgraph sampling. Finally, careful
resource isolation reduces contention between different data preprocessing
stages. Extensive experiments on various GNN models and large graph datasets
show that BGL significantly outperforms existing GNN training systems by 20.68x

We develop an integrated guidance and control system that in conjunction with
a stabilized seeker and landing site detection software can achieve precise and
safe planetary landing. The seeker tracks the designated landing site by
adjusting seeker elevation and azimuth angles to center the designated landing
site in the sensor field of view. The seeker angles, closing speed, and range
to the designated landing site are used to formulate a velocity field that is
used by the guidance and control system to achieve a safe landing at the
designated landing site. The guidance and control system maps this velocity
field, attitude, and rotational velocity directly to a commanded thrust vector
for the lander's four engines. The guidance and control system is implemented
as a policy optimized using reinforcement meta learning. We demonstrate that
the guidance and control system is compatible with multiple diverts during the
powered descent phase, and is robust to seeker lag, actuator lag and
degradation, and center of mass variation induced by fuel consumption. We
outline several concepts of operations, including an approach using a preplaced
landing beacon.

The underlying loss landscapes of deep neural networks have a great impact on
their training, but they have mainly been studied theoretically due to
computational constraints. This work vastly reduces the time required to
compute such loss landscapes, and uses them to study winning lottery tickets
found via iterative magnitude pruning. We also share results that contradict
previously claimed correlations between certain loss landscape projection
methods and model trainability and generalization error.

We introduce the Momentum Transformer, an attention-based deep learning
architecture which outperforms benchmark momentum and mean-reversion trading
strategies. Unlike state-of-the-art Long Short-Term Memory (LSTM)
architectures, which are sequential in nature, the attention mechanism provides
our architecture with a direct connection to all previous time-steps. Our
architecture enables us to learn longer-term dependencies, improves performance
when considering returns net of transaction costs and naturally adapts to new
market regimes, such as during the SARS-CoV-2 crisis. The Momentum Transformer
is inherently interpretable, providing us with greater insights into our deep
learning momentum trading strategy, including how it blends different classical
strategies and the past time-steps which are of the greatest significance to
the model.

Transformer networks are the de facto standard architecture in natural
language processing. To date, there are no theoretical analyses of the
Transformer's ability to capture tree structures. We focus on the ability of
Transformer networks to learn tree structures that are important for tree
transduction problems. We first analyze the theoretical capability of the
standard Transformer architecture to learn tree structures given enumeration of
all possible tree backbones, which we define as trees without labels. We then
prove that two linear layers with ReLU activation function can recover any tree
backbone from any two nonzero, linearly independent starting backbones. This
implies that a Transformer can learn tree structures well in theory. We conduct
experiments with synthetic data and find that the standard Transformer achieves
similar accuracy compared to a Transformer where tree position information is
explicitly encoded, albeit with slower convergence. This confirms empirically
that Transformers can learn tree structures.

A robot's deployment environment often involves perceptual changes that
differ from what it has experienced during training. Standard practices such as
data augmentation attempt to bridge this gap by augmenting source images in an
effort to extend the support of the training distribution to better cover what
the agent might experience at test time. In many cases, however, it is
impossible to know test-time distribution-shift a priori, making these schemes
infeasible. In this paper, we introduce a general approach, called Invariance
Through Latent Alignment (ILA), that improves the test-time performance of a
visuomotor control policy in deployment environments with unknown perceptual
variations. ILA performs unsupervised adaptation at deployment-time by matching
the distribution of latent features on the target domain to the agent's prior
experience, without relying on paired data. Although simple, we show that this
idea leads to surprising improvements on a variety of challenging adaptation
scenarios, including changes in lighting conditions, the content in the scene,
and camera poses. We present results on calibrated control benchmarks in
simulation -- the distractor control suite -- and a physical robot under a
sim-to-real setup.

We address the relatively unexplored problem of hyper-parameter optimization
(HPO) for federated learning (FL-HPO). We introduce Federated Loss suRface
Aggregation (FLoRA), the first FL-HPO solution framework that can address use
cases of tabular data and gradient boosting training algorithms in addition to
stochastic gradient descent/neural networks commonly addressed in the FL
literature. The framework enables single-shot FL-HPO, by first identifying a
good set of hyper-parameters that are used in a **single** FL training. Thus,
it enables FL-HPO solutions with minimal additional communication overhead
compared to FL training without HPO. Our empirical evaluation of FLoRA for
Gradient Boosted Decision Trees on seven OpenML data sets demonstrates
significant model accuracy improvements over the considered baseline, and
robustness to increasing number of parties involved in FL-HPO training.

Bringing dynamic robots into the wild requires a tenuous balance between
performance and safety. Yet controllers designed to provide robust safety
guarantees often result in conservative behavior, and tuning these controllers
to find the ideal trade-off between performance and safety typically requires
domain expertise or a carefully constructed reward function. This work presents
a design paradigm for systematically achieving behaviors that balance
performance and robust safety by integrating safety-aware Preference-Based
Learning (PBL) with Control Barrier Functions (CBFs). Fusing these concepts --
safety-aware learning and safety-critical control -- gives a robust means to
achieve safe behaviors on complex robotic systems in practice. We demonstrate
the capability of this design paradigm to achieve safe and performant
perception-based autonomous operation of a quadrupedal robot both in simulation
and experimentally on hardware.

The ever-increasing amount of user-generated content online has led, in
recent years, to an expansion in research and investment in automated content
analysis tools. Scrutiny of automated content analysis has accelerated during
the COVID-19 pandemic, as social networking services have placed a greater
reliance on these tools due to concerns about health risks to their moderation
staff from in-person work. At the same time, there are important policy debates
around the world about how to improve content moderation while protecting free
expression and privacy. In order to advance these debates, we need to
understand the potential role of automated content analysis tools.
  This paper explains the capabilities and limitations of tools for analyzing
online multimedia content and highlights the potential risks of using these
tools at scale without accounting for their limitations. It focuses on two main
categories of tools: matching models and computer prediction models. Matching
models include cryptographic and perceptual hashing, which compare
user-generated content with existing and known content. Predictive models
(including computer vision and computer audition) are machine learning
techniques that aim to identify characteristics of new or previously unknown
content.

With the recent advances in optical phase change material (PCM), photonic
in-memory neurocomputing has demonstrated its superiority in optical neural
network (ONN) designs with near-zero static power consumption, time-of-light
latency, and compact footprint. However, photonic tensor cores require massive
hardware reuse to implement large matrix multiplication due to the limited
single-core scale. The resultant large number of PCM writes leads to serious
dynamic power and overwhelms the fragile PCM with limited write endurance. In
this work, we propose a synergistic optimization framework, ELight, to minimize
the overall write efforts for efficient and reliable optical in-memory
neurocomputing. We first propose write-aware training to encourage the
similarity among weight blocks, and combine it with a post-training
optimization method to reduce programming efforts by eliminating redundant
writes. Experiments show that ELight can achieve over 20X reduction in the
total number of writes and dynamic power with comparable accuracy. With our
ELight, photonic in-memory neurocomputing will step forward towards viable
applications in machine learning with preserved accuracy, order-of-magnitude
longer lifetime, and lower programming energy.

Hyperparameter tuning in machine learning algorithms is a computationally
challenging task due to the large-scale nature of the problem. In order to
develop an efficient strategy for hyper-parameter tuning, one promising
solution is to use swarm intelligence algorithms. Artificial Bee Colony (ABC)
optimization lends itself as a promising and efficient optimization algorithm
for this purpose. However, in some cases, ABC can suffer from a slow
convergence rate or execution time due to the poor initial population of
solutions and expensive objective functions. To address these concerns, a novel
algorithm, OptABC, is proposed to help ABC algorithm in faster convergence
toward a near-optimum solution. OptABC integrates artificial bee colony
algorithm, K-Means clustering, greedy algorithm, and opposition-based learning
strategy for tuning the hyper-parameters of different machine learning models.
OptABC employs these techniques in an attempt to diversify the initial
population, and hence enhance the convergence ability without significantly
decreasing the accuracy. In order to validate the performance of the proposed
method, we compare the results with previous state-of-the-art approaches.
Experimental results demonstrate the effectiveness of the OptABC compared to
existing approaches in the literature.

Multi-armed bandit algorithms like Thompson Sampling can be used to conduct
adaptive experiments, in which maximizing reward means that data is used to
progressively assign more participants to more effective arms. Such assignment
strategies increase the risk of statistical hypothesis tests identifying a
difference between arms when there is not one, and failing to conclude there is
a difference in arms when there truly is one. We present simulations for 2-arm
experiments that explore two algorithms that combine the benefits of uniform
randomization for statistical analysis, with the benefits of reward
maximization achieved by Thompson Sampling (TS). First, Top-Two Thompson
Sampling adds a fixed amount of uniform random allocation (UR) spread evenly
over time. Second, a novel heuristic algorithm, called TS PostDiff (Posterior
Probability of Difference). TS PostDiff takes a Bayesian approach to mixing TS
and UR: the probability a participant is assigned using UR allocation is the
posterior probability that the difference between two arms is `small' (below a
certain threshold), allowing for more UR exploration when there is little or no
reward to be gained. We find that TS PostDiff method performs well across
multiple effect sizes, and thus does not require tuning based on a guess for
the true effect size.

In low-income settings, the most critical piece of information for electric
utilities is the anticipated consumption of a customer. Electricity consumption
assessment is difficult to do in settings where a significant fraction of
households do not yet have an electricity connection. In such settings the
absolute levels of anticipated consumption can range from 5-100 kWh/month,
leading to high variability amongst these customers. Precious resources are at
stake if a significant fraction of low consumers are connected over those with
higher consumption.
  This is the first study of it's kind in low-income settings that attempts to
predict a building's consumption and not that of an aggregate administrative
area. We train a Convolutional Neural Network (CNN) over pre-electrification
daytime satellite imagery with a sample of utility bills from 20,000
geo-referenced electricity customers in Kenya (0.01% of Kenya's residential
customers). This is made possible with a two-stage approach that uses a novel
building segmentation approach to leverage much larger volumes of no-cost
satellite imagery to make the most of scarce and expensive customer data. Our
method shows that competitive accuracies can be achieved at the building level,
addressing the challenge of consumption variability. This work shows that the
building's characteristics and it's surrounding context are both important in
predicting consumption levels. We also evaluate the addition of lower
resolution geospatial datasets into the training process, including nighttime
lights and census-derived data. The results are already helping inform site
selection and distribution-level planning, through granular predictions at the
level of individual structures in Kenya and there is no reason this cannot be
extended to other countries.

We consider the problem of selecting confounders for adjustment from a
potentially large set of covariates, when estimating a causal effect. Recently,
the high-dimensional Propensity Score (hdPS) method was developed for this
task; hdPS ranks potential confounders by estimating an importance score for
each variable and selects the top few variables. However, this ranking
procedure is limited: it requires all variables to be binary. We propose an
extension of the hdPS to general types of response and confounder variables. We
further develop a group importance score, allowing us to rank groups of
potential confounders. The main challenge is that our parameter requires either
the propensity score or response model; both vulnerable to model
misspecification. We propose a targeted maximum likelihood estimator (TMLE)
which allows the use of nonparametric, machine learning tools for fitting these
intermediate models. We establish asymptotic normality of our estimator, which
consequently allows constructing confidence intervals. We complement our work
with numerical studies on simulated and real data.

Discovering meaningful directions in the latent space of GANs to manipulate
semantic attributes typically requires large amounts of labeled data. Recent
work aims to overcome this limitation by leveraging the power of Contrastive
Language-Image Pre-training (CLIP), a joint text-image model. While promising,
these methods require several hours of preprocessing or training to achieve the
desired manipulations. In this paper, we present StyleMC, a fast and efficient
method for text-driven image generation and manipulation. StyleMC uses a
CLIP-based loss and an identity loss to manipulate images via a single text
prompt without significantly affecting other attributes. Unlike prior work,
StyleMC requires only a few seconds of training per text prompt to find stable
global directions, does not require prompt engineering and can be used with any
pre-trained StyleGAN2 model. We demonstrate the effectiveness of our method and
compare it to state-of-the-art methods. Our code can be found at
http://catlab-team.github.io/stylemc.

This paper studies how to generalize Tukey's depth to problems defined in a
restricted space that may be curved or have boundaries, and to problems with a
nondifferentiable objective. First, using a manifold approach, we propose a
broad class of Riemannian depth for smooth problems defined on a Riemannian
manifold, and showcase its applications in spherical data analysis, principal
component analysis, and multivariate orthogonal regression. Moreover, for
nonsmooth problems, we introduce additional slack variables and inequality
constraints to define a novel slacked data depth, which can perform
center-outward rankings of estimators arising from sparse learning and reduced
rank regression. Real data examples illustrate the usefulness of some proposed
data depths.

Outliers widely occur in big-data applications and may severely affect
statistical estimation and inference. In this paper, a framework of
outlier-resistant estimation is introduced to robustify an arbitrarily given
loss function. It has a close connection to the method of trimming and includes
explicit outlyingness parameters for all samples, which in turn facilitates
computation, theory, and parameter tuning. To tackle the issues of nonconvexity
and nonsmoothness, we develop scalable algorithms with implementation ease and
guaranteed fast convergence. In particular, a new technique is proposed to
alleviate the requirement on the starting point such that on regular datasets,
the number of data resamplings can be substantially reduced. Based on combined
statistical and computational treatments, we are able to perform nonasymptotic
analysis beyond M-estimation. The obtained resistant estimators, though not
necessarily globally or even locally optimal, enjoy minimax rate optimality in
both low dimensions and high dimensions. Experiments in regression,
classification, and neural networks show excellent performance of the proposed
methodology at the occurrence of gross outliers.

Neural network classifiers have become the de-facto choice for current
"pre-train then fine-tune" paradigms of visual classification. In this paper,
we investigate k-Nearest-Neighbor (k-NN) classifiers, a classical model-free
learning method from the pre-deep learning era, as an augmentation to modern
neural network based approaches. As a lazy learning method, k-NN simply
aggregates the distance between the test image and top-k neighbors in a
training set. We adopt k-NN with pre-trained visual representations produced by
either supervised or self-supervised methods in two steps: (1) Leverage k-NN
predicted probabilities as indications for easy vs. hard examples during
training. (2) Linearly interpolate the k-NN predicted distribution with that of
the augmented classifier. Via extensive experiments on a wide range of
classification tasks, our study reveals the generality and flexibility of k-NN
integration with additional insights: (1) k-NN achieves competitive results,
sometimes even outperforming a standard linear classifier. (2) Incorporating
k-NN is especially beneficial for tasks where parametric classifiers perform
poorly and / or in low-data regimes. We hope these discoveries will encourage
people to rethink the role of pre-deep learning, classical methods in computer
vision. Our code is available at: https://github.com/KMnP/nn-revisit.

The reliable prediction of the temporal behavior of complex systems is
required in numerous scientific fields. This strong interest is however
hindered by modeling issues: often, the governing equations describing the
physics of the system under consideration are not accessible or, when known,
their solution might require a computational time incompatible with the
prediction time constraints. Nowadays, approximating complex systems at hand in
a generic functional format and informing it ex--nihilo from available
observations has become a common practice, as illustrated by the enormous
amount of scientific work appeared in the last years. Numerous successful
examples based on deep neural networks are already available, although
generalizability of the models and margins of guarantee are often overlooked.
Here, we consider Long-Short Term Memory neural networks and thoroughly
investigate the impact of the training set and its structure on the quality of
the long-term prediction. Leveraging insights from ergodic theory, we perform a
thorough computational analysis to assess the amount of data sufficient for a
priori guaranteeing a faithful model of the physical system. We show how an
informed design of the training set, based on invariants of the system and the
structure of the underlying attractor, significantly improves the resulting
models, opening up avenues for research within the context of active learning.
Further, the non-trivial effects of the memory initializations when relying on
memory-capable models will be illustrated. Our findings provide evidence-based
good-practice on the amount and the choice of data required for an effective
data-driven modeling of any complex dynamical system.

We introduce a method to learn unsupervised semantic visual information based
on the premise that complex events (e.g., minutes) can be decomposed into
simpler events (e.g., a few seconds), and that these simple events are shared
across several complex events. We split a long video into short frame sequences
to extract their latent representation with three-dimensional convolutional
neural networks. A clustering method is used to group representations producing
a visual codebook (i.e., a long video is represented by a sequence of integers
given by the cluster labels). A dense representation is learned by encoding the
co-occurrence probability matrix for the codebook entries. We demonstrate how
this representation can leverage the performance of the dense video captioning
task in a scenario with only visual features. As a result of this approach, we
are able to replace the audio signal in the Bi-Modal Transformer (BMT) method
and produce temporal proposals with comparable performance. Furthermore, we
concatenate the visual signal with our descriptor in a vanilla transformer
method to achieve state-of-the-art performance in captioning compared to the
methods that explore only visual features, as well as a competitive performance
with multi-modal methods. Our code is available at
https://github.com/valterlej/dvcusi.

Given the growing use of Artificial Intelligence (AI) and machine learning
(ML) methods across all aspects of environmental sciences, it is imperative
that we initiate a discussion about the ethical and responsible use of AI. In
fact, much can be learned from other domains where AI was introduced, often
with the best of intentions, yet often led to unintended societal consequences,
such as hard coding racial bias in the criminal justice system or increasing
economic inequality through the financial system. A common misconception is
that the environmental sciences are immune to such unintended consequences when
AI is being used, as most data come from observations, and AI algorithms are
based on mathematical formulas, which are often seen as objective. In this
article, we argue the opposite can be the case. Using specific examples, we
demonstrate many ways in which the use of AI can introduce similar consequences
in the environmental sciences. This article will stimulate discussion and
research efforts in this direction. As a community, we should avoid repeating
any foreseeable mistakes made in other domains through the introduction of AI.
In fact, with proper precautions, AI can be a great tool to help {\it reduce}
climate and environmental injustice. We primarily focus on weather and climate
examples but the conclusions apply broadly across the environmental sciences.

Reinforcement learning studies how an agent should interact with an
environment to maximize its cumulative reward. A standard way to study this
question abstractly is to ask how many samples an agent needs from the
environment to learn an optimal policy for a $\gamma$-discounted Markov
decision process (MDP). For such an MDP, we design quantum algorithms that
approximate an optimal policy ($\pi^*$), the optimal value function ($v^*$),
and the optimal $Q$-function ($q^*$), assuming the algorithms can access
samples from the environment in quantum superposition. This assumption is
justified whenever there exists a simulator for the environment; for example,
if the environment is a video game or some other program. Our quantum
algorithms, inspired by value iteration, achieve quadratic speedups over the
best-possible classical sample complexities in the approximation accuracy
($\epsilon$) and two main parameters of the MDP: the effective time horizon
($\frac{1}{1-\gamma}$) and the size of the action space ($A$). Moreover, we
show that our quantum algorithm for computing $q^*$ is optimal by proving a
matching quantum lower bound.

Enhancing classical machine learning (ML) algorithms through quantum kernels
is a rapidly growing research topic in quantum machine learning (QML). A key
challenge in using kernels -- both classical and quantum -- is that ML
workflows involve acquiring new observations, for which new kernel values need
to be calculated. Transferring data back-and-forth between where the new
observations are generated & a quantum computer incurs a time delay; this delay
may exceed the timescales relevant for using the QML algorithm in the first
place. In this work, we show quantum kernel matrices can be extended to
incorporate new data using a classical (chordal-graph-based) matrix completion
algorithm. The minimal sample complexity needed for perfect completion is
dependent on matrix rank. We empirically show that (a) quantum kernel matrices
can be completed using this algorithm when the minimal sample complexity is
met, (b) the error of the completion degrades gracefully in the presence of
finite-sampling noise, and (c) the rank of quantum kernel matrices depends
weakly on the expressibility of the quantum feature map generating the kernel.
Further, on a real-world, industrially-relevant data set, the completion error
behaves gracefully even when the minimal sample complexity is not reached.

Approximating wind flows using computational fluid dynamics (CFD) methods can
be time-consuming. Creating a tool for interactively designing prototypes while
observing the wind flow change requires simpler models to simulate faster.
Instead of running numerical approximations resulting in detailed calculations,
data-driven methods and deep learning might be able to give similar results in
a fraction of the time. This work rephrases the problem from computing 3D flow
fields using CFD to a 2D image-to-image translation-based problem on the
building footprints to predict the flow field at pedestrian height level. We
investigate the use of generative adversarial networks (GAN), such as Pix2Pix
[1] and CycleGAN [2] representing state-of-the-art for image-to-image
translation task in various domains as well as U-Net autoencoder [3]. The
models can learn the underlying distribution of a dataset in a data-driven
manner, which we argue can help the model learn the underlying
Reynolds-averaged Navier-Stokes (RANS) equations from CFD. We experiment on
novel simulated datasets on various three-dimensional bluff-shaped buildings
with and without height information. Moreover, we present an extensive
qualitative and quantitative evaluation of the generated images for a selection
of models and compare their performance with the simulations delivered by CFD.
We then show that adding positional data to the input can produce more accurate
results by proposing a general framework for injecting such information on the
different architectures. Furthermore, we show that the models performances
improve by applying attention mechanisms and spectral normalization to
facilitate stable training.

We employ crowdsourcing to acquire time-continuous affective annotations for
movie clips, and refine noisy models trained from these crowd annotations
incorporating expert information within a Multi-task Learning (MTL) framework.
We propose a novel \textbf{e}xpert \textbf{g}uided MTL (EG-MTL) algorithm,
which minimizes the loss with respect to both crowd and expert labels to learn
a set of weights corresponding to each movie clip for which crowd annotations
are acquired. We employ EG-MTL to solve two problems, namely,
\textbf{\texttt{P1}}: where dynamic annotations acquired from both experts and
crowdworkers for the \textbf{Validation} set are used to train a regression
model with audio-visual clip descriptors as features, and predict dynamic
arousal and valence levels on 5--15 second snippets derived from the clips; and
\textbf{\texttt{P2}}: where a classification model trained on the
\textbf{Validation} set using dynamic crowd and expert annotations (as
features) and static affective clip labels is used for binary emotion
recognition on the \textbf{Evaluation} set for which only dynamic crowd
annotations are available. Observed experimental results confirm the
effectiveness of the EG-MTL algorithm, which is reflected via improved arousal
and valence estimation for \textbf{\texttt{P1}}, and higher recognition
accuracy for \textbf{\texttt{P2}}.

Modern deep learning frameworks provide imperative, eager execution
programming interfaces embedded in Python to provide a productive development
experience. However, deep learning practitioners sometimes need to capture and
transform program structure for performance optimization, visualization,
analysis, and hardware integration. We study the different designs for program
capture and transformation used in deep learning. By designing for typical deep
learning use cases rather than long tail ones, it is possible to create a
simpler framework for program capture and transformation. We apply this
principle in torch.fx, a program capture and transformation library for PyTorch
written entirely in Python and optimized for high developer productivity by ML
practitioners. We present case studies showing how torch.fx enables workflows
previously inaccessible in the PyTorch ecosystem.

We present a novel machine learning method for predicting the baryonic
properties of dark matter only subhalos from N-body simulations. Our model is
built using the extremely randomized tree (ERT) algorithm and takes subhalo
properties over a wide range of redshifts as its input features. We train our
model using the IllustrisTNG simulations to predict blackhole mass, gas mass,
magnitudes, star formation rate, stellar mass, and metallicity. We compare the
results of our method with a baseline model from previous works, and against a
model that only considers the mass history of the subhalo. We find that our new
model significantly outperforms both of the other models. We then investigate
the predictive power of each input by looking at feature importance scores from
the ERT algorithm. We produce feature importance plots for each baryonic
property, and find that they differ significantly. We identify low redshifts as
being most important for predicting star formation rate and gas mass, with high
redshifts being most important for predicting stellar mass and metallicity, and
consider what this implies for nature vs nurture. We find that the physical
properties of galaxies investigated in this study are all driven by nurture and
not nature. The only property showing a somewhat stronger impact of nature is
the present-day star formation rate of galaxies. Finally we verify that the
feature importance plots are discovering physical patterns, and that the trends
shown are not an artefact of the ERT algorithm.

In this paper, a white-Box support vector machine (SVM) framework and its
swarm-based optimization is presented for supervision of toothed milling cutter
through characterization of real-time spindle vibrations. The anomalous moments
of vibration evolved due to in-process tool failures (i.e., flank and nose
wear, crater and notch wear, edge fracture) have been investigated through
time-domain response of acceleration and statistical features. The Recursive
Feature Elimination with Cross-Validation (RFECV) with decision trees as the
estimator has been implemented for feature selection. Further, the competence
of standard SVM has been examined for tool health monitoring followed by its
optimization through application of swarm based algorithms. The comparative
analysis of performance of five meta-heuristic algorithms (Elephant Herding
Optimization, Monarch Butterfly Optimization, Harris Hawks Optimization, Slime
Mould Algorithm, and Moth Search Algorithm) has been carried out. The white-box
approach has been presented considering global and local representation that
provides insight into the performance of machine learning models in tool
condition monitoring.

Power flow analysis is used to evaluate the flow of electricity in the power
system network. Power flow calculation is used to determine the steady-state
variables of the system, such as the voltage magnitude/phase angle of each bus
and the active/reactive power flow on each branch. The DC power flow model is a
popular linear power flow model that is widely used in the power industry.
Although it is fast and robust, it may lead to inaccurate line flow results for
some transmission lines. Since renewable energy sources such as solar farms or
offshore wind farms are usually located far away from the main grid, accurate
line flow results on these critical lines are essential for power flow analysis
due to the unpredictable nature of renewable energy. Data-driven methods can be
used to partially address these inaccuracies by taking advantage of historical
grid profiles. In this paper, a neural network (NN) model is trained to predict
power flow results using historical power system data. Although the training
process may take time, once trained, it is very fast to estimate line flows. A
comprehensive performance analysis between the proposed NN-based power flow
model and the traditional DC power flow model is conducted. It can be concluded
that the proposed NN-based power flow model can find solutions quickly and more
accurately than DC power flow model.

Intelligent Tutoring Systems have become critically important in future
learning environments. Knowledge Tracing (KT) is a crucial part of that system.
It is about inferring the skill mastery of students and predicting their
performance to adjust the curriculum accordingly. Deep Learning-based KT models
have shown significant predictive performance compared with traditional models.
However, it is difficult to extract psychologically meaningful explanations
from the tens of thousands of parameters in neural networks, that would relate
to cognitive theory. There are several ways to achieve high accuracy in student
performance prediction but diagnostic and prognostic reasoning is more critical
in learning sciences. Since KT problem has few observable features (problem ID
and student's correctness at each practice), we extract meaningful latent
features from students' response data by using machine learning and data mining
techniques. In this work, we present Interpretable Knowledge Tracing (IKT), a
simple model that relies on three meaningful latent features: individual skill
mastery, ability profile (learning transfer across skills), and problem
difficulty. IKT's prediction of future student performance is made using a
Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are
easier to explain than deep learning-based student models. IKT also shows
better student performance prediction than deep learning-based student models
without requiring a huge amount of parameters. We conduct ablation studies on
each feature to examine their contribution to student performance prediction.
Thus, IKT has great potential for providing adaptive and personalized
instructions with causal reasoning in real-world educational systems.

Generalizing directed maximal ancestral graphs, we introduce a class of
graphical models for representing time lag specific causal relationships and
independencies among finitely many regularly sampled and regularly subsampled
time steps of multivariate time series with unobserved variables. We completely
characterize these graphs and show that they entail constraints beyond those
that have previously been considered in the literature. This allows for
stronger causal inferences without having imposed additional assumptions. In
generalization of directed partial ancestral graphs we further introduce a
graphical representation of Markov equivalence classes of the novel type of
graphs and show that these are more informative than what current
state-of-the-art causal discovery algorithms learn. We also analyze the
additional information gained by increasing the number of observed time steps.

Astronomical transients are stellar objects that become temporarily brighter
on various timescales and have led to some of the most significant discoveries
in cosmology and astronomy. Some of these transients are the explosive deaths
of stars known as supernovae while others are rare, exotic, or entirely new
kinds of exciting stellar explosions. New astronomical sky surveys are
observing unprecedented numbers of multi-wavelength transients, making standard
approaches of visually identifying new and interesting transients infeasible.
To meet this demand, we present two novel methods that aim to quickly and
automatically detect anomalous transient light curves in real-time. Both
methods are based on the simple idea that if the light curves from a known
population of transients can be accurately modelled, any deviations from model
predictions are likely anomalies. The first approach is a probabilistic neural
network built using Temporal Convolutional Networks (TCNs) and the second is an
interpretable Bayesian parametric model of a transient. We show that the
flexibility of neural networks, the attribute that makes them such a powerful
tool for many regression tasks, is what makes them less suitable for anomaly
detection when compared with our parametric model.

Accurate models of real quantum systems are important for investigating their
behaviour, yet are difficult to distill empirically. Here, we report an
algorithm -- the Quantum Model Learning Agent (QMLA) -- to reverse engineer
Hamiltonian descriptions of a target system. We test the performance of QMLA on
a number of simulated experiments, demonstrating several mechanisms for the
design of candidate Hamiltonian models and simultaneously entertaining numerous
hypotheses about the nature of the physical interactions governing the system
under study. QMLA is shown to identify the true model in the majority of
instances, when provided with limited a priori information, and control of the
experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard
families of models in parallel, reliably identifying the family which best
describes the system dynamics. We demonstrate QMLA operating on large model
spaces by incorporating a genetic algorithm to formulate new hypothetical
models. The selection of models whose features propagate to the next generation
is based upon an objective function inspired by the Elo rating scheme,
typically used to rate competitors in games such as chess and football. In all
instances, our protocol finds models that exhibit $F_1$-score $\geq 0.88$ when
compared with the true model, and it precisely identifies the true model in 72%
of cases, whilst exploring a space of over $250,000$ potential models. By
testing which interactions actually occur in the target system, QMLA is a
viable tool for both the exploration of fundamental physics and the
characterisation and calibration of quantum devices.

Intersecting branes provide a useful mechanism to construct particle physics
models from string theory with a wide variety of desirable characteristics. The
landscape of such models can be enormous, and navigating towards regions which
are most phenomenologically interesting is potentially challenging. Machine
learning techniques can be used to efficiently construct large numbers of
consistent and phenomenologically desirable models. In this work we phrase the
problem of finding consistent intersecting D-brane models in terms of genetic
algorithms, which mimic natural selection to evolve a population collectively
towards optimal solutions. For a four-dimensional ${\cal N}=1$ supersymmetric
type IIA orientifold with intersecting D6-branes, we demonstrate that
$\mathcal{O}(10^6)$ unique, fully consistent models can be easily constructed,
and, by a judicious choice of search environment and hyper-parameters,
$\mathcal{O}(30\%)$ of the found models contain the desired Standard Model
gauge group factor. Having a sizable sample allows us to draw some preliminary
landscape statistics of intersecting brane models both with and without the
restriction of having the Standard Model gauge factor.

Motion prediction of surrounding agents is an important task in context of
autonomous driving since it is closely related to driver's safety. Vehicle
Motion Prediction (VMP) track of Shifts Challenge focuses on developing models
which are robust to distributional shift and able to measure uncertainty of
their predictions. In this work we present the approach that significantly
improved provided benchmark and took 2nd place on the leaderboard.

In many application domains such as medicine, information retrieval,
cybersecurity, social media, etc., datasets used for inducing classification
models often have an unequal distribution of the instances of each class. This
situation, known as imbalanced data classification, causes low predictive
performance for the minority class examples. Thus, the prediction model is
unreliable although the overall model accuracy can be acceptable. Oversampling
and undersampling techniques are well-known strategies to deal with this
problem by balancing the number of examples of each class. However, their
effectiveness depends on several factors mainly related to data intrinsic
characteristics, such as imbalance ratio, dataset size and dimensionality,
overlapping between classes or borderline examples. In this work, the impact of
these factors is analyzed through a comprehensive comparative study involving
40 datasets from different application areas. The objective is to obtain models
for automatic selection of the best resampling strategy for any dataset based
on its characteristics. These models allow us to check several factors
simultaneously considering a wide range of values since they are induced from
very varied datasets that cover a broad spectrum of conditions. This differs
from most studies that focus on the individual analysis of the characteristics
or cover a small range of values. In addition, the study encompasses both basic
and advanced resampling strategies that are evaluated by means of eight
different performance metrics, including new measures specifically designed for
imbalanced data classification. The general nature of the proposal allows the
choice of the most appropriate method regardless of the domain, avoiding the
search for special purpose techniques that could be valid for the target data.

We present a textless speech-to-speech translation (S2ST) system that can
translate speech from one language into another language and can be built
without the need of any text data. Different from existing work in the
literature, we tackle the challenge in modeling multi-speaker target speech and
train the systems with real-world S2ST data. The key to our approach is a
self-supervised unit-based speech normalization technique, which finetunes a
pre-trained speech encoder with paired audios from multiple speakers and a
single reference speaker to reduce the variations due to accents, while
preserving the lexical content. With only 10 minutes of paired data for speech
normalization, we obtain on average 3.2 BLEU gain when training the S2ST model
on the VoxPopuli S2ST dataset, compared to a baseline trained on un-normalized
speech target. We also incorporate automatically mined S2ST data and show an
additional 2.0 BLEU gain. To our knowledge, we are the first to establish a
textless S2ST technique that can be trained with real-world data and works for
multiple language pairs. Audio samples are available at
https://facebookresearch.github.io/speech_translation/textless_s2st_real_data/index.html .

As task-oriented dialog systems are becoming increasingly popular in our
lives, more realistic tasks have been proposed and explored. However, new
practical challenges arise. For instance, current dialog systems cannot
effectively handle multiple search results when querying a database, due to the
lack of such scenarios in existing public datasets. In this paper, we propose
Database Search Result (DSR) Disambiguation, a novel task that focuses on
disambiguating database search results, which enhances user experience by
allowing them to choose from multiple options instead of just one. To study
this task, we augment the popular task-oriented dialog datasets (MultiWOZ and
SGD) with turns that resolve ambiguities by (a) synthetically generating turns
through a pre-defined grammar, and (b) collecting human paraphrases for a
subset. We find that training on our augmented dialog data improves the model's
ability to deal with ambiguous scenarios, without sacrificing performance on
unmodified turns. Furthermore, pre-fine tuning and multi-task learning help our
model to improve performance on DSR-disambiguation even in the absence of
in-domain data, suggesting that it can be learned as a universal dialog skill.
Our data and code will be made publicly available.

Large pre-trained language models are often trained on large volumes of
internet data, some of which may contain toxic or abusive language.
Consequently, language models encode toxic information, which makes the
real-world usage of these language models limited. Current methods aim to
prevent toxic features from appearing generated text. We hypothesize the
existence of a low-dimensional toxic subspace in the latent space of
pre-trained language models, the existence of which suggests that toxic
features follow some underlying pattern and are thus removable. To construct
this toxic subspace, we propose a method to generalize toxic directions in the
latent space. We also provide a methodology for constructing parallel datasets
using a context based word masking system. Through our experiments, we show
that when the toxic subspace is removed from a set of sentence representations,
almost no toxic representations remain in the result. We demonstrate
empirically that the subspace found using our method generalizes to multiple
toxicity corpora, indicating the existence of a low-dimensional toxic subspace.

We present a new challenging dataset, CPPE - 5 (Medical Personal Protective
Equipment), with the goal to allow the study of subordinate categorization of
medical personal protective equipments, which is not possible with other
popular data sets that focus on broad level categories (such as PASCAL VOC,
ImageNet, Microsoft COCO, OpenImages, etc). To make it easy for models trained
on this dataset to be used in practical scenarios in complex scenes, our
dataset mainly contains images that show complex scenes with several objects in
each scene in their natural context. The image collection for this dataset
focusing on: obtaining as many non-iconic images as possible and making sure
all the images are real-life images unlike other existing datasets in this
area. Our dataset includes 5 object categories (coveralls, face shield, gloves,
mask, and goggles) and each image is annotated with a set of bounding boxes and
positive labels. We present a detailed analysis of the dataset in comparison to
other popular broad category datasets as well as datasets focusing on personal
protective equipments, we also find that at present there exist no such
publicly available datasets. Finally we also analyze performance and compare
model complexities on baseline and state-of-the-art models for bounding box
results. Our code, data, and trained models are available at
https://git.io/cppe5-dataset .

Structured and grounded representation of text is typically formalized by
closed information extraction, the problem of extracting an exhaustive set of
(subject, relation, object) triplets that are consistent with a predefined set
of entities and relations from a knowledge base schema. Most existing works are
pipelines prone to error accumulation, and all approaches are only applicable
to unrealistically small numbers of entities and relations. We introduce GenIE
(generative information extraction), the first end-to-end autoregressive
formulation of closed information extraction. GenIE naturally exploits the
language knowledge from the pre-trained transformer by autoregressively
generating relations and entities in textual form. Thanks to a new bi-level
constrained generation strategy, only triplets consistent with the predefined
knowledge base schema are produced. Our experiments show that GenIE is
state-of-the-art on closed information extraction, generalizes from fewer
training data points than baselines, and scales to a previously unmanageable
number of entities and relations. With this work, closed information extraction
becomes practical in realistic scenarios, providing new opportunities for
downstream tasks. Finally, this work paves the way towards a unified end-to-end
approach to the core tasks of information extraction. Code, data and models
available at https://github.com/epfl-dlab/GenIE.

Nowadays, huge amounts of data are generated every second, and a quantity of
that data can be defined as sensitive. Blockchain technology has private,
secure, transparent and decentralized exchange of data as native. It is
adaptable and can be used in a wide range of Internet-based interactive systems
in academic and industrial settings. The essential part of programmable
distributed ledgers such as Ethereum, Polkadot, Cardano and other Web 3.0
technologies are smart contracts. Smart contracts are programs executed on the
global blockchain, the code is public as well as all of the data managed within
the transactions, thus creating a system that is reliable and cannot be cheated
if designed properly. In this paper, in order to make the educational system
more transparent and versatile we will describe an educational learning
platform designed as a distributed system.

A commonly observed problem of the state-of-the-art natural language
technologies, such as Amazon Alexa and Apple Siri, is that their services do
not extend to most developing countries' citizens due to language barriers.
Such populations suffer due to the lack of available resources in their
languages to build NLP products. This paper presents AllWOZ, a multilingual
multi-domain task-oriented customer service dialog dataset covering eight
languages: English, Mandarin, Korean, Vietnamese, Hindi, French, Portuguese,
and Thai. Furthermore, we create a benchmark for our multilingual dataset by
applying mT5 with meta-learning.

Many real-world data come in the form of graphs. Graph neural networks
(GNNs), a new family of machine learning (ML) models, have been proposed to
fully leverage graph data to build powerful applications. In particular, the
inductive GNNs, which can generalize to unseen data, become mainstream in this
direction. Machine learning models have shown great potential in various tasks
and have been deployed in many real-world scenarios. To train a good model, a
large amount of data as well as computational resources are needed, leading to
valuable intellectual property. Previous research has shown that ML models are
prone to model stealing attacks, which aim to steal the functionality of the
target models. However, most of them focus on the models trained with images
and texts. On the other hand, little attention has been paid to models trained
with graph data, i.e., GNNs. In this paper, we fill the gap by proposing the
first model stealing attacks against inductive GNNs. We systematically define
the threat model and propose six attacks based on the adversary's background
knowledge and the responses of the target models. Our evaluation on six
benchmark datasets shows that the proposed model stealing attacks against GNNs
achieve promising performance.

Recursive noun phrases (NPs) have interesting semantic properties. For
example, "my favorite new movie" is not necessarily my favorite movie, whereas
"my new favorite movie" is. This is common sense to humans, yet it is unknown
whether language models have such knowledge. We introduce the Recursive Noun
Phrase Challenge (RNPC), a dataset of three textual inference tasks involving
textual entailment and event plausibility comparison, precisely targeting the
understanding of recursive NPs. When evaluated on RNPC, state-of-the-art
Transformer models only perform around chance. Still, we show that such
knowledge is learnable with appropriate data. We further probe the models for
relevant linguistic features that can be learned from our tasks, including
modifier semantic category and modifier scope. Finally, models trained on RNPC
achieve strong zero-shot performance on an extrinsic Harm Detection evaluation
task, showing the usefulness of the understanding of recursive NPs in
downstream applications.

Recent works that revealed the vulnerability of dialogue state tracking (DST)
models to distributional shifts have made holistic comparisons on robustness
and qualitative analyses increasingly important for understanding their
relative performance. We present our findings from standardized and
comprehensive DST diagnoses, which have previously been sparse and
uncoordinated, using our toolkit, CheckDST, a collection of robustness tests
and failure mode analytics. We discover that different classes of DST models
have clear strengths and weaknesses, where generation models are more promising
for handling language variety while span-based classification models are more
robust to unseen entities. Prompted by this discovery, we also compare
checkpoints from the same model and find that the standard practice of
selecting checkpoints using validation loss/accuracy is prone to overfitting
and each model class has distinct patterns of failure. Lastly, we demonstrate
how our diagnoses motivate a pre-finetuning procedure with non-dialogue data
that offers comprehensive improvements to generation models by alleviating the
impact of distributional shifts through transfer learning.

As NLP models achieved state-of-the-art performances over benchmarks and
gained wide applications, it has been increasingly important to ensure the safe
deployment of these models in the real world, e.g., making sure the models are
robust against unseen or challenging scenarios. Despite robustness being an
increasingly studied topic, it has been separately explored in applications
like vision and NLP, with various definitions, evaluation and mitigation
strategies in multiple lines of research. In this paper, we aim to provide a
unifying survey of how to define, measure and improve robustness in NLP. We
first connect multiple definitions of robustness, then unify various lines of
work on identifying robustness failures and evaluating models' robustness.
Correspondingly, we present mitigation strategies that are data-driven,
model-driven, and inductive-prior-based, with a more systematic view of how to
effectively improve robustness in NLP models. Finally, we conclude by outlining
open challenges and future directions to motivate further research in this
area.

Deep generative models have demonstrated effectiveness in learning compact
and expressive design representations that significantly improve geometric
design optimization. However, these models do not consider the uncertainty
introduced by manufacturing or fabrication. Past work that quantifies such
uncertainty often makes simplified assumptions on geometric variations, while
the "real-world" uncertainty and its impact on design performance are difficult
to quantify due to the high dimensionality. To address this issue, we propose a
Generative Adversarial Network-based Design under Uncertainty Framework
(GAN-DUF), which contains a deep generative model that simultaneously learns a
compact representation of nominal (ideal) designs and the conditional
distribution of fabricated designs given any nominal design. We demonstrated
the framework on two real-world engineering design examples and showed its
capability of finding the solution that possesses better performances after
fabrication.

Recent advances in protein function prediction exploit graph-based deep
learning approaches to correlate the structural and topological features of
proteins with their molecular functions. However, proteins in vivo are not
static but dynamic molecules that alter conformation for functional purposes.
Here we apply normal mode analysis to native protein conformations and augment
protein graphs by connecting edges between dynamically correlated residue
pairs. In the multilabel function classification task, our method demonstrates
a remarkable performance gain based on this dynamics-informed representation.
The proposed graph neural network, ProDAR, increases the interpretability and
generalizability of residue-level annotations and robustly reflects structural
nuance in proteins. We elucidate the importance of dynamic information in graph
representation by comparing class activation maps for hMTH1, nitrophorin, and
SARS-CoV-2 receptor binding domain. Our model successfully learns the dynamic
fingerprints of proteins and pinpoints the residues of functional impacts, with
vast untapped potential for broad biotechnology and pharmaceutical

Improving the robustness of deep neural networks (DNNs) to adversarial
examples is an important yet challenging problem for secure deep learning.
Across existing defense techniques, adversarial training with Projected
Gradient Decent (PGD) is amongst the most effective. Adversarial training
solves a min-max optimization problem, with the \textit{inner maximization}
generating adversarial examples by maximizing the classification loss, and the
\textit{outer minimization} finding model parameters by minimizing the loss on
adversarial examples generated from the inner maximization. A criterion that
measures how well the inner maximization is solved is therefore crucial for
adversarial training. In this paper, we propose such a criterion, namely
First-Order Stationary Condition for constrained optimization (FOSC), to
quantitatively evaluate the convergence quality of adversarial examples found
in the inner maximization. With FOSC, we find that to ensure better robustness,
it is essential to use adversarial examples with better convergence quality at
the \textit{later stages} of training. Yet at the early stages, high
convergence quality adversarial examples are not necessary and may even lead to
poor robustness. Based on these observations, we propose a \textit{dynamic}
training strategy to gradually increase the convergence quality of the
generated adversarial examples, which significantly improves the robustness of
adversarial training. Our theoretical and empirical results show the

Understanding the black-box prediction for neural networks is challenging. To
achieve this, early studies have designed influence function (IF) to measure
the effect of removing a single training point on neural networks. However, the
classic implicit Hessian-vector product (IHVP) method for calculating IF is
fragile, and theoretical analysis of IF in the context of neural networks is
still lacking. To this end, we utilize the neural tangent kernel (NTK) theory
to calculate IF for the neural network trained with regularized mean-square
loss, and prove that the approximation error can be arbitrarily small when the
width is sufficiently large for two-layer ReLU networks. We analyze the error
bound for the classic IHVP method in the over-parameterized regime to
understand when and why it fails or not. In detail, our theoretical analysis
reveals that (1) the accuracy of IHVP depends on the regularization term, and
is pretty low under weak regularization; (2) the accuracy of IHVP has a
significant correlation with the probability density of corresponding training
points. We further borrow the theory from NTK to understand the IFs better,
including quantifying the complexity for influential samples and depicting the
variation of IFs during the training dynamics. Numerical experiments on
real-world data confirm our theoretical results and demonstrate our findings.

Blockchain technology provides a private, secure, transparent decentralized
exchange of data. Also, blockchain is not limited to a particular area, but it
has a wide range of applications and can be integrated into a variety of
Internet interactive systems. For example, the Internet of Things (IoT), supply
chain tracking, Electronic Health Records (EHR), digital forensics, identity
management, trustless payments, and other key business elements will all
benefit from its implementation. Next layer solutions such as Ethereum 2.0,
Polkadot, Cardano, and other Web 3.0 technologies provide developers
versatility. Moreover, these platforms utilize smart contracts which are
similar to standard, traditionalized software during development but offer key
utilities to end-users such as online wallets, secure data with transparent
rules. Blockchain is receiving a lot of attention in educational technology
(EduTech) as it aims to achieve a more transparent and multipurpose educational
system. In addition to smart contract technology which defines how data should
be registered, gathered and processed, blockchain can be used as an IoT
intermediary for mobile usage. Therefore, we implemented an educational
learning platform powered by blockchain technology to examine feasibility in
industry and academic environment. In essence, this is a web application which
is adapted to mobile platform and connected to blockchain for crucial data
exchanges. In this paper we want to emphasize the potential of blockchain
technology in multiple sectors as well as the need to really understand the
underlying principles which are allowing disruptability of traditional
centralized software solutions.

Variational Autoencoders (VAEs) suffer from degenerated performance, when
learning several successive tasks. This is caused by catastrophic forgetting.
In order to address the knowledge loss, VAEs are using either Generative Replay
(GR) mechanisms or Expanding Network Architectures (ENA). In this paper we
study the forgetting behaviour of VAEs using a joint GR and ENA methodology, by
deriving an upper bound on the negative marginal log-likelihood. This
theoretical analysis provides new insights into how VAEs forget the previously
learnt knowledge during lifelong learning. The analysis indicates the best
performance achieved when considering model mixtures, under the ENA framework,
where there are no restrictions on the number of components. However, an
ENA-based approach may require an excessive number of parameters. This
motivates us to propose a novel Dynamic Expansion Graph Model (DEGM). DEGM
expands its architecture, according to the novelty associated with each new
databases, when compared to the information already learnt by the network from
previous tasks. DEGM training optimizes knowledge structuring, characterizing
the joint probabilistic representations corresponding to the past and more
recently learned tasks. We demonstrate that DEGM guarantees optimal performance
for each task while also minimizing the required number of parameters.
Supplementary materials (SM) and source code are available in

We consider two problems of NMT domain adaptation using meta-learning. First,
we want to reach domain robustness, i.e., we want to reach high quality on both
domains seen in the training data and unseen domains. Second, we want our
systems to be adaptive, i.e., making it possible to finetune systems with just
hundreds of in-domain parallel sentences. We study the domain adaptability of
meta-learning when improving the domain robustness of the model. In this paper,
we propose a novel approach, RMLNMT (Robust Meta-Learning Framework for Neural
Machine Translation Domain Adaptation), which improves the robustness of
existing meta-learning models. More specifically, we show how to use a domain
classifier in curriculum learning and we integrate the word-level domain mixing
model into the meta-learning framework with a balanced sampling strategy.
Experiments on English$\rightarrow$German and English$\rightarrow$Chinese
translation show that RMLNMT improves in terms of both domain robustness and
domain adaptability in seen and unseen domains. Our source code is available at
https://github.com/lavine-lmu/RMLNMT.

Driven by the fact that many of us experienced softer or not-so-soft
lockdown, the intention of a couple of instructors at our university was to
develop a collaborative tool that could help in online delivery and
gamification on two courses that are delivered in the Business and IT
curriculums we are offering to our students. That tool could be described as a
decentralized web application that simulates Internet marketing principles and
helps in gamification of the learning process for our students. We planned our
web application for Internet marketing simulation as the gamification of the
learning process, which is one of the basics for active learning for Internet
Marketing course for International Business students, to gain new class
activities by online simulation competing in the field of Internet marketing
principles; and for IT students in developing the Web application and also on
adopting Blockchain technologies for the distributed reports which need to have
a consensus of all teams included in the simulation. The proposed solution
includes the design of business logic simulation and using four main digital
marketing tools social networking, content creating and sharing, search engine
marketing, and display advertising in use of such application for hands-on
online class exercises.

The detection of object states in images (State Detection - SD) is a problem
of both theoretical and practical importance and it is tightly interwoven with
other important computer vision problems, such as action recognition and
affordance detection. It is also highly relevant to any entity that needs to
reason and act in dynamic domains, such as robotic systems and intelligent
agents. Despite its importance, up to now, the research on this problem has
been limited. In this paper, we attempt a systematic study of the SD problem.
First, we introduce the Object State Detection Dataset (OSDD), a new publicly
available dataset consisting of more than 19,000 annotations for 18 object
categories and 9 state classes. Second, using a standard deep learning
framework used for Object Detection (OD), we conduct a number of appropriately
designed experiments, towards an in-depth study of the behavior of the SD
problem. This study enables the setup of a baseline on the performance of SD,
as well as its relative performance in comparison to OD, in a variety of
scenarios. Overall, the experimental outcomes confirm that SD is harder than OD
and that tailored SD methods need to be developed for addressing effectively
this significant problem.

In this work, we present SeqFormer for video instance segmentation. SeqFormer
follows the principle of vision transformer that models instance relationships
among video frames. Nevertheless, we observe that a stand-alone instance query
suffices for capturing a time sequence of instances in a video, but attention
mechanisms shall be done with each frame independently. To achieve this,
SeqFormer locates an instance in each frame and aggregates temporal information
to learn a powerful representation of a video-level instance, which is used to
predict the mask sequences on each frame dynamically. Instance tracking is
achieved naturally without tracking branches or post-processing. On
YouTube-VIS, SeqFormer achieves 47.4 AP with a ResNet-50 backbone and 49.0 AP
with a ResNet-101 backbone without bells and whistles. Such achievement
significantly exceeds the previous state-of-the-art performance by 4.6 and 4.4,
respectively. In addition, integrated with the recently-proposed Swin
transformer, SeqFormer achieves a much higher AP of 59.3. We hope SeqFormer
could be a strong baseline that fosters future research in video instance
segmentation, and in the meantime, advances this field with a more robust,
accurate, neat model. The code is available at
https://github.com/wjf5203/SeqFormer.

In modern software development, the JavaScript ecosystem of various
frameworks and libraries used to develop contemporary web applications presents
many advantages. JavaScript is a widely known interpreted programming language,
simple to learn and start development, and with numerous third-party libraries
and extensions. However, with the rise of highly user-interactive websites and
browser-based games, in some cases, JavaScripts executable engine could lack in
performance. Therefore, developers could combine several other programming
languages to create a polyglot user-interactive interoperable system to develop
efficient modern web applications. The interoperability modules offer
significant advantages but also present challenges in the execution due to high
complexity and longer compilation times. This paper explores WebAssembly, a
binary format compilation target with a low-level assembly-like language used
for targeting from other programming languages. The binary format allows
near-native performance level due to its compactness, as it prioritizes usage
of low-level languages. Moreover, as a continuation of our previous research of
the GraalVM ecosystem, we analyzed a guest language implementation of a
WebAssembly based system, TruffleWasm, hosted on GraalVM and Truffle Java
framework. This paper presents the architecture and review of the TruffleWasm
within the GraalVM-based ecosystem as well as from performance test results
within our academic environment.

Recent applications of machine learning (ML) reveal a noticeable shift from
its use for predictive modeling in the sense of a data-driven construction of
models mainly used for the purpose of prediction (of ground-truth facts) to its
use for prescriptive modeling. What is meant by this is the task of learning a
model that stipulates appropriate decisions about the right course of action in
real-world scenarios: Which medical therapy should be applied? Should this
person be hired for the job? As argued in this article, prescriptive modeling
comes with new technical conditions for learning and new demands regarding
reliability, responsibility, and the ethics of decision making. Therefore, to
support the data-driven design of decision-making agents that act in a rational
but at the same time responsible manner, a rigorous methodological foundation
of prescriptive ML is needed. The purpose of this short paper is to elaborate
on specific characteristics of prescriptive ML and to highlight some key
challenges it implies. Besides, drawing connections to other branches of
contemporary AI research, the grounding of prescriptive ML in a (generalized)
decision-theoretic framework is advocated.

Customer service chatbots are conversational systems designed to provide
information to customers about products/services offered by different
companies. Particularly, intent recognition is one of the core components in
the natural language understating capabilities of a chatbot system. Among the
different intents that a chatbot is trained to recognize, there is a set of
them that is universal to any customer service chatbot. Universal intents may
include salutation, switch the conversation to a human agent, farewells, among
others. A system to recognize those universal intents will be very helpful to
optimize the training process of specific customer service chatbots. We propose
the development of a universal intent recognition system, which is trained to
recognize a selected group of 11 intents that are common in 28 different
chatbots. The proposed system is trained considering state-of-the-art
word-embedding models such as word2vec and BERT, and deep classifiers based on
convolutional and recurrent neural networks. The proposed model is able to
discriminate between those universal intents with a balanced accuracy up to
80.4\%. In addition, the proposed system is equally accurate to recognize
intents expressed both in short and long text requests. At the same time,
misclassification errors often occurs between intents with very similar
semantic fields such as farewells and positive comments. The proposed system
will be very helpful to optimize the training process of a customer service
chatbot because some of the intents will be already available and detected by
our system. At the same time, the proposed approach will be a suitable base
model to train more specific chatbots by applying transfer learning strategies.

Contemporary software often becomes vastly complex, and we are required to
use a variety of technologies and different programming languages for its
development. As interoperability between programming languages could cause high
overhead resulting in a performance loss, it is important to examine how a
current polyglot virtual machine with a compiler written in a high-level
object-oriented language deals with it. OpenJDK's Project Metropolis presented
the GraalVM, an open-source, high-performance polyglot virtual machine, mostly
written in Java. This paper presents GraalVM's architecture and its features;
furthermore, examining how it resolves common interoperability and performance
problems. GraalVM makes software ecosystem productive when combining various
programming languages, for example, Java, JavaScript, C/C++, Python, Ruby, R,
and others. The vital part of GraalVM is the Graal compiler written in Java,
which allows developers to maintain and optimize code faster, simpler, and more
efficient, in comparison to traditional compilers in C/C++ languages. Graal can
be used as a just-in-time (JIT) or as static, ahead-of-time (AOT) compiler.
Graal is an aggressively optimizing compiler implementing common compiler
optimizations, with emphasis on outstanding inlining and escape analysis
algorithms. This paper compares Graal with some of the best-specialized
competitors, and presents our results tested within an academic environment.

Current AI/ML methods for data-driven engineering use models that are mostly
trained offline. Such models can be expensive to build in terms of
communication and computing cost, and they rely on data that is collected over
extended periods of time. Further, they become out-of-date when changes in the
system occur. To address these challenges, we investigate online learning
techniques that automatically reduce the number of available data sources for
model training. We present an online algorithm called Online Stable Feature Set
Algorithm (OSFS), which selects a small feature set from a large number of
available data sources after receiving a small number of measurements. The
algorithm is initialized with a feature ranking algorithm, a feature set
stability metric, and a search policy. We perform an extensive experimental
evaluation of this algorithm using traces from an in-house testbed and from a
data center in operation. We find that OSFS achieves a massive reduction in the
size of the feature set by 1-3 orders of magnitude on all investigated
datasets. Most importantly, we find that the accuracy of a predictor trained on
a OSFS-produced feature set is somewhat better than when the predictor is
trained on a feature set obtained through offline feature selection. OSFS is
thus shown to be effective as an online feature selection algorithm and robust
regarding the sample interval used for feature selection. We also find that,
when concept drift in the data underlying the model occurs, its effect can be
mitigated by recomputing the feature set and retraining the prediction model.

Black box optimization requires specifying a search space to explore for
solutions, e.g. a d-dimensional compact space, and this choice is critical for
getting the best results at a reasonable budget. Unfortunately, determining a
high quality search space can be challenging in many applications. For example,
when tuning hyperparameters for machine learning pipelines on a new problem
given a limited budget, one must strike a balance between excluding potentially
promising regions and keeping the search space small enough to be tractable.
The goal of this work is to motivate -- through example applications in tuning
deep neural networks -- the problem of predicting the quality of search spaces
conditioned on budgets, as well as to provide a simple scoring method based on
a utility function applied to a probabilistic response surface model, similar
to Bayesian optimization. We show that the method we present can compute
meaningful budget-conditional scores in a variety of situations. We also
provide experimental evidence that accurate scores can be useful in
constructing and pruning search spaces. Ultimately, we believe scoring search
spaces should become standard practice in the experimental workflow for deep
learning.

Accurate segmentation of the liver is a prerequisite for the diagnosis of
disease. Automated segmentation is an important application of computer-aided
detection and diagnosis of liver disease. In recent years, automated processing
of medical images has gained breakthroughs. However, the low contrast of
abdominal scan CT images and the complexity of liver morphology make accurate
automatic segmentation challenging. In this paper, we propose RA V-Net, which
is an improved medical image automatic segmentation model based on U-Net. It
has the following three main innovations. CofRes Module (Composite Original
Feature Residual Module) is proposed. With more complex convolution layers and
skip connections to make it obtain a higher level of image feature extraction
capability and prevent gradient disappearance or explosion. AR Module
(Attention Recovery Module) is proposed to reduce the computational effort of
the model. In addition, the spatial features between the data pixels of the
encoding and decoding modules are sensed by adjusting the channels and LSTM
convolution. Finally, the image features are effectively retained. CA Module
(Channel Attention Module) is introduced, which used to extract relevant
channels with dependencies and strengthen them by matrix dot product, while
weakening irrelevant channels without dependencies. The purpose of channel
attention is achieved. The attention mechanism provided by LSTM convolution and
CA Module are strong guarantees for the performance of the neural network. The
accuracy of U-Net network: 0.9862, precision: 0.9118, DSC: 0.8547, JSC: 0.82.
The evaluation metrics of RA V-Net, accuracy: 0.9968, precision: 0.9597, DSC:
0.9654, JSC: 0.9414. The most representative metric for the segmentation effect
is DSC, which improves 0.1107 over U-Net, and JSC improves 0.1214.

Background Sepsis is one of the most life-threatening circumstances for
critically ill patients in the US, while a standardized criteria for sepsis
identification is still under development. Disparities in social determinants
of sepsis patients can interfere with the risk prediction performances using
machine learning. Methods Disparities in social determinants, including race,
gender, marital status, insurance types and languages, among patients
identified by six available sepsis criteria were revealed by forest plots.
Sixteen machine learning classifiers were trained to predict in-hospital
mortality for sepsis patients. The performance of the trained model was tested
on the entire randomly conducted test set and each sub-population built based
on each of the following social determinants: race, gender, marital status,
insurance type, and language. Results We analyzed a total of 11,791 critical
care patients from the MIMIC-III database. Within the population identified by
each sepsis identification method, significant differences were observed among
sub-populations regarding race, marital status, insurance type, and language.
On the 5,783 sepsis patients identified by the Sepsis-3 criteria statistically
significant performance decreases for mortality prediction were observed when
applying the trained machine learning model on Asian and Hispanic patients.
With pairwise comparison, we detected performance discrepancies in mortality
prediction between Asian and White patients, Asians and patients of other
races, as well as English-speaking and Spanish-speaking patients. Conclusions
Disparities in proportions of patients identified by various sepsis criteria
were detected among the different social determinant groups. To achieve
accurate diagnosis, a versatile diagnostic system for sepsis is needed to
overcome the social determinant disparities of patients.

This paper presents an approach to trajectory-centric learning control based
on contraction metrics and disturbance estimation for nonlinear systems subject
to matched uncertainties. The approach uses deep neural networks to learn
uncertain dynamics while still providing guarantees of transient tracking
performance throughout the learning phase. Within the proposed approach, a
disturbance estimation law is adopted to estimate the pointwise value of the
uncertainty, with pre-computable estimation error bounds (EEBs). The learned
dynamics, the estimated disturbances, and the EEBs are then incorporated in a
robust Riemann energy condition to compute the control law that guarantees
exponential convergence of actual trajectories to desired ones throughout the
learning phase, even when the learned model is poor. On the other hand, with
improved accuracy, the learned model can help improve the robustness of the
tracking controller, e.g., against input delays, and can be incorporated to
plan better trajectories with improved performance, e.g., lower energy
consumption and shorter travel time.The proposed framework is validated on a
planar quadrotor example.

Representation is the way in which human beings re-present the reality of
what is happening, both externally and internally. Thus, visual representation
as a means of communication uses elements to build a narrative, just as spoken
and written language do. We propose using computer analysis to perform a
quantitative analysis of the elements used in the visual creations that have
been produced in reference to the epidemic, using the images compiled in The
Covid Art Museum's Instagram account to analyze the different elements used to
represent subjective experiences with regard to a global event. This process
has been carried out with techniques based on machine learning to detect
objects in the images so that the algorithm can be capable of learning and
detecting the objects contained in each study image. This research reveals that
the elements that are repeated in images to create narratives and the relations
of association that are established in the sample, concluding that, despite the
subjectivity that all creation entails, there are certain parameters of shared
and reduced decisions when it comes to selecting objects to be included in
visual representations

Generative networks are often trained to minimize a statistical divergence
between the reference distribution and the generative one in an adversarial
setting. Some works trained instead generative networks to minimize Scoring
Rules, functions assessing how well the generative distribution matches each
training sample individually. We show how the Scoring Rule formulation easily
extends to the so-called prequential (predictive-sequential) score, whose
minimization allows performing probabilistic forecasting with generative
networks. This objective leads to adversarial-free training, therefore easily
avoiding uncertainty underestimation due to mode collapse, which is a common
issue in the adversarial setting and undesirable for probabilistic forecasting.
We provide consistency guarantees for the minimizer of the prequential score
and employ that to perform probabilistic forecasting for two chaotic dynamical
models and a benchmark dataset of global weather observations. For this last
example, we define scoring rules for spatial data by drawing from the relevant
literature, with which we obtain better uncertainty quantification with little
hyperparameter tuning compared to adversarial training.

Purpose The purpose of the research is to conduct an exploratory
investigation of the material handling activities of an Italian logistics hub.
Wearable sensors and other smart tools were used for collecting human and
environmental features during working activities. These factors were correlated
with workers' performance and well-being.
  Design/methodology/approach Human and environmental factors play an important
role in operations management activities since they significantly influence
employees' performance, well-being and safety. Surprisingly, empirical studies
about the impact of such aspects on logistics operations are still very
limited. Trying to fill this gap, the research empirically explores human and
environmental factors affecting the performance of logistics workers exploiting
smart tools.
  Findings Results suggest that human attitudes, interactions, emotions and
environmental conditions remarkably influence workers' performance and
well-being, however, showing different relationships depending on individual
characteristics of each worker.
  Practical implications The authors' research opens up new avenues for
profiling employees and adopting an individualized human resource management,
providing managers with an operational system capable to potentially check and
improve workers' well-being and performance.
  Originality/value The originality of the study comes from the in-depth
exploration of human and environmental factors using body-worn sensors during
work activities, by recording individual, collaborative and environmental data
in real-time. To the best of the authors' knowledge, the current paper is the
first time that such a detailed analysis has been carried out in real-world
logistics operations.

A major impediment to successful drug development is the complexity, cost,
and scale of clinical trials. The detailed internal structure of clinical trial
data can make conventional optimization difficult to achieve. Recent advances
in machine learning, specifically graph-structured data analysis, have the
potential to enable significant progress in improving the clinical trial
design. TrialGraph seeks to apply these methodologies to produce a
proof-of-concept framework for developing models which can aid drug development
and benefit patients. In this work, we first introduce a curated clinical trial
data set compiled from the CT.gov, AACT and TrialTrove databases (n=1191
trials; representing one million patients) and describe the conversion of this
data to graph-structured formats. We then detail the mathematical basis and
implementation of a selection of graph machine learning algorithms, which
typically use standard machine classifiers on graph data embedded in a
low-dimensional feature space. We trained these models to predict side effect
information for a clinical trial given information on the disease, existing
medical conditions, and treatment. The MetaPath2Vec algorithm performed
exceptionally well, with standard Logistic Regression, Decision Tree, Random
Forest, Support Vector, and Neural Network classifiers exhibiting typical
ROC-AUC scores of 0.85, 0.68, 0.86, 0.80, and 0.77, respectively. Remarkably,
the best performing classifiers could only produce typical ROC-AUC scores of
0.70 when trained on equivalent array-structured data. Our work demonstrates
that graph modelling can significantly improve prediction accuracy on
appropriate datasets. Successive versions of the project that refine modelling
assumptions and incorporate more data types can produce excellent predictors
with real-world applications in drug development.

Reinforcement learning (RL) techniques have shown great success in many
challenging quantitative trading tasks, such as portfolio management and
algorithmic trading. Especially, intraday trading is one of the most profitable
and risky tasks because of the intraday behaviors of the financial market that
reflect billions of rapidly fluctuating capitals. However, a vast majority of
existing RL methods focus on the relatively low frequency trading scenarios
(e.g., day-level) and fail to capture the fleeting intraday investment
opportunities due to two major challenges: 1) how to effectively train
profitable RL agents for intraday investment decision-making, which involves
high-dimensional fine-grained action space; 2) how to learn meaningful
multi-modality market representation to understand the intraday behaviors of
the financial market at tick-level. Motivated by the efficient workflow of
professional human intraday traders, we propose DeepScalper, a deep
reinforcement learning framework for intraday trading to tackle the above
challenges. Specifically, DeepScalper includes four components: 1) a dueling
Q-network with action branching to deal with the large action space of intraday
trading for efficient RL optimization; 2) a novel reward function with a
hindsight bonus to encourage RL agents making trading decisions with a
long-term horizon of the entire trading day; 3) an encoder-decoder architecture
to learn multi-modality temporal market embedding, which incorporates both
macro-level and micro-level market information; 4) a risk-aware auxiliary task
to maintain a striking balance between maximizing profit and minimizing risk.
Through extensive experiments on real-world market data spanning over three
years on six financial futures, we demonstrate that DeepScalper significantly
outperforms many state-of-the-art baselines in terms of four financial
criteria.

The inter/intra-subject variability of electroencephalography (EEG) makes the
practical use of the brain-computer interface (BCI) difficult. In general, the
BCI system requires a calibration procedure to tune the model every time the
system is used. This problem is recognized as a major obstacle to BCI, and to
overcome it, approaches based on transfer learning (TL) have recently emerged.
However, many BCI paradigms are limited in that they consist of a structure
that shows labels first and then measures "imagery", the negative effects of
source subjects containing data that do not contain control signals have been
ignored in many cases of the subject-to-subject TL process. The main purpose of
this paper is to propose a method of excluding subjects that are expected to
have a negative impact on subject-to-subject TL training, which generally uses
data from as many subjects as possible. In this paper, we proposed a BCI
framework using only high-confidence subjects for TL training. In our
framework, a deep neural network selects useful subjects for the TL process and
excludes noisy subjects, using a co-teaching algorithm based on the small-loss
trick. We experimented with leave-one-subject-out validation on two public
datasets (2020 international BCI competition track 4 and OpenBMI dataset). Our
experimental results showed that confidence-aware TL, which selects subjects
with small loss instances, improves the generalization performance of BCI.

Minimizing prediction uncertainty on unlabeled data is a key factor to
achieve good performance in semi-supervised learning (SSL). The prediction
uncertainty is typically expressed as the \emph{entropy} computed by the
transformed probabilities in output space. Most existing works distill
low-entropy prediction by either accepting the determining class (with the
largest probability) as the true label or suppressing subtle predictions (with
the smaller probabilities). Unarguably, these distillation strategies are
usually heuristic and less informative for model training. From this
discernment, this paper proposes a dual mechanism, named ADaptive Sharpening
(\ADS), which first applies a soft-threshold to adaptively mask out determinate
and negligible predictions, and then seamlessly sharpens the informed
predictions, distilling certain predictions with the informed ones only. More
importantly, we theoretically analyze the traits of \ADS by comparing with
various distillation strategies. Numerous experiments verify that \ADS
significantly improves the state-of-the-art SSL methods by making it a plug-in.
Our proposed \ADS forges a cornerstone for future distillation-based SSL
research.

Transformers are groundbreaking architectures that have changed a flow of
deep learning, and many high-performance models are developing based on
transformer architectures. Transformers implemented only with attention with
encoder-decoder structure following seq2seq without using RNN, but had better
performance than RNN. Herein, we investigate the decoding technique for
electroencephalography (EEG) composed of self-attention module from transformer
architecture during imagined speech and overt speech. We performed
classification of nine subjects using convolutional neural network based on
EEGNet that captures temporal-spectral-spatial features from EEG of imagined
speech and overt speech. Furthermore, we applied the self-attention module to
decoding EEG to improve the performance and lower the number of parameters. Our
results demonstrate the possibility of decoding brain activities of imagined
speech and overt speech using attention modules. Also, only single channel EEG
or ear-EEG can be used to decode the imagined speech for practical BCIs.

Accelerating the neural network inference by FPGA has emerged as a popular
option, since the reconfigurability and high performance computing capability
of FPGA intrinsically satisfies the computation demand of the fast-evolving
neural algorithms. However, the popular neural accelerators on FPGA (e.g.,
Xilinx DPU) mainly utilize the DSP resources for constructing their processing
units, while the rich LUT resources are not well exploited. Via the
software-hardware co-design approach, in this work, we develop an FPGA-based
heterogeneous computing system for neural network acceleration. From the
hardware perspective, the proposed accelerator consists of DSP- and LUT-based
GEneral Matrix-Multiplication (GEMM) computing cores, which forms the entire
computing system in a heterogeneous fashion. The DSP- and LUT-based GEMM cores
are computed w.r.t a unified Instruction Set Architecture (ISA) and unified
buffers. Along the data flow of the neural network inference path, the
computation of the convolution/fully-connected layer is split into two
portions, handled by the DSP- and LUT-based GEMM cores asynchronously. From the
software perspective, we mathematically and systematically model the latency
and resource utilization of the proposed heterogeneous accelerator, regarding
varying system design configurations. Through leveraging the reinforcement
learning technique, we construct a framework to achieve end-to-end selection
and optimization of the design specification of target heterogeneous
accelerator, including workload split strategy, mixed-precision quantization
scheme, and resource allocation of DSP- and LUT-core. In virtue of the proposed
design framework and heterogeneous computing system, our design outperforms the
state-of-the-art Mix&Match design with latency reduced by 1.12-1.32x with
higher inference accuracy. The N3H-core is open-sourced at:
https://github.com/elliothe/N3H_Core.

Millions of people around the world can not access content on the Web because
most of the content is not readily available in their language. Machine
translation (MT) systems have the potential to change this for many languages.
Current MT systems provide very accurate results for high resource language
pairs, e.g., German and English. However, for many low resource languages, MT
is still under active research. The key challenge is lack of datasets to build
these systems. We present Lesan, an MT system for low resource languages. Our
pipeline solves the key bottleneck to low resource MT by leveraging online and
offline sources, a custom OCR system for Ethiopic and an automatic alignment
module. The final step in the pipeline is a sequence to sequence model that
takes parallel corpus as input and gives us a translation model. Lesan's
translation model is based on the Transformer architecture. After constructing
a base model, back translation, is used to leverage monolingual corpora.
Currently Lesan supports translation to and from Tigrinya, Amharic and English.
We perform extensive human evaluation and show that Lesan outperforms
state-of-the-art systems such as Google Translate and Microsoft Translator
across all six pairs. Lesan is freely available and has served more than 10
million translations so far. At the moment, there are only 217 Tigrinya and
15,009 Amharic Wikipedia articles. We believe that Lesan will contribute
towards democratizing access to the Web through MT for millions of people.

We present DR.DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual
Representation), a new cross-lingual information retrieval (CLIR) system
trained using multi-stage knowledge distillation (KD). The teacher of DR.DECR
relies on a highly effective but computationally expensive two-stage inference
process consisting of query translation and monolingual IR, while the student,
DR.DECR, executes a single CLIR step. We teach DR.DECR powerful multilingual
representations as well as CLIR by optimizing two corresponding KD objectives.
Learning useful representations of non-English text from an English-only
retriever is accomplished through a cross-lingual token alignment algorithm
that relies on the representation capabilities of the underlying multilingual
encoders. In both in-domain and zero-shot out-of-domain evaluation, DR.DECR
demonstrates far superior accuracy over direct fine-tuning with labeled CLIR
data. It is also the best single-model retriever on the XOR-TyDi benchmark at
the time of this writing.

Neural memory enables fast adaptation to new tasks with just a few training
samples. Existing memory models store features only from the single last layer,
which does not generalize well in presence of a domain shift between training
and test distributions. Rather than relying on a flat memory, we propose a
hierarchical alternative that stores features at different semantic levels. We
introduce a hierarchical prototype model, where each level of the prototype
fetches corresponding information from the hierarchical memory. The model is
endowed with the ability to flexibly rely on features at different semantic
levels if the domain shift circumstances so demand. We meta-learn the model by
a newly derived hierarchical variational inference framework, where
hierarchical memory and prototypes are jointly optimized. To explore and
exploit the importance of different semantic levels, we further propose to
learn the weights associated with the prototype at each level in a data-driven
way, which enables the model to adaptively choose the most generalizable
features. We conduct thorough ablation studies to demonstrate the effectiveness
of each component in our model. The new state-of-the-art performance on
cross-domain and competitive performance on traditional few-shot classification
further substantiates the benefit of hierarchical variational memory.

Today, many image coding scenarios do not have a human as final intended
user, but rather a machine fulfilling computer vision tasks on the decoded
image. Thereby, the primary goal is not to keep visual quality but maintain the
task accuracy of the machine for a given bitrate. Due to the tremendous
progress of deep neural networks setting benchmarking results, mostly neural
networks are employed to solve the analysis tasks at the decoder side.
Moreover, neural networks have also found their way into the field of image
compression recently. These two developments allow for an end-to-end training
of the neural compression network for an analysis network as information sink.
Therefore, we first roll out such a training with a task-specific loss to
enhance the coding performance of neural compression networks. Compared to the
standard VVC, 41.4% of bitrate are saved by this method for Mask R-CNN as
analysis network on the uncompressed Cityscapes dataset. As a main
contribution, we propose LSMnet, a network that runs in parallel to the encoder
network and masks out elements of the latent space that are presumably not
required for the analysis network. By this approach, additional 27.3% of
bitrate are saved compared to the basic neural compression network optimized
with the task loss. In addition, we are the first to utilize a feature-based
distortion in the training loss within the context of machine-to-machine
communication, which allows for a training without annotated data. We provide
extensive analyses on the Cityscapes dataset including cross-evaluation with
different analysis networks and present exemplary visual results. Inference
code and pre-trained models are published at
https://github.com/FAU-LMS/NCN_for_M2M.

Individual vocal differences are ubiquitous in the animal kingdom. In humans,
these differences pervade the entire vocal repertoire and constitute a "voice
print". Apes, our closest-living relatives, possess individual signatures
within specific call types, but the potential for a unique voice print has been
little investigated. This is partially attributed to the limitations associated
with extracting meaningful features from small data sets. Advances in machine
learning have highlighted an alternative to traditional acoustic features,
namely pre-trained learnt extractors. Here, we present an approach building on
these developments: leveraging a feature extractor based on a deep neural
network trained on over 10,000 human voice prints to provide an informative
space over which we identify chimpanzee voice prints. We compare our results
with those obtained by using traditional acoustic features and discuss the
benefits of our methodology and the significance of our findings for the
identification of "voice prints" in non-human animals.

Preserving privacy in contemporary NLP models allows us to work with
sensitive data, but unfortunately comes at a price. We know that stricter
privacy guarantees in differentially-private stochastic gradient descent
(DP-SGD) generally degrade model performance. However, previous research on the
efficiency of DP-SGD in NLP is inconclusive or even counter-intuitive. In this
short paper, we provide an extensive analysis of different privacy preserving
strategies on seven downstream datasets in five different `typical' NLP tasks
with varying complexity using modern neural models based on BERT and
XtremeDistil architectures. We show that unlike standard non-private approaches
to solving NLP tasks, where bigger is usually better, privacy-preserving
strategies do not exhibit a winning pattern, and each task and privacy regime
requires a special treatment to achieve adequate performance.

$k$NN based neural machine translation ($k$NN-MT) has achieved
state-of-the-art results in a variety of MT tasks. One significant shortcoming
of $k$NN-MT lies in its inefficiency in identifying the $k$ nearest neighbors
of the query representation from the entire datastore, which is prohibitively
time-intensive when the datastore size is large. In this work, we propose
\textbf{Faster $k$NN-MT} to address this issue. The core idea of Faster
$k$NN-MT is to use a hierarchical clustering strategy to approximate the
distance between the query and a data point in the datastore, which is
decomposed into two parts: the distance between the query and the center of the
cluster that the data point belongs to, and the distance between the data point
and the cluster center. We propose practical ways to compute these two parts in
a significantly faster manner. Through extensive experiments on different MT
benchmarks, we show that \textbf{Faster $k$NN-MT} is faster than Fast $k$NN-MT
\citep{meng2021fast} and only slightly (1.2 times) slower than its vanilla
counterpart while preserving model performance as $k$NN-MT. Faster $k$NN-MT
enables the deployment of $k$NN-MT models on real-world MT services.

Conversational recommendation systems (CRS) engage with users by inferring
user preferences from dialog history, providing accurate recommendations, and
generating appropriate responses. Previous CRSs use knowledge graph (KG) based
recommendation modules and integrate KG with language models for response
generation. Although KG-based approaches prove effective, two issues remain to
be solved. First, KG-based approaches ignore the information in the
conversational context but only rely on entity relations and bag of words to
recommend items. Second, it requires substantial engineering efforts to
maintain KGs that model domain-specific relations, thus leading to less
flexibility. In this paper, we propose a simple yet effective architecture
comprising a pre-trained language model (PLM) and an item metadata encoder. The
encoder learns to map item metadata to embeddings that can reflect the semantic
information in the dialog context. The PLM then consumes the semantic-aligned
item embeddings together with dialog context to generate high-quality
recommendations and responses. Instead of modeling entity relations with KGs,
our model reduces engineering complexity by directly converting each item to an
embedding. Experimental results on the benchmark dataset ReDial show that our
model obtains state-of-the-art results on both recommendation and response
generation tasks.

Our work reveals a structured shortcoming of the existing mainstream
self-supervised learning methods. Whereas self-supervised learning frameworks
usually take the prevailing perfect instance level invariance hypothesis for
granted, we carefully investigate the pitfalls behind. Particularly, we argue
that the existing augmentation pipeline for generating multiple positive views
naturally introduces out-of-distribution (OOD) samples that undermine the
learning of the downstream tasks. Generating diverse positive augmentations on
the input does not always pay off in benefiting downstream tasks. To overcome
this inherent deficiency, we introduce a lightweight latent variable model
UOTA, targeting the view sampling issue for self-supervised learning. UOTA
adaptively searches for the most important sampling region to produce views,
and provides viable choice for outlier-robust self-supervised learning
approaches. Our method directly generalizes to many mainstream self-supervised
learning approaches, regardless of the loss's nature contrastive or not. We
empirically show UOTA's advantage over the state-of-the-art self-supervised
paradigms with evident margin, which well justifies the existence of the OOD
sample issue embedded in the existing approaches. Especially, we theoretically
prove that the merits of the proposal boil down to guaranteed estimator
variance and bias reduction. Code is available: at
https://github.com/ssl-codelab/uota.

We construct and analyze approximation rates of deep operator networks
(ONets) between infinite-dimensional spaces that emulate with an exponential
rate of convergence the coefficient-to-solution map of elliptic second-order
partial differential equations. In particular, we consider problems set in
$d$-dimensional periodic domains, $d=1, 2, \dots$, and with analytic right-hand
sides and coefficients. Our analysis covers linear, elliptic second order
divergence-form PDEs as, e.g., diffusion-reaction problems, parametric
diffusion equations, and elliptic systems such as linear isotropic
elastostatics in heterogeneous materials.
  We leverage the exponential convergence of spectral collocation methods for
boundary value problems whose solutions are analytic. In the present periodic
and analytic setting, this follows from classical elliptic regularity. Within
the ONet branch and trunk construction of [Chen and Chen, 1993] and of [Lu et
al., 2021], we show the existence of deep ONets which emulate the
coefficient-to-solution map to a desired accuracy in the $H^1$ norm, uniformly
over the coefficient set. We prove that the neural networks in the ONet have
size $\mathcal{O}(\left|\log(\varepsilon)\right|^\kappa)$, where
$\varepsilon>0$ is the approximation accuracy, for some $\kappa>0$ depending on
the physical space dimension.

Recently, self-supervised learning technology has been applied to calculate
depth and ego-motion from monocular videos, achieving remarkable performance in
autonomous driving scenarios. One widely adopted assumption of depth and
ego-motion self-supervised learning is that the image brightness remains
constant within nearby frames. Unfortunately, the endoscopic scene does not
meet this assumption because there are severe brightness fluctuations induced
by illumination variations, non-Lambertian reflections and interreflections
during data collection, and these brightness fluctuations inevitably
deteriorate the depth and ego-motion estimation accuracy. In this work, we
introduce a novel concept referred to as appearance flow to address the
brightness inconsistency problem. The appearance flow takes into consideration
any variations in the brightness pattern and enables us to develop a
generalized dynamic image constraint. Furthermore, we build a unified
self-supervised framework to estimate monocular depth and ego-motion
simultaneously in endoscopic scenes, which comprises a structure module, a
motion module, an appearance module and a correspondence module, to accurately
reconstruct the appearance and calibrate the image brightness. Extensive
experiments are conducted on the SCARED dataset and EndoSLAM dataset, and the
proposed unified framework exceeds other self-supervised approaches by a large
margin. To validate our framework's generalization ability on different
patients and cameras, we train our model on SCARED but test it on the SERV-CT
and Hamlyn datasets without any fine-tuning, and the superior results reveal
its strong generalization ability. Code will be available at:
\url{https://github.com/ShuweiShao/AF-SfMLearner}.

The presence of mislabeled observations in data is a notoriously challenging
problem in statistics and machine learning, associated with poor generalization
properties for both traditional classifiers and, perhaps even more so, flexible
classifiers like neural networks. Here we propose a novel double regularization
of the neural network training loss that combines a penalty on the complexity
of the classification model and an optimal reweighting of training
observations. The combined penalties result in improved generalization
properties and strong robustness against overfitting in different settings of
mislabeled training data and also against variation in initial parameter values
when training. We provide a theoretical justification for our proposed method
derived for a simple case of logistic regression. We demonstrate the double
regularization model, here denoted by DRFit, for neural net classification of
(i) MNIST and (ii) CIFAR-10, in both cases with simulated mislabeling. We also
illustrate that DRFit identifies mislabeled data points with very good
precision. This provides strong support for DRFit as a practical of-the-shelf
classifier, since, without any sacrifice in performance, we get a classifier
that simultaneously reduces overfitting against mislabeling and gives an
accurate measure of the trustworthiness of the labels.

Large amounts of annotated data have become more important than ever,
especially since the rise of deep learning techniques. However, manual
annotations are costly. We propose a tool that enables researchers to create
large, high-quality, annotated datasets with only a few manual annotations,
thus strongly reducing annotation cost and effort. For this purpose, we combine
an active learning (AL) approach with a pre-trained language model to
semi-automatically identify annotation categories in the given text documents.
To highlight our research direction's potential, we evaluate the approach on
the task of identifying frames in news articles. Our preliminary results show
that employing AL strongly reduces the number of annotations for correct
classification of even these complex and subtle frames. On the framing dataset,
the AL approach needs only 16.3\% of the annotations to reach the same
performance as a model trained on the full dataset.

In this work, we unify several existing decoding strategies for punctuation
prediction in one framework and introduce a novel strategy which utilises
multiple predictions at each word across different windows. We show that
significant improvements can be achieved by optimising these strategies after
training a model, only leading to a potential increase in inference time, with
no requirement for retraining. We further use our decoding strategy framework
for the first comparison of tagging and classification approaches for
punctuation prediction in a real-time setting. Our results show that a
classification approach for punctuation prediction can be beneficial when
little or no right-side context is available.

The emergence of the novel coronavirus (COVID-19) has generated a need to
quickly and accurately assemble up-to-date information related to its spread.
While it is possible to use deaths to provide a reliable information feed, the
latency of data derived from deaths is significant. Confirmed cases derived
from positive test results potentially provide a lower latency data feed.
However, the sampling of those tested varies with time and the reason for
testing is often not recorded. Hospital admissions typically occur around 1-2
weeks after infection and can be considered out of date in relation to the time
of initial infection. The extent to which these issues are problematic is
likely to vary over time and between countries.
  We use a machine learning algorithm for natural language processing, trained
in multiple languages, to identify symptomatic individuals derived from social
media and, in particular Twitter, in real-time. We then use an extended SEIRD
epidemiological model to fuse combinations of low-latency feeds, including the
symptomatic counts from Twitter, with death data to estimate parameters of the
model and nowcast the number of people in each compartment. The model is
implemented in the probabilistic programming language Stan and uses a bespoke
numerical integrator. We present results showing that using specific
low-latency data feeds along with death data provides more consistent and
accurate forecasts of COVID-19 related deaths than using death data alone.

Complex scientific models where the likelihood cannot be evaluated present a
challenge for statistical inference. Over the past two decades, a wide range of
algorithms have been proposed for learning parameters in computationally
feasible ways, often under the heading of approximate Bayesian computation or
likelihood-free inference. There is, however, no consensus on how to rigorously
evaluate the performance of these algorithms. Here, we argue for scoring
algorithms by the mean squared error in estimating expectations of functions
with respect to the posterior. We show that score implies common alternatives,
including the acceptance rate and effective sample size, as limiting special
cases. We then derive asymptotically optimal distributions for choosing or
sampling discrete or continuous simulation parameters, respectively. Our
recommendations differ significantly from guidelines based on alternative
scores outside of their region of validity. As an application, we show
sequential Monte Carlo in this context can be made more accurate with no new
samples by accepting particles from all rounds.

Optimal setting of several hyper-parameters in machine learning algorithms is
key to make the most of available data. To this aim, several methods such as
evolutionary strategies, random search, Bayesian optimization and heuristic
rules of thumb have been proposed. In reinforcement learning (RL), the
information content of data gathered by the learning agent while interacting
with its environment is heavily dependent on the setting of many
hyper-parameters. Therefore, the user of an RL algorithm has to rely on
search-based optimization methods, such as grid search or the Nelder-Mead
simplex algorithm, that are very inefficient for most RL tasks, slows down
significantly the learning curve and leaves to the user the burden of
purposefully biasing data gathering. In this work, in order to make an RL
algorithm more user-independent, a novel approach for autonomous
hyper-parameter setting using Bayesian optimization is proposed. Data from past
episodes and different hyper-parameter values are used at a meta-learning level
by performing behavioral cloning which helps improving the effectiveness in
maximizing a reinforcement learning variant of an acquisition function. Also,
by tightly integrating Bayesian optimization in a reinforcement learning agent
design, the number of state transitions needed to converge to the optimal
policy for a given task is reduced. Computational experiments reveal promising
results compared to other manual tweaking and optimization-based approaches
which highlights the benefits of changing the algorithm hyper-parameters to
increase the information content of generated data.

We propose novel methods to develop action controllable agent that behaves
like a human and has the ability to align with human players in Multiplayer
Online Battle Arena (MOBA) games. By modeling the control problem as an action
generation process, we devise a deep latent alignment neural network model for
training agent, and a corresponding sampling algorithm for controlling an
agent's action. Particularly, we propose deterministic and stochastic attention
implementations of the core latent alignment model. Both simulated and online
experiments in the game Honor of Kings demonstrate the efficacy of the proposed
methods.

Ensuring solution feasibility is a key challenge in developing Deep Neural
Network (DNN) schemes for solving constrained optimization problems, due to
inherent DNN prediction errors. In this paper, we propose a "preventive
learning'" framework to systematically guarantee DNN solution feasibility for
problems with convex constraints and general objective functions. We first
apply a predict-and-reconstruct design to not only guarantee equality
constraints but also exploit them to reduce the number of variables to be
predicted by DNN. Then, as a key methodological contribution, we systematically
calibrate inequality constraints used in DNN training, thereby anticipating
prediction errors and ensuring the resulting solutions remain feasible. We
characterize the calibration magnitudes and the DNN size sufficient for
ensuring universal feasibility. We propose a new Adversary-Sample Aware
training algorithm to improve DNN's optimality performance without sacrificing
feasibility guarantee. Overall, the framework provides two DNNs. The first one
from characterizing the sufficient DNN size can guarantee universal feasibility
while the other from the proposed training algorithm further improves
optimality and maintains DNN's universal feasibility simultaneously. We apply
the preventive learning framework to develop DeepOPF+ for solving the essential
DC optimal power flow problem in grid operation. It improves over existing
DNN-based schemes in ensuring feasibility and attaining consistent desirable
speedup performance in both light-load and heavy-load regimes. Simulation
results over IEEE Case-30/118/300 test cases show that DeepOPF+ generates
$100\%$ feasible solutions with $<$0.5% optimality loss and up to two orders of
magnitude computational speedup, as compared to a state-of-the-art iterative
solver.

Though deep learning-based object detection methods have achieved promising
results on the conventional datasets, it is still challenging to locate objects
from the low-quality images captured in adverse weather conditions. The
existing methods either have difficulties in balancing the tasks of image
enhancement and object detection, or often ignore the latent information
beneficial for detection. To alleviate this problem, we propose a novel
Image-Adaptive YOLO (IA-YOLO) framework, where each image can be adaptively
enhanced for better detection performance. Specifically, a differentiable image
processing (DIP) module is presented to take into account the adverse weather
conditions for YOLO detector, whose parameters are predicted by a small
convolutional neural net-work (CNN-PP). We learn CNN-PP and YOLOv3 jointly in
an end-to-end fashion, which ensures that CNN-PP can learn an appropriate DIP
to enhance the image for detection in a weakly supervised manner. Our proposed
IA-YOLO approach can adaptively process images in both normal and adverse
weather conditions. The experimental results are very encouraging,
demonstrating the effectiveness of our proposed IA-YOLO method in both foggy
and low-light scenarios.

To generalize in object-centric tasks, a reinforcement learning (RL) agent
needs to exploit the structure that objects induce. Prior work has either
hard-coded object-centric features, used complex object-centric generative
models, or updated state using local spatial features. However, these
approaches have had limited success in enabling general RL agents. Motivated by
this, we introduce ``Feature-Attending Recurrent Modules'' (FARM), an
architecture for learning state representations that relies on simple, broadly
applicable inductive biases for capturing spatial and temporal regularities.
FARM learns a state representation that is distributed across multiple modules
that each attend to spatiotemporal features with an expressive feature
attention mechanism. This enables FARM to represent diverse object-induced
spatial and temporal regularities across subsets of modules. We hypothesize
that this enables an RL agent to flexibly recombine its experiences for
generalization. We study task suites in both 2D and 3D environments and find
that FARM better generalizes compared to competing architectures that leverage
attention or multiple modules.

Dynamic demand prediction is crucial for the efficient operation and
management of urban transportation systems. Extensive research has been
conducted on single-mode demand prediction, ignoring the fact that the demands
for different transportation modes can be correlated with each other. Despite
some recent efforts, existing approaches to multimodal demand prediction are
generally not flexible enough to account for multiplex networks with diverse
spatial units and heterogeneous spatiotemporal correlations across different
modes. To tackle these issues, this study proposes a multi-relational
spatiotemporal graph neural network (ST-MRGNN) for multimodal demand
prediction. Specifically, the spatial dependencies across modes are encoded
with multiple intra- and inter-modal relation graphs. A multi-relational graph
neural network (MRGNN) is introduced to capture cross-mode heterogeneous
spatial dependencies, consisting of generalized graph convolution networks to
learn the message passing mechanisms within relation graphs and an
attention-based aggregation module to summarize different relations. We further
integrate MRGNNs with temporal gated convolution layers to jointly model
heterogeneous spatiotemporal correlations. Extensive experiments are conducted
using real-world subway and ride-hailing datasets from New York City, and the
results verify the improved performance of our proposed approach over existing
methods across modes. The improvement is particularly large for demand-sparse
locations. Further analysis of the attention mechanisms of ST-MRGNN also
demonstrates its good interpretability for understanding cross-mode

Computational models of atrial fibrillation have successfully been used to
predict optimal ablation sites. A critical step to assess the effect of an
ablation pattern is to pace the model from different, potentially random,
locations to determine whether arrhythmias can be induced in the atria. In this
work, we propose to use multi-fidelity Gaussian process classification on
Riemannian manifolds to efficiently determine the regions in the atria where
arrhythmias are inducible. We build a probabilistic classifier that operates
directly on the atrial surface. We take advantage of lower resolution models to
explore the atrial surface and combine seamlessly with high-resolution models
to identify regions of inducibility. When trained with 40 samples, our
multi-fidelity classifier shows a balanced accuracy that is 10% higher than a
nearest neighbor classifier used as a baseline atrial fibrillation model, and
9% higher in presence of atrial fibrillation with ablations. We hope that this
new technique will allow faster and more precise clinical applications of
computational models for atrial fibrillation.

Depth estimation is a cornerstone of a vast number of applications requiring
3D assessment of the environment, such as robotics, augmented reality, and
autonomous driving to name a few. One prominent technique for depth estimation
is stereo matching which has several advantages: it is considered more
accessible than other depth-sensing technologies, can produce dense depth
estimates in real-time, and has benefited greatly from the advances of deep
learning in recent years. However, current techniques for depth estimation from
stereoscopic images still suffer from a built-in drawback. To reconstruct
depth, a stereo matching algorithm first estimates the disparity map between
the left and right images before applying a geometric triangulation. A simple
analysis reveals that the depth error is quadratically proportional to the
object's distance. Therefore, constant disparity errors are translated to large
depth errors for objects far from the camera. To mitigate this quadratic
relation, we propose a simple but effective method that uses a refinement
network for depth estimation. We show analytical and empirical results
suggesting that the proposed learning procedure reduces this quadratic
relation. We evaluate the proposed refinement procedure on well-known
benchmarks and datasets, like Sceneflow and KITTI datasets, and demonstrate
significant improvements in the depth accuracy metric.

Normalizing flows are diffeomorphic, typically dimension-preserving, models
trained using the likelihood of the model. We use the SurVAE framework to
construct dimension reducing surjective flows via a new layer, known as the
funnel. We demonstrate its efficacy on a variety of datasets, and show it
improves upon or matches the performance of existing flows while having a
reduced latent space size. The funnel layer can be constructed from a wide
range of transformations including restricted convolution and feed forward
layers.

We demonstrate the utility of elementary head-motion units termed kinemes for
behavioral analytics to predict personality and interview traits. Transforming
head-motion patterns into a sequence of kinemes facilitates discovery of latent
temporal signatures characterizing the targeted traits, thereby enabling both
efficient and explainable trait prediction. Utilizing Kinemes and Facial Action
Coding System (FACS) features to predict (a) OCEAN personality traits on the
First Impressions Candidate Screening videos, and (b) Interview traits on the
MIT dataset, we note that: (1) A Long-Short Term Memory (LSTM) network trained
with kineme sequences performs better than or similar to a Convolutional Neural
Network (CNN) trained with facial images; (2) Accurate predictions and
explanations are achieved on combining FACS action units (AUs) with kinemes,
and (3) Prediction performance is affected by the time-length over which head
and facial movements are observed.

Generative models synthesize image data with great success regarding sampling
quality, diversity and feature disentanglement. Generative models for time
series lack these benefits due to a missing representation, which captures
temporal dynamics and allows inversion for sampling. The paper proposes the
intertemporal return plot (IRP) representation to facilitate the use of
image-based generative adversarial networks for time series generation. The
representation proves effective in capturing time series characteristics and,
compared to alternative representations, benefits from invertibility and
scale-invariance. Empirical benchmarks confirm these features and demonstrate
that the IRP enables an off-the-shelf Wasserstein GAN with gradient penalty to
sample realistic time series, which outperform a specialized RNN-based GAN,
while simultaneously reducing model complexity.

Finding the closest separable state to a given target state is a notoriously
difficult task, even more difficult than deciding whether a state is entangled
or separable. To tackle this task, we parametrize separable states with a
neural network and train it to minimize the distance to a given target state,
with respect to a differentiable distance, such as the trace distance or
Hilbert--Schmidt distance. By examining the output of the algorithm, we obtain
an upper bound on the entanglement of the target state, and construct an
approximation for its closest separable state. We benchmark the method on a
variety of well-known classes of bipartite states and find excellent agreement,
even up to local dimension of $d=10$, while providing conjectures and analytic
insight for isotropic and Werner states. Moreover, we show our method to be
efficient in the multipartite case, considering different notions of
separability. Examining three and four-party GHZ and W states we recover known
bounds and obtain additional ones, for instance for triseparability.

In the context of time series forecasting, it is a common practice to
evaluate multiple methods and choose one of these methods or an ensemble for
producing the best forecasts. However, choosing among different ensembles over
multiple methods remains a challenging task that undergoes a combinatorial
explosion as the number of methods increases. In the context of demand
forecasting or revenue forecasting, this challenge is further exacerbated by a
large number of time series as well as limited historical data points available
due to changing business context. Although deep learning forecasting methods
aim to simultaneously forecast large collections of time series, they become
challenging to apply in such scenarios due to the limited history available and
might not yield desirable results. We propose a framework for forecasting short
high-dimensional time series data by combining low-rank temporal matrix
factorization and optimal model selection on latent time series using
cross-validation. We demonstrate that forecasting the latent factors leads to
significant performance gains as compared to directly applying different
uni-variate models on time series. Performance has been validated on a
truncated version of the M4 monthly dataset which contains time series data
from multiple domains showing the general applicability of the method.
Moreover, it is amenable to incorporating the analyst view of the future owing
to the low number of latent factors which is usually impractical when applying
forecasting methods directly to high dimensional datasets.

The rapid progression of Generative Adversarial Networks (GANs) has raised a
concern of their misuse for malicious purposes, especially in creating fake
face images. Although many proposed methods succeed in detecting GAN-based
synthetic images, they are still limited by the need for large quantities of
the training fake image dataset and challenges for the detector's
generalizability to unknown facial images. In this paper, we propose a new
approach that explores the asynchronous frequency spectra of color channels,
which is simple but effective for training both unsupervised and supervised
learning models to distinguish GAN-based synthetic images. We further
investigate the transferability of a training model that learns from our
suggested features in one source domain and validates on another target domains
with prior knowledge of the features' distribution. Our experimental results
show that the discrepancy of spectra in the frequency domain is a practical
artifact to effectively detect various types of GAN-based generated images.

Unlike molecular crystals, soft self-assembled fibres, micelles, vesicles,
etc., exhibit a certain order in the arrangement of their constitutive
monomers, but also high structural dynamicity and variability. Defects and
disordered local domains that continuously form-and-repair in their structures
impart to such materials unique adaptive and dynamical properties, which make
them, e.g., capable to communicate with each other. However, objective criteria
to compare such complex dynamical features and to classify soft supramolecular
materials are non-trivial to attain. Here we show a data-driven workflow
allowing us to achieve this goal. Building on unsupervised clustering of Smooth
Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular
dynamics simulations, we can compare a variety of soft supramolecular
assemblies via a robust SOAP metric. This provides us with a data-driven
"defectometer" to classify different types of supramolecular materials based on
the structural dynamics of the ordered/disordered local molecular environments
that statistically emerge within them.

Conventional static knowledge graphs model entities in relational data as
nodes, connected by edges of specific relation types. However, information and
knowledge evolve continuously, and temporal dynamics emerge, which are expected
to influence future situations. In temporal knowledge graphs, time information
is integrated into the graph by equipping each edge with a timestamp or a time
range. Embedding-based methods have been introduced for link prediction on
temporal knowledge graphs, but they mostly lack explainability and
comprehensible reasoning chains. Particularly, they are usually not designed to
deal with link forecasting -- event prediction involving future timestamps. We
address the task of link forecasting on temporal knowledge graphs and introduce
TLogic, an explainable framework that is based on temporal logical rules
extracted via temporal random walks. We compare TLogic with state-of-the-art
baselines on three benchmark datasets and show better overall performance while
our method also provides explanations that preserve time consistency.
Furthermore, in contrast to most state-of-the-art embedding-based methods,
TLogic works well in the inductive setting where already learned rules are
transferred to related datasets with a common vocabulary.

A robotic solution for the unmanned ground vehicles (UGVs) to execute the
highly complex task of object manipulation in an autonomous mode is presented.
This paper primarily focuses on developing an autonomous robotic system capable
of assembling elementary blocks to build the large 3D structures in GPS-denied
environments. The key contributions of this system paper are i) Designing of a
deep learning-based unified multi-task visual perception system for object
detection, part-detection, instance segmentation, and tracking, ii) an
electromagnetic gripper design for robust grasping, and iii) system integration
in which multiple system components are integrated to develop an optimized
software stack. The entire mechatronic and algorithmic design of UGV for the
above application is detailed in this work. The performance and efficacy of the
overall system are reported through several rigorous experiments.

In this paper we propose a novel socio-inspired convolutional neural network
(CNN) deep learning model for image splicing detection. Based on the premise
that learning from the detection of coarsely spliced image regions can improve
the detection of visually imperceptible finely spliced image forgeries, the
proposed model referred to as, MissMarple, is a twin CNN network involving
feature-transfer learning. Results obtained from training and testing the
proposed model using the benchmark datasets like Columbia splicing, WildWeb,
DSO1 and a proposed dataset titled AbhAS consisting of realistic splicing
forgeries revealed improvement in detection accuracy over the existing deep

Motion compensated inter prediction is a common component of all video
coders. The concept was established in traditional hybrid coding and
successfully transferred to learning-based video compression. To compress the
residual signal after prediction, usually the difference of the two signals is
compressed using a standard autoencoder. However, information theory tells us
that a general conditional coder is more efficient. In this paper, we provide a
solid foundation based on information theory and Shannon entropy to show the
potentials but also the limits of conditional coding. Building on those
results, we then propose the generalized difference coder, a special case of a
conditional coder designed to avoid limiting bottlenecks. With this coder, we
are able to achieve average rate savings of 27.8% compared to a standard
autoencoder, by only adding a moderate complexity overhead of less than 7%.

In this paper, we study the well-known team orienteering problem where a
fleet of robots collects rewards by visiting locations. Usually, the rewards
are assumed to be known to the robots; however, in applications such as
environmental monitoring or scene reconstruction, the rewards are often
subjective and specifying them is challenging. We propose a framework to learn
the unknown preferences of the user by presenting alternative solutions to
them, and the user provides a ranking on the proposed alternative solutions. We
consider the two cases for the user: 1) a deterministic user which provides the
optimal ranking for the alternative solutions, and 2) a noisy user which
provides the optimal ranking according to an unknown probability distribution.
For the deterministic user we propose a framework to minimize a bound on the
maximum deviation from the optimal solution, namely regret. We adapt the
approach to capture the noisy user and minimize the expected regret. Finally,
we demonstrate the importance of learning user preferences and the performance
of the proposed methods in an extensive set of experimental results using real
world datasets for environmental monitoring problems.

Capturing dynamics of operational similarity among terrorist groups is
critical to provide actionable insights for counter-terrorism and intelligence
monitoring. Yet, in spite of its theoretical and practical relevance, research
addressing this problem is currently lacking. We tackle this problem proposing
a novel computational framework for detecting clusters of terrorist groups
sharing similar behaviors, focusing on groups' yearly repertoire of deployed
tactics, attacked targets, and utilized weapons. Specifically considering those
organizations that have plotted at least 50 attacks from 1997 to 2018,
accounting for a total of 105 groups responsible for more than 42,000 events
worldwide, we offer three sets of results. First, we show that over the years
global terrorism has been characterized by increasing operational cohesiveness.
Second, we highlight that year-to-year stability in co-clustering among groups
has been particularly high from 2009 to 2018, indicating temporal consistency
of similarity patterns in the last decade. Third, we demonstrate that
operational similarity between two organizations is driven by three factors:
(a) their overall activity; (b) the difference in the diversity of their
operational repertoires; (c) the difference in a combined measure of diversity
and activity. Groups' operational preferences, geographical homophily and
ideological affinity have no consistent role in determining operational
similarity.

This work proposes a domain-informed neural network architecture for
experimental particle physics, using particle interaction localization with the
time-projection chamber (TPC) technology for dark matter research as an example
application. A key feature of the signals generated within the TPC is that they
allow localization of particle interactions through a process called
reconstruction. While multilayer perceptrons (MLPs) have emerged as a leading
contender for reconstruction in TPCs, such a black-box approach does not
reflect prior knowledge of the underlying scientific processes. This paper
looks anew at neural network-based interaction localization and encodes prior
detector knowledge, in terms of both signal characteristics and detector
geometry, into the feature encoding and the output layers of a multilayer
neural network. The resulting Domain-informed Neural Network (DiNN) limits the
receptive fields of the neurons in the initial feature encoding layers in order
to account for the spatially localized nature of the signals produced within
the TPC. This aspect of the DiNN, which has similarities with the emerging area
of graph neural networks in that the neurons in the initial layers only connect
to a handful of neurons in their succeeding layer, significantly reduces the
number of parameters in the network in comparison to an MLP. In addition, in
order to account for the detector geometry, the output layers of the network
are modified using two geometric transformations to ensure the DiNN produces
localizations within the interior of the detector. The end result is a neural
network architecture that has 60% fewer parameters than an MLP, but that still
achieves similar localization performance and provides a path to future
architectural developments with improved performance because of their ability
to encode additional domain knowledge into the architecture.

Sparsity priors are commonly used in denoising and image reconstruction. For
analysis-type priors, a dictionary defines a representation of signals that is
likely to be sparse. In most situations, this dictionary is not known, and is
to be recovered from pairs of ground-truth signals and measurements, by
minimizing the reconstruction error. This defines a hierarchical optimization
problem, which can be cast as a bi-level optimization. Yet, this problem is
unsolvable, as reconstructions and their derivative wrt the dictionary have no
closed-form expression. However, reconstructions can be iteratively computed
using the Forward-Backward splitting (FB) algorithm. In this paper, we
approximate reconstructions by the output of the aforementioned FB algorithm.
Then, we leverage automatic differentiation to evaluate the gradient of this
output wrt the dictionary, which we learn with projected gradient descent.
Experiments show that our algorithm successfully learns the 1D Total Variation
(TV) dictionary from piecewise constant signals. For the same case study, we
propose to constrain our search to dictionaries of 0-centered columns, which
removes undesired local minima and improves numerical stability.

Predicting the success of startup companies is of great importance for both
startup companies and investors. It is difficult due to the lack of available
data and appropriate general methods. With data platforms like Crunchbase
aggregating the information of startup companies, it is possible to predict
with machine learning algorithms. Existing research suffers from the data
sparsity problem as most early-stage startup companies do not have much data
available to the public. We try to leverage the recent algorithms to solve this
problem. We investigate several machine learning algorithms with a large
dataset from Crunchbase. The results suggest that LightGBM and XGBoost perform
best and achieve 53.03% and 52.96% F1 scores. We interpret the predictions from
the perspective of feature contribution. We construct portfolios based on the
models and achieve high success rates. These findings have substantial
implications on how machine learning methods can help startup companies and
investors.

Within this work, we investigate how data-driven numerical approximation
methods of the Koopman operator can be used in practical control engineering
applications. We refer to the method Extended Dynamic Mode Decomposition
(EDMD), which approximates a nonlinear dynamical system as a linear model. This
makes the method ideal for control engineering applications, because a linear
system description is often assumed for this purpose. Using academic examples,
we simulatively analyze the prediction performance of the learned EDMD models
and show how relevant system properties like stability, controllability, and
observability are reflected by the EDMD model, which is a critical requirement
for a successful control design process. Subsequently, we present our
experimental results on a mechatronic test bench and evaluate the applicability
to the control engineering design process. As a result, the investigated
methods are suitable as a low-effort alternative for the design steps of model
building and adaptation in the classical model-based controller design method.

This paper presents a novel learning-based clothing deformation method to
generate rich and reasonable detailed deformations for garments worn by bodies
of various shapes in various animations. In contrast to existing learning-based
methods, which require numerous trained models for different garment topologies
or poses and are unable to easily realize rich details, we use a unified
framework to produce high fidelity deformations efficiently and easily. To
address the challenging issue of predicting deformations influenced by
multi-source attributes, we propose three strategies from novel perspectives.
Specifically, we first found that the fit between the garment and the body has
an important impact on the degree of folds. We then designed an attribute
parser to generate detail-aware encodings and infused them into the graph
neural network, therefore enhancing the discrimination of details under diverse
attributes. Furthermore, to achieve better convergence and avoid overly smooth
deformations, we proposed output reconstruction to mitigate the complexity of
the learning task. Experiment results show that our proposed deformation method
achieves better performance over existing methods in terms of generalization
ability and quality of details.

The aim of the Memorability-EEG pilot subtask at MediaEval'2021 is to promote
interest in the use of neural signals -- either alone or in combination with
other data sources -- in the context of predicting video memorability by
highlighting the utility of EEG data. The dataset created consists of
pre-extracted features from EEG recordings of subjects while watching a subset
of videos from Predicting Media Memorability subtask 1. This demonstration
pilot gives interested researchers a sense of how neural signals can be used
without any prior domain knowledge, and enables them to do so in a future
memorability task. The dataset can be used to support the exploration of novel
machine learning and processing strategies for predicting video memorability,
while potentially increasing interdisciplinary interest in the subject of
memorability, and opening the door to new combined EEG-computer vision

Currently, next location recommendation plays a vital role in location-based
social network applications and services. Although many methods have been
proposed to solve this problem, three important challenges have not been well
addressed so far: (1) most existing methods are based on recurrent network,
which is time-consuming to train long sequences due to not allowing for full
parallelism; (2) personalized preferences generally are not considered
reasonably; (3) existing methods rarely systematically studied how to
efficiently utilize various auxiliary information (e.g., user ID and timestamp)
in trajectory data and the spatio-temporal relations among non-consecutive
locations. To address the above challenges, we propose a novel method named
SanMove, a self-attention network based model, to predict the next location via
capturing the long- and short-term mobility patterns of users. Specifically,
SanMove introduces a long-term preference learning module, and it uses a
self-attention module to capture the users long-term mobility pattern which can
represent personalized location preferences of users. Meanwhile, SanMove uses a
spatial-temporal guided non-invasive self-attention (STNOVA) to exploit
auxiliary information to learn short-term preferences. We evaluate SanMove with
two real-world datasets, and demonstrate SanMove is not only faster than the
state-of-the-art RNN-based predict model but also outperforms the baselines for
next location prediction.

This paper tackles the Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR)
problem from the viewpoint of cross-modality metric learning. This task has two
characteristics: 1) the zero-shot setting requires a metric space with good
within-class compactness and the between-class discrepancy for recognizing the
novel classes and 2) the sketch query and the photo gallery are in different
modalities. The metric learning viewpoint benefits ZS-SBIR from two aspects.
First, it facilitates improvement through recent good practices in deep metric
learning (DML). By combining two fundamental learning approaches in DML, e.g.,
classification training and pairwise training, we set up a strong baseline for
ZS-SBIR. Without bells and whistles, this baseline achieves competitive
retrieval accuracy. Second, it provides an insight that properly suppressing
the modality gap is critical. To this end, we design a novel method named
Modality-Aware Triplet Hard Mining (MATHM). MATHM enhances the baseline with
three types of pairwise learning, e.g., a cross-modality sample pair, a
within-modality sample pair, and their combination.\We also design an adaptive
weighting method to balance these three components during training dynamically.
Experimental results confirm that MATHM brings another round of significant
improvement based on the strong baseline and sets up new state-of-the-art
performance. For example, on the TU-Berlin dataset, we achieve 47.88+2.94%
mAP@all and 58.28+2.34% Prec@100. Code will be publicly available at:
https://github.com/huangzongheng/MATHM.

Active learning selects the most informative samples to exploit limited
annotation budgets. Existing work follows a cumbersome pipeline that repeats
the time-consuming model training and batch data selection multiple times. In
this paper, we challenge this status quo by proposing a novel general and
efficient active learning (GEAL) method following our designed new pipeline.
Utilizing a publicly available pretrained model, our method selects data from
different datasets with a single-pass inference of the same model without extra
training or supervision. To capture subtle local information, we propose
knowledge clusters extracted from intermediate features. Free from the
troublesome batch selection strategy, all data samples are selected in one-shot
through a distance-based sampling in the fine-grained knowledge cluster level.
This whole process is faster than prior arts by hundreds of times. Extensive
experiments verify the effectiveness of our method on object detection, image
classification, and semantic segmentation. Our code is publicly available in
https://github.com/yichen928/GEAL_active_learning.

In this paper, we propose a machine learning approach to recognise
engineering shape features such as holes, slots, etc. in a CAD mesh model. With
the advent of digital archiving, newer manufacturing techniques such as 3D
printing, scanning of components and reverse engineering, CAD data is
proliferated in the form of mesh model representation. As the number of nodes
and edges become larger in a mesh model as well as the possibility of presence
of noise, direct application of graph-based approaches would not only be
expensive but also difficult to be tuned for noisy data. Hence, this calls for
newer approaches to be devised for feature recognition for CAD models
represented in the form of mesh. Here, we show that a discrete version of Gauss
map can be used as a signature for a feature learning. We show that this
approach not only requires fewer memory requirements but also the training time
is quite less. As no network architecture is involved, the number of
hyperparameters are much lesser and can be tuned in a much faster time. The
recognition accuracy is also very similar to that of the one obtained using 3D
convolutional neural networks (CNN) but in much lesser running time and storage
requirements. A comparison has been done with other non-network based machine
learning approaches to show that our approach has the highest accuracy. We also
show the recognition results for CAD models having multiple features as well as
complex/interacting features obtained from public benchmarks. The ability to
handle noisy data has also been demonstrated.

Oscillator output generally has phase noise causing the output power spectral
density (PSD) to disperse around a Dirac delta function. In this paper, the
AWGN channel is considered, where the sent signal accompanying with phase noise
is added to the channel Gaussian noise and received at the receiver.
Conventional channel estimation algorithms such as least mean square (LMS) and
mean MSE criterion are not suitable for this channel estimation. We (i) analyze
this phase noise channel estimation with information theoretic learning (ITL)
criterion, i.e., maximum correntropy criterion (MCC), leading to robustness in
the channel estimator's steady state behavior; and (ii) improve the convergence
rate by combining MSE and MCC as a novel mixed-LMS algorithm.

We propose a framework to continuously learn object-centric representations
for visual learning and understanding. Existing object-centric representations
either rely on supervisions that individualize objects in the scene, or perform
unsupervised disentanglement that can hardly deal with complex scenes in the
real world. To mitigate the annotation burden and relax the constraints on the
statistical complexity of the data, our method leverages interactions to
effectively sample diverse variations of an object and the corresponding
training signals while learning the object-centric representations. Throughout
learning, objects are streamed one by one in random order with unknown
identities, and are associated with latent codes that can synthesize
discriminative weights for each object through a convolutional hypernetwork.
Moreover, re-identification of learned objects and forgetting prevention are
employed to make the learning process efficient and robust. We perform an
extensive study of the key features of the proposed framework and analyze the
characteristics of the learned representations. Furthermore, we demonstrate the
capability of the proposed framework in learning representations that can
improve label efficiency in downstream tasks. Our code and trained models are
made publicly available at: https://github.com/pptrick/Object-Pursuit.

Affinity maturation is crucial for improving the binding affinity of
antibodies to antigens. This process is mainly driven by point substitutions
caused by somatic hypermutations of the immunoglobulin gene. It also includes
deletions and insertions of genomic material known as indels. While the
landscape of point substitutions has been extensively studied, a detailed
statistical description of indels is still lacking. Here we present a
probabilistic inference tool to learn the statistics of indels from repertoire
sequencing data, which overcomes the pitfalls and biases of standard annotation
methods. The model includes antibody-specific maturation ages to account for
variable mutational loads in the repertoire. After validation on synthetic
data, we applied our tool to a large dataset of human immunoglobulin heavy
chains. The inferred model allows us to identify universal statistical features
of indels in heavy chains. We report distinct insertion and deletion hotspots,
and show that the distribution of lengths of indels follows a geometric
distribution, which puts constraints on future mechanistic models of the
hypermutation process.

With the periodic rise and fall of COVID-19 and numerous countries being
affected by its ramifications, there has been a tremendous amount of work that
has been done by scientists, researchers, and doctors all over the world.
Prompt intervention is keenly needed to tackle the unconscionable dissemination
of the disease. The implementation of Artificial Intelligence (AI) has made a
significant contribution to the digital health district by applying the
fundamentals of deep learning algorithms. In this study, a novel approach is
proposed to automatically diagnose the COVID-19 by the utilization of
Electrocardiogram (ECG) data with the integration of deep learning algorithms,
specifically the Convolutional Neural Network (CNN) models. Several CNN models
have been utilized in this proposed framework, including VGG16, VGG19,
InceptionResnetv2, InceptionV3, Resnet50, and Densenet201. The VGG16 model has
outperformed the rest of the models, with an accuracy of 85.92%. Our results
show a relatively low accuracy in the rest of the models compared to the VGG16
model, which is due to the small size of the utilized dataset, in addition to
the exclusive utilization of the Grid search hyperparameters optimization
approach for the VGG16 model only. Moreover, our results are preparatory, and
there is a possibility to enhance the accuracy of all models by further
expanding the dataset and adapting a suitable hyperparameters optimization

Unsupervised generation of high-quality multi-view-consistent images and 3D
shapes using only collections of single-view 2D photographs has been a
long-standing challenge. Existing 3D GANs are either compute-intensive or make
approximations that are not 3D-consistent; the former limits quality and
resolution of the generated images and the latter adversely affects multi-view
consistency and shape quality. In this work, we improve the computational
efficiency and image quality of 3D GANs without overly relying on these
approximations. We introduce an expressive hybrid explicit-implicit network
architecture that, together with other design choices, synthesizes not only
high-resolution multi-view-consistent images in real time but also produces
high-quality 3D geometry. By decoupling feature generation and neural
rendering, our framework is able to leverage state-of-the-art 2D CNN
generators, such as StyleGAN2, and inherit their efficiency and expressiveness.
We demonstrate state-of-the-art 3D-aware synthesis with FFHQ and AFHQ Cats,
among other experiments.

Radio channel modeling is one of the most fundamental aspects in the process
of designing, optimizing, and simulating wireless communication networks. In
this field, long-established approaches such as analytical channel models and
ray tracing techniques represent the de-facto standard methodologies. However,
as demonstrated by recent results, there remains an untapped potential to
innovate this research field by enriching model-based approaches with machine
learning techniques. In this paper, we present Deep RAdio channel modeling from
GeOinformatioN (DRaGon) as a novel machine learning-enabled method for
automatic generation of Radio Environmental Maps (REMs) from geographical data.
For achieving accurate path loss prediction results, DRaGon combines
determining features extracted from a three-dimensional model of the radio
propagation environment with raw images of the receiver area within a deep
learning model. In a comprehensive performance evaluation and validation
campaign, we compare the accuracy of the proposed approach with real world
measurements, ray tracing analyses, and well-known channel models. It is found
that the combination of expert knowledge from the communications domain and the
data analysis capabilities of deep learning allows to achieve a significantly
higher prediction accuracy than the reference methods.

Accurate drug response prediction (DRP) is a crucial yet challenging task in
precision medicine. This paper presents a novel Attention-Guided Multi-omics
Integration (AGMI) approach for DRP, which first constructs a Multi-edge Graph
(MeG) for each cell line, and then aggregates multi-omics features to predict
drug response using a novel structure, called Graph edge-aware Network (GeNet).
For the first time, our AGMI approach explores gene constraint based
multi-omics integration for DRP with the whole-genome using GNNs. Empirical
experiments on the CCLE and GDSC datasets show that our AGMI largely
outperforms state-of-the-art DRP methods by 8.3%--34.2% on four metrics. Our
data and code are available at https://github.com/yivan-WYYGDSG/AGMI.

Motivated by the heterogeneous nature of devices participating in large-scale
Federated Learning (FL) optimization, we focus on an asynchronous server-less
FL solution empowered by blockchain technology. In contrast to mostly adopted
FL approaches, which assume synchronous operation, we advocate an asynchronous
method whereby model aggregation is done as clients submit their local updates.
The asynchronous setting fits well with the federated optimization idea in
practical large-scale settings with heterogeneous clients. Thus, it potentially
leads to higher efficiency in terms of communication overhead and idle periods.
To evaluate the learning completion delay of BC-enabled FL, we provide an
analytical model based on batch service queue theory. Furthermore, we provide
simulation results to assess the performance of both synchronous and
asynchronous mechanisms. Important aspects involved in the BC-enabled FL
optimization, such as the network size, link capacity, or user requirements,
are put together and analyzed. As our results show, the synchronous setting
leads to higher prediction accuracy than the asynchronous case. Nevertheless,
asynchronous federated optimization provides much lower latency in many cases,
thus becoming an appealing solution for FL when dealing with large datasets,
tough timing constraints (e.g., near-real-time applications), or highly varying

With the rise of contrastive learning, unsupervised graph representation
learning has been booming recently, even surpassing the supervised counterparts
in some machine learning tasks. Most of existing contrastive models for graph
representation learning either focus on maximizing mutual information between
local and global embeddings, or primarily depend on contrasting embeddings at
node level. However, they are still not exquisite enough to comprehensively
explore the local and global views of network topology. Although the former
considers local-global relationship, its coarse global information leads to
grudging cooperation between local and global views. The latter pays attention
to node-level feature alignment, so that the role of global view appears
inconspicuous. To avoid falling into these two extreme cases, we propose a
novel unsupervised graph representation model by contrasting cluster
assignments, called as GRCCA. It is motivated to make good use of local and
global information synthetically through combining clustering algorithms and
contrastive learning. This not only facilitates the contrastive effect, but
also provides the more high-quality graph information. Meanwhile, GRCCA further
excavates cluster-level information, which make it get insight to the elusive
association between nodes beyond graph topology. Specifically, we first
generate two augmented graphs with distinct graph augmentation strategies, then
employ clustering algorithms to obtain their cluster assignments and prototypes
respectively. The proposed GRCCA further compels the identical nodes from
different augmented graphs to recognize their cluster assignments mutually by
minimizing a cross entropy loss. To demonstrate its effectiveness, we compare
with the state-of-the-art models in three different downstream tasks. The
experimental results show that GRCCA has strong competitiveness in most tasks.

Machine learning methods exploiting multi-parametric biomarkers, especially
based on neuroimaging, have huge potential to improve early diagnosis of
dementia and to predict which individuals are at-risk of developing dementia.
To benchmark algorithms in the field of machine learning and neuroimaging in
dementia and assess their potential for use in clinical practice and clinical
trials, seven grand challenges have been organized in the last decade.
  The seven grand challenges addressed questions related to screening, clinical
status estimation, prediction and monitoring in (pre-clinical) dementia. There
was little overlap in clinical questions, tasks and performance metrics.
Whereas this aids providing insight on a broad range of questions, it also
limits the validation of results across challenges. The validation process
itself was mostly comparable between challenges, using similar methods for
ensuring objective comparison, uncertainty estimation and statistical testing.
In general, winning algorithms performed rigorous data preprocessing and
combined a wide range of input features.
  Despite high state-of-the-art performances, most of the methods evaluated by
the challenges are not clinically used. To increase impact, future challenges
could pay more attention to statistical analysis of which factors relate to
higher performance, to clinical questions beyond Alzheimer's disease, and to
using testing data beyond the Alzheimer's Disease Neuroimaging Initiative.
Grand challenges would be an ideal venue for assessing the generalizability of
algorithm performance to unseen data of other cohorts. Key for increasing
impact in this way are larger testing data sizes, which could be reached by
sharing algorithms rather than data to exploit data that cannot be shared.

Image semantic segmentation technology is one of the key technologies for
intelligent systems to understand natural scenes. As one of the important
research directions in the field of visual intelligence, this technology has
broad application scenarios in the fields of mobile robots, drones, smart
driving, and smart security. However, in the actual application of mobile
robots, problems such as inaccurate segmentation semantic label prediction and
loss of edge information of segmented objects and background may occur. This
paper proposes an improved structure of a semantic segmentation network based
on a deep learning network that combines self-attention neural network and
neural network architecture search methods. First, a neural network search
method NAS (Neural Architecture Search) is used to find a semantic segmentation
network with multiple resolution branches. In the search process, combine the
self-attention network structure module to adjust the searched neural network
structure, and then combine the semantic segmentation network searched by
different branches to form a fast semantic segmentation network structure, and
input the picture into the network structure to get the final forecast result.
The experimental results on the Cityscapes dataset show that the accuracy of
the algorithm is 69.8%, and the segmentation speed is 48/s. It achieves a good
balance between real-time and accuracy, can optimize edge segmentation, and has
a better performance in complex scenes. Good robustness is suitable for
practical application.

The World Health Organization (WHO) has recommended wearing face masks as one
of the most effective measures to prevent COVID-19 transmission. In many
countries, it is now mandatory to wear face masks, specially in public places.
Since manual monitoring of face masks is often infeasible in the middle of the
crowd, automatic detection can be beneficial. To facilitate that, we explored a
number of deep learning models (i.e., VGG1, VGG19, ResNet50) for face-mask
detection and evaluated them on two benchmark datasets. We also evaluated
transfer learning (i.e., VGG19, ResNet50 pre-trained on ImageNet) in this
context. We find that while the performances of all the models are quite good,
transfer learning models achieve the best performance. Transfer learning
improves the performance by 0.10\%--0.40\% with 30\% less training time. Our
experiment also shows these high-performing models are not quite robust for
real-world cases where the test dataset comes from a different distribution.
Without any fine-tuning, the performance of these models drops by 47\% in
cross-domain settings.

Despite the rapid progress of neuromorphic computing, inadequate capacity and
insufficient representation power of spiking neural networks (SNNs) severely
restrict their application scope in practice. Residual learning and shortcuts
have been evidenced as an important approach for training deep neural networks,
but rarely did previous work assess their applicability to the characteristics
of spike-based communication and spatiotemporal dynamics. In this paper, we
first identify that this negligence leads to impeded information flow and
accompanying degradation problem in previous residual SNNs. Then we propose a
novel SNN-oriented residual block, MS-ResNet, which is able to significantly
extend the depth of directly trained SNNs, e.g. up to 482 layers on CIFAR-10
and 104 layers on ImageNet, without observing any slight degradation problem.
We validate the effectiveness of MS-ResNet on both frame-based and neuromorphic
datasets, and MS-ResNet104 achieves a superior result of 76.02% accuracy on
ImageNet, the first time in the domain of directly trained SNNs. Great energy
efficiency is also observed that on average only one spike per neuron is needed
to classify an input sample. We believe our powerful and scalable models will
provide a strong support for further exploration of SNNs.

The recent developments in wearable devices and the Internet of Medical
Things (IoMT) allow real-time monitoring and recording of electrocardiogram
(ECG) signals. However, continuous monitoring of ECG signals is challenging in
low-power wearable devices due to energy and memory constraints. Therefore, in
this paper, we present a novel and energy-efficient methodology for
continuously monitoring the heart for low-power wearable devices. The proposed
methodology is composed of three different layers: 1) a Noise/Artifact
detection layer to grade the quality of the ECG signals; 2) a Normal/Abnormal
beat classification layer to detect the anomalies in the ECG signals, and 3) an
Abnormal beat classification layer to detect diseases from ECG signals.
Moreover, a distributed multi-output Convolutional Neural Network (CNN)
architecture is used to decrease the energy consumption and latency between the
edge-fog/cloud. Our methodology reaches an accuracy of 99.2% on the well-known
MIT-BIH Arrhythmia dataset. Evaluation on real hardware shows that our
methodology is suitable for devices having a minimum RAM of 32KB. Moreover, the
proposed methodology achieves $7\times$ more energy efficiency compared to
state-of-the-art works.

Given a partition of a graph into connected components, the membership oracle
asserts whether any two vertices of the graph lie in the same component or not.
We prove that for $n\ge k\ge 2$, learning the components of an $n$-vertex
hidden graph with $k$ components requires at least $(k-1)n-\binom k2$
membership queries. Our result improves on the best known information-theoretic
bound of $\Omega(n\log k)$ queries, and exactly matches the query complexity of
the algorithm introduced by [Reyzin and Srivastava, 2007] for this problem.
Additionally, we introduce an oracle, with access to which one can learn the
number of components of $G$ in asymptotically fewer queries than learning the
full partition, thus answering another question posed by the same authors.
Lastly, we introduce a more applicable version of this oracle, and prove
asymptotically tight bounds of $\widetilde\Theta(m)$ queries for both learning
and verifying an $m$-edge hidden graph $G$ using it.

Recovering a dense depth image from sparse LiDAR scans is a challenging task.
Despite the popularity of color-guided methods for sparse-to-dense depth
completion, they treated pixels equally during optimization, ignoring the
uneven distribution characteristics in the sparse depth map and the accumulated
outliers in the synthesized ground truth. In this work, we introduce
uncertainty-driven loss functions to improve the robustness of depth completion
and handle the uncertainty in depth completion. Specifically, we propose an
explicit uncertainty formulation for robust depth completion with Jeffrey's
prior. A parametric uncertain-driven loss is introduced and translated to new
loss functions that are robust to noisy or missing data. Meanwhile, we propose
a multiscale joint prediction model that can simultaneously predict depth and
uncertainty maps. The estimated uncertainty map is also used to perform
adaptive prediction on the pixels with high uncertainty, leading to a residual
map for refining the completion results. Our method has been tested on KITTI
Depth Completion Benchmark and achieved the state-of-the-art robustness
performance in terms of MAE, IMAE, and IRMSE metrics.

Student performance prediction is a critical research problem to understand
the students' needs, present proper learning opportunities/resources, and
develop the teaching quality. However, traditional machine learning methods
fail to produce stable and accurate prediction results. In this paper, we
propose a graph-based ensemble machine learning method that aims to improve the
stability of single machine learning methods via the consensus of multiple
methods. To be specific, we leverage both supervised prediction methods and
unsupervised clustering methods, build an iterative approach that propagates in
a bipartite graph as well as converges to more stable and accurate prediction
results. Extensive experiments demonstrate the effectiveness of our proposed
method in predicting more accurate student performance. Specifically, our model
outperforms the best traditional machine learning algorithms by up to 14.8% in

Deep learning techniques for separating audio into different sound sources
face several challenges. Standard architectures require training separate
models for different types of audio sources. Although some universal separators
employ a single model to target multiple sources, they have difficulty
generalizing to unseen sources. In this paper, we propose a three-component
pipeline to train a universal audio source separator from a large, but
weakly-labeled dataset: AudioSet. First, we propose a transformer-based sound
event detection system for processing weakly-labeled training data. Second, we
devise a query-based audio separation model that leverages this data for model
training. Third, we design a latent embedding processor to encode queries that
specify audio targets for separation, allowing for zero-shot generalization.
Our approach uses a single model for source separation of multiple sound types,
and relies solely on weakly-labeled data for training. In addition, the
proposed audio separator can be used in a zero-shot setting, learning to
separate types of audio sources that were never seen in training. To evaluate
the separation performance, we test our model on MUSDB18, while training on the
disjoint AudioSet. We further verify the zero-shot performance by conducting
another experiment on audio source types that are held-out from training. The
model achieves comparable Source-to-Distortion Ratio (SDR) performance to
current supervised models in both cases.

In medical science, it is very important to gather multiple data on different
diseases and one of the most important objectives of the data is to investigate
the diseases. Myocardial infarction is a serious risk factor in mortality and
in previous studies, the main emphasis has been on people with heart disease
and measuring the likelihood of myocardial infarction in them through
demographic features, echocardiography, and electrocardiogram. In contrast, the
purpose of the present study is to utilize data analysis algorithms and compare
their accuracy in patients with a heart attack in order to identify the heart
muscle strength during myocardial infarction by taking into account emergency
operations and consequently predict myocardial infarction. For this purpose,
105 medical records of myocardial infarction patients with fourteen features
including age, the time of emergency operation, Creatine Phosphokinase (CPK)
test, heart rate, blood sugar, and vein are gathered and investigated through
classification techniques of data analysis including random decision forests,
decision tree, support vector machine (SVM), k-nearest neighbor, and ordinal
logistic regression. Finally, the model of random decision forests with an
accuracy of 76% is selected as the best model in terms of the mean evaluation
indicator. Also, seven features of the creatine Phosphokinase test, urea, white
and red blood cell count, blood sugar, time, and hemoglobin are identified as
the most effective features of the ejection fraction variable.

Entity linking faces significant challenges such as prolific variations and
prevalent ambiguities, especially in high-value domains with myriad entities.
Standard classification approaches suffer from the annotation bottleneck and
cannot effectively handle unseen entities. Zero-shot entity linking has emerged
as a promising direction for generalizing to new entities, but it still
requires example gold entity mentions during training and canonical
descriptions for all entities, both of which are rarely available outside of
Wikipedia. In this paper, we explore Knowledge-RIch Self-Supervision ($\tt
KRISS$) for biomedical entity linking, by leveraging readily available domain
knowledge. In training, it generates self-supervised mention examples on
unlabeled text using a domain ontology and trains a contextual encoder using
contrastive learning. For inference, it samples self-supervised mentions as
prototypes for each entity and conducts linking by mapping the test mention to
the most similar prototype. Our approach can easily incorporate entity
descriptions and gold mention labels if available. We conducted extensive
experiments on seven standard datasets spanning biomedical literature and
clinical notes. Without using any labeled information, our method produces $\tt
KRISSBERT$, a universal entity linker for four million UMLS entities that
attains new state of the art, outperforming prior self-supervised methods by as
much as 20 absolute points in accuracy.

An increasing number of communication and computational schemes with quantum
advantages have recently been proposed, which implies that quantum technology
has fertile application prospects. However, demonstrating these schemes
experimentally continues to be a central challenge because of the difficulty in
preparing high-dimensional states or highly entangled states. In this study, we
introduce and analyse a quantum coupon collector protocol by employing coherent
states and simple linear optical elements, which was successfully demonstrated
using realistic experimental equipment. We showed that our protocol can
significantly reduce the number of samples needed to learn a specific set
compared with the classical limit of the coupon collector problem. We also
discuss the potential values and expansions of the quantum coupon collector by
constructing a quantum blind box game. The information transmitted by the
proposed game also broke the classical limit. These results strongly prove the
advantages of quantum mechanics in machine learning and communication

Contactless and efficient systems are implemented rapidly to advocate
preventive methods in the fight against the COVID-19 pandemic. Despite the
positive benefits of such systems, there is potential for exploitation by
invading user privacy. In this work, we analyse the privacy invasiveness of
face biometric systems by predicting privacy-sensitive soft-biometrics using
masked face images. We train and apply a CNN based on the ResNet-50
architecture with 20,003 synthetic masked images and measure the privacy
invasiveness. Despite the popular belief of the privacy benefits of wearing a
mask among people, we show that there is no significant difference to privacy
invasiveness when a mask is worn. In our experiments we were able to accurately
predict sex (94.7%),race (83.1%) and age (MAE 6.21 and RMSE 8.33) from masked
face images. Our proposed approach can serve as a baseline utility to evaluate
the privacy-invasiveness of artificial intelligence systems that make use of
privacy-sensitive information. We open-source all contributions for
re-producibility and broader use by the research community.

We present a novel multistream network that learns robust eye representations
for gaze estimation. We first create a synthetic dataset containing eye region
masks detailing the visible eyeball and iris using a simulator. We then perform
eye region segmentation with a U-Net type model which we later use to generate
eye region masks for real-world eye images. Next, we pretrain an eye image
encoder in the real domain with self-supervised contrastive learning to learn
generalized eye representations. Finally, this pretrained eye encoder, along
with two additional encoders for visible eyeball region and iris, are used in
parallel in our multistream framework to extract salient features for gaze
estimation from real-world images. We demonstrate the performance of our method
on the EYEDIAP dataset in two different evaluation settings and achieve
state-of-the-art results, outperforming all the existing benchmarks on this
dataset. We also conduct additional experiments to validate the robustness of
our self-supervised network with respect to different amounts of labeled data
used for training.

We propose a transition-based system to transpile Abstract Meaning
Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).
This allows to delegate part of the abstraction problem to a strongly
pre-trained semantic parser, while learning transpiling with small amount of
paired data. We departure from recent work relating AMR and SPARQL constructs,
but rather than applying a set of rules, we teach the BART model to selectively
use these relations. Further, we avoid explicitly encoding AMR but rather
encode the parser state in the attention mechanism of BART, following recent
semantic parsing works. The resulting model is simple, provides supporting text
for its decisions, and outperforms recent progress in AMR-based KBQA in LC-QuAD
(F1 53.4), matching it in QALD (F1 30.8), while exploiting the same inductive
biases.

We analyze the ability of pre-trained language models to transfer knowledge
among datasets annotated with different type systems and to generalize beyond
the domain and dataset they were trained on. We create a meta task, over
multiple datasets focused on the prediction of rhetorical roles. Prediction of
the rhetorical role a sentence plays in a case decision is an important and
often studied task in AI & Law. Typically, it requires the annotation of a
large number of sentences to train a model, which can be time-consuming and
expensive. Further, the application of the models is restrained to the same
dataset it was trained on. We fine-tune language models and evaluate their
performance across datasets, to investigate the models' ability to generalize
across domains. Our results suggest that the approach could be helpful in
overcoming the cold-start problem in active or interactvie learning, and shows
the ability of the models to generalize across datasets and domains.

Recruiters can easily shortlist candidates for jobs via viewing their
curriculum vitae document. Unstructured document CV beholds candidates
portfolio and named entities listing details. The main aim of this study is to
design and propose a web oriented, highly responsive, computational pipeline
that systematically predicts CV entities using hierarchically refined label
attention networks.

Motivation: A perennial challenge for biomedical researchers and clinical
practitioners is to stay abreast with the rapid growth of publications and
medical notes. Natural language processing (NLP) has emerged as a promising
direction for taming information overload. In particular, large neural language
models facilitate transfer learning by pretraining on unlabeled text, as
exemplified by the successes of BERT models in various NLP applications.
However, fine-tuning such models for an end task remains challenging,
especially with small labeled datasets, which are common in biomedical NLP.
  Results: We conduct a systematic study on fine-tuning stability in biomedical
NLP. We show that finetuning performance may be sensitive to pretraining
settings, especially in low-resource domains. Large models have potential to
attain better performance, but increasing model size also exacerbates
finetuning instability. We thus conduct a comprehensive exploration of
techniques for addressing fine-tuning instability. We show that these
techniques can substantially improve fine-tuning performance for lowresource
biomedical NLP applications. Specifically, freezing lower layers is helpful for
standard BERT-BASE models, while layerwise decay is more effective for
BERT-LARGE and ELECTRA models. For low-resource text similarity tasks such as
BIOSSES, reinitializing the top layer is the optimal strategy. Overall,
domainspecific vocabulary and pretraining facilitate more robust models for
fine-tuning. Based on these findings, we establish new state of the art on a
wide range of biomedical NLP applications.
  Availability and implementation: To facilitate progress in biomedical NLP, we
release our state-of-the-art pretrained and fine-tuned models:
https://aka.ms/BLURB.

How can an end-user provide feedback if a deployed structured prediction
model generates inconsistent output, ignoring the structural complexity of
human language? This is an emerging topic with recent progress in synthetic or
constrained settings, and the next big leap would require testing and tuning
models in real-world settings. We present a new dataset, Interscript,
containing user feedback on a deployed model that generates complex everyday
tasks. Interscript contains 8,466 data points -- the input is a possibly
erroneous script and a user feedback, and the output is a modified script. We
posit two use-cases of \ours that might significantly advance the
state-of-the-art in interactive learning. The dataset is available at:
https://github.com/allenai/interscript.

We discuss the prediction of critical behavior of lattice observables in
SU(2) and SU(3) gauge theories. We show that feed-forward neural network,
trained on the lattice configurations of gauge fields as input data, finds
correlations with the target observable, which is also true in the critical
region where the neural network has not been trained. We have verified that the
neural network constructs a gauge-invariant function and this property does not
change over the entire range of the parameter space.

Our goal is to efficiently compute low-dimensional latent coordinates for
nodes in an input graph -- known as graph embedding -- for subsequent data
processing such as clustering. Focusing on finite graphs that are interpreted
as uniform samples on continuous manifolds (called manifold graphs), we
leverage existing fast extreme eigenvector computation algorithms for speedy
execution. We first pose a generalized eigenvalue problem for sparse matrix
pair $(\A,\B)$, where $\A = \L - \mu \Q + \epsilon \I$ is a sum of graph
Laplacian $\L$ and disconnected two-hop difference matrix $\Q$. Eigenvector
$\v$ minimizing Rayleigh quotient $\frac{\v^{\top} \A \v}{\v^{\top} \v}$ thus
minimizes $1$-hop neighbor distances while maximizing distances between
disconnected $2$-hop neighbors, preserving graph structure. Matrix $\B =
\text{diag}(\{\b_i\})$ that defines eigenvector orthogonality is then chosen so
that boundary / interior nodes in the sampling domain have the same generalized
degrees. $K$-dimensional latent vectors for the $N$ graph nodes are the first
$K$ generalized eigenvectors for $(\A,\B)$, computed in $\cO(N)$ using LOBPCG,
where $K \ll N$. Experiments show that our embedding is among the fastest in
the literature, while producing the best clustering performance for manifold
graphs.

Learning in stochastic games is arguably the most standard and fundamental
setting in multi-agent reinforcement learning (MARL). In this paper, we
consider decentralized MARL in stochastic games in the non-asymptotic regime.
In particular, we establish the finite-sample complexity of fully decentralized
Q-learning algorithms in a significant class of general-sum stochastic games
(SGs) - weakly acyclic SGs, which includes the common cooperative MARL setting
with an identical reward to all agents (a Markov team problem) as a special
case. We focus on the practical while challenging setting of fully
decentralized MARL, where neither the rewards nor the actions of other agents
can be observed by each agent. In fact, each agent is completely oblivious to
the presence of other decision makers. Both the tabular and the linear function
approximation cases have been considered. In the tabular setting, we analyze
the sample complexity for the decentralized Q-learning algorithm to converge to
a Markov perfect equilibrium (Nash equilibrium). With linear function
approximation, the results are for convergence to a linear approximated
equilibrium - a new notion of equilibrium that we propose - which describes
that each agent's policy is a best reply (to other agents) within a linear
space. Numerical experiments are also provided for both settings to demonstrate

Using computational notebooks (e.g., Jupyter Notebook), data scientists
rationalize their exploratory data analysis (EDA) based on their prior
experience and external knowledge such as online examples. For novices or data
scientists who lack specific knowledge about the dataset or problem to
investigate, effectively obtaining and understanding the external information
is critical to carry out EDA. This paper presents EDAssistant, a JupyterLab
extension that supports EDA with in-situ search of example notebooks and
recommendation of useful APIs, powered by novel interactive visualization of
search results. The code search and recommendation are enabled by
state-of-the-art machine learning models, trained on a large corpus of EDA
notebooks collected online. A user study is conducted to investigate both
EDAssistant and data scientists' current practice (i.e., using external search
engines). The results demonstrate the effectiveness and usefulness of
EDAssistant, and participants appreciated its smooth and in-context support of
EDA. We also report several design implications regarding code recommendation
tools.

Data assimilation (DA) in the geophysical sciences remains the cornerstone of
robust forecasts from numerical models. Indeed, DA plays a crucial role in the
quality of numerical weather prediction, and is a crucial building block that
has allowed dramatic improvements in weather forecasting over the past few
decades. DA is commonly framed in a variational setting, where one solves an
optimization problem within a Bayesian formulation using raw model forecasts as
a prior, and observations as likelihood. This leads to a DA objective function
that needs to be minimized, where the decision variables are the initial
conditions specified to the model. In traditional DA, the forward model is
numerically and computationally expensive. Here we replace the forward model
with a low-dimensional, data-driven, and differentiable emulator. Consequently,
gradients of our DA objective function with respect to the decision variables
are obtained rapidly via automatic differentiation. We demonstrate our approach
by performing an emulator-assisted DA forecast of geopotential height. Our
results indicate that emulator-assisted DA is faster than traditional
equation-based DA forecasts by four orders of magnitude, allowing computations
to be performed on a workstation rather than a dedicated high-performance
computer. In addition, we describe accuracy benefits of emulator-assisted DA
when compared to simply using the emulator for forecasting (i.e., without DA).

Minimizing privacy leakage while ensuring data utility is a critical problem
to data holders in a privacy-preserving data publishing task. Most prior
research concerns only with one type of data and resorts to a single obscuring
method, \eg, obfuscation or generalization, to achieve a privacy-utility
tradeoff, which is inadequate for protecting real-life heterogeneous data and
is hard to defend ever-growing machine learning based inference attacks. This
work takes a pilot study on privacy-preserving data publishing when both
generalization and obfuscation operations are employed for heterogeneous data
protection. To this end, we first propose novel measures for privacy and
utility quantification and formulate the hybrid privacy-preserving data
obscuring problem to account for the joint effect of generalization and
obfuscation. We then design a novel hybrid protection mechanism called
HyObscure, to cross-iteratively optimize the generalization and obfuscation
operations for maximum privacy protection under a certain utility guarantee.
The convergence of the iterative process and the privacy leakage bound of
HyObscure are also provided in theory. Extensive experiments demonstrate that
HyObscure significantly outperforms a variety of state-of-the-art baseline
methods when facing various inference attacks under different scenarios.
HyObscure also scales linearly to the data size and behaves robustly with
varying key parameters.

Federated learning (FL) is a promising machine learning paradigm that enables
cross-party data collaboration for real-world AI applications in a
privacy-preserving and law-regulated way. How to valuate parties' data is a
critical but challenging FL issue. In the literature, data valuation either
relies on running specific models for a given task or is just task irrelevant;
however, it is often requisite for party selection given a specific task when
FL models have not been determined yet. This work thus fills the gap and
proposes \emph{FedValue}, to our best knowledge, the first privacy-preserving,
task-specific but model-free data valuation method for vertical FL tasks.
Specifically, FedValue incorporates a novel information-theoretic metric termed
Shapley-CMI to assess data values of multiple parties from a game-theoretic
perspective. Moreover, a novel server-aided federated computation mechanism is
designed to compute Shapley-CMI and meanwhile protects each party from data
leakage. We also propose several techniques to accelerate Shapley-CMI
computation in practice. Extensive experiments on six open datasets validate
the effectiveness and efficiency of FedValue for data valuation of vertical FL
tasks. In particular, Shapley-CMI as a model-free metric performs comparably
with the measures that depend on running an ensemble of well-performing models.

The quality of underlying training data is very crucial for building
performant machine learning models with wider generalizabilty. However, current
machine learning (ML) tools lack streamlined processes for improving the data
quality. So, getting data quality insights and iteratively pruning the errors
to obtain a dataset which is most representative of downstream use cases is
still an ad-hoc manual process. Our work addresses this data tooling gap,
required to build improved ML workflows purely through data-centric techniques.
More specifically, we introduce a systematic framework for (1) finding noisy or
mislabelled samples in the dataset and, (2) identifying the most informative
samples, which when included in training would provide maximal model
performance lift. We demonstrate the efficacy of our framework on public as
well as private enterprise datasets of two Fortune 500 companies, and are
confident this work will form the basis for ML teams to perform more
intelligent data discovery and pruning.

Online transient stability assessment (TSA) is essential for secure and
stable power system operations. The growing number of Phasor Measurement Units
(PMUs) brings about massive sources of data that can enhance online TSA.
However, conventional data-driven methods require large amounts of transient
data to correctly assess the transient stability state of a system. In this
paper, a new data-driven TSA approach is developed for TSA with fewer data
compared to the conventional methods. The data reduction is enabled by learning
the dynamic behaviors of the historical transient data using generative and
adversarial networks (GAN). This knowledge is used online to predict the
voltage time series data after a transient event. A classifier embedded in the
generative network deploys the predicted post-contingency data to determine the
stability of the system following a fault. The developed GAN-based TSA approach
preserves the spatial and temporal correlations that exist in multivariate PMU
time series data. Hence, in comparison with the state-of-the-art TSA methods,
it achieves a higher assessment accuracy using only one sample of the measured
data and a shorter response time. Case studies conducted on the IEEE 118-bus
system demonstrate the superior performance of the GAN-based method compared to
the conventional data-driven techniques.

Federated optimization (FedOpt), which targets at collaboratively training a
learning model across a large number of distributed clients, is vital for
federated learning. The primary concerns in FedOpt can be attributed to the
model divergence and communication efficiency, which significantly affect the
performance. In this paper, we propose a new method, i.e., LoSAC, to learn from
heterogeneous distributed data more efficiently. Its key algorithmic insight is
to locally update the estimate for the global full gradient after {each}
regular local model update. Thus, LoSAC can keep clients' information refreshed
in a more compact way. In particular, we have studied the convergence result
for LoSAC. Besides, the bonus of LoSAC is the ability to defend the information
leakage from the recent technique Deep Leakage Gradients (DLG). Finally,
experiments have verified the superiority of LoSAC comparing with
state-of-the-art FedOpt algorithms. Specifically, LoSAC significantly improves
communication efficiency by more than $100\%$ on average, mitigates the model
divergence problem and equips with the defense ability against DLG.

Predicting drug-drug interactions (DDI) is the problem of predicting side
effects (unwanted outcomes) of a pair of drugs using drug information and known
side effects of many pairs. This problem can be formulated as predicting labels
(i.e. side effects) for each pair of nodes in a DDI graph, of which nodes are
drugs and edges are interacting drugs with known labels. State-of-the-art
methods for this problem are graph neural networks (GNNs), which leverage
neighborhood information in the graph to learn node representations. For DDI,
however, there are many labels with complicated relationships due to the nature
of side effects. Usual GNNs often fix labels as one-hot vectors that do not
reflect label relationships and potentially do not obtain the highest
performance in the difficult cases of infrequent labels. In this paper, we
formulate DDI as a hypergraph where each hyperedge is a triple: two nodes for
drugs and one node for a label. We then present CentSmoothie, a hypergraph
neural network that learns representations of nodes and labels altogether with
a novel central-smoothing formulation. We empirically demonstrate the
performance advantages of CentSmoothie in simulations as well as real datasets.

We consider large scale distributed optimization over a set of edge devices
connected to a central server, where the limited communication bandwidth
between the server and edge devices imposes a significant bottleneck for the
optimization procedure. Inspired by recent advances in federated learning, we
propose a distributed stochastic gradient descent (SGD) type algorithm that
exploits the sparsity of the gradient, when possible, to reduce communication
burden. At the heart of the algorithm is to use compressed sensing techniques
for the compression of the local stochastic gradients at the device side; and
at the server side, a sparse approximation of the global stochastic gradient is
recovered from the noisy aggregated compressed local gradients. We conduct
theoretical analysis on the convergence of our algorithm in the presence of
noise perturbation incurred by the communication channels, and also conduct
numerical experiments to corroborate its effectiveness.

A digital finite impulse response (FIR) filter design is fully synthesizable,
thanks to the mature CAD support of digital circuitry. On the contrary, analog
mixed-signal (AMS) filter design is mostly a manual process, including
architecture selection, schematic design, and layout. This work presents a
systematic design methodology to automate AMS FIR filter design using a time
approximation architecture without any tunable passive component, such as
switched capacitor or resistor. It not only enhances the flexibility of the
filter but also facilitates design automation with reduced analog complexity.
The proposed design flow features a hybrid approximation scheme that
automatically optimize the filter's impulse response in light of time
quantization effects, which shows significant performance improvement with
minimum designer's efforts in the loop. Additionally, a layout-aware regression
model based on an artificial neural network (ANN), in combination with
gradient-based search algorithm, is used to automate and expedite the filter
design. With the proposed framework, we demonstrate rapid synthesis of AMS FIR
filters in 65nm process from specification to layout.

Analog mixed-signal (AMS) circuit architecture has evolved towards more
digital friendly due to technology scaling and demand for higher
flexibility/reconfigurability. Meanwhile, the design complexity and cost of AMS
circuits has substantially increased due to the necessity of optimizing the
circuit sizing, layout, and verification of a complex AMS circuit. On the other
hand, machine learning (ML) algorithms have been under exponential growth over
the past decade and actively exploited by the electronic design automation
(EDA) community. This paper will identify the opportunities and challenges
brought about by this trend and overview several emerging AMS design
methodologies that are enabled by the recent evolution of AMS circuit
architectures and machine learning algorithms. Specifically, we will focus on
using neural-network-based surrogate models to expedite the circuit design
parameter search and layout iterations. Lastly, we will demonstrate the rapid
synthesis of several AMS circuit examples from specification to silicon
prototype, with significantly reduced human intervention.

Contrastive learning has become a key component of self-supervised learning
approaches for graph-structured data. Despite their success, existing graph
contrastive learning methods are incapable of uncertainty quantification for
node representations or their downstream tasks, limiting their application in
high-stakes domains. In this paper, we propose a novel Bayesian perspective of
graph contrastive learning methods showing random augmentations leads to
stochastic encoders. As a result, our proposed method represents each node by a
distribution in the latent space in contrast to existing techniques which embed
each node to a deterministic vector. By learning distributional
representations, we provide uncertainty estimates in downstream graph analytics
tasks and increase the expressive power of the predictive model. In addition,
we propose a Bayesian framework to infer the probability of perturbations in
each view of the contrastive model, eliminating the need for a computationally
expensive search for hyperparameter tuning. We empirically show a considerable
improvement in performance compared to existing state-of-the-art methods on
several benchmark datasets.

Most weed species can adversely impact agricultural productivity by competing
for nutrients required by high-value crops. Manual weeding is not practical for
large cropping areas. Many studies have been undertaken to develop automatic
weed management systems for agricultural crops. In this process, one of the
major tasks is to recognise the weeds from images. However, weed recognition is
a challenging task. It is because weed and crop plants can be similar in
colour, texture and shape which can be exacerbated further by the imaging
conditions, geographic or weather conditions when the images are recorded.
Advanced machine learning techniques can be used to recognise weeds from
imagery. In this paper, we have investigated five state-of-the-art deep neural
networks, namely VGG16, ResNet-50, Inception-V3, Inception-ResNet-v2 and
MobileNetV2, and evaluated their performance for weed recognition. We have used
several experimental settings and multiple dataset combinations. In particular,
we constructed a large weed-crop dataset by combining several smaller datasets,
mitigating class imbalance by data augmentation, and using this dataset in
benchmarking the deep neural networks. We investigated the use of transfer
learning techniques by preserving the pre-trained weights for extracting the
features and fine-tuning them using the images of crop and weed datasets. We
found that VGG16 performed better than others on small-scale datasets, while
ResNet-50 performed better than other deep networks on the large combined
dataset.

We present results of using a basic binary classification neural network
model to identify likely catastrophic outlier photometric redshift estimates of
individual galaxies, based only on the galaxies' measured photometric band
magnitude values. We find that a simple implementation of this classification
can identify a significant fraction of galaxies with catastrophic outlier
photometric redshift estimates while falsely categorizing only a much smaller
fraction of non-outliers. These methods have the potential to reduce the errors
introduced into science analyses by catastrophic outlier photometric redshift
estimates.

It is now a standard for neural network representations to be trained on
large, publicly available datasets, and used for new problems. The reasons for
why neural network representations have been so successful for transfer,
however, are still not fully understood. In this paper we show that, after
training, neural network representations align their top singular vectors to
the targets. We investigate this representation alignment phenomenon in a
variety of neural network architectures and find that (a) alignment emerges
across a variety of different architectures and optimizers, with more alignment
arising from depth (b) alignment increases for layers closer to the output and
(c) existing high-performance deep CNNs exhibit high levels of alignment. We
then highlight why alignment between the top singular vectors and the targets
can speed up learning and show in a classic synthetic transfer problem that
representation alignment correlates with positive and negative transfer to
similar and dissimilar tasks.

Neural architecture search enables automation of architecture design. Despite
its success, it is computationally costly and does not provide an insight on
how to design a desirable architecture. Here we propose a new way of searching
neural network where we search neural architecture by rewiring the
corresponding graph and predict the architecture performance by graph
properties. Because we do not perform machine learning over the entire graph
space and use predicted architecture performance to search architecture, the
searching process is remarkably efficient. We find graph based search can give
a reasonably good prediction of desirable architecture. In addition, we find
graph properties that are effective to predict architecture performance. Our
work proposes a new way of searching neural architecture and provides insights
on neural architecture design.

A wide variety of deep generative models has been developed in the past
decade. Yet, these models often struggle with simultaneously addressing three
key requirements including: high sample quality, mode coverage, and fast
sampling. We call the challenge imposed by these requirements the generative
learning trilemma, as the existing models often trade some of them for others.
Particularly, denoising diffusion models have shown impressive sample quality
and diversity, but their expensive sampling does not yet allow them to be
applied in many real-world applications. In this paper, we argue that slow
sampling in these models is fundamentally attributed to the Gaussian assumption
in the denoising step which is justified only for small step sizes. To enable
denoising with large steps, and hence, to reduce the total number of denoising
steps, we propose to model the denoising distribution using a complex
multimodal distribution. We introduce denoising diffusion generative
adversarial networks (denoising diffusion GANs) that model each denoising step
using a multimodal conditional GAN. Through extensive evaluations, we show that
denoising diffusion GANs obtain sample quality and diversity competitive with
original diffusion models while being 2000$\times$ faster on the CIFAR-10
dataset. Compared to traditional GANs, our model exhibits better mode coverage
and sample diversity. To the best of our knowledge, denoising diffusion GAN is
the first model that reduces sampling cost in diffusion models to an extent
that allows them to be applied to real-world applications inexpensively.
Project page and code can be found at
https://nvlabs.github.io/denoising-diffusion-gan

The transport of traffic flow can be modeled by the advection equation.
Finite difference and finite volumes methods have been used to numerically
solve this hyperbolic equation on a mesh. Advection has also been modeled
discretely on directed graphs using the graph advection operator [4, 18]. In
this paper, we first show that we can reformulate this graph advection operator
as a finite difference scheme. We then propose the Directed Graph Advection
Mat\'ern Gaussian Process (DGAMGP) model that incorporates the dynamics of this
graph advection operator into the kernel of a trainable Mat\'ern Gaussian
Process to effectively model traffic flow and its uncertainty as an advective
process on a directed graph.

The application of machine learning techniques for anomaly detection in
particle accelerators has gained popularity in recent years. These efforts have
ranged from the analysis of quenches in radio frequency cavities and
superconducting magnets to anomalous beam position monitors, and even losses in
rings. Using machine learning for anomaly detection can be challenging owing to
the inherent imbalance in the amount of data collected during normal operations
as compared to during faults. Additionally, the data are not always labeled and
therefore supervised learning is not possible. Autoencoders, neural networks
that form a compressed representation and reconstruction of the input data, are
a useful tool for such situations. Here we explore the use of autoencoder
reconstruction analysis for the prediction of magnet faults in the Advanced
Photon Source (APS) storage ring at Argonne National Laboratory.

While knowledge graphs contain rich semantic knowledge of various entities
and the relational information among them, temporal knowledge graphs (TKGs)
further indicate the interactions of the entities over time. To study how to
better model TKGs, automatic temporal knowledge graph completion (TKGC) has
gained great interest. Recent TKGC methods aim to integrate advanced deep
learning techniques, e.g., attention mechanism and Transformer, to boost model
performance. However, we find that compared to adopting various kinds of
complex modules, it is more beneficial to better utilize the whole amount of
temporal information along the time axis. In this paper, we propose a simple
but powerful graph encoder TARGCN for TKGC. TARGCN is parameter-efficient, and
it extensively utilizes the information from the whole temporal context. We
perform experiments on three benchmark datasets. Our model can achieve a more
than 42% relative improvement on GDELT dataset compared with the
state-of-the-art model. Meanwhile, it outperforms the strongest baseline on
ICEWS05-15 dataset with around 18.5% fewer parameters.

AMR parsing has experienced an unprecendented increase in performance in the
last three years, due to a mixture of effects including architecture
improvements and transfer learning. Self-learning techniques have also played a
role in pushing performance forward. However, for most recent high performant
parsers, the effect of self-learning and silver data augmentation seems to be
fading. In this paper we propose to overcome this diminishing returns of silver
data by combining Smatch-based ensembling techniques with ensemble
distillation. In an extensive experimental setup, we push single model English
parser performance to a new state-of-the-art, 85.9 (AMR2.0) and 84.3 (AMR3.0),
and return to substantial gains from silver data augmentation. We also attain a
new state-of-the-art for cross-lingual AMR parsing for Chinese, German, Italian
and Spanish. Finally we explore the impact of the proposed technique on domain
adaptation, and show that it can produce gains rivaling those of human
annotated data for QALD-9 and achieve a new state-of-the-art for BioAMR.

We introduce the arbitrary rectangle-range generalized elastic net penalty
method, abbreviated to ARGEN, for performing constrained variable selection and
regularization in high-dimensional sparse linear models. As a natural extension
of the nonnegative elastic net penalty method, ARGEN is proved to have variable
selection consistency and estimation consistency under some conditions. The
asymptotic behavior in distribution of the ARGEN estimators have been studied.
We also propose an algorithm called MU-QP-RR-W-$l_1$ to efficiently solve
ARGEN. By conducting simulation study we show that ARGEN outperforms the
elastic net in a number of settings. Finally an application of S&P 500 index
tracking with constraints on the stock allocations is performed to provide
general guidance for adapting ARGEN to solve real-world problems.

There is a massive underserved market for small business lending in the US
with the Federal Reserve estimating over \$650B in unmet annual financing
needs. Assessing the credit risk of a small business is key to making good
decisions whether to lend and at what terms. Large corporations have a
well-established credit assessment ecosystem, but small businesses suffer from
limited publicly available data and few (if any) credit analysts who cover them
closely. We explore the applicability of (DL-based) large corporate credit risk
models to small business credit rating.

In response to pathogens, the adaptive immune system generates specific
antibodies that bind and neutralize foreign antigens. Understanding the
composition of an individual's immune repertoire can provide insights into this
process and reveal potential therapeutic antibodies. In this work, we explore
the application of antibody-specific language models to aid understanding of
immune repertoires. We introduce AntiBERTy, a language model trained on 558M
natural antibody sequences. We find that within repertoires, our model clusters
antibodies into trajectories resembling affinity maturation. Importantly, we
show that models trained to predict highly redundant sequences under a multiple
instance learning framework identify key binding residues in the process. With
further development, the methods presented here will provide new insights into
antigen binding from repertoire sequences alone.

Flocking control of multi-agents system is challenging for agents with
partially unknown dynamics. This paper proposes an online learning-based
controller to stabilize flocking motion of double-integrator agents with
additional unknown nonlinear dynamics by using Gaussian processes (GP). Agents
interaction is described by a time-invariant infinitesimally minimally rigid
undirected graph. We provide a decentralized control law that exponentially
stabilizes the motion of the agents and captures Reynolds boids motion for
swarms by using GPs as an online learning-based oracle for the prediction of
the unknown dynamics. In particular the presented approach guarantees a
probabilistic bounded tracking error with high probability.

Artificial intelligence systems increasingly involve continual learning to
enable flexibility in general situations that are not encountered during system
training. Human interaction with autonomous systems is broadly studied, but
research has hitherto under-explored interactions that occur while the system
is actively learning, and can noticeably change its behaviour in minutes. In
this pilot study, we investigate how the interaction between a human and a
continually learning prediction agent develops as the agent develops
competency. Additionally, we compare two different agent architectures to
assess how representational choices in agent design affect the human-agent
interaction. We develop a virtual reality environment and a time-based
prediction task wherein learned predictions from a reinforcement learning (RL)
algorithm augment human predictions. We assess how a participant's performance
and behaviour in this task differs across agent types, using both quantitative
and qualitative analyses. Our findings suggest that human trust of the system
may be influenced by early interactions with the agent, and that trust in turn
affects strategic behaviour, but limitations of the pilot study rule out any
conclusive statement. We identify trust as a key feature of interaction to
focus on when considering RL-based technologies, and make several
recommendations for modification to this study in preparation for a
larger-scale investigation. A video summary of this paper can be found at
https://youtu.be/oVYJdnBqTwQ .

We propose DrBoost, a dense retrieval ensemble inspired by boosting. DrBoost
is trained in stages: each component model is learned sequentially and
specialized by focusing only on retrieval mistakes made by the current
ensemble. The final representation is the concatenation of the output vectors
of all the component models, making it a drop-in replacement for standard dense
retrievers at test time. DrBoost enjoys several advantages compared to standard
dense retrieval models. It produces representations which are 4x more compact,
while delivering comparable retrieval results. It also performs surprisingly
well under approximate search with coarse quantization, reducing latency and
bandwidth needs by another 4x. In practice, this can make the difference
between serving indices from disk versus from memory, paving the way for much
cheaper deployments.

Dynamic graphs with ordered sequences of events between nodes are prevalent
in real-world industrial applications such as e-commerce and social platforms.
However, representation learning for dynamic graphs has posed great
computational challenges due to the time and structure dependency and irregular
nature of the data, preventing such models from being deployed to real-world
applications. To tackle this challenge, we propose an efficient algorithm,
Efficient Dynamic Graph lEarning (EDGE), which selectively expresses certain
temporal dependency via training loss to improve the parallelism in
computations. We show that EDGE can scale to dynamic graphs with millions of
nodes and hundreds of millions of temporal events and achieve new
state-of-the-art (SOTA) performance.

This paper deals with the problem of finite-time learning for unknown
discrete-time nonlinear systems' dynamics, without the requirement of the
persistence of excitation. Two finite-time concurrent learning methods are
presented to approximate the uncertainties of the discrete-time nonlinear
systems in an online fashion by employing current data along with recorded
experienced data satisfying an easy-to-check rank condition on the richness of
the recorded data which is less restrictive in comparison with persistence of
excitation condition. For the proposed finite-time concurrent learning methods,
rigorous proofs guarantee the finite-time convergence of the estimated
parameters to their optimal values based on the discrete-time Lyapunov
analysis. Compared with the existing work in the literature, simulation results
illustrate that the proposed methods can timely and precisely approximate the

Cities have become primary actors on climate change and are increasingly
setting goals aimed at net-zero emissions. The rapid proliferation of
subnational governments "racing to zero" emissions and articulating their own
climate mitigation plans warrants closer examination to understand how these
actors intend to meet these goals. The scattered, incomplete and heterogeneous
nature of city climate policy documents, however, has made their systemic
analysis challenging. We analyze 318 climate action documents from cities that
have pledged net-zero targets or joined a transnational climate initiative with
this goal using machine learning-based natural language processing (NLP)
techniques. We use these approaches to accomplish two primary goals: 1)
determine text patterns that predict "ambitious" net-zero targets, where we
define an ambitious target as one that encompasses a subnational government's
economy-wide emissions; and 2) perform a sectoral analysis to identify patterns
and trade-offs in climate action themes (i.e., land-use, industry, buildings,
etc.). We find that cities that have defined ambitious climate actions tend to
emphasize quantitative metrics and specific high-emitting sectors in their
plans, supported by mentions of governance and citizen participation. Cities
predominantly emphasize energy-related actions in their plans, particularly in
the buildings, transport and heating sectors, but often at the expense of other
sectors, including land-use and climate impacts. The method presented in this
paper provides a replicable, scalable approach to analyzing climate action
plans and a first step towards facilitating cross-city learning.

If we changed the rules, would the wise become fools? Different groups
formalize reinforcement learning (RL) in different ways. If an agent in one RL
framework is to run within another RL framework's environments, the agent must
first be converted, or mapped, into that other framework. Whether or not this
is possible depends on the RL frameworks in question and on how intelligence is
measured. In this paper, we lay foundations for studying
relative-intelligence-preserving mappability between RL frameworks.

Nonlinear parametric systems have been widely used in modeling nonlinear
dynamics in science and engineering. Bifurcation analysis of these nonlinear
systems on the parameter space are usually used to study the solution structure
such as the number of solutions and the stability. In this paper, we develop a
new machine learning approach to compute the bifurcations via so-called
equation-driven neural networks (EDNNs). The EDNNs consist of a two-step
optimization: the first step is to approximate the solution function of the
parameter by training empirical solution data; the second step is to compute
bifurcations by using the approximated neural network obtained in the first
step. Both theoretical convergence analysis and numerical implementation on
several examples have been performed to demonstrate the feasibility of the

Effective representation learning is the key in improving model performance
for medical image analysis. In training deep learning models, a compromise
often must be made between performance and trust, both of which are essential
for medical applications. Moreover, models optimized with cross-entropy loss
tend to suffer from unwarranted overconfidence in the majority class and
over-cautiousness in the minority class. In this work, we integrate a new
surrogate loss with self-supervised learning for computer-aided screening of
COVID-19 patients using radiography images. In addition, we adopt a new
quantification score to measure a model's trustworthiness. Ablation study is
conducted for both the performance and the trust on feature learning methods
and loss functions. Comparisons show that leveraging the new surrogate loss on
self-supervised models can produce label-efficient networks that are both
high-performing and trustworthy.

Current state-of-the-art model-based reinforcement learning algorithms use
trajectory sampling methods, such as the Cross-Entropy Method (CEM), for
planning in continuous control settings. These zeroth-order optimizers require
sampling a large number of trajectory rollouts to select an optimal action,
which scales poorly for large prediction horizons or high dimensional action
spaces. First-order methods that use the gradients of the rewards with respect
to the actions as an update can mitigate this issue, but suffer from local
optima due to the non-convex optimization landscape. To overcome these issues
and achieve the best of both worlds, we propose a novel planner, Cross-Entropy
Method with Gradient Descent (CEM-GD), that combines first-order methods with
CEM. At the beginning of execution, CEM-GD uses CEM to sample a significant
amount of trajectory rollouts to explore the optimization landscape and avoid
poor local minima. It then uses the top trajectories as initialization for
gradient descent and applies gradient updates to each of these trajectories to
find the optimal action sequence. At each subsequent time step, however, CEM-GD
samples much fewer trajectories from CEM before applying gradient updates. We
show that as the dimensionality of the planning problem increases, CEM-GD
maintains desirable performance with a constant small number of samples by
using the gradient information, while avoiding local optima using initially
well-sampled trajectories. Furthermore, CEM-GD achieves better performance than
CEM on a variety of continuous control benchmarks in MuJoCo with 100x fewer
samples per time step, resulting in around 25% less computation time and 10%
less memory usage. The implementation of CEM-GD is available at
$\href{https://github.com/KevinHuang8/CEM-GD}{\text{https://github.com/KevinHuang8/CEM-GD}}$.

This work introduces a neural architecture for learning forward models of
stochastic environments. The task is achieved solely through learning from
temporal unstructured observations in the form of images. Once trained, the
model allows for tracking of the environment state in the presence of noise or
with new percepts arriving intermittently. Additionally, the state estimate can
be propagated in observation-blind mode, thus allowing for long-term
predictions. The network can output both expectation over future observations
and samples from belief distribution. The resulting functionalities are similar
to those of a Particle Filter (PF). The architecture is evaluated in an
environment where we simulate objects moving. As the forward and sensor models
are available, we implement a PF to gauge the quality of the models learnt from
the data.

In the modern age of social media and networks, graph representations of
real-world phenomena have become an incredibly useful source to mine insights.
Often, we are interested in understanding how entities in a graph are
interconnected. The Graph Neural Network (GNN) has proven to be a very useful
tool in a variety of graph learning tasks including node classification, link
prediction, and edge classification. However, in most of these tasks, the graph
data we are working with may be noisy and may contain spurious edges. That is,
there is a lot of uncertainty associated with the underlying graph structure.
Recent approaches to modeling uncertainty have been to use a Bayesian framework
and view the graph as a random variable with probabilities associated with
model parameters. Introducing the Bayesian paradigm to graph-based models,
specifically for semi-supervised node classification, has been shown to yield
higher classification accuracies. However, the method of graph inference
proposed in recent work does not take into account the structure of the graph.
In this paper, we propose a novel algorithm called Bayesian Graph Convolutional
Network using Neighborhood Random Walk Sampling (BGCN-NRWS), which uses a
Markov Chain Monte Carlo (MCMC) based graph sampling algorithm utilizing graph
structure, reduces overfitting by using a variational inference layer, and
yields consistently competitive classification results compared to the
state-of-the-art in semi-supervised node classification.

It is an essential product requirement of Yahoo Mail to distinguish between
personal and machine-generated emails. The old production classifier in Yahoo
Mail was based on a simple logistic regression model. That model was trained by
aggregating features at the SMTP address level. We propose building deep
learning models at the message level. We built and trained four individual CNN
models: (1) a content model with subject and content as input; (2) a sender
model with sender email address and name as input; (3) an action model by
analyzing email recipients' action patterns and correspondingly generating
target labels based on senders' opening/deleting behaviors; (4) a salutation
model by utilizing senders' "explicit salutation" signal as positive labels.
Next, we built a final full model after exploring different combinations of the
above four models. Experimental results on editorial data show that our full
model improves the adjusted-recall from 70.5% to 78.8% compared to the old
production model, while at the same time lifts the precision from 94.7% to
96.0%. Our full model also significantly beats the state-of-the-art Bert model
at this task. This full model has been deployed into the current production
system (Yahoo Mail 6).

Generating realistic vehicle speed trajectories is a crucial component in
evaluating vehicle fuel economy and in predictive control of self-driving cars.
Traditional generative models rely on Markov chain methods and can produce
accurate synthetic trajectories but are subject to the curse of dimensionality.
They do not allow to include conditional input variables into the generation
process. In this paper, we show how extensions to deep generative models allow
accurate and scalable generation. Proposed architectures involve recurrent and
feed-forward layers and are trained using adversarial techniques. Our models
are shown to perform well on generating vehicle trajectories using a model
trained on GPS data from Chicago metropolitan area.

Federated machine learning is a technique for training a model across
multiple devices without exchanging data between them. Because data remains
local to each compute node, federated learning is well-suited for use-cases in
fields where data is carefully controlled, such as medicine, or in domains with
bandwidth constraints. One weakness of this approach is that most federated
learning tools rely upon a central server to perform workload delegation and to
produce a single shared model. Here, we suggest a flexible framework for
decentralizing the federated learning pattern, and provide an open-source,
reference implementation compatible with PyTorch.

Alchemy is a new meta-learning environment rich enough to contain interesting
abstractions, yet simple enough to make fine-grained analysis tractable.
Further, Alchemy provides an optional symbolic interface that enables meta-RL
research without a large compute budget. In this work, we take the first steps
toward using Symbolic Alchemy to identify design choices that enable deep-RL
agents to learn various types of abstraction. Then, using a variety of
behavioral and introspective analyses we investigate how our trained agents use
and represent abstract task variables, and find intriguing connections to the
neuroscience of abstraction. We conclude by discussing the next steps for using
meta-RL and Alchemy to better understand the representation of abstract
variables in the brain.

Dense retrievers for open-domain question answering (ODQA) have been shown to
achieve impressive performance by training on large datasets of
question-passage pairs. In this work we ask whether this dependence on labeled
data can be reduced via unsupervised pretraining that is geared towards ODQA.
We show this is in fact possible, via a novel pretraining scheme designed for
retrieval. Our "recurring span retrieval" approach uses recurring spans across
passages in a document to create pseudo examples for contrastive learning. Our
pretraining scheme directly controls for term overlap across pseudo queries and
relevant passages, thus allowing to model both lexical and semantic relations
between them. The resulting model, named Spider, performs surprisingly well
without any labeled training examples on a wide range of ODQA datasets.
Specifically, it significantly outperforms all other pretrained baselines in a
zero-shot setting, and is competitive with BM25, a strong sparse baseline.
Moreover, a hybrid retriever over Spider and BM25 improves over both, and is
often competitive with DPR models, which are trained on tens of thousands of
examples. Last, notable gains are observed when using Spider as an
initialization for supervised training.

Reinforcement Learning (RL) agents in the real world must satisfy safety
constraints in addition to maximizing a reward objective. Model-based RL
algorithms hold promise for reducing unsafe real-world actions: they may
synthesize policies that obey all constraints using simulated samples from a
learned model. However, imperfect models can result in real-world constraint
violations even for actions that are predicted to satisfy all constraints. We
propose Conservative and Adaptive Penalty (CAP), a model-based safe RL
framework that accounts for potential modeling errors by capturing model
uncertainty and adaptively exploiting it to balance the reward and the cost
objectives. First, CAP inflates predicted costs using an uncertainty-based
penalty. Theoretically, we show that policies that satisfy this conservative
cost constraint are guaranteed to also be feasible in the true environment. We
further show that this guarantees the safety of all intermediate solutions
during RL training. Further, CAP adaptively tunes this penalty during training
using true cost feedback from the environment. We evaluate this conservative
and adaptive penalty-based approach for model-based safe RL extensively on
state and image-based environments. Our results demonstrate substantial gains
in sample-efficiency while incurring fewer violations than prior safe RL
algorithms. Code is available at: https://github.com/Redrew/CAP

In this paper we investigate how massive central galaxies cease their star
formation by comparing theoretical predictions from cosmological simulations:
EAGLE, Illustris and IllustrisTNG with observations of the local Universe from
the Sloan Digital Sky Survey (SDSS). Our machine learning (ML) classification
reveals supermassive black hole mass ($M_{\rm BH}$) as the most predictive
parameter in determining whether a galaxy is star forming or quenched at
redshift $z=0$ in all three simulations. This predicted consequence of active
galactic nucleus (AGN) quenching is reflected in the observations, where it is
true for a range of indirect estimates of $M_{\rm BH}$ via proxies as well as
its dynamical measurements. Our partial correlation analysis shows that other
galactic parameters lose their strong association with quiescence, once their
correlations with $M_{\rm BH}$ are accounted for. In simulations we demonstrate
that it is the integrated power output of the AGN, rather than its
instantaneous activity, which causes galaxies to quench. Finally, we analyse
the change in molecular gas content of galaxies from star forming to passive
populations. We find that both gas fractions ($f_{\rm gas}$) and star formation
efficiencies (SFEs) decrease upon transition to quiescence in the observations
but SFE is more predictive than $f_{\rm gas}$ in the ML passive/star-forming
classification. These trends in the SDSS are most closely recovered in
IllustrisTNG and are in direct contrast with the predictions made by Illustris.
We conclude that a viable AGN feedback prescription can be achieved by a
combination of preventative feedback and turbulence injection which together
quench star formation in central galaxies.

We propose a simple method to identify a continuous Lie algebra symmetry in a
dataset through regression by an artificial neural network. Our proposal takes
advantage of the $ \mathcal{O}(\epsilon^2)$ scaling of the output variable
under infinitesimal symmetry transformations on the input variables. As
symmetry transformations are generated post-training, the methodology does not
rely on sampling of the full representation space or binning of the dataset,
and the possibility of false identification is minimised. We demonstrate our
method in the SU(3)-symmetric (non-) linear $\Sigma$ model.

We report the detection and characterization of a new magnetospheric star, HD
135348, based on photometric and spectropolarimetric observations. The TESS
light curve of this star exhibited variations consistent with stars known to
possess rigidly rotating magnetospheres (RRMs), so we obtained
spectropolarimetric observations using the Robert Stobie Spectrograph (RSS) on
the Southern African Large Telescope (SALT) at four different rotational
phases. From these observations, we calculated the longitudinal magnetic field
of the star $\langle B_z \rangle$, as well as the Alfv\'en and Kepler radii,
and deduced that this star contains a centrifugal magnetosphere. However, an
archival spectrum does not exhibit the characteristic "double-horned" emission
profile for H$\alpha$ and the Brackett series that has been observed in many
other RRM stars. This could be due to the insufficient rotational phase
coverage of the available set of observations, as the spectra of these stars
significantly vary with the star's rotation. Our analysis underscores the use
of TESS in photometrically identifying magnetic star candidates for
spectropolarimetric follow-up using ground-based instruments. We are evaluating
the implementation of a machine learning classifier to search for more examples
of RRM stars in TESS data.

The success of deep learning has enabled advances in multimodal tasks that
require non-trivial fusion of multiple input domains. Although multimodal
models have shown potential in many problems, their increased complexity makes
them more vulnerable to attacks. A Backdoor (or Trojan) attack is a class of
security vulnerability wherein an attacker embeds a malicious secret behavior
into a network (e.g. targeted misclassification) that is activated when an
attacker-specified trigger is added to an input. In this work, we show that
multimodal networks are vulnerable to a novel type of attack that we refer to
as Dual-Key Multimodal Backdoors. This attack exploits the complex fusion
mechanisms used by state-of-the-art networks to embed backdoors that are both
effective and stealthy. Instead of using a single trigger, the proposed attack
embeds a trigger in each of the input modalities and activates the malicious
behavior only when both the triggers are present. We present an extensive study
of multimodal backdoors on the Visual Question Answering (VQA) task with
multiple architectures and visual feature backbones. A major challenge in
embedding backdoors in VQA models is that most models use visual features
extracted from a fixed pretrained object detector. This is challenging for the
attacker as the detector can distort or ignore the visual trigger entirely,
which leads to models where backdoors are over-reliant on the language trigger.
We tackle this problem by proposing a visual trigger optimization strategy
designed for pretrained object detectors. Through this method, we create
Dual-Key Backdoors with over a 98% attack success rate while only poisoning 1%
of the training data. Finally, we release TrojVQA, a large collection of clean
and trojan VQA models to enable research in defending against multimodal
backdoors.

In this letter we propose a data-driven approach to optimizing the algebraic
connectivity of a team of robots. While a considerable amount of research has
been devoted to this problem, we lack a method that scales in a manner suitable
for online applications for more than a handful of agents. To that end, we
propose a supervised learning approach with a convolutional neural network
(CNN) that learns to place communication agents from an expert that uses an
optimization-based strategy. We demonstrate the performance of our CNN on
canonical line and ring topologies, 105k randomly generated test cases, and
larger teams not seen during training. We also show how our system can be
applied to dynamic robot teams through a Unity-based simulation. After
training, our system produces connected configurations over an order of
magnitude faster than the optimization-based scheme for teams of 10-20 agents.

Out-of-distribution detection seeks to identify novelties, samples that
deviate from the norm. The task has been found to be quite challenging,
particularly in the case where the normal data distribution consists of
multiple semantic classes (e.g., multiple object categories). To overcome this
challenge, current approaches require manual labeling of the normal images
provided during training. In this work, we tackle multi-class novelty detection
without class labels. Our simple but effective solution consists of two stages:
we first discover "pseudo-class" labels using unsupervised clustering. Then
using these pseudo-class labels, we are able to use standard supervised
out-of-distribution detection methods. We verify the performance of our method
by a favorable comparison to the state-of-the-art, and provide extensive
analysis and ablations.

We introduce A-ViT, a method that adaptively adjusts the inference cost of
vision transformer (ViT) for images of different complexity. A-ViT achieves
this by automatically reducing the number of tokens in vision transformers that
are processed in the network as inference proceeds. We reformulate Adaptive
Computation Time (ACT) for this task, extending halting to discard redundant
spatial tokens. The appealing architectural properties of vision transformers
enables our adaptive token reduction mechanism to speed up inference without
modifying the network architecture or inference hardware. We demonstrate that
A-ViT requires no extra parameters or sub-network for halting, as we base the
learning of adaptive halting on the original network parameters. We further
introduce distributional prior regularization that stabilizes training compared
to prior ACT approaches. On the image classification task (ImageNet1K), we show
that our proposed A-ViT yields high efficacy in filtering informative spatial
features and cutting down on the overall compute. The proposed method improves
the throughput of DeiT-Tiny by 62% and DeiT-Small by 38% with only 0.3%
accuracy drop, outperforming prior art by a large margin. Project page at
https://a-vit.github.io/

Gravitational acceleration fields can be deduced from the collisionless
Boltzmann equation, once the distribution function is known. This can be
constructed via the method of normalizing flows from datasets of the positions
and velocities of stars. Here, we consider application of this technique to the
solar neighbourhood. We construct mock data from a linear superposition of
multiple `quasi-isothermal' distribution functions, representing stellar
populations in the equilibrium Milky Way disc. We show that given a mock
dataset comprising a million stars within 1 kpc of the Sun, the underlying
acceleration field can be measured with excellent, sub-percent level accuracy,
even in the face of realistic errors and missing line-of-sight velocities. The
effects of disequilibrium can lead to bias in the inferred acceleration field.
This can be diagnosed by the presence of a phase space spiral, which can be
extracted simply and cleanly from the learned distribution function. We carry
out a comparison with two other popular methods of finding the local
acceleration field (Jeans analysis and 1D distribution function fitting). We
show our method most accurately measures accelerations from a given mock
dataset, particularly in the presence of disequilibria.

Recent progresses in artificial intelligence and machine learning make it
possible to automatically identify seismic phases from exponentially growing
seismic data. Despite some exciting successes in automatic picking of the first
P- and S-wave arrivals, auto-identification of later seismic phases such as the
Moho-reflected PmP waves remains a significant challenge in matching the
performance of experienced analysts. The main difficulty of machine-identifying
PmP waves is that the identifiable PmP waves are rare, making the problem of
identifying the PmP waves from a massive seismic database inherently
unbalanced. In this work, by utilizing a high-quality PmP dataset (10,192
manual picks) in southern California, we develop PmPNet, a
deep-neural-network-based algorithm to automatically identify PmP waves
efficiently; by doing so, we accelerate the process of identifying the PmP
waves. PmPNet applies similar techniques in the machine learning community to
address the unbalancement of PmP datasets. The architecture of PmPNet is a
residual neural network (ResNet)-autoencoder with additional predictor block,
where encoder, decoder, and predictor are equipped with ResNet connection. We
conduct systematic research with field data, concluding that PmPNet can
efficiently achieve high precision and high recall simultaneously to
automatically identify PmP waves from a massive seismic database. Applying the
pre-trained PmPNet to the seismic database from January 1990 to December 1999
in southern California, we obtain nearly twice more PmP picks than the original
PmP dataset, providing valuable data for other studies such as mapping the
topography of the Moho discontinuity and imaging the lower crust structures of
southern California.

Spoken language understanding (SLU) tasks involve mapping from speech audio
signals to semantic labels. Given the complexity of such tasks, good
performance might be expected to require large labeled datasets, which are
difficult to collect for each new task and domain. However, recent advances in
self-supervised speech representations have made it feasible to consider
learning SLU models with limited labeled data. In this work we focus on
low-resource spoken named entity recognition (NER) and address the question:
Beyond self-supervised pre-training, how can we use external speech and/or text
data that are not annotated for the task? We draw on a variety of approaches,
including self-training, knowledge distillation, and transfer learning, and
consider their applicability to both end-to-end models and pipeline (speech
recognition followed by text NER model) approaches. We find that several of
these approaches improve performance in resource-constrained settings beyond
the benefits from pre-trained representations alone. Compared to prior work, we
find improved F1 scores of up to 16%. While the best baseline model is a
pipeline approach, the best performance when using external data is ultimately
achieved by an end-to-end model. We provide detailed comparisons and analyses,
showing for example that end-to-end models are able to focus on the more
NER-specific words.

The Morris worm was one of the first worms spread via the internet. It was
spread on November 2, 1988, and changed how computer security was viewed by
computer professionals as well as general public. Since its inception the
Morris worm has been studied extensively from the security point of view and is
still a point of interest. This paper summarizes the effects, impacts, and
lessons learned from the episode. There are other copies of this paper present.
However, I recommend using the arXiv version only.

The usage of automated learning agents is becoming increasingly prevalent in
many online economic applications such as online auctions and automated
trading. Motivated by such applications, this paper is dedicated to fundamental
modeling and analysis of the strategic situations that the users of automated
learning agents are facing. We consider strategic settings where several users
engage in a repeated online interaction, assisted by regret-minimizing learning
agents that repeatedly play a "game" on their behalf. We propose to view the
outcomes of the agents' dynamics as inducing a "meta-game" between the users.
Our main focus is on whether users can benefit in this meta-game from
"manipulating" their own agents by misreporting their parameters to them. We
define a general framework to model and analyze these strategic interactions
between users of learning agents for general games and analyze the equilibria
induced on the users in three classes of games.

Query-focused summarization (QFS) aims to produce summaries that answer
particular questions of interest, enabling greater user control and
personalization. While recently released datasets, such as QMSum or AQuaMuSe,
facilitate research efforts in QFS, the field lacks a comprehensive study of
the broad space of applicable modeling methods. In this paper we conduct a
systematic exploration of neural approaches to QFS, considering two general
classes of methods: two-stage extractive-abstractive solutions and end-to-end
models. Within those categories, we investigate existing models and explore
strategies for transfer learning. We also present two modeling extensions that
achieve state-of-the-art performance on the QMSum dataset, up to a margin of
3.38 ROUGE-1, 3.72 ROUGE2, and 3.28 ROUGE-L when combined with transfer
learning strategies. Results from human evaluation suggest that the best models
produce more comprehensive and factually consistent summaries compared to a
baseline model. Code and checkpoints are made publicly available:
https://github.com/salesforce/query-focused-sum.

We consider the problem of training a multi-layer over-parametrized neural
networks to minimize the empirical risk induced by a loss function. In the
typical setting of over-parametrization, the network width $m$ is much larger
than the data dimension $d$ and number of training samples $n$
($m=\mathrm{poly}(n,d)$), which induces a prohibitive large weight matrix $W\in
\mathbb{R}^{m\times m}$ per layer. Naively, one has to pay $O(m^2)$ time to
read the weight matrix and evaluate the neural network function in both forward
and backward computation. In this work, we show how to reduce the training cost
per iteration, specifically, we propose a framework that uses $m^2$ cost only
in the initialization phase and achieves a truly subquadratic cost per
iteration in terms of $m$, i.e., $m^{2-\Omega(1)}$ per iteration.
  To obtain this result, we make use of various techniques, including a shifted
ReLU-based sparsifier, a lazy low rank maintenance data structure, fast
rectangular matrix multiplication, tensor-based sketching techniques and
preconditioning.

Music mood classification has been a challenging problem in comparison with
other music classification problems (e.g., genre, composer, or period). One
solution for addressing this challenge is to use an ensemble of machine
learning models. In this paper, we show that visualization techniques can
effectively convey the popular prediction as well as uncertainty at different
music sections along the temporal axis while enabling the analysis of
individual ML models in conjunction with their application to different musical
data. In addition to the traditional visual designs, such as stacked line
graph, ThemeRiver, and pixel-based visualization, we introduce a new variant of
ThemeRiver, called "dual-flux ThemeRiver", which allows viewers to observe and
measure the most popular prediction more easily than stacked line graph and
ThemeRiver. Together with pixel-based visualization, dual-flux ThemeRiver plots
can also assist in model-development workflows, in addition to annotating music
using ensemble model predictions.

We develop a theoretical framework for $S_n$-equivariant quantum
convolutional circuits, building on and significantly generalizing Jordan's
Permutational Quantum Computing (PQC) formalism. We show that quantum circuits
are a natural choice for Fourier space neural architectures affording a
super-exponential speedup in computing the matrix elements of $S_n$-Fourier
coefficients compared to the best known classical Fast Fourier Transform (FFT)
over the symmetric group. In particular, we utilize the Okounkov-Vershik
approach to prove Harrow's statement (Ph.D. Thesis 2005 p.160) on the
equivalence between $\operatorname{SU}(d)$- and $S_n$-irrep bases and to
establish the $S_n$-equivariant Convolutional Quantum Alternating Ans\"atze
($S_n$-CQA) using Young-Jucys-Murphy (YJM) elements. We prove that $S_n$-CQA
are dense, thus expressible within each $S_n$-irrep block, which may serve as a
universal model for potential future quantum machine learning and optimization
applications. Our method provides another way to prove the universality of
Quantum Approximate Optimization Algorithm (QAOA), from the
representation-theoretical point of view. Our framework can be naturally
applied to a wide array of problems with global $\operatorname{SU}(d)$
symmetry. We present numerical simulations to showcase the effectiveness of the
ans\"atze to find the sign structure of the ground state of the $J_1$-$J_2$
antiferromagnetic Heisenberg model on the rectangular and Kagome lattices. Our
work identifies quantum advantage for a specific machine learning problem, and
provides the first application of the celebrated Okounkov-Vershik's
representation theory to machine learning and quantum physics.

A central obstacle in the objective assessment of treatment effect (TE)
estimators in randomized control trials (RCTs) is the lack of ground truth (or
validation set) to test their performance. In this paper, we propose a novel
cross-validation-like methodology to address this challenge. The key insight of
our procedure is that the noisy (but unbiased) difference-of-means estimate can
be used as a ground truth "label" on a portion of the RCT, to test the
performance of an estimator trained on the other portion. We combine this
insight with an aggregation scheme, which borrows statistical strength across a
large collection of RCTs, to present an end-to-end methodology for judging an
estimator's ability to recover the underlying treatment effect as well as
produce an optimal treatment "roll out" policy. We evaluate our methodology
across 699 RCTs implemented in the Amazon supply chain. In this heavy-tailed
setting, our methodology suggests that procedures that aggressively downweight
or truncate large values, while introducing bias, lower the variance enough to
ensure that the treatment effect is more accurately estimated.

We propose a solution to the novel task of rendering sharp videos from new
viewpoints from a single motion-blurred image of a face. Our method handles the
complexity of face blur by implicitly learning the geometry and motion of faces
through the joint training on three large datasets: FFHQ and 300VW, which are
publicly available, and a new Bern Multi-View Face Dataset (BMFD) that we
built. The first two datasets provide a large variety of faces and allow our
model to generalize better. BMFD instead allows us to introduce multi-view
constraints, which are crucial to synthesizing sharp videos from a new camera
view. It consists of high frame rate synchronized videos from multiple views of
several subjects displaying a wide range of facial expressions. We use the high
frame rate videos to simulate realistic motion blur through averaging. Thanks
to this dataset, we train a neural network to reconstruct a 3D video
representation from a single image and the corresponding face gaze. We then
provide a camera viewpoint relative to the estimated gaze and the blurry image
as input to an encoder-decoder network to generate a video of sharp frames with
a novel camera viewpoint. We demonstrate our approach on test subjects of our
multi-view dataset and VIDTIMIT.

A common task is the determination of system parameters from spectroscopy,
where one compares the experimental spectrum with calculated spectra, that
depend on the desired parameters. Here we discuss an approach based on a
machine learning technique, where the parameters for the numerical calculations
are chosen from Gaussian Process Regression (GPR). This approach does not only
quickly converge to an optimal parameter set, but in addition provides
information about the complete parameter space, which allows for example to
identify extended parameter regions where numerical spectra are consistent with
the experimental one. We consider as example dimers of organic molecules and
aim at extracting in particular the interaction between the monomers, and their
mutual orientation. We find that indeed the GPR gives reliable results which
are in agreement with direct calculations of these parameters using quantum
chemical methods.

Document summarization condenses a long document into a short version with
salient information and accurate semantic descriptions. The main issue is how
to make the output summary semantically consistent with the input document. To
reach this goal, recently, researchers have focused on supervised end-to-end
hybrid approaches, which contain an extractor module and abstractor module.
Among them, the extractor identifies the salient sentences from the input
document, and the abstractor generates a summary from the salient sentences.
This model successfully keeps the consistency between the generated summary and
the reference summary via various strategies (e.g., reinforcement learning).
There are two semantic gaps when training the hybrid model (one is between
document and extracted sentences, and the other is between extracted sentences
and summary). However, they are not explicitly considered in the existing
methods, which usually results in a semantic bias of summary. To mitigate the
above issue, in this paper, a new \textbf{r}einforcing
s\textbf{e}mantic-\textbf{sy}mmetry learning \textbf{m}odel is proposed for
document summarization (\textbf{ReSyM}). ReSyM introduces a
semantic-consistency reward in the extractor to bridge the first gap. A
semantic dual-reward is designed to bridge the second gap in the abstractor.
The whole document summarization process is implemented via reinforcement
learning with a hybrid reward mechanism (combining the above two rewards).
Moreover, a comprehensive sentence representation learning method is presented
to sufficiently capture the information from the original document. A series of
experiments have been conducted on two wildly used benchmark datasets CNN/Daily
Mail and BigPatent. The results have shown the superiority of ReSyM by
comparing it with the state-of-the-art baselines in terms of various evaluation
metrics.

Graph neural networks (GNNs) have recently been demonstrated to perform well
on a variety of network-based tasks such as decentralized control and resource
allocation, and provide computationally efficient methods for these tasks which
have traditionally been challenging in that regard. However, like many
neural-network based systems, GNNs are susceptible to shifts and perturbations
on their inputs, which can include both node attributes and graph structure. In
order to make them more useful for real-world applications, it is important to
ensure their robustness post-deployment. Motivated by controlling the Lipschitz
constant of GNN filters with respect to the node attributes, we propose to
constrain the frequency response of the GNN's filter banks. We extend this
formulation to the dynamic graph setting using a continuous frequency response
constraint, and solve a relaxed variant of the problem via the scenario
approach. This allows for the use of the same computationally efficient
algorithm on sampled constraints, which provides PAC-style guarantees on the
stability of the GNN using results in scenario optimization. We also highlight
an important connection between this setup and GNN stability to graph
perturbations, and provide experimental results which demonstrate the efficacy
and broadness of our approach.

This work proposes the M3E2, a multi-task learning neural network model to
estimate the effect of multiple treatments. In contrast to existing methods,
M3E2 can handle multiple treatment effects applied simultaneously to the same
unit, continuous and binary treatments, and many covariates. We compared M3E2
with three baselines in three synthetic benchmark datasets: two with multiple
treatments and one with one treatment. Our analysis showed that our method has
superior performance, making more assertive estimations of the multiple
treatment effects.

We study a class of deterministic flows in ${\mathbb R}^{d\times k}$,
parametrized by a random matrix ${\boldsymbol X}\in {\mathbb R}^{n\times d}$
with i.i.d. centered subgaussian entries. We characterize the asymptotic
behavior of these flows over bounded time horizons, in the high-dimensional
limit in which $n,d\to\infty$ with $k$ fixed and converging aspect ratios
$n/d\to\delta$. The asymptotic characterization we prove is in terms of a
system of a nonlinear stochastic process in $k$ dimensions, whose parameters
are determined by a fixed point condition. This type of characterization is
known in physics as dynamical mean field theory. Rigorous results of this type
have been obtained in the past for a few spin glass models. Our proof is based
on time discretization and a reduction to certain iterative schemes known as
approximate message passing (AMP) algorithms, as opposed to earlier work that
was based on large deviations theory and stochastic processes theory. The new
approach allows for a more elementary proof and implies that the
high-dimensional behavior of the flow is universal with respect to the
distribution of the entries of ${\boldsymbol X}$. As specific applications, we
obtain high-dimensional characterizations of gradient flow in some classical
models from statistics and machine learning, under a random design assumption.

Large scale self-supervised pre-training of Transformer language models has
advanced the field of Natural Language Processing and shown promise in
cross-application to the biological `languages' of proteins and DNA. Learning
effective representations of DNA sequences using large genomic sequence
corpuses may accelerate the development of models of gene regulation and
function through transfer learning. However, to accurately model cell
type-specific gene regulation and function, it is necessary to consider not
only the information contained in DNA nucleotide sequences, which is mostly
invariant between cell types, but also how the local chemical and structural
`epigenetic state' of chromosomes varies between cell types. Here, we introduce
a Bidirectional Encoder Representations from Transformers (BERT) model that
learns representations based on both DNA sequence and paired epigenetic state
inputs, which we call Epigenomic BERT (or EBERT). We pre-train EBERT with a
masked language model objective across the entire human genome and across 127
cell types. Training this complex model with a previously prohibitively large
dataset was made possible for the first time by a partnership with Cerebras
Systems, whose CS-1 system powered all pre-training experiments. We show
EBERT's transfer learning potential by demonstrating strong performance on a
cell type-specific transcription factor binding prediction task. Our fine-tuned
model exceeds state of the art performance on 4 of 13 evaluation datasets from
ENCODE-DREAM benchmarks and earns an overall rank of 3rd on the challenge
leaderboard. We explore how the inclusion of epigenetic data and task specific
feature augmentation impact transfer learning performance.

Graph Theory and Topological Data Analytics, while powerful, have many
drawbacks related to their sensitivity and consistency with TDA & Graph Network
Analytics. In this paper, we aim to propose a novel approach for encoding
vectorized associations between data points for the purpose of enabling smooth
transitions between Graph and Topological Data Analytics. We conclusively
reveal effective ways of converting such vectorized associations to simplicial
complexes representing micro-states in a Phase-Space, resulting in filter
specific, homotopic self-expressive, event-driven unique topological signatures
which we have referred as Roy-Kesselman Diagrams or R-K Diagrams with
persistent homology, which emerge from filter-based encodings of R-K Models.
The validity and impact of this approach were tested specifically on
high-dimensional raw and derived measures of Gravitational Wave Data from the
latest LIGO datasets published by the LIGO Open Science Centre along with
testing a generalized approach for a non-scientific use-case, which has been
demonstrated using the Tableau Superstore Sales dataset. We believe the
findings of our work will lay the foundation for many future scientific and
engineering applications of stable, high-dimensional data analysis with the
combined effectiveness of Topological Graph Theory transformations.

Improvements in autonomy offer the potential for positive outcomes in a
number of domains, yet guaranteeing their safe deployment is difficult. This
work investigates how humans can intelligently supervise agents to achieve some
level of safety even when performance guarantees are elusive. The motivating
research question is: In safety-critical settings, can we avoid the need to
have one human supervise one machine at all times? The paper formalizes this
'scaling supervision' problem, and investigates its application to the
safety-critical context of autonomous vehicles (AVs) merging into traffic. It
proposes a conservative, reachability-based method to reduce the burden on the
AVs' human supervisors, which allows for the establishment of high-confidence
upper bounds on the supervision requirements in this setting. Order statistics
and traffic simulations with deep reinforcement learning show analytically and
numerically that teaming of AVs enables supervision time sublinear in AV
adoption. A key takeaway is that, despite present imperfections of AVs,
supervision becomes more tractable as AVs are deployed en masse. While this
work focuses on AVs, the scalable supervision framework is relevant to a
broader array of autonomous control challenges.

The Extreme Universe Space Observatory Supper Pressure Balloon 2 (EUSO-SPB2)
is under development, and will prototype instrumentation for future
satellite-based missions, including the Probe of Extreme Multi-Messenger
Astrophysics (POEMMA). EUSO-SPB2 will consist of two telescopes. The first is a
Cherenkov telescope (CT) being developed to identify and estimate the
background sources for future below-the-limb very high energy (E>10 PeV)
astrophysical neutrino observations, as well as above-the-limb cosmic ray
induced signals (E>1 PeV). The second is a fluorescence telescope (FT) being
developed for detection of Ultra High Energy Cosmic Rays (UHECRs). In
preparation for the expected launch in 2023, extensive simulations tuned by
preliminary laboratory measurements have been preformed to understand the FT
capabilities. The energy threshold has been estimated at $10^{18.2}$ eV, and
results in a maximum detection rate at $10^{18.6}$ eV when taking into account
the shape of the UHECR spectrum. In addition, onboard software has been
developed based on the simulations as well as experience with previous EUSO
missions. This includes a level 1 trigger to be run on the computationally
limited flight hardware, as well as a deep learning based prioritization
algorithm in order to accommodate the balloon's telemetry budget. These
techniques could also be used later for future, space-based missions.

Systemic lupus erythematosus (SLE) is an autoimmune disease in which the
immune system of the patient starts attacking healthy tissues of the body.
Lupus Nephritis (LN) refers to the inflammation of kidney tissues resulting in
renal failure due to these attacks. The International Society of
Nephrology/Renal Pathology Society (ISN/RPS) has released a classification
system based on various patterns observed during renal injury in SLE.
Traditional methods require meticulous pathological assessment of the renal
biopsy and are time-consuming. Recently, computational techniques have helped
to alleviate this issue by using virtual microscopy or Whole Slide Imaging
(WSI). With the use of deep learning and modern computer vision techniques, we
propose a pipeline that is able to automate the process of 1) detection of
various glomeruli patterns present in these whole slide images and 2)
classification of each image using the extracted glomeruli features.

The emergence of novel hardware accelerators has powered the tremendous
growth of machine learning in recent years. These accelerators deliver
incomparable performance gains in processing high-volume matrix operators,
particularly matrix multiplication, a core component of neural network training
and inference. In this work, we explored opportunities of accelerating database
systems using NVIDIA's Tensor Core Units (TCUs). We present TCUDB, a
TCU-accelerated query engine processing a set of query operators including
natural joins and group-by aggregates as matrix operators within TCUs. Matrix
multiplication was considered inefficient in the past; however, this strategy
has remained largely unexplored in conventional GPU-based databases, which
primarily rely on vector or scalar processing. We demonstrate the significant
performance gain of TCUDB in a range of real-world applications including
entity matching, graph query processing, and matrix-based data analytics. TCUDB
achieves up to 288x speedup compared to a baseline GPU-based query engine.

The rapid characterisation of earthquake parameters such as its magnitude is
at the heart of Earthquake Early Warning (EEW). In traditional EEW methods the
robustness in the estimation of earthquake parameters have been observed to
increase with the length of input data. Since time is a crucial factor in EEW
applications, in this paper we propose a deep learning based magnitude
classifier and, further we investigate the effect of using five different
durations of seismic waveform data after first P wave arrival of length 1s, 3s,
10s, 20s and 30s. This is accomplished by testing the performance of the
proposed model that combines Convolution and Bidirectional Long-Short Term
Memory units to classify waveforms based on their magnitude into three classes
"noise", "low magnitude events" and "high magnitude events". Herein, any
earthquake signal with magnitude equal to or above 5.0 is labelled as high
magnitude. We show that the variation in the results produced by changing the
length of the data, is no more than the inherent randomness in the trained
models, due to their initialisation.

We consider the task of building strong but human-like policies in
multi-agent decision-making problems, given examples of human behavior.
Imitation learning is effective at predicting human actions but may not match
the strength of expert humans, while self-play learning and search techniques
(e.g. AlphaZero) lead to strong performance but may produce policies that are
difficult for humans to understand and coordinate with. We show in chess and Go
that regularizing search based on the KL divergence from an imitation-learned
policy results in higher human prediction accuracy and stronger performance
than imitation learning alone. We then introduce a novel regret minimization
algorithm that is regularized based on the KL divergence from an
imitation-learned policy, and show that using this algorithm for search in
no-press Diplomacy yields a policy that matches the human prediction accuracy
of imitation learning while being substantially stronger.

The use of reinforcement learning (RL) in scientific applications, such as
materials design and automated chemistry, is increasing. A major challenge,
however, lies in fact that measuring the state of the system is often costly
and time consuming in scientific applications, whereas policy learning with RL
requires a measurement after each time step. In this work, we make the
measurement costs explicit in the form of a costed reward and propose a
framework that enables off-the-shelf deep RL algorithms to learn a policy for
both selecting actions and determining whether or not to measure the current
state of the system at each time step. In this way, the agents learn to balance
the need for information with the cost of information. Our results show that
when trained under this regime, the Dueling DQN and PPO agents can learn
optimal action policies whilst making up to 50\% fewer state measurements, and
recurrent neural networks can produce a greater than 50\% reduction in
measurements. We postulate the these reduction can help to lower the barrier to
applying RL to real-world scientific applications.

Document summarization, as a fundamental task in natural language generation,
aims to generate a short and coherent summary for a given document.
Controllable summarization, especially of the length, is an important issue for
some practical applications, especially how to trade-off the length constraint
and information integrity. In this paper, we propose an \textbf{A}daptive
\textbf{L}ength \textbf{C}ontrolling \textbf{O}ptimization (\textbf{ALCO})
method to leverage two-stage abstractive summarization model via reinforcement
learning. ALCO incorporates length constraint into the stage of sentence
extraction to penalize the overlength extracted sentences. Meanwhile, a
saliency estimation mechanism is designed to preserve the salient information
in the generated sentences. A series of experiments have been conducted on a
wildly-used benchmark dataset \textit{CNN/Daily Mail}. The results have shown
that ALCO performs better than the popular baselines in terms of length
controllability and content preservation.

Since the beginning of the COVID-19 pandemic, researchers have developed deep
learning models to classify COVID-19 induced pneumonia. As with many medical
imaging tasks, the quality and quantity of the available data is often limited.
In this work we train a deep learning model on publicly available COVID-19
image data and evaluate the model on local hospital chest X-ray data. The data
has been reviewed and labeled by two radiologists to ensure a high quality
estimation of the generalization capabilities of the model. Furthermore, we are
using a Generative Adversarial Network to generate synthetic X-ray images based
on this data. Our results show that using those synthetic images for data
augmentation can improve the model's performance significantly. This can be a
promising approach for many sparse data domains.

We present n-CPS - a generalisation of the recent state-of-the-art cross
pseudo supervision (CPS) approach for the task of semi-supervised semantic
segmentation. In n-CPS, there are n simultaneously trained subnetworks that
learn from each other through one-hot encoding perturbation and consistency
regularisation. We also show that ensembling techniques applied to subnetworks
outputs can significantly improve the performance. To the best of our
knowledge, n-CPS paired with CutMix outperforms CPS and sets the new
state-of-the-art for Pascal VOC 2012 with (1/16, 1/8, 1/4, and 1/2 supervised
regimes) and Cityscapes (1/16 supervised).

Vast efforts have been devoted to creating high-performance few-shot
learners, i.e., large-scale pretrained language models (PLMs) that perform well
with little downstream task training data. Training PLMs has incurred
significant cost, but utilizing the few-shot learners is still challenging due
to their enormous size. This work focuses on a crucial question: How to make
effective use of these few-shot learners? We propose LMTurk, a novel approach
that treats few-shot learners as crowdsourcing workers. The rationale is that
crowdsourcing workers are in fact few-shot learners: They are shown a few
illustrative examples to learn about a task and then start annotating. LMTurk
employs few-shot learners built upon PLMs as workers. We show that the
resulting annotations can be utilized to train models that solve the task well
and are small enough to be deployable in practical scenarios. Active learning
is integrated into LMTurk to reduce the amount of queries made to PLMs,
minimizing the computational cost of running PLM inference passes. Altogether,
LMTurk is an important step towards making effective use of current PLMs.

Mainstream state-of-the-art domain generalization algorithms tend to
prioritize the assumption on semantic invariance across domains. Meanwhile, the
inherent intra-domain style invariance is usually underappreciated and put on
the shelf. In this paper, we reveal that leveraging intra-domain style
invariance is also of pivotal importance in improving the efficiency of domain
generalization. We verify that it is critical for the network to be informative
on what domain features are invariant and shared among instances, so that the
network sharpens its understanding and improves its semantic discriminative
ability. Correspondingly, we also propose a novel "jury" mechanism, which is
particularly effective in learning useful semantic feature commonalities among
domains. Our complete model called STEAM can be interpreted as a novel
probabilistic graphical model, for which the implementation requires convenient
constructions of two kinds of memory banks: semantic feature bank and style
feature bank. Empirical results show that our proposed framework surpasses the
state-of-the-art methods by clear margins.

Self-supervised learning (SSL) has recently become the favorite among feature
learning methodologies. It is therefore appealing for domain adaptation
approaches to consider incorporating SSL. The intuition is to enforce
instance-level feature consistency such that the predictor becomes somehow
invariant across domains. However, most existing SSL methods in the regime of
domain adaptation usually are treated as standalone auxiliary components,
leaving the signatures of domain adaptation unattended. Actually, the optimal
region where the domain gap vanishes and the instance level constraint that SSL
peruses may not coincide at all. From this point, we present a particular
paradigm of self-supervised learning tailored for domain adaptation, i.e.,
Transferrable Contrastive Learning (TCL), which links the SSL and the desired
cross-domain transferability congruently. We find contrastive learning
intrinsically a suitable candidate for domain adaptation, as its instance
invariance assumption can be conveniently promoted to cross-domain class-level
invariance favored by domain adaptation tasks. Based on particular memory bank
constructions and pseudo label strategies, TCL then penalizes cross-domain
intra-class domain discrepancy between source and target through a clean and
novel contrastive loss. The free lunch is, thanks to the incorporation of
contrastive learning, TCL relies on a moving-averaged key encoder that
naturally achieves a temporally ensembled version of pseudo labels for target
data, which avoids pseudo label error propagation at no extra cost. TCL
therefore efficiently reduces cross-domain gaps. Through extensive experiments
on benchmarks (Office-Home, VisDA-2017, Digits-five, PACS and DomainNet) for
both single-source and multi-source domain adaptation tasks, TCL has
demonstrated state-of-the-art performances.

Localizing text instances in natural scenes is regarded as a fundamental
challenge in computer vision. Nevertheless, owing to the extremely varied
aspect ratios and scales of text instances in real scenes, most conventional
text detectors suffer from the sub-text problem that only localizes the
fragments of text instance (i.e., sub-texts). In this work, we quantitatively
analyze the sub-text problem and present a simple yet effective design,
COntrastive RElation (CORE) module, to mitigate that issue. CORE first
leverages a vanilla relation block to model the relations among all text
proposals (sub-texts of multiple text instances) and further enhances
relational reasoning via instance-level sub-text discrimination in a
contrastive manner. Such way naturally learns instance-aware representations of
text proposals and thus facilitates scene text detection. We integrate the CORE
module into a two-stage text detector of Mask R-CNN and devise our text
detector CORE-Text. Extensive experiments on four benchmarks demonstrate the
superiority of CORE-Text. Code is available:
\url{https://github.com/jylins/CORE-Text}.

Extreme Multilabel Text Classification (XMTC) is a text classification
problem in which, (i) the output space is extremely large, (ii) each data point
may have multiple positive labels, and (iii) the data follows a strongly
imbalanced distribution. With applications in recommendation systems and
automatic tagging of web-scale documents, the research on XMTC has been focused
on improving prediction accuracy and dealing with imbalanced data. However, the
robustness of deep learning based XMTC models against adversarial examples has
been largely underexplored.
  In this paper, we investigate the behaviour of XMTC models under adversarial
attacks. To this end, first, we define adversarial attacks in multilabel text
classification problems. We categorize attacking multilabel text classifiers as
(a) positive-targeted, where the target positive label should fall out of top-k
predicted labels, and (b) negative-targeted, where the target negative label
should be among the top-k predicted labels. Then, by experiments on APLC-XLNet
and AttentionXML, we show that XMTC models are highly vulnerable to
positive-targeted attacks but more robust to negative-targeted ones.
Furthermore, our experiments show that the success rate of positive-targeted
adversarial attacks has an imbalanced distribution. More precisely, tail
classes are highly vulnerable to adversarial attacks for which an attacker can
generate adversarial samples with high similarity to the actual data-points. To
overcome this problem, we explore the effect of rebalanced loss functions in
XMTC where not only do they increase accuracy on tail classes, but they also
improve the robustness of these classes against adversarial attacks. The code
for our experiments is available at https://github.com/xmc-aalto/adv-xmtc

Money laundering is a global problem that concerns legitimizing proceeds from
serious felonies (1.7-4 trillion euros annually) such as drug dealing, human
trafficking, or corruption. The anti-money laundering systems deployed by
financial institutions typically comprise rules aligned with regulatory
frameworks. Human investigators review the alerts and report suspicious cases.
Such systems suffer from high false-positive rates, undermining their
effectiveness and resulting in high operational costs. We propose a machine
learning triage model, which complements the rule-based system and learns to
predict the risk of an alert accurately. Our model uses both entity-centric
engineered features and attributes characterizing inter-entity relations in the
form of graph-based features. We leverage time windows to construct the dynamic
graph, optimizing for time and space efficiency. We validate our model on a
real-world banking dataset and show how the triage model can reduce the number
of false positives by 80% while detecting over 90% of true positives. In this
way, our model can significantly improve anti-money laundering operations.

Singular-value-decomposition-based coherent integrated photonic neural
networks (SC-IPNNs) have a large footprint, suffer from high static power
consumption for training and inference, and cannot be pruned using conventional
DNN pruning techniques. We leverage the lottery ticket hypothesis to propose
the first hardware-aware pruning method for SC-IPNNs that alleviates these
challenges by minimizing the number of weight parameters. We prune a
multi-layer perceptron-based SC-IPNN and show that up to 89% of the phase
angles, which correspond to weight parameters in SC-IPNNs, can be pruned with a
negligible accuracy loss (smaller than 5%) while reducing the static power
consumption by up to 86%.

Traditional 3D morphable face models (3DMMs) provide fine-grained control
over expression but cannot easily capture geometric and appearance details.
Neural volumetric representations approach photorealism but are hard to animate
and do not generalize well to unseen expressions. To tackle this problem, we
propose IMavatar (Implicit Morphable avatar), a novel method for learning
implicit head avatars from monocular videos. Inspired by the fine-grained
control mechanisms afforded by conventional 3DMMs, we represent the expression-
and pose- related deformations via learned blendshapes and skinning fields.
These attributes are pose-independent and can be used to morph the canonical
geometry and texture fields given novel expression and pose parameters. We
employ ray marching and iterative root-finding to locate the canonical surface
intersection for each pixel. A key contribution is our novel analytical
gradient formulation that enables end-to-end training of IMavatars from videos.
We show quantitatively and qualitatively that our method improves geometry and
covers a more complete expression space compared to state-of-the-art methods.

As consensus across the various published AI ethics principles is approached,
a gap remains between high-level principles and practical techniques that can
be readily adopted to design and develop responsible AI systems. We examine the
practices and experiences of researchers and engineers from Australia's
national scientific research agency (CSIRO), who are involved in designing and
developing AI systems for a range of purposes. Semi-structured interviews were
used to examine how the practices of the participants relate to and align with
a set of high-level AI ethics principles proposed by the Australian Government.
The principles comprise: Privacy Protection & Security, Reliability & Safety,
Transparency & Explainability, Fairness, Contestability, Accountability,
Human-centred Values, and Human, Social & Environmental Wellbeing. The insights
of the researchers and engineers as well as the challenges that arose for them
in the practical application of the principles are examined. Finally, we
propose a set of organisational responses (learning, process, practice,
strategy, policy) to support the implementation of high-level AI ethics
principles into practice.

Recent advances in neural-network architecture allow for seamless integration
of convex optimization problems as differentiable layers in an end-to-end
trainable neural network. Integrating medium and large scale quadratic programs
into a deep neural network architecture, however, is challenging as solving
quadratic programs exactly by interior-point methods has worst-case cubic
complexity in the number of variables. In this paper, we present an alternative
network layer architecture based on the alternating direction method of
multipliers (ADMM) that is capable of scaling to problems with a moderately
large number of variables. Backward differentiation is performed by implicit
differentiation of the residual map of a modified fixed-point iteration.
Simulated results demonstrate the computational advantage of the ADMM layer,
which for medium scaled problems is approximately an order of magnitude faster
than the OptNet quadratic programming layer. Furthermore, our novel
backward-pass routine is efficient, from both a memory and computation
standpoint, in comparison to the standard approach based on unrolled
differentiation or implicit differentiation of the KKT optimality conditions.
We conclude with examples from portfolio optimization in the integrated
prediction and optimization paradigm.

Speaker diarization is connected to semantic segmentation in computer vision.
Inspired from MaskFormer \cite{cheng2021per} which treats semantic segmentation
as a set-prediction problem, we propose an end-to-end approach to predict a set
of targets consisting of binary masks, vocal activities and speaker vectors.
Our model, which we coin \textit{DiFormer}, is mainly based on a speaker
encoder and a feature pyramid network (FPN) module to extract multi-scale
speaker features which are then fed into a transformer encoder-decoder to
predict a set of diarization targets from learned query embedding. To account
for temporal characteristics of speech signal, bidirectional LSTMs are inserted
into the mask prediction module to improve temporal consistency. Our model
handles unknown number of speakers, speech overlaps, as well as vocal activity
detection in a unified way. Experiments on multimedia and meeting datasets
demonstrate the effectiveness of our approach.

Multivariate time series (MTS) forecasting has attracted much attention in
many intelligent applications. It is not a trivial task, as we need to consider
both intra-variable dependencies and inter-variable dependencies. However,
existing works are designed for specific scenarios, and require much domain
knowledge and expert efforts, which is difficult to transfer between different
scenarios. In this paper, we propose a scale-aware neural architecture search
framework for MTS forecasting (SNAS4MTF). A multi-scale decomposition module
transforms raw time series into multi-scale sub-series, which can preserve
multi-scale temporal patterns. An adaptive graph learning module infers the
different inter-variable dependencies under different time scales without any
prior knowledge. For MTS forecasting, a search space is designed to capture
both intra-variable dependencies and inter-variable dependencies at each time
scale. The multi-scale decomposition, adaptive graph learning, and neural
architecture search modules are jointly learned in an end-to-end framework.
Extensive experiments on two real-world datasets demonstrate that SNAS4MTF
achieves a promising performance compared with the state-of-the-art methods.

Bayesian optimization involves "inner optimization" over a new-data
acquisition criterion which is non-convex/highly multi-modal, may be
non-differentiable, or may otherwise thwart local numerical optimizers. In such
cases it is common to replace continuous search with a discrete one over random
candidates. Here we propose using candidates based on a Delaunay triangulation
of the existing input design. We detail the construction of these "tricands"
and demonstrate empirically how they outperform both numerically optimized
acquisitions and random candidate-based alternatives, and are well-suited for
hybrid schemes, on benchmark synthetic and real simulation experiments.

Quantum Optimal Control is an established field of research which is
necessary for the development of Quantum Technologies. In recent years, Machine
Learning techniques have been proved usefull to tackle a variety of quantum
problems. In particular, Reinforcement Learning has been employed to address
typical problems of control of quantum systems. In this tutorial we introduce
the methods of Quantum Optimal Control and Reinforcement Learning by applying
them to the problem of three-level population transfer. The jupyter notebooks
to reproduce some of our results are open-sourced and available on github.

An increasing awareness of biased patterns in natural language processing
resources, like BERT, has motivated many metrics to quantify `bias' and
`fairness'. But comparing the results of different metrics and the works that
evaluate with such metrics remains difficult, if not outright impossible. We
survey the existing literature on fairness metrics for pretrained language
models and experimentally evaluate compatibility, including both biases in
language models as in their downstream tasks. We do this by a mixture of
traditional literature survey and correlation analysis, as well as by running
empirical evaluations. We find that many metrics are not compatible and highly
depend on (i) templates, (ii) attribute and target seeds and (iii) the choice
of embeddings. These results indicate that fairness or bias evaluation remains
challenging for contextualized language models, if not at least highly
subjective. To improve future comparisons and fairness evaluations, we
recommend avoiding embedding-based metrics and focusing on fairness evaluations
in downstream tasks.

We propose a constrained linear data-feature-mapping model as an
interpretable mathematical model for image classification using a convolutional
neural network (CNN). From this viewpoint, we establish detailed connections
between the traditional iterative schemes for linear systems and the
architectures of the basic blocks of ResNet- and MgNet-type models. Using these
connections, we present some modified ResNet models that compared with the
original models have fewer parameters and yet can produce more accurate
results, thereby demonstrating the validity of this constrained learning
data-feature-mapping assumption. Based on this assumption, we further propose a
general data-feature iterative scheme to show the rationality of MgNet. We also
provide a systematic numerical study on MgNet to show its success and
advantages in image classification problems and demonstrate its advantages in
comparison with established networks.

The complexity of game play in online multiplayer games has generated strong
interest in modeling the different play styles or strategies used by players
for success. We develop a hierarchical Bayesian regression approach for the
online multiplayer game Battlefield 3 where performance is modeled as a
function of the roles, game type, and map taken on by that player in each of
their matches. We use a Dirichlet process prior that enables the clustering of
players that have similar player-specific coefficients in our regression model,
which allows us to discover common play styles amongst our sample of
Battlefield 3 players. This Bayesian semi-parametric clustering approach has
several advantages: the number of common play styles do not need to be
specified, players can move between multiple clusters, and the resulting
groupings often have a straight-forward interpretations. We examine the most
common play styles among Battlefield 3 players in detail and find groups of
players that exhibit overall high performance, as well as groupings of players
that perform particularly well in specific game types, maps and roles. We are
also able to differentiate between players that are stable members of a
particular play style from hybrid players that exhibit multiple play styles
across their matches. Modeling this landscape of different play styles will aid
game developers in developing specialized tutorials for new participants as
well as improving the construction of complementary teams in their online
matching queues.

The convolution operator at the core of many modern neural architectures can
effectively be seen as performing a dot product between an input matrix and a
filter. While this is readily applicable to data such as images, which can be
represented as regular grids in the Euclidean space, extending the convolution
operator to work on graphs proves more challenging, due to their irregular
structure. In this paper, we propose to use graph kernels, i.e., kernel
functions that compute an inner product on graphs, to extend the standard
convolution operator to the graph domain. This allows us to define an entirely
structural model that does not require computing the embedding of the input
graph. Our architecture allows to plug-in any type and number of graph kernels
and has the added benefit of providing some interpretability in terms of the
structural masks that are learned during the training process, similarly to
what happens for convolutional masks in traditional convolutional neural
networks. We perform an extensive ablation study to investigate the impact of
the model hyper-parameters and we show that our model achieves competitive
performance on standard graph classification datasets.

One of the core components of goal-oriented dialog systems is the task of
Intent Detection. Few-shot Learning upon Intent Detection is challenging due to
the scarcity of available annotated utterances. Although recent works making
use of metric-based and optimization-based methods have been proposed, the task
is still challenging in large label spaces and much smaller number of shots.
Generalized Few-shot learning is more difficult due to the presence of both
novel and seen classes during the testing phase. In this work, we propose a
simple and effective method based on Natural Language Inference that not only
tackles the problem of few shot intent detection, but also proves useful in
zero-shot and generalized few shot learning problems. Our extensive experiments
on a number of Natural Language Understanding (NLU) and Spoken Language
Understanding (SLU) datasets show the effectiveness of our approach. In
addition, we highlight the settings in which our NLI based method outperforms
the baselines by huge margins.

In this work we continue to build upon recent advances in reinforcement
learning for finite Markov processes. A common approach among previous existing
algorithms, both single-actor and distributed, is to either clip rewards or to
apply a transformation method on Q-functions to handle a large variety of
magnitudes in real discounted returns. We theoretically show that one of the
most successful methods may not yield an optimal policy if we have a
non-deterministic process. As a solution, we argue that distributional
reinforcement learning lends itself to remedy this situation completely. By the
introduction of a conjugated distributional operator we may handle a large
class of transformations for real returns with guaranteed theoretical
convergence. We propose an approximating single-actor algorithm based on this
operator that trains agents directly on unaltered rewards using a proper
distributional metric given by the Cram\'er distance. To evaluate its
performance in a stochastic setting we train agents on a suite of 55 Atari 2600
games using sticky-actions and obtain state-of-the-art performance compared to
other well-known algorithms in the Dopamine framework.

Multi-modal fusion is proven to be an effective method to improve the
accuracy and robustness of speaker tracking, especially in complex scenarios.
However, how to combine the heterogeneous information and exploit the
complementarity of multi-modal signals remains a challenging issue. In this
paper, we propose a novel Multi-modal Perception Tracker (MPT) for speaker
tracking using both audio and visual modalities. Specifically, a novel acoustic
map based on spatial-temporal Global Coherence Field (stGCF) is first
constructed for heterogeneous signal fusion, which employs a camera model to
map audio cues to the localization space consistent with the visual cues. Then
a multi-modal perception attention network is introduced to derive the
perception weights that measure the reliability and effectiveness of
intermittent audio and video streams disturbed by noise. Moreover, a unique
cross-modal self-supervised learning method is presented to model the
confidence of audio and visual observations by leveraging the complementarity
and consistency between different modalities. Experimental results show that
the proposed MPT achieves 98.6% and 78.3% tracking accuracy on the standard and
occluded datasets, respectively, which demonstrates its robustness under
adverse conditions and outperforms the current state-of-the-art methods.

Reference texts such as encyclopedias and news articles can manifest biased
language when objective reporting is substituted by subjective writing.
Existing methods to detect bias mostly rely on annotated data to train machine
learning models. However, low annotator agreement and comparability is a
substantial drawback in available media bias corpora. To evaluate data
collection options, we collect and compare labels obtained from two popular
crowdsourcing platforms. Our results demonstrate the existing crowdsourcing
approaches' lack of data quality, underlining the need for a trained expert
framework to gather a more reliable dataset. By creating such a framework and
gathering a first dataset, we are able to improve Krippendorff's $\alpha$ =
0.144 (crowdsourcing labels) to $\alpha$ = 0.419 (expert labels). We conclude
that detailed annotator training increases data quality, improving the
performance of existing bias detection systems. We will continue to extend our
dataset in the future.

Large deformations of organs, caused by diverse shapes and nonlinear shape
changes, pose a significant challenge for medical image registration.
Traditional registration methods need to iteratively optimize an objective
function via a specific deformation model along with meticulous parameter
tuning, but which have limited capabilities in registering images with large
deformations. While deep learning-based methods can learn the complex mapping
from input images to their respective deformation field, it is regression-based
and is prone to be stuck at local minima, particularly when large deformations
are involved. To this end, we present Stochastic Planner-Actor-Critic (SPAC), a
novel reinforcement learning-based framework that performs step-wise
registration. The key notion is warping a moving image successively by each
time step to finally align to a fixed image. Considering that it is challenging
to handle high dimensional continuous action and state spaces in the
conventional reinforcement learning (RL) framework, we introduce a new concept
`Plan' to the standard Actor-Critic model, which is of low dimension and can
facilitate the actor to generate a tractable high dimensional action. The
entire framework is based on unsupervised training and operates in an
end-to-end manner. We evaluate our method on several 2D and 3D medical image
datasets, some of which contain large deformations. Our empirical results
highlight that our work achieves consistent, significant gains and outperforms

Electroconvulsive Therapy (ECT) is arguably the most effective intervention
for treatment-resistant depression. While large interindividual variability
exists, a theory capable of predicting individual response to ECT remains
elusive. To address this, we posit a quantitative, mechanistic framework of ECT
response based on Network Control Theory (NCT). Then, we empirically test our
approach and employ it to predict ECT treatment response. To this end, we
derive a formal association between Postictal Suppression Index (PSI) - an ECT
seizure quality index - and whole-brain modal and average controllability, NCT
metrics based on white matter brain network architecture, respectively.
Exploiting the known association of ECT response and PSI, we then hypothesized
an association between our controllability metrics and ECT response mediated by
PSI. We formally tested this conjecture in N=50 depressive patients undergoing
ECT. We show that whole-brain controllability metrics based on pre-ECT
structural connectome data predict ECT response in accordance with our
hypotheses. In addition, we show the expected mediation effects via PSI.
Importantly, our theoretically motivated metrics are at least on par with
extensive machine learning models based on pre-ECT connectome data. In summary,
we derived and tested a control-theoretic framework capable of predicting ECT
response based on individual brain network architecture. It makes testable,
quantitative predictions regarding individual therapeutic response, which are
corroborated by strong empirical evidence. Our work might constitute a starting
point for a comprehensive, quantitative theory of personalized ECT
interventions rooted in control theory.

Branching Time Active Inference (Champion et al., 2021b,a) is a framework
proposing to look at planning as a form of Bayesian model expansion. Its root
can be found in Active Inference (Friston et al., 2016; Da Costa et al., 2020;
Champion et al., 2021c), a neuroscientific framework widely used for brain
modelling, as well as in Monte Carlo Tree Search (Browne et al., 2012), a
method broadly applied in the Reinforcement Learning literature. Up to now, the
inference of the latent variables was carried out by taking advantage of the
flexibility offered by Variational Message Passing (Winn and Bishop, 2005), an
iterative process that can be understood as sending messages along the edges of
a factor graph (Forney, 2001). In this paper, we harness the efficiency of an
alternative method for inference called Bayesian Filtering (Fox et al., 2003),
which does not require the iteration of the update equations until convergence
of the Variational Free Energy. Instead, this scheme alternates between two
phases: integration of evidence and prediction of future states. Both of those
phases can be performed efficiently and this provides a seventy times speed up
over the state-of-the-art.

Training a model-free deep reinforcement learning model to solve
image-to-image translation is difficult since it involves high-dimensional
continuous state and action spaces. In this paper, we draw inspiration from the
recent success of the maximum entropy reinforcement learning framework designed
for challenging continuous control problems to develop stochastic policies over
high dimensional continuous spaces including image representation, generation,
and control simultaneously. Central to this method is the Stochastic
Actor-Executor-Critic (SAEC) which is an off-policy actor-critic model with an
additional executor to generate realistic images. Specifically, the actor
focuses on the high-level representation and control policy by a stochastic
latent action, as well as explicitly directs the executor to generate low-level
actions to manipulate the state. Experiments on several image-to-image
translation tasks have demonstrated the effectiveness and robustness of the
proposed SAEC when facing high-dimensional continuous space problems.

In the past few years, it has been shown that deep learning systems are
highly vulnerable under attacks with adversarial examples. Neural-network-based
automatic speech recognition (ASR) systems are no exception. Targeted and
untargeted attacks can modify an audio input signal in such a way that humans
still recognise the same words, while ASR systems are steered to predict a
different transcription. In this paper, we propose a defense mechanism against
targeted adversarial attacks consisting in removing fast-changing features from
the audio signals, either by applying slow feature analysis, a low-pass filter,
or both, before feeding the input to the ASR system. We perform an empirical
analysis of hybrid ASR models trained on data pre-processed in such a way.
While the resulting models perform quite well on benign data, they are
significantly more robust against targeted adversarial attacks: Our final,
proposed model shows a performance on clean data similar to the baseline model,
while being more than four times more robust.

Human-Object Interaction (HOI) detection is a fundamental task in high-level
human-centric scene understanding. We propose PhraseHOI, containing a HOI
branch and a novel phrase branch, to leverage language prior and improve
relation expression. Specifically, the phrase branch is supervised by semantic
embeddings, whose ground truths are automatically converted from the original
HOI annotations without extra human efforts. Meanwhile, a novel label
composition method is proposed to deal with the long-tailed problem in HOI,
which composites novel phrase labels by semantic neighbors. Further, to
optimize the phrase branch, a loss composed of a distilling loss and a balanced
triplet loss is proposed. Extensive experiments are conducted to prove the
effectiveness of the proposed PhraseHOI, which achieves significant improvement
over the baseline and surpasses previous state-of-the-art methods on Full and
NonRare on the challenging HICO-DET benchmark.

We present a customized 3D mesh Transformer model for the pose transfer task.
As the 3D pose transfer essentially is a deformation procedure dependent on the
given meshes, the intuition of this work is to perceive the geometric
inconsistency between the given meshes with the powerful self-attention
mechanism. Specifically, we propose a novel geometry-contrastive Transformer
that has an efficient 3D structured perceiving ability to the global geometric
inconsistencies across the given meshes. Moreover, locally, a simple yet
efficient central geodesic contrastive loss is further proposed to improve the
regional geometric-inconsistency learning. At last, we present a latent
isometric regularization module together with a novel semi-synthesized dataset
for the cross-dataset 3D pose transfer task towards unknown spaces. The massive
experimental results prove the efficacy of our approach by showing
state-of-the-art quantitative performances on SMPL-NPT, FAUST and our new
proposed dataset SMG-3D datasets, as well as promising qualitative results on
MG-cloth and SMAL datasets. It's demonstrated that our method can achieve
robust 3D pose transfer and be generalized to challenging meshes from unknown
spaces on cross-dataset tasks. The code and dataset are made available. Code is
available: https://github.com/mikecheninoulu/CGT.

User identity linkage, which aims to link identities of a natural person
across different social platforms, has attracted increasing research interest
recently. Existing approaches usually first embed the identities as
deterministic vectors in a shared latent space, and then learn a classifier
based on the available annotations. However, the formation and characteristics
of real-world social platforms are full of uncertainties, which makes these
deterministic embedding based methods sub-optimal. In addition, it is
intractable to collect sufficient linkage annotations due to the tremendous
gaps between different platforms. Semi-supervised models utilize the unlabeled
data to help capture the intrinsic data distribution, which are more promising
in practical usage. However, the existing semi-supervised linkage methods
heavily rely on the heuristically defined similarity measurements to
incorporate the innate closeness between labeled and unlabeled samples. Such
manually designed assumptions may not be consistent with the actual linkage
signals and further introduce the noises. To address the mentioned limitations,
in this paper we propose a novel Noise-aware Semi-supervised Variational User
Identity Linkage (NSVUIL) model. Specifically, we first propose a novel
supervised linkage module to incorporate the available annotations. Each social
identity is represented by a Gaussian distribution in the Wasserstein space to
simultaneously preserve the fine-grained social profiles and model the
uncertainty of identities. Then, a noise-aware self-learning module is designed
to faithfully augment the few available annotations, which is capable of
filtering noises from the pseudo-labels generated by the supervised module.

This manuscript presents an approach for multi zone sound field reproduction
using supervised learning. Traditional multi zone sound field reproduction
methods assume constant speed of sound, neglecting nonlinear effects like wind
and temperature stratification. We show how to overcome these restrictions
using supervised learning of transfer functions. The quality of the solution is
measured by the acoustic contrast and the reproduction error. Our results show
that for the chosen setup, even with relatively small wind speeds, the acoustic
contrast and reproduction error can be improved by up to 16 dB, when wind is
considered in the trained model.

In this paper, we propose the SCORE (self-concordant regularization)
framework for unconstrained minimization problems which incorporates
second-order information in the Newton decrement framework for convex
optimization. We propose the generalized Gauss-Newton with Self-Concordant
Regularization (GGN-SCORE) algorithm that updates the minimization variables
each time it receives a new input batch. The proposed algorithm exploits the
structure of the second-order information in the Hessian matrix, thereby
reducing computational overhead. GGN-SCORE demonstrates how we may speed up
convergence while also improving model generalization for problems that involve
regularized minimization under the SCORE framework. Numerical experiments show
the efficiency of our method and its fast convergence, which compare favorably
against baseline first-order and quasi-Newton methods. Additional experiments
involving non-convex (overparameterized) neural network training problems show
similar convergence behaviour thereby highlighting the promise of the proposed
algorithm for non-convex optimization.

We introduce in this paper new and very effective numerical methods based on
neural networks for the approximation of the mean curvature flow of either
oriented or non-orientable surfaces. To learn the correct interface evolution
law, our neural networks are trained on phase field representations of exact
evolving interfaces. The structures of the networks draw inspiration from
splitting schemes used for the discretization of the Allen-Cahn equation. But
when the latter approximate the mean curvature motion of oriented interfaces
only, the approach we propose extends very naturally to the non-orientable
case. Through a variety of examples, we show that our networks, trained only on
flows of smooth and simplistic interfaces, generalize very well to more complex
interfaces, either oriented or non-orientable, and possibly with singularities.
Furthermore, they can be coupled easily with additional constraints which opens
the way to various applications illustrating the flexibility and effectiveness
of our approach: mean curvature flows with volume constraint, multiphase mean
curvature flows, numerical approximation of Steiner trees or minimal surfaces.

We are interested in interactive agents that learn to coordinate, namely, a
$builder$ -- which performs actions but ignores the goal of the task, i.e. has
no access to rewards -- and an $architect$ which guides the builder towards the
goal of the task. We define and explore a formal setting where artificial
agents are equipped with mechanisms that allow them to simultaneously learn a
task while at the same time evolving a shared communication protocol. Ideally,
such learning should only rely on high-level communication priors and be able
to handle a large variety of tasks and meanings while deriving communication
protocols that can be reused across tasks. We present the Architect-Builder
Problem (ABP): an asymmetrical setting in which an architect must learn to
guide a builder towards constructing a specific structure. The architect knows
the target structure but cannot act in the environment and can only send
arbitrary messages to the builder. The builder on the other hand can act in the
environment, but receives no rewards nor has any knowledge about the task, and
must learn to solve it relying only on the messages sent by the architect.
Crucially, the meaning of messages is initially not defined nor shared between
the agents but must be negotiated throughout learning. Under these constraints,
we propose Architect-Builder Iterated Guiding (ABIG), a solution to ABP where
the architect leverages a learned model of the builder to guide it while the
builder uses self-imitation learning to reinforce its guided behavior. We
analyze the key learning mechanisms of ABIG and test it in 2D tasks involving
grasping cubes, placing them at a given location, or building various shapes.
ABIG results in a low-level, high-frequency, guiding communication protocol
that not only enables an architect-builder pair to solve the task at hand, but
that can also generalize to unseen tasks.

Using techniques from deep learning (cf. [B\"uh+19]), we show that neural
networks can be trained successfully to replicate the modified payoff functions
that were first derived in the context of partial hedging by [FL00]. Not only
does this approach better accommodate the realistic setting of hedging in
discrete time, it also allows for the inclusion of transaction costs as well as
general market dynamics.

Articulated objects are pervasive in daily life. However, due to the
intrinsic high-DoF structure, the joint states of the articulated objects are
hard to be estimated. To model articulated objects, two kinds of shape
deformations namely the geometric and the pose deformation should be
considered. In this work, we present a novel category-specific parametric
representation called Object Model with Articulated Deformations (OMAD) to
explicitly model the articulated objects. In OMAD, a category is associated
with a linear shape function with shared shape basis and a non-linear joint
function. Both functions can be learned from a large-scale object model dataset
and fixed as category-specific priors. Then we propose an OMADNet to predict
the shape parameters and the joint states from an object's single observation.
With the full representation of the object shape and joint states, we can
address several tasks including category-level object pose estimation and the
articulated object retrieval. To evaluate these tasks, we create a synthetic
dataset based on PartNet-Mobility. Extensive experiments show that our simple
OMADNet can serve as a strong baseline for both tasks.

As a decisive part in the success of Mobility-as-a-Service (MaaS),
spatio-temporal predictive modeling for crowd movements is a challenging task
particularly considering scenarios where societal events drive mobility
behavior deviated from the normality. While tremendous progress has been made
to model high-level spatio-temporal regularities with deep learning, most, if
not all of the existing methods are neither aware of the dynamic interactions
among multiple transport modes nor adaptive to unprecedented volatility brought
by potential societal events. In this paper, we are therefore motivated to
improve the canonical spatio-temporal network (ST-Net) from two perspectives:
(1) design a heterogeneous mobility information network (HMIN) to explicitly
represent intermodality in multimodal mobility; (2) propose a memory-augmented
dynamic filter generator (MDFG) to generate sequence-specific parameters in an
on-the-fly fashion for various scenarios. The enhanced event-aware
spatio-temporal network, namely EAST-Net, is evaluated on several real-world
datasets with a wide variety and coverage of societal events. Both quantitative
and qualitative experimental results verify the superiority of our approach
compared with the state-of-the-art baselines. Code and data are published on
https://github.com/underdoc-wang/EAST-Net.

Despite the empirical success of meta reinforcement learning (meta-RL), there
are still a number poorly-understood discrepancies between theory and practice.
Critically, biased gradient estimates are almost always implemented in
practice, whereas prior theory on meta-RL only establishes convergence under
unbiased gradient estimates. In this work, we investigate such a discrepancy.
In particular, (1) We show that unbiased gradient estimates have variance
$\Theta(N)$ which linearly depends on the sample size $N$ of the inner loop
updates; (2) We propose linearized score function (LSF) gradient estimates,
which have bias $\mathcal{O}(1/\sqrt{N})$ and variance $\mathcal{O}(1/N)$; (3)
We show that most empirical prior work in fact implements variants of the LSF
gradient estimates. This implies that practical algorithms "accidentally"
introduce bias to achieve better performance; (4) We establish theoretical
guarantees for the LSF gradient estimates in meta-RL regarding its convergence
to stationary points, showing better dependency on $N$ than prior work when $N$
is large.

Adversarial training is a popular method to robustify models against
adversarial attacks. However, it exhibits much more severe overfitting than
training on clean inputs. In this work, we investigate this phenomenon from the
perspective of training instances, i.e., training input-target pairs. Based on
a quantitative metric measuring instances' difficulty, we analyze the model's
behavior on training instances of different difficulty levels. This lets us
show that the decay in generalization performance of adversarial training is a
result of the model's attempt to fit hard adversarial instances. We
theoretically verify our observations for both linear and general nonlinear
models, proving that models trained on hard instances have worse generalization
performance than ones trained on easy instances. Furthermore, we prove that the
difference in the generalization gap between models trained by instances of
different difficulty levels increases with the size of the adversarial budget.
Finally, we conduct case studies on methods mitigating adversarial overfitting
in several scenarios. Our analysis shows that methods successfully mitigating
adversarial overfitting all avoid fitting hard adversarial instances, while
ones fitting hard adversarial instances do not achieve true robustness.

Herein we report a multi-zone, heating, ventilation and air-conditioning
(HVAC) control case study of an industrial plant responsible for cooling a
hospital surgery center. The adopted approach to guaranteeing thermal comfort
and reducing electrical energy consumption is based on a statistical
non-parametric, non-linear regression technique named Gaussian processes. Our
study aimed at assessing the suitability of the aforementioned technique to
learning the building dynamics and yielding models for our model predictive
control (MPC) scheme. Experimental results gathered while the building was
under regular use showcase the final controller performance while subject to a
number of measured and unmeasured disturbances. Finally, we provide readers
with practical details and recommendations on how to manage the computational
complexity of the on-line optimization problem and obtain high-quality
solutions from solvers.

Fast and reliable connectivity is essential to enhancing situational
awareness and operational efficiency for public safety mission-critical (MC)
users. In emergency or disaster circumstances, where existing cellular network
coverage and capacity may not be available to meet MC communication demands,
deployable-network-based solutions such as cells-on-wheels/wings can be
utilized swiftly to ensure reliable connection for MC users. In this paper, we
consider a scenario where a macro base station (BS) is destroyed due to a
natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up
to provide temporary coverage for users in the disaster area. The UAV-BS is
integrated into the mobile network using the 5G integrated access and backhaul
(IAB) technology. We propose a framework and signalling procedure for applying
machine learning to this use case. A deep reinforcement learning algorithm is
designed to jointly optimize the access and backhaul antenna tilt as well as
the three-dimensional location of the UAV-BS in order to best serve the
on-ground MC users while maintaining a good backhaul connection. Our result
shows that the proposed algorithm can autonomously navigate and configure the
UAV-BS to improve the throughput and reduce the drop rate of MC users.

We deal with the scenario of conversational search, where user queries are
under-specified or ambiguous. This calls for a mixed-initiative setup.
User-asks (queries) and system-answers, as well as system-asks (clarification
questions) and user response, in order to clarify her information needs. We
focus on the task of selecting the next clarification question, given the
conversation context. Our method leverages passage retrieval from a background
content to fine-tune two deep-learning models for ranking candidate
clarification questions. We evaluated our method on two different use-cases.
The first is an open domain conversational search in a large web collection.
The second is a task-oriented customer-support setup. We show that our method
performs well on both use-cases.

Context. The Javalambre Photometric Local Universe Survey (J-PLUS) has
obtained precise photometry in twelve specially designed filters for large
numbers of Galactic stars. Deriving their precise stellar atmospheric
parameters and individual elemental abundances is crucial for studies of
Galactic structure, and the assembly history and chemical evolution of our
Galaxy. Aims. Our goal is to estimate not only stellar parameters (effective
temperature, Teff, surface gravity, log g, and metallicity, [Fe/H]), but also
[{\alpha}/Fe] and four elemental abundances ([C/Fe], [N/Fe], [Mg/Fe], and
[Ca/Fe]) using data from J-PLUS DR1. Methods. By combining recalibrated
photometric data from J-PLUS DR1, Gaia DR2, and spectroscopic labels from
LAMOST, we design and train a set of cost-sensitive neural networks, the CSNet,
to learn the non-linear mapping from stellar colors to their labels. Results.
We have achieved precisions of {\delta}Teff {\sim}55K,
{\delta}logg{\sim}0.15dex, and {\delta}[Fe/H]{\sim}0.07dex, respectively, over
a wide range of temperature, surface gravity, and metallicity. The
uncertainties of the abundance estimates for [{\alpha}/Fe] and the four
individual elements are in the range 0.04-0.08 dex. We compare our parameter
and abundance estimates with those from other spectroscopic catalogs such as
APOGEE and GALAH, and find an overall good agreement. Conclusions. Our results
demonstrate the potential of well-designed, high-quality photometric data for
determinations of stellar parameters as well as individual elemental
abundances. Applying the method to J-PLUS DR1, we have obtained the
aforementioned parameters for about two million stars, providing an outstanding
data set for chemo-dynamic analyses of the Milky Way. The catalog of the
estimated parameters is publicly accessible.

The use of low-cost sensors in conjunction with high-precision
instrumentation for air pollution monitoring has shown promising results in
recent years. One of the main challenges for these sensors has been the quality
of their data, which is why the main efforts have focused on calibrating the
sensors using machine learning techniques to improve the data quality. However,
there is one aspect that has been overlooked, that is, these sensors are
mounted on nodes that may have energy consumption restrictions if they are
battery-powered. In this paper, we show the usual sensor data gathering process
and we study the existing trade-offs between the sampling of such sensors, the
quality of the sensor calibration, and the power consumption involved. To this
end, we conduct experiments on prototype nodes measuring tropospheric ozone,
nitrogen dioxide, and nitrogen monoxide at high frequency. The results show
that the sensor sampling strategy directly affects the quality of the air
pollution estimation and that each type of sensor may require different
sampling strategies. In addition, duty cycles of 0.1 can be achieved when the
sensors have response times in the order of two minutes, and duty cycles
between 0.01 and 0.02 can be achieved when the sensor response times are
negligible, calibrating with hourly reference values and maintaining a quality
of calibrated data similar to when the node is connected to an uninterruptible
power supply.

Many innovative applications require establishing correspondences among 3D
geometric objects. However, the countless possible deformations of smooth
surfaces make shape matching a challenging task. Finding an embedding to
represent the different shapes in high-dimensional space where the matching is
easier to solve is a well-trodden path that has given many outstanding
solutions. Recently, a new trend has shown advantages in learning such
representations. This novel idea motivated us to investigate which properties
differentiate these data-driven embeddings and which ones promote
state-of-the-art results. In this study, we analyze, for the first time,
properties that arise in data-driven learned embedding and their relation to
the shape-matching task. Our discoveries highlight the close link between
matching and smoothness, which naturally emerge from training. Also, we
demonstrate the relation between the orthogonality of the embedding and the
bijectivity of the correspondence. Our experiments show exciting results,
overcoming well-established alternatives and shedding a different light on
relevant contexts and properties for learned embeddings.

The issue in respiratory sound classification has attained good attention
from the clinical scientists and medical researcher's group in the last year to
diagnosing COVID-19 disease. To date, various models of Artificial Intelligence
(AI) entered into the real-world to detect the COVID-19 disease from
human-generated sounds such as voice/speech, cough, and breath. The
Convolutional Neural Network (CNN) model is implemented for solving a lot of
real-world problems on machines based on Artificial Intelligence (AI). In this
context, one dimension (1D) CNN is suggested and implemented to diagnose
respiratory diseases of COVID-19 from human respiratory sounds such as a voice,
cough, and breath. An augmentation-based mechanism is applied to improve the
preprocessing performance of the COVID-19 sounds dataset and to automate
COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a
DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound
features such as the input function to the 1D CNN instead of adopting the
standard input of MFCC (Mel-frequency cepstral coefficient), and it is
performed better accuracy and performance than previous models.

Answering semantically-complicated questions according to an image is
challenging in Visual Question Answering (VQA) task. Although the image can be
well represented by deep learning, the question is always simply embedded and
cannot well indicate its meaning. Besides, the visual and textual features have
a gap for different modalities, it is difficult to align and utilize the
cross-modality information. In this paper, we focus on these two problems and
propose a Graph Matching Attention (GMA) network. Firstly, it not only builds
graph for the image, but also constructs graph for the question in terms of
both syntactic and embedding information. Next, we explore the intra-modality
relationships by a dual-stage graph encoder and then present a bilateral
cross-modality graph matching attention to infer the relationships between the
image and the question. The updated cross-modality features are then sent into
the answer prediction module for final answer prediction. Experiments
demonstrate that our network achieves state-of-the-art performance on the GQA
dataset and the VQA 2.0 dataset. The ablation studies verify the effectiveness
of each modules in our GMA network.

Workflow scheduling is a long-studied problem in parallel and distributed
computing (PDC), aiming to efficiently utilize compute resources to meet user's
service requirements. Recently proposed scheduling methods leverage the low
response times of edge computing platforms to optimize application Quality of
Service (QoS). However, scheduling workflow applications in mobile edge-cloud
systems is challenging due to computational heterogeneity, changing latencies
of mobile devices and the volatile nature of workload resource requirements. To
overcome these difficulties, it is essential, but at the same time challenging,
to develop a long-sighted optimization scheme that efficiently models the QoS
objectives. In this work, we propose MCDS: Monte Carlo Learning using Deep
Surrogate Models to efficiently schedule workflow applications in mobile
edge-cloud computing systems. MCDS is an Artificial Intelligence (AI) based
scheduling approach that uses a tree-based search strategy and a deep neural
network-based surrogate model to estimate the long-term QoS impact of immediate
actions for robust optimization of scheduling decisions. Experiments on
physical and simulated edge-cloud testbeds show that MCDS can improve over the
state-of-the-art methods in terms of energy consumption, response time, SLA
violations and cost by at least 6.13, 4.56, 45.09 and 30.71 percent

Model-based Deep Reinforcement Learning (RL) assumes the availability of a
model of an environment's underlying transition dynamics. This model can be
used to predict future effects of an agent's possible actions. When no such
model is available, it is possible to learn an approximation of the real
environment, e.g. by using generative neural networks, sometimes also called
World Models. As most real-world environments are stochastic in nature and the
transition dynamics are oftentimes multimodal, it is important to use a
modelling technique that is able to reflect this multimodal uncertainty. In
order to safely deploy such learning systems in the real world, especially in
an industrial context, it is paramount to consider these uncertainties. In this
work, we analyze existing and propose new metrics for the detection and
quantification of multimodal uncertainty in RL based World Models. The correct
modelling & detection of uncertain future states lays the foundation for
handling critical situations in a safe way, which is a prerequisite for
deploying RL systems in real-world settings.

In this paper, we tackle the inductive semi-supervised learning problem that
aims to obtain label predictions for out-of-sample data. The proposed approach,
called Optimal Transport Induction (OTI), extends efficiently an optimal
transport based transductive algorithm (OTP) to inductive tasks for both binary
and multi-class settings. A series of experiments are conducted on several
datasets in order to compare the proposed approach with state-of-the-art
methods. Experiments demonstrate the effectiveness of our approach. We make our
code publicly available (Code is available at:
https://github.com/MouradElHamri/OTI).

Long story generation (LSG) is one of the coveted goals in natural language
processing. Different from most text generation tasks, LSG requires to output a
long story of rich content based on a much shorter text input, and often
suffers from information sparsity. In this paper, we propose \emph{TopNet} to
alleviate this problem, by leveraging the recent advances in neural topic
modeling to obtain high-quality skeleton words to complement the short input.
In particular, instead of directly generating a story, we first learn to map
the short text input to a low-dimensional topic distribution (which is
pre-assigned by a topic model). Based on this latent topic distribution, we can
use the reconstruction decoder of the topic model to sample a sequence of
inter-related words as a skeleton for the story. Experiments on two benchmark
datasets show that our proposed framework is highly effective in skeleton word
selection and significantly outperforms the state-of-the-art models in both
automatic evaluation and human evaluation.

Chimney fires constitute one of the most commonly occurring fire types.
Precise prediction and prompt prevention are crucial in reducing the harm they
cause. In this paper, we develop a combined machine learning and statistical
modeling process to predict chimney fires. Firstly, we use random forests and
permutation importance techniques to identify the most informative explanatory
variables. Secondly, we design a Poisson point process model and apply
associated logistic regression estimation to estimate the parameters. Moreover,
we validate the Poisson model assumption using second-order summary statistics
and residuals. We implement the modeling process on data collected by the
Twente Fire Brigade and obtain plausible predictions. Compared to similar
studies, our approach has two advantages: i) with random forests, we can select
explanatory variables non-parametrically considering variable dependence; ii)
using logistic regression estimation, we can fit the statistical model
efficiently by tuning it to focus on important regions and times of the fire
data.

Machine learning (ML) and Deep Learning (DL) methods are being adopted
rapidly, especially in computer network security, such as fraud detection,
network anomaly detection, intrusion detection, and much more. However, the
lack of transparency of ML and DL based models is a major obstacle to their
implementation and criticized due to its black-box nature, even with such
tremendous results. Explainable Artificial Intelligence (XAI) is a promising
area that can improve the trustworthiness of these models by giving
explanations and interpreting its output. If the internal working of the ML and
DL based models is understandable, then it can further help to improve its
performance. The objective of this paper is to show that how XAI can be used to
interpret the results of the DL model, the autoencoder in this case. And, based
on the interpretation, we improved its performance for computer network anomaly
detection. The kernel SHAP method, which is based on the shapley values, is
used as a novel feature selection technique. This method is used to identify
only those features that are actually causing the anomalous behaviour of the
set of attack/anomaly instances. Later, these feature sets are used to train
and validate the autoencoder but on benign data only. Finally, the built
SHAP_Model outperformed the other two models proposed based on the feature
selection method. This whole experiment is conducted on the subset of the
latest CICIDS2017 network dataset. The overall accuracy and AUC of SHAP_Model
is 94% and 0.969, respectively.

Automatic Sleep Staging study is presently done with the help of
Electroencephalogram (EEG) signals. Recently, Deep Learning (DL) based
approaches have enabled significant progress in this area, allowing for
near-human accuracy in automated sleep staging. However, EEG based sleep
staging requires an extensive as well as an expensive clinical setup. Moreover,
the requirement of an expert for setup and the added inconvenience to the
subject under study renders it unfavourable in a point of care context.
Electrocardiogram (ECG), an unobtrusive alternative to EEG, is more suitable,
but its performance, unsurprisingly, remains sub-par compared to EEG-based
sleep staging. Naturally, it would be helpful to transfer knowledge from EEG to
ECG, ultimately enhancing the model's performance on ECG based inputs.
Knowledge Distillation (KD) is a renowned concept in DL that looks to transfer
knowledge from a better but potentially more cumbersome teacher model to a
compact student model. Building on this concept, we propose a cross-modal KD
framework to improve ECG-based sleep staging performance with assistance from
features learned through models trained on EEG. Additionally, we also conducted
multiple experiments on the individual components of the proposed model to get
better insight into the distillation approach. Data of 200 subjects from the
Montreal Archive of Sleep Studies (MASS) was utilized for our study. The
proposed model showed a 14.3\% and 13.4\% increase in weighted-F1-score in
4-class and 3-class sleep staging, respectively. This demonstrates the
viability of KD for performance improvement of single-channel ECG based sleep
staging in 4-class(W-L-D-R) and 3-class(W-N-R) classification.

A frequent challenge encountered with ecological data is how to interpret,
analyze, or model data having a high proportion of zeros. Much attention has
been given to zero-inflated count data, whereas models for non-negative
continuous data with an abundance of 0s are lacking. We consider zero-inflated
data on the unit interval and provide modeling to capture two types of 0s in
the context of the Beta regression model. We model 0s due to missing by chance
through left censoring of a latent regression, and 0s due to unsuitability
using an independent Bernoulli specification to create a point mass at 0. We
first develop the model as a spatial regression in environmental features and
then extend to introduce spatial random effects. We specify models
hierarchically, employing latent variables, fit them within a Bayesian
framework, and present new model comparison tools. Our motivating dataset
consists of percent cover abundance of two plant species at a collection of
sites in the Cape Floristic Region of South Africa. We find that environmental
features enable learning about the incidence of both types of 0s as well as the
positive percent covers. We also show that the spatial random effects model
improves predictive performance. The proposed modeling enables ecologists,
using environmental regressors, to extract a better understanding of the
presence/absence of species in terms of absence due to unsuitability vs.
missingness by chance, as well as abundance when present.

With increasing appealing to privacy issues in face recognition, federated
learning has emerged as one of the most prevalent approaches to study the
unconstrained face recognition problem with private decentralized data.
However, conventional decentralized federated algorithm sharing whole
parameters of networks among clients suffers from privacy leakage in face
recognition scene. In this work, we introduce a framework, FedGC, to tackle
federated learning for face recognition and guarantees higher privacy. We
explore a novel idea of correcting gradients from the perspective of backward
propagation and propose a softmax-based regularizer to correct gradients of
class embeddings by precisely injecting a cross-client gradient term.
Theoretically, we show that FedGC constitutes a valid loss function similar to
standard softmax. Extensive experiments have been conducted to validate the
superiority of FedGC which can match the performance of conventional
centralized methods utilizing full training dataset on several popular

Deep learning-based approaches have shown remarkable performance in the 3D
object detection task. However, they suffer from a catastrophic performance
drop on the originally trained classes when incrementally learning new classes
without revisiting the old data. This "catastrophic forgetting" phenomenon
impedes the deployment of 3D object detection approaches in real-world
scenarios, where continuous learning systems are needed. In this paper, we
study the unexplored yet important class-incremental 3D object detection
problem and present the first solution - SDCoT, a novel static-dynamic
co-teaching method. Our SDCoT alleviates the catastrophic forgetting of old
classes via a static teacher, which provides pseudo annotations for old classes
in the new samples and regularizes the current model by extracting previous
knowledge with a distillation loss. At the same time, SDCoT consistently learns
the underlying knowledge from new data via a dynamic teacher. We conduct
extensive experiments on two benchmark datasets and demonstrate the superior
performance of our SDCoT over baseline approaches in several incremental

Electronic healthcare records are an important source of information which
can be used in patient stratification to discover novel disease phenotypes.
However, they can be challenging to work with as data is often sparse and
irregularly sampled. One approach to solve these limitations is learning dense
embeddings that represent individual patient trajectories using a recurrent
neural network autoencoder (RNN-AE). This process can be susceptible to
unwanted data biases. We show that patient embeddings and clusters using
previously proposed RNN-AE models might be impacted by a trajectory bias,
meaning that results are dominated by the amount of data contained in each
patients trajectory, instead of clinically relevant details. We investigate
this bias on 2 datasets (from different hospitals) and 2 disease areas as well
as using different parts of the patient trajectory. Our results using 2
previously published baseline methods indicate a particularly strong bias in
case of an event-to-end trajectory. We present a method that can overcome this
issue using an adversarial training scheme on top of a RNN-AE. Our results show
that our approach can reduce the trajectory bias in all cases.

Much more attention has been paid to unsupervised feature selection nowadays
due to the emergence of massive unlabeled data. The distribution of samples and
the latent effect of training a learning method using samples in more effective
order need to be considered so as to improve the robustness of the method.
Self-paced learning is an effective method considering the training order of
samples. In this study, an unsupervised feature selection is proposed by
integrating the framework of self-paced learning and subspace learning.
Moreover, the local manifold structure is preserved and the redundancy of
features is constrained by two regularization terms. $L_{2,1/2}$-norm is
applied to the projection matrix, which aims to retain discriminative features
and further alleviate the effect of noise in the data. Then, an iterative
method is presented to solve the optimization problem. The convergence of the
method is proved theoretically and experimentally. The proposed method is
compared with other state of the art algorithms on nine real-world datasets.
The experimental results show that the proposed method can improve the
performance of clustering methods and outperform other compared algorithms.

The long-tailed class distribution in visual recognition tasks poses great
challenges for neural networks on how to handle the biased predictions between
head and tail classes, i.e., the model tends to classify tail classes as head
classes. While existing research focused on data resampling and loss function
engineering, in this paper, we take a different perspective: the classification
margins. We study the relationship between the margins and logits
(classification scores) and empirically observe the biased margins and the
biased logits are positively correlated. We propose MARC, a simple yet
effective MARgin Calibration function to dynamically calibrate the biased
margins for unbiased logits. We validate MARC through extensive experiments on
common long-tailed benchmarks including CIFAR-LT, ImageNet-LT, Places-LT, and
iNaturalist-LT. Experimental results demonstrate that our MARC achieves
favorable results on these benchmarks. In addition, MARC is extremely easy to
implement with just three lines of code. We hope this simple method will
motivate people to rethink the biased margins and biased logits in long-tailed
visual recognition.

Designing an effective communication mechanism among agents in reinforcement
learning has been a challenging task, especially for real-world applications.
The number of agents can grow or an environment sometimes needs to interact
with a changing number of agents in real-world scenarios. To this end, a
multi-agent framework needs to handle various scenarios of agents, in terms of
both scales and dynamics, for being practical to real-world applications. We
formulate the multi-agent environment with a different number of agents as a
multi-tasking problem and propose a meta reinforcement learning (meta-RL)
framework to tackle this problem. The proposed framework employs a meta-learned
Communication Pattern Recognition (CPR) module to identify communication
behavior and extract information that facilitates the training process.
Experimental results are poised to demonstrate that the proposed framework (a)
generalizes to an unseen larger number of agents and (b) allows the number of
agents to change between episodes. The ablation study is also provided to
reason the proposed CPR design and show such design is effective.

Embedding models have been an effective learning paradigm for
high-dimensional data. However, one open issue of embedding models is that
their representations (latent factors) often result in large parameter space.
We observe that existing distributed training frameworks face a scalability
issue of embedding models since updating and retrieving the shared embedding
parameters from servers usually dominates the training cycle. In this paper, we
propose HET, a new system framework that significantly improves the scalability
of huge embedding model training. We embrace skewed popularity distributions of
embeddings as a performance opportunity and leverage it to address the
communication bottleneck with an embedding cache. To ensure consistency across
the caches, we incorporate a new consistency model into HET design, which
provides fine-grained consistency guarantees on a per-embedding basis. Compared
to previous work that only allows staleness for read operations, HET also
utilizes staleness for write operations. Evaluations on six representative
tasks show that HET achieves up to 88% embedding communication reductions and
up to 20.68x performance speedup over the state-of-the-art baselines.

Artificial intelligence (AI) enables machines to learn from human experience,
adjust to new inputs, and perform human-like tasks. AI is progressing rapidly
and is transforming the way businesses operate, from process automation to
cognitive augmentation of tasks and intelligent process/data analytics.
However, the main challenge for human users would be to understand and
appropriately trust the result of AI algorithms and methods. In this paper, to
address this challenge, we study and analyze the recent work done in
Explainable Artificial Intelligence (XAI) methods and tools. We introduce a
novel XAI process, which facilitates producing explainable models while
maintaining a high level of learning performance. We present an interactive
evidence-based approach to assist human users in comprehending and trusting the
results and output created by AI-enabled algorithms. We adopt a typical
scenario in the Banking domain for analyzing customer transactions. We develop
a digital dashboard to facilitate interacting with the algorithm results and
discuss how the proposed XAI method can significantly improve the confidence of
data scientists in understanding the result of AI-enabled algorithms.

Nowadays on E-commerce platforms, products are presented to the customers
with multiple modalities. These multiple modalities are significant for a
retrieval system while providing attracted products for customers. Therefore,
how to take into account those multiple modalities simultaneously to boost the
retrieval performance is crucial. This problem is a huge challenge to us due to
the following reasons: (1) the way of extracting patch features with the
pre-trained image model (e.g., CNN-based model) has much inductive bias. It is
difficult to capture the efficient information from the product image in
E-commerce. (2) The heterogeneity of multimodal data makes it challenging to
construct the representations of query text and product including title and
image in a common subspace. We propose a novel Adversarial Cross-modal Enhanced
BERT (ACE-BERT) for efficient E-commerce retrieval. In detail, ACE-BERT
leverages the patch features and pixel features as image representation. Thus
the Transformer architecture can be applied directly to the raw image
sequences. With the pre-trained enhanced BERT as the backbone network, ACE-BERT
further adopts adversarial learning by adding a domain classifier to ensure the
distribution consistency of different modality representations for the purpose
of narrowing down the representation gap between query and product.
Experimental results demonstrate that ACE-BERT outperforms the state-of-the-art
approaches on the retrieval task. It is remarkable that ACE-BERT has already
been deployed in our E-commerce's search engine, leading to 1.46% increase in
revenue.

Deep learning frameworks have become increasingly popular in brain computer
interface (BCI) study thanks to their outstanding performance. However, in
terms of the classification model alone, they are treated as black box as they
do not provide any information on what led them to reach a particular decision.
In other words, we cannot convince whether the high performance was aroused by
the neuro-physiological factors or simply noise. Because of this disadvantage,
it is difficult to ensure adequate reliability compared to their high
performance. In this study, we propose an explainable deep learning model for
BCI. Specifically, we aim to classify EEG signal which is obtained from the
motor-imagery (MI) task. In addition, we adopted layer-wise relevance
propagation (LRP) to the model to interpret the reason that the model derived
certain classification output. We visualized the heatmap which indicates the
output of the LRP in form of topography to certify neuro-physiological factors.
Furthermore, we classified EEG with the subject-independent manner to learn
robust and generalized EEG features by avoiding subject dependency. The
methodology also provides the advantage of avoiding the expense of building
training data for each subject. With our proposed model, we obtained
generalized heatmap patterns for all subjects. As a result, we can conclude
that our proposed model provides neuro-physiologically reliable interpretation.

All Lossy compression algorithms employ similar compression schemes --
frequency domain transform followed by quantization and lossless encoding
schemes. They target tradeoffs by quantizating high frequency data to increase
compression rates which come at the cost of higher image distortion. We propose
a new method of optimizing quantization tables using Deep Learning and a
minimax loss function that more accurately measures the tradeoffs between rate
and distortion parameters (RD) than previous methods. We design a convolutional
neural network (CNN) that learns a mapping between image blocks and
quantization tables in an unsupervised manner. By processing images across all
channels at once, we can achieve stronger performance by also measuring
tradeoffs in information loss between different channels. We initially target
optimization on JPEG images but feel that this can be expanded to any lossy
compressor.

We propose a random forest (RF) machine learning approach to determine the
accreted stellar mass fractions ($f_\mathrm{acc}$) of central galaxies, based
on various dark matter halo and galaxy features. The RF is trained and tested
using 2,710 galaxies with stellar mass $\log_{10}M_\ast/M_\odot>10.16$ from the
TNG100 simulation. Galaxy size is the most important individual feature when
calculated in 3-dimensions, which becomes less important after accounting for
observational effects. For smaller galaxies, the rankings for features related
to merger histories increase. When an entire set of halo and galaxy features
are used, the prediction is almost unbiased, with root-mean-square error (RMSE)
of $\sim$0.068. A combination of up to three features with different types
(galaxy size, merger history and morphology) already saturates the power of
prediction. If using observable features, the RMSE increases to $\sim$0.104,
and a combined usage of stellar mass, galaxy size plus galaxy concentration
achieves similar predictions. Lastly, when using galaxy density, velocity and
velocity dispersion profiles as features, which approximately represent the
maximum amount of information extracted from galaxy images and velocity maps,
the prediction is not improved much. Hence the limiting precision of predicting
$f_\mathrm{acc}$ is $\sim$0.1 with observables, and the multi-component
decomposition of galaxy images should have similar or larger uncertainties. If
the central black hole mass and the spin parameter of galaxies can be
accurately measured in future observations, the RMSE is promising to be further
decreased by $\sim$20%.

The development of online economics arouses the demand of generating images
of models on product clothes, to display new clothes and promote sales.
However, the expensive proprietary model images challenge the existing image
virtual try-on methods in this scenario, as most of them need to be trained on
considerable amounts of model images accompanied with paired clothes images. In
this paper, we propose a cheap yet scalable weakly-supervised method called
Deep Generative Projection (DGP) to address this specific scenario. Lying in
the heart of the proposed method is to imitate the process of human predicting
the wearing effect, which is an unsupervised imagination based on life
experience rather than computation rules learned from supervisions. Here a
pretrained StyleGAN is used to capture the practical experience of wearing.
Experiments show that projecting the rough alignment of clothing and body onto
the StyleGAN space can yield photo-realistic wearing results. Experiments on
real scene proprietary model images demonstrate the superiority of DGP over
several state-of-the-art supervised methods when generating clothing model
images.

Pre-trained Language Models (PLMs) have achieved great success in various
Natural Language Processing (NLP) tasks under the pre-training and fine-tuning
paradigm. With large quantities of parameters, PLMs are computation-intensive
and resource-hungry. Hence, model pruning has been introduced to compress
large-scale PLMs. However, most prior approaches only consider task-specific
knowledge towards downstream tasks, but ignore the essential task-agnostic
knowledge during pruning, which may cause catastrophic forgetting problem and
lead to poor generalization ability. To maintain both task-agnostic and
task-specific knowledge in our pruned model, we propose ContrAstive Pruning
(CAP) under the paradigm of pre-training and fine-tuning. It is designed as a
general framework, compatible with both structured and unstructured pruning.
Unified in contrastive learning, CAP enables the pruned model to learn from the
pre-trained model for task-agnostic knowledge, and fine-tuned model for
task-specific knowledge. Besides, to better retain the performance of the
pruned model, the snapshots (i.e., the intermediate models at each pruning
iteration) also serve as effective supervisions for pruning. Our extensive
experiments show that adopting CAP consistently yields significant
improvements, especially in extremely high sparsity scenarios. With only 3%
model parameters reserved (i.e., 97% sparsity), CAP successfully achieves 99.2%
and 96.3% of the original BERT performance in QQP and MNLI tasks. In addition,
our probing experiments demonstrate that the model pruned by CAP tends to
achieve better generalization ability.

Data-driven algorithms, in particular neural networks, can emulate the
effects of unresolved processes in coarse-resolution climate models when
trained on high-resolution simulation data; however, they often make large
generalization errors when evaluated in conditions they were not trained on.
Here, we propose to physically rescale the inputs and outputs of machine
learning algorithms to help them generalize to unseen climates. Applied to
offline parameterizations of subgrid-scale thermodynamics in three distinct
climate models, we show that rescaled or "climate-invariant" neural networks
make accurate predictions in test climates that are 4K and 8K warmer than their
training climates. Additionally, "climate-invariant" neural nets facilitate
generalization between Aquaplanet and Earth-like simulations. Through
visualization and attribution methods, we show that compared to standard
machine learning models, "climate-invariant" algorithms learn more local and
robust relations between storm-scale convection, radiation, and their synoptic
thermodynamic environment. Overall, these results suggest that explicitly
incorporating physical knowledge into data-driven models of Earth system
processes can improve their consistency and ability to generalize across
climate regimes.

Most music emotion recognition approaches perform classification or
regression that estimates a general emotional category from a distribution of
music samples, but without considering emotional variations (e.g., happiness
can be further categorised into much, moderate or little happiness). We propose
an embedding-based music emotion recognition approach that associates music
samples with emotions in a common embedding space by considering both general
emotional categories and fine-grained discrimination within each category.
Since the association of music samples with emotions is uncertain due to
subjective human perceptions, we compute composite loss-based embeddings
obtained to maximise two statistical characteristics, one being the correlation
between music samples and emotions based on canonical correlation analysis, and
the other being a probabilistic similarity between a music sample and an
emotion with KL-divergence. The experiments on two benchmark datasets
demonstrate the effectiveness of our embedding-based approach, the composite
loss and learned acoustic features. In addition, detailed analysis shows that
our approach can accomplish robust bidirectional music emotion recognition that
not only identifies music samples matching with a specific emotion but also
detects emotions expressed in a certain music sample.

Graph neural networks (GNNs) have been widely applied in the recommendation
tasks and have obtained very appealing performance. However, most GNN-based
recommendation methods suffer from the problem of data sparsity in practice.
Meanwhile, pre-training techniques have achieved great success in mitigating
data sparsity in various domains such as natural language processing (NLP) and
computer vision (CV). Thus, graph pre-training has the great potential to
alleviate data sparsity in GNN-based recommendations. However, pre-training
GNNs for recommendations face unique challenges. For example, user-item
interaction graphs in different recommendation tasks have distinct sets of
users and items, and they often present different properties. Therefore, the
successful mechanisms commonly used in NLP and CV to transfer knowledge from
pre-training tasks to downstream tasks such as sharing learned embeddings or
feature extractors are not directly applicable to existing GNN-based
recommendations models. To tackle these challenges, we delicately design an
adaptive graph pre-training framework for localized collaborative filtering
(ADAPT). It does not require transferring user/item embeddings, and is able to
capture both the common knowledge across different graphs and the uniqueness
for each graph. Extensive experimental results have demonstrated the
effectiveness and superiority of ADAPT.

Recently, generalization bounds of the non-convex empirical risk minimization
paradigm using Stochastic Gradient Langevin Dynamics (SGLD) have been
extensively studied. Several theoretical frameworks have been presented to
study this problem from different perspectives, such as information theory and
stability. In this paper, we present a unified view from privacy leakage
analysis to investigate the generalization bounds of SGLD, along with a
theoretical framework for re-deriving previous results in a succinct manner.
  Aside from theoretical findings, we conduct various numerical studies to
empirically assess the information leakage issue of SGLD. Additionally, our
theoretical and empirical results provide explanations for prior works that
study the membership privacy of SGLD.

Accurate probabilistic predictions can be characterized by two properties --
calibration and sharpness. However, standard maximum likelihood training yields
models that are poorly calibrated and thus inaccurate -- a 90% confidence
interval typically does not contain the true outcome 90% of the time. This
paper argues that calibration is important in practice and is easy to maintain
by performing low-dimensional density estimation. We introduce a simple
training procedure based on recalibration that yields calibrated models without
sacrificing overall performance; unlike previous approaches, ours ensures the
most general property of distribution calibration and applies to any model,
including neural networks. We formally prove the correctness of our procedure
assuming that we can estimate densities in low dimensions and we establish
uniform convergence bounds. Our results yield empirical performance
improvements on linear and deep Bayesian models and suggest that calibration
should be increasingly leveraged across machine learning.

Logic locking has received considerable interest as a prominent technique for
protecting the design intellectual property from untrusted entities, especially
the foundry. Recently, machine learning (ML)-based attacks have questioned the
security guarantees of logic locking, and have demonstrated considerable
success in deciphering the secret key without relying on an oracle, hence,
proving to be very useful for an adversary in the fab. Such ML-based attacks
have triggered the development of learning-resilient locking techniques. The
most advanced state-of-the-art deceptive MUX-based locking (D-MUX) and the
symmetric MUX-based locking techniques have recently demonstrated resilience
against existing ML-based attacks. Both defense techniques obfuscate the design
by inserting key-controlled MUX logic, ensuring that all the secret inputs to
the MUXes are equiprobable.
  In this work, we show that these techniques primarily introduce local and
limited changes to the circuit without altering the global structure of the
design. By leveraging this observation, we propose a novel graph neural network
(GNN)-based link prediction attack, MuxLink, that successfully breaks both the
D-MUX and symmetric MUX-locking techniques, relying only on the underlying
structure of the locked design, i.e., in an oracle-less setting. Our trained
GNN model learns the structure of the given circuit and the composition of
gates around the non-obfuscated wires, thereby generating meaningful link
embeddings that help decipher the secret inputs to the MUXes. The proposed
MuxLink achieves key prediction accuracy and precision up to 100% on D-MUX and
symmetric MUX-locked ISCAS-85 and ITC-99 benchmarks, fully unlocking the
designs. We open-source MuxLink [1].

Reward design is a fundamental problem in reinforcement learning (RL). A
misspecified or poorly designed reward can result in low sample efficiency and
undesired behaviors. In this paper, we propose the idea of programmatic reward
design, i.e. using programs to specify the reward functions in RL environments.
Programs allow human engineers to express sub-goals and complex task scenarios
in a structured and interpretable way. The challenge of programmatic reward
design, however, is that while humans can provide the high-level structures,
properly setting the low-level details, such as the right amount of reward for
a specific sub-task, remains difficult. A major contribution of this paper is a
probabilistic framework that can infer the best candidate programmatic reward
function from expert demonstrations. Inspired by recent generative-adversarial
approaches, our framework searches for the most likely programmatic reward
function under which the optimally generated trajectories cannot be
differentiated from the demonstrated trajectories. Experimental results show
that programmatic reward functionslearned using this framework can
significantly outperform those learned using existing reward learning
algo-rithms, and enable RL agents to achieve state-of-the-artperformance on
highly complex tasks.

In learning action recognition, models are typically pre-trained on object
recognition with images, such as ImageNet, and later fine-tuned on target
action recognition with videos. This approach has achieved good empirical
performance especially with recent transformer-based video architectures. While
recently many works aim to design more advanced transformer architectures for
action recognition, less effort has been made on how to train video
transformers. In this work, we explore several training paradigms and present
two findings. First, video transformers benefit from joint training on diverse
video datasets and label spaces (e.g., Kinetics is appearance-focused while
SomethingSomething is motion-focused). Second, by further co-training with
images (as single-frame videos), the video transformers learn even better video
representations. We term this approach as Co-training Videos and Images for
Action Recognition (CoVeR). In particular, when pretrained on ImageNet-21K
based on the TimeSFormer architecture, CoVeR improves Kinetics-400 Top-1
Accuracy by 2.4%, Kinetics-600 by 2.3%, and SomethingSomething-v2 by 2.3%. When
pretrained on larger-scale image datasets following previous state-of-the-art,
CoVeR achieves best results on Kinetics-400 (87.2%), Kinetics-600 (87.9%),
Kinetics-700 (79.8%), SomethingSomething-v2 (70.9%), and Moments-in-Time
(46.1%), with a simple spatio-temporal video transformer.

Medical applications have driven many areas of engineering to optimize
diagnostic capabilities and convenience. In the near future, wireless body area
networks (WBANs) are expected to have widespread impact in medicine. To achieve
this impact, however, significant advances in research are needed to cope with
the changes of the human body's state, which make coherent communications
difficult or even impossible. In this paper, we consider a realistic
noncoherent WBAN system model where transmissions and receptions are conducted
without any channel state information due to the fast-varying channels of the
human body. Using distributed reception, we propose several symbol detection
approaches where on-off keying (OOK) modulation is exploited, among which a
supervised-learning-based approach is developed to overcome the noncoherent
system issue. Through simulation results, we compare and verify the performance
of the proposed techniques for noncoherent WBANs with OOK transmissions. We
show that the well-defined detection techniques with a
supervised-learning-based approach enable robust communications for noncoherent
WBAN systems.

Self-supervised learning is a powerful way to learn useful representations
from natural data. It has also been suggested as one possible means of building
visual representation in humans, but the specific objective and algorithm are
unknown. Currently, most self-supervised methods encourage the system to learn
an invariant representation of different transformations of the same image in
contrast to those of other images. However, such transformations are generally
non-biologically plausible, and often consist of contrived perceptual schemes
such as random cropping and color jittering. In this paper, we attempt to
reverse-engineer these augmentations to be more biologically or perceptually
plausible while still conferring the same benefits for encouraging robust
representation. Critically, we find that random cropping can be substituted by
cortical magnification, and saccade-like sampling of the image could also
assist the representation learning. The feasibility of these transformations
suggests a potential way that biological visual systems could implement
self-supervision. Further, they break the widely accepted spatially-uniform
processing assumption used in many computer vision algorithms, suggesting a
role for spatially-adaptive computation in humans and machines alike. Our code
and demo can be found here.

Legal texts routinely use concepts that are difficult to understand. Lawyers
elaborate on the meaning of such concepts by, among other things, carefully
investigating how have they been used in past. Finding text snippets that
mention a particular concept in a useful way is tedious, time-consuming, and,
hence, expensive. We assembled a data set of 26,959 sentences, coming from
legal case decisions, and labeled them in terms of their usefulness for
explaining selected legal concepts. Using the dataset we study the
effectiveness of transformer-based models pre-trained on large language corpora
to detect which of the sentences are useful. In light of models' predictions,
we analyze various linguistic properties of the explanatory sentences as well
as their relationship to the legal concept that needs to be explained. We show
that the transformer-based models are capable of learning surprisingly
sophisticated features and outperform the prior approaches to the task.

Numerical evaluations have definitively shown that, for deep learning
optimizers such as stochastic gradient descent, momentum, and adaptive methods,
the number of steps needed to train a deep neural network halves for each
doubling of the batch size and that there is a region of diminishing returns
beyond the critical batch size. In this paper, we determine the actual critical
batch size by using the global minimizer of the stochastic first-order oracle
(SFO) complexity of the optimizer. To prove the existence of the actual
critical batch size, we set the lower and upper bounds of the SFO complexity
and prove that there exist critical batch sizes in the sense of minimizing the
lower and upper bounds. This proof implies that, if the SFO complexity fits the
lower and upper bounds, then the existence of these critical batch sizes
demonstrates the existence of the actual critical batch size. We also discuss
the conditions needed for the SFO complexity to fit the lower and upper bounds
and provide numerical results that support our theoretical results.

Many improvements on GNNs can be deemed as operations on the spectrum of the
underlying graph matrix, which motivates us to directly study the
characteristics of the spectrum and their effects on GNN performance. By
generalizing most existing GNN architectures, we show that the correlation
issue caused by the $unsmooth$ spectrum becomes the obstacle to leveraging more
powerful graph filters as well as developing deep architectures, which
therefore restricts GNNs' performance. Inspired by this, we propose the
correlation-free architecture which naturally removes the correlation issue
among different channels, making it possible to utilize more sophisticated
filters within each channel. The final correlation-free architecture with more
powerful filters consistently boosts the performance of learning graph
representations. Code is available at https://github.com/qslim/gnn-spectrum.

Social distancing can reduce the infection rates in respiratory pandemics
such as COVID-19. Traffic intersections are particularly suitable for
monitoring and evaluation of social distancing behavior in metropolises. We
propose and evaluate a privacy-preserving social distancing analysis system
(B-SDA), which uses bird's-eye view video recordings of pedestrians who cross
traffic intersections. We devise algorithms for video pre-processing, object
detection and tracking which are rooted in the known computer-vision and deep
learning techniques, but modified to address the problem of detecting very
small objects/pedestrians captured by a highly elevated camera. We propose a
method for incorporating pedestrian grouping for detection of social distancing
violations. B-SDA is used to compare pedestrian behavior based on pre-pandemic
and pandemic videos in a major metropolitan area. The accomplished pedestrian
detection performance is $63.0\%$ $AP_{50}$ and the tracking performance is
$47.6\%$ MOTA. The social distancing violation rate of $15.6\%$ during the
pandemic is notably lower than $31.4\%$ pre-pandemic baseline, indicating that
pedestrians followed CDC-prescribed social distancing recommendations. The
proposed system is suitable for deployment in real-world applications.

The mathematical formalization of a neurological mechanism in the olfactory
circuit of a fruit-fly as a locality sensitive hash (Flyhash) and bloom filter
(FBF) has been recently proposed and "reprogrammed" for various machine
learning tasks such as similarity search, outlier detection and text
embeddings. We propose a novel reprogramming of this hash and bloom filter to
emulate the canonical nearest neighbor classifier (NNC) in the challenging
Federated Learning (FL) setup where training and test data are spread across
parties and no data can leave their respective parties. Specifically, we
utilize Flyhash and FBF to create the FlyNN classifier, and theoretically
establish conditions where FlyNN matches NNC. We show how FlyNN is trained
exactly in a FL setup with low communication overhead to produce FlyNNFL, and
how it can be differentially private. Empirically, we demonstrate that (i)
FlyNN matches NNC accuracy across 70 OpenML datasets, (ii) FlyNNFL training is
highly scalable with low communication overhead, providing up to $8\times$
speedup with $16$ parties.

We introduce ImportantAug, a technique to augment training data for speech
classification and recognition models by adding noise to unimportant regions of
the speech and not to important regions. Importance is predicted for each
utterance by a data augmentation agent that is trained to maximize the amount
of noise it adds while minimizing its impact on recognition performance. The
effectiveness of our method is illustrated on version two of the Google Speech
Commands (GSC) dataset. On the standard GSC test set, it achieves a 23.3%
relative error rate reduction compared to conventional noise augmentation which
applies noise to speech without regard to where it might be most effective. It
also provides a 25.4% error rate reduction compared to a baseline without data
augmentation. Additionally, the proposed ImportantAug outperforms the
conventional noise augmentation and the baseline on two test sets with
additional noise added.

One of the challenges of modern neuroscience is creating a "living computer"
based on neural networks grown in vitro. Such an artificial device is supposed
to perform neurocomputational tasks and interact with the environment when
embodied in a robot. Recent studies have identified the most critical
challenge, the search for a neural network architecture to implement
associative learning. This work proposes a model of modular architecture with
spiking neural networks connected by unidirectional couplings. We show that the
model enables training a neuro-robot according to Pavlovian conditioning. The
robot's performance in obstacle avoidance depends on the ratio of the weights
in inter-network couplings. We show that besides STDP, critical factors for
successful learning are synaptic and neuronal competitions. We use the recently
discovered shortest path rule to implement the synaptic competition. This
method is ready for experimental testing. Strong inhibitory couplings implement
the neuronal competition in the subnetwork responsible for the unconditional
response. Empirical testing of this approach requires a technique for growing
neural networks with a given ratio of excitatory and inhibitory neurons not
available yet. An alternative is building a hybrid system with in vitro neural
networks coupled through hardware memristive connections.

As the COVID-19 pandemic rampages across the world, the demands of video
conferencing surge. To this end, real-time portrait segmentation becomes a
popular feature to replace backgrounds of conferencing participants. While
feature-rich datasets, models and algorithms have been offered for segmentation
that extract body postures from life scenes, portrait segmentation has yet not
been well covered in a video conferencing context. To facilitate the progress
in this field, we introduce an open-source solution named PP-HumanSeg. This
work is the first to construct a large-scale video portrait dataset that
contains 291 videos from 23 conference scenes with 14K fine-labeled frames and
extensions to multi-camera teleconferencing. Furthermore, we propose a novel
Semantic Connectivity-aware Learning (SCL) for semantic segmentation, which
introduces a semantic connectivity-aware loss to improve the quality of
segmentation results from the perspective of connectivity. And we propose an
ultra-lightweight model with SCL for practical portrait segmentation, which
achieves the best trade-off between IoU and the speed of inference. Extensive
evaluations on our dataset demonstrate the superiority of SCL and our model.
The source code is available at https://github.com/PaddlePaddle/PaddleSeg.

Datasets containing both categorical and continuous variables are frequently
encountered in many areas, and with the rapid development of modern measurement
technologies, the dimensions of these variables can be very high. Despite the
recent progress made in modelling high-dimensional data for continuous
variables, there is a scarcity of methods that can deal with a mixed set of
variables. To fill this gap, this paper develops a novel approach for
classifying high-dimensional observations with mixed variables. Our framework
builds on a location model, in which the distributions of the continuous
variables conditional on categorical ones are assumed Gaussian. We overcome the
challenge of having to split data into exponentially many cells, or
combinations of the categorical variables, by kernel smoothing, and provide new
perspectives for its bandwidth choice to ensure an analogue of Bochner's Lemma,
which is different to the usual bias-variance tradeoff. We show that the two
sets of parameters in our model can be separately estimated and provide
penalized likelihood for their estimation. Results on the estimation accuracy
and the misclassification rates are established, and the competitive
performance of the proposed classifier is illustrated by extensive simulation
and real data studies.

In recent geospatial research, the importance of modeling large-scale human
mobility data and predicting trajectories is rising, in parallel with progress
in text generation using large-scale corpora in natural language processing.
Whereas there are already plenty of feasible approaches applicable to
geospatial sequence modeling itself, there seems to be room to improve with
regard to evaluation, specifically about measuring the similarity between
generated and reference trajectories. In this work, we propose a novel
similarity measure, GEO-BLEU, which can be especially useful in the context of
geospatial sequence modeling and generation. As the name suggests, this work is
based on BLEU, one of the most popular measures used in machine translation
research, while introducing spatial proximity to the idea of n-gram. We compare
this measure with an established baseline, dynamic time warping, applying it to
actual generated geospatial sequences. Using crowdsourced annotated data on the
similarity between geospatial sequences collected from over 12,000 cases, we
quantitatively and qualitatively show the proposed method's superiority.

Fuzzing is one of the prevailing methods for vulnerability detection.
However, even state-of-the-art fuzzing methods become ineffective after some
period of time, i.e., the coverage hardly improves as existing methods are
ineffective to focus the attention of fuzzing on covering the hard-to-trigger
program paths. In other words, they cannot generate inputs that can break the
bottleneck due to the fundamental difficulty in capturing the complex relations
between the test inputs and program coverage. In particular, existing fuzzers
suffer from the following main limitations: 1) lacking an overall analysis of
the program to identify the most "rewarding" seeds, and 2) lacking an effective
mutation strategy which could continuously select and mutates the more relevant
"bytes" of the seeds.
  In this work, we propose an approach called ATTuzz to address these two
issues systematically. First, we propose a lightweight dynamic analysis
technique which estimates the "reward" of covering each basic block and selects
the most rewarding seeds accordingly. Second, we mutate the selected seeds
according to a neural network model which predicts whether a certain
"rewarding" block will be covered given certain mutation on certain bytes of a
seed. The model is a deep learning model equipped with attention mechanism
which is learned and updated periodically whilst fuzzing. Our evaluation shows
that ATTuzz significantly outperforms 5 state-of-the-art grey-box fuzzers on 13
popular real-world programs at achieving higher edge coverage and finding new
bugs. In particular, ATTuzz achieved 2X edge coverage and 4X bugs detected than
AFL over 24-hour runs. Moreover, ATTuzz persistently improves the edge coverage
in the long run, i.e., achieving 50% more coverage than AFL in 5 days.

Studies in the area of neuroscience have revealed the relationship between
emotional patterns and brain functional regions, demonstrating that dynamic
relationships between different brain regions are an essential factor affecting
emotion recognition determined through electroencephalography (EEG). Moreover,
in EEG emotion recognition, we can observe that clearer boundaries exist
between coarse-grained emotions than those between fine-grained emotions, based
on the same EEG data; this indicates the concurrence of large coarse- and small
fine-grained emotion variations. Thus, the progressive classification process
from coarse- to fine-grained categories may be helpful for EEG emotion
recognition. Consequently, in this study, we propose a progressive graph
convolution network (PGCN) for capturing this inherent characteristic in EEG
emotional signals and progressively learning the discriminative EEG features.
To fit different EEG patterns, we constructed a dual-graph module to
characterize the intrinsic relationship between different EEG channels,
containing the dynamic functional connections and static spatial proximity
information of brain regions from neuroscience research. Moreover, motivated by
the observation of the relationship between coarse- and fine-grained emotions,
we adopt a dual-head module that enables the PGCN to progressively learn more
discriminative EEG features, from coarse-grained (easy) to fine-grained
categories (difficult), referring to the hierarchical characteristic of
emotion. To verify the performance of our model, extensive experiments were
conducted on two public datasets: SEED-IV and multi-modal physiological emotion
database (MPED).

We propose CLIP-Lite, an information efficient method for visual
representation learning by feature alignment with textual annotations. Compared
to the previously proposed CLIP model, CLIP-Lite requires only one negative
image-text sample pair for every positive image-text sample during the
optimization of its contrastive learning objective. We accomplish this by
taking advantage of an information efficient lower-bound to maximize the mutual
information between the two input modalities. This allows CLIP-Lite to be
trained with significantly reduced amounts of data and batch sizes while
obtaining better performance than CLIP. We evaluate CLIP-Lite by pretraining on
the COCO-Captions dataset and testing transfer learning to other datasets.
CLIP-Lite obtains a +15.4% mAP absolute gain in performance on Pascal VOC
classification, and a +22.1% top-1 accuracy gain on ImageNet, while being
comparable or superior to other, more complex, text-supervised models.
CLIP-Lite is also superior to CLIP on image and text retrieval, zero-shot
classification, and visual grounding. Finally, by performing explicit
image-text alignment during representation learning, we show that CLIP-Lite can
leverage language semantics to encourage bias-free visual representations that
can be used in downstream tasks.

We present VPVnet, a deep neural network method for the Stokes' equations
under reduced regularity. Different with recently proposed deep learning
methods [40,51] which are based on the original form of PDEs, VPVnet uses the
least square functional of the first-order velocity-pressure-vorticity (VPV)
formulation ([30]) as loss functions. As such, only first-order derivative is
required in the loss functions, hence the method is applicable to a much larger
class of problems, e.g. problems with non-smooth solutions. Despite that
several methods have been proposed recently to reduce the regularity
requirement by transforming the original problem into a corresponding
variational form, while for the Stokes' equations, the choice of approximating
spaces for the velocity and the pressure has to satisfy the LBB condition
additionally. Here by making use of the VPV formulation, lower regularity
requirement is achieved with no need for considering the LBB condition.
Convergence and error estimates have been established for the proposed method.
It is worth emphasizing that the VPVnet method is divergence-free and
pressure-robust, while classical inf-sup stable mixed finite elements for the
Stokes' equations are not pressure-robust. Various numerical experiments
including 2D and 3D lid-driven cavity test cases are conducted to demonstrate
its efficiency and accuracy.

Concerns regarding the impacts of climate change on marginalised communities
in the Global South have led to calls for affected communities to be more
active as agents in the process of planning for climate change. While the value
of involving communities in risk management is increasingly accepted, the
development of appropriate tools to support community engagement in flood risk
management projects remains nascent. Using the Revitalising Informal
Settlements and their Environment (RISE) Program as a case study, the article
interrogates the potential of citizen science to include disadvantaged urban
communities in project-level flood risk reduction planning processes. This
project collected more than 5000 photos taken by 26 community members living in
13 informal settlements in Fiji and Indonesia between 2018 and 2020. The case
study documents the method used as well as the results achieved within this
2-year project. It discusses the method developed and implemented, outlines the
main results, and provides lessons learned for others embarking on citizen
science environmental monitoring projects. The case study indicates that the
engagement model and the technology used were key to the success of the
flood-monitoring project. The experiences with the practice of monitoring
floods in collaboration with communities in Fiji and Indonesia provide insights
into how similar projects could advance more participatory risk management
practices. The article identifies how this kind of approach can collect
valuable flood data while also promoting opportunities for local communities to
be heard in the arena of risk reduction and climate change adaptation.

In this paper, we propose a new joint object detection and tracking (JoDT)
framework for 3D object detection and tracking based on camera and LiDAR
sensors. The proposed method, referred to as 3D DetecTrack, enables the
detector and tracker to cooperate to generate a spatio-temporal representation
of the camera and LiDAR data, with which 3D object detection and tracking are
then performed. The detector constructs the spatio-temporal features via the
weighted temporal aggregation of the spatial features obtained by the camera
and LiDAR fusion. Then, the detector reconfigures the initial detection results
using information from the tracklets maintained up to the previous time step.
Based on the spatio-temporal features generated by the detector, the tracker
associates the detected objects with previously tracked objects using a graph
neural network (GNN). We devise a fully-connected GNN facilitated by a
combination of rule-based edge pruning and attention-based edge gating, which
exploits both spatial and temporal object contexts to improve tracking
performance. The experiments conducted on both KITTI and nuScenes benchmarks
demonstrate that the proposed 3D DetecTrack achieves significant improvements
in both detection and tracking performances over baseline methods and achieves
state-of-the-art performance among existing methods through collaboration
between the detector and tracker.

The gradient noise of Stochastic Gradient Descent (SGD) is considered to play
a key role in its properties (e.g. escaping low potential points and
regularization). Past research has indicated that the covariance of the SGD
error done via minibatching plays a critical role in determining its
regularization and escape from low potential points. It is however not much
explored how much the distribution of the error influences the behavior of the
algorithm. Motivated by some new research in this area, we prove universality
results by showing that noise classes that have the same mean and covariance
structure of SGD via minibatching have similar properties. We mainly consider
the Multiplicative Stochastic Gradient Descent (M-SGD) algorithm as introduced
by Wu et al., which has a much more general noise class than the SGD algorithm
done via minibatching. We establish nonasymptotic bounds for the M-SGD
algorithm mainly with respect to the Stochastic Differential Equation
corresponding to SGD via minibatching. We also show that the M-SGD error is
approximately a scaled Gaussian distribution with mean $0$ at any fixed point
of the M-SGD algorithm. We also establish bounds for the convergence of the
M-SGD algorithm in the strongly convex regime.

The Benders' decomposition algorithm is a technique in mathematical
programming for complex mixed-integer linear programming (MILP) problems with a
particular block structure. The strategy of Benders' decomposition can be
described as a strategy of divide and conquer. The Benders' decomposition
algorithm has been employed in a variety of applications such as communication,
networking, and machine learning. However, the master problem in Benders'
decomposition is still NP-hard, which motivates us to employ quantum computing.
In the paper, we propose a hybrid quantum Benders' decomposition algorithm. We
transfer the Benders' decomposition's master problem into the quadratic
unconstrained binary optimization (QUBO) model and solve it by the
state-of-the-art quantum annealer. Then, we analyze the computational results
and discuss the feasibility of the proposed algorithm. Due to our reformulation
in the master problem in Benders' decomposition, our hybrid algorithm, which
takes advantage of both classical and quantum computers, can guarantee the
solution quality for solving MILP problems.

Modern semantic segmentation methods devote much attention to adjusting
feature representations to improve the segmentation performance in various
ways, such as metric learning, architecture design, etc. However, almost all
those methods neglect the particularity of boundary pixels. These pixels are
prone to obtain confusing features from both sides due to the continuous
expansion of receptive fields in CNN networks. In this way, they will mislead
the model optimization direction and make the class weights of such categories
that tend to share many adjacent pixels lack discrimination, which will damage
the overall performance. In this work, we dive deep into this problem and
propose a novel method named Embedded Superpixel CRF (ES-CRF) to address it.
ES-CRF involves two main aspects. On the one hand, ES-CRF innovatively fuses
the CRF mechanism into the CNN network as an organic whole for more effective
end-to-end optimization. It utilizes CRF to guide the message passing between
pixels in high-level features to purify the feature representation of boundary
pixels, with the help of inner pixels belong to the same object. On the other
hand, superpixel is integrated into ES-CRF to exploit the local object prior
for more reliable message passing. Finally, our proposed method yields new
records on two challenging benchmarks, i.e., Cityscapes and ADE20K. Moreover,
we make detailed theoretical analysis to verify the superiority of ES-CRF.

In this study, we propose a three-stage training approach of neural networks
for both photometric redshift estimation of galaxies and detection of
out-of-distribution (OOD) objects. Our approach comprises supervised and
unsupervised learning, which enables using unlabeled (UL) data for OOD
detection in training the networks. Employing the UL data, which is the dataset
most similar to the real-world data, ensures a reliable usage of the trained
model in practice. We quantitatively assess the model performance of
photometric redshift estimation and OOD detection using in-distribution (ID)
galaxies and labeled OOD (LOOD) samples such as stars and quasars. Our model
successfully produces photometric redshifts matched with spectroscopic
redshifts for the ID samples and identifies well the LOOD objects with more
than 98% accuracy. Although quantitative assessment with the UL samples is
impracticable due to the lack of labels and spectroscopic redshifts, we also
find that our model successfully estimates reasonable photometric redshifts for
ID-like UL samples and filter OOD-like UL objects. The code for the model
implementation is available at https://github.com/GooLee0123/MBRNN_OOD.

In the research, we developed a computer vision solution to support
diagnostic radiology in differentiating between COVID-19 pneumonia, influenza
virus pneumonia, and normal biomarkers. The chest radiograph appearance of
COVID-19 pneumonia is thought to be nonspecific, having presented a challenge
to identify an optimal architecture of a convolutional neural network (CNN)
that would classify with a high sensitivity among the pulmonary inflammation
features of COVID-19 and non-COVID-19 types of pneumonia. Rahman (2021) states
that COVID-19 radiography images observe unavailability and quality issues
impacting the diagnostic process and affecting the accuracy of the deep
learning detection models. A significant scarcity of COVID-19 radiography
images introduced an imbalance in data motivating us to use over-sampling
techniques. In the study, we include an extensive set of X-ray imaging of human
lungs (CXR) with COVID-19 pneumonia, influenza virus pneumonia, and normal
biomarkers to achieve an extensible and accurate CNN model. In the
experimentation phase of the research, we evaluated a variety of convolutional
network architectures, selecting a sequential convolutional network with two
traditional convolutional layers and two pooling layers with maximum function.
In its classification performance, the best performing model demonstrated a
validation accuracy of 93% and an F1 score of 0.95. We chose the Azure Machine
Learning service to perform network experimentation and solution deployment.
The auto-scaling compute clusters offered a significant time reduction in
network training. We would like to see scientists across fields of artificial
intelligence and human biology collaborating and expanding on the proposed
solution to provide rapid and comprehensive diagnostics, effectively mitigating
the spread of the virus

This paper considers a challenging scenario of machine type communications,
where we assume internet of things (IoT) devices send short packets
sporadically to an access point (AP) and the devices are not synchronized in
the packet level. High transmission efficiency and low latency are concerned.
Motivated by the great potential of multiple-input multiple-output
non-orthogonal multiple access (MIMO-NOMA) in massive access, we design a
grant-free MIMO-NOMA scheme, and in particular differential modulation is used
so that expensive channel estimation at the receiver (AP) can be bypassed. The
receiver at AP needs to carry out active device detection and multi-device data
detection. The active user detection is formulated as the estimation of the
common support of sparse signals, and a message passing based sparse Bayesian
learning (SBL) algorithm is designed to solve the problem. Due to the use of
differential modulation, we investigate the problem of non-coherent
multi-device data detection, and develop a message passing based Bayesian data
detector, where the constraint of differential modulation is exploited to
drastically improve the detection performance, compared to the conventional
non-coherent detection scheme. Simulation results demonstrate the effectiveness
of the proposed active device detector and non-coherent multi-device data
detector.

We propose a scalable framework for the learning of high-dimensional
parametric maps via adaptively constructed residual network (ResNet) maps
between reduced bases of the inputs and outputs. When just few training data
are available, it is beneficial to have a compact parametrization in order to
ameliorate the ill-posedness of the neural network training problem. By
linearly restricting high-dimensional maps to informed reduced bases of the
inputs, one can compress high-dimensional maps in a constructive way that can
be used to detect appropriate basis ranks, equipped with rigorous error
estimates. A scalable neural network learning framework is thus to learn the
nonlinear compressed reduced basis mapping. Unlike the reduced basis
construction, however, neural network constructions are not guaranteed to
reduce errors by adding representation power, making it difficult to achieve
good practical performance. Inspired by recent approximation theory that
connects ResNets to sequential minimizing flows, we present an adaptive ResNet
construction algorithm. This algorithm allows for depth-wise enrichment of the
neural network approximation, in a manner that can achieve good practical
performance by first training a shallow network and then adapting. We prove
universal approximation of the associated neural network class for $L^2_\nu$
functions on compact sets. Our overall framework allows for constructive means
to detect appropriate breadth and depth, and related compact parametrizations
of neural networks, significantly reducing the need for architectural
hyperparameter tuning. Numerical experiments for parametric PDE problems and a
3D CFD wing design optimization parametric map demonstrate that the proposed
methodology can achieve remarkably high accuracy for limited training data, and
outperformed other neural network strategies we compared against.

Human pose estimation from single images is a challenging problem that is
typically solved by supervised learning. Unfortunately, labeled training data
does not yet exist for many human activities since 3D annotation requires
dedicated motion capture systems. Therefore, we propose an unsupervised
approach that learns to predict a 3D human pose from a single image while only
being trained with 2D pose data, which can be crowd-sourced and is already
widely available. To this end, we estimate the 3D pose that is most likely over
random projections, with the likelihood estimated using normalizing flows on 2D
poses. While previous work requires strong priors on camera rotations in the
training data set, we learn the distribution of camera angles which
significantly improves the performance. Another part of our contribution is to
stabilize training with normalizing flows on high-dimensional 3D pose data by
first projecting the 2D poses to a linear subspace. We outperform the
state-of-the-art unsupervised human pose estimation methods on the benchmark
datasets Human3.6M and MPI-INF-3DHP in many metrics.

In recent years, people from all over the world are suffering from one of the
most severe diseases in history, known as Coronavirus disease 2019, COVID-19
for short. When the virus reaches the lungs, it has a higher probability to
cause lung pneumonia and sepsis. X-ray image is a powerful tool in identifying
the typical features of the infection for COVID-19 patients. The radiologists
and pathologists observe that ground-glass opacity appears in the chest X-ray
for infected patient \cite{cozzi2021ground}, and it could be used as one of the
criteria during the diagnosis process. In the past few years, deep learning has
proven to be one of the most powerful methods in the field of image
classification. Due to significant differences in Chest X-Ray between normal
and infected people \cite{rousan2020chest}, deep models could be used to
identify the presence of the disease given a patient's Chest X-Ray. Many deep
models are complex, and it evolves with lots of input parameters. Designers
sometimes struggle with the tuning process for deep models, especially when
they build up the model from scratch. Genetic Algorithm, inspired by the
biological evolution process, plays a key role in solving such complex
problems. In this paper, I proposed a genetic-based approach to optimize the
Convolutional Neural Network(CNN) for the Chest X-Ray classification task.

KBQA is a task that requires to answer questions by using semantic structured
information in knowledge base. Previous work in this area has been restricted
due to the lack of large semantic parsing dataset and the exponential growth of
searching space with the increasing hops of relation paths. In this paper, we
propose an efficient pipeline method equipped with a pre-trained language
model. By adopting Beam Search algorithm, the searching space will not be
restricted in subgraph of 3 hops. Besides, we propose a data generation
strategy, which enables our model to generalize well from few training samples.
We evaluate our model on an open-domain complex Chinese Question Answering task
CCKS2019 and achieve F1-score of 62.55% on the test dataset. In addition, in
order to test the few-shot learning capability of our model, we ramdomly select
10% of the primary data to train our model, the result shows that our model can
still achieves F1-score of 58.54%, which verifies the capability of our model
to process KBQA task and the advantage in few-shot Learning.

Unsupervised learning of feature representations is a challenging yet
important problem for analyzing a large collection of multimedia data that do
not have semantic labels. Recently proposed neural network-based unsupervised
learning approaches have succeeded in obtaining features appropriate for
classification of multimedia data. However, unsupervised learning of feature
representations adapted to content-based matching, comparison, or retrieval of
multimedia data has not been explored well. To obtain such retrieval-adapted
features, we introduce the idea of combining diffusion distance on a feature
manifold with neural network-based unsupervised feature learning. This idea is
realized as a novel algorithm called DeepDiffusion (DD). DD simultaneously
optimizes two components, a feature embedding by a deep neural network and a
distance metric that leverages diffusion on a latent feature manifold,
together. DD relies on its loss function but not encoder architecture. It can
thus be applied to diverse multimedia data types with their respective encoder
architectures. Experimental evaluation using 3D shapes and 2D images
demonstrates versatility as well as high accuracy of the DD algorithm. Code is
available at https://github.com/takahikof/DeepDiffusion

One of the fundamental challenges in realizing the potential of legged robots
is generating plans to traverse challenging terrains. Control actions must be
carefully selected so the robot will not crash or slip. The high dimensionality
of the joint space makes directly planning low-level actions from onboard
perception difficult, and control stacks that do not consider the low-level
mechanisms of the robot in planning are ill-suited to handle fine-grained
obstacles. One method for dealing with this is selecting footstep locations
based on terrain characteristics. However, incorporating robot dynamics into
footstep planning requires significant computation, much more than in the
quasi-static case. In this work, we present an LSTM-based planning framework
that learns probability distributions over likely footstep locations using both
terrain lookahead and the robot's dynamics, and leverages the LSTM's sequential
nature to find footsteps in linear time. Our framework can also be used as a
module to speed up sampling-based planners. We validate our approach on a
simulated one-legged hopper over a variety of uneven terrains.

Automatic speech recognition systems have created exciting possibilities for
applications, however they also enable opportunities for systematic
eavesdropping. We propose a method to camouflage a person's voice over-the-air
from these systems without inconveniencing the conversation between people in
the room. Standard adversarial attacks are not effective in real-time streaming
situations because the characteristics of the signal will have changed by the
time the attack is executed. We introduce predictive attacks, which achieve
real-time performance by forecasting the attack that will be the most effective
in the future. Under real-time constraints, our method jams the established
speech recognition system DeepSpeech 3.9x more than baselines as measured
through word error rate, and 6.6x more as measured through character error
rate. We furthermore demonstrate our approach is practically effective in
realistic environments over physical distances.

In this paper, we explore the possibility of building a unified foundation
model that can be adapted to both vision-only and text-only tasks. Starting
from BERT and ViT, we design a unified transformer consisting of
modality-specific tokenizers, a shared transformer encoder, and task-specific
output heads. To efficiently pre-train the proposed model jointly on unpaired
images and text, we propose two novel techniques: (i) We employ the
separately-trained BERT and ViT models as teachers and apply knowledge
distillation to provide additional, accurate supervision signals for the joint
training; (ii) We propose a novel gradient masking strategy to balance the
parameter updates from the image and text pre-training losses. We evaluate the
jointly pre-trained transformer by fine-tuning it on image classification tasks
and natural language understanding tasks, respectively. The experiments show
that the resultant unified foundation transformer works surprisingly well on
both the vision-only and text-only tasks, and the proposed knowledge
distillation and gradient masking strategy can effectively lift the performance
to approach the level of separately-trained models.

This paper describes BACON, a basic prototype of an automatic poetry
generator with author linguistic style transfer. It combines concepts and
techniques from finite state machinery, probabilistic models, artificial neural
networks and deep learning, to write original poetry with rich
aesthetic-qualities in the style of any given author. Extrinsic evaluation of
the output generated by BACON shows that participants were unable to tell the
difference between human and AI-generated poems in any statistically
significant way.

Score-based generative models (SGMs) have demonstrated remarkable synthesis
quality. SGMs rely on a diffusion process that gradually perturbs the data
towards a tractable distribution, while the generative model learns to denoise.
The complexity of this denoising task is, apart from the data distribution
itself, uniquely determined by the diffusion process. We argue that current
SGMs employ overly simplistic diffusions, leading to unnecessarily complex
denoising processes, which limit generative modeling performance. Based on
connections to statistical mechanics, we propose a novel critically-damped
Langevin diffusion (CLD) and show that CLD-based SGMs achieve superior
performance. CLD can be interpreted as running a joint diffusion in an extended
space, where the auxiliary variables can be considered "velocities" that are
coupled to the data variables as in Hamiltonian dynamics. We derive a novel
score matching objective for CLD and show that the model only needs to learn
the score function of the conditional distribution of the velocity given data,
an easier task than learning scores of the data directly. We also derive a new
sampling scheme for efficient synthesis from CLD-based diffusion models. We
find that CLD outperforms previous SGMs in synthesis quality for similar
network architectures and sampling compute budgets. We show that our novel
sampler for CLD significantly outperforms solvers such as Euler--Maruyama. Our
framework provides new insights into score-based denoising diffusion models and
can be readily used for high-resolution image synthesis. Project page and code:
https://nv-tlabs.github.io/CLD-SGM.

We develop methods to learn the correlation potential for a time-dependent
Kohn-Sham (TDKS) system in one spatial dimension. We start from a
low-dimensional two-electron system for which we can numerically solve the
time-dependent Schr\"odinger equation; this yields electron densities suitable
for training models of the correlation potential. We frame the learning problem
as one of optimizing a least-squares objective subject to the constraint that
the dynamics obey the TDKS equation. Applying adjoints, we develop efficient
methods to compute gradients and thereby learn models of the correlation
potential. Our results show that it is possible to learn values of the
correlation potential such that the resulting electron densities match ground
truth densities. We also show how to learn correlation potential functionals
with memory, demonstrating one such model that yields reasonable results for
trajectories outside the training set.

The mixing time of the Markov chain induced by a policy limits performance in
real-world continual learning scenarios. Yet, the effect of mixing times on
learning in continual reinforcement learning (RL) remains underexplored. In
this paper, we characterize problems that are of long-term interest to the
development of continual RL, which we call scalable MDPs, through the lens of
mixing times. In particular, we theoretically establish that scalable MDPs have
mixing times that scale polynomially with the size of the problem. We go on to
demonstrate that polynomial mixing times present significant difficulties for
existing approaches, which suffer from myopic bias and stale bootstrapped
estimates. To validate our theory, we study the empirical scaling behavior of
mixing times with respect to the number of tasks and task duration for high
performing policies deployed across multiple Atari games. Our analysis
demonstrates both that polynomial mixing times do emerge in practice and how
their existence may lead to unstable learning behavior like catastrophic
forgetting in continual learning settings.

Natural Language Processing (NLP) has become one of the leading application
areas in the current Artificial Intelligence boom. Transfer learning has
enabled large deep learning neural networks trained on the language modeling
task to vastly improve performance in almost all downstream language tasks.
Interestingly, when the language models are trained with data that includes
software code, they demonstrate remarkable abilities in generating functioning
computer code from natural language specifications. We argue that this creates
a conundrum for the claim that eliminative neural models are a radical
restructuring in our understanding of cognition in that they eliminate the need
for symbolic abstractions like generative phrase structure grammars. Because
the syntax of programming languages is by design determined by phrase structure
grammars, neural models that produce syntactic code are apparently
uninformative about the theoretical foundations of programming languages. The
demonstration that neural models perform well on tasks that involve clearly
symbolic systems, proves that they cannot be used as an argument that language
and other cognitive systems are not symbolic. Finally, we argue as a corollary
that the term language model is misleading and propose the adoption of the
working term corpus model instead, which better reflects the genesis and
contents of the model.

In this work, we propose an end-to-end graph network that learns forward and
inverse models of particle-based physics using interpretable inductive biases.
Physics-informed neural networks are often engineered to solve specific
problems through problem-specific regularization and loss functions. Such
explicit learning biases the network to learn data specific patterns and may
require a change in the loss function or neural network architecture hereby
limiting their generalizabiliy. Our graph network is implicitly biased by
learning to solve several tasks, thereby sharing representations between tasks
in order to learn the forward dynamics as well as infer the probability
distribution of unknown particle specific properties. We evaluate our approach
on one-step next state prediction tasks across diverse datasets. Our comparison
against related data-driven physics learning approaches reveals that our model
is able to predict the forward dynamics with at least an order of magnitude
higher accuracy. We also show that our approach is able to recover multi-modal
probability distributions of unknown physical parameters.

We discuss how VMware is solving the following challenges to harness data to
operate our ML-based anomaly detection system to detect performance issues in
our Software Defined Data Center (SDDC) enterprise deployments: (i) label
scarcity and label bias due to heavy dependency on unscalable human annotators,
and (ii) data drifts due to ever-changing workload patterns, software stack and
underlying hardware. Our anomaly detection system has been deployed in
production for many years and has successfully detected numerous major
performance issues. We demonstrate that by addressing these data challenges, we
not only improve the accuracy of our performance anomaly detection model by
30%, but also ensure that the model performance to never degrade over time.

A recent line of work has focused on training machine learning (ML) models in
the performative setting, i.e. when the data distribution reacts to the
deployed model. The goal in this setting is to learn a model which both induces
a favorable data distribution and performs well on the induced distribution,
thereby minimizing the test loss. Previous work on finding an optimal model
assumes that the data distribution immediately adapts to the deployed model. In
practice, however, this may not be the case, as the population may take time to
adapt to the model. In many applications, the data distribution depends on both
the currently deployed ML model and on the "state" that the population was in
before the model was deployed. In this work, we propose a new algorithm,
Stateful Performative Gradient Descent (Stateful PerfGD), for minimizing the
performative loss even in the presence of these effects. We provide theoretical
guarantees for the convergence of Stateful PerfGD. Our experiments confirm that
Stateful PerfGD substantially outperforms previous state-of-the-art methods.

This survey draws a broad-stroke, panoramic picture of the State of the Art
(SoTA) of the research in generative methods for the analysis of social media
data. It fills a void, as the existing survey articles are either much narrower
in their scope or are dated. We included two important aspects that currently
gain importance in mining and modeling social media: dynamics and networks.
Social dynamics are important for understanding the spreading of influence or
diseases, formation of friendships, the productivity of teams, etc. Networks,
on the other hand, may capture various complex relationships providing
additional insight and identifying important patterns that would otherwise go
unnoticed.

Social networks have become one of the main information channels for human
beings due to the immediate and social interactivity they offer, allowing in
some cases to publish what each user considers relevant. This has brought with
it the generation of false news or Fake News, publications that only seek to
generate uncertainty, misinformation or skew the opinion of readers. It has
been shown that the human being is not capable of fully identifying whether an
article is really a fact or a Fake News, due to this it is that models arise
that seek to characterize and identify articles based on data mining and
machine learning. This article proposes a three-layer framework, the main
objective of which is to characterize the emotions present in Fake News and to
be a tool for future work that identifies the emotional state and intentional
state of the public.

Classical control techniques such as PID and LQR have been used effectively
in maintaining a system state, but these techniques become more difficult to
implement when the model dynamics increase in complexity and sensitivity. For
adaptive robotic locomotion tasks with several degrees of freedom, this task
becomes infeasible with classical control techniques. Instead, reinforcement
learning can train optimal walking policies with ease. We apply deep Q-learning
and augmented random search (ARS) to teach a simulated two-dimensional bipedal
robot how to walk using the OpenAI Gym BipedalWalker-v3 environment. Deep
Q-learning did not yield a high reward policy, often prematurely converging to
suboptimal local maxima likely due to the coarsely discretized action space.
ARS, however, resulted in a better trained robot, and produced an optimal
policy which officially "solves" the BipedalWalker-v3 problem. Various naive
policies, including a random policy, a manually encoded inch forward policy,
and a stay still policy, were used as benchmarks to evaluate the proficiency of
the learning algorithm results.

While the consequences of mathematically-based software, algorithms and
strategies have become ever wider and better appreciated, ethical reflection on
mathematics has remained primitive. We review the somewhat disconnected
suggestions of commentators in recent decades with a view to piecing together a
coherent approach to ethics in mathematics. Calls for a Hippocratic Oath for
mathematicians are examined and it is concluded that while lessons can be
learned from the medical profession, the relation of mathematicians to those
affected by their work is significantly different. There is something to be
learned also from the codes of conduct of cognate but professionalised
quantitative disciplines such as engineering and accountancy, as well as from
legal principles bearing on professional work. We conclude with recommendations
that professional societies in mathematics should sponsor an (international)
code of ethics, institutional mission statements for mathematicians and
syllabuses for incorporation into mathematics degrees.

The shape of many objects in the built environment is dictated by their
relationships to the human body: how will a person interact with this object?
Existing data-driven generative models of 3D shapes produce plausible objects
but do not reason about the relationship of those objects to the human body. In
this paper, we learn body-aware generative models of 3D shapes. Specifically,
we train generative models of chairs, an ubiquitous shape category, which can
be conditioned on a given body shape or sitting pose. The
body-shape-conditioned models produce chairs which will be comfortable for a
person with the given body shape; the pose-conditioned models produce chairs
which accommodate the given sitting pose. To train these models, we define a
"sitting pose matching" metric and a novel "sitting comfort" metric.
Calculating these metrics requires an expensive optimization to sit the body
into the chair, which is too slow to be used as a loss function for training a
generative model. Thus, we train neural networks to efficiently approximate
these metrics. We use our approach to train three body-aware generative shape
models: a structured part-based generator, a point cloud generator, and an
implicit surface generator. In all cases, our approach produces models which
adapt their output chair shapes to input human body specifications.

In still image human action recognition, existing studies have mainly
leveraged extra bounding box information along with class labels to mitigate
the lack of temporal information in still images; however, preparing extra data
with manual annotation is time-consuming and also prone to human errors.
Moreover, the existing studies have not addressed action recognition with
long-tailed distribution. In this paper, we propose a two-phase multi-expert
classification method for human action recognition to cope with long-tailed
distribution by means of super-class learning and without any extra
information. To choose the best configuration for each super-class and
characterize inter-class dependency between different action classes, we
propose a novel Graph-Based Class Selection (GCS) algorithm. In the proposed
approach, a coarse-grained phase selects the most relevant fine-grained
experts. Then, the fine-grained experts encode the intricate details within
each super-class so that the inter-class variation increases. Extensive
experimental evaluations are conducted on various public human action
recognition datasets, including Stanford40, Pascal VOC 2012 Action, BU101+, and
IHAR datasets. The experimental results demonstrate that the proposed method
yields promising improvements. To be more specific, in IHAR, Sanford40, Pascal
VOC 2012 Action, and BU101+ benchmarks, the proposed approach outperforms the
state-of-the-art studies by 8.92%, 0.41%, 0.66%, and 2.11 % with much less
computational cost and without any auxiliary annotation information. Besides,
it is proven that in addressing action recognition with long-tailed
distribution, the proposed method outperforms its counterparts by a significant
margin.

We present PantheonRL, a multiagent reinforcement learning software package
for dynamic training interactions such as round-robin, adaptive, and ad-hoc
training. Our package is designed around flexible agent objects that can be
easily configured to support different training interactions, and handles fully
general multiagent environments with mixed rewards and n agents. Built on top
of StableBaselines3, our package works directly with existing powerful deep RL
algorithms. Finally, PantheonRL comes with an intuitive yet functional web user
interface for configuring experiments and launching multiple asynchronous jobs.
Our package can be found at https://github.com/Stanford-ILIAD/PantheonRL.

We study optimization problems where the objective function is modeled
through feedforward neural networks with rectified linear unit (ReLU)
activation. Recent literature has explored the use of a single neural network
to model either uncertain or complex elements within an objective function.
However, it is well known that ensembles of neural networks produce more stable
predictions and have better generalizability than models with single neural
networks, which motivates the investigation of ensembles of neural networks
rather than single neural networks in decision-making pipelines. We study how
to incorporate a neural network ensemble as the objective function of an
optimization model and explore computational approaches for the ensuing
problem. We present a mixed-integer linear program based on existing popular
big-M formulations for optimizing over a single neural network. We develop a
two-phase approach for our model that combines preprocessing procedures to
tighten bounds for critical neurons in the neural networks with a Lagrangian
relaxation-based branch-and-bound approach. Experimental evaluations of our
solution methods suggest that using ensembles of neural networks yields more
stable and higher quality solutions, compared to single neural networks, and
that our optimization algorithm outperforms (the adaption of) a
state-of-the-art approach in terms of computational time and optimality gaps.

Predicting the geographical location of users of social media like Twitter
has found several applications in health surveillance, emergency monitoring,
content personalization, and social studies in general. In this work we
contribute to the research in this area by designing and evaluating new methods
based on the literature of weighted multigraphs combined with state-of-the-art
deep learning techniques. The explored methods depart from a similar underlying
structure (that of an extended mention and/or follower network) but use
different information processing strategies, e.g., information diffusion
through transductive and inductive algorithms -- RGCNs and GraphSAGE,
respectively -- and node embeddings with Node2vec+. These graphs are then
combined with attention mechanisms to incorporate the users' text view into the
models. We assess the performance of each of these methods and compare them to
baseline models in the publicly available Twitter-US dataset; we also make a
new dataset available based on a large Twitter capture in Latin America.
Finally, our work discusses the limitations and validity of the comparisons
among methods in the context of different label definitions and metrics.

Normalizing flows have grown more popular over the last few years; however,
they continue to be computationally expensive, making them difficult to be
accepted into the broader machine learning community. In this paper, we
introduce a simple one-dimensional one-layer network that has closed form
Lipschitz constants; using this, we introduce a new Exact-Lipschitz Flow (ELF)
that combines the ease of sampling from residual flows with the strong
performance of autoregressive flows. Further, we show that ELF is provably a
universal density approximator, more computationally and parameter efficient
compared to a multitude of other flows, and achieves state-of-the-art
performance on multiple large-scale datasets.

We propose a grid-like computational model of tubular reactors. The
architecture is inspired by the computations performed by solvers of partial
differential equations which describe the dynamics of the chemical process
inside a tubular reactor. The proposed model may be entirely based on the known
form of the partial differential equations or it may contain generic machine
learning components such as multi-layer perceptrons. We show that the proposed
model can be trained using limited amounts of data to describe the state of a
fixed-bed catalytic reactor. The trained model can reconstruct unmeasured
states such as the catalyst activity using the measurements of inlet
concentrations and temperatures along the reactor.

Machine Learning has been successfully applied in systems applications such
as memory prefetching and caching, where learned models have been shown to
outperform heuristics. However, the lack of understanding the inner workings of
these models -- interpretability -- remains a major obstacle for adoption in
real-world deployments. Understanding a model's behavior can help system
administrators and developers gain confidence in the model, understand risks,
and debug unexpected behavior in production. Interpretability for models used
in computer systems poses a particular challenge: Unlike ML models trained on
images or text, the input domain (e.g., memory access patterns, program
counters) is not immediately interpretable. A major challenge is therefore to
explain the model in terms of concepts that are approachable to a human
practitioner. By analyzing a state-of-the-art caching model, we provide
evidence that the model has learned concepts beyond simple statistics that can
be leveraged for explanations. Our work provides a first step towards
explanability of system ML models and highlights both promises and challenges
of this emerging research area.

Motion deblurring is a highly ill-posed problem due to the loss of motion
information in the blur degradation process. Since event cameras can capture
apparent motion with a high temporal resolution, several attempts have explored
the potential of events for guiding deblurring. These methods generally assume
that the exposure time is the same as the reciprocal of the video frame rate.
However, this is not true in real situations, and the exposure time might be
unknown and dynamically varies depending on the video shooting
environment(e.g., illumination condition). In this paper, we address the
event-guided motion deblurring assuming dynamically variable unknown exposure
time of the frame-based camera. To this end, we first derive a new formulation
for event-guided motion deblurring by considering the exposure and readout time
in the video frame acquisition process. We then propose a novel end-to-end
learning framework for event-guided motion deblurring. In particular, we design
a novel Exposure Time-based Event Selection(ETES) module to selectively use
event features by estimating the cross-modal correlation between the features
from blurred frames and the events. Moreover, we propose a feature fusion
module to fuse the selected features from events and blur frames effectively.
We conduct extensive experiments on various datasets and demonstrate that our
method achieves state-of-the-art performance.

The increasing deployment of advanced digital technologies such as Internet
of Things (IoT) devices and Cyber-Physical Systems (CPS) in industrial
environments is enabling the productive use of machine learning (ML) algorithms
in the manufacturing domain. As ML applications transcend from research to
productive use in real-world industrial environments, the question of
reliability arises. Since the majority of ML models are trained and evaluated
on static datasets, continuous online monitoring of their performance is
required to build reliable systems. Furthermore, concept and sensor drift can
lead to degrading accuracy of the algorithm over time, thus compromising
safety, acceptance and economics if undetected and not properly addressed. In
this work, we exemplarily highlight the severity of the issue on a publicly
available industrial dataset which was recorded over the course of 36 months
and explain possible sources of drift. We assess the robustness of ML
algorithms commonly used in manufacturing and show, that the accuracy strongly
declines with increasing drift for all tested algorithms. We further
investigate how uncertainty estimation may be leveraged for online performance
estimation as well as drift detection as a first step towards continually
learning applications. The results indicate, that ensemble algorithms like
random forests show the least decay of confidence calibration under drift.

Recently, the discovery of interpretable directions in the latent spaces of
pre-trained GANs has become a popular topic. While existing works mostly
consider directions for semantic image manipulations, we focus on an abstract
property: creativity. Can we manipulate an image to be more or less creative?
We build our work on the largest AI-based creativity platform, Artbreeder,
where users can generate images using pre-trained GAN models. We explore the
latent dimensions of images generated on this platform and present a novel
framework for manipulating images to make them more creative. Our code and
dataset are available at http://github.com/catlab-team/latentcreative.

In this paper, we use a large-scale play scripts dataset to propose the novel
task of theatrical cue generation from dialogues. Using over one million lines
of dialogue and cues, we approach the problem of cue generation as a controlled
text generation task, and show how cues can be used to enhance the impact of
dialogue using a language model conditioned on a dialogue/cue discriminator. In
addition, we explore the use of topic keywords and emotions for controlled text
generation. Extensive quantitative and qualitative experiments show that
language models can be successfully used to generate plausible and
attribute-controlled texts in highly specialised domains such as play scripts.
Supporting materials can be found at: https://catlab-team.github.io/cuegen.

We use artificial intelligence (AI) to learn and infer the physics of higher
order gravitational wave modes of quasi-circular, spinning, non precessing
binary black hole mergers. We trained AI models using 14 million waveforms,
produced with the surrogate model NRHybSur3dq8, that include modes up to $\ell
\leq 4$ and $(5,5)$, except for $(4,0)$ and $(4,1)$, that describe binaries
with mass-ratios $q\leq8$, individual spins $s^z_{\{1,2\}}\in[-0.8, 0.8]$, and
inclination angle $\theta\in[0,\pi]$.Our probabilistic AI surrogates can
accurately constrain the mass-ratio, individual spins, effective spin, and
inclination angle of numerical relativity waveforms that describe such signal
manifold. We compared the predictions of our AI models with Gaussian process
regression, random forest, k-nearest neighbors, and linear regression, and with
traditional Bayesian inference methods through the PyCBC Inference toolkit,
finding that AI outperforms all these approaches in terms of accuracy, and are
between three to four orders of magnitude faster than traditional Bayesian
inference methods. Our AI surrogates were trained within 3.4 hours using
distributed training on 1,536 NVIDIA V100 GPUs in the Summit supercomputer.

Quantum machine learning is at the crossroads of two of the most exciting
current areas of research; quantum computing and classical machine learning. It
explores the interaction between quantum computing and machine learning,
investigating how results and techniques from one field can be used to solve
the problems of the other. Here, we investigate quantum support vector machine
(QSVM) algorithm and its circuit version on present quantum computers. We
propose a general encoding procedure extending QSVM algorithm, which would
allow one to feed vectors with higher dimension in the training-data oracle of
QSVM. We compute the efficiency of the QSVM circuit implementation method by
encoding training and testing data sample in quantum circuits and running them
on quantum simulator and real chip for two datasets; 6/9 and banknote. We
highlight the technical difficulties one would face while applying the QSVM
algorithm on current NISQ era devices. Then we propose a new method to classify
these datasets with enhanced efficiencies for the above datasets both on
simulator and real chips.

Establishing dense correspondence between two images is a fundamental
computer vision problem, which is typically tackled by matching local feature
descriptors. However, without global awareness, such local features are often
insufficient for disambiguating similar regions. And computing the pairwise
feature correlation across images is both computation-expensive and
memory-intensive. To make the local features aware of the global context and
improve their matching accuracy, we introduce DenseGAP, a new solution for
efficient Dense correspondence learning with a Graph-structured neural network
conditioned on Anchor Points. Specifically, we first propose a graph structure
that utilizes anchor points to provide sparse but reliable prior on inter- and
intra-image context and propagates them to all image points via directed edges.
We also design a graph-structured network to broadcast multi-level contexts via
light-weighted message-passing layers and generate high-resolution feature maps
at low memory cost. Finally, based on the predicted feature maps, we introduce
a coarse-to-fine framework for accurate correspondence prediction using cycle
consistency. Our feature descriptors capture both local and global information,
thus enabling a continuous feature field for querying arbitrary points at high
resolution. Through comprehensive ablative experiments and evaluations on
large-scale indoor and outdoor datasets, we demonstrate that our method
advances the state-of-the-art of correspondence learning on most benchmarks.

What does human pose tell us about a scene? We propose a task to answer this
question: given human pose as input, hallucinate a compatible scene. Subtle
cues captured by human pose -- action semantics, environment affordances,
object interactions -- provide surprising insight into which scenes are
compatible. We present a large-scale generative adversarial network for
pose-conditioned scene generation. We significantly scale the size and
complexity of training data, curating a massive meta-dataset containing over 19
million frames of humans in everyday environments. We double the capacity of
our model with respect to StyleGAN2 to handle such complex data, and design a
pose conditioning mechanism that drives our model to learn the nuanced
relationship between pose and scene. We leverage our trained model for various
applications: hallucinating pose-compatible scene(s) with or without humans,
visualizing incompatible scenes and poses, placing a person from one generated
image into another scene, and animating pose. Our model produces diverse
samples and outperforms pose-conditioned StyleGAN2 and Pix2Pix/Pix2PixHD
baselines in terms of accurate human placement (percent of correct keypoints)
and quality (Frechet inception distance).

Scaling language models with more data, compute and parameters has driven
significant progress in natural language processing. For example, thanks to
scaling, GPT-3 was able to achieve strong results on in-context learning tasks.
However, training these large dense models requires significant amounts of
computing resources. In this paper, we propose and develop a family of language
models named GLaM (Generalist Language Model), which uses a sparsely activated
mixture-of-experts architecture to scale the model capacity while also
incurring substantially less training cost compared to dense variants. The
largest GLaM has 1.2 trillion parameters, which is approximately 7x larger than
GPT-3. It consumes only 1/3 of the energy used to train GPT-3 and requires half
of the computation flops for inference, while still achieving better overall
zero-shot and one-shot performance across 29 NLP tasks.

Capturing and rendering life-like hair is particularly challenging due to its
fine geometric structure, the complex physical interaction and its non-trivial
visual appearance.Yet, hair is a critical component for believable avatars. In
this paper, we address the aforementioned problems: 1) we use a novel,
volumetric hair representation that is com-posed of thousands of primitives.
Each primitive can be rendered efficiently, yet realistically, by building on
the latest advances in neural rendering. 2) To have a reliable control signal,
we present a novel way of tracking hair on the strand level. To keep the
computational effort manageable, we use guide hairs and classic techniques to
expand those into a dense hood of hair. 3) To better enforce temporal
consistency and generalization ability of our model, we further optimize the 3D
scene flow of our representation with multi-view optical flow, using volumetric
ray marching. Our method can not only create realistic renders of recorded
multi-view sequences, but also create renderings for new hair configurations by
providing new control signals. We compare our method with existing work on
viewpoint synthesis and drivable animation and achieve state-of-the-art
results. Please check out our project website at
https://ziyanw1.github.io/hvh/.

The reservoir computing networks (RCNs) have been successfully employed as a
tool in learning and complex decision-making tasks. Despite their efficiency
and low training cost, practical applications of RCNs rely heavily on empirical
design. In this paper, we develop an algorithm to design RCNs using the
realization theory of linear dynamical systems. In particular, we introduce the
notion of $\alpha$-stable realization, and provide an efficient approach to
prune the size of a linear RCN without deteriorating the training accuracy.
Furthermore, we derive a necessary and sufficient condition on the
irreducibility of number of hidden nodes in linear RCNs based on the concepts
of controllability and observability matrices. Leveraging the linear RCN
design, we provide a tractable procedure to realize RCNs with nonlinear
activation functions. Finally, we present numerical experiments on forecasting
time-delay systems and chaotic systems to validate the proposed RCN design
methods and demonstrate their efficacy.

Knowledge-Based Visual Question Answering (KBVQA) is a bi-modal task
requiring external world knowledge in order to correctly answer a text question
and associated image. Recent single modality text work has shown knowledge
injection into pre-trained language models, specifically entity enhanced
knowledge graph embeddings, can improve performance on downstream
entity-centric tasks. In this work, we empirically study how and whether such
methods, applied in a bi-modal setting, can improve an existing VQA system's
performance on the KBVQA task. We experiment with two large publicly available
VQA datasets, (1) KVQA which contains mostly rare Wikipedia entities and (2)
OKVQA which is less entity-centric and more aligned with common sense
reasoning. Both lack explicit entity spans and we study the effect of different
weakly supervised and manual methods for obtaining them. Additionally we
analyze how recently proposed bi-modal and single modal attention explanations
are affected by the incorporation of such entity enhanced representations. Our
results show substantial improved performance on the KBVQA task without the
need for additional costly pre-training and we provide insights for when entity
knowledge injection helps improve a model's understanding. We provide code and
enhanced datasets for reproducibility.

Rubric-based admissions are claimed to help make the graduate admissions
process more equitable, possibly helping to address the historical and ongoing
inequities in the U.S. physics graduate school admissions process that have
often excluded applicants from minoritized races, ethnicities, genders, and
backgrounds. Yet, no studies have examined whether rubric-based admissions
methods represent a fundamental change of the admissions process or simply
represent a new tool that achieves the same outcome. To address that, we
developed supervised machine learning models of graduate admissions data
collected from our department over a seven-year period. During the first four
years, our department used a traditional admissions process and switched to a
rubric-based process for the following three years, allowing us to compare
which parts of the applications were used to drive admissions decisions. We
find that faculty focused on applicants' physics GRE scores and grade point
averages when making admissions decisions before the implementation of the
rubric. While we were able to develop a sufficiently good model whose results
we could trust for the data before the implementation of the rubric, we were
unable to do so for the data collected after the implementation of the rubric,
despite multiple modifications to the algorithms and data such as implementing
Tomek Links. Our inability to model the second data set despite being able to
model the first combined with model comparison analyses suggests that
rubric-based admissions does change the underlying process. These results
suggest that rubric-based holistic review is a method that could make the
graduate admissions process in physics more equitable.

Inspired by recent strides in empirical efficacy of implicit learning in many
robotics tasks, we seek to understand the theoretical benefits of implicit
formulations in the face of nearly discontinuous functions, common
characteristics for systems that make and break contact with the environment
such as in legged locomotion and manipulation. We present and motivate three
formulations for learning a function: one explicit and two implicit. We derive
generalization bounds for each of these three approaches, exposing where
explicit and implicit methods alike based on prediction error losses typically
fail to produce tight bounds, in contrast to other implicit methods with
violation-based loss definitions that can be fundamentally more robust to steep
slopes. Furthermore, we demonstrate that this violation implicit loss can
tightly bound graph distance, a quantity that often has physical roots and
handles noise in inputs and outputs alike, instead of prediction losses which
consider output noise only. Our insights into the generalizability and physical
relevance of violation implicit formulations match evidence from prior works
and are validated through a toy problem, inspired by rigid-contact models and
referenced throughout our theoretical analysis.

In this paper, an unsupervised and cognitively driven weighted-entropy method
for embedding semantic categories in hyperbolic geometry is proposed. The model
is driven by two fields of research in cognitive linguistics: the first is the
statistical learning theory of language acquisition and the proposal of using
high-dimensional networks to represent semantic knowledge in cognition, and the
second is the domain-specific approach to semantic communication. Weighted
conditional entropy of word co-occurrence is proposed as the embedding metric,
and the two weighting parameters are collocation diversity and conditional
probability ranking in the corresponding statistical distribution. The
Boltzmann distribution is then used on the weighted-entropy metric and embedded
into a hyperbolic Poincare disk model. Testing has been in particular performed
in the domains of basic color and kinship words, which belong to the classes
that domain-specificity focused research in cognitive semantics has most
intensively investigated. Results show that this new approach can successfully
model and map the semantic relationships of popularity and similarity for most
of the basic color and kinship words in English and have potential to be
generalized to other semantic domains and different languages. Generally, this
paper contributes to both computational cognitive semantics and the research on
network and geometry-driven language embedding in computational linguistics and
NLP.

Federated machine learning leverages edge computing to develop models from
network user data, but privacy in federated learning remains a major challenge.
Techniques using differential privacy have been proposed to address this, but
bring their own challenges -- many require a trusted third party or else add
too much noise to produce useful models. Recent advances in \emph{secure
aggregation} using multiparty computation eliminate the need for a third party,
but are computationally expensive especially at scale. We present a new
federated learning protocol that leverages a novel differentially private,
malicious secure aggregation protocol based on techniques from Learning With
Errors. Our protocol outperforms current state-of-the art techniques, and
empirical results show that it scales to a large number of parties, with
optimal accuracy for any differentially private federated learning scheme.

Variational Autoencoders are one of the most commonly used generative models,
particularly for image data. A prominent difficulty in training VAEs is data
that is supported on a lower-dimensional manifold. Recent work by Dai and Wipf
(2020) proposes a two-stage training algorithm for VAEs, based on a conjecture
that in standard VAE training the generator will converge to a solution with 0
variance which is correctly supported on the ground truth manifold. They gave
partial support for that conjecture by showing that some optima of the VAE loss
do satisfy this property, but did not analyze the training dynamics. In this
paper, we show that for linear encoders/decoders, the conjecture is true-that
is the VAE training does recover a generator with support equal to the ground
truth manifold-and does so due to an implicit bias of gradient descent rather
than merely the VAE loss itself. In the nonlinear case, we show that VAE
training frequently learns a higher-dimensional manifold which is a superset of
the ground truth manifold.

Anomaly detection is concerned with identifying examples in a dataset that do
not conform to the expected behaviour. While a vast amount of anomaly detection
algorithms exist, little attention has been paid to explaining why these
algorithms flag certain examples as anomalies. However, such an explanation
could be extremely useful to anyone interpreting the algorithms' output. This
paper develops a method to explain the anomaly predictions of the
state-of-the-art Isolation Forest anomaly detection algorithm. The method
outputs an explanation vector that captures how important each attribute of an
example is to identifying it as anomalous. A thorough experimental evaluation
on both synthetic and real-world datasets shows that our method is more
accurate and more efficient than most contemporary state-of-the-art
explainability methods.

Machine learning is rapidly becoming an integral part of experimental
physical discovery via automated and high-throughput synthesis, and active
experiments in scattering and electron/probe microscopy. This, in turn,
necessitates the development of active learning methods capable of exploring
relevant parameter spaces with the smallest number of steps. Here we introduce
an active learning approach based on co-navigation of the hypothesis and
experimental spaces. This is realized by combining the structured Gaussian
Processes containing probabilistic models of the possible system's behaviors
(hypotheses) with reinforcement learning policy refinement (discovery). This
approach closely resembles classical human-driven physical discovery, when
several alternative hypotheses realized via models with adjustable parameters
are tested during an experiment. We demonstrate this approach for exploring
concentration-induced phase transitions in combinatorial libraries of Sm-doped
BiFeO3 using Piezoresponse Force Microscopy, but it is straightforward to
extend it to higher-dimensional parameter spaces and more complex physical
problems once the experimental workflow and hypothesis-generation are

Information-theoretic lower bounds are often encountered in several branches
of computer science, including learning theory and cryptography. In the quantum
setting, Holevo's and Nayak's bounds give an estimate of the amount of
classical information that can be stored in a quantum state. Previous works
have shown how to combine information-theoretic tools with a counting argument
to lower bound the sample complexity of distribution-free quantum probably
approximately correct (PAC) learning. In our work, we establish the notion of
Probably Approximately Correct Source Coding and we show two novel applications
in quantum learning theory and delegated quantum computation with a purely
classical client. In particular, we provide a lower bound of the sample
complexity of a quantum learner for arbitrary functions under the Zipf
distribution, and we improve the security guarantees of a classically-driven
delegation protocol for measurement-based quantum computation (MBQC).

There has been a lot of interest in understanding what information is
captured by hidden representations of language models (LMs). Typically,
interpretation methods i) do not guarantee that the model actually uses the
encoded information, and ii) do not discover small subsets of neurons
responsible for a considered phenomenon. Inspired by causal mediation analysis,
we propose a method that discovers within a neural LM a small subset of neurons
responsible for a particular linguistic phenomenon, i.e., subsets causing a
change in the corresponding token emission probabilities. We use a
differentiable relaxation to approximately search through the combinatorial
space. An $L_0$ regularization term ensures that the search converges to
discrete and sparse solutions. We apply our method to analyze subject-verb
number agreement and gender bias detection in LSTMs. We observe that it is fast
and finds better solutions than the alternative (REINFORCE). Our experiments
confirm that each of these phenomenons is mediated through a small subset of
neurons that do not play any other discernible role.

We explore the promises and challenges of employing sequential
decision-making algorithms - such as bandits, reinforcement learning, and
active learning - in law and public policy. While such algorithms have
well-characterized performance in the private sector (e.g., online
advertising), their potential in law and the public sector remains largely
unexplored, due in part to distinct methodological challenges of the policy
setting. Public law, for instance, can pose multiple objectives, necessitate
batched and delayed feedback, and require systems to learn rational, causal
decision-making policies, each of which presents novel questions at the
research frontier. We highlight several applications of sequential
decision-making algorithms in regulation and governance, and discuss areas for
needed research to render such methods policy-compliant, more widely
applicable, and effective in the public sector. We also note the potential
risks of such deployments and describe how sequential decision systems can also
facilitate the discovery of harms. We hope our work inspires more investigation
of sequential decision making in law and public policy, which provide unique
challenges for machine learning researchers with tremendous potential for
social benefit.

How do we imagine visual objects and combine them to create new forms? To
answer this question, we need to explore the cognitive, computational and
neural mechanisms underlying imagery and creativity. The body of research on
deep learning models with creative behaviors is growing. However, in this paper
we suggest that the complexity of such models and their training sets is an
impediment to using them as tools to understand human aspects of creativity. We
propose using simpler models, inspired by neural and cognitive mechanisms, that
are trained with smaller data sets. We show that a standard deep learning
architecture can demonstrate imagery by generating shape/color combinations
using only symbolic codes as input. However, generating a new combination that
was not experienced by the model was not possible. We discuss the limitations
of such models, and explain how creativity could be embedded by incorporating
memory mechanisms to combine the output of the network into new combinations
and use that as new training data.

Most general-purpose classification methods, such as support-vector machine
(SVM) and random forest (RF), fail to account for an unusual characteristic of
astronomical data: known measurement error uncertainties. In astronomical data,
this information is often given in the data but discarded because popular
machine learning classifiers cannot incorporate it. We propose a
simulation-based approach that incorporates heteroscedastic measurement error
into existing classification method to better quantify uncertainty in
classification. The proposed method first simulates perturbed realizations of
the data from a Bayesian posterior predictive distribution of a Gaussian
measurement error model. Then, a chosen classifier is fit to each simulation.
The variation across the simulations naturally reflects the uncertainty
propagated from the measurement errors in both labeled and unlabeled data sets.
We demonstrate the use of this approach via two numerical studies. The first is
a thorough simulation study applying the proposed procedure to SVM and RF,
which are well-known hard and soft classifiers, respectively. The second study
is a realistic classification problem of identifying high-$z$ $(2.9 \leq z \leq
5.1)$ quasar candidates from photometric data. The data are from merged
catalogs of the Sloan Digital Sky Survey, the $Spitzer$ IRAC Equatorial Survey,
and the $Spitzer$-HETDEX Exploratory Large-Area Survey. The proposed approach
reveals that out of 11,847 high-$z$ quasar candidates identified by a random
forest without incorporating measurement error, 3,146 are potential
misclassifications with measurement error. Additionally, out of $1.85$ million
objects not identified as high-$z$ quasars without measurement error, 936 can
be considered new candidates with measurement error.

Simplicial neural networks (SNN) have recently emerged as the newest
direction in graph learning which expands the idea of convolutional
architectures from node space to simplicial complexes on graphs. Instead of
pre-dominantly assessing pairwise relations among nodes as in the current
practice, simplicial complexes allow us to describe higher-order interactions
and multi-node graph structures. By building upon connection between the
convolution operation and the new block Hodge-Laplacian, we propose the first
SNN for link prediction. Our new Block Simplicial Complex Neural Networks
(BScNets) model generalizes the existing graph convolutional network (GCN)
frameworks by systematically incorporating salient interactions among multiple
higher-order graph structures of different dimensions. We discuss theoretical
foundations behind BScNets and illustrate its utility for link prediction on
eight real-world and synthetic datasets. Our experiments indicate that BScNets
outperforms the state-of-the-art models by a significant margin while
maintaining low computation costs. Finally, we show utility of BScNets as the
new promising alternative for tracking spread of infectious diseases such as
COVID-19 and measuring the effectiveness of the healthcare risk mitigation

Recently, fine-tuning language models pre-trained on large text corpora have
provided huge improvements on vision-and-language (V&L) tasks as well as on
pure language tasks. However, fine-tuning the entire parameter set of
pre-trained models becomes impractical since the model size is growing rapidly.
Hence, in this paper, we introduce adapter-based parameter-efficient transfer
learning techniques to V&L models such as VL-BART and VLT5. We evaluate our
methods in a unified multi-task setup on both image-text and video-text
benchmarks. For the image-text tasks, we use four diverse V&L datasets: VQAv2,
GQA, NLVR2 , and MSCOCO image captioning. For video-text tasks, we use TVQA,
How2QA, TVC, and YC2C. With careful training and thorough experiments, we
benchmark three popular adapter-based methods (Adapter, Hyperformer, Compacter)
against the standard full fine-tuning and the recently proposed prompt-tuning
approach. We also enhance the efficiency and performance of adapters by sharing
their weights to attain knowledge across tasks. Our results demonstrate that
training the adapter with the weight-sharing technique (4.18% of total
parameters for image-text tasks and 3.39% for video-text tasks) can match the
performance of fine-tuning the entire model. Lastly, we present a comprehensive
analysis including the combination of adapter and task-specific prompts and the
impact of V&L pre-training on adapters. Our code is available at:
https://github.com/ylsung/VL_adapter.

We construct realistic spot and equity option market simulators for a single
underlying on the basis of normalizing flows. We address the
high-dimensionality of market observed call prices through an arbitrage-free
autoencoder that approximates efficient low-dimensional representations of the
prices while maintaining no static arbitrage in the reconstructed surface.
Given a multi-asset universe, we leverage the conditional invertibility
property of normalizing flows and introduce a scalable method to calibrate the
joint distribution of a set of independent simulators while preserving the
dynamics of each simulator. Empirical results highlight the goodness of the
calibrated simulators and their fidelity.

We demonstrate that embedding physics-driven constraints into machine
learning process can dramatically improve accuracy and generalizability of the
resulting model. Physics-informed learning is illustrated on the example of
analysis of optical modes propagating through a spatially periodic composite.
The approach presented can be readily utilized in other situations mapped onto
an eigenvalue problem, a known bottleneck of computational electrodynamics.
Physics-informed learning can be used to improve machine-learning-driven
design, optimization, and characterization, in particular in situations where
exact solutions are scarce or are slow to come up with.

Farquhar et al. [2021] show that correcting for active learning bias with
underparameterised models leads to improved downstream performance. For
overparameterised models such as NNs, however, correction leads either to
decreased or unchanged performance. They suggest that this is due to an
"overfitting bias" which offsets the active learning bias. We show that depth
uncertainty networks operate in a low overfitting regime, much like
underparameterised models. They should therefore see an increase in performance
with bias correction. Surprisingly, they do not. We propose that this negative
result, as well as the results Farquhar et al. [2021], can be explained via the
lens of the bias-variance decomposition of generalisation error.

Population imaging studies rely upon good quality medical imagery before
downstream image quantification. This study provides an automated approach to
assess image quality from cardiovascular magnetic resonance (CMR) imaging at
scale. We identify four common CMR imaging artefacts, including respiratory
motion, cardiac motion, Gibbs ringing, and aliasing. The model can deal with
images acquired in different views, including two, three, and four-chamber
long-axis and short-axis cine CMR images. Two deep learning-based models in
spatial and frequency domains are proposed. Besides recognising these
artefacts, the proposed models are suitable to the common challenges of not
having access to data labels. An unsupervised domain adaptation method and a
Fourier-based convolutional neural network are proposed to overcome these
challenges. We show that the proposed models reliably allow for CMR image
quality assessment. The accuracies obtained for the spatial model in supervised
and weakly supervised learning are 99.41+0.24 and 96.37+0.66 for the UK Biobank
dataset, respectively. Using unsupervised domain adaptation can somewhat
overcome the challenge of not having access to the data labels. The maximum
achieved domain gap coverage in unsupervised domain adaptation is 16.86%.
Domain adaptation can significantly improve a 5-class classification task and
deal with considerable domain shift without data labels. Increasing the speed
of training and testing can be achieved with the proposed model in the
frequency domain. The frequency-domain model can achieve the same accuracy yet
1.548 times faster than the spatial model. This model can also be used directly
on k-space data, and there is no need for image reconstruction.

In active learning, the size and complexity of the training dataset changes
over time. Simple models that are well specified by the amount of data
available at the start of active learning might suffer from bias as more points
are actively sampled. Flexible models that might be well suited to the full
dataset can suffer from overfitting towards the start of active learning. We
tackle this problem using Depth Uncertainty Networks (DUNs), a BNN variant in
which the depth of the network, and thus its complexity, is inferred. We find
that DUNs outperform other BNN variants on several active learning tasks.
Importantly, we show that on the tasks in which DUNs perform best they present
notably less overfitting than baselines.

In high-background or calibration measurements with cryogenic particle
detectors, a significant share of the exposure is lost due to pile-up of recoil
events. We propose a method for the separation of pile-up events with an LSTM
neural network and evaluate its performance on an exemplary data set. Despite a
non-linear detector response function, we can reconstruct the ground truth of a
severely distorted energy spectrum reasonably well.

Fascination in topological materials originates from their remarkable
response properties and exotic quasiparticles which can be utilized in quantum
technologies. In particular, large-scale efforts are currently focused on
realizing topological superconductors and their Majorana excitations. However,
determining the topological nature of superconductors with current experimental
probes is an outstanding challenge. This shortcoming has become increasingly
pressing due to rapidly developing designer platforms which are theorized to
display very rich topology and are better accessed by local probes rather than
transport experiments. We introduce a robust machine-learning protocol for
classifying the topological states of two-dimensional (2D) chiral
superconductors and insulators from local density of states (LDOS) data. Since
the LDOS can be measured with standard experimental techniques, our protocol
overcomes the almost three decades standing problem of identifying the topology
of 2D superconductors with broken time-reversal symmetry.

We argue that, when establishing and benchmarking Machine Learning (ML)
models, the research community should favour evaluation metrics that better
capture the value delivered by their model in practical applications. For a
specific class of use cases -- selective classification -- we show that not
only can it be simple enough to do, but that it has import consequences and
provides insights what to look for in a ``good'' ML model.

The optimal transport problem has many applications in machine learning,
physics, biology, economics, etc. Although its goal is very clear and
mathematically well-defined, finding its optimal solution can be challenging
for large datasets in high-dimensional space. Here, we propose a homotopy
algorithm that first transforms the problem into an easy form, by changing the
target distribution. It then transforms the problem back to the original form
through a series of iterations, tracing a path of solutions until it finds the
optimal solution for the original problem. We define the homotopy path as a
subspace rotation based on the orthogonal Procrustes problem, and then we
discretize the homotopy path using eigenvalue decomposition of the rotation
matrix. Our goal is to provide an algorithm with complexity bound
$\mathcal{O}(n^2 \log(n))$, faster than the existing methods in the literature.

Principal component analysis (PCA) is a popular dimension reduction technique
for vector data. Factored PCA (FPCA) is a probabilistic extension of PCA for
matrix data, which can substantially reduce the number of parameters in PCA
while yield satisfactory performance. However, FPCA is based on the Gaussian
assumption and thereby susceptible to outliers. Although the multivariate $t$
distribution as a robust modeling tool for vector data has a very long history,
its application to matrix data is very limited. The main reason is that the
dimension of the vectorized matrix data is often very high and the higher the
dimension, the lower the breakdown point that measures the robustness. To solve
the robustness problem suffered by FPCA and make it applicable to matrix data,
in this paper we propose a robust extension of FPCA (RFPCA), which is built
upon a $t$-type distribution called matrix-variate $t$ distribution. Like the
multivariate $t$ distribution, the matrix-variate $t$ distribution can
adaptively down-weight outliers and yield robust estimates. We develop a fast
EM-type algorithm for parameter estimation. Experiments on synthetic and
real-world datasets reveal that RFPCA is compared favorably with several
related methods and RFPCA is a simple but powerful tool for matrix-valued
outlier detection.

Recently, deep learning has attracted more and more attention in phase
unwrapping of fringe projection three-dimensional (3D) measurement, with the
aim to improve the performance leveraging the powerful Convolutional Neural
Network (CNN) models. In this paper, for the first time (to the best of our
knowledge), we introduce the Transformer into the phase unwrapping which is
different from CNN and propose Hformer model dedicated to phase unwrapping via
fringe order prediction. The proposed model has a hybrid CNN-Transformer
architecture that is mainly composed of backbone, encoder and decoder to take
advantage of both CNN and Transformer. Encoder and decoder with cross attention
are designed for the fringe order prediction. Experimental results show that
the proposed Hformer model achieves better performance in fringe order
prediction compared with the CNN models such as U-Net and DCNN. Moreover,
ablation study on Hformer is made to verify the improved feature pyramid
networks (FPN) and testing strategy with flipping in the predicted fringe
order. Our work opens an alternative way to deep learning based phase
unwrapping methods, which are dominated by CNN in fringe projection 3D

Deep reinforcement learning (DRL) has shown huge potentials in building
financial market simulators recently. However, due to the highly complex and
dynamic nature of real-world markets, raw historical financial data often
involve large noise and may not reflect the future of markets, degrading the
fidelity of DRL-based market simulators. Moreover, the accuracy of DRL-based
market simulators heavily relies on numerous and diverse DRL agents, which
increases demand for a universe of market environments and imposes a challenge
on simulation speed. In this paper, we present a FinRL-Meta framework that
builds a universe of market environments for data-driven financial
reinforcement learning. First, FinRL-Meta separates financial data processing
from the design pipeline of DRL-based strategy and provides open-source data
engineering tools for financial big data. Second, FinRL-Meta provides hundreds
of market environments for various trading tasks. Third, FinRL-Meta enables
multiprocessing simulation and training by exploiting thousands of GPU cores.
Our codes are available online at
https://github.com/AI4Finance-Foundation/FinRL-Meta.

Adaptation to external and internal changes is major for robotic systems in
uncertain environments. Here we present a novel multisensory active inference
torque controller for industrial arms that shows how prediction can be used to
resolve adaptation. Our controller, inspired by the predictive brain
hypothesis, improves the capabilities of current active inference approaches by
incorporating learning and multimodal integration of low and high-dimensional
sensor inputs (e.g., raw images) while simplifying the architecture. We
performed a systematic evaluation of our model on a 7DoF Franka Emika Panda
robot arm by comparing its behavior with previous active inference baselines
and classic controllers, analyzing both qualitatively and quantitatively
adaptation capabilities and control accuracy. Results showed improved control
accuracy in goal-directed reaching with high noise rejection due to multimodal
filtering, and adaptability to dynamical inertial changes, elasticity
constraints and human disturbances without the need to relearn the model nor
parameter retuning.

Recent work has shown the potential benefit of selective prediction systems
that can learn to defer to a human when the predictions of the AI are
unreliable, particularly to improve the reliability of AI systems in
high-stakes applications like healthcare or conservation. However, most prior
work assumes that human behavior remains unchanged when they solve a prediction
task as part of a human-AI team as opposed to by themselves. We show that this
is not the case by performing experiments to quantify human-AI interaction in
the context of selective prediction. In particular, we study the impact of
communicating different types of information to humans about the AI system's
decision to defer. Using real-world conservation data and a selective
prediction system that improves expected accuracy over that of the human or AI
system working individually, we show that this messaging has a significant
impact on the accuracy of human judgements. Our results study two components of
the messaging strategy: 1) Whether humans are informed about the prediction of
the AI system and 2) Whether they are informed about the decision of the
selective prediction system to defer. By manipulating these messaging
components, we show that it is possible to significantly boost human
performance by informing the human of the decision to defer, but not revealing
the prediction of the AI. We therefore show that it is vital to consider how
the decision to defer is communicated to a human when designing selective
prediction systems, and that the composite accuracy of a human-AI team must be
carefully evaluated using a human-in-the-loop framework.

In this paper, a new non-parametric empirical Bayes approach called CGAN-EB
is proposed for approximating empirical Bayes (EB) estimates in traffic
locations (e.g., road segments) which benefits from the modeling advantages of
deep neural networks, and its performance is compared in a simulation study
with the traditional approach based on negative binomial model (NB-EB). The
NB-EB uses negative binomial model in order to model the crash data and is the
most common approach in practice. To model the crash data in the proposed
CGAN-EB, conditional generative adversarial network is used, which is a
powerful deep neural network based method that can model any types of
distributions. A number of simulation experiments are designed and conducted to
evaluate the CGAN-EB performance in different conditions and compare it with
the NB-EB. The results show that CGAN-EB performs as well as NB-EB when
conditions favor the NB-EB model (i.e. data conform to the assumptions of the
NB model) and outperforms NB-EB in experiments reflecting conditions frequently
encountered in practice, specifically low sample means, and when crash
frequency does not follow a log-linear relationship with covariates.

In this paper we propose a new generative model of text, Step-unrolled
Denoising Autoencoder (SUNDAE), that does not rely on autoregressive models.
Similarly to denoising diffusion techniques, SUNDAE is repeatedly applied on a
sequence of tokens, starting from random inputs and improving them each time
until convergence. We present a simple new improvement operator that converges
in fewer iterations than diffusion methods, while qualitatively producing
better samples on natural language datasets. SUNDAE achieves state-of-the-art
results (among non-autoregressive methods) on the WMT'14 English-to-German
translation task and good qualitative results on unconditional language
modeling on the Colossal Cleaned Common Crawl dataset and a dataset of Python
code from GitHub. The non-autoregressive nature of SUNDAE opens up
possibilities beyond left-to-right prompted generation, by filling in arbitrary
blank patterns in a template.

Imitation Learning (IL) is an effective learning paradigm exploiting the
interactions between agents and environments. It does not require explicit
reward signals and instead tries to recover desired policies using expert
demonstrations. In general, IL methods can be categorized into Behavioral
Cloning (BC) and Inverse Reinforcement Learning (IRL). In this work, a novel
reward function based on probability density estimation is proposed for IRL,
which can significantly reduce the complexity of existing IRL methods.
Furthermore, we prove that the theoretically optimal policy derived from our
reward function is identical to the expert policy as long as it is
deterministic. Consequently, an IRL problem can be gracefully transformed into
a probability density estimation problem. Based on the proposed reward
function, we present a "watch-try-learn" style framework named Probability
Density Estimation based Imitation Learning (PDEIL), which can work in both
discrete and continuous action spaces. Finally, comprehensive experiments in
the Gym environment show that PDEIL is much more efficient than existing
algorithms in recovering rewards close to the ground truth.

While huge volumes of unlabeled data are generated and made available in many
domains, the demand for automated understanding of visual data is higher than
ever before. Most existing machine learning models typically rely on massive
amounts of labeled training data to achieve high performance. Unfortunately,
such a requirement cannot be met in real-world applications. The number of
labels is limited and manually annotating data is expensive and time-consuming.
It is often necessary to transfer knowledge from an existing labeled domain to
a new domain. However, model performance degrades because of the differences
between domains (domain shift or dataset bias). To overcome the burden of
annotation, Domain Adaptation (DA) aims to mitigate the domain shift problem
when transferring knowledge from one domain into another similar but different
domain. Unsupervised DA (UDA) deals with a labeled source domain and an
unlabeled target domain. The principal objective of UDA is to reduce the domain
discrepancy between the labeled source data and unlabeled target data and to
learn domain-invariant representations across the two domains during training.
In this paper, we first define UDA problem. Secondly, we overview the
state-of-the-art methods for different categories of UDA from both traditional
methods and deep learning based methods. Finally, we collect frequently used
benchmark datasets and report results of the state-of-the-art methods of UDA on
visual recognition problem.

We present a numerical method to model dynamical systems from data. We use
the recently introduced method Scalable Probabilistic Approximation (SPA) to
project points from a Euclidean space to convex polytopes and represent these
projected states of a system in new, lower-dimensional coordinates denoting
their position in the polytope. We then introduce a specific nonlinear
transformation to construct a model of the dynamics in the polytope and to
transform back into the original state space. To overcome the potential loss of
information from the projection to a lower-dimensional polytope, we use memory
in the sense of the delay-embedding theorem of Takens. By construction, our
method produces stable models. We illustrate the capacity of the method to
reproduce even chaotic dynamics and attractors with multiple connected
components on various examples.

In this work, we introduce a novel strategy for long-tail recognition that
addresses the tail classes' few-shot problem via training-free knowledge
transfer. Our objective is to transfer knowledge acquired from information-rich
common classes to semantically similar, and yet data-hungry, rare classes in
order to obtain stronger tail class representations. We leverage the fact that
class prototypes and learned cosine classifiers provide two different,
complementary representations of class cluster centres in feature space, and
use an attention mechanism to select and recompose learned classifier features
from common classes to obtain higher quality rare class representations. Our
knowledge transfer process is training free, reducing overfitting risks, and
can afford continual extension of classifiers to new classes. Experiments show
that our approach can achieve significant performance boosts on rare classes
while maintaining robust common class performance, outperforming directly
comparable state-of-the-art models.

In this paper with study phase transitions of the $q$-state Potts model,
through a number of unsupervised machine learning techniques, namely Principal
Component Analysis (PCA), $k$-means clustering, Uniform Manifold Approximation
and Projection (UMAP), and Topological Data Analysis (TDA). Even though in all
cases we are able to retrieve the correct critical temperatures $T_c(q)$, for
$q = 3, 4$ and $5$, results show that non-linear methods as UMAP and TDA are
less dependent on finite size effects, while still being able to distinguish
between first and second order phase transitions. This study may be considered
as a benchmark for the use of different unsupervised machine learning
algorithms in the investigation of phase transitions.

Fact-checking systems have become important tools to verify fake and
misguiding news. These systems become more trustworthy when human-readable
explanations accompany the veracity labels. However, manual collection of such
explanations is expensive and time-consuming. Recent works frame explanation
generation as extractive summarization, and propose to automatically select a
sufficient subset of the most important facts from the ruling comments (RCs) of
a professional journalist to obtain fact-checking explanations. However, these
explanations lack fluency and sentence coherence. In this work, we present an
iterative edit-based algorithm that uses only phrase-level edits to perform
unsupervised post-editing of disconnected RCs. To regulate our editing
algorithm, we use a scoring function with components including fluency and
semantic preservation. In addition, we show the applicability of our approach
in a completely unsupervised setting. We experiment with two benchmark
datasets, LIAR-PLUS and PubHealth. We show that our model generates
explanations that are fluent, readable, non-redundant, and cover important
information for the fact check.

Multi-armed bandits (MAB) are extensively studied in various settings where
the objective is to \textit{maximize} the actions' outcomes (i.e., rewards)
over time. Since safety is crucial in many real-world problems, safe versions
of MAB algorithms have also garnered considerable interest. In this work, we
tackle a different critical task through the lens of \textit{linear stochastic
bandits}, where the aim is to keep the actions' outcomes close to a target
level while respecting a \textit{two-sided} safety constraint, which we call
\textit{leveling}. Such a task is prevalent in numerous domains. Many
healthcare problems, for instance, require keeping a physiological variable in
a range and preferably close to a target level. The radical change in our
objective necessitates a new acquisition strategy, which is at the heart of a
MAB algorithm. We propose SALE-LTS: Safe Leveling via Linear Thompson Sampling
algorithm, with a novel acquisition strategy to accommodate our task and show
that it achieves sublinear regret with the same time and dimension dependence
as previous works on the classical reward maximization problem absent any
safety constraint. We demonstrate and discuss our algorithm's empirical
performance in detail via thorough experiments.

In the last decade, the blossom of deep learning has witnessed the rapid
development of scene text recognition. However, the recognition of
low-resolution scene text images remains a challenge. Even though some
super-resolution methods have been proposed to tackle this problem, they
usually treat text images as general images while ignoring the fact that the
visual quality of strokes (the atomic unit of text) plays an essential role for
text recognition. According to Gestalt Psychology, humans are capable of
composing parts of details into the most similar objects guided by prior
knowledge. Likewise, when humans observe a low-resolution text image, they will
inherently use partial stroke-level details to recover the appearance of
holistic characters. Inspired by Gestalt Psychology, we put forward a
Stroke-Aware Scene Text Image Super-Resolution method containing a
Stroke-Focused Module (SFM) to concentrate on stroke-level internal structures
of characters in text images. Specifically, we attempt to design rules for
decomposing English characters and digits at stroke-level, then pre-train a
text recognizer to provide stroke-level attention maps as positional clues with
the purpose of controlling the consistency between the generated
super-resolution image and high-resolution ground truth. The extensive
experimental results validate that the proposed method can indeed generate more
distinguishable images on TextZoom and manually constructed Chinese character
dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to
provide stroke-level guidance when training, it will not bring any time
overhead during the test phase. Code is available at
https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt.

Spiking neural networks (SNNs) are different from the classical networks used
in deep learning: the neurons communicate using electrical impulses called
spikes, just like biological neurons. SNNs are appealing for AI technology,
because they could be implemented on low power neuromorphic chips. However,
SNNs generally remain less accurate than their analog counterparts. In this
report, we examine various regularization and normalization techniques with the
goal of improving surrogate gradient learning in SNNs.

Animal vocalisations and natural soundscapes are fascinating objects of
study, and contain valuable evidence about animal behaviours, populations and
ecosystems. They are studied in bioacoustics and ecoacoustics, with signal
processing and analysis an important component. Computational bioacoustics has
accelerated in recent decades due to the growth of affordable digital sound
recording devices, and to huge progress in informatics such as big data, signal
processing and machine learning. Methods are inherited from the wider field of
deep learning, including speech and image processing. However, the tasks,
demands and data characteristics are often different from those addressed in
speech or music analysis. There remain unsolved problems, and tasks for which
evidence is surely present in many acoustic signals, but not yet realised. In
this paper I perform a review of the state of the art in deep learning for
computational bioacoustics, aiming to clarify key concepts and identify and
analyse knowledge gaps. Based on this, I offer a subjective but principled
roadmap for computational bioacoustics with deep learning: topics that the
community should aim to address, in order to make the most of future
developments in AI and informatics, and to use audio data in answering
zoological and ecological questions.

Consonant and vowel reduction are often encountered in speech, which might
cause performance degradation in automatic speech recognition (ASR). Our
recently proposed learning strategy based on masking, Phone Masking Training
(PMT), alleviates the impact of such phenomenon in Uyghur ASR. Although PMT
achieves remarkably improvements, there still exists room for further gains due
to the granularity mismatch between the masking unit of PMT (phoneme) and the
modeling unit (word-piece). To boost the performance of PMT, we propose
multi-modeling unit training (MMUT) architecture fusion with PMT (PM-MMUT). The
idea of MMUT framework is to split the Encoder into two parts including
acoustic feature sequences to phoneme-level representation (AF-to-PLR) and
phoneme-level representation to word-piece-level representation (PLR-to-WPLR).
It allows AF-to-PLR to be optimized by an intermediate phoneme-based CTC loss
to learn the rich phoneme-level context information brought by PMT.
Experimental results on Uyghur ASR show that the proposed approaches outperform
obviously the pure PMT. We also conduct experiments on the 960-hour Librispeech
benchmark using ESPnet1, which achieves about 10% relative WER reduction on all
the test set without LM fusion comparing with the latest official ESPnet1
pre-trained model.

Text-based person search aims to retrieve images of a certain pedestrian by a
textual description. The key challenge of this task is to eliminate the
inter-modality gap and achieve the feature alignment across modalities. In this
paper, we propose a semantic-aligned embedding method for text-based person
search, in which the feature alignment across modalities is achieved by
automatically learning the semantic-aligned visual features and textual
features. First, we introduce two Transformer-based backbones to encode robust
feature representations of the images and texts. Second, we design a
semantic-aligned feature aggregation network to adaptively select and aggregate
features with the same semantics into part-aware features, which is achieved by
a multi-head attention module constrained by a cross-modality part alignment
loss and a diversity loss. Experimental results on the CUHK-PEDES and Flickr30K
datasets show that our method achieves state-of-the-art performances.

There is increasing interest in the development of gate-based quantum
circuits for the training of machine learning models. Yet, little is understood
concerning the parameters of circuit design, and the effects of noise and other
measurement errors on the performance of quantum machine learning models. In
this paper, we explore the practical implications of key circuit design
parameters (number of qubits, depth etc.) using several standard machine
learning datasets and IBM's Qiskit simulator. In total we evaluate over 6500
unique circuits with $n \approx 120700$ individual runs. We find that in
general shallow (low depth) wide (more qubits) circuit topologies tend to
outperform deeper ones in settings without noise. We also explore the
implications and effects of different notions of noise and discuss circuit
topologies that are more / less robust to noise for classification machine
learning tasks. Based on the findings we define guidelines for circuit
topologies that show near-term promise for the realisation of quantum machine
learning algorithms using gate-based NISQ quantum computer.

Attractor neural networks (ANNs) are one of the leading theoretical
frameworks for the formation and retrieval of memories in networks of
biological neurons. In this framework, a pattern imposed by external inputs to
the network is said to be learned when this pattern becomes a fixed point
attractor of the network dynamics. The storage capacity is the maximum number
of patterns that can be learned by the network. In this paper, we study the
storage capacity of fully-connected and sparsely-connected networks with a
binarized Hebbian rule, for arbitrary coding levels. Our results show that a
network with discrete synapses has a similar storage capacity as the model with
continuous synapses, and that this capacity tends asymptotically towards the
optimal capacity, in the space of all possible binary connectivity matrices, in
the sparse coding limit. We also derive finite coding level corrections for the
asymptotic solution in the sparse coding limit. The result indicates the
capacity of network with Hebbian learning rules converges to the optimal
capacity extremely slowly when the coding level becomes small. Our results also
show that in networks with sparse binary connectivity matrices, the information
capacity per synapse is larger than in the fully connected case, and thus such
networks store information more efficiently.

Gravitational-wave population studies have become more important in
gravitational-wave astronomy because of the rapid growth of the observed
catalog. In recent studies, emulators based on different machine learning
techniques are used to emulate the outcomes of the population synthesis
simulation with fast speed. In this study, we benchmark the performance of two
emulators that learn the truncated power-law phenomenological model by using
Gaussian process regression and normalizing flows techniques to see which one
is a more capable likelihood emulator in the population inference. We benchmark
the characteristic of the emulators by comparing their performance in the
population inference to the phenomenological model using mock and real
observation data. Our results suggest that the normalizing flows emulator can
recover the posterior distribution by using the phenomenological model in the
population inference with up to 300 mock injections. The normalizing flows
emulator also underestimates the uncertainty for some posterior distributions
in the population inference on real observation data. On the other hand, the
Gaussian process regression emulator has poor performance on the same task and
can only be used effectively in low-dimension cases.

This paper deals with the highly challenging problem of reconstructing the
shape of a refracting object from a single image of its resulting caustic. Due
to the ubiquity of transparent refracting objects in everyday life,
reconstruction of their shape entails a multitude of practical applications.
The recent Shape from Caustics (SfC) method casts the problem as the inverse of
a light propagation simulation for synthesis of the caustic image, that can be
solved by a differentiable renderer. However, the inherent complexity of light
transport through refracting surfaces currently limits the practicability with
respect to reconstruction speed and robustness. To address these issues, we
introduce Neural-Shape from Caustics (N-SfC), a learning-based extension that
incorporates two components into the reconstruction pipeline: a denoising
module, which alleviates the computational cost of the light transport
simulation, and an optimization process based on learned gradient descent,
which enables better convergence using fewer iterations. Extensive experiments
demonstrate the effectiveness of our neural extensions in the scenario of
quality control in 3D glass printing, where we significantly outperform the
current state-of-the-art in terms of computational speed and final surface
error.

Federated Learning (FL) incurs high communication overhead, which can be
greatly alleviated by compression for model updates. Yet the tradeoff between
compression and model accuracy in the networked environment remains unclear
and, for simplicity, most implementations adopt a fixed compression rate only.
In this paper, we for the first time systematically examine this tradeoff,
identifying the influence of the compression error on the final model accuracy
with respect to the learning rate. Specifically, we factor the compression
error of each global iteration into the convergence rate analysis under both
strongly convex and non-convex loss functions. We then present an adaptation
framework to maximize the final model accuracy by strategically adjusting the
compression rate in each iteration. We have discussed the key implementation
issues of our framework in practical networks with representative compression
algorithms. Experiments over the popular MNIST and CIFAR-10 datasets confirm
that our solution effectively reduces network traffic yet maintains high model
accuracy in FL.

Despite the superior performance of Deep Learning (DL) on numerous
segmentation tasks, the DL-based approaches are notoriously overconfident about
their prediction with highly polarized label probability. This is often not
desirable for many applications with the inherent label ambiguity even in human
annotations. This challenge has been addressed by leveraging multiple
annotations per image and the segmentation uncertainty. However, multiple
per-image annotations are often not available in a real-world application and
the uncertainty does not provide full control on segmentation results to users.
In this paper, we propose novel methods to improve the segmentation probability
estimation without sacrificing performance in a real-world scenario that we
have only one ambiguous annotation per image. We marginalize the estimated
segmentation probability maps of networks that are encouraged to
under-/over-segment with the varying Tversky loss without penalizing balanced
segmentation. Moreover, we propose a unified hypernetwork ensemble method to
alleviate the computational burden of training multiple networks. Our
approaches successfully estimated the segmentation probability maps that
reflected the underlying structures and provided the intuitive control on
segmentation for the challenging 3D medical image segmentation. Although the
main focus of our proposed methods is not to improve the binary segmentation
performance, our approaches marginally outperformed the state-of-the-arts. The
codes are available at \url{https://github.com/sh4174/HypernetEnsemble}.

The field of neural networks has seen significant advances in recent years
with the development of deep and convolutional neural networks. Although many
of the current works address real-valued models, recent studies reveal that
neural networks with hypercomplex-valued parameters can better capture,
generalize, and represent the complexity of multidimensional data. This paper
explores the quaternion-valued convolutional neural network application for a
pattern recognition task from medicine, namely, the diagnosis of acute
lymphoblastic leukemia. Precisely, we compare the performance of real-valued
and quaternion-valued convolutional neural networks to classify lymphoblasts
from the peripheral blood smear microscopic images. The quaternion-valued
convolutional neural network achieved better or similar performance than its
corresponding real-valued network but using only 34% of its parameters. This
result confirms that quaternion algebra allows capturing and extracting
information from a color image with fewer parameters.

Classifier chains are an effective technique for modeling label dependencies
in multi-label classification. However, the method requires a fixed, static
order of the labels. While in theory, any order is sufficient, in practice,
this order has a substantial impact on the quality of the final prediction.
Dynamic classifier chains denote the idea that for each instance to classify,
the order in which the labels are predicted is dynamically chosen. The
complexity of a naive implementation of such an approach is prohibitive,
because it would require to train a sequence of classifiers for every possible
permutation of the labels. To tackle this problem efficiently, we propose a new
approach based on random decision trees which can dynamically select the label
ordering for each prediction. We show empirically that a dynamic selection of
the next label improves over the use of a static ordering under an otherwise
unchanged random decision tree model. % and experimental environment. In
addition, we also demonstrate an alternative approach based on extreme gradient
boosted trees, which allows for a more target-oriented training of dynamic
classifier chains. Our results show that this variant outperforms random
decision trees and other tree-based multi-label classification methods. More
importantly, the dynamic selection strategy allows to considerably speed up
training and prediction.

While Deep Learning (DL) technologies are a promising tool to solve
networking problems that map to classification tasks, their computational
complexity is still too high with respect to real-time traffic measurements
requirements. To reduce the DL inference cost, we propose a novel caching
paradigm, that we named approximate-key caching, which returns approximate
results for lookups of selected input based on cached DL inference results.
While approximate cache hits alleviate DL inference workload and increase the
system throughput, they however introduce an approximation error. As such, we
couple approximate-key caching with an error-correction principled algorithm,
that we named auto-refresh. We analytically model our caching system
performance for classic LRU and ideal caches, we perform a trace-driven
evaluation of the expected performance, and we compare the benefits of our
proposed approach with the state-of-the-art similarity caching -- testifying
the practical interest of our proposal.

Device-to-device (D2D) communication that allows proximity users to
communicate directly has been recently proposed to improve spectral efficiency
of cellular networks. In this paper, we assume a cellular network consisting of
multiple cellular user equipments (CUEs), which are the primary users, and a
cognitive D2D pair, which is the secondary user. The D2D pair needs a bandwidth
for data transmission that can be obtained via spectrum trading. We introduce a
bandwidth-auction game for the spectrum trading problem. The base station (BS)
and CUEs are able to sell their spectrum or share it with the D2D pair, which
allows the D2D pair to operate in orthogonal sharing, cellular, or
non-orthogonal sharing (NOS) modes. Operation of the D2D pair in the NOS mode
causes interference to the CUEs, which is possible under low interference
condition. In the auction, the D2D pair can buy its required spectrum from
three different service providers (SPs) corresponding to each mode that
operateon different frequency spectrums. The D2D pair bids a price bandwidth
demand curve and the SPs offer a price-demand supply curve. Since each player
is not aware of the strategy of other players in practical scenarios, the game
is assumed to be an incomplete information repeated one. A best response based
learning method is proposed for the decision making procedure of all players,
the D2D pair and SPs. It is shown that the proposed method converges to the
Nash equilibrium (NE) point of the game more rapidly than the state-of-the-art
methods when the game is played repeatedly. The sensitivity of the proposed
method to the learning rate variable is also less than the state-of-the-art
methods and hence can be considered as a robust one.

Sequential recommendation methods play an important role in real-world
recommender systems. These systems are able to catch user preferences by taking
advantage of historical records and then performing recommendations.
Contrastive learning(CL) is a cutting-edge technology that can assist us in
obtaining informative user representations, but these CL-based models need
subtle negative sampling strategies, tedious data augmentation methods, and
heavy hyper-parameters tuning work. In this paper, we introduce another way to
generate better user representations and recommend more attractive items to
users. Particularly, we put forward an effective \textbf{C}onsistency
\textbf{C}onstraint for sequential \textbf{Rec}ommendation(C$^2$-Rec) in which
only two extra training objectives are used without any structural
modifications and data augmentation strategies. Substantial experiments have
been conducted on three benchmark datasets and one real industrial dataset,
which proves that our proposed method outperforms SOTA models substantially.
Furthermore, our method needs much less training time than those CL-based
models. Online AB-test on real-world recommendation systems also achieves
10.141\% improvement on the click-through rate and 10.541\% increase on the
average click number per capita. The code is available at
\url{https://github.com/zhengrongqin/C2-Rec}.

Physical Unclonable Functions (PUFs) have been proposed as a way to identify
and authenticate electronic devices. Recently, several ideas have been
presented that aim to achieve the same for quantum devices. Some of these
constructions apply single-qubit gates in order to provide a secure fingerprint
of the quantum device. In this work, we formalize the class of Classical
Readout Quantum PUFs (CR-QPUFs) using the statistical query (SQ) model and
explicitly show insufficient security for CR-QPUFs based on single qubit
rotation gates, when the adversary has SQ access to the CR-QPUF. We demonstrate
how a malicious party can learn the CR-QPUF characteristics and forge the
signature of a quantum device through a modelling attack using a simple
regression of low-degree polynomials. The proposed modelling attack was
successfully implemented in a real-world scenario on real IBM Q quantum
machines. We thoroughly discuss the prospects and problems of CR-QPUFs where
quantum device imperfections are used as a secure fingerprint.

In this paper, we first address the dynamics of the elegant multi-valued
quaternionic Hopfield neural network (MV-QHNN) proposed by Minemoto and
collaborators. Contrary to what was expected, we show that the MV-QHNN, as well
as one of its variation, does not always come to rest at an equilibrium state
under the usual conditions. In fact, we provide simple examples in which the
network yields a periodic sequence of quaternionic state vectors. Afterward, we
turn our attention to the continuous-valued quaternionic Hopfield neural
network (CV-QHNN), which can be derived from the MV-QHNN by means of a limit
process. The CV-QHNN can be implemented more easily than the MV-QHNN model.
Furthermore, the asynchronous CV-QHNN always settles down into an equilibrium
state under the usual conditions. Theoretical issues are all illustrated by
examples in this paper.

We present a simple, efficient "direct learning" approach to train Volterra
series-based digital pre-distortion filters using neural networks. We show its
superior performance over conventional training methods using a 64-QAM 64-GBaud
simulated transmitter with varying transmitter nonlinearity and noisy

Unsupervised domain adaptive person re-identification (ReID) has been
extensively investigated to mitigate the adverse effects of domain gaps. Those
works assume the target domain data can be accessible all at once. However, for
the real-world streaming data, this hinders the timely adaptation to changing
data statistics and sufficient exploitation of increasing samples. In this
paper, to address more practical scenarios, we propose a new task, Lifelong
Unsupervised Domain Adaptive (LUDA) person ReID. This is challenging because it
requires the model to continuously adapt to unlabeled data in the target
environments while alleviating catastrophic forgetting for such a fine-grained
person retrieval task. We design an effective scheme for this task, dubbed
CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the
adaptation. Specifically, a meta-based Coordinated Data Replay strategy is
proposed to replay old data and update the network with a coordinated
optimization direction for both adaptation and memorization. Moreover, we
propose Relational Consistency Learning for old knowledge
distillation/inheritance in line with the objective of retrieval-based tasks.
We set up two evaluation settings to simulate the practical application
scenarios. Extensive experiments demonstrate the effectiveness of our
CLUDA-ReID for both scenarios with stationary target streams and scenarios with
dynamic target streams.

Fast and reliable K-Nearest Neighbor Graph algorithms are more important than
ever due to their widespread use in many data processing techniques. This paper
presents a runtime optimized C implementation of the heuristic "NN-Descent"
algorithm by Wei Dong et al. for the l2-distance metric. Various implementation
optimizations are explained which improve performance for low-dimensional as
well as high dimensional datasets. Optimizations to speed up the selection of
which datapoint pairs to evaluate the distance for are primarily impactful for
low-dimensional datasets. A heuristic which exploits the iterative nature of
NN-Descent to reorder data in memory is presented which enables better use of
locality and thereby improves the runtime. The restriction to the l2-distance
metric allows for the use of blocked distance evaluations which significantly
increase performance for high dimensional datasets. In combination the
optimizations yield an implementation which significantly outperforms a widely
used implementation of NN-Descent on all considered datasets. For instance, the
runtime on the popular MNIST handwritten digits dataset is halved.

The exotic nature of quantum mechanics makes machine learning (ML) be
different in the quantum realm compared to classical applications. ML can be
used for knowledge discovery using information continuously extracted from a
quantum system in a broad range of tasks. The model receives streaming quantum
information for learning and decision-making, resulting in instant feedback on
the quantum system. As a stream learning approach, we present a deep
reinforcement learning on streaming data from a continuously measured qubit at
the presence of detuning, dephasing, and relaxation. We also investigate how
the agent adapts to another quantum noise pattern by transfer learning. Stream
learning provides a better understanding of closed-loop quantum control, which
may pave the way for advanced quantum technologies.

Learning in a lifelong setting, where the dynamics continually evolve, is a
hard challenge for current reinforcement learning algorithms. Yet this would be
a much needed feature for practical applications. In this paper, we propose an
approach which learns a hyper-policy, whose input is time, that outputs the
parameters of the policy to be queried at that time. This hyper-policy is
trained to maximize the estimated future performance, efficiently reusing past
data by means of importance sampling, at the cost of introducing a controlled
bias. We combine the future performance estimate with the past performance to
mitigate catastrophic forgetting. To avoid overfitting the collected data, we
derive a differentiable variance bound that we embed as a penalization term.
Finally, we empirically validate our approach, in comparison with
state-of-the-art algorithms, on realistic environments, including water
resource management and trading.

As a core technology of the autonomous driving system, pedestrian trajectory
prediction can significantly enhance the function of active vehicle safety and
reduce road traffic injuries. In traffic scenes, when encountering with
oncoming people, pedestrians may make sudden turns or stop immediately, which
often leads to complicated trajectories. To predict such unpredictable
trajectories, we can gain insights into the interaction between pedestrians. In
this paper, we present a novel generative method named Spatial Interaction
Transformer (SIT), which learns the spatio-temporal correlation of pedestrian
trajectories through attention mechanisms. Furthermore, we introduce the
conditional variational autoencoder (CVAE) framework to model the future latent
motion states of pedestrians. In particular, the experiments based on
large-scale trafc dataset nuScenes [2] show that SIT has an outstanding
performance than state-of-the-art (SOTA) methods. Experimental evaluation on
the challenging ETH and UCY datasets conrms the robustness of our proposed
model

Automatic vulnerability detection on C/C++ source code has benefitted from
the introduction of machine learning to the field, with many recent
publications considering this combination. In contrast, assembly language or
machine code artifacts receive little attention, although there are compelling
reasons to study them. They are more representative of what is executed, more
easily incorporated in dynamic analysis and in the case of closed-source code,
there is no alternative. We propose ROMEO, a publicly available, reproducible
and reusable binary vulnerability detection benchmark dataset derived from the
Juliet test suite. Alongside, we introduce a simple text-based assembly
language representation that includes context for function-spanning
vulnerability detection and semantics to detect high-level vulnerabilities.
Finally, we show that this representation, combined with an off-the-shelf
classifier, compares favorably to state-of-the-art methods, including those
operating on the full C/C++ code.

Coherent WaveBurst is a generic, multidetector gravitational wave burst
search based on the excess power approach. The coherent WaveBurst algorithm
currently employed in the all-sky short-duration gravitational wave burst
search uses a conditional approach on selected attributes in the
multidimensional event attribute space to distinguish between noisy events from
that of astrophysical origin. We have been developing a supervised machine
learning approach based on the Gaussian mixture modeling to model the attribute
space for signals as well as noise events to enhance the probability of burst
detection [Gayathri et al.Phys. Rev. D 102, 104023 (2020)]. We further extend
the GMM approach to the all-sky short-duration coherent WaveBurst search as a
postprocessing step on events from the first half of the third observing run
(O3a). We show an improvement in sensitivity to generic gravitational wave
burst signal morphologies as well as the astrophysical source such as
core-collapse supernova models due to the application of our Gaussian mixture
model approach to coherent WaveBurst triggers. The Gaussian mixture model
method recovers the gravitational wave signals from massive compact binary
coalescences identified by coherent WaveBurst targeted for binary black holes
in GWTC-2, with better significance than the all-sky coherent WaveBurst search.
No additional significant gravitational wave bursts are observed.

Large pretrained language models (LMs) have become the central building block
of many NLP applications. Training these models requires ever more
computational resources and most of the existing models are trained on English
text only. It is exceedingly expensive to train these models in other
languages. To alleviate this problem, we introduce a novel method -- called
WECHSEL -- to efficiently and effectively transfer pretrained LMs to new
languages. WECHSEL can be applied to any model which uses subword-based
tokenization and learns an embedding for each subword. The tokenizer of the
source model (in English) is replaced with a tokenizer in the target language
and token embeddings are initialized such that they are semantically similar to
the English tokens by utilizing multilingual static word embeddings covering
English and the target language. We use WECHSEL to transfer the English RoBERTa
and GPT-2 models to four languages (French, German, Chinese and Swahili). We
also study the benefits of our method on very low-resource languages. WECHSEL
improves over proposed methods for cross-lingual parameter transfer and
outperforms models of comparable size trained from scratch with up to 64x less
training effort. Our method makes training large language models for new
languages more accessible and less damaging to the environment. We make our
code and models publicly available.

We introduce an end-to-end learning framework for image-to-image composition,
aiming to seamlessly compose an object represented as a cropped patch from an
object image into a background scene image. As our approach emphasizes more on
semantic and structural coherence of the composed images, rather than their
pixel-level RGB accuracies, we tailor the input and output of our network with
structure-aware features and design our network losses accordingly, with ground
truth established in a self-supervised setting through the object cropping.
Specifically, our network takes the semantic layout features from the input
scene image, features encoded from the edges and silhouette in the input object
patch, as well as a latent code as inputs, and generates a 2D spatial affine
transform defining the translation and scaling of the object patch. The learned
parameters are further fed into a differentiable spatial transformer network to
transform the object patch into the target image, where our model is trained
adversarially using an affine transform discriminator and a layout
discriminator. We evaluate our network, coined SAC-GAN, for various image
composition scenarios in terms of quality, composability, and generalizability
of the composite images. Comparisons are made to state-of-the-art alternatives,
including Instance Insertion, ST-GAN, CompGAN and PlaceNet, confirming

The quality of face images significantly influences the performance of
underlying face recognition algorithms. Face image quality assessment (FIQA)
estimates the utility of the captured image in achieving reliable and accurate
recognition performance. In this work, we propose a novel learning paradigm
that learns internal network observations during the training process. Based on
that, our proposed CR-FIQA uses this paradigm to estimate the face image
quality of a sample by predicting its relative classifiability. This
classifiability is measured based on the allocation of the training sample
feature representation in angular space with respect to its class center and
the nearest negative class center. We experimentally illustrate the correlation
between the face image quality and the sample relative classifiability. As such
property is only observable for the training dataset, we propose to learn this
property from the training dataset and utilize it to predict the quality
measure on unseen samples. This training is performed simultaneously while
optimizing the class centers by an angular margin penalty-based softmax loss
used for face recognition model training. Through extensive evaluation
experiments on eight benchmarks and four face recognition models, we
demonstrate the superiority of our proposed CR-FIQA over state-of-the-art
(SOTA) FIQA algorithms.

Quantum computers promise to surpass the most powerful classical
supercomputers when it comes to solving many critically important practical
problems, such as pharmaceutical and fertilizer design, supply chain and
traffic optimization, or optimization for machine learning tasks. Because
quantum computers function fundamentally differently from classical computers,
the emergence of quantum computing technology will lead to a new evolutionary
branch of statistical and data analytics methodologies. This review provides an
introduction to quantum computing designed to be accessible to statisticians
and data scientists, aiming to equip them with an overarching framework of
quantum computing, the basic language and building blocks of quantum
algorithms, and an overview of existing quantum applications in statistics and
data analysis. Our goal is to enable statisticians and data scientists to
follow quantum computing literature relevant to their fields, to collaborate
with quantum algorithm designers, and, ultimately, to bring forth the next
generation of statistical and data analytics tools.

The generalisation performance of a convolutional neural network (CNN) is
influenced by the quantity, quality, and variety of the training images.
Training images must be annotated, and this is time consuming and expensive.
The goal of our work was to reduce the number of annotated images needed to
train a CNN while maintaining its performance. We hypothesised that the
performance of a CNN can be improved faster by ensuring that the set of
training images contains a large fraction of hard-to-classify images. The
objective of our study was to test this hypothesis with an active learning
method that can automatically select the hard-to-classify images. We developed
an active learning method for Mask Region-based CNN (Mask R-CNN) and named this
method MaskAL. MaskAL involved the iterative training of Mask R-CNN, after
which the trained model was used to select a set of unlabelled images about
which the model was most uncertain. The selected images were then annotated and
used to retrain Mask R-CNN, and this was repeated for a number of sampling
iterations. In our study, MaskAL was compared to a random sampling method on a
broccoli dataset with five visually similar classes. MaskAL performed
significantly better than the random sampling. In addition, MaskAL had the same
performance after sampling 900 images as the random sampling had after 2300
images. Compared to a Mask R-CNN model that was trained on the entire training
set (14,000 images), MaskAL achieved 93.9% of that model's performance with
17.9% of its training data. The random sampling achieved 81.9% of that model's
performance with 16.4% of its training data. We conclude that by using MaskAL,
the annotation effort can be reduced for training Mask R-CNN on a broccoli
dataset with visually similar classes. Our software is available on
https://github.com/pieterblok/maskal.

As neural networks (NNs) are increasingly introduced into safety-critical
domains, there is a growing need to formally verify NNs before deployment. In
this work we focus on the formal verification problem of NN equivalence which
aims to prove that two NNs (e.g. an original and a compressed version) show
equivalent behavior. Two approaches have been proposed for this problem: Mixed
integer linear programming and interval propagation. While the first approach
lacks scalability, the latter is only suitable for structurally similar NNs
with small weight changes.
  The contribution of our paper has four parts. First, we show a theoretical
result by proving that the epsilon-equivalence problem is coNP-complete.
Secondly, we extend Tran et al.'s single NN geometric path enumeration
algorithm to a setting with multiple NNs. In a third step, we implement the
extended algorithm for equivalence verification and evaluate optimizations
necessary for its practical use. Finally, we perform a comparative evaluation
showing use-cases where our approach outperforms the previous state of the art,
both, for equivalence verification as well as for counter-example finding.

$k$-means and $k$-median clustering are powerful unsupervised machine
learning techniques. However, due to complicated dependences on all the
features, it is challenging to interpret the resulting cluster assignments.
Moshkovitz, Dasgupta, Rashtchian, and Frost [ICML 2020] proposed an elegant
model of explainable $k$-means and $k$-median clustering. In this model, a
decision tree with $k$ leaves provides a straightforward characterization of
the data set into clusters.
  We study two natural algorithmic questions about explainable clustering. (1)
For a given clustering, how to find the "best explanation" by using a decision
tree with $k$ leaves? (2) For a given set of points, how to find a decision
tree with $k$ leaves minimizing the $k$-means/median objective of the resulting
explainable clustering? To address the first question, we introduce a new model
of explainable clustering. Our model, inspired by the notion of outliers in
robust statistics, is the following. We are seeking a small number of points
(outliers) whose removal makes the existing clustering well-explainable. For
addressing the second question, we initiate the study of the model of
Moshkovitz et al. from the perspective of multivariate complexity. Our rigorous
algorithmic analysis sheds some light on the influence of parameters like the
input size, dimension of the data, the number of outliers, the number of
clusters, and the approximation ratio, on the computational complexity of
explainable clustering.

In many numerical simulations stochastic gradient descent (SGD) type
optimization methods perform very effectively in the training of deep neural
networks (DNNs) but till this day it remains an open problem of research to
provide a mathematical convergence analysis which rigorously explains the
success of SGD type optimization methods in the training of DNNs. In this work
we study SGD type optimization methods in the training of fully-connected
feedforward DNNs with rectified linear unit (ReLU) activation. We first
establish general regularity properties for the risk functions and their
generalized gradient functions appearing in the training of such DNNs and,
thereafter, we investigate the plain vanilla SGD optimization method in the
training of such DNNs under the assumption that the target function under
consideration is a constant function. Specifically, we prove under the
assumption that the learning rates (the step sizes of the SGD optimization
method) are sufficiently small but not $L^1$-summable and under the assumption
that the target function is a constant function that the expectation of the
riskof the considered SGD process converges in the training of such DNNs to
zero as the number of SGD steps increases to infinity.

Multi-label learning in the presence of missing labels (MLML) is a
challenging problem. Existing methods mainly focus on the design of network
structures or training schemes, which increase the complexity of
implementation. This work seeks to fulfill the potential of loss function in
MLML without increasing the procedure and complexity. Toward this end, we
propose two simple yet effective methods via robust loss design based on an
observation that a model can identify missing labels during training with a
high precision. The first is a novel robust loss for negatives, namely the Hill
loss, which re-weights negatives in the shape of a hill to alleviate the effect
of false negatives. The second is a self-paced loss correction (SPLC) method,
which uses a loss derived from the maximum likelihood criterion under an
approximate distribution of missing labels. Comprehensive experiments on a vast
range of multi-label image classification datasets demonstrate that our methods
can remarkably boost the performance of MLML and achieve new state-of-the-art
loss functions in MLML.

The exponential rise in wearable sensors has garnered significant interest in
assessing the physiological parameters during day-to-day activities.
Respiration rate is one of the vital parameters used in the performance
assessment of lifestyle activities. However, obtrusive setup for measurement,
motion artifacts, and other noises complicate the process. This paper presents
a multitasking architecture based on Deep Learning (DL) for estimating
instantaneous and average respiration rate from ECG and accelerometer signals,
such that it performs efficiently under daily living activities like cycling,
walking, etc. The multitasking network consists of a combination of
Encoder-Decoder and Encoder-IncResNet, to fetch the average respiration rate
and the respiration signal. The respiration signal can be leveraged to obtain
the breathing peaks and instantaneous breathing cycles. Mean absolute
error(MAE), Root mean square error (RMSE), inference time, and parameter count
analysis has been used to compare the network with the current state of art
Machine Learning (ML) model and other DL models developed in previous studies.
Other DL configurations based on a variety of inputs are also developed as a
part of the work. The proposed model showed better overall accuracy and gave
better results than individual modalities during different activities.

Deep learning has been utilized for the statistical downscaling of climate
data. Specifically, a two-dimensional (2D) convolutional neural network (CNN)
has been successfully applied to precipitation estimation. This study
implements a three-dimensional (3D) CNN to estimate watershed-scale daily
precipitation from 3D atmospheric data and compares the results with those for
a 2D CNN. The 2D CNN is extended along the time direction (3D-CNN-Time) and the
vertical direction (3D-CNN-Vert). The precipitation estimates of these extended
CNNs are compared with those of the 2D CNN in terms of the root-mean-square
error (RMSE), Nash-Sutcliffe efficiency (NSE), and 99th percentile RMSE. It is
found that both 3D-CNN-Time and 3D-CNN-Vert improve the model accuracy for
precipitation estimation compared to the 2D CNN. 3D-CNN-Vert provided the best
estimates during the training and test periods in terms of RMSE and NSE.

Adoption of recently developed methods from machine learning has given rise
to creation of drug-discovery knowledge graphs (KG) that utilize the
interconnected nature of the domain. Graph-based modelling of the data,
combined with KG embedding (KGE) methods, are promising as they provide a more
intuitive representation and are suitable for inference tasks such as
predicting missing links. One common application is to produce ranked lists of
genes for a given disease, where the rank is based on the perceived likelihood
of association between the gene and the disease. It is thus critical that these
predictions are not only pertinent but also biologically meaningful. However,
KGs can be biased either directly due to the underlying data sources that are
integrated or due to modeling choices in the construction of the graph, one
consequence of which is that certain entities can get topologically
overrepresented. We demonstrate the effect of these inherent structural
imbalances, resulting in densely-connected entities being highly ranked no
matter the context. We provide support for this observation across different
datasets, models as well as predictive tasks. Further, we present various graph
perturbation experiments which yield more support to the observation that KGE
models can be more influenced by the frequency of entities rather than any
biological information encoded within the relations. Our results highlight the
importance of data modeling choices, and emphasizes the need for practitioners
to be mindful of these issues when interpreting model outputs and during KG
composition.

Bloom Filters are a fundamental and pervasive data structure. Within the
growing area of Learned Data Structures, several Learned versions of Bloom
Filters have been considered, yielding advantages over classic Filters. Each of
them uses a classifier, which is the Learned part of the data structure.
Although it has a central role in those new filters, and its space footprint as
well as classification time may affect the performance of the Learned Filter,
no systematic study of which specific classifier to use in which circumstances
is available. We report progress in this area here, providing also initial
guidelines on which classifier to choose among five classic classification
paradigms.

HiClass is an open-source Python library for local hierarchical
classification entirely compatible with scikit-learn. It contains
implementations of the most common design patterns for hierarchical machine
learning models found in the literature, i.e., the local classifiers per node,
per parent node and per level. Additionally, the package contains
implementations of hierarchical metrics, which are more appropriate for
evaluating classification performance on hierarchical data. The documentation
includes installation and usage instructions, examples within tutorials and
interactive notebooks, and a complete description of the API. HiClass is
released under the simplified BSD license, encouraging its use in both academic
and commercial environments. Source code and documentation are available at
https://github.com/mirand863/hiclass.

Group synchronization refers to estimating a collection of group elements
from the noisy pairwise measurements. Such a nonconvex problem has received
much attention from numerous scientific fields including computer vision,
robotics, and cryo-electron microscopy. In this paper, we focus on the
orthogonal group synchronization problem with general additive noise models
under incomplete measurements, which is much more general than the commonly
considered setting of complete measurements. Characterizations of the
orthogonal group synchronization problem are given from perspectives of
optimality conditions as well as fixed points of the projected gradient ascent
method which is also known as the generalized power method (GPM). It is well
worth noting that these results still hold even without generative models. In
the meantime, we derive the local error bound property for the orthogonal group
synchronization problem which is useful for the convergence rate analysis of
different algorithms and can be of independent interest. Finally, we prove the
linear convergence result of the GPM to a global maximizer under a general
additive noise model based on the established local error bound property. Our
theoretical convergence result holds under several deterministic conditions
which can cover certain cases with adversarial noise, and as an example we
specialize it to the setting of the Erd\"os-R\'enyi measurement graph and
Gaussian noise.

Glioblastomas are the most aggressive fast-growing primary brain cancer which
originate in the glial cells of the brain. Accurate identification of the
malignant brain tumor and its sub-regions is still one of the most challenging
problems in medical image segmentation. The Brain Tumor Segmentation Challenge
(BraTS) has been a popular benchmark for automatic brain glioblastomas
segmentation algorithms since its initiation. In this year, BraTS 2021
challenge provides the largest multi-parametric (mpMRI) dataset of 2,000
pre-operative patients. In this paper, we propose a new aggregation of two deep
learning frameworks namely, DeepSeg and nnU-Net for automatic glioblastoma
recognition in pre-operative mpMRI. Our ensemble method obtains Dice similarity
scores of 92.00, 87.33, and 84.10 and Hausdorff Distances of 3.81, 8.91, and
16.02 for the enhancing tumor, tumor core, and whole tumor regions,
respectively, on the BraTS 2021 validation set, ranking us among the top ten
teams. These experimental findings provide evidence that it can be readily
applied clinically and thereby aiding in the brain cancer prognosis, therapy
planning, and therapy response monitoring. A docker image for reproducing our
segmentation results is available online at
(https://hub.docker.com/r/razeineldin/deepseg21).

We present a quantitatively accurate machine-learning (ML) model for the
computational prediction of core-electron binding energies, from which x-ray
photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model
combines density functional theory (DFT) with $GW$ and uses kernel ridge
regression for the ML predictions. We apply the new approach to materials and
molecules containing carbon, hydrogen and oxygen, and obtain qualitative and
quantitative agreement with experiment, resolving spectral features within 0.1
eV of reference experimental spectra. The method only requires the user to
provide a structural model for the material under study to obtain an XPS
prediction within seconds. Our new tool is freely available online through the
XPS Prediction Server.

Language is an essential factor of emancipation. Unfortunately, most of the
more than 2,000 African languages are low-resourced. The community has recently
used machine translation to revive and strengthen several African languages.
However, the trained models are often bilingual, resulting in a potentially
exponential number of models to train and maintain to cover all possible
translation directions. Additionally, bilingual models do not leverage the
similarity between some of the languages. Consequently, multilingual neural
machine translation (NMT) is gaining considerable interest, especially for
low-resourced languages. Nevertheless, its adoption by the community is still
limited. This paper introduces English2Gbe, a multilingual NMT model capable of
translating from English to Ewe or Fon. Using the BLEU, CHRF, and TER scores
computed with the Sacrebleu (Post, 2018) package for reproducibility, we show
that English2Gbe outperforms bilingual models (English to Ewe and English to
Fon) and gives state-of-the-art results on the JW300 benchmark for Fon
established by Nekoto et al. (2020). We hope this work will contribute to the
massive adoption of Multilingual models inside the community. Our code is made
accessible from Github.

Quantum Information Technologies and a Practical Module is a new course we
launch at Shanghai Jiao Tong University targeting at the undergraduate students
who major in a variety of engineering disciplines. We develop a holistic
curriculum for quantum computing covering the quantum hardware, quantum
algorithms and applications. The quantum computing approaches include the
universal digital quantum computing, analog quantum computing and the hybrid
quantum-classical variational quantum computing that is tailored to the noisy
intermediate-scale quantum (NISQ) technologies nowadays. Besides, we set a
practical module to bring student closer to the real industry needs. The
students would form a team of three to use any quantum approach to solve a
problem in fields like optimization, finance, machine learning, chemistry and
biology. Further, this course is selected into the Jiao Tong Global Virtual
Classroom Initiative, so that it is open to global students in Association of
Pacific Rim Universities at the same time with the offline students, in a
specifically updated classroom. The efforts in curriculum development,
practical module setting and blended learning make this course a good case
study for education on quantum sciences and technologies.

Weather forecast plays an essential role in multiple aspects of the daily
life of human beings. Currently, physics based numerical weather prediction is
used to predict the weather and requires enormous amount of computational
resources. In recent years, deep learning based models have seen wide success
in many weather-prediction related tasks. In this paper we describe our
experiments for the Weather4cast 2021 Challenge, where 8 hours of
spatio-temporal weather data is predicted based on an initial one hour of
spatio-temporal data. We focus on SmaAt-UNet, an efficient U-Net based
autoencoder. With this model we achieve competent results whilst maintaining
low computational resources. Furthermore, several approaches and possible
future work is discussed at the end of the paper.

The 3D LiDAR place recognition aims to estimate a coarse localization in a
previously seen environment based on a single scan from a rotating 3D LiDAR
sensor. The existing solutions to this problem include hand-crafted point cloud
descriptors (e.g., ScanContext, M2DP, LiDAR IRIS) and deep learning-based
solutions (e.g., PointNetVLAD, PCAN, LPDNet, DAGC, MinkLoc3D), which are often
only evaluated on accumulated 2D scans from the Oxford RobotCar dataset. We
introduce MinkLoc3D-SI, a sparse convolution-based solution that utilizes
spherical coordinates of 3D points and processes the intensity of 3D LiDAR
measurements, improving the performance when a single 3D LiDAR scan is used.
Our method integrates the improvements typical for hand-crafted descriptors
(like ScanContext) with the most efficient 3D sparse convolutions (MinkLoc3D).
Our experiments show improved results on single scans from 3D LiDARs (USyd
Campus dataset) and great generalization ability (KITTI dataset). Using
intensity information on accumulated 2D scans (RobotCar Intensity dataset)
improves the performance, even though spherical representation doesn't produce
a noticeable improvement. As a result, MinkLoc3D-SI is suited for single scans
obtained from a 3D LiDAR, making it applicable in autonomous vehicles.

Graph neural networks (GNNs) have been used to tackle the few-shot learning
(FSL) problem and shown great potentials under the transductive setting.
However under the inductive setting, existing GNN based methods are less
competitive. This is because they use an instance GNN as a label
propagation/classification module, which is jointly meta-learned with a feature
embedding network. This design is problematic because the classifier needs to
adapt quickly to new tasks while the embedding does not. To overcome this
problem, in this paper we propose a novel hybrid GNN (HGNN) model consisting of
two GNNs, an instance GNN and a prototype GNN. Instead of label propagation,
they act as feature embedding adaptation modules for quick adaptation of the
meta-learned feature embedding to new tasks. Importantly they are designed to
deal with a fundamental yet often neglected challenge in FSL, that is, with
only a handful of shots per class, any few-shot classifier would be sensitive
to badly sampled shots which are either outliers or can cause inter-class
distribution overlapping. %Our two GNNs are designed to address these two types
of poorly sampled few-shots respectively and their complementarity is exploited
in the hybrid GNN model. Extensive experiments show that our HGNN obtains new
state-of-the-art on three FSL benchmarks.

One of the challenges of logo recognition lies in the diversity of forms,
such as symbols, texts or a combination of both; further, logos tend to be
extremely concise in design while similar in appearance, suggesting the
difficulty of learning discriminative representations. To investigate the
variety and representation of logo, we introduced Makeup216, the largest and
most complex logo dataset in the field of makeup, captured from the real world.
It comprises of 216 logos and 157 brands, including 10,019 images and 37,018
annotated logo objects. In addition, we found that the marginal background
around the pure logo can provide a important context information and proposed
an adversarial attention representation framework (AAR) to attend on the logo
subject and auxiliary marginal background separately, which can be combined for
better representation. Our proposed framework achieved competitive results on
Makeup216 and another large-scale open logo dataset, which could provide fresh
thinking for logo recognition. The dataset of Makeup216 and the code of the
proposed framework will be released soon.

We give a complete characterisation of families of probability distributions
that are invariant under the action of ReLU neural network layers. The need for
such families arises during the training of Bayesian networks or the analysis
of trained neural networks, e.g., in the context of uncertainty quantification
(UQ) or explainable artificial intelligence (XAI). We prove that no invariant
parametrised family of distributions can exist unless at least one of the
following three restrictions holds: First, the network layers have a width of
one, which is unreasonable for practical neural networks. Second, the
probability measures in the family have finite support, which basically amounts
to sampling distributions. Third, the parametrisation of the family is not
locally Lipschitz continuous, which excludes all computationally feasible
families. Finally, we show that these restrictions are individually necessary.
For each of the three cases we can construct an invariant family exploiting
exactly one of the restrictions but not the other two.

In many applications of aerial/satellite image analysis (remote sensing), the
generation of exact shapes of objects is a cumbersome task. In most remote
sensing applications such as counting objects requires only location estimation
of objects. Hence, locating object centroids in aerial/satellite images is an
easy solution for tasks where the object's exact shape is not necessary. Thus,
this study focuses on assessing the feasibility of using deep neural networks
for locating object centroids in satellite images. Name of our model is
Centroid-UNet. The Centroid-UNet model is based on classic U-Net semantic
segmentation architecture. We modified and adapted the U-Net semantic
segmentation architecture into a centroid detection model preserving the
simplicity of the original model. Furthermore, we have tested and evaluated our
model with two case studies involving aerial/satellite images. Those two case
studies are building centroid detection case study and coconut tree centroid
detection case study. Our evaluation results have reached comparably good
accuracy compared to other methods, and also offer simplicity. The code and
models developed under this study are also available in the Centroid-UNet
GitHub repository: https://github.com/gicait/centroid-unet

Existing human mobility forecasting models follow the standard design of the
time-series prediction model which takes a series of numerical values as input
to generate a numerical value as a prediction. Although treating this as a
regression problem seems straightforward, incorporating various contextual
information such as the semantic category information of each Place-of-Interest
(POI) is a necessary step, and often the bottleneck, in designing an effective
mobility prediction model. As opposed to the typical approach, we treat
forecasting as a translation problem and propose a novel forecasting through a
language generation pipeline. The paper aims to address the human mobility
forecasting problem as a language translation task in a sequence-to-sequence
manner. A mobility-to-language template is first introduced to describe the
numerical mobility data as natural language sentences. The core intuition of
the human mobility forecasting translation task is to convert the input
mobility description sentences into a future mobility description from which
the prediction target can be obtained. Under this pipeline, a two-branch
network, SHIFT (Translating Human Mobility Forecasting), is designed.
Specifically, it consists of one main branch for language generation and one
auxiliary branch to directly learn mobility patterns. During the training, we
develop a momentum mode for better connecting and training the two branches.
Extensive experiments on three real-world datasets demonstrate that the
proposed SHIFT is effective and presents a new revolutionary approach to
forecasting human mobility.

We consider a multi-armed bandit setting where, at the beginning of each
round, the learner receives noisy independent, and possibly biased,
\emph{evaluations} of the true reward of each arm and it selects $K$ arms with
the objective of accumulating as much reward as possible over $T$ rounds. Under
the assumption that at each round the true reward of each arm is drawn from a
fixed distribution, we derive different algorithmic approaches and theoretical
guarantees depending on how the evaluations are generated. First, we show a
$\widetilde{O}(T^{2/3})$ regret in the general case when the observation
functions are a genearalized linear function of the true rewards. On the other
hand, we show that an improved $\widetilde{O}(\sqrt{T})$ regret can be derived
when the observation functions are noisy linear functions of the true rewards.
Finally, we report an empirical validation that confirms our theoretical
findings, provides a thorough comparison to alternative approaches, and further
supports the interest of this setting in practice.

As the third-generation neural networks, Spiking Neural Networks (SNNs) have
great potential on neuromorphic hardware because of their high
energy-efficiency. However, Deep Spiking Reinforcement Learning (DSRL), i.e.,
the Reinforcement Learning (RL) based on SNNs, is still in its preliminary
stage due to the binary output and the non-differentiable property of the
spiking function. To address these issues, we propose a Deep Spiking Q-Network
(DSQN) in this paper. Specifically, we propose a directly-trained deep spiking
reinforcement learning architecture based on the Leaky Integrate-and-Fire (LIF)
neurons and Deep Q-Network (DQN). Then, we adapt a direct spiking learning
algorithm for the Deep Spiking Q-Network. We further demonstrate the advantages
of using LIF neurons in DSQN theoretically. Comprehensive experiments have been
conducted on 17 top-performing Atari games to compare our method with the
state-of-the-art conversion method. The experimental results demonstrate the
superiority of our method in terms of performance, stability, robustness and
energy-efficiency. To the best of our knowledge, our work is the first one to
achieve state-of-the-art performance on multiple Atari games with the
directly-trained SNN.

Learning continually from non-stationary data streams is a challenging
research topic of growing popularity in the last few years. Being able to
learn, adapt, and generalize continually in an efficient, effective, and
scalable way is fundamental for a sustainable development of Artificial
Intelligent systems. However, an agent-centric view of continual learning
requires learning directly from raw data, which limits the interaction between
independent agents, the efficiency, and the privacy of current approaches.
Instead, we argue that continual learning systems should exploit the
availability of compressed information in the form of trained models. In this
paper, we introduce and formalize a new paradigm named "Ex-Model Continual
Learning" (ExML), where an agent learns from a sequence of previously trained
models instead of raw data. We further contribute with three ex-model continual
learning algorithms and an empirical setting comprising three datasets (MNIST,
CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are
extensively tested. Finally, we highlight the peculiarities of the ex-model
paradigm and we point out interesting future research directions.

Current state-of-the-art NLP systems use large neural networks that require
lots of computational resources for training. Inspired by human knowledge
acquisition, researchers have proposed curriculum learning, - sequencing of
tasks (task-based curricula) or ordering and sampling of the datasets
(data-based curricula) that facilitate training. This work investigates the
benefits of data-based curriculum learning for large modern language models
such as BERT and T5. We experiment with various curricula based on a range of
complexity measures and different sampling strategies. Extensive experiments on
different NLP tasks show that curricula based on various complexity measures
rarely has any benefits while random sampling performs either as well or better
than curricula.

We present the shift-dimension of multipersistence modules and investigate
its algebraic properties. This gives rise to a new invariant of multigraded
modules over the multivariate polynomial ring arising from the hierarchical
stabilization of the zeroth total multigraded Betti number. We give a fast
algorithm for the computation of the shift-dimension of interval modules in the
bivariate case. We construct multipersistence contours that are parameterized
by multivariate functions and hence provide a rich class of feature maps for
machine learning tasks.

Fake news, misinformation, and unverifiable facts on social media platforms
propagate disharmony and affect society, especially when dealing with an
epidemic like COVID-19. The task of Fake News Detection aims to tackle the
effects of such misinformation by classifying news items as fake or real. In
this paper, we propose a novel approach that improves over the current
automatic fake news detection approaches by automatically gathering evidence
for each claim. Our approach extracts supporting evidence from the web articles
and then selects appropriate text to be treated as evidence sets. We use a
pre-trained summarizer on these evidence sets and then use the extracted
summary as supporting evidence to aid the classification task. Our experiments,
using both machine learning and deep learning-based methods, help perform an
extensive evaluation of our approach. The results show that our approach
outperforms the state-of-the-art methods in fake news detection to achieve an
F1-score of 99.25 over the dataset provided for the CONSTRAINT-2021 Shared
Task. We also release the augmented dataset, our code and models for any

Generative Adversarial Networks (GANs) with high computation costs, e.g.,
BigGAN and StyleGAN2, have achieved remarkable results in synthesizing high
resolution and diverse images with high fidelity from random noises. Reducing
the computation cost of GANs while keeping generating photo-realistic images is
an urgent and challenging field for their broad applications on computational
resource-limited devices. In this work, we propose a novel yet simple {\bf
D}iscriminator {\bf G}uided {\bf L}earning approach for compressing vanilla
{\bf GAN}, dubbed {\bf DGL-GAN}. Motivated by the phenomenon that the teacher
discriminator may contain some meaningful information, we transfer the
knowledge merely from the teacher discriminator via the adversarial function.
We show DGL-GAN is valid since empirically, learning from the teacher
discriminator could facilitate the performance of student GANs, verified by
extensive experimental findings. Furthermore, we propose a two-stage training
strategy for training DGL-GAN, which can largely stabilize its training process
and achieve superior performance when we apply DGL-GAN to compress the two most
representative large-scale vanilla GANs, i.e., StyleGAN2 and BigGAN.
Experiments show that DGL-GAN achieves state-of-the-art (SOTA) results on both
StyleGAN2 (FID 2.92 on FFHQ with nearly $1/3$ parameters of StyleGAN2) and
BigGAN (IS 93.29 and FID 9.92 on ImageNet with nearly $1/4$ parameters of
BigGAN) and also outperforms several existing vanilla GAN compression
techniques. Moreover, DGL-GAN is also effective in boosting the performance of
original uncompressed GANs, original uncompressed StyleGAN2 boosted with
DGL-GAN achieves FID 2.65 on FFHQ, which achieves a new state-of-the-art
performance. Code and models are available at
\url{https://github.com/yuesongtian/DGL-GAN}.

We present Native Chinese Reader (NCR), a new machine reading comprehension
(MRC) dataset with particularly long articles in both modern and classical
Chinese. NCR is collected from the exam questions for the Chinese course in
China's high schools, which are designed to evaluate the language proficiency
of native Chinese youth. Existing Chinese MRC datasets are either
domain-specific or focusing on short contexts of a few hundreds of characters
in modern Chinese only. By contrast, NCR contains 8390 documents with an
average length of 1024 characters covering a wide range of Chinese writing
styles, including modern articles, classical literature and classical poetry. A
total of 20477 questions on these documents also require strong reasoning
abilities and common sense to figure out the correct answers. We implemented
multiple baseline models using popular Chinese pre-trained models and
additionally launched an online competition using our dataset to examine the
limit of current methods. The best model achieves 59% test accuracy while human
evaluation shows an average accuracy of 79%, which indicates a significant
performance gap between current MRC models and native Chinese speakers. We
release the dataset at https://sites.google.com/view/native-chinese-reader/.

In this paper, we adopt the maximizing mutual information (MI) approach to
tackle the problem of unsupervised learning of binary hash codes for efficient
cross-modal retrieval. We proposed a novel method, dubbed Cross-Modal Info-Max
Hashing (CMIMH). First, to learn informative representations that can preserve
both intra- and inter-modal similarities, we leverage the recent advances in
estimating variational lower-bound of MI to maximize the MI between the binary
representations and input features and between binary representations of
different modalities. By jointly maximizing these MIs under the assumption that
the binary representations are modelled by multivariate Bernoulli
distributions, we can learn binary representations, which can preserve both
intra- and inter-modal similarities, effectively in a mini-batch manner with
gradient descent. Furthermore, we find out that trying to minimize the modality
gap by learning similar binary representations for the same instance from
different modalities could result in less informative representations. Hence,
balancing between reducing the modality gap and losing modality-private
information is important for the cross-modal retrieval tasks. Quantitative
evaluations on standard benchmark datasets demonstrate that the proposed method
consistently outperforms other state-of-the-art cross-modal retrieval methods.

Radio Astronomy is undergoing a renaissance, as the next-generation of
instruments provides a massive leap forward in collecting area and therefore
raw sensitivity. However, to achieve this theoretical level of sensitivity in
the science data products we need to address the much more pernicious
systematic effects, which are the true limitation. These become all the more
significant when we consider that much of the time used by survey instruments,
such as the SKA, will be dedicated to deep surveys.
  CHILES is a deep HI survey of the COSMOS field, with 1,000 hours of VLA time.
We present our approach for creating the image cubes from the first Epoch, with
discussions of the methods and quantification of the data quality from 946 to
1420MHz -- a redshift range of 0.5 to 0. We layout the problems we had to solve
and describe how we tackled them. These are of importance as CHILES is the
first deep wideband multi-epoch HI survey and it has relevance for ongoing and
future surveys.
  We focus on the accumulated systematic errors in the imaging, as the goal is
to deliver a high-fidelity image that is only limited by the random thermal
errors. To understand and correct these systematic effects we ideally manage
them in the domain in which they arise, and that is predominately the
visibility domain. CHILES is a perfect test bed for many of the issues we can
expect for deep imaging with the SKA or ngVLA and we discuss the lessons we
have learned.

Brain-computer interface (BCI) is the technology that enables the
communication between humans and devices by reflecting status and intentions of
humans. When conducting imagined speech, the users imagine the pronunciation as
if actually speaking. In the case of decoding imagined speech-based EEG
signals, complex task can be conducted more intuitively, but decoding
performance is lower than that of other BCI paradigms. We modified our previous
model for decoding imagined speech-based EEG signals. Ten subjects participated
in the experiment. The average accuracy of our proposed method was 0.5648 for
classifying four words. In other words, our proposed method has significant
strength in learning local features. Hence, we demonstrated the feasibility of
decoding imagined speech-based EEG signals with robust performance.

Background and Objective: Advances in electron microscopy (EM) now allow
three-dimensional (3D) imaging of hundreds of micrometers of tissue with
nanometer-scale resolution, providing new opportunities to study the
ultrastructure of the brain. In this work, we introduce a freely available
Matlab-based gACSON software for visualization, segmentation, assessment, and
morphology analysis of myelinated axons in 3D-EM volumes of brain tissue
samples. Methods: The software is equipped with a graphical user interface
(GUI). It automatically segments the intra-axonal space of myelinated axons and
their corresponding myelin sheaths and allows manual segmentation,
proofreading, and interactive correction of the segmented components. gACSON
analyzes the morphology of myelinated axons, such as axonal diameter, axonal
eccentricity, myelin thickness, or g-ratio. Results: We illustrate the use of
the software by segmenting and analyzing myelinated axons in six 3D-EM volumes
of rat somatosensory cortex after sham surgery or traumatic brain injury (TBI).
Our results suggest that the equivalent diameter of myelinated axons in
somatosensory cortex was decreased in TBI animals five months after the injury.
Conclusions: Our results indicate that gACSON is a valuable tool for
visualization, segmentation, assessment, and morphology analysis of myelinated
axons in 3D-EM volumes. It is freely available at
https://github.com/AndreaBehan/g-ACSON under the MIT license.

Deep neural networks are effective feature extractors but they are
prohibitively large for deployment scenarios. Due to the huge number of
parameters, interpretability of parameters in different layers is not
straight-forward. This is why neural networks are sometimes considered black
boxes. Although simpler models are easier to explain, finding them is not easy.
If found, a sparse network that can fit to a data from scratch would help to
interpret parameters of a neural network. To this end, lottery ticket
hypothesis states that typical dense neural networks contain a small sparse
sub-network that can be trained to a reach similar test accuracy in an equal
number of steps. The goal of this work is to assess whether such a trainable
subnetwork exists for natural language models (NLM)s. To achieve this goal we
will review state-of-the-art compression techniques such as quantization,
knowledge distillation, and pruning.

Porosity has been identified as the key indicator of the durability
properties of concrete exposed to aggressive environments. This paper applies
ensemble learning to predict porosity of high-performance concrete containing
supplementary cementitious materials. The concrete samples utilized in this
study are characterized by eight composition features including w/b ratio,
binder content, fly ash, GGBS, superplasticizer, coarse/fine aggregate ratio,
curing condition and curing days. The assembled database consists of 240 data
records, featuring 74 unique concrete mixture designs. The proposed machine
learning algorithms are trained on 180 observations (75%) chosen randomly from
the data set and then tested on the remaining 60 observations (25%). The
numerical experiments suggest that the regression tree ensembles can accurately
predict the porosity of concrete from its mixture compositions. Gradient
boosting trees generally outperforms random forests in terms of prediction
accuracy. For random forests, the out-of-bag error based hyperparameter tuning
strategy is found to be much more efficient than k-Fold Cross-Validation.

Directly training spiking neural networks (SNNs) has remained challenging due
to complex neural dynamics and intrinsic non-differentiability in firing
functions. The well-known backpropagation through time (BPTT) algorithm
proposed to train SNNs suffers from large memory footprint and prohibits
backward and update unlocking, making it impossible to exploit the potential of
locally-supervised training methods. This work proposes an efficient and direct
training algorithm for SNNs that integrates a locally-supervised training
method with a temporally-truncated BPTT algorithm. The proposed algorithm
explores both temporal and spatial locality in BPTT and contributes to
significant reduction in computational cost including GPU memory utilization,
main memory access and arithmetic operations. We thoroughly explore the design
space concerning temporal truncation length and local training block size and
benchmark their impact on classification accuracy of different networks running
different types of tasks. The results reveal that temporal truncation has a
negative effect on the accuracy of classifying frame-based datasets, but leads
to improvement in accuracy on dynamic-vision-sensor (DVS) recorded datasets. In
spite of resulting information loss, local training is capable of alleviating
overfitting. The combined effect of temporal truncation and local training can
lead to the slowdown of accuracy drop and even improvement in accuracy. In
addition, training deep SNNs models such as AlexNet classifying CIFAR10-DVS
dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory,
10.79% reduction in memory access, and 99.64% reduction in MAC operations
compared to the standard end-to-end BPTT.

Many studies have discussed the difference in brain activity related to
encoding and retrieval of working memory (WM) tasks. However, it remains
unclear if there is a change in brain activation associated with re-encoding.
The main objective of this study was to compare different brain states (rest,
encoding, and re-encoding) during the WM task. We recorded brain activity from
thirty-seven participants using an electroencephalogram and calculated power
spectral density (PSD) and phase-locking value (PLV) for different frequencies.
In addition, the difference in phase-amplitude coupling (PAC) between encoding
and re-encoding was investigated. Our results showed that alpha PSD decreased
as the learning progressed, and theta PLV, beta PLV, and gamma PLV showed
differences between brain regions. Also, there was a statistically significant
difference in PAC. These findings suggest the possibility of improving the
efficiency of learning during re-encoding by understanding the differences in
neural correlation related to learning.

Multilingual speakers tend to alternate between languages within a
conversation, a phenomenon referred to as "code-switching" (CS). CS is a
complex phenomenon that not only encompasses linguistic challenges, but also
contains a great deal of complexity in terms of its dynamic behaviour across
speakers. This dynamic behaviour has been studied by sociologists and
psychologists, identifying factors affecting CS. In this paper, we provide an
empirical user study on Arabic-English CS, where we show the correlation
between users' CS frequency and character traits. We use machine learning (ML)
to validate the findings, informing and confirming existing theories. The
predictive models were able to predict users' CS frequency with an accuracy
higher than 55%, where travel experiences and personality traits played the
biggest role in the modeling process.

Sequential recommendation can capture user chronological preferences from
their historical behaviors, yet the learning of short sequences (cold-start
problem) in many benchmark datasets is still an open challenge. Recently, data
augmentation with pseudo-prior items generated by Transformers has drawn
considerable attention. These methods can generate pseudo-prior items
sequentially in reverse chronological order to extend the original sequences.
Nevertheless, the performance may still dramatically degrade in very short
sequences; most notably, the generation of pseudo-prior items does not take
into account the forward direction (from the past to the future), and so the
underlying temporal correlations are not preserved in terms of conditional
probabilities. Motivated by this, we propose a Bidirectional Chronological
Augmentation of Transformer (BiCAT) that uses a forward learning constraint in
the reverse generative process to capture contextual information more
effectively. Then, self-knowledge distillation is adopted in augmented and
original sequences to bridge the gap between data augmentation and model
representation, which enhances the robustness of sequence encoder. Moreover, an
informative positive and negative sampling strategy is proposed to accelerate
optimization and prevent overfitting. Extensive experiments on two popular
real-world datasets demonstrate the efficacy of our method on very short
sequences (L < 3) and long sequences (20 < L < 50) as well, our approach
outperforms state-of-the-art models by an average of 35.04% and 8.76%
respectively, in terms of Recall@5. Source code is available at
https://github.com/juyongjiang/BiCAT.

Heart disease is one of the significant challenges in today's world and one
of the leading causes of many deaths worldwide. Recent advancement of machine
learning (ML) application demonstrates that using electrocardiogram (ECG) and
patient data, detecting heart disease during the early stage is feasible.
However, both ECG and patient data are often imbalanced, which ultimately
raises a challenge for the traditional ML to perform unbiasedly. Over the
years, several data level and algorithm level solutions have been exposed by
many researchers and practitioners. To provide a broader view of the existing
literature, this study takes a systematic literature review (SLR) approach to
uncover the challenges associated with imbalanced data in heart diseases
predictions. Before that, we conducted a meta-analysis using 451 referenced
literature acquired from the reputed journals between 2012 and November 15,
2021. For in-depth analysis, 49 referenced literature has been considered and
studied, taking into account the following factors: heart disease type,
algorithms, applications, and solutions. Our SLR study revealed that the
current approaches encounter various open problems/issues when dealing with
imbalanced data, eventually hindering their practical applicability and
functionality.

Crime rate is increasing proportionally with the increasing rate of the
population. The most prominent approach was to introduce Closed-Circuit
Television (CCTV) camera-based surveillance to tackle the issue. Video
surveillance cameras have added a new dimension to detect crime. Several
research works on autonomous security camera surveillance are currently
ongoing, where the fundamental goal is to discover violent activity from video
feeds. From the technical viewpoint, this is a challenging problem because
analyzing a set of frames, i.e., videos in temporal dimension to detect
violence might need careful machine learning model training to reduce false
results. This research focuses on this problem by integrating state-of-the-art
Deep Learning methods to ensure a robust pipeline for autonomous surveillance
for detecting violent activities, e.g., kicking, punching, and slapping.
Initially, we designed a dataset of this specific interest, which contains 600
videos (200 for each action). Later, we have utilized existing pre-trained
model architectures to extract features, and later used deep learning network
for classification. Also, We have classified our models' accuracy, and
confusion matrix on different pre-trained architectures like VGG16,
InceptionV3, ResNet50, Xception and MobileNet V2 among which VGG16 and
MobileNet V2 performed better.

The ethics of Machine Learning has become an unavoidable topic in the AI
Community. The deployment of machine learning systems in multiple social
contexts has resulted in a closer ethical scrutiny of the design, development,
and application of these systems. The AI/ML community has come to terms with
the imperative to think about the ethical implications of machine learning, not
only as a product but also as a practice (Birhane, 2021; Shen et al. 2021). The
critical question that is troubling many debates is what can constitute an
ethically accountable machine learning system. In this paper we explore
possibilities for ethical evaluation of machine learning methodologies. We
scrutinize techniques, methods and technical practices in machine learning from
a relational ethics perspective, taking into consideration how machine learning
systems are part of the world and how they relate to different forms of agency.
Taking a page from Phil Agre (1997) we use the notion of a critical technical
practice as a means of analysis of machine learning approaches. Our radical
proposal is that supervised learning appears to be the only machine learning
method that is ethically defensible.

Deep discriminative models (DDMs), e.g. deep regression forests and deep
decision forests, have been extensively studied recently to solve problems such
as facial age estimation, head pose estimation, etc.. Due to a shortage of
well-labeled data that does not have noise and imbalanced distribution
problems, learning DDMs is always challenging. Existing methods usually tackle
these challenges through learning more discriminative features or re-weighting
samples. We argue that learning DDMs gradually, from easy to hard, is more
reasonable, for two reasons. First, this is more consistent with the cognitive
process of human beings. Second, noisy as well as underrepresented examples can
be distinguished by virtue of previously learned knowledge. Thus, we resort to
a gradual learning strategy -- self-paced learning (SPL). Then, a natural
question arises: can SPL lead DDMs to achieve more robust and less biased
solutions? To answer this question, this paper proposes a new SPL method: easy
and underrepresented examples first, for learning DDMs. This tackles the
fundamental ranking and selection problem in SPL from a new perspective:
fairness. Our idea is fundamental and can be easily combined with a variety of
DDMs. Extensive experimental results on three computer vision tasks, i.e.,
facial age estimation, head pose estimation, and gaze estimation, show our new
method gains considerable performance improvement in both accuracy and
fairness. Source code is available at https://github.com/learninginvision/SPU.

Deep reinforcement learning has enabled human-level or even super-human
performance in various types of games. However, the amount of exploration
required for learning is often quite large. Deep reinforcement learning also
has super-human performance in that no human being would be able to achieve
such amounts of exploration. To address this problem, we focus on the
\textit{satisficing} policy, which is a qualitatively different approach from
that of existing optimization algorithms. Thus, we propose Linear RS (LinRS),
which is a type of satisficing algorithm and a linear extension of
risk-sensitive satisficing (RS), for application to a wider range of tasks. The
generalization of RS provides an algorithm to reduce the volume of exploratory
actions by adopting a different approach from existing optimization algorithms.
LinRS utilizes linear regression and multiclass classification to linearly
approximate both the action value and proportion of action selections required
in the RS calculation. The results of our experiments indicate that LinRS
reduced the number of explorations and run time compared to those of existing
algorithms in contextual bandit problems. These results suggest that a further
generalization of satisficing algorithms may be useful for complex
environments, including those that are to be handled with deep reinforcement
learning.

Low-light image enhancement (LLE) remains challenging due to the unfavorable
prevailing low-contrast and weak-visibility problems of single RGB images. In
this paper, we respond to the intriguing learning-related question -- if
leveraging both accessible unpaired over/underexposed images and high-level
semantic guidance, can improve the performance of cutting-edge LLE models?
Here, we propose an effective semantically contrastive learning paradigm for
LLE (namely SCL-LLE). Beyond the existing LLE wisdom, it casts the image
enhancement task as multi-task joint learning, where LLE is converted into
three constraints of contrastive learning, semantic brightness consistency, and
feature preservation for simultaneously ensuring the exposure, texture, and
color consistency. SCL-LLE allows the LLE model to learn from unpaired
positives (normal-light)/negatives (over/underexposed), and enables it to
interact with the scene semantics to regularize the image enhancement network,
yet the interaction of high-level semantic knowledge and the low-level signal
prior is seldom investigated in previous methods. Training on readily available
open data, extensive experiments demonstrate that our method surpasses the
state-of-the-arts LLE models over six independent cross-scenes datasets.
Moreover, SCL-LLE's potential to benefit the downstream semantic segmentation
under extremely dark conditions is discussed. Source Code:
https://github.com/LingLIx/SCL-LLE.

We achieved contact-rich flexible object manipulation, which was difficult to
control with vision alone. In the unzipping task we chose as a validation task,
the gripper grasps the puller, which hides the bag state such as the direction
and amount of deformation behind it, making it difficult to obtain information
to perform the task by vision alone. Additionally, the flexible fabric bag
state constantly changes during operation, so the robot needs to dynamically
respond to the change. However, the appropriate robot behavior for all bag
states is difficult to prepare in advance. To solve this problem, we developed
a model that can perform contact-rich flexible object manipulation by real-time
prediction of vision with tactility. We introduced a point-based attention
mechanism for extracting image features, softmax transformation for predicting
motions, and convolutional neural network for extracting tactile features. The
results of experiments using a real robot arm revealed that our method can
realize motions responding to the deformation of the bag while reducing the
load on the zipper. Furthermore, using tactility improved the success rate from
56.7% to 93.3% compared with vision alone, demonstrating the effectiveness and
high performance of our method.

Data-centric AI is a new and exciting research topic in the AI community, but
many organizations already build and maintain various "data-centric"
applications whose goal is to produce high quality data. These range from
traditional business data processing applications (e.g., "how much should we
charge each of our customers this month?") to production ML systems such as
recommendation engines. The fields of data and ML engineering have arisen in
recent years to manage these applications, and both include many interesting
novel tools and processes. In this paper, we discuss several lessons from data
and ML engineering that could be interesting to apply in data-centric AI, based
on our experience building data and ML platforms that serve thousands of
applications at a range of organizations.

The detection of ancient settlements is a key focus in landscape archaeology.
Traditionally, settlements were identified through pedestrian survey, as
researchers physically traversed the landscape and recorded settlement
locations. Recently the manual identification and labeling of ancient remains
in satellite imagery have increased the scale of archaeological data
collection, but the process remains tremendously time-consuming and arduous.
The development of self-supervised learning (e.g., contrastive learning) offers
a scalable learning scheme in locating archaeological sites using unlabeled
satellite and historical aerial images. However, archaeology sites are only
present in a very small proportion of the whole landscape, while the modern
contrastive-supervised learning approach typically yield inferior performance
on the highly balanced dataset, such as identifying sparsely localized ancient
urbanization on a large area using satellite images. In this work, we propose a
framework to solve this long-tail problem. As opposed to the existing
contrastive learning approaches that typically treat the labeled and unlabeled
data separately, the proposed method reforms the learning paradigm under a
semi-supervised setting to fully utilize the precious annotated data (<7% in
our setting). Specifically, the highly unbalanced nature of the data is
employed as the prior knowledge to form pseudo negative pairs by ranking the
similarities between unannotated image patches and annotated anchor images. In
this study, we used 95,358 unlabeled images and 5,830 labeled images to solve
the problem of detecting ancient buildings from a long-tailed satellite image
dataset. From the results, our semi-supervised contrastive learning model
achieved a promising testing balanced accuracy of 79.0%, which is 3.8%
improvement over state-of-the-art approaches.

This paper presents a novel planning and control strategy for competing with
multiple vehicles in a car racing scenario. The proposed racing strategy
switches between two modes. When there are no surrounding vehicles, a
learning-based model predictive control (MPC) trajectory planner is used to
guarantee that the ego vehicle achieves better lap timing performance. When the
ego vehicle is competing with other surrounding vehicles to overtake, an
optimization-based planner generates multiple dynamically-feasible trajectories
through parallel computation. Each trajectory is optimized under a MPC
formulation with different homotopic Bezier-curve reference paths lying
laterally between surrounding vehicles. The time-optimal trajectory among these
different homotopic trajectories is selected and a low-level MPC controller
with control barrier function constraints for obstacle avoidance is used to
guarantee system's safety-critical performance. The proposed algorithm has the
capability to generate collision-free trajectories and track them while
enhancing the lap timing performance with steady low computational complexity,
outperforming existing approaches in both timing and performance for a
autonomous racing environment. To demonstrate the performance of our racing
strategy, we simulate with multiple randomly generated moving vehicles on the
track and test the ego vehicle's overtake maneuvers.

As details are missing in most representations of structures, the lack of
controllability to details is one of the major weaknesses in structure-based
controllable point cloud generation. It is observable that definitions of
details and structures are subjective. Details can be treated as structures on
small scales. To represent structures in different scales at the same time, we
present a graph-based representation of structures called the Multiscale
Structure Graph(MSG). Given structures in multiple scales, similar patterns of
local structures can be found at different scales, positions, and angles. The
knowledge learned from a local structure pattern shall be transferred to other
similar patterns. An encoding and generation mechanism, namely the Multiscale
Structure-based Point Cloud Generator(MSPCG) is proposed, which can
simultaneously learn point cloud generation from local patterns with
miscellaneous spatial properties. By editing the MSG, the proposed method
supports multiscale editions on point clouds. By generating point clouds from
local structures and learning simultaneously in multiple scales, our MSPCG has
better generalization ability and scalability. Trained on the ShapeNet, our
MSPCG is able to generate point clouds for unseen categories and generate
indoor scenes from a given structure. The experimental results show that our
method significantly outperforms baseline methods.

Brain-computer interface uses brain signals to communicate with external
devices without actual control. Many studies have been conducted to classify
motor imagery based on machine learning. However, classifying imagery data with
sparse spatial characteristics, such as single-arm motor imagery, remains a
challenge. In this paper, we proposed a method to factorize EEG signals into
two groups to classify motor imagery even if spatial features are sparse. Based
on adversarial learning, we focused on extracting common features of EEG
signals which are robust to noise and extracting only signal features. In
addition, class-specific features were extracted which are specialized for
class classification. Finally, the proposed method classifies the classes by
representing the features of the two groups as one embedding space. Through
experiments, we confirmed the feasibility that extracting features into two
groups is advantageous for datasets that contain sparse spatial features.

While generative adversarial networks (GAN) are popular for their higher
sample quality as opposed to other generative models like the variational
autoencoders (VAE) and Boltzmann machines, they suffer from the same difficulty
of the evaluation of generated samples. Various aspects must be kept in mind,
such as the quality of generated samples, the diversity of classes (within a
class and among classes), the use of disentangled latent spaces, agreement of
said evaluation metric with human perception, etc. In this paper, we propose a
new score, namely, GM Score, which takes into various factors such as sample
quality, disentangled representation, intra-class and inter-class diversity,
and other metrics such as precision, recall, and F1 score are employed for
discriminability of latent space of deep belief network (DBN) and restricted
Boltzmann machine (RBM). The evaluation is done for different GANs (GAN, DCGAN,
BiGAN, CGAN, CoupledGAN, LSGAN, SGAN, WGAN, and WGAN Improved) trained on the
benchmark MNIST dataset.

Figuring out the price of a listed Airbnb rental is an important and
difficult task for both the host and the customer. For the former, it can
enable them to set a reasonable price without compromising on their profits.
For the customer, it helps understand the key drivers for price and also
provides them with similarly priced places. This price prediction regression
task can also have multiple downstream uses, such as in recommendation of
similar rentals based on price. We propose to use geolocation, temporal, visual
and natural language features to create a reliable and accurate price
prediction algorithm.

A ubiquitous requirement in many practical reinforcement learning (RL)
applications, including medical treatment, recommendation system, education and
robotics, is that the deployed policy that actually interacts with the
environment cannot change frequently. Such an RL setting is called
low-switching-cost RL, i.e., achieving the highest reward while reducing the
number of policy switches during training. Despite the recent trend of
theoretical studies aiming to design provably efficient RL algorithms with low
switching costs, none of the existing approaches have been thoroughly evaluated
in popular RL testbeds. In this paper, we systematically studied a wide
collection of policy-switching approaches, including theoretically guided
criteria, policy-difference-based methods, and non-adaptive baselines. Through
extensive experiments on a medical treatment environment, the Atari games, and
robotic control tasks, we present the first empirical benchmark for
low-switching-cost RL and report novel findings on how to decrease the
switching cost while maintain a similar sample efficiency to the case without
the low-switching-cost constraint. We hope this benchmark could serve as a
starting point for developing more practically effective low-switching-cost RL
algorithms. We release our code and complete results in
https://sites.google.com/view/low-switching-cost-rl.

We consider the upper confidence bound strategy for Gaussian multi-armed
bandits with known control horizon sizes $N$ and build its limiting description
with a system of stochastic differential equations and ordinary differential
equations. Rewards for the arms are assumed to have unknown expected values and
known variances. A set of Monte-Carlo simulations was performed for the case of
close distributions of rewards, when mean rewards differ by the magnitude of
order $N^{-1/2}$, as it yields the highest normalized regret, to verify the
validity of the obtained description. The minimal size of the control horizon
when the normalized regret is not noticeably larger than maximum possible was
estimated.

Computational fluid dynamics (CFD) simulations are broadly applied in
engineering and physics. A standard description of fluid dynamics requires
solving the Navier-Stokes (N-S) equations in different flow regimes. However,
applications of CFD simulations are computationally-limited by the
availability, speed, and parallelism of high-performance computing. To improve
computational efficiency, machine learning techniques have been used to create
accelerated data-driven approximations for CFD. A majority of such approaches
rely on large labeled CFD datasets that are expensive to obtain at the scale
necessary to build robust data-driven models. We develop a weakly-supervised
approach to solve the steady-state N-S equations under various boundary
conditions, using a multi-channel input with boundary and geometric conditions.
We achieve state-of-the-art results without any labeled simulation data, but
using a custom data-driven and physics-informed loss function by using and
small-scale solutions to prime the model to solve the N-S equations. To improve
the resolution and predictability, we train stacked models of increasing
complexity generating the numerical solutions for N-S equations. Without
expensive computations, our model achieves high predictability with a variety
of obstacles and boundary conditions. Given its high flexibility, the model can
generate a solution on a 64 x 64 domain within 5 ms on a regular desktop
computer which is 1000 times faster than a regular CFD solver. Translation of
interactive CFD simulation on local consumer computing hardware enables new
applications in real-time predictions on the internet of things devices where
data transfer is prohibitive and can increase the scale, speed, and
computational cost of boundary-value fluid problems.

Recent learning-based lossless image compression methods encode an image in
the unit of subimages and achieve comparable performances to conventional
non-learning algorithms. However, these methods do not consider the performance
drop in the high-frequency region, giving equal consideration to the low and
high-frequency areas. In this paper, we propose a new lossless image
compression method that proceeds the encoding in a coarse-to-fine manner to
separate and process low and high-frequency regions differently. We initially
compress the low-frequency components and then use them as additional input for
encoding the remaining high-frequency region. The low-frequency components act
as a strong prior in this case, which leads to improved estimation in the
high-frequency area. In addition, we design the frequency decomposition process
to be adaptive to color channel, spatial location, and image characteristics.
As a result, our method derives an image-specific optimal ratio of
low/high-frequency components. Experiments show that the proposed method
achieves state-of-the-art performance for benchmark high-resolution datasets.

While in real life everyone behaves themselves at least to some extent, it is
much more difficult to expect people to behave themselves on the internet,
because there are few checks or consequences for posting something toxic to
others. Yet, for people on the other side, toxic texts often lead to serious
psychological consequences. Detecting such toxic texts is challenging. In this
paper, we attempt to build a toxicity detector using machine learning methods
including CNN, Naive Bayes model, as well as LSTM. While there has been
numerous groundwork laid by others, we aim to build models that provide higher
accuracy than the predecessors. We produced very high accuracy models using
LSTM and CNN, and compared them to the go-to solutions in language processing,
the Naive Bayes model. A word embedding approach is also applied to empower the
accuracy of our models.

We provide guarantees for approximate Gaussian Process (GP) regression
resulting from two common low-rank kernel approximations: based on random
Fourier features, and based on truncating the kernel's Mercer expansion. In
particular, we bound the Kullback-Leibler divergence between an exact GP and
one resulting from one of the afore-described low-rank approximations to its
kernel, as well as between their corresponding predictive densities, and we
also bound the error between predictive mean vectors and between predictive
covariance matrices computed using the exact versus using the approximate GP.
We provide experiments on both simulated data and standard benchmarks to
evaluate the effectiveness of our theoretical bounds.

Conversational Information Seeking (CIS) is a relatively new research area
within conversational AI that attempts to seek information from end-users in
order to understand and satisfy users' needs. If realized, such a system has
far-reaching benefits in the real world; for example, a CIS system can assist
clinicians in pre-screening or triaging patients in healthcare. A key open
sub-problem in CIS that remains unaddressed in the literature is generating
Information Seeking Questions (ISQs) based on a short initial query from the
end-user. To address this open problem, we propose Information SEEking Question
generator (ISEEQ), a novel approach for generating ISQs from just a short user
query, given a large text corpus relevant to the user query. Firstly, ISEEQ
uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the
knowledge-enriched query to retrieve relevant context passages to ask coherent
ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep
generative-adversarial reinforcement learning-based approach for generating
ISQs. We show that ISEEQ can generate high-quality ISQs to promote the
development of CIS agents. ISEEQ significantly outperforms comparable baselines
on five ISQ evaluation metrics across four datasets having user queries from
diverse domains. Further, we argue that ISEEQ is transferable across domains
for generating ISQs, as it shows the acceptable performance when trained and
tested on different pairs of domains. The qualitative human evaluation confirms
ISEEQ-generated ISQs are comparable in quality to human-generated questions and
outperform the best comparable baseline.

Data-centric AI is at the center of a fundamental shift in software
engineering where machine learning becomes the new software, powered by big
data and computing infrastructure. Here software engineering needs to be
re-thought where data becomes a first-class citizen on par with code. One
striking observation is that a significant portion of the machine learning
process is spent on data preparation. Without good data, even the best machine
learning algorithms cannot perform well. As a result, data-centric AI practices
are now becoming mainstream. Unfortunately, many datasets in the real world are
small, dirty, biased, and even poisoned. In this survey, we study the research
landscape for data collection and data quality primarily for deep learning
applications. Data collection is important because there is lesser need for
feature engineering for recent deep learning approaches, but instead more need
for large amounts of data. For data quality, we study data validation,
cleaning, and integration techniques. Even if the data cannot be fully cleaned,
we can still cope with imperfect data during model training using robust model
training techniques. In addition, while bias and fairness have been less
studied in traditional data management research, these issues become essential
topics in modern machine learning applications. We thus study fairness measures
and unfairness mitigation techniques that can be applied before, during, or
after model training. We believe that the data management community is well
poised to solve these problems.

Registration-based atlas building often poses computational challenges in
high-dimensional image spaces. In this paper, we introduce a novel hybrid atlas
building algorithm that fast estimates atlas from large-scale image datasets
with much reduced computational cost. In contrast to previous approaches that
iteratively perform registration tasks between an estimated atlas and
individual images, we propose to use learned priors of registration from
pre-trained neural networks. This newly developed hybrid framework features
several advantages of (i) providing an efficient way of atlas building without
losing the quality of results, and (ii) offering flexibility in utilizing a
wide variety of deep learning based registration methods. We demonstrate the
effectiveness of this proposed model on 3D brain magnetic resonance imaging
(MRI) scans.

In order to achieve reliable communication with a high data rate of massive
multiple-input multiple-output (MIMO) systems in frequency division duplex
(FDD) mode, the estimated channel state information (CSI) at the receiver needs
to be fed back to the transmitter. However, the feedback overhead becomes
exorbitant with the increasing number of antennas. In this paper, a two stages
low rank (TSLR) CSI feedback scheme for millimeter wave (mmWave) massive MIMO
systems is proposed to reduce the feedback overhead based on model-driven deep
learning. Besides, we design a deep iterative neural network, named FISTA-Net,
by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to
achieve more efficient CSI feedback. Moreover, a shrinkage thresholding network
(ST-Net) is designed in FISTA-Net based on the attention mechanism, which can
choose the threshold adaptively. Simulation results show that the proposed TSLR
CSI feedback scheme and FISTA-Net outperform the existing algorithms in various

Guided filter is a fundamental tool in computer vision and computer graphics
which aims to transfer structure information from guidance image to target
image. Most existing methods construct filter kernels from the guidance itself
without considering the mutual dependency between the guidance and the target.
However, since there typically exist significantly different edges in the two
images, simply transferring all structural information of the guidance to the
target would result in various artifacts. To cope with this problem, we propose
an effective framework named deep attentional guided image filtering, the
filtering process of which can fully integrate the complementary information
contained in both images. Specifically, we propose an attentional kernel
learning module to generate dual sets of filter kernels from the guidance and
the target, respectively, and then adaptively combine them by modeling the
pixel-wise dependency between the two images. Meanwhile, we propose a
multi-scale guided image filtering module to progressively generate the
filtering result with the constructed kernels in a coarse-to-fine manner.
Correspondingly, a multi-scale fusion strategy is introduced to reuse the
intermediate results in the coarse-to-fine process. Extensive experiments show
that the proposed framework compares favorably with the state-of-the-art
methods in a wide range of guided image filtering applications, such as guided
super-resolution, cross-modality restoration, texture removal, and semantic

Few-shot recognition aims to recognize novel categories under low-data
regimes. Some recent few-shot recognition methods introduce auxiliary semantic
modality, i.e., category attribute information, into representation learning,
which enhances the feature discrimination and improves the recognition
performance. Most of these existing methods only consider the attribute
information of support set while ignoring the query set, resulting in a
potential loss of performance. In this letter, we propose a novel
attribute-shaped learning (ASL) framework, which can jointly perform query
attributes generation and discriminative visual representation learning for
few-shot recognition. Specifically, a visual-attribute predictor (VAP) is
constructed to predict the attributes of queries. By leveraging the attributes
information, an attribute-visual attention module (AVAM) is designed, which can
adaptively utilize attributes and visual representations to learn more
discriminative features. Under the guidance of attribute modality, our method
can learn enhanced semantic-aware representation for classification.
Experiments demonstrate that our method can achieve competitive results on CUB
and SUN benchmarks. Our source code is available at:
\url{https://github.com/chenhaoxing/ASL}.

We present a novel mesh-based learning approach (N-Cloth) for plausible 3D
cloth deformation prediction. Our approach is general and can handle cloth or
obstacles represented by triangle meshes with arbitrary topologies. We use
graph convolution to transform the cloth and object meshes into a latent space
to reduce the non-linearity in the mesh space. Our network can predict the
target 3D cloth mesh deformation based on the initial state of the cloth mesh
template and the target obstacle mesh. Our approach can handle complex cloth
meshes with up to 100K triangles and scenes with various objects corresponding
to SMPL humans, non-SMPL humans or rigid bodies. In practice, our approach can
be used to generate plausible cloth simulation at 30-45 fps on an NVIDIA
GeForce RTX 3090 GPU. We highlight its benefits over prior learning-based
methods and physically-based cloth simulators.

Fully Homomorphic Encryption (FHE) allows arbitrarily complex computations on
encrypted data without ever needing to decrypt it, thus enabling us to maintain
data privacy on third-party systems. Unfortunately, sustaining deep
computations with FHE requires a periodic noise reduction step known as
bootstrapping. The cost of the bootstrapping operation is one of the primary
barriers to the wide-spread adoption of FHE. In this paper, we present an
in-depth architectural analysis of the bootstrapping step in FHE. First, we
observe that secure implementations of bootstrapping exhibit a low arithmetic
intensity (<1 Op/byte), require large caches (>100 MB), and are heavily bound
by the main memory bandwidth. Consequently, we demonstrate that existing
workloads observe marginal performance gains from the design of bespoke
high-throughput arithmetic units tailored to FHE. Second, we propose several
cache-friendly algorithmic optimizations that improve the throughput in FHE
bootstrapping by enabling up to 3.2x higher arithmetic intensity and 4.6x lower
memory bandwidth. Our optimizations apply to a wide range of structurally
similar computations such as private evaluation and training of machine
learning models. Finally, we incorporate these optimizations into an
architectural tool which, given a cache size, memory subsystem, the number of
functional units and a desired security level, selects optimal cryptosystem
parameters to maximize the bootstrapping throughput. Our optimized
bootstrapping implementation represents a best-case scenario for compute
acceleration of FHE. We show that despite these optimizations, bootstrapping
continues to be bottlenecked by main memory bandwidth. We propose new research
directions to address the underlying memory bottleneck. In summary, our answer
to the titular question is: yes, but only after addressing the memory
bottleneck!

We introduce PartGlot, a neural framework and associated architectures for
learning semantic part segmentation of 3D shape geometry, based solely on part
referential language. We exploit the fact that linguistic descriptions of a
shape can provide priors on the shape's parts -- as natural language has
evolved to reflect human perception of the compositional structure of objects,
essential to their recognition and use. For training, we use the paired
geometry / language data collected in the ShapeGlot work for their reference
game, where a speaker creates an utterance to differentiate a target shape from
two distractors and the listener has to find the target based on this
utterance. Our network is designed to solve this target discrimination problem,
carefully incorporating a Transformer-based attention module so that the output
attention can precisely highlight the semantic part or parts described in the
language. Furthermore, the network operates without any direct supervision on
the 3D geometry itself. Surprisingly, we further demonstrate that the learned
part information is generalizable to shape classes unseen during training. Our
approach opens the possibility of learning 3D shape parts from language alone,
without the need for large-scale part geometry annotations, thus facilitating
annotation acquisition.

This paper addresses the 3D point cloud reconstruction and 3D pose estimation
of the human hand from a single RGB image. To that end, we present a novel
pipeline for local and global point cloud reconstruction using a 3D hand
template while learning a latent representation for pose estimation. To
demonstrate our method, we introduce a new multi-view hand posture dataset to
obtain complete 3D point clouds of the hand in the real world. Experiments on
our newly proposed dataset and four public benchmarks demonstrate the model's
strengths. Our method outperforms competitors in 3D pose estimation while
reconstructing realistic-looking complete 3D hand point clouds.

Recently, graph neural networks (GNNs) have been widely used for document
classification. However, most existing methods are based on static word
co-occurrence graphs without sentence-level information, which poses three
challenges:(1) word ambiguity, (2) word synonymity, and (3) dynamic contextual
dependency. To address these challenges, we propose a novel GNN-based sparse
structure learning model for inductive document classification. Specifically, a
document-level graph is initially generated by a disjoint union of
sentence-level word co-occurrence graphs. Our model collects a set of trainable
edges connecting disjoint words between sentences and employs structure
learning to sparsely select edges with dynamic contextual dependencies. Graphs
with sparse structures can jointly exploit local and global contextual
information in documents through GNNs. For inductive learning, the refined
document graph is further fed into a general readout function for graph-level
classification and optimization in an end-to-end manner. Extensive experiments
on several real-world datasets demonstrate that the proposed model outperforms
most state-of-the-art results, and reveal the necessity to learn sparse
structures for each document.

The training and test data for deep-neural-network-based classifiers are
usually assumed to be sampled from the same distribution. When part of the test
samples are drawn from a distribution that is sufficiently far away from that
of the training samples (a.k.a. out-of-distribution (OOD) samples), the trained
neural network has a tendency to make high confidence predictions for these OOD
samples. Detection of the OOD samples is critical when training a neural
network used for image classification, object detection, etc. It can enhance
the classifier's robustness to irrelevant inputs, and improve the system
resilience and security under different forms of attacks. Detection of OOD
samples has three main challenges: (i) the proposed OOD detection method should
be compatible with various architectures of classifiers (e.g., DenseNet,
ResNet), without significantly increasing the model complexity and requirements
on computational resources; (ii) the OOD samples may come from multiple
distributions, whose class labels are commonly unavailable; (iii) a score
function needs to be defined to effectively separate OOD samples from
in-distribution (InD) samples. To overcome these challenges, we propose a
Wasserstein-based out-of-distribution detection (WOOD) method. The basic idea
is to define a Wasserstein-distance-based score that evaluates the
dissimilarity between a test sample and the distribution of InD samples. An
optimization problem is then formulated and solved based on the proposed score
function. The statistical learning bound of the proposed method is investigated
to guarantee that the loss value achieved by the empirical optimizer
approximates the global optimum. The comparison study results demonstrate that
the proposed WOOD consistently outperforms other existing OOD detection
methods.

Maximum likelihood estimation furnishes powerful insights into voting theory,
and the design of voting rules. However the MLE can usually be badly corrupted
by a single outlying sample. This means that a single voter or a group of
colluding voters can vote strategically and drastically affect the outcome.
Motivated by recent progress in algorithmic robust statistics, we revisit the
fundamental problem of estimating the central ranking in a Mallows model, but
ask for an estimator that is provably robust, unlike the MLE.
  Our main result is an efficiently computable estimator that achieves nearly
optimal robustness guarantees. In particular the robustness guarantees are
dimension-independent in the sense that our overall accuracy does not depend on
the number of alternatives being ranked. As an immediate consequence, we show
that while the landmark Gibbard-Satterthwaite theorem tells us a strong
impossiblity result about designing strategy-proof voting rules, there are
quantitatively strong ways to protect against large coalitions if we assume
that the remaining voters voters are honest and their preferences are sampled
from a Mallows model. Our work also makes technical contributions to
algorithmic robust statistics by designing new spectral filtering techniques
that can exploit the intricate combinatorial dependencies in the Mallows model.

Fast-developing fields such as Artificial Intelligence (AI) often outpace the
efforts of encyclopedic sources such as Wikipedia, which either do not
completely cover recently-introduced topics or lack such content entirely. As a
result, methods for automatically producing content are valuable tools to
address this information overload. We show that recent advances in pretrained
language modeling can be combined for a two-stage extractive and abstractive
approach for Wikipedia lead paragraph generation. We extend this approach to
generate longer Wikipedia-style summaries with sections and examine how such
methods struggle in this application through detailed studies with 100
reference human-collected surveys. This is the first study on utilizing web
resources for long Wikipedia-style summaries to the best of our knowledge.

Reliable robotic grasping, especially with deformable objects such as fruits,
remains a challenging task due to underactuated contact interactions with a
gripper, unknown object dynamics and geometries. In this study, we propose a
Transformer-based robotic grasping framework for rigid grippers that leverage
tactile and visual information for safe object grasping. Specifically, the
Transformer models learn physical feature embeddings with sensor feedback
through performing two pre-defined explorative actions (pinching and sliding)
and predict a grasping outcome through a multilayer perceptron (MLP) with a
given grasping strength. Using these predictions, the gripper predicts a safe
grasping strength via inference. Compared with convolutional recurrent networks
(CNN), the Transformer models can capture the long-term dependencies across the
image sequences and process spatial-temporal features simultaneously. We first
benchmark the Transformer models on a public dataset for slip detection.
Following that, we show that the Transformer models outperform a CNN+LSTM model
in terms of grasping accuracy and computational efficiency. We also collect our
fruit grasping dataset and conduct online grasping experiments using the
proposed framework for both seen and unseen fruits. Our codes and dataset are
public on GitHub.

Given the fact of a case, Legal Judgment Prediction (LJP) involves a series
of sub-tasks such as predicting violated law articles, charges and term of
penalty. We propose leveraging a unified text-to-text Transformer for LJP,
where the dependencies among sub-tasks can be naturally established within the
auto-regressive decoder. Compared with previous works, it has three advantages:
(1) it fits in the pretraining pattern of masked language models, and thereby
can benefit from the semantic prompts of each sub-task rather than treating
them as atomic labels, (2) it utilizes a single unified architecture, enabling
full parameter sharing across all sub-tasks, and (3) it can incorporate both
classification and generative sub-tasks. We show that this unified transformer,
albeit pretrained on general-domain text, outperforms pretrained models
tailored specifically for the legal domain. Through an extensive set of
experiments, we find that the best order to capture dependencies is different
from human intuitions, and the most reasonable logical order for humans can be
sub-optimal for the model. We further include two more auxiliary tasks: court
view generation and article content prediction, showing they can not only
improve the prediction accuracy, but also provide interpretable explanations
for model outputs even when an error is made. With the best configuration, our
model outperforms both previous SOTA and a single-tasked version of the unified
transformer by a large margin.

eHealth systems deliver critical digital healthcare and wellness services for
users by continuously monitoring physiological and contextual data. eHealth
applications use multi-modal machine learning kernels to analyze data from
different sensor modalities and automate decision-making. Noisy inputs and
motion artifacts during sensory data acquisition affect the i) prediction
accuracy and resilience of eHealth services and ii) energy efficiency in
processing garbage data. Monitoring raw sensory inputs to identify and drop
data and features from noisy modalities can improve prediction accuracy and
energy efficiency. We propose a closed-loop monitoring and control framework
for multi-modal eHealth applications, AMSER, that can mitigate garbage-in
garbage-out by i) monitoring input modalities, ii) analyzing raw input to
selectively drop noisy data and features, and iii) choosing appropriate machine
learning models that fit the configured data and feature vector - to improve
prediction accuracy and energy efficiency. We evaluate our AMSER approach using
multi-modal eHealth applications of pain assessment and stress monitoring over
different levels and types of noisy components incurred via different sensor
modalities. Our approach achieves up to 22\% improvement in prediction accuracy
and 5.6$\times$ energy consumption reduction in the sensing phase against the
state-of-the-art multi-modal monitoring application.

Quantum process tomography (QPT), where a quantum channel is reconstructed
through the analysis of repeated quantum measurements, is an important tool for
validating the operation of a quantum processor. We detail the combined use of
an existing QPT approach based on tensor networks (TNs) and unsupervised
learning with TN contraction path finding algorithms in order to use TNs of
arbitrary topologies for reconstruction. Experiments were conducted on the
7-qubit IBM Quantum Falcon Processor ibmq_casablanca, where we demonstrate this
technique by matching the topology of the tensor networks used for
reconstruction with the topology of the processor, allowing us to extend past
the characterisation of linear nearest neighbour circuits. Furthermore, we
conduct single-qubit gate set tomography (GST) on each individual qubit to
correct for separable errors during the state preparation and measurement
phases of QPT, which are separate from the channel under consideration but may
negatively impact the quality of its reconstruction. We are able to report a
fidelity of 0.89 against the ideal unitary channel of a single-cycle random
quantum circuit performed on ibmq_casablanca, after obtaining just $1.1 \times
10^5$ measurements for the reconstruction of this 7-qubit process. This
represents more than five orders of magnitude fewer total measurements than the
number needed to conduct full, traditional QPT on a 7-qubit process.

We provide a decision theoretic analysis of bandit experiments. Working
within the framework of diffusion asymptotics, we define suitable notions of
asymptotic Bayes and minimax risk for these experiments. For normally
distributed rewards, the minimal Bayes risk can be characterized as the
solution to a second-order partial differential equation (PDE). Using a limit
of experiments approach, we show that this PDE characterization also holds
asymptotically under both parametric and non-parametric distributions of the
rewards. The approach further describes the state variables it is
asymptotically sufficient to restrict attention to, and thereby suggests a
practical strategy for dimension reduction. The PDEs characterizing minimal
Bayes risk can be solved efficiently using sparse matrix routines. We derive
the optimal Bayes and minimax policies from their numerical solutions. These
optimal policies substantially dominate existing methods such as Thompson
sampling and UCB, often by a factor of two. The framework also covers time
discounting and pure exploration.

In this paper, we study scheduling in multi-class, multi-server queueing
systems with stochastic rewards of job-server assignments following a bilinear
model in feature vectors characterizing jobs and servers. A bilinear model
allows capturing pairwise interactions of features of jobs and servers. Our
goal is regret minimization for the objective of maximizing cumulative reward
of job-server assignments over a time horizon against an oracle policy that has
complete information about system parameters, while maintaining queueing system
stable and allowing for different job priorities. The scheduling problem we
study is motivated by various applications including matching in online
platforms, such as crowdsourcing and labour platforms, and cluster computing
systems. We study a scheduling algorithm based on weighted proportionally fair
allocation criteria augmented with marginal costs for reward maximization,
along with a linear bandit algorithm for estimating rewards of job-server
assignments. For a baseline setting, in which jobs have identical mean service
times, we show that our algorithm has a sub-linear regret, as well as a
sub-linear bound on the mean queue length, in the time horizon. We show that
similar bounds hold under more general assumptions, allowing for mean service
times to be different across job classes and a time-varying set of server
classes. We also show stability conditions for distributed iterative algorithms
for computing allocations, which is of interest in large-scale system
applications. We demonstrate the efficiency of our algorithms by numerical
experiments using both synthetic randomly generated data and a real-world
cluster computing data trace.

Learning the dynamics of spatiotemporal events is a fundamental problem.
Neural point processes enhance the expressivity of point process models with
deep neural networks. However, most existing methods only consider temporal
dynamics without spatial modeling. We propose Deep Spatiotemporal Point Process
(\ours{}), a deep dynamics model that integrates spatiotemporal point
processes. Our method is flexible, efficient, and can accurately forecast
irregularly sampled events over space and time. The key construction of our
approach is the nonparametric space-time intensity function, governed by a
latent process. The intensity function enjoys closed form integration for the
density. The latent process captures the uncertainty of the event sequence. We
use amortized variational inference to infer the latent process with deep
networks. Using synthetic datasets, we validate our model can accurately learn
the true intensity function. On real-world benchmark datasets, our model
demonstrates superior performance over state-of-the-art baselines. Our code and
data can be found at the https://github.com/Rose-STL-Lab/DeepSTPP.

We revisit the classic database of weighted-P4s which admit Calabi-Yau 3-fold
hypersurfaces equipped with a diverse set of tools from the machine-learning
toolbox. Unsupervised techniques identify an unanticipated almost linear
dependence of the topological data on the weights. This then allows us to
identify a previously unnoticed clustering in the Calabi-Yau data. Supervised
techniques are successful in predicting the topological parameters of the
hypersurface from its weights with an accuracy of R^2 > 95%. Supervised
learning also allows us to identify weighted-P4s which admit Calabi-Yau
hypersurfaces to 100% accuracy by making use of partitioning supported by the
clustering behaviour.

Building a socially intelligent agent involves many challenges, one of which
is to teach the agent to speak guided by its value like a human. However,
value-driven chatbots are still understudied in the area of dialogue systems.
Most existing datasets focus on commonsense reasoning or social norm modeling.
In this work, we present a new large-scale human value dataset called ValueNet,
which contains human attitudes on 21,374 text scenarios. The dataset is
organized in ten dimensions that conform to the basic human value theory in
intercultural research. We further develop a Transformer-based value regression
model on ValueNet to learn the utility distribution. Comprehensive empirical
results show that the learned value model could benefit a wide range of
dialogue tasks. For example, by teaching a generative agent with reinforcement
learning and the rewards from the value model, our method attains
state-of-the-art performance on the personalized dialog generation dataset:
Persona-Chat. With values as additional features, existing emotion recognition
models enable capturing rich human emotions in the context, which further
improves the empathetic response generation performance in the
EmpatheticDialogues dataset. To the best of our knowledge, ValueNet is the
first large-scale text dataset for human value modeling, and we are the first
one trying to incorporate a value model into emotionally intelligent dialogue
systems. The dataset is available at https://liang-qiu.github.io/ValueNet/.

Population-level societal events, such as civil unrest and crime, often have
a significant impact on our daily life. Forecasting such events is of great
importance for decision-making and resource allocation. Event prediction has
traditionally been challenging due to the lack of knowledge regarding the true
causes and underlying mechanisms of event occurrence. In recent years, research
on event forecasting has made significant progress due to two main reasons: (1)
the development of machine learning and deep learning algorithms and (2) the
accessibility of public data such as social media, news sources, blogs,
economic indicators, and other meta-data sources. The explosive growth of data
and the remarkable advancement in software/hardware technologies have led to
applications of deep learning techniques in societal event studies. This paper
is dedicated to providing a systematic and comprehensive overview of deep
learning technologies for societal event predictions. We focus on two domains
of societal events: \textit{civil unrest} and \textit{crime}. We first
introduce how event forecasting problems are formulated as a machine learning
prediction task. Then, we summarize data resources, traditional methods, and
recent development of deep learning models for these problems. Finally, we
discuss the challenges in societal event forecasting and put forward some
promising directions for future research.

This paper shows how a single mechanism allows knowledge to be constructed
layer by layer directly from an agent's raw sensorimotor stream. This
mechanism, the General Value Function (GVF) or "forecast," captures high-level,
abstract knowledge as a set of predictions about existing features and
knowledge, based exclusively on the agent's low-level senses and actions.
  Thus, forecasts provide a representation for organizing raw sensorimotor data
into useful abstractions over an unlimited number of layers--a long-sought goal
of AI and cognitive science.
  The heart of this paper is a detailed thought experiment providing a
concrete, step-by-step formal illustration of how an artificial agent can build
true, useful, abstract knowledge from its raw sensorimotor experience alone.
The knowledge is represented as a set of layered predictions (forecasts) about
the agent's observed consequences of its actions. This illustration shows
twelve separate layers: the lowest consisting of raw pixels, touch and force
sensors, and a small number of actions; the higher layers increasing in
abstraction, eventually resulting in rich knowledge about the agent's world,
corresponding roughly to doorways, walls, rooms, and floor plans. I then argue
that this general mechanism may allow the representation of a broad spectrum of
everyday human knowledge.

We propose the deep progressive image compression using trit-planes (DPICT)
algorithm, which is the first learning-based codec supporting fine granular
scalability (FGS). First, we transform an image into a latent tensor using an
analysis network. Then, we represent the latent tensor in ternary digits
(trits) and encode it into a compressed bitstream trit-plane by trit-plane in
the decreasing order of significance. Moreover, within each trit-plane, we sort
the trits according to their rate-distortion priorities and transmit more
important information first. Since the compression network is less optimized
for the cases of using fewer trit-planes, we develop a postprocessing network
for refining reconstructed images at low rates. Experimental results show that
DPICT outperforms conventional progressive codecs significantly, while enabling
FGS transmission. Codes are available at
https://github.com/jaehanlee-mcl/DPICT.

We propose near-optimal overlay networks based on $d$-regular expander graphs
to accelerate decentralized federated learning (DFL) and improve its
generalization. In DFL a massive number of clients are connected by an overlay
network, and they solve machine learning problems collaboratively without
sharing raw data. Our overlay network design integrates spectral graph theory
and the theoretical convergence and generalization bounds for DFL. As such, our
proposed overlay networks accelerate convergence, improve generalization, and
enhance robustness to clients failures in DFL with theoretical guarantees.
Also, we present an efficient algorithm to convert a given graph to a practical
overlay network and maintaining the network topology after potential client
failures. We numerically verify the advantages of DFL with our proposed
networks on various benchmark tasks, ranging from image classification to
language modeling using hundreds of clients.

Video anomaly detection aims to identify abnormal events that occurred in
videos. Since anomalous events are relatively rare, it is not feasible to
collect a balanced dataset and train a binary classifier to solve the task.
Thus, most previous approaches learn only from normal videos using unsupervised
or semi-supervised methods. Obviously, they are limited in capturing and
utilizing discriminative abnormal characteristics, which leads to compromised
anomaly detection performance. In this paper, to address this issue, we propose
a new learning paradigm by making full use of both normal and abnormal videos
for video anomaly detection. In particular, we formulate a new learning task:
cross-domain few-shot anomaly detection, which can transfer knowledge learned
from numerous videos in the source domain to help solve few-shot abnormality
detection in the target domain. Concretely, we leverage self-supervised
training on the target normal videos to reduce the domain gap and devise a meta
context perception module to explore the video context of the event in the
few-shot setting. Our experiments show that our method significantly
outperforms baseline methods on DoTA and UCF-Crime datasets, and the new task
contributes to a more practical training paradigm for anomaly detection.

Humans use natural language to compose common concepts from their environment
into plausible, day-to-day scene descriptions. However, such generative
commonsense reasoning (GCSR) skills are lacking in state-of-the-art text
generation methods. Descriptive sentences about arbitrary concepts generated by
neural text generation models (e.g., pre-trained text-to-text Transformers) are
often grammatically fluent but may not correspond to human common sense,
largely due to their lack of mechanisms to capture concept relations, to
identify implicit concepts, and to perform generalizable reasoning about unseen
concept compositions. In this paper, we propose an Imagine-and-Verbalize (I&V)
method, which learns to imagine a relational scene knowledge graph (SKG) with
relations between the input concepts, and leverage the SKG as a constraint when
generating a plausible scene description. We collect and harmonize a set of
knowledge resources from different domains and modalities, providing a rich
auxiliary supervision signal for I&V. The experiments demonstrate the
effectiveness of I&V in improving language models on both concept-to-sentence
and concept-to-story generation tasks, while enabling the model to learn well
from fewer task examples and generate SKGs that make common sense to human
annotators.

The Zurich Cognitive Language Processing Corpus (ZuCo) provides eye-tracking
and EEG signals from two reading paradigms, normal reading and task-specific
reading. We analyze whether machine learning methods are able to classify these
two tasks using eye-tracking and EEG features. We implement models with
aggregated sentence-level features as well as fine-grained word-level features.
We test the models in within-subject and cross-subject evaluation scenarios.
All models are tested on the ZuCo 1.0 and ZuCo 2.0 data subsets, which are
characterized by differing recording procedures and thus allow for different
levels of generalizability. Finally, we provide a series of control experiments
to analyze the results in more detail.

Observations of seabed texture with synthetic aperture sonar are dependent
upon several factors. In this work, we focus on collection geometry with
respect to isotropic and anisotropic textures. The low grazing angle of the
collection geometry, combined with orientation of the sonar path relative to
anisotropic texture, poses a significant challenge for image-alignment and
other multi-view scene understanding frameworks. We previously proposed using
features captured from estimated seabed relief to improve scene understanding.
While several methods have been developed to estimate seabed relief via
intensity, no large-scale study exists in the literature. Furthermore, a
dataset of coregistered seabed relief maps and sonar imagery is nonexistent to
learn this domain translation. We address these problems by producing a large
simulated dataset containing coregistered pairs of seabed relief and intensity
maps from two unique sonar data simulation techniques. We apply three types of
models, with varying complexity, to translate intensity imagery to seabed
relief: a Gaussian Markov Random Field approach (GMRF), a conditional
Generative Adversarial Network (cGAN), and UNet architectures. Methods are
compared in reference to the coregistered simulated datasets using L1 error.
Additionally, predictions on simulated and real SAS imagery are shown. Finally,
models are compared on two datasets of hand-aligned SAS imagery and evaluated
in terms of L1 error across multiple aspects in comparison to using intensity.
Our comprehensive experiments show that the proposed UNet architectures
outperform the GMRF and pix2pix cGAN models on seabed relief estimation for
simulated and real SAS imagery.

Distributional forecasts are important for a wide variety of applications,
including forecasting epidemics. Often, forecasts are miscalibrated, or
unreliable in assigning uncertainty to future events. We present a
recalibration method that can be applied to a black-box forecaster given
retrospective forecasts and observations, as well as an extension to make this
method more effective in recalibrating epidemic forecasts. This method is
guaranteed to improve calibration and log score performance when trained and
measured in-sample. We also prove that the increase in expected log score of a
recalibrated forecaster is equal to the entropy of the PIT distribution. We
apply this recalibration method to the 27 influenza forecasters in the FluSight
Network and show that recalibration reliably improves forecast accuracy and
calibration. This method is effective, robust, and easy to use as a
post-processing tool to improve epidemic forecasts.

The main objective of this paper is to outline a theoretical framework to
characterise humans' decision-making strategies under uncertainty, in
particular active learning in a black-box optimization task and trading-off
between information gathering (exploration) and reward seeking (exploitation).
Humans' decisions making according to these two objectives can be modelled in
terms of Pareto rationality. If a decision set contains a Pareto efficient
strategy, a rational decision maker should always select the dominant strategy
over its dominated alternatives. A distance from the Pareto frontier determines
whether a choice is Pareto rational. To collect data about humans' strategies
we have used a gaming application that shows the game field, with previous
decisions and observations, as well as the score obtained. The key element in
this paper is the representation of behavioural patterns of human learners as a
discrete probability distribution. This maps the problem of the
characterization of humans' behaviour into a space whose elements are
probability distributions structured by a distance between histograms, namely
the Wasserstein distance (WST). The distributional analysis gives new insights
about human search strategies and their deviations from Pareto rationality.
Since the uncertainty is one of the two objectives defining the Pareto
frontier, the analysis has been performed for three different uncertainty
quantification measures to identify which better explains the Pareto compliant
behavioural patterns. Beside the analysis of individual patterns WST has also
enabled a global analysis computing the barycenters and WST k-means clustering.
A further analysis has been performed by a decision tree to relate non-Paretian
behaviour, characterized by exasperated exploitation, to the dynamics of the
evolution of the reward seeking process.

MeTTa (Meta Type Talk) is a novel programming language created for use in the
OpenCog Hyperon AGI system. It is designed as a meta-language with very basic
and general facilities for handling symbols, groundings, variables, types,
substitutions and pattern matching. Primitives exist for creating new type
systems and associated DSLs. IInformally, MeTTa is Hyperon's lowest-level
"language of thought" -- the meta-language in which algorithms for learning
more particular knowledge representations, will operate, and in which these
algorithms themselves may be represented. Here we explain how one might go
about formalizing the MeTTa language as a system of metagraph rewrite rules, an
approach that fits in naturally to the Hyperon framework given that the
latter's core component is a distributed metagraph knowledge store (the
Atomspace). The metagraph rewrite rules constituting MeTTa programs can also be
represented as metagraphs, giving a natural model for MeTTa reflection and
self-modifying code. Considering MeTTa programs that compute equivalences
between execution traces of other MeTTa programs allows us to model spaces of
MeTTa execution traces using Homotopy Type Theory. Considering the limit of
MeTTa programs mapping between execution traces of MeTTa programs that map
between execution traces of MeTTa programs that ..., we find that a given MeTTa
codebase is effectively modeled as an infinity-groupoid, and the space of all
MeTTa codebases as an (infinity,1)-topis This topos is basically the same as
the so-called "Ruliad" previously derived from rewrite rules on hypergraphs, in
a discrete physics context. The formalization of MeTTA as metagraph rewrite
rules may also provide a useful framework for structuring the implementation of
efficient methods for pattern matching and equality inference within the MeTTa
interpreter.

While conventional ranking systems focus solely on maximizing the utility of
the ranked items to users, fairness-aware ranking systems additionally try to
balance the exposure for different protected attributes such as gender or race.
To achieve this type of group fairness for ranking, we derive a new ranking
system based on the first principles of distributional robustness. We formulate
a minimax game between a player choosing a distribution over rankings to
maximize utility while satisfying fairness constraints against an adversary
seeking to minimize utility while matching statistics of the training data. We
show that our approach provides better utility for highly fair rankings than
existing baseline methods.

We study potential biases of popular cluster quality metrics, such as
conductance or modularity. We propose a method that uses both stochastic and
preferential attachment block models construction to generate networks with
preset community structures, to which quality metrics will be applied. These
models also allow us to generate multi-level structures of varying strength,
which will show if metrics favour partitions into a larger or smaller number of
clusters. Additionally, we propose another quality metric, the density ratio.
  We observed that most of the studied metrics tend to favour partitions into a
smaller number of big clusters, even when their relative internal and external
connectivity are the same. The metrics found to be less biased are modularity
and density ratio.

When subjected to automated decision-making, decision subjects may
strategically modify their observable features in ways they believe will
maximize their chances of receiving a favorable decision. In many practical
situations, the underlying assessment rule is deliberately kept secret to avoid
gaming and maintain competitive advantage. The resulting opacity forces the
decision subjects to rely on incomplete information when making strategic
feature modifications. We capture such settings as a game of Bayesian
persuasion, in which the decision maker offers a form of recourse to the
decision subject by providing them with an action recommendation (or signal) to
incentivize them to modify their features in desirable ways. We show that when
using persuasion, the decision maker and decision subject are never worse off
in expectation, while the decision maker can be significantly better off. While
the decision maker's problem of finding the optimal Bayesian
incentive-compatible (BIC) signaling policy takes the form of optimization over
infinitely-many variables, we show that this optimization can be cast as a
linear program over finitely-many regions of the space of possible assessment
rules. While this reformulation simplifies the problem dramatically, solving
the linear program requires reasoning about exponentially-many variables, even
in relatively simple cases. Motivated by this observation, we provide a
polynomial-time approximation scheme that recovers a near-optimal signaling
policy. Finally, our numerical simulations on semi-synthetic data empirically
demonstrate the benefits of using persuasion in the algorithmic recourse
setting.

Bayesian neural networks (BNNs) have become a principal approach to alleviate
overconfident predictions in deep learning, but they often suffer from scaling
issues due to a large number of distribution parameters. In this paper, we
discover that the first layer of a deep network possesses multiple disparate
optima when solely retrained. This indicates a large posterior variance when
the first layer is altered by a Bayesian layer, which motivates us to design a
spatial-temporal-fusion BNN (STF-BNN) for efficiently scaling BNNs to large
models: (1) first normally train a neural network from scratch to realize fast
training; and (2) the first layer is converted to Bayesian and inferred by
employing stochastic variational inference, while other layers are fixed.
Compared to vanilla BNNs, our approach can greatly reduce the training time and
the number of parameters, which contributes to scale BNNs efficiently. We
further provide theoretical guarantees on the generalizability and the
capability of mitigating overconfidence of STF-BNN. Comprehensive experiments
demonstrate that STF-BNN (1) achieves the state-of-the-art performance on
prediction and uncertainty quantification; (2) significantly improves
adversarial robustness and privacy preservation; and (3) considerably reduces
training time and memory costs.

Modern classification algorithms are susceptible to adversarial
examples--perturbations to inputs that cause the algorithm to produce
undesirable behavior. In this work, we seek to understand and extend
adversarial examples across domains in which inputs are discrete, particularly
across new domains, such as computational biology. As a step towards this goal,
we formalize a notion of synonymous adversarial examples that applies in any
discrete setting and describe a simple domain-agnostic algorithm to construct
such examples. We apply this algorithm across multiple domains--including
sentiment analysis and DNA sequence classification--and find that it
consistently uncovers adversarial examples. We seek to understand their
prevalence theoretically and we attribute their existence to spurious token
correlations, a statistical phenomenon that is specific to discrete spaces. Our
work is a step towards a domain-agnostic treatment of discrete adversarial
examples analogous to that of continuous inputs.

Federated learning is inherently vulnerable to model poisoning attacks
because its decentralized nature allows attackers to participate with
compromised devices. In model poisoning attacks, the attacker reduces the
model's performance on targeted sub-tasks (e.g. classifying planes as birds) by
uploading "poisoned" updates. In this report we introduce \algoname{}, a novel
defense that uses global top-k update sparsification and device-level gradient
clipping to mitigate model poisoning attacks. We propose a theoretical
framework for analyzing the robustness of defenses against poisoning attacks,
and provide robustness and convergence analysis of our algorithm. To validate
its empirical efficacy we conduct an open-source evaluation at scale across
multiple benchmark datasets for computer vision and federated learning.

Cloud applications are increasingly shifting from large monolithic services,
to complex graphs of loosely-coupled microservices. Despite their advantages,
microservices also introduce cascading QoS violations in cloud applications,
which are difficult to diagnose and correct.
  We present Sage, a ML-driven root cause analysis system for interactive cloud
microservices. Sage leverages unsupervised learning models to circumvent the
overhead of trace labeling, determines the root cause of unpredictable
performance online, and applies corrective actions to restore performance. On
experiments on both dedicated local clusters and large GCE clusters we show
that Sage achieves high root cause detection accuracy and predictable

In this paper, we discuss the impact of the Covid-19 pandemic on alcohol
consumption habit changes among healthcare workers in the United States. We
utilize multiple supervised and unsupervised machine learning methods and
models such as Decision Trees, Logistic Regression, Naive Bayes classifier,
k-Nearest Neighbors, Support Vector Machines, Multilayer perceptron, XGBoost,
CatBoost, LightGBM, Chi-Squared Test and mutual information method on a mental
health survey data obtained from the University of Michigan Inter-University
Consortium for Political and Social Research to find out relationships between
COVID-19 related negative effects and alcohol consumption habit changes among
healthcare workers. Our findings suggest that COVID-19-related school closures,
COVID-19-related work schedule changes and COVID-related news exposure may lead
to an increase in alcohol use among healthcare workers in the United States.

Data-free knowledge distillation (DFKD) has recently been attracting
increasing attention from research communities, attributed to its capability to
compress a model only using synthetic data. Despite the encouraging results
achieved, state-of-the-art DFKD methods still suffer from the inefficiency of
data synthesis, making the data-free training process extremely time-consuming
and thus inapplicable for large-scale tasks. In this work, we introduce an
efficacious scheme, termed as FastDFKD, that allows us to accelerate DFKD by a
factor of orders of magnitude. At the heart of our approach is a novel strategy
to reuse the shared common features in training data so as to synthesize
different data instances. Unlike prior methods that optimize a set of data
independently, we propose to learn a meta-synthesizer that seeks common
features as the initialization for the fast data synthesis. As a result,
FastDFKD achieves data synthesis within only a few steps, significantly
enhancing the efficiency of data-free training. Experiments over CIFAR, NYUv2,
and ImageNet demonstrate that the proposed FastDFKD achieves 10$\times$ and
even 100$\times$ acceleration while preserving performances on par with state
of the art. Code is available at
\url{https://github.com/zju-vipa/Fast-Datafree}.

Independent component analysis is intended to recover the mutually
independent components from their linear mixtures. This technique has been
widely used in many fields, such as data analysis, signal processing, and
machine learning. To alleviate the dependency on prior knowledge concerning
unknown sources, many nonparametric methods have been proposed. In this paper,
we present a novel boosting-based algorithm for independent component analysis.
Our algorithm consists of maximizing likelihood estimation via boosting and
seeking unmixing matrix by the fixed-point method. A variety of experiments
validate its performance compared with many of the presently known algorithms.

We propose a new regression algorithm that learns from a set of input-output
pairs. Our algorithm is designed for populations where the relation between the
input variables and the output variable exhibits a heterogeneous behavior
across the predictor space. The algorithm starts with generating subsets that
are concentrated around random points in the input space. This is followed by
training a local predictor for each subset. Those predictors are then combined
in a novel way to yield an overall predictor. We call this algorithm ``LEarning
with Subset Stacking'' or LESS, due to its resemblance to the method of
stacking regressors. We compare the testing performance of LESS with
state-of-the-art methods on several datasets. Our comparison shows that LESS is
a competitive supervised learning method. Moreover, we observe that LESS is
also efficient in terms of computation time and it allows a straightforward
parallel implementation.

Due to its powerful automatic feature extraction, deep learning (DL) has been
widely used in source code vulnerability detection. However, although it
performs well on artificial datasets, its performance is not satisfactory when
detecting real-world vulnerabilities due to the high complexity of real-world
samples. In this paper, we propose to train DL-based vulnerability detection
models in a human-learning manner, that is, start with the simplest samples and
then gradually transition to difficult knowledge. Specifically, we design a
novel framework (Humer) that can enhance the detection ability of DL-based
vulnerability detectors. To validate the effectiveness of Humer, we select five
state-of-the-art DL-based vulnerability detection models (TokenCNN,
VulDeePecker, StatementGRU, ASTGRU, and Devign) to complete our evaluations.
Through the results, we find that the use of Humer can increase the F1 of these
models by an average of 10.5%. Moreover, Humer can make the model detect up to
16.7% more real-world vulnerabilities. Meanwhile, we also conduct a case study
to uncover vulnerabilities from real-world open source products by using these
enhanced DL-based vulnerability detectors. Through the results, we finally
discover 281 unreported vulnerabilities in NVD, of which 98 have been silently
patched by vendors in the latest version of corresponding products, but 159
still exist in the products.

Time series (TS) anomaly detection (AD) plays an essential role in various
applications, e.g., fraud detection in finance and healthcare monitoring. Due
to the inherently unpredictable and highly varied nature of anomalies and the
lack of anomaly labels in historical data, the AD problem is typically
formulated as an unsupervised learning problem. The performance of existing
solutions is often not satisfactory, especially in data-scarce scenarios. To
tackle this problem, we propose a novel self-supervised learning technique for
AD in time series, namely \emph{DeepFIB}. We model the problem as a \emph{Fill
In the Blank} game by masking some elements in the TS and imputing them with
the rest. Considering the two common anomaly shapes (point- or
sequence-outliers) in TS data, we implement two masking strategies with many
self-generated training samples. The corresponding self-imputation networks can
extract more robust temporal relations than existing AD solutions and
effectively facilitate identifying the two types of anomalies. For continuous
outliers, we also propose an anomaly localization algorithm that dramatically
reduces AD errors. Experiments on various real-world TS datasets demonstrate
that DeepFIB outperforms state-of-the-art methods by a large margin, achieving
up to $65.2\%$ relative improvement in F1-score.

Many real-world graphs (networks) are heterogeneous with different types of
nodes and edges. Heterogeneous graph embedding, aiming at learning the
low-dimensional node representations of a heterogeneous graph, is vital for
various downstream applications. Many meta-path based embedding methods have
been proposed to learn the semantic information of heterogeneous graphs in
recent years. However, most of the existing techniques overlook the graph
structure information when learning the heterogeneous graph embeddings. This
paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network
(SHGNN) to address the above limitations. In detail, we first utilize a feature
propagation module to capture the local structure information of intermediate
nodes in the meta-path. Next, we use a tree-attention aggregator to incorporate
the graph structure information into the aggregation module on the meta-path.
Finally, we leverage a meta-path aggregator to fuse the information aggregated
from different meta-paths. We conducted experiments on node classification and
clustering tasks and achieved state-of-the-art results on the benchmark
datasets, which shows the effectiveness of our proposed method.

Event cameras are novel bio-inspired sensors that measure per-pixel
brightness differences asynchronously. Recovering brightness from events is
appealing since the reconstructed images inherit the high dynamic range (HDR)
and high-speed properties of events; hence they can be used in many robotic
vision applications and to generate slow-motion HDR videos. However,
state-of-the-art methods tackle this problem by training an event-to-image
recurrent neural network (RNN), which lacks explainability and is difficult to
tune. In this work we show, for the first time, how tackling the joint problem
of motion and brightness estimation leads us to formulate event-based image
reconstruction as a linear inverse problem that can be solved without training
an image reconstruction RNN. Instead, classical and learning-based image priors
can be used to solve the problem and remove artifacts from the reconstructed
images. The experiments show that the proposed approach generates images with
visual quality on par with state-of-the-art methods despite only using data
from a short time interval. The proposed linear formulation and solvers have a
unifying character because they can be applied also to reconstruct brightness
from the second derivative. Additionally, the linear formulation is attractive
because it can be naturally combined with super-resolution, motion-segmentation
and color demosaicing.

Logical Natural Language Generation, i.e., generating textual descriptions
that can be logically entailed by a structured table, has been a challenge due
to the low fidelity of the generation. \citet{chen2020logic2text} have
addressed this problem by annotating interim logical programs to control the
generation contents and semantics, and presented the task of table-aware
logical form to text (Logic2text) generation. However, although table instances
are abundant in the real world, logical forms paired with textual descriptions
require costly human annotation work, which limits the performance of neural
models. To mitigate this, we propose topic-conditioned data augmentation
(TopicDA), which utilizes GPT-2 to generate unpaired logical forms and textual
descriptions directly from tables. We further introduce logical form generation
(LG), a dual task of Logic2text that requires generating a valid logical form
based on a text description of a table. We also propose a semi-supervised
learning approach to jointly train a Logic2text and an LG model with both
labeled and augmented data. The two models benefit from each other by providing
extra supervision signals through back-translation. Experimental results on the
Logic2text dataset and the LG task demonstrate that our approach can
effectively utilize the augmented data and outperform supervised baselines by a
substantial margin.

Hyperspectral imaging is an essential imaging modality for a wide range of
applications, especially in remote sensing, agriculture, and medicine. Inspired
by existing hyperspectral cameras that are either slow, expensive, or bulky,
reconstructing hyperspectral images (HSIs) from a low-budget snapshot
measurement has drawn wide attention. By mapping a truncated numerical
optimization algorithm into a network with a fixed number of phases, recent
deep unfolding networks (DUNs) for spectral snapshot compressive sensing (SCI)
have achieved remarkable success. However, DUNs are far from reaching the scope
of industrial applications limited by the lack of cross-phase feature
interaction and adaptive parameter adjustment. In this paper, we propose a
novel Hyperspectral Explicable Reconstruction and Optimal Sampling deep Network
for SCI, dubbed HerosNet, which includes several phases under the
ISTA-unfolding framework. Each phase can flexibly simulate the sensing matrix
and contextually adjust the step size in the gradient descent step, and
hierarchically fuse and interact the hidden states of previous phases to
effectively recover current HSI frames in the proximal mapping step.
Simultaneously, a hardware-friendly optimal binary mask is learned end-to-end
to further improve the reconstruction performance. Finally, our HerosNet is
validated to outperform the state-of-the-art methods on both simulation and
real datasets by large margins. The source code is available at
https://github.com/jianzhangcs/HerosNet.

During the past years,deep convolutional neural networks have achieved
impressive success in low-light Image Enhancement.Existing deep learning
methods mostly enhance the ability of feature extraction by stacking network
structures and deepening the depth of the network.which causes more runtime
cost on single image.In order to reduce inference time while fully extracting
local features and global features.Inspired by SGN,we propose a Attention based
Broadly self-guided network (ABSGN) for real world low-light image
Enhancement.such a broadly strategy is able to handle the noise at different
exposures.The proposed network is validated by many mainstream
benchmark.Additional experimental results show that the proposed network
outperforms most of state-of-the-art low-light image Enhancement solutions.

Confidence intervals are a standard technique for analyzing data. When
applied to time series, confidence intervals are computed for each time point
separately. Alternatively, we can compute confidence bands, where we are
required to find the smallest area enveloping $k$ time series, where $k$ is a
user parameter. Confidence bands can be then used to detect abnormal time
series, not just individual observations within the time series. We will show
that despite being an NP-hard problem it is possible to find optimal confidence
band for some $k$. We do this by considering a different problem: discovering
regularized bands, where we minimize the envelope area minus the number of
included time series weighted by a parameter $\alpha$. Unlike normal confidence
bands we can solve the problem exactly by using a minimum cut. By varying
$\alpha$ we can obtain solutions for various $k$. If we have a constraint $k$
for which we cannot find appropriate $\alpha$, we demonstrate a simple
algorithm that yields $O(\sqrt{n})$ approximation guarantee by connecting the
problem to a minimum $k$-union problem. This connection also implies that we
cannot approximate the problem better than $O(n^{1/4})$ under some (mild)
assumptions. Finally, we consider a variant where instead of minimizing the
area we minimize the maximum width. Here, we demonstrate a simple
2-approximation algorithm and show that we cannot achieve better approximation

Deep learning (DL) has been pervasive in a wide spectrum of nowadays software
systems and applications. The rich features of these DL based software
applications (i.e., DL software) usually rely on powerful DL models. To train
powerful DL models with large datasets efficiently, it has been a common
practice for developers to parallelize and distribute the computation and
memory over multiple devices in the training process, which is known as
distributed training. However, existing efforts in the software engineering
(SE) research community mainly focus on issues in the general process of
training DL models. In contrast, to the best of our knowledge, issues that
developers encounter in distributed training have never been well studied.
Given the surging importance of distributed training in the current practice of
developing DL software, this paper fills in the knowledge gap and presents the
first comprehensive study on developers' issues in distributed training. To
this end, we extract and analyze 1,054 real-world developers' issues in
distributed training from Stack Overflow and GitHub, two commonly used data
sources for studying software issues. We construct a fine-grained taxonomy
consisting of 30 categories regarding the fault symptoms and summarize common
fix patterns for different symptoms. Based on the results, we suggest
actionable implications and research avenues that can potentially facilitate
the future development of distributed training.

Estimating quality of transmitted speech is known to be a non-trivial task.
While traditionally, test participants are asked to rate the quality of
samples; nowadays, automated methods are available. These methods can be
divided into: 1) intrusive models, which use both, the original and the
degraded signals, and 2) non-intrusive models, which only require the degraded
signal. Recently, non-intrusive models based on neural networks showed to
outperform signal processing based models. However, the advantages of deep
learning based models come with the cost of being more challenging to
interpret. To get more insight into the prediction models the non-intrusive
speech quality prediction model NISQA is analyzed in this paper. NISQA is
composed of a convolutional neural network (CNN) and a recurrent neural network
(RNN). The task of the CNN is to compute relevant features for the speech
quality prediction on a frame level, while the RNN models time-dependencies
between the individual speech frames. Different explanation algorithms are used
to understand the automatically learned features of the CNN. In this way,
several interpretable features could be identified, such as the sensitivity to
noise or strong interruptions. On the other hand, it was found that multiple
features carry redundant information.

In recent years, research on near-term quantum machine learning has explored
how classical machine learning algorithms endowed with access to quantum
kernels (similarity measures) can outperform their purely classical
counterparts. Although theoretical work has shown provable advantage on
synthetic data sets, no work done to date has studied empirically whether
quantum advantage is attainable and with what kind of data set. In this paper,
we report the first systematic investigation of empirical quantum advantage
(EQA) in healthcare and life sciences and propose an end-to-end framework to
study EQA. We selected electronic health records (EHRs) data subsets and
created a configuration space of 5-20 features and 200-300 training samples.
For each configuration coordinate, we trained classical support vector machine
(SVM) models based on radial basis function (RBF) kernels and quantum models
with custom kernels using an IBM quantum computer, making this one of the
largest quantum machine learning experiments to date. We empirically identified
regimes where quantum kernels could provide advantage on a particular data set
and introduced a terrain ruggedness index, a metric to help quantitatively
estimate how the accuracy of a given model will perform as a function of the
number of features and sample size. The generalizable framework introduced here
represents a key step towards a priori identification of data sets where
quantum advantage could exist.

In this paper, we introduce the Hessian-Schatten total variation (HTV) -- a
novel seminorm that quantifies the total "rugosity" of multivariate functions.
Our motivation for defining HTV is to assess the complexity of
supervised-learning schemes. We start by specifying the adequate matrix-valued
Banach spaces that are equipped with suitable classes of mixed norms. We then
show that the HTV is invariant to rotations, scalings, and translations.
Additionally, its minimum value is achieved for linear mappings, which supports
the common intuition that linear regression is the least complex learning
model. Next, we present closed-form expressions of the HTV for two general
classes of functions. The first one is the class of Sobolev functions with a
certain degree of regularity, for which we show that the HTV coincides with the
Hessian-Schatten seminorm that is sometimes used as a regularizer for image
reconstruction. The second one is the class of continuous and piecewise-linear
(CPWL) functions. In this case, we show that the HTV reflects the total change
in slopes between linear regions that have a common facet. Hence, it can be
viewed as a convex relaxation (l1-type) of the number of linear regions
(l0-type) of CPWL mappings. Finally, we illustrate the use of our proposed
seminorm.

A major challenge in collaborative filtering methods is how to produce
recommendations for cold items (items with no ratings), or integrate cold item
into an existing catalog. Over the years, a variety of hybrid recommendation
models have been proposed to address this problem by utilizing items' metadata
and content along with their ratings or usage patterns. In this work, we wish
to revisit the cold start problem in order to draw attention to an overlooked
challenge: the ability to integrate and balance between (regular) warm items
and completely cold items. In this case, two different challenges arise: (1)
preserving high quality performance on warm items, while (2) learning to
promote cold items to relevant users. First, we show that these two objectives
are in fact conflicting, and the balance between them depends on the business
needs and the application at hand. Next, we propose a novel hybrid
recommendation algorithm that bridges these two conflicting objectives and
enables a harmonized balance between preserving high accuracy for warm items
while effectively promoting completely cold items. We demonstrate the
effectiveness of the proposed algorithm on movies, apps, and articles
recommendations, and provide an empirical analysis of the cold-warm trade-off.

Reconstructing spectral functions from Euclidean Green's functions is an
important inverse problem in physics. The prior knowledge for specific physical
systems routinely offers essential regularization schemes for solving the
ill-posed problem approximately. Aiming at this point, we propose an automatic
differentiation framework as a generic tool for the reconstruction from
observable data. We represent the spectra by neural networks and set chi-square
as loss function to optimize the parameters with backward automatic
differentiation unsupervisedly. In the training process, there is no explicit
physical prior embedding into neural networks except the positive-definite
form. The reconstruction accuracy is assessed through Kullback-Leibler(KL)
divergence and mean square error(MSE) at multiple noise levels. It should be
noted that the automatic differential framework and the freedom of introducing
regularization are inherent advantages of the present approach and may lead to
improvements of solving inverse problem in the future.

Training a model to provide natural language explanations (NLEs) for its
predictions usually requires the acquisition of task-specific NLEs, which is
time- and resource-consuming. A potential solution is the few-shot
out-of-domain transfer of NLEs from a parent task with many NLEs to a child
task. In this work, we examine the setup in which the child task has few NLEs
but abundant labels. We establish four few-shot transfer learning methods that
cover the possible fine-tuning combinations of the labels and NLEs for the
parent and child tasks. We transfer explainability from a large natural
language inference dataset (e-SNLI) separately to two child tasks: (1) hard
cases of pronoun resolution, where we introduce the small-e-WinoGrande dataset
of NLEs on top of the WinoGrande dataset, and (2)~commonsense validation
(ComVE). Our results demonstrate that the parent task helps with NLE generation
and we establish the best methods for this setup.

Smoking in public has been proven to be more harmful to nonsmokers, making it
a huge public health concern with urgent need for proactive measures and
attention by authorities. With the world moving towards the 4th Industrial
Revolution, there is a need for reliable eco-friendly detective measures
towards this harmful intoxicating behavior to public health in and out of smart
cities. We developed an Interpretable feature learning framework for smoking
behavior detection which utilizes a Deep Learning VGG-16 pretrained network to
predict and classify the input Image class and a Layer-wise Relevance
Propagation (LRP) to explain the network detection or prediction of smoking
behavior based on the most relevant learned features or pixels or neurons. The
network's classification decision is based mainly on features located at the
mouth especially the smoke seems to be of high importance to the network's
decision. The outline of the smoke is highlighted as evidence for the
corresponding class. Some elements are seen as having a negative effect on the
smoke neuron and are consequently highlighted differently. It is interesting to
see that the network distinguishes important from unimportant features based on
the image regions. The technology can also detect other smokeable drugs like
weed, shisha, marijuana etc. The framework allows for reliable identification
of action-based smokers in unsafe zones like schools, shopping malls, bus
stops, railway compartments or other violated places for smoking as per the
government's regulatory health policies. With installation clearly defined in
smoking zones, this technology can detect smokers out of range.

The introduction of Information and Communication Technology (ICT) in
transportation systems leads to several advantages (efficiency of transport,
mobility, traffic management). However, it may bring some drawbacks in terms of
increasing security challenges, also related to human behaviour. As an example
, in the last decades attempts to characterize drivers' behaviour have been
mostly targeted. This paper presents Secure Routine, a paradigm that uses
driver's habits to driver identification and, in particular, to distinguish the
vehicle's owner from other drivers. We evaluate Secure Routine in combination
with other three existing research works based on machine learning techniques.
Results are measured using well-known metrics and show that Secure Routine
outperforms the compared works.

This paper describes foundational efforts with SautiDB-Naija, a novel corpus
of non-native (L2) Nigerian English speech. We describe how the corpus was
created and curated as well as preliminary experiments with accent
classification and learning Nigerian accent embeddings. The initial version of
the corpus includes over 900 recordings from L2 English speakers of Nigerian
languages, such as Yoruba, Igbo, Edo, Efik-Ibibio, and Igala. We further
demonstrate how fine-tuning on a pre-trained model like wav2vec can yield
representations suitable for related speech tasks such as accent
classification. SautiDB-Naija has been published to Zenodo for general use
under a flexible Creative Commons License.

Educational Institutions have an essential role in promoting the teaching and
learning process, within universities, colleges, and communities. Due to the
recent coronavirus COVID 19 pandemic, many educational institutions adopted
hybrid learning (HL), which is a combination of classic and online learning. It
integrates the advantages of both, and it is a fundamental factor to ensure
continued learning. Technological innovations such as HL are changing the
teaching process, and how students, lecturers, and administrators interact.
Based on this, the Community College of Qatar (CCQ) focused on researching the
structures and elements related to the adoption of HL. Thus, the goal of this
research paper is to reveal the impact of HL on the learning process in CCQ,
and the effective instructional technology (INST) tools required for a
successful HL program. Our research questions for the survey were designed to
measure the opinions of the students, instructors, and administrators about the
HL program. It is observed from the results that the majority of students,
instructors, and administrators showed a positive attitude toward HL, but some
had negative views and experienced challenges. The results were analyzed and
discussed to better utilize HL to meet the growing demands of the community.

Federated Learning (FL) is a suitable solution for making use of sensitive
data belonging to patients, people, companies, or industries that are
obligatory to work under rigid privacy constraints. FL mainly or partially
supports data privacy and security issues and provides an alternative to model
problems facilitating multiple edge devices or organizations to contribute a
training of a global model using a number of local data without having them.
Non-IID data of FL caused from its distributed nature presents a significant
performance degradation and stabilization skews. This paper introduces a novel
method dynamically balancing the data distributions of clients by augmenting
images to address the non-IID data problem of FL. The introduced method
remarkably stabilizes the model training and improves the model's test accuracy
from 83.22% to 89.43% for multi-chest diseases detection of chest X-ray images
in highly non-IID FL setting. The results of IID, non-IID and non-IID with
proposed method federated trainings demonstrated that the proposed method might
help to encourage organizations or researchers in developing better systems to
get values from data with respect to data privacy not only for healthcare but
also other fields.

Recent years have witnessed great advances in object segmentation research.
In addition to generic objects, aquatic animals have attracted research
attention. Deep learning-based methods are widely used for aquatic animal
segmentation and have achieved promising performance. However, there is a lack
of challenging datasets for benchmarking. In this work, we build a new dataset
dubbed "Aquatic Animal Species." We also devise a novel GUided mixup
augmeNtatioN and multi-viEw fusion for aquatic animaL segmentation (GUNNEL)
that leverages the advantages of multiple view segmentation models to
effectively segment aquatic animals and improves the training performance by
synthesizing hard samples. Extensive experiments demonstrated the superiority
of our proposed framework over existing state-of-the-art instance segmentation
methods.

The homepage recommendation on most E-commerce applications places items in a
hierarchical manner, where different channels display items in different
styles. Existing algorithms usually optimize the performance of a single
channel. So designing the model to achieve the optimal recommendation list
which maximize the Click-Through Rate (CTR) of whole homepage is a challenge
problem. Other than the accuracy objective, display diversity on the homepage
is also important since homogeneous display usually hurts user experience. In
this paper, we propose a two-stage architecture of the homepage recommendation
system. In the first stage, we develop efficient algorithms for recommending
items to proper channels while maintaining diversity. The two methods can be
combined: user-channel-item predictive model with diversity constraint. In the
second stage, we provide an ordered list of items in each channel. Existing
re-ranking models are hard to describe the mutual influence between items in
both intra-channel and inter-channel. Therefore, we propose a Deep \&
Hierarchical Attention Network Re-ranking (DHANR) model for homepage
recommender systems. The Hierarchical Attention Network consists of an item
encoder, an item-level attention layer, a channel encoder and a channel-level
attention layer. Our method achieves a significant improvement in terms of
precision, intra-list average distance(ILAD) and channel-wise Precision@k in
offline experiments and in terms of CTR and ILAD in our online systems.

Large-scale knowledge graphs provide structured representations of human
knowledge. However, as it is impossible to collect all knowledge, knowledge
graphs are usually incomplete. Reasoning based on existing facts paves a way to
discover missing facts. In this paper, we study the problem of learning logical
rules for reasoning on knowledge graphs for completing missing factual
triplets. Learning logical rules equips a model with strong interpretability as
well as the ability to generalize to similar tasks. We propose a model able to
fully use training data which also considers multi-target scenarios. In
addition, considering the deficiency in evaluating the performance of models
and the quality of mined rules, we further propose two novel indicators to help
with the problem. Experimental results empirically demonstrate that our model
outperforms state-of-the-art methods on five benchmark datasets. The results
also prove the effectiveness of the indicators.

Variable names are important to understand and maintain code. If a variable
name and the value stored in the variable do not match, then the program
suffers from a name-value inconsistency, which is due to one of two situations
that developers may want to fix: Either a correct value is referred to through
a misleading name, which negatively affects code understandability and
maintainability, or the correct name is bound to a wrong value, which may cause
unexpected runtime behavior. Finding name-value inconsistencies is hard because
it requires an understanding of the meaning of names and knowledge about the
values assigned to a variable at runtime. This paper presents Nalin, a
technique to automatically detect name-value inconsistencies. The approach
combines a dynamic analysis that tracks assignments of values to names with a
neural machine learning model that predicts whether a name and a value fit
together. To the best of our knowledge, this is the first work to formulate the
problem of finding coding issues as a classification problem over names and
runtime values. We apply Nalin to 106,652 real-world Python programs, where
meaningful names are particularly important due to the absence of statically
declared types. Our results show that the classifier detects name-value
inconsistencies with high accuracy, that the warnings reported by Nalin have a
precision of 80% and a recall of 76% w.r.t. a ground truth created in a user
study, and that our approach complements existing techniques for finding coding
issues.

Discovering hazardous scenarios is crucial in testing and further improving
driving policies. However, conducting efficient driving policy testing faces
two key challenges. On the one hand, the probability of naturally encountering
hazardous scenarios is low when testing a well-trained autonomous driving
strategy. Thus, discovering these scenarios by purely real-world road testing
is extremely costly. On the other hand, a proper determination of accident
responsibility is necessary for this task. Collecting scenarios with
wrong-attributed responsibilities will lead to an overly conservative
autonomous driving strategy. To be more specific, we aim to discover hazardous
scenarios that are autonomous-vehicle responsible (AV-responsible), i.e., the
vulnerabilities of the under-test driving policy.
  To this end, this work proposes a Safety Test framework by finding
Av-Responsible Scenarios (STARS) based on multi-agent reinforcement learning.
STARS guides other traffic participants to produce Av-Responsible Scenarios and
make the under-test driving policy misbehave via introducing Hazard Arbitration
Reward (HAR). HAR enables our framework to discover diverse, complex, and
AV-responsible hazardous scenarios. Experimental results against four different
driving policies in three environments demonstrate that STARS can effectively
discover AV-responsible hazardous scenarios. These scenarios indeed correspond
to the vulnerabilities of the under-test driving policies, thus are meaningful
for their further improvements.

Current non-rigid object keypoint detectors perform well on a chosen kind of
species and body parts, and require a large amount of labelled keypoints for
training. Moreover, their heatmaps, tailored to specific body parts, cannot
recognize novel keypoints (keypoints not labelled for training) on unseen
species. We raise an interesting yet challenging question: how to detect both
base (annotated for training) and novel keypoints for unseen species given a
few annotated samples? Thus, we propose a versatile Few-shot Keypoint Detection
(FSKD) pipeline, which can detect a varying number of keypoints of different
kinds. Our FSKD provides the uncertainty estimation of predicted keypoints.
Specifically, FSKD involves main and auxiliary keypoint representation
learning, similarity learning, and keypoint localization with uncertainty
modeling to tackle the localization noise. Moreover, we model the uncertainty
across groups of keypoints by multivariate Gaussian distribution to exploit
implicit correlations between neighboring keypoints. We show the effectiveness
of our FSKD on (i) novel keypoint detection for unseen species, and (ii)
few-shot Fine-Grained Visual Recognition (FGVR) and (iii) Semantic Alignment
(SA) downstream tasks. For FGVR, detected keypoints improve the classification
accuracy. For SA, we showcase a novel thin-plate-spline warping that uses
estimated keypoint uncertainty under imperfect keypoint corespondences.

The increasing prevalence of security attacks on software-intensive systems
calls for new, effective methods for detecting and responding to these attacks.
As one promising approach, game theory provides analytical tools for modeling
the interaction between the system and the adversarial environment and
designing reliable defense. In this paper, we propose an approach for securing
software-intensive systems using a rigorous game-theoretical framework. First,
a self-adaptation framework is deployed on a component-based software intensive
system, which periodically monitors the system for anomalous behaviors. A
learning-based method is proposed to detect possible on-going attacks on the
system components and predict potential threats to components. Then, an
algorithm is designed to automatically build a \emph{Bayesian game} based on
the system architecture (of which some components might have been compromised)
once an attack is detected, in which the system components are modeled as
independent players in the game. Finally, an optimal defensive policy is
computed by solving the Bayesian game to achieve the best system utility, which
amounts to minimizing the impact of the attack. We conduct two sets of
experiments on two general benchmark tasks for security domain. Moreover, we
systematically present a case study on a real-world water treatment testbed,
i.e. the Secure Water Treatment System. Experiment results show the
applicability and the effectiveness of our approach.

We analyze the convergence properties of the two-timescale fictitious play
combining the classical fictitious play with the Q-learning for two-player
zero-sum stochastic games with player-dependent learning rates. We show its
almost sure convergence under the standard assumptions in two-timescale
stochastic approximation methods when the discount factor is less than the
product of the ratios of player-dependent step sizes. To this end, we formulate
a novel Lyapunov function formulation and present a one-sided asynchronous
convergence result.

Providing omnidirectional depth along with RGB information is important for
numerous applications, eg, VR/AR. However, as omnidirectional RGB-D data is not
always available, synthesizing RGB-D panorama data from limited information of
a scene can be useful. Therefore, some prior works tried to synthesize RGB
panorama images from perspective RGB images; however, they suffer from limited
image quality and can not be directly extended for RGB-D panorama synthesis. In
this paper, we study a new problem: RGB-D panorama synthesis under the
arbitrary configurations of cameras and depth sensors. Accordingly, we propose
a novel bi-modal (RGB-D) panorama synthesis (BIPS) framework. Especially, we
focus on indoor environments where the RGB-D panorama can provide a complete 3D
model for many applications. We design a generator that fuses the bi-modal
information and train it with residual-aided adversarial learning (RDAL). RDAL
allows to synthesize realistic indoor layout structures and interiors by
jointly inferring RGB panorama, layout depth, and residual depth. In addition,
as there is no tailored evaluation metric for RGB-D panorama synthesis, we
propose a novel metric to effectively evaluate its perceptual quality.
Extensive experiments show that our method synthesizes high-quality indoor
RGB-D panoramas and provides realistic 3D indoor models than prior methods.
Code will be released upon acceptance.

CMOS sensors employ row-wise acquisition mechanism while imaging a scene,
which can result in undesired motion artifacts known as rolling shutter (RS)
distortions in the captured image. Existing single image RS rectification
methods attempt to account for these distortions by either using algorithms
tailored for specific class of scenes which warrants information of intrinsic
camera parameters or a learning-based framework with known ground truth motion
parameters. In this paper, we propose an end-to-end deep neural network for the
challenging task of single image RS rectification. Our network consists of a
motion block, a trajectory module, a row block, an RS rectification module and
an RS regeneration module (which is used only during training). The motion
block predicts camera pose for every row of the input RS distorted image while
the trajectory module fits estimated motion parameters to a third-order
polynomial. The row block predicts the camera motion that must be associated
with every pixel in the target i.e, RS rectified image. Finally, the RS
rectification module uses motion trajectory and the output of row block to warp
the input RS image to arrive at a distortionfree image. For faster convergence
during training, we additionally use an RS regeneration module which compares
the input RS image with the ground truth image distorted by estimated motion
parameters. The end-to-end formulation in our model does not constrain the
estimated motion to ground-truth motion parameters, thereby successfully
rectifying the RS images with complex real-life camera motion. Experiments on
synthetic and real datasets reveal that our network outperforms prior art both

Semi-supervised domain adaptation (SSDA) is quite a challenging problem
requiring methods to overcome both 1) overfitting towards poorly annotated data
and 2) distribution shift across domains. Unfortunately, a simple combination
of domain adaptation (DA) and semi-supervised learning (SSL) methods often fail
to address such two objects because of training data bias towards labeled
samples. In this paper, we introduce an adaptive structure learning method to
regularize the cooperation of SSL and DA. Inspired by the multi-views learning,
our proposed framework is composed of a shared feature encoder network and two
classifier networks, trained for contradictory purposes. Among them, one of the
classifiers is applied to group target features to improve intra-class density,
enlarging the gap of categorical clusters for robust representation learning.
Meanwhile, the other classifier, serviced as a regularizer, attempts to scatter
the source features to enhance the smoothness of the decision boundary. The
iterations of target clustering and source expansion make the target features
being well-enclosed inside the dilated boundary of the corresponding source
points. For the joint address of cross-domain features alignment and partially
labeled data learning, we apply the maximum mean discrepancy (MMD) distance
minimization and self-training (ST) to project the contradictory structures
into a shared view to make the reliable final decision. The experimental
results over the standard SSDA benchmarks, including DomainNet and Office-home,
demonstrate both the accuracy and robustness of our method over the

Measuring the performance of a classifier is a vital task in machine
learning. The running time of an algorithm that computes the measure plays a
very small role in an offline setting, for example, when the classifier is
being developed by a researcher. However, the running time becomes more crucial
if our goal is to monitor the performance of a classifier over time.
  In this paper we study three algorithms for maintaining two measures. The
first algorithm maintains area under the ROC curve (AUC) under addition and
deletion of data points in $O(\log n)$ time. This is done by maintaining the
data points sorted in a self-balanced search tree. In addition, we augment the
search tree that allows us to query the ROC coordinates of a data point in
$O(\log n)$ time. In doing so we are able to maintain AUC in $O(\log n)$ time.
Our next two algorithms involve in maintaining $H$-measure, an alternative
measure based on the ROC curve. Computing the measure is a two-step process:
first we need to compute a convex hull of the ROC curve, followed by a sum over
the convex hull. We demonstrate that we can maintain the convex hull using a
minor modification of the classic convex hull maintenance algorithm. We then
show that under certain conditions, we can compute the $H$-measure exactly in
$O(\log^2 n)$ time, and if the conditions are not met, then we can estimate the
$H$-measure in $O((\log n + \epsilon^{-1})\log n)$ time. We show empirically
that our methods are significantly faster than the baselines.

In image retrieval, deep local features learned in a data-driven manner have
been demonstrated effective to improve retrieval performance. To realize
efficient retrieval on large image database, some approaches quantize deep
local features with a large codebook and match images with aggregated match
kernel. However, the complexity of these approaches is non-trivial with large
memory footprint, which limits their capability to jointly perform feature
learning and aggregation. To generate compact global representations while
maintaining regional matching capability, we propose a unified framework to
jointly learn local feature representation and aggregation. In our framework,
we first extract deep local features using CNNs. Then, we design a tokenizer
module to aggregate them into a few visual tokens, each corresponding to a
specific visual pattern. This helps to remove background noise, and capture
more discriminative regions in the image. Next, a refinement block is
introduced to enhance the visual tokens with self-attention and
cross-attention. Finally, different visual tokens are concatenated to generate
a compact global representation. The whole framework is trained end-to-end with
image-level labels. Extensive experiments are conducted to evaluate our
approach, which outperforms the state-of-the-art methods on the Revisited
Oxford and Paris datasets.

Recent techniques to solve photorealistic style transfer within deep
convolutional neural networks (CNNs) generally require intensive training from
large-scale datasets, thus having limited applicability and poor generalization
ability to unseen images or styles. To overcome this, we propose a novel
framework, dubbed Deep Translation Prior (DTP), to accomplish photorealistic
style transfer through test-time training on given input image pair with
untrained networks, which learns an image pair-specific translation prior and
thus yields better performance and generalization. Tailored for such test-time
training for style transfer, we present novel network architectures, with two
sub-modules of correspondence and generation modules, and loss functions
consisting of contrastive content, style, and cycle consistency losses. Our
framework does not require offline training phase for style transfer, which has
been one of the main challenges in existing methods, but the networks are to be
solely learned during test-time. Experimental results prove that our framework
has a better generalization ability to unseen image pairs and even outperforms
the state-of-the-art methods.

Surrogates, models that mimic the behavior of programs, form the basis of a
variety of development workflows. We study three surrogate-based design
patterns, evaluating each in case studies on a large-scale CPU simulator.
  With surrogate compilation, programmers develop a surrogate that mimics the
behavior of a program to deploy to end-users in place of the original program.
Surrogate compilation accelerates the CPU simulator under study by $1.6\times$.
With surrogate adaptation, programmers develop a surrogate of a program then
retrain that surrogate on a different task. Surrogate adaptation decreases the
simulator's error by up to $50\%$. With surrogate optimization, programmers
develop a surrogate of a program, optimize input parameters of the surrogate,
then plug the optimized input parameters back into the original program.
Surrogate optimization finds simulation parameters that decrease the
simulator's error by $5\%$ compared to the error induced by expert-set
  In this paper we formalize this taxonomy of surrogate-based design patterns.
We further describe the programming methodology common to all three design
patterns. Our work builds a foundation for the emerging class of workflows
based on programming with surrogates of programs.

RGB-Infrared person re-identification (RGB-IR ReID) aims to associate people
across disjoint RGB and IR camera views. Currently, state-of-the-art
performance of RGB-IR ReID is not as impressive as that of conventional ReID.
Much of that is due to the notorious modality bias training issue brought by
the single-modality ImageNet pre-training, which might yield RGB-biased
representations that severely hinder the cross-modality image retrieval. This
paper makes first attempt to tackle the task from a pre-training perspective.
We propose a self-supervised pre-training solution, named Modality-Aware
Multiple Granularity Learning (MMGL), which directly trains models from scratch
only on multi-modal ReID datasets, but achieving competitive results against
ImageNet pre-training, without using any external data or sophisticated tuning
tricks. First, we develop a simple-but-effective 'permutation recovery' pretext
task that globally maps shuffled RGB-IR images into a shared latent permutation
space, providing modality-invariant global representations for downstream ReID
tasks. Second, we present a part-aware cycle-contrastive (PCC) learning
strategy that utilizes cross-modality cycle-consistency to maximize agreement
between semantically similar RGB-IR image patches. This enables contrastive
learning for the unpaired multi-modal scenarios, further improving the
discriminability of local features without laborious instance augmentation.
Based on these designs, MMGL effectively alleviates the modality bias training
problem. Extensive experiments demonstrate that it learns better
representations (+8.03% Rank-1 accuracy) with faster training speed (converge
only in few hours) and higher data efficiency (<5% data size) than ImageNet
pre-training. The results also suggest it generalizes well to various existing
models, losses and has promising transferability across datasets. The code will
be released.

The misunderstanding and incorrect configurations of cryptographic primitives
have exposed severe security vulnerabilities to attackers. Due to the
pervasiveness and diversity of cryptographic misuses, a comprehensive and
accurate understanding of how cryptographic misuses can undermine the security
of an Android app is critical to the subsequent mitigation strategies but also
challenging. Although various approaches have been proposed to detect
cryptographic misuses in Android apps, seldom studies have focused on
estimating the security risks introduced by cryptographic misuses. To address
this problem, we present an extensible framework for deciding the threat level
of cryptographic misuses in Android apps. Firstly, we propose a unified
specification for representing cryptographic misuses to make our framework
extensible and develop adapters to unify the detection results of the
state-of-the-art cryptographic misuse detectors, resulting in an adapter-based
detection toolchain for a more comprehensive list of cryptographic misuses.
Secondly, we employ a misuse-originating data-flow analysis to connect each
cryptographic misuse to a set of data-flow sinks in an app, based on which we
propose a quantitative data-flow-driven metric for assessing the overall risk
of the app introduced by cryptographic misuses. To make the per-app assessment
more useful in the app vetting at the app-store level, we apply unsupervised
learning to predict and classify the top risky threats, to guide more efficient
subsequent mitigations. In the experiments on an instantiated implementation of
the framework, we evaluate the accuracy of our detection and the effect of
data-flow-driven risk assessment of our framework. Our empirical study on over
40,000 apps as well as the analysis of popular apps reveals important security
observations on the real threats of cryptographic misuses in Android apps.

Data driven generative machine learning models have recently emerged as one
of the most promising approaches for new materials discovery. While the
generator models can generate millions of candidates, it is critical to train
fast and accurate machine learning models to filter out stable, synthesizable
materials with desired properties. However, such efforts to build supervised
regression or classification screening models have been severely hindered by
the lack of unstable or unsynthesizable samples, which usually are not
collected and deposited in materials databases such as ICSD and Materials
Project (MP). At the same time, there are a significant amount of unlabelled
data available in these databases. Here we propose a semi-supervised deep
neural network (TSDNN) model for high-performance formation energy and
synthesizability prediction, which is achieved via its unique teacher-student
dual network architecture and its effective exploitation of the large amount of
unlabeled data. For formation energy based stability screening, our
semi-supervised classifier achieves an absolute 10.3\% accuracy improvement
compared to the baseline CGCNN regression model. For synthesizability
prediction, our model significantly increases the baseline PU learning's true
positive rate from 87.9\% to 97.9\% using 1/49 model parameters.
  To further prove the effectiveness of our models, we combined our
TSDNN-energy and TSDNN-synthesizability models with our CubicGAN generator to
discover novel stable cubic structures. Out of 1000 recommended candidate
samples by our models, 512 of them have negative formation energies as
validated by our DFT formation energy calculations. Our experimental results
show that our semi-supervised deep neural networks can significantly improve
the screening accuracy in large-scale generative materials design.

Training neural machine translation (NMT) models in federated learning (FL)
settings could be inefficient both computationally and communication-wise, due
to the large size of translation engines as well as the multiple rounds of
updates required to train clients and a central server. In this paper, we
explore how to efficiently build NMT models in an FL setup by proposing a novel
solution. In order to reduce the communication overhead, out of all neural
layers we only exchange what we term "Controller" layers. Controllers are a
small number of additional neural components connected to our pre-trained
architectures. These new components are placed in between original layers. They
act as liaisons to communicate with the central server and learn minimal
information that is sufficient enough to update clients.
  We evaluated the performance of our models on five datasets from different
domains to translate from German into English. We noted that the models
equipped with Controllers preform on par with those trained in a central and
non-FL setting. In addition, we observed a substantial reduction in the
communication traffic of the FL pipeline, which is a direct consequence of
using Controllers. Based on our experiments, Controller-based models are ~6
times less expensive than their other peers. This reduction is significantly
important when we consider the number of parameters in large models and it
becomes even more critical when such parameters need to be exchanged for
multiple rounds in FL settings.

Subsampling is an important technique to tackle the computational challenges
brought by big data. Many subsampling procedures fall within the framework of
importance sampling, which assigns high sampling probabilities to the samples
appearing to have big impacts. When the noise level is high, those sampling
procedures tend to pick many outliers and thus often do not perform
satisfactorily in practice. To tackle this issue, we design a new Markov
subsampling strategy based on Huber criterion (HMS) to construct an informative
subset from the noisy full data; the constructed subset then serves as a
refined working data for efficient processing. HMS is built upon a
Metropolis-Hasting procedure, where the inclusion probability of each sampling
unit is determined using the Huber criterion to prevent over scoring the
outliers. Under mild conditions, we show that the estimator based on the
subsamples selected by HMS is statistically consistent with a sub-Gaussian
deviation bound. The promising performance of HMS is demonstrated by extensive
studies on large scale simulations and real data examples.

We present MVLayoutNet, an end-to-end network for holistic 3D reconstruction
from multi-view panoramas. Our core contribution is to seamlessly combine
learned monocular layout estimation and multi-view stereo (MVS) for accurate
layout reconstruction in both 3D and image space. We jointly train a layout
module to produce an initial layout and a novel MVS module to obtain accurate
layout geometry. Unlike standard MVSNet [33], our MVS module takes a
newly-proposed layout cost volume, which aggregates multi-view costs at the
same depth layer into corresponding layout elements. We additionally provide an
attention-based scheme that guides the MVS module to focus on structural
regions. Such a design considers both local pixel-level costs and global
holistic information for better reconstruction. Experiments show that our
method outperforms state-of-the-arts in terms of depth rmse by 21.7% and 20.6%
on the 2D-3D-S [1] and ZInD [5] datasets. Finally, our method leads to coherent
layout geometry that enables the reconstruction of an entire scene.

With the development of deep learning, Neural Network is commonly adopted to
the License Plate Detection (LPD) task and achieves much better performance and
precision, especially CNN-based networks can achieve state of the art
RetinaNet[1]. For a single object detection task such as LPD, modified general
object detection would be time-consuming, unable to cope with complex scenarios
and a cumbersome weights file that is too hard to deploy on the embedded
device.

The shift towards end-to-end deep learning has brought unprecedented advances
in many areas of computer vision. However, deep neural networks are trained on
images with resolutions that rarely exceed $1,000 \times 1,000$ pixels. The
growing use of scanners that create images with extremely high resolutions
(average can be $100,000 \times 100,000$ pixels) thereby presents novel
challenges to the field. Most of the published methods preprocess
high-resolution images into a set of smaller patches, imposing an a priori
belief on the best properties of the extracted patches (magnification, field of
view, location, etc.). Herein, we introduce Magnifying Networks (MagNets) as an
alternative deep learning solution for gigapixel image analysis that does not
rely on a preprocessing stage nor requires the processing of billions of
pixels. MagNets can learn to dynamically retrieve any part of a gigapixel
image, at any magnification level and field of view, in an end-to-end fashion
with minimal ground truth (a single global, slide-level label). Our results on
the publicly available Camelyon16 and Camelyon17 datasets corroborate to the
effectiveness and efficiency of MagNets and the proposed optimization framework
for whole slide image classification. Importantly, MagNets process far less
patches from each slide than any of the existing approaches ($10$ to $300$
times less).

We study the effect of adversarial perturbations of images on deep stereo
matching networks for the disparity estimation task. We present a method to
craft a single set of perturbations that, when added to any stereo image pair
in a dataset, can fool a stereo network to significantly alter the perceived
scene geometry. Our perturbation images are "universal" in that they not only
corrupt estimates of the network on the dataset they are optimized for, but
also generalize to different architectures trained on different datasets. We
evaluate our approach on multiple benchmark datasets where our perturbations
can increase the D1-error (akin to fooling rate) of state-of-the-art stereo
networks from 1% to as much as 87%. We investigate the effect of perturbations
on the estimated scene geometry and identify object classes that are most
vulnerable. Our analysis on the activations of registered points between left
and right images led us to find architectural components that can increase
robustness against adversaries. By simply designing networks with such
components, one can reduce the effect of adversaries by up to 60.5%, which
rivals the robustness of networks fine-tuned with costly adversarial data
augmentation. Our design principle also improves their robustness against
common image corruptions by an average of 70%.

Current pre-training methods in computer vision focus on natural images in
the daily-life context. However, abstract diagrams such as icons and symbols
are common and important in the real world. This work is inspired by Tangram, a
game that requires replicating an abstract pattern from seven dissected shapes.
By recording human experience in solving tangram puzzles, we present the
Tangram dataset and show that a pre-trained neural model on the Tangram helps
solve some mini visual tasks based on low-resolution vision. Extensive
experiments demonstrate that our proposed method generates intelligent
solutions for aesthetic tasks such as folding clothes and evaluating room
layouts. The pre-trained feature extractor can facilitate the convergence of
few-shot learning tasks on human handwriting and improve the accuracy in
identifying icons by their contours. The Tangram dataset is available at
https://github.com/yizhouzhao/Tangram.

Convolutional neural networks (CNNs) can now achieve human-level performance
on challenging object recognition tasks. CNNs are also the leading quantitative
models in terms of predicting neural and behavioral responses in visual
recognition tasks. However, there is a widely accepted critique of CNN models:
unlike newborn animals, which learn rapidly and efficiently, CNNs are thought
to be "data hungry," requiring massive amounts of training data to develop
accurate models for object recognition. This critique challenges the promise of
using CNNs as models of visual development. Here, we directly examined whether
CNNs are more data hungry than newborn animals by performing parallel
controlled-rearing experiments on newborn chicks and CNNs. We raised newborn
chicks in strictly controlled visual environments, then simulated the training
data available in that environment by constructing a virtual animal chamber in
a video game engine. We recorded the visual images acquired by an agent moving
through the virtual chamber and used those images to train CNNs. When CNNs
received similar visual training data as chicks, the CNNs successfully solved
the same challenging view-invariant object recognition tasks as the chicks.
Thus, the CNNs were not more data hungry than animals: both CNNs and chicks
successfully developed robust object models from training data of a single
object.

Many historical map sheets are publicly available for studies that require
long-term historical geographic data. The cartographic design of these maps
includes a combination of map symbols and text labels. Automatically reading
text labels from map images could greatly speed up the map interpretation and
helps generate rich metadata describing the map content. Many text detection
algorithms have been proposed to locate text regions in map images
automatically, but most of the algorithms are trained on out-ofdomain datasets
(e.g., scenic images). Training data determines the quality of machine learning
models, and manually annotating text regions in map images is labor-extensive
and time-consuming. On the other hand, existing geographic data sources, such
as Open- StreetMap (OSM), contain machine-readable map layers, which allow us
to separate out the text layer and obtain text label annotations easily.
However, the cartographic styles between OSM map tiles and historical maps are
significantly different. This paper proposes a method to automatically generate
an unlimited amount of annotated historical map images for training text
detection models. We use a style transfer model to convert contemporary map
images into historical style and place text labels upon them. We show that the
state-of-the-art text detection models (e.g., PSENet) can benefit from the
synthetic historical maps and achieve significant improvement for historical
map text detection.

A focused crawler aims at discovering as many web pages relevant to a target
topic as possible, while avoiding irrelevant ones. Reinforcement Learning (RL)
has been utilized to optimize focused crawling. In this paper, we propose TRES,
an RL-empowered framework for focused crawling. We model the crawling
environment as a Markov Decision Process, which the RL agent aims at solving by
determining a good crawling strategy. Starting from a few human provided
keywords and a small text corpus, that are expected to be relevant to the
target topic, TRES follows a keyword set expansion procedure, which guides
crawling, and trains a classifier that constitutes the reward function. To
avoid a computationally infeasible brute force method for selecting a best
action, we propose Tree-Frontier, a decision-tree-based algorithm that
adaptively discretizes the large state and action spaces and finds only a few
representative actions. Tree-Frontier allows the agent to be likely to select
near-optimal actions by being greedy over selecting the best representative
action. Experimentally, we show that TRES significantly outperforms
state-of-the-art methods in terms of harvest rate (ratio of relevant pages
crawled), while Tree-Frontier reduces by orders of magnitude the number of
actions needed to be evaluated at each timestep.

This paper proposes a working recipe of using Vision Transformer (ViT) in
class incremental learning. Although this recipe only combines existing
techniques, developing the combination is not trivial. Firstly, naive
application of ViT to replace convolutional neural networks (CNNs) in
incremental learning results in serious performance degradation. Secondly, we
nail down three issues of naively using ViT: (a) ViT has very slow convergence
when the number of classes is small, (b) more bias towards new classes is
observed in ViT than CNN-based architectures, and (c) the conventional learning
rate of ViT is too low to learn a good classifier layer. Finally, our solution,
named ViTIL (ViT for Incremental Learning) achieves new state-of-the-art on
both CIFAR and ImageNet datasets for all three class incremental learning
setups by a clear margin. We believe this advances the knowledge of transformer
in the incremental learning community. Code will be publicly released.

We show that the byproducts of the standard training process of a random
forest yield not only the well known and almost computationally free out-of-bag
point estimate of the model generalization error, but also give a direct path
to compute confidence intervals for the generalization error which avoids
processes of data splitting and model retraining. Besides the low computational
cost involved in their construction, these confidence intervals are shown
through simulations to have good coverage and appropriate shrinking rate of
their width in terms of the training sample size.

We propose a novel hardware-aware magnitude pruning technique for coherent
photonic neural networks. The proposed technique can prune 99.45% of network
parameters and reduce the static power consumption by 98.23% with a negligible
accuracy loss.

The study of network data in the social and health sciences frequently
concentrates on two distinct tasks (1) detecting community structures among
nodes and (2) associating covariate information to edge formation. In much of
this data, it is likely that the effects of covariates on edge formation differ
between communities (e.g. age might play a different role in friendship
formation in communities across a city). In this work, we introduce a latent
space network model where coefficients associated with certain covariates can
depend on latent community membership of the nodes. We show that ignoring such
structure can lead to either over- or under-estimation of covariate importance
to edge formation and propose a Markov Chain Monte Carlo approach for
simultaneously learning the latent community structure and the community
specific coefficients. We leverage efficient spectral methods to improve the
computational tractability of our approach.

Continuously-growing data volumes lead to larger generic models. Specific
use-cases are usually left out, since generic models tend to perform poorly in
domain-specific cases. Our work addresses this gap with a method for selecting
in-domain data from generic-domain (parallel text) corpora, for the task of
machine translation. The proposed method ranks sentences in parallel
general-domain data according to their cosine similarity with a monolingual
domain-specific data set. We then select the top K sentences with the highest
similarity score to train a new machine translation system tuned to the
specific in-domain data. Our experimental results show that models trained on
this in-domain data outperform models trained on generic or a mixture of
generic and domain data. That is, our method selects high-quality
domain-specific training instances at low computational cost and data size.

The advent of switches with programmable dataplanes has enabled the rapid
development of new network functionality, as well as providing a platform for
acceleration of a broad range of application-level functionality. However,
existing switch hardware was not designed with application acceleration in
mind, and thus applications requiring operations or datatypes not used in
traditional network protocols must resort to expensive workarounds.
Applications involving floating point data, including distributed training for
machine learning and distributed query processing, are key examples.
  In this paper, we propose FPISA, a floating point representation designed to
work efficiently in programmable switches. We first implement FPISA on an Intel
Tofino switch, but find that it has limitations that impact throughput and
accuracy. We then propose hardware changes to address these limitations based
on the open-source Banzai switch architecture, and synthesize them in a 15-nm
standard-cell library to demonstrate their feasibility. Finally, we use FPISA
to implement accelerators for training for machine learning and for query
processing, and evaluate their performance on a switch implementing our changes
using emulation. We find that FPISA allows distributed training to use 25-75%
fewer CPU cores and provide up to 85.9% better throughput in a CPU-constrained
environment than SwitchML. For distributed query processing with floating point
data, FPISA enables up to 2.7x better throughput than Spark.

Noisy intermediate-scale quantum (NISQ) devices build the first generation of
quantum computers. Quantum neural networks (QNNs) gained high interest as one
of the few suitable quantum algorithms to run on these NISQ devices. Most of
the QNNs exploit supervised training algorithms with quantum states in form of
pairs to learn their underlying relation. However, only little attention has
been given to unsupervised training algorithms despite interesting applications
where the quantum data does not occur in pairs. Here we propose an approach to
unsupervised learning and reproducing characteristics of any given set of
quantum states. We build a generative adversarial model using two dissipative
quantum neural networks (DQNNs), leading to the dissipative quantum generative
adversarial network (DQGAN). The generator DQNN aims to produce quantum states
similar to the training data while the discriminator DQNN aims to distinguish
the generator's output from the training data. We find that training both parts
in a competitive manner results in a well trained generative DQNN. We see our
contribution as a proof of concept for using DQGANs to learn and extend
unlabeled training sets.

Belief propagation is a fundamental message-passing algorithm for numerous
applications in machine learning. It is known that belief propagation algorithm
is exact on tree graphs. However, belief propagation is run on loopy graphs in
most applications. So, understanding the behavior of belief propagation on
loopy graphs has been a major topic for researchers in different areas. In this
paper, we study the convergence behavior of generalized belief propagation
algorithm on graphs with motifs (triangles, loops, etc.) We show under a
certain initialization, generalized belief propagation converges to the global
optimum of the Bethe free energy for ferromagnetic Ising models on graphs with
motifs.

Deep image prior (DIP) and its variants have showed remarkable potential for
solving inverse problems in computer vision, without any extra training data.
Practical DIP models are often substantially overparameterized. During the
fitting process, these models learn mostly the desired visual content first,
and then pick up the potential modeling and observational noise, i.e.,
overfitting. Thus, the practicality of DIP often depends critically on good
early stopping (ES) that captures the transition period. In this regard, the
majority of DIP works for vision tasks only demonstrates the potential of the
models -- reporting the peak performance against the ground truth, but provides
no clue about how to operationally obtain near-peak performance without access
to the groundtruth. In this paper, we set to break this practicality barrier of
DIP, and propose an efficient ES strategy, which consistently detects near-peak
performance across several vision tasks and DIP variants. Based on a simple
measure of dispersion of consecutive DIP reconstructions, our ES method not
only outpaces the existing ones -- which only work in very narrow domains, but
also remains effective when combined with a number of methods that try to
mitigate the overfitting. The code is available at
https://github.com/sun-umn/Early_Stopping_for_DIP.

A new multi-attention based method for solving the MIL problem (MAMIL), which
takes into account the neighboring patches or instances of each analyzed patch
in a bag, is proposed. In the method, one of the attention modules takes into
account adjacent patches or instances, several attention modules are used to
get a diverse feature representation of patches, and one attention module is
used to unite different feature representations to provide an accurate
classification of each patch (instance) and the whole bag. Due to MAMIL, a
combined representation of patches and their neighbors in the form of
embeddings of a small dimensionality for simple classification is realized.
Moreover, different types of patches are efficiently processed, and a diverse
feature representation of patches in a bag by using several attention modules
is implemented. A simple approach for explaining the classification predictions
of patches is proposed. Numerical experiments with various datasets illustrate

Graph neural networks (GNNs) learn node representations by passing and
aggregating messages between neighboring nodes. GNNs have been applied
successfully in several application domains and achieved promising performance.
However, GNNs could be vulnerable to structural noise because of the message
passing mechanism where noise may be propagated through the entire graph.
Although a series of robust GNNs have been proposed, they are evaluated with
different structural noises, and it lacks a systematic comparison with
consistent settings. In this work, we conduct a comprehensive and systematical
comparative study on different types of robust GNNs under consistent structural
noise settings. From the noise aspect, we design three different levels of
structural noises, i.e., local, community, and global noises. From the model
aspect, we select some representative models from sample-based, revision-based,
and construction-based robust GNNs. Based on the empirical results, we provide
some practical suggestions for robust GNNs selection.

Deep neural networks (DNNs) have been broadly adopted in health risk
prediction to provide healthcare diagnoses and treatments. To evaluate their
robustness, existing research conducts adversarial attacks in the
white/gray-box setting where model parameters are accessible. However, a more
realistic black-box adversarial attack is ignored even though most real-world
models are trained with private data and released as black-box services on the
cloud. To fill this gap, we propose the first black-box adversarial attack
method against health risk prediction models named MedAttacker to investigate
their vulnerability. MedAttacker addresses the challenges brought by EHR data
via two steps: hierarchical position selection which selects the attacked
positions in a reinforcement learning (RL) framework and substitute selection
which identifies substitute with a score-based principle. Particularly, by
considering the temporal context inside EHRs, it initializes its RL position
selection policy by using the contribution score of each visit and the saliency
score of each code, which can be well integrated with the deterministic
substitute selection process decided by the score changes. In experiments,
MedAttacker consistently achieves the highest average success rate and even
outperforms a recent white-box EHR adversarial attack technique in certain
cases when attacking three advanced health risk prediction models in the
black-box setting across multiple real-world datasets. In addition, based on
the experiment results we include a discussion on defending EHR adversarial
attacks.

Muscle-actuated control is a research topic that spans multiple domains,
including biomechanics, neuroscience, reinforcement learning, robotics, and
graphics. This type of control is particularly challenging as bodies are often
overactuated and dynamics are delayed and non-linear. It is however a very well
tested and tuned actuation mechanism that has undergone millions of years of
evolution with interesting properties exploiting passive forces and efficient
energy storage of muscle-tendon units. To facilitate research on
muscle-actuated simulation, we release a 3D musculoskeletal simulation of an
ostrich based on the MuJoCo physics engine. The ostrich is one of the fastest
bipeds on earth and therefore makes an excellent model for studying
muscle-actuated bipedal locomotion. The model is based on CT scans and
dissections used to collect actual muscle data, such as insertion sites,
lengths, and pennation angles. Along with this model, we also provide a set of
reinforcement learning tasks, including reference motion tracking, running, and
neck control, used to infer muscle actuation patterns. The reference motion
data is based on motion capture clips of various behaviors that we preprocessed
and adapted to our model. This paper describes how the model was built and
iteratively improved using the tasks. We also evaluate the accuracy of the
muscle actuation patterns by comparing them to experimentally collected
electromyographic data from locomoting birds. The results demonstrate the need
for rich reward signals or regularization techniques to constrain muscle
excitations and produce realistic movements. Overall, we believe that this work
can provide a useful bridge between fields of research interested in muscle
actuation.

With the recent success of deep learning algorithms, many researchers have
focused on generative models for human motion animation. However, the research
community lacks a platform for training and benchmarking various algorithms,
and the animation industry needs a toolkit for implementing advanced motion
synthesizing techniques. To facilitate the study of deep motion synthesis
methods for skeleton-based human animation and their potential applications in
practical animation making, we introduce \genmotion: a library that provides
unified pipelines for data loading, model training, and animation sampling with
various deep learning algorithms. Besides, by combining Python coding in the
animation software \genmotion\ can assist animators in creating real-time 3D
character animation. Source code is available at
https://github.com/realvcla/GenMotion/.

The next generation of satellite constellations is designed to better address
the future needs of our connected society: highly-variable data demand, mobile
connectivity, and reaching more under-served regions. Artificial Intelligence
(AI) and learning-based methods are expected to become key players in the
industry, given the poor scalability and slow reaction time of current resource
allocation mechanisms. While AI frameworks have been validated for isolated
communication tasks or subproblems, there is still not a clear path to achieve
fully-autonomous satellite systems. Part of this issue results from the focus
on subproblems when designing models, instead of the necessary system-level
perspective. In this paper we try to bridge this gap by characterizing the
system-level needs that must be met to increase satellite autonomy, and
introduce three AI-based components (Demand Estimator, Offline Planner, and
Real Time Engine) that jointly address them. We first do a broad literature
review on the different subproblems and identify the missing links to the
system-level goals. In response to these gaps, we outline the three necessary
components and highlight their interactions. We also discuss how current models
can be incorporated into the framework and possible directions of future work.

Sample efficiency is crucial for imitation learning methods to be applicable
in real-world applications. Many studies improve sample efficiency by extending
adversarial imitation to be off-policy regardless of the fact that these
off-policy extensions could either change the original objective or involve
complicated optimization. We revisit the foundation of adversarial imitation
and propose an off-policy sample efficient approach that requires no
adversarial training or min-max optimization. Our formulation capitalizes on
two key insights: (1) the similarity between the Bellman equation and the
stationary state-action distribution equation allows us to derive a novel
temporal difference (TD) learning approach; and (2) the use of a deterministic
policy simplifies the TD learning. Combined, these insights yield a practical
algorithm, Deterministic and Discriminative Imitation (D2-Imitation), which
operates by first partitioning samples into two replay buffers and then
learning a deterministic policy via off-policy reinforcement learning. Our
empirical results show that D2-Imitation is effective in achieving good sample
efficiency, outperforming several off-policy extension approaches of
adversarial imitation on many control tasks.

Traditionally, clustered federated learning groups clients with the same data
distribution into a cluster, so that every client is uniquely associated with
one data distribution and helps train a model for this distribution. We relax
this hard association assumption to soft clustered federated learning, which
allows every local dataset to follow a mixture of multiple source
distributions. We propose FedSoft, which trains both locally personalized
models and high-quality cluster models in this setting. FedSoft limits client
workload by using proximal updates to require the completion of only one
optimization task from a subset of clients in every communication round. We
show, analytically and empirically, that FedSoft effectively exploits
similarities between the source distributions to learn personalized and cluster
models that perform well.

The state-of-the-art speech enhancement has limited performance in speech
estimation accuracy. Recently, in deep learning, the Transformer shows the
potential to exploit the long-range dependency in speech by self-attention.
Therefore, it is introduced in speech enhancement to improve the speech
estimation accuracy from a noise mixture. However, to address the computational
cost issue in Transformer with self-attention, the axial attention is the
option i.e., to split a 2D attention into two 1D attentions. Inspired by the
axial attention, in the proposed method we calculate the attention map along
both time- and frequency-axis to generate time and frequency sub-attention
maps. Moreover, different from the axial attention, the proposed method
provides two parallel multi-head attentions for time- and frequency-axis.
Furthermore, it is proven in the literature that the lower frequency-band in
speech, generally, contains more desired information than the higher
frequency-band, in a noise mixture. Therefore, the frequency-band aware
attention is proposed i.e., high frequency-band attention (HFA), and low
frequency-band attention (LFA). The U-shaped Transformer is also first time
introduced in the proposed method to further improve the speech estimation
accuracy. The extensive evaluations over four public datasets, confirm the

In the process industry, condition monitoring systems with automated fault
diagnosis methods assist human experts and thereby improve maintenance
efficiency, process sustainability, and workplace safety. Improving the
automated fault diagnosis methods using data and machine learning-based models
is a central aspect of intelligent fault diagnosis (IFD). A major challenge in
IFD is to develop realistic datasets with accurate labels needed to train and
validate models, and to transfer models trained with labeled lab data to
heterogeneous process industry environments. However, fault descriptions and
work-orders written by domain experts are increasingly digitised in modern
condition monitoring systems, for example in the context of rotating equipment
monitoring. Thus, domain-specific knowledge about fault characteristics and
severities exists as technical language annotations in industrial datasets.
Furthermore, recent advances in natural language processing enable weakly
supervised model optimisation using natural language annotations, most notably
in the form of natural language supervision (NLS). This creates a timely
opportunity to develop technical language supervision (TLS) solutions for IFD
systems grounded in industrial data, for example as a complement to
pre-training with lab data to address problems like overfitting and inaccurate
out-of-sample generalisation. We surveyed the literature and identify a
considerable improvement in the maturity of NLS over the last two years,
facilitating applications beyond natural language; a rapid development of weak
supervision methods; and transfer learning as a current trend in IFD which can
benefit from these developments. Finally we describe a general framework for
TLS and implement a TLS case study based on SentenceBERT and contrastive
learning based zero-shot inference on annotated industry data.

As data lakes become increasingly popular in large enterprises today, there
is a growing need to tag or classify data assets (e.g., files and databases) in
data lakes with additional metadata (e.g., semantic column-types), as the
inferred metadata can enable a range of downstream applications like data
governance (e.g., GDPR compliance), and dataset search. Given the sheer size of
today's enterprise data lakes with petabytes of data and millions of data
assets, it is imperative that data assets can be ``auto-tagged'', using
lightweight inference algorithms and minimal user input. In this work, we
develop Auto-Tag, a corpus-driven approach that automates data-tagging of
\textit{custom} data types in enterprise data lakes. Using Auto-Tag, users only
need to provide \textit{one} example column to demonstrate the desired
data-type to tag. Leveraging an index structure built offline using a
lightweight scan of the data lake, which is analogous to pre-training in
machine learning, Auto-Tag can infer suitable data patterns to best
``describe'' the underlying ``domain'' of the given column at an interactive
speed, which can then be used to tag additional data of the same ``type'' in
data lakes. The Auto-Tag approach can adapt to custom data-types, and is shown
to be both accurate and efficient. Part of Auto-Tag ships as a
``custom-classification'' feature in a cloud-based data governance and catalog
solution \textit{Azure Purview}.

Similar to how differences in the proficiency of the cardiovascular and
musculoskeletal system predict an individual's athletic ability, differences in
how the same brain region encodes information across individuals may explain
their behavior. However, when studying how the brain encodes information,
researchers choose different neuroimaging tasks (e.g., language or motor
tasks), which can rely on processing different types of information and can
modulate different brain regions. We hypothesize that individual differences in
how information is encoded in the brain are task-specific and predict different
behavior measures. We propose a framework using encoding-models to identify
individual differences in brain encoding and test if these differences can
predict behavior. We evaluate our framework using task functional magnetic
resonance imaging data. Our results indicate that individual differences
revealed by encoding-models are a powerful tool for predicting behavior, and
that researchers should optimize their choice of task and encoding-model for
their behavior of interest.

The advancement of deep learning has led to the development of neural
decoders for low latency communications. However, neural decoders can be very
complex which can lead to increased computation and latency. We consider
iterative pruning approaches (such as the lottery ticket hypothesis algorithm)
to prune weights in neural decoders. Decoders with fewer number of weights can
have lower latency and lower complexity while retaining the accuracy of the
original model. This will make neural decoders more suitable for mobile and
other edge devices with limited computational power. We also propose semi-soft
decision decoding for neural decoders which can be used to improve the bit
error rate performance of the pruned network.

Unsupervised domain adaptation (UDA) has shown remarkable results in bearing
fault diagnosis under changing working conditions in recent years. However,
most UDA methods do not consider the geometric structure of the data.
Furthermore, the global domain adaptation technique is commonly applied, which
ignores the relation between subdomains. This paper addresses mentioned
challenges by presenting the novel deep subdomain adaptation graph convolution
neural network (DSAGCN), which has two key characteristics: First, graph
convolution neural network (GCNN) is employed to model the structure of data.
Second, adversarial domain adaptation and local maximum mean discrepancy (LMMD)
methods are applied concurrently to align the subdomain's distribution and
reduce structure discrepancy between relevant subdomains and global domains.
CWRU and Paderborn bearing datasets are used to validate the DSAGCN method's
efficiency and superiority between comparison models. The experimental results
demonstrate the significance of aligning structured subdomains along with
domain adaptation methods to obtain an accurate data-driven model in
unsupervised fault diagnosis.

We study whether a planner can robustly implement a state-contingent social
choice function when (i) agents must incur a cost to learn the state and (ii)
the planner faces uncertainty regarding agents' preferences over outcomes,
information costs, and beliefs and higher-order beliefs about one another's
payoffs. We propose mechanisms that can approximately implement any desired
social choice function when the perturbations concerning agents' payoffs have
small ex ante probability. The mechanism is also robust to trembles in agents'
strategies and when agents receive noisy information about the state.

Data augmentation is an important technique to reduce overfitting and improve
learning performance, but existing works on data augmentation for 3D point
cloud data are based on heuristics. In this work, we instead propose to
automatically learn a data augmentation strategy using bilevel optimization. An
augmentor is designed in a similar fashion to a conditional generator and is
optimized by minimizing a base model's loss on a validation set when the
augmented input is used for training the model. This formulation provides a
more principled way to learn data augmentation on 3D point clouds. We evaluate
our approach on standard point cloud classification tasks and a more
challenging setting with pose misalignment between training and validation/test
sets. The proposed strategy achieves competitive performance on both tasks and
we provide further insight into the augmentor's ability to learn the validation
set distribution.

Retrosynthetic planning problem is to analyze a complex molecule and give a
synthetic route using simple building blocks. The huge number of chemical
reactions leads to a combinatorial explosion of possibilities, and even the
experienced chemists could not select the most promising transformations. The
current approaches rely on human-defined or machine-trained score functions
which have limited chemical knowledge or use expensive estimation methods such
as rollout to guide the search. In this paper, we propose {\tt MCTS}, a novel
MCTS-based retrosynthetic planning approach, to deal with retrosynthetic
planning problem. Instead of exploiting rollout, we build an Experience
Guidance Network to learn knowledge from synthetic experiences during the
search. Experiments on benchmark USPTO datasets show that, our {\tt MCTS} gains
significant improvement over state-of-the-art approaches both in efficiency and

The interpretation of the electrocardiogram (ECG) gives clinical information
and helps in assessing heart function. There are distinct ECG patterns
associated with a specific class of arrythmia. The convolutional neural network
is currently one of the most commonly employed deep learning algorithms for ECG
processing. However, deep learning models require many hyperparameters to tune.
Selecting an optimal or best hyperparameter for the convolutional neural
network algorithm is a highly challenging task. Often, we end up tuning the
model manually with different possible ranges of values until a best fit model
is obtained. Automatic hyperparameters tuning using Bayesian optimisation (BO)
and evolutionary algorithms can provide an effective solution to current
labour-intensive manual configuration approaches. In this paper, we propose to
optimise the Residual one Dimensional Convolutional Neural Network model
(R-1D-CNN) at two levels. At the first level, a residual convolutional layer
and one-dimensional convolutional neural layers are trained to learn
patient-specific ECG features over which multilayer perceptron layers can learn
to produce the final class vectors of each input. This level is manual and aims
to lower the search space. The second level is automatic and based on our
proposed BO-based algorithm. Our proposed optimised R-1D-CNN architecture is
evaluated on two publicly available ECG Datasets. Comparative experimental
results demonstrate that our BO-based algorithm achieves an optimal rate of
99.95%, while the baseline model achieves 99.70% for the MIT-BIH database.
Moreover, experiments demonstrate that the proposed architecture fine-tuned
with BO achieves a higher accuracy than the other proposed architectures. Our
optimised architecture achieves excellent results compared to previous works on

We aim to improve the performance of multi-agent flocking behavior by
quantifying the structural significance of each agent. We designed a confidence
score(ConfScore) to measure the spatial significance of each agent. The score
will be used by an auxiliary controller to refine the velocity of agents. The
agents will be enforced to follow the motion of the leader agents whose
ConfScores are high. We demonstrate the efficacy of the auxiliary controller by
applying it to several existing algorithms including learning-based and
non-learning-based methods. Furthermore, we examined how the auxiliary
controller can help improve the performance under different settings of
communication radius, number of agents and maximum initial velocity.

We study real-time collaborative robot (cobot) handling, where the cobot
maneuvers a workpiece under human commands. This is useful when it is risky for
humans to directly handle the workpiece. However, it is hard to make the cobot
both easy to command and flexible in possible operations. In this work, we
propose a Real-Time Collaborative Robot Handling (RTCoHand) framework that
allows the control of cobot via user-customized dynamic gestures. This is hard
due to variations among users, human motion uncertainties, and noisy human
input. We model the task as a probabilistic generative process, referred to as
Conditional Collaborative Handling Process (CCHP), and learn from human-human
collaboration. We thoroughly evaluate the adaptability and robustness of CCHP
and apply our approach to a real-time cobot handling task with Kinova Gen3
robot arm. We achieve seamless human-robot collaboration with both experienced
and new users. Compared to classical controllers, RTCoHand allows significantly
more complex maneuvers and lower user cognitive burden. It also eliminates the
need for trial-and-error, rendering it advantageous in safety-critical tasks.

We present an architecture where a feedback controller derived on an
approximate model of the environment assists the learning process to enhance
its data efficiency. This architecture, which we term as Control-Tutored
Q-learning (CTQL), is presented in two alternative flavours. The former is
based on defining the reward function so that a Boolean condition can be used
to determine when the control tutor policy is adopted, while the latter, termed
as probabilistic CTQL (pCTQL), is instead based on executing calls to the tutor
with a certain probability during learning. Both approaches are validated, and
thoroughly benchmarked against Q-Learning, by considering the stabilization of
an inverted pendulum as defined in OpenAI Gym as a representative problem.

We introduce a three stage pipeline: resized-diverse-inputs (RDIM),
diversity-ensemble (DEM) and region fitting, that work together to generate
transferable adversarial examples. We first explore the internal relationship
between existing attacks, and propose RDIM that is capable of exploiting this
relationship. Then we propose DEM, the multi-scale version of RDIM, to generate
multi-scale gradients. After the first two steps we transform value fitting
into region fitting across iterations. RDIM and region fitting do not require
extra running time and these three steps can be well integrated into other
attacks. Our best attack fools six black-box defenses with a 93% success rate
on average, which is higher than the state-of-the-art gradient-based attacks.
Besides, we rethink existing attacks rather than simply stacking new methods on
the old ones to get better performance. It is expected that our findings will
serve as the beginning of exploring the internal relationship between attack
methods. Codes are available at https://github.com/278287847/DEM.

Contextual bandit algorithms are widely used in domains where it is desirable
to provide a personalized service by leveraging contextual information, that
may contain sensitive information that needs to be protected. Inspired by this
scenario, we study the contextual linear bandit problem with differential
privacy (DP) constraints. While the literature has focused on either
centralized (joint DP) or local (local DP) privacy, we consider the shuffle
model of privacy and we show that is possible to achieve a privacy/utility
trade-off between JDP and LDP. By leveraging shuffling from privacy and
batching from bandits, we present an algorithm with regret bound
$\widetilde{\mathcal{O}}(T^{2/3}/\varepsilon^{1/3})$, while guaranteeing both
central (joint) and local privacy. Our result shows that it is possible to
obtain a trade-off between JDP and LDP by leveraging the shuffle model while
preserving local privacy.

Stochastic gradient descent (SGD) is a cornerstone of machine learning. When
the number N of data items is large, SGD relies on constructing an unbiased
estimator of the gradient of the empirical risk using a small subset of the
original dataset, called a minibatch. Default minibatch construction involves
uniformly sampling a subset of the desired size, but alternatives have been
explored for variance reduction. In particular, experimental evidence suggests
drawing minibatches from determinantal point processes (DPPs), distributions
over minibatches that favour diversity among selected items. However, like in
recent work on DPPs for coresets, providing a systematic and principled
understanding of how and why DPPs help has been difficult. In this work, we
contribute an orthogonal polynomial-based DPP paradigm for minibatch sampling
in SGD. Our approach leverages the specific data distribution at hand, which
endows it with greater sensitivity and power over existing data-agnostic
methods. We substantiate our method via a detailed theoretical analysis of its
convergence properties, interweaving between the discrete data set and the
underlying continuous domain. In particular, we show how specific DPPs and a
string of controlled approximations can lead to gradient estimators with a
variance that decays faster with the batchsize than under uniform sampling.
Coupled with existing finite-time guarantees for SGD on convex objectives, this
entails that, DPP minibatches lead to a smaller bound on the mean square
approximation error than uniform minibatches. Moreover, our estimators are
amenable to a recent algorithm that directly samples linear statistics of DPPs
(i.e., the gradient estimator) without sampling the underlying DPP (i.e., the
minibatch), thereby reducing computational overhead. We provide detailed
synthetic as well as real data experiments to substantiate our theoretical
claims.

Recent advances in Internet of Things (IoT) and the rising of the Internet of
Behavior (IoB) have made it possible to develop real-time improved traveler
assistance tools for mobile phones, assisted by cloud-based machine learning,
and using fog computing in between IoT and the Cloud. Within the
Horizon2020-funded mF2C project an Android app has been developed exploiting
the proximity marketing concept and covers the essential path through the
airport onto the flight, from the least busy security queue through to the time
to walk to gate, gate changes, and other obstacles. It gives chance to
travelers to discover the facilities of the airport, aided by a recommender
system using machine learning, that can make recommendations and offer voucher
according with the traveler's preferences or on similarities to other
travelers. The system provides obvious benefits to the airport planners, not
only people tracking in the shops area, but also aggregated and anonymized
view, like heat maps that can highlight bottlenecks in the infrastructure, or
suggest situations that require intervention, such as emergencies. With the
emerging of the COVID pandemic the tool could be adapted to help in the social
distancing to guarantee safety. The use of the fog-to-cloud platform and the
fulfilling of all centricity and privacy requirements of the IoB give evidence
of the impact of the solution in a smart city environment.

Multi-view stereo (MVS) is a crucial task for precise 3D reconstruction. Most
recent studies tried to improve the performance of matching cost volume in MVS
by designing aggregated 3D cost volumes and their regularization. This paper
focuses on learning a robust feature extraction network to enhance the
performance of matching costs without heavy computation in the other steps. In
particular, we present a dynamic scale feature extraction network, namely,
CDSFNet. It is composed of multiple novel convolution layers, each of which can
select a proper patch scale for each pixel guided by the normal curvature of
the image surface. As a result, CDFSNet can estimate the optimal patch scales
to learn discriminative features for accurate matching computation between
reference and source images. By combining the robust extracted features with an
appropriate cost formulation strategy, our resulting MVS architecture can
estimate depth maps more precisely. Extensive experiments showed that the
proposed method outperforms other state-of-the-art methods on complex outdoor
scenes. It significantly improves the completeness of reconstructed models. As
a result, the method can process higher resolution inputs within faster
run-time and lower memory than other MVS methods. Our source code is available
at url{https://github.com/TruongKhang/cds-mvsnet}.

We present a formalisation of finite Markov decision processes with rewards
in the Isabelle theorem prover. We focus on the foundations required for
dynamic programming and the use of reinforcement learning agents over such
processes. In particular, we derive the Bellman equation from first principles
(in both scalar and vector form), derive a vector calculation that produces the
expected value of any policy p, and go on to prove the existence of a
universally optimal policy where there is a discounting factor less than one.
Lastly, we prove that the value iteration and the policy iteration algorithms
work in finite time, producing an epsilon-optimal and a fully optimal policy

While physical activity has been described as a primary prevention against
chronic diseases, strenuous physical exertion under adverse ambient conditions
has also been reported as a major contributor to exacerbation of chronic
respiratory conditions. Maintaining a balance by monitoring the type and the
level of physical activities of affected individuals, could help in reducing
the cost and burden of managing respiratory ailments. This paper explores the
potentiality of motion sensors in Smartphones to estimate physical activity
thresholds that could trigger symptoms of exercise induced respiratory
conditions (EiRCs). The focus is on the extraction of measurements from the
embedded motion sensors to determine the activity level and the type of
activity that is tolerable to individuals respiratory health. The calculations
are based on the correlation between Signal Magnitude Area (SMA) and Energy
Expenditure (EE). We also consider the effect of changes in the ambient
conditions like temperature and humidity, as contributing factors to
respiratory distress during physical exercise. Real time data collected from
healthy individuals were used to demonstrate the potentiality of a mobile phone
as tool to regulate the level of physical activities of individuals with EiRCs.
We describe a practical situation where the experimental outcomes can be
applied to promote good respiratory health.

This paper aims to tackle the challenging task of one-shot object counting.
Given an image containing novel, previously unseen category objects, the goal
of the task is to count all instances in the desired category with only one
supporting bounding box example. To this end, we propose a counting model by
which you only need to Look At One instance (LaoNet). First, a feature
correlation module combines the Self-Attention and Correlative-Attention
modules to learn both inner-relations and inter-relations. It enables the
network to be robust to the inconsistency of rotations and sizes among
different instances. Second, a Scale Aggregation mechanism is designed to help
extract features with different scale information. Compared with existing
few-shot counting methods, LaoNet achieves state-of-the-art results while
learning with a high convergence speed. The code will be available soon.

We present a new active model-learning approach to generating abstractions of
a system implementation, as finite state automata (FSAs), from execution
traces. Given an implementation and a set of observable system variables, the
generated automata admit all system behaviours over the given variables and
provide useful insight in the form of invariants that hold on the
implementation. To achieve this, the proposed approach uses a pluggable model
learning component that can generate an FSA from a given set of traces.
Conditions that encode a completeness hypothesis are then extracted from the
FSA under construction and used to evaluate its degree of completeness by
checking their truth value against the system using software model checking.
This generates new traces that express any missing behaviours. The new trace
data is used to iteratively refine the abstraction, until all system behaviours
are admitted by the learned abstraction. To evaluate the approach, we
reverse-engineer a set of publicly available Simulink Stateflow models from
their C implementations.

Interpretability has attracted increasing attention in earth observation
problems. We apply interactive visualization and representation analysis to
guide interpretation of glacier segmentation models. We visualize the
activations from a U-Net to understand and evaluate the model performance. We
build an online interface using the Shiny R package to provide comprehensive
error analysis of the predictions. Users can interact with the panels and
discover model failure modes. Further, we discuss how visualization can provide
sanity checks during data preprocessing and model training.

This paper uses techniques from Random Matrix Theory to find the ideal
training-testing data split for a simple linear regression with m data points,
each an independent n-dimensional multivariate Gaussian. It defines "ideal" as
satisfying the integrity metric, i.e. the empirical model error is the actual
measurement noise, and thus fairly reflects the value or lack of same of the
model. This paper is the first to solve for the training and test size for any
model in a way that is truly optimal. The number of data points in the training
set is the root of a quartic polynomial Theorem 1 derives which depends only on
m and n; the covariance matrix of the multivariate Gaussian, the true model
parameters, and the true measurement noise drop out of the calculations. The
critical mathematical difficulties were realizing that the problems herein were
discussed in the context of the Jacobi Ensemble, a probability distribution
describing the eigenvalues of a known random matrix model, and evaluating a new
integral in the style of Selberg and Aomoto. Mathematical results are supported
with thorough computational evidence. This paper is a step towards automatic
choices of training/test set sizes in machine learning.

Acquiring the most representative examples via active learning (AL) can
benefit many data-dependent computer vision tasks by minimizing efforts of
image-level or pixel-wise annotations. In this paper, we propose a novel
Collaborative Panoptic-Regional Active Learning framework (CPRAL) to address
the semantic segmentation task. For a small batch of images initially sampled
with pixel-wise annotations, we employ panoptic information to initially select
unlabeled samples. Considering the class imbalance in the segmentation dataset,
we import a Regional Gaussian Attention module (RGA) to achieve
semantics-biased selection. The subset is highlighted by vote entropy and then
attended by Gaussian kernels to maximize the biased regions. We also propose a
Contextual Labels Extension (CLE) to boost regional annotations with contextual
attention guidance. With the collaboration of semantics-agnostic panoptic
matching and regionbiased selection and extension, our CPRAL can strike a
balance between labeling efforts and performance and compromise the semantics
distribution. We perform extensive experiments on Cityscapes and BDD10K
datasets and show that CPRAL outperforms the cutting-edge methods with
impressive results and less labeling proportion.

This paper proposes a hybrid quantum-classical algorithm that learns a
suitable quantum feature map that separates unlabelled data that is originally
non linearly separable in the classical space using a Variational quantum
feature map and q-means as a subroutine for unsupervised learning. The
objective of the Variational circuit is to maximally separate the clusters in
the quantum feature Hilbert space. First part of the circuit embeds the
classical data into quantum states. Second part performs unsupervised learning
on the quantum states in the quantum feature Hilbert space using the q-means
quantum circuit. The output of the quantum circuit are characteristic cluster
quantum states that represent a superposition of all quantum states belonging
to a particular cluster. The final part of the quantum circuit performs
measurements on the characteristic cluster quantum states to output the
inter-cluster overlap based on fidelity. The output of the complete quantum
circuit is used to compute the value of the cost function that is based on the
Hilbert-Schmidt distance between the density matrices of the characteristic
cluster quantum states. The gradient of the expectation value is used to
optimize the parameters of the variational circuit to learn a better quantum
feature map.

The generalization gap on the long-tailed data sets is largely owing to most
categories only occupying a few training samples. Decoupled training achieves
better performance by training backbone and classifier separately. What causes
the poorer performance of end-to-end model training (e.g., logits margin-based
methods)? In this work, we identify a key factor that affects the learning of
the classifier: the channel-correlated features with low entropy before
inputting into the classifier. From the perspective of information theory, we
analyze why cross-entropy loss tends to produce highly correlated features on
the imbalanced data. In addition, we theoretically analyze and prove its
impacts on the gradients of classifier weights, the condition number of
Hessian, and logits margin-based approach. Therefore, we firstly propose to use
Channel Whitening to decorrelate ("scatter") the classifier's inputs for
decoupling the weight update and reshaping the skewed decision boundary, which
achieves satisfactory results combined with logits margin-based method.
However, when the number of minor classes are large, batch imbalance and more
participation in training cause over-fitting of the major classes. We also
propose two novel modules, Block-based Relatively Balanced Batch Sampler (B3RS)
and Batch Embedded Training (BET) to solve the above problems, which makes the
end-to-end training achieve even better performance than decoupled training.
Experimental results on the long-tailed classification benchmarks, CIFAR-LT and
ImageNet-LT, demonstrate the effectiveness of our method.

Wire harnesses are essential connecting components in manufacturing industry
but are challenging to be automated in industrial tasks such as bin picking.
They are long, flexible and tend to get entangled when randomly placed in a
bin. This makes the robot struggle to pick a single one from the clutter.
Besides, modeling wire harnesses is difficult due to the complex structures of
combining deformable cables with rigid components, making it unsuitable for
training or collecting data in simulation. In this work, instead of directly
lifting wire harnesses, we proposed to grasp and extract the target following
circle-like trajectories until it is separated from the clutter. We learn a
policy from real-world data to infer the optimal action and grasp from visual
observation. Our policy enables the robot to perform non-tangle pickings
efficiently by maximizing success rates and reducing the execution time. To
evaluate our policy, we present a set of real-world experiments on picking wire
harnesses. Results show a significant improvement in success rates from 49.2%
to 84.6% over the tangle-agnostic bin picking method. We also evaluate the
effectiveness of our policy under different clutter scenarios using unseen
types of wire harnesses. The proposed method is expected to provide a practical
solution for automating manufacturing processes with wire harnesses.

Spontaneous parametric down-conversion in quantum optics is an invaluable
resource for the realization of high-dimensional qudits with spatial modes of
light. One of the main open challenges is how to directly generate a desirable
qudit state in the SPDC process. This problem can be addressed through advanced
computational learning methods; however, due to difficulties in modeling the
SPDC process by a fully differentiable algorithm that takes into account all
interaction effects, progress has been limited. Here, we overcome these
limitations and introduce a physically-constrained and differentiable model,
validated against experimental results for shaped pump beams and structured
crystals, capable of learning every interaction parameter in the process. We
avoid any restrictions induced by the stochastic nature of our physical model
and integrate the dynamic equations governing the evolution under the SPDC
Hamiltonian. We solve the inverse problem of designing a nonlinear quantum
optical system that achieves the desired quantum state of down-converted photon
pairs. The desired states are defined using either the second-order
correlations between different spatial modes or by specifying the required
density matrix. By learning nonlinear volume holograms as well as different
pump shapes, we successfully show how to generate maximally entangled states.
Furthermore, we simulate all-optical coherent control over the generated
quantum state by actively changing the profile of the pump beam. Our work can
be useful for applications such as novel designs of high-dimensional quantum
key distribution and quantum information processing protocols. In addition, our
method can be readily applied for controlling other degrees of freedom of light
in the SPDC process, such as the spectral and temporal properties, and may even
be used in condensed-matter systems having a similar interaction Hamiltonian.

In recent years, there have been great advances in the field of decentralized
learning with private data. Federated learning (FL) and split learning (SL) are
two spearheads possessing their pros and cons, and are suited for many user
clients and large models, respectively. To enjoy both benefits, hybrid
approaches such as SplitFed have emerged of late, yet their fundamentals have
still been illusive. In this work, we first identify the fundamental
bottlenecks of SL, and thereby propose a scalable SL framework, coined SGLR.
The server under SGLR broadcasts a common gradient averaged at the split-layer,
emulating FL without any additional communication across clients as opposed to
SplitFed. Meanwhile, SGLR splits the learning rate into its server-side and
client-side rates, and separately adjusts them to support many clients in
parallel. Simulation results corroborate that SGLR achieves higher accuracy
than other baseline SL methods including SplitFed, which is even on par with FL
consuming higher energy and communication costs. As a secondary result, we
observe greater reduction in leakage of sensitive information via mutual
information using SLGR over the baselines.

Recent years have witnessed a large amount of decentralized data in multiple
(edge) devices of end-users, while the aggregation of the decentralized data
remains difficult for machine learning jobs due to laws or regulations.
Federated Learning (FL) emerges as an effective approach to handling
decentralized data without sharing the sensitive raw data, while
collaboratively training global machine learning models. The servers in FL need
to select (and schedule) devices during the training process. However, the
scheduling of devices for multiple jobs with FL remains a critical and open
problem. In this paper, we propose a novel multi-job FL framework to enable the
parallel training process of multiple jobs. The framework consists of a system
model and two scheduling methods. In the system model, we propose a parallel
training process of multiple jobs, and construct a cost model based on the
training time and the data fairness of various devices during the training
process of diverse jobs. We propose a reinforcement learning-based method and a
Bayesian optimization-based method to schedule devices for multiple jobs while
minimizing the cost. We conduct extensive experimentation with multiple jobs
and datasets. The experimental results show that our proposed approaches
significantly outperform baseline approaches in terms of training time (up to
8.67 times faster) and accuracy (up to 44.6% higher).

The rapid uptake of intelligent applications is pushing deep learning (DL)
capabilities to Internet-of-Things (IoT). Despite the emergence of new tools
for embedding deep neural networks (DNNs) into IoT devices, providing
satisfactory Quality of Experience (QoE) to users is still challenging due to
the heterogeneity in DNN architectures, IoT devices, and user preferences. This
paper studies automated customization for DL inference on IoT devices (termed
as on-thing inference), and our goal is to enhance user QoE by configuring the
on-thing inference with an appropriate DNN for users under different usage
scenarios. The core of our method is a DNN selection module that learns user
QoE patterns on-the-fly and identifies the best-fit DNN for on-thing inference
with the learned knowledge. It leverages a novel online learning algorithm,
NeuralUCB, that has excellent generalization ability for handling various user
QoE patterns. We also embed the knowledge transfer technique in NeuralUCB to
expedite the learning process. However, NeuralUCB frequently solicits QoE
ratings from users, which incurs non-negligible inconvenience. To address this
problem, we design feedback solicitation schemes to reduce the number of QoE
solicitations while maintaining the learning efficiency of NeuralUCB. A
pragmatic problem, aggregated QoE, is further investigated to improve the
practicality of our framework. We conduct experiments on both synthetic and
real-world data. The results indicate that our method efficiently learns the
user QoE pattern with few solicitations and provides drastic QoE enhancement
for IoT devices.

Deep reinforcement learning (DRL) has revolutionized learning and actuation
in applications such as game playing and robotic control. The cost of data
collection, i.e., generating transitions from agent-environment interactions,
remains a major challenge for wider DRL adoption in complex real-world
problems. Following a cloud-native paradigm to train DRL agents on a GPU cloud
platform is a promising solution. In this paper, we present a scalable and
elastic library ElegantRL-podracer for cloud-native deep reinforcement
learning, which efficiently supports millions of GPU cores to carry out
massively parallel training at multiple levels. At a high-level,
ElegantRL-podracer employs a tournament-based ensemble scheme to orchestrate
the training process on hundreds or even thousands of GPUs, scheduling the
interactions between a leaderboard and a training pool with hundreds of pods.
At a low-level, each pod simulates agent-environment interactions in parallel
by fully utilizing nearly 7,000 GPU CUDA cores in a single GPU. Our
ElegantRL-podracer library features high scalability, elasticity and
accessibility by following the development principles of containerization,
microservices and MLOps. Using an NVIDIA DGX SuperPOD cloud, we conduct
extensive experiments on various tasks in locomotion and stock trading and show
that ElegantRL-podracer substantially outperforms RLlib. Our codes are

Many vision-language applications contain long articles of text paired with
images (e.g., news or Wikipedia articles). Prior work learning to encode and/or
generate these articles has primarily focused on understanding the article
itself and some related metadata like the title or date it was written.
However, the images and their captions or alt-text often contain crucial
information such as named entities that are difficult to be correctly
recognized and predicted by language models. To address this shortcoming, this
paper introduces an ENtity-aware article Generation method with Image
iNformation, ENGIN, to incorporate an article's image information into language
models. ENGIN represents articles that can be conditioned on metadata used by
prior work and information such as captions and named entities extracted from
images. Our key contribution is a novel Entity-aware mechanism to help our
model better recognize and predict the entity names in articles. We perform
experiments on three public datasets, GoodNews, VisualNews, and WikiText.
Quantitative results show that our approach improves generated article
perplexity by 4-5 points over the base models. Qualitative results demonstrate
the text generated by ENGIN is more consistent with embedded article images. We
also perform article quality annotation experiments on the generated articles
to validate that our model produces higher-quality articles. Finally, we
investigate the effect ENGIN has on methods that automatically detect
machine-generated articles.

Modern recommender systems are required to adapt to the change in user
preferences and item popularity. Such a problem is known as the temporal
dynamics problem, and it is one of the main challenges in recommender system
modeling. Different from the popular recurrent modeling approach, we propose a
new solution named LeapRec to the temporal dynamic problem by using
trajectory-based meta-learning to model time dependencies. LeapRec
characterizes temporal dynamics by two complement components named global time
leap (GTL) and ordered time leap (OTL). By design, GTL learns long-term
patterns by finding the shortest learning path across unordered temporal data.
Cooperatively, OTL learns short-term patterns by considering the sequential
nature of the temporal data. Our experimental results show that LeapRec
consistently outperforms the state-of-the-art methods on several datasets and
recommendation metrics. Furthermore, we provide an empirical study of the
interaction between GTL and OTL, showing the effects of long- and short-term
modeling.

This paper proposes a data-driven method for learning convergent control
policies from offline data using Contraction theory. Contraction theory enables
constructing a policy that makes the closed-loop system trajectories inherently
convergent towards a unique trajectory. At the technical level, identifying the
contraction metric, which is the distance metric with respect to which a
robot's trajectories exhibit contraction is often non-trivial. We propose to
jointly learn the control policy and its corresponding contraction metric while
enforcing contraction. To achieve this, we learn an implicit dynamics model of
the robotic system from an offline data set consisting of the robot's state and
input trajectories. Using this learned dynamics model, we propose a data
augmentation algorithm for learning contraction policies. We randomly generate
samples in the state-space and propagate them forward in time through the
learned dynamics model to generate auxiliary sample trajectories. We then learn
both the control policy and the contraction metric such that the distance
between the trajectories from the offline data set and our generated auxiliary
sample trajectories decreases over time. We evaluate the performance of our
proposed framework on simulated robotic goal-reaching tasks and demonstrate
that enforcing contraction results in faster convergence and greater robustness
of the learned policy.

Relation extraction is a fundamental problem in natural language processing.
Most existing models are defined for relation extraction in the general domain.
However, their performance on specific domains (e.g., biomedicine) is yet
unclear. To fill this gap, this paper carries out an empirical study on
relation extraction in biomedical research articles. Specifically, we consider
both sentence-level and document-level relation extraction, and run a few
state-of-the-art methods on several benchmark datasets. Our results show that
(1) current document-level relation extraction methods have strong
generalization ability; (2) existing methods require a large amount of labeled
data for model fine-tuning in biomedicine. Our observations may inspire people
in this field to develop more effective models for biomedical relation

Attention is a state of arousal capable of dealing with limited processing
bottlenecks in human beings by focusing selectively on one piece of information
while ignoring other perceptible information. For decades, concepts and
functions of attention have been studied in philosophy, psychology,
neuroscience, and computing. Currently, this property has been widely explored
in deep neural networks. Many different neural attention models are now
available and have been a very active research area over the past six years.
From the theoretical standpoint of attention, this survey provides a critical
analysis of major neural attention models. Here we propose a taxonomy that
corroborates with theoretical aspects that predate Deep Learning. Our taxonomy
provides an organizational structure that asks new questions and structures the
understanding of existing attentional mechanisms. In particular, 17 criteria
derived from psychology and neuroscience classic studies are formulated for
qualitative comparison and critical analysis on the 51 main models found on a
set of more than 650 papers analyzed. Also, we highlight several theoretical
issues that have not yet been explored, including discussions about biological
plausibility, highlight current research trends, and provide insights for the
future.

Modern cyber-physical architectures use data collected from systems at
different physical locations to learn appropriate behaviors and adapt to
uncertain environments. However, an important challenge arises as communication
exchanges at the edge of networked systems are costly due to limited resources.
This paper considers a setup where multiple agents need to communicate
efficiently in order to jointly solve a reinforcement learning problem over
time-series data collected in a distributed manner. This is posed as learning
an approximate value function over a communication network. An algorithm for
achieving communication efficiency is proposed, supported with theoretical
guarantees, practical implementations, and numerical evaluations. The approach
is based on the idea of communicating only when sufficiently informative data
is collected.

Face-swapping models have been drawing attention for their compelling
generation quality, but their complex architectures and loss functions often
require careful tuning for successful training. We propose a new face-swapping
model called `Smooth-Swap', which excludes complex handcrafted designs and
allows fast and stable training. The main idea of Smooth-Swap is to build
smooth identity embedding that can provide stable gradients for identity
change. Unlike the one used in previous models trained for a purely
discriminative task, the proposed embedding is trained with a supervised
contrastive loss promoting a smoother space. With improved smoothness,
Smooth-Swap suffices to be composed of a generic U-Net-based generator and
three basic loss functions, a far simpler design compared with the previous
models. Extensive experiments on face-swapping benchmarks (FFHQ,
FaceForensics++) and face images in the wild show that our model is also
quantitatively and qualitatively comparable or even superior to the existing
methods.

On-device directional hearing requires audio source separation from a given
direction while achieving stringent human-imperceptible latency requirements.
While neural nets can achieve significantly better performance than traditional
beamformers, all existing models fall short of supporting low-latency causal
inference on computationally-constrained wearables. We present DeepBeam, a
hybrid model that combines traditional beamformers with a custom lightweight
neural net. The former reduces the computational burden of the latter and also
improves its generalizability, while the latter is designed to further reduce
the memory and computational overhead to enable real-time and low-latency
operations. Our evaluation shows comparable performance to state-of-the-art
causal inference models on synthetic data while achieving a 5x reduction of
model size, 4x reduction of computation per second, 5x reduction in processing
time and generalizing better to real hardware data. Further, our real-time
hybrid model runs in 8 ms on mobile CPUs designed for low-power wearable
devices and achieves an end-to-end latency of 17.5 ms.

Group Activity Recognition detects the activity collectively performed by a
group of actors, which requires compositional reasoning of actors and objects.
We approach the task by modeling the video as tokens that represent the
multi-scale semantic concepts in the video. We propose COMPOSER, a Multiscale
Transformer based architecture that performs attention-based reasoning over
tokens at each scale and learns group activity compositionally. In addition,
prior works suffer from scene biases with privacy and ethical concerns. We only
use the keypoint modality which reduces scene biases and prevents acquiring
detailed visual data that may contain private or biased information of users.
We improve the multiscale representations in COMPOSER by clustering the
intermediate scale representations, while maintaining consistent cluster
assignments between scales. Finally, we use techniques such as auxiliary
prediction and data augmentations tailored to the keypoint signals to aid model
training. We demonstrate the model's strength and interpretability on two
widely-used datasets (Volleyball and Collective Activity). COMPOSER achieves up
to +5.4% improvement with just the keypoint modality. Code is available at
https://github.com/hongluzhou/composer

In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates
for stochastic processes with complex distributions. Conventional inferential
methods for DGP models can suffer from high computational complexity as they
require large-scale operations with kernel matrices for training and inference.
In this work, we propose an efficient scheme for accurate inference and
efficient training based on a range of Gaussian Processes, called the Tensor
Markov Gaussian Processes (TMGP). We construct an induced approximation of TMGP
referred to as the hierarchical expansion. Next, we develop a deep TMGP (DTMGP)
model as the composition of multiple hierarchical expansion of TMGPs. The
proposed DTMGP model has the following properties: (1) the outputs of each
activation function are deterministic while the weights are chosen
independently from standard Gaussian distribution; (2) in training or
prediction, only polylog(M) (out of M) activation functions have non-zero
outputs, which significantly boosts the computational efficiency. Our numerical
experiments on synthetic models and real datasets show the superior
computational efficiency of DTMGP over existing DGP models.

Graph learning is often a necessary step in processing or representing
structured data, when the underlying graph is not given explicitly. Graph
learning is generally performed centrally with a full knowledge of the graph
signals, namely the data that lives on the graph nodes. However, there are
settings where data cannot be collected easily or only with a non-negligible
communication cost. In such cases, distributed processing appears as a natural
solution, where the data stays mostly local and all processing is performed
among neighbours nodes on the communication graph. We propose here a novel
distributed graph learning algorithm, which permits to infer a graph from
signal observations on the nodes under the assumption that the data is smooth
on the target graph. We solve a distributed optimization problem with local
projection constraints to infer a valid graph while limiting the communication
costs. Our results show that the distributed approach has a lower communication
cost than a centralised algorithm without compromising the accuracy in the
inferred graph. It also scales better in communication costs with the increase
of the network size, especially for sparse networks.

Recent self-supervised video representation learning methods have found
significant success by exploring essential properties of videos, e.g. speed,
temporal order, etc. This work exploits an essential yet under-explored
property of videos, the video continuity, to obtain supervision signals for
self-supervised representation learning. Specifically, we formulate three novel
continuity-related pretext tasks, i.e. continuity justification, discontinuity
localization, and missing section approximation, that jointly supervise a
shared backbone for video representation learning. This self-supervision
approach, termed as Continuity Perception Network (CPNet), solves the three
tasks altogether and encourages the backbone network to learn local and
long-ranged motion and context representations. It outperforms prior arts on
multiple downstream tasks, such as action recognition, video retrieval, and
action localization. Additionally, the video continuity can be complementary to
other coarse-grained video properties for representation learning, and
integrating the proposed pretext task to prior arts can yield much performance
gains.

Space free-flyers like the Astrobee robots currently operating aboard the
International Space Station must operate with inherent system uncertainties.
Parametric uncertainties like mass and moment of inertia are especially
important to quantify in these safety-critical space systems and can change in
scenarios such as on-orbit cargo movement, where unknown grappled payloads
significantly change the system dynamics. Cautiously learning these
uncertainties en route can potentially avoid time- and fuel-consuming pure
system identification maneuvers. Recognizing this, this work proposes RATTLE,
an online information-aware motion planning algorithm that explicitly weights
parametric model-learning coupled with real-time replanning capability that can
take advantage of improved system models. The method consists of a two-tiered
(global and local) planner, a low-level model predictive controller, and an
online parameter estimator that produces estimates of the robot's inertial
properties for more informed control and replanning on-the-fly; all levels of
the planning and control feature online update-able models. Simulation results
of RATTLE for the Astrobee free-flyer grappling an uncertain payload are
presented alongside results of a hardware demonstration showcasing the ability
to explicitly encourage model parametric learning while achieving otherwise
useful motion.

Historical processes manifest remarkable diversity. Nevertheless, scholars
have long attempted to identify patterns and categorize historical actors and
influences with some success. A stochastic process framework provides a
structured approach for the analysis of large historical datasets that allows
for detection of sometimes surprising patterns, identification of relevant
causal actors both endogenous and exogenous to the process, and comparison
between different historical cases. The combination of data, analytical tools
and the organizing theoretical framework of stochastic processes complements
traditional narrative approaches in history and archaeology.

The human hippocampus possesses "concept cells", neurons that fire when
presented with stimuli belonging to a specific concept, regardless of the
modality. Recently, similar concept cells were discovered in a multimodal
network called CLIP (Radford et at., 2021). Here, we ask whether CLIP can
explain the fMRI activity of the human hippocampus better than a purely visual
(or linguistic) model. We extend our analysis to a range of publicly available
uni- and multi-modal models. We demonstrate that "multimodality" stands out as
a key component when assessing the ability of a network to explain the
multivoxel activity in the hippocampus.

Learning from set-structured data is an essential problem with many
applications in machine learning and computer vision. This paper focuses on
non-parametric and data-independent learning from set-structured data using
approximate nearest neighbor (ANN) solutions, particularly locality-sensitive
hashing. We consider the problem of set retrieval from an input set query. Such
retrieval problem requires: 1) an efficient mechanism to calculate the
distances/dissimilarities between sets, and 2) an appropriate data structure
for fast nearest neighbor search. To that end, we propose Sliced-Wasserstein
set embedding as a computationally efficient "set-2-vector" mechanism that
enables downstream ANN, with theoretical guarantees. The set elements are
treated as samples from an unknown underlying distribution, and the
Sliced-Wasserstein distance is used to compare sets. We demonstrate the
effectiveness of our algorithm, denoted as Set-LOcality Sensitive Hashing
(SLOSH), on various set retrieval datasets and compare our proposed embedding
with standard set embedding approaches, including Generalized Mean (GeM)
embedding/pooling, Featurewise Sort Pooling (FSPool), and Covariance Pooling
and show consistent improvement in retrieval results. The code for replicating
our results is available here:
\href{https://github.com/mint-vu/SLOSH}{https://github.com/mint-vu/SLOSH}.

Knowledge Graph Question Answering (KGQA) involves retrieving facts from a
Knowledge Graph (KG) using natural language queries. A KG is a curated set of
facts consisting of entities linked by relations. Certain facts include also
temporal information forming a Temporal KG (TKG). Although many natural
questions involve explicit or implicit time constraints, question answering
(QA) over TKGs has been a relatively unexplored area. Existing solutions are
mainly designed for simple temporal questions that can be answered directly by
a single TKG fact. This paper puts forth a comprehensive embedding-based
framework for answering complex questions over TKGs. Our method termed temporal
question reasoning (TempoQR) exploits TKG embeddings to ground the question to
the specific entities and time scope it refers to. It does so by augmenting the
question embeddings with context, entity and time-aware information by
employing three specialized modules. The first computes a textual
representation of a given question, the second combines it with the entity
embeddings for entities involved in the question, and the third generates
question-specific time embeddings. Finally, a transformer-based encoder learns
to fuse the generated temporal information with the question representation,
which is used for answer predictions. Extensive experiments show that TempoQR
improves accuracy by 25--45 percentage points on complex temporal questions
over state-of-the-art approaches and it generalizes better to unseen question
types.

Many of the recent advances in speech separation are primarily aimed at
synthetic mixtures of short audio utterances with high degrees of overlap. Most
of these approaches need an additional stitching step to stitch the separated
speech chunks for long form audio. Since most of the approaches involve
Permutation Invariant training (PIT), the order of separated speech chunks is
nondeterministic and leads to difficulty in accurately stitching homogenous
speaker chunks for downstream tasks like Automatic Speech Recognition (ASR).
Also, most of these models are trained with synthetic mixtures and do not
generalize to real conversational data. In this paper, we propose a speaker
conditioned separator trained on speaker embeddings extracted directly from the
mixed signal using an over-clustering based approach. This model naturally
regulates the order of the separated chunks without the need for an additional
stitching step. We also introduce a data sampling strategy with real and
synthetic mixtures which generalizes well to real conversation speech. With
this model and data sampling technique, we show significant improvements in
speaker-attributed word error rate (SA-WER) on Hub5 data.

MANA-2.0 is a scalable, future-proof design for transparent checkpointing of
MPI-based computations. Its network transparency ("network-agnostic") feature
ensures that MANA-2.0 will provide a viable, efficient mechanism for
transparently checkpointing MPI applications on current and future
supercomputers. MANA-2.0 is an enhancement of previous work, the original MANA,
which interposes MPI calls, and is a work in progress intended for production
deployment. MANA-2.0 implements a series of new algorithms and features that
improve MANA's scalability and reliability, enabling transparent
checkpoint-restart over thousands of MPI processes. MANA-2.0 is being tested on
today's Cori supercomputer at NERSC using Cray MPICH library over the Cray GNI
network, but it is designed to work over any standard MPI running over an
arbitrary network. Two widely-used HPC applications were selected to
demonstrate the enhanced features of MANA-2.0: GROMACS, a molecular dynamics
simulation code with frequent point-to-point communication, and VASP, a
materials science code with frequent MPI collective communication. Perhaps the
most important lesson to be learned from MANA-2.0 is a series of algorithms and
data structures for library-based transformations that enable MPI-based
computations over MANA-2.0 to reliably survive the checkpoint-restart

Being spontaneous, micro-expressions are useful in the inference of a
person's true emotions even if an attempt is made to conceal them. Due to their
short duration and low intensity, the recognition of micro-expressions is a
difficult task in affective computing. The early work based on handcrafted
spatio-temporal features which showed some promise, has recently been
superseded by different deep learning approaches which now compete for the
state of the art performance. Nevertheless, the problem of capturing both local
and global spatio-temporal patterns remains challenging. To this end, herein we
propose a novel spatio-temporal transformer architecture -- to the best of our
knowledge, the first purely transformer based approach (i.e. void of any
convolutional network use) for micro-expression recognition. The architecture
comprises a spatial encoder which learns spatial patterns, a temporal
aggregator for temporal dimension analysis, and a classification head. A
comprehensive evaluation on three widely used spontaneous micro-expression data
sets, namely SMIC-HS, CASME II and SAMM, shows that the proposed approach
consistently outperforms the state of the art, and is the first framework in
the published literature on micro-expression recognition to achieve the
unweighted F1-score greater than 0.9 on any of the aforementioned data sets.

We employ Proximal Iteration for value-function optimization in deep
reinforcement learning. Proximal Iteration is a computationally efficient
technique that enables biasing the optimization procedure towards desirable
solutions. As a concrete application, we endow the objective function of Deep
Q-Network (DQN) and Rainbow agents with a proximal term to ensure robustness in
presence of large noise. The resultant agents, which we call DQN Pro and
Rainbow Pro, exhibit significant improvements over their original counterparts
on the Atari benchmark. Our results accentuate the power of employing sound
optimization techniques for deep reinforcement learning.

As more users across the world are interacting with dialog agents in their
daily life, there is a need for better speech understanding that calls for
renewed attention to the dynamics between research in automatic speech
recognition (ASR) and natural language understanding (NLU). We briefly review
these research areas and lay out the current relationship between them. In
light of the observations we make in this paper, we argue that (1) NLU should
be cognizant of the presence of ASR models being used upstream in a dialog
system's pipeline, (2) ASR should be able to learn from errors found in NLU,
(3) there is a need for end-to-end datasets that provide semantic annotations
on spoken input, (4) there should be stronger collaboration between ASR and NLU
research communities.

The idea of representing symbolic knowledge in connectionist systems has been
a long-standing endeavour which has attracted much attention recently with the
objective of combining machine learning and scalable sound reasoning. Early
work has shown a correspondence between propositional logic and symmetrical
neural networks which nevertheless did not scale well with the number of
variables and whose training regime was inefficient. In this paper, we
introduce Logical Boltzmann Machines (LBM), a neurosymbolic system that can
represent any propositional logic formula in strict disjunctive normal form. We
prove equivalence between energy minimization in LBM and logical satisfiability
thus showing that LBM is capable of sound reasoning. We evaluate reasoning
empirically to show that LBM is capable of finding all satisfying assignments
of a class of logical formulae by searching fewer than 0.75% of the possible
(approximately 1 billion) assignments. We compare learning in LBM with a
symbolic inductive logic programming system, a state-of-the-art neurosymbolic
system and a purely neural network-based system, achieving better learning
performance in five out of seven data sets.

The problem of communicating sensor measurements over shared networks is
prevalent in many modern large-scale distributed systems such as cyber-physical
systems, wireless sensor networks, and the internet of things. Due to bandwidth
constraints, the system designer must jointly design decentralized medium
access transmission and estimation policies that accommodate a very large
number of devices in extremely contested environments such that the collection
of all observations is reproduced at the destination with the best possible
fidelity. We formulate a remote estimation problem in the mean-field regime
where a very large number of sensors communicate their observations to an
access point, or base station, under a strict constraint on the maximum
fraction of transmitting devices. We show that in the mean-field regime, this
problem exhibits a structure that enables tractable optimization algorithms.
More importantly, we obtain a data-driven learning scheme that admits a finite
sample-complexity guarantee on the performance of the resulting estimation
system under minimal assumptions on the data's probability density function.

We present a quality-aware multimodal recognition framework that combines
representations from multiple biometric traits with varying quality and number
of samples to achieve increased recognition accuracy by extracting
complimentary identification information based on the quality of the samples.
We develop a quality-aware framework for fusing representations of input
modalities by weighting their importance using quality scores estimated in a
weakly-supervised fashion. This framework utilizes two fusion blocks, each
represented by a set of quality-aware and aggregation networks. In addition to
architecture modifications, we propose two task-specific loss functions:
multimodal separability loss and multimodal compactness loss. The first loss
assures that the representations of modalities for a class have comparable
magnitudes to provide a better quality estimation, while the multimodal
representations of different classes are distributed to achieve maximum
discrimination in the embedding space. The second loss, which is considered to
regularize the network weights, improves the generalization performance by
regularizing the framework. We evaluate the performance by considering three
multimodal datasets consisting of face, iris, and fingerprint modalities. The
efficacy of the framework is demonstrated through comparison with the
state-of-the-art algorithms. In particular, our framework outperforms the rank-
and score-level fusion of modalities of BIOMDATA by more than 30% for true
acceptance rate at false acceptance rate of $10^{-4}$.

In this work, we develop new self-learning techniques with an attention-based
sequence-to-sequence (seq2seq) model for automatic speech recognition (ASR).
For untranscribed speech data, the hypothesis from an ASR system must be used
as a label. However, the imperfect ASR result makes unsupervised learning
difficult to consistently improve recognition performance especially in the
case that multiple powerful teacher models are unavailable. In contrast to
conventional unsupervised learning approaches, we adopt the \emph{multi-task
learning} (MTL) framework where the $n$-th best ASR hypothesis is used as the
label of each task. The seq2seq network is updated through the MTL framework so
as to find the common representation that can cover multiple hypotheses. By
doing so, the effect of the \emph{hard-decision} errors can be alleviated.
  We first demonstrate the effectiveness of our self-learning methods through
ASR experiments in an accent adaptation task between the US and British English
speech. Our experiment results show that our method can reduce the WER on the
British speech data from 14.55\% to 10.36\% compared to the baseline model
trained with the US English data only. Moreover, we investigate the effect of
our proposed methods in a federated learning scenario.

Consistency regularization is one of the most widely-used techniques for
semi-supervised learning (SSL). Generally, the aim is to train a model that is
invariant to various data augmentations. In this paper, we revisit this idea
and find that enforcing invariance by decreasing distances between features
from differently augmented images leads to improved performance. However,
encouraging equivariance instead, by increasing the feature distance, further
improves performance. To this end, we propose an improved consistency
regularization framework by a simple yet effective technique, FeatDistLoss,
that imposes consistency and equivariance on the classifier and the feature
level, respectively. Experimental results show that our model defines a new
state of the art for various datasets and settings and outperforms previous
work by a significant margin, particularly in low data regimes. Extensive
experiments are conducted to analyze the method, and the code will be
published.

The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity
with a little computational complexity. In this work, we investigate how
multi-lingual Automatic Speech Recognition (ASR) networks can be scaled up with
a simple routing algorithm in order to achieve better accuracy. More
specifically, we apply the sparsely-gated MoE technique to two types of
networks: Sequence-to-Sequence Transformer (S2S-T) and Transformer Transducer
(T-T). We demonstrate through a set of ASR experiments on multiple language
data that the MoE networks can reduce the relative word error rates by 16.3%
and 4.6% with the S2S-T and T-T, respectively. Moreover, we thoroughly
investigate the effect of the MoE on the T-T architecture in various
conditions: streaming mode, non-streaming mode, the use of language ID and the
label decoder with the MoE.

In two-alternative forced choice tasks, prior knowledge can improve
performance, especially when operating near the psychophysical threshold. For
instance, if subjects know that one choice is much more likely than the other,
they can make that choice when evidence is weak. A common hypothesis for these
kinds of tasks is that the prior is stored in neural activity. Here we propose
a different hypothesis: the prior is stored in synaptic strengths. We study the
International Brain Laboratory task, in which a grating appears on either the
right or left side of a screen, and a mouse has to move a wheel to bring the
grating to the center. The grating is often low in contrast which makes the
task relatively difficult, and the prior probability that the grating appears
on the right is either 80% or 20%, in (unsignaled) blocks of about 50 trials.
We model this as a reinforcement learning task, using a feedforward neural
network to map states to actions, and adjust the weights of the network to
maximize reward, learning via policy gradient. Our model uses an internal state
that stores an estimate of the grating and confidence, and follows Bayesian
updates, and can switch between engaged and disengaged states to mimic animal
behavior. This model reproduces the main experimental finding - that the
psychometric curve with respect to contrast shifts after a block switch in
about 10 trials. Also, as seen in the experiments, in our model the difference
in neuronal activity in the right and left blocks is small - it is virtually
impossible to decode block structure from activity on single trials if noise is
about 2%. The hypothesis that priors are stored in weights is difficult to
test, but the technology to do so should be available in the not so distant
future.

The pandemic has shattered the traditional enclosures of learning. The
post-pandemic university (PPU) will no longer be contained within the 4 walls
of a lecture theatre, and finish once students have left the premises. The use
of online services has now blended home and university life, and the PPU needs
to reflect this. Our proposal of a continuous learning model will take
advantage of the newfound omnipresence of learning, while being dynamic enough
to continually adapt to the ever-evolving virus situation. Universities
restricting themselves to fixed subject themes that are then forgotten once
completed, will miss out on the "fresh start" presented by the virus.

Most reinforcement learning (RL) recommendation systems designed for edge
computing must either synchronize during recommendation selection or depend on
an unprincipled patchwork collection of algorithms. In this work, we build on
asynchronous coagent policy gradient algorithms \citep{kostas2020asynchronous}
to propose a principled solution to this problem. The class of algorithms that
we propose can be distributed over the internet and run asynchronously and in
real-time. When a given edge fails to respond to a request for data with
sufficient speed, this is not a problem; the algorithm is designed to function
and learn in the edge setting, and network issues are part of this setting. The
result is a principled, theoretically grounded RL algorithm designed to be
distributed in and learn in this asynchronous environment. In this work, we
describe this algorithm and a proposed class of architectures in detail, and
demonstrate that they work well in practice in the asynchronous setting, even
as the network quality degrades.

Recent advancements in technology have led to a boost in social media usage
which has ultimately led to large amounts of user-generated data which also
includes hateful and offensive speech. The language used in social media is
often a combination of English and the native language in the region. In India,
Hindi is used predominantly and is often code-switched with English, giving
rise to the Hinglish (Hindi+English) language. Various approaches have been
made in the past to classify the code-mixed Hinglish hate speech using
different machine learning and deep learning-based techniques. However, these
techniques make use of recurrence on convolution mechanisms which are
computationally expensive and have high memory requirements. Past techniques
also make use of complex data processing making the existing techniques very
complex and non-sustainable to change in data. We propose a much simpler
approach which is not only at par with these complex networks but also exceeds
performance with the use of subword tokenization algorithms like BPE and
Unigram along with multi-head attention-based technique giving an accuracy of
87.41% and F1 score of 0.851 on standard datasets. Efficient use of BPE and
Unigram algorithms help handle the non-conventional Hinglish vocabulary making
our technique simple, efficient and sustainable to use in the real world.

This paper illustrates locality sensitive hasing (LSH) models for the
identification and removal of nearly redundant data in a text dataset. To
evaluate the different models, we create an artificial dataset for data
deduplication using English Wikipedia articles. Area-Under-Curve (AUC) over 0.9
were observed for most models, with the best model reaching 0.96. Deduplication
enables more effective model training by preventing the model from learning a
distribution that differs from the real one as a result of the repeated data.

In this paper, we present a method of building strong, explainable
classifiers in the form of Boolean search rules. We developed an interactive
environment called CASE (Computer Assisted Semantic Exploration) which exploits
word co-occurrence to guide human annotators in selection of relevant search
terms. The system seamlessly facilitates iterative evaluation and improvement
of the classification rules. The process enables the human annotators to
leverage the benefits of statistical information while incorporating their
expert intuition into the creation of such rules. We evaluate classifiers
created with our CASE system on 4 datasets, and compare the results to machine
learning methods, including SKOPE rules, Random forest, Support Vector Machine,
and fastText classifiers. The results drive the discussion on trade-offs
between superior compactness, simplicity, and intuitiveness of the Boolean
search rules versus the better performance of state-of-the-art machine learning
models for text classification.

Occurrences of catastrophes such as natural or man-made disasters trigger the
spread of rumours over social media at a rapid pace. Presenting a trustworthy
and summarized account of the unfolding event in near real-time to the
consumers of such potentially unreliable information thus becomes an important
task. In this work, we propose MTLTS, the first end-to-end solution for the
task that jointly determines the credibility and summary-worthiness of tweets.
Our credibility verifier is designed to recursively learn the structural
properties of a Twitter conversation cascade, along with the stances of replies
towards the source tweet. We then take a hierarchical multi-task learning
approach, where the verifier is trained at a lower layer, and the summarizer is
trained at a deeper layer where it utilizes the verifier predictions to
determine the salience of a tweet. Different from existing disaster-specific
summarizers, we model tweet summarization as a supervised task. Such an
approach can automatically learn summary-worthy features, and can therefore
generalize well across domains. When trained on the PHEME dataset [29], not
only do we outperform the strongest baselines for the auxiliary task of
verification/rumour detection, we also achieve 21 - 35% gains in the verified
ratio of summary tweets, and 16 - 20% gains in ROUGE1-F1 scores over the
existing state-of-the-art solutions for the primary task of trustworthy
summarization.

Quantum computing has promised significant improvement in solving difficult
computational tasks over classical computers. Designing quantum circuits for
practical use, however, is not a trivial objective and requires expert-level
knowledge. To aid this endeavor, this paper proposes a machine learning-based
method to construct quantum circuit architectures. Previous works have
demonstrated that classical deep reinforcement learning (DRL) algorithms can
successfully construct quantum circuit architectures without encoded physics
knowledge. However, these DRL-based works are not generalizable to settings
with changing device noises, thus requiring considerable amounts of training
resources to keep the RL models up-to-date. With this in mind, we incorporated
continual learning to enhance the performance of our algorithm. In this paper,
we present the Probabilistic Policy Reuse with deep Q-learning (PPR-DQL)
framework to tackle this circuit design challenge. By conducting numerical
simulations over various noise patterns, we demonstrate that the RL agent with
PPR was able to find the quantum gate sequence to generate the two-qubit Bell
state faster than the agent that was trained from scratch. The proposed
framework is general and can be applied to other quantum gate synthesis or
control problems -- including the automatic calibration of quantum devices.

We present a deep machine learning (ML) approach to constraining cosmological
parameters with multi-wavelength observations of galaxy clusters. The ML
approach has two components: an encoder that builds a compressed representation
of each galaxy cluster and a flexible CNN to estimate the cosmological model
from a cluster sample. It is trained and tested on simulated cluster catalogs
built from the Magneticum simulations. From the simulated catalogs, the ML
method estimates the amplitude of matter fluctuations, sigma_8, at
approximately the expected theoretical limit. More importantly, the deep ML
approach can be interpreted. We lay out three schemes for interpreting the ML
technique: a leave-one-out method for assessing cluster importance, an average
saliency for evaluating feature importance, and correlations in the terse layer
for understanding whether an ML technique can be safely applied to
observational data. These interpretation schemes led to the discovery of a
previously unknown self-calibration mode for flux- and volume-limited cluster
surveys. We describe this new mode, which uses the amplitude and peak of the
cluster mass PDF as anchors for mass calibration. We introduce the term
"overspecialized" to describe a common pitfall in astronomical applications of
machine learning in which the ML method learns simulation-specific details, and
we show how a carefully constructed architecture can be used to check for this
source of systematic error.

Automatic evaluation of the retinal fundus image is emerging as one of the
most important tools for early detection and treatment of progressive eye
diseases like Glaucoma. Glaucoma results to a progressive degeneration of
vision and is characterized by the deformation of the shape of optic cup and
the degeneration of the blood vessels resulting in the formation of a notch
along the neuroretinal rim. In this paper, we propose a deep learning-based
pipeline for automatic segmentation of optic disc (OD) and optic cup (OC)
regions from Digital Fundus Images (DFIs), thereby extracting distinct features
necessary for prediction of Glaucoma. This methodology has utilized focal notch
analysis of neuroretinal rim along with cup-to-disc ratio values as classifying
parameters to enhance the accuracy of Computer-aided design (CAD) systems in
analyzing glaucoma. Support Vector-based Machine Learning algorithm is used for
classification, which classifies DFIs as Glaucomatous or Normal based on the
extracted features. The proposed pipeline was evaluated on the freely available
DRISHTI-GS dataset with a resultant accuracy of 93.33% for detecting Glaucoma
from DFIs.

Representation learners that disentangle factors of variation have already
proven to be important in addressing various real world concerns such as
fairness and interpretability. Initially consisting of unsupervised models with
independence assumptions, more recently, weak supervision and correlated
features have been explored, but without a causal view of the generative
process. In contrast, we work under the regime of a causal generative process
where generative factors are either independent or can be potentially
confounded by a set of observed or unobserved confounders. We present an
analysis of disentangled representations through the notion of disentangled
causal process. We motivate the need for new metrics and datasets to study
causal disentanglement and propose two evaluation metrics and a dataset. We
show that our metrics capture the desiderata of disentangled causal process.
Finally, we perform an empirical study on state of the art disentangled
representation learners using our metrics and dataset to evaluate them from
causal perspective.

In this work, we analyze an efficient sampling-based algorithm for
general-purpose reachability analysis, which remains a notoriously challenging
problem with applications ranging from neural network verification to safety
analysis of dynamical systems. By sampling inputs, evaluating their images in
the true reachable set, and taking their $\epsilon$-padded convex hull as a set
estimator, this algorithm applies to general problem settings and is simple to
implement. Our main contribution is the derivation of asymptotic and
finite-sample accuracy guarantees using random set theory. This analysis
informs algorithmic design to obtain an $\epsilon$-close reachable set
approximation with high probability, provides insights into which reachability
problems are most challenging, and motivates safety-critical applications of
the technique. On a neural network verification task, we show that this
approach is more accurate and significantly faster than prior work. Informed by
our analysis, we also design a robust model predictive controller that we
demonstrate in hardware experiments.

We present methods to rigorously extract parameter combinations that are
constrained by data from posterior distributions. The standard approach uses
linear methods that apply to Gaussian distributions. We show the limitations of
the linear methods for current surveys, and develop non-linear methods that can
be used with non-Gaussian distributions, and are independent of the parameter
basis. These are made possible by the use of machine-learning models,
normalizing flows, to learn posterior distributions from their samples. These
models allow us to obtain the local covariance of the posterior at all
positions in parameter space and use its inverse, the Fisher matrix, as a local
metric over parameter space. The posterior distribution can then be
non-linearly decomposed into the leading constrained parameter combinations via
parallel transport in the metric space. We test our methods on two
non-Gaussian, benchmark examples, and then apply them to the parameter
posteriors of the Dark Energy Survey and Planck CMB lensing. We illustrate how
our method automatically learns the survey-specific, best constrained effective
amplitude parameter $S_8$ for cosmic shear alone, cosmic shear and galaxy
clustering, and CMB lensing. We also identify constrained parameter
combinations in the full parameter space, and as an application we estimate the
Hubble constant, $H_0$, from large-structure data alone.

Detecting anomalies in time-varying multivariate data is crucial in various
industries for the predictive maintenance of equipment. Numerous machine
learning (ML) algorithms have been proposed to support automated anomaly
identification. However, a significant amount of human knowledge is still
required to interpret, analyze, and calibrate the results of automated
analysis. This paper investigates current practices used to detect and
investigate anomalies in time series data in industrial contexts and identifies
corresponding needs. Through iterative design and working with nine experts
from two industry domains (aerospace and energy), we characterize six design
elements required for a successful visualization system that supports effective
detection, investigation, and annotation of time series anomalies. We summarize
an ideal human-AI collaboration workflow that streamlines the process and
supports efficient and collaborative analysis. We introduce MTV (Multivariate
Time Series Visualization), a visual analytics system to support such workflow.
The system incorporates a set of novel visualization and interaction designs to
support multi-faceted time series exploration, efficient in-situ anomaly
annotation, and insight communication. Two user studies, one with 6 spacecraft
experts (with routine anomaly analysis tasks) and one with 25 general end-users
(without such tasks), are conducted to demonstrate the effectiveness and
usefulness of MTV.

Complex systems often contain feedback loops that can be described as cyclic
causal models. Intervening in such systems may lead to counter-intuitive
effects, which cannot be inferred directly from the graph structure. After
establishing a framework for differentiable interventions based on Lie groups,
we take advantage of modern automatic differentiation techniques and their
application to implicit functions in order to optimize interventions in cyclic
causal models. We illustrate the use of this framework by investigating
scenarios of transition to sustainable economies.

What are the symmetries of a dataset? Whereas the symmetries of an individual
data element can be characterized by its invariance under various
transformations, the symmetries of an ensemble of data elements are ambiguous
due to Jacobian factors introduced while changing coordinates. In this paper,
we provide a rigorous statistical definition of the symmetries of a dataset,
which involves inertial reference densities, in analogy to inertial frames in
classical mechanics. We then propose SymmetryGAN as a novel and powerful
approach to automatically discover symmetries using a deep learning method
based on generative adversarial networks (GANs). When applied to Gaussian
examples, SymmetryGAN shows excellent empirical performance, in agreement with
expectations from the analytic loss landscape. SymmetryGAN is then applied to
simulated dijet events from the Large Hadron Collider (LHC) to demonstrate the
potential utility of this method in high energy collider physics applications.
Going beyond symmetry discovery, we consider procedures to infer the underlying
symmetry group from empirical data.

We explore the use of Deep Learning to infer the temperature of the
intergalactic medium from the transmitted flux in the high redshift Lyman-alpha
forest. We train Neural Networks on sets of simulated spectra from redshift
z=2-3 outputs of cosmological hydrodynamic simulations, including high
temperature regions added in post-processing to approximate bubbles heated by
Helium-II reionization. We evaluate how well the trained networks are able to
reconstruct the temperature from the effect of Doppler broadening in the
simulated input Lyman-alpha forest absorption spectra. We find that for spectra
with high resolution (10 km/s pixel) and moderate signal to noise (20-50), the
neural network is able to reconstruct the IGM temperature smoothed on scales of
6 Mpc/h quite well. Concentrating on discontinuities we find that high
temperature regions of width 25 Mpc/h and temperature 20,000 K can be fairly
easily detected and characterized. We show an example where multiple sightlines
are combined to yield tomographic images of hot bubbles. Deep Learning
techniques may be useful in this way to help us understand the complex
temperature structure of the intergalactic medium around the time of Helium
reionization.

Current efficient fine-tuning methods (e.g., adapters, prefix-tuning, etc.)
have optimized conditional text generation via training a small set of extra
parameters of the neural language model, while freezing the rest for
efficiency. While showing strong performance on some generation tasks, they
don't generalize across all generation tasks. We show that soft-prompt based
conditional text generation can be improved with simple and efficient methods
that simulate modeling the discourse structure of human written text. We
investigate two design choices: First, we apply \textit{hierarchical blocking}
on the prefix parameters to simulate a higher-level discourse structure of
human written text. Second, we apply \textit{attention sparsity} on the prefix
parameters at different layers of the network and learn sparse transformations
on the softmax-function. We show that structured design of prefix parameters
yields more coherent, faithful and relevant generations than the baseline
prefix-tuning on all generation tasks.

Network and system security are incredibly critical issues now. Due to the
rapid proliferation of malware, traditional analysis methods struggle with
enormous samples.
  In this paper, we propose four easy-to-extract and small-scale features,
including sizes and permissions of Windows PE sections, content complexity, and
import libraries, to classify malware families, and use automatic machine
learning to search for the best model and hyper-parameters for each feature and
their combinations. Compared with detailed behavior-related features like API
sequences, proposed features provide macroscopic information about malware. The
analysis is based on static disassembly scripts and hexadecimal machine code.
Unlike dynamic behavior analysis, static analysis is resource-efficient and
offers complete code coverage, but is vulnerable to code obfuscation and
encryption.
  The results demonstrate that features which work well in dynamic analysis are
not necessarily effective when applied to static analysis. For instance, API
4-grams only achieve 57.96% accuracy and involve a relatively high dimensional
feature set (5000 dimensions). In contrast, the novel proposed features
together with a classical machine learning algorithm (Random Forest) presents
very good accuracy at 99.40% and the feature vector is of much smaller
dimension (40 dimensions). We demonstrate the effectiveness of this approach
through integration in IDA Pro, which also facilitates the collection of new
training samples and subsequent model retraining.

We present TFF, which is a Transformer framework for the analysis of
functional Magnetic Resonance Imaging (fMRI) data. TFF employs a two-phase
training approach. First, self-supervised training is applied to a collection
of fMRI scans, where the model is trained to reconstruct 3D volume data.
Second, the pre-trained model is fine-tuned on specific tasks, utilizing ground
truth labels. Our results show state-of-the-art performance on a variety of
fMRI tasks, including age and gender prediction, as well as schizophrenia
recognition. Our code for the training, network architecture, and results is
attached as supplementary material.

The use of analog resistance states for storing weights in neuromorphic
systems is impeded by fabrication imprecision and device stochasticity that
limit the precision of synapse weights. This challenge can be resolved by
emulating analog behavior with the stochastic switching of the binary states of
spin-transfer torque magnetoresistive random-access memory (STT-MRAM). However,
previous approaches based on STT-MRAM operate in an asynchronous manner that is
difficult to implement experimentally. This paper proposes a synchronous
spiking neural network system with clocked circuits that perform unsupervised
learning leveraging the stochastic switching of STT-MRAM. The proposed system
enables a single-layer network to achieve 90% inference accuracy on the MNIST
dataset.

The sizes of pretrained language models make them challenging and expensive
to use when there are multiple desired downstream tasks. In this work, we adopt
recent strategies for model pruning during finetuning to explore the question
of whether it is possible to prune a single encoder so that it can be used for
multiple tasks. We allocate a fixed parameter budget and compare pruning a
single model with a multitask objective against the best ensemble of
single-task models. We find that under two pruning strategies (element-wise and
rank pruning), the approach with the multitask objective outperforms training
models separately when averaged across all tasks, and it is competitive on each
individual one. Additional analysis finds that using a multitask objective
during pruning can also be an effective method for reducing model sizes for
low-resource tasks.

Energy-Based Models (EBMs) allow for extremely flexible specifications of
probability distributions. However, they do not provide a mechanism for
obtaining exact samples from these distributions. Monte Carlo techniques can
aid us in obtaining samples if some proposal distribution that we can easily
sample from is available. For instance, rejection sampling can provide exact
samples but is often difficult or impossible to apply due to the need to find a
proposal distribution that upper-bounds the target distribution everywhere.
Approximate Markov chain Monte Carlo sampling techniques like
Metropolis-Hastings are usually easier to design, exploiting a local proposal
distribution that performs local edits on an evolving sample. However, these
techniques can be inefficient due to the local nature of the proposal
distribution and do not provide an estimate of the quality of their samples. In
this work, we propose a new approximate sampling technique, Quasi Rejection
Sampling (QRS), that allows for a trade-off between sampling efficiency and
sampling quality, while providing explicit convergence bounds and diagnostics.
QRS capitalizes on the availability of high-quality global proposal
distributions obtained from deep learning models. We demonstrate the
effectiveness of QRS sampling for discrete EBMs over text for the tasks of
controlled text generation with distributional constraints and paraphrase
generation. We show that we can sample from such EBMs with arbitrary precision
at the cost of sampling efficiency.

In a world of daily emerging scientific inquisition and discovery, the
prolific launch of machine learning across industries comes to little surprise
for those familiar with the potential of ML. Neither so should the congruent
expansion of ethics-focused research that emerged as a response to issues of
bias and unfairness that stemmed from those very same applications. Fairness
research, which focuses on techniques to combat algorithmic bias, is now more
supported than ever before. A large portion of fairness research has gone to
producing tools that machine learning practitioners can use to audit for bias
while designing their algorithms. Nonetheless, there is a lack of application
of these fairness solutions in practice. This systematic review provides an
in-depth summary of the algorithmic bias issues that have been defined and the
fairness solution space that has been proposed. Moreover, this review provides
an in-depth breakdown of the caveats to the solution space that have arisen
since their release and a taxonomy of needs that have been proposed by machine
learning practitioners, fairness researchers, and institutional stakeholders.
These needs have been organized and addressed to the parties most influential
to their implementation, which includes fairness researchers, organizations
that produce ML algorithms, and the machine learning practitioners themselves.
These findings can be used in the future to bridge the gap between
practitioners and fairness experts and inform the creation of usable fair ML
toolkits.

Data-driven societal event forecasting methods exploit relevant historical
information to predict future events. These methods rely on historical labeled
data and cannot accurately predict events when data are limited or of poor
quality. Studying causal effects between events goes beyond correlation
analysis and can contribute to a more robust prediction of events. However,
incorporating causality analysis in data-driven event forecasting is
challenging due to several factors: (i) Events occur in a complex and dynamic
social environment. Many unobserved variables, i.e., hidden confounders, affect
both potential causes and outcomes. (ii) Given spatiotemporal non-independent
and identically distributed (non-IID) data, modeling hidden confounders for
accurate causal effect estimation is not trivial. In this work, we introduce a
deep learning framework that integrates causal effect estimation into event
forecasting. We first study the problem of Individual Treatment Effect (ITE)
estimation from observational event data with spatiotemporal attributes and
present a novel causal inference model to estimate ITEs. We then incorporate
the learned event-related causal information into event prediction as prior
knowledge. Two robust learning modules, including a feature reweighting module
and an approximate constraint loss, are introduced to enable prior knowledge
injection. We evaluate the proposed causal inference model on real-world event
datasets and validate the effectiveness of proposed robust learning modules in
event prediction by feeding learned causal information into different deep
learning methods. Experimental results demonstrate the strengths of the
proposed causal inference model for ITE estimation in societal events and
showcase the beneficial properties of robust learning modules in societal event

Sparse Blind Source Separation (BSS) has become a well established tool for a
wide range of applications - for instance, in astrophysics and remote sensing.
Classical sparse BSS methods, such as the Proximal Alternating Linearized
Minimization (PALM) algorithm, nevertheless often suffer from a difficult
hyperparameter choice, which undermines their results. To bypass this pitfall,
we propose in this work to build on the thriving field of algorithm
unfolding/unrolling. Unrolling PALM enables to leverage the data-driven
knowledge stemming from realistic simulations or ground-truth data by learning
both PALM hyperparameters and variables. In contrast to most existing unrolled
algorithms, which assume a fixed known dictionary during the training and
testing phases, this article further emphasizes on the ability to deal with
variable mixing matrices (a.k.a. dictionaries). The proposed Learned PALM
(LPALM) algorithm thus enables to perform semi-blind source separation, which
is key to increase the generalization of the learnt model in real-world
applications. We illustrate the relevance of LPALM in astrophysical
multispectral imaging: the algorithm not only needs up to $10^4-10^5$ times
fewer iterations than PALM, but also improves the separation quality, while
avoiding the cumbersome hyperparameter and initialization choice of PALM. We
further show that LPALM outperforms other unrolled source separation methods in
the semi-blind setting.

User interface modeling is inherently multimodal, which involves several
distinct types of data: images, structures and language. The tasks are also
diverse, including object detection, language generation and grounding. In this
paper, we present VUT, a Versatile UI Transformer that takes multimodal input
and simultaneously accomplishes 5 distinct tasks with the same model. Our model
consists of a multimodal Transformer encoder that jointly encodes UI images and
structures, and performs UI object detection when the UI structures are absent
in the input. Our model also consists of an auto-regressive Transformer model
that encodes the language input and decodes output, for both question-answering
and command grounding with respect to the UI. Our experiments show that for
most of the tasks, when trained jointly for multi-tasks, VUT substantially
reduces the number of models and footprints needed for performing multiple
tasks, while achieving accuracy exceeding or on par with baseline models
trained for each individual task.

Federated learning is a distributed machine learning mechanism where local
devices collaboratively train a shared global model under the orchestration of
a central server, while keeping all private data decentralized. In the system,
model parameters and its updates are transmitted instead of raw data, and thus
the communication bottleneck has become a key challenge. Besides, recent larger
and deeper machine learning models also pose more difficulties in deploying
them in a federated environment. In this paper, we design a federated two-stage
learning framework that augments prototypical federated learning with a cut
layer on devices and uses sign-based stochastic gradient descent with the
majority vote method on model updates. Cut layer on devices learns informative
and low-dimension representations of raw data locally, which helps reduce
global model parameters and prevents data leakage. Sign-based SGD with the
majority vote method for model updates also helps alleviate communication
limitations. Empirically, we show that our system is an efficient and privacy
preserving federated learning scheme and suits for general application

Teleconferencing is becoming essential during the COVID-19 pandemic. However,
in real-world applications, speech quality can deteriorate due to, for example,
background interference, noise, or reverberation. To solve this problem, target
speech extraction from the mixture signals can be performed with the aid of the
user's vocal features. Various features are accounted for in this study's
proposed system, including speaker embeddings derived from user enrollment and
a novel long-short-term spatial coherence feature pertaining to the target
speaker activity. As a learning-based approach, a target speech sifting network
was employed to extract the relevant features. The network trained with LSTSC
in the proposed approach is robust to microphone array geometries and the
number of microphones. Furthermore, the proposed enhancement system was
compared with a baseline system with speaker embeddings and interchannel phase
difference. The results demonstrated the superior performance of the proposed
system over the baseline in enhancement performance and robustness.

Central to active learning (AL) is what data should be selected for
annotation. Existing works attempt to select highly uncertain or informative
data for annotation. Nevertheless, it remains unclear how selected data impacts
the test performance of the task model used in AL. In this work, we explore
such an impact by theoretically proving that selecting unlabeled data of higher
gradient norm leads to a lower upper-bound of test loss, resulting in better
test performance. However, due to the lack of label information, directly
computing gradient norm for unlabeled data is infeasible. To address this
challenge, we propose two schemes, namely expected-gradnorm and
entropy-gradnorm. The former computes the gradient norm by constructing an
expected empirical loss while the latter constructs an unsupervised loss with
entropy. Furthermore, we integrate the two schemes in a universal AL framework.
We evaluate our method on classical image classification and semantic
segmentation tasks. To demonstrate its competency in domain applications and
its robustness to noise, we also validate our method on a cellular imaging
analysis task, namely cryo-Electron Tomography subtomogram classification.
Results demonstrate that our method achieves superior performance against the
state of the art. Our source code is available at
https://github.com/xulabs/aitom/blob/master/doc/projects/al_gradnorm.md.

We present a very simple algorithm for attention that requires $O(1)$ memory
with respect to sequence length and an extension to self-attention that
requires $O(\log n)$ memory. This is in contrast with the frequently stated
belief that self-attention requires $O(n^2)$ memory. While the time complexity
is still $O(n^2)$, device memory rather than compute capability is often the
limiting factor on modern accelerators. Thus, reducing the memory requirements
of attention allows processing of longer sequences than might otherwise be
feasible. We provide a practical implementation for accelerators that requires
$O(\sqrt{n})$ memory, is numerically stable, and is within a few percent of the
runtime of the standard implementation of attention. We also demonstrate how to
differentiate the function while remaining memory-efficient. For sequence
length 16384, the memory overhead of self-attention is reduced by 59X for
inference and by 32X for differentiation.

The potential of deep learning based image-to-image translations has recently
drawn a lot of attention; one intriguing possibility is that of generating
cosmological predictions with a drastic reduction in computational cost. Such
an effort requires optimization of neural networks with loss functions beyond
low-order statistics like pixel-wise mean square error, and validation of
results beyond simple visual comparisons and summary statistics. In order to
study learning-based cosmological mappings, we choose a tractable analytical
prescription - the Zel'dovich approximation - modeled using U-Net, a
convolutional image translation framework. A comprehensive list of metrics is
proposed, including higher-order correlation functions, conservation laws,
topological indicators, dynamical robustness, and statistical independence of
density fields. We find that the U-Net approach does well with some metrics but
has difficulties with others. In addition to validating AI approaches using
rigorous physical benchmarks, this study motivates advancements in
domain-specific optimization schemes for scientific machine learning.

Concept-oriented deep learning (CODL) is a general approach to meet the
future challenges for deep learning: (1) learning with little or no external
supervision, (2) coping with test examples that come from a different
distribution than the training examples, and (3) integrating deep learning with
symbolic AI. In CODL, as in human learning, concept representations are learned
based on concept exemplars. Contrastive self-supervised learning (CSSL)
provides a promising approach to do so, since it: (1) uses data-driven
associations, to get away from semantic labels, (2) supports incremental and
continual learning, to get away from (large) fixed datasets, and (3)
accommodates emergent objectives, to get away from fixed objectives (tasks). We
discuss major aspects of concept representation learning using CSSL. These
include dual-level concept representations, CSSL for feature representations,
exemplar similarity measures and self-supervised relational reasoning,
incremental and continual CSSL, and contrastive self-supervised concept (class)
incremental learning. The discussion leverages recent findings from cognitive
neural science and CSSL.

In this work we propose the use of quantile regression and dilated recurrent
neural networks with temporal scaling (MQ-DRNN-s) and apply it to the inventory
management task. This model showed a better performance of up to 3.2\% over a
statistical benchmark (the quantile autoregressive model with exogenous
variables, QAR-X), being better than the MQ-DRNN without temporal scaling by
6\%. The above on a set of 10,000 time series of sales of El Globo over a
53-week horizon using rolling windows of 7-day ahead each week.

Accurate evidence retrieval is essential for automated fact checking. Little
previous research has focused on the differences between true and false claims
and how they affect evidence retrieval. This paper shows that, compared with
true claims, false claims more frequently contain irrelevant entities which can
distract evidence retrieval model. A BERT-based retrieval model made more
mistakes in retrieving refuting evidence for false claims than supporting
evidence for true claims. When tested with adversarial false claims
(synthetically generated) containing irrelevant entities, the recall of the
retrieval model is significantly lower than that for original claims. These
results suggest that the vanilla BERT-based retrieval model is not robust to
irrelevant entities in the false claims. By augmenting the training data with
synthetic false claims containing irrelevant entities, the trained model
achieved higher evidence recall, including that of false claims with irrelevant
entities. In addition, using separate models to retrieve refuting and
supporting evidence and then aggregating them can also increase the evidence
recall, including that of false claims with irrelevant entities. These results
suggest that we can increase the BERT-based retrieval model's robustness to
false claims with irrelevant entities via data augmentation and model ensemble.

Hand hygiene is crucial for preventing viruses and infections. Due to the
pervasive outbreak of COVID-19, wearing a mask and hand hygiene appear to be
the most effective ways for the public to curb the spread of these viruses. The
World Health Organization (WHO) recommends a guideline for alcohol-based hand
rub in eight steps to ensure that all surfaces of hands are entirely clean. As
these steps involve complex gestures, human assessment of them lacks enough
accuracy. However, Deep Neural Network (DNN) and machine vision have made it
possible to accurately evaluate hand rubbing quality for the purposes of
training and feedback. In this paper, an automated deep learning based hand rub
assessment system with real-time feedback is presented. The system evaluates
the compliance with the 8-step guideline using a DNN architecture trained on a
dataset of videos collected from volunteers with various skin tones and hand
characteristics following the hand rubbing guideline. Various DNN architectures
were tested, and an Inception-ResNet model led to the best results with 97%
test accuracy. In the proposed system, an NVIDIA Jetson AGX Xavier embedded
board runs the software. The efficacy of the system is evaluated in a concrete
situation of being used by various users, and challenging steps are identified.
In this experiment, the average time taken by the hand rubbing steps among
volunteers is 27.2 seconds, which conforms to the WHO guidelines.

In this paper, we propose an ensemble of deep neural networks along with data
augmentation (DA) learned using effective speech-based features to recognize
emotions from speech. Our ensemble model is built on three deep neural
network-based models. These neural networks are built using the basic local
feature acquiring blocks (LFAB) which are consecutive layers of dilated 1D
Convolutional Neural networks followed by the max pooling and batch
normalization layers. To acquire the long-term dependencies in speech signals
further two variants are proposed by adding Gated Recurrent Unit (GRU) and Long
Short Term Memory (LSTM) layers respectively. All three network models have
consecutive fully connected layers before the final softmax layer for
classification. The ensemble model uses a weighted average to provide the final
classification. We have utilized five standard benchmark datasets: TESS,
EMO-DB, RAVDESS, SAVEE, and CREMA-D for evaluation. We have performed DA by
injecting Additive White Gaussian Noise, pitch shifting, and stretching the
signal level to generalize the models, and thus increasing the accuracy of the
models and reducing the overfitting as well. We handcrafted five categories of
features: Mel-frequency cepstral coefficients, Log Mel-Scaled Spectrogram,
Zero-Crossing Rate, Chromagram, and statistical Root Mean Square Energy value
from each audio sample. These features are used as the input to the LFAB blocks
that further extract the hidden local features which are then fed to either
fully connected layers or to LSTM or GRU based on the model type to acquire the
additional long-term contextual representations. LFAB followed by GRU or LSTM
results in better performance compared to the baseline model. The ensemble
model achieves the state-of-the-art weighted average accuracy in all the
datasets.

Ubiquitous positioning for pedestrian in adverse environment has served a
long standing challenge. Despite dramatic progress made by Deep Learning,
multi-sensor deep odometry systems yet pose a high computational cost and
suffer from cumulative drifting errors over time. Thanks to the increasing
computational power of edge devices, we propose a novel ubiquitous positioning
solution by integrating state-of-the-art deep odometry models on edge with an
EKF (Extended Kalman Filter)-LoRa backend. We carefully compare and select
three sensor modalities, i.e., an Inertial Measurement Unit (IMU), a
millimetre-wave (mmWave) radar, and a thermal infrared camera, and realise
their deep odometry inference engines which runs in real-time. A pipeline of
deploying deep odometry considering accuracy, complexity, and edge platform is
proposed. We design a LoRa link for positional data backhaul and projecting
aggregated positions of deep odometry into the global frame. We find that a
simple EKF based fusion module is sufficient for generic positioning
calibration with over 34% accuracy gains against any standalone deep odometry
system. Extensive tests in different environments validate the efficiency and
efficacy of our proposed positioning system.

Non-negative matrix factorization with transform learning (TL-NMF) is a
recent idea that aims at learning data representations suited to NMF. In this
work, we relate TL-NMF to the classical matrix joint-diagonalization (JD)
problem. We show that, when the number of data realizations is sufficiently
large, TL-NMF can be replaced by a two-step approach -- termed as JD+NMF --
that estimates the transform through JD, prior to NMF computation. In contrast,
we found that when the number of data realizations is limited, not only is
JD+NMF no longer equivalent to TL-NMF, but the inherent low-rank constraint of
TL-NMF turns out to be an essential ingredient to learn meaningful transforms
for NMF.

Computation in several real-world applications like probabilistic machine
learning, sparse linear algebra, and robotic navigation, can be modeled as
irregular directed acyclic graphs (DAGs). The irregular data dependencies in
DAGs pose challenges to parallel execution on general-purpose CPUs and GPUs,
resulting in severe under-utilization of the hardware. This paper proposes DPU,
a specialized processor designed for the efficient execution of irregular DAGs.
The DPU is equipped with parallel compute units that execute different
subgraphs of a DAG independently. The compute units can synchronize within a
cycle using a hardware-supported synchronization primitive, and communicate via
an efficient interconnect to a global banked scratchpad. Furthermore, a
precision-scalable posit arithmetic unit is developed to enable
application-dependent precision. The DPU is taped-out in 28nm CMOS, achieving a
speedup of 5.1$\times$ and 20.6$\times$ over state-of-the-art CPU and GPU
implementations on DAGs of sparse linear algebra and probabilistic machine
learning workloads. This performance is achieved while operating at a power
budget of 0.23W, as opposed to 55W and 98W of the CPU and GPU, resulting in a
peak efficiency of 538 GOPS/W with DPU, which is 1350$\times$ and 9000$\times$
higher than the CPU and GPU, respectively. Thus, with specialized architecture,
DPU enables low-power execution of irregular DAG workloads.

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine
Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern,
Germany. Topics of the conference are applications of Artificial Intellgence in
life sciences, intelligent systems, industry 4.0, mobility and others. The
TriRhenaTech alliance is a network of universities in the Upper-Rhine
Trinational Metropolitan Region comprising of the German universities of
applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier,
the Baden-Wuerttemberg Cooperative State University Loerrach, the French
university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the
fields of engineering, architecture and management) and the University of
Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal
is to reinforce the transfer of knowledge, research, and technology, as well as
the cross-border mobility of students.

Clustering is a popular unsupervised learning tool often used to discover
groups within a larger population such as customer segments, or patient
subtypes. However, despite its use as a tool for subgroup discovery and
description - few state-of-the-art algorithms provide any rationale or
description behind the clusters found. We propose a novel approach for
interpretable clustering that both clusters data points and constructs
polytopes around the discovered clusters to explain them. Our framework allows
for additional constraints on the polytopes - including ensuring that the
hyperplanes constructing the polytope are axis-parallel or sparse with integer
coefficients. We formulate the problem of constructing clusters via polytopes
as a Mixed-Integer Non-Linear Program (MINLP). To solve our formulation we
propose a two phase approach where we first initialize clusters and polytopes
using alternating minimization, and then use coordinate descent to boost
clustering performance. We benchmark our approach on a suite of synthetic and
real world clustering problems, where our algorithm outperforms state of the
art interpretable and non-interpretable clustering algorithms.

Task embeddings are low-dimensional representations that are trained to
capture task properties. In this paper, we propose MetaEval, a collection of
$101$ NLP tasks. We fit a single transformer to all MetaEval tasks jointly
while conditioning it on learned embeddings. The resulting task embeddings
enable a novel analysis of the space of tasks. We then show that task aspects
can be mapped to task embeddings for new tasks without using any annotated
examples.
  Predicted embeddings can modulate the encoder for zero-shot inference and
outperform a zero-shot baseline on GLUE tasks. The provided multitask setup can
function as a benchmark for future transfer learning research.

Realistic 3D indoor scene datasets have enabled significant recent progress
in computer vision, scene understanding, autonomous navigation, and 3D
reconstruction. But the scale, diversity, and customizability of existing
datasets is limited, and it is time-consuming and expensive to scan and
annotate more. Fortunately, combinatorics is on our side: there are enough
individual rooms in existing 3D scene datasets, if there was but a way to
recombine them into new layouts. In this paper, we propose the task of
generating novel 3D floor plans from existing 3D rooms. We identify three
sub-tasks of this problem: generation of 2D layout, retrieval of compatible 3D
rooms, and deformation of 3D rooms to fit the layout. We then discuss different
strategies for solving the problem, and design two representative pipelines:
one uses available 2D floor plans to guide selection and deformation of 3D
rooms; the other learns to retrieve a set of compatible 3D rooms and combine
them into novel layouts. We design a set of metrics that evaluate the generated
results with respect to each of the three subtasks and show that different
methods trade off performance on these subtasks. Finally, we survey downstream
tasks that benefit from generated 3D scenes and discuss strategies in selecting
the methods most appropriate for the demands of these tasks.

Neuroevolution is one of the methodologies that can be used for learning
optimal architecture during training. It uses evolutionary algorithms to
generate the topology of artificial neural networks and its parameters. The
main benefits are that it is scalable and can be fully or partially non
gradient method. In this work, a modified neuroevolution technique is presented
which incorporates multi-level optimisation. The presented approach adapts
evolution strategies for evolving an ensemble model based on the bagging
technique, using genetic operators for optimising single anomaly detection
models, reducing the training dataset to speedup the search process and perform
non-gradient fine tuning. Multivariate anomaly detection as an unsupervised
learning task is the case study upon which the presented approach is tested.
Single model optimisation is based on mutation and crossover operators and is
focused on finding optimal window sizes, the number of layers, layer depths,
hyperparameters etc. to boost the anomaly detection scores of new and already
known models. The proposed framework and its protocol shows that it is possible
to find architecture within a reasonable time frame which can boost all well
known multivariate anomaly detection deep learning architectures. The work
concentrates on improvements to the multi-level neuroevolution approach for
anomaly detection. The main modifications are in the methods of mixing groups
and single model evolution, non-gradient fine tuning and a voting mechanism.
The presented framework can be used as an efficient learning network
architecture method for any different unsupervised task where autoencoder
architectures can be used. The tests were run on SWAT, WADI, MSL and SMAP
datasets and the presented approach evolved the architectures that achieved the
best scores among other deep learning models.

Contrastive learning has been proven suitable for learning sentence
embeddings and can significantly improve the semantic textual similarity (STS)
tasks. Recently, large contrastive learning models, e.g., Sentence-T5, tend to
be proposed to learn more powerful sentence embeddings. Though effective, such
large models are hard to serve online due to computational resources or time
cost limits. To tackle that, knowledge distillation (KD) is commonly adopted,
which can compress a large "teacher" model into a small "student" model but
generally suffer from some performance loss. Here we propose an enhanced KD
framework termed Distill-Contrast (DisCo). The proposed DisCo framework firstly
utilizes KD to transfer the capability of a large sentence embedding model to a
small student model on large unlabelled data, and then finetunes the student
model with contrastive learning on labelled training data. For the KD process
in DisCo, we further propose Contrastive Knowledge Distillation (CKD) to
enhance the consistencies among teacher model training, KD, and student model
finetuning, which can probably improve performance like prompt learning.
Extensive experiments on 7 STS benchmarks show that student models trained with
the proposed DisCo and CKD suffer from little or even no performance loss and
consistently outperform the corresponding counterparts of the same parameter
size. Amazingly, our 110M student model can even outperform the latest
state-of-the-art (SOTA) model, i.e., Sentence-T5(11B), with only 1% parameters.

Unsupervised learning has made substantial progress over the last few years,
especially by means of contrastive self-supervised learning. The dominating
dataset for benchmarking self-supervised learning has been ImageNet, for which
recent methods are approaching the performance achieved by fully supervised
training. The ImageNet dataset is however largely object-centric, and it is not
clear yet what potential those methods have on widely different datasets and
tasks that are not object-centric, such as in digital pathology. While
self-supervised learning has started to be explored within this area with
encouraging results, there is reason to look closer at how this setting differs
from natural images and ImageNet. In this paper we make an in-depth analysis of
contrastive learning for histopathology, pin-pointing how the contrastive
objective will behave differently due to the characteristics of histopathology
data. We bring forward a number of considerations, such as view generation for
the contrastive objective and hyper-parameter tuning. In a large battery of
experiments, we analyze how the downstream performance in tissue classification
will be affected by these considerations. The results point to how contrastive
learning can reduce the annotation effort within digital pathology, but that
the specific dataset characteristics need to be considered. To take full
advantage of the contrastive learning objective, different calibrations of view
generation and hyper-parameters are required. Our results pave the way for
realizing the full potential of self-supervised learning for histopathology

Deep neural networks have become the driving force of modern image
recognition systems. However, the vulnerability of neural networks against
adversarial attacks poses a serious threat to the people affected by these
systems. In this paper, we focus on a real-world threat model where a
Man-in-the-Middle adversary maliciously intercepts and perturbs images web
users upload online. This type of attack can raise severe ethical concerns on
top of simple performance degradation. To prevent this attack, we devise a
novel bi-level optimization algorithm that finds points in the vicinity of
natural images that are robust to adversarial perturbations. Experiments on
CIFAR-10 and ImageNet show our method can effectively robustify natural images
within the given modification budget. We also show the proposed method can
improve robustness when jointly used with randomized smoothing.

Discrimination in selection problems such as hiring or college admission is
often explained by implicit bias from the decision maker against disadvantaged
demographic groups. In this paper, we consider a model where the decision maker
receives a noisy estimate of each candidate's quality, whose variance depends
on the candidate's group -- we argue that such differential variance is a key
feature of many selection problems. We analyze two notable settings: in the
first, the noise variances are unknown to the decision maker who simply picks
the candidates with the highest estimated quality independently of their group;
in the second, the variances are known and the decision maker picks candidates
having the highest expected quality given the noisy estimate. We show that both
baseline decision makers yield discrimination, although in opposite directions:
the first leads to underrepresentation of the low-variance group while the
second leads to underrepresentation of the high-variance group. We study the
effect on the selection utility of imposing a fairness mechanism that we term
the $\gamma$-rule (it is an extension of the classical four-fifths rule and it
also includes demographic parity). In the first setting (with unknown
variances), we prove that under mild conditions, imposing the $\gamma$-rule
increases the selection utility -- here there is no trade-off between fairness
and utility. In the second setting (with known variances), imposing the
$\gamma$-rule decreases the utility but we prove a bound on the utility loss
due to the fairness mechanism.

Training robot manipulation policies is a challenging and open problem in
robotics and artificial intelligence. In this paper we propose a novel and
compact state representation based on the rewards predicted from an image-based
task success classifier. Our experiments, using the Pepper robot in simulation
with two deep reinforcement learning algorithms on a grab-and-lift task, reveal
that our proposed state representation can achieve up to 97% task success using
our best policies.

The advent of scientific machine learning (SciML) has opened up a new field
with many promises and challenges in the field of simulation science by
developing approaches at the interface of physics- and data-based modelling. To
this end, physics-informed neural networks (PINNs) have been introduced in
recent years, which cope for the scarcity in training data by incorporating
physics knowledge of the problem at so-called collocation points. In this work,
we investigate the prediction performance of PINNs with respect to the number
of collocation points used to enforce the physics-based penalty terms. We show
that PINNs can fail, learning a trivial solution that fulfills the
physics-derived penalty term by definition. We have developed an alternative
sampling approach and a new penalty term enabling us to remedy this core
problem of PINNs in data-scarce settings with competitive results while
reducing the amount of collocation points needed by up to 80 \% for benchmark
problems.

Understanding the fundamental principles behind the massive success of neural
networks is one of the most important open questions in deep learning. However,
due to the highly complex nature of the problem, progress has been relatively
slow. In this note, through the lens of infinite-width networks, a.k.a. neural
kernels, we present one such principle resulting from hierarchical localities.
It is well-known that the eigenstructure of infinite-width multilayer
perceptrons (MLPs) depends solely on the concept frequency, which measures the
order of interactions. We show that the topologies from deep convolutional
networks (CNNs) restructure the associated eigenspaces into finer subspaces. In
addition to frequency, the new structure also depends on the concept space,
which measures the spatial distance among nonlinear interaction terms. The
resulting fine-grained eigenstructure dramatically improves the network's
learnability, empowering them to simultaneously model a much richer class of
interactions, including Long-Range-Low-Frequency interactions,
Short-Range-High-Frequency interactions, and various interpolations and
extrapolations in-between. Additionally, model scaling can improve the
resolutions of interpolations and extrapolations and, therefore, the network's
learnability. Finally, we prove a sharp characterization of the generalization
error for infinite-width CNNs of any depth in the high-dimensional setting. Two
corollaries follow: (1) infinite-width deep CNNs can break the curse of
dimensionality without losing their expressivity, and (2) scaling improves
performance in both the finite and infinite data regimes.

An important issue during an engineering design process is to develop an
understanding which design parameters have the most influence on the
performance. Especially in the context of optimization approaches this
knowledge is crucial in order to realize an efficient design process and
achieve high-performing results. Information theory provides powerful tools to
investigate these relationships because measures are model-free and thus also
capture non-linear relationships, while requiring only minimal assumptions on
the input data. We therefore propose to use recently introduced
information-theoretic methods and estimation algorithms to find the most
influential input parameters in optimization results. The proposed methods are
in particular able to account for interactions between parameters, which are
often neglected but may lead to redundant or synergistic contributions of
multiple parameters. We demonstrate the application of these methods on
optimization data from aerospace engineering, where we first identify the most
relevant optimization parameters using a recently introduced
information-theoretic feature-selection algorithm that accounts for
interactions between parameters. Second, we use the novel partial information
decomposition (PID) framework that allows to quantify redundant and synergistic
contributions between selected parameters with respect to the optimization
outcome to identify parameter interactions. We thus demonstrate the power of
novel information-theoretic approaches in identifying relevant parameters in
optimization runs and highlight how these methods avoid the selection of
redundant parameters, while detecting interactions that result in synergistic
contributions of multiple parameters.

We investigate the use of a certain class of functional inequalities known as
weak Poincar\'e inequalities to bound convergence of Markov chains to
equilibrium. We show that this enables the straightforward and transparent
derivation of subgeometric convergence bounds for methods such as the
Independent Metropolis--Hastings sampler and pseudo-marginal methods for
intractable likelihoods, the latter being subgeometric in many practical
settings. These results rely on novel quantitative comparison theorems between
Markov chains. Associated proofs are simpler than those relying on
drift/minorization conditions and the tools developed allow us to recover and
further extend known results as particular cases. We are then able to provide
new insights into the practical use of pseudo-marginal algorithms, analyse the
effect of averaging in Approximate Bayesian Computation (ABC) and the use of
products of independent averages, and also to study the case of lognormal
weights relevant to particle marginal Metropolis--Hastings (PMMH).

Gradient descent ascent (GDA), the simplest single-loop algorithm for
nonconvex minimax optimization, is widely used in practical applications such
as generative adversarial networks (GANs) and adversarial training. Albeit its
desirable simplicity, recent work shows inferior convergence rates of GDA in
theory even assuming strong concavity of the objective on one side. This paper
establishes new convergence results for two alternative single-loop algorithms
-- alternating GDA and smoothed GDA -- under the mild assumption that the
objective satisfies the Polyak-Lojasiewicz (PL) condition about one variable.
We prove that, to find an $\epsilon$-stationary point, (i) alternating GDA and
its stochastic variant (without mini batch) respectively require $O(\kappa^{2}
\epsilon^{-2})$ and $O(\kappa^{4} \epsilon^{-4})$ iterations, while (ii)
smoothed GDA and its stochastic variant (without mini batch) respectively
require $O(\kappa \epsilon^{-2})$ and $O(\kappa^{2} \epsilon^{-4})$ iterations.
The latter greatly improves over the vanilla GDA and gives the hitherto best
known complexity results among single-loop algorithms under similar settings.
We further showcase the empirical efficiency of these algorithms in training
GANs and robust nonlinear regression.

Population ageing and pandemics recently demonstrate to cause isolation of
elderly people in their houses, generating the need for a reliable assistive
figure. Robotic assistants are the new frontier of innovation for domestic
welfare, and elderly monitoring is one of the services a robot can handle for
collective well-being. Despite these emerging needs, in the actual landscape of
robotic assistants there are no platform which successfully combines a reliable
mobility in cluttered domestic spaces, with lightweight and offline Artificial
Intelligence (AI) solutions for perception and interaction. In this work, we
present Marvin, a novel assistive robotic platform we developed with a modular
layer-based architecture, merging a flexible mechanical design with
cutting-edge AI for perception and vocal control. We focus the design of Marvin
on three target service functions: monitoring of elderly and reduced-mobility
subjects, remote presence and connectivity, and night assistance. Compared to
previous works, we propose a tiny omnidirectional platform, which enables agile
mobility and effective obstacle avoidance. Moreover, we design a controllable
positioning device, which easily allows the user to access the interface for
connectivity and extends the visual range of the camera sensor. Nonetheless, we
delicately consider the privacy issues arising from private data collection on
cloud services, a critical aspect of commercial AI-based assistants. To this
end, we demonstrate how lightweight deep learning solutions for visual
perception and vocal command can be adopted, completely running offline on the
embedded hardware of the robot.

Evidence-based medicine, the practice in which healthcare professionals refer
to the best available evidence when making decisions, forms the foundation of
modern healthcare. However, it relies on labour-intensive systematic reviews,
where domain specialists must aggregate and extract information from thousands
of publications, primarily of randomised controlled trial (RCT) results, into
evidence tables. This paper investigates automating evidence table generation
by decomposing the problem across two language processing tasks: \textit{named
entity recognition}, which identifies key entities within text, such as drug
names, and \textit{relation extraction}, which maps their relationships for
separating them into ordered tuples. We focus on the automatic tabulation of
sentences from published RCT abstracts that report the results of the study
outcomes. Two deep neural net models were developed as part of a joint
extraction pipeline, using the principles of transfer learning and
transformer-based language representations. To train and test these models, a
new gold-standard corpus was developed, comprising almost 600 result sentences
from six disease areas. This approach demonstrated significant advantages, with
our system performing well across multiple natural language processing tasks
and disease areas, as well as in generalising to disease domains unseen during
training. Furthermore, we show these results were achievable through training
our models on as few as 200 example sentences. The final system is a proof of
concept that the generation of evidence tables can be semi-automated,
representing a step towards fully automating systematic reviews.

The commercial availability of low-cost millimeter wave (mmWave)
communication and radar devices is starting to improve the penetration of such
technologies in consumer markets, paving the way for large-scale and dense
deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the
same time, pervasive mmWave access will enable device localization and
device-free sensing with unprecedented accuracy, especially with respect to
sub-6 GHz commercial-grade devices. This paper surveys the state of the art in
device-based localization and device-free sensing using mmWave communication
and radar devices, with a focus on indoor deployments. We first overview key
concepts about mmWave signal propagation and system design. Then, we provide a
detailed account of approaches and algorithms for localization and sensing
enabled by mmWaves. We consider several dimensions in our analysis, including
the main objectives, techniques, and performance of each work, whether each
research reached some degree of implementation, and which hardware platforms
were used for this purpose. We conclude by discussing that better algorithms
for consumer-grade devices, data fusion methods for dense deployments, as well
as an educated application of machine learning methods are promising, relevant
and timely research directions.

Representation learning of the task-oriented attention while tracking
instrument holds vast potential in image-guided robotic surgery. Incorporating
cognitive ability to automate the camera control enables the surgeon to
concentrate more on dealing with surgical instruments. The objective is to
reduce the operation time and facilitate the surgery for both surgeons and
patients. We propose an end-to-end trainable Spatio-Temporal Multi-Task
Learning (ST-MTL) model with a shared encoder and spatio-temporal decoders for
the real-time surgical instrument segmentation and task-oriented saliency
detection. In the MTL model of shared parameters, optimizing multiple loss
functions into a convergence point is still an open challenge. We tackle the
problem with a novel asynchronous spatio-temporal optimization (ASTO) technique
by calculating independent gradients for each decoder. We also design a
competitive squeeze and excitation unit by casting a skip connection that
retains weak features, excites strong features, and performs dynamic spatial
and channel-wise feature recalibration. To capture better long term
spatio-temporal dependencies, we enhance the long-short term memory (LSTM)
module by concatenating high-level encoder features of consecutive frames. We
also introduce Sinkhorn regularized loss to enhance task-oriented saliency
detection by preserving computational efficiency. We generate the task-aware
saliency maps and scanpath of the instruments on the dataset of the MICCAI 2017
robotic instrument segmentation challenge. Compared to the state-of-the-art
segmentation and saliency methods, our model outperforms most of the evaluation
metrics and produces an outstanding performance in the challenge.

Understanding capabilities and limitations of different network architectures
is of fundamental importance to machine learning. Bayesian inference on
Gaussian processes has proven to be a viable approach for studying recurrent
and deep networks in the limit of infinite layer width, $n\to\infty$. Here we
present a unified and systematic derivation of the mean-field theory for both
architectures that starts from first principles by employing established
methods from statistical physics of disordered systems. The theory elucidates
that while the mean-field equations are different with regard to their temporal
structure, they yet yield identical Gaussian kernels when readouts are taken at
a single time point or layer, respectively. Bayesian inference applied to
classification then predicts identical performance and capabilities for the two
architectures. Numerically, we find that convergence towards the mean-field
theory is typically slower for recurrent networks than for deep networks and
the convergence speed depends non-trivially on the parameters of the weight
prior as well as the depth or number of time steps, respectively. Our method
exposes that Gaussian processes are but the lowest order of a systematic
expansion in $1/n$ and we compute next-to-leading-order corrections which turn
out to be architecture-specific. The formalism thus paves the way to
investigate the fundamental differences between recurrent and deep
architectures at finite widths $n$.

Deep learning (DL) models, especially those large-scale and high-performance
ones, can be very costly to train, demanding a great amount of data and
computational resources. Unauthorized reproduction of DL models can lead to
copyright infringement and cause huge economic losses to model owners. Existing
copyright protection techniques are mostly based on watermarking, which embeds
an owner-specified watermark into the model. While being able to provide exact
ownership verification, these techniques are 1) invasive, as they need to
tamper with the training process, which may affect the utility or introduce new
security risks; 2) prone to adaptive attacks that attempt to remove the
watermark; and 3) not robust to the emerging model extraction attacks. Latest
fingerprinting work, though being non-invasive, also falls short when facing
the diverse and ever-growing attack scenarios. In this paper, we propose a
novel testing framework for DL copyright protection: DEEPJUDGE. DEEPJUDGE
quantitatively tests the similarities between two DL models: a victim model and
a suspect model. It leverages a diverse set of testing metrics and test case
generation methods to produce a chain of supporting evidence to help determine
whether a suspect model is a copy of the victim model. Advantages of DEEPJUDGE
include: 1) non-invasive, as it works directly on the model and does not tamper
with the training process; 2) efficient, as it only needs a small set of test
cases and a quick scan of models; 3) flexible, as it can easily incorporate new
metrics or generation methods to obtain more confident judgement; and 4) fairly
robust to model extraction and adaptive attacks. We verify the effectiveness of
DEEPJUDGE under typical copyright infringement scenarios, including model
finetuning, pruning and extraction, via extensive experiments on both image and
speech datasets with a variety of model architectures.

Most existing vision-language pre-training methods focus on understanding
tasks and use BERT-like objectives (masked language modeling and image-text
matching) during pretraining. Although they perform well in many understanding
downstream tasks, e.g., visual question answering, image-text retrieval and
visual entailment, they do not possess the ability to generate. To tackle this
problem, we propose Unified multimodal pre-training for both Vision-Language
understanding and generation (UniVL). The proposed UniVL is capable of handling
both understanding tasks and generative tasks. We augment existing pretraining
paradigms that only use random masks with causal masks, i.e., triangular masks
that mask out future tokens, such that the pre-trained models can have
autoregressive generation abilities by design. We formulate several previous
understanding tasks as a text generation task and propose to use prompt-based
method for fine-tuning on different downstream tasks. Our experiments show that
there is a trade-off between understanding tasks and generation tasks while
using the same model, and a feasible way to improve both tasks is to use more
data. Our UniVL framework attains comparable performance to recent
vision-language pre-training methods on both understanding tasks and generation
tasks. Moreover, we demostrate that prompt-based finetuning is more
data-efficient - it outperforms discriminative methods in few-shot scenarios.

The aim of this work is to detect and automatically generate high-level
explanations of anomalous events in video. Understanding the cause of an
anomalous event is crucial as the required response is dependant on its nature
and severity. Recent works typically use object or action classifier to detect
and provide labels for anomalous events. However, this constrains detection
systems to a finite set of known classes and prevents generalisation to unknown
objects or behaviours. Here we show how to robustly detect anomalies without
the use of object or action classifiers yet still recover the high level reason
behind the event. We make the following contributions: (1) a method using
saliency maps to decouple the explanation of anomalous events from object and
action classifiers, (2) show how to improve the quality of saliency maps using
a novel neural architecture for learning discrete representations of video by
predicting future frames and (3) beat the state-of-the-art anomaly explanation
methods by 60\% on a subset of the public benchmark X-MAN dataset.

Undergraduate physics education has greatly benefited from the introduction
of computational activities. However, despite the benefits computation has
delivered, we still lack a complete understanding of the computationally
integrated learning experience from the student perspective. In particular, we
are interested in investigating how students develop expert-like perceptions of
computation as a practice within the professional physics community. To
investigate this aspect of student development, we employ the Communities of
Practice framework, which describes how students navigate through a
professional community by appropriating the community's practices and goals as
personally important. We introduce the construct of a COP-Model as a student's
internal representation of a professional community that they develop as they
navigate that community. We used this construct to formulate a set of research
questions and semistructured interview protocols to explore how five physics
students represent the use of computation in their mental models of the global
physics community. We foreground these representations in the local academic
community established by their instructors in three concurrent computationally
integrated physics courses. We find that these students saw computation as a
normal and valuable part of physics practice, identified benefits of using
computation in alignment with the physics community, struggle with confidence
with regards to computation, and demonstrate some expectations for
computational proficiency that are misaligned with the physics community. We
establish these themes with interview excerpts and discuss implications for
instruction and future research.

In this paper we present a computational modeling account of an active self
in artificial agents. In particular we focus on how an agent can be equipped
with a sense of control and how it arises in autonomous situated action and, in
turn, influences action control. We argue that this requires laying out an
embodied cognitive model that combines bottom-up processes (sensorimotor
learning and fine-grained adaptation of control) with top-down processes
(cognitive processes for strategy selection and decision-making). We present
such a conceptual computational architecture based on principles of predictive
processing and free energy minimization. Using this general model, we describe
how a sense of control can form across the levels of a control hierarchy and
how this can support action control in an unpredictable environment. We present
an implementation of this model as well as first evaluations in a simulated
task scenario, in which an autonomous agent has to cope with un-/predictable
situations and experiences corresponding sense of control. We explore different
model parameter settings that lead to different ways of combining low-level and
high-level action control. The results show the importance of appropriately
weighting information in situations where the need for low/high-level action
control varies and they demonstrate how the sense of control can facilitate
this.

The use of trigonometric polynomials as Lagrange multipliers in the harmonic
mortar method enables an efficient and elegant treatment of relative motion in
the stator-rotor coupling of electric machine simulation. Explicit formulas for
the torque computation are derived by energetic considerations, and their
realization by harmonic mortar finite element and isogemetric analysis
discretizations is discussed. Numerical tests are presented to illustrate the
theoretical results and demonstrate the potential of harmonic mortar methods
for the evaluation of torque ripples.

We introduce Shennong, a Python toolbox and command-line utility for speech
features extraction. It implements a wide range of well-established state of
art algorithms including spectro-temporal filters such as Mel-Frequency
Cepstral Filterbanks or Predictive Linear Filters, pre-trained neural networks,
pitch estimators as well as speaker normalization methods and post-processing
algorithms. Shennong is an open source, easy-to-use, reliable and extensible
framework. The use of Python makes the integration to others speech modeling
and machine learning tools easy. It aims to replace or complement several
heterogeneous software, such as Kaldi or Praat. After describing the Shennong
software architecture, its core components and implemented algorithms, this
paper illustrates its use on three applications: a comparison of speech
features performances on a phones discrimination task, an analysis of a Vocal
Tract Length Normalization model as a function of the speech duration used for
training and a comparison of pitch estimation algorithms under various noise

The ultimate performance of machine learning algorithms for classification
tasks is usually measured in terms of the empirical error probability (or
accuracy) based on a testing dataset. Whereas, these algorithms are optimized
through the minimization of a typically different--more convenient--loss
function based on a training set. For classification tasks, this loss function
is often the negative log-loss that leads to the well-known cross-entropy risk
which is typically better behaved (from a numerical perspective) than the error
probability. Conventional studies on the generalization error do not usually
take into account the underlying mismatch between losses at training and
testing phases. In this work, we introduce an analysis based on point-wise PAC
approach over the generalization gap considering the mismatch of testing based
on the accuracy metric and training on the negative log-loss. We label this
analysis PACMAN. Building on the fact that the mentioned mismatch can be
written as a likelihood ratio, concentration inequalities can be used to
provide some insights for the generalization problem in terms of some
point-wise PAC bounds depending on some meaningful information-theoretic
quantities. An analysis of the obtained bounds and a comparison with available
results in the literature are also provided.

Software development companies organize hackathons to encourage innovation.
Despite many benefits of hackathons, in large-scale agile organizations where
many teams work together, stopping the ongoing work results in a significant
decrease in the immediate output. Motivated by the need to understand whether
and how to run hackathons, we investigated how the practice affects
productivity on the individual and organizational levels. By mapping the
benefits and challenges to an established productivity framework, we found that
hackathons improve developers' satisfaction and well-being, strengthen the
company culture, improve performance (as many ideas are tested), increase
activity (as the ideas are developed quickly), and improve communication and
collaboration (because the social network is strengthened). Addressing
managerial concerns, we found that hackathons also increase efficiency and flow
because people learn to complete work and make progress quickly, and they build
new competence. Finally, with respect to virtual hackathons we found that
developers work more in isolation because tasks are split between team members
resulting in less collaboration. This means that some important, expected
hackathon values in virtual contexts require extra effort and cannot be taken
for granted.

Among all fluids, water has always been of special concern for scientists
from a broad variety of research fields due to its rich behavior. In
particular, some questions remain unanswered nowadays concerning the
temperature dependence of bulk and interfacial transport properties of
supercooled and liquid water, e.g. regarding the fundamentals of the violation
of the Stokes-Einstein relation in the supercooled regime or the subtle
relation between structure and dynamical properties. Here we investigated the
temperature dependence of the bulk transport properties from ab initio
molecular dynamics based on density functional theory, down to the supercooled
regime. We determined from a selection of functionals, that SCAN better
describes the experimental viscosity and self-diffusion coefficient, although
we found disagreements at the lowest temperatures. For a limited set of
temperatures, we also explored the role of nuclear quantum effects on water
dynamics using ab initio molecular dynamics that has been accelerated via a
recently introduced machine learning approach. We then investigated the
molecular mechanisms underlying the different functionals performance and
assessed the validity of the Stokes-Einstein relation. We also explored the
connection between structural properties and the transport coefficients,
verifying the validity of the excess entropy scaling relations for all the
functionals. These results pave the way to predict the transport coefficients
from the radial distribution function, helping to develop better functionals.
On this line, they indicate the importance of describing the long-range
features of the radial distribution function.

Reinforcement learning (RL) has gained increasing attraction in the academia
and tech industry with launches to a variety of impactful applications and
products. Although research is being actively conducted on many fronts (e.g.,
offline RL, performance, etc.), many RL practitioners face a challenge that has
been largely ignored: determine whether a designed Markov Decision Process
(MDP) is valid and meaningful. This study proposes a heuristic-based feature
analysis method to validate whether an MDP is well formulated. We believe an
MDP suitable for applying RL should contain a set of state features that are
both sensitive to actions and predictive in rewards. We tested our method in
constructed environments showing that our approach can identify certain invalid
environment formulations. As far as we know, performing validity analysis for
RL problem formulation is a novel direction. We envision that our tool will
serve as a motivational example to help practitioners apply RL in real-world
problems more easily.

Fuzzy skill multimaps can describe individuals' knowledge states from the
perspective of latent cognitive abilities. The significance of discriminative
knowledge structure is reducing repeated testing and the workload for students,
which responds to the so-called `double reduction policy'. As an application in
knowledge assessment, the bi-discriminative knowledge structure is useful for
evaluating `relative independence' items or knowledge points. Moreover, it is
of great significance that which kind of fuzzy skill multimap delineates a
discriminative or bi-discriminative knowledge structure. Moreover, we give a
characterization of fuzzy skill multimaps such that the delineated knowledge
structures are knowledge spaces, learning spaces and simple closure spaces
respectively. Further, there are some interesting applications in the meshing
of the delineated knowledge structures and the distributed fuzzy skill
multimaps. We also give a more precise analysis with regard to the separability
(resp. bi-separability) on which condition the information collected within a
local assessment can reflect an assessment at the global level, and which
condition any of the local domains can localize from given global assessment.

We propose AnonyGAN, a GAN-based solution for face anonymisation which
replaces the visual information corresponding to a source identity with a
condition identity provided as any single image. With the goal to maintain the
geometric attributes of the source face, i.e., the facial pose and expression,
and to promote more natural face generation, we propose to exploit a Bipartite
Graph to explicitly model the relations between the facial landmarks of the
source identity and the ones of the condition identity through a deep model. We
further propose a landmark attention model to relax the manual selection of
facial landmarks, allowing the network to weight the landmarks for the best
visual naturalness and pose preservation. Finally, to facilitate the appearance
learning, we propose a hybrid training strategy to address the challenge caused
by the lack of direct pixel-level supervision. We evaluate our method and its
variants on two public datasets, CelebA and LFW, in terms of visual
naturalness, facial pose preservation and of its impacts on face detection and
re-identification. We prove that AnonyGAN significantly outperforms the
state-of-the-art methods in terms of visual naturalness, face detection and
pose preservation.

Reinforcement Learning (RL) enables agents to learn how to perform various
tasks from scratch. In domains like autonomous driving, recommendation systems,
and more, optimal RL policies learned could cause a privacy breach if the
policies memorize any part of the private reward. We study the set of existing
differentially-private RL policies derived from various RL algorithms such as
Value Iteration, Deep Q Networks, and Vanilla Proximal Policy Optimization. We
propose a new Privacy-Aware Inverse RL (PRIL) analysis framework, that performs
reward reconstruction as an adversarial attack on private policies that the
agents may deploy. For this, we introduce the reward reconstruction attack,
wherein we seek to reconstruct the original reward from a privacy-preserving
policy using an Inverse RL algorithm. An adversary must do poorly at
reconstructing the original reward function if the agent uses a tightly private
policy. Using this framework, we empirically test the effectiveness of the
privacy guarantee offered by the private algorithms on multiple instances of
the FrozenLake domain of varying complexities. Based on the analysis performed,
we infer a gap between the current standard of privacy offered and the standard
of privacy needed to protect reward functions in RL. We do so by quantifying
the extent to which each private policy protects the reward function by
measuring distances between the original and reconstructed rewards.

Neural network pruning has remarkable performance for reducing the complexity
of deep network models. Recent network pruning methods usually focused on
removing unimportant or redundant filters in the network. In this paper, by
exploring the similarities between feature maps, we propose a novel filter
pruning method, Central Filter (CF), which suggests that a filter is
approximately equal to a set of other filters after appropriate adjustments.
Our method is based on the discovery that the average similarity between
feature maps changes very little, regardless of the number of input images.
Based on this finding, we establish similarity graphs on feature maps and
calculate the closeness centrality of each node to select the Central Filter.
Moreover, we design a method to directly adjust weights in the next layer
corresponding to the Central Filter, effectively minimizing the error caused by
pruning. Through experiments on various benchmark networks and datasets, CF
yields state-of-the-art performance. For example, with ResNet-56, CF reduces
approximately 39.7% of FLOPs by removing 47.1% of the parameters, with even
0.33% accuracy improvement on CIFAR-10. With GoogLeNet, CF reduces
approximately 63.2% of FLOPs by removing 55.6% of the parameters, with only a
small loss of 0.35% in top-1 accuracy on CIFAR-10. With ResNet-50, CF reduces
approximately 47.9% of FLOPs by removing 36.9% of the parameters, with only a
small loss of 1.07% in top-1 accuracy on ImageNet. The codes can be available
at https://github.com/8ubpshLR23/Central-Filter.

Neural networks can be used as surrogates for PDE models. They can be made
physics-aware by penalizing underlying equations or the conservation of
physical properties in the loss function during training. Current approaches
allow to additionally respect data from numerical simulations or experiments in
the training process. However, this data is frequently expensive to obtain and
thus only scarcely available for complex models. In this work, we investigate
how physics-aware models can be enriched with computationally cheaper, but
inexact, data from other surrogate models like Reduced-Order Models (ROMs). In
order to avoid trusting too-low-fidelity surrogate solutions, we develop an
approach that is sensitive to the error in inexact data. As a proof of concept,
we consider the one-dimensional wave equation and show that the training
accuracy is increased by two orders of magnitude when inexact data from ROMs is
incorporated.

In current Internet-of-Things (IoT) deployments, a mix of traditional IP
networking and IoT specific protocols, both relying on the TCP protocol, can be
used to transport data from a source to a destination. Therefore, TCP-specific
attacks, such as the Distributed Denial of Service (DDoS) using the TCP SYN
attack, are one of the most plausible tools that attackers can use on
Cyber-Physical Systems (CPS). This may be done by launching an attack from its
IoT subsystem, here referred to as the "CPS-IoT", with potential propagation to
the different servers located in both fog and the cloud infrastructures of the
CPS. This study compares the effectiveness of supervised, unsupervised, and
semi-supervised machine learning algorithms for detecting DDoS attacks in
CPS-IoT, particularly during data transmission to and from the physical space
to the cyber space via the Internet. The algorithms considered are broadly
grouped into two: i) Detection algorithms, which include Logistic Regression
(LGR), K-Means, and Artificial Neural Networks (ANN). We also looked into the
effectiveness of semi-supervised hybrid learning models, which use unsupervised
K-Means to label data, then feed the output to a supervised learning model for
attack detection. ii.) Prediction algorithms - LGR, Kernel Ridge Regression
(KRR) and Support Vector Regression (SVR), which were used to predict imminent
attacks. Experimental tests were carried out and obtained results showed that
the hybrid model was able to achieve 100% accuracy with zero false positives;
while all the prediction models were able to achieve over 94% attack prediction
accuracy.

Sentiment Analysis is one of the most classical and primarily studied natural
language processing tasks. This problem had a notable advance with the
proposition of more complex and scalable machine learning models. Despite this
progress, the Brazilian Portuguese language still disposes only of limited
linguistic resources, such as datasets dedicated to sentiment classification,
especially when considering the existence of predefined partitions in training,
testing, and validation sets that would allow a more fair comparison of
different algorithm alternatives. Motivated by these issues, this work analyzes
the predictive performance of a range of document embedding strategies,
assuming the polarity as the system outcome. This analysis includes five
sentiment analysis datasets in Brazilian Portuguese, unified in a single
dataset, and a reference partitioning in training, testing, and validation
sets, both made publicly available through a digital repository. A
cross-evaluation of dataset-specific models over different contexts is
conducted to evaluate their generalization capabilities and the feasibility of
adopting a unique model for addressing all scenarios.

Autonomous vehicles and robots require increasingly more robustness and
reliability to meet the demands of modern tasks. These requirements specially
apply to cameras because they are the predominant sensors to acquire
information about the environment and support actions. A camera must maintain
proper functionality and take automatic countermeasures if necessary. However,
there is little work that examines the practical use of a general condition
monitoring approach for cameras and designs countermeasures in the context of
an envisaged high-level application. We propose a generic and interpretable
self-health-maintenance framework for cameras based on data- and
physically-grounded models. To this end, we determine two reliable, real-time
capable estimators for typical image effects of a camera in poor condition
(defocus blur, motion blur, different noise phenomena and most common
combinations) by comparing traditional and retrained machine learning-based
approaches in extensive experiments. Furthermore, we demonstrate how one can
adjust the camera parameters (e.g., exposure time and ISO gain) to achieve
optimal whole-system capability based on experimental (non-linear and
non-monotonic) input-output performance curves, using object detection, motion
blur and sensor noise as examples. Our framework not only provides a practical
ready-to-use solution to evaluate and maintain the health of cameras, but can
also serve as a basis for extensions to tackle more sophisticated problems that
combine additional data sources (e.g., sensor or environment parameters)
empirically in order to attain fully reliable and robust machines.

Gaussian process regression is increasingly applied for learning unknown
dynamical systems. In particular, the implicit quantification of the
uncertainty of the learned model makes it a promising approach for
safety-critical applications. When using Gaussian process regression to learn
unknown systems, a commonly considered approach consists of learning the
residual dynamics after applying some generic discretization technique, which
might however disregard properties of the underlying physical system.
Variational integrators are a less common yet promising approach to
discretization, as they retain physical properties of the underlying system,
such as energy conservation and satisfaction of explicit kinematic constraints.
In this work, we present a novel structure-preserving learning-based modelling
approach that combines a variational integrator for the nominal dynamics of a
mechanical system and learning residual dynamics with Gaussian process
regression. We extend our approach to systems with known kinematic constraints
and provide formal bounds on the prediction uncertainty. The simulative
evaluation of the proposed method shows desirable energy conservation
properties in accordance with general theoretical results and demonstrates
exact constraint satisfaction for constrained dynamical systems.

We consider mixtures of $k\geq 2$ Gaussian components with unknown means and
unknown covariance (identical for all components) that are well-separated,
i.e., distinct components have statistical overlap at most $k^{-C}$ for a large
enough constant $C\ge 1$. Previous statistical-query lower bounds [DKS17] give
formal evidence that even distinguishing such mixtures from (pure) Gaussians
may be exponentially hard (in $k$).
  We show that this kind of hardness can only appear if mixing weights are
allowed to be exponentially small, and that for polynomially lower bounded
mixing weights non-trivial algorithmic guarantees are possible in
quasi-polynomial time.
  Concretely, we develop an algorithm based on the sum-of-squares method with
running time quasi-polynomial in the minimum mixing weight. The algorithm can
reliably distinguish between a mixture of $k\ge 2$ well-separated Gaussian
components and a (pure) Gaussian distribution. As a certificate, the algorithm
computes a bipartition of the input sample that separates a pair of mixture
components, i.e., both sides of the bipartition contain most of the sample
points of at least one component.
  For the special case of colinear means, our algorithm outputs a $k$
clustering of the input sample that is approximately consistent with the
components of the mixture.
  A significant challenge for our results is that they appear to be inherently
sensitive to small fractions of adversarial outliers unlike most previous
results for Gaussian mixtures. The reason is that such outliers can simulate
exponentially small mixing weights even for mixtures with polynomially lower
bounded mixing weights.
  A key technical ingredient is a characterization of separating directions for
well-separated Gaussian components in terms of ratios of polynomials that
correspond to moments of two carefully chosen orders logarithmic in the minimum
mixing weight.

In clinical medicine, magnetic resonance imaging (MRI) is one of the most
important tools for diagnosis, triage, prognosis, and treatment planning.
However, MRI suffers from an inherent slow data acquisition process because
data is collected sequentially in k-space. In recent years, most MRI
reconstruction methods proposed in the literature focus on holistic image
reconstruction rather than enhancing the edge information. This work steps
aside this general trend by elaborating on the enhancement of edge information.
Specifically, we introduce a novel parallel imaging coupled dual discriminator
generative adversarial network (PIDD-GAN) for fast multi-channel MRI
reconstruction by incorporating multi-view information. The dual discriminator
design aims to improve the edge information in MRI reconstruction. One
discriminator is used for holistic image reconstruction, whereas the other one
is responsible for enhancing edge information. An improved U-Net with local and
global residual learning is proposed for the generator. Frequency channel
attention blocks (FCA Blocks) are embedded in the generator for incorporating
attention mechanisms. Content loss is introduced to train the generator for
better reconstruction quality. We performed comprehensive experiments on
Calgary-Campinas public brain MR dataset and compared our method with
state-of-the-art MRI reconstruction methods. Ablation studies of residual
learning were conducted on the MICCAI13 dataset to validate the proposed
modules. Results show that our PIDD-GAN provides high-quality reconstructed MR
images, with well-preserved edge information. The time of single-image
reconstruction is below 5ms, which meets the demand of faster processing.

High-amplitude events of the out-of-plane vorticity component $\omega_z$ are
analyzed by stereoscopic particle image velocimetry (PIV) in the bulk region of
turbulent Rayleigh-B\'{e}nard convection in air. The Rayleigh numbers ${\rm
Ra}$ vary from $1.7 \times 10^4$ to $5.1 \times 10^5$. The experimental
investigation is connected with a comprehensive statistical analysis of
long-term time series of $\omega_z$ and individual velocity derivatives
$\partial u_i/\partial x_j$. A statistical convergence for derivative moments
up to an order of 6 is demonstrated. Our results are found to agree well with
existing high-resolution direct numerical simulation data in the same range of
parameters, including the extreme vorticity events which appear in the far
exponential tails of the corresponding probability density functions. The
transition from a Gaussian to a non-Gaussian velocity derivative statistics in
the bulk of a convection flow is confirmed experimentally. The experimental
data are used to train a reservoir computing model, one implementation of a
recurrent neural network, to reproduce highly intermittent experimental time
series of the vorticity and thus reconstruct extreme out-of-plane vorticity
events. After training the model with high-resolution PIV data, the machine
learning model is run with sparsely seeded, continually available, and unseen
measurement data in the reconstruction phase. The dependence of the
reconstruction quality on the sparsity of the partial observations is also
documented. Our latter result paves the way to machine--learning--assisted
experimental analyses of small-scale turbulence for which time series of
missing velocity derivatives can be provided by generative algorithms.

Subjects change frequently in moderated debates with several participants,
such as in parliamentary sessions, electoral debates, and trials. Partitioning
a debate into blocks with the same subject is essential for understanding.
Often a moderator is responsible for defining when a new block begins so that
the task of automatically partitioning a moderated debate can focus solely on
the moderator's behavior. In this paper, we (i) propose a new algorithm,
DEBACER, which partitions moderated debates; (ii) carry out a comparative study
between conventional and BERTimbau pipelines; and (iii) validate DEBACER
applying it to the minutes of the Assembly of the Republic of Portugal. Our
results show the effectiveness of DEBACER. Keywords: Natural Language
Processing, Political Documents, Spoken Text Processing, Speech Split, Dialogue
Partitioning.

Neural networks have shown state-of-the-art performance in designing
auctions, where the network learns the optimal allocations and payment rule to
ensure desirable properties. Motivated by the same, we focus on learning fair
division of resources, with no payments involved. Our goal is to allocate the
items, goods and/or chores efficiently among the fair allocations. By fair, we
mean an allocation that is Envy-free (EF). However, such an allocation may not
always exist for indivisible resources. Therefore, we consider the relaxed
notion, Envy-freeness up to one item (EF1) that is guaranteed to exist.
However, it is not enough to guarantee EF1 since the allocation of empty
bundles is also EF1. Hence, we add the further constraint of efficiency,
maximum utilitarian social welfare (USW). In general finding, USW allocations
among EF1 is an NP-Hard problem even when valuations are additive. In this
work, we design a network for this task which we refer to as EEF1-NN. We
propose an UNet inspired architecture, Lagrangian loss function, and training
procedure to obtain desired results. We show that EEF1-NN finds allocation
close to optimal USW allocation and ensures EF1 with a high probability for
different distributions over input valuations. Compared to existing approaches
EEF1-NN empirically guarantees higher USW. Moreover, EEF1-NN is scalable and
determines the allocations much faster than solving it as a constrained

With the emerging technologies in Intelligent Transportation System (ITS),
the adaptive operation of road space is likely to be realised within decades.
An intelligent street can learn and improve its decision-making on the
right-of-way (ROW) for road users, liberating more active pedestrian space
while maintaining traffic safety and efficiency. However, there is a lack of
effective controlling techniques for these adaptive street infrastructures. To
fill this gap in existing studies, we formulate this control problem as a
Markov Game and develop a solution based on the multi-agent Deep Deterministic
Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign
ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street
parking areas in real-time. Integrated with the SUMO traffic simulator, this
model was evaluated using the road network of the South Kensington District
against three cases of divergent traffic conditions: pedestrian flow rates, AVs
traffic flow rates and parking demands. Results reveal that our model can
achieve an average reduction of 3.87% and 6.26% in street space assigned for
on-street parking and vehicular operations. Combined with space gained by
limiting the number of driving lanes, the average proportion of sidewalks to
total widths of streets can significantly increase by 10.13%.

Natural language generation (NLG) applications have gained great popularity
due to the powerful deep learning techniques and large training corpus. The
deployed NLG models may be stolen or used without authorization, while
watermarking has become a useful tool to protect Intellectual Property (IP) of
deep models. However, existing watermarking technologies using backdoors are
easily detected or harmful for NLG applications. In this paper, we propose a
semantic and robust watermarking scheme for NLG models that utilize unharmful
phrase pairs as watermarks for IP protection. The watermarks give NLG models
personal preference for some special phrase combinations. Specifically, we
generate watermarks by following a semantic combination pattern and
systematically augment the watermark corpus to enhance the robustness. Then, we
embed these watermarks into an NLG model without misleading its original
attention mechanism. We conduct extensive experiments and the results
demonstrate the effectiveness, robustness, and undetectability of the proposed
scheme.

Recent privacy awareness initiatives such as the EU General Data Protection
Regulation subdued Machine Learning (ML) to privacy and security assessments.
Federated Learning (FL) grants a privacy-driven, decentralized training scheme
that improves ML models' security. The industry's fast-growing adaptation and
security evaluations of FL technology exposed various vulnerabilities that
threaten FL's confidentiality, integrity, or availability (CIA). This work
assesses the CIA of FL by reviewing the state-of-the-art (SoTA) and creating a
threat model that embraces the attack's surface, adversarial actors,
capabilities, and goals. We propose the first unifying taxonomy for attacks and
defenses and provide promising future research directions.

Exploring unknown environments is a fundamental task in many domains, e.g.,
robot navigation, network security, and internet search. We initiate the study
of a learning-augmented variant of the classical, notoriously hard online graph
exploration problem by adding access to machine-learned predictions. We propose
an algorithm that naturally integrates predictions into the well-known Nearest
Neighbor (NN) algorithm and significantly outperforms any known online
algorithm if the prediction is of high accuracy while maintaining good
guarantees when the prediction is of poor quality. We provide theoretical
worst-case bounds that gracefully degrade with the prediction error, and we
complement them by computational experiments that confirm our results. Further,
we extend our concept to a general framework to robustify algorithms. By
interpolating carefully between a given algorithm and NN, we prove new
performance bounds that leverage the individual good performance on particular
inputs while establishing robustness to arbitrary inputs.

In recent years, we have seen significant steps taken in the development of
self-driving cars. Multiple companies are starting to roll out impressive
systems that work in a variety of settings. These systems can sometimes give
the impression that full self-driving is just around the corner and that we
would soon build cars without even a steering wheel. The increase in the level
of autonomy and control given to an AI provides an opportunity for new modes of
human-vehicle interaction. However, surveys have shown that giving more control
to an AI in self-driving cars is accompanied by a degree of uneasiness by
passengers. In an attempt to alleviate this issue, recent works have taken a
natural language-oriented approach by allowing the passenger to give commands
that refer to specific objects in the visual scene. Nevertheless, this is only
half the task as the car should also understand the physical destination of the
command, which is what we focus on in this paper. We propose an extension in
which we annotate the 3D destination that the car needs to reach after
executing the given command and evaluate multiple different baselines on
predicting this destination location. Additionally, we introduce a model that
outperforms the prior works adapted for this particular setting.

We study the minimum vertex cover problem in the following stochastic
setting. Let $G$ be an arbitrary given graph, $p \in (0, 1]$ a parameter of the
problem, and let $G_p$ be a random subgraph that includes each edge of $G$
independently with probability $p$. We are unaware of the realization $G_p$,
but can learn if an edge $e$ exists in $G_p$ by querying it. The goal is to
find an approximate minimum vertex cover (MVC) of $G_p$ by querying few edges
of $G$ non-adaptively.
  This stochastic setting has been studied extensively for various problems
such as minimum spanning trees, matroids, shortest paths, and matchings. To our
knowledge, however, no non-trivial bound was known for MVC prior to our work.
In this work, we present a:
  * $(2+\epsilon)$-approximation for general graphs which queries
$O(\frac{1}{\epsilon^3 p})$ edges per vertex, and a
  * $1.367$-approximation for bipartite graphs which queries $poly(1/p)$ edges
per vertex.
  Additionally, we show that at the expense of a triple-exponential dependence
on $p^{-1}$ in the number of queries, the approximation ratio can be improved
down to $(1+\epsilon)$ for bipartite graphs.
  Our techniques also lead to improved bounds for bipartite stochastic
matching. We obtain a $0.731$-approximation with nearly-linear in $1/p$
per-vertex queries. This is the first result to break the prevalent $(2/3 \sim
0.66)$-approximation barrier in the $poly(1/p)$ query regime, improving
algorithms of [Behnezhad et al; SODA'19] and [Assadi and Bernstein; SOSA'19].

The diffusion of atoms and radicals on interstellar dust grains is a
fundamental ingredient for predicting accurate molecular abundances in
astronomical environments. Quantitative values of diffusivity and diffusion
barriers usually rely heavily on empirical rules. In this paper, we compute the
diffusion coefficients of adsorbed nitrogen atoms by combining machine-learned
interatomic potentials, metadynamics, and kinetic Monte Carlo simulations. With
this approach, we obtain a diffusion coefficient of nitrogen atoms on the
surface of amorphous solid water of merely $(3.5 \pm
1.1)10^{-34}$cm$^2$s$^{-1}$ at 10 K for a bare ice surface. Thus, we find that
nitrogen, as a paradigmatic case for light and weakly bound adsorbates, is
unable to diffuse on bare amorphous solid water at 10 K. Surface coverage has a
strong effect on the diffusion coefficient by modulating its value over 9--12
orders of magnitude at 10 K and enables diffusion for specific conditions. In
addition, we have found that atom tunneling has a negligible effect. Average
diffusion barriers of the potential energy surface (2.56 kJ mol$^{-1}$) differ
strongly from the effective diffusion barrier obtained from the diffusion
coefficient for a bare surface (6.06 kJ mol$^{-1}$) and are, thus,
inappropriate for diffusion modeling. Our findings suggest that the thermal
diffusion of N on water ice is a process that is highly dependent on the
physical conditions of the ice.

In a vertical federated learning (VFL) scenario where features and model are
split into different parties, communications of sample-specific updates are
required for correct gradient calculations but can be used to deduce important
sample-level label information. An immediate defense strategy is to protect
sample-level messages communicated with Homomorphic Encryption (HE), and in
this way only the batch-averaged local gradients are exposed to each party
(termed black-boxed VFL). In this paper, we first explore the possibility of
recovering labels in the vertical federated learning setting with HE-protected
communication, and show that private labels can be reconstructed with high
accuracy by training a gradient inversion model. Furthermore, we show that
label replacement backdoor attacks can be conducted in black-boxed VFL by
directly replacing encrypted communicated messages (termed gradient-replacement
attack). As it is a common presumption that batch-averaged information is safe
to share, batch label inference and replacement attacks are a severe challenge
to VFL. To defend against batch label inference attack, we further evaluate
several defense strategies, including confusional autoencoder (CoAE), a
technique we proposed based on autoencoder and entropy regularization. We
demonstrate that label inference and replacement attacks can be successfully
blocked by this technique without hurting as much main task accuracy as
compared to existing methods.

In this work, we present the Large Labelled Logo Dataset (L3D), a
multipurpose, hand-labelled, continuously growing dataset. It is composed of
around 770k of color 256x256 RGB images extracted from the European Union
Intellectual Property Office (EUIPO) open registry. Each of them is associated
to multiple labels that classify the figurative and textual elements that
appear in the images. These annotations have been classified by the EUIPO
evaluators using the Vienna classification, a hierarchical classification of
figurative marks. We suggest two direct applications of this dataset, namely,
logo classification and logo generation.

Car-following (CF) modeling, an essential component in simulating human CF
behaviors, has attracted increasing research interest in the past decades. This
paper pushes the state of the art by proposing a novel generative hybrid CF
model, which achieves high accuracy in characterizing dynamic human CF
behaviors and is able to generate realistic human CF behaviors for any given
observed or even unobserved driving style. Specifically, the ability of
accurately capturing human CF behaviors is ensured by designing and calibrating
an Intelligent Driver Model (IDM) with time-varying parameters. The reason
behind is that such time-varying parameters can express both the inter-driver
heterogeneity, i.e., diverse driving styles of different drivers, and the
intra-driver heterogeneity, i.e., changing driving styles of the same driver.
The ability of generating realistic human CF behaviors of any given observed
driving style is achieved by applying a neural process (NP) based model. The
ability of inferring CF behaviors of unobserved driving styles is supported by
exploring the relationship between the calibrated time-varying IDM parameters
and an intermediate variable of NP. To demonstrate the effectiveness of our
proposed models, we conduct extensive experiments and comparisons, including CF
model parameter calibration, CF behavior prediction, and trajectory simulation
for different driving styles.

The task of transforming a furnished room image into a background-only is
extremely challenging since it requires making large changes regarding the
scene context while still preserving the overall layout and style. In order to
acquire photo-realistic and structural consistent background, existing deep
learning methods either employ image inpainting approaches or incorporate the
learning of the scene layout as an individual task and leverage it later in a
not fully differentiable semantic region-adaptive normalization module. To
tackle these drawbacks, we treat scene layout generation as a feature linear
transformation problem and propose a simple yet effective adjusted fully
differentiable soft semantic region-adaptive normalization module (softSEAN)
block. We showcase the applicability in diminished reality and depth estimation
tasks, where our approach besides the advantages of mitigating training
complexity and non-differentiability issues, surpasses the compared methods
both quantitatively and qualitatively. Our softSEAN block can be used as a
drop-in module for existing discriminative and generative models.
Implementation is available on vcl3d.github.io/PanoDR/.

Graph representation learning received increasing attentions in recent years.
Most of existing methods ignore the complexity of the graph structures and
restrict graphs in a single constant-curvature representation space, which is
only suitable to particular kinds of graph structure indeed. Additionally,
these methods follow the supervised or semi-supervised learning paradigm, and
thereby notably limit their deployment on the unlabeled graphs in real
applications. To address these aforementioned limitations, we take the first
attempt to study the self-supervised graph representation learning in the
mixed-curvature spaces. In this paper, we present a novel Self-supervised
Mixed-curvature Graph Neural Network (SelfMGNN). Instead of working on one
single constant-curvature space, we construct a mixed-curvature space via the
Cartesian product of multiple Riemannian component spaces and design
hierarchical attention mechanisms for learning and fusing the representations
across these component spaces. To enable the self-supervisd learning, we
propose a novel dual contrastive approach. The mixed-curvature Riemannian space
actually provides multiple Riemannian views for the contrastive learning. We
introduce a Riemannian projector to reveal these views, and utilize a
well-designed Riemannian discriminator for the single-view and cross-view
contrastive learning within and across the Riemannian views. Finally, extensive
experiments show that SelfMGNN captures the complicated graph structures in
reality and outperforms state-of-the-art baselines.

Gradient-based methods for the distributed training of residual networks
(ResNets) typically require a forward pass of the input data, followed by
back-propagating the error gradient to update model parameters, which becomes
time-consuming as the network goes deeper. To break the algorithmic locking and
exploit synchronous module parallelism in both the forward and backward modes,
auxiliary-variable methods have attracted much interest lately but suffer from
significant communication overhead and lack of data augmentation. In this work,
a novel joint learning framework for training realistic ResNets across multiple
compute devices is established by trading off the storage and recomputation of
external auxiliary variables. More specifically, the input data of each
independent processor is generated from its low-capacity auxiliary network
(AuxNet), which permits the use of data augmentation and realizes forward
unlocking. The backward passes are then executed in parallel, each with a local
loss function that originates from the penalty or augmented Lagrangian (AL)
methods. Finally, the proposed AuxNet is employed to reproduce the updated
auxiliary variables through an end-to-end training process. We demonstrate the
effectiveness of our methods on ResNets and WideResNets across CIFAR-10,
CIFAR-100, and ImageNet datasets, achieving speedup over the traditional
layer-serial training method while maintaining comparable testing accuracy.

While deep learning algorithms demonstrate a great potential in scientific
computing, its application to multi-scale problems remains to be a big
challenge. This is manifested by the "frequency principle" that neural networks
tend to learn low frequency components first. Novel architectures such as
multi-scale deep neural network (MscaleDNN) were proposed to alleviate this
problem to some extent. In this paper, we construct a subspace decomposition
based DNN (dubbed SD$^2$NN) architecture for a class of multi-scale problems by
combining traditional numerical analysis ideas and MscaleDNN algorithms. The
proposed architecture includes one low frequency normal DNN submodule, and one
(or a few) high frequency MscaleDNN submodule(s), which are designed to capture
the smooth part and the oscillatory part of the multi-scale solutions,
respectively. In addition, a novel trigonometric activation function is
incorporated in the SD$^2$NN model. We demonstrate the performance of the
SD$^2$NN architecture through several benchmark multi-scale problems in regular
or irregular geometric domains. Numerical results show that the SD$^2$NN model
is superior to existing models such as MscaleDNN.

We introduce UNIST, the first deep neural implicit model for general-purpose,
unpaired shape-to-shape translation, in both 2D and 3D domains. Our model is
built on autoencoding implicit fields, rather than point clouds which
represents the state of the art. Furthermore, our translation network is
trained to perform the task over a latent grid representation which combines
the merits of both latent-space processing and position awareness, to not only
enable drastic shape transforms but also well preserve spatial features and
fine local details for natural shape translations. With the same network
architecture and only dictated by the input domain pairs, our model can learn
both style-preserving content alteration and content-preserving style transfer.
We demonstrate the generality and quality of the translation results, and
compare them to well-known baselines. Code is available at
https://qiminchen.github.io/unist/.

Recent studies have shown that adversarial examples hand-crafted on one
white-box model can be used to attack other black-box models. Such cross-model
transferability makes it feasible to perform black-box attacks, which has
raised security concerns for real-world DNNs applications. Nevertheless,
existing works mostly focus on investigating the adversarial transferability
across different deep models that share the same modality of input data. The
cross-modal transferability of adversarial perturbation has never been
explored. This paper investigates the transferability of adversarial
perturbation across different modalities, i.e., leveraging adversarial
perturbation generated on white-box image models to attack black-box video
models. Specifically, motivated by the observation that the low-level feature
space between images and video frames are similar, we propose a simple yet
effective cross-modal attack method, named as Image To Video (I2V) attack. I2V
generates adversarial frames by minimizing the cosine similarity between
features of pre-trained image models from adversarial and benign examples, then
combines the generated adversarial frames to perform black-box attacks on video
recognition models. Extensive experiments demonstrate that I2V can achieve high
attack success rates on different black-box video recognition models. On
Kinetics-400 and UCF-101, I2V achieves an average attack success rate of 77.88%
and 65.68%, respectively, which sheds light on the feasibility of cross-modal

Data-driven methods for remaining useful life (RUL) prediction normally learn
features from a fixed window size of a priori of degradation, which may lead to
less accurate prediction results on different datasets because of the variance
of local features. This paper proposes a method for RUL prediction which
depends on a trend feature representing the overall time sequence of
degradation. Complete ensemble empirical mode decomposition, followed by a
reconstruction procedure, is created to build the trend features. The
probability distribution of sensors' measurement learned by conditional neural
processes is used to evaluate the trend features. With the best trend feature,
a data-driven model using long short-term memory is developed to predict the
RUL. To prove the effectiveness of the proposed method, experiments on a
benchmark C-MAPSS dataset are carried out and compared with other
state-of-the-art methods. Comparison results show that the proposed method
achieves the smallest root mean square values in prediction of all RUL.

Sample average approximation (SAA), a popular method for tractably solving
stochastic optimization problems, enjoys strong asymptotic performance
guarantees in settings with independent training samples. However, these
guarantees are not known to hold generally with dependent samples, such as in
online learning with time series data or distributed computing with Markovian
training samples. In this paper, we show that SAA remains tractable when the
distribution of unknown parameters is only observable through dependent
instances and still enjoys asymptotic consistency and finite sample guarantees.
Specifically, we provide a rigorous probability error analysis to derive $1 -
\beta$ confidence bounds for the out-of-sample performance of SAA estimators
and show that these estimators are asymptotically consistent. We then, using
monotone operator theory, study the performance of a class of stochastic
first-order algorithms trained on a dependent source of data. We show that
approximation error for these algorithms is bounded and concentrates around
zero, and establish deviation bounds for iterates when the underlying
stochastic process is $\phi$-mixing. The algorithms presented can be used to
handle numerically inconvenient loss functions such as the sum of a smooth and
non-smooth function or of non-smooth functions with constraints. To illustrate
the usefulness of our results, we present several stochastic versions of
popular algorithms such as stochastic proximal gradient descent (S-PGD),
stochastic relaxed Peaceman--Rachford splitting algorithms (S-rPRS), and
numerical experiment.

Contextual bandit algorithms have many applicants in a variety of scenarios.
In order to develop trustworthy contextual bandit systems, understanding the
impacts of various adversarial attacks on contextual bandit algorithms is
essential. In this paper, we propose a new class of attacks: action poisoning
attacks, where an adversary can change the action signal selected by the agent.
We design action poisoning attack schemes against linear contextual bandit
algorithms in both white-box and black-box settings. We further analyze the
cost of the proposed attack strategies for a very popular and widely used
bandit algorithm: LinUCB. We show that, in both white-box and black-box
settings, the proposed attack schemes can force the LinUCB agent to pull a
target arm very frequently by spending only logarithm cost.

Transfer adversarial attack is a non-trivial black-box adversarial attack
that aims to craft adversarial perturbations on the surrogate model and then
apply such perturbations to the victim model. However, the transferability of
perturbations from existing methods is still limited, since the adversarial
perturbations are easily overfitting with a single surrogate model and specific
data pattern. In this paper, we propose a Learning to Learn Transferable Attack
(LLTA) method, which makes the adversarial perturbations more generalized via
learning from both data and model augmentation. For data augmentation, we adopt
simple random resizing and padding. For model augmentation, we randomly alter
the back propagation instead of the forward propagation to eliminate the effect
on the model prediction. By treating the attack of both specific data and a
modified model as a task, we expect the adversarial perturbations to adopt
enough tasks for generalization. To this end, the meta-learning algorithm is
further introduced during the iteration of perturbation generation. Empirical
results on the widely-used dataset demonstrate the effectiveness of our attack
method with a 12.85% higher success rate of transfer attack compared with the
state-of-the-art methods. We also evaluate our method on the real-world online
system, i.e., Google Cloud Vision API, to further show the practical potentials

Transformer-based models are not efficient in processing long sequences due
to the quadratic space and time complexity of the self-attention modules. To
address this limitation, Linformer and Informer are proposed to reduce the
quadratic complexity to linear (modulo logarithmic factors) via low-dimensional
projection and row selection respectively. These two models are intrinsically
connected, and to understand their connection, we introduce a theoretical
framework of matrix sketching. Based on the theoretical analysis, we propose
Skeinformer to accelerate self-attention and further improve the accuracy of
matrix approximation to self-attention with three carefully designed
components: column sampling, adaptive row normalization and pilot sampling
reutilization. Experiments on the Long Range Arena (LRA) benchmark demonstrate
that our methods outperform alternatives with a consistently smaller time/space

Many well-established anomaly detection methods use the distance of a sample
to those in its local neighbourhood: so-called `local outlier methods', such as
LOF and DBSCAN. They are popular for their simple principles and strong
performance on unstructured, feature-based data that is commonplace in many
practical applications. However, they cannot learn to adapt for a particular
set of data due to their lack of trainable parameters. In this paper, we begin
by unifying local outlier methods by showing that they are particular cases of
the more general message passing framework used in graph neural networks. This
allows us to introduce learnability into local outlier methods, in the form of
a neural network, for greater flexibility and expressivity: specifically, we
propose LUNAR, a novel, graph neural network-based anomaly detection method.
LUNAR learns to use information from the nearest neighbours of each node in a
trainable way to find anomalies. We show that our method performs significantly
better than existing local outlier methods, as well as state-of-the-art deep
baselines. We also show that the performance of our method is much more robust
to different settings of the local neighbourhood size.

This paper considers the recently popular beyond-worst-case algorithm
analysis model which integrates machine-learned predictions with online
algorithm design. We consider the online Steiner tree problem in this model for
both directed and undirected graphs. Steiner tree is known to have strong lower
bounds in the online setting and any algorithm's worst-case guarantee is far
from desirable. This paper considers algorithms that predict which terminal
arrives online. The predictions may be incorrect and the algorithms'
performance is parameterized by the number of incorrectly predicted terminals.
These guarantees ensure that algorithms break through the online lower bounds
with good predictions and the competitive ratio gracefully degrades as the
prediction error grows. We then observe that the theory is predictive of what
will occur empirically. We show on graphs where terminals are drawn from a
distribution, the new online algorithms have strong performance even with
modestly correct predictions.

Is the center position fully capable of representing a pixel? There is
nothing wrong to represent pixels with their centers in a discrete image
representation, but it makes more sense to consider each pixel as the
aggregation of signals from a local area in an image super-resolution (SR)
context. Despite the great capability of coordinate-based implicit
representation in the field of arbitrary-scale image SR, this area's nature of
pixels is not fully considered. To this end, we propose integrated positional
encoding (IPE), extending traditional positional encoding by aggregating
frequency information over the pixel area. We apply IPE to the state-of-the-art
arbitrary-scale image super-resolution method: local implicit image function
(LIIF), presenting IPE-LIIF. We show the effectiveness of IPE-LIIF by
quantitative and qualitative evaluations, and further demonstrate the
generalization ability of IPE to larger image scales and multiple
implicit-based methods. Code will be released.

Conversation disentanglement, the task to identify separate threads in
conversations, is an important pre-processing step in multi-party
conversational NLP applications such as conversational question answering and
conversation summarization. Framing it as a utterance-to-utterance
classification problem -- i.e. given an utterance of interest (UOI), find which
past utterance it replies to -- we explore a number of transformer-based models
and found that BERT in combination with handcrafted features remains a strong
baseline. We then build a multi-task learning model that jointly learns
utterance-to-utterance and utterance-to-thread classification. Observing that
the ground truth label (past utterance) is in the top candidates when our model
makes an error, we experiment with using bipartite graphs as a post-processing
step to learn how to best match a set of UOIs to past utterances. Experiments
on the Ubuntu IRC dataset show that this approach has the potential to
outperform the conventional greedy approach of simply selecting the highest
probability candidate for each UOI independently, indicating a promising future
research direction.

This paper proposes a new sequential model learning architecture to solve
partially observable Markov decision problems. Rather than compressing
sequential information at every timestep as in conventional recurrent neural
network-based methods, the proposed architecture generates a latent variable in
each data block with a length of multiple timesteps and passes the most
relevant information to the next block for policy optimization. The proposed
blockwise sequential model is implemented based on self-attention, making the
model capable of detailed sequential learning in partial observable settings.
The proposed model builds an additional learning network to efficiently
implement gradient estimation by using self-normalized importance sampling,
which does not require the complex blockwise input data reconstruction in the
model learning. Numerical results show that the proposed method significantly
outperforms previous methods in various partially observable environments.

Contrastive learning has made considerable progress in computer vision,
outperforming supervised pretraining on a range of downstream datasets.
However, is contrastive learning the better choice in all situations? We
demonstrate two cases where it is not. First, under sufficiently small
pretraining budgets, supervised pretraining on ImageNet consistently
outperforms a comparable contrastive model on eight diverse image
classification datasets. This suggests that the common practice of comparing
pretraining approaches at hundreds or thousands of epochs may not produce
actionable insights for those with more limited compute budgets. Second, even
with larger pretraining budgets we identify tasks where supervised learning
prevails, perhaps because the object-centric bias of supervised pretraining
makes the model more resilient to common corruptions and spurious
foreground-background correlations. These results underscore the need to
characterize tradeoffs of different pretraining objectives across a wider range
of contexts and training regimes.

Despite recent advances in deep-learning based semantic segmentation,
automatic building detection from remotely sensed imagery is still a
challenging problem owing to large variability in the appearance of buildings
across the globe. The errors occur mostly around the boundaries of the building
footprints, in shadow areas, and when detecting buildings whose exterior
surfaces have reflectivity properties that are very similar to those of the
surrounding regions. To overcome these problems, we propose a generative
adversarial network based segmentation framework with uncertainty attention
unit and refinement module embedded in the generator. The refinement module,
composed of edge and reverse attention units, is designed to refine the
predicted building map. The edge attention enhances the boundary features to
estimate building boundaries with greater precision, and the reverse attention
allows the network to explore the features missing in the previously estimated
regions. The uncertainty attention unit assists the network in resolving
uncertainties in classification. As a measure of the power of our approach, as
of December 4, 2021, it ranks at the second place on DeepGlobe's public
leaderboard despite the fact that main focus of our approach -- refinement of
the building edges -- does not align exactly with the metrics used for
leaderboard rankings. Our overall F1-score on DeepGlobe's challenging dataset
is 0.745. We also report improvements on the previous-best results for the
challenging INRIA Validation Dataset for which our network achieves an overall
IoU of 81.28% and an overall accuracy of 97.03%. Along the same lines, for the
official INRIA Test Dataset, our network scores 77.86% and 96.41% in overall
IoU and accuracy.

Supersymmetric quantum gauge theories are important mathematical tools in
high energy physics. As an example, supersymmetric matrix models can be used as
a holographic description of quantum black holes. The wave function of such
supersymmetric gauge theories is not known and it is challenging to obtain with
traditional techniques. We employ a neural quantum state ansatz for the wave
function of a supersymmetric matrix model and use a variational quantum Monte
Carlo approach to discover the ground state of the system. We discuss the
difficulty of including bosonic particles and fermionic particles, as well as
gauge degrees of freedom.

Open quantum systems can undergo dissipative phase transitions, and their
critical behavior can be exploited in sensing applications. For example, it can
be used to enhance the fidelity of superconducting qubit readout measurements,
a central problem toward the creation of reliable quantum hardware. A recently
introduced measurement protocol, named ``critical parametric quantum sensing'',
uses the parametric (two-photon driven) Kerr resonator's driven-dissipative
phase transition to reach single-qubit detection fidelity of 99.9\%
[arXiv:2107.04503]. In this work, we improve upon the previous protocol by
using machine learning-based classification algorithms to \textit{efficiently
and rapidly} extract information from this critical dynamics, which has so far
been neglected to focus only on stationary properties. These classification
algorithms are applied to the time series data of weak quantum measurements
(homodyne detection) of a circuit-QED implementation of the Kerr resonator
coupled to a superconducting qubit. This demonstrates how machine learning
methods enable a faster and more reliable measurement protocol in critical open
quantum systems.

Speech-driven 3D facial animation is challenging due to the complex geometry
of human faces and the limited availability of 3D audio-visual data. Prior
works typically focus on learning phoneme-level features of short audio windows
with limited context, occasionally resulting in inaccurate lip movements. To
tackle this limitation, we propose a Transformer-based autoregressive model,
FaceFormer, which encodes the long-term audio context and autoregressively
predicts a sequence of animated 3D face meshes. To cope with the data scarcity
issue, we integrate the self-supervised pre-trained speech representations.
Also, we devise two biased attention mechanisms well suited to this specific
task, including the biased cross-modal multi-head (MH) attention and the biased
causal MH self-attention with a periodic positional encoding strategy. The
former effectively aligns the audio-motion modalities, whereas the latter
offers abilities to generalize to longer audio sequences. Extensive experiments
and a perceptual user study show that our approach outperforms the existing
state-of-the-arts. The code will be made available.

Quantum phase transitions are ubiquitous in quantum many body systems. The
quantum fluctuations that occur at very low temperatures are known to be
responsible for driving the system across different phases as a function of an
external control parameter. The XY Hamiltonian with a transverse field is a
basic model that manifests two distinct quantum phase transitions, including
spontaneous $Z_2$ symmetry breaking from an ordered to a disordered state.
While programmable quantum devices have shown great success in investigating
the various exotic quantum phases of matter, in parallel, the quest for
harnessing machine learning tools in learning quantum phases of matter is
ongoing. In this paper, we present a numerical study of the power of a
quantum-inspired generative model known as the Born machine in learning quantum
phases of matter. Data obtained from the system under open and periodic
boundary conditions is considered. Our results indicate that a Born machine
based on matrix product states can successfully capture the quantum state
across various phases of the XY Hamiltonian and close to a critical point,
despite the existence of long-range correlations. We further impose boundary
conditions on the Born machine and show that matching the boundary condition of
the Born machine and that of the training data improves performance when
limited data is available and a small bond dimension is employed.

Reliable plasma transport modeling for magnetic confinement fusion depends on
accurately resolving the ion charge state distribution and radiative power
losses of the plasma. These quantities can be obtained from solutions of a
collisional-radiative (CR) model at each time step within a plasma transport
simulation. However, even compact, approximate CR models can be computationally
onerous to evaluate, and in-situ evaluations of these models within a coupled
plasma transport code can lead to a rigid bottleneck. A way to bypass this
bottleneck is to deploy artificial neural network surrogates for rapid
evaluations of the necessary plasma quantities. However, one issue with
training an accurate artificial neural network surrogate is the reliance on a
sufficiently large and representative data set for both training and
validation, which can be time-consuming to generate. In this study we further
explore a data-driven active learning and training scheme to allow autonomous
adaptive sampling of the problem parameter space that ensures a sufficiently
large and meaningful set of training data assembled for the surrogate training.
Our numerical experiments show that in order to produce a comparably accurate
CR surrogate, the proposed approach requires a total number of data samples
that is an order-of-magnitude smaller than a conventional approach.

We present PyTorch Connectomics (PyTC), an open-source deep-learning
framework for the semantic and instance segmentation of volumetric microscopy
images, built upon PyTorch. We demonstrate the effectiveness of PyTC in the
field of connectomics, which aims to segment and reconstruct neurons, synapses,
and other organelles like mitochondria at nanometer resolution for
understanding neuronal communication, metabolism, and development in animal
brains. PyTC is a scalable and flexible toolbox that tackles datasets at
different scales and supports multi-task and semi-supervised learning to better
exploit expensive expert annotations and the vast amount of unlabeled data
during training. Those functionalities can be easily realized in PyTC by
changing the configuration options without coding and adapted to other 2D and
3D segmentation tasks for different tissues and imaging modalities.
Quantitatively, our framework achieves the best performance in the CREMI
challenge for synaptic cleft segmentation (outperforms existing best result by
relatively 6.1$\%$) and competitive performance on mitochondria and neuronal
nuclei segmentation. Code and tutorials are publicly available at
https://connectomics.readthedocs.io.

Federated machine learning is a versatile and flexible tool to utilize
distributed data from different sources, especially when communication
technology develops rapidly and an unprecedented amount of data could be
collected on mobile devices nowadays. Federated learning method exploits not
only the data but the computational power of all devices in the network to
achieve more efficient model training. Nevertheless, while most traditional
federated learning methods work well for homogeneous data and tasks, adapting
the method to a different heterogeneous data and task distribution is
challenging. This limitation has constrained the applications of federated
learning in real-world contexts, especially in healthcare settings. Inspired by
the fundamental idea of meta-learning, in this study we propose a new
algorithm, which is an integration of federated learning and meta-learning, to
tackle this issue. In addition, owing to the advantage of transfer learning for
model generalization, we further improve our algorithm by introducing partial
parameter sharing. We name this method partial meta-federated learning (PMFL).
Finally, we apply the algorithms to two medical datasets. We show that our
algorithm could obtain the fastest training speed and achieve the best
performance when dealing with heterogeneous medical datasets.

The paper presents classification and analysis of the mathematical models of
COVID-19 spread in different groups of populations such as the family, school,
office (3-100 people), neighborhood (100-5000 people), city, region (0.5-15
million people), country, continent and the world. The classification covers
the main types of models including time-series, differential, imitation ones,
and their combinations. The time-series models are built from analysis of the
time series derived using filtration, regression and network methods (Section
2). The differential models include those derived from systems of ordinary and
stochastic differential equations as well as partial-derivative equations
(Section 3). The imitation models include cellular automata and agent-based
models (Section 4). The fourth group in the classification is combinations of
nonlinear Markov chains and optimal control theory, derived within the
framework of the mean-field game theory. Due to the novelty of the disease and
the difficulties it causes, the parameters of most models are, as a rule,
unknown, which necessitates one to solve the inverse problem, so the paper also
analyses the main algorithms to solve the inverse problem such as stochastic
optimization; nature-like algorithms (genetic; differential evolution; particle
swarm, etc.); the understanding method; big-data analysis, and machine
learning.

Many scientific prediction problems have spatiotemporal data- and
modeling-related challenges in handling complex variations in space and time
using only sparse and unevenly distributed observations. This paper presents a
novel deep learning architecture, Deep learning predictions for
LocATion-dependent Time-sEries data (DeepLATTE), that explicitly incorporates
theories of spatial statistics into neural networks to address these
challenges. In addition to a feature selection module and a spatiotemporal
learning module, DeepLATTE contains an autocorrelation-guided semi-supervised
learning strategy to enforce both local autocorrelation patterns and global
autocorrelation trends of the predictions in the learned spatiotemporal
embedding space to be consistent with the observed data, overcoming the
limitation of sparse and unevenly distributed observations. During the training
process, both supervised and semi-supervised losses guide the updates of the
entire network to: 1) prevent overfitting, 2) refine feature selection, 3)
learn useful spatiotemporal representations, and 4) improve overall prediction.
We conduct a demonstration of DeepLATTE using publicly available data for an
important public health topic, air quality prediction, in a well-studied,
complex physical environment - Los Angeles. The experiment demonstrates that
the proposed approach provides accurate fine-spatial-scale air quality
predictions and reveals the critical environmental factors affecting the
results.

Implicit neural networks are a general class of learning models that replace
the layers in traditional feedforward models with implicit algebraic equations.
Compared to traditional learning models, implicit networks offer competitive
performance and reduced memory consumption. However, they can remain brittle
with respect to input adversarial perturbations.
  This paper proposes a theoretical and computational framework for robustness
verification of implicit neural networks; our framework blends together mixed
monotone systems theory and contraction theory. First, given an implicit neural
network, we introduce a related embedded network and show that, given an
$\ell_\infty$-norm box constraint on the input, the embedded network provides
an $\ell_\infty$-norm box overapproximation for the output of the given
network. Second, using $\ell_{\infty}$-matrix measures, we propose sufficient
conditions for well-posedness of both the original and embedded system and
design an iterative algorithm to compute the $\ell_{\infty}$-norm box
robustness margins for reachability and classification problems. Third, of
independent value, we propose a novel relative classifier variable that leads
to tighter bounds on the certified adversarial robustness in classification
problems. Finally, we perform numerical simulations on a Non-Euclidean Monotone
Operator Network (NEMON) trained on the MNIST dataset. In these simulations, we
compare the accuracy and run time of our mixed monotone contractive approach
with the existing robustness verification approaches in the literature for
estimating the certified adversarial robustness.

Differential privacy is a de facto privacy framework that has seen adoption
in practice via a number of mature software platforms. Implementation of
differentially private (DP) mechanisms has to be done carefully to ensure
end-to-end security guarantees. In this paper we study two implementation flaws
in the noise generation commonly used in DP systems. First we examine the
Gaussian mechanism's susceptibility to a floating-point representation attack.
The premise of this first vulnerability is similar to the one carried out by
Mironov in 2011 against the Laplace mechanism. Our experiments show attack's
success against DP algorithms, including deep learning models trained using
differentially-private stochastic gradient descent.
  In the second part of the paper we study discrete counterparts of the Laplace
and Gaussian mechanisms that were previously proposed to alleviate the
shortcomings of floating-point representation of real numbers. We show that
such implementations unfortunately suffer from another side channel: a novel
timing attack. An observer that can measure the time to draw (discrete) Laplace
or Gaussian noise can predict the noise magnitude, which can then be used to
recover sensitive attributes. This attack invalidates differential privacy
guarantees of systems implementing such mechanisms.
  We demonstrate that several commonly used, state-of-the-art implementations
of differential privacy are susceptible to these attacks. We report success
rates up to 92.56% for floating point attacks on DP-SGD, and up to 99.65% for
end-to-end timing attacks on private sum protected with discrete Laplace.
Finally, we evaluate and suggest partial mitigations.

Recently 3D point cloud learning has been a hot topic in computer vision and
autonomous driving. Due to the fact that it is difficult to manually annotate a
qualitative large-scale 3D point cloud dataset, unsupervised domain adaptation
(UDA) is popular in 3D point cloud learning which aims to transfer the learned
knowledge from the labeled source domain to the unlabeled target domain.
However, the generalization and reconstruction errors caused by domain shift
with simply-learned model are inevitable which substantially hinder the model's
capability from learning good representations. To address these issues, we
propose an end-to-end self-ensembling network (SEN) for 3D point cloud domain
adaption tasks. Generally, our SEN resorts to the advantages of Mean Teacher
and semi-supervised learning, and introduces a soft classification loss and a
consistency loss, aiming to achieve consistent generalization and accurate
reconstruction. In SEN, a student network is kept in a collaborative manner
with supervised learning and self-supervised learning, and a teacher network
conducts temporal consistency to learn useful representations and ensure the
quality of point clouds reconstruction. Extensive experiments on several 3D
point cloud UDA benchmarks show that our SEN outperforms the state-of-the-art
methods on both classification and segmentation tasks. Moreover, further
analysis demonstrates that our SEN also achieves better reconstruction results.

Differentiable rendering is an essential operation in modern vision, allowing
inverse graphics approaches to 3D understanding to be utilized in modern
machine learning frameworks. Explicit shape representations (voxels, point
clouds, or meshes), while relatively easily rendered, often suffer from limited
geometric fidelity or topological constraints. On the other hand, implicit
representations (occupancy, distance, or radiance fields) preserve greater
fidelity, but suffer from complex or inefficient rendering processes, limiting
scalability. In this work, we endeavour to address both shortcomings with a
novel shape representation that allows fast differentiable rendering within an
implicit architecture. Building on implicit distance representations, we define
Directed Distance Fields (DDFs), which map an oriented point (position and
direction) to surface visibility and depth. Such a field can render a depth map
with a single forward pass per pixel, enable differential surface geometry
extraction (e.g., surface normals and curvatures) via network derivatives, be
easily composed, and permit extraction of classical unsigned distance fields.
Using probabilistic DDFs (PDDFs), we show how to model inherent discontinuities
in the underlying field. Finally, we apply our method to fitting single shapes,
unpaired 3D-aware generative image modelling, and single-image 3D
reconstruction tasks, showcasing strong performance with simple architectural
components via the versatility of our representation.

While deep reinforcement learning (RL) agents have demonstrated incredible
potential in attaining dexterous behaviours for robotics, they tend to make
errors when deployed in the real world due to mismatches between the training
and execution environments. In contrast, the classical robotics community have
developed a range of controllers that can safely operate across most states in
the real world given their explicit derivation. These controllers however lack
the dexterity required for complex tasks given limitations in analytical
modelling and approximations. In this paper, we propose Bayesian Controller
Fusion (BCF), a novel uncertainty-aware deployment strategy that combines the
strengths of deep RL policies and traditional handcrafted controllers. In this
framework, we can perform zero-shot sim-to-real transfer, where our uncertainty
based formulation allows the robot to reliably act within out-of-distribution
states by leveraging the handcrafted controller while gaining the dexterity of
the learned system otherwise. We show promising results on two real-world
continuous control tasks, where BCF outperforms both the standalone policy and
controller, surpassing what either can achieve independently. A supplementary
video demonstrating our system is provided at https://bit.ly/bcf_deploy.

Building embodied intelligent agents that can interact with 3D indoor
environments has received increasing research attention in recent years. While
most works focus on single-object or agent-object visual functionality and
affordances, our work proposes to study a new kind of visual relationship that
is also important to perceive and model -- inter-object functional
relationships (e.g., a switch on the wall turns on or off the light, a remote
control operates the TV). Humans often spend little or no effort to infer these
relationships, even when entering a new room, by using our strong prior
knowledge (e.g., we know that buttons control electrical devices) or using only
a few exploratory interactions in cases of uncertainty (e.g., multiple switches
and lights in the same room). In this paper, we take the first step in building
AI system learning inter-object functional relationships in 3D indoor
environments with key technical contributions of modeling prior knowledge by
training over large-scale scenes and designing interactive policies for
effectively exploring the training scenes and quickly adapting to novel test
scenes. We create a new benchmark based on the AI2Thor and PartNet datasets and
perform extensive experiments that prove the effectiveness of our proposed
method. Results show that our model successfully learns priors and
fast-interactive-adaptation strategies for exploring inter-object functional
relationships in complex 3D scenes. Several ablation studies further validate
the usefulness of each proposed module.

Weakly supervised object localization (WSOL) aims to learn object localizer
solely by using image-level labels. The convolution neural network (CNN) based
techniques often result in highlighting the most discriminative part of objects
while ignoring the entire object extent. Recently, the transformer architecture
has been deployed to WSOL to capture the long-range feature dependencies with
self-attention mechanism and multilayer perceptron structure. Nevertheless,
transformers lack the locality inductive bias inherent to CNNs and therefore
may deteriorate local feature details in WSOL. In this paper, we propose a
novel framework built upon the transformer, termed LCTR (Local Continuity
TRansformer), which targets at enhancing the local perception capability of
global features among long-range feature dependencies. To this end, we propose
a relational patch-attention module (RPAM), which considers cross-patch
information on a global basis. We further design a cue digging module (CDM),
which utilizes local features to guide the learning trend of the model for
highlighting the weak local responses. Finally, comprehensive experiments are
carried out on two widely used datasets, ie, CUB-200-2011 and ILSVRC, to verify

To enhance the coverage and transmission reliability, repetitions adopted by
Narrowband Internet of Things (NB-IoT) allow repeating transmissions several
times. However, this results in a waste of radio resources when the signal
strength is high. In addition, in low signal quality, the selection of a higher
modulation and coding scheme (MCS) level leads to a huge packet loss in the
network. Moreover, the number of physical resource blocks (PRBs) per-user needs
to be chosen dynamically, such that the utilization of radio resources can be
improved on per-user basis. Therefore, in NB-IoT systems, dynamic adaptation of
repetitions, MCS, and radio resources, known as auto link-configuration, is
crucial. Accordingly, in this paper, we propose SmartCon which is a Generative
Adversarial Network (GAN)-based deep learning approach for auto
link-configuration during uplink or downlink scheduling, such that the packet
loss rate is significantly reduced in NB-IoT networks. For the training purpose
of the GAN, we use a Multi-Armed Bandit (MAB)-based reinforcement learning
mechanism that intelligently tunes its output depending on the present network
condition. The performance of SmartCon is thoroughly evaluated through
simulations where it is shown to significantly improve the performance of
NB-IoT systems compared to baseline schemes.

Machine learning models are critically susceptible to evasion attacks from
adversarial examples. Generally, adversarial examples, modified inputs
deceptively similar to the original input, are constructed under whitebox
settings by adversaries with full access to the model. However, recent attacks
have shown a remarkable reduction in query numbers to craft adversarial
examples using blackbox attacks. Particularly, alarming is the ability to
exploit the classification decision from the access interface of a trained
model provided by a growing number of Machine Learning as a Service providers
including Google, Microsoft, IBM and used by a plethora of applications
incorporating these models. The ability of an adversary to exploit only the
predicted label from a model to craft adversarial examples is distinguished as
a decision-based attack. In our study, we first deep dive into recent
state-of-the-art decision-based attacks in ICLR and SP to highlight the costly
nature of discovering low distortion adversarial employing gradient estimation
methods. We develop a robust query efficient attack capable of avoiding
entrapment in a local minimum and misdirection from noisy gradients seen in
gradient estimation methods. The attack method we propose, RamBoAttack,
exploits the notion of Randomized Block Coordinate Descent to explore the
hidden classifier manifold, targeting perturbations to manipulate only
localized input features to address the issues of gradient estimation methods.
Importantly, the RamBoAttack is more robust to the different sample inputs
available to an adversary and the targeted class. Overall, for a given target
class, RamBoAttack is demonstrated to be more robust at achieving a lower
distortion within a given query budget. We curate our extensive results using
the large-scale high-resolution ImageNet dataset and open-source our attack,
test samples and artifacts on GitHub.

In the coming years, a new generation of sky surveys, in particular, Euclid
Space Telescope (2022), and the Rubin Observatory's Legacy Survey of Space and
Time (LSST, 2023) will discover more than 200,000 new strong gravitational
lenses, which represents an increase of more than two orders of magnitude
compared to currently known sample sizes. Accurate and fast analysis of such
large volumes of data under a statistical framework is therefore crucial for
all sciences enabled by strong lensing. Here, we report on the application of
simulation-based inference methods, in particular, density estimation
techniques, to the predictions of the set of parameters of strong lensing
systems from neural networks. This allows us to explicitly impose desired
priors on lensing parameters, while guaranteeing convergence to the optimal
posterior in the limit of perfect performance.

Causal inference from longitudinal studies is central to epidemiologic
research. Targeted Maximum Likelihood Estimation (TMLE) is an established
double-robust causal effect estimation method, but how missing data should be
handled when using TMLE with data-adaptive approaches is unclear. Based on
motivating data from the Victorian Adolescent Health Cohort Study, we conducted
simulation and case studies to evaluate the performance of methods for handling
missing data when using TMLE. These were complete-case analysis; an extended
TMLE method incorporating a model for outcome missingness mechanism; missing
indicator method for missing covariate data; and six multiple imputation (MI)
approaches using parametric or machine-learning approaches to handle missing
outcome, exposure, and covariate data. The simulation study considered a simple
scenario (the exposure and outcome generated from main-effects regressions),
and two complex scenarios (models also included interactions), alongside eleven
missingness mechanisms defined using causal diagrams. No approach performed
well across all scenarios and missingness mechanisms. For non-MI methods, bias
depended on missingness mechanism (little when outcome did not influence
missingness in any variable). For parametric MI, bias depended on missingness
mechanism (smaller when outcome did not directly influence outcome missingness)
and data generation scenario (larger for the complex scenarios). Including
interaction terms in the imputation model improved performance. For MI using
machine learning, bias depended on missingness mechanism (smaller when no
variable with missing data directly influenced outcome missingness). We
recommend considering missing data mechanism and, if using MI, opting for a
saturated parametric or data-adaptive imputation model for handling missing
data in TMLE estimation.

The standard simplex in R^n, also known as the probability simplex, is the
set of nonnegative vectors whose entries sum up to 1. They frequently appear as
constraints in optimization problems that arise in machine learning,
statistics, data science, operations research, and beyond. We convert the
standard simplex to the unit sphere and thus transform the corresponding
constrained optimization problem into an optimization problem on a simple,
smooth manifold. We show that KKT points and strict-saddle points of the
minimization problem on the standard simplex all correspond to those of the
transformed problem, and vice versa. So, solving one problem is equivalent to
solving the other problem. Then, we propose several simple, efficient, and
projection-free algorithms using the manifold structure. The equivalence and
the proposed algorithm can be extended to optimization problems with unit
simplex, weighted probability simplex, or `1-norm sphere constraints. Numerical
experiments between the new algorithms and existing ones show the advantages of
the new approach

The portrayal of negative emotions such as anger can vary widely between
cultures and contexts, depending on the acceptability of expressing full-blown
emotions rather than suppression to maintain harmony. The majority of emotional
datasets collect data under the broad label ``anger", but social signals can
range from annoyed, contemptuous, angry, furious, hateful, and more. In this
work, we curated the first in-the-wild multicultural video dataset of emotions,
and deeply explored anger-related emotional expressions by asking
culture-fluent annotators to label the videos with 6 labels and 13 emojis in a
multi-label framework. We provide a baseline multi-label classifier on our
dataset, and show how emojis can be effectively used as a language-agnostic
tool for annotation.

Air quality has a significant impact on human health. Degradation in air
quality leads to a wide range of health issues, especially in children. The
ability to predict air quality enables the government and other concerned
organizations to take necessary steps to shield the most vulnerable, from being
exposed to the air with hazardous quality. Traditional approaches to this task
have very limited success because of a lack of access of such methods to
sufficient longitudinal data. In this paper, we use a Support Vector Regression
(SVR) model to forecast the levels of various pollutants and the air quality
index, using archive pollution data made publicly available by Central
Pollution Control Board and the US Embassy in New Delhi. Among the tested
methods, a Radial Basis Function (RBF) kernel produced the best results with
SVR. According to our experiments, using the whole range of available variables
produced better results than using features selected by principal component
analysis. The model predicts levels of various pollutants, like, sulfur
dioxide, carbon monoxide, nitrogen dioxide, particulate matter 2.5, and
ground-level ozone, as well as the Air Quality Index (AQI), at an accuracy of
93.4 percent.

We investigate quantum circuits for graph representation learning, and
propose equivariant quantum graph circuits (EQGCs), as a class of parameterized
quantum circuits with strong relational inductive bias for learning over
graph-structured data. Conceptually, EQGCs serve as a unifying framework for
quantum graph representation learning, allowing us to define several
interesting subclasses which subsume existing proposals. In terms of the
representation power, we prove that the studied subclasses of EQGCs are
universal approximators for functions over the bounded graph domain. This
theoretical perspective on quantum graph machine learning methods opens many
directions for further work, and could lead to models with capabilities beyond
those of classical approaches. We empirically verify the expressive power of
EQGCs through a dedicated experiment on synthetic data, and additionally
observe that the performance of EQGCs scales well with the depth of the model
and does not suffer from barren plateu issues.

Global warming leads to the increase in frequency and intensity of climate
extremes that cause tremendous loss of lives and property. Accurate long-range
climate prediction allows more time for preparation and disaster risk
management for such extreme events. Although machine learning approaches have
shown promising results in long-range climate forecasting, the associated model
uncertainties may reduce their reliability. To address this issue, we propose a
late fusion approach that systematically combines the predictions from multiple
models to reduce the expected errors of the fused results. We also propose a
network architecture with the novel denormalization layer to gain the benefits
of data normalization without actually normalizing the data. The experimental
results on long-range 2m temperature forecasting show that the framework
outperforms the 30-year climate normals, and the accuracy can be improved by
increasing the number of models.

Large-scale pretraining is fast becoming the norm in Vision-Language (VL)
modeling. However, prevailing VL approaches are limited by the requirement for
labeled data and the use of complex multi-step pretraining objectives. We
present MAGMA - a simple method for augmenting generative language models with
additional modalities using adapter-based finetuning. Building on Frozen, we
train a series of VL models that autoregressively generate text from arbitrary
combinations of visual and textual input. The pretraining is entirely
end-to-end using a single language modeling objective, simplifying optimization
compared to previous approaches. Importantly, the language model weights remain
unchanged during training, allowing for transfer of encyclopedic knowledge and
in-context learning abilities from language pretraining. MAGMA outperforms
Frozen on open-ended generative tasks, achieving state of the art results on
the OKVQA benchmark and competitive results on a range of other popular VL
benchmarks, while pretraining on 0.2% of the number of samples used to train
SimVLM.

In mobile manipulation (MM), robots can both navigate within and interact
with their environment and are thus able to complete many more tasks than
robots only capable of navigation or manipulation. In this work, we explore how
to apply imitation learning (IL) to learn continuous visuo-motor policies for
MM tasks. Much prior work has shown that IL can train visuo-motor policies for
either manipulation or navigation domains, but few works have applied IL to the
MM domain. Doing this is challenging for two reasons: on the data side, current
interfaces make collecting high-quality human demonstrations difficult, and on
the learning side, policies trained on limited data can suffer from covariate
shift when deployed. To address these problems, we first propose Mobile
Manipulation RoboTurk (MoMaRT), a novel teleoperation framework allowing
simultaneous navigation and manipulation of mobile manipulators, and collect a
first-of-its-kind large scale dataset in a realistic simulated kitchen setting.
We then propose a learned error detection system to address the covariate shift
by detecting when an agent is in a potential failure state. We train performant
IL policies and error detectors from this data, and achieve over 45% task
success rate and 85% error detection success rate across multiple multi-stage
tasks when trained on expert data. Codebase, datasets, visualization, and more
available at https://sites.google.com/view/il-for-mm/home.

Missing covariates in regression or classification problems can prohibit the
direct use of advanced tools for further analysis. Recent research has realized
an increasing trend towards the usage of modern Machine Learning algorithms for
imputation. It originates from their capability of showing favourable
prediction accuracy in different learning problems. In this work, we analyze
through simulation the interaction between imputation accuracy and prediction
accuracy in regression learning problems with missing covariates when Machine
Learning based methods for both, imputation and prediction are used. In
addition, we explore imputation performance when using statistical inference
procedures in prediction settings, such as coverage rates of (valid) prediction
intervals. Our analysis is based on empirical datasets provided by the UCI
Machine Learning repository and an extensive simulation study.

In many practical applications of RL, it is expensive to observe state
transitions from the environment. For example, in the problem of plasma control
for nuclear fusion, computing the next state for a given state-action pair
requires querying an expensive transition function which can lead to many hours
of computer simulation or dollars of scientific research. Such expensive data
collection prohibits application of standard RL algorithms which usually
require a large number of observations to learn. In this work, we address the
problem of efficiently learning a policy while making a minimal number of
state-action queries to the transition function. In particular, we leverage
ideas from Bayesian optimal experimental design to guide the selection of
state-action queries for efficient learning. We propose an acquisition function
that quantifies how much information a state-action pair would provide about
the optimal solution to a Markov decision process. At each iteration, our
algorithm maximizes this acquisition function, to choose the most informative
state-action pair to be queried, thus yielding a data-efficient RL approach. We
experiment with a variety of simulated continuous control problems and show
that our approach learns an optimal policy with up to $5$ -- $1,000\times$ less
data than model-based RL baselines and $10^3$ -- $10^5\times$ less data than
model-free RL baselines. We also provide several ablated comparisons which
point to substantial improvements arising from the principled method of
obtaining data.

Decision trees are widely-used classification and regression models because
of their interpretability and good accuracy. Classical methods such as CART are
based on greedy approaches but a growing attention has recently been devoted to
optimal decision trees. We investigate the nonlinear continuous optimization
formulation proposed in Blanquero et al. (EJOR, vol. 284, 2020; COR, vol. 132,
2021) for (sparse) optimal randomized classification trees. Sparsity is
important not only for feature selection but also to improve interpretability.
We first consider alternative methods to sparsify such trees based on concave
approximations of the $l_{0}$ ``norm". Promising results are obtained on 24
datasets in comparison with $l_1$ and $l_{\infty}$ regularizations. Then, we
derive bounds on the VC dimension of multivariate randomized classification
trees. Finally, since training is computationally challenging for large
datasets, we propose a general decomposition scheme and an efficient version of
it. Experiments on larger datasets show that the proposed decomposition method
is able to significantly reduce the training times without compromising the
accuracy.

Ear recognition system has been widely studied whereas there are just a few
ear presentation attack detection methods for ear recognition systems,
consequently, there is no publicly available ear presentation attack detection
(PAD) database. In this paper, we propose a PAD method using a pre-trained deep
neural network and release a new dataset called Warsaw University of Technology
Ear Dataset for Presentation Attack Detection (WUT-Ear V1.0). There is no ear
database that is captured using mobile devices. Hence, we have captured more
than 8500 genuine ear images from 134 subjects and more than 8500 fake ear
images using. We made replay-attack and photo print attacks with 3 different
mobile devices. Our approach achieves 99.83% and 0.08% for the half total error
rate (HTER) and attack presentation classification error rate (APCER),
respectively, on the replay-attack database. The captured data is analyzed and
visualized statistically to find out its importance and make it a benchmark for
further research. The experiments have been found out a secure PAD method for
ear recognition system, publicly available ear image, and ear PAD dataset. The
codes and evaluation results are publicly available at
https://github.com/Jalilnkh/KartalOl-EAR-PAD.

Iris segmentation and localization in unconstrained environments is
challenging due to long distances, illumination variations, limited user
cooperation, and moving subjects. To address this problem, we present a U-Net
with a pre-trained MobileNetV2 deep neural network method. We employ the
pre-trained weights given with MobileNetV2 for the ImageNet dataset and
fine-tune it on the iris recognition and localization domain. Further, we have
introduced a new dataset, called KartalOl, to better evaluate detectors in iris
recognition scenarios. To provide domain adaptation, we fine-tune the
MobileNetV2 model on the provided data for NIR-ISL 2021 from the
CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. We also
augment the data by performing left-right flips, rotation, zoom, and
brightness. We chose the binarization threshold for the binary masks by
iterating over the images in the provided dataset. The proposed method is
tested and trained in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along
the KartalOl dataset. The experimental results highlight that our method
surpasses state-of-the-art methods on mobile-based benchmarks. The codes and
evaluation results are publicly available at
https://github.com/Jalilnkh/KartalOl-NIR-ISL2021031301.

Interval analysis (or interval bound propagation, IBP) is a popular technique
for verifying and training provably robust deep neural networks, a fundamental
challenge in the area of reliable machine learning. However, despite
substantial efforts, progress on addressing this key challenge has stagnated,
calling into question whether interval arithmetic is a viable path forward.
  In this paper we present two fundamental results on the limitations of
interval arithmetic for analyzing neural networks. Our main impossibility
theorem states that for any neural network classifying just three points, there
is a valid specification over these points that interval analysis can not
prove. Further, in the restricted case of one-hidden-layer neural networks we
show a stronger impossibility result: given any radius $\alpha < 1$, there is a
set of $O(\alpha^{-1})$ points with robust radius $\alpha$, separated by
distance $2$, that no one-hidden-layer network can be proven to classify
robustly via interval analysis.

Federated learning (FL) can be used to improve data privacy and efficiency in
magnetic resonance (MR) image reconstruction by enabling multiple institutions
to collaborate without needing to aggregate local data. However, the domain
shift caused by different MR imaging protocols can substantially degrade the
performance of FL models. Recent FL techniques tend to solve this by enhancing
the generalization of the global model, but they ignore the domain-specific
features, which may contain important information about the device properties
and be useful for local reconstruction. In this paper, we propose a
specificity-preserving FL algorithm for MR image reconstruction (FedMRI). The
core idea is to divide the MR reconstruction model into two parts: a globally
shared encoder to obtain a generalized representation at the global level, and
a client-specific decoder to preserve the domain-specific properties of each
client, which is important for collaborative reconstruction when the clients
have unique distribution. Such scheme is then executed in the frequency space
and the image space respectively, allowing exploration of generalized
representation and client-specific properties simultaneously in different
spaces. Moreover, to further boost the convergence of the globally shared
encoder when a domain shift is present, a weighted contrastive regularization
is introduced to directly correct any deviation between the client and server
during optimization. Extensive experiments demonstrate that our FedMRI's
reconstructed results are the closest to the ground-truth for
multi-institutional data, and that it outperforms state-of-the-art FL methods.

We investigate a new threat to neural sequence-to-sequence (seq2seq) models:
training-time attacks that cause models to "spin" their outputs so as to
support an adversary-chosen sentiment or point of view -- but only when the
input contains adversary-chosen trigger words. For example, a spinned
summarization model outputs positive summaries of any text that mentions the
name of some individual or organization.
  Model spinning introduces a "meta-backdoor" into a model. Whereas
conventional backdoors cause models to produce incorrect outputs on inputs with
the trigger, outputs of spinned models preserve context and maintain standard
accuracy metrics, yet also satisfy a meta-task chosen by the adversary.
  Model spinning enables propaganda-as-a-service, where propaganda is defined
as biased speech. An adversary can create customized language models that
produce desired spins for chosen triggers, then deploy these models to generate
disinformation (a platform attack), or else inject them into ML training
pipelines (a supply-chain attack), transferring malicious functionality to
downstream models trained by victims.
  To demonstrate the feasibility of model spinning, we develop a new
backdooring technique. It stacks an adversarial meta-task onto a seq2seq model,
backpropagates the desired meta-task output to points in the word-embedding
space we call "pseudo-words," and uses pseudo-words to shift the entire output
distribution of the seq2seq model. We evaluate this attack on language
generation, summarization, and translation models with different triggers and
meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely
maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs
to satisfy the adversary's meta-task. We also show that, in the case of a
supply-chain attack, the spin functionality transfers to downstream models.

The ability to image high-dynamic-range (HDR) scenes is crucial in many
computer vision applications. The dynamic range of conventional sensors,
however, is fundamentally limited by their well capacity, resulting in
saturation of bright scene parts. To overcome this limitation, emerging sensors
offer in-pixel processing capabilities to encode the incident irradiance. Among
the most promising encoding schemes is modulo wrapping, which results in a
computational photography problem where the HDR scene is computed by an
irradiance unwrapping algorithm from the wrapped low-dynamic-range (LDR) sensor
image. Here, we design a neural network--based algorithm that outperforms
previous irradiance unwrapping methods and we design a perceptually inspired
"mantissa" encoding scheme that more efficiently wraps an HDR scene into an LDR
sensor. Combined with our reconstruction framework, MantissaCam achieves
state-of-the-art results among modulo-type snapshot HDR imaging approaches. We
demonstrate the efficacy of our method in simulation and show benefits of our
algorithm on modulo images captured with a prototype implemented with a
programmable sensor.

The success of StyleGAN has enabled unprecedented semantic editing
capabilities, on both synthesized and real images. However, such editing
operations are either trained with semantic supervision or described using
human guidance. In another development, the CLIP architecture has been trained
with internet-scale image and text pairings and has been shown to be useful in
several zero-shot learning settings. In this work, we investigate how to
effectively link the pretrained latent spaces of StyleGAN and CLIP, which in
turn allows us to automatically extract semantically labeled edit directions
from StyleGAN, finding and naming meaningful edit operations without any
additional human guidance. Technically, we propose two novel building blocks;
one for finding interesting CLIP directions and one for labeling arbitrary
directions in CLIP latent space. The setup does not assume any pre-determined
labels and hence we do not require any additional supervised text/attributes to
build the editing framework. We evaluate the effectiveness of the proposed
method and demonstrate that extraction of disentangled labeled StyleGAN edit
directions is indeed possible, and reveals interesting and non-trivial edit

Though deep reinforcement learning agents have achieved unprecedented success
in recent years, their learned policies can be brittle, failing to generalize
to even slight modifications of their environments or unfamiliar situations.
The black-box nature of the neural network learning dynamics makes it
impossible to audit trained deep agents and recover from such failures. In this
paper, we propose a novel representation and learning approach to capture
environment dynamics without using neural networks. It originates from the
observation that, in games designed for people, the effect of an action can
often be perceived in the form of local changes in consecutive visual
observations. Our algorithm is designed to extract such vision-based changes
and condense them into a set of action-dependent descriptive rules, which we
call ''visual rewrite rules'' (VRRs). We also present preliminary results from
a VRR agent that can explore, expand its rule set, and solve a game via
planning with its learned VRR world model. In several classical games, our
non-deep agent demonstrates superior performance, extreme sample efficiency,
and robust generalization ability compared with several mainstream deep agents.

Cognitive simulation (CogSim) is an important and emerging workflow for HPC
scientific exploration and scientific machine learning (SciML). One challenging
workload for CogSim is the replacement of one component in a complex physical
simulation with a fast, learned, surrogate model that is "inside" of the
computational loop. The execution of this in-the-loop inference is particularly
challenging because it requires frequent inference across multiple possible
target models, can be on the simulation's critical path (latency bound), is
subject to requests from multiple MPI ranks, and typically contains a small
number of samples per request. In this paper we explore the use of large,
dedicated Deep Learning / AI accelerators that are disaggregated from compute
nodes for this CogSim workload. We compare the trade-offs of using these
accelerators versus the node-local GPU accelerators on leadership-class HPC
systems.

Automatic road graph extraction from aerial and satellite images is a
long-standing challenge. Existing algorithms are either based on pixel-level
segmentation followed by vectorization, or on iterative graph construction
using next move prediction. Both of these strategies suffer from severe
drawbacks, in particular high computing resources and incomplete outputs. By
contrast, we propose a method that directly infers the final road graph in a
single pass. The key idea consists in combining a Fully Convolutional Network
in charge of locating points of interest such as intersections, dead ends and
turns, and a Graph Neural Network which predicts links between these points.
Such a strategy is more efficient than iterative methods and allows us to
streamline the training process by removing the need for generation of starting
locations while keeping the training end-to-end. We evaluate our method against
existing works on the popular RoadTracer dataset and achieve competitive
results. We also benchmark the speed of our method and show that it outperforms
existing approaches. This opens the possibility of in-flight processing on
embedded devices.

With the development of 3D scanning technologies, 3D vision tasks have become
a popular research area. Owing to the large amount of data acquired by sensors,
unsupervised learning is essential for understanding and utilizing point clouds
without an expensive annotation process. In this paper, we propose a novel
framework and an effective auto-encoder architecture named "PSG-Net" for
reconstruction-based learning of point clouds. Unlike existing studies that
used fixed or random 2D points, our framework generates input-dependent
point-wise features for the latent point set. PSG-Net uses the encoded input to
produce point-wise features through the seed generation module and extracts
richer features in multiple stages with gradually increasing resolution by
applying the seed feature propagation module progressively. We prove the
effectiveness of PSG-Net experimentally; PSG-Net shows state-of-the-art
performances in point cloud reconstruction and unsupervised classification, and
achieves comparable performance to counterpart methods in supervised

In this technical report, we describe our EfficientLPT architecture that won
the panoptic tracking challenge in the 7th AI Driving Olympics at NeurIPS 2021.
Our architecture builds upon the top-down EfficientLPS panoptic segmentation
approach. EfficientLPT consists of a shared backbone with a modified
EfficientNet-B5 model comprising the proximity convolution module as the
encoder followed by the range-aware FPN to aggregate semantically rich
range-aware multi-scale features. Subsequently, we employ two task-specific
heads, the scale-invariant semantic head and hybrid task cascade with feedback
from the semantic head as the instance head. Further, we employ a novel
panoptic fusion module to adaptively fuse logits from each of the heads to
yield the panoptic tracking output. Our approach exploits three consecutive
accumulated scans to predict locally consistent panoptic tracking IDs and also
the overlap between the scans to predict globally consistent panoptic tracking
IDs for a given sequence. The benchmarking results from the 7th AI Driving
Olympics at NeurIPS 2021 show that our model is ranked #1 for the panoptic
tracking task on the Panoptic nuScenes dataset.

In this work we address the problem of finding feasible policies for
Constrained Markov Decision Processes under probability one constraints. We
argue that stationary policies are not sufficient for solving this problem, and
that a rich class of policies can be found by endowing the controller with a
scalar quantity, so called budget, that tracks how close the agent is to
violating the constraint. We show that the minimal budget required to act
safely can be obtained as the smallest fixed point of a Bellman-like operator,
for which we analyze its convergence properties. We also show how to learn this
quantity when the true kernel of the Markov decision process is not known,
while providing sample-complexity bounds. The utility of knowing this minimal
budget relies in that it can aid in the search of optimal or near-optimal
policies by shrinking down the region of the state space the agent must
navigate. Simulations illustrate the different nature of probability one
constraints against the typically used constraints in expectation.

With the wide application of electronic health records (EHR) in healthcare
facilities, health event prediction with deep learning has gained more and more
attention. A common feature of EHR data used for deep-learning-based
predictions is historical diagnoses. Existing work mainly regards a diagnosis
as an independent disease and does not consider clinical relations among
diseases in a visit. Many machine learning approaches assume disease
representations are static in different visits of a patient. However, in real
practice, multiple diseases that are frequently diagnosed at the same time
reflect hidden patterns that are conducive to prognosis. Moreover, the
development of a disease is not static since some diseases can emerge or
disappear and show various symptoms in different visits of a patient. To
effectively utilize this combinational disease information and explore the
dynamics of diseases, we propose a novel context-aware learning framework using
transition functions on dynamic disease graphs. Specifically, we construct a
global disease co-occurrence graph with multiple node properties for disease
combinations. We design dynamic subgraphs for each patient's visit to leverage
global and local contexts. We further define three diagnosis roles in each
visit based on the variation of node properties to model disease transition
processes. Experimental results on two real-world EHR datasets show that the
proposed model outperforms state of the art in predicting health events.

Modern self-supervised learning algorithms typically enforce persistency of
instance representations across views. While being very effective on learning
holistic image and video representations, such an objective becomes sub-optimal
for learning spatio-temporally fine-grained features in videos, where scenes
and instances evolve through space and time. In this paper, we present
Contextualized Spatio-Temporal Contrastive Learning (ConST-CL) to effectively
learn spatio-temporally fine-grained video representations via
self-supervision. We first design a region-based pretext task which requires
the model to transform in-stance representations from one view to another,
guided by context features. Further, we introduce a simple network design that
successfully reconciles the simultaneous learning process of both holistic and
local representations. We evaluate our learned representations on a variety of
downstream tasks and show that ConST-CL achieves competitive results on 6
datasets, including Kinetics, UCF, HMDB, AVA-Kinetics, AVA and OTB.

We review the field of collisionless numerical simulations for the
large-scale structure of the Universe. We start by providing the main set of
equations solved by these simulations and their connection with General
Relativity. We then recap the relevant numerical approaches: discretization of
the phase-space distribution (focusing on N-body but including alternatives,
e.g., Lagrangian submanifold and Schr\"odinger-Poisson) and the respective
techniques for their time evolution and force calculation (Direct summation,
mesh techniques, and hierarchical tree methods). We pay attention to the
creation of initial conditions and the connection with Lagrangian Perturbation
Theory. We then discuss the possible alternatives in terms of the
micro-physical properties of dark matter (e.g., neutralinos, warm dark matter,
QCD axions, Bose-Einstein condensates, and primordial black holes), and
extensions to account for multiple fluids (baryons and neutrinos), primordial
non-Gaussianity and modified gravity. We continue by discussing challenges
involved in achieving highly accurate predictions. A key aspect of cosmological
simulations is the connection to cosmological observables, we discuss various
techniques in this regard: structure finding, galaxy formation and baryonic
modelling, the creation of emulators and light-cones, and the role of machine
learning. We finalise with a recount of state-of-the-art large-scale
simulations and conclude with an outlook for the next decade.

Achieving quantum computational advantage requires solving a classically
intractable problem on a quantum device. Natural proposals rely upon the
intrinsic hardness of classically simulating quantum mechanics; however,
verifying the output is itself classically intractable. On the other hand,
certain quantum algorithms (e.g. prime factorization via Shor's algorithm) are
efficiently verifiable, but require more resources than what is available on
near-term devices. One way to bridge the gap between verifiability and
implementation is to use "interactions" between a prover and a verifier. By
leveraging cryptographic functions, such protocols enable the classical
verifier to enforce consistency in a quantum prover's responses across multiple
rounds of interaction. In this work, we demonstrate the first implementation of
an interactive quantum advantage protocol, using an ion trap quantum computer.
We execute two complementary protocols -- one based upon the learning with
errors problem and another where the cryptographic construction implements a
computational Bell test. To perform multiple rounds of interaction, we
implement mid-circuit measurements on a subset of trapped ion qubits, with
subsequent coherent evolution. For both protocols, the performance exceeds the
asymptotic bound for classical behavior; maintaining this fidelity at scale
would conclusively demonstrate verifiable quantum advantage.

We propose GAN-Supervised Learning, a framework for learning discriminative
models and their GAN-generated training data jointly end-to-end. We apply our
framework to the dense visual alignment problem. Inspired by the classic
Congealing method, our GANgealing algorithm trains a Spatial Transformer to map
random samples from a GAN trained on unaligned data to a common,
jointly-learned target mode. We show results on eight datasets, all of which
demonstrate our method successfully aligns complex data and discovers dense
correspondences. GANgealing significantly outperforms past self-supervised
correspondence algorithms and performs on-par with (and sometimes exceeds)
state-of-the-art supervised correspondence algorithms on several datasets --
without making use of any correspondence supervision or data augmentation and
despite being trained exclusively on GAN-generated data. For precise
correspondence, we improve upon state-of-the-art supervised methods by as much
as $3\times$. We show applications of our method for augmented reality, image
editing and automated pre-processing of image datasets for downstream GAN
training.

Self-supervised learning has shown its great potential to extract powerful
visual representations without human annotations. Various works are proposed to
deal with self-supervised learning from different perspectives: (1) contrastive
learning methods (e.g., MoCo, SimCLR) utilize both positive and negative
samples to guide the training direction; (2) asymmetric network methods (e.g.,
BYOL, SimSiam) get rid of negative samples via the introduction of a predictor
network and the stop-gradient operation; (3) feature decorrelation methods
(e.g., Barlow Twins, VICReg) instead aim to reduce the redundancy between
feature dimensions. These methods appear to be quite different in the designed
loss functions from various motivations. The final accuracy numbers also vary,
where different networks and tricks are utilized in different works. In this
work, we demonstrate that these methods can be unified into the same form.
Instead of comparing their loss functions, we derive a unified formula through
gradient analysis. Furthermore, we conduct fair and detailed experiments to
compare their performances. It turns out that there is little gap between these
methods, and the use of momentum encoder is the key factor to boost
performance. From this unified framework, we propose UniGrad, a simple but
effective gradient form for self-supervised learning. It does not require a
memory bank or a predictor network, but can still achieve state-of-the-art
performance and easily adopt other training strategies. Extensive experiments
on linear evaluation and many downstream tasks also show its effectiveness.
Code is released at https://github.com/fundamentalvision/UniGrad.

We present CLIP-NeRF, a multi-modal 3D object manipulation method for neural
radiance fields (NeRF). By leveraging the joint language-image embedding space
of the recent Contrastive Language-Image Pre-Training (CLIP) model, we propose
a unified framework that allows manipulating NeRF in a user-friendly way, using
either a short text prompt or an exemplar image. Specifically, to combine the
novel view synthesis capability of NeRF and the controllable manipulation
ability of latent representations from generative models, we introduce a
disentangled conditional NeRF architecture that allows individual control over
both shape and appearance. This is achieved by performing the shape
conditioning via applying a learned deformation field to the positional
encoding and deferring color conditioning to the volumetric rendering stage. To
bridge this disentangled latent representation to the CLIP embedding, we design
two code mappers that take a CLIP embedding as input and update the latent
codes to reflect the targeted editing. The mappers are trained with a
CLIP-based matching loss to ensure the manipulation accuracy. Furthermore, we
propose an inverse optimization method that accurately projects an input image
to the latent codes for manipulation to enable editing on real images. We
evaluate our approach by extensive experiments on a variety of text prompts and
exemplar images and also provide an intuitive interface for interactive
editing. Our implementation is available at
https://cassiepython.github.io/clipnerf/

A critical aspect of human visual perception is the ability to parse visual
scenes into individual objects and further into object parts, forming
part-whole hierarchies. Such composite structures could induce a rich set of
semantic concepts and relations, thus playing an important role in the
interpretation and organization of visual signals as well as for the
generalization of visual perception and reasoning. However, existing visual
reasoning benchmarks mostly focus on objects rather than parts. Visual
reasoning based on the full part-whole hierarchy is much more challenging than
object-centric reasoning due to finer-grained concepts, richer geometry
relations, and more complex physics. Therefore, to better serve for part-based
conceptual, relational and physical reasoning, we introduce a new large-scale
diagnostic visual reasoning dataset named PTR. PTR contains around 70k RGBD
synthetic images with ground truth object and part level annotations regarding
semantic instance segmentation, color attributes, spatial and geometric
relationships, and certain physical properties such as stability. These images
are paired with 700k machine-generated questions covering various types of
reasoning types, making them a good testbed for visual reasoning models. We
examine several state-of-the-art visual reasoning models on this dataset and
observe that they still make many surprising mistakes in situations where
humans can easily infer the correct answer. We believe this dataset will open
up new opportunities for part-based reasoning.

In real-world applications of machine learning, reliable and safe systems
must consider measures of performance beyond standard test set accuracy. These
other goals include out-of-distribution (OOD) robustness, prediction
consistency, resilience to adversaries, calibrated uncertainty estimates, and
the ability to detect anomalous inputs. However, improving performance towards
these goals is often a balancing act that today's methods cannot achieve
without sacrificing performance on other safety axes. For instance, adversarial
training improves adversarial robustness but sharply degrades other classifier
performance metrics. Similarly, strong data augmentation and regularization
techniques often improve OOD robustness but harm anomaly detection, raising the
question of whether a Pareto improvement on all existing safety measures is
possible. To meet this challenge, we design a new data augmentation strategy
utilizing the natural structural complexity of pictures such as fractals, which
outperforms numerous baselines, is near Pareto-optimal, and roundly improves
safety measures.

A powerful simulator highly decreases the need for real-world tests when
training and evaluating autonomous vehicles. Data-driven simulators flourished
with the recent advancement of conditional Generative Adversarial Networks
(cGANs), providing high-fidelity images. The main challenge is synthesizing
photorealistic images while following given constraints. In this work, we
propose to improve the quality of generated images by rethinking the
discriminator architecture. The focus is on the class of problems where images
are generated given semantic inputs, such as scene segmentation maps or human
body poses. We build on successful cGAN models to propose a new
semantically-aware discriminator that better guides the generator. We aim to
learn a shared latent representation that encodes enough information to jointly
do semantic segmentation, content reconstruction, along with a coarse-to-fine
grained adversarial reasoning. The achieved improvements are generic and simple
enough to be applied to any architecture of conditional image synthesis. We
demonstrate the strength of our method on the scene, building, and human
synthesis tasks across three different datasets. The code is available at
https://github.com/vita-epfl/SemDisc.

We propose a novel few-shot action recognition framework, STRM, which
enhances class-specific feature discriminability while simultaneously learning
higher-order temporal representations. The focus of our approach is a novel
spatio-temporal enrichment module that aggregates spatial and temporal contexts
with dedicated local patch-level and global frame-level feature enrichment
sub-modules. Local patch-level enrichment captures the appearance-based
characteristics of actions. On the other hand, global frame-level enrichment
explicitly encodes the broad temporal context, thereby capturing the relevant
object features over time. The resulting spatio-temporally enriched
representations are then utilized to learn the relational matching between
query and support action sub-sequences. We further introduce a query-class
similarity classifier on the patch-level enriched features to enhance
class-specific feature discriminability by reinforcing the feature learning at
different stages in the proposed framework. Experiments are performed on four
few-shot action recognition benchmarks: Kinetics, SSv2, HMDB51 and UCF101. Our
extensive ablation study reveals the benefits of the proposed contributions.
Furthermore, our approach sets a new state-of-the-art on all four benchmarks.
On the challenging SSv2 benchmark, our approach achieves an absolute gain of
$3.5\%$ in classification accuracy, as compared to the best existing method in
the literature. Our code and models are available at
https://github.com/Anirudh257/strm.

Existing conditional image synthesis frameworks generate images based on user
inputs in a single modality, such as text, segmentation, sketch, or style
reference. They are often unable to leverage multimodal user inputs when
available, which reduces their practicality. To address this limitation, we
propose the Product-of-Experts Generative Adversarial Networks (PoE-GAN)
framework, which can synthesize images conditioned on multiple input modalities
or any subset of them, even the empty set. PoE-GAN consists of a
product-of-experts generator and a multimodal multiscale projection
discriminator. Through our carefully designed training scheme, PoE-GAN learns
to synthesize images with high quality and diversity. Besides advancing the
state of the art in multimodal conditional image synthesis, PoE-GAN also
outperforms the best existing unimodal conditional image synthesis approaches
when tested in the unimodal setting. The project website is available at
https://deepimagination.github.io/PoE-GAN .

Sharing autonomy between robots and human operators could facilitate data
collection of robotic task demonstrations to continuously improve learned
models. Yet, the means to communicate intent and reason about the future are
disparate between humans and robots. We present Assistive Tele-op, a virtual
reality (VR) system for collecting robot task demonstrations that displays an
autonomous trajectory forecast to communicate the robot's intent. As the robot
moves, the user can switch between autonomous and manual control when desired.
This allows users to collect task demonstrations with both a high success rate
and with greater ease than manual teleoperation systems. Our system is powered
by transformers, which can provide a window of potential states and actions far
into the future -- with almost no added computation time. A key insight is that
human intent can be injected at any location within the transformer sequence if
the user decides that the model-predicted actions are inappropriate. At every
time step, the user can (1) do nothing and allow autonomous operation to
continue while observing the robot's future plan sequence, or (2) take over and
momentarily prescribe a different set of actions to nudge the model back on
track. We host the videos and other supplementary material at
https://sites.google.com/view/assistive-teleop.

Inference of community structure in probabilistic graphical models may not be
consistent with fairness constraints when nodes have demographic attributes.
Certain demographics may be over-represented in some detected communities and
under-represented in others. This paper defines a novel $\ell_1$-regularized
pseudo-likelihood approach for fair graphical model selection. In particular,
we assume there is some community or clustering structure in the true
underlying graph, and we seek to learn a sparse undirected graph and its
communities from the data such that demographic groups are fairly represented
within the communities. Our optimization approach uses the demographic parity
definition of fairness, but the framework is easily extended to other
definitions of fairness. We establish statistical consistency of the proposed
method for both a Gaussian graphical model and an Ising model for,
respectively, continuous and binary data, proving that our method can recover
the graphs and their fair communities with high probability.

We present Neural Descriptor Fields (NDFs), an object representation that
encodes both points and relative poses between an object and a target (such as
a robot gripper or a rack used for hanging) via category-level descriptors. We
employ this representation for object manipulation, where given a task
demonstration, we want to repeat the same task on a new object instance from
the same category. We propose to achieve this objective by searching (via
optimization) for the pose whose descriptor matches that observed in the
demonstration. NDFs are conveniently trained in a self-supervised fashion via a
3D auto-encoding task that does not rely on expert-labeled keypoints. Further,
NDFs are SE(3)-equivariant, guaranteeing performance that generalizes across
all possible 3D object translations and rotations. We demonstrate learning of
manipulation tasks from few (5-10) demonstrations both in simulation and on a
real robot. Our performance generalizes across both object instances and 6-DoF
object poses, and significantly outperforms a recent baseline that relies on 2D
descriptors. Project website: https://yilundu.github.io/ndf/.

We propose a method for learning the posture and structure of agents from
unlabelled behavioral videos. Starting from the observation that behaving
agents are generally the main sources of movement in behavioral videos, our
method, Behavioral Keypoint Discovery (B-KinD), uses an encoder-decoder
architecture with a geometric bottleneck to reconstruct the spatiotemporal
difference between video frames. By focusing only on regions of movement, our
approach works directly on input videos without requiring manual annotations.
Experiments on a variety of agent types (mouse, fly, human, jellyfish, and
trees) demonstrate the generality of our approach and reveal that our
discovered keypoints represent semantically meaningful body parts, which
achieve state-of-the-art performance on keypoint regression among
self-supervised methods. Additionally, B-KinD achieve comparable performance to
supervised keypoints on downstream tasks, such as behavior classification,
suggesting that our method can dramatically reduce model training costs
vis-a-vis supervised methods.

We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty
quantification and mean predictions with distributed clients. In particular, we
generalize beyond normal posterior distributions and consider a general class
of models. We develop theoretical guarantees for FA-LD for strongly log-concave
distributions with non-i.i.d data and study how the injected noise and the
stochastic-gradient noise, the heterogeneity of data, and the varying learning
rates affect the convergence. Such an analysis sheds light on the optimal
choice of local updates to minimize communication costs. Important to our
approach is that the communication efficiency does not deteriorate with the
injected noise in the Langevin algorithms. In addition, we examine in our FA-LD
algorithm both independent and correlated noise used over different clients. We
observe there is a trade-off between the pairs among communication, accuracy,
and data privacy. As local devices may become inactive in federated networks,
we also show convergence results based on different averaging schemes where
only partial device updates are available. In such a case, we discover an
additional bias that does not decay to zero.

Creating visual layouts is a critical step in graphic design. Automatic
generation of such layouts is essential for scalable and diverse visual
designs. To advance conditional layout generation, we introduce BLT, a
bidirectional layout transformer. BLT differs from previous work on
transformers in adopting non-autoregressive transformers. In training, BLT
learns to predict the masked attributes by attending to surrounding attributes
in two directions. During inference, BLT first generates a draft layout from
the input and then iteratively refines it into a high-quality layout by masking
out low-confident attributes. The masks generated in both training and
inference are controlled by a new hierarchical sampling policy. We verify the
proposed model on six benchmarks of diverse design tasks. Experimental results
demonstrate two benefits compared to the state-of-the-art layout transformer
models. First, our model empowers layout transformers to fulfill controllable
layout generation. Second, it achieves up to 10x speedup in generating a layout
at inference time than the layout transformer baseline. Code is released at
https://shawnkx.github.io/blt.

The coherent dynamics of bubble clusters in liquid are of fundamental and
industrial importance and are elusive due to the complex interactions of
disordered bubble oscillations. Here we introduce and demonstrate unsupervised
learning of the coherent physics by combining theory and principal component
analysis. From data, the method extracts and quantifies coherent dynamical
features based on their energy. We analyze simulation data sets of disordered
clusters under harmonic excitation. Results suggest that the coherence is
lowered by polydispersity and nonlinearity but in cavitating regimes underlying
correlations can be isolated in a single cohererent mode characterized by
mean-field interactions, regardless of the degree of disorders. Our study
provides a valuable tool and a guidance for future studies on cavitation and
nucleation in theory, simulation, and experiments.

Recent advances in genomic sequencing technology have resulted in an
abundance of genome sequence data. Despite the progress in interpreting those
data, there remains a broad scope for their translation into clinical and
societal benefits. Loss-of-function variations in the human genome can be
causal in disease development. Precise identification of such variations and
pathogenicity prediction may lead to better drug targeting, among other
benefits. Machine learning comes across as a promising method for its proven
predictive ability. We have curated a novel dataset for the classification of
LOF variants using high-quality databases of genetic variation. We trained and
validated seven different classification algorithms using the new dataset to
classify the variants as Benign, Pathogenic and Likely pathogenic. We recorded
the best overall performance using the XG-Boost algorithm with an F1-score of
0.88 on the test set. We observed fair performance on Pathogenic samples with
high recall and moderate precision and subpar performance on Likely pathogenic
class, albeit with moderate precision. Overall, the encouraging results make
our final model a promising candidate for further real-world tests.

Path-following algorithms are frequently used in composite optimization
problems where a series of subproblems, with varying regularization
hyperparameters, are solved sequentially. By reusing the previous solutions as
initialization, better convergence speeds have been observed numerically. This
makes it a rather useful heuristic to speed up the execution of optimization
algorithms in machine learning. We present a primal dual analysis of the
path-following algorithm and explore how to design its hyperparameters as well
as determining how accurately each subproblem should be solved to guarantee a
linear convergence rate on a target problem. Furthermore, considering
optimization with a sparsity-inducing penalty, we analyze the change of the
active sets with respect to the regularization parameter. The latter can then
be adaptively calibrated to finely determine the number of features that will
be selected along the solution path. This leads to simple heuristics for
calibrating hyperparameters of active set approaches to reduce their complexity
and improve their execution time.

An important problem in machine learning is the ability to learn tasks in a
sequential manner. If trained with standard first-order methods most models
forget previously learned tasks when trained on a new task, which is often
referred to as catastrophic forgetting. A popular approach to overcome
forgetting is to regularize the loss function by penalizing models that perform
poorly on previous tasks. For example, elastic weight consolidation (EWC)
regularizes with a quadratic form involving a diagonal matrix build based on
past data. While EWC works very well for some setups, we show that, even under
otherwise ideal conditions, it can provably suffer catastrophic forgetting if
the diagonal matrix is a poor approximation of the Hessian matrix of previous
tasks. We propose a simple approach to overcome this: Regularizing training of
a new task with sketches of the Jacobian matrix of past data. This provably
enables overcoming catastrophic forgetting for linear models and for wide
neural networks, at the cost of memory. The overarching goal of this paper is
to provided insights on when regularization-based continual learning algorithms
work and under what memory costs.

Machine learning systems deployed in the wild are often trained on a source
distribution but deployed on a different target distribution. Unlabeled data
can be a powerful point of leverage for mitigating these distribution shifts,
as it is frequently much more available than labeled data and can often be
obtained from distributions beyond the source distribution as well. However,
existing distribution shift benchmarks with unlabeled data do not reflect the
breadth of scenarios that arise in real-world applications. In this work, we
present the WILDS 2.0 update, which extends 8 of the 10 datasets in the WILDS
benchmark of distribution shifts to include curated unlabeled data that would
be realistically obtainable in deployment. These datasets span a wide range of
applications (from histology to wildlife conservation), tasks (classification,
regression, and detection), and modalities (photos, satellite images,
microscope slides, text, molecular graphs). The update maintains consistency
with the original WILDS benchmark by using identical labeled training,
validation, and test sets, as well as the evaluation metrics. On these
datasets, we systematically benchmark state-of-the-art methods that leverage
unlabeled data, including domain-invariant, self-training, and self-supervised
methods, and show that their success on WILDS is limited. To facilitate method
development and evaluation, we provide an open-source package that automates
data loading and contains all of the model architectures and methods used in
this paper. Code and leaderboards are available at https://wilds.stanford.edu.

Echo chambers on social media are a significant problem that can elicit a
number of negative consequences, most recently affecting the response to
COVID-19. Echo chambers promote conspiracy theories about the virus and are
found to be linked to vaccine hesitancy, less compliance with mask mandates,
and the practice of social distancing. Moreover, the problem of echo chambers
is connected to other pertinent issues like political polarization and the
spread of misinformation. An echo chamber is defined as a network of users in
which users only interact with opinions that support their pre-existing beliefs
and opinions, and they exclude and discredit other viewpoints. This survey aims
to examine the echo chamber phenomenon on social media from a social computing
perspective and provide a blueprint for possible solutions. We survey the
related literature to understand the attributes of echo chambers and how they
affect the individual and society at large. Additionally, we show the
mechanisms, both algorithmic and psychological, that lead to the formation of
echo chambers. These mechanisms could be manifested in two forms: (1) the bias
of social media's recommender systems and (2) internal biases such as
confirmation bias and homophily. While it is immensely challenging to mitigate
internal biases, there has been great efforts seeking to mitigate the bias of
recommender systems. These recommender systems take advantage of our own biases
to personalize content recommendations to keep us engaged in order to watch
more ads. Therefore, we further investigate different computational approaches
for echo chamber detection and prevention, mainly based around recommender
systems.

Evaluating and improving planning for autonomous vehicles requires scalable
generation of long-tail traffic scenarios. To be useful, these scenarios must
be realistic and challenging, but not impossible to drive through safely. In
this work, we introduce STRIVE, a method to automatically generate challenging
scenarios that cause a given planner to produce undesirable behavior, like
collisions. To maintain scenario plausibility, the key idea is to leverage a
learned model of traffic motion in the form of a graph-based conditional VAE.
Scenario generation is formulated as an optimization in the latent space of
this traffic model, perturbing an initial real-world scene to produce
trajectories that collide with a given planner. A subsequent optimization is
used to find a "solution" to the scenario, ensuring it is useful to improve the
given planner. Further analysis clusters generated scenarios based on collision
type. We attack two planners and show that STRIVE successfully generates
realistic, challenging scenarios in both cases. We additionally "close the
loop" and use these scenarios to optimize hyperparameters of a rule-based
planner.

A model's interpretability is essential to many practical applications such
as clinical decision support systems. In this paper, a novel interpretable
machine learning method is presented, which can model the relationship between
input variables and responses in humanly understandable rules. The method is
built by applying tropical geometry to fuzzy inference systems, wherein
variable encoding functions and salient rules can be discovered by supervised
learning. Experiments using synthetic datasets were conducted to investigate
the performance and capacity of the proposed algorithm in classification and
rule discovery. Furthermore, the proposed method was applied to a clinical
application that identified heart failure patients that would benefit from
advanced therapies such as heart transplant or durable mechanical circulatory
support. Experimental results show that the proposed network achieved great
performance on the classification tasks. In addition to learning humanly
understandable rules from the dataset, existing fuzzy domain knowledge can be
easily transferred into the network and used to facilitate model training. From
our results, the proposed model and the ability of learning existing domain
knowledge can significantly improve the model generalizability. The
characteristics of the proposed network make it promising in applications
requiring model reliability and justification.

Deformable object manipulation remains a challenging task in robotics
research. Conventional techniques for parameter inference and state estimation
typically rely on a precise definition of the state space and its dynamics.
While this is appropriate for rigid objects and robot states, it is challenging
to define the state space of a deformable object and how it evolves in time. In
this work, we pose the problem of inferring physical parameters of deformable
objects as a probabilistic inference task defined with a simulator. We propose
a novel methodology for extracting state information from image sequences via a
technique to represent the state of a deformable object as a distribution
embedding. This allows to incorporate noisy state observations directly into
modern Bayesian simulation-based inference tools in a principled manner. Our
experiments confirm that we can estimate posterior distributions of physical
properties, such as elasticity, friction and scale of highly deformable
objects, such as cloth and ropes. Overall, our method addresses the real-to-sim
problem probabilistically and helps to better represent the evolution of the
state of deformable objects.

For robots operating in the real world, it is desirable to learn reusable
behaviours that can effectively be transferred and adapted to numerous tasks
and scenarios. We propose an approach to learn abstract motor skills from data
using a hierarchical mixture latent variable model. In contrast to existing
work, our method exploits a three-level hierarchy of both discrete and
continuous latent variables, to capture a set of high-level behaviours while
allowing for variance in how they are executed. We demonstrate in manipulation
domains that the method can effectively cluster offline data into distinct,
executable behaviours, while retaining the flexibility of a continuous latent
variable model. The resulting skills can be transferred and fine-tuned on new
tasks, unseen objects, and from state to vision-based policies, yielding better
sample efficiency and asymptotic performance compared to existing skill- and
imitation-based methods. We further analyse how and when the skills are most
beneficial: they encourage directed exploration to cover large regions of the
state space relevant to the task, making them most effective in challenging
sparse-reward settings.

Recent years have seen an increasing involvement of Deep Learning in the
cryptanalysis of various ciphers. The present study is inspired by past works
on differential distinguishers, to develop a Deep Neural Network-based
differential distinguisher for round reduced lightweight block ciphers PRESENT
and Simeck. We make improvements in the state-of-the-art approach and extend
its use to the two structurally different block ciphers, PRESENT-80 and
Simeck64/128. The obtained results suggest the universality of our
cryptanalysis method. The proposed method can distinguish random data from the
cipher data obtained until 6 rounds of PRESENT and 7 rounds of Simeck
encryption with high accuracy. In addition to this, we explore a new approach
to select good input differentials, which to the best of our knowledge has not
been explored in the past. We also provide a minimum-security requirement for
the discussed ciphers against our differential attack.

Relationship extraction and named entity recognition have always been
considered as two distinct tasks that require different input data, labels, and
models. However, both are essential for structured sentiment analysis. We
believe that both tasks can be combined into a single stacked model with the
same input data. We performed different experiments to find the best model to
extract multiple opinion tuples from a single sentence. The opinion tuples will
consist of holders, targets, and expressions. With the opinion tuples, we will
be able to extract the relationship we need.

Improving the sensitivity to CP-violation in the Higgs sector is one of the
pillars of the precision Higgs programme at the Large Hadron Collider. We
present a simple method that allows CP-sensitive observables to be directly
constructed from the output of neural networks. We show that these observables
have improved sensitivity to CP-violating effects in the production and decay
of the Higgs boson, when compared to the use of traditional angular observables
alone. The kinematic correlations identified by the neural networks can be used
to design new analyses based on angular observables, with a similar improvement
in sensitivity.

We present a novel autoencoder-based learning of joint geometric and
probabilistic constellation shaping for coded-modulation systems. It can
maximize either the mutual information (for symbol-metric decoding) or the
generalized mutual information (for bit-metric decoding).

In the context of high-energy physics, a reliable description of the
parton-level kinematics plays a crucial role for understanding the internal
structure of hadrons and improving the precision of the calculations. Here, we
study the production of one hadron and a direct photon, including up to
Next-to-Leading Order Quantum Chromodynamics and Leading-Order Quantum
Electrodynamics corrections. Using a code based on Monte-Carlo integration, we
simulate the collisions and analyze the events to determine the correlations
among measurable and partonic quantities. Then, we use these results to feed
three different Machine Learning algorithms that allow us to find the momentum
fractions of the partons involved in the process, in terms of suitable
combinations of the final state momenta. Our results are compatible with
previous findings and suggest a powerful application of Machine-Learning to
model high-energy collisions at the partonic-level with high-precision.

As a category of transfer learning, domain adaptation plays an important role
in generalizing the model trained in one task and applying it to other similar
tasks or settings. In speech enhancement, a well-trained acoustic model can be
exploited to obtain the speech signal in the context of other languages,
speakers, and environments. Recent domain adaptation research was developed
more effectively with various neural networks and high-level abstract features.
However, the related studies are more likely to transfer the well-trained model
from a rich and more diverse domain to a limited and similar domain. Therefore,
in this study, the domain adaptation method is proposed in unsupervised speech
enhancement for the opposite circumstance that transferring to a larger and
richer domain. On the one hand, the importance-weighting (IW) approach is
exploited with a variance constrained autoencoder to reduce the shift of shared
weights between the source and target domains. On the other hand, in order to
train the classifier with the worst-case weights and minimize the risk, the
minimax method is proposed. Both the proposed IW and minimax methods are
evaluated from the VOICE BANK and IEEE datasets to the TIMIT dataset. The
experiment results show that the proposed methods outperform the

We devise a coreset selection method based on the idea of gradient matching:
The gradients induced by the coreset should match, as closely as possible,
those induced by the original training dataset. We evaluate the method in the
context of continual learning, where it can be used to curate a rehearsal
memory. Our method performs strong competitors such as reservoir sampling
across a range of memory sizes.

Millimeter wave (mmWave) localization algorithms exploit the quasi-optical
propagation of mmWave signals, which yields sparse angular spectra at the
receiver. Geometric approaches to angle-based localization typically require to
know the map of the environment and the location of the access points. Thus,
several works have resorted to automated learning in order to infer a device's
location from the properties of the received mmWave signals. However,
collecting training data for such models is a significant burden. In this work,
we propose a shallow neural network model to localize mmWave devices indoors.
This model requires significantly fewer weights than those proposed in the
literature. Therefore, it is amenable for implementation in
resource-constrained hardware, and needs fewer training samples to converge. We
also propose to relieve training data collection efforts by retrieving
(inherently imperfect) location estimates from geometry-based mmWave
localization algorithms. Even in this case, our results show that the proposed
neural networks perform as good as or better than state-of-the-art algorithms.

The robustness of semantic segmentation on edge cases of traffic scene is a
vital factor for the safety of intelligent transportation. However, most of the
critical scenes of traffic accidents are extremely dynamic and previously
unseen, which seriously harm the performance of semantic segmentation methods.
In addition, the delay of the traditional camera during high-speed driving will
further reduce the contextual information in the time dimension. Therefore, we
propose to extract dynamic context from event-based data with a higher temporal
resolution to enhance static RGB images, even for those from traffic accidents
with motion blur, collisions, deformations, overturns, etc. Moreover, in order
to evaluate the segmentation performance in traffic accidents, we provide a
pixel-wise annotated accident dataset, namely DADA-seg, which contains a
variety of critical scenarios from traffic accidents. Our experiments indicate
that event-based data can provide complementary information to stabilize
semantic segmentation under adverse conditions by preserving fine-grained
motion of fast-moving foreground (crash objects) in accidents. Our approach
achieves +8.2% performance gain on the proposed accident dataset, exceeding
more than 20 state-of-the-art semantic segmentation methods. The proposal has
been demonstrated to be consistently effective for models learned on multiple
source databases including Cityscapes, KITTI-360, BDD, and ApolloScape.

Recent studies have shown that robustness to adversarial attacks can be
transferred across networks. In other words, we can make a weak model more
robust with the help of a strong teacher model. We ask if instead of learning
from a static teacher, can models "learn together" and "teach each other" to
achieve better robustness? In this paper, we study how interactions among
models affect robustness via knowledge distillation. We propose mutual
adversarial training (MAT), in which multiple models are trained together and
share the knowledge of adversarial examples to achieve improved robustness. MAT
allows robust models to explore a larger space of adversarial samples, and find
more robust feature spaces and decision boundaries. Through extensive
experiments on CIFAR-10 and CIFAR-100, we demonstrate that MAT can effectively
improve model robustness and outperform state-of-the-art methods under
white-box attacks, bringing $\sim$8% accuracy gain to vanilla adversarial
training (AT) under PGD-100 attacks. In addition, we show that MAT can also
mitigate the robustness trade-off among different perturbation types, bringing
as much as 13.1% accuracy gain to AT baselines against the union of $l_\infty$,
$l_2$ and $l_1$ attacks. These results show the superiority of the proposed
method and demonstrate that collaborative learning is an effective strategy for
designing robust models.

Wikidata is the largest general-interest knowledge base that is openly
available. It is collaboratively edited by thousands of volunteer editors and
has thus evolved considerably since its inception in 2012. In this paper, we
present Wikidated 1.0, a dataset of Wikidata's full revision history, which
encodes changes between Wikidata revisions as sets of deletions and additions
of RDF triples. To the best of our knowledge, it constitutes the first large
dataset of an evolving knowledge graph, a recently emerging research subject in
the Semantic Web community. We introduce the methodology for generating
Wikidated 1.0 from dumps of Wikidata, discuss its implementation and
limitations, and present statistical characteristics of the dataset.

Uncertainty estimation (UE) techniques -- such as the Gaussian process (GP),
Bayesian neural networks (BNN), Monte Carlo dropout (MCDropout) -- aim to
improve the interpretability of machine learning models by assigning an
estimated uncertainty value to each of their prediction outputs. However, since
too high uncertainty estimates can have fatal consequences in practice, this
paper analyzes the above techniques.
  Firstly, we show that GP methods always yield high uncertainty estimates on
out of distribution (OOD) data. Secondly, we show on a 2D toy example that both
BNNs and MCDropout do not give high uncertainty estimates on OOD samples.
Finally, we show empirically that this pitfall of BNNs and MCDropout holds on
real world datasets as well. Our insights (i) raise awareness for the more
cautious use of currently popular UE methods in Deep Learning, (ii) encourage
the development of UE methods that approximate GP-based methods -- instead of
BNNs and MCDropout, and (iii) our empirical setups can be used for verifying
the OOD performances of any other UE method. The source code is available at
https://github.com/epfml/uncertainity-estimation.

Data sparsity problem is a key challenge of Natural Language Understanding
(NLU), especially for a new target domain. By training an NLU model in source
domains and applying the model to an arbitrary target domain directly (even
without fine-tuning), few-shot NLU becomes crucial to mitigate the data
scarcity issue. In this paper, we propose to improve prototypical networks with
vector projection distance and abstract triangular Conditional Random Field
(CRF) for the few-shot NLU. The vector projection distance exploits projections
of contextual word embeddings on label vectors as word-label similarities,
which is equivalent to a normalized linear model. The abstract triangular CRF
learns domain-agnostic label transitions for joint intent classification and
slot filling tasks. Extensive experiments demonstrate that our proposed methods
can significantly surpass strong baselines. Specifically, our approach can
achieve a new state-of-the-art on two few-shot NLU benchmarks (Few-Joint and
SNIPS) in Chinese and English without fine-tuning on target domains.

Deep learning-based diagnostic performance increases with more annotated
data, but large-scale manual annotations are expensive and labour-intensive.
Experts evaluate diagnostic images during clinical routine, and write their
findings in reports. Leveraging unlabelled exams paired with clinical reports
could overcome the manual labelling bottleneck. We hypothesise that detection
models can be trained semi-supervised with automatic annotations generated
using model predictions, guided by sparse information from clinical reports. To
demonstrate efficacy, we train clinically significant prostate cancer (csPCa)
segmentation models, where automatic annotations are guided by the number of
clinically significant findings in the radiology reports. We included 7,756
prostate MRI examinations, of which 3,050 were manually annotated. We evaluated
prostate cancer detection performance on 300 exams from an external centre with
histopathology-confirmed ground truth. Semi-supervised training improved
patient-based diagnostic area under the receiver operating characteristic curve
from $87.2 \pm 0.8\%$ to $89.4 \pm 1.0\%$ ($P<10^{-4}$) and improved
lesion-based sensitivity at one false positive per case from $76.4 \pm 3.8\%$
to $83.6 \pm 2.3\%$ ($P<10^{-4}$). Semi-supervised training was 14$\times$ more
annotation-efficient for case-based performance and 6$\times$ more
annotation-efficient for lesion-based performance. This improved performance
demonstrates the feasibility of our training procedure. Source code is publicly
available at github.com/DIAGNijmegen/Report-Guided-Annotation. Best csPCa
detection algorithm is available at
grand-challenge.org/algorithms/bpmri-cspca-detection-report-guided-annotations/.

The world is currently experiencing an ongoing pandemic of an infectious
disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed
Tomography (CT) plays an important role in assessing the severity of the
infection and can also be used to identify those symptomatic and asymptomatic
COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients,
radiologists are increasingly stressed to examine the CT scans manually.
Therefore, an automated 3D CT scan recognition tool is highly in demand since
the manual analysis is time-consuming for radiologists and their fatigue can
cause possible misjudgment. However, due to various technical specifications of
CT scanners located in different hospitals, the appearance of CT images can be
significantly different leading to the failure of many automated image
recognition approaches. The multi-domain shift problem for the multi-center and
multi-scanner studies is therefore nontrivial that is also crucial for a
dependable recognition and critical for reproducible and objective diagnosis
and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model
namely coronavirus information fusion and diagnosis network (CIFD-Net) that can
efficiently handle the multi-domain shift problem via a new robust weakly
supervised learning paradigm. Our model can resolve the problem of different
appearance in CT scan images reliably and efficiently while attaining higher
accuracy compared to other state-of-the-art methods.

Vision transformer architectures have been demonstrated to work very
effectively for image classification tasks. Efforts to solve more challenging
vision tasks with transformers rely on convolutional backbones for feature
extraction. In this paper we investigate the use of a pure transformer
architecture (i.e., one with no CNN backbone) for the problem of 2D body pose
estimation. We evaluate two ViT architectures on the COCO dataset. We
demonstrate that using an encoder-decoder transformer architecture yields state
of the art results on this estimation problem.

Fluid flows are omnipresent in nature and engineering disciplines. The
reliable computation of fluids has been a long-lasting challenge due to
nonlinear interactions over multiple spatio-temporal scales. The compressible
Navier-Stokes equations govern compressible flows and allow for complex
phenomena like turbulence and shocks. Despite tremendous progress in hardware
and software, capturing the smallest length-scales in fluid flows still
introduces prohibitive computational cost for real-life applications. We are
currently witnessing a paradigm shift towards machine learning supported design
of numerical schemes as a means to tackle aforementioned problem. While prior
work has explored differentiable algorithms for one- or two-dimensional
incompressible fluid flows, we present a fully-differentiable three-dimensional
framework for the computation of compressible fluid flows using high-order
state-of-the-art numerical methods. Firstly, we demonstrate the efficiency of
our solver by computing classical two- and three-dimensional test cases,
including strong shocks and transition to turbulence. Secondly, and more
importantly, our framework allows for end-to-end optimization to improve
existing numerical schemes inside computational fluid dynamics algorithms. In
particular, we are using neural networks to substitute a conventional numerical
flux function.

Artificial intelligence has not yet revolutionized the design of materials
and molecules. In this perspective, we identify four barriers preventing the
integration of atomistic deep learning, molecular science, and high-performance
computing. We outline focused research efforts to address the opportunities
presented by these challenges.

This work presents an initial proof of concept of how Music Emotion
Recognition (MER) systems could be intentionally biased with respect to
annotations of musically induced emotions in a political context. In specific,
we analyze traditional Colombian music containing politically charged lyrics of
two types: (1) vallenatos and social songs from the "left-wing" guerrilla
Fuerzas Armadas Revolucionarias de Colombia (FARC) and (2) corridos from the
"right-wing" paramilitaries Autodefensas Unidas de Colombia (AUC). We train
personalized machine learning models to predict induced emotions for three
users with diverse political views - we aim at identifying the songs that may
induce negative emotions for a particular user, such as anger and fear. To this
extent, a user's emotion judgements could be interpreted as problematizing data
- subjective emotional judgments could in turn be used to influence the user in
a human-centered machine learning environment. In short, highly desired
"emotion regulation" applications could potentially deviate to "emotion
manipulation" - the recent discredit of emotion recognition technologies might
transcend ethical issues of diversity and inclusion.

To improve instance-level detection/segmentation performance, existing
self-supervised and semi-supervised methods extract either task-unrelated or
task-specific training signals from unlabeled data. We show that these two
approaches, at the two extreme ends of the task-specificity spectrum, are
suboptimal for the task performance. Utilizing too little task-specific
training signals causes underfitting to the ground-truth labels of downstream
tasks, while the opposite causes overfitting to the ground-truth labels. To
this end, we propose a novel Class-Agnostic Semi-Supervised Learning (CA-SSL)
framework to achieve a more favorable task-specificity balance in extracting
training signals from unlabeled data. CA-SSL has three training stages that act
on either ground-truth labels (labeled data) or pseudo labels (unlabeled data).
This decoupling strategy avoids the complicated scheme in traditional SSL
methods that balances the contributions from both data types. Especially, we
introduce a warmup training stage to achieve a more optimal balance in task
specificity by ignoring class information in the pseudo labels, while
preserving localization training signals. As a result, our warmup model can
better avoid underfitting/overfitting when fine-tuned on the ground-truth
labels in detection and segmentation tasks. Using 3.6M unlabeled data, we
achieve a significant performance gain of 4.7% over ImageNet-pretrained
baseline on FCOS object detection. In addition, our warmup model demonstrates
excellent transferability to other detection and segmentation frameworks.

Numerical weather prediction (NWP) and machine learning (ML) methods are
popular for solar forecasting. However, NWP models have multiple possible
physical parameterizations, which requires site-specific NWP optimization. This
is further complicated when regional NWP models are used with global climate
models with different possible parameterizations. In this study, an alternative
approach is proposed and evaluated for four radiation models. Weather Research
and Forecasting (WRF) model is run in both global and regional mode to provide
an estimate for solar irradiance. This estimate is then post-processed using ML
to provide a final prediction. Normalized root-mean-square error from WRF is
reduced by up to 40-50% with this ML error correction model. Results obtained
using CAM, GFDL, New Goddard and RRTMG radiation models were comparable after
this correction, negating the need for WRF parameterization tuning. Other
models incorporating nearby locations and sensor data are also evaluated, with
the latter being particularly promising.

We present mechanoChemML, a machine learning software library for
computational materials physics. mechanoChemML is designed to function as an
interface between platforms that are widely used for machine learning on one
hand, and others for solution of partial differential equations-based models of
physics. Of special interest here, and the focus of mechanoChemML, are
applications to computational materials physics. These typically feature the
coupled solution of material transport, reaction, phase transformation,
mechanics, heat transport and electrochemistry. Central to the organization of
mechanoChemML are machine learning workflows that arise in the context of
data-driven computational materials physics. The mechanoChemML code structure
is described, the machine learning workflows are laid out and their application
to the solution of several problems in materials physics is outlined.

The lack of evidence for new interactions and particles at the Large Hadron
Collider has motivated the high-energy physics community to explore
model-agnostic data-analysis approaches to search for new physics. Autoencoders
are unsupervised machine learning models based on artificial neural networks,
capable of learning background distributions. We study quantum autoencoders
based on variational quantum circuits for the problem of anomaly detection at
the LHC. For a QCD $t\bar{t}$ background and resonant heavy Higgs signals, we
find that a simple quantum autoencoder outperforms classical autoencoders for
the same inputs and trains very efficiently. Moreover, this performance is
reproducible on present quantum devices. This shows that quantum autoencoders
are good candidates for analysing high-energy physics data in future LHC runs.

Automated persuasion systems (APS) aim to persuade a user to believe
something by entering into a dialogue in which arguments and counterarguments
are exchanged. To maximize the probability that an APS is successful in
persuading a user, it can identify a global policy that will allow it to select
the best arguments it presents at each stage of the dialogue whatever arguments
the user presents. However, in real applications, such as for healthcare, it is
unlikely the utility of the outcome of the dialogue will be the same, or the
exact opposite, for the APS and user. In order to deal with this situation,
games in extended form have been harnessed for argumentation in Bi-party
Decision Theory. This opens new problems that we address in this paper: (1) How
can we use Machine Learning (ML) methods to predict utility functions for
different subpopulations of users? and (2) How can we identify for a new user
the best utility function from amongst those that we have learned? To this
extent, we develop two ML methods, EAI and EDS, that leverage information
coming from the users to predict their utilities. EAI is restricted to a fixed
amount of information, whereas EDS can choose the information that best detects
the subpopulations of a user. We evaluate EAI and EDS in a simulation setting
and in a realistic case study concerning healthy eating habits. Results are
promising in both cases, but EDS is more effective at predicting useful utility

We use the machine learning technique to search the polytope which can result
in an orientifold Calabi-Yau hypersurface and the "naive Type IIB string
vacua". We show that neural networks can be trained to give a high accuracy for
classifying the orientifold property and vacua based on the newly generated
orientifold Calabi-Yau database with $h^{1,1}(X) \leq 6$ arXiv:2111.03078. This
indicates the orientifold symmetry may already be encoded in the polytope
structure. In the end, we try to use the trained neural networks model to go
beyond the database and predict the orientifold signal of polytope for higher
$h^{1,1}(X)$.

The security of Deep Learning classifiers is a critical field of study
because of the existence of adversarial attacks. Such attacks usually rely on
the principle of transferability, where an adversarial example crafted on a
surrogate classifier tends to mislead the target classifier trained on the same
dataset even if both classifiers have quite different architecture. Ensemble
methods against adversarial attacks demonstrate that an adversarial example is
less likely to mislead multiple classifiers in an ensemble having diverse
decision boundaries. However, recent ensemble methods have either been shown to
be vulnerable to stronger adversaries or shown to lack an end-to-end
evaluation. This paper attempts to develop a new ensemble methodology that
constructs multiple diverse classifiers using a Pairwise Adversarially Robust
Loss (PARL) function during the training procedure. PARL utilizes gradients of
each layer with respect to input in every classifier within the ensemble
simultaneously. The proposed training procedure enables PARL to achieve higher
robustness against black-box transfer attacks compared to previous ensemble
methods without adversely affecting the accuracy of clean examples. We also
evaluate the robustness in the presence of white-box attacks, where adversarial
examples are crafted using parameters of the target classifier. We present
extensive experiments using standard image classification datasets like
CIFAR-10 and CIFAR-100 trained using standard ResNet20 classifier against
proposed ensemble methodology.

The prosperous development of cloud computing and machine learning as a
service has led to the widespread use of media software to process confidential
media data. This paper explores an adversary's ability to launch side channel
analyses (SCA) against media software to reconstruct confidential media inputs.
Recent advances in representation learning and perceptual learning inspired us
to consider the reconstruction of media inputs from side channel traces as a
cross-modality manifold learning task that can be addressed in a unified manner
with an autoencoder framework trained to learn the mapping between media inputs
and side channel observations. We further enhance the autoencoder with
attention to localize the program points that make the primary contribution to
SCA, thus automatically pinpointing information-leakage points in media
software. We also propose a novel and highly effective defensive technique
called perception blinding that can perturb media inputs with perception masks
and mitigate manifold learning-based SCA.
  Our evaluation exploits three popular media software to reconstruct inputs in
image, audio, and text formats. We analyze three common side channels - cache
bank, cache line, and page tables - and userspace-only cache set accesses
logged by standard Prime+Probe. Our framework successfully reconstructs
high-quality confidential inputs from the assessed media software and
automatically pinpoint their vulnerable program points, many of which are
unknown to the public. We further show that perception blinding can mitigate
manifold learning-based SCA with negligible extra cost.

Probabilistic circuits (PCs) are a powerful modeling framework for
representing tractable probability distributions over combinatorial spaces. In
machine learning and probabilistic programming, one is often interested in
understanding whether the distributions learned using PCs are close to the
desired distribution. Thus, given two probabilistic circuits, a fundamental
problem of interest is to determine whether their distributions are close to
  The primary contribution of this paper is a closeness test for PCs with
respect to the total variation distance metric. Our algorithm utilizes two
common PC queries, counting and sampling. In particular, we provide a poly-time
probabilistic algorithm to check the closeness of two PCs when the PCs support
tractable approximate counting and sampling. We demonstrate the practical
efficiency of our algorithmic framework via a detailed experimental evaluation
of a prototype implementation against a set of 475 PC benchmarks. We find that
our test correctly decides the closeness of all 475 PCs within 3600 seconds.

Tagging based relational triple extraction methods are attracting growing
research attention recently. However, most of these methods take a
unidirectional extraction framework that first extracts all subjects and then
extracts objects and relations simultaneously based on the subjects extracted.
This framework has an obvious deficiency that it is too sensitive to the
extraction results of subjects. To overcome this deficiency, we propose a
bidirectional extraction framework based method that extracts triples based on
the entity pairs extracted from two complementary directions. Concretely, we
first extract all possible subject-object pairs from two paralleled directions.
These two extraction directions are connected by a shared encoder component,
thus the extraction features from one direction can flow to another direction
and vice versa. By this way, the extractions of two directions can boost and
complement each other. Next, we assign all possible relations for each entity
pair by a biaffine model. During training, we observe that the share structure
will lead to a convergence rate inconsistency issue which is harmful to
performance. So we propose a share-aware learning mechanism to address it. We
evaluate the proposed model on multiple benchmark datasets. Extensive
experimental results show that the proposed model is very effective and it
achieves state-of-the-art results on all of these datasets. Moreover,
experiments show that both the proposed bidirectional extraction framework and
the share-aware learning mechanism have good adaptability and can be used to
improve the performance of other tagging based methods. The source code of our
work is available at: https://github.com/neukg/BiRTE.

Deep learning-based speech enhancement has shown unprecedented performance in
recent years. The most popular mono speech enhancement frameworks are
end-to-end networks mapping the noisy mixture into an estimate of the clean
speech. With growing computational power and availability of multichannel
microphone recordings, prior works have aimed to incorporate spatial statistics
along with spectral information to boost up performance. Despite an improvement
in enhancement performance of mono output, the spatial image preservation and
subjective evaluations have not gained much attention in the literature. This
paper proposes a novel stereo-aware framework for speech enhancement, i.e., a
training loss for deep learning-based speech enhancement to preserve the
spatial image while enhancing the stereo mixture. The proposed framework is
model independent, hence it can be applied to any deep learning based
architecture. We provide an extensive objective and subjective evaluation of
the trained models through a listening test. We show that by regularizing for
an image preservation loss, the overall performance is improved, and the stereo
aspect of the speech is better preserved.

In this paper, we make the very first attempt to investigate the integration
of deep hash learning with vehicle re-identification. We propose a deep
hash-based vehicle re-identification framework, dubbed DVHN, which
substantially reduces memory usage and promotes retrieval efficiency while
reserving nearest neighbor search accuracy. Concretely,~DVHN directly learns
discrete compact binary hash codes for each image by jointly optimizing the
feature learning network and the hash code generating module. Specifically, we
directly constrain the output from the convolutional neural network to be
discrete binary codes and ensure the learned binary codes are optimal for
classification. To optimize the deep discrete hashing framework, we further
propose an alternating minimization method for learning binary
similarity-preserved hashing codes. Extensive experiments on two widely-studied
vehicle re-identification datasets- \textbf{VehicleID} and \textbf{VeRi}-~have
demonstrated the superiority of our method against the state-of-the-art deep
hash methods. \textbf{DVHN} of $2048$ bits can achieve 13.94\% and 10.21\%
accuracy improvement in terms of \textbf{mAP} and \textbf{Rank@1} for
\textbf{VehicleID (800)} dataset. For \textbf{VeRi}, we achieve 35.45\% and
32.72\% performance gains for \textbf{Rank@1} and \textbf{mAP}, respectively.

Recently, Convolutional Neural Network (CNN) has achieved excellent
performance in the classification task. It is widely known that CNN is deemed
as a 'black-box', which is hard for understanding the prediction mechanism and
debugging the wrong prediction. Some model debugging and explanation works are
developed for solving the above drawbacks. However, those methods focus on
explanation and diagnosing possible causes for model prediction, based on which
the researchers handle the following optimization of models manually. In this
paper, we propose the first completely automatic model diagnosing and treating
tool, termed as Model Doctor. Based on two discoveries that 1) each category is
only correlated with sparse and specific convolution kernels, and 2)
adversarial samples are isolated while normal samples are successive in the
feature space, a simple aggregate gradient constraint is devised for
effectively diagnosing and optimizing CNN classifiers. The aggregate gradient
strategy is a versatile module for mainstream CNN classifiers. Extensive
experiments demonstrate that the proposed Model Doctor applies to all existing
CNN classifiers, and improves the accuracy of $16$ mainstream CNN classifiers
by 1%-5%.

Time series processing is an essential aspect of wind turbine health
monitoring. Despite the progress in this field, there is still room for new
methods to improve modeling quality. In this paper, we propose two new
approaches for the analysis of wind turbine health. Both approaches are based
on abstract concepts, implemented using fuzzy sets, which summarize and
aggregate the underlying raw data. By observing the change in concepts, we
infer about the change in the turbine's health. Analyzes are carried out
separately for different external conditions (wind speed and temperature). We
extract concepts that represent relative low, moderate, and high power
production. The first method aims at evaluating the decrease or increase in
relatively high and low power production. This task is performed using a
regression-like model. The second method evaluates the overall drift of the
extracted concepts. Large drift indicates that the power production process
undergoes fluctuations in time. Concepts are labeled using linguistic labels,
thus equipping our model with improved interpretability features. We applied
the proposed approach to process publicly available data describing four wind
turbines. The simulation results have shown that the aging process is not
homogeneous in all wind turbines.

A comprehensive understanding of vision and language and their interrelation
are crucial to realize the underlying similarities and differences between
these modalities and to learn more generalized, meaningful representations. In
recent years, most of the works related to Text-to-Image synthesis and
Image-to-Text generation, focused on supervised generative deep architectures
to solve the problems, where very little interest was placed on learning the
similarities between the embedding spaces across modalities. In this paper, we
propose a novel self-supervised deep learning based approach towards learning
the cross-modal embedding spaces; for both image to text and text to image
generations. In our approach, we first obtain dense vector representations of
images using StackGAN-based autoencoder model and also dense vector
representations on sentence-level utilizing LSTM based text-autoencoder; then
we study the mapping from embedding space of one modality to embedding space of
the other modality utilizing GAN and maximum mean discrepancy based generative
networks. We, also demonstrate that our model learns to generate textual
description from image data as well as images from textual data both

Nesterov's accelerated gradient method (NAG) is widely used in problems with
machine learning background including deep learning, and is corresponding to a
continuous-time differential equation. From this connection, the property of
the differential equation and its numerical approximation can be investigated
to improve the accelerated gradient method. In this work we present a new
improvement of NAG in terms of stability inspired by numerical analysis. We
give the precise order of NAG as a numerical approximation of its
continuous-time limit and then present a new method with higher order. We show
theoretically that our new method is more stable than NAG for large step size.
Experiments of matrix completion and handwriting digit recognition demonstrate
that the stability of our new method is better. Furthermore, better stability
leads to higher computational speed in experiments.

We present the results of a time-coincident event search for low-energy
electron antineutrinos in the KamLAND detector with gamma-ray bursts from the
Gamma-ray Coordinates Network and Fermi Gamma-ray Burst Monitor. Using a
variable coincidence time window of $\pm$500s plus the duration of each
gamma-ray burst, no statistically significant excess above background is
observed. We place the world's most stringent 90% confidence level upper limit
on the electron antineutrino fluence below 17.5 MeV. Assuming a Fermi-Dirac
neutrino energy spectrum from the gamma-ray burst source, we use the available
redshift data to constrain the electron antineutrino luminosity and effective

Learning intra-region contexts and inter-region relations are two effective
strategies to strengthen feature representations for point cloud analysis.
However, unifying the two strategies for point cloud representation is not
fully emphasized in existing methods. To this end, we propose a novel framework
named Point Relation-Aware Network (PRA-Net), which is composed of an
Intra-region Structure Learning (ISL) module and an Inter-region Relation
Learning (IRL) module. The ISL module can dynamically integrate the local
structural information into the point features, while the IRL module captures
inter-region relations adaptively and efficiently via a differentiable region
partition scheme and a representative point-based strategy. Extensive
experiments on several 3D benchmarks covering shape classification, keypoint
estimation, and part segmentation have verified the effectiveness and the
generalization ability of PRA-Net. Code will be available at
https://github.com/XiwuChen/PRA-Net .

The success of deep neural nets heavily relies on their ability to encode
complex relations between their input and their output. While this property
serves to fit the training data well, it also obscures the mechanism that
drives prediction. This study aims to reveal hidden concepts by employing an
intervention mechanism that shifts the predicted class based on discrete
variational autoencoders. An explanatory model then visualizes the encoded
information from any hidden layer and its corresponding intervened
representation. By the assessment of differences between the original
representation and the intervened representation, one can determine the
concepts that can alter the class, hence providing interpretability. We
demonstrate the effectiveness of our approach on CelebA, where we show various
visualizations for bias in the data and suggest different interventions to
reveal and change bias.

Recently, deep learning with Convolutional Neural Networks (CNNs) and
Transformers has shown encouraging results in fully supervised medical image
segmentation. However, it is still challenging for them to achieve good
performance with limited annotations for training. In this work, we present a
very simple yet efficient framework for semi-supervised medical image
segmentation by introducing the cross teaching between CNN and Transformer.
Specifically, we simplify the classical deep co-training from consistency
regularization to cross teaching, where the prediction of a network is used as
the pseudo label to supervise the other network directly end-to-end.
Considering the difference in learning paradigm between CNN and Transformer, we
introduce the Cross Teaching between CNN and Transformer rather than just using
CNNs. Experiments on a public benchmark show that our method outperforms eight
existing semi-supervised learning methods just with a simpler framework.
Notably, this work may be the first attempt to combine CNN and transformer for
semi-supervised medical image segmentation and achieve promising results on a
public benchmark. The code will be released at:
https://github.com/HiLab-git/SSL4MIS.

In this paper, we consider the forecast evaluation of realized volatility
measures under cross-section dependence using equal predictive accuracy testing
procedures. We evaluate the predictive accuracy of the model based on the
augmented cross-section when forecasting Realized Volatility. Under the null
hypothesis of equal predictive accuracy the benchmark model employed is a
standard HAR model while under the alternative of non-equal predictive accuracy
the forecast model is an augmented HAR model estimated via the LASSO shrinkage.
We study the sensitivity of forecasts to the model specification by
incorporating a measurement error correction as well as cross-sectional jump
component measures. The out-of-sample forecast evaluation of the models is
assessed with numerical implementations.

Over the last years, many 'explainable artificial intelligence' (xAI)
approaches have been developed, but these have not always been objectively
evaluated. To evaluate the quality of heatmaps generated by various saliency
methods, we developed a framework to generate artificial data with synthetic
lesions and a known ground truth map. Using this framework, we evaluated two
data sets with different backgrounds, Perlin noise and 2D brain MRI slices, and
found that the heatmaps vary strongly between saliency methods and backgrounds.
We strongly encourage further evaluation of saliency maps and xAI methods using
this framework before applying these in clinical or other safety-critical
settings.

Learning the embeddings of knowledge graphs (KG) is vital in artificial
intelligence, and can benefit various downstream applications, such as
recommendation and question answering. In recent years, many research efforts
have been proposed for knowledge graph embedding (KGE). However, most previous
KGE methods ignore the semantic similarity between the related entities and
entity-relation couples in different triples since they separately optimize
each triple with the scoring function. To address this problem, we propose a
simple yet efficient contrastive learning framework for tensor decomposition
based (TDB) KGE, which can shorten the semantic distance of the related
entities and entity-relation couples in different triples and thus improve the
performance of KGE. We evaluate our proposed method on three standard KGE
datasets: WN18RR, FB15k-237 and YAGO3-10. Our method can yield some new
state-of-the-art results, achieving 51.2% MRR, 46.8% Hits@1 on the WN18RR
dataset, 37.8% MRR, 28.6% Hits@1 on FB15k-237 dataset, and 59.1% MRR, 51.8%
Hits@1 on the YAGO3-10 dataset.

General point clouds have been increasingly investigated for different tasks,
and recently Transformer-based networks are proposed for point cloud analysis.
However, there are barely related works for medical point clouds, which are
important for disease detection and treatment. In this work, we propose an
attention-based model specifically for medical point clouds, namely 3D medical
point Transformer (3DMedPT), to examine the complex biological structures. By
augmenting contextual information and summarizing local responses at query, our
attention module can capture both local context and global content feature
interactions. However, the insufficient training samples of medical data may
lead to poor feature learning, so we apply position embeddings to learn
accurate local geometry and Multi-Graph Reasoning (MGR) to examine global
knowledge propagation over channel graphs to enrich feature representations.
Experiments conducted on IntrA dataset proves the superiority of 3DMedPT, where
we achieve the best classification and segmentation results. Furthermore, the
promising generalization ability of our method is validated on general 3D point
cloud benchmarks: ModelNet40 and ShapeNetPart. Code is released.

We propose UWash, an intelligent solution upon smartwatches, to assess
handwashing for the purpose of raising users' awareness and cultivating habits
in high-quality handwashing. UWash can identify the onset/offset of
handwashing, measure the duration of each gesture, and score each gesture as
well as the entire procedure in accordance with the WHO guidelines.
Technically, we address the task of handwashing assessment as the semantic
segmentation problem in computer vision, and propose a lightweight UNet-like
network, only 496KBits, to achieve it effectively. Experiments over 51 subjects
show that UWash achieves the accuracy of 92.27\% on sample-wise handwashing
gesture recognition, $<$0.5 \textit{seconds} error in onset/offset detection,
and $<$5 out of 100 \textit{points} error in scoring in the user-dependent
setting, while remains promising in the cross-user evaluation and in the
cross-user-cross-location evaluation.

Recent work [4] analyses the local convergence of Adam in a neighbourhood of
an optimal solution for a twice-differentiable function. It is found that the
learning rate has to be sufficiently small to ensure local stability of the
optimal solution. The above convergence results also hold for AdamW. In this
work, we propose a new adaptive optimisation method by extending AdamW in two
aspects with the purpose to relax the requirement on small learning rate for
local stability, which we refer to as Aida. Firstly, we consider tracking the
2nd moment r_t of the pth power of the gradient-magnitudes. r_t reduces to v_t
of AdamW when p=2. Suppose {m_t} is the first moment of AdamW. It is known that
the update direction m_{t+1}/(v_{t+1}+epsilon)^0.5 (or
m_{t+1}/(v_{t+1}^0.5+epsilon) of AdamW (or Adam) can be decomposed as the sign
vector sign(m_{t+1}) multiplied elementwise by a vector of magnitudes
|m_{t+1}|/(v_{t+1}+epsilon)^0.5 (or |m_{t+1}|/(v_{t+1}^0.5+epsilon)). Aida is
designed to compute the qth power of the magnitude in the form of
|m_{t+1}|^q/(r_{t+1}+epsilon)^(q/p) (or |m_{t+1}|^q/((r_{t+1})^(q/p)+epsilon)),
which reduces to that of AdamW when (p,q)=(2,1).
  Suppose the origin 0 is a local optimal solution of a twice-differentiable
function. It is found theoretically that when q>1 and p>1 in Aida, the origin 0
is locally stable only when the weight-decay is non-zero. Experiments are
conducted for solving ten toy optimisation problems and training Transformer
and Swin-Transformer for two deep learning (DL) tasks. The empirical study
demonstrates that in a number of scenarios (including the two DL tasks), Aida
with particular setups of (p,q) not equal to (2,1) outperforms the setup
(p,q)=(2,1) of AdamW.

While recently many designs have been proposed to improve the model
efficiency of convolutional neural networks (CNNs) on a fixed resource budget,
theoretical understanding of these designs is still conspicuously lacking. This
paper aims to provide a new framework for answering the question: Is there
still any remaining model redundancy in a compressed CNN? We begin by
developing a general statistical formulation of CNNs and compressed CNNs via
the tensor decomposition, such that the weights across layers can be summarized
into a single tensor. Then, through a rigorous sample complexity analysis, we
reveal an important discrepancy between the derived sample complexity and the
naive parameter counting, which serves as a direct indicator of the model
redundancy. Motivated by this finding, we introduce a new model redundancy
measure for compressed CNNs, called the $K/R$ ratio, which further allows for
nonlinear activations. The usefulness of this new measure is supported by
ablation studies on popular block designs and datasets.

Graph representation learning (GRL) on attribute-missing graphs, which is a
common yet challenging problem, has recently attracted considerable attention.
We observe that existing literature: 1) isolates the learning of attribute and
structure embedding thus fails to take full advantages of the two types of
information; 2) imposes too strict distribution assumption on the latent space
variables, leading to less discriminative feature representations. In this
paper, based on the idea of introducing intimate information interaction
between the two information sources, we propose our Siamese Attribute-missing
Graph Auto-encoder (SAGA). Specifically, three strategies have been conducted.
First, we entangle the attribute embedding and structure embedding by
introducing a siamese network structure to share the parameters learned by both
processes, which allows the network training to benefit from more abundant and
diverse information. Second, we introduce a K-nearest neighbor (KNN) and
structural constraint enhanced learning mechanism to improve the quality of
latent features of the missing attributes by filtering unreliable connections.
Third, we manually mask the connections on multiple adjacent matrices and force
the structural information embedding sub-network to recover the true adjacent
matrix, thus enforcing the resulting network to be able to selectively exploit
more high-order discriminative features for data completion. Extensive
experiments on six benchmark datasets demonstrate the superiority of our SAGA
against the state-of-the-art methods.

For personalized ranking models, the well-calibrated probability of an item
being preferred by a user has great practical value. While existing work shows
promising results in image classification, probability calibration has not been
much explored for personalized ranking. In this paper, we aim to estimate the
calibrated probability of how likely a user will prefer an item. We investigate
various parametric distributions and propose two parametric calibration
methods, namely Gaussian calibration and Gamma calibration. Each proposed
method can be seen as a post-processing function that maps the ranking scores
of pre-trained models to well-calibrated preference probabilities, without
affecting the recommendation performance. We also design the unbiased empirical
risk minimization framework that guides the calibration methods to learning of
true preference probability from the biased user-item interaction dataset.
Extensive evaluations with various personalized ranking models on real-world
datasets show that both the proposed calibration methods and the unbiased
empirical risk minimization significantly improve the calibration performance.

In this paper we consider how to evaluate survival distribution predictions
with measures of discrimination. This is a non-trivial problem as
discrimination measures are the most commonly used in survival analysis and yet
there is no clear method to derive a risk prediction from a distribution
prediction. We survey methods proposed in literature and software and consider
their respective advantages and disadvantages. Whilst distributions are
frequently evaluated by discrimination measures, we find that the method for
doing so is rarely described in the literature and often leads to unfair
comparisons. We find that the most robust method of reducing a distribution to
a risk is to sum over the predicted cumulative hazard. We recommend that
machine learning survival analysis software implements clear transformations
between distribution and risk predictions in order to allow more transparent
and accessible model evaluation. The code used in the final experiment is
available at https://github.com/RaphaelS1/distribution_discrimination.

Protein-protein interactions (PPIs) are essentials for many biological
processes where two or more proteins physically bind together to achieve their
functions. Modeling PPIs is useful for many biomedical applications, such as
vaccine design, antibody therapeutics, and peptide drug discovery. Pre-training
a protein model to learn effective representation is critical for PPIs. Most
pre-training models for PPIs are sequence-based, which naively adopt the
language models used in natural language processing to amino acid sequences.
More advanced works utilize the structure-aware pre-training technique, taking
advantage of the contact maps of known protein structures. However, neither
sequences nor contact maps can fully characterize structures and functions of
the proteins, which are closely related to the PPI problem. Inspired by this
insight, we propose a multimodal protein pre-training model with three
modalities: sequence, structure, and function (S2F). Notably, instead of using
contact maps to learn the amino acid-level rigid structures, we encode the
structure feature with the topology complex of point clouds of heavy atoms. It
allows our model to learn structural information about not only the backbones
but also the side chains. Moreover, our model incorporates the knowledge from
the functional description of proteins extracted from literature or manual
annotations. Our experiments show that the S2F learns protein embeddings that
achieve good performances on a variety of PPIs tasks, including cross-species
PPI, antibody-antigen affinity prediction, antibody neutralization prediction
for SARS-CoV-2, and mutation-driven binding affinity change prediction.

limitation by learning a latent space capturing the key stance phases of a
particular gait, via a generative model trained on a single trot style. This
encourages disentanglement such that application of a drive signal to a single
dimension of the latent state induces holistic plans synthesising a continuous
variety of trot styles. In fact properties of this drive signal map directly to
gait parameters such as cadence, footstep height and full stance duration. The
use of a generative model facilitates the detection and mitigation of
disturbances to provide a versatile and robust planning framework. We evaluate
our approach on a real ANYmal quadruped robot and demonstrate that our method
achieves a continuous blend of dynamic trot styles whilst being robust and
reactive to external perturbations.

Making statements about the performance of trained models on tasks involving
new data is one of the primary goals of machine learning, i.e., to understand
the generalization power of a model. Various capacity measures try to capture
this ability, but usually fall short in explaining important characteristics of
models that we observe in practice. In this study, we propose the local
effective dimension as a capacity measure which seems to correlate well with
generalization error on standard data sets. Importantly, we prove that the
local effective dimension bounds the generalization error and discuss the
aptness of this capacity measure for machine learning models.

Automotive companies are increasingly looking for ways to make their products
lighter, using novel materials and novel bonding processes to join these
materials together. Finding the optimal process parameters for such adhesive
bonding process is challenging. In this research, we successfully applied
Bayesian optimization using Gaussian Process Regression and Logistic
Regression, to efficiently (i.e., requiring few experiments) guide the design
of experiments to the Pareto-optimal process parameter settings.

The detection of offensive, hateful and profane language has become a
critical challenge since many users in social networks are exposed to
cyberbullying activities on a daily basis. In this paper, we present an
analysis of combining different textual features for the detection of hateful
or offensive posts on Twitter. We provide a detailed experimental evaluation to
understand the impact of each building block in a neural network architecture.
The proposed architecture is evaluated on the English Subtask 1A: Identifying
Hate, offensive and profane content from the post datasets of HASOC-2021
dataset under the team name TIB-VA. We compared different variants of the
contextual word embeddings combined with the character level embeddings and the
encoding of collected hate terms.

Choosing an appropriate programming paradigm for high-performance computing
on low-power devices can be useful to speed up calculations. Many Android
devices have an integrated GPU and - although not officially supported - the
OpenCL framework can be used on Android devices for addressing these GPUs.
OpenCL supports thread and data parallelism. Applications that use the GPU must
account for the fact that they can be suspended by the user or the Android
operating system at any moment. We have created a wrapper library that allows
to use OpenCL on Android devices. Already written OpenCL programs can be
executed with almost no modification. We have used this library to compare the
performance of the DBSCAN and Kmeans algorithms on an integrated GPU of an
Arm-v7 tablet with other single and multithreaded implementations on the same
device. We have investigated which programming paradigm and language allows the
best tradeoff between execution speed and energy consumption. Using the GPU for
HPC on Android devices can help to carry out computationally intensive machine
learning or data mining tasks in remote areas, under harsh environmental
conditions and in areas where energy supply is an issue.

Research shows that exposure to suicide-related news media content is
associated with suicide rates, with some content characteristics likely having
harmful and others potentially protective effects. Although good evidence
exists for a few selected characteristics, systematic large scale
investigations are missing in general, and in particular for social media data.
We apply machine learning methods to classify large quantities of Twitter data
according to a novel annotation scheme that distinguishes 12 categories of
suicide-related tweets. We then trained a benchmark of machine learning models
including a majority classifier, an approach based on word frequency (TF-IDF
with a linear SVM) and two state-of-the-art deep learning models (BERT, XLNet).
The two deep learning models achieved the best performance in two
classification tasks: In the first task, we classified six main content
categories, including personal stories about either suicidal ideation and
attempts or coping, calls for action intending to spread either problem
awareness or prevention-related information, reporting of suicide cases, and
other tweets irrelevant to these categories. The deep learning models reached
accuracy scores above 73% on average across the six categories, and F1-scores
in between 0.70 and 0.85 for all but the suicidal ideation and attempts
category (0.51-0.55). In the second task, separating tweets referring to actual
suicide from off-topic tweets, they correctly labeled around 88% of tweets,
with BERT achieving F1-scores of 0.93 and 0.74 for the two categories,
respectively. These classification performances are comparable to the
state-of-the-art on similar tasks. By making data labeling more efficient, this
work has enabled large-scale investigations on harmful and protective
associations of social media content with suicide rates and help-seeking
behavior.

The LHCb experiment at the Large Hadron Collider (LHC) at CERN has
successfully performed a large number of physics measurements during Runs 1 and
2 of the LHC. Monte Carlo simulation is key to the interpretation of these and
future measurements. The LHCb experiment is currently undergoing a major
detector upgrade for Run 3 of the LHC to process events with five times higher
luminosity. New simulation software technologies have to be introduced to
produce simulated data samples of sufficient size within the computing
resources allocated for the next few years. Therefore, the LHCb collaboration
is currently preparing an upgraded version of its Gauss simulation framework.
The new version provides the LHCb specific functionality while its generic
simulation infrastructure has been encapsulated in an experiment independent
framework, Gaussino. The latter combines the Gaudi core software framework and
the Geant4 simulation toolkit and fully exploits their multi-threading
capabilities. A prototype of a fast simulation interface to the simulation
toolkit is being developed as the latest addition to Gaussino to provide an
extensive palette of fast simulation models, including new deep learning-based
options.

A novel simulator called VMAgent is introduced to help RL researchers better
explore new methods, especially for virtual machine scheduling. VMAgent is
inspired by practical virtual machine (VM) scheduling tasks and provides an
efficient simulation platform that can reflect the real situations of cloud
computing. Three scenarios (fading, recovering, and expansion) are concluded
from practical cloud computing and corresponds to many reinforcement learning
challenges (high dimensional state and action spaces, high non-stationarity,
and life-long demand). VMAgent provides flexible configurations for RL
researchers to design their customized scheduling environments considering
different problem features. From the VM scheduling perspective, VMAgent also
helps to explore better learning-based scheduling solutions.

Modal regression, a widely used regression protocol, has been extensively
investigated in statistical and machine learning communities due to its
robustness to outliers and heavy-tailed noises. Understanding modal
regression's theoretical behavior can be fundamental in learning theory.
Despite significant progress in characterizing its statistical property, the
majority of the results are based on the assumption that samples are
independent and identical distributed (i.i.d.), which is too restrictive for
real-world applications. This paper concerns the statistical property of
regularized modal regression (RMR) within an important dependence structure -
Markov dependent. Specifically, we establish the upper bound for RMR estimator
under moderate conditions and give an explicit learning rate. Our results show
that the Markov dependence impacts on the generalization error in the way that
sample size would be discounted by a multiplicative factor depending on the
spectral gap of underlying Markov chain. This result shed a new light on
characterizing the theoretical underpinning for robust regression.

Analysis of XRD diffraction patterns is one of the keystones of materials
science and materials research. With the advancement of data-driven methods for
materials design, candidate materials can be quickly screened for the study of
a desired physical property. Efficient methods to automatically analyze and
identify phases present in a given pattern, are paramount for the success of
this new paradigm. To aid this process, the open source python package Xray
Estimation and Refinement Using Similarity (XERUS) for semi-automatic/automatic
phase identification is presented. XERUS takes advantages of open crystal
structure databases, not relying on proprietary databases, to obtain crystal
structures on the fly, being then chemical space agnostic. By wrapping around
GSASII, it can automatically simulate patterns and calculate similarity
measures used for phase identification. Our approach is simple and quick but
also applicable to multiphase identification, by coupling the similarity
calculations with quick refinements followed by an iterative peak removal
process. XERUS is shown in action in four different experimental datasets and
also it is benchmarked against a recently proposed deep learning method for a
mixture dataset covering the Li-Mn-O-F chemical space. XERUS will be freely
available on https://www.github.com/pedrobcst/Xerus/

Domain generalization involves learning a classifier from a heterogeneous
collection of training sources such that it generalizes to data drawn from
similar unknown target domains, with applications in large-scale learning and
personalized inference. In many settings, privacy concerns prohibit obtaining
domain labels for the training data samples, and instead only have an
aggregated collection of training points. Existing approaches that utilize
domain labels to create domain-invariant feature representations are
inapplicable in this setting, requiring alternative approaches to learn
generalizable classifiers. In this paper, we propose a domain-adaptive approach
to this problem, which operates in two steps: (a) we cluster training data
within a carefully chosen feature space to create pseudo-domains, and (b) using
these pseudo-domains we learn a domain-adaptive classifier that makes
predictions using information about both the input and the pseudo-domain it
belongs to. Our approach achieves state-of-the-art performance on a variety of
domain generalization benchmarks without using domain labels whatsoever.
Furthermore, we provide novel theoretical guarantees on domain generalization
using cluster information. Our approach is amenable to ensemble-based methods
and provides substantial gains even on large-scale benchmark datasets. The code
can be found at: https://github.com/xavierohan/AdaClust_DomainBed

As 3D object detection on point clouds relies on the geometrical
relationships between the points, non-standard object shapes can hinder a
method's detection capability. However, in safety-critical settings, robustness
to out-of-domain and long-tail samples is fundamental to circumvent dangerous
issues, such as the misdetection of damaged or rare cars. In this work, we
substantially improve the generalization of 3D object detectors to
out-of-domain data by deforming point clouds during training. We achieve this
with 3D-VField: a novel data augmentation method that plausibly deforms objects
via vector fields learned in an adversarial fashion. Our approach constrains 3D
points to slide along their sensor view rays while neither adding nor removing
any of them. The obtained vectors are transferable, sample-independent and
preserve shape and occlusions. Despite training only on a standard dataset,
such as KITTI, augmenting with our vector fields significantly improves the
generalization to differently shaped objects and scenes. Towards this end, we
propose and share CrashD: a synthetic dataset of realistic damaged and rare
cars, with a variety of crash scenarios. Extensive experiments on KITTI, Waymo,
our CrashD and SUN RGB-D show the generalizability of our techniques to
out-of-domain data, different models and sensors, namely LiDAR and ToF cameras,
for both indoor and outdoor scenes. Our CrashD dataset is available at
https://crashd-cars.github.io.

Triplet loss is a widely adopted loss function in ReID task which pulls the
hardest positive pairs close and pushes the hardest negative pairs far away.
However, the selected samples are not the hardest globally, but the hardest
only in a mini-batch, which will affect the performance. In this report, a hard
batch mining method is proposed to mine the hardest samples globally to make
triplet harder. More specifically, the most similar classes are selected into a
same mini-batch so that the similar classes could be pushed further away.
Besides, an adversarial scene removal module composed of a scene classifier and
an adversarial loss is used to learn scene invariant feature representations.
Experiments are conducted on dataset MSMT17 to prove the effectiveness, and our
method surpasses all of the previous methods and sets state-of-the-art result.

Deformable image registration is one of the fundamental tasks in medical
imaging. Classical registration algorithms usually require a high computational
cost for iterative optimizations. Although deep-learning-based methods have
been developed for fast image registration, it is still challenging to obtain
realistic continuous deformations from a moving image to a fixed image with
less topological folding problem. To address this, here we present a novel
diffusion-model-based image registration method, called DiffuseMorph.
DiffuseMorph not only generates synthetic deformed images through reverse
diffusion but also allows image registration by deformation fields.
Specifically, the deformation fields are generated by the conditional score
function of the deformation between the moving and fixed images, so that the
registration can be performed from continuous deformation by simply scaling the
latent feature of the score. Experimental results on 2D facial and 3D medical
image registration tasks demonstrate that our method provides flexible
deformations with topology preservation capability.

Recent years have witnessed the great success of multi-agent systems (MAS).
Value decomposition, which decomposes joint action values into individual
action values, has been an important work in MAS. However, many value
decomposition methods ignore the coordination among different agents, leading
to the notorious "lazy agents" problem. To enhance the coordination in MAS,
this paper proposes HyperGraph CoNvolution MIX (HGCN-MIX), a method that
incorporates hypergraph convolution with value decomposition. HGCN-MIX models
agents as well as their relationships as a hypergraph, where agents are nodes
and hyperedges among nodes indicate that the corresponding agents can
coordinate to achieve larger rewards. Then, it trains a hypergraph that can
capture the collaborative relationships among agents. Leveraging the learned
hypergraph to consider how other agents' observations and actions affect their
decisions, the agents in a MAS can better coordinate. We evaluate HGCN-MIX in
the StarCraft II multi-agent challenge benchmark. The experimental results
demonstrate that HGCN-MIX can train joint policies that outperform or achieve a
similar level of performance as the current state-of-the-art techniques. We
also observe that HGCN-MIX has an even more significant improvement of
performance in the scenarios with a large amount of agents. Besides, we conduct
additional analysis to emphasize that when the hypergraph learns more
relationships, HGCN-MIX can train stronger joint policies.

While automated driving is often advertised with better-than-human driving
performance, this work reviews that it is nearly impossible to provide direct
statistical evidence on the system level that this is actually the case. The
amount of labeled data needed would exceed dimensions of present day technical
and economical capabilities. A commonly used strategy therefore is the use of
redundancy along with the proof of sufficient subsystems' performances. As it
is known, this strategy is efficient especially for the case of subsystems
operating independently, i.e. the occurrence of errors is independent in a
statistical sense. Here, we give some first considerations and experimental
evidence that this strategy is not a free ride as the errors of neural networks
fulfilling the same computer vision task, at least for some cases, show
correlated occurrences of errors. This remains true, if training data,
architecture, and training are kept separate or independence is trained using
special loss functions. Using data from different sensors (realized by up to
five 2D projections of the 3D MNIST data set) in our experiments is more
efficiently reducing correlations, however not to an extent that is realizing
the potential of reduction of testing data that can be obtained for redundant
and statistically independent subsystems.

This paper proposes a Deep Reinforcement Learning algorithm for financial
portfolio trading based on Deep Q-learning. The algorithm is capable of trading
high-dimensional portfolios from cross-sectional datasets of any size which may
include data gaps and non-unique history lengths in the assets. We sequentially
set up environments by sampling one asset for each environment while rewarding
investments with the resulting asset's return and cash reservation with the
average return of the set of assets. This enforces the agent to strategically
assign capital to assets that it predicts to perform above-average. We apply
our methodology in an out-of-sample analysis to 48 US stock portfolio setups,
varying in the number of stocks from ten up to 500 stocks, in the selection
criteria and in the level of transaction costs. The algorithm on average
outperforms all considered passive and active benchmark investment strategies
by a large margin using only one hyperparameter setup for all portfolios.

Vision-based deep learning models can be promising for
speech-and-hearing-impaired and secret communications. While such non-verbal
communications are primarily investigated with hand-gestures and facial
expressions, no research endeavour is tracked so far for the lips state (i.e.,
open/close)-based interpretation/translation system. In support of this
development, this paper reports two new Convolutional Neural Network (CNN)
models for lips state detection. Building upon two prominent lips landmark
detectors, DLIB and MediaPipe, we simplify lips-state model with a set of six
key landmarks, and use their distances for the lips state classification.
Thereby, both the models are developed to count the opening and closing of lips
and thus, they can classify a symbol with the total count. Varying frame-rates,
lips-movements and face-angles are investigated to determine the effectiveness
of the models. Our early experimental results demonstrate that the model with
DLIB is relatively slower in terms of an average of 6 frames per second (FPS)
and higher average detection accuracy of 95.25%. In contrast, the model with
MediaPipe offers faster landmark detection capability with an average FPS of 20
and detection accuracy of 94.4%. Both models thus could effectively interpret
the lips state for non-verbal semantics into a natural language.

The aim of this work is to investigate the impact of crossmodal
self-supervised pre-training for speech reconstruction (video-to-audio) by
leveraging the natural co-occurrence of audio and visual streams in videos. We
propose LipSound2 which consists of an encoder-decoder architecture and
location-aware attention mechanism to map face image sequences to mel-scale
spectrograms directly without requiring any human annotations. The proposed
LipSound2 model is firstly pre-trained on $\sim$2400h multi-lingual (e.g.
English and German) audio-visual data (VoxCeleb2). To verify the
generalizability of the proposed method, we then fine-tune the pre-trained
model on domain-specific datasets (GRID, TCD-TIMIT) for English speech
reconstruction and achieve a significant improvement on speech quality and
intelligibility compared to previous approaches in speaker-dependent and
-independent settings. In addition to English, we conduct Chinese speech
reconstruction on the CMLR dataset to verify the impact on transferability.
Lastly, we train the cascaded lip reading (video-to-text) system by fine-tuning
the generated audios on a pre-trained speech recognition system and achieve
state-of-the-art performance on both English and Chinese benchmark datasets.

Building damage detection after natural disasters like earthquakes is crucial
for initiating effective emergency response actions. Remotely sensed very high
spatial resolution (VHR) imagery can provide vital information due to their
ability to map the affected buildings with high geometric precision. Many
approaches have been developed to detect damaged buildings due to earthquakes.
However, little attention has been paid to exploiting rich features represented
in VHR images using Deep Neural Networks (DNN). This paper presents a novel
superpixel based approach combining DNN and a modified segmentation method, to
detect damaged buildings from VHR imagery. Firstly, a modified Fast Scanning
and Adaptive Merging method is extended to create initial over-segmentation.
Secondly, the segments are merged based on the Region Adjacent Graph (RAG),
considered an improved semantic similarity criterion composed of Local Binary
Patterns (LBP) texture, spectral, and shape features. Thirdly, a pre-trained
DNN using Stacked Denoising Auto-Encoders called SDAE-DNN is presented, to
exploit the rich semantic features for building damage detection. Deep-layer
feature abstraction of SDAE-DNN could boost detection accuracy through learning
more intrinsic and discriminative features, which outperformed other methods
using state-of-the-art alternative classifiers. We demonstrate the feasibility
and effectiveness of our method using a subset of WorldView-2 imagery, in the
complex urban areas of Bhaktapur, Nepal, which was affected by the Nepal
Earthquake of April 25, 2015.

There is a growing interest in learning a velocity command tracking
controller of quadruped robot using reinforcement learning due to its
robustness and scalability. However, a single policy, trained end-to-end,
usually shows a single gait regardless of the command velocity. This could be a
suboptimal solution considering the existence of optimal gait according to the
velocity for quadruped animals. In this work, we propose a hierarchical
controller for quadruped robot that could generate multiple gaits (i.e. pace,
trot, bound) while tracking velocity command. Our controller is composed of two
policies, each working as a central pattern generator and local feedback
controller, and trained with hierarchical reinforcement learning. Experiment
results show 1) the existence of optimal gait for specific velocity range 2)
the efficiency of our hierarchical controller compared to a controller composed
of a single policy, which usually shows a single gait. Codes are publicly

Acoustic classification of frogs has gotten a lot of attention recently due
to its potential applicability in ecological investigations. Numerous studies
have been presented for identifying frog species, although the majority of
recorded species are thought to be monotypic. The purpose of this study is to
demonstrate a method for classifying various frog species using an audio
recording. To be more exact, continuous frog recordings are cut into audio
snippets first (10 seconds). Then, for each ten-second recording, several
time-frequency representations are constructed. Following that, rather than
using manually created features, Machine Learning methods are employed to
classify the frog species. Data reduction techniques; Principal Component
Analysis (PCA) and Independent Component Analysis (ICA) are used to extract the
most important features before classification. Finally, to validate our
classification accuracy, cross validation and prediction accuracy are used.
Experimental results show that PCA extracted features that achieved better
classification accuracy both with cross validation and prediction accuracy.

Federated edge learning (FEEL) has drawn much attention as a
privacy-preserving distributed learning framework for mobile edge networks. In
this work, we investigate a novel semi-decentralized FEEL (SD-FEEL)
architecture where multiple edge servers collaborate to incorporate more data
from edge devices in training. Despite the low training latency enabled by fast
edge aggregation, the device heterogeneity in computational resources
deteriorates the efficiency. This paper proposes an asynchronous training
algorithm for SD-FEEL to overcome this issue, where edge servers can
independently set deadlines for the associated client nodes and trigger the
model aggregation. To deal with different levels of staleness, we design a
staleness-aware aggregation scheme and analyze its convergence performance.
Simulation results demonstrate the effectiveness of our proposed algorithm in
achieving faster convergence and better learning performance.

Cross-lingual Machine Reading Comprehension (xMRC) is challenging due to the
lack of training data in low-resource languages. The recent approaches use
training data only in a resource-rich language like English to fine-tune
large-scale cross-lingual pre-trained language models. Due to the big
difference between languages, a model fine-tuned only by a source language may
not perform well for target languages. Interestingly, we observe that while the
top-1 results predicted by the previous approaches may often fail to hit the
ground-truth answers, the correct answers are often contained in the top-k
predicted results. Based on this observation, we develop a two-stage approach
to enhance the model performance. The first stage targets at recall: we design
a hard-learning (HL) algorithm to maximize the likelihood that the top-k
predictions contain the accurate answer. The second stage focuses on precision:
an answer-aware contrastive learning (AA-CL) mechanism is developed to learn
the fine difference between the accurate answer and other candidates. Our
extensive experiments show that our model significantly outperforms a series of
strong baselines on two cross-lingual MRC benchmark datasets.

Multi-task learning has been observed by many researchers, which supposes
that different tasks can share a low-rank common yet latent subspace. It means
learning multiple tasks jointly is better than learning them independently. In
this paper, we propose two novel multi-task learning formulations based on two
regularization terms, which can learn the optimal shared latent subspace by
minimizing the exactly $k$ minimal singular values. The proposed regularization
terms are the more tight approximations of rank minimization than trace norm.
But it's an NP-hard problem to solve the exact rank minimization problem.
Therefore, we design a novel re-weighted based iterative strategy to solve our
models, which can tactically handle the exact rank minimization problem by
setting a large penalizing parameter. Experimental results on benchmark
datasets demonstrate that our methods can correctly recover the low-rank
structure shared across tasks, and outperform related multi-task learning
methods.

Class Incremental Learning (CIL) aims at learning a multi-class classifier in
a phase-by-phase manner, in which only data of a subset of the classes are
provided at each phase. Previous works mainly focus on mitigating forgetting in
phases after the initial one. However, we find that improving CIL at its
initial phase is also a promising direction. Specifically, we experimentally
show that directly encouraging CIL Learner at the initial phase to output
similar representations as the model jointly trained on all classes can greatly
boost the CIL performance. Motivated by this, we study the difference between a
na\"ively-trained initial-phase model and the oracle model. Specifically, since
one major difference between these two models is the number of training
classes, we investigate how such difference affects the model representations.
We find that, with fewer training classes, the data representations of each
class lie in a long and narrow region; with more training classes, the
representations of each class scatter more uniformly. Inspired by this
observation, we propose Class-wise Decorrelation (CwD) that effectively
regularizes representations of each class to scatter more uniformly, thus
mimicking the model jointly trained with all classes (i.e., the oracle model).
Our CwD is simple to implement and easy to plug into existing methods.
Extensive experiments on various benchmark datasets show that CwD consistently
and significantly improves the performance of existing state-of-the-art methods
by around 1\% to 3\%. Code will be released.

A lifelong learning agent is able to continually learn from potentially
infinite streams of pattern sensory data. One major historic difficulty in
building agents that adapt in this way is that neural systems struggle to
retain previously-acquired knowledge when learning from new samples. This
problem is known as catastrophic forgetting (interference) and remains an
unsolved problem in the domain of machine learning to this day. While
forgetting in the context of feedforward networks has been examined extensively
over the decades, far less has been done in the context of alternative
architectures such as the venerable self-organizing map (SOM), an unsupervised
neural model that is often used in tasks such as clustering and dimensionality
reduction. Although the competition among its internal neurons might carry the
potential to improve memory retention, we observe that a fixed-sized SOM
trained on task incremental data, i.e., it receives data points related to
specific classes at certain temporal increments, experiences significant
forgetting. In this study, we propose the continual SOM (c-SOM), a model that
is capable of reducing its own forgetting when processing information.

Deep learning has shown astonishing performance in accelerated magnetic
resonance imaging (MRI). Most state-of-the-art deep learning reconstructions
adopt the powerful convolutional neural network and perform 2D convolution
since many magnetic resonance images or their corresponding k-space are in 2D.
In this work, we present a new approach that explores the 1D convolution,
making the deep network much easier to be trained and generalized. We further
integrate the 1D convolution into the proposed deep network, named as
One-dimensional Deep Low-rank and Sparse network (ODLS), which unrolls the
iteration procedure of a low-rank and sparse reconstruction model. Extensive
results on in vivo knee and brain datasets demonstrate that, the proposed ODLS
is very suitable for the case of limited training subjects and provides
improved reconstruction performance than state-of-the-art methods both visually
and quantitatively. Additionally, ODLS also shows nice robustness to different
undersampling scenarios and some mismatches between the training and test data.
In summary, our work demonstrates that the 1D deep learning scheme is
memory-efficient and robust in fast MRI.

Despite the rapid progress of neuromorphic computing, the inadequate depth
and the resulting insufficient representation power of spiking neural networks
(SNNs) severely restrict their application scope in practice. Residual learning
and shortcuts have been evidenced as an important approach for training deep
neural networks, but rarely did previous work assess their applicability to the
characteristics of spike-based communication and spatiotemporal dynamics. This
negligence leads to impeded information flow and the accompanying degradation
problem. In this paper, we identify the crux and then propose a novel residual
block for SNNs, which is able to significantly extend the depth of directly
trained SNNs, e.g., up to 482 layers on CIFAR-10 and 104 layers on ImageNet,
without observing any slight degradation problem. We validate the effectiveness
of our methods on both frame-based and neuromorphic datasets, and our
SRM-ResNet104 achieves a superior result of 76.02% accuracy on ImageNet, the
first time in the domain of directly trained SNNs. The great energy efficiency
is estimated and the resulting networks need on average only one spike per
neuron for classifying an input sample. We believe our powerful and scalable
modeling will provide a strong support for further exploration of SNNs.

Enhancing the quality of low-light images plays a very important role in many
image processing and multimedia applications. In recent years, a variety of
deep learning techniques have been developed to address this challenging task.
A typical framework is to simultaneously estimate the illumination and
reflectance, but they disregard the scene-level contextual information
encapsulated in feature spaces, causing many unfavorable outcomes, e.g.,
details loss, color unsaturation, artifacts, and so on. To address these
issues, we develop a new context-sensitive decomposition network architecture
to exploit the scene-level contextual dependencies on spatial scales. More
concretely, we build a two-stream estimation mechanism including reflectance
and illumination estimation network. We design a novel context-sensitive
decomposition connection to bridge the two-stream mechanism by incorporating
the physical principle. The spatially-varying illumination guidance is further
constructed for achieving the edge-aware smoothness property of the
illumination component. According to different training patterns, we construct
CSDNet (paired supervision) and CSDGAN (unpaired supervision) to fully evaluate
our designed architecture. We test our method on seven testing benchmarks to
conduct plenty of analytical and evaluated experiments. Thanks to our designed
context-sensitive decomposition connection, we successfully realized excellent
enhanced results, which fully indicates our superiority against existing
state-of-the-art approaches. Finally, considering the practical needs for
high-efficiency, we develop a lightweight CSDNet (named LiteCSDNet) by reducing
the number of channels. Further, by sharing an encoder for these two
components, we obtain a more lightweight version (SLiteCSDNet for short).
SLiteCSDNet just contains 0.0301M parameters but achieves the almost same
performance as CSDNet.

While adversarial attacks on deep image classification models pose serious
security concerns in practice, this paper suggests a novel paradigm where the
concept of adversarial attacks can benefit classification performance, which we
call amicable aid. We show that by taking the opposite search direction of
perturbation, an image can be converted to another yielding higher confidence
by the classification model and even a wrongly classified image can be made to
be correctly classified. Furthermore, with a large amount of perturbation, an
image can be made unrecognizable by human eyes, while it is correctly
recognized by the model. The mechanism of the amicable aid is explained in the
viewpoint of the underlying natural image manifold. We also consider universal
amicable perturbations, i.e., a fixed perturbation can be applied to multiple
images to improve their classification results. While it is challenging to find
such perturbations, we show that making the decision boundary as perpendicular
to the image manifold as possible via training with modified data is effective
to obtain a model for which universal amicable perturbations are more easily
found. Finally, we discuss several application scenarios where the amicable aid
can be useful, including secure image communication, privacy-preserving image
communication, and protection against adversarial attacks.

Images captured from low-light scenes often suffer from severe degradations,
including low visibility, color cast and intensive noises, etc. These factors
not only affect image qualities, but also degrade the performance of downstream
Low-Light Vision (LLV) applications. A variety of deep learning methods have
been proposed to enhance the visual quality of low-light images. However, these
approaches mostly rely on significant architecture engineering to obtain proper
low-light models and often suffer from high computational burden. Furthermore,
it is still challenging to extend these enhancement techniques to handle other
LLVs. To partially address above issues, we establish Retinex-inspired
Unrolling with Architecture Search (RUAS), a general learning framework, which
not only can address low-light enhancement task, but also has the flexibility
to handle other more challenging downstream vision applications. Specifically,
we first establish a nested optimization formulation, together with an
unrolling strategy, to explore underlying principles of a series of LLV tasks.
Furthermore, we construct a differentiable strategy to cooperatively search
specific scene and task architectures for RUAS. Last but not least, we
demonstrate how to apply RUAS for both low- and high-level LLV applications
(e.g., enhancement, detection and segmentation). Extensive experiments verify
the flexibility, effectiveness, and efficiency of RUAS.

Metal halides perovskites (MHPs) are attracting considerable interest for
optoelectronic applications, with Cs$_2$AgBiBr$_6$ one of the main contenders
among lead-free systems. Cs$_2$AgBiBr$_6$ crystallizes in a nominally
double-perovskite structure, but exhibits a soft lattice with large atomic
fluctuations characteristic of MHPs. While crucial to understand
electron-phonon and phonon-phonon couplings, the spatio-temporal correlations
of these fluctuations remain largely unknown. Here, we reveal these
correlations using comprehensive neutron and x-ray scattering measurements on
Cs$_2$AgBiBr$_6$ single-crystals, complemented with first-principles
simulations augmented with machine-learned neural-network potentials. We report
the discovery of an unexpected complex modulated ground state structure
containing several hundred atoms, arising from a soft-phonon instability of the
low-temperature tetragonal phase. Further, our experiments and simulations both
reveal extensive correlated 2D fluctuations of Br octahedra at finite
temperature, arising from soft anharmonic optic phonons, reflecting very
shallow potential wells. These results provide new insights into the atomic
structure and fluctuations in MHPs, critical to understand and control their
thermal and optoelectronic properties.

Despite overparameterization, deep networks trained via supervised learning
are easy to optimize and exhibit excellent generalization. One hypothesis to
explain this is that overparameterized deep networks enjoy the benefits of
implicit regularization induced by stochastic gradient descent, which favors
parsimonious solutions that generalize well on test inputs. It is reasonable to
surmise that deep reinforcement learning (RL) methods could also benefit from
this effect. In this paper, we discuss how the implicit regularization effect
of SGD seen in supervised learning could in fact be harmful in the offline deep
RL setting, leading to poor generalization and degenerate feature
representations. Our theoretical analysis shows that when existing models of
implicit regularization are applied to temporal difference learning, the
resulting derived regularizer favors degenerate solutions with excessive
"aliasing", in stark contrast to the supervised learning case. We back up these
findings empirically, showing that feature representations learned by a deep
network value function trained via bootstrapping can indeed become degenerate,
aliasing the representations for state-action pairs that appear on either side
of the Bellman backup. To address this issue, we derive the form of this
implicit regularizer and, inspired by this derivation, propose a simple and
effective explicit regularizer, called DR3, that counteracts the undesirable
effects of this implicit regularizer. When combined with existing offline RL
methods, DR3 substantially improves performance and stability, alleviating
unlearning in Atari 2600 games, D4RL domains and robotic manipulation from
images.

High fidelity behavior prediction of human drivers is crucial for efficient
and safe deployment of autonomous vehicles, which is challenging due to the
stochasticity, heterogeneity, and time-varying nature of human behaviors. On
one hand, the trained prediction model can only capture the motion pattern in
an average sense, while the nuances among individuals can hardly be reflected.
On the other hand, the prediction model trained on the training set may not
generalize to the testing set which may be in a different scenario or data
distribution, resulting in low transferability and generalizability. In this
paper, we applied a $\tau$-step modified Extended Kalman Filter parameter
adaptation algorithm (MEKF$_\lambda$) to the driving behavior prediction task,
which has not been studied before in literature. With the feedback of the
observed trajectory, the algorithm is applied to neural-network-based models to
improve the performance of driving behavior predictions across different human
subjects and scenarios. A new set of metrics is proposed for systematic
evaluation of online adaptation performance in reducing the prediction error
for different individuals and scenarios. Empirical studies on the best layer in
the model and steps of observation to adapt are also provided.

We proposed a multivariate time series anomaly detection frame-work Ymir,
which leverages ensemble learning and supervisedlearning technology to
efficiently learn and adapt to anomaliesin real-world system applications. Ymir
integrates several currentlywidely used unsupervised anomaly detection models
through anensemble learning method, and thus can provide robust frontalanomaly
detection results in unsupervised scenarios. In a super-vised setting, domain
experts and system users discuss and providelabels (anomalous or not) for the
training data, which reflects theiranomaly detection criteria for the specific
system. Ymir leveragesthe aforementioned unsupervised methods to extract rich
and usefulfeature representations from the raw multivariate time series
data,then combines the features and labels with a supervised classifier todo
anomaly detection. We evaluated Ymir on internal multivariatetime series
datasets from large monitoring systems and achievedgood anomaly detection

When 5G began its commercialisation journey around 2020, the discussion on
the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth,
coverage, reliability, energy efficiency, lower latency, and, more importantly,
an integrated "human-centric" network system powered by artificial intelligence
(AI). Such a 6G network will lead to an excessive number of automated decisions
made every second. These decisions can range widely, from network resource
allocation to collision avoidance for self-driving cars. However, the risk of
losing control over decision-making may increase due to high-speed
data-intensive AI decision-making beyond designers and users' comprehension.
The promising explainable AI (XAI) methods can mitigate such risks by enhancing
the transparency of the black box AI decision-making process. This survey paper
highlights the need for XAI towards the upcoming 6G age in every aspect,
including 6G technologies (e.g., intelligent radio, zero-touch network
management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the
lessons learned from the recent attempts and outlined important research
challenges in applying XAI for building 6G systems. This research aligns with
goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals
(UN-SDG), promoting innovation and building infrastructure, sustainable and
inclusive human settlement, advancing justice and strong institutions, and
fostering partnership at the global level.

Diffusion models have recently attained significant interest within the
community owing to their strong performance as generative models. Furthermore,
its application to inverse problems have demonstrated state-of-the-art
performance. Unfortunately, diffusion models have a critical downside - they
are inherently slow to sample from, needing few thousand steps of iteration to
generate images from pure Gaussian noise. In this work, we show that starting
from Gaussian noise is unnecessary. Instead, starting from a single forward
diffusion with better initialization significantly reduces the number of
sampling steps in the reverse conditional diffusion. This phenomenon is
formally explained by the contraction theory of the stochastic difference
equations like our conditional diffusion strategy - the alternating
applications of reverse diffusion followed by a non-expansive data consistency
step. The new sampling strategy, dubbed Come-Closer-Diffuse-Faster (CCDF), also
reveals a new insight on how the existing feed-forward neural network
approaches for inverse problems can be synergistically combined with the
diffusion models. Experimental results with super-resolution, image inpainting,
and compressed sensing MRI demonstrate that our method can achieve
state-of-the-art reconstruction performance at significantly reduced sampling
steps.

The information detection of complex systems from data is currently
undergoing a revolution, driven by the emergence of big data and machine
learning methodology. Discovering governing equations and quantifying dynamical
properties of complex systems are among central challenges. In this work, we
devise a nonparametric approach to learn the relative entropy rate from
observations of stochastic differential equations with different drift
functions.The estimator corresponding to the relative entropy rate then is
presented via the Gaussian process kernel theory. Meanwhile, this approach
enables to extract the governing equations. We illustrate our approach in
several examples. Numerical experiments show the proposed approach performs
well for rational drift functions, not only polynomial drift functions.

Reinforcement learning techniques can provide substantial insights into the
desired behaviors of future autonomous driving systems. By optimizing for
societal metrics of traffic such as increased throughput and reduced energy
consumption, such methods can derive maneuvers that, if adopted by even a small
portion of vehicles, may significantly improve the state of traffic for all
vehicles involved. These methods, however, are hindered in practice by the
difficulty of designing efficient and accurate models of traffic, as well as
the challenges associated with optimizing for the behaviors of dozens of
interacting agents. In response to these challenges, this paper tackles the
problem of learning generalizable traffic control strategies in simple
representations of vehicle driving dynamics. In particular, we look to
mixed-autonomy ring roads as depictions of instabilities that result in the
formation of congestion. Within this problem, we design a curriculum learning
paradigm that exploits the natural extendability of the network to effectively
learn behaviors that reduce congestion over long horizons. Next, we study the
implications of modeling lane changing on the transferability of policies. Our
findings suggest that introducing lane change behaviors that even approximately
match trends in more complex systems can significantly improve the
generalizability of subsequent learned models to more accurate multi-lane
models of traffic.

Music source separation (MSS) shows active progress with deep learning models
in recent years. Many MSS models perform separations on spectrograms by
estimating bounded ratio masks and reusing the phases of the mixture. When
using convolutional neural networks (CNN), weights are usually shared within a
spectrogram during convolution regardless of the different patterns between
frequency bands. In this study, we propose a new MSS model, channel-wise
subband phase-aware ResUNet (CWS-PResUNet), to decompose signals into subbands
and estimate an unbound complex ideal ratio mask (cIRM) for each source.
CWS-PResUNet utilizes a channel-wise subband (CWS) feature to limit unnecessary
global weights sharing on the spectrogram and reduce computational resource
consumptions. The saved computational cost and memory can in turn allow for a
larger architecture. On the MUSDB18HQ test set, we propose a 276-layer
CWS-PResUNet and achieve state-of-the-art (SoTA) performance on vocals with an
8.92 signal-to-distortion ratio (SDR) score. By combining CWS-PResUNet and
Demucs, our ByteMSS system ranks the 2nd on vocals score and 5th on average
score in the 2021 ISMIR Music Demixing (MDX) Challenge limited training data
track (leaderboard A). Our code and pre-trained models are publicly available
at: https://github.com/haoheliu/2021-ISMIR-MSS-Challenge-CWS-PResUNet

We present a reward-predictive, model-based deep learning method featuring
trajectory-constrained visual attention for local planning in visual navigation
tasks. Our method learns to place visual attention at locations in latent image
space which follow trajectories caused by vehicle control actions to enhance
predictive accuracy during planning. The attention model is jointly optimized
by the task-specific loss and an additional trajectory-constraint loss,
allowing adaptability yet encouraging a regularized structure for improved
generalization and reliability. Importantly, visual attention is applied in
latent feature map space instead of raw image space to promote efficient
planning. We validated our model in visual navigation tasks of planning low
turbulence, collision-free trajectories in off-road settings and hill climbing
with locking differentials in the presence of slippery terrain. Experiments
involved randomized procedural generated simulation and real-world
environments. We found our method improved generalization and learning
efficiency when compared to no-attention and self-attention alternatives.

Nowadays many cities around the world have introduced electric buses to
optimize urban traffic and reduce local carbon emissions. In order to cut
carbon emissions and maximize the utility of electric buses, it is important to
choose suitable routes for them. Traditionally, route selection is on the basis
of dedicated surveys, which are costly in time and labor. In this paper, we
mainly focus attention on planning electric bus routes intelligently, depending
on the unique needs of each region throughout the city. We propose
Clairvoyance, a route planning system that leverages a deep neural network and
a multilayer perceptron to predict the future people's trips and the future
transportation carbon emission in the whole city, respectively. Given the
future information of people's trips and transportation carbon emission, we
utilize a greedy mechanism to recommend bus routes for electric buses that will
depart in an ideal state. Furthermore, representative features of the two
neural networks are extracted from the heterogeneous urban datasets. We
evaluate our approach through extensive experiments on real-world data sources
in Zhuhai, China. The results show that our designed neural network-based
algorithms are consistently superior to the typical baselines. Additionally,
the recommended routes for electric buses are helpful in reducing the peak
value of carbon emissions and making full use of electric buses in the city.

Entropy production (EP) is a central measure in nonequilibrium
thermodynamics, as it can quantify the irreversibility of a process as well as
its energy dissipation in special cases. Using the time-reversal asymmetry in a
system's path probability distribution, many methods have been developed to
estimate EP from only trajectory data. However, estimating the EP of a system
with odd-parity variables, which prevails in nonequilibrium systems, has not
been covered. In this study, we develop a machine learning method for
estimating the EP in a stochastic system with odd-parity variables through
multiple neural networks. We demonstrate our method with two systems, an
underdamped bead-spring model and a one-particle odd-parity Markov jump
process.

Pre-training has become a standard paradigm in many computer vision tasks.
However, most of the methods are generally designed on the RGB image domain.
Due to the discrepancy between the two-dimensional image plane and the
three-dimensional space, such pre-trained models fail to perceive spatial
information and serve as sub-optimal solutions for 3D-related tasks. To bridge
this gap, we aim to learn a spatial-aware visual representation that can
describe the three-dimensional space and is more suitable and effective for
these tasks. To leverage point clouds, which are much more superior in
providing spatial information compared to images, we propose a simple yet
effective 2D Image and 3D Point cloud Unsupervised pre-training strategy,
called SimIPU. Specifically, we develop a multi-modal contrastive learning
framework that consists of an intra-modal spatial perception module to learn a
spatial-aware representation from point clouds and an inter-modal feature
interaction module to transfer the capability of perceiving spatial information
from the point cloud encoder to the image encoder, respectively. Positive pairs
for contrastive losses are established by the matching algorithm and the
projection matrix. The whole framework is trained in an unsupervised end-to-end
fashion. To the best of our knowledge, this is the first study to explore
contrastive learning pre-training strategies for outdoor multi-modal datasets,
containing paired camera images and LIDAR point clouds. Codes and models are
available at https://github.com/zhyever/SimIPU.

We comment on a recent TKDE paper "Linear Approximation of F-measure for the
Performance Evaluation of Classification Algorithms on Imbalanced Data Sets",
and make two improvements related to comparison of F-measures for two
prediction rules.

While transformers have shown great potential on video recognition tasks with
their strong capability of capturing long-range dependencies, they often suffer
high computational costs induced by self-attention operation on the huge number
of 3D tokens in a video. In this paper, we propose a new transformer
architecture, termed DualFormer, which can effectively and efficiently perform
space-time attention for video recognition. Specifically, our DualFormer
stratifies the full space-time attention into dual cascaded levels, i.e., to
first learn fine-grained local space-time interactions among nearby 3D tokens,
followed by the capture of coarse-grained global dependencies between the query
token and the coarse-grained global pyramid contexts. Different from existing
methods that apply space-time factorization or restrict attention computations
within local windows for improving efficiency, our local-global stratified
strategy can well capture both short- and long-range spatiotemporal
dependencies, and meanwhile greatly reduces the number of keys and values in
attention computation to boost efficiency. Experimental results show the
superiority of DualFormer on five video benchmarks against existing methods. In
particular, DualFormer sets new state-of-the-art 82.9%/85.2% top-1 accuracy on
Kinetics-400/600 with around 1000G inference FLOPs which is at least 3.2 times
fewer than existing methods with similar performances. We have released our
code at https://github.com/sail-sg/dualformer.

Learned sparse and dense representations capture different successful
approaches to text retrieval and the fusion of their results has proven to be
more effective and robust. Prior work combines dense and sparse retrievers by
fusing their model scores. As an alternative, this paper presents a simple
approach to densifying sparse representations for text retrieval that does not
involve any training. Our densified sparse representations (DSRs) are
interpretable and can be easily combined with dense representations for
end-to-end retrieval. We demonstrate that our approach can jointly learn sparse
and dense representations within a single model and then combine them for dense
retrieval. Experimental results suggest that combining our DSRs and dense
representations yields a balanced tradeoff between effectiveness and

In this paper, we tackle the problem of one-shot unsupervised domain
adaptation (OSUDA) for semantic segmentation where the segmentors only see one
unlabeled target image during training. In this case, traditional unsupervised
domain adaptation models usually fail since they cannot adapt to the target
domain with over-fitting to one (or few) target samples. To address this
problem, existing OSUDA methods usually integrate a style-transfer module to
perform domain randomization based on the unlabeled target sample, with which
multiple domains around the target sample can be explored during training.
However, such a style-transfer module relies on an additional set of images as
style reference for pre-training and also increases the memory demand for
domain adaptation. Here we propose a new OSUDA method that can effectively
relieve such computational burden. Specifically, we integrate several
style-mixing layers into the segmentor which play the role of style-transfer
module to stylize the source images without introducing any learned parameters.
Moreover, we propose a patchwise prototypical matching (PPM) method to weighted
consider the importance of source pixels during the supervised training to
relieve the negative adaptation. Experimental results show that our method
achieves new state-of-the-art performance on two commonly used benchmarks for
domain adaptive semantic segmentation under the one-shot setting and is more
efficient than all comparison approaches.

Recently, cluster contrastive learning has been proven effective for object
ReID by computing the contrastive loss between the individual features and the
cluster memory. However, existing methods that use the individual features to
momentum update the cluster memory will fluctuate over the training examples,
especially for the outlier samples. Unlike the individual-based updating
mechanism, the centroid-based updating mechanism that applies the mean feature
of each cluster to update the cluster memory can reduce the impact of
individual samples. Therefore, we formulate the individual-based updating and
centroid-based updating mechanisms in a unified cluster contrastive framework,
named Dual Cluster Contrastive framework (DCC), which maintains two types of
memory banks: individual and centroid cluster memory banks. Significantly, the
individual cluster memory considers just one individual at a time to take a
single step for updating. The centroid cluster memory applies the mean feature
of each cluster to update the corresponding cluster memory. During
optimization, besides the vallina contrastive loss of each memory, a cross-view
consistency constraint is applied to exchange the benefits of two memories for
generating a discriminative description for the object ReID. Note that DCC can
be easily applied for unsupervised or supervised object ReID by using
ground-truth labels or the generated pseudo-labels. Extensive experiments on
three benchmarks, \emph{e.g.,} Market-1501, MSMT17, and VeRi-776, under
\textbf{supervised Object ReID} and \textbf{unsupervised Object ReID}
demonstrate the superiority of the proposed DCC.

In this paper, we propose a new Hessian inverse free Fully Single Loop
Algorithm (FSLA) for bilevel optimization problems. Classic algorithms for
bilevel optimization admit a double loop structure which is computationally
expensive. Recently, several single loop algorithms have been proposed with
optimizing the inner and outer variable alternatively. However, these
algorithms not yet achieve fully single loop. As they overlook the loop needed
to evaluate the hyper-gradient for a given inner and outer state. In order to
develop a fully single loop algorithm, we first study the structure of the
hyper-gradient and identify a general approximation formulation of
hyper-gradient computation that encompasses several previous common approaches,
e.g. back-propagation through time, conjugate gradient, \emph{etc.} Based on
this formulation, we introduce a new state variable to maintain the historical
hyper-gradient information. Combining our new formulation with the alternative
update of the inner and outer variables, we propose an efficient fully single
loop algorithm. We theoretically show that the error generated by the new state
can be bounded and our algorithm converges with the rate of $O(\epsilon^{-2})$.
Finally, we verify the efficacy our algorithm empirically through multiple
bilevel optimization based machine learning tasks.

We present an algorithm for the calibration of local volatility from market
option prices through deep self-consistent learning, by approximating market
option prices and local volatility using deep neural networks. Our method uses
the initial-boundary value problem of the underlying Dupire's partial
differential equation solved by the parameterized option prices to bring
corrections to the parameterization in a self-consistent way. By exploiting the
differentiability of the neural networks, we can evaluate Dupire's equation
locally at each maturity-strike pair; while by exploiting their continuity, we
sample maturity-strike pairs uniformly from a given domain, going beyond the
discrete points where the options are quoted. For comparison with existing
approaches, the proposed method is tested on both synthetic and market option
prices, which shows an improved performance in terms of repricing error, no
violation of the no-arbitrage constraints, and smoothness of the calibrated
local volatility.

Coordinate-based networks have emerged as a powerful tool for 3D
representation and scene reconstruction. These networks are trained to map
continuous input coordinates to the value of a signal at each point. Still,
current architectures are black boxes: their spectral characteristics cannot be
easily analyzed, and their behavior at unsupervised points is difficult to
predict. Moreover, these networks are typically trained to represent a signal
at a single scale, so naive downsampling or upsampling results in artifacts. We
introduce band-limited coordinate networks (BACON), a network architecture with
an analytical Fourier spectrum. BACON has constrained behavior at unsupervised
points, can be designed based on the spectral characteristics of the
represented signal, and can represent signals at multiple scales without
per-scale supervision. We demonstrate BACON for multiscale neural
representation of images, radiance fields, and 3D scenes using signed distance
functions and show that it outperforms conventional single-scale coordinate
networks in terms of interpretability and quality.

Numerous applications of machine learning involve representing probability
distributions over high-dimensional data. We propose autoregressive quantile
flows, a flexible class of normalizing flow models trained using a novel
objective based on proper scoring rules. Our objective does not require
calculating computationally expensive determinants of Jacobians during training
and supports new types of neural architectures, such as neural autoregressive
flows from which it is easy to sample.
  We leverage these models in quantile flow regression, an approach that
parameterizes predictive conditional distributions with flows, resulting in
improved probabilistic predictions on tasks such as time series forecasting and
object detection. Our novel objective functions and neural flow
parameterizations also yield improvements on popular generation and density
estimation tasks, and represent a step beyond maximum likelihood learning of
flows.

Learning from continuous data streams via classification/regression is
prevalent in many domains. Adapting to evolving data characteristics (concept
drift) while protecting data owners' private information is an open challenge.
We present a differentially private ensemble solution to this problem with two
distinguishing features: it allows an \textit{unbounded} number of ensemble
updates to deal with the potentially never-ending data streams under a fixed
privacy budget, and it is \textit{model agnostic}, in that it treats any
pre-trained differentially private classification/regression model as a
black-box. Our method outperforms competitors on real-world and simulated
datasets for varying settings of privacy, concept drift, and data distribution.

Safe autonomous navigation in unknown environments is an important problem
for mobile robots. This paper proposes techniques to learn the dynamics model
of a mobile robot from trajectory data and synthesize a tracking controller
with safety and stability guarantees. The state of a rigid-body robot usually
contains its position, orientation, and generalized velocity and satisfies
Hamilton's equations of motion. Instead of a hand-derived dynamics model, we
use a dataset of state-control trajectories to train a translation-equivariant
nonlinear Hamiltonian model represented as a neural ordinary differential
equation (ODE) network. The learned Hamiltonian model is used to synthesize an
energy-shaping passivity-based controller and derive conditions which guarantee
safe regulation to a desired reference pose. We enable adaptive tracking of a
desired path, subject to safety constraints obtained from obstacle distance
measurements. The trade-off between the robot's energy and the distance to
safety constraint violation is used to adaptively govern a reference pose along
the desired path. Our safe adaptive controller is demonstrated on a simulated
hexarotor robot navigating in an unknown environments.

Graph neural networks (GNNs) are composed of layers consisting of graph
convolutions and pointwise nonlinearities. Due to their invariance and
stability properties, GNNs are provably successful at learning representations
from data supported on moderate-scale graphs. However, they are difficult to
learn on large-scale graphs. In this paper, we study the problem of training
GNNs on graphs of moderate size and transferring them to large-scale graphs. We
use graph limits called graphons to define limit objects for graph filters and
GNNs -- graphon filters and graphon neural networks (WNNs) -- which we
interpret as generative models for graph filters and GNNs. We then show that
graphon filters and WNNs can be approximated by graph filters and GNNs sampled
from them on weighted and stochastic graphs. Because the error of these
approximations can be upper bounded, by a triangle inequality argument we can
further bound the error of transferring a graph filter or a GNN across graphs.
Our results show that (i) the transference error decreases with the graph size,
and (ii) that graph filters have a transferability-discriminability tradeoff
that in GNNs is alleviated by the scattering behavior of the nonlinearity.
These findings are demonstrated empirically in a movie recommendation problem
and in a decentralized control task.

Monocular 3D object detection aims to localize 3D bounding boxes in an input
single 2D image. It is a highly challenging problem and remains open,
especially when no extra information (e.g., depth, lidar and/or multi-frames)
can be leveraged in training and/or inference. This paper proposes a simple yet
effective formulation for monocular 3D object detection without exploiting any
extra information. It presents the MonoCon method which learns Monocular
Contexts, as auxiliary tasks in training, to help monocular 3D object
detection. The key idea is that with the annotated 3D bounding boxes of objects
in an image, there is a rich set of well-posed projected 2D supervision signals
available in training, such as the projected corner keypoints and their
associated offset vectors with respect to the center of 2D bounding box, which
should be exploited as auxiliary tasks in training. The proposed MonoCon is
motivated by the Cramer-Wold theorem in measure theory at a high level. In
implementation, it utilizes a very simple end-to-end design to justify the
effectiveness of learning auxiliary monocular contexts, which consists of three
components: a Deep Neural Network (DNN) based feature backbone, a number of
regression head branches for learning the essential parameters used in the 3D
bounding box prediction, and a number of regression head branches for learning
auxiliary contexts. After training, the auxiliary context regression branches
are discarded for better inference efficiency. In experiments, the proposed
MonoCon is tested in the KITTI benchmark (car, pedestrain and cyclist). It
outperforms all prior arts in the leaderboard on car category and obtains
comparable performance on pedestrian and cyclist in terms of accuracy. Thanks
to the simple design, the proposed MonoCon method obtains the fastest inference
speed with 38.7 fps in comparisons

Understanding how adult humans learn to categorize can shed novel insights
into the mechanisms underlying experience-dependent brain plasticity.
Drift-diffusion processes are popular in such contexts for their ability to
mimic underlying neural mechanisms but require data on both category responses
and associated response times for inference. Category response accuracies are,
however, often the only reliable measure recorded by behavioral scientists to
describe human learning. Building carefully on drift-diffusion models with
latent response times, we derive a biologically interpretable inverse-probit
categorical probability model for such data. The model, however, presents
significant identifiability and inference challenges. We address these
challenges via a novel projection-based approach with a symmetry preserving
identifiability constraint that allows us to work with conjugate priors in an
unconstrained space. We adapt the model for group and individual level
inference in longitudinal settings. Building again on the model's latent
variable representation, we design an efficient Markov chain Monte Carlo
algorithm for posterior computation. We evaluate the method's empirical
performances through simulation experiments. The method's practical efficacy is
illustrated in applications to longitudinal tone learning studies.

Bayesian optimization is a sequential procedure for obtaining the global
optimum of black-box functions without knowing a priori their true form. Good
uncertainty estimates over the shape of the objective function are essential in
guiding the optimization process. However, these estimates can be inaccurate if
the true objective function violates assumptions made by its model (e.g.,
Gaussianity). This paper studies which uncertainties are needed in Bayesian
optimization models and argues that ideal uncertainties should be calibrated --
i.e., an 80% predictive interval should contain the true outcome 80% of the
time. We propose a simple algorithm for enforcing this property and show that
it enables Bayesian optimization to arrive at the global optimum in fewer
steps. We provide theoretical insights into the role of calibrated
uncertainties and demonstrate the improved performance of our method on
standard benchmark functions and hyperparameter optimization tasks.

As neural networks have become more powerful, there has been a rising desire
to deploy them in the real world; however, the power and accuracy of neural
networks is largely due to their depth and complexity, making them difficult to
deploy, especially in resource-constrained devices. Neural network quantization
has recently arisen to meet this demand of reducing the size and complexity of
neural networks by reducing the precision of a network. With smaller and
simpler networks, it becomes possible to run neural networks within the
constraints of their target hardware. This paper surveys the many neural
network quantization techniques that have been developed in the last decade.
Based on this survey and comparison of neural network quantization techniques,
we propose future directions of research in the area.

Estimating a time-varying spatial covariance matrix for a beamforming
algorithm is a challenging task, especially for wearable devices, as the
algorithm must compensate for time-varying signal statistics due to rapid
pose-changes. In this paper, we propose Neural Integrated Covariance Estimators
for Beamformers, NICE-Beam. NICE-Beam is a general technique for learning how
to estimate time-varying spatial covariance matrices, which we apply to joint
speech enhancement and dereverberation. It is based on training a neural
network module to non-linearly track and leverage scene information across
time. We integrate our solution into a beamforming pipeline, which enables
simple training, faster than real-time inference, and a variety of test-time
adaptation options. We evaluate the proposed model against a suite of baselines
in scenes with both stationary and moving microphones. Our results show that
the proposed method can outperform a hand-tuned estimator, despite the
hand-tuned estimator using oracle source separation knowledge.

We propose a method for learning constraints represented as Gaussian
processes (GPs) from locally-optimal demonstrations. Our approach uses the
Karush-Kuhn-Tucker (KKT) optimality conditions to determine where on the
demonstrations the constraint is tight, and a scaling of the constraint
gradient at those states. We then train a GP representation of the constraint
which is consistent with and which generalizes this information. We further
show that the GP uncertainty can be used within a kinodynamic RRT to plan
probabilistically-safe trajectories, and that we can exploit the GP structure
within the planner to exactly achieve a specified safety probability. We
demonstrate our method can learn complex, nonlinear constraints demonstrated on
a 5D nonholonomic car, a 12D quadrotor, and a 3-link planar arm, all while
requiring minimal prior information on the constraint. Our results suggest the
learned GP constraint is accurate, outperforming previous constraint learning
methods that require more a priori knowledge.

Visual scanpath is the sequence of fixation points that the human gaze
travels while observing an image, and its prediction helps in modeling the
visual attention of an image. To this end several models were proposed in the
literature using complex deep learning architectures and frameworks. Here, we
explore the efficiency of using common deep learning architectures, in a simple
fully convolutional regressive manner. We experiment how well these models can
predict the scanpaths on 2 datasets. We compare with other models using
different metrics and show competitive results that sometimes surpass previous
complex architectures. We also compare the different leveraged backbone
architectures based on their performances on the experiment to deduce which
ones are the most suitable for the task.

Half of long-term care (LTC) residents are malnourished increasing
hospitalization, mortality, morbidity, with lower quality of life. Current
tracking methods are subjective and time consuming. This paper presents the
automated food imaging and nutrient intake tracking (AFINI-T) technology
designed for LTC. We propose a novel convolutional autoencoder for food
classification, trained on an augmented UNIMIB2016 dataset and tested on our
simulated LTC food intake dataset (12 meal scenarios; up to 15 classes each;
top-1 classification accuracy: 88.9%; mean intake error: -0.4 mL$\pm$36.7 mL).
Nutrient intake estimation by volume was strongly linearly correlated with
nutrient estimates from mass ($r^2$ 0.92 to 0.99) with good agreement between
methods ($\sigma$= -2.7 to -0.01; zero within each of the limits of agreement).
The AFINI-T approach is a deep-learning powered computational nutrient sensing
system that may provide a novel means for more accurately and objectively
tracking LTC resident food intake to support and prevent malnutrition tracking

We are interested in representation learning in self-supervised, supervised,
and semi-supervised settings. Some recent self-supervised learning methods like
mean-shift (MSF) cluster images by pulling the embedding of a query image to be
closer to its nearest neighbors (NNs). Since most NNs are close to the query by
design, the averaging may not affect the embedding of the query much. On the
other hand, far away NNs may not be semantically related to the query. We
generalize the mean-shift idea by constraining the search space of NNs using
another source of knowledge so that NNs are far from the query while still
being semantically related. We show that our method (1) outperforms MSF in SSL
setting when the constraint utilizes a different augmentation of an image from
the previous epoch, and (2) outperforms PAWS in semi-supervised setting with
less training resources when the constraint ensures that the NNs have the same
pseudo-label as the query.

We have created a knowledge graph based on major data sources used in
ecotoxicological risk assessment. We have applied this knowledge graph to an
important task in risk assessment, namely chemical effect prediction. We have
evaluated nine knowledge graph embedding models from a selection of geometric,
decomposition, and convolutional models on this prediction task. We show that
using knowledge graph embeddings can increase the accuracy of effect prediction
with neural networks. Furthermore, we have implemented a fine-tuning
architecture that adapts the knowledge graph embeddings to the effect
prediction task and leads to better performance. Finally, we evaluate certain
characteristics of the knowledge graph embedding models to shed light on the
individual model performance.

The problem of forecasting the whole 24 profile of the Italian electric load
is addressed as a multitask learning problem, whose complexity is kept under
control via alternative regularization methods. In view of the quarter-hourly
samplings, 96 predictors are used, each of which linearly depends on 96
regressors. The 96x96 matrix weights form a 96x96 matrix, that can be seen and
displayed as a surface sampled on a square domain. Different regularization and
sparsity approaches to reduce the degrees of freedom of the surface were
explored, comparing the obtained forecasts with those of the Italian
Transmission System Operator Terna. Besides outperforming Terna in terms of
quarter-hourly mean absolute percentage error and mean absolute error, the
prediction residuals turned out to be weakly correlated with Terna, which
suggests that further improvement could ensue from forecasts aggregation. In
fact, the aggregated forecasts yielded further relevant drops in terms of
quarter-hourly and daily mean absolute percentage error, mean absolute error
and root mean square error (up to 30%) over the three test years considered.

Generation of photo-realistic images, semantic editing and representation
learning are a few of many potential applications of high resolution generative
models. Recent progress in GANs have established them as an excellent choice
for such tasks. However, since they do not provide an inference model, image
editing or downstream tasks such as classification can not be done on real
images using the GAN latent space. Despite numerous efforts to train an
inference model or design an iterative method to invert a pre-trained
generator, previous methods are dataset (e.g. human face images) and
architecture (e.g. StyleGAN) specific. These methods are nontrivial to extend
to novel datasets or architectures. We propose a general framework that is
agnostic to architecture and datasets. Our key insight is that, by training the
inference and the generative model together, we allow them to adapt to each
other and to converge to a better quality model. Our \textbf{InvGAN}, short for
Invertible GAN, successfully embeds real images to the latent space of a high
quality generative model. This allows us to perform image inpainting, merging,
interpolation and online data augmentation. We demonstrate this with extensive
qualitative and quantitative experiments.

This paper discusses basic results and recent developments on variational
regularization methods, as developed for inverse problems. In a typical setup
we review basic properties needed to obtain a convergent regularization scheme
and further discuss the derivation of quantitative estimates respectively
needed ingredients such as Bregman distances for convex functionals.
  In addition to the approach developed for inverse problems we will also
discuss variational regularization in machine learning and work out some
connections to the classical regularization theory. In particular we will
discuss a reinterpretation of machine learning problems in the framework of
regularization theory and a reinterpretation of variational methods for inverse
problems in the framework of risk minimization. Moreover, we establish some
previously unknown connections between error estimates in Bregman distances and
generalization errors.

Van Rooyen et al. introduced a notion of convex loss functions being robust
to random classification noise, and established that the "unhinged" loss
function is robust in this sense. In this note we study the accuracy of binary
classifiers obtained by minimizing the unhinged loss, and observe that even for
simple linearly separable data distributions, minimizing the unhinged loss may
only yield a binary classifier with accuracy no better than random guessing.

We propose MASTAF, a Model-Agnostic Spatio-Temporal Attention Fusion network
for few-shot video classification. MASTAF takes input from a general video
spatial and temporal representation,e.g., using 2D CNN, 3D CNN, and Video
Transformer. Then, to make the most of such representations, we use self- and
cross-attention models to highlight the critical spatio-temporal region to
increase the inter-class variations and decrease the intra-class variations.
Last, MASTAF applies a lightweight fusion network and a nearest neighbor
classifier to classify each query video. We demonstrate that MASTAF improves
the state-of-the-art performance on three few-shot video classification
benchmarks(UCF101, HMDB51, and Something-Something-V2), e.g., by up to 91.6%,
69.5%, and 60.7% for five-way one-shot video classification, respectively.

The problem of estimating the divergence between 2 high dimensional
distributions with limited samples is an important problem in various fields
such as machine learning. Although previous methods perform well with moderate
dimensional data, their accuracy starts to degrade in situations with 100s of
binary variables. Therefore, we propose the use of decomposable models for
estimating divergences in high dimensional data. These allow us to factorize
the estimated density of the high-dimensional distribution into a product of
lower dimensional functions. We conduct formal and experimental analyses to
explore the properties of using decomposable models in the context of
divergence estimation. To this end, we show empirically that estimating the
Kullback-Leibler divergence using decomposable models from a maximum likelihood
estimator outperforms existing methods for divergence estimation in situations
where dimensionality is high and useful decomposable models can be learnt from
the available data.

Generating high-quality random numbers with a Gaussian probability
distribution function is an important and resource consuming computational task
for many applications in the fields of machine learning and Monte Carlo
algorithms. Recently, CMOS-based digital hardware architectures have been
explored as specialized Gaussian random number generators (GRNGs). These
CMOS-based GRNGs have a large area and require entropy sources at their input
which increase the computing cost. Here, we propose a GRNG that works on the
principle of the Boltzmann law in a physical system made from an interconnected
network of thermally unstable magnetic tunnel junctions. The proposed hardware
can produce multi-bit Gaussian random numbers at a gigahertz speed and can be
configured to generate distributions with a desired mean and variance. An
analytical derivation of the required interconnection and bias strengths is
provided followed by numerical simulations to demonstrate the functionalities
of the GRNG.

Graph neural networks (GNNs) have demonstrated great success in
representation learning for graph-structured data. The layer-wise graph
convolution in GNNs is shown to be powerful at capturing graph topology. During
this process, GNNs are usually guided by pre-defined kernels such as Laplacian
matrix, adjacency matrix, or their variants. However, the adoptions of
pre-defined kernels may restrain the generalities to different graphs: mismatch
between graph and kernel would entail sub-optimal performance. For example,
GNNs that focus on low-frequency information may not achieve satisfactory
performance when high-frequency information is significant for the graphs, and
vice versa. To solve this problem, in this paper, we propose a novel framework
- i.e., namely Adaptive Kernel Graph Neural Network (AKGNN) - which learns to
adapt to the optimal graph kernel in a unified manner at the first attempt. In
the proposed AKGNN, we first design a data-driven graph kernel learning
mechanism, which adaptively modulates the balance between all-pass and low-pass
filters by modifying the maximal eigenvalue of the graph Laplacian. Through
this process, AKGNN learns the optimal threshold between high and low frequency
signals to relieve the generality problem. Later, we further reduce the number
of parameters by a parameterization trick and enhance the expressive power by a
global readout function. Extensive experiments are conducted on acknowledged
benchmark datasets and promising results demonstrate the outstanding
performance of our proposed AKGNN by comparison with state-of-the-art GNNs. The
source code is publicly available at: https://github.com/jumxglhf/AKGNN.

Machine-part interaction classification is a key capability required by
Cyber-Physical Systems (CPS), a pivotal enabler of Smart Manufacturing (SM).
While previous relevant studies on the subject have primarily focused on time
series classification, change point detection is equally important because it
provides temporal information on changes in behavior of the machine. In this
work, we address point detection and time series classification for
machine-part interactions with a deep Convolutional Neural Network (CNN) based
framework. The CNN in this framework utilizes a two-stage encoder-classifier
structure for efficient feature representation and convenient deployment
customization for CPS. Though data-driven, the design and optimization of the
framework are Subject Matter Expertise (SME) guided. An SME defined Finite
State Machine (FSM) is incorporated into the framework to prohibit intermittent
misclassifications. In the case study, we implement the framework to perform
machine-part interaction classification on a milling machine, and the
performance is evaluated using a testing dataset and deployment simulations.
The implementation achieved an average F1-Score of 0.946 across classes on the
testing dataset and an average delay of 0.24 seconds on the deployment

A main research goal in various studies is to use an observational data set
and provide a new set of counterfactual guidelines that can yield causal
improvements. Dynamic Treatment Regimes (DTRs) are widely studied to formalize
this process. However, available methods in finding optimal DTRs often rely on
assumptions that are violated in real-world applications (e.g., medical
decision-making or public policy), especially when (a) the existence of
unobserved confounders cannot be ignored, and (b) the unobserved confounders
are time-varying (e.g., affected by previous actions). When such assumptions
are violated, one often faces ambiguity regarding the underlying causal model
that is needed to be assumed to obtain an optimal DTR. This ambiguity is
inevitable, since the dynamics of unobserved confounders and their causal
impact on the observed part of the data cannot be understood from the observed
data. Motivated by a case study of finding superior treatment regimes for
patients who underwent transplantation in our partner hospital and faced a
medical condition known as New Onset Diabetes After Transplantation (NODAT), we
extend DTRs to a new class termed Ambiguous Dynamic Treatment Regimes (ADTRs),
in which the causal impact of treatment regimes is evaluated based on a "cloud"
of potential causal models. We then connect ADTRs to Ambiguous Partially
Observable Mark Decision Processes (APOMDPs) proposed by Saghafian (2018), and
develop two Reinforcement Learning methods termed Direct Augmented V-Learning
(DAV-Learning) and Safe Augmented V-Learning (SAV-Learning), which enable using
the observed data to efficiently learn an optimal treatment regime. We
establish theoretical results for these learning methods, including (weak)
consistency and asymptotic normality. We further evaluate the performance of
these learning methods both in our case study and in simulation experiments.

In this paper, we propose a novel co-learning framework (CoSSL) with
decoupled representation learning and classifier learning for imbalanced SSL.
To handle the data imbalance, we devise Tail-class Feature Enhancement (TFE)
for classifier learning. Furthermore, the current evaluation protocol for
imbalanced SSL focuses only on balanced test sets, which has limited
practicality in real-world scenarios. Therefore, we further conduct a
comprehensive evaluation under various shifted test distributions. In
experiments, we show that our approach outperforms other methods over a large
range of shifted distributions, achieving state-of-the-art performance on
benchmark datasets ranging from CIFAR-10, CIFAR-100, ImageNet, to Food-101. Our

The rapid adoption of facial recognition (FR) technology by both government
and commercial entities in recent years has raised concerns about civil
liberties and privacy. In response, a broad suite of so-called "anti-facial
recognition" (AFR) tools has been developed to help users avoid unwanted facial
recognition. The set of AFR tools proposed in the last few years is
wide-ranging and rapidly evolving, necessitating a step back to consider the
broader design space of AFR systems and long-term challenges. This paper aims
to fill that gap and provides the first comprehensive analysis of the AFR
research landscape. Using the operational stages of FR systems as a starting
point, we create a systematic framework for analyzing the benefits and
tradeoffs of different AFR approaches. We then consider both technical and
social challenges facing AFR tools and propose directions for future research

Human annotations play a crucial role in machine learning (ML) research and
development. However, the ethical considerations around the processes and
decisions that go into building ML datasets has not received nearly enough
the crowdsourcing platforms and what that relationship affords them. Finally,
we put forth a concrete set of recommendations and considerations for dataset
developers at various stages of the ML data pipeline: task formulation,
selection of annotators, platform and infrastructure choices, dataset analysis
and evaluation, and dataset documentation and release.

The rapid changes in the finance industry due to the increasing amount of
data have revolutionized the techniques on data processing and data analysis
and brought new theoretical and computational challenges. In contrast to
classical stochastic control theory and other analytical approaches for solving
financial decision-making problems that heavily reply on model assumptions, new
developments from reinforcement learning (RL) are able to make full use of the
large amount of financial data with fewer model assumptions and to improve
decisions in complex financial environments. This survey paper aims to review
the recent developments and use of RL approaches in finance. We give an
introduction to Markov decision processes, which is the setting for many of the
commonly used RL approaches. Various algorithms are then introduced with a
focus on value and policy based methods that do not require any model
assumptions. Connections are made with neural networks to extend the framework
to encompass deep RL algorithms. Our survey concludes by discussing the
application of these RL algorithms in a variety of decision-making problems in
finance, including optimal execution, portfolio optimization, option pricing
and hedging, market making, smart order routing, and robo-advising.

In this paper, we propose PATO-a producibility-aware topology optimization
(TO) framework to help efficiently explore the design space of components
fabricated using metal additive manufacturing (AM), while ensuring
manufacturability with respect to cracking. Specifically, parts fabricated
through Laser Powder Bed Fusion are prone to defects such as warpage or
cracking due to high residual stress values generated from the steep thermal
gradients produced during the build process. Maturing the design for such parts
and planning their fabrication can span months to years, often involving
multiple handoffs between design and manufacturing engineers. PATO is based on
the a priori discovery of crack-free designs, so that the optimized part can be
built defect-free at the outset. To ensure that the design is crack free during
optimization, producibility is explicitly encoded within the standard
formulation of TO, using a crack index. Multiple crack indices are explored and
using experimental validation, maximum shear strain index (MSSI) is shown to be
an accurate crack index. Simulating the build process is a coupled,
multi-physics computation and incorporating it in the TO loop can be
computationally prohibitive. We leverage the current advances in deep
convolutional neural networks and present a high-fidelity surrogate model based
on an Attention-based U-Net architecture to predict the MSSI values as a
spatially varying field over the part's domain. Further, we employ automatic
differentiation to directly compute the gradient of maximum MSSI with respect
to the input design variables and augment it with the performance-based
sensitivity field to optimize the design while considering the trade-off
between weight, manufacturability, and functionality. We demonstrate the
effectiveness of the proposed method through benchmark studies in 3D as well as
experimental validation.

Recognizing unseen relations with no training instances is a challenging task
in the real world. In this paper, we propose a prompt-based model with semantic
knowledge augmentation (ZS-SKA) to recognize unseen relations under the
zero-shot setting. We generate augmented instances with unseen relations from
instances with seen relations following a new word-level sentence translation
rule. We design prompts based on an external knowledge graph to integrate
semantic knowledge information learned from seen relations. Instead of using
the actual label sets in the prompt template, we construct weighted virtual
label words. By generating the representations of both seen and unseen
relations with augmented instances and prompts through prototypical networks,
distance is calculated to predict unseen relations. Extensive experiments
conducted on three public datasets show that ZS-SKA outperforms
state-of-the-art methods under the zero-shot scenarios. Our experimental
results also demonstrate the effectiveness and robustness of ZS-SKA.

An approach to field theory is studied in which fields are comprised of $N$
constituent random neurons. Gaussian theories arise in the infinite-$N$ limit
when neurons are independently distributed, via the Central Limit Theorem,
while interactions arise due to finite-$N$ effects or non-independently
distributed neurons. Euclidean-invariant ensembles of neurons are engineered,
with tunable two-point function, yielding families of Euclidean-invariant field
theories. Some Gaussian, Euclidean invariant theories are reflection positive,
which allows for analytic continuation to a Lorentz-invariant quantum field
theory. Examples are presented that yield dual theories at infinite-$N$, but
have different symmetries at finite-$N$. Landscapes of classical field
configurations are determined by local maxima of parameter distributions.
Predictions arise from mixed field-neuron correlators. Near-Gaussianity is
exhibited at large-$N$, potentially explaining a feature of field theories in
Nature.

This work presents a self-supervised learning framework named TeG to explore
Temporal Granularity in learning video representations. In TeG, we sample a
long clip from a video and a short clip that lies inside the long clip. We then
extract their dense temporal embeddings. The training objective consists of two
parts: a fine-grained temporal learning objective to maximize the similarity
between corresponding temporal embeddings in the short clip and the long clip,
and a persistent temporal learning objective to pull together global embeddings
of the two clips. Our study reveals the impact of temporal granularity with
three major findings. 1) Different video tasks may require features of
different temporal granularities. 2) Intriguingly, some tasks that are widely
considered to require temporal awareness can actually be well addressed by
temporally persistent features. 3) The flexibility of TeG gives rise to
state-of-the-art results on 8 video benchmarks, outperforming supervised
pre-training in most cases.

Automated experiments in 4D Scanning Transmission Electron Microscopy are
implemented for rapid discovery of local structures, symmetry-breaking
distortions, and internal electric and magnetic fields in complex materials.
Deep kernel learning enables active learning of the relationship between local
structure and a 4D-STEM based descriptors. With this, efficient and
"intelligent" probing of dissimilar structural elements to discover desired
physical functionality is made possible. This approach allows effective
navigation of the sample in an automated fashion guided by either a
pre-determined physical phenomenon, such as strongest electric field magnitude,
or in an exploratory fashion. We verify the approach first on pre-acquired
4D-STEM data, and further implement it experimentally on an operational STEM.
The experimental discovery workflow is demonstrated using graphene, and
subsequently extended towards a lesser-known layered 2D van der Waal material,
MnPS3. This approach establishes a paradigm for physics-driven automated
4D-STEM experiments that enable probing the physics of strongly correlated
systems and quantum materials and devices, as well as exploration of beam
sensitive materials.

Image-based visual-language (I-VL) pre-training has shown great success for
learning joint visual-textual representations from large-scale web data,
revealing remarkable ability for zero-shot generalisation. This paper presents
a simple but strong baseline to efficiently adapt the pre-trained I-VL model,
and exploit its powerful ability for resource-hungry video understanding tasks,
with minimal training. Specifically, we propose to optimise a few random
vectors, termed as continuous prompt vectors, that convert video-related tasks
into the same format as the pre-training objectives. In addition, to bridge the
gap between static images and videos, temporal information is encoded with
lightweight Transformers stacking on top of frame-wise visual features.
Experimentally, we conduct extensive ablation studies to analyse the critical
components. On 10 public benchmarks of action recognition, action localisation,
and text-video retrieval, across closed-set, few-shot, and zero-shot scenarios,
we achieve competitive or state-of-the-art performance to existing methods,
despite optimising significantly fewer parameters.

We study a localized notion of uniform convergence known as an "optimistic
rate" (Panchenko 2002; Srebro et al. 2010) for linear regression with Gaussian
data. Our refined analysis avoids the hidden constant and logarithmic factor in
existing results, which are known to be crucial in high-dimensional settings,
especially for understanding interpolation learning. As a special case, our
analysis recovers the guarantee from Koehler et al. (2021), which tightly
characterizes the population risk of low-norm interpolators under the benign
overfitting conditions. Our optimistic rate bound, though, also analyzes
predictors with arbitrary training error. This allows us to recover some
classical statistical guarantees for ridge and LASSO regression under random
designs, and helps us obtain a precise understanding of the excess risk of
near-interpolators in the over-parameterized regime.

As a seminal tool in self-supervised representation learning, contrastive
learning has gained unprecedented attention in recent years. In essence,
contrastive learning aims to leverage pairs of positive and negative samples
for representation learning, which relates to exploiting neighborhood
information in a feature space. By investigating the connection between
contrastive learning and neighborhood component analysis (NCA), we provide a
novel stochastic nearest neighbor viewpoint of contrastive learning and
subsequently propose a series of contrastive losses that outperform the
existing ones. Under our proposed framework, we show a new methodology to
design integrated contrastive losses that could simultaneously achieve good
accuracy and robustness on downstream tasks. With the integrated framework, we
achieve up to 6\% improvement on the standard accuracy and 17\% improvement on
the robust accuracy.

We develop a new continual meta-learning method to address challenges in
sequential multi-task learning. In this setting, the agent's goal is to achieve
high reward over any sequence of tasks quickly. Prior meta-reinforcement
learning algorithms have demonstrated promising results in accelerating the
acquisition of new tasks. However, they require access to all tasks during
training. Beyond simply transferring past experience to new tasks, our goal is
to devise continual reinforcement learning algorithms that learn to learn,
using their experience on previous tasks to learn new tasks more quickly. We
introduce a new method, continual meta-policy search (CoMPS), that removes this
limitation by meta-training in an incremental fashion, over each task in a
sequence, without revisiting prior tasks. CoMPS continuously repeats two
subroutines: learning a new task using RL and using the experience from RL to
perform completely offline meta-learning to prepare for subsequent task
learning. We find that CoMPS outperforms prior continual learning and
off-policy meta-reinforcement methods on several sequences of challenging
continuous control tasks.

Unlike traditional supervised learning, in many settings only partial
feedback is available. We may only observe outcomes for the chosen actions, but
not the counterfactual outcomes associated with other alternatives. Such
settings encompass a wide variety of applications including pricing, online
marketing and precision medicine. A key challenge is that observational data
are influenced by historical policies deployed in the system, yielding a biased
data distribution. We approach this task as a domain adaptation problem and
propose a self-training algorithm which imputes outcomes with categorical
values for finite unseen actions in the observational data to simulate a
randomized trial through pseudolabeling, which we refer to as Counterfactual
Self-Training (CST). CST iteratively imputes pseudolabels and retrains the
model. In addition, we show input consistency loss can further improve CST
performance which is shown in recent theoretical analysis of pseudolabeling. We
demonstrate the effectiveness of the proposed algorithms on both synthetic and

Training speaker-discriminative and robust speaker verification systems
without speaker labels is still challenging and worthwhile to explore. In this
study, we propose an effective self-supervised learning framework and a novel
regularization strategy to facilitate self-supervised speaker representation
learning. Different from contrastive learning-based self-supervised learning
methods, the proposed self-supervised regularization (SSReg) focuses
exclusively on the similarity between the latent representations of positive
data pairs. We also explore the effectiveness of alternative online data
augmentation strategies on both the time domain and frequency domain. With our
strong online data augmentation strategy, the proposed SSReg shows the
potential of self-supervised learning without using negative pairs and it can
significantly improve the performance of self-supervised speaker representation
learning with a simple Siamese network architecture. Comprehensive experiments
on the VoxCeleb datasets demonstrate that our proposed self-supervised approach
obtains a 23.4% relative improvement by adding the effective self-supervised
regularization and outperforms other previous works.

Due to the representation limitation of the joint Q value function,
multi-agent reinforcement learning methods with linear value decomposition
(LVD) or monotonic value decomposition (MVD) suffer from relative
overgeneralization. As a result, they can not ensure optimal consistency (i.e.,
the correspondence between individual greedy actions and the maximal true Q
value). In this paper, we derive the expression of the joint Q value function
of LVD and MVD. According to the expression, we draw a transition diagram,
where each self-transition node (STN) is a possible convergence. To ensure
optimal consistency, the optimal node is required to be the unique STN.
Therefore, we propose the greedy-based value representation (GVR), which turns
the optimal node into an STN via inferior target shaping and further eliminates
the non-optimal STNs via superior experience replay. In addition, GVR achieves
an adaptive trade-off between optimality and stability. Our method outperforms
state-of-the-art baselines in experiments on various benchmarks. Theoretical
proofs and empirical results on matrix games demonstrate that GVR ensures
optimal consistency under sufficient exploration.

We initiate the first empirical study on the use of MLP architectures for
vision-and-language (VL) fusion. Through extensive experiments on 5 VL tasks
and 5 robust VQA benchmarks, we find that: (i) Without pre-training, using MLPs
for multimodal fusion has a noticeable performance gap compared to
transformers; (ii) However, VL pre-training can help close the performance gap;
(iii) Instead of heavy multi-head attention, adding tiny one-head attention to
MLPs is sufficient to achieve comparable performance to transformers. Moreover,
we also find that the performance gap between MLPs and transformers is not
widened when being evaluated on the harder robust VQA benchmarks, suggesting
using MLPs for VL fusion can generalize roughly to a similar degree as using
transformers. These results hint that MLPs can effectively learn to align
vision and text features extracted from lower-level encoders without heavy
reliance on self-attention. Based on this, we ask an even bolder question: can
we have an all-MLP architecture for VL modeling, where both VL fusion and the
vision encoder are replaced with MLPs? Our result shows that an all-MLP VL
model is sub-optimal compared to state-of-the-art full-featured VL models when
both of them get pre-trained. However, pre-training an all-MLP can surprisingly
achieve a better average score than full-featured transformer models without
pre-training. This indicates the potential of large-scale pre-training of
MLP-like architectures for VL modeling and inspires the future research
direction on simplifying well-established VL modeling with less inductive
design bias. Our code is publicly available at:
https://github.com/easonnie/mlp-vil

Multi-modal learning from video data has seen increased attention recently as
it allows to train semantically meaningful embeddings without human annotation
enabling tasks like zero-shot retrieval and classification. In this work, we
present a multi-modal, modality agnostic fusion transformer approach that
learns to exchange information between multiple modalities, such as video,
audio, and text, and integrate them into a joined multi-modal representation to
obtain an embedding that aggregates multi-modal temporal information. We
propose to train the system with a combinatorial loss on everything at once,
single modalities as well as pairs of modalities, explicitly leaving out any
add-ons such as position or modality encoding. At test time, the resulting
model can process and fuse any number of input modalities. Moreover, the
implicit properties of the transformer allow to process inputs of different
lengths. To evaluate the proposed approach, we train the model on the large
scale HowTo100M dataset and evaluate the resulting embedding space on four
challenging benchmark datasets obtaining state-of-the-art results in zero-shot
video retrieval and zero-shot video action localization.

This paper presents a novel approach for the joint design of a reconfigurable
intelligent surface (RIS) and a transmitter-receiver pair that are trained
together as a set of deep neural networks (DNNs) to optimize the end-to-end
communication performance at the receiver. The RIS is a software-defined array
of unit cells that can be controlled in terms of the scattering and reflection
profiles to focus the incoming signals from the transmitter to the receiver.
The benefit of the RIS is to improve the coverage and spectral efficiency for
wireless communications by overcoming physical obstructions of the
line-of-sight (LoS) links. The selection process of the RIS beam codeword (out
of a pre-defined codebook) is formulated as a DNN, while the operations of the
transmitter-receiver pair are modeled as two DNNs, one for the encoder (at the
transmitter) and the other one for the decoder (at the receiver) of an
autoencoder, by accounting for channel effects including those induced by the
RIS in between. The underlying DNNs are jointly trained to minimize the symbol
error rate at the receiver. Numerical results show that the proposed design
achieves major gains in error performance with respect to various baseline
schemes, where no RIS is used or the selection of the RIS beam is separated
from the design of the transmitter-receiver pair.

We enhance auto-regressive language models by conditioning on document chunks
retrieved from a large corpus, based on local similarity with preceding tokens.
With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO)
obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite
using 25$\times$ fewer parameters. After fine-tuning, RETRO performance
translates to downstream knowledge-intensive tasks such as question answering.
RETRO combines a frozen Bert retriever, a differentiable encoder and a chunked
cross-attention mechanism to predict tokens based on an order of magnitude more
data than what is typically consumed during training. We typically train RETRO
from scratch, yet can also rapidly RETROfit pre-trained transformers with
retrieval and still achieve good performance. Our work opens up new avenues for
improving language models through explicit memory at unprecedented scale.

Unsupervised Zero-Shot Voice Conversion (VC) aims to modify the speaker
characteristic of an utterance to match an unseen target speaker without
relying on parallel training data. Recently, self-supervised learning of speech
representation has been shown to produce useful linguistic units without using
transcripts, which can be directly passed to a VC model. In this paper, we
showed that high-quality audio samples can be achieved by using a length
resampling decoder, which enables the VC model to work in conjunction with
different linguistic feature extractors and vocoders without requiring them to
operate on the same sequence length. We showed that our method can outperform
many baselines on the VCTK dataset. Without modifying the architecture, we
further demonstrated that a) using pairs of different audio segments from the
same speaker, b) adding a cycle consistency loss, and c) adding a speaker
classification loss can help to learn a better speaker embedding. Our model
trained on LibriTTS using these techniques achieves the best performance,
producing audio samples transferred well to the target speaker's voice, while
preserving the linguistic content that is comparable with actual human
utterances in terms of Character Error Rate.

In recent years, there is an influx of deep learning models for 3D vehicle
object detection. However, little attention was paid to orientation prediction.
Existing research work proposed various vehicle orientation representation
methods for deep learning, however a holistic, systematic review has not been
conducted. Through our experiments, we categorize and compare the accuracy
performance of various existing orientation representations using the KITTI 3D
object detection dataset, and propose a new form of orientation representation:
Tricosine. Among these, the 2D Cartesian-based representation, or Single Bin,
achieves the highest accuracy, with additional channeled inputs (positional
encoding and depth map) not boosting prediction performance. Our code is
published on GitHub:
https://github.com/umd-fire-coml/KITTI-orientation-learning

Matching on covariates is a well-established framework for estimating causal
effects in observational studies. The principal challenge stems from the often
high-dimensional structure of the problem. Many methods have been introduced to
address this, with different advantages and drawbacks in computational and
statistical performance as well as interpretability. This article introduces a
natural optimal matching method based on multimarginal unbalanced optimal
transport that possesses many useful properties in this regard. It provides
interpretable weights based on the distance of matched individuals, can be
efficiently implemented via the iterative proportional fitting procedure, and
can match several treatment arms simultaneously. Importantly, the proposed
method only selects good matches from either group, hence is competitive with
the classical k-nearest neighbors approach in terms of bias and variance in
finite samples. Moreover, we prove a central limit theorem for the empirical
process of the potential functions of the optimal coupling in the unbalanced
optimal transport problem with a fixed penalty term. This implies a parametric
rate of convergence of the empirically obtained weights to the optimal weights
in the population for a fixed penalty term.

We introduce a variant of the speaker localization problem, which we call
device arbitration. In the device arbitration problem, a user utters a keyword
that is detected by multiple distributed microphone arrays (smart home
devices), and we want to determine which device was closest to the user. Rather
than solving the full localization problem, we propose an end-to-end machine
learning system. This system learns a feature embedding that is computed
independently on each device. The embeddings from each device are then
aggregated together to produce the final arbitration decision. We use a
large-scale room simulation to generate training and evaluation data, and
compare our system against a signal processing baseline.

Objective: Continuous monitoring of biosignals via wearable sensors has
quickly expanded in the medical and wellness fields. At rest, automatic
detection of vital parameters is generally accurate. However, in conditions
such as high-intensity exercise, sudden physiological changes occur to the
signals, compromising the robustness of standard algorithms. Methods: Our
method, called BayeSlope, is based on unsupervised learning, Bayesian
filtering, and non-linear normalization to enhance and correctly detect the R
peaks according to their expected positions in the ECG. Furthermore, as
BayeSlope is computationally heavy and can drain the device battery quickly, we
propose an online design that adapts its robustness to sudden physiological
changes, and its complexity to the heterogeneous resources of modern embedded
platforms. This method combines BayeSlope with a lightweight algorithm,
executed in cores with different capabilities, to reduce the energy consumption
while preserving the accuracy. Results: BayeSlope achieves an F1 score of 99.3%
in experiments during intense cycling exercise with 20 subjects. Additionally,
the online adaptive process achieves an F1 score of 99% across five different
exercise intensities, with a total energy consumption of 1.55+-0.54~mJ.
Conclusion: We propose a highly accurate and robust method, and a complete
energy-efficient implementation in a modern ultra-low-power embedded platform
to improve R peak detection in challenging conditions, such as during
high-intensity exercise. Significance: The experiments show that BayeSlope
outperforms a state-of-the-art algorithm up to 8.4% in F1 score, while our
online adaptive method can reach energy savings up to 38.7% on modern
heterogeneous wearable platforms.

In informational recommenders, many challenges arise from the need to handle
the semantic and hierarchical structure between knowledge areas. This work aims
to advance towards building a state-aware educational recommendation system
that incorporates semantic relatedness between knowledge topics, propagating
latent information across semantically related topics. We introduce a novel
learner model that exploits this semantic relatedness between knowledge
components in learning resources using the Wikipedia link graph, with the aim
to better predict learner engagement and latent knowledge in a lifelong
learning scenario. In this sense, Semantic TrueLearn builds a humanly intuitive
knowledge representation while leveraging Bayesian machine learning to improve
the predictive performance of the educational engagement. Our experiments with
a large dataset demonstrate that this new semantic version of TrueLearn
algorithm achieves statistically significant improvements in terms of
predictive performance with a simple extension that adds semantic awareness to
the model.

Recent self-supervision methods have found success in learning feature
representations that could rival ones from full supervision, and have been
shown to be beneficial to the model in several ways: for example improving
models robustness and out-of-distribution detection. In our paper, we conduct
an empirical study to understand more precisely in what way can self-supervised
learning - as a pre-training technique or part of adversarial training -
affects model robustness to $l_2$ and $l_{\infty}$ adversarial perturbations
and natural image corruptions. Self-supervision can indeed improve model
robustness, however it turns out the devil is in the details. If one simply
adds self-supervision loss in tandem with adversarial training, then one sees
improvement in accuracy of the model when evaluated with adversarial
perturbations smaller or comparable to the value of $\epsilon_{train}$ that the
robust model is trained with. However, if one observes the accuracy for
$\epsilon_{test} \ge \epsilon_{train}$, the model accuracy drops. In fact, the
larger the weight of the supervision loss, the larger the drop in performance,
i.e. harming the robustness of the model. We identify primary ways in which
self-supervision can be added to adversarial training, and observe that using a
self-supervised loss to optimize both network parameters and find adversarial
examples leads to the strongest improvement in model robustness, as this can be
viewed as a form of ensemble adversarial training. Although self-supervised
pre-training yields benefits in improving adversarial training as compared to
random weight initialization, we observe no benefit in model robustness or
accuracy if self-supervision is incorporated into adversarial training.

Motivated by the learned iterative soft thresholding algorithm (LISTA), we
introduce a general class of neural networks suitable for sparse reconstruction
from few linear measurements. By allowing a wide range of degrees of
weight-sharing between the layers, we enable a unified analysis for very
different neural network types, ranging from recurrent ones to networks more
similar to standard feedforward neural networks. Based on training samples, via
empirical risk minimization we aim at learning the optimal network parameters
and thereby the optimal network that reconstructs signals from their
low-dimensional linear measurements. We derive generalization bounds by
analyzing the Rademacher complexity of hypothesis classes consisting of such
deep networks, that also take into account the thresholding parameters. We
obtain estimates of the sample complexity that essentially depend only linearly
on the number of parameters and on the depth. We apply our main result to
obtain specific generalization bounds for several practical examples, including
different algorithms for (implicit) dictionary learning, and convolutional

We introduce a closed-form method for identification of discrete-time linear
time-variant systems from data, formulating the learning problem as a
regularized least squares problem where the regularizer favors smooth solutions
within a trajectory. We develop a closed-form algorithm with guarantees of
optimality and with a complexity that increases linearly with the number of
instants considered per trajectory. The COSMIC algorithm achieves the desired
result even in the presence of large volumes of data. Our method solved the
problem using two orders of magnitude less computational power than a general
purpose convex solver and was about 3 times faster than a Stochastic Block
Coordinate Descent especially designed method. Computational times of our
method remained in the order of magnitude of the second even for 10k and 100k
time instants, where the general purpose solver crashed. To prove its
applicability to real world systems, we test with spring-mass-damper system and
use the estimated model to find the optimal control path. Our algorithm was
applied to both a Low Fidelity and Functional Engineering Simulators for the
Comet Interceptor mission, that requires precise pointing of the on-board
cameras in a fast dynamics environment. Thus, this paper provides a fast
alternative to classical system identification techniques for linear
time-variant systems, while proving to be a solid base for applications in the
Space industry and a step forward to the incorporation of algorithms that
leverage data in such a safety-critical environment.

We report a digital twin (DT) framework of electrical tomography (ET) to
address the challenge of real-time quantitative multiphase flow imaging based
on non-invasive and non-radioactive technologies. Multiphase flow is ubiquitous
in nature, industry, and research. Accurate flow imaging is the key to
understanding this complex phenomenon. Existing non-radioactive multiphase flow
imaging methods based on electrical tomography are limited to providing
qualitative images. The proposed DT framework, building upon a synergistic
integration of 3D field coupling simulation, model-based deep learning, and
edge computing, allows ET to dynamically learn the flow features in the virtual
space and implement the model in the physical system, thus providing
unprecedented resolution and accuracy. The DT framework is demonstrated on
gas-liquid two-phase flow and electrical capacitance tomography (ECT). It can
be readily extended to various tomography modalities, scenarios, and scales in
biomedical, energy, and aerospace applications as an effective alternative to
radioactive solutions for precise flow visualization and characterization.

In the context of online privacy, many methods propose complex privacy and
security preserving measures to protect sensitive data. In this paper, we argue
that: not storing any sensitive data is the best form of security. Thus we
propose an online framework that "burns after reading", i.e. each online sample
is immediately deleted after it is processed. Meanwhile, we tackle the
inevitable distribution shift between the labeled public data and unlabeled
private data as a problem of unsupervised domain adaptation. Specifically, we
propose a novel algorithm that aims at the most fundamental challenge of the
online adaptation setting--the lack of diverse source-target data pairs.
Therefore, we design a Cross-Domain Bootstrapping approach, called CroDoBo, to
increase the combined diversity across domains. Further, to fully exploit the
valuable discrepancies among the diverse combinations, we employ the training
strategy of multiple learners with co-supervision. CroDoBo achieves
state-of-the-art online performance on four domain adaptation benchmarks.

In this work, our aim is to provide a structured answer in natural language
to a complex information need. Particularly, we envision using generative
models from the perspective of data-to-text generation. We propose the use of a
content selection and planning pipeline which aims at structuring the answer by
generating intermediate plans. The experimental evaluation is performed using
the TREC Complex Answer Retrieval (CAR) dataset. We evaluate both the generated
answer and its corresponding structure and show the effectiveness of
planning-based models in comparison to a text-to-text model.

We consider the problem of signal estimation in generalized linear models
defined via rotationally invariant design matrices. Since these matrices can
have an arbitrary spectral distribution, this model is well suited for
capturing complex correlation structures which often arise in applications. We
propose a novel family of approximate message passing (AMP) algorithms for
signal estimation, and rigorously characterize their performance in the
high-dimensional limit via a state evolution recursion. Our rotationally
invariant AMP has complexity of the same order as the existing AMP derived
under the restrictive assumption of a Gaussian design; our algorithm also
recovers this existing AMP as a special case. Numerical results showcase a
performance close to Vector AMP (which is conjectured to be Bayes-optimal in
some settings), but obtained with a much lower complexity, as the proposed
algorithm does not require a computationally expensive singular value

During probabilistic learning organisms often apply a sub-optimal
"probability-matching" strategy, where selection rates match reward
probabilities, rather than engaging in the optimal "maximization" strategy,
where the option with the highest reward probability is always selected.
Despite decades of research, the mechanisms contributing to
probability-matching are still under debate, and particularly noteworthy is
that no differences between probability-matching and maximization strategies
have been reported at the level of the brain. Here, we provide theoretical
proof for a computational model that explains the complete range of behaviors
between pure maximization and pure probability-matching. Fitting this model to
behavior of 60 participants performing a probabilistic reinforcement learning
task during fMRI scanning confirmed the model-derived prediction that
probability-matching relates to an increased integration of negative outcomes
during learning, as indicated by a stronger coupling between midbrain BOLD
signal and negative prediction errors. Because the degree of
probability-matching was consistent within an individual across nine different
conditions, our results further suggest that the tendency to express a
particular learning strategy is a trait-like feature of an individual.

Long-standing data sparsity and cold-start constitute thorny and perplexing
problems for the recommendation systems. Cross-domain recommendation as a
domain adaptation framework has been utilized to efficiently address these
challenging issues, by exploiting information from multiple domains. In this
study, an item-level relevance cross-domain recommendation task is explored,
where two related domains, that is, the source and the target domain contain
common items without sharing sensitive information regarding the users'
behavior, and thus avoiding the leak of user privacy. In light of this
scenario, two novel coupled autoencoder-based deep learning methods are
proposed for cross-domain recommendation. The first method aims to
simultaneously learn a pair of autoencoders in order to reveal the intrinsic
representations of the items in the source and target domains, along with a
coupled mapping function to model the non-linear relationships between these
representations, thus transferring beneficial information from the source to
the target domain. The second method is derived based on a new joint
regularized optimization problem, which employs two autoencoders to generate in
a deep and non-linear manner the user and item-latent factors, while at the
same time a data-driven function is learnt to map the item-latent factors
across domains. Extensive numerical experiments on two publicly available
benchmark datasets are conducted illustrating the superior performance of our
proposed methods compared to several state-of-the-art cross-domain
recommendation frameworks.

In model-based medical image analysis, three features of interest are the
shape of structures of interest, their relative pose, and image intensity
profiles representative of some physical property. Often, these are modelled
separately through statistical models by decomposing the object's features into
a set of basis functions through principal geodesic analysis or principal
component analysis. This study presents a statistical modelling method for
automatic learning of shape, pose and intensity features in medical images
which we call the Dynamic multi feature-class Gaussian process models
(DMFC-GPM). A DMFC-GPM is a Gaussian process (GP)-based model with a shared
latent space that encodes linear and non-linear variation. Our method is
defined in a continuous domain with a principled way to represent shape, pose
and intensity feature classes in a linear space, based on deformation fields. A
deformation field-based metric is adapted in the method for modelling shape and
intensity feature variation as well as for comparing rigid transformations
(pose). Moreover, DMFC-GPMs inherit properties intrinsic to GPs including
marginalisation and regression. Furthermore, they allow for adding additional
pose feature variability on top of those obtained from the image acquisition
process; what we term as permutation modelling. For image analysis tasks using
DMFC-GPMs, we adapt Metropolis-Hastings algorithms making the prediction of
features fully probabilistic. We validate the method using controlled synthetic
data and we perform experiments on bone structures from CT images of the
shoulder to illustrate the efficacy of the model for pose and shape feature
prediction. The model performance results suggest that this new modelling
paradigm is robust, accurate, accessible, and has potential applications
including the management of musculoskeletal disorders and clinical decision
making

A recent paper by Davies et al (2021) describes how deep learning (DL)
technology was used to find plausible hypotheses that have led to two original
mathematical results: one in knot theory, one in representation theory. I argue
here that the significance and novelty of this application of DL technology to
mathematics is significantly overstated in the paper under review and has been
wildly overstated in some of the accounts in the popular science press. In the
knot theory result, the role of DL was small, and a conventional statistical
analysis would probably have sufficed. In the representation theory result, the
role of DL is much larger; however, it is not very different in kind from what
has been done in experimental mathematics for decades. Moreover, it is not
clear whether the distinctive features of DL that make it useful here will
apply across a wide range of mathematical problems. Finally, I argue that the
DL here "guides human intuition" is unhelpful and misleading; what the DL does
primarily does is to mark many possible conjectures as false and a few others
as possibly worthy of study.
  Certainly the representation theory result represents an original and
interesting application of DL to mathematical research, but its larger
significance is uncertain.

Copy detection, which is a task to determine whether an image is a modified
copy of any image in a database, is an unsolved problem. Thus, we addressed
copy detection by training convolutional neural networks (CNNs) with
contrastive learning. Training with a large memory-bank and hard data
augmentation enables the CNNs to obtain more discriminative representation. Our
proposed negative embedding subtraction further boosts the copy detection
accuracy. Using our methods, we achieved 1st place in the Facebook AI Image
Similarity Challenge: Descriptor Track. Our code is publicly available here:
\url{https://github.com/lyakaap/ISC21-Descriptor-Track-1st}

In this work, we study the emergence of sparsity and multiway structures in
second-order statistical characterizations of dynamical processes governed by
partial differential equations (PDEs). We consider several state-of-the-art
multiway covariance and inverse covariance (precision) matrix estimators and
examine their pros and cons in terms of accuracy and interpretability in the
context of physics-driven forecasting when incorporated into the ensemble
Kalman filter (EnKF). In particular, we show that multiway data generated from
the Poisson and the convection-diffusion types of PDEs can be accurately
tracked via EnKF when integrated with appropriate covariance and precision
matrix estimators.

Market makers play a key role in financial markets by providing liquidity.
They usually fill order books with buy and sell limit orders in order to
provide traders alternative price levels to operate. This paper focuses
precisely on the study of these markets makers strategies from an agent-based
perspective. In particular, we propose the application of Reinforcement
Learning (RL) for the creation of intelligent market markers in simulated stock
markets. This research analyzes how RL market maker agents behaves in
non-competitive (only one RL market maker learning at the same time) and
competitive scenarios (multiple RL market markers learning at the same time),
and how they adapt their strategies in a Sim2Real scope with interesting
results. Furthermore, it covers the application of policy transfer between
different experiments, describing the impact of competing environments on RL
agents performance. RL and deep RL techniques are proven as profitable market
maker approaches, leading to a better understanding of their behavior in stock
markets.

We present the SCR framework for enhancing the training of graph neural
networks (GNNs) with consistency regularization. Regularization is a set of
strategies used in Machine Learning to reduce overfitting and improve the
generalization ability. However, it is unclear how to best design the
generalization strategies in GNNs, as it works in a semi-supervised setting for
graph data. The major challenge lies in how to efficiently balance the
trade-off between the error from the labeled data and that from the unlabeled
data. SCR is a simple yet general framework in which we introduce two
strategies of consistency regularization to address the challenge above. One is
to minimize the disagreements among the perturbed predictions by different
versions of a GNN model. The other is to leverage the Mean Teacher paradigm to
estimate a consistency loss between teacher and student models instead of the
disagreement of the predictions. We conducted experiments on three large-scale
node classification datasets in the Open Graph Benchmark (OGB). Experimental
results demonstrate that the proposed SCR framework is a general one that can
enhance various GNNs to achieve better performance. Finally, SCR has been the
top-1 entry on all three OGB leaderboards as of this submission.

Random data augmentations (RDAs) are state of the art regarding practical
graph neural networks that are provably universal. There is great diversity
regarding terminology, methodology, benchmarks, and evaluation metrics used
among existing RDAs. Not only does this make it increasingly difficult for
practitioners to decide which technique to apply to a given problem, but it
also stands in the way of systematic improvements. We propose a new
comprehensive framework that captures all previous RDA techniques. On the
theoretical side, among other results, we formally prove that under natural
conditions all instantiations of our framework are universal. On the practical
side, we develop a method to systematically and automatically train RDAs. This
in turn enables us to impartially and objectively compare all existing RDAs.
New RDAs naturally emerge from our approach, and our experiments demonstrate
that they improve the state of the art.

In this work we develop a generalizable and efficient Neural Radiance Field
(NeRF) pipeline for high-fidelity free-viewpoint human body synthesis under
settings with sparse camera views. Though existing NeRF-based methods can
synthesize rather realistic details for human body, they tend to produce poor
results when the input has self-occlusion, especially for unseen humans under
sparse views. Moreover, these methods often require a large number of sampling
points for rendering, which leads to low efficiency and limits their real-world
applicability. To address these challenges, we propose a Geometry-guided
Progressive NeRF (GP-NeRF). In particular, to better tackle self-occlusion, we
devise a geometry-guided multi-view feature integration approach that utilizes
the estimated geometry prior to integrate the incomplete information from input
views and construct a complete geometry volume for the target human body.
Meanwhile, for achieving higher rendering efficiency, we introduce a
progressive rendering pipeline through geometry guidance, which leverages the
geometric feature volume and the predicted density values to progressively
reduce the number of sampling points and speed up the rendering process.
Experiments on the ZJU-MoCap and THUman datasets show that our method
outperforms the state-of-the-arts significantly across multiple generalization
settings, while the time cost is reduced > 70% via applying our efficient
progressive rendering pipeline.

Electroencephalogram (EEG) decoding aims to identify the perceptual,
semantic, and cognitive content of neural processing based on non-invasively
measured brain activity. Traditional EEG decoding methods have achieved
moderate success when applied to data acquired in static, well-controlled lab
environments. However, an open-world environment is a more realistic setting,
where situations affecting EEG recordings can emerge unexpectedly,
significantly weakening the robustness of existing methods. In recent years,
deep learning (DL) has emerged as a potential solution for such problems due to
its superior capacity in feature extraction. It overcomes the limitations of
defining `handcrafted' features or features extracted using shallow
architectures, but typically requires large amounts of costly,
expertly-labelled data - something not always obtainable. Combining DL with
domain-specific knowledge may allow for development of robust approaches to
decode brain activity even with small-sample data. Although various DL methods
have been proposed to tackle some of the challenges in EEG decoding, a
systematic tutorial overview, particularly for open-world applications, is
currently lacking. This article therefore provides a comprehensive survey of DL
methods for open-world EEG decoding, and identifies promising research
directions to inspire future studies for EEG decoding in real-world

Forensic analysis of manipulated pixels requires the identification of
various hidden and subtle features from images. Conventional image recognition
models generally fail at this task because they are biased and more attentive
toward the dominant local and spatial features. In this paper, we propose a
novel Gated Context Attention Network (GCA-Net) that utilizes non-local
attention in conjunction with a gating mechanism in order to capture the finer
image discrepancies and better identify forged regions. The proposed framework
uses high dimensional embeddings to filter and aggregate the relevant context
from coarse feature maps at various stages of the decoding process. This
improves the network's understanding of global differences and reduces
false-positive localizations. Our evaluation on standard image forensic
benchmarks shows that GCA-Net can both compete against and improve over
state-of-the-art networks by an average of 4.7% AUC. Additional ablation
studies also demonstrate the method's robustness against attributions and
resilience to false-positive predictions.

Twitter is one of the most popular social networks attracting millions of
users, while a considerable proportion of online discourse is captured. It
provides a simple usage framework with short messages and an efficient
application programming interface (API) enabling the research community to
study and analyze several aspects of this social network. However, the Twitter
usage simplicity can lead to malicious handling by various bots. The malicious
handling phenomenon expands in online discourse, especially during the
electoral periods, where except the legitimate bots used for dissemination and
communication purposes, the goal is to manipulate the public opinion and the
electorate towards a certain direction, specific ideology, or political party.
This paper focuses on the design of a novel system for identifying Twitter bots
based on labeled Twitter data. To this end, a supervised machine learning (ML)
framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm,
where the hyper-parameters are tuned via cross-validation. Our study also
deploys Shapley Additive Explanations (SHAP) for explaining the ML model
predictions by calculating feature importance, using the game theoretic-based
Shapley values. Experimental evaluation on distinct Twitter datasets
demonstrate the superiority of our approach, in terms of bot detection
accuracy, when compared against a recent state-of-the-art Twitter bot detection
method.

Deep learning models have been widely used for anomaly detection in
surveillance videos. Typical models are equipped with the capability to
reconstruct normal videos and evaluate the reconstruction errors on anomalous
videos to indicate the extent of abnormalities. However, existing approaches
suffer from two disadvantages. Firstly, they can only encode the movements of
each identity independently, without considering the interactions among
identities which may also indicate anomalies. Secondly, they leverage
inflexible models whose structures are fixed under different scenes, this
configuration disables the understanding of scenes. In this paper, we propose a
Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to
address these problems, the HSTGCNN is composed of multiple branches that
correspond to different levels of graph representations. High-level graph
representations encode the trajectories of people and the interactions among
multiple identities while low-level graph representations encode the local body
postures of each person. Furthermore, we propose to weightedly combine multiple
branches that are better at different scenes. An improvement over single-level
graph representations is achieved in this way. An understanding of scenes is
achieved and serves anomaly detection. High-level graph representations are
assigned higher weights to encode moving speed and directions of people in
low-resolution videos while low-level graph representations are assigned higher
weights to encode human skeletons in high-resolution videos. Experimental
results show that the proposed HSTGCNN significantly outperforms current
state-of-the-art models on four benchmark datasets (UCSD Pedestrian,
ShanghaiTech, CUHK Avenue and IITB-Corridor) by using much less learnable

Human activities within smart infrastructures generate a vast amount of IMU
data from the wearables worn by individuals. Many existing studies rely on such
sensory data for human activity recognition (HAR); however, one of the major
bottlenecks is their reliance on pre-annotated or labeled data. Manual
human-driven annotations are neither scalable nor efficient, whereas existing
auto-annotation techniques heavily depend on video signatures. Still,
video-based auto-annotation needs high computation resources and has privacy
concerns when the data from a personal space, like a smart-home, is transferred
to the cloud. This paper exploits the acoustic signatures generated from human
activities to label the wearables' IMU data at the edge, thus mitigating
resource requirement and data privacy concerns. We utilize acoustic-based
pre-trained HAR models for cross-modal labeling of the IMU data even when two
individuals perform simultaneous but different activities under the same
environmental context. We observe that non-overlapping acoustic gaps exist with
a high probability during the simultaneous activities performed by two
individuals in the environment's acoustic context, which helps us resolve the
overlapping activity signatures to label them individually. A principled
evaluation of the proposed approach on two real-life in-house datasets further
augmented to create a dual occupant setup, shows that the framework can
correctly annotate a significant volume of unlabeled IMU data from both
individuals with an accuracy of $\mathbf{82.59\%}$ ($\mathbf{\pm 17.94\%}$) and
$\mathbf{98.32\%}$ ($\mathbf{\pm 3.68\%}$), respectively, for a workshop and a
kitchen environment.

With the rapid increase of big data, distributed Machine Learning (ML) has
been widely applied in training large-scale models. Stochastic Gradient Descent
(SGD) is arguably the workhorse algorithm of ML. Distributed ML models trained
by SGD involve large amounts of gradient communication, which limits the
scalability of distributed ML. Thus, it is important to compress the gradients
for reducing communication. In this paper, we propose FastSGD, a Fast
compressed SGD framework for distributed ML. To achieve a high compression
ratio at a low cost, FastSGD represents the gradients as key-value pairs, and
compresses both the gradient keys and values in linear time complexity. For the
gradient value compression, FastSGD first uses a reciprocal mapper to transform
original values into reciprocal values, and then, it utilizes a logarithm
quantization to further reduce reciprocal values to small integers. Finally,
FastSGD filters reduced gradient integers by a given threshold. For the
gradient key compression, FastSGD provides an adaptive fine-grained delta
encoding method to store gradient keys with fewer bits. Extensive experiments
on practical ML models and datasets demonstrate that FastSGD achieves the
compression ratio up to 4 orders of magnitude, and accelerates the convergence
time up to 8x, compared with state-of-the-art methods.

The great diversity of end-user tasks ranging from manufacturing environments
to personal homes makes pre-programming robots for general purpose applications
extremely challenging. In fact, teaching robots new actions from scratch that
can be reused for previously unseen tasks remains a difficult challenge and is
generally left up to robotics experts. In this work, we present iRoPro, an
interactive Robot Programming framework that allows end-users with little to no
technical background to teach a robot new reusable actions. We combine
Programming by Demonstration and Automated Planning techniques to allow the
user to construct the robot's knowledge base by teaching new actions by
kinesthetic demonstration. The actions are generalised and reused with a task
planner to solve previously unseen problems defined by the user. We implement
iRoPro as an end-to-end system on a Baxter Research Robot to simultaneously
teach low- and high-level actions by demonstration that the user can customise
via a Graphical User Interface to adapt to their specific use case. To evaluate
the feasibility of our approach, we first conducted pre-design experiments to
better understand the user's adoption of involved concepts and the proposed
robot programming process. We compare results with post-design experiments,
where we conducted a user study to validate the usability of our approach with
real end-users. Overall, we showed that users with different programming levels
and educational backgrounds can easily learn and use iRoPro and its robot
programming process.

In causality, estimating the effect of a treatment without confounding
inference remains a major issue because requires to assess the outcome in both
case with and without treatment. Not being able to observe simultaneously both
of them, the estimation of potential outcome remains a challenging task. We
propose an innovative approach where the problem is reformulated as a missing
data model. The aim is to estimate the hidden distribution of \emph{causal
populations}, defined as a function of treatment and outcome. A Causal
Auto-Encoder (CAE), enhanced by a prior dependent on treatment and outcome
information, assimilates the latent space to the probability distribution of
the target populations. The features are reconstructed after being reduced to a
latent space and constrained by a mask introduced in the intermediate layer of
the network, containing treatment and outcome information.

Hand-encoding PDDL domains is generally accepted as difficult, tedious and
error-prone. The difficulty is even greater when temporal domains have to be
encoded. Indeed, actions have a duration and their effects are not
instantaneous. In this paper, we present TempAMLSI, an algorithm based on the
AMLSI approach able to learn temporal domains. TempAMLSI is based on the
classical assumption done in temporal planning that it is possible to convert a
non-temporal domain into a temporal domain. TempAMLSI is the first approach
able to learn temporal domain with single hard envelope and Cushing's
intervals. We show experimentally that TempAMLSI is able to learn accurate
temporal domains, i.e., temporal domain that can be used directly to solve new
planning problem, with different forms of action concurrency.

Radar shows great potential for autonomous driving by accomplishing
long-range sensing under diverse weather conditions. But radar is also a
particularly challenging sensing modality due to the radar noises. Recent works
have made enormous progress in classifying free and occupied spaces in radar
images by leveraging lidar label supervision. However, there are still several
unsolved issues. Firstly, the sensing distance of the results is limited by the
sensing range of lidar. Secondly, the performance of the results is degenerated
by lidar due to the physical sensing discrepancies between the two sensors. For
example, some objects visible to lidar are invisible to radar, and some objects
occluded in lidar scans are visible in radar images because of the radar's
penetrating capability. These sensing differences cause false positive and
penetrating capability degeneration, respectively.
  In this paper, we propose training data preprocessing and polar sliding
window inference to solve the issues. The data preprocessing aims to reduce the
effect caused by radar-invisible measurements in lidar scans. The polar sliding
window inference aims to solve the limited sensing range issue by applying a
near-range trained network to the long-range region. Instead of using common
Cartesian representation, we propose to use polar representation to reduce the
shape dissimilarity between long-range and near-range data. We find that
extending a near-range trained network to long-range region inference in the
polar space has 4.2 times better IoU than in Cartesian space. Besides, the
polar sliding window inference can preserve the radar penetrating capability by
changing the viewpoint of the inference region, which makes some occluded
measurements seem non-occluded for a pretrained network.

Prediction using the ground truth sounds like an oxymoron in machine
learning. However, such an unrealistic setting was used in hundreds, if not
thousands of papers in the area of finding graph representations. To evaluate
the multi-label problem of node classification by using the obtained
representations, many works assume in the prediction stage that the number of
labels of each test instance is known. In practice such ground truth
information is rarely available, but we point out that such an inappropriate
setting is now ubiquitous in this research area. We detailedly investigate why
the situation occurs. Our analysis indicates that with unrealistic information,
the performance is likely over-estimated. To see why suitable predictions were
not used, we identify difficulties in applying some multi-label techniques. For
the use in future studies, we propose simple and effective settings without
using practically unknown information. Finally, we take this chance to conduct
a fair and serious comparison of major graph-representation learning methods on
multi-label node classification.

In the context of smart grids and load balancing, daily peak load forecasting
has become a critical activity for stakeholders of the energy industry. An
understanding of peak magnitude and timing is paramount for the implementation
of smart grid strategies such as peak shaving. The modelling approach proposed
in this paper leverages high-resolution and low-resolution information to
forecast daily peak demand size and timing. The resulting multi-resolution
modelling framework can be adapted to different model classes. The key
contributions of this paper are a) a general and formal introduction to the
multi-resolution modelling approach, b) a discussion on modelling approaches at
different resolutions implemented via Generalised Additive Models and Neural
Networks and c) experimental results on real data from the UK electricity
market. The results confirm that the predictive performance of the proposed
modelling approach is competitive with that of low- and high-resolution

The quantitative analysis of non-invasive electrophysiology signals from
electroencephalography (EEG) and magnetoencephalography (MEG) boils down to the
identification of temporal patterns such as evoked responses, transient bursts
of neural oscillations but also blinks or heartbeats for data cleaning. Several
works have shown that these patterns can be extracted efficiently in an
unsupervised way, e.g., using Convolutional Dictionary Learning. This leads to
an event-based description of the data. Given these events, a natural question
is to estimate how their occurrences are modulated by certain cognitive tasks
and experimental manipulations. To address it, we propose a point process
approach. While point processes have been used in neuroscience in the past, in
particular for single cell recordings (spike trains), techniques such as
Convolutional Dictionary Learning make them amenable to human studies based on
EEG/MEG signals. We develop a novel statistical point process model-called
driven temporal point processes (DriPP)-where the intensity function of the
point process model is linked to a set of point processes corresponding to
stimulation events. We derive a fast and principled expectation-maximization
(EM) algorithm to estimate the parameters of this model. Simulations reveal
that model parameters can be identified from long enough signals. Results on
standard MEG datasets demonstrate that our methodology reveals event-related
neural responses-both evoked and induced-and isolates non-task specific
temporal patterns.

Recently Implicit Neural Representations (INRs) gained attention as a novel
and effective representation for various data types. Thus far, prior work
mostly focused on optimizing their reconstruction performance. This work
investigates INRs from a novel perspective, i.e., as a tool for image
compression. To this end, we propose the first comprehensive compression
pipeline based on INRs including quantization, quantization-aware retraining
and entropy coding. Encoding with INRs, i.e. overfitting to a data sample, is
typically orders of magnitude slower. To mitigate this drawback, we leverage
meta-learned initializations based on MAML to reach the encoding in fewer
gradient updates which also generally improves rate-distortion performance of
INRs. We find that our approach to source compression with INRs vastly
outperforms similar prior work, is competitive with common compression
algorithms designed specifically for images and closes the gap to
state-of-the-art learned approaches based on Rate-Distortion Autoencoders.
Moreover, we provide an extensive ablation study on the importance of
individual components of our method which we hope facilitates future research
on this novel approach to image compression.

Comprehending the interplay between spatial and temporal characteristics of
neural dynamics can contribute to our understanding of information processing
in the human brain. Graph neural networks (GNNs) provide a new possibility to
interpret graph structured signals like those observed in complex brain
networks. In our study we compare different spatio-temporal GNN architectures
and study their ability to model neural activity distributions obtained in
functional MRI (fMRI) studies. We evaluate the performance of the GNN models on
a variety of scenarios in MRI studies and also compare it to a VAR model, which
is currently often used for directed functional connectivity analysis. We show
that by learning localized functional interactions on the anatomical substrate,
GNN based approaches are able to robustly scale to large network studies, even
when available data are scarce. By including anatomical connectivity as the
physical substrate for information propagation, such GNNs also provide a
multi-modal perspective on directed connectivity analysis, offering a novel
possibility to investigate the spatio-temporal dynamics in brain networks.

Mobile networks (MN) are anticipated to provide unprecedented opportunities
to enable a new world of connected experiences and radically shift the way
people interact with everything. MN are becoming more and more complex, driven
by ever-increasingly complicated configuration issues and blossoming new
service requirements. This complexity poses significant challenges in
deployment, management, operation, optimization, and maintenance, since they
require a complete understanding and cognition of MN. Artificial intelligence
(AI), which deals with the simulation of intelligent behavior in computers, has
demonstrated enormous success in many application domains, suggesting its
potential in cognizing the state of MN and making intelligent decisions. In
this paper, we first propose an AI-powered mobile network architecture and
discuss challenges in terms of cognition complexity, decisions with
high-dimensional action space, and self-adaption to system dynamics. Then,
potential solutions that are associated with AI are discussed. Finally, we
propose a deep learning approach that directly maps the state of MN to
perceived QoS, integrating cognition with the decision. Our proposed approach
helps operators in making more intelligent decisions to guarantee QoS.
Meanwhile, the effectiveness and advantages of our proposed approach are
demonstrated on a real-world dataset, involving $31261$ users over $77$
stations within $5$ days.

More and more orgainizations and institutions make efforts on using external
data to improve the performance of AI services. To address the data privacy and
security concerns, federated learning has attracted increasing attention from
both academia and industry to securely construct AI models across multiple
isolated data providers. In this paper, we studied the efficiency problem of
adapting widely used XGBoost model in real-world applications to vertical
federated learning setting. State-of-the-art vertical federated XGBoost
frameworks requires large number of encryption operations and ciphertext
transmissions, which makes the model training much less efficient than training
XGBoost models locally. To bridge this gap, we proposed a novel batch
homomorphic encryption method to cut the cost of encryption-related computation
and transmission in nearly half. This is achieved by encoding the first-order
derivative and the second-order derivative into a single number for encryption,
ciphertext transmission, and homomorphic addition operations. The sum of
multiple first-order derivatives and second-order derivatives can be
simultaneously decoded from the sum of encoded values. We are motivated by the
batch idea in the work of BatchCrypt for horizontal federated learning, and
design a novel batch method to address the limitations of allowing quite few
number of negative numbers. The encode procedure of the proposed batch method
consists of four steps, including shifting, truncating, quantizing and
batching, while the decoding procedure consists of de-quantization and shifting
back. The advantages of our method are demonstrated through theoretical
analysis and extensive numerical experiments.

Crowd flow forecasting, which aims to predict the crowds entering or leaving
certain regions, is a fundamental task in smart cities. One of the key
properties of crowd flow data is periodicity: a pattern that occurs at regular
time intervals, such as a weekly pattern. To capture such periodicity, existing
studies either fuse the periodic hidden states into channels for networks to
learn or apply extra periodic strategies to the network architecture. In this
paper, we devise a novel periodic residual learning network (PRNet) for a
better modeling of periodicity in crowd flow data. Unlike existing methods,
PRNet frames the crowd flow forecasting as a periodic residual learning problem
by modeling the variation between the inputs (the previous time period) and the
outputs (the future time period). Compared to directly predicting crowd flows
that are highly dynamic, learning more stationary deviation is much easier,
which thus facilitates the model training. Besides, the learned variation
enables the network to produce the residual between future conditions and its
corresponding weekly observations at each time interval, and therefore
contributes to substantially more accurate multi-step ahead predictions.
Extensive experiments show that PRNet can be easily integrated into existing
models to enhance their predictive performance.

[Context & motivation] Eliciting requirements that are detailed and logical
enough to be amenable to formal verification is a difficult task. Multiple
tools exist for requirements elicitation and some of these also support
formalisation of requirements in a way that is useful for formal methods.
[Question/problem] This paper reports on our experience of using the FRET
alongside our industrial partner. The use case that we investigate is an
aircraft engine controller. In this context, we evaluate the use of FRET to
bridge the communication gap between formal methods experts and aerospace
industry specialists. [Principal ideas/results] We describe our journey from
ambiguous, natural-language requirements to concise, formalised FRET
requirements. We include our analysis of the formalised requirements from the
perspective of patterns, translation into other formal methods and the
relationship between parent-child requirements in this set. We also provide
insight into lessons learned throughout this process and identify future
improvements to FRET. [Contribution] Previous experience reports have been
published by the FRET team, but this is the first such report of an industrial
use case that was written by researchers that have not been involved FRET's

A comprehensive and precise analysis of shale gas production performance is
crucial for evaluating resource potential, designing field development plan,
and making investment decisions. However, quantitative analysis can be
challenging because production performance is dominated by a complex
interaction among a series of geological and engineering factors. In this
study, we propose a hybrid data-driven procedure for analyzing shale gas
production performance, which consists of a complete workflow for dominant
factor analysis, production forecast, and development optimization. More
specifically, game theory and machine learning models are coupled to determine
the dominating geological and engineering factors. The Shapley value with
definite physical meanings is employed to quantitatively measure the effects of
individual factors. A multi-model-fused stacked model is trained for production
forecast, on the basis of which derivative-free optimization algorithms are
introduced to optimize the development plan. The complete workflow is validated
with actual production data collected from the Fuling shale gas field, Sichuan
Basin, China. The validation results show that the proposed procedure can draw
rigorous conclusions with quantified evidence and thereby provide specific and
reliable suggestions for development plan optimization. Comparing with
traditional and experience-based approaches, the hybrid data-driven procedure
is advanced in terms of both efficiency and accuracy.

The large variety of digital payment choices available to consumers today has
been a key driver of e-commerce transactions in the past decade. Unfortunately,
this has also given rise to cybercriminals and fraudsters who are constantly
looking for vulnerabilities in these systems by deploying increasingly
sophisticated fraud attacks. A typical fraud detection system employs standard
supervised learning methods where the focus is on maximizing the fraud recall
rate. However, we argue that such a formulation can lead to sub-optimal
solutions. The design requirements for these fraud models requires that they
are robust to the high-class imbalance in the data, adaptive to changes in
fraud patterns, maintain a balance between the fraud rate and the decline rate
to maximize revenue, and be amenable to asynchronous feedback since usually
there is a significant lag between the transaction and the fraud realization.
To achieve this, we formulate fraud detection as a sequential decision-making
problem by including the utility maximization within the model in the form of
the reward function. The historical decline rate and fraud rate define the
state of the system with a binary action space composed of approving or
declining the transaction. In this study, we primarily focus on utility
maximization and explore different reward functions to this end. The
performance of the proposed Reinforcement Learning system has been evaluated
for two publicly available fraud datasets using Deep Q-learning and compared
with different classifiers. We aim to address the rest of the issues in future
work.

We address the problem of sequentially selecting and observing processes from
a given set to find the anomalies among them. The decision-maker observes a
subset of the processes at any given time instant and obtains a noisy binary
indicator of whether or not the corresponding process is anomalous. In this
setting, we develop an anomaly detection algorithm that chooses the processes
to be observed at a given time instant, decides when to stop taking
observations, and declares the decision on anomalous processes. The objective
of the detection algorithm is to identify the anomalies with an accuracy
exceeding the desired value while minimizing the delay in decision making. We
devise a centralized algorithm where the processes are jointly selected by a
common agent as well as a decentralized algorithm where the decision of whether
to select a process is made independently for each process. Our algorithms rely
on a Markov decision process defined using the marginal probability of each
process being normal or anomalous, conditioned on the observations. We
implement the detection algorithms using the deep actor-critic reinforcement
learning framework. Unlike prior work on this topic that has exponential
complexity in the number of processes, our algorithms have computational and
memory requirements that are both polynomial in the number of processes. We
demonstrate the efficacy of these algorithms using numerical experiments by
comparing them with state-of-the-art methods.

This works considers challenges of building and usage a formal knowledge base
(model), which unites the ATT&CK, CAPEC, CWE, CVE security enumerations. The
proposed model can be used to learn relations between attack techniques, attack
pattern, weaknesses, and vulnerabilities in order to build various threat
landscapes, in particular, for threat modeling. The model is created as an
ontology with freely available datasets in the OWL and RDF formats. The use of
ontologies is an alternative of structural and graph based approaches to
integrate the security enumerations. In this work we consider an approach of
threat modeling with the data components of ATT&CK based on the knowledge base
and an ontology driven threat modeling framework. Also, some evaluations are
made, how it can be possible to use the ontological approach of threat modeling
and which challenges this can be faced.

Experience replay \citep{lin1993reinforcement, mnih2015human} is a widely
used technique to achieve efficient use of data and improved performance in RL
algorithms. In experience replay, past transitions are stored in a memory
buffer and re-used during learning. Various suggestions for sampling schemes
from the replay buffer have been suggested in previous works, attempting to
optimally choose those experiences which will most contribute to the
convergence to an optimal policy. Here, we give some conditions on the replay
sampling scheme that will ensure convergence, focusing on the well-known
Q-learning algorithm in the tabular setting. After establishing sufficient
conditions for convergence, we turn to suggest a slightly different usage for
experience replay - replaying memories in a biased manner as a means to change
the properties of the resulting policy. We initiate a rigorous study of
experience replay as a tool to control and modify the properties of the
resulting policy. In particular, we show that using an appropriate biased
sampling scheme can allow us to achieve a \emph{safe} policy. We believe that
using experience replay as a biasing mechanism that allows controlling the
resulting policy in desirable ways is an idea with promising potential for many

Advanced deep learning (DL) algorithms may predict the patient's risk of
developing breast cancer based on the Breast Imaging Reporting and Data System
(BI-RADS) and density standards. Recent studies have suggested that the
combination of multi-view analysis improved the overall breast exam
classification. In this paper, we propose a novel multi-view DL approach for
BI-RADS and density assessment of mammograms. The proposed approach first
deploys deep convolutional networks for feature extraction on each view
separately. The extracted features are then stacked and fed into a Light
Gradient Boosting Machine (LightGBM) classifier to predict BI-RADS and density
scores. We conduct extensive experiments on both the internal mammography
dataset and the public dataset Digital Database for Screening Mammography
(DDSM). The experimental results demonstrate that the proposed approach
outperforms the single-view classification approach on two benchmark datasets
by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM
dataset). These results highlight the vital role of combining multi-view
information to improve the performance of breast cancer risk prediction.

Fine-grained Visual Classification (FGVC) aims to identify objects from
subcategories. It is a very challenging task because of the subtle inter-class
differences. Existing research applies large-scale convolutional neural
networks or visual transformers as the feature extractor, which is extremely
computationally expensive. In fact, real-world scenarios of fine-grained
recognition often require a more lightweight mobile network that can be
utilized offline. However, the fundamental mobile network feature extraction
capability is weaker than large-scale models. In this paper, based on the
lightweight MobilenetV2, we propose a Progressive Multi-Stage Interactive
training method with a Recursive Mosaic Generator (RMG-PMSI). First, we propose
a Recursive Mosaic Generator (RMG) that generates images with different
granularities in different phases. Then, the features of different stages pass
through a Multi-Stage Interaction (MSI) module, which strengthens and
complements the corresponding features of different stages. Finally, using the
progressive training (P), the features extracted by the model in different
stages can be fully utilized and fused with each other. Experiments on three
prestigious fine-grained benchmarks show that RMG-PMSI can significantly
improve the performance with good robustness and transferability.

This paper proposes a nonlinear policy architecture for control of
partially-observed linear dynamical systems providing built-in closed-loop
stability guarantees. The policy is based on a nonlinear version of the Youla
parameterization, and augments a known stabilizing linear controller with a
nonlinear operator from a recently developed class of dynamic neural network
models called the recurrent equilibrium network (REN). We prove that RENs are
universal approximators of contracting and Lipschitz nonlinear systems, and
subsequently show that the the proposed Youla-REN architecture is a universal
approximator of stabilizing nonlinear controllers. The REN architecture
simplifies learning since unconstrained optimization can be applied, and we
consider both a model-based case where exact gradients are available and
reinforcement learning using random search with zeroth-order oracles. In
simulation examples our method converges faster to better controllers and is
more scalable than existing methods, while guaranteeing stability during
learning transients.

A long-cherished vision in robotics is to equip robots with skills that match
the versatility and precision of humans. For example, when playing table
tennis, a robot should be capable of returning the ball in various ways while
precisely placing it at the desired location. A common approach to model such
versatile behavior is to use a Mixture of Experts (MoE) model, where each
expert is a contextual motion primitive. However, learning such MoEs is
challenging as most objectives force the model to cover the entire context
space, which prevents specialization of the primitives resulting in rather
low-quality components. Starting from maximum entropy reinforcement learning
(RL), we decompose the objective into optimizing an individual lower bound per
mixture component. Further, we introduce a curriculum by allowing the
components to focus on a local context region, enabling the model to learn
highly accurate skill representations. To this end, we use local context
distributions that are adapted jointly with the expert primitives. Our lower
bound advocates an iterative addition of new components, where new components
will concentrate on local context regions not covered by the current MoE. This
local and incremental learning results in a modular MoE model of high accuracy
and versatility, where both properties can be scaled by adding more components
on the fly. We demonstrate this by an extensive ablation and on two challenging
simulated robot skill learning tasks. We compare our achieved performance to
LaDiPS and HiREPS, a known hierarchical policy search method for learning
diverse skills.

Self-supervised models have been shown to produce comparable or better visual
representations than their supervised counterparts when trained offline on
unlabeled data at scale. However, their efficacy is catastrophically reduced in
a Continual Learning (CL) scenario where data is presented to the model
sequentially. In this paper, we show that self-supervised loss functions can be
seamlessly converted into distillation mechanisms for CL by adding a predictor
network that maps the current state of the representations to their past state.
This enables us to devise a framework for Continual self-supervised visual
representation Learning that (i) significantly improves the quality of the
learned representations, (ii) is compatible with several state-of-the-art
self-supervised objectives, and (iii) needs little to no hyperparameter tuning.
We demonstrate the effectiveness of our approach empirically by training six
popular self-supervised models in various CL settings.

Audio representations for music information retrieval are typically learned
via supervised learning in a task-specific fashion. Although effective at
producing state-of-the-art results, this scheme lacks flexibility with respect
to the range of applications a model can have and requires extensively
annotated datasets. In this work, we pose the question of whether it may be
possible to exploit weakly aligned text as the only supervisory signal to learn
general-purpose music audio representations. To address this question, we
design a multimodal architecture for music and language pre-training (MuLaP)
optimised via a set of proxy tasks. Weak supervision is provided in the form of
noisy natural language descriptions conveying the overall musical content of
the track. After pre-training, we transfer the audio backbone of the model to a
set of music audio classification and regression tasks. We demonstrate the
usefulness of our approach by comparing the performance of audio
representations produced by the same audio backbone with different training
strategies and show that our pre-training method consistently achieves
comparable or higher scores on all tasks and datasets considered. Our
experiments also confirm that MuLaP effectively leverages audio-caption pairs
to learn representations that are competitive with audio-only and cross-modal
self-supervised methods in the literature.

A commonly used heuristic in RL is experience replay
(e.g.~\citet{lin1993reinforcement, mnih2015human}), in which a learner stores
and re-uses past trajectories as if they were sampled online. In this work, we
initiate a rigorous study of this heuristic in the setting of tabular
Q-learning. We provide a convergence rate guarantee, and discuss how it
compares to the convergence of Q-learning depending on important parameters
such as the frequency and number of replay iterations. We also provide
theoretical evidence showing when we might expect this heuristic to strictly
improve performance, by introducing and analyzing a simple class of MDPs.
Finally, we provide some experiments to support our theoretical findings.

Synthetic aperture radar tomography (TomoSAR) has been extensively employed
in 3-D reconstruction in dense urban areas using high-resolution SAR
acquisitions. Compressive sensing (CS)-based algorithms are generally
considered as the state of the art in super-resolving TomoSAR, in particular in
the single look case. This superior performance comes at the cost of extra
computational burdens, because of the sparse reconstruction, which cannot be
solved analytically and we need to employ computationally expensive iterative
solvers. In this paper, we propose a novel deep learning-based super-resolving
TomoSAR inversion approach, $\boldsymbol{\gamma}$-Net, to tackle this
challenge. $\boldsymbol{\gamma}$-Net adopts advanced complex-valued learned
iterative shrinkage thresholding algorithm (CV-LISTA) to mimic the iterative
optimization step in sparse reconstruction. Simulations show the height
estimate from a well-trained $\boldsymbol{\gamma}$-Net approaches the
Cram\'er-Rao lower bound while improving the computational efficiency by 1 to 2
orders of magnitude comparing to the first-order CS-based methods. It also
shows no degradation in the super-resolution power comparing to the
state-of-the-art second-order TomoSAR solvers, which are much more
computationally expensive than the first-order methods. Specifically,
$\boldsymbol{\gamma}$-Net reaches more than $90\%$ detection rate in moderate
super-resolving cases at 25 measurements at 6dB SNR. Moreover, simulation at
limited baselines demonstrates that the proposed algorithm outperforms the
second-order CS-based method by a fair margin. Test on real TerraSAR-X data
with just 6 interferograms also shows high-quality 3-D reconstruction with
high-density detected double scatterers.

The Skinned Multi-Person Linear (SMPL) model can represent a human body by
mapping pose and shape parameters to body meshes. This has been shown to
facilitate inferring 3D human pose and shape from images via different learning
models. However, not all pose and shape parameter values yield
physically-plausible or even realistic body meshes. In other words, SMPL is
under-constrained and may thus lead to invalid results when used to reconstruct
humans from images, either by directly optimizing its parameters, or by
learning a mapping from the image to these parameters.
  In this paper, we therefore learn a prior that restricts the SMPL parameters
to values that produce realistic poses via adversarial training. We show that
our learned prior covers the diversity of the real-data distribution,
facilitates optimization for 3D reconstruction from 2D keypoints, and yields
better pose estimates when used for regression from images. We found that the
prior based on spherical distribution gets the best results. Furthermore, in
all these tasks, it outperforms the state-of-the-art VAE-based approach to
constraining the SMPL parameters.

We present results from the fastsum collaboration's programme to determine
the spectrum of the bottomonium system as a function of temperature. Three
different methods of extracting spectral information are discussed: a Maximum
Likelihood approach using a Gaussian spectral function for the ground state,
the Backus Gilbert method, and the Kernel Ridge Regression machine learning
procedure. We employ the fastsum anisotropic lattices with 2+1 dynamical quark
flavours, with temperatures ranging from 47 to 375 MeV.

We consider the problem of Combinatorial Pure Exploration (CPE), which deals
with finding a combinatorial set or arms with a high reward, when the rewards
of individual arms are unknown in advance and must be estimated using arm
pulls. Previous algorithms for this problem, while obtaining sample complexity
reductions in many cases, are highly computationally intensive, thus making
them impractical even for mildly large problems. In this work, we propose a new
CPE algorithm in the PAC setting, which is computationally light weight, and so
can easily be applied to problems with tens of thousands of arms. This is
achieved since the proposed algorithm requires a very small number of
combinatorial oracle calls. The algorithm is based on successive acceptance of
arms, along with elimination which is based on the combinatorial structure of
the problem. We provide sample complexity guarantees for our algorithm, and
demonstrate in experiments its usefulness on large problems, whereas previous
algorithms are impractical to run on problems of even a few dozen arms. The
code for the algorithms and experiments is provided at
https://github.com/noabdavid/csale.

With the booming of pre-trained transformers, representation-based models
based on Siamese transformer encoders have become mainstream techniques for
efficient text matching. However, these models suffer from severe performance
degradation due to the lack of interaction between the text pair, compared with
interaction-based models. Prior arts attempt to address this through performing
extra interaction for Siamese encoded representations, while the interaction
during encoding is still ignored. To remedy this, we propose a \textit{Virtual}
InteRacTion mechanism (VIRT) to transfer interactive knowledge from
interaction-based models into Siamese encoders through attention map
distillation. As a train-time-only component, VIRT could completely maintain
the high efficiency of the Siamese structure and brings no extra computation
cost during inference. To fully utilize the learned interactive knowledge, we
further design a VIRT-adapted interaction strategy. Experimental results on
multiple text matching datasets demonstrate that our method outperforms
state-of-the-art representation-based models. What's more, VIRT can be easily
integrated into existing representation-based methods to achieve further

Image registration is a fundamental medical image analysis task, and a wide
variety of approaches have been proposed. However, only a few studies have
comprehensively compared medical image registration approaches on a wide range
of clinically relevant tasks. This limits the development of registration
methods, the adoption of research advances into practice, and a fair benchmark
across competing approaches. The Learn2Reg challenge addresses these
limitations by providing a multi-task medical image registration data set for
comprehensive characterisation of deformable registration algorithms. A
continuous evaluation will be possible at
https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of
anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR),
availability of annotations, as well as intra- and inter-patient registration
evaluation. We established an easily accessible framework for training and
validation of 3D registration methods, which enabled the compilation of results
of over 65 individual method submissions from more than 20 unique teams. We
used a complementary set of metrics, including robustness, accuracy,
plausibility, and runtime, enabling unique insight into the current
state-of-the-art of medical image registration. This paper describes datasets,
tasks, evaluation methods and results of the challenge, as well as results of
further analysis of transferability to new datasets, the importance of label
supervision, and resulting bias. While no single approach worked best across
all tasks, many methodological aspects could be identified that push the
performance of medical image registration to new state-of-the-art performance.
Furthermore, we demystified the common belief that conventional registration
methods have to be much slower than deep-learning-based methods.

Kernel principal component analysis (KPCA) is a well-recognized nonlinear
dimensionality reduction method that has been widely used in nonlinear fault
detection tasks. As a kernel trick-based method, KPCA inherits two major
problems. First, the form and the parameters of the kernel function are usually
selected blindly, depending seriously on trial-and-error. As a result, there
may be serious performance degradation in case of inappropriate selections.
Second, at the online monitoring stage, KPCA has much computational burden and
poor real-time performance, because the kernel method requires to leverage all
the offline training data. In this work, to deal with the two drawbacks, a
learnable faster realization of the conventional KPCA is proposed. The core
idea is to parameterize all feasible kernel functions using the novel nonlinear
DAE-FE (deep autoencoder based feature extraction) framework and propose
DAE-PCA (deep autoencoder based principal component analysis) approach in
detail. The proposed DAE-PCA method is proved to be equivalent to KPCA but has
more advantage in terms of automatic searching of the most suitable nonlinear
high-dimensional space according to the inputs. Furthermore, the online
computational efficiency improves by approximately 100 times compared with the
conventional KPCA. With the Tennessee Eastman (TE) process benchmark, the
effectiveness and superiority of the proposed method is illustrated.

The extraction of relevant information carried out by named entities in
handwriting documents is still a challenging task. Unlike traditional
information extraction approaches that usually face text transcription and
named entity recognition as separate subsequent tasks, we propose in this paper
an end-to-end transformer-based approach to jointly perform these two tasks.
The proposed approach operates at the paragraph level, which brings two main
benefits. First, it allows the model to avoid unrecoverable early errors due to
line segmentation. Second, it allows the model to exploit larger bi-dimensional
context information to identify the semantic categories, reaching a higher
final prediction accuracy. We also explore different training scenarios to show
their effect on the performance and we demonstrate that a two-stage learning
strategy can make the model reach a higher final prediction accuracy. As far as
we know, this work presents the first approach that adopts the transformer
networks for named entity recognition in handwritten documents. We achieve the
new state-of-the-art performance in the ICDAR 2017 Information Extraction
competition using the Esposalles database, for the complete task, even though
the proposed technique does not use any dictionaries, language modeling, or
post-processing.

Distributed Constraint Optimization Problems (DCOPs) are an important
subclass of combinatorial optimization problems, where information and controls
are distributed among multiple autonomous agents. Previously, Machine Learning
(ML) has been largely applied to solve combinatorial optimization problems by
learning effective heuristics. However, existing ML-based heuristic methods are
often not generalizable to different search algorithms. Most importantly, these
methods usually require full knowledge about the problems to be solved, which
are not suitable for distributed settings where centralization is not realistic
due to geographical limitations or privacy concerns. To address the generality
issue, we propose a novel directed acyclic graph representation schema for
DCOPs and leverage the Graph Attention Networks (GATs) to embed graph
representations. Our model, GAT-PCM, is then pretrained with optimally labelled
data in an offline manner, so as to construct effective heuristics to boost a
broad range of DCOP algorithms where evaluating the quality of a partial
assignment is critical, such as local search or backtracking search.
Furthermore, to enable decentralized model inference, we propose a distributed
embedding schema of GAT-PCM where each agent exchanges only embedded vectors,
and show its soundness and complexity. Finally, we demonstrate the
effectiveness of our model by combining it with a local search or a
backtracking search algorithm. Extensive empirical evaluations indicate that
the GAT-PCM-boosted algorithms significantly outperform the state-of-the-art
methods in various benchmarks. The pretrained model is available at
https://github.com/dyc941126/GAT-PCM.

In this article, we study large-dimensional matrix factor models and estimate
the factor loading matrices and factor score matrix by minimizing square loss
function. Interestingly, the resultant estimators coincide with our Projected
Estimators (PE) in Yu et al.(2021), which was proposed from the perspective of
simultaneous reduction of the dimensionality and the magnitudes of the
idiosyncratic error matrix. In other word, we provide a least-square
interpretation of the PE for matrix factor model, which parallels to the
least-square interpretation of the PCA for the vector factor model. We derive
the convergence rate of the theoretical minimizers under sub-Gaussian tails.
Considering the robustness to the heavy tails of the idiosyncratic errors, we
extend the least squares to minimizing the Huber loss function, which leads to
a weighted iterative projection approach to compute and learn the parameters.
We conduct extensive numerical studies to investigate the empirical performance
of the proposed Huber estimators relative to the sate-of-the-art ones. It shows
that the Huber estimators perform robustly and much better than the existing
ones when the data are heavy-tailed, and as a result can be used as a safe
replacement of the existing ones in practice. An application to a Fama-French
financial portfolios illustrates the empirical advantage of the Huber

Anomaly detection is a well-established research area that seeks to identify
samples outside of a predetermined distribution. An anomaly detection pipeline
is comprised of two main stages: (1) feature extraction and (2) normality score
assignment. Recent papers used pre-trained networks for feature extraction
achieving state-of-the-art results. However, the use of pre-trained networks
does not fully-utilize the normal samples that are available at train time.
This paper suggests taking advantage of this information by using
teacher-student training. In our setting, a pretrained teacher network is used
to train a student network on the normal training samples. Since the student
network is trained only on normal samples, it is expected to deviate from the
teacher network in abnormal cases. This difference can serve as a complementary
representation to the pre-trained feature vector. Our method -- Transformaly --
exploits a pre-trained Vision Transformer (ViT) to extract both feature
vectors: the pre-trained (agnostic) features and the teacher-student
(fine-tuned) features. We report state-of-the-art AUROC results in both the
common unimodal setting, where one class is considered normal and the rest are
considered abnormal, and the multimodal setting, where all classes but one are
considered normal, and just one class is considered abnormal. The code is
available at https://github.com/MatanCohen1/Transformaly.

Face recognition is a crucial task in various multimedia applications such as
security check, credential access and motion sensing games. However, the task
is challenging when an input face is noisy (e.g. poor-condition RGB image) or
lacks certain information (e.g. 3D face without color). In this work, we
propose a Multimodal Training Unimodal Test (MTUT) framework for robust face
classification, which exploits the cross-modality relationship during training
and applies it as a complementary of the imperfect single modality input during
testing. Technically, during training, the framework (1) builds both
intra-modality and cross-modality autoencoders with the aid of facial
attributes to learn latent embeddings as multimodal descriptors, (2) proposes a
novel multimodal embedding divergence loss to align the heterogeneous features
from different modalities, which also adaptively avoids the useless modality
(if any) from confusing the model. This way, the learned autoencoders can
generate robust embeddings in single-modality face classification on test
stage. We evaluate our framework in two face classification datasets and two
kinds of testing input: (1) poor-condition image and (2) point cloud or 3D face
mesh, when both 2D and 3D modalities are available for training. We
experimentally show that our MTUT framework consistently outperforms ten
baselines on 2D and 3D settings of both datasets.

The immunohistochemical (IHC) staining of the human epidermal growth factor
receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis,
preclinical studies and diagnostic decisions, guiding cancer treatment and
investigation of pathogenesis. HER2 staining demands laborious tissue treatment
and chemical processing performed by a histotechnologist, which typically takes
one day to prepare in a laboratory, increasing analysis time and associated
costs. Here, we describe a deep learning-based virtual HER2 IHC staining method
using a conditional generative adversarial network that is trained to rapidly
transform autofluorescence microscopic images of unlabeled/label-free breast
tissue sections into bright-field equivalent microscopic images, matching the
standard HER2 IHC staining that is chemically performed on the same tissue
sections. The efficacy of this virtual HER2 staining framework was demonstrated
by quantitative analysis, in which three board-certified breast pathologists
blindly graded the HER2 scores of virtually stained and immunohistochemically
stained HER2 whole slide images (WSIs) to reveal that the HER2 scores
determined by inspecting virtual IHC images are as accurate as their
immunohistochemically stained counterparts. A second quantitative blinded study
performed by the same diagnosticians further revealed that the virtually
stained HER2 images exhibit a comparable staining quality in the level of
nuclear detail, membrane clearness, and absence of staining artifacts with
respect to their immunohistochemically stained counterparts. This virtual HER2
staining framework bypasses the costly, laborious, and time-consuming IHC
staining procedures in laboratory, and can be extended to other types of
biomarkers to accelerate the IHC tissue staining used in life sciences and
biomedical workflow.

One-step abductive multi-target learning (OSAMTL) was proposed to handle
complex noisy labels. In this paper, giving definition of diverse noisy label
samples (DNLS), we propose one-step abductive multi-target learning with DNLS
(OSAMTL-DNLS) to expand the methodology of original OSAMTL to better handle
complex noisy labels.

While self-supervised representation learning (SSL) has proved to be
effective in the large model, there is still a huge gap between the SSL and
supervised method in the lightweight model when following the same solution. We
delve into this problem and find that the lightweight model is prone to
collapse in semantic space when simply performing instance-wise contrast. To
address this issue, we propose a relation-wise contrastive paradigm with
Relation Knowledge Distillation (ReKD). We introduce a heterogeneous teacher to
explicitly mine the semantic information and transferring a novel relation
knowledge to the student (lightweight model). The theoretical analysis supports
our main concern about instance-wise contrast and verify the effectiveness of
our relation-wise contrastive learning. Extensive experimental results also
demonstrate that our method achieves significant improvements on multiple
lightweight models. Particularly, the linear evaluation on AlexNet obviously
improves the current state-of-art from 44.7% to 50.1%, which is the first work
to get close to the supervised 50.5%. Code will be made available.

Finding shortest paths in a graph is relevant for numerous problems in
computer vision and graphics, including image segmentation, shape matching, or
the computation of geodesic distances on discrete surfaces. Traditionally, the
concept of a shortest path is considered for graphs with scalar edge weights,
which makes it possible to compute the length of a path by adding up the
individual edge weights. Yet, graphs with scalar edge weights are severely
limited in their expressivity, since oftentimes edges are used to encode
significantly more complex interrelations. In this work we compensate for this
modelling limitation and introduce the novel graph-theoretic concept of a
shortest path in a graph with matrix-valued edges. To this end, we define a
meaningful way for quantifying the path length for matrix-valued edges, and we
propose a simple yet effective algorithm to compute the respective shortest
path. While our formalism is universal and thus applicable to a wide range of
settings in vision, graphics and beyond, we focus on demonstrating its merits
in the context of 3D multi-shape analysis.

Assessing the performance of Generative Adversarial Networks (GANs) has been
an important topic due to its practical significance. Although several
evaluation metrics have been proposed, they generally assess the quality of the
whole generated image distribution. For Reference-guided Image Synthesis (RIS)
tasks, i.e., rendering a source image in the style of another reference image,
where assessing the quality of a single generated image is crucial, these
metrics are not applicable. In this paper, we propose a general learning-based
framework, Reference-guided Image Synthesis Assessment (RISA) to quantitatively
evaluate the quality of a single generated image. Notably, the training of RISA
does not require human annotations. In specific, the training data for RISA are
acquired by the intermediate models from the training procedure in RIS, and
weakly annotated by the number of models' iterations, based on the positive
correlation between image quality and iterations. As this annotation is too
coarse as a supervision signal, we introduce two techniques: 1) a pixel-wise
interpolation scheme to refine the coarse labels, and 2) multiple binary
classifiers to replace a na\"ive regressor. In addition, an unsupervised
contrastive loss is introduced to effectively capture the style similarity
between a generated image and its reference image. Empirical results on various
datasets demonstrate that RISA is highly consistent with human preference and
transfers well across models.

Symmetry is omnipresent in nature and perceived by the visual system of many
species, as it facilitates detecting ecologically important classes of objects
in our environment. Symmetry perception requires abstraction of long-range
spatial dependencies between image regions, and its underlying neural
mechanisms remain elusive. In this paper, we evaluate Deep Neural Network (DNN)
architectures on the task of learning symmetry perception from examples. We
demonstrate that feed-forward DNNs that excel at modelling human performance on
object recognition tasks, are unable to acquire a general notion of symmetry.
This is the case even when the DNNs are architected to capture long-range
spatial dependencies, such as through `dilated' convolutions and the recently
introduced `transformers' design. By contrast, we find that recurrent
architectures are capable of learning to perceive symmetry by decomposing the
long-range spatial dependencies into a sequence of local operations, that are
reusable for novel images. These results suggest that recurrent connections
likely play an important role in symmetry perception in artificial systems, and
possibly, biological ones too.

In statistical decision theory, a model is said to be Pareto optimal (or
admissible) if no other model carries less risk for at least one state of
nature while presenting no more risk for others. How can you rationally
aggregate/combine a finite set of Pareto optimal models while preserving Pareto
efficiency? This question is nontrivial because weighted model averaging does
not, in general, preserve Pareto efficiency. This paper presents an answer in
four logical steps: (1) A rational aggregation rule should preserve Pareto
efficiency (2) Due to the complete class theorem, Pareto optimal models must be
Bayesian, i.e., they minimize a risk where the true state of nature is averaged
with respect to some prior. Therefore each Pareto optimal model can be
associated with a prior, and Pareto efficiency can be maintained by aggregating
Pareto optimal models through their priors. (3) A prior can be interpreted as a
preference ranking over models: prior $\pi$ prefers model A over model B if the
average risk of A is lower than the average risk of B. (4) A
rational/consistent aggregation rule should preserve this preference ranking:
If both priors $\pi$ and $\pi'$ prefer model A over model B, then the prior
obtained by aggregating $\pi$ and $\pi'$ must also prefer A over B. Under these
four steps, we show that all rational/consistent aggregation rules are as
follows: Give each individual Pareto optimal model a weight, introduce a weak
order/ranking over the set of Pareto optimal models, aggregate a finite set of
models S as the model associated with the prior obtained as the weighted
average of the priors of the highest-ranked models in S. This result shows that
all rational/consistent aggregation rules must follow a generalization of
hierarchical Bayesian modeling. Following our main result, we present
applications to Kernel smoothing, time-depreciating models, and voting

Learning to reconstruct 3D garments is important for dressing 3D human bodies
of different shapes in different poses. Previous works typically rely on 2D
images as input, which however suffer from the scale and pose ambiguities. To
circumvent the problems caused by 2D images, we propose a principled framework,
Garment4D, that uses 3D point cloud sequences of dressed humans for garment
reconstruction. Garment4D has three dedicated steps: sequential garments
registration, canonical garment estimation, and posed garment reconstruction.
The main challenges are two-fold: 1) effective 3D feature learning for fine
details, and 2) capture of garment dynamics caused by the interaction between
garments and the human body, especially for loose garments like skirts. To
unravel these problems, we introduce a novel Proposal-Guided Hierarchical
Feature Network and Iterative Graph Convolution Network, which integrate both
high-level semantic features and low-level geometric features for fine details
reconstruction. Furthermore, we propose a Temporal Transformer for smooth
garment motions capture. Unlike non-parametric methods, the reconstructed
garment meshes by our method are separable from the human body and have strong
interpretability, which is desirable for downstream tasks. As the first attempt
at this task, high-quality reconstruction results are qualitatively and
quantitatively illustrated through extensive experiments. Codes are available
at https://github.com/hongfz16/Garment4D.

Using a model of the environment and a value function, an agent can construct
many estimates of a state's value, by unrolling the model for different lengths
and bootstrapping with its value function. Our key insight is that one can
treat this set of value estimates as a type of ensemble, which we call an
\emph{implicit value ensemble} (IVE). Consequently, the discrepancy between
these estimates can be used as a proxy for the agent's epistemic uncertainty;
we term this signal \emph{model-value inconsistency} or
\emph{self-inconsistency} for short. Unlike prior work which estimates
uncertainty by training an ensemble of many models and/or value functions, this
approach requires only the single model and value function which are already
being learned in most model-based reinforcement learning algorithms. We provide
empirical evidence in both tabular and function approximation settings from
pixels that self-inconsistency is useful (i) as a signal for exploration, (ii)
for acting safely under distribution shifts, and (iii) for robustifying
value-based planning with a learned model.

The Arcade Learning Environment (ALE) is proposed as an evaluation platform
for empirically assessing the generality of agents across dozens of Atari 2600
games. ALE offers various challenging problems and has drawn significant
attention from the deep reinforcement learning (RL) community. From Deep
Q-Networks (DQN) to Agent57, RL agents seem to achieve superhuman performance
in ALE. However, is this the case? In this paper, to explore this problem, we
first review the current evaluation metrics in the Atari benchmarks and then
reveal that the current evaluation criteria of achieving superhuman performance
are inappropriate, which underestimated the human performance relative to what
is possible. To handle those problems and promote the development of RL
research, we propose a novel Atari benchmark based on human world records
(HWR), which puts forward higher requirements for RL agents on both final
performance and learning efficiency. Furthermore, we summarize the
state-of-the-art (SOTA) methods in Atari benchmarks and provide benchmark
results over new evaluation metrics based on human world records. We concluded
that at least four open challenges hinder RL agents from achieving superhuman
performance from those new benchmark results. Finally, we also discuss some
promising ways to handle those problems.

Deep learning methods have shown outstanding performance in many
applications, including single-image super-resolution (SISR). With residual
connection architecture, deeply stacked convolutional neural networks provide a
substantial performance boost for SISR, but their huge parameters and
computational loads are impractical for real-world applications. Thus,
designing lightweight models with acceptable performance is one of the major
tasks in current SISR research. The objective of lightweight network design is
to balance a computational load and reconstruction performance. Most of the
previous methods have manually designed complex and predefined fixed
structures, which generally required a large number of experiments and lacked
flexibility in the diversity of input image statistics. In this paper, we
propose a dynamic residual self-attention network (DRSAN) for lightweight SISR,
while focusing on the automated design of residual connections between building
blocks. The proposed DRSAN has dynamic residual connections based on dynamic
residual attention (DRA), which adaptively changes its structure according to
input statistics. Specifically, we propose a dynamic residual module that
explicitly models the DRA by finding the interrelation between residual paths
and input image statistics, as well as assigning proper weights to each
residual path. We also propose a residual self-attention (RSA) module to
further boost the performance, which produces 3-dimensional attention maps
without additional parameters by cooperating with residual structures. The
proposed dynamic scheme, exploiting the combination of DRA and RSA, shows an
efficient trade-off between computational complexity and network performance.
Experimental results show that the DRSAN performs better than or comparable to
existing state-of-the-art lightweight models for SISR.

The vision-language navigation (VLN) task requires an agent to reach a target
with the guidance of natural language instruction. Previous works learn to
navigate step-by-step following an instruction. However, these works may fail
to discriminate the similarities and discrepancies across
instruction-trajectory pairs and ignore the temporal continuity of
sub-instructions. These problems hinder agents from learning distinctive
vision-and-language representations, harming the robustness and
generalizability of the navigation policy. In this paper, we propose a
Contrastive Instruction-Trajectory Learning (CITL) framework that explores
invariance across similar data samples and variance across different ones to
learn distinctive representations for robust navigation. Specifically, we
propose: (1) a coarse-grained contrastive learning objective to enhance
vision-and-language representations by contrasting semantics of full trajectory
observations and instructions, respectively; (2) a fine-grained contrastive
learning objective to perceive instructions by leveraging the temporal
information of the sub-instructions; (3) a pairwise sample-reweighting
mechanism for contrastive learning to mine hard samples and hence mitigate the
influence of data sampling bias in contrastive learning. Our CITL can be easily
integrated with VLN backbones to form a new learning paradigm and achieve
better generalizability in unseen environments. Extensive experiments show that
the model with CITL surpasses the previous state-of-the-art methods on R2R,
R4R, and RxR.

Domain adaptation (DA) attempts to transfer the knowledge from a labeled
source domain to an unlabeled target domain that follows different distribution
from the source. To achieve this, DA methods include a source classification
objective to extract the source knowledge and a domain alignment objective to
diminish the domain shift, ensuring knowledge transfer. Typically, former DA
methods adopt some weight hyper-parameters to linearly combine the training
objectives to form an overall objective. However, the gradient directions of
these objectives may conflict with each other due to domain shift. Under such
circumstances, the linear optimization scheme might decrease the overall
objective value at the expense of damaging one of the training objectives,
leading to restricted solutions. In this paper, we rethink the optimization
scheme for DA from a gradient-based perspective. We propose a Pareto Domain
Adaptation (ParetoDA) approach to control the overall optimization direction,
aiming to cooperatively optimize all training objectives. Specifically, to
reach a desirable solution on the target domain, we design a surrogate loss
mimicking target classification. To improve target-prediction accuracy to
support the mimicking, we propose a target-prediction refining mechanism which
exploits domain labels via Bayes' theorem. On the other hand, since prior
knowledge of weighting schemes for objectives is often unavailable to guide
optimization to approach the optimal solution on the target domain, we propose
a dynamic preference mechanism to dynamically guide our cooperative
optimization by the gradient of the surrogate loss on a held-out unlabeled
target dataset. Extensive experiments on image classification and semantic
segmentation benchmarks demonstrate the effectiveness of ParetoDA

Recently, learned image compression methods have outperformed traditional
hand-crafted ones including BPG. One of the keys to this success is learned
entropy models that estimate the probability distribution of the quantized
latent representation. Like other vision tasks, most recent learned entropy
models are based on convolutional neural networks (CNNs). However, CNNs have a
limitation in modeling long-range dependencies due to their nature of local
connectivity, which can be a significant bottleneck in image compression where
reducing spatial redundancy is a key point. To overcome this issue, we propose
a novel entropy model called Information Transformer (Informer) that exploits
both global and local information in a content-dependent manner using an
attention mechanism. Our experiments show that Informer improves
rate--distortion performance over the state-of-the-art methods on the Kodak and
Tecnick datasets without the quadratic computational complexity problem. Our
source code is available at https://github.com/naver-ai/informer.

We propose a deep learning-based foreign language learning platform, named
FreeTalky, for people who experience anxiety dealing with foreign languages, by
employing a humanoid robot NAO and various deep learning models. A
persona-based dialogue system that is embedded in NAO provides an interesting
and consistent multi-turn dialogue for users. Also, an grammar error correction
system promotes improvement in grammar skills of the users. Thus, our system
enables personalized learning based on persona dialogue and facilitates grammar
learning of a user using grammar error feedback. Furthermore, we verified
whether FreeTalky provides practical help in alleviating xenoglossophobia by
replacing the real human in the conversation with a NAO robot, through human

We present ShinRL, an open-source library specialized for the evaluation of
reinforcement learning (RL) algorithms from both theoretical and practical
perspectives. Existing RL libraries typically allow users to evaluate practical
performances of deep RL algorithms through returns. Nevertheless, these
libraries are not necessarily useful for analyzing if the algorithms perform as
theoretically expected, such as if Q learning really achieves the optimal Q
function. In contrast, ShinRL provides an RL environment interface that can
compute metrics for delving into the behaviors of RL algorithms, such as the
gap between learned and the optimal Q values and state visitation frequencies.
In addition, we introduce a flexible solver interface for evaluating both
theoretically justified algorithms (e.g., dynamic programming and tabular RL)
and practically effective ones (i.e., deep RL, typically with some additional
extensions and regularizations) in a consistent fashion. As a case study, we
show that how combining these two features of ShinRL makes it easier to analyze
the behavior of deep Q learning. Furthermore, we demonstrate that ShinRL can be
used to empirically validate recent theoretical findings such as the effect of
KL regularization for value iteration and for deep Q learning, and the
robustness of entropy-regularized policies to adversarial rewards. The source
code for ShinRL is available on GitHub: https://github.com/omron-sinicx/ShinRL.

Securing enterprise networks presents challenges in terms of both their size
and distributed structure. Data required to detect and characterize malicious
activities may be diffused and may be located across network and endpoint
devices. Further, cyber-relevant data routinely exceeds total available
storage, bandwidth, and analysis capability, often by several orders of
magnitude. Real-time detection of threats within or across very large
enterprise networks is not simply an issue of scale, but also a challenge due
to the variable nature of malicious activities and their presentations. The
system seeks to develop a hierarchy of cyber reasoning layers to detect
malicious behavior, characterize novel attack vectors and present an analyst
with a contextualized human-readable output from a series of machine learning
models. We developed machine learning algorithms for scalable throughput and
improved recall for our Multi-Resolution Joint Optimization for Enterprise
Security and Forensics (ESAFE) solution. This Paper will provide an overview of
ESAFE's Machine Learning Modules, Attack Ontologies, and Automated Smart Alert
generation which provide multi-layer reasoning over cross-correlated sensors
for analyst consumption.

Restoring reasonable and realistic content for arbitrary missing regions in
images is an important yet challenging task. Although recent image inpainting
models have made significant progress in generating vivid visual details, they
can still lead to texture blurring or structural distortions due to contextual
ambiguity when dealing with more complex scenes. To address this issue, we
propose the Semantic Pyramid Network (SPN) motivated by the idea that learning
multi-scale semantic priors from specific pretext tasks can greatly benefit the
recovery of locally missing content in images. SPN consists of two components.
First, it distills semantic priors from a pretext model into a multi-scale
feature pyramid, achieving a consistent understanding of the global context and
local structures. Within the prior learner, we present an optional module for
variational inference to realize probabilistic image inpainting driven by
various learned priors. The second component of SPN is a fully context-aware
image generator, which adaptively and progressively refines low-level visual
representations at multiple scales with the (stochastic) prior pyramid. We
train the prior learner and the image generator as a unified model without any
post-processing. Our approach achieves the state of the art on multiple
datasets, including Places2, Paris StreetView, CelebA, and CelebA-HQ, under
both deterministic and probabilistic inpainting setups.

Accurate prediction of the number of daily or weekly confirmed cases of
COVID-19 is critical to the control of the pandemic. Existing mechanistic
models nicely capture the disease dynamics. However, to forecast the future,
they require the transmission rate to be known, limiting their prediction
power. Typically, a hypothesis is made on the form of the transmission rate
with respect to time. Yet the real form is too complex to be mechanistically
modeled due to the unknown dynamics of many influential factors. We tackle this
problem by using a hypothesis-free machine-learning algorithm to estimate the
transmission rate from data on non-pharmaceutical policies, and in turn
forecast the confirmed cases using a mechanistic disease model. More
specifically, we build a hybrid model consisting of a mechanistic ordinary
differential equation (ODE) model and a generalized boosting model (GBM). To
calibrate the parameters, we develop an "inverse method" that obtains the
transmission rate inversely in time from the other variables in the ODE model
and then feed it into the GBM to connect with the policy data. The resulting
model forecasted the number of daily confirmed cases up to 35 days in the
future in the United States with an averaged mean absolute percentage error of
27%. Being partly data-driven, the method is more accurate than typical
mechanistic models and meanwhile more intuitive, and possibly reliable, than
purely data-based machine learning models. Moreover, it can identify the most
informative predictive variables, which can be helpful in designing improved
forecasters as well as informing policymakers.

Entity linking aims to establish a link between entity mentions in a document
and the corresponding entities in knowledge graphs (KGs). Previous work has
shown the effectiveness of global coherence for entity linking. However, most
of the existing global linking methods based on sequential decisions focus on
how to utilize previously linked entities to enhance the later decisions. In
those methods, the order of mention is fixed, making the model unable to adjust
the subsequent linking targets according to the previously linked results,
which will cause the previous information to be unreasonably utilized. To
address the problem, we propose a novel model, called DyMen, to dynamically
adjust the subsequent linking target based on the previously linked entities
via reinforcement learning, enabling the model to select a link target that can
fully use previously linked information. We sample mention by sliding window to
reduce the action sampling space of reinforcement learning and maintain the
semantic coherence of mention. Experiments conducted on several benchmark
datasets have shown the effectiveness of the proposed model.

This work considers the problem of learning the Markov parameters of a linear
system from observed data. Recent non-asymptotic system identification results
have characterized the sample complexity of this problem in the single and
multi-rollout setting. In both instances, the number of samples required in
order to obtain acceptable estimates can produce optimization problems with an
intractably large number of decision variables for a second-order algorithm. We
show that a randomized and distributed Newton algorithm based on
Hessian-sketching can produce $\epsilon$-optimal solutions and converges
geometrically. Moreover, the algorithm is trivially parallelizable. Our results
hold for a variety of sketching matrices and we illustrate the theory with

Hidden community is a useful concept proposed recently for social network
analysis. To handle the rapid growth of network scale, in this work, we explore
the detection of hidden communities from the local perspective, and propose a
new method that detects and boosts each layer iteratively on a subgraph sampled
from the original network. We first expand the seed set from a single seed node
based on our modified local spectral method and detect an initial dominant
local community. Then we temporarily remove the members of this community as
well as their connections to other nodes, and detect all the neighborhood
communities in the remaining subgraph, including some "broken communities" that
only contain a fraction of members in the original network. The local community
and neighborhood communities form a dominant layer, and by reducing the edge
weights inside these communities, we weaken this layer's structure to reveal
the hidden layers. Eventually, we repeat the whole process and all communities
containing the seed node can be detected and boosted iteratively. We
theoretically show that our method can avoid some situations that a broken
community and the local community are regarded as one community in the
subgraph, leading to the inaccuracy on detection which can be caused by global
hidden community detection methods. Extensive experiments show that our method
could significantly outperform the state-of-the-art baselines designed for
either global hidden community detection or multiple local community detection.

Column Generation (CG) is an effective method for solving large-scale
optimization problems. CG starts by solving a sub-problem with a subset of
columns (i.e., variables) and gradually includes new columns that can improve
the solution of the current subproblem. The new columns are generated as needed
by repeatedly solving a pricing problem, which is often NP-hard and is a
bottleneck of the CG approach. To tackle this, we propose a
Machine-Learning-based Pricing Heuristic (MLPH)that can generate many
high-quality columns efficiently. In each iteration of CG, our MLPH leverages
an ML model to predict the optimal solution of the pricing problem, which is
then used to guide a sampling method to efficiently generate multiple
high-quality columns. Using the graph coloring problem, we empirically show
that MLPH significantly enhancesCG as compared to six state-of-the-art methods,
and the improvement in CG can lead to substantially better performance of the
branch-and-price exact method.

Overparameterization is shown to result in poor test accuracy on rare
subgroups under a variety of settings where subgroup information is known. To
gain a more complete picture, we consider the case where subgroup information
is unknown. We investigate the effect of model size on worst-group
generalization under empirical risk minimization (ERM) across a wide range of
settings, varying: 1) architectures (ResNet, VGG, or BERT), 2) domains (vision
or natural language processing), 3) model size (width or depth), and 4)
initialization (with pre-trained or random weights). Our systematic evaluation
reveals that increasing model size does not hurt, and may help, worst-group
test performance under ERM across all setups. In particular, increasing
pre-trained model size consistently improves performance on Waterbirds and
MultiNLI. We advise practitioners to use larger pre-trained models when
subgroup labels are unknown.

One of the most significant challenges involved in efforts to understand the
equation of state of dense neutron-rich matter is the uncertain density
dependence of the nuclear symmetry energy. Because of its broad impact, pinning
down the density dependence of the nuclear symmetry energy has been a
longstanding goal of both nuclear physics and astrophysics. Recent observations
of neutron stars, in both electromagnetic and gravitational-wave spectra, have
already constrained significantly the nuclear symmetry energy at high
densities. Training deep neural networks to learn a computationally efficient
representation of the mapping between astrophysical observables of neutron
stars, such as masses, radii, and tidal deformabilities, and the nuclear
symmetry energy allows its density dependence to be determined reliably and
accurately. In this work we use a deep learning approach to determine the
nuclear symmetry energy as a function of density directly from observational
neutron star data. We show for the first time that artificial neural networks
can precisely reconstruct the nuclear symmetry energy from a set of available
neutron star observables, such as, masses and radii as those measured by, e.g.,
the NICER mission, or masses and tidal deformabilities as measured by the
LIGO/VIRGO/KAGRA gravitational-wave detectors. These results demonstrate the
potential of artificial neural networks to reconstruct the symmetry energy, and
the equation of state, directly from neutron star observational data, and
emphasize the importance of the deep learning approach in the era of
Multi-Messenger Astrophysics.

Stochastic optimization algorithms implemented on distributed computing
architectures are increasingly used to tackle large-scale machine learning
applications. A key bottleneck in such distributed systems is the communication
overhead for exchanging information such as stochastic gradients between
different workers. Sparse communication with memory and the adaptive
aggregation methodology are two successful frameworks among the various
techniques proposed to address this issue. In this paper, we exploit the
advantages of Sparse communication and Adaptive aggregated Stochastic Gradients
to design a communication-efficient distributed algorithm named SASG.
Specifically, we determine the workers who need to communicate with the
parameter server based on the adaptive aggregation rule and then sparsify the
transmitted information. Therefore, our algorithm reduces both the overhead of
communication rounds and the number of communication bits in the distributed
system. We define an auxiliary sequence and provide convergence results of the
algorithm with the help of Lyapunov function analysis. Experiments on training
deep neural networks show that our algorithm can significantly reduce the
communication overhead compared to the previous methods, with little impact on
training and testing accuracy.

Representation learning models for Knowledge Graphs (KG) have proven to be
effective in encoding structural information and performing reasoning over KGs.
In this paper, we propose a novel pre-training-then-fine-tuning framework for
knowledge graph representation learning, in which a KG model is firstly
pre-trained with triple classification task, followed by discriminative
fine-tuning on specific downstream tasks such as entity type prediction and
entity alignment. Drawing on the general ideas of learning deep contextualized
word representations in typical pre-trained language models, we propose SCoP to
learn pre-trained KG representations with structural and contextual triples of
the target triple encoded. Experimental results demonstrate that fine-tuning
SCoP not only outperforms results of baselines on a portfolio of downstream
tasks but also avoids tedious task-specific model design and parameter
training.

System representations inspired by the infinite-dimensional Koopman operator
(generator) are increasingly considered for predictive modeling. Due to the
operator's linearity, a range of nonlinear systems admit linear predictor
representations - allowing for simplified prediction, analysis and control.
However, finding meaningful finite-dimensional representations for prediction
is difficult as it involves determining features that are both
Koopman-invariant (evolve linearly under the dynamics) as well as relevant
(spanning the original state) - a generally unsupervised problem. In this work,
we present Koopmanizing Flows - a novel continuous-time framework for
supervised learning of linear predictors for a class of nonlinear dynamics. In
our model construction a latent diffeomorphically related linear system unfolds
into a linear predictor through the composition with a monomial basis. The
lifting, its linear dynamics and state reconstruction are learned
simultaneously, while an unconstrained parameterization of Hurwitz matrices
ensures asymptotic stability regardless of the operator approximation accuracy.
The superior efficacy of Koopmanizing Flows is demonstrated in comparison to a
state-of-the-art method on the well-known LASA handwriting benchmark.

Hyper-parameter optimization is a crucial problem in machine learning as it
aims to achieve the state-of-the-art performance in any model. Great efforts
have been made in this field, such as random search, grid search, Bayesian
optimization. In this paper, we model hyper-parameter optimization process as a
Markov decision process, and tackle it with reinforcement learning. A novel
hyper-parameter optimization method based on soft actor critic and hierarchical
mixture regularization has been proposed. Experiments show that the proposed
method can obtain better hyper-parameters in a shorter time.

We consider a variant of the best arm identification (BAI) problem in
multi-armed bandits (MAB) in which there are two sets of arms (source and
target), and the objective is to determine the best target arm while only
pulling source arms. In this paper, we study the setting when, despite the
means being unknown, there is a known additive relationship between the source
and target MAB instances. We show how our framework covers a range of
previously studied pure exploration problems and additionally captures new
problems. We propose and theoretically analyze an LUCB-style algorithm to
identify an $\epsilon$-optimal target arm with high probability. Our
theoretical analysis highlights aspects of this transfer learning problem that
do not arise in the typical BAI setup, and yet recover the LUCB algorithm for
single domain BAI as a special case.

This paper proposes a deep learning approach to a class of active sensing
problems in wireless communications in which an agent sequentially interacts
with an environment over a predetermined number of time frames to gather
information in order to perform a sensing or actuation task for maximizing some
utility function. In such an active learning setting, the agent needs to design
an adaptive sensing strategy sequentially based on the observations made so
far. To tackle such a challenging problem in which the dimension of historical
observations increases over time, we propose to use a long short-term memory
(LSTM) network to exploit the temporal correlations in the sequence of
observations and to map each observation to a fixed-size state information
vector. We then use a deep neural network (DNN) to map the LSTM state at each
time frame to the design of the next measurement step. Finally, we employ
another DNN to map the final LSTM state to the desired solution. We investigate
the performance of the proposed framework for adaptive channel sensing problems
in wireless communications. In particular, we consider the adaptive beamforming
problem for mmWave beam alignment and the adaptive reconfigurable intelligent
surface sensing problem for reflection alignment. Numerical results demonstrate
that the proposed deep active sensing strategy outperforms the existing
adaptive or nonadaptive sensing schemes.

Robotic Surgical Assistants (RSAs) are commonly used to perform minimally
invasive surgeries by expert surgeons. However, long procedures filled with
tedious and repetitive tasks such as suturing can lead to surgeon fatigue,
motivating the automation of suturing. As visual tracking of a thin reflective
needle is extremely challenging, prior work has modified the needle with
nonreflective contrasting paint. As a step towards automation of a suturing
subtask without modifying the needle, we propose HOUSTON: Handoff of
Unmodified, Surgical, Tool-Obstructed Needles, a problem and algorithm that
uses a learned active sensing policy with a stereo camera to localize and align
the needle into a visible and accessible pose for the other arm. To compensate
for robot positioning and needle perception errors, the algorithm then executes
a high-precision grasping motion that uses multiple cameras. In physical
experiments using the da Vinci Research Kit (dVRK), HOUSTON successfully passes
unmodified surgical needles with a success rate of 96.7% and is able to perform
handover sequentially between the arms 32.4 times on average before failure. On
needles unseen in training, HOUSTON achieves a success rate of 75 - 92.9%. To
our knowledge, this work is the first to study handover of unmodified surgical
needles. See https://tinyurl.com/houston-surgery for additional materials.

In sequential recommender system applications, it is important to develop
models that can capture users' evolving interest over time to successfully
recommend future items that they are likely to interact with. For users with
long histories, typical models based on recurrent neural networks tend to
forget important items in the distant past. Recent works have shown that
storing a small sketch of past items can improve sequential recommendation
tasks. However, these works all rely on static sketching policies, i.e.,
heuristics to select items to keep in the sketch, which are not necessarily
optimal and cannot improve over time with more training data. In this paper, we
propose a differentiable policy for sketching (DiPS), a framework that learns a
data-driven sketching policy in an end-to-end manner together with the
recommender system model to explicitly maximize recommendation quality in the
future. We also propose an approximate estimator of the gradient for optimizing
the sketching algorithm parameters that is computationally efficient. We verify
the effectiveness of DiPS on real-world datasets under various practical
settings and show that it requires up to $50\%$ fewer sketch items to reach the
same predictive quality than existing sketching policies.

Visual odometry aims to track the incremental motion of an object using the
information captured by visual sensors. In this work, we study the point cloud
odometry problem, where only the point cloud scans obtained by the LiDAR (Light
Detection And Ranging) are used to estimate object's motion trajectory. A
lightweight point cloud odometry solution is proposed and named the green point
cloud odometry (GreenPCO) method. GreenPCO is an unsupervised learning method
that predicts object motion by matching features of consecutive point cloud
scans. It consists of three steps. First, a geometry-aware point sampling
scheme is used to select discriminant points from the large point cloud.
Second, the view is partitioned into four regions surrounding the object, and
the PointHop++ method is used to extract point features. Third, point
correspondences are established to estimate object motion between two
consecutive scans. Experiments on the KITTI dataset are conducted to
demonstrate the effectiveness of the GreenPCO method. It is observed that
GreenPCO outperforms benchmarking deep learning methods in accuracy while it
has a significantly smaller model size and less training time.

With the rapid development of intelligent vehicles and Advanced
Driver-Assistance Systems (ADAS), a new trend is that mixed levels of human
driver engagements will be involved in the transportation system. Therefore,
necessary visual guidance for drivers is vitally important under this situation
to prevent potential risks. To advance the development of visual guidance
systems, we introduce a novel vision-cloud data fusion methodology, integrating
camera image and Digital Twin information from the cloud to help intelligent
vehicles make better decisions. Target vehicle bounding box is drawn and
matched with the help of the object detector (running on the ego-vehicle) and
position information (received from the cloud). The best matching result, a
79.2% accuracy under 0.7 intersection over union threshold, is obtained with
depth images served as an additional feature source. A case study on lane
change prediction is conducted to show the effectiveness of the proposed data
fusion methodology. In the case study, a multi-layer perceptron algorithm is
proposed with modified lane change prediction approaches. Human-in-the-loop
simulation results obtained from the Unity game engine reveal that the proposed
model can improve highway driving performance significantly in terms of safety,
comfort, and environmental sustainability.

Multi-Chip-Modules (MCMs) reduce the design and fabrication cost of machine
learning (ML) accelerators while delivering performance and energy efficiency
on par with a monolithic large chip. However, ML compilers targeting MCMs need
to solve complex optimization problems optimally and efficiently to achieve
this high performance. One such problem is the multi-chip partitioning problem
where compilers determine the optimal partitioning and placement of operations
in tensor computation graphs on chiplets in MCMs. Partitioning ML graphs for
MCMs is particularly hard as the search space grows exponentially with the
number of chiplets available and the number of nodes in the neural network.
Furthermore, the constraints imposed by the underlying hardware produce a
search space where valid solutions are extremely sparse. In this paper, we
present a strategy using a deep reinforcement learning (RL) framework to emit a
possibly invalid candidate partition that is then corrected by a constraint
solver. Using the constraint solver ensures that RL encounters valid solutions
in the sparse space frequently enough to converge with fewer samples as
compared to non-learned strategies. The architectural choices we make for the
policy network allow us to generalize across different ML graphs. Our
evaluation of a production-scale model, BERT, on real hardware reveals that the
partitioning generated using RL policy achieves 6.11% and 5.85% higher
throughput than random search and simulated annealing. In addition, fine-tuning
the pre-trained RL policy reduces the search time from 3 hours to only 9
minutes, while achieving the same throughput as training RL policy from
scratch.

An ML model based on precomputed per-channel SCI is proposed. Due to its
superior accuracy over closed-form GN, an average SNR gain of 1.1 dB in an
end-to-end link optimization and a 40% reduction in required lightpaths to meet
traffic requests in a network planning scenario are shown.

Deep Neural Networks (DNNs) are used in a wide variety of applications.
However, as in any software application, DNN-based apps are afflicted with
bugs. Previous work observed that DNN bug fix patterns are different from
traditional bug fix patterns. Furthermore, those buggy models are non-trivial
to diagnose and fix due to inexplicit errors with several options to fix them.
To support developers in locating and fixing bugs, we propose DeepDiagnosis, a
novel debugging approach that localizes the faults, reports error symptoms and
suggests fixes for DNN programs. In the first phase, our technique monitors a
training model, periodically checking for eight types of error conditions.
Then, in case of problems, it reports messages containing sufficient
information to perform actionable repairs to the model. In the evaluation, we
thoroughly examine 444 models -53 real-world from GitHub and Stack Overflow,
and 391 curated by AUTOTRAINER. DeepDiagnosis provides superior accuracy when
compared to UMLUAT and DeepLocalize. Our technique is faster than AUTOTRAINER
for fault localization. The results show that our approach can support
additional types of models, while state-of-the-art was only able to handle
classification ones. Our technique was able to report bugs that do not manifest
as numerical errors during training. Also, it can provide actionable insights
for fix whereas DeepLocalize can only report faults that lead to numerical
errors during training. DeepDiagnosis manifests the best capabilities of fault
detection, bug localization, and symptoms identification when compared to other

Many deep neural network architectures loosely based on brain networks have
recently been shown to replicate neural firing patterns observed in the brain.
One of the most exciting and promising novel architectures, the Transformer
neural network, was developed without the brain in mind. In this work, we show
that transformers, when equipped with recurrent position encodings, replicate
the precisely tuned spatial representations of the hippocampal formation; most
notably place and grid cells. Furthermore, we show that this result is no
surprise since it is closely related to current hippocampal models from
neuroscience. We additionally show the transformer version offers dramatic
performance gains over the neuroscience version. This work continues to bind
computations of artificial and brain networks, offers a novel understanding of
the hippocampal-cortical interaction, and suggests how wider cortical areas may
perform complex tasks beyond current neuroscience models such as language
comprehension.

The sensitivity of image classifiers to small perturbations in the input is
often viewed as a defect of their construction. We demonstrate that this
sensitivity is a fundamental property of classifiers. For any arbitrary
classifier over the set of $n$-by-$n$ images, we show that for all but one
class it is possible to change the classification of all but a tiny fraction of
the images in that class with a perturbation of size $O(n^{1/\max{(p,1)}})$
when measured in any $p$-norm for $p \geq 0$. We then discuss how this
phenomenon relates to human visual perception and the potential implications
for the design considerations of computer vision systems.

We demonstrate QoT estimation in a live network utilizing neural networks
trained on synthetic data spanning a large parameter space. The ML-model
predicts the measured lightpath performance with <0.5dB SNR error over a wide
configuration range.

We consider the problem of learning free-form symbolic expressions from raw
data, such as that produced by an experiment in any scientific domain. Accurate
and interpretable models of scientific phenomena are the cornerstone of
scientific research. Simple yet interpretable models, such as linear or
logistic regression and decision trees often lack predictive accuracy.
Alternatively, accurate blackbox models such as deep neural networks provide
high predictive accuracy, but do not readily admit human understanding in a way
that would enrich the scientific theory of the phenomenon. Many great
breakthroughs in science revolve around the development of parsimonious
equational models with high predictive accuracy, such as Newton's laws,
universal gravitation, and Maxwell's equations. Previous work on automating the
search of equational models from data combine domain-specific heuristics as
well as computationally expensive techniques, such as genetic programming and
Monte-Carlo search. We develop a deep neural network (MACSYMA) to address the
symbolic regression problem as an end-to-end supervised learning problem.
MACSYMA can generate symbolic expressions that describe a dataset. The
computational complexity of the task is reduced to the feedforward computation
of a neural network. We train our neural network on a synthetic dataset
consisting of data tables of varying length and varying levels of noise, for
which the neural network must learn to produce the correct symbolic expression
token by token. Finally, we validate our technique by running on a public
dataset from behavioral science.

Face identification (FI) is ubiquitous and drives many high-stake decisions
made by law enforcement. State-of-the-art FI approaches compare two images by
taking the cosine similarity between their image embeddings. Yet, such an
approach suffers from poor out-of-distribution (OOD) generalization to new
types of images (e.g., when a query face is masked, cropped, or rotated) not
included in the training set or the gallery. Here, we propose a re-ranking
approach that compares two faces using the Earth Mover's Distance on the deep,
spatial features of image patches. Our extra comparison stage explicitly
examines image similarity at a fine-grained level (e.g., eyes to eyes) and is
more robust to OOD perturbations and occlusions than traditional FI.
Interestingly, without finetuning feature extractors, our method consistently
improves the accuracy on all tested OOD queries: masked, cropped, rotated, and
adversarial while obtaining similar results on in-distribution images.

We present neural activation coding (NAC) as a novel approach for learning
deep representations from unlabeled data for downstream applications. We argue
that the deep encoder should maximize its nonlinear expressivity on the data
for downstream predictors to take full advantage of its representation power.
To this end, NAC maximizes the mutual information between activation patterns
of the encoder and the data over a noisy communication channel. We show that
learning for a noise-robust activation code increases the number of distinct
linear regions of ReLU encoders, hence the maximum nonlinear expressivity. More
interestingly, NAC learns both continuous and discrete representations of data,
which we respectively evaluate on two downstream tasks: (i) linear
classification on CIFAR-10 and ImageNet-1K and (ii) nearest neighbor retrieval
on CIFAR-10 and FLICKR-25K. Empirical results show that NAC attains better or
comparable performance on both tasks over recent baselines including SimCLR and
DistillHash. In addition, NAC pretraining provides significant benefits to the
training of deep generative models. Our code is available at
https://github.com/yookoon/nac.

Functional connectivity (FC) studies have demonstrated the overarching value
of studying the brain and its disorders through the undirected weighted graph
of fMRI correlation matrix. Most of the work with the FC, however, depends on
the way the connectivity is computed, and further depends on the manual
post-hoc analysis of the FC matrices. In this work we propose a deep learning
architecture BrainGNN that learns the connectivity structure as part of
learning to classify subjects. It simultaneously applies a graphical neural
network to this learned graph and learns to select a sparse subset of brain
regions important to the prediction task. We demonstrate the model's
state-of-the-art classification performance on a schizophrenia fMRI dataset and
demonstrate how introspection leads to disorder relevant findings. The graphs
learned by the model exhibit strong class discrimination and the sparse subset
of relevant regions are consistent with the schizophrenia literature.

Despite the outstanding success of self-supervised pretraining methods for
video representation learning, they generalise poorly when the unlabeled
dataset for pretraining is small or the domain difference between unlabelled
data in source task (pretraining) and labeled data in target task (finetuning)
is significant. To mitigate these issues, we propose a novel approach to
complement self-supervised pretraining via an auxiliary pretraining phase,
based on knowledge similarity distillation, auxSKD, for better generalisation
with a significantly smaller amount of video data, e.g. Kinetics-100 rather
than Kinetics-400. Our method deploys a teacher network that iteratively
distills its knowledge to the student model by capturing the similarity
information between segments of unlabelled video data. The student model
meanwhile solves a pretext task by exploiting this prior knowledge. We also
introduce a novel pretext task, Video Segment Pace Prediction or VSPP, which
requires our model to predict the playback speed of a randomly selected segment
of the input video to provide more reliable self-supervised representations.
Our experimental results show superior results to the state of the art on both
UCF101 and HMDB51 datasets when pretraining on K100 in apple-to-apple
comparisons. Additionally, we show that our auxiliary pretraining, auxSKD, when
added as an extra pretraining phase to recent state of the art self-supervised
methods (i.e. VCOP, VideoPace, and RSPNet), improves their results on UCF101
and HMDB51. Our code is available at https://github.com/Plrbear/auxSKD.

Address parsing consists of identifying the segments that make up an address,
such as a street name or a postal code. Because of its importance for tasks
like record linkage, address parsing has been approached with many techniques,
the latest relying on neural networks. While these models yield notable
results, previous work on neural networks has only focused on parsing addresses
from a single source country. This paper explores the possibility of
transferring the address parsing knowledge acquired by training deep learning
models on some countries' addresses to others with no further training in a
zero-shot transfer learning setting. We also experiment using an attention
mechanism and a domain adversarial training algorithm in the same zero-shot
transfer setting to improve performance. Both methods yield state-of-the-art
performance for most of the tested countries while giving good results to the
remaining countries. We also explore the effect of incomplete addresses on our
best model, and we evaluate the impact of using incomplete addresses during
training. In addition, we propose an open-source Python implementation of some
of our trained models.

Since there are a number of Internet-of-Things (IoT) applications that need
to collect data sets from a large number of sensors or devices in real-time,
sensing and communication need to be integrated for efficient uploading from
devices. In this paper, we introduce the notion of data-aided sensing (DAS)
where a base station (BS) utilizes a subset of data that is already uploaded
and available to select the next device for efficient data collection or
sensing. Thus, using DAS, certain tasks in IoT applications, including
federated learning, can be completed by uploading from a small number of
selected devices. Two different types of DAS are considered: one is centralized
DAS and the other is distributed DAS. In centralized DAS, the BS decides the
uploading order, while each device can decide when to upload its own local data
set among multiple uploading rounds in distributed DAS. In distributed DAS,
random access is employed where the access probability of each device is
decided according to its local measurement for efficient uploading.

Sparse shrunk additive models and sparse random feature models have been
developed separately as methods to learn low-order functions, where there are
few interactions between variables, but neither offers computational
efficiency. On the other hand, $\ell_2$-based shrunk additive models are
efficient but do not offer feature selection as the resulting coefficient
vectors are dense. Inspired by the success of the iterative magnitude pruning
technique in finding lottery tickets of neural networks, we propose a new
method -- Sparser Random Feature Models via IMP (ShRIMP) -- to efficiently fit
high-dimensional data with inherent low-dimensional structure in the form of
sparse variable dependencies. Our method can be viewed as a combined process to
construct and find sparse lottery tickets for two-layer dense networks. We
explain the observed benefit of SHRIMP through a refined analysis on the
generalization error for thresholded Basis Pursuit and resulting bounds on
eigenvalues.
  From function approximation experiments on both synthetic data and real-world
benchmark datasets, we show that SHRIMP obtains better than or competitive test
accuracy compared to state-of-art sparse feature and additive methods such as
SRFE-S, SSAM, and SALSA. Meanwhile, SHRIMP performs feature selection with low
computational complexity and is robust to the pruning rate, indicating a
robustness in the structure of the obtained subnetworks. We gain insight into
the lottery ticket hypothesis through SHRIMP by noting a correspondence between
our model and weight/neuron subnetworks.

Digital pathology is one of the most significant developments in modern
medicine. Pathological examinations are the gold standard of medical protocols
and play a fundamental role in diagnosis. Recently, with the advent of digital
scanners, tissue histopathology slides can now be digitized and stored as
digital images. As a result, digitized histopathological tissues can be used in
computer-aided image analysis programs and machine learning techniques.
Detection and segmentation of nuclei are some of the essential steps in the
diagnosis of cancers. Recently, deep learning has been used for nuclei
segmentation. However, one of the problems in deep learning methods for nuclei
segmentation is the lack of information from out of the patches. This paper
proposes a deep learning-based approach for nuclei segmentation, which
addresses the problem of misprediction in patch border areas. We use both local
and global patches to predict the final segmentation map. Experimental results
on the Multi-organ histopathology dataset demonstrate that our method
outperforms the baseline nuclei segmentation and popular segmentation models.

In recent years, the field of precision medicine has seen many advancements.
Significant focus has been placed on creating algorithms to estimate
individualized treatment rules (ITR), which map from patient covariates to the
space of available treatments with the goal of maximizing patient outcome.
Direct Learning (D-Learning) is a recent one-step method which estimates the
ITR by directly modeling the treatment-covariate interaction. However, when the
variance of the outcome is heterogeneous with respect to treatment and
covariates, D-Learning does not leverage this structure. Stabilized Direct
Learning (SD-Learning), proposed in this paper, utilizes potential
heteroscedasticity in the error term through a residual reweighting which
models the residual variance via flexible machine learning algorithms such as
XGBoost and random forests. We also develop an internal cross-validation scheme
which determines the best residual model amongst competing models. SD-Learning
improves the efficiency of D-Learning estimates in binary and multi-arm
treatment scenarios. The method is simple to implement and an easy way to
improve existing algorithms within the D-Learning family, including original
D-Learning, Angle-based D-Learning (AD-Learning), and Robust D-Learning
(RD-Learning). We provide theoretical properties and justification of the
optimality of SD-Learning. Head-to-head performance comparisons with D-Learning
methods are provided through simulations, which demonstrate improvement in
terms of average prediction error (APE), misclassification rate, and empirical
value, along with data analysis of an AIDS randomized clinical trial.

Learning-based predictive control is a promising alternative to
optimization-based MPC. However, efficiently learning the optimal control
policy, the optimal value function, or the Q-function requires suitable
function approximators. Often, artificial neural networks (ANN) are considered
but choosing a suitable topology is also non-trivial. Against this background,
it has recently been shown that tailored ANN allow, in principle, to exactly
describe the optimal control policy in linear MPC by exploiting its piecewise
affine structure. In this paper, we provide a similar result for representing
the optimal value function and the Q-function that are both known to be
piecewise quadratic for linear MPC.

We present a unified approach to study continuous measurement based quantum
thermal machines in static as well as adiabatically driven systems. We
investigate both steady state and transient dynamics for the time-independent
case. In the adiabatically driven case, we show how measurement based
thermodynamic quantities can be attributed geometric characteristics. We also
provide the appropriate definition for heat transfer and dissipation owing to
continous measurement in the presence and absence of adiabatic driving. We
illustrate the aforementioned ideas and study the phenomena of refrigeration in
two different paradigmatic examples: a coupled quantum dot and a coupled qubit
system, both undergoing continuous measurement and slow driving. In the
time-independent case, we show that quantum coherence can improve the cooling
power of measurement based quantum refrigerators. Exclusively for the case of
coupled qubits, we consider linear as well as non-linear system-bath couplings.
We observe that non-linear coupling produces cooling effects in certain regime
where otherwise heating is expected. In the adiabatically driven case, we
observe that quantum measurement can provide significant boost to the power of
adiabatic quantum refrigerators. We also observe that the obtained boost can be
larger than the sum of power due to individual effects. The measurement based
refrigerators can have similar or better coefficient of performance (COP) in
the driven case compared to the static one in the regime where heat extraction
is maximum. Our results have potential significance for future application in
devices ranging from measurement based quantum thermal machines to
refrigeration in quantum processing networks.

In this chapter, we will mainly focus on collaborative training across
wireless devices. Training a ML model is equivalent to solving an optimization
problem, and many distributed optimization algorithms have been developed over
the last decades. These distributed ML algorithms provide data locality; that
is, a joint model can be trained collaboratively while the data available at
each participating device remains local. This addresses, to some extend, the
privacy concern. They also provide computational scalability as they allow
exploiting computational resources distributed across many edge devices.
However, in practice, this does not directly lead to a linear gain in the
overall learning speed with the number of devices. This is partly due to the
communication bottleneck limiting the overall computation speed. Additionally,
wireless devices are highly heterogeneous in their computational capabilities,
and both their computation speed and communication rate can be highly
time-varying due to physical factors. Therefore, distributed learning
algorithms, particularly those to be implemented at the wireless network edge,
must be carefully designed taking into account the impact of time-varying
communication network as well as the heterogeneous and stochastic computation
capabilities of devices.

In recent years, several results in the supervised learning setting suggested
that classical statistical learning-theoretic measures, such as VC dimension,
do not adequately explain the performance of deep learning models which
prompted a slew of work in the infinite-width and iteration regimes. However,
there is little theoretical explanation for the success of neural networks
beyond the supervised setting. In this paper we argue that, under some
distributional assumptions, classical learning-theoretic measures can
sufficiently explain generalization for graph neural networks in the
transductive setting. In particular, we provide a rigorous analysis of the
performance of neural networks in the context of transductive inference,
specifically by analysing the generalisation properties of graph convolutional
networks for the problem of node classification. While VC Dimension does result
in trivial generalisation error bounds in this setting as well, we show that
transductive Rademacher complexity can explain the generalisation properties of
graph convolutional networks for stochastic block models. We further use the
generalisation error bounds based on transductive Rademacher complexity to
demonstrate the role of graph convolutions and network architectures in
achieving smaller generalisation error and provide insights into when the graph
structure can help in learning. The findings of this paper could re-new the
interest in studying generalisation in neural networks in terms of
learning-theoretic measures, albeit in specific problems.

Few-shot learning remains a challenging problem, with unsatisfactory 1-shot
accuracies for most real-world data. Here, we present a different perspective
for data distributions in the feature space of a deep network and show how to
exploit it for few-shot learning. First, we observe that nearest neighbors in
the feature space are with high probability members of the same class while
generally two random points from one class are not much closer to each other
than points from different classes. This observation suggests that classes in
feature space form sparse, loosely connected graphs instead of dense clusters.
To exploit this property, we propose using a small amount of label propagation
into the unlabeled space and then using a kernel PCA reconstruction error as
decision boundary for the feature-space data distribution of each class. Using
this method, which we call "K-Prop," we demonstrate largely improved few-shot
learning performances (e.g., 83% accuracy for 1-shot 5-way classification on
the RESISC45 satellite-images dataset) for datasets for which a backbone
network can be trained with high within-class nearest-neighbor probabilities.
We demonstrate this relationship using six different datasets.

Imitation learning is a powerful approach for learning autonomous driving
policy by leveraging data from expert driver demonstrations. However, driving
policies trained via imitation learning that neglect the causal structure of
expert demonstrations yield two undesirable behaviors: inertia and collision.
In this paper, we propose Causal Imitative Model (CIM) to address inertia and
collision problems. CIM explicitly discovers the causal model and utilizes it
to train the policy. Specifically, CIM disentangles the input to a set of
latent variables, selects the causal variables, and determines the next
position by leveraging the selected variables. Our experiments show that our
method outperforms previous work in terms of inertia and collision rates.
Moreover, thanks to exploiting the causal structure, CIM shrinks the input
dimension to only two, hence, can adapt to new environments in a few-shot
setting. Code is available at https://github.com/vita-epfl/CIM.

Contrastive representation learning of videos highly relies on the
availability of millions of unlabelled videos. This is practical for videos
available on web but acquiring such large scale of videos for real-world
applications is very expensive and laborious. Therefore, in this paper we focus
on designing video augmentation for self-supervised learning, we first analyze
the best strategy to mix videos to create a new augmented video sample. Then,
the question remains, can we make use of the other modalities in videos for
data mixing? To this end, we propose Cross-Modal Manifold Cutmix (CMMC) that
inserts a video tesseract into another video tesseract in the feature space
across two different modalities. We find that our video mixing strategy
STC-mix, i.e. preliminary mixing of videos followed by CMMC across different
modalities in a video, improves the quality of learned video representations.
We conduct thorough experiments for two downstream tasks: action recognition
and video retrieval on two small scale video datasets UCF101, and HMDB51. We
also demonstrate the effectiveness of our STC-mix on NTU dataset where domain
knowledge is limited. We show that the performance of our STC-mix on both the
downstream tasks is on par with the other self-supervised approaches while
requiring less training data.

Learning self-supervised video representation predominantly focuses on
discriminating instances generated from simple data augmentation schemes.
However, the learned representation often fails to generalize over unseen
camera viewpoints. To this end, we propose ViewCLR, that learns self-supervised
video representation invariant to camera viewpoint changes. We introduce a
view-generator that can be considered as a learnable augmentation for any
self-supervised pre-text tasks, to generate latent viewpoint representation of
a video. ViewCLR maximizes the similarities between the latent viewpoint
representation with its representation from the original viewpoint, enabling
the learned video encoder to generalize over unseen camera viewpoints.
Experiments on cross-view benchmark datasets including NTU RGB+D dataset show
that ViewCLR stands as a state-of-the-art viewpoint invariant self-supervised
method.

Hypergraphs capture multi-way relationships in data, and they have
consequently seen a number of applications in higher-order network analysis,
computer vision, geometry processing, and machine learning. In this paper, we
develop theoretical foundations for studying the space of hypergraphs using
ingredients from optimal transport. By enriching a hypergraph with probability
measures on its nodes and hyperedges, as well as relational information
capturing local and global structures, we obtain a general and robust framework
for studying the collection of all hypergraphs. First, we introduce a
hypergraph distance based on the co-optimal transport framework of Redko et al.
and study its theoretical properties. Second, we formalize common methods for
transforming a hypergraph into a graph as maps between the space of hypergraphs
and the space of graphs, and study their functorial properties and Lipschitz
bounds. Finally, we demonstrate the versatility of our Hypergraph Co-Optimal
Transport (HyperCOT) framework through various examples.

Action detection is an essential and challenging task, especially for densely
labelled datasets of untrimmed videos. The temporal relation is complex in
those datasets, including challenges like composite action, and co-occurring
action. For detecting actions in those complex videos, efficiently capturing
both short-term and long-term temporal information in the video is critical. To
this end, we propose a novel ConvTransformer network for action detection. This
network comprises three main components: (1) Temporal Encoder module
extensively explores global and local temporal relations at multiple temporal
resolutions. (2) Temporal Scale Mixer module effectively fuses the multi-scale
features to have a unified feature representation. (3) Classification module is
used to learn the instance center-relative position and predict the frame-level
classification scores. The extensive experiments on multiple datasets,
including Charades, TSU and MultiTHUMOS, confirm the effectiveness of our
proposed method. Our network outperforms the state-of-the-art methods on all
three datasets.

Humans and animals explore their environment and acquire useful skills even
in the absence of clear goals, exhibiting intrinsic motivation. The study of
intrinsic motivation in artificial agents is concerned with the following
question: what is a good general-purpose objective for an agent? We study this
question in dynamic partially-observed environments, and argue that a compact
and general learning objective is to minimize the entropy of the agent's state
visitation estimated using a latent state-space model. This objective induces
an agent to both gather information about its environment, corresponding to
reducing uncertainty, and to gain control over its environment, corresponding
to reducing the unpredictability of future world states. We instantiate this
approach as a deep reinforcement learning agent equipped with a deep
variational Bayes filter. We find that our agent learns to discover, represent,
and exercise control of dynamic objects in a variety of partially-observed
environments sensed with visual observations without extrinsic reward.

Clustering is a fundamental primitive in unsupervised learning which gives
rise to a rich class of computationally-challenging inference tasks. In this
work, we focus on the canonical task of clustering d-dimensional Gaussian
mixtures with unknown (and possibly degenerate) covariance. Recent works (Ghosh
et al. '20; Mao, Wein '21; Davis, Diaz, Wang '21) have established lower bounds
against the class of low-degree polynomial methods and the sum-of-squares (SoS)
hierarchy for recovering certain hidden structures planted in Gaussian
clustering instances. Prior work on many similar inference tasks portends that
such lower bounds strongly suggest the presence of an inherent
statistical-to-computational gap for clustering, that is, a parameter regime
where the clustering task is statistically possible but no polynomial-time
algorithm succeeds.
  One special case of the clustering task we consider is equivalent to the
problem of finding a planted hypercube vector in an otherwise random subspace.
We show that, perhaps surprisingly, this particular clustering model does not
exhibit a statistical-to-computational gap, even though the aforementioned
low-degree and SoS lower bounds continue to apply in this case. To achieve
this, we give a polynomial-time algorithm based on the Lenstra--Lenstra--Lovasz
lattice basis reduction method which achieves the statistically-optimal sample
complexity of d+1 samples. This result extends the class of problems whose
conjectured statistical-to-computational gaps can be "closed" by "brittle"
polynomial-time algorithms, highlighting the crucial but subtle role of noise
in the onset of statistical-to-computational gaps.

We consider distributed online min-max resource allocation with a set of
parallel agents and a parameter server. Our goal is to minimize the pointwise
maximum over a set of time-varying and decreasing cost functions, without a
priori information about these functions. We propose a novel online algorithm,
termed Distributed Online resource Re-Allocation (DORA), where non-stragglers
learn to relinquish resource and share resource with stragglers. A notable
feature of DORA is that it does not require gradient calculation or projection
operation, unlike most existing online optimization strategies. This allows it
to substantially reduce the computation overhead in large-scale and distributed
networks. We show that the dynamic regret of the proposed algorithm is upper
bounded by $O\left(T^{\frac{3}{4}}(1+P_T)^{\frac{1}{4}}\right)$, where $T$ is
the total number of rounds and $P_T$ is the path-length of the instantaneous
minimizers. We further consider an application to the bandwidth allocation
problem in distributed online machine learning. Our numerical study
demonstrates the efficacy of the proposed solution and its performance
advantage over gradient- and/or projection-based resource allocation algorithms
in reducing wall-clock time.

Nowadays, due to advanced digital imaging technologies and internet
accessibility to the public, the number of generated digital images has
increased dramatically. Thus, the need for automatic image enhancement
techniques is quite apparent. In recent years, deep learning has been used
effectively. Here, after introducing some recently developed works on image
enhancement, an image enhancement system based on convolutional neural networks
is presented. Our goal is to make an effective use of two available approaches,
convolutional neural network and bilateral grid. In our approach, we increase
the training data and the model dimensions and propose a variable rate during
the training process. The enhancement results produced by our proposed method,
while incorporating 5 different experts, show both quantitative and qualitative
improvements as compared to other available methods.

We develop a tool that extracts emotions from social media text data. Our
methodology has three main advantages. First, it is tailored for financial
context; second, it incorporates key aspects of social media data, such as
non-standard phrases, emojis and emoticons; and third, it operates by
sequentially learning a latent representation that includes features such as
word order, word usage, and local context. This tool, along with a user guide
is available at: https://github.com/dvamossy/EmTract. Using EmTract, we explore
the relationship between investor emotions expressed on social media and asset
prices. We document a number of interesting insights. First, we confirm some of
the findings of controlled laboratory experiments relating investor emotions to
asset price movements. Second, we show that investor emotions are predictive of
daily price movements. These impacts are larger when volatility or short
interest are higher, and when institutional ownership or liquidity are lower.
Third, increased investor enthusiasm prior to the IPO contributes to the large
first-day return and long-run underperformance of IPO stocks. To corroborate
our results, we provide a number of robustness checks, including using an
alternative emotion model. Our findings reinforce the intuition that emotions
and market dynamics are closely related, and highlight the importance of
considering investor emotions when assessing a stock's short-term value.

When arranging for third-party data annotation, it can be hard to compare how
well the competing providers apply best practices to create high-quality
datasets. This leads to a "race to the bottom," where competition based solely
on price makes it hard for vendors to charge for high-quality annotation. We
propose a voluntary rubric which can be used (a) as a scorecard to compare
vendors' offerings, (b) to communicate our expectations of the vendors more
clearly and consistently than today, (c) to justify the expense of choosing
someone other than the lowest bidder, and (d) to encourage annotation providers
to improve their practices.

Weak supervision (WS) frameworks are a popular way to bypass hand-labeling
large datasets for training data-hungry models. These approaches synthesize
multiple noisy but cheaply-acquired estimates of labels into a set of
high-quality pseudolabels for downstream training. However, the synthesis
technique is specific to a particular kind of label, such as binary labels or
sequences, and each new label type requires manually designing a new synthesis
algorithm. Instead, we propose a universal technique that enables weak
supervision over any label type while still offering desirable properties,
including practical flexibility, computational efficiency, and theoretical
guarantees. We apply this technique to important problems previously not
tackled by WS frameworks including learning to rank, regression, and learning
in hyperbolic space. Theoretically, our synthesis approach produces a
consistent estimators for learning some challenging but important
generalizations of the exponential family model. Experimentally, we validate
our framework and show improvement over baselines in diverse settings including
real-world learning-to-rank and regression problems along with learning on
hyperbolic manifolds.

Deep generative models such as GANs and normalizing flows are powerful
priors. They can regularize inverse problems to reduce ill-posedness and attain
high-quality results. However, the latent vector of such deep generative models
can fall out of the desired high-dimensional standard Gaussian distribution
during an inversion, particularly in the presence of noise in data or
inaccurate forward models. In such a case, deep generative models are
ineffective in attaining high-fidelity solutions. To address this issue, we
propose to reparameterize and Gaussianize the latent vector using novel
differentiable data-dependent layers wherein custom operators are defined by
solving optimization problems. These proposed layers constrain an inversion to
find feasible in-distribution solutions. We tested and validated our technique
on three inversion tasks: compressive-sensing MRI, image deblurring, and
eikonal tomography (a nonlinear PDE-constrained inverse problem), using two
representative deep generative models: StyleGAN2 and Glow, and achieved

Missing data are prevalent and present daunting challenges in real data
analysis. While there is a growing body of literature on fairness in analysis
of fully observed data, there has been little theoretical work on investigating
fairness in analysis of incomplete data. In practice, a popular analytical
approach for dealing with missing data is to use only the set of complete
cases, i.e., observations with all features fully observed to train a
prediction algorithm. However, depending on the missing data mechanism, the
distribution of complete cases and the distribution of the complete data may be
substantially different. When the goal is to develop a fair algorithm in the
complete data domain where there are no missing values, an algorithm that is
fair in the complete case domain may show disproportionate bias towards some
marginalized groups in the complete data domain. To fill this significant gap,
we study the problem of estimating fairness in the complete data domain for an
arbitrary model evaluated merely using complete cases. We provide upper and
lower bounds on the fairness estimation error and conduct numerical experiments
to assess our theoretical results. Our work provides the first known
theoretical results on fairness guarantee in analysis of incomplete data.

This paper presents a grounded language-image pre-training (GLIP) model for
learning object-level, language-aware, and semantic-rich visual
representations. GLIP unifies object detection and phrase grounding for
pre-training. The unification brings two benefits: 1) it allows GLIP to learn
from both detection and grounding data to improve both tasks and bootstrap a
good grounding model; 2) GLIP can leverage massive image-text pairs by
generating grounding boxes in a self-training fashion, making the learned
representation semantic-rich. In our experiments, we pre-train GLIP on 27M
grounding data, including 3M human-annotated and 24M web-crawled image-text
pairs. The learned representations demonstrate strong zero-shot and few-shot
transferability to various object-level recognition tasks. 1) When directly
evaluated on COCO and LVIS (without seeing any images in COCO during
pre-training), GLIP achieves 49.8 AP and 26.9 AP, respectively, surpassing many
supervised baselines. 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on val
and 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13
downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervised
Dynamic Head. Code is released at https://github.com/microsoft/GLIP.

Policy Search and Model Predictive Control~(MPC) are two different paradigms
for robot control: policy search has the strength of automatically learning
complex policies using experienced data, while MPC can offer optimal control
performance using models and trajectory optimization. An open research question
is how to leverage and combine the advantages of both approaches. In this work,
we provide an answer by using policy search for automatically choosing
high-level decision variables for MPC, which leads to a novel
policy-search-for-model-predictive-control framework. Specifically, we
formulate the MPC as a parameterized controller, where the hard-to-optimize
decision variables are represented as high-level policies. Such a formulation
allows optimizing policies in a self-supervised fashion. We validate this
framework by focusing on a challenging problem in agile drone flight: flying a
quadrotor through fast-moving gates. Experiments show that our controller
achieves robust and real-time control performance in both simulation and the
real world. The proposed framework offers a new perspective for merging
learning and control.

With the increased availability of 3D data, the need for solutions processing
those also increased rapidly. However, adding dimension to already reliably
accurate 2D approaches leads to immense memory consumption and higher
computational complexity. These issues cause current hardware to reach its
limitations, with most methods forced to reduce the input resolution
drastically. Our main contribution is a novel deep 3D semantic segmentation
method for gun detection in baggage CT scans that enables fast training and low
video memory consumption for high-resolution voxelized volumes. We introduce a
moving pyramid approach that utilizes multiple forward passes at inference time
for segmenting an instance.

Intrinsic imaging or intrinsic image decomposition has traditionally been
described as the problem of decomposing an image into two layers: a
reflectance, the albedo invariant color of the material; and a shading,
produced by the interaction between light and geometry. Deep learning
techniques have been broadly applied in recent years to increase the accuracy
of those separations. In this survey, we overview those results in context of
well-known intrinsic image data sets and relevant metrics used in the
literature, discussing their suitability to predict a desirable intrinsic image
decomposition. Although the Lambertian assumption is still a foundational basis
for many methods, we show that there is increasing awareness on the potential
of more sophisticated physically-principled components of the image formation
process, that is, optically accurate material models and geometry, and more
complete inverse light transport estimations. We classify these methods in
terms of the type of decomposition, considering the priors and models used, as
well as the learning architecture and methodology driving the decomposition
process. We also provide insights about future directions for research, given
the recent advances in neural, inverse and differentiable rendering techniques.

Data scarcity and noise are important issues in industrial applications of
machine learning. However, it is often challenging to devise a scalable and
generalized approach to address the fundamental distributional and semantic
properties of dataset with black box models. For this reason, data-centric
approaches are crucial for the automation of machine learning operation
pipeline. In order to serve as the basis for this automation, we suggest a
domain-agnostic pipeline for refining the quality of data in image
classification problems. This pipeline contains data valuation, cleansing, and
augmentation. With an appropriate combination of these methods, we could
achieve 84.711% test accuracy (ranked #6, Honorable Mention in the Most
Innovative) in the Data-Centric AI competition only with the provided dataset.

Event processing is the cornerstone of the dynamic and responsive Internet of
Things (IoT). Recent approaches in this area are based on representational
state transfer (REST) principles, which allow event processing tasks to be
placed at any device that follows the same principles. However, the tasks
should be properly distributed among edge devices to ensure fair resources
utilization and guarantee seamless execution. This article investigates the use
of deep learning to fairly distribute the tasks. An attention-based neural
network model is proposed to generate efficient load balancing solutions under
different scenarios. The proposed model is based on the Transformer and Pointer
Network architectures, and is trained by an advantage actor-critic
reinforcement learning algorithm. The model is designed to scale to the number
of event processing tasks and the number of edge devices, with no need for
hyperparameters re-tuning or even retraining. Extensive experimental results
show that the proposed model outperforms conventional heuristics in many key
performance indicators. The generic design and the obtained results show that
the proposed model can potentially be applied to several other load balancing
problem variations, which makes the proposal an attractive option to be used in
real-world scenarios due to its scalability and efficiency.

Deep learning has brought the most profound contribution towards biomedical
image segmentation to automate the process of delineation in medical imaging.
To accomplish such task, the models are required to be trained using huge
amount of annotated or labelled data that highlights the region of interest
with a binary mask. However, efficient generation of the annotations for such
huge data requires expert biomedical analysts and extensive manual effort. It
is a tedious and expensive task, while also being vulnerable to human error. To
address this problem, a self-supervised learning framework, BT-Unet is proposed
that uses the Barlow Twins approach to pre-train the encoder of a U-Net model
via redundancy reduction in an unsupervised manner to learn data
representation. Later, complete network is fine-tuned to perform actual
segmentation. The BT-Unet framework can be trained with a limited number of
annotated samples while having high number of unannotated samples, which is
mostly the case in real-world problems. This framework is validated over
multiple U-Net models over diverse datasets by generating scenarios of a
limited number of labelled samples using standard evaluation metrics. With
exhaustive experiment trials, it is observed that the BT-Unet framework
enhances the performance of the U-Net models with significant margin under such

In this study, Au+Au collisions with the impact parameter of $0 \leq b \leq
12.5$ fm at $\sqrt{s_{NN}} = 200$ GeV are simulated by the AMPT model to
provide the preliminary final-state information. After transforming these
information into appropriate input data (the energy spectra of final-state
charged hadrons), we construct a deep neural network (DNN) and a convolutional
neural network (CNN) to connect final-state observables with impact parameters.
The results show that both the DNN and CNN can reconstruct the impact
parameters with a mean absolute error about $0.4$ fm with CNN behaving slightly
better. Then, we test the neural networks for different beam energies and
pseudorapidity ranges in this task. It turns out that these two models work
well for both low and high energies. But when making test for a larger
pseudorapidity window, we observe that the CNN shows higher prediction accuracy
than the DNN. With the method of Grad-CAM, we shed light on the `attention'
mechanism of the CNN model.

This work analyses the so-called "Relativization Barrier" with respect to the
Baker-Gill-Solovay oracle Turing machine. We show that the {\em
diagonalization} technique is a valid mathematical proof technique, but it has
some prerequisites when referring to the "relativization barrier."

Expensive sensors and inefficient algorithmic pipelines significantly affect
the overall cost of autonomous machines. However, affordable robotic solutions
are essential to practical usage, and their financial impact constitutes a
fundamental requirement to employ service robotics in most fields of
application. Among all, researchers in the precision agriculture domain strive
to devise robust and cost-effective autonomous platforms in order to provide
genuinely large-scale competitive solutions. In this article, we present a
complete algorithmic pipeline for row-based crops autonomous navigation,
specifically designed to cope with low-range sensors and seasonal variations.
Firstly, we build on a robust data-driven methodology to generate a viable path
for the autonomous machine, covering the full extension of the crop with only
the occupancy grid map information of the field. Moreover, our solution
leverages on latest advancement of deep learning optimization techniques and
synthetic generation of data to provide an affordable solution that efficiently
tackles the well-known Global Navigation Satellite System unreliability and
degradation due to vegetation growing inside rows. Extensive experimentation
and simulations against computer-generated environments and real-world crops
demonstrated the robustness and intrinsic generalizability of our methodology
that opens the possibility of highly affordable and fully autonomous machines.

We propose an unsupervised convolutional neural network (CNN) for relaxation
parameter estimation. This network incorporates signal relaxation and Bloch
simulations while taking advantage of residual learning and spatial relations
across neighboring voxels. Quantification accuracy and robustness to noise is
shown to be significantly improved compared to standard parameter estimation
methods in numerical simulations and in vivo data for multi-echo T2 and T2*
mapping. The combination of the proposed network with subspace modeling and MR
fingerprinting (MRF) from highly undersampled data permits high quality T1 and
T2 mapping.

Incremental learning represents a crucial task in aerial image processing,
especially given the limited availability of large-scale annotated datasets. A
major issue concerning current deep neural architectures is known as
catastrophic forgetting, namely the inability to faithfully maintain past
knowledge once a new set of data is provided for retraining. Over the years,
several techniques have been proposed to mitigate this problem for image
classification and object detection. However, only recently the focus has
shifted towards more complex downstream tasks such as instance or semantic
segmentation. Starting from incremental-class learning for semantic
segmentation tasks, our goal is to adapt this strategy to the aerial domain,
exploiting a peculiar feature that differentiates it from natural images,
namely the orientation. In addition to the standard knowledge distillation
approach, we propose a contrastive regularization, where any given input is
compared with its augmented version (i.e. flipping and rotations) in order to
minimize the difference between the segmentation features produced by both
inputs. We show the effectiveness of our solution on the Potsdam dataset,
outperforming the incremental baseline in every test. Code available at:
https://github.com/edornd/contrastive-distillation.

Choosing the best treatment-plan for each individual patient requires
accurate forecasts of their outcome trajectories as a function of the
treatment, over time. While large observational data sets constitute rich
sources of information to learn from, they also contain biases as treatments
are rarely assigned randomly in practice. To provide accurate and unbiased
forecasts, we introduce the Disentangled Counterfactual Recurrent Network
(DCRN), a novel sequence-to-sequence architecture that estimates treatment
outcomes over time by learning representations of patient histories that are
disentangled into three separate latent factors: a treatment factor,
influencing only treatment selection; an outcome factor, influencing only the
outcome; and a confounding factor, influencing both. With an architecture that
is completely inspired by the causal structure of treatment influence over
time, we advance forecast accuracy and disease understanding, as our
architecture allows for practitioners to infer which patient features influence
which part in a patient's trajectory, contrasting other approaches in this
domain. We demonstrate that DCRN outperforms current state-of-the-art methods
in forecasting treatment responses, on both real and simulated data.

Light has many properties that vision sensors can passively measure.
Colour-band separated wavelength and intensity are arguably the most commonly
used for monocular 6D object pose estimation. This paper explores how
complementary polarisation information, i.e. the orientation of light wave
oscillations, influences the accuracy of pose predictions. A hybrid model that
leverages physical priors jointly with a data-driven learning strategy is
designed and carefully tested on objects with different levels of photometric
complexity. Our design significantly improves the pose accuracy compared to
state-of-the-art photometric approaches and enables object pose estimation for
highly reflective and transparent objects. A new multi-modal instance-level 6D
object pose dataset with highly accurate pose annotations for multiple objects
with varying photometric complexity is introduced as a benchmark.

Multi-agent reinforcement learning (MARL) has received increasing attention
for its applications in various domains. Researchers have paid much attention
on its partially observable and cooperative settings for meeting real-world
requirements. For testing performance of different algorithms, standardized
environments are designed such as the StarCraft Multi-Agent Challenge, which is
one of the most successful MARL benchmarks. To our best knowledge, most of
current environments are synchronous, where agents execute actions in the same
pace. However, heterogeneous agents usually have their own action spaces and
there is no guarantee for actions from different agents to have the same
executed cycle, which leads to asynchronous multi-agent cooperation. Inspired
from the Wargame, a confrontation game between two armies abstracted from real
world environment, we propose the first Partially Observable Asynchronous
multi-agent Cooperation challenge (POAC) for the MARL community. Specifically,
POAC supports two teams of heterogeneous agents to fight with each other, where
an agent selects actions based on its own observations and cooperates
asynchronously with its allies. Moreover, POAC is a light weight, flexible and
easy to use environment, which can be configured by users to meet different
experimental requirements such as self-play model, human-AI model and so on.
Along with our benchmark, we offer six game scenarios of varying difficulties
with the built-in rule-based AI as opponents. Finally, since most MARL
algorithms are designed for synchronous agents, we revise several
representatives to meet the asynchronous setting, and the relatively poor
experimental results validate the challenge of POAC. Source code is released in
\url{http://turingai.ia.ac.cn/data\_center/show}.

This paper presents raceBERT -- a transformer-based model for predicting race
and ethnicity from character sequences in names, and an accompanying python
package. Using a transformer-based model trained on a U.S. Florida voter
registration dataset, the model predicts the likelihood of a name belonging to
5 U.S. census race categories (White, Black, Hispanic, Asian & Pacific
Islander, American Indian & Alaskan Native). I build on Sood and Laohaprapanon
(2018) by replacing their LSTM model with transformer-based models (pre-trained
BERT model, and a roBERTa model trained from scratch), and compare the results.
To the best of my knowledge, raceBERT achieves state-of-the-art results in race
prediction using names, with an average f1-score of 0.86 -- a 4.1% improvement
over the previous state-of-the-art, and improvements between 15-17% for
non-white names.

Representing data by means of graph structures identifies one of the most
valid approach to extract information in several data analysis applications.
This is especially true when multimodal datasets are investigated, as records
collected by means of diverse sensing strategies are taken into account and
explored. Nevertheless, classic graph signal processing is based on a model for
information propagation that is configured according to heat diffusion
mechanism. This system provides several constraints and assumptions on the data
properties that might be not valid for multimodal data analysis, especially
when large scale datasets collected from heterogeneous sources are considered,
so that the accuracy and robustness of the outcomes might be severely
jeopardized. In this paper, we introduce a novel model for graph definition
based on fluid diffusion. The proposed approach improves the ability of
graph-based data analysis to take into account several issues of modern data
analysis in operational scenarios, so to provide a platform for precise,
versatile, and efficient understanding of the phenomena underlying the records
under exam, and to fully exploit the potential provided by the diversity of the
records in obtaining a thorough characterization of the data and their
significance. In this work, we focus our attention to using this fluid
diffusion model to drive a community detection scheme, i.e., to divide
multimodal datasets into many groups according to similarity among nodes in an
unsupervised fashion. Experimental results achieved by testing real multimodal
datasets in diverse application scenarios show that our method is able to
strongly outperform state-of-the-art schemes for community detection in
multimodal data analysis.

Graph neural networks (GNNs) have achieved impressive performance when
testing and training graph data come from identical distribution. However,
existing GNNs lack out-of-distribution generalization abilities so that their
performance substantially degrades when there exist distribution shifts between
testing and training graph data. To solve this problem, in this work, we
propose an out-of-distribution generalized graph neural network (OOD-GNN) for
achieving satisfactory performance on unseen testing graphs that have different
distributions with training graphs. Our proposed OOD-GNN employs a novel
nonlinear graph representation decorrelation method utilizing random Fourier
features, which encourages the model to eliminate the statistical dependence
between relevant and irrelevant graph representations through iteratively
optimizing the sample graph weights and graph encoder. We further present a
global weight estimator to learn weights for training graphs such that
variables in graph representations are forced to be independent. The learned
weights help the graph encoder to get rid of spurious correlations and, in
turn, concentrate more on the true connection between learned discriminative
graph representations and their ground-truth labels. We conduct extensive
experiments to validate the out-of-distribution generalization abilities on two
synthetic and 12 real-world datasets with distribution shifts. The results
demonstrate that our proposed OOD-GNN significantly outperforms

Feedforward control is essential to achieving good tracking performance in
positioning systems. The aim of this paper is to develop an identification
strategy for inverse models of systems with nonlinear dynamics of unknown
structure using input-output data, which directly delivers feedforward signals
for a-priori unknown tasks. To this end, inverse systems are regarded as
noncausal nonlinear finite impulse response (NFIR) systems and modeled as a
Gaussian Process with a stationary kernel function that imposes properties such
as smoothness and periodicity. The approach is validated experimentally on a
consumer printer with friction and shown to lead to improved tracking
performance with respect to linear feedforward.

Despite the great progress in video understanding made by deep convolutional
neural networks, feature representation learned by existing methods may be
biased to static visual cues. To address this issue, we propose a novel method
to suppress static visual cues (SSVC) based on probabilistic analysis for
self-supervised video representation learning. In our method, video frames are
first encoded to obtain latent variables under standard normal distribution via
normalizing flows. By modelling static factors in a video as a random variable,
the conditional distribution of each latent variable becomes shifted and scaled
normal. Then, the less-varying latent variables along time are selected as
static cues and suppressed to generate motion-preserved videos. Finally,
positive pairs are constructed by motion-preserved videos for contrastive
learning to alleviate the problem of representation bias to static cues. The
less-biased video representation can be better generalized to various
downstream tasks. Extensive experiments on publicly available benchmarks
demonstrate that the proposed method outperforms the state of the art when only
single RGB modality is used for pre-training.

In this work, we study the information content learned by a convolutional
neural network (CNN) when trained to carry out the inverse mapping between a
database of synthetic Ca II intensity spectra and the vertical stratification
of the temperature of the atmospheres used to generate such spectra. In
particular, we evaluate the ability of the neural network to extract
information about the sensitivity of the spectral line to temperature as a
function of height. By training the CNN on sufficiently narrow wavelength
intervals across the Ca II spectral profiles, we find that the error in the
temperature prediction shows an inverse relationship to the response function
of the spectral line to temperature, this is, different regions of the spectrum
yield a better temperature prediction at their expected regions of formation.
This work shows that the function that the CNN learns during the training
process contains a physically-meaningful mapping between wavelength and
atmospheric height.

On-policy deep reinforcement learning algorithms have low data utilization
and require significant experience for policy improvement. This paper proposes
a proximal policy optimization algorithm with prioritized trajectory replay
(PTR-PPO) that combines on-policy and off-policy methods to improve sampling
efficiency by prioritizing the replay of trajectories generated by old
policies. We first design three trajectory priorities based on the
characteristics of trajectories: the first two being max and mean trajectory
priorities based on one-step empirical generalized advantage estimation (GAE)
values and the last being reward trajectory priorities based on normalized
undiscounted cumulative reward. Then, we incorporate the prioritized trajectory
replay into the PPO algorithm, propose a truncated importance weight method to
overcome the high variance caused by large importance weights under multistep
experience, and design a policy improvement loss function for PPO under
off-policy conditions. We evaluate the performance of PTR-PPO in a set of Atari
discrete control tasks, achieving state-of-the-art performance. In addition, by
analyzing the heatmap of priority changes at various locations in the priority
memory during training, we find that memory size and rollout length can have a
significant impact on the distribution of trajectory priorities and, hence, on
the performance of the algorithm.

Human-centered artificial intelligence (AI) posits that machine learning and
AI should be developed and applied in a socially aware way. In this article, we
argue that qualitative analysis (QA) can be a valuable tool in this process,
supplementing, informing, and extending the possibilities of AI models. We show
this by describing how QA can be integrated in the current prediction paradigm
of AI, assisting scientists in the process of selecting data, variables, and
model architectures. Furthermore, we argue that QA can be a part of novel
paradigms towards Human Centered AI. QA can support scientists and
practitioners in practical problem solving and situated model development. It
can also promote participatory design approaches, reveal understudied and
emerging issues in AI systems, and assist policy making.

Appropriate weight initialization has been of key importance to successfully
train neural networks. Recently, batch normalization has diminished the role of
weight initialization by simply normalizing each layer based on batch
statistics. Unfortunately, batch normalization has several drawbacks when
applied to small batch sizes, as they are required to cope with memory
limitations when learning on point clouds. While well-founded weight
initialization strategies can render batch normalization unnecessary and thus
avoid these drawbacks, no such approaches have been proposed for point
convolutional networks. To fill this gap, we propose a framework to unify the
multitude of continuous convolutions. This enables our main contribution,
variance-aware weight initialization. We show that this initialization can
avoid batch normalization while achieving similar and, in some cases, better

Multiple techniques for producing calibrated predictive probabilities using
deep neural networks in supervised learning settings have emerged that leverage
approaches to ensemble diverse solutions discovered during cyclic training or
training from multiple random starting points (deep ensembles). However, only a
limited amount of work has investigated the utility of exploring the local
region around each diverse solution (posterior mode). Using three well-known
deep architectures on the CIFAR-10 dataset, we evaluate several simple methods
for exploring local regions of the weight space with respect to Brier score,
accuracy, and expected calibration error. We consider both Bayesian inference
techniques (variational inference and Hamiltonian Monte Carlo applied to the
softmax output layer) as well as utilizing the stochastic gradient descent
trajectory near optima. While adding separate modes to the ensemble uniformly
improves performance, we show that the simple mode exploration methods
considered here produce little to no improvement over ensembles without mode

Machine learning plays a crucial role in enhancing and accelerating the
search for new fundamental physics. We review the state of machine learning
methods and applications for new physics searches in the context of terrestrial
high energy physics experiments, including the Large Hadron Collider, rare
event searches, and neutrino experiments. While machine learning has a long
history in these fields, the deep learning revolution (early 2010s) has yielded
a qualitative shift in terms of the scope and ambition of research. These
modern machine learning developments are the focus of the present review.

Gas turbine engines are complex machines that typically generate a vast
amount of data, and require careful monitoring to allow for cost-effective
preventative maintenance. In aerospace applications, returning all measured
data to ground is prohibitively expensive, often causing useful, high value,
data to be discarded. The ability to detect, prioritise, and return useful data
in real-time is therefore vital. This paper proposes that system output
measurements, described by a convolutional neural network model of normality,
are prioritised in real-time for the attention of preventative maintenance
decision makers.
  Due to the complexity of gas turbine engine time-varying behaviours, deriving
accurate physical models is difficult, and often leads to models with low
prediction accuracy and incompatibility with real-time execution. Data-driven
modelling is a desirable alternative producing high accuracy, asset specific
models without the need for derivation from first principles.
  We present a data-driven system for online detection and prioritisation of
anomalous data. Biased data assessment deriving from novel operating conditions
is avoided by uncertainty management integrated into the deep neural predictive
model. Testing is performed on real and synthetic data, showing sensitivity to
both real and synthetic faults. The system is capable of running in real-time
on low-power embedded hardware and is currently in deployment on the
Rolls-Royce Pearl 15 engine flight trials.

A common vision from science fiction is that robots will one day inhabit our
physical spaces, sense the world as we do, assist our physical labours, and
communicate with us through natural language. Here we study how to design
artificial agents that can interact naturally with humans using the
simplification of a virtual environment. We show that imitation learning of
human-human interactions in a simulated world, in conjunction with
self-supervised learning, is sufficient to produce a multimodal interactive
agent, which we call MIA, that successfully interacts with non-adversarial
humans 75% of the time. We further identify architectural and algorithmic
techniques that improve performance, such as hierarchical action selection.
Altogether, our results demonstrate that imitation of multi-modal, real-time
human behaviour may provide a straightforward and surprisingly effective means
of imbuing agents with a rich behavioural prior from which agents might then be
fine-tuned for specific purposes, thus laying a foundation for training capable
agents for interactive robots or digital assistants. A video of MIA's behaviour
may be found at https://youtu.be/ZFgRhviF7mY

As robots venture into the real world, they are subject to unmodeled dynamics
and disturbances. Traditional model-based control approaches have been proven
successful in relatively static and known operating environments. However, when
an accurate model of the robot is not available, model-based design can lead to
suboptimal and even unsafe behaviour. In this work, we propose a method that
bridges the model-reality gap and enables the application of model-based
approaches even if dynamic uncertainties are present. In particular, we present
a learning-based model reference adaptation approach that makes a robot system,
with possibly uncertain dynamics, behave as a predefined reference model. In
turn, the reference model can be used for model-based controller design. In
contrast to typical model reference adaptation control approaches, we leverage
the representative power of neural networks to capture highly nonlinear
dynamics uncertainties and guarantee stability by encoding a certifying
Lipschitz condition in the architectural design of a special type of neural
network called the Lipschitz network. Our approach applies to a general class
of nonlinear control-affine systems even when our prior knowledge about the
true robot system is limited. We demonstrate our approach in flying inverted
pendulum experiments, where an off-the-shelf quadrotor is challenged to balance
an inverted pendulum while hovering or tracking circular trajectories.

Optimization problems with continuous data appear in, e.g., robust machine
learning, functional data analysis, and variational inference. Here, the target
function is given as an integral over a family of (continuously) indexed target
functions - integrated with respect to a probability measure. Such problems can
often be solved by stochastic optimization methods: performing optimization
steps with respect to the indexed target function with randomly switched
indices. In this work, we study a continuous-time variant of the stochastic
gradient descent algorithm for optimization problems with continuous data. This
so-called stochastic gradient process consists in a gradient flow minimizing an
indexed target function that is coupled with a continuous-time index process
determining the index. Index processes are, e.g., reflected diffusions, pure
jump processes, or other L\'evy processes on compact spaces. Thus, we study
multiple sampling patterns for the continuous data space and allow for data
simulated or streamed at runtime of the algorithm. We analyze the approximation
properties of the stochastic gradient process and study its longtime behavior
and ergodicity under constant and decreasing learning rates. We end with
illustrating the applicability of the stochastic gradient process in a
polynomial regression problem with noisy functional data, as well as in a
physics-informed neural network.

Inferring the abstract relational and causal structure of the world is a
major challenge for reinforcement-learning (RL) agents. For humans,
language--particularly in the form of explanations--plays a considerable role
in overcoming this challenge. Here, we show that language can play a similar
role for deep RL agents in complex environments. While agents typically
struggle to acquire relational and causal knowledge, augmenting their
experience by training them to predict language descriptions and explanations
can overcome these limitations. We show that language can help agents learn
challenging relational tasks, and examine which aspects of language contribute
to its benefits. We then show that explanations can help agents to infer not
only relational but also causal structure. Language can shape the way that
agents to generalize out-of-distribution from ambiguous, causally-confounded
training, and explanations even allow agents to learn to perform experimental
interventions to identify causal relationships. Our results suggest that
language description and explanation may be powerful tools for improving agent
learning and generalization.

Deep learning-based music source separation has gained a lot of interest in
the last decades. Most of the existing methods operate with either spectrograms
or waveforms. Spectrogram based models learn suitable masks for separating
magnitude spectrogram into different sources, and waveform-based models
directly generate waveforms of individual sources. The two types of models have
complementary strengths; the former is superior given harmonic sources such as
vocals, while the latter demonstrates better results for percussion and bass
instruments. In this work, we improved upon the state-of-the-art (SoTA) models
and successfully combined the best of both worlds. The backbones of the
proposed framework, dubbed Danna-Sep, are two spectrogram-based models
including a modified X-UMX and U-Net, and an enhanced Demucs as the
waveform-based model. Given an input of mixture, we linearly combined
respective outputs from the three models to obtain the final result. We showed
in the experiments that, despite its simplicity, Danna-Sep surpassed the SoTA
models by a large margin in terms of Source-to-Distortion Ratio.

Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth
estimation for mobile devices due to its small size and affordable price.
Previous works have mainly focused on quality improvement for I-ToF imaging
especially curing the effect of Multi Path Interference (MPI). These
investigations are typically done in specifically constrained scenarios at
close distance, indoors and under little ambient light. Surprisingly little
work has investigated I-ToF quality improvement in real-life scenarios where
strong ambient light and far distances pose difficulties due to an extreme
amount of induced shot noise and signal sparsity, caused by the attenuation
with limited sensor power and light scattering. In this work, we propose a new
learning based end-to-end depth prediction network which takes noisy raw I-ToF
signals as well as an RGB image and fuses their latent representation based on
a multi step approach involving both implicit and explicit alignment to predict
a high quality long range depth map aligned to the RGB viewpoint. We test our
approach on challenging real-world scenes and show more than 40% RMSE
improvement on the final depth map compared to the baseline approach.

Solving high-dimensional partial differential equations is a recurrent
challenge in economics, science and engineering. In recent years, a great
number of computational approaches have been developed, most of them relying on
a combination of Monte Carlo sampling and deep learning based approximation.
For elliptic and parabolic problems, existing methods can broadly be classified
into those resting on reformulations in terms of $\textit{backward stochastic
differential equations}$ (BSDEs) and those aiming to minimize a regression-type
$L^2$-error ($\textit{physics-informed neural networks}$, PINNs). In this
paper, we review the literature and suggest a methodology based on the novel
$\textit{diffusion loss}$ that interpolates between BSDEs and PINNs. Our
contribution opens the door towards a unified understanding of numerical
approaches for high-dimensional PDEs, as well as for implementations that
combine the strengths of BSDEs and PINNs. We also provide generalizations to
eigenvalue problems and perform extensive numerical studies, including
calculations of the ground state for nonlinear Schr\"odinger operators and
committor functions relevant in molecular dynamics.

Top like vector quarks arising from underlying strong sectors are expected to
have large decay widths pushing them beyond the narrow width approximation. In
this paper we consider a broad colored vector quark that strongly couples to an
exotic pseudoscalar. We use the full 1PI resummed propagator for the exotic
quark to recast the present LHC constraints ruling out masses below
$\sim1.2~(1.1)$ TeV for width to mass ratio of $0.1~(0.6)$. We utilize machine
learning techniques that are demonstratively more efficient than traditional
cut based searches to present the reach of HL-LHC on the parameter space of
this broad resonance. We find that at $3~ab^{-1}$ the HL-LHC has a discovery
potential up to $1.6$ TeV dominated by the pair production channel. We study
the feasibility of using machine learning techniques to analyze the broad
resonance peaks expected from these exotic quarks at collider experiments like
the LHC.

Deep learning (DL) image registration methods amortize the costly pair-wise
iterative optimization by training deep neural networks to predict the optimal
transformation in one fast forward-pass. In this work, we bridge the gap
between traditional iterative energy optimization-based registration and
network-based registration, and propose Gradient Descent Network for Image
Registration (GraDIRN). Our proposed approach trains a DL network that embeds
unrolled multiresolution gradient-based energy optimization in its forward
pass, which explicitly enforces image dissimilarity minimization in its update
steps. Extensive evaluations were performed on registration tasks using 2D
cardiac MR and 3D brain MR images. We demonstrate that our approach achieved
state-of-the-art registration performance while using fewer learned parameters,
with good data efficiency and domain robustness.

In recent years, the task of mining important information from social media
posts during crises has become a focus of research for the purposes of
assisting emergency response (ES). The TREC Incident Streams (IS) track is a
research challenge organised for this purpose. The track asks participating
systems to both classify a stream of crisis-related tweets into humanitarian
aid related information types and estimate their importance regarding
criticality. The former refers to a multi-label information type classification
task and the latter refers to a priority estimation task. In this paper, we
report on the participation of the University College Dublin School of Computer
Science (UCD-CS) in TREC-IS 2021. We explored a variety of approaches,
including simple machine learning algorithms, multi-task learning techniques,
text augmentation, and ensemble approaches. The official evaluation results
indicate that our runs achieve the highest scores in many metrics. To aid
reproducibility, our code is publicly available at
https://github.com/wangcongcong123/crisis-mtl.

In this paper, we propose to employ a Gaussian map representation to estimate
precise location and count of 3D surface features, addressing the limitations
of state-of-the-art methods based on density estimation which struggle in
presence of local disturbances. Gaussian maps indicate probable object location
and can be generated directly from keypoint annotations avoiding laborious and
costly per-pixel annotations. We apply this method to the 3D spheroidal class
of objects which can be projected into 2D shape representation enabling
efficient processing by a neural network GNet, an improved UNet architecture,
which generates the likely locations of surface features and their precise
count. We demonstrate a practical use of this technique for counting strawberry
achenes which is used as a fruit quality measure in phenotyping applications.
The results of training the proposed system on several hundreds of 3D scans of
strawberries from a publicly available dataset demonstrate the accuracy and
precision of the system which outperforms the state-of-the-art density-based
methods for this application.

Not until recently, robust robot locomotion has been achieved by deep
reinforcement learning (DRL). However, for efficient learning of parametrized
bipedal walking, developed references are usually required, limiting the
performance to that of the references. In this paper, we propose to design an
adaptive reward function for imitation learning from the references. The agent
is encouraged to mimic the references when its performance is low, while to
pursue high performance when it reaches the limit of references. We further
demonstrate that developed references can be replaced by low-quality references
that are generated without laborious tuning and infeasible to deploy by
themselves, as long as they can provide a priori knowledge to expedite the

When analyzing parametric statistical models, a useful approach consists in
modeling geometrically the parameter space. However, even for very simple and
commonly used hierarchical models like statistical mixtures or stochastic deep
neural networks, the smoothness assumption of manifolds is violated at singular
points which exhibit non-smooth neighborhoods in the parameter space. These
singular models have been analyzed in the context of learning dynamics, where
singularities can act as attractors on the learning trajectory and, therefore,
negatively influence the convergence speed of models. We propose a general
approach to circumvent the problem arising from singularities by using
stratifolds, a concept from algebraic topology, to formally model singular
parameter spaces. We use the property that specific stratifolds are equipped
with a resolution method to construct a smooth manifold approximation of the
singular space. We empirically show that using (natural) gradient descent on
the smooth manifold approximation instead of the singular space allows us to
avoid the attractor behavior and therefore improve the convergence speed in
learning.

The use of deep learning methods for solving PDEs is a field in full
expansion. In particular, Physical Informed Neural Networks, that implement a
sampling of the physical domain and use a loss function that penalizes the
violation of the partial differential equation, have shown their great
potential. Yet, to address large scale problems encountered in real
applications and compete with existing numerical methods for PDEs, it is
important to design parallel algorithms with good scalability properties. In
the vein of traditional domain decomposition methods (DDM), we consider the
recently proposed deep-ddm approach. We present an extension of this method
that relies on the use of a coarse space correction, similarly to what is done
in traditional DDM solvers. Our investigations shows that the coarse correction
is able to alleviate the deterioration of the convergence of the solver when
the number of subdomains is increased thanks to an instantaneous information
exchange between subdomains at each iteration. Experimental results demonstrate
that our approach induces a remarkable acceleration of the original deep-ddm
method, at a reduced additional computational cost.

Feed-forward only convolutional neural networks (CNNs) may ignore intrinsic
relationships and potential benefits of feedback connections in vision tasks
such as saliency detection, despite their significant representation
capabilities. In this work, we propose a feedback-recursive convolutional
framework (SalFBNet) for saliency detection. The proposed feedback model can
learn abundant contextual representations by bridging a recursive pathway from
higher-level feature blocks to low-level layer. Moreover, we create a
large-scale Pseudo-Saliency dataset to alleviate the problem of data deficiency
in saliency detection. We first use the proposed feedback model to learn
saliency distribution from pseudo-ground-truth. Afterwards, we fine-tune the
feedback model on existing eye-fixation datasets. Furthermore, we present a
novel Selective Fixation and Non-Fixation Error (sFNE) loss to make proposed
feedback model better learn distinguishable eye-fixation-based features.
Extensive experimental results show that our SalFBNet with fewer parameters
achieves competitive results on the public saliency detection benchmarks, which
demonstrate the effectiveness of proposed feedback model and Pseudo-Saliency
data. Source codes and Pseudo-Saliency dataset can be found at
https://github.com/gqding/SalFBNet

Learning the physical dynamics of deformable objects with particle-based
representation has been the objective of many computational models in machine
learning. While several state-of-the-art models have achieved this objective in
simulated environments, most existing models impose a precondition, such that
the input is a sequence of ordered point sets. That is, the order of the points
in each point set must be the same across the entire input sequence. This
precondition restrains the model from generalizing to real-world data, which is
considered to be a sequence of unordered point sets. In this paper, we propose
a model named time-wise PointNet (TP-Net) that solves this problem by directly
consuming a sequence of unordered point sets to infer the future state of a
deformable object with particle-based representation. Our model consists of a
shared feature extractor that extracts global features from each input point
set in parallel and a prediction network that aggregates and reasons on these
features for future prediction. The key concept of our approach is that we use
global features rather than local features to achieve invariance to input
permutations and ensure the stability and scalability of our model. Experiments
demonstrate that our model achieves state-of-the-art performance with real-time
prediction speed in both synthetic dataset and real-world dataset. In addition,
we provide quantitative and qualitative analysis on why our approach is more
effective and efficient than existing approaches.

Online learning algorithms have become a ubiquitous tool in the machine
learning toolbox and are frequently used in small, resource-constraint
environments. Among the most successful online learning methods are Decision
Tree (DT) ensembles. DT ensembles provide excellent performance while adapting
to changes in the data, but they are not resource efficient. Incremental tree
learners keep adding new nodes to the tree but never remove old ones increasing
the memory consumption over time. Gradient-based tree learning, on the other
hand, requires the computation of gradients over the entire tree which is
costly for even moderately sized trees. In this paper, we propose a novel
memory-efficient online classification ensemble called shrub ensembles for
resource-constraint systems. Our algorithm trains small to medium-sized
decision trees on small windows and uses stochastic proximal gradient descent
to learn the ensemble weights of these `shrubs'. We provide a theoretical
analysis of our algorithm and include an extensive discussion on the behavior
of our approach in the online setting. In a series of 2~959 experiments on 12
different datasets, we compare our method against 8 state-of-the-art methods.
Our Shrub Ensembles retain an excellent performance even when only little
memory is available. We show that SE offers a better accuracy-memory trade-off
in 7 of 12 cases, while having a statistically significant better performance
than most other methods. Our implementation is available under
https://github.com/sbuschjaeger/se-online .

Local volatility is a versatile option pricing model due to its state
dependent diffusion coefficient. Calibration is, however, non-trivial as it
involves both proposing a hypothesis model of the latent function and a method
for fitting it to data. In this paper we present novel Bayesian inference with
Gaussian process priors. We obtain a rich representation of the local
volatility function with a probabilistic notion of uncertainty attached to the
calibrate. We propose an inference algorithm and apply our approach to S&P 500
market data.

Locating the promoter region in DNA sequences is of paramount importance in
the field of bioinformatics. This is a problem widely studied in the
literature, however, not yet fully resolved. Some researchers have presented
remarkable results using convolution networks, that allowed the automatic
extraction of features from a DNA chain. However, a universal architecture that
could generalize to several organisms has not yet been achieved, and thus,
requiring researchers to seek new architectures and hyperparameters for each
new organism evaluated. In this work, we propose a versatile architecture,
based on capsule network, that can accurately identify promoter sequences in
raw DNA data from seven different organisms, eukaryotic, and prokaryotic. Our
model, the CapsProm, could assist in the transfer of learning between organisms
and expand its applicability. Furthermore the CapsProm showed competitive
results, overcoming the baseline method in five out of seven of the tested
datasets (F1-score). The models and source code are made available at
https://github.com/lauromoraes/CapsNet-promoter.

We investigate the feasability of improving the semi-empirical density
functional based tight-binding method (DFTB) through a general and transferable
many-body repulsive potential for pure silicon using a common machine-learning
framework. Atomic environments using atom centered symmetry functions fed into
flexible high-dimensional neural-networks allow to overcome the limited pair
potentials used until now, with the ability to train simultaneously on a large
variety of systems. We achieve an improvement on bulk systems, with good
performance on energetic, vibrational and structural properties. Contrarily,
there are difficulties for clusters due to surface effects. To deepen the
discussion, we also put these results into perspective with two fully
machine-learned numerical potentials for silicon from the literature. This
allows us to identify both the transferability of such approaches together with
the impact of narrowing the role of machine-learning models to reproduce only a
part of the total energy.

Tensor decomposition is one of the fundamental technique for model
compression of deep convolution neural networks owing to its ability to reveal
the latent relations among complex structures. However, most existing methods
compress the networks layer by layer, which cannot provide a satisfactory
solution to achieve global optimization. In this paper, we proposed a model
reduction method to compress the pre-trained networks using low-rank tensor
decomposition of the convolution layers. Our method is based on the
optimization techniques to select the proper ranks of decomposed network
layers. A new regularization method, called funnel function, is proposed to
suppress the unimportant factors during the compression, so the proper ranks
can be revealed much easier. The experimental results show that our algorithm
can reduce more model parameters than other tensor compression methods. For
ResNet18 with ImageNet2012, our reduced model can reach more than twi times
speed up in terms of GMAC with merely 0.7% Top-1 accuracy drop, which
outperforms most existing methods in both metrics.

By training a model on multiple observed source domains, domain
generalization aims to generalize well to arbitrary unseen target domains
without further training. Existing works mainly focus on learning
domain-invariant features to improve the generalization ability. However, since
target domain is not available during training, previous methods inevitably
suffer from overfitting in source domains. To tackle this issue, we develop an
effective dropout-based framework to enlarge the region of the model's
attention, which can effectively mitigate the overfitting problem.
Particularly, different from the typical dropout scheme, which normally
conducts the dropout on the fixed layer, first, we randomly select one layer,
and then we randomly select its channels to conduct dropout. Besides, we
leverage the progressive scheme to add the ratio of the dropout during
training, which can gradually boost the difficulty of training model to enhance
the robustness of the model. Moreover, to further alleviate the impact of the
overfitting issue, we leverage the augmentation schemes on image-level and
feature-level to yield a strong baseline model. We conduct extensive
experiments on multiple benchmark datasets, which show our method can
outperform the state-of-the-art methods.

Information theory is concerned with the study of transmission, processing,
extraction, and utilization of information. In its most abstract form,
information is conceived as a means of resolving uncertainty. Shannon and
Weaver (1949) were among the first to develop a conceptual framework for
information theory. One of the key assumptions of the model is that uncertainty
increases linearly with the amount of complexity (in bit units) of information
transmitted or generated. A whole body of data from the cognitive neurosciences
has shown since that the time of human response or action increases in a
similar fashion as a function of information complexity. This paper will
discuss some of the implications of what is known about the limitations of
human information processing for the development of reliable Artificial
Intelligence. It is concluded that novel conceptual frameworks are needed to
inspire future studies on this complex problem space.

Cross-domain cold-start recommendation is an increasingly emerging issue for
recommender systems. Existing works mainly focus on solving either cross-domain
user recommendation or cold-start content recommendation. However, when a new
domain evolves at its early stage, it has potential users similar to the source
domain but with much fewer interactions. It is critical to learn a user's
preference from the source domain and transfer it into the target domain,
especially on the newly arriving contents with limited user feedback. To bridge
this gap, we propose a self-trained Cross-dOmain User Preference LEarning
(COUPLE) framework, targeting cold-start recommendation with various semantic
tags, such as attributes of items or genres of videos. More specifically, we
consider three levels of preferences, including user history, user content and
user group to provide reliable recommendation. With user history represented by
a domain-aware sequential model, a frequency encoder is applied to the
underlying tags for user content preference learning. Then, a hierarchical
memory tree with orthogonal node representation is proposed to further
generalize user group preference across domains. The whole framework updates in
a contrastive way with a First-In-First-Out (FIFO) queue to obtain more
distinctive representations. Extensive experiments on two datasets demonstrate
the efficiency of COUPLE in both user and content cold-start situations. By
deploying an online A/B test for a week, we show that the Click-Through-Rate
(CTR) of COUPLE is superior to other baselines used on Taobao APP. Now the
method is serving online for the cross-domain cold micro-video recommendation.

This paper discusses the estimation of the generalization gap, the difference
between a generalization error and an empirical error, for overparameterized
models (e.g., neural networks). We first show that a functional variance, a key
concept in defining a widely-applicable information criterion, characterizes
the generalization gap even in overparameterized settings where a conventional
theory cannot be applied. We also propose a computationally efficient
approximation of the function variance, the Langevin approximation of the
functional variance (Langevin FV). This method leverages only the $1$st-order
gradient of the squared loss function, without referencing the $2$nd-order
gradient; this ensures that the computation is efficient and the implementation
is consistent with gradient-based optimization algorithms. We demonstrate the
Langevin FV numerically by estimating the generalization gaps of
overparameterized linear regression and non-linear neural network models.

Blockchain technology has the characteristics of decentralization,
traceability and tamper proof, which creates a reliable decentralized
transaction mode, further accelerating the development of the blockchain
platforms. However, with the popularization of various financial applications,
security problems caused by blockchain digital assets, such as money
laundering, illegal fundraising and phishing fraud, are constantly on the rise.
Therefore, financial security has become an important issue in the blockchain
ecosystem, and identifying the types of accounts in blockchain (e.g. miners,
phishing accounts, Ponzi contracts, etc.) is of great significance in risk
assessment and market supervision. In this paper, we construct an account
interaction graph using raw blockchain data in a graph perspective, and
proposes a joint learning framework for account identity inference on
blockchain with graph contrast. We first capture transaction feature and
correlation feature from interaction graph, and then perform sampling and data
augmentation to generate multiple views for account subgraphs, finally jointly
train the subgraph contrast and account classification task. Extensive
experiments on Ethereum datasets show that our method achieves significant
advantages in account identity inference task in terms of classification
performance, scalability and generalization.

Deep learning has achieved many breakthroughs in modern classification tasks.
Numerous architectures have been proposed for different data structures but
when it comes to the loss function, the cross-entropy loss is the predominant
choice. Recently, several alternative losses have seen revived interests for
deep classifiers. In particular, empirical evidence seems to promote square
loss but a theoretical justification is still lacking. In this work, we
contribute to the theoretical understanding of square loss in classification by
systematically investigating how it performs for overparametrized neural
networks in the neural tangent kernel (NTK) regime. Interesting properties
regarding the generalization error, robustness, and calibration error are
revealed. We consider two cases, according to whether classes are separable or
not. In the general non-separable case, fast convergence rate is established
for both misclassification rate and calibration error. When classes are
separable, the misclassification rate improves to be exponentially fast.
Further, the resulting margin is proven to be lower bounded away from zero,
providing theoretical guarantees for robustness. We expect our findings to hold
beyond the NTK regime and translate to practical settings. To this end, we
conduct extensive empirical studies on practical neural networks, demonstrating
the effectiveness of square loss in both synthetic low-dimensional data and
real image data. Comparing to cross-entropy, square loss has comparable
generalization error but noticeable advantages in robustness and model

Encrypted control seeks confidential controller evaluation in cloud-based or
networked systems. Many existing approaches build on homomorphic encryption
(HE) that allow simple mathematical operations to be carried out on encrypted
data. Unfortunately, HE is computationally demanding and many control laws (in
particular non-polynomial ones) cannot be efficiently implemented with this
  We show in this paper that secure two-party computation using garbled
circuits provides a powerful alternative to HE for encrypted control. More
precisely, we present a novel scheme that allows to efficiently implement
(non-polynomial) max-out neural networks with one hidden layer in a secure
fashion. These networks are of special interest for control since they allow,
in principle, to exactly describe piecewise affine control laws resulting from,
e.g., linear model predictive control (MPC). However, exact fits require
high-dimensional preactivations of the neurons. Fortunately, we illustrate that
even low-dimensional learning-based approximations are sufficiently accurate
for linear MPC. In addition, these approximations can be securely evaluated
using garbled circuit in less than 100~ms for our numerical example. Hence, our
approach opens new opportunities for applying encrypted control.

Existing deep learning-based Unsupervised Salient Object Detection (USOD)
methods rely on supervised pre-trained deep models. Moreover, they generate
pseudo labels based on hand-crafted features, which lack high-level semantic
information. In order to overcome these shortcomings, we propose a new
two-stage Activation-to-Saliency (A2S) framework that effectively excavates
high-quality saliency cues to train a robust saliency detector. It is worth
noting that our method does not require any manual annotation, even in the
pre-training phase. In the first stage, we transform an unsupervisedly
pre-trained network to aggregate multi-level features to a single activation
map, where an Adaptive Decision Boundary (ADB) is proposed to assist the
training of the transformed network. Moreover, a new loss function is proposed
to facilitate the generation of high-quality pseudo labels. In the second
stage, a self-rectification learning paradigm strategy is developed to train a
saliency detector and refine the pseudo labels online. In addition, we
construct a lightweight saliency detector using two Residual Attention Modules
(RAMs) to largely reduce the risk of overfitting. Extensive experiments on
several SOD benchmarks prove that our framework reports significant performance
compared with existing USOD methods. Moreover, training our framework on 3,000
images consumes about 1 hour, which is over 30$\times$ faster than previous

Anomaly detection in surveillance videos is challenging and important for
ensuring public security. Different from pixel-based anomaly detection methods,
pose-based methods utilize highly-structured skeleton data, which decreases the
computational burden and also avoids the negative impact of background noise.
However, unlike pixel-based methods, which could directly exploit explicit
motion features such as optical flow, pose-based methods suffer from the lack
of alternative dynamic representation. In this paper, a novel Motion Embedder
(ME) is proposed to provide a pose motion representation from the probability
perspective. Furthermore, a novel task-specific Spatial-Temporal Transformer
(STT) is deployed for self-supervised pose sequence reconstruction. These two
modules are then integrated into a unified framework for pose regularity
learning, which is referred to as Motion Prior Regularity Learner (MoPRL).
MoPRL achieves the state-of-the-art performance by an average improvement of
4.7% AUC on several challenging datasets. Extensive experiments validate the
versatility of each proposed module.

The robotics community has seen an exponential growth in the level of
complexity of the theoretical tools presented for the modeling of soft robotics
devices. Different solutions have been presented to overcome the difficulties
related to the modeling of soft robots, often leveraging on other scientific
disciplines, such as continuum mechanics, computational mechanics and computer
graphics. These theoretical and computational foundations are often taken for
granted and this leads to an intricate literature that, consequently, has
rarely been the subject of a complete review. For the first time, we present
here a structured overview of all the approaches proposed so far to model soft
robots. The chosen classification, which is based on their theoretical and
numerical grounds, allows us to provide a critical analysis about their uses
and applicability. This will enable robotics researchers to learn the basics of
these modeling techniques and their associated numerical methods, but also to
have a critical perspective on their uses.

Cascade prediction aims at modeling information diffusion in the network.
Most previous methods concentrate on mining either structural or sequential
features from the network and the propagation path. Recent efforts devoted to
combining network structure and sequence features by graph neural networks and
recurrent neural networks. Nevertheless, the limitation of spectral or spatial
methods restricts the improvement of prediction performance. Moreover,
recurrent neural networks are time-consuming and computation-expensive, which
causes the inefficiency of prediction. Here, we propose a novel method CCasGNN
considering the individual profile, structural features, and sequence
information. The method benefits from using a collaborative framework of GAT
and GCN and stacking positional encoding into the layers of graph neural
networks, which is different from all existing ones and demonstrates good
performance. The experiments conducted on two real-world datasets confirm that
our method significantly improves the prediction accuracy compared to
state-of-the-art approaches. What's more, the ablation study investigates the
contribution of each component in our method.

In this research, we extend the universal reinforcement learning (URL) agent
models of artificial general intelligence to quantum environments. The utility
function of a classical exploratory stochastic Knowledge Seeking Agent, KL-KSA,
is generalized to distance measures from quantum information theory on density
matrices. Quantum process tomography (QPT) algorithms form the tractable subset
of programs for modeling environmental dynamics. The optimal QPT policy is
selected based on a mutable cost function based on algorithmic complexity as
well as computational resource complexity. Instead of Turing machines, we
estimate the cost metrics on a high-level language to allow realistic
experimentation. The entire agent design is encapsulated in a self-replicating
quine which mutates the cost function based on the predictive value of the
optimal policy choosing scheme. Thus, multiple agents with pareto-optimal QPT
policies evolve using genetic programming, mimicking the development of
physical theories each with different resource trade-offs. This formal
framework is termed Quantum Knowledge Seeking Agent (QKSA).
  Despite its importance, few quantum reinforcement learning models exist in
contrast to the current thrust in quantum machine learning. QKSA is the first
proposal for a framework that resembles the classical URL models. Similar to
how AIXI-tl is a resource-bounded active version of Solomonoff universal
induction, QKSA is a resource-bounded participatory observer framework to the
recently proposed algorithmic information-based reconstruction of quantum
mechanics. QKSA can be applied for simulating and studying aspects of quantum
information theory. Specifically, we demonstrate that it can be used to
accelerate quantum variational algorithms which include tomographic
reconstruction as its integral subroutine.

Deep discrete structured models have seen considerable progress recently, but
traditional inference using dynamic programming (DP) typically works with a
small number of states (less than hundreds), which severely limits model
capacity. At the same time, across machine learning, there is a recent trend of
using randomized truncation techniques to accelerate computations involving
large sums. Here, we propose a family of randomized dynamic programming (RDP)
algorithms for scaling structured models to tens of thousands of latent states.
Our method is widely applicable to classical DP-based inference (partition,
marginal, reparameterization, entropy) and different graph structures (chains,
trees, and more general hypergraphs). It is also compatible with automatic
differentiation: it can be integrated with neural networks seamlessly and
learned with gradient-based optimizers. Our core technique approximates the
sum-product by restricting and reweighting DP on a small subset of nodes, which
reduces computation by orders of magnitude. We further achieve low bias and
variance via Rao-Blackwellization and importance sampling. Experiments over
different graphs demonstrate the accuracy and efficiency of our approach.
Furthermore, when using RDP for training a structured variational autoencoder
with a scaled inference network, we achieve better test likelihood than
baselines and successfully prevent posterior collapse. code at:
https://github.com/FranxYao/RDP

We present Godot Reinforcement Learning (RL) Agents, an open-source interface
for developing environments and agents in the Godot Game Engine. The Godot RL
Agents interface allows the design, creation and learning of agent behaviors in
challenging 2D and 3D environments with various on-policy and off-policy Deep
RL algorithms. We provide a standard Gym interface, with wrappers for learning
in the Ray RLlib and Stable Baselines RL frameworks. This allows users access
to over 20 state of the art on-policy, off-policy and multi-agent RL
algorithms. The framework is a versatile tool that allows researchers and game
designers the ability to create environments with discrete, continuous and
mixed action spaces. The interface is relatively performant, with 12k
interactions per second on a high end laptop computer, when parallized on 4 CPU
cores. An overview video is available here: https://youtu.be/g1MlZSFqIj4

Enabling highly secure applications (such as border crossing) with face
recognition requires extensive biometric performance tests through large scale
data. However, using real face images raises concerns about privacy as the laws
do not allow the images to be used for other purposes than originally intended.
Using representative and subsets of face data can also lead to unwanted
demographic biases and cause an imbalance in datasets. One possible solution to
overcome these issues is to replace real face images with synthetically
generated samples. While generating synthetic images has benefited from recent
advancements in computer vision, generating multiple samples of the same
synthetic identity resembling real-world variations is still unaddressed, i.e.,
mated samples. This work proposes a non-deterministic method for generating
mated face images by exploiting the well-structured latent space of StyleGAN.
Mated samples are generated by manipulating latent vectors, and more precisely,
we exploit Principal Component Analysis (PCA) to define semantically meaningful
directions in the latent space and control the similarity between the original
and the mated samples using a pre-trained face recognition system. We create a
new dataset of synthetic face images (SymFace) consisting of 77,034 samples
including 25,919 synthetic IDs. Through our analysis using well-established
face image quality metrics, we demonstrate the differences in the biometric
quality of synthetic samples mimicking characteristics of real biometric data.
The analysis and results thereof indicate the use of synthetic samples created
using the proposed approach as a viable alternative to replacing real biometric
data.

Makeup transfer is not only to extract the makeup style of the reference
image, but also to render the makeup style to the semantic corresponding
position of the target image. However, most existing methods focus on the
former and ignore the latter, resulting in a failure to achieve desired
results. To solve the above problems, we propose a unified Symmetric
Semantic-Aware Transformer (SSAT) network, which incorporates semantic
correspondence learning to realize makeup transfer and removal simultaneously.
In SSAT, a novel Symmetric Semantic Corresponding Feature Transfer (SSCFT)
module and a weakly supervised semantic loss are proposed to model and
facilitate the establishment of accurate semantic correspondence. In the
generation process, the extracted makeup features are spatially distorted by
SSCFT to achieve semantic alignment with the target image, then the distorted
makeup features are combined with unmodified makeup irrelevant features to
produce the final result. Experiments show that our method obtains more
visually accurate makeup transfer results, and user study in comparison with
other state-of-the-art makeup transfer methods reflects the superiority of our
method. Besides, we verify the robustness of the proposed method in the
difference of expression and pose, object occlusion scenes, and extend it to
video makeup transfer. Code will be available at
https://gitee.com/sunzhaoyang0304/ssat-msp.

When the unknown regression function of a single variable is known to have
derivatives up to the $(\gamma+1)$th order bounded in absolute values by a
common constant everywhere or a.e., the classical minimax optimal rate of the
mean integrated squared error (MISE)
$\left(\frac{1}{n}\right)^{\frac{2\gamma+2}{2\gamma+3}}$ leads one to conclude
that, as $\gamma$ gets larger, the rate gets closer to $\frac{1}{n}$. This
paper shows that: (i) if $n\leq\left(\gamma+1\right)^{2\gamma+3}$, the minimax
optimal MISE rate is roughly $\frac{\log n}{n}$ and the optimal degree of
smoothness to exploit is roughly $\left\lceil \frac{\log n}{2}\right\rceil -2$;
(ii) if $n>\left(\gamma+1\right)^{2\gamma+3}$, the minimax optimal MISE rate is
$\left(\frac{1}{n}\right)^{\frac{2\gamma+2}{2\gamma+3}}$ and the optimal degree
of smoothness to exploit is $\gamma+1$.
  The building blocks of our minimax optimality results are a set of metric
entropy bounds we develop in this paper for smooth function classes. Some of
our bounds are original, and some of them improve and/or generalize the ones in
the literature. Our metric entropy bounds allow us to explore the minimax
optimal MISE rates associated with some commonly seen smoothness classes and
also several non-standard smoothness classes, and can also be of independent
interest even if one does not care about the nonparametric regressions.

With an increase of dataset availability, the potential for learning from a
variety of data sources has increased. One particular method to improve
learning from multiple data sources is to embed the data source during
training. This allows the model to learn generalizable features as well as
distinguishing features between datasets. However, these dataset embeddings
have mostly been used before contextualized transformer-based embeddings were
introduced in the field of Natural Language Processing. In this work, we
compare two methods to embed datasets in a transformer-based multilingual
dependency parser, and perform an extensive evaluation. We show that: 1)
embedding the dataset is still beneficial with these models 2) performance
increases are highest when embedding the dataset at the encoder level 3)
unsurprisingly, we confirm that performance increases are highest for small
datasets and datasets with a low baseline score. 4) we show that training on
the combination of all datasets performs similarly to designing smaller
clusters based on language-relatedness.

We introduce a novel self-supervised contrastive learning method to learn
representations from unlabelled videos. Existing approaches ignore the
specifics of input distortions, e.g., by learning invariance to temporal
transformations. Instead, we argue that video representation should preserve
video dynamics and reflect temporal manipulations of the input. Therefore, we
exploit novel constraints to build representations that are equivariant to
temporal transformations and better capture video dynamics. In our method,
relative temporal transformations between augmented clips of a video are
encoded in a vector and contrasted with other transformation vectors. To
support temporal equivariance learning, we additionally propose the
self-supervised classification of two clips of a video into 1. overlapping 2.
ordered, or 3. unordered. Our experiments show that time-equivariant
representations achieve state-of-the-art results in video retrieval and action
recognition benchmarks on UCF101, HMDB51, and Diving48.

One of the most discussed issues in graph generative modeling is the ordering
of the representation. One solution consists of using equivariant generative
functions, which ensure the ordering invariance. After having discussed some
properties of such functions, we propose 3G-GAN, a 3-stages model relying on
GANs and equivariant functions. The model is still under development. However,
we present some encouraging exploratory experiments and discuss the issues
still to be addressed.

Deep learning models have shown incredible performance on numerous image
recognition, classification, and reconstruction tasks. Although very appealing
and valuable due to their predictive capabilities, one common threat remains
challenging to resolve. A specifically trained attacker can introduce malicious
input perturbations to fool the network, thus causing potentially harmful
mispredictions. Moreover, these attacks can succeed when the adversary has full
access to the target model (white-box) and even when such access is limited
(black-box setting). The ensemble of models can protect against such attacks
but might be brittle under shared vulnerabilities in its members (attack
transferability). To that end, this work proposes a novel diversity-promoting
learning approach for the deep ensembles. The idea is to promote saliency map
diversity (SMD) on ensemble members to prevent the attacker from targeting all
ensemble members at once by introducing an additional term in our learning
objective. During training, this helps us minimize the alignment between model
saliencies to reduce shared member vulnerabilities and, thus, increase ensemble
robustness to adversaries. We empirically show a reduced transferability
between ensemble members and improved performance compared to the
state-of-the-art ensemble defense against medium and high strength white-box
attacks. In addition, we demonstrate that our approach combined with existing
methods outperforms state-of-the-art ensemble algorithms for defense under
white-box and black-box attacks.

To alleviate green house effect, high network energy efficiency (EE) has
increasingly become an important research target in wireless green
communications. Therefore, the investigation for resource management to
mitigate the co-tier interference in the small cell network (SCN) is provided.
Moreover, with the merits of cloud radio access network (C-RAN), small cell
base stations (SBSs) can be decomposed of a central small cell (CSC) and remote
small cells (RSCs). To achieve the coordination, the split medium access
control (MAC) based functional splitting is adopted with scheduler deployed at
CSCs and retransmission functions left at RSCs. However, limited fronthaul has
a compelling impact at RSCs due to requirements of user quality-of-service
(QoS). Accordingly, a traffic control-based user association and resource
allocation (TURA) scheme is proposed for a centralized resource management. To
deal with the infeasibility to control all RSCs by CSC, we propose a hybrid
controlled user and resource management (HARM) scheme. A CSC performs TURA for
RSCs to mitigate intra-group interference within localized C-RANs, whereas the
CSCs among separate C-RANs conduct cooperative resource competition (CRC) game
for alleviating inter-group interference. Based on regret-based learning
algorithm, the proposed schemes are analytically proved to reach the correlated
equilibrium (CE). Simulation results have validated the effect of traffic
control in TURA scheme and the convergence of CRC. Moreover, the comparison of
the proposed TURA, HARM, and CRC schemes with the benchmark is revealed. It is
observed that the TURA scheme outperforms the other schemes under ideal
fronthaul control, whilst the proposed HARM scheme can sustain EE performance
considering feasible implementation.

In the last years decision-focused learning framework, also known as
predict-and-optimize, have received increasing attention. In this setting, the
predictions of a machine learning model are used as estimated cost coefficients
in the objective function of a discrete combinatorial optimization problem for
decision making. Decision-focused learning proposes to train the ML models,
often neural network models, by directly optimizing the quality of decisions
made by the optimization solvers. Based on a recent work that proposed a noise
contrastive estimation loss over a subset of the solution space, we observe
that decision-focused learning can more generally be seen as a learning-to-rank
problem, where the goal is to learn an objective function that ranks the
feasible points correctly. This observation is independent of the optimization
method used and of the form of the objective function. We develop pointwise,
pairwise and listwise ranking loss functions, which can be differentiated in
closed form given a subset of solutions. We empirically investigate the quality
of our generic methods compared to existing decision-focused learning
approaches with competitive results. Furthermore, controlling the subset of
solutions allows controlling the runtime considerably, with limited effect on
regret.

The analysis of ultrasonic NDE data has traditionally been addressed by a
trained operator manually interpreting data with the support of rudimentary
automation tools. Recently, many demonstrations of deep learning (DL)
techniques that address individual NDE tasks (data pre-processing, defect
detection, defect characterisation, and property measurement) have started to
emerge in the research community. These methods have the potential to offer
high flexibility, efficiency, and accuracy subject to the availability of
sufficient training data. Moreover, they enable the automation of complex
processes that span one or more NDE steps (e.g. detection, characterisation,
and sizing). There is, however, a lack of consensus on the direction and
requirements that these new methods should follow. These elements are critical
to help achieve automation of ultrasonic NDE driven by artificial intelligence
such that the research community, industry, and regulatory bodies embrace it.
This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by
DL methodologies. The review is organised by the NDE tasks that are addressed
by means of DL approaches. Key remaining challenges for each task are noted.
Basic axiomatic principles for DL methods in NDE are identified based on the
literature review, relevant international regulations, and current industrial
needs. By placing DL methods in the context of general NDE automation levels,
this paper aims to provide a roadmap for future research and development in the
area.

Handwritten mathematical expression recognition aims to automatically
generate LaTeX sequences from given images. Currently, attention-based
encoder-decoder models are widely used in this task. They typically generate
target sequences in a left-to-right (L2R) manner, leaving the right-to-left
(R2L) contexts unexploited. In this paper, we propose an Attention aggregation
based Bi-directional Mutual learning Network (ABM) which consists of one shared
encoder and two parallel inverse decoders (L2R and R2L). The two decoders are
enhanced via mutual distillation, which involves one-to-one knowledge transfer
at each training step, making full use of the complementary information from
two inverse directions. Moreover, in order to deal with mathematical symbols in
diverse scales, an Attention Aggregation Module (AAM) is proposed to
effectively integrate multi-scale coverage attentions. Notably, in the
inference phase, given that the model already learns knowledge from two inverse
directions, we only use the L2R branch for inference, keeping the original
parameter size and inference speed. Extensive experiments demonstrate that our
proposed approach achieves the recognition accuracy of 56.85 % on CROHME 2014,
52.92 % on CROHME 2016, and 53.96 % on CROHME 2019 without data augmentation
and model ensembling, substantially outperforming the state-of-the-art methods.
The source code is available in https://github.com/XH-B/ABM.

In this paper, we describe our proposed methodology to approach the
predict+optimise challenge introduced in the IEEE CIS 3rd Technical Challenge.
The predictive model employs an ensemble of LightGBM models and the
prescriptive analysis employs mathematical optimisation to efficiently
prescribe solutions that minimise the average cost over multiple scenarios. Our
solutions ranked 1st in the optimisation and 2nd in the prediction challenge of
the competition.

In recent years, self-supervised representation learning for skeleton-based
action recognition has been developed with the advance of contrastive learning
methods. The existing contrastive learning methods use normal augmentations to
construct similar positive samples, which limits the ability to explore novel
movement patterns. In this paper, to make better use of the movement patterns
introduced by extreme augmentations, a Contrastive Learning framework utilizing
Abundant Information Mining for self-supervised action Representation (AimCLR)
is proposed. First, the extreme augmentations and the Energy-based
Attention-guided Drop Module (EADM) are proposed to obtain diverse positive
samples, which bring novel movement patterns to improve the universality of the
learned representations. Second, since directly using extreme augmentations may
not be able to boost the performance due to the drastic changes in original
identity, the Dual Distributional Divergence Minimization Loss (D$^3$M Loss) is
proposed to minimize the distribution divergence in a more gentle way. Third,
the Nearest Neighbors Mining (NNM) is proposed to further expand positive
samples to make the abundant information mining process more reasonable.
Exhaustive experiments on NTU RGB+D 60, PKU-MMD, NTU RGB+D 120 datasets have
verified that our AimCLR can significantly perform favorably against
state-of-the-art methods under a variety of evaluation protocols with observed
higher quality action representations. Our code is available at
https://github.com/Levigty/AimCLR.

We show that deep learning models, and especially architectures like the
Transformer, originally intended for natural language, can be trained on
randomly generated datasets to predict to very high accuracy both the
qualitative and quantitative features of metabolic networks. Using standard
mathematical techniques, we create large sets (40 million elements) of random
networks that can be used to train our models. These trained models can predict
network equilibrium on random graphs in more than 99% of cases. They can also
generalize to graphs with different structure than those encountered at
training. Finally, they can predict almost perfectly the equilibria of a small
set of known biological networks. Our approach is both very economical in
experimental data and uses only small and shallow deep-learning model, far from
the large architectures commonly used in machine translation. Such results pave
the way for larger use of deep learning models for problems related to
biological networks in key areas such as quantitative systems pharmacology,
systems biology, and synthetic biology.

Video self-supervised learning is a challenging task, which requires
significant expressive power from the model to leverage rich spatial-temporal
knowledge and generate effective supervisory signals from large amounts of
unlabeled videos. However, existing methods fail to increase the temporal
diversity of unlabeled videos and ignore elaborately modeling multi-scale
temporal dependencies in an explicit way. To overcome these limitations, we
take advantage of the multi-scale temporal dependencies within videos and
proposes a novel video self-supervised learning framework named Temporal
Contrastive Graph Learning (TCGL), which jointly models the inter-snippet and
intra-snippet temporal dependencies for temporal representation learning with a
hybrid graph contrastive learning strategy. Specifically, a Spatial-Temporal
Knowledge Discovering (STKD) module is first introduced to extract
motion-enhanced spatial-temporal representations from videos based on the
frequency domain analysis of discrete cosine transform. To explicitly model
multi-scale temporal dependencies of unlabeled videos, our TCGL integrates the
prior knowledge about the frame and snippet orders into graph structures, i.e.,
the intra-/inter- snippet Temporal Contrastive Graphs (TCG). Then, specific
contrastive learning modules are designed to maximize the agreement between
nodes in different graph views. To generate supervisory signals for unlabeled
videos, we introduce an Adaptive Snippet Order Prediction (ASOP) module which
leverages the relational knowledge among video snippets to learn the global
context representation and recalibrate the channel-wise features adaptively.
Experimental results demonstrate the superiority of our TCGL over the
state-of-the-art methods on large-scale action recognition and video retrieval
benchmarks.The code is publicly available at
https://github.com/YangLiu9208/TCGL.

Quantum computing devices are inevitably subject to errors. To leverage
quantum technologies for computational benefits in practical applications,
quantum algorithms and protocols must be implemented reliably under noise and
imperfections. Since noise and imperfections limit the size of quantum circuits
that can be realized on a quantum device, developing quantum error mitigation
techniques that do not require extra qubits and gates is of critical
importance. In this work, we present a deep learning-based protocol for
reducing readout errors on quantum hardware. Our technique is based on training
an artificial neural network with the measurement results obtained from
experiments with simple quantum circuits consisting of singe-qubit gates only.
With the neural network and deep learning, non-linear noise can be corrected,
which is not possible with the existing linear inversion methods. The advantage
of our method against the existing methods is demonstrated through quantum
readout error mitigation experiments performed on IBM five-qubit quantum
devices.

Learning rational behaviors in open-world games like Minecraft remains to be
challenging for Reinforcement Learning (RL) research due to the compound
challenge of partial observability, high-dimensional visual perception and
delayed reward. To address this, we propose JueWu-MC, a sample-efficient
hierarchical RL approach equipped with representation learning and imitation
learning to deal with perception and exploration. Specifically, our approach
includes two levels of hierarchy, where the high-level controller learns a
policy to control over options and the low-level workers learn to solve each
sub-task. To boost the learning of sub-tasks, we propose a combination of
techniques including 1) action-aware representation learning which captures
underlying relations between action and representation, 2) discriminator-based
self-imitation learning for efficient exploration, and 3) ensemble behavior
cloning with consistency filtering for policy robustness. Extensive experiments
show that JueWu-MC significantly improves sample efficiency and outperforms a
set of baselines by a large margin. Notably, we won the championship of the
NeurIPS MineRL 2021 research competition and achieved the highest performance
score ever.

The multi-task learning (MTL) paradigm can be traced back to an early paper
of Caruana (1997) in which it was argued that data from multiple tasks can be
used with the aim to obtain a better performance over learning each task
independently. A solution of MTL with conflicting objectives requires modelling
the trade-off among them which is generally beyond what a straight linear
combination can achieve. A theoretically principled and computationally
effective strategy is finding solutions which are not dominated by others as it
is addressed in the Pareto analysis. Multi-objective optimization problems
arising in the multi-task learning context have specific features and require
adhoc methods. The analysis of these features and the proposal of a new
computational approach represent the focus of this work. Multi-objective
evolutionary algorithms (MOEAs) can easily include the concept of dominance and
therefore the Pareto analysis. The major drawback of MOEAs is a low sample
efficiency with respect to function evaluations. The key reason for this
drawback is that most of the evolutionary approaches do not use models for
approximating the objective function. Bayesian Optimization takes a radically
different approach based on a surrogate model, such as a Gaussian Process. In
this thesis the solutions in the Input Space are represented as probability
distributions encapsulating the knowledge contained in the function
evaluations. In this space of probability distributions, endowed with the
metric given by the Wasserstein distance, a new algorithm MOEA/WST can be
designed in which the model is not directly on the objective function but in an
intermediate Information Space where the objects from the input space are
mapped into histograms. Computational results show that the sample efficiency
and the quality of the Pareto set provided by MOEA/WST are significantly better
than in the standard MOEA.

In this paper, pragmatic implementation of an indoor autonomous delivery
system that exploits Reinforcement Learning algorithms for path planning and
collision avoidance is audited. The proposed system is a cost-efficient
approach that is implemented to facilitate a Raspberry Pi controlled
four-wheel-drive non-holonomic robot map a grid. This approach computes and
navigates the shortest path from a source key point to a destination key point
to carry out the desired delivery. Q learning and Deep-Q learning are used to
find the optimal path while avoiding collision with static obstacles. This work
defines an approach to deploy these two algorithms on a robot. A novel
algorithm to decode an array of directions into accurate movements in a certain
action space is also proposed. The procedure followed to dispatch this system
with the said requirements is described, ergo presenting our proof of concept
for indoor autonomous delivery vehicles.

Safe exploration is critical for using reinforcement learning (RL) in
risk-sensitive environments. Recent work learns risk measures which measure the
probability of violating constraints, which can then be used to enable safety.
However, learning such risk measures requires significant interaction with the
environment, resulting in excessive constraint violations during learning.
Furthermore, these measures are not easily transferable to new environments. We
cast safe exploration as an offline meta-RL problem, where the objective is to
leverage examples of safe and unsafe behavior across a range of environments to
quickly adapt learned risk measures to a new environment with previously unseen
dynamics. We then propose MEta-learning for Safe Adaptation (MESA), an approach
for meta-learning a risk measure for safe RL. Simulation experiments across 5
continuous control domains suggest that MESA can leverage offline data from a
range of different environments to reduce constraint violations in unseen
environments by up to a factor of 2 while maintaining task performance. See
https://tinyurl.com/safe-meta-rl for code and supplementary material.

The usage and amount of information available on the internet increase over
the past decade. This digitization leads to the need for automated answering
system to extract fruitful information from redundant and transitional
knowledge sources. Such systems are designed to cater the most prominent answer
from this giant knowledge source to the user query using natural language
understanding (NLU) and thus eminently depends on the Question-answering(QA)
field.
  Question answering involves but not limited to the steps like mapping of user
question to pertinent query, retrieval of relevant information, finding the
best suitable answer from the retrieved information etc. The current
improvement of deep learning models evince compelling performance improvement
in all these tasks.
  In this review work, the research directions of QA field are analyzed based
on the type of question, answer type, source of evidence-answer, and modeling
approach. This detailing followed by open challenges of the field like
automatic question generation, similarity detection and, low resource
availability for a language. In the end, a survey of available datasets and
evaluation measures is presented.

Artificial neural network (ANN) ability to learn, correct errors, and
transform a large amount of raw data into useful medical decisions for
treatment and care have increased its popularity for enhanced patient safety
and quality of care. Therefore, this paper reviews the critical role of ANNs in
providing valuable insights for patients' healthcare decisions and efficient
disease diagnosis. We thoroughly review different types of ANNs presented in
the existing literature that advanced ANNs adaptation for complex applications.
Moreover, we also investigate ANN's advances for various disease diagnoses and
treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a
novel deep Convolutional Neural Network (CNN) model called ConXNet for
improving the detection accuracy of COVID-19 disease. ConXNet is trained and
tested using different datasets, and it achieves more than 97% detection
accuracy and precision, which is significantly better than existing models.
Finally, we highlight future research directions and challenges such as
complexity of the algorithms, insufficient available data, privacy and
security, and integration of biosensing with ANNs. These research directions
require considerable attention for improving the scope of ANNs for medical
diagnostic and treatment applications.

A membership inference attack allows an adversary to query a trained machine
learning model to predict whether or not a particular example was contained in
the model's training dataset. These attacks are currently evaluated using
average-case "accuracy" metrics that fail to characterize whether the attack
can confidently identify any members of the training set. We argue that attacks
should instead be evaluated by computing their true-positive rate at low (e.g.,
<0.1%) false-positive rates, and find most prior attacks perform poorly when
evaluated in this way. To address this we develop a Likelihood Ratio Attack
(LiRA) that carefully combines multiple ideas from the literature. Our attack
is 10x more powerful at low false-positive rates, and also strictly dominates
prior attacks on existing metrics.

Visual scenes are extremely rich in diversity, not only because there are
infinite combinations of objects and background, but also because the
observations of the same scene may vary greatly with the change of viewpoints.
When observing a visual scene that contains multiple objects from multiple
viewpoints, humans are able to perceive the scene in a compositional way from
each viewpoint, while achieving the so-called "object constancy" across
different viewpoints, even though the exact viewpoints are untold. This ability
is essential for humans to identify the same object while moving and to learn
from vision efficiently. It is intriguing to design models that have the
similar ability. In this paper, we consider a novel problem of learning
compositional scene representations from multiple unspecified viewpoints
without using any supervision, and propose a deep generative model which
separates latent representations into a viewpoint-independent part and a
viewpoint-dependent part to solve this problem. To infer latent
representations, the information contained in different viewpoints is
iteratively integrated by neural networks. Experiments on several specifically
designed synthetic datasets have shown that the proposed method is able to
effectively learn from multiple unspecified viewpoints.

This paper attempts to analyze the effectiveness of deep learning for tabular
data processing. It is believed that decision trees and their ensembles is the
leading method in this domain, and deep neural networks must be content with
computer vision and so on. But the deep neural network is a framework for
building gradient-based hierarchical representations, and this key feature
should be able to provide the best processing of generic structured (tabular)
data, not just image matrices and audio spectrograms. This problem is
considered through the prism of the Weather Prediction track in the Yandex
Shifts challenge (in other words, the Yandex Shifts Weather task). This task is
a variant of the classical tabular data regression problem. It is also
connected with another important problem: generalization and uncertainty in
machine learning. This paper proposes an end-to-end algorithm for solving the
problem of regression with uncertainty on tabular data, which is based on the
combination of four ideas: 1) deep ensemble of self-normalizing neural
networks, 2) regression as parameter estimation of the Gaussian target error
distribution, 3) hierarchical multitask learning, and 4) simple data
preprocessing. Three modifications of the proposed algorithm form the top-3
leaderboard of the Yandex Shifts Weather challenge respectively. This paper
considers that this success has occurred due to the fundamental properties of
the deep learning algorithm, and tries to prove this.

In this work, we explore the potential of multi-domain multi-branch
convolutional neural networks (CNNs) for identifying comparatively rare giant
radio galaxies from large volumes of survey data, such as those expected for
new-generation radio telescopes like the SKA and its precursors. The approach
presented here allows models to learn jointly from multiple survey inputs, in
this case NVSS and FIRST, as well as incorporating numerical redshift
information. We find that the inclusion of multi-resolution survey data results
in correction of 39% of the misclassifications seen from equivalent single
domain networks for the classification problem considered in this work. We also
show that the inclusion of redshift information can moderately improve the
classification of giant radio galaxies.

Modern Web systems such as social media and e-commerce contain rich contents
expressed in images and text. Leveraging information from multi-modalities can
improve the performance of machine learning tasks such as classification and
recommendation. In this paper, we propose the Cross-Modality Attention
Contrastive Language-Image Pre-training (CMA-CLIP), a new framework which
unifies two types of cross-modality attentions, sequence-wise attention and
modality-wise attention, to effectively fuse information from image and text
pairs. The sequence-wise attention enables the framework to capture the
fine-grained relationship between image patches and text tokens, while the
modality-wise attention weighs each modality by its relevance to the downstream
tasks. In addition, by adding task specific modality-wise attentions and
multilayer perceptrons, our proposed framework is capable of performing
multi-task classification with multi-modalities.
  We conduct experiments on a Major Retail Website Product Attribute (MRWPA)
dataset and two public datasets, Food101 and Fashion-Gen. The results show that
CMA-CLIP outperforms the pre-trained and fine-tuned CLIP by an average of 11.9%
in recall at the same level of precision on the MRWPA dataset for multi-task
classification. It also surpasses the state-of-the-art method on Fashion-Gen
Dataset by 5.5% in accuracy and achieves competitive performance on Food101
Dataset. Through detailed ablation studies, we further demonstrate the
effectiveness of both cross-modality attention modules and our method's
robustness against noise in image and text inputs, which is a common challenge

Traffic forecasting is one of the most popular spatio-temporal tasks in the
field of machine learning. A prevalent approach in the field is to combine
graph convolutional networks and recurrent neural networks for the
spatio-temporal processing. There has been fierce competition and many novel
methods have been proposed. In this paper, we present the method of
spatio-temporal graph neural controlled differential equation (STG-NCDE).
Neural controlled differential equations (NCDEs) are a breakthrough concept for
processing sequential data. We extend the concept and design two NCDEs: one for
the temporal processing and the other for the spatial processing. After that,
we combine them into a single framework. We conduct experiments with 6
benchmark datasets and 20 baselines. STG-NCDE shows the best accuracy in all
cases, outperforming all those 20 baselines by non-trivial margins.

We present a methodology to train our multi-speaker emotional text-to-speech
synthesizer that can express speech for 10 speakers' 7 different emotions. All
silences from audio samples are removed prior to learning. This results in fast
learning by our model. Curriculum learning is applied to train our model
efficiently. Our model is first trained with a large single-speaker neutral
dataset, and then trained with neutral speech from all speakers. Finally, our
model is trained using datasets of emotional speech from all speakers. In each
stage, training samples of each speaker-emotion pair have equal probability to
appear in mini-batches. Through this procedure, our model can synthesize speech
for all targeted speakers and emotions. Our synthesized audio sets are
available on our web page.

Causal discovery aims to learn a causal graph from observational data. To
date, most causal discovery methods require data to be stored in a central
server. However, data owners gradually refuse to share their personalized data
to avoid privacy leakage, making this task more troublesome by cutting off the
first step. A puzzle arises: $\textit{how do we infer causal relations from
decentralized data?}$ In this paper, with the additive noise model assumption
of data, we take the first step in developing a gradient-based learning
framework named DAG-Shared Federated Causal Discovery (DS-FCD), which can learn
the causal graph without directly touching local data and naturally handle the
data heterogeneity. DS-FCD benefits from a two-level structure of each local
model. The first level learns the causal graph and communicates with the server
to get model information from other clients, while the second level
approximates causal mechanisms and personally updates from its own data to
accommodate the data heterogeneity. Moreover, DS-FCD formulates the overall
learning task as a continuous optimization problem by taking advantage of an
equality acyclicity constraint, which can be naturally solved by gradient
descent methods. Extensive experiments on both synthetic and real-world
datasets verify the efficacy of the proposed method.

This report proposes a combined optimal control and perception framework for
Micro Aerial Vehicle (MAV) autonomous navigation in novel indoor enclosed
environments, relying exclusively on on-board sensor data. We use privileged
information from a simulator to generate optimal waypoints in 3D space that our
perception system learns to imitate. The trained learning based perception
module in turn is able to generate similar obstacle avoiding waypoints from
sensor data (RGB + IMU) alone. We demonstrate the efficacy of the framework
across novel scenes in the iGibson simulation environment.

Despite significant advancements of deep learning-based forgery detectors for
distinguishing manipulated deepfake images, most detection approaches suffer
from moderate to significant performance degradation with low-quality
compressed deepfake images. Because of the limited information in low-quality
images, detecting low-quality deepfake remains an important challenge. In this
work, we apply frequency domain learning and optimal transport theory in
knowledge distillation (KD) to specifically improve the detection of
low-quality compressed deepfake images. We explore transfer learning capability
in KD to enable a student network to learn discriminative features from
low-quality images effectively. In particular, we propose the Attention-based
Deepfake detection Distiller (ADD), which consists of two novel distillations:
1) frequency attention distillation that effectively retrieves the removed
high-frequency components in the student network, and 2) multi-view attention
distillation that creates multiple attention vectors by slicing the teacher's
and student's tensors under different views to transfer the teacher tensor's
distribution to the student more efficiently. Our extensive experimental
results demonstrate that our approach outperforms state-of-the-art baselines in
detecting low-quality compressed deepfake images.

Recently, vision Transformers (ViTs) are developing rapidly and starting to
challenge the domination of convolutional neural networks (CNNs) in the realm
of computer vision (CV). With the general-purpose Transformer architecture
replacing the hard-coded inductive biases of convolution, ViTs have surpassed
CNNs, especially in data-sufficient circumstances. However, ViTs are prone to
over-fit on small datasets and thus rely on large-scale pre-training, which
expends enormous time. In this paper, we strive to liberate ViTs from
pre-training by introducing CNNs' inductive biases back to ViTs while
preserving their network architectures for higher upper bound and setting up
more suitable optimization objectives. To begin with, an agent CNN is designed
based on the given ViT with inductive biases. Then a bootstrapping training
algorithm is proposed to jointly optimize the agent and ViT with weight
sharing, during which the ViT learns inductive biases from the intermediate
features of the agent. Extensive experiments on CIFAR-10/100 and ImageNet-1k
with limited training data have shown encouraging results that the inductive
biases help ViTs converge significantly faster and outperform conventional CNNs
with even fewer parameters. Our code is publicly available at
https://github.com/zhfeing/Bootstrapping-ViTs-pytorch.

Constant rise in energy consumption that comes with the population growth and
introduction of new technologies has posed critical issues such as efficient
energy management on the consumer side. That has elevated the importance of the
use of renewable energy sources, particularly photovoltaic (PV) system and wind
turbine. This work models and discusses design options based on the hybrid
power system of grid and battery storage. The effects of installed capacity on
renewable penetration (RP) and cost of electricity (COE) are investigated for
each modality. For successful operation of hybrid power system and electricity
trading in power market, accurate predictions of PV power production and load
demand are taken into account. A machine learning (ML) model is introduced for
scheduling, and predicting variations of the PV power production and load
demand. Fitness of the ML model shows, when employing a linear regression
model, the mean squared error (MSE) of 0.000012, root mean square error (RMSE)
of 0.003560 and R2 of 0.999379. Using predicted PV power production and load
demand, reduction of electricity cost is 37.5 % when PV and utility grid are
utilized, and is 43.06% when PV, utility grid, and storage system are utilized.

We give the first polynomial time and sample $(\epsilon,
\delta)$-differentially private (DP) algorithm to estimate the mean, covariance
and higher moments in the presence of a constant fraction of adversarial
outliers. Our algorithm succeeds for families of distributions that satisfy two
well-studied properties in prior works on robust estimation: certifiable
subgaussianity of directional moments and certifiable hypercontractivity of
degree 2 polynomials. Our recovery guarantees hold in the "right
affine-invariant norms": Mahalanobis distance for mean, multiplicative spectral
and relative Frobenius distance guarantees for covariance and injective norms
for higher moments. Prior works obtained private robust algorithms for mean
estimation of subgaussian distributions with bounded covariance. For covariance
estimation, ours is the first efficient algorithm (even in the absence of
outliers) that succeeds without any condition-number assumptions.
  Our algorithms arise from a new framework that provides a general blueprint
for modifying convex relaxations for robust estimation to satisfy strong
worst-case stability guarantees in the appropriate parameter norms whenever the
algorithms produce witnesses of correctness in their run. We verify such
guarantees for a modification of standard sum-of-squares (SoS) semidefinite
programming relaxations for robust estimation. Our privacy guarantees are
obtained by combining stability guarantees with a new "estimate dependent"
noise injection mechanism in which noise scales with the eigenvalues of the
estimated covariance. We believe this framework will be useful more generally
in obtaining DP counterparts of robust estimators.
  Independently of our work, Ashtiani and Liaw [AL21] also obtained a
polynomial time and sample private robust estimation algorithm for Gaussian

The success of deep learning methods relies on the availability of
well-labeled large-scale datasets. However, for medical images, annotating such
abundant training data often requires experienced radiologists and consumes
their limited time. Few-shot learning is developed to alleviate this burden,
which achieves competitive performances with only several labeled data.
However, a crucial yet previously overlooked problem in few-shot learning is
about the selection of template images for annotation before learning, which
affects the final performance. We herein propose a novel Sample Choosing Policy
(SCP) to select "the most worthy" images for annotation, in the context of
few-shot medical landmark detection. SCP consists of three parts: 1)
Self-supervised training for building a pre-trained deep model to extract
features from radiological images, 2) Key Point Proposal for localizing
informative patches, and 3) Representative Score Estimation for searching the
most representative samples or templates. The advantage of SCP is demonstrated
by various experiments on three widely-used public datasets. For one-shot
medical landmark detection, its use reduces the mean radial errors on
Cephalometric and HandXray datasets by 14.2% (from 3.595mm to 3.083mm) and
35.5% (4.114mm to 2.653mm), respectively.

Coordination graph is a promising approach to model agent collaboration in
multi-agent reinforcement learning. It conducts a graph-based value
factorization and induces explicit coordination among agents to complete
complicated tasks. However, one critical challenge in this paradigm is the
complexity of greedy action selection with respect to the factorized values. It
refers to the decentralized constraint optimization problem (DCOP), which and
whose constant-ratio approximation are NP-hard problems. To bypass this
systematic hardness, this paper proposes a novel method, named Self-Organized
Polynomial-time Coordination Graphs (SOP-CG), which uses structured graph
classes to guarantee the accuracy and the computational efficiency of
collaborated action selection. SOP-CG employs dynamic graph topology to ensure
sufficient value function expressiveness. The graph selection is unified into
an end-to-end learning paradigm. In experiments, we show that our approach
learns succinct and well-adapted graph topologies, induces effective
coordination, and improves performance across a variety of cooperative
multi-agent tasks.

Objective: Improving geographical access remains a key issue in determining
the sufficiency of regional medical resources during health policy design.
However, patient choices can be the result of the complex interactivity of
various factors. The aim of this study is to propose a deep neural network
approach to model the complex decision of patient choice in travel distance to
access care, which is an important indicator for policymaking in allocating
resources. Method: We used the 4-year nationwide insurance data of Taiwan and
accumulated the possible features discussed in earlier literature. This study
proposes the use of a convolutional neural network (CNN)-based framework to
make predictions. The model performance was tested against other machine
learning methods. The proposed framework was further interpreted using
Integrated Gradients (IG) to analyze the feature weights. Results: We
successfully demonstrated the effectiveness of using a CNN-based framework to
predict the travel distance of patients, achieving an accuracy of 0.968, AUC of
0.969, sensitivity of 0.960, and specificity of 0.989. The CNN-based framework
outperformed all other methods. In this research, the IG weights are
potentially explainable; however, the relationship does not correspond to known
indicators in public health, similar to common consensus. Conclusions: Our
results demonstrate the feasibility of the deep learning-based travel distance
prediction model. It has the potential to guide policymaking in resource
allocation.

Portrait photo retouching is a photo retouching task that emphasizes
human-region priority and group-level consistency. The lookup table-based
method achieves promising retouching performance by learning image-adaptive
weights to combine 3-dimensional lookup tables (3D LUTs) and conducting
pixel-to-pixel color transformation. However, this paradigm ignores the local
context cues and applies the same transformation to portrait pixels and
background pixels when they exhibit the same raw RGB values. In contrast, an
expert usually conducts different operations to adjust the color temperatures
and tones of portrait regions and background regions. This inspires us to model
local context cues to improve the retouching quality explicitly. Firstly, we
consider an image patch and predict pixel-adaptive lookup table weights to
precisely retouch the center pixel. Secondly, as neighboring pixels exhibit
different affinities to the center pixel, we estimate a local attention mask to
modulate the influence of neighboring pixels. Thirdly, the quality of the local
attention mask can be further improved by applying supervision, which is based
on the affinity map calculated by the groundtruth portrait mask. As for
group-level consistency, we propose to directly constrain the variance of mean
color components in the Lab space. Extensive experiments on PPR10K dataset
verify the effectiveness of our method, e.g. on high-resolution photos, the
PSNR metric receives over 0.5 gains while the group-level consistency metric
obtains at least 2.1 decreases.

These two synthetic datasets comprise vital signs, laboratory test results,
administered fluid boluses and vasopressors for 3,910 patients with acute
hypotension and for 2,164 patients with sepsis in the Intensive Care Unit
(ICU). The patient cohorts were built using previously published inclusion and
exclusion criteria and the data were created using Generative Adversarial
Networks (GANs) and the MIMIC-III Clinical Database. The risk of identity
disclosure associated with the release of these data was estimated to be very
low (0.045%). The datasets were generated and published as part of the Health
Gym, a project aiming to publicly distribute synthetic longitudinal health data
for developing machine learning algorithms (with a particular focus on offline
reinforcement learning) and for educational purposes.

The use of machine learning (ML)-based language models (LMs) to monitor
content online is on the rise. For toxic text identification, task-specific
fine-tuning of these models are performed using datasets labeled by annotators
who provide ground-truth labels in an effort to distinguish between offensive
and normal content. These projects have led to the development, improvement,
and expansion of large datasets over time, and have contributed immensely to
research on natural language. Despite the achievements, existing evidence
suggests that ML models built on these datasets do not always result in
desirable outcomes. Therefore, using a design science research (DSR) approach,
this study examines selected toxic text datasets with the goal of shedding
light on some of the inherent issues and contributing to discussions on
navigating these challenges for existing and future projects. To achieve the
goal of the study, we re-annotate samples from three toxic text datasets and
find that a multi-label approach to annotating toxic text samples can help to
improve dataset quality. While this approach may not improve the traditional
metric of inter-annotator agreement, it may better capture dependence on
context and diversity in annotators. We discuss the implications of these
results for both theory and practice.

Deep learning based generative models such as deepfake have been able to
generate amazing images and videos. However, these models may need significant
transformation when applied to generate crystal materials structures in which
the building blocks, the physical atoms are very different from the pixels.
Naively transferred generative models tend to generate a large portion of
physically infeasible crystal structures that are not stable or synthesizable.
Herein we show that by exploiting and adding physically oriented data
augmentation, loss function terms, and post processing, our deep adversarial
network (GAN) based generative models can now generate crystal structures with
higher physical feasibility and expand our previous models which can only
create cubic structures.

From an engineering perspective, a design should not only perform well in an
ideal condition, but should also resist noises. Such a design methodology,
namely robust design, has been widely implemented in the industry for product
quality control. However, classic robust design requires a lot of evaluations
for a single design target, while the results of these evaluations could not be
reused for a new target. To achieve a data-efficient robust design, we propose
Robust Inverse Design under Noise (RID-Noise), which can utilize existing noisy
data to train a conditional invertible neural network (cINN). Specifically, we
estimate the robustness of a design parameter by its predictability, measured
by the prediction error of a forward neural network. We also define a
sample-wise weight, which can be used in the maximum weighted likelihood
estimation of an inverse model based on a cINN. With the visual results from
experiments, we clearly justify how RID-Noise works by learning the
distribution and robustness from data. Further experiments on several
real-world benchmark tasks with noises confirm that our method is more
effective than other state-of-the-art inverse design methods. Code and
supplementary is publicly available at
https://github.com/ThyrixYang/rid-noise-aaai22

A genetic algorithm is suitable for exploring large search spaces as it finds
an approximate solution. Because of this advantage, genetic algorithm is
effective in exploring vast and unknown space such as molecular search space.
Though the algorithm is suitable for searching vast chemical space, it is
difficult to optimize pharmacological properties while maintaining molecular
substructure. To solve this issue, we introduce a genetic algorithm featuring a
constrained molecular inverse design. The proposed algorithm successfully
produces valid molecules for crossover and mutation. Furthermore, it optimizes
specific properties while adhering to structural constraints using a two-phase
optimization. Experiments prove that our algorithm effectively finds molecules
that satisfy specific properties while maintaining structural constraints.

In order to support a variety of missions and deal with different flight
environments, drone control programs typically provide configurable control
parameters. However, such a flexibility introduces vulnerabilities. One such
vulnerability, referred to as range specification bugs, has been recently
identified. The vulnerability originates from the fact that even though each
individual parameter receives a value in the recommended value range, certain
combinations of parameter values may affect the drone physical stability. In
this paper we develop a novel learning-guided search system to find such
combinations, that we refer to as incorrect configurations. Our system applies
metaheuristic search algorithms mutating configurations to detect the
configuration parameters that have values driving the drone to unstable
physical states. To guide the mutations, our system leverages a machine
learning predictor as the fitness evaluator. Finally, by utilizing
multi-objective optimization, our system returns the feasible ranges based on
the mutation search results. Because in our system the mutations are guided by
a predictor, evaluating the parameter configurations does not require
realistic/simulation executions. Therefore, our system supports a comprehensive
and yet efficient detection of incorrect configurations. We have carried out an
experimental evaluation of our system. The evaluation results show that the
system successfully reports potentially incorrect configurations, of which over
85% lead to actual unstable physical states.

In this paper a novel model-free algorithm is proposed. This algorithm can
learn the nearly optimal control law of constrained-input systems from online
data without requiring any a priori knowledge of system dynamics. Based on the
concept of generalized policy iteration method, there are two neural networks
(NNs), namely actor and critic NN to approximate the optimal value function and
optimal policy. The stability of closed-loop systems and the convergence of
weights are also guaranteed by Lyapunov analysis.

Although deep models achieve high predictive performance, it is difficult for
humans to understand the predictions they made. Explainability is important for
real-world applications to justify their reliability. Many example-based
explanation methods have been proposed, such as representer point selection,
where an explanation model defined by a set of training examples is used for
explaining a prediction model. For improving the interpretability, reducing the
number of examples in the explanation model is important. However, the
explanations with fewer examples can be unfaithful since it is difficult to
approximate prediction models well by such example-based explanation models.
The unfaithful explanations mean that the predictions by the explainable model
are different from those by the prediction model. We propose a method for
training deep models such that their predictions are faithfully explained by
explanation models with a small number of examples. We train the prediction and
explanation models simultaneously with a sparse regularizer for reducing the
number of examples. The proposed method can be incorporated into any neural
network-based prediction models. Experiments using several datasets demonstrate
that the proposed method improves faithfulness while keeping the predictive

We propose a novel, structured pruning algorithm for neural networks -- the
iterative, Sparse Structured Pruning algorithm, dubbed as i-SpaSP. Inspired by
ideas from sparse signal recovery, i-SpaSP operates by iteratively identifying
a larger set of important parameter groups (e.g., filters or neurons) within a
network that contribute most to the residual between pruned and dense network
output, then thresholding these groups based on a smaller, pre-defined pruning
ratio. For both two-layer and multi-layer network architectures with ReLU
activations, we show the error induced by pruning with i-SpaSP decays
polynomially, where the degree of this polynomial becomes arbitrarily large
based on the sparsity of the dense network's hidden representations. In our
experiments, i-SpaSP is evaluated across a variety of datasets (i.e., MNIST,
ImageNet, and XNLI) and architectures (i.e., feed forward networks, ResNet34,
MobileNetV2, and BERT), where it is shown to discover high-performing
sub-networks and improve upon the pruning efficiency of provable baseline
methodologies by several orders of magnitude. Put simply, i-SpaSP is easy to
implement with automatic differentiation, achieves strong empirical results,
comes with theoretical convergence guarantees, and is efficient, thus
distinguishing itself as one of the few computationally efficient, practical,
and provable pruning algorithms.

With the rapidly growing model complexity and data volume, training deep
generative models (DGMs) for better performance has becoming an increasingly
more important challenge. Previous research on this problem has mainly focused
on improving DGMs by either introducing new objective functions or designing
more expressive model architectures. However, such approaches often introduce
significantly more computational and/or designing overhead. To resolve such
issues, we introduce in this paper a generic framework called {\em
generative-model inference} that is capable of enhancing pre-trained GANs
effectively and seamlessly in a variety of application scenarios. Our basic
idea is to efficiently infer the optimal latent distribution for the given
requirements using Wasserstein gradient flow techniques, instead of re-training
or fine-tuning pre-trained model parameters. Extensive experimental results on
applications like image generation, image translation, text-to-image
generation, image inpainting, and text-guided image editing suggest the
effectiveness and superiority of our proposed framework.

With the rise of the gig economy, online language tutoring platforms are
becoming increasingly popular. They provide temporary and flexible jobs for
native speakers as tutors and allow language learners to have one-on-one
speaking practices on demand. However, the lack of stable relationships hinders
tutors and learners from building long-term trust. "Distributed tutorship" --
temporally discontinuous learning experience with different tutors -- has been
underexplored yet has many implications for modern learning platforms. In this
paper, we analyzed tutorship sequences of 15,959 learners and found that around
40% of learners change to new tutors every session; 44% learners change to new
tutors while reverting to previous tutors sometimes; only 16% learners change
to new tutors and then fix on one tutor. We also found suggestive evidence that
higher distributedness -- higher diversity and lower continuity in tutorship --
is correlated to slower improvements in speaking performance scores with a
similar number of sessions. We further surveyed 519 and interviewed 40 learners
and found that more learners preferred fixed tutorship while some do not have
it due to various reasons. Finally, we conducted semi-structured interviews
with three tutors and one product manager to discuss the implications for
improving the continuity in learning under distributed tutorship.

Graph Neural Networks (GNNs) exploit signals from node features and the input
graph topology to improve node classification task performance. However, these
models tend to perform poorly on heterophilic graphs, where connected nodes
have different labels. Recently proposed GNNs work across graphs having varying
levels of homophily. Among these, models relying on polynomial graph filters
have shown promise. We observe that solutions to these polynomial graph filter
models are also solutions to an overdetermined system of equations. It suggests
that in some instances, the model needs to learn a reasonably high order
polynomial. On investigation, we find the proposed models ineffective at
learning such polynomials due to their designs. To mitigate this issue, we
perform an eigendecomposition of the graph and propose to learn multiple
adaptive polynomial filters acting on different subsets of the spectrum. We
theoretically and empirically show that our proposed model learns a better
filter, thereby improving classification accuracy. We study various aspects of
our proposed model including, dependency on the number of eigencomponents
utilized, latent polynomial filters learned, and performance of the individual
polynomials on the node classification task. We further show that our model is
scalable by evaluating over large graphs. Our model achieves performance gains
of up to 5% over the state-of-the-art models and outperforms existing
polynomial filter-based approaches in general.

Interactions involving multiple objects simultaneously are ubiquitous across
many domains. The systems these interactions inhabit can be modelled using
hypergraphs, a generalization of traditional graphs in which each edge can
connect any number of nodes. Analyzing the global and static properties of
these hypergraphs has led to a plethora of novel findings regarding how these
modelled systems are structured. However, less is known about the localized
structure of these systems and how they evolve over time. In this paper, we
propose the study of hypergraph ego-networks, a structure that can be used to
model higher-order interactions involving a single node. We also propose the
temporal reconstruction of hypergraph ego-networks as a benchmark problem for
models that aim to predict the local temporal structure of hypergraphs. By
combining a deep learning binary classifier with a hill-climbing algorithm, we
will present a model for reconstructing hypergraph ego-networks by
incorporating structural patterns found across multiple domains.

Learning and generalizing to novel concepts with few samples (Few-Shot
Learning) is still an essential challenge to real-world applications. A
principle way of achieving few-shot learning is to realize a model that can
rapidly adapt to the context of a given task. Dynamic networks have been shown
capable of learning content-adaptive parameters efficiently, making them
suitable for few-shot learning. In this paper, we propose to learn the dynamic
kernels of a convolution network as a function of the task at hand, enabling
faster generalization. To this end, we obtain our dynamic kernels based on the
entire task and each sample and develop a mechanism further conditioning on
each individual channel and position independently. This results in dynamic
kernels that simultaneously attend to the global information whilst also
considering minuscule details available. We empirically show that our model
improves performance on few-shot classification and detection tasks, achieving
a tangible improvement over several baseline models. This includes
state-of-the-art results on 4 few-shot classification benchmarks:
mini-ImageNet, tiered-ImageNet, CUB and FC100 and competitive results on a
few-shot detection dataset: MS COCO-PASCAL-VOC.

The combination of neural networks and quantum Monte Carlo methods has arisen
as a path forward for highly accurate electronic structure calculations.
Previous proposals have combined equivariant neural network layers with an
antisymmetric layer to satisfy the antisymmetry requirements of the electronic
wavefunction. However, to date it is unclear if one can represent antisymmetric
functions of physical interest, and it is difficult to measure the
expressiveness of the antisymmetric layer. This work attempts to address this
problem by introducing explicitly antisymmetrized universal neural network
layers as a diagnostic tool. We first introduce a generic antisymmetric (GA)
layer, which we use to replace the entire antisymmetric layer of the highly
accurate ansatz known as the FermiNet. We demonstrate that the resulting
FermiNet-GA architecture can yield effectively the exact ground state energy
for small systems. We then consider a factorized antisymmetric (FA) layer which
more directly generalizes the FermiNet by replacing products of determinants
with products of antisymmetrized neural networks. Interestingly, the resulting
FermiNet-FA architecture does not outperform the FermiNet. This suggests that
the sum of products of antisymmetries is a key limiting aspect of the FermiNet
architecture. To explore this further, we investigate a slight modification of
the FermiNet called the full determinant mode, which replaces each product of
determinants with a single combined determinant. The full single-determinant
FermiNet closes a large part of the gap between the standard single-determinant
FermiNet and FermiNet-GA. Surprisingly, on the nitrogen molecule at a
dissociating bond length of 4.0 Bohr, the full single-determinant FermiNet can
significantly outperform the standard 64-determinant FermiNet, yielding an
energy within 0.4 kcal/mol of the best available computational benchmark.

Feature selection has been an essential step in developing industry-scale
deep Click-Through Rate (CTR) prediction systems. The goal of neural feature
selection (NFS) is to choose a relatively small subset of features with the
best explanatory power as a means to remove redundant features and reduce
computational cost. Inspired by gradient-based neural architecture search (NAS)
and network pruning methods, people have tackled the NFS problem with Gating
approach that inserts a set of differentiable binary gates to drop less
informative features. The binary gates are optimized along with the network
parameters in an efficient end-to-end manner. In this paper, we analyze the
gradient-based solution from an exploration-exploitation perspective and use
empirical results to show that Gating approach might suffer from insufficient
exploration. To improve the exploration capacity of gradient-based solutions,
we propose a simple but effective ensemble learning approach, named Ensemble
Gating. We choose two public datasets, namely Avazu and Criteo, to evaluate
this approach. Our experiments show that, without adding any computational
overhead or introducing any hyper-parameter (except the size of the ensemble),
our method is able to consistently improve Gating approach and find a better
subset of features on the two datasets with three different underlying deep CTR

Real-world tasks of interest are generally poorly defined by human-readable
descriptions and have no pre-defined reward signals unless it is defined by a
human designer. Conversely, data-driven algorithms are often designed to solve
a specific, narrowly defined, task with performance metrics that drives the
agent's learning. In this work, we present the solution that won first place
and was awarded the most human-like agent in the 2021 NeurIPS Competition
MineRL BASALT Challenge: Learning from Human Feedback in Minecraft, which
challenged participants to use human data to solve four tasks defined only by a
natural language description and no reward function. Our approach uses the
available human demonstration data to train an imitation learning policy for
navigation and additional human feedback to train an image classifier. These
modules, combined with an estimated odometry map, become a powerful
state-machine designed to utilize human knowledge in a natural hierarchical
paradigm. We compare this hybrid intelligence approach to both end-to-end
machine learning and pure engineered solutions, which are then judged by human
evaluators. Codebase is available at
https://github.com/viniciusguigo/kairos_minerl_basalt.

There has been a major advance in the field of Data Science in the last few
decades, and these have been utilized for different engineering disciplines and
applications. Artificial Intelligence (AI), Machine Learning (ML) and Deep
Learning (DL) algorithms have been utilized for civil Structural Health
Monitoring (SHM) especially for damage detection applications using sensor
data. Although ML and DL methods show superior learning skills for complex data
structures, they require plenty of data for training. However, in SHM, data
collection from civil structures can be expensive and time taking; particularly
getting useful data (damage associated data) can be challenging. The objective
of this study is to address the data scarcity problem for damage detection
applications. This paper employs 1-D Wasserstein Deep Convolutional Generative
Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic
labelled acceleration data generation. Then, the generated data is augmented
with varying ratios for the training dataset of a 1-D Deep Convolutional Neural
Network (1-D DCNN) for damage detection application. The damage detection
results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data
scarcity in vibration-based damage detection applications of civil structures.
  Keywords: Structural Health Monitoring (SHM), Structural Damage Detection,
1-D Deep Convolutional Neural Networks (1-D DCNN), 1-D Generative Adversarial
Networks (1-D GAN), Wasserstein Generative Adversarial Networks with Gradient
Penalty (WGAN-GP)

Adversarial bit-flip attack (BFA) on Neural Network weights can result in
catastrophic accuracy degradation by flipping a very small number of bits. A
major drawback of prior bit flip attack techniques is their reliance on test
data. This is frequently not possible for applications that contain sensitive
or proprietary data. In this paper, we propose Blind Data Adversarial Bit-flip
Attack (BDFA), a novel technique to enable BFA without any access to the
training or testing data. This is achieved by optimizing for a synthetic
dataset, which is engineered to match the statistics of batch normalization
across different layers of the network and the targeted label. Experimental
results show that BDFA could decrease the accuracy of ResNet50 significantly
from 75.96\% to 13.94\% with only 4 bits flips.

Obtaining a well-trained model involves expensive data collection and
training procedures, therefore the model is a valuable intellectual property.
Recent studies revealed that adversaries can `steal' deployed models even when
they have no training samples and can not get access to the model parameters or
structures. Currently, there were some defense methods to alleviate this
threat, mostly by increasing the cost of model stealing. In this paper, we
explore the defense from another angle by verifying whether a suspicious model
contains the knowledge of defender-specified \emph{external features}.
Specifically, we embed the external features by tempering a few training
samples with style transfer. We then train a meta-classifier to determine
whether a model is stolen from the victim. This approach is inspired by the
understanding that the stolen models should contain the knowledge of features
learned by the victim model. We examine our method on both CIFAR-10 and
ImageNet datasets. Experimental results demonstrate that our method is
effective in detecting different types of model stealing simultaneously, even
if the stolen model is obtained via a multi-stage stealing process. The codes
for reproducing main results are available at Github
(https://github.com/zlh-thu/StealingVerification).

Current state-of-the-art cross-lingual summarization models employ multi-task
learning paradigm, which works on a shared vocabulary module and relies on the
self-attention mechanism to attend among tokens in two languages. However,
correlation learned by self-attention is often loose and implicit, inefficient
in capturing crucial cross-lingual representations between languages. The
matter worsens when performing on languages with separate morphological or
structural features, making the cross-lingual alignment more challenging,
resulting in the performance drop. To overcome this problem, we propose a novel
Knowledge-Distillation-based framework for Cross-Lingual Summarization, seeking
to explicitly construct cross-lingual correlation by distilling the knowledge
of the monolingual summarization teacher into the cross-lingual summarization
student. Since the representations of the teacher and the student lie on two
different vector spaces, we further propose a Knowledge Distillation loss using
Sinkhorn Divergence, an Optimal-Transport distance, to estimate the discrepancy
between those teacher and student representations. Due to the intuitively
geometric nature of Sinkhorn Divergence, the student model can productively
learn to align its produced cross-lingual hidden states with monolingual hidden
states, hence leading to a strong correlation between distant languages.
Experiments on cross-lingual summarization datasets in pairs of distant
languages demonstrate that our method outperforms state-of-the-art models under
both high and low-resourced settings.

Structural Health Monitoring (SHM) has been continuously benefiting from the
advancements in the field of data science. Various types of Artificial
Intelligence (AI) methods have been utilized for the assessment and evaluation
of civil structures. In AI, Machine Learning (ML) and Deep Learning (DL)
algorithms require plenty of datasets to train; particularly, the more data DL
models are trained with, the better output it yields. Yet, in SHM applications,
collecting data from civil structures through sensors is expensive and
obtaining useful data (damage associated data) is challenging. In this paper,
1-D Wasserstein loss Deep Convolutional Generative Adversarial Networks using
Gradient Penalty (1-D WDCGAN-GP) is utilized to generate damage associated
vibration datasets that are similar to the input. For the purpose of
vibration-based damage diagnostics, a 1-D Deep Convolutional Neural Network
(1-D DCNN) is built, trained, and tested on both real and generated datasets.
The classification results from the 1-D DCNN on both datasets resulted to be
very similar to each other. The presented work in this paper shows that for the
cases of insufficient data in DL or ML-based damage diagnostics, 1-D WDCGAN-GP
can successfully generate data for the model to be trained on. Keywords: 1-D
Generative Adversarial Networks (GAN), Deep Convolutional Generative
Adversarial Networks (DCGAN), Wasserstein Generative Adversarial Networks with
Gradient Penalty (WGAN-GP), 1-D Convolutional Neural Networks (CNN), Structural
Health Monitoring (SHM), Structural Damage Diagnostics, Structural Damage
Detection

As Structural Health Monitoring (SHM) being implemented more over the years,
the use of operational modal analysis of civil structures has become more
significant for the assessment and evaluation of engineering structures.
Machine Learning (ML) and Deep Learning (DL) algorithms have been in use for
structural damage diagnostics of civil structures in the last couple of
decades. While collecting vibration data from civil structures is a challenging
and expensive task for both undamaged and damaged cases, in this paper, the
authors are introducing Generative Adversarial Networks (GAN) that is built on
the Deep Convolutional Neural Network (DCNN) and using Wasserstein Distance for
generating artificial labelled data to be used for structural damage diagnostic
purposes. The authors named the developed model 1D W-DCGAN and successfully
generated vibration data which is very similar to the input. The methodology
presented in this paper will pave the way for vibration data generation for
numerous future applications in the SHM domain.

We use a data-driven approach to model a three-dimensional turbulent flow
using cutting-edge Deep Learning techniques. The deep learning framework
incorporates physical constraints on the flow, such as preserving
incompressibility and global statistical invariants of velocity gradient
tensor. The accuracy of the model is assessed using statistical and
physics-based metrics. The data set comes from Direct Numerical Simulation of
an incompressible, statistically stationary, isotropic turbulent flow in a
cubic box. Since the size of the dataset is memory intensive, we first generate
a low-dimensional representation of the velocity data, and then pass it to a
sequence prediction network that learns the spatial and temporal correlations
of the underlying data. The dimensionality reduction is performed via
extraction using Vector-Quantized Autoencoder (VQ-AE), which learns the
discrete latent variables. For the sequence forecasting, the idea of
Transformer architecture from natural language processing is used, and its
performance compared against more standard Recurrent Networks (such as
Convolutional LSTM). These architectures are designed and trained to perform a
sequence to sequence multi-class classification task in which they take an
input sequence with a fixed length (k) and predict a sequence with a fixed
length (p), representing the future time instants of the flow. Our results for
the short-term predictions show that the accuracy of results for both models
deteriorates across predicted snapshots due to autoregressive nature of the
predictions. Based on our diagnostics tests, the trained Conv-Transformer model
outperforms the Conv-LSTM one and can accurately, both quantitatively and
qualitatively, retain the large scales and capture well the inertial scales of
flow but fails at recovering the small and intermittent fluid motions.

Complex-valued neural networks (CVNNs) have been widely applied to various
fields, especially signal processing and image recognition. However, few works
focus on the generalization of CVNNs, albeit it is vital to ensure the
performance of CVNNs on unseen data. This paper is the first work that proves a
generalization bound for the complex-valued neural network. The bound scales
with the spectral complexity, the dominant factor of which is the spectral norm
product of weight matrices. Further, our work provides a generalization bound
for CVNNs when training data is sequential, which is also affected by the
spectral complexity. Theoretically, these bounds are derived via Maurey
Sparsification Lemma and Dudley Entropy Integral. Empirically, we conduct
experiments by training complex-valued convolutional neural networks on
different datasets: MNIST, FashionMNIST, CIFAR-10, CIFAR-100, Tiny ImageNet,
and IMDB. Spearman's rank-order correlation coefficients and the corresponding
p values on these datasets give strong proof that the spectral complexity of
the network, measured by the weight matrices spectral norm product, has a
statistically significant correlation with the generalization ability.

Next Generation (NextG) networks are expected to support demanding tactile
internet applications such as augmented reality and connected autonomous
vehicles. Whereas recent innovations bring the promise of larger link capacity,
their sensitivity to the environment and erratic performance defy traditional
model-based control rationales. Zero-touch data-driven approaches can improve
the ability of the network to adapt to the current operating conditions. Tools
such as reinforcement learning (RL) algorithms can build optimal control policy
solely based on a history of observations. Specifically, deep RL (DRL), which
uses a deep neural network (DNN) as a predictor, has been shown to achieve good
performance even in complex environments and with high dimensional inputs.
However, the training of DRL models require a large amount of data, which may
limit its adaptability to ever-evolving statistics of the underlying
environment. Moreover, wireless networks are inherently distributed systems,
where centralized DRL approaches would require excessive data exchange, while
fully distributed approaches may result in slower convergence rates and
performance degradation. In this paper, to address these challenges, we propose
a federated learning (FL) approach to DRL, which we refer to federated DRL
(F-DRL), where base stations (BS) collaboratively train the embedded DNN by
only sharing models' weights rather than training data. We evaluate two
distinct versions of F-DRL, value and policy based, and show the superior
performance they achieve compared to distributed and centralized DRL.

Graph neural architecture search has received a lot of attention as Graph
Neural Networks (GNNs) has been successfully applied on the non-Euclidean data
recently. However, exploring all possible GNNs architectures in the huge search
space is too time-consuming or impossible for big graph data. In this paper, we
propose a parallel graph architecture search (GraphPAS) framework for graph
neural networks. In GraphPAS, we explore the search space in parallel by
designing a sharing-based evolution learning, which can improve the search
efficiency without losing the accuracy. Additionally, architecture information
entropy is adopted dynamically for mutation selection probability, which can
reduce space exploration. The experimental result shows that GraphPAS
outperforms state-of-art models with efficiency and accuracy simultaneously.

Since its invention in 2014, the Adam optimizer has received tremendous
attention. On one hand, it has been widely used in deep learning and many
variants have been proposed, while on the other hand their theoretical
convergence property remains to be a mystery. It is far from satisfactory in
the sense that some studies require strong assumptions about the updates, which
are not necessarily applicable in practice, while other studies still follow
the original problematic convergence analysis of Adam, which was shown to be
not sufficient to ensure convergence. Although rigorous convergence analysis
exists for Adam, they impose specific requirements on the update of the
adaptive step size, which are not generic enough to cover many other variants
of Adam. To address theses issues, in this extended abstract, we present a
simple and generic proof of convergence for a family of Adam-style methods
(including Adam, AMSGrad, Adabound, etc.). Our analysis only requires an
increasing or large "momentum" parameter for the first-order moment, which is
indeed the case used in practice, and a boundness condition on the adaptive
factor of the step size, which applies to all variants of Adam under mild
conditions of stochastic gradients. We also establish a variance diminishing
result for the used stochastic gradient estimators. Indeed, our analysis of
Adam is so simple and generic that it can be leveraged to establish the
convergence for solving a broader family of non-convex optimization problems,
including min-max, compositional, and bilevel optimization problems. For the
full (earlier) version of this extended abstract, please refer to
arXiv:2104.14840.

Recently, a massive number of deep learning based approaches have been
successfully applied to various remote sensing image (RSI) recognition tasks.
However, most existing advances of deep learning methods in the RSI field
heavily rely on the features extracted by the manually designed backbone
network, which severely hinders the potential of deep learning models due the
complexity of RSI and the limitation of prior knowledge. In this paper, we
research a new design paradigm for the backbone architecture in RSI recognition
tasks, including scene classification, land-cover classification and object
detection. A novel one-shot architecture search framework based on
weight-sharing strategy and evolutionary algorithm is proposed, called RSBNet,
which consists of three stages: Firstly, a supernet constructed in a layer-wise
search space is pretrained on a self-assembled large-scale RSI dataset based on
an ensemble single-path training strategy. Next, the pre-trained supernet is
equipped with different recognition heads through the switchable recognition
module and respectively fine-tuned on the target dataset to obtain
task-specific supernet. Finally, we search the optimal backbone architecture
for different recognition tasks based on the evolutionary algorithm without any
network training. Extensive experiments have been conducted on five benchmark
datasets for different recognition tasks, the results show the effectiveness of
the proposed search paradigm and demonstrate that the searched backbone is able
to flexibly adapt different RSI recognition tasks and achieve impressive

Histopathological cancer diagnostics has become more complex, and the
increasing number of biopsies is a challenge for most pathology laboratories.
Thus, development of automatic methods for evaluation of histopathological
cancer sections would be of value. In this study, we used 624 whole slide
images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded
convolutional neural network design, called H2G-Net, for semantic segmentation
of gigapixel histopathological images. The design involves a detection stage
using a patch-wise method, and a refinement stage using a convolutional
autoencoder. To validate the design, we conducted an ablation study to assess
the impact of selected components in the pipeline on tumour segmentation.
Guiding segmentation, using hierarchical sampling and deep heatmap refinement,
proved to be beneficial when segmenting the histopathological images. We found
a significant improvement when using a refinement network for postprocessing
the generated tumour segmentation heatmaps. The overall best design achieved a
Dice score of 0.933 on an independent test set of 90 WSIs. The design
outperformed single-resolution approaches, such as cluster-guided, patch-wise
high-resolution classification using MobileNetV2 (0.872) and a low-resolution
U-Net (0.874). In addition, segmentation on a representative x400 WSI took ~58
seconds, using only the CPU. The findings demonstrate the potential of
utilizing a refinement network to improve patch-wise predictions. The solution
is efficient and does not require overlapping patch inference or ensembling.
Furthermore, we showed that deep neural networks can be trained using a random
sampling scheme that balances on multiple different labels simultaneously,
without the need of storing patches on disk. Future work should involve more
efficient patch generation and sampling, as well as improved clustering.

Over the recent years, various deep learning-based methods were proposed for
extracting a fixed-dimensional embedding vector from speech signals. Although
the deep learning-based embedding extraction methods have shown good
performance in numerous tasks including speaker verification, language
identification and anti-spoofing, their performance is limited when it comes to
mismatched conditions due to the variability within them unrelated to the main
task. In order to alleviate this problem, we propose a novel training strategy
that regularizes the embedding network to have minimum information about the
nuisance attributes. To achieve this, our proposed method directly incorporates
the information bottleneck scheme into the training process, where the mutual
information is estimated using the main task classifier and an auxiliary
normalizing flow network. The proposed method was evaluated on different speech
processing tasks and showed improvement over the standard training strategy in
all experimentation.

We consider the problem of predicting cellular network performance (signal
maps) from measurements collected by several mobile devices. We formulate the
problem within the online federated learning framework: (i) federated learning
(FL) enables users to collaboratively train a model, while keeping their
training data on their devices; (ii) measurements are collected as users move
around over time and are used for local training in an online fashion. We
consider an honest-but-curious server, who observes the updates from target
users participating in FL and infers their location using a deep leakage from
gradients (DLG) type of attack, originally developed to reconstruct training
data of DNN image classifiers. We make the key observation that a DLG attack,
applied to our setting, infers the average location of a batch of local data,
and can thus be used to reconstruct the target users' trajectory at a coarse
granularity. We show that a moderate level of privacy protection is already
offered by the averaging of gradients, which is inherent to Federated
Averaging. Furthermore, we propose an algorithm that devices can apply locally
to curate the batches used for local updates, so as to effectively protect
their location privacy without hurting utility. Finally, we show that the
effect of multiple users participating in FL depends on the similarity of their
trajectories. To the best of our knowledge, this is the first study of DLG
attacks in the setting of FL from crowdsourced spatio-temporal data.

Box-supervised instance segmentation has recently attracted lots of research
efforts while little attention is received in aerial image domain. In contrast
to the general object collections, aerial objects have large intra-class
variances and inter-class similarity with complex background. Moreover, there
are many tiny objects in the high-resolution satellite images. This makes the
recent pairwise affinity modeling method inevitably to involve the noisy
supervision with the inferior results. To tackle these problems, we propose a
novel aerial instance segmentation approach, which drives the network to learn
a series of level set functions for the aerial objects with only box
annotations in an end-to-end fashion. Instead of learning the pairwise
affinity, the level set method with the carefully designed energy functions
treats the object segmentation as curve evolution, which is able to accurately
recover the object's boundaries and prevent the interference from the
indistinguishable background and similar objects. The experimental results
demonstrate that the proposed approach outperforms the state-of-the-art
box-supervised instance segmentation methods. The source code is available at

Interpreting 21cm measurements from current and upcoming experiments like
HERA and the SKA will provide new scientific insights and exciting implications
for astrophysics and cosmology regarding the Epoch of Reionization (EoR).
Several recent works have proposed using machine learning methods, such as
convolutions neural networks (CNNs), to analyze images of reionization
generated by these experiments since they could take full advantage of
information contained in the image. Generally, these studies have used only a
single semi-numeric method to generate the input 21cm data. In this work, we
investigate the extent to which training CNNs for reionization applications
depends on the underlying semi-numeric models. Working in the context of
predicting CMB optical depth from 21cm images, we compare networks trained on
similar datasets from 21cmfast and zreion, two widely used semi-numeric
reionization methods. We show that neural networks trained on input data from
only one model produce poor predictions on data from the other model.
Satisfactory results are only achieved when both models are included in the
training data. This finding has important implications for future analyses on
observation data, and encourages the use of multiple models to produce images
that capture the full complexity of the EoR.

Computing Nash equilibrium in multi-agent games is a longstanding challenge
at the interface of game theory and computer science. It is well known that a
general normal form game in N players and k strategies requires exponential
space simply to write down. This Curse of Multi-Agents prompts the study of
succinct games which can be written down efficiently. A canonical example of a
succinct game is the graphical game which models players as nodes in a graph
interacting with only their neighbors in direct analogy with markov random
fields. Graphical games have found applications in wireless, financial, and
social networks. However, computing the nash equilbrium of graphical games has
proven challenging. Even for polymatrix games, a model where payoffs to an
agent can be written as the sum of payoffs of interactions with the agent's
neighbors, it has been shown that computing an epsilon approximate nash
equilibrium is PPAD hard for epsilon smaller than a constant. The focus of this
work is to circumvent this computational hardness by considering average case
graph models i.e random graphs. We provide a quasipolynomial time approximation
scheme (QPTAS) for computing an epsilon approximate nash equilibrium of
polymatrix games on random graphs with edge density greater than poly(k,
1/epsilon, ln(N))$ with high probability. Furthermore, with the same runtime we
can compute an epsilon-approximate Nash equilibrium that epsilon-approximates
the maximum social welfare of any nash equilibrium of the game. Our primary
technical innovation is an "accelerated rounding" of a novel hierarchical
convex program for the nash equilibrium problem. Our accelerated rounding also
yields faster algorithms for Max-2CSP on the same family of random graphs,
which may be of independent interest.

Hyperscanning with functional near-infrared spectroscopy (fNIRS) is an
emerging neuroimaging application that measures the nuanced neural signatures
underlying social interactions. Researchers have assessed the effect of sex and
task type (e.g., cooperation versus competition) on inter-brain coherence
during human-to-human interactions. However, no work has yet used deep
learning-based approaches to extract insights into sex and task-based
differences in an fNIRS hyperscanning context. This work proposes a
convolutional neural network-based approach to dyadic sex composition and task
classification for an extensive hyperscanning dataset with $N = 222$
participants. Inter-brain signal similarity computed using dynamic time warping
is used as the input data. The proposed approach achieves a maximum
classification accuracy of greater than $80$ percent, thereby providing a new
avenue for exploring and understanding complex brain behavior.

Binary density ratio estimation (DRE), the problem of estimating the ratio
$p_1/p_2$ given their empirical samples, provides the foundation for many
state-of-the-art machine learning algorithms such as contrastive representation
learning and covariate shift adaptation. In this work, we consider a
generalized setting where given samples from multiple distributions $p_1,
\ldots, p_k$ (for $k > 2$), we aim to efficiently estimate the density ratios
between all pairs of distributions. Such a generalization leads to important
new applications such as estimating statistical discrepancy among multiple
random variables like multi-distribution $f$-divergence, and bias correction
via multiple importance sampling. We then develop a general framework from the
perspective of Bregman divergence minimization, where each strictly convex
multivariate function induces a proper loss for multi-distribution DRE.
Moreover, we rederive the theoretical connection between multi-distribution
density ratio estimation and class probability estimation, justifying the use
of any strictly proper scoring rule composite with a link function for
multi-distribution DRE. We show that our framework leads to methods that
strictly generalize their counterparts in binary DRE, as well as new methods
that show comparable or superior performance on various downstream tasks.

Obtaining first-order regret bounds -- regret bounds scaling not as the
worst-case but with some measure of the performance of the optimal policy on a
given instance -- is a core question in sequential decision-making. While such
bounds exist in many settings, they have proven elusive in reinforcement
learning with large state spaces. In this work we address this gap, and show
that it is possible to obtain regret scaling as
$\widetilde{\mathcal{O}}(\sqrt{d^3 H^3 \cdot V_1^\star \cdot K} +
d^{3.5}H^3\log K )$ in reinforcement learning with large state spaces, namely
the linear MDP setting. Here $V_1^\star$ is the value of the optimal policy and
$K$ is the number of episodes. We demonstrate that existing techniques based on
least squares estimation are insufficient to obtain this result, and instead
develop a novel robust self-normalized concentration bound based on the robust
Catoni mean estimator, which may be of independent interest.

It is often of interest to estimate regression functions non-parametrically.
Penalized regression (PR) is one statistically-effective, well-studied solution
to this problem. Unfortunately, in many cases, finding exact solutions to PR
problems is computationally intractable. In this manuscript, we propose a
mesh-based approximate solution (MBS) for those scenarios. MBS transforms the
complicated functional minimization of NPR, to a finite parameter, discrete
convex minimization; and allows us to leverage the tools of modern convex
optimization. We show applications of MBS in a number of explicit examples
(including both uni- and multi-variate regression), and explore how the number
of parameters must increase with our sample-size in order for MBS to maintain
the rate-optimality of NPR. We also give an efficient algorithm to minimize the
MBS objective while effectively leveraging the sparsity inherent in MBS.

Modern spectroscopic surveys of galaxies such as MaNGA consist of millions of
diverse spectra covering different regions of thousands of galaxies. We propose
and implement a deep unsupervised machine learning method to summarize the
entire diversity of MaNGA spectra onto a 15x15 map (DESOM-1), where
neighbouring points on the map represent similar spectra. We demonstrate our
method as an alternative to conventional full spectral fitting for deriving
physical quantities, as well as their full probability distributions, much more
efficiently than traditional resource-intensive Bayesian methods. Since spectra
are grouped by similarity, the distribution of spectra onto the map for a
single galaxy, i.e., its "fingerprint", reveals the presence of distinct
stellar populations within the galaxy indicating smoother or episodic
star-formation histories. We further map the diversity of galaxy fingerprints
onto a second map (DESOM-2). Using galaxy images and independent measures of
galaxy morphology, we confirm that galaxies with similar fingerprints have
similar morphologies and inclination angles. Since morphological information
was not used in the mapping algorithm, relating galaxy morphology to the
star-formation histories encoded in the fingerprints is one example of how the
DESOM maps can be used to make scientific inferences.

We present an approach to solving hard geometric optimization problems in the
RANSAC framework. The hard minimal problems arise from relaxing the original
geometric optimization problem into a minimal problem with many spurious
solutions. Our approach avoids computing large numbers of spurious solutions.
We design a learning strategy for selecting a starting problem-solution pair
that can be numerically continued to the problem and the solution of interest.
We demonstrate our approach by developing a RANSAC solver for the problem of
computing the relative pose of three calibrated cameras, via a minimal
relaxation using four points in each view. On average, we can solve a single
problem in under 70 $\mu s.$ We also benchmark and study our engineering
choices on the very familiar problem of computing the relative pose of two
calibrated cameras, via the minimal case of five points in two views.

Event-based vision sensors encode local pixel-wise brightness changes in
streams of events rather than image frames and yield sparse, energy-efficient
encodings of scenes, in addition to low latency, high dynamic range, and lack
of motion blur. Recent progress in object recognition from event-based sensors
has come from conversions of deep neural networks, trained with
backpropagation. However, using these approaches for event streams requires a
transformation to a synchronous paradigm, which not only loses computational
efficiency, but also misses opportunities to extract spatio-temporal features.
In this article we propose a hybrid architecture for end-to-end training of
deep neural networks for event-based pattern recognition and object detection,
combining a spiking neural network (SNN) backbone for efficient event-based
feature extraction, and a subsequent analog neural network (ANN) head to solve
synchronous classification and detection tasks. This is achieved by combining
standard backpropagation with surrogate gradient training to propagate
gradients through the SNN. Hybrid SNN-ANNs can be trained without conversion,
and result in highly accurate networks that are substantially more
computationally efficient than their ANN counterparts. We demonstrate results
on event-based classification and object detection datasets, in which only the
architecture of the ANN heads need to be adapted to the tasks, and no
conversion of the event-based input is necessary. Since ANNs and SNNs require
different hardware paradigms to maximize their efficiency, we envision that SNN
backbone and ANN head can be executed on different processing units, and thus
analyze the necessary bandwidth to communicate between the two parts. Hybrid
networks are promising architectures to further advance machine learning
approaches for event-based vision, without having to compromise on efficiency.

Return caching is a recent strategy that enables efficient minibatch training
with multistep estimators (e.g. the {\lambda}-return) for deep reinforcement
learning. By precomputing return estimates in sequential batches and then
storing the results in an auxiliary data structure for later sampling, the
average computation spent per estimate can be greatly reduced. Still, the
efficiency of return caching could be improved, particularly with regard to its
large memory usage and repetitive data copies. We propose a new data structure,
the Virtual Replay Cache (VRC), to address these shortcomings. When learning to
play Atari 2600 games, the VRC nearly eliminates DQN({\lambda})'s cache memory
footprint and slightly reduces the total training time on our hardware.

Many learning tasks require observing a sequence of images and making a
decision. In a transportation problem of designing and planning for shipping
boxes between nodes, we show how to treat the network of nodes and the flows
between them as images. These images have useful structural information that
can be statistically summarized. Using image compression techniques, we reduce
an image down to a set of numbers that contain interpretable geographic
information that we call geographic signatures. Using geographic signatures, we
learn network structure that can be utilized to recommend future network
connectivity. We develop a Bayesian reinforcement algorithm that takes
advantage of statistically summarized network information as priors and
user-decisions to reinforce an agent's probabilistic decision.

We study the functional task of deep learning image classification models and
show that image classification requires extrapolation capabilities. This
suggests that new theories have to be developed for the understanding of deep
learning as the current theory assumes models are solely interpolating, leaving
many questions about them unanswered. We investigate the pixel space and also
the feature spaces extracted from images by trained models (in their hidden
layers, including the 64-dimensional feature space in the last hidden layer of
pre-trained residual neural networks), and also the feature space extracted by
wavelets/shearlets. In all these domains, testing samples considerably fall
outside the convex hull of training sets, and image classification requires
extrapolation. In contrast to the deep learning literature, in cognitive
science, psychology, and neuroscience, extrapolation and learning are often
studied in tandem. Moreover, many aspects of human visual cognition and
behavior are reported to involve extrapolation. We propose a novel
extrapolation framework for the mathematical study of deep learning models. In
our framework, we use the term extrapolation in this specific way of
extrapolating outside the convex hull of training set (in the pixel space or
feature space) but within the specific scope defined by the training data, the
same way extrapolation is defined in many studies in cognitive science. We
explain that our extrapolation framework can provide novel answers to open
research problems about deep learning including their over-parameterization,
their training regime, out-of-distribution detection, etc. We also see that the
extent of extrapolation is negligible in learning tasks where deep learning is
reported to have no advantage over simple models.

Deep clustering (DC) leverages the representation power of deep architectures
to learn embedding spaces that are optimal for cluster analysis. This approach
filters out low-level information irrelevant for clustering and has proven
remarkably successful for high dimensional data spaces. Some DC methods employ
Generative Adversarial Networks (GANs), motivated by the powerful latent
representations these models are able to learn implicitly. In this work, we
propose HC-MGAN, a new technique based on GANs with multiple generators
(MGANs), which have not been explored for clustering. Our method is inspired by
the observation that each generator of a MGAN tends to generate data that
correlates with a sub-region of the real data distribution. We use this
clustered generation to train a classifier for inferring from which generator a
given image came from, thus providing a semantically meaningful clustering for
the real distribution. Additionally, we design our method so that it is
performed in a top-down hierarchical clustering tree, thus proposing the first
hierarchical DC method, to the best of our knowledge. We conduct several
experiments to evaluate the proposed method against recent DC methods,
obtaining competitive results. Last, we perform an exploratory analysis of the
hierarchical clustering tree that highlights how accurately it organizes the
data in a hierarchy of semantically coherent patterns.

Today deep learning is widely used for building software. A software
engineering problem with deep learning is that finding an appropriate
convolutional neural network (CNN) model for the task can be a challenge for
developers. Recent work on AutoML, more precisely neural architecture search
(NAS), embodied by tools like Auto-Keras aims to solve this problem by
essentially viewing it as a search problem where the starting point is a
default CNN model, and mutation of this CNN model allows exploration of the
space of CNN models to find a CNN model that will work best for the problem.
These works have had significant success in producing high-accuracy CNN models.
There are two problems, however. First, NAS can be very costly, often taking
several hours to complete. Second, CNN models produced by NAS can be very
complex that makes it harder to understand them and costlier to train them. We
propose a novel approach for NAS, where instead of starting from a default CNN
model, the initial model is selected from a repository of models extracted from
GitHub. The intuition being that developers solving a similar problem may have
developed a better starting point compared to the default model. We also
analyze common layer patterns of CNN models in the wild to understand changes
that the developers make to improve their models. Our approach uses commonly
occurring changes as mutation operators in NAS. We have extended Auto-Keras to
implement our approach. Our evaluation using 8 top voted problems from Kaggle
for tasks including image classification and image regression shows that given
the same search time, without loss of accuracy, Manas produces models with
42.9% to 99.6% fewer number of parameters than Auto-Keras' models. Benchmarked
on GPU, Manas' models train 30.3% to 641.6% faster than Auto-Keras' models.

While modern policy optimization methods can do complex manipulation from
sensory data, they struggle on problems with extended time horizons and
multiple sub-goals. On the other hand, task and motion planning (TAMP) methods
scale to long horizons but they are computationally expensive and need to
precisely track world state. We propose a method that draws on the strength of
both methods: we train a policy to imitate a TAMP solver's output. This
produces a feed-forward policy that can accomplish multi-step tasks from
sensory data. First, we build an asynchronous distributed TAMP solver that can
produce supervision data fast enough for imitation learning. Then, we propose a
hierarchical policy architecture that lets us use partially trained control
policies to speed up the TAMP solver. In robotic manipulation tasks with 7-DoF
joint control, the partially trained policies reduce the time needed for
planning by a factor of up to 2.6. Among these tasks, we can learn a policy
that solves the RoboSuite 4-object pick-place task 88% of the time from object
pose observations and a policy that solves the RoboDesk 9-goal benchmark 79% of
the time from RGB images (averaged across the 9 disparate tasks).

Molecular Dynamics (MD) simulation is a powerful tool for understanding the
dynamics and structure of matter. Since the resolution of MD is atomic-scale,
achieving long time-scale simulations with femtosecond integration is very
expensive. In each MD step, numerous iterative computations are performed to
calculate energy based on different types of interaction and their
corresponding spatial gradients. These repetitive computations can be learned
and surrogated by a deep learning model like a Graph Neural Network (GNN). In
this work, we developed a GNN Accelerated Molecular Dynamics (GAMD) model that
directly predicts forces given the state of the system (atom positions, atom
types), bypassing the evaluation of potential energy. By training the GNN on a
variety of data sources (simulation data derived from classical MD and density
functional theory), we show that GAMD can predict the dynamics of two typical
molecular systems, Lennard-Jones system and Water system, in the NVT ensemble
with velocities regulated by a thermostat. We further show that GAMD's learning
and inference are agnostic to the scale, where it can scale to much larger
systems at test time. We also perform a comprehensive benchmark test comparing
our implementation of GAMD to production-level MD softwares, showing GAMD's
competitive performance on the large-scale simulation.

We introduce an unsupervised motion-compensated reconstruction scheme for
high-resolution free-breathing pulmonary MRI. We model the image frames in the
time series as the deformed version of the 3D template image volume. We assume
the deformation maps to be points on a smooth manifold in high-dimensional
space. Specifically, we model the deformation map at each time instant as the
output of a CNN-based generator that has the same weight for all time-frames,
driven by a low-dimensional latent vector. The time series of latent vectors
account for the dynamics in the dataset, including respiratory motion and bulk
motion. The template image volume, the parameters of the generator, and the
latent vectors are learned directly from the k-t space data in an unsupervised
fashion. Our experimental results show improved reconstructions compared to
state-of-the-art methods, especially in the context of bulk motion during the
scans.

Discrete undirected graphical models, also known as Markov Random Fields
(MRFs), can flexibly encode probabilistic interactions of multiple variables,
and have enjoyed successful applications to a wide range of problems. However,
a well-known yet little studied limitation of discrete MRFs is that they cannot
capture context-specific independence (CSI). Existing methods require carefully
developed theories and purpose-built inference methods, which limit their
applications to only small-scale problems. In this paper, we propose the Markov
Attention Model (MAM), a family of discrete MRFs that incorporates an attention
mechanism. The attention mechanism allows variables to dynamically attend to
some other variables while ignoring the rest, and enables capturing of CSIs in
MRFs. A MAM is formulated as an MRF, allowing it to benefit from the rich set
of existing MRF inference methods and scale to large models and datasets. To
demonstrate MAM's capabilities to capture CSIs at scale, we apply MAMs to
capture an important type of CSI that is present in a symbolic approach to
recurrent computations in perceptual grouping. Experiments on two recently
proposed synthetic perceptual grouping tasks and on realistic images
demonstrate the advantages of MAMs in sample-efficiency, interpretability and
generalizability when compared with strong recurrent neural network baselines,
and validate MAM's capabilities to efficiently capture CSIs at scale.

Deep learning in molecular and materials sciences is limited by the lack of
integration between applied science, artificial intelligence, and
high-performance computing. Bottlenecks with respect to the amount of training
data, the size and complexity of model architectures, and the scale of the
compute infrastructure are all key factors limiting the scaling of deep
learning for molecules and materials. Here, we present $\textit{LitMatter}$, a
lightweight framework for scaling molecular deep learning methods. We train
four graph neural network architectures on over 400 GPUs and investigate the
scaling behavior of these methods. Depending on the model architecture,
training time speedups up to $60\times$ are seen. Empirical neural scaling
relations quantify the model-dependent scaling and enable optimal compute
resource allocation and the identification of scalable molecular geometric deep
learning model implementations.

Timeseries partitioning is an essential step in most machine-learning driven,
sensor-based IoT applications. This paper introduces a sample-efficient,
robust, time-series segmentation model and algorithm. We show that by learning
a representation specifically with the segmentation objective based on maximum
mean discrepancy (MMD), our algorithm can robustly detect time-series events
across different applications. Our loss function allows us to infer whether
consecutive sequences of samples are drawn from the same distribution (null
hypothesis) and determines the change-point between pairs that reject the null
hypothesis (i.e., come from different distributions). We demonstrate its
applicability in a real-world IoT deployment for ambient-sensing based activity
recognition. Moreover, while many works on change-point detection exist in the
literature, our model is significantly simpler and can be fully trained in 9-93
seconds on average with little variation in hyperparameters for data across
different applications. We empirically evaluate Cadence on four popular change
point detection (CPD) datasets where Cadence matches or outperforms existing
CPD techniques.

Simulations of complex-valued Hopfield networks based on spin-torque
oscillators can recover phase-encoded images. Sequences of memristor-augmented
inverters provide tunable delay elements that implement complex weights by
phase shifting the oscillatory output of the oscillators. Pseudo-inverse
training suffices to store at least 12 images in a set of 192 oscillators,
representing 16$\times$12 pixel images. The energy required to recover an image
depends on the desired error level. For the oscillators and circuitry
considered here, 5 % root mean square deviations from the ideal image require
approximately 5 $\mu$s and consume roughly 130 nJ. Simulations show that the
network functions well when the resonant frequency of the oscillators can be
tuned to have a fractional spread less than $10^{-3}$, depending on the
strength of the feedback.

Backdoor (Trojan) attacks are emerging threats against deep neural networks
(DNN). A DNN being attacked will predict to an attacker-desired target class
whenever a test sample from any source class is embedded with a backdoor
pattern; while correctly classifying clean (attack-free) test samples. Existing
backdoor defenses have shown success in detecting whether a DNN is attacked and
in reverse-engineering the backdoor pattern in a "post-training" regime: the
defender has access to the DNN to be inspected and a small, clean dataset
collected independently, but has no access to the (possibly poisoned) training
set of the DNN. However, these defenses neither catch culprits in the act of
triggering the backdoor mapping, nor mitigate the backdoor attack at test-time.
In this paper, we propose an "in-flight" defense against backdoor attacks on
image classification that 1) detects use of a backdoor trigger at test-time;
and 2) infers the class of origin (source class) for a detected trigger
example. The effectiveness of our defense is demonstrated experimentally
against different strong backdoor attacks.

Atomistic simulations have now established themselves as an indispensable
tool in understanding deformation mechanisms of materials at the atomic scale.
Large scale simulations are regularly used to study the behavior of
polycrystalline materials at the nanoscale. In this work, we propose a method
for grain segmentation of an atomistic configuration using an unsupervised
machine learning algorithm that clusters atoms into individual grains based on
their orientation. The proposed method, called the Orisodata algorithm, is
based on the iterative self-organizing data analysis technique and is modified
to work in the orientation space. The working of the algorithm is demonstrated
on a 122 grain nanocrystalline thin film sample in both undeformed and deformed
states. The Orisodata algorithm is also compared with two other grain
segmentation algorithms available in the open-source visualization tool Ovito.
The results show that the Orisodata algorithm is able to correctly identify
deformation twins as well as regions separated by low angle grain boundaries.
The model parameters have intuitive physical meaning and relate to similar
thresholds used in experiments, which not only helps obtain optimal values but
also facilitates easy interpretation and validation of results.

Approaches to keeping a dynamical system within state constraints typically
rely on a model-based safety condition to limit the control signals. In the
face of significant modeling uncertainty, the system can suffer from important
performance penalties due to the safety condition becoming overly conservative.
Machine learning can be employed to reduce the uncertainty around the system
dynamics, and allow for higher performance. In this article, we propose the
safe uncertainty learning principle, and argue that the learning must be
properly structured to preserve safety guarantees. For instance, robust safety
conditions are necessary, and they must be initialized with conservative
uncertainty bounds prior to learning. Also, the uncertainty bounds should only
be tightened if the collected data sufficiently capture the future system
behavior. To support the principle, two example problems are solved with
control barrier functions: a lane-change controller for an autonomous vehicle,
and an adaptive cruise controller. This work offers a way to evaluate if
machine learning preserves safety guarantees during the control of uncertain
dynamical systems. It also highlights challenging aspects of learning for
control.

Recent advances in learning for control allow to synthesize vehicle
controllers from learned system dynamics and maintain robust stability
guarantees. However, no approach is well-suited for training linear
time-invariant (LTI) controllers using arbitrary learned models of the
dynamics. This article introduces a method to do so. It uses a robust control
framework to derive robust stability criteria. It also uses simulated policy
rollouts to obtain gradients on the controller parameters, which serve to
improve the closed-loop performance. By formulating the stability criteria as
penalties with computable gradients, they can be used to guide the controller
parameters toward robust stability during gradient descent. The approach is
flexible as it does not restrict the type of learned model for the simulated
rollouts. The robust control framework ensures that the controller is already
robustly stabilizing when first implemented on the actual system and no data is
yet collected. It also ensures that the system stays stable in the event of a
shift in dynamics, given the system behavior remains within assumed uncertainty
bounds. We demonstrate the approach by synthesizing a controller for simulated
autonomous lane change maneuvers. This work thus presents a flexible approach
to learning robustly stabilizing LTI controllers that take advantage of modern
machine learning techniques.

This paper proposes that Lipschitz continuity is a natural outcome of
regularized least squares in kernel-based learning. Lipschitz continuity is an
important proxy for robustness of input-output operators. It is also
instrumental for guaranteeing closed-loop stability of kernel-based
controlllers through small incremental gain arguments. We introduce a new class
of nonexpansive kernels that are shown to induce Hilbert spaces consisting of
only Lipschitz continuous operators. The Lipschitz constant of estimated
operators within such Hilbert spaces can be tuned by suitable selection of a
regularization parameter. As is typical for kernel-based models, input-output
operators are estimated from data by solving tractable systems of linear
equations. The approach thus constitutes a promising alternative to
Lipschitz-bounded neural networks, that have recently been investigated but are
computationally expensive to train.

Few-shot classification requires adapting knowledge learned from a large
annotated base dataset to recognize novel unseen classes, each represented by
few labeled examples. In such a scenario, pretraining a network with high
capacity on the large dataset and then finetuning it on the few examples causes
severe overfitting. At the same time, training a simple linear classifier on
top of "frozen" features learned from the large labeled dataset fails to adapt
the model to the properties of the novel classes, effectively inducing
underfitting. In this paper we propose an alternative approach to both of these
two popular strategies. First, our method pseudo-labels the entire large
dataset using the linear classifier trained on the novel classes. This
effectively "hallucinates" the novel classes in the large dataset, despite the
novel categories not being present in the base database (novel and base classes
are disjoint). Then, it finetunes the entire model with a distillation loss on
the pseudo-labeled base examples, in addition to the standard cross-entropy
loss on the novel dataset. This step effectively trains the network to
recognize contextual and appearance cues that are useful for the novel-category
recognition but using the entire large-scale base dataset and thus overcoming
the inherent data-scarcity problem of few-shot learning. Despite the simplicity
of the approach, we show that that our method outperforms the state-of-the-art
on four well-established few-shot classification benchmarks.

The energy Casimir method is an effective controller design approach to
stabilize port-Hamiltonian systems at a desired equilibrium. However, its
application relies on the availability of suitable Casimir and Lyapunov
functions, whose computation are generally intractable. In this paper, we
propose a neural network-based framework to learn these functions. We show how
to achieve equilibrium assignment by adding suitable regularization terms in
the training cost. We also propose a parameterization of Casimir functions for
reducing the training complexity. Moreover, the distance between the
equilibrium of the learned Lyapunov function and the desired equilibrium is
analyzed, which indicates that for small suboptimality gaps, the distance
decreases linearly with respect to the training loss. Our methods are backed up
by simulations on a pendulum system.

Learning graph convolutional networks (GCNs) is an emerging field which aims
at generalizing convolutional operations to arbitrary non-regular domains. In
particular, GCNs operating on spatial domains show superior performances
compared to spectral ones, however their success is highly dependent on how the
topology of input graphs is defined. In this paper, we introduce a novel
framework for graph convolutional networks that learns the topological
properties of graphs. The design principle of our method is based on the
optimization of a constrained objective function which learns not only the
usual convolutional parameters in GCNs but also a transformation basis that
conveys the most relevant topological relationships in these graphs.
Experiments conducted on the challenging task of skeleton-based action
recognition shows the superiority of the proposed method compared to
handcrafted graph design as well as the related work.

Camera calibration is integral to robotics and computer vision algorithms
that seek to infer geometric properties of the scene from visual input streams.
In practice, calibration is a laborious procedure requiring specialized data
collection and careful tuning. This process must be repeated whenever the
parameters of the camera change, which can be a frequent occurrence for mobile
robots and autonomous vehicles. In contrast, self-supervised depth and
ego-motion estimation approaches can bypass explicit calibration by inferring
per-frame projection models that optimize a view synthesis objective. In this
paper, we extend this approach to explicitly calibrate a wide range of cameras
from raw videos in the wild. We propose a learning algorithm to regress
per-sequence calibration parameters using an efficient family of general camera
models. Our procedure achieves self-calibration results with sub-pixel
reprojection error, outperforming other learning-based methods. We validate our
approach on a wide variety of camera geometries, including perspective,
fisheye, and catadioptric. Finally, we show that our approach leads to
improvements in the downstream task of depth estimation, achieving
state-of-the-art results on the EuRoC dataset with greater computational
efficiency than contemporary methods.

Recent work on neuro-symbolic inductive logic programming has led to
promising approaches that can learn explanatory rules from noisy, real-world
data. While some proposals approximate logical operators with differentiable
operators from fuzzy or real-valued logic that are parameter-free thus
diminishing their capacity to fit the data, other approaches are only loosely
based on logic making it difficult to interpret the learned "rules". In this
paper, we propose learning rules with the recently proposed logical neural
networks (LNN). Compared to others, LNNs offer strong connection to classical
Boolean logic thus allowing for precise interpretation of learned rules while
harboring parameters that can be trained with gradient-based optimization to
effectively fit the data. We extend LNNs to induce rules in first-order logic.
Our experiments on standard benchmarking tasks confirm that LNN rules are
highly interpretable and can achieve comparable or higher accuracy due to their
flexible parameterization.

Progress in machine learning (ML) stems from a combination of data
availability, computational resources, and an appropriate encoding of inductive
biases. Useful biases often exploit symmetries in the prediction problem, such
as convolutional networks relying on translation equivariance. Automatically
discovering these useful symmetries holds the potential to greatly improve the
performance of ML systems, but still remains a challenge. In this work, we
focus on sequential prediction problems and take inspiration from Noether's
theorem to reduce the problem of finding inductive biases to meta-learning
useful conserved quantities. We propose Noether Networks: a new type of
architecture where a meta-learned conservation loss is optimized inside the
prediction function. We show, theoretically and experimentally, that Noether
Networks improve prediction quality, providing a general framework for
discovering inductive biases in sequential problems.

Due to their massive success in various domains, deep learning techniques are
increasingly used to design network intrusion detection solutions that detect
and mitigate unknown and known attacks with high accuracy detection rates and
minimal feature engineering. However, it has been found that deep learning
models are vulnerable to data instances that can mislead the model to make
incorrect classification decisions so-called (adversarial examples). Such
vulnerability allows attackers to target NIDSs by adding small crafty
perturbations to the malicious traffic to evade detection and disrupt the
system's critical functionalities. The problem of deep adversarial learning has
been extensively studied in the computer vision domain; however, it is still an
area of open research in network security applications. Therefore, this survey
explores the researches that employ different aspects of adversarial machine
learning in the area of network intrusion detection in order to provide
directions for potential solutions. First, the surveyed studies are categorized
based on their contribution to generating adversarial examples, evaluating the
robustness of ML-based NIDs towards adversarial examples, and defending these
models against such attacks. Second, we highlight the characteristics
identified in the surveyed research. Furthermore, we discuss the applicability
of the existing generic adversarial attacks for the NIDS domain, the
feasibility of launching the proposed attacks in real-world scenarios, and the
limitations of the existing mitigation solutions.

Periodic variables illuminate the physical processes of stars throughout
their lifetime. Wide-field surveys continue to increase our discovery rates of
periodic variable stars. Automated approaches are essential to identify
interesting periodic variable stars for multi-wavelength and spectroscopic
follow-up. Here, we present a novel unsupervised machine learning approach to
hunt for anomalous periodic variables using phase-folded light curves presented
in the Zwicky Transient Facility Catalogue of Periodic Variable Stars by
\citet{Chen_2020}. We use a convolutional variational autoencoder to learn a
low dimensional latent representation, and we search for anomalies within this
latent dimension via an isolation forest. We identify anomalies with irregular
variability. Most of the top anomalies are likely highly variable Red Giants or
Asymptotic Giant Branch stars concentrated in the Milky Way galactic disk; a
fraction of the identified anomalies are more consistent with Young Stellar
Objects. Detailed spectroscopic follow-up observations are encouraged to reveal
the nature of these anomalies.

We are at a unique timeline in the history of human evolution where we may be
able to discover earth-like planets around stars outside our solar system where
conditions can support life or even find evidence of life on those planets.
With the launch of several satellites in recent years by NASA, ESA, and other
major space agencies, an ample amount of datasets are at our disposal which can
be utilized to train machine learning models that can automate the arduous
tasks of exoplanet detection, its identification, and habitability
determination. Automating these tasks can save a considerable amount of time
and minimize human errors due to manual intervention. To achieve this aim, we
first analyze the light intensity curves from stars captured by the Kepler
telescope to detect the potential curves that exhibit the characteristics of an
existence of a possible planetary system. For this detection, along with
training conventional models, we propose a stacked GBDT model that can be
trained on multiple representations of the light signals simultaneously.
Subsequently, we address the automation of exoplanet identification and
habitability determination by leveraging several state-of-art machine learning
and ensemble approaches. The identification of exoplanets aims to distinguish
false positive instances from the actual instances of exoplanets whereas the
habitability assessment groups the exoplanet instances into different clusters
based on their habitable characteristics. Additionally, we propose a new metric
called Adequate Thermal Adequacy (ATA) score to establish a potential linear
relationship between habitable and non-habitable instances. Experimental
results suggest that the proposed stacked GBDT model outperformed the
conventional models in detecting transiting exoplanets. Furthermore, the
incorporation of ATA scores in habitability classification enhanced the
performance of models.

We propose a simple architecture for deep reinforcement learning by embedding
inputs into a learned Fourier basis and show that it improves the sample
efficiency of both state-based and image-based RL. We perform infinite-width
analysis of our architecture using the Neural Tangent Kernel and theoretically
show that tuning the initial variance of the Fourier basis is equivalent to
functional regularization of the learned deep network. That is, these learned
Fourier features allow for adjusting the degree to which networks underfit or
overfit different frequencies in the training data, and hence provide a
controlled mechanism to improve the stability and performance of RL
optimization. Empirically, this allows us to prioritize learning low-frequency
functions and speed up learning by reducing networks' susceptibility to noise
in the optimization process, such as during Bellman updates. Experiments on
standard state-based and image-based RL benchmarks show clear benefits of our
architecture over the baselines. Website at
https://alexanderli.com/learned-fourier-features

Most of today's AI systems focus on using self-attention mechanisms and
transformer architectures on large amounts of diverse data to achieve
impressive performance gains. In this paper, we propose to augment the
transformer architecture with an external attention mechanism to bring external
knowledge and context to bear. By integrating external information into the
prediction process, we hope to reduce the need for ever-larger models and
increase the democratization of AI systems. We find that the proposed external
attention mechanism can significantly improve the performance of existing AI
systems, allowing practitioners to easily customize foundation AI models to
many diverse downstream applications. In particular, we focus on the task of
Commonsense Reasoning, demonstrating that the proposed external attention
mechanism can augment existing transformer models and significantly improve the
model's reasoning capabilities. The proposed system, Knowledgeable External
Attention for commonsense Reasoning (KEAR), reaches human parity on the open
CommonsenseQA research benchmark with an accuracy of 89.4\% in comparison to
the human accuracy of 88.9\%.

With the rapid advances in generative adversarial networks (GANs), the visual
quality of synthesised scenes keeps improving, including for complex urban
scenes with applications to automated driving. We address in this work a
continual scene generation setup in which GANs are trained on a stream of
distinct domains; ideally, the learned models should eventually be able to
generate new scenes in all seen domains. This setup reflects the real-life
scenario where data are continuously acquired in different places at different
times. In such a continual setup, we aim for learning with zero forgetting,
\IE, with no degradation in synthesis quality over earlier domains due to
catastrophic forgetting. To this end, we introduce a novel framework that not
only (i) enables seamless knowledge transfer in continual training but also
(ii) guarantees zero forgetting with a small overhead cost. While being more
memory efficient, thanks to continual learning, our model obtains better
synthesis quality as compared against the brute-force solution that trains one
full model for each domain. Especially, under extreme low-data regimes, our
approach outperforms the brute-force one by a large margin.

Recent strides in interpretable machine learning (ML) research reveal that
models exploit undesirable patterns in the data to make predictions, which
potentially causes harms in deployment. However, it is unclear how we can fix
these models. We present our ongoing work, GAM Changer, an open-source
interactive system to help data scientists and domain experts easily and
responsibly edit their Generalized Additive Models (GAMs). With novel
visualization techniques, our tool puts interpretability into action --
empowering human users to analyze, validate, and align model behaviors with
their knowledge and values. Built using modern web technologies, our tool runs
locally in users' computational notebooks or web browsers without requiring
extra compute resources, lowering the barrier to creating more responsible ML
models. GAM Changer is available at https://interpret.ml/gam-changer.

Semantic segmentation plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of
an image. Yet, the state-of-the-art models rely on large amount of annotated
samples, which are more expensive to obtain than in tasks such as image
classification. Since unlabelled data is instead significantly cheaper to
obtain, it is not surprising that Unsupervised Domain Adaptation reached a
broad success within the semantic segmentation community.
  This survey is an effort to summarize five years of this incredibly rapidly
growing field, which embraces the importance of semantic segmentation itself
and a critical need of adapting segmentation models to new environments. We
present the most important semantic segmentation methods; we provide a
comprehensive survey on domain adaptation techniques for semantic segmentation;
we unveil newer trends such as multi-domain learning, domain generalization,
test-time adaptation or source-free domain adaptation; we conclude this survey
by describing datasets and benchmarks most widely used in semantic segmentation
research. We hope that this survey will provide researchers across academia and
industry with a comprehensive reference guide and will help them in fostering
new research directions in the field.

The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science.

The NASA InSight mission payload includes the Heat Flow and Physical
Properties Package HP$^3$ to measure the surface heat flow. The package was
designed to use a small penetrator -- nicknamed the mole -- to implement a
string of temperature sensors in the soil to a depth of 5m. The mole itself is
equipped with sensors to measure a thermal conductivity as it proceeds to
depth. The heat flow would be calculated from the product of the temperature
gradient and the thermal conductivity. To avoid the perturbation caused by
annual surface temperature variations, the measurements would be taken at a
depth between 3 m and 5 m. The mole was designed to penetrate cohesionless soil
similar to Quartz sand which was expected to provide a good analogue material
for Martian sand. The sand would provide friction to the buried mole hull to
balance the remaining recoil of the mole hammer mechanism that drives the mole
forward. Unfortunately, the mole did not penetrate more than a mole length of
40 cm. The failure to penetrate deeper was largely due to a few tens of
centimeter thick cohesive duricrust that failed to provide the required
friction. Although a suppressor mass and spring in the hammer mechanism
absorbed much of the recoil, the available mass did not allow a system that
would have eliminated the recoil. The mole penetrated to 40 cm depth benefiting
from friction provided by springs in the support structure from which it was
deployed. It was found in addition that the Martian soil provided unexpected
levels of penetration resistance that would have motivated to designing a more
powerful mole. It is concluded that more mass would have allowed to design a
more robust system with little or no recoil, more energy of the mole hammer
mechanism and a more massive support structure.

A risk-averse preview-based $Q$-learning planner is presented for navigation
of autonomous vehicles. To this end, the multi-lane road ahead of a vehicle is
represented by a finite-state non-stationary Markov decision process (MDP). A
risk assessment unit module is then presented that leverages the preview
information provided by sensors along with a stochastic reachability module to
assign reward values to the MDP states and update them as scenarios develop. A
sampling-based risk-averse preview-based Q-learning algorithm is finally
developed that generates samples using the preview information and reward
function to learn risk-averse optimal planning strategies without actual
interaction with the environment. The risk factor is imposed on the objective
function to avoid fluctuation of the Q values, which can jeopardize the
vehicle's safety and/or performance. The overall hybrid automaton model of the
system is leveraged to develop a feasibility check unit module that detects
unfeasible plans and enables the planner system to react proactively to the
changes of the environment. Finally, to verify the efficiency of the presented
algorithm, its implementation on two highway driving scenarios of an autonomous
vehicle in a varying traffic density is considered.

Gaussian Process state-space models capture complex temporal dependencies in
a principled manner by placing a Gaussian Process prior on the transition
function. These models have a natural interpretation as discretized stochastic
differential equations, but inference for long sequences with fast and slow
transitions is difficult. Fast transitions need tight discretizations whereas
slow transitions require backpropagating the gradients over long
subtrajectories. We propose a novel Gaussian process state-space architecture
composed of multiple components, each trained on a different resolution, to
model effects on different timescales. The combined model allows traversing
time on adaptive scales, providing efficient inference for arbitrarily long
sequences with complex dynamics. We benchmark our novel method on
semi-synthetic data and on an engine modeling task. In both experiments, our
approach compares favorably against its state-of-the-art alternatives that
operate on a single time-scale only.

General-purpose robots coexisting with humans in their environment must learn
to relate human language to their perceptions and actions to be useful in a
range of daily tasks. Moreover, they need to acquire a diverse repertoire of
general-purpose skills that allow composing long-horizon tasks by following
unconstrained language instructions. In this paper, we present CALVIN
(Composing Actions from Language and Vision), an open-source simulated
benchmark to learn long-horizon language-conditioned tasks. Our aim is to make
it possible to develop agents that can solve many robotic manipulation tasks
over a long horizon, from onboard sensors, and specified only via human
language. CALVIN tasks are more complex in terms of sequence length, action
space, and language than existing vision-and-language task datasets and
supports flexible specification of sensor suites. We evaluate the agents in
zero-shot to novel language instructions and to novel environments and objects.
We show that a baseline model based on multi-context imitation learning
performs poorly on CALVIN, suggesting that there is significant room for
developing innovative agents that learn to relate human language to their world
models with this benchmark.

A multitude of explainability methods and associated fidelity performance
metrics have been proposed to help better understand how modern AI systems make
decisions. However, much of the current work has remained theoretical --
without much consideration for the human end-user. In particular, it is not yet
known (1) how useful current explainability methods are in practice for more
real-world scenarios and (2) how well associated performance metrics accurately
predict how much knowledge individual explanations contribute to a human
end-user trying to understand the inner-workings of the system. To fill this
gap, we conducted psychophysics experiments at scale to evaluate the ability of
human participants to leverage representative attribution methods for
understanding the behavior of different image classifiers representing three
real-world scenarios: identifying bias in an AI system, characterizing the
visual strategy it uses for tasks that are too difficult for an untrained
non-expert human observer as well as understanding its failure cases. Our
results demonstrate that the degree to which individual attribution methods
help human participants better understand an AI system varied widely across
these scenarios. This suggests a critical need for the field to move past
quantitative improvements of current attribution methods towards the
development of complementary approaches that provide qualitatively different
sources of information to human end-users.

Blackbox transfer attacks for image classifiers have been extensively studied
in recent years. In contrast, little progress has been made on transfer attacks
for object detectors. Object detectors take a holistic view of the image and
the detection of one object (or lack thereof) often depends on other objects in
the scene. This makes such detectors inherently context-aware and adversarial
attacks in this space are more challenging than those targeting image
classifiers. In this paper, we present a new approach to generate context-aware
attacks for object detectors. We show that by using co-occurrence of objects
and their relative locations and sizes as context information, we can
successfully generate targeted mis-categorization attacks that achieve higher
transfer success rates on blackbox object detectors than the state-of-the-art.
We test our approach on a variety of object detectors with images from PASCAL
VOC and MS COCO datasets and demonstrate up to $20$ percentage points
improvement in performance compared to the other state-of-the-art methods.

We consider the classic 1-center problem: Given a set P of n points in a
metric space find the point in P that minimizes the maximum distance to the
other points of P. We study the complexity of this problem in d-dimensional
$\ell_p$-metrics and in edit and Ulam metrics over strings of length d. Our
results for the 1-center problem may be classified based on d as follows.
  $\bullet$ Small d: We provide the first linear-time algorithm for 1-center
problem in fixed-dimensional $\ell_1$ metrics. On the other hand, assuming the
hitting set conjecture (HSC), we show that when $d=\omega(\log n)$, no
subquadratic algorithm can solve 1-center problem in any of the
$\ell_p$-metrics, or in edit or Ulam metrics.
  $\bullet$ Large d. When $d=\Omega(n)$, we extend our conditional lower bound
to rule out sub quartic algorithms for 1-center problem in edit metric
(assuming Quantified SETH). On the other hand, we give a
$(1+\epsilon)$-approximation for 1-center in Ulam metric with running time
$\tilde{O_{\epsilon}}(nd+n^2\sqrt{d})$.
  We also strengthen some of the above lower bounds by allowing approximations
or by reducing the dimension d, but only against a weaker class of algorithms
which list all requisite solutions. Moreover, we extend one of our hardness
results to rule out subquartic algorithms for the well-studied 1-median problem
in the edit metric, where given a set of n strings each of length n, the goal
is to find a string in the set that minimizes the sum of the edit distances to
the rest of the strings in the set.

In this work, we develop intuitive controls for editing the style of 3D
objects. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and
local geometric details which conform to a target text prompt. We consider a
disentangled representation of a 3D object using a fixed mesh input (content)
coupled with a learned neural network, which we term neural style field
network. In order to modify style, we obtain a similarity score between a text
prompt (describing style) and a stylized mesh by harnessing the
representational power of CLIP. Text2Mesh requires neither a pre-trained
generative model nor a specialized 3D mesh dataset. It can handle low-quality
meshes (non-manifold, boundaries, etc.) with arbitrary genus, and does not
require UV parameterization. We demonstrate the ability of our technique to
synthesize a myriad of styles over a wide variety of 3D meshes.

Cross-validation is the standard approach for tuning parameter selection in
many non-parametric regression problems. However its use is less common in
change-point regression, perhaps as its prediction error-based criterion may
appear to permit small spurious changes and hence be less well-suited to
estimation of the number and location of change-points. We show that in fact
the problems of cross-validation with squared error loss are more severe and
can lead to systematic under- or over-estimation of the number of
change-points, and highly suboptimal estimation of the mean function in simple
settings where changes are easily detectable. We propose two simple approaches
to remedy these issues, the first involving the use of absolute error rather
than squared error loss, and the second involving modifying the holdout sets
used. For the latter, we provide conditions that permit consistent estimation
of the number of change-points for a general change-point estimation procedure.
We show these conditions are satisfied for optimal partitioning using new
results on its performance when supplied with the incorrect number of
change-points. Numerical experiments show that the absolute error approach in
particular is competitive with common change-point methods using classical
tuning parameter choices when error distributions are well-specified, but can
substantially outperform these in misspecified models. An implementation of our
methodology is available in the R package crossvalidationCP on CRAN.

A key challenge in building theoretical foundations for deep learning is the
complex optimization dynamics of neural networks, resulting from the
high-dimensional interactions between the large number of network parameters.
Such non-trivial dynamics lead to intriguing behaviors such as the phenomenon
of "double descent" of the generalization error. The more commonly studied
aspect of this phenomenon corresponds to model-wise double descent where the
test error exhibits a second descent with increasing model complexity, beyond
the classical U-shaped error curve. In this work, we investigate the origins of
the less studied epoch-wise double descent in which the test error undergoes
two non-monotonous transitions, or descents as the training time increases. By
leveraging tools from statistical physics, we study a linear teacher-student
setup exhibiting epoch-wise double descent similar to that in deep neural
networks. In this setting, we derive closed-form analytical expressions for the
evolution of generalization error over training. We find that double descent
can be attributed to distinct features being learned at different scales: as
fast-learning features overfit, slower-learning features start to fit,
resulting in a second descent in test error. We validate our findings through
numerical experiments where our theory accurately predicts empirical findings
and remains consistent with observations in deep neural networks.

A data set of recorded single played tones of a concert grand piano is
investigated using Machine Learning (ML) on psychoacoustic timbre features. The
examined instrument has been recorded at two stages: firstly right after
manufacture and secondly after being played in a concert hall for one year. A
previous study [Plath2019] revealed that listeners clearly distinguished both
stages but no clear correlation with acoustics, signal processing tools or
verbalizations of perceived differences could be found. Using a Self-Organizing
Map (SOM), training single as well as double feature sets, it can be shown that
spectral flux is able to perfectly cluster the two stages. Sound Pressure Level
(SPL), roughness, and fractal correlation dimension (as a measure for initial
transient chaoticity) are furthermore able to order the keys with respect to
high and low notes. Combining spectral flux with the three other features in
double-feature training sets maintains stage clustering only for SPL and
fractal dimension, showing sub-clusters for both stages. These sub-clusters
point to a homogenization of SPL for stage 2 with respect to stage 1 and a
pronounced ordering and sub-clustering of key regions with respect to initial
transient chaoticity.

Due to their high energy intensity, buildings play a major role in the
current worldwide energy transition. Building models are ubiquitous since they
are needed at each stage of the life of buildings, i.e. for design,
retrofitting, and control operations. Classical white-box models, based on
physical equations, are bound to follow the laws of physics but the specific
design of their underlying structure might hinder their expressiveness and
hence their accuracy. On the other hand, black-box models are better suited to
capture nonlinear building dynamics and thus can often achieve better accuracy,
but they require a lot of data and might not follow the laws of physics, a
problem that is particularly common for neural network (NN) models. To counter
this known generalization issue, physics-informed NNs have recently been
introduced, where researchers introduce prior knowledge in the structure of NNs
to ground them in known underlying physical laws and avoid classical NN
generalization issues.
  In this work, we present a novel physics-informed NN architecture, dubbed
Physically Consistent NN (PCNN), which only requires past operational data and
no engineering overhead, including prior knowledge in a linear module running
in parallel to a classical NN. We formally prove that such networks are
physically consistent - by design and even on unseen data - with respect to
different control inputs and temperatures outside and in neighboring zones. We
demonstrate their performance on a case study, where the PCNN attains an
accuracy up to 40% better than a classical physics-based resistance-capacitance
model on 3-day long prediction horizons. Furthermore, despite their constrained
structure, PCNNs attain similar performance to classical NNs on the validation
data, overfitting the training data less and retaining high expressiveness to
tackle the generalization issue.

Virtual support agents have grown in popularity as a way for businesses to
provide better and more accessible customer service. Some challenges in this
domain include ambiguous user queries as well as changing support topics and
user behavior (non-stationarity). We do, however, have access to partial
feedback provided by the user (clicks, surveys, and other events) which can be
leveraged to improve the user experience. Adaptable learning techniques, like
contextual bandits, are a natural fit for this problem setting. In this paper,
we discuss real-world implementations of contextual bandits (CB) for the
Microsoft virtual agent. It includes intent disambiguation based on
neural-linear bandits (NLB) and contextual recommendations based on a
collection of multi-armed bandits (MAB). Our solutions have been deployed to
production and have improved key business metrics of the Microsoft virtual
agent, as confirmed by A/B experiments. Results include a relative increase of
over 12% in problem resolution rate and relative decrease of over 4% in
escalations to a human operator. While our current use cases focus on intent
disambiguation and contextual recommendation for support bots, we believe our
methods can be extended to other domains.

Analyzing the morphology of cells in microscopy images can provide insights
into the mechanism of compounds or the function of genes. Addressing this task
requires methods that can not only extract biological information from the
images, but also ignore technical variations, ie, changes in experimental
procedure or differences between equipments used to collect microscopy images.
We propose Treatment ExemplArs with Mixture-of-experts (TEAMs), an embedding
learning approach that learns a set of experts that are specialized in
capturing technical variations in our training set and then aggregates
specialist's predictions at test time. Thus, TEAMs can learn powerful
embeddings with less technical variation bias by minimizing the noise from
every expert. To train our model, we leverage Treatment Exemplars that enable
our approach to capture the distribution of the entire dataset in every
minibatch while still fitting into GPU memory. We evaluate our approach on
three datasets for tasks like drug discovery, boosting performance on
identifying the true mechanism of action of cell treatments by 5.5-11% over the

When a neural language model (LM) is adapted to perform a new task, what
aspects of the task predict the eventual performance of the model? In NLP,
systematic features of LM generalization to individual examples are well
characterized, but systematic aspects of LM adaptability to new tasks are not
nearly as well understood. We present a large-scale empirical study of the
features and limits of LM adaptability using a new benchmark, TaskBench500,
built from 500 procedurally generated sequence modeling tasks. These tasks
combine core aspects of language processing, including lexical semantics,
sequence processing, memorization, logical reasoning, and world knowledge.
Using TaskBench500, we evaluate three facets of adaptability, finding that: (1)
adaptation procedures differ dramatically in their ability to memorize small
datasets; (2) within a subset of task types, adaptation procedures exhibit
compositional adaptability to complex tasks; and (3) failure to match training
label distributions is explained by mismatches in the intrinsic difficulty of
predicting individual labels. Our experiments show that adaptability to new
tasks, like generalization to new examples, can be systematically described and
understood, and we conclude with a discussion of additional aspects of
adaptability that could be studied using the new benchmark.

Text summarization methods have attracted much attention all the time. In
recent years, deep learning has been applied to text summarization, and it
turned out to be pretty effective. Most of the current text summarization
methods based on deep learning are supervised methods which need large-scale
datasets. However, large-scale datasets are difficult to obtain in practical
applications. In this paper, an unsupervised extractive text summarization
method based on multi-round calculation is proposed. Based on the directed
graph algorithm, we change the common method which calculates the sentence
ranking at one time to multi-round calculation, and we dynamically optimize the
relation of sentences after each round of calculation to reduce the redundancy
of summarization. Experiments are carried out on four data sets, each
separately containing Chinese, English, long and short texts. The experiment
results show that our method has better performance than other unsupervised
methods.

Understanding kinetics and thermodynamics profile of biomolecules is
necessary to understand their functional roles which has a major impact in
mechanism driven drug discovery. Molecular dynamics simulation has been
routinely used to understand conformational dynamics and molecular recognition
in biomolecules. Statistical analysis of high-dimensional spatiotemporal data
generated from molecular dynamics simulation requires identification of few
low-dimensional variables which can describe essential dynamics of a system
without significant loss of informations. In physical chemistry, these
low-dimensional variables often called collective variables. Collective
variables are used to generated reduced representation of free energy surface
and calculate transition probabilities between different metastable basins.
However the choice of collective variables is not trivial for complex systems.
Collective variables ranges from geometric criteria's such as distances,
dihedral angles to abstract ones such as weighted linear combinations of
multiple geometric variables. Advent of machine learning algorithms led to
increasing use of abstract collective variables to represent biomolecular
dynamics. In this review, I will highlight several nuances of commonly used
collective variables ranging from geometric to abstract ones. Further, I will
put forward some cases where machine learning based collective variables were
used to describe simple systems which in principle could have been described by
geometric ones. Finally, I will put forward my thoughts on artificial general
intelligence and how it can be used to discover and predict collective
variables from spatiotemporal data generated by molecular dynamics simulations.

Quantum algorithms can be used to perform unsupervised machine learning tasks
like data clustering by mapping the distance between data points to a graph
optimization problem (i.e. MAXCUT) and finding optimal solution through energy
minimization using hybrid quantum classical methods. Taking advantage of the
IBM runtime environment, we benchmark the performance of the "Warm-Start" (ws)
variant of Quantum Approximate Optimization Algorithm (QAOA) versus the
standard implementation of QAOA and the variational quantum eigensolver (VQE)
for unstructured clustering problems using real world dataset with respect to
accuracy and execution time. Our numerical results show a strong speedup in
execution time for different optimization algorithms using the IBM Qiskit
Runtime architecture and increased speedup in classification accuracy in
ws-QAOA algorithm

This article proposes novel rules for false discovery rate control (FDRC)
geared towards online anomaly detection in time series. Online FDRC rules allow
to control the properties of a sequence of statistical tests. In the context of
anomaly detection, the null hypothesis is that an observation is normal and the
alternative is that it is anomalous. FDRC rules allow users to target a lower
bound on precision in unsupervised settings. The methods proposed in this
article overcome short-comings of previous FDRC rules in the context of anomaly
detection, in particular ensuring that power remains high even when the
alternative is exceedingly rare (typical in anomaly detection) and the test
statistics are serially dependent (typical in time series). We show the
soundness of these rules in both theory and experiments.

In the domain of medical image processing, medical device manufacturers
protect their intellectual property in many cases by shipping only compiled
software, i.e. binary code which can be executed but is difficult to be
understood by a potential attacker. In this paper, we investigate how well this
procedure is able to protect image processing algorithms. In particular, we
investigate whether the computation of mono-energetic images and iodine maps
from dual energy CT data can be reverse-engineered by machine learning methods.
Our results indicate that both can be approximated using only one single slice
image as training data at a very high accuracy with structural similarity
greater than 0.98 in all investigated cases.

The classic Cluster Editing problem (also known as Correlation Clustering)
asks to transform a given graph into a disjoint union of cliques (clusters) by
a small number of edge modifications. When applied to vertex-colored graphs
(the colors representing subgroups), standard algorithms for the NP-hard
Cluster Editing problem may yield solutions that are biased towards subgroups
of data (e.g., demographic groups), measured in the number of modifications
incident to the members of the subgroups. We propose a modification fairness
constraint which ensures that the number of edits incident to each subgroup is
proportional to its size. To start with, we study Modification-Fair Cluster
Editing for graphs with two vertex colors. We show that the problem is NP-hard
even if one may only insert edges within a subgroup; note that in the classic
"non-fair" setting, this case is trivially polynomial-time solvable. However,
in the more general editing form, the modification-fair variant remains
fixed-parameter tractable with respect to the number of edge edits. We
complement these and further theoretical results with an empirical analysis of
our model on real-world social networks where we find that the price of
modification-fairness is surprisingly low, that is, the cost of optimal
modification-fair differs from the cost of optimal "non-fair" solutions only by
a small percentage.

Games have a long history of serving as a benchmark for progress in
artificial intelligence. Recently, approaches using search and learning have
shown strong performance across a set of perfect information games, and
approaches using game-theoretic reasoning and learning have shown strong
performance for specific imperfect information poker variants. We introduce
Player of Games, a general-purpose algorithm that unifies previous approaches,
combining guided search, self-play learning, and game-theoretic reasoning.
Player of Games is the first algorithm to achieve strong empirical performance
in large perfect and imperfect information games -- an important step towards
truly general algorithms for arbitrary environments. We prove that Player of
Games is sound, converging to perfect play as available computation time and
approximation capacity increases. Player of Games reaches strong performance in
chess and Go, beats the strongest openly available agent in heads-up no-limit
Texas hold'em poker (Slumbot), and defeats the state-of-the-art agent in
Scotland Yard, an imperfect information game that illustrates the value of
guided search, learning, and game-theoretic reasoning.

This paper elucidates a model for acoustic single and multi-tone
classification in resource constrained edge devices. The proposed model is of
State-of-the-art Fast Accurate Stable Tiny Gated Recurrent Neural Network. This
model has resulted in improved performance metrics and lower size compared to
previous hypothesized methods by using lesser parameters with higher efficiency
and employment of a noise reduction algorithm. The model is implemented as an
acoustic AI module, focused for the application of sound identification,
localization, and deployment on AI systems like that of an autonomous car.
Further, the inclusion of localization techniques carries the potential of
adding a new dimension to the multi-tone classifiers present in autonomous
vehicles, as its demand increases in urban cities and developing countries in

Technology has an important role to play in the field of Rehabilitation,
improving patient outcomes and reducing healthcare costs. However, existing
approaches lack clinical validation, robustness and ease of use. We propose
Tele-EvalNet, a novel system consisting of two components: a live feedback
model and an overall performance evaluation model. The live feedback model
demonstrates feedback on exercise correctness with easy to understand
instructions highlighted using color markers. The overall performance
evaluation model learns a mapping of joint data to scores, given to the
performance by clinicians. The model does this by extracting clinically
approved features from joint data. Further, these features are encoded to a
lower dimensional space with an autoencoder. A novel multi-scale CNN-LSTM
network is proposed to learn a mapping of performance data to the scores by
leveraging features extracted at multiple scales. The proposed system shows a
high degree of improvement in score predictions and outperforms the
state-of-the-art rehabilitation models.

Modeling trajectories generated by robot joints is complex and required for
high level activities like trajectory generation, clustering, and
classification. Disentagled representation learning promises advances in
unsupervised learning, but they have not been evaluated in robot-generated
trajectories. In this paper we evaluate three disentangling VAEs ($\beta$-VAE,
Decorr VAE, and a new $\beta$-Decorr VAE) on a dataset of 1M robot trajectories
generated from a 3 DoF robot arm. We find that the decorrelation-based
formulations perform the best in terms of disentangling metrics, trajectory
quality, and correlation with ground truth latent features. We expect that
these results increase the use of unsupervised learning in robot control.

We focus on controllable disentangled representation learning (C-Dis-RL),
where users can control the partition of the disentangled latent space to
factorize dataset attributes (concepts) for downstream tasks. Two general
problems remain under-explored in current methods: (1) They lack comprehensive
disentanglement constraints, especially missing the minimization of mutual
information between different attributes across latent and observation domains.
(2) They lack convexity constraints, which is important for meaningfully
manipulating specific attributes for downstream tasks. To encourage both
comprehensive C-Dis-RL and convexity simultaneously, we propose a simple yet
efficient method: Controllable Interpolation Regularization (CIR), which
creates a positive loop where disentanglement and convexity can help each
other. Specifically, we conduct controlled interpolation in latent space during
training, and we reuse the encoder to help form a 'perfect disentanglement'
regularization. In that case, (a) disentanglement loss implicitly enlarges the
potential understandable distribution to encourage convexity; (b) convexity can
in turn improve robust and precise disentanglement. CIR is a general module and
we merge CIR with three different algorithms: ELEGANT, I2I-Dis, and GZS-Net to
show the compatibility and effectiveness. Qualitative and quantitative
experiments show improvement in C-Dis-RL and latent convexity by CIR. This
further improves downstream tasks: controllable image synthesis, cross-modality
image translation, and zero-shot synthesis.

Text Style Transfer (TST) aims to alter the underlying style of the source
text to another specific style while keeping the same content. Due to the
scarcity of high-quality parallel training data, unsupervised learning has
become a trending direction for TST tasks. In this paper, we propose a novel
VAE based Text Style Transfer with pivOt Words Enhancement leaRning (VT-STOWER)
method which utilizes Variational AutoEncoder (VAE) and external style
embeddings to learn semantics and style distribution jointly. Additionally, we
introduce pivot words learning, which is applied to learn decisive words for a
specific style and thereby further improve the overall performance of the style
transfer. The proposed VT-STOWER can be scaled to different TST scenarios given
very limited and non-parallel training data with a novel and flexible style
strength control mechanism. Experiments demonstrate that the VT-STOWER
outperforms the state-of-the-art on sentiment, formality, and code-switching
TST tasks.

Markov chain Monte Carlo (MCMC) provides asymptotically consistent estimates
of intractable posterior expectations as the number of iterations tends to
infinity. However, in large data applications, MCMC can be computationally
expensive per iteration. This has catalyzed interest in sampling methods such
as approximate MCMC, which trade off asymptotic consistency for improved
computational speed. In this article, we propose estimators based on couplings
of Markov chains to assess the quality of such asymptotically biased sampling
methods. The estimators give empirical upper bounds of the Wassertein distance
between the limiting distribution of the asymptotically biased sampling method
and the original target distribution of interest. We establish theoretical
guarantees for our upper bounds and show that our estimators can remain
effective in high dimensions. We apply our quality measures to stochastic
gradient MCMC, variational Bayes, and Laplace approximations for tall data and
to approximate MCMC for Bayesian logistic regression in 4500 dimensions and
Bayesian linear regression in 50000 dimensions.

Integrated Information Theory (IIT) is an audacious attempt to pin down the
abstract, phenomenological experiences of consciousness into a rigorous,
mathematical framework. We show that IIT's stance in regards to neuronal noise
is inconsistent with experimental data demonstrating that neuronal noise in the
brain plays a critical role in learning, visual recognition, and even
categorical representation. IIT predicts that entropy due to noise will reduce
the information integration of a physical system, which is inconsistent with
experimental data demonstrating that decision-related noise is a necessary
condition for learning and visual recognition tasks. IIT must therefore be
reformulated to accommodate experimental evidence showing both the successes
and failures of noise.

Deep reinforcement learning is an effective tool to learn robot control
policies from scratch. However, these methods are notorious for the enormous
amount of required training data which is prohibitively expensive to collect on
real robots. A highly popular alternative is to learn from simulations,
allowing to generate the data much faster, safer, and cheaper. Since all
simulators are mere models of reality, there are inevitable differences between
the simulated and the real data, often referenced as the 'reality gap'. To
bridge this gap, many approaches learn one policy from a distribution over
simulators. In this paper, we propose to combine reinforcement learning from
randomized physics simulations with policy distillation. Our algorithm, called
Distilled Domain Randomization (DiDoR), distills so-called teacher policies,
which are experts on domains that have been sampled initially, into a student
policy that is later deployed. This way, DiDoR learns controllers which
transfer directly from simulation to reality, i.e., without requiring data from
the target domain. We compare DiDoR against three baselines in three sim-to-sim
as well as two sim-to-real experiments. Our results show that the target domain
performance of policies trained with DiDoR is en par or better than the
baselines'. Moreover, our approach neither increases the required memory
capacity nor the time to compute an action, which may well be a point of
failure for successfully deploying the learned controller.

Machine learning algorithms underpin modern diagnostic-aiding software, which
has proved valuable in clinical practice, particularly in radiology. However,
inaccuracies, mainly due to the limited availability of clinical samples for
training these algorithms, hamper their wider applicability, acceptance, and
recognition amongst clinicians. We present an analysis of state-of-the-art
automatic quality control (QC) approaches that can be implemented within these
algorithms to estimate the certainty of their outputs. We validated the most
promising approaches on a brain image segmentation task identifying white
matter hyperintensities (WMH) in magnetic resonance imaging data. WMH are a
correlate of small vessel disease common in mid-to-late adulthood and are
particularly challenging to segment due to their varied size, and
distributional patterns. Our results show that the aggregation of uncertainty
and Dice prediction were most effective in failure detection for this task.
Both methods independently improved mean Dice from 0.82 to 0.84. Our work
reveals how QC methods can help to detect failed segmentation cases and
therefore make automatic segmentation more reliable and suitable for clinical
practice.

Compact data representations are one approach for improving generalization of
learned functions. We explicitly illustrate the relationship between entropy
and cardinality, both measures of compactness, including how gradient descent
on the former reduces the latter. Whereas entropy is distribution sensitive,
cardinality is not. We propose a third compactness measure that is a compromise
between the two: expected cardinality, or the expected number of unique states
in any finite number of draws, which is more meaningful than standard
cardinality as it discounts states with negligible probability mass. We show
that minimizing entropy also minimizes expected cardinality.

Generalized zero shot learning (GZSL) is still a technical challenge of deep
learning as it has to recognize both source and target classes without data
from target classes. To preserve the semantic relation between source and
target classes when only trained with data from source classes, we address the
quantification of the knowledge transfer and semantic relation from an
information-theoretic viewpoint. To this end, we follow the prototypical model
and format the variables of concern as a probability vector. Leveraging on the
proposed probability vector representation, the information measurement such as
mutual information and entropy, can be effectively evaluated with simple closed
forms. We discuss the choice of common embedding space and distance function
when using the prototypical model. Then We propose three information-theoretic
loss functions for deterministic GZSL model: a mutual information loss to
bridge seen data and target classes; an uncertainty-aware entropy constraint
loss to prevent overfitting when using seen data to learn the embedding of
target classes; a semantic preserving cross entropy loss to preserve the
semantic relation when mapping the semantic representations to the common
space. Simulation shows that, as a deterministic model, our proposed method
obtains state of the art results on GZSL benchmark datasets. We achieve 21%-64%
improvements over the baseline model -- deep calibration network (DCN) and for
the first time demonstrate a deterministic model can perform as well as
generative ones. Moreover, our proposed model is compatible with generative
models. Simulation studies show that by incorporating with f-CLSWGAN, we obtain
comparable results compared with advanced generative models.

Denoising diffusion probabilistic models have recently received much research
attention since they outperform alternative approaches, such as GANs, and
currently provide state-of-the-art generative performance. The superior
performance of diffusion models has made them an appealing tool in several
applications, including inpainting, super-resolution, and semantic editing. In
this paper, we demonstrate that diffusion models can also serve as an
instrument for semantic segmentation, especially in the setup when labeled data
is scarce. In particular, for several pretrained diffusion models, we
investigate the intermediate activations from the networks that perform the
Markov step of the reverse diffusion process. We show that these activations
effectively capture the semantic information from an input image and appear to
be excellent pixel-level representations for the segmentation problem. Based on
these observations, we describe a simple segmentation method, which can work
even if only a few training images are provided. Our approach significantly
outperforms the existing alternatives on several datasets for the same amount
of human supervision.

This paper offers a retrospective of what we learnt from organizing the
workshop *Ethical Considerations in Creative applications of Computer Vision*
at CVPR 2021 conference and, prior to that, a series of workshops on *Computer
Vision for Fashion, Art and Design* at ECCV 2018, ICCV 2019, and CVPR 2020. We
hope this reflection will bring artists and machine learning researchers into
conversation around the ethical and social dimensions of creative applications
of computer vision.

How to learn a universal facial representation that boosts all face analysis
tasks? This paper takes one step toward this goal. In this paper, we study the
transfer performance of pre-trained models on face analysis tasks and introduce
a framework, called FaRL, for general Facial Representation Learning in a
visual-linguistic manner. On one hand, the framework involves a contrastive
loss to learn high-level semantic meaning from image-text pairs. On the other
hand, we propose exploring low-level information simultaneously to further
enhance the face representation, by adding a masked image modeling. We perform
pre-training on LAION-FACE, a dataset containing large amount of face
image-text pairs, and evaluate the representation capability on multiple
downstream tasks. We show that FaRL achieves better transfer performance
compared with previous pre-trained models. We also verify its superiority in
the low-data regime. More importantly, our model surpasses the state-of-the-art
methods on face analysis tasks including face parsing and face alignment.

It is important to forecast dam inflow for flood damage mitigation. The
hydrograph provides critical information such as the start time, peak level,
and volume. Particularly, dam management requires a 6-h lead time of the dam
inflow forecast based on a future hydrograph. The authors propose novel target
inflow weights to create an ocean feature vector extracted from the analyzed
images of the sea surface. We extracted 4,096 elements of the dimension vector
in the fc6 layer of the pre-trained VGG16 network. Subsequently, we reduced it
to three dimensions of t-SNE. Furthermore, we created the principal component
of the sea temperature weights using PCA. We found that these weights
contribute to the stability of predictor importance by numerical experiments.
As base regression models, we calibrate the least squares with kernel
expansion, the quantile random forest minimized out-of bag error, and the
support vector regression with a polynomial kernel. When we compute the
predictor importance, we visualize the stability of each variable importance
introduced by our proposed weights, compared with other results without
weights. We apply our method to a dam at Kanto region in Japan and focus on the
trained term from 2007 to 2018, with a limited flood term from June to October.
We test the accuracy over the 2019 flood term. Finally, we present the applied
results and further statistical learning for unknown flood forecast.

Hierarchical reinforcement learning (HRL) holds great potential for
sample-efficient learning on challenging long-horizon tasks. In particular,
letting a higher level assign subgoals to a lower level has been shown to
enable fast learning on difficult problems. However, such subgoal-based methods
have been designed with static reinforcement learning environments in mind and
consequently struggle with dynamic elements beyond the immediate control of the
agent even though they are ubiquitous in real-world problems. In this paper, we
introduce Hierarchical reinforcement learning with Timed Subgoals (HiTS), an
HRL algorithm that enables the agent to adapt its timing to a dynamic
environment by not only specifying what goal state is to be reached but also
when. We discuss how communicating with a lower level in terms of such timed
subgoals results in a more stable learning problem for the higher level. Our
experiments on a range of standard benchmarks and three new challenging dynamic
reinforcement learning environments show that our method is capable of
sample-efficient learning where an existing state-of-the-art subgoal-based HRL
method fails to learn stable solutions.

Temporal abstraction in reinforcement learning (RL), offers the promise of
improving generalization and knowledge transfer in complex environments, by
propagating information more efficiently over time. Although option learning
was initially formulated in a way that allows updating many options
simultaneously, using off-policy, intra-option learning (Sutton, Precup &
Singh, 1999), many of the recent hierarchical reinforcement learning approaches
only update a single option at a time: the option currently executing. We
revisit and extend intra-option learning in the context of deep reinforcement
learning, in order to enable updating all options consistent with current
primitive action choices, without introducing any additional estimates. Our
method can therefore be naturally adopted in most hierarchical RL frameworks.
When we combine our approach with the option-critic algorithm for option
discovery, we obtain significant improvements in performance and
data-efficiency across a wide variety of domains.

Matrix factorization is a widely adopted recommender system technique that
fits scalar rating values by dot products of user feature vectors and item
feature vectors. However, the formulation of matrix factorization as a scalar
fitting problem is not friendly to side information incorporation or multi-task
learning. In this paper, we replace the scalar values of the user rating matrix
by matrices, and fit the matrix values by matrix products of user feature
matrix and item feature matrix. Our framework is friendly to multitask learning
and side information incorporation. We use popularity data as side information
in our paper in particular to enhance the performance of matrix factorization
techniques. In the experiment section, we prove the competence of our method
compared with other approaches using both accuracy and fairness metrics. Our
framework is an ideal substitute for tensor factorization in context-aware
recommendation and many other scenarios.

Effective water resource management requires information on water
availability, both in terms of quality and quantity, spatially and temporally.
In this paper, we study the methodology behind Transfer Learning (TL) through
fine-tuning and parameter transferring for better generalization performance of
streamflow prediction in data-sparse regions. We propose a standard recurrent
neural network in the form of Long Short-Term Memory (LSTM) to fit on a
sufficiently large source domain dataset and repurpose the learned weights to a
significantly smaller, yet similar target domain datasets. We present a
methodology to implement transfer learning approaches for spatiotemporal
applications by separating the spatial and temporal components of the model and
training the model to generalize based on categorical datasets representing
spatial variability. The framework is developed on a rich benchmark dataset
from the US and evaluated on a smaller dataset collected by The Nature
Conservancy in Kenya. The LSTM model exhibits generalization performance
through our TL technique. Results from this current experiment demonstrate the
effective predictive skill of forecasting streamflow responses when knowledge
transferring and static descriptors are used to improve hydrologic model
generalization in data-sparse regions.

The science of causality explains/determines 'cause-effect' relationship
between the entities of a system by providing mathematical tools for the
purpose. In spite of all the success and widespread applications of
machine-learning (ML) algorithms, these algorithms are based on statistical
learning alone. Currently, they are nowhere close to 'human-like' intelligence
as they fail to answer and learn based on the important "Why?" questions.
Hence, researchers are attempting to integrate ML with the science of
causality. Among the many causal learning issues encountered by ML, one is that
these algorithms are dumb to the temporal order or structure in data. In this
work we develop a machine learning pipeline based on a recently proposed
'neurochaos' feature learning technique (ChaosFEX feature extractor), that
helps us to learn generalized causal-structure in given time-series data.

Given the rapid rise in energy demand by data centers and computing systems
in general, it is fundamental to incorporate energy considerations when
designing (scheduling) algorithms. Machine learning can be a useful approach in
practice by predicting the future load of the system based on, for example,
historical data. However, the effectiveness of such an approach highly depends
on the quality of the predictions and can be quite far from optimal when
predictions are sub-par. On the other hand, while providing a worst-case
guarantee, classical online algorithms can be pessimistic for large classes of
inputs arising in practice.
  This paper, in the spirit of the new area of machine learning augmented
algorithms, attempts to obtain the best of both worlds for the classical,
deadline based, online speed-scaling problem: Based on the introduction of a
novel prediction setup, we develop algorithms that (i) obtain provably low
energy-consumption in the presence of adequate predictions, and (ii) are robust
against inadequate predictions, and (iii) are smooth, i.e., their performance
gradually degrades as the prediction error increases.

Computer aided diagnostics often requires analysis of a region of interest
(ROI) within a radiology scan, and the ROI may be an organ or a suborgan.
Although deep learning algorithms have the ability to outperform other methods,
they rely on the availability of a large amount of annotated data. Motivated by
the need to address this limitation, an approach to localisation and detection
of multiple organs based on supervised and semi-supervised learning is
presented here. It draws upon previous work by the authors on localising the
thoracic and lumbar spine region in CT images. The method generates six
bounding boxes of organs of interest, which are then fused to a single bounding
box. The results of experiments on localisation of the Spleen, Left and Right
Kidneys in CT Images using supervised and semi supervised learning (SSL)
demonstrate the ability to address data limitations with a much smaller data
set and fewer annotations, compared to other state-of-the-art methods. The SSL
performance was evaluated using three different mixes of labelled and
unlabelled data (i.e.30:70,35:65,40:60) for each of lumbar spine, spleen left
and right kidneys respectively. The results indicate that SSL provides a
workable alternative especially in medical imaging where it is difficult to
obtain annotated data.

Testing practices within the machine learning (ML) community have centered
around assessing a learned model's predictive performance measured against a
test dataset, often drawn from the same distribution as the training dataset.
While recent work on robustness and fairness testing within the ML community
has pointed to the importance of testing against distributional shifts, these
efforts also focus on estimating the likelihood of the model making an error
against a reference dataset/distribution. We argue that this view of testing
actively discourages researchers and developers from looking into other sources
of robustness failures, for instance corner cases which may have severe
undesirable impacts. We draw parallels with decades of work within software
engineering testing focused on assessing a software system against various
stress conditions, including corner cases, as opposed to solely focusing on
average-case behaviour. Finally, we put forth a set of recommendations to
broaden the view of machine learning testing to a rigorous practice.

Current approaches for deformable medical image registration often struggle
to fulfill all of the following criteria: versatile applicability, small
computation or training times, and the being able to estimate large
deformations. Furthermore, end-to-end networks for supervised training of
registration often become overly complex and difficult to train. For the
Learn2Reg2021 challenge, we aim to address these issues by decoupling feature
learning and geometric alignment. First, we introduce a new very fast and
accurate optimisation method. By using discretised displacements and a coupled
convex optimisation procedure, we are able to robustly cope with large
deformations. With the help of an Adam-based instance optimisation, we achieve
very accurate registration performances and by using regularisation, we obtain
smooth and plausible deformation fields. Second, to be versatile for different
registration tasks, we extract hand-crafted features that are modality and
contrast invariant and complement them with semantic features from a
task-specific segmentation U-Net. With our results we were able to achieve the
overall Learn2Reg2021 challenge's second place, winning Task 1 and being second
and third in the other two tasks.

We address efficient calculation of influence functions for tracking
predictions back to the training data. We propose and analyze a new approach to
speeding up the inverse Hessian calculation based on Arnoldi iteration. With
this improvement, we achieve, to the best of our knowledge, the first
successful implementation of influence functions that scales to full-size
(language and vision) Transformer models with several hundreds of millions of
parameters. We evaluate our approach on image classification and
sequence-to-sequence tasks with tens to a hundred of millions of training
examples. Our code will be available at
https://github.com/google-research/jax-influence.

Depth and ego-motion estimations are essential for the localization and
navigation of autonomous robots and autonomous driving. Recent studies make it
possible to learn the per-pixel depth and ego-motion from the unlabeled
monocular video. A novel unsupervised training framework is proposed with 3D
hierarchical refinement and augmentation using explicit 3D geometry. In this
framework, the depth and pose estimations are hierarchically and mutually
coupled to refine the estimated pose layer by layer. The intermediate view
image is proposed and synthesized by warping the pixels in an image with the
estimated depth and coarse pose. Then, the residual pose transformation can be
estimated from the new view image and the image of the adjacent frame to refine
the coarse pose. The iterative refinement is implemented in a differentiable
manner in this paper, making the whole framework optimized uniformly.
Meanwhile, a new image augmentation method is proposed for the pose estimation
by synthesizing a new view image, which creatively augments the pose in 3D
space but gets a new augmented 2D image. The experiments on KITTI demonstrate
that our depth estimation achieves state-of-the-art performance and even
surpasses recent approaches that utilize other auxiliary tasks. Our visual
odometry outperforms all recent unsupervised monocular learning-based methods
and achieves competitive performance to the geometry-based method, ORB-SLAM2
with back-end optimization.

Deep generative models of molecules have grown immensely in popularity,
trained on relevant datasets, these models are used to search through chemical
space. The downstream utility of generative models for the inverse design of
novel functional compounds depends on their ability to learn a training
distribution of molecules. The most simple example is a language model that
takes the form of a recurrent neural network and generates molecules using a
string representation. More sophisticated are graph generative models, which
sequentially construct molecular graphs and typically achieve state of the art
results. However, recent work has shown that language models are more capable
than once thought, particularly in the low data regime. In this work, we
investigate the capacity of simple language models to learn distributions of
molecules. For this purpose, we introduce several challenging generative
modeling tasks by compiling especially complex distributions of molecules. On
each task, we evaluate the ability of language models as compared with two
widely used graph generative models. The results demonstrate that language
models are powerful generative models, capable of adeptly learning complex
molecular distributions -- and yield better performance than the graph models.
Language models can accurately generate: distributions of the highest scoring
penalized LogP molecules in ZINC15, multi-modal molecular distributions as well
as the largest molecules in PubChem.

Deep reinforcement learning (RL) agents are becoming increasingly proficient
in a range of complex control tasks. However, the agent's behavior is usually
difficult to interpret due to the introduction of black-box function, making it
difficult to acquire the trust of users. Although there have been some
interesting interpretation methods for vision-based RL, most of them cannot
uncover temporal causal information, raising questions about their reliability.
To address this problem, we present a temporal-spatial causal interpretation
(TSCI) model to understand the agent's long-term behavior, which is essential
for sequential decision-making. TSCI model builds on the formulation of
temporal causality, which reflects the temporal causal relations between
sequential observations and decisions of RL agent. Then a separate causal
discovery network is employed to identify temporal-spatial causal features,
which are constrained to satisfy the temporal causality. TSCI model is
applicable to recurrent agents and can be used to discover causal features with
high efficiency once trained. The empirical results show that TSCI model can
produce high-resolution and sharp attention masks to highlight task-relevant
temporal-spatial information that constitutes most evidence about how
vision-based RL agents make sequential decisions. In addition, we further
demonstrate that our method is able to provide valuable causal interpretations
for vision-based RL agents from the temporal perspective.

Estimating individualized treatment effects (ITEs) from observational data is
crucial for decision-making. In order to obtain unbiased ITE estimates, a
common assumption is that all confounders are observed. However, in practice,
it is unlikely that we observe these confounders directly. Instead, we often
observe noisy measurements of true confounders, which can serve as valid
proxies. In this paper, we address the problem of estimating ITE in the
longitudinal setting where we observe noisy proxies instead of true
confounders. To this end, we develop the Deconfounding Temporal Autoencoder, a
novel method that leverages observed noisy proxies to learn a hidden embedding
that reflects the true hidden confounders. In particular, the DTA combines a
long short-term memory autoencoder with a causal regularization penalty that
renders the potential outcomes and treatment assignment conditionally
independent given the learned hidden embedding. Once the hidden embedding is
learned via DTA, state-of-the-art outcome models can be used to control for it
and obtain unbiased estimates of ITE. Using synthetic and real-world medical
data, we demonstrate the effectiveness of our DTA by improving over
state-of-the-art benchmarks by a substantial margin.

We present a new approach to instill 4D dynamic object priors into learned 3D
representations by unsupervised pre-training. We observe that dynamic movement
of an object through an environment provides important cues about its
objectness, and thus propose to imbue learned 3D representations with such
dynamic understanding, that can then be effectively transferred to improved
performance in downstream 3D semantic scene understanding tasks. We propose a
new data augmentation scheme leveraging synthetic 3D shapes moving in static 3D
environments, and employ contrastive learning under 3D-4D constraints that
encode 4D invariances into the learned 3D representations. Experiments
demonstrate that our unsupervised representation learning results in
improvement in downstream 3D semantic segmentation, object detection, and
instance segmentation tasks, and moreover, notably improves performance in
data-scarce scenarios.

Flexible-joint manipulators are frequently used for increased safety during
human-robot collaboration and shared workspace tasks. However, joint
flexibility significantly reduces the accuracy of motion, especially at high
velocities and with inexpensive actuators. In this paper, we present a
learning-based approach to identify the unknown dynamics of a flexible-joint
manipulator and improve the trajectory tracking at high velocities. We propose
a two-stage model which is composed of a one-step forward dynamics future
predictor and an inverse dynamics estimator. The second part is based on linear
time-invariant dynamical operators to approximate the feed-forward joint
position and velocity commands. We train the model end-to-end on real-world
data and evaluate it on the Baxter robot. Our experiments indicate that
augmenting the input with one-step future state prediction improves the
performance, compared to the same model without prediction. We compare joint
position, joint velocity and end-effector position tracking accuracy against
the classical baseline controller and several simpler models.

In these notes we will tackle the problem of finding optimal policies for
Markov decision processes (MDPs) which are not fully known to us. Our intention
is to slowly transition from an offline setting to an online (learning)
setting. Namely, we are moving towards reinforcement learning.

Anomaly detection is concerned with a wide range of applications such as
fault detection, system monitoring, and event detection. Identifying anomalies
from metering data obtained from smart metering system is a critical task to
enhance reliability, stability, and efficiency of the power system. This paper
presents an anomaly detection process to find outliers observed in the smart
metering system. In the proposed approach, bi-directional long short-term
memory (BiLSTM) based autoencoder is used and finds the anomalous data point.
It calculates the reconstruction error through autoencoder with the
non-anomalous data, and the outliers to be classified as anomalies are
separated from the non-anomalous data by predefined threshold. Anomaly
detection method based on the BiLSTM autoencoder is tested with the metering
data corresponding to 4 types of energy sources electricity/water/heating/hot
water collected from 985 households.

Tabular data are ubiquitous in real world applications. Although many
commonly-used neural components (e.g., convolution) and extensible neural
networks (e.g., ResNet) have been developed by the machine learning community,
few of them were effective for tabular data and few designs were adequately
tailored for tabular data structures. In this paper, we propose a novel and
flexible neural component for tabular data, called Abstract Layer (AbstLay),
which learns to explicitly group correlative input features and generate
higher-level features for semantics abstraction. Also, we design a structure
re-parameterization method to compress the learned AbstLay, thus reducing the
computational complexity by a clear margin in the reference phase. A special
basic block is built using AbstLays, and we construct a family of Deep Abstract
Networks (DANets) for tabular data classification and regression by stacking
such blocks. In DANets, a special shortcut path is introduced to fetch
information from raw tabular features, assisting feature interactions across
different levels. Comprehensive experiments on seven real-world tabular
datasets show that our AbstLay and DANets are effective for tabular data
classification and regression, and the computational complexity is superior to
competitive methods. Besides, we evaluate the performance gains of DANet as it
goes deep, verifying the extendibility of our method. Our code is available at
https://github.com/WhatAShot/DANet.

Noisy labels damage the performance of deep networks. For robust learning, a
prominent two-stage pipeline alternates between eliminating possible incorrect
labels and semi-supervised training. However, discarding part of noisy labels
could result in a loss of information, especially when the corruption is not
completely random, e.g., class-dependent or instance-dependent. Moreover, from
the training dynamics of a representative two-stage method DivideMix, we
identify the domination of confirmation bias: pseudo-labels fail to correct a
considerable amount of noisy labels, and consequently, the errors accumulate.
To sufficiently exploit information from noisy labels and mitigate wrong
corrections, we propose Robust Label Refurbishment (Robust LR) a new hybrid
method that integrates pseudo-labeling and confidence estimation techniques to
refurbish noisy labels. We show that our method successfully alleviates the
damage of both label noise and confirmation bias. As a result, it achieves
state-of-the-art results across datasets and noise types. For example, Robust
LR achieves up to 4.5% absolute top-1 accuracy improvement over the previous
best on the real-world noisy dataset WebVision.

The rapid rise in demand for training large neural network architectures has
brought into focus the need for partitioning strategies, for example by using
data, model, or pipeline parallelism. Implementing these methods is
increasingly supported through program primitives, but identifying efficient
partitioning strategies requires expensive experimentation and expertise. We
present the prototype of an automated partitioner that seamlessly integrates
into existing compilers and existing user workflows. Our partitioner enables
SPMD-style parallelism that encompasses data parallelism and
parameter/activation sharding. Through a combination of inductive tactics and
search in a platform-independent partitioning IR, automap can recover expert
partitioning strategies such as Megatron sharding for transformer layers.

This paper presents a technique for navigation of mobile robot with Deep
Q-Network (DQN) combined with Gated Recurrent Unit (GRU). The DQN integrated
with the GRU allows action skipping for improved navigation performance. This
technique aims at efficient navigation of mobile robot such as autonomous
parking robot. Framework for reinforcement learning can be applied to the DQN
combined with the GRU in a real environment, which can be modeled by the
Partially Observable Markov Decision Process (POMDP). By allowing action
skipping, the ability of the DQN combined with the GRU in learning key-action
can be improved. The proposed algorithm is applied to explore the feasibility
of solution in real environment by the ROS-Gazebo simulator, and the simulation
results show that the proposed algorithm achieves improved performance in
navigation and collision avoidance as compared to the results obtained by DQN
alone and DQN combined with GRU without allowing action skipping.

Abstract This project presents a system of neural networks to translate
between images and melodies. Autoencoders compress the information in samples
to abstract representation. A translation network learns a set of
correspondences between musical and visual concepts from repeated joint
exposure. The resulting "artificial synesthete" generates simple melodies
inspired by images, and images from music. These are novel interpretation (not
transposed data), expressing the machine' perception and understanding.
Observing the work, one explores the machine's perception and thus, by
contrast, one's own.

Univariate and multivariate general linear regression models, subject to
linear inequality constraints, arise in many scientific applications. The
linear inequality restrictions on model parameters are often available from
phenomenological knowledge and motivated by machine learning applications of
high-consequence engineering systems (Agrell, 2019; Veiga and Marrel, 2012).
Some studies on the multiple linear models consider known linear combinations
of the regression coefficient parameters restricted between upper and lower
bounds. In the present paper, we consider both univariate and multivariate
general linear models subjected to this kind of linear restrictions. So far,
research on univariate cases based on Bayesian methods is all under the
condition that the coefficient matrix of the linear restrictions is a square
matrix of full rank. This condition is not, however, always feasible. Another
difficulty arises at the estimation step by implementing the Gibbs algorithm,
which exhibits, in most cases, slow convergence. This paper presents a Bayesian
method to estimate the regression parameters when the matrix of the constraints
providing the set of linear inequality restrictions undergoes no condition. For
the multivariate case, our Bayesian method estimates the regression parameters
when the number of the constrains is less than the number of the regression
coefficients in each multiple linear models. We examine the efficiency of our
Bayesian method through simulation studies for both univariate and multivariate
regressions. After that, we illustrate that the convergence of our algorithm is
relatively faster than the previous methods. Finally, we use our approach to
analyze two real datasets.

In many reinforcement learning applications, the underlying environment
reward and transition functions are explicitly known differentiable functions.
This enables us to use recent research which applies machine learning tools to
stochastic control to find optimal action functions. In this paper, we define
differentiable reinforcement learning as a particular case of this research. We
find that incorporating deep learning in this framework leads to more accurate
and stable solutions than those obtained from more generic actor critic
algorithms. We apply this deep differentiable reinforcement learning (DDRL)
algorithm to the problem of one asset optimal trading strategies in various
environments where the market dynamics are known. Thanks to the stability of
this method, we are able to efficiently find optimal strategies for complex
multi-scale market models. We also extend these methods to simultaneously find
optimal action functions for a wide range of environment parameters. This makes
it applicable to real life financial signals and portfolio optimization where
the expected return has multiple time scales. In the case of a slow and a fast
alpha signal, we find that the optimal trading strategy consists in using the
fast signal to time the trades associated to the slow signal.

In this paper, we aim at providing an effective Pairwise Learning Neural Link
Prediction (PLNLP) framework. The framework treats link prediction as a
pairwise learning to rank problem and consists of four main components, i.e.,
neighborhood encoder, link predictor, negative sampler and objective function.
The framework is flexible that any generic graph neural convolution or link
prediction specific neural architecture could be employed as neighborhood
encoder. For link predictor, we design different scoring functions, which could
be selected based on different types of graphs. In negative sampler, we provide
several sampling strategies, which are problem specific. As for objective
function, we propose to use an effective ranking loss, which approximately
maximizes the standard ranking metric AUC. We evaluate the proposed PLNLP
framework on 4 link property prediction datasets of Open Graph Benchmark,
including ogbl-ddi, ogbl-collab, ogbl-ppa and ogbl-ciation2. PLNLP achieves top
1 performance on ogbl-ddi and ogbl-collab, and top 2 performance on
ogbl-ciation2 only with basic neural architecture. The performance demonstrates
the effectiveness of PLNLP.

Applications of deep learning for audio effects often focus on modeling
analog effects or learning to control effects to emulate a trained audio
engineer. However, deep learning approaches also have the potential to expand
creativity through neural audio effects that enable new sound transformations.
While recent work demonstrated that neural networks with random weights produce
compelling audio effects, control of these effects is limited and unintuitive.
To address this, we introduce a method for the steerable discovery of neural
audio effects. This method enables the design of effects using example
recordings provided by the user. We demonstrate how this method produces an
effect similar to the target effect, along with interesting inaccuracies, while
also providing perceptually relevant controls.

The ability of a model to learn continually can be empirically assessed in
different continual learning scenarios. Each scenario defines the constraints
and the opportunities of the learning environment. Here, we challenge the
current trend in the continual learning literature to experiment mainly on
class-incremental scenarios, where classes present in one experience are never
revisited. We posit that an excessive focus on this setting may be limiting for
future research on continual learning, since class-incremental scenarios
artificially exacerbate catastrophic forgetting, at the expense of other
important objectives like forward transfer and computational efficiency. In
many real-world environments, in fact, repetition of previously encountered
concepts occurs naturally and contributes to softening the disruption of
previous knowledge. We advocate for a more in-depth study of alternative
continual learning scenarios, in which repetition is integrated by design in
the stream of incoming information. Starting from already existing proposals,
we describe the advantages such class-incremental with repetition scenarios
could offer for a more comprehensive assessment of continual learning models.

Increasing deployment of photovoltaic (PV) plants requires methods for
automatic detection of faulty PV modules in modalities, such as infrared (IR)
images. Recently, deep learning has become popular for this. However, related
works typically sample train and test data from the same distribution ignoring
the presence of domain shift between data of different PV plants. Instead, we
frame fault detection as more realistic unsupervised domain adaptation problem
where we train on labelled data of one source PV plant and make predictions on
another target plant. We train a ResNet-34 convolutional neural network with a
supervised contrastive loss, on top of which we employ a k-nearest neighbor
classifier to detect anomalies. Our method achieves a satisfactory area under
the receiver operating characteristic (AUROC) of 73.3 % to 96.6 % on nine
combinations of four source and target datasets with 2.92 million IR images of
which 8.5 % are anomalous. It even outperforms a binary cross-entropy
classifier in some cases. With a fixed decision threshold this results in 79.4
% and 77.1 % correctly classified normal and anomalous images, respectively.
Most misclassified anomalies are of low severity, such as hot diodes and small
hot spots. Our method is insensitive to hyperparameter settings, converges
quickly and reliably detects unknown types of anomalies making it well suited
for practice. Possible uses are in automatic PV plant inspection systems or to
streamline manual labelling of IR datasets by filtering out normal images.
Furthermore, our work serves the community with a more realistic view on PV
module fault detection using unsupervised domain adaptation to develop more
performant methods with favorable generalization capabilities.

In federated learning (FL), data does not leave personal devices when they
are jointly training a machine learning model. Instead, these devices share
gradients with a central party (e.g., a company). Because data never "leaves"
personal devices, FL is presented as privacy-preserving. Yet, recently it was
shown that this protection is but a thin facade, as even a passive attacker
observing gradients can reconstruct data of individual users. In this paper, we
argue that prior work still largely underestimates the vulnerability of FL.
This is because prior efforts exclusively consider passive attackers that are
honest-but-curious. Instead, we introduce an active and dishonest attacker
acting as the central party, who is able to modify the shared model's weights
before users compute model gradients. We call the modified weights "trap
weights". Our active attacker is able to recover user data perfectly and at
near zero costs: the attack requires no complex optimization objectives.
Instead, it exploits inherent data leakage from model gradients and amplifies
this effect by maliciously altering the weights of the shared model. These
specificities enable our attack to scale to models trained with large
mini-batches of data. Where attackers from prior work require hours to recover
a single data point, our method needs milliseconds to capture the full
mini-batch of data from both fully-connected and convolutional deep neural
networks. Finally, we consider mitigations. We observe that current
implementations of differential privacy (DP) in FL are flawed, as they
explicitly trust the central party with the crucial task of adding DP noise,
and thus provide no protection against a malicious central party. We also
consider other defenses and explain why they are similarly inadequate. A
significant redesign of FL is required for it to provide any meaningful form of
data privacy to users.

In 2003, Benedetto and Fickus introduced a vivid intuition for an objective
function called the frame potential, whose global minimizers are fundamental
objects known today as unit norm tight frames. Their main result was that the
frame potential exhibits no spurious local minimizers, suggesting local
optimization as an approach to construct these objects. Local optimization has
since become the workhorse of cutting-edge signal processing and machine
learning, and accordingly, the community has identified a variety of techniques
to study optimization landscapes. This chapter applies some of these techniques
to obtain three modern proofs of the Benedetto-Fickus theorem.

We propose an adaptation of the curriculum training framework, applicable to
state-of-the-art meta learning techniques for few-shot classification.
Curriculum-based training popularly attempts to mimic human learning by
progressively increasing the training complexity to enable incremental concept
learning. As the meta-learner's goal is learning how to learn from as few
samples as possible, the exact number of those samples (i.e. the size of the
support set) arises as a natural proxy of a given task's difficulty. We define
a simple yet novel curriculum schedule that begins with a larger support size
and progressively reduces it throughout training to eventually match the
desired shot-size of the test setup. This proposed method boosts the learning
efficiency as well as the generalization capability. Our experiments with the
MAML algorithm on two few-shot image classification tasks show significant
gains with the curriculum training framework. Ablation studies corroborate the
independence of our proposed method from the model architecture as well as the
meta-learning hyperparameters

In this paper, we address a crucial problem in fashion e-commerce (with
respect to customer experience, as well as revenue): color variants
identification, i.e., identifying fashion products that match exactly in their
design (or style), but only to differ in their color. We propose a generic
framework, that leverages deep visual Representation Learning at its heart, to
address this problem for our fashion e-commerce platform. Our framework could
be trained with supervisory signals in the form of triplets, that are obtained
manually. However, it is infeasible to obtain manual annotations for the entire
huge collection of data usually present in fashion e-commerce platforms, such
as ours, while capturing all the difficult corner cases. But, to our rescue,
interestingly we observed that this crucial problem in fashion e-commerce could
also be solved by simple color jitter based image augmentation, that recently
became widely popular in the contrastive Self-Supervised Learning (SSL)
literature, that seeks to learn visual representations without using manual
labels. This naturally led to a question in our mind: Could we leverage SSL in
our use-case, and still obtain comparable performance to our supervised
framework? The answer is, Yes! because, color variant fashion objects are
nothing but manifestations of a style, in different colors, and a model trained
to be invariant to the color (with, or without supervision), should be able to
recognize this! This is what the paper further demonstrates, both
qualitatively, and quantitatively, while evaluating a couple of
state-of-the-art SSL techniques, and also proposing a novel method.

We present a deep neural network method for learning a personal
representation for individuals that are performing a self neuromodulation task,
guided by functional MRI (fMRI). This neurofeedback task (watch vs. regulate)
provides the subjects with a continuous feedback contingent on down regulation
of their Amygdala signal and the learning algorithm focuses on this region's
time-course of activity. The representation is learned by a self-supervised
recurrent neural network, that predicts the Amygdala activity in the next fMRI
frame given recent fMRI frames and is conditioned on the learned individual
representation. It is shown that the individuals' representation improves the
next-frame prediction considerably. Moreover, this personal representation,
learned solely from fMRI images, yields good performance in linear prediction
of psychiatric traits, which is better than performing such a prediction based
on clinical data and personality tests. Our code is attached as supplementary
and the data would be shared subject to ethical approvals.

Probabilistic time series forecasting is crucial in many application domains
such as retail, ecommerce, finance, or biology. With the increasing
availability of large volumes of data, a number of neural architectures have
been proposed for this problem. In particular, Transformer-based methods
achieve state-of-the-art performance on real-world benchmarks. However, these
methods require a large number of parameters to be learned, which imposes high
memory requirements on the computational resources for training such models.
  To address this problem, we introduce a novel Bidirectional Temporal
Convolutional Network (BiTCN), which requires an order of magnitude less
parameters than a common Transformer-based approach. Our model combines two
Temporal Convolutional Networks (TCNs): the first network encodes future
covariates of the time series, whereas the second network encodes past
observations and covariates. We jointly estimate the parameters of an output
distribution via these two networks.
  Experiments on four real-world datasets show that our method performs on par
with four state-of-the-art probabilistic forecasting methods, including a
Transformer-based approach and WaveNet, on two point metrics (sMAPE, NRMSE) as
well as on a set of range metrics (quantile loss percentiles) in the majority
of cases. Secondly, we demonstrate that our method requires significantly less
parameters than Transformer-based methods, which means the model can be trained
faster with significantly lower memory requirements, which as a consequence
reduces the infrastructure cost for deploying these models.

We investigate data-driven forward-inverse problems for Yajima-Oikawa (YO)
system by employing two technologies which improve the performance of neural
network in deep physics-informed neural network (PINN), namely neuron-wise
locally adaptive activation functions and $L^2$ norm parameter regularization.
Indeed, we not only recover three different forms of vector rogue waves (RWs)
by means of three distinct initial-boundary value conditions in the forward
problem of YO system, including bright-bright RWs, intermediate-bright RWs and
dark-bright RWs, but also study the inverse problem of YO system by using
training data with different noise intensity. In order to deal with the problem
that the capacity of learning unknown parameters is not ideal when the PINN
with only locally adaptive activation functions utilizes training data with
noise interference in the inverse problem of YO system, thus we introduce $L^2$
norm regularization, which can drive the weights closer to origin, into PINN
with locally adaptive activation functions, then find that the PINN model with
two strategies shows amazing training effect by using training data with noise
interference to investigate the inverse problem of YO system.

We introduce ProtoPool, an interpretable image classification model with a
pool of prototypes shared by the classes. The training is more straightforward
than in the existing methods because it does not require the pruning stage. It
is obtained by introducing a fully differentiable assignment of prototypes to
particular classes. Moreover, we introduce a novel focal similarity function to
focus the model on the rare foreground features. We show that ProtoPool obtains
state-of-the-art accuracy on the CUB-200-2011 and the Stanford Cars datasets,
substantially reducing the number of prototypes. We provide a theoretical
analysis of the method and a user study to show that our prototypes are more
distinctive than those obtained with competitive methods.

In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US
imaging system can visualize a volume along three axial planes. This allows for
a full view of the anatomy, which is useful for gynecological (GYN) and
obstetrical (OB) applications. Unfortunately, the 3-D US has an inherent
limitation in resolution compared to the 2-D US. In the case of 3-D US with a
3-D mechanical probe, for example, the image quality is comparable along the
beam direction, but significant deterioration in image quality is often
observed in the other two axial image planes. To address this, here we propose
a novel unsupervised deep learning approach to improve 3-D US image quality. In
particular, using {\em unmatched} high-quality 2-D US images as a reference, we
trained a recently proposed switchable CycleGAN architecture so that every
mapping plane in 3-D US can learn the image quality of 2-D US images. Thanks to
the switchable architecture, our network can also provide real-time control of
image enhancement level based on user preference, which is ideal for a
user-centric scanner setup. Extensive experiments with clinical evaluation
confirm that our method offers significantly improved image quality as well
user-friendly flexibility.

Object Detection, a fundamental computer vision problem, has paramount
importance in smart camera systems. However, a truly reliable camera system
could be achieved if and only if the underlying object detection component is
robust enough across varying imaging conditions (or domains), for instance,
different times of the day, adverse weather conditions, etc. In an effort to
achieving a reliable camera system, in this paper, we make an attempt to train
such a robust detector. Unfortunately, to build a well-performing detector
across varying imaging conditions, one would require labeled training images
(often in large numbers) from a plethora of corner cases. As manually obtaining
such a large labeled dataset may be infeasible, we suggest using synthetic
images, to mimic different training image domains. We propose a novel,
contrastive learning method to align the latent representations of a pair of
real and synthetic images, to make the detector robust to the different
domains. However, we found that merely contrasting the embeddings may lead to
catastrophic forgetting of the information essential for object detection.
Hence, we employ a continual learning based penalty, to alleviate the issue of
forgetting, while contrasting the representations. We showcase that our
proposed method outperforms a wide range of alternatives to address the
extremely challenging, yet under-studied scenario of object detection at
night-time.

Contrastive learning has proven effective for pre-training image models on
unlabeled data with promising results for tasks such as medical image
classification. Using paired text (like radiological reports) during
pre-training improves the results even further. Still, most existing methods
target image classification downstream tasks and may not be optimal for
localized tasks like semantic segmentation or object detection. We therefore
propose Localized representation learning from Vision and Text (LoVT), to our
best knowledge, the first text-supervised pre-training method that targets
localized medical imaging tasks. Our method combines instance-level
image-report contrastive learning with local contrastive learning on image
region and report sentence representations. We evaluate LoVT and commonly used
pre-training methods on an evaluation framework of 18 localized tasks on chest
X-rays from five public datasets. LoVT performs best on 10 of the 18 studied
tasks making it the preferred method of choice for localized tasks.

We propose a new algorithm for training deep neural networks (DNNs) with
binary weights. In particular, we first cast the problem of training binary
neural networks (BiNNs) as a bilevel optimization instance and subsequently
construct flexible relaxations of this bilevel program. The resulting training
method shares its algorithmic simplicity with several existing approaches to
train BiNNs, in particular with the straight-through gradient estimator
successfully employed in BinaryConnect and subsequent methods. In fact, our
proposed method can be interpreted as an adaptive variant of the original
straight-through estimator that conditionally (but not always) acts like a
linear mapping in the backward pass of error propagation. Experimental results
demonstrate that our new algorithm offers favorable performance compared to

As Machine Learning (ML) models are becoming increasingly complex, one of the
central challenges is their deployment at scale, such that companies and
organizations can create value through Artificial Intelligence (AI). An
emerging paradigm in ML is a federated approach where the learning model is
delivered to a group of heterogeneous agents partially, allowing agents to
train the model locally with their own data. However, the problem of valuation
of models, as well the questions of incentives for collaborative training and
trading of data/models, have received limited treatment in the literature. In
this paper, a new ecosystem of ML model trading over a trusted Blockchain-based
network is proposed. The buyer can acquire the model of interest from the ML
market, and interested sellers spend local computations on their data to
enhance that model's quality. In doing so, the proportional relation between
the local data and the quality of trained models is considered, and the
valuations of seller's data in training the models are estimated through the
distributed Data Shapley Value (DSV). At the same time, the trustworthiness of
the entire trading process is provided by the distributed Ledger Technology
(DLT). Extensive experimental evaluation of the proposed approach shows a
competitive run-time performance, with a 15\% drop in the cost of execution,
and fairness in terms of incentives for the participants.

Numerous works have proven that existing neighbor-averaging Graph Neural
Networks cannot efficiently catch structure features, and many works show that
injecting structure, distance, position or spatial features can significantly
improve performance of GNNs, however, injecting overall structure and distance
into GNNs is an intuitive but remaining untouched idea. In this work, we shed
light on the direction. We first extracting hop-wise structure information and
compute distance distributional information, gathering with node's intrinsic
features, embedding them into same vector space and then adding them up. The
derived embedding vectors are then fed into GATs(like GAT, AGDN) and then
Correct and Smooth, experiments show that the DHSEGATs achieve competitive
result. The code is available at https://github.com/hzg0601/DHSEGATs.

We investigate a nonstochastic bandit setting in which the loss of an action
is not immediately charged to the player, but rather spread over the subsequent
rounds in an adversarial way. The instantaneous loss observed by the player at
the end of each round is then a sum of many loss components of previously
played actions. This setting encompasses as a special case the easier task of
bandits with delayed feedback, a well-studied framework where the player
observes the delayed losses individually.
  Our first contribution is a general reduction transforming a standard bandit
algorithm into one that can operate in the harder setting: We bound the regret
of the transformed algorithm in terms of the stability and regret of the
original algorithm. Then, we show that the transformation of a suitably tuned
FTRL with Tsallis entropy has a regret of order $\sqrt{(d+1)KT}$, where $d$ is
the maximum delay, $K$ is the number of arms, and $T$ is the time horizon.
Finally, we show that our results cannot be improved in general by exhibiting a
matching (up to a log factor) lower bound on the regret of any algorithm
operating in this setting.

The production of dark matter particles from confining dark sectors may lead
to many novel experimental signatures. Depending on the details of the theory,
dark quark production in proton-proton collisions could result in semivisible
jets of particles: collimated sprays of dark hadrons of which only some are
detectable by particle collider experiments. The experimental signature is
characterised by the presence of reconstructed missing momentum collinear with
the visible components of the jets. This complex topology is sensitive to
detector inefficiencies and mis-reconstruction that generate artificial missing
momentum. With this work, we propose a signal-agnostic strategy to reject
ordinary jets and identify semivisible jets via anomaly detection techniques. A
deep neural autoencoder network with jet substructure variables as input proves
highly useful for analyzing anomalous jets. The study focuses on the
semivisible jet signature; however, the technique can apply to any new physics
model that predicts signatures with anomalous jets from non-SM particles.

Data augmentation (DA) has been widely investigated to facilitate model
optimization in many tasks. However, in most cases, data augmentation is
randomly performed for each training sample with a certain probability, which
might incur content destruction and visual ambiguities. To eliminate this, in
this paper, we propose an effective approach, dubbed SelectAugment, to select
samples to be augmented in a deterministic and online manner based on the
sample contents and the network training status. Specifically, in each batch,
we first determine the augmentation ratio, and then decide whether to augment
each training sample under this ratio. We model this process as a two-step
Markov decision process and adopt Hierarchical Reinforcement Learning (HRL) to
learn the augmentation policy. In this way, the negative effects of the
randomness in selecting samples to augment can be effectively alleviated and
the effectiveness of DA is improved. Extensive experiments demonstrate that our
proposed SelectAugment can be adapted upon numerous commonly used DA methods,
e.g., Mixup, Cutmix, AutoAugment, etc, and improve their performance on
multiple benchmark datasets of image classification and fine-grained image

We consider online no-regret learning in unknown games with bandit feedback,
where each player can only observe its reward at each time -- determined by all
players' current joint action -- rather than its gradient. We focus on the
class of smooth and strongly monotone games and study optimal no-regret
learning therein. Leveraging self-concordant barrier functions, we first
construct a new bandit learning algorithm and show that it achieves the
single-agent optimal regret of $\tilde{\Theta}(n\sqrt{T})$ under smooth and
strongly concave reward functions ($n \geq 1$ is the problem dimension). We
then show that if each player applies this no-regret learning algorithm in
strongly monotone games, the joint action converges in the last iterate to the
unique Nash equilibrium at a rate of $\tilde{\Theta}(\sqrt{\frac{n^2}{T}})$.
Prior to our work, the best-known convergence rate in the same class of games
is $\tilde{O}(\sqrt[3]{\frac{n^2}{T}})$ (achieved by a different algorithm),
thus leaving open the problem of optimal no-regret learning algorithms (since
the known lower bound is $\Omega(\sqrt{\frac{n^2}{T}})$). Our results thus
settle this open problem and contribute to the broad landscape of bandit
game-theoretical learning by identifying the first doubly optimal bandit
learning algorithm, in that it achieves (up to log factors) both optimal regret
in the single-agent learning and optimal last-iterate convergence rate in the
multi-agent learning. We also present results on several application studies --
Cournot competition, Kelly auctions, and distributed regularized logistic
regression -- to demonstrate the efficacy of our algorithm.

Soft Actor-Critic (SAC) is considered the state-of-the-art algorithm in
continuous action space settings. It uses the maximum entropy framework for
efficiency and stability, and applies a heuristic temperature Lagrange term to
tune the temperature $\alpha$, which determines how "soft" the policy should
be. It is counter-intuitive that empirical evidence shows SAC does not perform
well in discrete domains. In this paper we investigate the possible
explanations for this phenomenon and propose Target Entropy Scheduled SAC
(TES-SAC), an annealing method for the target entropy parameter applied on SAC.
Target entropy is a constant in the temperature Lagrange term and represents
the target policy entropy in discrete SAC. We compare our method on Atari 2600
games with different constant target entropy SAC, and analyze on how our
scheduling affects SAC.

Electron transfer with changing occupation in the 4f subshell poses a
considerable challenge for quantitative predictions in quantum chemistry. Using
the example of cerium oxide, we identify the main deficiencies of common
parameter-dependent one-electron approaches, such as density functional theory
(DFT) with a Hubbard correction, or hybrid functionals. As a response, we
present the first benchmark of ab initio many-electron theory for electron
transfer energies and lattice parameters under periodic boundary conditions. We
show that the direct random phase approximation clearly outperforms all DFT
variations. From this foundation, we, then, systematically improve even
further. Periodic second-order M{\o}ller-Plesset perturbation theory meanwhile
manages to recover standard hybrid functional values. Using these approaches to
eliminate parameter bias allows for highly accurate benchmarks of strongly
correlated materials, the reliable assessment of various density functionals,
and functional fitting via machine-learning.

Multivariate time series forecasting is a challenging task because the data
involves a mixture of long- and short-term patterns, with dynamic
spatio-temporal dependencies among variables. Existing graph neural networks
(GNN) typically model multivariate relationships with a pre-defined spatial
graph or learned fixed adjacency graph. It limits the application of GNN and
fails to handle the above challenges. In this paper, we propose a novel
framework, namely static- and dynamic-graph learning-neural network (SDGL). The
model acquires static and dynamic graph matrices from data to model long- and
short-term patterns respectively. Static matric is developed to capture the
fixed long-term association pattern via node embeddings, and we leverage graph
regularity for controlling the quality of the learned static graph. To capture
dynamic dependencies among variables, we propose dynamic graphs learning method
to generate time-varying matrices based on changing node features and static
node embeddings. And in the method, we integrate the learned static graph
information as inductive bias to construct dynamic graphs and local
spatio-temporal patterns better. Extensive experiments are conducted on two
traffic datasets with extra structural information and four time series
datasets, which show that our approach achieves state-of-the-art performance on
almost all datasets. If the paper is accepted, I will open the source code on
github.

The ability to successfully grasp objects is crucial in robotics, as it
enables several interactive downstream applications. To this end, most
approaches either compute the full 6D pose for the object of interest or learn
to predict a set of grasping points. While the former approaches do not scale
well to multiple object instances or classes yet, the latter require large
annotated datasets and are hampered by their poor generalization capabilities
to new geometries. To overcome these shortcomings, we propose to teach a robot
how to grasp an object with a simple and short human demonstration. Hence, our
approach neither requires many annotated images nor is it restricted to a
specific geometry. We first present a small sequence of RGB-D images displaying
a human-object interaction. This sequence is then leveraged to build associated
hand and object meshes that represent the depicted interaction. Subsequently,
we complete missing parts of the reconstructed object shape and estimate the
relative transformation between the reconstruction and the visible object in
the scene. Finally, we transfer the a-priori knowledge from the relative pose
between object and human hand with the estimate of the current object pose in
the scene into necessary grasping instructions for the robot. Exhaustive
evaluations with Toyota's Human Support Robot (HSR) in real and synthetic
environments demonstrate the applicability of our proposed methodology and its
advantage in comparison to previous approaches.

Offline reinforcement learning leverages previously-collected offline
datasets to learn optimal policies with no necessity to access the real
environment. Such a paradigm is also desirable for multi-agent reinforcement
learning (MARL) tasks, given the increased interactions among agents and with
the enviroment. Yet, in MARL, the paradigm of offline pre-training with online
fine-tuning has not been studied, nor datasets or benchmarks for offline MARL
research are available. In this paper, we facilitate the research by providing
large-scale datasets, and use them to examine the usage of the Decision
Transformer in the context of MARL. We investigate the generalisation of MARL
offline pre-training in the following three aspects: 1) between single agents
and multiple agents, 2) from offline pretraining to the online fine-tuning, and
3) to that of multiple downstream tasks with few-shot and zero-shot
capabilities. We start by introducing the first offline MARL dataset with
diverse quality levels based on the StarCraftII environment, and then propose
the novel architecture of multi-agent decision transformer (MADT) for effective
offline learning. MADT leverages transformer's modelling ability of sequence
modelling and integrates it seamlessly with both offline and online MARL tasks.
A crucial benefit of MADT is that it learns generalisable policies that can
transfer between different types of agents under different task scenarios. On
StarCraft II offline dataset, MADT outperforms the state-of-the-art offline RL
baselines. When applied to online tasks, the pre-trained MADT significantly
improves sample efficiency, and enjoys strong performance both few-short and
zero-shot cases. To our best knowledge, this is the first work that studies and
demonstrates the effectiveness of offline pre-trained models in terms of sample
efficiency and generalisability enhancements in MARL.

In cooperative localization, communicating mobile agents use inter-agent
relative measurements to improve their dead-reckoning-based global
localization. Measurement scheduling enables an agent to decide which subset of
available inter-agent relative measurements it should process when its
computational resources are limited. Optimal measurement scheduling is an
NP-hard combinatorial optimization problem. The so-called sequential greedy
(SG) algorithm is a popular suboptimal polynomial-time solution for this
problem. However, the merit function evaluation for the SG algorithms requires
access to the state estimate vector and error covariance matrix of all the
landmark agents (teammates that an agent can take measurements from). This
paper proposes a measurement scheduling for CL that follows the SG approach but
reduces the communication and computation cost by using a neural network-based
surrogate model as a proxy for the SG algorithm's merit function. The
significance of this model is that it is driven by local information and only a
scalar metadata from the landmark agents. This solution addresses the time and
memory complexity issues of running the SG algorithm in three ways: (a)
reducing the inter-agent communication message size, (b) decreasing the
complexity of function evaluations by using a simpler surrogate (proxy)
function, (c) reducing the required memory size.Simulations demonstrate our
results.

Quantizing the floating-point weights and activations of deep convolutional
neural networks to fixed-point representation yields reduced memory footprints
and inference time. Recently, efforts have been afoot towards zero-shot
quantization that does not require original unlabelled training samples of a
given task. These best-published works heavily rely on the learned batch
normalization (BN) parameters to infer the range of the activations for
quantization. In particular, these methods are built upon either empirical
estimation framework or the data distillation approach, for computing the range
of the activations. However, the performance of such schemes severely degrades
when presented with a network that does not accommodate BN layers. In this line
of thought, we propose a generalized zero-shot quantization (GZSQ) framework
that neither requires original data nor relies on BN layer statistics. We have
utilized the data distillation approach and leveraged only the pre-trained
weights of the model to estimate enriched data for range calibration of the
activations. To the best of our knowledge, this is the first work that utilizes
the distribution of the pretrained weights to assist the process of zero-shot
quantization. The proposed scheme has significantly outperformed the existing
zero-shot works, e.g., an improvement of ~ 33% in classification accuracy for
MobileNetV2 and several other models that are w & w/o BN layers, for a variety
of tasks. We have also demonstrated the efficacy of the proposed work across
multiple open-source quantization frameworks. Importantly, our work is the
first attempt towards the post-training zero-shot quantization of futuristic
unnormalized deep neural networks.

Recent advances in computationally efficient non-myopic Bayesian optimization
(BO) improve query efficiency over traditional myopic methods like expected
improvement while only modestly increasing computational cost. These advances
have been largely limited, however, to unconstrained optimization. For
constrained optimization, the few existing non-myopic BO methods require heavy
computation. For instance, one existing non-myopic constrained BO method [Lam
and Willcox, 2017] relies on computationally expensive unreliable brute-force
derivative-free optimization of a Monte Carlo rollout acquisition function.
Methods that use the reparameterization trick for more efficient
derivative-based optimization of non-myopic acquisition functions in the
unconstrained setting, like sample average approximation and infinitesimal
perturbation analysis, do not extend: constraints introduce discontinuities in
the sampled acquisition function surface that hinder its optimization.
Moreover, we argue here that being non-myopic is even more important in
constrained problems because fear of violating constraints pushes myopic
methods away from sampling the boundary between feasible and infeasible
regions, slowing the discovery of optimal solutions with tight constraints. In
this paper, we propose a computationally efficient two-step lookahead
constrained Bayesian optimization acquisition function (2-OPT-C) supporting
both sequential and batch settings. To enable fast acquisition function
optimization, we develop a novel likelihood-ratio-based unbiased estimator of
the gradient of the two-step optimal acquisition function that does not use the
reparameterization trick. In numerical experiments, 2-OPT-C typically improves
query efficiency by 2x or more over previous methods, and in some cases by 10x
or more.

Grant-free random access has the potential to support massive connectivity in
Internet of Things (IoT) networks, where joint activity detection and channel
estimation (JADCE) is a key issue that needs to be tackled. The existing
methods for JADCE usually suffer from one of the following limitations: high
computational complexity, ineffective in inducing sparsity, and incapable of
handling complex matrix estimation. To mitigate all the aforementioned
limitations, we in this paper develop an effective unfolding neural network
framework built upon the proximal operator method to tackle the JADCE problem
in IoT networks, where the base station is equipped with multiple antennas.
Specifically, the JADCE problem is formulated as a group-sparse-matrix
estimation problem, which is regularized by non-convex minimax concave penalty
(MCP). This problem can be iteratively solved by using the proximal operator
method, based on which we develop a unfolding neural network structure by
parameterizing the algorithmic iterations. By further exploiting the coupling
structure among the training parameters as well as the analytical computation,
we develop two additional unfolding structures to reduce the training
complexity. We prove that the proposed algorithm achieves a linear convergence
rate. Results show that our proposed three unfolding structures not only
achieve a faster convergence rate but also obtain a higher estimation accuracy
than the baseline methods.

With the emergence of deep learning in the last years, new opportunities
arose in Earth observation research. Nevertheless, they also brought with them
new challenges. The data-hungry training processes of deep learning models
demand large, resource expensive, annotated data sets and partly replaced
knowledge-driven approaches so that model behaviour and the final prediction
process became a black box. The proposed SyntEO approach enables Earth
observation researchers to automatically generate large deep learning ready
data sets by merging existing and procedural data. SyntEO does this by
including expert knowledge in the data generation process in a highly
structured manner to control the automatic image and label generation by
employing an ontology. In this way, fully controllable experiment environments
are set up, which support insights in the model training on the synthetic data
sets. Thus, SyntEO makes the learning process approachable, which is an
important cornerstone for explainable machine learning. We demonstrate the
SyntEO approach by predicting offshore wind farms in Sentinel-1 images on two
of the worlds largest offshore wind energy production sites. The largest
generated data set has 90,000 training examples. A basic convolutional neural
network for object detection, that is only trained on this synthetic data,
confidently detects offshore wind farms by minimising false detections in
challenging environments. In addition, four sequential data sets are generated,
demonstrating how the SyntEO approach can precisely define the data set
structure and influence the training process. SyntEO is thus a hybrid approach
that creates an interface between expert knowledge and data-driven image
analysis.

As the concept and implementation of cutting-edge technologies like
artificial intelligence and machine learning has become relevant, academics,
researchers and information professionals involve research in this area. The
objective of this systematic literature review is to provide a synthesis of
empirical studies exploring application of artificial intelligence and machine
learning in libraries. To achieve the objectives of the study, a systematic
literature review was conducted based on the original guidelines proposed by
Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA
and LISTA databases. Following the rigorous/ established selection process, a
total of thirty-two articles were finally selected, reviewed and analyzed to
summarize on the application of AI and ML domain and techniques which are most
often used in libraries. Findings show that the current state of the AI and ML
research that is relevant with the LIS domain mainly focuses on theoretical
works. However, some researchers also emphasized on implementation projects or
case studies. This study will provide a panoramic view of AI and ML in
libraries for researchers, practitioners and educators for furthering the more
technology-oriented approaches, and anticipating future innovation pathways.

Despite great strides made on fine-grained visual classification (FGVC),
current methods are still heavily reliant on fully-supervised paradigms where
ample expert labels are called for. Semi-supervised learning (SSL) techniques,
acquiring knowledge from unlabeled data, provide a considerable means forward
and have shown great promise for coarse-grained problems. However, exiting SSL
paradigms mostly assume in-distribution (i.e., category-aligned) unlabeled
data, which hinders their effectiveness when re-proposed on FGVC. In this
paper, we put forward a novel design specifically aimed at making
out-of-distribution data work for semi-supervised FGVC, i.e., to "clue them
in". We work off an important assumption that all fine-grained categories
naturally follow a hierarchical structure (e.g., the phylogenetic tree of
"Aves" that covers all bird species). It follows that, instead of operating on
individual samples, we can instead predict sample relations within this tree
structure as the optimization goal of SSL. Beyond this, we further introduced
two strategies uniquely brought by these tree structures to achieve
inter-sample consistency regularization and reliable pseudo-relation. Our
experimental results reveal that (i) the proposed method yields good robustness
against out-of-distribution data, and (ii) it can be equipped with prior arts,
boosting their performance thus yielding state-of-the-art results. Code is
available at https://github.com/PRIS-CV/RelMatch.

Model-based reinforcement learning methods achieve significant sample
efficiency in many tasks, but their performance is often limited by the
existence of the model error. To reduce the model error, previous works use a
single well-designed network to fit the entire environment dynamics, which
treats the environment dynamics as a black box. However, these methods lack to
consider the environmental decomposed property that the dynamics may contain
multiple sub-dynamics, which can be modeled separately, allowing us to
construct the world model more accurately. In this paper, we propose the
Environment Dynamics Decomposition (ED2), a novel world model construction
framework that models the environment in a decomposing manner. ED2 contains two
key components: sub-dynamics discovery (SD2) and dynamics decomposition
prediction (D2P). SD2 discovers the sub-dynamics in an environment and then D2P
constructs the decomposed world model following the sub-dynamics. ED2 can be
easily combined with existing MBRL algorithms and empirical results show that
ED2 significantly reduces the model error and boosts the performance of the
state-of-the-art MBRL algorithms on various tasks.

Object detection has achieved a huge breakthrough with deep neural networks
and massive annotated data. However, current detection methods cannot be
directly transferred to the scenario where the annotated data is scarce due to
the severe overfitting problem. Although few-shot learning and zero-shot
learning have been extensively explored in the field of image classification,
it is indispensable to design new methods for object detection in the
data-scarce scenario since object detection has an additional challenging
localization task. Low-Shot Object Detection (LSOD) is an emerging research
topic of detecting objects from a few or even no annotated samples, consisting
of One-Shot Object Detection (OSOD), Few-Shot Object Detection (FSOD) and
Zero-Shot Object Detection (ZSD). This survey provides a comprehensive review
of LSOD methods. First, we propose a thorough taxonomy of LSOD methods and
analyze them systematically, comprising some extensional topics of LSOD
(semi-supervised LSOD, weakly-supervised LSOD and incremental LSOD). Then, we
indicate the pros and cons of current LSOD methods with a comparison of their
performance. Finally, we discuss the challenges and promising directions of
LSOD to provide guidance for future works.

We propose a novel policy gradient method for multi-agent reinforcement
learning, which leverages two different variance-reduction techniques and does
not require large batches over iterations. Specifically, we propose a
momentum-based decentralized policy gradient tracking (MDPGT) where a new
momentum-based variance reduction technique is used to approximate the local
policy gradient surrogate with importance sampling, and an intermediate
parameter is adopted to track two consecutive policy gradient surrogates.
Moreover, MDPGT provably achieves the best available sample complexity of
$\mathcal{O}(N^{-1}\epsilon^{-3})$ for converging to an $\epsilon$-stationary
point of the global average of $N$ local performance functions (possibly
nonconcave). This outperforms the state-of-the-art sample complexity in
decentralized model-free reinforcement learning, and when initialized with a
single trajectory, the sample complexity matches those obtained by the existing
decentralized policy gradient methods. We further validate the theoretical
claim for the Gaussian policy function. When the required error tolerance
$\epsilon$ is small enough, MDPGT leads to a linear speed up, which has been
previously established in decentralized stochastic optimization, but not for
reinforcement learning. Lastly, we provide empirical results on a multi-agent
reinforcement learning benchmark environment to support our theoretical
findings.

How to extract meaningful information in user historical behavior plays a
crucial role in recommendation. User behavior sequence often contains multiple
conceptually distinct items that belong to different item groups and the number
of the item groups is changing over time. It is necessary to learn a dynamic
group of representations according the item groups in a user historical
behavior. However, current works only learns a predefined and fixed number
representations which includes single representation methods and multi
representations methods from the user context that could lead to suboptimal
recommendation quality. In this paper we propose a model that can automatically
and adaptively generates a dynamic group of representations from the user
behavior accordingly. To be specific, AutoRep is composed of an informative
representation construct (IRC) module and a dynamic representations construct
(DRC) module. The IRC module learns the overall sequential characteristics of
user behavior with a bi-directional architecture transformer. The DRC module
dynamically allocate the item in the user behavior into different item groups
and form a dynamic group of representations in a differentiable method. Such
design improves the model recommendation performance. We evaluate the proposed
model on five benchmark datasets. The results show that AutoRep outperforms
representative baselines. Further ablation study has been conducted to deepen
our understandings of AutoRep, including the proposed module IRC and DRC.

The Markov decision process (MDP) provides a mathematical framework for
modeling sequential decision-making problems, many of which are crucial to
security and safety, such as autonomous driving and robot control. The rapid
development of artificial intelligence research has created efficient methods
for solving MDPs, such as deep neural networks (DNNs), reinforcement learning
(RL), and imitation learning (IL). However, these popular models for solving
MDPs are neither thoroughly tested nor rigorously reliable.
  We present MDPFuzzer, the first blackbox fuzz testing framework for models
solving MDPs. MDPFuzzer forms testing oracles by checking whether the target
model enters abnormal and dangerous states. During fuzzing, MDPFuzzer decides
which mutated state to retain by measuring if it can reduce cumulative rewards
or form a new state sequence. We design efficient techniques to quantify the
"freshness" of a state sequence using Gaussian mixture models (GMMs) and
dynamic expectation-maximization (DynEM). We also prioritize states with high
potential of revealing crashes by estimating the local sensitivity of target
models over states.
  MDPFuzzer is evaluated on five state-of-the-art models for solving MDPs,
including supervised DNN, RL, IL, and multi-agent RL. Our evaluation includes
scenarios of autonomous driving, aircraft collision avoidance, and two games
that are often used to benchmark RL. During a 12-hour run, we find over 80
crash-triggering state sequences on each model. We show inspiring findings that
crash-triggering states, though look normal, induce distinct neuron activation
patterns compared with normal states. We further develop an abnormal behavior
detector to harden all the evaluated models and repair them with the findings
of MDPFuzzer to significantly enhance their robustness without sacrificing
accuracy.

Three experiments, with an IBM superconducting quantum computer, are
presented, where the setting combinations on a three qubit GHZ(like) state were
selected by two additional assistant qubits. The average of the polynomial of
Mermin for the three entangled qubits was calculated; the results showed
violation of the inequality of Mermin. However, given that the assistant qubits
selected, imposed and informed the type of settings, it was possible to
interpret the results in terms of arranged relations among hidden variables of
the assistants and the entanglement BEFORE each shot; the hidden variables may
or may not be local depending on the way the qubits were initialized.

Initial assessment tests are crucial in capturing learner knowledge states in
a consistent manner. Aside from crafting questions itself, putting together
relevant problems to form a question sheet is also a time-consuming process. In
this work, we present a generic formulation of question assembly and a genetic
algorithm based method that can generate assessment tests from raw
problem-solving history. First, we estimate the learner-question knowledge
matrix (snapshot). Each matrix element stands for the probability that a
learner correctly answers a specific question. We formulate the task as a
combinatorial search over this snapshot. To ensure representative and
discriminative diagnostic tests, questions are selected (1) that has a low root
mean squared error against the whole question pool and (2) high standard
deviation among learner performances. Experimental results show that the
proposed method outperforms greedy and random baseline by a large margin in one
private dataset and four public datasets. We also performed qualitative
analysis on the generated assessment test for 9th graders, which enjoys good
problem scatterness across the whole 9th grader curriculum and decent
difficulty level distribution.

In visual retrieval systems, updating the embedding model requires
recomputing features for every piece of data. This expensive process is
referred to as backfilling. Recently, the idea of backward compatible training
(BCT) was proposed. To avoid the cost of backfilling, BCT modifies training of
the new model to make its representations compatible with those of the old
model. However, BCT can significantly hinder the performance of the new model.
In this work, we propose a new learning paradigm for representation learning:
forward compatible training (FCT). In FCT, when the old model is trained, we
also prepare for a future unknown version of the model. We propose learning
side-information, an auxiliary feature for each sample which facilitates future
updates of the model. To develop a powerful and flexible framework for model
compatibility, we combine side-information with a forward transformation from
old to new embeddings. Training of the new model is not modified, hence, its
accuracy is not degraded. We demonstrate significant retrieval accuracy
improvement compared to BCT for various datasets: ImageNet-1k (+18.1%),
Places-365 (+5.4%), and VGG-Face2 (+8.3%). FCT obtains model compatibility when
the new and old models are trained across different datasets, losses, and

Many state-of-the-art ML models have outperformed humans in various tasks
such as image classification. With such outstanding performance, ML models are
widely used today. However, the existence of adversarial attacks and data
poisoning attacks really questions the robustness of ML models. For instance,
Engstrom et al. demonstrated that state-of-the-art image classifiers could be
easily fooled by a small rotation on an arbitrary image. As ML systems are
being increasingly integrated into safety and security-sensitive applications,
adversarial attacks and data poisoning attacks pose a considerable threat. This
chapter focuses on the two broad and important areas of ML security:
adversarial attacks and data poisoning attacks.

Variational autoencoder-based voice conversion (VAE-VC) has the advantage of
requiring only pairs of speeches and speaker labels for training. Unlike the
majority of the research in VAE-VC which focuses on utilizing auxiliary losses
or discretizing latent variables, this paper investigates how an increasing
model expressiveness has benefits and impacts on the VAE-VC. Specifically, we
first analyze VAE-VC from a rate-distortion perspective, and point out that
model expressiveness is significant for VAE-VC because rate and distortion
reflect similarity and naturalness of converted speeches. Based on the
analysis, we propose a novel VC method using a deep hierarchical VAE, which has
high model expressiveness as well as having fast conversion speed thanks to its
non-autoregressive decoder. Also, our analysis reveals another problem that
similarity can be degraded when the latent variable of VAEs has redundant
information. We address the problem by controlling the information contained in
the latent variable using $\beta$-VAE objective. In the experiment using VCTK
corpus, the proposed method achieved mean opinion scores higher than 3.5 on
both naturalness and similarity in inter-gender settings, which are higher than
the scores of existing autoencoder-based VC methods.

In this paper, we focus on learning effective entity matching models over
multi-source large-scale data. For real applications, we relax typical
assumptions that data distributions/spaces, or entity identities are shared
between sources, and propose a Relaxed Multi-source Large-scale Entity-matching
(RMLE) problem. Challenges of the problem include 1) how to align large-scale
entities between sources to share information and 2) how to mitigate negative
transfer from joint learning multi-source data. What's worse, one practical
issue is the entanglement between both challenges. Specifically, incorrect
alignments may increase negative transfer; while mitigating negative transfer
for one source may result in poorly learned representations for other sources
and then decrease alignment accuracy. To handle the entangled challenges, we
point out that the key is to optimize information sharing first based on Pareto
front optimization, by showing that information sharing significantly
influences the Pareto front which depicts lower bounds of negative transfer.
Consequently, we proposed an Incentive Compatible Pareto Alignment (ICPA)
method to first optimize cross-source alignments based on Pareto front
optimization, then mitigate negative transfer constrained on the optimized
alignments. This mechanism renders each source can learn based on its true
preference without worrying about deteriorating representations of other
sources. Specifically, the Pareto front optimization encourages minimizing
lower bounds of negative transfer, which optimizes whether and which to align.
Comprehensive empirical evaluation results on four large-scale datasets are
provided to demonstrate the effectiveness and superiority of ICPA. Online A/B
test results at a search advertising platform also demonstrate the
effectiveness of ICPA in production environments.

In this paper, we present the texture reformer, a fast and universal
neural-based framework for interactive texture transfer with user-specified
guidance. The challenges lie in three aspects: 1) the diversity of tasks, 2)
the simplicity of guidance maps, and 3) the execution efficiency. To address
these challenges, our key idea is to use a novel feed-forward multi-view and
multi-stage synthesis procedure consisting of I) a global view structure
alignment stage, II) a local view texture refinement stage, and III) a holistic
effect enhancement stage to synthesize high-quality results with coherent
structures and fine texture details in a coarse-to-fine fashion. In addition,
we also introduce a novel learning-free view-specific texture reformation
(VSTR) operation with a new semantic map guidance strategy to achieve more
accurate semantic-guided and structure-preserved texture transfer. The
experimental results on a variety of application scenarios demonstrate the
effectiveness and superiority of our framework. And compared with the
state-of-the-art interactive texture transfer algorithms, it not only achieves
higher quality results but, more remarkably, also is 2-5 orders of magnitude
faster. Code is available at https://github.com/EndyWon/Texture-Reformer.

Recently, sequential recommendation systems are important in solving the
information overload in many online services. Current methods in sequential
recommendation focus on learning a fixed number of representations for each
user at any time, with a single representation or multi representations for the
user. However, when a user is exploring items on an e-commerce recommendation
system, the number of this user's hobbies may change overtime (e.g.
increase/reduce one more interest), affected by the user's evolving self needs.
Moreover, different users may have various number of interests. In this paper,
we argue that it is meaningful to explore a personalized dynamic number of user
interests, and learn a dynamic group of user interest representations
accordingly. We propose a sequential model with dynamic number of
representations for recommendation systems (RDRSR). Specifically, RDRSR is
composed of a dynamic interest discriminator (DID) module and a dynamic
interest allocator (DIA) module. The DID module explores the number of a user's
interests by learning the overall sequential characteristics with
bi-directional self-attention and Gumbel-Softmax. The DIA module make the
historical clicked items into a group of item groups and constructs user's
dynamic interest representation. Additionally, experiments on the real-world
datasets demonstrates our model's effectiveness.

Age-of-Information (AoI) is an application layer metric that has been widely
adopted to quantify the information freshness of each information source.
However, few works address the impact of probabilistic transmission failures on
satisfying the harsh AoI requirement of each source, which is of critical
importance in a great number of wireless-powered real-time applications. In
this paper, we investigate the transmission scheduling problem of maximizing
throughput over wireless channels under different time-average AoI requirements
for heterogeneous information sources. When the channel reliability for each
source is known as prior, the global optimal transmission scheduling policy is
proposed. Moreover, when channel reliabilities are unknown, it is modeled as an
AoI-constrained Multi-Armed Bandit (MAB) problem. Then a learning algorithm
that meets the AoI requirement with probability 1 and incurs up to
O(K\sqrt{T\log T}) accumulated regret is proposed, where K is the number of
arms/information sources, and T is the time horizon. Numerical results show
that the accumulated regret of our learning algorithm is strictly bounded by
K\sqrt{T\log T} and outperforms the AoI-constraint-aware baseline, and the AoI
requirement of every source is robustly satisfied.

Semileptonic exclusive tau decays in the Standard Model are reviewed. As it
is well-known, they are a privileged arena to learn about low-energy
hadronization. They also allow for a number of clean new physics tests, which
have attracted much attention recently, probing scales as high as a few TeV. In
light of forthcoming Belle-II data, perspectives on this area are bright.

Deep learning-based approaches to delineating 3D structure depend on accurate
annotations to train the networks. Yet, in practice, people, no matter how
conscientious, have trouble precisely delineating in 3D and on a large scale,
in part because the data is often hard to interpret visually and in part
because the 3D interfaces are awkward to use. In this paper, we introduce a
method that explicitly accounts for annotation inaccuracies. To this end, we
treat the annotations as active contour models that can deform themselves while
preserving their topology. This enables us to jointly train the network and
correct potential errors in the original annotations. The result is an approach
that boosts performance of deep networks trained with potentially inaccurate

Spinning LiDAR data are prevalent for 3D vision tasks. Since LiDAR data is
presented in the form of point clouds, expensive 3D operations are usually
required. This paper revisits spinning LiDAR scan formation and presents a
cylindrical range image representation with a ray-wise projection/unprojection
model. It is built upon raw scans and supports lossless conversion from 2D to
3D, allowing fast 2D operations, including 2D index-based neighbor search and
downsampling. We then propose, to the best of our knowledge, the first
multi-scale registration and dense signed distance function (SDF)
reconstruction system for LiDAR range images. We further collect a dataset of
indoor and outdoor LiDAR scenes in the posed range image format. A
comprehensive evaluation of registration and reconstruction is conducted on the
proposed dataset and the KITTI dataset. Experiments demonstrate that our
approach outperforms surface reconstruction baselines and achieves similar
performance to state-of-the-art LiDAR registration methods, including a modern
learning-based registration approach. Thanks to the simplicity, our
registration runs at 100Hz and SDF reconstruction in real time. The dataset and
a modularized C++/Python toolbox will be released.

Traditional depth sensors generate accurate real world depth estimates that
surpass even the most advanced learning approaches trained only on simulation
domains. Since ground truth depth is readily available in the simulation domain
but quite difficult to obtain in the real domain, we propose a method that
leverages the best of both worlds. In this paper we present a new framework,
ActiveZero, which is a mixed domain learning solution for active stereovision
systems that requires no real world depth annotation. First, we demonstrate the
transferability of our method to out-of-distribution real data by using a mixed
domain learning strategy. In the simulation domain, we use a combination of
supervised disparity loss and self-supervised losses on a shape primitives
dataset. By contrast, in the real domain, we only use self-supervised losses on
a dataset that is out-of-distribution from either training simulation data or
test real data. Second, our method introduces a novel self-supervised loss
called temporal IR reprojection to increase the robustness and accuracy of our
reprojections in hard-to-perceive regions. Finally, we show how the method can
be trained end-to-end and that each module is important for attaining the end
result. Extensive qualitative and quantitative evaluations on real data
demonstrate state of the art results that can even beat a commercial depth
sensor.

Data-to-text generation systems aim to generate text descriptions based on
input data (often represented in the tabular form). A typical system uses huge
training samples for learning the correspondence between tables and texts.
However, large training sets are expensive to obtain, limiting the
applicability of these approaches in real-world scenarios. In this work, we
focus on few-shot data-to-text generation. We observe that, while fine-tuned
pretrained language models may generate plausible sentences, they suffer from
the low semantic coverage problem in the few-shot setting. In other words,
important input slots tend to be missing in the generated text. To this end, we
propose a search-and-learning approach that leverages pretrained language
models but inserts the missing slots to improve the semantic coverage. We
further fine-tune our system based on the search results to smooth out the
search noise, yielding better-quality text and improving inference efficiency
to a large extent. Experiments show that our model achieves high performance on
E2E and WikiBio datasets. Especially, we cover 98.35% of input slots on E2E,
largely alleviating the low coverage problem.

Interests in digital image processing are growing enormously in recent
decades. As a result, different data compression techniques have been proposed
which are concerned mostly with the minimization of information used for the
representation of images. With the advances of deep neural networks, image
compression can be achieved to a higher degree. This paper describes an
overview of JPEG Compression, Discrete Fourier Transform (DFT), Convolutional
Neural Network (CNN), quality metrics to measure the performance of image
compression and discuss the advancement of deep learning for image compression
mostly focused on JPEG, and suggests that adaptation of model improve the
compression.

Recent advanced methods for fashion landmark detection are mainly driven by
training convolutional neural networks on large-scale fashion datasets, which
has a large number of annotated landmarks. However, such large-scale
annotations are difficult and expensive to obtain in real-world applications,
thus models that can generalize well from a small amount of labelled data are
desired. We investigate this problem of few-shot fashion landmark detection,
where only a few labelled samples are available for an unseen task. This work
proposes a novel framework named MetaCloth via meta-learning, which is able to
learn unseen tasks of dense fashion landmark detection with only a few
annotated samples. Unlike previous meta-learning work that focus on solving
"N-way K-shot" tasks, where each task predicts N number of classes by training
with K annotated samples for each class (N is fixed for all seen and unseen
tasks), a task in MetaCloth detects N different landmarks for different
clothing categories using K samples, where N varies across tasks, because
different clothing categories usually have various number of landmarks.
Therefore, numbers of parameters are various for different seen and unseen
tasks in MetaCloth. MetaCloth is carefully designed to dynamically generate
different numbers of parameters for different tasks, and learn a generalizable
feature extraction network from a few annotated samples with a set of good
initialization parameters. Extensive experiments show that MetaCloth
outperforms its counterparts by a large margin.

A structural equation model (SEM) is an effective framework to reason over
causal relationships represented via a directed acyclic graph (DAG). Recent
advances have enabled effective maximum-likelihood point estimation of DAGs
from observational data. However, a point estimate may not accurately capture
the uncertainty in inferring the underlying graph in practical scenarios,
wherein the true DAG is non-identifiable and/or the observed dataset is
limited. We propose Bayesian Causal Discovery Nets (BCD Nets), a variational
inference framework for estimating a distribution over DAGs characterizing a
linear-Gaussian SEM. Developing a full Bayesian posterior over DAGs is
challenging due to the the discrete and combinatorial nature of graphs. We
analyse key design choices for scalable VI over DAGs, such as 1) the
parametrization of DAGs via an expressive variational family, 2) a continuous
relaxation that enables low-variance stochastic optimization, and 3) suitable
priors over the latent variables. We provide a series of experiments on real
and synthetic data showing that BCD Nets outperform maximum-likelihood methods
on standard causal discovery metrics such as structural Hamming distance in low
data regimes.

Distributed training has become a pervasive and effective approach for
training a large neural network (NN) model with processing massive data.
However, it is very challenging to satisfy requirements from various NN models,
diverse computing resources, and their dynamic changes during a training job.
In this study, we design our distributed training framework in a systematic
end-to-end view to provide the built-in adaptive ability for different
scenarios, especially for industrial applications and production environments,
by fully considering resource allocation, model partition, task placement, and
distributed execution. Based on the unified distributed graph and the unified
cluster object, our adaptive framework is equipped with a global cost model and
a global planner, which can enable arbitrary parallelism, resource-aware
placement, multi-mode execution, fault-tolerant, and elastic distributed
training. The experiments demonstrate that our framework can satisfy various
requirements from the diversity of applications and the heterogeneity of
resources with highly competitive performance. The ERNIE language model with
260 billion parameters is efficiently trained on thousands of AI processors
with 91.7% weak scalability. The throughput of the model from the recommender
system by employing the heterogeneous pipeline asynchronous execution can be
increased up to 2.1 times and 3.3 times that of the GPU-only and CPU-only
training respectively. Moreover, the fault-tolerant and elastic distributed
training have been successfully applied to the online industrial applications,
which give a reduction of 34.49% in the number of failed long-term training
jobs and an increase of 33.91% for the global scheduling efficiency in the
production environment.

State estimation from limited sensor measurements is ubiquitously found as a
common challenge in a broad range of fields including mechanics, astronomy, and
geophysics. Fluid mechanics is no exception -- state estimation of fluid flows
is particularly important for flow control and processing of experimental data.
However, strong nonlinearities and spatio-temporal high degrees of freedom of
fluid flows cause difficulties in reasonable estimations. To handle these
issues, neural networks (NNs) have recently been applied to the fluid flow
estimation instead of conventional linear methods. The present study focuses on
the capability of NNs to various fluid flow estimation problems from a
practical viewpoint regarding robust training. Three types of unsteady laminar
and turbulent flows are considered for the present demonstration: 1. square
cylinder wake, 2. turbulent channel flow, and 3. laminar to turbulent
transitional boundary layer. We utilize a convolutional neural network (CNN) to
estimate velocity fields from sectional sensor measurements. To assess the
practicability of the CNN models, physical quantities required for the input
and robustness against lack of sensors are investigated. We also examine the
effectiveness of several considerable approaches for model training to gain
more robustness against the lack of sensors. The knowledge acquired through the
present study in terms of effective training approaches can be transferred
towards practical machine learning in fluid flow modeling.

Audio-driven one-shot talking face generation methods are usually trained on
video resources of various persons. However, their created videos often suffer
unnatural mouth shapes and asynchronous lips because those methods struggle to
learn a consistent speech style from different speakers. We observe that it
would be much easier to learn a consistent speech style from a specific
speaker, which leads to authentic mouth movements. Hence, we propose a novel
one-shot talking face generation framework by exploring consistent correlations
between audio and visual motions from a specific speaker and then transferring
audio-driven motion fields to a reference image. Specifically, we develop an
Audio-Visual Correlation Transformer (AVCT) that aims to infer talking motions
represented by keypoint based dense motion fields from an input audio. In
particular, considering audio may come from different identities in deployment,
we incorporate phonemes to represent audio signals. In this manner, our AVCT
can inherently generalize to audio spoken by other identities. Moreover, as
face keypoints are used to represent speakers, AVCT is agnostic against
appearances of the training speaker, and thus allows us to manipulate face
images of different identities readily. Considering different face shapes lead
to different motions, a motion field transfer module is exploited to reduce the
audio-driven dense motion field gap between the training identity and the
one-shot reference. Once we obtained the dense motion field of the reference
image, we employ an image renderer to generate its talking face videos from an
audio clip. Thanks to our learned consistent speaking style, our method
generates authentic mouth shapes and vivid movements. Extensive experiments
demonstrate that our synthesized videos outperform the state-of-the-art in
terms of visual quality and lip-sync.

As powerful as fine-grained visual classification (FGVC) is, responding your
query with a bird name of "Whip-poor-will" or "Mallard" probably does not make
much sense. This however commonly accepted in the literature, underlines a
fundamental question interfacing AI and human -- what constitutes transferable
knowledge for human to learn from AI? This paper sets out to answer this very
question using FGVC as a test bed. Specifically, we envisage a scenario where a
trained FGVC model (the AI expert) functions as a knowledge provider in
enabling average people (you and me) to become better domain experts ourselves,
i.e. those capable in distinguishing between "Whip-poor-will" and "Mallard".
Fig. 1 lays out our approach in answering this question. Assuming an AI expert
trained using expert human labels, we ask (i) what is the best transferable
knowledge we can extract from AI, and (ii) what is the most practical means to
measure the gains in expertise given that knowledge? On the former, we propose
to represent knowledge as highly discriminative visual regions that are
expert-exclusive. For that, we devise a multi-stage learning framework, which
starts with modelling visual attention of domain experts and novices before
discriminatively distilling their differences to acquire the expert exclusive
knowledge. For the latter, we simulate the evaluation process as book guide to
best accommodate the learning practice of what is accustomed to humans. A
comprehensive human study of 15,000 trials shows our method is able to
consistently improve people of divergent bird expertise to recognise once
unrecognisable birds. Interestingly, our approach also leads to improved
conventional FGVC performance when the extracted knowledge defined is utilised
as means to achieve discriminative localisation. Codes are available at:
https://github.com/PRIS-CV/Making-a-Bird-AI-Expert-Work-for-You-and-Me

This paper studies continual learning (CL) of a sequence of aspect sentiment
classification (ASC) tasks. Although some CL techniques have been proposed for
document sentiment classification, we are not aware of any CL work on ASC. A CL
system that incrementally learns a sequence of ASC tasks should address the
following two issues: (1) transfer knowledge learned from previous tasks to the
new task to help it learn a better model, and (2) maintain the performance of
the models for previous tasks so that they are not forgotten. This paper
proposes a novel capsule network based model called B-CL to address these
issues. B-CL markedly improves the ASC performance on both the new task and the
old tasks via forward and backward knowledge transfer. The effectiveness of
B-CL is demonstrated through extensive experiments.

The use of algorithmic decision making systems in domains which impact the
financial, social, and political well-being of people has created a demand for
these decision making systems to be "fair" under some accepted notion of
equity. This demand has in turn inspired a large body of work focused on the
development of fair learning algorithms which are then used in lieu of their
conventional counterparts. Most analysis of such fair algorithms proceeds from
the assumption that the people affected by the algorithmic decisions are
represented as immutable feature vectors. However, strategic agents may possess
both the ability and the incentive to manipulate this observed feature vector
in order to attain a more favorable outcome. We explore the impact that
strategic agent behavior could have on fair classifiers and derive conditions
under which this behavior leads to fair classifiers becoming less fair than
their conventional counterparts under the same measure of fairness that the
fair classifier takes into account. These conditions are related to the the way
in which the fair classifier remedies unfairness on the original unmanipulated
data: fair classifiers which remedy unfairness by becoming more selective than
their conventional counterparts are the ones that become less fair than their
counterparts when agents are strategic. We further demonstrate that both the
increased selectiveness of the fair classifier, and consequently the loss of
fairness, arises when performing fair learning on domains in which the
advantaged group is overrepresented in the region near (and on the beneficial
side of) the decision boundary of conventional classifiers. Finally, we observe
experimentally, using several datasets and learning methods, that this fairness
reversal is common, and that our theoretical characterization of the fairness
reversal conditions indeed holds in most such cases.

Automatic delineation of organ-at-risk (OAR) and gross-tumor-volume (GTV) is
of great significance for radiotherapy planning. However, it is a challenging
task to learn powerful representations for accurate delineation under limited
pixel (voxel)-wise annotations. Contrastive learning at pixel-level can
alleviate the dependency on annotations by learning dense representations from
unlabeled data. Recent studies in this direction design various contrastive
losses on the feature maps, to yield discriminative features for each pixel in
the map. However, pixels in the same map inevitably share semantics to be
closer than they actually are, which may affect the discrimination of pixels in
the same map and lead to the unfair comparison to pixels in other maps. To
address these issues, we propose a separated region-level contrastive learning
scheme, namely SepaReg, the core of which is to separate each image into
regions and encode each region separately. Specifically, SepaReg comprises two
components: a structure-aware image separation (SIS) module and an intra- and
inter-organ distillation (IID) module. The SIS is proposed to operate on the
image set to rebuild a region set under the guidance of structural information.
The inter-organ representation will be learned from this set via typical
contrastive losses cross regions. On the other hand, the IID is proposed to
tackle the quantity imbalance in the region set as tiny organs may produce
fewer regions, by exploiting intra-organ representations. We conducted
extensive experiments to evaluate the proposed model on a public dataset and
two private datasets. The experimental results demonstrate the effectiveness of
the proposed model, consistently achieving better performance than
https://github.com/jcwang123/Separate_CL.

This paper introduces the proposed automatic minuting system of the Hitachi
team for the First Shared Task on Automatic Minuting (AutoMin-2021). We utilize
a reference-free approach (i.e., without using training minutes) for automatic
minuting (Task A), which first splits a transcript into blocks on the basis of
topics and subsequently summarizes those blocks with a pre-trained BART model
fine-tuned on a summarization corpus of chat dialogue. In addition, we apply a
technique of argument mining to the generated minutes, reorganizing them in a
well-structured and coherent way. We utilize multiple relevance scores to
determine whether or not a minute is derived from the same meeting when either
a transcript or another minute is given (Task B and C). On top of those scores,
we train a conventional machine learning model to bind them and to make final
decisions. Consequently, our approach for Task A achieve the best adequacy
score among all submissions and close performance to the best system in terms
of grammatical correctness and fluency. For Task B and C, the proposed model
successfully outperformed a majority vote baseline.

Traffic forecasting is crucial for public safety and resource optimization,
yet is very challenging due to three aspects: i) current existing works mostly
exploit intricate temporal patterns (e.g., the short-term thunderstorm and
long-term daily trends) within a single method, which fail to accurately
capture spatio-temporal dependencies under different schemas; ii) the
under-exploration of the graph positional encoding limit the extraction of
spatial information in the commonly used full graph attention network; iii) the
quadratic complexity of the full graph attention introduces heavy computational
needs. To achieve the effective traffic flow forecasting, we propose an
efficient spectral graph attention network with disentangled traffic sequences.
Specifically, the discrete wavelet transform is leveraged to obtain the low-
and high-frequency components of traffic sequences, and a dual-channel encoder
is elaborately designed to accurately capture the spatio-temporal dependencies
under long- and short-term schemas of the low- and high-frequency components.
Moreover, a novel wavelet-based graph positional encoding and a query sampling
strategy are introduced in our spectral graph attention to effectively guide
message passing and efficiently calculate the attention. Extensive experiments
on four real-world datasets show the superiority of our model, i.e., the higher
traffic forecasting precision with lower computational cost.

Traffic forecasting is important in intelligent transportation systems of
webs and beneficial to traffic safety, yet is very challenging because of the
complex and dynamic spatio-temporal dependencies in real-world traffic systems.
Prior methods use the pre-defined or learnable static graph to extract spatial
correlations. However, the static graph-based methods fail to mine the
evolution of the traffic network. Researchers subsequently generate the dynamic
graph for each time slice to reflect the changes of spatial correlations, but
they follow the paradigm of independently modeling spatio-temporal
dependencies, ignoring the cross-time spatial influence. In this paper, we
propose a novel cross-time dynamic graph-based deep learning model, named
CDGNet, for traffic forecasting. The model is able to effectively capture the
cross-time spatial dependence between each time slice and its historical time
slices by utilizing the cross-time dynamic graph. Meanwhile, we design a gating
mechanism to sparse the cross-time dynamic graph, which conforms to the sparse
spatial correlations in the real world. Besides, we propose a novel
encoder-decoder architecture to incorporate the cross-time dynamic graph-based
GCN for multi-step traffic forecasting. Experimental results on three
real-world public traffic datasets demonstrate that CDGNet outperforms the
state-of-the-art baselines. We additionally provide a qualitative study to
analyze the effectiveness of our architecture.

Decentralized Finance (DeFi) is a system of financial products and services
built and delivered through smart contracts on various blockchains. In the past
year, DeFi has gained popularity and market capitalization. However, it has
also been connected to crime, in particular, various types of securities
violations. The lack of Know Your Customer requirements in DeFi poses
challenges to governments trying to mitigate potential offending in this space.
This study aims to uncover whether this problem is suited to a machine learning
approach, namely, whether we can identify DeFi projects potentially engaging in
securities violations based on their tokens' smart contract code. We adapt
prior work on detecting specific types of securities violations across
Ethereum, building a random forest classifier based on features extracted from
DeFi projects' tokens' smart contract code. The final classifier achieves a
98.6% F1-score. From further feature-level analysis, we find a single feature
makes this a highly detectable problem. The high reliance on a single feature
means that, at this stage, a complex machine learning model may not be
necessary or desirable for this problem. However, this may change as DeFi
securities violations become more sophisticated. Another contribution of our
study is a new dataset, comprised of (a) a verified ground truth dataset for
tokens involved in securities violations and (b) a set of legitimate tokens
from a reputable DeFi aggregator. This paper further discusses the potential
use of a model like ours by prosecutors in enforcement efforts and connects it
to the wider legal context.

We present a Fourier-based machine learning technique that characterizes and
detects facial emotions. The main challenging task in the development of
machine learning (ML) models for classifying facial emotions is the detection
of accurate emotional features from a set of training samples, and the
generation of feature vectors for constructing a meaningful feature space and
building ML models. In this paper, we hypothesis that the emotional features
are hidden in the frequency domain; hence, they can be captured by leveraging
the frequency domain and masking techniques. We also make use of the conjecture
that a facial emotions are convoluted with the normal facial features and the
other emotional features; however, they carry linearly separable spatial
frequencies (we call computational emotional frequencies). Hence, we propose a
technique by leveraging fast Fourier transform (FFT) and rectangular
narrow-band frequency kernels, and the widely used Yale-Faces image dataset. We
test the hypothesis using the performance scores of the random forest (RF) and
the artificial neural network (ANN) classifiers as the measures to validate the
effectiveness of the captured emotional frequencies. Our finding is that the
computational emotional frequencies discovered by the proposed approach
provides meaningful emotional features that help RF and ANN achieve a high
precision scores above 93%, on average.

Data augmentation is an important component in the robustness evaluation of
models in natural language processing (NLP) and in enhancing the diversity of
the data they are trained on. In this paper, we present NL-Augmenter, a new
participatory Python-based natural language augmentation framework which
supports the creation of both transformations (modifications to the data) and
filters (data splits according to specific features). We describe the framework
and an initial set of 117 transformations and 23 filters for a variety of
natural language tasks. We demonstrate the efficacy of NL-Augmenter by using
several of its transformations to analyze the robustness of popular natural
language models. The infrastructure, datacards and robustness analysis results
are available publicly on the NL-Augmenter repository
(https://github.com/GEM-benchmark/NL-Augmenter).

Digitized documents such as scientific articles, tax forms, invoices,
contract papers, historic texts are widely used nowadays. These document images
could be degraded or damaged due to various reasons including poor lighting
conditions, shadow, distortions like noise and blur, aging, ink stain,
bleed-through, watermark, stamp, etc. Document image enhancement plays a
crucial role as a pre-processing step in many automated document analysis and
recognition tasks such as character recognition. With recent advances in deep
learning, many methods are proposed to enhance the quality of these document
images. In this paper, we review deep learning-based methods, datasets, and
metrics for six main document image enhancement tasks, including binarization,
debluring, denoising, defading, watermark removal, and shadow removal. We
summarize the recent works for each task and discuss their features,
challenges, and limitations. We introduce multiple document image enhancement
tasks that have received little to no attention, including over and under
exposure correction, super resolution, and bleed-through removal. We identify
several promising research directions and opportunities for future research.

classification(ASC) tasks in a particular CL setting called domain incremental
learning (DIL). Each task is from a different domain or product. The DIL
setting is particularly suited to ASC because in testing the system needs not
know the task/domain to which the test data belongs. To our knowledge, this
setting has not been studied before for ASC. This paper proposes a novel model
called CLASSIC. The key novelty is a contrastive continual learning method that
enables both knowledge transfer across tasks and knowledge distillation from
old tasks to the new task, which eliminates the need for task ids in testing.
Experimental results show the high effectiveness of CLASSIC.

Despite the success of deep functional maps in non-rigid 3D shape matching,
there exists no learning framework that models both self-symmetry and shape
matching simultaneously. This is despite the fact that errors due to symmetry
mismatch are a major challenge in non-rigid shape matching. In this paper, we
propose a novel framework that simultaneously learns both self symmetry as well
as a pairwise map between a pair of shapes. Our key idea is to couple a self
symmetry map and a pairwise map through a regularization term that provides a
joint constraint on both of them, thereby, leading to more accurate maps. We
validate our method on several benchmarks where it outperforms many competitive
baselines on both tasks.

Continual learning (CL) learns a sequence of tasks incrementally with the
goal of achieving two main objectives: overcoming catastrophic forgetting (CF)
and encouraging knowledge transfer (KT) across tasks. However, most existing
techniques focus only on overcoming CF and have no mechanism to encourage KT,
and thus do not do well in KT. Although several papers have tried to deal with
both CF and KT, our experiments show that they suffer from serious CF when the
tasks do not have much shared knowledge. Another observation is that most
current CL methods do not use pre-trained models, but it has been shown that
such models can significantly improve the end task performance. For example, in
natural language processing, fine-tuning a BERT-like pre-trained language model
is one of the most effective approaches. However, for CL, this approach suffers
from serious CF. An interesting question is how to make the best use of
pre-trained models for CL. This paper proposes a novel model called CTR to
solve these problems. Our experimental results demonstrate the effectiveness of
CTR

In this paper we criticize the robustness measure traditionally employed to
assess the performance of machine learning models deployed in adversarial
settings. To mitigate the limitations of robustness, we introduce a new measure
called resilience and we focus on its verification. In particular, we discuss
how resilience can be verified by combining a traditional robustness
verification technique with a data-independent stability analysis, which
identifies a subset of the feature space where the model does not change its
predictions despite adversarial manipulations. We then introduce a formally
sound data-independent stability analysis for decision trees and decision tree
ensembles, which we experimentally assess on public datasets and we leverage
for resilience verification. Our results show that resilience verification is
useful and feasible in practice, yielding a more reliable security assessment
of both standard and robust decision tree models.

The Internet generates large volumes of data at a high rate, in particular,
posts on social networks. Although social network data has numerous semantic
adulterations, and is not intended to be a source of geo-spatial information,
in the text of posts we find pieces of important information about how people
relate to their environment, which can be used to identify interesting aspects
of how human beings interact with portions of land based on their activities.
This research proposes a methodology for the identification of land uses using
Natural Language Processing (NLP) from the contents of the popular social
network Twitter. It will be approached by identifying keywords with linguistic
patterns from the text, and the geographical coordinates associated with the
publication. Context-specific innovations are introduced to deal with data
across South America and, in particular, in the city of Arequipa, Peru. The
objective is to identify the five main land uses: residential, commercial,
institutional-governmental, industrial-offices and unbuilt land. Within the
framework of urban planning and sustainable urban management, the methodology
contributes to the optimization of the identification techniques applied for
the updating of land use cadastres, since the results achieved an accuracy of
about 90%, which motivates its application in the real context. In addition, it
would allow the identification of land use categories at a more detailed level,
in situations such as a complex/mixed distribution building based on the amount
of data collected. Finally, the methodology makes land use information
available in a more up-to-date fashion and, above all, avoids the high economic
cost of the non-automatic production of land use maps for cities, mostly in
developing countries.

Reinforcement Learning (RL) based solutions are being adopted in a variety of
domains including robotics, health care and industrial automation. Most focus
is given to when these solutions work well, but they fail when presented with
out of distribution inputs. RL policies share the same faults as most machine
learning models. Out of distribution detection for RL is generally not well
covered in the literature, and there is a lack of benchmarks for this task. In
this work we propose a benchmark to evaluate OOD detection methods in a
Reinforcement Learning setting, by modifying the physical parameters of
non-visual standard environments or corrupting the state observation for visual
environments. We discuss ways to generate custom RL environments that can
produce OOD data, and evaluate three uncertainty methods for the OOD detection
task. Our results show that ensemble methods have the best OOD detection
performance with a lower standard deviation across multiple environments.

Probabilistic machine learning increasingly informs critical decisions in
medicine, economics, politics, and beyond. We need evidence to support that the
resulting decisions are well-founded. To aid development of trust in these
decisions, we develop a taxonomy delineating where trust in an analysis can
break down: (1) in the translation of real-world goals to goals on a particular
set of available training data, (2) in the translation of abstract goals on the
training data to a concrete mathematical problem, (3) in the use of an
algorithm to solve the stated mathematical problem, and (4) in the use of a
particular code implementation of the chosen algorithm. We detail how trust can
fail at each step and illustrate our taxonomy with two case studies: an
analysis of the efficacy of microcredit and The Economist's predictions of the
2020 US presidential election. Finally, we describe a wide variety of methods
that can be used to increase trust at each step of our taxonomy. The use of our
taxonomy highlights steps where existing research work on trust tends to
concentrate and also steps where establishing trust is particularly
challenging.

Ontology alignment (a.k.a ontology matching (OM)) plays a critical role in
knowledge integration. Owing to the success of machine learning in many
domains, it has been applied in OM. However, the existing methods, which often
adopt ad-hoc feature engineering or non-contextual word embeddings, have not
yet outperformed rule-based systems especially in an unsupervised setting. In
this paper, we propose a novel OM system named BERTMap which can support both
unsupervised and semi-supervised settings. It first predicts mappings using a
classifier based on fine-tuning the contextual embedding model BERT on text
semantics corpora extracted from ontologies, and then refines the mappings
through extension and repair by utilizing the ontology structure and logic. Our
evaluation with three alignment tasks on biomedical ontologies demonstrates
that BERTMap can often perform better than the leading OM systems LogMap and
AML.

Everyday, more and more people are turning to online learning, which has
altered our traditional classroom method. Recording lectures has always been a
normal task for online educators, and it has lately become even more important
during the epidemic because actual lessons are still being postponed in several
countries. When recording lectures, a graphics tablet is a great substitute for
a whiteboard because of its portability and ability to interface with
computers. This graphic tablet, however, is too expensive for the majority of
instructors. In this paper, we propose a computer vision-based alternative to
the graphics tablet for instructors and educators, which functions largely in
the same way as a graphic tablet but just requires a pen, paper, and a laptop's
webcam. We call it "Do-It-Yourself Graphics Tab" or "DIY Graphics Tab". Our
system receives a sequence of images of a person's writing on paper acquired by
a camera as input and outputs the screen containing the contents of the writing
from the paper. The task is not straightforward since there are many obstacles
such as occlusion due to the person's hand, random movement of the paper, poor
lighting condition, perspective distortion due to the angle of view, etc. A
pipeline is used to route the input recording through our system, which
conducts instance segmentation and preprocessing before generating the
appropriate output. We also conducted user experience evaluations from the
teachers and students, and their responses are examined in this paper.

Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the
scarcity of abnormal cases. Additionally, the use of real patients' ECGs is
highly regulated due to privacy issues. Therefore, there is always a need for
more ECG data, especially for the training of automatic diagnosis machine
learning models, which perform better when trained on a balanced dataset. We
studied the synthetic ECG generation capability of 5 different models from the
generative adversarial network (GAN) family and compared their performances,
the focus being only on Normal cardiac cycles. Dynamic Time Warping (DTW),
Fr\'echet, and Euclidean distance functions were employed to quantitatively
measure performance. Five different methods for evaluating generated beats were
proposed and applied. We also proposed 3 new concepts (threshold, accepted beat
and productivity rate) and employed them along with the aforementioned methods
as a systematic way for comparison between models. The results show that all
the tested models can, to an extent, successfully mass-generate acceptable
heartbeats with high similarity in morphological features, and potentially all
of them can be used to augment imbalanced datasets. However, visual inspections
of generated beats favors BiLSTM-DC GAN and WGAN, as they produce statistically
more acceptable beats. Also, with regards to productivity rate, the Classic GAN
is superior with a 72% productivity rate. We also designed a simple experiment
with the state-of-the-art classifier (ECGResNet34) to show empirically that the
augmentation of the imbalanced dataset by synthetic ECG signals could improve
the performance of classification significantly.

We consider the problem of understanding the coordinated movements of
biological or artificial swarms. In this regard, we propose a learning scheme
to estimate the coordination laws of the interacting agents from observations
of the swarm's density over time. We describe the dynamics of the swarm based
on pairwise interactions according to a Cucker-Smale flocking model, and
express the swarm's density evolution as the solution to a system of mean-field
hydrodynamic equations. We propose a new family of parametric functions to
model the pairwise interactions, which allows for the mean-field macroscopic
system of integro-differential equations to be efficiently solved as an
augmented system of PDEs. Finally, we incorporate the augmented system in an
iterative optimization scheme to learn the dynamics of the interacting agents
from observations of the swarm's density evolution over time. The results of
this work can offer an alternative approach to study how animal flocks
coordinate, create new control schemes for large networked systems, and serve
as a central part of defense mechanisms against adversarial drone attacks.

This work explores the potency of stochastic competition-based activations,
namely Stochastic Local Winner-Takes-All (LWTA), against powerful
(gradient-based) white-box and black-box adversarial attacks; we especially
focus on Adversarial Training settings. In our work, we replace the
conventional ReLU-based nonlinearities with blocks comprising locally and
stochastically competing linear units. The output of each network layer now
yields a sparse output, depending on the outcome of winner sampling in each
block. We rely on the Variational Bayesian framework for training and
inference; we incorporate conventional PGD-based adversarial training arguments
to increase the overall adversarial robustness. As we experimentally show, the
arising networks yield state-of-the-art robustness against powerful adversarial
attacks while retaining very high classification rate in the benign case.

With the motive of training all the parameters of a neural network, we study
why and when one can achieve this by iteratively creating, training, and
combining randomly selected subnetworks. Such scenarios have either implicitly
or explicitly emerged in the recent literature: see e.g., the Dropout family of
regularization techniques, or some distributed ML training protocols that
reduce communication/computation complexities, such as the Independent Subnet
Training protocol. While these methods are studied empirically and utilized in
practice, they often enjoy partial or no theoretical support, especially when
applied on neural network-based objectives.
  In this manuscript, our focus is on overparameterized single hidden layer
neural networks with ReLU activations in the lazy training regime. By carefully
analyzing $i)$ the subnetworks' neural tangent kernel, $ii)$ the surrogate
functions' gradient, and $iii)$ how we sample and combine the surrogate
functions, we prove linear convergence rate of the training error -- up to a
neighborhood around the optimal point -- for an overparameterized single-hidden
layer perceptron with a regression loss. Our analysis reveals a dependency of
the size of the neighborhood around the optimal point on the number of
surrogate models and the number of local training steps for each selected
subnetwork. Moreover, the considered framework generalizes and provides new
insights on dropout training, multi-sample dropout training, as well as
Independent Subnet Training; for each case, we provide convergence results as
corollaries of our main theorem.

Query Expansion (QE) is a well established method for improving retrieval
metrics in image search applications. When using QE, the search is conducted on
a new query vector, constructed using an aggregation function over the query
and images from the database. Recent works gave rise to QE techniques in which
the aggregation function is learned, whereas previous techniques were based on
hand-crafted aggregation functions, e.g., taking the mean of the query's
nearest neighbors. However, most QE methods have focused on aggregation
functions that work directly over the query and its immediate nearest
neighbors. In this work, a hierarchical model, Graph Query Expansion (GQE), is
presented, which is learned in a supervised manner and performs aggregation
over an extended neighborhood of the query, thus increasing the information
used from the database when computing the query expansion, and using the
structure of the nearest neighbors graph. The technique achieves
state-of-the-art results over known benchmarks.

Short-term load forecasting (STLF) is challenging due to complex time series
(TS) which express three seasonal patterns and a nonlinear trend. This paper
proposes a novel hybrid hierarchical deep learning model that deals with
multiple seasonality and produces both point forecasts and predictive intervals
(PIs). It combines exponential smoothing (ES) and a recurrent neural network
(RNN). ES extracts dynamically the main components of each individual TS and
enables on-the-fly deseasonalization, which is particularly useful when
operating on a relatively small data set. A multi-layer RNN is equipped with a
new type of dilated recurrent cell designed to efficiently model both short and
long-term dependencies in TS. To improve the internal TS representation and
thus the model's performance, RNN learns simultaneously both the ES parameters
and the main mapping function transforming inputs into forecasts. We compare
our approach against several baseline methods, including classical statistical
methods and machine learning (ML) approaches, on STLF problems for 35 European
countries. The empirical study clearly shows that the proposed model has high
expressive power to solve nonlinear stochastic forecasting problems with TS
including multiple seasonality and significant random fluctuations. In fact, it
outperforms both statistical and state-of-the-art ML models in terms of
accuracy.

Today, Neural Networks are the basis of breakthroughs in virtually every
technical domain. Their application to accelerators has recently resulted in
better performance and efficiency in these systems. At the same time, the
increasing hardware failures due to the latest (shrinked) semiconductor
technology needs to be addressed. Since accelerator systems are often used to
back time-critical applications such as self-driving cars or medical diagnosis
applications, these hardware failures must be eliminated. Our research
evaluates these failures from a systemic point of view. Based on our results,
we find critical results for the system reliability enhancement and we further
put forth an efficient method to avoid these failures with minimal hardware
overhead.

Federated learning is a rapidly-growing area of research which enables a
large number of clients to jointly train a machine learning model on
privately-held data. One of the largest barriers to wider adoption of federated
learning is the communication cost of sending model updates from and to the
clients, which is accentuated by the fact that many of these devices are
bandwidth-constrained. In this paper, we aim to address this issue by
optimizing networks within a subspace of their full parameter space, an idea
known as intrinsic dimension in the machine learning theory community. We use a
correspondence between the notion of intrinsic dimension and gradient
compressibility to derive a family of low-bandwidth optimization algorithms,
which we call intrinsic gradient compression algorithms. Specifically, we
present three algorithms in this family with different levels of upload and
download bandwidth for use in various federated settings, along with
theoretical guarantees on their performance. Finally, in large-scale federated
learning experiments with models containing up to 100M parameters, we show that
our algorithms perform extremely well compared to current state-of-the-art
gradient compression methods.

In this work we introduce a novel approach to the pulsar classification
problem in time-domain radio astronomy using a Born machine, often referred to
as a quantum neural network. Using a single-qubit architecture, we show that
the pulsar classification problem maps well to the Bloch sphere and that
comparable accuracies to more classical machine learning approaches are
achievable. We introduce a novel single-qubit encoding for the pulsar data used
in this work and show that this performs comparably to a multi-qubit QAOA
encoding.

Variable names are critical for conveying intended program behavior. Machine
learning-based program analysis methods use variable name representations for a
wide range of tasks, such as suggesting new variable names and bug detection.
Ideally, such methods could capture semantic relationships between names beyond
syntactic similarity, e.g., the fact that the names average and mean are
similar. Unfortunately, previous work has found that even the best of previous
representation approaches primarily capture relatedness (whether two variables
are linked at all), rather than similarity (whether they actually have the same
meaning).
  We propose VarCLR, a new approach for learning semantic representations of
variable names that effectively captures variable similarity in this stricter
sense. We observe that this problem is an excellent fit for contrastive
learning, which aims to minimize the distance between explicitly similar
inputs, while maximizing the distance between dissimilar inputs. This requires
labeled training data, and thus we construct a novel, weakly-supervised
variable renaming dataset mined from GitHub edits. We show that VarCLR enables
the effective application of sophisticated, general-purpose language models
like BERT, to variable name representation and thus also to related downstream
tasks like variable name similarity search or spelling correction. VarCLR
produces models that significantly outperform the state-of-the-art on IdBench,
an existing benchmark that explicitly captures variable similarity (as distinct
from relatedness). Finally, we contribute a release of all data, code, and
pre-trained models, aiming to provide a drop-in replacement for variable
representations used in either existing or future program analyses that rely on
variable names.

To interpret uncertainty estimates from differentiable probabilistic models,
recent work has proposed generating a single Counterfactual Latent Uncertainty
Explanation (CLUE) for a given data point where the model is uncertain,
identifying a single, on-manifold change to the input such that the model
becomes more certain in its prediction. We broaden the exploration to examine
$\delta$-CLUE, the set of potential CLUEs within a $\delta$ ball of the
original input in latent space. We study the diversity of such sets and find
that many CLUEs are redundant; as such, we propose DIVerse CLUE
($\nabla$-CLUE), a set of CLUEs which each propose a distinct explanation as to
how one can decrease the uncertainty associated with an input. We then further
propose GLobal AMortised CLUE (GLAM-CLUE), a distinct and novel method which
learns amortised mappings on specific groups of uncertain inputs, taking them
and efficiently transforming them in a single function call into inputs for
which a model will be certain. Our experiments show that $\delta$-CLUE,
$\nabla$-CLUE, and GLAM-CLUE all address shortcomings of CLUE and provide
beneficial explanations of uncertainty estimates to practitioners.

Malware ascription is a relatively unexplored area, and it is rather
difficult to attribute malware and detect authorship. In this paper, we employ
various Static and Dynamic features of malicious executables to classify
malware based on their family. We leverage Cuckoo Sandbox and machine learning
to make progress in this research. Post analysis, classification is performed
using various deep learning and machine learning algorithms. Using the features
gathered from VirusTotal (static) and Cuckoo (dynamic) reports, we ran the
vectorized data against Multinomial Naive Bayes, Support Vector Machine, and
Bagging using Decision Trees as the base estimator. For each classifier, we
tuned the hyper-parameters using exhaustive search methods. Our reports can be
extremely useful in malware ascription.

Social media, professional sports, and video games are driving rapid growth
in live video streaming, on platforms such as Twitch and YouTube Live. Live
streaming experience is very susceptible to short-time-scale network congestion
since client playback buffers are often no more than a few seconds.
Unfortunately, identifying such streams and measuring their QoE for network
management is challenging, since content providers largely use the same
delivery infrastructure for live and video-on-demand (VoD) streaming, and
packet inspection techniques (including SNI/DNS query monitoring) cannot always
distinguish between the two.
  In this paper, we design, build, and deploy ReCLive: a machine learning
method for live video detection and QoE measurement based on network-level
behavioral characteristics. Our contributions are four-fold: (1) We analyze
about 23,000 video streams from Twitch and YouTube, and identify key features
in their traffic profile that differentiate live and on-demand streaming. We
release our traffic traces as open data to the public; (2) We develop an
LSTM-based binary classifier model that distinguishes live from on-demand
streams in real-time with over 95% accuracy across providers; (3) We develop a
method that estimates QoE metrics of live streaming flows in terms of
resolution and buffer stall events with overall accuracies of 93% and 90%,
respectively; and (4) Finally, we prototype our solution, train it in the lab,
and deploy it in a live ISP network serving more than 7,000 subscribers. Our
method provides ISPs with fine-grained visibility into live video streams,
enabling them to measure and improve user experience.

We derive criteria for the selection of datapoints used for data-driven
reduced-order modeling and other areas of supervised learning based on Gaussian
process regression (GPR). While this is a well-studied area in the fields of
active learning and optimal experimental design, most criteria in the
literature are empirical. Here we introduce an optimality condition for the
selection of a new input defined as the minimizer of the distance between the
approximated output probability density function (pdf) of the reduced-order
model and the exact one. Given that the exact pdf is unknown, we define the
selection criterion as the supremum over the unit sphere of the native Hilbert
space for the GPR. The resulting selection criterion, however, has a form that
is difficult to compute. We combine results from GPR theory and asymptotic
analysis to derive a computable form of the defined optimality criterion that
is valid in the limit of small predictive variance. The derived asymptotic form
of the selection criterion leads to convergence of the GPR model that
guarantees a balanced distribution of data resources between probable and
large-deviation outputs, resulting in an effective way for sampling towards
data-driven reduced-order modeling.

In the present work, a reinforcement learning (RL) based adaptive algorithm
to optimise the transmit beampattern for a colocated massive MIMO radar is
presented. Under the massive MIMO regime, a robust Wald type detector, able to
guarantee certain detection performances under a wide range of practical
disturbance models, has been recently proposed. Furthermore, an RL/cognitive
methodology has been exploited to improve the detection performance by learning
and interacting with the surrounding unknown environment. Building upon
previous findings, we develop here a fully adaptive and data driven scheme for
the selection of the hyper-parameters involved in the RL algorithm. Such an
adaptive selection makes the Wald RL based detector independent of any ad hoc,
and potentially suboptimal, manual tuning of the hyper-parameters. Simulation
results show the effectiveness of the proposed scheme in harsh scenarios with
strong clutter and low SNR values.

Spurious credit card transactions are a significant source of financial
losses and urge the development of accurate fraud detection algorithms. In this
paper, we use machine learning strategies for such an aim. First, we apply a
mixed learning technique that uses K-means preprocessing before trained
classification to the problem at hand. Next, we introduce an adapted detector
ensemble technique that uses OR-logic algorithm aggregation to enhance the
detection rate. Then, both strategies are deployed in tandem in numerical
simulations using real-world transactions data. We observed from simulation
results that the proposed methods diminished computational cost and enhanced
performance concerning state-of-the-art techniques.

The increasing availability of large collections of electronic health record
(EHR) data and unprecedented technical advances in deep learning (DL) have
sparked a surge of research interest in developing DL based clinical decision
support systems for diagnosis, prognosis, and treatment. Despite the
recognition of the value of deep learning in healthcare, impediments to further
adoption in real healthcare settings remain due to the black-box nature of DL.
Therefore, there is an emerging need for interpretable DL, which allows end
users to evaluate the model decision making to know whether to accept or reject
predictions and recommendations before an action is taken. In this review, we
focus on the interpretability of the DL models in healthcare. We start by
introducing the methods for interpretability in depth and comprehensively as a
methodological reference for future researchers or clinical practitioners in
this field. Besides the methods' details, we also include a discussion of
advantages and disadvantages of these methods and which scenarios each of them
is suitable for, so that interested readers can know how to compare and choose
among them for use. Moreover, we discuss how these methods, originally
developed for solving general-domain problems, have been adapted and applied to
healthcare problems and how they can help physicians better understand these
data-driven technologies. Overall, we hope this survey can help researchers and
practitioners in both artificial intelligence (AI) and clinical fields
understand what methods we have for enhancing the interpretability of their DL
models and choose the optimal one accordingly.

Vision Transformer (ViT) and its variants (e.g., Swin, PVT) have achieved
great success in various computer vision tasks, owing to their capability to
learn long-range contextual information. Layer Normalization (LN) is an
essential ingredient in these models. However, we found that the ordinary LN
makes tokens at different positions similar in magnitude because it normalizes
embeddings within each token. It is difficult for Transformers to capture
inductive bias such as the positional context in an image with LN. We tackle
this problem by proposing a new normalizer, termed Dynamic Token Normalization
(DTN), where normalization is performed both within each token (intra-token)
and across different tokens (inter-token). DTN has several merits. Firstly, it
is built on a unified formulation and thus can represent various existing
normalization methods. Secondly, DTN learns to normalize tokens in both
intra-token and inter-token manners, enabling Transformers to capture both the
global contextual information and the local positional context. {Thirdly, by
simply replacing LN layers, DTN can be readily plugged into various vision
transformers, such as ViT, Swin, PVT, LeViT, T2T-ViT, BigBird and Reformer.
Extensive experiments show that the transformer equipped with DTN consistently
outperforms baseline model with minimal extra parameters and computational
overhead. For example, DTN outperforms LN by $0.5\%$ - $1.2\%$ top-1 accuracy
on ImageNet, by $1.2$ - $1.4$ box AP in object detection on COCO benchmark, by
$2.3\%$ - $3.9\%$ mCE in robustness experiments on ImageNet-C, and by $0.5\%$ -
$0.8\%$ accuracy in Long ListOps on Long-Range Arena.} Codes will be made
public at \url{https://github.com/wqshao126/DTN}

Data-driven forecasts of air quality have recently achieved more accurate
short-term predictions. Despite their success, most of the current data-driven
solutions lack proper quantifications of model uncertainty that communicate how
much to trust the forecasts. Recently, several practical tools to estimate
uncertainty have been developed in probabilistic deep learning. However, there
have not been empirical applications and extensive comparisons of these tools
in the domain of air quality forecasts. Therefore, this work applies
state-of-the-art techniques of uncertainty quantification in a real-world
setting of air quality forecasts. Through extensive experiments, we describe
training probabilistic models and evaluate their predictive uncertainties based
on empirical performance, reliability of confidence estimate, and practical
applicability. We also propose improving these models using "free" adversarial
training and exploiting temporal and spatial correlation inherent in air
quality data. Our experiments demonstrate that the proposed models perform
better than previous works in quantifying uncertainty in data-driven air
quality forecasts. Overall, Bayesian neural networks provide a more reliable
uncertainty estimate but can be challenging to implement and scale. Other
scalable methods, such as deep ensemble, Monte Carlo (MC) dropout, and
stochastic weight averaging-Gaussian (SWAG), can perform well if applied
correctly but with different tradeoffs and slight variations in performance
metrics. Finally, our results show the practical impact of uncertainty
estimation and demonstrate that, indeed, probabilistic models are more suitable
for making informed decisions. Code and dataset are available at
\url{https://github.com/Abdulmajid-Murad/deep_probabilistic_forecast}

Efficient exploration is important for reinforcement learners to achieve high
rewards. In multi-agent systems, coordinated exploration and behaviour is
critical for agents to jointly achieve optimal outcomes. In this paper, we
introduce a new general framework for improving coordination and performance of
multi-agent reinforcement learners (MARL). Our framework, named Learnable
Intrinsic-Reward Generation Selection algorithm (LIGS) introduces an adaptive
learner, Generator that observes the agents and learns to construct intrinsic
rewards online that coordinate the agents' joint exploration and joint
behaviour. Using a novel combination of MARL and switching controls, LIGS
determines the best states to learn to add intrinsic rewards which leads to a
highly efficient learning process. LIGS can subdivide complex tasks making them
easier to solve and enables systems of MARL agents to quickly solve
environments with sparse rewards. LIGS can seamlessly adopt existing MARL
algorithms and, our theory shows that it ensures convergence to policies that
deliver higher system performance. We demonstrate its superior performance in
challenging tasks in Foraging and StarCraft II.

In this paper, we aim to efficiently and accurately predict the static
channel impulse response (CIR) with only the user's position information and a
set of channel instances obtained within a certain wireless communication
environment. Such a problem is by no means trivial since it needs to
reconstruct the high-dimensional information (here the CIR everywhere) from the
extremely low-dimensional data (here the location coordinates), which often
results in overfitting and large prediction error. To this end, we resort to a
novel physics-inspired generative approach. Specifically, we first use a
forward deep neural network to infer the positions of all possible images of
the source reflected by the surrounding scatterers within that environment, and
then use the well-known Gaussian Radial Basis Function network (GRBF) to
approximate the amplitudes of all possible propagation paths. We further
incorporate the most recently developed sinusoidal representation network
(SIREN) into the proposed network to implicitly represent the highly dynamic
phases of all possible paths, which usually cannot be well predicted by the
conventional neural networks with non-periodic activators. The resultant
framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also
extended to the MIMO channel case. Key performance measures including
prediction accuracy, convergence speed, network scale and robustness to channel
estimation error are comprehensively evaluated and compared with existing
popular networks, which show that our proposed network is much more efficient
in representing, learning and predicting wireless channels in a given
communication environment.

This paper proposes an algorithm (RMDA) for training neural networks (NNs)
with a regularization term for promoting desired structures. RMDA does not
incur computation additional to proximal SGD with momentum, and achieves
variance reduction without requiring the objective function to be of the
finite-sum form. Through the tool of manifold identification from nonlinear
optimization, we prove that after a finite number of iterations, all iterates
of RMDA possess a desired structure identical to that induced by the
regularizer at the stationary point of asymptotic convergence, even in the
presence of engineering tricks like data augmentation and dropout that
complicate the training process. Experiments on training NNs with structured
sparsity confirm that variance reduction is necessary for such an
identification, and show that RMDA thus significantly outperforms existing
methods for this task. For unstructured sparsity, RMDA also outperforms a
state-of-the-art pruning method, validating the benefits of training structured
NNs through regularization.

Mining user-generated content--e.g., for the early detection of outbreaks or
for extracting personal observations--often suffers from the lack of enough
training data, short document length, and informal language model. We propose a
novel multi-view active learning model, called Context-aware Co-testing with
Bagging (COCOBA), to address these issues in the classification tasks tailored
for a query word--e.g., detecting illness reports given the disease name.
COCOBA employs the context of user postings to construct two views. Then it
uses the distribution of the representations in each view to detect the regions
that are assigned to the opposite classes. This effectively leads to detecting
the contexts that the two base learners disagree on. Our model also employs a
query-by-committee model to address the usually noisy language of user
postings. The experiments testify that our model is applicable to multiple
important representative Twitter tasks and also significantly outperforms the

Abstract. Purpose: This paper presents a scheme for generating virtual
intraoperative CT scans in order to improve surgical completeness in Endoscopic
Sinus Surgeries (ESS). Approach: The work presents three methods, the tip
motion-based, the tip trajectory-based, and the instrument based, along with
non-parametric smoothing and Gaussian Process Regression, for virtual
intraoperative CT generation. Results: The proposed methods studied and
compared on ESS performed on cadavers. Surgical results show all three methods
improve the Dice Similarity Coefficients > 86%, with F-score > 92% and
precision > 89.91%. The tip trajectory-based method was found to have best
performance and reached 96.87% precision in surgical completeness evaluation.
Conclusions: This work demonstrated that virtual intraoperative CT scans
improves the consistency between the actual surgical scene and the reference
model, and improves surgical completeness in ESS. Comparing with actual
intraoperative CT scans, the proposed scheme has no impact on existing surgical
protocols, does not require extra hardware other than the one is already
available in most ESS overcome the high costs, the repeated radiation, and the
elongated anesthesia caused by actual intraoperative CTs, and is practical in
ESS.

There is growing interest in the role of sentiment in economic
decision-making. However, most research on the subject has focused on positive
and negative valence. Conviction Narrative Theory (CNT) places Approach and
Avoidance sentiment (that which drives action) at the heart of real-world
decision-making, and argues that it better captures emotion in financial
markets. This research, bringing together psychology and machine learning,
introduces new techniques to differentiate Approach and Avoidance from positive
and negative sentiment on a fundamental level of meaning. It does this by
comparing word-lists, previously constructed to capture these concepts in text
data, across a large range of semantic features. The results demonstrate that
Avoidance in particular is well defined as a separate type of emotion, which is
evaluative/cognitive and action-orientated in nature. Refining the Avoidance
word-list according to these features improves macroeconomic models, suggesting
that they capture the essence of Avoidance and that it plays a crucial role in
driving real-world economic decision-making.

Cross-modal retrieval is to utilize one modality as a query to retrieve data
from another modality, which has become a popular topic in information
retrieval, machine learning, and database. How to effectively measure the
similarity between different modality data is the major challenge of
cross-modal retrieval. Although several reasearch works have calculated the
correlation between different modality data via learning a common subspace
representation, the encoder's ability to extract features from multi-modal
information is not satisfactory. In this paper, we present a novel variational
autoencoder (VAE) architecture for audio-visual cross-modal retrieval, by
learning paired audio-visual correlation embedding and category correlation
embedding as constraints to reinforce the mutuality of audio-visual
information. On the one hand, audio encoder and visual encoder separately
encode audio data and visual data into two different latent spaces. Further,
two mutual latent spaces are respectively constructed by canonical correlation
analysis (CCA). On the other hand, probabilistic modeling methods is used to
deal with possible noise and missing information in the data. Additionally, in
this way, the cross-modal discrepancy from intra-modal and inter-modal
information are simultaneously eliminated in the joint embedding subspace. We
conduct extensive experiments over two benchmark datasets. The experimental
outcomes exhibit that the proposed architecture is effective in learning
audio-visual correlation and is appreciably better than the existing
cross-modal retrieval methods.

Endoscopic Sinus and Skull Base Surgeries (ESSBSs) is a challenging and
potentially dangerous surgical procedure, and objective skill assessment is the
key components to improve the effectiveness of surgical training, to
re-validate surgeons' skills, and to decrease surgical trauma and the
complication rate in operating rooms. Because of the complexity of surgical
procedures, the variation of operation styles, and the fast development of new
surgical skills, the surgical skill assessment remains a challenging problem.
This work presents a novel Gaussian Process Learning-based heuristic automatic
objective surgical skill assessment method for ESSBSs. Different with classical
surgical skill assessment algorithms, the proposed method 1) utilizes the
kinematic features in surgical instrument relative movements, instead of using
specific surgical tasks or the statistics to assess skills in real-time; 2)
provide informative feedback, instead of a summative scores; 3) has the ability
to incrementally learn from new data, instead of depending on a fixed dataset.
The proposed method projects the instrument movements into the endoscope
coordinate to reduce the data dimensionality. It then extracts the kinematic
features of the projected data and learns the relationship between surgical
skill levels and the features with the Gaussian Process learning technique. The
proposed method was verified in full endoscopic skull base and sinus surgeries
on cadavers. These surgeries have different pathology, requires different
treatment and has different complexities. The experimental results show that
the proposed method reaches 100\% prediction precision for complete surgical
procedures and 90\% precision for real-time prediction assessment.

User modeling plays a fundamental role in industrial recommender systems,
either in the matching stage and the ranking stage, in terms of both the
customer experience and business revenue. How to extract users' multiple
interests effectively from their historical behavior sequences to improve the
relevance and personalization of the recommend results remains an open problem
for user modeling.Most existing deep-learning based approaches exploit item-ids
and category-ids but neglect fine-grained features like color and mate-rial,
which hinders modeling the fine granularity of users' interests.In the paper,
we present Multiple interest and Fine granularity Net-work (MFN), which tackle
users' multiple and fine-grained interests and construct the model from both
the similarity relationship and the combination relationship among the users'
multiple interests.Specifically, for modeling the similarity relationship, we
leverage two sets of embeddings, where one is the fixed embedding from
pre-trained models (e.g. Glove) to give the attention weights and the other is
trainable embedding to be trained with MFN together.For modeling the
combination relationship, self-attentive layers are exploited to build the
higher order combinations of different interest representations. In the
construction of network, we design an interest-extract module using attention
mechanism to capture multiple interest representations from user historical
behavior sequences and leverage an auxiliary loss to boost the distinction of
the interest representations. Then a hierarchical network is applied to model
the attention relation between the multiple interest vectors of different
granularities and the target item. We evaluate MFNon both public and industrial
datasets. The experimental results demonstrate that the proposed MFN achieves
superior performance than other existed representing methods.

In this paper, we propose a gradient boosting algorithm called
\textit{adaptive boosting histogram transform} (\textit{ABHT}) for regression
to illustrate the local adaptivity of gradient boosting algorithms in histogram
transform ensemble learning. From the theoretical perspective, when the target
function lies in a locally H\"older continuous space, we show that our ABHT can
filter out the regions with different orders of smoothness. Consequently, we
are able to prove that the upper bound of the convergence rates of ABHT is
strictly smaller than the lower bound of \textit{parallel ensemble histogram
transform} (\textit{PEHT}). In the experiments, both synthetic and real-world
data experiments empirically validate the theoretical results, which
demonstrates the advantageous performance and local adaptivity of our ABHT.

Refractory high-entropy alloys (RHEAs) are a promising class of alloys that
show elevated-temperature yield strengths and have potential to use as
high-performance materials in gas turbine engines. However, exploring the vast
RHEA compositional space experimentally is challenging, and only a small
fraction of this space has been explored to date. The work demonstrates the
development of a state-of-the-art machine learning (ML) predictive framework
coupled with optimization methods to intelligently explore the vast
compositional space and drive the search in a direction that improves
high-temperature yield strengths. Our forward yield strength model is shown to
have a significantly improved predictive accuracy relative to the
state-of-the-art approach, and also provides inherent uncertainty
quantification through the use of repeated k-fold cross-validation. Upon
development of a robust yield strength prediction model, the coupled framework
is used to discover new RHEAs with superior high temperature yield strength. We
have shown that RHEA compositions can be customized to have maximum yield
strength at a specific temperature.

The Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging
vision problem that aims to predict panoptic segmentation and depth in a video
simultaneously. The previous work solves this task by extending the existing
panoptic segmentation method with an extra dense depth prediction and instance
tracking head. However, the relationship between the depth and panoptic
segmentation is not well explored -- simply combining existing methods leads to
competition and needs carefully weight balancing. In this paper, we present
PolyphonicFormer, a vision transformer to unify these sub-tasks under the DVPS
task and lead to more robust results. Our principal insight is that the depth
can be harmonized with the panoptic segmentation with our proposed new paradigm
of predicting instance level depth maps with object queries. Then the
relationship between the two tasks via query-based learning is explored. From
the experiments, we demonstrate the benefits of our design from both depth
estimation and panoptic segmentation aspects. Since each thing query also
encodes the instance-wise information, it is natural to perform tracking
directly with appearance learning. Our method achieves state-of-the-art results
on two DVPS datasets (Semantic KITTI, Cityscapes), and ranks 1st on the
ICCV-2021 BMTT Challenge video + depth track. Code is available at
https://github.com/HarborYuan/PolyphonicFormer .

We present a novel machine learning-based approach to generate fast-executing
virtual radiofrequency quadrupole (RFQ) particle accelerators using surrogate
modelling. These could potentially be used as on-line feedback tools during
beam commissioning and operation, and to optimize the RFQ beam dynamics design
prior to construction. Since surrogate models execute orders of magnitude
faster than corresponding physics beam dynamics simulations using standard
tools like PARMTEQM and RFQGen, the computational complexity of the
multi-objective optimization problem reduces significantly. Ultimately, this
presents a computationally inexpensive and time efficient method to perform
sensitivity studies and an optimization of the crucial RFQ beam output
parameters like transmission and emittances. Two different methods of surrogate
model creation (polynomial chaos expansion and neural networks) are discussed
and the achieved model accuracy is evaluated for different study cases with
gradually increasing complexity, ranging from a simple FODO cell example to the
full RFQ optimization. We find that variations of the beam input Twiss
parameters can be reproduced well. The prediction of the beam with respect to
hardware changes, e.g. of the electrode modulation, are challenging on the
other hand. We discuss possible reasons.

The conventional fishing industry has several difficulties: water
contamination, temperature instability, nutrition, area, expense, etc. In fish
farming, Biofloc technology turns traditional farming into a sophisticated
infrastructure that enables the utilization of leftover food by turning it into
bacterial biomass. The purpose of our study is to propose an intelligent IoT
Biofloc system that improves efficiency and production. This article introduced
a system that gathers data from sensors, store data in the cloud, analyses it
using a machine learning model such as a Decision regression tree model to
predict the water condition, and provides real-time monitoring through an
android app. The proposed system has achieved a satisfactory accuracy of 79%
during the experiment.

We present a Siamese-like Dual-branch network based on solely Transformers
for tracking. Given a template and a search image, we divide them into
non-overlapping patches and extract a feature vector for each patch based on
its matching results with others within an attention window. For each token, we
estimate whether it contains the target object and the corresponding size. The
advantage of the approach is that the features are learned from matching, and
ultimately, for matching. So the features are aligned with the object tracking
task. The method achieves better or comparable results as the best-performing
methods which first use CNN to extract features and then use Transformer to
fuse them. It outperforms the state-of-the-art methods on the GOT-10k and
VOT2020 benchmarks. In addition, the method achieves real-time inference speed
(about $40$ fps) on one GPU. The code and models will be released.

Mobile devices are indispensable sources of big data. Federated learning (FL)
has a great potential in exploiting these private data by exchanging locally
trained models instead of their raw data. However, mobile devices are often
energy limited and wirelessly connected, and FL cannot cope flexibly with their
heterogeneous and time-varying energy capacity and communication throughput,
limiting the adoption. Motivated by these issues, we propose a novel energy and
communication efficient FL framework, coined SlimFL. To resolve the
heterogeneous energy capacity problem, each device in SlimFL runs a
width-adjustable slimmable neural network (SNN). To address the heterogeneous
communication throughput problem, each full-width (1.0x) SNN model and its
half-width ($0.5$x) model are superposition-coded before transmission, and
successively decoded after reception as the 0.5x or $1.0$x model depending on
the channel quality. Simulation results show that SlimFL can simultaneously
train both $0.5$x and $1.0$x models with reasonable accuracy and convergence
speed, compared to its vanilla FL counterpart separately training the two
models using $2$x more communication resources. Surprisingly, SlimFL achieves
even higher accuracy with lower energy footprints than vanilla FL for poor
channels and non-IID data distributions, under which vanilla FL converges
slowly.

Goal-achieving problems are puzzles that set up a specific situation with a
clear objective. An example that is well-studied is the category of
life-and-death (L&D) problems for Go, which helps players hone their skill of
identifying region safety. Many previous methods like lambda search try null
moves first, then derive so-called relevance zones (RZs), outside of which the
opponent does not need to search. This paper first proposes a novel RZ-based
approach, called the RZ-Based Search (RZS), to solving L&D problems for Go. RZS
tries moves before determining whether they are null moves post-hoc. This means
we do not need to rely on null move heuristics, resulting in a more elegant
algorithm, so that it can also be seamlessly incorporated into AlphaZero's
super-human level play in our solver. To repurpose AlphaZero for solving, we
also propose a new training method called Faster to Life (FTL), which modifies
AlphaZero to entice it to win more quickly. We use RZS and FTL to solve L&D
problems on Go, namely solving 68 among 106 problems from a professional L&D
book while a previous program solves 11 only. Finally, we discuss that the
approach is generic in the sense that RZS is applicable to solving many other
goal-achieving problems for board games.

Unmanned aerospace vehicles usually carry sensors (i.e., electro-optical
and/or infrared imaging cameras) as their primary payload. These sensors are
used for image processing, target tracking, surveillance, mapping, and
providing high-resolution imagery for environmental surveys. It is crucial to
obtain a steady image in all these applications. This is typically accomplished
by using multi-axis gimbal systems. This paper concentrates on the modeling and
control of a multi-axis gimbal system. A novel and fully outlined procedure is
proposed to derive the nonlinear and highly coupled Equations of Motion of the
two-axis gimbal system. Different from the existing literature, Forward
Dynamics of the two-axis gimbal system is modeled using multi-body dynamics
modeling techniques. In addition to the Forward Dynamics model, the Inverse
Dynamics model is developed to estimate the complementary torques associated
with the state and mechanism-dependent, complex disturbances acting on the
system. A disturbance compensator based on multilayer perceptron (MLP)
structure is implemented to cope with external and internal disturbances and
parameter uncertainties through the torque input channel. Our initial
simulations and experimental work show that the new NN (neural network)-based
controller is performs better in the full operational range without requiring
any tuning or adjustment when compared with well-known controllers such as
cascaded PID, ADRC (Active Disturbance Rejection Control), Inverse Dynamics
based controllers.

Muilti-modality data are ubiquitous in biology, especially that we have
entered the multi-omics era, when we can measure the same biological object
(cell) from different aspects (omics) to provide a more comprehensive insight
into the cellular system. When dealing with such multi-omics data, the first
step is to determine the correspondence among different modalities. In other
words, we should match data from different spaces corresponding to the same
object. This problem is particularly challenging in the single-cell multi-omics
scenario because such data are very sparse with extremely high dimensions.
Secondly, matched single-cell multi-omics data are rare and hard to collect.
Furthermore, due to the limitations of the experimental environment, the data
are usually highly noisy. To promote the single-cell multi-omics research, we
overcome the above challenges, proposing a novel framework to align and
integrate single-cell RNA-seq data and single-cell ATAC-seq data. Our approach
can efficiently map the above data with high sparsity and noise from different
spaces to a low-dimensional manifold in a unified space, making the downstream
alignment and integration straightforward. Compared with the other
state-of-the-art methods, our method performs better in both simulated and real
single-cell data. The proposed method is helpful for the single-cell
multi-omics research. The improvement for integration on the simulated data is

Contextual word representations generated by language models (LMs) learn
spurious associations present in the training corpora. Recent findings reveal
that adversaries can exploit these associations to reverse-engineer the private
attributes of entities mentioned within the corpora. These findings have led to
efforts towards minimizing the privacy risks of language models. However,
existing approaches lack interpretability, compromise on data utility and fail
to provide privacy guarantees. Thus, the goal of my doctoral research is to
develop interpretable approaches towards privacy preservation of text
representations that retain data utility while guaranteeing privacy. To this
end, I aim to study and develop methods to incorporate steganographic
modifications within the vector geometry to obfuscate underlying spurious
associations and preserve the distributional semantic properties learnt during
training.

The generalized eigenvalue (GE) problems are of particular importance in
various areas of science engineering and machine learning. We present a
variational quantum algorithm for finding the desired generalized eigenvalue of
the GE problem, $\mathcal{A}|\psi\rangle=\lambda\mathcal{B}|\psi\rangle$, by
choosing suitable loss functions. Our approach imposes the superposition of the
trial state and the obtained eigenvectors with respect to the weighting matrix
$\mathcal{B}$ on the Rayleigh-quotient. Furthermore, both the values and
derivatives of the loss functions can be calculated on near-term quantum
devices with shallow quantum circuit. Finally, we propose a full quantum
generalized eigensolver (FQGE) to calculate the minimal generalized eigenvalue
with quantum gradient descent algorithm. As a demonstration of the principle,
we numerically implement our algorithms to conduct a 2-qubit simulation and
successfully find the generalized eigenvalues of the matrix pencil
$(\mathcal{A},\,\mathcal{B})$. The numerically experimental result indicates
that FQGE is robust under Gaussian noise.

Facing the difficulty of expensive and trivial data collection and
annotation, how to make a deep learning-based short-term voltage stability
assessment (STVSA) model work well on a small training dataset is a challenging
and urgent problem. Although a big enough dataset can be directly generated by
contingency simulation, this data generation process is usually cumbersome and
inefficient; while data augmentation provides a low-cost and efficient way to
artificially inflate the representative and diversified training datasets with
label preserving transformations. In this respect, this paper proposes a novel
deep-learning intelligent system incorporating data augmentation for STVSA of
power systems. First, due to the unavailability of reliable quantitative
criteria to judge the stability status for a specific power system,
semi-supervised cluster learning is leveraged to obtain labeled samples in an
original small dataset. Second, to make deep learning applicable to the small
dataset, conditional least squares generative adversarial networks
(LSGAN)-based data augmentation is introduced to expand the original dataset
via artificially creating additional valid samples. Third, to extract temporal
dependencies from the post-disturbance dynamic trajectories of a system, a
bi-directional gated recurrent unit with attention mechanism based assessment
model is established, which bi-directionally learns the significant time
dependencies and automatically allocates attention weights. The test results
demonstrate the presented approach manages to achieve better accuracy and a
faster response time with original small datasets. Besides classification
accuracy, this work employs statistical measures to comprehensively examine the
performance of the proposal.

We present a mathematically well founded approach for the synthetic modeling
of turbulent flows using generative adversarial networks (GAN). Based on the
analysis of chaotic, deterministic systems in terms of ergodicity, we outline a
mathematical proof that GAN can actually learn to sample state snapshots form
the invariant measure of the chaotic system. Based on this analysis, we study a
hierarchy of chaotic systems starting with the Lorenz attractor and then carry
on to the modeling of turbulent flows with GAN. As training data, we use fields
of velocity fluctuations obtained from large eddy simulations (LES). Two
architectures are investigated in detail: we use a deep, convolutional GAN
(DCGAN) to synthesise the turbulent flow around a cylinder. We furthermore
simulate the flow around a low pressure turbine stator using the pix2pixHD
architecture for a conditional DCGAN being conditioned on the position of a
rotating wake in front of the stator. The settings of adversarial training and
the effects of using specific GAN architectures are explained. We thereby show
that GAN are efficient in simulating turbulence in technically challenging flow
problems on the basis of a moderate amount of training data. GAN training and
inference times significantly fall short when compared with classical numerical
methods, in particular LES, while still providing turbulent flows in high
resolution. We furthermore analyse the statistical properties of the
synthesized and LES flow fields, which agree excellently. We also show the
ability of the conditional GAN to generalize over changes of geometry by
generating turbulent flow fields for positions of the wake that are not
included in the training data.

This paper aims to integrate two synergetic technologies, federated learning
(FL) and width-adjustable slimmable neural network (SNN) architectures. FL
preserves data privacy by exchanging the locally trained models of mobile
devices. By adopting SNNs as local models, FL can flexibly cope with the
time-varying energy capacities of mobile devices. Combining FL and SNNs is
however non-trivial, particularly under wireless connections with time-varying
channel conditions. Furthermore, existing multi-width SNN training algorithms
are sensitive to the data distributions across devices, so are ill-suited to
FL. Motivated by this, we propose a communication and energy-efficient
SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for
global model aggregation and superposition training (ST) for updating local
models. By applying SC, SlimFL exchanges the superposition of multiple width
configurations that are decoded as many as possible for a given communication
throughput. Leveraging ST, SlimFL aligns the forward propagation of different
width configurations, while avoiding the inter-width interference during
backpropagation. We formally prove the convergence of SlimFL. The result
reveals that SlimFL is not only communication-efficient but also can counteract
non-IID data distributions and poor channel conditions, which is also
corroborated by simulations.

Active learning is an established technique to reduce the labeling cost to
build high-quality machine learning models. A core component of active learning
is the acquisition function that determines which data should be selected to
annotate. State-of-the-art acquisition functions -- and more largely, active
learning techniques -- have been designed to maximize the clean performance
(e.g. accuracy) and have disregarded robustness, an important quality property
that has received increasing attention. Active learning, therefore, produces
models that are accurate but not robust.
  In this paper, we propose \emph{robust active learning}, an active learning
process that integrates adversarial training -- the most established method to
produce robust models. Via an empirical study on 11 acquisition functions, 4
datasets, 6 DNN architectures, and 15105 trained DNNs, we show that robust
active learning can produce models with the robustness (accuracy on adversarial
examples) ranging from 2.35\% to 63.85\%, whereas standard active learning
systematically achieves negligible robustness (less than 0.20\%). Our study
also reveals, however, that the acquisition functions that perform well on
accuracy are worse than random sampling when it comes to robustness. We,
therefore, examine the reasons behind this and devise a new acquisition
function that targets both clean performance and robustness. Our acquisition
function -- named density-based robust sampling with entropy (DRE) --
outperforms the other acquisition functions (including random) in terms of
robustness by up to 24.40\% (3.84\% than random particularly), while remaining
competitive on accuracy. Additionally, we prove that DRE is applicable as a
test selection metric for model retraining and stands out from all compared
functions by up to 8.21\% robustness.

Deep learning-based histopathology image classification is a key technique to
help physicians in improving the accuracy and promptness of cancer diagnosis.
However, the noisy labels are often inevitable in the complex manual annotation
process, and thus mislead the training of the classification model. In this
work, we introduce a novel hard sample aware noise robust learning method for
histopathology image classification. To distinguish the informative hard
samples from the harmful noisy ones, we build an easy/hard/noisy (EHN)
detection model by using the sample training history. Then we integrate the EHN
into a self-training architecture to lower the noise rate through gradually
label correction. With the obtained almost clean dataset, we further propose a
noise suppressing and hard enhancing (NSHE) scheme to train the noise robust
model. Compared with the previous works, our method can save more clean samples
and can be directly applied to the real-world noisy dataset scenario without
using a clean subset. Experimental results demonstrate that the proposed scheme
outperforms the current state-of-the-art methods in both the synthetic and
real-world noisy datasets. The source code and data are available at
https://github.com/bupt-ai-cz/HSA-NRL/.

Voice disorders significantly undermine people's ability to speak in their
daily lives. Without early diagnoses and treatments, these disorders may
drastically deteriorate. Thus, automatic detection systems at home are desired
for people inaccessible to disease assessments. However, more accurate systems
usually require more cumbersome machine learning models, whereas the memory and
computational resources of the systems at home are limited. Moreover, the
performance of the systems may be weakened due to domain mismatch between
clinic and real-world data. Therefore, we aimed to develop a compressed and
domain-robust pathological voice detection system. Domain adversarial training
was utilized to address domain mismatch by extracting domain-invariant
features. In addition, factorized convolutional neural networks were exploited
to compress the feature extractor model. The results showed that only 4% of
degradation of unweighted average recall occurred in the target domain compared
to the source domain, indicating that the domain mismatch was effectively
eliminated. Furthermore, our system reduced both usages of memory and
computation by over 73.9%. We concluded that this proposed system successfully
resolved domain mismatch and may be applicable to embedded systems at home with
limited resources.

Artificial intelligence (AI) based device identification improves the
security of the internet of things (IoT), and accelerates the authentication
process. However, existing approaches rely on the assumption that we can learn
all the classes from the training set, namely, closed-set classification. To
overcome the closed-set limitation, we propose a novel open set RF device
identification method to classify unseen classes in the testing set. First, we
design a specific convolution neural network (CNN) with a short-time Fourier
transforming (STFT) pre-processing module, which efficiently recognizes the
differences of feature maps learned from various RF device signals. Then to
generate a representation of known class bounds, we estimate the probability
map of the open-set via the OpenMax function. We conduct experiments on sampled
data and voice signal sets, considering various pre-processing schemes, network
structures, distance metrics, tail sizes, and openness degrees. The simulation
results show the superiority of the proposed method in terms of robustness and
accuracy.

Embedding real-world networks presents challenges because it is not clear how
to identify their latent geometries. Embedding some disassortative networks,
such as scale-free networks, to the Euclidean space has been shown to incur
distortions. Embedding scale-free networks to hyperbolic spaces offer an
exciting alternative but incurs distortions when embedding assortative networks
with latent geometries not hyperbolic. We propose an inductive model that
leverages both the expressiveness of GCNs and trivial bundle to learn inductive
node representations for networks with or without node features. A trivial
bundle is a simple case of fiber bundles,a space that is globally a product
space of its base space and fiber. The coordinates of base space and those of
fiber can be used to express the assortative and disassortative factors in
generating edges. Therefore, the model has the ability to learn embeddings that
can express those factors. In practice, it reduces errors for link prediction
and node classification when compared to the Euclidean and hyperbolic GCNs.

Many physical and mathematical models involve random fields in their input
data. Examples are ordinary differential equations, partial differential
equations and integro--differential equations with uncertainties in the
coefficient functions described by random fields. They also play a dominant
role in problems in machine learning. In this article, we do not assume to have
knowledge of the moments or expansion terms of the random fields but we instead
have only given discretized samples for them. We thus model some measurement
process for this discrete information and then approximate the covariance
operator of the original random field. Of course, the true covariance operator
is of infinite rank and hence we can not assume to get an accurate
approximation from a finite number of spatially discretized observations. On
the other hand, smoothness of the true (unknown) covariance function results in
effective low rank approximations to the true covariance operator. We derive
explicit error estimates that involve the finite rank approximation error of
the covariance operator, the Monte-Carlo-type errors for sampling in the
stochastic domain and the numerical discretization error in the physical
domain. This permits to give sufficient conditions on the three discretization
parameters to guarantee that an error below a prescribed accuracy $\varepsilon$
is achieved.

The modeling, computational cost, and accuracy of traditional Spatio-temporal
networks are the three most concentrated research topics in video action
recognition. The traditional 2D convolution has a low computational cost, but
it cannot capture the time relationship; the convolutional neural networks
(CNNs) model based on 3D convolution can obtain good performance, but its
computational cost is high, and the amount of parameters is large. In this
paper, we propose a plug-and-play Spatio-temporal Shift Module (STSM), which is
a generic module that is both effective and high-performance. Specifically,
after STSM is inserted into other networks, the performance of the network can
be improved without increasing the number of calculations and parameters. In
particular, when the network is 2D CNNs, our STSM module allows the network to
learn efficient Spatio-temporal features. We conducted extensive evaluations of
the proposed module, conducted numerous experiments to study its effectiveness
in video action recognition, and achieved state-of-the-art results on the
kinetics-400 and Something-Something V2 datasets.

This paper study the reconstruction of High Dynamic Range (HDR) video from
snapshot-coded LDR video. Constructing an HDR video requires restoring the HDR
values for each frame and maintaining the consistency between successive
frames. HDR image acquisition from single image capture, also known as snapshot
HDR imaging, can be achieved in several ways. For example, the reconfigurable
snapshot HDR camera is realized by introducing an optical element into the
optical stack of the camera; by placing a coded mask at a small standoff
distance in front of the sensor. High-quality HDR image can be recovered from
the captured coded image using deep learning methods. This study utilizes
3D-CNNs to perform a joint demosaicking, denoising, and HDR video
reconstruction from coded LDR video. We enforce more temporally consistent HDR
video reconstruction by introducing a temporal loss function that considers the
short-term and long-term consistency. The obtained results are promising and
could lead to affordable HDR video capture using conventional cameras.

One of the most effective methods of channel pruning is to trim on the basis
of the importance of each neuron. However, measuring the importance of each
neuron is an NP-hard problem. Previous works have proposed to trim by
considering the statistics of a single layer or a plurality of successive
layers of neurons. These works cannot eliminate the influence of different data
on the model in the reconstruction error, and currently, there is no work to
prove that the absolute values of the parameters can be directly used as the
basis for judging the importance of the weights. A more reasonable approach is
to eliminate the difference between batch data that accurately measures the
weight of influence. In this paper, we propose to use ensemble learning to
train a model for different batches of data and use the influence function (a
classic technique from robust statistics) to learn the algorithm to track the
model's prediction and return its training parameter gradient, so that we can
determine the responsibility for each parameter, which we call "influence", in
the prediction process. In addition, we theoretically prove that the
back-propagation of the deep network is a first-order Taylor approximation of
the influence function of the weights. We perform extensive experiments to
prove that pruning based on the influence function using the idea of ensemble
learning will be much more effective than just focusing on error
reconstruction. Experiments on CIFAR shows that the influence pruning achieves
the state-of-the-art result.

We present a deep learning-based method for propagating spatially-varying
visual material attributes (e.g. texture maps or image stylizations) to larger
samples of the same or similar materials. For training, we leverage images of
the material taken under multiple illuminations and a dedicated data
augmentation policy, making the transfer robust to novel illumination
conditions and affine deformations. Our model relies on a supervised
image-to-image translation framework and is agnostic to the transferred domain;
we showcase a semantic segmentation, a normal map, and a stylization. Following
an image analogies approach, the method only requires the training data to
contain the same visual structures as the input guidance. Our approach works at
interactive rates, making it suitable for material edit applications. We
thoroughly evaluate our learning methodology in a controlled setup providing
quantitative measures of performance. Last, we demonstrate that training the
model on a single material is enough to generalize to materials of the same
type without the need for massive datasets.

Intention recognition is an important step to facilitate collaboration among
multiple agents. Existing work mainly focuses on intention recognition in a
single-agent setting and uses a descriptive model, e.g. Bayesian networks, in
the recognition process. In this article, we develop a new approach of
identifying intentions for multiple agents through a clustering algorithm. We
first define an intention model for multiple agents of interest. We use a
prescriptive approach to model agents' behaviours where their intentions are
hidden in the implementation of their plans. We introduce landmarks into the
behavioural model therefore enhancing informative features to identify common
intentions for multiple agents. Subsequently, we further refine the model by
focusing only action sequences in their plan and provide a light model for
identifying and comparing their intentions. The new models provide a simple
approach of grouping agents' common intentions upon partial plans observed in
agents' interactions. Then, we transform the intention recognition into an
un-supervised learning problem and adapt a clustering algorithm to group
intentions of multiple agents through comparing their behavioural models. We
conduct the clustering process by measuring similarity of probability
distributions over potential landmarks in intention models so as to discover
agents' common intentions. Finally, we examine the new intention recognition
approaches in two problem domains. We demonstrate importance of recognising
common intentions of multiple agents in achieving their goals and provide
experimental results to show performance of the new approaches.

Attention mechanism plays a more and more important role in point cloud
analysis and channel attention is one of the hotspots. With so much channel
information, it is difficult for neural networks to screen useful channel
information. Thus, an adaptive channel encoding mechanism is proposed to
capture channel relationships in this paper. It improves the quality of the
representation generated by the network by explicitly encoding the
interdependence between the channels of its features. Specifically, a
channel-wise convolution (Channel-Conv) is proposed to adaptively learn the
relationship between coordinates and features, so as to encode the channel.
Different from the popular attention weight schemes, the Channel-Conv proposed
in this paper realizes adaptability in convolution operation, rather than
simply assigning different weights for channels. Extensive experiments on
existing benchmarks verify our method achieves the state of the arts.

clinical approaches. Semi-supervised learning has been widely applied to
medical image segmentation tasks since it alleviates the heavy burden of
acquiring expert-examined annotations and takes the advantage of unlabeled data
which is much easier to acquire. Although consistency learning has been proven
to be an effective approach by enforcing an invariance of predictions under
different distributions, existing approaches cannot make full use of
region-level shape constraint and boundary-level distance information from
unlabeled data. In this paper, we propose a novel uncertainty-guided mutual
consistency learning framework to effectively exploit unlabeled data by
integrating intra-task consistency learning from up-to-date predictions for
self-ensembling and cross-task consistency learning from task-level
regularization to exploit geometric shape information. The framework is guided
by the estimated segmentation uncertainty of models to select out relatively
certain predictions for consistency learning, so as to effectively exploit more
reliable information from unlabeled data. Experiments on two publicly available
benchmark datasets showed that: 1) Our proposed method can achieve significant
performance improvement by leveraging unlabeled data, with up to 4.13% and
9.82% in Dice coefficient compared to supervised baseline on left atrium
segmentation and brain tumor segmentation, respectively. 2) Compared with other
semi-supervised segmentation methods, our proposed method achieve better
segmentation performance under the same backbone network and task settings on
both datasets, demonstrating the effectiveness and robustness of our method and
potential transferability for other medical image segmentation tasks.

Distillation efforts have led to language models that are more compact and
efficient without serious drops in performance. The standard approach to
distillation trains a student model against two objectives: a task-specific
objective (e.g., language modeling) and an imitation objective that encourages
the hidden states of the student model to be similar to those of the larger
teacher model. In this paper, we show that it is beneficial to augment
distillation with a third objective that encourages the student to imitate the
causal computation process of the teacher through interchange intervention
training(IIT). IIT pushes the student model to become a causal abstraction of
the teacher model - a simpler model with the same causal structure. IIT is
fully differentiable, easily implemented, and combines flexibly with other
objectives. Compared with standard distillation of BERT, distillation via IIT
results in lower perplexity on Wikipedia (masked language modeling) and marked
improvements on the GLUE benchmark (natural language understanding), SQuAD
(question answering), and CoNLL-2003 (named entity recognition).

A wide range of optimization problems arising in machine learning can be
solved by gradient descent algorithms, and a central question in this area is
how to efficiently compress a large-scale dataset so as to reduce the
computational complexity. {\em Coreset} is a popular data compression technique
that has been extensively studied before. However, most of existing coreset
methods are problem-dependent and cannot be used as a general tool for a
broader range of applications. A key obstacle is that they often rely on the
pseudo-dimension and total sensitivity bound that can be very high or hard to
obtain. In this paper, based on the ''locality'' property of gradient descent
algorithms, we propose a new framework, termed ''sequential coreset'', which
effectively avoids these obstacles. Moreover, our method is particularly
suitable for sparse optimization whence the coreset size can be further reduced
to be only poly-logarithmically dependent on the dimension. In practice, the
experimental results suggest that our method can save a large amount of running
time compared with the baseline algorithms.

Person search aims to jointly localize and identify a query person from
natural, uncropped images, which has been actively studied over the past few
years. In this paper, we delve into the rich context information globally and
locally surrounding the target person, which we refer to as scene and group
context, respectively. Unlike previous works that treat the two types of
context individually, we exploit them in a unified global-local context network
(GLCNet) with the intuitive aim of feature enhancement. Specifically, re-ID
embeddings and context features are simultaneously learned in a multi-stage
fashion, ultimately leading to enhanced, discriminative features for person
search. We conduct the experiments on two person search benchmarks (i.e.,
CUHK-SYSU and PRW) as well as extend our approach to a more challenging setting
(i.e., character search on MovieNet). Extensive experimental results
demonstrate the consistent improvement of the proposed GLCNet over the
state-of-the-art methods on all three datasets. Our source codes, pre-trained
models, and the new setting w.r.t. character search are publicly available at:
https://github.com/ZhengPeng7/GLCNet.

This paper proposes a distributed weighted regularized least squares
algorithm (DWRLS) based on spherical radial basis functions and spherical
quadrature rules to tackle spherical data that are stored across numerous local
servers and cannot be shared with each other. Via developing a novel integral
operator approach, we succeed in deriving optimal approximation rates for DWRLS
and theoretically demonstrate that DWRLS performs similarly as running a
weighted regularized least squares algorithm with the whole data on a large
enough machine. This interesting finding implies that distributed learning is
capable of sufficiently exploiting potential values of distributively stored
spherical data, even though every local server cannot access all the data.

In this work, we present a new method for 3D face reconstruction from
sparse-view RGB images. Unlike previous methods which are built upon 3D
morphable models (3DMMs) with limited details, we leverage an implicit
representation to encode rich geometric features. Our overall pipeline consists
of two major components, including a geometry network, which learns a
deformable neural signed distance function (SDF) as the 3D face representation,
and a rendering network, which learns to render on-surface points of the neural
SDF to match the input images via self-supervised optimization. To handle
in-the-wild sparse-view input of the same target with different expressions at
test time, we propose residual latent code to effectively expand the shape
space of the learned implicit face representation as well as a novel
view-switch loss to enforce consistency among different views. Our experimental
results on several benchmark datasets demonstrate that our approach outperforms
alternative baselines and achieves superior face reconstruction results

Based on recent advances in realistic language modeling (GPT-3) and
cross-modal representations (CLIP), Gaud\'i was developed to help designers
search for inspirational images using natural language. In the early stages of
the design process, with the goal of eliciting a client's preferred creative
direction, designers will typically create thematic collections of
inspirational images called "mood-boards". Creating a mood-board involves
sequential image searches which are currently performed using keywords or
images. Gaud\'i transforms this process into a conversation where the user is
gradually detailing the mood-board's theme. This representation allows our AI
to generate new search queries from scratch, straight from a project briefing,
following a theme hypothesized by GPT-3. Compared to previous computational
approaches to mood-board creation, to the best of our knowledge, ours is the
first attempt to represent mood-boards as the stories that designers tell when
presenting a creative direction to a client.

The existing neural architecture search algorithms are mostly working on
search spaces with short-distance connections. We argue that such designs,
though safe and stable, obstacles the search algorithms from exploring more
complicated scenarios. In this paper, we build the search algorithm upon a
complicated search space with long-distance connections, and show that existing
weight-sharing search algorithms mostly fail due to the existence of
\textbf{interleaved connections}. Based on the observation, we present a simple
yet effective algorithm named \textbf{IF-NAS}, where we perform a periodic
sampling strategy to construct different sub-networks during the search
procedure, avoiding the interleaved connections to emerge in any of them. In
the proposed search space, IF-NAS outperform both random sampling and previous
weight-sharing search algorithms by a significant margin. IF-NAS also
generalizes to the micro cell-based spaces which are much easier. Our research
emphasizes the importance of macro structure and we look forward to further
efforts along this direction.

We propose an end-to-end architecture for facial expression recognition. Our
model learns an optimal tree topology for facial landmarks, whose traversal
generates a sequence from which we obtain an embedding to feed a sequential
learner. The proposed architecture incorporates two main streams, one focusing
on landmark positions to learn the structure of the face, while the other
focuses on patches around the landmarks to learn texture information. Each
stream is followed by an attention mechanism and the outputs are fed to a
two-stream fusion component to perform the final classification. We conduct
extensive experiments on two large-scale publicly available facial expression
datasets, AffectNet and FER2013, to evaluate the efficacy of our approach. Our
method outperforms other solutions in the area and sets new state-of-the-art
expression recognition rates on these datasets.

Background and Objective: Artificial intelligence (AI) methods coupled with
biomedical analysis has a critical role during pandemics as it helps to release
the overwhelming pressure from healthcare systems and physicians. As the
ongoing COVID-19 crisis worsens in countries having dense populations and
inadequate testing kits like Brazil and India, radiological imaging can act as
an important diagnostic tool to accurately classify covid-19 patients and
prescribe the necessary treatment in due time. With this motivation, we present
our study based on deep learning architecture for detecting covid-19 infected
lungs using chest X-rays. Dataset: We collected a total of 2470 images for
three different class labels, namely, healthy lungs, ordinary pneumonia, and
covid-19 infected pneumonia, out of which 470 X-ray images belong to the
covid-19 category. Methods: We first pre-process all the images using histogram
equalization techniques and segment them using U-net architecture. VGG-16
network is then used for feature extraction from the pre-processed images which
is further sampled by SMOTE oversampling technique to achieve a balanced
dataset. Finally, the class-balanced features are classified using a support
vector machine (SVM) classifier with 10-fold cross-validation and the accuracy
is evaluated. Result and Conclusion: Our novel approach combining well-known
pre-processing techniques, feature extraction methods, and dataset balancing
method, lead us to an outstanding rate of recognition of 98% for COVID-19
images over a dataset of 2470 X-ray images. Our model is therefore fit to be
utilized in healthcare facilities for screening purposes.

Knowledge distillation (KD) has recently emerged as a powerful strategy to
transfer knowledge from a pre-trained teacher model to a lightweight student,
and has demonstrated its unprecedented success over a wide spectrum of
applications. In spite of the encouraging results, the KD process per se poses
a potential threat to network ownership protection, since the knowledge
contained in network can be effortlessly distilled and hence exposed to a
malicious user. In this paper, we propose a novel framework, termed as Safe
Distillation Box (SDB), that allows us to wrap a pre-trained model in a virtual
box for intellectual property protection. Specifically, SDB preserves the
inference capability of the wrapped model to all users, but precludes KD from
unauthorized users. For authorized users, on the other hand, SDB carries out a
knowledge augmentation scheme to strengthen the KD performances and the results
of the student model. In other words, all users may employ a model in SDB for
inference, but only authorized users get access to KD from the model. The
proposed SDB imposes no constraints over the model architecture, and may
readily serve as a plug-and-play solution to protect the ownership of a
pre-trained network. Experiments across various datasets and architectures
demonstrate that, with SDB, the performance of an unauthorized KD drops
significantly while that of an authorized gets enhanced, demonstrating the
effectiveness of SDB.

Tweedie distributions are a special case of exponential dispersion models,
which are often used in classical statistics as distributions for generalized
linear models. Here, we reveal that Tweedie distributions also play key roles
in modern deep learning era, leading to a distribution independent
self-supervised image denoising formula without clean reference images.
Specifically, by combining with the recent Noise2Score self-supervised image
denoising approach and the saddle point approximation of Tweedie distribution,
we can provide a general closed-form denoising formula that can be used for
large classes of noise distributions without ever knowing the underlying noise
distribution. Similar to the original Noise2Score, the new approach is composed
of two successive steps: score matching using perturbed noisy images, followed
by a closed form image denoising formula via distribution-independent Tweedie's
formula. This also suggests a systematic algorithm to estimate the noise model
and noise parameters for a given noisy image data set. Through extensive
experiments, we demonstrate that the proposed method can accurately estimate
noise models and parameters, and provide the state-of-the-art self-supervised
image denoising performance in the benchmark dataset and real-world dataset.

Inspired by the recent success of self-supervised methods applied on images,
self-supervised learning on graph structured data has seen rapid growth
especially centered on augmentation-based contrastive methods. However, we
argue that without carefully designed augmentation techniques, augmentations on
graphs may behave arbitrarily in that the underlying semantics of graphs can
drastically change. As a consequence, the performance of existing
augmentation-based methods is highly dependent on the choice of augmentation
scheme, i.e., hyperparameters associated with augmentations. In this paper, we
propose a novel augmentation-free self-supervised learning framework for
graphs, named AFGRL. Specifically, we generate an alternative view of a graph
by discovering nodes that share the local structural information and the global
semantics with the graph. Extensive experiments towards various node-level
tasks, i.e., node classification, clustering, and similarity search on various
real-world datasets demonstrate the superiority of AFGRL. The source code for
AFGRL is available at https://github.com/Namkyeong/AFGRL.

Official government publications are key sources for understanding the
history of societies. Web publishing has fundamentally changed the scale and
processes by which governments produce and disseminate information.
Significantly, a range of web archiving programs have captured massive troves
of government publications. For example, hundreds of millions of unique U.S.
Government documents posted to the web in PDF form have been archived by
libraries to date. Yet, these PDFs remain largely unutilized and understudied
in part due to the challenges surrounding the development of scalable pipelines
for searching and analyzing them. This paper utilizes a Library of Congress
dataset of 1,000 government PDFs in order to offer initial approaches for
searching and analyzing these PDFs at scale. In addition to demonstrating the
utility of PDF metadata, this paper offers computationally-efficient machine
learning approaches to search and discovery that utilize the PDFs' textual and
visual features as well. We conclude by detailing how these methods can be
operationalized at scale in order to support systems for navigating millions of
PDFs.

Camera localization is a fundamental and crucial problem for many robotic
applications. In recent years, using deep-learning for camera-based
localization has become a popular research direction. However, they lack
robustness to large domain shifts, which can be caused by seasonal or
illumination changes between training and testing data sets. Data augmentation
is an attractive approach to tackle this problem, as it does not require
additional data to be provided. However, existing augmentation methods blindly
perturb all pixels and therefore cannot achieve satisfactory performance. To
overcome this issue, we proposed RADA, a system whose aim is to concentrate on
perturbing the geometrically informative parts of the image. As a result, it
learns to generate minimal image perturbations that are still capable of
perplexing the network. We show that when these examples are utilized as
augmentation, it greatly improves robustness. We show that our method
outperforms previous augmentation techniques and achieves up to two times
higher accuracy than the SOTA localization models (e.g., AtLoc and MapNet) when
tested on `unseen' challenging weather conditions.

Ice accumulation in the blades of wind turbines can cause them to describe
anomalous rotations or no rotations at all, thus affecting the generation of
electricity and power output. In this work, we investigate the problem of ice
accumulation in wind turbines by framing it as anomaly detection of
multi-variate time series. Our approach focuses on two main parts: first,
learning low-dimensional representations of time series using a Variational
Recurrent Autoencoder (VRAE), and second, using unsupervised clustering
algorithms to classify the learned representations as normal (no ice
accumulated) or abnormal (ice accumulated). We have evaluated our approach on a
custom wind turbine time series dataset, for the two-classes problem (one
normal versus one abnormal class), we obtained a classification accuracy of up
to 96$\%$ on test data. For the multiple-class problem (one normal versus
multiple abnormal classes), we present a qualitative analysis of the
low-dimensional learned latent space, providing insights into the capacities of
our approach to tackle such problem. The code to reproduce this work can be
found here https://github.com/agrija9/Wind-Turbines-VRAE-Paper.

Gaussian process regression (GPR) is a powerful machine learning method which
has recently enjoyed wider use, in particular in physical sciences. In its
original formulation, GPR uses a square matrix of covariances among training
data and can be viewed as linear regression problem with equal numbers of
training data and basis functions. When data are sparse, avoidance of
overfitting and optimization of hyperparameters of GPR are difficult, in
particular in high-dimensional spaces where the data sparsity issue cannot
practically be resolved by adding more data. Optimal choice of hyperparameters,
however, determines success or failure of the application of the GPR method. We
show that parameter optimization is facilitated by rectangularization of the
defining equation of GPR. On the example of a 15-dimensional molecular
potential energy surface we demonstrate that this approach allows effective
hyperparameter tuning even with very sparse data.

This technical report presents a text-to-image neural network "Emojich" that
generates emojis using captions in Russian language as a condition. We aim to
keep the generalization ability of a pretrained big model ruDALL-E Malevich
(XL) 1.3B parameters at the fine-tuning stage, while giving special style to
the images generated. Here are presented some engineering methods, code
realization, all hyper-parameters for reproducing results and a Telegram bot
where everyone can create their own customized sets of stickers. Also, some
newly generated emojis obtained by "Emojich" model are demonstrated.

Predicting wildfire spread is critical for land management and disaster
preparedness. To this end, we present `Next Day Wildfire Spread,' a curated,
large-scale, multivariate data set of historical wildfires aggregating nearly a
decade of remote-sensing data across the United States. In contrast to existing
fire data sets based on Earth observation satellites, our data set combines 2D
fire data with multiple explanatory variables (e.g., topography, vegetation,
weather, drought index, population density) aligned over 2D regions, providing
a feature-rich data set for machine learning. To demonstrate the usefulness of
this data set, we implement a neural network that takes advantage of the
spatial information of this data to predict wildfire spread. We compare the
performance of the neural network with other machine learning models: logistic
regression and random forest. This data set can be used as a benchmark for
developing wildfire propagation models based on remote sensing data for a lead
time of one day.

Many deep learning tasks have to deal with graphs (e.g., protein structures,
social networks, source code abstract syntax trees). Due to the importance of
these tasks, people turned to Graph Neural Networks (GNNs) as the de facto
method for learning on graphs. GNNs have become widely applied due to their
convincing performance. Unfortunately, one major barrier to using GNNs is that
GNNs require substantial time and resources to train. Recently, a new method
for learning on graph data is Graph Neural Tangent Kernel (GNTK) [Du, Hou,
Salakhutdinov, Poczos, Wang and Xu 19]. GNTK is an application of Neural
Tangent Kernel (NTK) [Jacot, Gabriel and Hongler 18] (a kernel method) on graph
data, and solving NTK regression is equivalent to using gradient descent to
train an infinite-wide neural network. The key benefit of using GNTK is that,
similar to any kernel method, GNTK's parameters can be solved directly in a
single step. This can avoid time-consuming gradient descent. Meanwhile,
sketching has become increasingly used in speeding up various optimization
problems, including solving kernel regression. Given a kernel matrix of $n$
graphs, using sketching in solving kernel regression can reduce the running
time to $o(n^3)$. But unfortunately such methods usually require extensive
knowledge about the kernel matrix beforehand, while in the case of GNTK we find
that the construction of the kernel matrix is already $O(n^2N^4)$, assuming
each graph has $N$ nodes. The kernel matrix construction time can be a major
performance bottleneck when the size of graphs $N$ increases. A natural
question to ask is thus whether we can speed up the kernel matrix construction
to improve GNTK regression's end-to-end running time. This paper provides the
first algorithm to construct the kernel matrix in $o(n^2N^3)$ running time.

A prominent challenge to the safe and optimal operation of the modern power
grid arises due to growing uncertainties in loads and renewables. Stochastic
optimal power flow (SOPF) formulations provide a mechanism to handle these
uncertainties by computing dispatch decisions and control policies that
maintain feasibility under uncertainty. Most SOPF formulations consider simple
control policies such as affine policies that are mathematically simple and
resemble many policies used in current practice. Motivated by the efficacy of
machine learning (ML) algorithms and the potential benefits of general control
policies for cost and constraint enforcement, we put forth a deep neural
network (DNN)-based policy that predicts the generator dispatch decisions in
real time in response to uncertainty. The weights of the DNN are learnt using
stochastic primal-dual updates that solve the SOPF without the need for prior
generation of training labels and can explicitly account for the feasibility
constraints in the SOPF. The advantages of the DNN policy over simpler policies
and their efficacy in enforcing safety limits and producing near optimal
solutions are demonstrated in the context of a chance constrained formulation
on a number of test cases.

We develop a stochastic volatility framework for modeling multiple currencies
based on CBI-time-changed L\'evy processes. The proposed framework captures the
typical risk characteristics of FX markets and is coherent with the symmetries
of FX rates. Moreover, due to the self-exciting behavior of CBI processes, the
volatilities of FX rates exhibit self-exciting dynamics. By relying on the
theory of affine processes, we show that our approach is analytically tractable
and that the model structure is invariant under a suitable class of
risk-neutral measures. A semi-closed pricing formula for currency options is
obtained by Fourier methods. We propose two calibration methods, also by
relying on deep-learning techniques, and show that a simple specification of
the model can achieve a good fit to market data on a currency triangle.

Deep Neural Network (DNN) is becoming adopted for video analytics on mobile
devices. To reduce the delay of running DNNs, many mobile devices are equipped
with Neural Processing Units (NPU). However, due to the resource limitations of
NPU, these DNNs have to be compressed to increase the processing speed at the
cost of accuracy. To address the low accuracy problem, we propose a Confidence
Based Offloading (CBO) framework for deep learning video analytics. The major
challenge is to determine when to return the NPU classification result based on
the confidence level of running the DNN, and when to offload the video frames
to the server for further processing to increase the accuracy. We first
identify the problem of using existing confidence scores to make offloading
decisions, and propose confidence score calibration techniques to improve the
performance. Then, we formulate the CBO problem where the goal is to maximize
accuracy under some time constraint, and propose an adaptive solution that
determines which frames to offload at what resolution based on the confidence
score and the network condition. Through real implementations and extensive
evaluations, we demonstrate that the proposed solution can significantly
outperform other approaches.

The Quantitative Group Testing (QGT) is about learning a (hidden) subset $K$
of some large domain $N$ using a sequence of queries, where a result of a query
provides information about the size of the intersection of the query with the
unknown subset $K$. Almost all previous work focused on randomized algorithms
minimizing the number of queries; however, in case of large domains $N$,
randomization may result in a significant deviation from the expected
precision. Others assumed unlimited computational power (existential results)
or adaptiveness of queries. In this work we propose efficient non-adaptive
deterministic QGT algorithms for constructing queries and deconstructing a
hidden set $K$ from the results of the queries, without using randomization,
adaptiveness or unlimited computational power. The efficiency is three-fold.
First, in terms of almost-optimal number of queries - we improve it by factor
nearly $|K|$ comparing to previous constructive results. Second, our algorithms
construct the queries and reconstruct set $K$ in polynomial time. Third, they
work for any hidden set $K$, as well as multi-sets, and even if the results of
the queries are capped at $\sqrt{|K|}$. We also analyze how often elements
occur in queries and its impact to parallelization and fault-tolerance of the
query system.

We draw insights from the social psychology literature to identify two facets
of Twitter deliberations about migrants, i.e., perceptions about migrants and
behaviors towards mi-grants. Our theoretical anchoring helped us in identifying
two prevailing perceptions (i.e., sympathy and antipathy) and two dominant
behaviors (i.e., solidarity and animosity) of social media users towards
migrants. We have employed unsuper-vised and supervised approaches to identify
these perceptions and behaviors. In the domain of applied NLP, our study
of-fers a nuanced understanding of migrant-related Twitter de-liberations. Our
proposed transformer-based model, i.e., BERT + CNN, has reported an F1-score of
0.76 and outper-formed other models. Additionally, we argue that tweets
con-veying antipathy or animosity can be broadly considered hate speech towards
migrants, but they are not the same. Thus, our approach has fine-tuned the
binary hate speech detection task by highlighting the granular differences
between perceptual and behavioral aspects of hate speeches.

Wasserstein gradient flow has emerged as a promising approach to solve
optimization problems over the space of probability distributions. A recent
trend is to use the well-known JKO scheme in combination with input convex
neural networks to numerically implement the proximal step. The most
challenging step, in this setup, is to evaluate functions involving density
explicitly, such as entropy, in terms of samples. This paper builds on the
recent works with a slight but crucial difference: we propose to utilize a
variational formulation of the objective function formulated as maximization
over a parametric class of functions. Theoretically, the proposed variational
formulation allows the construction of gradient flows directly for empirical
distributions with a well-defined and meaningful objective function.
Computationally, this approach replaces the computationally expensive step in
existing methods, to handle objective functions involving density, with inner
loop updates that only require a small batch of samples and scale well with the
dimension. The performance and scalability of the proposed method are
illustrated with the aid of several numerical experiments involving
high-dimensional synthetic and real datasets.

The Gaussian-smoothed optimal transport (GOT) framework, pioneered in
Goldfeld et al. (2020) and followed up by a series of subsequent papers, has
quickly caught attention among researchers in statistics, machine learning,
information theory, and related fields. One key observation made therein is
that, by adapting to the GOT framework instead of its unsmoothed counterpart,
the curse of dimensionality for using the empirical measure to approximate the
true data generating distribution can be lifted. The current paper shows that a
related observation applies to the estimation of nonparametric mixing
distributions in discrete exponential family models, where under the GOT cost
the estimation accuracy of the nonparametric MLE can be accelerated to a
polynomial rate. This is in sharp contrast to the classical sub-polynomial
rates based on unsmoothed metrics, which cannot be improved from an
information-theoretical perspective. A key step in our analysis is the
establishment of a new Jackson-type approximation bound of Gaussian-convoluted
Lipschitz functions. This insight bridges existing techniques of analyzing the
nonparametric MLEs and the new GOT framework.

Predicting the bandwidth utilization on network links can be extremely useful
for detecting congestion in order to correct them before they occur. In this
paper, we present a solution to predict the bandwidth utilization between
different network links with a very high accuracy. A simulated network is
created to collect data related to the performance of the network links on
every interface. These data are processed and expanded with feature engineering
in order to create a training set. We evaluate and compare three types of
machine learning algorithms, namely ARIMA (AutoRegressive Integrated Moving
Average), MLP (Multi Layer Perceptron) and LSTM (Long Short-Term Memory), in
order to predict the future bandwidth consumption. The LSTM outperforms ARIMA
and MLP with very accurate predictions, rarely exceeding a 3\% error (40\% for
ARIMA and 20\% for the MLP). We then show that the proposed solution can be
used in real time with a reaction managed by a Software-Defined Networking
(SDN) platform.

Gated cameras hold promise as an alternative to scanning LiDAR sensors with
high-resolution 3D depth that is robust to back-scatter in fog, snow, and rain.
Instead of sequentially scanning a scene and directly recording depth via the
photon time-of-flight, as in pulsed LiDAR sensors, gated imagers encode depth
in the relative intensity of a handful of gated slices, captured at megapixel
resolution. Although existing methods have shown that it is possible to decode
high-resolution depth from such measurements, these methods require
synchronized and calibrated LiDAR to supervise the gated depth decoder --
prohibiting fast adoption across geographies, training on large unpaired
datasets, and exploring alternative applications outside of automotive use
cases. In this work, we fill this gap and propose an entirely self-supervised
depth estimation method that uses gated intensity profiles and temporal
consistency as a training signal. The proposed model is trained end-to-end from
gated video sequences, does not require LiDAR or RGB data, and learns to
estimate absolute depth values. We take gated slices as input and disentangle
the estimation of the scene albedo, depth, and ambient light, which are then
used to learn to reconstruct the input slices through a cyclic loss. We rely on
temporal consistency between a given frame and neighboring gated slices to
estimate depth in regions with shadows and reflections. We experimentally
validate that the proposed approach outperforms existing supervised and
self-supervised depth estimation methods based on monocular RGB and stereo
images, as well as supervised methods based on gated images.

Recently, zero-shot and few-shot learning via Contrastive Vision-Language
Pre-training (CLIP) have shown inspirational performance on 2D visual
recognition, which learns to match images with their corresponding texts in
open-vocabulary settings. However, it remains under explored that whether CLIP,
pre-trained by large-scale image-text pairs in 2D, can be generalized to 3D
recognition. In this paper, we identify such a setting is feasible by proposing
PointCLIP, which conducts alignment between CLIP-encoded point cloud and 3D
category texts. Specifically, we encode a point cloud by projecting it into
multi-view depth maps without rendering, and aggregate the view-wise zero-shot
prediction to achieve knowledge transfer from 2D to 3D. On top of that, we
design an inter-view adapter to better extract the global feature and
adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in
2D. By just fine-tuning the lightweight adapter in the few-shot settings, the
performance of PointCLIP could be largely improved. In addition, we observe the
complementary property between PointCLIP and classical 3D-supervised networks.
By simple ensembling, PointCLIP boosts baseline's performance and even
surpasses state-of-the-art models. Therefore, PointCLIP is a promising
alternative for effective 3D point cloud understanding via CLIP under low
resource cost and data regime. We conduct thorough experiments on
widely-adopted ModelNet10, ModelNet40 and the challenging ScanObjectNN to
demonstrate the effectiveness of PointCLIP. The code is released at
https://github.com/ZrrSkywalker/PointCLIP.

Reliable traffic flow prediction is crucial to creating intelligent
transportation systems. Many big-data-based prediction approaches have been
developed but they do not reflect complicated dynamic interactions between
roads considering time and location. In this study, we propose a dynamically
localised long short-term memory (LSTM) model that involves both spatial and
temporal dependence between roads. To do so, we use a localised dynamic spatial
weight matrix along with its dynamic variation. Moreover, the LSTM model can
deal with sequential data with long dependency as well as complex non-linear
features. Empirical results indicated superior prediction performances of the
proposed model compared to two different baseline methods.

Contrastive Language-Image Pre-training (CLIP) has drawn increasing attention
recently for its transferable visual representation learning. However, due to
the semantic gap within datasets, CLIP's pre-trained image-text alignment
becomes sub-optimal on downstream tasks, which severely harms its transferring
performance. To better adapt the cross-modality embedding space, we propose to
enhance CLIP via Visual-guided Texts, named VT-CLIP. Specifically, we guide
textual features of different categories to adaptively explore informative
regions on the image and aggregate visual features by attention mechanisms. In
this way, the texts become visual-guided, namely, more semantically correlated
with downstream images, which greatly benefits the category-wise matching
process. In few-shot settings, we evaluate our VT-CLIP on 11 well-known
classification datasets to demonstrate its effectiveness.

We consider the problem of detecting latent community information of mixed
membership weighted networks in which nodes have mixed memberships and edge
weights connecting between nodes can be finite real numbers. We propose a
general mixed membership distribution-free model for this problem. The model
has no distribution constraints of adjacency matrix's elements but only the
expected values and can be viewed as generalizations of some previous models
including the famous mixed membership stochastic blockmodels. Especially,
signed networks in which nodes can belong to multiple communities can be
generated from our model. We use an efficient spectral algorithm to estimate
community memberships under the model. We derive the convergence rate of the
proposed algorithm under the model using spectral analysis. We demonstrate the
advantages of the mixed membership distribution-free model and the algorithm
with applications to a small scale of simulated networks when adjacency
matrix's elements follow different distributions. We have also applied the
algorithm to five real-world weighted network data sets with encouraging
results.

Depth separation results propose a possible theoretical explanation for the
benefits of deep neural networks over shallower architectures, establishing
that the former possess superior approximation capabilities. However, there are
no known results in which the deeper architecture leverages this advantage into
a provable optimization guarantee. We prove that when the data are generated by
a distribution with radial symmetry which satisfies some mild assumptions,
gradient descent can efficiently learn ball indicator functions using a depth 2
neural network with two layers of sigmoidal activations, and where the hidden
layer is held fixed throughout training. By building on and refining existing
techniques for approximation lower bounds of neural networks with a single
layer of non-linearities, we show that there are $d$-dimensional radial
distributions on the data such that ball indicators cannot be learned
efficiently by any algorithm to accuracy better than $\Omega(d^{-4})$, nor by a
standard gradient descent implementation to accuracy better than a constant.
These results establish what is to the best of our knowledge, the first
optimization-based separations where the approximation benefits of the stronger
architecture provably manifest in practice. Our proof technique introduces new
tools and ideas that may be of independent interest in the theoretical study of
both the approximation and optimization of neural networks.

Wearables that constantly collect various sensor data of their users increase
the chances for inferences of unintentional and sensitive information such as
passwords typed on a physical keyboard. We take a thorough look at the
potential of using electromyographic (EMG) data, a sensor modality which is new
to the market but has lately gained attention in the context of wearables for
augmented reality (AR), for a keylogging side-channel attack. Our approach is
based on neural networks for a between-subject attack in a realistic scenario
using the Myo Armband to collect the sensor data. In our approach, the EMG data
has proven to be the most prominent source of information compared to the
accelerometer and gyroscope, increasing the keystroke detection performance.
For our end-to-end approach on raw data, we report a mean balanced accuracy of
about 76 % for the keystroke detection and a mean top-3 key accuracy of about
32 % on 52 classes for the key identification on passwords of varying
strengths. We have created an extensive dataset including more than 310 000
keystrokes recorded from 37 volunteers, which is available as open access along
with the source code used to create the given results.

In many applications of long-range imaging, we are faced with a scenario
where a person appearing in the captured imagery is often degraded by
atmospheric turbulence. However, restoring such degraded images for face
verification is difficult since the degradation causes images to be
geometrically distorted and blurry. To mitigate the turbulence effect, in this
paper, we propose the first turbulence mitigation method that makes use of
visual priors encapsulated by a well-trained GAN. Based on the visual priors,
we propose to learn to preserve the identity of restored images on a spatial
periodic contextual distance. Such a distance can keep the realism of restored
images from the GAN while considering the identity difference at the network
learning. In addition, hierarchical pseudo connections are proposed for
facilitating the identity-preserving learning by introducing more appearance
variance without identity changing. Extensive experiments show that our method
significantly outperforms prior art in both the visual quality and face
verification accuracy of restored results.

As a basic task of computer vision, image similarity retrieval is facing the
challenge of large-scale data and image copy attacks. This paper presents our
3rd place solution to the matching track of Image Similarity Challenge (ISC)
2021 organized by Facebook AI. We propose a multi-branch retrieval method of
combining global descriptors and local descriptors to cover all attack cases.
Specifically, we attempt many strategies to optimize global descriptors,
including abundant data augmentations, self-supervised learning with a single
Transformer model, overlay detection preprocessing. Moreover, we introduce the
robust SIFT feature and GPU Faiss for local retrieval which makes up for the
shortcomings of the global retrieval. Finally, KNN-matching algorithm is used
to judge the match and merge scores. We show some ablation experiments of our
method, which reveals the complementary advantages of global and local
features.

The prosperity of mobile and financial technologies has bred and expanded
various kinds of financial products to a broader scope of people, which
contributes to advocating financial inclusion. It has non-trivial social
benefits of diminishing financial inequality. However, the technical challenges
in individual financial risk evaluation caused by the distinct characteristic
distribution and limited credit history of new users, as well as the
inexperience of newly-entered companies in handling complex data and obtaining
accurate labels, impede further promoting financial inclusion. To tackle these
challenges, this paper develops a novel transfer learning algorithm (i.e.,
TransBoost) that combines the merits of tree-based models and kernel methods.
The TransBoost is designed with a parallel tree structure and efficient weights
updating mechanism with theoretical guarantee, which enables it to excel in
tackling real-world data with high dimensional features and sparsity in $O(n)$
time complexity. We conduct extensive experiments on two public datasets and a
unique large-scale dataset from Tencent Mobile Payment. The results show that
the TransBoost outperforms other state-of-the-art benchmark transfer learning
algorithms in terms of prediction accuracy with superior efficiency, shows
stronger robustness to data sparsity, and provides meaningful model
interpretation. Besides, given a financial risk level, the TransBoost enables
financial service providers to serve the largest number of users including
those who would otherwise be excluded by other algorithms. That is, the
TransBoost improves financial inclusion.

We consider the novel problem of unsupervised domain adaptation of source
models, without access to the source data for semantic segmentation.
Unsupervised domain adaptation aims to adapt a model learned on the labeled
source data, to a new unlabeled target dataset. Existing methods assume that
the source data is available along with the target data during adaptation.
However, in practical scenarios, we may only have access to the source model
and the unlabeled target data, but not the labeled source, due to reasons such
as privacy, storage, etc. In this work, we propose a self-training approach to
extract the knowledge from the source model. To compensate for the distribution
shift from source to target, we first update only the normalization parameters
of the network with the unlabeled target data. Then we employ
confidence-filtered pseudo labeling and enforce consistencies against certain
transformations. Despite being very simple and intuitive, our framework is able
to achieve significant performance gains compared to directly applying the
source model on the target data as reflected in our extensive experiments and
ablation studies. In fact, the performance is just a few points away from the
recent state-of-the-art methods which use source data for adaptation. We
further demonstrate the generalisability of the proposed approach for fully
test-time adaptation setting, where we do not need any target training data and
adapt only during test-time.

Hierarchical classification aims to sort the object into a hierarchy of
categories. For example, a bird can be categorized according to a three-level
hierarchy of order, family, and species. Existing methods commonly address
hierarchical classification by decoupling it into several multi-class
classification tasks. However, such a multi-task learning strategy fails to
fully exploit the correlation among various categories across different
hierarchies. In this paper, we propose Label Hierarchy Transition, a unified
probabilistic framework based on deep learning, to address hierarchical
classification. Specifically, we explicitly learn the label hierarchy
transition matrices, whose column vectors represent the conditional label
distributions of classes between two adjacent hierarchies and could be capable
of encoding the correlation embedded in class hierarchies. We further propose a
confusion loss, which encourages the classification network to learn the
correlation across different label hierarchies during training. The proposed
framework can be adapted to any existing deep network with only minor
modifications. We experiment with three public benchmark datasets with various
class hierarchies, and the results demonstrate the superiority of our approach
beyond the prior arts. Source code will be made publicly available.

FPGA-specific DNN architectures using the native LUTs as independently
trainable inference operators have been shown to achieve favorable
area-accuracy and energy-accuracy tradeoffs. The first work in this area,
LUTNet, exhibited state-of-the-art performance for standard DNN benchmarks. In
this paper, we propose the learned optimization of such LUT-based topologies,
resulting in higher-efficiency designs than via the direct use of
off-the-shelf, hand-designed networks. Existing implementations of this class
of architecture require the manual specification of the number of inputs per
LUT, K. Choosing appropriate K a priori is challenging, and doing so at even
high granularity, e.g. per layer, is a time-consuming and error-prone process
that leaves FPGAs' spatial flexibility underexploited. Furthermore, prior works
see LUT inputs connected randomly, which does not guarantee a good choice of
network topology. To address these issues, we propose logic shrinkage, a
fine-grained netlist pruning methodology enabling K to be automatically learned
for every LUT in a neural network targeted for FPGA inference. By removing LUT
inputs determined to be of low importance, our method increases the efficiency
of the resultant accelerators. Our GPU-friendly solution to LUT input removal
is capable of processing large topologies during their training with negligible
slowdown. With logic shrinkage, we better the area and energy efficiency of the
best-performing LUTNet implementation of the CNV network classifying CIFAR-10
by 1.54x and 1.31x, respectively, while matching its accuracy. This
implementation also reaches 2.71x the area efficiency of an equally accurate,
heavily pruned BNN. On ImageNet with the Bi-Real Net architecture, employment
of logic shrinkage results in a post-synthesis area reduction of 2.67x vs
LUTNet, allowing for implementation that was previously impossible on today's
largest FPGAs.

Learning from a sequence of tasks for a lifetime is essential for an agent
towards artificial general intelligence. This requires the agent to
continuously learn and memorize new knowledge without interference. This paper
first demonstrates a fundamental issue of lifelong learning using neural
networks, named anterograde forgetting, i.e., preserving and transferring
memory may inhibit the learning of new knowledge. This is attributed to the
fact that the learning capacity of a neural network will be reduced as it keeps
memorizing historical knowledge, and the fact that conceptual confusion may
occur as it transfers irrelevant old knowledge to the current task. This work
proposes a general framework named Cycled Memory Networks (CMN) to address the
anterograde forgetting in neural networks for lifelong learning. The CMN
consists of two individual memory networks to store short-term and long-term
memories to avoid capacity shrinkage. A transfer cell is designed to connect
these two memory networks, enabling knowledge transfer from the long-term
memory network to the short-term memory network to mitigate the conceptual
confusion, and a memory consolidation mechanism is developed to integrate
short-term knowledge into the long-term memory network for knowledge
accumulation. Experimental results demonstrate that the CMN can effectively
address the anterograde forgetting on several task-related, task-conflict,
class-incremental and cross-domain benchmarks.

We propose Unified Model of Saliency and Scanpaths (UMSS) -- a model that
learns to predict visual saliency and scanpaths (i.e. sequences of eye
fixations) on information visualisations. Although scanpaths provide rich
information about the importance of different visualisation elements during the
visual exploration process, prior work has been limited to predicting
aggregated attention statistics, such as visual saliency. We present in-depth
analyses of gaze behaviour for different information visualisation elements
(e.g. Title, Label, Data) on the popular MASSVIS dataset. We show that while,
overall, gaze patterns are surprisingly consistent across visualisations and
viewers, there are also structural differences in gaze dynamics for different
elements. Informed by our analyses, UMSS first predicts multi-duration
element-level saliency maps, then probabilistically samples scanpaths from
them. Extensive experiments on MASSVIS show that our method consistently
outperforms state-of-the-art methods with respect to several, widely used
scanpath and saliency evaluation metrics. Our method achieves a relative
improvement in sequence score of 11.5% for scanpath prediction, and a relative
improvement in Pearson correlation coefficient of up to 23.6% for saliency
prediction. These results are auspicious and point towards richer user models
and simulations of visual attention on visualisations without the need for any
eye tracking equipment.

Multi-view Stereo (MVS) with known camera parameters is essentially a 1D
search problem within a valid depth range. Recent deep learning-based MVS
methods typically densely sample depth hypotheses in the depth range, and then
construct prohibitively memory-consuming 3D cost volumes for depth prediction.
Although coarse-to-fine sampling strategies alleviate this overhead issue to a
certain extent, the efficiency of MVS is still an open challenge. In this work,
we propose a novel method for highly efficient MVS that remarkably decreases
the memory footprint, meanwhile clearly advancing state-of-the-art depth
prediction performance. We investigate what a search strategy can be reasonably
optimal for MVS taking into account of both efficiency and effectiveness. We
first formulate MVS as a binary search problem, and accordingly propose a
generalized binary search network for MVS. Specifically, in each step, the
depth range is split into 2 bins with extra 1 error tolerance bin on both
sides. A classification is performed to identify which bin contains the true
depth. We also design three mechanisms to respectively handle classification
errors, deal with out-of-range samples and decrease the training memory. The
new formulation makes our method only sample a very small number of depth
hypotheses in each step, which is highly memory efficient, and also greatly
facilitates quick training convergence. Experiments on competitive benchmarks
show that our method achieves state-of-the-art accuracy with much less memory.
Particularly, our method obtains an overall score of 0.289 on DTU dataset and
tops the first place on challenging Tanks and Temples advanced dataset among
all the learning-based methods. The trained models and code will be released at
https://github.com/MiZhenxing/GBi-Net.

Since conventional approaches could not adapt to dynamic traffic conditions,
reinforcement learning (RL) has attracted more attention to help solve the
traffic signal control (TSC) problem. However, existing RL-based methods are
rarely deployed considering that they are neither cost-effective in terms of
computing resources nor more robust than traditional approaches, which raises a
critical research question: how to construct an adaptive controller for TSC
with less training and reduced complexity based on RL-based approach? To
address this question, in this paper, we (1) innovatively specify the traffic
movement representation as a simple but efficient pressure of vehicle queues in
a traffic network, namely efficient pressure (EP); (2) build a traffic signal
settings protocol, including phase duration, signal phase number and EP for
TSC; (3) design a TSC approach based on the traditional max pressure (MP)
approach, namely efficient max pressure (Efficient-MP) using the EP to capture
the traffic state; and (4) develop a general RL-based TSC algorithm template:
efficient Xlight (Efficient-XLight) under EP. Through comprehensive experiments
on multiple real-world datasets in our traffic signal settings' protocol for
TSC, we demonstrate that efficient pressure is complementary to traditional and
RL-based modeling to design better TSC methods. Our code is released on Github.

Although many pretrained models exist for text or images, there have been
relatively fewer attempts to train representations specifically for dialog
understanding. Prior works usually relied on finetuned representations based on
generic text representation models like BERT or GPT-2. But such language
modeling pretraining objectives do not take the structural information of
conversational text into consideration. Although generative dialog models can
learn structural features too, we argue that the structure-unaware word-by-word
generation is not suitable for effective conversation modeling. We empirically
demonstrate that such representations do not perform consistently across
various dialog understanding tasks. Hence, we propose a structure-aware Mutual
Information based loss-function DMI (Discourse Mutual Information) for training
dialog-representation models, that additionally captures the inherent
uncertainty in response prediction. Extensive evaluation on nine diverse dialog
modeling tasks shows that our proposed DMI-based models outperform strong
baselines by significant margins.

Advances in machine learning methods provide tools that have broad
applicability in scientific research. These techniques are being applied across
the diversity of nuclear physics research topics, leading to advances that will
facilitate scientific discoveries and societal applications.
  This Review gives a snapshot of nuclear physics research which has been
transformed by machine learning techniques.

Establishing patient-specific finite element analysis (FEA) models for
computational fluid dynamics (CFD) of double stenosed artery models involves
time and effort, restricting physicians' ability to respond quickly in
time-critical medical applications. Such issues might be addressed by training
deep learning (DL) models to learn and predict blood flow characteristics using
a dataset generated by CFD simulations of simplified double stenosed artery
models with different configurations. When blood flow patterns are compared
through an actual double stenosed artery model, derived from IVUS imaging, it
is revealed that the sinusoidal approximation of stenosed neck geometry, which
has been widely used in previous research works, fails to effectively represent
the effects of a real constriction. As a result, a novel geometric
representation of the constricted neck is proposed which, in terms of a
generalized simplified model, outperforms the former assumption. The sequential
change in artery lumen diameter and flow parameters along the length of the
vessel presented opportunities for the use of LSTM and GRU DL models. However,
with the small dataset of short lengths of doubly constricted blood arteries,
the basic neural network model outperforms the specialized RNNs for most flow
properties. LSTM, on the other hand, performs better for predicting flow
properties with large fluctuations, such as varying blood pressure over the
length of the vessels. Despite having good overall accuracies in training and
testing across all the properties for the vessels in the dataset, the GRU model
underperforms for an individual vessel flow prediction in all cases. The
results also point to the need of individually optimized hyperparameters for
each property in any model rather than aiming to achieve overall good
performance across all outputs with a single set of hyperparameters.

The majority of prior monocular depth estimation methods without groundtruth
depth guidance focus on driving scenarios. We show that such methods generalize
poorly to unseen complex indoor scenes, where objects are cluttered and
arbitrarily arranged in the near field. To obtain more robustness, we propose a
structure distillation approach to learn knacks from an off-the-shelf relative
depth estimator that produces structured but metric-agnostic depth. By
combining structure distillation with a branch that learns metrics from
left-right consistency, we attain structured and metric depth for generic
indoor scenes and make inferences in real-time. To facilitate learning and
evaluation, we collect SimSIN, a dataset from simulation with thousands of
environments, and UniSIN, a dataset that contains about 500 real scan sequences
of generic indoor environments. We experiment in both sim-to-real and
real-to-real settings, and show improvements, as well as in downstream
applications using our depth maps. This work provides a full study, covering
methods, data, and applications aspects.

Existing online multi-label classification works cannot well handle the
online label thresholding problem and lack the regret analysis for their online
algorithms. This paper proposes a novel framework of adaptive label
thresholding algorithms for online multi-label classification, with the aim to
overcome the drawbacks of existing methods. The key feature of our framework is
that both scoring and thresholding models are included as important components
of the online multi-label classifier and are incorporated into one online
optimization problem. Further, in order to establish the relationship between
scoring and thresholding models, a novel multi-label classification loss
function is derived, which measures to what an extent the multi-label
classifier can distinguish between relevant labels and irrelevant ones for an
incoming instance. Based on this new framework and loss function, we present a
first-order linear algorithm and a second-order one, which both enjoy closed
form update, but rely on different techniques for updating the multi-label
classifier. Both algorithms are proved to achieve a sub-linear regret. Using
Mercer kernels, our first-order algorithm has been extended to deal with
nonlinear multi-label prediction tasks. Experiments show the advantage of our
linear and nonlinear algorithms, in terms of various multi-label performance
metrics.

The ability to generalize learned representations across significantly
different visual domains, such as between real photos, clipart, paintings, and
sketches, is a fundamental capacity of the human visual system. In this paper,
different from most cross-domain works that utilize some (or full) source
domain supervision, we approach a relatively new and very practical
Unsupervised Domain Generalization (UDG) setup of having no training
supervision in neither source nor target domains. Our approach is based on
self-supervised learning of a Bridge Across Domains (BrAD) - an auxiliary
bridge domain accompanied by a set of semantics preserving visual
(image-to-image) mappings to BrAD from each of the training domains. The BrAD
and mappings to it are learned jointly (end-to-end) with a contrastive
self-supervised representation model that semantically aligns each of the
domains to its BrAD-projection, and hence implicitly drives all the domains
(seen or unseen) to semantically align to each other. In this work, we show how
using an edge-regularized BrAD our approach achieves significant gains across
multiple benchmarks and a range of tasks, including UDG, Few-shot UDA, and
unsupervised generalization across multi-domain datasets (including
generalization to unseen domains and classes).

This project focuses on the self-supervised training of convolutional neural
networks (CNNs) and transformer networks for the task of image recognition. A
simple siamese network with different backbones is used in order to maximize
the similarity of two augmented transformed images from the same source image.
In this way, the backbone is able to learn visual information without
supervision. Finally, the method is evaluated on three image recognition
datasets.

Building a fault-tolerant edge system that can quickly react to node
overloads or failures is challenging due to the unreliability of edge devices
and the strict service deadlines of modern applications. Moreover, unnecessary
task migrations can stress the system network, giving rise to the need for a
smart and parsimonious failure recovery scheme. Prior approaches often fail to
adapt to highly volatile workloads or accurately detect and diagnose faults for
optimal remediation. There is thus a need for a robust and proactive
fault-tolerance mechanism to meet service level objectives. In this work, we
propose PreGAN, a composite AI model using a Generative Adversarial Network
(GAN) to predict preemptive migration decisions for proactive fault-tolerance
in containerized edge deployments. PreGAN uses co-simulations in tandem with a
GAN to learn a few-shot anomaly classifier and proactively predict migration
decisions for reliable computing. Extensive experiments on a Raspberry-Pi based
edge environment show that PreGAN can outperform state-of-the-art baseline
methods in fault-detection, diagnosis and classification, thus achieving high
quality of service. PreGAN accomplishes this by 5.1% more accurate fault
detection, higher diagnosis scores and 23.8% lower overheads compared to the
best method among the considered baselines.

Time-series classification approaches based on deep neural networks are easy
to be overfitting on UCR datasets, which is caused by the few-shot problem of
those datasets. Therefore, in order to alleviate the overfitting phenomenon for
further improving the accuracy, we first propose Label Smoothing for
InceptionTime (LSTime), which adopts the information of soft labels compared to
just hard labels. Next, instead of manually adjusting soft labels by LSTime,
Knowledge Distillation for InceptionTime (KDTime) is proposed in order to
automatically generate soft labels by the teacher model. At last, in order to
rectify the incorrect predicted soft labels from the teacher model, Knowledge
Distillation with Calibration for InceptionTime (KDCTime) is proposed, where it
contains two optional calibrating strategies, i.e. KDC by Translating (KDCT)
and KDC by Reordering (KDCR). The experimental results show that the accuracy
of KDCTime is promising, while its inference time is two orders of magnitude
faster than ROCKET with an acceptable training time overhead.

Learning visual concepts from raw images without strong supervision is a
challenging task. In this work, we show the advantages of prototype
representations for understanding and revising the latent space of neural
concept learners. For this purpose, we introduce interactive Concept Swapping
Networks (iCSNs), a novel framework for learning concept-grounded
representations via weak supervision and implicit prototype representations.
iCSNs learn to bind conceptual information to specific prototype slots by
swapping the latent representations of paired images. This semantically
grounded and discrete latent space facilitates human understanding and
human-machine interaction. We support this claim by conducting experiments on
our novel data set "Elementary Concept Reasoning" (ECR), focusing on visual
concepts shared by geometric objects.

We introduce a machine-learning (ML) framework for high-throughput
benchmarking of diverse representations of chemical systems against datasets of
materials and molecules. The guiding principle underlying the benchmarking
approach is to evaluate raw descriptor performance by limiting model complexity
to simple regression schemes while enforcing best ML practices, allowing for
unbiased hyperparameter optimization, and assessing learning progress through
learning curves along series of synchronized train-test splits. The resulting
models are intended as baselines that can inform future method development,
next to indicating how easily a given dataset can be learnt. Through a
comparative analysis of the training outcome across a diverse set of
physicochemical, topological and geometric representations, we glean insight
into the relative merits of these representations as well as their
interrelatedness.

While Transformer has achieved remarkable performance in various high-level
vision tasks, it is still challenging to exploit the full potential of
Transformer in image restoration. The crux lies in the limited depth of
applying Transformer in the typical encoder-decoder framework for image
restoration, resulting from heavy self-attention computation load and
inefficient communications across different depth (scales) of layers. In this
paper, we present a deep and effective Transformer-based network for image
restoration, termed as U2-Former, which is able to employ Transformer as the
core operation to perform image restoration in a deep encoding and decoding
space. Specifically, it leverages the nested U-shaped structure to facilitate
the interactions across different layers with different scales of feature maps.
Furthermore, we optimize the computational efficiency for the basic Transformer
block by introducing a feature-filtering mechanism to compress the token
representation. Apart from the typical supervision ways for image restoration,
our U2-Former also performs contrastive learning in multiple aspects to further
decouple the noise component from the background image. Extensive experiments
on various image restoration tasks, including reflection removal, rain streak
removal and dehazing respectively, demonstrate the effectiveness of the
proposed U2-Former.

In few-shot imitation learning (FSIL), using behavioral cloning (BC) to solve
unseen tasks with few expert demonstrations becomes a popular research
direction. The following capabilities are essential in robotics applications:
(1) Behaving in compound tasks that contain multiple stages. (2) Retrieving
knowledge from few length-variant and misalignment demonstrations. (3) Learning
from a different expert. No previous work can achieve these abilities at the
same time. In this work, we conduct FSIL problem under the union of above
settings and introduce a novel stage conscious attention network (SCAN) to
retrieve knowledge from few demonstrations simultaneously. SCAN uses an
attention module to identify each stage in length-variant demonstrations.
Moreover, it is designed under demonstration-conditioned policy that learns the
relationship between experts and agents. Experiment results show that SCAN can
learn from different experts without fine-tuning and outperform baselines in
complicated compound tasks with explainable visualization.

Thermal infrared (TIR) image has proven effectiveness in providing
temperature cues to the RGB features for multispectral pedestrian detection.
Most existing methods directly inject the TIR modality into the RGB-based
framework or simply ensemble the results of two modalities. This, however,
could lead to inferior detection performance, as the RGB and TIR features
generally have modality-specific noise, which might worsen the features along
with the propagation of the network. Therefore, this work proposes an effective
and efficient cross-modality fusion module called Bi-directional Adaptive
Attention Gate (BAA-Gate). Based on the attention mechanism, the BAA-Gate is
devised to distill the informative features and recalibrate the representations
asymptotically. Concretely, a bi-direction multi-stage fusion strategy is
adopted to progressively optimize features of two modalities and retain their
specificity during the propagation. Moreover, an adaptive interaction of
BAA-Gate is introduced by the illumination-based weighting strategy to
adaptively adjust the recalibrating and aggregating strength in the BAA-Gate
and enhance the robustness towards illumination changes. Considerable
experiments on the challenging KAIST dataset demonstrate the superior
performance of our method with satisfactory speed.

Cold-start problem is a fundamental challenge for recommendation tasks. The
recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model,
PT-GNN, pre-trains the GNN model to reconstruct the cold-start embeddings and
has shown great potential for cold-start recommendation. However, due to the
over-smoothing problem, PT-GNN can only capture up to 3-order relation, which
can not provide much useful auxiliary information to depict the target
cold-start user or item. Besides, the embedding reconstruction task only
considers the intra-correlations within the subgraph of users and items, while
ignoring the inter-correlations across different subgraphs. To solve the above
challenges, we propose a multi-strategy based pre-training method for
cold-start recommendation (MPT), which extends PT-GNN from the perspective of
model architecture and pretext tasks to improve the cold-start recommendation
performance. Specifically, in terms of the model architecture, in addition to
the short-range dependencies of users and items captured by the GNN encoder, we
introduce a Transformer encoder to capture long-range dependencies. In terms of
the pretext task, in addition to considering the intra-correlations of users
and items by the embedding reconstruction task, we add embedding contrastive
learning task to capture inter-correlations of users and items. We train the
GNN and Transformer encoders on these pretext tasks under the meta-learning
setting to simulate the real cold-start scenario, making the model easily and
rapidly being adapted to new cold-start users and items. Experiments on three
public recommendation datasets show the superiority of the proposed MPT model
against the vanilla GNN models, the pre-training GNN model on user/item
embedding inference and the recommendation task.

As an important branch in Recommender System, occasional group recommendation
has received more and more attention. In this scenario, each occasional group
(cold-start group) has no or few historical interacted items. As each
occasional group has extremely sparse interactions with items, traditional
group recommendation methods can not learn high-quality group representations.
The recent proposed Graph Neural Networks (GNNs), which incorporate the
high-order neighbors of the target occasional group, can alleviate the above
problem in some extent. However, these GNNs still can not explicitly strengthen
the embedding quality of the high-order neighbors with few interactions.
Motivated by the Self-supervised Learning technique, which is able to find the
correlations within the data itself, we propose a self-supervised graph
learning framework, which takes the user/item/group embedding reconstruction as
the pretext task to enhance the embeddings of the cold-start
users/items/groups. In order to explicitly enhance the high-order cold-start
neighbors' embedding quality, we further introduce an embedding enhancer, which
leverages the self-attention mechanism to improve the embedding quality for
them. Comprehensive experiments show the advantages of our proposed framework
than the state-of-the-art methods.

With the great success of pre-trained models, the pretrain-then-finetune
paradigm has been widely adopted on downstream tasks for source code
understanding. However, compared to costly training a large-scale model from
scratch, how to effectively adapt pre-trained models to a new task has not been
fully explored. In this paper, we propose an approach to bridge pre-trained
models and code-related tasks. We exploit semantic-preserving transformation to
enrich downstream data diversity, and help pre-trained models learn semantic
features invariant to these semantically equivalent transformations. Further,
we introduce curriculum learning to organize the transformed data in an
easy-to-hard manner to fine-tune existing pre-trained models.
  We apply our approach to a range of pre-trained models, and they
significantly outperform the state-of-the-art models on tasks for source code
understanding, such as algorithm classification, code clone detection, and code
search. Our experiments even show that without heavy pre-training on code data,
natural language pre-trained model RoBERTa fine-tuned with our lightweight
approach could outperform or rival existing code pre-trained models fine-tuned
on the above tasks, such as CodeBERT and GraphCodeBERT. This finding suggests
that there is still much room for improvement in code pre-trained models.

Traffic forecasting is a problem of intelligent transportation systems (ITS)
and crucial for individuals and public agencies. Therefore, researches pay
great attention to deal with the complex spatio-temporal dependencies of
traffic system for accurate forecasting. However, there are two challenges: 1)
Most traffic forecasting studies mainly focus on modeling correlations of
neighboring sensors and ignore correlations of remote sensors, e.g., business
districts with similar spatio-temporal patterns; 2) Prior methods which use
static adjacency matrix in graph convolutional networks (GCNs) are not enough
to reflect the dynamic spatial dependence in traffic system. Moreover,
fine-grained methods which use self-attention to model dynamic correlations of
all sensors ignore hierarchical information in road networks and have quadratic
computational complexity. In this paper, we propose a novel dynamic multi-graph
convolution recurrent network (DMGCRN) to tackle above issues, which can model
the spatial correlations of distance, the spatial correlations of structure,
and the temporal correlations simultaneously. We not only use the
distance-based graph to capture spatial information from nodes are close in
distance but also construct a novel latent graph which encoded the structure
correlations among roads to capture spatial information from nodes are similar
in structure. Furthermore, we divide the neighbors of each sensor into
coarse-grained regions, and dynamically assign different weights to each region
at different times. Meanwhile, we integrate the dynamic multi-graph convolution
network into the gated recurrent unit (GRU) to capture temporal dependence.
Extensive experiments on three real-world traffic datasets demonstrate that our
proposed algorithm outperforms state-of-the-art baselines.

The natural exponential function is widely used in modeling many engineering
and scientific systems. It is also an integral part of many neural network
activation function such as sigmoid, tanh, ELU, RBF etc. Dedicated hardware
accelerator and processors are designed for faster execution of such
applications. Such accelerators can immensely benefit from an optimal
implementation of exponential function. This can be achieved for most
applications with the knowledge that the exponential function for a negative
domain is more widely used than the positive domain. This paper presents an
optimized implementation of exponential function for variable precision fixed
point negative input. The implementation presented here significantly reduces
the number of multipliers and adders. This is further optimized using mixed
world-length implementation for the series expansion. The reduction in area and
power consumption is more than 30% and 50% respectively over previous
equivalent method.

Traffic prediction has gradually attracted the attention of researchers
because of the increase in traffic big data. Therefore, how to mine the complex
spatio-temporal correlations in traffic data to predict traffic conditions more
accurately become a difficult problem. Previous works combined graph
convolution networks (GCNs) and self-attention mechanism with deep time series
models (e.g. recurrent neural networks) to capture the spatio-temporal
correlations separately, ignoring the relationships across time and space.
Besides, GCNs are limited by over-smoothing issue and self-attention is limited
by quadratic problem, result in GCNs lack global representation capabilities,
and self-attention inefficiently capture the global spatial dependence. In this
paper, we propose a novel deep learning model for traffic forecasting, named
Multi-Context Aware Spatio-Temporal Joint Linear Attention (STJLA), which
applies linear attention to the spatio-temporal joint graph to capture global
dependence between all spatio-temporal nodes efficiently. More specifically,
STJLA utilizes static structural context and dynamic semantic context to
improve model performance. The static structure context based on node2vec and
one-hot encoding enriches the spatio-temporal position information.
Furthermore, the multi-head diffusion convolution network based dynamic spatial
context enhances the local spatial perception ability, and the GRU based
dynamic temporal context stabilizes sequence position information of the linear
attention, respectively. Experiments on two real-world traffic datasets,
England and PEMSD7, demonstrate that our STJLA can achieve up to 9.83% and
3.08% accuracy improvement in MAE measure over state-of-the-art baselines.

In this paper, we propose a robust sample generation scheme to construct
informative triplets. The proposed hard sample generation is a two-stage
synthesis framework that produces hard samples through effective positive and
negative sample generators in two stages, respectively. The first stage
stretches the anchor-positive pairs with piecewise linear manipulation and
enhances the quality of generated samples by skillfully designing a conditional
generative adversarial network to lower the risk of mode collapse. The second
stage utilizes an adaptive reverse metric constraint to generate the final hard
samples. Extensive experiments on several benchmark datasets verify that our
method achieves superior performance than the existing hard-sample generation
algorithms. Besides, we also find that our proposed hard sample generation
method combining the existing triplet mining strategies can further boost the
deep metric learning performance.

The existence of a universal learning architecture in human cognition is a
widely spread conjecture supported by experimental findings from neuroscience.
While no low-level implementation can be specified yet, an abstract outline of
human perception and learning is believed to entail three basic properties: (a)
hierarchical attention and processing, (b) memory-based knowledge
representation, and (c) progressive learning and knowledge compaction. We
approach the design of such a learning architecture from a system-theoretic
viewpoint, developing a closed-loop system with three main components: (i) a
multi-resolution analysis pre-processor, (ii) a group-invariant feature
extractor, and (iii) a progressive knowledge-based learning module.
Multi-resolution feedback loops are used for learning, i.e., for adapting the
system parameters to online observations. To design (i) and (ii), we build upon
the established theory of wavelet-based multi-resolution analysis and the
properties of group convolution operators. Regarding (iii), we introduce a
novel learning algorithm that constructs progressively growing knowledge
representations in multiple resolutions. The proposed algorithm is an extension
of the Online Deterministic Annealing (ODA) algorithm based on annealing
optimization, solved using gradient-free stochastic approximation. ODA has
inherent robustness and regularization properties and provides a means to
progressively increase the complexity of the learning model i.e. the number of
the neurons, as needed, through an intuitive bifurcation phenomenon. The
proposed multi-resolution approach is hierarchical, progressive,
knowledge-based, and interpretable. We illustrate the properties of the
proposed architecture in the context of the state-of-the-art learning
algorithms and deep learning methods.

Multimodal fusion and multitask learning are two vital topics in machine
learning. Despite the fruitful progress, existing methods for both problems are
still brittle to the same challenge -- it remains dilemmatic to integrate the
common information across modalities (resp. tasks) meanwhile preserving the
specific patterns of each modality (resp. task). Besides, while they are
actually closely related to each other, multimodal fusion and multitask
learning are rarely explored within the same methodological framework before.
In this paper, we propose Channel-Exchanging-Network (CEN) which is
self-adaptive, parameter-free, and more importantly, applicable for multimodal
and multitask dense image prediction. At its core, CEN adaptively exchanges
channels between subnetworks of different modalities. Specifically, the channel
exchanging process is self-guided by individual channel importance that is
measured by the magnitude of Batch-Normalization (BN) scaling factor during
training. For the application of dense image prediction, the validity of CEN is
tested by four different scenarios: multimodal fusion, cycle multimodal fusion,
multitask learning, and multimodal multitask learning. Extensive experiments on
semantic segmentation via RGB-D data and image translation through multi-domain
input verify the effectiveness of CEN compared to state-of-the-art methods.
Detailed ablation studies have also been carried out, which demonstrate the
advantage of each component we propose. Our code is available at
https://github.com/yikaiw/CEN.

Edge detection is the basis of many computer vision applications. State of
the art predominantly relies on deep learning with two decisive factors:
dataset content and network's architecture. Most of the publicly available
datasets are not curated for edge detection tasks. Here, we offer a solution to
this constraint. First, we argue that edges, contours and boundaries, despite
their overlaps, are three distinct visual features requiring separate benchmark
datasets. To this end, we present a new dataset of edges. Second, we propose a
novel architecture, termed Dense Extreme Inception Network for Edge Detection
(DexiNed), that can be trained from scratch without any pre-trained weights.
DexiNed outperforms other algorithms in the presented dataset. It also
generalizes well to other datasets without any fine-tuning. The higher quality
of DexiNed is also perceptually evident thanks to the sharper and finer edges
it outputs.

Deformable image registration is able to achieve fast and accurate alignment
between a pair of images and thus plays an important role in many medical image
studies. The current deep learning (DL)-based image registration approaches
directly learn the spatial transformation from one image to another by
leveraging a convolutional neural network, requiring ground truth or similarity
metric. Nevertheless, these methods only use a global similarity energy
function to evaluate the similarity of a pair of images, which ignores the
similarity of regions of interest (ROIs) within images. Moreover, DL-based
methods often estimate global spatial transformations of image directly, which
never pays attention to region spatial transformations of ROIs within images.
In this paper, we present a novel dual-flow transformation network with region
consistency constraint which maximizes the similarity of ROIs within a pair of
images and estimates both global and region spatial transformations
simultaneously. Experiments on four public 3D MRI datasets show that the
proposed method achieves the best registration performance in accuracy and
generalization compared with other state-of-the-art methods.

Graph convolutional networks (GCNs) have achieved remarkable learning ability
for dealing with various graph structural data recently. In general, deep GCNs
do not work well since graph convolution in conventional GCNs is a special form
of Laplacian smoothing, which makes the representation of different nodes
indistinguishable. In the literature, multi-scale information was employed in
GCNs to enhance the expressive power of GCNs. However, over-smoothing
phenomenon as a crucial issue of GCNs remains to be solved and investigated. In
this paper, we propose two novel multi-scale GCN frameworks by incorporating
self-attention mechanism and multi-scale information into the design of GCNs.
Our methods greatly improve the computational efficiency and prediction
accuracy of the GCNs model. Extensive experiments on both node classification
and graph classification demonstrate the effectiveness over several
state-of-the-art GCNs. Notably, the proposed two architectures can efficiently
mitigate the over-smoothing problem of GCNs, and the layer of our model can
even be increased to $64$.

3D Morphable Models (3DMMs) are generative models for face shape and
appearance. However, the shape parameters of traditional 3DMMs satisfy the
multivariate Gaussian distribution while the identity embeddings satisfy the
hypersphere distribution, and this conflict makes it challenging for face
reconstruction models to preserve the faithfulness and the shape consistency
simultaneously. To address this issue, we propose the Sphere Face Model(SFM), a
novel 3DMM for monocular face reconstruction, which can preserve both shape
fidelity and identity consistency. The core of our SFM is the basis matrix
which can be used to reconstruct 3D face shapes, and the basic matrix is
learned by adopting a two-stage training approach where 3D and 2D training data
are used in the first and second stages, respectively. To resolve the
distribution mismatch, we design a novel loss to make the shape parameters have
a hyperspherical latent space. Extensive experiments show that SFM has high
representation ability and shape parameter space's clustering performance.
Moreover, it produces fidelity face shapes, and the shapes are consistent in
challenging conditions in monocular face reconstruction.

Recent studies have shown that StyleGANs provide promising prior models for
downstream tasks on image synthesis and editing. However, since the latent
codes of StyleGANs are designed to control global styles, it is hard to achieve
a fine-grained control over synthesized images. We present SemanticStyleGAN,
where a generator is trained to model local semantic parts separately and
synthesizes images in a compositional way. The structure and texture of
different local parts are controlled by corresponding latent codes.
Experimental results demonstrate that our model provides a strong
disentanglement between different spatial areas. When combined with editing
methods designed for StyleGANs, it can achieve a more fine-grained control to
edit synthesized or real images. The model can also be extended to other
domains via transfer learning. Thus, as a generic prior model with built-in
disentanglement, it could facilitate the development of GAN-based applications
and enable more potential downstream tasks.

Data used to train machine learning (ML) models can be sensitive. Membership
inference attacks (MIAs), attempting to determine whether a particular data
record was used to train an ML model, risk violating membership privacy. ML
model builders need a principled definition of a metric to quantify the
membership privacy risk of (a) individual training data records, (b) computed
independently of specific MIAs, (c) which assesses susceptibility to different
MIAs, (d) can be used for different applications, and (e) efficiently. None of
the prior membership privacy risk metrics simultaneously meet all these
  We present SHAPr, a membership privacy metric based on Shapley values which
is a leave-one-out (LOO) technique, originally intended to measure the
contribution of a training data record on model utility. We conjecture that
contribution to model utility can act as a proxy for memorization, and hence
represent membership privacy risk.
  Using ten benchmark datasets, we show that SHAPr is indeed effective in
estimating susceptibility of training data records to MIAs. We also show that,
unlike prior work, SHAPr is significantly better in estimating susceptibility
to newer, and more effective MIA. We apply SHAPr to evaluate the efficacy of
several defenses against MIAs: using regularization and removing high risk
training data records. Moreover, SHAPr is versatile: it can be used for
estimating vulnerability of different subgroups to MIAs, and inherits
applications of Shapley values (e.g., data valuation). We show that SHAPr has
an acceptable computational cost (compared to naive LOO), varying from a few
minutes for the smallest dataset to ~92 minutes for the largest dataset.

Phishing attacks continue to be a significant threat on the Internet. Prior
studies show that it is possible to determine whether a website is phishing or
not just by analyzing its URL more carefully. A major advantage of the URL
based approach is that it can identify a phishing website even before the web
page is rendered in the browser, thus avoiding other potential problems such as
cryptojacking and drive-by downloads. However, traditional URL based approaches
have their limitations. Blacklist based approaches are prone to zero-hour
phishing attacks, advanced machine learning based approaches consume high
resources, and other approaches send the URL to a remote server which
compromises user's privacy. In this paper, we present a layered anti-phishing
defense, PhishMatch, which is robust, accurate, inexpensive, and client-side.
We design a space-time efficient Aho-Corasick algorithm for exact string
matching and n-gram based indexing technique for approximate string matching to
detect various cybersquatting techniques in the phishing URL. To reduce false
positives, we use a global whitelist and personalized user whitelists. We also
determine the context in which the URL is visited and use that information to
classify the input URL more accurately. The last component of PhishMatch
involves a machine learning model and controlled search engine queries to
classify the URL. A prototype plugin of PhishMatch, developed for the Chrome
browser, was found to be fast and lightweight. Our evaluation shows that
PhishMatch is both efficient and effective.

Deep hashing has shown promising performance in large-scale image retrieval.
However, latent codes extracted by Deep Neural Networks (DNNs) will inevitably
lose semantic information during the binarization process, which damages the
retrieval accuracy and makes it challenging. Although many existing approaches
perform regularization to alleviate quantization errors, we figure out an
incompatible conflict between metric learning and quantization learning. The
metric loss penalizes the inter-class distances to push different classes
unconstrained far away. Worse still, it tends to map the latent code deviate
from ideal binarization point and generate severe ambiguity in the binarization
process. Based on the minimum distance of the binary linear code, we creatively
propose Hashing-guided Hinge Function (HHF) to avoid such conflict. In detail,
the carefully-designed inflection point, which relies on the hash bit length
and category numbers, is explicitly adopted to balance the metric term and
quantization term. Such a modification prevents the network from falling into
local metric optimal minima in deep hashing. Extensive experiments in CIFAR-10,
CIFAR-100, ImageNet, and MS-COCO show that HHF consistently outperforms
existing techniques, and is robust and flexible to transplant into other
methods. Code is available at https://github.com/JerryXu0129/HHF.

To support the newly introduced multimedia services with ultra-low latency
and extensive computation requirements, resource-constrained end user devices
should utilize the ubiquitous computing resources available at network edge for
augmenting on-board (local) processing with edge computing. In this regard, the
capability of cell-free massive MIMO to provide reliable access links by
guaranteeing uniform quality of service without cell edge can be exploited for
seamless parallel processing. Taking this into account, we consider a cell-free
massive MIMO-enabled mobile edge network to meet the stringent requirements of
the advanced services. For the considered mobile edge network, we formulate a
joint communication and computing resource allocation (JCCRA) problem with the
objective of minimizing energy consumption of the users while meeting the tight
delay constraints. We then propose a fully distributed cooperative solution
approach based on multiagent deep deterministic policy gradient (MADDPG)
algorithm. The simulation results demonstrate that the performance of the
proposed distributed approach has converged to that of a centralized deep
deterministic policy gradient (DDPG)-based target benchmark, while alleviating
the large overhead associated with the latter. Furthermore, it has been shown
that our approach significantly outperforms heuristic baselines in terms of
energy efficiency, roughly up to 5 times less total energy consumption.

Knowledge distillation aims at transferring the knowledge from a large
teacher model to a small student model with great improvements of the
performance of the student model. Therefore, the student network can replace
the teacher network to deploy on low-resource devices since the higher
performance, lower number of parameters and shorter inference time.
Self-knowledge distillation (SKD) attracts a great attention recently that a
student model itself is a teacher model distilling knowledge from. To the best
of our knowledge, self knowledge distillation can be divided into two main
streams: data augmentation and refined knowledge auxiliary. In this paper, we
purpose a novel SKD method in a different way from the main stream methods. Our
method distills knowledge from multilevel abstraction features. Experiments and
ablation studies show its great effectiveness and generalization on various
kinds of tasks with various kinds of model structures. Our codes have been
released on GitHub.

Objectives: To develop and validate a deep learning (DL)-based primary tumor
biopsy signature for predicting axillary lymph node (ALN) metastasis
preoperatively in early breast cancer (EBC) patients with clinically negative
ALN.
  Methods: A total of 1,058 EBC patients with pathologically confirmed ALN
status were enrolled from May 2010 to August 2020. A DL core-needle biopsy
(DL-CNB) model was built on the attention-based multiple instance-learning
(AMIL) framework to predict ALN status utilizing the DL features, which were
extracted from the cancer areas of digitized whole-slide images (WSIs) of
breast CNB specimens annotated by two pathologists. Accuracy, sensitivity,
specificity, receiver operating characteristic (ROC) curves, and areas under
the ROC curve (AUCs) were analyzed to evaluate our model.
  Results: The best-performing DL-CNB model with VGG16_BN as the feature
extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865)
in predicting positive ALN metastasis in the independent test cohort.
Furthermore, our model incorporating the clinical data, which was called
DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially
for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The
interpretation of DL-CNB model showed that the top signatures most predictive
of ALN metastasis were characterized by the nucleus features including density
($p$ = 0.015), circumference ($p$ = 0.009), circularity ($p$ = 0.010), and
orientation ($p$ = 0.012).
  Conclusion: Our study provides a novel DL-based biomarker on primary tumor
CNB slides to predict the metastatic status of ALN preoperatively for patients
with EBC. The codes and dataset are available at
https://github.com/bupt-ai-cz/BALNMP

GANs have matured in recent years and are able to generate high-resolution,
realistic images. However, the computational resources and the data required
for the training of high-quality GANs are enormous, and the study of transfer
learning of these models is therefore an urgent topic. Many of the available
high-quality pretrained GANs are unconditional (like StyleGAN). For many
applications, however, conditional GANs are preferable, because they provide
more control over the generation process, despite often suffering more training
difficulties. Therefore, in this paper, we focus on transferring from
high-quality pretrained unconditional GANs to conditional GANs. This requires
architectural adaptation of the pretrained GAN to perform the conditioning. To
this end, we propose hyper-modulated generative networks that allow for shared
and complementary supervision. To prevent the additional weights of the
hypernetwork to overfit, with subsequent mode collapse on small target domains,
we introduce a self-initialization procedure that does not require any real
data to initialize the hypernetwork parameters. To further improve the sample
efficiency of the transfer, we apply contrastive learning in the discriminator,
which effectively works on very limited batch sizes. In extensive experiments,
we validate the efficiency of the hypernetworks, self-initialization and
contrastive loss for knowledge transfer on standard benchmarks.

We present a Reinforcement Learning (RL) based framework for optimizing
long-term discounted reward problems with large combinatorial action space and
state dependent constraints. These characteristics are common to many
operations management problems, e.g., network inventory replenishment, where
managers have to deal with uncertain demand, lost sales, and capacity
constraints that results in more complex feasible action spaces. Our proposed
Programmable Actor Reinforcement Learning (PARL) uses a deep-policy iteration
method that leverages neural networks (NNs) to approximate the value function
and combines it with mathematical programming (MP) and sample average
approximation (SAA) to solve the per-step-action optimally while accounting for
combinatorial action spaces and state-dependent constraint sets.
  We show how the proposed methodology can be applied to complex inventory
replenishment problems where analytical solutions are intractable. We also
benchmark the proposed algorithm against state-of-the-art RL algorithms and
commonly used replenishment heuristics and find that the proposed algorithm
considerably outperforms existing methods by as much as 14.7\% on average in
various supply chain settings.
  This improvement in performance of PARL over benchmark algorithms can be
attributed to better inventory cost management, especially in inventory
constrained settings. Furthermore, in a simpler back order setting where the
optimal solution is tractable, we find that the RL based policy also converges
to the optimal policy. Finally, to make RL algorithms more accessible for
inventory management researchers, we also discuss a modular Python library
developed that can be used to test the performance of RL algorithms with
various supply chain structures. This library can spur future research in
developing practical and near-optimal algorithms for inventory management
problems.

Speech-driven 3D facial animation with accurate lip synchronization has been
widely studied. However, synthesizing realistic motions for the entire face
during speech has rarely been explored. In this work, we present a joint
audio-text model to capture the contextual information for expressive
speech-driven 3D facial animation. The existing datasets are collected to cover
as many different phonemes as possible instead of sentences, thus limiting the
capability of the audio-based model to learn more diverse contexts. To address
this, we propose to leverage the contextual text embeddings extracted from the
powerful pre-trained language model that has learned rich contextual
representations from large-scale text data. Our hypothesis is that the text
features can disambiguate the variations in upper face expressions, which are
not strongly correlated with the audio. In contrast to prior approaches which
learn phoneme-level features from the text, we investigate the high-level
contextual text features for speech-driven 3D facial animation. We show that
the combined acoustic and textual modalities can synthesize realistic facial
expressions while maintaining audio-lip synchronization. We conduct the
quantitative and qualitative evaluations as well as the perceptual user study.
The results demonstrate the superior performance of our model against existing

In the fourth industrial revolution, securing the protection of the supply
chain has become an ever-growing concern. One such cyber threat is a hardware
Trojan (HT), a malicious modification to an IC. HTs are often identified in the
hardware manufacturing process, but should be removed earlier, when the design
is being specified. Machine learning-based HT detection in gate-level netlists
is an efficient approach to identify HTs at the early stage. However,
feature-based modeling has limitations in discovering an appropriate set of HT
features. We thus propose NHTD-GL in this paper, a novel node-wise HT detection
method based on graph learning (GL). Given the formal analysis of HT features
obtained from domain knowledge, NHTD-GL bridges the gap between graph
representation learning and feature-based HT detection. The experimental
results demonstrate that NHTD-GL achieves 0.998 detection accuracy and
outperforms state-of-the-art node-wise HT detection methods. NHTD-GL extracts
HT features without heuristic feature engineering.

Machine learning models are known to be susceptible to adversarial attacks
which can cause misclassification by introducing small but well designed
perturbations. In this paper, we consider a classical hypothesis testing
problem in order to develop fundamental insight into defending against such
adversarial perturbations. We interpret an adversarial perturbation as a
nuisance parameter, and propose a defense based on applying the generalized
likelihood ratio test (GLRT) to the resulting composite hypothesis testing
problem, jointly estimating the class of interest and the adversarial
perturbation. While the GLRT approach is applicable to general multi-class
hypothesis testing, we first evaluate it for binary hypothesis testing in white
Gaussian noise under $\ell_{\infty}$ norm-bounded adversarial perturbations,
for which a known minimax defense optimizing for the worst-case attack provides
a benchmark. We derive the worst-case attack for the GLRT defense, and show
that its asymptotic performance (as the dimension of the data increases)
approaches that of the minimax defense. For non-asymptotic regimes, we show via
simulations that the GLRT defense is competitive with the minimax approach
under the worst-case attack, while yielding a better robustness-accuracy
tradeoff under weaker attacks. We also illustrate the GLRT approach for a
multi-class hypothesis testing problem, for which a minimax strategy is not
known, evaluating its performance under both noise-agnostic and noise-aware
adversarial settings, by providing a method to find optimal noise-aware
attacks, and heuristics to find noise-agnostic attacks that are close to
optimal in the high SNR regime.

Multi-fidelity modeling and calibration are data fusion tasks that
ubiquitously arise in engineering design. In this paper, we introduce a novel
approach based on latent-map Gaussian processes (LMGPs) that enables efficient
and accurate data fusion. In our approach, we convert data fusion into a latent
space learning problem where the relations among different data sources are
automatically learned. This conversion endows our approach with attractive
advantages such as increased accuracy, reduced costs, flexibility to jointly
fuse any number of data sources, and ability to visualize correlations between
data sources. This visualization allows the user to detect model form errors or
determine the optimum strategy for high-fidelity emulation by fitting LMGP only
to the subset of the data sources that are well-correlated. We also develop a
new kernel function that enables LMGPs to not only build a probabilistic
multi-fidelity surrogate but also estimate calibration parameters with high
accuracy and consistency. The implementation and use of our approach are
considerably simpler and less prone to numerical issues compared to existing
technologies. We demonstrate the benefits of LMGP-based data fusion by
comparing its performance against competing methods on a wide range of
examples.

Machine learning has now become an integral part of research and innovation.
The field of machine learning density functional theory has continuously
expanded over the years while making several noticeable advances. We briefly
discuss the status of this field and point out some current and future
challenges. We also talk about how state-of-the-art science and technology
tools can help overcome these challenges. This draft is a part of the "Roadmap
on Machine Learning in Electronic Structure" to be published in Electronic
Structure (EST).

Advances in LiDAR sensors provide rich 3D data that supports 3D scene
understanding. However, due to occlusion and signal miss, LiDAR point clouds
are in practice 2.5D as they cover only partial underlying shapes, which poses
a fundamental challenge to 3D perception. To tackle the challenge, we present a
novel LiDAR-based 3D object detection model, dubbed Behind the Curtain Detector
(BtcDet), which learns the object shape priors and estimates the complete
object shapes that are partially occluded (curtained) in point clouds. BtcDet
first identifies the regions that are affected by occlusion and signal miss. In
these regions, our model predicts the probability of occupancy that indicates
if a region contains object shapes. Integrated with this probability map,
BtcDet can generate high-quality 3D proposals. Finally, the probability of
occupancy is also integrated into a proposal refinement module to generate the
final bounding boxes. Extensive experiments on the KITTI Dataset and the Waymo
Open Dataset demonstrate the effectiveness of BtcDet. Particularly, for the 3D
detection of both cars and cyclists on the KITTI benchmark, BtcDet surpasses
all of the published state-of-the-art methods by remarkable margins. Code is
released
(https://github.com/Xharlie/BtcDet}{https://github.com/Xharlie/BtcDet).

Deep learning and hardware for it has garnered immense academic and industry
interest in the past 5 years -- including almost 100 startups, more than $5B of
VC investment -- and a re-relevance of the role of architecture. However, the
state-of-art remains NVIDIA's TensorCore-based systems that provide i)
top-of-line performance, ii) turnkey software stack, and iii) coverage across a
wide-spectrum of DL network styles (DL-architecture in AI parlance). Other
academic and industry efforts have included novel approaches like spatial
dataflow, CGRAs, systolic arrays, blended FPGA LUTs with fixed function units
and more. These have all necessitated their own innovations in architecture,
compiler, and software stack integration. However, none of these have yet
satisfied all the 3 metrics that NVIDIA's TensorCore and software stack
provides, and generally seem to perform worse. In this paper, we systematically
investigate the behavior of DL workloads and imputed needs on
hardware/compiler/software. We show that SIMD/short-vector, caching, and
synchronization in a fairly well-understood multicore chip organization we call
UPCYCLE can achieve day-zero software maturity, and provide big integer factor
speedups over the state-of-art NVIDIA solutions. Compared to an A100, UPCYCLE
at small-batch size is geo-mean 3.8X faster for inference, geo-mean 4.2X faster
at training, while consuming only half the power. Second, the UPCYCLE
architecture requires no new compiler or software stack innovation. Third, it
provides full DL-architecture coverage, and can be instantiated to provide
training-optimized, inference-optimized, or balanced training and inference
systems. Overall, this paper motivates the treatment of software maturity as a
first class design constraint in developing new architectures for DL. This is
achieved by revisiting well understood ideas, upcycling them for future DL
architectures...

Finding high-quality solutions to mixed-integer linear programming problems
(MILPs) is of great importance for many practical applications. In this
respect, the refinement heuristic local branching (LB) has been proposed to
produce improving solutions and has been highly influential for the development
of local search methods in MILP. The algorithm iteratively explores a sequence
of solution neighborhoods defined by the so-called local branching constraint,
namely, a linear inequality limiting the distance from a reference solution.
For a LB algorithm, the choice of the neighborhood size is critical to
performance. In this work, we study the relation between the size of the search
neighborhood and the behavior of the underlying LB algorithm, and we devise a
leaning based framework for predicting the best size for the specific instance
to be solved. Furthermore, we have also investigated the relation between the
time limit for exploring the LB neighborhood and the actual performance of LB
scheme, and devised a strategy for adapting the time limit. We computationally
show that the neighborhood size and time limit can indeed be learned, leading
to improved performances and that the overall algorithm generalizes well both
with respect to the instance size and, remarkably, across instances.

In tracking multiple objects, it is often assumed that each observation
(measurement) is originated from one and only one object. However, we may
encounter a situation that each measurement may or may not be associated with
multiple objects at each time step --spawning. Therefore, the association of
each measurement to multiple objects is a crucial task to perform in order to
track multiple objects with birth and death. In this paper, we introduce a
novel Bayesian nonparametric approach that models a scenario where each
observation may be drawn from an unknown number of objects for which it
provides a tractable Markov chain Monte Carlo (MCMC) approach to sample from
the posterior distribution. The number of objects at each time step, itself, is
also assumed to be unknown. We, then, show through experiments the advantage of
nonparametric modeling to scenarios with spawning events. Our experiment
results also demonstrate the advantages of our framework over the existing
methods.

We present ALX, an open-source library for distributed matrix factorization
using Alternating Least Squares, written in JAX. Our design allows for
efficient use of the TPU architecture and scales well to matrix factorization
problems of O(B) rows/columns by scaling the number of available TPU cores. In
order to spur future research on large scale matrix factorization methods and
to illustrate the scalability properties of our own implementation, we also
built a real world web link prediction dataset called WebGraph. This dataset
can be easily modeled as a matrix factorization problem. We created several
variants of this dataset based on locality and sparsity properties of
sub-graphs. The largest variant of WebGraph has around 365M nodes and training
a single epoch finishes in about 20 minutes with 256 TPU cores. We include
speed and performance numbers of ALX on all variants of WebGraph. Both the
framework code and the dataset is open-sourced.

Non-linear operations such as GELU, Layer normalization, and Softmax are
essential yet costly building blocks of Transformer models. Several prior works
simplified these operations with look-up tables or integer computations, but
such approximations suffer inferior accuracy or considerable hardware cost with
long latency. This paper proposes an accurate and hardware-friendly
approximation framework for efficient Transformer inference. Our framework
employs a simple neural network as a universal approximator with its structure
equivalently transformed into a LUT. The proposed framework called NN-LUT can
accurately replace all the non-linear operations in popular BERT models with
significant reductions in area, power consumption, and latency.

Multi-agent simulation is commonly used across multiple disciplines,
specifically in artificial intelligence in recent years, which creates an
environment for downstream machine learning or reinforcement learning tasks. In
many practical scenarios, however, only the output series that result from the
interactions of simulation agents are observable. Therefore, simulators need to
be calibrated so that the simulated output series resemble historical -- which
amounts to solving a complex simulation optimization problem. In this paper, we
propose a simple and efficient framework for calibrating simulator parameters
from historical output series observations. First, we consider a novel concept
of eligibility set to bypass the potential non-identifiability issue. Second,
we generalize the two-sample Kolmogorov-Smirnov (K-S) test with Bonferroni
correction to test the similarity between two high-dimensional distributions,
which gives a simple yet effective distance metric between the output series
sample sets. Third, we suggest using Bayesian optimization (BO) and
trust-region BO (TuRBO) to minimize the aforementioned distance metric.
Finally, we demonstrate the efficiency of our framework using numerical
experiments both on a multi-agent financial market simulator.

We study a class of classification problems best exemplified by the
\emph{bank loan} problem, where a lender decides whether or not to issue a
loan. The lender only observes whether a customer will repay a loan if the loan
is issued to begin with, and thus modeled decisions affect what data is
available to the lender for future decisions. As a result, it is possible for
the lender's algorithm to ``get stuck'' with a self-fulfilling model. This
model never corrects its false negatives, since it never sees the true label
for rejected data, thus accumulating infinite regret. In the case of linear
models, this issue can be addressed by adding optimism directly into the model
predictions. However, there are few methods that extend to the function
approximation case using Deep Neural Networks. We present Pseudo-Label Optimism
(PLOT), a conceptually and computationally simple method for this setting
applicable to DNNs. \PLOT{} adds an optimistic label to the subset of decision
points the current model is deciding on, trains the model on all data so far
(including these points along with their optimistic labels), and finally uses
the resulting \emph{optimistic} model for decision making. \PLOT{} achieves
competitive performance on a set of three challenging benchmark problems,
requiring minimal hyperparameter tuning. We also show that \PLOT{} satisfies a
logarithmic regret guarantee, under a Lipschitz and logistic mean label model,
and under a separability condition on the data.

Conferences are deeply connected to research fields, in this case
bibliometrics. As such, they are a venue to present and discuss current and
innovative research, and play an important role for the scholarly community. In
this article, we provide an overview on the history of conferences in
bibliometrics. We conduct an analysis to list the most prominent conferences
that were announced in the newsletter by ISSI, the International Society for
Scientometrics and Informetrics. Furthermore, we describe how conferences are
connected to learned societies and journals. Finally, we provide an outlook on
how conferences might change in future.

Sets of bilinear constraints are important in various machine learning
models. Mathematically, they are hyperbolas in a product space. In this paper,
we give a complete formula for projections onto sets of bilinear constraints or
hyperbolas in a general Hilbert space.

Unfairness in mortgage lending has created generational inequality among
racial and ethnic groups in the US. Many studies address this problem, but most
existing work focuses on correlation-based techniques. In our work, we use the
framework of counterfactual fairness to train fair machine learning models. We
propose a new causal graph for the variables available in the Home Mortgage
Disclosure Act (HMDA) data. We use a matching-based approach instead of the
latent variable modeling approach, because the former approach does not rely on
any modeling assumptions. Furthermore, matching provides us with counterfactual
pairs in which the race variable is isolated. We first demonstrate the
unfairness in mortgage approval and interest rates between African-American and
non-Hispanic White sub-populations. Then, we show that having balanced data
using matching does not guarantee perfect counterfactual fairness of the
machine learning models.

Predicting UV-visible absorption spectra is essential to understanding
photochemical processes and designing energy materials. Quantum chemical
methods can deliver accurate calculations of UV-visible absorption spectra, but
they are computationally expensive, especially for large systems or when one
computes line shapes from thermal averages. Here, we present an approach to
predicting UV-visible absorption spectra of solvated aromatic molecules by
quantum chemistry (QC) and machine learning (ML). We show that a ML model,
trained on the high-level QC calculation of the excitation energy of a set of
aromatic molecules, can accurately predict the line shape of the lowest-energy
UV-visible absorption band of several related molecules with less than 0.1 eV
deviation with respect to reference experimental spectra. Applying linear
decomposition analysis on the excitation energies, we unveil that our ML models
probe vertical excitations of these aromatic molecules primarily by learning
the atomic environment of their phenyl rings, which align with the physical
origin of the $\pi\rightarrow\pi^\star$ electronic transition. Our study
provides an effective workflow that combines ML with quantum chemical methods
to accelerate the calculations of UV-visible absorption spectra for various

We consider the problem of contextual bandits where actions are subsets of a
ground set and mean rewards are modeled by an unknown monotone submodular
function that belongs to a class $\mathcal{F}$. We allow time-varying matroid
constraints to be placed on the feasible sets. Assuming access to an online
regression oracle with regret $\mathsf{Reg}(\mathcal{F})$, our algorithm
efficiently randomizes around local optima of estimated functions according to
the Inverse Gap Weighting strategy. We show that cumulative regret of this
procedure with time horizon $n$ scales as $O(\sqrt{n
\mathsf{Reg}(\mathcal{F})})$ against a benchmark with a multiplicative factor
$1/2$. On the other hand, using the techniques of (Filmus and Ward 2014), we
show that an $\epsilon$-Greedy procedure with local randomization attains
regret of $O(n^{2/3} \mathsf{Reg}(\mathcal{F})^{1/3})$ against a stronger
$(1-e^{-1})$ benchmark.

Prostate cancer is the second deadliest cancer for American men. While
Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted
biopsies for prostate cancer diagnosis, its utility remains limited due to high
rates of false positives and false negatives as well as low inter-reader
agreements. Machine learning methods to detect and localize cancer on prostate
MRI can help standardize radiologist interpretations. However, existing machine
learning methods vary not only in model architecture, but also in the ground
truth labeling strategies used for model training. In this study, we compare
different labeling strategies, namely, pathology-confirmed radiologist labels,
pathologist labels on whole-mount histopathology images, and lesion-level and
pixel-level digital pathologist labels (previously validated deep learning
algorithm on histopathology images to predict pixel-level Gleason patterns) on
whole-mount histopathology images. We analyse the effects these labels have on
the performance of the trained machine learning models. Our experiments show
that (1) radiologist labels and models trained with them can miss cancers, or
underestimate cancer extent, (2) digital pathologist labels and models trained
with them have high concordance with pathologist labels, and (3) models trained
with digital pathologist labels achieve the best performance in prostate cancer
detection in two different cohorts with different disease distributions,
irrespective of the model architecture used. Digital pathologist labels can
reduce challenges associated with human annotations, including labor, time,
inter- and intra-reader variability, and can help bridge the gap between
prostate radiology and pathology by enabling the training of reliable machine
learning models to detect and localize prostate cancer on MRI.

Virtual reality (VR) has seen increased use for training and instruction.
Designers can enable VR users to gain insights into their own performance by
visualizing telemetry data from their actions in VR. Our ability to detect
patterns and trends visually suggests the use of data visualization as a tool
for users to identify strategies for improved performance. Typical tasks in VR
training scenarios are manipulation of 3D objects (e.g., for learning how to
maintain a jet engine) and navigation (e.g., to learn the geography of a
building or landscape before traveling on-site). In this paper, we present the
results of the RUI VR (84 subjects) and Luddy VR studies (68 subjects), where
participants were divided into experiment and control cohorts. All subjects
performed a series of tasks: 44 cube-matching tasks in RUI VR and 48 navigation
tasks through a virtual building in Luddy VR (all divided into two sets). All
Luddy VR subjects used VR gear; RUI VR subjects were divided across three
setups: 2D Desktop (with laptop and mouse), VR Tabletop (in VR, sitting at a
table), and VR Standup (in VR, standing). In an intervention called "Reflective
phase," the experiment cohorts were presented with data visualizations,
designed with the Data Visualization Literacy Framework (DVL-FW), of the data
they generated during the first set of tasks before continuing to the second
part of the study. For Luddy VR, we found that experiment users had
significantly faster completion times in their second trial (p = 0.014) while
scoring higher in a mid-questionnaire about the virtual building (p = 0.009).
For RUI VR, we found no significant differences for completion time and
accuracy between the two cohorts in the VR setups; however, 2D Desktop subjects
in the experiment cohort had significantly higher rotation accuracy as well as
satisfaction (p(rotation) = 0.031, p(satisfaction) = 0.040).

We consider computations of a Turing machine subjected to noise. In every
step, the action (the new state and the new content of the observed cell, the
direction of the head movement) can differ from that prescribed by the
transition function with a small probability (independently of previous such
events). We construct a universal 1-tape Turing machine that for a low enough
(constant) noise probability performs arbitrarily large computations. For this
unavoidably, the input needs to be encoded -- by a simple code depending on its
size. The work uses a technique familiar from reliable cellular automata,
complemented by some new ones.

Recently, data-driven inertial navigation approaches have demonstrated their
capability of using well-trained neural networks to obtain accurate position
estimates from inertial measurement units (IMU) measurements. In this paper, we
propose a novel robust Contextual Transformer-based network for Inertial
Navigation~(CTIN) to accurately predict velocity and trajectory. To this end,
we first design a ResNet-based encoder enhanced by local and global multi-head
self-attention to capture spatial contextual information from IMU measurements.
Then we fuse these spatial representations with temporal knowledge by
leveraging multi-head attention in the Transformer decoder. Finally, multi-task
learning with uncertainty reduction is leveraged to improve learning efficiency
and prediction accuracy of velocity and trajectory. Through extensive
experiments over a wide range of inertial datasets~(e.g. RIDI, OxIOD, RoNIN,
IDOL, and our own), CTIN is very robust and outperforms state-of-the-art
models.

The prediction of residential power usage is essential in assisting a smart
grid to manage and preserve energy to ensure efficient use. An accurate energy
forecasting at the customer level will reflect directly into efficiency
improvements across the power grid system, however forecasting building energy
use is a complex task due to many influencing factors, such as meteorological
and occupancy patterns. In addiction, high-dimensional time series increasingly
arise in the Internet of Energy (IoE), given the emergence of multi-sensor
environments and the two way communication between energy consumers and the
smart grid. Therefore, methods that are capable of computing high-dimensional
time series are of great value in smart building and IoE applications. Fuzzy
Time Series (FTS) models stand out as data-driven non-parametric models of easy
implementation and high accuracy. Unfortunately, the existing FTS models can be
unfeasible if all features were used to train the model. We present a new
methodology for handling high-dimensional time series, by projecting the
original high-dimensional data into a low dimensional embedding space and using
multivariate FTS approach in this low dimensional representation. Combining
these techniques enables a better representation of the complex content of
multivariate time series and more accurate forecasts.

Variational AutoEncoders (VAE) employ deep learning models to learn a
continuous latent z-space that is subjacent to a high-dimensional observed
dataset. With that, many tasks are made possible, including face reconstruction
and face synthesis. In this work, we investigated how face masks can help the
training of VAEs for face reconstruction, by restricting the learning to the
pixels selected by the face mask. An evaluation of the proposal using the
celebA dataset shows that the reconstructed images are enhanced with the face
masks, especially when SSIM loss is used either with l1 or l2 loss functions.
We noticed that the inclusion of a decoder for face mask prediction in the
architecture affected the performance for l1 or l2 loss functions, while this
was not the case for the SSIM loss. Besides, SSIM perceptual loss yielded the
crispest samples between all hypotheses tested, although it shifts the original
color of the image, making the usage of the l1 or l2 losses together with SSIM
helpful to solve this issue.

Using an enhanced Self-Organizing Map method, we provided suboptimal
solutions to the Traveling Salesman Problem. Besides, we employed
hyperparameter tuning to identify the most critical features in the algorithm.
All improvements in the benchmark work brought consistent results and may
inspire future efforts to improve this algorithm and apply it to different
problems.

This work examines adaptive distributed learning strategies designed to
operate under communication constraints. We consider a network of agents that
must solve an online optimization problem from continual observation of
streaming data. The agents implement a distributed cooperative strategy where
each agent is allowed to perform local exchange of information with its
neighbors. In order to cope with communication constraints, the exchanged
information must be unavoidably compressed. We propose a diffusion strategy
nicknamed as ACTC (Adapt-Compress-Then-Combine), which relies on the following
steps: i) an adaptation step where each agent performs an individual
stochastic-gradient update with constant step-size; ii) a compression step that
leverages a recently introduced class of stochastic compression operators; and
iii) a combination step where each agent combines the compressed updates
received from its neighbors. The distinguishing elements of this work are as
follows. First, we focus on adaptive strategies, where constant (as opposed to
diminishing) step-sizes are critical to respond in real time to nonstationary
variations. Second, we consider the general class of directed graphs and
left-stochastic combination policies, which allow us to enhance the interplay
between topology and learning. Third, in contrast with related works that
assume strong convexity for all individual agents' cost functions, we require
strong convexity only at a network level, a condition satisfied even if a
single agent has a strongly-convex cost and the remaining agents have
non-convex costs. Fourth, we focus on a diffusion (as opposed to consensus)
strategy. Under the demanding setting of compressed information, we establish
that the ACTC iterates fluctuate around the desired optimizer, achieving
remarkable savings in terms of bits exchanged between neighboring agents.

We present regression and compression algorithms for lattice QCD data
utilizing the efficient binary optimization ability of quantum annealers. In
the regression algorithm, we encode the correlation between the input and
output variables into a sparse coding machine learning algorithm. The trained
correlation pattern is used to predict lattice QCD observables of unseen
lattice configurations from other observables measured on the lattice. In the
compression algorithm, we define a mapping from lattice QCD data of
floating-point numbers to the binary coefficients that closely reconstruct the
input data from a set of basis vectors. Since the reconstruction is not exact,
the mapping defines a lossy compression, but, a reasonably small number of
binary coefficients are able to reconstruct the input vector of lattice QCD
data with the reconstruction error much smaller than the statistical
fluctuation. In both applications, we use D-Wave quantum annealers to solve the
NP-hard binary optimization problems of the machine learning algorithms.

We discuss results for bottomonium at nonzero temperature obtained using
NRQCD on Fastsum Generation 2L ensembles, as part of the Fastsum
collaboration's programme to determine the spectrum of the bottomonium system
as a function of temperature using a variety of approaches. Here we give an
update on results for spectral functions obtained using Kernel Ridge
Regression. We pay in particular attention to the generation of training data
and introduce the notion of using lattice QCD ensembles to learn how to improve
the generation of training data. A practical implementation is given.

We exploit the complementary strengths of vision and proprioception to
develop a point-goal navigation system for legged robots, called VP-Nav. Legged
systems are capable of traversing more complex terrain than wheeled robots, but
to fully utilize this capability, we need a high-level path planner in the
navigation system to be aware of the walking capabilities of the low-level
locomotion policy in varying environments. We achieve this by using
proprioceptive feedback to ensure the safety of the planned path by sensing
unexpected obstacles like glass walls, terrain properties like slipperiness or
softness of the ground and robot properties like extra payload that are likely
missed by vision. The navigation system uses onboard cameras to generate an
occupancy map and a corresponding cost map to reach the goal. A fast marching
planner then generates a target path. A velocity command generator takes this
as input to generate the desired velocity for the walking policy. A safety
advisor module adds sensed unexpected obstacles to the occupancy map and
environment-determined speed limits to the velocity command generator. We show
superior performance compared to wheeled robot baselines, and ablation studies
which have disjoint high-level planning and low-level control. We also show the
real-world deployment of VP-Nav on a quadruped robot with onboard sensors and
computation. Videos at https://navigation-locomotion.github.io

Accurately predicting possible behaviors of traffic participants is an
essential capability for autonomous vehicles. Since autonomous vehicles need to
navigate in dynamically changing environments, they are expected to make
accurate predictions regardless of where they are and what driving
circumstances they encountered. Therefore, generalization capability to unseen
domains is crucial for prediction models when autonomous vehicles are deployed
in the real world. In this paper, we aim to address the domain generalization
problem for vehicle intention prediction tasks and a causal-based time series
domain generalization (CTSDG) model is proposed. We construct a structural
causal model for vehicle intention prediction tasks to learn an invariant
representation of input driving data for domain generalization. We further
integrate a recurrent latent variable model into our structural causal model to
better capture temporal latent dependencies from time-series input data. The
effectiveness of our approach is evaluated via real-world driving data. We
demonstrate that our proposed method has consistent improvement on prediction
accuracy compared to other state-of-the-art domain generalization and behavior
prediction methods.

We introduce REDO, a class-agnostic framework to REconstruct the Dynamic
Objects from RGBD or calibrated videos. Compared to prior work, our problem
setting is more realistic yet more challenging for three reasons: 1) due to
occlusion or camera settings an object of interest may never be entirely
visible, but we aim to reconstruct the complete shape; 2) we aim to handle
different object dynamics including rigid motion, non-rigid motion, and
articulation; 3) we aim to reconstruct different categories of objects with one
unified framework. To address these challenges, we develop two novel modules.
First, we introduce a canonical 4D implicit function which is pixel-aligned
with aggregated temporal visual cues. Second, we develop a 4D transformation
module which captures object dynamics to support temporal propagation and
aggregation. We study the efficacy of REDO in extensive experiments on
synthetic RGBD video datasets SAIL-VOS 3D and DeformingThings4D++, and on
real-world video data 3DPW. We find REDO outperforms state-of-the-art dynamic
reconstruction methods by a margin. In ablation studies we validate each
developed component.

We present a Newton-type method that converges fast from any initialization
and for arbitrary convex objectives with Lipschitz Hessians. We achieve this by
merging the ideas of cubic regularization with a certain adaptive
Levenberg--Marquardt penalty. In particular, we show that the iterates given by
$x^{k+1}=x^k - \bigl(\nabla^2 f(x^k) + \sqrt{H\|\nabla f(x^k)\|}
\mathbf{I}\bigr)^{-1}\nabla f(x^k)$, where $H>0$ is a constant, converge
globally with a $\mathcal{O}(\frac{1}{k^2})$ rate. Our method is the first
variant of Newton's method that has both cheap iterations and provably fast
global convergence. Moreover, we prove that locally our method converges
superlinearly when the objective is strongly convex. To boost the method's
performance, we present a line search procedure that does not need
hyperparameters and is provably efficient.

In most modern cities, traffic congestion is one of the most salient societal
challenges. Past research has shown that inserting a limited number of
autonomous vehicles (AVs) within the traffic flow, with driving policies
learned specifically for the purpose of reducing congestion, can significantly
improve traffic conditions. However, to date these AV policies have generally
been evaluated under the same limited conditions under which they were trained.
On the other hand, to be considered for practical deployment, they must be
robust to a wide variety of traffic conditions. This paper establishes for the
first time that a multiagent driving policy can be trained in such a way that
it generalizes to different traffic flows, AV penetration, and road geometries,
including on multi-lane roads.

This paper aims to explore the feasibility of neural architecture search
(NAS) given only a pre-trained model without using any original training data.
This is an important circumstance for privacy protection, bias avoidance, etc.,
in real-world scenarios. To achieve this, we start by synthesizing usable data
through recovering the knowledge from a pre-trained deep neural network. Then
we use the synthesized data and their predicted soft-labels to guide neural
architecture search. We identify that the NAS task requires the synthesized
data (we target at image domain here) with enough semantics, diversity, and a
minimal domain gap from the natural images. For semantics, we propose recursive
label calibration to produce more informative outputs. For diversity, we
propose a regional update strategy to generate more diverse and
semantically-enriched synthetic data. For minimal domain gap, we use input and
feature-level regularization to mimic the original data distribution in latent
space. We instantiate our proposed framework with three popular NAS algorithms:
DARTS, ProxylessNAS and SPOS. Surprisingly, our results demonstrate that the
architectures discovered by searching with our synthetic data achieve accuracy
that is comparable to, or even higher than, architectures discovered by
searching from the original ones, for the first time, deriving the conclusion
that NAS can be done effectively with no need of access to the original or
called natural data if the synthesis method is well designed.

Although PIFu-based 3D human reconstruction methods are popular, the quality
of recovered details is still unsatisfactory. In a sparse (e.g., 3 RGBD
sensors) capture setting, the depth noise is typically amplified in the PIFu
representation, resulting in flat facial surfaces and geometry-fallible bodies.
In this paper, we propose a novel geometry-aware two-scale PIFu for 3D human
reconstruction from sparse, noisy inputs. Our key idea is to exploit the
complementary properties of depth denoising and 3D reconstruction, for learning
a two-scale PIFu representation to reconstruct high-frequency facial details
and consistent bodies separately. To this end, we first formulate depth
denoising and 3D reconstruction as a multi-task learning problem. The depth
denoising process enriches the local geometry information of the reconstruction
features, while the reconstruction process enhances depth denoising with global
topology information. We then propose to learn the two-scale PIFu
representation using two MLPs based on the denoised depth and geometry-aware
features. Extensive experiments demonstrate the effectiveness of our approach
in reconstructing facial details and bodies of different poses and its
superiority over state-of-the-art methods.

In this paper, we propose a novel approach for unsupervised domain
adaptation, that relates notions of optimal transport, learning probability
measures and unsupervised learning. The proposed approach, HOT-DA, is based on
a hierarchical formulation of optimal transport, that leverages beyond the
geometrical information captured by the ground metric, richer structural
information in the source and target domains. The additional information in the
labeled source domain is formed instinctively by grouping samples into
structures according to their class labels. While exploring hidden structures
in the unlabeled target domain is reduced to the problem of learning
probability measures through Wasserstein barycenter, which we prove to be
equivalent to spectral clustering. Experiments on a toy dataset with
controllable complexity and two challenging visual adaptation datasets show the
superiority of the proposed approach over the state-of-the-art.

We introduce a novel method for identifying the mass composition of
ultra-high-energy cosmic rays using deep learning. The key idea of the method
is to use a chain of two neural networks. The first network predicts the type
of a primary particle for individual events, while the second infers the mass
composition of an ensemble of events. We apply this method to the Monte-Carlo
data for the Telescope Array Surface Detectors readings, on which it yields an
unprecedented low error of 7% for 4-component approximation. We also discuss
the problems of applying the developed method to the experimental data, and the
way they can be resolved.

Based on various existing wireless fingerprint location algorithms in
intelligent sports venues, a high-precision and fast indoor location algorithm
improved weighted k-nearest neighbor (I-WKNN) is proposed. In order to meet the
complex environment of sports venues and the demand of high-speed sampling,
this paper proposes an AP selection algorithm for offline and online stages.
Based on the characteristics of the signal intensity distribution in
intelligent venues, an asymmetric Gaussian filter algorithm is proposed. This
paper introduces the application of the positioning algorithm in the
intelligent stadium system, and completes the data acquisition and real-time
positioning of the stadium. Compared with traditional WKNN and KNN algorithms,
the I-WKNN algorithm has advantages in fingerprint positioning database
processing, environmental noise adaptability, real-time positioning accuracy
and positioning speed, etc. The experimental results show that the I-WKNN
algorithm has obvious advantages in positioning accuracy and positioning time
in a complex noise environment and has obvious application potential in a smart
stadium.

Recently, graph neural networks (GNNs), as the backbone of graph-based
machine learning, demonstrate great success in various domains (e.g.,
e-commerce). However, the performance of GNNs is usually unsatisfactory due to
the highly sparse and irregular graph-based operations. To this end, we
propose, TC-GNN, the first GPU Tensor Core Unit (TCU) based GNN acceleration
framework. The core idea is to reconcile the "Sparse" GNN computation with
"Dense" TCU. Specifically, we conduct an in-depth analysis of the sparse
operations in mainstream GNN computing frameworks. We introduce a novel sparse
graph translation technique to facilitate TCU processing of sparse GNN
workload. We also implement an effective CUDA core and TCU collaboration design
to fully utilize GPU resources. We fully integrate TC-GNN with the Pytorch
framework for ease of programming. Rigorous experiments show an average of
1.70X speedup over the state-of-the-art Deep Graph Library framework across
various GNN models and dataset settings.

The determination of charged particle trajectories in collisions at the CERN
Large Hadron Collider (LHC) is an important but challenging problem, especially
in the high interaction density conditions expected during the future
high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a
type of geometric deep learning algorithm that has successfully been applied to
this task by embedding tracker data as a graph -- nodes represent hits, while
edges represent possible track segments -- and classifying the edges as true or
fake track segments. However, their study in hardware- or software-based
trigger applications has been limited due to their large computational cost. In
this paper, we introduce an automated translation workflow, integrated into a
broader tool called $\texttt{hls4ml}$, for converting GNNs into firmware for
field-programmable gate arrays (FPGAs). We use this translation tool to
implement GNNs for charged particle tracking, trained using the TrackML
challenge dataset, on FPGAs with designs targeting different graph sizes, task
complexites, and latency/throughput requirements. This work could enable the
inclusion of charged particle tracking GNNs at the trigger level for HL-LHC

We present an analytical policy update rule that is independent of parametric
function approximators. The policy update rule is suitable for optimizing
general stochastic policies and has a monotonic improvement guarantee. It is
derived from a closed-form solution to trust-region optimization using calculus
of variation, following a new theoretical result that tightens existing bounds
for policy improvement using trust-region methods. The update rule builds a
connection between policy search methods and value function methods. Moreover,
off-policy reinforcement learning algorithms can be derived from the update
rule since it does not need to compute integration over on-policy states. In
addition, the update rule extends immediately to cooperative multi-agent
systems when policy updates are performed by one agent at a time.

Well-trained machine-learning models, which leverage large amounts of
open-source software data, have now become an interesting approach to
automating many software engineering tasks. Several SE tasks have all been
subject to this approach, with performance gradually improving over the past
several years with better models and training methods. More, and more diverse,
clean, labeled data is better for training; but constructing good-quality
datasets is time-consuming and challenging. Ways of augmenting the volume and
diversity of clean, labeled data generally have wide applicability. For some
languages (e.g., Ruby) labeled data is less abundant; in others (e.g.,
JavaScript) the available data maybe more focused on some application domains,
and thus less diverse. As a way around such data bottlenecks, we present
evidence suggesting that human-written code in different languages (which
performs the same function), is rather similar, and particularly preserving of
identifier naming patterns; we further present evidence suggesting that
identifiers are a very important element of training data for software
engineering tasks. We leverage this rather fortuitous phenomenon to find
evidence that available multilingual training data (across different languages)
can be used to amplify performance. We study this for 3 different tasks: code
summarization, code retrieval, and function naming. We note that this
data-augmenting approach is broadly compatible with different tasks, languages,
and machine-learning models.

In the field of Connectomics, a primary problem is that of 3D neuron
segmentation. Although deep learning-based methods have achieved remarkable
accuracy, errors still exist, especially in regions with image defects. One
common type of defect is that of consecutive missing image sections. Here, data
is lost along some axis, and the resulting neuron segmentations are split
across the gap. To address this problem, we propose a novel method based on
point cloud representations of neurons. We formulate the problem as a
classification problem and train CurveNet, a state-of-the-art point cloud
classification model, to identify which neurons should be merged. We show that
our method not only performs strongly but also scales reasonably to gaps well
beyond what other methods have attempted to address. Additionally, our point
cloud representations are highly efficient in terms of data, maintaining high
performance with an amount of data that would be unfeasible for other methods.
We believe that this is an indicator of the viability of using point cloud
representations for other proofreading tasks.

Understanding and controlling quantum transport in low-dimensional systems is
pivotal for heat management at the nanoscale. One promising strategy to obtain
the desired transport properties is to engineer particular spectral structures.
In this work we are interested in quasiperiodic disorder - incommensurate with
the underlying periodicity of the lattice - which induces fractality in the
energy spectrum. A well known example is the Fibonacci model which, despite
being non-interacting, yields anomalous diffusion with a continuously varying
dynamical exponent smoothly crossing over from superdiffusive to subdiffusive
regime as a function of potential strength. We study the finite-temperature
electric and heat transport of this model in linear response in the absence and
in the presence of dephasing noise due to inelastic scattering. The dephasing
causes both thermal and electric transport to become diffusive, thereby making
thermal and electrical conductivities finite in the thermodynamic limit. Thus,
in the subdiffusive regime it leads to enhancement of transport. We find that
the thermal and electric conductivities have multiple peaks as a function of
dephasing strength. Remarkably, we observe that the thermal and electrical
conductivities are not proportional to each other, a clear violation of
Wiedemann-Franz law, and the position of their maxima can differ. We argue that
this feature can be utilized to enhance performance of quantum thermal
machines. In particular, we show that by tuning the strength of the dephasing
noise we can enhance the performance of the device in regimes where it acts as
an autonomous refrigerator.

Artificial Intelligence (AI) in Education has been said to have the potential
for building more personalised curricula, as well as democratising education
worldwide and creating a Renaissance of new ways of teaching and learning.
Millions of students are already starting to benefit from the use of these
technologies, but millions more around the world are not. If this trend
continues, the first delivery of AI in Education could be greater educational
inequality, along with a global misallocation of educational resources
motivated by the current technological determinism narrative. In this paper, we
focus on speculating and posing questions around the future of AI in Education,
with the aim of starting the pressing conversation that would set the right
foundations for the new generation of education that is permeated by
technology. This paper starts by synthesising how AI might change how we learn
and teach, focusing specifically on the case of personalised learning
companions, and then move to discuss some socio-technical features that will be
crucial for avoiding the perils of these AI systems worldwide (and perhaps
ensuring their success). This paper also discusses the potential of using AI
together with free, participatory and democratic resources, such as Wikipedia,
Open Educational Resources and open-source tools. We also emphasise the need
for collectively designing human-centered, transparent, interactive and
collaborative AI-based algorithms that empower and give complete agency to
stakeholders, as well as support new emerging pedagogies. Finally, we ask what
would it take for this educational revolution to provide egalitarian and
empowering access to education, beyond any political, cultural, language,
geographical and learning ability barriers.

Artificial neural systems trained using reinforcement, supervised, and
unsupervised learning all acquire internal representations of high dimensional
input. To what extent these representations depend on the different learning
objectives is largely unknown. Here we compare the representations learned by
eight different convolutional neural networks, each with identical ResNet
architectures and trained on the same family of egocentric images, but embedded
within different learning systems. Specifically, the representations are
trained to guide action in a compound reinforcement learning task; to predict
one or a combination of three task-related targets with supervision; or using
one of three different unsupervised objectives. Using representational
similarity analysis, we find that the network trained with reinforcement
learning differs most from the other networks. Using metrics inspired by the
neuroscience literature, we find that the model trained with reinforcement
learning has a sparse and high-dimensional representation wherein individual
images are represented with very different patterns of neural activity. Further
analysis suggests these representations may arise in order to guide long-term
behavior and goal-seeking in the RL agent. Finally, we compare the
representations learned by the RL agent to neural activity from mouse visual
cortex and find it to perform as well or better than other models. Our results
provide insights into how the properties of neural representations are
influenced by objective functions and can inform transfer learning approaches.

Longitudinal interaction studies with Socially Assistive Robots are crucial
to ensure that the robot is relevant for long-term use and its perceptions are
not prone to the novelty effect. In this paper, we present a dynamic Bayesian
network (DBN) to capture the longitudinal interactions participants had with a
teleoperated robot coach (RC) delivering mindfulness sessions. The DBN model is
used to study complex, temporal interactions between the participants
self-reported personality traits, weekly baseline wellbeing scores, session
ratings, and facial AUs elicited during the sessions in a 5-week longitudinal
study. DBN modelling involves learning a graphical representation that
facilitates intuitive understanding of how multiple components contribute to
the longitudinal changes in session ratings corresponding to the perceptions of
the RC, and participants relaxation and calm levels. The learnt model captures
the following within and between sessions aspects of the longitudinal
interaction study: influence of the 5 personality dimensions on the facial AU
states and the session ratings, influence of facial AU states on the session
ratings, and the influences within the items of the session ratings. The DBN
structure is learnt using first 3 time points and the obtained model is used to
predict the session ratings of the last 2 time points of the 5-week
longitudinal data. The predictions are quantified using subject-wise RMSE and
R2 scores. We also demonstrate two applications of the model, namely,
imputation of missing values in the dataset and estimation of longitudinal
session ratings of a new participant with a given personality profile. The
obtained DBN model thus facilitates learning of conditional dependency
structure between variables in the longitudinal data and offers inferences and
conceptual understanding which are not possible through other regression

Emergency response is highly dependent on the time of incident reporting.
Unfortunately, the traditional approach to receiving incident reports (e.g.,
calling 911 in the USA) has time delays. Crowdsourcing platforms such as Waze
provide an opportunity for early identification of incidents. However,
detecting incidents from crowdsourced data streams is difficult due to the
challenges of noise and uncertainty associated with such data. Further, simply
optimizing over detection accuracy can compromise spatial-temporal localization
of the inference, thereby making such approaches infeasible for real-world
deployment. This paper presents a novel problem formulation and solution
approach for practitioner-centered incident detection using crowdsourced data
by using emergency response management as a case-study. The proposed approach
CROME (Crowdsourced Multi-objective Event Detection) quantifies the
relationship between the performance metrics of incident classification (e.g.,
F1 score) and the requirements of model practitioners (e.g., 1 km. radius for
incident detection). First, we show how crowdsourced reports, ground-truth
historical data, and other relevant determinants such as traffic and weather
can be used together in a Convolutional Neural Network (CNN) architecture for
early detection of emergency incidents. Then, we use a Pareto
optimization-based approach to optimize the output of the CNN in tandem with
practitioner-centric parameters to balance detection accuracy and
spatial-temporal localization. Finally, we demonstrate the applicability of
this approach using crowdsourced data from Waze and traffic accident reports
from Nashville, TN, USA. Our experiments demonstrate that the proposed approach
outperforms existing approaches in incident detection while simultaneously
optimizing the needs for real-world deployment and usability.

Broadcast/multicast communication systems are typically designed to optimize
the outage rate criterion, which neglects the performance of the fraction of
clients with the worst channel conditions. Targeting ultra-reliable
communication scenarios, this paper takes a complementary approach by
introducing the conditional value-at-risk (CVaR) rate as the expected rate of a
worst-case fraction of clients. To support differential quality-of-service
(QoS) levels in this class of clients, layered division multiplexing (LDM) is
applied, which enables decoding at different rates. Focusing on a practical
scenario in which the transmitter does not know the fading distribution, layer
allocation is optimized based on a dataset sampled during deployment. The
optimality gap caused by the availability of limited data is bounded via a
generalization analysis, and the sample complexity is shown to increase as the
designated fraction of worst-case clients decreases. Considering this
theoretical result, meta-learning is introduced as a means to reduce sample
complexity by leveraging data from previous deployments. Numerical experiments
demonstrate that LDM improves spectral efficiency even for small datasets;
that, for sufficiently large datasets, the proposed mirror-descent-based layer
optimization scheme achieves a CVaR rate close to that achieved when the
transmitter knows the fading distribution; and that meta-learning can
significantly reduce data requirements.

This thesis contributes a structured inquiry into the open actuarial
mathematics problem of modelling user behaviour using machine learning methods,
in order to predict purchase intent of non-life insurance products. It is
valuable for a company to understand user interactions with their website as it
provides rich and individualized insight into consumer behaviour. Most of
existing research in user behaviour modelling aims to explain or predict clicks
on a search engine result page or to estimate click-through rate in sponsored
search. These models are based on concepts about users' examination patterns of
a web page and the web page's representation of items. Investigating the
problem of modelling user behaviour to predict purchase intent on a business
website, we observe that a user's intention yields high dependency on how the
user navigates the website in terms of how many different web pages the user
visited, what kind of web pages the user interacted with, and how much time the
user spent on each web page. Inspired by these findings, we propose two
different ways of representing features of a user session leading to two models
for user click-based purchase prediction: one based on a Feed Forward Neural
Network, and another based on a Recurrent Neural Network. We examine the
discriminativeness of user-clicks for predicting purchase intent by comparing
the above two models with a model using demographic features of the user. Our
experimental results show that our click-based models significantly outperform
the demographic model, in terms of standard classification evaluation metrics,
and that a model based on a sequential representation of user clicks yields
slightly greater performance than a model based on feature engineering of
clicks.

Concept drift in process mining (PM) is a challenge as classical methods
assume processes are in a steady-state, i.e., events share the same process
version. We conducted a systematic literature review on the intersection of
these areas, and thus, we review concept drift in process mining and bring
forward a taxonomy of existing techniques for drift detection and online
process mining for evolving environments. Existing works depict that (i) PM
still primarily focuses on offline analysis, and (ii) the assessment of concept
drift techniques in processes is cumbersome due to the lack of common
evaluation protocol, datasets, and metrics.

Inclusion of high throughput technologies in the field of biology has
generated massive amounts of biological data in the recent years. Now,
transforming these huge volumes of data into knowledge is the primary challenge
in computational biology. The traditional methods of data analysis have failed
to carry out the task. Hence, researchers are turning to machine learning based
approaches for the analysis of high-dimensional big data. In machine learning,
once a model is trained with a training dataset, it can be applied on a testing
dataset which is independent. In current times, deep learning algorithms
further promote the application of machine learning in several field of biology
including plant virology. Considering a significant progress in the application
of machine learning in understanding plant virology, this review highlights an
introductory note on machine learning and comprehensively discusses the trends
and prospects of machine learning in diagnosis of viral diseases, understanding
host-virus interplay and emergence of plant viruses.

We present a relativistic heavy-quark action tuning for the charm sector on
ensembles generated by the CLS consortium. We tune a particular 5-parameter
action in an entirely non-perturbative and -- up to the chosen experimental
input -- model-independent way using machine learning and the continuum
experimental charmonium ground-state masses with various quantum numbers. In
the end we are reasonably successful; obtaining a set of simulation parameters
that we then verify produces the expected spectrum. In the future, we will use
this action for finite-volume calculations of hadron-hadron scattering.

Convolutional neural networks (CNN) have demonstrated their ability to
segment 2D cardiac ultrasound images. However, despite recent successes
according to which the intra-observer variability on end-diastole and
end-systole images has been reached, CNNs still struggle to leverage temporal
information to provide accurate and temporally consistent segmentation maps
across the whole cycle. Such consistency is required to accurately describe the
cardiac function, a necessary step in diagnosing many cardiovascular diseases.
In this paper, we propose a framework to learn the 2D+time apical long-axis
cardiac shape such that the segmented sequences can benefit from temporal and
anatomical consistency constraints. Our method is a post-processing that takes
as input segmented echocardiographic sequences produced by any state-of-the-art
method and processes it in two steps to (i) identify spatio-temporal
inconsistencies according to the overall dynamics of the cardiac sequence and
(ii) correct the inconsistencies. The identification and correction of cardiac
inconsistencies relies on a constrained autoencoder trained to learn a
physiologically interpretable embedding of cardiac shapes, where we can both
detect and fix anomalies. We tested our framework on 98 full-cycle sequences
from the CAMUS dataset, which are available alongside this paper. Our temporal
regularization method not only improves the accuracy of the segmentation across
the whole sequences, but also enforces temporal and anatomical consistency.

Wikidata is a frequently updated, community-driven, and multilingual
knowledge graph. Hence, Wikidata is an attractive basis for Entity Linking,
which is evident by the recent increase in published papers. This survey
focuses on four subjects: (1) Which Wikidata Entity Linking datasets exist, how
widely used are they and how are they constructed? (2) Do the characteristics
of Wikidata matter for the design of Entity Linking datasets and if so, how?
(3) How do current Entity Linking approaches exploit the specific
characteristics of Wikidata? (4) Which Wikidata characteristics are unexploited
by existing Entity Linking approaches? This survey reveals that current
Wikidata-specific Entity Linking datasets do not differ in their annotation
scheme from schemes for other knowledge graphs like DBpedia. Thus, the
potential for multilingual and time-dependent datasets, naturally suited for
Wikidata, is not lifted. Furthermore, we show that most Entity Linking
approaches use Wikidata in the same way as any other knowledge graph missing
the chance to leverage Wikidata-specific characteristics to increase quality.
Almost all approaches employ specific properties like labels and sometimes
descriptions but ignore characteristics such as the hyper-relational structure.
Hence, there is still room for improvement, for example, by including
hyper-relational graph embeddings or type information. Many approaches also
include information from Wikipedia, which is easily combinable with Wikidata
and provides valuable textual information, which Wikidata lacks.

We present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD
models from a shape database to a single input image. This enables 3D
perception of an observed scene from a 2D RGB observation, characterized as a
lightweight, compact, clean CAD representation. Core to our approach is our
differentiable alignment optimization based on dense 2D-3D object
correspondences and Procrustes alignment. ROCA can thus provide a robust CAD
alignment while simultaneously informing CAD retrieval by leveraging the 2D-3D
correspondences to learn geometrically similar CAD models. Experiments on
challenging, real-world imagery from ScanNet show that ROCA significantly
improves on state of the art, from 9.5% to 17.6% in retrieval-aware CAD
alignment accuracy.

Animals move in three dimensions (3D). Thus, 3D measurement is necessary to
report the true kinematics of animal movement. Existing 3D measurement
techniques draw on specialized hardware, such as motion capture or depth
cameras, as well as deep multi-view and monocular computer vision. Continued
advances at the intersection of deep learning and computer vision will
facilitate 3D tracking across more anatomical features, with less training
data, in additional species, and within more natural, occlusive environments.
3D behavioral measurement enables unique applications in phenotyping,
investigating the neural basis of behavior, and designing artificial agents
capable of imitating animal behavior.

We extend neural 3D representations to allow for intuitive and interpretable
user control beyond novel view rendering (i.e. camera control). We allow the
user to annotate which part of the scene one wishes to control with just a
small number of mask annotations in the training images. Our key idea is to
treat the attributes as latent variables that are regressed by the neural
network given the scene encoding. This leads to a few-shot learning framework,
where attributes are discovered automatically by the framework, when
annotations are not provided. We apply our method to various scenes with
different types of controllable attributes (e.g. expression control on human
faces, or state control in movement of inanimate objects). Overall, we
demonstrate, to the best of our knowledge, for the first time novel view and
novel attribute re-rendering of scenes from a single video.

The study introduces a new analysis scheme to analyze trace data and
visualize students' self-regulated learning strategies in a mastery-based
online learning modules platform. The pedagogical design of the platform
resulted in fewer event types and less variability in student trace data. The
current analysis scheme overcomes those challenges by conducting three levels
of clustering analysis. On the event level, mixture-model fitting is employed
to distinguish between abnormally short and normal assessment attempts and
study events. On the module level, trace level clustering is performed with
three different methods for generating distance metrics between traces, with
the best performing output used in the next step. On the sequence level, trace
level clustering is performed on top of module-level clusters to reveal
students' change of learning strategy over time. We demonstrated that distance
metrics generated based on learning theory produced better clustering results
than pure data-driven or hybrid methods. The analysis showed that most students
started the semester with productive learning strategies, but a significant
fraction shifted to a multitude of less productive strategies in response to
increasing content difficulty and stress. The observations could prompt
instructors to rethink conventional course structure and implement
interventions to improve self-regulation at optimal times.

Efficient high-performance decoding of topological stabilizer codes has the
potential to crucially improve the balance between logical failure rates and
the number and individual error rates of the constituent qubits. High-threshold
maximum-likelihood decoders require an explicit error model for Pauli errors to
decode a specific syndrome, whereas lower-threshold heuristic approaches such
as minimum weight matching are "error agnostic". Here we consider an
intermediate approach, formulating a decoder that depends on the bias, i.e.,
the relative probability of phase-flip to bit-flip errors, but is agnostic to
error rate. Our decoder is based on counting the number and effective weight of
the most likely error chains in each equivalence class of a given syndrome. We
use Metropolis-based Monte Carlo sampling to explore the space of error chains
and find unique chains, that are efficiently identified using a hash table.
Using the error-rate invariance the decoder can sample chains effectively at an
error rate which is higher than the physical error rate and without the need
for "thermalization" between chains in different equivalence classes. Applied
to the surface code and the XZZX code, the decoder matches maximum-likelihood
decoders for moderate code sizes or low error rates. We anticipate that,
because of the compressed information content per syndrome, it can be taken
full advantage of in combination with machine-learning methods to extrapolate
Monte Carlo-generated data.

Here, we report measurements of detailed dynamic cohesive properties (DCPs)
beyond the dynamic fracture toughness of a bicontinuously nanostructured
copolymer, polyurea, under an extremely loading rate, from deep-learning
analyses of a dynamic big-data-generating experiment. We first describe a new
Dynamic Line-Image Shearing Interferometer (DL-ISI), which uses a streak camera
to record optical fringes of displacement-gradient vs time profile along a line
on sample's rear surface. This system enables us to detect crack initiation and
growth processes in plate-impact experiments. Then, we present a convolutional
neural network (CNN) based deep-learning framework, trained by extensive
finite-element simulations, that inversely determines the accurate DCPs from
the DL-ISI fringe images. For the measurements, plate-impact experiments were
performed on a set of samples with a mid-plane crack. A Conditional Generative
Adversarial Networks (cGAN) was employed first to reconstruct missing DL-ISI
fringes with recorded partial DL-ISI fringes. Then, the CNN and a correlation
method were applied to the fully reconstructed fringes to get the dynamic
fracture toughness, 12.1kJ/m^2, cohesive strength, 302 MPa, and maximum
cohesive separation, 80.5 um, within 0.4%, 2.7%, and 2.2% differences,
respectively. For the first time, the DCPs of polyurea have been successfully
obtained by the DL-ISI with the pre-trained CNN and correlation analyses of
cGAN-reconstructed data sets. The dynamic cohesive strength is found to be
nearly three times higher than the dynamic-failure-initiation strength. The
high dynamic fracture toughness is found to stem from both high dynamic
cohesive strength and high ductility of the dynamic cohesive separation.

The positive outcome of a trauma intervention depends on an intraoperative
evaluation of inserted metallic implants. Due to occurring metal artifacts, the
quality of this evaluation heavily depends on the performance of so-called
Metal Artifact Reduction methods (MAR). The majority of these MAR methods
require prior segmentation of the inserted metal objects. Therefore, typically
a rather simple thresholding-based segmentation method in the reconstructed 3D
volume is applied, despite some major disadvantages. With this publication, the
potential of shifting the segmentation task to a learning-based,
view-consistent 2D projection-based method on the downstream MAR's outcome is
investigated. For segmenting the present metal, a rather simple learning-based
2D projection-wise segmentation network that is trained using real data
acquired during cadaver studies, is examined. To overcome the disadvantages
that come along with a 2D projection-wise segmentation, a Consistency Filter is
proposed. The influence of the shifted segmentation domain is investigated by
comparing the results of the standard fsMAR with a modified fsMAR version using
the new segmentation masks. With a quantitative and qualitative evaluation on
real cadaver data, the investigated approach showed an increased MAR
performance and a high insensitivity against metal artifacts. For cases with
metal outside the reconstruction's FoV or cases with vanishing metal, a
significant reduction in artifacts could be shown. Thus, increases of up to
roughly 3 dB w.r.t. the mean PSNR metric over all slices and up to 9 dB for
single slices were achieved. The shown results reveal a beneficial influence of
the shift to a 2D-based segmentation method on real data for downstream use
with a MAR method, like the fsMAR.

We develop DEEPTRAVERSAL, a feedback-driven framework to test DNNs.
DEEPTRAVERSAL first launches an offline phase to map media data of various
forms to manifolds. Then, in its online testing phase, DEEPTRAVERSAL traverses
the prepared manifold space to maximize DNN coverage criteria and trigger
prediction errors. In our evaluation, DNNs executing various tasks (e.g.,
classification, self-driving, machine translation) and media data of different
types (image, audio, text) were used. DEEPTRAVERSAL exhibits better performance
than prior methods with respect to popular DNN coverage criteria and it can
discover a larger number and higher quality of error-triggering inputs. The
tested DNN models, after being repaired with findings of DEEPTRAVERSAL, achieve
better accuracy

This paper summarizes eight design requirements for DNN testing criteria,
taking into account distribution properties and practical concerns. We then
propose a new criterion, NLC, that satisfies all of these design requirements.
NLC treats a single DNN layer as the basic computational unit (rather than a
single neuron) and captures four critical features of neuron output
distributions. Thus, NLC is denoted as NeuraL Coverage, which more accurately
describes how neural networks comprehend inputs via approximated distributions
rather than neurons. We demonstrate that NLC is significantly correlated with
the diversity of a test suite across a number of tasks (classification and
generation) and data formats (image and text). Its capacity to discover DNN
prediction errors is promising. Test input mutation guided by NLC result in a
greater quality and diversity of exposed erroneous behaviors.

A reinforcement learning (RL) control policy could fail in a new/perturbed
environment that is different from the training environment, due to the
presence of dynamic variations. For controlling systems with continuous state
and action spaces, we propose an add-on approach to robustifying a pre-trained
RL policy by augmenting it with an $\mathcal{L}_{1}$ adaptive controller
($\mathcal{L}_{1}$AC). Leveraging the capability of an $\mathcal{L}_{1}$AC for
fast estimation and active compensation of dynamic variations, the proposed
approach can improve the robustness of an RL policy which is trained either in
a simulator or in the real world without consideration of a broad class of
dynamic variations. Numerical and real-world experiments empirically
demonstrate the efficacy of the proposed approach in robustifying RL policies
trained using both model-free and model-based methods.

By leveraging data from a fully labeled source domain, unsupervised domain
adaptation (UDA) improves classification performance on an unlabeled target
domain through explicit discrepancy minimization of data distribution or
adversarial learning. As an enhancement, category alignment is involved during
adaptation to reinforce target feature discrimination by utilizing model
prediction. However, there remain unexplored problems about pseudo-label
inaccuracy incurred by wrong category predictions on target domain, and
distribution deviation caused by overfitting on source domain. In this paper,
we propose a model-agnostic two-stage learning framework, which greatly reduces
flawed model predictions using soft pseudo-label strategy and avoids
overfitting on source domain with a curriculum learning strategy.
Theoretically, it successfully decreases the combined risk in the upper bound
of expected error on the target domain. At the first stage, we train a model
with distribution alignment-based UDA method to obtain soft semantic label on
target domain with rather high confidence. To avoid overfitting on source
domain, at the second stage, we propose a curriculum learning strategy to
adaptively control the weighting between losses from the two domains so that
the focus of the training stage is gradually shifted from source distribution
to target distribution with prediction confidence boosted on the target domain.
Extensive experiments on two well-known benchmark datasets validate the
universal effectiveness of our proposed framework on promoting the performance
of the top-ranked UDA algorithms and demonstrate its consistent superior

Due to the promising advantages in space compression and inference
acceleration, quantized representation learning for recommender systems has
become an emerging research direction recently. As the target is to embed
latent features in the discrete embedding space, developing quantization for
user-item representations with a few low-precision integers confronts the
challenge of high information loss, thus leading to unsatisfactory performance
in Top-K recommendation.
  In this work, we study the problem of representation learning for
recommendation with 1-bit quantization. We propose a model named Low-loss
Quantized Graph Convolutional Network (L^2Q-GCN). Different from previous work
that plugs quantization as the final encoder of user-item embeddings, L^2Q-GCN
learns the quantized representations whilst capturing the structural
information of user-item interaction graphs at different semantic levels. This
achieves the substantial retention of intermediate interactive information,
alleviating the feature smoothing issue for ranking caused by numerical
quantization. To further improve the model performance, we also present an
advanced solution named L^2Q-GCN-anl with quantization approximation and
annealing training strategy. We conduct extensive experiments on four
benchmarks over Top-K recommendation task. The experimental results show that,
with nearly 9x representation storage compression, L^2Q-GCN-anl attains about
90~99% performance recovery compared to the state-of-the-art model.

In this survey, we present the current status on robots performing
manipulation tasks that require varying contact with the environment, such that
the robot must either implicitly or explicitly control the contact force with
the environment to complete the task. Robots can perform more and more
manipulation tasks that are still done by humans, and there is a growing number
of publications on the topics of 1) performing tasks that always require
contact and 2) mitigating uncertainty by leveraging the environment in tasks
that, under perfect information, could be performed without contact. The recent
trends have seen robots perform tasks earlier left for humans, such as massage,
and in the classical tasks, such as peg-in-hole, there is a more efficient
generalization to other similar tasks, better error tolerance, and faster
planning or learning of the tasks. Thus, in this survey we cover the current
stage of robots performing such tasks, starting from surveying all the
different in-contact tasks robots can perform, observing how these tasks are
controlled and represented, and finally presenting the learning and planning of
the skills required to complete these tasks.

We study the problem of estimating precision matrices in Gaussian
distributions that are multivariate totally positive of order two
($\mathrm{MTP}_2$). The precision matrix in such a distribution is an M-matrix.
The problem can be formulated as a sign-constrained log-determinant program.
The existing algorithms designed for solving this problem are based on the
block coordinate descent method, which are computationally prohibitive in
high-dimensional cases, because of the need to solve a large number of
nonnegative quadratic programs. We propose a novel algorithm based on the
two-metric projection method, with a well-designed search direction and
variable partitioning scheme. Our algorithm reduces the computational
complexity significantly in solving this problem, and its theoretical
convergence is established. Experiments involving synthetic and real-world data
demonstrate that our proposed algorithm is significantly more efficient, from a
computational time perspective, than the state-of-the-art methods.

Emotion Recognition in Conversations (ERC) is an important and active
research area. Recent work has shown the benefits of using multiple modalities
(e.g., text, audio, and video) for the ERC task. In a conversation,
participants tend to maintain a particular emotional state unless some stimuli
evokes a change. There is a continuous ebb and flow of emotions in a
conversation. Inspired by this observation, we propose a multimodal ERC model
and augment it with an emotion-shift component that improves performance. The
proposed emotion-shift component is modular and can be added to any existing
multimodal ERC model (with a few modifications). We experiment with different
variants of the model, and results show that the inclusion of emotion shift
signal helps the model to outperform existing models for ERC on MOSEI and
IEMOCAP datasets.

In last-mile routing, the task of finding a route is often framed as a
Traveling Salesman Problem to minimize travel time and associated cost.
However, solutions stemming from this approach do not match the realized paths
as drivers deviate due to navigational considerations and preferences. To
prescribe routes that incorporate this tacit knowledge, a data-driven model is
proposed that aligns well with the hierarchical structure of delivery data
wherein each stop belongs to a zone - a geographical area. First, on the global
level, a zone sequence is established as a result of a minimization over a cost
matrix which is a weighted combination of historical information and distances
(travel times) between zones. Subsequently, within zones, sequences of stops
are determined, such that, integrated with the predetermined zone sequence, a
full solution is obtained.
  The methodology is particularly promising as it propels itself within the
top-tier of submissions to the Last-Mile Routing Research Challenge, while it
maintains an elegant decomposition that ensures a feasible implementation into
practice. The concurrence between prescribed and realized routes underpins the
adequateness of a hierarchical breakdown of the problem and the fact that
drivers make a series of locally optimal decisions when navigating.
Furthermore, experimenting with the balance between historical information and
distance exposes that historic information is pivotal in deciding a starting
zone of a route. The experiments also reveal that at the end of a route,
historical information can best be discarded, making the time it takes to
return to the station the primary concern.

Generative Adversarial Networks (GANs) are gaining increasing attention as a
means for synthesising data. So far much of this work has been applied to use
cases outside of the data confidentiality domain with a common application
being the production of artificial images. Here we consider the potential
application of GANs for the purpose of generating synthetic census microdata.
We employ a battery of utility metrics and a disclosure risk metric (the
Targeted Correct Attribution Probability) to compare the data produced by
tabular GANs with those produced using orthodox data synthesis methods.

Detecting critical nodes in sparse networks is important in a variety of
application domains. A Critical Node Problem (CNP) aims to find a set of
critical nodes from a network whose deletion maximally degrades the pairwise
connectivity of the residual network. Due to its general NP-hard nature,
state-of-the-art CNP solutions are based on heuristic approaches. Domain
knowledge and trial-and-error are usually required when designing such
approaches, thus consuming considerable effort and time. This work proposes a
feature importance-aware graph attention network for node representation and
combines it with dueling double deep Q-network to create an end-to-end
algorithm to solve CNP for the first time. It does not need any
problem-specific knowledge or labeled datasets as required by most of existing
methods. Once the model is trained, it can be generalized to cope with various
types of CNPs (with different sizes and topological structures) without
re-training. Extensive experiments on 28 real-world networks show that the
proposed method is highly comparable to state-of-the-art methods. It does not
require any problem-specific knowledge and, hence, can be applicable to many
applications including those impossible ones by using the existing approaches.
It can be combined with some local search methods to further improve its
solution quality. Extensive comparison results are given to show its
effectiveness in solving CNP.

The recent prosperity of learning-based image rain and noise removal is
mainly due to the well-designed neural network architectures and large labeled
datasets. However, we find that current image rain and noise removal methods
result in low utilization of images. To alleviate the reliance on large labeled
datasets, we propose the task-driven image rain and noise removal (TRNR) based
on the introduced patch analysis strategy. The patch analysis strategy provides
image patches with various spatial and statistical properties for training and
has been verified to increase the utilization of images. Further, the patch
analysis strategy motivates us to consider learning image rain and noise
removal task-driven instead of data-driven. Therefore we introduce the
N-frequency-K-shot learning task for TRNR. Each N-frequency-K-shot learning
task is based on a tiny dataset containing NK image patches sampled by the
patch analysis strategy. TRNR enables neural networks to learn from abundant
N-frequency-K-shot learning tasks other than from adequate data. To verify the
effectiveness of TRNR, we build a light Multi-Scale Residual Network (MSResNet)
with about 0.9M parameters to learn image rain removal and use a simple ResNet
with about 1.2M parameters dubbed DNNet for blind gaussian noise removal with a
few images (for example, 20.0% train-set of Rain100H). Experimental results
demonstrate that TRNR enables MSResNet to learn better from fewer images. In
addition, MSResNet and DNNet utilizing TRNR have obtained better performance
than most recent deep learning methods trained data-driven on large labeled
datasets. These experimental results have confirmed the effectiveness and
superiority of the proposed TRNR. The codes of TRNR will be public soon.

The recent explosion of question answering (QA) datasets and models has
increased the interest in the generalization of models across multiple domains
and formats by either training on multiple datasets or by combining multiple
models. Despite the promising results of multi-dataset models, some domains or
QA formats may require specific architectures, and thus the adaptability of
these models might be limited. In addition, current approaches for combining
models disregard cues such as question-answer compatibility. In this work, we
propose to combine expert agents with a novel, flexible, and training-efficient
architecture that considers questions, answer predictions, and
answer-prediction confidence scores to select the best answer among a list of
answer candidates. Through quantitative and qualitative experiments we show
that our model i) creates a collaboration between agents that outperforms
previous multi-agent and multi-dataset approaches in both in-domain and
out-of-domain scenarios, ii) is highly data-efficient to train, and iii) can be
adapted to any QA format. We release our code and a dataset of answer
predictions from expert agents for 16 QA datasets to foster future developments
of multi-agent systems on https://github.com/UKPLab/MetaQA.

Quality control and quality assurance are challenges in Direct Metal Laser
Melting (DMLM). Intermittent machine diagnostics and downstream part
inspections catch problems after undue cost has been incurred processing
defective parts. In this paper we demonstrate two methodologies for in-process
fault detection and part quality prediction that can be readily deployed on
existing commercial DMLM systems with minimal hardware modification. Novel
features were derived from the time series of common photodiode sensors along
with standard machine control signals. A Bayesian approach attributes
measurements to one of multiple process states and a least squares regression
model predicts severity of certain material defects.

Learning a well-informed heuristic function for hard task planning domains is
an elusive problem. Although there are known neural network architectures to
represent such heuristic knowledge, it is not obvious what concrete information
is learned and whether techniques aimed at understanding the structure help in
improving the quality of the heuristics. This paper presents a network model to
learn a heuristic capable of relating distant parts of the state space via
optimal plan imitation using the attention mechanism, which drastically
improves the learning of a good heuristic function. To counter the limitation
of the method in the creation of problems of increasing difficulty, we
demonstrate the use of curriculum learning, where newly solved problem
instances are added to the training set, which, in turn, helps to solve
problems of higher complexities and far exceeds the performances of all
existing baselines including classical planning heuristics. We demonstrate its
effectiveness for grid-type PDDL domains.

Implicit neural representations (INRs) have recently emerged as a promising
alternative to classical discretized representations of signals. Nevertheless,
despite their practical success, we still do not understand how INRs represent
signals. We propose a novel unified perspective to theoretically analyse INRs.
Leveraging results from harmonic analysis and deep learning theory, we show
that most INR families are analogous to structured signal dictionaries whose
atoms are integer harmonics of the set of initial mapping frequencies. This
structure allows INRs to express signals with an exponentially increasing
frequency support using a number of parameters that only grows linearly with
depth. We also explore the inductive bias of INRs exploiting recent results
about the empirical neural tangent kernel (NTK). Specifically, we show that the
eigenfunctions of the NTK can be seen as dictionary atoms whose inner product
with the target signal determines the final performance of their
reconstruction. In this regard, we reveal that meta-learning has a reshaping
effect on the NTK analogous to dictionary learning, building dictionary atoms
as a combination of the examples seen during meta-training. Our results permit
to design and tune novel INR architectures, but can also be of interest for the
wider deep learning theory community.

Monocular 3D object detection aims to predict the object location, dimension
and orientation in 3D space alongside the object category given only a
monocular image. It poses a great challenge due to its ill-posed property which
is critically lack of depth information in the 2D image plane. While there
exist approaches leveraging off-the-shelve depth estimation or relying on LiDAR
sensors to mitigate this problem, the dependence on the additional depth model
or expensive equipment severely limits their scalability to generic 3D
perception. In this paper, we propose a stereo-guided monocular 3D object
detection framework, dubbed SGM3D, adapting the robust 3D features learned from
stereo inputs to enhance the feature for monocular detection. We innovatively
present a multi-granularity domain adaptation (MG-DA) mechanism to exploit the
network's ability to generate stereo-mimicking features given only on monocular
cues. Coarse BEV feature-level, as well as the fine anchor-level domain
adaptation, are both leveraged for guidance in the monocular domain.In
addition, we introduce an IoU matching-based alignment (IoU-MA) method for
object-level domain adaptation between the stereo and monocular predictions to
alleviate the mismatches while adopting the MG-DA. Extensive experiments
demonstrate state-of-the-art results on KITTI and Lyft datasets.

In combating climate change, an effective demand-based energy supply
operation of the district energy system (DES) for heating or cooling is
indispensable. As a consequence, an accurate forecast of heat consumption on
the consumer side poses an important first step towards an optimal energy
supply. However, due to the non-linearity and non-stationarity of heat
consumption data, the prediction of the thermal energy demand of DES remains
challenging. In this work, we propose a forecasting framework for thermal
energy consumption within a district heating system (DHS) based on kernel
Support Vector Regression (kSVR) using real-world smart meter data. Particle
Swarm Optimization (PSO) is employed to find the optimal hyper-parameter for
the kSVR model which leads to the superiority of the proposed methods when
compared to a state-of-the-art ARIMA model. The average MAPE is reduced to
2.07% and 2.64% for the individual meter-specific forecasting and for
forecasting of societal consumption, respectively.

It was recently shown that under smoothness conditions, the squared
Wasserstein distance between two distributions could be efficiently computed
with appealing statistical error upper bounds. However, rather than the
distance itself, the object of interest for applications such as generative
modeling is the underlying optimal transport map. Hence, computational and
statistical guarantees need to be obtained for the estimated maps themselves.
In this paper, we propose the first tractable algorithm for which the
statistical $L^2$ error on the maps nearly matches the existing minimax
lower-bounds for smooth map estimation. Our method is based on solving the
semi-dual formulation of optimal transport with an infinite-dimensional
sum-of-squares reformulation, and leads to an algorithm which has
dimension-free polynomial rates in the number of samples, with potentially
exponentially dimension-dependent constants.

The onset of rheumatic diseases such as rheumatoid arthritis is typically
subclinical, which results in challenging early detection of the disease.
However, characteristic changes in the anatomy can be detected using imaging
techniques such as MRI or CT. Modern imaging techniques such as chemical
exchange saturation transfer (CEST) MRI drive the hope to improve early
detection even further through the imaging of metabolites in the body. To image
small structures in the joints of patients, typically one of the first regions
where changes due to the disease occur, a high resolution for the CEST MR
imaging is necessary. Currently, however, CEST MR suffers from an inherently
low resolution due to the underlying physical constraints of the acquisition.
In this work we compared established up-sampling techniques to neural
network-based super-resolution approaches. We could show, that neural networks
are able to learn the mapping from low-resolution to high-resolution
unsaturated CEST images considerably better than present methods. On the test
set a PSNR of 32.29dB (+10%), a NRMSE of 0.14 (+28%), and a SSIM of 0.85 (+15%)
could be achieved using a ResNet neural network, improving the baseline
considerably. This work paves the way for the prospective investigation of
neural networks for super-resolution CEST MRI and, followingly, might lead to a
earlier detection of the onset of rheumatic diseases.

Making an updated and as-built model plays an important role in the
life-cycle of a process plant. In particular, Digital Twin models must be
precise to guarantee the efficiency and reliability of the systems. Data-driven
models can simulate the latest behavior of the sub-systems by considering
uncertainties and life-cycle related changes. This paper presents a
step-by-step concept for hybrid Digital Twin models of process plants using an
early implemented prototype as an example. It will detail the steps for
updating the first-principles model and Digital Twin of a brownfield process
system using data-driven models of the process equipment. The challenges for
generation of an as-built hybrid Digital Twin will also be discussed. With the
help of process history data to teach Machine Learning models, the implemented
Digital Twin can be continually improved over time and this work in progress
can be further optimized.

We introduce a new setting of Novel Class Discovery in Semantic Segmentation
(NCDSS), which aims at segmenting unlabeled images containing new classes given
prior knowledge from a labeled set of disjoint classes. In contrast to existing
approaches that look at novel class discovery in image classification, we focus
on the more challenging semantic segmentation. In NCDSS, we need to distinguish
the objects and background, and to handle the existence of multiple classes
within an image, which increases the difficulty in using the unlabeled data. To
tackle this new setting, we leverage the labeled base data and a saliency model
to coarsely cluster novel classes for model training in our basic framework.
Additionally, we propose the Entropy-based Uncertainty Modeling and
Self-training (EUMS) framework to overcome noisy pseudo-labels, further
improving the model performance on the novel classes. Our EUMS utilizes an
entropy ranking technique and a dynamic reassignment to distill clean labels,
thereby making full use of the noisy data via self-supervised learning. We
build the NCDSS benchmark on the PASCAL-5$^i$ dataset and COCO-20$^i$ dataset.
Extensive experiments demonstrate the feasibility of the basic framework
(achieving an average mIoU of 49.81% on PASCAL-5$^i$) and the effectiveness of
EUMS framework (outperforming the basic framework by 9.28% mIoU on
PASCAL-5$^i$).

Transformers can learn to perform numerical computations from examples only.
I study nine problems of linear algebra, from basic matrix operations to
eigenvalue decomposition and inversion, and introduce and discuss four encoding
schemes to represent real numbers. On all problems, transformers trained on
sets of random matrices achieve high accuracies (over 90%). The models are
robust to noise, and can generalize out of their training distribution. In
particular, models trained to predict Laplace-distributed eigenvalues
generalize to different classes of matrices: Wigner matrices or matrices with
positive eigenvalues. The reverse is not true.

Estimation of the value-at-risk (VaR) of a large portfolio of assets is an
important task for financial institutions. As the joint log-returns of asset
prices can often be projected to a latent space of a much smaller dimension,
the use of a variational autoencoder (VAE) for estimating the VaR is a natural
suggestion. To ensure the bottleneck structure of autoencoders when learning
sequential data, we use a temporal VAE (TempVAE) that avoids an auto-regressive
structure for the observation variables. However, the low signal- to-noise
ratio of financial data in combination with the auto-pruning property of a VAE
typically makes the use of a VAE prone to posterior collapse. Therefore, we
propose to use annealing of the regularization to mitigate this effect. As a
result, the auto-pruning of the TempVAE works properly which also results in
excellent estimation results for the VaR that beats classical GARCH-type and
historical simulation approaches when applied to real data.

Although existing semantic segmentation approaches achieve impressive
results, they still struggle to update their models incrementally as new
categories are uncovered. Furthermore, pixel-by-pixel annotations are expensive
and time-consuming. This paper proposes a novel framework for Weakly
Incremental Learning for Semantic Segmentation, that aims at learning to
segment new classes from cheap and largely available image-level labels. As
opposed to existing approaches, that need to generate pseudo-labels offline, we
use an auxiliary classifier, trained with image-level labels and regularized by
the segmentation model, to obtain pseudo-supervision online and update the
model incrementally. We cope with the inherent noise in the process by using
soft-labels generated by the auxiliary classifier. We demonstrate the
effectiveness of our approach on the Pascal VOC and COCO datasets,
outperforming offline weakly-supervised methods and obtaining results
comparable with incremental learning methods with full supervision. Code can be
found at https://github.com/fcdl94/WILSON.

The marginal Bayesian predictive classifiers (mBpc) as opposed to the
simultaneous Bayesian predictive classifiers (sBpc), handle each data
separately and hence tacitly assumes the independence of the observations.
However, due to saturation in learning of generative model parameters, the
adverse effect of this false assumption on the accuracy of mBpc tends to wear
out in face of increasing amount of training data; guaranteeing the convergence
of these two classifiers under de Finetti type of exchangeability. This result
however, is far from trivial for the sequences generated under Partition
exchangeability (PE), where even umpteen amount of training data is not ruling
out the possibility of an unobserved outcome (Wonderland!). We provide a
computational scheme that allows the generation of the sequences under PE.
Based on that, with controlled increase of the training data, we show the
convergence of the sBpc and mBpc. This underlies the use of simpler yet
computationally more efficient marginal classifiers instead of simultaneous. We
also provide a parameter estimation of the generative model giving rise to the
partition exchangeable sequence as well as a testing paradigm for the equality
of this parameter across different samples. The package for Bayesian predictive
supervised classifications, parameter estimation and hypothesis testing of the
Ewens Sampling formula generative model is deposited on CRAN as PEkit package
and free available from https://github.com/AmiryousefiLab/PEkit.

Previous studies on automatic berthing systems based on artificial neural
network (ANN) showed great berthing performance by training the ANN with ship
berthing data as training data. However, because the ANN requires a large
amount of training data to yield robust performance, the ANN-based automatic
berthing system is somewhat limited due to the difficulty in obtaining the
berthing data. In this study, to overcome this difficulty, the automatic
berthing system based on one of the reinforcement learning (RL) algorithms,
proximal policy optimization (PPO), is proposed because the RL algorithms can
learn an optimal control policy through trial-and-error by interacting with a
given environment and does not require any pre-obtained training data, where
the control policy in the proposed PPO-based automatic berthing system controls
revolutions per second (RPS) and rudder angle of a ship. Finally, it is shown
that the proposed PPO-based automatic berthing system eliminates the need for
obtaining the training dataset and shows great potential for the actual
berthing application.

Repeatedly solving the parameterized optimal mass transport (pOMT) problem is
a frequent task in applications such as image registration and adaptive grid
generation. It is thus critical to develop a highly efficient reduced solver
that is equally accurate as the full order model. In this paper, we propose
such a machine learning-like method for pOMT by adapting a new reduced basis
(RB) technique specifically designed for nonlinear equations, the reduced
residual reduced over-collocation (R2-ROC) approach, to the parameterized
Monge-Amp$\grave{\rm e}$re equation. It builds on top of a narrow-stencil
finite different method (FDM), a so-called truth solver, which we propose in
this paper for the Monge-Amp$\grave{\rm e}$re equation with a transport
boundary. Together with the R2-ROC approach, it allows us to handle the strong
and unique nonlinearity pertaining to the Monge-Amp$\grave{\rm e}$re equation
achieving online efficiency without resorting to any direct approximation of
the nonlinearity. Several challenging numerical tests demonstrate the accuracy
and high efficiency of our method for solving the Monge-Amp$\grave{\rm e}$re
equation with various parametric boundary conditions.

Decision trees are often preferred when implementing Machine Learning in
embedded systems for their simplicity and scalability. Hoeffding Trees are a
type of Decision Trees that take advantage of the Hoeffding Bound to allow them
to learn patterns in data without having to continuously store the data samples
for future reprocessing. This makes them especially suitable for deployment on
embedded devices. In this work we highlight the features of an HLS
implementation of the Hoeffding Tree. The implementation parameters include the
feature size of the samples (D), the number of output classes (K), and the
maximum number of nodes to which the tree is allowed to grow (Nd). We target a
Xilinx MPSoC ZCU102, and evaluate: the design's resource requirements and clock
frequency for different numbers of classes and feature size, the execution time
on several synthetic datasets of varying sample sizes (N), number of output
classes and the execution time and accuracy for two datasets from UCI. For a
problem size of D3, K5, and N40000, a single decision tree operating at 103MHz
is capable of 8.3x faster inference than the 1.2GHz ARM Cortex-A53 core.
Compared to a reference implementation of the Hoeffding tree, we achieve
comparable classification accuracy for the UCI datasets.

which can change the wireless propagation environment through design of their
reflection coefficients. We consider a practical setting where (i) the IRS
reflection coefficients are achieved by adjusting tunable elements embedded in
the meta-atoms, (ii) the IRS reflection coefficients are affected by the
incident angles of the incoming signals, (iii) the IRS is deployed in
multi-path, time-varying channels, and (iv) the feedback link from the base
station to the IRS has a low data rate. Conventional optimization-based IRS
control protocols, which rely on channel estimation and conveying the optimized
variables to the IRS, are not applicable in this setting due to the difficulty
of channel estimation and the low feedback rate. Therefore, we develop a novel
adaptive codebook-based limited feedback protocol where only a codeword index
is transferred to the IRS. We propose two solutions for adaptive codebook
design: random adjacency (RA) and deep neural network policy-based IRS control
(DPIC), both of which only require the end-to-end compound channels. We further
develop several augmented schemes based on the RA and DPIC. Numerical
evaluations show that the data rate and average data rate over one coherence
time are improved substantially by our schemes.

Image-to-image (I2I) translation is an established way of translating data
from one domain to another but the usability of the translated images in the
target domain when working with such dissimilar domains as the SAR/optical
satellite imagery ones and how much of the origin domain is translated to the
target domain is still not clear enough. This article address this by
performing translations of labelled datasets from the optical domain to the SAR
domain with different I2I algorithms from the state-of-the-art, learning from
transferred features in the destination domain and evaluating later how much
from the original dataset was transferred. Added to this, stacking is proposed
as a way of combining the knowledge learned from the different I2I translations
and evaluated against single models.

Active inference is a mathematical framework which originated in
computational neuroscience as a theory of how the brain implements action,
perception and learning. Recently, it has been shown to be a promising approach
to the problems of state-estimation and control under uncertainty, as well as a
foundation for the construction of goal-driven behaviours in robotics and
artificial agents in general. Here, we review the state-of-the-art theory and
implementations of active inference for state-estimation, control, planning and
learning; describing current achievements with a particular focus on robotics.
We showcase relevant experiments that illustrate its potential in terms of
adaptation, generalization and robustness. Furthermore, we connect this
approach with other frameworks and discuss its expected benefits and
challenges: a unified framework with functional biological plausibility using
variational Bayesian inference.

In this article, we introduce a novel type of spatio-temporal sequential
patterns called Constricted Spatio-Temporal Sequential (CSTS) patterns and
thoroughly analyze their properties. We demonstrate that the set of CSTS
patterns is a concise representation of all spatio-temporal sequential patterns
that can be discovered in a given dataset. To measure significance of the
discovered CSTS patterns we adapt the participation index measure. We also
provide CSTS-Miner: an algorithm that discovers all participation index strong
CSTS patterns in event data. We experimentally evaluate the proposed algorithms
using two crime-related datasets: Pittsburgh Police Incident Blotter Dataset
and Boston Crime Incident Reports Dataset. In the experiments, the CSTS-Miner
algorithm is compared with the other four state-of-the-art algorithms:
STS-Miner, CSTPM, STBFM and CST-SPMiner. As the results of experiments suggest,
the proposed algorithm discovers much fewer patterns than the other selected
algorithms. Finally, we provide the examples of interesting crime-related
patterns discovered by the proposed CSTS-Miner algorithm.

We introduce a novel training procedure for policy gradient methods wherein
episodic memory is used to optimize the hyperparameters of reinforcement
learning algorithms on-the-fly. Unlike other hyperparameter searches, we
formulate hyperparameter scheduling as a standard Markov Decision Process and
use episodic memory to store the outcome of used hyperparameters and their
training contexts. At any policy update step, the policy learner refers to the
stored experiences, and adaptively reconfigures its learning algorithm with the
new hyperparameters determined by the memory. This mechanism, dubbed as
Episodic Policy Gradient Training (EPGT), enables an episodic learning process,
and jointly learns the policy and the learning algorithm's hyperparameters
within a single run. Experimental results on both continuous and discrete
environments demonstrate the advantage of using the proposed method in boosting
the performance of various policy gradient algorithms.

Similarly to other connectionist models, Graph Neural Networks (GNNs) lack
transparency in their decision-making. A number of sub-symbolic approaches have
been developed to provide insights into the GNN decision making process. These
are first important steps on the way to explainability, but the generated
explanations are often hard to understand for users that are not AI experts. To
overcome this problem, we introduce a conceptual approach combining
sub-symbolic and symbolic methods for human-centric explanations, that
incorporate domain knowledge and causality. We furthermore introduce the notion
of fidelity as a metric for evaluating how close the explanation is to the
GNN's internal decision making process. The evaluation with a chemical dataset
and ontology shows the explanatory value and reliability of our method.

In this work we deal with the funding costs rising from hedging the risky
securities underlying a target volatility strategy (TVS), a portfolio of risky
assets and a risk-free one dynamically rebalanced in order to keep the realized
volatility of the portfolio on a certain level. The uncertainty in the TVS
risky portfolio composition along with the difference in hedging costs for each
component requires to solve a control problem to evaluate the option prices. We
derive an analytical solution of the problem in the Black and Scholes (BS)
scenario. Then we use Reinforcement Learning (RL) techniques to determine the
fund composition leading to the most conservative price under the local
volatility (LV) model, for which an a priori solution is not available. We show
how the performances of the RL agents are compatible with those obtained by
applying path-wise the BS analytical strategy to the TVS dynamics, which
therefore appears competitive also in the LV scenario.

Unsupervised person re-identification (Re-ID) attracts increasing attention
due to its potential to resolve the scalability problem of supervised Re-ID
models. Most existing unsupervised methods adopt an iterative clustering
mechanism, where the network was trained based on pseudo labels generated by
unsupervised clustering. However, clustering errors are inevitable. To generate
high-quality pseudo-labels and mitigate the impact of clustering errors, we
propose a novel clustering relationship modeling framework for unsupervised
person Re-ID. Specifically, before clustering, the relation between unlabeled
images is explored based on a graph correlation learning (GCL) module and the
refined features are then used for clustering to generate high-quality
pseudo-labels.Thus, GCL adaptively mines the relationship between samples in a
mini-batch to reduce the impact of abnormal clustering when training. To train
the network more effectively, we further propose a selective contrastive
learning (SCL) method with a selective memory bank update policy. Extensive
experiments demonstrate that our method shows much better results than most
state-of-the-art unsupervised methods on Market1501, DukeMTMC-reID and MSMT17
datasets. We will release the code for model reproduction.

Recent developments in transformer models for visual data have led to
significant improvements in recognition and detection tasks. In particular,
using learnable queries in place of region proposals has given rise to a new
class of one-stage detection models, spearheaded by the Detection Transformer
(DETR). Variations on this one-stage approach have since dominated human-object
interaction (HOI) detection. However, the success of such one-stage HOI
detectors can largely be attributed to the representation power of
transformers. We discovered that when equipped with the same transformer, their
two-stage counterparts can be more performant and memory-efficient, while
taking a fraction of the time to train. In this work, we propose the
Unary-Pairwise Transformer, a two-stage detector that exploits unary and
pairwise representations for HOIs. We observe that the unary and pairwise parts
of our transformer network specialise, with the former preferentially
increasing the scores of positive examples and the latter decreasing the scores
of negative examples. We evaluate our method on the HICO-DET and V-COCO
datasets, and significantly outperform state-of-the-art approaches. At
inference time, our model with ResNet50 approaches real-time performance on a
single GPU.

Legal documents are unstructured, use legal jargon, and have considerable
length, making them difficult to process automatically via conventional text
processing techniques. A legal document processing system would benefit
substantially if the documents could be segmented into coherent information
units. This paper proposes a new corpus of legal documents annotated (with the
help of legal experts) with a set of 13 semantically coherent units labels
(referred to as Rhetorical Roles), e.g., facts, arguments, statute, issue,
precedent, ruling, and ratio. We perform a thorough analysis of the corpus and
the annotations. For automatically segmenting the legal documents, we
experiment with the task of rhetorical role prediction: given a document,
predict the text segments corresponding to various roles. Using the created
corpus, we experiment extensively with various deep learning-based baseline
models for the task. Further, we develop a multitask learning (MTL) based deep
model with document rhetorical role label shift as an auxiliary task for
segmenting a legal document. The proposed model shows superior performance over
the existing models. We also experiment with model performance in the case of
domain transfer and model distillation techniques to see the model performance
in limited data conditions.

Enterprise data typically involves multiple heterogeneous data sources and
external data that respectively record business activities, transactions,
customer demographics, status, behaviors, interactions and communications with
the enterprise, and the consumption and feedback of its products, services,
production, marketing, operations, and management, etc. A critical challenge in
enterprise data science is to enable an effective whole-of-enterprise data
understanding and data-driven discovery and decision-making on all-round
enterprise DNA. We introduce a neural encoder Table2Vec for automated universal
representation learning of entities such as customers from all-round enterprise
DNA with automated data characteristics analysis and data quality augmentation.
The learned universal representations serve as representative and benchmarkable
enterprise data genomes and can be used for enterprise-wide and domain-specific
learning tasks. Table2Vec integrates automated universal representation
learning on low-quality enterprise data and downstream learning tasks. We
illustrate Table2Vec in characterizing all-round customer data DNA in an
enterprise on complex heterogeneous multi-relational big tables to build
universal customer vector representations. The learned universal representation
of each customer is all-round, representative and benchmarkable to support both
enterprise-wide and domain-specific learning goals and tasks in enterprise data
science. Table2Vec significantly outperforms the existing shallow, boosting and
deep learning methods typically used for enterprise analytics. We further
discuss the research opportunities, directions and applications of automated
universal enterprise representation and learning and the learned enterprise
data DNA for automated, all-purpose, whole-of-enterprise and ethical machine
learning and data science.

The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescope system
consists of two imaging atmospheric Cherenkov telescopes (IACTs) and is located
on the Canary island of La Palma. IACTs are excellent tools to inspect the
very-high-energy (few tens of GeV and above) gamma-ray sky by capturing images
of the air showers, originated by the absorption of gamma rays and cosmic rays
by the atmosphere, through the detection of Cherenkov photons emitted in the
shower. One of the main factors determining the sensitivity of IACTs to
gamma-ray sources, in general, is how well reconstructed the properties (type,
energy, and incoming direction) of the primary particle triggering the air
shower are. We present how deep convolutional neural networks (CNNs) are being
explored as a promising method for IACT full-event reconstruction. The
performance of the method is evaluated on observational data using the standard
MAGIC Analysis and Reconstruction Software, MARS, and CTLearn, a package for
IACT event reconstruction through deep learning.

This paper proposes a method for the automatic creation of variables (in the
case of regression) that complement the information contained in the initial
input vector. The method works as a pre-processing step in which the continuous
values of the variable to be regressed are discretized into a set of intervals
which are then used to define value thresholds. Then classifiers are trained to
predict whether the value to be regressed is less than or equal to each of
these thresholds. The different outputs of the classifiers are then
concatenated in the form of an additional vector of variables that enriches the
initial vector of the regression problem. The implemented system can thus be
considered as a generic pre-processing tool. We tested the proposed enrichment
method with 5 types of regressors and evaluated it in 33 regression datasets.
Our experimental results confirm the interest of the approach.

This paper studies an instance of the multi-armed bandit (MAB) problem,
specifically where several causal MABs operate chronologically in the same
dynamical system. Practically the reward distribution of each bandit is
governed by the same non-trivial dependence structure, which is a dynamic
causal model. Dynamic because we allow for each causal MAB to depend on the
preceding MAB and in doing so are able to transfer information between agents.
Our contribution, the Chronological Causal Bandit (CCB), is useful in discrete
decision-making settings where the causal effects are changing across time and
can be informed by earlier interventions in the same system. In this paper, we
present some early findings of the CCB as demonstrated on a toy problem.

Recently simulation methods have been developed for optical tactile sensors
to enable the Sim2Real learning, i.e., firstly training models in simulation
before deploying them on the real robot. However, some artefacts in the real
objects are unpredictable, such as imperfections caused by fabrication
processes, or scratches by the natural wear and tear, and thus cannot be
represented in the simulation, resulting in a significant gap between the
simulated and real tactile images. To address this Sim2Real gap, we propose a
novel texture generation network that maps the simulated images into
photorealistic tactile images that resemble a real sensor contacting a real
imperfect object. Each simulated tactile image is first divided into two types
of regions: areas that are in contact with the object and areas that are not.
The former is applied with generated textures learned from real textures in the
real tactile images, whereas the latter maintains its appearance as when the
sensor is not in contact with any object. This makes sure that the artefacts
are only applied to the deformed regions of the sensor. Our extensive
experiments show that the proposed texture generation network can generate
these realistic artefacts on the deformed regions of the sensor, while avoiding
leaking the textures into areas of no contact. Quantitative experiments further
reveal that when using the adapted images generated by our proposed network for
a Sim2Real classification task, the drop in accuracy caused by the Sim2Real gap
is reduced from 38.43% to merely 0.81%. As such, this work has the potential to
accelerate the Sim2Real learning for robotic tasks requiring tactile sensing.

Dance choreography for a piece of music is a challenging task, having to be
creative in presenting distinctive stylistic dance elements while taking into
account the musical theme and rhythm. It has been tackled by different
approaches such as similarity retrieval, sequence-to-sequence modeling and
generative adversarial networks, but their generated dance sequences are often
short of motion realism, diversity and music consistency. In this paper, we
propose a Music-to-Dance with Optimal Transport Network (MDOT-Net) for learning
to generate 3D dance choreographies from music. We introduce an optimal
transport distance for evaluating the authenticity of the generated dance
distribution and a Gromov-Wasserstein distance to measure the correspondence
between the dance distribution and the input music. This gives a well defined
and non-divergent training objective that mitigates the limitation of standard
GAN training which is frequently plagued with instability and divergent
generator loss issues. Extensive experiments demonstrate that our MDOT-Net can
synthesize realistic and diverse dances which achieve an organic unity with the
input music, reflecting the shared intentionality and matching the rhythmic
articulation. Sample results are found at
https://www.youtube.com/watch?v=dErfBkrlUO8.

Theoretically, the conditional expectation of a square-integrable random
variable $Y$ given a $d$-dimensional random vector $X$ can be obtained by
minimizing the mean squared distance between $Y$ and $f(X)$ over all Borel
measurable functions $f \colon \mathbb{R}^d \to \mathbb{R}$. However, in many
applications this minimization problem cannot be solved exactly, and instead, a
numerical method that computes an approximate minimum over a suitable subfamily
of Borel functions has to be used. The quality of the result depends on the
adequacy of the subfamily and the performance of the numerical method. In this
paper, we derive an expected value representation of the minimal mean square
distance which in many applications can efficiently be approximated with a
standard Monte Carlo average. This enables us to provide guarantees for the
accuracy of any numerical approximation of a given conditional expectation. We
illustrate the method by assessing the quality of approximate conditional
expectations obtained by linear, polynomial as well as neural network
regression in different concrete examples.

Geometric feature learning for 3D meshes is central to computer graphics and
highly important for numerous vision applications. However, deep learning
currently lags in hierarchical modeling of heterogeneous 3D meshes due to the
lack of required operations and/or their efficient implementations. In this
paper, we propose a series of modular operations for effective geometric deep
learning over heterogeneous 3D meshes. These operations include mesh
convolutions, (un)pooling and efficient mesh decimation. We provide open source
implementation of these operations, collectively termed \textit{Picasso}. The
mesh decimation module of Picasso is GPU-accelerated, which can process a batch
of meshes on-the-fly for deep learning. Our (un)pooling operations compute
features for newly-created neurons across network layers of varying resolution.
Our mesh convolutions include facet2vertex, vertex2facet, and facet2facet
convolutions that exploit vMF mixture and Barycentric interpolation to
incorporate fuzzy modelling. Leveraging the modular operations of Picasso, we
contribute a novel hierarchical neural network, PicassoNet-II, to learn highly
discriminative features from 3D meshes. PicassoNet-II accepts primitive
geometrics and fine textures of mesh facets as input features, while processing
full scene meshes. Our network achieves highly competitive performance for
shape analysis and scene parsing on a variety of benchmarks. We release Picasso
and PicassoNet-II on Github https://github.com/EnyaHermite/Picasso.

With the rapid development of deep learning technology and improvement in
computing capability, deep learning has been widely used in the field of
hyperspectral image (HSI) classification. In general, deep learning models
often contain many trainable parameters and require a massive number of labeled
samples to achieve optimal performance. However, in regard to HSI
classification, a large number of labeled samples is generally difficult to
acquire due to the difficulty and time-consuming nature of manual labeling.
Therefore, many research works focus on building a deep learning model for HSI
classification with few labeled samples. In this article, we concentrate on
this topic and provide a systematic review of the relevant literature.
Specifically, the contributions of this paper are twofold. First, the research
progress of related methods is categorized according to the learning paradigm,
including transfer learning, active learning and few-shot learning. Second, a
number of experiments with various state-of-the-art approaches has been carried
out, and the results are summarized to reveal the potential research
directions. More importantly, it is notable that although there is a vast gap
between deep learning models (that usually need sufficient labeled samples) and
the HSI scenario with few labeled samples, the issues of small-sample sets can
be well characterized by fusion of deep learning methods and related
techniques, such as transfer learning and a lightweight model. For
reproducibility, the source codes of the methods assessed in the paper can be
found at https://github.com/ShuGuoJ/HSI-Classification.git.

Computed Tomography Angiography is a key modality providing insights into the
cerebrovascular vessel tree that are crucial for the diagnosis and treatment of
ischemic strokes, in particular in cases of large vessel occlusions (LVO).
Thus, the clinical workflow greatly benefits from an automated detection of
patients suffering from LVOs. This work uses convolutional neural networks for
case-level classification trained with elastic deformation of the vessel tree
segmentation masks to artificially augment training data. Using only masks as
the input to our model uniquely allows us to apply such deformations much more
aggressively than one could with conventional image volumes while retaining
sample realism. The neural network classifies the presence of an LVO and the
affected hemisphere. In a 5-fold cross validated ablation study, we demonstrate
that the use of the suggested augmentation enables us to train robust models
even from few data sets. Training the EfficientNetB1 architecture on 100 data
sets, the proposed augmentation scheme was able to raise the ROC AUC to 0.85
from a baseline value of 0.56 using no augmentation. The best performance was
achieved using a 3D-DenseNet yielding an AUC of 0.87. The augmentation had
positive impact in classification of the affected hemisphere as well, where the
3D-DenseNet reached an AUC of 0.93 on both sides.

Controversial content refers to any content that attracts both positive and
negative feedback. Its automatic identification, especially on social media, is
a challenging task as it should be done on a large number of continuously
evolving posts, covering a large variety of topics. Most of the existing
approaches rely on the graph structure of a topic-discussion and/or the content
of messages. This paper proposes a controversy detection approach based on both
graph structure of a discussion and text features. Our proposed approach relies
on Graph Neural Network (gnn) to encode the graph representation (including its
texts) in an embedding vector before performing a graph classification task.
The latter will classify the post as controversial or not. Two controversy
detection strategies are proposed. The first one is based on a hierarchical
graph representation learning. Graph user nodes are embedded hierarchically and
iteratively to compute the whole graph embedding vector. The second one is
based on the attention mechanism, which allows each user node to give more or
less importance to its neighbors when computing node embeddings. We conduct
experiments to evaluate our approach using different real-world datasets.
Conducted experiments show the positive impact of combining textual features
and structural information in terms of performance.

Designing code to be simplistic yet to offer choice is a tightrope walk.
Additional modules such as optimizers and data sets make a framework useful to
a broader audience, but the added complexity quickly becomes a problem.
Framework parameters may apply only to some modules but not others, be mutually
exclusive or depend on each other, often in unclear ways. Even so, many
frameworks are limited to a few specific use cases. This paper presents the
underlying concept of UniNAS, a framework designed to incorporate a variety of
Neural Architecture Search approaches. Since they differ in the number of
optimizers and networks, hyper-parameter optimization, network designs,
candidate operations, and more, a traditional approach can not solve the task.
Instead, every module defines its own hyper-parameters and a local tree
structure of module requirements. A configuration file specifies which modules
are used, their used parameters, and which other modules they use in turn This
concept of argument trees enables combining and reusing modules in complex
configurations while avoiding many problems mentioned above. Argument trees can
also be configured from a graphical user interface so that designing and
changing experiments becomes possible without writing a single line of code.
UniNAS is publicly available at https://github.com/cogsys-tuebingen/uninas

The label-embedded dictionary learning (DL) algorithms generate influential
dictionaries by introducing discriminative information. However, there exists a
limitation: All the label-embedded DL methods rely on the labels due that this
way merely achieves ideal performances in supervised learning. While in
semi-supervised and unsupervised learning, it is no longer sufficient to be
effective. Inspired by the concept of self-supervised learning (e.g., setting
the pretext task to generate a universal model for the downstream task), we
propose a Self-Supervised Dictionary Learning (SSDL) framework to address this
challenge. Specifically, we first design a $p$-Laplacian Attention Hypergraph
Learning (pAHL) block as the pretext task to generate pseudo soft labels for
DL. Then, we adopt the pseudo labels to train a dictionary from a primary
label-embedded DL method. We evaluate our SSDL on two human activity
recognition datasets. The comparison results with other state-of-the-art
methods have demonstrated the efficiency of SSDL.

Face detection is to search all the possible regions for faces in images and
locate the faces if there are any. Many applications including face
recognition, facial expression recognition, face tracking and head-pose
estimation assume that both the location and the size of faces are known in the
image. In recent decades, researchers have created many typical and efficient
face detectors from the Viola-Jones face detector to current CNN-based ones.
However, with the tremendous increase in images and videos with variations in
face scale, appearance, expression, occlusion and pose, traditional face
detectors are challenged to detect various "in the wild" faces. The emergence
of deep learning techniques brought remarkable breakthroughs to face detection
along with the price of a considerable increase in computation. This paper
introduces representative deep learning-based methods and presents a deep and
thorough analysis in terms of accuracy and efficiency. We further compare and
discuss the popular and challenging datasets and their evaluation metrics. A
comprehensive comparison of several successful deep learning-based face
detectors is conducted to uncover their efficiency using two metrics: FLOPs and
latency. The paper can guide to choose appropriate face detectors for different
applications and also to develop more efficient and accurate detectors.

This paper addresses the problem of fast learning of radar detectors with a
limited amount of training data. In current data-driven approaches for radar
detection, re-training is generally required when the operating environment
changes, incurring large overhead in terms of data collection and training
time. In contrast, this paper proposes two novel deep learning-based approaches
that enable fast adaptation of detectors based on few data samples from a new
environment. The proposed methods integrate prior knowledge regarding
previously encountered radar operating environments in two different ways. One
approach is based on transfer learning: it first pre-trains a detector such
that it works well on data collected in previously observed environments, and
then it adapts the pre-trained detector to the specific current environment.
The other approach targets explicitly few-shot training via meta-learning:
based on data from previous environments, it finds a common initialization that
enables fast adaptation to a new environment. Numerical results validate the
benefits of the proposed two approaches compared with the conventional method
based on training with no prior knowledge. Furthermore, the meta-learning-based
detector outperforms the transfer learning-based detector when the clutter is
Gaussian.

Considering the models that apply the contextual information of time-series
data could improve the fault diagnosis performance, some neural network
structures such as RNN, LSTM, and GRU were proposed to model the fault
diagnosis effectively. However, these models are restricted by their serial
computation and hence cannot achieve high diagnostic efficiency. Also the
parallel CNN is difficult to implement fault diagnosis in an efficient way
because it requires larger convolution kernels or deep structure to achieve
long-term feature extraction capabilities. Besides, BERT model applies absolute
position embedding to introduce contextual information to the model, which
would bring noise to the raw data and therefore cannot be applied to fault
diagnosis directly. In order to address the above problems, a fault diagnosis
model named deep parallel time-series relation network(DPTRN) has been proposed
in this paper. There are mainly three advantages for DPTRN: (1) Our proposed
time relationship unit is based on full multilayer perceptron(MLP) structure,
therefore, DPTRN performs fault diagnosis in a parallel way and improves
computing efficiency significantly. (2) By improving the absolute position
embedding, our novel decoupling position embedding unit could be applied on the
fault diagnosis directly and learn contextual information. (3) Our proposed
DPTRN has obvious advantage in feature interpretability. We confirm the effect
of the proposed method on four datasets, and the results show the
effectiveness, efficiency and interpretability of the proposed DPTRN model.

Transferability of adversarial samples became a serious concern due to their
impact on the reliability of machine learning system deployments, as they find
their way into many critical applications. Knowing factors that influence
transferability of adversarial samples can assist experts to make informed
decisions on how to build robust and reliable machine learning systems. The
goal of this study is to provide insights on the mechanisms behind the
transferability of adversarial samples through an attack-centric approach. This
attack-centric perspective interprets how adversarial samples would transfer by
assessing the impact of machine learning attacks (that generated them) on a
given input dataset. To achieve this goal, we generated adversarial samples
using attacker models and transferred these samples to victim models. We
analyzed the behavior of adversarial samples on victim models and outlined four
factors that can influence the transferability of adversarial samples. Although
these factors are not necessarily exhaustive, they provide useful insights to
researchers and practitioners of machine learning systems.

In this work, we present a general purpose deep neural network package for
representing energies, forces, dipole moments, and polarizabilities of
atomistic systems. This so-called recursively embedded atom neural network
model takes both advantages of the physically inspired atomic descriptor based
neural networks and the message-passing based neural networks. Implemented in
the PyTorch framework, the training process is parallelized on both CPU and GPU
with high efficiency and low memory, in which all hyperparameters can be
optimized automatically. We demonstrate the state-of-the-art accuracy, high
efficiency, scalability, and universality of this package by learning not only
energies (with or without forces), but also dipole moment vectors and
polarizability tensors, in various molecular, reactive, and periodic systems.
An interface between a trained model and LAMMPs is provided for large scale
molecular dynamics simulations. We hope that this open-source toolbox will
allow future method development and applications of machine learned potential
energy surfaces and quantum-chemical properties of molecules, reactions, and

The time-dependent quadratic minimization (TDQM) problem appears in many
applications and research projects. It has been reported that the zeroing
neural network (ZNN) models can effectively solve the TDQM problem. However,
the convergent and robust performance of the existing ZNN models are restricted
for lack of a joint-action mechanism of adaptive coefficient and integration
enhanced term. Consequently, the residual-based adaption coefficient zeroing
neural network (RACZNN) model with integration term is proposed in this paper
for solving the TDQM problem. The adaptive coefficient is proposed to improve
the performance of convergence and the integration term is embedded to ensure
the RACZNN model can maintain reliable robustness while perturbed by variant
measurement noises. Compared with the state-of-the-art models, the proposed
RACZNN model owns faster convergence and more reliable robustness. Then,
theorems are provided to prove the convergence of the RACZNN model. Finally,
corresponding quantitative numerical experiments are designed and performed in
this paper to verify the performance of the proposed RACZNN model.

Deep learning (DL) has been widely applied to many domains. Unique challenges
in engineering DL systems are posed by the programming paradigm shift from
traditional systems to DL systems, and performance is one of the challenges.
Performance problems (PPs) in DL systems can cause severe consequences such as
excessive resource consumption and financial loss. While bugs in DL systems
have been extensively investigated, PPs in DL systems have hardly been
explored. To bridge this gap, we present the first comprehensive study to i)
characterize symptoms, root causes, and introducing and exposing stages of PPs
in DL systems developed in TensorFLow and Keras, with 224 PPs collected from
210 StackOverflow posts, and to ii) assess the capability of existing
performance analysis approaches in tackling PPs, with a constructed benchmark
of 58 PPs in DL systems. Our findings shed light on the implications on
developing high-performance DL systems, and detecting and localizing PPs in DL
systems. To demonstrate the usefulness of our findings, we develop a static
checker Deep-Perf to detect three types of PPs. It has detected 488 new PPs in
130 GitHub projects. 105 and 27 PPs have been confirmed and fixed.

Prescriptive process monitoring methods seek to optimize a business process
by recommending interventions at runtime to prevent negative outcomes or poorly
performing cases. In recent years, various prescriptive process monitoring
methods have been proposed. This paper studies existing methods in this field
via a Systematic Literature Review (SLR). In order to structure the field, the
paper proposes a framework for characterizing prescriptive process monitoring
methods according to their performance objective, performance metrics,
intervention types, modeling techniques, data inputs, and intervention
policies. The SLR provides insights into challenges and areas for future
research that could enhance the usefulness and applicability of prescriptive
process monitoring methods. The paper highlights the need to validate existing
and new methods in real-world settings, to extend the types of interventions
beyond those related to the temporal and cost perspectives, and to design
policies that take into account causality and second-order effects.

Sparse events, such as malign attacks in real-time network traffic, have
caused big organisations an immense hike in revenue loss. This is due to the
excessive growth of the network and its exposure to a plethora of people. The
standard methods used to detect intrusions are not promising and have
significant failure to identify new malware. Moreover, the challenges in
handling high volume data with sparsity, high false positives, fewer detection
rates in minor class, training time and feature engineering of the
dimensionality of data has promoted deep learning to take over the task with
less time and great results. The existing system needs improvement in solving
real-time network traffic issues along with feature engineering. Our proposed
work overcomes these challenges by giving promising results using deep-stacked
autoencoders in two stages. The two-stage deep learning combines with shallow
learning using the random forest for classification in the second stage. This
made the model get well with the latest Canadian Institute for Cybersecurity -
Intrusion Detection System 2017 (CICIDS-2017) dataset. Zero false positives
with admirable detection accuracy were achieved.

Recent advances in automated skin cancer diagnosis have yielded performance
on par with board-certified dermatologists. However, these approaches
formulated skin cancer diagnosis as a simple classification task, dismissing
the potential benefit from lesion segmentation. We argue that an accurate
lesion segmentation can supplement the classification task with additive lesion
information, such as asymmetry, border, intensity, and physical size; in turn,
a faithful lesion classification can support the segmentation task with
discriminant lesion features. To this end, this paper proposes a new multi-task
framework, named MT-TransUNet, which is capable of segmenting and classifying
skin lesions collaboratively by mediating multi-task tokens in Transformers.
Furthermore, we have introduced dual-task and attended region consistency
losses to take advantage of those images without pixel-level annotation,
ensuring the model's robustness when it encounters the same image with an
account of augmentation. Our MT-TransUNet exceeds the previous state of the art
for lesion segmentation and classification tasks in ISIC-2017 and PH2; more
importantly, it preserves compelling computational efficiency regarding model
parameters (48M~vs.~130M) and inference speed (0.17s~vs.~2.02s per image). Code
will be available at https://github.com/JingyeChen/MT-TransUNet.

Deep learning-based methods for low-light image enhancement typically require
enormous paired training data, which are impractical to capture in real-world
scenarios. Recently, unsupervised approaches have been explored to eliminate
the reliance on paired training data. However, they perform erratically in
diverse real-world scenarios due to the absence of priors. To address this
issue, we propose an unsupervised low-light image enhancement method based on
an effective prior termed histogram equalization prior (HEP). Our work is
inspired by the interesting observation that the feature maps of histogram
equalization enhanced image and the ground truth are similar. Specifically, we
formulate the HEP to provide abundant texture and luminance information.
Embedded into a Light Up Module (LUM), it helps to decompose the low-light
images into illumination and reflectance maps, and the reflectance maps can be
regarded as restored images. However, the derivation based on Retinex theory
reveals that the reflectance maps are contaminated by noise. We introduce a
Noise Disentanglement Module (NDM) to disentangle the noise and content in the
reflectance maps with the reliable aid of unpaired clean images. Guided by the
histogram equalization prior and noise disentanglement, our method can recover
finer details and is more capable to suppress noise in real-world low-light
scenarios. Extensive experiments demonstrate that our method performs favorably
against the state-of-the-art unsupervised low-light enhancement algorithms and
even matches the state-of-the-art supervised algorithms.

A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are
envisaged to provide a global coverage SAT network in beyond fifth-generation
(5G) cellular systems. LEO SAT networks exhibit extremely long link distances
of many users under time-varying SAT network topology. This makes existing
multiple access protocols, such as random access channel (RACH) based cellular
protocol designed for fixed terrestrial network topology, ill-suited. To
overcome this issue, in this paper, we propose a novel grant-free random access
solution for LEO SAT networks, dubbed emergent random access channel protocol
(eRACH). In stark contrast to existing model-based and standardized protocols,
eRACH is a model-free approach that emerges through interaction with the
non-stationary network environment, using multi-agent deep reinforcement
learning (MADRL). Furthermore, by exploiting known SAT orbiting patterns, eRACH
does not require central coordination or additional communication across users,
while training convergence is stabilized through the regular orbiting patterns.
Compared to RACH, we show from various simulations that our proposed eRACH
yields 54.6% higher average network throughput with around two times lower
average access delay while achieving 0.989 Jain's fairness index.

This paper introduces the system submitted by the Yidun NISP team to the
video keyword wakeup challenge. We propose a mandarin keyword spotting system
(KWS) with several novel and effective improvements, including a big backbone
(B) model, a keyword biasing (B) mechanism and the introduction of syllable
modeling units (S). By considering this, we term the total system BBS-KWS as an
abbreviation. The BBS-KWS system consists of an end-to-end automatic speech
recognition (ASR) module and a KWS module. The ASR module converts speech
features to text representations, which applies a big backbone network to the
acoustic model and takes syllable modeling units into consideration as well. In
addition, the keyword biasing mechanism is used to improve the recall rate of
keywords in the ASR inference stage. The KWS module applies multiple criteria
to determine the absence or presence of the keywords, such as multi-stage
matching, fuzzy matching, and connectionist temporal classification (CTC)
prefix score. To further improve our system, we conduct semi-supervised
learning on the CN-Celeb dataset for better generalization. In the VKW task,
the BBS-KWS system achieves significant gains over the baseline and won the
first place in two tracks.

Probabilistic numerical methods (PNMs) solve numerical problems via
probabilistic inference. They have been developed for linear algebra,
optimization, integration and differential equation simulation. PNMs naturally
incorporate prior information about a problem and quantify uncertainty due to
finite computational resources as well as stochastic input. In this paper, we
present ProbNum: a Python library providing state-of-the-art probabilistic
numerical solvers. ProbNum enables custom composition of PNMs for specific
problem classes via a modular design as well as wrappers for off-the-shelf use.
Tutorials, documentation, developer guides and benchmarks are available online
at www.probnum.org.

Progress in pre-trained language models has led to a surge of impressive
results on downstream tasks for natural language understanding. Recent work on
probing pre-trained language models uncovered a wide range of linguistic
properties encoded in their contextualized representations. However, it is
unclear whether they encode semantic knowledge that is crucial to symbolic
inference methods. We propose a methodology for probing linguistic information
for logical inference in pre-trained language model representations. Our
probing datasets cover a list of linguistic phenomena required by major
symbolic inference systems. We find that (i) pre-trained language models do
encode several types of linguistic information for inference, but there are
also some types of information that are weakly encoded, (ii) language models
can effectively learn missing linguistic information through fine-tuning.
Overall, our findings provide insights into which aspects of linguistic
information for logical inference do language models and their pre-training
procedures capture. Moreover, we have demonstrated language models' potential
as semantic and background knowledge bases for supporting symbolic inference
methods.

Transformer based language models have led to impressive results across all
domains in Natural Language Processing. Pretraining these models on language
modeling tasks and finetuning them on downstream tasks such as Text
Classification, Question Answering and Neural Machine Translation has
consistently shown exemplary results. In this work, we propose a Multitask
Finetuning methodology which combines the Bilingual Machine Translation task
with an auxiliary Causal Language Modeling task to improve performance on the
former task on Indian Languages. We conduct an empirical study on three
language pairs, Marathi-Hindi, Marathi-English and Hindi-English, where we
compare the multitask finetuning approach to the standard finetuning approach,
for which we use the mBART50 model. Our study indicates that the multitask
finetuning method could be a better technique than standard finetuning, and
could improve Bilingual Machine Translation across language pairs.

The task of shape space learning involves mapping a train set of shapes to
and from a latent representation space with good generalization properties.
Often, real-world collections of shapes have symmetries, which can be defined
as transformations that do not change the essence of the shape. A natural way
to incorporate symmetries in shape space learning is to ask that the mapping to
the shape space (encoder) and mapping from the shape space (decoder) are
equivariant to the relevant symmetries.
  In this paper, we present a framework for incorporating equivariance in
encoders and decoders by introducing two contributions: (i) adapting the recent
Frame Averaging (FA) framework for building generic, efficient, and maximally
expressive Equivariant autoencoders; and (ii) constructing autoencoders
equivariant to piecewise Euclidean motions applied to different parts of the
shape. To the best of our knowledge, this is the first fully piecewise
Euclidean equivariant autoencoder construction. Training our framework is
simple: it uses standard reconstruction losses and does not require the
introduction of new losses. Our architectures are built of standard (backbone)
architectures with the appropriate frame averaging to make them equivariant.
Testing our framework on both rigid shapes dataset using implicit neural
representations, and articulated shape datasets using mesh-based neural
networks show state-of-the-art generalization to unseen test shapes, improving
relevant baselines by a large margin. In particular, our method demonstrates
significant improvement in generalizing to unseen articulated poses.

Few-shot object detection has attracted increasing attention and rapidly
progressed in recent years. However, the requirement of an exhaustive offline
fine-tuning stage in existing methods is time-consuming and significantly
hinders their usage in online applications such as autonomous exploration of
low-power robots. We find that their major limitation is that the little but
valuable information from a few support images is not fully exploited. To solve
this problem, we propose a brand new architecture, AirDet, and surprisingly
find that, by learning class-agnostic relation with the support images in all
modules, including cross-scale object proposal network, shots aggregation
module, and localization network, AirDet without fine-tuning achieves
comparable or even better results than many fine-tuned methods, reaching up to
30-40% improvements. We also present solid results of onboard tests on
real-world exploration data from the DARPA Subterranean Challenge, which
strongly validate the feasibility of AirDet in robotics. To the best of our
knowledge, AirDet is the first feasible few-shot detection method for
autonomous exploration of low-power robots. The code and pre-trained models are
released at https://github.com/Jaraxxus-Me/AirDet.

In multiuser communication systems, user scheduling and beamforming (US-BF)
design are two fundamental problems that are usually studied separately in the
existing literature. In this work, we focus on the joint US-BF design with the
goal of maximizing the set cardinality of scheduled users, which is
computationally challenging due to the non-convex objective function and the
coupled constraints with discrete-continuous variables. To tackle these
difficulties, a successive convex approximation based US-BF (SCA-USBF)
optimization algorithm is firstly proposed. Then, inspired by wireless
intelligent communication, a graph neural network based joint US-BF (J-USBF)
learning algorithm is developed by combining the joint US and power allocation
network model with the BF analytical solution. The effectiveness of SCA-USBF
and J-USBF is verified by various numerical results, the latter achieves close
performance and higher computational efficiency. Furthermore, the proposed
J-USBF also enjoys the generalizability in dynamic wireless network scenarios.

Permutationally invariant polynomial (PIP) regression has been used to obtain
machine-learned (ML) potential energy surfaces, including analytical gradients,
for many molecules and chemical reactions. Recently, the approach has been
extended to moderate size molecules and applied to systems up to 15 atoms. The
algorithm, including "purification of the basis", is computationally efficient
for energies; however, we found that the recent extension to obtain analytical
gradients, despite being a remarkable advance over previous methods, could be
further improved. Here we report developments to compact further a purified
basis and, more significantly, to use the reverse gradient approach to greatly
speed up gradient evaluation. We demonstrate this for our recent 4-body water
interaction potential. Comparisons of training and testing precision on the
MD17 database of energies and gradients (forces) for ethanol against GP-SOAP,
ANI, sGDML, PhysNet, pKREG, KRR, and other methods, which were recently
assessed by Dral and co-workers, are given. The PIP fits are as precise as
those using these methods, but the PIP computation time for energy and force
evaluation is shown to be 10 to 1000 times faster. Finally, a new PIP PES is
reported for ethanol based on a more extensive dataset of energies and
gradients than in the MD17 database. Diffusion Monte Carlo calculations which
fail on MD17-based PESs are successful using the new PES.

Deep Learning (DL)-based malware detectors are increasingly adopted for early
detection of malicious behavior in cybersecurity. However, their sensitivity to
adversarial malware variants has raised immense security concerns. Generating
such adversarial variants by the defender is crucial to improving the
resistance of DL-based malware detectors against them. This necessity has given
rise to an emerging stream of machine learning research, Adversarial Malware
example Generation (AMG), which aims to generate evasive adversarial malware
variants that preserve the malicious functionality of a given malware. Within
AMG research, black-box method has gained more attention than white-box
methods. However, most black-box AMG methods require numerous interactions with
the malware detectors to generate adversarial malware examples. Given that most
malware detectors enforce a query limit, this could result in generating
non-realistic adversarial examples that are likely to be detected in practice
due to lack of stealth. In this study, we show that a novel DL-based causal
language model enables single-shot evasion (i.e., with only one query to
malware detector) by treating the content of the malware executable as a byte
sequence and training a Generative Pre-Trained Transformer (GPT). Our proposed
method, MalGPT, significantly outperformed the leading benchmark methods on a
real-world malware dataset obtained from VirusTotal, achieving over 24.51\%
evasion rate. MalGPT enables cybersecurity researchers to develop advanced
defense capabilities by emulating large-scale realistic AMG.

Data collected by Earth-observing (EO) satellites are often afflicted by
cloud cover. Detecting the presence of clouds -- which is increasingly done
using deep learning -- is crucial preprocessing in EO applications. In fact,
advanced EO satellites perform deep learning-based cloud detection on board the
satellites and downlink only clear-sky data to save precious bandwidth. In this
paper, we highlight the vulnerability of deep learning-based cloud detection
towards adversarial attacks. By optimising an adversarial pattern and
superimposing it into a cloudless scene, we bias the neural network into
detecting clouds in the scene. Since the input spectra of cloud detectors
include the non-visible bands, we generated our attacks in the multispectral
domain. This opens up the potential of multi-objective attacks, specifically,
adversarial biasing in the cloud-sensitive bands and visual camouflage in the
visible bands. We also investigated mitigation strategies against the
adversarial attacks. We hope our work further builds awareness of the potential
of adversarial attacks in the EO community.

Learning and generalizing from limited examples, i,e, few-shot learning, is
of core importance to many real-world vision applications. A principal way of
achieving few-shot learning is to realize an embedding where samples from
different classes are distinctive. Recent studies suggest that embedding via
hyperbolic geometry enjoys low distortion for hierarchical and structured data,
making it suitable for few-shot learning. In this paper, we propose to learn a
context-aware hyperbolic metric to characterize the distance between a point
and a set associated with a learned set to set distance. To this end, we
formulate the metric as a weighted sum on the tangent bundle of the hyperbolic
space and develop a mechanism to obtain the weights adaptively and based on the
constellation of the points. This not only makes the metric local but also
dependent on the task in hand, meaning that the metric will adapt depending on
the samples that it compares. We empirically show that such metric yields
robustness in the presence of outliers and achieves a tangible improvement over
baseline models. This includes the state-of-the-art results on five popular
few-shot classification benchmarks, namely mini-ImageNet, tiered-ImageNet,
Caltech-UCSD Birds-200-2011 (CUB), CIFAR-FS, and FC100.

Multi-label learning predicts a subset of labels from a given label set for
an unseen instance while considering label correlations. A known challenge with
multi-label classification is the long-tailed distribution of labels. Many
studies focus on improving the overall predictions of the model and thus do not
prioritise tail-end labels. Improving the tail-end label predictions in
multi-label classifications of medical text enables the potential to understand
patients better and improve care. The knowledge gained by one or more
infrequent labels can impact the cause of medical decisions and treatment
plans. This research presents variations of concatenated domain-specific
language models, including multi-BioMed-Transformers, to achieve two primary
goals. First, to improve F1 scores of infrequent labels across multi-label
problems, especially with long-tail labels; second, to handle long medical text
and multi-sourced electronic health records (EHRs), a challenging task for
standard transformers designed to work on short input sequences. A vital
contribution of this research is new state-of-the-art (SOTA) results obtained
using TransformerXL for predicting medical codes. A variety of experiments are
performed on the Medical Information Mart for Intensive Care (MIMIC-III)
database. Results show that concatenated BioMed-Transformers outperform
standard transformers in terms of overall micro and macro F1 scores and
individual F1 scores of tail-end labels, while incurring lower training times
than existing transformer-based solutions for long input sequences.

Benchmark datasets play a central role in the organization of machine
learning research. They coordinate researchers around shared research problems
and serve as a measure of progress towards shared goals. Despite the
foundational role of benchmarking practices in this field, relatively little
attention has been paid to the dynamics of benchmark dataset use and reuse,
within or across machine learning subcommunities. In this paper, we dig into
these dynamics. We study how dataset usage patterns differ across machine
learning subcommunities and across time from 2015-2020. We find increasing
concentration on fewer and fewer datasets within task communities, significant
adoption of datasets from other tasks, and concentration across the field on
datasets that have been introduced by researchers situated within a small
number of elite institutions. Our results have implications for scientific
evaluation, AI ethics, and equity/access within the field.

Self-supervised learning aims to learn image feature representations without
the usage of manually annotated labels. It is often used as a precursor step to
obtain useful initial network weights which contribute to faster convergence
and superior performance of downstream tasks. While self-supervision allows one
to reduce the domain gap between supervised and unsupervised learning without
the usage of labels, the self-supervised objective still requires a strong
inductive bias to downstream tasks for effective transfer learning. In this
work, we present our material and texture based self-supervision method named
MATTER (MATerial and TExture Representation Learning), which is inspired by
classical material and texture methods. Material and texture can effectively
describe any surface, including its tactile properties, color, and specularity.
By extension, effective representation of material and texture can describe
other semantic classes strongly associated with said material and texture.
MATTER leverages multi-temporal, spatially aligned remote sensing imagery over
unchanged regions to learn invariance to illumination and viewing angle as a
mechanism to achieve consistency of material and texture representation. We
show that our self-supervision pre-training method allows for up to 24.22% and
6.33% performance increase in unsupervised and fine-tuned setups, and up to 76%
faster convergence on change detection, land cover classification, and semantic
segmentation tasks.

In this paper, we propose a spatial graph convolution (GC) to classify
signals on a graph. Existing GC methods are limited to using the structural
information in the feature space. Additionally, the single step of GCs only
uses features on the one-hop neighboring nodes from the target node. In this
paper, we propose two methods to improve the performance of GCs: 1) Utilizing
structural information in the feature space, and 2) exploiting the multi-hop
information in one GC step. In the first method, we define three structural
features in the feature space: feature angle, feature distance, and relational
embedding. The second method aggregates the node-wise features of multi-hop
neighbors in a GC. Both methods can be simultaneously used. We also propose
graph neural networks (GNNs) integrating the proposed GC for classifying nodes
in 3D point clouds and citation networks. In experiments, the proposed GNNs
exhibited a higher classification accuracy than existing methods.

Classical machine learners are designed only to tackle one task without
capability of adopting new emerging tasks or classes whereas such capacity is
more practical and human-like in the real world. To address this shortcoming,
continual machine learners are elaborated to commendably learn a stream of
tasks with domain and class shifts among different tasks. In this paper, we
propose a general feature-propagation based contrastive continual learning
method which is capable of handling multiple continual learning scenarios.
Specifically, we align the current and previous representation spaces by means
of feature propagation and contrastive representation learning to bridge the
domain shifts among distinct tasks. To further mitigate the class-wise shifts
of the feature representation, a supervised contrastive loss is exploited to
make the example embeddings of the same class closer than those of different
classes. The extensive experimental results demonstrate the outstanding
performance of the proposed method on six continual learning benchmarks
compared to a group of cutting-edge continual learning methods.

Predicting emergency-braking distance is important for the collision
avoidance related features, which are the most essential and popular safety
features for vehicles. In this study, we first gathered a large data set
including a three-dimensional acceleration data and the corresponding
emergency-braking distance. Using this data set, we propose a deep-learning
model to predict emergency-braking distance, which only requires 0.25 seconds
three-dimensional vehicle acceleration data before the break as input. We
consider two road surfaces, our deep learning approach is robust to both road
surfaces and have accuracy within 3 feet.

As the fourth largest language family in the world, the Dravidian languages
have become a research hotspot in natural language processing (NLP). Although
the Dravidian languages contain a large number of languages, there are
relatively few public available resources. Besides, text classification task,
as a basic task of natural language processing, how to combine it to multiple
languages in the Dravidian languages, is still a major difficulty in Dravidian
Natural Language Processing. Hence, to address these problems, we proposed a
multilingual text classification framework for the Dravidian languages. On the
one hand, the framework used the LaBSE pre-trained model as the base model.
Aiming at the problem of text information bias in multi-task learning, we
propose to use the MLM strategy to select language-specific words, and used
adversarial training to perturb them. On the other hand, in view of the problem
that the model cannot well recognize and utilize the correlation among
languages, we further proposed a language-specific representation module to
enrich semantic information for the model. The experimental results
demonstrated that the framework we proposed has a significant performance in
multilingual text classification tasks with each strategy achieving certain

Differential privacy (DP) has become the de facto standard of privacy
preservation due to its strong protection and sound mathematical foundation,
which is widely adopted in different applications such as big data analysis,
graph data process, machine learning, deep learning, and federated learning.
Although DP has become an active and influential area, it is not the best
remedy for all privacy problems in different scenarios. Moreover, there are
also some misunderstanding, misuse, and great challenges of DP in specific
applications. In this paper, we point out a series of limits and open
challenges of corresponding research areas. Besides, we offer potentially new
insights and avenues on combining differential privacy with other effective
dimension reduction techniques and secure multiparty computing to clearly
define various privacy models.

We propose a new information aggregation method which called Localized
Feature Aggregation Module based on the similarity between the feature maps of
an encoder and a decoder. The proposed method recovers positional information
by emphasizing the similarity between decoder's feature maps with superior
semantic information and encoder's feature maps with superior positional
information. The proposed method can learn positional information more
efficiently than conventional concatenation in the U-net and attention U-net.
Additionally, the proposed method also uses localized attention range to reduce
the computational cost. Two innovations contributed to improve the segmentation
accuracy with lower computational cost. By experiments on the Drosophila cell
image dataset and COVID-19 image dataset, we confirmed that our method
outperformed conventional methods.

Many open-world applications require the detection of novel objects, yet
state-of-the-art object detection and instance segmentation networks do not
excel at this task. The key issue lies in their assumption that regions without
any annotations should be suppressed as negatives, which teaches the model to
treat the unannotated objects as background. To address this issue, we propose
a simple yet surprisingly powerful data augmentation and training scheme we
call Learning to Detect Every Thing (LDET). To avoid suppressing hidden
objects, background objects that are visible but unlabeled, we paste annotated
objects on a background image sampled from a small region of the original
image. Since training solely on such synthetically-augmented images suffers
from domain shift, we decouple the training into two parts: 1) training the
region classification and regression head on augmented images, and 2)~training
the mask heads on original images. In this way, a model does not learn to
classify hidden objects as background while generalizing well to real images.
LDET leads to significant improvements on many datasets in the open-world
instance segmentation task, outperforming baselines on cross-category
generalization on COCO, as well as cross-dataset evaluation on UVO and
Cityscapes.

Learning modality-fused representations and processing unaligned multimodal
sequences are meaningful and challenging in multimodal emotion recognition.
Existing approaches use directional pairwise attention or a message hub to fuse
language, visual, and audio modalities. However, those approaches introduce
information redundancy when fusing features and are inefficient without
considering the complementarity of modalities. In this paper, we propose an
efficient neural network to learn modality-fused representations with
CB-Transformer (LMR-CBT) for multimodal emotion recognition from unaligned
multimodal sequences. Specifically, we first perform feature extraction for the
three modalities respectively to obtain the local structure of the sequences.
Then, we design a novel transformer with cross-modal blocks (CB-Transformer)
that enables complementary learning of different modalities, mainly divided
into local temporal learning,cross-modal feature fusion and global
self-attention representations. In addition, we splice the fused features with
the original features to classify the emotions of the sequences. Finally, we
conduct word-aligned and unaligned experiments on three challenging datasets,
IEMOCAP, CMU-MOSI, and CMU-MOSEI. The experimental results show the superiority
and efficiency of our proposed method in both settings. Compared with the
mainstream methods, our approach reaches the state-of-the-art with a minimum
number of parameters.

The recently developed physics-informed machine learning has made great
progress for solving nonlinear partial differential equations (PDEs), however,
it may fail to provide reasonable approximations to the PDEs with discontinuous
solutions. In this paper, we focus on the discrete time physics-informed neural
network (PINN), and propose a hybrid PINN scheme for the nonlinear PDEs. In
this approach, the local solution structures are classified as smooth and
nonsmooth scales by introducing a discontinuity indicator, and then the
automatic differentiation technique is employed for resolving smooth scales,
while an improved weighted essentially non-oscillatory (WENO) scheme is adopted
to capture discontinuities. We then test the present approach by considering
the viscous and inviscid Burgers equations , and it is shown that compared with
original discrete time PINN, the present hybrid approach has a better
performance in approximating the discontinuous solution even at a relatively
larger time step.

Recently, transformer-based image segmentation methods have achieved notable
success against previous solutions. While for video domains, how to effectively
model temporal context with the attention of object instances across frames
remains an open problem. In this paper, we propose an online video instance
segmentation framework with a novel instance-aware temporal fusion method. We
first leverages the representation, i.e., a latent code in the global context
(instance code) and CNN feature maps to represent instance- and pixel-level
features. Based on this representation, we introduce a cropping-free temporal
fusion approach to model the temporal consistency between video frames.
Specifically, we encode global instance-specific information in the instance
code and build up inter-frame contextual fusion with hybrid attentions between
the instance codes and CNN feature maps. Inter-frame consistency between the
instance codes are further enforced with order constraints. By leveraging the
learned hybrid temporal consistency, we are able to directly retrieve and
maintain instance identities across frames, eliminating the complicated
frame-wise instance matching in prior methods. Extensive experiments have been
conducted on popular VIS datasets, i.e. Youtube-VIS-19/21. Our model achieves
the best performance among all online VIS methods. Notably, our model also
eclipses all offline methods when using the ResNet-50 backbone.

Adversarial robustness is a critical property in a variety of modern machine
learning applications. While it has been the subject of several recent
theoretical studies, many important questions related to adversarial robustness
are still open. In this work, we study a fundamental question regarding Bayes
optimality for adversarial robustness. We provide general sufficient conditions
under which the existence of a Bayes optimal classifier can be guaranteed for
adversarial robustness. Our results can provide a useful tool for a subsequent
study of surrogate losses in adversarial robustness and their consistency
properties. This manuscript is the extended version of the paper "On the
Existence of the Adversarial Bayes Classifier" published in NeurIPS. The
results of the original paper did not apply to some non-strictly convex norms.
Here we extend our results to all possible norms. Additionally, we clarify a
missing step in one of our proofs.

Hyperbolic neural networks have been popular in the recent past due to their
ability to represent hierarchical data sets effectively and efficiently. The
challenge in developing these networks lies in the nonlinearity of the
embedding space namely, the Hyperbolic space. Hyperbolic space is a homogeneous
Riemannian manifold of the Lorentz group. Most existing methods (with some
exceptions) use local linearization to define a variety of operations
paralleling those used in traditional deep neural networks in Euclidean spaces.
In this paper, we present a novel fully hyperbolic neural network which uses
the concept of projections (embeddings) followed by an intrinsic aggregation
and a nonlinearity all within the hyperbolic space. The novelty here lies in
the projection which is designed to project data on to a lower-dimensional
embedded hyperbolic space and hence leads to a nested hyperbolic space
representation independently useful for dimensionality reduction. The main
theoretical contribution is that the proposed embedding is proved to be
isometric and equivariant under the Lorentz transformations. This projection is
computationally efficient since it can be expressed by simple linear
operations, and, due to the aforementioned equivariance property, it allows for
weight sharing. The nested hyperbolic space representation is the core
component of our network and therefore, we first compare this ensuing nested
hyperbolic space representation with other dimensionality reduction methods
such as tangent PCA, principal geodesic analysis (PGA) and HoroPCA. Based on
this equivariant embedding, we develop a novel fully hyperbolic graph
convolutional neural network architecture to learn the parameters of the
projection. Finally, we present experiments demonstrating comparative
performance of our network on several publicly available data sets.

Interest in unmanned aerial system (UAS) powered solutions for 6G
communication networks has grown immensely with the widespread availability of
machine learning based autonomy modules and embedded graphical processing units
(GPUs). While these technologies have revolutionized the possibilities of UAS
solutions, designing an operable, robust autonomy framework for UAS remains a
multi-faceted and difficult problem. In this work, we present our novel,
modular framework for UAS autonomy, entitled MR-iFLY, and discuss how it may be
extended to enable 6G swarm solutions. We begin by detailing the challenges
associated with machine learning based UAS autonomy on resource constrained
devices. Next, we describe in depth, how MR-iFLY's novel depth estimation and
collision avoidance technology meets these challenges. Lastly, we describe the
various evaluation criteria we have used to measure performance, show how our
optimized machine vision components provide up to 15X speedup over baseline
models and present a flight demonstration video of MR-iFLY's vision-based
collision avoidance technology. We argue that these empirical results
substantiate MR-iFLY as a candidate for use in reducing communication overhead
between nodes in 6G communication swarms by providing standalone collision
avoidance and navigation capabilities.

Advanced manufacturing techniques have enabled the production of materials
with state-of-the-art properties. In many cases however, the development of
physics-based models of these techniques lags behind their use in the lab. This
means that designing and running experiments proceeds largely via trial and
error. This is sub-optimal since experiments are cost-, time-, and
labor-intensive. In this work we propose a machine learning framework,
differential property classification (DPC), which enables an experimenter to
leverage machine learning's unparalleled pattern matching capability to pursue
data-driven experimental design. DPC takes two possible experiment parameter
sets and outputs a prediction of which will produce a material with a more
desirable property specified by the operator. We demonstrate the success of DPC
on AA7075 tube manufacturing process and mechanical property data using shear
assisted processing and extrusion (ShAPE), a solid phase processing technology.
We show that by focusing on the experimenter's need to choose between multiple
candidate experimental parameters, we can reframe the challenging regression
task of predicting material properties from processing parameters, into a
classification task on which machine learning models can achieve good

The vision transformer splits each image into a sequence of tokens with fixed
length and processes the tokens in the same way as words in natural language
processing. More tokens normally lead to better performance but considerably
increased computational cost. Motivated by the proverb "A picture is worth a
thousand words" we aim to accelerate the ViT model by making a long image
short. To this end, we propose a novel approach to assign token length
adaptively during inference. Specifically, we first train a ViT model, called
Resizable-ViT (ReViT), that can process any given input with diverse token
lengths. Then, we retrieve the "token-length label" from ReViT and use it to
train a lightweight Token-Length Assigner (TLA). The token-length labels are
the smallest number of tokens to split an image that the ReViT can make the
correct prediction, and TLA is learned to allocate the optimal token length
based on these labels. The TLA enables the ReViT to process the image with the
minimum sufficient number of tokens during inference. Thus, the inference speed
is boosted by reducing the token numbers in the ViT model. Our approach is
general and compatible with modern vision transformer architectures and can
significantly reduce computational expanse. We verified the effectiveness of
our methods on multiple representative ViT models (DeiT, LV-ViT, and
TimesFormer) across two tasks (image classification and action recognition).

Zero-shot learning (ZSL) aims to recognize novel classes by transferring
semantic knowledge from seen classes to unseen ones. Semantic knowledge is
learned from attribute descriptions shared between different classes, which act
as strong priors for localizing object attributes that represent discriminative
region features, enabling significant visual-semantic interaction. Although
some attention-based models have attempted to learn such region features in a
single image, the transferability and discriminative attribute localization of
visual features are typically neglected. In this paper, we propose an
attribute-guided Transformer network, termed TransZero, to refine visual
features and learn attribute localization for discriminative visual embedding
representations in ZSL. Specifically, TransZero takes a feature augmentation
encoder to alleviate the cross-dataset bias between ImageNet and ZSL
benchmarks, and improves the transferability of visual features by reducing the
entangled relative geometry relationships among region features. To learn
locality-augmented visual features, TransZero employs a visual-semantic decoder
to localize the image regions most relevant to each attribute in a given image,
under the guidance of semantic attribute information. Then, the
locality-augmented visual features and semantic vectors are used to conduct
effective visual-semantic interaction in a visual-semantic embedding network.
Extensive experiments show that TransZero achieves the new state of the art on
three ZSL benchmarks. The codes are available at:
\url{https://github.com/shiming-chen/TransZero}.

Approximate Bayesian deep learning methods hold significant promise for
addressing several issues that occur when deploying deep learning components in
intelligent systems, including mitigating the occurrence of over-confident
errors and providing enhanced robustness to out of distribution examples.
However, the computational requirements of existing approximate Bayesian
inference methods can make them ill-suited for deployment in intelligent IoT
systems that include lower-powered edge devices. In this paper, we present a
range of approximate Bayesian inference methods for supervised deep learning
and highlight the challenges and opportunities when applying these methods on
current edge hardware. We highlight several potential solutions to decreasing
model storage requirements and improving computational scalability, including
model pruning and distillation methods.

Modeling non-Euclidean data is drawing attention along with the unprecedented
successes of deep neural networks in diverse fields. In particular, symmetric
positive definite (SPD) matrix is being actively studied in computer vision,
signal processing, and medical image analysis, thanks to its ability to learn
appropriate statistical representations. However, due to its strong
constraints, it remains challenging for optimization problems or inefficient
computation costs, especially, within a deep learning framework. In this paper,
we propose to exploit a diffeomorphism mapping between Riemannian manifolds and
a Cholesky space, by which it becomes feasible not only to efficiently solve
optimization problems but also to reduce computation costs greatly. Further, in
order for dynamics modeling in time series data, we devise a continuous
manifold learning method by integrating a manifold ordinary differential
equation and a gated recurrent neural network in a systematic manner. It is
noteworthy that because of the nice parameterization of matrices in a Cholesky
space, it is straightforward to train our proposed network with Riemannian
geometric metrics equipped. We demonstrate through experiments that the
proposed model can be efficiently and reliably trained as well as outperform
existing manifold methods and state-of-the-art methods in two classification
tasks: action recognition and sleep staging classification.

Browser fingerprinting is a stateless tracking technique that attempts to
combine information exposed by multiple different web APIs to create a unique
identifier for tracking users across the web. Over the last decade, trackers
have abused several existing and newly proposed web APIs to further enhance the
browser fingerprint. Existing approaches are limited to detecting a specific
fingerprinting technique(s) at a particular point in time. Thus, they are
unable to systematically detect novel fingerprinting techniques that abuse
different web APIs. In this paper, we propose FP-Radar, a machine learning
approach that leverages longitudinal measurements of web API usage on top-100K
websites over the last decade, for early detection of new and evolving browser
fingerprinting techniques. The results show that FP-Radar is able to early
detect the abuse of newly introduced properties of already known (e.g., WebGL,
Sensor) and as well as previously unknown (e.g., Gamepad, Clipboard) APIs for
browser fingerprinting. To the best of our knowledge, FP-Radar is also the
first to detect the abuse of the Visibility API for ephemeral fingerprinting in
the wild.

Sequential training from task to task is becoming one of the major objects in
deep learning applications such as continual learning and transfer learning.
Nevertheless, it remains unclear under what conditions the trained model's
performance improves or deteriorates. To deepen our understanding of sequential
training, this study provides a theoretical analysis of generalization
performance in a solvable case of continual learning. We consider neural
networks in the neural tangent kernel (NTK) regime that continually learn
target functions from task to task, and investigate the generalization by using
an established statistical mechanical analysis of kernel ridge-less regression.
We first show characteristic transitions from positive to negative transfer.
More similar targets above a specific critical value can achieve positive
knowledge transfer for the subsequent task while catastrophic forgetting occurs
even with very similar targets. Next, we investigate a variant of continual
learning which supposes the same target function in multiple tasks. Even for
the same target, the trained model shows some transfer and forgetting depending
on the sample size of each task. We can guarantee that the generalization error
monotonically decreases from task to task for equal sample sizes while
unbalanced sample sizes deteriorate the generalization. We respectively refer
to these improvement and deterioration as self-knowledge transfer and
forgetting, and empirically confirm them in realistic training of deep neural
networks as well.

This paper considers the problem of regulating a linear dynamical system to
the solution of a convex optimization problem with an unknown or
partially-known cost. We design a data-driven feedback controller - based on
gradient flow dynamics - that (i) is augmented with learning methods to
estimate the cost function based on infrequent (and possibly noisy) functional
evaluations; and, concurrently, (ii) is designed to drive the inputs and
outputs of the dynamical system to the optimizer of the problem. We derive
sufficient conditions on the learning error and the controller gain to ensure
that the error between the optimizer of the problem and the state of the
closed-loop system is ultimately bounded; the error bound accounts for the
functional estimation errors and the temporal variability of the unknown
disturbance affecting the linear dynamical system. Our results directly lead to
exponential input-to-state stability of the closed-loop system. The proposed
method and the theoretical bounds are validated numerically.

Meme is an interesting word. Internet memes offer unique insights into the
changes in our perception of the world, the media and our own lives. If you
surf the Internet for long enough, you will see it somewhere on the Internet.
With the rise of social media platforms and convenient image dissemination,
Image Meme has gained fame. Image memes have become a kind of pop culture and
they play an important role in communication over social media, blogs, and open
messages. With the development of artificial intelligence and the widespread
use of deep learning, Natural Language Processing (NLP) and Computer Vision
(CV) can also be used to solve more problems in life, including meme
generation. An Internet meme commonly takes the form of an image and is created
by combining a meme template (image) and a caption (natural language sentence).
In our project, we propose an end-to-end encoder-decoder architecture meme
generator. For a given input sentence, we use the Meme template selection model
to determine the emotion it expresses and select the image template. Then
generate captions and memes through to the meme caption generator. Code and
models are available at github

This paper proposes a probabilistic contrastive loss function for
self-supervised learning. The well-known contrastive loss is deterministic and
involves a temperature hyperparameter that scales the inner product between two
normed feature embeddings. By reinterpreting the temperature hyperparameter as
a quantity related to the radius of the hypersphere, we derive a new loss
function that involves a confidence measure which quantifies uncertainty in a
mathematically grounding manner. Some intriguing properties of the proposed
loss function are empirically demonstrated, which agree with human-like
predictions. We believe the present work brings up a new prospective to the
area of contrastive learning.

We introduce \textit{HALO} -- a deep generative model utilising HAmiltonian
Latent Operators to reliably disentangle content and motion information in
image sequences. The \textit{content} represents summary statistics of a
sequence, and \textit{motion} is a dynamic process that determines how
information is expressed in any part of the sequence. By modelling the dynamics
as a Hamiltonian motion, important desiderata are ensured: (1) the motion is
reversible, (2) the symplectic, volume-preserving structure in phase space
means paths are continuous and are not divergent in the latent space.
Consequently, the nearness of sequence frames is realised by the nearness of
their coordinates in the phase space, which proves valuable for disentanglement
and long-term sequence generation. The sequence space is generally comprised of
different types of dynamical motions. To ensure long-term separability and
allow controlled generation, we associate every motion with a unique
Hamiltonian that acts in its respective subspace. We demonstrate the utility of
\textit{HALO} by swapping the motion of a pair of sequences, controlled
generation, and image rotations.

The scientific claim verification task requires an NLP system to label
scientific documents which Support or Refute an input claim, and to select
evidentiary sentences (or rationales) justifying each predicted label. In this
work, we present MultiVerS, which predicts a fact-checking label and identifies
rationales in a multitask fashion based on a shared encoding of the claim and
full document context. This approach accomplishes two key modeling goals.
First, it ensures that all relevant contextual information is incorporated into
each labeling decision. Second, it enables the model to learn from instances
annotated with a document-level fact-checking label, but lacking sentence-level
rationales. This allows MultiVerS to perform weakly-supervised domain
adaptation by training on scientific documents labeled using high-precision
heuristics. Our approach outperforms two competitive baselines on three
scientific claim verification datasets, with particularly strong performance in
zero / few-shot domain adaptation experiments. Our code and data are available
at https://github.com/dwadden/multivers.

Distributed deep learning frameworks like federated learning (FL) and its
variants are enabling personalized experiences across a wide range of web
clients and mobile/IoT devices. However, FL-based frameworks are constrained by
computational resources at clients due to the exploding growth of model
parameters (eg. billion parameter model). Split learning (SL), a recent
framework, reduces client compute load by splitting the model training between
client and server. This flexibility is extremely useful for low-compute setups
but is often achieved at cost of increase in bandwidth consumption and may
result in sub-optimal convergence, especially when client data is
heterogeneous. In this work, we introduce AdaSplit which enables efficiently
scaling SL to low resource scenarios by reducing bandwidth consumption and
improving performance across heterogeneous clients. To capture and benchmark
this multi-dimensional nature of distributed deep learning, we also introduce
C3-Score, a metric to evaluate performance under resource budgets. We validate
the effectiveness of AdaSplit under limited resources through extensive
experimental comparison with strong federated and split learning baselines. We
also present a sensitivity analysis of key design choices in AdaSplit which
validates the ability of AdaSplit to provide adaptive trade-offs across
variable resource budgets.

The search for new high-performance organic semiconducting molecules is
challenging due to the vastness of the chemical space, machine learning
methods, particularly deep learning models like graph neural networks (GNNs),
have shown promising potential to address such challenge. However, practical
applications of GNNs for chemistry are often limited by the availability of
labelled data. Meanwhile, unlabelled molecular data is abundant and could
potentially be utilized to alleviate the scarcity issue of labelled data. Here,
we advocate the use of self-supervised learning to improve the performance of
GNNs by pre-training them with unlabeled molecular data. We investigate
regression problems involving ground and excited state properties, both
relevant for optoelectronic properties of organic semiconductors. Additionally,
we extend the self-supervised learning strategy to molecules in non-equilibrium
configurations which are important for studying the effects of disorder. In all
cases, we obtain considerable performance improvement over results without
pre-training, in particular when labelled training data is limited, and such
improvement is attributed to the capability of self-supervised learning in
identifying structural similarity among unlabeled molecules.

A desire to achieve large medical imaging datasets keeps increasing as
machine learning algorithms, parallel computing, and hardware technology
evolve. Accordingly, there is a growing demand in pooling data from multiple
clinical and academic institutes to enable large-scale clinical or
translational research studies. Magnetic resonance imaging (MRI) is a
frequently used, non-invasive imaging modality. However, constructing a big MRI
data repository has multiple challenges related to privacy, data size, DICOM
format, logistics, and non-standardized images. Not only building the data
repository is difficult, but using data pooled from the repository is also
challenging, due to heterogeneity in image acquisition, reconstruction, and
processing pipelines across MRI vendors and imaging sites. This position paper
describes challenges in constructing a large MRI data repository and using data
downloaded from such data repositories in various aspects. To help address the
challenges, the paper proposes introducing a quality assessment pipeline, with
considerations and general design principles.

Radiography is often used to probe complex, evolving density fields in
dynamic systems and in so doing gain insight into the underlying physics. This
technique has been used in numerous fields including materials science, shock
physics, inertial confinement fusion, and other national security applications.
In many of these applications, however, complications resulting from noise,
scatter, complex beam dynamics, etc. prevent the reconstruction of density from
being accurate enough to identify the underlying physics with sufficient
confidence. As such, density reconstruction from static/dynamic radiography has
typically been limited to identifying discontinuous features such as cracks and
voids in a number of these applications.
  In this work, we propose a fundamentally new approach to reconstructing
density from a temporal sequence of radiographic images. Using only the robust
features identifiable in radiographs, we combine them with the underlying
hydrodynamic equations of motion using a machine learning approach, namely,
conditional generative adversarial networks (cGAN), to determine the density
fields from a dynamic sequence of radiographs. Next, we seek to further enhance
the hydrodynamic consistency of the ML-based density reconstruction through a
process of parameter estimation and projection onto a hydrodynamic manifold. In
this context, we note that the distance from the hydrodynamic manifold given by
the training data to the test data in the parameter space considered both
serves as a diagnostic of the robustness of the predictions and serves to
augment the training database, with the expectation that the latter will
further reduce future density reconstruction errors. Finally, we demonstrate
the ability of this method to outperform a traditional radiographic
reconstruction in capturing allowable hydrodynamic paths even when relatively
small amounts of scatter are present.

Photo-acid generators (PAGs) are compounds that release acids ($H^+$ ions)
when exposed to light. These compounds are critical components of the
photolithography processes that are used in the manufacture of semiconductor
logic and memory chips. The exponential increase in the demand for
semiconductors has highlighted the need for discovering novel photo-acid
generators. While de novo molecule design using deep generative models has been
widely employed for drug discovery and material design, its application to the
creation of novel photo-acid generators poses several unique challenges, such
as lack of property labels. In this paper, we highlight these challenges and
propose a generative modeling approach that utilizes conditional generation
from a pre-trained deep autoencoder and expert-in-the-loop techniques. The
validity of the proposed approach was evaluated with the help of subject matter
experts, indicating the promise of such an approach for applications beyond the
creation of novel photo-acid generators.

This paper deals with differentiable dynamical models congruent with neural
process theories that cast brain function as the hierarchical refinement of an
internal generative model explaining observations. Our work extends existing
implementations of gradient-based predictive coding with automatic
differentiation and allows to integrate deep neural networks for non-linear
state parameterization. Gradient-based predictive coding optimises inferred
states and weights locally in for each layer by optimising precision-weighted
prediction errors that propagate from stimuli towards latent states.
Predictions flow backwards, from latent states towards lower layers. The model
suggested here optimises hierarchical and dynamical predictions of latent
states. Hierarchical predictions encode expected content and hierarchical
structure. Dynamical predictions capture changes in the encoded content along
with higher order derivatives. Hierarchical and dynamical predictions interact
and address different aspects of the same latent states. We apply the model to
various perception and planning tasks on sequential data and show their mutual
dependence. In particular, we demonstrate how learning sampling distances in
parallel address meaningful locations data sampled at discrete time steps. We
discuss possibilities to relax the assumption of linear hierarchies in favor of
more flexible graph structure with emergent properties. We compare the granular
structure of the model with canonical microcircuits describing predictive
coding in biological networks and review the connection to Markov Blankets as a
tool to characterize modularity. A final section sketches out ideas for
efficient perception and planning in nested spatio-temporal hierarchies.

Label noise detection has been widely studied in Machine Learning because of
its importance in improving training data quality. Satisfactory noise detection
has been achieved by adopting ensembles of classifiers. In this approach, an
instance is assigned as mislabeled if a high proportion of members in the pool
misclassifies it. Previous authors have empirically evaluated this approach;
nevertheless, they mostly assumed that label noise is generated completely at
random in a dataset. This is a strong assumption since other types of label
noise are feasible in practice and can influence noise detection results. This
work investigates the performance of ensemble noise detection under two
different noise models: the Noisy at Random (NAR), in which the probability of
label noise depends on the instance class, in comparison to the Noisy
Completely at Random model, in which the probability of label noise is entirely
independent. In this setting, we investigate the effect of class distribution
on noise detection performance since it changes the total noise level observed
in a dataset under the NAR assumption. Further, an evaluation of the ensemble
vote threshold is conducted to contrast with the most common approaches in the
literature. In many performed experiments, choosing a noise generation model
over another can lead to different results when considering aspects such as
class imbalance and noise level ratio among different classes.

In signal processing, several applications involve the recovery of a function
given noisy modulo samples. The setting considered in this paper is that the
samples corrupted by an additive Gaussian noise are wrapped due to the modulo
operation. Typical examples of this problem arise in phase unwrapping problems
or in the context of self-reset analog to digital converters. We consider a
fixed design setting where the modulo samples are given on a regular grid.
Then, a three stage recovery strategy is proposed to recover the ground truth
signal up to a global integer shift. The first stage denoises the modulo
samples by using local polynomial estimators. In the second stage, an
unwrapping algorithm is applied to the denoised modulo samples on the grid.
Finally, a spline based quasi-interpolant operator is used to yield an estimate
of the ground truth function up to a global integer shift. For a function in
H\"older class, uniform error rates are given for recovery performance with
high probability. This extends recent results obtained by Fanuel and Tyagi for
Lipschitz smooth functions wherein $k$NN regression was used in the denoising
step.

In many applications of computer vision it is important to accurately
estimate the trajectory of an object over time by fusing data from a number of
sources, of which 2D and 3D imagery is only one. In this paper, we show how to
use a deep feature encoding in conjunction with generative densities over the
features in a factor-graph based, probabilistic tracking framework. We present
a likelihood model that combines a learned feature encoder with generative
densities over them, both trained in a supervised manner. We also experiment
with directly inferring probability through the use of image classification
models that feed into the likelihood formulation. These models are used to
implement deep factors that are added to the factor graph to complement other
factors that represent domain-specific knowledge such as motion models and/or
other prior information. Factors are then optimized together in a non-linear
least-squares tracking framework that takes the form of an Extended Kalman
Smoother with a Gaussian prior. A key feature of our likelihood model is that
it leverages the Lie group properties of the tracked target's pose to apply the
feature encoding on an image patch, extracted through a differentiable warp
function inspired by spatial transformer networks. To illustrate the proposed
approach we evaluate it on a challenging social insect behavior dataset, and
show that using deep features does outperform these earlier linear appearance
models used in this setting.

Binary networks are extremely efficient as they use only two symbols to
define the network: $\{+1,-1\}$. One can make the prior distribution of these
symbols a design choice. The recent IR-Net of Qin et al. argues that imposing a
Bernoulli distribution with equal priors (equal bit ratios) over the binary
weights leads to maximum entropy and thus minimizes information loss. However,
prior work cannot precisely control the binary weight distribution during
training, and therefore cannot guarantee maximum entropy. Here, we show that
quantizing using optimal transport can guarantee any bit ratio, including equal
ratios. We investigate experimentally that equal bit ratios are indeed
preferable and show that our method leads to optimization benefits. We show
that our quantization method is effective when compared to state-of-the-art
binarization methods, even when using binary weight pruning.

A cognitive architecture aimed at cumulative learning must provide the
necessary information and control structures to allow agents to learn
incrementally and autonomously from their experience. This involves managing an
agent's goals as well as continuously relating sensory information to these in
its perception-cognition information stack. The more varied the environment of
a learning agent is, the more general and flexible must be these mechanisms to
handle a wider variety of relevant patterns, tasks, and goal structures. While
many researchers agree that information at different levels of abstraction
likely differs in its makeup and structure and processing mechanisms, agreement
on the particulars of such differences is not generally shared in the research
community. A binary processing architecture (often referred to as System-1 and
System-2) has been proposed as a model of cognitive processing for low- and
high-level information, respectively. We posit that cognition is not binary in
this way and that knowledge at any level of abstraction involves what we refer
to as neurosymbolic information, meaning that data at both high and low levels
must contain both symbolic and subsymbolic information. Further, we argue that
the main differentiating factor between the processing of high and low levels
of data abstraction can be largely attributed to the nature of the involved
attention mechanisms. We describe the key arguments behind this view and review
relevant evidence from the literature.

For cyber-physical systems, finding a set of test cases with the least cost
by exploring multiple goals is a complex task. For example, Arrieta et al.
reported that state-of-the-art optimizers struggle to find minimal test suites
for this task. To better manage this task, we propose DoLesS (Domination with
Least Squares Approximation) which uses a domination predicate to sort the
space of possible goals to a small number of representative examples.
Multi-objective domination then divides these examples into a "best" set and
the remaining "rest" set. After that, DoLesS applies an inverted least squares
approximation approach to learn a minimal set of tests that can distinguish
best from rest in the reduced example space. DoLesS has been tested on four
cyber-physical models: a tank flow model; a model of electric car windows; a
safety feature of an AC engine; and a continuous PID controller combined with a
discrete state machine. Comparing to the recent state-of-the-art paper
attempted the same task, DoLesS performs as well or even better as
state-of-the-art, while running 80-360 times faster on average (seconds instead
of hours). Hence, we recommend DoLesSas a fast method to find minimal test
suites for multi-goal cyber-physical systems. For replication purposes, all our
code is on-line:https://github.com/hellonull123/Test_Selection_2021.

Throughout the Covid-19 pandemic, a significant amount of effort had been put
into developing techniques that predict the number of infections under various
assumptions about the public policy and non-pharmaceutical interventions. While
both the available data and the sophistication of the AI models and available
computing power exceed what was available in previous years, the overall
success of prediction approaches was very limited. In this paper, we start from
prediction algorithms proposed for XPrize Pandemic Response Challenge and
consider several directions that might allow their improvement. Then, we
investigate their performance over medium-term predictions extending over
several months. We find that augmenting the algorithms with additional
information about the culture of the modeled region, incorporating traditional
compartmental models and up-to-date deep learning architectures can improve the
performance for short term predictions, the accuracy of medium-term predictions
is still very low and a significant amount of future research is needed to make
such models a reliable component of a public policy toolbox.

In the online (time-series) search problem, a player is presented with a
sequence of prices which are revealed in an online manner. In the standard
definition of the problem, for each revealed price, the player must decide
irrevocably whether to accept or reject it, without knowledge of future prices
(other than an upper and a lower bound on their extreme values), and the
objective is to minimize the competitive ratio, namely the worst-case ratio
between the maximum price in the sequence and the one selected by the player.
The problem formulates several applications of decision-making in the face of
uncertainty on the revealed samples.
  Previous work on this problem has largely assumed extreme scenarios in which
either the player has almost no information about the input, or the player is
provided with some powerful, and error-free advice. In this work, we study
learning-augmented algorithms, in which there is a potentially erroneous
prediction concerning the input. Specifically, we consider two different
settings: the setting in which the prediction is related to the maximum price
in the sequence, as well as the setting in which the prediction is obtained as
a response to a number of binary queries. For both settings, we provide tight,
or near-tight upper and lower bounds on the worst-case performance of search
algorithms as a function of the prediction error. We also provide experimental
results on data obtained from stock exchange markets that confirm the
theoretical analysis, and explain how our techniques can be applicable to other
learning-augmented applications.

Wikipedia is an important free source of intelligible knowledge. Despite
that, Brazilian Portuguese Wikipedia still lacks descriptions for many
subjects. In an effort to expand the Brazilian Wikipedia, we contribute PLSum,
a framework for generating wiki-like abstractive summaries from multiple
descriptive websites. The framework has an extractive stage followed by an
abstractive one. In particular, for the abstractive stage, we fine-tune and
compare two recent variations of the Transformer neural network, PTT5, and
Longformer. To fine-tune and evaluate the model, we created a dataset with
thousands of examples, linking reference websites to Wikipedia. Our results
show that it is possible to generate meaningful abstractive summaries from
Brazilian Portuguese web content.

Dropout is conventionally used during the training phase as regularization
method and for quantifying uncertainty in deep learning. We propose to use
dropout during training as well as inference steps, and average multiple
predictions to improve the accuracy, while reducing and quantifying the
uncertainty. The results are evaluated for fractional anisotropy (FA) and mean
diffusivity (MD) maps which are obtained from only 3 direction scans. With our
method, accuracy can be improved significantly compared to network outputs
without dropout, especially when the training dataset is small. Moreover,
confidence maps are generated which may aid in diagnosis of unseen pathology or

We propose using Normalizing Flows as a trainable kernel within the molecular
dynamics update of Hamiltonian Monte Carlo (HMC). By learning (invertible)
transformations that simplify our dynamics, we can outperform traditional
methods at generating independent configurations. We show that, using a
carefully constructed network architecture, our approach can be easily scaled
to large lattice volumes with minimal retraining effort. The source code for
our implementation is publicly available online at
https://github.com/nftqcd/fthmc.

This paper studies privacy-preserving exploration in Markov Decision
Processes (MDPs) with linear representation. We first consider the setting of
linear-mixture MDPs (Ayoub et al., 2020) (a.k.a.\ model-based setting) and
provide an unified framework for analyzing joint and local differential private
(DP) exploration. Through this framework, we prove a
$\widetilde{O}(K^{3/4}/\sqrt{\epsilon})$ regret bound for
$(\epsilon,\delta)$-local DP exploration and a
$\widetilde{O}(\sqrt{K/\epsilon})$ regret bound for $(\epsilon,\delta)$-joint
DP. We further study privacy-preserving exploration in linear MDPs (Jin et al.,
2020) (a.k.a.\ model-free setting) where we provide a
$\widetilde{O}\left(K^{\frac{3}{5}}/\epsilon^{\frac{2}{5}}\right)$ regret bound
for $(\epsilon,\delta)$-joint DP, with a novel algorithm based on
low-switching. Finally, we provide insights into the issues of designing local
DP algorithms in this model-free setting.

We introduce a divergence measure between data distributions based on
operators in reproducing kernel Hilbert spaces defined by kernels. The
empirical estimator of the divergence is computed using the eigenvalues of
positive definite Gram matrices that are obtained by evaluating the kernel over
pairs of data points. The new measure shares similar properties to
Jensen-Shannon divergence. Convergence of the proposed estimators follows from
concentration results based on the difference between the ordered spectrum of
the Gram matrices and the integral operators associated with the population
quantities. The proposed measure of divergence avoids the estimation of the
probability distribution underlying the data. Numerical experiments involving
comparing distributions and applications to sampling unbalanced data for
classification show that the proposed divergence can achieve state of the art
results.

We introduce LeapfrogLayers, an invertible neural network architecture that
can be trained to efficiently sample the topology of a 2D $U(1)$ lattice gauge
theory. We show an improvement in the integrated autocorrelation time of the
topological charge when compared with traditional HMC, and look at how
different quantities transform under our model. Our implementation is open
source, and is publicly available on github at
https://github.com/saforem2/l2hmc-qcd.

Bayesian quadrature (BQ) is a model-based numerical integration method that
is able to increase sample efficiency by encoding and leveraging known
structure of the integration task at hand. In this paper, we explore priors
that encode invariance of the integrand under a set of bijective
transformations in the input domain, in particular some unitary
transformations, such as rotations, axis-flips, or point symmetries. We show
initial results on superior performance in comparison to standard Bayesian
quadrature on several synthetic and one real world application.

The current study explores an artificial intelligence framework for measuring
the structural features from microscopy images of the bacterial biofilms.
Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a
model for sulfate reducing bacteria that are implicated in microbiologically
influenced corrosion problems. Our goal is to automate the process of
extracting the geometrical properties of the DA-G20 cells from the scanning
electron microscopy (SEM) images, which is otherwise a laborious and costly
process. These geometric properties are a biofilm phenotype that allow us to
understand how the biofilm structurally adapts to the surface properties of the
underlying metals, which can lead to better corrosion prevention solutions. We
adapt two deep learning models: (a) a deep convolutional neural network (DCNN)
model to achieve semantic segmentation of the cells, (d) a mask
region-convolutional neural network (Mask R-CNN) model to achieve instance
segmentation of the cells. These models are then integrated with moment
invariants approach to measure the geometric characteristics of the segmented
cells. Our numerical studies confirm that the Mask-RCNN and DCNN methods are
227x and 70x faster respectively, compared to the traditional method of manual
identification and measurement of the cell geometric properties by the domain
experts.

An unsupervised online streaming model is considered where samples arrive in
an online fashion over $T$ slots. There are $M$ classifiers, whose confusion
matrices are unknown a priori. In each slot, at most one sample can be labeled
by any classifier. The accuracy of a sample is a function of the set of labels
obtained for it from various classifiers. The utility of a sample is a scalar
multiple of its accuracy minus the response time (difference of the departure
slot and the arrival slot), where the departure slot is also decided by the
algorithm. Since each classifier can label at most one sample per slot, there
is a tradeoff between obtaining a larger set of labels for a particular sample
to improve its accuracy, and its response time. The problem of maximizing the
sum of the utilities of all samples is considered, where learning the confusion
matrices, sample-classifier matching assignment, and sample departure slot
decisions depend on each other. The proposed algorithm first learns the
confusion matrices, and then uses a greedy algorithm for sample-classifier
matching. A sample departs once its incremental utility turns non-positive. We
show that the competitive ratio of the proposed algorithm is
$\frac{1}{2}-{\mathcal O}\left(\frac{\log T}{T}\right)$.

Reinforcement learning (RL) and brain-computer interfaces (BCI) are two
fields that have been growing over the past decade. Until recently, these
fields have operated independently of one another. With the rising interest in
human-in-the-loop (HITL) applications, RL algorithms have been adapted to
account for human guidance giving rise to the sub-field of interactive
reinforcement learning (IRL). Adjacently, BCI applications have been long
interested in extracting intrinsic feedback from neural activity during
human-computer interactions. These two ideas have set RL and BCI on a collision
course for one another through the integration of BCI into the IRL framework
where intrinsic feedback can be utilized to help train an agent. This
intersection has created a new and emerging paradigm denoted as intrinsic IRL.
To further help facilitate deeper ingratiation of BCI and IRL, we provide a
tutorial and review of intrinsic IRL so far with an emphasis on its parent
field of feedback-driven IRL along with discussions concerning validity,
challenges, and open problems.

This paper investigates the estimation and inference of the average treatment
effect (ATE) using deep neural networks (DNNs) in the potential outcomes
framework. Under some regularity conditions, the observed response can be
formulated as the response of a mean regression problem with both the
confounding variables and the treatment indicator as the independent variables.
Using such formulation, we investigate two methods for ATE estimation and
inference based on the estimated mean regression function via DNN regression
using a specific network architecture. We show that both DNN estimates of ATE
are consistent with dimension-free consistency rates under some assumptions on
the underlying true mean regression model. Our model assumptions accommodate
the potentially complicated dependence structure of the observed response on
the covariates, including latent factors and nonlinear interactions between the
treatment indicator and confounding variables. We also establish the asymptotic
normality of our estimators based on the idea of sample splitting, ensuring
precise inference and uncertainty quantification. Simulation studies and real
data application justify our theoretical findings and support our DNN
estimation and inference methods.

Generating images from natural language instructions is an intriguing yet
highly challenging task. We approach text-to-image generation by combining the
power of the retrained CLIP representation with an off-the-shelf image
generator (GANs), optimizing in the latent space of GAN to find images that
achieve maximum CLIP score with the given input text. Compared to traditional
methods that train generative models from text to image starting from scratch,
the CLIP+GAN approach is training-free, zero shot and can be easily customized
with different generators.
  However, optimizing CLIP score in the GAN space casts a highly challenging
optimization problem and off-the-shelf optimizers such as Adam fail to yield
satisfying results. In this work, we propose a FuseDream pipeline, which
improves the CLIP+GAN approach with three key techniques: 1) an AugCLIP score
which robustifies the CLIP objective by introducing random augmentation on
image. 2) a novel initialization and over-parameterization strategy for
optimization which allows us to efficiently navigate the non-convex landscape
in GAN space. 3) a composed generation technique which, by leveraging a novel
bi-level optimization formulation, can compose multiple images to extend the
GAN space and overcome the data-bias.
  When promoted by different input text, FuseDream can generate high-quality
images with varying objects, backgrounds, artistic styles, even novel
counterfactual concepts that do not appear in the training data of the GAN we
use. Quantitatively, the images generated by FuseDream yield top-level
Inception score and FID score on MS COCO dataset, without additional
architecture design or training. Our code is publicly available at
\url{https://github.com/gnobitab/FuseDream}.

Advancements in Intelligent Traffic Systems (ITS) have made huge amounts of
traffic data available through automatic data collection. A big part of this
data is stored as trajectories of moving vehicles and road users. Automatic
analysis of this data with minimal human supervision would both lower the costs
and eliminate subjectivity of the analysis. Trajectory clustering is an
unsupervised task.
  In this paper, we perform a comprehensive comparison of similarity measures,
clustering algorithms and evaluation measures using trajectory data from seven
intersections. We also propose a method to automatically generate trajectory
reference clusters based on their origin and destination points to be used for
label-based evaluation measures. Therefore, the entire procedure remains
unsupervised both in clustering and evaluation levels. Finally, we use a
combination of evaluation measures to find the top performing similarity
measures and clustering algorithms for each intersection. The results show that
there is no single combination of distance and clustering algorithm that is
always among the top ten clustering setups.

This paper is an extension of our work where we presented a three-stage
XGBoost algorithm for forecasting sales under product cannibalization scenario.
Previously we developed the model based on our intuition and provided empirical
evidence on its performance. In this study we would briefly go over the
algorithm and then provide mathematical reasoning behind its working.

Graph sparsification concerns data reduction where an edge-reduced graph of a
similar structure is preferred. Existing methods are mostly sampling-based,
which introduce high computation complexity in general and lack of flexibility
for a different reduction objective. We present SparRL, the first general and
effective reinforcement learning-based framework for graph sparsification.
SparRL can easily adapt to different reduction goals and promise
graph-size-independent complexity. Extensive experiments show that SparRL
outperforms all prevailing sparsification methods in producing high-quality
sparsified graphs concerning a variety of objectives.

Approximate computing is known for its effectiveness in improvising the
energy efficiency of deep neural network (DNN) accelerators at the cost of
slight accuracy loss. Very recently, the inexact nature of approximate
components, such as approximate multipliers have also been reported successful
in defending adversarial attacks on DNNs models. Since the approximation errors
traverse through the DNN layers as masked or unmasked, this raises a key
research question-can approximate computing always offer a defense against
adversarial attacks in DNNs, i.e., are they universally defensive? Towards
this, we present an extensive adversarial robustness analysis of different
approximate DNN accelerators (AxDNNs) using the state-of-the-art approximate
multipliers. In particular, we evaluate the impact of ten adversarial attacks
on different AxDNNs using the MNIST and CIFAR-10 datasets. Our results
demonstrate that adversarial attacks on AxDNNs can cause 53% accuracy loss
whereas the same attack may lead to almost no accuracy loss (as low as 0.06%)
in the accurate DNN. Thus, approximate computing cannot be referred to as a
universal defense strategy against adversarial attacks.

We present Neural Head Avatars, a novel neural representation that explicitly
models the surface geometry and appearance of an animatable human avatar that
can be used for teleconferencing in AR/VR or other applications in the movie or
games industry that rely on a digital human. Our representation can be learned
from a monocular RGB portrait video that features a range of different
expressions and views. Specifically, we propose a hybrid representation
consisting of a morphable model for the coarse shape and expressions of the
face, and two feed-forward networks, predicting vertex offsets of the
underlying mesh as well as a view- and expression-dependent texture. We
demonstrate that this representation is able to accurately extrapolate to
unseen poses and view points, and generates natural expressions while providing
sharp texture details. Compared to previous works on head avatars, our method
provides a disentangled shape and appearance model of the complete human head
(including hair) that is compatible with the standard graphics pipeline.
Moreover, it quantitatively and qualitatively outperforms current state of the
art in terms of reconstruction quality and novel-view synthesis.

We present "Extending the Satellites Around Galactic Analogs Survey" (xSAGA),
a method for identifying low-$z$ galaxies on the basis of optical imaging, and
results on the spatial distributions of xSAGA satellites around host galaxies.
Using spectroscopic redshift catalogs from the SAGA Survey as a training data
set, we have optimized a convolutional neural network (CNN) to identify $z <
0.03$ galaxies from more distant objects using image cutouts from the DESI
Legacy Imaging Surveys. From the sample of $> 100,000$ CNN-selected low-$z$
galaxies, we identify $>20,000$ probable satellites located between 36-300
projected kpc from NASA-Sloan Atlas central galaxies in the stellar mass range
$9.5 < \log(M_\star/M_\odot) < 11$. We characterize the incompleteness and
contamination for CNN-selected samples, and apply corrections in order to
estimate the true number of satellites as a function of projected radial
distance from their hosts. Satellite richness depends strongly on host stellar
mass, such that more massive host galaxies have more satellites, and on host
morphology, such that elliptical hosts have more satellites than disky hosts
with comparable stellar masses. We also find a strong inverse correlation
between satellite richness and the magnitude gap between a host and its
brightest satellite. The normalized satellite radial distribution between
36-300 kpc does not depend strongly on host stellar mass, morphology, or
magnitude gap. The satellite abundances and radial distributions we measure are
in reasonable agreement with predictions from hydrodynamic simulations. Our
results deliver unprecedented statistical power for studying satellite galaxy
populations, and highlight the promise of using machine learning for extending
galaxy samples of wide-area surveys.

Large-scale astronomical surveys have the potential to capture data on large
numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate
timely analysis and spectroscopic follow-up before the supernova fades, an LSN
needs to be identified soon after it begins. To quickly identify LSNe in
optical survey datasets, we designed ZipperNet, a multi-branch deep neural
network that combines convolutional layers (traditionally used for images) with
long short-term memory (LSTM) layers (traditionally used for time series). We
tested ZipperNet on the task of classifying objects from four categories -- no
lens, galaxy-galaxy lens, lensed type Ia supernova, lensed core-collapse
supernova -- within high-fidelity simulations of three cosmic survey data sets
-- the Dark Energy Survey (DES), Rubin Observatory's Legacy Survey of Space and
Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey.
Among our results, we find that for the LSST-like dataset, ZipperNet classifies
LSNe with a receiver operating characteristic area under the curve of 0.97,
predicts the spectroscopic type of the lensed supernovae with 79\% accuracy,
and demonstrates similarly high performance for LSNe 1-2 epochs after first
detection. We anticipate that a model like ZipperNet, which simultaneously
incorporates spatial and temporal information, can play a significant role in
the rapid identification of lensed transient systems in cosmic survey

This paper studies the BERT pretraining of video transformers. It is a
straightforward but worth-studying extension given the recent success from BERT
pretraining of image transformers. We introduce BEVT which decouples video
representation learning into spatial representation learning and temporal
dynamics learning. In particular, BEVT first performs masked image modeling on
image data, and then conducts masked image modeling jointly with masked video
modeling on video data. This design is motivated by two observations: 1)
transformers learned on image datasets provide decent spatial priors that can
ease the learning of video transformers, which are often times
computationally-intensive if trained from scratch; 2) discriminative clues,
i.e., spatial and temporal information, needed to make correct predictions vary
among different videos due to large intra-class and inter-class variations. We
conduct extensive experiments on three challenging video benchmarks where BEVT
achieves very promising results. On Kinetics 400, for which recognition mostly
relies on discriminative spatial representations, BEVT achieves comparable
results to strong supervised baselines. On Something-Something-V2 and Diving
48, which contain videos relying on temporal dynamics, BEVT outperforms by
clear margins all alternative baselines and achieves state-of-the-art
performance with a 71.4\% and 87.2\% Top-1 accuracy respectively. Code will be
made available at \url{https://github.com/xyzforever/BEVT}.

Image segmentation is about grouping pixels with different semantics, e.g.,
category or instance membership, where each choice of semantics defines a task.
While only the semantics of each task differ, current research focuses on
designing specialized architectures for each task. We present Masked-attention
Mask Transformer (Mask2Former), a new architecture capable of addressing any
image segmentation task (panoptic, instance or semantic). Its key components
include masked attention, which extracts localized features by constraining
cross-attention within predicted mask regions. In addition to reducing the
research effort by at least three times, it outperforms the best specialized
architectures by a significant margin on four popular datasets. Most notably,
Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on
COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7
mIoU on ADE20K).

While Knowledge Distillation (KD) has been recognized as a useful tool in
many visual tasks, such as supervised classification and self-supervised
representation learning, the main drawback of a vanilla KD framework is its
mechanism, which consumes the majority of the computational overhead on
forwarding through the giant teacher networks, making the entire learning
procedure inefficient and costly. ReLabel, a recently proposed solution,
suggests creating a label map for the entire image. During training, it
receives the cropped region-level label by RoI aligning on a pre-generated
entire label map, allowing for efficient supervision generation without having
to pass through the teachers many times. However, as the KD teachers are from
conventional multi-crop training, there are various mismatches between the
global label-map and region-level label in this technique, resulting in
performance deterioration. In this study, we present a Fast Knowledge
Distillation (FKD) framework that replicates the distillation training phase
and generates soft labels using the multi-crop KD approach, while training
faster than ReLabel since no post-processes such as RoI align and softmax
operations are used. When conducting multi-crop in the same image for data
loading, our FKD is even more efficient than the traditional image
classification framework. On ImageNet-1K, we obtain 79.8% with ResNet-50,
outperforming ReLabel by ~1.0% while being faster. On the self-supervised
learning task, we also show that FKD has an efficiency advantage. Our project
page: http://zhiqiangshen.com/projects/FKD/index.html, source code and models
are available at: https://github.com/szq0214/FKD.

We study complex-valued scaling as a type of symmetry natural and unique to
complex-valued measurements and representations. Deep Complex Networks (DCN)
extends real-valued algebra to the complex domain without addressing
complex-valued scaling. SurReal takes a restrictive manifold view of complex
numbers, adopting a distance metric to achieve complex-scaling invariance while
losing rich complex-valued information. We analyze complex-valued scaling as a
co-domain transformation and design novel equivariant and invariant neural
network layer functions for this special transformation. We also propose novel
complex-valued representations of RGB images, where complex-valued scaling
indicates hue shift or correlated changes across color channels. Benchmarked on
MSTAR, CIFAR10, CIFAR100, and SVHN, our co-domain symmetric (CDS) classifiers
deliver higher accuracy, better generalization, robustness to co-domain
transformations, and lower model bias and variance than DCN and SurReal with
far fewer parameters.

We present an approach for 3D global human mesh recovery from monocular
videos recorded with dynamic cameras. Our approach is robust to severe and
long-term occlusions and tracks human bodies even when they go outside the
camera's field of view. To achieve this, we first propose a deep generative
motion infiller, which autoregressively infills the body motions of occluded
humans based on visible motions. Additionally, in contrast to prior work, our
approach reconstructs human meshes in consistent global coordinates even with
dynamic cameras. Since the joint reconstruction of human motions and camera
poses is underconstrained, we propose a global trajectory predictor that
generates global human trajectories based on local body movements. Using the
predicted trajectories as anchors, we present a global optimization framework
that refines the predicted trajectories and optimizes the camera poses to match
the video evidence such as 2D keypoints. Experiments on challenging indoor and
in-the-wild datasets with dynamic cameras demonstrate that the proposed
approach outperforms prior methods significantly in terms of motion infilling
and global mesh recovery.

Neural radiance fields (NeRFs) produce state-of-the-art view synthesis
results. However, they are slow to render, requiring hundreds of network
evaluations per pixel to approximate a volume rendering integral. Baking NeRFs
into explicit data structures enables efficient rendering, but results in a
large increase in memory footprint and, in many cases, a quality reduction. In
this paper, we propose a novel neural light field representation that, in
contrast, is compact and directly predicts integrated radiance along rays. Our
method supports rendering with a single network evaluation per pixel for small
baseline light field datasets and can also be applied to larger baselines with
only a few evaluations per pixel. At the core of our approach is a ray-space
embedding network that maps the 4D ray-space manifold into an intermediate,
interpolable latent space. Our method achieves state-of-the-art quality on
dense forward-facing datasets such as the Stanford Light Field dataset. In
addition, for forward-facing scenes with sparser inputs we achieve results that
are competitive with NeRF-based approaches in terms of quality while providing
a better speed/quality/memory trade-off with far fewer network evaluations.

Biological intelligence systems of animals perceive the world by integrating
information in different modalities and processing simultaneously for various
tasks. In contrast, current machine learning research follows a task-specific
paradigm, leading to inefficient collaboration between tasks and high marginal
costs of developing perception models for new tasks. In this paper, we present
a generic perception architecture named Uni-Perceiver, which processes a
variety of modalities and tasks with unified modeling and shared parameters.
Specifically, Uni-Perceiver encodes different task inputs and targets from
arbitrary modalities into a unified representation space with a
modality-agnostic Transformer encoder and lightweight modality-specific
tokenizers. Different perception tasks are modeled as the same formulation,
that is, finding the maximum likelihood target for each input through the
similarity of their representations. The model is pre-trained on several
uni-modal and multi-modal tasks, and evaluated on a variety of downstream
tasks, including novel tasks that did not appear in the pre-training stage.
Results show that our pre-trained model without any tuning can achieve
reasonable performance even on novel tasks. The performance can be improved to
a level close to state-of-the-art methods by conducting prompt tuning on 1% of
downstream task data. Full-data fine-tuning further delivers results on par
with or better than state-of-the-art results. Code shall be released.

With the advent of deep learning, estimating depth from a single RGB image
has recently received a lot of attention, being capable of empowering many
different applications ranging from path planning for robotics to computational
cinematography. Nevertheless, while the depth maps are in their entirety fairly
reliable, the estimates around object discontinuities are still far from
satisfactory. This can be contributed to the fact that the convolutional
operator naturally aggregates features across object discontinuities, resulting
in smooth transitions rather than clear boundaries. Therefore, in order to
circumvent this issue, we propose a novel convolutional operator which is
explicitly tailored to avoid feature aggregation of different object parts. In
particular, our method is based on estimating per-part depth values by means of
superpixels. The proposed convolutional operator, which we dub "Instance
Convolution", then only considers each object part individually on the basis of
the estimated superpixels. Our evaluation with respect to the NYUv2 as well as
the iBims dataset clearly demonstrates the superiority of Instance Convolutions
over the classical convolution at estimating depth around occlusion boundaries,
while producing comparable results elsewhere. Code will be made publicly

Humans can perceive scenes in 3D from a handful of 2D views. For AI agents,
the ability to recognize a scene from any viewpoint given only a few images
enables them to efficiently interact with the scene and its objects. In this
work, we attempt to endow machines with this ability. We propose a model which
takes as input a few RGB images of a new scene and recognizes the scene from
novel viewpoints by segmenting it into semantic categories. All this without
access to the RGB images from those views. We pair 2D scene recognition with an
implicit 3D representation and learn from multi-view 2D annotations of hundreds
of scenes without any 3D supervision beyond camera poses. We experiment on
challenging datasets and demonstrate our model's ability to jointly capture
semantics and geometry of novel scenes with diverse layouts, object types and
shapes.

This paper aims to reduce the rendering time of generalizable radiance
fields. Some recent works equip neural radiance fields with image encoders and
are able to generalize across scenes, which avoids the per-scene optimization.
However, their rendering process is generally very slow. A major factor is that
they sample lots of points in empty space when inferring radiance fields. In
this paper, we present a hybrid scene representation which combines the best of
implicit radiance fields and explicit depth maps for efficient rendering.
Specifically, we first build the cascade cost volume to efficiently predict the
coarse geometry of the scene. The coarse geometry allows us to sample few
points near the scene surface and significantly improves the rendering speed.
This process is fully differentiable, enabling us to jointly learn the depth
prediction and radiance field networks from only RGB images. Experiments show
that the proposed approach exhibits state-of-the-art performance on the DTU,
Real Forward-facing and NeRF Synthetic datasets, while being at least 50 times
faster than previous generalizable radiance field methods. We also demonstrate
the capability of our method to synthesize free-viewpoint videos of dynamic
human performers in real-time. The code will be available at
https://zju3dv.github.io/enerf/.

Recent progress has shown that large-scale pre-training using contrastive
image-text pairs can be a promising alternative for high-quality visual
representation learning from natural language supervision. Benefiting from a
broader source of supervision, this new paradigm exhibits impressive
transferability to downstream classification tasks and datasets. However, the
problem of transferring the knowledge learned from image-text pairs to more
complex dense prediction tasks has barely been visited. In this work, we
present a new framework for dense prediction by implicitly and explicitly
leveraging the pre-trained knowledge from CLIP. Specifically, we convert the
original image-text matching problem in CLIP to a pixel-text matching problem
and use the pixel-text score maps to guide the learning of dense prediction
models. By further using the contextual information from the image to prompt
the language model, we are able to facilitate our model to better exploit the
pre-trained knowledge. Our method is model-agnostic, which can be applied to
arbitrary dense prediction systems and various pre-trained visual backbones
including both CLIP models and ImageNet pre-trained models. Extensive
experiments demonstrate the superior performance of our methods on semantic
segmentation, object detection, and instance segmentation tasks. Code is
available at https://github.com/raoyongming/DenseCLIP

Machine learning generated content such as image artworks, textual poems and
music become prominent in recent years. These tools attract much attention from
the media, artists, researchers, and investors. Because these tools are
data-driven, they are inherently different than the traditional creative tools
which arises the question - who may own the content that is generated by these
tools? In this paper we aim to address this question, we start by providing a
background to this problem, raising several candidates that may own the content
and arguments for each one of them. Then we propose a possible algorithmic
solution in the vision-based model's regime. Finally, we discuss the broader
implications of this problem.

Unsupervised semantic segmentation aims to obtain high-level semantic
representation on low-level visual features without manual annotations. Most
existing methods are bottom-up approaches that try to group pixels into regions
based on their visual cues or certain predefined rules. As a result, it is
difficult for these bottom-up approaches to generate fine-grained semantic
segmentation when coming to complicated scenes with multiple objects and some
objects sharing similar visual appearance. In contrast, we propose the first
top-down unsupervised semantic segmentation framework for fine-grained
segmentation in extremely complicated scenarios. Specifically, we first obtain
rich high-level structured semantic concept information from large-scale vision
data in a self-supervised learning manner, and use such information as a prior
to discover potential semantic categories presented in target datasets.
Secondly, the discovered high-level semantic categories are mapped to low-level
pixel features by calculating the class activate map (CAM) with respect to
certain discovered semantic representation. Lastly, the obtained CAMs serve as
pseudo labels to train the segmentation module and produce the final semantic
segmentation. Experimental results on multiple semantic segmentation benchmarks
show that our top-down unsupervised segmentation is robust to both
object-centric and scene-centric datasets under different semantic granularity
levels, and outperforms all the current state-of-the-art bottom-up methods. Our
code is available at \url{https://github.com/damo-cv/TransFGU}.

Open-world object detection (OWOD) is a challenging computer vision problem,
where the task is to detect a known set of object categories while
simultaneously identifying unknown objects. Additionally, the model must
incrementally learn new classes that become known in the next training
episodes. Distinct from standard object detection, the OWOD setting poses
significant challenges for generating quality candidate proposals on
potentially unknown objects, separating the unknown objects from the background
and detecting diverse unknown objects. Here, we introduce a novel end-to-end
transformer-based framework, OW-DETR, for open-world object detection. The
proposed OW-DETR comprises three dedicated components namely, attention-driven
pseudo-labeling, novelty classification and objectness scoring to explicitly
address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes
multi-scale contextual information, possesses less inductive bias, enables
knowledge transfer from known classes to the unknown class and can better
discriminate between unknown objects and background. Comprehensive experiments
are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive
ablations reveal the merits of our proposed contributions. Further, our model
outperforms the recently introduced OWOD approach, ORE, with absolute gains
ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of
incremental object detection, OW-DETR outperforms the state-of-the-art for all
settings on PASCAL VOC. Our code is available at
https://github.com/akshitac8/OW-DETR.

While visual imitation learning offers one of the most effective ways of
learning from visual demonstrations, generalizing from them requires either
hundreds of diverse demonstrations, task specific priors, or large,
hard-to-train parametric models. One reason such complexities arise is because
standard visual imitation frameworks try to solve two coupled problems at once:
learning a succinct but good representation from the diverse visual data, while
simultaneously learning to associate the demonstrated actions with such
representations. Such joint learning causes an interdependence between these
two problems, which often results in needing large amounts of demonstrations
for learning. To address this challenge, we instead propose to decouple
representation learning from behavior learning for visual imitation. First, we
learn a visual representation encoder from offline data using standard
supervised and self-supervised learning methods. Once the representations are
trained, we use non-parametric Locally Weighted Regression to predict the
actions. We experimentally show that this simple decoupling improves the
performance of visual imitation models on both offline demonstration datasets
and real-robot door opening compared to prior work in visual imitation. All of
our generated data, code, and robot videos are publicly available at
https://jyopari.github.io/VINN/.

The Robust Markov Decision Process (RMDP) framework focuses on designing
control policies that are robust against the parameter uncertainties due to the
mismatches between the simulator model and real-world settings. An RMDP problem
is typically formulated as a max-min problem, where the objective is to find
the policy that maximizes the value function for the worst possible model that
lies in an uncertainty set around a nominal model. The standard robust dynamic
programming approach requires the knowledge of the nominal model for computing
the optimal robust policy. In this work, we propose a model-based reinforcement
learning (RL) algorithm for learning an $\epsilon$-optimal robust policy when
the nominal model is unknown. We consider three different forms of uncertainty
sets, characterized by the total variation distance, chi-square divergence, and
KL divergence. For each of these uncertainty sets, we give a precise
characterization of the sample complexity of our proposed algorithm. In
addition to the sample complexity results, we also present a formal analytical
argument on the benefit of using robust policies. Finally, we demonstrate the
performance of our algorithm on two benchmark problems.

Coronary heart disease, which is a form of cardiovascular disease (CVD), is
the leading cause of death worldwide. The odds of survival are good if it is
found or diagnosed early. The current report discusses a comparative approach
to the classification of coronary heart disease datasets using machine learning
(ML) algorithms. The current study created and tested several
machine-learning-based classification models. The dataset was subjected to
Smote to handle unbalanced classes and feature selection technique in order to
assess the impact on two distinct performance metrics. The results show that
logistic regression produced the highest performance score on the original
dataset compared to the other algorithms employed. In conclusion, this study
suggests that LR on a well-processed and standardized dataset can predict
coronary heart disease with greater accuracy than the other algorithms.

We introduce a way to learn to estimate a scene representation from a single
image by predicting a low-dimensional subspace of optical flow for each
training example, which encompasses the variety of possible camera and object
movement. Supervision is provided by a novel loss which measures the distance
between this predicted flow subspace and an observed optical flow. This
provides a new approach to learning scene representation tasks, such as
monocular depth prediction or instance segmentation, in an unsupervised fashion
using in-the-wild input videos without requiring camera poses, intrinsics, or
an explicit multi-view stereo step. We evaluate our method in multiple
settings, including an indoor depth prediction task where it achieves
comparable performance to recent methods trained with more supervision.

Under voltage load shedding has been considered as a standard approach to
recover the voltage stability of the electric power grid under emergency
conditions, yet this scheme usually trips a massive amount of load
inefficiently. Reinforcement learning (RL) has been adopted as a promising
approach to circumvent the issues; however, RL approach usually cannot
guarantee the safety of the systems under control. In this paper, we discuss a
couple of novel safe RL approaches, namely constrained optimization approach
and Barrier function-based approach, that can safely recover voltage under
emergency events. This method is general and can be applied to other
safety-critical control problems. Numerical simulations on the 39-bus IEEE
benchmark are performed to demonstrate the effectiveness of the proposed safe
RL emergency control.

We address the problem of active speaker detection through a new framework,
called SPELL, that learns long-range multimodal graphs to encode the
inter-modal relationship between audio and visual data. We cast active speaker
detection as a node classification task that is aware of longer-term
dependencies. We first construct a graph from a video so that each node
corresponds to one person. Nodes representing the same identity share edges
between them within a defined temporal window. Nodes within the same video
frame are also connected to encode inter-person interactions. Through extensive
experiments on the Ava-ActiveSpeaker dataset, we demonstrate that learning
graph-based representation, owing to its explicit spatial and temporal
structure, significantly improves the overall performance. SPELL outperforms
several relevant baselines and performs at par with state of the art models
while requiring an order of magnitude lower computation cost.

Within the last few years, there has been a move towards using statistical
models in conjunction with neural networks with the end goal of being able to
better answer the question, "what do our models know?". From this trend,
classical metrics such as Prediction Interval Coverage Probability (PICP) and
new metrics such as calibration error have entered the general repertoire of
model evaluation in order to gain better insight into how the uncertainty of
our model compares to reality. One important component of uncertainty modeling
is model uncertainty (epistemic uncertainty), a measurement of what the model
does and does not know. However, current evaluation techniques tends to
conflate model uncertainty with aleatoric uncertainty (irreducible error),
leading to incorrect conclusions. In this paper, using posterior predictive
checks, we show how calibration error and its variants are almost always
incorrect to use given model uncertainty, and further show how this mistake can
lead to trust in bad models and mistrust in good models. Though posterior
predictive checks has often been used for in-sample evaluation of Bayesian
models, we show it still has an important place in the modern deep learning
world.

This study presents a broad perspective of hybrid process modeling and
optimization combining the scientific knowledge and data analytics in
bioprocessing and chemical engineering with a science-guided machine learning
(SGML) approach. We divide the approach into two major categories. The first
refers to the case where a data-based ML model compliments and makes the
first-principle science-based model more accurate in prediction, and the second
corresponds to the case where scientific knowledge helps make the ML model more
scientifically consistent. We present a detailed review of scientific and
engineering literature relating to the hybrid SGML approach, and propose a
systematic classification of hybrid SGML models. For applying ML to improve
science-based models, we present expositions of the sub-categories of direct
serial and parallel hybrid modeling and their combinations, inverse modeling,
reduced-order modeling, quantifying uncertainty in the process and even
discovering governing equations of the process model. For applying scientific
principles to improve ML models, we discuss the sub-categories of
science-guided design, learning and refinement. For each sub-category, we
identify its requirements, advantages and limitations, together with their
published and potential areas of applications in bioprocessing and chemical
engineering.We also present several examples to illustrate different hybrid
SGML methodologies for modeling polymer processes.

We introduce Neural Point Light Fields that represent scenes implicitly with
a light field living on a sparse point cloud. Combining differentiable volume
rendering with learned implicit density representations has made it possible to
synthesize photo-realistic images for novel views of small scenes. As neural
volumetric rendering methods require dense sampling of the underlying
functional scene representation, at hundreds of samples along a ray cast
through the volume, they are fundamentally limited to small scenes with the
same objects projected to hundreds of training views. Promoting sparse point
clouds to neural implicit light fields allows us to represent large scenes
effectively with only a single radiance evaluation per ray. These point light
fields are a function of the ray direction, and local point feature
neighborhood, allowing us to interpolate the light field conditioned training
images without dense object coverage and parallax. We assess the proposed
method for novel view synthesis on large driving scenarios, where we synthesize
realistic unseen views that existing implicit approaches fail to represent. We
validate that Neural Point Light Fields make it possible to predict videos
along unseen trajectories previously only feasible to generate by explicitly
modeling the scene.

We combine neural rendering with multi-modal image and text representations
to synthesize diverse 3D objects solely from natural language descriptions. Our
method, Dream Fields, can generate the geometry and color of a wide range of
objects without 3D supervision. Due to the scarcity of diverse, captioned 3D
data, prior methods only generate objects from a handful of categories, such as
ShapeNet. Instead, we guide generation with image-text models pre-trained on
large datasets of captioned images from the web. Our method optimizes a Neural
Radiance Field from many camera views so that rendered images score highly with
a target caption according to a pre-trained CLIP model. To improve fidelity and
visual quality, we introduce simple geometric priors, including
sparsity-inducing transmittance regularization, scene bounds, and new MLP
architectures. In experiments, Dream Fields produce realistic, multi-view
consistent object geometry and color from a variety of natural language
captions.

Target Propagation (TP) algorithms compute targets instead of gradients along
neural networks and propagate them backward in a way that is similar yet
different than gradient back-propagation (BP). The idea was first presented as
a perturbative alternative to back-propagation that may achieve greater
accuracy in gradient evaluation when training multi-layer neural networks
(LeCun et al., 1989). However, TP has remained more of a template algorithm
with many variations than a well-identified algorithm. Revisiting insights of
LeCun et al., (1989) and more recently of Lee et al. (2015), we present a
simple version of target propagation based on regularized inversion of network
layers, easily implementable in a differentiable programming framework. We
compare its computational complexity to the one of BP and delineate the regimes
in which TP can be attractive compared to BP. We show how our TP can be used to
train recurrent neural networks with long sequences on various sequence
modeling problems. The experimental results underscore the importance of
regularization in TP in practice.

We consider a multi-armed bandit problem specified by a set of
one-dimensional family exponential distributions endowed with a unimodal
structure. We introduce IMED-UB, a algorithm that optimally exploits the
unimodal-structure, by adapting to this setting the Indexed Minimum Empirical
Divergence (IMED) algorithm introduced by Honda and Takemura [2015]. Owing to
our proof technique, we are able to provide a concise finite-time analysis of
IMED-UB algorithm. Numerical experiments show that IMED-UB competes with the

To meet the growing interest in Deep Reinforcement Learning (DRL), we sought
to construct a DRL-driven Atari Pong agent and accompanying visualization tool.
Existing approaches do not support the flexibility required to create an
interactive exhibit with easily-configurable physics and a human-controlled
player. Therefore, we constructed a new Pong game environment, discovered and
addressed a number of unique data deficiencies that arise when applying DRL to
a new environment, architected and tuned a policy gradient based DRL model,
developed a real-time network visualization, and combined these elements into
an interactive display to help build intuition and awareness of the mechanics
of DRL inference.

A comprehensive understanding of the behaviours of the various geophysical
processes requires, among others, detailed investigations across temporal
scales. In this work, we propose a new time series feature compilation for
advancing and enriching such investigations in a hydroclimatic context. This
specific compilation can facilitate largely interpretable feature
investigations and comparisons in terms of temporal dependence, temporal
variation, "forecastability", lumpiness, stability, nonlinearity (and
linearity), trends, spikiness, curvature and seasonality. Detailed
quantifications and multifaceted characterizations are herein obtained by
computing the values of the proposed feature compilation across nine temporal
resolutions (i.e., the 1-day, 2-day, 3-day, 7-day, 0.5-month, 1-month, 2-month,
3-month and 6-month ones) and three hydroclimatic time series types (i.e.,
temperature, precipitation and streamflow) for 34-year-long time series records
originating from 511 geographical locations across the continental United
States. Based on the acquired information and knowledge, similarities and
differences between the examined time series types with respect to the
evolution patterns characterizing their feature values with increasing (or
decreasing) temporal resolution are identified. To our view, the similarities
in these patterns are rather surprising. We also find that the spatial patterns
emerging from feature-based time series clustering are largely analogous across
temporal scales, and compare the features with respect to their usefulness in
clustering the time series at the various temporal resolutions. For most of the
features, this usefulness can vary to a notable degree across temporal
resolutions and time series types, thereby pointing out the need for conducting
multifaceted time series characterizations for the study of hydroclimatic

We study the problem of large-scale network embedding, which aims to learn
low-dimensional latent representations for network mining applications. Recent
research in the field of network embedding has led to significant progress such
as DeepWalk, LINE, NetMF, NetSMF. However, the huge size of many real-world
networks makes it computationally expensive to learn network embedding from the
entire network. In this work, we present a novel network embedding method
called "NES", which learns network embedding from a small representative
subgraph. NES leverages theories from graph sampling to efficiently construct
representative subgraph with smaller size which can be used to make inferences
about the full network, enabling significantly improved efficiency in embedding
learning. Then, NES computes the network embedding from this representative
subgraph, efficiently. Compared with well-known methods, extensive experiments
on networks of various scales and types demonstrate that NES achieves
comparable performance and significant efficiency superiority.

Due to the curse of dimensionality and the limitation on training data,
approximating high-dimensional functions is a very challenging task even for
powerful deep neural networks. Inspired by the Nonlinear Level set Learning
(NLL) method that uses the reversible residual network (RevNet), in this paper
we propose a new method of Dimension Reduction via Learning Level Sets (DRiLLS)
for function approximation. Our method contains two major components: one is
the pseudo-reversible neural network (PRNN) module that effectively transforms
high-dimensional input variables to low-dimensional active variables, and the
other is the synthesized regression module for approximating function values
based on the transformed data in the low-dimensional space. The PRNN not only
relaxes the invertibility constraint of the nonlinear transformation present in
the NLL method due to the use of RevNet, but also adaptively weights the
influence of each sample and controls the sensitivity of the function to the
learned active variables. The synthesized regression uses Euclidean distance in
the input space to select neighboring samples, whose projections on the space
of active variables are used to perform local least-squares polynomial fitting.
This helps to resolve numerical oscillation issues present in traditional local
and global regressions. Extensive experimental results demonstrate that our
DRiLLS method outperforms both the NLL and Active Subspace methods, especially
when the target function possesses critical points in the interior of its input
domain.

We use telemetry data from the Gemini North ALTAIR adaptive optics system to
investigate how well the commands for wavefront correction (both Tip/Tilt and
high-order turbulence) can be forecasted in order to reduce lag error (due to
wavefront sensor averaging and computational delays) and improve delivered
image quality. We show that a high level of reduction ($\sim$ 5 for Tip-Tilt
and $\sim$ 2 for high-order modes) in RMS wavefront error can be achieved by
using a "forecasting filter" based on a linear auto-regressive model with only
a few coefficients ($\sim$ 30 for Tip-Tilt and $\sim$ 5 for high-order modes)
to complement the existing integral servo-controller. Updating this filter to
adapt to evolving observing conditions is computationally inexpensive and
requires less than 10 seconds worth of telemetry data. We also use several
machine learning models (Long-Short Term Memory and dilated convolutional
models) to evaluate whether further improvements could be achieved with a more
sophisticated non-linear model. Our attempts showed no perceptible improvements
over linear auto-regressive predictions, even for large lags where residuals
from the linear models are high, suggesting that non-linear wavefront
distortions for ALTAIR at the Gemini North telescope may not be forecasted with
the current setup

Distributed Deep Learning (DDL) is essential for large-scale Deep Learning
(DL) training. Synchronous Stochastic Gradient Descent (SSGD) 1 is the de facto
DDL optimization method. Using a sufficiently large batch size is critical to
achieving DDL runtime speedup. In a large batch setting, the learning rate must
be increased to compensate for the reduced number of parameter updates.
However, a large learning rate may harm convergence in SSGD and training could
easily diverge. Recently, Decentralized Parallel SGD (DPSGD) has been proposed
to improve distributed training speed. In this paper, we find that DPSGD not
only has a system-wise run-time benefit but also a significant convergence
benefit over SSGD in the large batch setting. Based on a detailed analysis of
the DPSGD learning dynamics, we find that DPSGD introduces additional
landscape-dependent noise that automatically adjusts the effective learning
rate to improve convergence. In addition, we theoretically show that this noise
smoothes the loss landscape, hence allowing a larger learning rate. We conduct
extensive studies over 18 state-of-the-art DL models/tasks and demonstrate that
DPSGD often converges in cases where SSGD diverges for large learning rates in
the large batch setting. Our findings are consistent across two different
application domains: Computer Vision (CIFAR10 and ImageNet-1K) and Automatic
Speech Recognition (SWB300 and SWB2000), and two different types of neural
network models: Convolutional Neural Networks and Long Short-Term Memory
Recurrent Neural Networks.

Anomaly detection and localization is an important vision problem, having
multiple applications. Effective and generic semantic segmentation of anomalous
regions on various different surfaces, where most anomalous regions inherently
do not have any obvious pattern, is still under active research. Periodic
health monitoring and fault (anomaly) detection in vast infrastructures, which
is an important safety-related task, is one such application area of
vision-based anomaly segmentation. However, the task is quite challenging due
to large variations in surface faults, texture-less construction
material/background, lighting conditions etc. Cracks are critical and frequent
surface faults that manifest as extreme zigzag-shaped thin, elongated regions.
They are among the hardest faults to detect, even with deep learning. In this
work, we address an open aspect of automatic crack segmentation problem, that
of generalizing and improving the performance of segmentation across a variety
of scenarios, by modeling the problem differently. We carefully study and
abstract the sub-problems involved and solve them in a broader context, making
our solution generic. On a variety of datasets related to surveillance of
different infrastructures, under varying conditions, our model consistently
outperforms the state-of-the-art algorithms by a significant margin, without
any bells-and-whistles. This performance advantage easily carried over in two
deployments of our model, tested against industry-provided datasets. Even
further, we could establish our model's performance for two manufacturing
quality inspection scenarios as well, where the defect types are not just crack
equivalents, but much more and different. Hence we hope that our model is
indeed a truly generic defect segmentation model.

Deep Learning has revolutionized machine learning and artificial
intelligence, achieving superhuman performance in several standard benchmarks.
It is well-known that deep learning models are inefficient to train; they learn
by processing millions of training data multiple times and require powerful
computational resources to process large batches of data in parallel at the
same time rather than sequentially. Deep learning models also have unexpected
failure modes; they can be fooled into misbehaviour, producing unexpectedly
incorrect predictions.
  In this thesis, we study approaches to improve the training efficiency and
robustness of deep learning models. In the context of learning visual-semantic
embeddings, we find that prioritizing learning on more informative training
data increases convergence speed and improves generalization performance on
test data. We formalize a simple trick called hard negative mining as a
modification to the learning objective function with no computational overhead.
Next, we seek improvements to optimization speed in general-purpose
optimization methods in deep learning. We show that a redundancy-aware
modification to the sampling of training data improves the training speed and
develops an efficient method for detecting the diversity of training signal,
namely, gradient clustering. Finally, we study adversarial robustness in deep
learning and approaches to achieve maximal adversarial robustness without
training with additional data. For linear models, we prove guaranteed maximal
robustness achieved only by appropriate choice of the optimizer,
regularization, or architecture.

Generative Neural Radiance Field (GNeRF) models, which extract implicit 3D
representations from 2D images, have recently been shown to produce realistic
images representing rigid/semi-rigid objects, such as human faces or cars.
However, they usually struggle to generate high-quality images representing
non-rigid objects, such as the human body, which is of a great interest for
many computer graphics applications. This paper proposes a 3D-aware
Semantic-Guided Generative Model (3D-SGAN) for human image synthesis, which
combines a GNeRF with a texture generator. The former learns an implicit 3D
representation of the human body and outputs a set of 2D semantic segmentation
masks. The latter transforms these semantic masks into a real image, adding a
realistic texture to the human appearance. Without requiring additional 3D
information, our model can learn 3D human representations with a
photo-realistic, controllable generation. Our experiments on the DeepFashion
dataset show that 3D-SGAN significantly outperforms the most recent baselines.
The code is available at https://github.com/zhangqianhui/3DSGAN

LiDAR (short for "Light Detection And Ranging" or "Laser Imaging, Detection,
And Ranging") technology can be used to provide detailed three-dimensional
elevation maps of urban and rural landscapes. To date, airborne LiDAR imaging
has been predominantly confined to the environmental and archaeological
domains. However, the geographically granular and open-source nature of this
data also lends itself to an array of societal, organizational and business
applications where geo-demographic type data is utilised. Arguably, the
complexity involved in processing this multi-dimensional data has thus far
restricted its broader adoption. In this paper, we propose a series of
convenient task-agnostic tile elevation embeddings to address this challenge,
using recent advances from unsupervised Deep Learning. We test the potential of
our embeddings by predicting seven English indices of deprivation (2019) for
small geographies in the Greater London area. These indices cover a range of
socio-economic outcomes and serve as a proxy for a wide variety of downstream
tasks to which the embeddings can be applied. We consider the suitability of
this data not just on its own but also as an auxiliary source of data in
combination with demographic features, thus providing a realistic use case for
the embeddings. Having trialled various model/embedding configurations, we find
that our best performing embeddings lead to Root-Mean-Squared-Error (RMSE)
improvements of up to 21% over using standard demographic features alone. We
also demonstrate how our embedding pipeline, using Deep Learning combined with
K-means clustering, produces coherent tile segments which allow the latent
embedding features to be interpreted.

Hyperspectral feature spaces are useful for many remote sensing applications
ranging from spectral mixture modeling to discrete thematic classification. In
such cases, characterization of the feature space dimensionality, geometry and
topology can provide guidance for effective model design. The objective of this
study is to compare and contrast two approaches for identifying feature space
basis vectors via dimensionality reduction. These approaches can be combined to
render a joint characterization that reveals spectral properties not apparent
using either approach alone. We use a diverse collection of AVIRIS-NG
reflectance spectra of the snow-firn-ice continuum to illustrate the utility of
joint characterization and identify physical properties inferred from the
spectra. Spectral feature spaces combining principal components (PCs) and
t-distributed Stochastic Neighbor Embeddings (t-SNEs) provide physically
interpretable dimensions representing the global (PC) structure of cryospheric
reflectance properties and local (t-SNE) manifold structures revealing
clustering not resolved in the global continuum. Joint characterization reveals
distinct continua for snow-firn gradients on different parts of the Greenland
Ice Sheet and multiple clusters of ice reflectance properties common to both
glacier and sea ice in different locations. Clustering revealed in t-SNE
feature spaces, and extended to the joint characterization, distinguishes
differences in spectral curvature specific to location within the snow
accumulation zone, and BRDF effects related to view geometry. The ability of
PC+t-SNE joint characterization to produce a physically interpretable spectral
feature spaces revealing global topology while preserving local manifold
structures suggests that this characterization might be extended to the much
higher dimensional hyperspectral feature space of all terrestrial land cover.

Unsupervised domain adaptation has recently emerged as an effective paradigm
for generalizing deep neural networks to new target domains. However, there is
still enormous potential to be tapped to reach the fully supervised
performance. In this paper, we present a novel active learning strategy to
assist knowledge transfer in the target domain, dubbed active domain
adaptation. We start from an observation that energy-based models exhibit
\textit{free energy biases} when training (source) and test (target) data come
from different distributions. Inspired by this inherent mechanism, we
empirically reveal that a simple yet efficient energy-based sampling strategy
sheds light on selecting the most valuable target samples than existing
approaches requiring particular architectures or computation of the distances.
Our algorithm, Energy-based Active Domain Adaptation (EADA), queries groups of
target data that incorporate both domain characteristic and instance
uncertainty into every selection round. Meanwhile, by aligning the free energy
of target data compact around the source domain via a regularization term,
domain gap can be implicitly diminished. Through extensive experiments, we show
that EADA surpasses state-of-the-art methods on well-known challenging
benchmarks with substantial improvements, making it a useful option in the open
world. Code is available at https://github.com/BIT-DA/EADA.

The robustness of federated learning (FL) is vital for the distributed
training of an accurate global model that is shared among large number of
clients. The collaborative learning framework by typically aggregating model
updates is vulnerable to model poisoning attacks from adversarial clients.
Since the shared information between the global server and participants are
only limited to model parameters, it is challenging to detect bad model
updates. Moreover, real-world datasets are usually heterogeneous and not
independent and identically distributed (Non-IID) among participants, which
makes the design of such robust FL pipeline more difficult. In this work, we
propose a novel robust aggregation method, Federated Robust Adaptive
Distillation (FedRAD), to detect adversaries and robustly aggregate local
models based on properties of the median statistic, and then performing an
adapted version of ensemble Knowledge Distillation. We run extensive
experiments to evaluate the proposed method against recently published works.
The results show that FedRAD outperforms all other aggregators in the presence
of adversaries, as well as in heterogeneous data distributions.

Temporal action segmentation classifies the action of each frame in (long)
video sequences. Due to the high cost of frame-wise labeling, we propose the
first semi-supervised method for temporal action segmentation. Our method
hinges on unsupervised representation learning, which, for temporal action
segmentation, poses unique challenges. Actions in untrimmed videos vary in
length and have unknown labels and start/end times. Ordering of actions across
videos may also vary. We propose a novel way to learn frame-wise
representations from temporal convolutional networks (TCNs) by clustering input
features with added time-proximity condition and multi-resolution similarity.
By merging representation learning with conventional supervised learning, we
develop an "Iterative-Contrast-Classify (ICC)" semi-supervised learning scheme.
With more labelled data, ICC progressively improves in performance; ICC
semi-supervised learning, with 40% labelled videos, performs similar to
fully-supervised counterparts. Our ICC improves MoF by {+1.8, +5.6, +2.5}% on
Breakfast, 50Salads and GTEA respectively for 100% labelled videos.

Training of convolutional neural networks is a high dimensional and a
non-convex optimization problem. At present, it is inefficient in situations
where parametric learning rates can not be confidently set. Some past works
have introduced Newton methods for training deep neural networks. Newton
methods for convolutional neural networks involve complicated operations.
Finding the Hessian matrix in second-order methods becomes very complex as we
mainly use the finite differences method with the image data. Newton methods
for convolutional neural networks deals with this by using the sub-sampled
Hessian Newton methods. In this paper, we have used the complete data instead
of the sub-sampled methods that only handle partial data at a time. Further, we
have used parallel processing instead of serial processing in mini-batch
computations. The results obtained using parallel processing in this study,
outperform the time taken by the previous approach.

This work aims at improving instance retrieval with self-supervision. We find
that fine-tuning using the recently developed self-supervised (SSL) learning
methods, such as SimCLR and MoCo, fails to improve the performance of instance
retrieval. In this work, we identify that the learnt representations for
instance retrieval should be invariant to large variations in viewpoint and
background etc., whereas self-augmented positives applied by the current SSL
methods can not provide strong enough signals for learning robust
instance-level representations. To overcome this problem, we propose InsCLR, a
new SSL method that builds on the \textit{instance-level} contrast, to learn
the intra-class invariance by dynamically mining meaningful pseudo positive
samples from both mini-batches and a memory bank during training. Extensive
experiments demonstrate that InsCLR achieves similar or even better performance
than the state-of-the-art SSL methods on instance retrieval. Code is available
at https://github.com/zeludeng/insclr.

There is often a trade-off between building deep learning systems that are
expressive enough to capture the nuances of the reality, and having the right
inductive biases for efficient learning. We introduce Residual Pathway Priors
(RPPs) as a method for converting hard architectural constraints into soft
priors, guiding models towards structured solutions, while retaining the
ability to capture additional complexity. Using RPPs, we construct neural
network priors with inductive biases for equivariances, but without limiting
flexibility. We show that RPPs are resilient to approximate or misspecified
symmetries, and are as effective as fully constrained models even when
symmetries are exact. We showcase the broad applicability of RPPs with
dynamical systems, tabular data, and reinforcement learning. In Mujoco
locomotion tasks, where contact forces and directional rewards violate strict
equivariance assumptions, the RPP outperforms baseline model-free RL agents,
and also improves the learned transition models for model-based RL.

Learning personalized decision policies that generalize to the target
population is of great relevance. Since training data is often not
representative of the target population, standard policy learning methods may
yield policies that do not generalize target population. To address this
challenge, we propose a novel framework for learning policies that generalize
to the target population. For this, we characterize the difference between the
training data and the target population as a sample selection bias using a
selection variable. Over an uncertainty set around this selection variable, we
optimize the minimax value of a policy to achieve the best worst-case policy
value on the target population. In order to solve the minimax problem, we
derive an efficient algorithm based on a convex-concave procedure and prove
convergence for parametrized spaces of policies such as logistic policies. We
prove that, if the uncertainty set is well-specified, our policies generalize
to the target population as they can not do worse than on the training data.
Using simulated data and a clinical trial, we demonstrate that, compared to
standard policy learning methods, our framework improves the generalizability
of policies substantially.

Fusion technique is a key research topic in multimodal sentiment analysis.
The recent attention-based fusion demonstrates advances over simple
operation-based fusion. However, these fusion works adopt single-scale, i.e.,
token-level or utterance-level, unimodal representation. Such single-scale
fusion is suboptimal because that different modality should be aligned with
different granularities. This paper proposes a fusion model named ScaleVLAD to
gather multi-Scale representation from text, video, and audio with shared
Vectors of Locally Aggregated Descriptors to improve unaligned multimodal
sentiment analysis. These shared vectors can be regarded as shared topics to
align different modalities. In addition, we propose a self-supervised shifted
clustering loss to keep the fused feature differentiation among samples. The
backbones are three Transformer encoders corresponding to three modalities, and
the aggregated features generated from the fusion module are feed to a
Transformer plus a full connection to finish task predictions. Experiments on
three popular sentiment analysis benchmarks, IEMOCAP, MOSI, and MOSEI,
demonstrate significant gains over baselines.

Accelerated gradient methods are the cornerstones of large-scale, data-driven
optimization problems that arise naturally in machine learning and other fields
concerning data analysis. We introduce a gradient-based optimization framework
for achieving acceleration, based on the recently introduced notion of
fixed-time stability of dynamical systems. The method presents itself as a
generalization of simple gradient-based methods suitably scaled to achieve
convergence to the optimizer in a fixed-time, independent of the
initialization. We achieve this by first leveraging a continuous-time framework
for designing fixed-time stable dynamical systems, and later providing a
consistent discretization strategy, such that the equivalent discrete-time
algorithm tracks the optimizer in a practically fixed number of iterations. We
also provide a theoretical analysis of the convergence behavior of the proposed
gradient flows, and their robustness to additive disturbances for a range of
functions obeying strong convexity, strict convexity, and possibly nonconvexity
but satisfying the Polyak-{\L}ojasiewicz inequality. We also show that the
regret bound on the convergence rate is constant by virtue of the fixed-time
convergence. The hyperparameters have intuitive interpretations and can be
tuned to fit the requirements on the desired convergence rates. We validate the
accelerated convergence properties of the proposed schemes on a range of
numerical examples against the state-of-the-art optimization algorithms. Our
work provides insights on developing novel optimization algorithms via
discretization of continuous-time flows.

Time Slotted Channel Hopping (TSCH) behavioural mode has been introduced in
IEEE 802.15.4e standard to address the ultra-high reliability and ultra-low
power communication requirements of Industrial Internet of Things (IIoT)
networks. Scheduling the packet transmissions in IIoT networks is a difficult
task owing to the limited resources and dynamic topology. In this paper, we
propose a phasic policy gradient (PPG) based TSCH schedule learning algorithm.
The proposed PPG based scheduling algorithm overcomes the drawbacks of totally
distributed and totally centralized deep reinforcement learning-based
scheduling algorithms by employing the actor-critic policy gradient method that
learns the scheduling algorithm in two phases, namely policy phase and
auxiliary phase.

Object detection in autonomous driving applications implies that the
detection and tracking of semantic objects are commonly native to urban driving
environments, as pedestrians and vehicles. One of the major challenges in
state-of-the-art deep-learning based object detection is false positive which
occurrences with overconfident scores. This is highly undesirable in autonomous
driving and other critical robotic-perception domains because of safety
concerns. This paper proposes an approach to alleviate the problem of
overconfident predictions by introducing a novel probabilistic layer to deep
object detection networks in testing. The suggested approach avoids the
traditional Sigmoid or Softmax prediction layer which often produces
overconfident predictions. It is demonstrated that the proposed technique
reduces overconfidence in the false positives without degrading the performance
on the true positives. The approach is validated on the 2D-KITTI objection
detection through the YOLOV4 and SECOND (Lidar-based detector). The proposed
approach enables enabling interpretable probabilistic predictions without the
requirement of re-training the network and therefore is very practical.

The training phase is the most important stage during the machine learning
process. In the case of labeled data and supervised learning, machine training
consists in minimizing the loss function subject to different constraints. In
an abstract setting, it can be formulated as a multiple criteria optimization
model in which each criterion measures the distance between the output
associated with a specific input and its label. Therefore, the fitting term is
a vector function and its minimization is intended in the Pareto sense. We
provide stability results of the efficient solutions with respect to
perturbations of input and output data. We then extend the same approach to the
case of learning with multiple datasets. The multiple dataset environment is
relevant when reducing the bias due to the choice of a specific training set.
We propose a scalarization approach to implement this model and numerical
experiments in digit classification using MNIST data.

Shifts Challenge: Robustness and Uncertainty under Real-World Distributional
Shift is a competition held by NeurIPS 2021. The objective of this competition
is to search for methods to solve the motion prediction problem in
cross-domain. In the real world dataset, It exists variance between input data
distribution and ground-true data distribution, which is called the domain
shift problem. In this report, we propose a new architecture inspired by state
of the art papers. The main contribution is the backbone architecture with
self-attention mechanism and predominant loss function. Subsequently, we won
3rd place as shown on the leaderboard.

In the Group Testing problem, the objective is to learn a subset K of some
much larger domain N, using the shortest-possible sequence of queries Q. A
feedback to a query provides some information about the intersection between
the query and subset K. Several specific feedbacks have been studied in the
literature, often proving different formulas for the estimate of the query
complexity of the problem, defined as the shortest length of queries' sequence
solving Group Testing problem with specific feedback. In this paper we study
what are the properties of the feedback that influence the query complexity of
Group Testing and what is their measurable impact. We propose a generic
framework that covers a vast majority of relevant settings considered in the
literature, which depends on two fundamental parameters of the feedback: input
capacity $\alpha$ and output expressiveness $\beta$. They upper bound the
logarithm of the size of the feedback function domain and image, respectively.
To justify the value of the framework, we prove upper bounds on query
complexity of non adaptive, deterministic Group Testing under some "efficient"
feedbacks, for minimum, maximum and general expressiveness, and complement them
with a lower bound on all feedbacks with given parameters $\alpha,\beta$. Our
upper bounds also hold if the feedback function could get an input twisted by a
malicious adversary, in case the intersection of a query and the hidden set is
bigger than the feedback capacity $\alpha$. We also show that slight change in
the feedback function may result in substantial worsening of the query
complexity. Additionally, we analyze explicitly constructed randomized
counterparts of the deterministic results. Our results provide some insights to
what are the most useful bits of information an output-restricted feedback
could provide, and open a number of challenging research directions.

Although many graph-based clustering methods attempt to model the stationary
diffusion state in their objectives, their performance limits to using a
predefined graph. We argue that the estimation of the stationary diffusion
state can be achieved by gradient descent over neural networks. We specifically
design the Stationary Diffusion State Neural Estimation (SDSNE) to exploit
multiview structural graph information for co-supervised learning. We explore
how to design a graph neural network specially for unsupervised multiview
learning and integrate multiple graphs into a unified consensus graph by a
shared self-attentional module. The view-shared self-attentional module
utilizes the graph structure to learn a view-consistent global graph.
Meanwhile, instead of using auto-encoder in most unsupervised learning graph
neural networks, SDSNE uses a co-supervised strategy with structure information
to supervise the model learning. The co-supervised strategy as the loss
function guides SDSNE in achieving the stationary state. With the help of the
loss and the self-attentional module, we learn to obtain a graph in which nodes
in each connected component fully connect by the same weight. Experiments on
several multiview datasets demonstrate effectiveness of SDSNE in terms of six
clustering evaluation metrics.

Interval and large invasive breast cancers, which are associated with worse
prognosis than other cancers, are usually detected at a late stage due to false
negative assessments of screening mammograms. The missed screening-time
detection is commonly caused by the tumor being obscured by its surrounding
breast tissues, a phenomenon called masking. To study and benchmark
mammographic masking of cancer, in this work we introduce CSAW-M, the largest
public mammographic dataset, collected from over 10,000 individuals and
annotated with potential masking. In contrast to the previous approaches which
measure breast image density as a proxy, our dataset directly provides
annotations of masking potential assessments from five specialists. We also
trained deep learning models on CSAW-M to estimate the masking level and showed
that the estimated masking is significantly more predictive of screening
participants diagnosed with interval and large invasive cancers -- without
being explicitly trained for these tasks -- than its breast density

Image harmonization aims at adjusting the appearance of the foreground to
make it more compatible with the background. Due to a lack of understanding of
the background illumination direction, existing works are incapable of
generating a realistic foreground shading. In this paper, we decompose the
image harmonization into two sub-problems: 1) illumination estimation of
background images and 2) rendering of foreground objects. Before solving these
two sub-problems, we first learn a direction-aware illumination descriptor via
a neural rendering framework, of which the key is a Shading Module that
decomposes the shading field into multiple shading components given depth
information. Then we design a Background Illumination Estimation Module to
extract the direction-aware illumination descriptor from the background.
Finally, the illumination descriptor is used in conjunction with the neural
rendering framework to generate the harmonized foreground image containing a
novel harmonized shading. Moreover, we construct a photo-realistic synthetic
image harmonization dataset that contains numerous shading variations by
image-based lighting. Extensive experiments on this dataset demonstrate the
effectiveness of the proposed method. Our dataset and code will be made

Understanding the role of regularization is a central question in Statistical
Inference. Empirically, well-chosen regularization schemes often dramatically
improve the quality of the inferred models by avoiding overfitting of the
training data. We consider here the particular case of L 2 and L 1
regularizations in the Maximum A Posteriori (MAP) inference of generative
pairwise graphical models. Based on analytical calculations on Gaussian
multivariate distributions and numerical experiments on Gaussian and Potts
models we study the likelihoods of the training, test, and 'generated data'
(with the inferred models) sets as functions of the regularization strengths.
We show in particular that, at its maximum, the test likelihood and the
'generated' likelihood, which quantifies the quality of the generated samples,
have remarkably close values. The optimal value for the regularization strength
is found to be approximately equal to the inverse sum of the squared couplings
incoming on sites on the underlying network of interactions. Our results seem
largely independent of the structure of the true underlying interactions that
generated the data, of the regularization scheme considered, and are valid when
small fluctuations of the posterior distribution around the MAP estimator are
taken into account. Connections with empirical works on protein models learned
from homologous sequences are discussed.

LINC-NIRVANA (LN) is one of the instruments on-board the Large Binocular
Telescope (LBT). LN is a high-resolution, near-infrared imager equipped with an
advanced adaptive optics module. LN implements layer-oriented Multi-Conjugate
Adaptive Optics (MCAO) approach using two independent wavefront sensors per
side of the binocular telescope measuring the turbulence volume above the
telescope. The capability of acquiring up to 20 Natural Guide Stars
simultaneously from two distinct fields of view, and using them for wavefront
sensing with 20 separate pyramids per side of the telescope makes the LN MCAO
system one of a kind.
  Commissioning of the left MCAO channel is almost complete, while that of the
right arm is on-going. The Science Verification on the left side is expected to
start soon after the MCAO performance is optimised for faint guide stars. In
this article, we put together the lessons learned during the commissioning of
the LN MCAO module. We hope and believe that this article will help the future
MCAO instrument commissioning teams.

Software developers embed logging statements inside the source code as an
imperative duty in modern software development as log files are necessary for
tracking down runtime system issues and troubleshooting system management
tasks. However, the current logging process is mostly manual, and thus, proper
placement and content of logging statements remain as challenges. To overcome
these challenges, methods that aim to automate log placement and predict its
content, i.e., 'where and what to log', are of high interest. Thus, we focus on
predicting the location (i.e., where) and description (i.e., what) for log
statements by utilizing source code clones and natural language processing
(NLP), as these approaches provide additional context and advantage for log
prediction. Specifically, we guide our research with three research questions
(RQs): (RQ1) how similar code snippets, i.e., code clones, can be leveraged for
log statements prediction? (RQ2) how the approach can be extended to automate
log statements' descriptions? and (RQ3) how effective the proposed methods are
for log location and description prediction? To pursue our RQs, we perform an
experimental study on seven open-source Java projects. We introduce an updated
and improved log-aware code-clone detection method to predict the location of
logging statements (RQ1). Then, we incorporate natural language processing
(NLP) and deep learning methods to automate the log statements' description
prediction (RQ2). Our analysis shows that our hybrid NLP and code-clone
detection approach (NLP CC'd) outperforms conventional clone detectors in
finding log statement locations on average by 15.60% and achieves 40.86% higher
performance on BLEU and ROUGE scores for predicting the description of logging
statements when compared to prior research (RQ3).

The neural network-based approach to solving partial differential equations
has attracted considerable attention due to its simplicity and flexibility in
representing the solution of the partial differential equation. In training a
neural network, the network learns global features corresponding to
low-frequency components while high-frequency components are approximated at a
much slower rate. For a class of equations in which the solution contains a
wide range of scales, the network training process can suffer from slow
convergence and low accuracy due to its inability to capture the high-frequency
components. In this work, we propose a hierarchical approach to improve the
convergence rate and accuracy of the neural network solution to partial
differential equations. The proposed method comprises multi-training levels in
which a newly introduced neural network is guided to learn the residual of the
previous level approximation. By the nature of neural networks' training
process, the high-level correction is inclined to capture the high-frequency
components. We validate the efficiency and robustness of the proposed
hierarchical approach through a suite of linear and nonlinear partial

This paper presents a parameterization of nonlinear controllers for uncertain
systems building on a recently developed neural network architecture, called
the recurrent equilibrium network (REN), and a nonlinear version of the Youla
parameterization. The proposed framework has "built-in" guarantees of
stability, i.e., all policies in the search space result in a contracting
(globally exponentially stable) closed-loop system. Thus, it requires very mild
assumptions on the choice of cost function and the stability property can be
generalized to unseen data. Another useful feature of this approach is that
policies are parameterized directly without any constraints, which simplifies
learning by a broad range of policy-learning methods based on unconstrained
optimization (e.g. stochastic gradient descent). We illustrate the proposed
approach with a variety of simulation examples.

Code embedding is a keystone in the application of machine learning on
several Software Engineering (SE) tasks. To effectively support a plethora of
SE tasks, the embedding needs to capture program syntax and semantics in a way
that is generic. To this end, we propose the first self-supervised pre-training
approach (called GraphCode2Vec) which produces task-agnostic embedding of
lexical and program dependence features. GraphCode2Vec achieves this via a
synergistic combination of code analysis and Graph Neural Networks.
GraphCode2Vec is generic, it allows pre-training, and it is applicable to
several SE downstream tasks. We evaluate the effectiveness of GraphCode2Vec on
four (4) tasks (method name prediction, solution classification, mutation
testing and overfitted patch classification), and compare it with four (4)
similarly generic code embedding baselines (Code2Seq, Code2Vec, CodeBERT,
GraphCodeBERT) and 7 task-specific, learning-based methods. In particular,
GraphCode2Vec is more effective than both generic and task-specific
learning-based baselines. It is also complementary and comparable to
GraphCodeBERT (a larger and more complex model). We also demonstrate through a
probing and ablation study that GraphCode2Vec learns lexical and program
dependence features and that self-supervised pre-training improves

As an effective algorithm for solving complex optimization problems,
artificial bee colony (ABC) algorithm has shown to be competitive, but the same
as other population-based algorithms, it is poor at balancing the abilities of
global searching in the whole solution space (named as exploration) and quick
searching in local solution space which is defined as exploitation. For
improving the performance of ABC, an adaptive group collaborative ABC (AgABC)
algorithm is introduced where the population in different phases is divided to
specific groups and different search strategies with different abilities are
assigned to the members in groups, and the member or strategy which obtains the
best solution will be employed for further searching. Experimental results on
benchmark functions show that the proposed algorithm with dynamic mechanism is
superior to other algorithms in searching accuracy and stability. Furthermore,
numerical experiments show that the proposed method can generate the optimal
solution for the complex scheduling problem.

The goal of local citation recommendation is to recommend a missing reference
from the local citation context and optionally also from the global context. To
balance the tradeoff between speed and accuracy of citation recommendation in
the context of a large-scale paper database, a viable approach is to first
prefetch a limited number of relevant documents using efficient ranking methods
and then to perform a fine-grained reranking using more sophisticated models.
In that vein, BM25 has been found to be a tough-to-beat approach to
prefetching, which is why recent work has focused mainly on the reranking step.
Even so, we explore prefetching with nearest neighbor search among text
embeddings constructed by a hierarchical attention network. When coupled with a
SciBERT reranker fine-tuned on local citation recommendation tasks, our
hierarchical Attention encoder (HAtten) achieves high prefetch recall for a
given number of candidates to be reranked. Consequently, our reranker requires
fewer prefetch candidates to rerank, yet still achieves state-of-the-art
performance on various local citation recommendation datasets such as ACL-200,
FullTextPeerRead, RefSeer, and arXiv.

Imperfect labels are ubiquitous in real-world datasets and seriously harm the
model performance. Several recent effective methods for handling noisy labels
have two key steps: 1) dividing samples into cleanly labeled and wrongly
labeled sets by training loss, 2) using semi-supervised methods to generate
pseudo-labels for samples in the wrongly labeled set. However, current methods
always hurt the informative hard samples due to the similar loss distribution
between the hard samples and the noisy ones. In this paper, we proposed PGDF
(Prior Guided Denoising Framework), a novel framework to learn a deep model to
suppress noise by generating the samples' prior knowledge, which is integrated
into both dividing samples step and semi-supervised step. Our framework can
save more informative hard clean samples into the cleanly labeled set. Besides,
our framework also promotes the quality of pseudo-labels during the
semi-supervised step by suppressing the noise in the current pseudo-labels
generating scheme. To further enhance the hard samples, we reweight the samples
in the cleanly labeled set during training. We evaluated our method using
synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the
real-world datasets WebVision and Clothing1M. The results demonstrate
substantial improvements over state-of-the-art methods.

Recent advances in reinforcement learning have demonstrated its ability to
solve hard agent-environment interaction tasks on a super-human level. However,
the application of reinforcement learning methods to practical and real-world
tasks is currently limited due to most RL state-of-art algorithms' sample
inefficiency, i.e., the need for a vast number of training episodes. For
example, OpenAI Five algorithm that has beaten human players in Dota 2 has
trained for thousands of years of game time. Several approaches exist that
tackle the issue of sample inefficiency, that either offers a more efficient
usage of already gathered experience or aim to gain a more relevant and diverse
experience via a better exploration of an environment. However, to our
knowledge, no such approach exists for model-based algorithms, that showed
their high sample efficiency in solving hard control tasks with
high-dimensional state space. This work connects exploration techniques and
model-based reinforcement learning. We have designed a novel exploration method
that takes into account features of the model-based approach. We also
demonstrate through experiments that our method significantly improves the
performance of the model-based algorithm Dreamer.

Video-Text pre-training aims at learning transferable representations from
large-scale video-text pairs via aligning the semantics between visual and
textual information. State-of-the-art approaches extract visual features from
raw pixels in an end-to-end fashion. However, these methods operate at
frame-level directly and thus overlook the spatio-temporal structure of objects
in video, which yet has a strong synergy with nouns in textual descriptions. In
this work, we propose a simple yet effective module for video-text
representation learning, namely RegionLearner, which can take into account the
structure of objects during pre-training on large-scale video-text pairs. Given
a video, our module (1) first quantizes visual features into semantic clusters,
then (2) generates learnable masks and uses them to aggregate the features
belonging to the same semantic region, and finally (3) models the interactions
between different aggregated regions. In contrast to using off-the-shelf object
detectors, our proposed module does not require explicit supervision and is
much more computationally efficient. We pre-train the proposed approach on the
public WebVid2M and CC3M datasets. Extensive evaluations on four downstream
video-text retrieval benchmarks clearly demonstrate the effectiveness of our
RegionLearner. The code will be available at
https://github.com/ruiyan1995/Region_Learner.

Heterogeneous computing is the strategy of deploying multiple types of
processing elements within a single workflow, and allowing each to perform the
tasks to which is best suited. To fully harness the power of heterogeneity, we
want to be able to dynamically schedule portions of the code and migrate
processes at runtime between the architectures. Also, migration includes
transforming the execution state of the process, which induces a significant
overhead that offsets the benefits of migrating in the first place. The goal of
my PhD is to work on techniques that allow applications to run on heterogeneous
cores under a shared programming model, and to tackle the generic problem of
creating a uniform address space between processes running on these highly
diverse systems. This would greatly simplify the migration process. We will
start by examining a common stack layout between x86 and ARM binaries, focusing
on these two widely spread architectures, and later we will try to generalize
to other more diverse execution environments. On top of that, the performance
and energy efficiency of the above effort compared to current approaches will
be benchmarked.

A class or taxonomic hierarchy is often manually constructed, and part of our
knowledge about the world. In this paper, we propose a novel algorithm for
automatically acquiring a class hierarchy from a classifier which is often a
large neural network these days. The information that we need from a classifier
is its confusion matrix which contains, for each pair of base classes, the
number of errors the classifier makes by mistaking one for another. Our
algorithm produces surprisingly good hierarchies for some well-known deep
neural network models trained on the CIFAR-10 dataset, a neural network model
for predicting the native language of a non-native English speaker, a neural
network model for detecting the language of a written text, and a classifier
for identifying music genre. In the literature, such class hierarchies have
been used to provide interpretability to the neural networks. We also discuss
some other potential uses of the acquired hierarchies.

Aggregating multi-modality data to obtain accurate and reliable data
representation attracts more and more attention. The pristine researchers
generally adopt the CNN to extract features of independent modality and
aggregate them with a fusion module. However, the overall performance is
becoming saturated due to limited local convolutional features. Recent studies
demonstrate that Transformer models usually work comparable or even better than
CNN for multi-modality task, but they simply adopt concatenation or
cross-attention for feature fusion which may just obtain sub-optimal results.
In this work, we re-thinking the self-attention based Transformer and propose a
novel MutualFormer for multi-modality data fusion and representation. The core
of MutualFormer is the design of both token mixer and modality mixer to conduct
the communication among both tokens and modalities. Specifically, it contains
three main modules, i.e., i) Self-attention (SA) for intra-modality token
mixer, ii) Cross-diffusion attention (CDA) for inter-modality mixer and iii)
Aggregation module. The main advantage of the proposed CDA is that it is
defined based on individual domain similarities in the metric space which thus
can naturally avoid the issue of domain/modality gap in cross-modality
similarities computation. We successfully apply the MutualFormer to the
saliency detection problem and propose a novel approach to obtain the
reinforced features of RGB and Depth images. Extensive experiments on six
popular datasets demonstrate that our model achieves comparable results with 16
SOTA models.

Graph convolutional networks have made great progress in graph-based
semi-supervised learning. Existing methods mainly assume that nodes connected
by graph edges are prone to have similar attributes and labels, so that the
features smoothed by local graph structures can reveal the class similarities.
However, there often exist mismatches between graph structures and labels in
many real-world scenarios, where the structures may propagate misleading
features or labels that eventually affect the model performance. In this paper,
we propose a multi-task self-distillation framework that injects
self-supervised learning and self-distillation into graph convolutional
networks to separately address the mismatch problem from the structure side and
the label side. First, we formulate a self-supervision pipeline based on
pre-text tasks to capture different levels of similarities in graphs. The
feature extraction process is encouraged to capture more complex proximity by
jointly optimizing the pre-text task and the target task. Consequently, the
local feature aggregations are improved from the structure side. Second,
self-distillation uses soft labels of the model itself as additional
supervision, which has similar effects as label smoothing. The knowledge from
the classification pipeline and the self-supervision pipeline is collectively
distilled to improve the generalization ability of the model from the label
side. Experiment results show that the proposed method obtains remarkable
performance gains under several classic graph convolutional architectures.

In this paper, we investigate the problem of predicting the future volatility
of Forex currency pairs using the deep learning techniques. We show
step-by-step how to construct the deep-learning network by the guidance of the
empirical patterns of the intra-day volatility. The numerical results show that
the multiscale Long Short-Term Memory (LSTM) model with the input of
multi-currency pairs consistently achieves the state-of-the-art accuracy
compared with both the conventional baselines, i.e. autoregressive and GARCH
model, and the other deep learning models.

Graph representation learning (GRL) has emerged as a powerful technique for
solving graph analytics tasks. It can effectively convert discrete graph data
into a low-dimensional space where the graph structural information and graph
properties are maximumly preserved. While there is rich literature on node and
whole-graph representation learning, GRL for link is relatively less studied
and less understood. One common practice in previous works is to generate link
representations by directly aggregating the representations of their incident
nodes, which is not capable of capturing effective link features. Moreover,
common GRL methods usually rely on full-graph training, suffering from poor
scalability and high resource consumption on large-scale graphs. In this paper,
we design Subgraph Contrastive Link Representation Learning (SCLRL) -- a
self-supervised link embedding framework, which utilizes the strong correlation
between central links and their neighborhood subgraphs to characterize links.
We extract the "link-centric induced subgraphs" as input, with a subgraph-level
contrastive discrimination as pretext task, to learn the intrinsic and
structural link features via subgraph mini-batch training. Extensive
experiments conducted on five datasets demonstrate that SCLRL has significant
performance advantages in link representation learning on benchmark datasets
and prominent efficiency advantages in terms of training speed and memory
consumption on large-scale graphs, when compared with existing link
representation learning methods.

Modeling the world can benefit robot learning by providing a rich training
signal for shaping an agent's latent state space. However, learning world
models in unconstrained environments over high-dimensional observation spaces
such as images is challenging. One source of difficulty is the presence of
irrelevant but hard-to-model background distractions, and unimportant visual
details of task-relevant entities. We address this issue by learning a
recurrent latent dynamics model which contrastively predicts the next
observation. This simple model leads to surprisingly robust robotic control
even with simultaneous camera, background, and color distractions. We
outperform alternatives such as bisimulation methods which impose
state-similarity measures derived from divergence in future reward or future
optimal actions. We obtain state-of-the-art results on the Distracting Control
Suite, a challenging benchmark for pixel-based robotic control.

The ubiquity of implicit feedback makes it indispensable for building
recommender systems. However, it does not actually reflect the actual
satisfaction of users. For example, in E-commerce, a large portion of clicks do
not translate to purchases, and many purchases end up with negative reviews. As
such, it is of importance to account for the inevitable noises in implicit
feedback. However, little work on recommendation has taken the noisy nature of
implicit feedback into consideration. In this work, we explore the central
theme of denoising implicit feedback for recommender learning, including
training and inference. By observing the process of normal recommender
training, we find that noisy feedback typically has large loss values in the
early stages. Inspired by this observation, we propose a new training strategy
named Adaptive Denoising Training (ADT), which adaptively prunes the noisy
interactions by two paradigms (i.e., Truncated Loss and Reweighted Loss).
Furthermore, we consider extra feedback (e.g., rating) as auxiliary signal and
propose three strategies to incorporate extra feedback into ADT: finetuning,
warm-up training, and colliding inference. We instantiate the two paradigms on
the widely used binary cross-entropy loss and test them on three representative
recommender models. Extensive experiments on three benchmarks demonstrate that
ADT significantly improves the quality of recommendation over normal training
without using extra feedback. Besides, the proposed three strategies for using
extra feedback largely enhance the denoising ability of ADT.

The generation of feasible adversarial examples is necessary for properly
assessing models that work in constrained feature space. However, it remains a
challenging task to enforce constraints into attacks that were designed for
computer vision. We propose a unified framework to generate feasible
adversarial examples that satisfy given domain constraints. Our framework can
handle both linear and non-linear constraints. We instantiate our framework
into two algorithms: a gradient-based attack that introduces constraints in the
loss function to maximize, and a multi-objective search algorithm that aims for
misclassification, perturbation minimization, and constraint satisfaction. We
show that our approach is effective in four different domains, with a success
rate of up to 100%, where state-of-the-art attacks fail to generate a single
feasible example. In addition to adversarial retraining, we propose to
introduce engineered non-convex constraints to improve model adversarial
robustness. We demonstrate that this new defense is as effective as adversarial
retraining. Our framework forms the starting point for research on constrained
adversarial attacks and provides relevant baselines and datasets that future
research can exploit.

Generating factual-consistent summaries is a challenging task for abstractive
summarization. Previous works mainly encode factual information or perform
post-correct/rank after decoding. In this paper, we provide a
factual-consistent solution from the perspective of contrastive learning, which
is a natural extension of previous works. We propose CO2Sum (Contrastive for
Consistency), a contrastive learning scheme that can be easily applied on
sequence-to-sequence models for factual-consistent abstractive summarization,
proving that the model can be fact-aware without modifying the architecture.
CO2Sum applies contrastive learning on the encoder, which can help the model be
aware of the factual information contained in the input article, or performs
contrastive learning on the decoder, which makes the model to generate
factual-correct output summary. What's more, these two schemes are orthogonal
and can be combined to further improve faithfulness. Comprehensive experiments
on public benchmarks demonstrate that CO2Sum improves the faithfulness on large
pre-trained language models and reaches competitive results compared to other
strong factual-consistent summarization baselines.

In this paper, we study the stochastic combinatorial multi-armed bandit
problem under semi-bandit feedback. While much work has been done on algorithms
that optimize the expected reward for linear as well as some general reward
functions, we study a variant of the problem, where the objective is to be
risk-aware. More specifically, we consider the problem of maximizing the
Conditional Value-at-Risk (CVaR), a risk measure that takes into account only
the worst-case rewards. We propose new algorithms that maximize the CVaR of the
rewards obtained from the super arms of the combinatorial bandit for the two
cases of Gaussian and bounded arm rewards. We further analyze these algorithms
and provide regret bounds. We believe that our results provide the first
theoretical insights into combinatorial semi-bandit problems in the risk-aware
case.

Carotid artery vessel wall thickness measurement is an essential step in the
monitoring of patients with atherosclerosis. This requires accurate
segmentation of the vessel wall, i.e., the region between an artery's lumen and
outer wall, in black-blood magnetic resonance (MR) images. Commonly used
convolutional neural networks (CNNs) for semantic segmentation are suboptimal
for this task as their use does not guarantee a contiguous ring-shaped
segmentation. Instead, in this work, we cast vessel wall segmentation as a
multi-task regression problem in a polar coordinate system. For each carotid
artery in each axial image slice, we aim to simultaneously find two
non-intersecting nested contours that together delineate the vessel wall. CNNs
applied to this problem enable an inductive bias that guarantees ring-shaped
vessel walls. Moreover, we identify a problem-specific training data
augmentation technique that substantially affects segmentation performance. We
apply our method to segmentation of the internal and external carotid artery
wall, and achieve top-ranking quantitative results in a public challenge, i.e.,
a median Dice similarity coefficient of 0.813 for the vessel wall and median
Hausdorff distances of 0.552 mm and 0.776 mm for lumen and outer wall,
respectively. Moreover, we show how the method improves over a conventional
semantic segmentation approach. These results show that it is feasible to
automatically obtain anatomically plausible segmentations of the carotid vessel
wall with high accuracy.

3D object detection has been wildly studied in recent years, especially for
robot perception systems. However, existing 3D object detection is under a
closed-set condition, meaning that the network can only output boxes of trained
classes. Unfortunately, this closed-set condition is not robust enough for
practical use, as it will identify unknown objects as known by mistake.
Therefore, in this paper, we propose an open-set 3D object detector, which aims
to (1) identify known objects, like the closed-set detection, and (2) identify
unknown objects and give their accurate bounding boxes. Specifically, we divide
the open-set 3D object detection problem into two steps: (1) finding out the
regions containing the unknown objects with high probability and (2) enclosing
the points of these regions with proper bounding boxes. The first step is
solved by the finding that unknown objects are often classified as known
objects with low confidence, and we show that the Euclidean distance sum based
on metric learning is a better confidence score than the naive softmax
probability to differentiate unknown objects from known objects. On this basis,
unsupervised clustering is used to refine the bounding boxes of unknown
objects. The proposed method combining metric learning and unsupervised
clustering is called the MLUC network. Our experiments show that our MLUC
network achieves state-of-the-art performance and can identify both known and
unknown objects as expected.

The availability and interactive nature of social media have made them the
primary source of news around the globe. The popularity of social media tempts
criminals to pursue their immoral intentions by producing and disseminating
fake news using seductive text and misleading images. Therefore, verifying
social media news and spotting fakes is crucial. This work aims to analyze
multi-modal features from texts and images in social media for detecting fake
news. We propose a Fake News Revealer (FNR) method that utilizes transform
learning to extract contextual and semantic features and contrastive loss to
determine the similarity between image and text. We applied FNR on two real
social media datasets. The results show the proposed method achieves higher
accuracies in detecting fake news compared to the previous works.

Modeling law search and retrieval as prediction problems has recently emerged
as a predominant approach in law intelligence. Focusing on the law article
retrieval task, we present a deep learning framework named LamBERTa, which is
designed for civil-law codes, and specifically trained on the Italian civil
code. To our knowledge, this is the first study proposing an advanced approach
to law article prediction for the Italian legal system based on a BERT
(Bidirectional Encoder Representations from Transformers) learning framework,
which has recently attracted increased attention among deep learning
approaches, showing outstanding effectiveness in several natural language
processing and learning tasks. We define LamBERTa models by fine-tuning an
Italian pre-trained BERT on the Italian civil code or its portions, for law
article retrieval as a classification task. One key aspect of our LamBERTa
framework is that we conceived it to address an extreme classification
scenario, which is characterized by a high number of classes, the few-shot
learning problem, and the lack of test query benchmarks for Italian legal
prediction tasks. To solve such issues, we define different methods for the
unsupervised labeling of the law articles, which can in principle be applied to
any law article code system. We provide insights into the explainability and
interpretability of our LamBERTa models, and we present an extensive
experimental analysis over query sets of different type, for single-label as
well as multi-label evaluation tasks. Empirical evidence has shown the
effectiveness of LamBERTa, and also its superiority against widely used
deep-learning text classifiers and a few-shot learner conceived for an
attribute-aware prediction task.

Biases can filter into AI technology without our knowledge. Oftentimes,
seminal deep learning networks champion increased accuracy above all else. In
this paper, we attempt to alleviate biases encountered by semantic segmentation
models in urban driving scenes, via an iteratively trained unlearning
algorithm. Convolutional neural networks have been shown to rely on colour and
texture rather than geometry. This raises issues when safety-critical
applications, such as self-driving cars, encounter images with covariate shift
at test time - induced by variations such as lighting changes or seasonality.
Conceptual proof of bias unlearning has been shown on simple datasets such as
MNIST. However, the strategy has never been applied to the safety-critical
domain of pixel-wise semantic segmentation of highly variable training data -
such as urban scenes. Trained models for both the baseline and bias unlearning
scheme have been tested for performance on colour-manipulated validation sets
showing a disparity of up to 85.50% in mIoU from the original RGB images -
confirming segmentation networks strongly depend on the colour information in
the training data to make their classification. The bias unlearning scheme
shows improvements of handling this covariate shift of up to 61% in the best
observed case - and performs consistently better at classifying the "human" and
"vehicle" classes compared to the baseline model.

We study the complexity of optimizing highly smooth convex functions. For a
positive integer $p$, we want to find an $\epsilon$-approximate minimum of a
convex function $f$, given oracle access to the function and its first $p$
derivatives, assuming that the $p$th derivative of $f$ is Lipschitz.
  Recently, three independent research groups (Jiang et al., PLMR 2019;
Gasnikov et al., PLMR 2019; Bubeck et al., PLMR 2019) developed a new algorithm
that solves this problem with $\tilde{O}(1/\epsilon^{\frac{2}{3p+1}})$ oracle
calls for constant $p$. This is known to be optimal (up to log factors) for
deterministic algorithms, but known lower bounds for randomized algorithms do
not match this bound. We prove a new lower bound that matches this bound (up to
log factors), and holds not only for randomized algorithms, but also for
quantum algorithms.

Graph Neural Networks (GNNs) have achieved great success in processing graph
data by extracting and propagating structure-aware features. Existing GNN
research designs various propagation schemes to guide the aggregation of
neighbor information. Recently the field has advanced from local propagation
schemes that focus on local neighbors towards extended propagation schemes that
can directly deal with extended neighbors consisting of both local and
high-order neighbors. Despite the impressive performance, existing approaches
are still insufficient to build an efficient and learnable extended propagation
scheme that can adaptively adjust the influence of local and high-order
neighbors. This paper proposes an efficient yet effective end-to-end framework,
namely Contrastive Adaptive Propagation Graph Neural Networks (CAPGNN), to
address these issues by combining Personalized PageRank and attention
techniques. CAPGNN models the learnable extended propagation scheme with a
polynomial of a sparse local affinity matrix, where the polynomial relies on
Personalized PageRank to provide superior initial coefficients. In order to
adaptively adjust the influence of both local and high-order neighbors, a
coefficient-attention model is introduced to learn to adjust the coefficients
of the polynomial. In addition, we leverage self-supervised learning techniques
and design a negative-free entropy-aware contrastive loss to explicitly take
advantage of unlabeled data for training. We implement CAPGNN as two different
versions named CAPGCN and CAPGAT, which use static and dynamic sparse local
affinity matrices, respectively. Experiments on graph benchmark datasets
suggest that CAPGNN can consistently outperform or match state-of-the-art
baselines. The source code is publicly available at
https://github.com/hujunxianligong/CAPGNN.

In a conventional experiment, inductive inferences between source and target
systems are typically justified with reference to a uniformity principle
between systems of the same material type. In an analogue quantum simulation,
by contrast, scientists aim to learn about target quantum systems of one
material type via an experiment on a source quantum system of a different
material type. In this paper, we argue that such an inference can be justified
by reference to the two quantum systems being of the same empirical type. We
illustrate this novel experimental practice of wavefunction engineering with
reference to the example of Bose-Hubbard systems.

Investigating efficiently the data collected from a system's activity can
help to detect malicious attempts and better understand the context behind past
incident occurrences. Nowadays, several solutions can be used to monitor system
activities to detect probable abnormalities and malfunctions. However, most of
these systems overwhelm their users with vast amounts of information, making it
harder for them to perceive incident occurrences and their context. Our
approach combines a dynamic and intuitive user interface with Machine Learning
forecasts to provide an intelligent investigation tool that facilitates the
security operator's work. Our system can also act as an enhanced and fully
automated decision support mechanism that provides suggestions about possible
incident occurrences.

Machine learning models are widely used for real-world applications, such as
document analysis and vision. Constrained machine learning problems are
problems where learned models have to both be accurate and respect constraints.
For continuous convex constraints, many works have been proposed, but learning
under combinatorial constraints is still a hard problem. The goal of this paper
is to broaden the modeling capacity of constrained machine learning problems by
incorporating existing work from combinatorial optimization. We propose first a
general framework called BaGeL (Branch, Generate and Learn) which applies
Branch and Bound to constrained learning problems where a learning problem is
generated and trained at each node until only valid models are obtained.
Because machine learning has specific requirements, we also propose an extended
table constraint to split the space of hypotheses. We validate the approach on
two examples: a linear regression under configuration constraints and a
non-negative matrix factorization with prior knowledge for latent semantics
analysis.

In the institutional research mode, in order to explore which characteristics
are the best indicators for predicting academic risk from the student behavior
data sets that have high-dimensional, unbalanced classified small sample, it
transforms the academic risk prediction of college students into a binary
classification task. It predicts academic risk based on the LightGBM model and
the interpretable machine learning method of Shapley value. The simulation
results show that from the global perspective of the prediction model,
characteristics such as the quality of academic partners, the seating position
in classroom, the dormitory study atmosphere, the English scores of the college
entrance examination, the quantity of academic partners, the addiction level of
video games, the mobility of academic partners, and the degree of truancy are
the best 8 predictors for academic risk. It is contrary to intuition that
characteristics such as living in campus or not, work-study, lipstick
addiction, student leader or not, lover amount, and smoking have little
correlation with university academic risk in this experiment. From the local
perspective of the sample, the factors affecting academic risk vary from person
to person. It can perform personalized interpretable analysis through Shapley
values, which cannot be done by traditional mathematical statistical prediction
models. The academic contributions of this research are mainly in two aspects:
First, the learning interaction networks is proposed for the first time, so
that social behavior can be used to compensate for the one-sided individual
behavior and improve the performance of academic risk prediction. Second, the
introduction of Shapley value calculation makes machine learning that lacks a
clear reasoning process visualized, and provides intuitive decision support for
education managers.

Intention sharing is crucial for efficient cooperation under partially
observable environments in multi-agent reinforcement learning (MARL). However,
message deceiving, i.e., a mismatch between the propagated intentions and the
final decisions, may happen when agents change strategies simultaneously
according to received intentions. Message deceiving leads to potential
miscoordination and difficulty for policy learning. This paper proposes the
leader-follower forest (LFF) to learn the hierarchical relationship between
agents based on interdependencies, achieving one-sided intention sharing in
multi-agent communication. By limiting the flowings of intentions through
directed edges, intention sharing via LFF (IS-LFF) can eliminate message
deceiving effectively and achieve better coordination. In addition, a twostage
learning algorithm is proposed to train the forest and the agent network. We
evaluate IS-LFF on multiple partially observable MARL benchmarks, and the
experimental results show that our method outperforms state-of-the-art
communication algorithms.

Modern large-scale deep learning workloads highlight the need for parallel
execution across many devices in order to fit model data into hardware
accelerator memories. In these settings, array redistribution may be required
during a computation, but can also become a bottleneck if not done efficiently.
In this paper we address the problem of redistributing multi-dimensional array
data in SPMD computations, the most prevalent form of parallelism in deep
learning. We present a type-directed approach to synthesizing array
redistributions as sequences of MPI-style collective operations. We prove
formally that our synthesized redistributions are memory-efficient and perform
no excessive data transfers. Array redistribution for SPMD computations using
collective operations has also been implemented in the context of the XLA SPMD
partitioner, a production-grade tool for partitioning programs across
accelerator systems. We evaluate our approach against the XLA implementation
and find that our approach delivers a geometric mean speedup of $1.22\times$,
with maximum speedups as a high as $5.7\times$, while offering provable memory
guarantees, making our system particularly appealing for large-scale models.

This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine
Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML
models to work on integrated datasets, empowering the learning process with
diverse information from different datasets. Therefore, R-NIDS targets the
design of more robust models, that generalize better than traditional
approaches. We also propose a new dataset, called UNK21. It is built from three
of the most well-known network datasets (UGR'16, USNW-NB15 and NLS-KDD), each
one gathered from its own network environment, with different features and
classes, by using a data aggregation approach present in R-NIDS. Following
R-NIDS, in this work we propose to build two well-known ML models (a linear and
a non-linear one) based on the information of three of the most common datasets
in the literature for NIDS evaluation, those integrated in UNK21. The results
that the proposed methodology offers show how these two ML models trained as a
NIDS solution could benefit from this approach, being able to generalize better
when training on the newly proposed UNK21 dataset. Furthermore, these results
are carefully analyzed with statistical tools that provide high confidence on

Recently, Graph Neural Networks (GNNs) have gained popularity in a variety of
real-world scenarios. Despite the great success, the architecture design of
GNNs heavily relies on manual labor. Thus, automated graph neural network
(AutoGNN) has attracted interest and attention from the research community,
which makes significant performance improvements in recent years. However,
existing AutoGNN works mainly adopt an implicit way to model and leverage the
link information in the graphs, which is not well regularized to the link
prediction task on graphs, and limits the performance of AutoGNN for other
graph tasks. In this paper, we present a novel AutoGNN work that explicitly
models the link information, abbreviated to AutoGEL. In such a way, AutoGEL can
handle the link prediction task and improve the performance of AutoGNNs on the
node classification and graph classification task. Specifically, AutoGEL
proposes a novel search space containing various design dimensions at both
intra-layer and inter-layer designs and adopts a more robust differentiable
search algorithm to further improve efficiency and effectiveness. Experimental
results on benchmark data sets demonstrate the superiority of AutoGEL on
several tasks.

This paper is devoted to the problem of detection of forest and non-forest
areas on Earth images. We propose two statistical methods to tackle this
problem: one based on multiple hypothesis testing with parametric distribution
families, another one -- on non-parametric tests. The parametric approach is
novel in the literature and relevant to a larger class of problems -- detection
of natural objects, as well as anomaly detection. We develop mathematical
background for each of the two methods, build self-sufficient detection
algorithms using them and discuss practical aspects of their implementation. We
also compare our algorithms with those from standard machine learning using

Enhancing the diversity of sentences to describe video contents is an
important problem arising in recent video captioning research. In this paper,
we explore this problem from a novel perspective of customizing video captions
by imitating exemplar sentence syntaxes. Specifically, given a video and any
syntax-valid exemplar sentence, we introduce a new task of Syntax Customized
Video Captioning (SCVC) aiming to generate one caption which not only
semantically describes the video contents but also syntactically imitates the
given exemplar sentence. To tackle the SCVC task, we propose a novel video
captioning model, where a hierarchical sentence syntax encoder is firstly
designed to extract the syntactic structure of the exemplar sentence, then a
syntax conditioned caption decoder is devised to generate the syntactically
structured caption expressing video semantics. As there is no available syntax
customized groundtruth video captions, we tackle such a challenge by proposing
a new training strategy, which leverages the traditional pairwise video
captioning data and our collected exemplar sentences to accomplish the model
learning. Extensive experiments, in terms of semantic, syntactic, fluency, and
diversity evaluations, clearly demonstrate our model capability to generate
syntax-varied and semantics-coherent video captions that well imitate different
exemplar sentences with enriched diversities.

We use machine learning tools to model the line interaction of failure
cascading in power grid networks. We first collect data sets of simulated
trajectories of possible consecutive line failure following an initial random
failure and considering actual constraints in a model power network until the
system settles at a steady state. We use weighted $l_1$-regularized logistic
regression-based models to find static and dynamic models that capture pairwise
and latent higher-order lines' failure interactions using pairwise statistical
data. The static model captures the failures' interactions near the steady
states of the network, and the dynamic model captures the failure unfolding in
a time series of consecutive network states. We test models over independent
trajectories of failure unfolding in the network to evaluate their failure
predictive power. We observe asymmetric, strongly positive, and negative
interactions between different lines' states in the network. We use the static
interaction model to estimate the distribution of cascade size and identify
groups of lines that tend to fail together, and compare against the data. The
dynamic interaction model successfully predicts the network state for
long-lasting failure propagation trajectories after an initial failure.

Conversational analysis systems are trained using noisy human labels and
often require heavy preprocessing during multi-modal feature extraction. Using
noisy labels in single-task learning increases the risk of over-fitting.
Auxiliary tasks could improve the performance of the primary task learning
during the same training -- this approach sits in the intersection of transfer
learning and multi-task learning (MTL). In this paper, we explore how the
preprocessed data used for feature engineering can be re-used as auxiliary
tasks, thereby promoting the productive use of data. Our main contributions
are: (1) the identification of sixteen beneficially auxiliary tasks, (2)
studying the method of distributing learning capacity between the primary and
auxiliary tasks, and (3) studying the relative supervision hierarchy between
the primary and auxiliary tasks. Extensive experiments on IEMOCAP and SEMAINE
data validate the improvements over single-task approaches, and suggest that it
may generalize across multiple primary tasks.

Contrastive learning techniques have been widely used in the field of
computer vision as a means of augmenting datasets. In this paper, we extend the
use of these contrastive learning embeddings to sentiment analysis tasks and
demonstrate that fine-tuning on these embeddings provides an improvement over
fine-tuning on BERT-based embeddings to achieve higher benchmarks on the task
of sentiment analysis when evaluated on the DynaSent dataset. We also explore
how our fine-tuned models perform on cross-domain benchmark datasets.
Additionally, we explore upsampling techniques to achieve a more balanced class
distribution to make further improvements on our benchmark tasks.

Point clouds are gaining prominence as a method for representing 3D shapes,
but their irregular structure poses a challenge for deep learning methods. In
this paper we propose CloudWalker, a novel method for learning 3D shapes using
random walks. Previous works attempt to adapt Convolutional Neural Networks
(CNNs) or impose a grid or mesh structure to 3D point clouds. This work
presents a different approach for representing and learning the shape from a
given point set. The key idea is to impose structure on the point set by
multiple random walks through the cloud for exploring different regions of the
3D object. Then we learn a per-point and per-walk representation and aggregate
multiple walk predictions at inference. Our approach achieves state-of-the-art
results for two 3D shape analysis tasks: classification and retrieval.

Optimizing expensive to evaluate black-box functions over an input space
consisting of all permutations of d objects is an important problem with many
real-world applications. For example, placement of functional blocks in
hardware design to optimize performance via simulations. The overall goal is to
minimize the number of function evaluations to find high-performing
permutations. The key challenge in solving this problem using the Bayesian
optimization (BO) framework is to trade-off the complexity of statistical model
and tractability of acquisition function optimization. In this paper, we
propose and evaluate two algorithms for BO over Permutation Spaces (BOPS).
First, BOPS-T employs Gaussian process (GP) surrogate model with Kendall
kernels and a Tractable acquisition function optimization approach based on
Thompson sampling to select the sequence of permutations for evaluation.
Second, BOPS-H employs GP surrogate model with Mallow kernels and a Heuristic
search approach to optimize expected improvement acquisition function. We
theoretically analyze the performance of BOPS-T to show that their regret grows
sub-linearly. Our experiments on multiple synthetic and real-world benchmarks
show that both BOPS-T and BOPS-H perform better than the state-of-the-art BO
algorithm for combinatorial spaces. To drive future research on this important
problem, we make new resources and real-world benchmarks available to the

Lack of labeled data is a main obstacle in relation extraction.
Semi-supervised relation extraction (SSRE) has been proven to be a promising
way for this problem through annotating unlabeled samples as additional
training data. Almost all prior researches along this line adopt multiple
models to make the annotations more reliable by taking the intersection set of
predicted results from these models. However, the difference set, which
contains rich information about unlabeled data, has been long neglected by
prior studies.
  In this paper, we propose to learn not only from the consensus but also the
disagreement among different models in SSRE. To this end, we develop a simple
and general multi-teacher distillation (MTD) framework, which can be easily
integrated into any existing SSRE methods. Specifically, we first let the
teachers correspond to the multiple models and select the samples in the
intersection set of the last iteration in SSRE methods to augment labeled data
as usual. We then transfer the class distributions for samples in the
difference set as soft labels to guide the student. We finally perform
prediction using the trained student model. Experimental results on two public
datasets demonstrate that our framework significantly promotes the performance
of the base SSRE methods with pretty low computational cost.

The increasing demand for analyzing the insights in sports has stimulated a
line of productive studies from a variety of perspectives, e.g., health state
monitoring, outcome prediction. In this paper, we focus on objectively judging
what and where to return strokes, which is still unexplored in turn-based
sports. By formulating stroke forecasting as a sequence prediction task,
existing works can tackle the problem but fail to model information based on
the characteristics of badminton. To address these limitations, we propose a
novel Position-aware Fusion of Rally Progress and Player Styles framework
(ShuttleNet) that incorporates rally progress and information of the players by
two modified encoder-decoder extractors. Moreover, we design a fusion network
to integrate rally contexts and contexts of the players by conditioning on
information dependency and different positions. Extensive experiments on the
badminton dataset demonstrate that ShuttleNet significantly outperforms the
state-of-the-art methods and also empirically validates the feasibility of each
component in ShuttleNet. On top of that, we provide an analysis scenario for
the stroke forecasting problem.

We introduce N-ImageNet, a large-scale dataset targeted for robust,
fine-grained object recognition with event cameras. The dataset is collected
using programmable hardware in which an event camera consistently moves around
a monitor displaying images from ImageNet. N-ImageNet serves as a challenging
benchmark for event-based object recognition, due to its large number of
classes and samples. We empirically show that pretraining on N-ImageNet
improves the performance of event-based classifiers and helps them learn with
few labeled data. In addition, we present several variants of N-ImageNet to
test the robustness of event-based classifiers under diverse camera
trajectories and severe lighting conditions, and propose a novel event
representation to alleviate the performance degradation. To the best of our
knowledge, we are the first to quantitatively investigate the consequences
caused by various environmental conditions on event-based object recognition
algorithms. N-ImageNet and its variants are expected to guide practical
implementations for deploying event-based object recognition algorithms in the

Knowledge graph (KG) inference aims to address the natural incompleteness of
KGs, including rule learning-based and KG embedding (KGE) models. However, the
rule learning-based models suffer from low efficiency and generalization while
KGE models lack interpretability. To address these challenges, we propose a
novel and effective closed-loop neural-symbolic learning framework EngineKG via
incorporating our developed KGE and rule learning modules. KGE module exploits
symbolic rules and paths to enhance the semantic association between entities
and relations for improving KG embeddings and interpretability. A novel rule
pruning mechanism is proposed in the rule learning module by leveraging paths
as initial candidate rules and employing KG embeddings together with concepts
for extracting more high-quality rules. Experimental results on four real-world
datasets show that our model outperforms the relevant baselines on link
prediction tasks, demonstrating the superiority of our KG inference model in a
neural-symbolic learning fashion.

Federated learning (FL) has recently emerged as a transformative paradigm
that jointly train a model with distributed data sets in IoT while avoiding the
need for central data collection. Due to the limited observation range, such
data sets can only reflect local information, which limits the quality of
trained models. In practice, the global information and local observations
would require a joint consideration for learning to make a reasonable policy.
However, in horizontal FL, the central agency only acts as a model aggregator
without utilizing its global observation to further improve the model. This
could significantly degrade the performance in some missions such as traffic
flow prediction in network systems, where the global information may enhance
the accuracy. Meanwhile, the global feature may not be directly transmitted to
agents for data security. How to utilize the global observation residing in the
central agency while protecting its safety thus rises up as an important
problem in FL. In this paper, we develop a vertical-horizontal federated
learning (VHFL) process, where the global feature is shared with the agents in
a procedure similar to that of vertical FL without any extra communication
rounds. By considering the delay and packet loss, we will analyze VHFL
convergence and validate its performance by experiments. It is shown that the
proposed VHFL could enhance the accuracy compared with horizontal FL while
still protecting the security of global data.

First-person action recognition is a challenging task in video understanding.
Because of strong ego-motion and a limited field of view, many backgrounds or
noisy frames in a first-person video can distract an action recognition model
during its learning process. To encode more discriminative features, the model
needs to have the ability to focus on the most relevant part of the video for
action recognition. Previous works explored to address this problem by applying
temporal attention but failed to consider the global context of the full video,
which is critical for determining the relatively significant parts. In this
work, we propose a simple yet effective Stacked Temporal Attention Module
(STAM) to compute temporal attention based on the global knowledge across clips
for emphasizing the most discriminative features. We achieve this by stacking
multiple self-attention layers. Instead of naive stacking, which is
experimentally proven to be ineffective, we carefully design the input to each
self-attention layer so that both the local and global context of the video is
considered during generating the temporal attention weights. Experiments
demonstrate that our proposed STAM can be built on top of most existing
backbones and boost the performance in various datasets.

Segmenting an image into its parts is a frequent preprocess for high-level
vision tasks such as image editing. However, annotating masks for supervised
training is expensive. Weakly-supervised and unsupervised methods exist, but
they depend on the comparison of pairs of images, such as from multi-views,
frames of videos, and image augmentation, which limits their applicability. To
address this, we propose a GAN-based approach that generates images conditioned
on latent masks, thereby alleviating full or weak annotations required in
previous approaches. We show that such mask-conditioned image generation can be
learned faithfully when conditioning the masks in a hierarchical manner on
latent keypoints that define the position of parts explicitly. Without
requiring supervision of masks or points, this strategy increases robustness to
viewpoint and object positions changes. It also lets us generate image-mask
pairs for training a segmentation network, which outperforms the
state-of-the-art unsupervised segmentation methods on established benchmarks.

In recent years, owing to the outstanding performance in graph representation
learning, graph neural network (GNN) techniques have gained considerable
interests in many real-world scenarios, such as recommender systems and social
networks. In recommender systems, the main challenge is to learn the effective
user/item representations from their interactions. However, many recent
publications using GNNs for recommender systems cannot be directly compared,
due to their difference on datasets and evaluation metrics. Furthermore, many
of them only provide a demo to conduct experiments on small datasets, which is
far away to be applied in real-world recommender systems. To address this
problem, we introduce Graph4Rec, a universal toolkit that unifies the paradigm
to train GNN models into the following parts: graphs input, random walk
generation, ego graphs generation, pairs generation and GNNs selection. From
this training pipeline, one can easily establish his own GNN model with a few
configurations. Besides, we develop a large-scale graph engine and a parameter
server to support distributed GNN training. We conduct a systematic and
comprehensive experiment to compare the performance of different GNN models on
several scenarios in different scale. Extensive experiments are demonstrated to
identify the key components of GNNs. We also try to figure out how the sparse
and dense parameters affect the performance of GNNs. Finally, we investigate
methods including negative sampling, ego graph construction order, and warm
start strategy to find a more effective and efficient GNNs practice on
recommender systems. Our toolkit is based on PGL
https://github.com/PaddlePaddle/PGL and the code is opened source in
https://github.com/PaddlePaddle/PGL/tree/main/apps/Graph4Rec.

The human gaze is a cost-efficient physiological data that reveals human
underlying attentional patterns. The selective attention mechanism helps the
cognition system focus on task-relevant visual clues by ignoring the presence
of distractors. Thanks to this ability, human beings can efficiently learn from
a very limited number of training samples. Inspired by this mechanism, we aim
to leverage gaze for medical image analysis tasks with small training data. Our
proposed framework includes a backbone encoder and a Selective Attention
Network (SAN) that simulates the underlying attention. The SAN implicitly
encodes information such as suspicious regions that is relevant to the medical
diagnose tasks by estimating the actual human gaze. Then we design a novel
Auxiliary Attention Block (AAB) to allow information from SAN to be utilized by
the backbone encoder to focus on selective areas. Specifically, this block uses
a modified version of a multi-head attention layer to simulate the human visual
search procedure. Note that the SAN and AAB can be plugged into different
backbones, and the framework can be used for multiple medical image analysis
tasks when equipped with task-specific heads. Our method is demonstrated to
achieve superior performance on both 3D tumor segmentation and 2D chest X-ray
classification tasks. We also show that the estimated gaze probability map of
the SAN is consistent with an actual gaze fixation map obtained by
board-certified doctors.

The ARIANNA experiment is an Askaryan detector designed to record radio
signals induced by neutrino interactions in the Antarctic ice. Because of the
low neutrino flux at high energies ($E > 10^{16} $), the physics output is
limited by statistics. Hence, an increase in sensitivity significantly improves
the interpretation of data and offers the ability to probe new parameter
spaces. The amplitudes of the trigger threshold are limited by the rate of
triggering on unavoidable thermal noise fluctuations. We present a real-time
thermal noise rejection algorithm that enables the trigger thresholds to be
lowered, which increases the sensitivity to neutrinos by up to a factor of two
(depending on energy) compared to the current ARIANNA capabilities. A deep
learning discriminator, based on a Convolutional Neural Network (CNN), is
implemented to identify and remove thermal events in real time. We describe a
CNN trained on MC data that runs on the current ARIANNA microcomputer and
retains 95 percent of the neutrino signal at a thermal noise rejection factor
of $10^5$, compared to a template matching procedure which reaches only $10^2$
for the same signal efficiency. Then the results are verified in a lab
measurement by feeding in generated neutrino-like signal pulses and thermal
noise directly into the ARIANNA data acquisition system. Lastly, the same CNN
is used to classify cosmic-rays events to make sure they are not rejected. The
network classified 102 out of 104 cosmic-ray events as signal.

In this paper, we propose TransMEF, a transformer-based multi-exposure image
fusion framework that uses self-supervised multi-task learning. The framework
is based on an encoder-decoder network, which can be trained on large natural
image datasets and does not require ground truth fusion images. We design three
self-supervised reconstruction tasks according to the characteristics of
multi-exposure images and conduct these tasks simultaneously using multi-task
learning; through this process, the network can learn the characteristics of
multi-exposure images and extract more generalized features. In addition, to
compensate for the defect in establishing long-range dependencies in CNN-based
architectures, we design an encoder that combines a CNN module with a
transformer module. This combination enables the network to focus on both local
and global information. We evaluated our method and compared it to 11
competitive traditional and deep learning-based methods on the latest released
multi-exposure image fusion benchmark dataset, and our method achieved the best
performance in both subjective and objective evaluations.

This paper presents a novel deep learning architecture for acoustic model in
the context of Automatic Speech Recognition (ASR), termed as MixNet. Besides
the conventional layers, such as fully connected layers in DNN-HMM and memory
cells in LSTM-HMM, the model uses two additional layers based on Mixture of
Experts (MoE). The first MoE layer operating at the input is based on
pre-defined broad phonetic classes and the second layer operating at the
penultimate layer is based on automatically learned acoustic classes. In
natural speech, overlap in distribution across different acoustic classes is
inevitable, which leads to inter-class mis-classification. The ASR accuracy is
expected to improve if the conventional architecture of acoustic model is
modified to make them more suitable to account for such overlaps. MixNet is
developed keeping this in mind. Analysis conducted by means of scatter diagram
verifies that MoE indeed improves the separation between classes that
translates to better ASR accuracy. Experiments are conducted on a large
vocabulary ASR task which show that the proposed architecture provides 13.6%
and 10.0% relative reduction in word error rates compared to the conventional
models, namely, DNN and LSTM respectively, trained using sMBR criteria. In
comparison to an existing method developed for phone-classification (by Eigen
et al), our proposed method yields a significant improvement.

Despite their remarkable ability to generalize with over-capacity networks,
deep neural networks often learn to abuse spurious biases in the data instead
of using the actual task-related information. Since such shortcuts are only
effective within the collected dataset, the resulting biased model
underperforms on real-world inputs, or cause unintended social repercussions
such as gender discrimination. To counteract the influence of bias, existing
methods either exploit auxiliary information which is rarely obtainable in
practice, or sift for bias-free samples in the training data, hoping for the
sufficient existence of clean samples. However, such presumptions about the
data are not always guaranteed. In this paper, we propose Contrastive Debiasing
via Generative Bias-transformation~(CDvG) which is capable of operating in more
general environments where existing methods break down due to unmet
presumptions such as insufficient bias-free samples. Motivated by our
observation that not only discriminative models, as previously known, but also
generative models tend to focus on the bias when possible, CDvG uses a
translation model to transform the bias in the sample to another mode of bias
while preserving task-relevant information. Through contrastive learning, we
set transformed biased views against another, learning bias-invariant
representations. Experimental results on synthetic and real-world datasets
demonstrate that our framework outperforms the current state-of-the-arts, and
effectively prevents the models from being biased even when bias-free samples
are extremely scarce.

Checklists are simple decision aids that are often used to promote safety and
reliability in clinical applications. In this paper, we present a method to
learn checklists for clinical decision support. We represent predictive
checklists as discrete linear classifiers with binary features and unit
weights. We then learn globally optimal predictive checklists from data by
solving an integer programming problem. Our method allows users to customize
checklists to obey complex constraints, including constraints to enforce group
fairness and to binarize real-valued features at training time. In addition, it
pairs models with an optimality gap that can inform model development and
determine the feasibility of learning sufficiently accurate checklists on a
given dataset. We pair our method with specialized techniques that speed up its
ability to train a predictive checklist that performs well and has a small
optimality gap. We benchmark the performance of our method on seven clinical
classification problems, and demonstrate its practical benefits by training a
short-form checklist for PTSD screening. Our results show that our method can
fit simple predictive checklists that perform well and that can easily be
customized to obey a rich class of custom constraints.

Face sketch generation has attracted much attention in the field of visual
computing. However, existing methods either are limited to constrained
conditions or heavily rely on various preprocessing steps to deal with
in-the-wild cases. In this paper, we argue that accurately perceiving facial
region and facial components is crucial for unconstrained sketch synthesis. To
this end, we propose a novel Perception-Adaptive Network (PANet), which can
generate high-quality face sketches under unconstrained conditions in an
end-to-end scheme. Specifically, our PANet is composed of i) a Fully
Convolutional Encoder for hierarchical feature extraction, ii) a Face-Adaptive
Perceiving Decoder for extracting potential facial region and handling face
variations, and iii) a Component-Adaptive Perceiving Module for facial
component aware feature representation learning. To facilitate further
researches of unconstrained face sketch synthesis, we introduce a new benchmark
termed WildSketch, which contains 800 pairs of face photo-sketch with large
variations in pose, expression, ethnic origin, background, and illumination.
Extensive experiments demonstrate that the proposed method is capable of
achieving state-of-the-art performance under both constrained and unconstrained
conditions. Our source codes and the WildSketch benchmark are resealed on the
project page http://lingboliu.com/unconstrained_face_sketch.html.

Explainable AI (XAI) research has been booming, but the question "$\textbf{To
whom}$ are we making AI explainable?" is yet to gain sufficient attention. Not
much of XAI is comprehensible to non-AI experts, who nonetheless, are the
primary audience and major stakeholders of deployed AI systems in practice. The
gap is glaring: what is considered "explained" to AI-experts versus non-experts
are very different in practical scenarios. Hence, this gap produced two
distinct cultures of expectations, goals, and forms of XAI in real-life AI
deployments.
  We advocate that it is critical to develop XAI methods for non-technical
audiences. We then present a real-life case study, where AI experts provided
non-technical explanations of AI decisions to non-technical stakeholders, and
completed a successful deployment in a highly regulated industry. We then
synthesize lessons learned from the case, and share a list of suggestions for
AI experts to consider when explaining AI decisions to non-technical
stakeholders.

Question Generation (QG) receives increasing research attention in NLP
community. One motivation for QG is that QG significantly facilitates the
preparation of educational reading practice and assessments. While the
significant advancement of QG techniques was reported, current QG results are
not ideal for educational reading practice assessment in terms of
\textit{controllability} and \textit{question difficulty}. This paper reports
our results toward the two issues. First, we report a state-of-the-art
exam-like QG model by advancing the current best model from 11.96 to 20.19 (in
terms of BLEU 4 score). Second, we propose to investigate a variant of QG
setting by allowing users to provide keywords for guiding QG direction. We also
present a simple but effective model toward the QG controllability task.
Experiments are also performed and the results demonstrate the feasibility and
potentials of improving QG diversity and controllability by the proposed
keyword provision QG model.

We consider the problem of spatial path planning. In contrast to the
classical solutions which optimize a new plan from scratch and assume access to
the full map with ground truth obstacle locations, we learn a planner from the
data in a differentiable manner that allows us to leverage statistical
regularities from past data. We propose Spatial Planning Transformers (SPT),
which given an obstacle map learns to generate actions by planning over
long-range spatial dependencies, unlike prior data-driven planners that
propagate information locally via convolutional structure in an iterative
manner. In the setting where the ground truth map is not known to the agent, we
leverage pre-trained SPTs in an end-to-end framework that has the structure of
mapper and planner built into it which allows seamless generalization to
out-of-distribution maps and goals. SPTs outperform prior state-of-the-art
differentiable planners across all the setups for both manipulation and
navigation tasks, leading to an absolute improvement of 7-19%.

We present a methodology for modifying the behavior of a classifier by
directly rewriting its prediction rules. Our approach requires virtually no
additional data collection and can be applied to a variety of settings,
including adapting a model to new environments, and modifying it to ignore
spurious features. Our code is available at
https://github.com/MadryLab/EditingClassifiers .

In this paper, we explore how we can build upon the data and models of
Internet images and use them to adapt to robot vision without requiring any
extra labels. We present a framework called Self-supervised Embodied Active
Learning (SEAL). It utilizes perception models trained on internet images to
learn an active exploration policy. The observations gathered by this
exploration policy are labelled using 3D consistency and used to improve the
perception model. We build and utilize 3D semantic maps to learn both action
and perception in a completely self-supervised manner. The semantic map is used
to compute an intrinsic motivation reward for training the exploration policy
and for labelling the agent observations using spatio-temporal 3D consistency
and label propagation. We demonstrate that the SEAL framework can be used to
close the action-perception loop: it improves object detection and instance
segmentation performance of a pretrained perception model by just moving around
in training environments and the improved perception model can be used to
improve Object Goal Navigation.

Cross-domain recommendation (CDR) aims to provide better recommendation
results in the target domain with the help of the source domain, which is
widely used and explored in real-world systems. However, CDR in the matching
(i.e., candidate generation) module struggles with the data sparsity and
popularity bias issues in both representation learning and knowledge transfer.
In this work, we propose a novel Contrastive Cross-Domain Recommendation (CCDR)
framework for CDR in matching. Specifically, we build a huge diversified
preference network to capture multiple information reflecting user diverse
interests, and design an intra-domain contrastive learning (intra-CL) and three
inter-domain contrastive learning (inter-CL) tasks for better representation
learning and knowledge transfer. The intra-CL enables more effective and
balanced training inside the target domain via a graph augmentation, while the
inter-CL builds different types of cross-domain interactions from user,
taxonomy, and neighbor aspects. In experiments, CCDR achieves significant
improvements on both offline and online evaluations in a real-world system.
Currently, we have deployed our CCDR on WeChat Top Stories, affecting plenty of
users. The source code is in https://github.com/lqfarmer/CCDR.

Recently Transformer has been largely explored in tracking and shown
state-of-the-art (SOTA) performance. However, existing efforts mainly focus on
fusing and enhancing features generated by convolutional neural networks
(CNNs). The potential of Transformer in representation learning remains
under-explored. In this paper, we aim to further unleash the power of
Transformer by proposing a simple yet efficient fully-attentional tracker,
dubbed SwinTrack, within classic Siamese framework. In particular, both
representation learning and feature fusion in SwinTrack leverage the
Transformer architecture, enabling better feature interactions for tracking
than pure CNN or hybrid CNN-Transformer frameworks. Besides, to further enhance
robustness, we present a novel motion token that embeds historical target
trajectory to improve tracking by providing temporal context. Our motion token
is lightweight with negligible computation but brings clear gains. In our
thorough experiments, SwinTrack exceeds existing approaches on multiple
benchmarks. Particularly, on the challenging LaSOT, SwinTrack sets a new record
with 0.713 SUC score. It also achieves SOTA results on other benchmarks. We
expect SwinTrack to serve as a solid baseline for Transformer tracking and
facilitate future research. Our codes and results are released at
https://github.com/LitingLin/SwinTrack.

High-fidelity, AI-based simulated classroom systems enable teachers to
rehearse effective teaching strategies. However, dialogue-oriented open-ended
conversations such as teaching a student about scale factors can be difficult
to model. This paper builds a text-based interactive conversational agent to
help teachers practice mathematical questioning skills based on the well-known
Instructional Quality Assessment. We take a human-centered approach to
designing our system, relying on advances in deep learning, uncertainty
quantification, and natural language processing while acknowledging the
limitations of conversational agents for specific pedagogical needs. Using
experts' input directly during the simulation, we demonstrate how conversation
success rate and high user satisfaction can be achieved.

Electroencephalogram (EEG) recordings are often contaminated with artifacts.
Various methods have been developed to eliminate or weaken the influence of
artifacts. However, most of them rely on prior experience for analysis. Here,
we propose an deep learning framework to separate neural signal and artifacts
in the embedding space and reconstruct the denoised signal, which is called
DeepSeparator. DeepSeparator employs an encoder to extract and amplify the
features in the raw EEG, a module called decomposer to extract the trend,
detect and suppress artifact and a decoder to reconstruct the denoised signal.
Besides, DeepSeparator can extract the artifact, which largely increases the
model interpretability. The proposed method is tested with a semi-synthetic EEG
dataset and a real task-related EEG dataset, suggesting that DeepSeparator
outperforms the conventional models in both EOG and EMG artifact removal.
DeepSeparator can be extended to multi-channel EEG and data of any length. It
may motivate future developments and application of deep learning-based EEG
denoising. The code for DeepSeparator is available at
https://github.com/ncclabsustech/DeepSeparator.

Federated Learning (FL) has recently become an effective approach for
cyberattack detection systems, especially in Internet-of-Things (IoT) networks.
By distributing the learning process across IoT gateways, FL can improve
learning efficiency, reduce communication overheads and enhance privacy for
cyberattack detection systems. Challenges in implementation of FL in such
systems include unavailability of labeled data and dissimilarity of data
features in different IoT networks. In this paper, we propose a novel
collaborative learning framework that leverages Transfer Learning (TL) to
overcome these challenges. Particularly, we develop a novel collaborative
learning approach that enables a target network with unlabeled data to
effectively and quickly learn knowledge from a source network that possesses
abundant labeled data. It is important that the state-of-the-art studies
require the participated datasets of networks to have the same features, thus
limiting the efficiency, flexibility as well as scalability of intrusion
detection systems. However, our proposed framework can address these problems
by exchanging the learning knowledge among various deep learning models, even
when their datasets have different features. Extensive experiments on recent
real-world cybersecurity datasets show that the proposed framework can improve
more than 40% as compared to the state-of-the-art deep learning based

We study the statistical properties of the dynamic trajectory of stochastic
gradient descent (SGD). We approximate the mini-batch SGD and the momentum SGD
as stochastic differential equations (SDEs). We exploit the continuous
formulation of SDE and the theory of Fokker-Planck equations to develop new
results on the escaping phenomenon and the relationship with large batch and
sharp minima. In particular, we find that the stochastic process solution tends
to converge to flatter minima regardless of the batch size in the asymptotic
regime. However, the convergence rate is rigorously proven to depend on the
batch size. These results are validated empirically with various datasets and
models.

A large body of research demonstrates how teachers' questioning strategies
can improve student learning outcomes. However, developing new scenarios is
challenging because of the lack of training data for a specific scenario and
the costs associated with labeling. This paper presents a high-fidelity,
AI-based classroom simulator to help teachers rehearse research-based
mathematical questioning skills. Using a human-in-the-loop approach, we
collected a high-quality training dataset for a mathematical questioning
scenario. Using recent advances in uncertainty quantification, we evaluated our
conversational agent for usability and analyzed the practicality of
incorporating a human-in-the-loop approach for data collection and system
evaluation for a mathematical questioning scenario.

The computer-aided diagnosis of focal liver lesions (FLLs) can help improve
workflow and enable correct diagnoses; FLL detection is the first step in such
a computer-aided diagnosis. Despite the recent success of deep-learning-based
approaches in detecting FLLs, current methods are not sufficiently robust for
assessing misaligned multiphase data. By introducing an attention-guided
multiphase alignment in feature space, this study presents a fully automated,
end-to-end learning framework for detecting FLLs from multiphase computed
tomography (CT) images. Our method is robust to misaligned multiphase images
owing to its complete learning-based approach, which reduces the sensitivity of
the model's performance to the quality of registration and enables a standalone
deployment of the model in clinical practice. Evaluation on a large-scale
dataset with 280 patients confirmed that our method outperformed previous
state-of-the-art methods and significantly reduced the performance degradation
for detecting FLLs using misaligned multiphase CT images. The robustness of the
proposed method can enhance the clinical adoption of the deep-learning-based
computer-aided detection system.

In this paper, we focus on a typical two-phase phenomenon in the learning of
multi-layer perceptrons (MLPs), and we aim to explain the reason for the
decrease of feature diversity in the first phase. Specifically, people find
that, in the training of MLPs, the training loss does not decrease
significantly until the second phase. To this end, we further explore the
reason why the diversity of features over different samples keeps decreasing in
the first phase, which hurts the optimization of MLPs. We explain such a
phenomenon in terms of the learning dynamics of MLPs. Furthermore, we
theoretically explain why four typical operations can alleviate the decrease of
the feature diversity.

Recommendation systems are a key modern application of machine learning, but
they have the downside that they often draw upon sensitive user information in
making their predictions. We show how to address this deficiency by basing a
service's recommendation engine upon recommendations from other existing
services, which contain no sensitive information by nature. Specifically, we
introduce a contextual multi-armed bandit recommendation framework where the
agent has access to recommendations for other services. In our setting, the
user's (potentially sensitive) information belongs to a high-dimensional latent
space, and the ideal recommendations for the source and target tasks (which are
non-sensitive) are given by unknown linear transformations of the user
information. So long as the tasks rely on similar segments of the user
information, we can decompose the target recommendation problem into systematic
components that can be derived from the source recommendations, and
idiosyncratic components that are user-specific and cannot be derived from the
source, but have significantly lower dimensionality. We propose an
explore-then-refine approach to learning and utilizing this decomposition; then
using ideas from perturbation theory and statistical concentration of measure,
we prove our algorithm achieves regret comparable to a strong skyline that has
full knowledge of the source and target transformations. We also consider a
generalization of our algorithm to a model with many simultaneous targets and
no source. Our methods obtain superior empirical results on synthetic

Multi-label classification (MLC) is a prediction task where each sample can
have more than one label. We propose a novel contrastive learning boosted
multi-label prediction model based on a Gaussian mixture variational
autoencoder (C-GMVAE), which learns a multimodal prior space and employs a
contrastive loss. Many existing methods introduce extra complex neural modules
like graph neural networks to capture the label correlations, in addition to
the prediction modules. We find that by using contrastive learning in the
supervised setting, we can exploit label information effectively in a
data-driven manner, and learn meaningful feature and label embeddings which
capture the label correlations and enhance the predictive power. Our method
also adopts the idea of learning and aligning latent spaces for both features
and labels. In contrast to previous works based on a unimodal prior, C-GMVAE
imposes a Gaussian mixture structure on the latent space, to alleviate the
posterior collapse and over-regularization issues. C-GMVAE outperforms existing
methods on multiple public datasets and can often match other models' full
performance with only 50% of the training data. Furthermore, we show that the
learnt embeddings provide insights into the interpretation of label-label

The contemporary visual captioning models frequently hallucinate objects that
are not actually in a scene, due to the visual misclassification or
over-reliance on priors that resulting in the semantic inconsistency between
the visual information and the target lexical words. The most common way is to
encourage the captioning model to dynamically link generated object words or
phrases to appropriate regions of the image, i.e., the grounded image
captioning (GIC). However, GIC utilizes an auxiliary task (grounding objects)
that has not solved the key issue of object hallucination, i.e., the semantic
inconsistency. In this paper, we take a novel perspective on the issue above -
exploiting the semantic coherency between the visual and language modalities.
Specifically, we propose the Consensus Rraph Representation Learning framework
(CGRL) for GIC that incorporates a consensus representation into the grounded
captioning pipeline. The consensus is learned by aligning the visual graph
(e.g., scene graph) to the language graph that consider both the nodes and
edges in a graph. With the aligned consensus, the captioning model can capture
both the correct linguistic characteristics and visual relevance, and then
grounding appropriate image regions further. We validate the effectiveness of
our model, with a significant decline in object hallucination (-9% CHAIRi) on
the Flickr30k Entities dataset. Besides, our CGRL also evaluated by several
automatic metrics and human evaluation, the results indicate that the proposed
approach can simultaneously improve the performance of image captioning (+2.9
Cider) and grounding (+2.3 F1LOC).

Adversarial attacks, e.g., adversarial perturbations of the input and
adversarial samples, pose significant challenges to machine learning and deep
learning techniques, including interactive recommendation systems. The latent
embedding space of those techniques makes adversarial attacks difficult to
detect at an early stage. Recent advance in causality shows that counterfactual
can also be considered one of ways to generate the adversarial samples drawn
from different distribution as the training samples. We propose to explore
adversarial examples and attack agnostic detection on reinforcement
learning-based interactive recommendation systems. We first craft different
types of adversarial examples by adding perturbations to the input and
intervening on the casual factors. Then, we augment recommendation systems by
detecting potential attacks with a deep learning-based classifier based on the
crafted data. Finally, we study the attack strength and frequency of
adversarial examples and evaluate our model on standard datasets with multiple
crafting methods. Our extensive experiments show that most adversarial attacks
are effective, and both attack strength and attack frequency impact the attack
performance. The strategically-timed attack achieves comparative attack
performance with only 1/3 to 1/2 attack frequency. Besides, our black-box
detector trained with one crafting method has the generalization ability over
several other crafting methods.

Policy learning is a quickly growing area. As robotics and computers control
day-to-day life, their error rate needs to be minimized and controlled. There
are many policy learning methods and bandit methods with provable error rates
that accompany them. We show an error or regret bound and convergence of the
Deep Epsilon Greedy method which chooses actions with a neural network's
prediction. We also show that Epsilon Greedy method regret upper bound is
minimized with cubic root exploration. In experiments with the real-world
dataset MNIST, we construct a nonlinear reinforcement learning problem. We
witness how with either high or low noise, some methods do and some do not
converge which agrees with our proof of convergence.

The technologies used in smart homes have recently improved to learn the user
preferences from feedback in order to enhance the user convenience and quality
of experience. Most smart homes learn a uniform model to represent the thermal
preferences of users, which generally fails when the pool of occupants includes
people with different sensitivities to temperature, for instance due to age and
physiological factors. Thus, a smart home with a single optimal policy may fail
to provide comfort when a new user with a different preference is integrated
into the home. In this paper, we propose a Bayesian Reinforcement learning
framework that can approximate the current occupant state in a partially
observable smart home environment using its thermal preference, and then
identify the occupant as a new user or someone is already known to the system.
Our proposed framework can be used to identify users based on the temperature
and humidity preferences of the occupant when performing different activities
to enable personalization and improve comfort. We then compare the proposed
framework with a baseline long short-term memory learner that learns the
thermal preference of the user from the sequence of actions which it takes. We
perform these experiments with up to 5 simulated human models each based on
hierarchical reinforcement learning. The results show that our framework can
approximate the belief state of the current user just by its temperature and
humidity preferences across different activities with a high degree of
accuracy.

Image captioning is a longstanding problem in the field of computer vision
and natural language processing. To date, researchers have produced impressive
state-of-the-art performance in the age of deep learning. Most of these
state-of-the-art, however, requires large volume of annotated image-caption
pairs in order to train their models. When given an image dataset of interests,
practitioner needs to annotate the caption for each image in the training set
and this process needs to happen for each newly collected image dataset. In
this paper, we explore the task of unsupervised image captioning which utilizes
unpaired images and texts to train the model so that the texts can come from
different sources than the images. A main school of research on this topic that
has been shown to be effective is to construct pairs from the images and texts
in the training set according to their overlap of objects. Unlike in the
supervised setting, these constructed pairings are however not guaranteed to
have fully overlapping set of objects. Our work in this paper overcomes this by
harvesting objects corresponding to a given sentence from the training set,
even if they don't belong to the same image. When used as input to a
transformer, such mixture of objects enables larger if not full object
coverage, and when supervised by the corresponding sentence, produced results
that outperform current state of the art unsupervised methods by a significant
margin. Building upon this finding, we further show that (1) additional
information on relationship between objects and attributes of objects also
helps in boosting performance; and (2) our method also extends well to
non-English image captioning, which usually suffers from a scarcer level of
annotations. Our findings are supported by strong empirical results. Our code
is available at https://github.com/zihangm/obj-centric-unsup-caption.

Grounded video description (GVD) encourages captioning models to attend to
appropriate video regions (e.g., objects) dynamically and generate a
description. Such a setting can help explain the decisions of captioning models
and prevents the model from hallucinating object words in its description.
However, such design mainly focuses on object word generation and thus may
ignore fine-grained information and suffer from missing visual concepts.
Moreover, relational words (e.g., "jump left or right") are usual
spatio-temporal inference results, i.e., these words cannot be grounded on
certain spatial regions. To tackle the above limitations, we design a novel
relational graph learning framework for GVD, in which a language-refined scene
graph representation is designed to explore fine-grained visual concepts.
Furthermore, the refined graph can be regarded as relational inductive
knowledge to assist captioning models in selecting the relevant information it
needs to generate correct words. We validate the effectiveness of our model
through automatic metrics and human evaluation, and the results indicate that
our approach can generate more fine-grained and accurate description, and it
solves the problem of object hallucination to some extent.

Transformer is a potentially powerful architecture for vision tasks. Although
equipped with more parameters and attention mechanism, its performance is not
as dominant as CNN currently. CNN is usually computationally cheaper and still
the leading competitor in various vision tasks. One research direction is to
adopt the successful ideas of CNN and improve transformer, but it often relies
on elaborated and heuristic network design. Observing that transformer and CNN
are complementary in representation learning and convergence speed, we propose
an efficient training framework called Vision Pair Learning (VPL) for image
classification task. VPL builds up a network composed of a transformer branch,
a CNN branch and pair learning module. With multi-stage training strategy, VPL
enables the branches to learn from their partners during the appropriate stage
of the training process, and makes them both achieve better performance with
less time cost. Without external data, VPL promotes the top-1 accuracy of
ViT-Base and ResNet-50 on the ImageNet-1k validation set to 83.47% and 79.61%
respectively. Experiments on other datasets of various domains prove the
efficacy of VPL and suggest that transformer performs better when paired with
the differently structured CNN in VPL. we also analyze the importance of
components through ablation study.

A fully nonparametric approach for making probabilistic predictions in
multi-response regression problems is introduced. Random forests are used as
marginal models for each response variable and, as novel contribution of the
present work, the dependence between the multiple response variables is modeled
by a generative neural network. This combined modeling approach of random
forests, corresponding empirical marginal residual distributions and a
generative neural network is referred to as RafterNet. Multiple datasets serve
as examples to demonstrate the flexibility of the approach and its impact for
making probabilistic forecasts.

Automated Driving Systems (ADSs) have seen rapid progress in recent years. To
ensure the safety and reliability of these systems, extensive testings are
being conducted before their future mass deployment. Testing the system on the
road is the closest to real-world and desirable approach, but it is incredibly
costly. Also, it is infeasible to cover rare corner cases using such real-world
testing. Thus, a popular alternative is to evaluate an ADS's performance in
some well-designed challenging scenarios, a.k.a. scenario-based testing.
High-fidelity simulators have been widely used in this setting to maximize
flexibility and convenience in testing what-if scenarios. Although many works
have been proposed offering diverse frameworks/methods for testing specific
systems, the comparisons and connections among these works are still missing.
To bridge this gap, in this work, we provide a generic formulation of
scenario-based testing in high-fidelity simulation and conduct a literature
review on the existing works. We further compare them and present the open
challenges as well as potential future research directions.

Earnings call (EC), as a periodic teleconference of a publicly-traded
company, has been extensively studied as an essential market indicator because
of its high analytical value in corporate fundamentals. The recent emergence of
deep learning techniques has shown great promise in creating automated
pipelines to benefit the EC-supported financial applications. However, these
methods presume all included contents to be informative without refining
valuable semantics from long-text transcript and suffer from EC scarcity issue.
Meanwhile, these black-box methods possess inherent difficulties in providing
human-understandable explanations. To this end, in this paper, we propose a
Multi-Domain Transformer-Based Counterfactual Augmentation, named MTCA, to
address the above problems. Specifically, we first propose a transformer-based
EC encoder to attentively quantify the task-inspired significance of critical
EC content for market inference. Then, a multi-domain counterfactual learning
framework is developed to evaluate the gradient-based variations after we
perturb limited EC informative texts with plentiful cross-domain documents,
enabling MTCA to perform unsupervised data augmentation. As a bonus, we
discover a way to use non-training data as instance-based explanations for
which we show the result with case studies. Extensive experiments on the
real-world financial datasets demonstrate the effectiveness of interpretable
MTCA for improving the volatility evaluation ability of the state-of-the-art by
14.2\% in accuracy.

Extra-label drug use in food animal medicine is authorized by the US Animal
Medicinal Drug Use Clarification Act (AMDUCA), and estimated withdrawal
intervals are based on published scientific pharmacokinetic data. Occasionally
there is a paucity of scientific data on which to base a withdrawal interval or
a large number of animals being treated, driving the need to test for drug
residues. Rapid assay commercial farm-side tests are essential for monitoring
drug residues in animal products to protect human health. Active ingredients,
sensitivity, matrices, and species that have been evaluated for commercial
rapid assay tests are typically reported on manufacturers' websites or in PDF
documents that are available to consumers but may require a special access
request. Additionally, this information is not always correlated with
FDA-approved tolerances. Furthermore, parameter changes for these tests can be
very challenging to regularly identify, especially those listed on websites or
in documents that are not publicly available. Therefore, artificial
intelligence plays a critical role in efficiently extracting the data and
ensure current information. Extracting tables from PDF and HTML documents has
been investigated both by academia and commercial tool builders. Research in
text mining of such documents has become a widespread yet challenging arena in
implementing natural language programming. However, techniques of extracting
tables are still in their infancy and being investigated and improved by
researchers. In this study, we developed and evaluated a data-mining method for
automatically extracting rapid assay data from electronic documents. Our
automatic electronic data extraction method includes a software package module,
a developed pattern recognition tool, and a data mining engine. Assay details
were provided by several commercial entities that produce these rapid drug
residue assay

We present HIPNet, a neural implicit pose network trained on multiple
subjects across many poses. HIPNet can disentangle subject-specific details
from pose-specific details, effectively enabling us to retarget motion from one
subject to another or to animate between keyframes through latent space
interpolation. To this end, we employ a hierarchical skeleton-based
representation to learn a signed distance function on a canonical unposed
space. This joint-based decomposition enables us to represent subtle details
that are local to the space around the body joint. Unlike previous neural
implicit method that requires ground-truth SDF for training, our model we only
need a posed skeleton and the point cloud for training, and we have no
dependency on a traditional parametric model or traditional skinning
approaches. We achieve state-of-the-art results on various single-subject and
multi-subject benchmarks.

Autonomous vehicles (AVs) must interact with a diverse set of human drivers
in heterogeneous geographic areas. Ideally, fleets of AVs should share
trajectory data to continually re-train and improve trajectory forecasting
models from collective experience using cloud-based distributed learning. At
the same time, these robots should ideally avoid uploading raw driver
interaction data in order to protect proprietary policies (when sharing
insights with other companies) or protect driver privacy from insurance
companies. Federated learning (FL) is a popular mechanism to learn models in
cloud servers from diverse users without divulging private local data. However,
FL is often not robust -- it learns sub-optimal models when user data comes
from highly heterogeneous distributions, which is a key hallmark of human-robot
interactions. In this paper, we present a novel variant of personalized FL to
specialize robust robot learning models to diverse user distributions. Our
algorithm outperforms standard FL benchmarks by up to 2x in real user studies
that we conducted where human-operated vehicles must gracefully merge lanes
with simulated AVs in the standard CARLA and CARLO AV simulators.

Graph Neural Networks (GNNs) have achieved great success on a variety of
tasks with graph-structural data, among which node classification is an
essential one. Unsupervised Graph Domain Adaptation (UGDA) shows its practical
value of reducing the labeling cost for node classification. It leverages
knowledge from a labeled graph (i.e., source domain) to tackle the same task on
another unlabeled graph (i.e., target domain). Most existing UGDA methods
heavily rely on the labeled graph in the source domain. They utilize labels
from the source domain as the supervision signal and are jointly trained on
both the source graph and the target graph. However, in some real-world
scenarios, the source graph is inaccessible because of either unavailability or
privacy issues. Therefore, we propose a novel scenario named Source Free
Unsupervised Graph Domain Adaptation (SFUGDA). In this scenario, the only
information we can leverage from the source domain is the well-trained source
model, without any exposure to the source graph and its labels. As a result,
existing UGDA methods are not feasible anymore. To address the non-trivial
adaptation challenges in this practical scenario, we propose a model-agnostic
algorithm for domain adaptation to fully exploit the discriminative ability of
the source model while preserving the consistency of structural proximity on
the target graph. We prove the effectiveness of the proposed algorithm both
theoretically and empirically. The experimental results on four cross-domain
tasks show consistent improvements of the Macro-F1 score up to 0.17.

Robust model fitting is a fundamental problem in computer vision: used to
pre-process raw data in the presence of outliers. Maximisation of Consensus
(MaxCon) is one of the most popular robust criteria and widely used. Recently
(Tennakoon et al. CVPR2021), a connection has been made between MaxCon and
estimation of influences of a Monotone Boolean function. Equipping the Boolean
cube with different measures and adopting different sampling strategies (two
sides of the same coin) can have differing effects: which leads to the current
study. This paper studies the concept of weighted influences for solving
MaxCon. In particular, we study endowing the Boolean cube with the Bernoulli
measure and performing biased (as opposed to uniform) sampling. Theoretically,
we prove the weighted influences, under this measure, of points belonging to
larger structures are smaller than those of points belonging to smaller
structures in general. We also consider another "natural" family of
sampling/weighting strategies, sampling with uniform measure concentrated on a
particular (Hamming) level of the cube.
  Based on weighted sampling, we modify the algorithm of Tennakoon et al., and
test on both synthetic and real datasets. This paper is not promoting a new
approach per se, but rather studying the issue of weighted sampling.
Accordingly, we are not claiming to have produced a superior algorithm: rather
we show some modest gains of Bernoulli sampling, and we illuminate some of the
interactions between structure in data and weighted sampling.

New intelligence applications are driving increasing interest in deploying
deep neural networks (DNN) in a distributed way. To set up distributed deep
learning involves alterations of a great number of the parameter configurations
of network/edge devices and DNN models, which are crucial to achieve best
performances. Simulations measure scalability of intelligence applications in
the early stage, as well as to determine the effects of different
configurations, thus highly desired. However, work on simulating the
distributed intelligence environment is still in its infancy. The existing
simulation frameworks, such as NS-3, etc., cannot extended in a straightforward
way to support simulations of distributed learning. In this paper, we propose a
novel discrete event simulator, sim4DistrDL, which includes a deep learning
module and a network simulation module to facilitate simulation of DNN-based
distributed applications. Specifically, we give the design and implementation
of the proposed learning simulator and present an illustrative use case.

We introduce quantile filtered imitation learning (QFIL), a novel policy
improvement operator designed for offline reinforcement learning. QFIL performs
policy improvement by running imitation learning on a filtered version of the
offline dataset. The filtering process removes $ s,a $ pairs whose estimated Q
values fall below a given quantile of the pushforward distribution over values
induced by sampling actions from the behavior policy. The definitions of both
the pushforward Q distribution and resulting value function quantile are key
contributions of our method. We prove that QFIL gives us a safe policy
improvement step with function approximation and that the choice of quantile
provides a natural hyperparameter to trade off bias and variance of the
improvement step. Empirically, we perform a synthetic experiment illustrating
how QFIL effectively makes a bias-variance tradeoff and we see that QFIL
performs well on the D4RL benchmark.

Modeling semantic information is helpful for scene text recognition. In this
work, we propose to model semantic and visual information jointly with a
Visual-Semantic Transformer (VST). The VST first explicitly extracts primary
semantic information from visual feature maps with a transformer module and a
primary visual-semantic alignment module. The semantic information is then
joined with the visual feature maps (viewed as a sequence) to form a pseudo
multi-domain sequence combining visual and semantic information, which is
subsequently fed into an transformer-based interaction module to enable
learning of interactions between visual and semantic features. In this way, the
visual features can be enhanced by the semantic information and vice versus.
The enhanced version of visual features are further decoded by a secondary
visual-semantic alignment module which shares weights with the primary one.
Finally, the decoded visual features and the enhanced semantic features are
jointly processed by the third transformer module obtaining the final text
prediction. Experiments on seven public benchmarks including regular/ irregular
text recognition datasets verifies the effectiveness our proposed model,
reaching state of the art on four of the seven benchmarks.

The recently developed Particle-based Variational Inference (ParVI) methods
drive the empirical distribution of a set of \emph{fixed-weight} particles
towards a given target distribution $\pi$ by iteratively updating particles'
positions. However, the fixed weight restriction greatly confines the empirical
distribution's approximation ability, especially when the particle number is
limited. In this paper, we propose to dynamically adjust particles' weights
according to a Fisher-Rao reaction flow. We develop a general Dynamic-weight
Particle-based Variational Inference (DPVI) framework according to a novel
continuous composite flow, which evolves the positions and weights of particles
simultaneously. We show that the mean-field limit of our composite flow is
actually a Wasserstein-Fisher-Rao gradient flow of certain dissimilarity
functional $\mathcal{F}$, which leads to a faster decrease of $\mathcal{F}$
than the Wasserstein gradient flow underlying existing fixed-weight ParVIs. By
using different finite-particle approximations in our general framework, we
derive several efficient DPVI algorithms. The empirical results demonstrate the
superiority of our derived DPVI algorithms over their fixed-weight

We investigate how effective an attacker can be when it only learns from its
victim's actions, without access to the victim's reward. In this work, we are
motivated by the scenario where the attacker wants to behave strategically when
the victim's motivations are unknown. We argue that one heuristic approach an
attacker can use is to maximize the entropy of the victim's policy. The policy
is generally not obfuscated, which implies it may be extracted simply by
passively observing the victim. We provide such a strategy in the form of a
reward-free exploration algorithm that maximizes the attacker's entropy during
the exploration phase, and then maximizes the victim's empirical entropy during
the planning phase. In our experiments, the victim agents are subverted through
policy entropy maximization, implying an attacker might not need access to the
victim's reward to succeed. Hence, reward-free attacks, which are based only on
observing behavior, show the feasibility of an attacker to act strategically
without knowledge of the victim's motives even if the victim's reward
information is protected.

It is natural to represent objects in terms of their parts. This has the
potential to improve the performance of algorithms for object recognition and
segmentation but can also help for downstream tasks like activity recognition.
Research on part-based models, however, is hindered by the lack of datasets
with per-pixel part annotations. This is partly due to the difficulty and high
cost of annotating object parts so it has rarely been done except for humans
(where there exists a big literature on part-based models). To help address
this problem, we propose PartImageNet, a large, high-quality dataset with part
segmentation annotations. It consists of $158$ classes from ImageNet with
approximately $24,000$ images. PartImageNet is unique because it offers
part-level annotations on a general set of classes including non-rigid,
articulated objects, while having an order of magnitude larger size compared to
existing part datasets (excluding datasets of humans). It can be utilized for
many vision tasks including Object Segmentation, Semantic Part Segmentation,
Few-shot Learning and Part Discovery. We conduct comprehensive experiments
which study these tasks and set up a set of baselines. The dataset and scripts
are released at https://github.com/TACJu/PartImageNet.

Conventional synchronous machines are gradually replaced by converter-based
renewable resources. As a result, synchronous inertia, an important
time-varying quantity, has substantially more impact on modern power systems
stability. The increasing integration of renewable energy resources imports
different dynamics into traditional power systems; therefore, the estimation of
system inertia using mathematical model becomes more difficult. In this paper,
we propose a novel learning-assisted inertia estimation model based on
long-term recurrent convolutional network (LRCN) that uses system wide
frequency and phase voltage measurements. The proposed approach uses a
non-intrusive probing signal to perturb the system and collects ambient
measurements with phasor measurement units (PMU) to train the proposed LRCN
model. Case studies are conducted on the IEEE 24-bus system. Under a
signal-to-noise ratio (SNR) of 60dB condition, the proposed LRCN based inertia
estimation model achieves an accuracy of 97.56% with a mean squared error (MSE)
of 0.0552. Furthermore, with a low SNR of 45dB, the proposed learning-assisted
inertia estimation model is still able to achieve a high accuracy of 93.07%.

Federated Learning (FL) has been considered as an appealing framework to
tackle data privacy issues of mobile devices compared to conventional Machine
Learning (ML). Using Edge Servers (ESs) as intermediaries to perform model
aggregation in proximity can reduce the transmission overhead, and it enables
great potentials in low-latency FL, where the hierarchical architecture of FL
(HFL) has been attracted more attention. Designing a proper client selection
policy can significantly improve training performance, and it has been
extensively used in FL studies. However, to the best of our knowledge, there
are no studies focusing on HFL. In addition, client selection for HFL faces
more challenges than conventional FL, e.g., the time-varying connection of
client-ES pairs and the limited budget of the Network Operator (NO). In this
paper, we investigate a client selection problem for HFL, where the NO learns
the number of successful participating clients to improve the training
performance (i.e., select as many clients in each round) as well as under the
limited budget on each ES. An online policy, called Context-aware Online Client
Selection (COCS), is developed based on Contextual Combinatorial Multi-Armed
Bandit (CC-MAB). COCS observes the side-information (context) of local
computing and transmission of client-ES pairs and makes client selection
decisions to maximize NO's utility given a limited budget. Theoretically, COCS
achieves a sublinear regret compared to an Oracle policy on both strongly
convex and non-convex HFL. Simulation results also support the efficiency of
the proposed COCS policy on real-world datasets.

Probabilistic circuits (PCs) are a family of generative models which allows
for the computation of exact likelihoods and marginals of its probability
distributions. PCs are both expressive and tractable, and serve as popular
choices for discrete density estimation tasks. However, large PCs are
susceptible to overfitting, and only a few regularization strategies (e.g.,
dropout, weight-decay) have been explored. We propose HyperSPNs: a new paradigm
of generating the mixture weights of large PCs using a small-scale neural
network. Our framework can be viewed as a soft weight-sharing strategy, which
combines the greater expressiveness of large models with the better
generalization and memory-footprint properties of small models. We show the
merits of our regularization strategy on two state-of-the-art PC families
introduced in recent literature -- RAT-SPNs and EiNETs -- and demonstrate
generalization improvements in both models on a suite of density estimation
benchmarks in both discrete and continuous domains.

Deep learning based methods hold state-of-the-art results in low-level image
processing tasks, but remain difficult to interpret due to their black-box
construction. Unrolled optimization networks present an interpretable
alternative to constructing deep neural networks by deriving their architecture
from classical iterative optimization methods without use of tricks from the
standard deep learning tool-box. So far, such methods have demonstrated
performance close to that of state-of-the-art models while using their
interpretable construction to achieve a comparably low learned parameter count.
In this work, we propose an unrolled convolutional dictionary learning network
(CDLNet) and demonstrate its competitive denoising and joint denoising and
demosaicing (JDD) performance both in low and high parameter count regimes.
Specifically, we show that the proposed model outperforms state-of-the-art
fully convolutional denoising and JDD models when scaled to a similar parameter
count. In addition, we leverage the model's interpretable construction to
propose a noise-adaptive parameterization of thresholds in the network that
enables state-of-the-art blind denoising performance, and near perfect
generalization on noise-levels unseen during training. Furthermore, we show
that such performance extends to the JDD task and unsupervised learning.

Despite the recent progress in Graph Neural Networks (GNNs), it remains
challenging to explain the predictions made by GNNs. Existing explanation
methods mainly focus on post-hoc explanations where another explanatory model
is employed to provide explanations for a trained GNN. The fact that post-hoc
methods fail to reveal the original reasoning process of GNNs raises the need
of building GNNs with built-in interpretability. In this work, we propose
Prototype Graph Neural Network (ProtGNN), which combines prototype learning
with GNNs and provides a new perspective on the explanations of GNNs. In
ProtGNN, the explanations are naturally derived from the case-based reasoning
process and are actually used during classification. The prediction of ProtGNN
is obtained by comparing the inputs to a few learned prototypes in the latent
space. Furthermore, for better interpretability and higher efficiency, a novel
conditional subgraph sampling module is incorporated to indicate which part of
the input graph is most similar to each prototype in ProtGNN+. Finally, we
evaluate our method on a wide range of datasets and perform concrete case
studies. Extensive results show that ProtGNN and ProtGNN+ can provide inherent
interpretability while achieving accuracy on par with the non-interpretable

Index modulation (IM) reduces the power consumption and hardware cost of the
multiple-input multiple-output (MIMO) system by activating part of the antennas
for data transmission. However, IM significantly increases the complexity of
the receiver and needs accurate channel estimation to guarantee its
performance. To tackle these challenges, in this paper, we design a deep
learning (DL) based detector for the IM aided MIMO (IM-MIMO) systems. We first
formulate the detection process as a sparse reconstruction problem by utilizing
the inherent attributes of IM. Then, based on greedy strategy, we design a DL
based detector, called IMRecoNet, to realize this sparse reconstruction
process. Different from the general neural networks, we introduce complex value
operations to adapt the complex signals in communication systems. To the best
of our knowledge, this is the first attempt that introduce complex valued
neural network to the design of detector for the IM-MIMO systems. Finally, to
verify the adaptability and robustness of the proposed detector, simulations
are carried out with consideration of inaccurate channel state information
(CSI) and correlated MIMO channels. The simulation results demonstrate that the
proposed detector outperforms existing algorithms in terms of antenna
recognition accuracy and bit error rate under various scenarios.

Meta-reinforcement learning (meta-RL) has proven to be a successful framework
for leveraging experience from prior tasks to rapidly learn new related tasks,
however, current meta-RL approaches struggle to learn in sparse reward
environments. Although existing meta-RL algorithms can learn strategies for
adapting to new sparse reward tasks, the actual adaptation strategies are
learned using hand-shaped reward functions, or require simple environments
where random exploration is sufficient to encounter sparse reward. In this
paper, we present a formulation of hindsight relabeling for meta-RL, which
relabels experience during meta-training to enable learning to learn entirely
using sparse reward. We demonstrate the effectiveness of our approach on a
suite of challenging sparse reward goal-reaching environments that previously
required dense reward during meta-training to solve. Our approach solves these
environments using the true sparse reward function, with performance comparable
to training with a proxy dense reward function.

Computer-generated holograms (CGHs) are used in holographic three-dimensional
(3D) displays and holographic projections. The quality of the reconstructed
images using phase-only CGHs is degraded because the amplitude of the
reconstructed image is difficult to control. Iterative optimization methods
such as the Gerchberg-Saxton (GS) algorithm are one option for improving image
quality. They optimize CGHs in an iterative fashion to obtain a higher image
quality. However, such iterative computation is time consuming, and the
improvement in image quality is often stagnant. Recently, deep learning-based
hologram computation has been proposed. Deep neural networks directly infer
CGHs from input image data. However, it is limited to reconstructing images
that are the same size as the hologram. In this study, we use deep learning to
optimize phase-only CGHs generated using scaled diffraction computations and
the random phase-free method. By combining the random phase-free method with
the scaled diffraction computation, it is possible to handle a zoomable
reconstructed image larger than the hologram. In comparison to the GS
algorithm, the proposed method optimizes both high quality and speed.

Counterfactual explanations represent the minimal change to a data sample
that alters its predicted classification, typically from an unfavorable initial
class to a desired target class. Counterfactuals help answer questions such as
"what needs to change for this application to get accepted for a loan?". A
number of recently proposed approaches to counterfactual generation give
varying definitions of "plausible" counterfactuals and methods to generate
them. However, many of these methods are computationally intensive and provide
unconvincing explanations. Here we introduce SharpShooter, a method for binary
classification that starts by creating a projected version of the input that
classifies as the target class. Counterfactual candidates are then generated in
latent space on the interpolation line between the input and its projection. We
then demonstrate that our framework translates core characteristics of a sample
to its counterfactual through the use of learned representations. Furthermore,
we show that SharpShooter is competitive across common quality metrics on
tabular and image datasets while being orders of magnitude faster than two
comparable methods and excels at measures of realism, making it well-suited for
high velocity machine learning applications which require timely explanations.

Safe reinforcement learning is extremely challenging--not only must the agent
explore an unknown environment, it must do so while ensuring no safety
constraint violations. We formulate this safe reinforcement learning (RL)
problem using the framework of a finite-horizon Constrained Markov Decision
Process (CMDP) with an unknown transition probability function, where we model
the safety requirements as constraints on the expected cumulative costs that
must be satisfied during all episodes of learning. We propose a model-based
safe RL algorithm that we call Doubly Optimistic and Pessimistic Exploration
(DOPE), and show that it achieves an objective regret
$\tilde{O}(|\mathcal{S}|\sqrt{|\mathcal{A}| K})$ without violating the safety
constraints during learning, where $|\mathcal{S}|$ is the number of states,
$|\mathcal{A}|$ is the number of actions, and $K$ is the number of learning
episodes. Our key idea is to combine a reward bonus for exploration (optimism)
with a conservative constraint (pessimism), in addition to the standard
optimistic model-based exploration. DOPE is not only able to improve the
objective regret bound, but also shows a significant empirical performance
improvement as compared to earlier optimism-pessimism approaches.

Value function approximation is a crucial module for policy evaluation in
reinforcement learning when the state space is large or continuous. The present
paper takes a generative perspective on policy evaluation via
temporal-difference (TD) learning, where a Gaussian process (GP) prior is
presumed on the sought value function, and instantaneous rewards are
probabilistically generated based on value function evaluations at two
consecutive states. Capitalizing on a random feature-based approximant of the
GP prior, an online scalable (OS) approach, termed {OS-GPTD}, is developed to
estimate the value function for a given policy by observing a sequence of
state-reward pairs. To benchmark the performance of OS-GPTD even in an
adversarial setting, where the modeling assumptions are violated, complementary
worst-case analyses are performed by upper-bounding the cumulative Bellman
error as well as the long-term reward prediction error, relative to their
counterparts from a fixed value function estimator with the entire state-reward
trajectory in hindsight. Moreover, to alleviate the limited expressiveness
associated with a single fixed kernel, a weighted ensemble (E) of GP priors is
employed to yield an alternative scheme, termed OS-EGPTD, that can jointly
infer the value function, and select interactively the EGP kernel on-the-fly.
Finally, performances of the novel OS-(E)GPTD schemes are evaluated on two

Spurious correlations allow flexible models to predict well during training
but poorly on related test distributions. Recent work has shown that models
that satisfy particular independencies involving correlation-inducing
\textit{nuisance} variables have guarantees on their test performance.
Enforcing such independencies requires nuisances to be observed during
training. However, nuisances, such as demographics or image background labels,
are often missing. Enforcing independence on just the observed data does not
imply independence on the entire population. Here we derive \acrshort{mmd}
estimators used for invariance objectives under missing nuisances. On
simulations and clinical data, optimizing through these estimates achieves test
performance similar to using estimators that make use of the full data.

Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for
solving multi-stage stochastic optimization, widely used for modeling
real-world process optimization tasks. Unfortunately, SDDP has a worst-case
complexity that scales exponentially in the number of decision variables, which
severely limits applicability to only low dimensional problems. To overcome
this limitation, we extend SDDP by introducing a trainable neural model that
learns to map problem instances to a piece-wise linear value function within
intrinsic low-dimension space, which is architected specifically to interact
with a base SDDP solver, so that can accelerate optimization performance on new
instances. The proposed Neural Stochastic Dual Dynamic Programming ($\nu$-SDDP)
continually self-improves by solving successive problems. An empirical
investigation demonstrates that $\nu$-SDDP can significantly reduce problem
solving cost without sacrificing solution quality over competitors such as SDDP
and reinforcement learning algorithms, across a range of synthetic and
real-world process optimization problems.

Computing sample means on Riemannian manifolds is typically computationally
costly as exemplified by computation of the Fr\'echet mean which often requires
finding minimizing geodesics to each data point for each step of an iterative
optimization scheme. When closed-form expressions for geodesics are not
available, this leads to a nested optimization problem that is costly to solve.
The implied computational cost impacts applications in both geometric
statistics and in geometric deep learning. The weighted diffusion mean offers
an alternative to the weighted Fr\'echet mean. We show how the diffusion mean
and the weighted diffusion mean can be estimated with a stochastic simulation
scheme that does not require nested optimization. We achieve this by
conditioning a Brownian motion in a product manifold to hit the diagonal at a
predetermined time. We develop the theoretical foundation for the
sampling-based mean estimation, we develop two simulation schemes, and we
demonstrate the applicability of the method with examples of sampled means on
two manifolds.

Over the past decade, cryogenic electron microscopy (cryo-EM) has emerged as
a primary method for determining near-native, near-atomic resolution 3D
structures of biological macromolecules. In order to meet increasing demand for
cryo-EM, automated methods to improve throughput and efficiency while lowering
costs are needed. Currently, all high-magnification cryo-EM data collection
softwares require human input and manual tuning of parameters. Expert operators
must navigate low- and medium-magnification images to find good
high-magnification collection locations. Automating this is non-trivial: the
images suffer from low signal-to-noise ratio and are affected by a range of
experimental parameters that can differ for each collection session. Here, we
use various computer vision algorithms, including mixture models, convolutional
neural networks, and U-Nets to develop the first pipeline to automate low- and
medium-magnification targeting. Learned models in this pipeline are trained on
a large internal dataset of images from real world cryo-EM data collection
sessions, labeled with locations that were selected by operators. Using these
models, we show that we can effectively detect and classify regions of interest
in low- and medium-magnification images, and can generalize to unseen sessions,
as well as to images captured using different microscopes from external
facilities. We expect our open-source pipeline, Ptolemy, will be both
immediately useful as a tool for automation of cryo-EM data collection, and
serve as a foundation for future advanced methods for efficient and automated
cryo-EM microscopy.

Given the broad capabilities of large language models, it should be possible
to work towards a general-purpose, text-based assistant that is aligned with
human values, meaning that it is helpful, honest, and harmless. As an initial
foray in this direction we study simple baseline techniques and evaluations,
such as prompting. We find that the benefits from modest interventions increase
with model size, generalize to a variety of alignment evaluations, and do not
compromise the performance of large models. Next we investigate scaling trends
for several training objectives relevant to alignment, comparing imitation
learning, binary discrimination, and ranked preference modeling. We find that
ranked preference modeling performs much better than imitation learning, and
often scales more favorably with model size. In contrast, binary discrimination
typically performs and scales very similarly to imitation learning. Finally we
study a `preference model pre-training' stage of training, with the goal of
improving sample efficiency when finetuning on human preferences.

Bugs in Scratch programs can spoil the fun and inhibit learning success. Many
common bugs are the result of recurring patterns of bad code. In this paper we
present a collection of common code patterns that typically hint at bugs in
Scratch programs, and the LitterBox tool which can automatically detect them.
We empirically evaluate how frequently these patterns occur, and how severe
their consequences usually are. While fixing bugs inevitably is part of
learning, the possibility to identify the bugs automatically provides the
potential to support learners

Uncertainty estimation is a key component in any deployed machine learning
system. One way to evaluate uncertainty estimation is using
"out-of-distribution" (OoD) detection, that is, distinguishing between the
training data distribution and an unseen different data distribution using
uncertainty. In this work, we show that current feature density based
uncertainty estimators cannot perform well consistently across different OoD
detection settings. To solve this, we propose to decompose the learned
representations and integrate the uncertainties estimated on them separately.
Through experiments, we demonstrate that we can greatly improve the performance
and the interpretability of the uncertainty estimation.

Advances in generative modeling based on GANs has motivated the community to
find their use beyond image generation and editing tasks. In particular,
several recent works have shown that GAN representations can be re-purposed for
discriminative tasks such as part segmentation, especially when training data
is limited. But how do these improvements stack-up against recent advances in
self-supervised learning? Motivated by this we present an alternative approach
based on contrastive learning and compare their performance on standard
few-shot part segmentation benchmarks. Our experiments reveal that not only do
the GAN-based approach offer no significant performance advantage, their
multi-step training is complex, nearly an order-of-magnitude slower, and can
introduce additional bias. These experiments suggest that the inductive biases
of generative models, such as their ability to disentangle shape and texture,
are well captured by standard feed-forward networks trained using contrastive
learning. These experiments suggest that the inductive biases present in
current generative models, such as their ability to disentangle shape and
texture, are well captured by standard feed-forward networks trained using

Pancreatic cancer will soon be the second leading cause of cancer-related
death in Western society. Imaging techniques such as CT, MRI and ultrasound
typically help providing the initial diagnosis, but histopathological
assessment is still the gold standard for final confirmation of disease
presence and prognosis. In recent years machine learning approaches and
pathomics pipelines have shown potential in improving diagnostics and
prognostics in other cancerous entities, such as breast and prostate cancer. A
crucial first step in these pipelines is typically identification and
segmentation of the tumour area. Ideally this step is done automatically to
prevent time consuming manual annotation. We propose a multi-task convolutional
neural network to balance disease detection and segmentation accuracy. We
validated our approach on a dataset of 29 patients (for a total of 58 slides)
at different resolutions. The best single task segmentation network achieved a
median Dice of 0.885 (0.122) IQR at a resolution of 15.56 $\mu$m. Our
multi-task network improved on that with a median Dice score of 0.934 (0.077)
IQR.

Iris recognition of living individuals is a mature biometric modality that
has been adopted globally from governmental ID programs, border crossing, voter
registration and de-duplication, to unlocking mobile phones. On the other hand,
the possibility of recognizing deceased subjects with their iris patterns has
emerged recently. In this paper, we present an end-to-end deep learning-based
method for postmortem iris segmentation and recognition with a special
visualization technique intended to support forensic human examiners in their
efforts. The proposed postmortem iris segmentation approach outperforms the
state of the art and in addition to iris annulus, as in case of classical iris
segmentation methods - detects abnormal regions caused by eye decomposition
processes, such as furrows or irregular specular highlights present on the
drying and wrinkling cornea. The method was trained and validated with data
acquired from 171 cadavers, kept in mortuary conditions, and tested on
subject-disjoint data acquired from 259 deceased subjects. To our knowledge,
this is the largest corpus of data used in postmortem iris recognition research
to date. The source code of the proposed method are offered with the paper. The
test data will be available through the National Archive of Criminal Justice
Data (NACJD) archives.

Learning effective visual representations without human supervision is a
long-standing problem in computer vision. Recent advances in self-supervised
learning algorithms have utilized contrastive learning, with methods such as
SimCLR, which applies a composition of augmentations to an image, and minimizes
a contrastive loss between the two augmented images. In this paper, we present
CLAWS, an annotation-efficient learning framework, addressing the problem of
manually labeling large-scale agricultural datasets along with potential
applications such as anomaly detection and plant growth analytics. CLAWS uses a
network backbone inspired by SimCLR and weak supervision to investigate the
effect of contrastive learning within class clusters. In addition, we inject a
hard attention mask to the cropped input image before maximizing agreement
between the image pairs using a contrastive loss function. This mask forces the
network to focus on pertinent object features and ignore background features.
We compare results between a supervised SimCLR and CLAWS using an agricultural
dataset with 227,060 samples consisting of 11 different crop classes. Our
experiments and extensive evaluations show that CLAWS achieves a competitive
NMI score of 0.7325. Furthermore, CLAWS engenders the creation of low
dimensional representations of very large datasets with minimal parameter
tuning and forming well-defined clusters, which lends themselves to using
efficient, transparent, and highly interpretable clustering methods such as
Gaussian Mixture Models.

To protect sensitive training data, differentially private stochastic
gradient descent (DP-SGD) has been adopted in deep learning to provide
rigorously defined privacy. However, DP-SGD requires the injection of an amount
of noise that scales with the number of gradient dimensions, resulting in large
performance drops compared to non-private training. In this work, we propose
random freeze which randomly freezes a progressively increasing subset of
parameters and results in sparse gradient updates while maintaining or
increasing accuracy. We theoretically prove the convergence of random freeze
and find that random freeze exhibits a signal loss and perturbation moderation
trade-off in DP-SGD. Applying random freeze across various DP-SGD frameworks,
we maintain accuracy within the same number of iterations while achieving up to
70% representation sparsity, which demonstrates that the trade-off exists in a
variety of DP-SGD methods. We further note that random freeze significantly
improves accuracy, in particular for large networks. Additionally, axis-aligned
sparsity induced by random freeze leads to various advantages for projected
DP-SGD or federated learning in terms of computational cost, memory footprint
and communication overhead.

Experiments were performed at the Fermilab Accelerator Science and Technology
(FAST) facility to elucidate the effects of long-range wakefields (LRWs) in
TESLA-type superconducting rf cavities. In particular, we investigated the
higher-order modes (HOMs) generated in the eight cavities of a cryomodule (CM)
due to off-axis steering with correctors located ~4 m upstream of the CM. We
have observed correlated submacropulse centroid slews of a few-hundred microns
and centroid oscillations at ~240 kHz in the rf BPM data after the CM. The
entrance energy into the CM was 25 MeV, and the exit energy was 100 MeV with
125 pC/b and 400 pC/b in 50-bunch pulse trains. These experimental results were
evaluated for machine learning training aspects which will be used to inform
the commissioning plan for the Linac Coherent Light Source-II injector CM.

In this work we propose a batch version of the Greenkhorn algorithm for
multimarginal regularized optimal transport problems. Our framework is general
enough to cover, as particular cases, some existing algorithms like Sinkhorn
and Greenkhorn algorithm for the bi-marginal setting, and (greedy)
MultiSinkhorn for multimarginal optimal transport. We provide a complete
convergence analysis, which is based on the properties of the iterative Bregman
projections (IBP) method with greedy control. Global linear rate of convergence
and explicit bound on the iteration complexity are obtained. When specialized
to above mentioned algorithms, our results give new insights and/or improve

In many areas, we have well-founded insights about causal structure that
would be useful to bring into our trained models while still allowing them to
learn in a data-driven fashion. To achieve this, we present the new method of
interchange intervention training (IIT). In IIT, we (1) align variables in a
causal model (e.g., a deterministic program or Bayesian network) with
representations in a neural model and (2) train the neural model to match the
counterfactual behavior of the causal model on a base input when aligned
representations in both models are set to be the value they would be for a
source input. IIT is fully differentiable, flexibly combines with other
objectives, and guarantees that the target causal model is a causal abstraction
of the neural model when its loss is zero. We evaluate IIT on a structural
vision task (MNIST-PVR), a navigational language task (ReaSCAN), and a natural
language inference task (MQNLI). We compare IIT against multi-task training
objectives and data augmentation. In all our experiments, IIT achieves the best
results and produces neural models that are more interpretable in the sense
that they more successfully realize the target causal model.

Many scientific and engineering problems require accurate models of dynamical
systems with rare and extreme events. Such problems present a challenging task
for data-driven modelling, with many naive machine learning methods failing to
predict or accurately quantify such events. One cause for this difficulty is
that systems with extreme events, by definition, yield imbalanced datasets and
that standard loss functions easily ignore rare events. That is, metrics for
goodness of fit used to train models are not designed to ensure accuracy on
rare events. This work seeks to improve the performance of regression models
for extreme events by considering loss functions designed to highlight
outliers. We propose a novel loss function, the adjusted output weighted loss,
and extend the applicability of relative entropy based loss functions to
systems with low dimensional output. The proposed functions are tested using
several cases of dynamical systems exhibiting extreme events and shown to
significantly improve accuracy in predictions of extreme events.

With wearable IMU sensors, one can estimate human poses from wearable devices
without requiring visual input~\cite{von2017sparse}. In this work, we pose the
question: Can we reason about object structure in real-world environments
solely from human trajectory information? Crucially, we observe that human
motion and interactions tend to give strong information about the objects in a
scene -- for instance a person sitting indicates the likely presence of a chair
or sofa. To this end, we propose P2R-Net to learn a probabilistic 3D model of
the objects in a scene characterized by their class categories and oriented 3D
bounding boxes, based on an input observed human trajectory in the environment.
P2R-Net models the probability distribution of object class as well as a deep
Gaussian mixture model for object boxes, enabling sampling of multiple,
diverse, likely modes of object configurations from an observed human
trajectory. In our experiments we show that P2R-Net can effectively learn
multi-modal distributions of likely objects for human motions, and produce a
variety of plausible object structures of the environment, even without any
visual information. The results demonstrate that P2R-Net consistently
outperforms the baselines on the PROX dataset and the VirtualHome platform.

In many real-world situations, data is distributed across multiple locations
and can't be combined for training. Federated learning is a novel distributed
learning approach that allows multiple federating agents to jointly learn a
model. While this approach might reduce the error each agent experiences, it
also raises questions of fairness: to what extent can the error experienced by
one agent be significantly lower than the error experienced by another agent?
In this work, we consider two notions of fairness that each may be appropriate
in different circumstances: egalitarian fairness (which aims to bound how
dissimilar error rates can be) and proportional fairness (which aims to reward
players for contributing more data). For egalitarian fairness, we obtain a
tight multiplicative bound on how widely error rates can diverge between agents
federating together. For proportional fairness, we show that sub-proportional
error (relative to the number of data points contributed) is guaranteed for any
individually rational federating coalition.

It has been shown that the apparent advantage of some quantum machine
learning algorithms may be efficiently replicated using classical algorithms
with suitable data access -- a process known as dequantization. Existing works
on dequantization compare quantum algorithms which take copies of an n-qubit
quantum state $|x\rangle = \sum_{i} x_i |i\rangle$ as input to classical
algorithms which have sample and query (SQ) access to the vector $x$. In this
note, we prove that classical algorithms with SQ access can accomplish some
learning tasks exponentially faster than quantum algorithms with quantum state
inputs. Because classical algorithms are a subset of quantum algorithms, this
demonstrates that SQ access can sometimes be significantly more powerful than
quantum state inputs. Our findings suggest that the absence of exponential
quantum advantage in some learning tasks may be due to SQ access being too
powerful relative to quantum state inputs. If we compare quantum algorithms
with quantum state inputs to classical algorithms with access to measurement
data on quantum states, the landscape of quantum advantage can be dramatically
different. We remark that when the quantum states are constructed from
exponential-size classical data, comparing SQ access and quantum state inputs
is appropriate since both require exponential time to prepare.

The generalized g-formula can be used to estimate the probability of survival
under a sustained treatment strategy. When treatment strategies are
deterministic, estimators derived from the so-called efficient influence
function (EIF) for the g-formula will be doubly robust to model
misspecification. In recent years, several practical applications have
motivated estimation of the g-formula under non-deterministic treatment
strategies where treatment assignment at each time point depends on the
observed treatment process. In this case, EIF-based estimators may or may not
be doubly robust. In this paper, we provide sufficient conditions to ensure
existence of doubly robust estimators for intervention treatment distributions
that depend on the observed treatment process for point treatment
interventions, and give a class of intervention treatment distributions
dependent on the observed treatment process that guarantee model doubly and
multiply robust estimators in longitudinal settings. Motivated by an
application to pre-exposure prophylaxis (PrEP) initiation studies, we propose a
new treatment intervention dependent on the observed treatment process. We show
there exist 1) estimators that are doubly and multiply robust to model
misspecification, and 2) estimators that when used with machine learning
algorithms can attain fast convergence rates for our proposed intervention.
Theoretical results are confirmed via simulation studies.

Reverse engineering an integrated circuit netlist is a powerful tool to help
detect malicious logic and counteract design piracy. A critical challenge in
this domain is the correct classification of data-path and control-logic
registers in a design. We present ReIGNN, a novel learning-based register
classification methodology that combines graph neural networks (GNNs) with
structural analysis to classify the registers in a circuit with high accuracy
and generalize well across different designs. GNNs are particularly effective
in processing circuit netlists in terms of graphs and leveraging properties of
the nodes and their neighborhoods to learn to efficiently discriminate between
different types of nodes. Structural analysis can further rectify any registers
misclassified as state registers by the GNN by analyzing strongly connected
components in the netlist graph. Numerical results on a set of benchmarks show
that ReIGNN can achieve, on average, 96.5% balanced accuracy and 97.7%
sensitivity across different designs.

Videos are a rich source for self-supervised learning (SSL) of visual
representations due to the presence of natural temporal transformations of
objects. However, current methods typically randomly sample video clips for
learning, which results in an imperfect supervisory signal. In this work, we
propose PreViTS, an SSL framework that utilizes an unsupervised tracking signal
for selecting clips containing the same object, which helps better utilize
temporal transformations of objects. PreViTS further uses the tracking signal
to spatially constrain the frame regions to learn from and trains the model to
locate meaningful objects by providing supervision on Grad-CAM attention maps.
To evaluate our approach, we train a momentum contrastive (MoCo) encoder on
VGG-Sound and Kinetics-400 datasets with PreViTS. Training with PreViTS
outperforms representations learnt by contrastive strategy alone on video
downstream tasks, obtaining state-of-the-art performance on action
classification. PreViTS helps learn feature representations that are more
robust to changes in background and context, as seen by experiments on datasets
with background changes. Learning from large-scale videos with PreViTS could
lead to more accurate and robust visual feature representations.

Sparse decision tree optimization has been one of the most fundamental
problems in AI since its inception and is a challenge at the core of
interpretable machine learning. Sparse decision tree optimization is
computationally hard, and despite steady effort since the 1960's, breakthroughs
have only been made on the problem within the past few years, primarily on the
problem of finding optimal sparse decision trees. However, current
state-of-the-art algorithms often require impractical amounts of computation
time and memory to find optimal or near-optimal trees for some real-world
datasets, particularly those having several continuous-valued features. Given
that the search spaces of these decision tree optimization problems are
massive, can we practically hope to find a sparse decision tree that competes
in accuracy with a black box machine learning model? We address this problem
via smart guessing strategies that can be applied to any optimal
branch-and-bound-based decision tree algorithm. We show that by using these
guesses, we can reduce the run time by multiple orders of magnitude, while
providing bounds on how far the resulting trees can deviate from the black
box's accuracy and expressive power. Our approach enables guesses about how to
bin continuous features, the size of the tree, and lower bounds on the error
for the optimal decision tree. Our experiments show that in many cases we can
rapidly construct sparse decision trees that match the accuracy of black box
models. To summarize: when you are having trouble optimizing, just guess.

Variational segmentation algorithms require a prior imposed in the form of a
regularisation term to enforce smoothness of the solution. Recently, it was
shown in the Deep Image Prior work that the explicit regularisation in a model
can be removed and replaced by the implicit regularisation captured by the
architecture of a neural network. The Deep Image Prior approach is competitive,
but is only tailored to one specific image and does not allow us to predict
future images. We propose to incorporate the ideas from Deep Image Prior into a
more traditional learning algorithm to allow us to use the implicit
regularisation offered by the Deep Image Prior, but still be able to predict
future images.

Machine learning is shifting towards general-purpose pretrained generative
models, trained in a self-supervised manner on large amounts of data, which can
then be applied to solve a large number of tasks. However, due to their generic
training methodology, these models often fail to meet some of the downstream
requirements (e.g., hallucinations in abstractive summarization or style
violations in code generation). This raises the important question of how to
adapt pre-trained generative models to meet all requirements without destroying
their general capabilities ("catastrophic forgetting"). Recent work has
proposed to solve this problem by representing task-specific requirements
through energy-based models (EBMs) and approximating these EBMs using
distributional policy gradients (DPG). Despite its effectiveness, this approach
is however limited to unconditional distributions. In this paper, we extend DPG
to conditional tasks by proposing Conditional DPG (CDPG). We evaluate CDPG on
four different control objectives across three tasks (translation,
summarization and code generation) and two pretrained models (T5 and GPT-Neo).
Our results show that fine-tuning using CDPG robustly moves these pretrained
models closer towards meeting control objectives and -- in contrast with
baseline approaches -- does not result in catastrophic forgetting.

Out-of-distribution (OOD) detection is important for deploying machine
learning models in the real world, where test data from shifted distributions
can naturally arise. While a plethora of algorithmic approaches have recently
emerged for OOD detection, a critical gap remains in theoretical understanding.
In this work, we develop an analytical framework that characterizes and unifies
the theoretical understanding for OOD detection. Our analytical framework
motivates a novel OOD detection method for neural networks, GEM, which
demonstrates both theoretical and empirical superiority. In particular, on
CIFAR-100 as in-distribution data, our method outperforms a competitive
baseline by 16.57% (FPR95). Lastly, we formally provide provable guarantees and
comprehensive analysis of our method, underpinning how various properties of
data distribution affect the performance of OOD detection.

Quantum technology has the potential to revolutionize how we acquire and
process experimental data to learn about the physical world. An experimental
setup that transduces data from a physical system to a stable quantum memory,
and processes that data using a quantum computer, could have significant
advantages over conventional experiments in which the physical system is
measured and the outcomes are processed using a classical computer. We prove
that, in various tasks, quantum machines can learn from exponentially fewer
experiments than those required in conventional experiments. The exponential
advantage holds in predicting properties of physical systems, performing
quantum principal component analysis on noisy states, and learning approximate
models of physical dynamics. In some tasks, the quantum processing needed to
achieve the exponential advantage can be modest; for example, one can
simultaneously learn about many noncommuting observables by processing only two
copies of the system. Conducting experiments with up to 40 superconducting
qubits and 1300 quantum gates, we demonstrate that a substantial quantum
advantage can be realized using today's relatively noisy quantum processors.
Our results highlight how quantum technology can enable powerful new strategies
to learn about nature.

The task of multimodal learning has seen a growing interest recently as it
allows for training neural architectures based on different modalities such as
vision, text, and audio. One challenge in training such models is that they
need to jointly learn semantic concepts and their relationships across
different input representations. Capsule networks have been shown to perform
well in context of capturing the relation between low-level input features and
higher-level concepts. However, capsules have so far mainly been used only in
small-scale fully supervised settings due to the resource demand of
conventional routing algorithms. We present a new multimodal capsule network
that allows us to leverage the strength of capsules in the context of a
multimodal learning framework on large amounts of video data. To adapt the
capsules to large-scale input data, we propose a novel routing by
self-attention mechanism that selects relevant capsules which are then used to
generate a final joint multimodal feature representation. This allows not only
for robust training with noisy video data, but also to scale up the size of the
capsule network compared to traditional routing methods while still being
computationally efficient. We evaluate the proposed architecture by pretraining
it on a large-scale multimodal video dataset and applying it on four datasets
in two challenging downstream tasks. Results show that the proposed multimodal
capsule network is not only able to improve results compared to other routing
techniques, but also achieves competitive performance on the task of multimodal
learning.

The proliferation of harmful and offensive content is a problem that many
online platforms face today. One of the most common approaches for moderating
offensive content online is via the identification and removal after it has
been posted, increasingly assisted by machine learning algorithms. More
recently, platforms have begun employing moderation approaches which seek to
intervene prior to offensive content being posted. In this paper, we conduct an
online randomized controlled experiment on Twitter to evaluate a new
intervention that aims to encourage participants to reconsider their offensive
content and, ultimately, seeks to reduce the amount of offensive content on the
platform. The intervention prompts users who are about to post harmful content
with an opportunity to pause and reconsider their Tweet. We find that users in
our treatment prompted with this intervention posted 6% fewer offensive Tweets
than non-prompted users in our control. This decrease in the creation of
offensive content can be attributed not just to the deletion and revision of
prompted Tweets -- we also observed a decrease in both the number of offensive
Tweets that prompted users create in the future and the number of offensive
replies to prompted Tweets. We conclude that interventions allowing users to
reconsider their comments can be an effective mechanism for reducing offensive
content online.

Quantum computing holds significant potential for applications in biology and
medicine, spanning from the simulation of biomolecules to machine learning
approaches for subtyping cancers on the basis of clinical features. This
potential is encapsulated by the concept of a quantum advantage, which is
typically contingent on a reduction in the consumption of a computational
resource, such as time, space, or data. Here, we distill the concept of a
quantum advantage into a simple framework that we hope will aid researchers in
biology and medicine pursuing the development of quantum applications. We then
apply this framework to a wide variety of computational problems relevant to
these domains in an effort to i) assess the potential of quantum advantages in
specific application areas and ii) identify gaps that may be addressed with
novel quantum approaches. Bearing in mind the rapid pace of change in the
fields of quantum computing and classical algorithms, we aim to provide an
extensive survey of applications in biology and medicine that may lead to
practical quantum advantages.

What can neural networks learn about the visual world from a single image?
While it obviously cannot contain the multitudes of possible objects, scenes
and lighting conditions that exist - within the space of all possible
256^(3x224x224) 224-sized square images, it might still provide a strong prior
for natural images. To analyze this hypothesis, we develop a framework for
training neural networks from scratch using a single image by means of
knowledge distillation from a supervisedly pretrained teacher. With this, we
find that the answer to the above question is: 'surprisingly, a lot'. In
quantitative terms, we find top-1 accuracies of 94%/74% on CIFAR-10/100, 66% on
ImageNet, and by extending this method to video and audio, 77% on UCF-101 and
84% on SpeechCommands. In extensive analyses we disentangle the effect of
augmentations, choice of source image and network architectures and also
discover "panda neurons" in networks that have never seen a panda. This work
shows that one image can be used to extrapolate to thousands of object classes
and motivates a renewed research agenda on the fundamental interplay of
augmentations and image.

We study infinite limits of neural network quantum states ($\infty$-NNQS),
which exhibit representation power through ensemble statistics, and also
tractable gradient descent dynamics. Ensemble averages of Renyi entropies are
expressed in terms of neural network correlators, and architectures that
exhibit volume-law entanglement are presented. A general framework is developed
for studying the gradient descent dynamics of neural network quantum states
(NNQS), using a quantum state neural tangent kernel (QS-NTK). For $\infty$-NNQS
the training dynamics is simplified, since the QS-NTK becomes deterministic and
constant. An analytic solution is derived for quantum state supervised
learning, which allows an $\infty$-NNQS to recover any target wavefunction.
Numerical experiments on finite and infinite NNQS in the transverse field Ising
model and Fermi Hubbard model demonstrate excellent agreement with theory.
$\infty$-NNQS opens up new opportunities for studying entanglement and training
dynamics in other physics applications, such as in finding ground states.

The success of Generative Adversarial Networks (GANs) is largely built upon
the adversarial training between a generator (G) and a discriminator (D). They
are expected to reach a certain equilibrium where D cannot distinguish the
generated images from the real ones. However, such an equilibrium is rarely
achieved in practical GAN training, instead, D almost always surpasses G. We
attribute one of its sources to the information asymmetry between D and G. We
observe that D learns its own visual attention when determining whether an
image is real or fake, but G has no explicit clue on which regions to focus on
for a particular synthesis. To alleviate the issue of D dominating the
competition in GANs, we aim to raise the spatial awareness of G. Randomly
sampled multi-level heatmaps are encoded into the intermediate layers of G as
an inductive bias. Thus G can purposefully improve the synthesis of certain
image regions. We further propose to align the spatial awareness of G with the
attention map induced from D. Through this way we effectively lessen the
information gap between D and G. Extensive results show that our method pushes
the two-player game in GANs closer to the equilibrium, leading to a better
synthesis performance. As a byproduct, the introduced spatial awareness
facilitates interactive editing over the output synthesis. Demo video and code
are available at https://genforce.github.io/eqgan-sa/.

Autonomous Land Vehicles (ALV) shall efficiently recognize the ground in
unknown environments. A novel $\mathcal{GP}$-based method is proposed for the
ground segmentation task in rough driving scenarios. A non-stationary
covariance function is utilized as the kernel for the $\mathcal{GP}$. The
ground surface behavior is assumed to only demonstrate local-smoothness. Thus,
point estimates of the kernel's length-scales are obtained. Thus, two Gaussian
processes are introduced to separately model the observation and local
characteristics of the data. While, the \textit{observation process} is used to
model the ground, the \textit{latent process} is put on length-scale values to
estimate point values of length-scales at each input location. Input locations
for this latent process are chosen in a physically-motivated procedure to
represent an intuition about ground condition. Furthermore, an intuitive guess
of length-scale value is represented by assuming the existence of hypothetical
surfaces in the environment that every bunch of data points may be assumed to
be resulted from measurements from this surfaces. Bayesian inference is
implemented using \textit{maximum a Posteriori} criterion. The log-marginal
likelihood function is assumed to be a multi-task objective function, to
represent a whole-frame unbiased view of the ground at each frame. Simulation
results shows the effectiveness of the proposed method even in an uneven, rough
scene which outperforms similar Gaussian process based ground segmentation
methods. While adjacent segments do not have similar ground structure in an
uneven scene, the proposed method gives an efficient ground estimation based on
a whole-frame viewpoint instead of just estimating segment-wise probable ground
surfaces.

We consider the problem of clustering mixtures of mean-separated Gaussians in
high dimensions. We are given samples from a mixture of $k$ identity covariance
Gaussians, so that the minimum pairwise distance between any two pairs of means
is at least $\Delta$, for some parameter $\Delta > 0$, and the goal is to
recover the ground truth clustering of these samples. It is folklore that
separation $\Delta = \Theta (\sqrt{\log k})$ is both necessary and sufficient
to recover a good clustering, at least information theoretically. However, the
estimators which achieve this guarantee are inefficient. We give the first
algorithm which runs in polynomial time, and which almost matches this
guarantee. More precisely, we give an algorithm which takes polynomially many
samples and time, and which can successfully recover a good clustering, so long
as the separation is $\Delta = \Omega (\log^{1/2 + c} k)$, for any $c > 0$.
Previously, polynomial time algorithms were only known for this problem when
the separation was polynomial in $k$, and all algorithms which could tolerate
$\textsf{poly}( \log k )$ separation required quasipolynomial time. We also
extend our result to mixtures of translations of a distribution which satisfies
the Poincar\'{e} inequality, under additional mild assumptions. Our main
technical tool, which we believe is of independent interest, is a novel way to
implicitly represent and estimate high degree moments of a distribution, which
allows us to extract important information about high-degree moments without
ever writing down the full moment tensors explicitly.

We present Mirable's submission to the 2021 Emotions and Themes in Music
challenge. In this work, we intend to address the question: can we leverage
semi-supervised learning techniques on music emotion recognition? With that, we
experiment with noisy student training, which has improved model performance in
the image classification domain. As the noisy student method requires a strong
teacher model, we further delve into the factors including (i) input training
length and (ii) complementary music representations to further boost the
performance of the teacher model. For (i), we find that models trained with
short input length perform better in PR-AUC, whereas those trained with long
input length perform better in ROC-AUC. For (ii), we find that using harmonic
pitch class profiles (HPCP) consistently improve tagging performance, which
suggests that harmonic representation is useful for music emotion tagging.
Finally, we find that noisy student method only improves tagging results for
the case of long training length. Additionally, we find that ensembling
representations trained with different training lengths can improve tagging
results significantly, which suggest a possible direction to explore
incorporating multiple temporal resolutions in the network architecture for

Due to the advent of modern embedded systems and mobile devices with
constrained resources, there is a great demand for incredibly efficient deep
neural networks for machine learning purposes. There is also a growing concern
of privacy and confidentiality of user data within the general public when
their data is processed and stored in an external server which has further
fueled the need for developing such efficient neural networks for real-time
inference on local embedded systems. The scope of our work presented in this
paper is limited to image classification using a convolutional neural network.
A Convolutional Neural Network (CNN) is a class of Deep Neural Network (DNN)
widely used in the analysis of visual images captured by an image sensor,
designed to extract information and convert it into meaningful representations
for real-time inference of the input data. In this paper, we propose a neoteric
variant of deep convolutional neural network architecture to ameliorate the
performance of existing CNN architectures for real-time inference on embedded
systems. We show that this architecture, dubbed CondenseNeXt, is remarkably
efficient in comparison to the baseline neural network architecture,
CondenseNet, by reducing trainable parameters and FLOPs required to train the
network whilst maintaining a balance between the trained model size of less
than 3.0 MB and accuracy trade-off resulting in an unprecedented computational

Direction finding and positioning systems based on RF signals are
significantly impacted by multipath propagation, particularly in indoor
environments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angle
of Arrival (AoA) in the presence of multipath or when operating in a weak
signal regime. We note that digitally sampled RF frontends allow for the easy
analysis of signals, and their delayed components. Low-cost Software-Defined
Radio (SDR) modules enable Channel State Information (CSI) extraction across a
wide spectrum, motivating the design of an enhanced Angle-of-Arrival (AoA)
solution. We propose a Deep Learning approach to deriving AoA from a single
snapshot of the SDR multichannel data. We compare and contrast deep-learning
based angle classification and regression models, to estimate up to two AoAs
accurately. We have implemented the inference engines on different platforms to
extract AoAs in real-time, demonstrating the computational tractability of our
approach. To demonstrate the utility of our approach we have collected IQ
(In-phase and Quadrature components) samples from a four-element Universal
Linear Array (ULA) in various Light-of-Sight (LOS) and Non-Line-of-Sight (NLOS)
environments, and published the dataset. Our proposed method demonstrates
excellent reliability in determining number of impinging signals and realized
mean absolute AoA errors less than $2^{\circ}$.

Label-free model evaluation, or AutoEval, estimates model accuracy on
unlabeled test sets, and is critical for understanding model behaviors in
various unseen environments. In the absence of image labels, based on dataset
representations, we estimate model performance for AutoEval with regression. On
the one hand, image feature is a straightforward choice for such
representations, but it hampers regression learning due to being unstructured
(\ie no specific meanings for component at certain location) and of
large-scale. On the other hand, previous methods adopt simple structured
representations (like average confidence or average feature), but insufficient
to capture the data characteristics given their limited dimensions. In this
work, we take the best of both worlds and propose a new semi-structured dataset
representation that is manageable for regression learning while containing rich
information for AutoEval. Based on image features, we integrate distribution
shapes, clusters, and representative samples for a semi-structured dataset
representation. Besides the structured overall description with distribution
shapes, the unstructured description with clusters and representative samples
include additional fine-grained information facilitating the AutoEval task. On
three existing datasets and 25 newly introduced ones, we experimentally show
that the proposed representation achieves competitive results. Code and dataset
are available at
https://github.com/sxzrt/Semi-Structured-Dataset-Representations.

In recent years, researchers pay growing attention to the few-shot learning
(FSL) task to address the data-scarce problem. A standard FSL framework is
composed of two components: i) Pre-train. Employ the base data to generate a
CNN-based feature extraction model (FEM). ii) Meta-test. Apply the trained FEM
to the novel data (category is different from base data) to acquire the feature
embeddings and recognize them. Although researchers have made remarkable
breakthroughs in FSL, there still exists a fundamental problem. Since the
trained FEM with base data usually cannot adapt to the novel class flawlessly,
the novel data's feature may lead to the distribution shift problem. To address
this challenge, we hypothesize that even if most of the decisions based on
different FEMs are viewed as weak decisions, which are not available for all
classes, they still perform decently in some specific categories. Inspired by
this assumption, we propose a novel method Multi-Decision Fusing Model (MDFM),
which comprehensively considers the decisions based on multiple FEMs to enhance
the efficacy and robustness of the model. MDFM is a simple, flexible,
non-parametric method that can directly apply to the existing FEMs. Besides, we
extend the proposed MDFM to two FSL settings (i.e., supervised and
semi-supervised settings). We evaluate the proposed method on five benchmark
datasets and achieve significant improvements of 3.4%-7.3% compared with

Can deep learning models achieve greater generalization if their training is
guided by reference to human perceptual abilities? And how can we implement
this in a practical manner? This paper proposes a training strategy to ConveY
Brain Oversight to Raise Generalization (CYBORG). This new approach
incorporates human-annotated saliency maps into a loss function that guides the
model's learning to focus on image regions that humans deem salient for the
task. The Class Activation Mapping (CAM) mechanism is used to probe the model's
current saliency in each training batch, juxtapose this model saliency with
human saliency, and penalize large differences. Results on the task of
synthetic face detection, selected to illustrate the effectiveness of the
approach, show that CYBORG leads to significant improvement in accuracy on
unseen samples consisting of face images generated from six Generative
Adversarial Networks across multiple classification network architectures. We
also show that scaling to even seven times the training data, or using
non-human-saliency auxiliary information, such as segmentation masks, and
standard loss cannot beat the performance of CYBORG-trained models. As a side
effect of this work, we observe that the addition of explicit region annotation
to the task of synthetic face detection increased human classification
accuracy. This work opens a new area of research on how to incorporate human
visual saliency into loss functions in practice. All data, code and pre-trained
models used in this work are offered with this paper.

Deep learning techniques have shown the capability to identify the degree of
energy loss of high-energy jets traversing hot QCD medium on a jet-by-jet
basis. The average amount of quenching of quark and gluon jets in hot QCD
medium actually have different characteristics, such as their dependence on the
in-medium traversed length and the early-developed jet substructures in the
evolution. These observations motivate us to consider these two types of jets
separately and classify them from jet images with deep learning techniques. We
find that the classification performance gradually decreases with increasing
degree of jet modification. In addition, we discuss the predictive power of
different jet observables, such as the jet shape, jet fragmentation function,
jet substructures as well as their combinations, in order to address the
interpretability of the classification task.

With deep learning techniques, the degree of modification of energetic jets
that traversed hot QCD medium can be identified on a jet-by-jet basis. Due to
the strong correlations between the degree of jet modification and its
traversed length in the medium, we demonstrate the power of our novel method to
locate the creation point of a dijet pair in the nuclear overlap region. In
particular, jet properties, such as jet width and orientation can serve as
additional handles to locate the creation points to a higher level of
precision, which constitutes a significant development towards the
long-standing goal of using jets as tomographic probes of the quark-gluon
plasma.

We present an open-source Python framework for NeuroEvolution Optimization
with Reinforcement Learning (NEORL) developed at the Massachusetts Institute of
Technology. NEORL offers a global optimization interface of state-of-the-art
algorithms in the field of evolutionary computation, neural networks through
reinforcement learning, and hybrid neuroevolution algorithms. NEORL features
diverse set of algorithms, user-friendly interface, parallel computing support,
automatic hyperparameter tuning, detailed documentation, and demonstration of
applications in mathematical and real-world engineering optimization. NEORL
encompasses various optimization problems from combinatorial, continuous, mixed
discrete/continuous, to high-dimensional, expensive, and constrained
engineering optimization. NEORL is tested in variety of engineering
applications relevant to low carbon energy research in addressing solutions to
climate change. The examples include nuclear reactor control and fuel cell
power production. The results demonstrate NEORL competitiveness against other
algorithms and optimization frameworks in the literature, and a potential tool
to solve large-scale optimization problems. More examples and benchmarking of
NEORL can be found here: https://neorl.readthedocs.io/en/latest/index.html

A fundamental problem in statistics is to compare the outcomes attained by
members of subpopulations. This problem arises in the analysis of randomized
controlled trials, in the analysis of A/B tests, and in the assessment of
fairness and bias in the treatment of sensitive subpopulations, especially when
measuring the effects of algorithms and machine learning. Often the comparison
makes the most sense when performed separately for individuals who are similar
according to certain characteristics given by the values of covariates of
interest; the separate comparisons can also be aggregated in various ways to
compare across all values of the covariates. Separating, segmenting, or
stratifying into those with similar values of the covariates is also known as
"conditioning on" or "controlling for" those covariates; controlling for age or
annual income is common.
  Two standard methods of controlling for covariates are (1) binning and (2)
regression modeling. Binning requires making fairly arbitrary, yet frequently
highly influential choices, and is unsatisfactorily temperamental in multiple
dimensions, with multiple covariates. Regression analysis works wonderfully
when there is good reason to believe in a particular parameterized regression
model or classifier (such as logistic regression). Thus, there appears to be no
extant canonical fully non-parametric regression for the comparison of
subpopulations, not while conditioning on multiple specified covariates.
Existing methods rely on analysts to make choices, and those choices can be
debatable; analysts can deceive others or even themselves. The present paper
aims to fill the gap, combining two ingredients: (1) recently developed
methodologies for such comparisons that already exist when conditioning on a
single scalar covariate and (2) the Hilbert space-filling curve that maps
continuously from one dimension to multiple dimensions.

A thermal simulation methodology is developed for interconnects enabled by a
data-driven learning algorithm accounting for variations of material
properties, heat sources and boundary conditions (BCs). The methodology is
based on the concepts of model order reduction and domain decomposition to
construct a multi-block approach. A generic block model is built to represent a
group of interconnect blocks that are used to wire standard cells in the
integrated circuits (ICs). The blocks in this group possess identical geometry
with various metal/via routings. The data-driven model reduction method is thus
applied to learn material property variations induced by different metal/via
routings in the blocks, in addition to the variations of heat sources and BCs.
The approach is investigated in two very different settings. It is first
applied to thermal simulation of a single interconnect block with similar BCs
to those in the training of the generic block. It is then implemented in
multi-block thermal simulation of a FinFET IC, where the interconnect structure
is partitioned into several blocks each modeled by the generic block model.
Accuracy of the generic block model is examined in terms of the metal/via
routings, BCs and thermal discontinuities at the block interfaces.

Ransomware defense solutions that can quickly detect and classify different
ransomware classes to formulate rapid response plans have been in high demand
in recent years. Though the applicability of adopting deep learning techniques
to provide automation and self-learning provision has been proven in many
application domains, the lack of data available for ransomware (and other
malware)samples has been raised as a barrier to developing effective deep
learning-based solutions. To address this concern, we propose a few-shot
meta-learning based Siamese Neural Network that not only detects ransomware
attacks but is able to classify them into different classes. Our proposed model
utilizes the entropy feature directly extracted from ransomware binary files to
retain more fine-grained features associated with different ransomware
signatures. These entropy features are used further to train and optimize our
model using a pre-trained network (e.g. VGG-16) in a meta-learning fashion.
This approach generates more accurate weight factors, compared to feature
images are used, to avoid the bias typically associated with a model trained
with a limited number of training samples. Our experimental results show that
our proposed model is highly effective in providing a weighted F1-score
exceeding the rate>86% compared

Natural Language Understanding (NLU) is a branch of Natural Language
Processing (NLP) that uses intelligent computer software to understand texts
that encode human knowledge. Recent years have witnessed notable progress
across various NLU tasks with deep learning techniques, especially with
pretrained language models. Besides proposing more advanced model
architectures, constructing more reliable and trustworthy datasets also plays a
huge role in improving NLU systems, without which it would be impossible to
train a decent NLU model. It's worth noting that the human ability of
understanding natural language is flexible and robust. On the contrary, most of
existing NLU systems fail to achieve desirable performance on out-of-domain
data or struggle on handling challenging items (e.g., inherently ambiguous
items, adversarial items) in the real world. Therefore, in order to have NLU
models understand human language more effectively, it is expected to prioritize
the study on robust natural language understanding. In this thesis, we deem
that NLU systems are consisting of two components: NLU models and NLU datasets.
As such, we argue that, to achieve robust NLU, the model architecture/training
and the dataset are equally important. Specifically, we will focus on three NLU
tasks to illustrate the robustness problem in different NLU tasks and our
contributions (i.e., novel models and new datasets) to help achieve more robust
natural language understanding. Moving forward, the ultimate goal for robust
natural language understanding is to build NLU models which can behave humanly.
That is, it's expected that robust NLU systems are capable to transfer the
knowledge from training corpus to unseen documents more reliably and survive
when encountering challenging items even if the system doesn't know a priori of
users' inputs.

Saliency Object Detection (SOD) has several applications in image analysis.
The methods have evolved from image-intrinsic to object-inspired
(deep-learning-based) models. When a model fail, however, there is no
alternative to enhance its saliency map. We fill this gap by introducing a
hybrid approach, named \textit{Iterative Saliency Enhancement over Superpixel
Similarity} (ISESS), that iteratively generates enhanced saliency maps by
executing two operations alternately: object-based superpixel segmentation and
superpixel-based saliency estimation -- cycling operations never exploited.
ISESS estimates seeds for superpixel delineation from a given saliency map and
defines superpixel queries in the foreground and background. A new saliency map
results from color similarities between queries and superpixels at each
iteration. The process repeats and, after a given number of iterations, the
generated saliency maps are combined into one by cellular automata. Finally,
the resulting map is merged with the initial one by the maximum bewteen their
average values per superpixel. We demonstrate that our hybrid model can
consistently outperform three state-of-the-art deep-learning-based methods on
five image datasets.

Transformer architectures have been successfully used in learning source code
representations. The fusion between a graph representation like Abstract Syntax
Tree (AST) and a source code sequence makes the use of current approaches
computationally intractable for large input sequence lengths. Source code can
have long-range dependencies that require larger sequence lengths to model
effectively. Current approaches have a quadratic growth in computational and
memory costs with respect to the sequence length. Using such models in
practical scenarios is difficult. In this work, we propose the conditioning of
a source code snippet with its graph modality by using the graph adjacency
matrix as an attention mask for a sparse self-attention mechanism and the use
of a graph diffusion mechanism to model longer-range token dependencies. Our
model reaches state-of-the-art results in BLEU, METEOR, and ROUGE-L metrics for
the code summarization task and near state-of-the-art accuracy in the variable
misuse task. The memory use and inference time of our model have linear growth
with respect to the input sequence length as compared to the quadratic growth
of previous works.

Patients regularly continue assessment or treatment in other facilities than
they began them in, receiving their previous imaging studies as a CD-ROM and
requiring clinical staff at the new hospital to import these studies into their
local database. However, between different facilities, standards for
nomenclature, contents, or even medical procedures may vary, often requiring
human intervention to accurately classify the received studies in the context
of the recipient hospital's standards. In this study, the authors present MOMO
(MOdality Mapping and Orchestration), a deep learning-based approach to
automate this mapping process utilizing metadata substring matching and a
neural network ensemble, which is trained to recognize the 76 most common
imaging studies across seven different modalities. A retrospective study is
performed to measure the accuracy that this algorithm can provide. To this end,
a set of 11,934 imaging series with existing labels was retrieved from the
local hospital's PACS database to train the neural networks. A set of 843
completely anonymized external studies was hand-labeled to assess the
performance of our algorithm. Additionally, an ablation study was performed to
measure the performance impact of the network ensemble in the algorithm, and a
comparative performance test with a commercial product was conducted. In
comparison to a commercial product (96.20% predictive power, 82.86% accuracy,
1.36% minor errors), a neural network ensemble alone performs the
classification task with less accuracy (99.05% predictive power, 72.69%
accuracy, 10.3% minor errors). However, MOMO outperforms either by a large
margin in accuracy and with increased predictive power (99.29% predictive
power, 92.71% accuracy, 2.63% minor errors).

We show that coupled autonomous thermal machines, in the presence of three
heat reservoirs and following a global linear-irreversible description, provide
a unified framework to accommodate the variety of expressions for the
efficiency at maximum power (EMP). The efficiency is expressible in terms of
the Carnot efficiency of the global set up if the intermediate reservoir
temperature is an algebraic mean of the hot and cold temperatures. We give an
explanation of the universal properties of EMP near equilibrium in terms of the
properties of symmetric algebraic means. For the case of broken time reversal
symmetry, a universal second order coefficient of 6/49 is predicted in the
series expansion of EMP, analogous to the 1/8 coefficient in the time-reversal
symmetric case.

Certified robustness guarantee gauges a model's robustness to test-time
attacks and can assess the model's readiness for deployment in the real world.
In this work, we critically examine how the adversarial robustness guarantees
from randomized smoothing-based certification methods change when
state-of-the-art certifiably robust models encounter out-of-distribution (OOD)
data. Our analysis demonstrates a previously unknown vulnerability of these
models to low-frequency OOD data such as weather-related corruptions, rendering
these models unfit for deployment in the wild. To alleviate this issue, we
propose a novel data augmentation scheme, FourierMix, that produces
augmentations to improve the spectral coverage of the training data.
Furthermore, we propose a new regularizer that encourages consistent
predictions on noise perturbations of the augmented data to improve the quality
of the smoothed models. We find that FourierMix augmentations help eliminate
the spectral bias of certifiably robust models enabling them to achieve
significantly better robustness guarantees on a range of OOD benchmarks. Our
evaluation also uncovers the inability of current OOD benchmarks at
highlighting the spectral biases of the models. To this end, we propose a
comprehensive benchmarking suite that contains corruptions from different
regions in the spectral domain. Evaluation of models trained with popular
augmentation methods on the proposed suite highlights their spectral biases and
establishes the superiority of FourierMix trained models at achieving
better-certified robustness guarantees under OOD shifts over the entire
frequency spectrum.

Random walks are a fundamental primitive used in many machine learning
algorithms with several applications in clustering and semi-supervised
learning. Despite their relevance, the first efficient parallel algorithm to
compute random walks has been introduced very recently (Lacki et al.).
Unfortunately their method has a fundamental shortcoming: their algorithm is
non-local in that it heavily relies on computing random walks out of all nodes
in the input graph, even though in many practical applications one is
interested in computing random walks only from a small subset of nodes in the
graph. In this paper, we present a new algorithm that overcomes this limitation
by building random walk efficiently and locally at the same time. We show that
our technique is both memory and round efficient, and in particular yields an
efficient parallel local clustering algorithm. Finally, we complement our
theoretical analysis with experimental results showing that our algorithm is
significantly more scalable than previous approaches.

We introduce the dynamic grasp synthesis task: given an object with a known
6D pose and a grasp reference, our goal is to generate motions that move the
object to a target 6D pose. This is challenging, because it requires reasoning
about the complex articulation of the human hand and the intricate physical
interaction with the object. We propose a novel method that frames this problem
in the reinforcement learning framework and leverages a physics simulation,
both to learn and to evaluate such dynamic interactions. A hierarchical
approach decomposes the task into low-level grasping and high-level motion
synthesis. It can be used to generate novel hand sequences that approach,
grasp, and move an object to a desired location, while retaining
human-likeness. We show that our approach leads to stable grasps and generates
a wide range of motions. Furthermore, even imperfect labels can be corrected by
our method to generate dynamic interaction sequences.

Electron density $\rho(\vec{r})$ is the fundamental variable in the
calculation of ground state energy with density functional theory (DFT). Beyond
total energy, features and changes in $\rho(\vec{r})$ distributions are often
used to capture critical physicochemical phenomena in functional materials. We
present a machine learning framework for the prediction of $\rho(\vec{r})$. The
model is based on equivariant graph neural networks and the electron density is
predicted at special query point vertices that are part of the message passing
graph, but only receive messages. The model is tested across multiple data sets
of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and
LixNiyMnzCo(1-y-z)O2 lithium ion battery cathodes (NMC). For QM9 molecules, the
accuracy of the proposed model exceeds typical variability in $\rho(\vec{r})$
obtained from DFT done with different exchange-correlation functionals. The
accuracy on all three datasets is beyond state of the art and the computation
time is orders of magnitude faster than DFT.

The COVID19 pandemic has demonstrated a need for remote learning and virtual
learning applications such as virtual reality (VR) and tablet-based solutions.
Creating complex learning scenarios by developers is highly time-consuming and
can take over a year. It is also costly to employ teams of system analysts,
developers and 3D artists. There is a requirement to provide a simple method to
enable lecturers to create their own content for their laboratory tutorials.
Research has been undertaken into developing generic models to enable the
semi-automatic creation of a virtual learning tools for subjects that require
practical interactions with the lab resources. In addition to the system for
creating digital twins, a case study describing the creation of a virtual
learning application for an electrical laboratory tutorial has been presented.

To create heterogeneous, multiscale structures with unprecedented
functionalities, recent topology optimization approaches design either fully
aperiodic systems or functionally graded structures, which compete in terms of
design freedom and efficiency. We propose to inherit the advantages of both
through a data-driven framework for multiclass functionally graded structures
that mixes several families, i.e., classes, of microstructure topologies to
create spatially-varying designs with guaranteed feasibility. The key is a new
multiclass shape blending scheme that generates smoothly graded microstructures
without requiring compatible classes or connectivity and feasibility
constraints. Moreover, it transforms the microscale problem into an efficient,
low-dimensional one without confining the design to predefined shapes.
Compliance and shape matching examples using common truss geometries and
diversity-based freeform topologies demonstrate the versatility of our
framework, while studies on the effect of the number and diversity of classes
illustrate the effectiveness. The generality of the proposed methods supports
future extensions beyond the linear applications presented.

Deep neural networks for computer vision tasks are deployed in increasingly
safety-critical and socially-impactful applications, motivating the need to
close the gap in model performance under varied, naturally occurring imaging
conditions. Robustness, ambiguously used in multiple contexts including
adversarial machine learning, here then refers to preserving model performance
under naturally-induced image corruptions or alterations.
  We perform a systematic review to identify, analyze, and summarize current
definitions and progress towards non-adversarial robustness in deep learning
for computer vision. We find that this area of research has received
disproportionately little attention relative to adversarial machine learning,
yet a significant robustness gap exists that often manifests in performance
degradation similar in magnitude to adversarial conditions.
  To provide a more transparent definition of robustness across contexts, we
introduce a structural causal model of the data generating process and
interpret non-adversarial robustness as pertaining to a model's behavior on
corrupted images which correspond to low-probability samples from the unaltered
data distribution. We then identify key architecture-, data augmentation-, and
optimization tactics for improving neural network robustness. This causal view
of robustness reveals that common practices in the current literature, both in
regards to robustness tactics and evaluations, correspond to causal concepts,
such as soft interventions resulting in a counterfactually-altered distribution
of imaging conditions. Through our findings and analysis, we offer perspectives
on how future research may mind this evident and significant non-adversarial
robustness gap.

As a consequence of the COVID-19 pandemic, the demand for telecommunication
for remote learning/working and telemedicine has significantly increased.
Mobile Edge Caching (MEC) in the 6G networks has been evolved as an efficient
solution to meet the phenomenal growth of the global mobile data traffic by
bringing multimedia content closer to the users. Although massive connectivity
enabled by MEC networks will significantly increase the quality of
communications, there are several key challenges ahead. The limited storage of
edge nodes, the large size of multimedia content, and the time-variant users'
preferences make it critical to efficiently and dynamically predict the
popularity of content to store the most upcoming requested ones before being
requested. Recent advancements in Deep Neural Networks (DNNs) have drawn much
research attention to predict the content popularity in proactive caching
schemes. Existing DNN models in this context, however, suffer from longterm
dependencies, computational complexity, and unsuitability for parallel
computing. To tackle these challenges, we propose an edge caching framework
incorporated with the attention-based Vision Transformer (ViT) neural network,
referred to as the Transformer-based Edge (TEDGE) caching, which to the best of
our knowledge, is being studied for the first time. Moreover, the TEDGE caching
framework requires no data pre-processing and additional contextual
information. Simulation results corroborate the effectiveness of the proposed
TEDGE caching framework in comparison to its counterparts.

This paper explores the use of deep neural networks for semiparametric
estimation of economic models of maximizing behavior in production or discrete
choice. We argue that certain deep networks are particularly well suited as a
nonparametric sieve to approximate regression functions that result from
nonlinear latent variable models of continuous or discrete optimization.
Multi-stage models of this type will typically generate rich interaction
effects between regressors ("inputs") in the regression function so that there
may be no plausible separability restrictions on the "reduced-form" mapping
form inputs to outputs to alleviate the curse of dimensionality. Rather,
economic shape, sparsity, or separability restrictions either at a global level
or intermediate stages are usually stated in terms of the latent variable
model. We show that restrictions of this kind are imposed in a more
straightforward manner if a sufficiently flexible version of the latent
variable model is in fact used to approximate the unknown regression function.

An autonomous experimentation platform in manufacturing is supposedly capable
of conducting a sequential search for finding suitable manufacturing conditions
for advanced materials by itself or even for discovering new materials with
minimal human intervention. The core of the intelligent control of such
platforms is the policy directing sequential experiments, namely, to decide
where to conduct the next experiment based on what has been done thus far. Such
policy inevitably trades off exploitation versus exploration and the current
practice is under the Bayesian optimization framework using the expected
improvement criterion or its variants. We discuss whether it is beneficial to
trade off exploitation versus exploration by measuring the element and degree
of surprise associated with the immediate past observation. We devise a
surprise-reacting policy using two existing surprise metrics, known as the
Shannon surprise and Bayesian surprise. Our analysis shows that the
surprise-reacting policy appears to be better suited for quickly characterizing
the overall landscape of a response surface or a design place under resource
constraints. We argue that such capability is much needed for futuristic
autonomous experimentation platforms. We do not claim that we have a fully
autonomous experimentation platform, but believe that our current effort sheds
new lights or provides a different view angle as researchers are racing to
elevate the autonomy of various primitive autonomous experimentation systems.

Complex sequential tasks in continuous-control settings often require agents
to successfully traverse a set of "narrow passages" in their state space.
Solving such tasks with a sparse reward in a sample-efficient manner poses a
challenge to modern reinforcement learning (RL) due to the associated
long-horizon nature of the problem and the lack of sufficient positive signal
during learning. Various tools have been applied to address this challenge.
When available, large sets of demonstrations can guide agent exploration.
Hindsight relabelling on the other hand does not require additional sources of
information. However, existing strategies explore based on task-agnostic goal
distributions, which can render the solution of long-horizon tasks impractical.
In this work, we extend hindsight relabelling mechanisms to guide exploration
along task-specific distributions implied by a small set of successful
demonstrations. We evaluate the approach on four complex, single and dual arm,
robotics manipulation tasks against strong suitable baselines. The method
requires far fewer demonstrations to solve all tasks and achieves a
significantly higher overall performance as task complexity increases. Finally,
we investigate the robustness of the proposed solution with respect to the
quality of input representations and the number of demonstrations.

The existing search tools for exploring the NASA Astrophysics Data System
(ADS) can be quite rich and empowering (e.g., similar and trending operators),
but researchers are not yet allowed to fully leverage semantic search. For
example, a query for "results from the Planck mission" should be able to
distinguish between all the various meanings of Planck (person, mission,
constant, institutions and more) without further clarification from the user.
At ADS, we are applying modern machine learning and natural language processing
techniques to our dataset of recent astronomy publications to train astroBERT,
a deeply contextual language model based on research at Google. Using
astroBERT, we aim to enrich the ADS dataset and improve its discoverability,
and in particular we are developing our own named entity recognition tool. We
present here our preliminary results and lessons learned.

An outlier is an event or observation that is defined as an unusual activity,
intrusion, or a suspicious data point that lies at an irregular distance from a
population. The definition of an outlier event, however, is subjective and
depends on the application and the domain (Energy, Health, Wireless Network,
etc.). It is important to detect outlier events as carefully as possible to
avoid infrastructure failures because anomalous events can cause minor to
severe damage to infrastructure. For instance, an attack on a cyber-physical
system such as a microgrid may initiate voltage or frequency instability,
thereby damaging a smart inverter which involves very expensive repairing.
Unusual activities in microgrids can be mechanical faults, behavior changes in
the system, human or instrument errors or a malicious attack. Accordingly, and
due to its variability, Outlier Detection (OD) is an ever-growing research
field. In this chapter, we discuss the progress of OD methods using AI
techniques. For that, the fundamental concepts of each OD model are introduced
via multiple categories. Broad range of OD methods are categorized into six
major categories: Statistical-based, Distance-based, Density-based,
Clustering-based, Learning-based, and Ensemble methods. For every category, we
discuss recent state-of-the-art approaches, their application areas, and
performances. After that, a brief discussion regarding the advantages,
disadvantages, and challenges of each technique is provided with
recommendations on future research directions. This survey aims to guide the
reader to better understand recent progress of OD methods for the assurance of
AI.

In this paper, we introduce a novel deep learning method for photo-realistic
manipulation of the emotional state of actors in "in-the-wild" videos. The
proposed method is based on a parametric 3D face representation of the actor in
the input scene that offers a reliable disentanglement of the facial identity
from the head pose and facial expressions. It then uses a novel deep domain
translation framework that alters the facial expressions in a consistent and
plausible manner, taking into account their dynamics. Finally, the altered
facial expressions are used to photo-realistically manipulate the facial region
in the input scene based on an especially-designed neural face renderer. To the
best of our knowledge, our method is the first to be capable of controlling the
actor's facial expressions by even using as a sole input the semantic labels of
the manipulated emotions, while at the same time preserving the speech-related
lip movements. We conduct extensive qualitative and quantitative evaluations
and comparisons, which demonstrate the effectiveness of our approach and the
especially promising results that we obtain. Our method opens a plethora of new
possibilities for useful applications of neural rendering technologies, ranging
from movie post-production and video games to photo-realistic affective
avatars.

We present the Shape Part Slot Machine, a new method for assembling novel 3D
shapes from existing parts by performing contact-based reasoning. Our method
represents each shape as a graph of ``slots,'' where each slot is a region of
contact between two shape parts. Based on this representation, we design a
graph-neural-network-based model for generating new slot graphs and retrieving
compatible parts, as well as a gradient-descent-based optimization scheme for
assembling the retrieved parts into a complete shape that respects the
generated slot graph. This approach does not require any semantic part labels;
interestingly, it also does not require complete part geometries -- reasoning
about the slots proves sufficient to generate novel, high-quality 3D shapes. We
demonstrate that our method generates shapes that outperform existing
modeling-by-assembly approaches regarding quality, diversity, and structural

Most approaches to deep reinforcement learning (DRL) attempt to solve a
single task at a time. As a result, most existing research benchmarks consist
of individual games or suites of games that have common interfaces but little
overlap in their perceptual features, objectives, or reward structures. To
facilitate research into knowledge transfer among trained agents (e.g. via
multi-task and meta-learning), more environment suites that provide
configurable tasks with enough commonality to be studied collectively are
needed. In this paper we present Meta Arcade, a tool to easily define and
configure custom 2D arcade games that share common visuals, state spaces,
action spaces, game components, and scoring mechanisms. Meta Arcade differs
from prior environments in that both task commonality and configurability are
prioritized: entire sets of games can be constructed from common elements, and
these elements are adjustable through exposed parameters. We include a suite of
24 predefined games that collectively illustrate the possibilities of this
framework and discuss how these games can be configured for research
applications. We provide several experiments that illustrate how Meta Arcade
could be used, including single-task benchmarks of predefined games, sample
curriculum-based approaches that change game parameters over a set schedule,
and an exploration of transfer learning between games.

Most geophysical inverse problems are nonlinear and rely upon numerical
forward solvers involving discretization and simplified representations of the
underlying physics. As a result, forward modeling errors are inevitable. In
practice, such model errors tend to be either completely ignored, which leads
to biased and over-confident inversion results, or only partly taken into
account using restrictive Gaussian assumptions. Here, we rely on deep
generative neural networks to learn problem-specific low-dimensional
probabilistic representations of the discrepancy between high-fidelity and
low-fidelity forward solvers. These representations are then used to
probabilistically invert for the model error jointly with the target
geophysical property field, using the computationally-cheap, low-fidelity
forward solver. To this end, we combine a Markov-chain-Monte-Carlo (MCMC)
inversion algorithm with a trained convolutional neural network of the spatial
generative adversarial network (SGAN) type, whereby at each MCMC step, the
simulated low-fidelity forward response is corrected using a proposed
model-error realization. Considering the crosshole ground-penetrating radar
traveltime tomography inverse problem, we train SGAN networks on traveltime
discrepancy images between: (1) curved-ray (high fidelity) and straight-ray
(low fidelity) forward solvers; and (2) finite-difference-time-domain (high
fidelity) and straight-ray (low fidelity) forward solvers. We demonstrate that
the SGAN is able to learn the spatial statistics of the model error and that
suitable representations of both the subsurface model and model error can be
recovered by MCMC. In comparison with inversion results obtained when model
errors are either ignored or approximated by a Gaussian distribution, we find
that our method has lower posterior parameter bias and better explains the
observed traveltime data.[...]

Weakly supervised object localization (WSOL) aims to localize objects using
only image-level labels. Recently a new paradigm has emerged by generating a
foreground prediction map (FPM) to achieve localization task. Existing
FPM-based methods use cross-entropy (CE) to evaluate the foreground prediction
map and to guide the learning of generator. We argue for using activation value
to achieve more efficient learning. It is based on the experimental observation
that, for a trained network, CE converges to zero when the foreground mask
covers only part of the object region. While activation value increases until
the mask expands to the object boundary, which indicates that more object areas
can be learned by using activation value. In this paper, we propose a
Background Activation Suppression (BAS) method. Specifically, an Activation Map
Constraint module (AMC) is designed to facilitate the learning of generator by
suppressing the background activation value. Meanwhile, by using the foreground
region guidance and the area constraint, BAS can learn the whole region of the
object. In the inference phase, we consider the prediction maps of different
categories together to obtain the final localization results. Extensive
experiments show that BAS achieves significant and consistent improvement over
the baseline methods on the CUB-200-2011 and ILSVRC datasets. Code and models
are available at https://github.com/wpy1999/BAS.

Owing to the benefits for customers (lower prices), drivers (higher
revenues), aggregation companies (higher revenues) and the environment (fewer
vehicles), on-demand ride pooling (e.g., Uber pool, Grab Share) has become
quite popular. The significant computational complexity of matching vehicles to
combinations of requests has meant that traditional ride pooling approaches are
myopic in that they do not consider the impact of current matches on future
value for vehicles/drivers. Recently, Neural Approximate Dynamic Programming
(NeurADP) has employed value decomposition with Approximate Dynamic Programming
(ADP) to outperform leading approaches by considering the impact of an
individual agent's (vehicle) chosen actions on the future value of that agent.
However, in order to ensure scalability and facilitate city-scale ride pooling,
NeurADP completely ignores the impact of other agents actions on individual
agent/vehicle value. As demonstrated in our experimental results, ignoring the
impact of other agents actions on individual value can have a significant
impact on the overall performance when there is increased competition among
vehicles for demand. Our key contribution is a novel mechanism based on
computing conditional expectations through joint conditional probabilities for
capturing dependencies on other agents actions without increasing the
complexity of training or decision making. We show that our new approach,
Conditional Expectation based Value Decomposition (CEVD) outperforms NeurADP by
up to 9.76% in terms of overall requests served, which is a significant
improvement on a city wide benchmark taxi dataset.

Recent research suggests that systematic generalization in natural language
understanding remains a challenge for state-of-the-art neural models such as
Transformers and Graph Neural Networks. To tackle this challenge, we propose
Edge Transformer, a new model that combines inspiration from Transformers and
rule-based symbolic AI. The first key idea in Edge Transformers is to associate
vector states with every edge, that is, with every pair of input nodes -- as
opposed to just every node, as it is done in the Transformer model. The second
major innovation is a triangular attention mechanism that updates edge
representations in a way that is inspired by unification from logic
programming. We evaluate Edge Transformer on compositional generalization
benchmarks in relational reasoning, semantic parsing, and dependency parsing.
In all three settings, the Edge Transformer outperforms Relation-aware,
Universal and classical Transformer baselines.

Motivated by the success of pre-trained language models such as BERT in a
broad range of natural language processing (NLP) tasks, recent research efforts
have been made for adapting these models for different application domains.
Along this line, existing domain-oriented models have primarily followed the
vanilla BERT architecture and have a straightforward use of the domain corpus.
However, domain-oriented tasks usually require accurate understanding of domain
phrases, and such fine-grained phrase-level knowledge is hard to be captured by
existing pre-training scheme. Also, the word co-occurrences guided semantic
learning of pre-training models can be largely augmented by entity-level
association knowledge. But meanwhile, by doing so there is a risk of
introducing noise due to the lack of groundtruth word-level alignment. To
address the above issues, we provide a generalized domain-oriented approach,
which leverages auxiliary domain knowledge to improve the existing pre-training
framework from two aspects. First, to preserve phrase knowledge effectively, we
build a domain phrase pool as auxiliary training tool, meanwhile we introduce
Adaptive Hybrid Masked Model to incorporate such knowledge. It integrates two
learning modes, word learning and phrase learning, and allows them to switch
between each other. Second, we introduce Cross Entity Alignment to leverage
entity association as weak supervision to augment the semantic learning of
pre-trained models. To alleviate the potential noise in this process, we
introduce an interpretable Optimal Transport based approach to guide alignment
learning. Experiments on four domain-oriented tasks demonstrate the superiority
of our framework.

Background: Twelve lead ECGs are a core diagnostic tool for cardiovascular
diseases. Here, we describe and analyse an ensemble deep neural network
architecture to classify 24 cardiac abnormalities from 12-lead ECGs.
  Method: We proposed a squeeze and excite ResNet to automatically learn deep
features from 12-lead ECGs, in order to identify 24 cardiac conditions. The
deep features were augmented with age and gender features in the final fully
connected layers. Output thresholds for each class were set using a constrained
grid search. To determine why the model made incorrect predictions, two expert
clinicians independently interpreted a random set of 100 misclassified ECGs
concerning Left Axis Deviation.
  Results: Using the bespoke weighted accuracy metric, we achieved a 5-fold
cross validation score of 0.684, and sensitivity and specificity of 0.758 and
0.969, respectively. We scored 0.520 on the full test data, and ranked 2nd out
of 41 in the official challenge rankings. On a random set of misclassified
ECGs, agreement between two clinicians and training labels was poor (clinician
1: kappa = -0.057, clinician 2: kappa = -0.159). In contrast, agreement between
the clinicians was very high (kappa = 0.92).
  Discussion: The proposed prediction model performed well on the validation
and hidden test data in comparison to models trained on the same data. We also
discovered considerable inconsistency in training labels, which is likely to
hinder development of more accurate models.

Recent progress in self-supervision shows that pre-training large neural
networks on vast amounts of unsupervised data can lead to impressive increases
in generalisation for downstream tasks. Such models, recently coined as
foundation models, have been transformational to the field of natural language
processing. While similar models have also been trained on large corpuses of
images, they are not well suited for remote sensing data. To stimulate the
development of foundation models for Earth monitoring, we propose to develop a
new benchmark comprised of a variety of downstream tasks related to climate
change. We believe that this can lead to substantial improvements in many
existing applications and facilitate the development of new applications. This
proposal is also a call for collaboration with the aim of developing a better
evaluation process to mitigate potential downsides of foundation models for
Earth monitoring.

Face anti-spoofing (FAS) plays a vital role in preventing face recognition
systems from presentation attacks. Existing face anti-spoofing datasets lack
diversity due to the insufficient identity and insignificant variance, which
limits the generalization ability of FAS model. In this paper, we propose Dual
Spoof Disentanglement Generation (DSDG) framework to tackle this challenge by
"anti-spoofing via generation". Depending on the interpretable factorized
latent disentanglement in Variational Autoencoder (VAE), DSDG learns a joint
distribution of the identity representation and the spoofing pattern
representation in the latent space. Then, large-scale paired live and spoofing
images can be generated from random noise to boost the diversity of the
training set. However, some generated face images are partially distorted due
to the inherent defect of VAE. Such noisy samples are hard to predict precise
depth values, thus may obstruct the widely-used depth supervised optimization.
To tackle this issue, we further introduce a lightweight Depth Uncertainty
Module (DUM), which alleviates the adverse effects of noisy samples by depth
uncertainty learning. DUM is developed without extra-dependency, thus can be
flexibly integrated with any depth supervised network for face anti-spoofing.
We evaluate the effectiveness of the proposed method on five popular benchmarks
and achieve state-of-the-art results under both intra- and inter- test
settings. The codes are available at
https://github.com/JDAI-CV/FaceX-Zoo/tree/main/addition_module/DSDG.

Deep language models have achieved remarkable success in the NLP domain. The
standard way to train a deep language model is to employ unsupervised learning
from scratch on a large unlabeled corpus. However, such large corpora are only
available for widely-adopted and high-resource languages and domains. This
study presents the first deep language model, DPRK-BERT, for the DPRK language.
We achieve this by compiling the first unlabeled corpus for the DPRK language
and fine-tuning a preexisting the ROK language model. We compare the proposed
model with existing approaches and show significant improvements on two DPRK
datasets. We also present a cross-lingual version of this model which yields
better generalization across the two Korean languages. Finally, we provide
various NLP tools related to the DPRK language that would foster future
research.

DaSciM (Data Science and Mining) part of LIX at Ecole Polytechnique,
established in 2013 and since then producing research results in the area of
large scale data analysis via methods of machine and deep learning. The group
has been specifically active in the area of NLP and text mining with
interesting results at methodological and resources level. Here follow our
different contributions of interest to the AFIA community.

In this paper, we consider sampling from a class of distributions with thin
tails supported on $\mathbb{R}^d$ and make two primary contributions. First, we
propose a new Metropolized Algorithm With Optimization Step (MAO), which is
well suited for such targets. Our algorithm is capable of sampling from
distributions where the Metropolis-adjusted Langevin algorithm (MALA) is not
converging or lacking in theoretical guarantees. Second, we derive upper bounds
on the mixing time of MAO. Our results are supported by simulations on multiple
target distributions.

Monolithic software encapsulates all functional capabilities into a single
deployable unit. But managing it becomes harder as the demand for new
functionalities grow. Microservice architecture is seen as an alternate as it
advocates building an application through a set of loosely coupled small
services wherein each service owns a single functional responsibility. But the
challenges associated with the separation of functional modules, slows down the
migration of a monolithic code into microservices. In this work, we propose a
representation learning based solution to tackle this problem. We use a
heterogeneous graph to jointly represent software artifacts (like programs and
resources) and the different relationships they share (function calls,
inheritance, etc.), and perform a constraint-based clustering through a novel
heterogeneous graph neural network. Experimental studies show that our approach
is effective on monoliths of different types.

With the rapid development of computer graphics and vision, several
three-dimensional (3D) reconstruction techniques have been proposed and used to
obtain the 3D representation of objects in the form of point cloud models, mesh
models, and geometric models. The cost of 3D reconstruction is declining due to
the maturing of this technology, however, the inexpensive 3D reconstruction
scanners on the market may not be able to generate a clear point cloud model as
expected. This study systematically reviews some basic types of 3D
reconstruction technology and introduces an easy implementation using a linear
laser scanner, a camera, and a turntable. The implementation is based on the
monovision with laser and has tested several objects like wiki and mug. The
accuracy and resolution of the point cloud result are quite satisfying. It
turns out everyone can build such a 3D reconstruction system with appropriate
procedures.

In many real world applications of machine learning, models have to meet
certain domain-based requirements that can be expressed as constraints (e.g.,
safety-critical constraints in autonomous driving systems). Such constraints
are often handled by including them in a regularization term, while learning a
model. This approach, however, does not guarantee 100% satisfaction of the
constraints: it only reduces violations of the constraints on the training set
rather than ensuring that the predictions by the model will always adhere to
them. In this paper, we present a framework for learning models that provably
fulfil the constraints under all circumstances (i.e., also on unseen data). To
achieve this, we cast learning as a maximum satisfiability problem, and solve
it using a novel SaDe algorithm that combines constraint satisfaction with
gradient descent. We compare our method against regularization based baselines
on linear models and show that our method is capable of enforcing different
types of domain constraints effectively on unseen data, without sacrificing

Molecular representation learning contributes to multiple downstream tasks
such as molecular property prediction and drug design. To properly represent
molecules, graph contrastive learning is a promising paradigm as it utilizes
self-supervision signals and has no requirements for human annotations.
However, prior works fail to incorporate fundamental domain knowledge into
graph semantics and thus ignore the correlations between atoms that have common
attributes but are not directly connected by bonds. To address these issues, we
construct a Chemical Element Knowledge Graph (KG) to summarize microscopic
associations between elements and propose a novel Knowledge-enhanced
Contrastive Learning (KCL) framework for molecular representation learning. KCL
framework consists of three modules. The first module, knowledge-guided graph
augmentation, augments the original molecular graph based on the Chemical
Element KG. The second module, knowledge-aware graph representation, extracts
molecular representations with a common graph encoder for the original
molecular graph and a Knowledge-aware Message Passing Neural Network (KMPNN) to
encode complex information in the augmented molecular graph. The final module
is a contrastive objective, where we maximize agreement between these two views
of molecular graphs. Extensive experiments demonstrated that KCL obtained
superior performances against state-of-the-art baselines on eight molecular
datasets. Visualization experiments properly interpret what KCL has learned
from atoms and attributes in the augmented molecular graphs. Our codes and data
are available at https://github.com/ZJU-Fangyin/KCL.

We introduce "entanglement", a novel metric to measure how synchronized
communication between team members is. This measure calculates the Euclidean
distance among team members' social network metrics timeseries. We validate the
metric with four case studies. The first case study uses entanglement of 11
medical innovation teams to predict team performance and learning behavior. The
second case looks at the e-mail communication of 113 senior executives of an
international services firm, predicting employee turnover through lack of
entanglement of an employee. The third case analyzes the individual employee
performance of 81 managers. The fourth case study predicts performance of 13
customer-dedicated teams at a big international company by comparing
entanglement in the e-mail interactions with satisfaction of their customers
measured through Net Promoter Score (NPS). While we can only speculate about
what is causing the entanglement effect, we find that it is a new and versatile
indicator for the analysis of employees' communication, analyzing the hitherto
underused temporal dimension of online social networks which could be used as a
powerful predictor of employee and team performance, employee turnover, and
customer satisfaction.

This work presents a detailed comparison of the performance of deep learning
models such as convolutional neural networks (CNN), long short-term memory
(LSTM), gated recurrent units (GRU), their hybrids, and a selection of shallow
learning classifiers for sentiment analysis of Arabic reviews. Additionally,
the comparison includes state-of-the-art models such as the transformer
architecture and the araBERT pre-trained model. The datasets used in this study
are multi-dialect Arabic hotel and book review datasets, which are some of the
largest publicly available datasets for Arabic reviews. Results showed deep
learning outperforming shallow learning for binary and multi-label
classification, in contrast with the results of similar work reported in the
literature. This discrepancy in outcome was caused by dataset size as we found
it to be proportional to the performance of deep learning models. The
performance of deep and shallow learning techniques was analyzed in terms of
accuracy and F1 score. The best performing shallow learning technique was
Random Forest followed by Decision Tree, and AdaBoost. The deep learning models
performed similarly using a default embedding layer, while the transformer
model performed best when augmented with araBERT.

During a multi-speed transmission development process, the final calibration
of the gearshift controller parameters is usually performed on a physical test
bench. Engineers typically treat the mapping from the controller parameters to
the gearshift quality as a black-box, and use methods rooted in experimental
design -- a purely statistical approach -- to infer the parameter combination
that will maximize a chosen gearshift performance indicator. This approach
unfortunately requires thousands of gearshift trials, ultimately discouraging
the exploration of different control strategies. In this work, we calibrate the
feedforward and feedback parameters of a gearshift controller using a
model-based reinforcement learning algorithm adapted from Pilco. Experimental
results show that the method optimizes the controller parameters with few
gearshift trials. This approach can accelerate the exploration of gearshift
control strategies, which is especially important for the emerging technology
of multi-speed transmissions for electric vehicles.

Person search unifies person detection and person re-identification (Re-ID)
to locate query persons from the panoramic gallery images. One major challenge
comes from the imbalanced long-tail person identity distributions, which
prevents the one-step person search model from learning discriminative person
features for the final re-identification. However, it is under-explored how to
solve the heavy imbalanced identity distributions for the one-step person
search. Techniques designed for the long-tail classification task, for example,
image-level re-sampling strategies, are hard to be effectively applied to the
one-step person search which jointly solves person detection and Re-ID subtasks
with a detection-based multi-task framework. To tackle this problem, we propose
a Subtask-dominated Transfer Learning (STL) method. The STL method solves the
long-tail problem in the pretraining stage of the dominated Re-ID subtask and
improves the one-step person search by transfer learning of the pretrained
model. We further design a Multi-level RoI Fusion Pooling layer to enhance the
discrimination ability of person features for the one-step person search.
Extensive experiments on CUHK-SYSU and PRW datasets demonstrate the superiority
and effectiveness of the proposed method.

In this paper, we consider the problem of multi-view clustering on incomplete
views. Compared with complete multi-view clustering, the view-missing problem
increases the difficulty of learning common representations from different
views. To address the challenge, we propose a novel incomplete multi-view
clustering framework, which incorporates cross-view relation transfer and
multi-view fusion learning. Specifically, based on the consistency existing in
multi-view data, we devise a cross-view relation transfer-based completion
module, which transfers known similar inter-instance relationships to the
missing view and recovers the missing data via graph networks based on the
transferred relationship graph. Then the view-specific encoders are designed to
extract the recovered multi-view data, and an attention-based fusion layer is
introduced to obtain the common representation. Moreover, to reduce the impact
of the error caused by the inconsistency between views and obtain a better
clustering structure, a joint clustering layer is introduced to optimize
recovery and clustering simultaneously. Extensive experiments conducted on
several real datasets demonstrate the effectiveness of the proposed method.

Graph Neural Networks (GNNs) have been shown to be a powerful tool for
generating predictions from biological data. Their application to neuroimaging
data such as functional magnetic resonance imaging (fMRI) scans has been
limited. However, applying GNNs to fMRI scans may substantially improve
predictive accuracy and could be used to inform clinical diagnosis in the
future. In this paper, we present a novel approach to representing
resting-state fMRI data as a graph containing nodes and edges without omitting
any of the voxels and thus reducing information loss. We compare multiple GNN
architectures and show that they can successfully predict the disease and sex
of a person. We hope to provide a basis for future work to exploit the power of
GNNs when applied to brain imaging data.

Quantization is emerging as an efficient approach to promote
hardware-friendly deep learning and run deep neural networks on
resource-limited hardware. However, it still causes a significant decrease to
the network in accuracy. We summarize challenges of quantization into two
categories: Quantization for Diverse Architectures and Quantization on Complex
Scenes. Our studies focus mainly on applying quantization on various
architectures and scenes and pushing the limit of quantization to extremely
compress and accelerate networks. The comprehensive research on quantization
will achieve more powerful, more efficient, and more flexible hardware-friendly
deep learning, and make it better suited to more real-world applications.

Detecting oriented objects along with estimating their rotation information
is one crucial step for analyzing remote sensing images. Despite that many
methods proposed recently have achieved remarkable performance, most of them
directly learn to predict object directions under the supervision of only one
(e.g. the rotation angle) or a few (e.g. several coordinates) groundtruth
values individually. Oriented object detection would be more accurate and
robust if extra constraints, with respect to proposal and rotation information
regression, are adopted for joint supervision during training. To this end, we
innovatively propose a mechanism that simultaneously learns the regression of
horizontal proposals, oriented proposals, and rotation angles of objects in a
consistent manner, via naive geometric computing, as one additional steady
constraint (see Figure 1). An oriented center prior guided label assignment
strategy is proposed for further enhancing the quality of proposals, yielding
better performance. Extensive experiments demonstrate the model equipped with
our idea significantly outperforms the baseline by a large margin to achieve a
new state-of-the-art result without any extra computational burden during
inference. Our proposed idea is simple and intuitive that can be readily
implemented. Source codes and trained models are involved in supplementary
files.

We target the task of cross-lingual Machine Reading Comprehension (MRC) in
the direct zero-shot setting, by incorporating syntactic features from
Universal Dependencies (UD), and the key features we use are the syntactic
relations within each sentence. While previous work has demonstrated effective
syntax-guided MRC models, we propose to adopt the inter-sentence syntactic
relations, in addition to the rudimentary intra-sentence relations, to further
utilize the syntactic dependencies in the multi-sentence input of the MRC task.
In our approach, we build the Inter-Sentence Dependency Graph (ISDG) connecting
dependency trees to form global syntactic relations across sentences. We then
propose the ISDG encoder that encodes the global dependency graph, addressing
the inter-sentence relations via both one-hop and multi-hop dependency paths
explicitly. Experiments on three multilingual MRC datasets (XQuAD, MLQA,
TyDiQA-GoldP) show that our encoder that is only trained on English is able to
improve the zero-shot performance on all 14 test sets covering 8 languages,
with up to 3.8 F1 / 5.2 EM improvement on-average, and 5.2 F1 / 11.2 EM on
certain languages. Further analysis shows the improvement can be attributed to
the attention on the cross-linguistically consistent syntactic path.

Machine learning and deep learning methods have become essential for
computer-assisted prediction in medicine, with a growing number of applications
also in the field of mammography. Typically these algorithms are trained for a
specific task, e.g., the classification of lesions or the prediction of a
mammogram's pathology status. To obtain a comprehensive view of a patient,
models which were all trained for the same task(s) are subsequently ensembled
or combined. In this work, we propose a pipeline approach, where we first train
a set of individual, task-specific models and subsequently investigate the
fusion thereof, which is in contrast to the standard model ensembling strategy.
We fuse model predictions and high-level features from deep learning models
with hybrid patient models to build stronger predictors on patient level. To
this end, we propose a multi-branch deep learning model which efficiently fuses
features across different tasks and mammograms to obtain a comprehensive
patient-level prediction. We train and evaluate our full pipeline on public
mammography data, i.e., DDSM and its curated version CBIS-DDSM, and report an
AUC score of 0.962 for predicting the presence of any lesion and 0.791 for
predicting the presence of malignant lesions on patient level. Overall, our
fusion approaches improve AUC scores significantly by up to 0.04 compared to
standard model ensembling. Moreover, by providing not only global patient-level
predictions but also task-specific model results that are related to
radiological features, our pipeline aims to closely support the reading
workflow of radiologists.

Representing a label distribution as a one-hot vector is a common practice in
training node classification models. However, the one-hot representation may
not adequately reflect the semantic characteristics of a node in different
classes, as some nodes may be semantically close to their neighbors in other
classes. It would cause over-confidence since the models are encouraged to
assign full probabilities when classifying every node. While training models
with label smoothing can ease this problem to some degree, it still fails to
capture the nodes' semantic characteristics implied by the graph structures. In
this work, we propose a novel SALS (\textit{Structure-Aware Label Smoothing})
method as an enhancement component to popular node classification models. SALS
leverages the graph structures to capture the semantic correlations between the
connected nodes and generate the structure-aware label distribution to replace
the original one-hot label vectors, thus improving the node classification
performance without inference costs. Extensive experiments on seven node
classification benchmark datasets reveal the effectiveness of our SALS on
improving both transductive and inductive node classification. Empirical
results show that SALS is superior to the label smoothing method and enhances
the node classification models to outperform the baseline methods.

The pretrain-finetune paradigm is a classical pipeline in visual learning.
Recent progress on unsupervised pretraining methods shows superior transfer
performance to their supervised counterparts. This paper revisits this
phenomenon and sheds new light on understanding the transferability gap between
unsupervised and supervised pretraining from a multilayer perceptron (MLP)
perspective. While previous works focus on the effectiveness of MLP on
unsupervised image classification where pretraining and evaluation are
conducted on the same dataset, we reveal that the MLP projector is also the key
factor to better transferability of unsupervised pretraining methods than
supervised pretraining methods. Based on this observation, we attempt to close
the transferability gap between supervised and unsupervised pretraining by
adding an MLP projector before the classifier in supervised pretraining. Our
analysis indicates that the MLP projector can help retain intra-class variation
of visual features, decrease the feature distribution distance between
pretraining and evaluation datasets, and reduce feature redundancy. Extensive
experiments on public benchmarks demonstrate that the added MLP projector
significantly boosts the transferability of supervised pretraining, e.g. +7.2%
top-1 accuracy on the concept generalization task, +5.8% top-1 accuracy for
linear evaluation on 12-domain classification tasks, and +0.8% AP on COCO
object detection task, making supervised pretraining comparable or even better
than unsupervised pretraining.

The goal of this paper is Human-object Interaction (HO-I) detection. HO-I
detection aims to find interacting human-objects regions and classify their
interaction from an image. Researchers obtain significant improvement in recent
years by relying on strong HO-I alignment supervision from [5]. HO-I alignment
supervision pairs humans with their interacted objects, and then aligns
human-object pair(s) with their interaction categories. Since collecting such
annotation is expensive, in this paper, we propose to detect HO-I without
alignment supervision. We instead rely on image-level supervision that only
enumerates existing interactions within the image without pointing where they
happen. Our paper makes three contributions: i) We propose Align-Former, a
visual-transformer based CNN that can detect HO-I with only image-level
supervision. ii) Align-Former is equipped with HO-I align layer, that can learn
to select appropriate targets to allow detector supervision. iii) We evaluate
Align-Former on HICO-DET [5] and V-COCO [13], and show that Align-Former
outperforms existing image-level supervised HO-I detectors by a large margin
(4.71% mAP improvement from 16.14% to 20.85% on HICO-DET [5]).

The pair-contact process with diffusion (PCPD), a generalized model of the
ordinary pair-contact process (PCP) without diffusion, exhibits a continuous
absorbing phase transition. Unlike the PCP, whose nature of phase transition is
clearly classified into the directed percolation (DP) universality class, the
model of PCPD has been controversially discussed since its infancy. To our best
knowledge, there is so far no consensus on whether the phase transition of the
PCPD falls into the unknown university classes or else conveys a new kind of
non-equilibrium phase transition. In this paper, both unsupervised and
supervised learning are employed to study the PCPD with scrutiny. Firstly, two
unsupervised learning methods, principal component analysis (PCA) and
autoencoder, are taken. Our results show that both methods can cluster the
original configurations of the model and provide reasonable estimates of
thresholds. Therefore, no matter whether the non-equilibrium lattice model is a
random process of unitary (for instance the DP) or binary (for instance the
PCP), or whether it contains the diffusion motion of particles, unsupervised
leaning can capture the essential, hidden information. Beyond that, supervised
learning is also applied to learning the PCPD at different diffusion rates. We
proposed a more accurate numerical method to determine the spatial correlation
exponent $\nu_{\perp}$, which, to a large degree, avoids the uncertainty of
data collapses through naked eyes. Our extensive calculations reveal that
$\nu_{\perp}$ of PCPD depends continuously on the diffusion rate $D$, which
supports the viewpoint that the PCPD may lead to a new type of absorbing phase

Objective image quality evaluation is a challenging task, which aims to
measure the quality of a given image automatically. According to the
availability of the reference images, there are Full-Reference and No-Reference
IQA tasks, respectively. Most deep learning approaches use regression from deep
features extracted by Convolutional Neural Networks. For the FR task, another
option is conducting a statistical comparison on deep features. For all these
methods, non-local information is usually neglected. In addition, the
relationship between FR and NR tasks is less explored. Motivated by the recent
success of transformers in modeling contextual information, we propose a
unified IQA framework that utilizes CNN backbone and transformer encoder to
extract features. The proposed framework is compatible with both FR and NR
modes and allows for a joint training scheme. Evaluation experiments on three
standard IQA datasets, i.e., LIVE, CSIQ and TID2013, and KONIQ-10K, show that
our proposed model can achieve state-of-the-art FR performance. In addition,
comparable NR performance is achieved in extensive experiments, and the results
show that the NR performance can be leveraged by the joint training scheme.

Although considerable progress has been made in semantic scene understanding
under clear weather, it is still a tough problem under adverse weather
conditions, such as dense fog, due to the uncertainty caused by imperfect
observations. Besides, difficulties in collecting and labeling foggy images
hinder the progress of this field. Considering the success in semantic scene
understanding under clear weather, we think it is reasonable to transfer
knowledge learned from clear images to the foggy domain. As such, the problem
becomes to bridge the domain gap between clear images and foggy images. Unlike
previous methods that mainly focus on closing the domain gap caused by fog --
defogging the foggy images or fogging the clear images, we propose to alleviate
the domain gap by considering fog influence and style variation simultaneously.
The motivation is based on our finding that the style-related gap and the
fog-related gap can be divided and closed respectively, by adding an
intermediate domain. Thus, we propose a new pipeline to cumulatively adapt
style, fog and the dual-factor (style and fog). Specifically, we devise a
unified framework to disentangle the style factor and the fog factor
separately, and then the dual-factor from images in different domains.
Furthermore, we collaborate the disentanglement of three factors with a novel
cumulative loss to thoroughly disentangle these three factors. Our method
achieves the state-of-the-art performance on three benchmarks and shows
generalization ability in rainy and snowy scenes.

Consistency is the theoretical property of a meta learning algorithm that
ensures that, under certain assumptions, it can adapt to any task at test time.
An open question is whether and how theoretical consistency translates into
practice, in comparison to inconsistent algorithms. In this paper, we
empirically investigate this question on a set of representative meta-RL
algorithms. We find that theoretically consistent algorithms can indeed usually
adapt to out-of-distribution (OOD) tasks, while inconsistent ones cannot,
although they can still fail in practice for reasons like poor exploration. We
further find that theoretically inconsistent algorithms can be made consistent
by continuing to update all agent components on the OOD tasks, and adapt as
well or better than originally consistent ones. We conclude that theoretical
consistency is indeed a desirable property, and inconsistent meta-RL algorithms
can easily be made consistent to enjoy the same benefits.

We target at the task of weakly-supervised video object grounding (WSVOG),
where only video-sentence annotations are available during model learning. It
aims to localize objects described in the sentence to visual regions in the
video, which is a fundamental capability needed in pattern analysis and machine
learning. Despite the recent progress, existing methods all suffer from the
severe problem of spurious association, which will harm the grounding
performance. In this paper, we start from the definition of WSVOG and pinpoint
the spurious association from two aspects: (1) the association itself is not
object-relevant but extremely ambiguous due to weak supervision, and (2) the
association is unavoidably confounded by the observational bias when taking the
statistics-based matching strategy in existing methods. With this in mind, we
design a unified causal framework to learn the deconfounded object-relevant
association for more accurate and robust video object grounding. Specifically,
we learn the object-relevant association by causal intervention from the
perspective of video data generation process. To overcome the problems of
lacking fine-grained supervision in terms of intervention, we propose a novel
spatial-temporal adversarial contrastive learning paradigm. To further remove
the accompanying confounding effect within the object-relevant association, we
pursue the true causality by conducting causal intervention via backdoor
adjustment. Finally, the deconfounded object-relevant association is learned
and optimized under a unified causal framework in an end-to-end manner.
Extensive experiments on both IID and OOD testing sets of three benchmarks
demonstrate its accurate and robust grounding performance against

The Facebook network allows its users to record their reactions to text via a
typology of emotions. This network, taken at scale, is therefore a prime data
set of annotated sentiment data. This paper uses millions of such reactions,
derived from a decade worth of Facebook post data centred around a Sri Lankan
context, to model an eye of the beholder approach to sentiment detection for
online Sinhala textual content. Three different sentiment analysis models are
built, taking into account a limited subset of reactions, all reactions, and
another that derives a positive/negative star rating value. The efficacy of
these models in capturing the reactions of the observers are then computed and
discussed. The analysis reveals that binary classification of reactions, for
Sinhala content, is significantly more accurate than the other approaches.
Furthermore, the inclusion of the like reaction hinders the capability of
accurately predicting other reactions.

Domain adaptation is crucial to adapt a learned model to new scenarios, such
as domain shifts or changing data distributions. Current approaches usually
require a large amount of labeled or unlabeled data from the shifted domain.
This can be a hurdle in fields which require continuous dynamic adaptation or
suffer from scarcity of data, e.g. autonomous driving in challenging weather
conditions. To address this problem of continuous adaptation to distribution
shifts, we propose Dynamic Unsupervised Adaptation (DUA). By continuously
adapting the statistics of the batch normalization layers we modify the feature
representations of the model. We show that by sequentially adapting a model
with only a fraction of unlabeled data, a strong performance gain can be
achieved. With even less than 1% of unlabeled data from the target domain, DUA
already achieves competitive results to strong baselines. In addition, the
computational overhead is minimal in contrast to previous approaches. Our
approach is simple, yet effective and can be applied to any architecture which
uses batch normalization as one of its components. We show the utility of DUA
by evaluating it on a variety of domain adaptation datasets and tasks including
object recognition, digit recognition and object detection.

Hadron spectral functions carry all the information of hadrons and are
encoded in the Euclidean two-point correlation functions. The extraction of
hadron spectral functions from the correlator is a typical ill-posed inverse
problem and infinite number of solutions to this problem exists. We propose a
novel neural network (sVAE) based on the Variation Auto-Encoder (VAE) and
Bayesian theorem. Inspired by the maximum entropy method (MEM) we construct the
loss function of the neural work such that it includes a Shannon-Jaynes entropy
term and a likelihood term. The sVAE is then trained to provide the most
probable spectral functions. For the training samples of spectral function we
used general spectral functions produced from the Gaussian Mixture Model. After
the training is done we performed the mock data tests with input spectral
functions consisting 1) only a free continuum, 2) only a resonance peak, 3) a
resonance peak plus a free continuum and 4) a NRQCD motivated spectral
function. From the mock data test we find that the sVAE in most cases is
comparable to the maximum entropy method in the quality of reconstructing
spectral functions and even outperforms the MEM in the case where the spectral
function has sharp peaks with insufficient number of data points in the
correlator. By applying to temporal correlation functions of charmonium in the
pseudoscalar channel obtained in the quenched lattice QCD at 0.75 $T_c$ on
$128^3\times96$ lattices and $1.5$ $T_c$ on $128^3\times48$ lattices, we find
that the resonance peak of $\eta_c$ extracted from both the sVAE and MEM has a
substantial dependence on the number of points in the temporal direction
($N_\tau$) adopted in the lattice simulation and $N_\tau$ larger than 48 is
needed to resolve the fate of $\eta_c$ at 1.5 $T_c$.

Benefit from the promising features of second-order correlation, ghost
imaging (GI) has received extensive attentions in recent years. Simultaneously,
GI is affected by the poor trade-off between sampling rate and imaging quality.
The traditional image reconstruction method in GI is to accumulate the action
result of each speckle and the corresponding bucket signal. We found that the
image reconstruction process of GI is very similar to the Recurrent Neural
Network (RNN), which is one of the deep learning algorithm. In this paper, we
proposed a novel method that effectively implements GI on the RNN architecture,
called GI-RNN. The state of each layer in RNN is determined by the output of
the previous layer and the input of this layer, and the output of the network
is the sum of all previous states. Therefore, we take the speckle of each
illumination and the corresponding bucket signal as the input of each layer,
and the output of the network is the sum of all previous speckle and bucket
signal, which is the image of the target. The testing results show that the
proposed method can achieve image reconstruction at a very low sampling rate
(0.38$\%$). Moreover, we compare GI-RNN with traditional GI algorithm and
compressed sensing algorithm. The results of different targets show that GI-RNN
is 6.61 dB higher than compressed sensing algorithm and 12.58 dB higher than
traditional GI algorithm on average. In our view, the proposed method makes an
important step to applications of GI.

Einstein-Podolsky-Rosen(EPR)steering is a kind of powerful nonlocal quantum
resource in quantum information processing such as quantum cryptography and
quantum communication. Many criteria have been proposed in the past few years
to detect the steerability both analytically and numerically. Supervised
machine learning such as support vector machines and neural networks have also
been trained to detect the EPR steerability. To implement supervised machine
learning, one needs a lot of labeled quantum states by using the semidefinite
programming, which is very time consuming. We present a semi-supervised support
vector machine method which only uses a small portion of labeled quantum states
in detecting quantum steering. We show that our approach can significantly
improve the accuracies by detailed examples.

Producing densely annotated data is a difficult and tedious task for medical
imaging applications. To address this problem, we propose a novel approach to
generate supervision for semi-supervised semantic segmentation. We argue that
visually similar regions between labeled and unlabeled images likely contain
the same semantics and therefore should share their label. Following this
thought, we use a small number of labeled images as reference material and
match pixels in an unlabeled image to the semantics of the best fitting pixel
in a reference set. This way, we avoid pitfalls such as confirmation bias,
common in purely prediction-based pseudo-labeling. Since our method does not
require any architectural changes or accompanying networks, one can easily
insert it into existing frameworks. We achieve the same performance as a
standard fully supervised model on X-ray anatomy segmentation, albeit 95% fewer
labeled images. Aside from an in-depth analysis of different aspects of our
proposed method, we further demonstrate the effectiveness of our
reference-guided learning paradigm by comparing our approach against existing
methods for retinal fluid segmentation with competitive performance as we
improve upon recent work by up to 15% mean IoU.

Automatic identification of script is an essential component of a
multilingual OCR engine. In this paper, we present an efficient, lightweight,
real-time and on-device spatial attention based CNN-LSTM network for scene text
script identification, feasible for deployment on resource constrained mobile
devices. Our network consists of a CNN, equipped with a spatial attention
module which helps reduce the spatial distortions present in natural images.
This allows the feature extractor to generate rich image representations while
ignoring the deformities and thereby, enhancing the performance of this fine
grained classification task. The network also employs residue convolutional
blocks to build a deep network to focus on the discriminative features of a
script. The CNN learns the text feature representation by identifying each
character as belonging to a particular script and the long term spatial
dependencies within the text are captured using the sequence learning
capabilities of the LSTM layers. Combining the spatial attention mechanism with
the residue convolutional blocks, we are able to enhance the performance of the
baseline CNN to build an end-to-end trainable network for script
identification. The experimental results on several standard benchmarks
demonstrate the effectiveness of our method. The network achieves competitive
accuracy with state-of-the-art methods and is superior in terms of network
size, with a total of just 1.1 million parameters and inference time of 2.7

Growing evidence points to recurring influence campaigns on social media,
often sponsored by state actors aiming to manipulate public opinion on
sensitive political topics. Typically, campaigns are performed through
instrumented accounts, known as troll accounts; despite their prominence,
however, little work has been done to detect these accounts in the wild. In
this paper, we present TROLLMAGNIFIER, a detection system for troll accounts.
Our key observation, based on analysis of known Russian-sponsored troll
accounts identified by Reddit, is that they show loose coordination, often
interacting with each other to further specific narratives. Therefore, troll
accounts controlled by the same actor often show similarities that can be
leveraged for detection. TROLLMAGNIFIER learns the typical behavior of known
troll accounts and identifies more that behave similarly. We train
TROLLMAGNIFIER on a set of 335 known troll accounts and run it on a large
dataset of Reddit accounts. Our system identifies 1,248 potential troll
accounts; we then provide a multi-faceted analysis to corroborate the
correctness of our classification. In particular, 66% of the detected accounts
show signs of being instrumented by malicious actors (e.g., they were created
on the same exact day as a known troll, they have since been suspended by
Reddit, etc.). They also discuss similar topics as the known troll accounts and
exhibit temporal synchronization in their activity. Overall, we show that using
TROLLMAGNIFIER, one can grow the initial knowledge of potential trolls provided
by Reddit by over 300%.

In this paper, we explore model-based approach to training robust and
interpretable binarized regression models for multiclass classification tasks
using Mixed-Integer Programming (MIP). Our MIP model balances the optimization
of prediction margin and model size by using a weighted objective that:
minimizes the total margin of incorrectly classified training instances,
maximizes the total margin of correctly classified training instances, and
maximizes the overall model regularization. We conduct two sets of experiments
to test the classification accuracy of our MIP model over standard and
corrupted versions of multiple classification datasets, respectively. In the
first set of experiments, we show that our MIP model outperforms an equivalent
Pseudo-Boolean Optimization (PBO) model and achieves competitive results to
Logistic Regression (LR) and Gradient Descent (GD) in terms of classification
accuracy over the standard datasets. In the second set of experiments, we show
that our MIP model outperforms the other models (i.e., GD and LR) in terms of
classification accuracy over majority of the corrupted datasets. Finally, we
visually demonstrate the interpretability of our MIP model in terms of its
learned parameters over the MNIST dataset. Overall, we show the effectiveness
of training robust and interpretable binarized regression models using MIP.

In this work we contribute a distribution shift benchmark for a computer
vision task; monocular depth estimation. Our differentiation is the
decomposition of the wider distribution shift of uncontrolled testing on
in-the-wild data, to three distinct distribution shifts. Specifically, we
generate data via synthesis and analyze them to produce covariate (color
input), prior (depth output) and concept (their relationship) distribution
shifts. We also synthesize combinations and show how each one is indeed a
different challenge to address, as stacking them produces increased performance
drops and cannot be addressed horizontally using standard approaches.

The recent and increasing interest in video-language research has driven the
development of large-scale datasets that enable data-intensive machine learning
techniques. In comparison, limited effort has been made at assessing the
fitness of these datasets for the video-language grounding task. Recent works
have begun to discover significant limitations in these datasets, suggesting
that state-of-the-art techniques commonly overfit to hidden dataset biases. In
this work, we present MAD (Movie Audio Descriptions), a novel benchmark that
departs from the paradigm of augmenting existing video datasets with text
annotations and focuses on crawling and aligning available audio descriptions
of mainstream movies. MAD contains over 384,000 natural language sentences
grounded in over 1,200 hours of videos and exhibits a significant reduction in
the currently diagnosed biases for video-language grounding datasets. MAD's
collection strategy enables a novel and more challenging version of
video-language grounding, where short temporal moments (typically seconds long)
must be accurately grounded in diverse long-form videos that can last up to
three hours. We have released MAD's data and baselines code at
https://github.com/Soldelli/MAD.

Whereas adversarial training can be useful against specific adversarial
perturbations, they have also proven ineffective in generalizing towards
attacks deviating from those used for training. However, we observe that this
ineffectiveness is intrinsically connected to domain adaptability, another
crucial issue in deep learning for which adversarial domain adaptation appears
to be a promising solution. Consequently, we proposed Adv-4-Adv as a novel
adversarial training method that aims to retain robustness against unseen
adversarial perturbations. Essentially, Adv-4-Adv treats attacks incurring
different perturbations as distinct domains, and by leveraging the power of
adversarial domain adaptation, it aims to remove the domain/attack-specific
features. This forces a trained model to learn a robust domain-invariant
representation, which in turn enhances its generalization ability. Extensive
evaluations on Fashion-MNIST, SVHN, CIFAR-10, and CIFAR-100 demonstrate that a
model trained by Adv-4-Adv based on samples crafted by simple attacks (e.g.,
FGSM) can be generalized to more advanced attacks (e.g., PGD), and the
performance exceeds state-of-the-art proposals on these datasets.

There is a growing interest in applying machine learning techniques to
healthcare. Recently, federated learning (FL) is gaining popularity since it
allows researchers to train powerful models without compromising data privacy
and security. However, the performance of existing FL approaches often
deteriorates when encountering non-iid situations where there exist
distribution gaps among clients, and few previous efforts focus on
personalization in healthcare. In this article, we propose FedAP to tackle
domain shifts and then obtain personalized models for local clients. FedAP
learns the similarity between clients based on the statistics of the batch
normalization layers while preserving the specificity of each client with
different local batch normalization. Comprehensive experiments on five
healthcare benchmarks demonstrate that FedAP achieves better accuracy compared
to state-of-the-art methods (e.g., 10% accuracy improvement for PAMAP2) with
faster convergence speed.

Medical automatic diagnosis aims to imitate human doctors in real-world
diagnostic processes and to achieve accurate diagnoses by interacting with the
patients. The task is formulated as a sequential decision-making problem with a
series of symptom inquiring steps and the final diagnosis. Recent research has
studied incorporating reinforcement learning for symptom inquiring and
classification techniques for disease diagnosis, respectively. However, studies
on efficiently and effectively combining the two procedures are still lacking.
To address this issue, we devise an adaptive mechanism to align reinforcement
learning and classification methods using distribution entropy as the medium.
Additionally, we created a new dataset for patient simulation to address the
lacking of large-scale evaluation benchmarks. The dataset is extracted from the
MedlinePlus knowledge base and contains significantly more diseases and more
comprehensive symptoms and examination information than existing datasets.
Experimental evaluation shows that our method outperforms three current
state-of-the-art methods on different datasets by achieving higher medical
diagnosis accuracy with fewer inquiring turns.

Reinforcement learning (RL) has been used in a range of simulated real-world
tasks, e.g., sensor coordination, traffic light control, and on-demand mobility
services. However, real world deployments are rare, as RL struggles with
dynamic nature of real world environments, requiring time for learning a task
and adapting to changes in the environment. Transfer Learning (TL) can help
lower these adaptation times. In particular, there is a significant potential
of applying TL in multi-agent RL systems, where multiple agents can share
knowledge with each other, as well as with new agents that join the system. To
obtain the most from inter-agent transfer, transfer roles (i.e., determining
which agents act as sources and which as targets), as well as relevant transfer
content parameters (e.g., transfer size) should be selected dynamically in each
particular situation. As a first step towards fully dynamic transfers, in this
paper we investigate the impact of TL transfer parameters with fixed source and
target roles. Specifically, we label every agent-environment interaction with
agent's epistemic confidence, and we filter the shared examples using varying
threshold levels and sample sizes. We investigate impact of these parameters in
two scenarios, a standard predator-prey RL benchmark and a simulation of a
ride-sharing system with 200 vehicle agents and 10,000 ride-requests.

Comparing probability distributions is at the crux of many machine learning
algorithms. Maximum Mean Discrepancies (MMD) and Optimal Transport distances
(OT) are two classes of distances between probability measures that have
attracted abundant attention in past years. This paper establishes some
conditions under which the Wasserstein distance can be controlled by MMD norms.
Our work is motivated by the compressive statistical learning (CSL) theory, a
general framework for resource-efficient large scale learning in which the
training data is summarized in a single vector (called sketch) that captures
the information relevant to the considered learning task. Inspired by existing
results in CSL, we introduce the H\"older Lower Restricted Isometric Property
(H\"older LRIP) and show that this property comes with interesting guarantees
for compressive statistical learning. Based on the relations between the MMD
and the Wasserstein distance, we provide guarantees for compressive statistical
learning by introducing and studying the concept of Wasserstein learnability of
the learning task, that is when some task-specific metric between probability
distributions can be bounded by a Wasserstein distance.

A law of large numbers for the empirical distribution of parameters of a
one-layer artificial neural networks with sparse connectivity is derived for a
simultaneously increasing number of both, neurons and training iterations of
the stochastic gradient descent.

Current approaches to Zero-Shot Learning (ZSL) struggle to learn
generalizable semantic knowledge capable of capturing complex correlations.
Inspired by \emph{Spiral Curriculum}, which enhances learning processes by
revisiting knowledge, we propose a form of spiral learning which revisits
visual representations based on a sequence of attribute groups (e.g., a
combined group of \emph{color} and \emph{shape}). Spiral learning aims to learn
generalized local correlations, enabling models to gradually enhance global
learning and thus understand complex correlations. Our implementation is based
on a 2-stage \emph{Reinforced Self-Revised (RSR)} framework: \emph{preview} and
\emph{review}. RSR first previews visual information to construct diverse
attribute groups in a weakly-supervised manner. Then, it spirally learns
refined localities based on attribute groups and uses localities to revise
global semantic correlations. Our framework outperforms state-of-the-art
algorithms on four benchmark datasets in both zero-shot and generalized
zero-shot settings, which demonstrates the effectiveness of spiral learning in
learning generalizable and complex correlations. We also conduct extensive
analysis to show that attribute groups and reinforced decision processes can
capture complementary semantic information to improve predictions and aid

Federated Learning is a widely adopted method to train neural networks over
distributed data. One main limitation is the performance degradation that
occurs when data is heterogeneously distributed. While many works have
attempted to address this problem, these methods under-perform because they are
founded on a limited understanding of neural networks. In this work, we verify
that only certain important layers in a neural network require regularization
for effective training. We additionally verify that Centered Kernel Alignment
(CKA) most accurately calculates similarity between layers of neural networks
trained on different data. By applying CKA-based regularization to important
layers during training, we significantly improve performance in heterogeneous
settings. We present FedCKA: a simple framework that out-performs previous
state-of-the-art methods on various deep learning tasks while also improving
efficiency and scalability.

Learning the structure of a Bayesian Network (BN) with score-based solutions
involves exploring the search space of possible graphs and moving towards the
graph that maximises a given objective function. Some algorithms offer exact
solutions that guarantee to return the graph with the highest objective score,
while others offer approximate solutions in exchange for reduced computational
complexity. This paper describes an approximate BN structure learning
algorithm, which we call Model Averaging Hill-Climbing (MAHC), that combines
two novel strategies with hill-climbing search. The algorithm starts by pruning
the search space of graphs, where the pruning strategy can be viewed as an
aggressive version of the pruning strategies that are typically applied to
combinatorial optimisation structure learning problems. It then performs model
averaging in the hill-climbing search process and moves to the neighbouring
graph that maximises the objective function, on average, for that neighbouring
graph and over all its valid neighbouring graphs. Comparisons with other
algorithms spanning different classes of learning suggest that the combination
of aggressive pruning with model averaging is both effective and efficient,
particularly in the presence of data noise.

The film industry is one of the most popular entertainment industries and one
of the biggest markets for business. Among the contributing factors to this
would be the success of a movie in terms of its popularity as well as its box
office performance. Hence, we create a comprehensive comparison between the
various machine learning models to predict the rate of success of a movie. The
effectiveness of these models along with their statistical significance is
studied to conclude which of these models is the best predictor. Some insights
regarding factors that affect the success of the movies are also found. The
models studied include some Regression models, Machine Learning models, a Time
Series model and a Neural Network with the Neural Network being the best
performing model with an accuracy of about 86%. Additionally, as part of the
testing data for the movies released in 2020 are analysed.

Diffusion Probabilistic Methods are employed for state-of-the-art image
generation. In this work, we present a method for extending such models for
performing image segmentation. The method learns end-to-end, without relying on
a pre-trained backbone. The information in the input image and in the current
estimation of the segmentation map is merged by summing the output of two
encoders. Additional encoding layers and a decoder are then used to iteratively
refine the segmentation map, using a diffusion model. Since the diffusion model
is probabilistic, it is applied multiple times, and the results are merged into
a final segmentation map. The new method produces state-of-the-art results on
the Cityscapes validation set, the Vaihingen building segmentation benchmark,
and the MoNuSeg dataset.

The problem of approximating a dense matrix by a product of sparse factors is
a fundamental problem for many signal processing and machine learning tasks. It
can be decomposed into two subproblems: finding the position of the non-zero
coefficients in the sparse factors, and determining their values. While the
first step is usually seen as the most challenging one due to its combinatorial
nature, this paper focuses on the second step, referred to as sparse matrix
approximation with fixed support. First, we show its NP-hardness, while also
presenting a nontrivial family of supports making the problem practically
tractable with a dedicated algorithm. Then, we investigate the landscape of its
natural optimization formulation, proving the absence of spurious local valleys
and spurious local minima, whose presence could prevent local optimization
methods to achieve global optimality. The advantages of the proposed algorithm
over state-of-the-art first-order optimization methods are discussed.

Precise measurement of flow velocity in microfluidic channels is of
importance in microfluidic applications, such as quantitative chemical
analysis, sample preparation and drug synthesis. However, simple approaches for
quickly and precisely measuring the flow velocity in microchannels are still
lacking. Herein, we propose a deep neural networks assisted scalar image
velocimetry (DNN-SIV) for quick and precise extraction of fluid velocity in a
shallow microfluidic channel with a high aspect ratio, which is a basic
geometry for cell culture, from a dye concentration field with spatiotemporal
gradients. DNN-SIV is built on physics-informed neural networks and residual
neural networks that integrate data of scalar field and physics laws to
determine the velocity in the height direction. The underlying enforcing
physics laws are derived from the Navier-Stokes equation and the scalar
transport equation. Apart from this, dynamic concentration boundary condition
is adopted to improve the velocity measurement of laminar flow with small
Reynolds Number in microchannels. The proposed DNN-SIV is validated and
analyzed by numerical simulations. Compared to integral minimization algorithm
used in conventional SIV, DNN-SIV is robust to noise in the measured scalar
field and more efficiently allowing real-time flow visualization. Furthermore,
the fundamental significance of rational construction of concentration field in
microchannels is also underscored. The proposed DNN-SIV in this paper is
agnostic to initial and boundary conditions that can be a promising velocity
measurement approach for many potential applications in microfluidic chips.

Recently, vector-quantized image modeling has demonstrated impressive
performance on generation tasks such as text-to-image generation. However, we
discover that the current image quantizers do not satisfy translation
equivariance in the quantized space due to aliasing, degrading performance in
the downstream text-to-image generation and image-to-text generation, even in
simple experimental setups. Instead of focusing on anti-aliasing, we take a
direct approach to encourage translation equivariance in the quantized space.
In particular, we explore a desirable property of image quantizers, called
'Translation Equivariance in the Quantized Space' and propose a simple but
effective way to achieve translation equivariance by regularizing orthogonality
in the codebook embedding vectors. Using this method, we improve accuracy by
+22% in text-to-image generation and +26% in image-to-text generation,
outperforming the VQGAN.

Measurement update rules for Bayes filters often contain hand-crafted
heuristics to compute observation probabilities for high-dimensional sensor
data, like images. In this work, we propose the novel approach Deep Measurement
Update (DMU) as a general update rule for a wide range of systems. DMU has a
conditional encoder-decoder neural network structure to process depth images as
raw inputs. Even though the network is trained only on synthetic data, the
model shows good performance at evaluation time on real-world data. With our
proposed training scheme primed data training , we demonstrate how the DMU
models can be trained efficiently to be sensitive to condition variables
without having to rely on a stochastic information bottleneck. We validate the
proposed methods in multiple scenarios of increasing complexity, beginning with
the pose estimation of a single object to the joint estimation of the pose and
the internal state of an articulated system. Moreover, we provide a benchmark
against Articulated Signed Distance Functions(A-SDF) on the RBO dataset as a
baseline comparison for articulation state estimation.

training is one of the most effective approaches in training robust models
against such attacks. However, it is much slower than vanilla training of
neural networks since it needs to construct adversarial examples for the entire
training data at every iteration, hampering its effectiveness. Recently, Fast
Adversarial Training (FAT) was proposed that can obtain robust models
efficiently. However, the reasons behind its success are not fully understood,
and more importantly, it can only train robust models for $\ell_\infty$-bounded
attacks as it uses FGSM during training. In this paper, by leveraging the
theory of coreset selection, we show how selecting a small subset of training
data provides a general, more principled approach toward reducing the time
complexity of robust training. Unlike existing methods, our approach can be
adapted to a wide variety of training objectives, including TRADES,
$\ell_p$-PGD, and Perceptual Adversarial Training (PAT). Our experimental
results indicate that our approach speeds up adversarial training by 2-3 times
while experiencing a slight reduction in the clean and robust accuracy.

The Intelligent decision of the unmanned combat aerial vehicle (UCAV) has
long been a challenging problem. The conventional search method can hardly
satisfy the real-time demand during high dynamics air combat scenarios. The
reinforcement learning (RL) method can significantly shorten the decision time
via using neural networks. However, the sparse reward problem limits its
convergence speed and the artificial prior experience reward can easily deviate
its optimal convergent direction of the original task, which raises great
difficulties for the RL air combat application. In this paper, we propose a
homotopy-based soft actor-critic method (HSAC) which focuses on addressing
these problems via following the homotopy path between the original task with
sparse reward and the auxiliary task with artificial prior experience reward.
The convergence and the feasibility of this method are also proved in this
paper. To confirm our method feasibly, we construct a detailed 3D air combat
simulation environment for the RL-based methods training firstly, and we
implement our method in both the attack horizontal flight UCAV task and the
self-play confrontation task. Experimental results show that our method
performs better than the methods only utilizing the sparse reward or the
artificial prior experience reward. The agent trained by our method can reach
more than 98.3% win rate in the attack horizontal flight UCAV task and average
67.4% win rate when confronted with the agents trained by the other two
methods.

This paper makes two contributions. Firstly, it introduces mixed
compositional kernels and mixed neural network Gaussian processes (NGGPs).
Mixed compositional kernels are generated by composition of probability
generating functions (PGFs). A mixed NNGP is a Gaussian process (GP) with a
mixed compositional kernel, arising in the infinite-width limit of multilayer
perceptrons (MLPs) that have a different activation function for each layer.
Secondly, $\theta$ activation functions for neural networks and $\theta$
compositional kernels are introduced by building upon the theory of branching
processes, and more specifically upon $\theta$ PGFs. While $\theta$
compositional kernels are recursive, they are expressed in closed form. It is
shown that $\theta$ compositional kernels have non-degenerate asymptotic
properties under certain conditions. Thus, GPs with $\theta$ compositional
kernels do not require non-explicit recursive kernel evaluations and have
controllable infinite-depth asymptotic properties. An open research question is
whether GPs with $\theta$ compositional kernels are limits of infinitely-wide
MLPs with $\theta$ activation functions.

Categorical attributes are those that can take a discrete set of values,
e.g., colours. This work is about compressing vectors over categorical
attributes to low-dimension discrete vectors. The current hash-based methods
compressing vectors over categorical attributes to low-dimension discrete
vectors do not provide any guarantee on the Hamming distances between the
compressed representations. Here we present FSketch to create sketches for
sparse categorical data and an estimator to estimate the pairwise Hamming
distances among the uncompressed data only from their sketches. We claim that
these sketches can be used in the usual data mining tasks in place of the
original data without compromising the quality of the task. For that, we ensure
that the sketches also are categorical, sparse, and the Hamming distance
estimates are reasonably precise. Both the sketch construction and the Hamming
distance estimation algorithms require just a single-pass; furthermore, changes
to a data point can be incorporated into its sketch in an efficient manner. The
compressibility depends upon how sparse the data is and is independent of the
original dimension -- making our algorithm attractive for many real-life
scenarios. Our claims are backed by rigorous theoretical analysis of the
properties of FSketch and supplemented by extensive comparative evaluations
with related algorithms on some real-world datasets. We show that FSketch is
significantly faster, and the accuracy obtained by using its sketches are among
the top for the standard unsupervised tasks of RMSE, clustering and similarity
search.

In this paper, we explore the tokenized representation of musical scores
using the Transformer model to automatically generate musical scores. Thus far,
sequence models have yielded fruitful results with note-level (MIDI-equivalent)
symbolic representations of music. Although the note-level representations can
comprise sufficient information to reproduce music aurally, they cannot contain
adequate information to represent music visually in terms of notation. Musical
scores contain various musical symbols (e.g., clef, key signature, and notes)
and attributes (e.g., stem direction, beam, and tie) that enable us to visually
comprehend musical content. However, automated estimation of these elements has
yet to be comprehensively addressed. In this paper, we first design score token
representation corresponding to the various musical elements. We then train the
Transformer model to transcribe note-level representation into appropriate
music notation. Evaluations of popular piano scores show that the proposed
method significantly outperforms existing methods on all 12 musical aspects
that were investigated. We also explore an effective notation-level token
representation to work with the model and determine that our proposed
representation produces the steadiest results.

In this paper, we propose an approach to quantitatively analyze impacts of
different training label errors to RNN-T based ASR models. The result shows
deletion errors are more harmful than substitution and insertion label errors
in RNN-T training data. We also examined label error impact mitigation
approaches on RNN-T and found that, though all the methods mitigate the
label-error-caused degradation to some extent, they could not remove the
performance gap between the models trained with and without the presence of
label errors. Based on the analysis results, we suggest to design data
pipelines for RNN-T with higher priority on reducing deletion label errors. We
also find that ensuring high-quality training labels remains important, despite
of the existence of the label error mitigation approaches.

BlockSystems.jl and NetworkDynamics.jl are two novel software packages which
facilitate highly efficient transient stability simulations of power networks.
Users may specify inputs and power system design in a convenient modular and
equation-based manner without compromising on speed or model detail. Written in
the high-level, high-performance programming language Julia a rich open-source
package ecosystem is available, which provides state-of-the-art solvers and
machine learning algorithms. Motivated by the recent interest in the Nordic
inertia challenge we have implemented the Nordic5 test case and tuned its
control parameters by making use of the machine learning and automatic
differentiation capabilities of our software stack.

As cloud applications shift from monoliths to loosely coupled microservices,
application developers must decide how many compute resources (e.g., number of
replicated containers) to assign to each microservice within an application.
This decision affects both (1) the dollar cost to the application developer and
(2) the end-to-end latency perceived by the application user. Today, individual
microservices are autoscaled independently by adding VMs whenever
per-microservice CPU or memory utilization crosses a configurable threshold.
However, an application user's end-to-end latency consists of time spent on
multiple microservices and each microservice might need a different number of
VMs to achieve an overall end-to-end latency.
  We present COLA, an autoscaler for microservice-based applications, which
collectively allocates VMs to microservices with a global goal of minimizing
dollar cost while keeping end-to-end application latency under a given target.
Using 5 open-source applications, we compared COLA to several utilization and
machine learning based autoscalers. We evaluate COLA across different compute
settings on Google Kubernetes Engine (GKE) in which users manage compute
resources, GKE standard, and a new mode of operation in which the cloud
provider manages compute infrastructure, GKE Autopilot. COLA meets a desired
median or tail latency target on 53 of 63 workloads where it provides a cost
reduction of 19.3%, on average, over the next cheapest autoscaler. COLA is the
most cost effective autoscaling policy for 48 of these 53 workloads. The cost
savings from managing a cluster with COLA result in COLA paying for its
training cost in a few days. On smaller applications, for which we can
exhaustively search microservice configurations, we find that COLA is optimal
for 90% of cases and near optimal otherwise.

The capability of accurate prediction of protein functions and properties is
essential in the biotechnology industry, e.g. drug development and artificial
protein synthesis, etc. The main challenges of protein function prediction are
the large label space and the lack of labeled training data. Our method
leverages unsupervised sequence embedding and the success of deep convolutional
neural network to overcome these challenges. In contrast, most of the existing
methods delete the rare protein functions to reduce the label space.
Furthermore, some existing methods require additional bio-information (e.g.,
the 3-dimensional structure of the proteins) which is difficult to be
determined in biochemical experiments. Our proposed method significantly
outperforms the other methods on the publicly available benchmark using only
protein sequences as input. This allows the process of identifying protein
functions to be sped up.

Deep neural networks have achieved outstanding performance over various
tasks, but they have a critical issue: over-confident predictions even for
completely unknown samples. Many studies have been proposed to successfully
filter out these unknown samples, but they only considered narrow and specific
tasks, referred to as misclassification detection, open-set recognition, or
out-of-distribution detection. In this work, we argue that these tasks should
be treated as fundamentally an identical problem because an ideal model should
possess detection capability for all those tasks. Therefore, we introduce the
unknown detection task, an integration of previous individual tasks, for a
rigorous examination of the detection capability of deep neural networks on a
wide spectrum of unknown samples. To this end, unified benchmark datasets on
different scales were constructed and the unknown detection capabilities of
existing popular methods were subject to comparison. We found that Deep
Ensemble consistently outperforms the other approaches in detecting unknowns;
however, all methods are only successful for a specific type of unknown. The
reproducible code and benchmark datasets are available at
https://github.com/daintlab/unknown-detection-benchmarks .

Bagging and boosting are two popular ensemble methods in machine learning
(ML) that produce many individual decision trees. Due to the inherent ensemble
characteristic of these methods, they typically outperform single decision
trees or other ML models in predictive performance. However, numerous decision
paths are generated for each decision tree, increasing the overall complexity
of the model and hindering its use in domains that require trustworthy and
explainable decisions, such as finance, social care, and health care. Thus, the
interpretability of bagging and boosting algorithms, such as random forest and
adaptive boosting, reduces as the number of decisions rises. In this paper, we
propose a visual analytics tool that aims to assist users in extracting
decisions from such ML models via a thorough visual inspection workflow that
includes selecting a set of robust and diverse models (originating from
different ensemble learning algorithms), choosing important features according
to their global contribution, and deciding which decisions are essential for
global explanation (or locally, for specific cases). The outcome is a final
decision based on the class agreement of several models and the explored manual
decisions exported by users. We evaluated the applicability and effectiveness
of VisRuler via a use case, a usage scenario, and a user study. The evaluation
revealed that most users managed to successfully use our system to explore
decision rules visually, performing the proposed tasks and answering the given
questions in a satisfying way.

Employing unmanned aerial vehicles (UAVs) has attracted growing interests and
emerged as the state-of-the-art technology for data collection in
Internet-of-Things (IoT) networks. In this paper, with the objective of
minimizing the total energy consumption of the UAV-IoT system, we formulate the
problem of jointly designing the UAV's trajectory and selecting cluster heads
in the IoT network as a constrained combinatorial optimization problem which is
classified as NP-hard and challenging to solve. We propose a novel deep
reinforcement learning (DRL) with a sequential model strategy that can
effectively learn the policy represented by a sequence-to-sequence neural
network for the UAV's trajectory design in an unsupervised manner. Through
extensive simulations, the obtained results show that the proposed DRL method
can find the UAV's trajectory that requires much less energy consumption when
compared to other baseline algorithms and achieves close-to-optimal
performance. In addition, simulation results show that the trained model by our
proposed DRL algorithm has an excellent generalization ability to larger
problem sizes without the need to retrain the model.

Developing an accurate tourism forecasting model is essential for making
desirable policy decisions for tourism management. Early studies on tourism
management focus on discovering external factors related to tourism demand.
Recent studies utilize deep learning in demand forecasting along with these
external factors. They mainly use recursive neural network models such as LSTM
and RNN for their frameworks. However, these models are not suitable for use in
forecasting tourism demand. This is because tourism demand is strongly affected
by changes in various external factors, and recursive neural network models
have limitations in handling these multivariate inputs. We propose a multi-head
attention CNN model (MHAC) for addressing these limitations. The MHAC uses
1D-convolutional neural network to analyze temporal patterns and the attention
mechanism to reflect correlations between input variables. This model makes it
possible to extract spatiotemporal characteristics from time-series data of
various variables. We apply our forecasting framework to predict inbound
tourist changes in South Korea by considering external factors such as
politics, disease, season, and attraction of Korean culture. The performance
results of extensive experiments show that our method outperforms other
deep-learning-based prediction frameworks in South Korea tourism forecasting.

In-memory deep learning computes neural network models where they are stored,
thus avoiding long distance communication between memory and computation units,
resulting in considerable savings in energy and time. In-memory deep learning
has already demonstrated orders of magnitude higher performance density and
energy efficiency. The use of emerging memory technology promises to increase
the gains in density, energy, and performance even further. However, emerging
memory technology is intrinsically unstable, resulting in random fluctuations
of data reads. This can translate to non-negligible accuracy loss, potentially
nullifying the gains. In this paper, we propose three optimization techniques
that can mathematically overcome the instability problem of emerging memory
technology. They can improve the accuracy of the in-memory deep learning model
while maximizing its energy efficiency. Experiments show that our solution can
fully recover most models' state-of-the-art accuracy, and achieves at least an
order of magnitude higher energy efficiency than the state-of-the-art.

Purpose: To propose a novel deep learning-based method called RG-Net
(reconstruction and generation network) for highly accelerated MR parametric
mapping by undersampling k-space and reducing the acquired contrast number
  Methods: The proposed framework consists of a reconstruction module and a
generative module. The reconstruction module reconstructs MR images from the
acquired few undersampled k-space data with the help of a data prior. The
generative module then synthesizes the remaining multi-contrast images from the
reconstructed images, where the exponential model is implicitly incorporated
into the image generation through the supervision of fully sampled labels. The
RG-Net was evaluated on the T1\r{ho} mapping data of knee and brain at
different acceleration rates. Regional T1\r{ho} analysis for cartilage and the
brain was performed to access the performance of RG-Net.
  Results: RG-Net yields a high-quality T1\r{ho} map at a high acceleration
rate of 17. Compared with the competing methods that only undersample k-space,
our framework achieves better performance in T1\r{ho} value analysis. Our
method also improves quality of T1\r{ho} maps on patient with glioma.
  Conclusion: The proposed RG-Net that adopted a new strategy by undersampling
k-space and reducing the contrast number simultaneously for fast MR parametric
mapping, can achieve a high acceleration rate while maintaining good
reconstruction quality. The generative module of our framework can also be used
as an insert module in other fast MR parametric mapping methods.
  Keywords: Deep learning, convolutional neural network, fast MR parametric
mapping

A core component of the recent success of self-supervised learning is
cropping data augmentation, which selects sub-regions of an image to be used as
positive views in the self-supervised loss. The underlying assumption is that
randomly cropped and resized regions of a given image share information about
the objects of interest, which the learned representation will capture. This
assumption is mostly satisfied in datasets such as ImageNet where there is a
large, centered object, which is highly likely to be present in random crops of
the full image. However, in other datasets such as OpenImages or COCO, which
are more representative of real world uncurated data, there are typically
multiple small objects in an image. In this work, we show that self-supervised
learning based on the usual random cropping performs poorly on such datasets.
We propose replacing one or both of the random crops with crops obtained from
an object proposal algorithm. This encourages the model to learn both object
and scene level semantic representations. Using this approach, which we call
object-aware cropping, results in significant improvements over scene cropping
on classification and object detection benchmarks. For example, on OpenImages,
our approach achieves an improvement of 8.8% mAP over random scene-level
cropping using MoCo-v2 based pre-training. We also show significant
improvements on COCO and PASCAL-VOC object detection and segmentation tasks
over the state-of-the-art self-supervised learning approaches. Our approach is
efficient, simple and general, and can be used in most existing contrastive and
non-contrastive self-supervised learning frameworks.

Brain computer interface applications can be used to overcome learning
problems, especially student anxiety, lack of focus, and lack of attention.
This paper introduces a system based on brain computer interface (BCI) to be
used in education to measure intended learning outcomes and measure the impact
of noise on the degree of system accuracy. This system works online and is
based on recorded brain signal dataset. The system can be considered as a
special case of P300 speller accepting only letters from A to D. These are the
possible answers to multiple-choice questions MCQ. The teacher makes exams,
stores them in an exam database and delivers them to students. Students enroll
into the system and record their brain signals. Brain signals go through
preprocessing phase in which signals undergo low and high pass filter. Then the
signals undergo a subsampling and segmentation. The features obtained are used
as inputs to Linear Discriminant Analysis (LDA). Gained accuracy is 91%.

Existing person re-identification (ReID) methods typically directly load the
pre-trained ImageNet weights for initialization. However, as a fine-grained
classification task, ReID is more challenging and exists a large domain gap
between ImageNet classification. Inspired by the great success of
self-supervised representation learning with contrastive objectives, in this
paper, we design an Unsupervised Pre-training framework for ReID based on the
contrastive learning (CL) pipeline, dubbed UP-ReID. During the pre-training, we
attempt to address two critical issues for learning fine-grained ReID features:
(1) the augmentations in CL pipeline may distort the discriminative clues in
person images. (2) the fine-grained local features of person images are not
fully-explored. Therefore, we introduce an intra-identity
(I$^2$-)regularization in the UP-ReID, which is instantiated as two constraints
coming from global image aspect and local patch aspect: a global consistency is
enforced between augmented and original person images to increase robustness to
augmentation, while an intrinsic contrastive constraint among local patches of
each image is employed to fully explore the local discriminative clues.
Extensive experiments on multiple popular Re-ID datasets, including PersonX,
Market1501, CUHK03, and MSMT17, demonstrate that our UP-ReID pre-trained model
can significantly benefit the downstream ReID fine-tuning and achieve
state-of-the-art performance. Codes and models will be released to
https://github.com/Frost-Yang-99/UP-ReID.

Label noise in data has long been an important problem in supervised learning
applications as it affects the effectiveness of many widely used classification
methods. Recently, important real-world applications, such as medical diagnosis
and cybersecurity, have generated renewed interest in the Neyman-Pearson (NP)
classification paradigm, which constrains the more severe type of error (e.g.,
the type I error) under a preferred level while minimizing the other (e.g., the
type II error). However, there has been little research on the NP paradigm
under label noise. It is somewhat surprising that even when common NP
classifiers ignore the label noise in the training stage, they are still able
to control the type I error with high probability. However, the price they pay
is excessive conservativeness of the type I error and hence a significant drop
in power (i.e., $1 - $ type II error). Assuming that domain experts provide
lower bounds on the corruption severity, we propose the first theory-backed
algorithm that adapts most state-of-the-art classification methods to the
training label noise under the NP paradigm. The resulting classifiers not only
control the type I error with high probability under the desired level but also
improve power.

Quantum machine learning (QML) algorithms have obtained great relevance in
the machine learning (ML) field due to the promise of quantum speedups when
performing basic linear algebra subroutines (BLAS), a fundamental element in
most ML algorithms. By making use of BLAS operations, we propose, implement and
analyze a quantum k-means (qk-means) algorithm with a low time complexity of
$\mathcal{O}(NKlog(D)I/C)$ to apply it to the fundamental problem of
discriminating quantum states at readout. Discriminating quantum states allows
the identification of quantum states $|0\rangle$ and $|1\rangle$ from low-level
in-phase and quadrature signal (IQ) data, and can be done using custom ML
models. In order to reduce dependency on a classical computer, we use the
qk-means to perform state discrimination on the IBMQ Bogota device and managed
to find assignment fidelities of up to 98.7% that were only marginally lower
than that of the k-means algorithm. Inspection of assignment fidelity scores
resulting from applying both algorithms to a combination of quantum states
showed concordance to our correlation analysis using Pearson Correlation
coefficients, where evidence shows cross-talk in the (1, 2) and (2, 3)
neighboring qubit couples for the analyzed device.

Generative models which use explicit density modeling (e.g., variational
autoencoders, flow-based generative models) involve finding a mapping from a
known distribution, e.g. Gaussian, to the unknown input distribution. This
often requires searching over a class of non-linear functions (e.g.,
representable by a deep neural network). While effective in practice, the
associated runtime/memory costs can increase rapidly, usually as a function of
the performance desired in an application. We propose a much cheaper (and
simpler) strategy to estimate this mapping based on adapting known results in
kernel transfer operators. We show that our formulation enables highly
efficient distribution approximation and sampling, and offers surprisingly good
empirical performance that compares favorably with powerful baselines, but with
significant runtime savings. We show that the algorithm also performs well in
small sample size settings (in brain imaging).

The Nesterov's accelerated quasi-Newton (L)NAQ method has shown to accelerate
the conventional (L)BFGS quasi-Newton method using the Nesterov's accelerated
gradient in several neural network (NN) applications. However, the calculation
of two gradients per iteration increases the computational cost. The Momentum
accelerated Quasi-Newton (MoQ) method showed that the Nesterov's accelerated
gradient can be approximated as a linear combination of past gradients. This
abstract extends the MoQ approximation to limited memory NAQ and evaluates the
performance on a function approximation problem.

Reducing the radiation exposure for patients in Total-body CT scans has
attracted extensive attention in the medical imaging community. Given the fact
that low radiation dose may result in increased noise and artifacts, which
greatly affected the clinical diagnosis. To obtain high-quality Total-body
Low-dose CT (LDCT) images, previous deep-learning-based research work has
introduced various network architectures. However, most of these methods only
adopt Normal-dose CT (NDCT) images as ground truths to guide the training of
the denoising network. Such simple restriction leads the model to less
effectiveness and makes the reconstructed images suffer from over-smoothing
effects. In this paper, we propose a novel intra-task knowledge transfer method
that leverages the distilled knowledge from NDCT images to assist the training
process on LDCT images. The derived architecture is referred to as the
Teacher-Student Consistency Network (TSC-Net), which consists of the teacher
network and the student network with identical architecture. Through the
supervision between intermediate features, the student network is encouraged to
imitate the teacher network and gain abundant texture details. Moreover, to
further exploit the information contained in CT scans, a contrastive
regularization mechanism (CRM) built upon contrastive learning is
introduced.CRM performs to pull the restored CT images closer to the NDCT
samples and push far away from the LDCT samples in the latent space. In
addition, based on the attention and deformable convolution mechanism, we
design a Dynamic Enhancement Module (DEM) to improve the network transformation

Temporal action localization has long been researched in computer vision.
Existing state-of-the-art action localization methods divide each video into
multiple action units (i.e., proposals in two-stage methods and segments in
one-stage methods) and then perform action recognition/regression on each of
them individually, without explicitly exploiting their relations during
learning. In this paper, we claim that the relations between action units play
an important role in action localization, and a more powerful action detector
should not only capture the local content of each action unit but also allow a
wider field of view on the context related to it. To this end, we propose a
general graph convolutional module (GCM) that can be easily plugged into
existing action localization methods, including two-stage and one-stage
paradigms. To be specific, we first construct a graph, where each action unit
is represented as a node and their relations between two action units as an
edge. Here, we use two types of relations, one for capturing the temporal
connections between different action units, and the other one for
characterizing their semantic relationship. Particularly for the temporal
connections in two-stage methods, we further explore two different kinds of
edges, one connecting the overlapping action units and the other one connecting
surrounding but disjointed units. Upon the graph we built, we then apply graph
convolutional networks (GCNs) to model the relations among different action
units, which is able to learn more informative representations to enhance
action localization. Experimental results show that our GCM consistently
improves the performance of existing action localization methods, including
two-stage methods (e.g., CBR and R-C3D) and one-stage methods (e.g., D-SSAD),
verifying the generality and effectiveness of our GCM.

Much attention has been paid to algorithms related to sentencing, the setting
of bail, parole decisions and recidivism while less attention has been paid to
carceral algorithms, those algorithms used to determine an incarcerated
individual's lived experience. In this paper we study one such algorithm, the
Pennsylvania Additive Classification Tool (PACT) that assigns custody levels to
incarcerated individuals. We analyze the PACT in ways that criminal justice
algorithms are often analyzed: namely, we train an accurate machine learning
model for the PACT; we study its fairness across sex, age and race; and we
determine which features are most important. In addition to these conventional
computations, we propose and carry out some new ways to study such algorithms.
Instead of focusing on the outcomes themselves, we propose shifting our
attention to the variability in the outcomes, especially because many carceral
algorithms are used repeatedly and there can be a propagation of uncertainty.
By carrying out several simulations of assigning custody levels, we shine light
on problematic aspects of tools like the PACT.

Johnson-Lindenstrauss guarantees certain topological structure is preserved
under random projections when project high dimensional deterministic vectors to
low dimensional vectors. In this work, we try to understand how random matrix
affect norms of random vectors. In particular we prove the distribution of the
norm of random vector $X \in \mathbb{R}^n$, whose entries are i.i.d. random
variables, is preserved by random projection $S:\mathbb{R}^n \to \mathbb{R}^m$.
More precisely, \[ \frac{X^TS^TSX - mn}{\sqrt{\sigma^2 m^2n+2mn^2}}
\xrightarrow[\quad m/n\to 0 \quad ]{ m,n\to \infty } \mathcal{N}(0,1) \] We
also prove a concentration of the random norm transformed by either random
projection or random embedding. Overall, our results showed random matrix has
low distortion for the norm of random vectors with i.i.d. entries.

Understanding the details of small-scale convection and storm formation is
crucial to accurately represent the larger-scale planetary dynamics. Presently,
atmospheric scientists run high-resolution, storm-resolving simulations to
capture these kilometer-scale weather details. However, because they contain
abundant information, these simulations can be overwhelming to analyze using
conventional approaches. This paper takes a data-driven approach and jointly
embeds spatial arrays of vertical wind velocities, temperatures, and water
vapor information as three "channels" of a VAE architecture. Our "multi-channel
VAE" results in more interpretable and robust latent structures than earlier
work analyzing vertical velocities in isolation. Analyzing and clustering the
VAE's latent space identifies weather patterns and their geographical
manifestations in a fully unsupervised fashion. Our approach shows that VAEs
can play essential roles in analyzing high-dimensional simulation data and
extracting critical weather and climate characteristics.

Multi-agent behavior modeling and trajectory forecasting are crucial for the
safe navigation of autonomous agents in interactive scenarios. Variational
Autoencoder (VAE) has been widely applied in multi-agent interaction modeling
to generate diverse behavior and learn a low-dimensional representation for
interacting systems. However, existing literature did not formally discuss if a
VAE-based model can properly encode interaction into its latent space. In this
work, we argue that one of the typical formulations of VAEs in multi-agent
modeling suffers from an issue we refer to as social posterior collapse, i.e.,
the model is prone to ignoring historical social context when predicting the
future trajectory of an agent. It could cause significant prediction errors and
poor generalization performance. We analyze the reason behind this
under-explored phenomenon and propose several measures to tackle it. Afterward,
we implement the proposed framework and experiment on real-world datasets for
multi-agent trajectory prediction. In particular, we propose a novel sparse
graph attention message-passing (sparse-GAMP) layer, which helps us detect
social posterior collapse in our experiments. In the experiments, we verify
that social posterior collapse indeed occurs. Also, the proposed measures are
effective in alleviating the issue. As a result, the model attains better
generalization performance when historical social context is informative for

Dormant season grapevine pruning requires skilled seasonal workers during the
winter season which are becoming less available. As workers hasten to prune
more vines in less time amid to the short-term seasonal hiring culture and low
wages, vines are often pruned inconsistently leading to imbalanced grapevines.
In addition to this, currently existing mechanical methods cannot selectively
prune grapevines and manual follow-up operations are often required that
further increase production cost. In this paper, we present the design and
field evaluation of a rugged, and fully autonomous robot for end-to-end pruning
of dormant season grapevines. The proposed design incorporates novel camera
systems, a kinematically redundant manipulator, a ground robot, and novel
algorithms in the perception system. The presented research prototype robot
system was able to spur prune a row of vines from both sides completely in 213
sec/vine with a total pruning accuracy of 87%. Initial field tests of the
autonomous system in a commercial vineyard have shown significant variability
reduction in dormant season pruning when compared to mechanical pre-pruning
trials. The design approach, system components, lessons learned, future
enhancements as well as a brief economic analysis are described in the
manuscript.

Die analysis is an essential numismatic method, and an important tool of
ancient economic history. Yet, manual die studies are too labor-intensive to
comprehensively study large coinages such as those of the Roman Empire. We
address this problem by proposing a model for unsupervised computational die
analysis, which can reduce the time investment necessary for large-scale die
studies by several orders of magnitude, in many cases from years to weeks. From
a computer vision viewpoint, die studies present a challenging unsupervised
clustering problem, because they involve an unknown and large number of highly
similar semantic classes of imbalanced sizes. We address these issues through
determining dissimilarities between coin faces derived from specifically
devised Gaussian process-based keypoint features in a Bayesian distance
clustering framework. The efficacy of our method is demonstrated through an
analysis of 1135 Roman silver coins struck between 64-66 C.E..

We utilize a recently developed topic modeling method called SeNMFk,
extending the standard Non-negative Matrix Factorization (NMF) methods by
incorporating the semantic structure of the text, and adding a robust system
for determining the number of topics. With SeNMFk, we were able to extract
coherent topics validated by human experts. From these topics, a few are
relatively general and cover broad concepts, while the majority can be
precisely mapped to specific scientific effects or measurement techniques. The
topics also differ by ubiquity, with only three topics prevalent in almost 40
percent of the abstract, while each specific topic tends to dominate a small
subset of the abstracts. These results demonstrate the ability of SeNMFk to
produce a layered and nuanced analysis of large scientific corpora.

While transfer learning has become a ubiquitous technique used across Natural
Language Processing (NLP) tasks, it is often unable to replicate the
performance of pre-trained models on text of niche domains like Automotive. In
this paper we aim to understand the main characteristics of the distribution
shift with automotive domain text (describing technical functionalities such as
Cruise Control) and attempt to explain the potential reasons for the gap in
performance. We focus on performing the Named Entity Recognition (NER) task as
it requires strong lexical, syntactic and semantic understanding by the model.
Our experiments with 2 different encoders, namely BERT-Base-Uncased and
SciBERT-Base-Scivocab-Uncased have lead to interesting findings that showed: 1)
The performance of SciBERT is better than BERT when used for automotive domain,
2) Fine-tuning the language models with automotive domain text did not make
significant improvements to the NER performance, 3) The distribution shift is
challenging as it is characterized by lack of repeating contexts, sparseness of
entities, large number of Out-Of-Vocabulary (OOV) words and class overlap due
to domain specific nuances.

Human pose transfer aims at transferring the appearance of the source person
to the target pose. Existing methods utilizing flow-based warping for non-rigid
human image generation have achieved great success. However, they fail to
preserve the appearance details in synthesized images since the spatial
correlation between the source and target is not fully exploited. To this end,
we propose the Flow-based Dual Attention GAN (FDA-GAN) to apply occlusion- and
deformation-aware feature fusion for higher generation quality. Specifically,
deformable local attention and flow similarity attention, constituting the dual
attention mechanism, can derive the output features responsible for deformable-
and occlusion-aware fusion, respectively. Besides, to maintain the pose and
global position consistency in transferring, we design a pose normalization
network for learning adaptive normalization from the target pose to the source
person. Both qualitative and quantitative results show that our method
outperforms state-of-the-art models in public iPER and DeepFashion datasets.

We investigate the optimal design of experimental studies that have
pre-treatment outcome data available. The average treatment effect is estimated
as the difference between the weighted average outcomes of the treated and
control units. A number of commonly used approaches fit this formulation,
including the difference-in-means estimator and a variety of synthetic-control
techniques. We propose several methods for choosing the set of treated units in
conjunction with the weights. Observing the NP-hardness of the problem, we
introduce a mixed-integer programming formulation which selects both the
treatment and control sets and unit weightings. We prove that these proposed
approaches lead to qualitatively different experimental units being selected
for treatment. We use simulations based on publicly available data from the US
Bureau of Labor Statistics that show improvements in terms of mean squared
error and statistical power when compared to simple and commonly used
alternatives such as randomized trials.

Learning from mistakes is an effective learning approach widely used in human
learning, where a learner pays greater focus on mistakes to circumvent them in
the future to improve the overall learning outcomes. In this work, we aim to
investigate how effectively we can leverage this exceptional learning ability
to improve machine learning models. We propose a simple and effective
multi-level optimization framework called learning from mistakes using class
weighting (LFM-CW), inspired by mistake-driven learning to train better machine
learning models. In this formulation, the primary objective is to train a model
to perform effectively on target tasks by using a re-weighting technique. We
learn the class weights by minimizing the validation loss of the model and
re-train the model with the synthetic data from the image generator weighted by
class-wise performance and real data. We apply our LFM-CW framework with
differential architecture search methods on image classification datasets such
as CIFAR and ImageNet, where the results show that our proposed strategy
achieves lower error rate than the baselines.

Accelerating finite automata processing is critical for advancing real-time
analytic in pattern matching, data mining, bioinformatics, intrusion detection,
and machine learning. Recent in-memory automata accelerators leveraging SRAMs
and DRAMs have shown exciting improvements over conventional digital designs.
However, the bit-vector representation of state transitions used by all SOTA
designs is only optimal in processing worst-case completely random patterns,
while a significant amount of memory and energy is wasted in running most
real-world benchmarks. We present CAMA, a Content-Addressable Memory (CAM)
enabled Automata accelerator for processing homogeneous non-deterministic
finite automata (NFA). A radically different state representation scheme, along
with co-designed novel circuits and data encoding schemes, greatly reduces
energy, memory, and chip area for most realistic NFAs. CAMA is holistically
optimized with the following major contributions: (1) a 16x256 8-transistor
(8T) CAM array for state matching, replacing the 256x256 6T SRAM array or two
16x256 6T SRAM banks in SOTA designs; (2) a novel encoding scheme that enables
content searching within 8T SRAMs and adapts to different applications; (3) a
reconfigurable and scalable architecture that improves efficiency on all tested
benchmarks, without losing support for any NFA that is compatible with SOTA
designs; (4) an optimization framework that automates the choice of encoding
schemes and maps a given NFA to the proposed hardware. Two versions of CAMA,
one optimized for energy (CAMA-E) and the other for throughput (CAMA-T), are
comprehensively evaluated in a 28nm CMOS process, and across 21 real-world and
synthetic benchmarks. CAMA-E achieves 2.1x, 2.8x, and 2.04x lower energy than
CA, 2-stride Impala, and eAP. CAMA-T shows 2.68x, 3.87x and 2.62x higher
average compute density than 2-stride Impala, CA, and eAP.

This work investigates the usage of batch normalization in neural
architecture search (NAS). Specifically, Frankle et al. find that training
BatchNorm only can achieve nontrivial performance. Furthermore, Chen et al.
claim that training BatchNorm only can speed up the training of the one-shot
NAS supernet over ten times. Critically, there is no effort to understand 1)
why training BatchNorm only can find the perform-well architectures with the
reduced supernet-training time, and 2) what is the difference between the
train-BN-only supernet and the standard-train supernet. We begin by showing
that the train-BN-only networks converge to the neural tangent kernel regime,
obtain the same training dynamics as train all parameters theoretically. Our
proof supports the claim to train BatchNorm only on supernet with less training
time. Then, we empirically disclose that train-BN-only supernet provides an
advantage on convolutions over other operators, cause unfair competition
between architectures. This is due to only the convolution operator being
attached with BatchNorm. Through experiments, we show that such unfairness
makes the search algorithm prone to select models with convolutions. To solve
this issue, we introduce fairness in the search space by placing a BatchNorm
layer on every operator. However, we observe that the performance predictor in
Chen et al. is inapplicable on the new search space. To this end, we propose a
novel composite performance indicator to evaluate networks from three
perspectives: expressivity, trainability, and uncertainty, derived from the
theoretical property of BatchNorm. We demonstrate the effectiveness of our
approach on multiple NAS-benchmarks (NAS-Bench101, NAS-Bench-201) and search
spaces (DARTS search space and MobileNet search space).

Recent progress in few-shot learning promotes a more realistic cross-domain
setting, where the source and target datasets are from different domains. Due
to the domain gap and disjoint label spaces between source and target datasets,
their shared knowledge is extremely limited. This encourages us to explore more
information in the target domain rather than to overly elaborate training
strategies on the source domain as in many existing methods. Hence, we start
from a generic representation pre-trained by a cross-entropy loss and a
conventional distance-based classifier, along with an image retrieval view, to
employ a re-ranking process for calibrating a target distance matrix by
discovering the reciprocal k-nearest neighbours within the task. Assuming the
pre-trained representation is biased towards the source, we construct a
non-linear subspace to minimise task-irrelevant features therewithin while keep
more transferrable discriminative information by a hyperbolic tangent
transformation. The calibrated distance in this target-aware non-linear
subspace is complementary to that in the pre-trained representation. To impose
such distance calibration information onto the pre-trained representation, a
Kullback-Leibler divergence loss is employed to gradually guide the model
towards the calibrated distance-based distribution. Extensive evaluations on
eight target domains show that this target ranking calibration process can
improve conventional distance-based classifiers in few-shot learning.

Memristive systems and devices are potentially available for implementing
reservoir computing (RC) systems applied to pattern recognition. However, the
computational ability of memristive RC systems depends on intertwined factors
such as system architectures and physical properties of memristive elements,
which complicates identifying the key factor for system performance. Here we
develop a simulation platform for RC with memristor device networks, which
enables testing different system designs for performance improvement. Numerical
simulations show that the memristor-network-based RC systems can yield high
computational performance comparable to that of state-of-the-art methods in
three time series classification tasks. We demonstrate that the excellent and
robust computation under device-to-device variability can be achieved by
appropriately setting network structures, nonlinearity of memristors, and
pre/post-processing, which increases the potential for reliable computation
with unreliable component devices. Our results contribute to an establishment
of a design guide for memristive reservoirs toward a realization of
energy-efficient machine learning hardware.

Deep generative models have gained much attention given their ability to
generate data for applications as varied as healthcare to financial technology
to surveillance, and many more - the most popular models being generative
adversarial networks and variational auto-encoders. Yet, as with all machine
learning models, ever is the concern over security breaches and privacy leaks
and deep generative models are no exception. These models have advanced so
rapidly in recent years that work on their security is still in its infancy. In
an attempt to audit the current and future threats against these models, and to
provide a roadmap for defense preparations in the short term, we prepared this
comprehensive and specialized survey on the security and privacy preservation
of GANs and VAEs. Our focus is on the inner connection between attacks and
model architectures and, more specifically, on five components of deep
generative models: the training data, the latent code, the generators/decoders
of GANs/ VAEs, the discriminators/encoders of GANs/ VAEs, and the generated
data. For each model, component and attack, we review the current research
progress and identify the key challenges. The paper concludes with a discussion
of possible future attacks and research directions in the field.

Perceiving and interacting with 3D articulated objects, such as cabinets,
doors, and faucets, pose particular challenges for future home-assistant robots
performing daily tasks in human environments. Besides parsing the articulated
parts and joint parameters, researchers recently advocate learning manipulation
affordance over the input shape geometry which is more task-aware and
geometrically fine-grained. However, taking only passive observations as
inputs, these methods ignore many hidden but important kinematic constraints
(e.g., joint location and limits) and dynamic factors (e.g., joint friction and
restitution), therefore losing significant accuracy for test cases with such
uncertainties. In this paper, we propose a novel framework, named AdaAfford,
that learns to perform very few test-time interactions for quickly adapting the
affordance priors to more accurate instance-specific posteriors. We conduct
large-scale experiments using the PartNet-Mobility dataset and prove that our
system performs better than baselines.

It is difficult for humans to distinguish the true and false of rumors, but
current deep learning models can surpass humans and achieve excellent accuracy
on many rumor datasets. In this paper, we investigate whether deep learning
models that seem to perform well actually learn to detect rumors. We evaluate
models on their generalization ability to out-of-domain examples by fine-tuning
BERT-based models on five real-world datasets and evaluating against all test
sets. The experimental results indicate that the generalization ability of the
models on other unseen datasets are unsatisfactory, even common-sense rumors
cannot be detected. Moreover, we found through experiments that models take
shortcuts and learn absurd knowledge when the rumor datasets have serious data
pitfalls. This means that simple modifications to the rumor text based on
specific rules will lead to inconsistent model predictions. To more
realistically evaluate rumor detection models, we proposed a new evaluation
method called paired test (PairT), which requires models to correctly predict a
pair of test samples at the same time. Furthermore, we make recommendations on
how to better create rumor dataset and evaluate rumor detection model at the
end of this paper.

Fueled by the widespread adoption of Machine Learning (ML) and the
high-throughput screening of materials, the data-centric approach to materials
design has asserted itself as a robust and powerful tool for the in-silico
prediction of materials properties. When training models to predict material
properties, researchers often face a difficult choice between a model's
interpretability or its performance. We study this trade-off by leveraging four
different state-of-the-art ML techniques: XGBoost, SISSO, Roost, and TPOT for
the prediction of structural and electronic properties of perovskites and 2D
materials. We then assess the future outlook of the continued integration of ML
into materials discovery and identify key problems that will continue to
challenge researchers as the size of the literature's datasets and complexity
of models increases. Finally, we offer several possible solutions to these
challenges with a focus on retaining interpretability and share our thoughts on
magnifying the impact of ML on materials design.

Graph Neural Networks (GNNs) have achieved unprecedented success in
identifying categorical labels of graphs. However, most existing graph
classification problems with GNNs follow the protocol of balanced data
splitting, which misaligns with many real-world scenarios in which some classes
have much fewer labels than others. Directly training GNNs under this
imbalanced scenario may lead to uninformative representations of graphs in
minority classes, and compromise the overall classification performance, which
signifies the importance of developing effective GNNs towards handling
imbalanced graph classification. Existing methods are either tailored for
non-graph structured data or designed specifically for imbalanced node
classification while few focus on imbalanced graph classification. To this end,
we introduce a novel framework, Graph-of-Graph Neural Networks (G$^2$GNN),
which alleviates the graph imbalance issue by deriving extra supervision
globally from neighboring graphs and locally from stochastic augmentations of
graphs. Globally, we construct a graph of graphs (GoG) based on kernel
similarity and perform GoG propagation to aggregate neighboring graph
representations. Locally, we employ topological augmentation via masking node
features or dropping edges with self-consistency regularization to generate
stochastic augmentations of each graph that improve the model generalibility.
Extensive graph classification experiments conducted on seven benchmark
datasets demonstrate our proposed G$^2$GNN outperforms numerous baselines by
roughly 5\% in both F1-macro and F1-micro scores. The implementation of
G$^2$GNN is available at
https://github.com/YuWVandy/G2GNN}{https://github.com/YuWVandy/G2GNN

Recent volumetric 3D reconstruction methods can produce very accurate
results, with plausible geometry even for unobserved surfaces. However, they
face an undesirable trade-off when it comes to multi-view fusion. They can fuse
all available view information by global averaging, thus losing fine detail, or
they can heuristically cluster views for local fusion, thus restricting their
ability to consider all views jointly. Our key insight is that greater detail
can be retained without restricting view diversity by learning a view-fusion
function conditioned on camera pose and image content. We propose to learn this
multi-view fusion using a transformer. To this end, we introduce VoRTX, an
end-to-end volumetric 3D reconstruction network using transformers for
wide-baseline, multi-view feature fusion. Our model is occlusion-aware,
leveraging the transformer architecture to predict an initial, projective scene
geometry estimate. This estimate is used to avoid backprojecting image features
through surfaces into occluded regions. We train our model on ScanNet and show
that it produces better reconstructions than state-of-the-art methods. We also
demonstrate generalization without any fine-tuning, outperforming the same
state-of-the-art methods on two other datasets, TUM-RGBD and ICL-NUIM.

In late December 2019, the novel coronavirus (Sars-Cov-2) and the resulting
disease COVID-19 were first identified in Wuhan China. The disease slipped
through containment measures, with the first known case in the United States
being identified on January 20th, 2020. In this paper, we utilize survey data
from the Inter-university Consortium for Political and Social Research and
apply several statistical and machine learning models and techniques such as
Decision Trees, Multinomial Logistic Regression, Naive Bayes, k-Nearest
Neighbors, Support Vector Machines, Neural Networks, Random Forests, Gradient
Tree Boosting, XGBoost, CatBoost, LightGBM, Synthetic Minority Oversampling,
and Chi-Squared Test to analyze the impacts the COVID-19 pandemic has had on
the mental health of frontline workers in the United States. Through the
interpretation of the many models applied to the mental health survey data, we
have concluded that the most important factor in predicting the mental health
decline of a frontline worker is the healthcare role the individual is in
(Nurse, Emergency Room Staff, Surgeon, etc.), followed by the amount of sleep
the individual has had in the last week, the amount of COVID-19 related news an
individual has consumed on average in a day, the age of the worker, and the
usage of alcohol and cannabis.

Training generative adversarial networks (GANs) is known to be difficult,
especially for financial time series. This paper first analyzes the
well-posedness problem in GANs minimax games and the convexity issue in GANs
objective functions. It then proposes a stochastic control framework for
hyper-parameters tuning in GANs training. The weak form of dynamic programming
principle and the uniqueness and the existence of the value function in the
viscosity sense for the corresponding minimax game are established. In
particular, explicit forms for the optimal adaptive learning rate and batch
size are derived and are shown to depend on the convexity of the objective
function, revealing a relation between improper choices of learning rate and
explosion in GANs training. Finally, empirical studies demonstrate that
training algorithms incorporating this adaptive control approach outperform the
standard ADAM method in terms of convergence and robustness.
  From GANs training perspective, the analysis in this paper provides
analytical support for the popular practice of ``clipping'', and suggests that
the convexity and well-posedness issues in GANs may be tackled through
appropriate choices of hyper-parameters.

In this paper, we leverage the recent advances in physics-informed neural
network (PINN) and develop a generic PINN-based framework to assess the
reliability of multi-state systems (MSSs). The proposed methodology consists of
two major steps. In the first step, we recast the reliability assessment of MSS
as a machine learning problem using the framework of PINN. A feedforward neural
network with two individual loss groups are constructed to encode the initial
condition and state transitions governed by ordinary differential equations
(ODEs) in MSS. Next, we tackle the problem of high imbalance in the magnitude
of the back-propagated gradients in PINN from a multi-task learning
perspective. Particularly, we treat each element in the loss function as an
individual task, and adopt a gradient surgery approach named projecting
conflicting gradients (PCGrad), where a task's gradient is projected onto the
norm plane of any other task that has a conflicting gradient. The gradient
projection operation significantly mitigates the detrimental effects caused by
the gradient interference when training PINN, thus accelerating the convergence
speed of PINN to high-precision solutions to MSS reliability assessment. With
the proposed PINN-based framework, we investigate its applications for MSS
reliability assessment in several different contexts in terms of
time-independent or dependent state transitions and system scales varying from
small to medium. The results demonstrate that the proposed PINN-based framework
shows generic and remarkable performance in MSS reliability assessment, and the
incorporation of PCGrad in PINN leads to substantial improvement in solution
quality and convergence speed.

In autonomous driving, there has been an explosion in the use of deep neural
networks for perception, prediction and planning tasks. As autonomous vehicles
(AVs) move closer to production, multi-modal sensor inputs and heterogeneous
vehicle fleets with different sets of sensor platforms are becoming
increasingly common in the industry. However, neural network architectures
typically target specific sensor platforms and are not robust to changes in
input, making the problem of scaling and model deployment particularly
difficult. Furthermore, most players still treat the problem of optimizing
software and hardware as entirely independent problems. We propose a new end to
end architecture, Generalized Sensor Fusion (GSF), which is designed in such a
way that both sensor inputs and target tasks are modular and modifiable. This
enables AV system designers to easily experiment with different sensor
configurations and methods and opens up the ability to deploy on heterogeneous
fleets using the same models that are shared across a large engineering
organization. Using this system, we report experimental results where we
demonstrate near-parity of an expensive high-density (HD) LiDAR sensor with a
cheap low-density (LD) LiDAR plus camera setup in the 3D object detection task.
This paves the way for the industry to jointly design hardware and software
architectures as well as large fleets with heterogeneous configurations.

Reconstructing the 3D pose of a person in metric scale from a single view
image is a geometrically ill-posed problem. For example, we can not measure the
exact distance of a person to the camera from a single view image without
additional scene assumptions (e.g., known height). Existing learning based
approaches circumvent this issue by reconstructing the 3D pose up to scale.
However, there are many applications such as virtual telepresence, robotics,
and augmented reality that require metric scale reconstruction. In this paper,
we show that audio signals recorded along with an image, provide complementary
information to reconstruct the metric 3D pose of the person.
  The key insight is that as the audio signals traverse across the 3D space,
their interactions with the body provide metric information about the body's
pose. Based on this insight, we introduce a time-invariant transfer function
called pose kernel -- the impulse response of audio signals induced by the body
pose. The main properties of the pose kernel are that (1) its envelope highly
correlates with 3D pose, (2) the time response corresponds to arrival time,
indicating the metric distance to the microphone, and (3) it is invariant to
changes in the scene geometry configurations. Therefore, it is readily
generalizable to unseen scenes. We design a multi-stage 3D CNN that fuses audio
and visual signals and learns to reconstruct 3D pose in a metric scale. We show
that our multi-modal method produces accurate metric reconstruction in real
world scenes, which is not possible with state-of-the-art lifting approaches
including parametric mesh regression and depth regression.

We propose to deliberate the hypothesis alignment of a streaming RNN-T model
with the previously proposed Align-Refine non-autoregressive decoding method
and its improved versions. The method performs a few refinement steps, where
each step shares a transformer decoder that attends to both text features
(extracted from alignments) and audio features, and outputs complete updated
alignments. The transformer decoder is trained with the CTC loss which
facilitates parallel greedy decoding, and performs full-context attention to
capture label dependencies. We improve Align-Refine by introducing cascaded
encoder that captures more audio context before refinement, and alignment
augmentation which enforces learning label dependency. We show that,
conditioned on hypothesis alignments of a streaming RNN-T model, our method
obtains significantly more accurate recognition results than the first-pass
RNN-T, with only small amount of model parameters.

We study a minimax risk of estimating inverse functions on a plane, while
keeping an estimator is also invertible. Learning invertibility from data and
exploiting an invertible estimator are used in many domains, such as
statistics, econometrics, and machine learning. Although the consistency and
universality of invertible estimators have been well investigated, analysis on
the efficiency of these methods is still under development. In this study, we
study a minimax risk for estimating invertible bi-Lipschitz functions on a
square in a $2$-dimensional plane. We first introduce an inverse $L^2$-risk to
evaluate an estimator which preserves invertibility. Then, we derive lower and
upper rates for a minimax inverse risk by exploiting a representation of
invertible functions using level-sets. To obtain an upper bound, we develop an
estimator asymptotically almost everywhere invertible, whose risk attains the
derived minimax lower rate up to logarithmic factors. The derived minimax rate
corresponds to that of the non-invertible bi-Lipschitz function, which rejects
the expectation of whether invertibility improves the minimax rate, similar to
other shape constraints.

An onomatopoeic word, which is a character sequence that phonetically
imitates a sound, is effective in expressing characteristics of sound such as
duration, pitch, and timbre. We propose an environmental-sound-extraction
method using onomatopoeic words to specify the target sound to be extracted. By
this method, we estimate a time-frequency mask from an input mixture
spectrogram and an onomatopoeic word using a U-Net architecture, then extract
the corresponding target sound by masking the spectrogram. Experimental results
indicate that the proposed method can extract only the target sound
corresponding to the onomatopoeic word and performs better than conventional
methods that use sound-event classes to specify the target sound.

Face recognition is the very significant field in pattern recognition area.
It has multiple applications in military and finance, to name a few. In this
paper, the combination of the sparse PCA with the nearest-neighbor method (and
with the kernel ridge regression method) will be proposed and will be applied
to solve the face recognition problem. Experimental results illustrate that the
accuracy of the combination of the sparse PCA method (using the proximal
gradient method and the FISTA method) and one specific classification system
may be lower than the accuracy of the combination of the PCA method and one
specific classification system but sometimes the combination of the sparse PCA
method (using the proximal gradient method or the FISTA method) and one
specific classification system leads to better accuracy. Moreover, we recognize
that the process computing the sparse PCA algorithm using the FISTA method is
always faster than the process computing the sparse PCA algorithm using the
proximal gradient method.

We present 3DVNet, a novel multi-view stereo (MVS) depth-prediction method
that combines the advantages of previous depth-based and volumetric MVS
approaches. Our key idea is the use of a 3D scene-modeling network that
iteratively updates a set of coarse depth predictions, resulting in highly
accurate predictions which agree on the underlying scene geometry. Unlike
existing depth-prediction techniques, our method uses a volumetric 3D
convolutional neural network (CNN) that operates in world space on all depth
maps jointly. The network can therefore learn meaningful scene-level priors.
Furthermore, unlike existing volumetric MVS techniques, our 3D CNN operates on
a feature-augmented point cloud, allowing for effective aggregation of
multi-view information and flexible iterative refinement of depth maps.
Experimental results show our method exceeds state-of-the-art accuracy in both
depth prediction and 3D reconstruction metrics on the ScanNet dataset, as well
as a selection of scenes from the TUM-RGBD and ICL-NUIM datasets. This shows
that our method is both effective and generalizes to new settings.

In this paper, we propose a deep learning-based model to detect extratropical
cyclones (ETCs) of northern hemisphere, while developing a novel workflow of
processing images and generating labels for ETCs. We first label the cyclone
center by adapting an approach from Bonfanti et.al. [1] and set up criteria of
labeling ETCs of three categories: developing, mature, and declining stages. We
then propose a framework of labeling and preprocessing the images in our
dataset. Once the images and labels are ready to serve as inputs, we create our
object detection model named Single Shot Detector (SSD) to fit the format of
our dataset. We train and evaluate our model with our labeled dataset on two
settings (binary and multiclass classifications), while keeping a record of the
results. Finally, we achieved relatively high performance with detecting ETCs
of mature stage (mean Average Precision is 86.64%), and an acceptable result
for detecting ETCs of all three categories (mean Average Precision 79.34%). We
conclude that the single-shot detector model can succeed in detecting ETCs of
different stages, and it has demonstrated great potential in the future
applications of ETC detection in other relevant settings.

In this paper we present a new algorithm for online (sequential) inference in
Bayesian neural networks, and show its suitability for tackling contextual
bandit problems. The key idea is to combine the extended Kalman filter (which
locally linearizes the likelihood function at each time step) with a (learned
or random) low-dimensional affine subspace for the parameters; the use of a
subspace enables us to scale our algorithm to models with $\sim 1M$ parameters.
While most other neural bandit methods need to store the entire past dataset in
order to avoid the problem of "catastrophic forgetting", our approach uses
constant memory. This is possible because we represent uncertainty about all
the parameters in the model, not just the final linear layer. We show good
results on the "Deep Bayesian Bandit Showdown" benchmark, as well as MNIST and
a recommender system.

In this paper, we revisit the problem of using in-distribution public data to
improve the privacy/utility trade-offs for differentially private (DP) model
training. (Here, public data refers to auxiliary data sets that have no privacy
concerns.) We design a natural variant of DP mirror descent, where the DP
gradients of the private/sensitive data act as the linear term, and the loss
generated by the public data as the mirror map.
  We show that, for linear regression with feature vectors drawn from a
non-isotropic sub-Gaussian distribution, our algorithm, PDA-DPMD (a variant of
mirror descent), provides population risk guarantees that are asymptotically
better than the best known guarantees under DP (without having access to public
data), when the number of public data samples ($n_{\sf pub}$) is sufficiently
large. We further show that our algorithm has natural "noise stability"
properties that control the variance due to noise added to ensure DP.
  We demonstrate the efficacy of our algorithm by showing privacy/utility
trade-offs on four benchmark datasets (StackOverflow, WikiText-2, CIFAR-10, and
EMNIST). We show that our algorithm not only significantly improves over
traditional DP-SGD, which does not have access to public data, but to our
knowledge is the first to improve over DP-SGD on models that have been
pre-trained with public data.

With the global issue of plastic debris ever expanding, it is about time that
the technology industry stepped in. This study aims to assess whether deep
learning can successfully distinguish between marine life and man-made debris
underwater. The aim is to find if we are safely able to clean up our oceans
with Artificial Intelligence without disrupting the delicate balance of the
aquatic ecosystems. The research explores the use of Convolutional Neural
Networks from the perspective of protecting the ecosystem, rather than
primarily collecting rubbish. We did this by building a custom-built, deep
learning model, with an original database including 1,644 underwater images and
used a binary classification to sort synthesised material from aquatic life. We
concluded that although it is possible to safely distinguish between debris and
life, further exploration with a larger database and stronger CNN structure has
the potential for much more promising results.

Inspired by the recent advances in implicitly representing signals with
trained neural networks, we aim to learn a continuous representation for
narrow-baseline 4D light fields. We propose an implicit representation model
for 4D light fields which is conditioned on a sparse set of input views. Our
model is trained to output the light field values for a continuous range of
query spatio-angular coordinates. Given a sparse set of input views, our scheme
can super-resolve the input in both spatial and angular domains by flexible
factors. consists of a feature extractor and a decoder which are trained on a
dataset of light field patches. The feature extractor captures per-pixel
features from the input views. These features can be resized to a desired
spatial resolution and fed to the decoder along with the query coordinates.
This formulation enables us to reconstruct light field views at any desired
spatial and angular resolution. Additionally, our network can handle scenarios
in which input views are either of low-resolution or with missing pixels.
Experiments show that our method achieves state-of-the-art performance for the
task of view synthesis while being computationally fast.

Community detection is one of the most important methodological fields of
network science, and one which has attracted a significant amount of attention
over the past decades. This area deals with the automated division of a network
into fundamental building blocks, with the objective of providing a summary of
its large-scale structure. Despite its importance and widespread adoption,
there is a noticeable gap between what is arguably the state-of-the-art and the
methods that are actually used in practice in a variety of fields. Here we
attempt to address this discrepancy by dividing existing methods according to
whether they have a "descriptive" or an "inferential" goal. While descriptive
methods find patterns in networks based on context-dependent notions of
community structure, inferential methods articulate generative models, and
attempt to fit them to data. In this way, they are able to provide insights
into the mechanisms of network formation, and separate structure from
randomness in a manner supported by statistical evidence. We review how
employing descriptive methods with inferential aims is riddled with pitfalls
and misleading answers, and thus should be in general avoided. We argue that
inferential methods are more typically aligned with clearer scientific
questions, yield more robust results, and should be in many cases preferred. We
attempt to dispel some myths and half-truths often believed when community
detection is employed in practice, in an effort to improve both the use of such
methods as well as the interpretation of their results.

We consider data-visualization systems where a middleware layer translates a
frontend request to a SQL query to a backend database to compute visual
results. We study the problem of answering a visualization request within a
limited time constraint due to the responsiveness requirement. We explore the
optimization options of rewriting an original query by adding hints and/or
doing approximations so that the total time is within the time constraint. We
develop a novel middleware solution called Maliva based on machine learning
(ML) techniques. It applies the Markov Decision Process (MDP) model to decide
how to rewrite queries and uses training instances to learn an agent to make a
sequence of decisions judiciously for an online request. We give a full
specification of the technique, including how to construct an MDP model, how to
train an agent, and how to use approximating rewrite options. Our experiments
on both real and synthetic datasets show that Maliva performs significantly
better than a baseline solution that does not do any rewriting, in terms of
both the probability of serving requests interactively and query execution
time.

Recently, large pretrained models (e.g., BERT, StyleGAN, CLIP) have shown
great knowledge transfer and generalization capability on various downstream
tasks within their domains. Inspired by these efforts, in this paper we propose
a unified model for open-domain image editing focusing on color and tone
adjustment of open-domain images while keeping their original content and
structure. Our model learns a unified editing space that is more semantic,
intuitive, and easy to manipulate than the operation space (e.g., contrast,
brightness, color curve) used in many existing photo editing softwares. Our
model belongs to the image-to-image translation framework which consists of an
image encoder and decoder, and is trained on pairs of before- and after-images
to produce multimodal outputs. We show that by inverting image pairs into
latent codes of the learned editing space, our model can be leveraged for
various downstream editing tasks such as language-guided image editing,
personalized editing, editing-style clustering, retrieval, etc. We extensively
study the unique properties of the editing space in experiments and demonstrate
superior performance on the aforementioned tasks.

A collection of the accepted abstracts for the Machine Learning for Health
(ML4H) symposium 2021. This index is not complete, as some accepted abstracts
chose to opt-out of inclusion.

We propose a new, more general approach to the design of stochastic
gradient-based optimization methods for machine learning. In this new
framework, optimizers assume access to a batch of gradient estimates per
iteration, rather than a single estimate. This better reflects the information
that is actually available in typical machine learning setups. To demonstrate
the usefulness of this generalized approach, we develop Eve, an adaptation of
the Adam optimizer which uses examplewise gradients to obtain more accurate
second-moment estimates. We provide preliminary experiments, without
hyperparameter tuning, which show that the new optimizer slightly outperforms
Adam on a small scale benchmark and performs the same or worse on larger scale
benchmarks. Further work is needed to refine the algorithm and tune

In differentiable neural architecture search (NAS) algorithms like DARTS, the
training set used to update model weight and the validation set used to update
model architectures are sampled from the same data distribution. Thus, the
uncommon features in the dataset fail to receive enough attention during
training. In this paper, instead of introducing more complex NAS algorithms, we
explore the idea that adding quality synthesized datasets into training can
help the classification model identify its weakness and improve recognition
accuracy. We introduce a training strategy called ``Differentiable Architecture
Search with a Generative Model(DASGM)." In DASGM, the training set is used to
update the classification model weight, while a synthesized dataset is used to
train its architecture. The generated images have different distributions from
the training set, which can help the classification model learn better features
to identify its weakness. We formulate DASGM into a multi-level optimization
framework and develop an effective algorithm to solve it. Experiments on
CIFAR-10, CIFAR-100, and ImageNet have demonstrated the effectiveness of DASGM.
Code will be made available.

Visual content creation has spurred a soaring interest given its applications
in mobile photography and AR / VR. Style transfer and single-image 3D
photography as two representative tasks have so far evolved independently. In
this paper, we make a connection between the two, and address the challenging
task of 3D photo stylization - generating stylized novel views from a single
image given an arbitrary style. Our key intuition is that style transfer and
view synthesis have to be jointly modeled for this task. To this end, we
propose a deep model that learns geometry-aware content features for
stylization from a point cloud representation of the scene, resulting in
high-quality stylized images that are consistent across views. Further, we
introduce a novel training protocol to enable the learning using only 2D
images. We demonstrate the superiority of our method via extensive qualitative
and quantitative studies, and showcase key applications of our method in light
of the growing demand for 3D content creation from 2D image assets.

Deep neural networks based object detectors have shown great success in a
variety of domains like autonomous vehicles, biomedical imaging, etc. It is
known that their success depends on a large amount of data from the domain of
interest. While deep models often perform well in terms of overall accuracy,
they often struggle in performance on rare yet critical data slices. For
example, data slices like "motorcycle at night" or "bicycle at night" are often
rare but very critical slices for self-driving applications and false negatives
on such rare slices could result in ill-fated failures and accidents. Active
learning (AL) is a well-known paradigm to incrementally and adaptively build
training datasets with a human in the loop. However, current AL based
acquisition functions are not well-equipped to tackle real-world datasets with
rare slices, since they are based on uncertainty scores or global descriptors
of the image. We propose TALISMAN, a novel framework for Targeted Active
Learning or object detectIon with rare slices using Submodular MutuAl
iNformation. Our method uses the submodular mutual information functions
instantiated using features of the region of interest (RoI) to efficiently
target and acquire data points with rare slices. We evaluate our framework on
the standard PASCAL VOC07+12 and BDD100K, a real-world self-driving dataset. We
observe that TALISMAN outperforms other methods by in terms of average
precision on rare slices, and in terms of mAP.

Generic pre-trained speech and text representations promise to reduce the
need for large labeled datasets on specific speech and language tasks. However,
it is not clear how to effectively adapt these representations for speech
emotion recognition. Recent public benchmarks show the efficacy of several
popular self-supervised speech representations for emotion classification. In
this study, we show that the primary difference between the top-performing
representations is in predicting valence while the differences in predicting
activation and dominance dimensions are less pronounced. However, we show that
even the best-performing HuBERT representation underperforms on valence
prediction compared to a multimodal model that also incorporates text
representation. We address this shortcoming by injecting lexical information
into the speech representation using the multimodal model as a teacher. To
improve the efficacy of our approach, we propose a novel estimate of the
quality of the emotion predictions, to condition teacher-student training. We
report new audio-only state-of-the-art concordance correlation coefficient
(CCC) values of 0.757, 0.627, 0.671 for activation, valence and dominance
predictions, respectively, on the MSP-Podcast corpus, and also state-of-the-art
values of 0.667, 0.582, 0.545 on the IEMOCAP corpus.

Uncrewed autonomous vehicles (UAVs) have made significant contributions to
reconnaissance and surveillance missions in past US military campaigns. As the
prevalence of UAVs increases, there has also been improvements in counter-UAV
technology that makes it difficult for them to successfully obtain valuable
intelligence within an area of interest. Hence, it has become important that
modern UAVs can accomplish their missions while maximizing their chances of
survival. In this work, we specifically study the problem of identifying a
short path from a designated start to a goal, while collecting all rewards and
avoiding adversaries that move randomly on the grid. We also provide a possible
application of the framework in a military setting, that of autonomous casualty
evacuation. We present a comparison of three methods to solve this problem:
namely we implement a Deep Q-Learning model, an $\varepsilon$-greedy tabular
Q-Learning model, and an online optimization framework. Our computational
experiments, designed using simple grid-world environments with random
adversaries showcase how these approaches work and compare them in terms of
performance, accuracy, and computational time.

Extracting information from stochastic fields or textures is a ubiquitous
task in science, from exploratory data analysis to classification and parameter
estimation. From physics to biology, it tends to be done either through a power
spectrum analysis, which is often too limited, or the use of convolutional
neural networks (CNNs), which require large training sets and lack
interpretability. In this paper, we advocate for the use of the scattering
transform (Mallat 2012), a powerful statistic which borrows mathematical ideas
from CNNs but does not require any training, and is interpretable. We show that
it provides a relatively compact set of summary statistics with visual
interpretation and which carries most of the relevant information in a wide
range of scientific applications. We present a non-technical introduction to
this estimator and we argue that it can benefit data analysis, comparison to
models and parameter inference in many fields of science. Interestingly,
understanding the core operations of the scattering transform allows one to
decipher many key aspects of the inner workings of CNNs.

Optimization of Top-1 ImageNet promotes enormous networks that may be
impractical in inference settings. Binary neural networks (BNNs) have the
potential to significantly lower the compute intensity but existing models
suffer from low quality. To overcome this deficiency, we propose PokeConv, a
binary convolution block which improves quality of BNNs by techniques such as
adding multiple residual paths, and tuning the activation function. We apply it
to ResNet-50 and optimize ResNet's initial convolutional layer which is hard to
binarize. We name the resulting network family PokeBNN. These techniques are
chosen to yield favorable improvements in both top-1 accuracy and the network's
cost. In order to enable joint optimization of the cost together with accuracy,
we define arithmetic computation effort (ACE), a hardware- and energy-inspired
cost metric for quantized and binarized networks. We also identify a need to
optimize an under-explored hyper-parameter controlling the binarization
gradient approximation.
  We establish a new, strong state-of-the-art (SOTA) on top-1 accuracy together
with commonly-used CPU64 cost, ACE cost and network size metrics.
ReActNet-Adam, the previous SOTA in BNNs, achieved a 70.5% top-1 accuracy with
7.9 ACE. A small variant of PokeBNN achieves 70.5% top-1 with 2.6 ACE, more
than 3x reduction in cost; a larger PokeBNN achieves 75.6% top-1 with 7.8 ACE,
more than 5% improvement in accuracy without increasing the cost. PokeBNN
implementation in JAX/Flax and reproduction instructions are available in AQT
repository: https://github.com/google/aqt

Worldwide 2019 million people have been infected and 4.5 million have lost
their lives in the ongoing Covid-19 pandemic. Until vaccines became widely
available, precautions and safety measures like wearing masks, physical
distancing, avoiding face touching were some of the primary means to curb the
spread of virus. Face touching is a compulsive human begavior that can not be
prevented without making a continuous consious effort, even then it is
inevitable. To address this problem, we have designed a smartwatch-based
solution, CovidAlert, that leverages Random Forest algorithm trained on
accelerometer and gyroscope data from the smartwatch to detects hand transition
to face and sends a quick haptic alert to the users. CovidALert is highly
energy efficient as it employs STA/LTA algorithm as a gatekeeper to curtail the
usage of Random Forest model on the watch when user is inactive. The overall
accuracy of our system is 88.4% with low false negatives and false positives.
We also demonstrated the system viability by implementing it on a commercial
Fossil Gen 5 smartwatch.

Attractor networks are an influential theory for memory storage in brain
systems. This theory has recently been challenged by the observation of strong
temporal variability in neuronal recordings during memory tasks. In this work,
we study a sparsely connected attractor network where memories are learned
according to a Hebbian synaptic plasticity rule. After recapitulating known
results for the continuous, sparsely connected Hopfield model, we investigate a
model in which new memories are learned continuously and old memories are
forgotten, using an online synaptic plasticity rule. We show that for a
forgetting time scale that optimizes storage capacity, the qualitative features
of the network's memory retrieval dynamics are age-dependent: most recent
memories are retrieved as fixed-point attractors while older memories are
retrieved as chaotic attractors characterized by strong heterogeneity and
temporal fluctuations. Therefore, fixed-point and chaotic attractors co-exist
in the network phase space. The network presents a continuum of statistically
distinguishable memory states, where chaotic fluctuations appear abruptly above
a critical age and then increase gradually until the memory disappears. We
develop a dynamical mean field theory (DMFT) to analyze the age-dependent
dynamics and compare the theory with simulations of large networks. Our
numerical simulations show that a high-degree of sparsity is necessary for the
DMFT to accurately predict the network capacity. Finally, our theory provides
specific predictions for delay response tasks with aging memoranda. Our theory
of attractor networks that continuously learn new information at the price of
forgetting old memories can account for the observed diversity of retrieval
states in the cortex, and in particular the strong temporal fluctuations of
cortical activity.

Autonomous systems are becoming ubiquitous and gaining momentum within the
marine sector. Since the electrification of transport is happening
simultaneously, autonomous marine vessels can reduce environmental impact,
lower costs, and increase efficiency. Although close monitoring is still
required to ensure safety, the ultimate goal is full autonomy. One major
milestone is to develop a control system that is versatile enough to handle any
weather and encounter that is also robust and reliable. Additionally, the
control system must adhere to the International Regulations for Preventing
Collisions at Sea (COLREGs) for successful interaction with human sailors.
Since the COLREGs were written for the human mind to interpret, they are
written in ambiguous prose and therefore not machine-readable or verifiable.
Due to these challenges and the wide variety of situations to be tackled,
classical model-based approaches prove complicated to implement and
computationally heavy. Within machine learning (ML), deep reinforcement
learning (DRL) has shown great potential for a wide range of applications. The
model-free and self-learning properties of DRL make it a promising candidate
for autonomous vessels. In this work, a subset of the COLREGs is incorporated
into a DRL-based path following and obstacle avoidance system using collision
risk theory. The resulting autonomous agent dynamically interpolates between
path following and COLREG-compliant collision avoidance in the training
scenario, isolated encounter situations, and AIS-based simulations of
real-world scenarios.

Large pre-trained language models perform remarkably well on tasks that can
be done "in one pass", such as generating realistic text or synthesizing
computer programs. However, they struggle with tasks that require unbounded
multi-step computation, such as adding integers or executing programs.
Surprisingly, we find that these same models are able to perform complex
multi-step computations -- even in the few-shot regime -- when asked to perform
the operation "step by step", showing the results of intermediate computations.
In particular, we train transformers to perform multi-step computations by
asking them to emit intermediate computation steps into a "scratchpad". On a
series of increasingly complex tasks ranging from long addition to the
execution of arbitrary programs, we show that scratchpads dramatically improve
the ability of language models to perform multi-step computations.

Pre-training on large-scale databases consisting of natural images and then
fine-tuning them to fit the application at hand, or transfer-learning, is a
popular strategy in computer vision. However, Kataoka et al., 2020 introduced a
technique to eliminate the need for natural images in supervised deep learning
by proposing a novel synthetic, formula-based method to generate 2D fractals as
training corpus. Using one synthetically generated fractal for each class, they
achieved transfer learning results comparable to models pre-trained on natural
images. In this project, we take inspiration from their work and build on this
idea -- using 3D procedural object renders. Since the image formation process
in the natural world is based on its 3D structure, we expect pre-training with
3D mesh renders to provide an implicit bias leading to better generalization
capabilities in a transfer learning setting and that invariances to 3D rotation
and illumination are easier to be learned based on 3D data. Similar to the
previous work, our training corpus will be fully synthetic and derived from
simple procedural strategies; we will go beyond classic data augmentation and
also vary illumination and pose which are controllable in our setting and study
their effect on transfer learning capabilities in context to prior work. In
addition, we will compare the 2D fractal and 3D procedural object networks to
human and non-human primate brain data to learn more about the 2D vs. 3D nature
of biological vision.

We conducted semi-structured interviews with members of the LGBTQ community
about their privacy practices and concerns on social networking sites.
Participants used different social media sites for different needs and adapted
to not being completely out on each site. We would value the opportunity to
discuss the unique privacy and security needs of this population with workshop
participants and learn more about the privacy needs of other marginalized user
groups from researchers who have worked in those communities.

The topological patterns exhibited by many real-world networks motivate the
development of topology-based methods for assessing the similarity of networks.
However, extracting topological structure is difficult, especially for large
and dense networks whose node degrees range over multiple orders of magnitude.
In this paper, we propose a novel and computationally practical topological
clustering method that clusters complex networks with intricate topology using
principled theory from persistent homology and optimal transport. Such networks
are aggregated into clusters through a centroid-based clustering strategy based
on both their topological and geometric structure, preserving correspondence
between nodes in different networks. The notions of topological proximity and
centroid are characterized using a novel and efficient approach to computation
of the Wasserstein distance and barycenter for persistence barcodes associated
with connected components and cycles. The proposed method is demonstrated to be
effective using both simulated networks and measured functional brain networks.

Designing models that produce accurate predictions is the fundamental
objective of machine learning (ML). This work presents methods demonstrating
that when the derivatives of target variables (outputs) with respect to inputs
can be extracted from processes of interest, e.g., neural networks (NN) based
surrogate models, they can be leveraged to further improve the accuracy of
differentiable ML models. This paper generalises the idea and provides
practical methodologies that can be used to leverage gradient information (GI)
across a variety of applications including: (1) Improving the performance of
generative adversarial networks (GANs); (2) efficiently tuning NN model
complexity; (3) regularising linear regressions. Numerical results show that GI
can effective enhance ML models with existing datasets, demonstrating its value
for a variety of applications.

Radioactivity induced by cosmic muon spallation is a dominant source of
backgrounds for $\mathcal{O}(10)~$MeV neutrino interactions in water Cherenkov
detectors. In particular, it is crucial to reduce backgrounds to measure the
solar neutrino spectrum and find neutrino interactions from distant supernovae.
In this paper we introduce new techniques to locate muon-induced hadronic
showers and efficiently reject spallation backgrounds. Applying these
techniques to the solar neutrino analysis with an exposure of
$2790\times22.5$~kton.day increases the signal efficiency by $12.6\%$,
approximately corresponding to an additional year of detector running.
Furthermore, we present the first spallation simulation at SK, where we model
hadronic interactions using FLUKA. The agreement between the isotope yields and
shower pattern in this simulation and in the data gives confidence in the
accuracy of this simulation, and thus opens the door to use it to optimize muon
spallation removal in new data with gadolinium-enhanced neutron capture

We propose methods for the analysis of hierarchical clustering that fully use
the multi-resolution structure provided by a dendrogram. Specifically, we
propose a loss for choosing between clustering methods, a feature importance
score and a graphical tool for visualizing the segmentation of features in a
dendrogram. Current approaches to these tasks lead to loss of information since
they require the user to generate a single partition of the instances by
cutting the dendrogram at a specified level. Our proposed methods, instead, use
the full structure of the dendrogram. The key insight behind the proposed
methods is to view a dendrogram as a phylogeny. This analogy permits the
assignment of a feature value to each internal node of a tree through ancestral
state reconstruction. Real and simulated datasets provide evidence that our
proposed framework has desirable outcomes. We provide an R package that
implements our methods.

Causal analysis and classification of injury severity applying non-parametric
methods for traffic crashes has received limited attention. This study presents
a methodological framework for causal inference, using Granger causality
analysis, and injury severity classification of traffic crashes, occurring on
interstates, with different machine learning techniques including decision
trees (DT), random forest (RF), extreme gradient boosting (XGBoost), and deep
neural network (DNN). The data used in this study were obtained for traffic
crashes on all interstates across the state of Texas from a period of six years
between 2014 and 2019. The output of the proposed severity classification
approach includes three classes for fatal and severe injury (KA) crashes,
non-severe and possible injury (BC) crashes, and property damage only (PDO)
crashes. While Granger Causality helped identify the most influential factors
affecting crash severity, the learning-based models predicted the severity
classes with varying performance. The results of Granger causality analysis
identified the speed limit, surface and weather conditions, traffic volume,
presence of workzones, workers in workzones, and high occupancy vehicle (HOV)
lanes, among others, as the most important factors affecting crash severity.
The prediction performance of the classifiers yielded varying results across
the different classes. Specifically, while decision tree and random forest
classifiers provided the greatest performance for PDO and BC severities,
respectively, for the KA class, the rarest class in the data, deep neural net
classifier performed superior than all other algorithms, most likely due to its
capability of approximating nonlinear models. This study contributes to the
limited body of knowledge pertaining to causal analysis and classification
prediction of traffic crash injury severity using non-parametric approaches.

Graph embedding is a transformation of nodes of a network into a set of
vectors. A good embedding should capture the underlying graph topology and
structure, node-to-node relationship, and other relevant information about the
graph, its subgraphs, and nodes themselves. If these objectives are achieved,
an embedding is a meaningful, understandable, and often compressed
representation of a network. Unfortunately, selecting the best embedding is a
challenging task and very often requires domain experts. In this paper, we
extend the framework for evaluating graph embeddings that was recently
introduced by the authors. Now, the framework assigns two scores, local and
global, to each embedding that measure the quality of an evaluated embedding
for tasks that require good representation of local and, respectively, global
properties of the network. The best embedding, if needed, can be selected in an
unsupervised way, or the framework can identify a few embeddings that are worth
further investigation. The framework is flexible, scalable, and can deal with
undirected/directed, weighted/unweighted graphs.

Learning from rationales seeks to augment model prediction accuracy using
human-annotated rationales (i.e. subsets of input tokens) that justify their
chosen labels, often in the form of intermediate or multitask supervision.
While intuitive, this idea has proven elusive in practice. We make two
observations about human rationales via empirical analyses: 1) maximizing
rationale supervision accuracy is not necessarily the optimal objective for
improving model accuracy; 2) human rationales vary in whether they provide
sufficient information for the model to exploit for prediction. Building on
these insights, we propose several novel loss functions and learning
strategies, and evaluate their effectiveness on three datasets with human
rationales. Our results demonstrate consistent improvements over baselines in
both label and rationale accuracy, including a 3% accuracy improvement on
MultiRC. Our work highlights the importance of understanding properties of
human explanations and exploiting them accordingly in model training.

Diabetic foot ulcers are a common manifestation of lesions on the diabetic
foot, a syndrome acquired as a long-term complication of diabetes mellitus.
Accompanying neuropathy and vascular damage promote acquisition of pressure
injuries and tissue death due to ischaemia. Affected areas are prone to
infections, hindering the healing progress. The research at hand investigates
an approach on classification of infection and ischaemia, conducted as part of
the Diabetic Foot Ulcer Challenge (DFUC) 2021. Different models of the
EfficientNet family are utilized in ensembles. An extension strategy for the
training data is applied, involving pseudo-labeling for unlabeled images, and
extensive generation of synthetic images via pix2pixHD to cope with severe
class imbalances. The resulting extended training dataset features $8.68$ times
the size of the baseline and shows a real to synthetic image ratio of $1:3$.
Performances of models and ensembles trained on the baseline and extended
training dataset are compared. Synthetic images featured a broad qualitative
variety. Results show that models trained on the extended training dataset as
well as their ensemble benefit from the large extension. F1-Scores for rare
classes receive outstanding boosts, while those for common classes are either
not harmed or boosted moderately. A critical discussion concretizes benefits
and identifies limitations, suggesting improvements. The work concludes that
classification performance of individual models as well as that of ensembles
can be boosted utilizing synthetic images. Especially performance for rare
classes benefits notably.

Misinformation is now a major problem due to its potential high risks to our
core democratic and societal values and orders. Out-of-context misinformation
is one of the easiest and effective ways used by adversaries to spread viral
false stories. In this threat, a real image is re-purposed to support other
narratives by misrepresenting its context and/or elements. The internet is
being used as the go-to way to verify information using different sources and
modalities. Our goal is an inspectable method that automates this
time-consuming and reasoning-intensive process by fact-checking the
image-caption pairing using Web evidence. To integrate evidence and cues from
both modalities, we introduce the concept of 'multi-modal cycle-consistency
check'; starting from the image/caption, we gather textual/visual evidence,
which will be compared against the other paired caption/image, respectively.
Moreover, we propose a novel architecture, Consistency-Checking Network (CCN),
that mimics the layered human reasoning across the same and different
modalities: the caption vs. textual evidence, the image vs. visual evidence,
and the image vs. caption. Our work offers the first step and benchmark for
open-domain, content-based, multi-modal fact-checking, and significantly
outperforms previous baselines that did not leverage external evidence.

Gradient inversion attack (or input recovery from gradient) is an emerging
threat to the security and privacy preservation of Federated learning, whereby
malicious eavesdroppers or participants in the protocol can recover (partially)
the clients' private data. This paper evaluates existing attacks and defenses.
We find that some attacks make strong assumptions about the setup. Relaxing
such assumptions can substantially weaken these attacks. We then evaluate the
benefits of three proposed defense mechanisms against gradient inversion
attacks. We show the trade-offs of privacy leakage and data utility of these
defense methods, and find that combining them in an appropriate manner makes
the attack less effective, even under the original strong assumptions. We also
estimate the computation cost of end-to-end recovery of a single image under
each evaluated defense. Our findings suggest that the state-of-the-art attacks
can currently be defended against with minor data utility loss, as summarized
in a list of potential strategies. Our code is available at:
https://github.com/Princeton-SysML/GradAttack.

Pre-training models on Imagenet or other massive datasets of real images has
led to major advances in computer vision, albeit accompanied with shortcomings
related to curation cost, privacy, usage rights, and ethical issues. In this
paper, for the first time, we study the transferability of pre-trained models
based on synthetic data generated by graphics simulators to downstream tasks
from very different domains. In using such synthetic data for pre-training, we
find that downstream performance on different tasks are favored by different
configurations of simulation parameters (e.g. lighting, object pose,
backgrounds, etc.), and that there is no one-size-fits-all solution. It is thus
better to tailor synthetic pre-training data to a specific downstream task, for
best performance. We introduce Task2Sim, a unified model mapping downstream
task representations to optimal simulation parameters to generate synthetic
pre-training data for them. Task2Sim learns this mapping by training to find
the set of best parameters on a set of "seen" tasks. Once trained, it can then
be used to predict best simulation parameters for novel "unseen" tasks in one
shot, without requiring additional training. Given a budget in number of images
per class, our extensive experiments with 20 diverse downstream tasks show
Task2Sim's task-adaptive pre-training data results in significantly better
downstream performance than non-adaptively choosing simulation parameters on
both seen and unseen tasks. It is even competitive with pre-training on real
images from Imagenet.

Background: The COVID-19 pandemic has affected our society and human
well-being in various ways. In this study, we investigate how the pandemic has
influenced people's emotions and psychological states compared to a
pre-pandemic period using real-world data from social media.
  Method: We collected Reddit social media data from 2019 (pre-pandemic) and
2020 (pandemic) from the subreddits communities associated with eight
universities. We applied the pre-trained Robustly Optimized BERT pre-training
approach (RoBERTa) to learn text embedding from the Reddit messages, and
leveraged the relational information among posted messages to train a graph
attention network (GAT) for sentiment classification. Finally, we applied model
stacking to combine the prediction probabilities from RoBERTa and GAT to yield
the final classification on sentiment. With the model-predicted sentiment
labels on the collected data, we used a generalized linear mixed-effects model
to estimate the effects of pandemic and in-person teaching during the pandemic
on sentiment.
  Results: The results suggest that the odds of negative sentiments in 2020
(pandemic) were 25.7% higher than the odds in 2019 (pre-pandemic) with a
$p$-value $<0.001$; and the odds of negative sentiments associated in-person
learning were 48.3% higher than with remote learning in 2020 with a $p$-value
of 0.029.
  Conclusions: Our study results are consistent with the findings in the
literature on the negative impacts of the pandemic on people's emotions and
psychological states. Our study contributes to the growing real-world evidence
on the various negative impacts of the pandemic on our society; it also
provides a good example of using both ML techniques and statistical modeling
and inference to make better use of real-world data.

The Lipschitz constant of the map between the input and output space
represented by a neural network is a natural metric for assessing the
robustness of the model. We present a new method to constrain the Lipschitz
constant of dense deep learning models that can also be generalized to other
architectures. The method relies on a simple weight normalization scheme during
training that ensures the Lipschitz constant of every layer is below an upper
limit specified by the analyst. A simple residual connection can then be used
to make the model monotonic in any subset of its inputs, which is useful in
scenarios where domain knowledge dictates such dependence. Examples can be
found in algorithmic fairness requirements or, as presented here, in the
classification of the decays of subatomic particles produced at the CERN Large
Hadron Collider. Our normalization is minimally constraining and allows the
underlying architecture to maintain higher expressiveness compared to other
techniques which aim to either control the Lipschitz constant of the model or
ensure its monotonicity. We show how the algorithm was used to train a
powerful, robust, and interpretable discriminator for heavy-flavor decays in
the LHCb realtime data-processing system.

Overparameterized neural networks generalize well but are expensive to train.
Ideally, one would like to reduce their computational cost while retaining
their generalization benefits. Sparse model training is a simple and promising
approach to achieve this, but there remain challenges as existing methods
struggle with accuracy loss, slow training runtime, or difficulty in
sparsifying all model components. The core problem is that searching for a
sparsity mask over a discrete set of sparse matrices is difficult and
expensive. To address this, our main insight is to optimize over a continuous
superset of sparse matrices with a fixed structure known as products of
butterfly matrices. As butterfly matrices are not hardware efficient, we
propose simple variants of butterfly (block and flat) to take advantage of
modern hardware. Our method (Pixelated Butterfly) uses a simple fixed sparsity
pattern based on flat block butterfly and low-rank matrices to sparsify most
network layers (e.g., attention, MLP). We empirically validate that Pixelated
Butterfly is 3x faster than butterfly and speeds up training to achieve
favorable accuracy--efficiency tradeoffs. On the ImageNet classification and
WikiText-103 language modeling tasks, our sparse models train up to 2.5x faster
than the dense MLP-Mixer, Vision Transformer, and GPT-2 medium with no drop in
accuracy.

We use deep neural networks to machine learn correlations between knot
invariants in various dimensions. The three-dimensional invariant of interest
is the Jones polynomial $J(q)$, and the four-dimensional invariants are the
Khovanov polynomial $\text{Kh}(q,t)$, smooth slice genus $g$, and Rasmussen's
$s$-invariant. We find that a two-layer feed-forward neural network can predict
$s$ from $\text{Kh}(q,-q^{-4})$ with greater than $99\%$ accuracy. A
theoretical explanation for this performance exists in knot theory via the now
disproven knight move conjecture, which is obeyed by all knots in our dataset.
More surprisingly, we find similar performance for the prediction of $s$ from
$\text{Kh}(q,-q^{-2})$, which suggests a novel relationship between the
Khovanov and Lee homology theories of a knot. The network predicts $g$ from
$\text{Kh}(q,t)$ with similarly high accuracy, and we discuss the extent to
which the machine is learning $s$ as opposed to $g$, since there is a general
inequality $|s| \leq 2g$. The Jones polynomial, as a three-dimensional
invariant, is not obviously related to $s$ or $g$, but the network achieves
greater than $95\%$ accuracy in predicting either from $J(q)$. Moreover,
similar accuracy can be achieved by evaluating $J(q)$ at roots of unity. This
suggests a relationship with $SU(2)$ Chern--Simons theory, and we review the
gauge theory construction of Khovanov homology which may be relevant for
explaining the network's performance.

Lightning is a destructive and highly visible product of severe storms, yet
there is still much to be learned about the conditions under which lightning is
most likely to occur. The GOES-16 and GOES-17 satellites, launched in 2016 and
2018 by NOAA and NASA, collect a wealth of data regarding individual lightning
strike occurrence and potentially related atmospheric variables. The acute
nature and inherent spatial correlation in lightning data renders standard
regression analyses inappropriate. Further, computational considerations are
foregrounded by the desire to analyze the immense and rapidly increasing volume
of lightning data. We present a new computationally feasible method that
combines spectral and Laplace approximations in an EM algorithm, denoted SLEM,
to fit the widely popular log-Gaussian Cox process model to large spatial point
pattern datasets. In simulations, we find SLEM is competitive with contemporary
techniques in terms of speed and accuracy. When applied to two lightning
datasets, SLEM provides better out-of-sample prediction scores and quicker
runtimes, suggesting its particular usefulness for analyzing lightning data,
which tend to have sparse signals.

Determining the poverty levels of various regions throughout the world is
crucial in identifying interventions for poverty reduction initiatives and
directing resources fairly. However, reliable data on global economic
livelihoods is hard to come by, especially for areas in the developing world,
hampering efforts to both deploy services and monitor/evaluate progress. This
is largely due to the fact that this data is obtained from traditional
door-to-door surveys, which are time consuming and expensive. Overhead
satellite imagery contain characteristics that make it possible to estimate the
region's poverty level. In this work, I develop deep learning computer vision
methods that can predict a region's poverty level from an overhead satellite
image. I experiment with both daytime and nighttime imagery. Furthermore,
because data limitations are often the barrier to entry in poverty prediction
from satellite imagery, I explore the impact that data quantity and data
augmentation have on the representational power and overall accuracy of the
networks. Lastly, to evaluate the robustness of the networks, I evaluate them
on data from continents that were absent in the development set.

Built on top of self-attention mechanisms, vision transformers have
demonstrated remarkable performance on a variety of vision tasks recently.
While achieving excellent performance, they still require relatively intensive
computational cost that scales up drastically as the numbers of patches,
self-attention heads and transformer blocks increase. In this paper, we argue
that due to the large variations among images, their need for modeling
long-range dependencies between patches differ. To this end, we introduce
AdaViT, an adaptive computation framework that learns to derive usage policies
on which patches, self-attention heads and transformer blocks to use throughout
the backbone on a per-input basis, aiming to improve inference efficiency of
vision transformers with a minimal drop of accuracy for image recognition.
Optimized jointly with a transformer backbone in an end-to-end manner, a
light-weight decision network is attached to the backbone to produce decisions
on-the-fly. Extensive experiments on ImageNet demonstrate that our method
obtains more than 2x improvement on efficiency compared to state-of-the-art
vision transformers with only 0.8% drop of accuracy, achieving good
efficiency/accuracy trade-offs conditioned on different computational budgets.
We further conduct quantitative and qualitative analysis on learned usage
polices and provide more insights on the redundancy in vision transformers.

The inversion of real images into StyleGAN's latent space is a well-studied
problem. Nevertheless, applying existing approaches to real-world scenarios
remains an open challenge, due to an inherent trade-off between reconstruction
and editability: latent space regions which can accurately represent real
images typically suffer from degraded semantic control. Recent work proposes to
mitigate this trade-off by fine-tuning the generator to add the target image to
well-behaved, editable regions of the latent space. While promising, this
fine-tuning scheme is impractical for prevalent use as it requires a lengthy
training phase for each new image. In this work, we introduce this approach
into the realm of encoder-based inversion. We propose HyperStyle, a
hypernetwork that learns to modulate StyleGAN's weights to faithfully express a
given image in editable regions of the latent space. A naive modulation
approach would require training a hypernetwork with over three billion
parameters. Through careful network design, we reduce this to be in line with
existing encoders. HyperStyle yields reconstructions comparable to those of
optimization techniques with the near real-time inference capabilities of
encoders. Lastly, we demonstrate HyperStyle's effectiveness on several
applications beyond the inversion task, including the editing of out-of-domain
images which were never seen during training.

Understanding document images (e.g., invoices) is a core but challenging task
since it requires complex functions such as reading text and a holistic
understanding of the document. Current Visual Document Understanding (VDU)
methods outsource the task of reading text to off-the-shelf Optical Character
Recognition (OCR) engines and focus on the understanding task with the OCR
outputs. Although such OCR-based approaches have shown promising performance,
they suffer from 1) high computational costs for using OCR; 2) inflexibility of
OCR models on languages or types of document; 3) OCR error propagation to the
subsequent process. To address these issues, in this paper, we introduce a
novel OCR-free VDU model named Donut, which stands for Document understanding
transformer. As the first step in OCR-free VDU research, we propose a simple
architecture (i.e., Transformer) with a pre-training objective (i.e.,
cross-entropy loss). Donut is conceptually simple yet effective. Through
extensive experiments and analyses, we show a simple OCR-free VDU model, Donut,
achieves state-of-the-art performances on various VDU tasks in terms of both
speed and accuracy. In addition, we offer a synthetic data generator that helps
the model pre-training to be flexible in various languages and domains. The
code, trained model and synthetic data are available at
https://github.com/clovaai/donut.

HOTTBOX is a Python library for exploratory analysis and visualisation of
multi-dimensional arrays of data, also known as tensors. The library includes
methods ranging from standard multi-way operations and data manipulation
through to multi-linear algebra based tensor decompositions. HOTTBOX also
comprises sophisticated algorithms for generalised multi-linear classification
and data fusion, such as Support Tensor Machine (STM) and Tensor Ensemble
Learning (TEL). For user convenience, HOTTBOX offers a unifying API which
establishes a self-sufficient ecosystem for various forms of efficient
representation of multi-way data and the corresponding decomposition and
association algorithms. Particular emphasis is placed on scalability and
interactive visualisation, to support multidisciplinary data analysis
communities working on big data and tensors. HOTTBOX also provides means for
integration with other popular data science libraries for visualisation and
data manipulation. The source code, examples and documentation ca be found at
https://github.com/hottbox/hottbox.

The prediction of stock price movement direction is significant in financial
circles and academic. Stock price contains complex, incomplete, and fuzzy
information which makes it an extremely difficult task to predict its
development trend. Predicting and analysing financial data is a nonlinear,
time-dependent problem. With rapid development in machine learning and deep
learning, this task can be performed more effectively by a purposely designed
network. This paper aims to improve prediction accuracy and minimizing
forecasting error loss through deep learning architecture by using Generative
Adversarial Networks. It was proposed a generic model consisting of Phase-space
Reconstruction (PSR) method for reconstructing price series and Generative
Adversarial Network (GAN) which is a combination of two neural networks which
are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural
Network (CNN) as Discriminative model for adversarial training to forecast the
stock market. LSTM will generate new instances based on historical basic
indicators information and then CNN will estimate whether the data is predicted
by LSTM or is real. It was found that the Generative Adversarial Network (GAN)
has performed well on the enhanced root mean square error to LSTM, as it was
4.35% more accurate in predicting the direction and reduced processing time and
RMSE by 78 secs and 0.029, respectively. This study provides a better result in
the accuracy of the stock index. It seems that the proposed system concentrates
on minimizing the root mean square error and processing time and improving the
direction prediction accuracy, and provides a better result in the accuracy of
the stock index.

Hadronization is a non-perturbative process, which theoretical description
can not be deduced from first principles. Modeling hadron formation requires
several assumptions and various phenomenological approaches. Utilizing
state-of-the-art Computer Vision and Deep Learning algorithms, it is eventually
possible to train neural networks to learn non-linear and non-perturbative
features of the physical processes. In this study, results of two ResNet
networks are presented by investigating global and kinematical quantities,
indeed jet- and event-shape variables. The widely used Lund string
fragmentation model is applied as a baseline in $\sqrt{s}= 7$ TeV proton-proton
collisions to predict the most relevant observables at further LHC energies.

Recently, methods have been developed to accurately predict the testing
performance of a Deep Neural Network (DNN) on a particular task, given
statistics of its underlying topological structure. However, further leveraging
this newly found insight for practical applications is intractable due to the
high computational cost in terms of time and memory. In this work, we define a
new class of topological features that accurately characterize the progress of
learning while being quick to compute during running time. Additionally, our
proposed topological features are readily equipped for backpropagation, meaning
that they can be incorporated in end-to-end training. Our newly developed
practical topological characterization of DNNs allows for an additional set of
applications. We first show we can predict the performance of a DNN without a
testing set and without the need for high-performance computing. We also
demonstrate our topological characterization of DNNs is effective in estimating
task similarity. Lastly, we show we can induce learning in DNNs by actively
constraining the DNN's topological structure. This opens up new avenues in
constricting the underlying structure of DNNs in a meta-learning framework.

An important property for deep neural networks is the ability to perform
robust out-of-distribution detection on previously unseen data. This property
is essential for safety purposes when deploying models for real world
applications. Recent studies show that probabilistic generative models can
perform poorly on this task, which is surprising given that they seek to
estimate the likelihood of training data. To alleviate this issue, we propose
the exponentially tilted Gaussian prior distribution for the Variational
Autoencoder (VAE) which pulls points onto the surface of a hyper-sphere in
latent space. This achieves state-of-the art results on the area under the
curve-receiver operator characteristics metric using just the log-likelihood
that the VAE naturally assigns. Because this prior is a simple modification of
the traditional VAE prior, it is faster and easier to implement than
competitive methods.

For strongly convex objectives that are smooth, the classical theory of
gradient descent ensures linear convergence relative to the number of gradient
evaluations. An analogous nonsmooth theory is challenging. Even when the
objective is smooth at every iterate, the corresponding local models are
unstable and the number of cutting planes invoked by traditional remedies is
difficult to bound, leading to convergences guarantees that are sublinear
relative to the cumulative number of gradient evaluations. We instead propose a
multipoint generalization of the gradient descent iteration for local
optimization. While designed with general objectives in mind, we are motivated
by a ``max-of-smooth'' model that captures the subdifferential dimension at
optimality. We prove linear convergence when the objective is itself
max-of-smooth, and experiments suggest a more general phenomenon.

Diffusion probabilistic models (DPMs) have achieved remarkable quality in
image generation that rivals GANs'. But unlike GANs, DPMs use a set of latent
variables that lack semantic meaning and cannot serve as a useful
representation for other tasks. This paper explores the possibility of using
DPMs for representation learning and seeks to extract a meaningful and
decodable representation of an input image via autoencoding. Our key idea is to
use a learnable encoder for discovering the high-level semantics, and a DPM as
the decoder for modeling the remaining stochastic variations. Our method can
encode any image into a two-part latent code, where the first part is
semantically meaningful and linear, and the second part captures stochastic
details, allowing near-exact reconstruction. This capability enables
challenging applications that currently foil GAN-based methods, such as
attribute manipulation on real images. We also show that this two-level
encoding improves denoising efficiency and naturally facilitates various
downstream tasks including few-shot conditional sampling. Please visit our
project page: https://Diff-AE.github.io/

The Biocreative VII Track-2 challenge consists of named entity recognition,
entity-linking (or entity-normalization), and topic indexing tasks -- with
entities and topics limited to chemicals for this challenge. Named entity
recognition is a well-established problem and we achieve our best performance
with BERT-based BioMegatron models. We extend our BERT-based approach to the
entity linking task. After the second stage of pretraining BioBERT with a
metric-learning loss strategy called self-alignment pretraining (SAP), we link
entities based on the cosine similarity between their SAP-BioBERT word
embeddings. Despite the success of our named entity recognition experiments, we
find the chemical indexing task generally more challenging.
  In addition to conventional NER methods, we attempt both named entity
recognition and entity linking with a novel text-to-text or "prompt" based
method that uses generative language models such as T5 and GPT. We achieve
encouraging results with this new approach.

A number of applications, such as mobile robots or automated vehicles, use
LiDAR sensors to obtain detailed information about their three-dimensional
surroundings. Many methods use image-like projections to efficiently process
these LiDAR measurements and use deep convolutional neural networks to predict
semantic classes for each point in the scan. The spatial stationary assumption
enables the usage of convolutions. However, LiDAR scans exhibit large
differences in appearance over the vertical axis. Therefore, we propose semi
local convolution (SLC), a convolution layer with reduced amount of
weight-sharing along the vertical dimension. We are first to investigate the
usage of such a layer independent of any other model changes. Our experiments
did not show any improvement over traditional convolution layers in terms of
segmentation IoU or accuracy.

Deep learning has led to many recent advances in object detection and
instance segmentation, among other computer vision tasks. These advancements
have led to wide application of deep learning based methods and related
methodologies in object detection tasks for satellite imagery. In this paper,
we introduce MIS Check-Dam, a new dataset of check-dams from satellite imagery
for building an automated system for the detection and mapping of check-dams,
focusing on the importance of irrigation structures used for agriculture. We
review some of the most recent object detection and instance segmentation
methods and assess their performance on our new dataset. We evaluate several
single stage, two-stage and attention based methods under various network
configurations and backbone architectures. The dataset and the pre-trained
models are available at https://www.cse.iitb.ac.in/gramdrishti/.

Single-Agent (SA) Reinforcement Learning systems have shown outstanding
re-sults on non-stationary problems. However, Multi-Agent Reinforcement
Learning(MARL) can surpass SA systems generally and when scaling. Furthermore,
MAsystems can be super-powered by collaboration, which can happen through
ob-serving others, or a communication system used to share information
betweencollaborators. Here, we developed a distributed MA learning mechanism
withthe ability to communicate based on decentralised partially observable
Markovdecision processes (Dec-POMDPs) and Graph Neural Networks (GNNs).
Minimis-ing the time and energy consumed by training Machine Learning models
whileimproving performance can be achieved by collaborative MA mechanisms.
Wedemonstrate this in a real-world scenario, an offshore wind farm, including a
set ofdistributed wind turbines, where the objective is to maximise collective
efficiency.Compared to a SA system, MA collaboration has shown significantly
reducedtraining time and higher cumulative rewards in unseen and scaled

Point cloud registration for 3D objects is a challenging task due to sparse
and noisy measurements, incomplete observations and large transformations. In
this work, we propose \textbf{G}raph \textbf{M}atching \textbf{C}onsensus
\textbf{Net}work (\textbf{GMCNet}), which estimates pose-invariant
correspondences for full-range Partial-to-Partial point cloud Registration
(PPR) in the object-level registration scenario. To encode robust point
descriptors, \textbf{1)} we first comprehensively investigate
transformation-robustness and noise-resilience of various geometric features.
\textbf{2)} Then, we employ a novel {T}ransformation-robust {P}oint
{T}ransformer (\textbf{TPT}) module to adaptively aggregate local features
regarding the structural relations, which takes advantage from both handcrafted
rotation-invariant ({\textit{RI}}) features and noise-resilient spatial
coordinates. \textbf{3)} Based on a synergy of hierarchical graph networks and
graphical modeling, we propose the {H}ierarchical {G}raphical {M}odeling
(\textbf{HGM}) architecture to encode robust descriptors consisting of i) a
unary term learned from {\textit{RI}} features; and ii) multiple smoothness
terms encoded from neighboring point relations at different scales through our
TPT modules. Moreover, we construct a challenging PPR dataset (\textbf{MVP-RG})
based on the recent MVP dataset that features high-quality scans. Extensive
experiments show that GMCNet outperforms previous state-of-the-art methods for
PPR. Notably, GMCNet encodes point descriptors for each point cloud
individually without using cross-contextual information, or ground truth
correspondences for training. Our code and datasets are available at:
https://github.com/paul007pl/GMCNet.

The availability of high-resolution weather radar images underpins effective
forecasting and decision-making. In regions beyond traditional radar coverage,
generative models have emerged as an important synthetic capability, fusing
more ubiquitous data sources, such as satellite imagery and numerical weather
models, into accurate radar-like products. Here, we demonstrate methods to
augment conventional convolutional neural networks with quantum-assisted models
for generative tasks in global synthetic weather radar. We show that quantum
kernels can, in principle, perform fundamentally more complex tasks than
classical learning machines on the relevant underlying data. Our results
establish synthetic weather radar as an effective heuristic benchmark for
quantum computing capabilities and set the stage for detailed quantum advantage
benchmarking on a high-impact operationally relevant problem.

Parameter identification for marine ecosystem models is important for the
assessment and validation of marine ecosystem models against observational
data. The surrogate-based optimization (SBO) is a computationally efficient
method to optimize complex models. SBO replaces the computationally expensive
(high-fidelity) model by a surrogate constructed from a less accurate but
computationally cheaper (low-fidelity) model in combination with an appropriate
correction approach, which improves the accuracy of the low-fidelity model. To
construct a computationally cheap low-fidelity model, we tested three different
approaches to compute an approximation of the annual periodic solution (i.e., a
steady annual cycle) of a marine ecosystem model: firstly, a reduced number of
spin-up iterations (several decades instead of millennia), secondly, an
artificial neural network (ANN) approximating the steady annual cycle and,
finally, a combination of both approaches. Except for the low-fidelity model
using only the ANN, the SBO yielded a solution close to the target and reduced
the computational effort significantly. If an ANN approximating appropriately a
marine ecosystem model is available, the SBO using this ANN as low-fidelity
model presents a promising and computational efficient method for the
validation.

Gravitational wave science is a dynamical, fast-expanding research field
founded on results, tools and methodologies drawn from different research areas
and communities. Early career scientists entering this field must learn and
combine knowledge and techniques from a range of disciplines. The Workshop on
Gravitational-Wave Astrophysics for Early Career Scientists (GWAECS), held
virtually in May 2021, planted the seeds of an interdisciplinary,
well-connected and all-inclusive community of early career scientists working
on gravitational waves, able to exchange relevant information and ideas, build
a healthy professional and international environment, share and learn valuable
skills, and ensure that ongoing research efforts are perpetuated and expanded
in order to attain the main scientific goals envisioned by the whole community.
GWAECS was the first event unifying early career scientists belonging to
different communities, historically associated with different large-scale
gravitational wave experiments. It provided a broad perspective on the future
of gravitational waves, offered training on soft and transferable skills and
allowed ample time for informal discussions between early career scientists and
well-known research experts. The essence of those activities is summarised and
collected in the present document, which presents a recap of each session of
the workshop and aims to provide all early career scientists with a
long-lasting, useful reference which constitutes the legacy of all the ideas
that circulated at GWAECS.

Density functional theory is the standard theory for computing the electronic
structure of materials, which is based on a functional that maps the electron
density to the energy. However, a rigorous form of the functional is not known
and has been heuristically constructed by interpolating asymptotic constraints
known for extreme situations, such as isolated atoms and uniform electron gas.
Recent studies have demonstrated that the functional can be effectively
approximated using machine learning (ML) approaches. However, most ML models do
not satisfy asymptotic constraints. In this study, by applying a novel ML model
architecture, we demonstrate a neural network-based exchange-correlation
functional satisfying physical asymptotic constraints. Calculations reveal that
the trained functional is applicable to various materials with an accuracy
higher than that of existing functionals, even for materials whose electronic
properties are different from the properties of materials in the training
dataset. Our proposed approach thus improves the accuracy and generalization
performance of the ML-based functional by combining the advantages of ML and
analytical modeling.

We present MapReader, a free, open-source software library written in Python
for analyzing large map collections (scanned or born-digital). This library
transforms the way historians can use maps by turning extensive, homogeneous
map sets into searchable primary sources. MapReader allows users with little or
no computer vision expertise to i) retrieve maps via web-servers; ii)
preprocess and divide them into patches; iii) annotate patches; iv) train,
fine-tune, and evaluate deep neural network models; and v) create structured
data about map content. We demonstrate how MapReader enables historians to
interpret a collection of $\approx$16K nineteenth-century Ordnance Survey map
sheets ($\approx$30.5M patches), foregrounding the challenge of translating
visual markers into machine-readable data. We present a case study focusing on
British rail infrastructure and buildings as depicted on these maps. We also
show how the outputs from the MapReader pipeline can be linked to other,
external datasets, which we use to evaluate as well as enrich and interpret the
results. We release $\approx$62K manually annotated patches used here for
training and evaluating the models.

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is
traditionally considered an unsupervised learning task. In recent years, the
use of background knowledge to improve the cluster quality and promote
interpretability of the clustering process has become a hot research topic at
the intersection of mathematical optimization and machine learning research.
The problem of taking advantage of background information in data clustering is
called semi-supervised or constrained clustering. In this paper, we present a
branch-and-cut algorithm for semi-supervised MSSC, where background knowledge
is incorporated as pairwise must-link and cannot-link constraints. For the
lower bound procedure, we solve the semidefinite programming relaxation of the
MSSC discrete optimization model, and we use a cutting-plane procedure for
strengthening the bound. For the upper bound, instead, by using integer
programming tools, we use an adaptation of the k-means algorithm to the
constrained case. For the first time, the proposed global optimization
algorithm efficiently manages to solve real-world instances up to 800 data
points with different combinations of must-link and cannot-link constraints and
with a generic number of features. This problem size is about four times larger
than the one of the instances solved by state-of-the-art exact algorithms.

Algorithmic stability is a concept from learning theory that expresses the
degree to which changes to the input data (e.g., removal of a single data
point) may affect the outputs of a regression algorithm. Knowing an algorithm's
stability properties is often useful for many downstream applications -- for
example, stability is known to lead to desirable generalization properties and
predictive inference guarantees. However, many modern algorithms currently used
in practice are too complex for a theoretical analysis of their stability
properties, and thus we can only attempt to establish these properties through
an empirical exploration of the algorithm's behavior on various data sets. In
this work, we lay out a formal statistical framework for this kind of "black
box testing" without any assumptions on the algorithm or the data distribution,
and establish fundamental bounds on the ability of any black box test to
identify algorithmic stability.

Motivated by the recent empirical success of policy-based reinforcement
learning (RL), there has been a research trend studying the performance of
policy-based RL methods on standard control benchmark problems. In this paper,
we examine the effectiveness of policy-based RL methods on an important robust
control problem, namely $\mu$ synthesis. We build a connection between robust
adversarial RL and $\mu$ synthesis, and develop a model-free version of the
well-known $DK$-iteration for solving state-feedback $\mu$ synthesis with
static $D$-scaling. In the proposed algorithm, the $K$ step mimics the
classical central path algorithm via incorporating a recently-developed
double-loop adversarial RL method as a subroutine, and the $D$ step is based on
model-free finite difference approximation. Extensive numerical study is also
presented to demonstrate the utility of our proposed model-free algorithm. Our
study sheds new light on the connections between adversarial RL and robust
control.

This paper presents a deep learning-based video compression framework
(ViSTRA3). The proposed framework intelligently adapts video format parameters
of the input video before encoding, subsequently employing a CNN at the decoder
to restore their original format and enhance reconstruction quality. ViSTRA3
has been integrated with the H.266/VVC Test Model VTM 14.0, and evaluated under
the Joint Video Exploration Team Common Test Conditions. Bj{\o}negaard Delta
(BD) measurement results show that the proposed framework consistently
outperforms the original VVC VTM, with average BD-rate savings of 1.8% and 3.7%
based on the assessment of PSNR and VMAF.

We prove a general Embedding Principle of loss landscape of deep neural
networks (NNs) that unravels a hierarchical structure of the loss landscape of
NNs, i.e., loss landscape of an NN contains all critical points of all the
narrower NNs. This result is obtained by constructing a class of critical
embeddings which map any critical point of a narrower NN to a critical point of
the target NN with the same output function. By discovering a wide class of
general compatible critical embeddings, we provide a gross estimate of the
dimension of critical submanifolds embedded from critical points of narrower
NNs. We further prove an irreversiblility property of any critical embedding
that the number of negative/zero/positive eigenvalues of the Hessian matrix of
a critical point may increase but never decrease as an NN becomes wider through
the embedding. Using a special realization of general compatible critical
embedding, we prove a stringent necessary condition for being a "truly-bad"
critical point that never becomes a strict-saddle point through any critical
embedding. This result implies the commonplace of strict-saddle points in wide
NNs, which may be an important reason underlying the easy optimization of wide
NNs widely observed in practice.

A human hand can grasp a desired number of objects at once from a pile based
solely on tactile sensing. To do so, a robot needs to grasp within a pile,
sense the number of objects in the grasp before lifting, and predict the number
of objects that will remain in the grasp after lifting. It is a challenging
problem because when making the prediction, the robotic hand is still in the
pile and the objects in the grasp are not observable to vision systems.
Moreover, some objects that are grasped by the hand before lifting from the
pile may fall out of the grasp when the hand is lifted. This occurs because
they were supported by other objects in the pile instead of the fingers of the
hand. Therefore, a robotic hand should sense the number of objects in a grasp
using its tactile sensors before lifting. This paper presents novel
multi-object grasping analyzing methods for solving this problem. They include
a grasp volume calculation, tactile force analysis, and a data-driven deep
learning approach. The methods have been implemented on a Barrett hand and then
evaluated in simulations and a real setup with a robotic system. The evaluation
results conclude that once the Barrett hand grasps multiple objects in the
pile, the data-driven model can predict, before lifting, the number of objects
that will remain in the hand after lifting. The root-mean-square errors for our
approach are 0.74 for balls and 0.58 for cubes in simulations, and 1.06 for
balls, and 1.45 for cubes in the real system.

Time-of-Flight (ToF) cameras are subject to high levels of noise and
distortions due to Multi-Path-Interference (MPI). While recent research showed
that 2D neural networks are able to outperform previous traditional
State-of-the-Art (SOTA) methods on denoising ToF-Data, little research on
learning-based approaches has been done to make direct use of the 3D
information present in depth images. In this paper, we propose an iterative
denoising approach operating in 3D space, that is designed to learn on 2.5D
data by enabling 3D point convolutions to correct the points' positions along
the view direction. As labeled real world data is scarce for this task, we
further train our network with a self-training approach on unlabeled real world
data to account for real world statistics. We demonstrate that our method is
able to outperform SOTA methods on several datasets, including two real world
datasets and a new large-scale synthetic data set introduced in this paper.

Decision support systems based on clinical notes have the potential to
improve patient care by pointing doctors towards overseen risks. Predicting a
patient's outcome is an essential part of such systems, for which the use of
deep neural networks has shown promising results. However, the patterns learned
by these networks are mostly opaque and previous work revealed flaws regarding
the reproduction of unintended biases. We thus introduce an extendable testing
framework that evaluates the behavior of clinical outcome models regarding
changes of the input. The framework helps to understand learned patterns and
their influence on model decisions. In this work, we apply it to analyse the
change in behavior with regard to the patient characteristics gender, age and
ethnicity. Our evaluation of three current clinical NLP models demonstrates the
concrete effects of these characteristics on the models' decisions. They show
that model behavior varies drastically even when fine-tuned on the same data
and that allegedly best-performing models have not always learned the most
medically plausible patterns.

This project was the first attempt to use big data analytics approaches and
machine learning along with Natural Language Processing (NLP) of tweets for
flood risk assessment and decision making. Multiple Python packages were
developed and integrated within the Flood Analytics Information System (FAIS).
FAIS workflow includes the use of IoTs-APIs and various machine learning
approaches for transmitting, processing, and loading big data through which the
application gathers information from various data servers and replicates it to
a data warehouse (IBM database service). Users are allowed to directly stream
and download flood related images/videos from the US Geological Survey (USGS)
and Department of Transportation (DOT) and save the data on a local storage.
The outcome of the river measurement, imagery, and tabular data is displayed on
a web based remote dashboard and the information can be plotted in real-time.
FAIS proved to be a robust and user-friendly tool for flood data analysis at
regional scale that could help stakeholders for rapid assessment of flood
situation and damages. FAIS also provides flood frequency analysis (FFA) to
estimate flood quantiles including the associated uncertainties that combine
the elements of observational analysis, stochastic probability distribution and
design return periods. FAIS is publicly available and deployed on the
Clemson-IBM cloud service.

Dimensional data reduction methods are fundamental to explore and visualize
large data sets. Basic requirements for unsupervised data exploration are
simplicity, flexibility and scalability. However, current methods show complex
parameterizations and strong computational limitations when exploring large
data structures across scales. Here, we focus on the t-SNE algorithm and show
that a simplified parameter setup with a single control parameter, namely the
perplexity, can effectively balance local and global data structure
visualization. We also designed a chunk\&mix protocol to efficiently
parallelize t-SNE and explore data structure across a much wide range of scales
than currently available. Our parallel version of the BH-tSNE, namely pt-SNE,
converges to good global embedding, comparable to state-of-the-art solutions,
though the chunk\&mix protocol adds little noise and decreases the accuracy at
the local scale. Nonetheless, we show that simple post-processing can
efficiently restore local scale visualization, without any loss of precision at
the global scales. We expect the same approach to apply to faster embedding
algorithms other than BH-tSNE, like FIt-SNE or UMAP, thus, extending the
state-of-the-art and leading to more comprehensive data structure visualization
and analysis.

Machine Learning methods can learn how to reconstruct Magnetic Resonance
Images and thereby accelerate acquisition, which is of paramount importance to
the clinical workflow. Physics-informed networks incorporate the forward model
of accelerated MRI reconstruction in the learning process. With increasing
network complexity, robustness is not ensured when reconstructing data unseen
during training. We aim to embed data consistency (DC) in deep networks while
balancing the degree of network complexity. While doing so, we will assess
whether either explicit or implicit enforcement of DC in varying network
architectures is preferred to optimize performance. We propose a scheme called
Cascades of Independently Recurrent Inference Machines (CIRIM) to assess DC
through unrolled optimization. Herein we assess DC both implicitly by gradient
descent and explicitly by a designed term. Extensive comparison of the CIRIM to
CS as well as to other methods is performed: the E2EVN, CascadeNet, KIKINet,
LPDNet, RIM, IRIM, and UNet. Models were trained and evaluated on T1-weighted
and FLAIR contrast brain data, and T2-weighted knee data. Both 1D and 2D
undersampling patterns were evaluated. Robustness was tested by reconstructing
7.5x prospectively undersampled 3D FLAIR MRI data of Multiple Sclerosis (MS)
patients with white matter lesions. The CIRIM performed best when implicitly
enforcing DC, while the E2EVN required an explicit DC formulation. In
reconstructing MS patient data, prospectively acquired with a sampling pattern
unseen during model training, the CIRIM maintained lesion contrast while
efficiently denoising the images. The CIRIM showed highly promising
generalization capabilities maintaining a very fair trade-off between
reconstructed image quality and fast reconstruction times, which is crucial in
the clinical workflow.

Power curves capture the relationship between wind speed and output power for
a specific wind turbine. Accurate regression models of this function prove
useful in monitoring, maintenance, design, and planning. In practice, however,
the measurements do not always correspond to the ideal curve: power
curtailments will appear as (additional) functional components. Such
multivalued relationships cannot be modelled by conventional regression, and
the associated data are usually removed during pre-processing. The current work
suggests an alternative method to infer multivalued relationships in curtailed
power data. Using a population-based approach, an overlapping mixture of
probabilistic regression models is applied to signals recorded from turbines
within an operational wind farm. The model is shown to provide an accurate
representation of practical power data across the population.

Recent advances in state-of-the-art ultra-low power embedded devices for
machine learning (ML) have permitted a new class of products whose key features
enable ML capabilities on microcontrollers with less than 1 mW power
consumption (TinyML). TinyML provides a unique solution by aggregating and
analyzing data at the edge on low-power embedded devices. However, we have only
recently been able to run ML on microcontrollers, and the field is still in its
infancy, which means that hardware, software, and research are changing
extremely rapidly. Consequently, many TinyML frameworks have been developed for
different platforms to facilitate the deployment of ML models and standardize
the process. Therefore, in this paper, we focus on bench-marking two popular
frameworks: Tensorflow Lite Micro (TFLM) on the Arduino Nano BLE and CUBE AI on
the STM32-NucleoF401RE to provide a standardized framework selection criterion
for specific applications.

Previous portrait image generation methods roughly fall into two categories:
2D GANs and 3D-aware GANs. 2D GANs can generate high fidelity portraits but
with low view consistency. 3D-aware GAN methods can maintain view consistency
but their generated images are not locally editable. To overcome these
limitations, we propose FENeRF, a 3D-aware generator that can produce
view-consistent and locally-editable portrait images. Our method uses two
decoupled latent codes to generate corresponding facial semantics and texture
in a spatial aligned 3D volume with shared geometry. Benefiting from such
underlying 3D representation, FENeRF can jointly render the boundary-aligned
image and semantic mask and use the semantic mask to edit the 3D volume via GAN
inversion. We further show such 3D representation can be learned from widely
available monocular image and semantic mask pairs. Moreover, we reveal that
joint learning semantics and texture helps to generate finer geometry. Our
experiments demonstrate that FENeRF outperforms state-of-the-art methods in
various face editing tasks.

Nowadays, classification and Out-of-Distribution (OoD) detection in the
few-shot setting remain challenging aims due to rarity and the limited samples
in the few-shot setting, and because of adversarial attacks. Accomplishing
these aims is important for critical systems in safety, security, and defence.
In parallel, OoD detection is challenging since deep neural network classifiers
set high confidence to OoD samples away from the training data. To address such
limitations, we propose the Few-shot ROBust (FROB) model for classification and
few-shot OoD detection. We devise FROB for improved robustness and reliable
confidence prediction for few-shot OoD detection. We generate the support
boundary of the normal class distribution and combine it with few-shot Outlier
Exposure (OE). We propose a self-supervised learning few-shot confidence
boundary methodology based on generative and discriminative models. The
contribution of FROB is the combination of the generated boundary in a
self-supervised learning manner and the imposition of low confidence at this
learned boundary. FROB implicitly generates strong adversarial samples on the
boundary and forces samples from OoD, including our boundary, to be less
confident by the classifier. FROB achieves generalization to unseen OoD with
applicability to unknown, in the wild, test sets that do not correlate to the
training datasets. To improve robustness, FROB redesigns OE to work even for
zero-shots. By including our boundary, FROB reduces the threshold linked to the
model's few-shot robustness; it maintains the OoD performance approximately
independent of the number of few-shots. The few-shot robustness analysis
evaluation of FROB on different sets and on One-Class Classification (OCC) data
shows that FROB achieves competitive performance and outperforms benchmarks in
terms of robustness to the outlier few-shot sample population and variability.

Over the last decades, light-emitting diodes (LED) have replaced common light
bulbs in almost every application, from flashlights in smartphones to
automotive headlights. Illuminating nightly streets requires LEDs to emit a
light spectrum that is perceived as pure white by the human eye. The power
associated with such a white light spectrum is not only distributed over the
contributing wavelengths but also over the angles of vision. For many
applications, the usable light rays are required to exit the LED in forward
direction, namely under small angles to the perpendicular. In this work, we
demonstrate that a specifically designed multi-layer thin film on top of a
white LED increases the power of pure white light emitted in forward direction.
Therefore, the deduced multi-objective optimization problem is reformulated via
a real-valued physics-guided objective function that represents the
hierarchical structure of our engineering problem. Variants of Bayesian
optimization are employed to maximize this non-deterministic objective function
based on ray tracing simulations. Eventually, the investigation of optical
properties of suitable multi-layer thin films allowed to identify the mechanism
behind the increased directionality of white light: angle and wavelength
selective filtering causes the multi-layer thin film to play ping pong with
rays of light.

Video frame interpolation (VFI) is currently a very active research topic,
with applications spanning computer vision, post production and video encoding.
VFI can be extremely challenging, particularly in sequences containing large
motions, occlusions or dynamic textures, where existing approaches fail to
offer perceptually robust interpolation performance. In this context, we
present a novel deep learning based VFI method, ST-MFNet, based on a
Spatio-Temporal Multi-Flow architecture. ST-MFNet employs a new multi-scale
multi-flow predictor to estimate many-to-one intermediate flows, which are
combined with conventional one-to-one optical flows to capture both large and
complex motions. In order to enhance interpolation performance for various
textures, a 3D CNN is also employed to model the content dynamics over an
extended temporal window. Moreover, ST-MFNet has been trained within an ST-GAN
framework, which was originally developed for texture synthesis, with the aim
of further improving perceptual interpolation quality. Our approach has been
comprehensively evaluated -- compared with fourteen state-of-the-art VFI
algorithms -- clearly demonstrating that ST-MFNet consistently outperforms
these benchmarks on varied and representative test datasets, with significant
gains up to 1.09dB in PSNR for cases including large motions and dynamic
textures. Project page: https://danielism97.github.io/ST-MFNet.

In recent years, the proliferation of unmanned aerial vehicles (UAVs) has
increased dramatically. UAVs can accomplish complex or dangerous tasks in a
reliable and cost-effective way but are still limited by power consumption
problems, which pose serious constraints on the flight duration and completion
of energy-demanding tasks. The possibility of providing UAVs with advanced
decision-making capabilities in an energy-effective way would be extremely
beneficial. In this paper, we propose a practical solution to this problem that
exploits deep learning on the edge. The developed system integrates an OpenMV
microcontroller into a DJI Tello Micro Aerial Vehicle (MAV). The
microcontroller hosts a set of machine learning-enabled inference tools that
cooperate to control the navigation of the drone and complete a given mission
objective. The goal of this approach is to leverage the new opportunistic
features of TinyML through OpenMV including offline inference, low latency,
energy efficiency, and data security. The approach is successfully validated on
a practical application consisting of the onboard detection of people wearing
protection masks in a crowded environment.

Accurate channel modeling is the foundation of communication system design.
However, the traditional measurement-based modeling approach has increasing
challenges for the scenarios with insufficient measurement data. To obtain
enough data for channel modeling, the Artificial Neural Network (ANN) is used
in this paper to predict channel data. The high mobility railway channel is
considered, which is a typical scenario where it is challenging to obtain
enough data for modeling within a short sampling interval. Three types of ANNs,
the Back Propagation Network, Radial Basis Function Neural Network and Extreme
Learning Machine, are considered to predict channel path loss and shadow
fading. The Root-Mean-Square error is used to evaluate prediction accuracy. The
factors that may influence prediction accuracy are compared and discussed,
including the type of network, number of neurons and proportion of training
data. It is found that a larger number of neurons can significantly reduce
prediction error, whereas the influence of proportion of training data is
relatively small. The results can be used to improve modeling accuracy of path
loss and shadow fading when measurement data is reduced.

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces
(STAR-RISs) has been considered as a promising auxiliary device to enhance the
performance of the wireless network, where users located at the different sides
of the surfaces can be simultaneously served by the transmitting and reflecting
signals. In this paper, the energy efficiency (EE) maximization problem for a
non-orthogonal multiple access (NOMA) assisted STAR-RIS downlink network is
investigated. Due to the fractional form of the EE, it is challenging to solve
the EE maximization problem by the traditional convex optimization solutions.
In this work, a deep deterministic policy gradient (DDPG)-based algorithm is
proposed to maximize the EE by jointly optimizing the transmission beamforming
vectors at the base station and the coefficients matrices at the STAR-RIS.
Simulation results demonstrate that the proposed algorithm can effectively
maximize the system EE considering the time-varying channels.

Encoder-Decoder networks such as U-Nets have been applied successfully in a
wide range of computer vision tasks, especially for image segmentation of
different flavours across different fields. Nevertheless, most applications
lack of a satisfying quantification of the uncertainty of the prediction. Yet,
a well calibrated segmentation uncertainty can be a key element for scientific
applications such as precision cosmology. In this on-going work, we explore the
use of the probabilistic version of the U-Net, recently proposed by Kohl et al
(2018), and adapt it to automate the segmentation of galaxies for large
photometric surveys. We focus especially on the probabilistic segmentation of
overlapping galaxies, also known as blending. We show that, even when training
with a single ground truth per input sample, the model manages to properly
capture a pixel-wise uncertainty on the segmentation map. Such uncertainty can
then be propagated further down the analysis of the galaxy properties. To our
knowledge, this is the first time such an experiment is applied for galaxy
deblending in astrophysics.

Mixup is a popular data-dependent augmentation technique for deep neural
networks, which contains two sub-tasks, mixup generation, and classification.
The community typically confines mixup to supervised learning (SL) and the
objective of the generation sub-task is fixed to selected sample pair instead
of considering the whole data manifold. To overcome such limitations, we
systematically study the mixup generation objective and propose
Scenario-Agnostic Mixup for both SL and Self-supervised Learning (SSL)
scenarios, named SAMix. Specifically, we hypothesize and verify the objective
function of mixup generation as optimizing local smoothness between two mixed
classes subject to global discrimination from other classes. Therefore, we
propose $\eta$-balanced mixup loss for complementary learning of the two
sub-objectives. Meanwhile, we parameterize the generation sub-task as a
learnable sub-network, Mixer, with mixing attention which avoids trivial
solutions and improves transferable abilities. To eliminate the computational
cost of online training, we introduce a pre-trained version,
SAMix$^\mathcal{P}$, that achieves efficient performance in various tasks.
Extensive experiments on SL and SSL benchmarks demonstrate that SAMix
consistently outperforms leading methods.

Climate change is expected to increase the likelihood of drought events, with
severe implications for food security. Unlike other natural disasters, droughts
have a slow onset and depend on various external factors, making drought
detection in climate data difficult. In contrast to existing works that rely on
simple relative drought indices as ground-truth data, we build upon soil
moisture index (SMI) obtained from a hydrological model. This index is directly
related to insufficiently available water to vegetation. Given ERA5-Land
climate input data of six months with land use information from MODIS satellite
observation, we compare different models with and without sequential inductive
bias in classifying droughts based on SMI. We use PR-AUC as the evaluation
measure to account for the class imbalance and obtain promising results despite
a challenging time-based split. We further show in an ablation study that the
models retain their predictive capabilities given input data of coarser
resolutions, as frequently encountered in climate models.

The increasing use of Machine Learning (ML) components embedded in autonomous
systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the
pressing need to assure their functional safety. As for traditional functional
safety, the emerging consensus within both, industry and academia, is to use
assurance cases for this purpose. Typically assurance cases support claims of
reliability in support of safety, and can be viewed as a structured way of
organising arguments and evidence generated from safety analysis and
reliability modelling activities. While such assurance activities are
traditionally guided by consensus-based standards developed from vast
engineering experience, LESs pose new challenges in safety-critical application
due to the characteristics and design of ML models. In this article, we first
present an overall assurance framework for LESs with an emphasis on
quantitative aspects, e.g., breaking down system-level safety targets to
component-level requirements and supporting claims stated in reliability
metrics. We then introduce a novel model-agnostic Reliability Assessment Model
(RAM) for ML classifiers that utilises the operational profile and robustness
verification evidence. We discuss the model assumptions and the inherent
challenges of assessing ML reliability uncovered by our RAM and propose
solutions to practical use. Probabilistic safety argument templates at the
lower ML component-level are also developed based on the RAM. Finally, to
evaluate and demonstrate our methods, we not only conduct experiments on
synthetic/benchmark datasets but also scope our methods with case studies on
simulated Autonomous Underwater Vehicles and physical Unmanned Ground Vehicles.

Radio continuum observations of external galaxies provide us with an
excellent outside view on the distribution of cosmic-ray electrons in the disc
and halo. In this review, we summarise the current state of what we have
learned from modelling such observations with cosmic-ray transport, paying
particular attention to the question to what extent we can exploit radio haloes
when studying galactic winds. We have developed the user-friendly framework
SPINNAKER to model radio haloes with either pure advection or diffusion,
allowing us to study both diffusion coefficients and advection speeds in nearby
galaxies. Using these models, we show that we can identify galaxies with winds
using both morphology and radio spectral indices of radio haloes. Advective
radio haloes are ubiquitous, indicating that already fairly low values of the
star formation rate (SFR) surface density ($\Sigma_{\rm SFR}$) can trigger
galactic winds. The advection speeds scale with SFR, $\Sigma_{\rm SFR}$, and
rotation speed as expected for stellar feedback-driven winds. Accelerating
winds are in agreement with our radio spectral index data, but this is
sensitive to the magnetic field parametrisation, so that constant wind speeds
cannot be ruled out either. The question to what extent cosmic rays can be a
driving force behind winds is still an open issue and we discuss only in
passing how a simple iso-thermal wind model could fit our data. Nevertheless,
the comparison with inferences from observations and theory looks promising
with radio continuum offering a complementary view on galactic winds. We finish
with a perspective on future observations and challenges lying ahead.

The inception of modeling contextual information using models such as BERT,
ELMo, and Flair has significantly improved representation learning for words.
It has also given SOTA results in almost every NLP task - Machine Translation,
Text Summarization and Named Entity Recognition, to name a few. In this work,
in addition to using these dominant context-aware representations, we propose a
Knowledge Aware Representation Learning (KARL) Network for Named Entity
Recognition (NER). We discuss the challenges of using existing methods in
incorporating world knowledge for NER and show how our proposed methods could
be leveraged to overcome those challenges. KARL is based on a Transformer
Encoder that utilizes large knowledge bases represented as fact triplets,
converts them to a graph context, and extracts essential entity information
residing inside to generate contextualized triplet representation for feature
augmentation. Experimental results show that the augmentation done using KARL
can considerably boost the performance of our NER system and achieve
significantly better results than existing approaches in the literature on
three publicly available NER datasets, namely CoNLL 2003, CoNLL++, and
OntoNotes v5. We also observe better generalization and application to a
real-world setting from KARL on unseen entities.

Unsupervised anomaly detection tackles the problem of finding anomalies
inside datasets without the labels availability; since data tagging is
typically hard or expensive to obtain, such approaches have seen huge
applicability in recent years. In this context, Isolation Forest is a popular
algorithm able to define an anomaly score by means of an ensemble of peculiar
trees called isolation trees. These are built using a random partitioning
procedure that is extremely fast and cheap to train. However, we find that the
standard algorithm might be improved in terms of memory requirements, latency
and performances; this is of particular importance in low resources scenarios
and in TinyML implementations on ultra-constrained microprocessors. Moreover,
Anomaly Detection approaches currently do not take advantage of weak
supervisions: being typically consumed in Decision Support Systems, feedback
from the users, even if rare, can be a valuable source of information that is
currently unexplored. Beside showing iForest training limitations, we propose
here TiWS-iForest, an approach that, by leveraging weak supervision is able to
reduce Isolation Forest complexity and to enhance detection performances. We
showed the effectiveness of TiWS-iForest on real word datasets and we share the
code in a public repository to enhance reproducibility.

We consider independent component analysis of binary data. While fundamental
in practice, this case has been much less developed than ICA for continuous
data. We start by assuming a linear mixing model in a continuous-valued latent
space, followed by a binary observation model. Importantly, we assume that the
sources are non-stationary; this is necessary since any non-Gaussianity would
essentially be destroyed by the binarization. Interestingly, the model allows
for closed-form likelihood by employing the cumulative distribution function of
the multivariate Gaussian distribution. In stark contrast to the
continuous-valued case, we prove non-identifiability of the model with few
observed variables; our empirical results imply identifiability when the number
of observed variables is higher. We present a practical method for binary ICA
that uses only pairwise marginals, which are faster to compute than the full
multivariate likelihood. Experiments give insight into the requirements for the
number of observed variables, segments, and latent sources that allow the model
to be estimated.

In spite of the dominant performances of deep neural networks, recent works
have shown that they are poorly calibrated, resulting in over-confident
predictions. Miscalibration can be exacerbated by overfitting due to the
minimization of the cross-entropy during training, as it promotes the predicted
softmax probabilities to match the one-hot label assignments. This yields a
pre-softmax activation of the correct class that is significantly larger than
the remaining activations. Recent evidence from the literature suggests that
loss functions that embed implicit or explicit maximization of the entropy of
predictions yield state-of-the-art calibration performances. We provide a
unifying constrained-optimization perspective of current state-of-the-art
calibration losses. Specifically, these losses could be viewed as
approximations of a linear penalty (or a Lagrangian) imposing equality
constraints on logit distances. This points to an important limitation of such
underlying equality constraints, whose ensuing gradients constantly push
towards a non-informative solution, which might prevent from reaching the best
compromise between the discriminative performance and calibration of the model
during gradient-based optimization. Following our observations, we propose a
simple and flexible generalization based on inequality constraints, which
imposes a controllable margin on logit distances. Comprehensive experiments on
a variety of image classification, semantic segmentation and NLP benchmarks
demonstrate that our method sets novel state-of-the-art results on these tasks
in terms of network calibration, without affecting the discriminative
performance. The code is available at https://github.com/by-liu/MbLS .

Logistic regression is a widely used statistical model to describe the
relationship between a binary response variable and predictor variables in data
sets. It is often used in machine learning to identify important predictor
variables. This task, variable selection, typically amounts to fitting a
logistic regression model regularized by a convex combination of $\ell_1$ and
$\ell_{2}^{2}$ penalties. Since modern big data sets can contain hundreds of
thousands to billions of predictor variables, variable selection methods depend
on efficient and robust optimization algorithms to perform well.
State-of-the-art algorithms for variable selection, however, were not
traditionally designed to handle big data sets; they either scale poorly in
size or are prone to produce unreliable numerical results. It therefore remains
challenging to perform variable selection on big data sets without access to
adequate and costly computational resources. In this paper, we propose a
nonlinear primal-dual algorithm that addresses these shortcomings.
Specifically, we propose an iterative algorithm that provably computes a
solution to a logistic regression problem regularized by an elastic net penalty
in $O(T(m,n)\log(1/\epsilon))$ operations, where $\epsilon \in (0,1)$ denotes
the tolerance and $T(m,n)$ denotes the number of arithmetic operations required
to perform matrix-vector multiplication on a data set with $m$ samples each
comprising $n$ features. This result improves on the known complexity bound of
$O(\min(m^2n,mn^2)\log(1/\epsilon))$ for first-order optimization methods such
as the classic primal-dual hybrid gradient or forward-backward splitting
methods.

Despite significant advances in graph representation learning, little
attention has been paid to the more practical continual learning scenario in
which new categories of nodes (e.g., new research areas in citation networks,
or new types of products in co-purchasing networks) and their associated edges
are continuously emerging, causing catastrophic forgetting on previous
categories. Existing methods either ignore the rich topological information or
sacrifice plasticity for stability. To this end, we present Hierarchical
Prototype Networks (HPNs) which extract different levels of abstract knowledge
in the form of prototypes to represent the continuously expanded graphs.
Specifically, we first leverage a set of Atomic Feature Extractors (AFEs) to
encode both the elemental attribute information and the topological structure
of the target node. Next, we develop HPNs to adaptively select relevant AFEs
and represent each node with three levels of prototypes. In this way, whenever
a new category of nodes is given, only the relevant AFEs and prototypes at each
level will be activated and refined, while others remain uninterrupted to
maintain the performance over existing nodes. Theoretically, we first
demonstrate that the memory consumption of HPNs is bounded regardless of how
many tasks are encountered. Then, we prove that under mild constraints,
learning new tasks will not alter the prototypes matched to previous data,
thereby eliminating the forgetting problem. The theoretical results are
supported by experiments on five datasets, showing that HPNs not only
outperform state-of-the-art baseline techniques but also consume relatively
less memory.

Regularization can mitigate the generalization gap between training and
inference by introducing inductive bias. Existing works have already proposed
various inductive biases from diverse perspectives. However, none of them
explores inductive bias from the perspective of class-dependent response
distribution of individual neurons. In this paper, we conduct a substantial
analysis of the characteristics of such distribution. Based on the analysis
results, we articulate the Neuron Steadiness Hypothesis: the neuron with
similar responses to instances of the same class leads to better
generalization. Accordingly, we propose a new regularization method called
Neuron Steadiness Regularization (NSR) to reduce neuron intra-class response
variance. Based on the Complexity Measure, we theoretically guarantee the
effectiveness of NSR for improving generalization. We conduct extensive
experiments on Multilayer Perceptron, Convolutional Neural Networks, and Graph
Neural Networks with popular benchmark datasets of diverse domains, which show
that our Neuron Steadiness Regularization consistently outperforms the vanilla
version of models with significant gain and low additional computational
overhead.

Many downstream applications are using dependency trees, and are thus relying
on dependency parsers producing correct, or at least consistent, output.
However, dependency parsers are trained using machine learning, and are
therefore susceptible to unwanted inconsistencies due to biases in the training
data. This paper explores the effects of such biases in four languages -
English, Swedish, Russian, and Ukrainian - though an experiment where we study
the effect of replacing numerals in sentences. We show that such seemingly
insignificant changes in the input can cause large differences in the output,
and suggest that data augmentation can remedy the problems.

Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC)
but is challenging as lesions are often small and poorly defined on
contrast-enhanced computed tomography scans (CE-CT). Deep learning can
facilitate PDAC diagnosis, however current models still fail to identify small
(<2cm) lesions. In this study, state-of-the-art deep learning models were used
to develop an automatic framework for PDAC detection, focusing on small
lesions. Additionally, the impact of integrating surrounding anatomy was
investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients
and a cohort of 123 patients without PDAC were used to train a nnUnet for
automatic lesion detection and segmentation (nnUnet_T). Two additional nnUnets
were trained to investigate the impact of anatomy integration: (1) segmenting
the pancreas and tumor (nnUnet_TP), (2) segmenting the pancreas, tumor, and
multiple surrounding anatomical structures (nnUnet_MS). An external, publicly
available test set was used to compare the performance of the three networks.
The nnUnet_MS achieved the best performance, with an area under the receiver
operating characteristic curve of 0.91 for the whole test set and 0.88 for
tumors <2cm, showing that state-of-the-art deep learning can detect small PDAC
and benefits from anatomy information.

Deep learning on the point cloud is increasingly developing. Grouping the
point with its neighbors and conducting convolution-like operation on them can
learn the local feature of the point cloud, but this method is weak to extract
the long-distance global feature. Performing the attention-based transformer on
the whole point cloud can effectively learn the global feature of it, but this
method is hardly to extract the local detailed feature. In this paper, we
propose a novel module that can simultaneously extract and fuse local and
global features, which is named as CT-block. The CT-block is composed of two
branches, where the letter C represents the convolution-branch and the letter T
represents the transformer-branch. The convolution-branch performs convolution
on the grouped neighbor points to extract the local feature. Meanwhile, the
transformer-branch performs offset-attention process on the whole point cloud
to extract the global feature. Through the bridge constructed by the feature
transmission element in the CT-block, the local and global features guide each
other during learning and are fused effectively. We apply the CT-block to
construct point cloud classification and segmentation networks, and evaluate
the performance of them by several public datasets. The experimental results
show that, because the features learned by CT-block are much expressive, the
performance of the networks constructed by the CT-block on the point cloud
classification and segmentation tasks achieve state of the art.

Motivated by learning from experience and exploiting existing knowledge in
civil nuclear operations, we have developed in-house generic PSA models for
pressurized and boiling water reactors. The models are computationally light,
handy, transparent, user-friendly, and easily adaptable to account for major
plant-specific differences. They cover the common internal initiating events,
frontline and support systems reliability and dependencies, human-factors,
common-cause failures, and account for new factors typically overlooked in many
developed PSAs. For quantification, the models use generic US reliability data,
precursor analysis reports and studies, the ETHZ Curated Nuclear Events
Database, and experts' opinions. Moreover, uncertainties in the most
influential basic events are addressed. The generated results show good
agreement with assessments available in the literature with detailed PSAs. We
envision the models as an unbiased framework to measure nuclear operational
risk with the same "ruler", and hence support inter-plant risk comparisons that
are usually not possible due to differences in plant-specific PSA assumptions
and scopes. The models can be used for initial risk screening,
order-of-magnitude precursor analysis, and other research and pedagogic
applications especially when no plant-specific PSAs are available. Finally, we
are using the generic models for large-scale precursor analysis that will
generate big picture trends, lessons, and insights.

The recent success of the generative model shows that leveraging the
multi-modal embedding space can manipulate an image using text information.
However, manipulating an image with other sources rather than text, such as
sound, is not easy due to the dynamic characteristics of the sources.
Especially, sound can convey vivid emotions and dynamic expressions of the real
world. Here, we propose a framework that directly encodes sound into the
multi-modal (image-text) embedding space and manipulates an image from the
space. Our audio encoder is trained to produce a latent representation from an
audio input, which is forced to be aligned with image and text representations
in the multi-modal embedding space. We use a direct latent optimization method
based on aligned embeddings for sound-guided image manipulation. We also show
that our method can mix text and audio modalities, which enrich the variety of
the image modification. We verify the effectiveness of our sound-guided image
manipulation quantitatively and qualitatively. We also show that our method can
mix different modalities, i.e., text and audio, which enrich the variety of the
image modification. The experiments on zero-shot audio classification and
semantic-level image classification show that our proposed model outperforms
other text and sound-guided state-of-the-art methods.

The growth of deep reinforcement learning (RL) has brought multiple exciting
tools and methods to the field. This rapid expansion makes it important to
understand the interplay between individual elements of the RL toolbox. We
approach this task from an empirical perspective by conducting a study in the
continuous control setting. We present multiple insights of fundamental nature,
including: an average of multiple actors trained from the same data boosts
performance; the existing methods are unstable across training runs, epochs of
training, and evaluation runs; a commonly used additive action noise is not
required for effective training; a strategy based on posterior sampling
explores better than the approximated UCB combined with the weighted Bellman
backup; the weighted Bellman backup alone cannot replace the clipped double
Q-Learning; the critics' initialization plays the major role in ensemble-based
actor-critic exploration. As a conclusion, we show how existing tools can be
brought together in a novel way, giving rise to the Ensemble Deep Deterministic
Policy Gradients (ED2) method, to yield state-of-the-art results on continuous
control tasks from OpenAI Gym MuJoCo. From the practical side, ED2 is
conceptually straightforward, easy to code, and does not require knowledge
outside of the existing RL toolbox.

This paper presents the novel way combining the BERT embedding method and the
graph convolutional neural network. This combination is employed to solve the
text classification problem. Initially, we apply the BERT embedding method to
the texts (in the BBC news dataset and the IMDB movie reviews dataset) in order
to transform all the texts to numerical vector. Then, the graph convolutional
neural network will be applied to these numerical vectors to classify these
texts into their ap-propriate classes/labels. Experiments show that the
performance of the graph convolutional neural network model is better than the
perfor-mances of the combination of the BERT embedding method with clas-sical

CLIP (Contrastive Language-Image Pretraining) is an efficient method for
learning computer vision tasks from natural language supervision that has
powered a recent breakthrough in deep learning due to its zero-shot transfer
capabilities. By training from image-text pairs available on the internet, the
CLIP model transfers non-trivially to most tasks without the need for any data
set specific training. In this work, we use CLIP to implement the engine of the
popular game "Guess who?", so that the player interacts with the game using
natural language prompts and CLIP automatically decides whether an image in the
game board fulfills that prompt or not. We study the performance of this
approach by benchmarking on different ways of prompting the questions to CLIP,
and show the limitations of its zero-shot capabilites.

Multi-view projection methods have demonstrated promising performance on 3D
understanding tasks like 3D classification and segmentation. However, it
remains unclear how to combine such multi-view methods with the widely
available 3D point clouds. Previous methods use unlearned heuristics to combine
features at the point level. To this end, we introduce the concept of the
multi-view point cloud (Voint cloud), representing each 3D point as a set of
features extracted from several view-points. This novel 3D Voint cloud
representation combines the compactness of 3D point cloud representation with
the natural view-awareness of multi-view representation. Naturally, we can
equip this new representation with convolutional and pooling operations. We
deploy a Voint neural network (VointNet) with a theoretically established
functional form to learn representations in the Voint space. Our novel
representation achieves state-of-the-art performance on 3D classification and
retrieval on ScanObjectNN, ModelNet40, and ShapeNet Core55. Additionally, we
achieve competitive performance for 3D semantic segmentation on ShapeNet Parts.
Further analysis shows that VointNet improves the robustness to rotation and
occlusion compared to other methods.

Cross-Database Micro-Expression Recognition (CDMER) aims to develop the
Micro-Expression Recognition (MER) methods with strong domain adaptability,
i.e., the ability to recognize the Micro-Expressions (MEs) of different
subjects captured by different imaging devices in different scenes. The
development of CDMER is faced with two key problems: 1) the severe feature
distribution gap between the source and target databases; 2) the feature
representation bottleneck of ME such local and subtle facial expressions. To
solve these problems, this paper proposes a novel Transfer Group Sparse
Regression method, namely TGSR, which aims to 1) optimize the measurement and
better alleviate the difference between the source and target databases, and 2)
highlight the valid facial regions to enhance extracted features, by the
operation of selecting the group features from the raw face feature, where each
region is associated with a group of raw face feature, i.e., the salient facial
region selection. Compared with previous transfer group sparse methods, our
proposed TGSR has the ability to select the salient facial regions, which is
effective in alleviating the aforementioned problems for better performance and
reducing the computational cost at the same time. We use two public ME
databases, i.e., CASME II and SMIC, to evaluate our proposed TGSR method.
Experimental results show that our proposed TGSR learns the discriminative and
explicable regions, and outperforms most state-of-the-art
subspace-learning-based domain-adaptive methods for CDMER.

Recognition techniques allow robots to make proper planning and control
strategies to manipulate various objects. Object recognition is more reliable
when made by combining several percepts, e.g., vision and haptics. One of the
distinguishing features of each object's material is its heat properties, and
classification can exploit heat transfer, similarly to human thermal sensation.
Thermal-based recognition has the advantage of obtaining contact surface
information in realtime by simply capturing temperature change using a tiny and
cheap sensor. However, heat transfer between a robot surface and a contact
object is strongly affected by the initial temperature and environmental
conditions. A given object's material cannot be recognized when its temperature
is the same as the robotic grippertip. We present a material classification
system using active temperature controllable robotic gripper to induce heat
flow. Subsequently, our system can recognize materials independently from their
ambient temperature. The robotic gripper surface can be regulated to any
temperature that differentiates it from the touched object's surface. We
conducted some experiments by integrating the temperature control system with
the Academic SCARA Robot, classifying them based on a long short-term memory
(LSTM) using temperature data obtained from grasping target objects.

The challenges presented in an autonomous racing situation are distinct from
those faced in regular autonomous driving and require faster end-to-end
algorithms and consideration of a longer horizon in determining optimal current
actions keeping in mind upcoming maneuvers and situations. In this paper, we
propose an end-to-end method for autonomous racing that takes in as inputs
video information from an onboard camera and determines final steering and
throttle control actions. We use the following split to construct such a method
(1) learning a low dimensional representation of the scene, (2) pre-generating
the optimal trajectory for the given scene, and (3) tracking the predicted
trajectory using a classical control method. In learning a low-dimensional
representation of the scene, we use intermediate representations with a novel
unsupervised trajectory planner to generate expert trajectories, and hence
utilize them to directly predict race lines from a given front-facing input
image. Thus, the proposed algorithm employs the best of two worlds - the
robustness of learning-based approaches to perception and the accuracy of
optimization-based approaches for trajectory generation in an end-to-end
learning-based framework. We deploy and demonstrate our framework on CARLA, a
photorealistic simulator for testing self-driving cars in realistic

Review articles are a means to structure state-of-the-art literature and to
organize the growing number of scholarly publications. However, review articles
are suffering from numerous limitations, weakening the impact the articles
could potentially have. A key limitation is the inability of machines to access
and process knowledge presented within review articles. In this work, we
present SmartReviews, a review authoring and publishing tool, specifically
addressing the limitations of review articles. The tool enables community-based
authoring of living articles, leveraging a scholarly knowledge graph to provide
machine-actionable knowledge. We evaluate the approach and tool by means of a
SmartReview use case. The results indicate that the evaluated article is
successfully addressing the weaknesses of the current review practices.

In this paper, we are concerned with rotation equivariance on 2D point cloud
data. We describe a particular set of functions able to approximate any
continuous rotation equivariant and permutation invariant function. Based on
this result, we propose a novel neural network architecture for processing 2D
point clouds and we prove its universality for approximating functions
exhibiting these symmetries.
  We also show how to extend the architecture to accept a set of 2D-2D
correspondences as indata, while maintaining similar equivariance properties.
Experiments are presented on the estimation of essential matrices in stereo
vision.

Self-supervised pretraining is the method of choice for natural language
processing models and is rapidly gaining popularity in many vision tasks.
Recently, self-supervised pretraining has shown to outperform supervised
pretraining for many downstream vision applications, marking a milestone in the
area. This superiority is attributed to the negative impact of incomplete
labelling of the training images, which convey multiple concepts, but are
annotated using a single dominant class label. Although Self-Supervised
Learning (SSL), in principle, is free of this limitation, the choice of pretext
task facilitating SSL is perpetuating this shortcoming by driving the learning
process towards a single concept output. This study aims to investigate the
possibility of modelling all the concepts present in an image without using
labels. In this aspect the proposed SSL frame-work MC-SSL0.0 is a step towards
Multi-Concept Self-Supervised Learning (MC-SSL) that goes beyond modelling
single dominant label in an image to effectively utilise the information from
all the concepts present in it. MC-SSL0.0 consists of two core design concepts,
group masked model learning and learning of pseudo-concept for data token using
a momentum encoder (teacher-student) framework. The experimental results on
multi-label and multi-class image classification downstream tasks demonstrate
that MC-SSL0.0 not only surpasses existing SSL methods but also outperforms
supervised transfer learning. The source code will be made publicly available
for community to train on bigger corpus.

We introduce a new real-valued invariant called the natural slope of a
hyperbolic knot in the 3-sphere, which is defined in terms of its cusp
geometry. We show that twice the knot signature and the natural slope differ by
at most a constant times the hyperbolic volume divided by the cube of the
injectivity radius. This inequality was discovered using machine learning to
detect relationships between various knot invariants. It has applications to
Dehn surgery and to 4-ball genus. We also show a refined version of the
inequality where the upper bound is a linear function of the volume, and the
slope is corrected by terms corresponding to short geodesics that link the knot
an odd number of times.

To further develop the statistical inference problem for heterogeneous
treatment effects, this paper builds on Breiman's (2001) random forest tree
(RFT)and Wager et al.'s (2018) causal tree to parameterize the nonparametric
problem using the excellent statistical properties of classical OLS and the
division of local linear intervals based on covariate quantile points, while
preserving the random forest trees with the advantages of constructible
confidence intervals and asymptotic normality properties [Athey and Imbens
(2016),Efron (2014),Wager et al.(2014)\citep{wager2014asymptotic}], we propose
a decision tree using quantile classification according to fixed rules combined
with polynomial estimation of local samples, which we call the quantile local
linear causal tree (QLPRT) and forest (QLPRF).

Differentiable physics is a powerful tool in computer vision and robotics for
scene understanding and reasoning about interactions. Existing approaches have
frequently been limited to objects with simple shape or shapes that are known
in advance. In this paper, we propose a novel approach to differentiable
physics with frictional contacts which represents object shapes implicitly
using signed distance fields (SDFs). Our simulation supports contact point
calculation even when the involved shapes are nonconvex. Moreover, we propose
ways for differentiating the dynamics for the object shape to facilitate shape
optimization using gradient-based methods. In our experiments, we demonstrate
that our approach allows for model-based inference of physical parameters such
as friction coefficients, mass, forces or shape parameters from trajectory and
depth image observations in several challenging synthetic scenarios and a real
image sequence.

Modern deep learning (DL) architectures are trained using variants of the SGD
algorithm that is run with a $\textit{manually}$ defined learning rate
schedule, i.e., the learning rate is dropped at the pre-defined epochs,
typically when the training loss is expected to saturate. In this paper we
develop an algorithm that realizes the learning rate drop
$\textit{automatically}$. The proposed method, that we refer to as AutoDrop, is
motivated by the observation that the angular velocity of the model parameters,
i.e., the velocity of the changes of the convergence direction, for a fixed
learning rate initially increases rapidly and then progresses towards soft
saturation. At saturation the optimizer slows down thus the angular velocity
saturation is a good indicator for dropping the learning rate. After the drop,
the angular velocity "resets" and follows the previously described pattern - it
increases again until saturation. We show that our method improves over SOTA
training approaches: it accelerates the training of DL models and leads to a
better generalization. We also show that our method does not require any extra
hyperparameter tuning. AutoDrop is furthermore extremely simple to implement
and computationally cheap. Finally, we develop a theoretical framework for
analyzing our algorithm and provide convergence guarantees.

Artificial neural network (ANN) is a versatile tool to study the neural
representation in the ventral visual stream, and the knowledge in neuroscience
in return inspires ANN models to improve performance in the task. However, it
is still unclear how to merge these two directions into a unified framework. In
this study, we propose an integrated framework called Deep Autoencoder with
Neural Response (DAE-NR), which incorporates information from ANN and the
visual cortex to achieve better image reconstruction performance and higher
neural representation similarity between biological and artificial neurons. The
same visual stimuli (i.e., natural images) are input to both the mice brain and
DAE-NR. The encoder of DAE-NR jointly learns the dependencies from neural spike
encoding and image reconstruction. For the neural spike encoding task, the
features derived from a specific hidden layer of the encoder are transformed by
a mapping function to predict the ground-truth neural response under the
constraint of image reconstruction. Simultaneously, for the image
reconstruction task, the latent representation obtained by the encoder is
assigned to a decoder to restore the original image under the guidance of
neural information. In DAE-NR, the learning process of encoder, mapping
function and decoder are all implicitly constrained by these two tasks. Our
experiments demonstrate that if and only if with the joint learning, DAE-NRs
can improve the performance of visual image reconstruction and increase the
representation similarity between biological neurons and artificial neurons.
The DAE-NR offers a new perspective on the integration of computer vision and

In this work we explore multi-arm bandit streaming model, especially in cases
where the model faces resource bottleneck. We build over existing algorithms
conditioned by limited arm memory at any instance of time. Specifically, we
improve the amount of streaming passes it takes for a bandit algorithm to incur
a $O(\sqrt{T\log(T)})$ regret by a logarithmic factor, and also provide 2-pass
algorithms with some initial conditions to incur a similar order of regret.

Due to the difficulty of obtaining ground-truth labels, learning from
virtual-world datasets is of great interest for real-world applications like
semantic segmentation. From domain adaptation perspective, the key challenge is
to learn domain-agnostic representation of the inputs in order to benefit from
virtual data. In this paper, we propose a novel trident-like architecture that
enforces a shared feature encoder to satisfy confrontational source and target
constraints simultaneously, thus learning a domain-invariant feature space.
Moreover, we also introduce a novel training pipeline enabling self-induced
cross-domain data augmentation during the forward pass. This contributes to a
further reduction of the domain gap. Combined with a self-training process, we
obtain state-of-the-art results on benchmark datasets (e.g. GTA5 or Synthia to
Cityscapes adaptation). Code and pre-trained models are available at
https://github.com/HMRC-AEL/TridentAdapt

Through recent advancements in speech technologies and introduction of smart
assistants, such as Amazon Alexa, Apple Siri and Google Home, increasing number
of users are interacting with various applications through voice commands.
E-commerce companies typically display short product titles on their webpages,
either human-curated or algorithmically generated, when brevity is required.
However, these titles are dissimilar from natural spoken language. For example,
"Lucky Charms Gluten Free Break-fast Cereal, 20.5 oz a box Lucky Charms Gluten
Free" is acceptable to display on a webpage, while a similar title cannot be
used in a voice based text-to-speech application. In such conversational
systems, an easy to comprehend sentence, such as "a 20.5 ounce box of lucky
charms gluten free cereal" is preferred. Compared to display devices, where
images and detailed product information can be presented to users, short titles
for products which convey the most important information, are necessary when
interfacing with voice assistants. We propose eBERT, a sequence-to-sequence
approach by further pre-training the BERT embeddings on an e-commerce product
description corpus, and then fine-tuning the resulting model to generate short,
natural, spoken language titles from input web titles. Our extensive
experiments on a real-world industry dataset, as well as human evaluation of
model output, demonstrate that eBERT summarization outperforms comparable
baseline models. Owing to the efficacy of the model, a version of this model
has been deployed in real-world setting.

Sampling random variables following a Boltzmann distribution is an NP-hard
problem involved in various applications such as training of \textit{Boltzmann
machines}, a specific kind of neural network. Several attempts have been made
to use a D-Wave quantum computer to sample such a distribution, as this could
lead to significant speedup in these applications. Yet, at present, several
challenges remain to efficiently perform such sampling. We detail the various
obstacles and explain the remaining difficulties in solving the sampling
problem on a D-wave machine.

Efficient exploration of the chemical space to search the candidate drugs
that satisfy various constraints is a fundamental task of drug discovery.
Advanced deep generative methods attempt to optimize the molecules in the
compact latent space instead of the discrete original space, but the mapping
between the original and latent spaces is always kept unchanged during the
entire optimization process. The unchanged mapping makes those methods
challenging to fast adapt to various optimization scenes and leads to the great
demand for assessed molecules (samples) to provide optimization direction,
which is a considerable expense for drug discovery. To this end, we design a
sample-efficient molecular generative method, HelixMO, which explores the
scene-sensitive latent space to promote sample efficiency. The scene-sensitive
latent space focuses more on modeling the promising molecules by dynamically
adjusting the space mapping by leveraging the correlations between the general
and scene-specific characteristics during the optimization process. Extensive
experiments demonstrate that HelixMO can achieve competitive performance with
only a few assessed samples on four molecular optimization scenes. Ablation
studies verify the positive impact of the scene-specific latent space, which is
capable of identifying the critical characteristics of the promising molecules.
We also deployed HelixMO on the website PaddleHelix
(https://paddlehelix.baidu.com/app/drug/drugdesign/forecast) to provide drug
design service.

Probabilistic topic models like Latent Dirichlet Allocation (LDA) have been
previously extended to the bilingual setting. A fundamental modeling assumption
in several of these extensions is that the input corpora are in the form of
document pairs whose constituent documents share a single topic distribution.
However, this assumption is strong for comparable corpora that consist of
documents thematically similar to an extent only, which are, in turn, the most
commonly available or easy to obtain. In this paper we relax this assumption by
proposing for the paired documents to have separate, yet bound topic
distributions. % a binding mechanism between the distributions of the paired
documents. We suggest that the strength of the bound should depend on each
pair's semantic similarity. To estimate the similarity of documents that are
written in different languages we use cross-lingual word embeddings that are
learned with shallow neural networks. We evaluate the proposed binding
mechanism by extending two topic models: a bilingual adaptation of LDA that
assumes bag-of-words inputs and a model that incorporates part of the text
structure in the form of boundaries of semantically coherent segments. To
assess the performance of the novel topic models we conduct intrinsic and
extrinsic experiments on five bilingual, comparable corpora of English
documents with French, German, Italian, Spanish and Portuguese documents. The
results demonstrate the efficiency of our approach in terms of both topic
coherence measured by the normalized point-wise mutual information, and
generalization performance measured by perplexity and in terms of Mean
Reciprocal Rank in a cross-lingual document retrieval task for each of the

Human affect recognition is a well-established research area with numerous
applications, e.g., in psychological care, but existing methods assume that all
emotions-of-interest are given a priori as annotated training examples.
However, the rising granularity and refinements of the human emotional spectrum
through novel psychological theories and the increased consideration of
emotions in context brings considerable pressure to data collection and
labeling work. In this paper, we conceptualize one-shot recognition of emotions
in context -- a new problem aimed at recognizing human affect states in finer
particle level from a single support sample. To address this challenging task,
we follow the deep metric learning paradigm and introduce a multi-modal emotion
embedding approach which minimizes the distance of the same-emotion embeddings
by leveraging complementary information of human appearance and the semantic
scene context obtained through a semantic segmentation network. All streams of
our context-aware model are optimized jointly using weighted triplet loss and
weighted cross entropy loss. We conduct thorough experiments on both,
categorical and numerical emotion recognition tasks of the Emotic dataset
adapted to our one-shot recognition problem, revealing that categorizing human
affect from a single example is a hard task. Still, all variants of our model
clearly outperform the random baseline, while leveraging the semantic scene
context consistently improves the learnt representations, setting
state-of-the-art results in one-shot emotion recognition. To foster research of
more universal representations of human affect states, we will make our
benchmark and models publicly available to the community under
https://github.com/KPeng9510/Affect-DML.

Video-based automatic depression analysis provides a fast, objective and
repeatable self-assessment solution, which has been widely developed in recent
years. While depression clues may be reflected by human facial behaviours of
various temporal scales, most existing approaches either focused on modelling
depression from short-term or video-level facial behaviours. In this sense, we
propose a two-stage framework that models depression severity from multi-scale
short-term and video-level facial behaviours. The short-term depressive
behaviour modelling stage first deep learns depression-related facial
behavioural features from multiple short temporal scales, where a Depression
Feature Enhancement (DFE) module is proposed to enhance the depression-related
clues for all temporal scales and remove non-depression noises. Then, the
video-level depressive behaviour modelling stage proposes two novel graph
encoding strategies, i.e., Sequential Graph Representation (SEG) and Spectral
Graph Representation (SPG), to re-encode all short-term features of the target
video into a video-level graph representation, summarizing depression-related
multi-scale video-level temporal information. As a result, the produced graph
representations predict depression severity using both short-term and long-term
facial beahviour patterns. The experimental results on AVEC 2013 and AVEC 2014
datasets show that the proposed DFE module constantly enhanced the depression
severity estimation performance for various CNN models while the SPG is
superior than other video-level modelling methods. More importantly, the result
achieved for the proposed two-stage framework shows its promising and solid
performance compared to widely-used one-stage modelling approaches.

Advances in computer vision are pushing the limits of im-age manipulation,
with generative models sampling detailed images on various tasks. However, a
specialized model is often developed and trained for each specific task, even
though many image edition tasks share similarities. In denoising, inpainting,
or image compositing, one always aims at generating a realistic image from a
low-quality one. In this paper, we aim at making a step towards a unified
approach for image editing. To do so, we propose EdiBERT, a bi-directional
transformer trained in the discrete latent space built by a vector-quantized
auto-encoder. We argue that such a bidirectional model is suited for image
manipulation since any patch can be re-sampled conditionally to the whole
image. Using this unique and straightforward training objective, we show that
the resulting model matches state-of-the-art performances on a wide variety of
tasks: image denoising, image completion, and image composition.

We present DeepAL, a Python library that implements several common strategies
for active learning, with a particular emphasis on deep active learning. DeepAL
provides a simple and unified framework based on PyTorch that allows users to
easily load custom datasets, build custom data handlers, and design custom
strategies without much modification of codes. DeepAL is open-source on Github
and welcome any contribution.

We argue the Fisher information matrix (FIM) of one hidden layer networks
with the ReLU activation function. Let $W$ denote the $d \times p$ weight
matrix from the $d$-dimensional input to the hidden layer consisting of $p$
neurons, and $v$ the $p$-dimensional weight vector from the hidden layer to the
scalar output. We focus on the FIM of $v$, which we denote as $I$. When $p$ is
large, under certain conditions, the following approximately holds. 1) There
are three major clusters in the eigenvalue distribution. 2) Since $I$ is
non-negative owing to the ReLU, the first eigenvalue is the Perron-Frobenius
eigenvalue. 3) For the cluster of the next maximum values, the eigenspace is
spanned by the row vectors of $W$. 4) The direct sum of the eigenspace of the
first eigenvalue and that of the third cluster is spanned by the set of all the
vectors obtained as the Hadamard product of any pair of the row vectors of $W$.
We confirmed by numerical calculation that the above is approximately correct
when the number of hidden nodes is about 10000.

Transferring knowledge learned from the labeled source domain to the raw
target domain for unsupervised domain adaptation (UDA) is essential to the
scalable deployment of an autonomous driving system. State-of-the-art methods
in UDA often employ a key idea: utilizing joint supervision signals from both
of the source domain (with ground-truth) and target domain (with pseudo-labels)
for self-training. In this work, we improve and extend on this aspect. We
present ConDA, a concatenation-based domain adaptation framework for LiDAR
segmentation that: 1) constructs an intermediate domain consisting of
fine-grained interchange signals from both source and target domains without
destabilizing the semantic coherency of objects and background around the
ego-vehicle; and 2) utilizes the intermediate domain for self-training. To
improve both the network training on the source domain and self-training on the
intermediate domain, we propose an anti-aliasing regularizer and an entropy
aggregator to reduce the negative effect caused by the aliasing artifacts and
noisy pseudo labels. Through extensive experiments, we demonstrate that ConDA
significantly outperforms prior arts in mitigating the domain gap not only in
UDA, but also in other DAs with minimum supervisions, such as
semi-/weakly-supervised DAs.

Owing to the growth of interest in Reinforcement Learning in the last few
years, gradient based policy control methods have been gaining popularity for
Control problems as well. And rightly so, since gradient policy methods have
the advantage of optimizing a metric of interest in an end-to-end manner, along
with being relatively easy to implement without complete knowledge of the
underlying system. In this paper, we study the global convergence of
gradient-based policy optimization methods for quadratic control of
discrete-time and model-free Markovian jump linear systems (MJLS). We surmount
myriad challenges that arise because of more than one states coupled with lack
of knowledge of the system dynamics and show global convergence of the policy
using gradient descent and natural policy gradient methods. We also provide
simulation studies to corroborate our claims.

In this work we compare the performance of several machine learning
algorithms applied to the problem of modelling air transport demand.
Forecasting in the air transport industry is an essential part of planning and
managing because of the economic and financial aspects of the industry. The
traditional approach used in airline operations as specified by the
International Civil Aviation Organization is the use of a multiple linear
regression (MLR) model, utilizing cost variables and economic factors. Here,
the performance of models utilizing an artificial neural network (ANN), an
adaptive neuro-fuzzy inference system (ANFIS), a genetic algorithm, a support
vector machine, and a regression tree are compared to MLR. The ANN and ANFIS
had the best performance in terms of the lowest mean squared error.

Point cloud instance segmentation has achieved huge progress with the
emergence of deep learning. However, these methods are usually data-hungry with
expensive and time-consuming dense point cloud annotations. To alleviate the
annotation cost, unlabeled or weakly labeled data is still less explored in the
task. In this paper, we introduce the first semi-supervised point cloud
instance segmentation framework (SPIB) using both labeled and unlabelled
bounding boxes as supervision. To be specific, our SPIB architecture involves a
two-stage learning procedure. For stage one, a bounding box proposal generation
network is trained under a semi-supervised setting with perturbation
consistency regularization (SPCR). The regularization works by enforcing an
invariance of the bounding box predictions over different perturbations applied
to the input point clouds, to provide self-supervision for network learning.
For stage two, the bounding box proposals with SPCR are grouped into some
subsets, and the instance masks are mined inside each subset with a novel
semantic propagation module and a property consistency graph module. Moreover,
we introduce a novel occupancy ratio guided refinement module to refine the
instance masks. Extensive experiments on the challenging ScanNet v2 dataset
demonstrate our method can achieve competitive performance compared with the
recent fully-supervised methods.

There has been recently a growing interest for implicit shape
representations. Contrary to explicit representations, they have no resolution
limitations and they easily deal with a wide variety of surface topologies. To
learn these implicit representations, current approaches rely on a certain
level of shape supervision (e.g., inside/outside information or
distance-to-shape knowledge), or at least require a dense point cloud (to
approximate well enough the distance-to-shape). In contrast, we introduce
NeeDrop, a self-supervised method for learning shape representations from
possibly extremely sparse point clouds. Like in Buffon's needle problem, we
"drop" (sample) needles on the point cloud and consider that, statistically,
close to the surface, the needle end points lie on opposite sides of the
surface. No shape knowledge is required and the point cloud can be highly
sparse, e.g., as lidar point clouds acquired by vehicles. Previous
self-supervised shape representation approaches fail to produce good-quality
results on this kind of data. We obtain quantitative results on par with
existing supervised approaches on shape reconstruction datasets and show
promising qualitative results on hard autonomous driving datasets such as
KITTI.

Nowadays, malware and malware incidents are increasing daily, even with
various antivirus systems and malware detection or classification
methodologies. Machine learning techniques have been the main focus of the
security experts to detect malware and determine their families. Many static,
dynamic, and hybrid techniques have been presented for that purpose. In this
study, the static analysis technique has been applied to malware samples to
extract API calls, which is one of the most used features in machine/deep
learning models as it represents the behavior of malware samples.
  Since the rapid increase and continuous evolution of malware affect the
detection capacity of antivirus scanners, recent and updated datasets of
malicious software became necessary to overcome this drawback. This paper
introduces two new datasets: One with 14,616 samples obtained and compiled from
VirusShare and one with 9,795 samples from VirusSample. In addition, benchmark
results based on static API calls of malware samples are presented using
several machine and deep learning models on these datasets. We believe that
these two datasets and benchmark results enable researchers to test and
validate their methods and approaches in this field.

Magnetic Resonance Imaging (MRI) is an important medical imaging modality,
while it requires a long acquisition time. To reduce the acquisition time,
various methods have been proposed. However, these methods failed to
reconstruct images with a clear structure for two main reasons. Firstly,
similar patches widely exist in MR images, while most previous deep
learning-based methods ignore this property and only adopt CNN to learn local
information. Secondly, the existing methods only use clear images to constrain
the upper bound of the solution space, while the lower bound is not
constrained, so that a better parameter of the network cannot be obtained. To
address these problems, we propose a Contrastive Learning for Local and Global
Learning MRI Reconstruction Network (CLGNet). Specifically, according to the
Fourier theory, each value in the Fourier domain is calculated from all the
values in Spatial domain. Therefore, we propose a Spatial and Fourier Layer
(SFL) to simultaneously learn the local and global information in Spatial and
Fourier domains. Moreover, compared with self-attention and transformer, the
SFL has a stronger learning ability and can achieve better performance in less
time. Based on the SFL, we design a Spatial and Fourier Residual block as the
main component of our model. Meanwhile, to constrain the lower bound and upper
bound of the solution space, we introduce contrastive learning, which can pull
the result closer to the clear image and push the result further away from the
undersampled image. Extensive experimental results on different datasets and
acceleration rates demonstrate that the proposed CLGNet achieves new

We developed Convolutional Neural Networks (CNNs) to rapidly and directly
infer the planet mass from radio dust continuum images. Substructures induced
by young planets in protoplanetary disks can be used to infer the potential
young planets' properties. Hydrodynamical simulations have been used to study
the relationships between the planet's properties and these disk features.
However, these attempts either fine-tuned numerical simulations to fit one
protoplanetary disk at a time, which was time-consuming, or azimuthally
averaged simulation results to derive some linear relationships between the gap
width/depth and the planet mass, which lost information on asymmetric features
in disks. To cope with these disadvantages, we developed Planet Gap neural
Networks (PGNets) to infer the planet mass from 2D images. We first fit the
gridded data in Zhang et al. (2018) as a classification problem. Then, we
quadrupled the data set by running additional simulations with near-randomly
sampled parameters, and derived the planet mass and disk viscosity together as
a regression problem. The classification approach can reach an accuracy of
92\%, whereas the regression approach can reach 1$\sigma$ as 0.16 dex for
planet mass and 0.23 dex for disk viscosity. We can reproduce the degeneracy
scaling $\alpha$ $\propto$ $M_p^3$ found in the linear fitting method, which
means that the CNN method can even be used to find degeneracy relationship. The
gradient-weighted class activation mapping effectively confirms that PGNets use
proper disk features to constrain the planet mass. We provide programs for
PGNets and the traditional fitting method from Zhang et al. (2018), and discuss
each method's advantages and disadvantages.

Recent Vision Transformer~(ViT) models have demonstrated encouraging results
across various computer vision tasks, thanks to their competence in modeling
long-range dependencies of image patches or tokens via self-attention. These
models, however, usually designate the similar receptive fields of each token
feature within each layer. Such a constraint inevitably limits the ability of
each self-attention layer in capturing multi-scale features, thereby leading to
performance degradation in handling images with multiple objects of different
scales. To address this issue, we propose a novel and generic strategy, termed
shunted self-attention~(SSA), that allows ViTs to model the attentions at
hybrid scales per attention layer. The key idea of SSA is to inject
heterogeneous receptive field sizes into tokens: before computing the
self-attention matrix, it selectively merges tokens to represent larger object
features while keeping certain tokens to preserve fine-grained features. This
novel merging scheme enables the self-attention to learn relationships between
objects with different sizes and simultaneously reduces the token numbers and
the computational cost. Extensive experiments across various tasks demonstrate
the superiority of SSA. Specifically, the SSA-based transformer achieves 84.0\%
Top-1 accuracy and outperforms the state-of-the-art Focal Transformer on
ImageNet with only half of the model size and computation cost, and surpasses
Focal Transformer by 1.3 mAP on COCO and 2.9 mIOU on ADE20K under similar
parameter and computation cost. Code has been released at
https://github.com/OliverRensu/Shunted-Transformer.

Stereo matching is an important task in computer vision which has drawn
tremendous research attention for decades. While in terms of disparity
accuracy, density and data size, public stereo datasets are difficult to meet
the requirements of models. In this paper, we aim to address the issue between
datasets and models and propose a large scale stereo dataset with high accuracy
disparity ground truth named PlantStereo. We used a semi-automatic way to
construct the dataset: after camera calibration and image registration, high
accuracy disparity images can be obtained from the depth images. In total,
PlantStereo contains 812 image pairs covering a diverse set of plants: spinach,
tomato, pepper and pumpkin. We firstly evaluated our PlantStereo dataset on
four different stereo matching methods. Extensive experiments on different
models and plants show that compared with ground truth in integer accuracy,
high accuracy disparity images provided by PlantStereo can remarkably improve
the training effect of deep learning models. This paper provided a feasible and
reliable method to realize plant surface dense reconstruction. The PlantStereo
dataset and relative code are available at:
https://www.github.com/wangqingyu985/PlantStereo

Laser-induced breakdown spectroscopy is a preferred technique for fast and
direct multi-elemental mapping of samples under ambient pressure, without any
limitation on the targeted element. However, LIBS mapping data have two
peculiarities: an intrinsically low signal-to-noise ratio due to single-shot
measurements, and a high dimensionality due to the high number of spectra
acquired for imaging. This is all the truer as lateral resolution gets higher:
in this case, the ablation spot diameter is reduced, as well as the ablated
mass and the emission signal, while the number of spectra for a given surface
increases. Therefore, efficient extraction of physico-chemical information from
a noisy and large dataset is a major issue. Multivariate approaches were
introduced by several authors as a means to cope with such data, particularly
Principal Component Analysis. This technique is useful to analyse correlations
between different elements, but it is limited to low signal-to-noise ratios. In
this paper, we introduce HyperPCA, a new analysis tool for hyperspectral images
based on a sparse representation of the data using Discrete Wavelet Transform
and kernel-based sparse PCA to reduce the impact of noise on the data and to
consistently extract the spectroscopic signal, with a particular emphasis on
LIBS data. The method is first illustrated using simulated LIBS mapping
datasets to emphasise its performances with an extremely low shot-to-shot
signal-to-noise ratio, and with a variable degree of spectral interference.
Comparisons to standard PCA and to traditional univariate data analyses are
provided. Finally, it is used to process real data in two cases that clearly
illustrate the potential of the proposed algorithm. We show that the method
presents advantages both in quantity and quality of the information recovered,
thus improving the physico-chemical characterization of analysed surfaces.

Obtaining annotations for large training sets is expensive, especially in
settings where domain knowledge is required, such as behavior analysis. Weak
supervision has been studied to reduce annotation costs by using weak labels
from task-specific labeling functions (LFs) to augment ground truth labels.
However, domain experts still need to hand-craft different LFs for different
tasks, limiting scalability. To reduce expert effort, we present AutoSWAP: a
framework for automatically synthesizing data-efficient task-level LFs. The key
to our approach is to efficiently represent expert knowledge in a reusable
domain-specific language and more general domain-level LFs, with which we use
state-of-the-art program synthesis techniques and a small labeled dataset to
generate task-level LFs. Additionally, we propose a novel structural diversity
cost that allows for efficient synthesis of diverse sets of LFs, further
improving AutoSWAP's performance. We evaluate AutoSWAP in three behavior
analysis domains and demonstrate that AutoSWAP outperforms existing approaches
using only a fraction of the data. Our results suggest that AutoSWAP is an
effective way to automatically generate LFs that can significantly reduce
expert effort for behavior analysis.

Biological data and knowledge bases increasingly rely on Semantic Web
technologies and the use of knowledge graphs for data integration, retrieval
and federated queries. We propose a solution for automatically semantifying
biological assays. Our solution contrasts the problem of automated
semantification as labeling versus clustering where the two methods are on
opposite ends of the method complexity spectrum. Characteristically modeling
our problem, we find the clustering solution significantly outperforms a deep
neural network state-of-the-art labeling approach. This novel contribution is
based on two factors: 1) a learning objective closely modeled after the data
outperforms an alternative approach with sophisticated semantic modeling; 2)
automatically semantifying biological assays achieves a high performance F1 of
nearly 83%, which to our knowledge is the first reported standardized
evaluation of the task offering a strong benchmark model.

Fully supervised semantic segmentation technologies bring a paradigm shift in
scene understanding. However, the burden of expensive labeling cost remains as
a challenge. To solve the cost problem, recent studies proposed language model
based zero-shot semantic segmentation (L-ZSSS) approaches. In this paper, we
address L-ZSSS has a limitation in generalization which is a virtue of
zero-shot learning. Tackling the limitation, we propose a language-model-free
zero-shot semantic segmentation framework, Spatial and Multi-scale aware Visual
Class Embedding Network (SM-VCENet). Furthermore, leveraging vision-oriented
class embedding SM-VCENet enriches visual information of the class embedding by
multi-scale attention and spatial attention. We also propose a novel benchmark
(PASCAL2COCO) for zero-shot semantic segmentation, which provides
generalization evaluation by domain adaptation and contains visually
challenging samples. In experiments, our SM-VCENet outperforms zero-shot
semantic segmentation state-of-the-art by a relative margin in PASCAL-5i
benchmark and shows generalization-robustness in PASCAL2COCO benchmark.

Compression has emerged as one of the essential deep learning research
topics, especially for the edge devices that have limited computation power and
storage capacity. Among the main compression techniques, low-rank compression
via matrix factorization has been known to have two problems. First, an
extensive tuning is required. Second, the resulting compression performance is
typically not impressive. In this work, we propose a low-rank compression
method that utilizes a modified beam-search for an automatic rank selection and
a modified stable rank for a compression-friendly training. The resulting BSR
(Beam-search and Stable Rank) algorithm requires only a single hyperparameter
to be tuned for the desired compression ratio. The performance of BSR in terms
of accuracy and compression ratio trade-off curve turns out to be superior to
the previously known low-rank compression methods. Furthermore, BSR can perform
on par with or better than the state-of-the-art structured pruning methods. As
with pruning, BSR can be easily combined with quantization for an additional

Computer-generated holography (CGH) has broad applications such as
direct-view display, virtual and augmented reality, as well as optical
microscopy. CGH usually utilizes a spatial light modulator that displays a
computer-generated phase mask, modulating the phase of coherent light in order
to generate customized patterns. The algorithm that computes the phase mask is
the core of CGH and is usually tailored to meet different applications. CGH for
optical microscopy usually requires 3D accessibility (i.e., generating
overlapping patterns along the $z$-axis) and micron-scale spatial precision.
Here, we propose a CGH algorithm using an unsupervised generative model
designed for optical microscopy to synthesize 3D selected illumination. The
algorithm, named sparse deep CGH, is able to generate sparsely distributed
points in a large 3D volume with higher contrast than conventional CGH

We study the characteristic probability density distribution of random flat
band models by machine learning. The models considered here are constructed on
the basis of the molecular-orbital representation, which guarantees the
existence of the macroscopically degenerate zero-energy modes even in the
presence of randomness. We find that flat band states are successfully
distinguished from conventional extended and localized states, indicating the
characteristic feature of the flat band states. We also find that the flat band
states can be detected when the target data are defined in the different
lattice from the training data, which implies the universal feature of the flat
band states constructed by the molecular-orbital representation.

Molecular dynamics (MD) simulation predicts the trajectory of atoms by
solving Newton's equation of motion with a numeric integrator. Due to physical
constraints, the time step of the integrator need to be small to maintain
sufficient precision. This limits the efficiency of simulation. To this end, we
introduce a graph neural network (GNN) based model, MDNet, to predict the
evolution of coordinates and momentum with large time steps. In addition, MDNet
can easily scale to a larger system, due to its linear complexity with respect
to the system size. We demonstrate the performance of MDNet on a 4000-atom
system with large time steps, and show that MDNet can predict good equilibrium
and transport properties, well aligned with standard MD simulations.

Referring image segmentation aims to segment a referent via a natural
linguistic expression.Due to the distinct data properties between text and
image, it is challenging for a network to well align text and pixel-level
features. Existing approaches use pretrained models to facilitate learning, yet
separately transfer the language/vision knowledge from pretrained models,
ignoring the multi-modal corresponding information. Inspired by the recent
advance in Contrastive Language-Image Pretraining (CLIP), in this paper, we
propose an end-to-end CLIP-Driven Referring Image Segmentation framework
(CRIS). To transfer the multi-modal knowledge effectively, CRIS resorts to
vision-language decoding and contrastive learning for achieving the
text-to-pixel alignment. More specifically, we design a vision-language decoder
to propagate fine-grained semantic information from textual representations to
each pixel-level activation, which promotes consistency between the two
modalities. In addition, we present text-to-pixel contrastive learning to
explicitly enforce the text feature similar to the related pixel-level features
and dissimilar to the irrelevances. The experimental results on three benchmark
datasets demonstrate that our proposed framework significantly outperforms the
state-of-the-art performance without any post-processing. The code will be
released.

In this paper, we explore machine translation improvement via Generative
Adversarial Network (GAN) architecture. We take inspiration from RelGAN, a
model for text generation, and NMT-GAN, an adversarial machine translation
model, to implement a model that learns to transform awkward, non-fluent
English sentences to fluent ones, while only being trained on monolingual
corpora. We utilize a parameter $\lambda$ to control the amount of deviation
from the input sentence, i.e. a trade-off between keeping the original tokens
and modifying it to be more fluent. Our results improved upon phrase-based
machine translation in some cases. Especially, GAN with a transformer generator
shows some promising results. We suggests some directions for future works to
build upon this proof-of-concept.

For video captioning, "pre-training and fine-tuning" has become a de facto
paradigm, where ImageNet Pre-training (INP) is usually used to encode the video
content, then a task-oriented network is fine-tuned from scratch to cope with
caption generation. This paper first investigates the impact of the recently
proposed CLIP (Contrastive Language-Image Pre-training) on video captioning.
Through the empirical study on INP vs. CLIP, we identify the potential
deficiencies of INP and explore the key factors for accurate description
generation. The results show that the INP-based model is tricky to capture
concepts' semantics and sensitive to irrelevant background information. By
contrast, the CLIP-based model significantly improves the caption quality and
highlights the importance of concept-aware representation learning. With these
findings, we propose Dual Concept Detection (DCD) further to inject concept
knowledge into the model during training. DCD is an auxiliary task that
requires a caption model to learn the correspondence between video content and
concepts and the co-occurrence relations between concepts. Experiments on
MSR-VTT and VATEX demonstrate the effectiveness of DCD, and the visualization
results further reveal the necessity of learning concept-aware representations.

Machine learning models are vulnerable to adversarial attacks. In this paper,
we consider the scenario where a model is to be distributed to many users,
among which a malicious user attempts to attack another user. The malicious
user probes its unique copy of the model to search for adversarial samples,
presenting found samples to the victim's model in order to replicate the
attack. By distributing different copies of the model to different users, we
can mitigate such attacks wherein adversarial samples found on one copy would
not work on another copy. We propose a flexible parameter rewriting method that
directly modifies the model's parameters. This method does not require training
and is able to generate a large number of copies, where each copy induces
different sets of adversarial samples. Experimentation studies show that our
approach can significantly mitigate the attacks while retaining high accuracy.

In this study, we explore the transformer's ability to capture
intra-relations among frames by augmenting the receptive field of models.
Concretely, we propose a CycleGAN-based model with the transformer and
investigate its ability in the emotional voice conversion task. In the training
procedure, we adopt curriculum learning to gradually increase the frame length
so that the model can see from the short segment till the entire speech. The
proposed method was evaluated on the Japanese emotional speech dataset and
compared to several baselines (ACVAE, CycleGAN) with objective and subjective
evaluations. The results show that our proposed model is able to convert
emotion with higher strength and quality.

English proficiency assessments have become a necessary metric for filtering
and selecting prospective candidates for both academia and industry. With the
rise in demand for such assessments, it has become increasingly necessary to
have the automated human-interpretable results to prevent inconsistencies and
ensure meaningful feedback to the second language learners. Feature-based
classical approaches have been more interpretable in understanding what the
scoring model learns. Therefore, in this work, we utilize classical machine
learning models to formulate a speech scoring task as both a classification and
a regression problem, followed by a thorough study to interpret and study the
relation between the linguistic cues and the English proficiency level of the
speaker. First, we extract linguist features under five categories (fluency,
pronunciation, content, grammar and vocabulary, and acoustic) and train models
to grade responses. In comparison, we find that the regression-based models
perform equivalent to or better than the classification approach. Second, we
perform ablation studies to understand the impact of each of the feature and
feature categories on the performance of proficiency grading. Further, to
understand individual feature contributions, we present the importance of top
features on the best performing algorithm for the grading task. Third, we make
use of Partial Dependence Plots and Shapley values to explore feature
importance and conclude that the best performing trained model learns the
underlying rubrics used for grading the dataset used in this study.

$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure
learning. It provides functionalities of generating data from either simulator
or real-world dataset, learning causal structure from the data, and evaluating
the learned graph, together with useful practices such as prior knowledge
insertion, preliminary neighborhood selection, and post-processing to remove
false discoveries. Compared with related packages, $\texttt{gCastle}$ includes
many recently developed gradient-based causal discovery methods with optional
GPU acceleration. $\texttt{gCastle}$ brings convenience to researchers who may
directly experiment with the code as well as practitioners with graphical user
interference. Three real-world datasets in telecommunications are also provided
in the current version. $\texttt{gCastle}$ is available under Apache License
2.0 at \url{https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle}.

Fast and safe voltage regulation algorithms can serve as fundamental schemes
for achieving a high level of renewable penetration in the modern distribution
power grids. Faced with uncertain or even unknown distribution grid models and
fast-changing power injections, model-free deep reinforcement learning (DRL)
algorithms have been proposed to find the reactive power injections for
inverters while optimizing the voltage profiles. However, such data-driven
controllers can not guarantee satisfaction of the hard operational constraints,
such as maintaining voltage profiles within a certain range of the nominal
value. To this end, we propose SAVER: SAfe VoltagE Regulator, which is composed
of an RL learner and a specifically designed, computational efficient safety
projection layer. SAVER provides a plug-and-play interface for a set of DRL
algorithms that guarantees the system voltages to be within safe bounds.
Numerical simulations on real-world data validate the performance of the

Optimizing chemical molecules for desired properties lies at the core of drug
development. Despite initial successes made by deep generative models and
reinforcement learning methods, these methods were mostly limited by the
requirement of predefined attribute functions or parallel data with manually
pre-compiled pairs of original and optimized molecules. In this paper, for the
first time, we formulate molecular optimization as a style transfer problem and
present a novel generative model that could automatically learn internal
differences between two groups of non-parallel data through adversarial
training strategies. Our model further enables both preservation of molecular
contents and optimization of molecular properties through combining auxiliary
guided-variational autoencoders and generative flow techniques. Experiments on
two molecular optimization tasks, toxicity modification and synthesizability
improvement, demonstrate that our model significantly outperforms several

Protein-ligand interactions (PLIs) are fundamental to biochemical research
and their identification is crucial for estimating biophysical and biochemical
properties for rational therapeutic design. Currently, experimental
characterization of these properties is the most accurate method, however, this
is very time-consuming and labor-intensive. A number of computational methods
have been developed in this context but most of the existing PLI prediction
heavily depends on 2D protein sequence data. Here, we present a novel parallel
graph neural network (GNN) to integrate knowledge representation and reasoning
for PLI prediction to perform deep learning guided by expert knowledge and
informed by 3D structural data. We develop two distinct GNN architectures, GNNF
is the base implementation that employs distinct featurization to enhance
domain-awareness, while GNNP is a novel implementation that can predict with no
prior knowledge of the intermolecular interactions. The comprehensive
evaluation demonstrated that GNN can successfully capture the binary
interactions between ligand and proteins 3D structure with 0.979 test accuracy
for GNNF and 0.958 for GNNP for predicting activity of a protein-ligand
complex. These models are further adapted for regression tasks to predict
experimental binding affinities and pIC50 is crucial for drugs potency and
efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on
experimental affinity and 0.50 and 0.51 on pIC50 with GNNF and GNNP,
respectively, outperforming similar 2D sequence-based models. Our method can
serve as an interpretable and explainable artificial intelligence (AI) tool for
predicted activity, potency, and biophysical properties of lead candidates. To
this end, we show the utility of GNNP on SARS-Cov-2 protein targets by
screening a large compound library and comparing our prediction with the
experimentally measured data.

This paper presents a novel attention-based neural network for structured
reconstruction, which takes a 2D raster image as an input and reconstructs a
planar graph depicting an underlying geometric structure. The approach detects
corners and classifies edge candidates between corners in an end-to-end manner.
Our contribution is a holistic edge classification architecture, which 1)
initializes the feature of an edge candidate by a trigonometric positional
encoding of its end-points; 2) fuses image feature to each edge candidate by
deformable attention; 3) employs two weight-sharing Transformer decoders to
learn holistic structural patterns over the graph edge candidates; and 4) is
trained with a masked learning strategy. The corner detector is a variant of
the edge classification architecture, adapted to operate on pixels as corner
candidates. We conduct experiments on two structured reconstruction tasks:
outdoor building architecture and indoor floorplan planar graph reconstruction.
Extensive qualitative and quantitative evaluations demonstrate the superiority
of our approach over the state of the art. Code and pre-trained models are
available at https://heat-structured-reconstruction.github.io.

We present Path Integral Sampler~(PIS), a novel algorithm to draw samples
from unnormalized probability density functions. The PIS is built on the
Schr\"odinger bridge problem which aims to recover the most likely evolution of
a diffusion process given its initial distribution and terminal distribution.
The PIS draws samples from the initial distribution and then propagates the
samples through the Schr\"odinger bridge to reach the terminal distribution.
Applying the Girsanov theorem, with a simple prior diffusion, we formulate the
PIS as a stochastic optimal control problem whose running cost is the control
energy and terminal cost is chosen according to the target distribution. By
modeling the control as a neural network, we establish a sampling algorithm
that can be trained end-to-end. We provide theoretical justification of the
sampling quality of PIS in terms of Wasserstein distance when sub-optimal
control is used. Moreover, the path integrals theory is used to compute
importance weights of the samples to compensate for the bias induced by the
sub-optimality of the controller and time-discretization. We experimentally
demonstrate the advantages of PIS compared with other start-of-the-art sampling
methods on a variety of tasks.

Conditional gradient methods (CGM) are widely used in modern machine
learning. CGM's overall running time usually consists of two parts: the number
of iterations and the cost of each iteration. Most efforts focus on reducing
the number of iterations as a means to reduce the overall running time. In this
work, we focus on improving the per iteration cost of CGM. The bottleneck step
in most CGM is maximum inner product search (MaxIP), which requires a linear
scan over the parameters. In practice, approximate MaxIP data-structures are
found to be helpful heuristics. However, theoretically, nothing is known about
the combination of approximate MaxIP data-structures and CGM. In this work, we
answer this question positively by providing a formal framework to combine the
locality sensitive hashing type approximate MaxIP data-structures with CGM
algorithms. As a result, we show the first algorithm, where the cost per
iteration is sublinear in the number of parameters, for many fundamental
optimization algorithms, e.g., Frank-Wolfe, Herding algorithm, and policy
gradient.

Coordinate-MLPs are emerging as an effective tool for modeling
multidimensional continuous signals, overcoming many drawbacks associated with
discrete grid-based approximations. However, coordinate-MLPs with ReLU
activations, in their rudimentary form, demonstrate poor performance in
representing signals with high fidelity, promoting the need for positional
embedding layers. Recently, Sitzmann et al. proposed a sinusoidal activation
function that has the capacity to omit positional embedding from
coordinate-MLPs while still preserving high signal fidelity. Despite its
potential, ReLUs are still dominating the space of coordinate-MLPs; we
speculate that this is due to the hyper-sensitivity of networks -- that employ
such sinusoidal activations -- to the initialization schemes. In this paper, we
attempt to broaden the current understanding of the effect of activations in
coordinate-MLPs, and show that there exists a broader class of activations that
are suitable for encoding signals. We affirm that sinusoidal activations are
only a single example in this class, and propose several non-periodic functions
that empirically demonstrate more robust performance against random
initializations than sinusoids. Finally, we advocate for a shift towards
coordinate-MLPs that employ these non-traditional activation functions due to
their high performance and simplicity.

Investigations into the loss landscapes of deep neural networks are often
laborious. This work documents our user-driven approach to create a platform
for semi-automating this process. LossPlot accepts data in the form of a csv,
and allows multiple trained minimizers of the loss function to be manipulated
in sync. Other features include a simple yet intuitive checkbox UI, summary
statistics, and the ability to control clipping which other methods do not
offer.

Skeleton-based action recognition attracts practitioners and researchers due
to the lightweight, compact nature of datasets. Compared with RGB-video-based
action recognition, skeleton-based action recognition is a safer way to protect
the privacy of subjects while having competitive recognition performance.
However, due to improvements in skeleton estimation algorithms as well as
motion- and depth-sensors, more details of motion characteristics can be
preserved in the skeleton dataset, leading to potential privacy leakage. To
investigate the potential privacy leakage from skeleton datasets, we first
train a classifier to categorize sensitive private information from
trajectories of joints. Our preliminary experiments show that the gender
classifier achieves 87% accuracy on average and the re-identification task
achieves 80% accuracy on average for three baseline models: Shift-GCN, MS-G3D,
and 2s-AGCN. We propose an adversarial anonymization algorithm to protect
potential privacy leakage from the skeleton dataset. Experimental results show
that an anonymized dataset can reduce the risk of privacy leakage while having
marginal effects on action recognition performance.

Recently, vision transformer (ViT) and its variants have achieved promising
performances in various computer vision tasks. Yet the high computational costs
and training data requirements of ViTs limit their application in
resource-constrained settings. Model compression is an effective method to
speed up deep learning models, but the research of compressing ViTs has been
less explored. Many previous works concentrate on reducing the number of
tokens. However, this line of attack breaks down the spatial structure of ViTs
and is hard to be generalized into downstream tasks. In this paper, we design a
unified framework for structural pruning of both ViTs and its variants, namely
UP-ViTs. Our method focuses on pruning all ViTs components while maintaining
the consistency of the model structure. Abundant experimental results show that
our method can achieve high accuracy on compressed ViTs and variants, e.g.,
UP-DeiT-T achieves 75.79% accuracy on ImageNet, which outperforms the vanilla
DeiT-T by 3.59% with the same computational cost. UP-PVTv2-B0 improves the
accuracy of PVTv2-B0 by 4.83% for ImageNet classification. Meanwhile, UP-ViTs
maintains the consistency of the token representation and gains consistent
improvements on object detection tasks.

Medical applications have benefited greatly from the rapid advancement in
computer vision. Considering patient monitoring in particular, in-bed human
posture estimation offers important health-related metrics with potential value
in medical condition assessments. Despite great progress in this domain, it
remains challenging due to substantial ambiguity during occlusions, and the
lack of large corpora of manually labeled data for model training, particularly
with domains such as thermal infrared imaging which are privacy-preserving, and
thus of great interest. Motivated by the effectiveness of self-supervised
methods in learning features directly from data, we propose a multi-modal
conditional variational autoencoder (MC-VAE) capable of reconstructing features
from missing modalities seen during training. This approach is used with HRNet
to enable single modality inference for in-bed pose estimation. Through
extensive evaluations, we demonstrate that body positions can be effectively
recognized from the available modality, achieving on par results with baseline
models that are highly dependent on having access to multiple modes at
inference time. The proposed framework supports future research towards
self-supervised learning that generates a robust model from a single source,
and expects it to generalize over many unknown distributions in clinical

Land remote sensing analysis is a crucial research in earth science. In this
work, we focus on a challenging task of land analysis, i.e., automatic
extraction of traffic roads from remote sensing data, which has widespread
applications in urban development and expansion estimation. Nevertheless,
conventional methods either only utilized the limited information of aerial
images, or simply fused multimodal information (e.g., vehicle trajectories),
thus cannot well recognize unconstrained roads. To facilitate this problem, we
introduce a novel neural network framework termed Cross-Modal Message
Propagation Network (CMMPNet), which fully benefits the complementary different
modal data (i.e., aerial images and crowdsourced trajectories). Specifically,
CMMPNet is composed of two deep Auto-Encoders for modality-specific
representation learning and a tailor-designed Dual Enhancement Module for
cross-modal representation refinement. In particular, the complementary
information of each modality is comprehensively extracted and dynamically
propagated to enhance the representation of another modality. Extensive
experiments on three real-world benchmarks demonstrate the effectiveness of our
CMMPNet for robust road extraction benefiting from blending different modal
data, either using image and trajectory data or image and Lidar data. From the
experimental results, we observe that the proposed approach outperforms current
state-of-the-art methods by large margins.Our source code is resealed on the
project page http://lingboliu.com/multimodal_road_extraction.html.

EfficientPose is an impressive 3D object detection model. It has been
demonstrated to be quick, scalable, and accurate, especially when considering
that it uses only RGB inputs. In this paper we try to improve on EfficientPose
by giving it the ability to infer an object's size, and by simplifying both the
data collection and loss calculations. We evaluated ePose using the Linemod
dataset and a new subset of it called "Occlusion 1-class". We also outline our
current progress and thoughts about using ePose with the NuScenes and the 2017
KITTI 3D Object Detection datasets. The source code is available at
https://github.com/tbd-clip/EfficientPose.

Machine learning (ML) has demonstrated the promise for accurate and efficient
property prediction of molecules and crystalline materials. To develop highly
accurate ML models for chemical structure property prediction, datasets with
sufficient samples are required. However, obtaining clean and sufficient data
of chemical properties can be expensive and time-consuming, which greatly
limits the performance of ML models. Inspired by the success of data
augmentations in computer vision and natural language processing, we developed
AugLiChem: the data augmentation library for chemical structures. Augmentation
methods for both crystalline systems and molecules are introduced, which can be
utilized for fingerprint-based ML models and Graph Neural Networks(GNNs). We
show that using our augmentation strategies significantly improves the
performance of ML models, especially when using GNNs. In addition, the
augmentations that we developed can be used as a direct plug-in module during
training and have demonstrated the effectiveness when implemented with
different GNN models through the AugliChem library. The Python-based package
for our implementation of Auglichem: Data augmentation library for chemical
structures, is publicly available at: https://github.com/BaratiLab/AugLiChem.

There is an increasing demand in medical industries to have automated systems
for detection and localization which are manually inefficient otherwise. In
dentistry, it bears great interest to trace the pathway of mandibular canals
accurately. Proper localization of the position of the mandibular canals, which
surrounds the inferior alveolar nerve (IAN), reduces the risk of damaging it
during dental implantology. Manual detection of canal paths is not an efficient
way in terms of time and labor. Here, we propose a deep learning-based
framework to detect mandibular canals from CBCT data. It is a 3-stage process
fully automatic end-to-end. Ground truths are generated in the preprocessing
stage. Instead of using commonly used fixed diameter tubular-shaped ground
truth, we generate centerlines of the mandibular canals and used them as ground
truths in the training process. A 3D U-Net architecture is used for model
training. An efficient post-processing stage is developed to rectify the
initial prediction. The precision, recall, F1-score, and IoU are measured to
analyze the voxel-level segmentation performance. However, to analyze the
distance-based measurements, mean curve distance (MCD) both from ground truth
to prediction and prediction to ground truth is calculated. Extensive
experiments are conducted to demonstrate the effectiveness of the model.

Modern deep neural networks must demonstrate state-of-the-art accuracy while
exhibiting low latency and energy consumption. As such, neural architecture
search (NAS) algorithms take these two constraints into account when generating
a new architecture. However, efficiency metrics such as latency are typically
hardware dependent requiring the NAS algorithm to either measure or predict the
architecture latency. Measuring the latency of every evaluated architecture
adds a significant amount of time to the NAS process. Here we propose
Microprocessor A Priori for Latency Estimation MAPLE that does not rely on
transfer learning or domain adaptation but instead generalizes to new hardware
by incorporating a prior hardware characteristics during training. MAPLE takes
advantage of a novel quantitative strategy to characterize the underlying
microprocessor by measuring relevant hardware performance metrics, yielding a
fine-grained and expressive hardware descriptor. Moreover, the proposed MAPLE
benefits from the tightly coupled I/O between the CPU and GPU and their
dependency to predict DNN latency on GPUs while measuring microprocessor
performance hardware counters from the CPU feeding the GPU hardware. Through
this quantitative strategy as the hardware descriptor, MAPLE can generalize to
new hardware via a few shot adaptation strategy where with as few as 3 samples
it exhibits a 6% improvement over state-of-the-art methods requiring as much as
10 samples. Experimental results showed that, increasing the few shot
adaptation samples to 10 improves the accuracy significantly over the
state-of-the-art methods by 12%. Furthermore, it was demonstrated that MAPLE
exhibiting 8-10% better accuracy, on average, compared to relevant baselines at
any number of adaptation samples.

Raw data sizes are growing and proliferating in scientific research, driven
by the success of data-hungry computational methods, such as machine learning.
The preponderance of proprietary and shoehorned data formats make computations
slower and make it harder to reproduce research and to port methods to new
platforms. Here we present the RawArray format: a simple, fast, and extensible
format for archival storage of multidimensional numeric arrays on disk.
  The RawArray file format is a simple concatenation of a header array and a
data array. The header comprises seven or more 64-bit unsigned integers. The
array data can be anything. Arbitrary user metadata can be appended to an
RawArray file if desired, for example to store measurement details, color
palettes, or geolocation data.
  We present benchmarks showing a factor of 2--3$\times$ speedup over HDF5 for
a range of array sizes and a speedup of up to 20$\times$ in reading the common
deep learning datasets MNIST and CIFAR10.

Appropriate dosing of radiation is crucial to patient safety in radiotherapy.
Current quality assurance depends heavily on a peer-review process, where the
physicians' peer review on each patient's treatment plan, including dose and
fractionation. However, such a process is manual and laborious. Physicians may
not identify errors due to time constraints and caseload. We designed a novel
prescription anomaly detection algorithm that utilizes historical data to
predict anomalous cases. Such a tool can serve as an electronic peer who will
assist the peer-review process providing extra safety to the patients. In our
primary model, we created two dissimilarity metrics, R and F. R defining how
far a new patient's prescription is from historical prescriptions. F represents
how far away a patient's feature set is from the group with an identical or
similar prescription. We flag prescription if either metric is greater than
specific optimized cut-off values. We used thoracic cancer patients (n=2356) as
an example and extracted seven features. Here, we report our testing f1 score,
between 75%-94% for different treatment technique groups. We also independently
validate our results by conducting a mock peer review with three thoracic
specialists. Our model has a lower type 2 error rate compared to manual
peer-review physicians. Our model has many advantages over traditional machine
learning algorithms, particularly in that it does not suffer from class
imbalance. It can also explain why it flags each case and separate prescription
and non-prescription-related features without learning from the data.

Inspired by ideas from optimal transport theory we present Trust the Critics
(TTC), a new algorithm for generative modelling. This algorithm eliminates the
trainable generator from a Wasserstein GAN; instead, it iteratively modifies
the source data using gradient descent on a sequence of trained critic
networks. This is motivated in part by the misalignment which we observed
between the optimal transport directions provided by the gradients of the
critic and the directions in which data points actually move when parametrized
by a trainable generator. Previous work has arrived at similar ideas from
different viewpoints, but our basis in optimal transport theory motivates the
choice of an adaptive step size which greatly accelerates convergence compared
to a constant step size. Using this step size rule, we prove an initial
geometric convergence rate in the case of source distributions with densities.
These convergence rates cease to apply only when a non-negligible set of
generated data is essentially indistinguishable from real data. Resolving the
misalignment issue improves performance, which we demonstrate in experiments
that show that given a fixed number of training epochs, TTC produces higher
quality images than a comparable WGAN, albeit at increased memory requirements.
In addition, TTC provides an iterative formula for the transformed density,
which traditional WGANs do not. Finally, TTC can be applied to map any source
distribution onto any target; we demonstrate through experiments that TTC can
obtain competitive performance in image generation, translation, and denoising
without dedicated algorithms.

Generative adversarial networks (GANs) have proven successful in image
generation tasks. However, GAN training is inherently unstable. Although many
works try to stabilize it by manually modifying GAN architecture, it requires
much expertise. Neural architecture search (NAS) has become an attractive
solution to search GANs automatically. The early NAS-GANs search only
generators to reduce search complexity but lead to a sub-optimal GAN. Some
recent works try to search both generator (G) and discriminator (D), but they
suffer from the instability of GAN training. To alleviate the instability, we
propose an efficient two-stage evolutionary algorithm-based NAS framework to
search GANs, namely EAGAN. We decouple the search of G and D into two stages,
where stage-1 searches G with a fixed D and adopts the many-to-one training
strategy, and stage-2 searches D with the optimal G found in stage-1 and adopts
the one-to-one training and weight-resetting strategies to enhance the
stability of GAN training. Both stages use the non-dominated sorting method to
produce Pareto-front architectures under multiple objectives (e.g., model size,
Inception Score (IS), and Fr\'echet Inception Distance (FID)). EAGAN is applied
to the unconditional image generation task and can efficiently finish the
search on the CIFAR-10 dataset in 1.2 GPU days. Our searched GANs achieve
competitive results (IS=8.81$\pm$0.10, FID=9.91) on the CIFAR-10 dataset and
surpass prior NAS-GANs on the STL-10 dataset (IS=10.44$\pm$0.087, FID=22.18).
Source code: https://github.com/marsggbo/EAGAN.

Personalizing dialogue agents is important for dialogue systems to generate
more specific, consistent, and engaging responses. However, most current
dialogue personalization approaches rely on explicit persona descriptions
during inference, which severely restricts its application. In this paper, we
propose a novel approach that learns to predict persona information based on
the dialogue history to personalize the dialogue agent without relying on any
explicit persona descriptions during inference. Experimental results on the
PersonaChat dataset show that the proposed method can improve the consistency
of generated responses when conditioning on the predicted profile of the
dialogue agent (i.e. "self persona"), and improve the engagingness of the
generated responses when conditioning on the predicted persona of the dialogue
partner (i.e. "their persona"). We also find that a trained persona prediction
model can be successfully transferred to other datasets and help generate more
relevant responses.

In over-parameterized deep neural networks there can be many possible
parameter configurations that fit the training data exactly. However, the
properties of these interpolating solutions are poorly understood. We argue
that over-parameterized neural networks trained with stochastic gradient
descent are subject to a Geometric Occam's Razor; that is, these networks are
implicitly regularized by the geometric model complexity. For one-dimensional
regression, the geometric model complexity is simply given by the arc length of
the function. For higher-dimensional settings, the geometric model complexity
depends on the Dirichlet energy of the function. We explore the relationship
between this Geometric Occam's Razor, the Dirichlet energy and other known
forms of implicit regularization. Finally, for ResNets trained on CIFAR-10, we
observe that Dirichlet energy measurements are consistent with the action of
this implicit Geometric Occam's Razor.

Traffic optimization challenges, such as load balancing, flow scheduling, and
improving packet delivery time, are difficult online decision-making problems
in wide area networks (WAN). Complex heuristics are needed for instance to find
optimal paths that improve packet delivery time and minimize interruptions
which may be caused by link failures or congestion. The recent success of
reinforcement learning (RL) algorithms can provide useful solutions to build
better robust systems that learn from experience in model-free settings.
  In this work, we consider a path optimization problem, specifically for
packet routing, in large complex networks. We develop and evaluate a model-free
approach, applying multi-agent meta reinforcement learning (MAMRL) that can
determine the next-hop of each packet to get it delivered to its destination
with minimum time overall. Specifically, we propose to leverage and compare
deep policy optimization RL algorithms for enabling distributed model-free
control in communication networks and present a novel meta-learning-based
framework, MAMRL, for enabling quick adaptation to topology changes. To
evaluate the proposed framework, we simulate with various WAN topologies. Our
extensive packet-level simulation results show that compared to classical
shortest path and traditional reinforcement learning approaches, MAMRL
significantly reduces the average packet delivery time even when network demand
increases; and compared to a non-meta deep policy optimization algorithm, our
results show the reduction of packet loss in much fewer episodes when link
failures occur while offering comparable average packet delivery time.

With the increasingly available large-scale cancer genomics datasets, machine
learning approaches have played an important role in revealing novel insights
into cancer development. Existing methods have shown encouraging performance in
identifying genes that are predictive for cancer survival, but are still
limited in modeling the distribution over genes. Here, we proposed a novel
method that can simulate the gene expression distribution at any given time
point, including those that are out of the range of the observed time points.
In order to model the irregular time series where each patient is one
observation, we integrated a neural ordinary differential equation (neural ODE)
with cox regression into our framework. We evaluated our method on eight cancer
types on TCGA and observed a substantial improvement over existing approaches.
Our visualization results and further analysis indicate how our method can be
used to simulate expression at the early cancer stage, offering the possibility
for early cancer identification.

Sketch-based image manipulation is an interactive image editing task to
modify an image based on input sketches from users. Existing methods typically
formulate this task as a conditional inpainting problem, which requires users
to draw an extra mask indicating the region to modify in addition to sketches.
The masked regions are regarded as holes and filled by an inpainting model
conditioned on the sketch. With this formulation, paired training data can be
easily obtained by randomly creating masks and extracting edges or contours.
Although this setup simplifies data preparation and model design, it
complicates user interaction and discards useful information in masked regions.
To this end, we investigate a new paradigm of sketch-based image manipulation:
mask-free local image manipulation, which only requires sketch inputs from
users and utilizes the entire original image. Given an image and sketch, our
model automatically predicts the target modification region and encodes it into
a structure agnostic style vector. A generator then synthesizes the new image
content based on the style vector and sketch. The manipulated image is finally
produced by blending the generator output into the modification region of the
original image. Our model can be trained in a self-supervised fashion by
learning the reconstruction of an image region from the style vector and
sketch. The proposed method offers simpler and more intuitive user workflows
for sketch-based image manipulation and provides better results than previous
approaches. More results, code and interactive demo will be available at
\url{https://zengxianyu.github.io/sketchedit}.

This paper proposes the Transition Motion Tensor, a data-driven framework
that creates novel and physically accurate transitions outside of the motion
dataset. It enables simulated characters to adopt new motion skills efficiently
and robustly without modifying existing ones. Given several physically
simulated controllers specializing in different motions, the tensor serves as a
temporal guideline to transition between them. Through querying the tensor for
transitions that best fit user-defined preferences, we can create a unified
controller capable of producing novel transitions and solving complex tasks
that may require multiple motions to work coherently. We apply our framework on
both quadrupeds and bipeds, perform quantitative and qualitative evaluations on
transition quality, and demonstrate its capability of tackling complex motion
planning problems while following user control directives.

In this paper, we present a communication-efficient federated learning
framework inspired by quantized compressed sensing. The presented framework
consists of gradient compression for wireless devices and gradient
reconstruction for a parameter server (PS). Our strategy for gradient
compression is to sequentially perform block sparsification, dimensional
reduction, and quantization. Thanks to gradient sparsification and
quantization, our strategy can achieve a higher compression ratio than one-bit
gradient compression. For accurate aggregation of the local gradients from the
compressed signals at the PS, we put forth an approximate minimum mean square
error (MMSE) approach for gradient reconstruction using the
expectation-maximization generalized-approximate-message-passing (EM-GAMP)
algorithm. Assuming Bernoulli Gaussian-mixture prior, this algorithm
iteratively updates the posterior mean and variance of local gradients from the
compressed signals. We also present a low-complexity approach for the gradient
reconstruction. In this approach, we use the Bussgang theorem to aggregate
local gradients from the compressed signals, then compute an approximate MMSE
estimate of the aggregated gradient using the EM-GAMP algorithm. We also
provide a convergence rate analysis of the presented framework. Using the MNIST
dataset, we demonstrate that the presented framework achieves almost identical
performance with the case that performs no compression, while significantly
reducing communication overhead for federated learning.

CTR prediction is essential for modern recommender systems. Ranging from
early factorization machines to deep learning based models in recent years,
existing CTR methods focus on capturing useful feature interactions or mining
important behavior patterns. Despite the effectiveness, we argue that these
methods suffer from the risk of label sparsity (i.e., the user-item
interactions are highly sparse with respect to the feature space), label noise
(i.e., the collected user-item interactions are usually noisy), and the
underuse of domain knowledge (i.e., the pairwise correlations between samples).
To address these challenging problems, we propose a novel Multi-Interest
Self-Supervised learning (MISS) framework which enhances the feature embeddings
with interest-level self-supervision signals. With the help of two novel
CNN-based multi-interest extractors,self-supervision signals are discovered
with full considerations of different interest representations (point-wise and
union-wise), interest dependencies (short-range and long-range), and interest
correlations (inter-item and intra-item). Based on that, contrastive learning
losses are further applied to the augmented views of interest representations,
which effectively improves the feature representation learning. Furthermore,
our proposed MISS framework can be used as an plug-in component with existing
CTR prediction models and further boost their performances. Extensive
experiments on three large-scale datasets show that MISS significantly
outperforms the state-of-the-art models, by up to 13.55% in AUC, and also
enjoys good compatibility with representative deep CTR models.

Independent Component Analysis (ICA) is intended to recover the mutually
independent sources from their linear mixtures, and F astICA is one of the most
successful ICA algorithms. Although it seems reasonable to improve the
performance of F astICA by introducing more nonlinear functions to the
negentropy estimation, the original fixed-point method (approximate Newton
method) in F astICA degenerates under this circumstance. To alleviate this
problem, we propose a novel method based on the second-order approximation of
minimum discrimination information (MDI). The joint maximization in our method
is consisted of minimizing single weighted least squares and seeking unmixing
matrix by the fixed-point method. Experimental results validate its efficiency
compared with other popular ICA algorithms.

Existing 3D human pose estimation algorithms trained on distortion-free
datasets suffer performance drop when applied to new scenarios with a specific
camera distortion. In this paper, we propose a simple yet effective model for
3D human pose estimation in video that can quickly adapt to any distortion
environment by utilizing MAML, a representative optimization-based
meta-learning algorithm. We consider a sequence of 2D keypoints in a particular
distortion as a single task of MAML. However, due to the absence of a
large-scale dataset in a distorted environment, we propose an efficient method
to generate synthetic distorted data from undistorted 2D keypoints. For the
evaluation, we assume two practical testing situations depending on whether a
motion capture sensor is available or not. In particular, we propose Inference
Stage Optimization using bone-length symmetry and consistency. Extensive
evaluation shows that our proposed method successfully adapts to various
degrees of distortion in the testing phase and outperforms the existing
state-of-the-art approaches. The proposed method is useful in practice because
it does not require camera calibration and additional computations in a testing
set-up.

The vibrations generated from scratching and tapping on surfaces can be
highly expressive and recognizable, and have therefore been proposed as a
method of natural user interface (NUI). Previous systems require custom sensor
hardware such as contact microphones and have struggled with gesture
classification accuracy.
  We propose a deep learning approach to scratch input. Using smartphones and
tablets laid on tabletops or other similar surfaces, our system achieved a
gesture classification accuracy of 95.8\%, substantially reducing gesture
misclassification from previous works. Further, our system achieved this
performance when tested on a wide variety of surfaces, mobile devices, and in
high noise environments.
  The results indicate high potential for the application of deep learning
techniques to natural user interface (NUI) systems that can readily convert
large unpowered surfaces into a user interface using just a smartphone with no
special-purpose sensors or hardware.

Precise continuum normalisation of merged \'{e}chelle spectra is a demanding
task necessary for various detailed spectroscopic analyses. Automatic methods
have limited effectiveness due to the variety of features present in the
spectra of stars. This complexity often leads to the necessity of manual
normalisation which is a time demanding task. The aim of this work is to
develop a fully automated normalisation tool that works with order-merged
spectra and offers flexible manual fine-tuning, if necessary. The core of the
proposed method uses the novel fully convolutional deep neural network (SUPP
Network) that was trained to predict a pseudo-continuum. The post-processing
step uses smoothing splines that gives access to regressed knots useful for
optional manual corrections. The active learning technique was applied to deal
with possible biases that may arise from training with synthetic spectra and to
extend the applicability of the proposed method to features absent in this kind
of spectra. The developed normalisation method was tested with high-resolution
spectra of stars having spectral types from O to G, and gave root mean squared
(RMS) error over the set of test stars equal $0.0128$ in the spectral range
from $3900\,\r{A}$ to $7000\,\r{A}$ and $0.0081$ in the range from
$4200\,\r{A}$ to $7000\,\r{A}$. Experiments with synthetic spectra give RMS of
the order of $0.0050$. The proposed method gives results comparable to careful
manual normalisation. Additionally, this approach is general and can be used in
other fields of astronomy where background modelling or trend removal is a part
of data processing. The algorithm is available online at https://git.io/JqJhf.

We study the problem of online learning in predictive control of an unknown
linear dynamical system with time varying cost functions which are unknown
apriori. Specifically, we study the online learning problem where the control
algorithm does not know the true system model and has only access to a
fixed-length (that does not grow with the control horizon) preview of the
future cost functions. The goal of the online algorithm is to minimize the
dynamic regret, defined as the difference between the cumulative cost incurred
by the algorithm and that of the best sequence of actions in hindsight. We
propose two different online Model Predictive Control (MPC) algorithms to
address this problem, namely Certainty Equivalence MPC (CE-MPC) algorithm and
Optimistic MPC (O-MPC) algorithm. We show that under the standard stability
assumption for the model estimate, the CE-MPC algorithm achieves
$\mathcal{O}(T^{2/3})$ dynamic regret. We then extend this result to the
setting where the stability assumption holds only for the true system model by
proposing the O-MPC algorithm. We show that the O-MPC algorithm also achieves
$\mathcal{O}(T^{2/3})$ dynamic regret, at the cost of some additional
computation. We also present numerical studies to demonstrate the performance
of our algorithm.

X-ray imaging is the most popular medical imaging technology. While x-ray
radiography is rather cost-effective, tissue structures are superimposed along
the x-ray paths. On the other hand, computed tomography (CT) reconstructs
internal structures but CT increases radiation dose, is complicated and
expensive. Here we propose "x-ray dissectography" to extract a target
organ/tissue digitally from few radiographic projections for stereographic and
tomographic analysis in the deep learning framework. As an exemplary
embodiment, we propose a general X-ray dissectography network, a dedicated
X-ray stereotography network, and the X-ray imaging systems to implement these
functionalities. Our experiments show that x-ray stereography can be achieved
of an isolated organ such as the lungs in this case, suggesting the feasibility
of transforming conventional radiographic reading to the stereographic
examination of the isolated organ, which potentially allows higher sensitivity
and specificity, and even tomographic visualization of the target. With further
improvements, x-ray dissectography promises to be a new x-ray imaging modality
for CT-grade diagnosis at radiation dose and system cost comparable to that of
radiographic or tomosynthetic imaging.

In recent years, enterprises have been targeted by advanced adversaries who
leverage creative ways to infiltrate their systems and move laterally to gain
access to critical data. One increasingly common evasive method is to hide the
malicious activity behind a benign program by using tools that are already
installed on user computers. These programs are usually part of the operating
system distribution or another user-installed binary, therefore this type of
attack is called "Living-Off-The-Land". Detecting these attacks is challenging,
as adversaries may not create malicious files on the victim computers and
anti-virus scans fail to detect them. We propose the design of an Active
Learning framework called LOLAL for detecting Living-Off-the-Land attacks that
iteratively selects a set of uncertain and anomalous samples for labeling by a
human analyst. LOLAL is specifically designed to work well when a limited
number of labeled samples are available for training machine learning models to
detect attacks. We investigate methods to represent command-line text using
word-embedding techniques, and design ensemble boosting classifiers to
distinguish malicious and benign samples based on the embedding representation.
We leverage a large, anonymized dataset collected by an endpoint security
product and demonstrate that our ensemble classifiers achieve an average F1
score of 0.96 at classifying different attack classes. We show that our active
learning method consistently improves the classifier performance, as more
training data is labeled, and converges in less than 30 iterations when
starting with a small number of labeled instances.

Hyperbolic space can naturally embed hierarchies that often exist in
real-world data and semantics. While high-dimensional hyperbolic embeddings
lead to better representations, most hyperbolic models utilize low-dimensional
embeddings, due to non-trivial optimization and visualization of
high-dimensional hyperbolic data.
  We propose CO-SNE, which extends the Euclidean space visualization tool,
t-SNE, to hyperbolic space. Like t-SNE, it converts distances between data
points to joint probabilities and tries to minimize the Kullback-Leibler
divergence between the joint probabilities of high-dimensional data $X$ and
low-dimensional embedding $Y$. However, unlike Euclidean space, hyperbolic
space is inhomogeneous: A volume could contain a lot more points at a location
far from the origin. CO-SNE thus uses hyperbolic normal distributions for $X$
and hyperbolic \underline{C}auchy instead of t-SNE's Student's t-distribution
for $Y$, and it additionally seeks to preserve $X$'s individual distances to
the \underline{O}rigin in $Y$.
  We apply CO-SNE to naturally hyperbolic data and supervisedly learned
hyperbolic features. Our results demonstrate that CO-SNE deflates
high-dimensional hyperbolic data into a low-dimensional space without losing
their hyperbolic characteristics, significantly outperforming popular
visualization tools such as PCA, t-SNE, UMAP, and HoroPCA which is also
designed for hyperbolic data.

Malware family classification is a significant issue with public safety and
research implications that has been hindered by the high cost of expert labels.
The vast majority of corpora use noisy labeling approaches that obstruct
definitive quantification of results and study of deeper interactions. In order
to provide the data needed to advance further, we have created the Malware
Open-source Threat Intelligence Family (MOTIF) dataset. MOTIF contains 3,095
malware samples from 454 families, making it the largest and most diverse
public malware dataset with ground truth family labels to date, nearly 3x
larger than any prior expert-labeled corpus and 36x larger than the prior
Windows malware corpus. MOTIF also comes with a mapping from malware samples to
threat reports published by reputable industry sources, which both validates
the labels and opens new research opportunities in connecting opaque malware
samples to human-readable descriptions. This enables important evaluations that
are normally infeasible due to non-standardized reporting in industry. For
example, we provide aliases of the different names used to describe the same
malware family, allowing us to benchmark for the first time accuracy of
existing tools when names are obtained from differing sources. Evaluation
results obtained using the MOTIF dataset indicate that existing tasks have
significant room for improvement, with accuracy of antivirus majority voting
measured at only 62.10% and the well-known AVClass tool having just 46.78%
accuracy. Our findings indicate that malware family classification suffers a
type of labeling noise unlike that studied in most ML literature, due to the
large open set of classes that may not be known from the sample under
consideration

Heterogeneous radio access networks require efficient traffic steering
methods to reach near-optimal results in order to maximize network capacity.
This paper aims to propose a novel traffic steering algorithm for usage in
HetNets, which utilizes a reinforcement learning algorithm in combination with
an artificial neural network to maximize total user satisfaction in the
simulated cellular network. The novel algorithm was compared with two reference
algorithms using network simulation results. The results prove that the novel
algorithm provides noticeably better efficiency in comparison with reference
algorithms, especially in terms of the number of served users with limited
frequency resources of the radio access network.

This work focuses on an efficient Agile design methodology for
domain-specific accelerators. We employ feature-by-feature enhancement of a
vertical development stack and apply it to the TVM/VTA inference accelerator.
We have enhanced the VTA design space and enabled end-to-end support for
additional workloads. This has been accomplished by augmenting the VTA
micro-architecture and instruction set architecture (ISA), as well as by
enhancing the TVM compilation stack to support a wide range of VTA configs.
  The VTA tsim implementation (CHISEL-based) has been enhanced with fully
pipelined versions of the ALU/GEMM execution units. In tsim, memory width can
now range between 8-64 bytes. Field widths have been made more flexible to
support larger scratchpads. New instructions have been added: element-wise
8-bit multiplication to support depthwise convolution, and load with a choice
of pad values to support max pooling. Support for more layers and better double
buffering has also been added.
  Fully pipelining ALU/GEMM helps significantly: 4.9x fewer cycles with minimal
area change to run ResNet-18 under the default config. Configs featuring a
further 11.5x decrease in cycle count at a cost of 12x greater area can be
instantiated. Many points on the area-performance pareto curve are shown,
showcasing the balance of execution unit sizing, memory interface width, and
scratchpad sizing. Finally, VTA is now able to run Mobilenet 1.0 and all layers
for ResNets, including the previously disabled pooling and fully connected
layers.
  The TVM/VTA architecture has always featured end-to-end workload evaluation
on RTL in minutes. With our modifications, it now offers a much greater number
of feasible configurations with a wide range of cost vs. performance. All
capabilities mentioned are available in opensource forks while a subset of
these capabilities have already been upstreamed.

We present a machine learning framework to train and validate neural networks
to predict the anisotropic elastic response of the monoclinic organic molecular
crystal $\beta$-HMX in the geometrical nonlinear regime. A filtered molecular
dynamic (MD) simulations database is used to train the neural networks with a
Sobolev norm that uses the stress measure and a reference configuration to
deduce the elastic stored energy functional. To improve the accuracy of the
elasticity tangent predictions originating from the learned stored energy, a
transfer learning technique is used to introduce additional tangential
constraints from the data while necessary conditions (e.g. strong ellipticity,
crystallographic symmetry) for the correctness of the model are either
introduced as additional physical constraints or incorporated in the validation
tests. Assessment of the neural networks is based on (1) the accuracy with
which they reproduce the bottom-line constitutive responses predicted by MD,
(2) detailed examination of their stability and uniqueness, and (3)
admissibility of the predicted responses with respect to continuum mechanics
theory in the finite-deformation regime. We compare the neural networks'
training efficiency under different Sobolev constraints and assess the models'
accuracy and robustness against MD benchmarks for $\beta$-HMX.

Image captioning is the task of automatically generating sentences that
describe an input image in the best way possible. The most successful
techniques for automatically generating image captions have recently used
attentive deep learning models. There are variations in the way deep learning
models with attention are designed. In this survey, we provide a review of
literature related to attentive deep learning models for image captioning.
Instead of offering a comprehensive review of all prior work on deep image
captioning models, we explain various types of attention mechanisms used for
the task of image captioning in deep learning models. The most successful deep
learning models used for image captioning follow the encoder-decoder
architecture, although there are differences in the way these models employ
attention mechanisms. Via analysis on performance results from different
attentive deep models for image captioning, we aim at finding the most
successful types of attention mechanisms in deep models for image captioning.
Soft attention, bottom-up attention, and multi-head attention are the types of
attention mechanism widely used in state-of-the-art attentive deep learning
models for image captioning. At the current time, the best results are achieved
from variants of multi-head attention with bottom-up attention.

Highly dynamic mobile ad-hoc networks (MANETs) remain as one of the most
challenging environments to develop and deploy robust, efficient, and scalable
routing protocols. In this paper, we present DeepCQ+ routing protocol which, in
a novel manner integrates emerging multi-agent deep reinforcement learning
(MADRL) techniques into existing Q-learning-based routing protocols and their
variants and achieves persistently higher performance across a wide range of
topology and mobility configurations. While keeping the overall protocol
structure of the Q-learning-based routing protocols, DeepCQ+ replaces
statically configured parameterized thresholds and hand-written rules with
carefully designed MADRL agents such that no configuration of such parameters
is required a priori. Extensive simulation shows that DeepCQ+ yields
significantly increased end-to-end throughput with lower overhead and no
apparent degradation of end-to-end delays (hop counts) compared to its
Q-learning based counterparts. Qualitatively, and perhaps more significantly,
DeepCQ+ maintains remarkably similar performance gains under many scenarios
that it was not trained for in terms of network sizes, mobility conditions, and
traffic dynamics. To the best of our knowledge, this is the first successful
application of the MADRL framework for the MANET routing problem that
demonstrates a high degree of scalability and robustness even under
environments that are outside the trained range of scenarios. This implies that
our MARL-based DeepCQ+ design solution significantly improves the performance
of Q-learning based CQ+ baseline approach for comparison and increases its
practicality and explainability because the real-world MANET environment will
likely vary outside the trained range of MANET scenarios. Additional techniques
to further increase the gains in performance and scalability are discussed.

Competitive online games use rating systems to match players with similar
skills to ensure a satisfying experience for players. In this paper, we focus
on the importance of addressing different aspects of playing behavior when
modeling players for creating match-ups. To this end, we engineer several
behavioral features from a dataset of over 75,000 battle royale matches and
create player models based on the retrieved features. We then use the created
models to predict ranks for different groups of players in the data. The
predicted ranks are compared to those of three popular rating systems. Our
results show the superiority of simple behavioral models over mainstream rating
systems. Some behavioral features provided accurate predictions for all groups
of players while others proved useful for certain groups of players. The
results of this study highlight the necessity of considering different aspects
of the player's behavior such as goals, strategy, and expertise when making
assignments.

While deep learning has enabled significant progress in designing general
purpose robot grasping systems, there remain objects which still pose
challenges for these systems. Recent work on Exploratory Grasping has
formalized the problem of systematically exploring grasps on these adversarial
objects and explored a multi-armed bandit model for identifying high-quality
grasps on each object stable pose. However, these systems are still limited to
exploring a small number or grasps on each object. We present Learned Efficient
Grasp Sets (LEGS), an algorithm that efficiently explores thousands of possible
grasps by maintaining small active sets of promising grasps and determining
when it can stop exploring the object with high confidence. Experiments suggest
that LEGS can identify a high-quality grasp more efficiently than prior
algorithms which do not use active sets. In simulation experiments, we measure
the gap between the success probability of the best grasp identified by LEGS,
baselines, and the most-robust grasp (verified ground truth). After 3000
exploration steps, LEGS outperforms baseline algorithms on 10/14 and 25/39
objects on the Dex-Net Adversarial and EGAD! datasets respectively. We then
evaluate LEGS in physical experiments; trials on 3 challenging objects suggest
that LEGS converges to high-performing grasps significantly faster than
baselines. See https://sites.google.com/view/legs-exp-grasping for supplemental
material and videos.

We present a deformable prototypical part network (Deformable ProtoPNet), an
interpretable image classifier that integrates the power of deep learning and
the interpretability of case-based reasoning. This model classifies input
images by comparing them with prototypes learned during training, yielding
explanations in the form of "this looks like that." However, while previous
methods use spatially rigid prototypes, we address this shortcoming by
proposing spatially flexible prototypes. Each prototype is made up of several
prototypical parts that adaptively change their relative spatial positions
depending on the input image. Consequently, a Deformable ProtoPNet can
explicitly capture pose variations and context, improving both model accuracy
and the richness of explanations provided. Compared to other case-based
interpretable models using prototypes, our approach achieves state-of-the-art
accuracy and gives an explanation with greater context. The code is available
at https://github.com/jdonnelly36/Deformable-ProtoPNet.

We develop multiple Deep Learning (DL) models that advance the
state-of-the-art predictions of the global auroral particle precipitation. We
use observations from low Earth orbiting spacecraft of the electron energy flux
to develop a model that improves global nowcasts (predictions at the time of
observation) of the accelerated particles. Multiple Machine Learning (ML)
modeling approaches are compared, including a novel multi-task model, models
with tail- and distribution-based loss functions, and a spatio-temporally
sparse 2D-convolutional model. We detail the data preparation process as well
as the model development that will be illustrative for many similar time series
global regression problems in space weather and across domains. Our ML
improvements are three-fold: 1) loss function engineering; 2) multi-task
learning; and 3) transforming the task from time series prediction to
spatio-temporal prediction. Notably, the ML models improve prediction of the
extreme events, historically obstinate to accurate specification and indicate
that increased expressive capacity provided by ML innovation can address grand
challenges in the science of space weather.

Structuring medical data in France remains a challenge mainly because of the
lack of medical data due to privacy concerns and the lack of methods and
approaches on processing the French language. One of these challenges is
structuring drug-related information in French clinical documents. To our
knowledge, over the last decade, there are less than five relevant papers that
study French prescriptions. This paper proposes a new approach for extracting
drug-related information from French clinical scanned documents while
preserving patients' privacy. In addition, we deployed our method in a health
data management platform where it is used to structure drug medical data and
help patients organize their drug schedules. It can be implemented on any web
or mobile platform. This work closes the gap between theoretical and practical
work by creating an application adapted to real production problems. It is a
combination of a rule-based phase and a Deep Learning approach. Finally,
numerical results show the outperformance and relevance of the proposed

Federated Learning (FL) is a distributed machine learning protocol that
allows a set of agents to collaboratively train a model without sharing their
datasets. This makes FL particularly suitable for settings where data privacy
is desired. However, it has been observed that the performance of FL is closely
related to the similarity of the local data distributions of agents.
Particularly, as the data distributions of agents differ, the accuracy of the
trained models drop. In this work, we look at how variations in local data
distributions affect the fairness and the robustness properties of the trained
models in addition to the accuracy. Our experimental results indicate that, the
trained models exhibit higher bias, and become more susceptible to attacks as
local data distributions differ. Importantly, the degradation in the fairness,
and robustness can be much more severe than the accuracy. Therefore, we reveal
that small variations that have little impact on the accuracy could still be
important if the trained model is to be deployed in a fairness/security
critical context.

The increasing popularity of the Web has subsequently increased the abundance
of reviews on products and services. Mining these reviews for expressed
sentiment is beneficial for both companies and consumers, as quality can be
improved based on this information. In this paper, we consider the
state-of-the-art HAABSA++ algorithm for aspect-based sentiment analysis tasked
with identifying the sentiment expressed towards a given aspect in review
sentences. Specifically, we train the neural network part of this algorithm
using an adversarial network, a novel machine learning training method where a
generator network tries to fool the classifier network by generating highly
realistic new samples, as such increasing robustness. This method, as of yet
never in its classical form applied to aspect-based sentiment analysis, is
found to be able to considerably improve the out-of-sample accuracy of
HAABSA++: for the SemEval 2015 dataset, accuracy was increased from 81.7% to
82.5%, and for the SemEval 2016 task, accuracy increased from 84.4% to 87.3%.

Machine learning-based data-driven modeling can allow computationally
efficient time-dependent solutions of PDEs, such as those that describe
subsurface multiphysical problems. In this work, our previous approach of
conditional generative adversarial networks (cGAN) developed for the solution
of steady-state problems involving highly heterogeneous material properties is
extended to time-dependent problems by adopting the concept of continuous cGAN
(CcGAN). The CcGAN that can condition continuous variables is developed to
incorporate the time domain through either element-wise addition or conditional
batch normalization. We note that this approach can accommodate other
continuous variables (e.g., Young's modulus) similar to the time domain, which
makes this framework highly flexible and extendable. Moreover, this framework
can handle training data that contain different timestamps and then predict
timestamps that do not exist in the training data. As a numerical example, the
transient response of the coupled poroelastic process is studied in two
different permeability fields: Zinn \& Harvey transformation and a bimodal
transformation. The proposed CcGAN uses heterogeneous permeability fields as
input parameters while pressure and displacement fields over time are model
output. Our results show that the model provides sufficient accuracy with
computational speed-up. This robust framework will enable us to perform
real-time reservoir management and robust uncertainty quantification in
realistic problems.

Initial fault detection and diagnostics are imperative measures to improve
the efficiency, safety, and stability of vehicle operation. In recent years,
numerous studies have investigated data-driven approaches to improve the
vehicle diagnostics process using available vehicle data. Moreover, data-driven
methods are employed to enhance customer-service agent interactions. In this
study, we demonstrate a machine learning pipeline to improve automated vehicle
diagnostics. First, Natural Language Processing (NLP) is used to automate the
extraction of crucial information from free-text failure reports (generated
during customers' calls to the service department). Then, deep learning
algorithms are employed to validate service requests and filter vague or
misleading claims. Ultimately, different classification algorithms are
implemented to classify service requests so that valid service requests can be
directed to the relevant service department. The proposed model- Bidirectional
Long Short Term Memory (BiLSTM) along with Convolution Neural Network (CNN)-
shows more than 18\% accuracy improvement in validating service requests
compared to technicians' capabilities. In addition, using domain-based NLP
techniques at preprocessing and feature extraction stages along with CNN-BiLSTM
based request validation enhanced the accuracy ($>25\%$), sensitivity
($>39\%$), specificity ($>11\%$), and precision ($>11\%$) of Gradient Tree
Boosting (GTB) service classification model. The Receiver Operating
Characteristic Area Under the Curve (ROC-AUC) reached 0.82.

Despite governing heat management in any realistic device, the microscopic
mechanisms of heat transport in all-solid-state electrolytes are poorly known:
existing calculations, all based on simplistic semi-empirical models, are
unreliable for superionic conductors and largely overestimate their thermal
conductivity. In this work, we deploy a combination of state-of-the-art methods
to calculate the thermal conductivity of a prototypical Li-ion conductor, the
Li$_3$ClO antiperovskite. By leveraging ab initio, machine-learning, and
force-field descriptions of inter-atomic forces, we are able to reveal the
massive role of anharmonic interactions and diffusive defects on the thermal
conductivity and its temperature dependence, and to eventually embed their
effects into a simple rationale which is likely applicable to a wide class of
ionic conductors.

Predicting the future behavior of road users is one of the most challenging
and important problems in autonomous driving. Applying deep learning to this
problem requires fusing heterogeneous world state in the form of rich
perception signals and map information, and inferring highly multi-modal
distributions over possible futures. In this paper, we present MultiPath++, a
future prediction model that achieves state-of-the-art performance on popular
benchmarks. MultiPath++ improves the MultiPath architecture by revisiting many
design choices. The first key design difference is a departure from dense
image-based encoding of the input world state in favor of a sparse encoding of
heterogeneous scene elements: MultiPath++ consumes compact and efficient
polylines to describe road features, and raw agent state information directly
(e.g., position, velocity, acceleration). We propose a context-aware fusion of
these elements and develop a reusable multi-context gating fusion component.
Second, we reconsider the choice of pre-defined, static anchors, and develop a
way to learn latent anchor embeddings end-to-end in the model. Lastly, we
explore ensembling and output aggregation techniques -- common in other ML
domains -- and find effective variants for our probabilistic multimodal output
representation. We perform an extensive ablation on these design choices, and
show that our proposed model achieves state-of-the-art performance on the
Argoverse Motion Forecasting Competition and the Waymo Open Dataset Motion
Prediction Challenge.

To protect tropical forest biodiversity, we need to be able to detect it
reliably, cheaply, and at scale. Automated species detection from passively
recorded soundscapes via machine-learning approaches is a promising technique
towards this goal, but it is constrained by the necessity of large training
data sets. Using soundscapes from a tropical forest in Borneo and a
Convolutional Neural Network model (CNN) created with transfer learning, we
investigate i) the minimum viable training data set size for accurate
prediction of call types ('sonotypes'), and ii) the extent to which data
augmentation can overcome the issue of small training data sets. We found that
even relatively high sample sizes (> 80 per call type) lead to mediocre
accuracy, which however improves significantly with data augmentation,
including at extremely small sample sizes, regardless of taxonomic group or
call characteristics. Our results suggest that transfer learning and data
augmentation can make the use of CNNs to classify species' vocalizations
feasible even for small soundscape-based projects with many rare species.
Retraining our open-source model requires only basic programming skills which
makes it possible for individual conservation initiatives to match their local
context, in order to enable more evidence-informed management of biodiversity.

We study the problem of causal structure learning with essentially no
assumptions on the functional relationships and noise. We develop DAG-FOCI, a
computationally fast algorithm for this setting that is based on the FOCI
variable selection algorithm in~\cite{azadkia2021simple}. DAG-FOCI outputs the
set of parents of a response variable of interest. We provide theoretical
guarantees of our procedure when the underlying graph does not contain any
(undirected) cycle containing the response variable of interest. Furthermore,
in the absence of this assumption, we give a conservative guarantee against
false positive causal claims when the set of parents is identifiable. We
demonstrate the applicability of DAG-FOCI on simulated as well as a real
dataset from computational biology~\cite{sachs2005causal}.

Brain tumor segmentation is a critical task for tumor volumetric analyses and
AI algorithms. However, it is a time-consuming process and requires
neuroradiology expertise. While there has been extensive research focused on
optimizing brain tumor segmentation in the adult population, studies on AI
guided pediatric tumor segmentation are scarce. Furthermore, MRI signal
characteristics of pediatric and adult brain tumors differ, necessitating the
development of segmentation algorithms specifically designed for pediatric
brain tumors. We developed a segmentation model trained on magnetic resonance
imaging (MRI) of pediatric patients with low-grade gliomas (pLGGs) from The
Hospital for Sick Children (Toronto, Ontario, Canada). The proposed model
utilizes deep Multitask Learning (dMTL) by adding tumor's genetic alteration
classifier as an auxiliary task to the main network, ultimately improving the
accuracy of the segmentation results.

System-on-chip (SoC) developers increasingly rely on pre-verified hardware
intellectual property (IP) blocks acquired from untrusted third-party vendors.
These IPs might contain hidden malicious functionalities or hardware Trojans to
compromise the security of the fabricated SoCs. Recently, supervised machine
learning (ML) techniques have shown promising capability in identifying nets of
potential Trojans in third party IPs (3PIPs). However, they bring several major
challenges. First, they do not guide us to an optimal choice of features that
reliably covers diverse classes of Trojans. Second, they require multiple
Trojan-free/trusted designs to insert known Trojans and generate a trained
model. Even if a set of trusted designs are available for training, the suspect
IP could be inherently very different from the set of trusted designs, which
may negatively impact the verification outcome. Third, these techniques only
identify a set of suspect Trojan nets that require manual intervention to
understand the potential threat. In this paper, we present VIPR, a systematic
machine learning (ML) based trust verification solution for 3PIPs that
eliminates the need for trusted designs for training. We present a
comprehensive framework, associated algorithms, and a tool flow for obtaining
an optimal set of features, training a targeted machine learning model,
detecting suspect nets, and identifying Trojan circuitry from the suspect nets.
We evaluate the framework on several Trust-Hub Trojan benchmarks and provide a
comparative analysis of detection performance across different trained models,
selection of features, and post-processing techniques. The proposed
post-processing algorithms reduce false positives by up to 92.85%.

In the last decade, data-driven algorithms outperformed traditional
optimization-based algorithms in many research areas, such as computer vision,
natural language processing, etc. However, extensive data usages bring a new
challenge or even threat to deep learning algorithms, i.e., privacy-preserving.
Distributed training strategies have recently become a promising approach to
ensure data privacy when training deep models. This paper extends conventional
serverless platforms with serverless edge learning architectures and provides
an efficient distributed training framework from the networking perspective.
This framework dynamically orchestrates available resources among heterogeneous
physical units to efficiently fulfill deep learning objectives. The design
jointly considers learning task requests and underlying infrastructure
heterogeneity, including last-mile transmissions, computation abilities of
mobile devices, edge and cloud computing centers, and devices battery status.
Furthermore, to significantly reduce distributed training overheads,
small-scale data training is proposed by integrating with a general, simple
data classifier. This low-load enhancement can seamlessly work with various
distributed deep models to improve communications and computation efficiencies
during the training phase. Finally, open challenges and future research
directions encourage the research community to develop efficient distributed
deep learning techniques.

There is an increasing interest from ML and HCI communities in empowering
creators with better generative models and more intuitive interfaces with which
to control them. In music, ML researchers have focused on training models
capable of generating pieces with increasing long-range structure and musical
coherence, while HCI researchers have separately focused on designing steering
interfaces that support user control and ownership. In this study, we
investigate through a common framework how developments in both models and user
interfaces are important for empowering co-creation where the goal is to create
music that communicates particular imagery or ideas (e.g., as is common for
other purposeful tasks in music creation like establishing mood or creating
accompanying music for another media). Our study is distinguished in that it
measures communication through both composer's self-reported experiences, and
how listeners evaluate this communication through the music. In an evaluation
study with 26 composers creating 100+ pieces of music and listeners providing
1000+ head-to-head comparisons, we find that more expressive models and more
steerable interfaces are important and complementary ways to make a difference
in composers communicating through music and supporting their creative
empowerment.

Deriving metallicities for solar-like stars follows well-established methods,
but for cooler stars such as M dwarfs, the determination is much more
complicated due to forests of molecular lines that are present. Several methods
have been developed in recent years to determine accurate stellar parameters
for these cool stars ($T_{\rm eff} \lesssim$ 4000 K). However, significant
differences can be found at times when comparing metallicities for the same
star derived using different methods. In this work, we determine the effective
temperatures, surface gravities, and metallicities of 18 well-studied M dwarfs
observed with the CARMENES high-resolution spectrograph following different
approaches, including synthetic spectral fitting, analysis of pseudo-equivalent
widths, and machine learning. We analyzed the discrepancies in the derived
stellar parameters, including metallicity, in several analysis runs. Our goal
is to minimize these discrepancies and find stellar parameters that are more
consistent with the literature values. We attempted to achieve this consistency
by standardizing the most commonly used components, such as wavelength ranges,
synthetic model spectra, continuum normalization methods, and stellar
parameters. We conclude that although such modifications work quite well for
hotter main-sequence stars, they do not improve the consistency in stellar
parameters for M dwarfs, leading to mean deviations of around 50-200 K in
temperature and 0.1-0.3 dex in metallicity. In particular, M dwarfs are much
more complex and a standardization of the aforementioned components cannot be
considered as a straightforward recipe for bringing consistency to the derived
parameters. Further in-depth investigations of the employed methods would be
necessary in order to identify and correct for the discrepancies that remain.

Classical image denoising methods utilize the non-local self-similarity
principle to effectively recover image content from noisy images. Current
state-of-the-art methods use deep convolutional neural networks (CNNs) to
effectively learn the mapping from noisy to clean images. Deep denoising CNNs
manifest a high learning capacity and integrate non-local information owing to
the large receptive field yielded by numerous cascade of hidden layers.
However, deep networks are also computationally complex and require large data
for training. To address these issues, this study draws the focus on the
Self-organized Operational Neural Networks (Self-ONNs) empowered by a novel
neuron model that can achieve a similar or better denoising performance with a
compact and shallow model. Recently, the concept of super-neurons has been
introduced which augment the non-linear transformations of generative neurons
by utilizing non-localized kernel locations for an enhanced receptive field
size. This is the key accomplishment which renders the need for a deep network
configuration. As the integration of non-local information is known to benefit
denoising, in this work we investigate the use of super neurons for both
synthetic and real-world image denoising. We also discuss the practical issues
in implementing the super neuron model on GPUs and propose a trade-off between
the heterogeneity of non-localized operations and computational complexity. Our
results demonstrate that with the same width and depth, Self-ONNs with super
neurons provide a significant boost of denoising performance over the networks
with generative and convolutional neurons for both denoising tasks. Moreover,
results demonstrate that Self-ONNs with super neurons can achieve a competitive
and superior synthetic denoising performances than well-known deep CNN
denoisers for synthetic and real-world denoising, respectively.

In this paper, we consider the challenge of face morphing attacks, which
substantially undermine the integrity of face recognition systems such as those
adopted for use in border protection agencies. Morph detection can be
formulated as extracting fine-grained representations, where local
discriminative features are harnessed for learning a hypothesis. To acquire
discriminative features at different granularity as well as a decoupled
spectral information, we leverage wavelet domain analysis to gain insight into
the spatial-frequency content of a morphed face. As such, instead of using
images in the RGB domain, we decompose every image into its wavelet sub-bands
using 2D wavelet decomposition and a deep supervised feature selection scheme
is employed to find the most discriminative wavelet sub-bands of input images.
To this end, we train a Deep Neural Network (DNN) morph detector using the
decomposed wavelet sub-bands of the morphed and bona fide images. In the
training phase, our structured group sparsity-constrained DNN picks the most
discriminative wavelet sub-bands out of all the sub-bands, with which we
retrain our DNN, resulting in a precise detection of morphed images when
inference is achieved on a probe image. The efficacy of our deep morph detector
which is enhanced by structured group lasso is validated through experiments on
three facial morph image databases, i.e., VISAPP17, LMA, and MorGAN.

Traditional AI approaches in customized (personalized) contextual pricing
applications assume that the data distribution at the time of online pricing is
similar to that observed during training. However, this assumption may be
violated in practice because of the dynamic nature of customer buying patterns,
particularly due to unanticipated system shocks such as COVID-19. We study the
changes in customer behavior for a major airline during the COVID-19 pandemic
by framing it as a covariate shift and concept drift detection problem. We
identify which customers changed their travel and purchase behavior and the
attributes affecting that change using (i) Fast Generalized Subset Scanning and
(ii) Causal Forests. In our experiments with simulated and real-world data, we
present how these two techniques can be used through qualitative analysis.

Lithium-ion batteries degrade due to usage and exposure to environmental
conditions, which affects their capability to store energy and supply power.
Accurately predicting the capacity and power fade of lithium-ion battery cells
is challenging due to intrinsic manufacturing variances and coupled nonlinear
ageing mechanisms. In this paper, we propose a data-driven prognostics
framework to predict both capacity and power fade simultaneously with
multi-task learning. The model is able to predict the degradation trajectory of
both capacity and internal resistance together with knee-points and end-of-life
points accurately at early-life stage. The validation shows an average
percentage error of 2.37% and 1.24% for the prediction of capacity fade and
resistance rise, respectively. The model's ability to accurately predict the
degradation, facing capacity and resistance estimation errors, further
demonstrates the model's robustness and generalizability. Compared with
single-task learning models for forecasting capacity and power degradation, the
model shows a significant prediction accuracy improvement and computational
cost reduction. This work presents the highlights of multi-task learning in the
degradation prognostics for lithium-ion batteries.

Sketches are abstract representations of visual perception and visuospatial
construction. In this work, we proposed a new framework, Generative Adversarial
Networks with Conditional Neural Movement Primitives (GAN-CNMP), that
incorporates a novel adversarial loss on CNMP to increase sketch smoothness and
consistency. Through the experiments, we show that our model can be trained
with few unlabeled samples, can construct distributions automatically in the
latent space, and produces better results than the base model in terms of shape
consistency and smoothness.

Recent studies on learning with noisy labels have shown remarkable
performance by exploiting a small clean dataset. In particular, model agnostic
meta-learning-based label correction methods further improve performance by
correcting noisy labels on the fly. However, there is no safeguard on the label
miscorrection, resulting in unavoidable performance degradation. Moreover,
every training step requires at least three back-propagations, significantly
slowing down the training speed. To mitigate these issues, we propose a robust
and efficient method that learns a label transition matrix on the fly.
Employing the transition matrix makes the classifier skeptical about all the
corrected samples, which alleviates the miscorrection issue. We also introduce
a two-head architecture to efficiently estimate the label transition matrix
every iteration within a single back-propagation, so that the estimated matrix
closely follows the shifting noise distribution induced by label correction.
Extensive experiments demonstrate that our approach shows the best performance
in training efficiency while having comparable or better accuracy than existing
methods.

Awareness detection technologies have been gaining traction in a variety of
enterprises; most often used for driver fatigue detection, recent research has
shifted towards using computer vision technologies to analyze user attention in
environments such as online classrooms. This paper aims to extend previous
research on distraction detection by analyzing which visual features contribute
most to predicting awareness and fatigue. We utilized the open source facial
analysis toolkit OpenFace in order to analyze visual data of subjects at
varying levels of attentiveness. Then, using a Support-Vector Machine (SVM) we
created several prediction models for user attention and identified Histogram
of Oriented Gradients (HOG) and Action Units to be the greatest predictors of
the features we tested. We also compared the performance of this SVM to deep
learning approaches that utilize Convolutional and/or Recurrent neural networks
(CNN's and CRNN's). Interestingly, CRNN's did not appear to perform
significantly better than their CNN counterparts. While deep learning methods
achieved greater prediction accuracy, SVMs utilized less resources and, using
certain parameters, were able to approach the performance of deep learning
methods.

One barrier to more widespread adoption of differentially private neural
networks is the entailed accuracy loss. To address this issue, the relationship
between neural network architectures and model accuracy under differential
privacy constraints needs to be better understood. As a first step, we test
whether extant knowledge on architecture design also holds in the
differentially private setting. Our findings show that it does not;
architectures that perform well without differential privacy, do not
necessarily do so with differential privacy. Consequently, extant knowledge on
neural network architecture design cannot be seamlessly translated into the
differential privacy context. Future research is required to better understand
the relationship between neural network architectures and model accuracy to
enable better architecture design choices under differential privacy

We describe Countersynth, a conditional generative model of diffeomorphic
deformations that induce label-driven, biologically plausible changes in
volumetric brain images. The model is intended to synthesise counterfactual
training data augmentations for downstream discriminative modelling tasks where
fidelity is limited by data imbalance, distributional instability, confounding,
or underspecification, and exhibits inequitable performance across distinct
subpopulations. Focusing on demographic attributes, we evaluate the quality of
synthesized counterfactuals with voxel-based morphometry, classification and
regression of the conditioning attributes, and the Fr\'{e}chet inception
distance. Examining downstream discriminative performance in the context of
engineered demographic imbalance and confounding, we use UK Biobank magnetic
resonance imaging data to benchmark CounterSynth augmentation against current
solutions to these problems. We achieve state-of-the-art improvements, both in
overall fidelity and equity. The source code for CounterSynth is available
online.

Formal reasoning about hashing-based probabilistic data structures often
requires reasoning about random variables where when one variable gets larger
(such as the number of elements hashed into one bucket), the others tend to be
smaller (like the number of elements hashed into the other buckets). This is an
example of negative dependence, a generalization of probabilistic independence
that has recently found interesting applications in algorithm design and
machine learning. Despite the usefulness of negative dependence for the
analyses of probabilistic data structures, existing verification methods cannot
establish this property for randomized programs.
  To fill this gap, we design LINA, a probabilistic separation logic for
reasoning about negative dependence. Following recent works on probabilistic
separation logic using separating conjunction to reason about the probabilistic
independence of random variables, we use separating conjunction to reason about
negative dependence. Our assertion logic features two separating conjunctions,
one for independence and one for negative dependence. We generalize the logic
of bunched implications (BI) to support multiple separating conjunctions, and
provide a sound and complete proof system. Notably, the semantics for
separating conjunction relies on a non-deterministic, rather than partial,
operation for combining resources. By drawing on closure properties for
negative dependence, our program logic supports a Frame-like rule for negative
dependence and monotone operations. We demonstrate how LINA can verify
probabilistic properties of hash-based data structures and balls-into-bins

Physical simulation-based optimization is a common task in science and
engineering. Many such simulations produce image- or tensor-based outputs where
the desired objective is a function of those outputs, and optimization is
performed over a high-dimensional parameter space. We develop a Bayesian
optimization method leveraging tensor-based Gaussian process surrogates and
trust region Bayesian optimization to effectively model the image outputs and
to efficiently optimize these types of simulations, including a radio-frequency
tower configuration problem and an optical design problem.

Simulations of colloidal suspensions consisting of mesoscopic particles and
smaller species such as ions or depletants are computationally challenging as
different length and time scales are involved. Here, we introduce a machine
learning (ML) approach in which the degrees of freedom of the microscopic
species are integrated out and the mesoscopic particles interact with effective
many-body potentials, which we fit as a function of all colloid coordinates
with a set of symmetry functions. We apply this approach to a colloid-polymer
mixture. Remarkably, the ML potentials can be assumed to be effectively
state-independent and can be used in direct-coexistence simulations. We show
that our ML method reduces the computational cost by several orders of
magnitude compared to a numerical evaluation and accurately describes the phase
behavior and structure, even for state points where the effective potential is
largely determined by many-body contributions.

We introduce the RadixStringSpline (RSS) learned index structure for
efficiently indexing strings. RSS is a tree of radix splines each indexing a
fixed number of bytes. RSS approaches or exceeds the performance of traditional
string indexes while using 7-70$\times$ less memory. RSS achieves this by using
the minimal string prefix to sufficiently distinguish the data unlike most
learned approaches which index the entire string. Additionally, the
bounded-error nature of RSS accelerates the last mile search and also enables a
memory-efficient hash-table lookup accelerator. We benchmark RSS on several
real-world string datasets against ART and HOT. Our experiments suggest this
line of research may be promising for future memory-intensive database

Despite the recent advances in multi-task learning of dense prediction
problems, most methods rely on expensive labelled datasets. In this paper, we
present a label efficient approach and look at jointly learning of multiple
dense prediction tasks on partially annotated data (i.e. not all the task
labels are available for each image), which we call multi-task
partially-supervised learning. We propose a multi-task training procedure that
successfully leverages task relations to supervise its multi-task learning when
data is partially annotated. In particular, we learn to map each task pair to a
joint pairwise task-space which enables sharing information between them in a
computationally efficient way through another network conditioned on task
pairs, and avoids learning trivial cross-task relations by retaining high-level
information about the input image. We rigorously demonstrate that our proposed
method effectively exploits the images with unlabelled tasks and outperforms
existing semi-supervised learning approaches and related methods on three
standard benchmarks.

Koopman operators are infinite-dimensional operators that globally linearize
nonlinear dynamical systems, making their spectral information useful for
understanding dynamics. However, Koopman operators can have continuous spectra
and infinite-dimensional invariant subspaces, making computing their spectral
information a considerable challenge. This paper describes data-driven
algorithms with rigorous convergence guarantees for computing spectral
information of Koopman operators from trajectory data. We introduce residual
dynamic mode decomposition (ResDMD), which provides the first scheme for
computing the spectra and pseudospectra of general Koopman operators from
snapshot data without spectral pollution. Using the resolvent operator and
ResDMD, we also compute smoothed approximations of spectral measures associated
with measure-preserving dynamical systems. We prove explicit convergence
theorems for our algorithms, which can achieve high-order convergence even for
chaotic systems, when computing the density of the continuous spectrum and the
discrete spectrum. We demonstrate our algorithms on the tent map, Gauss
iterated map, nonlinear pendulum, double pendulum, Lorenz system, and an
$11$-dimensional extended Lorenz system. Finally, we provide kernelized
variants of our algorithms for dynamical systems with a high-dimensional
state-space. This allows us to compute the spectral measure associated with the
dynamics of a protein molecule that has a 20,046-dimensional state-space, and
compute nonlinear Koopman modes with error bounds for turbulent flow past
aerofoils with Reynolds number $>10^5$ that has a 295,122-dimensional
state-space.

As acquiring pixel-wise annotations of real-world images for semantic
segmentation is a costly process, a model can instead be trained with more
accessible synthetic data and adapted to real images without requiring their
annotations. This process is studied in unsupervised domain adaptation (UDA).
Even though a large number of methods propose new adaptation strategies, they
are mostly based on outdated network architectures. As the influence of recent
network architectures has not been systematically studied, we first benchmark
different network architectures for UDA and newly reveal the potential of
Transformers for UDA semantic segmentation. Based on the findings, we propose a
novel UDA method, DAFormer. The network architecture of DAFormer consists of a
Transformer encoder and a multi-level context-aware feature fusion decoder. It
is enabled by three simple but crucial training strategies to stabilize the
training and to avoid overfitting to the source domain: While (1) Rare Class
Sampling on the source domain improves the quality of the pseudo-labels by
mitigating the confirmation bias of self-training toward common classes, (2) a
Thing-Class ImageNet Feature Distance and (3) a learning rate warmup promote
feature transfer from ImageNet pretraining. DAFormer represents a major advance
in UDA. It improves the state of the art by 10.8 mIoU for GTA-to-Cityscapes and
5.4 mIoU for Synthia-to-Cityscapes and enables learning even difficult classes
such as train, bus, and truck well. The implementation is available at
https://github.com/lhoyer/DAFormer.

We present new constraints on the masses of the halos hosting the Milky Way
and Andromeda galaxies derived using graph neural networks. Our models, trained
on thousands of state-of-the-art hydrodynamic simulations of the CAMELS
project, only make use of the positions, velocities and stellar masses of the
galaxies belonging to the halos, and are able to perform likelihood-free
inference on halo masses while accounting for both cosmological and
astrophysical uncertainties. Our constraints are in agreement with estimates
from other traditional methods.

The nonuniform quantization strategy for compressing neural networks usually
achieves better performance than its counterpart, i.e., uniform strategy, due
to its superior representational capacity. However, many nonuniform
quantization methods overlook the complicated projection process in
implementing the nonuniformly quantized weights/activations, which incurs
non-negligible time and space overhead in hardware deployment. In this study,
we propose Nonuniform-to-Uniform Quantization (N2UQ), a method that can
maintain the strong representation ability of nonuniform methods while being
hardware-friendly and efficient as the uniform quantization for model
inference. We achieve this through learning the flexible in-equidistant input
thresholds to better fit the underlying distribution while quantizing these
real-valued inputs into equidistant output levels. To train the quantized
network with learnable input thresholds, we introduce a generalized
straight-through estimator (G-STE) for intractable backward derivative
calculation w.r.t. threshold parameters. Additionally, we consider entropy
preserving regularization to further reduce information loss in weight
quantization. Even under this adverse constraint of imposing uniformly
quantized weights and activations, our N2UQ outperforms state-of-the-art
nonuniform quantization methods by 0.5~1.7 on ImageNet, demonstrating the
contribution of N2UQ design. Code and models are available at:
https://github.com/liuzechun/Nonuniform-to-Uniform-Quantization.

Recent advances in high-fidelity semantic image editing heavily rely on the
presumably disentangled latent spaces of the state-of-the-art generative
models, such as StyleGAN. Specifically, recent works show that it is possible
to achieve decent controllability of attributes in face images via linear
shifts along with latent directions. Several recent methods address the
discovery of such directions, implicitly assuming that the state-of-the-art
GANs learn the latent spaces with inherently linearly separable attribute
distributions and semantic vector arithmetic properties.
  In our work, we show that nonlinear latent code manipulations realized as
flows of a trainable Neural ODE are beneficial for many practical non-face
image domains with more complex non-textured factors of variation. In
particular, we investigate a large number of datasets with known attributes and
demonstrate that certain attribute manipulations are challenging to obtain with
linear shifts only.

Fitting parametric models of human bodies, hands or faces to sparse input
signals in an accurate, robust, and fast manner has the promise of
significantly improving immersion in AR and VR scenarios. A common first step
in systems that tackle these problems is to regress the parameters of the
parametric model directly from the input data. This approach is fast, robust,
and is a good starting point for an iterative minimization algorithm. The
latter searches for the minimum of an energy function, typically composed of a
data term and priors that encode our knowledge about the problem's structure.
While this is undoubtedly a very successful recipe, priors are often hand
defined heuristics and finding the right balance between the different terms to
achieve high quality results is a non-trivial task. Furthermore, converting and
optimizing these systems to run in a performant way requires custom
implementations that demand significant time investments from both engineers
and domain experts. In this work, we build upon recent advances in learned
optimization and propose an update rule inspired by the classic
Levenberg-Marquardt algorithm. We show the effectiveness of the proposed neural
optimizer on three problems, 3D body estimation from a head-mounted device, 3D
body estimation from sparse 2D keypoints and face surface estimation from dense
2D landmarks. Our method can easily be applied to new model fitting problems
and offers a competitive alternative to well-tuned 'traditional' model fitting
pipelines, both in terms of accuracy and speed.

We present the vector quantized diffusion (VQ-Diffusion) model for
text-to-image generation. This method is based on a vector quantized
variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional
variant of the recently developed Denoising Diffusion Probabilistic Model
(DDPM). We find that this latent-space method is well-suited for text-to-image
generation tasks because it not only eliminates the unidirectional bias with
existing methods but also allows us to incorporate a mask-and-replace diffusion
strategy to avoid the accumulation of errors, which is a serious problem with
existing methods. Our experiments show that the VQ-Diffusion produces
significantly better text-to-image generation results when compared with
conventional autoregressive (AR) models with similar numbers of parameters.
Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can
handle more complex scenes and improve the synthesized image quality by a large
margin. Finally, we show that the image generation computation in our method
can be made highly efficient by reparameterization. With traditional AR
methods, the text-to-image generation time increases linearly with the output
image resolution and hence is quite time consuming even for normal size images.
The VQ-Diffusion allows us to achieve a better trade-off between quality and
speed. Our experiments indicate that the VQ-Diffusion model with the
reparameterization is fifteen times faster than traditional AR methods while
achieving a better image quality.

The referring video object segmentation task (RVOS) involves segmentation of
a text-referred object instance in the frames of a given video. Due to the
complex nature of this multimodal task, which combines text reasoning, video
understanding, instance segmentation and tracking, existing approaches
typically rely on sophisticated pipelines in order to tackle it. In this paper,
we propose a simple Transformer-based approach to RVOS. Our framework, termed
Multimodal Tracking Transformer (MTTR), models the RVOS task as a sequence
prediction problem. Following recent advancements in computer vision and
natural language processing, MTTR is based on the realization that video and
text can be processed together effectively and elegantly by a single multimodal
Transformer model. MTTR is end-to-end trainable, free of text-related inductive
bias components and requires no additional mask-refinement post-processing
steps. As such, it simplifies the RVOS pipeline considerably compared to
existing methods. Evaluation on standard benchmarks reveals that MTTR
significantly outperforms previous art across multiple metrics. In particular,
MTTR shows impressive +5.7 and +5.0 mAP gains on the A2D-Sentences and
JHMDB-Sentences datasets respectively, while processing 76 frames per second.
In addition, we report strong results on the public validation set of
Refer-YouTube-VOS, a more challenging RVOS dataset that has yet to receive the
attention of researchers. The code to reproduce our experiments is available at
https://github.com/mttr2021/MTTR

Learning behavioral patterns from observational data has been a de-facto
approach to motion forecasting. Yet, the current paradigm suffers from two
shortcomings: brittle under distribution shifts and inefficient for knowledge
transfer. In this work, we propose to address these challenges from a causal
representation perspective. We first introduce a causal formalism of motion
forecasting, which casts the problem as a dynamic process with three groups of
latent variables, namely invariant variables, style confounders, and spurious
features. We then introduce a learning framework that treats each group
separately: (i) unlike the common practice mixing datasets collected from
different locations, we exploit their subtle distinctions by means of an
invariance loss encouraging the model to suppress spurious correlations; (ii)
we devise a modular architecture that factorizes the representations of
invariant mechanisms and style confounders to approximate a sparse causal
graph; (iii) we introduce a style contrastive loss that not only enforces the
structure of style representations but also serves as a self-supervisory signal
for test-time refinement on the fly. Experiments on synthetic and real datasets
show that our proposed method improves the robustness and reusability of
learned motion representations, significantly outperforming prior
state-of-the-art motion forecasting models for out-of-distribution
generalization and low-shot transfer.

We present Point-BERT, a new paradigm for learning Transformers to generalize
the concept of BERT to 3D point cloud. Inspired by BERT, we devise a Masked
Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically,
we first divide a point cloud into several local point patches, and a point
cloud Tokenizer with a discrete Variational AutoEncoder (dVAE) is designed to
generate discrete point tokens containing meaningful local information. Then,
we randomly mask out some patches of input point clouds and feed them into the
backbone Transformers. The pre-training objective is to recover the original
point tokens at the masked locations under the supervision of point tokens
obtained by the Tokenizer. Extensive experiments demonstrate that the proposed
BERT-style pre-training strategy significantly improves the performance of
standard point cloud Transformers. Equipped with our pre-training strategy, we
show that a pure Transformer architecture attains 93.8% accuracy on ModelNet40
and 83.1% accuracy on the hardest setting of ScanObjectNN, surpassing carefully
designed point cloud models with much fewer hand-made designs. We also
demonstrate that the representations learned by Point-BERT transfer well to new
tasks and domains, where our models largely advance the state-of-the-art of
few-shot point cloud classification task. The code and pre-trained models are
available at https://github.com/lulutang0608/Point-BERT

Natural language offers a highly intuitive interface for image editing. In
this paper, we introduce the first solution for performing local (region-based)
edits in generic natural images, based on a natural language description along
with an ROI mask. We achieve our goal by leveraging and combining a pretrained
language-image model (CLIP), to steer the edit towards a user-provided text
prompt, with a denoising diffusion probabilistic model (DDPM) to generate
natural-looking results. To seamlessly fuse the edited region with the
unchanged parts of the image, we spatially blend noised versions of the input
image with the local text-guided diffusion latent at a progression of noise
levels. In addition, we show that adding augmentations to the diffusion process
mitigates adversarial results. We compare against several baselines and related
methods, both qualitatively and quantitatively, and show that our method
outperforms these solutions in terms of overall realism, ability to preserve
the background and matching the text. Finally, we show several text-driven
editing applications, including adding a new object to an image,
removing/replacing/altering existing objects, background replacement, and image
extrapolation. Code is available at:
https://omriavrahami.com/blended-diffusion-page/

A colored Gaussian graphical model is a linear concentration model in which
equalities among the concentrations are specified by a coloring of an
underlying graph. The model is called RCOP if this coloring is given by the
edge and vertex orbits of a subgroup of the automorphism group of the graph. We
show that RCOP Gaussian graphical models on block graphs are toric in the space
of covariance matrices and we describe Markov bases for them. To this end, we
learn more about the combinatorial structure of these models and their
connection with Jordan algebras.

Millimeter-wave (mmWave) and terahertz (THz) communications require
beamforming to acquire adequate receive signal-to-noise ratio (SNR). To find
the optimal beam, current beam management solutions perform beam training over
a large number of beams in pre-defined codebooks. The beam training overhead
increases the access latency and can become infeasible for high-mobility
applications. To reduce or even eliminate this beam training overhead, we
propose to utilize the visual data, captured for example by cameras at the base
stations, to guide the beam tracking/refining process. We propose a machine
learning (ML) framework, based on an encoder-decoder architecture, that can
predict the future beams using the previously obtained visual sensing
information. Our proposed approach is evaluated on a large-scale real-world
dataset, where it achieves an accuracy of $64.47\%$ (and a normalized receive
power of $97.66\%$) in predicting the future beam. This is achieved while
requiring less than $1\%$ of the beam training overhead of a corresponding
baseline solution that uses a sequence of previous beams to predict the future
one. This high performance and low overhead obtained on the real-world dataset
demonstrate the potential of the proposed vision-aided beam tracking approach

Generic Event Boundary Detection (GEBD) is a newly suggested video
understanding task that aims to find one level deeper semantic boundaries of
events. Bridging the gap between natural human perception and video
understanding, it has various potential applications, including interpretable
and semantically valid video parsing. Still at an early development stage,
existing GEBD solvers are simple extensions of relevant video understanding
tasks, disregarding GEBD's distinctive characteristics. In this paper, we
propose a novel framework for unsupervised/supervised GEBD, by using the
Temporal Self-similarity Matrix (TSM) as the video representation. The new
Recursive TSM Parsing (RTP) algorithm exploits local diagonal patterns in TSM
to detect boundaries, and it is combined with the Boundary Contrastive (BoCo)
loss to train our encoder to generate more informative TSMs. Our framework can
be applied to both unsupervised and supervised settings, with both achieving
state-of-the-art performance by a huge margin in GEBD benchmark. Especially,
our unsupervised method outperforms the previous state-of-the-art "supervised"
model, implying its exceptional efficacy.

Recent advances show that semi-supervised implicit representation learning
can be achieved through physical constraints like Eikonal equations. However,
this scheme has not yet been successfully used for LiDAR point cloud data, due
to its spatially varying sparsity. In this paper, we develop a novel
formulation that conditions the semi-supervised implicit function on localized
shape embeddings. It exploits the strong representation learning power of
sparse convolutional networks to generate shape-aware dense feature volumes,
while still allows semi-supervised signed distance function learning without
knowing its exact values at free space. With extensive quantitative and
qualitative results, we demonstrate intrinsic properties of this new learning
system and its usefulness in real-world road scenes. Notably, we improve IoU
from 26.3% to 51.0% on SemanticKITTI. Moreover, we explore two paradigms to
integrate semantic label predictions, achieving implicit semantic completion.
Code and models can be accessed at https://github.com/OPEN-AIR-SUN/SISC.

Chart question answering (CQA) is a task used for assessing chart
comprehension, which is fundamentally different from understanding natural
images. CQA requires analyzing the relationships between the textual and the
visual components of a chart, in order to answer general questions or infer
numerical values. Most existing CQA datasets and models are based on
simplifying assumptions that often enable surpassing human performance. In this
work, we address this outcome and propose a new model that jointly learns
classification and regression. Our language-vision setup uses co-attention
transformers to capture the complex real-world interactions between the
question and the textual elements. We validate our design with extensive
experiments on the realistic PlotQA dataset, outperforming previous approaches
by a large margin, while showing competitive performance on FigureQA. Our model
is particularly well suited for realistic questions with out-of-vocabulary
answers that require regression.

Vision Transformers (ViT)s have shown great performance in self-supervised
learning of global and local representations that can be transferred to
downstream applications. Inspired by these results, we introduce a novel
self-supervised learning framework with tailored proxy tasks for medical image
analysis. Specifically, we propose: (i) a new 3D transformer-based model,
dubbed Swin UNEt TRansformers (Swin UNETR), with a hierarchical encoder for
self-supervised pre-training; (ii) tailored proxy tasks for learning the
underlying pattern of human anatomy. We demonstrate successful pre-training of
the proposed model on 5,050 publicly available computed tomography (CT) images
from various body organs. The effectiveness of our approach is validated by
fine-tuning the pre-trained models on the Beyond the Cranial Vault (BTCV)
Segmentation Challenge with 13 abdominal organs and segmentation tasks from the
Medical Segmentation Decathlon (MSD) dataset. Our model is currently the
state-of-the-art (i.e. ranked 1st) on the public test leaderboards of both MSD
and BTCV datasets. Code: https://monai.io/research/swin-unetr

The need to approximate functions is ubiquitous in science, either due to
empirical constraints or high computational cost of accessing the function. In
high-energy physics, the precise computation of the scattering cross-section of
a process requires the evaluation of computationally intensive integrals. A
wide variety of methods in machine learning have been used to tackle this
problem, but often the motivation of using one method over another is lacking.
Comparing these methods is typically highly dependent on the problem at hand,
so we specify to the case where we can evaluate the function a large number of
times, after which quick and accurate evaluation can take place. We consider
four interpolation and three machine learning techniques and compare their
performance on three toy functions, the four-point scalar Passarino-Veltman
$D_0$ function, and the two-loop self-energy master integral $M$. We find that
in low dimensions ($d = 3$), traditional interpolation techniques like the
Radial Basis Function perform very well, but in higher dimensions ($d=5, 6, 9$)
we find that multi-layer perceptrons (a.k.a neural networks) do not suffer as
much from the curse of dimensionality and provide the fastest and most accurate

In many real-world applications of combinatorial bandits such as content
caching, rewards must be maximized while satisfying minimum service
requirements. In addition, base arm availabilities vary over time, and actions
need to be adapted to the situation to maximize the rewards. We propose a new
bandit model called Contextual Combinatorial Volatile Bandits with Group
Thresholds to address these challenges. Our model subsumes combinatorial
bandits by considering super arms to be subsets of groups of base arms. We seek
to maximize super arm rewards while satisfying thresholds of all base arm
groups that constitute a super arm. To this end, we define a new notion of
regret that merges super arm reward maximization with group reward
satisfaction. To facilitate learning, we assume that the mean outcomes of base
arms are samples from a Gaussian Process indexed by the context set ${\cal X}$,
and the expected reward is Lipschitz continuous in expected base arm outcomes.
We propose an algorithm, called Thresholded Combinatorial Gaussian Process
Upper Confidence Bounds (TCGP-UCB), that balances between maximizing cumulative
reward and satisfying group reward thresholds and prove that it incurs
$\tilde{O}(K\sqrt{T\overline{\gamma}_{T}} )$ regret with high probability,
where $\overline{\gamma}_{T}$ is the maximum information gain associated with
the set of base arm contexts that appeared in the first $T$ rounds and $K$ is
the maximum super arm cardinality of any feasible action over all rounds. We
show in experiments that our algorithm accumulates a reward comparable with
that of the state-of-the-art combinatorial bandit algorithm while picking
actions whose groups satisfy their thresholds.

Advection-diffusion equations describe a large family of natural transport
processes, e.g., fluid flow, heat transfer, and wind transport. They are also
used for optical flow and perfusion imaging computations. We develop a machine
learning model, D^2-SONATA, built upon a stochastic advection-diffusion
equation, which predicts the velocity and diffusion fields that drive 2D/3D
image time-series of transport. In particular, our proposed model incorporates
a model of transport atypicality, which isolates abnormal differences between
expected normal transport behavior and the observed transport. In a medical
context such a normal-abnormal decomposition can be used, for example, to
quantify pathologies. Specifically, our model identifies the advection and
diffusion contributions from the transport time-series and simultaneously
predicts an anomaly value field to provide a decomposition into normal and
abnormal advection and diffusion behavior. To achieve improved estimation
performance for the velocity and diffusion-tensor fields underlying the
advection-diffusion process and for the estimation of the anomaly fields, we
create a 2D/3D anomaly-encoded advection-diffusion simulator, which allows for
supervised learning. We further apply our model on a brain perfusion dataset
from ischemic stroke patients via transfer learning. Extensive comparisons
demonstrate that our model successfully distinguishes stroke lesions (abnormal)
from normal brain regions, while reconstructing the underlying velocity and
diffusion tensor fields.

We introduce NeuralProphet, a successor to Facebook Prophet, which set an
industry standard for explainable, scalable, and user-friendly forecasting
frameworks. With the proliferation of time series data, explainable forecasting
remains a challenging task for business and operational decision making. Hybrid
solutions are needed to bridge the gap between interpretable classical methods
and scalable deep learning models. We view Prophet as a precursor to such a
solution. However, Prophet lacks local context, which is essential for
forecasting the near-term future and is challenging to extend due to its Stan
backend.
  NeuralProphet is a hybrid forecasting framework based on PyTorch and trained
with standard deep learning methods, making it easy for developers to extend
the framework. Local context is introduced with auto-regression and covariate
modules, which can be configured as classical linear regression or as Neural
Networks. Otherwise, NeuralProphet retains the design philosophy of Prophet and
provides the same basic model components.
  Our results demonstrate that NeuralProphet produces interpretable forecast
components of equivalent or superior quality to Prophet on a set of generated
time series. NeuralProphet outperforms Prophet on a diverse collection of
real-world datasets. For short to medium-term forecasts, NeuralProphet improves
forecast accuracy by 55 to 92 percent.

The design of efficient hardware accelerators for high-throughput
data-processing applications, e.g., deep neural networks, is a challenging task
in computer architecture design. In this regard, High-Level Synthesis (HLS)
emerges as a solution for fast prototyping application-specific hardware
starting from a behavioural description of the application computational flow.
This Design-Space Exploration (DSE) aims at identifying Pareto optimal
synthesis configurations whose exhaustive search is often unfeasible due to the
design-space dimensionality and the prohibitive computational cost of the
synthesis process. Within this framework, we effectively and efficiently
address the design problem by proposing, for the first time in the literature,
graph neural networks that jointly predict acceleration performance and
hardware costs of a synthesized behavioral specification given optimization
directives. The learned model can be used to rapidly approach the Pareto curve
by guiding the DSE, taking into account performance and cost estimates. The
proposed method outperforms traditional HLS-driven DSE approaches, by
accounting for arbitrary length of computer programs and the invariant
properties of the input. We propose a novel hybrid control and data flow graph
representation that enables training the graph neural network on specifications
of different hardware accelerators; the methodology naturally transfers to
unseen data-processing applications too. Moreover, we show that our approach
achieves prediction accuracy comparable with that of commonly used simulators
without having access to analytical models of the HLS compiler and the target
FPGA, while being orders of magnitude faster. Finally, the learned
representation can be exploited for DSE in unexplored configuration spaces by
fine-tuning on a small number of samples from the new target domain.

The solution methods used to realize artificial general intelligence (AGI)
may not contain the formalism needed to adequately model and characterize AGI.
In particular, current approaches to learning hold notions of problem domain
and problem task as fundamental precepts, but it is hardly apparent that an AGI
encountered in the wild will be discernable into a set of domain-task pairings.
Nor is it apparent that the outcomes of AGI in a system can be well expressed
in terms of domain and task, or as consequences thereof. Thus, there is both a
practical and theoretical use for meta-theories of learning which do not
express themselves explicitly in terms of solution methods. General systems
theory offers such a meta-theory. Herein, Mesarovician abstract systems theory
is used as a super-structure for learning. Abstract learning systems are
formulated. Subsequent elaboration stratifies the assumptions of learning
systems into a hierarchy and considers the hierarchy such stratification
projects onto learning theory. The presented Mesarovician abstract learning
systems theory calls back to the founding motivations of artificial
intelligence research by focusing on the thinking participants directly, in
this case, learning systems, in contrast to the contemporary focus on the
problems thinking participants solve.

In this paper, we consider both first- and second-order techniques to address
continuous optimization problems arising in machine learning. In the
first-order case, we propose a framework of transition from deterministic or
semi-deterministic to stochastic quadratic regularization methods. We leverage
the two-phase nature of stochastic optimization to propose a novel first-order
algorithm with adaptive sampling and adaptive step size. In the second-order
case, we propose a novel stochastic damped L-BFGS method that improves on
previous algorithms in the highly nonconvex context of deep learning. Both
algorithms are evaluated on well-known deep learning datasets and exhibit
promising performance.

important inverse problem in many-body physics. However, the inversion is
proved to be ill-posed in the realistic systems with noisy Green's functions.
In this Letter, we propose an automatic differentiation(AD) framework as a
generic tool for the spectral reconstruction from propagator observable.
Exploiting the neural networks' regularization as a non-local smoothness
regulator of the spectral function, we represent spectral functions by neural
networks and use the propagator's reconstruction error to optimize the network
parameters unsupervisedly. In the training process, except for the
positive-definite form for the spectral function, there are no other explicit
physical priors embedded into the neural networks. The reconstruction
performance is assessed through relative entropy and mean square error for two
different network representations. Compared to the maximum entropy method, the
AD framework achieves better performance in the large-noise situation. It is
noted that the freedom of introducing non-local regularization is an inherent
advantage of the present framework and may lead to substantial improvements in
solving inverse problems.

Automated hyperparameter optimization (HPO) has gained great popularity and
is an important ingredient of most automated machine learning frameworks. The
process of designing HPO algorithms, however, is still an unsystematic and
manual process: Limitations of prior work are identified and the improvements
proposed are -- even though guided by expert knowledge -- still somewhat
arbitrary. This rarely allows for gaining a holistic understanding of which
algorithmic components are driving performance, and carries the risk of
overlooking good algorithmic design choices. We present a principled approach
to automated benchmark-driven algorithm design applied to multifidelity HPO
(MF-HPO): First, we formalize a rich space of MF-HPO candidates that includes,
but is not limited to common HPO algorithms, and then present a configurable
framework covering this space. To find the best candidate automatically and
systematically, we follow a programming-by-optimization approach and search
over the space of algorithm candidates via Bayesian optimization. We challenge
whether the found design choices are necessary or could be replaced by more
naive and simpler ones by performing an ablation analysis. We observe that
using a relatively simple configuration, in some ways simpler than established
methods, performs very well as long as some critical configuration parameters
have the right value.

Traditional statistical estimation, or statistical inference in general, is
static, in the sense that the estimate of the quantity of interest does not
change the future evolution of the quantity. In some sequential estimation
problems however, we encounter the situation where the future values of the
quantity to be estimated depend on the estimate of its current value. Examples
include stock price prediction by big investors, interactive product
recommendation, and behavior prediction in multi-agent systems. We may call
such problems as dynamic inference. In this work, a formulation of this problem
under a Bayesian probabilistic framework is given, and the optimal estimation
strategy is derived as the solution to minimize the overall inference loss. How
the optimal estimation strategy works is illustrated through two examples,
stock trend prediction and vehicle behavior prediction. When the underlying
models for dynamic inference are unknown, we can consider the problem of
learning for dynamic inference. This learning problem can potentially unify
several familiar machine learning problems, including supervised learning,
imitation learning, and reinforcement learning.

The visual world naturally exhibits a long-tailed distribution of open
classes, which poses great challenges to modern visual systems. Existing
approaches either perform class re-balancing strategies or directly improve
network modules to address the problem. However, they still train models with a
finite set of predefined labels, limiting their supervision information and
restricting their transferability to novel instances. Recent advances in
large-scale contrastive visual-language pretraining shed light on a new pathway
for visual recognition. With open-vocabulary supervisions, pretrained
contrastive vision-language models learn powerful multimodal representations
that are promising to handle data deficiency and unseen concepts. By
calculating the semantic similarity between visual and text inputs, visual
recognition is converted to a vision-language matching problem. Inspired by
this, we propose BALLAD to leverage contrastive vision-language models for
long-tailed recognition. We first continue pretraining the vision-language
backbone through contrastive learning on a specific long-tailed target dataset.
Afterward, we freeze the backbone and further employ an additional adapter
layer to enhance the representations of tail classes on balanced training
samples built with re-sampling strategies. Extensive experiments have been
conducted on three popular long-tailed recognition benchmarks. As a result, our
simple and effective approach sets the new state-of-the-art performances and
outperforms competitive baselines with a large margin. Code is released at
https://github.com/gaopengcuhk/BALLAD.

When an image classifier outputs a wrong class label, it can be helpful to
see what changes in the image would lead to a correct classification. This is
the aim of algorithms generating counterfactual explanations. However, there is
no easily scalable method to generate such counterfactuals. We develop a new
algorithm providing counterfactual explanations for large image classifiers
trained with spectral normalisation at low computational cost. We empirically
compare this algorithm against baselines from the literature; our novel
algorithm consistently finds counterfactuals that are much closer to the
original inputs. At the same time, the realism of these counterfactuals is
comparable to the baselines. The code for all experiments is available at
https://github.com/benedikthoeltgen/DeDUCE.

One of the key criticisms of deep learning is that large amounts of expensive
and difficult-to-acquire training data are required in order to train models
with high performance and good generalization capabilities. Focusing on the
task of monocular camera pose estimation via scene coordinate regression (SCR),
we describe a novel method, Domain Adaptation of Networks for Camera pose
Estimation (DANCE), which enables the training of models without access to any
labels on the target task. DANCE requires unlabeled images (without known
poses, ordering, or scene coordinate labels) and a 3D representation of the
space (e.g., a scanned point cloud), both of which can be captured with minimal
effort using off-the-shelf commodity hardware. DANCE renders labeled synthetic
images from the 3D model, and bridges the inevitable domain gap between
synthetic and real images by applying unsupervised image-level domain
adaptation techniques (unpaired image-to-image translation). When tested on
real images, the SCR model trained with DANCE achieved comparable performance
to its fully supervised counterpart (in both cases using PnP-RANSAC for final
pose estimation) at a fraction of the cost. Our code and dataset are available
at https://github.com/JackLangerman/dance

In this paper, we provide a novel and simple algorithm, Clairvoyant
Multiplicative Weights Updates (CMWU) for regret minimization in general games.
CMWU effectively corresponds to the standard MWU algorithm but where all
agents, when updating their mixed strategies, use the payoff profiles based on
tomorrow's behavior, i.e. the agents are clairvoyant. CMWU achieves constant
regret of $\ln(m)/\eta$ in all normal-form games with m actions and fixed
step-sizes $\eta$. Although CMWU encodes in its definition a fixed point
computation, which in principle could result in dynamics that are neither
computationally efficient nor uncoupled, we show that both of these issues can
be largely circumvented. Specifically, as long as the step-size $\eta$ is upper
bounded by $\frac{1}{(n-1)V}$, where $n$ is the number of agents and $[0,V]$ is
the payoff range, then the CMWU updates can be computed linearly fast via a
contraction map. This implementation results in an uncoupled online learning
dynamic that admits a $O (\log T)$-sparse sub-sequence where each agent
experiences at most $O(nV\log m)$ regret. This implies that the CMWU dynamics
converge with rate $O(nV \log m \log T / T)$ to a \textit{Coarse Correlated
Equilibrium}. The latter improves on the current state-of-the-art convergence
rate of \textit{uncoupled online learning dynamics}
\cite{daskalakis2021near,anagnostides2021near}.

Existing approaches to the crime prediction problem are unsuccessful in
expressing the details since they assign the probability values to large
regions. This paper introduces a new architecture with the graph convolutional
networks (GCN) and multivariate Gaussian distributions to perform
high-resolution forecasting that applies to any spatiotemporal data. We tackle
the sparsity problem in high resolution by leveraging the flexible structure of
GCNs and providing a subdivision algorithm. We build our model with Graph
Convolutional Gated Recurrent Units (Graph-ConvGRU) to learn spatial, temporal,
and categorical relations. In each node of the graph, we learn a multivariate
probability distribution from the extracted features of GCNs. We perform
experiments on real-life and synthetic datasets, and our model obtains the best
validation and the best test score among the baseline models with significant
improvements. We show that our model is not only generative but also precise.

Despite machine learning models' success in Natural Language Processing (NLP)
tasks, predictions from these models frequently fail on out-of-distribution
(OOD) samples. Prior works have focused on developing state-of-the-art methods
for detecting OOD. The fundamental question of how OOD samples differ from
in-distribution samples remains unanswered. This paper explores how data
dynamics in training models can be used to understand the fundamental
differences between OOD and in-distribution samples in extensive detail. We
found that syntactic characteristics of the data samples that the model
consistently predicts incorrectly in both OOD and in-distribution cases
directly contradict each other. In addition, we observed preliminary evidence
supporting the hypothesis that models are more likely to latch on trivial
syntactic heuristics (e.g., overlap of words between two sentences) when making
predictions on OOD samples. We hope our preliminary study accelerates the
data-centric analysis on various machine learning phenomena.

Unmanned Aerial Vehicles (UAVs), which are at the forefront of cutting-edge
technology, have unmatched potential for pioneering applications in a wide
range of disciplines. In particular, in the field of cognitive radio (CR),
which is a key aspect in the implementation of the new 5G telecommunication
technology. The integration between the drone and CR consolidates the drone's
capabilities at the heart of the remarkably promising Internet-of-Things (IoT)
technology supported by CR. The highly dynamic network topologies, weakly
networked communication links, reliable line-of-sight (LOS) communication
links, and orbital or flight paths are characteristic features of UAV
communication compared to terrestrial wireless networks. Nevertheless, the
implementation of this system is constrained by several severe challenges, such
as energy efficiency, battery power limitation, spectrum handover, propagation
channel modeling, routing protocols, security policy, and delay setbacks. In
this paper, we consider the impact of energy scarcity faced by the UAV in
various CR applications. We also analyze the impact of energy scarcity on
communication-based applications and present the general problem of battery
limitation. Finally, we give an overview and comparison between recent
solutions proposed by researchers both in the field of communication and based
on batteries and consider possible future directions according to the state of
the art, such as novel Graph Signal Processing (GSP) and machine learning (ML).

An evaluation criterion for safe and trustworthy deep learning is how well
the invariances captured by representations of deep neural networks (DNNs) are
shared with humans. We identify challenges in measuring these invariances.
Prior works used gradient-based methods to generate \textit{identically
represented inputs} (IRIs), \ie, inputs which have identical representations
(on a given layer) of a neural network, and thus capture invariances of a given
network. One necessary criterion for a network's invariances to align with
human perception is for its IRIs look `similar` to humans. Prior works,
however, have mixed takeaways; some argue that later layers of DNNs do not
learn human-like invariances (\cite{jenelle2019metamers}) yet others seem to
indicate otherwise (\cite{mahendran2014understanding}). We argue that the loss
function used to generate IRIs can heavily affect takeaways about invariances
of the network and is the primary reason for these conflicting findings. We
propose an \textit{adversarial} regularizer on the IRI generation loss that
finds IRIs that make any model appear to have very little shared invariance
with humans. Based on this evidence, we argue that there is scope for improving
models to have human-like invariances, and further, to have meaningful
comparisons between models one should use IRIs generated using the
\textit{regularizer-free} loss. We then conduct an in-depth investigation of
how different components (\eg~architectures, training losses, data
augmentations) of the deep learning pipeline contribute to learning models that
have good alignment with humans. We find that architectures with residual
connections trained using a (self-supervised) contrastive loss with $\ell_p$
ball adversarial data augmentation tend to learn invariances that are most
aligned with humans.

In many scientific disciplines, coarse-grained causal models are used to
explain and predict the dynamics of more fine-grained systems. Naturally, such
models require appropriate macrovariables. Automated procedures to detect
suitable variables would be useful to leverage increasingly available
high-dimensional observational datasets. This work introduces a novel
algorithmic approach that is inspired by a new characterisation of causal
macrovariables as information bottlenecks between microstates. Its general form
can be adapted to address individual needs of different scientific goals. After
a further transformation step, the causal relationships between learned
variables can be investigated through additive noise models. Experiments on
both simulated data and on a real climate dataset are reported. In a synthetic
dataset, the algorithm robustly detects the ground-truth variables and
correctly infers the causal relationships between them. In a real climate
dataset, the algorithm robustly detects two variables that correspond to the
two known variations of the El Nino phenomenon.

Edge computing is a fast-growing computing paradigm where data is processed
at the local site where it is generated, close to the end-devices. This can
benefit a set of disruptive applications like autonomous driving, augmented
reality, and collaborative machine learning, which produce incredible amounts
of data that need to be shared, processed and stored at the edge to meet low
latency requirements. However, edge storage poses new challenges due to the
scarcity and heterogeneity of edge infrastructures and the diversity of edge
applications. In particular, edge applications may impose conflicting
constraints and optimizations that are hard to be reconciled on the limited,
hard-to-scale edge resources. In this vision paper we argue that a new
middleware for constrained edge resources is needed, providing a unified
storage service for diverse edge applications. We identify programmability as a
critical feature that should be leveraged to optimize the resource sharing
while delivering the specialization needed for edge applications. Following
this line, we make a case for eBPF and present the design for Griffin - a
flexible, lightweight programmable edge storage middleware powered by eBPF.

Changepoint analysis deals with unsupervised detection and/or estimation of
time-points in time-series data, when the distribution generating the data
changes. In this article, we consider \emph{offline} changepoint detection in
the context of large scale textual data. We build a specialised temporal topic
model with provisions for changepoints in the distribution of topic
proportions. As full likelihood based inference in this model is
computationally intractable, we develop a computationally tractable approximate
inference procedure. More specifically, we use sample splitting to estimate
topic polytopes first and then apply a likelihood ratio statistic together with
a modified version of the wild binary segmentation algorithm of Fryzlewicz et
al. (2014). Our methodology facilitates automated detection of structural
changes in large corpora without the need of manual processing by domain
experts. As changepoints under our model correspond to changes in topic
structure, the estimated changepoints are often highly interpretable as marking
the surge or decline in popularity of a fashionable topic. We apply our
procedure on two large datasets: (i) a corpus of English literature from the
period 1800-1922 (Underwoodet al., 2015); (ii) abstracts from the High Energy
Physics arXiv repository (Clementet al., 2019). We obtain some historically
well-known changepoints and discover some new ones.

Magnetic materials are crucial components of many technologies that could
drive the ecological transition, including electric motors, wind turbine
generators and magnetic refrigeration systems. Discovering materials with large
magnetic moments is therefore an increasing priority. Here, using
state-of-the-art machine learning methods, we scan the Inorganic Crystal
Structure Database (ICSD) of hundreds of thousands of existing materials to
find those that are ferromagnetic and have large magnetic moments. Crystal
graph convolutional neural networks (CGCNN), materials graph network (MEGNet)
and random forests are trained on the Materials Project database that contains
the results of high-throughput DFT predictions. For random forests, we use a
stochastic method to select nearly one hundred relevant descriptors based on
chemical composition and crystal structure. This gives results that are
comparable to those of neural networks. The comparison between these different
machine learning approaches gives an estimate of the errors for our predictions
on the ICSD database. Validating our final predictions by comparisons with
available experimental data, we found 15 materials that are likely to have
large magnetic moments and have not been yet studied experimentally.

Over the last decade, e-learning has revolutionized how students learn by
providing them access to quality education whenever and wherever they want.
However, students often get distracted because of various reasons, which affect
the learning capacity to a great extent. Many researchers have been trying to
improve the quality of online education, but we need a holistic approach to
address this issue. This paper intends to provide a mechanism that uses the
camera feed and microphone input to monitor the real-time attention level of
students during online classes. We explore various image processing techniques
and machine learning algorithms throughout this study. We propose a system that
uses five distinct non-verbal features to calculate the attention score of the
student during computer based tasks and generate real-time feedback for both
students and the organization. We can use the generated feedback as a heuristic
value to analyze the overall performance of students as well as the teaching
standards of the lecturers.

Computational drug repositioning technology is an effective tool to
accelerate drug development. Although this technique has been widely used and
successful in recent decades, many existing models still suffer from multiple
drawbacks such as the massive number of unvalidated drug-disease associations
and the inner product. The limitations of these works are mainly due to the
following two reasons: firstly, previous works used negative sampling
techniques to treat unvalidated drug-disease associations as negative samples,
which is invalid in real-world settings; secondly, the inner product cannot
fully take into account the feature information contained in the latent factor
of drug and disease. In this paper, we propose a novel PUON framework for
addressing the above deficiencies, which models the risk estimator of
computational drug repositioning only using validated (Positive) and
unvalidated (Unlabelled) drug-disease associations without employing negative
sampling techniques. The PUON also proposed an Outer Neighborhood-based
classifier for modeling the cross-feature information of the latent facotor.
For a comprehensive comparison, we considered 8 popular baselines. Extensive
experiments in four real-world datasets showed that PUON model achieved the
best performance based on 6 evaluation metrics.

Building general-purpose robots to perform a diverse range of tasks in a
large variety of environments in the physical world at the human level is
extremely challenging. It requires the robot learning to be sample-efficient,
generalizable, compositional, and incremental. In this work, we introduce a
systematic learning framework called SAGCI-system towards achieving these above
four requirements. Our system first takes the raw point clouds gathered by the
camera mounted on the robot's wrist as the inputs and produces initial modeling
of the surrounding environment represented as a file of Unified Robot
Description Format (URDF). Our system adopts a learning-augmented
differentiable simulation that loads the URDF. The robot then utilizes the
interactive perception to interact with the environment to online verify and
modify the URDF. Leveraging the differentiable simulation, we propose a
model-based learning algorithm combining object-centric and robot-centric
stages to efficiently produce policies to accomplish manipulation tasks. We
apply our system to perform articulated object manipulation tasks, both in the
simulation and the real world. Extensive experiments demonstrate the
effectiveness of our proposed learning framework. Supplemental materials and
videos are available on https://sites.google.com/view/egci.

Ensemble forecasting is, so far, the most successful approach to produce
relevant forecasts with an estimation of their uncertainty. The main
limitations of ensemble forecasting are the high computational cost and the
difficulty to capture and quantify different sources of uncertainty,
particularly those associated with model errors. In this work we perform
toy-model and state-of-the-art model experiments to analyze to what extent
artificial neural networks (ANNs) are able to model the different sources of
uncertainty present in a forecast. In particular those associated with the
accuracy of the initial conditions and those introduced by the model error. We
also compare different training strategies: one based on a direct training
using the mean and spread of an ensemble forecast as target, the other ones
rely on an indirect training strategy using an analyzed state as target in
which the uncertainty is implicitly learned from the data. Experiments using
the Lorenz'96 model show that the ANNs are able to emulate some of the
properties of ensemble forecasts like the filtering of the most unpredictable
modes and a state-dependent quantification of the forecast uncertainty.
Moreover, ANNs provide a reliable estimation of the forecast uncertainty in the
presence of model error. Preliminary experiments conducted with a
state-of-the-art forecasting system also confirm the ability of ANNs to produce
a reliable quantification of the forecast uncertainty.

Excessive sleepiness in attention-critical contexts can lead to adverse
events, such as car crashes. Detecting and monitoring sleepiness can help
prevent these adverse events from happening. In this paper, we use the Voiceome
dataset to extract speech from 1,828 participants to develop a deep transfer
learning model using Hidden-Unit BERT (HuBERT) speech representations to detect
sleepiness from individuals. Speech is an under-utilized source of data in
sleep detection, but as speech collection is easy, cost-effective, and
non-invasive, it provides a promising resource for sleepiness detection. Two
complementary techniques were conducted in order to seek converging evidence
regarding the importance of individual speech tasks. Our first technique,
masking, evaluated task importance by combining all speech tasks, masking
selected responses in the speech, and observing systematic changes in model
accuracy. Our second technique, separate training, compared the accuracy of
multiple models, each of which used the same architecture, but was trained on a
different subset of speech tasks. Our evaluation shows that the best-performing
model utilizes the memory recall task and categorical naming task from the
Boston Naming Test, which achieved an accuracy of 80.07% (F1-score of 0.85) and
81.13% (F1-score of 0.89), respectively.

Inserting a backdoor into the joint model in federated learning (FL) is a
recent threat raising concerns. Existing studies mostly focus on developing
effective countermeasures against this threat, assuming that backdoored local
models, if any, somehow reveal themselves by anomalies in their gradients.
However, this assumption needs to be elaborated by identifying specifically
which gradients are more likely to indicate an anomaly to what extent under
which conditions. This is an important issue given that neural network models
usually have huge parametric space and consist of a large number of weights. In
this study, we make a deep gradient-level analysis on the expected variations
in model gradients under several backdoor attack scenarios against FL. Our main
novel finding is that backdoor-induced anomalies in local model updates
(weights or gradients) appear in the final layer bias weights of the malicious
local models. We support and validate our findings by both theoretical and
experimental analysis in various FL settings. We also investigate the impact of
the number of malicious clients, learning rate, and malicious data rate on the
observed anomaly. Our implementation is publicly available\footnote{\url{
https://github.com/ArcelikAcikKaynak/Federated_Learning.git}}.

The notion of symbiosis has been increasingly mentioned in research on
physically coupled human-machine systems. Yet, a uniform specification on which
aspects constitute human-machine symbiosis is missing. By combining the
expertise of different disciplines, we elaborate on a multivariate perspective
of symbiosis as the highest form of physically coupled human-machine systems.
Four dimensions are considered: Task, interaction, performance, and experience.
First, human and machine work together to accomplish a common task
conceptualized on both a decision and an action level (task dimension). Second,
each partner possesses an internal representation of own as well as the other
partner's intentions and influence on the environment. This alignment, which is
the core of the interaction, constitutes the symbiotic understanding between
both partners, being the basis of a joint, highly coordinated and effective
action (interaction dimension). Third, the symbiotic interaction leads to
synergetic effects regarding the intention recognition and complementary
strengths of the partners, resulting in a higher overall performance
(performance dimension). Fourth, symbiotic systems specifically change the
user's experiences, like flow, acceptance, sense of agency, and embodiment
(experience dimension). This multivariate perspective is flexible and generic
and is also applicable in diverse human-machine scenarios, helping to bridge
barriers between different disciplines.

Learning the manifold structure of remote sensing images is of paramount
relevance for modeling and understanding processes, as well as to encapsulate
the high dimensionality in a reduced set of informative features for subsequent
classification, regression, or unmixing. Manifold learning methods have shown
excellent performance to deal with hyperspectral image (HSI) analysis but,
unless specifically designed, they cannot provide an explicit embedding map
readily applicable to out-of-sample data. A common assumption to deal with the
problem is that the transformation between the high-dimensional input space and
the (typically low) latent space is linear. This is a particularly strong
assumption, especially when dealing with hyperspectral images due to the
well-known nonlinear nature of the data. To address this problem, a manifold
learning method based on High Dimensional Model Representation (HDMR) is
proposed, which enables to present a nonlinear embedding function to project
out-of-sample samples into the latent space. The proposed method is compared to
manifold learning methods along with its linear counterparts and achieves
promising performance in terms of classification accuracy of a representative
set of hyperspectral images.

In this paper, we introduce the online and streaming MAP inference and
learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where
data points arrive in an arbitrary order and the algorithms are constrained to
use a single-pass over the data as well as sub-linear memory. The online
setting has an additional requirement of maintaining a valid solution at any
point in time. For solving these new problems, we propose algorithms with
theoretical guarantees, evaluate them on several real-world datasets, and show
that they give comparable performance to state-of-the-art offline algorithms
that store the entire data in memory and take multiple passes over it.

In the last two years, the U.S. government has emphasized the importance of
accelerating artificial intelligence (AI) and machine learning (ML) within the
government and across the nation. In particular, the National Artificial
Intelligence Initiative Act of 2020, which became law on January 1, 2021,
provides for a coordinated program across the entire federal government to
accelerate AI research and application. The U.S. government can benefit from
public artificial intelligence and machine learning challenges through the
development of novel algorithms and participation in experiential training.
Although the public, private, and non-profit sectors have a history of
leveraging crowdsourcing initiatives to generate novel solutions to difficult
problems and engage stakeholders, interest in public competitions has waned in
recent years as a result of at least three major factors: (1) a lack of
high-quality, high-impact data; (2) a narrow engagement focus on specialized
groups; and (3) insufficient operationalization of challenge results. Herein we
identify common issues and recommend approaches to increase the effectiveness
of challenges. To address these barriers, enabling the use of public
competitions for accelerating AI and ML practice, the U.S. government must
leverage methods that protect sensitive data while enabling modelling, enable
easier participation, empower deployment of validated models, and incentivize
engagement from broad sections of the population.

Parts represent a basic unit of geometric and semantic similarity across
different objects. We argue that part knowledge should be composable beyond the
observed object classes. Towards this, we present 3D Compositional Zero-shot
Learning as a problem of part generalization from seen to unseen object classes
for semantic segmentation. We provide a structured study through benchmarking
the task with the proposed Compositional-PartNet dataset. This dataset is
created by processing the original PartNet to maximize part overlap across
different objects. The existing point cloud part segmentation methods fail to
generalize to unseen object classes in this setting. As a solution, we propose
DeCompositional Consensus, which combines a part segmentation network with a
part scoring network. The key intuition to our approach is that a segmentation
mask over some parts should have a consensus with its part scores when each
part is taken apart. The two networks reason over different part combinations
defined in a per-object part prior to generate the most suitable segmentation
mask. We demonstrate that our method allows compositional zero-shot
segmentation and generalized zero-shot classification, and establishes the
state of the art on both tasks.

Capturing the dynamically deforming 3D shape of clothed human is essential
for numerous applications, including VR/AR, autonomous driving, and
human-computer interaction. Existing methods either require a highly
specialized capturing setup, such as expensive multi-view imaging systems, or
they lack robustness to challenging body poses. In this work, we propose a
method capable of capturing the dynamic 3D human shape from a monocular video
featuring challenging body poses, without any additional input. We first build
a 3D template human model of the subject based on a learned regression model.
We then track this template model's deformation under challenging body
articulations based on 2D image observations. Our method outperforms
state-of-the-art methods on an in-the-wild human video dataset 3DPW. Moreover,
we demonstrate its efficacy in robustness and generalizability on videos from
iPER datasets.

Numerical simulations of Earth's weather and climate require substantial
amounts of computation. This has led to a growing interest in replacing
subroutines that explicitly compute physical processes with approximate machine
learning (ML) methods that are fast at inference time. Within weather and
climate models, atmospheric radiative transfer (RT) calculations are especially
expensive. This has made them a popular target for neural network-based
emulators. However, prior work is hard to compare due to the lack of a
comprehensive dataset and standardized best practices for ML benchmarking. To
fill this gap, we build a large dataset, ClimART, with more than \emph{10
million samples from present, pre-industrial, and future climate conditions},
based on the Canadian Earth System Model. ClimART poses several methodological
challenges for the ML community, such as multiple out-of-distribution test
sets, underlying domain physics, and a trade-off between accuracy and inference
speed. We also present several novel baselines that indicate shortcomings of
datasets and network architectures used in prior work. Download instructions,
baselines, and code are available at: https://github.com/RolnickLab/climart

Visual navigation by mobile robots is classically tackled through SLAM plus
optimal planning, and more recently through end-to-end training of policies
implemented as deep networks. While the former are often limited to waypoint
planning, but have proven their efficiency even on real physical environments,
the latter solutions are most frequently employed in simulation, but have been
shown to be able learn more complex visual reasoning, involving complex
semantical regularities. Navigation by real robots in physical environments is
still an open problem. End-to-end training approaches have been thoroughly
tested in simulation only, with experiments involving real robots being
restricted to rare performance evaluations in simplified laboratory conditions.
In this work we present an in-depth study of the performance and reasoning
capacities of real physical agents, trained in simulation and deployed to two
different physical environments. Beyond benchmarking, we provide insights into
the generalization capabilities of different agents training in different
conditions. We visualize sensor usage and the importance of the different types
of signals. We show, that for the PointGoal task, an agent pre-trained on wide
variety of tasks and fine-tuned on a simulated version of the target
environment can reach competitive performance without modelling any sim2real
transfer, i.e. by deploying the trained agent directly from simulation to a
real physical robot.

Geosteering of wells requires fast interpretation of geophysical logs, which
is a non-unique inverse problem. Current work presents a proof-of-concept
approach to multi-modal probabilistic inversion of logs using a single
evaluation of an artificial deep neural network (DNN). A mixture density DNN
(MDN) is trained using the "multiple-trajectory-prediction" (MTP) loss
functions, which avoids mode collapse typical for traditional MDNs, and allows
multi-modal prediction ahead of data. The proposed approach is verified on the
real-time stratigraphic inversion of gamma-ray logs. The multi-modal predictor
outputs several likely inverse solutions/predictions, providing more accurate
and realistic solutions than a deterministic regression using a DNN. For these
likely stratigraphic curves, the model simultaneously predicts their
probabilities, which are implicitly learned from the training geological data.
The stratigraphy predictions and their probabilities obtained in milliseconds
from the MDN can enable better real-time decisions under geological

Recent advances at the intersection of dense large graph limits and mean
field games have begun to enable the scalable analysis of a broad class of
dynamical sequential games with large numbers of agents. So far, results have
been largely limited to graphon mean field systems with continuous-time
diffusive or jump dynamics, typically without control and with little focus on
computational methods. We propose a novel discrete-time formulation for graphon
mean field games as the limit of non-linear dense graph Markov games with weak
interaction. On the theoretical side, we give extensive and rigorous existence
and approximation properties of the graphon mean field solution in sufficiently
large systems. On the practical side, we provide general learning schemes for
graphon mean field equilibria by either introducing agent equivalence classes
or reformulating the graphon mean field system as a classical mean field
system. By repeatedly finding a regularized optimal control solution and its
generated mean field, we successfully obtain plausible approximate Nash
equilibria in otherwise infeasible large dense graph games with many agents.
Empirically, we are able to demonstrate on a number of examples that the
finite-agent behavior comes increasingly close to the mean field behavior for
our computed equilibria as the graph or system size grows, verifying our
theory. More generally, we successfully apply policy gradient reinforcement
learning in conjunction with sequential Monte Carlo methods.

Standard spatial convolutions assume input data with a regular neighborhood
structure. Existing methods typically generalize convolution to the irregular
point cloud domain by fixing a regular "view" through e.g. a fixed neighborhood
size, where the convolution kernel size remains the same for each point.
However, since point clouds are not as structured as images, the fixed neighbor
number gives an unfortunate inductive bias. We present a novel graph
convolution named Difference Graph Convolution (diffConv), which does not rely
on a regular view. diffConv operates on spatially-varying and density-dilated
neighborhoods, which are further adapted by a learned masked attention
mechanism. Experiments show that our model is very robust to the noise,
obtaining state-of-the-art performance in 3D shape classification and scene
understanding tasks, along with a faster inference speed.

One underlying assumption of recent federated learning (FL) paradigms is that
all local models usually share the same network architecture and size, which
becomes impractical for devices with different hardware resources. A scalable
federated learning framework should address the heterogeneity that clients have
different computing capacities and communication capabilities. To this end,
this paper proposes FedHM, a novel heterogeneous federated model compression
framework, distributing the heterogeneous low-rank models to clients and then
aggregating them into a full-rank model. Our solution enables the training of
heterogeneous models with varying computational complexities and aggregates
them into a single global model. Furthermore, FedHM significantly reduces the
communication cost by using low-rank models. Extensive experimental results
demonstrate that FedHM is superior in the performance and robustness of models
of different sizes, compared with state-of-the-art heterogeneous FL methods
under various FL settings. Additionally, the convergence guarantee of FL for
heterogeneous devices is first theoretically analyzed.

Graph Neural Network (GNN) has achieved state-of-the-art performance in
various high-stake prediction tasks, but multiple layers of aggregations on
graphs with irregular structures make GNN a less interpretable model. Prior
methods use simpler subgraphs to simulate the full model, or counterfactuals to
identify the causes of a prediction. The two families of approaches aim at two
distinct objectives, "simulatability" and "counterfactual relevance", but it is
not clear how the objectives can jointly influence the human understanding of
an explanation. We design a user study to investigate such joint effects and
use the findings to design a multi-objective optimization (MOO) algorithm to
find Pareto optimal explanations that are well-balanced in simulatability and
counterfactual. Since the target model can be of any GNN variants and may not
be accessible due to privacy concerns, we design a search algorithm using
zeroth-order information without accessing the architecture and parameters of
the target model. Quantitative experiments on nine graphs from four
applications demonstrate that the Pareto efficient explanations dominate
single-objective baselines that use first-order continuous optimization or
discrete combinatorial search. The explanations are further evaluated in
robustness and sensitivity to show their capability of revealing convincing
causes while being cautious about the possible confounders. The diverse
dominating counterfactuals can certify the feasibility of algorithmic recourse,
that can potentially promote algorithmic fairness where humans are
participating in the decision-making using GNN.

Buildings classification using satellite images is becoming more important
for several applications such as damage assessment, resource allocation, and
population estimation. We focus, in this work, on buildings damage assessment
(BDA) and buildings type classification (BTC) of residential and
non-residential buildings. We propose to rely solely on RGB satellite images
and follow a 2-stage deep learning-based approach, where first, buildings'
footprints are extracted using a semantic segmentation model, followed by
classification of the cropped images. Due to the lack of an appropriate dataset
for the residential/non-residential building classification, we introduce a new
dataset of high-resolution satellite images. We conduct extensive experiments
to select the best hyper-parameters, model architecture, and training paradigm,
and we propose a new transfer learning-based approach that outperforms
classical methods. Finally, we validate the proposed approach on two
applications showing excellent accuracy and F1-score metrics.

The task of semi-supervised video object segmentation (VOS) has been greatly
advanced and state-of-the-art performance has been made by dense matching-based
methods. The recent methods leverage space-time memory (STM) networks and learn
to retrieve relevant information from all available sources, where the past
frames with object masks form an external memory and the current frame as the
query is segmented using the mask information in the memory. However, when
forming the memory and performing matching, these methods only exploit the
appearance information while ignoring the motion information. In this paper, we
advocate the return of the \emph{motion information} and propose a motion
uncertainty-aware framework (MUNet) for semi-supervised VOS. First, we propose
an implicit method to learn the spatial correspondences between neighboring
frames, building upon a correlation cost volume. To handle the challenging
cases of occlusion and textureless regions during constructing dense
correspondences, we incorporate the uncertainty in dense matching and achieve
motion uncertainty-aware feature representation. Second, we introduce a
motion-aware spatial attention module to effectively fuse the motion feature
with the semantic feature. Comprehensive experiments on challenging benchmarks
show that \textbf{\textit{using a small amount of data and combining it with
powerful motion information can bring a significant performance boost}}. We
achieve ${76.5\%}$ $\mathcal{J} \& \mathcal{F}$ only using DAVIS17 for
training, which significantly outperforms the \textit{SOTA} methods under the
low-data protocol. \textit{The code will be released.}

Orbital angular momentum (OAM) at radio-frequency provides a novel approach
of multiplexing a set of orthogonal modes on the same frequency channel to
achieve high spectral efficiencies. However, classical phase gradient-based OAM
mode detection methods require perfect alignment of transmit and receive
antennas, which greatly challenges the practical application of OAM
communications. In this paper, we first show the effect of non-parallel
misalignment on the OAM phase structure, and then propose the OAM mode
detection method based on uniform circular array (UCA) samples learning for the
more general alignment or non-parallel misalignment case. Specifically, we
applied three classifiers: K-nearest neighbor (KNN), support vector machine
(SVM), and back-propagation neural network (BPNN) to both single-mode and
multi-mode OAM detection. The simulation results validate that the proposed
learning-based OAM mode detection methods are robust to misalignment errors and
especially BPNN classifier has the best generalization performance.

Joint representation of geometry, colour and semantics using a 3D neural
field enables accurate dense labelling from ultra-sparse interactions as a user
reconstructs a scene in real-time using a handheld RGB-D sensor. Our iLabel
system requires no training data, yet can densely label scenes more accurately
than standard methods trained on large, expensively labelled image datasets.
Furthermore, it works in an 'open set' manner, with semantic classes defined on
the fly by the user.
  ILabel's underlying model is a multilayer perceptron (MLP) trained from
scratch in real-time to learn a joint neural scene representation. The scene
model is updated and visualised in real-time, allowing the user to focus
interactions to achieve efficient labelling. A room or similar scene can be
accurately labelled into 10+ semantic categories with only a few tens of
clicks. Quantitative labelling accuracy scales powerfully with the number of
clicks, and rapidly surpasses standard pre-trained semantic segmentation
methods. We also demonstrate a hierarchical labelling variant.

Reinforcement learning (RL) agents are widely used for solving complex
sequential decision making tasks, but still exhibit difficulty in generalizing
to scenarios not seen during training. While prior online approaches
demonstrated that using additional signals beyond the reward function can lead
to better generalization capabilities in RL agents, i.e. using self-supervised
learning (SSL), they struggle in the offline RL setting, i.e. learning from a
static dataset. We show that performance of online algorithms for
generalization in RL can be hindered in the offline setting due to poor
estimation of similarity between observations. We propose a new
theoretically-motivated framework called Generalized Similarity Functions
(GSF), which uses contrastive learning to train an offline RL agent to
aggregate observations based on the similarity of their expected future
behavior, where we quantify this similarity using \emph{generalized value
functions}. We show that GSF is general enough to recover existing SSL
objectives while also improving zero-shot generalization performance on a
complex offline RL benchmark, offline Procgen.

We introduce time-ordered multibody interactions to describe complex systems
manifesting temporal as well as multibody dependencies. First, we show how the
dynamics of multivariate Markov chains can be decomposed in ensembles of
time-ordered multibody interactions. Then, we present an algorithm to extract
combined interactions from data and a measure to characterize the complexity of
interaction ensembles. Finally, we experimentally validate the robustness of
our algorithm against statistical errors and its efficiency at obtaining simple
interaction ensembles.

Weakly-supervised learning has become a popular technology in recent years.
In this paper, we propose a novel medical image classification algorithm,
called Weakly-Supervised Generative Adversarial Networks (WSGAN), which only
uses a small number of real images without labels to generate fake images or
mask images to enlarge the sample size of the training set. First, we combine
with MixMatch to generate pseudo labels for the fake images and unlabeled
images to do the classification. Second, contrastive learning and
self-attention mechanism are introduced into the proposed problem to enhance
the classification accuracy. Third, the problem of mode collapse is well
addressed by cyclic consistency loss. Finally, we design global and local
classifiers to complement each other with the key information needed for
classification. The experimental results on four medical image datasets show
that WSGAN can obtain relatively high learning performance by using few labeled
and unlabeled data. For example, the classification accuracy of WSGAN is 11%
higher than that of the second-ranked MIXMATCH with 100 labeled images and 1000
unlabeled images on the OCT dataset. In addition, we also conduct ablation
experiments to verify the effectiveness of our algorithm.

Devices based on arrays of interconnected magnetic nano-rings with emergent
magnetization dynamics have recently been proposed for use in reservoir
computing applications, but for them to be computationally useful it must be
possible to optimise their dynamical responses. Here, we use a phenomenological
model to demonstrate that such reservoirs can be optimised for classification
tasks by tuning hyperparameters that control the scaling and input rate of data
into the system using rotating magnetic fields. We use task-independent metrics
to assess the rings' computational capabilities at each set of these
hyperparameters and show how these metrics correlate directly to performance in
spoken and written digit recognition tasks. We then show that these metrics,
and performance in tasks, can be further improved by expanding the reservoir's
output to include multiple, concurrent measures of the ring arrays magnetic
states.

Safety is the primary concern when it comes to air traffic. In-flight safety
between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise
separation minima, utilizing conflict detection and resolution methods.
Existing methods mainly deal with pairwise conflicts, however due to an
expected increase in traffic density, encounters with more than two UAVs are
likely to happen. In this paper, we model multi-UAV conflict resolution as a
multi-agent reinforcement learning problem. We implement an algorithm based on
graph neural networks where cooperative agents can communicate to jointly
generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4
present agents. Results show that agents are able to successfully solve the
multi-UAV conflicts through a cooperative strategy.

A fundamental goal in neuroscience is to understand the relationship between
neural activity and behavior. For example, the ability to extract behavioral
intentions from neural data, or neural decoding, is critical for developing
effective brain machine interfaces. Although simple linear models have been
applied to this challenge, they cannot identify important non-linear
relationships. Thus, a self-supervised means of identifying non-linear
relationships between neural dynamics and behavior, in order to compute neural
representations, remains an important open problem. To address this challenge,
we generated a new multimodal dataset consisting of the spontaneous behaviors
generated by fruit flies, Drosophila melanogaster -- a popular model organism
in neuroscience research. The dataset includes 3D markerless motion capture
data from six camera views of the animal generating spontaneous actions, as
well as synchronously acquired two-photon microscope images capturing the
activity of descending neuron populations that are thought to drive actions.
Standard contrastive learning and unsupervised domain adaptation techniques
struggle to learn neural action representations (embeddings computed from the
neural data describing action labels) due to large inter-animal differences in
both neural and behavioral modalities. To overcome this deficiency, we
developed simple yet effective augmentations that close the inter-animal domain
gap, allowing us to extract behaviorally relevant, yet domain agnostic,
information from neural data. This multimodal dataset and our new set of
augmentations promise to accelerate the application of self-supervised learning
methods in neuroscience.

Pre-trained models have proved to be powerful in enhancing task-oriented
dialog systems. However, current pre-training methods mainly focus on enhancing
dialog understanding and generation tasks while neglecting the exploitation of
dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog
model that explicitly learns dialog policy from limited labeled dialogs and
large-scale unlabeled dialog corpora via semi-supervised learning.
Specifically, we introduce a dialog act prediction task for policy optimization
during pre-training and employ a consistency regularization term to refine the
learned representation with the help of unlabeled dialogs. We also implement a
gating mechanism to weigh suitable unlabeled dialog samples. Empirical results
show that GALAXY substantially improves the performance of task-oriented dialog
systems, and achieves new state-of-the-art results on benchmark datasets:
In-Car, MultiWOZ2.0 and MultiWOZ2.1, improving their end-to-end combined scores
by 2.5, 5.3 and 5.5 points, respectively. We also show that GALAXY has a
stronger few-shot ability than existing models under various low-resource
settings.

Jets produced in high-energy heavy-ion collisions are modified compared to
those in proton-proton collisions due to their interaction with the deconfined,
strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine
learning techniques to identify important features that distinguish jets
produced in heavy-ion collisions from jets produced in proton-proton
collisions. We formulate the problem using binary classification and focus on
leveraging machine learning in ways that inform theoretical calculations of jet
modification: (i) we quantify the information content in terms of Infrared
Collinear (IRC)-safety and in terms of hard vs. soft emissions, (ii) we
identify optimally discriminating observables that are in principle calculable
in perturbative QCD, and (iii) we assess the information loss due to the
heavy-ion underlying event and background subtraction algorithms. We illustrate
our methodology using Monte Carlo event generators, where we find that
important information about jet quenching is contained not only in hard
splittings but also in soft emissions and IRC-unsafe physics inside the jet.
This information appears to be significantly reduced by the presence of the
underlying event. We discuss the implications of this for the prospect of using
jet quenching to extract properties of the QGP. Since the training labels are
exactly known, this methodology can be used directly on experimental data
without reliance on modeling. We outline a proposal for how such an
experimental analysis can be carried out, and how it can guide future

Contrastive representation learning has proven to be an effective
self-supervised learning method. Most successful approaches are based on Noise
Contrastive Estimation (NCE) and use different views of an instance as
positives that should be contrasted with other instances, called negatives,
that are considered as noise. However, several instances in a dataset are drawn
from the same distribution and share underlying semantic information. A good
data representation should contain relations, or semantic similarity, between
the instances. Contrastive learning implicitly learns relations but considering
all negatives as noise harms the quality of the learned relations. To
circumvent this issue, we propose a novel formulation of contrastive learning
using semantic similarity between instances called Similarity Contrastive
Estimation (SCE). Our training objective is a soft contrastive learning one.
Instead of hard classifying positives and negatives, we estimate from one view
of a batch a continuous distribution to push or pull instances based on their
semantic similarities. This target similarity distribution is sharpened to
eliminate noisy relations. The model predicts for each instance, from another
view, the target distribution while contrasting its positive with negatives.
Experimental results show that SCE is Top-1 on the ImageNet linear evaluation
protocol at 100 pretraining epochs with 72.1% accuracy and is competitive with
state-of-the-art algorithms by reaching 75.4% for 200 epochs with multi-crop.
We also show that SCE is able to generalize to several tasks. Source code is
available here: https://github.com/CEA-LIST/SCE.

Audio-visual navigation combines sight and hearing to navigate to a
sound-emitting source in an unmapped environment. While recent approaches have
demonstrated the benefits of audio input to detect and find the goal, they
focus on clean and static sound sources and struggle to generalize to unheard
sounds. In this work, we propose the novel dynamic audio-visual navigation
benchmark which requires catching a moving sound source in an environment with
noisy and distracting sounds, posing a range of new challenges. We introduce a
reinforcement learning approach that learns a robust navigation policy for
these complex settings. To achieve this, we propose an architecture that fuses
audio-visual information in the spatial feature space to learn correlations of
geometric information inherent in both local maps and audio signals. We
demonstrate that our approach consistently outperforms the current
state-of-the-art by a large margin across all tasks of moving sounds, unheard
sounds, and noisy environments, on two challenging 3D scanned real-world
environments, namely Matterport3D and Replica. The benchmark is available at
http://dav-nav.cs.uni-freiburg.de.

Search engines are considered the primary tool to assist and empower learners
in finding information relevant to their learning goals-be it learning
something new, improving their existing skills, or just fulfilling a curiosity.
While several approaches for improving search engines for the learning scenario
have been proposed, instructional scaffolding has not been studied in the
context of search as learning, despite being shown to be effective for
improving learning in both digital and traditional learning contexts. When
scaffolding is employed, instructors provide learners with support throughout
their autonomous learning process. We hypothesize that the usage of scaffolding
techniques within a search system can be an effective way to help learners
achieve their learning objectives whilst searching. As such, this paper
investigates the incorporation of scaffolding into a search system employing
three different strategies (as well as a control condition): (I) AQE_{SC}, the
automatic expansion of user queries with relevant subtopics; (ii) CURATED_{SC},
the presenting of a manually curated static list of relevant subtopics on the
search engine result page; and (iii) FEEDBACK_{SC}, which projects real-time
feedback about a user's exploration of the topic space on top of the
CURATED_{SC} visualization. To investigate the effectiveness of these
approaches with respect to human learning, we conduct a user study (N=126)
where participants were tasked with searching and learning about topics such as
`genetically modified organisms'. We find that (I) the introduction of the
proposed scaffolding methods does not significantly improve learning gains.
However, (ii) it does significantly impact search behavior. Furthermore, (iii)
immediate feedback of the participants' learning leads to undesirable user
behavior, with participants focusing on the feedback gauges instead of
learning.

Spoken language understanding (SLU) tasks are usually solved by first
transcribing an utterance with automatic speech recognition (ASR) and then
feeding the output to a text-based model. Recent advances in self-supervised
representation learning for speech data have focused on improving the ASR
component. We investigate whether representation learning for speech has
matured enough to replace ASR in SLU. We compare learned speech features from
wav2vec 2.0, state-of-the-art ASR transcripts, and the ground truth text as
input for a novel speech-based named entity recognition task, a cardiac arrest
detection task on real-world emergency calls and two existing SLU benchmarks.
We show that learned speech features are superior to ASR transcripts on three
classification tasks. For machine translation, ASR transcripts are still the
better choice. We highlight the intrinsic robustness of wav2vec 2.0
representations to out-of-vocabulary words as key to better performance.

Recently, fairness-aware learning have become increasingly crucial, but most
of those methods operate by assuming the availability of fully annotated
demographic group labels. We emphasize that such assumption is unrealistic for
real-world applications since group label annotations are expensive and can
conflict with privacy issues. In this paper, we consider a more practical
scenario, dubbed as Algorithmic Group Fairness with the Partially annotated
Group labels (Fair-PG). We observe that the existing methods to achieve group
fairness perform even worse than the vanilla training, which simply uses full
data only with target labels, under Fair-PG. To address this problem, we
propose a simple Confidence-based Group Label assignment (CGL) strategy that is
readily applicable to any fairness-aware learning method. CGL utilizes an
auxiliary group classifier to assign pseudo group labels, where random labels
are assigned to low confident samples. We first theoretically show that our
method design is better than the vanilla pseudo-labeling strategy in terms of
fairness criteria. Then, we empirically show on several benchmark datasets that
by combining CGL and the state-of-the-art fairness-aware in-processing methods,
the target accuracies and the fairness metrics can be jointly improved compared
to the baselines. Furthermore, we convincingly show that CGL enables to
naturally augment the given group-labeled dataset with external target
label-only datasets so that both accuracy and fairness can be improved. Code is
available at https://github.com/naver-ai/cgl_fairness.

We study a class of algorithms for solving bilevel optimization problems in
both stochastic and deterministic settings when the inner-level objective is
strongly convex. Specifically, we consider algorithms based on inexact implicit
differentiation and we exploit a warm-start strategy to amortize the estimation
of the exact gradient. We then introduce a unified theoretical framework
inspired by the study of singularly perturbed systems (Habets, 1974) to analyze
such amortized algorithms. By using this framework, our analysis shows these
algorithms to match the computational complexity of oracle methods that have
access to an unbiased estimate of the gradient, thus outperforming many
existing results for bilevel optimization. We illustrate these findings on
synthetic experiments and demonstrate the efficiency of these algorithms on
hyper-parameter optimization experiments involving several thousands of

Although convolutional neural networks (CNNs) showed remarkable results in
many vision tasks, they are still strained by simple yet challenging visual
reasoning problems. Inspired by the recent success of the Transformer network
in computer vision, in this paper, we introduce the Recurrent Vision
Transformer (RViT) model. Thanks to the impact of recurrent connections and
spatial attention in reasoning tasks, this network achieves competitive results
on the same-different visual reasoning problems from the SVRT dataset. The
weight-sharing both in spatial and depth dimensions regularizes the model,
allowing it to learn using far fewer free parameters, using only 28k training
samples. A comprehensive ablation study confirms the importance of a hybrid CNN
+ Transformer architecture and the role of the feedback connections, which
iteratively refine the internal representation until a stable prediction is
obtained. In the end, this study can lay the basis for a deeper understanding
of the role of attention and recurrent connections for solving visual abstract
reasoning tasks.

Pattern recognition based on a high-dimensional predictor is considered. A
classifier is defined which is based on a Transformer encoder. The rate of
convergence of the misclassification probability of the classifier towards the
optimal misclassification probability is analyzed. It is shown that this
classifier is able to circumvent the curse of dimensionality provided the
aposteriori probability satisfies a suitable hierarchical composition model.
Furthermore, the difference between Transformer classifiers analyzed
theoretically in this paper and Transformer classifiers used nowadays in
practice are illustrated by considering classification problems in natural
language processing.

Astronomy is entering an era of data-driven discovery, due in part to modern
machine learning (ML) techniques enabling powerful new ways to interpret
observations. This shift in our scientific approach requires us to consider
whether we can trust the black box. Here, we overview methods for an
often-overlooked step in the development of ML models: building community trust
in the algorithms. Trust is an essential ingredient not just for creating more
robust data analysis techniques, but also for building confidence within the
astronomy community to embrace machine learning methods and results.

This technical report is devoted to the continuous estimation of an
epsilon-assignment. Roughly speaking, an epsilon assignment between two sets V1
and V2 may be understood as a bijective mapping between a sub part of V1 and a
sub part of V2 . The remaining elements of V1 (not included in this mapping)
are mapped onto an epsilon pseudo element of V2 . We say that such elements are
deleted. Conversely, the remaining elements of V2 correspond to the image of
the epsilon pseudo element of V1. We say that these elements are inserted. As a
result our method provides a result similar to the one of the Sinkhorn
algorithm with the additional ability to reject some elements which are either
inserted or deleted. It thus naturally handles sets V1 and V2 of different
sizes and decides mappings/insertions/deletions in a unified way. Our
algorithms are iterative and differentiable and may thus be easily inserted
within a backpropagation based learning framework such as artificial neural
networks.

Deep neural networks are discovered to be non-robust when attacked by
imperceptible adversarial examples, which is dangerous for it applied into
medical diagnostic system that requires high reliability. However, the defense
methods that have good effect in natural images may not be suitable for medical
diagnostic tasks. The preprocessing methods (e.g., random resizing,
compression) may lead to the loss of the small lesions feature in the medical
image. Retraining the network on the augmented data set is also not practical
for medical models that have already been deployed online. Accordingly, it is
necessary to design an easy-to-deploy and effective defense framework for
medical diagnostic tasks. In this paper, we propose a Robust and Retrain-Less
Diagnostic Framework for Medical pretrained models against adversarial attack
(i.e., MedRDF). It acts on the inference time of the pertained medical model.
Specifically, for each test image, MedRDF firstly creates a large number of
noisy copies of it, and obtains the output labels of these copies from the
pretrained medical diagnostic model. Then, based on the labels of these copies,
MedRDF outputs the final robust diagnostic result by majority voting. In
addition to the diagnostic result, MedRDF produces the Robust Metric (RM) as
the confidence of the result. Therefore, it is convenient and reliable to
utilize MedRDF to convert pre-trained non-robust diagnostic models into robust
ones. The experimental results on COVID-19 and DermaMNIST datasets verify the
effectiveness of our MedRDF in improving the robustness of medical diagnostic
models.

Blood pressure (BP) is one of the most influential bio-markers for
cardiovascular diseases and stroke; therefore, it needs to be regularly
monitored to diagnose and prevent any advent of medical complications. Current
cuffless approaches to continuous BP monitoring, though non-invasive and
unobtrusive, involve explicit feature engineering surrounding fingertip
Photoplethysmogram (PPG) signals. To circumvent this, we present an end-to-end
deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP
(SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate
continuous Arterial BP (ABP) waveform. Under the terms of the British
Hypertension Society (BHS) standard, BP-Net achieves Grade A for DBP and MAP
estimation and Grade B for SBP estimation. BP-Net also satisfies Advancement of
Medical Instrumentation (AAMI) criteria for DBP and MAP estimation and achieves
Mean Absolute Error (MAE) of 5.16 mmHg and 2.89 mmHg for SBP and DBP,
respectively. Further, we establish the ubiquitous potential of our approach by
deploying BP-Net on a Raspberry Pi 4 device and achieve 4.25 ms inference time
for our model to translate the PPG waveform to ABP waveform.

Convolution and self-attention are two powerful techniques for representation
learning, and they are usually considered as two peer approaches that are
distinct from each other. In this paper, we show that there exists a strong
underlying relation between them, in the sense that the bulk of computations of
these two paradigms are in fact done with the same operation. Specifically, we
first show that a traditional convolution with kernel size k x k can be
decomposed into k^2 individual 1x1 convolutions, followed by shift and
summation operations. Then, we interpret the projections of queries, keys, and
values in self-attention module as multiple 1x1 convolutions, followed by the
computation of attention weights and aggregation of the values. Therefore, the
first stage of both two modules comprises the similar operation. More
importantly, the first stage contributes a dominant computation complexity
(square of the channel size) comparing to the second stage. This observation
naturally leads to an elegant integration of these two seemingly distinct
paradigms, i.e., a mixed model that enjoys the benefit of both self-Attention
and Convolution (ACmix), while having minimum computational overhead compared
to the pure convolution or self-attention counterpart. Extensive experiments
show that our model achieves consistently improved results over competitive
baselines on image recognition and downstream tasks. Code and pre-trained
models will be released at https://github.com/LeapLabTHU/ACmix and
https://gitee.com/mindspore/models.

Reinforcement learning (RL) algorithms are often categorized as either
on-policy or off-policy depending on whether they use data from a target policy
of interest or from a different behavior policy. In this paper, we study a
subtle distinction between on-policy data and on-policy sampling in the context
of the RL sub-problem of policy evaluation. We observe that on-policy sampling
may fail to match the expected distribution of on-policy data after observing
only a finite number of trajectories and this failure hinders data-efficient
policy evaluation. Towards improved data-efficiency, we show how non-i.i.d.,
off-policy sampling can produce data that more closely matches the expected
on-policy data distribution and consequently increases the accuracy of the
Monte Carlo estimator for policy evaluation. We introduce a method called
Robust On-Policy Sampling and demonstrate theoretically and empirically that it
produces data that converges faster to the expected on-policy distribution
compared to on-policy sampling. Empirically, we show that this faster
convergence leads to lower mean squared error policy value estimates.

The coalescences of massive black hole binaries (MBHBs) are one of the main
targets of space-based gravitational wave observatories. Such gravitational
wave sources are expected to be accompanied by electromagnetic emission. Low
latency time of gravitational wave searches and accurate sky localization are
keys in triggering successful follow-up observations on the electromagnetic
counterparts. Here we present a deep learning method for the first time to
rapidly search for MBHB signals in the strain data. Our model is capable to
process 1-year of data in just several seconds, identifying all MBHB
coalescences with no false alarms. We test the performance of our model on the
simulated data from the LISA data challenge. We demonstrate that the model
shows a robust resistance for a wide range of generalization for MBHB signals.
This method is supposed to be an effective approach, which combined the
advances of artificial intelligence to open a new pathway for space-based
gravitational wave observations.

Matrix Factorization plays an important role in machine learning such as
Non-negative Matrix Factorization, Principal Component Analysis, Dictionary
Learning, etc. However, most of the studies aim to minimize the loss by
measuring the Euclidean distance, though in some fields, angle distance is
known to be more important and critical for analysis. In this paper, we propose
a method by adding constraints on factors to unify the Euclidean and angle
distance. However, due to non-convexity of the objective and constraints, the
optimized solution is not easy to obtain. In this paper we propose a general
framework to systematically solve it with provable convergence guarantee with
various constraints.

Most graph neural networks (GNNs) use the message passing paradigm, in which
node features are propagated on the input graph. Recent works pointed to the
distortion of information flowing from distant nodes as a factor limiting the
efficiency of message passing for tasks relying on long-distance interactions.
This phenomenon, referred to as 'over-squashing', has been heuristically
attributed to graph bottlenecks where the number of $k$-hop neighbors grows
rapidly with $k$. We provide a precise description of the over-squashing
phenomenon in GNNs and analyze how it arises from bottlenecks in the graph. For
this purpose, we introduce a new edge-based combinatorial curvature and prove
that negatively curved edges are responsible for the over-squashing issue. We
also propose and experimentally test a curvature-based graph rewiring method to
alleviate the over-squashing.

Meta-learners and ensembles aim to combine a set of relevant yet diverse base
models to improve predictive performance. However, determining an appropriate
set of base models is challenging, especially in online environments where the
underlying distribution of data can change over time. In this paper, we present
a novel approach for estimating the conceptual similarity of base models, which
is calculated using the Principal Angles (PAs) between their underlying
subspaces. We propose two methods that use conceptual similarity as a metric to
obtain a relevant yet diverse subset of base models: (i) parameterised
threshold culling and (ii) parameterless conceptual clustering. We evaluate
these methods against thresholding using common ensemble pruning metrics,
namely predictive performance and Mutual Information (MI), in the context of
online Transfer Learning (TL), using both synthetic and real-world data. Our
results show that conceptual similarity thresholding has a reduced
computational overhead, and yet yields comparable predictive performance to
thresholding using predictive performance and MI. Furthermore, conceptual
clustering achieves similar predictive performances without requiring
parameterisation, and achieves this with lower computational overhead than
thresholding using predictive performance and MI when the number of base models
becomes large.

We propose an AE-based transceiver for a WDM system impaired by hardware
imperfections. We design our AE following the architecture of conventional
communication systems. This enables to initialize the AE-based transceiver to
have similar performance to its conventional counterpart prior to training and
improves the training convergence rate. We first train the AE in a
single-channel system, and show that it achieves performance improvements by
putting energy outside the desired bandwidth, and therefore cannot be used for
a WDM system. We then train the AE in a WDM setup. Simulation results show that
the proposed AE significantly outperforms the conventional approach. More
specifically, it increases the spectral efficiency of the considered system by
reducing the guard band by 37\% and 50\% for a root-raised-cosine filter-based
matched filter with 10\% and 1\% roll-off, respectively. An ablation study
indicates that the performance gain can be ascribed to the optimization of the
symbol mapper, the pulse-shaping filter, and the symbol demapper. Finally, we
use reinforcement learning to learn the pulse-shaping filter under the
assumption that the channel model is unknown. Simulation results show that the
reinforcement-learning-based algorithm achieves similar performance to the
standard supervised end-to-end learning approach assuming perfect channel

An essential task of Automated Machine Learning (AutoML) is the problem of
automatically finding the pipeline with the best generalization performance on
a given dataset. This problem has been addressed with sophisticated black-box
optimization techniques such as Bayesian Optimization, Grammar-Based Genetic
Algorithms, and tree search algorithms. Most of the current approaches are
motivated by the assumption that optimizing the components of a pipeline in
isolation may yield sub-optimal results. We present Naive AutoML, an approach
that does precisely this: It optimizes the different algorithms of a
pre-defined pipeline scheme in isolation. The finally returned pipeline is
obtained by just taking the best algorithm of each slot. The isolated
optimization leads to substantially reduced search spaces, and, surprisingly,
this approach yields comparable and sometimes even better performance than
current state-of-the-art optimizers.

Translational invariance induced by pooling operations is an inherent
property of convolutional neural networks, which facilitates numerous computer
vision tasks such as classification. Yet to leverage rotational invariant
tasks, convolutional architectures require specific rotational invariant layers
or extensive data augmentation to learn from diverse rotated versions of a
given spatial configuration. Unwrapping the image into its polar coordinates
provides a more explicit representation to train a convolutional architecture
as the rotational invariance becomes translational, hence the visually distinct
but otherwise equivalent rotated versions of a given scene can be learnt from a
single image. We show with two common vision-based solar irradiance forecasting
challenges (i.e. using ground-taken sky images or satellite images), that this
preprocessing step significantly improves prediction results by standardising
the scene representation, while decreasing training time by a factor of 4
compared to augmenting data with rotations. In addition, this transformation
magnifies the area surrounding the centre of the rotation, leading to more
accurate short-term irradiance predictions.

Increasing the cardinality of categorical variables might decrease the
overall performance of machine learning (ML) algorithms. This paper presents a
novel computational preprocessing method to convert categorical to numerical
variables ML algorithms. It uses a supervised binary classifier to extract
additional context-related features from the categorical values. Up to two
numerical variables per categorical variable are created, depending on the
compression achieved by the Principal Component Analysis (PCA). The method
requires two hyperparameters: a threshold related to the distribution of
categories in the variables and the PCA representativeness. This paper applies
the proposed approach to the well-known cybersecurity NSLKDD dataset to select
and convert three categorical features to numerical features. After choosing
the threshold parameter, we use conditional probabilities to convert the three
categorical variables into six new numerical variables. After that, we feed
these numerical variables to the PCA algorithm and select the whole or partial
numbers of the Principal Components (PCs). Finally, by applying binary
classification with ten different classifiers, we measure the performance of
the new encoder and compare it with the other 17 well-known category encoders.
The new technique achieves the highest performance related to accuracy and Area
Under the Curve (AUC) on high cardinality categorical variables. Also, we
define the harmonic average metrics to find the best trade-off between train
and test performances and prevent underfitting and overfitting. Ultimately, the
number of newly created numerical variables is minimal. This data reduction
improves computational processing time in Internet of things (IoT) devices in
future telecommunication networks.

Neonatal seizures are a commonly encountered neurological condition. They are
the first clinical signs of a serious neurological disorder. Thus, rapid
recognition and treatment are necessary to prevent serious fatalities. The use
of electroencephalography (EEG) in the field of neurology allows precise
diagnosis of several medical conditions. However, interpreting EEG signals
needs the attention of highly specialized staff since the infant brain is
developmentally immature during the neonatal period. Detecting seizures on time
could potentially prevent the negative effects on the neurocognitive
development of the infants. In recent years, neonatal seizure detection using
machine learning algorithms have been gaining traction. Since there is a need
for the classification of bio-signals to be computationally inexpensive in the
case of seizure detection, this research presents a machine learning (ML) based
architecture that operates with comparable predictive performance as previous
models but with minimum level configuration. The proposed classifier was
trained and tested on a public dataset of NICU seizures recorded at the
Helsinki University Hospital. Our architecture achieved a best sensitivity of
87%, which is 6% more than that of the standard ML model chosen in this study.
The model size of the ML classifier is optimized to just 4.84 KB with minimum
prediction time of 182.61 milliseconds, thus enabling it to be deployed on
wearable ultra-edge devices for quick and accurate response and obviating the
need for cloud-based and other such exhaustive computational methods.

Deep neural networks represent the gold standard for image classification.
However, they usually need large amounts of data to reach superior performance.
In this work, we focus on image classification problems with a few labeled
examples per class and improve data efficiency by using an ensemble of
relatively small networks. For the first time, our work broadly studies the
existing concept of neural ensembling in domains with small data, through
extensive validation using popular datasets and architectures. We compare
ensembles of networks to their deeper or wider single competitors given a total
fixed computational budget. We show that ensembling relatively shallow networks
is a simple yet effective technique that is generally better than current
state-of-the-art approaches for learning from small datasets. Finally, we
present our interpretation according to which neural ensembles are more sample
efficient because they learn simpler functions.

With the advent of machine learning in applications of critical
infrastructure such as healthcare and energy, privacy is a growing concern in
the minds of stakeholders. It is pivotal to ensure that neither the model nor
the data can be used to extract sensitive information used by attackers against
individuals or to harm whole societies through the exploitation of critical
infrastructure. The applicability of machine learning in these domains is
mostly limited due to a lack of trust regarding the transparency and the
privacy constraints. Various safety-critical use cases (mostly relying on
time-series data) are currently underrepresented in privacy-related
considerations. By evaluating several privacy-preserving methods regarding
their applicability on time-series data, we validated the inefficacy of
encryption for deep learning, the strong dataset dependence of differential
privacy, and the broad applicability of federated methods.

State-of-the-art language models (LMs) represented by long-short term memory
recurrent neural networks (LSTM-RNNs) and Transformers are becoming
increasingly complex and expensive for practical applications. Low-bit neural
network quantization provides a powerful solution to dramatically reduce their
model size. Current quantization methods are based on uniform precision and
fail to account for the varying performance sensitivity at different parts of
LMs to quantization errors. To this end, novel mixed precision neural network
LM quantization methods are proposed in this paper. The optimal local precision
choices for LSTM-RNN and Transformer based neural LMs are automatically learned
using three techniques. The first two approaches are based on quantization
sensitivity metrics in the form of either the KL-divergence measured between
full precision and quantized LMs, or Hessian trace weighted quantization
perturbation that can be approximated efficiently using matrix free techniques.
The third approach is based on mixed precision neural architecture search. In
order to overcome the difficulty in using gradient descent methods to directly
estimate discrete quantized weights, alternating direction methods of
multipliers (ADMM) are used to efficiently train quantized LMs. Experiments
were conducted on state-of-the-art LF-MMI CNN-TDNN systems featuring speed
perturbation, i-Vector and learning hidden unit contribution (LHUC) based
speaker adaptation on two tasks: Switchboard telephone speech and AMI meeting
transcription. The proposed mixed precision quantization techniques achieved
"lossless" quantization on both tasks, by producing model size compression
ratios of up to approximately 16 times over the full precision LSTM and
Transformer baseline LMs, while incurring no statistically significant word
error rate increase.

In this work, we aim to classify nodes of unstructured peer-to-peer networks
with communication uncertainty, such as users of decentralized social networks.
Graph Neural Networks (GNNs) are known to improve the accuracy of simple
classifiers in centralized settings by leveraging naturally occurring network
links, but graph convolutional layers are challenging to implement in
decentralized settings when node neighbors are not constantly available. We
address this problem by employing decoupled GNNs, where base classifier
predictions and errors are diffused through graphs after training. For these,
we deploy pre-trained and gossip-trained base classifiers and implement
peer-to-peer graph diffusion under communication uncertainty. In particular, we
develop an asynchronous decentralized formulation of diffusion that converges
to centralized predictions in distribution and linearly with respect to
communication rates. We experiment on three real-world graphs with node
features and labels and simulate peer-to-peer networks with uniformly random
communication frequencies; given a portion of known labels, our decentralized
graph diffusion achieves comparable accuracy to centralized GNNs with minimal
communication overhead (less than 3% of what gossip training already adds).

In this paper, we consider recovering $n$ dimensional signals from $m$ binary
measurements corrupted by noises and sign flips under the assumption that the
target signals have low generative intrinsic dimension, i.e., the target
signals can be approximately generated via an $L$-Lipschitz generator $G:
\mathbb{R}^k\rightarrow\mathbb{R}^{n}, k\ll n$. Although the binary
measurements model is highly nonlinear, we propose a least square decoder and
prove that, up to a constant $c$, with high probability, the least square
decoder achieves a sharp estimation error $\mathcal{O} (\sqrt{\frac{k\log
(Ln)}{m}})$ as long as $m\geq \mathcal{O}( k\log (Ln))$. Extensive numerical
simulations and comparisons with state-of-the-art methods demonstrated the
least square decoder is robust to noise and sign flips, as indicated by our
theory. By constructing a ReLU network with properly chosen depth and width, we
verify the (approximately) deep generative prior, which is of independent
interest.

Generative algorithms such as GANs are at the cusp of next revolution in the
field of unsupervised learning and large-scale artificial data generation.
However, the adversarial (competitive) co-training of the discriminative and
generative networks in GAN makes them computationally intensive and hinders
their deployment on the resource-constrained IoT edge devices. Moreover, the
frequent data transfer between the discriminative and generative networks
during training significantly degrades the efficacy of the von-Neumann GAN
accelerators such as those based on GPU and FPGA. Therefore, there is an urgent
need for development of ultra-compact and energy-efficient hardware
accelerators for GANs. To this end, in this work, we propose to exploit the
passive RRAM crossbar arrays for performing key operations of a fully-connected
GAN: (a) true random noise generation for the generator network, (b)
vector-by-matrix-multiplication with unprecedented energy-efficiency during the
forward pass and backward propagation and (C) in-situ adversarial training
using a hardware friendly Manhattan's rule. Our extensive analysis utilizing an
experimentally calibrated phenomological model for passive RRAM crossbar array
reveals an unforeseen trade-off between the accuracy and the energy dissipated
while training the GAN network with different noise inputs to the generator.
Furthermore, our results indicate that the spatial and temporal variations and
true random noise, which are otherwise undesirable for memory application,
boost the energy-efficiency of the GAN implementation on passive RRAM crossbar
arrays without degrading its accuracy.

Recognition of overlapped speech has been a highly challenging task to date.
State-of-the-art multi-channel speech separation system are becoming
increasingly complex and expensive for practical applications. To this end,
low-bit neural network quantization provides a powerful solution to
dramatically reduce their model size. However, current quantization methods are
based on uniform precision and fail to account for the varying performance
sensitivity at different model components to quantization errors. In this
paper, novel mixed precision DNN quantization methods are proposed by applying
locally variable bit-widths to individual TCN components of a TF masking based
multi-channel speech separation system. The optimal local precision settings
are automatically learned using three techniques. The first two approaches
utilize quantization sensitivity metrics based on either the mean square error
(MSE) loss function curvature, or the KL-divergence measured between full
precision and quantized separation models. The third approach is based on mixed
precision neural architecture search. Experiments conducted on the LRS3-TED
corpus simulated overlapped speech data suggest that the proposed mixed
precision quantization techniques consistently outperform the uniform precision
baseline speech separation systems of comparable bit-widths in terms of SI-SNR
and PESQ scores as well as word error rate (WER) reductions up to 2.88%
absolute (8% relative).

The end-to-end learning-based video compression has attracted substantial
attentions by paving another way to compress video signals as stacked visual
features. This paper proposes an efficient end-to-end deep video codec with
jointly optimized compression and enhancement modules (JCEVC). First, we
propose a dual-path generative adversarial network (DPEG) to reconstruct video
details after compression. An $\alpha$-path facilitates the structure
information reconstruction with a large receptive field and multi-frame
references, while a $\beta$-path facilitates the reconstruction of local
textures. Both paths are fused and co-trained within a generative-adversarial
process. Second, we reuse the DPEG network in both motion compensation and
quality enhancement modules, which are further combined with other necessary
modules to formulate our JCEVC framework. Third, we employ a joint training of
deep video compression and enhancement that further improves the
rate-distortion (RD) performance of compression. Compared with x265 LDP very
fast mode, our JCEVC reduces the average bit-per-pixel (bpp) by 39.39\%/54.92\%
at the same PSNR/MS-SSIM, which outperforms the state-of-the-art deep video
codecs by a considerable margin.

We propose a method for jointly estimating the 3D motion, 3D shape, and
appearance of highly motion-blurred objects from a video. To this end, we model
the blurred appearance of a fast moving object in a generative fashion by
parametrizing its 3D position, rotation, velocity, acceleration, bounces,
shape, and texture over the duration of a predefined time window spanning
multiple frames. Using differentiable rendering, we are able to estimate all
parameters by minimizing the pixel-wise reprojection error to the input video
via backpropagating through a rendering pipeline that accounts for motion blur
by averaging the graphics output over short time intervals. For that purpose,
we also estimate the camera exposure gap time within the same optimization. To
account for abrupt motion changes like bounces, we model the motion trajectory
as a piece-wise polynomial, and we are able to estimate the specific time of
the bounce at sub-frame accuracy. Experiments on established benchmark datasets
demonstrate that our method outperforms previous methods for fast moving object
deblurring and 3D reconstruction.

The visual quality of photographs taken under imperfect lightness conditions
can be degenerated by multiple factors, e.g., low lightness, imaging noise,
color distortion and so on. Current low-light image enhancement models focus on
the improvement of low lightness only, or simply deal with all the degeneration
factors as a whole, therefore leading to a sub-optimal performance. In this
paper, we propose to decouple the enhancement model into two sequential stages.
The first stage focuses on improving the scene visibility based on a pixel-wise
non-linear mapping. The second stage focuses on improving the appearance
fidelity by suppressing the rest degeneration factors. The decoupled model
facilitates the enhancement in two aspects. On the one hand, the whole
low-light enhancement can be divided into two easier subtasks. The first one
only aims to enhance the visibility. It also helps to bridge the large
intensity gap between the low-light and normal-light images. In this way, the
second subtask can be shaped as the local appearance adjustment. On the other
hand, since the parameter matrix learned from the first stage is aware of the
lightness distribution and the scene structure, it can be incorporated into the
second stage as the complementary information. In the experiments, our model
demonstrates the state-of-the-art performance in both qualitative and
quantitative comparisons, compared with other low-light image enhancement
models. In addition, the ablation studies also validate the effectiveness of
our model in multiple aspects, such as model structure and loss function. The
trained model is available at
https://github.com/hanxuhfut/Decoupled-Low-light-Image-Enhancement.

With the outbreak of COVID-19 pandemic, a dire need to effectively identify
the individuals who may have come in close-contact to others who have been
infected with COVID-19 has risen. This process of identifying individuals, also
termed as 'Contact tracing', has significant implications for the containment
and control of the spread of this virus. However, manual tracing has proven to
be ineffective calling for automated contact tracing approaches. As such, this
research presents an automated machine learning system for identifying
individuals who may have come in contact with others infected with COVID-19
using sensor data transmitted through handheld devices. This paper describes
the different approaches followed in arriving at an optimal solution model that
effectually predicts whether a person has been in close proximity to an
infected individual using a gradient boosting algorithm and time series feature

Recent text-to-image matching models apply contrastive learning to large
corpora of uncurated pairs of images and sentences. While such models can
provide a powerful score for matching and subsequent zero-shot tasks, they are
not capable of generating caption given an image. In this work, we repurpose
such models to generate a descriptive text given an image at inference time,
without any further training or tuning steps. This is done by combining the
visual-semantic model with a large language model, benefiting from the
knowledge in both web-scale models. The resulting captions are much less
restrictive than those obtained by supervised captioning methods. Moreover, as
a zero-shot learning method, it is extremely flexible and we demonstrate its
ability to perform image arithmetic in which the inputs can be either images or
text, and the output is a sentence. This enables novel high-level vision
capabilities such as comparing two images or solving visual analogy tests. Our
code is available at: https://github.com/YoadTew/zero-shot-image-to-text.

We are presenting a novel, Deep Learning based approach to estimate the
normalized broadband spectral energy distribution (SED) of different stellar
populations in synthetic galaxies. In contrast to the non-parametric multiband
source separation algorithm, SCARLET - where the SED and morphology are
simultaneously fitted - in our study we provide a morphology-independent,
statistical determination of the SEDs, where we only use the color distribution
of the galaxy. We developed a neural network (sedNN) that accurately predicts
the SEDs of the old, red and young, blue stellar populations of realistic
synthetic galaxies from the color distribution of the galaxy-related pixels in
simulated broadband images. We trained and tested the network on a subset of
the recently published CosmoDC2 simulated galaxy catalog containing about 3,600
galaxies. The model performance was compared to the results of SCARLET, where
we found that sedNN can predict the SEDs with 4-5% accuracy on average, which
is about two times better than applying SCARLET. We also investigated the
effect of this improvement on the flux determination accuracy of the bulge and
disk. We found that using more accurate SEDs decreases the error in the flux
determination of the components by approximately 30%.

Online signatures are one of the most commonly used biometrics. Several
verification systems and public databases were presented in this field. This
paper presents a combination of k-nearest neighbor and dynamic time warping
algorithms as a verification system using the recently published DeepSignDB
database. Our algorithm was applied on both finger and stylus input signatures
which represent both office and mobile scenarios. The system was first tested
on the development set of the database. It achieved an error rate of 6.04% for
the stylus input signatures, 5.20% for the finger input signatures, and 6.00%
for a combination of both types. The system was also applied to the evaluation
set of the database and achieved very promising results, especially for finger
input signatures.

The computing device deployment explosion experienced in recent years,
motivated by the advances of technologies such as Internet-of-Things (IoT) and
5G, has led to a global scenario with increasing cybersecurity risks and
threats. Among them, device spoofing and impersonation cyberattacks stand out
due to their impact and, usually, low complexity required to be launched. To
solve this issue, several solutions have emerged to identify device models and
types based on the combination of behavioral fingerprinting and Machine/Deep
Learning (ML/DL) techniques. However, these solutions are not appropriated for
scenarios where data privacy and protection is a must, as they require data
centralization for processing. In this context, newer approaches such as
Federated Learning (FL) have not been fully explored yet, especially when
malicious clients are present in the scenario setup. The present work analyzes
and compares the device model identification performance of a centralized DL
model with an FL one while using execution time-based events. For experimental
purposes, a dataset containing execution-time features of 55 Raspberry Pis
belonging to four different models has been collected and published. Using this
dataset, the proposed solution achieved 0.9999 accuracy in both setups,
centralized and federated, showing no performance decrease while preserving
data privacy. Later, the impact of a label-flipping attack during the federated
model training is evaluated, using several aggregation mechanisms as
countermeasure. Zeno and coordinate-wise median aggregation show the best
performance, although their performance greatly degrades when the percentage of
fully malicious clients (all training samples poisoned) grows over 50%.

We investigate the generalization properties of a self-training algorithm
with halfspaces. The approach learns a list of halfspaces iteratively from
labeled and unlabeled training data, in which each iteration consists of two
steps: exploration and pruning. In the exploration phase, the halfspace is
found sequentially by maximizing the unsigned-margin among unlabeled examples
and then assigning pseudo-labels to those that have a distance higher than the
current threshold. The pseudo-labeled examples are then added to the training
set, and a new classifier is learned. This process is repeated until no more
unlabeled examples remain for pseudo-labeling. In the pruning phase,
pseudo-labeled samples that have a distance to the last halfspace greater than
the associated unsigned-margin are then discarded. We prove that the
misclassification error of the resulting sequence of classifiers is bounded and
show that the resulting semi-supervised approach never degrades performance
compared to the classifier learned using only the initial labeled training set.
Experiments carried out on a variety of benchmarks demonstrate the efficiency
of the proposed approach compared to state-of-the-art methods.

We describe an iterative active-learning algorithm to recognise rare traffic
signs. A standard ResNet is trained on a training set containing only a single
sample of the rare class. We demonstrate that by sorting the samples of a
large, unlabeled set by the estimated probability of belonging to the rare
class, we can efficiently identify samples from the rare class. This works
despite the fact that this estimated probability is usually quite low. A
reliable active-learning loop is obtained by labeling these candidate samples,
including them in the training set, and iterating the procedure. Further, we
show that we get similar results starting from a single synthetic sample. Our
results are important as they indicate a straightforward way of improving
traffic-sign recognition for automated driving systems. In addition, they show
that we can make use of the information hidden in low confidence outputs, which
is usually ignored.

Object visual navigation aims to steer an agent towards a target object based
on visual observations of the agent. It is highly desirable to reasonably
perceive the environment and accurately control the agent. In the navigation
task, we introduce an Agent-Centric Relation Graph (ACRG) for learning the
visual representation based on the relationships in the environment. ACRG is a
highly effective and reasonable structure that consists of two relationships,
i.e., the relationship among objects and the relationship between the agent and
the target. On the one hand, we design the Object Horizontal Relationship Graph
(OHRG) that stores the relative horizontal location among objects. Note that
the vertical relationship is not involved in OHRG, and we argue that OHRG is
suitable for the control strategy. On the other hand, we propose the
Agent-Target Depth Relationship Graph (ATDRG) that enables the agent to
perceive the distance to the target. To achieve ATDRG, we utilize image depth
to represent the distance. Given the above relationships, the agent can
perceive the environment and output navigation actions. Given the visual
representations constructed by ACRG and position-encoded global features, the
agent can capture the target position to perform navigation actions.
Experimental results in the artificial environment AI2-Thor demonstrate that
ACRG significantly outperforms other state-of-the-art methods in unseen testing

We present a novel deep-learning-based method for Multi-View Stereo. Our
method estimates high resolution and highly precise depth maps iteratively, by
traversing the continuous space of feasible depth values at each pixel in a
binary decision fashion. The decision process leverages a deep-network
architecture: this computes a pixelwise binary mask that establishes whether
each pixel actual depth is in front or behind its current iteration individual
depth hypothesis. Moreover, in order to handle occluded regions, at each
iteration the results from different source images are fused using pixelwise
weights estimated by a second network. Thanks to the adopted binary decision
strategy, which permits an efficient exploration of the depth space, our method
can handle high resolution images without trading resolution and precision.
This sets it apart from most alternative learning-based Multi-View Stereo
methods, where the explicit discretization of the depth space requires the
processing of large cost volumes. We compare our method with state-of-the-art
Multi-View Stereo methods on the DTU, Tanks and Temples and the challenging
ETH3D benchmarks and show competitive results.

State-of-the-art neural language models represented by Transformers are
becoming increasingly complex and expensive for practical applications. Low-bit
deep neural network quantization techniques provides a powerful solution to
dramatically reduce their model size. Current low-bit quantization methods are
sensitivity at different parts of the system to quantization errors. To this
end, novel mixed precision DNN quantization methods are proposed in this paper.
The optimal local precision settings are automatically learned using two
techniques. The first is based on a quantization sensitivity metric in the form
of Hessian trace weighted quantization perturbation. The second is based on
mixed precision Transformer architecture search. Alternating direction methods
of multipliers (ADMM) are used to efficiently train mixed precision quantized
DNN systems. Experiments conducted on Penn Treebank (PTB) and a Switchboard
corpus trained LF-MMI TDNN system suggest the proposed mixed precision
Transformer quantization techniques achieved model size compression ratios of
up to 16 times over the full precision baseline with no recognition performance
degradation. When being used to compress a larger full precision Transformer LM
with more layers, overall word error rate (WER) reductions up to 1.7% absolute
(18% relative) were obtained.

The absence of an algorithm that effectively monitors deep learning models
used in side-channel attacks increases the difficulty of evaluation. If the
attack is unsuccessful, the question is if we are dealing with a resistant
implementation or a faulty model. We propose an early stopping algorithm that
reliably recognizes the model's optimal state during training. The novelty of
our solution is an efficient implementation of guessing entropy estimation.
Additionally, we formalize two conditions, persistence and patience, for a deep
learning model to be optimal. As a result, the model converges with fewer
traces.

Person re-identification (reID) aims at retrieving a person from images
captured by different cameras. For deep-learning-based reID methods, it has
been proved that using local features together with global feature could help
to give robust representation for person retrieval. Human pose information
could provide the locations of human skeleton to effectively guide the network
to pay more attention on these key areas and could also help to reduce the
noise distractions from background or occlusion. However, methods proposed by
previous pose-based works might not be able to fully exploit the benefits of
pose information and few of them take into consideration the different
contributions of separate local features. In this paper, we propose a pose
guided graph attention network, a multi-branch architecture consisting of one
branch for global feature, one branch for mid-granular body features and one
branch for fine-granular key point features. We use a pre-trained pose
estimator to generate the key-point heatmaps for local feature learning and
carefully design a graph attention convolution layer to re-assign the
contribution weights of extracted local features by modeling the similarities
relations. Experiment results demonstrate the effectiveness of our approach on
discriminative feature learning and we show that our model achieves
state-of-the-art performances on several mainstream evaluation datasets. We
also conduct a plenty of ablation studies and design different kinds of
comparison experiments for our network to prove its effectiveness and
robustness, including occluded experiments and cross-domain tests.

The connection between Bayesian neural networks and Gaussian processes gained
a lot of attention in the last few years, with the flagship result that hidden
units converge to a Gaussian process limit when the layers width tends to
infinity. Underpinning this result is the fact that hidden units become
independent in the infinite-width limit. Our aim is to shed some light on
hidden units dependence properties in practical finite-width Bayesian neural
networks. In addition to theoretical results, we assess empirically the depth
and width impacts on hidden units dependence properties.

Indirect Time-of-Flight cameras (iToF) are low-cost devices that provide
depth images at an interactive frame rate. However, they are affected by
different error sources, with the spotlight taken by Multi-Path Interference
(MPI), a key challenge for this technology. Common data-driven approaches tend
to focus on a direct estimation of the output depth values, ignoring the
underlying transient propagation of the light in the scene. In this work
instead, we propose a very compact architecture, leveraging on the
direct-global subdivision of transient information for the removal of MPI and
for the reconstruction of the transient information itself. The proposed model
reaches state-of-the-art MPI correction performances both on synthetic and real
data and proves to be very competitive also at extreme levels of noise; at the
same time, it also makes a step towards reconstructing transient information
from multi-frequency iToF data.

The high memory consumption and computational costs of Recurrent neural
network language models (RNNLMs) limit their wider application on resource
constrained devices. In recent years, neural network quantization techniques
that are capable of producing extremely low-bit compression, for example,
binarized RNNLMs, are gaining increasing research interests. Directly training
of quantized neural networks is difficult. By formulating quantized RNNLMs
training as an optimization problem, this paper presents a novel method to
train quantized RNNLMs from scratch using alternating direction methods of
multipliers (ADMM). This method can also flexibly adjust the trade-off between
the compression rate and model performance using tied low-bit quantization
tables. Experiments on two tasks: Penn Treebank (PTB), and Switchboard (SWBD)
suggest the proposed ADMM quantization achieved a model size compression factor
of up to 31 times over the full precision baseline RNNLMs. Faster convergence
of 5 times in model training over the baseline binarized RNNLM quantization was
also obtained. Index Terms: Language models, Recurrent neural networks,
Quantization, Alternating direction methods of multipliers.

In this work we examine the performance enhancement in classification of
medical imaging data when image features are combined with associated non-image
data. We compare the performance of eight state-of-the-art deep neural networks
in classification tasks when using only image features, compared to when these
are combined with patient metadata. We utilise transfer learning with networks
pretrained on ImageNet used directly as feature extractors and fine tuned on
the target domain. Our experiments show that performance can be significantly
enhanced with the inclusion of metadata and use interpretability methods to
identify which features lead to these enhancements. Furthermore, our results
indicate that the performance enhancement for natural medical imaging (e.g.
optical images) benefit most from direct use of pre-trained models, whereas non
natural images (e.g. representations of non imaging data) benefit most from
fine tuning pre-trained networks. These enhancements come at a negligible
additional cost in computation time, and therefore is a practical method for
other applications.

The classic cake-cutting problem provides a model for addressing the fair and
efficient allocation of a divisible, heterogeneous resource among agents with
distinct preferences. Focusing on a standard formulation of cake cutting, in
which each agent must receive a contiguous piece of the cake in an offline
setting, this work instead focuses on online allocating non-contiguous pieces
of cake among agents and establishes algorithmic results for fairness measures.
In this regard, we made use of classic adversarial multi-armed bandits to
achieve sub-linear Fairness and Revenue Regret at the same time. Adversarial
bandits are powerful tools to model the adversarial reinforcement learning
environments, that provide strong upper-bounds for regret of learning with just
observing one action's reward in each step by applying smart trade-off between
exploration and exploitation. This work studies the power of the famous EXP_3
algorithm that is based on exponential wight{-}importance updating probability
distribution through time horizon.

In the past decade, we have witnessed the rise of deep learning to dominate
the field of artificial intelligence. Advances in artificial neural networks
alongside corresponding advances in hardware accelerators with large memory
capacity, together with the availability of large datasets enabled
practitioners to train and deploy sophisticated neural network models that
achieve state-of-the-art performance on tasks across several fields spanning
computer vision, natural language processing, and reinforcement learning.
However, as these neural networks become bigger, more complex, and more widely
used, fundamental problems with current deep learning models become more
apparent. State-of-the-art deep learning models are known to suffer from issues
that range from poor robustness, inability to adapt to novel task settings, to
requiring rigid and inflexible configuration assumptions. Collective behavior,
commonly observed in nature, tends to produce systems that are robust,
adaptable, and have less rigid assumptions about the environment configuration.
Collective intelligence, as a field, studies the group intelligence that
emerges from the interactions of many individuals. Within this field, ideas
such as self-organization, emergent behavior, swarm optimization, and cellular
automata were developed to model and explain complex systems. It is therefore
natural to see these ideas incorporated into newer deep learning methods. In
this review, we will provide a historical context of neural network research's
involvement with complex systems, and highlight several active areas in modern
deep learning research that incorporate the principles of collective
intelligence to advance its current capabilities. We hope this review can serve
as a bridge between the complex systems and deep learning communities.

This paper covers n-tuple-based reinforcement learning (RL) algorithms for
games. We present new algorithms for TD-, SARSA- and Q-learning which work
seamlessly on various games with arbitrary number of players. This is achieved
by taking a player-centered view where each player propagates his/her rewards
back to previous rounds. We add a new element called Final Adaptation RL (FARL)
to all these algorithms. Our main contribution is that FARL is a vitally
important ingredient to achieve success with the player-centered view in
various games. We report results on seven board games with 1, 2 and 3 players,
including Othello, ConnectFour and Hex. In most cases it is found that FARL is
important to learn a near-perfect playing strategy. All algorithms are
available in the GBG framework on GitHub.

The living body is composed of innumerable fine and complex structures and
although these structures have been studied in the past, a vast amount of
information pertaining to them still remains unknown. When attempting to
observe these ultra-structures, the use of electron microscopy (EM) has become
indispensable. However, conventional EM settings are limited to a narrow tissue
area that can bias observations. Recently, new trends in EM research have
emerged that provide coverage of far broader, nano-scale fields of view for
two-dimensional wide areas and three-dimensional large volumes. Together with
cutting-edge bioimage informatics conducted via deep learning, such techniques
have accelerated the quantification of complex morphological images. Moreover,
these advances have led to the comprehensive acquisition and quantification of
cellular morphology, which is now treated as a new omics science termed
'morphomics'. Moreover, by incorporating these new methodologies, the field of
traditional pathology is expected to advance, potentially with the
identification of previously unknown structures, quantification of rare events,
reclassification of diseases and automatic diagnosis of diseases. In this
review, we discuss these technological and analytical advances, which have
arisen from the need to analyse nano-scale bioimages, in detail, as well as
focusing on state-of-art image analysis involving deep learning.

Unsupervised domain adaptation (UDA) has successfully addressed the domain
shift problem for visual applications. Yet, these approaches may have limited
performance for time series data due to the following reasons. First, they
mainly rely on large-scale dataset (i.e., ImageNet) for the source pretraining,
which is not applicable for time-series data. Second, they ignore the temporal
dimension on the feature space of the source and target domains during the
domain alignment step. Last, most of prior UDA methods can only align the
global features without considering the fine-grained class distribution of the
target domain. To address these limitations, we propose a Self-supervised
Autoregressive Domain Adaptation (SLARDA) framework. In particular, we first
design a self-supervised learning module that utilizes forecasting as an
auxiliary task to improve the transferability of the source features. Second,
we propose a novel autoregressive domain adaptation technique that incorporates
temporal dependency of both source and target features during domain alignment.
Finally, we develop an ensemble teacher model to align the class-wise
distribution in the target domain via a confident pseudo labeling approach.
Extensive experiments have been conducted on three real-world time series
applications with 30 cross-domain scenarios. Results demonstrate that our
proposed SLARDA method significantly outperforms the state-of-the-art
approaches for time series domain adaptation.

Supervised learning-based methods yield robust denoising results, yet they
are inherently limited by the need for large-scale clean/noisy paired datasets.
The use of unsupervised denoisers, on the other hand, necessitates a more
detailed understanding of the underlying image statistics. In particular, it is
well known that apparent differences between clean and noisy images are most
prominent on high-frequency bands, justifying the use of low-pass filters as
part of conventional image preprocessing steps. However, most learning-based
denoising methods utilize only one-sided information from the spatial domain
without considering frequency domain information. To address this limitation,
in this study we propose a frequency-sensitive unsupervised denoising method.
To this end, a generative adversarial network (GAN) is used as a base
structure. Subsequently, we include spectral discriminator and frequency
reconstruction loss to transfer frequency knowledge into the generator. Results
using natural and synthetic datasets indicate that our unsupervised learning
method augmented with frequency information achieves state-of-the-art denoising
performance, suggesting that frequency domain information could be a viable
factor in improving the overall performance of unsupervised learning-based
methods.

Single-agent reinforcement learning algorithms in a multi-agent environment
are inadequate for fostering cooperation. If intelligent agents are to interact
and work together to solve complex problems, methods that counter
non-cooperative behavior are needed to facilitate the training of multiple
agents. This is the goal of cooperative AI. Recent research in adversarial
machine learning, however, shows that models (e.g., image classifiers) can be
easily deceived into making inferior decisions. Meanwhile, an important line of
research in cooperative AI has focused on introducing algorithmic improvements
that accelerate learning of optimally cooperative behavior. We argue that
prominent methods of cooperative AI are exposed to weaknesses analogous to
those studied in prior machine learning research. More specifically, we show
that three algorithms inspired by human-like social intelligence are, in
principle, vulnerable to attacks that exploit weaknesses introduced by
cooperative AI's algorithmic improvements and report experimental findings that
illustrate how these vulnerabilities can be exploited in practice.

The lack of large-scale noisy-clean image pairs restricts supervised
denoising methods' deployment in actual applications. While existing
unsupervised methods are able to learn image denoising without ground-truth
clean images, they either show poor performance or work under impractical
settings (e.g., paired noisy images). In this paper, we present a practical
unsupervised image denoising method to achieve state-of-the-art denoising
performance. Our method only requires single noisy images and a noise model,
which is easily accessible in practical raw image denoising. It performs two
steps iteratively: (1) Constructing a noisier-noisy dataset with random noise
from the noise model; (2) training a model on the noisier-noisy dataset and
using the trained model to refine noisy images to obtain the targets used in
the next round. We further approximate our full iterative method with a fast
algorithm for more efficient training while keeping its original high
performance. Experiments on real-world, synthetic, and correlated noise show
that our proposed unsupervised denoising approach has superior performances
over existing unsupervised methods and competitive performance with supervised
methods. In addition, we argue that existing denoising datasets are of low
quality and contain only a small number of scenes. To evaluate raw image
denoising performance in real-world applications, we build a high-quality raw
image dataset SenseNoise-500 that contains 500 real-life scenes. The dataset
can serve as a strong benchmark for better evaluating raw image denoising. Code
and dataset will be released at https://github.com/zhangyi-3/IDR

Knowledge distillation has become an important approach to obtain a compact
yet effective model. To achieve this goal, a small student model is trained to
exploit the knowledge of a large well-trained teacher model. However, due to
the capacity gap between the teacher and the student, the student's performance
is hard to reach the level of the teacher. Regarding this issue, existing
methods propose to reduce the difficulty of the teacher's knowledge via a proxy
way. We argue that these proxy-based methods overlook the knowledge loss of the
teacher, which may cause the student to encounter capacity bottlenecks. In this
paper, we alleviate the capacity gap problem from a new perspective with the
purpose of averting knowledge loss. Instead of sacrificing part of the
teacher's knowledge, we propose to build a more powerful student via
adversarial collaborative learning. To this end, we further propose an
Adversarial Collaborative Knowledge Distillation (ACKD) method that effectively
improves the performance of knowledge distillation. Specifically, we construct
the student model with multiple auxiliary learners. Meanwhile, we devise an
adversarial collaborative module (ACM) that introduces attention mechanism and
adversarial learning to enhance the capacity of the student. Extensive
experiments on four classification tasks show the superiority of the proposed
ACKD.

In this study, a machine learning model was developed for automatically
detecting respiratory system sounds such as sneezing and coughing in disease
diagnosis. The automatic model and approach development of breath sounds, which
carry valuable information, results in early diagnosis and treatment. A
successful machine learning model was developed in this study, which was a
strong response to the challenge called the "Pfizer digital medicine challenge"
on the "OSFHOME" open access platform. "Environmental sound classification"
called ESC-50 and AudioSet sound files were used to prepare the dataset. In
this dataset, which consisted of three parts, features that effectively showed
coughing and sneezing sound analysis were extracted from training, testing and
validating samples. Based on the Mel frequency cepstral coefficients (MFCC)
feature extraction method, mathematical and statistical features were prepared.
Three different classification techniques were considered to perform successful
respiratory sound classification in the dataset containing more than 3800
different sounds. Support vector machine (SVM) with radial basis function (RBF)
kernels, ensemble aggregation and decision tree classification methods were
used as classification techniques. In an attempt to classify coughing and
sneezing sounds from other sounds, SVM with RBF kernels was achieved with 83%
success.

Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new
domain with only a small set of labeled samples when a large labeled dataset is
given on a source domain. In this paper, we propose a pair-based SSDA method
that adapts a model to the target domain using self-distillation with sample
pairs. Each sample pair is composed of a teacher sample from a labeled dataset
(i.e., source or labeled target) and its student sample from an unlabeled
dataset (i.e., unlabeled target). Our method generates an assistant feature by
transferring an intermediate style between the teacher and the student, and
then train the model by minimizing the output discrepancy between the student
and the assistant. During training, the assistants gradually bridge the
discrepancy between the two domains, thus allowing the student to easily learn
from the teacher. Experimental evaluation on standard benchmarks shows that our
method effectively minimizes both the inter-domain and intra-domain
discrepancies, thus achieving significant improvements over recent methods.

In this work we propose a novel data-driven, real-time power system voltage
control method based on the physics-informed guided meta evolutionary strategy
(ES). The main objective is to quickly provide an adaptive control strategy to
mitigate the fault-induced delayed voltage recovery (FIDVR) problem.
Reinforcement learning methods have been developed for the same or similar
challenging control problems, but they suffer from training inefficiency and
lack of robustness for "corner or unseen" scenarios. On the other hand,
extensive physical knowledge has been developed in power systems but little has
been leveraged in learning-based approaches. To address these challenges, we
introduce the trainable action mask technique for flexibly embedding physical
knowledge into RL models to rule out unnecessary or unfavorable actions, and
achieve notable improvements in sample efficiency, control performance and
robustness. Furthermore, our method leverages past learning experience to
derive surrogate gradient to guide and accelerate the exploration process in
training. Case studies on the IEEE 300-bus system and comparisons with other
state-of-the-art benchmark methods demonstrate effectiveness and advantages of

In budget-constrained settings aimed at mitigating unfairness, like law
enforcement, it is essential to prioritize the sources of unfairness before
taking measures to mitigate them in the real world. Unlike previous works,
which only serve as a caution against possible discrimination and de-bias data
after data generation, this work provides a toolkit to mitigate unfairness
during data generation, given by the Unfair Edge Prioritization algorithm, in
addition to de-biasing data after generation, given by the Discrimination
Removal algorithm. We assume that a non-parametric Markovian causal model
representative of the data generation procedure is given. The edges emanating
from the sensitive nodes in the causal graph, such as race, are assumed to be
the sources of unfairness. We first quantify Edge Flow in any edge X -> Y,
which is the belief of observing a specific value of Y due to the influence of
a specific value of X along X -> Y. We then quantify Edge Unfairness by
formulating a non-parametric model in terms of edge flows. We then prove that
cumulative unfairness towards sensitive groups in a decision, like race in a
bail decision, is non-existent when edge unfairness is absent. We prove this
result for the non-trivial non-parametric model setting when the cumulative
unfairness cannot be expressed in terms of edge unfairness. We then measure the
Potential to mitigate the Cumulative Unfairness when edge unfairness is
decreased. Based on these measurements, we propose the Unfair Edge
Prioritization algorithm that can then be used by policymakers. We also propose
the Discrimination Removal Procedure that de-biases a data distribution by
eliminating optimization constraints that grow exponentially in the number of
sensitive attributes and values taken by them. Extensive experiments validate
the theorem and specifications used for quantifying the above measures.

As a promising distributed machine learning paradigm, Federated Learning (FL)
trains a central model with decentralized data without compromising user
privacy, which has made it widely used by Artificial Intelligence Internet of
Things (AIoT) applications. However, the traditional FL suffers from model
inaccuracy since it trains local models using hard labels of data and ignores
useful information of incorrect predictions with small probabilities. Although
various solutions try to tackle the bottleneck of the traditional FL, most of
them introduce significant communication and memory overhead, making the
deployment of large-scale AIoT devices a great challenge. To address the above
problem, this paper presents a novel Distillation-based Federated Learning
(DFL) architecture that enables efficient and accurate FL for AIoT
applications. Inspired by Knowledge Distillation (KD) that can increase the
model accuracy, our approach adds the soft targets used by KD to the FL model
training, which occupies negligible network resources. The soft targets are
generated by local sample predictions of each AIoT device after each round of
local training and used for the next round of model training. During the local
training of DFL, both soft targets and hard labels are used as approximation
objectives of model predictions to improve model accuracy by supplementing the
knowledge of soft targets. To further improve the performance of our DFL model,
we design a dynamic adjustment strategy for tuning the ratio of two loss
functions used in KD, which can maximize the use of both soft targets and hard
labels. Comprehensive experimental results on well-known benchmarks show that
our approach can significantly improve the model accuracy of FL with both
Independent and Identically Distributed (IID) and non-IID data.

Deep Reinforcement Learning (DRL) has demonstrated great potentials in
solving sequential decision making problems in many applications. Despite its
promising performance, practical gaps exist when deploying DRL in real-world
scenarios. One main barrier is the over-fitting issue that leads to poor
generalizability of the policy learned by DRL. In particular, for offline DRL
with observational data, model selection is a challenging task as there is no
ground truth available for performance demonstration, in contrast with the
online setting with simulated environments. In this work, we propose a
pessimistic model selection (PMS) approach for offline DRL with a theoretical
guarantee, which features a provably effective framework for finding the best
policy among a set of candidate models. Two refined approaches are also
proposed to address the potential bias of DRL model in identifying the optimal
policy. Numerical studies demonstrated the superior performance of our approach
over existing methods.

Federated learning~(FL) facilitates the training and deploying AI models on
edge devices. Preserving user data privacy in FL introduces several challenges,
including expensive communication costs, limited resources, and data
heterogeneity. In this paper, we propose SPATL, an FL method that addresses
these issues by: (a) introducing a salient parameter selection agent and
communicating selected parameters only; (b) splitting a model into a shared
encoder and a local predictor, and transferring its knowledge to heterogeneous
clients via the locally customized predictor. Additionally, we leverage a
gradient control mechanism to further speed up model convergence and increase
robustness of training processes. Experiments demonstrate that SPATL reduces
communication overhead, accelerates model inference, and enables stable
training processes with better results compared to state-of-the-art methods.
Our approach reduces communication cost by up to $86.45\%$, accelerates local
inference by reducing up to $39.7\%$ FLOPs on VGG-11, and requires $7.4 \times$
less communication overhead when training ResNet-20.

Visible-to-thermal face image matching is a challenging variate of
cross-modality recognition. The challenge lies in the large modality gap and
low correlation between visible and thermal modalities. Existing approaches
employ image preprocessing, feature extraction, or common subspace projection,
which are independent problems in themselves. In this paper, we propose an
end-to-end framework for cross-modal face recognition. The proposed algorithm
aims to learn identity-discriminative features from unprocessed facial images
and identify cross-modal image pairs. A novel Unit-Class Loss is proposed for
preserving identity information while discarding modality information. In
addition, a Cross-Modality Discriminator block is proposed for integrating
image-pair classification capability into the network. The proposed network can
be used to extract modality-independent vector representations or a
matching-pair classification for test images. Our cross-modality face
recognition experiments on five independent databases demonstrate that the
proposed method achieves marked improvement over existing state-of-the-art
methods.

Saliency methods have been widely used to highlight important input features
in model predictions. Most existing methods use backpropagation on a modified
gradient function to generate saliency maps. Thus, noisy gradients can result
in unfaithful feature attributions. In this paper, we tackle this issue and
introduce a {\it saliency guided training}procedure for neural networks to
reduce noisy gradients used in predictions while retaining the predictive
performance of the model. Our saliency guided training procedure iteratively
masks features with small and potentially noisy gradients while maximizing the
similarity of model outputs for both masked and unmasked inputs. We apply the
saliency guided training procedure to various synthetic and real data sets from
computer vision, natural language processing, and time series across diverse
neural architectures, including Recurrent Neural Networks, Convolutional
Networks, and Transformers. Through qualitative and quantitative evaluations,
we show that saliency guided training procedure significantly improves model
interpretability across various domains while preserving its predictive

Prioritized experience replay is a reinforcement learning technique whereby
agents speed up learning by replaying useful past experiences. This usefulness
is quantified as the expected gain from replaying the experience, a quantity
often approximated as the prediction error (TD-error). However, recent work in
neuroscience suggests that, in biological organisms, replay is prioritized not
only by gain, but also by "need" -- a quantity measuring the expected relevance
of each experience with respect to the current situation. Importantly, this
term is not currently considered in algorithms such as prioritized experience
replay. In this paper we present a new approach for prioritizing experiences
for replay that considers both gain and need. Our proposed algorithms show a
significant increase in performance in benchmarks including the Dyna-Q maze and
a selection of Atari games.

DETR is the first end-to-end object detector using a transformer
encoder-decoder architecture and demonstrates competitive performance but low
computational efficiency on high resolution feature maps. The subsequent work,
Deformable DETR, enhances the efficiency of DETR by replacing dense attention
with deformable attention, which achieves 10x faster convergence and improved
performance. Deformable DETR uses the multiscale feature to ameliorate
performance, however, the number of encoder tokens increases by 20x compared to
DETR, and the computation cost of the encoder attention remains a bottleneck.
In our preliminary experiment, we observe that the detection performance hardly
deteriorates even if only a part of the encoder token is updated. Inspired by
this observation, we propose Sparse DETR that selectively updates only the
tokens expected to be referenced by the decoder, thus help the model
effectively detect objects. In addition, we show that applying an auxiliary
detection loss on the selected tokens in the encoder improves the performance
while minimizing computational overhead. We validate that Sparse DETR achieves
better performance than Deformable DETR even with only 10% encoder tokens on
the COCO dataset. Albeit only the encoder tokens are sparsified, the total
computation cost decreases by 38% and the frames per second (FPS) increases by
42% compared to Deformable DETR.
  Code is available at https://github.com/kakaobrain/sparse-detr

Machine learning (ML) is finding its way into safety-critical systems (SCS).
Current safety standards and practice were not designed to cope with ML
techniques, and it is difficult to be confident that SCSs that contain ML
components are safe. Our hypothesis was that there has been a rush to deploy ML
techniques at the expense of a thorough examination as to whether the use of ML
techniques introduces safety problems that we are not yet adequately able to
detect and mitigate against. We thus conducted a targeted literature survey to
determine the research effort that has been expended in applying ML to SCS
compared with that spent on evaluating the safety of SCSs that deploy ML
components. This paper presents the (surprising) results of the survey.

In recent years, there have been several advancements in the task of image
super-resolution using the state of the art Deep Learning-based architectures.
Many super-resolution-based techniques previously published, require high-end
and top-of-the-line Graphics Processing Unit (GPUs) to perform image
super-resolution. With the increasing advancements in Deep Learning approaches,
neural networks have become more and more compute hungry. We took a step back
and, focused on creating a real-time efficient solution. We present an
architecture that is faster and smaller in terms of its memory footprint. The
proposed architecture uses Depth-wise Separable Convolutions to extract
features and, it performs on-par with other super-resolution GANs (Generative
Adversarial Networks) while maintaining real-time inference and a low memory
footprint. A real-time super-resolution enables streaming high resolution media
content even under poor bandwidth conditions. While maintaining an efficient
trade-off between the accuracy and latency, we are able to produce a comparable
performance model which is one-eighth (1/8) the size of super-resolution GANs
and computes 74 times faster than super-resolution GANs.

A critical aspect in the manufacturing process is the visual quality
inspection of manufactured components for defects and flaws. Human-only visual
inspection can be very time-consuming and laborious, and is a significant
bottleneck especially for high-throughput manufacturing scenarios. Given
significant advances in the field of deep learning, automated visual quality
inspection can lead to highly efficient and reliable detection of defects and
flaws during the manufacturing process. However, deep learning-driven visual
inspection methods often necessitate significant computational resources, thus
limiting throughput and act as a bottleneck to widespread adoption for enabling
smart factories. In this study, we investigated the utilization of a
machine-driven design exploration approach to create TinyDefectNet, a highly
compact deep convolutional network architecture tailored for high-throughput
manufacturing visual quality inspection. TinyDefectNet comprises of just ~427K
parameters and has a computational complexity of ~97M FLOPs, yet achieving a
detection accuracy of a state-of-the-art architecture for the task of surface
defect detection on the NEU defect benchmark dataset. As such, TinyDefectNet
can achieve the same level of detection performance at 52$\times$ lower
architectural complexity and 11x lower computational complexity. Furthermore,
TinyDefectNet was deployed on an AMD EPYC 7R32, and achieved 7.6x faster
throughput using the native Tensorflow environment and 9x faster throughput
using AMD ZenDNN accelerator library. Finally, explainability-driven
performance validation strategy was conducted to ensure correct decision-making
behaviour was exhibited by TinyDefectNet to improve trust in its usage by
operators and inspectors.

Person Search is a relevant task that aims to jointly solve Person Detection
and Person Re-identification(re-ID). Though most previous methods focus on
learning robust individual features for retrieval, it's still hard to
distinguish confusing persons because of illumination, large pose variance, and
occlusion. Contextual information is practically available in person search
task which benefits searching in terms of reducing confusion. To this end, we
present a novel contextual feature head named Attention Context-Aware
Embedding(ACAE) which enhances contextual information. ACAE repeatedly reviews
the person features within and across images to find similar pedestrian
patterns, allowing it to implicitly learn to find possible co-travelers and
efficiently model contextual relevant instances' relations. Moreover, we
propose Image Memory Bank to improve the training efficiency. Experimentally,
ACAE shows extensive promotion when built on different one-step methods. Our
overall methods achieve state-of-the-art results compared with previous
one-step methods.

are underway in an attempt to manage COTS populations to ecologically
sustainable levels. We release a large-scale, annotated underwater image
dataset from a COTS outbreak area on the GBR, to encourage research on Machine
Learning and AI-driven technologies to improve the detection, monitoring, and
management of COTS populations at reef scale. The dataset is released and
hosted in a Kaggle competition that challenges the international Machine
Learning community with the task of COTS detection from these underwater
images.

Complex systems show how surprising and beautiful phenomena can emerge from
structures or agents following simple rules. With the recent success of deep
reinforcement learning (RL), a natural path forward would be to use the
capabilities of multiple deep RL agents to produce emergent behavior of greater
benefit and sophistication. In general, this has proved to be an unreliable
strategy without significant computation due to the difficulties inherent in
multi-agent RL training. In this paper, we propose some criteria for creativity
in multi-agent RL. We hope this proposal will give artists applying multi-agent
RL a starting point, and provide a catalyst for further investigation guided by
philosophical discussion.

In this work, we propose a new and general framework to defend against
backdoor attacks, inspired by the fact that attack triggers usually follow a
\textsc{specific} type of attacking pattern, and therefore, poisoned training
examples have greater impacts on each other during training. We introduce the
notion of the {\it influence graph}, which consists of nodes and edges
respectively representative of individual training points and associated
pair-wise influences. The influence between a pair of training points
represents the impact of removing one training point on the prediction of
another, approximated by the influence function \citep{koh2017understanding}.
Malicious training points are extracted by finding the maximum average
sub-graph subject to a particular size. Extensive experiments on computer
vision and natural language processing tasks demonstrate the effectiveness and
generality of the proposed framework.

Acronym disambiguation means finding the correct meaning of an ambiguous
acronym from the dictionary in a given sentence, which is one of the key points
for scientific document understanding (SDU@AAAI-22). Recently, many attempts
have tried to solve this problem via fine-tuning the pre-trained masked
language models (MLMs) in order to obtain a better acronym representation.
However, the acronym meaning is varied under different contexts, whose
corresponding phrase representation mapped in different directions lacks
discrimination in the entire vector space. Thus, the original representations
of the pre-trained MLMs are not ideal for the acronym disambiguation task. In
this paper, we propose a Simple framework for Contrastive Learning of Acronym
Disambiguation (SimCLAD) method to better understand the acronym meanings.
Specifically, we design a continual contrastive pre-training method that
enhances the pre-trained model's generalization ability by learning the
phrase-level contrastive distributions between true meaning and ambiguous
phrases. The results on the acronym disambiguation of the scientific domain in
English show that the proposed method outperforms all other competitive
state-of-the-art (SOTA) methods.

Gating modules have been widely explored in dynamic network pruning to reduce
the run-time computational cost of deep neural networks while preserving the
representation of features. Despite the substantial progress, existing methods
remain ignoring the consistency between feature and gate distributions, which
may lead to distortion of gated features. In this paper, we propose a
feature-gate coupling (FGC) approach aiming to align distributions of features
and gates. FGC is a plug-and-play module, which consists of two steps carried
out in an iterative self-supervised manner. In the first step, FGC utilizes the
$k$-Nearest Neighbor method in the feature space to explore instance
neighborhood relationships, which are treated as self-supervisory signals. In
the second step, FGC exploits contrastive learning to regularize gating modules
with generated self-supervisory signals, leading to the alignment of instance
neighborhood relationships within the feature and gate spaces. Experimental
results validate that the proposed FGC method improves the baseline approach
with significant margins, outperforming the state-of-the-arts with better
accuracy-computation trade-off. Code is publicly available.

Computer-assisted diagnosis (CAD) based on deep learning has become a crucial
diagnostic technology in the medical industry, effectively improving diagnosis
accuracy. However, the scarcity of brain tumor Magnetic Resonance (MR) image
datasets causes the low performance of deep learning algorithms. The
distribution of transformed images generated by traditional data augmentation
(DA) intrinsically resembles the original ones, resulting in a limited
performance in terms of generalization ability. This work improves Progressive
Growing of GANs with a structural similarity loss function (PGGAN-SSIM) to
solve image blurriness problems and model collapse. We also explore other
GAN-based data augmentation to demonstrate the effectiveness of the proposed
model. Our results show that PGGAN-SSIM successfully generates 256x256
realistic brain tumor MR images which fill the real image distribution
uncovered by the original dataset. Furthermore, PGGAN-SSIM exceeds other
GAN-based methods, achieving promising performance improvement in Frechet
Inception Distance (FID) and Multi-scale Structural Similarity (MS-SSIM).

Autonomous driving has made great progress and been introduced in practical
use step by step. On the other hand, the concept of personal mobility is also
getting popular, and its autonomous driving specialized for individual drivers
is expected for a new step. However, it is difficult to collect a large driving
dataset, which is basically required for the learning of autonomous driving,
from the individual driver of the personal mobility. In addition, when the
driver is not familiar with the operation of the personal mobility, the dataset
will contain non-optimal data. This study therefore focuses on an autonomous
driving method for the personal mobility with such a small and noisy, so-called
personal, dataset. Specifically, we introduce a new loss function based on
Tsallis statistics that weights gradients depending on the original loss
function and allows us to exclude noisy data in the optimization phase. In
addition, we improve the visualization technique to verify whether the driver
and the controller have the same region of interest. From the experimental
results, we found that the conventional autonomous driving failed to drive
properly due to the wrong operations in the personal dataset, and the region of
interest was different from that of the driver. In contrast, the proposed
method learned robustly against the errors and successfully drove automatically
while paying attention to the similar region to the driver. Attached video is
also uploaded on youtube: https://youtu.be/KEq8-bOxYQA

Inspired by the foundational works by Spivak and Fong and Cruttwell et al.,
we introduce a categorical framework to formalize Bayesian inference and
learning. The two key ideas at play here are the notions of Bayesian inversions
and the functor GL as constructed by Cruttwell et al.. In this context, we find
that Bayesian learning is the simplest case of the learning paradigm. We then
obtain categorical formulations of batch and sequential Bayes updates while
also verifying that the two coincide in a specific example.

Domain generalizable person re-identification aims to apply a trained model
to unseen domains. Prior works either combine the data in all the training
domains to capture domain-invariant features, or adopt a mixture of experts to
investigate domain-specific information. In this work, we argue that both
domain-specific and domain-invariant features are crucial for improving the
generalization ability of re-id models. To this end, we design a novel
framework, which we name two-stream adaptive learning (TAL), to simultaneously
model these two kinds of information. Specifically, a domain-specific stream is
proposed to capture training domain statistics with batch normalization (BN)
parameters, while an adaptive matching layer is designed to dynamically
aggregate domain-level information. In the meantime, we design an adaptive BN
layer in the domain-invariant stream, to approximate the statistics of various
unseen domains. These two streams work adaptively and collaboratively to learn
generalizable re-id features. Our framework can be applied to both
single-source and multi-source domain generalization tasks, where experimental
results show that our framework notably outperforms the state-of-the-art
methods.

The Science4cast Competition consists of predicting new links in a semantic
network, with each node representing a concept and each edge representing a
link proposed by a paper relating two concepts. This network contains
information from 1994-2017, with a discretization of days (which represents the
publication date of the underlying papers). Team Hash Brown's final submission,
\emph{ee5a}, achieved a score of 0.92738 on the test set. Our team's score
ranks \emph{second place}, 0.01 below the winner's score. This paper details
our model, its intuition, and the performance of its variations in the test
set.

Prior work on sentiment analysis using weak supervision primarily focuses on
different reviews such as movies (IMDB), restaurants (Yelp), products
(Amazon).~One under-explored field in this regard is customer chat data for a
customer-agent chat in customer support due to the lack of availability of free
public data. Here, we perform sentiment analysis on customer chat using weak
supervision on our in-house dataset. We fine-tune the pre-trained language
model (LM) RoBERTa as a sentiment classifier using weak supervision. Our
contribution is as follows:1) We show that by using weak sentiment classifiers
along with domain-specific lexicon-based rules as Labeling Functions (LF), we
can train a fairly accurate customer chat sentiment classifier using weak
supervision. 2) We compare the performance of our custom-trained model with
off-the-shelf google cloud NLP API for sentiment analysis. We show that by
injecting domain-specific knowledge using LFs, even with weak supervision, we
can train a model to handle some domain-specific use cases better than
off-the-shelf google cloud NLP API. 3) We also present an analysis of how
customer sentiment in a chat relates to problem resolution.

Radio frequency fingerprint identification (RFFI) can uniquely classify
wireless devices by analyzing the received signal distortions caused by the
intrinsic hardware impairments. The state-of-the-art deep learning techniques
such as convolutional neural network (CNN) have been adopted to classify IoT
devices with high accuracy. However, deep learning-based RFFI requires input
data of a fixed size. In addition, many IoT devices work in low signal-to-noise
ratio (SNR) scenarios but the low SNR RFFI is rarely investigated. In this
paper, the state-of-the-art transformer model is used as the classifier, which
can process signals of variable length. Data augmentation is adopted to improve
low SNR RFFI performance. A multi-packet inference approach is further proposed
to improve the classification accuracy in low SNR scenarios. We take LoRa as a
case study and evaluate the system by classifying 10 commercial-off-the-shelf
LoRa devices in various SNR conditions. The online augmentation can boost the
low SNR RFFI performance by up to 50% and multi-packet inference can further
increase it by over 20%.

Off-policy policy evaluation methods for sequential decision making can be
used to help identify if a proposed decision policy is better than a current
baseline policy. However, a new decision policy may be better than a baseline
policy for some individuals but not others. This has motivated a push towards
personalization and accurate per-state estimates of heterogeneous treatment
effects (HTEs). Given the limited data present in many important applications,
individual predictions can come at a cost to accuracy and confidence in such
predictions. We develop a method to balance the need for personalization with
confident predictions by identifying subgroups where it is possible to
confidently estimate the expected difference in a new decision policy relative
to a baseline. We propose a novel loss function that accounts for uncertainty
during the subgroup partitioning phase. In experiments, we show that our method
can be used to form accurate predictions of HTEs where other methods struggle.

Contrastive learning has led to substantial improvements in the quality of
learned embedding representations for tasks such as image classification.
However, a key drawback of existing contrastive augmentation methods is that
they may lead to the modification of the image content which can yield
undesired alterations of its semantics. This can affect the performance of the
model on downstream tasks. Hence, in this paper, we ask whether we can augment
image data in contrastive learning such that the task-relevant semantic content
of an image is preserved. For this purpose, we propose to leverage
saliency-based explanation methods to create content-preserving masked
augmentations for contrastive learning. Our novel explanation-driven supervised
contrastive learning (ExCon) methodology critically serves the dual goals of
encouraging nearby image embeddings to have similar content and explanation. To
quantify the impact of ExCon, we conduct experiments on the CIFAR-100 and the
Tiny ImageNet datasets. We demonstrate that ExCon outperforms vanilla
supervised contrastive learning in terms of classification, explanation
quality, adversarial robustness as well as probabilistic calibration in the
context of distributional shift.

In a clinical setting, epilepsy patients are monitored via video
electroencephalogram (EEG) tests. A video EEG records what the patient
experiences on videotape while an EEG device records their brainwaves.
Currently, there are no existing automated methods for tracking the patient's
location during a seizure, and video recordings of hospital patients are
substantially different from publicly available video benchmark datasets. For
example, the camera angle can be unusual, and patients can be partially covered
with bedding sheets and electrode sets. Being able to track a patient in
real-time with video EEG would be a promising innovation towards improving the
quality of healthcare. Specifically, an automated patient detection system
could supplement clinical oversight and reduce the resource-intensive efforts
of nurses and doctors who need to continuously monitor patients. We evaluate an
ImageNet pre-trained Mask R-CNN, a standard deep learning model for object
detection, on the task of patient detection using our own curated dataset of 45
videos of hospital patients. The dataset was aggregated and curated for this
work. We show that without fine-tuning, ImageNet pre-trained Mask R-CNN models
perform poorly on such data. By fine-tuning the models with a subset of our
dataset, we observe a substantial improvement in patient detection performance,
with a mean average precision of 0.64. We show that the results vary
substantially depending on the video clip.

Vision-and-Language Navigation (VLN) is a challenging task in the field of
artificial intelligence. Although massive progress has been made in this task
over the past few years attributed to breakthroughs in deep vision and language
models, it remains tough to build VLN models that can generalize as well as
humans. In this paper, we provide a new perspective to improve VLN models.
Based on our discovery that snapshots of the same VLN model behave
significantly differently even when their success rates are relatively the
same, we propose a snapshot-based ensemble solution that leverages predictions
among multiple snapshots. Constructed on the snapshots of the existing
state-of-the-art (SOTA) model $\circlearrowright$BERT and our past-action-aware
modification, our proposed ensemble achieves the new SOTA performance in the
R2R dataset challenge in Navigation Error (NE) and Success weighted by Path
Length (SPL).

Shortening acquisition time and reducing the motion artifacts are two of the
most essential concerns in magnetic resonance imaging. As a promising solution,
deep learning-based high-quality MR image restoration has been investigated to
generate highly-resolved and motion artifact-free MR images from lower
resolution images acquired with shortened acquisition time or motion
artifact-corrupted images. However, numerous problems still exist to prevent
deep learning approaches from becoming practical in the clinic environment.
Specifically, most of the prior works focus solely on the network but ignore
the impact of various down-sampling strategies on the acquisition time.
Besides, the long inference time and high GPU consumption are also the
bottlenecks to deploy most of the prior works in clinics. Furthermore, prior
studies employ random movement in retrospective motion artifact generation,
resulting in uncontrollable severity of motion artifact. More importantly,
doctors are unsure whether the generated MR images are trustworthy, making
diagnosis difficult. To overcome all these problems, we adopted a unified
framework of 2D deep learning neural network for both 3D MRI super-resolution
and motion artifact reduction, demonstrating such a framework can achieve
better performance in 3D MRI restoration tasks compared to other
state-of-the-art methods and remain the GPU consumption and inference time
significantly low, thus easier to deploy. We also analyzed several
down-sampling strategies based on the acceleration factor, including multiple
combinations of in-plane and through-plane down-sampling, and developed a
controllable and quantifiable motion artifact generation method. At last, the
pixel-wise uncertainty was calculated and used to estimate the accuracy of the
generated image, providing additional information for a reliable diagnosis.

Systolic arrays have been widely used for accelerating HPC and deep learning
applications. There is a plethora of previous works on the performance tuning
of systolic arrays, but usually based on a number of oversimplified assumptions
(e.g., only considering divisors for loop tiling, pruning based on off-chip
data communication) to reduce the design space.
  In this paper, we present a comprehensive design space exploration tool named
Odyssey for systolic array optimization. Odyssey does not rely on artificial
assumptions to limit the design space, and yet it is highly efficient and
scalable with a hybrid optimization technique. For example, for a
1024x1024x1024 matrix multiplication, it finds designs that reach 90% of the
optimal performance in 5 seconds with a single CPU thread. Moreover, using
Odyssey, we unveil and quantify the suboptimality introduced by multiple
commonly used oversimplifications in prior studies for systolic array design
space exploration. For example, Odyssey results show that limiting to divisors
for loop tiling leads to a 39% performance loss, and pruning based on off-chip
data movement results in a 45% performance loss. We applied Odyssey to explore
the architecture trade-offs for matrix multiplication and convolutional neural
network, providing inspiration into possible optimizations for these two

With the proliferation of smart devices and revolutions in communications,
electrical distribution systems are gradually shifting from passive,
manually-operated and inflexible ones, to a massively interconnected
cyber-physical smart grid to address the energy challenges of the future.
However, the integration of several cutting-edge technologies has introduced
several security and privacy vulnerabilities due to the large-scale complexity
and resource limitations of deployments. Recent research trends have shown that
False Data Injection (FDI) attacks are becoming one of the most malicious cyber
threats within the entire smart grid paradigm. Therefore, this paper presents a
comprehensive survey of the recent advances in FDI attacks within active
distribution systems and proposes a taxonomy to classify the FDI threats with
respect to smart grid targets. The related studies are contrasted and
summarized in terms of the attack methodologies and implications on the
electrical power distribution networks. Finally, we identify some research gaps
and recommend a number of future research directions to guide and motivate
prospective researchers.

The mechanisms involved in transforming early visual signals to curvature
representations in V4 are unknown. We propose a hierarchical model that reveals
V1/V2 encodings that are essential components for this transformation to the
reported curvature representations in V4. Then, by relaxing the often-imposed
prior of a single Gaussian, V4 shape selectivity is learned in the last layer
of the hierarchy from Macaque V4 responses. We found that V4 cells integrate
multiple shape parts from the full spatial extent of their receptive fields
with similar excitatory and inhibitory contributions. Our results uncover new
details in existing data about shape selectivity in V4 neurons that with
further experiments can enhance our understanding of processing in this area.
Accordingly, we propose designs for a stimulus set that allow removing shape
parts without disturbing the curvature signal to isolate part contributions to
V4 responses.

Federated learning learns from scattered data by fusing collaborative models
from local nodes. However, the conventional coordinate-based model averaging by
FedAvg ignored the random information encoded per parameter and may suffer from
structural feature misalignment. In this work, we propose Fed2, a
feature-aligned federated learning framework to resolve this issue by
establishing a firm structure-feature alignment across the collaborative
models. Fed2 is composed of two major designs: First, we design a
feature-oriented model structure adaptation method to ensure explicit feature
allocation in different neural network structures. Applying the structure
adaptation to collaborative models, matchable structures with similar feature
information can be initialized at the very early training stage. During the
federated learning process, we then propose a feature paired averaging scheme
to guarantee aligned feature distribution and maintain no feature fusion
conflicts under either IID or non-IID scenarios. Eventually, Fed2 could
effectively enhance the federated learning convergence performance under
extensive homo- and heterogeneous settings, providing excellent convergence
speed, accuracy, and computation/communication efficiency.

Deep Learning (DL) models have achieved superior performance in many
application domains, including vision, language, medical, commercial ads,
entertainment, etc. With the fast development, both DL applications and the
underlying serving hardware have demonstrated strong scaling trends, i.e.,
Model Scaling and Compute Scaling, for example, the recent pre-trained model
with hundreds of billions of parameters with ~TB level memory consumption, as
well as the newest GPU accelerators providing hundreds of TFLOPS. With both
scaling trends, new problems and challenges emerge in DL inference serving
systems, which gradually trends towards Large-scale Deep learning Serving
systems (LDS). This survey aims to summarize and categorize the emerging
challenges and optimization opportunities for large-scale deep learning serving
systems. By providing a novel taxonomy, summarizing the computing paradigms,
and elaborating the recent technique advances, we hope that this survey could
shed light on new optimization perspectives and motivate novel works in
large-scale deep learning system optimization.

In iterated games, a player can unilaterally exert influence over the outcome
through a careful choice of strategy. A powerful class of such "payoff control"
strategies was discovered by Press and Dyson (2012). Their so-called
"zero-determinant" (ZD) strategies allow a player to unilaterally enforce a
linear relationship between both players' payoffs. It was subsequently shown by
Chen and Zinger (2014) that when the slope of this linear relationship is
positive, ZD strategies are robustly effective against a selfishly optimizing
co-player, in that all adapting paths of the selfish player lead to the maximal
payoffs for both players (at least when there are certain restrictions on the
game parameters). In this paper, we investigate the efficacy of selfish
learning against a fixed player in more general settings, for both ZD and
non-ZD strategies. We first prove that in any symmetric $2\times 2$ game, the
selfish player's final strategy must be of a certain form and cannot be fully
stochastic. We then show that there are prisoner's dilemma interactions for
which selfish optimization does not always lead to maximal payoffs against
fixed ZD strategies with positive slope. We give examples of selfish adapting
paths that lead to locally but not globally optimal payoffs, undermining the
robustness of payoff control strategies. For non-ZD strategies, these
pathologies arise regardless of the original restrictions on the game
parameters. Our results illuminate the difficulty of implementing robust payoff
control and selfish optimization, even in the simplest context of playing
against a fixed strategy.

Gaussian mixture models find their place as a powerful tool, mostly in the
clustering problem, but with proper preparation also in feature extraction,
pattern recognition, image segmentation and in general machine learning. When
faced with the problem of schema matching, different mixture models computed on
different pieces of data can maintain crucial information about the structure
of the dataset. In order to measure or compare results from mixture models, the
Wasserstein distance can be very useful, however it is not easy to calculate
for mixture distributions. In this paper we derive one of possible
approximations for the Wasserstein distance between Gaussian mixture models and
reduce it to linear problem. Furthermore, application examples concerning real
world data are shown.

Intelligent edge devices with built-in processors vary widely in terms of
capability and physical form to perform advanced Computer Vision (CV) tasks
such as image classification and object detection, for example. With constant
advances in the field of autonomous cars and UAVs, embedded systems and mobile
devices, there has been an ever-growing demand for extremely efficient
Artificial Neural Networks (ANN) for real-time inference on these smart edge
devices with constrained computational resources. With unreliable network
connections in remote regions and an added complexity of data transmission, it
is of an utmost importance to capture and process data locally instead of
sending the data to cloud servers for remote processing. Edge devices on the
other hand, offer limited processing power due to their inexpensive hardware,
and limited cooling and computational resources. In this paper, we propose a
novel deep convolutional neural network architecture called EffCNet which is an
improved and an efficient version of CondenseNet Convolutional Neural Network
(CNN) for edge devices utilizing self-querying data augmentation and depthwise
separable convolutional strategies to improve real-time inference performance
as well as reduce the final trained model size, trainable parameters, and
Floating-Point Operations (FLOPs) of EffCNet CNN. Furthermore, extensive
supervised image classification analyses are conducted on two benchmarking
datasets: CIFAR-10 and CIFAR-100, to verify real-time inference performance of
our proposed CNN. Finally, we deploy these trained weights on NXP BlueBox which
is an intelligent edge development platform designed for self-driving vehicles
and UAVs, and conclusions will be extrapolated accordingly.

With the rapid advancement and increased use of deep learning models in image
identification, security becomes a major concern to their deployment in
safety-critical systems. Since the accuracy and robustness of deep learning
models are primarily attributed from the purity of the training samples,
therefore the deep learning architectures are often susceptible to adversarial
attacks. Adversarial attacks are often obtained by making subtle perturbations
to normal images, which are mostly imperceptible to humans, but can seriously
confuse the state-of-the-art machine learning models. What is so special in the
slightest intelligent perturbations or noise additions over normal images that
it leads to catastrophic classifications by the deep neural networks? Using
statistical hypothesis testing, we find that Conditional Variational
AutoEncoders (CVAE) are surprisingly good at detecting imperceptible image
perturbations. In this paper, we show how CVAEs can be effectively used to
detect adversarial attacks on image classification networks. We demonstrate our
results over MNIST, CIFAR-10 dataset and show how our method gives comparable
performance to the state-of-the-art methods in detecting adversaries while not
getting confused with noisy images, where most of the existing methods falter.

Deep learning has recently made remarkable progress in natural language
processing. Yet, the resulting algorithms remain far from competing with the
language abilities of the human brain. Predictive coding theory offers a
potential explanation to this discrepancy: while deep language algorithms are
optimized to predict adjacent words, the human brain would be tuned to make
long-range and hierarchical predictions. To test this hypothesis, we analyze
the fMRI brain signals of 304 subjects each listening to 70min of short
stories. After confirming that the activations of deep language algorithms
linearly map onto those of the brain, we show that enhancing these models with
long-range forecast representations improves their brain-mapping. The results
further reveal a hierarchy of predictions in the brain, whereby the
fronto-parietal cortices forecast more abstract and more distant
representations than the temporal cortices. Overall, this study strengthens
predictive coding theory and suggests a critical role of long-range and
hierarchical predictions in natural language processing.

A reservoir computer is a special type of neural network, where most of the
weights are randomly fixed and only a subset are trained.
  In this thesis we prove results about reservoir computers trained on
deterministic dynamical systems, and stochastic processes. We focus mostly on a
special type of reservoir computer called an Echo State Network (ESN).
  In the deterministic case, we prove (under some assumptions) that if a
reservoir computer has the Echo State Property (ESP), then there is a C1
generalised synchronisation between the input dynamical system and the dynamics
in the reservoir space. Furthermore, we prove that a reservoir computer with
the local ESP in several disjoint subsets of the reservoir space will admit
several distinct generalised synchronisations. In the special case that the
reservoir map is linear, and has the ESP, we prove that the generalised
synchronisation is generically an embedding. This result admits Takens'
embedding Theorem as a special case.
  We go to show that ESNs trained on scalar observations of an ergodic
dynamical system can approximate an arbitrary target function, including the
next step map used in time series forecasting. This universal approximation
property holds despite the training process being entirely linear.
  We prove analogous results for ESNs trained on observations of a stochastic
process, which are not be Markovian in general. We use these results to develop
supervised learning, and reinforcement learning algorithms supported by an ESN.
  In the penultimate chapter of this thesis, we use a reservoir computer to
numerically solve linear PDEs. In the final chapter, we conclude and discuss

The data-driven approach has been demonstrated as a promising technique to
solve complicated scientific problems. Full Waveform Inversion (FWI) is
commonly epitomized as an image-to-image translation task, which motivates the
use of deep neural networks as an end-to-end solution. Despite being trained
with synthetic data, the deep learning-driven FWI is expected to perform well
when evaluated with sufficient real-world data. In this paper, we study such
properties by asking: how robust are these deep neural networks and how do they
generalize? For robustness, we prove the upper bounds of the deviation between
the predictions from clean and noisy data. Moreover, we demonstrate an
interplay between the noise level and the additional gain of loss. For
generalization, we prove a norm-based generalization error upper bound via a
stability-generalization framework. Experimental results on seismic FWI
datasets corroborate with the theoretical results, shedding light on a better
understanding of utilizing Deep Learning for complicated scientific

In recent years, large-scale Bayesian learning draws a great deal of
attention. However, in big-data era, the amount of data we face is growing much
faster than our ability to deal with it. Fortunately, it is observed that
large-scale datasets usually own rich internal structure and is somewhat
redundant. In this paper, we attempt to simplify the Bayesian posterior via
exploiting this structure. Specifically, we restrict our interest to the
so-called well-clustered datasets and construct an \emph{approximate posterior}
according to the clustering information. Fortunately, the clustering structure
can be efficiently obtained via a particular clustering algorithm. When
constructing the approximate posterior, the data points in the same cluster are
all replaced by the centroid of the cluster. As a result, the posterior can be
significantly simplified. Theoretically, we show that under certain conditions
the approximate posterior we construct is close (measured by KL divergence) to
the exact posterior. Furthermore, thorough experiments are conducted to
validate the fact that the constructed posterior is a good approximation to the
true posterior and much easier to sample from.

Federated learning (FL) is a promising strategy for performing
privacy-preserving, distributed learning with a network of clients (i.e., edge
devices). However, the data distribution among clients is often non-IID in
nature, making efficient optimization difficult. To alleviate this issue, many
FL algorithms focus on mitigating the effects of data heterogeneity across
clients by introducing a variety of proximal terms, some incurring considerable
compute and/or memory overheads, to restrain local updates with respect to the
global model. Instead, we consider rethinking solutions to data heterogeneity
in FL with a focus on local learning generality rather than proximal
restriction. To this end, we first present a systematic study informed by
second-order indicators to better understand algorithm effectiveness in FL.
Interestingly, we find that standard regularization methods are surprisingly
strong performers in mitigating data heterogeneity effects. Based on our
findings, we further propose a simple and effective method, FedAlign, to
overcome data heterogeneity and the pitfalls of previous methods. FedAlign
achieves competitive accuracy with state-of-the-art FL methods across a variety
of settings while minimizing computation and memory overhead. Code is available
at https://github.com/mmendiet/FedAlign

Research on generalization bounds for deep networks seeks to give ways to
predict test error using just the training dataset and the network parameters.
While generalization bounds can give many insights about architecture design,
training algorithms, etc., what they do not currently do is yield good
predictions for actual test error. A recently introduced Predicting
Generalization in Deep Learning competition~\citep{jiang2020neurips} aims to
encourage discovery of methods to better predict test error. The current paper
investigates a simple idea: can test error be predicted using {\em synthetic
data,} produced using a Generative Adversarial Network (GAN) that was trained
on the same training dataset? Upon investigating several GAN models and
architectures, we find that this turns out to be the case. In fact, using GANs
pre-trained on standard datasets, the test error can be predicted without
requiring any additional hyper-parameter tuning. This result is surprising
because GANs have well-known limitations (e.g. mode collapse) and are known to
not learn the data distribution accurately. Yet the generated samples are good
enough to substitute for test data. Several additional experiments are
presented to explore reasons why GANs do well at this task. In addition to a
new approach for predicting generalization, the counter-intuitive phenomena
presented in our work may also call for a better understanding of GANs'
strengths and limitations.

Humans communicate with graphical sketches apart from symbolic languages.
Primarily focusing on the latter, recent studies of emergent communication
overlook the sketches; they do not account for the evolution process through
which symbolic sign systems emerge in the trade-off between iconicity and
symbolicity. In this work, we take the very first step to model and simulate
this process via two neural agents playing a visual communication game; the
sender communicates with the receiver by sketching on a canvas. We devise a
novel reinforcement learning method such that agents are evolved jointly
towards successful communication and abstract graphical conventions. To inspect
the emerged conventions, we define three fundamental properties -- iconicity,
symbolicity, and semanticity -- and design evaluation methods accordingly. Our
experimental results under different controls are consistent with the
observation in studies of human graphical conventions. Of note, we find that
evolved sketches can preserve the continuum of semantics under proper
environmental pressures. More interestingly, co-evolved agents can switch
between conventionalized and iconic communication based on their familiarity
with referents. We hope the present research can pave the path for studying
emergent communication with the modality of sketches.

Maximum Entropy Reinforcement Learning (MaxEnt RL) algorithms such as Soft
Q-Learning (SQL) and Soft Actor-Critic trade off reward and policy entropy,
which has the potential to improve training stability and robustness. Most
MaxEnt RL methods, however, use a constant tradeoff coefficient (temperature),
contrary to the intuition that the temperature should be high early in training
to avoid overfitting to noisy value estimates and decrease later in training as
we increasingly trust high value estimates to truly lead to good rewards.
Moreover, our confidence in value estimates is state-dependent, increasing
every time we use more evidence to update an estimate. In this paper, we
present a simple state-based temperature scheduling approach, and instantiate
it for SQL as Count-Based Soft Q-Learning (CBSQL). We evaluate our approach on
a toy domain as well as in several Atari 2600 domains and show promising
results.

In this work, we demonstrate how a publicly available, pre-trained Jukebox
model can be adapted for the problem of audio source separation from a single
mixed audio channel. Our neural network architecture, which is using transfer
learning, is quick to train and the results demonstrate performance comparable
to other state-of-the-art approaches that require a lot more compute resources,
training data, and time. We provide an open-source code implementation of our
architecture (https://github.com/wzaielamri/unmix)

Recent advances in digital pathology have led to the need for Histopathology
Image Retrieval (HIR) systems that search through databases of biopsy images to
find similar cases to a given query image. These HIR systems allow pathologists
to effortlessly and efficiently access thousands of previously diagnosed cases
in order to exploit the knowledge in the corresponding pathology reports. Since
HIR systems may have to deal with millions of gigapixel images, the extraction
of compact and expressive image features must be available to allow for
efficient and accurate retrieval. In this paper, we propose the application of
Gram barcodes as image features for HIR systems. Unlike most feature generation
schemes, Gram barcodes are based on high-order statistics that describe tissue
texture by summarizing the correlations between different feature maps in
layers of convolutional neural networks. We run HIR experiments on three public
datasets using a pre-trained VGG19 network for Gram barcode generation and
showcase highly competitive results.

The gravitational $N$-body problem, which is fundamentally important in
astrophysics to predict the motion of $N$ celestial bodies under the mutual
gravity of each other, is usually solved numerically because there is no known
general analytical solution for $N>2$. Can an $N$-body problem be solved
accurately by a neural network (NN)? Can a NN observe long-term conservation of
energy and orbital angular momentum? Inspired by Wistom & Holman (1991)'s
symplectic map, we present a neural $N$-body integrator for splitting the
Hamiltonian into a two-body part, solvable analytically, and an interaction
part that we approximate with a NN. Our neural symplectic $N$-body code
integrates a general three-body system for $10^{5}$ steps without diverting
from the ground truth dynamics obtained from a traditional $N$-body integrator.
Moreover, it exhibits good inductive bias by successfully predicting the
evolution of $N$-body systems that are no part of the training set.

We propose MALIGN, a novel malware family detection approach inspired by
genome sequence alignment. MALIGN encodes malware using four nucleotides and
then uses genome sequence alignment approaches to create a signature of a
malware family based on the code fragments conserved in the family making it
robust to evasion by modification and addition of content. Moreover, unlike
previous approaches based on sequence alignment, our method uses a multiple
whole-genome alignment tool that protects against adversarial attacks such as
code insertion, deletion or modification. Our approach outperforms
state-of-the-art machine learning based malware detectors and demonstrates
robustness against trivial adversarial attacks. MALIGN also helps identify the
techniques malware authors use to evade detection.

Most of the existing algorithms for zero-shot classification problems
typically rely on the attribute-based semantic relations among categories to
realize the classification of novel categories without observing any of their
instances. However, training the zero-shot classification models still requires
attribute labeling for each class (or even instance) in the training dataset,
which is also expensive. To this end, in this paper, we bring up a new problem
scenario: "Can we derive zero-shot learning for novel attribute
detectors/classifiers and use them to automatically annotate the dataset for
labeling efficiency?". Basically, given only a small set of detectors that are
learned to recognize some manually annotated attributes (i.e., the seen
attributes), we aim to synthesize the detectors of novel attributes in a
zero-shot learning manner. Our proposed method, Zero-Shot Learning for
Attributes (ZSLA), which is the first of its kind to the best of our knowledge,
tackles this new research problem by applying the set operations to first
decompose the seen attributes into their basic attributes and then recombine
these basic attributes into the novel ones. Extensive experiments are conducted
to verify the capacity of our synthesized detectors for accurately capturing
the semantics of the novel attributes and show their superior performance in
terms of detection and localization compared to other baseline approaches.
Moreover, we demonstrate the application of automatic annotation using our
synthesized detectors on Caltech-UCSD Birds-200-2011 dataset. Various
generalized zero-shot classification algorithms trained upon the dataset
re-annotated by ZSLA show comparable performance with those trained with the
manual ground-truth annotations. Please refer to our project page for source
code: https://yuhsuanli.github.io/ZSLA/

Multi-agent Reinforcement Learning (MARL) problems often require cooperation
among agents in order to solve a task. Centralization and decentralization are
two approaches used for cooperation in MARL. While fully decentralized methods
are prone to converge to suboptimal solutions due to partial observability and
nonstationarity, the methods involving centralization suffer from scalability
limitations and lazy agent problem. Centralized training decentralized
execution paradigm brings out the best of these two approaches; however,
centralized training still has an upper limit of scalability not only for
acquired coordination performance but also for model size and training time. In
this work, we adopt the centralized training with decentralized execution
paradigm and investigate the generalization and transfer capacity of the
trained models across variable number of agents. This capacity is assessed by
training variable number of agents in a specific MARL problem and then
performing greedy evaluations with variable number of agents for each training
configuration. Thus, we analyze the evaluation performance for each combination
of agent count for training versus evaluation. We perform experimental
evaluations on predator prey and traffic junction environments and demonstrate
that it is possible to obtain similar or higher evaluation performance by
training with less agents. We conclude that optimal number of agents to perform
training may differ from the target number of agents and argue that transfer
across large number of agents can be a more efficient solution to scaling up
than directly increasing number of agents during training.

To cope with the current pandemic situation and reinstate pseudo-normal daily
life, several measures have been deployed and maintained, such as mask wearing,
social distancing, hands sanitizing, etc. Since outdoor cultural events,
concerts, and picnics, are gradually allowed, a close monitoring of the crowd
activity is needed to avoid undesired contact and disease transmission. In this
context, intelligent unmanned aerial vehicles (UAVs) can be occasionally
deployed to ensure the surveillance of these activities, that health
restriction measures are applied, and to trigger alerts when the latter are not
respected. Consequently, we propose in this paper a complete UAV framework for
intelligent monitoring of post COVID-19 outdoor activities. Specifically, we
propose a three steps approach. In the first step, captured images by a UAV are
analyzed using machine learning to detect and locate individuals. The second
step consists of a novel coordinates mapping approach to evaluate distances
among individuals, then cluster them, while the third step provides an
energy-efficient and/or reliable UAV trajectory to inspect clusters for
restrictions violation such as mask wearing. Obtained results provide the
following insights: 1) Efficient detection of individuals depends on the angle
from which the image was captured, 2) coordinates mapping is very sensitive to
the estimation error in individuals' bounding boxes, and 3) UAV trajectory
design algorithm 2-Opt is recommended for practical real-time deployments due
to its low-complexity and near-optimal performance.

Existing deep learning based unsupervised video object segmentation methods
still rely on ground-truth segmentation masks to train. Unsupervised in this
context only means that no annotated frames are used during inference. As
obtaining ground-truth segmentation masks for real image scenes is a laborious
task, we envision a simple framework for dominant moving object segmentation
that neither requires annotated data to train nor relies on saliency priors or
pre-trained optical flow maps. Inspired by a layered image representation, we
introduce a technique to group pixel regions according to their affine
parametric motion. This enables our network to learn segmentation of the
dominant foreground object using only RGB image pairs as input for both
training and inference. We establish a baseline for this novel task using a new
MovingCars dataset and show competitive performance against recent methods that
require annotated masks to train.

Precision medicine is a clinical approach for disease prevention, detection
and treatment, which considers each individual's genetic background,
environment and lifestyle. The development of this tailored avenue has been
driven by the increased availability of omics methods, large cohorts of
temporal samples, and their integration with clinical data. Despite the immense
progression, existing computational methods for data analysis fail to provide
appropriate solutions for this complex, high-dimensional and longitudinal data.
In this work we have developed a new method termed TCAM, a dimensionality
reduction technique for multi-way data, that overcomes major limitations when
doing trajectory analysis of longitudinal omics data. Using real-world data, we
show that TCAM outperforms traditional methods, as well as state-of-the-art
tensor-based approaches for longitudinal microbiome data analysis. Moreover, we
demonstrate the versatility of TCAM by applying it to several different omics
datasets, and the applicability of it as a drop-in replacement within
straightforward ML tasks.

Convolutional Neural Networks(CNN) are inherently equivariant under
translations, however, they do not have an equivalent embedded mechanism to
handle other transformations such as rotations and change in scale. Several
approaches exist that make CNNs equivariant under other transformation groups
by design. Among these, steerable CNNs have been especially effective. However,
these approaches require redesigning standard networks with filters mapped from
combinations of predefined basis involving complex analytical functions. We
experimentally demonstrate that these restrictions in the choice of basis can
lead to model weights that are sub-optimal for the primary deep learning task
(e.g. classification). Moreover, such hard-baked explicit formulations make it
difficult to design composite networks comprising heterogeneous feature groups.
To circumvent such issues, we propose Implicitly Equivariant Networks (IEN)
which induce equivariance in the different layers of a standard CNN model by
optimizing a multi-objective loss function that combines the primary loss with
an equivariance loss term. Through experiments with VGG and ResNet models on
Rot-MNIST , Rot-TinyImageNet, Scale-MNIST and STL-10 datasets, we show that
IEN, even with its simple formulation, performs better than steerable networks.
Also, IEN facilitates construction of heterogeneous filter groups allowing
reduction in number of channels in CNNs by a factor of over 30% while
maintaining performance on par with baselines. The efficacy of IEN is further
validated on the hard problem of visual object tracking. We show that IEN
outperforms the state-of-the-art rotation equivariant tracking method while
providing faster inference speed.

Decreasing projection views to lower X-ray radiation dose usually leads to
severe streak artifacts. To improve image quality from sparse-view data, a
Multi-domain Integrative Swin Transformer network (MIST-net) was developed in
this article. First, MIST-net incorporated lavish domain features from data,
residual-data, image, and residual-image using flexible network architectures,
where residual-data and residual-image sub-network was considered as data
consistency module to eliminate interpolation and reconstruction errors.
Second, a trainable edge enhancement filter was incorporated to detect and
protect image edges. Third, a high-quality reconstruction Swin transformer
(i.e., Recformer) was designed to capture image global features. The experiment
results on numerical and real cardiac clinical datasets with 48-views
demonstrated that our proposed MIST-net provided better image quality with more
small features and sharp edges than other competitors.

Machine Learning (ML) has achieved great successes in recent decades, both in
research and in practice. In Cyber-Physical Systems (CPS), ML can for example
be used to optimize systems, to detect anomalies or to identify root causes of
system failures. However, existing algorithms suffer from two major drawbacks:
(i) They are hard to interpret by human experts. (ii) Transferring results from
one systems to another (similar) system is often a challenge. Concept learning,
or Representation Learning (RepL), is a solution to both of these drawbacks;
mimicking the human solution approach to explain-ability and transfer-ability:
By learning general concepts such as physical quantities or system states, the
model becomes interpretable by humans. Furthermore concepts on this abstract
level can normally be applied to a wide range of different systems. Modern ML
methods are already widely used in CPS, but concept learning and transfer
learning are hardly used so far. In this paper, we provide an overview of the
current state of research regarding methods for learning physical concepts in
time series data, which is the primary form of sensor data of CPS. We also
analyze the most important methods from the current state of the art using the
example of a three-tank system. Based on these concrete implementations1, we
discuss the advantages and disadvantages of the methods and show for which
purpose and under which conditions they can be used.

We consider the product of determinantal point processes (DPPs), a point
process whose probability mass is proportional to the product of principal
minors of multiple matrices, as a natural, promising generalization of DPPs. We
study the computational complexity of computing its normalizing constant, which
is among the most essential probabilistic inference tasks. Our
complexity-theoretic results (almost) rule out the existence of efficient
algorithms for this task unless the input matrices are forced to have favorable
structures. In particular, we prove the following:
  (1) Computing $\sum_S\det({\bf A}_{S,S})^p$ exactly for every (fixed)
positive even integer $p$ is UP-hard and Mod$_3$P-hard, which gives a negative
answer to an open question posed by Kulesza and Taskar.
  (2) $\sum_S\det({\bf A}_{S,S})\det({\bf B}_{S,S})\det({\bf C}_{S,S})$ is
NP-hard to approximate within a factor of $2^{O(|I|^{1-\epsilon})}$ or
$2^{O(n^{1/\epsilon})}$ for any $\epsilon>0$, where $|I|$ is the input size and
$n$ is the order of the input matrix. This result is stronger than the
#P-hardness for the case of two matrices derived by Gillenwater.
  (3) There exists a $k^{O(k)}n^{O(1)}$-time algorithm for computing
$\sum_S\det({\bf A}_{S,S})\det({\bf B}_{S,S})$, where $k$ is the maximum rank
of $\bf A$ and $\bf B$ or the treewidth of the graph formed by nonzero entries
of $\bf A$ and $\bf B$. Such parameterized algorithms are said to be
fixed-parameter tractable.
  These results can be extended to the fixed-size case. Further, we present two
applications of fixed-parameter tractable algorithms given a matrix $\bf A$ of
treewidth $w$:
  (4) We can compute a $2^{\frac{n}{2p-1}}$-approximation to $\sum_S\det({\bf
A}_{S,S})^p$ for any fractional number $p>1$ in $w^{O(wp)}n^{O(1)}$ time.
  (5) We can find a $2^{\sqrt n}$-approximation to unconstrained MAP inference
in $w^{O(w\sqrt n)}n^{O(1)}$ time.

The focus of this paper is on the problem of image retrieval with attribute
manipulation. Our proposed work is able to manipulate the desired attributes of
the query image while maintaining its other attributes. For example, the collar
attribute of the query image can be changed from round to v-neck to retrieve
similar images from a large dataset. A key challenge in e-commerce is that
images have multiple attributes where users would like to manipulate and it is
important to estimate discriminative feature representations for each of these
attributes. The proposed FashionSearchNet-v2 architecture is able to learn
attribute specific representations by leveraging on its weakly-supervised
localization module, which ignores the unrelated features of attributes in the
feature space, thus improving the similarity learning. The network is jointly
trained with the combination of attribute classification and triplet ranking
loss to estimate local representations. These local representations are then
merged into a single global representation based on the instructed attribute
manipulation where desired images can be retrieved with a distance metric. The
proposed method also provides explainability for its retrieval process to help
provide additional information on the attention of the network. Experiments
performed on several datasets that are rich in terms of the number of
attributes show that FashionSearchNet-v2 outperforms the other state-of-the-art
attribute manipulation techniques. Different than our earlier work
(FashionSearchNet), we propose several improvements in the learning procedure
and show that the proposed FashionSearchNet-v2 can be generalized to different
domains other than fashion.

Artificial intelligence through machine learning is increasingly used in the
digital society. Solutions based on machine learning bring both great
opportunities, thus coined "Software 2.0," but also great challenges for the
engineering community to tackle. Due to the experimental approach used by data
scientists when developing machine learning models, agility is an essential
characteristic. In this keynote address, we discuss two contemporary
development phenomena that are fundamental in machine learning development,
i.e., notebook interfaces and MLOps. First, we present a solution that can
remedy some of the intrinsic weaknesses of working in notebooks by supporting
easy transitions to integrated development environments. Second, we propose
reinforced engineering of AI systems by introducing metaphorical buttresses and
rebars in the MLOps context. Machine learning-based solutions are dynamic in
nature, and we argue that reinforced continuous engineering is required to
quality assure the trustworthy AI systems of tomorrow.

Overlapping speech diarization is always treated as a multi-label
classification problem. In this paper, we reformulate this task as a
single-label prediction problem by encoding the multi-speaker labels with power
set. Specifically, we propose the speaker embedding-aware neural diarization
(SEND) method, which predicts the power set encoded labels according to the
similarities between speech features and given speaker embeddings. Our method
is further extended and integrated with downstream tasks by utilizing the
textual information, which has not been well studied in previous literature.
The experimental results show that our method achieves lower diarization error
rate than the target-speaker voice activity detection. When textual information
is involved, the diarization errors can be further reduced. For the real
meeting scenario, our method can achieve 34.11% relative improvement compared
with the Bayesian hidden Markov model based clustering algorithm.

In the weakly supervised localization setting, supervision is given as an
image-level label. We propose to employ an image classifier $f$ and to train a
generative network $g$ that outputs, given the input image, a per-pixel weight
map that indicates the location of the object within the image. Network $g$ is
trained by minimizing the discrepancy between the output of the classifier $f$
on the original image and its output given the same image weighted by the
output of $g$. The scheme requires a regularization term that ensures that $g$
does not provide a uniform weight, and an early stopping criterion in order to
prevent $g$ from over-segmenting the image. Our results indicate that the
method outperforms existing localization methods by a sizable margin on the
challenging fine-grained classification datasets, as well as a generic image
recognition dataset. Additionally, the obtained weight map is also
state-of-the-art in weakly supervised segmentation in fine-grained
categorization datasets.

Portfolio optimization is one of the essential fields of focus in finance.
There has been an increasing demand for novel computational methods in this
area to compute portfolios with better returns and lower risks in recent years.
We present a novel computational method called Representation Portfolio
Selection (RPS) by redefining the distance matrix of financial assets using
Representation Learning and Clustering algorithms for portfolio selection to
increase diversification. RPS proposes a heuristic for getting closer to the
optimal subset of assets. Using empirical results in this paper, we demonstrate
that widely used portfolio optimization algorithms, such as MVO, CLA, and HRP,
can benefit from our asset subset selection.

The air that surrounds us is the cardinal source of respiration of all
life-forms. Therefore, it is undoubtedly vital to highlight that balanced air
quality is utmost important to the respiratory health of all living beings,
environmental homeostasis, and even economical equilibrium. Nevertheless, a
gradual deterioration of air quality has been observed in the last few decades,
due to the continuous increment of polluted emissions from automobiles and
industries into the atmosphere. Even though many people have scarcely
acknowledged the depth of the problem, the persistent efforts of determined
parties, including the World Health Organization, have consistently pushed the
boundaries for a qualitatively better global air homeostasis, by facilitating
technology-driven initiatives to timely detect and predict air quality in
regional and global scales. However, the existing frameworks for air quality
monitoring lack the capability of real-time responsiveness and flexible
semantic distribution. In this paper, a novel Internet of Things framework is
proposed which is easily implementable, semantically distributive, and
empowered by a machine learning model. The proposed system is equipped with a
NodeRED dashboard which processes, visualizes, and stores the primary sensor
data that are acquired through a public air quality sensor network, and
further, the dashboard is integrated with a machine-learning model to obtain
temporal and geo-spatial air quality predictions. ESP8266 NodeMCU is
incorporated as a subscriber to the NodeRED dashboard via a message queuing
telemetry transport broker to communicate quantitative air quality data or
alarming emails to the end-users through the developed web and mobile
applications. Therefore, the proposed system could become highly beneficial in
empowering public engagement in air quality through an unoppressive,
data-driven, and semantic framework.

Multi-task learning (MTL) is an active field in deep learning in which we
train a model to jointly learn multiple tasks by exploiting relationships
between the tasks. It has been shown that MTL helps the model share the learned
features between tasks and enhance predictions compared to when learning each
task independently. We propose a new learning framework for 2-task MTL problem
that uses the predictions of one task as inputs to another network to predict
the other task. We define two new loss terms inspired by cycle-consistency loss
and contrastive learning, alignment loss and cross-task consistency loss. Both
losses are designed to enforce the model to align the predictions of multiple
tasks so that the model predicts consistently. We theoretically prove that both
losses help the model learn more efficiently and that cross-task consistency
loss is better in terms of alignment with the straight-forward predictions.
Experimental results also show that our proposed model achieves significant
performance on the benchmark Cityscapes and NYU dataset.

Data imbalance, that is the disproportion between the number of training
observations coming from different classes, remains one of the most significant
challenges affecting contemporary machine learning. The negative impact of data
imbalance on traditional classification algorithms can be reduced by the data
preprocessing techniques, methods that manipulate the training data to
artificially reduce the degree of imbalance. However, the existing data
preprocessing techniques, in particular SMOTE and its derivatives, which
constitute the most prevalent paradigm of imbalanced data preprocessing, tend
to be susceptible to various data difficulty factors. This is in part due to
the fact that the original SMOTE algorithm does not utilize the information
about majority class observations. The focus of this thesis is development of
novel data resampling strategies natively utilizing the information about the
distribution of both minority and majority class. The thesis summarizes the
content of 12 research papers focused on the proposed binary data resampling
strategies, their translation to the multi-class setting, and the practical
application to the problem of histopathological data classification.

With the enrichment of literature resources, researchers are facing the
growing problem of information explosion and knowledge overload. To help
scholars retrieve literature and acquire knowledge successfully, clarifying the
semantic structure of the content in academic literature has become the
essential research question. In the research on identifying the structure
function of chapters in academic articles, only a few studies used the deep
learning model and explored the optimization for feature input. This limits the
application, optimization potential of deep learning models for the research
task. This paper took articles of the ACL conference as the corpus. We employ
the traditional machine learning models and deep learning models to construct
the classifiers based on various feature input. Experimental results show that
(1) Compared with the chapter content, the chapter title is more conducive to
identifying the structure function of academic articles. (2) Relative position
is a valuable feature for building traditional models. (3) Inspired by (2),
this paper further introduces contextual information into the deep learning
models and achieved significant results. Meanwhile, our models show good
migration ability in the open test containing 200 sampled non-training samples.
We also annotated the ACL main conference papers in recent five years based on
the best practice performing models and performed a time series analysis of the
overall corpus. This work explores and summarizes the practical features and
models for this task through multiple comparative experiments and provides a
reference for related text classification tasks. Finally, we indicate the
limitations and shortcomings of the current model and the direction of further

With the development of Internet technology, the phenomenon of information
overload is becoming more and more obvious. It takes a lot of time for users to
obtain the information they need. However, keyphrases that summarize document
information highly are helpful for users to quickly obtain and understand
documents. For academic resources, most existing studies extract keyphrases
through the title and abstract of papers. We find that title information in
references also contains author-assigned keyphrases. Therefore, this article
uses reference information and applies two typical methods of unsupervised
extraction methods (TF*IDF and TextRank), two representative traditional
supervised learning algorithms (Na\"ive Bayes and Conditional Random Field) and
a supervised deep learning model (BiLSTM-CRF), to analyze the specific
performance of reference information on keyphrase extraction. It is expected to
improve the quality of keyphrase recognition from the perspective of expanding
the source text. The experimental results show that reference information can
increase precision, recall, and F1 of automatic keyphrase extraction to a
certain extent. This indicates the usefulness of reference information on
keyphrase extraction of academic papers and provides a new idea for the
following research on automatic keyphrase extraction.

We demonstrate that Bayesian machine learning can be used to treat the vast
amount of experimental fission data which are noisy, incomplete, discrepant,
and correlated. As an example, the two-dimensional cumulative fission yields
(CFY) of neutron-induced fission of $^{238}$U are evaluated with energy
dependencies and uncertainty qualifications. For independent fission yields
(IFY) with very few experimental data, the heterogeneous data fusion of CFY and
IFY is employed to interpolate the energy dependence. This work shows that
Bayesian data fusion can facilitate the further utilization of imperfect raw
nuclear data.

Cross-domain sentiment classification has been a hot spot these years, which
aims to learn a reliable classifier using labeled data from a source domain and
evaluate it on a target domain. In this vein, most approaches utilized domain
adaptation that maps data from different domains into a common feature space.
To further improve the model performance, several methods targeted to mine
domain-specific information were proposed. However, most of them only utilized
a limited part of domain-specific information. In this study, we first develop
a method of extracting domain-specific words based on the topic information
derived from topic models. Then, we propose a Topic Driven Adaptive Network
(TDAN) for cross-domain sentiment classification. The network consists of two
sub-networks: a semantics attention network and a domain-specific word
attention network, the structures of which are based on transformers. These
sub-networks take different forms of input and their outputs are fused as the
feature vector. Experiments validate the effectiveness of our TDAN on sentiment
classification across domains. Case studies also indicate that topic models
have the potential to add value to cross-domain sentiment classification by
discovering interpretable and low-dimensional subspaces.

We propose Deep MAGSAC++ combining the advantages of traditional and deep
robust estimators. We introduce a novel loss function that exploits the
orientation and scale from partially affine covariant features, e.g., SIFT, in
a geometrically justifiable manner. The new loss helps in learning higher-order
information about the underlying scene geometry. Moreover, we propose a new
sampler for RANSAC that always selects the sample with the highest probability
of consisting only of inliers. After every unsuccessful iteration, the
probabilities are updated in a principled way via a Bayesian approach. The
prediction of the deep network is exploited as prior inside the sampler.
Benefiting from the new loss, the proposed sampler and a number of technical
advancements, Deep MAGSAC++ is superior to the state-of-the-art both in terms
of accuracy and run-time on thousands of image pairs from publicly available
real-world datasets for essential and fundamental matrix estimation.

Molecular representation learning is the first yet vital step in combining
deep learning and molecular science. To push the boundaries of molecular
representation learning, we present PhysChem, a novel neural architecture that
learns molecular representations via fusing physical and chemical information
of molecules. PhysChem is composed of a physicist network (PhysNet) and a
chemist network (ChemNet). PhysNet is a neural physical engine that learns
molecular conformations through simulating molecular dynamics with
parameterized forces; ChemNet implements geometry-aware deep message-passing to
learn chemical / biomedical properties of molecules. Two networks specialize in
their own tasks and cooperate by providing expertise to each other. By fusing
physical and chemical information, PhysChem achieved state-of-the-art
performances on MoleculeNet, a standard molecular machine learning benchmark.
The effectiveness of PhysChem was further corroborated on cutting-edge datasets
of SARS-CoV-2.

Deep Learning methods are renowned for their performances, yet their lack of
interpretability prevents them from high-stakes contexts. Recent model agnostic
methods address this problem by providing post-hoc interpretability methods by
reverse-engineering the model's inner workings. However, in many regulated
fields, interpretability should be kept in mind from the start, which means
that post-hoc methods are valid only as a sanity check after model training.
Interpretability from the start, in an abstract setting, means posing a set of
soft constraints on the model's behavior by injecting knowledge and
annihilating possible biases. We propose a Multicriteria technique that allows
to control the feature effects on the model's outcome by injecting knowledge in
the objective function. We then extend the technique by including a non-linear
knowledge function to account for more complex effects and local lack of
knowledge. The result is a Deep Learning model that embodies interpretability
from the start and aligns with the recent regulations. A practical empirical
example based on credit risk, suggests that our approach creates performant yet
robust models capable of overcoming biases derived from data scarcity.

Escaping saddle points is a central research topic in nonconvex optimization.
In this paper, we propose a simple gradient-based algorithm such that for a
smooth function $f\colon\mathbb{R}^n\to\mathbb{R}$, it outputs an
$\epsilon$-approximate second-order stationary point in $\tilde{O}(\log
n/\epsilon^{1.75})$ iterations. Compared to the previous state-of-the-art
algorithms by Jin et al. with $\tilde{O}((\log n)^{4}/\epsilon^{2})$ or
$\tilde{O}((\log n)^{6}/\epsilon^{1.75})$ iterations, our algorithm is
polynomially better in terms of $\log n$ and matches their complexities in
terms of $1/\epsilon$. For the stochastic setting, our algorithm outputs an
$\epsilon$-approximate second-order stationary point in $\tilde{O}((\log
n)^{2}/\epsilon^{4})$ iterations. Technically, our main contribution is an idea
of implementing a robust Hessian power method using only gradients, which can
find negative curvature near saddle points and achieve the polynomial speedup
in $\log n$ compared to the perturbed gradient descent methods. Finally, we
also perform numerical experiments that support our results.

Fingerprinting-based indoor localization is an emerging application domain
for enhanced positioning and tracking of people and assets within indoor
locales. The superior pairing of ubiquitously available WiFi signals with
computationally capable smartphones is set to revolutionize the area of indoor
localization. However, the observed signal characteristics from independently
maintained WiFi access points vary greatly over time. Moreover, some of the
WiFi access points visible at the initial deployment phase may be replaced or
removed over time. These factors are often ignored in indoor localization
frameworks and cause gradual and catastrophic degradation of localization
accuracy post-deployment (over weeks and months). To overcome these challenges,
we propose a Siamese neural encoder-based framework that offers up to 40%
reduction in degradation of localization accuracy over time compared to the
state-of-the-art in the area, without requiring any retraining.

The field of computer vision is rapidly evolving, particularly in the context
of new methods of neural architecture design. These models contribute to (1)
the Climate Crisis - increased CO2 emissions and (2) the Privacy Crisis - data
leakage concerns. To address the often overlooked impact the Computer Vision
(CV) community has on these crises, we outline a novel ethical framework,
\textit{P4AI}: Principlism for AI, an augmented principlistic view of ethical
dilemmas within AI. We then suggest using P4AI to make concrete recommendations
to the community to mitigate the climate and privacy crises.

Climate change continues to be a pressing issue that currently affects
society at-large. It is important that we as a society, including the Computer
Vision (CV) community take steps to limit our impact on the environment. In
this paper, we (a) analyze the effect of diminishing returns on CV methods, and
(b) propose a \textit{``NoFADE''}: a novel entropy-based metric to quantify
model--dataset--complexity relationships. We show that some CV tasks are
reaching saturation, while others are almost fully saturated. In this light,
NoFADE allows the CV community to compare models and datasets on a similar
basis, establishing an agnostic platform.

The task of hyper-parameter optimization (HPO) is burdened with heavy
computational costs due to the intractability of optimizing both a model's
weights and its hyper-parameters simultaneously. In this work, we introduce a
new class of HPO method and explore how the low-rank factorization of the
convolutional weights of intermediate layers of a convolutional neural network
can be used to define an analytical response surface for optimizing
hyper-parameters, using only training data. We quantify how this surface
behaves as a surrogate to model performance and can be solved using a
trust-region search algorithm, which we call autoHyper. The algorithm
outperforms state-of-the-art such as Bayesian Optimization and generalizes
across model, optimizer, and dataset selection. Our code can be found at
\url{https://github.com/MathieuTuli/autoHyper}.

Fast stereo based 3D object detectors have made great progress recently.
However, they lag far behind high-precision stereo based methods in accuracy.
We argue that the main reason is due to the poor geometry-aware feature
representation in 3D space. To solve this problem, we propose an efficient
stereo geometry network (ESGN). The key in our ESGN is an efficient
geometry-aware feature generation (EGFG) module. Our EGFG module first uses a
stereo correlation and reprojection module to construct multi-scale stereo
volumes in camera frustum space, second employs a multi-scale BEV projection
and fusion module to generate multiple geometry-aware features. In these two
steps, we adopt deep multi-scale information fusion for discriminative
geometry-aware feature generation, without any complex aggregation networks. In
addition, we introduce a deep geometry-aware feature distillation scheme to
guide stereo feature learning with a LiDAR-based detector. The experiments are
performed on the classical KITTI dataset. On KITTI test set, our ESGN
outperforms the fast state-of-art-art detector YOLOStereo3D by 5.14\% on
mAP$_{3d}$ at 62$ms$. To the best of our knowledge, our ESGN achieves a best
trade-off between accuracy and speed. We hope that our efficient stereo
geometry network can provide more possible directions for fast 3D object
detection. Our source code will be released.

In this paper, we utilized generative models, and reformulate it for problems
in molecular dynamics (MD) simulation, by introducing an MD potential energy
component to our generative model. By incorporating potential energy as
calculated from TorchMD into a conditional generative framework, we attempt to
construct a low-potential energy route of transformation between the
helix~$\rightarrow$~coil structures of a protein. We show how to add an
additional loss function to conditional generative models, motivated by
potential energy of molecular configurations, and also present an optimization
technique for such an augmented loss function. Our results show the benefit of
this additional loss term on synthesizing realistic molecular trajectories.

Energy harvesting (EH) IoT devices that operate intermittently without
batteries, coupled with advances in deep neural networks (DNNs), have opened up
new opportunities for enabling sustainable smart applications. Nevertheless,
implementing those computation and memory-intensive intelligent algorithms on
EH devices is extremely difficult due to the challenges of limited resources
and intermittent power supply that causes frequent failures. To address those
challenges, this paper proposes a methodology that enables fast deep learning
with low-energy accelerators for tiny energy harvesting devices. We first
propose $RAD$, a resource-aware structured DNN training framework, which
employs block circulant matrix and structured pruning to achieve high
compression for leveraging the advantage of various vector operation
accelerators. A DNN implementation method, $ACE$, is then proposed that employs
low-energy accelerators to profit maximum performance with small energy
consumption. Finally, we further design $FLEX$, the system support for
intermittent computation in energy harvesting situations. Experimental results
from three different DNN models demonstrate that $RAD$, $ACE$, and $FLEX$ can
enable fast and correct inference on energy harvesting devices with up to 4.26X
runtime reduction, up to 7.7X energy reduction with higher accuracy over the

In this work, we study the Neural Tangent Kernel (NTK) of Matrix Product
States (MPS) and the convergence of its NTK in the infinite bond dimensional
limit. We prove that the NTK of MPS asymptotically converges to a constant
matrix during the gradient descent (training) process (and also the
initialization phase) as the bond dimensions of MPS go to infinity by the
observation that the variation of the tensors in MPS asymptotically goes to
zero during training in the infinite limit. By showing the
positive-definiteness of the NTK of MPS, the convergence of MPS during the
training in the function space (space of functions represented by MPS) is
guaranteed without any extra assumptions of the data set. We then consider the
settings of (supervised) Regression with Mean Square Error (RMSE) and
(unsupervised) Born Machines (BM) and analyze their dynamics in the infinite
bond dimensional limit. The ordinary differential equations (ODEs) which
describe the dynamics of the responses of MPS in the RMSE and BM are derived
and solved in the closed-form. For the Regression, we consider Mercer Kernels
(Gaussian Kernels) and find that the evolution of the mean of the responses of
MPS follows the largest eigenvalue of the NTK. Due to the orthogonality of the
kernel functions in BM, the evolution of different modes (samples) decouples
and the "characteristic time" of convergence in training is obtained.

{\it Learning finite automata} (termed as {\it model learning}) has become an
important field in machine learning and has been useful realistic applications.
Quantum finite automata (QFA) are simple models of quantum computers with
finite memory. Due to their simplicity, QFA have well physical realizability,
but one-way QFA still have essential advantages over classical finite automata
with regard to state complexity (two-way QFA are more powerful than classical
finite automata in computation ability as well). As a different problem in {\it
quantum learning theory} and {\it quantum machine learning}, in this paper, our
purpose is to initiate the study of {\it learning QFA with queries} (naturally
it may be termed as {\it quantum model learning}), and the main results are
regarding learning two basic one-way QFA: (1) We propose a learning algorithm
for measure-once one-way QFA (MO-1QFA) with query complexity of polynomial
time; (2) We propose a learning algorithm for measure-many one-way QFA
(MM-1QFA) with query complexity of polynomial-time, as well.

In this paper, we study the generalization performance of global minima for
implementing empirical risk minimization (ERM) on over-parameterized deep ReLU
nets. Using a novel deepening scheme for deep ReLU nets, we rigorously prove
that there exist perfect global minima achieving almost optimal generalization
error bounds for numerous types of data under mild conditions. Since
over-parameterization is crucial to guarantee that the global minima of ERM on
deep ReLU nets can be realized by the widely used stochastic gradient descent
(SGD) algorithm, our results indeed fill a gap between optimization and

As wildfires are expected to become more frequent and severe, improved
prediction models are vital to mitigating risk and allocating resources. With
remote sensing data, valuable spatiotemporal statistical models can be created
and used for resource management practices. In this paper, we create a dynamic
model for future wildfire predictions of five locations within the western
United States through a deep neural network via historical burned area and
climate data. The proposed model has distinct features that address the
characteristic need in prediction evaluations, including dynamic online
estimation and time-series modeling. Between locations, local fire event
triggers are not isolated, and there are confounding factors when local data is
analyzed due to incomplete state observations. When compared to existing
approaches that do not account for incomplete state observation within wildfire
time-series data, on average, we are able to achieve higher prediction

With the rapid growth of malware attacks, more antivirus developers consider
deploying machine learning technologies into their productions. Researchers and
developers published various machine learning-based detectors with high
precision on malware detection in recent years. Although numerous machine
learning-based malware detectors are available, they face various machine
learning-targeted attacks, including evasion and adversarial attacks. This
project explores how and why adversarial examples evade malware detectors, then
proposes a randomised chaining method to defend against adversarial malware
statically. This research is crucial for working towards combating the
pertinent malware cybercrime.

Recently, Graph Neural Networks (GNNs) have proven their effectiveness for
recommender systems. Existing studies have applied GNNs to capture
collaborative relations in the data. However, in real-world scenarios, the
relations in a recommendation graph can be of various kinds. For example, two
movies may be associated either by the same genre or by the same
director/actor. If we use a single graph to elaborate all these relations, the
graph can be too complex to process. To address this issue, we bring the idea
of pre-training to process the complex graph step by step. Based on the idea of
divide-and-conquer, we separate the large graph into three sub-graphs: user
graph, item graph, and user-item interaction graph. Then the user and item
embeddings are pre-trained from user and item graphs, respectively. To conduct
pre-training, we construct the multi-relational user graph and item graph,
respectively, based on their attributes. In this paper, we propose a novel
Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the
pre-train user and item embeddings on the user and item graph prior to the
recommendation step. Specifically, we design a relation-level attention layer
to learn the importance of different relations. Next, a Reinforced Neighbor
Sampler (RNS) is applied to search the optimal filtering threshold for sampling
top-k similar neighbors in the graph, which avoids the over-smoothing issue. We
initialize the recommendation model with the pre-trained user/item embeddings.
Finally, an aggregation-based GNN model is utilized to learn from the
collaborative relations in the user-item interaction graph and provide
recommendations. Our experiments demonstrate that RAM-GNN outperforms other
state-of-the-art graph-based recommendation models and multi-relational graph

With the increasingly rapid development of new malicious computer software by
bad faith actors, both commercial and research-oriented antivirus detectors
have come to make greater use of machine learning tactics to identify such
malware as harmful before end users are exposed to their effects. This, in
turn, has spurred the development of tools that allow for known malware to be
manipulated such that they can evade being classified as dangerous by these
machine learning-based detectors, while retaining their malicious
functionality. These manipulations function by applying a set of changes that
can be made to Windows programs that result in a different file structure and
signature without altering the software's capabilities. Various proposals have
been made for the most effective way of applying these alterations to input
malware to deceive static malware detectors; the purpose of this research is to
examine these proposals and test their implementations to determine which
tactics tend to generate the most successful attacks.

The ability to reason with multiple hierarchical structures is an attractive
and desirable property of sequential inductive biases for natural language
processing. Do the state-of-the-art Transformers and LSTM architectures
implicitly encode for these biases? To answer this, we propose ORCHARD, a
diagnostic dataset for systematically evaluating hierarchical reasoning in
state-of-the-art neural sequence models. While there have been prior evaluation
frameworks such as ListOps or Logical Inference, our work presents a novel and
more natural setting where our models learn to reason with multiple explicit
hierarchical structures instead of only one, i.e., requiring the ability to do
both long-term sequence memorizing, relational reasoning while reasoning with
hierarchical structure. Consequently, backed by a set of rigorous experiments,
we show that (1) Transformer and LSTM models surprisingly fail in systematic
generalization, and (2) with increased references between hierarchies,
Transformer performs no better than random.

Inducing latent tree structures from sequential data is an emerging trend in
the NLP research landscape today, largely popularized by recent methods such as
Gumbel LSTM and Ordered Neurons (ON-LSTM). This paper proposes FASTTREES, a new
general purpose neural module for fast sequence encoding. Unlike most previous
works that consider recurrence to be necessary for tree induction, our work
explores the notion of parallel tree induction, i.e., imbuing our model with
hierarchical inductive biases in a parallelizable, non-autoregressive fashion.
To this end, our proposed FASTTREES achieves competitive or superior
performance to ON-LSTM on four well-established sequence modeling tasks, i.e.,
language modeling, logical inference, sentiment analysis and natural language
inference. Moreover, we show that the FASTTREES module can be applied to
enhance Transformer models, achieving performance gains on three sequence
transduction tasks (machine translation, subject-verb agreement and
mathematical language understanding), paving the way for modular tree induction
modules. Overall, we outperform existing state-of-the-art models on logical
inference tasks by +4% and mathematical language understanding by +8%.

Unsupervised domain adaptive person re-identification has received
significant attention due to its high practical value. In past years, by
following the clustering and finetuning paradigm, researchers propose to
utilize the teacher-student framework in their methods to decrease the domain
gap between different person re-identification datasets. Inspired by recent
teacher-student framework based methods, which try to mimic the human learning
process either by making the student directly copy behavior from the teacher or
selecting reliable learning materials, we propose to conduct further
exploration to imitate the human learning process from different aspects,
\textit{i.e.}, adaptively updating learning materials, selectively imitating
teacher behaviors, and analyzing learning materials structures. The explored
three components, collaborate together to constitute a new method for
unsupervised domain adaptive person re-identification, which is called Human
Learning Imitation framework. The experimental results on three benchmark
datasets demonstrate the efficacy of our proposed method.

In this paper, we propose \texttt{FGPR}: a Federated Gaussian process
($\mathcal{GP}$) regression framework that uses an averaging strategy for model
aggregation and stochastic gradient descent for local client computations.
Notably, the resulting global model excels in personalization as \texttt{FGPR}
jointly learns a global $\mathcal{GP}$ prior across all clients. The predictive
posterior then is obtained by exploiting this prior and conditioning on local
data which encodes personalized features from a specific client. Theoretically,
we show that \texttt{FGPR} converges to a critical point of the full
log-likelihood function, subject to statistical error. Through extensive case
studies we show that \texttt{FGPR} excels in a wide range of applications and
is a promising approach for privacy-preserving multi-fidelity data modeling.

Nonnegative matrix factorization (NMF) has been widely used to learn
low-dimensional representations of data. However, NMF pays the same attention
to all attributes of a data point, which inevitably leads to inaccurate
representation. For example, in a human-face data set, if an image contains a
hat on the head, the hat should be removed or the importance of its
corresponding attributes should be decreased during matrix factorizing. This
paper proposes a new type of NMF called entropy weighted NMF (EWNMF), which
uses an optimizable weight for each attribute of each data point to emphasize
their importance. This process is achieved by adding an entropy regularizer to
the cost function and then using the Lagrange multiplier method to solve the
problem. Experimental results with several data sets demonstrate the
feasibility and effectiveness of the proposed method. We make our code
available at https://github.com/Poisson-EM/Entropy-weighted-NMF.

Automatic question answering is an important yet challenging task in
E-commerce given the millions of questions posted by users about the product
that they are interested in purchasing. Hence, there is a great demand for
automatic answer generation systems that provide quick responses using related
information about the product. There are three sources of knowledge available
for answering a user posted query, they are reviews, duplicate or similar
questions, and specifications. Effectively utilizing these information sources
will greatly aid us in answering complex questions. However, there are two main
challenges present in exploiting these sources: (i) The presence of irrelevant
information and (ii) the presence of ambiguity of sentiment present in reviews
and similar questions. Through this work we propose a novel pipeline (MSQAP)
that utilizes the rich information present in the aforementioned sources by
separately performing relevancy and ambiguity prediction before generating a
response.
  Experimental results show that our relevancy prediction model (BERT-QA)
outperforms all other variants and has an improvement of 12.36% in F1 score
compared to the BERT-base baseline. Our generation model (T5-QA) outperforms
the baselines in all content preservation metrics such as BLEU, ROUGE and has
an average improvement of 35.02% in ROUGE and 198.75% in BLEU compared to the
highest performing baseline (HSSC-q). Human evaluation of our pipeline shows us
that our method has an overall improvement in accuracy of 30.7% over the
generation model (T5-QA), resulting in our full pipeline-based approach (MSQAP)
providing more accurate answers. To the best of our knowledge, this is the
first work in the e-commerce domain that automatically generates natural
language answers combining the information present in diverse sources such as
specifications, similar questions, and reviews data.

This manuscript proposes to extend the information set of time-series
regression trees with latent stationary factors extracted via state-space
methods. In doing so, this approach generalises time-series regression trees on
two dimensions. First, it allows to handle predictors that exhibit measurement
error, non-stationary trends, seasonality and/or irregularities such as missing
observations. Second, it gives a transparent way for using domain-specific
theory to inform time-series regression trees. As a byproduct, this technique
sets the foundations for structuring powerful ensembles. Their real-world
applicability is studied under the lenses of empirical macro-finance.

Real-world data often exhibits long tail distributions with heavy class
imbalance, where the majority classes can dominate the training process and
alter the decision boundaries of the minority classes. Recently, researchers
have investigated the potential of supervised contrastive learning for
long-tailed recognition, and demonstrated that it provides a strong performance
gain. In this paper, we show that while supervised contrastive learning can
help improve performance, past baselines suffer from poor uniformity brought in
by imbalanced data distribution. This poor uniformity manifests in samples from
the minority class having poor separability in the feature space. To address
this problem, we propose targeted supervised contrastive learning (TSC), which
improves the uniformity of the feature distribution on the hypersphere. TSC
first generates a set of targets uniformly distributed on a hypersphere. It
then makes the features of different classes converge to these distinct and
uniformly distributed targets during training. This forces all classes,
including minority classes, to maintain a uniform distribution in the feature
space, improves class boundaries, and provides better generalization even in
the presence of long-tail data. Experiments on multiple datasets show that TSC
achieves state-of-the-art performance on long-tailed recognition tasks.

We propose a novel scene representation that encodes reaching distance -- the
distance between any position in the scene to a goal along a feasible
trajectory. We demonstrate that this environment field representation can
directly guide the dynamic behaviors of agents in 2D mazes or 3D indoor scenes.
Our environment field is a continuous representation and learned via a neural
implicit function using discretely sampled training data. We showcase its
application for agent navigation in 2D mazes, and human trajectory prediction
in 3D indoor environments. To produce physically plausible and natural
trajectories for humans, we additionally learn a generative model that predicts
regions where humans commonly appear, and enforce the environment field to be
defined within such regions. Extensive experiments demonstrate that the
proposed method can generate both feasible and plausible trajectories
efficiently and accurately.

The process of wound healing has been an active area of research around the
world. The problem is the wounds of different patients heal differently. For
example, patients with a background of diabetes may have difficulties in
healing [1]. By clearly understanding this process, we can determine the type
and quantity of medicine to give to patients with varying types of wounds. In
this research, we use a variation of the Alternating Direction Implicit method
to solve a partial differential equation that models part of the wound healing
process. Wound images are used as the dataset that we analyze. To segment the
image's wound, we implement deep learning-based models. We show that the
combination of a variant of the Alternating Direction Implicit method and Deep
Learning provides a reasonably accurate model for the process of wound healing.
To the best of our knowledge, this is the first attempt to combine both
numerical PDE and deep learning techniques in an automated system to capture
the long-term behavior of wound healing.

The availability of multi-modality datasets provides a unique opportunity to
characterize the same object of interest using multiple viewpoints more
comprehensively. In this work, we investigate the use of canonical correlation
analysis (CCA) and penalized variants of CCA (pCCA) for the fusion of two
modalities. We study a simple graphical model for the generation of
two-modality data. We analytically show that, with known model parameters,
posterior mean estimators that jointly use both modalities outperform arbitrary
linear mixing of single modality posterior estimators in latent variable
prediction. Penalized extensions of CCA (pCCA) that incorporate domain
knowledge can discover correlations with high-dimensional, low-sample data,
whereas traditional CCA is inapplicable. To facilitate the generation of
multi-dimensional embeddings with pCCA, we propose two matrix deflation schemes
that enforce desirable properties exhibited by CCA. We propose a two-stage
prediction pipeline using pCCA embeddings generated with deflation for latent
variable prediction by combining all the above. On simulated data, our proposed
model drastically reduces the mean-squared error in latent variable prediction.
When applied to publicly available histopathology data and RNA-sequencing data
from The Cancer Genome Atlas (TCGA) breast cancer patients, our model can
outperform principal components analysis (PCA) embeddings of the same dimension
in survival prediction.

Optimizing nonconvex (NCVX) problems, especially nonsmooth and constrained
ones, is an essential part of machine learning. However, it can be hard to
reliably solve such problems without optimization expertise. Existing
general-purpose NCVX optimization packages are powerful but typically cannot
handle nonsmoothness. GRANSO is among the first optimization solvers targeting
general nonsmooth NCVX problems with nonsmooth constraints, but, as it is
implemented in MATLAB and requires the user to provide analytical gradients,
GRANSO is often not a convenient choice in machine learning (especially deep
learning) applications. To greatly lower the technical barrier, we introduce a
new software package called NCVX, whose initial release contains the solver
PyGRANSO, a PyTorch-enabled port of GRANSO incorporating auto-differentiation,
GPU acceleration, tensor input, and support for new QP solvers. NCVX is built
on freely available and widely used open-source frameworks, and as a highlight,
can solve general constrained deep learning problems, the first of its kind.
NCVX is available at https://ncvx.org, with detailed documentation and numerous
examples from machine learning and other fields.

Challenges inherent to autonomous wintertime navigation in forests include
lack of reliable a Global Navigation Satellite System (GNSS) signal, low
feature contrast, high illumination variations and changing environment. This
type of off-road environment is an extreme case of situations autonomous cars
could encounter in northern regions. Thus, it is important to understand the
impact of this harsh environment on autonomous navigation systems. To this end,
we present a field report analyzing teach-and-repeat navigation in a subarctic
forest while subject to fluctuating weather, including light and heavy snow,
rain and drizzle. First, we describe the system, which relies on point cloud
registration to localize a mobile robot through a boreal forest, while
simultaneously building a map. We experimentally evaluate this system in over
18.8 km of autonomous navigation in the teach-and-repeat mode. Over 14 repeat
runs, only four manual interventions were required, three of which were due to
localization failure and another one caused by battery power outage. We show
that dense vegetation perturbs the GNSS signal, rendering it unsuitable for
navigation in forest trails. Furthermore, we highlight the increased
uncertainty related to localizing using point cloud registration in forest
trails. We demonstrate that it is not snow precipitation, but snow
accumulation, that affects our system's ability to localize within the
environment. Finally, we expose some challenges and lessons learned from our
field campaign to support better experimental work in winter conditions. Our
dataset is available online.

Background: COVID-19 has become a challenge worldwide and properly planning
of medical resources is the key to combating COVID-19. In the US Veteran
Affairs Health Care System (VA), many of the enrollees are susceptible to
COVID-19. Predicting the COVID-19 to allocate medical resources promptly
becomes a critical issue. When the VA enrollees have COVID-19 symptoms, it is
recommended that their first step should be to call the VA Call Center. For
confirmed COVID-19 patients, the median time from the first symptom to hospital
admission was seven days. By predicting the number of COVID-19 related calls,
we could predict imminent surges in healthcare use and plan medical resources
ahead. Objective: The study aims to develop a method to forecast the daily
number of COVID-19 related calls for each of the 110 VA medical centers.
Methods: In the proposed method, we pre-trained a model using a cluster of
medical centers and fine-tuned it for individual medical centers. At the
cluster level, we performed feature selection to select significant features
and automatic hyper-parameter search to select optimal hyper-parameter value
combinations for the model. Conclusions: This study proposed an accurate method
to forecast the daily number of COVID-19 related calls for VA medical centers.
The proposed method was able to overcome modeling challenges by grouping
similar medical centers into clusters to enlarge the dataset for training
models, and using hyper-parameter search to automatically find optimal
hyper-parameter value combinations for models. With the proposed method, surges
in health care can be predicted ahead. This allows health care practitioners to
better plan medical resources and combat COVID-19.

The rise of the new generation of cyber threats demands more sophisticated
and intelligent cyber defense solutions equipped with autonomous agents capable
of learning to make decisions without the knowledge of human experts. Several
reinforcement learning methods (e.g., Markov) for automated network intrusion
tasks have been proposed in recent years. In this paper, we introduce a new
generation of network intrusion detection methods that combines a
Q-learning-based reinforcement learning with a deep-feed forward neural network
method for network intrusion detection. Our proposed Deep Q-Learning (DQL)
model provides an ongoing auto-learning capability for a network environment
that can detect different types of network intrusions using an automated
trial-error approach and continuously enhance its detection capabilities. We
provide the details of fine-tuning different hyperparameters involved in the
DQL model for more effective self-learning. According to our extensive
experimental results based on the NSL-KDD dataset, we confirm that the lower
discount factor which is set as 0.001 under 250 episodes of training yields the
best performance results. Our experimental results also show that our proposed
DQL is highly effective in detecting different intrusion classes and
outperforms other similar machine learning approaches.

Online hatred is a growing concern on many social media platforms. To address
this issue, different social media platforms have introduced moderation
policies for such content. They also employ moderators who can check the posts
violating moderation policies and take appropriate action. Academicians in the
abusive language research domain also perform various studies to detect such
content better. Although there is extensive research in abusive language
detection in English, there is a lacuna in abusive language detection in low
resource languages like Hindi, Urdu etc. In this FIRE 2021 shared task -
"HASOC- Abusive and Threatening language detection in Urdu" the organizers
propose an abusive language detection dataset in Urdu along with threatening
language detection. In this paper, we explored several machine learning models
such as XGboost, LGBM, m-BERT based models for abusive and threatening content
detection in Urdu based on the shared task. We observed the Transformer model
specifically trained on abusive language dataset in Arabic helps in getting the
best performance. Our model came First for both abusive and threatening content
detection with an F1scoreof 0.88 and 0.54, respectively.

Hate speech is considered to be one of the major issues currently plaguing
online social media. Repeated and repetitive exposure to hate speech has been
shown to create physiological effects on the target users. Thus, hate speech,
in all its forms, should be addressed on these platforms in order to maintain
good health. In this paper, we explored several Transformer based machine
learning models for the detection of hate speech and offensive content in
English and Indo-Aryan languages at FIRE 2021. We explore several models such
as mBERT, XLMR-large, XLMR-base by team name "Super Mario". Our models came 2nd
position in Code-Mixed Data set (Macro F1: 0.7107), 2nd position in Hindi
two-class classification(Macro F1: 0.7797), 4th in English four-class category
(Macro F1: 0.8006) and 12th in English two-class category (Macro F1: 0.6447).

Prepositions are frequently occurring polysemous words. Disambiguation of
prepositions is crucial in tasks like semantic role labelling, question
answering, text entailment, and noun compound paraphrasing. In this paper, we
propose a novel methodology for preposition sense disambiguation (PSD), which
does not use any linguistic tools. In a supervised setting, the machine
learning model is presented with sentences wherein prepositions have been
annotated with senses. These senses are IDs in what is called The Preposition
Project (TPP). We use the hidden layer representations from pre-trained BERT
and BERT variants. The latent representations are then classified into the
correct sense ID using a Multi Layer Perceptron. The dataset used for this task
is from SemEval-2007 Task-6. Our methodology gives an accuracy of 86.85% which
is better than the state-of-the-art.

Recent research in the field of computer vision strongly focuses on deep
learning architectures to tackle image processing problems. Deep neural
networks are often considered in complex image processing scenarios since
traditional computer vision approaches are expensive to develop or reach their
limits due to complex relations. However, a common criticism is the need for
large annotated datasets to determine robust parameters. Annotating images by
human experts is time-consuming, burdensome, and expensive. Thus, support is
needed to simplify annotation, increase user efficiency, and annotation
quality. In this paper, we propose a generic workflow to assist the annotation
process and discuss methods on an abstract level. Thereby, we review the
possibilities of focusing on promising samples, image pre-processing,
pre-labeling, label inspection, or post-processing of annotations. In addition,
we present an implementation of the proposal by means of a developed flexible
and extendable software prototype nested in hybrid touchscreen/laptop device.

This work presents a reinforcement learning-based switching control mechanism
to autonomously move a ferromagnetic object (representing a milliscale robot)
around obstacles within a constrained environment in the presence of
disturbances. This mechanism can be used to navigate objects (e.g., capsule
endoscopy, swarms of drug particles) through complex environments when active
control is a necessity but where direct manipulation can be hazardous. The
proposed control scheme consists of a switching control architecture
implemented by two sub-controllers. The first sub-controller is designed to
employs the robot's inverse kinematic solutions to do an environment search of
the to-be-carried ferromagnetic particle while being robust to disturbances.
The second sub-controller uses a customized rainbow algorithm to control a
robotic arm, i.e., the UR5 robot, to carry a ferromagnetic particle to a
desired position through a constrained environment. For the customized Rainbow
algorithm, Quantile Huber loss from the Implicit Quantile Networks (IQN)
algorithm and ResNet are employed. The proposed controller is first trained and
tested in a real-time physics simulation engine (PyBullet). Afterward, the
trained controller is transferred to a UR5 robot to remotely transport a
ferromagnetic particle in a real-world scenario to demonstrate the
applicability of the proposed approach. The experimental results show an
average success rate of 98.86\% calculated over 30 episodes for randomly
generated trajectories.

Autonomous driving technologies are expected to not only improve mobility and
road safety but also bring energy efficiency benefits. In the foreseeable
future, autonomous vehicles (AVs) will operate on roads shared with
human-driven vehicles. To maintain safety and liveness while simultaneously
minimizing energy consumption, the AV planning and decision-making process
should account for interactions between the autonomous ego vehicle and
surrounding human-driven vehicles. In this chapter, we describe a framework for
developing energy-efficient autonomous driving policies on shared roads by
exploiting human-driver behavior modeling based on cognitive hierarchy theory
and reinforcement learning.

In this work, we benchmark a variety of single- and multi-task graph neural
network (GNN) models against lower-bar and higher-bar traditional machine
learning approaches employing human engineered molecular features. We consider
four GNN variants -- Graph Convolutional Network (GCN), Graph Attention Network
(GAT), Message Passing Neural Network (MPNN), and Attentive Fingerprint
(AttentiveFP). So far deep learning models have been primarily benchmarked
using lower-bar traditional models solely based on fingerprints, while more
realistic benchmarks employing fingerprints, whole-molecule descriptors and
predictions from other related endpoints (e.g., LogD7.4) appear to be scarce
for industrial ADME datasets. In addition to time-split test sets based on
Genentech data, this study benefits from the availability of measurements from
an external chemical space (Roche data). We identify GAT as a promising
approach to implementing deep learning models. While all GNN models
significantly outperform lower-bar benchmark traditional models solely based on
fingerprints, only GATs seem to offer a small but consistent improvement over
higher-bar benchmark traditional models. Finally, the accuracy of in vitro
assays from different laboratories predicting the same experimental endpoints
appears to be comparable with the accuracy of GAT single-task models,
suggesting that most of the observed error from the models is a function of the
experimental error propagation.

Automated source code summarization is a task that generates summarized
information about the purpose, usage, and--or implementation of methods and
classes to support understanding of these code entities. Multiple approaches
and techniques have been proposed for supervised and unsupervised learning in
code summarization, however, they were mostly focused on generating a summary
for a piece of code. In addition, very few works have leveraged unofficial
documentation. This paper proposes an automatic and novel approach for
summarizing Android API methods discussed in Stack Overflow that we consider as
unofficial documentation in this research. Our approach takes the API method's
name as an input and generates a natural language summary based on Stack
Overflow discussions of that API method. We have conducted a survey that
involves 16 Android developers to evaluate the quality of our automatically
generated summaries and compare them with the official Android documentation.
Our results demonstrate that while developers find the official documentation
more useful in general, the generated summaries are also competitive, in
particular for offering implementation details, and can be used as a
complementary source for guiding developers in software development and
maintenance tasks.

Sparse Conditional Random Field (CRF) is a powerful technique in computer
vision and natural language processing for structured prediction. However,
solving sparse CRFs in large-scale applications remains challenging. In this
paper, we propose a novel safe dynamic screening method that exploits an
accurate dual optimum estimation to identify and remove the irrelevant features
during the training process. Thus, the problem size can be reduced
continuously, leading to great savings in the computational cost without
sacrificing any accuracy on the finally learned model. To the best of our
knowledge, this is the first screening method which introduces the dual optimum
estimation technique -- by carefully exploring and exploiting the strong
convexity and the complex structure of the dual problem -- in static screening
methods to dynamic screening. In this way, we can absorb the advantages of both
the static and dynamic screening methods and avoid their drawbacks. Our
estimation would be much more accurate than those developed based on the
duality gap, which contributes to a much stronger screening rule. Moreover, our
method is also the first screening method in sparse CRFs and even structure
prediction models. Experimental results on both synthetic and real-world
datasets demonstrate that the speedup gained by our method is significant.

Dense Retrieval (DR) reaches state-of-the-art results in first-stage
retrieval, but little is known about the mechanisms that contribute to its
success. Therefore, in this work, we conduct an interpretation study of
recently proposed DR models. Specifically, we first discretize the embeddings
output by the document and query encoders. Based on the discrete
representations, we analyze the attribution of input tokens. Both qualitative
and quantitative experiments are carried out on public test collections.
Results suggest that DR models pay attention to different aspects of input and
extract various high-level topic representations. Therefore, we can regard the
representations learned by DR models as a mixture of high-level topics.

Magnesium hydride (MgH$_2$) has been widely studied for effective hydrogen
storage. However, its bulk desorption temperature (553 K) is deemed too high
for practical applications. Besides doping, a strategy to decrease such
reaction energy for releasing hydrogen is the use of MgH$_2$-based
nanoparticles (NPs). Here, we investigate first the thermodynamic properties of
Mg$_n$H$_{2n}$ NPs ($n<10$) from first-principles, in particular by assessing
the anharmonic effects on the enthalpy, entropy and thermal expansion by means
of the Stochastic Self Consistent Harmonic Approximation (SSCHA). The latter
method goes beyond previous approaches, typically based on molecular mechanics
and the quasi-harmonic approximation, allowing the ab initio calculation of the
fully-anharmonic free energy. We find an almost linear dependence on
temperature of the interatomic bond lengths - with a relative variation of few
percent over 300K -, alongside with a bond distance decrease of the Mg-H bonds.
In order to increase the size of NPs toward experiments of hydrogen desorption
from MgH$_2$ we devise a computationally effective Machine Learning model
trained to accurately determine the forces and total energies (i.e. the
potential energy surfaces), integrating the latter with the SSCHA model to
fully include the anharmonic effects. We find a significative decrease of the
H-desorption temperature for sub-nanometric clusters Mg$_n$H$_{2n}$ with $n
\leq 10$, with a non-negligible, although little effect due to anharmonicities
(up to 10%).

Filling missing data in cloud-covered areas of satellite imaging is an
important task to improve data quantity and quality for enhanced earth
observation. Traditional cloud filling studies focused on continuous numerical
data such as temperature and cyanobacterial concentration in the open ocean.
Cloud data filling issues in coastal imaging is far less studied because of the
complex landscape. Inspired by the success of data imputation methods in
recommender systems that are designed for online shopping, the present study
explored their application to satellite cloud data filling tasks. A numerical
experiment was designed and conducted for a LandSat dataset with a range of
synthetic cloud covers to examine the performance of different data filling
schemes. The recommender system-inspired matrix factorization algorithm called
Funk-SVD showed superior performance in computational accuracy and efficiency
for the task of recovering landscape types in a complex coastal area than the
traditional data filling scheme of DINEOF (Data Interpolating Empirical
Orthogonal Functions) and the deep learning method of Datawig. The new method
achieved the best filling accuracy and reached a speed comparable to DINEOF and
much faster than deep learning. A theoretical framework was created to analyze
the error propagation in DINEOF and found the algorithm needs to be modified to
converge to the ground truth. The present study showed that Funk-SVD has great
potential to enhance cloud data filling performance and connects the fields of
recommender systems and cloud filling to promote the improvement and sharing of
useful algorithms.

Existing disentangled-based methods for generalizable person
re-identification aim at directly disentangling person representations into
domain-relevant interference and identity-relevant feature. However, they
ignore that some crucial characteristics are stubbornly entwined in both the
domain-relevant interference and identity-relevant feature, which are
intractable to decompose in an unsupervised manner. In this paper, we propose a
simple yet effective Calibrated Feature Decomposition (CFD) module that focuses
on improving the generalization capacity for person re-identification through a
more judicious feature decomposition and reinforcement strategy. Specifically,
a calibrated-and-standardized Batch normalization (CSBN) is designed to learn
calibrated person representation by jointly exploring intra-domain calibration
and inter-domain standardization of multi-source domain features. CSBN
restricts instance-level inconsistency of feature distribution for each domain
and captures intrinsic domain-level specific statistics. The calibrated person
representation is subtly decomposed into the identity-relevant feature, domain
feature, and the remaining entangled one. For enhancing the generalization
ability and ensuring high discrimination of the identity-relevant feature, a
calibrated instance normalization (CIN) is introduced to enforce discriminative
id-relevant information, and filter out id-irrelevant information, and
meanwhile the rich complementary clues from the remaining entangled feature are
further employed to strengthen it. Extensive experiments demonstrate the strong
generalization capability of our framework. Our models empowered by CFD modules
significantly outperform the state-of-the-art domain generalization approaches
on multiple widely-used benchmarks. Code will be made public:
https://github.com/zkcys001/CFD.

Many assumptions in the federated learning literature present a best-case
scenario that can not be satisfied in most real-world applications. An
asynchronous setting reflects the realistic environment in which federated
learning methods must be able to operate reliably. Besides varying amounts of
non-IID data at participants, the asynchronous setting models heterogeneous
client participation due to available computational power and battery
constraints and also accounts for delayed communications between clients and
the server. To reduce the communication overhead associated with asynchronous
online federated learning (ASO-Fed), we use the principles of
partial-sharing-based communication. In this manner, we reduce the
communication load of the participants and, therefore, render participation in
the learning task more accessible. We prove the convergence of the proposed
ASO-Fed and provide simulations to analyze its behavior further. The
simulations reveal that, in the asynchronous setting, it is possible to achieve
the same convergence as the federated stochastic gradient (Online-FedSGD) while
reducing the communication tenfold.

Contrastive learning has achieved remarkable success on various high-level
tasks, but there are fewer methods proposed for low-level tasks. It is
challenging to adopt vanilla contrastive learning technologies proposed for
high-level visual tasks straight to low-level visual tasks since the acquired
global visual representations are insufficient for low-level tasks requiring
rich texture and context information. In this paper, we propose a novel
contrastive learning framework for single image super-resolution (SISR). We
investigate the contrastive learning-based SISR from two perspectives: sample
construction and feature embedding. The existing methods propose some naive
sample construction approaches (e.g., considering the low-quality input as a
negative sample and the ground truth as a positive sample) and they adopt a
prior model (e.g., pre-trained VGG model) to obtain the feature embedding
instead of exploring a task-friendly one. To this end, we propose a practical
contrastive learning framework for SISR that involves the generation of many
informative positive and hard negative samples in frequency space. Instead of
utilizing an additional pre-trained network, we design a simple but effective
embedding network inherited from the discriminator network and can be
iteratively optimized with the primary SR network making it task-generalizable.
Finally, we conduct an extensive experimental evaluation of our method compared
with benchmark methods and show remarkable gains of up to 0.21 dB over the
current state-of-the-art approaches for SISR.

To solve the ill-posed problem of hyperspectral image super-resolution
(HSISR), an usually method is to use the prior information of the hyperspectral
images (HSIs) as a regularization term to constrain the objective function.
Model-based methods using hand-crafted priors cannot fully characterize the
properties of HSIs. Learning-based methods usually use a convolutional neural
network (CNN) to learn the implicit priors of HSIs. However, the learning
ability of CNN is limited, it only considers the spatial characteristics of the
HSIs and ignores the spectral characteristics, and convolution is not effective
for long-range dependency modeling. There is still a lot of room for
improvement. In this paper, we propose a novel HSISR method that uses
Transformer instead of CNN to learn the prior of HSIs. Specifically, we first
use the proximal gradient algorithm to solve the HSISR model, and then use an
unfolding network to simulate the iterative solution processes. The
self-attention layer of Transformer makes it have the ability of spatial global
interaction. In addition, we add 3D-CNN behind the Transformer layers to better
explore the spatio-spectral correlation of HSIs. Both quantitative and visual
results on two widely used HSI datasets and the real-world dataset demonstrate
that the proposed method achieves a considerable gain compared to all the
mainstream algorithms including the most competitive conventional methods and
the recently proposed deep learning-based methods.

In this work we propose a clustering framework based on the paradigm of
transform learning. In simple terms the representation from transform learning
is used for K-means clustering; however, the problem is not solved in such a
na\"ive piecemeal fashion. The K-means clustering loss is embedded into the
transform learning framework and the joint problem is solved using the
alternating direction method of multipliers. Results on document clustering
show that our proposed approach improves over the state-of-the-art.

Subspace clustering techniques have shown promise in hyperspectral image
segmentation. The fundamental assumption in subspace clustering is that the
samples belonging to different clusters/segments lie in separable subspaces.
What if this condition does not hold? We surmise that even if the condition
does not hold in the original space, the data may be nonlinearly transformed to
a space where it will be separable into subspaces. In this work, we propose a
transformation based on the tenets of deep dictionary learning (DDL). In
particular, we incorporate the sparse subspace clustering (SSC) loss in the DDL
formulation. Here DDL nonlinearly transforms the data such that the transformed
representation (of the data) is separable into subspaces. We show that the
proposed formulation improves over the state-of-the-art deep learning
techniques in hyperspectral image clustering.

This article is intended for physical scientists who wish to gain deeper
insights into machine learning algorithms which we present via the domain they
know best, physics. We begin with a review of two energy-based machine learning
algorithms, Hopfield networks and Boltzmann machines, and their connection to
the Ising model. This serves as a foundation to understand the phenomenon of
learning more generally. Equipped with this intuition we then delve into
additional, more "practical," machine learning architectures including
feedforward neural networks, convolutional neural networks, and autoencoders.
We also provide code that explicitly demonstrates training a neural network
with gradient descent.

Common cross-validation (CV) methods like k-fold cross-validation or
Monte-Carlo cross-validation estimate the predictive performance of a learner
by repeatedly training it on a large portion of the given data and testing on
the remaining data. These techniques have two major drawbacks. First, they can
be unnecessarily slow on large datasets. Second, beyond an estimation of the
final performance, they give almost no insights into the learning process of
the validated algorithm. In this paper, we present a new approach for
validation based on learning curves (LCCV). Instead of creating train-test
splits with a large portion of training data, LCCV iteratively increases the
number of instances used for training. In the context of model selection, it
discards models that are very unlikely to become competitive. We run a large
scale experiment on the 67 datasets from the AutoML benchmark and empirically
show that in over 90% of the cases using LCCV leads to similar performance (at
most 1.5% difference) as using 5/10-fold CV. However, it yields substantial
runtime reductions of over 20% on average. Additionally, it provides important
insights, which for example allow assessing the benefits of acquiring more
data. These results are orthogonal to other advances in the field of AutoML.

Data-driven character animation techniques rely on the existence of a
properly established model of motion, capable of describing its rich context.
However, commonly used motion representations often fail to accurately encode
the full articulation of motion, or present artifacts. In this work, we address
the fundamental problem of finding a robust pose representation for motion
modeling, suitable for deep character animation, one that can better constrain
poses and faithfully capture nuances correlated with skeletal characteristics.
Our representation is based on dual quaternions, the mathematical abstractions
with well-defined operations, which simultaneously encode rotational and
positional orientation, enabling a hierarchy-aware encoding, centered around
the root. We demonstrate that our representation overcomes common motion
artifacts, and assess its performance compared to other popular
representations. We conduct an ablation study to evaluate the impact of various
losses that can be incorporated during learning. Leveraging the fact that our
representation implicitly encodes skeletal motion attributes, we train a
network on a dataset comprising of skeletons with different proportions,
without the need to retarget them first to a universal skeleton, which causes
subtle motion elements to be missed. We show that smooth and natural poses can
be achieved, paving the way for fascinating applications.

Normalization like Batch Normalization (BN) is a milestone technique to
normalize the distributions of intermediate layers in deep learning, enabling
faster training and better generalization accuracy. However, in fidelity image
Super-Resolution (SR), it is believed that normalization layers get rid of
range flexibility by normalizing the features and they are simply removed from
modern SR networks. In this paper, we study this phenomenon quantitatively and
qualitatively. We found that the standard deviation of the residual feature
shrinks a lot after normalization layers, which causes the performance
degradation in SR networks. Standard deviation reflects the amount of variation
of pixel values. When the variation becomes smaller, the edges will become less
discriminative for the network to resolve. To address this problem, we propose
an Adaptive Deviation Modulator (AdaDM), in which a modulation factor is
adaptively predicted to amplify the pixel deviation. For better generalization
performance, we apply BN in state-of-the-art SR networks with the proposed
AdaDM. Meanwhile, the deviation amplification strategy in AdaDM makes the edge
information in the feature more distinguishable. As a consequence, SR networks
with BN and our AdaDM can get substantial performance improvements on benchmark
datasets. Extensive experiments have been conducted to show the effectiveness

Human-centered systems of systems such as social networks, Internet of
Things, or healthcare systems are growingly becoming major facets of modern
life. Realistic models of human behavior in such systems play a significant
role in their accurate modeling and prediction. Yet, human behavior under
uncertainty often violates the predictions by the conventional probabilistic
models. Recently, quantum-like decision theories have shown a considerable
potential to explain the contradictions in human behavior by applying quantum
probability. But providing a quantum-like decision theory that could predict,
rather than describe the current, state of human behavior is still one of the
unsolved challenges. The main novelty of our approach is introducing an
entangled Bayesian network inspired by the entanglement concept in quantum
information theory, in which each human is a part of the entire society.
Accordingly, society's effect on the dynamic evolution of the decision-making
process, which is less often considered in decision theories, is modeled by the
entanglement measures. The proposed predictive entangled quantum-like Bayesian
network (PEQBN) is evaluated on 22 experimental tasks. Results confirm that
PEQBN provides more realistic predictions of human decisions under uncertainty,
when compared with classical Bayesian networks and three recent quantum-like

We propose a methodology to sample from time-integrated stochastic bridges,
namely random variables defined as $\int_{t_1}^{t_2} f(Y(t))dt$ conditioned on
$Y(t_1)\!=\!a$ and $Y(t_2)\!=\!b$, with $a,b\in R$. The Stochastic Collocation
Monte Carlo sampler and the Seven-League scheme are applied for this purpose.
Notably, the distribution of the time-integrated bridge is approximated
utilizing a polynomial chaos expansion built on a suitable set of stochastic
collocation points. Furthermore, artificial neural networks are employed to
learn the collocation points. The result is a robust, data-driven procedure for
the Monte Carlo sampling from conditional time-integrated processes, which
guarantees high accuracy and generates thousands of samples in milliseconds.
Applications, with a focus on finance, are presented here as well.

Differential privacy (DP) is an essential technique for privacy-preserving.
It was found that a large model trained for privacy preserving performs worse
than a smaller model (e.g. ResNet50 performs worse than ResNet18). To better
understand this phenomenon, we study high dimensional DP learning from the
viewpoint of generalization. Theoretically, we show that for the simple
Gaussian model with even small DP noise, if the dimension is large enough, then
the classification error can be as bad as the random guessing. Then we propose
a feature selection method to reduce the size of the model, based on a new
metric which trades off the classification accuracy and privacy preserving.
Experiments on real data support our theoretical results and demonstrate the
advantage of the proposed method.

Person search in media has seen increasing potential in Internet
applications, such as video clipping and character collection. This task is
common but overlooked by previous person search works which focus on
surveillance scenes. The media scenarios have some different challenges from
surveillance scenes. For example, a person may change his clothes frequently.
To alleviate this issue, this paper proposes a Unified Detector and Graph
Network (UDGNet) for person search in media. UDGNet is the first person search
framework to detect and re-identify the human body and head simultaneously.
Specifically, it first builds two branches based on a unified network to detect
the human body and head, then the detected body and head are used for
re-identification. This dual-task approach can significantly enhance
discriminative learning. To tackle the cloth-changing issue, UDGNet builds two
graphs to explore reliable links among cloth-changing samples and utilizes a
graph network to learn better embeddings. This design effectively enhances the
robustness of person search to cloth-changing challenges. Besides, we
demonstrate that UDGNet can be implemented with both anchor-based and
anchor-free person search frameworks and further achieve performance
improvement. This paper also contributes a large-scale dataset for Person
Search in Media (PSM), which provides both body and head annotations. It is by
far the largest dataset for person search in media. Experiments show that
UDGNet improves the anchor-free model AlignPS by 12.1% in mAP. Meanwhile, it
shows good generalization across surveillance and longterm scenarios. The
dataset and code will be available at: https://github.com/shuxjweb/PSM.git.

We propose a new method for testing antenna arrays that records the radiating
electromagnetic (EM) field using an absorbing material and evaluating the
resulting thermal image series through an AI using a conditional
encoder-decoder model. Given the power and phase of the signals fed into each
array element, we are able to reconstruct normal sequences through our trained
model and compare it to the real sequences observed by a thermal camera. These
thermograms only contain low-level patterns such as blobs of various shapes. A
contour-based anomaly detector can then map the reconstruction error matrix to
an anomaly score to identify faulty antenna arrays and increase the
classification F-measure (F-M) by up to 46%. We show our approach on the time
series thermograms collected by our antenna testing system. Conventionally, a
variational autoencoder (VAE) learning observation noise may yield better
results than a VAE with a constant noise assumption. However, we demonstrate
that this is not the case for anomaly detection on such low-level patterns for
two reasons. First, the baseline metric reconstruction probability, which
incorporates the learned observation noise, fails to differentiate anomalous
patterns. Second, the area under the receiver operating characteristic (ROC)
curve of a VAE with a lower observation noise assumption achieves 11.83% higher
than that of a VAE with learned noise.

Device mobility in dense Wi-Fi networks offers several challenges. Two
well-known problems related to device mobility are handover prediction and
access point selection. Due to the complex nature of the radio environment,
analytical models may not characterize the wireless channel, which makes the
solution of these problems very difficult. Recently, cognitive network
architectures using sophisticated learning techniques are increasingly being
applied to such problems. In this paper, we propose data-driven machine
learning (ML) schemes to efficiently solve these problems in wireless LAN
(WLAN) networks. The proposed schemes are evaluated and results are compared
with traditional approaches to the aforementioned problems. The results report
significant improvement in network performance by applying the proposed
schemes. The proposed scheme for handover prediction outperforms traditional
methods i.e. received signal strength method and traveling distance method by
reducing the number of unnecessary handovers by 60% and 50% respectively.
Similarly, in AP selection, the proposed scheme outperforms the strongest
signal first and least loaded first algorithms by achieving higher throughput
gains up to 9.2% and 8% respectively.

Square-root Lasso problems are proven robust regression problems.
Furthermore, square-root regression problems with structured sparsity also
plays an important role in statistics and machine learning. In this paper, we
focus on the numerical computation of large-scale linearly constrained sparse
group square-root Lasso problems. In order to overcome the difficulty that
there are two nonsmooth terms in the objective function, we propose a dual
semismooth Newton (SSN) based augmented Lagrangian method (ALM) for it. That
is, we apply the ALM to the dual problem with the subproblem solved by the SSN
method. To apply the SSN method, the positive definiteness of the generalized
Jacobian is very important. Hence we characterize the equivalence of its
positive definiteness and the constraint nondegeneracy condition of the
corresponding primal problem. In numerical implementation, we fully employ the
second order sparsity so that the Newton direction can be efficiently obtained.
Numerical experiments demonstrate the efficiency of the proposed algorithm.

We consider straggler-resilient learning. In many previous works, e.g., in
the coded computing literature, straggling is modeled as random delays that are
independent and identically distributed between workers. However, in many
practical scenarios, a given worker may straggle over an extended period of
time. We propose a latency model that captures this behavior and is
substantiated by traces collected on Microsoft Azure, Amazon Web Services
(AWS), and a small local cluster. Building on this model, we propose DSAG, a
mixed synchronous-asynchronous iterative optimization method, based on the
stochastic average gradient (SAG) method, that combines timely and stale
results. We also propose a dynamic load-balancing strategy to further reduce
the impact of straggling workers. We evaluate DSAG for principal component
analysis, cast as a finite-sum optimization problem, of a large genomics
dataset, and for logistic regression on a cluster composed of 100 workers on
AWS, and find that DSAG is up to about 50% faster than SAG, and more than twice
as fast as coded computing methods, for the particular scenario that we
consider.

Most existing methods usually formulate the non-blind deconvolution problem
into a maximum-a-posteriori framework and address it by manually designing
kinds of regularization terms and data terms of the latent clear images.
However, explicitly designing these two terms is quite challenging and usually
leads to complex optimization problems which are difficult to solve. In this
paper, we propose an effective non-blind deconvolution approach by learning
discriminative shrinkage functions to implicitly model these terms. In contrast
to most existing methods that use deep convolutional neural networks (CNNs) or
radial basis functions to simply learn the regularization term, we formulate
both the data term and regularization term and split the deconvolution model
into data-related and regularization-related sub-problems according to the
alternating direction method of multipliers. We explore the properties of the
Maxout function and develop a deep CNN model with a Maxout layer to learn
discriminative shrinkage functions to directly approximate the solutions of
these two sub-problems. Moreover, given the fast-Fourier-transform-based image
restoration usually leads to ringing artifacts while conjugate-gradient-based
approach is time-consuming, we develop the Conjugate Gradient Network to
restore the latent clear images effectively and efficiently. Experimental
results show that the proposed method performs favorably against the
state-of-the-art ones in terms of efficiency and accuracy.

Cooperation in settings where agents have both common and conflicting
interests (mixed-motive environments) has recently received considerable
attention in multi-agent learning. However, the mixed-motive environments
typically studied have a single cooperative outcome on which all agents can
agree. Many real-world multi-agent environments are instead bargaining problems
(BPs): they have several Pareto-optimal payoff profiles over which agents have
conflicting preferences. We argue that typical cooperation-inducing learning
algorithms fail to cooperate in BPs when there is room for normative
disagreement resulting in the existence of multiple competing cooperative
equilibria, and illustrate this problem empirically. To remedy the issue, we
introduce the notion of norm-adaptive policies. Norm-adaptive policies are
capable of behaving according to different norms in different circumstances,
creating opportunities for resolving normative disagreement. We develop a class
of norm-adaptive policies and show in experiments that these significantly
increase cooperation. However, norm-adaptiveness cannot address residual
bargaining failure arising from a fundamental tradeoff between exploitability
and cooperative robustness.

In the task of Chinese named entity recognition based on deep learning,
activation function plays an irreplaceable role, it introduces nonlinear
characteristics into neural network, so that the fitted model can be applied to
various tasks. However, the information density of industrial safety analysis
text is relatively high, and the correlation and similarity between the
information are large, which is easy to cause the problem of high deviation and
high standard deviation of the model, no specific activation function has been
designed in previous studies, and the traditional activation function has the
problems of gradient vanishing and negative region, which also lead to the
recognition accuracy of the model can not be further improved. To solve these
problems, a novel activation function AIS is proposed in this paper. AIS is an
activation function applied in industrial safety engineering, which is composed
of two piecewise nonlinear functions. In the positive region, the structure
combining exponential function and quadratic function is used to alleviate the
problem of deviation and standard deviation, and the linear function is added
to modify it, which makes the whole activation function smoother and overcomes
the problem of gradient vanishing. In the negative region, the cubic function
structure is used to solve the negative region problem and accelerate the
convergence of the model. Based on the deep learning model of BERT-BiLSTM-CRF,
the performance of AIS is evaluated. The results show that, compared with other
activation functions, AIS overcomes the problems of gradient vanishing and
negative region, reduces the deviation of the model, speeds up the model
fitting, and improves the extraction ability of the model for industrial
entities.

Deep learning oriented named entity recognition (DNER) has gradually become
the paradigm of knowledge discovery, which greatly promotes domain
intelligence. However, the current activation function of DNER fails to treat
gradient vanishing, no negative output or non-differentiable existence, which
may impede knowledge exploration caused by the omission and incomplete
representation of latent semantics. To break through the dilemma, we present a
novel activation function termed KDAC. Detailly, KDAC is an aggregation
function with multiple conversion modes. The backbone of the activation region
is the interaction between exponent and linearity, and the both ends extend
through adaptive linear divergence, which surmounts the obstacle of gradient
vanishing and no negative output. Crucially, the non-differentiable points are
alerted and eliminated by an approximate smoothing algorithm. KDAC has a series
of brilliant properties, including nonlinear, stable near-linear transformation
and derivative, as well as dynamic style, etc. We perform experiments based on
BERT-BiLSTM-CNN-CRF model on six benchmark datasets containing different domain
knowledge, such as Weibo, Clinical, E-commerce, Resume, HAZOP and People's
daily. The evaluation results show that KDAC is advanced and effective, and can
provide more generalized activation to stimulate the performance of DNER. We
hope that KDAC can be exploited as a promising activation function to devote
itself to the construction of knowledge.

Hazard and operability analysis (HAZOP) is a remarkable representative in
industrial safety engineering. However, a great storehouse of industrial safety
knowledge (ISK) in HAZOP reports has not been thoroughly exploited. In order to
reuse and unlock the value of ISK and optimize HAZOP, we have developed a novel
knowledge graph for industrial safety (ISKG) with HAZOP as the carrier through
bridging data science and engineering design. Specifically, firstly,
considering that the knowledge contained in HAZOP reports of different
processes in industry is not the same, we creatively develope a general ISK
standardization framework, it provides a practical scheme for integrating HAZOP
reports from various processes and uniformly representing the ISK with diverse
expressions. Secondly, we conceive a novel and reliable information extraction
model based on deep learning combined with data science, it can effectively
mine ISK from HAZOP reports, which alleviates the obstacle of ISK extraction
caused by the particularity of HAZOP text. Finally, we build ISK triples and
store them in the Neo4j graph database. We take indirect coal liquefaction
process as a case study to develop ISKG, and its oriented applications can
optimize HAZOP and mine the potential of ISK, which is of great significance to
improve the security of the system and enhance prevention awareness for people.
ISKG containing the ISK standardization framework and the information
extraction model sets an example of the interaction between data science and
engineering design, which can enlighten other researchers and extend the
perspectives of industrial safety.

The core of information retrieval (IR) is to identify relevant information
from large-scale resources and return it as a ranked list to respond to the
user's information need. In recent years, the resurgence of deep learning has
greatly advanced this field and leads to a hot topic named NeuIR (i.e., neural
information retrieval), especially the paradigm of pre-training methods (PTMs).
Owing to sophisticated pre-training objectives and huge model size, pre-trained
models can learn universal language representations from massive textual data,
which are beneficial to the ranking task of IR. Recently, a large number of
works, which are dedicated to the application of PTMs in IR, have been
introduced to promote the retrieval performance. Considering the rapid progress
of this direction, this survey aims to provide a systematic review of
pre-training methods in IR. To be specific, we present an overview of PTMs
applied in different components of an IR system, including the retrieval
component, the re-ranking component, and other components. In addition, we also
introduce PTMs specifically designed for IR, and summarize available datasets
as well as benchmark leaderboards. Moreover, we discuss some open challenges
and highlight several promising directions, with the hope of inspiring and
facilitating more works on these topics for future research.

We address end-to-end learned video compression with a special focus on
better learning and utilizing temporal contexts. For temporal context mining,
we propose to store not only the previously reconstructed frames, but also the
propagated features into the generalized decoded picture buffer. From the
stored propagated features, we propose to learn multi-scale temporal contexts,
and re-fill the learned temporal contexts into the modules of our compression
scheme, including the contextual encoder-decoder, the frame generator, and the
temporal context encoder. Our scheme discards the parallelization-unfriendly
auto-regressive entropy model to pursue a more practical decoding time. We
compare our scheme with x264 and x265 (representing industrial software for
H.264 and H.265, respectively) as well as the official reference software for
H.264, H.265, and H.266 (JM, HM, and VTM, respectively). When intra period is
32 and oriented to PSNR, our scheme outperforms H.265--HM by 14.4% bit rate
saving; when oriented to MS-SSIM, our scheme outperforms H.266--VTM by 21.1%
bit rate saving.

Adversarial attacks provide a good way to study the robustness of deep
learning models. One category of methods in transfer-based black-box attack
utilizes several image transformation operations to improve the transferability
of adversarial examples, which is effective, but fails to take the specific
characteristic of the input image into consideration. In this work, we propose
a novel architecture, called Adaptive Image Transformation Learner (AITL),
which incorporates different image transformation operations into a unified
framework to further improve the transferability of adversarial examples.
Unlike the fixed combinational transformations used in existing works, our
elaborately designed transformation learner adaptively selects the most
effective combination of image transformations specific to the input image.
Extensive experiments on ImageNet demonstrate that our method significantly
improves the attack success rates on both normally trained models and defense
models under various settings.

In recent years, the security of deep learning models achieves more and more
attentions with the rapid development of neural networks, which are vulnerable
to adversarial examples. Almost all existing gradient-based attack methods use
the sign function in the generation to meet the requirement of perturbation
budget on $L_\infty$ norm. However, we find that the sign function may be
improper for generating adversarial examples since it modifies the exact
gradient direction. We propose to remove the sign function and directly utilize
the exact gradient direction with a scaling factor for generating adversarial
perturbations, which improves the attack success rates of adversarial examples
even with fewer perturbations. Moreover, considering that the best scaling
factor varies across different images, we propose an adaptive scaling factor
generator to seek an appropriate scaling factor for each image, which avoids
the computational cost for manually searching the scaling factor. Our method
can be integrated with almost all existing gradient-based attack methods to
further improve the attack success rates. Extensive experiments on the CIFAR10
and ImageNet datasets show that our method exhibits higher transferability and

A fundamental challenge for machine learning models is generalizing to
out-of-distribution (OOD) data, in part due to spurious correlations. To tackle
this challenge, we first formalize the OOD generalization problem as
constrained optimization, called Disentanglement-constrained Domain
Generalization (DDG). We relax this non-trivial constrained optimization
problem to a tractable form with finite-dimensional parameterization and
empirical approximation. Then a theoretical analysis of the extent to which the
above transformations deviates from the original problem is provided. Based on
the transformation, we propose a primal-dual algorithm for joint representation
disentanglement and domain generalization. In contrast to traditional
approaches based on domain adversarial training and domain labels, DDG jointly
learns semantic and variation encoders for disentanglement, enabling flexible
manipulation and augmentation on training data. DDG aims to learn intrinsic
representations of semantic concepts that are invariant to nuisance factors and
generalizable across domains. Comprehensive experiments on popular benchmarks
show that DDG can achieve competitive OOD performance and uncover interpretable
salient structures within data.

Large-scale data analysis is growing at an exponential rate as data
proliferates in our societies. This abundance of data has the advantage of
allowing the decision-maker to implement complex models in scenarios that were
prohibitive before. At the same time, such an amount of data requires a
distributed thinking approach. In fact, Deep Learning models require plenty of
resources, and distributed training is needed. This paper presents a
Multicriteria approach for distributed learning. Our approach uses the Weighted
Goal Programming approach in its Chebyshev formulation to build an ensemble of
decision rules that optimize aprioristically defined performance metrics. Such
a formulation is beneficial because it is both model and metric agnostic and
provides an interpretable output for the decision-maker. We test our approach
by showing a practical application in electricity demand forecasting. Our
results suggest that when we allow for dataset split overlapping, the
performances of our methodology are consistently above the baseline model
trained on the whole dataset.

Incorporating personas information allows diverse and engaging responses in
dialogue response generation. Unfortunately, prior works have primarily focused
on self personas and have overlooked the value of partner personas. Moreover,
in practical applications, the availability of ground truth partner personas is
often not the case. This paper attempts to tackle these issues by offering a
novel framework that leverages automatic partner personas generation to enhance
the succeeding dialogue generation. We incorporate reinforcement learning with
a dedicatedly designed critic network for reward judgement. Experimental
results from both automatic and human evaluation demonstrate a) Our framework
is capable of generating relevant, informative and coherent partner personas,
even compared to the ground truth partner personas. b) Generated partner
personas enhance the succeeding response generation, thus surpassing our
baselines and comparison model when partner personas are missing during the
inference stage. c) Our framework generates responses that are more informative
and engaging than our baseline conditioned on the ground truth partner personas
during inference. d) Our dedicatedly designed critic network reinforces our
framework effectively. Finally, our framework gives better explainability and
reduces the demands for external databases for partner personas.

The periodic pulsations of stars teach us about their underlying physical
process. We present a convolutional autoencoder-based pipeline as an automatic
approach to search for out-of-distribution anomalous periodic variables within
The Zwicky Transient Facility Catalog of Periodic Variable Stars (ZTF CPVS). We
use an isolation forest to rank each periodic variable by its anomaly score.
Our overall most anomalous events have a unique physical origin: they are
mostly highly variable and irregular evolved stars. Multiwavelength data
suggest that they are most likely Red Giant or Asymptotic Giant Branch stars
concentrated in the Milky Way galactic disk. Furthermore, we show how the
learned latent features can be used for the classification of periodic
variables through a hierarchical random forest. This novel semi-supervised
approach allows astronomers to identify the most anomalous events within a
given physical class, significantly increasing the potential for scientific

Domain adaptation (DA) benefits from the rigorous theoretical works that
study its insightful characteristics and various aspects, e.g., learning
domain-invariant representations and its trade-off. However, it seems not the
case for the multiple source DA and domain generalization (DG) settings which
are remarkably more complicated and sophisticated due to the involvement of
multiple source domains and potential unavailability of target domain during
training. In this paper, we develop novel upper-bounds for the target general
loss which appeal to us to define two kinds of domain-invariant
representations. We further study the pros and cons as well as the trade-offs
of enforcing learning each domain-invariant representation. Finally, we conduct
experiments to inspect the trade-off of these representations for offering
practical hints regarding how to use them in practice and explore other
interesting properties of our developed theory.

Intensity diffraction tomography (IDT) refers to a class of optical
microscopy techniques for imaging the 3D refractive index (RI) distribution of
a sample from a set of 2D intensity-only measurements. The reconstruction of
artifact-free RI maps is a fundamental challenge in IDT due to the loss of
phase information and the missing cone problem. Neural fields (NF) has recently
emerged as a new deep learning (DL) approach for learning continuous
representations of physical fields. NF uses a coordinate-based neural network
to represent the field by mapping the spatial coordinates to the corresponding
physical quantities, in our case the complex-valued refractive index values. We
present DeCAF as the first NF-based IDT method that can learn a high-quality
continuous representation of a RI volume from its intensity-only and
limited-angle measurements. The representation in DeCAF is learned directly
from the measurements of the test sample by using the IDT forward model,
without any ground-truth RI maps. We qualitatively and quantitatively evaluate
DeCAF on the simulated and experimental biological samples. Our results show
that DeCAF can generate high-contrast and artifact-free RI maps and lead to up
to 2.1 times reduction in MSE over existing methods.

Various topological techniques and tools have been applied to neural networks
in terms of network complexity, explainability, and performance. One
fundamental assumption of this line of research is the existence of a global
(Euclidean) coordinate system upon which the topological layer is constructed.
Despite promising results, such a \textit{topologization} method has yet to be
widely adopted because the parametrization of a topologization layer takes a
considerable amount of time and more importantly, lacks a theoretical
foundation without which the performance of the neural network only achieves
suboptimal performance. This paper proposes a learnable topological layer for
neural networks without requiring a Euclidean space; Instead, the proposed
construction requires nothing more than a general metric space except for an
inner product, i.e., a Hilbert space. Accordingly, the according
parametrization for the proposed topological layer is free of user-specified
hyperparameters, which precludes the costly parametrization stage and the
corresponding possibility of suboptimal networks.

Intelligent service robots require the ability to perform a variety of tasks
in dynamic environments. Despite the significant progress in robotic grasping,
it is still a challenge for robots to decide grasping position when given
different tasks in unstructured real life environments. In order to overcome
this challenge, creating a proper knowledge representation framework is the
key. Unlike the previous work, in this paper, task is defined as a triplet
including grasping tool, desired action and target object. Our proposed
algorithm GATER (Grasp--Action--Target Embeddings and Relations) models the
relationship among grasping tools--action--target objects in embedding space.
To validate our method, a novel dataset is created for task-specific grasping.
GATER is trained on the new dataset and achieve task-specific grasping
inference with 94.6\% success rate. Finally, the effectiveness of GATER
algorithm is tested on a real service robot platform. GATER algorithm has its
potential in human behavior prediction and human-robot interaction.

Video content classification is an important research content in computer
vision, which is widely used in many fields, such as image and video retrieval,
computer vision. This paper presents a model that is a combination of
Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) which
develops, trains, and optimizes a deep learning network that can identify the
type of video content and classify them into categories such as "Animation,
Gaming, natural content, flat content, etc". To enhance the performance of the
model novel keyframe extraction method is included to classify only the
keyframes, thereby reducing the overall processing time without sacrificing any
significant performance.

The stochastic differential equation (SDE)-based random process models of
volatile renewable energy sources (RESs) jointly capture the evolving
probability distribution and temporal correlation in continuous time. It has
enabled recent studies to remarkably improve the performance of power system
dynamic uncertainty quantification and optimization. However, considering the
non-homogeneous random process nature of PV, there still remains a challenging
question: how can a realistic and accurate SDE model for PV power be obtained
that reflects its weather-dependent uncertainty in online operation, especially
when high-resolution numerical weather prediction (NWP) is unavailable for many
distributed plants? To fill this gap, this article finds that an accurate SDE
model for PV power can be constructed by only using the cheap data from
low-resolution public weather reports. Specifically, an hourly parameterized
Jacobi diffusion process is constructed to recreate the temporal patterns of PV
volatility during a day. Its parameters are mapped from the public weather
report using an ensemble of extreme learning machines (ELMs) to reflect the
varying weather conditions. The SDE model jointly captures intraday and
intrahour volatility. Statistical examination based on real-world data
collected in Macau shows the proposed approach outperforms a selection of
state-of-the-art deep learning-based time-series forecast methods.

There are several reasons why the fear of vaccination has caused population
rejection. Questions have been raised by students regarding the effectiveness
of vaccines, which in turn has led to vaccination hesitancy. Students
perceptions are influenced by vaccination hesitancy, which affects the
acceptance of e-learning platforms. Hence, this research aimed to examine the
post-acceptance of e-learning platforms on the basis of a conceptual model that
employs different variables. Distinct contribution is made by every variable to
the post-acceptance of e-learning platforms. A hybrid model was used in the
current study in which technology acceptance model (TAM) determinants were
employed along with other external factors such as fear of vaccination,
perceived routine use, perceived enjoyment, perceived critical mass, and
self-efficiency which are directly linked to post-acceptance of e-learning
platforms. The focus of earlier studies on this topic has been on the
significance of e-learning acceptance in various environments and countries.
However, in this study, the newly-spread use of e-learning platforms in the
gulf area was examined using a hybrid conceptual model. The empirical studies
carried out in the past mainly used structural equation modelling (SEM)
analysis; however, this study used an evolving hybrid analysis approach, in
which SEM and the artificial neural network (ANN) that are based on deep
learning were employed. The importance-performance map analysis (IPMA) was also
used in this study to determine the significance and performance of each
factor. The proposed model is backed by the findings of data analysis.

Offline policy learning (OPL) leverages existing data collected a priori for
policy optimization without any active exploration. Despite the prevalence and
recent interest in this problem, its theoretical and algorithmic foundations in
function approximation settings remain under-developed. In this paper, we
consider this problem on the axes of distributional shift, optimization, and
generalization in offline contextual bandits with neural networks. In
particular, we propose a provably efficient offline contextual bandit with
neural network function approximation that does not require any functional
assumption on the reward. We show that our method provably generalizes over
unseen contexts under a milder condition for distributional shift than the
existing OPL works. Notably, unlike any other OPL method, our method learns
from the offline data in an online manner using stochastic gradient descent,
allowing us to leverage the benefits of online learning into an offline
setting. Moreover, we show that our method is more computationally efficient
and has a better dependence on the effective dimension of the neural network
than an online counterpart. Finally, we demonstrate the empirical effectiveness
of our method in a range of synthetic and real-world OPL problems.

Astronomical data is full of holes. While there are many reasons for this
missing data, the data can be randomly missing, caused by things like data
corruptions or unfavourable observing conditions. We test some simple data
imputation methods(Mean, Median, Minimum, Maximum and k-Nearest Neighbours
(kNN)), as well as two more complex methods (Multivariate Imputation by using
Chained Equation (MICE) and Generative Adversarial Imputation Network (GAIN))
against data where increasing amounts are randomly set to missing. We then use
the imputed datasets to estimate the redshift of the galaxies, using the kNN
and Random Forest ML techniques. We find that the MICE algorithm provides the
lowest Root Mean Square Error and consequently the lowest prediction error,
with the GAIN algorithm the next best.

The Fourier Neural Operator (FNO) is a learning-based method for efficiently
simulating partial differential equations. We propose the Factorized Fourier
Neural Operator (F-FNO) that allows much better generalization with deeper
networks. With a careful combination of the Fourier factorization, a shared
kernel integral operator across all layers, the Markov property, and residual
connections, F-FNOs achieve a six-fold reduction in error on the most turbulent
setting of the Navier-Stokes benchmark dataset. We show that our model
maintains an error rate of 2% while still running an order of magnitude faster
than a numerical solver, even when the problem setting is extended to include
additional contexts such as viscosity and time-varying forces. This enables the
same pretrained neural network to model vastly different conditions.

Deep neural networks have demonstrated superior performance in short-term
traffic forecasting. However, most existing traffic forecasting systems assume
that the training and testing data are drawn from the same underlying
distribution, which limits their practical applicability. The NeurIPS 2021
Traffic4cast challenge is the first of its kind dedicated to benchmarking the
robustness of traffic forecasting models towards domain shifts in space and
time. This technical report describes our solution to this challenge. In
particular, we present a multi-task learning framework for temporal and
spatio-temporal domain adaptation of traffic forecasting models. Experimental
results demonstrate that our multi-task learning approach achieves strong
empirical performance, outperforming a number of baseline domain adaptation
methods, while remaining highly efficient. The source code for this technical
report is available at https://github.com/YichaoLu/Traffic4cast2021.

Feature selection is an extensively studied technique in the machine learning
literature where the main objective is to identify the subset of features that
provides the highest predictive power. However, in causal inference, our goal
is to identify the set of variables that are associated with both the treatment
variable and outcome (i.e., the confounders). While controlling for the
confounding variables helps us to achieve an unbiased estimate of causal
effect, recent research shows that controlling for purely outcome predictors
along with the confounders can reduce the variance of the estimate. In this
paper, we propose an Outcome Adaptive Elastic-Net (OAENet) method specifically
designed for causal inference to select the confounders and outcome predictors
for inclusion in the propensity score model or in the matching mechanism.
OAENet provides two major advantages over existing methods: it performs
superiorly on correlated data, and it can be applied to any matching method and
any estimates. In addition, OAENet is computationally efficient compared to

With multiple components and relations, financial data are often presented as
graph data, since it could represent both the individual features and the
complicated relations. Due to the complexity and volatility of the financial
market, the graph constructed on the financial data is often heterogeneous or
time-varying, which imposes challenges on modeling technology. Among the graph
modeling technologies, graph neural network (GNN) models are able to handle the
complex graph structure and achieve great performance and thus could be used to
solve financial tasks. In this work, we provide a comprehensive review of GNN
models in recent financial context. We first categorize the commonly-used
financial graphs and summarize the feature processing step for each node. Then
we summarize the GNN methodology for each graph type, application in each area,
and propose some potential research areas.

One of the major challenges in training text-to-image generation models is
the need of a large number of high-quality image-text pairs. While image
samples are often easily accessible, the associated text descriptions typically
require careful human captioning, which is particularly time- and
cost-consuming. In this paper, we propose the first work to train text-to-image
generation models without any text data. Our method leverages the well-aligned
multi-modal semantic space of the powerful pre-trained CLIP model: the
requirement of text-conditioning is seamlessly alleviated via generating text
features from image features. Extensive experiments are conducted to illustrate
the effectiveness of the proposed method. We obtain state-of-the-art results in
the standard text-to-image generation tasks. Importantly, the proposed
language-free model outperforms most existing models trained with full
image-text pairs. Furthermore, our method can be applied in fine-tuning
pre-trained models, which saves both training time and cost in training
text-to-image generation models. Our pre-trained model obtains competitive
results in zero-shot text-to-image generation on the MS-COCO dataset, yet with
around only 1% of the model size and training data size relative to the
recently proposed large DALL-E model.

We introduce the concept of structure-regions and their impact on student
experience in large-enrollment frosh courses. These regions involve three major
components: active learning, low-stakes assignments, and formative assessment.
We analyze performance data and show that high-structure courses have a
positive impact on class performance by decreasing the performance gap
throughout the duration of a course.

We outline emerging opportunities and challenges to enhance the utility of AI
for scientific discovery. The distinct goals of AI for industry versus the
goals of AI for science create tension between identifying patterns in data
versus discovering patterns in the world from data. If we address the
fundamental challenges associated with "bridging the gap" between domain-driven
scientific models and data-driven AI learning machines, then we expect that
these AI models can transform hypothesis generation, scientific discovery, and
the scientific process itself.

With the rapid development of high-throughput experimental technologies,
different types of omics (e.g., genomics, epigenomics, transcriptomics,
proteomics, and metabolomics) data can be produced from clinical samples. The
correlations between different omics types attracts a lot of research interest,
whereas the stduy on genome-wide omcis data translation (i.e, generation and
prediction of one type of omics data from another type of omics data) is almost
blank. Generative adversarial networks and the variants are one of the most
state-of-the-art deep learning technologies, which have shown great success in
image-to-image translation, text-to-image translation, etc. Here we proposed
OmiTrans, a deep learning framework adopted the idea of generative adversarial
networks to achieve omics-to-omics translation with promising results. OmiTrans
was able to faithfully reconstruct gene expression profiles from DNA
methylation data with high accuracy and great model generalisation, as
demonstrated in the experiments.

In this paper, we address reasoning tasks from open vocabulary Knowledge
Bases (openKBs) using state-of-the-art Neural Language Models (NLMs) with
applications in scientific literature. For this purpose, self-attention based
NLMs are trained using a common sense KB as a source task. The NLMs are then
tested on a target KB for open vocabulary reasoning tasks involving scientific
knowledge related to the most prevalent chronic diseases (also known as
non-communicable diseases, NCDs). Our results identified NLMs that performed
consistently and with significance in knowledge inference for both source and
target tasks. Furthermore, in our analysis by inspection we discussed the
semantic regularities and reasoning capabilities learned by the models, while
showing a first insight into the potential benefits of our approach to aid NCD
research.

Streaming data routinely generated by mobile phones, social networks,
e-commerce, and electronic health records present new opportunities for near
real-time surveillance of the impact of an intervention on an outcome of
interest via causal inference methods. However, as data grow rapidly in volume
and velocity, storing and combing data become increasingly challenging. The
amount of time and effort spent to update analyses can grow exponentially,
which defeats the purpose of instantaneous surveillance. Data sharing barriers
in multi-center studies bring additional challenges to rapid signal detection
and update. It is thus time to turn static causal inference to online causal
learning that can incorporate new information as it becomes available without
revisiting prior observations. In this paper, we present a framework for online
estimation and inference of treatment effects leveraging a series of datasets
that arrive sequentially without storing or re-accessing individual-level raw
data. We establish estimation consistency and asymptotic normality of the
proposed framework for online causal inference. In particular, our framework is
robust to biased data batches in the sense that the proposed online estimator
is asymptotically unbiased as long as the pooled data is a random sample of the
target population regardless of whether each data batch is. We also provide an
R package for analyzing streaming observational data that enjoys great
computation efficiency compared to existing software packages for offline
analyses. Our proposed methods are illustrated with extensive simulations and
an application to sequential monitoring of adverse events post COVID-19
vaccine.

Energy-based modeling is a promising approach to unsupervised learning, which
yields many downstream applications from a single model. The main difficulty in
learning energy-based models with the "contrastive approaches" is the
generation of samples from the current energy function at each iteration. Many
advances have been made to accomplish this subroutine cheaply. Nevertheless,
all such sampling paradigms run MCMC targeting the current model, which
requires infinitely long chains to generate samples from the true energy
distribution and is problematic in practice. This paper proposes an alternative
approach to getting these samples and avoiding crude MCMC sampling from the
current model. We accomplish this by viewing the evolution of the modeling
distribution as (i) the evolution of the energy function, and (ii) the
evolution of the samples from this distribution along some vector field. We
subsequently derive this time-dependent vector field such that the particles
following this field are approximately distributed as the current density
model. Thereby we match the evolution of the particles with the evolution of
the energy function prescribed by the learning procedure. Importantly, unlike
Monte Carlo sampling, our method targets to match the current distribution in a
finite time. Finally, we demonstrate its effectiveness empirically compared to
MCMC-based learning methods.

Kernel based Deep Learning using multi-layer kernel machines(MKMs) was
proposed by Y.Cho and L.K. Saul in \cite{saul}. In MKMs they used only one
kernel(arc-cosine kernel) at a layer for the kernel PCA-based feature
extraction. We propose to use multiple kernels in each layer by taking a convex
combination of many kernels following an unsupervised learning strategy.
Empirical study is conducted on \textit{mnist-back-rand},
\textit{mnist-back-image} and \textit{mnist-rot-back-image} datasets generated
by adding random noise in the image background of MNIST dataset. Experimental
results indicate that using MKL in MKMs earns a better representation of the
raw data and improves the classifier performance.

Data based materials science is the new promise to accelerate materials
design. Especially in computational materials science, data generation can
easily be automatized. Usually, the focus is on processing and evaluating the
data to derive rules or to discover new materials, while less attention is
being paid on the strategy to generate the data. In this work, we show that by
a sequential design of experiment scheme, the process of generating and
learning from the data can be combined to discover the relevant sections of the
parameter space. Our example is the energy of grain boundaries as a function of
their geometric degrees of freedom, calculated via atomistic simulations. The
sampling of this grain boundary energy space, or even subspaces of it,
represents a challenge due to the presence of deep cusps of the energy, which
are located at irregular intervals of the geometric parameters. Existing
approaches to sample grain boundary energy subspaces therefore either need a
huge amount of datapoints or a~priori knowledge of the positions of these
cusps. We combine statistical methods with atomistic simulations and a
sequential sampling technique and compare this strategy to a regular sampling
technique. We thereby demonstrate that this sequential design is able to sample
a subspace with a minimal amount of points while finding unknown cusps
automatically.

The SEparator for CApture Reactions (SECAR) is a next-generation recoil
separator system at the Facility for Rare Isotope Beams (FRIB) designed for the
direct measurement of capture reactions on unstable nuclei in inverse
kinematics. To maximize the performance of the device, careful beam alignment
to the central ion optical axis needs to be achieved. This can be difficult to
attain through manual tuning by human operators without potentially leaving the
system in a sub-optimal and irreproducible state. In this work, we present the
first development of online Bayesian optimization with a Gaussian process model
to tune an ion beam through a nuclear astrophysics recoil separator. We show
that the method achieves small incoming angular deviations (0-1 mrad) in an
efficient and reproducible manner that is at least 3 times faster than standard
hand-tuning. This method is now routinely used for all separator tuning.

Domain generalization algorithms use training data from multiple domains to
learn models that generalize well to unseen domains. While recently proposed
benchmarks demonstrate that most of the existing algorithms do not outperform
simple baselines, the established evaluation methods fail to expose the impact
of various factors that contribute to the poor performance. In this paper we
propose an evaluation framework for domain generalization algorithms that
allows decomposition of the error into components capturing distinct aspects of
generalization. Inspired by the prevalence of algorithms based on the idea of
domain-invariant representation learning, we extend the evaluation framework to
capture various types of failures in achieving invariance. We show that the
largest contributor to the generalization error varies across methods,
datasets, regularization strengths and even training lengths. We observe two
problems associated with the strategy of learning domain-invariant
representations. On Colored MNIST, most domain generalization algorithms fail
because they reach domain-invariance only on the training domains. On
Camelyon-17, domain-invariance degrades the quality of representations on
unseen domains. We hypothesize that focusing instead on tuning the classifier
on top of a rich representation can be a promising direction.

Variational quantum algorithms are expected to demonstrate the advantage of
quantum computing on near-term noisy quantum computers. However, training such
variational quantum algorithms suffers from gradient vanishing as the size of
the algorithm increases. Previous work cannot handle the gradient vanishing
induced by the inevitable noise effects on realistic quantum hardware. In this
paper, we propose a novel training scheme to mitigate such noise-induced
gradient vanishing. We first introduce a new cost function of which the
gradients are significantly augmented by employing traceless observables in
truncated subspace. We then prove that the same minimum can be reached by
optimizing the original cost function with the gradients from the new cost
function. Experiments show that our new training scheme is highly effective for
major variational quantum algorithms of various tasks.

There is a tendency across different subfields in AI to valorize a small
collection of influential benchmarks. These benchmarks operate as stand-ins for
a range of anointed common problems that are frequently framed as foundational
milestones on the path towards flexible and generalizable AI systems.
State-of-the-art performance on these benchmarks is widely understood as
indicative of progress towards these long-term goals. In this position paper,
we explore the limits of such benchmarks in order to reveal the construct
validity issues in their framing as the functionally "general" broad measures
of progress they are set up to be.

In this paper, we present our work participating in the BioCreative VII Track
3 - automatic extraction of medication names in tweets, where we implemented a
multi-task learning model that is jointly trained on text classification and
sequence labelling. Our best system run achieved a strict F1 of 80.4, ranking
first and more than 10 points higher than the average score of all
participants. Our analyses show that the ensemble technique, multi-task
learning, and data augmentation are all beneficial for medication detection in
tweets.

During 2020 and 2021, severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) transmission has been increasing amongst the world's population at
an alarming rate. Reducing the spread of SARS-CoV-2 and other diseases that are
spread in similar manners is paramount for public health officials as they seek
to effectively manage resources and potential population control measures such
as social distancing and quarantines. By analyzing the United States' county
network structure, one can model and interdict potential higher infection
areas. County officials can provide targeted information, preparedness
training, as well as increase testing in these areas. While these approaches
may provide adequate countermeasures for localized areas, they are inadequate
for the holistic United States. We solve this problem by collecting coronavirus
disease 2019 (COVID-19) infections and deaths from the Center for Disease
Control and Prevention{\color{black},} and {\color{black} adjacency between all
counties obtained} from the United States Census Bureau. Generalized network
autoregressive (GNAR) time series models have been proposed as an efficient
learning algorithm for networked datasets. This work fuses network science and
operations research techniques to univariately model COVID-19 cases, deaths,
and current survivors across the United States' county network structure.

While gravitational microlensing by planetary systems provides unique vistas
on the properties of exoplanets, observations of a given 2-body microlensing
event can often be interpreted with multiple distinct physical configurations.
Such ambiguities are typically attributed to the close-wide and inner-outer
types of degeneracies that arise from transformation invariances and symmetries
of microlensing caustics. However, there remain unexplained inconsistencies
between aforementioned theories and observations. Here, leveraging a fast
machine learning inference framework, we present the discovery of the offset
degeneracy, which concerns a magnification-matching behaviour on the lens-axis
and is formulated independent of caustics. This offset degeneracy unifies the
close-wide and inner-outer degeneracies, generalises to resonant topologies,
and upon reanalysis, not only appears ubiquitous in previously published
planetary events with 2-fold degenerate solutions, but also resolves prior
inconsistencies. Our analysis demonstrates that degenerate caustics do not
strictly result in degenerate magnifications and that the commonly invoked
close-wide degeneracy essentially never arises in actual events. Moreover, it
is shown that parameters in offset degenerate configurations are related by a
simple expression. This suggests the existence of a deeper symmetry in the
equations governing 2-body lenses than previously recognised.

Most image-to-image translation methods require a large number of training
images, which restricts their applicability. We instead propose ManiFest: a
framework for few-shot image translation that learns a context-aware
representation of a target domain from a few images only. To enforce feature
consistency, our framework learns a style manifold between source and proxy
anchor domains (assumed to be composed of large numbers of images). The learned
manifold is interpolated and deformed towards the few-shot target domain via
patch-based adversarial and feature statistics alignment losses. All of these
components are trained simultaneously during a single end-to-end loop. In
addition to the general few-shot translation task, our approach can
alternatively be conditioned on a single exemplar image to reproduce its
specific style. Extensive experiments demonstrate the efficacy of ManiFest on
multiple tasks, outperforming the state-of-the-art on all metrics and in both
the general- and exemplar-based scenarios. Our code is available at
https://github.com/cv-rits/Manifest .

Learning-based optical flow estimation has been dominated with the pipeline
of cost volume with convolutions for flow regression, which is inherently
limited to local correlations and thus is hard to address the long-standing
challenge of large displacements. To alleviate this, the state-of-the-art
framework RAFT gradually improves its prediction quality by using a large
number of iterative refinements, achieving remarkable performance but
introducing linearly increasing inference time. To enable both high accuracy
and efficiency, we completely revamp the dominant flow regression pipeline by
reformulating optical flow as a global matching problem, which identifies the
correspondences by directly comparing feature similarities. Specifically, we
propose a GMFlow framework, which consists of three main components: a
customized Transformer for feature enhancement, a correlation and softmax layer
for global feature matching, and a self-attention layer for flow propagation.
We further introduce a refinement step that reuses GMFlow at higher feature
resolution for residual flow prediction. Our new framework outperforms
31-refinements RAFT on the challenging Sintel benchmark, while using only one
refinement and running faster, suggesting a new paradigm for accurate and
efficient optical flow estimation. Code is available at
https://github.com/haofeixu/gmflow.

We present Neural Kernel Fields: a novel method for reconstructing implicit
3D shapes based on a learned kernel ridge regression. Our technique achieves
state-of-the-art results when reconstructing 3D objects and large scenes from
sparse oriented points, and can reconstruct shape categories outside the
training set with almost no drop in accuracy. The core insight of our approach
is that kernel methods are extremely effective for reconstructing shapes when
the chosen kernel has an appropriate inductive bias. We thus factor the problem
of shape reconstruction into two parts: (1) a backbone neural network which
learns kernel parameters from data, and (2) a kernel ridge regression that fits
the input points on-the-fly by solving a simple positive definite linear system
using the learned kernel. As a result of this factorization, our reconstruction
gains the benefits of data-driven methods under sparse point density while
maintaining interpolatory behavior, which converges to the ground truth shape
as input sampling density increases. Our experiments demonstrate a strong
generalization capability to objects outside the train-set category and scanned
scenes. Source code and pretrained models are available at
https://nv-tlabs.github.io/nkf.

Previous online 3D Multi-Object Tracking(3DMOT) methods terminate a tracklet
when it is not associated with new detections for a few frames. But if an
object just goes dark, like being temporarily occluded by other objects or
simply getting out of FOV, terminating a tracklet prematurely will result in an
identity switch. We reveal that premature tracklet termination is the main
cause of identity switches in modern 3DMOT systems. To address this, we propose
Immortal Tracker, a simple tracking system that utilizes trajectory prediction
to maintain tracklets for objects gone dark. We employ a simple Kalman filter
for trajectory prediction and preserve the tracklet by prediction when the
target is not visible. With this method, we can avoid 96% vehicle identity
switches resulting from premature tracklet termination. Without any learned
parameters, our method achieves a mismatch ratio at the 0.0001 level and
competitive MOTA for the vehicle class on the Waymo Open Dataset test set. Our
mismatch ratio is tens of times lower than any previously published method.
Similar results are reported on nuScenes. We believe the proposed Immortal
Tracker can offer a simple yet powerful solution for pushing the limit of
3DMOT. Our code is available at
https://github.com/ImmortalTracker/ImmortalTracker.

The thin-file borrowers are customers for whom a creditworthiness assessment
is uncertain due to their lack of credit history; many researchers have used
borrowers' relationships and interactions networks in the form of graphs as an
alternative data source to address this. Incorporating network data is
traditionally made by hand-crafted feature engineering, and lately, the graph
neural network has emerged as an alternative, but it still does not improve
over the traditional method's performance. Here we introduce a framework to
improve credit scoring models by blending several Graph Representation Learning
methods: feature engineering, graph embeddings, and graph neural networks. We
stacked their outputs to produce a single score in this approach. We validated
this framework using a unique multi-source dataset that characterizes the
relationships and credit history for the entire population of a Latin American
country, applying it to credit risk models, application, and behavior,
targeting both individuals and companies.
  Our results show that the graph representation learning methods should be
used as complements, and these should not be seen as self-sufficient methods as
is currently done. In terms of AUC and KS, we enhance the statistical
performance, outperforming traditional methods.
  In Corporate lending, where the gain is much higher, it confirms that
evaluating an unbanked company cannot solely consider its features. The
business ecosystem where these firms interact with their owners, suppliers,
customers, and other companies provides novel knowledge that enables financial
institutions to enhance their creditworthiness assessment.
  Our results let us know when and which group to use graph data and what
effects on performance to expect. They also show the enormous value of graph
data on the unbanked credit scoring problem, principally to help companies'
banking.

Today, various machine learning (ML) applications offer continuous data
processing and real-time data analytics at the edge of a wireless network.
Distributed real-time ML solutions are highly sensitive to the so-called
straggler effect caused by resource heterogeneity and alleviated by various
computation offloading mechanisms that seriously challenge the communication
efficiency, especially in large-scale scenarios. To decrease the communication
overhead, we rely on device-to-device (D2D) connectivity that improves spectrum
utilization and allows efficient data exchange between devices in proximity. In
particular, we design a novel D2D-aided coded federated learning method
(D2D-CFL) for efficient load balancing across devices. The proposed solution
captures system dynamics, including data (time-dependent learning model, varied
intensity of data arrivals), device (diverse computational resources and volume
of training data), and deployment (varied locations and D2D graph
connectivity). To minimize the number of communication rounds, we derive an
optimal compression rate for achieving minimum processing time and establish
its connection with the convergence time. The resulting optimization problem
provides suboptimal compression parameters, which improve the total training
time. Our proposed method is beneficial for real-time collaborative
applications, where the users continuously generate training data resulting in
the model drift.

Automation and robotisation of the agricultural sector are seen as a viable
solution to socio-economic challenges faced by this industry. This technology
often relies on intelligent perception systems providing information about
crops, plants and the entire environment. The challenges faced by traditional
2D vision systems can be addressed by modern 3D vision systems which enable
straightforward localisation of objects, size and shape estimation, or handling
of occlusions. So far, the use of 3D sensing was mainly limited to indoor or
structured environments. In this paper, we evaluate modern sensing technologies
including stereo and time-of-flight cameras for 3D perception of shape in
agriculture and study their usability for segmenting out soft fruit from
background based on their shape. To that end, we propose a novel 3D deep neural
network which exploits the organised nature of information originating from the
camera-based 3D sensors. We demonstrate the superior performance and efficiency
of the proposed architecture compared to the state-of-the-art 3D networks.
Through a simulated study, we also show the potential of the 3D sensing
paradigm for object segmentation in agriculture and provide insights and
analysis of what shape quality is needed and expected for further analysis of
crops. The results of this work should encourage researchers and companies to
develop more accurate and robust 3D sensing technologies to assure their wider
adoption in practical agricultural applications.

With the increasing adoption of machine learning (ML) models and systems in
high-stakes settings across different industries, guaranteeing a model's
performance after deployment has become crucial. Monitoring models in
production is a critical aspect of ensuring their continued performance and
reliability. We present Amazon SageMaker Model Monitor, a fully managed service
that continuously monitors the quality of machine learning models hosted on
Amazon SageMaker. Our system automatically detects data, concept, bias, and
feature attribution drift in models in real-time and provides alerts so that
model owners can take corrective actions and thereby maintain high quality
models. We describe the key requirements obtained from customers, system design
and architecture, and methodology for detecting different types of drift.
Further, we provide quantitative evaluations followed by use cases, insights,
and lessons learned from more than two years of production deployment.

Malaria, a fatal but curable disease claims hundreds of thousands of lives
every year. Early and correct diagnosis is vital to avoid health complexities,
however, it depends upon the availability of costly microscopes and trained
experts to analyze blood-smear slides. Deep learning-based methods have the
potential to not only decrease the burden of experts but also improve
diagnostic accuracy on low-cost microscopes. However, this is hampered by the
absence of a reasonable size dataset. One of the most challenging aspects is
the reluctance of the experts to annotate the dataset at low magnification on
low-cost microscopes. We present a dataset to further the research on malaria
microscopy over the low-cost microscopes at low magnification. Our large-scale
dataset consists of images of blood-smear slides from several malaria-infected
patients, collected through microscopes at two different cost spectrums and
multiple magnifications. Malarial cells are annotated for the localization and
life-stage classification task on the images collected through the high-cost
microscope at high magnification. We design a mechanism to transfer these
annotations from the high-cost microscope at high magnification to the low-cost
microscope, at multiple magnifications. Multiple object detectors and domain
adaptation methods are presented as the baselines. Furthermore, a partially
supervised domain adaptation method is introduced to adapt the object-detector
to work on the images collected from the low-cost microscope. The dataset will
be made publicly available after publication.

Do language models have beliefs about the world? Dennett (1995) famously
argues that even thermostats have beliefs, on the view that a belief is simply
an informational state decoupled from any motivational state. In this paper, we
discuss approaches to detecting when models have beliefs about the world, and
we improve on methods for updating model beliefs to be more truthful, with a
focus on methods based on learned optimizers or hypernetworks. Our main
contributions include: (1) new metrics for evaluating belief-updating methods
that focus on the logical consistency of beliefs, (2) a training objective for
Sequential, Local, and Generalizing model updates (SLAG) that improves the
performance of learned optimizers, and (3) the introduction of the belief
graph, which is a new form of interface with language models that shows the
interdependencies between model beliefs. Our experiments suggest that models
possess belief-like qualities to only a limited extent, but update methods can
both fix incorrect model beliefs and greatly improve their consistency.
Although off-the-shelf optimizers are surprisingly strong belief-updating
baselines, our learned optimizers can outperform them in more difficult
settings than have been considered in past work. Code is available at
https://github.com/peterbhase/SLAG-Belief-Updating

The goal of contrastive learning based pre-training is to leverage large
quantities of unlabeled data to produce a model that can be readily adapted
downstream. Current approaches revolve around solving an image discrimination
task: given an anchor image, an augmented counterpart of that image, and some
other images, the model must produce representations such that the distance
between the anchor and its counterpart is small, and the distances between the
anchor and the other images are large. There are two significant problems with
this approach: (i) by contrasting representations at the image-level, it is
hard to generate detailed object-sensitive features that are beneficial to
downstream object-level tasks such as instance segmentation; (ii) the
augmentation strategy of producing an augmented counterpart is fixed, making
learning less effective at the later stages of pre-training. In this work, we
introduce Curricular Contrastive Object-level Pre-training (CCOP) to tackle
these problems: (i) we use selective search to find rough object regions and
use them to build an inter-image object-level contrastive loss and an
intra-image object-level discrimination loss into our pre-training objective;
(ii) we present a curriculum learning mechanism that adaptively augments the
generated regions, which allows the model to consistently acquire a useful
learning signal, even in the later stages of pre-training. Our experiments show
that our approach improves on the MoCo v2 baseline by a large margin on
multiple object-level tasks when pre-training on multi-object scene image
datasets. Code is available at https://github.com/ChenhongyiYang/CCOP.

Fair representation learning transforms user data into a representation that
ensures fairness and utility regardless of the downstream application. However,
learning individually fair representations, i.e., guaranteeing that similar
individuals are treated similarly, remains challenging in high-dimensional
settings such as computer vision. In this work, we introduce LASSI, the first
representation learning method for certifying individual fairness of
high-dimensional data. Our key insight is to leverage recent advances in
generative modeling to capture the set of similar individuals in the generative
latent space. This enables us to learn individually fair representations that
map similar individuals close together by using adversarial training to
minimize the distance between their representations. Finally, we employ
randomized smoothing to provably map similar individuals close together, in
turn ensuring that local robustness verification of the downstream application
results in end-to-end fairness certification. Our experimental evaluation on
challenging real-world image data demonstrates that our method increases
certified individual fairness by up to 90% without significantly affecting task
utility.

This paper addresses the problem of mapping high-dimensional data to a
low-dimensional space, in the presence of other known features. This problem is
ubiquitous in science and engineering as there are often
controllable/measurable features in most applications. Furthermore, the
discovered features in previous analyses can become the known features in
subsequent analyses, repeatedly. To solve this problem, this paper proposes a
broad class of methods, which is referred to as conditional multidimensional
scaling. An algorithm for optimizing the objective function of conditional
multidimensional scaling is also developed. The proposed framework is
illustrated with kinship terms, facial expressions, and simulated car-brand
perception examples. These examples demonstrate the benefits of the framework
for being able to marginalize out the known features to uncover unknown,
unanticipated features in the reduced-dimension space and for enabling a
repeated, more straightforward knowledge discovery process. Computer codes for
this work are available in the open-source cml R package.

We show how to deal with uncertainties on the Standard Model predictions in
an agnostic new physics search strategy that exploits artificial neural
networks. Our approach builds directly on the specific Maximum Likelihood ratio
treatment of uncertainties as nuisance parameters for hypothesis testing that
is routinely employed in high-energy physics. After presenting the conceptual
foundations of our method, we first illustrate all aspects of its
implementation and extensively study its performances on a toy one-dimensional
problem. We then show how to implement it in a multivariate setup by studying
the impact of two typical sources of experimental uncertainties in two-body
final states at the LHC.

Even though many semantic segmentation methods exist that are able to perform
well on many medical datasets, often, they are not designed for direct use in
clinical practice. The two main concerns are generalization to unseen data with
a different visual appearance, e.g., images acquired using a different scanner,
and efficiency in terms of computation time and required Graphics Processing
Unit (GPU) memory. In this work, we employ a multi-organ segmentation model
based on the SpatialConfiguration-Net (SCN), which integrates prior knowledge
of the spatial configuration among the labelled organs to resolve spurious
responses in the network outputs. Furthermore, we modified the architecture of
the segmentation model to reduce its memory footprint as much as possible
without drastically impacting the quality of the predictions. Lastly, we
implemented a minimal inference script for which we optimized both, execution
time and required GPU memory.

Optimizing highly complex cost/energy functions over discrete variables is at
the heart of many open problems across different scientific disciplines and
industries. A major obstacle is the emergence of many-body effects among
certain subsets of variables in hard instances leading to critical slowing down
or collective freezing for known stochastic local search strategies. An
exponential computational effort is generally required to unfreeze such
variables and explore other unseen regions of the configuration space. Here, we
introduce a quantum-inspired family of nonlocal Nonequilibrium Monte Carlo
(NMC) algorithms by developing an adaptive gradient-free strategy that can
efficiently learn key instance-wise geometrical features of the cost function.
That information is employed on-the-fly to construct spatially inhomogeneous
thermal fluctuations for collectively unfreezing variables at various length
scales, circumventing costly exploration versus exploitation trade-offs. We
apply our algorithm to two of the most challenging combinatorial optimization
problems: random k-satisfiability (k-SAT) near the computational phase
transitions and Quadratic Assignment Problems (QAP). We observe significant
speedup and robustness over both specialized deterministic solvers and generic
stochastic solvers. In particular, for 90% of random 4-SAT instances we find
solutions that are inaccessible for the best specialized deterministic
algorithm known as Survey Propagation (SP) with an order of magnitude
improvement in the quality of solutions for the hardest 10% instances. We also
demonstrate two orders of magnitude improvement in time-to-solution over the
state-of-the-art generic stochastic solver known as Adaptive Parallel Tempering
(APT).

Correlations in streams of multivariate time series data means that
typically, only a small subset of the features are required for a given data
mining task. In this paper, we propose a technique which we call Merit Score
for Time-Series data (MSTS) that does feature subset selection based on the
correlation patterns of single feature classifier outputs. We assign a Merit
Score to the feature subsets which is used as the basis for selecting 'good'
feature subsets. The proposed technique is evaluated on datasets from the UEA
multivariate time series archive and is compared against a Wrapper approach for
feature subset selection. MSTS is shown to be effective for feature subset
selection and is in particular effective as a data reduction technique. MSTS is
shown here to be computationally more efficient than the Wrapper strategy in
selecting a suitable feature subset, being more than 100 times faster for some
larger datasets while also maintaining a good classification accuracy.

This work proposes a multi-agent filtering algorithm over graphs for
finite-state hidden Markov models (HMMs), which can be used for sequential
state estimation or for tracking opinion formation over dynamic social
networks. We show that the difference from the optimal centralized Bayesian
solution is asymptotically bounded for geometrically ergodic transition models.
Experiments illustrate the theoretical findings and in particular, demonstrate
the superior performance of the proposed algorithm compared to a
state-of-the-art social learning algorithm.

Generative Adversarial Networks (GANs) are among the most popular approaches
to generate synthetic data, especially images, for data sharing purposes. Given
the vital importance of preserving the privacy of the individual data points in
the original data, GANs are trained utilizing frameworks with robust privacy
guarantees such as Differential Privacy (DP). However, these approaches remain
widely unstudied beyond single performance metrics when presented with
imbalanced datasets. To this end, we systematically compare GANs trained with
the two best-known DP frameworks for deep learning, DP-SGD, and PATE, in
different data imbalance settings from two perspectives -- the size of the
classes in the generated synthetic data and their classification performance.
  Our analyses show that applying PATE, similarly to DP-SGD, has a disparate
effect on the under/over-represented classes but in a much milder magnitude
making it more robust. Interestingly, our experiments consistently show that
for PATE, unlike DP-SGD, the privacy-utility trade-off is not monotonically
decreasing but is much smoother and inverted U-shaped, meaning that adding a
small degree of privacy actually helps generalization. However, we have also
identified some settings (e.g., large imbalance) where PATE-GAN completely
fails to learn some subparts of the training data.

We establish an equivalence between a family of adversarial training problems
for non-parametric binary classification and a family of regularized risk
minimization problems where the regularizer is a nonlocal perimeter functional.
The resulting regularized risk minimization problems admit exact convex
relaxations of the type $L^1+$ (nonlocal) $\operatorname{TV}$, a form
frequently studied in image analysis and graph-based learning. A rich geometric
structure is revealed by this reformulation which in turn allows us to
establish a series of properties of optimal solutions of the original problem,
including the existence of minimal and maximal solutions (interpreted in a
suitable sense), and the existence of regular solutions (also interpreted in a
suitable sense). In addition, we highlight how the connection between
adversarial training and perimeter minimization problems provides a novel,
directly interpretable, statistical motivation for a family of regularized risk
minimization problems involving perimeter/total variation. The majority of our
theoretical results are independent of the distance used to define adversarial
attacks.

Biological signaling pathways based upon proteins binding to one another to
relay a signal for genetic expression, such as the Bone Morphogenetic Protein
(BMP) signaling pathway, can be modeled by mass action kinetics and
conservation laws that result in non-closed form polynomial equations.
Accurately determining parameters of biological pathways that represent
physically relevant features, such as binding affinity of proteins and their
associated uncertainty, presents a challenge for biological models lacking an
explicit likelihood function. Additionally, parameterizing non-closed form
biological models requires copious amounts of data from expensive
perturbation-response experiments to fit model parameters. We present an
algorithm (SBIDOEMAN) for determining optimal experiments and parameters of
systems biology models with implicit likelihoods. We evaluate our algorithm
using simulations of held-out true parameter values and demonstrate an
improvement in the rate of accurate parameter inference over random and
equidistant experimental designs when evaluated on two simple models of the BMP
signaling pathway with an implicit likelihood function.

With the rising extension of renewable energies, the intraday electricity
markets have recorded a growing popularity amongst traders as well as electric
utilities to cope with the induced volatility of the energy supply. Through
their short trading horizon and continuous nature, the intraday markets offer
the ability to adjust trading decisions from the day-ahead market or reduce
trading risk in a short-term notice. Producers of renewable energies utilize
the intraday market to lower their forecast risk, by modifying their provided
capacities based on current forecasts. However, the market dynamics are complex
due to the fact that the power grids have to remain stable and electricity is
only partly storable. Consequently, robust and intelligent trading strategies
are required that are capable to operate in the intraday market. In this work,
we propose a novel autonomous trading approach based on Deep Reinforcement
Learning (DRL) algorithms as a possible solution. For this purpose, we model
the intraday trade as a Markov Decision Problem (MDP) and employ the Proximal
Policy Optimization (PPO) algorithm as our DRL approach. A simulation framework
is introduced that enables the trading of the continuous intraday price in a
resolution of one minute steps. We test our framework in a case study from the
perspective of a wind park operator. We include next to general trade
information both price and wind forecasts. On a test scenario of German
intraday trading results from 2018, we are able to outperform multiple
baselines with at least 45.24% improvement, showing the advantage of the DRL
algorithm. However, we also discuss limitations and enhancements of the DRL
agent, in order to increase the performance in future works.

Score-based diffusion models have emerged as one of the most promising
frameworks for deep generative modelling. In this work we conduct a systematic
comparison and theoretical analysis of different approaches to learning
conditional probability distributions with score-based diffusion models. In
particular, we prove results which provide a theoretical justification for one
of the most successful estimators of the conditional score. Moreover, we
introduce a multi-speed diffusion framework, which leads to a new estimator for
the conditional score, performing on par with previous state-of-the-art
approaches. Our theoretical and experimental findings are accompanied by an
open source library MSDiff which allows for application and further research of
multi-speed diffusion models.

Backbone curves are used to characterize nonlinear responses of structural
elements by simplifying the cyclic force-deformation relationships. Accurate
modeling of cyclic behavior can be achieved with a reliable backbone curve
model. In this paper, a novel machine learning-based model is proposed to
predict the backbone curve of reinforced concrete shear (structural) walls
based on key wall design properties. Reported experimental responses of a
detailed test database consisting of 384 reinforced concrete shear walls under
cyclic loading were utilized to predict seven critical points to define the
backbone curves, namely: shear at cracking point; shear and displacement at
yielding point; and peak shear force and corresponding displacement; and
ultimate displacement and corresponding shear. The predictive models were
developed based on the Gaussian Process Regression method (GPR), which adopts a
non-parametric Bayesian approach. The ability of the proposed GPR-based model
to make accurate and robust estimations for the backbone curves was validated
based on unseen data using a hundred random sampling procedure. The prediction
accuracies (i.e., ratio of predicted/actual values) are close to 1.0, whereas
the coefficient of determination (R2) values range between 0.90-0.97 for all
backbone points. The proposed GPR-based backbone models are shown to reflect
cyclic behavior more accurately than the traditional methods, therefore, they
would serve the earthquake engineering community for better evaluation of the
seismic performance of existing buildings.

The high volume of increasingly sophisticated cyber threats is drawing
growing attention to cybersecurity, where many challenges remain unresolved.
Namely, for intrusion detection, new algorithms that are more robust,
effective, and able to use more information are needed. Moreover, the intrusion
detection task faces a serious challenge associated with the extreme class
imbalance between normal and malicious traffics. Recently, graph-neural network
(GNN) achieved state-of-the-art performance to model the network topology in
cybersecurity tasks. However, only a few works exist using GNNs to tackle the
intrusion detection problem. Besides, other promising avenues such as applying
the attention mechanism are still under-explored. This paper presents two novel
graph-based solutions for intrusion detection, the modified E-GraphSAGE, and
E-ResGATalgorithms, which rely on the established GraphSAGE and graph attention
network (GAT), respectively. The key idea is to integrate residual learning
into the GNN leveraging the available graph information. Residual connections
are added as a strategy to deal with the high-class imbalance, aiming at
retaining the original information and improving the minority classes'
performance. An extensive experimental evaluation of four recent intrusion
detection datasets shows the excellent performance of our approaches,
especially when predicting minority classes.

Deep learning-based models encounter challenges when processing long-tailed
data in the real world. Existing solutions usually employ some balancing
strategies or transfer learning to deal with the class imbalance problem, based
on the image modality. In this work, we present a visual-linguistic long-tailed
recognition framework, termed VL-LTR, and conduct empirical studies on the
benefits of introducing text modality for long-tailed recognition (LTR).
Compared to existing approaches, the proposed VL-LTR has the following merits.
(1) Our method can not only learn visual representation from images but also
learn corresponding linguistic representation from noisy class-level text
descriptions collected from the Internet; (2) Our method can effectively use
the learned visual-linguistic representation to improve the visual recognition
performance, especially for classes with fewer image samples. We also conduct
extensive experiments and set the new state-of-the-art performance on
widely-used LTR benchmarks. Notably, our method achieves 77.2% overall accuracy
on ImageNet-LT, which significantly outperforms the previous best method by
over 17 points, and is close to the prevailing performance training on the full
ImageNet. Code is available at https://github.com/ChangyaoTian/VL-LTR.

We consider the mobile localization problem in future millimeter-wave
wireless networks with distributed Base Stations (BSs) based on multi-antenna
channel state information (CSI). For this problem, we propose a Semi-supervised
tdistributed Stochastic Neighbor Embedding (St-SNE) algorithm to directly embed
the high-dimensional CSI samples into the 2D geographical map. We evaluate the
performance of St-SNE in a simulated urban outdoor millimeter-wave radio access
network. Our results show that St-SNE achieves a mean localization error of 6.8
m with only 5% of labeled CSI samples in a 200*200 m^2 area with a ray-tracing
channel model. St-SNE does not require accurate synchronization among multiple
BSs, and is promising for future large-scale millimeter-wave localization.

The Asian giant hornet (AGH) appeared in Washington State appears to have a
potential danger of bioinvasion. Washington State has collected public photos
and videos of detected insects for verification and further investigation. In
this paper, we analyze AGH using data analysis,statistics, discrete
mathematics, and deep learning techniques to process the data to controlAGH
spreading.First, we visualize the geographical distribution of insects in
Washington State. Then we investigate insect populations to varying months of
the year and different days of a month.Third, we employ wavelet analysis to
examine the periodic spread of AGH. Fourth, we apply ordinary differential
equations to examine AGH numbers at the different natural growthrate and
reaction speed and output the potential propagation coefficient. Next, we
leverage cellular automaton combined with the potential propagation coefficient
to simulate the geographical spread under changing potential propagation. To
update the model, we use delayed differential equations to simulate human
intervention. We use the time difference between detection time and submission
time to determine the unit of time to delay time. After that, we construct a
lightweight CNN called SqueezeNet and assess its classification performance. We
then relate several non-reference image quality metrics, including NIQE, image
gradient, entropy, contrast, and TOPSIS to judge the cause of
misclassification. Furthermore, we build a Random Forest classifier to identify
positive and negative samples based on image qualities only. We also display
the feature importance and conduct an error analysis. Besides, we present
sensitivity analysis to verify the robustness of our models. Finally, we show
the strengths and weaknesses of our model and derives the conclusions.

A hybrid teaching approach that relied on combining Project Based Learning
with Team Based Learning was trialled in an engineering module during the past
five years. Our motivation was to expose students to real-world authentic
engineering problems and to steer them away from the classical 'banking'
approach, with a view to developing their systems engineering skills via deeper
and more collaborative learning. Our third year module was called Team Design
and Project Skills and was concerned with 320 students dividing themselves in
teams to develop a smart electronic system. We reveal module design details and
discuss the effectiveness of our teaching approach via analysis of student
grades during the past five years, as well as data from surveys that were
completed by 68 students. 64% of surveyed students agreed that the module
helped broaden their perspective in electronic systems design. Moreover, 84%
recognised that this module was a valuable component in their degree programme.
Adopting this approach in an engineering curriculum enabled students to
integrate knowledge in areas that included control systems, image processing,
embedded systems, sensors, as well as team working, decision making, trouble
shooting and project planning.

This paper aims to discuss and analyze the potentialities of Recurrent Neural
Networks (RNN) in control design applications. The main families of RNN are
considered, namely Neural Nonlinear AutoRegressive eXogenous, (NNARX), Echo
State Networks (ESN), Long Short Term Memory (LSTM), and Gated Recurrent Units
(GRU). The goal is twofold. Firstly, to survey recent results concerning the
training of RNN that enjoy Input-to-State Stability (ISS) and Incremental
Input-to-State Stability ($\delta$ISS) guarantees. Secondly, to discuss the
issues that still hinder the widespread use of RNN for control, namely their
robustness, verifiability, and interpretability. The former properties are
related to the so-called generalization capabilities of the networks, i.e.
their consistency with the underlying real plants, even in presence of unseen
or perturbed input trajectories. The latter is instead related to the
possibility of providing a clear formal connection between the RNN model and
the plant. In this context, we illustrate how ISS and $\delta$ISS represent a
significant step towards the robustness and verifiability of the RNN models,
while the requirement of interpretability paves the way to the use of
physics-based networks. The design of model predictive controllers with RNN as
plant's model is also briefly discussed. Lastly, some of the main topics of the
paper are illustrated on a simulated chemical system.

Focusing on discriminative zero-shot learning, in this work we introduce a
novel mechanism that dynamically augments during training the set of seen
classes to produce additional fictitious classes. These fictitious classes
diminish the model's tendency to fixate during training on attribute
correlations that appear in the training set but will not appear in newly
exposed classes. The proposed model is tested within the two formulations of
the zero-shot learning framework; namely, generalized zero-shot learning (GZSL)
and classical zero-shot learning (CZSL). Our model improves the
state-of-the-art performance on the CUB dataset and reaches comparable results
on the other common datasets, AWA2 and SUN. We investigate the strengths and
weaknesses of our method, including the effects of catastrophic forgetting when
training an end-to-end zero-shot model.

The world is structured in countless ways. It may be prudent to enforce
corresponding structural properties to a learning algorithm's solution, such as
incorporating prior beliefs, natural constraints, or causal structures. Doing
so may translate to faster, more accurate, and more flexible models, which may
directly relate to real-world impact. In this dissertation, we consider two
different research areas that concern structuring a learning algorithm's
solution: when the structure is known and when it has to be discovered.

We study the problem of example-based procedural texture synthesis using
highly compact models. Given a sample image, we use differentiable programming
to train a generative process, parameterised by a recurrent Neural Cellular
Automata (NCA) rule. Contrary to the common belief that neural networks should
be significantly over-parameterised, we demonstrate that our model architecture
and training procedure allows for representing complex texture patterns using
just a few hundred learned parameters, making their expressivity comparable to
hand-engineered procedural texture generating programs. The smallest models
from the proposed $\mu$NCA family scale down to 68 parameters. When using
quantisation to one byte per parameter, proposed models can be shrunk to a size
range between 588 and 68 bytes. Implementation of a texture generator that uses
these parameters to produce images is possible with just a few lines of GLSL or
C code.

Many different studies have suggested that episodic memory is a generative
process, but most computational models adopt a storage view. In this work, we
propose a computational model for generative episodic memory. It is based on
the central hypothesis that the hippocampus stores and retrieves selected
aspects of an episode as a memory trace, which is necessarily incomplete. At
recall, the neocortex reasonably fills in the missing information based on
general semantic information in a process we call semantic completion.
  As episodes we use images of digits (MNIST) augmented by different
backgrounds representing context. Our model is based on a VQ-VAE which
generates a compressed latent representation in form of an index matrix, which
still has some spatial resolution. We assume that attention selects some part
of the index matrix while others are discarded, this then represents the gist
of the episode and is stored as a memory trace. At recall the missing parts are
filled in by a PixelCNN, modeling semantic completion, and the completed index
matrix is then decoded into a full image by the VQ-VAE.
  The model is able to complete missing parts of a memory trace in a
semantically plausible way up to the point where it can generate plausible
images from scratch. Due to the combinatorics in the index matrix, the model
generalizes well to images not trained on. Compression as well as semantic
completion contribute to a strong reduction in memory requirements and
robustness to noise. Finally we also model an episodic memory experiment and
can reproduce that semantically congruent contexts are always recalled better
than incongruent ones, high attention levels improve memory accuracy in both
cases, and contexts that are not remembered correctly are more often remembered
semantically congruently than completely wrong.

Telegram is one of the most used instant messaging apps worldwide. Some of
its success lies in providing high privacy protection and social network
features like the channels -- virtual rooms in which only the admins can post
and broadcast messages to all its subscribers. However, these same features
contributed to the emergence of borderline activities and, as is common with
Online Social Networks, the heavy presence of fake accounts. Telegram started
to address these issues by introducing the verified and scam marks for the
channels. Unfortunately, the problem is far from being solved. In this work, we
perform a large-scale analysis of Telegram by collecting 35,382 different
channels and over 130,000,000 messages. We study the channels that Telegram
marks as verified or scam, highlighting analogies and differences. Then, we
move to the unmarked channels. Here, we find some of the infamous activities
also present on privacy-preserving services of the Dark Web, such as carding,
sharing of illegal adult and copyright protected content. In addition, we
identify and analyze two other types of channels: the clones and the fakes.
Clones are channels that publish the exact content of another channel to gain
subscribers and promote services. Instead, fakes are channels that attempt to
impersonate celebrities or well-known services. Fakes are hard to identify even
by the most advanced users. To detect the fake channels automatically, we
propose a machine learning model that is able to identify them with an accuracy
of 86%. Lastly, we study Sabmyk, a conspiracy theory that exploited fakes and
clones to spread quickly on the platform reaching over 1,000,000 users.

The optimal design of federated learning (FL) algorithms for solving general
machine learning (ML) problems in practical edge computing systems with
quantized message passing remains an open problem. This paper considers an edge
computing system where the server and workers have possibly different computing
and communication capabilities and employ quantization before transmitting
messages. To explore the full potential of FL in such an edge computing system,
we first present a general FL algorithm, namely GenQSGD, parameterized by the
numbers of global and local iterations, mini-batch size, and step size
sequence. Then, we analyze its convergence for an arbitrary step size sequence
and specify the convergence results under three commonly adopted step size
rules, namely the constant, exponential, and diminishing step size rules. Next,
we optimize the algorithm parameters to minimize the energy cost under the time
constraint and convergence error constraint, with the focus on the overall
implementing process of FL. Specifically, for any given step size sequence
under each considered step size rule, we optimize the numbers of global and
local iterations and mini-batch size to optimally implement FL for applications
with preset step size sequences. We also optimize the step size sequence along
with these algorithm parameters to explore the full potential of FL. The
resulting optimization problems are challenging non-convex problems with
non-differentiable constraint functions. We propose iterative algorithms to
obtain KKT points using general inner approximation (GIA) and tricks for
solving complementary geometric programming (CGP). Finally, we numerically
demonstrate the remarkable gains of GenQSGD with optimized algorithm parameters
over existing FL algorithms and reveal the significance of optimally designing
general FL algorithms.

Deep learning has shown remarkable results on Euclidean data (e.g. audio,
images, text) however this type of data is limited in the amount of relational
information it can hold. In mathematics we can model more general relational
data in a graph structure while retaining Euclidean data as associated node or
edge features. Due to the ubiquity of graph data, and its ability to hold
multiple dimensions of information, graph deep learning has become a fast
emerging field. We look at applying and optimising graph deep learning on a
finance graph to produce more informed clusters of companies. Having clusters
produced from multiple streams of data can be highly useful in quantitative
finance; not only does it allow clusters to be tailored to the specific task
but the culmination of multiple streams allows for cross source pattern
recognition that would have otherwise gone unnoticed. This can provide
financial institutions with an edge over competitors which is crucial in the
heavily optimised world of trading. In this paper we use news co-occurrence and
stock price for our data combination. We optimise our model to achieve an
average testing precision of 78% and find a clear improvement in clustering
capabilities when dual data sources are used; cluster purity rises from 32% for
just vertex data and 42% for just edge data to 64% when both are used in
comparisons to ground-truth Bloomberg clusters. The framework we provide
utilises unsupervised learning which we view as key for future work due to the
volume of unlabelled data in financial markets.

Scene graph generation (SGG) aims to capture a wide variety of interactions
between pairs of objects, which is essential for full scene understanding.
Existing SGG methods trained on the entire set of relations fail to acquire
complex reasoning about visual and textual correlations due to various biases
in training data. Learning on trivial relations that indicate generic spatial
configuration like 'on' instead of informative relations such as 'parked on'
does not enforce this complex reasoning, harming generalization. To address
this problem, we propose a novel framework for SGG training that exploits
relation labels based on their informativeness. Our model-agnostic training
procedure imputes missing informative relations for less informative samples in
the training data and trains a SGG model on the imputed labels along with
existing annotations. We show that this approach can successfully be used in
conjunction with state-of-the-art SGG methods and improves their performance
significantly in multiple metrics on the standard Visual Genome benchmark.
Furthermore, we obtain considerable improvements for unseen triplets in a more
challenging zero-shot setting.

Shapley values are today extensively used as a model-agnostic explanation
framework to explain complex predictive machine learning models. Shapley values
have desirable theoretical properties and a sound mathematical foundation in
the field of cooperative game theory. Precise Shapley value estimates for
dependent data rely on accurate modeling of the dependencies between all
feature combinations. In this paper, we use a variational autoencoder with
arbitrary conditioning (VAEAC) to model all feature dependencies
simultaneously. We demonstrate through comprehensive simulation studies that
our VAEAC approach to Shapley value estimation outperforms the state-of-the-art
methods for a wide range of settings for both continuous and mixed dependent
features. For high-dimensional settings, our VAEAC approach with a non-uniform
masking scheme significantly outperforms competing methods. Finally, we apply
our VAEAC approach to estimate Shapley value explanations for the Abalone data
set from the UCI Machine Learning Repository.

The theory of urban traffic flow has been developed and new types of
meta-automata have emerged and simulate realistic traffic conditions relatively
well. Among these models, the brake light model can simulate the three-phase
traffic flow theory very well. However, the existing brake light model also has
certain shortcomings, in that the model will change the speed of congestion
propagation upward when the model is covariant, which is not realistic, and the
model also lacks simulation parameters for driver behavior. In this paper, we
propose a new model based on the brake light model, which can achieve a certain
degree of simulation of driver behavior by adjusting the parameters, and also
achieve a stabilization of the propagation speed of the congestion wave when
the parameters are changed.

We present an approach to learn dense, continuous 2D-3D correspondence
distributions over the surface of objects from data with no prior knowledge of
visual ambiguities like symmetry. We also present a new method for 6D pose
estimation of rigid objects using the learnt distributions to sample, score and
refine pose hypotheses. The correspondence distributions are learnt with a
contrastive loss, represented in object-specific latent spaces by an
encoder-decoder query model and a small fully connected key model. Our method
is unsupervised with respect to visual ambiguities, yet we show that the query-
and key models learn to represent accurate multi-modal surface distributions.
Our pose estimation method improves the state-of-the-art significantly on the
comprehensive BOP Challenge, trained purely on synthetic data, even compared
with methods trained on real data. The project site is at
https://surfemb.github.io/ .

The metaverse, enormous virtual-physical cyberspace, has brought
unprecedented opportunities for artists to blend every corner of our physical
surroundings with digital creativity. This article conducts a comprehensive
survey on computational arts, in which seven critical topics are relevant to
the metaverse, describing novel artworks in blended virtual-physical realities.
The topics first cover the building elements for the metaverse, e.g., virtual
scenes and characters, auditory, textual elements. Next, several remarkable
types of novel creations in the expanded horizons of metaverse cyberspace have
been reflected, such as immersive arts, robotic arts, and other user-centric
approaches fuelling contemporary creative outputs. Finally, we propose several
research agendas: democratising computational arts, digital privacy, and safety
for metaverse artists, ownership recognition for digital artworks,
technological challenges, and so on. The survey also serves as introductory
material for artists and metaverse technologists to begin creations in the
realm of surrealistic cyberspace.

Many practical applications of reinforcement learning require agents to learn
from sparse and delayed rewards. It challenges the ability of agents to
attribute their actions to future outcomes. In this paper, we consider the
problem formulation of episodic reinforcement learning with trajectory
feedback. It refers to an extreme delay of reward signals, in which the agent
can only obtain one reward signal at the end of each trajectory. A popular
paradigm for this problem setting is learning with a designed auxiliary dense
reward function, namely proxy reward, instead of sparse environmental signals.
Based on this framework, this paper proposes a novel reward redistribution
algorithm, randomized return decomposition (RRD), to learn a proxy reward
function for episodic reinforcement learning. We establish a surrogate problem
by Monte-Carlo sampling that scales up least-squares-based reward
redistribution to long-horizon problems. We analyze our surrogate loss function
by connection with existing methods in the literature, which illustrates the
algorithmic properties of our approach. In experiments, we extensively evaluate
our proposed method on a variety of benchmark tasks with episodic rewards and
demonstrate substantial improvement over baseline algorithms.

Reciprocating probes are a classic and widespread tool for the investigation
of the edge and Scrape-Off Layer of magnetic fusion plasmas. In the Wendelstein
7-X (W7-X) stellarator, the Multi-Purpose Manipulator serves as a multi-user
platform for probe measurements of various kinds. This paper presents a review
on reciprocating probe operation during the first operation phase of W7-X with
a test divertor (2017-2018). It gives an overview of the diverse zoo of probe
heads and presents lessons learned about probe operation in complex magnetic
geometries, operation safety, and probe head design. A few examples of probe
measurements with a focus on unexpected observations are presented.

A complete understanding of the brain requires an integrated description of
the numerous scales of neural organization. It means studying the interplay of
genes, synapses, and even whole brain regions which ultimately leads to
different types of behavior, from perception to action, while asleep or awake.
Yet, multiscale brain modeling is challenging, in part because of the
difficulty to access simultaneously to information from multiple spatiotemporal
scales. While some insights have been gained on the role of specific
microcircuits (e.g., thalamocortical), a comprehensive characterization of how
changes occurring at one scale can have an impact on other ones, remains poorly
understood. Recent efforts to address this gap include the development of new
analytical tools mostly adapted from network science and dynamical systems
theory. These theoretical contributions provide a powerful framework to analyze
and model interconnected complex systems exhibiting interactions within and
between different scales, or layers. Here, we present recent advances for the
characterization of the multiscale brain organization in terms of
structure-function, oscillation frequencies and temporal evolution. Efforts are
reviewed on the multilayer network properties underlying higher-order
organization of neuronal assemblies, as well as on the identification of
multimodal network-based biomarkers of brain pathologies, such as Alzheimer's
disease. We conclude this Colloquium with a perspective discussion on how
recent results from multilayer network theory, involving generative modeling,
controllability and machine learning, could be adopted to address new questions
in modern neuroscience.

Attention modules for Convolutional Neural Networks (CNNs) are an effective
method to enhance performance on multiple computer-vision tasks. While existing
methods appropriately model channel-, spatial- and self-attention, they
primarily operate in a feedforward bottom-up manner. Consequently, the
attention mechanism strongly depends on the local information of a single input
feature map and does not incorporate relatively semantically-richer contextual
information available at higher layers that can specify "what and where to
look" in lower-level feature maps through top-down information flow.
  Accordingly, in this work, we propose a lightweight top-down attention module
(TDAM) that iteratively generates a "visual searchlight" to perform channel and
spatial modulation of its inputs and outputs more contextually-relevant feature
maps at each computation step. Our experiments indicate that TDAM enhances the
performance of CNNs across multiple object-recognition benchmarks and
outperforms prominent attention modules while being more parameter and memory
efficient. Further, TDAM-based models learn to "shift attention" by localizing
individual objects or features at each computation step without any explicit
supervision resulting in a 5% improvement for ResNet50 on weakly-supervised
object localization. Source code and models are publicly available at:
https://github.com/shantanuj/TDAM_Top_down_attention_module .

In this paper, a turbulence model based on deep neural network is developed
for turbulent flow around airfoil at high Reynolds numbers. According to the
data got from the Spalart-Allmaras (SA) turbulence model, we build a neural
network model that maps flow features to eddy viscosity. The model is then used
to replace the SA turbulence model to mutually couple with the CFD solver. We
build this suitable data-driven turbulence model mainly from the inputs,
outputs features and loss function of the model. A feature selection method
based on feature importance is also implemented. The results show that this
feature selection method can effectively remove redundant features. The model
based on the new input features has better accuracy and stability in mutual
coupling with the CFD solver. The force coefficient obtained from solution can
match the sample data well. The developed model also shows strong
generalization at different inflow condition (angle of attack, Mach number,
Reynolds number and airfoil).

Content creators often use music to enhance their stories, as it can be a
powerful tool to convey emotion. In this paper, our goal is to help creators
find music to match the emotion of their story. We focus on text-based stories
that can be auralized (e.g., books), use multiple sentences as input queries,
and automatically retrieve matching music. We formalize this task as a
cross-modal text-to-music retrieval problem. Both the music and text domains
have existing datasets with emotion labels, but mismatched emotion vocabularies
prevent us from using mood or emotion annotations directly for matching. To
address this challenge, we propose and investigate several emotion embedding
spaces, both manually defined (e.g., valence/arousal) and data-driven (e.g.,
Word2Vec and metric learning) to bridge this gap. Our experiments show that by
leveraging these embedding spaces, we are able to successfully bridge the gap
between modalities to facilitate cross modal retrieval. We show that our method
can leverage the well established valence-arousal space, but that it can also
achieve our goal via data-driven embedding spaces. By leveraging data-driven
embeddings, our approach has the potential of being generalized to other
retrieval tasks that require broader or completely different vocabularies.

Log data anomaly detection is a core component in the area of artificial
intelligence for IT operations. However, the large amount of existing methods
makes it hard to choose the right approach for a specific system. A better
understanding of different kinds of anomalies, and which algorithms are
suitable for detecting them, would support researchers and IT operators.
Although a common taxonomy for anomalies already exists, it has not yet been
applied specifically to log data, pointing out the characteristics and
peculiarities in this domain.
  In this paper, we present a taxonomy for different kinds of log data
anomalies and introduce a method for analyzing such anomalies in labeled
datasets. We applied our taxonomy to the three common benchmark datasets
Thunderbird, Spirit, and BGL, and trained five state-of-the-art unsupervised
anomaly detection algorithms to evaluate their performance in detecting
different kinds of anomalies. Our results show, that the most common anomaly
type is also the easiest to predict. Moreover, deep learning-based approaches
outperform data mining-based approaches in all anomaly types, but especially
when it comes to detecting contextual anomalies.

Recently developed offline reinforcement learning algorithms have made it
possible to learn policies directly from pre-collected datasets, giving rise to
a new dilemma for practitioners: Since the performance the algorithms are able
to deliver depends greatly on the dataset that is presented to them,
practitioners need to pick the right dataset among the available ones. This
problem has so far not been discussed in the corresponding literature. We
discuss ideas how to select promising datasets and propose three very simple
indicators: Estimated relative return improvement (ERI) and estimated action
stochasticity (EAS), as well as a combination of the two (COI), and empirically
show that despite their simplicity they can be very effectively used for
dataset selection.

Permeability has a dominant influence on the flow properties of a natural
fluid. Lattice Boltzmann simulator determines permeability from the nano and
micropore network. The simulator holds millions of flow dynamics calculations
with its accumulated errors and high consumption of computing power. To
efficiently and consistently predict permeability, we propose a morphology
decoder, a parallel and serial flow reconstruction of machine learning
segmented heterogeneous Cretaceous texture from 3D micro computerized
tomography and nuclear magnetic resonance images. For 3D vision, we introduce
controllable-measurable-volume as new supervised segmentation, in which a
unique set of voxel intensity corresponds to grain and pore throat sizes. The
morphology decoder demarks and aggregates the morphologies boundaries in a
novel way to produce permeability. Morphology decoder method consists of five
novel processes, which describes in this paper, these novel processes are: (1)
Geometrical 3D Permeability, (2) Machine Learning guided 3D Properties
Recognition of Rock Morphology, (3) 3D Image Properties Integration Model for
Permeability, (4) MRI Permeability Imager, and (5) Morphology Decoder (the
process that integrates the other four novel processes).

Machine unlearning for security is studied in this context. Several spam
email detection methods exist, each of which employs a different algorithm to
detect undesired spam emails. But these models are vulnerable to attacks. Many
attackers exploit the model by polluting the data, which are trained to the
model in various ways. So to act deftly in such situations model needs to
readily unlearn the polluted data without the need for retraining. Retraining
is impractical in most cases as there is already a massive amount of data
trained to the model in the past, which needs to be trained again just for
removing a small amount of polluted data, which is often significantly less
than 1%. This problem can be solved by developing unlearning frameworks for all
spam detection models. In this research, unlearning module is integrated into
spam detection models that are based on Naive Bayes, Decision trees, and Random
Forests algorithms. To assess the benefits of unlearning over retraining, three
spam detection models are polluted and exploited by taking attackers' positions
and proving models' vulnerability. Reduction in accuracy and true positive
rates are shown in each case showing the effect of pollution on models. Then
unlearning modules are integrated into the models, and polluted data is
unlearned; on testing the models after unlearning, restoration of performance
is seen. Also, unlearning and retraining times are compared with different
pollution data sizes on all models. On analyzing the findings, it can be
concluded that unlearning is considerably superior to retraining. Results show
that unlearning is fast, easy to implement, easy to use, and effective.

Inconsistency in prediction problems occurs when instances that relate in a
certain way on condition attributes, do not follow the same relation on the
decision attribute. For example, in ordinal classification with monotonicity
constraints, it occurs when an instance dominating another instance on
condition attributes has been assigned to a worse decision class. It typically
appears as a result of perturbation in data caused by incomplete knowledge
(missing attributes) or by random effects that occur during data generation
(instability in the assessment of decision attribute values). Inconsistencies
with respect to a crisp preorder relation (expressing either dominance or
indiscernibility between instances) can be handled using symbolic approaches
like rough set theory and by using statistical/machine learning approaches that
involve optimization methods. Fuzzy rough sets can also be seen as a symbolic
approach to inconsistency handling with respect to a fuzzy relation. In this
article, we introduce a new machine learning method for inconsistency handling
with respect to a fuzzy preorder relation. The novel approach is motivated by
the existing machine learning approach used for crisp relations. We provide
statistical foundations for it and develop optimization procedures that can be
used to eliminate inconsistencies. The article also proves important properties
and contains didactic examples of those procedures.

Transfer learning is a classic paradigm by which models pretrained on large
"upstream" datasets are adapted to yield good results on "downstream"
specialized datasets. Generally, more accurate models on the "upstream" dataset
tend to provide better transfer accuracy "downstream". In this work, we perform
an in-depth investigation of this phenomenon in the context of convolutional
neural networks (CNNs) trained on the ImageNet dataset, which have been pruned
- that is, compressed by sparsifying their connections. We consider transfer
using unstructured pruned models obtained by applying several state-of-the-art
pruning methods, including magnitude-based, second-order, re-growth,
lottery-ticket, and regularization approaches, in the context of twelve
standard transfer tasks. In a nutshell, our study shows that sparse models can
match or even outperform the transfer performance of dense models, even at high
sparsities, and, while doing so, can lead to significant inference and even
training speedups. At the same time, we observe and analyze significant
differences in the behaviour of different pruning methods.

Prompt-based approaches are strong at few-shot learning. However, Perez et
al. (2021) have recently cast doubt on their performance because they had
difficulty getting good results in a "true" few-shot setting in which prompts
and hyperparameters cannot be tuned on a dev set. In view of this, we conduct
an extensive study of PET, a method that combines textual instructions with
example-based finetuning. We show that, if correctly configured, PET performs
strongly in a true few-shot setting, i.e., without a dev set. Crucial for this
strong performance is PET's ability to intelligently handle multiple prompts.
We then put our findings to a real-world test by running PET on RAFT, a
benchmark of tasks taken directly from realistic NLP applications for which no
labeled dev or test sets are available. PET achieves a new state of the art on
RAFT and performs close to non-expert humans for 7 out of 11 tasks. These
results demonstrate that prompt-based learners like PET excel at true few-shot
learning and underpin our belief that learning from instructions will play an
important role on the path towards human-like few-shot learning capabilities.

Clinical decision support for histopathology image data mainly focuses on
strongly supervised annotations, which offers intuitive interpretability, but
is bound by expert performance. Here, we propose an explainable cancer relapse
prediction network (eCaReNet) and show that end-to-end learning without strong
annotations offers state-of-the-art performance while interpretability can be
included through an attention mechanism. On the use case of prostate cancer
survival prediction, using 14,479 images and only relapse times as annotations,
we reach a cumulative dynamic AUC of 0.78 on a validation set, being on par
with an expert pathologist (and an AUC of 0.77 on a separate test set). Our
model is well-calibrated and outputs survival curves as well as a risk score
and group per patient. Making use of the attention weights of a multiple
instance learning layer, we show that malignant patches have a higher influence
on the prediction than benign patches, thus offering an intuitive
interpretation of the prediction. Our code is available at
www.github.com/imsb-uke/ecarenet.

Finding optimal parameter configurations for tunable GPU kernels is a
non-trivial exercise for large search spaces, even when automated. This poses
an optimization task on a non-convex search space, using an expensive to
evaluate function with unknown derivative. These characteristics make a good
candidate for Bayesian Optimization, which has not been applied to this problem
before. However, the application of Bayesian Optimization to this problem is
challenging. We demonstrate how to deal with the rough, discrete, constrained
search spaces, containing invalid configurations. We introduce a novel
contextual variance exploration factor, as well as new acquisition functions
with improved scalability, combined with an informed acquisition function
selection mechanism. By comparing the performance of our Bayesian Optimization
implementation on various test cases to the existing search strategies in
Kernel Tuner, as well as other Bayesian Optimization implementations, we
demonstrate that our search strategies generalize well and consistently
outperform other search strategies by a wide margin.

Evaluating side-channel analysis (SCA) security is a complex process,
involving applying several techniques whose success depends on human
engineering. Therefore, it is crucial to avoid a false sense of confidence
provided by non-optimal (failing) attacks. Different alternatives have emerged
lately trying to mitigate human dependency, among which deep learning (DL)
attacks are the most studied today. DL promise to simplify the procedure by
e.g. evading the need for point of interest selection or the capability of
bypassing noise and desynchronization, among other shortcuts. However,
including DL in the equation comes at a price, since working with neural
networks is not straightforward in this context. Recently, an alternative has
appeared with the potential to mitigate this dependence without adding extra
complexity: Estimation of Distribution Algorithm-based SCA. In this paper, we
compare these two relevant methods, supporting our findings by experiments on

High annotation costs are a substantial bottleneck in applying modern deep
learning architectures to clinically relevant medical use cases, substantiating
the need for novel algorithms to learn from unlabeled data. In this work, we
propose ContIG, a self-supervised method that can learn from large datasets of
unlabeled medical images and genetic data. Our approach aligns images and
several genetic modalities in the feature space using a contrastive loss. We
design our method to integrate multiple modalities of each individual person in
the same model end-to-end, even when the available modalities vary across
individuals. Our procedure outperforms state-of-the-art self-supervised methods
on all evaluated downstream benchmark tasks. We also adapt gradient-based
explainability algorithms to better understand the learned cross-modal
associations between the images and genetic modalities. Finally, we perform
genome-wide association studies on the features learned by our models,
uncovering interesting relationships between images and genetic data.

Confounders in deep learning are in general detrimental to model's
generalization where they infiltrate feature representations. Therefore,
learning causal features that are free of interference from confounders is
important. Most previous causal learning based approaches employ back-door
criterion to mitigate the adverse effect of certain specific confounder, which
require the explicit identification of confounder. However, in real scenarios,
confounders are typically diverse and difficult to be identified. In this
paper, we propose a novel Confounder Identification-free Causal Visual Feature
Learning (CICF) method, which obviates the need for identifying confounders.
CICF models the interventions among different samples based on front-door
criterion, and then approximates the global-scope intervening effect upon the
instance-level interventions from the perspective of optimization. In this way,
we aim to find a reliable optimization direction, which avoids the intervening
effects of confounders, to learn causal features. Furthermore, we uncover the
relation between CICF and the popular meta-learning strategy MAML, and provide
an interpretation of why MAML works from the theoretical perspective of causal
learning for the first time. Thanks to the effective learning of causal
features, our CICF enables models to have superior generalization capability.
Extensive experiments on domain generalization benchmark datasets demonstrate
the effectiveness of our CICF, which achieves the state-of-the-art performance.

We present the development of a dataset for Kazakh named entity recognition.
The dataset was built as there is a clear need for publicly available annotated
corpora in Kazakh, as well as annotation guidelines containing
straightforward--but rigorous--rules and examples. The dataset annotation,
based on the IOB2 scheme, was carried out on television news text by two native
Kazakh speakers under the supervision of the first author. The resulting
dataset contains 112,702 sentences and 136,333 annotations for 25 entity
classes. State-of-the-art machine learning models to automatise Kazakh named
entity recognition were also built, with the best-performing model achieving an
exact match F1-score of 97.22% on the test set. The annotated dataset,
guidelines, and codes used to train the models are freely available for
download under the CC BY 4.0 licence from https://github.com/IS2AI/KazNERD.

Finding an optimal individualized treatment regimen is considered one of the
most challenging precision medicine problems. Various patient characteristics
influence the response to the treatment, and hence, there is no
one-size-fits-all regimen. Moreover, the administration of an unsafe dose
during the treatment can have adverse effects on health. Therefore, a treatment
model must ensure patient \emph{safety} while \emph{efficiently} optimizing the
course of therapy. We study a prevalent medical problem where the treatment
aims to keep a physiological variable in a safe range and preferably close to a
target level, which we refer to as \emph{leveling}. Such a task may be relevant
in numerous other domains as well. We propose ESCADA, a novel and generic
multi-armed bandit (MAB) algorithm tailored for the leveling task, to make
safe, personalized, and context-aware dose recommendations. We derive high
probability upper bounds on its cumulative regret and safety guarantees.
Following ESCADA's design, we also describe its Thompson sampling-based
counterpart. We discuss why the straightforward adaptations of the classical
MAB algorithms such as GP-UCB may not be a good fit for the leveling task.
Finally, we make \emph{in silico} experiments on the bolus-insulin dose
allocation problem in type-1 diabetes mellitus disease and compare our
algorithms against the famous GP-UCB algorithm, the rule-based dose
calculators, and a clinician.

Recently, machine-learning methods have been shown to be successful in
identifying and classifying different phases of the square-lattice Ising model.
We study the performance and limits of classification and regression models. In
particular, we investigate how accurately the correlation length, energy and
magnetisation can be recovered from a given configuration. We find that a
supervised learning study of a regression model yields good predictions for
magnetisation and energy, and acceptable predictions for the correlation
length.

Relative attribute (RA), referring to the preference over two images on the
strength of a specific attribute, can enable fine-grained image-to-image
translation due to its rich semantic information. Existing work based on RAs
however failed to reconcile the goal for fine-grained translation and the goal
for high-quality generation. We propose a new model TRIP to coordinate these
two goals for high-quality fine-grained translation. In particular, we
simultaneously train two modules: a generator that translates an input image to
the desired image with smooth subtle changes with respect to the interested
attributes; and a ranker that ranks rival preferences consisting of the input
image and the desired image. Rival preferences refer to the adversarial ranking
process: (1) the ranker thinks no difference between the desired image and the
input image in terms of the desired attributes; (2) the generator fools the
ranker to believe that the desired image changes the attributes over the input
image as desired. RAs over pairs of real images are introduced to guide the
ranker to rank image pairs regarding the interested attributes only. With an
effective ranker, the generator would "win" the adversarial game by producing
high-quality images that present desired changes over the attributes compared
to the input image. The experiments on two face image datasets and one shoe
image dataset demonstrate that our TRIP achieves state-of-art results in
generating high-fidelity images which exhibit smooth changes over the
interested attributes.

Manual annotation of medical images is highly subjective, leading to
inevitable and huge annotation biases. Deep learning models may surpass human
performance on a variety of tasks, but they may also mimic or amplify these
biases. Although we can have multiple annotators and fuse their annotations to
reduce stochastic errors, we cannot use this strategy to handle the bias caused
by annotators' preferences. In this paper, we highlight the issue of
annotator-related biases on medical image segmentation tasks, and propose a
Preference-involved Annotation Distribution Learning (PADL) framework to
address it from the perspective of disentangling an annotator's preference from
stochastic errors using distribution learning so as to produce not only a meta
segmentation but also the segmentation possibly made by each annotator. Under
this framework, a stochastic error modeling (SEM) module estimates the meta
segmentation map and average stochastic error map, and a series of human
preference modeling (HPM) modules estimate each annotator's segmentation and
the corresponding stochastic error. We evaluated our PADL framework on two
medical image benchmarks with different imaging modalities, which have been
annotated by multiple medical professionals, and achieved promising performance
on all five medical image segmentation tasks.

Deep Learning has become overly complicated and has enjoyed stellar success
in solving several classical problems like image classification, object
detection, etc. Several methods for explaining these decisions have been
proposed. Black-box methods to generate saliency maps are particularly
interesting due to the fact that they do not utilize the internals of the model
to explain the decision. Most black-box methods perturb the input and observe
the changes in the output. We formulate saliency map generation as a sequential
search problem and leverage upon Reinforcement Learning (RL) to accumulate
evidence from input images that most strongly support decisions made by a
classifier. Such a strategy encourages to search intelligently for the
perturbations that will lead to high-quality explanations. While successful
black box explanation approaches need to rely on heavy computations and suffer
from small sample approximation, the deterministic policy learned by our method
makes it a lot more efficient during the inference. Experiments on three
benchmark datasets demonstrate the superiority of the proposed approach in
inference time over state-of-the-arts without hurting the performance. Project
Page: https://cvir.github.io/projects/rexl.html

Skin dynamics contributes to the enriched realism of human body models in
rendered scenes. Traditional methods rely on physics-based simulations to
accurately reproduce the dynamic behavior of soft tissues. Due to the model
complexity and thus the heavy computation, however, they do not directly offer
practical solutions to domains where real-time performance is desirable. The
quality shapes obtained by physics-based simulations are not fully exploited by
example-based or more recent datadriven methods neither, with most of them
having focused on the modeling of static skin shapes by leveraging quality
data. To address these limitations, we present a learningbased method for
dynamic skin deformation. At the core of our work is a recurrent neural network
that learns to predict the nonlinear, dynamics-dependent shape change over time
from pre-existing mesh deformation sequence data. Our network also learns to
predict the variation of skin dynamics across different individuals with
varying body shapes. After training the network delivers realistic,
high-quality skin dynamics that is specific to a person in a real-time course.
We obtain results that significantly saves the computational time, while
maintaining comparable prediction quality compared to state-of-the-art results.

Reaction-diffusion models have been proposed for decades to capture the
growth of gliomas, the most common primary brain tumours. However, severe
limitations regarding the estimation of the initial conditions and parameter
values of such models have restrained their clinical use as a personalised
tool. In this work, we investigate the ability of deep convolutional neural
networks (DCNNs) to address the pitfalls commonly encountered in the field.
Based on 1,200 synthetic tumours grown over real brain geometries derived from
magnetic resonance (MR) data of 6 healthy subjects, we demonstrate the ability
of DCNNs to reconstruct a whole tumour cell density distribution from only two
imaging contours at a single time point. With an additional imaging contour
extracted at a prior time point, we also demonstrate the ability of DCNNs to
accurately estimate the individual diffusivity and proliferation parameters of
the model. From this knowledge, the spatio-temporal evolution of the tumour
cell density distribution at later time points can ultimately be precisely
captured using the model. We finally show the applicability of our approach to
MR data of a real glioblastoma patient. This approach may open the perspective
of a clinical application of reaction-diffusion growth models for tumour
prognosis and treatment planning.

Electron Backscattering Diffraction (EBSD) provides important information to
discriminate phase transformation products in steels. This task is
conventionally performed by an expert, who carries a high degree of
subjectivity and requires time and effort. In this paper, we question if
Convolutional Neural Networks (CNNs) are able to extract meaningful features
from EBSD-based data in order to automatically classify the present phases
within a steel microstructure. The selected case of study is ferrite-martensite
discrimination and U-Net has been selected as the network architecture to work
with. Pixel-wise accuracies around ~95% have been obtained when inputting raw
orientation data, while ~98% has been reached with orientation-derived
parameters such as Kernel Average Misorientation (KAM) or pattern quality.
Compared to other available approaches in the literature for phase
discrimination, the models presented here provided higher accuracies in shorter
times. These promising results open a possibility to work on more complex steel
microstructures.

Diffuse Optical Tomography (DOT) is an emerging technology in medical imaging
which employs light in the NIR spectrum to estimate the distribution of optical
coefficients in biological tissues for diagnostic and monitoring purposes. DOT
reconstruction implies the solution of a severely ill-posed inverse problem,
for which regularization techniques are mandatory in order to achieve
reasonable results. Traditionally, regularization techniques put a variance
prior on the desired solution/gradient via regularization parameters, whose
choice requires a fine tuning, specific for each case. In this work we explore
deep learning techniques in a fully data-driven approach, able of
reconstructing the generating signal (optical absorption coefficient) in an
automated way. We base our approach on the so-called Learned Singular Value
Decomposition, which has been proposed for general inverse problems, and we
tailor it to the DOT application. We perform tests with increasing levels of
noise on the measure, and compare it with standard variational approaches.

Federated Learning is a novel framework that allows multiple devices or
institutions to train a machine learning model collaboratively while preserving
their data private. This decentralized approach is prone to suffer the
consequences of data statistical heterogeneity, both across the different
entities and over time, which may lead to a lack of convergence. To avoid such
issues, different methods have been proposed in the past few years. However,
data may be heterogeneous in lots of different ways, and current proposals do
not always determine the kind of heterogeneity they are considering. In this
work, we formally classify data statistical heterogeneity and review the most
remarkable learning strategies that are able to face it. At the same time, we
introduce approaches from other machine learning frameworks, such as Continual
Learning, that also deal with data heterogeneity and could be easily adapted to
the Federated Learning settings.

WiFi-based sensing has aroused immense attention over recent years. The
rationale is that the signal fluctuations caused by humans carry the
information of human behavior which can be extracted from the channel state
information of WiFi. Still, the prior studies mainly focus on single-task
sensing (STS), e.g., gesture recognition, indoor localization, user
identification. Since the fluctuations caused by gestures are highly coupling
with body features and the user's location, we propose a WiFi-based multi-task
sensing model (Wimuse) to perform gesture recognition, indoor localization, and
user identification tasks simultaneously. However, these tasks have different
difficulty levels (i.e., imbalance issue) and need task-specific information
(i.e., discrepancy issue). To address these issues, the knowledge distillation
technique and task-specific residual adaptor are adopted in Wimuse. We first
train the STS model for each task. Then, for solving the imbalance issue, the
extracted common feature in Wimuse is encouraged to get close to the
counterpart features of the STS models. Further, for each task, a task-specific
residual adaptor is applied to extract the task-specific compensation feature
which is fused with the common feature to address the discrepancy issue. We
conduct comprehensive experiments on three public datasets and evaluation
suggests that Wimuse achieves state-of-the-art performance with the average
accuracy of 85.20%, 98.39%, and 98.725% on the joint task of gesture
recognition, indoor localization, and user identification, respectively.

In this paper, we propose a deep learning based reduced order modeling method
for stochastic underground flow problems in highly heterogeneous media. We aim
to utilize supervised learning to build a reduced surrogate model from the
stochastic parameter space that characterizes the possible highly heterogeneous
media to the solution space of a stochastic flow problem to have fast online
simulations. Dominant POD modes obtained from a well-designed spectral problem
in a global snapshot space are used to represent the solution of the flow
problem. Due to the small dimension of the solution, the complexity of the
neural network is significantly reduced. We adopt the generalized multiscale
finite element method (GMsFEM), in which a set of local multiscale basis
functions that can capture the heterogeneity of the media and source
information are constructed to efficiently generate globally defined snapshot
space. Rigorous theoretical analyses are provided and extensive numerical
experiments for linear and nonlinear stochastic flows are provided to verify
the superior performance of the proposed method.

Policymakers face a broader challenge of how to view AI capabilities today
and where does society stand in terms of those capabilities. This paper surveys
AI capabilities and tackles this very issue, exploring it in context of
political security in digitally networked societies. We extend the ideas of
Information Management to better understand contemporary AI systems as part of
a larger and more complex information system. Comprehensively reviewing AI
capabilities and contemporary man-machine interactions, we undertake conceptual
development to suggest that better information management could allow states to
more optimally offset the risks of AI enabled influence and better utilise the
emerging capabilities which these systems have to offer to policymakers and
political institutions across the world. Hopefully this long essay will actuate
further debates and discussions over these ideas, and prove to be a useful
contribution towards governing the future of AI.

Origin-Destination Estimation plays an important role in the era of
Intelligent Transportation. Nevertheless, as a under-determined problem, OD
estimation confronts many challenges from cross-space inference to non-convex,
non-linear optimization. As a powerful nonlinear approximator, deep learning is
an ideal data-driven method to provide a novel perspective for OD estimation.
However, viewing multi-interval traffic counts as spatial-temporal inputs and
OD matrix as heterogeneous graph-structured output, the existing neural network
architecture is not suitable for the cross-space inference problem thus a new
deep learning architecture is needed. We propose CGAME, short for cyclic graph
attentive matching encoder, including bi-directional encoder-decoder networks
and a novel graph matcher in the hidden layer with double-layer attention
mechanism. It realizes effective information exchange between the forward
networks and backward networks and establishes coupling relations across
underlying feature space. The proposed model achieves state-of-the-art compared
with baselines in the designed experiments and offers a paradigm for inference
tasks across representation space.

The native macOS application PicArrange integrates state-of-the-art image
sorting and similarity search to enable users to get a better overview of their
images. Many file and image management features have been added to make it a
tool that addresses a full image management workflow. A modification of the
Self Sorting Map algorithm enables a list-like image arrangement without
loosing the visual sorting. Efficient calculation and storage of visual
features as well as the use of many macOS APIs result in an application that is
fluid to use.

Multimodal data provide complementary information of a natural phenomenon by
integrating data from various domains with very different statistical
properties. Capturing the intra-modality and cross-modality information of
multimodal data is the essential capability of multimodal learning methods. The
geometry-aware data analysis approaches provide these capabilities by
implicitly representing data in various modalities based on their geometric
underlying structures. Also, in many applications, data are explicitly defined
on an intrinsic geometric structure. Generalizing deep learning methods to the
non-Euclidean domains is an emerging research field, which has recently been
investigated in many studies. Most of those popular methods are developed for
unimodal data. In this paper, a multimodal multi-scaled graph wavelet
convolutional network (M-GWCN) is proposed as an end-to-end network. M-GWCN
simultaneously finds intra-modality representation by applying the multiscale
graph wavelet transform to provide helpful localization properties in the graph
domain of each modality, and cross-modality representation by learning
permutations that encode correlations among various modalities. M-GWCN is not
limited to either the homogeneous modalities with the same number of data, or
any prior knowledge indicating correspondences between modalities. Several
semi-supervised node classification experiments have been conducted on three
popular unimodal explicit graph-based datasets and five multimodal implicit
ones. The experimental results indicate the superiority and effectiveness of
the proposed methods compared with both spectral graph domain convolutional
neural networks and state-of-the-art multimodal methods.

Recently, table structure recognition has achieved impressive progress with
the help of deep graph models. Most of them exploit single visual cues of
tabular elements or simply combine visual cues with other modalities via early
fusion to reason their graph relationships. However, neither early fusion nor
individually reasoning in terms of multiple modalities can be appropriate for
all varieties of table structures with great diversity. Instead, different
modalities are expected to collaborate with each other in different patterns
for different table cases. In the community, the importance of intra-inter
modality interactions for table structure reasoning is still unexplored. In
this paper, we define it as heterogeneous table structure recognition
(Hetero-TSR) problem. With the aim of filling this gap, we present a novel
Neural Collaborative Graph Machines (NCGM) equipped with stacked collaborative
blocks, which alternatively extracts intra-modality context and models
inter-modality interactions in a hierarchical way. It can represent the
intra-inter modality relationships of tabular elements more robustly, which
significantly improves the recognition performance. We also show that the
proposed NCGM can modulate collaborative pattern of different modalities
conditioned on the context of intra-modality cues, which is vital for
diversified table cases. Experimental results on benchmarks demonstrate our
proposed NCGM achieves state-of-the-art performance and beats other
contemporary methods by a large margin especially under challenging scenarios.

Predicting the plausible future trajectories of nearby agents is a core
challenge for the safety of Autonomous Vehicles and it mainly depends on two
external cues: the dynamic neighbor agents and static scene context. Recent
approaches have made great progress in characterizing the two cues separately.
However, they ignore the correlation between the two cues and most of them are
difficult to achieve map-adaptive prediction. In this paper, we use lane as
scene data and propose a staged network that Jointly learning Agent and Lane
information for Multimodal Trajectory Prediction (JAL-MTP). JAL-MTP use a
Social to Lane (S2L) module to jointly represent the static lane and the
dynamic motion of the neighboring agents as instance-level lane, a Recurrent
Lane Attention (RLA) mechanism for utilizing the instance-level lanes to
predict the map-adaptive future trajectories and two selectors to identify the
typical and reasonable trajectories. The experiments conducted on the public
Argoverse dataset demonstrate that JAL-MTP significantly outperforms the
existing models in both quantitative and qualitative.

Event extraction (EE) plays an important role in many industrial application
scenarios, and high-quality EE methods require a large amount of manual
annotation data to train supervised learning models. However, the cost of
obtaining annotation data is very high, especially for annotation of domain
events, which requires the participation of experts from corresponding domain.
So we introduce active learning (AL) technology to reduce the cost of event
annotation. But the existing AL methods have two main problems, which make them
not well used for event extraction. Firstly, the existing pool-based selection
strategies have limitations in terms of computational cost and sample validity.
Secondly, the existing evaluation of sample importance lacks the use of local
sample information. In this paper, we present a novel deep AL method for EE. We
propose a batch-based selection strategy and a Memory-Based Loss Prediction
model (MBLP) to select unlabeled samples efficiently. During the selection
process, we use an internal-external sample loss ranking method to evaluate the
sample importance by using local information. Finally, we propose a delayed
training strategy to train the MBLP model. Extensive experiments are performed
on three domain datasets, and our method outperforms other state-of-the-art
methods.

Viral infections are causing significant morbidity and mortality worldwide.
Understanding the interaction patterns between a particular virus and human
proteins plays a crucial role in unveiling the underlying mechanism of viral
infection and pathogenesis. This could further help in the prevention and
treatment of virus-related diseases. However, the task of predicting
protein-protein interactions between a new virus and human cells is extremely
challenging due to scarce data on virus-human interactions and fast mutation
rates of most viruses.
  We developed a multitask transfer learning approach that exploits the
information of around 24 million protein sequences and the interaction patterns
from the human interactome to counter the problem of small training datasets.
Instead of using hand-crafted protein features, we utilize statistically rich
protein representations learned by a deep language modeling approach from a
massive source of protein sequences. Additionally, we employ an additional
objective which aims to maximize the probability of observing human
protein-protein interactions. This additional task objective acts as a
regularizer and also allows to incorporate domain knowledge to inform the
virus-human protein-protein interaction prediction model.
  Our approach achieved competitive results on 13 benchmark datasets and the
case study for the SAR-CoV-2 virus receptor. Experimental results show that our
proposed model works effectively for both virus-human and bacteria-human
protein-protein interaction prediction tasks. We share our code for
reproducibility and future research at
https://git.l3s.uni-hannover.de/dong/multitask-transfer.

XORNet-based low power controller is a popular technique to reduce circuit
transitions in scan-based testing. However, existing solutions construct the
XORNet evenly for scan chain control, and it may result in sub-optimal
solutions without any design guidance. In this paper, we propose a novel
testability-aware low power controller with evolutionary learning. The XORNet
generated from the proposed genetic algorithm (GA) enables adaptive control for
scan chains according to their usages, thereby significantly improving XORNet
encoding capacity, reducing the number of failure cases with ATPG and
decreasing test data volume. Experimental results indicate that under the same
control bits, our GA-guided XORNet design can improve the fault coverage by up
to 2.11%. The proposed GA-guided XORNets also allows reducing the number of
control bits, and the total testing time decreases by 20.78% on average and up
to 47.09% compared to the existing design without sacrificing test coverage.

Much research effort has been devoted to explaining the success of deep
learning. Random Matrix Theory (RMT) provides an emerging way to this end:
spectral analysis of large random matrices involved in a trained deep neural
network (DNN) such as weight matrices or Hessian matrices with respect to the
stochastic gradient descent algorithm. To have more comprehensive understanding
of weight matrices spectra, we conduct extensive experiments on weight matrices
in different modules, e.g., layers, networks and data sets. Following the
previous work of \cite{martin2018implicit}, we classify the spectra in the
terminal stage into three main types: Light Tail (LT), Bulk Transition period
(BT) and Heavy Tail(HT). These different types, especially HT, implicitly
indicate some regularization in the DNNs. A main contribution from the paper is
that we identify the difficulty of the classification problem as a driving
factor for the appearance of heavy tail in weight matrices spectra. Higher the
classification difficulty, higher the chance for HT to appear. Moreover, the
classification difficulty can be affected by the signal-to-noise ratio of the
dataset, or by the complexity of the classification problem (complex features,
large number of classes) as well. Leveraging on this finding, we further
propose a spectral criterion to detect the appearance of heavy tails and use it
to early stop the training process without testing data. Such early stopped
DNNs have the merit of avoiding overfitting and unnecessary extra training
while preserving a much comparable generalization ability. These findings from
the paper are validated in several NNs, using Gaussian synthetic data and real
data sets (MNIST and CIFAR10).

Over the past few years, deep neural networks (DNNs) have achieved tremendous
success and have been continuously applied in many application domains.
However, during the practical deployment in the industrial tasks, DNNs are
found to be erroneous-prone due to various reasons such as overfitting, lacking
robustness to real-world corruptions during practical usage. To address these
challenges, many recent attempts have been made to repair DNNs for version
updates under practical operational contexts by updating weights (i.e., network
parameters) through retraining, fine-tuning, or direct weight fixing at a
neural level. In this work, as the first attempt, we initiate to repair DNNs by
jointly optimizing the architecture and weights at a higher (i.e., block)
level.
  We first perform empirical studies to investigate the limitation of whole
network-level and layer-level repairing, which motivates us to explore a novel
repairing direction for DNN repair at the block level. To this end, we first
propose adversarial-aware spectrum analysis for vulnerable block localization
that considers the neurons' status and weights' gradients in blocks during the
forward and backward processes, which enables more accurate candidate block
localization for repairing even under a few examples. Then, we further propose
the architecture-oriented search-based repairing that relaxes the targeted
block to a continuous repairing search space at higher deep feature levels. By
jointly optimizing the architecture and weights in that space, we can identify
a much better block architecture. We implement our proposed repairing
techniques as a tool, named ArchRepair, and conduct extensive experiments to
validate the proposed method. The results show that our method can not only
repair but also enhance accuracy & robustness, outperforming the
state-of-the-art DNN repair techniques.

Hierarchical models with gamma hyperpriors provide a flexible,
sparse-promoting framework to bridge $L^1$ and $L^2$ regularizations in
Bayesian formulations to inverse problems. Despite the Bayesian motivation for
these models, existing methodologies are limited to \textit{maximum a
posteriori} estimation. The potential to perform uncertainty quantification has
not yet been realized. This paper introduces a variational iterative
alternating scheme for hierarchical inverse problems with gamma hyperpriors.
The proposed variational inference approach yields accurate reconstruction,
provides meaningful uncertainty quantification, and is easy to implement. In
addition, it lends itself naturally to conduct model selection for the choice
of hyperparameters. We illustrate the performance of our methodology in several
computed examples, including a deconvolution problem and sparse identification
of dynamical systems from time series data.

The StochAstic Recursive grAdient algoritHm (SARAH) algorithm is a variance
reduced variant of the Stochastic Gradient Descent (SGD) algorithm that needs a
gradient of the objective function from time to time. In this paper, we remove
the necessity of a full gradient computation. This is achieved by using a
randomized reshuffling strategy and aggregating stochastic gradients obtained
in each epoch. The aggregated stochastic gradients serve as an estimate of a
full gradient in the SARAH algorithm. We provide a theoretical analysis of the
proposed approach and conclude the paper with numerical experiments that
demonstrate the efficiency of this approach.

In recent years, neural network based methods have been proposed as a method
that cangenerate representations from music, but they are not human readable
and hardly analyzable oreditable by a human. To address this issue, we propose
a novel method to learn source-awarelatent representations of music through
Vector-Quantized Variational Auto-Encoder(VQ-VAE).We train our VQ-VAE to encode
an input mixture into a tensor of integers in a discrete latentspace, and
design them to have a decomposed structure which allows humans to manipulatethe
latent vector in a source-aware manner. This paper also shows that we can
generate basslines by estimating latent vectors in a discrete space.

Applying deep learning (DL) techniques in the electronic design automation
(EDA) field has become a trending topic. Most solutions apply well-developed DL
models to solve specific EDA problems. While demonstrating promising results,
they require careful model tuning for every problem. The fundamental question
on "How to obtain a general and effective neural representation of circuits?"
has not been answered yet. In this work, we take the first step towards solving
this problem. We propose DeepGate, a novel representation learning solution
that effectively embeds both logic function and structural information of a
circuit as vectors on each gate. Specifically, we propose transforming circuits
into unified and-inverter graph format for learning and using signal
probabilities as the supervision task in DeepGate. We then introduce a novel
graph neural network that uses strong inductive biases in practical circuits as
learning priors for signal probability prediction. Our experimental results
show the efficacy and generalization capability of DeepGate.

This study presents two supervised multiclassification machine learning
models to predict the poverty status of Costa Rican households as a way to
support government and business sectors make decisions in a rapidly changing
social and economic environment. Using the Costa Rican household dataset
collected via the proxy means test conducted by the Inter-American Development
Bank, Random Forest and Gradient Boosted Trees achieved F1 scores of 64.9% and
68.4%, respectively. This study also reveals that education has the greatest
impact on predicting poverty status.

Dynamic Time Warping (DTW), and its constrained (CDTW) and weighted (WDTW)
variants, are time series distances with a wide range of applications. They
minimize the cost of non-linear alignments between series. CDTW and WDTW have
been introduced because DTW is too permissive in its alignments. However, CDTW
uses a crude step function, allowing unconstrained flexibility within the
window, and none beyond it. WDTW's multiplicative weight is relative to the
distances between aligned points along a warped path, rather than being a
direct function of the amount of warping that is introduced. In this paper, we
introduce Amerced Dynamic Time Warping (ADTW), a new, intuitive, DTW variant
that penalizes the act of warping by a fixed additive cost. Like CDTW and WDTW,
ADTW constrains the amount of warping. However, it avoids both abrupt
discontinuities in the amount of warping allowed and the limitations of a
multiplicative penalty. We formally introduce ADTW, prove some of its
properties, and discuss its parameterization. We show on a simple example how
it can be parameterized to achieve an intuitive outcome, and demonstrate its
usefulness on a standard time series classification benchmark. We provide a
demonstration application in C++.

Numerous physical systems are described by ordinary or partial differential
equations whose solutions are given by holomorphic or meromorphic functions in
the complex domain. In many cases, only the magnitude of these functions are
observed on various points on the purely imaginary jw-axis since coherent
measurement of their phases is often expensive. However, it is desirable to
retrieve the lost phases from the magnitudes when possible. To this end, we
propose a physics-infused deep neural network based on the Blaschke products
for phase retrieval. Inspired by the Helson and Sarason Theorem, we recover
coefficients of a rational function of Blaschke products using a Blaschke
Product Neural Network (BPNN), based upon the magnitude observations as input.
The resulting rational function is then used for phase retrieval. We compare
the BPNN to conventional deep neural networks (NNs) on several phase retrieval
problems, comprising both synthetic and contemporary real-world problems (e.g.,
metamaterials for which data collection requires substantial expertise and is
time consuming). On each phase retrieval problem, we compare against a
population of conventional NNs of varying size and hyperparameter settings.
Even without any hyper-parameter search, we find that BPNNs consistently
outperform the population of optimized NNs in scarce data scenarios, and do so
despite being much smaller models. The results can in turn be applied to
calculate the refractive index of metamaterials, which is an important problem
in emerging areas of material science.

Binary perceptron is a fundamental model of supervised learning for the
non-convex optimization, which is a root of the popular deep learning. Binary
perceptron is able to achieve a classification of random high-dimensional data
by computing the marginal probabilities of binary synapses. The relationship
between the algorithmic instability and the equilibrium analysis of the model
remains elusive. Here, we establish the relationship by showing that the
instability condition around the algorithmic fixed point is identical to the
instability for breaking the replica symmetric saddle point solution of the
free energy function. Therefore, our analysis would hopefully provide insights
towards other learning systems in bridging the gap between non-convex learning
dynamics and statistical mechanics properties of more complex neural networks.

Self-supervised learning approach like contrastive learning is attached great
attention in natural language processing. It uses pairs of training data
augmentations to build a classification task for an encoder with well
representation ability. However, the construction of learning pairs over
contrastive learning is much harder in NLP tasks. Previous works generate
word-level changes to form pairs, but small transforms may cause notable
changes on the meaning of sentences as the discrete and sparse nature of
natural language. In this paper, adversarial training is performed to generate
challenging and harder learning adversarial examples over the embedding space
of NLP as learning pairs. Using contrastive learning improves the
generalization ability of adversarial training because contrastive loss can
uniform the sample distribution. And at the same time, adversarial training
also enhances the robustness of contrastive learning. Two novel frameworks,
supervised contrastive adversarial learning (SCAL) and unsupervised SCAL
(USCAL), are proposed, which yields learning pairs by utilizing the adversarial
training for contrastive learning. The label-based loss of supervised tasks is
exploited to generate adversarial examples while unsupervised tasks bring
contrastive loss. To validate the effectiveness of the proposed framework, we
employ it to Transformer-based models for natural language understanding,
sentence semantic textual similarity and adversarial learning tasks.
Experimental results on GLUE benchmark tasks show that our fine-tuned
supervised method outperforms BERT$_{base}$ over 1.75\%. We also evaluate our
unsupervised method on semantic textual similarity (STS) tasks, and our method
gets 77.29\% with BERT$_{base}$. The robustness of our approach conducts
state-of-the-art results under multiple adversarial datasets on NLI tasks.

Recently, phishing scams have posed a significant threat to blockchains.
Phishing detectors direct their efforts in hunting phishing addresses. Most of
the detectors extract target addresses' transaction behavior features by random
walking or constructing static subgraphs. The random walking
methods,unfortunately, usually miss structural information due to limited
sampling sequence length, while the static subgraph methods tend to ignore
temporal features lying in the evolving transaction behaviors. More
importantly, their performance undergoes severe degradation when the malicious
users intentionally hide phishing behaviors. To address these challenges, we
propose TEGDetector, a dynamic graph classifier that learns the evolving
behavior features from transaction evolution graphs (TEGs). First, we cast the
transaction series into multiple time slices, capturing the target address's
transaction behaviors in different periods. Then, we provide a fast
non-parametric phishing detector to narrow down the search space of suspicious
addresses. Finally, TEGDetector considers both the spatial and temporal
evolutions towards a complete characterization of the evolving transaction
behaviors. Moreover, TEGDetector utilizes adaptively learnt time coefficient to
pay distinct attention to different periods, which provides several novel
insights. Extensive experiments on the large-scale Ethereum transaction dataset
demonstrate that the proposed method achieves state-of-the-art detection

We propose a Transformer architecture for volumetric segmentation, a
challenging task that requires keeping a complex balance in encoding local and
global spatial cues, and preserving information along all axes of the volume.
Encoder of the proposed design benefits from self-attention mechanism to
simultaneously encode local and global cues, while the decoder employs a
parallel self and cross attention formulation to capture fine details for
boundary refinement. Empirically, we show that the proposed design choices
result in a computationally efficient model, with competitive and promising
results on the Medical Segmentation Decathlon (MSD) brain tumor segmentation
(BraTS) Task. We further show that the representations learned by our model are
robust against data corruptions.
\href{https://github.com/himashi92/VT-UNet}{Our code implementation is publicly
available}.

Liver cancer is one of the most common malignant diseases in the world.
Segmentation and labeling of liver tumors and blood vessels in CT images can
provide convenience for doctors in liver tumor diagnosis and surgical
intervention. In the past decades, many state-of-the-art medical image
segmentation algorithms appeared during this period. With the development of
embedded devices, embedded deployment for medical segmentation and automatic
reconstruction brings prospects for future automated surgical tasks. Yet, most
of the existing segmentation methods mostly care about the spatial feature
context and have a perception defect in the semantic relevance of medical
images, which significantly affects the segmentation accuracy of liver tumors
and blood vessels. Deploying large and complex models into embedded devices
requires a reasonable trade-off between model accuracy, reasoning speed and
model capacity. Given these problems, we introduce a multi-scale feature fusion
network called TransFusionNet based on Transformer. This network achieved very
competitive performance for liver vessel and liver tumor segmentation tasks,
meanwhile it can improve the recognition of morphologic margins of liver tumors
by exploiting the global information of CT images. Experiments show that in
vessel segmentation task TransFusionNet achieved mean Dice coefficients of
0.899 and in liver tumor segmentation task TransFusionNet achieved mean Dice
coefficients of 0.961. Compared with the state-of-the-art framework, our model
achieves the best segmentation result. In addition, we deployed the model into
an embedded micro-structure and constructed an integrated model for liver tumor
vascular segmentation and reconstruction. This proprietary structure will be
the exclusive component of the future medical field.

Memory replay may be key to learning in biological brains, which manage to
learn new tasks continually without catastrophically interfering with previous
knowledge. On the other hand, artificial neural networks suffer from
catastrophic forgetting and tend to only perform well on tasks that they were
recently trained on. In this work we explore the application of latent space
based memory replay for classification using artificial neural networks. We are
able to preserve good performance in previous tasks by storing only a small
percentage of the original data in a compressed latent space version.

Semiconductor device models are essential to understand the charge transport
in thin film transistors (TFTs). Using these TFT models to draw inference
involves estimating parameters used to fit to the experimental data. These
experimental data can involve extracted charge carrier mobility or measured
current. Estimating these parameters help us draw inferences about device
performance. Fitting a TFT model for a given experimental data using the model
parameters relies on manual fine tuning of multiple parameters by human
experts. Several of these parameters may have confounding effects on the
experimental data, making their individual effect extraction a non-intuitive
process during manual tuning. To avoid this convoluted process, we propose a
new method for automating the model parameter extraction process resulting in
an accurate model fitting. In this work, model choice based approximate
Bayesian computation (aBc) is used for generating the posterior distribution of
the estimated parameters using observed mobility at various gate voltage
values. Furthermore, it is shown that the extracted parameters can be
accurately predicted from the mobility curves using gradient boosted trees.
This work also provides a comparative analysis of the proposed framework with
fine-tuned neural networks wherein the proposed framework is shown to perform
better.

Many existing neural architecture search (NAS) solutions rely on downstream
training for architecture evaluation, which takes enormous computations.
Considering that these computations bring a large carbon footprint, this paper
aims to explore a green (namely environmental-friendly) NAS solution that
evaluates architectures without training. Intuitively, gradients, induced by
the architecture itself, directly decide the convergence and generalization
results. It motivates us to propose the gradient kernel hypothesis: Gradients
can be used as a coarse-grained proxy of downstream training to evaluate
random-initialized networks. To support the hypothesis, we conduct a
theoretical analysis and find a practical gradient kernel that has good
correlations with training loss and validation performance. According to this
hypothesis, we propose a new kernel based architecture search approach KNAS.
Experiments show that KNAS achieves competitive results with orders of
magnitude faster than "train-then-test" paradigms on image classification
tasks. Furthermore, the extremely low search cost enables its wide
applications. The searched network also outperforms strong baseline
RoBERTA-large on two text classification tasks. Codes are available at
\url{https://github.com/Jingjing-NLP/KNAS} .

This is a tutorial and survey paper on Generative Adversarial Network (GAN),
adversarial autoencoders, and their variants. We start with explaining
adversarial learning and the vanilla GAN. Then, we explain the conditional GAN
and DCGAN. The mode collapse problem is introduced and various methods,
including minibatch GAN, unrolled GAN, BourGAN, mixture GAN, D2GAN, and
Wasserstein GAN, are introduced for resolving this problem. Then, maximum
likelihood estimation in GAN are explained along with f-GAN, adversarial
variational Bayes, and Bayesian GAN. Then, we cover feature matching in GAN,
InfoGAN, GRAN, LSGAN, energy-based GAN, CatGAN, MMD GAN, LapGAN, progressive
GAN, triple GAN, LAG, GMAN, AdaGAN, CoGAN, inverse GAN, BiGAN, ALI, SAGAN,
Few-shot GAN, SinGAN, and interpolation and evaluation of GAN. Then, we
introduce some applications of GAN such as image-to-image translation
(including PatchGAN, CycleGAN, DeepFaceDrawing, simulated GAN, interactive
GAN), text-to-image translation (including StackGAN), and mixing image
characteristics (including FineGAN and MixNMatch). Finally, we explain the
autoencoders based on adversarial learning including adversarial autoencoder,
PixelGAN, and implicit autoencoder.

In a recorded situation, textual information is crucial for scene
interpretation and decision making. The ability to edit text directly on images
has a number of advantages, including error correction, text restoration, and
image reusability. This research shows how to change image text at the letter
and digits level. I devised a two-part letters-digits network (LDN) to encode
and decode digital images, as well as learn and transfer the font style of the
source characters to the target characters. This method allows you to update
the uppercase letters, lowercase letters and digits in the picture.

The need for learning from unlabeled data is increasing in contemporary
machine learning. Methods for unsupervised feature ranking, which identify the
most important features in such data are thus gaining attention, and so are
their applications in studying high throughput biological experiments or user
bases for recommender systems. We propose FRANe (Feature Ranking via Attribute
Networks), an unsupervised algorithm capable of finding key features in given
unlabeled data set. FRANe is based on ideas from network reconstruction and
network analysis. FRANe performs better than state-of-the-art competitors, as
we empirically demonstrate on a large collection of benchmarks. Moreover, we
provide the time complexity analysis of FRANe further demonstrating its
scalability. Finally, FRANe offers as the result the interpretable relational
structures used to derive the feature importances.

Hamilton and Moitra (2021) showed that, in certain regimes, it is not
possible to accelerate Riemannian gradient descent in the hyperbolic plane if
we restrict ourselves to algorithms which make queries in a (large) bounded
domain and which receive gradients and function values corrupted by a (small)
amount of noise. We show that acceleration remains unachievable for any
deterministic algorithm which receives exact gradient and function-value
information (unbounded queries, no noise). Our results hold for the classes of
strongly and nonstrongly geodesically convex functions, and for a large class
of Hadamard manifolds including hyperbolic spaces and the symmetric space
$\mathrm{SL}(n) / \mathrm{SO}(n)$ of positive definite $n \times n$ matrices of
determinant one. This cements a surprising gap between the complexity of convex
optimization and geodesically convex optimization: for hyperbolic spaces,
Riemannian gradient descent is optimal on the class of smooth and and strongly
geodesically convex functions, in the regime where the condition number scales
with the radius of the optimization domain. The key idea for proving the lower
bound consists of perturbing the hard functions of Hamilton and Moitra (2021)
with sums of bump functions chosen by a resisting oracle.

Semi-supervised video action recognition tends to enable deep neural networks
to achieve remarkable performance even with very limited labeled data. However,
existing methods are mainly transferred from current image-based methods (e.g.,
FixMatch). Without specifically utilizing the temporal dynamics and inherent
multimodal attributes, their results could be suboptimal. To better leverage
the encoded temporal information in videos, we introduce temporal gradient as
an additional modality for more attentive feature extraction in this paper. To
be specific, our method explicitly distills the fine-grained motion
representations from temporal gradient (TG) and imposes consistency across
different modalities (i.e., RGB and TG). The performance of semi-supervised
action recognition is significantly improved without additional computation or
parameters during inference. Our method achieves the state-of-the-art
performance on three video action recognition benchmarks (i.e., Kinetics-400,
UCF-101, and HMDB-51) under several typical semi-supervised settings (i.e.,
different ratios of labeled data).

Many tasks in deep learning involve optimizing over the \emph{inputs} to a
network to minimize or maximize some objective; examples include optimization
over latent spaces in a generative model to match a target image, or
adversarially perturbing an input to worsen classifier performance. Performing
such optimization, however, is traditionally quite costly, as it involves a
complete forward and backward pass through the network for each gradient step.
In a separate line of work, a recent thread of research has developed the deep
equilibrium (DEQ) model, a class of models that foregoes traditional network
depth and instead computes the output of a network by finding the fixed point
of a single nonlinear layer. In this paper, we show that there is a natural
synergy between these two settings. Although, naively using DEQs for these
optimization problems is expensive (owing to the time needed to compute a fixed
point for each gradient step), we can leverage the fact that gradient-based
optimization can \emph{itself} be cast as a fixed point iteration to
substantially improve the overall speed. That is, we \emph{simultaneously} both
solve for the DEQ fixed point \emph{and} optimize over network inputs, all
within a single ``augmented'' DEQ model that jointly encodes both the original
network and the optimization process. Indeed, the procedure is fast enough that
it allows us to efficiently \emph{train} DEQ models for tasks traditionally
relying on an ``inner'' optimization loop. We demonstrate this strategy on
various tasks such as training generative models while optimizing over latent
codes, training models for inverse problems like denoising and inpainting,
adversarial training and gradient based meta-learning.

We propose a method to detect outliers in empirically observed trajectories
on a discrete or discretized manifold modeled by a simplicial complex. Our
approach is similar to spectral embeddings such as diffusion-maps and Laplacian
eigenmaps, that construct vertex embeddings from the eigenvectors of the graph
Laplacian associated with low eigenvalues. Here we consider trajectories as
edge-flow vectors defined on a simplicial complex, a higher-order
generalization of graphs, and use the Hodge 1-Laplacian of the simplicial
complex to derive embeddings of these edge-flows. By projecting trajectory
vectors onto the eigenspace of the Hodge 1-Laplacian associated to small
eigenvalues, we can characterize the behavior of the trajectories relative to
the homology of the complex, which corresponds to holes in the underlying
space. This enables us to classify trajectories based on simply interpretable,
low-dimensional statistics. We show how this technique can single out
trajectories that behave (topologically) different compared to typical
trajectories, and illustrate the performance of our approach with both
synthetic and empirical data.

The identification of community structure in a social network is an important
problem tackled in the literature of network analysis. There are many solutions
to this problem using a static scenario, when facing a dynamic scenario some
solutions may be adapted but others simply do not fit, moreover when
considering the demand to analyze constantly growing networks. In this context,
we propose an approach to the problem of community detection in dynamic
networks based on a reinforcement learning strategy to deal with changes on big
networks using a local optimization on the modularity score of the changed
entities. An experiment using synthetic and real-world dynamic network data
shows results comparable to static scenarios.

Despite the success of deep learning in computer vision, algorithms to
recognize subtle and small objects (or regions) is still challenging. For
example, recognizing a baseball or a frisbee on a ground scene or a bone
fracture in an X-ray image can easily result in overfitting, unless a huge
amount of training data is available. To mitigate this problem, we need a way
to force a model should identify subtle regions in limited training data. In
this paper, we propose a simple but efficient supervised augmentation method
called Cut\&Remain. It achieved better performance on various medical image
domain (internally sourced- and public dataset) and a natural image domain
(MS-COCO$_s$) than other supervised augmentation and the explicit guidance
methods.
  In addition, using the class activation map, we identified that the
Cut\&Remain methods drive a model to focus on relevant subtle and small regions
efficiently. We also show that the performance monotonically increased along
the Cut\&Remain ratio, indicating that a model can be improved even though only
limited amount of Cut\&Remain is applied for, so that it allows low supervising
(annotation) cost for improvement.

Federated learning (FL) enables collaborative learning of a deep learning
model without sharing the data of participating sites. FL in medical image
analysis tasks is relatively new and open for enhancements. In this study, we
propose FedDropoutAvg, a new federated learning approach for training a
generalizable model. The proposed method takes advantage of randomness, both in
client selection and also in federated averaging process. We compare
FedDropoutAvg to several algorithms in an FL scenario for real-world multi-site
histopathology image classification task. We show that with FedDropoutAvg, the
final model can achieve performance better than other FL approaches and closer
to a classical deep learning model that requires all data to be shared for
centralized training. We test the trained models on a large dataset consisting
of 1.2 million image tiles from 21 different centers. To evaluate the
generalization ability of the proposed approach, we use held-out test sets from
centers whose data was used in the FL and for unseen data from other
independent centers whose data was not used in the federated training. We show
that the proposed approach is more generalizable than other state-of-the-art
federated training approaches. To the best of our knowledge, ours is the first
study to use a randomized client and local model parameter selection procedure
in a federated setting for a medical image analysis task.

Randomized clinical trials eliminate confounding but impose strict exclusion
criteria that limit recruitment to a subset of the population. Observational
datasets are more inclusive but suffer from confounding -- often providing
overly optimistic estimates of treatment response over time due to partially
optimized physician prescribing patterns. We therefore assume that the
unconfounded treatment response lies somewhere in-between the observational
estimate before and the observational estimate after treatment assignment. This
assumption allows us to extrapolate results from exclusive trials to the
broader population by analyzing observational and trial data simultaneously
using an algorithm called Optimum in Convex Hulls (OCH). OCH represents the
treatment effect either in terms of convex hulls of conditional expectations or
convex hulls (also known as mixtures) of conditional densities. The algorithm
first learns the component expectations or densities using the observational
data and then learns the linear mixing coefficients using trial data in order
to approximate the true treatment effect; theory importantly explains why this
linear combination should hold. OCH estimates the treatment effect in terms
both expectations and densities with state of the art accuracy.

Causal inference grows increasingly complex as the number of confounders
increases. Given treatments $X$, confounders $Z$ and outcomes $Y$, we develop a
non-parametric method to test the \textit{do-null} hypothesis $H_0:\;
p(y|\text{\it do}(X=x))=p(y)$ against the general alternative. Building on the
Hilbert Schmidt Independence Criterion (HSIC) for marginal independence
testing, we propose backdoor-HSIC (bd-HSIC) and demonstrate that it is
calibrated and has power for both binary and continuous treatments under a
large number of confounders. Additionally, we establish convergence properties
of the estimators of covariance operators used in bd-HSIC. We investigate the
advantages and disadvantages of bd-HSIC against parametric tests as well as the
importance of using the do-null testing in contrast to marginal independence
testing or conditional independence testing. A complete implementation can be
found at
\hyperlink{https://github.com/MrHuff/kgformula}{\texttt{https://github.com/MrHuff/kgformula}}.

We are interested in privatizing an approximate posterior inference algorithm
called Expectation Propagation (EP). EP approximates the posterior by
iteratively refining approximations to the local likelihoods, and is known to
provide better posterior uncertainties than those by variational inference
(VI). However, EP needs a large memory to maintain all local approximates
associated with each datapoint in the training data. To overcome this
challenge, stochastic expectation propagation (SEP) considers a single unique
local factor that captures the average effect of each likelihood term to the
posterior and refines it in a way analogous to EP. In terms of privacy, SEP is
more tractable than EP because at each refining step of a factor, the remaining
factors are fixed and do not depend on other datapoints as in EP, which makes
the sensitivity analysis straightforward. We provide a theoretical analysis of
the privacy-accuracy trade-off in the posterior estimates under our method,
called differentially private stochastic expectation propagation (DP-SEP).
Furthermore, we demonstrate the performance of our DP-SEP algorithm evaluated
on both synthetic and real-world datasets in terms of the quality of posterior
estimates at different levels of guaranteed privacy.

We address the task of domain adaptation in object detection, where there is
a domain gap between a domain with annotations (source) and a domain of
interest without annotations (target). As an effective semi-supervised learning
method, the teacher-student framework (a student model is supervised by the
pseudo labels from a teacher model) has also yielded a large accuracy gain in
cross-domain object detection. However, it suffers from the domain shift and
generates many low-quality pseudo labels (\textit{e.g.,} false positives),
which leads to sub-optimal performance. To mitigate this problem, we propose a
teacher-student framework named Adaptive Teacher (AT) which leverages domain
adversarial learning and weak-strong data augmentation to address the domain
gap. Specifically, we employ feature-level adversarial training in the student
model, allowing features derived from the source and target domains to share
similar distributions. This process ensures the student model produces
domain-invariant features. Furthermore, we apply weak-strong augmentation and
mutual learning between the teacher model (taking data from the target domain)
and the student model (taking data from both domains). This enables the teacher
model to learn the knowledge from the student model without being biased to the
source domain. We show that AT demonstrates superiority over existing
approaches and even Oracle (fully-supervised) models by a large margin. For
example, we achieve 50.9% (49.3%) mAP on Foggy Cityscape (Clipart1K), which is
9.2% (5.2%) and 8.2% (11.0%) higher than previous state-of-the-art and Oracle,

Cancelable biometrics refers to a group of techniques in which the biometric
inputs are transformed intentionally using a key before processing or storage.
This transformation is repeatable enabling subsequent biometric comparisons.
This paper introduces a new scheme for cancelable biometrics aimed at
protecting the templates against potential attacks, applicable to any
biometric-based recognition system. Our proposed scheme is based on
time-varying keys obtained from morphing random biometric information. An
experimental implementation of the proposed scheme is given for face
biometrics. The results confirm that the proposed approach is able to withstand
against leakage attacks while improving the recognition performance.

Current models on Explainable Artificial Intelligence (XAI) have shown an
evident and quantified lack of reliability for measuring feature-relevance when
statistically entangled features are proposed for training deep classifiers.
There has been an increase in the application of Deep Learning in clinical
trials to predict early diagnosis of neuro-developmental disorders, such as
Autism Spectrum Disorder (ASD). However, the inclusion of more reliable
saliency-maps to obtain more trustworthy and interpretable metrics using neural
activity features is still insufficiently mature for practical applications in
diagnostics or clinical trials. Moreover, in ASD research the inclusion of deep
classifiers that use neural measures to predict viewed facial emotions is
relatively unexplored. Therefore, in this study we propose the evaluation of a
Convolutional Neural Network (CNN) for electroencephalography (EEG)-based
facial emotion recognition decoding complemented with a novel
RemOve-And-Retrain (ROAR) methodology to recover highly relevant features used
in the classifier. Specifically, we compare well-known relevance maps such as
Layer-Wise Relevance Propagation (LRP), PatternNet, Pattern-Attribution, and
Smooth-Grad Squared. This study is the first to consolidate a more transparent
feature-relevance calculation for a successful EEG-based facial emotion
recognition using a within-subject-trained CNN in typically-developed and ASD
individuals.

We propose Characteristic-Neural Ordinary Differential Equations (C-NODEs), a
framework for extending Neural Ordinary Differential Equations (NODEs) beyond
ODEs. While NODEs model the evolution of a latent variables as the solution to
an ODE, C-NODE models the evolution of the latent variables as the solution of
a family of first-order quasi-linear partial differential equations (PDEs)
along curves on which the PDEs reduce to ODEs, referred to as characteristic
curves. This in turn allows the application of the standard frameworks for
solving ODEs, namely the adjoint method. Learning optimal characteristic curves
for given tasks improves the performance and computational efficiency, compared
to state of the art NODE models. We prove that the C-NODE framework extends the
classical NODE on classification tasks by demonstrating explicit C-NODE
representable functions not expressible by NODEs. Additionally, we present
C-NODE-based continuous normalizing flows, which describe the density evolution
of latent variables along multiple dimensions. Empirical results demonstrate
the improvements provided by the proposed method for classification and density
estimation on CIFAR-10, SVHN, and MNIST datasets under a similar computational
budget as the existing NODE methods. The results also provide empirical
evidence that the learned curves improve the efficiency of the system through a
lower number of parameters and function evaluations compared with baselines.

We introduce the notion of regularized Bayesian best response (RBBR) learning
dynamic in heterogeneous population games. We obtain such a dynamic via
perturbation by an arbitrary strongly convex regularizer in Bayesian population
games with finitely many strategies. We provide a sufficient condition for the
existence of rest points of the RBBR learning dynamic, and hence the existence
of regularized Bayesian equilibrium in Bayesian population games. We also
explore the fundamental properties of the RBBR learning dynamic, which includes
the existence of unique continuous solutions from arbitrary initial conditions,
as well as the continuity of the solution trajectories thus obtained with
respect to the initial conditions. Finally, as an application we introduce the
notions of Bayesian potential and Bayesian negative semidefinite games and
provide convergence results for such games.

Differentiable Architecture Search (DARTS) has received massive attention in
recent years, mainly because it significantly reduces the computational cost
through weight sharing and continuous relaxation. However, more recent works
find that existing differentiable NAS techniques struggle to outperform naive
baselines, yielding deteriorative architectures as the search proceeds. Rather
than directly optimizing the architecture parameters, this paper formulates the
neural architecture search as a distribution learning problem through relaxing
the architecture weights into Gaussian distributions. By leveraging the
natural-gradient variational inference (NGVI), the architecture distribution
can be easily optimized based on existing codebases without incurring more
memory and computational consumption. We demonstrate how the differentiable NAS
benefits from Bayesian principles, enhancing exploration and improving
stability. The experimental results on NAS-Bench-201 and NAS-Bench-1shot1
benchmark datasets confirm the significant improvements the proposed framework
can make. In addition, instead of simply applying the argmax on the learned
parameters, we further leverage the recently-proposed training-free proxies in
NAS to select the optimal architecture from a group architectures drawn from
the optimized distribution, where we achieve state-of-the-art results on the
NAS-Bench-201 and NAS-Bench-1shot1 benchmarks. Our best architecture in the
DARTS search space also obtains competitive test errors with 2.37\%, 15.72\%,
and 24.2\% on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively.

In this work, we extensively redesign the newly introduced method of token
mixing using Fourier Transforms (FNET) to replace the computationally expensive
self-attention mechanism in a full transformer implementation on a long
document summarization task (> 512 tokens). As a baseline, we also carried out
long document summarization using established methods such as Longformer and
Big Bird transformer models that are capable of processing over 8000 tokens and
are currently the state of the art methods for these type of problems. The
original FNET paper implemented this in an encoder only architecture while
abstractive summarization requires both an encoder and a decoder. Since such a
pretrained transformer model does not currently exist in the public domain, we
decided to implement a full transformer based on this Fourier token mixing
approach in an encoder/decoder architecture which we trained starting with
Glove embeddings for the individual words in the corpus. We investigated a
number of different extensions to the original FNET architecture and evaluated
them on their Rouge F1-score performance on a summarization task. All
modifications showed better performance on the summarization task than when
using the original FNET encoder in a transformer architecture.

The canonical approach to video captioning dictates a caption generation
model to learn from offline-extracted dense video features. These feature
extractors usually operate on video frames sampled at a fixed frame rate and
are often trained on image/video understanding tasks, without adaption to video
captioning data. In this work, we present SwinBERT, an end-to-end
transformer-based model for video captioning, which takes video frame patches
directly as inputs, and outputs a natural language description. Instead of
leveraging multiple 2D/3D feature extractors, our method adopts a video
transformer to encode spatial-temporal representations that can adapt to
variable lengths of video input without dedicated design for different frame
rates. Based on this model architecture, we show that video captioning can
benefit significantly from more densely sampled video frames as opposed to
previous successes with sparsely sampled video frames for video-and-language
understanding tasks (e.g., video question answering). Moreover, to avoid the
inherent redundancy in consecutive video frames, we propose adaptively learning
a sparse attention mask and optimizing it for task-specific performance
improvement through better long-range video sequence modeling. Through
extensive experiments on 5 video captioning datasets, we show that SwinBERT
achieves across-the-board performance improvements over previous methods, often
by a large margin. The learned sparse attention masks in addition push the
limit to new state of the arts, and can be transferred between different video
lengths and between different datasets. Code is available at
https://github.com/microsoft/SwinBERT

Similar to Open Data initiatives, data science as a community has launched
initiatives for sharing not only data but entire pipelines, derivatives,
artifacts, etc. (Open Data Science). However, the few efforts that exist focus
on the technical part on how to facilitate sharing, conversion, etc. This
vision paper goes a step further and proposes KEK, an open federated data
science platform that does not only allow for sharing data science pipelines
and their (meta)data but also provides methods for efficient search and, in the
ideal case, even allows for combining and defining pipelines across platforms
in a federated manner. In doing so, KEK addresses the so far neglected
challenge of actually finding artifacts that are semantically related and that
can be combined to achieve a certain goal.

Identifying meaningful and independent factors of variation in a dataset is a
challenging learning task frequently addressed by means of deep latent variable
models. This task can be viewed as learning symmetry transformations preserving
the value of a chosen property along latent dimensions. However, existing
approaches exhibit severe drawbacks in enforcing the invariance property in the
latent space. We address these shortcomings with a novel approach to cycle
consistency. Our method involves two separate latent subspaces for the target
property and the remaining input information, respectively. In order to enforce
invariance as well as sparsity in the latent space, we incorporate semantic
knowledge by using cycle consistency constraints relying on property side
information. The proposed method is based on the deep information bottleneck
and, in contrast to other approaches, allows using continuous target properties
and provides inherent model selection capabilities. We demonstrate on synthetic
and molecular data that our approach identifies more meaningful factors which
lead to sparser and more interpretable models with improved invariance

Statistical models are central to machine learning with broad applicability
across a range of downstream tasks. The models are controlled by free
parameters that are typically estimated from data by maximum-likelihood
estimation or approximations thereof. However, when faced with real-world
datasets many of the models run into a critical issue: they are formulated in
terms of fully-observed data, whereas in practice the datasets are plagued with
missing data. The theory of statistical model estimation from incomplete data
is conceptually similar to the estimation of latent-variable models, where
powerful tools such as variational inference (VI) exist. However, in contrast
to standard latent-variable models, parameter estimation with incomplete data
often requires estimating exponentially-many conditional distributions of the
missing variables, hence making standard VI methods intractable. We address
this gap by introducing variational Gibbs inference (VGI), a new
general-purpose method to estimate the parameters of statistical models from
incomplete data. We validate VGI on a set of synthetic and real-world
estimation tasks, estimating important machine learning models such as VAEs and
normalising flows from incomplete data. The proposed method, whilst
general-purpose, achieves competitive or better performance than existing
model-specific estimation methods.

Insider threats are costly, hard to detect, and unfortunately rising in
occurrence. Seeking to improve detection of such threats, we develop novel
techniques to enable us to extract powerful features and augment attack vectors
for greater classification power. Most importantly, we generate high quality
color image encodings of user behavior that do not have the downsides of
traditional greyscale image encodings. Combined, they form Computer Vision User
and Entity Behavior Analytics, a detection system designed from the ground up
to improve upon advancements in academia and mitigate the issues that prevent
the usage of advanced models in industry. The proposed system beats
state-of-art methods used in academia and as well as in industry on a gold
standard benchmarking dataset.

Disobeying the classical wisdom of statistical learning theory, modern deep
neural networks generalize well even though they typically contain millions of
parameters. Recently, it has been shown that the trajectories of iterative
optimization algorithms can possess fractal structures, and their
generalization error can be formally linked to the complexity of such fractals.
This complexity is measured by the fractal's intrinsic dimension, a quantity
usually much smaller than the number of parameters in the network. Even though
this perspective provides an explanation for why overparametrized networks
would not overfit, computing the intrinsic dimension (e.g., for monitoring
generalization during training) is a notoriously difficult task, where existing
methods typically fail even in moderate ambient dimensions. In this study, we
consider this problem from the lens of topological data analysis (TDA) and
develop a generic computational tool that is built on rigorous mathematical
foundations. By making a novel connection between learning theory and TDA, we
first illustrate that the generalization error can be equivalently bounded in
terms of a notion called the 'persistent homology dimension' (PHD), where,
compared with prior work, our approach does not require any additional
geometrical or statistical assumptions on the training dynamics. Then, by
utilizing recently established theoretical results and TDA tools, we develop an
efficient algorithm to estimate PHD in the scale of modern deep neural networks
and further provide visualization tools to help understand generalization in
deep learning. Our experiments show that the proposed approach can efficiently
compute a network's intrinsic dimension in a variety of settings, which is
predictive of the generalization error.

In this article, we employ a collection of stochastic differential equations
with drift and diffusion coefficients approximated by neural networks to
predict the trend of chaotic time series which has big jump properties. Our
contributions are, first, we propose a model called L\'evy induced stochastic
differential equation network, which explores compounded stochastic
differential equations with $\alpha$-stable L\'evy motion to model complex time
series data and solve the problem through neural network approximation. Second,
we theoretically prove that the numerical solution through our algorithm
converges in probability to the solution of corresponding stochastic
differential equation, without curse of dimensionality. Finally, we illustrate
our method by applying it to real financial time series data and find the
accuracy increases through the use of non-Gaussian L\'evy processes. We also
present detailed comparisons in terms of data patterns, various models,
different shapes of L\'evy motion and the prediction lengths.

In this paper, we propose a self-supervised visual representation learning
approach which involves both generative and discriminative proxies, where we
focus on the former part by requiring the target network to recover the
original image based on the mid-level features. Different from prior work that
mostly focuses on pixel-level similarity between the original and generated
images, we advocate for Semantic-aware Generation (SaGe) to facilitate richer
semantics rather than details to be preserved in the generated image. The core
idea of implementing SaGe is to use an evaluator, a deep network that is
pre-trained without labels, for extracting semantic-aware features. SaGe
complements the target network with view-specific features and thus alleviates
the semantic degradation brought by intensive data augmentations. We execute
SaGe on ImageNet-1K and evaluate the pre-trained models on five downstream
tasks including nearest neighbor test, linear classification, and fine-scaled
image recognition, demonstrating its ability to learn stronger visual

An increasing number of machine learning problems, such as robust or
adversarial variants of existing algorithms, require minimizing a loss function
that is itself defined as a maximum. Carrying a loop of stochastic gradient
ascent (SGA) steps on the (inner) maximization problem, followed by an SGD step
on the (outer) minimization, is known as Epoch Stochastic Gradient
\textit{Descent Ascent} (ESGDA). While successful in practice, the theoretical
analysis of ESGDA remains challenging, with no clear guidance on choices for
the inner loop size nor on the interplay between inner/outer step sizes. We
propose RSGDA (Randomized SGDA), a variant of ESGDA with stochastic loop size
with a simpler theoretical analysis. RSGDA comes with the first (among SGDA
algorithms) almost sure convergence rates when used on nonconvex
min/strongly-concave max settings. RSGDA can be parameterized using optimal
loop sizes that guarantee the best convergence rates known to hold for SGDA. We
test RSGDA on toy and larger scale problems, using distributionally robust
optimization and single-cell data matching using optimal transport as a
testbed.

With the advent of Transformer, which was used in translation models in 2017,
attention-based architectures began to attract attention. Furthermore, after
the emergence of BERT, which strengthened the NLU-specific encoder part, which
is a part of the Transformer, and the GPT architecture, which strengthened the
NLG-specific decoder part, various methodologies, data, and models for learning
the Pretrained Language Model began to appear. Furthermore, in the past three
years, various Pretrained Language Models specialized for Korean have appeared.
In this paper, we intend to numerically and qualitatively compare and analyze
various Korean PLMs released to the public.

With the popularity of Transformer architectures in computer vision, the
research focus has shifted towards developing computationally efficient
designs. Window-based local attention is one of the major techniques being
adopted in recent works. These methods begin with very small patch size and
small embedding dimensions and then perform strided convolution (patch merging)
in order to reduce the feature map size and increase embedding dimensions,
hence, forming a pyramidal Convolutional Neural Network (CNN) like design. In
this work, we investigate local and global information modelling in
transformers by presenting a novel isotropic architecture that adopts local
windows and special tokens, called Super tokens, for self-attention.
Specifically, a single Super token is assigned to each image window which
captures the rich local details for that window. These tokens are then employed
for cross-window communication and global representation learning. Hence, most
of the learning is independent of the image patches $(N)$ in the higher layers,
and the class embedding is learned solely based on the Super tokens $(N/M^2)$
where $M^2$ is the window size. In standard image classification on
Imagenet-1K, the proposed Super tokens based transformer (STT-S25) achieves
83.5\% accuracy which is equivalent to Swin transformer (Swin-B) with circa
half the number of parameters (49M) and double the inference time throughput.
The proposed Super token transformer offers a lightweight and promising
backbone for visual recognition tasks.

Monitoring and managing Earth's forests in an informed manner is an important
requirement for addressing challenges like biodiversity loss and climate
change. While traditional in situ or aerial campaigns for forest assessments
provide accurate data for analysis at regional level, scaling them to entire
countries and beyond with high temporal resolution is hardly possible. In this
work, we propose a Bayesian deep learning approach to densely estimate forest
structure variables at country-scale with 10-meter resolution, using freely
available satellite imagery as input. Our method jointly transforms Sentinel-2
optical images and Sentinel-1 synthetic aperture radar images into maps of five
different forest structure variables: 95th height percentile, mean height,
density, Gini coefficient, and fractional cover. We train and test our model on
reference data from 41 airborne laser scanning missions across Norway and
demonstrate that it is able to generalize to unseen test regions, achieving
normalized mean absolute errors between 11% and 15%, depending on the variable.
Our work is also the first to propose a Bayesian deep learning approach so as
to predict forest structure variables with well-calibrated uncertainty
estimates. These increase the trustworthiness of the model and its suitability
for downstream tasks that require reliable confidence estimates, such as
informed decision making. We present an extensive set of experiments to
validate the accuracy of the predicted maps as well as the quality of the
predicted uncertainties. To demonstrate scalability, we provide Norway-wide
maps for the five forest structure variables.

A classical problem in computer vision is to infer a 3D scene representation
from few images that can be used to render novel views at interactive rates.
Previous work focuses on reconstructing pre-defined 3D representations, e.g.
textured meshes, or implicit representations, e.g. radiance fields, and often
requires input images with precise camera poses and long processing times for
each novel scene.
  In this work, we propose the Scene Representation Transformer (SRT), a method
which processes posed or unposed RGB images of a new area, infers a "set-latent
scene representation", and synthesises novel views, all in a single
feed-forward pass. To calculate the scene representation, we propose a
generalization of the Vision Transformer to sets of images, enabling global
information integration, and hence 3D reasoning. An efficient decoder
transformer parameterizes the light field by attending into the scene
representation to render novel views. Learning is supervised end-to-end by
minimizing a novel-view reconstruction error.
  We show that this method outperforms recent baselines in terms of PSNR and
speed on synthetic datasets, including a new dataset created for the paper.
Further, we demonstrate that SRT scales to support interactive visualization
and semantic segmentation of real-world outdoor environments using Street View
imagery.

The digital transformation faces tremendous security challenges. In
particular, the growing number of cyber-attacks targeting Internet of Things
(IoT) systems restates the need for a reliable detection of malicious network
activity. This paper presents a comparative analysis of supervised,
unsupervised and reinforcement learning techniques on nine malware captures of
the IoT-23 dataset, considering both binary and multi-class classification
scenarios. The developed models consisted of Support Vector Machine (SVM),
Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine
(LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep
Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQN),
adapted to the intrusion detection context. The most reliable performance was
achieved by LightGBM. Nonetheless, iForest displayed good anomaly detection
results and the DRL model demonstrated the possible benefits of employing this
methodology to continuously improve the detection. Overall, the obtained
results indicate that the analyzed techniques are well suited for IoT intrusion

Split learning is a popular technique used for vertical federated learning
(VFL), where the goal is to jointly train a model on the private input and
label data held by two parties. This technique uses a split-model, trained
end-to-end, by exchanging the intermediate representations (IR) of the inputs
and gradients of the IR between the two parties. We propose ExPLoit - a
label-leakage attack that allows an adversarial input-owner to extract the
private labels of the label-owner during split-learning. ExPLoit frames the
attack as a supervised learning problem by using a novel loss function that
combines gradient-matching and several regularization terms developed using key
properties of the dataset and models. Our evaluations show that ExPLoit can
uncover the private labels with near-perfect accuracy of up to 99.96%. Our
findings underscore the need for better training techniques for VFL.

Task and motion planning problems in robotics combine symbolic planning over
discrete task variables with motion optimization over continuous state and
action variables. Recent works such as PDDLStream have focused on optimistic
planning with an incrementally growing set of objects until a feasible
trajectory is found. However, this set is exhaustively expanded in a
breadth-first manner, regardless of the logical and geometric structure of the
problem at hand, which makes long-horizon reasoning with large numbers of
objects prohibitively time-consuming. To address this issue, we propose a
geometrically informed symbolic planner that expands the set of objects and
facts in a best-first manner, prioritized by a Graph Neural Network that is
learned from prior search computations. We evaluate our approach on a diverse
set of problems and demonstrate an improved ability to plan in difficult
scenarios. We also apply our algorithm on a 7DOF robotic arm in block-stacking
manipulation tasks.

The increasing importance of resource-efficient production entails that
manufacturing companies have to create a more dynamic production environment,
with flexible manufacturing machines and processes. To fully utilize this
potential of dynamic manufacturing through automatic production planning,
formal skill descriptions of the machines are essential. However, generating
those skill descriptions in a manual fashion is labor-intensive and requires
extensive domain-knowledge. In this contribution an ontology-based
semi-automatic skill description system that utilizes production logs and
industrial ontologies through inductive logic programming is introduced and
benefits and drawbacks of the proposed solution are evaluated.

Simulation-based inference with conditional neural density estimators is a
powerful approach to solving inverse problems in science. However, these
methods typically treat the underlying forward model as a black box, with no
way to exploit geometric properties such as equivariances. Equivariances are
common in scientific models, however integrating them directly into expressive
inference networks (such as normalizing flows) is not straightforward. We here
describe an alternative method to incorporate equivariances under joint
transformations of parameters and data. Our method -- called group equivariant
neural posterior estimation (GNPE) -- is based on self-consistently
standardizing the "pose" of the data while estimating the posterior over
parameters. It is architecture-independent, and applies both to exact and
approximate equivariances. As a real-world application, we use GNPE for
amortized inference of astrophysical binary black hole systems from
gravitational-wave observations. We show that GNPE achieves state-of-the-art
accuracy while reducing inference times by three orders of magnitude.

Pretrained contextualized text representation models learn an effective
representation of a natural language to make it machine understandable. After
the breakthrough of the attention mechanism, a new generation of pretrained
models have been proposed achieving good performances since the introduction of
the Transformer. Bidirectional Encoder Representations from Transformers (BERT)
has become the state-of-the-art model for language understanding. Despite their
success, most of the available models have been trained on Indo-European
languages however similar research for under-represented languages and dialects
remains sparse.
  In this paper, we investigate the feasibility of training monolingual
Transformer-based language models for under represented languages, with a
specific focus on the Tunisian dialect. We evaluate our language model on
sentiment analysis task, dialect identification task and reading comprehension
question-answering task. We show that the use of noisy web crawled data instead
of structured data (Wikipedia, articles, etc.) is more convenient for such
non-standardized language. Moreover, results indicate that a relatively small
web crawled dataset leads to performances that are as good as those obtained
using larger datasets. Finally, our best performing TunBERT model reaches or
improves the state-of-the-art in all three downstream tasks. We release the
TunBERT pretrained model and the datasets used for fine-tuning.

Scene Graph Generation has gained much attention in computer vision research
with the growing demand in image understanding projects like visual question
answering, image captioning, self-driving cars, crowd behavior analysis,
activity recognition, and more. Scene graph, a visually grounded graphical
structure of an image, immensely helps to simplify the image understanding
tasks. In this work, we introduced a post-processing algorithm called Geometric
Context to understand the visual scenes better geometrically. We use this
post-processing algorithm to add and refine the geometric relationships between
object pairs to a prior model. We exploit this context by calculating the
direction and distance between object pairs. We use Knowledge Embedded Routing
Network (KERN) as our baseline model, extend the work with our algorithm, and
show comparable results on the recent state-of-the-art algorithms.

A core challenge for an autonomous agent acting in the real world is to adapt
its repertoire of skills to cope with its noisy perception and dynamics. To
scale learning of skills to long-horizon tasks, robots should be able to learn
and later refine their skills in a structured manner through trajectories
rather than making instantaneous decisions individually at each time step. To
this end, we propose the Soft Actor-Critic Gaussian Mixture Model (SAC-GMM), a
novel hybrid approach that learns robot skills through a dynamical system and
adapts the learned skills in their own trajectory distribution space through
interactions with the environment. Our approach combines classical robotics
techniques of learning from demonstration with the deep reinforcement learning
framework and exploits their complementary nature. We show that our method
utilizes sensors solely available during the execution of preliminarily learned
skills to extract relevant features that lead to faster skill refinement.
Extensive evaluations in both simulation and real-world environments
demonstrate the effectiveness of our method in refining robot skills by
leveraging physical interactions, high-dimensional sensory data, and sparse
task completion rewards. Videos, code, and pre-trained models are available at
http://sac-gmm.cs.uni-freiburg.de.

Common approaches for task-agnostic exploration learn tabula-rasa --the agent
assumes isolated environments and no prior knowledge or experience. However, in
the real world, agents learn in many environments and always come with prior
experiences as they explore new ones. Exploration is a lifelong process. In
this paper, we propose a paradigm change in the formulation and evaluation of
task-agnostic exploration. In this setup, the agent first learns to explore
across many environments without any extrinsic goal in a task-agnostic manner.
Later on, the agent effectively transfers the learned exploration policy to
better explore new environments when solving tasks. In this context, we
evaluate several baseline exploration strategies and present a simple yet
effective approach to learning task-agnostic exploration policies. Our key idea
is that there are two components of exploration: (1) an agent-centric component
encouraging exploration of unseen parts of the environment based on an agent's
belief; (2) an environment-centric component encouraging exploration of
inherently interesting objects. We show that our formulation is effective and
provides the most consistent exploration across several training-testing
environment pairs. We also introduce benchmarks and metrics for evaluating
task-agnostic exploration strategies. The source code is available at
https://github.com/sparisi/cbet/.

We use deep-learning strategies to study the 2D percolation model on a square
lattice. We employ standard image recognition tools with a multi-layered
convolutional neural network. We test how well these strategies can
characterise densities and correlation lengths of percolation states and
whether the essential role of the percolating cluster is recognised.

Neural Radiance Field (NeRF) is a popular method in data-driven 3D
reconstruction. Given its simplicity and high quality rendering, many NeRF
applications are being developed. However, NeRF's big limitation is its slow
speed. Many attempts are made to speeding up NeRF training and inference,
including intricate code-level optimization and caching, use of sophisticated
data structures, and amortization through multi-task and meta learning. In this
work, we revisit the basic building blocks of NeRF through the lens of classic
techniques before NeRF. We propose Voxel-Accelearated NeRF (VaxNeRF),
integrating NeRF with visual hull, a classic 3D reconstruction technique only
requiring binary foreground-background pixel labels per image. Visual hull,
which can be optimized in about 10 seconds, can provide coarse in-out field
separation to omit substantial amounts of network evaluations in NeRF. We
provide a clean fully-pythonic, JAX-based implementation on the popular JaxNeRF
codebase, consisting of only about 30 lines of code changes and a modular
visual hull subroutine, and achieve about 2-8x faster learning on top of the
highly-performative JaxNeRF baseline with zero degradation in rendering
quality. With sufficient compute, this effectively brings down full NeRF
training from hours to 30 minutes. We hope VaxNeRF -- a careful combination of
a classic technique with a deep method (that arguably replaced it) -- can
empower and accelerate new NeRF extensions and applications, with its
simplicity, portability, and reliable performance gains. Codes are available at
https://github.com/naruya/VaxNeRF .

QNNVerifier is the first open-source tool for verifying implementations of
neural networks that takes into account the finite word-length (i.e.
quantization) of their operands. The novel support for quantization is achieved
by employing state-of-the-art software model checking (SMC) techniques. It
translates the implementation of neural networks to a decidable fragment of
first-order logic based on satisfiability modulo theories (SMT). The effects of
fixed- and floating-point operations are represented through direct
implementations given a hardware-determined precision. Furthermore, QNNVerifier
allows to specify bespoke safety properties and verify the resulting model with
different verification strategies (incremental and k-induction) and SMT
solvers. Finally, QNNVerifier is the first tool that combines invariant
inference via interval analysis and discretization of non-linear activation
functions to speed up the verification of neural networks by orders of
magnitude. A video presentation of QNNVerifier is available at
https://youtu.be/7jMgOL41zTY

Deep neural networks notoriously suffer from dataset biases which are
detrimental to model robustness, generalization and fairness. In this work, we
propose a two-stage debiasing scheme to combat against the intractable unknown
biases. Starting by analyzing the factors of the presence of biased models, we
design a novel learning objective which cannot be reached by relying on biases
alone. Specifically, debiased models are achieved with the proposed Gradient
Alignment (GA) which dynamically balances the contributions of bias-aligned and
bias-conflicting samples (refer to samples with/without bias cues respectively)
throughout the whole training process, enforcing models to exploit intrinsic
cues to make fair decisions. While in real-world scenarios, the potential
biases are extremely hard to discover and prohibitively expensive to label
manually. We further propose an automatic bias-conflicting sample mining method
by peer-picking and training ensemble without prior knowledge of bias
information. Experiments conducted on multiple datasets in various settings
demonstrate the effectiveness and robustness of our proposed scheme, which
successfully alleviates the negative impact of unknown biases and achieves

Data-driven paradigms using machine learning are becoming ubiquitous in image
processing and communications. In particular, image-to-image (I2I) translation
is a generic and widely used approach to image processing problems, such as
image synthesis, style transfer, and image restoration. At the same time,
neural image compression has emerged as a data-driven alternative to
traditional coding approaches in visual communications. In this paper, we study
the combination of these two paradigms into a joint I2I compression and
translation framework, focusing on multi-domain image synthesis. We first
propose distributed I2I translation by integrating quantization and entropy
coding into an I2I translation framework (i.e. I2Icodec). In practice, the
image compression functionality (i.e. autoencoding) is also desirable,
requiring to deploy alongside I2Icodec a regular image codec. Thus, we further
propose a unified framework that allows both translation and autoencoding
capabilities in a single codec. Adaptive residual blocks conditioned on the
translation/compression mode provide flexible adaptation to the desired
functionality. The experiments show promising results in both I2I translation
and image compression using a single model.

We propose a learning-based method for light-path construction in path
tracing algorithms, which iteratively optimizes and samples from what we refer
to as spatio-directional Gaussian mixture models (SDMMs). In particular, we
approximate incident radiance as an online-trained $5$D mixture that is
accelerated by a $k$D-tree. Using the same framework, we approximate BSDFs as
pre-trained $n$D mixtures, where $n$ is the number of BSDF parameters. Such an
approach addresses two major challenges in path-guiding models. First, the $5$D
radiance representation naturally captures correlation between the spatial and
directional dimensions. Such correlations are present in e.g. parallax and
caustics. Second, by using a tangent-space parameterization of Gaussians, our
spatio-directional mixtures can perform approximate product sampling with
arbitrarily oriented BSDFs. Existing models are only able to do this by either
foregoing anisotropy of the mixture components or by representing the radiance
field in local (normal aligned) coordinates, which both make the radiance field
more difficult to learn. An additional benefit of the tangent-space
parameterization is that each individual Gaussian is mapped to the solid sphere
with low distortion near its center of mass. Our method performs especially
well on scenes with small, localized luminaires that induce high
spatio-directional correlation in the incident radiance.

In this paper, we propose a novel method for representation and
classification of two-person interactions from 3D skeleton sequences. The key
idea of our approach is to use Gaussian distributions to capture statistics on
R n and those on the space of symmetric positive definite (SPD) matrices. The
main challenge is how to parametrize those distributions. Towards this end, we
develop methods for embedding Gaussian distributions in matrix groups based on
the theory of Lie groups and Riemannian symmetric spaces. Our method relies on
the Riemannian geometry of the underlying manifolds and has the advantage of
encoding high-order statistics from 3D joint positions. We show that the
proposed method achieves competitive results in two-person interaction
recognition on three benchmarks for 3D human activity understanding.

In this paper, we propose a simple attention mechanism, we call
box-attention. It enables spatial interaction between grid features, as sampled
from boxes of interest, and improves the learning capability of transformers
for several vision tasks. Specifically, we present BoxeR, short for Box
Transformer, which attends to a set of boxes by predicting their transformation
from a reference window on an input feature map. The BoxeR computes attention
weights on these boxes by considering its grid structure. Notably, BoxeR-2D
naturally reasons about box information within its attention module, making it
suitable for end-to-end instance detection and segmentation tasks. By learning
invariance to rotation in the box-attention module, BoxeR-3D is capable of
generating discriminative information from a bird's-eye view plane for 3D
end-to-end object detection. Our experiments demonstrate that the proposed
BoxeR-2D achieves state-of-the-art results on COCO detection and instance
segmentation. Besides, BoxeR-3D improves over the end-to-end 3D object
detection baseline and already obtains a compelling performance for the vehicle
category of Waymo Open, without any class-specific optimization. Code is
available at https://github.com/kienduynguyen/BoxeR.

Collecting large clean-distorted training image pairs in real world is
non-trivial, which seriously limits the practical applications of these
supervised learning based image super-resolution (SR) methods. Previous works
attempt to address this problem by leveraging unsupervised learning
technologies to alleviate the dependency for paired training samples. However,
these methods typically suffer from unsatisfactory textures synthesis due to
the lack of clean image supervision. Compared with purely unsupervised
solution, the under-explored scheme with Few-Shot clean images (FS-RSR) is more
feasible to tackle this challenging Real Image Super-Resolution task. In this
paper, we are the first to investigate the few-shot real image super-resolution
and propose a Distortion-Relation guided Transfer Learning (termed as DRTL)
framework. DRTL assigns a knowledge graph to capture the distortion relation
between auxiliary tasks (i.e., synthetic distortions) and target tasks (i.e.,
real distortions with few images), and then adopt a gradient weighting strategy
to guide the knowledge transfer from auxiliary task to target task. In this
way, DRTL could quickly learn the most relevant knowledge from the prior
distortions for target distortion. We instantiate DRTL integrated with
pre-training and meta-learning pipelines as an embodiment to realize a
distortion-relation aware FS-RSR. Extensive experiments on multiple benchmarks
demonstrate the effectiveness of DRTL on few-shot real image super-resolution.

Despite their massive success, training successful deep neural networks still
largely relies on experimentally choosing an architecture, hyper-parameters,
initialization, and training mechanism. In this work, we focus on determining
the success of standard gradient descent method for training deep neural
networks on a specified dataset, architecture, and initialization (DAI)
combination. Through extensive systematic experiments, we show that the
evolution of singular values of the matrix obtained from the hidden layers of a
DNN can aid in determining the success of gradient descent technique to train a
DAI, even in the absence of validation labels in the supervised learning
paradigm. This phenomenon can facilitate early give-up, stopping the training
of neural networks which are predicted to not generalize well, early in the
training process. Our experimentation across multiple datasets, architectures,
and initializations reveals that the proposed scores can more accurately
predict the success of a DAI than simply relying on the validation accuracy at
earlier epochs to make a judgment.

Prior plays an important role in providing the plausible constraint on human
motion. Previous works design motion priors following a variety of paradigms
under different circumstances, leading to the lack of versatility. In this
paper, we first summarize the indispensable properties of the motion prior, and
accordingly, design a framework to learn the versatile motion prior, which
models the inherent probability distribution of human motions. Specifically,
for efficient prior representation learning, we propose a global orientation
normalization to remove redundant environment information in the original
motion data space. Also, a two-level, sequence-based and segment-based,
frequency guidance is introduced into the encoding stage. Then, we adopt a
denoising training scheme to disentangle the environment information from input
motion data in a learnable way, so as to generate consistent and
distinguishable representation. Embedding our motion prior into prevailing
backbones on three different tasks, we conduct extensive experiments, and both
quantitative and qualitative results demonstrate the versatility and
effectiveness of our motion prior. Our model and code are available at
https://github.com/JchenXu/human-motion-prior.

Understanding how activity in neural circuits reshapes following task
learning could reveal fundamental mechanisms of learning. Thanks to the recent
advances in neural imaging technologies, high-quality recordings can be
obtained from hundreds of neurons over multiple days or even weeks. However,
the complexity and dimensionality of population responses pose significant
challenges for analysis. Existing methods of studying neuronal adaptation and
learning often impose strong assumptions on the data or model, resulting in
biased descriptions that do not generalize. In this work, we use a variant of
deep generative models called - CycleGAN, to learn the unknown mapping between
pre- and post-learning neural activities recorded $\textit{in vivo}$. We
develop an end-to-end pipeline to preprocess, train and evaluate calcium
fluorescence signals, and a procedure to interpret the resulting deep learning
models. To assess the validity of our method, we first test our framework on a
synthetic dataset with known ground-truth transformation. Subsequently, we
applied our method to neural activities recorded from the primary visual cortex
of behaving mice, where the mice transition from novice to expert-level
performance in a visual-based virtual reality experiment. We evaluate model
performance on generated calcium signals and their inferred spike trains. To
maximize performance, we derive a novel approach to pre-sort neurons such that
convolutional-based networks can take advantage of the spatial information that
exists in neural activities. In addition, we incorporate visual explanation
methods to improve the interpretability of our work and gain insights into the
learning process as manifested in the cellular activities. Together, our
results demonstrate that analyzing neuronal learning processes with data-driven
deep unsupervised methods holds the potential to unravel changes in an unbiased
way.

Riots and protests, if gone out of control, can cause havoc in a country. We
have seen examples of this, such as the BLM movement, climate strikes, CAA
Movement, and many more, which caused disruption to a large extent. Our motive
behind creating this dataset was to use it to develop machine learning systems
that can give its users insight into the trending events going on and alert
them about the events that could lead to disruption in the nation. If any event
starts going out of control, it can be handled and mitigated by monitoring it
before the matter escalates. This dataset collects tweets of past or ongoing
events known to have caused disruption and labels these tweets as 1. We also
collect tweets that are considered non-eventful and label them as 0 so that
they can also be used to train a classification system. The dataset contains
94855 records of unique events and 168706 records of unique non-events, thus
giving the total dataset 263561 records. We extract multiple features from the
tweets, such as the user's follower count and the user's location, to
understand the impact and reach of the tweets. This dataset might be useful in
various event related machine learning problems such as event classification,
event recognition, and so on.

Machine learning in medical imaging during clinical routine is impaired by
changes in scanner protocols, hardware, or policies resulting in a
heterogeneous set of acquisition settings. When training a deep learning model
on an initial static training set, model performance and reliability suffer
from changes of acquisition characteristics as data and targets may become
inconsistent. Continual learning can help to adapt models to the changing
environment by training on a continuous data stream. However, continual manual
expert labelling of medical imaging requires substantial effort. Thus, ways to
use labelling resources efficiently on a well chosen sub-set of new examples is
necessary to render this strategy feasible.
  Here, we propose a method for continual active learning operating on a stream
of medical images in a multi-scanner setting. The approach automatically
recognizes shifts in image acquisition characteristics - new domains -, selects
optimal examples for labelling and adapts training accordingly. Labelling is
subject to a limited budget, resembling typical real world scenarios. To
demonstrate generalizability, we evaluate the effectiveness of our method on
three tasks: cardiac segmentation, lung nodule detection and brain age
estimation. Results show that the proposed approach outperforms other active
learning methods, while effectively counteracting catastrophic forgetting.

In this work, we present a catalog of cataclysmic variables (CVs) identified
from the Sixth Data Release (DR6) of the Large Sky Area Multi-Object Fiber
Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a
novel machine-learning algorithm called UMAP to screen out a total of 169,509
H$\alpha$-emission spectra, and obtain a classification accuracy of the
algorithm of over 99.6$\%$ from the cross-validation set. We then apply the
template matching program PyHammer v2.0 to the LAMOST spectra to obtain the
optimal spectral type with metallicity, which helps us identify the
chromospherically active stars and potential binary stars from the 169,509
spectra. After visually inspecting all the spectra, we identify 323 CV
candidates from the LAMOST database, among them 52 objects are new. We further
discuss the new CV candidates in subtypes based on their spectral features,
including five DN subtype during outbursts, five NL subtype and four magnetic
CVs (three AM Her type and one IP type). We also find two CVs that have been
previously identified by photometry, and confirm their previous classification
by the LAMOST spectra.

Recent works have shown that the task of wireless transmission of images can
be learned with the use of machine learning techniques. Very promising results
in end-to-end image quality, superior to popular digital schemes that utilize
source and channel coding separation, have been demonstrated through the
training of an autoencoder, with a non-trainable channel layer in the middle.
However, these methods assume that any complex value can be transmitted over
the channel, which can prevent the application of the algorithm in scenarios
where the hardware or protocol can only admit certain sets of channel inputs,
such as the use of a digital constellation. Herein, we propose DeepJSCC-Q, an
end-to-end optimized joint source-channel coding scheme for wireless image
transmission, which is able to operate with a fixed channel input alphabet. We
show that DeepJSCC-Q can achieve similar performance to models that use
continuous-valued channel input. Importantly, it preserves the graceful
degradation of image quality observed in prior work when channel conditions
worsen, making DeepJSCC-Q much more attractive for deployment in practical
systems.

A simple and interpretable way to learn a dynamical system from data is to
interpolate its vector-field with a kernel. In particular, this strategy is
highly efficient (both in terms of accuracy and complexity) when the kernel is
data-adapted using Kernel Flows (KF)~\cite{Owhadi19} (which uses gradient-based
optimization to learn a kernel based on the premise that a kernel is good if
there is no significant loss in accuracy if half of the data is used for
interpolation). Despite its previous successes, this strategy (based on
interpolating the vector field driving the dynamical system) breaks down when
the observed time series is not regularly sampled in time. In this work, we
propose to address this problem by directly approximating the vector field of
the dynamical system by incorporating time differences between observations in
the (KF) data-adapted kernels. We compare our approach with the classical one
over different benchmark dynamical systems and show that it significantly
improves the forecasting accuracy while remaining simple, fast, and robust.

We present DeepWiVe, the first-ever end-to-end joint source-channel coding
(JSCC) video transmission scheme that leverages the power of deep neural
networks (DNNs) to directly map video signals to channel symbols, combining
video compression, channel coding, and modulation steps into a single neural
transform. Our DNN decoder predicts residuals without distortion feedback,
which improves video quality by accounting for occlusion/disocclusion and
camera movements. We simultaneously train different bandwidth allocation
networks for the frames to allow variable bandwidth transmission. Then, we
train a bandwidth allocation network using reinforcement learning (RL) that
optimizes the allocation of limited available channel bandwidth among video
frames to maximize overall visual quality. Our results show that DeepWiVe can
overcome the cliff-effect, which is prevalent in conventional separation-based
digital communication schemes, and achieve graceful degradation with the
mismatch between the estimated and actual channel qualities. DeepWiVe
outperforms H.264 video compression followed by low-density parity check (LDPC)
codes in all channel conditions by up to 0.0462 on average in terms of the
multi-scale structural similarity index measure (MS-SSIM), while beating H.265
+ LDPC by up to 0.0058 on average. We also illustrate the importance of
optimizing bandwidth allocation in JSCC video transmission by showing that our
optimal bandwidth allocation policy is superior to the na\"ive uniform
allocation. We believe this is an important step towards fulfilling the
potential of an end-to-end optimized JSCC wireless video transmission system
that is superior to the current separation-based designs.

We present a machine learning (ML) framework for designing desired signal
power profiles over the spectral and spatial domains in the fiber span. The
proposed framework adjusts the Raman pump power values to obtain the desired
two-dimensional (2D) profiles using a convolutional neural network (CNN)
followed by the differential evolution (DE) technique. The CNN learns the
mapping between the 2D profiles and their corresponding pump power values using
a data-set generated by exciting the amplification setup. Nonetheless, its
performance is not accurate for designing 2D profiles of practical interest,
such as a 2D flat or a 2D symmetric (with respect to the middle point in
distance). To adjust the pump power values more accurately, the DE fine-tunes
the power values initialized by the CNN to design the proposed 2D profile with
a lower cost value. In the fine-tuning process, the DE employs the direct
amplification model which consists of 8 bidirectional propagating pumps,
including 2 second-order and 6 first order, in an 80 km fiber span. We evaluate
the framework to design broadband 2D flat and symmetric power profiles, as two
goals for wavelength division multiplexing (WDM) system performing over the
whole C-band. Results indicate the framework's ability to achieve maximum power
excursion of 2.81 dB for a 2D flat, and maximum asymmetry of 14% for a 2D
symmetric profile.

The fidelity bandits problem is a variant of the $K$-armed bandit problem in
which the reward of each arm is augmented by a fidelity reward that provides
the player with an additional payoff depending on how 'loyal' the player has
been to that arm in the past. We propose two models for fidelity. In the
loyalty-points model the amount of extra reward depends on the number of times
the arm has previously been played. In the subscription model the additional
reward depends on the current number of consecutive draws of the arm. We
consider both stochastic and adversarial problems. Since single-arm strategies
are not always optimal in stochastic problems, the notion of regret in the
adversarial setting needs careful adjustment. We introduce three possible
notions of regret and investigate which can be bounded sublinearly. We study in
detail the special cases of increasing, decreasing and coupon (where the player
gets an additional reward after every $m$ plays of an arm) fidelity rewards.
For the models which do not necessarily enjoy sublinear regret, we provide a
worst case lower bound. For those models which exhibit sublinear regret, we
provide algorithms and bound their regret.

We develop a rotation equivariant model for generating 3D hand meshes from 2D
RGB images. This guarantees that as the input image of a hand is rotated the
generated mesh undergoes a corresponding rotation. Furthermore, this removes
undesirable deformations in the meshes often generated by methods without
rotation equivariance. By building a rotation equivariant model, through
considering symmetries in the problem, we reduce the need for training on very
large datasets to achieve good mesh reconstruction.
  The encoder takes images defined on $\mathbb{Z}^{2}$ and maps these to latent
functions defined on the group $C_{8}$. We introduce a novel vector mapping
function to map the function defined on $C_{8}$ to a latent point cloud space
defined on the group $\mathrm{SO}(2)$. Further, we introduce a 3D projection
function that learns a 3D function from the $\mathrm{SO}(2)$ latent space.
Finally, we use an $\mathrm{SO}(3)$ equivariant decoder to ensure rotation
equivariance. Our rotation equivariant model outperforms state-of-the-art
methods on a real-world dataset and we demonstrate that it accurately captures
the shape and pose in the generated meshes under rotation of the input hand.

Machine learning algorithms dedicated to financial time series forecasting
have gained a lot of interest over the last few years. One difficulty lies in
the choice between several algorithms, as their estimation accuracy may be
unstable over time. Aggregation combines a finite set of forecasting models,
called experts, without making assumptions about the models and dynamically
adapts to market conditions. We apply expert aggregation to the construction of
long-short strategies, built from the individual stock return forecasts. The
online mixture outperforms individual algorithms in terms of both portfolio
performance and stability. Extensions to both expert and aggregation
specializations are also proposed and improve the overall mixture on portfolio

We address the problem of ensemble selection in transfer learning: Given a
large pool of source models we want to select an ensemble of models which,
after fine-tuning on the target training set, yields the best performance on
the target test set. Since fine-tuning all possible ensembles is
computationally prohibitive, we aim at predicting performance on the target
dataset using a computationally efficient transferability metric. We propose
several new transferability metrics designed for this task and evaluate them in
a challenging and realistic transfer learning setup for semantic segmentation:
we create a large and diverse pool of source models by considering 17 source
datasets covering a wide variety of image domain, two different architectures,
and two pre-training schemes. Given this pool, we then automatically select a
subset to form an ensemble performing well on a given target dataset. We
compare the ensemble selected by our method to two baselines which select a
single source model, either (1) from the same pool as our method; or (2) from a
pool containing large source models, each with similar capacity as an ensemble.
Averaged over 17 target datasets, we outperform these baselines by 6.0% and
2.5% relative mean IoU, respectively.

Recently many studies have been conducted on the topic of relation
extraction. The DrugProt track at BioCreative VII provides a manually-annotated
corpus for the purpose of the development and evaluation of relation extraction
systems, in which interactions between chemicals and genes are studied. We
describe the ensemble system that we used for our submission, which combines
predictions of fine-tuned bioBERT, sciBERT and const-bioBERT models by majority
voting. We specifically tested the contribution of syntactic information to
relation extraction with BERT. We observed that adding constituentbased
syntactic information to BERT improved precision, but decreased recall, since
relations rarely seen in the train set were less likely to be predicted by BERT
models in which the syntactic information is infused. Our code is available
online [https://github.com/Maple177/drugprot-relation-extraction].

We investigate a method to extract relations from texts based on global
alignment and syntactic information. Combined with SVM, this method is shown to
have a performance comparable or even better than LSTM on two RE tasks.

Understanding the glassy nature of neural networks is pivotal both for
theoretical and computational advances in Machine Learning and Theoretical
Artificial Intelligence. Keeping the focus on dense associative Hebbian neural
networks, the purpose of this paper is two-fold: at first we develop rigorous
mathematical approaches to address properly a statistical mechanical picture of
the phenomenon of {\em replica symmetry breaking} (RSB) in these networks, then
-- deepening results stemmed via these routes -- we aim to inspect the {\em
glassiness} that they hide. In particular, regarding the methodology, we
provide two techniques: the former is an adaptation of the transport PDE to the
case, while the latter is an extension of Guerra's interpolation breakthrough.
Beyond coherence among the results, either in replica symmetric and in the
one-step replica symmetry breaking level of description, we prove the Gardner's
picture and we identify the maximal storage capacity by a ground-state analysis
in the Baldi-Venkatesh high-storage regime.
  In the second part of the paper we investigate the glassy structure of these
networks: in contrast with the replica symmetric scenario (RS), RSB actually
stabilizes the spin-glass phase. We report huge differences w.r.t. the standard
pairwise Hopfield limit: in particular, it is known that it is possible to
express the free energy of the Hopfield neural network as a linear combination
of the free energies of an hard spin glass (i.e. the Sherrington-Kirkpatrick
model) and a soft spin glass (the Gaussian or "spherical" model). This is no
longer true when interactions are more than pairwise (whatever the level of
description, RS or RSB): for dense networks solely the free energy of the hard
spin glass survives, proving a huge diversity in the underlying glassiness of
associative neural networks.

Obtaining per-beat information is a key task in the analysis of cardiac
electrocardiograms (ECG), as many downstream diagnosis tasks are dependent on
ECG-based measurements. Those measurements, however, are costly to produce,
especially in recordings that change throughout long periods of time. However,
existing annotated databases for ECG delineation are small, being insufficient
in size and in the array of pathological conditions they represent. This
article delves has two main contributions. First, a pseudo-synthetic data
generation algorithm was developed, based in probabilistically composing ECG
traces given "pools" of fundamental segments, as cropped from the original
databases, and a set of rules for their arrangement into coherent synthetic
traces. The generation of conditions is controlled by imposing expert knowledge
on the generated trace, which increases the input variability for training the
model. Second, two novel segmentation-based loss functions have been developed,
which attempt at enforcing the prediction of an exact number of independent
structures and at producing closer segmentation boundaries by focusing on a
reduced number of samples. The best performing model obtained an $F_1$-score of
99.38\% and a delineation error of $2.19 \pm 17.73$ ms and $4.45 \pm 18.32$ ms
for all wave's fiducials (onsets and offsets, respectively), as averaged across
the P, QRS and T waves for three distinct freely available databases. The
excellent results were obtained despite the heterogeneous characteristics of
the tested databases, in terms of lead configurations (Holter, 12-lead),
sampling frequencies ($250$, $500$ and $2,000$ Hz) and represented
pathophysiologies (e.g., different types of arrhythmias, sinus rhythm with
structural heart disease), hinting at its generalization capabilities, while
outperforming current state-of-the-art delineation approaches.

Learning dynamical models from data plays a vital role in engineering design,
optimization, and predictions. Building models describing dynamics of complex
processes (e.g., weather dynamics, or reactive flows) using empirical knowledge
or first principles are onerous or infeasible. Moreover, these models are
high-dimensional but spatially correlated. It is, however, observed that the
dynamics of high-fidelity models often evolve in low-dimensional manifolds.
Furthermore, it is also known that for sufficiently smooth vector fields
defining the nonlinear dynamics, a quadratic model can describe it accurately
in an appropriate coordinate system, conferring to the McCormick relaxation
idea in nonconvex optimization. Here, we aim at finding a low-dimensional
embedding of high-fidelity dynamical data, ensuring a simple quadratic model to
explain its dynamics. To that aim, this work leverages deep learning to
identify low-dimensional quadratic embeddings for high-fidelity dynamical
systems. Precisely, we identify the embedding of data using an autoencoder to
have the desired property of the embedding. We also embed a Runge-Kutta method
to avoid the time-derivative computations, which is often a challenge. We
illustrate the ability of the approach by a couple of examples, arising in
describing flow dynamics and the oscillatory tubular reactor model.

Can we train a single transformer model capable of processing multiple
modalities and datasets, whilst sharing almost all of its learnable parameters?
We present PolyViT, a model trained on image, audio and video which answers
this question. By co-training different tasks on a single modality, we are able
to improve the accuracy of each individual task and achieve state-of-the-art
results on 5 standard video- and audio-classification datasets. Co-training
PolyViT on multiple modalities and tasks leads to a model that is even more
parameter-efficient, and learns representations that generalize across multiple
domains. Moreover, we show that co-training is simple and practical to
implement, as we do not need to tune hyperparameters for each combination of
datasets, but can simply adapt those from standard, single-task training.

Automatic brain tumor segmentation from Magnetic Resonance Imaging (MRI) data
plays an important role in assessing tumor response to therapy and personalized
treatment stratification.Manual segmentation is tedious and
subjective.Deep-learning-based algorithms for brain tumor segmentation have the
potential to provide objective and fast tumor segmentation.However, the
training of such algorithms requires large datasets which are not always
available. Data augmentation techniques may reduce the need for large
datasets.However current approaches are mostly parametric and may result in
suboptimal performance.We introduce two non-parametric methods of data
augmentation for brain tumor segmentation: the mixed structure regularization
(MSR) and shuffle pixels noise (SPN).We evaluated the added value of the MSR
and SPN augmentation on the brain tumor segmentation (BraTS) 2018 challenge
dataset with the encoder-decoder nnU-Net architecture as the segmentation
algorithm.Both MSR and SPN improve the nnU-Net segmentation accuracy compared
to parametric Gaussian noise augmentation.Mean dice score increased from 80% to
82% and p-values=0.0022, 0.0028 when comparing MSR to non-parametric
augmentation for the tumor core and whole tumor experiments respectively.The
proposed MSR and SPN augmentations have the potential to improve
neural-networks performance in other tasks as well.

Is intelligence realized by connectionist or classicist? While connectionist
approaches have achieved superhuman performance, there has been growing
evidence that such task-specific superiority is particularly fragile in
systematic generalization. This observation lies in the central debate between
connectionist and classicist, wherein the latter continually advocates an
algebraic treatment in cognitive architectures. In this work, we follow the
classicist's call and propose a hybrid approach to improve systematic
generalization in reasoning. Specifically, we showcase a prototype with
algebraic representation for the abstract spatial-temporal reasoning task of
Raven's Progressive Matrices (RPM) and present the ALgebra-Aware
Neuro-Semi-Symbolic (ALANS) learner. The ALANS learner is motivated by abstract
algebra and the representation theory. It consists of a neural visual
perception frontend and an algebraic abstract reasoning backend: the frontend
summarizes the visual information from object-based representation, while the
backend transforms it into an algebraic structure and induces the hidden
operator on the fly. The induced operator is later executed to predict the
answer's representation, and the choice most similar to the prediction is
selected as the solution. Extensive experiments show that by incorporating an
algebraic treatment, the ALANS learner outperforms various pure connectionist
models in domains requiring systematic generalization. We further show the
generative nature of the learned algebraic representation; it can be decoded by
isomorphism to generate an answer.

With the rapid proliferation of the Internet, the area of education has
undergone a massive transformation in terms of how students and instructors
interact in a classroom. Online learning now takes more than one form,
including the use of technology to enhance a face-to-face class, a hybrid class
that combines both face-to-face meetings and online work, and fully online
courses. Further, online classrooms are usually composed of an online education
forum (OEF) where students and instructor discuss open-ended questions for
gaining better understanding of the subject. However, empirical studies have
repeatedly shown that the dropout rates in these online courses are very high
partly due to the lack of motivation among the enrolled students. We undertake
an empirical comparison of student behavior in OEFs associated with a
graduate-level course during two terms. We identify key parameters dictating
the dynamics of OEFs like effective incentive design, student heterogeneity,
and super-posters phenomenon. Motivated by empirical observations, we propose
an analytical model based on continuous time Markov chains (CTMCs) to capture
instructor-student interactions in an OEF. Using concepts from lumpability of
CTMCs, we compute steady state and transient probabilities along with expected
net-rewards for the instructor and the students. We formulate a mixed-integer
linear program which views an OEF as a single-leader-multiple-followers
Stackelberg game. Through simulations, we observe that students exhibit varied
degree of non-monotonicity in their participation (with increasing instructor
involvement). We also study the effect of instructor bias and budget on the
student participation levels. Our model exhibits the empirically observed
super-poster phenomenon under certain parameter configurations and recommends
an optimal plan to the instructor for maximizing student participation in OEFs.

We present a method for the generation of Midi files of piano music. The
method models the right and left hands using two networks, where the left hand
is conditioned on the right hand. This way, the melody is generated before the
harmony. The Midi is represented in a way that is invariant to the musical
scale, and the melody is represented, for the purpose of conditioning the
harmony, by the content of each bar, viewed as a chord. Finally, notes are
added randomly, based on this chord representation, in order to enrich the
generated audio. Our experiments show a significant improvement over the state
of the art for training on such datasets, and demonstrate the contribution of
each of the novel components.

Graph neural networks (GNNs) are quickly becoming the standard approach for
learning on graph structured data across several domains, but they lack
transparency in their decision-making. Several perturbation-based approaches
have been developed to provide insights into the decision making process of
GNNs. As this is an early research area, the methods and data used to evaluate
the generated explanations lack maturity. We explore these existing approaches
and identify common pitfalls in three main areas: (1) synthetic data generation
process, (2) evaluation metrics, and (3) the final presentation of the
explanation. For this purpose, we perform an empirical study to explore these
pitfalls along with their unintended consequences and propose remedies to
mitigate their effects.

Metal artefacts in CT images may disrupt image quality and interfere with
diagnosis. Recently many deep-learning-based CT metal artefact reduction (MAR)
methods have been proposed. Current deep MAR methods may be troubled with
domain gap problem, where methods trained on simulated data cannot perform well
on practical data. In this work, we experimentally investigate two image-domain
supervised methods, two dual-domain supervised methods and two image-domain
unsupervised methods on a dental dataset and a torso dataset, to explore
whether domain gap problem exists or is overcome. We find that I-DL-MAR and
DudoNet are effective for practical data of the torso dataset, indicating the
domain gap problem is solved. However, none of the investigated methods perform
satisfactorily on practical data of the dental dataset. Based on the
experimental results, we further analyze the causes of domain gap problem for
each method and dataset, which may be beneficial for improving existing methods
or designing new ones. The findings suggest that the domain gap problem in deep
MAR methods remains to be addressed.

We give the first polynomial-time algorithm to estimate the mean of a
$d$-variate probability distribution with bounded covariance from
$\tilde{O}(d)$ independent samples subject to pure differential privacy. Prior
algorithms for this problem either incur exponential running time, require
$\Omega(d^{1.5})$ samples, or satisfy only the weaker concentrated or
approximate differential privacy conditions. In particular, all prior
polynomial-time algorithms require $d^{1+\Omega(1)}$ samples to guarantee small
privacy loss with "cryptographically" high probability, $1-2^{-d^{\Omega(1)}}$,
while our algorithm retains $\tilde{O}(d)$ sample complexity even in this
stringent setting.
  Our main technique is a new approach to use the powerful Sum of Squares
method (SoS) to design differentially private algorithms. SoS proofs to
algorithms is a key theme in numerous recent works in high-dimensional
algorithmic statistics -- estimators which apparently require exponential
running time but whose analysis can be captured by low-degree Sum of Squares
proofs can be automatically turned into polynomial-time algorithms with the
same provable guarantees. We demonstrate a similar proofs to private algorithms
phenomenon: instances of the workhorse exponential mechanism which apparently
require exponential time but which can be analyzed with low-degree SoS proofs
can be automatically turned into polynomial-time differentially private
algorithms. We prove a meta-theorem capturing this phenomenon, which we expect
to be of broad use in private algorithm design.
  Our techniques also draw new connections between differentially private and
robust statistics in high dimensions. In particular, viewed through our
proofs-to-private-algorithms lens, several well-studied SoS proofs from recent
works in algorithmic robust statistics directly yield key components of our
differentially private mean estimation algorithm.

This paper makes a first step towards a logic of learning from experiments.
For this, we investigate formal frameworks for modeling the interaction of
causal and (qualitative) epistemic reasoning. Crucial for our approach is the
idea that the notion of an intervention can be used as a formal expression of a
(real or hypothetical) experiment. In a first step we extend the well-known
causal models with a simple Hintikka-style representation of the epistemic
state of an agent. In the resulting setting, one can talk not only about the
knowledge of an agent about the values of variables and how interventions
affect them, but also about knowledge update. The resulting logic can model
reasoning about thought experiments. However, it is unable to account for
learning from experiments, which is clearly brought out by the fact that it
validates the no learning principle for interventions. Therefore, in a second
step, we implement a more complex notion of knowledge that allows an agent to
observe (measure) certain variables when an experiment is carried out. This
extended system does allow for learning from experiments. For all the proposed
logical systems, we provide a sound and complete axiomatization.

Bokeh effect is a natural shallow depth-of-field phenomenon that blurs the
out-of-focus part in photography. In pursuit of aesthetically pleasing photos,
people usually regard the bokeh effect as an indispensable part of the photo.
Due to its natural advantage and universality, as well as the fact that many
visual recognition tasks can already be negatively affected by the `natural
bokeh' phenomenon, in this work, we systematically study the bokeh effect from
a new angle, i.e., adversarial bokeh attack (AdvBokeh) that aims to embed
calculated deceptive information into the bokeh generation and produce a
natural adversarial example without any human-noticeable noise artifacts. To
this end, we first propose a Depth-guided Bokeh Synthesis Network (DebsNet)
that is able to flexibly synthesis, refocus, and adjust the level of bokeh of
the image, with a one-stage training procedure. The DebsNet allows us to tap
into the bokeh generation process and attack the depth map that is needed for
generating realistic bokeh (i.e., adversarially tuning the depth map) based on
subsequent visual tasks. To further improve the realisticity of the adversarial
bokeh, we propose depth-guided gradient-based attack to regularize the
gradient.We validate the proposed method on a popular adversarial image
classification dataset, i.e., NeurIPS-2017 DEV, and show that the proposed
method can penetrate four state-of-the-art (SOTA) image classification networks
i.e., ResNet50, VGG, DenseNet, and MobileNetV2 with a high success rate as well
as high image quality. The adversarial examples obtained by AdvBokeh also
exhibit high level of transferability under black-box settings. Moreover, the
adversarially generated defocus blur images from the AdvBokeh can actually be
capitalized to enhance the performance of SOTA defocus deblurring system, i.e.,
IFAN.

Recent studies focus on formulating the traffic forecasting as a
spatio-temporal graph modeling problem. They typically construct a static
spatial graph at each time step and then connect each node with itself between
adjacent time steps to construct the spatio-temporal graph. In such a graph,
the correlations between different nodes at different time steps are not
explicitly reflected, which may restrict the learning ability of graph neural
networks. Meanwhile, those models ignore the dynamic spatio-temporal
correlations among nodes as they use the same adjacency matrix at different
time steps. To overcome these limitations, we propose a Spatio-Temporal Joint
Graph Convolutional Networks (STJGCN) for traffic forecasting over several time
steps ahead on a road network. Specifically, we construct both pre-defined and
adaptive spatio-temporal joint graphs (STJGs) between any two time steps, which
represent comprehensive and dynamic spatio-temporal correlations. We further
design dilated causal spatio-temporal joint graph convolution layers on STJG to
capture the spatio-temporal dependencies from distinct perspectives with
multiple ranges. A multi-range attention mechanism is proposed to aggregate the
information of different ranges. Experiments on four public traffic datasets
demonstrate that STJGCN is computationally efficient and outperforms 11

Titanium dioxide has been extensively studied in the rutile or anatase
phases, while its high-pressure phases are less well understood, despite that
many are thought to have interesting optical, mechanical and electrochemical
properties. First-principles methods such as density functional theory (DFT)
are often used to compute the enthalpies of TiO$_2$ phases at 0~K, but they are
expensive and thus impractical for long time-scale and large system-size
simulations at finite temperatures. On the other hand, cheap empirical
potentials fail to capture the relative stablities of the various polymorphs.
To model the thermodynamic behaviors of ambient and high-pressure phases of
TiO$_2$, we design an empirical model as a baseline, and then train a machine
learning potential based on the difference between the DFT data and the
empirical model. This so-called $\Delta$-learning potential contains long-range
electrostatic interactions, and predicts the 0~K enthalpies of stable TiO$_2$
phases that are in good agreement with DFT. We construct a pressure-temperature
phase diagram of TiO$_2$ in the range $0<P<70$~GPa and $100<T<1500$~K. We then
simulate dynamic phase transition processes, by compressing anatase at
different temperatures. At 300~K, we observe predominantly
anatase-to-baddeleyite transformation at about 20~GPa, via a martensitic
two-step mechanism with highly ordered and collective atomic motion. At 2000~K,
anatase can transform into cotunnite around 45-55~GPa in a thermally-activated
and probabilistic manner, accompanied by diffusive movement of oxygen atoms.
The pressures computed for these transitions show good agreement with
experiments. Our results shed light on how to synthesize and stabilize
high-pressure TiO$_2$ phases, and our method is generally applicable to other
functional materials with multiple polymorphs.

One major goal of the AI security community is to securely and reliably
produce and deploy deep learning models for real-world applications. To this
end, data poisoning based backdoor attacks on deep neural networks (DNNs) in
the production stage (or training stage) and corresponding defenses are
extensively explored in recent years. Ironically, backdoor attacks in the
deployment stage, which can often happen in unprofessional users' devices and
are thus arguably far more threatening in real-world scenarios, draw much less
attention of the community. We attribute this imbalance of vigilance to the
weak practicality of existing deployment-stage backdoor attack algorithms and
the insufficiency of real-world attack demonstrations. To fill the blank, in
this work, we study the realistic threat of deployment-stage backdoor attacks
on DNNs. We base our study on a commonly used deployment-stage attack paradigm
-- adversarial weight attack, where adversaries selectively modify model
weights to embed backdoor into deployed DNNs. To approach realistic
practicality, we propose the first gray-box and physically realizable weights
attack algorithm for backdoor injection, namely subnet replacement attack
(SRA), which only requires architecture information of the victim model and can
support physical triggers in the real world. Extensive experimental simulations
and system-level real-world attack demonstrations are conducted. Our results
not only suggest the effectiveness and practicality of the proposed attack
algorithm, but also reveal the practical risk of a novel type of computer virus
that may widely spread and stealthily inject backdoor into DNN models in user
devices. By our study, we call for more attention to the vulnerability of DNNs
in the deployment stage.

Among the several paradigms of artificial intelligence (AI) or machine
learning (ML), a remarkably successful paradigm is deep learning. Deep
learning's phenomenal success has been hoped to be interpreted via fundamental
research on the theory of deep learning. Accordingly, applied research on deep
learning has spurred the theory of deep learning-oriented depth and breadth of
developments. Inspired by such developments, we pose these fundamental
questions: can we accurately approximate an arbitrary matrix-vector product
using deep rectified linear unit (ReLU) feedforward neural networks (FNNs)? If
so, can we bound the resulting approximation error? In light of these
questions, we derive error bounds in Lebesgue and Sobolev norms that comprise
our developed deep approximation theory. Guided by this theory, we have
successfully trained deep ReLU FNNs whose test results justify our developed
theory. The developed theory is also applicable for guiding and easing the
training of teacher deep ReLU FNNs in view of the emerging teacher-student AI
or ML paradigms that are essential for solving several AI or ML problems in
wireless communications and signal processing; network science and graph signal
processing; and network neuroscience and brain physics.

This paper studies a distributed policy gradient in collaborative multi-agent
reinforcement learning (MARL), where agents over a communication network aim to
find the optimal policy to maximize the average of all agents' local returns.
Due to the non-concave performance function of policy gradient, the existing
distributed stochastic optimization methods for convex problems cannot be
directly used for policy gradient in MARL. This paper proposes a distributed
policy gradient with variance reduction and gradient tracking to address the
high variances of policy gradient, and utilizes importance weight to solve the
non-stationary problem in the sampling process. We then provide an upper bound
on the mean-squared stationary gap, which depends on the number of iterations,
the mini-batch size, the epoch size, the problem parameters, and the network
topology. We further establish the sample and communication complexity to
obtain an $\epsilon$-approximate stationary point. Numerical experiments on the
control problem in MARL are performed to validate the effectiveness of the

State-of-the-art frameworks in self-supervised learning have recently shown
that fully utilizing transformer-based models can lead to performance boost
compared to conventional CNN models. Striving to maximize the mutual
information of two views of an image, existing works apply a contrastive loss
to the final representations. Motivated by self-distillation in the supervised
regime, we further exploit this by allowing the intermediate representations to
learn from the final layer via the contrastive loss. Through self-distillation,
the intermediate layers are better suited for instance discrimination, making
the performance of an early-exited sub-network not much degraded from that of
the full network. This renders the pretext task easier also for the final
layer, lead to better representations. Our method, Self-Distilled
Self-Supervised Learning (SDSSL), outperforms competitive baselines (SimCLR,
BYOL and MoCo v3) using ViT on various tasks and datasets. In the linear
evaluation and k-NN protocol, SDSSL not only leads to superior performance in
the final layers, but also in most of the lower layers. Furthermore, positive
and negative alignments are used to explain how representations are formed more
effectively. Code will be available.

In the trial-and-error mechanism of reinforcement learning (RL), a notorious
contradiction arises when we expect to learn a safe policy: how to learn a safe
policy without enough data and prior model about the dangerous region? Existing
methods mostly use the posterior penalty for dangerous actions, which means
that the agent is not penalized until experiencing danger. This fact causes
that the agent cannot learn a zero-violation policy even after convergence.
Otherwise, it would not receive any penalty and lose the knowledge about
danger. In this paper, we propose the safe set actor-critic (SSAC) algorithm,
which confines the policy update using safety-oriented energy functions, or the
safety indexes. The safety index is designed to increase rapidly for
potentially dangerous actions, which allows us to locate the safe set on the
action space, or the control safe set. Therefore, we can identify the dangerous
actions prior to taking them, and further obtain a zero constraint-violation
policy after convergence.We claim that we can learn the energy function in a
model-free manner similar to learning a value function. By using the energy
function transition as the constraint objective, we formulate a constrained RL
problem. We prove that our Lagrangian-based solutions make sure that the
learned policy will converge to the constrained optimum under some assumptions.
The proposed algorithm is evaluated on both the complex simulation environments
and a hardware-in-loop (HIL) experiment with a real controller from the
autonomous vehicle. Experimental results suggest that the converged policy in
all environments achieves zero constraint violation and comparable performance
with model-based baselines.

Graph Neural Networks (GNNs) have become increasingly popular and achieved
impressive results in many graph-based applications. However, extensive manual
work and domain knowledge are required to design effective architectures, and
the results of GNN models have high variance with different training setups,
which limits the application of existing GNN models. In this paper, we present
AutoHEnsGNN, a framework to build effective and robust models for graph tasks
without any human intervention. AutoHEnsGNN won first place in the AutoGraph
Challenge for KDD Cup 2020, and achieved the best rank score of five real-life
datasets in the final phase. Given a task, AutoHEnsGNN first applies a fast
proxy evaluation to automatically select a pool of promising GNN models. Then
it builds a hierarchical ensemble framework: 1) We propose graph self-ensemble
(GSE), which can reduce the variance of weight initialization and efficiently
exploit the information of local and global neighborhoods; 2) Based on GSE, a
weighted ensemble of different types of GNN models is used to effectively learn
more discriminative node representations. To efficiently search the
architectures and ensemble weights, we propose AutoHEnsGNN$_{\text{Gradient}}$,
which treats the architectures and ensemble weights as architecture parameters
and uses gradient-based architecture search to obtain optimal configurations,
and AutoHEnsGNN$_{\text{Adaptive}}$, which can adaptively adjust the ensemble
weight based on the model accuracy. Extensive experiments on node
classification, graph classification, edge prediction and KDD Cup challenge
demonstrate the effectiveness and generality of AutoHEnsGNN

The concern of overconfident mis-predictions under distributional shift
demands extensive reliability research on Graph Neural Networks used in
critical tasks in drug discovery. Here we first introduce CardioTox, a
real-world benchmark on drug cardio-toxicity to facilitate such efforts. Our
exploratory study shows overconfident mis-predictions are often distant from
training data. That leads us to develop distance-aware GNNs: GNN-SNGP. Through
evaluation on CardioTox and three established benchmarks, we demonstrate
GNN-SNGP's effectiveness in increasing distance-awareness, reducing
overconfident mis-predictions and making better calibrated predictions without
sacrificing accuracy performance. Our ablation study further reveals the
representation learned by GNN-SNGP improves distance-preservation over its base
architecture and is one major factor for improvements.

This paper proposes a deep representation learning using an
information-theoretic loss with an aim to increase the inter-class distances as
well as within-class similarity in the embedded space. Tasks such as anomaly
and out-of-distribution detection, in which test samples comes from classes
unseen in training, are problematic for deep neural networks. For such tasks,
it is not sufficient to merely discriminate between known classes. Our
intuition is to represent the known classes in compact and separated embedded
regions in order to decrease the possibility of known and unseen classes
overlapping in the embedded space. We derive a loss from Information Bottleneck
principle, which reflects the inter-class distances as well as the compactness
within classes, thus will extend the existing deep data description models. Our
empirical study shows that the proposed model improves the segmentation of
normal classes in the deep feature space, and subsequently contributes to
identifying out-of-distribution samples.

Spiking neural networks (SNNs) can utilize spatio-temporal information and
have a nature of energy efficiency which is a good alternative to deep neural
networks(DNNs). The event-driven information processing makes SNNs can reduce
the expensive computation of DNNs and save a lot of energy consumption.
However, high training and inference latency is a limitation of the development
of deeper SNNs. SNNs usually need tens or even hundreds of time steps during
the training and inference process which causes not only the increase of
latency but also the waste of energy consumption. To overcome this problem, we
proposed a novel training method based on backpropagation (BP) for ultra-low
latency(1-2 time steps) SNN with multi-threshold. In order to increase the
information capacity of each spike, we introduce the multi-threshold Leaky
Integrate and Fired (LIF) model. In our proposed training method, we proposed
three approximated derivative for spike activity to solve the problem of the
non-differentiable issue which cause difficulties for direct training SNNs
based on BP. The experimental results show that our proposed method achieves an
average accuracy of 99.56%, 93.08%, and 87.90% on MNIST, FashionMNIST, and
CIFAR10, respectively with only 2 time steps. For the CIFAR10 dataset, our
proposed method achieve 1.12% accuracy improvement over the previously reported
direct trained SNNs with fewer time steps.

Approximate inference methods like the Laplace method, Laplace approximations
and variational methods, amongst others, are popular methods when exact
inference is not feasible due to the complexity of the model or the abundance
of data. In this paper we propose a hybrid approximate method namely Low-Rank
Variational Bayes correction (VBC), that uses the Laplace method and
subsequently a Variational Bayes correction to the posterior mean. The cost is
essentially that of the Laplace method which ensures scalability of the method.
We illustrate the method and its advantages with simulated and real data, on
small and large scale.

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a
labeled source domain to an unlabeled target domain. Most existing UDA
approaches enable knowledge transfer via learning domain-invariant
representation and sharing one classifier across two domains. However, ignoring
the domain-specific information that are related to the task, and forcing a
unified classifier to fit both domains will limit the feature expressiveness in
each domain. In this paper, by observing that the Transformer architecture with
comparable parameters can generate more transferable representations than CNN
counterparts, we propose a Win-Win TRansformer framework (WinTR) that
separately explores the domain-specific knowledge for each domain and meanwhile
interchanges cross-domain knowledge. Specifically, we learn two different
mappings using two individual classification tokens in the Transformer, and
design for each one a domain-specific classifier. The cross-domain knowledge is
transferred via source guided label refinement and single-sided feature
alignment with respect to source or target, which keeps the integrity of
domain-specific information. Extensive experiments on three benchmark datasets
show that our method outperforms the state-of-the-art UDA methods, validating
the effectiveness of exploiting both domain-specific and invariant

Self-training has greatly facilitated domain adaptive semantic segmentation,
which iteratively generates pseudo labels on unlabeled target data and retrains
the network. However, realistic segmentation datasets are highly imbalanced,
pseudo labels are typically biased to the majority classes and basically noisy,
leading to an error-prone and suboptimal model. In this paper, we propose a
simple region-based active learning approach for semantic segmentation under a
domain shift, aiming to automatically query a small partition of image regions
to be labeled while maximizing segmentation performance. Our algorithm, Region
Impurity and Prediction Uncertainty (RIPU), introduces a new acquisition
strategy characterizing the spatial adjacency of image regions along with the
prediction confidence. We show that the proposed region-based selection
strategy makes more efficient use of a limited budget than image-based or
point-based counterparts. Further, we enforce local prediction consistency
between a pixel and its nearest neighbors on a source image. Alongside, we
develop a negative learning loss to make the features more discriminative.
Extensive experiments demonstrate that our method only requires very few
annotations to almost reach the supervised performance and substantially
outperforms state-of-the-art methods. The code is available at
https://github.com/BIT-DA/RIPU.

In the era of big data, standard analysis tools may be inadequate for making
inference and there is a growing need for more efficient and innovative ways to
collect, process, analyze and interpret the massive and complex data. We
provide an overview of challenges in big data problems and describe how
innovative analytical methods, machine learning tools and metaheuristics can
tackle general healthcare problems with a focus on the current pandemic. In
particular, we give applications of modern digital technology, statistical
methods, data platforms and data integration systems to improve diagnosis and
treatment of diseases in clinical research and novel epidemiologic tools to
tackle infection source problems, such as finding Patient Zero in the spread of
epidemics. We make the case that analyzing and interpreting big data is a very
challenging task that requires a multi-disciplinary effort to continuously
create more effective methodologies and powerful tools to transfer data
information into knowledge that enables informed decision making.

Airwriting Recognition is the problem of identifying letters written in free
space with finger movement. It is essentially a specialized case of gesture
recognition, wherein the vocabulary of gestures corresponds to letters as in a
particular language. With the wide adoption of smart wearables in the general
population, airwriting recognition using motion sensors from a smart-band can
be used as a medium of user input for applications in Human-Computer
Interaction. There has been limited work in the recognition of in-air
trajectories using motion sensors, and the performance of the techniques in the
case when the device used to record signals is changed has not been explored
hitherto. Motivated by these, a new paradigm for device and user-independent
airwriting recognition based on supervised contrastive learning is proposed. A
two stage classification strategy is employed, the first of which involves
training an encoder network with supervised contrastive loss. In the subsequent
stage, a classification head is trained with the encoder weights kept frozen.
The efficacy of the proposed method is demonstrated through experiments on a
publicly available dataset and also with a dataset recorded in our lab using a
different device. Experiments have been performed in both supervised and
unsupervised settings and compared against several state-of-the-art domain
adaptation techniques. Data and the code for our implementation will be made
available at https://github.com/ayushayt/SCLAiR.

In this paper, we introduce ML-Decoder, a new attention-based classification
head. ML-Decoder predicts the existence of class labels via queries, and
enables better utilization of spatial data compared to global average pooling.
By redesigning the decoder architecture, and using a novel group-decoding
scheme, ML-Decoder is highly efficient, and can scale well to thousands of
classes. Compared to using a larger backbone, ML-Decoder consistently provides
a better speed-accuracy trade-off. ML-Decoder is also versatile - it can be
used as a drop-in replacement for various classification heads, and generalize
to unseen classes when operated with word queries. Novel query augmentations
further improve its generalization ability. Using ML-Decoder, we achieve
state-of-the-art results on several classification tasks: on MS-COCO
multi-label, we reach 91.4% mAP; on NUS-WIDE zero-shot, we reach 31.1% ZSL mAP;
and on ImageNet single-label, we reach with vanilla ResNet50 backbone a new top
score of 80.7%, without extra data or distillation. Public code is available
at: https://github.com/Alibaba-MIIL/ML_Decoder

Nowadays, recommender systems already impact almost every facet of peoples
lives. To provide personalized high quality recommendation results,
conventional systems usually train pointwise rankers to predict the absolute
value of objectives and leverage a distinct shallow tower to estimate and
alleviate the impact of position bias. However, with such a training paradigm,
the optimization target differs a lot from the ranking metrics valuing the
relative order of top ranked items rather than the prediction precision of each
item. Moreover, as the existing system tends to recommend more relevant items
at higher positions, it is difficult for the shallow tower based methods to
precisely attribute the user feedback to the impact of position or relevance.
Therefore, there exists an exciting opportunity for us to get enhanced
performance if we manage to solve the aforementioned issues. Unbiased learning
to rank algorithms, which are verified to model the relative relevance
accurately based on noisy feedback, are appealing candidates and have already
been applied in many applications with single categorical labels, such as user
click signals. Nevertheless, the existing unbiased LTR methods cannot properly
handle multiple feedback incorporating both categorical and continuous labels.
Accordingly, we design a novel unbiased LTR algorithm to tackle the challenges,
which innovatively models position bias in the pairwise fashion and introduces
the pairwise trust bias to separate the position bias, trust bias, and user
relevance explicitly. Experiment results on public benchmark datasets and
internal live traffic show the superior results of the proposed method for both
categorical and continuous labels.

In the field of pattern classification, the training of deep learning
classifiers is mostly end-to-end learning, and the loss function is the
constraint on the final output (posterior probability) of the network, so the
existence of Softmax is essential. In the case of end-to-end learning, there is
usually no effective loss function that completely relies on the features of
the middle layer to restrict learning, resulting in the distribution of sample
latent features is not optimal, so there is still room for improvement in
classification accuracy. Based on the concept of Predefined Evenly-Distributed
Class Centroids (PEDCC), this article proposes a Softmax-free loss function
based on predefined optimal-distribution of latent features-POD Loss. The loss
function only restricts the latent features of the samples, including the
norm-adaptive Cosine distance between the latent feature vector of the sample
and the center of the predefined evenly-distributed class, and the correlation
between the latent features of the samples. Finally, Cosine distance is used
for classification. Compared with the commonly used Softmax Loss, some typical
Softmax related loss functions and PEDCC-Loss, experiments on several commonly
used datasets on several typical deep learning classification networks show
that the classification performance of POD Loss is always significant better
and easier to converge. Code is available in
https://github.com/TianYuZu/POD-Loss.

Although recent learning-based calibration methods can predict extrinsic and
intrinsic camera parameters from a single image, the accuracy of these methods
is degraded in fisheye images. This degradation is caused by mismatching
between the actual projection and expected projection. To address this problem,
we propose a generic camera model that has the potential to address various
types of distortion. Our generic camera model is utilized for learning-based
methods through a closed-form numerical calculation of the camera projection.
Simultaneously to recover rotation and fisheye distortion, we propose a
learning-based calibration method that uses the camera model. Furthermore, we
propose a loss function that alleviates the bias of the magnitude of errors for
four extrinsic and intrinsic camera parameters. Extensive experiments
demonstrated that our proposed method outperformed conventional methods on two
largescale datasets and images captured by off-the-shelf fisheye cameras.
Moreover, we are the first researchers to analyze the performance of
learning-based methods using various types of projection for off-the-shelf
cameras.

Images acquired from rainy scenes usually suffer from bad visibility which
may damage the performance of computer vision applications. The rainy scenarios
can be categorized into two classes: moderate rain and heavy rain scenes.
Moderate rain scene mainly consists of rain streaks while heavy rain scene
contains both rain streaks and the veiling effect (similar to haze). Although
existing methods have achieved excellent performance on these two cases
individually, it still lacks a general architecture to address both heavy rain
and moderate rain scenarios effectively. In this paper, we construct a
hierarchical multi-direction representation network by using the contourlet
transform (CT) to address both moderate rain and heavy rain scenarios. The CT
divides the image into the multi-direction subbands (MS) and the semantic
subband (SS). First, the rain streak information is retrieved to the MS based
on the multi-orientation property of the CT. Second, a hierarchical
architecture is proposed to reconstruct the background information including
damaged semantic information and the veiling effect in the SS. Last, the
multi-level subband discriminator with the feedback error map is proposed. By
this module, all subbands can be well optimized. This is the first architecture
that can address both of the two scenarios effectively. The code is available
in https://github.com/cctakaet/ContourletNet-BMVC2021.

We present the first learning-based framework for category-level 3D object
detection and implicit shape estimation based on a pair of stereo RGB images in
the wild. Previous stereo 3D object detection approaches cannot describe the
complete shape details of the detected objects and often fails for the small
objects. In contrast, we propose a new progressive approach that can (1)
perform precise localization as well as provide a complete and
resolution-agnostic shape description for the detected objects and (2) produce
significantly more accurate orientation predictions for the tiny instances.
This approach features a new instance-level network that explicitly models the
unseen surface hallucination problem using point-based representations and uses
a new geometric representation for orientation refinement. Extensive
experiments show that our approach achieves state-of-the-art performance using
various metrics on the KITTI benchmark. Code and pre-trained models will be
available at this https URL.

We search for new superhard B-N-O compounds with an iterative machine
learning (ML) procedure, where ML models are trained using sample crystal
structures from evolutionary algorithm. We first use cohesive energy to
evaluate the thermodynamic stability of varying B$_x$N$_y$O$_z$ compositions,
and then gradually focus on compositional regions with high cohesive energy and
high hardness. The results converge quickly after a few iterations. Our
resulting ML models show that B$_{x+2}$N$_{x}$O$_{3}$ compounds with $x \geq 3$
(like B$_5$N$_3$O$_3$, B$_6$N$_4$O$_3$, etc.) are potentially superhard and
thermodynamically favorable. Our meta-GGA density functional theory
calculations indicate that these materials are also wide bandgap ($\ge 4.4$ eV)
insulators, with the valence band maximum related to the $p$-orbitals of
nitrogen atoms near vacant sites. This study demonstrates that an iterative
method combining ML and ab initio simulations provides a powerful tool for
discovering novel materials.

Adversarial robustness has received increasing attention along with the study
of adversarial examples. So far, existing works show that robust models not
only obtain robustness against various adversarial attacks but also boost the
performance in some downstream tasks. However, the underlying mechanism of
adversarial robustness is still not clear. In this paper, we interpret
adversarial robustness from the perspective of linear components, and find that
there exist some statistical properties for comprehensively robust models.
Specifically, robust models show obvious hierarchical clustering effect on
their linearized sub-networks, when removing or replacing all non-linear
components (e.g., batch normalization, maximum pooling, or activation layers).
Based on these observations, we propose a novel understanding of adversarial
robustness and apply it on more tasks including domain adaption and robustness
boosting. Experimental evaluations demonstrate the rationality and superiority
of our proposed clustering strategy.

Effective semi-supervised learning (SSL) in medical image analysis (MIA) must
address two challenges: 1) work effectively on both multi-class (e.g., lesion
classification) and multi-label (e.g., multiple-disease diagnosis) problems,
and 2) handle imbalanced learning (because of the high variance in disease
prevalence). One strategy to explore in SSL MIA is based on the pseudo
labelling strategy, but it has a few shortcomings. Pseudo-labelling has in
general lower accuracy than consistency learning, it is not specifically
designed for both multi-class and multi-label problems, and it can be
challenged by imbalanced learning. In this paper, unlike traditional methods
that select confident pseudo label by threshold, we propose a new SSL
algorithm, called anti-curriculum pseudo-labelling (ACPL), which introduces
novel techniques to select informative unlabelled samples, improving training
balance and allowing the model to work for both multi-label and multi-class
problems, and to estimate pseudo labels by an accurate ensemble of classifiers
(improving pseudo label accuracy). We run extensive experiments to evaluate
ACPL on two public medical image classification benchmarks: Chest X-Ray14 for
thorax disease multi-label classification and ISIC2018 for skin lesion
multi-class classification. Our method outperforms previous SOTA SSL methods on
both datasets

Present-day deep learning-based motion deblurring methods utilize the pair of
synthetic blur and sharp data to regress any particular framework. This task is
designed for directly translating a blurry image input into its restored
version as output. The aforementioned approach relies heavily on the quality of
the synthetic blurry data, which are only available before the training stage.
Handling this issue by providing a large amount of data is expensive for common
usage. We answer this challenge by providing an on-the-fly blurry data
augmenter that can be run during training and test stages. To fully utilize it,
we incorporate an unorthodox scheme of deblurring framework that employs the
sequence of blur-deblur-reblur-deblur steps. The reblur step is assisted by a
reblurring module (synthesizer) that provides the reblurred version
(pseudo-blur) of its sharp or deblurred counterpart. The proposed module is
also equipped with hand-crafted prior extracted using the state-of-the-art
human body statistical model. This prior is employed to map human and non-human
regions during adversarial learning to fully perceive the characteristics of
human-articulated and scene motion blurs. By engaging this approach, our
deblurring module becomes adaptive and achieves superior outcomes compared to
recent state-of-the-art deblurring algorithms.

Deep generative models are attracting great attention for molecular design
with desired properties. Most existing models generate molecules by
sequentially adding atoms. This often renders generated molecules with less
correlation with target properties and low synthetic accessibility. Molecular
fragments such as functional groups are more closely related to molecular
properties and synthetic accessibility than atoms. Here, we propose a
fragment-based molecular generative model which designs new molecules with
target properties by sequentially adding molecular fragments to any given
starting molecule. A key feature of our model is a high generalization ability
in terms of property control and fragment types. The former becomes possible by
learning the contribution of individual fragments to the target properties in
an auto-regressive manner. For the latter, we used a deep neural network that
predicts the bonding probability of two molecules from the embedding vectors of
the two molecules as input. The high synthetic accessibility of the generated
molecules is implicitly considered while preparing the fragment library with
the BRICS decomposition method. We show that the model can generate molecules
with the simultaneous control of multiple target properties at a high success
rate. It also works equally well with unseen fragments even in the property
range where the training data is rare, verifying the high generalization
ability. As a practical application, we demonstrated that the model can
generate potential inhibitors with high binding affinities against the 3CL
protease of SARS-COV-2 in terms of docking score.

Adversarial examples can easily degrade the classification performance in
neural networks. Empirical methods for promoting robustness to such examples
have been proposed, but often lack both analytical insights and formal
guarantees. Recently, some robustness certificates have appeared in the
literature based on system theoretic notions. This work proposes an incremental
dissipativity-based robustness certificate for neural networks in the form of a
linear matrix inequality for each layer. We also propose an equivalent spectral
norm bound for this certificate which is scalable to neural networks with
multiple layers. We demonstrate the improved performance against adversarial
attacks on a feed-forward neural network trained on MNIST and an Alexnet
trained using CIFAR-10.

Historically, analysis for multiscale PDEs is largely unified while numerical
schemes tend to be equation-specific. In this paper, we propose a unified
framework for computing multiscale problems through random sampling. This is
achieved by incorporating randomized SVD solvers and manifold learning
techniques to numerically reconstruct the low-rank features of multiscale PDEs.
We use multiscale radiative transfer equation and elliptic equation with rough
media to showcase the application of this framework.

Consistency learning using input image, feature, or network perturbations has
shown remarkable results in semi-supervised semantic segmentation, but this
approach can be seriously affected by inaccurate predictions of unlabelled
training images. There are two consequences of these inaccurate predictions: 1)
the training based on the "strict" cross-entropy (CE) loss can easily overfit
prediction mistakes, leading to confirmation bias; and 2) the perturbations
applied to these inaccurate predictions will use potentially erroneous
predictions as training signals, degrading consistency learning. In this paper,
we address the prediction accuracy problem of consistency learning methods with
novel extensions of the mean-teacher (MT) model, which include a new auxiliary
teacher, and the replacement of MT's mean square error (MSE) by a stricter
confidence-weighted cross-entropy (Conf-CE) loss. The accurate prediction by
this model allows us to use a challenging combination of network, input data
and feature perturbations to improve the consistency learning generalisation,
where the feature perturbations consist of a new adversarial perturbation.
Results on public benchmarks show that our approach achieves remarkable
improvements over the previous SOTA methods in the field. Our code is available
at https://github.com/yyliu01/PS-MT.

Anomaly detection is a fundamental yet challenging problem in machine
learning due to the lack of label information. In this work, we propose a novel
and powerful framework, dubbed as SLA$^2$P, for unsupervised anomaly detection.
After extracting representative embeddings from raw data, we apply random
projections to the features and regard features transformed by different
projections as belonging to distinct pseudo classes. We then train a classifier
network on these transformed features to perform self-supervised learning. Next
we add adversarial perturbation to the transformed features to decrease their
softmax scores of the predicted labels and design anomaly scores based on the
predictive uncertainties of the classifier on these perturbed features. Our
motivation is that because of the relatively small number and the decentralized
modes of anomalies, 1) the pseudo label classifier's training concentrates more
on learning the semantic information of normal data rather than anomalous data;
2) the transformed features of the normal data are more robust to the
perturbations than those of the anomalies. Consequently, the perturbed
transformed features of anomalies fail to be classified well and accordingly
have lower anomaly scores than those of the normal samples. Extensive
experiments on image, text and inherently tabular benchmark datasets back up
our findings and indicate that SLA$^2$P achieves state-of-the-art results on
unsupervised anomaly detection tasks consistently.

This paper presents a new method to solve keypoint detection and instance
association by using Transformer. For bottom-up multi-person pose estimation
models, they need to detect keypoints and learn associative information between
keypoints. We argue that these problems can be entirely solved by Transformer.
Specifically, the self-attention in Transformer measures dependencies between
any pair of locations, which can provide association information for keypoints
grouping. However, the naive attention patterns are still not subjectively
controlled, so there is no guarantee that the keypoints will always attend to
the instances to which they belong. To address it we propose a novel approach
of supervising self-attention for multi-person keypoint detection and instance
association. By using instance masks to supervise self-attention to be
instance-aware, we can assign the detected keypoints to their corresponding
instances based on the pairwise attention scores, without using pre-defined
offset vector fields or embedding like CNN-based bottom-up models. An
additional benefit of our method is that the instance segmentation results of
any number of people can be directly obtained from the supervised attention
matrix, thereby simplifying the pixel assignment pipeline. The experiments on
the COCO multi-person keypoint detection challenge and person instance
segmentation task demonstrate the effectiveness and simplicity of the proposed
method and show a promising way to control self-attention behavior for specific
purposes.

The diagnosis of early stages of Alzheimer's disease (AD) is essential for
timely treatment to slow further deterioration. Visualizing the morphological
features for the early stages of AD is of great clinical value. In this work, a
novel Multidirectional Perception Generative Adversarial Network (MP-GAN) is
proposed to visualize the morphological features indicating the severity of AD
for patients of different stages. Specifically, by introducing a novel
multidirectional mapping mechanism into the model, the proposed MP-GAN can
capture the salient global features efficiently. Thus, by utilizing the
class-discriminative map from the generator, the proposed model can clearly
delineate the subtle lesions via MR image transformations between the source
domain and the pre-defined target domain. Besides, by integrating the
adversarial loss, classification loss, cycle consistency loss and \emph{L}1
penalty, a single generator in MP-GAN can learn the class-discriminative maps
for multiple-classes. Extensive experimental results on Alzheimer's Disease
Neuroimaging Initiative (ADNI) dataset demonstrate that MP-GAN achieves
superior performance compared with the existing methods. The lesions visualized
by MP-GAN are also consistent with what clinicians observe.

Building upon prior research that highlighted the need for standardizing
environments for building control research, and inspired by recently introduced
challenges for real life reinforcement learning control, here we propose a
non-exhaustive set of nine real world challenges for reinforcement learning
control in grid-interactive buildings. We argue that research in this area
should be expressed in this framework in addition to providing a standardized
environment for repeatability. Advanced controllers such as model predictive
control and reinforcement learning (RL) control have both advantages and
disadvantages that prevent them from being implemented in real world problems.
Comparisons between the two are rare, and often biased. By focusing on the
challenges, we can investigate the performance of the controllers under a
variety of situations and generate a fair comparison. As a demonstration, we
implement the offline learning challenge in CityLearn and study the impact of
different levels of domain knowledge and complexity of RL algorithms. We show
that the sequence of operations utilized in a rule based controller (RBC) used
for offline training affects the performance of the RL agents when evaluated on
a set of four energy flexibility metrics. Longer offline learning from an
optimized RBC leads to improved performance in the long run. RL agents that
learn from a simplified RBC risk poorer performance as the offline learning
period increases. We also observe no impact on performance from information
sharing amongst agents. We call for a more interdisciplinary effort of the
research community to address the real world challenges, and unlock the
potential of grid-interactive building

Active learning (AL) algorithms aim to identify an optimal subset of data for
annotation, such that deep neural networks (DNN) can achieve better performance
when trained on this labeled subset. AL is especially impactful in industrial
scale settings where data labeling costs are high and practitioners use every
tool at their disposal to improve model performance. The recent success of
self-supervised pretraining (SSP) highlights the importance of harnessing
abundant unlabeled data to boost model performance. By combining AL with SSP,
we can make use of unlabeled data while simultaneously labeling and training on
particularly informative samples.
  In this work, we study a combination of AL and SSP on ImageNet. We find that
performance on small toy datasets -- the typical benchmark setting in the
literature -- is not representative of performance on ImageNet due to the class
imbalanced samples selected by an active learner. Among the existing baselines
we test, popular AL algorithms across a variety of small and large scale
settings fail to outperform random sampling. To remedy the class-imbalance
problem, we propose Balanced Selection (BASE), a simple, scalable AL algorithm
that outperforms random sampling consistently by selecting more balanced
samples for annotation than existing methods. Our code is available at:
https://github.com/zeyademam/active_learning .

We present SyNoRiM, a novel way to jointly register multiple non-rigid shapes
by synchronizing the maps relating learned functions defined on the point
clouds. Even though the ability to process non-rigid shapes is critical in
various applications ranging from computer animation to 3D digitization, the
literature still lacks a robust and flexible framework to match and align a
collection of real, noisy scans observed under occlusions. Given a set of such
point clouds, our method first computes the pairwise correspondences
parameterized via functional maps. We simultaneously learn potentially
non-orthogonal basis functions to effectively regularize the deformations,
while handling the occlusions in an elegant way. To maximally benefit from the
multi-way information provided by the inferred pairwise deformation fields, we
synchronize the pairwise functional maps into a cycle-consistent whole thanks
to our novel and principled optimization formulation. We demonstrate via
extensive experiments that our method achieves a state-of-the-art performance
in registration accuracy, while being flexible and efficient as we handle both
non-rigid and multi-body cases in a unified framework and avoid the costly
optimization over point-wise permutations by the use of basis function maps.

This letter summarizes and proves the concept of bounded-input bounded-state
(BIBS) stability for weight convergence of a broad family of
in-parameter-linear nonlinear neural architectures as it generally applies to a
broad family of incremental gradient learning algorithms. A practical BIBS
convergence condition results from the derived proofs for every individual
learning point or batches for real-time applications.

We establish generalization error bounds for stochastic gradient Langevin
dynamics (SGLD) with constant learning rate under the assumptions of
dissipativity and smoothness, a setting that has received increased attention
in the sampling/optimization literature. Unlike existing bounds for SGLD in
non-convex settings, ours are time-independent and decay to zero as the sample
size increases. Using the framework of uniform stability, we establish
time-independent bounds by exploiting the Wasserstein contraction property of
the Langevin diffusion, which also allows us to circumvent the need to bound
gradients using Lipschitz-like assumptions. Our analysis also supports variants
of SGLD that use different discretization methods, incorporate Euclidean
projections, or use non-isotropic noise.

In this work we investigate how to achieve equivariance to input
transformations in deep networks, purely from data, without being given a model
of those transformations. Convolutional Neural Networks (CNNs), for example,
are equivariant to image translation, a transformation that can be easily
modelled (by shifting the pixels vertically or horizontally). Other
transformations, such as out-of-plane rotations, do not admit a simple analytic
model. We propose an auto-encoder architecture whose embedding obeys an
arbitrary set of equivariance relations simultaneously, such as translation,
rotation, colour changes, and many others. This means that it can take an input
image, and produce versions transformed by a given amount that were not
observed before (e.g. a different point of view of the same object, or a colour
variation). Despite extending to many (even non-geometric) transformations, our
model reduces exactly to a CNN in the special case of translation-equivariance.
Equivariances are important for the interpretability and robustness of deep
networks, and we demonstrate results of successful re-rendering of transformed
versions of input images on several synthetic and real datasets, as well as
results on object pose estimation.

Imitation learning, and robot learning in general, emerged due to
breakthroughs in machine learning, rather than breakthroughs in robotics. As
such, evaluation metrics for robot learning are deeply rooted in those for
machine learning, and focus primarily on data efficiency. We believe that a
better metric for real-world robot learning is time efficiency, which better
models the true cost to humans. This is a call to arms to the robot learning
community to develop our own evaluation metrics, tailored towards the long-term
goals of real-world robotics.

In the problem of structured prediction with graph representation learning
(GRL for short), the hypothesis returned by the algorithm maps the set of
features in the \emph{receptive field} of the targeted vertex to its label. To
understand the learnability of those algorithms, we introduce a weaker form of
uniform stability termed \emph{multi-fidelity stability} and give learning
guarantees for weakly dependent graphs. We testify that
~\citet{london2016stability}'s claim on the generalization of a single sample
holds for GRL when the receptive field is sparse. In addition, we study the
stability induced bound for two popular algorithms: \textbf{(1)} Stochastic
gradient descent under convex and non-convex landscape. In this example, we
provide non-asymptotic bounds that highly depend on the sparsity of the
receptive field constructed by the algorithm. \textbf{(2)} The constrained
regression problem on a 1-layer linear equivariant GNN. In this example, we
present lower bounds for the discrepancy between the two types of stability,
which justified the multi-fidelity design.

We propose a way of learning disentangled content-style representation of
image, allowing us to extrapolate images to any style as well as interpolate
between any pair of styles. By augmenting data set in a supervised setting and
imposing triplet loss, we ensure the separation of information encoded by
content and style representation. We also make use of cycle-consistency loss to
guarantee that images could be reconstructed faithfully by their

This paper presents a machine learning approach to model the electric
consumption of electric vehicles at macroscopic level, i.e., in the absence of
a speed profile, while preserving microscopic level accuracy. For this work, we
leveraged a high-performance, agent-based transportation tool to model trips
that occur in the Greater Chicago region under various scenario changes, along
with physics-based modeling and simulation tools to provide high-fidelity
energy consumption values. The generated results constitute a very large
dataset of vehicle-route energy outcomes that capture variability in vehicle
and routing setting, and in which high-fidelity time series of vehicle speed
dynamics is masked. We show that although all internal dynamics that affect
energy consumption are masked, it is possible to learn aggregate-level energy
consumption values quite accurately with a deep learning approach. When
large-scale data is available, and with carefully tailored feature engineering,
a well-designed model can overcome and retrieve latent information. This model
has been deployed and integrated within POLARIS Transportation System
Simulation Tool to support real-time behavioral transportation models for
individual charging decision-making, and rerouting of electric vehicles.

Deep networks provide state-of-the-art performance in multiple imaging
inverse problems ranging from medical imaging to computational photography.
However, most existing networks are trained with clean signals which are often
hard or impossible to obtain. Equivariant imaging (EI) is a recent
self-supervised learning framework that exploits the group invariance present
in signal distributions to learn a reconstruction function from partial
measurement data alone. While EI results are impressive, its performance
degrades with increasing noise. In this paper, we propose a Robust Equivariant
Imaging (REI) framework which can learn to image from noisy partial
measurements alone. The proposed method uses Stein's Unbiased Risk Estimator
(SURE) to obtain a fully unsupervised training loss that is robust to noise. We
show that REI leads to considerable performance gains on linear and nonlinear
inverse problems, thereby paving the way for robust unsupervised imaging with
deep networks. Code is available at: https://github.com/edongdongchen/REI.

We formally demonstrate that the relative seriality model of Kallman, et al.
maps exactly onto a simple type of convolutional neural network. This approach
leads to a natural interpretation of feedforward connections in the
convolutional layer and stacked intermediate pooling layers in terms of
bystander effects and hierarchical tissue organization, respectively. These
results serve as proof-of-principle for radiobiologically interpretable deep
learning of normal tissue complication probability using large-scale imaging
and dosimetry datasets.

Domain generalization (DG) is a difficult transfer learning problem aiming to
learn a generalizable model for unseen domains. Recent foundation models (FMs)
are robust to many distribution shifts and, therefore, should substantially
improve the performance of DG. In this work, we study generic ways to adopt
CLIP, a Visual-Language Foundation Model, for DG problems in image
classification. While ERM greatly improves the accuracy with bigger backbones
and training datasets using standard DG benchmarks, fine-tuning FMs is not
practical in many real-world situations. We propose Domain Prompt Learning
(DPL) as a novel approach for domain inference in the form of conditional
prompt generation. DPL achieved a significant accuracy improvement with only
training a lightweight prompt generator (a three-layer MLP), whose parameter is
of equivalent scale to the classification projector in the previous DG
literature. Combining \dplshort~with CLIP provides surprising performance,
raising the accuracy of zero-shot CLIP from 73.7% to 79.3% on several standard
datasets, namely PACS, VLCS, OfficeHome, and TerraIncognita. We hope the
simplicity and success of our approach lead to broader adoption and analysis of
foundation models in the domain generalization field. Our code is available at
https://github.com/shogi880/DPLCLIP.

Autoencoders have useful applications in high energy physics in anomaly
detection, particularly for jets - collimated showers of particles produced in
collisions such as those at the CERN Large Hadron Collider. We explore the use
of graph-based autoencoders, which operate on jets in their "particle cloud"
representations and can leverage the interdependencies among the particles
within a jet, for such tasks. Additionally, we develop a differentiable
approximation to the energy mover's distance via a graph neural network, which
may subsequently be used as a reconstruction loss function for autoencoders.

We study the classification of animal behavior using accelerometry data
through various recurrent neural network (RNN) models. We evaluate the
classification performance and complexity of the considered models, which
feature long short-time memory (LSTM) or gated recurrent unit (GRU)
architectures with varying depths and widths, using four datasets acquired from
cattle via collar or ear tags. We also include two state-of-the-art
convolutional neural network (CNN)-based time-series classification models in
the evaluations. The results show that the RNN-based models can achieve similar
or higher classification accuracy compared with the CNN-based models while
having less computational and memory requirements. We also observe that the
models with GRU architecture generally outperform the ones with LSTM
architecture in terms of classification accuracy despite being less complex. A
single-layer uni-directional GRU model with 64 hidden units appears to offer a
good balance between accuracy and complexity making it suitable for
implementation on edge/embedded devices.

The particle-flow (PF) algorithm is used in general-purpose particle
detectors to reconstruct a comprehensive particle-level view of the collision
by combining information from different subdetectors. A graph neural network
(GNN) model, known as the machine-learned particle-flow (MLPF) algorithm, has
been developed to substitute the rule-based PF algorithm. However,
understanding the model's decision making is not straightforward, especially
given the complexity of the set-to-set prediction task, dynamic graph building,
and message-passing steps. In this paper, we adapt the layerwise-relevance
propagation technique for GNNs and apply it to the MLPF algorithm to gauge the
relevant nodes and features for its predictions. Through this process, we gain
insight into the model's decision-making.

In this paper we introduce the SchemaDB data-set; a collection of relational
database schemata in both sql and graph formats. Databases are not commonly
shared publicly for reasons of privacy and security, so schemata are not
available for study. Consequently, an understanding of database structures in
the wild is lacking, and most examples found publicly belong to common
development frameworks or are derived from textbooks or engine benchmark
designs. SchemaDB contains 2,500 samples of relational schemata found in public
repositories which we have standardised to MySQL syntax. We provide our
gathering and transformation methodology, summary statistics, and structural
analysis, and discuss potential downstream research tasks in several domains.

We study fairness in the context of classification where the performance is
measured by the area under the curve (AUC) of the receiver operating
characteristic. AUC is commonly used to measure the performance of prediction
models. The same classifier can have significantly varying AUCs for different
protected groups and, in real-world applications, it is often desirable to
reduce such cross-group differences. We address the problem of how to acquire
additional features to most greatly improve AUC for the disadvantaged group. We
develop a novel approach, fairAUC, based on feature augmentation (adding
features) to mitigate bias between identifiable groups. The approach requires
only a few summary statistics to offer provable guarantees on AUC improvement,
and allows managers flexibility in determining where in the fairness-accuracy
tradeoff they would like to be. We evaluate fairAUC on synthetic and real-world
datasets and find that it significantly improves AUC for the disadvantaged
group relative to benchmarks maximizing overall AUC and minimizing bias between
groups.

Understanding the abundance of a species is the first step towards
understanding both its long-term sustainability and the impact that we may be
having upon it. Ecologists use camera traps to remotely survey for the presence
of specific animal species. Previous studies have shown that deep learning
models can be trained to automatically detect and classify animals within
camera trap imagery with high levels of confidence. However, the ability to
train these models is reliant upon having enough high-quality training data.
What happens when the animal is rare or the data sets are non-existent? This
research proposes an approach of using images of rare animals in captivity
(focusing on the Scottish wildcat) to generate the training dataset. We explore
the challenges associated with generalising a model trained on captivity data
when applied to data collected in the wild. The research is contextualised by
the needs of ecologists in planning/engineering. Following precedents from
other research, this project establishes an ensemble for object detection,
image segmentation and image classification models which are then tested using
different image manipulation and class structuring techniques to encourage
model generalisation. The research concludes, in the context of Scottish
wildcat, that models trained on captivity imagery cannot be generalised to wild
camera trap imagery using existing techniques. However, final model
performances based on a two-class model Wildcat vs Not Wildcat achieved an
overall accuracy score of 81.6% and Wildcat accuracy score of 54.8% on a test
set in which only 1% of images contained a wildcat. This suggests using
captivity images is feasible with further research. This is the first research
which attempts to generate a training set based on captivity data and the first
to explore the development of such models in the context of ecologists in
planning/engineering.

In this paper, we develop a Wasserstein autoencoder (WAE) with a
hyperspherical prior for multimodal data in the application of inertial
confinement fusion. Unlike a typical hyperspherical generative model that
requires computationally inefficient sampling from distributions like the von
Mis Fisher, we sample from a normal distribution followed by a projection layer
before the generator. Finally, to determine the validity of the generated
samples, we exploit a known relationship between the modalities in the dataset
as a scientific constraint, and study different properties of the proposed
model.

Different from fine-tuning models pre-trained on a large-scale dataset of
preset classes, class-incremental learning (CIL) aims to recognize novel
classes over time without forgetting pre-trained classes. However, a given
model will be challenged by test images with finer-grained classes, e.g., a
basenji is at most recognized as a dog. Such images form a new training set
(i.e., support set) so that the incremental model is hoped to recognize a
basenji (i.e., query) as a basenji next time. This paper formulates such a
hybrid natural problem of coarse-to-fine few-shot (C2FS) recognition as a CIL
problem named C2FSCIL, and proposes a simple, effective, and
theoretically-sound strategy Knowe: to learn, normalize, and freeze a
classifier's weights from fine labels, once learning an embedding space
contrastively from coarse labels. Besides, as CIL aims at a
stability-plasticity balance, new overall performance metrics are proposed. In
that sense, on CIFAR-100, BREEDS, and tieredImageNet, Knowe outperforms all
recent relevant CIL/FSCIL methods that are tailored to the new problem setting
for the first time.

Out-of-distribution (OOD) detection has received much attention lately due to
its practical importance in enhancing the safe deployment of neural networks.
One of the primary challenges is that models often produce highly confident
predictions on OOD data, which undermines the driving principle in OOD
detection that the model should only be confident about in-distribution
samples. In this work, we propose ReAct--a simple and effective technique for
reducing model overconfidence on OOD data. Our method is motivated by novel
analysis on internal activations of neural networks, which displays highly
distinctive signature patterns for OOD distributions. Our method can generalize
effectively to different network architectures and different OOD detection
scores. We empirically demonstrate that ReAct achieves competitive detection
performance on a comprehensive suite of benchmark datasets, and give
theoretical explication for our method's efficacy. On the ImageNet benchmark,
ReAct reduces the false positive rate (FPR95) by 25.05% compared to the
previous best method.

Identifying outlier documents, whose content is different from the majority
of the documents in a corpus, has played an important role to manage a large
text collection. However, due to the absence of explicit information about the
inlier (or target) distribution, existing unsupervised outlier detectors are
likely to make unreliable results depending on the density or diversity of the
outliers in the corpus. To address this challenge, we introduce a new task
referred to as out-of-category detection, which aims to distinguish the
documents according to their semantic relevance to the inlier (or target)
categories by using the category names as weak supervision. In practice, this
task can be widely applicable in that it can flexibly designate the scope of
target categories according to users' interests while requiring only the
target-category names as minimum guidance. In this paper, we present an
out-of-category detection framework, which effectively measures how confidently
each document belongs to one of the target categories based on its
category-specific relevance score. Our framework adopts a two-step approach;
(i) it first generates the pseudo-category label of all unlabeled documents by
exploiting the word-document similarity encoded in a text embedding space, then
(ii) it trains a neural classifier by using the pseudo-labels in order to
compute the confidence from its target-category prediction. The experiments on
real-world datasets demonstrate that our framework achieves the best detection
performance among all baseline methods in various scenarios specifying
different target categories.

Today, Machine Learning (ML) applications can have access to tens of
thousands of features. With such feature sets, efficiently browsing and
curating subsets of most relevant features is a challenge. In this paper, we
present a novel approach to visualize up to several thousands of features in a
single image. The image not only shows information on individual features, but
also expresses feature interactions via the relative positioning of features.

Imbalanced Learning (IL) is an important problem that widely exists in data
mining applications. Typical IL methods utilize intuitive class-wise resampling
or reweighting to directly balance the training set. However, some recent
research efforts in specific domains show that class-imbalanced learning can be
achieved without class-wise manipulation. This prompts us to think about the
relationship between the two different IL strategies and the nature of the
class imbalance. Fundamentally, they correspond to two essential imbalances
that exist in IL: the difference in quantity between examples from different
classes as well as between easy and hard examples within a single class, i.e.,
inter-class and intra-class imbalance. Existing works fail to explicitly take
both imbalances into account and thus suffer from suboptimal performance. In
light of this, we present Duple-Balanced Ensemble, namely DUBE , a versatile
ensemble learning framework. Unlike prevailing methods, DUBE directly performs
inter-class and intra-class balancing without relying on heavy distance-based
computation, which allows it to achieve competitive performance while being
computationally efficient. We also present a detailed discussion and analysis
about the pros and cons of different inter/intra-class balancing strategies
based on DUBE . Extensive experiments validate the effectiveness of the
proposed method. Code and examples are available at
https://github.com/ICDE2022Sub/duplebalance.

Recent advances in algorithm-hardware co-design for deep neural networks
(DNNs) have demonstrated their potential in automatically designing neural
architectures and hardware designs. Nevertheless, it is still a challenging
optimization problem due to the expensive training cost and the time-consuming
hardware implementation, which makes the exploration on the vast design space
of neural architecture and hardware design intractable. In this paper, we
demonstrate that our proposed approach is capable of locating designs on the
Pareto frontier. This capability is enabled by a novel three-phase co-design
framework, with the following new features: (a) decoupling DNN training from
the design space exploration of hardware architecture and neural architecture,
(b) providing a hardware-friendly neural architecture space by considering
hardware characteristics in constructing the search cells, (c) adopting
Gaussian process to predict accuracy, latency and power consumption to avoid
time-consuming synthesis and place-and-route processes. In comparison with the
manually-designed ResNet101, InceptionV2 and MobileNetV2, we can achieve up to
5% higher accuracy with up to 3x speed up on the ImageNet dataset. Compared
with other state-of-the-art co-design frameworks, our found network and
hardware configuration can achieve 2% ~ 6% higher accuracy, 2x ~ 26x smaller
latency and 8.5x higher energy efficiency.

Given a real-valued hypothesis class $\mathcal{H}$, we investigate under what
conditions there is a differentially private algorithm which learns an optimal
hypothesis from $\mathcal{H}$ given i.i.d. data. Inspired by recent results for
the related setting of binary classification (Alon et al., 2019; Bun et al.,
2020), where it was shown that online learnability of a binary class is
necessary and sufficient for its private learnability, Jung et al. (2020)
showed that in the setting of regression, online learnability of $\mathcal{H}$
is necessary for private learnability. Here online learnability of
$\mathcal{H}$ is characterized by the finiteness of its $\eta$-sequential fat
shattering dimension, ${\rm sfat}_\eta(\mathcal{H})$, for all $\eta > 0$. In
terms of sufficient conditions for private learnability, Jung et al. (2020)
showed that $\mathcal{H}$ is privately learnable if $\lim_{\eta \downarrow 0}
{\rm sfat}_\eta(\mathcal{H})$ is finite, which is a fairly restrictive
condition. We show that under the relaxed condition $\lim \inf_{\eta \downarrow
0} \eta \cdot {\rm sfat}_\eta(\mathcal{H}) = 0$, $\mathcal{H}$ is privately
learnable, establishing the first nonparametric private learnability guarantee
for classes $\mathcal{H}$ with ${\rm sfat}_\eta(\mathcal{H})$ diverging as
$\eta \downarrow 0$. Our techniques involve a novel filtering procedure to
output stable hypotheses for nonparametric function classes.

Transfer learning has become a popular method for leveraging pre-trained
models in computer vision. However, without performing computationally
expensive fine-tuning, it is difficult to quantify which pre-trained source
models are suitable for a specific target task, or, conversely, to which tasks
a pre-trained source model can be easily adapted to. In this work, we propose
Gaussian Bhattacharyya Coefficient (GBC), a novel method for quantifying
transferability between a source model and a target dataset. In a first step we
embed all target images in the feature space defined by the source model, and
represent them with per-class Gaussians. Then, we estimate their pairwise class
separability using the Bhattacharyya coefficient, yielding a simple and
effective measure of how well the source model transfers to the target task. We
evaluate GBC on image classification tasks in the context of dataset and
architecture selection. Further, we also perform experiments on the more
complex semantic segmentation transferability estimation task. We demonstrate
that GBC outperforms state-of-the-art transferability metrics on most
evaluation criteria in the semantic segmentation settings, matches the
performance of top methods for dataset transferability in image classification,
and performs best on architecture selection problems for image classification.

imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox
for quick implementing and deploying ensemble learning algorithms on
class-imbalanced data. It provides access to multiple state-of-art ensemble
imbalanced learning (EIL) methods, visualizer, and utility functions for
dealing with the class imbalance problem. These ensemble methods include
resampling-based, e.g., under/over-sampling, and reweighting-based ones, e.g.,
cost-sensitive learning. Beyond the implementation, we also extend conventional
binary EIL algorithms with new functionalities like multi-class support and
resampling scheduler, thereby enabling them to handle more complex tasks. The
package was developed under a simple, well-documented API design follows that
of scikit-learn for increased ease of use. imbens is released under the MIT
open-source license and can be installed from Python Package Index (PyPI).
Source code, binaries, detailed documentation, and usage examples are available
at https://github.com/ZhiningLiu1998/imbalanced-ensemble.

Physical products are often complex assemblies combining a multitude of 3D
parts modeled in computer-aided design (CAD) software. CAD designers build up
these assemblies by aligning individual parts to one another using constraints
called joints. In this paper we introduce JoinABLe, a learning-based method
that assembles parts together to form joints. JoinABLe uses the weak
supervision available in standard parametric CAD files without the help of
object class labels or human guidance. Our results show that by making network
predictions over a graph representation of solid models we can outperform
multiple baseline methods with an accuracy (79.53%) that approaches human
performance (80%). Finally, to support future research we release the Fusion
360 Gallery assembly dataset, containing assemblies with rich information on
joints, contact surfaces, holes, and the underlying assembly graph structure.

Parkinson's disease (PD) is debilitating, progressive, and clinically marked
by motor symptoms. As the second most common neurodegenerative disease in the
world, it affects over 10 million lives globally. Existing diagnoses methods
have limitations, such as the expense of visiting doctors and the challenge of
automated early detection, considering that behavioral differences in patients
and healthy individuals are often indistinguishable in the early stages.
However, micrographia, a handwriting disorder that leads to abnormally small
handwriting, tremors, dystonia, and slow movement in the hands and fingers, is
commonly observed in the early stages of PD. In this work, we apply machine
learning techniques to extract signs of micrographia from drawing samples
gathered from two open-source datasets and achieve a predictive accuracy of
94%. This work also sets the foundations for a publicly available and
user-friendly web portal that anyone with access to a pen, printer, and phone
can use for early PD detection.

Mega-constellations of small-size Low Earth Orbit (LEO) satellites are
currently planned and deployed by various private and public entities. While
global connectivity is the main rationale, these constellations also offer the
potential to gather immense amounts of data, e.g., for Earth observation. Power
and bandwidth constraints together with motives like privacy, limiting delay,
or resiliency make it desirable to process this data directly within the
constellation. We consider the implementation of on-board federated learning
(FL) orchestrated by an out-of-constellation parameter server (PS) and propose
a novel communication scheme tailored to support FL. It leverages intra-orbit
inter-satellite links, the predictability of satellite movements and partial
aggregating to massively reduce the training time and communication costs. In
particular, for a constellation with 40 satellites equally distributed among
five low Earth orbits and the PS in medium Earth orbit, we observe a 29x
speed-up in the training process time and a 8x traffic reduction at the PS over

Removing the background in ID Card images is a real challenge for remote
verification systems because many of the re-digitalised images present
cluttered backgrounds, poor illumination conditions, distortion and occlusions.
The background in ID Card images confuses the classifiers and the text
extraction. Due to the lack of available images for research, this field
represents an open problem in computer vision today. This work proposes a
method for removing the background using semantic segmentation of ID Cards. In
the end, images captured in the wild from the real operation, using a manually
labelled dataset consisting of 45,007 images, with five types of ID Cards from
three countries (Chile, Argentina and Mexico), including typical presentation
attack scenarios, were used. This method can help to improve the following
stages in a regular identity verification or document tampering detection
system. Two Deep Learning approaches were explored, based on MobileUNet and
DenseNet10. The best results were obtained using MobileUNet, with 6.5 million
parameters. A Chilean ID Card's mean Intersection Over Union (IoU) was 0.9926
on a private test dataset of 4,988 images. The best results for the fused
multi-country dataset of ID Card images from Chile, Argentina and Mexico
reached an IoU of 0.9911. The proposed methods are lightweight enough to be
used in real-time operation on mobile devices.

Large Transformer models yield impressive results on many tasks, but are
expensive to train, or even fine-tune, and so slow at decoding that their use
and study becomes out of reach. We address this problem by leveraging sparsity.
We study sparse variants for all layers in the Transformer and propose Scaling
Transformers, a family of next generation Transformer models that use sparse
layers to scale efficiently and perform unbatched decoding much faster than the
standard Transformer as we scale up the model size. Surprisingly, the sparse
layers are enough to obtain the same perplexity as the standard Transformer
with the same number of parameters. We also integrate with prior sparsity
approaches to attention and enable fast inference on long sequences even with
limited memory. This results in performance competitive to the state-of-the-art
on long text summarization.

Audio classification has seen great progress with the increasing availability
of large-scale datasets. These large datasets, however, are often only
partially labeled as collecting full annotations is a tedious and expensive
process. This paper presents two semi-supervised methods capable of learning
with missing labels and evaluates them on two publicly available, partially
labeled datasets. The first method relies on label enhancement by a two-stage
teacher-student learning process, while the second method utilizes the mean
teacher semi-supervised learning algorithm. Our results demonstrate the impact
of improperly handling missing labels and compare the benefits of using
different strategies leveraging data with few labels. Methods capable of
learning with partially labeled data have the potential to improve models for
audio classification by utilizing even larger amounts of data without the need
for complete annotations.

The huge domain gap between sketches and photos and the highly abstract
sketch representations pose challenges for sketch-based image retrieval
(\underline{SBIR}). The zero-shot sketch-based image retrieval
(\underline{ZS-SBIR}) is more generic and practical but poses an even greater
challenge because of the additional knowledge gap between the seen and unseen
categories. To simultaneously mitigate both gaps, we propose an
\textbf{A}pproaching-and-\textbf{C}entralizing \textbf{Net}work (termed
"\textbf{ACNet}") to jointly optimize sketch-to-photo synthesis and the image
retrieval. The retrieval module guides the synthesis module to generate large
amounts of diverse photo-like images which gradually approach the photo domain,
and thus better serve the retrieval module than ever to learn domain-agnostic
representations and category-agnostic common knowledge for generalizing to
unseen categories. These diverse images generated with retrieval guidance can
effectively alleviate the overfitting problem troubling concrete
category-specific training samples with high gradients. We also discover the
use of proxy-based NormSoftmax loss is effective in the zero-shot setting
because its centralizing effect can stabilize our joint training and promote
the generalization ability to unseen categories. Our approach is simple yet
effective, which achieves state-of-the-art performance on two widely used
ZS-SBIR datasets and surpasses previous methods by a large margin.

LUCI is an general-purpose spectral line-fitting pipeline which natively
integrates machine learning algorithms to initialize fit functions. LUCI
currently uses point-estimates obtained from a convolutional neural network
(CNN) to inform optimization algorithms; this methodology has shown great
promise by reducing computation time and reducing the chance of falling into a
local minimum using convex optimization methods. In this update to LUCI, we
expand upon the CNN developed in Rhea et al. 2020 so that it outputs Gaussian
posterior distributions of the fit parameters of interest (the velocity and
broadening) rather than simple point-estimates. Moreover, these posteriors are
then used to inform the priors in a Bayesian inference scheme, either emcee or
dynesty. The code is publicly available at https://github.com/crhea93/LUCI.

FCMpy is an open source package in Python for building and analyzing Fuzzy
Cognitive Maps. More specifically, the package allows 1) deriving fuzzy causal
weights from qualitative data, 2) simulating the system behavior, 3) applying
machine learning algorithms (e.g., Nonlinear Hebbian Learning, Active Hebbian
Learning, Genetic Algorithms and Deterministic Learning) to adjust the FCM
causal weight matrix and to solve classification problems, and 4) implementing
scenario analysis by simulating hypothetical interventions (i.e., analyzing
what-if scenarios).

We introduce layered controllable video generation, where we, without any
supervision, decompose the initial frame of a video into foreground and
background layers, with which the user can control the video generation process
by simply manipulating the foreground mask. The key challenges are the
unsupervised foreground-background separation, which is ambiguous, and ability
to anticipate user manipulations with access to only raw video sequences. We
address these challenges by proposing a two-stage learning procedure. In the
first stage, with the rich set of losses and dynamic foreground size prior, we
learn how to separate the frame into foreground and background layers and,
conditioned on these layers, how to generate the next frame using VQ-VAE
generator. In the second stage, we fine-tune this network to anticipate edits
to the mask, by fitting (parameterized) control to the mask from future frame.
We demonstrate the effectiveness of this learning and the more granular control
mechanism, while illustrating state-of-the-art performance on two benchmark
datasets. We provide a video abstract as well as some video results on
https://gabriel-huang.github.io/layered_controllable_video_generation

Increasing usage of Digital Manufacturing (DM) in safety-critical domains is
increasing attention on the cybersecurity of the manufacturing process, as
malicious third parties might aim to introduce defects in digital designs. In
general, the DM process involves creating a digital object (as CAD files)
before using a slicer program to convert the models into printing instructions
(e.g. g-code) suitable for the target printer. As the g-code is an intermediate
machine format, malicious edits may be difficult to detect, especially when the
golden (original) models are not available to the manufacturer. In this work we
aim to quantify this hypothesis through a red-team/blue-team case study,
whereby the red-team aims to introduce subtle defects that would impact the
properties (strengths) of the 3D printed parts, and the blue-team aims to
detect these modifications in the absence of the golden models. The case study
had two sets of models, the first with 180 designs (with 2 compromised using 2
methods) and the second with 4320 designs (with 60 compromised using 6
methods). Using statistical modelling and machine learning (ML), the blue-team
was able to detect all the compromises in the first set of data, and 50 of the
compromises in the second.

The goal of generative machine learning is to model the probability
distribution underlying a given data set. This probability distribution helps
to characterize the generation process of the data samples. While classical
generative machine learning is solely based on classical resources, generative
quantum machine learning can also employ quantum resources - such as
parameterized quantum channels and quantum operators - to learn and sample from
the probability model of interest.
  Applications of generative (quantum) models are multifaceted. The trained
model can generate new samples that are compatible with the given data and
extend the data set. Additionally, learning a model for the generation process
of a data set may provide interesting information about the corresponding
properties. With the help of quantum resources, the respective generative
models have access to functions that are difficult to evaluate with a classical
computer and may improve the performance or lead to new insights. Furthermore,
generative quantum machine learning can be applied to efficient, approximate
loading of classical data into a quantum state which may help to avoid -
potentially exponentially - expensive, exact quantum data loading.
  The aim of this doctoral thesis is to develop new generative quantum machine
learning algorithms, demonstrate their feasibility, and analyze their
performance. Additionally, we outline their potential application to efficient,
approximate quantum data loading. More specifically, we introduce a quantum
generative adversarial network and a quantum Boltzmann machine implementation,
both of which can be realized with parameterized quantum circuits. These
algorithms are compatible with first-generation quantum hardware and, thus,
enable us to study proof of concept implementations not only with numerical
quantum simulations but also real quantum hardware available today.

Classification and identification of different phases and the transitions
between them is a central task in condensed matter physics. Machine learning,
which has achieved dramatic success in a wide range of applications, holds the
promise to bring unprecedented perspectives for this challenging task. However,
despite the exciting progress made along this direction, the reliability of
machine-learning approaches likewise demands further investigation. Here, with
the nitrogen-vacancy center platform, we report the first proof-of-principle
experimental demonstration of adversarial examples in learning topological
phases. We show that, after adding a tiny amount of carefully-designed
perturbations, the experimentally observed adversarial examples can
successfully deceive a splendid phase classifier, whose prediction accuracy is
larger than $99.2\%$ on legitimate samples, with a notably high confidence. Our
results explicitly showcase the crucial vulnerability aspect of applying
machine learning techniques in classifying phases of matter, which provides an
indispensable guide for future studies in this interdisciplinary field.

We resurrect the infamous harmonic mean estimator for computing the marginal
likelihood (Bayesian evidence) and solve its problematic large variance. The
marginal likelihood is a key component of Bayesian model selection since it is
required to evaluate model posterior probabilities; however, its computation is
challenging. The original harmonic mean estimator, first proposed in 1994 by
Newton and Raftery, involves computing the harmonic mean of the likelihood
given samples from the posterior. It was immediately realised that the original
estimator can fail catastrophically since its variance can become very large
and may not be finite. A number of variants of the harmonic mean estimator have
been proposed to address this issue although none have proven fully
satisfactory. We present the learnt harmonic mean estimator, a variant of the
original estimator that solves its large variance problem. This is achieved by
interpreting the harmonic mean estimator as importance sampling and introducing
a new target distribution. The new target distribution is learned to
approximate the optimal but inaccessible target, while minimising the variance
of the resulting estimator. Since the estimator requires samples of the
posterior only it is agnostic to the strategy used to generate posterior
samples. We validate the estimator on a variety of numerical experiments,
including a number of pathological examples where the original harmonic mean
estimator fails catastrophically. In all cases our learnt harmonic mean
estimator is shown to be highly accurate. The estimator is computationally
scalable and can be applied to problems of dimension $\mathcal{O}(10^3)$ and
beyond. Code implementing the learnt harmonic mean estimator is made publicly

This paper explores a better codebook for BERT pre-training of vision
transformers. The recent work BEiT successfully transfers BERT pre-training
from NLP to the vision field. It directly adopts one simple discrete VAE as the
visual tokenizer, but has not considered the semantic level of the resulting
visual tokens. By contrast, the discrete tokens in NLP field are naturally
highly semantic. This difference motivates us to learn a perceptual codebook.
And we surprisingly find one simple yet effective idea: enforcing perceptual
similarity during the dVAE training. We demonstrate that the visual tokens
generated by the proposed perceptual codebook do exhibit better semantic
meanings, and subsequently help pre-training achieve superior transfer
performance in various downstream tasks. For example, we achieve 84.5% Top-1
accuracy on ImageNet-1K with ViT-B backbone, outperforming the competitive
method BEiT by +1.3 with the same pre-training epochs. It can also improve the
performance of object detection and segmentation tasks on COCO val by +1.3 box
AP and +1.0 mask AP, semantic segmentation on ADE20k by +1.0 mIoU. Equipped
with a larger backbone ViT-H, we achieve the state-of-the-art performance
(88.3% Top-1 accuracy) among the methods using only ImageNet-1K data. The code
and models will be available at https://github.com/microsoft/PeCo.

Estimating 3D human poses from monocular videos is a challenging task due to
depth ambiguity and self-occlusion. Most existing works attempt to solve both
issues by exploiting spatial and temporal relationships. However, those works
ignore the fact that it is an inverse problem where multiple feasible solutions
(i.e., hypotheses) exist. To relieve this limitation, we propose a
Multi-Hypothesis Transformer (MHFormer) that learns spatio-temporal
representations of multiple plausible pose hypotheses. In order to effectively
model multi-hypothesis dependencies and build strong relationships across
hypothesis features, the task is decomposed into three stages: (i) Generate
multiple initial hypothesis representations; (ii) Model self-hypothesis
communication, merge multiple hypotheses into a single converged representation
and then partition it into several diverged hypotheses; (iii) Learn
cross-hypothesis communication and aggregate the multi-hypothesis features to
synthesize the final 3D pose. Through the above processes, the final
representation is enhanced and the synthesized pose is much more accurate.
Extensive experiments show that MHFormer achieves state-of-the-art results on
two challenging datasets: Human3.6M and MPI-INF-3DHP. Without bells and
whistles, its performance surpasses the previous best result by a large margin
of 3% on Human3.6M. Code and models are available at
\url{https://github.com/Vegetebird/MHFormer}.

Synthetic images created by generative models increase in quality and
expressiveness as newer models utilize larger datasets and novel architectures.
Although this photorealism is a positive side-effect from a creative
standpoint, it becomes problematic when such generative models are used for
impersonation without consent. Most of these approaches are built on the
partial transfer between source and target pairs, or they generate completely
new samples based on an ideal distribution, still resembling the closest real
sample in the dataset. We propose MixSyn (read as " mixin' ") for learning
novel fuzzy compositions from multiple sources and creating novel images as a
mix of image regions corresponding to the compositions. MixSyn not only
combines uncorrelated regions from multiple source masks into a coherent
semantic composition, but also generates mask-aware high quality
reconstructions of non-existing images. We compare MixSyn to state-of-the-art
single-source sequential generation and collage generation approaches in terms
of quality, diversity, realism, and expressive power; while also showcasing
interactive synthesis, mix & match, and edit propagation tasks, with no mask

Whilst diffusion probabilistic models can generate high quality image
content, key limitations remain in terms of both generating high-resolution
imagery and their associated high computational requirements. Recent
Vector-Quantized image models have overcome this limitation of image resolution
but are prohibitively slow and unidirectional as they generate tokens via
element-wise autoregressive sampling from the prior. By contrast, in this paper
we propose a novel discrete diffusion probabilistic model prior which enables
parallel prediction of Vector-Quantized tokens by using an unconstrained
Transformer architecture as the backbone. During training, tokens are randomly
masked in an order-agnostic manner and the Transformer learns to predict the
original tokens. This parallelism of Vector-Quantized token prediction in turn
facilitates unconditional generation of globally consistent high-resolution and
diverse imagery at a fraction of the computational expense. In this manner, we
can generate image resolutions exceeding that of the original training set
samples whilst additionally provisioning per-image likelihood estimates (in a
departure from generative adversarial approaches). Our approach achieves
state-of-the-art results in terms of Density (LSUN Bedroom: 1.51; LSUN
Churches: 1.12; FFHQ: 1.20) and Coverage (LSUN Bedroom: 0.83; LSUN Churches:
0.73; FFHQ: 0.80), and performs competitively on FID (LSUN Bedroom: 3.64; LSUN
Churches: 4.07; FFHQ: 6.11) whilst offering advantages in terms of both
computation and reduced training set requirements.

Open-vocabulary instance segmentation aims at segmenting novel classes
without mask annotations. It is an important step toward reducing laborious
human supervision. Most existing works first pretrain a model on captioned
images covering many novel classes and then finetune it on limited base classes
with mask annotations. However, the high-level textual information learned from
caption pretraining alone cannot effectively encode the details required for
pixel-wise segmentation. To address this, we propose a cross-modal
pseudo-labeling framework, which generates training pseudo masks by aligning
word semantics in captions with visual features of object masks in images.
Thus, our framework is capable of labeling novel classes in captions via their
word semantics to self-train a student model. To account for noises in pseudo
masks, we design a robust student model that selectively distills mask
knowledge by estimating the mask noise levels, hence mitigating the adverse
impact of noisy pseudo masks. By extensive experiments, we show the
effectiveness of our framework, where we significantly improve mAP score by
4.5% on MS-COCO and 5.1% on the large-scale Open Images & Conceptual Captions
datasets compared to the state-of-the-art.

Existing deep learning-based human mesh reconstruction approaches have a
tendency to build larger networks in order to achieve higher accuracy.
Computational complexity and model size are often neglected, despite being key
characteristics for practical use of human mesh reconstruction models (e.g.
virtual try-on systems). In this paper, we present GTRS, a lightweight
pose-based method that can reconstruct human mesh from 2D human pose. We
propose a pose analysis module that uses graph transformers to exploit
structured and implicit joint correlations, and a mesh regression module that
combines the extracted pose feature with the mesh template to reconstruct the
final human mesh. We demonstrate the efficiency and generalization of GTRS by
extensive evaluations on the Human3.6M and 3DPW datasets. In particular, GTRS
achieves better accuracy than the SOTA pose-based method Pose2Mesh while only
using 10.2% of the parameters (Params) and 2.5% of the FLOPs on the challenging
in-the-wild 3DPW dataset. Code will be publicly available.

This paper presents the data-driven techniques and methodologies used to
predict the remaining useful life (RUL) of a fleet of aircraft engines that can
suffer failures of diverse nature. The solution presented is based on two Deep
Convolutional Neural Networks (DCNN) stacked in two levels. The first DCNN is
used to extract a low-dimensional feature vector using the normalized raw data
as input. The second DCNN ingests a list of vectors taken from the former DCNN
and estimates the RUL. Model selection was carried out by means of Bayesian
optimization using a repeated random subsampling validation approach. The
proposed methodology was ranked in the third place of the 2021 PHM Conference
Data Challenge.

We present EgoRenderer, a system for rendering full-body neural avatars of a
person captured by a wearable, egocentric fisheye camera that is mounted on a
cap or a VR headset. Our system renders photorealistic novel views of the actor
and her motion from arbitrary virtual camera locations. Rendering full-body
avatars from such egocentric images come with unique challenges due to the
top-down view and large distortions. We tackle these challenges by decomposing
the rendering process into several steps, including texture synthesis, pose
construction, and neural image translation. For texture synthesis, we propose
Ego-DPNet, a neural network that infers dense correspondences between the input
fisheye images and an underlying parametric body model, and to extract textures
from egocentric inputs. In addition, to encode dynamic appearances, our
approach also learns an implicit texture stack that captures detailed
appearance variation across poses and viewpoints. For correct pose generation,
we first estimate body pose from the egocentric view using a parametric model.
We then synthesize an external free-viewpoint pose image by projecting the
parametric model to the user-specified target viewpoint. We next combine the
target pose image and the textures into a combined feature image, which is
transformed into the output color image using a neural image translation
network. Experimental evaluations show that EgoRenderer is capable of
generating realistic free-viewpoint avatars of a person wearing an egocentric
camera. Comparisons to several baselines demonstrate the advantages of our
approach.

Two major challenges in demand forecasting are product cannibalization and
long term forecasting. Product cannibalization is a phenomenon in which high
demand of some products leads to reduction in sales of other products. Long
term forecasting involves forecasting the sales over longer time frame that is
critical for strategic business purposes. Also, conventional methods, for
instance, recurrent neural networks may be ineffective where train data size is
small as in the case in this study. This work presents XGBoost-based
three-stage framework that addresses product cannibalization and associated
long term error propagation problems. The performance of the proposed
three-stage XGBoost-based framework is compared to and is found superior than
that of regular XGBoost algorithm.

In recent years, researchers have made significant progress in devising
reinforcement-learning algorithms for optimizing linear temporal logic (LTL)
objectives and LTL-like objectives. Despite these advancements, there are
fundamental limitations to how well this problem can be solved. Previous
studies have alluded to this fact but have not examined it in depth. In this
paper, we address the tractability of reinforcement learning for general LTL
objectives from a theoretical perspective. We formalize the problem under the
probably approximately correct learning in Markov decision processes (PAC-MDP)
framework, a standard framework for measuring sample complexity in
reinforcement learning. In this formalization, we prove that the optimal policy
for any LTL formula is PAC-MDP-learnable if and only if the formula is in the
most limited class in the LTL hierarchy, consisting of formulas that are
decidable within a finite horizon. Practically, our result implies that it is
impossible for a reinforcement-learning algorithm to obtain a PAC-MDP guarantee
on the performance of its learned policy after finitely many interactions with
an unconstrained environment for LTL objectives that are not decidable within a
finite horizon.

Accurate value estimates are important for off-policy reinforcement learning.
Algorithms based on temporal difference learning typically are prone to an
over- or underestimation bias building up over time. In this paper, we propose
a general method called Adaptively Calibrated Critics (ACC) that uses the most
recent high variance but unbiased on-policy rollouts to alleviate the bias of
the low variance temporal difference targets. We apply ACC to Truncated
Quantile Critics, which is an algorithm for continuous control that allows
regulation of the bias with a hyperparameter tuned per environment. The
resulting algorithm adaptively adjusts the parameter during training rendering
hyperparameter search unnecessary and sets a new state of the art on the OpenAI
gym continuous control benchmark among all algorithms that do not tune
hyperparameters for each environment. ACC further achieves improved results on
different tasks from the Meta-World robot benchmark. Additionally, we
demonstrate the generality of ACC by applying it to TD3 and showing an improved
performance also in this setting.

The utility of classical neural networks as universal approximators suggests
that their quantum analogues could play an important role in quantum
generalizations of machine-learning methods. Inspired by the proposal in
[Torrontegui and Garc\'ia-Ripoll 2019 EPL 125 30004], we demonstrate a
superconducting qubit implementation of an adiabatic controlled gate, which
generalizes the action of a classical perceptron as the basic building block of
a quantum neural network. We show full control over the steepness of the
perceptron activation function, the input weight and the bias by tuning the
adiabatic gate length, the coupling between the qubits and the frequency of the
applied drive, respectively. In its general form, the gate realizes a
multi-qubit entangling operation in a single step, whose decomposition into
single- and two-qubit gates would require a number of gates that is exponential
in the number of qubits. Its demonstrated direct implementation as perceptron
in quantum hardware may therefore lead to more powerful quantum neural networks
when combined with suitable additional standard gates.

This paper considers a novel multi-agent linear stochastic approximation
algorithm driven by Markovian noise and general consensus-type interaction, in
which each agent evolves according to its local stochastic approximation
process which depends on the information from its neighbors. The
interconnection structure among the agents is described by a time-varying
directed graph. While the convergence of consensus-based stochastic
approximation algorithms when the interconnection among the agents is described
by doubly stochastic matrices (at least in expectation) has been studied, less
is known about the case when the interconnection matrix is simply stochastic.
For any uniformly strongly connected graph sequences whose associated
interaction matrices are stochastic, the paper derives finite-time bounds on
the mean-square error, defined as the deviation of the output of the algorithm
from the unique equilibrium point of the associated ordinary differential
equation. For the case of interconnection matrices being stochastic, the
equilibrium point can be any unspecified convex combination of the local
equilibria of all the agents in the absence of communication. Both the cases
with constant and time-varying step-sizes are considered. In the case when the
convex combination is required to be a straight average and interaction between
any pair of neighboring agents may be uni-directional, so that doubly
stochastic matrices cannot be implemented in a distributed manner, the paper
proposes a push-sum-type distributed stochastic approximation algorithm and
provides its finite-time bound for the time-varying step-size case by
leveraging the analysis for the consensus-type algorithm with stochastic
matrices and developing novel properties of the push-sum algorithm.

Self-supervised contrastive learning is one of the domains which has
progressed rapidly over the last few years. Most of the state-of-the-art
self-supervised algorithms use a large number of negative samples, momentum
updates, specific architectural modifications, or extensive training to learn
good representations. Such arrangements make the overall training process
complex and challenging to realize analytically. In this paper, we propose a
mutual information optimization based loss function for contrastive learning
where we model contrastive learning into a binary classification problem to
predict if a pair is positive or not. This formulation not only helps us to
track the problem mathematically but also helps us to outperform existing
algorithms. Unlike the existing methods that only maximize the mutual
information in a positive pair, the proposed loss function optimizes the mutual
information in both positive and negative pairs. We also present a mathematical
expression for the parameter gradients flowing into the projector and the
displacement of the feature vectors in the feature space. This helps us to get
a mathematical insight into the working principle of contrastive learning. An
additive $L_2$ regularizer is also used to prevent diverging of the feature
vectors and to improve performance. The proposed method outperforms the
state-of-the-art algorithms on benchmark datasets like STL-10, CIFAR-10,
CIFAR-100. After only 250 epochs of pre-training, the proposed model achieves
the best accuracy of 85.44\%, 60.75\%, 56.81\% on CIFAR-10, STL-10, CIFAR-100

Artificial neural networks have successfully tackled a large variety of
problems by training extremely deep networks via back-propagation. A direct
application of back-propagation to spiking neural networks contains
biologically implausible components, like the weight transport problem or
separate inference and learning phases. Various methods address different
components individually, but a complete solution remains intangible. Here, we
take an alternate approach that avoids back-propagation and its associated
issues entirely. Recent work in deep learning proposed independently training
each layer of a network via the information bottleneck (IB). Subsequent studies
noted that this layer-wise approach circumvents error propagation across
layers, leading to a biologically plausible paradigm. Unfortunately, the IB is
computed using a batch of samples. The prior work addresses this with a weight
update that only uses two samples (the current and previous sample). Our work
takes a different approach by decomposing the weight update into a local and
global component. The local component is Hebbian and only depends on the
current sample. The global component computes a layer-wise modulatory signal
that depends on a batch of samples. We show that this modulatory signal can be
learned by an auxiliary circuit with working memory (WM) like a reservoir.
Thus, we can use batch sizes greater than two, and the batch size determines
the required capacity of the WM. To the best of our knowledge, our rule is the
first biologically plausible mechanism to directly couple synaptic updates with
a WM of the task. We evaluate our rule on synthetic datasets and image
classification datasets like MNIST, and we explore the effect of the WM
capacity on learning performance. We hope our work is a first-step towards
understanding the mechanistic role of memory in learning.

Mapping and 3D detection are two major issues in vision-based robotics, and
self-driving. While previous works only focus on each task separately, we
present an innovative and efficient multi-task deep learning framework (SM3D)
for Simultaneous Mapping and 3D Detection by bridging the gap with robust depth
estimation and "Pseudo-LiDAR" point cloud for the first time. The Mapping
module takes consecutive monocular frames to generate depth and pose
estimation. In 3D Detection module, the depth estimation is projected into 3D
space to generate "Pseudo-LiDAR" point cloud, where LiDAR-based 3D detector can
be leveraged on point cloud for vehicular 3D detection and localization. By
end-to-end training of both modules, the proposed mapping and 3D detection
method outperforms the state-of-the-art baseline by 10.0% and 13.2% in
accuracy, respectively. While achieving better accuracy, our monocular
multi-task SM3D is more than 2 times faster than pure stereo 3D detector, and
18.3% faster than using two modules separately.

A key challenge in computer vision and deep learning is the definition of
robust strategies for the detection of adversarial examples. Here, we propose
the adoption of ensemble approaches to leverage the effectiveness of multiple
detectors in exploiting distinct properties of the input data. To this end, the
ENsemble Adversarial Detector (ENAD) framework integrates scoring functions
from state-of-the-art detectors based on Mahalanobis distance, Local Intrinsic
Dimensionality, and One-Class Support Vector Machines, which process the hidden
features of deep neural networks. ENAD is designed to ensure high
standardization and reproducibility to the computational workflow. Importantly,
extensive tests on benchmark datasets, models and adversarial attacks show that
ENAD outperforms all competing methods in the large majority of settings. The
improvement over the state-of-the-art and the intrinsic generality of the
framework, which allows one to easily extend ENAD to include any set of
detectors, set the foundations for the new area of ensemble adversarial

In recent years, there has been significant work on increasing both
interpretability and debuggability of a Deep Neural Network (DNN) by extracting
a rule-based model that approximates its decision boundary. Nevertheless,
current DNN rule extraction methods that consider a DNN's latent space when
extracting rules, known as decompositional algorithms, are either restricted to
single-layer DNNs or intractable as the size of the DNN or data grows. In this
paper, we address these limitations by introducing ECLAIRE, a novel
polynomial-time rule extraction algorithm capable of scaling to both large DNN
architectures and large training datasets. We evaluate ECLAIRE on a wide
variety of tasks, ranging from breast cancer prognosis to particle detection,
and show that it consistently extracts more accurate and comprehensible rule
sets than the current state-of-the-art methods while using orders of magnitude
less computational resources. We make all of our methods available, including a
rule set visualisation interface, through the open-source REMIX library
(https://github.com/mateoespinosa/remix).

Vision transformers (ViTs) have become the popular structures and
outperformed convolutional neural networks (CNNs) on various vision tasks.
However, such powerful transformers bring a huge computation burden, because of
the exhausting token-to-token comparison. The previous works focus on dropping
insignificant tokens to reduce the computational cost of ViTs. But when the
dropping ratio increases, this hard manner will inevitably discard the vital
tokens, which limits its efficiency. To solve the issue, we propose a generic
self-slimmed learning approach for vanilla ViTs, namely SiT. Specifically, we
first design a novel Token Slimming Module (TSM), which can boost the inference
efficiency of ViTs by dynamic token aggregation. As a general method of token
hard dropping, our TSM softly integrates redundant tokens into fewer
informative ones. It can dynamically zoom visual attention without cutting off
discriminative token relations in the images, even with a high slimming ratio.
Furthermore, we introduce a concise Feature Recalibration Distillation (FRD)
framework, wherein we design a reverse version of TSM (RTSM) to recalibrate the
unstructured token in a flexible auto-encoder manner. Due to the similar
structure between teacher and student, our FRD can effectively leverage
structure knowledge for better convergence. Finally, we conduct extensive
experiments to evaluate our SiT. It demonstrates that our method can speed up
ViTs by 1.7x with negligible accuracy drop, and even speed up ViTs by 3.6x
while maintaining 97% of their performance. Surprisingly, by simply arming
LV-ViT with our SiT, we achieve new state-of-the-art performance on ImageNet.
Code is available at https://github.com/Sense-X/SiT.

Deep learning's success has been attributed to the training of large,
overparameterized models on massive amounts of data. As this trend continues,
model training has become prohibitively costly, requiring access to powerful
computing systems to train state-of-the-art networks. A large body of research
has been devoted to addressing the cost per iteration of training through
various model compression techniques like pruning and quantization. Less effort
has been spent targeting the number of iterations. Previous work, such as
forget scores and GraNd/EL2N scores, address this problem by identifying
important samples within a full dataset and pruning the remaining samples,
thereby reducing the iterations per epoch. Though these methods decrease the
training time, they use expensive static scoring algorithms prior to training.
When accounting for the scoring mechanism, the total run time is often
increased. In this work, we address this shortcoming with dynamic data pruning
algorithms. Surprisingly, we find that uniform random dynamic pruning can
outperform the prior work at aggressive pruning rates. We attribute this to the
existence of "sometimes" samples -- points that are important to the learned
decision boundary only some of the training time. To better exploit the
subtlety of sometimes samples, we propose two algorithms, based on
reinforcement learning techniques, to dynamically prune samples and achieve
even higher accuracy than the random dynamic method. We test all our methods
against a full-dataset baseline and the prior work on CIFAR-10 and CIFAR-100,
and we can reduce the training time by up to 2x without significant performance
loss. Our results suggest that data pruning should be understood as a dynamic
process that is closely tied to a model's training trajectory, instead of a
static step based solely on the dataset alone.

Personalized search plays a crucial role in improving user search experience
owing to its ability to build user profiles based on historical behaviors.
Previous studies have made great progress in extracting personal signals from
the query log and learning user representations. However, neural personalized
search is extremely dependent on sufficient data to train the user model. Data
sparsity is an inevitable challenge for existing methods to learn high-quality
user representations. Moreover, the overemphasis on final ranking quality leads
to rough data representations and impairs the generalizability of the model. To
tackle these issues, we propose a Personalized Search framework with
Self-supervised Learning (PSSL) to enhance data representations. Specifically,
we adopt a contrastive sampling method to extract paired self-supervised
information from sequences of user behaviors in query logs. Four auxiliary
tasks are designed to pre-train the sentence encoder and the sequence encoder
used in the ranking model. They are optimized by contrastive loss which aims to
close the distance between similar user sequences, queries, and documents.
Experimental results on two datasets demonstrate that our proposed model PSSL
achieves state-of-the-art performance compared with existing baselines.

Training a good supernet in one-shot NAS methods is difficult since the
search space is usually considerably huge (e.g., $13^{21}$). In order to
enhance the supernet's evaluation ability, one greedy strategy is to sample
good paths, and let the supernet lean towards the good ones and ease its
evaluation burden as a result. However, in practice the search can be still
quite inefficient since the identification of good paths is not accurate enough
and sampled paths still scatter around the whole search space. In this paper,
we leverage an explicit path filter to capture the characteristics of paths and
directly filter those weak ones, so that the search can be thus implemented on
the shrunk space more greedily and efficiently. Concretely, based on the fact
that good paths are much less than the weak ones in the space, we argue that
the label of "weak paths" will be more confident and reliable than that of
"good paths" in multi-path sampling. In this way, we thus cast the training of
path filter in the positive and unlabeled (PU) learning paradigm, and also
encourage a \textit{path embedding} as better path/operation representation to
enhance the identification capacity of the learned filter. By dint of this
embedding, we can further shrink the search space by aggregating similar
operations with similar embeddings, and the search can be more efficient and
accurate. Extensive experiments validate the effectiveness of the proposed
method GreedyNASv2. For example, our obtained GreedyNASv2-L achieves $81.1\%$
Top-1 accuracy on ImageNet dataset, significantly outperforming the ResNet-50

Multi-task indoor scene understanding is widely considered as an intriguing
formulation, as the affinity of different tasks may lead to improved
performance. In this paper, we tackle the new problem of joint semantic,
affordance and attribute parsing. However, successfully resolving it requires a
model to capture long-range dependency, learn from weakly aligned data and
properly balance sub-tasks during training. To this end, we propose an
attention-based architecture named Cerberus and a tailored training framework.
Our method effectively addresses the aforementioned challenges and achieves
state-of-the-art performance on all three tasks. Moreover, an in-depth analysis
shows concept affinity consistent with human cognition, which inspires us to
explore the possibility of weakly supervised learning. Surprisingly, Cerberus
achieves strong results using only 0.1%-1% annotation. Visualizations further
confirm that this success is credited to common attention maps across tasks.
Code and models can be accessed at https://github.com/OPEN-AIR-SUN/Cerberus.

Genome engineering is undergoing unprecedented development and is now
becoming widely available. To ensure responsible biotechnology innovation and
to reduce misuse of engineered DNA sequences, it is vital to develop tools to
identify the lab-of-origin of engineered plasmids. Genetic engineering
attribution (GEA), the ability to make sequence-lab associations, would support
forensic experts in this process. Here, we propose a method, based on metric
learning, that ranks the most likely labs-of-origin whilst simultaneously
generating embeddings for plasmid sequences and labs. These embeddings can be
used to perform various downstream tasks, such as clustering DNA sequences and
labs, as well as using them as features in machine learning models. Our
approach employs a circular shift augmentation approach and is able to
correctly rank the lab-of-origin $90\%$ of the time within its top 10
predictions - outperforming all current state-of-the-art approaches. We also
demonstrate that we can perform few-shot-learning and obtain $76\%$ top-10
accuracy using only $10\%$ of the sequences. This means, we outperform the
previous CNN approach using only one-tenth of the data. We also demonstrate
that we are able to extract key signatures in plasmid sequences for particular
labs, allowing for an interpretable examination of the model's outputs.

This thesis is mainly concerned with state-space approaches for solving deep
(temporal) Gaussian process (DGP) regression problems. More specifically, we
represent DGPs as hierarchically composed systems of stochastic differential
equations (SDEs), and we consequently solve the DGP regression problem by using
state-space filtering and smoothing methods. The resulting state-space DGP
(SS-DGP) models generate a rich class of priors compatible with modelling a
number of irregular signals/functions. Moreover, due to their Markovian
structure, SS-DGPs regression problems can be solved efficiently by using
Bayesian filtering and smoothing methods. The second contribution of this
thesis is that we solve continuous-discrete Gaussian filtering and smoothing
problems by using the Taylor moment expansion (TME) method. This induces a
class of filters and smoothers that can be asymptotically exact in predicting
the mean and covariance of stochastic differential equations (SDEs) solutions.
Moreover, the TME method and TME filters and smoothers are compatible with
simulating SS-DGPs and solving their regression problems. Lastly, this thesis
features a number of applications of state-space (deep) GPs. These applications
mainly include, (i) estimation of unknown drift functions of SDEs from
partially observed trajectories and (ii) estimation of spectro-temporal
features of signals.

Models of human motion commonly focus either on trajectory prediction or
action classification but rarely both. The marked heterogeneity and intricate
compositionality of human motion render each task vulnerable to the data
degradation and distributional shift common to real-world scenarios. A
sufficiently expressive generative model of action could in theory enable data
conditioning and distributional resilience within a unified framework
applicable to both tasks. Here we propose a novel architecture based on
hierarchical variational autoencoders and deep graph convolutional neural
networks for generating a holistic model of action over multiple time-scales.
We show this Hierarchical Graph-convolutional Variational Autoencoder (HG-VAE)
to be capable of generating coherent actions, detecting out-of-distribution
data, and imputing missing data by gradient ascent on the model's posterior.
Trained and evaluated on H3.6M and the largest collection of open source human
motion data, AMASS, we show HG-VAE can facilitate downstream discriminative
learning better than baseline models.

We propose learning via retracing, a novel self-supervised approach for
learning the state representation (and the associated dynamics model) for
reinforcement learning tasks. In addition to the predictive (reconstruction)
supervision in the forward direction, we propose to include "retraced"
transitions for representation / model learning, by enforcing the
cycle-consistency constraint between the original and retraced states, hence
improve upon the sample efficiency of learning. Moreover, learning via
retracing explicitly propagates information about future transitions backward
for inferring previous states, thus facilitates stronger representation
learning for the downstream reinforcement learning tasks. We introduce
Cycle-Consistency World Model (CCWM), a concrete model-based instantiation of
learning via retracing. Additionally we propose a novel adaptive "truncation"
mechanism for counteracting the negative impacts brought by "irreversible"
transitions such that learning via retracing can be maximally effective.
Through extensive empirical studies on visual-based continuous control
benchmarks, we demonstrate that CCWM achieves state-of-the-art performance in
terms of sample efficiency and asymptotic performance, whilst exhibiting
behaviours that are indicative of stronger representation learning.

Object-centric representations are a promising path toward more systematic
generalization by providing flexible abstractions upon which compositional
world models can be built. Recent work on simple 2D and 3D datasets has shown
that models with object-centric inductive biases can learn to segment and
represent meaningful objects from the statistical structure of the data alone
without the need for any supervision. However, such fully-unsupervised methods
still fail to scale to diverse realistic data, despite the use of increasingly
complex inductive biases such as priors for the size of objects or the 3D
geometry of the scene. In this paper, we instead take a weakly-supervised
approach and focus on how 1) using the temporal dynamics of video data in the
form of optical flow and 2) conditioning the model on simple object location
cues can be used to enable segmenting and tracking objects in significantly
more realistic synthetic data. We introduce a sequential extension to Slot
Attention which we train to predict optical flow for realistic looking
synthetic scenes and show that conditioning the initial state of this model on
a small set of hints, such as center of mass of objects in the first frame, is
sufficient to significantly improve instance segmentation. These benefits
generalize beyond the training distribution to novel objects, novel
backgrounds, and to longer video sequences. We also find that such
initial-state-conditioning can be used during inference as a flexible interface
to query the model for specific objects or parts of objects, which could pave
the way for a range of weakly-supervised approaches and allow more effective
interaction with trained models.

We present Lepard, a Learning based approach for partial point cloud matching
in rigid and deformable scenes. The key characteristics are the following
techniques that exploit 3D positional knowledge for point cloud matching: 1) An
architecture that disentangles point cloud representation into feature space
and 3D position space. 2) A position encoding method that explicitly reveals 3D
relative distance information through the dot product of vectors. 3) A
repositioning technique that modifies the crosspoint-cloud relative positions.
Ablation studies demonstrate the effectiveness of the above techniques. In
rigid cases, Lepard combined with RANSAC and ICP demonstrates state-of-the-art
registration recall of 93.9% / 71.3% on the 3DMatch / 3DLoMatch. In deformable
cases, Lepard achieves +27.1% / +34.8% higher non-rigid feature matching recall
than the prior art on our newly constructed 4DMatch / 4DLoMatch benchmark.

A music piece is both comprehended hierarchically, from sonic events to
melodies, and sequentially, in the form of repetition and variation. Music from
different cultures establish different aesthetics by having different style
conventions on these two aspects. We propose a framework that could be used to
quantitatively compare music from different cultures by looking at these two
aspects.
  The framework is based on an Music Information Dynamics model, a Variable
Markov Oracle (VMO), and is extended with a variational representation learning
of audio. A variational autoencoder (VAE) is trained to map audio fragments
into a latent representation. The latent representation is fed into a VMO. The
VMO then learns a clustering of the latent representation via a threshold that
maximizes the information rate of the quantized latent representation sequence.
This threshold effectively controls the sensibility of the predictive step to
acoustic changes, which determines the framework's ability to track repetitions
on longer time scales. This approach allows characterization of the overall
information contents of a musical signal at each level of acoustic sensibility.
  Our findings under this framework show that sensibility to subtle acoustic
changes is higher for East-Asian musical traditions, while the Western works
exhibit longer motivic structures at higher thresholds of differences in the
latent space. This suggests that a profile of information contents, analyzed as
a function of the level of acoustic detail can serve as a possible cultural
characteristic.

Generative Adversarial Network (GAN) based localized image editing can suffer
from ambiguity between semantic attributes. We thus present a novel objective
function to evaluate the locality of an image edit. By introducing the
supervision from a pre-trained segmentation network and optimizing the
objective function, our framework, called Locally Effective Latent Space
Direction (LELSD), is applicable to any dataset and GAN architecture. Our
method is also computationally fast and exhibits a high extent of
disentanglement, which allows users to interactively perform a sequence of
edits on an image. Our experiments on both GAN-generated and real images
qualitatively demonstrate the high quality and advantages of our method.

Learning to estimate object pose often requires ground-truth (GT) labels,
such as CAD model and absolute-scale object pose, which is expensive and
laborious to obtain in the real world. To tackle this problem, we propose an
unsupervised domain adaptation (UDA) for category-level object pose estimation,
called UDA-COPE. Inspired by recent multi-modal UDA techniques, the proposed
method exploits a teacher-student self-supervised learning scheme to train a
pose estimation network without using target domain pose labels. We also
introduce a bidirectional filtering method between the predicted normalized
object coordinate space (NOCS) map and observed point cloud, to not only make
our teacher network more robust to the target domain but also to provide more
reliable pseudo labels for the student network training. Extensive experimental
results demonstrate the effectiveness of our proposed method both
quantitatively and qualitatively. Notably, without leveraging target-domain GT
labels, our proposed method achieved comparable or sometimes superior
performance to existing methods that depend on the GT labels.

Sickle cell anemia is a genetic disorder that is widespread in many regions
of the world. Early diagnosis through screening and preventive treatments are
known to reduce mortality in the case of sickle cell disease (SCD). In
addition, the screening of individuals with the largely asymptomatic condition
of sickle cell trait (SCT) is necessary to curtail the genetic propagation of
the disease. However, the cost and complexity of conventional diagnostic
methods limit the feasibility of early diagnosis of SCD and SCT in
resource-limited areas worldwide. Recently, our group developed a low-cost
UV-Vis absorbance spectroscopy based diagnostic test for SCD and SCT. Here, we
propose an AutoML based approach to classify the raw spectra data obtained from
the developed UV-Vis spectroscopy technique with high accuracy. The proposed
approach can detect the presence of sickle hemoglobin with 100% sensitivity and
93.84% specificity. This study demonstrates the potential utility of the
machine learning-based absorbance spectroscopy test for deployment in mass
screening programs in resource-limited settings.

Generative adversarial networks are a kind of deep generative model with the
potential to revolutionize biomedical imaging. This is because GANs have a
learned capacity to draw whole-image variates from a lower-dimensional
representation of an unknown, high-dimensional distribution that fully
describes the input training images. The overarching problem with GANs in
clinical applications is that there is not adequate or automatic means of
assessing the diagnostic quality of images generated by GANs. In this work, we
demonstrate several tests of the statistical accuracy of images output by two
popular GAN architectures. We designed several stochastic object models (SOMs)
of distinct features that can be recovered after generation by a trained GAN.
Several of these features are high-order, algorithmic pixel-arrangement rules
which are not readily expressed in covariance matrices. We designed and
validated statistical classifiers to detect the known arrangement rules. We
then tested the rates at which the different GANs correctly reproduced the
rules under a variety of training scenarios and degrees of feature-class
similarity. We found that ensembles of generated images can appear accurate
visually, and correspond to low Frechet Inception Distance scores (FID), while
not exhibiting the known spatial arrangements. Furthermore, GANs trained on a
spectrum of distinct spatial orders did not respect the given prevalence of
those orders in the training data. The main conclusion is that while low-order
ensemble statistics are largely correct, there are numerous quantifiable errors
per image that plausibly can affect subsequent use of the GAN-generated images.

Sparse matrix-vector multiplication (SpMV) multiplies a sparse matrix with a
dense vector. SpMV plays a crucial role in many applications, from graph
analytics to deep learning. The random memory accesses of the sparse matrix
make accelerator design challenging. However, high bandwidth memory (HBM) based
FPGAs are a good fit for designing accelerators for SpMV. In this paper, we
present Serpens, an HBM based accelerator for general-purpose SpMV.Serpens
features (1) a general-purpose design, (2) memory-centric processing engines,
and (3) index coalescing to support the efficient processing of arbitrary
SpMVs. From the evaluation of twelve large-size matrices, Serpens is 1.91x and
1.76x better in terms of geomean throughput than the latest accelerators
GraphLiLy and Sextans, respectively. We also evaluate 2,519 SuiteSparse
matrices, and Serpens achieves 2.10x higher throughput than a K80 GPU. For the
energy/bandwidth efficiency, Serpens is 1.71x/1.99x, 1.90x/2.69x, and
6.25x/4.06x better compared with GraphLily, Sextans, and K80, respectively.
After scaling up to 24 HBM channels, Serpens achieves up to 60.55~GFLOP/s
(30,204~MTEPS) and up to 3.79x over GraphLily. The code is available at
https://github.com/UCLA-VAST/Serpens.

Recently Liu et al. (Acta Mater., 2020) proposed a new divide-and-conquer
based machine learning method for predicting creep life of superalloys. The
idea is enlightening though, the prediction accuracy and intelligence remain to
be improved. In the present work, we modify the method by adding a
dimensionality reduction algorithm before the clustering step and introducing a
grid search algorithm to the regression model selection step. As a consequence,
the clustering result becomes much more desirable and the accuracy of predicted
creep life is dramatically improved. The root-mean-square error,
mean-absolute-percentage error and relevant coefficient of the original method
are 0.2341, 0.0595 and 0.9121, while those of the modified method are 0.0285,
0.0196, and 0.9806, respectively. Moreover, the ad-hoc determination of
hyperparameters in the original method is replaced by automated determination
of hyperparameters in the modified method, which considerably improves the
intelligence of the method.

Machine learning (ML) models need to be frequently retrained on changing
datasets in a wide variety of application scenarios, including data valuation
and uncertainty quantification. To efficiently retrain the model, linear
approximation methods such as influence function have been proposed to estimate
the impact of data changes on model parameters. However, these methods become
inaccurate for large dataset changes. In this work, we focus on convex learning
problems and propose a general framework to learn to estimate optimized model
parameters for different training sets using neural networks. We propose to
enforce the predicted model parameters to obey optimality conditions and
maintain utility through regularization techniques, which significantly improve
generalization. Moreover, we rigorously characterize the expressive power of
neural networks to approximate the optimizer of convex problems. Empirical
results demonstrate the advantage of the proposed method in accurate and
efficient model parameter estimation compared to the state-of-the-art.

The purpose of this work is to contribute to the state of the art of
deep-learning methods for diffeomorphic registration. We propose an adversarial
learning LDDMM method for pairs of 3D mono-modal images based on Generative
Adversarial Networks. The method is inspired by the recent literature for
deformable image registration with adversarial learning. We combine the best
performing generative, discriminative, and adversarial ingredients from the
state of the art within the LDDMM paradigm. We have successfully implemented
two models with the stationary and the EPDiff-constrained non-stationary
parameterizations of diffeomorphisms. Our unsupervised and data-hungry approach
has shown a competitive performance with respect to a benchmark supervised and
rich-data approach. In addition, our method has shown similar results to
model-based methods with a computational time under one second.

With growing societal acceptance and increasing cost efficiency due to mass
production, service robots are beginning to cross from the industrial to the
social domain. Currently, customer service robots tend to be digital and
emulate social interactions through on-screen text, but state-of-the-art
research points towards physical robots soon providing customer service in
person. This article explores two possibilities. Firstly, whether transfer
learning can aid in the improvement of customer service chatbots between
business domains. Second, the implementation of a framework for physical robots
for in-person interaction. Modelled on social interaction with Twitter customer
support accounts, transformer-based chatbot models are initially assigned to
learn one domain from an initial random weight distribution. Given shared
vocabulary, each model is then tasked with learning another domain by
transferring knowledge from the previous. Following studies on 19 different
businesses, results show that the majority of models are improved when
transferring weights from at least one other domain, in particular those that
are more data-scarce than others. General language transfer learning occurs, as
well as higher-level transfer of similar domain knowledge, in several cases.
The chatbots are finally implemented on Temi and Pepper robots, with
feasibility issues encountered and solutions are proposed to overcome them.

Local or reactive navigation is essential for autonomous mobile robots which
operate in an indoor environment. Techniques such as SLAM, computer vision
require significant computational power which increases cost. Similarly, using
rudimentary methods makes the robot susceptible to inconsistent behavior. This
paper aims to develop a robot that balances cost and accuracy by using machine
learning to predict the best obstacle avoidance move based on distance inputs
from four ultrasonic sensors that are strategically mounted on the front,
front-left, front-right, and back of the robot. The underlying hardware
consists of an Arduino Uno and a Raspberry Pi 3B. The machine learning model is
first trained on the data collected by the robot. Then the Arduino continuously
polls the sensors and calculates the distance values, and in case of critical
need for avoidance, a suitable maneuver is made by the Arduino. In other
scenarios, sensor data is sent to the Raspberry Pi using a USB connection and
the machine learning model generates the best move for navigation, which is
sent to the Arduino for driving motors accordingly. The system is mounted on a
2-WD robot chassis and tested in a cluttered indoor setting with most
impressive results.

We propose a paradigm shift in the data-driven modeling of the instrumental
response field of telescopes. By adding a differentiable optical forward model
pixels to the wavefront. This allows to transfer a great deal of complexity
from the instrumental response into the forward model while being able to adapt
to the observations, remaining data-driven. Our framework allows a way forward
to building powerful models that are physically motivated, interpretable, and
that do not require special calibration data. We show that for a simplified
setting of a space telescope, this framework represents a real performance
breakthrough compared to existing data-driven approaches with reconstruction
errors decreasing 5 fold at observation resolution and more than 10 fold for a
3x super-resolution. We successfully model chromatic variations of the
instrument's response only using noisy broad-band in-focus observations.

Non-intrusive speech intelligibility (SI) prediction from binaural signals is
useful in many applications. However, most existing signal-based measures are
designed to be applied to single-channel signals. Measures specifically
designed to take into account the binaural properties of the signal are often
intrusive - characterised by requiring access to a clean speech signal - and
typically rely on combining both channels into a single-channel signal before
making predictions. This paper proposes a non-intrusive SI measure that
computes features from a binaural input signal using a combination of vector
quantization (VQ) and contrastive predictive coding (CPC) methods. VQ-CPC
feature extraction does not rely on any model of the auditory system and is
instead trained to maximise the mutual information between the input signal and
output features. The computed VQ-CPC features are input to a predicting
function parameterized by a neural network. Two predicting functions are
considered in this paper. Both feature extractor and predicting functions are
trained on simulated binaural signals with isotropic noise. They are tested on
simulated signals with isotropic and real noise. For all signals, the ground
truth scores are the (intrusive) deterministic binaural STOI. Results are
presented in terms of correlations and MSE and demonstrate that VQ-CPC features
are able to capture information relevant to modelling SI and outperform all the
considered benchmarks - even when evaluating on data comprising of different
noise field types.

Recently, MLP-Like networks have been revived for image recognition. However,
whether it is possible to build a generic MLP-Like architecture on video domain
has not been explored, due to complex spatial-temporal modeling with large
computation burden. To fill this gap, we present an efficient self-attention
free backbone, namely MorphMLP, which flexibly leverages the concise
Fully-Connected (FC) layer for video representation learning. Specifically, a
MorphMLP block consists of two key layers in sequence, i.e., MorphFC_s and
MorphFC_t, for spatial and temporal modeling respectively. MorphFC_s can
effectively capture core semantics in each frame, by progressive token
interaction along both height and width dimensions. Alternatively, MorphFC_t
can adaptively learn long-term dependency over frames, by temporal token
aggregation on each spatial location. With such multi-dimension and multi-scale
factorization, our MorphMLP block can achieve a great accuracy-computation
balance. Finally, we evaluate our MorphMLP on a number of popular video
benchmarks. Compared with the recent state-of-the-art models, MorphMLP
significantly reduces computation but with better accuracy, e.g., MorphMLP-S
only uses 50% GFLOPs of VideoSwin-T but achieves 0.9% top-1 improvement on
Kinetics400, under ImageNet1K pretraining. MorphMLP-B only uses 43% GFLOPs of
MViT-B but achieves 2.4% top-1 improvement on SSV2, even though MorphMLP-B is
pretrained on ImageNet1K while MViT-B is pretrained on Kinetics400. Moreover,
our method adapted to the image domain outperforms previous SOTA MLP-Like
architectures. Code is available at https://github.com/MTLab/MorphMLP.

University evaluation and ranking is an extremely complex activity. Major
universities are struggling because of increasingly complex indicator systems
of world university rankings. So can we find the meta-indicators of the index
system by simplifying the complexity? This research discovered three
meta-indicators based on interpretable machine learning. The first one is time,
to be friends with time, and believe in the power of time, and accumulate
historical deposits; the second one is space, to be friends with city, and grow
together by co-develop; the third one is relationships, to be friends with
alumni, and strive for more alumni donations without ceiling.

Achieving the desired optical response from a multilayer thin-film structure
over a broad range of wavelengths and angles of incidence can be challenging.
An advanced thin-film structure can consist of multiple materials with
different thicknesses and numerous layers. Design and optimization of complex
thin-film structures with multiple variables is a computationally heavy problem
that is still under active research. To enable fast and easy experimentation
with new optimization techniques, we propose the Python package TMM-Fast which
enables parallelized computation of reflection and transmission of light at
different angles of incidence and wavelengths through the multilayer thin-film.
By decreasing computational time, generating datasets for machine learning
becomes feasible and evolutionary optimization can be used effectively.
Additionally, the sub-package TMM-Torch allows to directly compute analytical
gradients for local optimization by using PyTorch Autograd functionality.
Finally, an OpenAi Gym environment is presented which allows the user to train
reinforcement learning agents on the problem of finding multilayer thin-film

Deep learning models usually suffer from domain shift issues, where models
trained on one source domain do not generalize well to other unseen domains. In
this work, we investigate the single-source domain generalization problem:
training a deep network that is robust to unseen domains, under the condition
that training data is only available from one source domain, which is common in
medical imaging applications. We tackle this problem in the context of
cross-domain medical image segmentation. Under this scenario, domain shifts are
mainly caused by different acquisition processes. We propose a simple
causality-inspired data augmentation approach to expose a segmentation model to
synthesized domain-shifted training examples. Specifically, 1) to make the deep
model robust to discrepancies in image intensities and textures, we employ a
family of randomly-weighted shallow networks. They augment training images
using diverse appearance transformations. 2) Further we show that spurious
correlations among objects in an image are detrimental to domain robustness.
These correlations might be taken by the network as domain-specific clues for
making predictions, and they may break on unseen domains. We remove these
spurious correlations via causal intervention. This is achieved by resampling
the appearances of potentially correlated objects independently. The proposed
approach is validated on three cross-domain segmentation tasks: cross-modality
(CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI
segmentation, and cross-center prostate MRI segmentation. The proposed approach
yields consistent performance gains compared with competitive methods when
tested on unseen domains.

The dynamics in the photosphere is governed by the multi-scale turbulent
convection termed as granulation and supergranulation. It is important to
derive 3-dimensional velocity vectors to understand the nature of the turbulent
convection. However, it is difficult to obtain the velocity component
perpendicular to the line-of-sight, which corresponds to the horizontal
velocity in disk center observations. We developed a convolutional neural
network model with a multi-scale deep learning architecture. The method
consists of multiple convolutional kernels with various sizes of the receptive
fields, and it performs convolution for spatial and temporal axes. The network
is trained with data from three different numerical simulations of turbulent
convection, and we introduced a coherence spectrum to assess the horizontal
velocity fields that were derived at each spatial scale. The multi-scale deep
learning method successfully predicts the horizontal velocities for each
convection simulation in terms of the global-correlation-coefficient, which is
often used for evaluating the prediction accuracy of the methods. The coherence
spectrum reveals the strong dependence of the correlation coefficients on the
spatial scales. Although coherence spectra are higher than 0.9 for large-scale
structures, they drastically decrease to less than 0.3 for small-scale
structures wherein the global-correlation-coefficient indicates a high value of
approximately 0.95. We determined that this decrease in the coherence spectrum
occurs around the energy injection scales. The accuracy for the small-scale
structures is not guaranteed solely by the global-correlation-coefficient. To
improve the accuracy in small-scales, it is important to improve the loss
function for enhancing the small-scale structures and to utilize other physical
quantities related to the non-linear cascade of convective eddies as input
data.

Conditioned source separations have attracted significant attention because
of their flexibility, applicability and extensionality. Their performance was
usually inferior to the existing approaches, such as the single source
separation model. However, a recently proposed method called LaSAFT-Net has
shown that conditioned models can show comparable performance against existing
single-source separation models. This paper presents LightSAFT-Net, a
lightweight version of LaSAFT-Net. As a baseline, it provided a sufficient SDR
performance for comparison during the Music Demixing Challenge at ISMIR 2021.
This paper also enhances the existing LightSAFT-Net by replacing the LightSAFT
blocks in the encoder with TFC-TDF blocks. Our enhanced LightSAFT-Net
outperforms the previous one with fewer parameters.Conditioned source
separations have attracted significant attention because of their flexibility,
applicability and extensionality. Their performance was usually inferior to the
existing approaches, such as the single source separation model. However, a
recently proposed method called LaSAFT-Net has shown that conditioned models
can show comparable performance against existing single-source separation
models. This paper presents LightSAFT-Net, a lightweight version of LaSAFT-Net.
As a baseline, it provided a sufficient SDR performance for comparison during
the Music Demixing Challenge at ISMIR 2021.

We propose a non-intrusive Deep Learning-based Reduced Order Model (DL-ROM)
capable of capturing the complex dynamics of mechanical systems showing inertia
and geometric nonlinearities. In the first phase, a limited number of high
fidelity snapshots are used to generate a POD-Galerkin ROM which is
subsequently exploited to generate the data, covering the whole parameter
range, used in the training phase of the DL-ROM. A convolutional autoencoder is
employed to map the system response onto a low-dimensional representation and,
in parallel, to model the reduced nonlinear trial manifold. The system dynamics
on the manifold is described by means of a deep feedforward neural network that
is trained together with the autoencoder. The strategy is benchmarked against
high fidelity solutions on a clamped-clamped beam and on a real micromirror
with softening response and multiplicity of solutions. By comparing the
different computational costs, we discuss the impressive gain in performance
and show that the DL-ROM truly represents a real-time tool which can be
profitably and efficiently employed in complex system-level simulation
procedures for design and optimisation purposes.

Normalizing flows, diffusion normalizing flows and variational autoencoders
are powerful generative models. This chapter provides a unified framework to
handle these approaches via Markov chains. We consider stochastic normalizing
flows as a pair of Markov chains fulfilling some properties and show how many
state-of-the-art models for data generation fit into this framework. Indeed
numerical simulations show that including stochastic layers improves the
expressivity of the network and allows for generating multimodal distributions
from unimodal ones. The Markov chains point of view enables us to couple both
deterministic layers as invertible neural networks and stochastic layers as
Metropolis-Hasting layers, Langevin layers, variational autoencoders and
diffusion normalizing flows in a mathematically sound way. Our framework
establishes a useful mathematical tool to combine the various approaches.

Introductory mechanics ("physics 1") is a critical gateway course for
students desiring to pursue a STEM career. A major challenge with this course
is that there is a large spread in the students' incoming physics preparation,
and this level of preparation is strongly predictive of a students'
performance. The level of incoming preparation is also largely determined by a
student's educational privilege, and so this course can amplify inequities in
K-12 education and provide a barrier to a STEM career for students from
marginalized groups. Here, we present a novel introductory course design to
address such equity challenges in physics 1. We designed the course based on
the concept of deliberate practice to give students targeted, scaffolded, and
repeated opportunities to engage in research-identified practices and decisions
required for effective problem-solving. We used real-world problems, as they
carry less resemblance to physics high school problems, and so even the
students with the best high school physics instruction have little experience
or skill in solving them. The students learned the physics content knowledge
they needed in future courses, particularly in engineering, and their
problem-solving skills improved substantially. Furthermore, the success in the
course was not correlated with incoming physics preparation, in stark contrast
to the outcomes from traditional physics 1 courses. These findings suggest that
we made physics 1 more equitable by employing a deliberate practice approach in
the context of real-world problem-solving.

Stereo vision is an effective technique for depth estimation with broad
applicability in autonomous urban and highway driving. While various deep
learning-based approaches have been developed for stereo, the input data from a
binocular setup with a fixed baseline are limited. Addressing such a problem,
we present an end-to-end network for processing the data from a trinocular
setup, which is a combination of a narrow and a wide stereo pair. In this
design, two pairs of binocular data with a common reference image are treated
with shared weights of the network and a mid-level fusion. We also propose a
Guided Addition method for merging the 4D data of the two baselines.
Additionally, an iterative sequential self-supervised and supervised learning
on real and synthetic datasets is presented, making the training of the
trinocular system practical with no need to ground-truth data of the real
dataset. Experimental results demonstrate that the trinocular disparity network
surpasses the scenario where individual pairs are fed into a similar
architecture. Code and dataset:
https://github.com/cogsys-tuebingen/tristereonet.

Nuclei segmentation is a fundamental task in digital pathology analysis and
can be automated by deep learning-based methods. However, the development of
such an automated method requires a large amount of data with precisely
annotated masks which is hard to obtain. Training with weakly labeled data is a
popular solution for reducing the workload of annotation. In this paper, we
propose a novel meta-learning-based nuclei segmentation method which follows
the label correction paradigm to leverage data with noisy masks. Specifically,
we design a fully conventional meta-model that can correct noisy masks using a
small amount of clean meta-data. Then the corrected masks can be used to
supervise the training of the segmentation model. Meanwhile, a bi-level
optimization method is adopted to alternately update the parameters of the main
segmentation model and the meta-model in an end-to-end way. Extensive
experimental results on two nuclear segmentation datasets show that our method
achieves the state-of-the-art result. It even achieves comparable performance
with the model training on supervised data in some noisy settings.

In standard neural network training, the gradients in the backward pass are
determined by the forward pass. As a result, the two stages are coupled. This
is how most neural networks are trained currently. However, gradient
modification in the backward pass has seldom been studied in the literature. In
this paper we explore decoupled training, where we alter the gradients in the
backward pass. We propose a simple yet powerful method called PowerGrad
Transform, that alters the gradients before the weight update in the backward
pass and significantly enhances the predictive performance of the neural
network. PowerGrad Transform trains the network to arrive at a better optima at
convergence. It is computationally extremely efficient, virtually adding no
additional cost to either memory or compute, but results in improved final
accuracies on both the training and test sets. PowerGrad Transform is easy to
integrate into existing training routines, requiring just a few lines of code.
PowerGrad Transform accelerates training and makes it possible for the network
to better fit the training data. With decoupled training, PowerGrad Transform
improves baseline accuracies for ResNet-50 by 0.73%, for SE-ResNet-50 by 0.66%
and by more than 1.0% for the non-normalized ResNet-18 network on the ImageNet

A trained neural network can be interpreted as a structural causal model
(SCM) that provides the effect of changing input variables on the model's
output. However, if training data contains both causal and correlational
relationships, a model that optimizes prediction accuracy may not necessarily
learn the true causal relationships between input and output variables. On the
other hand, expert users often have prior knowledge of the causal relationship
between certain input variables and output from domain knowledge. Therefore, we
propose a regularization method that aligns the learned causal effects of a
neural network with domain priors, including both direct and total causal
effects. We show that this approach can generalize to different kinds of domain
priors, including monotonicity of causal effect of an input variable on output
or zero causal effect of a variable on output for purposes of fairness. Our
experiments on twelve benchmark datasets show its utility in regularizing a
neural network model to maintain desired causal effects, without compromising
on accuracy. Importantly, we also show that a model thus trained is robust and
gets improved accuracy on noisy inputs.

The use of autoencoders for shape editing or generation through latent space
manipulation suffers from unpredictable changes in the output shape. Our
autoencoder-based method enables intuitive shape editing in latent space by
disentangling latent sub-spaces into style variables and control points on the
surface that can be manipulated independently. The key idea is adding a
Lipschitz-type constraint to the loss function, i.e. bounding the change of the
output shape proportionally to the change in latent space, leading to
interpretable latent space representations. The control points on the surface
that are part of the latent code of an object can then be freely moved,
allowing for intuitive shape editing directly in latent space. We evaluate our
method by comparing to state-of-the-art data-driven shape editing methods. We
further demonstrate the expressiveness of our learned latent space by
leveraging it for unsupervised part segmentation.

There are good arguments to support the claim that deep neural networks
(DNNs) capture better feature representations than the previous hand-crafted
feature engineering, which leads to a significant performance improvement. In
this paper, we move a tiny step towards understanding the dynamics of feature
representations over layers. Specifically, we model the process of class
separation of intermediate representations in pre-trained DNNs as the evolution
of communities in dynamic graphs. Then, we introduce modularity, a generic
metric in graph theory, to quantify the evolution of communities. In the
preliminary experiment, we find that modularity roughly tends to increase as
the layer goes deeper and the degradation and plateau arise when the model
complexity is great relative to the dataset. Through an asymptotic analysis, we
prove that modularity can be broadly used for different applications. For
example, modularity provides new insights to quantify the difference between
feature representations. More crucially, we demonstrate that the degradation
and plateau in modularity curves represent redundant layers in DNNs and can be
pruned with minimal impact on performance, which provides theoretical guidance
for layer pruning. Our code is available at
https://github.com/yaolu-zjut/Dynamic-Graphs-Construction.

Pansharpening in remote sensing image aims at acquiring a high-resolution
multispectral (HRMS) image directly by fusing a low-resolution multispectral
(LRMS) image with a panchromatic (PAN) image. The main concern is how to
effectively combine the rich spectral information of LRMS image with the
abundant spatial information of PAN image. Recently, many methods based on deep
learning have been proposed for the pansharpening task. However, these methods
usually has two main drawbacks: 1) requiring HRMS for supervised learning; and
2) simply ignoring the latent relation between the MS and PAN image and fusing
them directly. To solve these problems, we propose a novel unsupervised network
based on learnable degradation processes, dubbed as LDP-Net. A reblurring block
and a graying block are designed to learn the corresponding degradation
processes, respectively. In addition, a novel hybrid loss function is proposed
to constrain both spatial and spectral consistency between the pansharpened
image and the PAN and LRMS images at different resolutions. Experiments on
Worldview2 and Worldview3 images demonstrate that our proposed LDP-Net can fuse
PAN and LRMS images effectively without the help of HRMS samples, achieving
promising performance in terms of both qualitative visual effects and

The cooperative bandit problem is increasingly becoming relevant due to its
applications in large-scale decision-making. However, most research for this
problem focuses exclusively on the setting with perfect communication, whereas
in most real-world distributed settings, communication is often over stochastic
networks, with arbitrary corruptions and delays. In this paper, we study
cooperative bandit learning under three typical real-world communication
scenarios, namely, (a) message-passing over stochastic time-varying networks,
(b) instantaneous reward-sharing over a network with random delays, and (c)
message-passing with adversarially corrupted rewards, including byzantine
communication. For each of these environments, we propose decentralized
algorithms that achieve competitive performance, along with near-optimal
guarantees on the incurred group regret as well. Furthermore, in the setting
with perfect communication, we present an improved delayed-update algorithm
that outperforms the existing state-of-the-art on various network topologies.
Finally, we present tight network-dependent minimax lower bounds on the group
regret. Our proposed algorithms are straightforward to implement and obtain
competitive empirical performance.

Autoregressive models have proven to be very powerful in NLP text generation
tasks and lately have gained popularity for image generation as well. However,
they have seen limited use for the synthesis of 3D shapes so far. This is
mainly due to the lack of a straightforward way to linearize 3D data as well as
to scaling problems with the length of the resulting sequences when describing
complex shapes. In this work we address both of these problems. We use octrees
as a compact hierarchical shape representation that can be sequentialized by
traversal ordering. Moreover, we introduce an adaptive compression scheme, that
significantly reduces sequence lengths and thus enables their effective
generation with a transformer, while still allowing fully autoregressive
sampling and parallel training. We demonstrate the performance of our model by
comparing against the state-of-the-art in shape generation.

Automatic monitoring of adverse drug events (ADEs) or reactions (ADRs) is
currently receiving significant attention from the biomedical community. In
recent years, user-generated data on social media has become a valuable
resource for this task. Neural models have achieved impressive performance on
automatic text classification for ADR detection. Yet, training and evaluation
of these methods are carried out on user-generated texts about a targeted drug.
In this paper, we assess the robustness of state-of-the-art neural
architectures across different drug groups. We investigate several strategies
to use pseudo-labeled data in addition to a manually annotated train set.
Out-of-dataset experiments diagnose the bottleneck of supervised models in
terms of breakdown performance, while additional pseudo-labeled data improves
overall results regardless of the text selection strategy.

Video captioning aims to generate natural language descriptions according to
the content, where representation learning plays a crucial role. Existing
methods are mainly developed within the supervised learning framework via
word-by-word comparison of the generated caption against the ground-truth text
without fully exploiting linguistic semantics. In this work, we propose a
hierarchical modular network to bridge video representations and linguistic
semantics from three levels before generating captions. In particular, the
hierarchy is composed of: (I) Entity level, which highlights objects that are
most likely to be mentioned in captions. (II) Predicate level, which learns the
actions conditioned on highlighted objects and is supervised by the predicate
in captions. (III) Sentence level, which learns the global semantic
representation and is supervised by the whole caption. Each level is
implemented by one module. Extensive experimental results show that the
proposed method performs favorably against the state-of-the-art models on the
two widely-used benchmarks: MSVD 104.0% and MSR-VTT 51.5% in CIDEr score.

Recent self-supervised models have demonstrated equal or better performance
than supervised methods, opening for AI systems to learn visual representations
from practically unlimited data. However, these methods are typically
classification-based and thus ineffective for learning high-resolution feature
maps that preserve precise spatial information. This work introduces
superpixels to improve self-supervised learning of dense semantically rich
visual concept embeddings. Decomposing images into a small set of visually
coherent regions reduces the computational complexity by $\mathcal{O}(1000)$
while preserving detail. We experimentally show that contrasting over regions
improves the effectiveness of contrastive learning methods, extends their
applicability to high-resolution images, improves overclustering performance,
superpixels are better than grids, and regional masking improves performance.
The expressiveness of our dense embeddings is demonstrated by improving the
SOTA unsupervised semantic segmentation benchmark on Cityscapes, and for
convolutional models on COCO.

Weakly supervised temporal action localization aims at learning the
instance-level action pattern from the video-level labels, where a significant
challenge is action-context confusion. To overcome this challenge, one recent
work builds an action-click supervision framework. It requires similar
annotation costs but can steadily improve the localization performance when
compared to the conventional weakly supervised methods. In this paper, by
revealing that the performance bottleneck of the existing approaches mainly
comes from the background errors, we find that a stronger action localizer can
be trained with labels on the background video frames rather than those on the
action frames. To this end, we convert the action-click supervision to the
background-click supervision and develop a novel method, called BackTAL.
Specifically, BackTAL implements two-fold modeling on the background video
frames, i.e. the position modeling and the feature modeling. In position
modeling, we not only conduct supervised learning on the annotated video frames
but also design a score separation module to enlarge the score differences
between the potential action frames and backgrounds. In feature modeling, we
propose an affinity module to measure frame-specific similarities among
neighboring frames and dynamically attend to informative neighbors when
calculating temporal convolution. Extensive experiments on three benchmarks are
conducted, which demonstrate the high performance of the established BackTAL
and the rationality of the proposed background-click supervision. Code is
available at https://github.com/VividLe/BackTAL.

Learning a disentangled, interpretable, and structured latent representation
in 3D generative models of faces and bodies is still an open problem. The
problem is particularly acute when control over identity features is required.
In this paper, we propose an intuitive yet effective self-supervised approach
to train a 3D shape variational autoencoder (VAE) which encourages a
disentangled latent representation of identity features. Curating the
mini-batch generation by swapping arbitrary features across different shapes
allows to define a loss function leveraging known differences and similarities
in the latent representations. Experimental results conducted on 3D meshes show
that state-of-the-art methods for latent disentanglement are not able to
disentangle identity features of faces and bodies. Our proposed method properly
decouples the generation of such features while maintaining good representation
and reconstruction capabilities.

The thriving of artificial intelligence (AI) applications is driving the
further evolution of wireless networks. It has been envisioned that 6G will be
transformative and will revolutionize the evolution of wireless from "connected
things" to "connected intelligence". However, state-of-the-art deep learning
and big data analytics based AI systems require tremendous computation and
communication resources, causing significant latency, energy consumption,
network congestion, and privacy leakage in both of the training and inference
processes. By embedding model training and inference capabilities into the
network edge, edge AI stands out as a disruptive technology for 6G to
seamlessly integrate sensing, communication, computation, and intelligence,
thereby improving the efficiency, effectiveness, privacy, and security of 6G
networks. In this paper, we shall provide our vision for scalable and
trustworthy edge AI systems with integrated design of wireless communication
strategies and decentralized machine learning models. New design principles of
wireless networks, service-driven resource allocation optimization methods, as
well as a holistic end-to-end system architecture to support edge AI will be
described. Standardization, software and hardware platforms, and application
scenarios are also discussed to facilitate the industrialization and
commercialization of edge AI systems.

Time series processing and feature extraction are crucial and time-intensive
steps in conventional machine learning pipelines. Existing packages are limited
in their applicability, as they cannot cope with irregularly-sampled or
asynchronous data and make strong assumptions about the data format. Moreover,
these packages do not focus on execution speed and memory efficiency, resulting
in considerable overhead. We present $\texttt{tsflex}$, a Python toolkit for
time series processing and feature extraction, that focuses on performance and
flexibility, enabling broad applicability. This toolkit leverages window-stride
arguments of the same data type as the sequence-index, and maintains the
sequence-index through all operations. $\texttt{tsflex}$ is flexible as it
supports (1) multivariate time series, (2) multiple window-stride
configurations, and (3) integrates with processing and feature functions from
other packages, while (4) making no assumptions about the data sampling
regularity, series alignment, and data type. Other functionalities include
multiprocessing, detailed execution logging, chunking sequences, and
serialization. Benchmarks show that $\texttt{tsflex}$ is faster and more
memory-efficient compared to similar packages, while being more permissive and
flexible in its utilization.

Image augmentations applied during training are crucial for the
generalization performance of image classifiers. Therefore, a large body of
research has focused on finding the optimal augmentation policy for a given
task. Yet, RandAugment [2], a simple random augmentation policy, has recently
been shown to outperform existing sophisticated policies. Only Adversarial
AutoAugment (AdvAA) [11], an approach based on the idea of adversarial
training, has shown to be better than RandAugment. In this paper, we show that
random augmentations are still competitive compared to an optimal adversarial
approach, as well as to simple curricula, and conjecture that the success of
AdvAA is due to the stochasticity of the policy controller network, which
introduces a mild form of curriculum.

Smart contract transactions are increasingly interleaved by cross-contract
calls. While many tools have been developed to identify a common set of
vulnerabilities, the cross-contract vulnerability is overlooked by existing
tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the
presence of more than two interacting contracts. Existing methods are however
limited to analyze a maximum of two contracts at the same time. Detecting
cross-contract vulnerabilities is highly non-trivial. With multiple interacting
contracts, the search space is much larger than that of a single contract. To
address this problem, we present xFuzz, a machine learning guided smart
contract fuzzing framework. The machine learning models are trained with novel
features (e.g., word vectors and instructions) and are used to filter likely
benign program paths. Comparing with existing static tools, machine learning
model is proven to be more robust, avoiding directly adopting manually-defined
rules in specific tools. We compare xFuzz with three state-of-the-art tools on
7,391 contracts. xFuzz detects 18 exploitable cross-contract vulnerabilities,
of which 15 vulnerabilities are exposed for the first time. Furthermore, our
approach is shown to be efficient in detecting non-cross-contract
vulnerabilities as well -- using less than 20% time as that of other fuzzing
tools, xFuzz detects twice as many vulnerabilities.

Despite the huge and continuous advances in computational linguistics, the
lack of annotated data for Named Entity Recognition (NER) is still a
challenging issue, especially in low-resource languages and when domain
knowledge is required for high-quality annotations. Recent findings in NLP show
the effectiveness of cloze-style questions in enabling language models to
leverage the knowledge they acquired during the pre-training phase. In our
work, we propose a simple and intuitive adaptation of Pattern-Exploiting
Training (PET), a recent approach which combines the cloze-questions mechanism
and fine-tuning for few-shot learning: the key idea is to rephrase the NER task
with patterns. Our approach achieves considerably better performance than
standard fine-tuning and comparable or improved results with respect to other
few-shot baselines without relying on manually annotated data or distant
supervision on three benchmark datasets: NCBI-disease, BC2GM and a private
Italian biomedical corpus.

Dataflow applications, such as machine learning algorithms, can run for days,
making it desirable to have assurances that they will work correctly. Current
tools are not good enough: too often the interactions between tasks are not
type-safe, leading to undesirable run-time errors. This paper presents a new
declarative Haskell Embedded DSL (eDSL) for dataflow programming: CircuitFlow.
Defined as a Symmetric Monoidal Preorder (SMP) on data that models dependencies
in the workflow, it has a strong mathematical basis, refocusing on how data
flows through an application, resulting in a more expressive solution that not
only catches errors statically, but also achieves competitive run-time
performance. In our preliminary evaluation, CircuitFlow outperforms the
industry-leading Luigi library of Spotify by scaling better with the number of
inputs. The innovative creation of CircuitFlow is also of note, exemplifying
how to create a modular eDSL whose semantics necessitates effects, and where
storing complex type information for program correctness is paramount.

Relative radiometric normalization(RRN) of different satellite images of the
same terrain is necessary for change detection, object
classification/segmentation, and map-making tasks. However, traditional RRN
models are not robust, disturbing by object change, and RRN models precisely
considering object change can not robustly obtain the no-change set. This paper
proposes auto robust relative radiometric normalization methods via latent
change noise modeling. They utilize the prior knowledge that no change points
possess small-scale noise under relative radiometric normalization and that
change points possess large-scale radiometric noise after radiometric
normalization, combining the stochastic expectation maximization method to
quickly and robustly extract the no-change set to learn the relative
radiometric normalization mapping functions. This makes our model theoretically
grounded regarding the probabilistic theory and mathematics deduction.
Specifically, when we select histogram matching as the relative radiometric
normalization learning scheme integrating with the mixture of Gaussian
noise(HM-RRN-MoG), the HM-RRN-MoG model achieves the best performance. Our
model possesses the ability to robustly against clouds/fogs/changes. Our method
naturally generates a robust evaluation indicator for RRN that is the no-change
set root mean square error. We apply the HM-RRN-MoG model to the latter
vegetation/water change detection task, which reduces the radiometric contrast
and NDVI/NDWI differences on the no-change set, generates consistent and
comparable results. We utilize the no-change set into the building change
detection task, efficiently reducing the pseudo-change and boosting the

Constrained tensor and matrix factorization models allow to extract
interpretable patterns from multiway data. Therefore identifiability properties
and efficient algorithms for constrained low-rank approximations are nowadays
important research topics. This work deals with columns of factor matrices of a
low-rank approximation being sparse in a known and possibly overcomplete basis,
a model coined as Dictionary-based Low-Rank Approximation (DLRA). While earlier
contributions focused on finding factor columns inside a dictionary of
candidate columns, i.e. one-sparse approximations, this work is the first to
tackle DLRA with sparsity larger than one. I propose to focus on the
sparse-coding subproblem coined Mixed Sparse-Coding (MSC) that emerges when
solving DLRA with an alternating optimization strategy. Several algorithms
based on sparse-coding heuristics (greedy methods, convex relaxations) are
provided to solve MSC. The performance of these heuristics is evaluated on
simulated data. Then, I show how to adapt an efficient MSC solver based on the
LASSO to compute Dictionary-based Matrix Factorization and Canonical Polyadic
Decomposition in the context of hyperspectral image processing and
chemometrics. These experiments suggest that DLRA extends the modeling
capabilities of low-rank approximations, helps reducing estimation variance and
enhances the identifiability and interpretability of estimated factors.

This is the paper for the first place winning solution of the Drone vs. Bird
Challenge, organized by AVSS 2021. As the usage of drones increases with
lowered costs and improved drone technology, drone detection emerges as a vital
object detection task. However, detecting distant drones under unfavorable
conditions, namely weak contrast, long-range, low visibility, requires
effective algorithms. Our method approaches the drone detection problem by
fine-tuning a YOLOv5 model with real and synthetically generated data using a
Kalman-based object tracker to boost detection confidence. Our results indicate
that augmenting the real data with an optimal subset of synthetic data can
increase the performance. Moreover, temporal information gathered by object
tracking methods can increase performance further.

Anomaly detection is important in many real-life applications. Recently,
self-supervised learning has greatly helped deep anomaly detection by
recognizing several geometric transformations. However these methods lack finer
features, usually highly depend on the anomaly type, and do not perform well on
fine-grained problems. To address these issues, we first introduce in this work
three novel and efficient discriminative and generative tasks which have
complementary strength: (i) a piece-wise jigsaw puzzle task focuses on
structure cues; (ii) a tint rotation recognition is used within each piece,
taking into account the colorimetry information; (iii) and a partial
re-colorization task considers the image texture. In order to make the
re-colorization task more object-oriented than background-oriented, we propose
to include the contextual color information of the image border via an
attention mechanism. We then present a new out-of-distribution detection
function and highlight its better stability compared to existing methods. Along
with it, we also experiment different score fusion functions. Finally, we
evaluate our method on an extensive protocol composed of various anomaly types,
from object anomalies, style anomalies with fine-grained classification to
local anomalies with face anti-spoofing datasets. Our model significantly
outperforms state-of-the-art with up to 36% relative error improvement on
object anomalies and 40% on face anti-spoofing problems.

Lipschitz learning is a graph-based semi-supervised learning method where one
extends labels from a labeled to an unlabeled data set by solving the infinity
Laplace equation on a weighted graph. In this work we prove uniform convergence
rates for solutions of the graph infinity Laplace equation as the number of
vertices grows to infinity. Their continuum limits are absolutely minimizing
Lipschitz extensions with respect to the geodesic metric of the domain where
the graph vertices are sampled from. We work under very general assumptions on
the graph weights, the set of labeled vertices, and the continuum domain. Our
main contribution is that we obtain quantitative convergence rates even for
very sparsely connected graphs, as they typically appear in applications like
semi-supervised learning. In particular, our framework allows for graph
bandwidths down to the connectivity radius. For proving this we first show a
quantitative convergence statement for graph distance functions to geodesic
distance functions in the continuum. Using the "comparison with distance
functions" principle, we can pass these convergence statements to infinity
harmonic functions and absolutely minimizing Lipschitz extensions.

We present a machine learning model to classify Active Galactic Nuclei (AGN)
and galaxies (AGN-galaxy classifier) and a model to identify type 1 (optically
unabsorbed) and type 2 (optically absorbed) AGN (type 1/2 classifier). We test
tree-based algorithms, using training samples built from the X-ray Multi-Mirror
Mission -Newton (XMM-Newton) catalogue and the Sloan Digital Sky Survey (SDSS),
with labels derived from the SDSS survey. The performance was tested making use
of simulations and of cross-validation techniques. With a set of features
including spectroscopic redshifts and X-ray parameters connected to source
properties (e.g. fluxes and extension), as well as features related to X-ray
instrumental conditions, the precision and recall for AGN identification are 94
and 93 per cent, while the type 1/2 classifier has a precision of 74 per cent
and a recall of 80 per cent for type 2 AGN. The performance obtained with
photometric redshifts is very similar to that achieved with spectroscopic
redshifts in both test cases, while there is a decrease in performance when
excluding redshifts. Our machine learning model trained on X-ray features can
accurately identify AGN in extragalactic surveys. The type 1/2 classifier has a
valuable performance for type 2 AGN, but its ability to generalise without
redshifts is hampered by the limited census of absorbed AGN at high redshift.

While the most visible part of the safety verification process of automated
vehicles concerns the planning and control system, it is often overlooked that
safety of the latter crucially depends on the fault-tolerance of the preceding
environment perception. Modern perception systems feature complex and often
machine-learning-based components with various failure modes that can
jeopardize the overall safety. At the same time, a verification by for example
redundant execution is not always feasible due to resource constraints. In this
paper, we address the need for feasible and efficient perception monitors and
propose a lightweight approach that helps to protect the integrity of the
perception system while keeping the additional compute overhead minimal. In
contrast to existing solutions, the monitor is realized by a well-balanced
combination of sensor checks -- here using LiDAR information -- and
plausibility checks on the object motion history. It is designed to detect
relevant errors in the distance and velocity of objects in the environment of
the automated vehicle. In conjunction with an appropriate planning system, such
a monitor can help to make safe automated driving feasible.

Although there is significant progress in supervised semantic segmentation,
it remains challenging to deploy the segmentation models to unseen domains due
to domain biases. Domain adaptation can help in this regard by transferring
knowledge from a labeled source domain to an unlabeled target domain. Previous
methods typically attempt to perform the adaptation on global features,
however, the local semantic affiliations accounting for each pixel in the
feature space are often ignored, resulting in less discriminability. To solve
this issue, we propose a novel semantic prototype-based contrastive learning
framework for fine-grained class alignment. Specifically, the semantic
prototypes provide supervisory signals for per-pixel discriminative
representation learning and each pixel of source and target domains in the
feature space is required to reflect the content of the corresponding semantic
prototype. In this way, our framework is able to explicitly make intra-class
pixel representations closer and inter-class pixel representations further
apart to improve the robustness of the segmentation model as well as alleviate
the domain shift problem. Our method is easy to implement and attains superior
results compared to state-of-the-art approaches, as is demonstrated with a
number of experiments. The code is publicly available at
https://github.com/BinhuiXie/SPCL.

Semantic information has been proved effective in scene text recognition.
Most existing methods tend to couple both visual and semantic information in an
attention-based decoder. As a result, the learning of semantic features is
prone to have a bias on the limited vocabulary of the training set, which is
called vocabulary reliance. In this paper, we propose a novel Visual-Semantic
Decoupling Network (VSDN) to address the problem. Our VSDN contains a Visual
Decoder (VD) and a Semantic Decoder (SD) to learn purer visual and semantic
feature representation respectively. Besides, a Semantic Encoder (SE) is
designed to match SD, which can be pre-trained together by additional
inexpensive large vocabulary via a simple word correction task. Thus the
semantic feature is more unbiased and precise to guide the visual feature
alignment and enrich the final character representation. Experiments show that
our method achieves state-of-the-art or competitive results on the standard
benchmarks, and outperforms the popular baseline by a large margin under
circumstances where the training set has a small size of vocabulary.

We improve the recently developed Neural DUDE, a neural network-based
adaptive discrete denoiser, by combining it with the supervised learning
framework. Namely, we make the supervised pre-training of Neural DUDE
compatible with the adaptive fine-tuning of the parameters based on the given
noisy data subject to denoising. As a result, we achieve a significant
denoising performance boost compared to the vanilla Neural DUDE, which only
carries out the adaptive fine-tuning step with randomly initialized parameters.
Moreover, we show the adaptive fine-tuning makes the algorithm robust such that
a noise-mismatched or blindly trained supervised model can still achieve the
performance of that of the matched model. Furthermore, we make a few
algorithmic advancements to make Neural DUDE more scalable and deal with
multi-dimensional data or data with larger alphabet size. We systematically
show our improvements on two very diverse datasets, binary images and DNA

Deep learning based virtual try-on system has achieved some encouraging
progress recently, but there still remain several big challenges that need to
be solved, such as trying on arbitrary clothes of all types, trying on the
clothes from one category to another and generating image-realistic results
with few artifacts. To handle this issue, we in this paper first collect a new
dataset with all types of clothes, \ie tops, bottoms, and whole clothes, each
one has multiple categories with rich information of clothing characteristics
such as patterns, logos, and other details. Based on this dataset, we then
propose the Arbitrary Virtual Try-On Network (AVTON) that is utilized for
all-type clothes, which can synthesize realistic try-on images by preserving
and trading off characteristics of the target clothes and the reference person.
Our approach includes three modules: 1) Limbs Prediction Module, which is
utilized for predicting the human body parts by preserving the characteristics
of the reference person. This is especially good for handling cross-category
try-on task (\eg long sleeves \(\leftrightarrow\) short sleeves or long pants
\(\leftrightarrow\) skirts, \etc), where the exposed arms or legs with the skin
colors and details can be reasonably predicted; 2) Improved Geometric Matching
Module, which is designed to warp clothes according to the geometry of the
target person. We improve the TPS based warping method with a compactly
supported radial function (Wendland's \(\Psi\)-function); 3) Trade-Off Fusion
Module, which is to trade off the characteristics of the warped clothes and the
reference person. This module is to make the generated try-on images look more
natural and realistic based on a fine-tune symmetry of the network structure.
Extensive simulations are conducted and our approach can achieve better
performance compared with the state-of-the-art virtual try-on methods.

Event cameras sense per-pixel intensity changes and produce asynchronous
event streams with high dynamic range and less motion blur, showing advantages
over conventional cameras. A hurdle of training event-based models is the lack
of large qualitative labeled data. Prior works learning end-tasks mostly rely
on labeled or pseudo-labeled datasets obtained from the active pixel sensor
(APS) frames; however, such datasets' quality is far from rivaling those based
on the canonical images. In this paper, we propose a novel approach, called
\textbf{EvDistill}, to learn a student network on the unlabeled and unpaired
event data (target modality) via knowledge distillation (KD) from a teacher
network trained with large-scale, labeled image data (source modality). To
enable KD across the unpaired modalities, we first propose a bidirectional
modality reconstruction (BMR) module to bridge both modalities and
simultaneously exploit them to distill knowledge via the crafted pairs, causing
no extra computation in the inference. The BMR is improved by the end-tasks and
KD losses in an end-to-end manner. Second, we leverage the structural
similarities of both modalities and adapt the knowledge by matching their
distributions. Moreover, as most prior feature KD methods are uni-modality and
less applicable to our problem, we propose to leverage an affinity graph KD
loss to boost the distillation. Our extensive experiments on semantic
segmentation and object recognition demonstrate that EvDistill achieves
significantly better results than the prior works and KD with only events and
APS frames.

Recent advances in reinforcement learning (RL) have made it possible to
develop sophisticated agents that excel in a wide range of applications.
Simulations using such agents can provide valuable information in scenarios
that are difficult to scientifically experiment in the real world. In this
paper, we examine the play-style characteristics of football RL agents and
uncover how strategies may develop during training. The learnt strategies are
then compared with those of real football players. We explore what can be
learnt from the use of simulated environments by using aggregated statistics
and social network analysis (SNA). As a result, we found that (1) there are
strong correlations between the competitiveness of an agent and various SNA
metrics and (2) aspects of the RL agents play style become similar to real
world footballers as the agent becomes more competitive. We discuss further
advances that may be necessary to improve our understanding necessary to fully
utilise RL for the analysis of football.

Deep neural networks (DNNs) are so over-parametrized that recent research has
found them to already contain a subnetwork with high accuracy at their randomly
initialized state. Finding these subnetworks is a viable alternative training
method to weight learning. In parallel, another line of work has hypothesized
that deep residual networks (ResNets) are trying to approximate the behaviour
of shallow recurrent neural networks (RNNs) and has proposed a way for
compressing them into recurrent models. This paper proposes blending these
lines of research into a highly compressed yet accurate model: Hidden-Fold
Networks (HFNs). By first folding ResNet into a recurrent structure and then
searching for an accurate subnetwork hidden within the randomly initialized
model, a high-performing yet tiny HFN is obtained without ever updating the
weights. As a result, HFN achieves equivalent performance to ResNet50 on
CIFAR100 while occupying 38.5x less memory, and similar performance to ResNet34
on ImageNet with a memory size 26.8x smaller. The HFN will become even more
attractive by minimizing data transfers while staying accurate when it runs on
highly-quantized and randomly-weighted DNN inference accelerators. Code
available at https://github.com/Lopez-Angel/hidden-fold-networks

Real-scale scene flow estimation has become increasingly important for 3D
computer vision. Some works successfully estimate real-scale 3D scene flow with
LiDAR. However, these ubiquitous and expensive sensors are still unlikely to be
equipped widely for real application. Other works use monocular images to
estimate scene flow, but their scene flow estimations are normalized with scale
ambiguity, where additional depth or point cloud ground truth are required to
recover the real scale. Even though they perform well in 2D, these works do not
provide accurate and reliable 3D estimates. We present a deep learning
architecture on permutohedral lattice - MonoPLFlowNet. Different from all
previous works, our MonoPLFlowNet is the first work where only two consecutive
monocular images are used as input, while both depth and 3D scene flow are
estimated in real scale. Our real-scale scene flow estimation outperforms all
state-of-the-art monocular-image based works recovered to real scale by ground
truth, and is comparable to LiDAR approaches. As a by-product, our real-scale
depth estimation also outperforms other state-of-the-art works.

To prevent private training data leakage in Fed?erated Learning systems, we
propose a novel se?cure aggregation scheme based on seed homomor?phic
pseudo-random generator (SHPRG), named SASH. SASH leverages the homomorphic
property of SHPRG to simplify the masking and demask?ing scheme, which for each
of the clients and for the server, entails an overhead linear w.r.t model size
and constant w.r.t number of clients. We prove that even against worst-case
colluding adversaries, SASH preserves training data privacy, while being
resilient to dropouts without extra overhead. We experimentally demonstrate
SASH significantly improves the efficiency to 20 times over baseline,
especially in the more realistic case where the numbers of clients and model
size become large, and a cer?tain percentage of clients drop out from the
system.

Face anti-spoofing (FAS) plays a crucial role in securing face recognition
systems. Empirically, given an image, a model with more consistent output on
different views of this image usually performs better, as shown in Fig.1.
Motivated by this exciting observation, we conjecture that encouraging feature
consistency of different views may be a promising way to boost FAS models. In
this paper, we explore this way thoroughly by enhancing both Embedding-level
and Prediction-level Consistency Regularization (EPCR) in FAS. Specifically, at
the embedding-level, we design a dense similarity loss to maximize the
similarities between all positions of two intermediate feature maps in a
self-supervised fashion; while at the prediction-level, we optimize the mean
square error between the predictions of two views. Notably, our EPCR is free of
annotations and can directly integrate into semi-supervised learning schemes.
Considering different application scenarios, we further design five diverse
semi-supervised protocols to measure semi-supervised FAS techniques. We conduct
extensive experiments to show that EPCR can significantly improve the
performance of several supervised and semi-supervised tasks on benchmark
datasets. The codes and protocols will be released at
https://github.com/clks-wzz/EPCR.

Reinforcement learning is a general methodology of adaptive optimal control
that has attracted much attention in various fields ranging from video game
industry to robot manipulators. Despite its remarkable performance
demonstrations, plain reinforcement learning controllers do not guarantee
stability which compromises their applicability in industry. To provide such
guarantees, measures have to be taken. This gives rise to what could generally
be called stabilizing reinforcement learning. Concrete approaches range from
employment of human overseers to filter out unsafe actions to formally verified
shields and fusion with classical stabilizing controllers. A line of attack
that utilizes elements of adaptive control has become fairly popular in the
recent years. In this note, we critically address such an approach in a fairly
general actor-critic setup for nonlinear time-continuous environments. The
actor network utilizes a so-called robustifying term that is supposed to
compensate for the neural network errors. The corresponding stability analysis
is based on the value function itself. We indicate a problem in such a
stability analysis and provide a counterexample to the overall control scheme.
Implications for such a line of attack in stabilizing reinforcement learning
are discussed. Furthermore, unfortunately the said problem possess no fix
without a substantial reconsideration of the whole approach. As a positive
message, we derive a stochastic critic neural network weight convergence
analysis provided that the environment was stabilized.

Spatial-temporal local binary pattern (STLBP) has been widely used in dynamic
texture recognition. STLBP often encounters the high-dimension problem as its
dimension increases exponentially, so that STLBP could only utilize a small
neighborhood. To tackle this problem, we propose a method for dynamic texture
recognition using PDV hashing and dictionary learning on multi-scale volume
local binary pattern (PHD-MVLBP). Instead of forming very high-dimensional LBP
histogram features, it first uses hash functions to map the pixel difference
vectors (PDVs) to binary vectors, then forms a dictionary using the derived
binary vector, and encodes them using the derived dictionary. In such a way,
the PDVs are mapped to feature vectors of the size of dictionary, instead of
LBP histograms of very high dimension. Such an encoding scheme could extract
the discriminant information from videos in a much larger neighborhood
effectively. The experimental results on two widely-used dynamic textures
datasets, DynTex++ and UCLA, show the superiority performance of the proposed
approach over the state-of-the-art methods.

This paper is concerned with the lossy compression of general random
variables, specifically with rate-distortion theory and quantization of random
variables taking values in general measurable spaces such as, e.g., manifolds
and fractal sets. Manifold structures are prevalent in data science, e.g., in
compressed sensing, machine learning, image processing, and handwritten digit
recognition. Fractal sets find application in image compression and in the
modeling of Ethernet traffic. Our main contributions are bounds on the
rate-distortion function and the quantization error. These bounds are very
general and essentially only require the existence of reference measures
satisfying certain regularity conditions in terms of small ball probabilities.
To illustrate the wide applicability of our results, we particularize them to
random variables taking values in i) manifolds, namely, hyperspheres and
Grassmannians, and ii) self-similar sets characterized by iterated function
systems satisfying the weak separation property.

Self-supervised methods (SSL) have achieved significant success via
maximizing the mutual information between two augmented views, where cropping
is a popular augmentation technique. Cropped regions are widely used to
construct positive pairs, while the left regions after cropping have rarely
been explored in existing methods, although they together constitute the same
image instance and both contribute to the description of the category. In this
paper, we make the first attempt to demonstrate the importance of both regions
in cropping from a complete perspective and propose a simple yet effective
pretext task called Region Contrastive Learning (RegionCL). Specifically, given
two different images, we randomly crop a region (called the paste view) from
each image with the same size and swap them to compose two new images together
with the left regions (called the canvas view), respectively. Then, contrastive
pairs can be efficiently constructed according to the following simple
criteria, i.e., each view is (1) positive with views augmented from the same
original image and (2) negative with views augmented from other images. With
minor modifications to popular SSL methods, RegionCL exploits those abundant
pairs and helps the model distinguish the regions features from both canvas and
paste views, therefore learning better visual representations. Experiments on
ImageNet, MS COCO, and Cityscapes demonstrate that RegionCL improves MoCo v2,
DenseCL, and SimSiam by large margins and achieves state-of-the-art performance
on classification, detection, and segmentation tasks. The code will be
available at https://github.com/Annbless/RegionCL.git.

We study the $K$-armed contextual dueling bandit problem, a sequential
decision making setting in which the learner uses contextual information to
make two decisions, but only observes \emph{preference-based feedback}
suggesting that one decision was better than the other. We focus on the regret
minimization problem under realizability, where the feedback is generated by a
pairwise preference matrix that is well-specified by a given function class
$\mathcal F$. We provide a new algorithm that achieves the optimal regret rate
for a new notion of best response regret, which is a strictly stronger
performance measure than those considered in prior works. The algorithm is also
computationally efficient, running in polynomial time assuming access to an
online oracle for square loss regression over $\mathcal F$. This resolves an
open problem of Dud\'ik et al. [2015] on oracle efficient, regret-optimal
algorithms for contextual dueling bandits.

The existing work shows that the neural network trained by naive
gradient-based optimization method is prone to adversarial attacks, adds small
malicious on the ordinary input is enough to make the neural network wrong. At
the same time, the attack against a neural network is the key to improving its
robustness. The training against adversarial examples can make neural networks
resist some kinds of adversarial attacks. At the same time, the adversarial
attack against a neural network can also reveal some characteristics of the
neural network, a complex high-dimensional non-linear function, as discussed in
  In This project, we develop a first-order method to attack the neural
network. Compare with other first-order attacks, our method has a much higher
success rate. Furthermore, it is much faster than second-order attacks and
multi-steps first-order attacks.

In hardware-aware Differentiable Neural Architecture Search (DNAS), it is
challenging to compute gradients of hardware metrics to perform architecture
search. Existing works rely on linear approximations with limited support to
customized hardware accelerators. In this work, we propose End-to-end
Hardware-aware DNAS (EH-DNAS), a seamless integration of end-to-end hardware
benchmarking, and fully automated DNAS to deliver hardware-efficient deep
neural networks on various platforms, including Edge GPUs, Edge TPUs, Mobile
CPUs, and customized accelerators. Given a desired hardware platform, we
propose to learn a differentiable model predicting the end-to-end hardware
performance of neural network architectures for DNAS. We also introduce
E2E-Perf, an end-to-end hardware benchmarking tool for customized accelerators.
Experiments on CIFAR10 and ImageNet show that EH-DNAS improves the hardware
performance by an average of $1.4\times$ on customized accelerators and
$1.6\times$ on existing hardware processors while maintaining the

We develop an end-to-end deep-neural-network-based algorithm for classifying
animal behavior using accelerometry data on the embedded system of an
artificial intelligence of things (AIoT) device installed in a wearable collar
tag. The proposed algorithm jointly performs feature extraction and
classification utilizing a set of infinite-impulse-response (IIR) and
finite-impulse-response (FIR) filters together with a multilayer perceptron.
The utilized IIR and FIR filters can be viewed as specific types of recurrent
and convolutional neural network layers, respectively. We evaluate the
performance of the proposed algorithm via two real-world datasets collected
from total eighteen grazing beef cattle using collar tags. The results show
that the proposed algorithm offers good intra- and inter-dataset classification
accuracy and outperforms its closest contenders including two state-of-the-art
convolutional-neural-network-based time-series classification algorithms, which
are significantly more complex. We implement the proposed algorithm on the
embedded system of the utilized collar tags' AIoT device to perform in-situ
classification of animal behavior. We achieve real-time in-situ behavior
inference from accelerometry data without imposing any strain on the available
computational, memory, or energy resources of the embedded system.

As a dominant paradigm, fine-tuning a pre-trained model on the target data is
widely used in many deep learning applications, especially for small data sets.
However, recent studies have empirically shown that training from scratch has
the final performance that is no worse than this pre-training strategy once the
number of training samples is increased in some vision tasks. In this work, we
revisit this phenomenon from the perspective of generalization analysis by
using excess risk bound which is popular in learning theory. The result reveals
that the excess risk bound may have a weak dependency on the pre-trained model.
The observation inspires us to leverage pre-training data for fine-tuning,
since this data is also available for fine-tuning. The generalization result of
using pre-training data shows that the excess risk bound on a target task can
be improved when the appropriate pre-training data is included in fine-tuning.
With the theoretical motivation, we propose a novel selection strategy to
select a subset from pre-training data to help improve the generalization on
the target task. Extensive experimental results for image classification tasks
on 8 benchmark data sets verify the effectiveness of the proposed data
selection based fine-tuning pipeline.

Over the last decade, there has been a spike in criminal activity all around
the globe. According to the Indian police department, vehicle theft is one of
the least solved offenses, and almost 19% of all recorded cases are related to
motor vehicle theft. To overcome these adversaries, we propose a real-time
vehicle surveillance system, which detects and tracks the suspect vehicle using
the CCTV video feed. The proposed system extracts various attributes of the
vehicle such as Make, Model, Color, License plate number, and type of the
license plate. Various image processing and deep learning algorithms are
employed to meet the objectives of the proposed system. The extracted features
can be used as evidence to report violations of law. Although the system uses
more parameters, it is still able to make real time predictions with minimal
latency and accuracy loss.

Vision transformers have delivered tremendous success in representation
learning. This is primarily due to effective token mixing through self
attention. However, this scales quadratically with the number of pixels, which
becomes infeasible for high-resolution inputs. To cope with this challenge, we
propose Adaptive Fourier Neural Operator (AFNO) as an efficient token mixer
that learns to mix in the Fourier domain. AFNO is based on a principled
foundation of operator learning which allows us to frame token mixing as a
continuous global convolution without any dependence on the input resolution.
This principle was previously used to design FNO, which solves global
convolution efficiently in the Fourier domain and has shown promise in learning
challenging PDEs. To handle challenges in visual representation learning such
as discontinuities in images and high resolution inputs, we propose principled
architectural modifications to FNO which results in memory and computational
efficiency. This includes imposing a block-diagonal structure on the channel
mixing weights, adaptively sharing weights across tokens, and sparsifying the
frequency modes via soft-thresholding and shrinkage. The resulting model is
highly parallel with a quasi-linear complexity and has linear memory in the
sequence size. AFNO outperforms self-attention mechanisms for few-shot
segmentation in terms of both efficiency and accuracy. For Cityscapes
segmentation with the Segformer-B3 backbone, AFNO can handle a sequence size of
65k and outperforms other efficient self-attention mechanisms.

This paper presents a novel training method for end-to-end scene text
recognition. End-to-end scene text recognition offers high recognition
accuracy, especially when using the encoder-decoder model based on Transformer.
To train a highly accurate end-to-end model, we need to prepare a large
image-to-text paired dataset for the target language. However, it is difficult
to collect this data, especially for resource-poor languages. To overcome this
difficulty, our proposed method utilizes well-prepared large datasets in
resource-rich languages such as English, to train the resource-poor
encoder-decoder model. Our key idea is to build a model in which the encoder
reflects knowledge of multiple languages while the decoder specializes in
knowledge of just the resource-poor language. To this end, the proposed method
pre-trains the encoder by using a multilingual dataset that combines the
resource-poor language's dataset and the resource-rich language's dataset to
learn language-invariant knowledge for scene text recognition. The proposed
method also pre-trains the decoder by using the resource-poor language's
dataset to make the decoder better suited to the resource-poor language.
Experiments on Japanese scene text recognition using a small, publicly
available dataset demonstrate the effectiveness of the proposed method.

Network quantization has gained increasing attention since it can
significantly reduce the model size and computational overhead. However, due to
the discrete nature of quantization, a small change in full-precision weights
might incur large change in quantized weights, which leads to severe loss
fluctuations and thus results in the sharp loss landscape. The fluctuating loss
makes the gradients unstable during training, resulting in considerable
performance degradation. Recently, Sharpness-Aware Minimization (SAM) has been
proposed to smooth the loss landscape and improve the generalization
performance of the models. Nevertheless, how to customize SAM to the quantized
models is non-trivial due to the effect of the clipping and discretization in
quantization. In this paper, we propose a novel method, dubbed Sharpness-Aware
Quantization (SAQ), to smooth the loss landscape and improve the generalization
performance of the quantized models, which explores the effect of SAM in model
compression, particularly quantization for the first time. Specifically, we
first propose a unified view for quantization and SAM, where we consider them
as introducing quantization noises and adversarial perturbations to the model
weights. According to whether the quantization noises and adversarial
perturbations depend on each other, SAQ can be divided into three cases. We
then analyze and compare different cases comprehensively. Extensive experiments
on both convolutional neural networks and Transformers show that SAQ improves
the generalization performance of the quantized models, yielding the SOTA
results in uniform quantization. For example, on ImageNet, our SAQ outperforms
the model trained with the conventional optimization procedure (i.e., SGD) by
1.1% on the Top-1 accuracy on 4-bit ResNet-50. Code is available at
https://github.com/zip-group/SAQ.

Since the increasing outspread of COVID-19 in the U.S., with the highest
number of confirmed cases and deaths in the world as of September 2020, most
states in the country have enforced travel restrictions resulting in sharp
reductions in mobility. However, the overall impact and long-term implications
of this crisis to travel and mobility remain uncertain. To this end, this study
develops an analytical framework that determines and analyzes the most dominant
factors impacting human mobility and travel in the U.S. during this pandemic.
In particular, the study uses Granger causality to determine the important
predictors influencing daily vehicle miles traveled and utilize linear
regularization algorithms, including Ridge and LASSO techniques, to model and
predict mobility. State-level time-series data were obtained from various
open-access sources for the period starting from March 1, 2020 through June 13,
2020 and the entire data set was divided into two parts for training and
testing purposes. The variables selected by Granger causality were used to
train the three different reduced order models by ordinary least square
regression, Ridge regression, and LASSO regression algorithms. Finally, the
prediction accuracy of the developed models was examined on the test data. The
results indicate that the factors including the number of new COVID cases,
social distancing index, population staying at home, percent of out of county
trips, trips to different destinations, socioeconomic status, percent of people
working from home, and statewide closure, among others, were the most important
factors influencing daily VMT. Also, among all the modeling techniques, Ridge
regression provides the most superior performance with the least error, while
LASSO regression also performed better than the ordinary least square model.

Analytic continuation maps imaginary-time Green's functions obtained by
various theoretical/numerical methods to real-time response functions that can
be directly compared with experiments. Analytic continuation is an important
bridge between many-body theories and experiments but is also a challenging
problem because such mappings are ill-conditioned. In this work, we develop a
neural network-based method for this problem. The training data is generated
either using synthetic Gaussian-type spectral functions or from exactly
solvable models where the analytic continuation can be obtained analytically.
Then, we applied the trained neural network to the testing data, either with
synthetic noise or intrinsic noise in Monte Carlo simulations. We conclude that
the best performance is always achieved when a proper amount of noise is added
to the training data. Moreover, our method can successfully capture multi-peak
structure in the resulting response function for the cases with the best
performance. The method can be combined with Monte Carlo simulations to compare
with experiments on real-time dynamics.

In recent visual self-supervision works, an imitated classification
objective, called pretext task, is established by assigning labels to
transformed or augmented input images. The goal of pretext can be predicting
what transformations are applied to the image. However, it is observed that
image transformations already present in the dataset might be less effective in
learning such self-supervised representations. Building on this observation, we
propose a framework based on generative adversarial network to automatically
find the transformations which are not present in the input dataset and thus
effective for the self-supervised learning. This automated policy allows to
estimate the transformation distribution of a dataset and also construct its
complementary distribution from which training pairs are sampled for the
pretext task. We evaluated our framework using several visual recognition
datasets to show the efficacy of our automated transformation policy.

State-of-the-art (SOTA) anomaly segmentation approaches on complex urban
driving scenes explore pixel-wise classification uncertainty learned from
outlier exposure, or external reconstruction models. However, previous
uncertainty approaches that directly associate high uncertainty to anomaly may
sometimes lead to incorrect anomaly predictions, and external reconstruction
models tend to be too inefficient for real-time self-driving embedded systems.
In this paper, we propose a new anomaly segmentation method, named pixel-wise
energy-biased abstention learning (PEBAL), that explores pixel-wise abstention
learning (AL) with a model that learns an adaptive pixel-level anomaly class,
and an energy-based model (EBM) that learns inlier pixel distribution. More
specifically, PEBAL is based on a non-trivial joint training of EBM and AL,
where EBM is trained to output high-energy for anomaly pixels (from outlier
exposure) and AL is trained such that these high-energy pixels receive adaptive
low penalty for being included to the anomaly class. We extensively evaluate
PEBAL against the SOTA and show that it achieves the best performance across
four benchmarks. Code is available at https://github.com/tianyu0207/PEBAL.

Few-shot semantic segmentation aims to segment novel-class objects in a given
query image with only a few labeled support images. Most advanced solutions
exploit a metric learning framework that performs segmentation through matching
each query feature to a learned class-specific prototype. However, this
framework suffers from biased classification due to incomplete feature
comparisons. To address this issue, we present an adaptive prototype
representation by introducing class-specific and class-agnostic prototypes and
thus construct complete sample pairs for learning semantic alignment with query
features. The complementary features learning manner effectively enriches
feature comparison and helps yield an unbiased segmentation model in the
few-shot setting. It is implemented with a two-branch end-to-end network (\ie,
a class-specific branch and a class-agnostic branch), which generates
prototypes and then combines query features to perform comparisons. In
addition, the proposed class-agnostic branch is simple yet effective. In
practice, it can adaptively generate multiple class-agnostic prototypes for
query images and learn feature alignment in a self-contrastive manner.
Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ demonstrate the
superiority of our method. At no expense of inference efficiency, our model
achieves state-of-the-art results in both 1-shot and 5-shot settings for

Recent advances in path-based explainable recommendation systems have
attracted increasing attention thanks to the rich information provided by
knowledge graphs. Most existing explainable recommendations only utilize static
knowledge graphs and ignore the dynamic user-item evolutions, leading to less
convincing and inaccurate explanations. Although there are some works that
realize that modelling user's temporal sequential behaviour could boost the
performance and explainability of the recommender systems, most of them either
only focus on modelling user's sequential interactions within a path or
independently and separately of the recommendation mechanism. In this paper, we
propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging
Reinforcement Learning (TMER-RL), which utilizes reinforcement item-item path
modelling between consecutive items with attention mechanisms to sequentially
model dynamic user-item evolutions on dynamic knowledge graph for explainable
recommendation. Compared with existing works that use heavy recurrent neural
networks to model temporal information, we propose simple but effective neural
networks to capture users' historical item features and path-based context to
characterize the next purchased item. Extensive evaluations of TMER on two
real-world datasets show state-of-the-art performance compared against recent

In this paper, we develop a dynamic detection network (DDNet) based detector
for multiple-input multiple-output (MIMO) systems. By constructing an improved
DetNet (IDetNet) detector and the OAMPNet detector as two independent network
branches, the DDNet detector performs sample-wise dynamic routing to adaptively
select a better one between the IDetNet and the OAMPNet detectors for every
samples under different system conditions. To avoid the prohibitive
transmission overhead of dataset collection in centralized learning (CL), we
propose the federated averaging (FedAve)-DDNet detector, where all raw data are
kept at local clients and only locally trained model parameters are transmitted
to the central server for aggregation. To further reduce the transmission
overhead, we develop the federated gradient sparsification (FedGS)-DDNet
detector by randomly sampling gradients with elaborately calculated probability
when uploading gradients to the central server. Based on simulation results,
the proposed DDNet detector consistently outperforms other detectors under all
system conditions thanks to the sample-wise dynamic routing. Moreover, the
federated DDNet detectors, especially the FedGS-DDNet detector, can reduce the
transmission overhead by at least 25.7\% while maintaining satisfactory
detection accuracy.

Internet of Medical Things (IoMT) is becoming ubiquitous with a proliferation
of smart medical devices and applications used in smart hospitals, smart-home
based care, and nursing homes. It utilizes smart medical devices and cloud
computing services along with core Internet of Things (IoT) technologies to
sense patients' vital body parameters, monitor health conditions and generate
multivariate data to support just-in-time health services. Mostly, this large
amount of data is analyzed in centralized servers. Anomaly Detection (AD) in a
centralized healthcare ecosystem is often plagued by significant delays in
response time with high performance overhead. Moreover, there are inherent
privacy issues associated with sending patients' personal health data to a
centralized server, which may also introduce several security threats to the AD
model, such as possibility of data poisoning. To overcome these issues with
centralized AD models, here we propose a Federated Learning (FL) based AD model
which utilizes edge cloudlets to run AD models locally without sharing
patients' data. Since existing FL approaches perform aggregation on a single
server which restricts the scope of FL, in this paper, we introduce a
hierarchical FL that allows aggregation at different levels enabling
multi-party collaboration. We introduce a novel disease-based grouping
mechanism where different AD models are grouped based on specific types of
diseases. Furthermore, we develop a new Federated Time Distributed (FedTimeDis)
Long Short-Term Memory (LSTM) approach to train the AD model. We present a
Remote Patient Monitoring (RPM) use case to demonstrate our model, and
illustrate a proof-of-concept implementation using Digital Twin (DT) and edge
cloudlets.

Precisely how humans process relational patterns of information in knowledge,
language, music, and society is not well understood. Prior work in the field of
statistical learning has demonstrated that humans process such information by
building internal models of the underlying network structure. However, these
mental maps are often inaccurate due to limitations in human information
processing. The existence of such limitations raises clear questions: Given a
target network that one wishes for a human to learn, what network should one
present to the human? Should one simply present the target network as-is, or
should one emphasize certain parts of the network to proactively mitigate
expected errors in learning? To answer these questions, we study the
optimization of network learnability. Evaluating an array of synthetic and
real-world networks, we find that learnability is enhanced by reinforcing
connections within modules or clusters. In contrast, when networks contain
significant core-periphery structure we find that learnability is best
optimized by reinforcing peripheral edges between low-degree nodes. Overall,
our findings suggest that the accuracy of human network learning can be
systematically enhanced by targeted emphasis and de-emphasis of prescribed
sectors of information.

We introduce definitions of computable PAC learning for binary classification
over computable metric spaces. We provide sufficient conditions for learners
that are empirical risk minimizers (ERM) to be computable, and bound the strong
Weihrauch degree of an ERM learner under more general conditions. We also give
a presentation of a hypothesis class that does not admit any proper computable
PAC learner with computable sample function, despite the underlying class being
PAC learnable.

Subspace clustering methods embrace a self-expressive model that represents
each data point as a linear combination of other data points in the dataset are
powerful unsupervised learning techniques. However, when dealing with
large-scale datasets, the representation of each data point by referring to all
data points as a dictionary suffers from high computational complexity. To
alleviate this issue, we introduce a parallelizable multi-subset based
self-expressive model (PMS) which represents each data point by combing
multiple subsets, with each consisting of only a small percentage of samples.
The adoption of PMS in subspace clustering (PMSSC) leads to computational
advantages because each optimization problem decomposed into each subset is
small, and can be solved efficiently in parallel. Besides, PMSSC is able to
combine multiple self-expressive coefficient vectors obtained from subsets,
which contributes to the improvement of self-expressiveness. Extensive
experiments on synthetic data and real-world datasets show the efficiency and
effectiveness of our approach against competitive methods.

Up to now, most existing steganalytic methods are designed for grayscale
images, and they are not suitable for color images that are widely used in
current social networks. In this paper, we design a universal color image
steganalysis network (called UCNet) in spatial and JPEG domains. The proposed
method includes preprocessing, convolutional, and classification modules. To
preserve the steganographic artifacts in each color channel, in preprocessing
module, we firstly separate the input image into three channels according to
the corresponding embedding spaces (i.e. RGB for spatial steganography and
YCbCr for JPEG steganography), and then extract the image residuals with 62
fixed high-pass filters, finally concatenate all truncated residuals for
subsequent analysis rather than adding them together with normal convolution
like existing CNN-based steganalyzers. To accelerate the network convergence
and effectively reduce the number of parameters, in convolutional module, we
carefully design three types of layers with different shortcut connections and
group convolution structures to further learn high-level steganalytic features.
In classification module, we employ a global average pooling and fully
connected layer for classification. We conduct extensive experiments on ALASKA
II to demonstrate that the proposed method can achieve state-of-the-art results
compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in
both spatial and JPEG domains, while keeping relatively few memory requirements
and training time. Furthermore, we also provide necessary descriptions and many
ablation experiments to verify the rationality of the network design.

Single-step adversarial training (AT) has received wide attention as it
proved to be both efficient and robust. However, a serious problem of
catastrophic overfitting exists, i.e., the robust accuracy against projected
gradient descent (PGD) attack suddenly drops to 0% during the training. In this
paper, we approach this problem from a novel perspective of optimization and
firstly reveal the close link between the fast-growing gradient of each sample
and overfitting, which can also be applied to understand robust overfitting in
multi-step AT. To control the growth of the gradient, we propose a new AT
method, Subspace Adversarial Training (Sub-AT), which constrains AT in a
carefully extracted subspace. It successfully resolves both kinds of
overfitting and significantly boosts the robustness. In subspace, we also allow
single-step AT with larger steps and larger radius, further improving the
robustness performance. As a result, we achieve state-of-the-art single-step AT
performance. Without any regularization term, our single-step AT can reach over
51% robust accuracy against strong PGD-50 attack of radius 8/255 on CIFAR-10,
reaching a competitive performance against standard multi-step PGD-10 AT with
huge computational advantages. The code is released at
https://github.com/nblt/Sub-AT.

This two-part paper investigates the application of artificial intelligence
(AI) and in particular machine learning (ML) to the study of wireless
propagation channels. In Part I, we introduced AI and ML as well as provided a
comprehensive survey on ML enabled channel characterization and antenna-channel
optimization, and in this part (Part II) we review state-of-the-art literature
on scenario identification and channel modeling here. In particular, the key
ideas of ML for scenario identification and channel modeling/prediction are
presented, and the widely used ML methods for propagation scenario
identification and channel modeling and prediction are analyzed and compared.
Based on the state-of-art, the future challenges of AI/ML-based channel data
processing techniques are given as well.

To provide higher data rates, as well as better coverage, cost efficiency,
security, adaptability, and scalability, the 5G and beyond 5G networks are
developed with various artificial intelligence techniques. In this two-part
paper, we investigate the application of artificial intelligence (AI) and in
particular machine learning (ML) to the study of wireless propagation channels.
It firstly provides a comprehensive overview of ML for channel characterization
and ML-based antenna-channel optimization in this first part, and then it gives
a state-of-the-art literature review of channel scenario identification and
channel modeling in Part II. Fundamental results and key concepts of ML for
communication networks are presented, and widely used ML methods for channel
data processing, propagation channel estimation, and characterization are
analyzed and compared. A discussion of challenges and future research
directions for ML-enabled next generation networks of the topics covered in
this part rounds off the paper.

Domain adaptation is crucial for transferring the knowledge from the source
labeled CT dataset to the target unlabeled MR dataset in abdominal multi-organ
segmentation. Meanwhile, it is highly desirable to avoid the high annotation
cost related to the target dataset and protect the source dataset privacy.
Therefore, we propose an effective source-free unsupervised domain adaptation
method for cross-modality abdominal multi-organ segmentation without source
dataset access. The proposed framework comprises two stages. In the first
stage, the feature map statistics-guided model adaptation combined with entropy
minimization is developed to help the top segmentation network reliably segment
the target images. The pseudo-labels output from the top segmentation network
are used to guide the style compensation network to generate source-like
images. The pseudo-labels output from the middle segmentation network is used
to supervise the learning progress of the desired model (bottom segmentation
network). In the second stage, the circular learning and pixel-adaptive mask
refinement are used to further improve the desired model performance. With this
approach, we achieved satisfactory abdominal multi-organ segmentation
performance, outperforming the existing state-of-the-art domain adaptation
methods. The proposed approach can be easily extended to situations in which
target annotation data exist. With only one labeled MR volume, the performance
can be levelled with that of supervised learning. Furthermore, the proposed
approach is proven to be effective for source-free unsupervised domain
adaptation in reverse direction.

It is generally believed that Grover search algorithm (GSA) with quantum
noise may quickly lose its quadratic speedup over its classical case. In this
paper, we partly agree with that by our new findings as follows. First, we
investigate different typical diagonalizable noises represented by Bloch
vectors, and the results demonstrate that the success probability decreases
exponentially to 1/2 and oscillates around 1/2 with the increase of the number
of iterations. Second, for some types of noises, such as bit flip and bit-phase
flip noises, can improve the performance of GSA for certain parts of the search
process. Third, we calculate and analyze the noise threshold of the bit-phase
flip noise for the requested success probability and the result shows that GSA
with noise within the threshold still outperforms its classical counterpart.
According to the above results, some interesting works in the noisy
intermediate-scale quantum (NISQ) computing are suggested, such as verifying
the correctness of quantum algorithms even with noises and machine learning

Understanding model predictions is critical in healthcare, to facilitate
rapid verification of model correctness and to guard against use of models that
exploit confounding variables. We introduce the challenging new task of
explainable multiple abnormality classification in volumetric medical images,
in which a model must indicate the regions used to predict each abnormality. To
solve this task, we propose a multiple instance learning convolutional neural
network, AxialNet, that allows identification of top slices for each
abnormality. Next we incorporate HiResCAM, an attention mechanism, to identify
sub-slice regions. We prove that for AxialNet, HiResCAM explanations are
guaranteed to reflect the locations the model used, unlike Grad-CAM which
sometimes highlights irrelevant locations. Armed with a model that produces
faithful explanations, we then aim to improve the model's learning through a
novel mask loss that leverages HiResCAM and 3D allowed regions to encourage the
model to predict abnormalities based only on the organs in which those
abnormalities appear. The 3D allowed regions are obtained automatically through
a new approach, PARTITION, that combines location information extracted from
radiology reports with organ segmentation maps obtained through morphological
image processing. Overall, we propose the first model for explainable
multi-abnormality prediction in volumetric medical images, and then use the
mask loss to achieve a 33% improvement in organ localization of multiple
abnormalities in the RAD-ChestCT data set of 36,316 scans, representing the
state of the art. This work advances the clinical applicability of multiple
abnormality modeling in chest CT volumes.

Inter-robot transfer of training data is a little explored topic in learning-
and vision-based robot control. Here we propose a transfer method from a robot
with a lower Degree-of-Freedom (DoF) to one with a higher DoF utilizing the
omnidirectional camera image. The virtual rotation of the robot camera enables
data augmentation in this transfer learning process. As an experimental
demonstration, a vision-based control policy for a 6-DoF robot is trained using
a dataset collected by a wheeled ground robot with only three DoFs. Towards the
application of robotic manipulations, we also demonstrate a control system of a
6-DoF arm robot using multiple policies with different fields of view to enable
object reaching tasks.

In this paper, we study the transmission design for reconfigurable
intelligent surface (RIS)-aided multiuser communication networks. Different
from most of the existing contributions, we consider long-term CSI-based
transmission design, where both the beamforming vectors at the base station
(BS) and the phase shifts at the RIS are designed based on long-term CSI, which
can significantly reduce the channel estimation overhead. Due to the lack of
explicit ergodic data rate expression, we propose a novel deep deterministic
policy gradient (DDPG) based algorithm to solve the optimization problem, which
was trained by using the channel vectors generated in an offline manner.
Simulation results demonstrate that the achievable net throughput is higher
than that achieved by the conventional instantaneous-CSI based scheme when
taking the channel estimation overhead into account.

The Observation--Hypothesis--Prediction--Experimentation loop paradigm for
scientific research has been practiced by researchers for years towards
scientific discoveries. However, with data explosion in both mega-scale and
milli-scale scientific research, it has been sometimes very difficult to
manually analyze the data and propose new hypotheses to drive the cycle for
scientific discovery. In this paper, we discuss the role of Explainable AI in
scientific discovery process by demonstrating an Explainable AI-based paradigm
for science discovery. The key is to use Explainable AI to help derive data or
model interpretations, hypotheses, as well as scientific discoveries or
insights. We show how computational and data-intensive methodology -- together
with experimental and theoretical methodology -- can be seamlessly integrated
for scientific research. To demonstrate the AI-based science discovery process,
and to pay our respect to some of the greatest minds in human history, we show
how Kepler's laws of planetary motion and Newton's law of universal gravitation
can be rediscovered by (Explainable) AI based on Tycho Brahe's astronomical
observation data, whose works were leading the scientific revolution in the
16-17th century. This work also highlights the important role of Explainable AI
(as compared to Blackbox AI) in science discovery to help humans prevent or
better prepare for the possible technological singularity that may happen in
the future, since science is not only about the know how, but also the know
why. Presentation of the work is available at
https://slideslive.com/38986142/from-kepler-to-newton-explainable-ai-for-science-discovery.

Recently, many methods based on deep learning have been proposed for music
source separation. Some state-of-the-art methods have shown that stacking many
layers with many skip connections improve the SDR performance. Although such a
deep and complex architecture shows outstanding performance, it usually
requires numerous computing resources and time for training and evaluation.
This paper proposes a two-stream neural network for music demixing, called
KUIELab-MDX-Net, which shows a good balance of performance and required
resources. The proposed model has a time-frequency branch and a time-domain
branch, where each branch separates stems, respectively. It blends results from
two streams to generate the final estimation. KUIELab-MDX-Net took second place
on leaderboard A and third place on leaderboard B in the Music Demixing
Challenge at ISMIR 2021. This paper also summarizes experimental results on
another benchmark, MUSDB18. Our source code is available online.

Cyber attacks are increasing in volume, frequency, and complexity. In
response, the security community is looking toward fully automating cyber
defense systems using machine learning. However, so far the resultant effects
on the coevolutionary dynamics of attackers and defenders have not been
examined. In this whitepaper, we hypothesise that increased automation on both
sides will accelerate the coevolutionary cycle, thus begging the question of
whether there are any resultant fixed points, and how they are characterised.
Working within the threat model of Locked Shields, Europe's largest
cyberdefense exercise, we study blackbox adversarial attacks on network
classifiers. Given already existing attack capabilities, we question the
utility of optimal evasion attack frameworks based on minimal evasion
distances. Instead, we suggest a novel reinforcement learning setting that can
be used to efficiently generate arbitrary adversarial perturbations. We then
argue that attacker-defender fixed points are themselves general-sum games with
complex phase transitions, and introduce a temporally extended multi-agent
reinforcement learning framework in which the resultant dynamics can be
studied. We hypothesise that one plausible fixed point of AI-NIDS may be a
scenario where the defense strategy relies heavily on whitelisted feature flow
subspaces. Finally, we demonstrate that a continual learning approach is
required to study attacker-defender dynamics in temporally extended general-sum
games.

Most set prediction models in deep learning use set-equivariant operations,
but they actually operate on multisets. We show that set-equivariant functions
cannot represent certain functions on multisets, so we introduce the more
appropriate notion of multiset-equivariance. We identify that the existing Deep
Set Prediction Network (DSPN) can be multiset-equivariant without being
hindered by set-equivariance and improve it with approximate implicit
differentiation, allowing for better optimization while being faster and saving
memory. In a range of toy experiments, we show that the perspective of
multiset-equivariance is beneficial and that our changes to DSPN achieve better
results in most cases. On CLEVR object property prediction, we substantially
improve over the state-of-the-art Slot Attention from 8% to 77% in one of the
strictest evaluation metrics because of the benefits made possible by implicit

Compositionally complex materials (CCMs) present a potential paradigm shift
in the design of magnetic materials. These alloys exhibit long-range structural
order coupled with limited or no chemical order. As a result, extreme local
environments exist with a large opposing magnetic energy term, which can
manifest large changes in the magnetic behavior. In the current work, the
magnetic properties of (Cr, Mn, Fe, Ni) alloys are presented. These materials
were prepared by room-temperature combinatorial sputtering, resulting in a
range of compositions with a single BCC structural phase and no chemical
ordering. The combinatorial growth technique allows CCMs to be prepared outside
of their thermodynamically stable phase, enabling the exploration of otherwise
inaccessible order. The mixed ferromagnetic and antiferromagnetic interactions
in these alloys causes frustrated magnetic behavior, which results in an
extremely low coercivity (<1 mT), which increases rapidly at 50 K. At low
temperatures, the coercivity achieves values of nearly 500 mT, which is
comparable to some high-anisotropy magnetic materials. Commensurate with the
divergent coercivity is an atypical drop in the temperature dependent
magnetization. These effects are explained by a mixed magnetic phase model,
consisting of ferro-, antiferro , and frustrated magnetic regions, and are
rationalized by simulations. A machine-learning algorithm is employed to
visualize the parameter space and inform the development of subsequent
compositions. Annealing the samples at 600 {\deg}C orders the sample, more-than
doubling the Curie temperature and increasing the saturation magnetization by
as much as 5x. Simultaneously, the large coercivities are suppressed, resulting
in magnetic behavior that is largely temperature independent over a range of
350 K.

The gradients of convex functions are expressive models of non-trivial vector
fields. For example, Brenier's theorem yields that the optimal transport map
between any two measures on Euclidean space under the squared distance is
realized as a convex gradient, which is a key insight used in recent generative
flow models. In this paper, we study how to model convex gradients by
integrating a Jacobian-vector product parameterized by a neural network, which
we call the Input Convex Gradient Network (ICGN). We theoretically study ICGNs
and compare them to taking the gradient of an Input-Convex Neural Network
(ICNN), empirically demonstrating that a single layer ICGN can fit a toy
example better than a single layer ICNN. Lastly, we explore extensions to
deeper networks and connections to constructions from Riemannian geometry.

A wide range of analysis and testing techniques targeting modern web apps
rely on the automated exploration of their state space by firing events that
mimic user interactions. However, finding out which elements are actionable in
web apps is not a trivial task. To improve the efficacy of exploring the event
space of web apps, we propose a browser-independent, instrumentation-free
approach based on structural and visual stylistic cues. Our approach,
implemented in a tool called StyleX, employs machine learning models, trained
on 700,000 web elements from 1,000 real-world websites, to predict actionable
elements on a webpage a priori. In addition, our approach uses stylistic cues
for ranking these actionable elements while exploring the app. Our actionable
predictor models achieve 90.14\% precision and 87.76\% recall when considering
the click event listener, and on average, 75.42\% precision and 77.76\% recall
when considering the five most-frequent event types. Our evaluations show that
StyleX can improve the JavaScript code coverage achieved by a general-purpose
crawler by up to 23\%.

Terms and conditions (T&Cs) are pervasive on the web and often contain
important information for consumers, but are rarely read. Previous research has
explored methods to surface alarming privacy policies using manual labelers,
natural language processing, and deep learning techniques. However, this prior
work used pre-determined categories for annotations, and did not investigate
what consumers really deem as important from their perspective. In this paper,
we instead combine crowdsourcing with an open definition of "what is important"
in T&Cs. We present a workflow consisting of pairwise comparisons, agreement
validation, and Bradley-Terry rank modeling, to effectively establish rankings
of T&C statements from non-expert crowdworkers on this open definition, and
further analyzed consumers' preferences. We applied this workflow to 1,551 T&C
statements from 27 e-commerce websites, contributed by 3,462 unique crowd
workers doing 203,068 pairwise comparisons, and conducted thematic and
readability analysis on the statements considered as important/unimportant. We
found that consumers especially cared about policies related to after-sales and
money, and tended to regard harder-to-understand statements as more important.
We also present machine learning models to identify T&C clauses that consumers
considered important, achieving at best a 92.7% balanced accuracy, 91.6%
recall, and 89.2% precision. We foresee using our workflow and model to
efficiently and reliably highlight important T&Cs on websites at a large scale,
improving consumers' awareness

In this paper, we present a Generative Adversarial Network (GAN) machine
learning model to interpolate irregularly distributed measurements across the
spatial domain to construct a smooth radio frequency map (RFMap) and then
perform localization using a deep neural network. Monitoring wireless spectrum
over spatial, temporal, and frequency domains will become a critical feature in
facilitating dynamic spectrum access (DSA) in beyond-5G and 6G communication
technologies. Localization, wireless signal detection, and spectrum
policy-making are several of the applications where distributed spectrum
sensing will play a significant role. Detection and positioning of wireless
emitters is a very challenging task in a large spectral and spatial area. In
order to construct a smooth RFMap database, a large number of measurements are
required which can be very expensive and time consuming. One approach to help
realize these systems is to collect finite localized measurements across a
given area and then interpolate the measurement values to construct the
database. Current methods in the literature employ channel modeling to
construct the radio frequency map, which lacks the granularity for accurate
localization whereas our proposed approach reconstructs a new generalized
RFMap. Localization results are presented and compared with conventional
channel models.

Transfer learning from large-scale pre-trained models has become essential
for many computer vision tasks. Recent studies have shown that datasets like
ImageNet are weakly labeled since images with multiple object classes present
are assigned a single label. This ambiguity biases models towards a single
prediction, which could result in the suppression of classes that tend to
co-occur in the data. Inspired by language emergence literature, we propose
multi-label iterated learning (MILe) to incorporate the inductive biases of
multi-label learning from single labels using the framework of iterated
learning. MILe is a simple yet effective procedure that builds a multi-label
description of the image by propagating binary predictions through successive
generations of teacher and student networks with a learning bottleneck.
Experiments show that our approach exhibits systematic benefits on ImageNet
accuracy as well as ReaL F1 score, which indicates that MILe deals better with
label ambiguity than the standard training procedure, even when fine-tuning
from self-supervised weights. We also show that MILe is effective reducing
label noise, achieving state-of-the-art performance on real-world large-scale
noisy data such as WebVision. Furthermore, MILe improves performance in class
incremental settings such as IIRC and it is robust to distribution shifts.
Code: https://github.com/rajeswar18/MILe

Recent advancements in self-supervised learning have reduced the gap between
supervised and unsupervised representation learning. However, most
self-supervised and deep clustering techniques rely heavily on data
augmentation, rendering them ineffective for many learning tasks where
insufficient domain knowledge exists for performing augmentation. We propose a
new self-distillation based algorithm for domain-agnostic clustering. Our
method builds upon the existing deep clustering frameworks and requires no
separate student model. The proposed method outperforms existing domain
agnostic (augmentation-free) algorithms on CIFAR-10. We empirically demonstrate
that knowledge distillation can improve unsupervised representation learning by
extracting richer `dark knowledge' from the model than using predicted labels
alone. Preliminary experiments also suggest that self-distillation improves the
convergence of DeepCluster-v2.

Rate-distortion (R-D) function, a key quantity in information theory,
characterizes the fundamental limit of how much a data source can be compressed
subject to a fidelity criterion, by any compression algorithm. As researchers
push for ever-improving compression performance, establishing the R-D function
of a given data source is not only of scientific interest, but also sheds light
on the possible room for improving compression algorithms. Previous work on
this problem relied on distributional assumptions on the data source (Gibson,
2017) or only applied to discrete data (Blahut, 1972; Arimoto, 1972). By
contrast, this paper makes the first attempt at an algorithm for sandwiching
the R-D function of a general (not necessarily discrete) source requiring only
i.i.d. data samples. We estimate R-D sandwich bounds for a variety of
artificial and real-world data sources, in settings far beyond the feasibility
of any known method, and shed light on the optimality of neural data
compression (Ball\'e et al., 2021; Yang et al., 2022). Our R-D upper bound on
natural images indicates theoretical room for improving state-of-the-art image
compression methods by at least one dB in PSNR at various bitrates. Our data
and code can be found at https://github.com/mandt-lab/empirical-RD-sandwich.

Synthesizing human motion with a global structure, such as a choreography, is
a challenging task. Existing methods tend to concentrate on local smooth pose
transitions and neglect the global context or the theme of the motion. In this
work, we present a music-driven motion synthesis framework that generates
long-term sequences of human motions which are synchronized with the input
beats, and jointly form a global structure that respects a specific dance
genre. In addition, our framework enables generation of diverse motions that
are controlled by the content of the music, and not only by the beat. Our
music-driven dance synthesis framework is a hierarchical system that consists
of three levels: pose, motif, and choreography. The pose level consists of an
LSTM component that generates temporally coherent sequences of poses. The motif
level guides sets of consecutive poses to form a movement that belongs to a
specific distribution using a novel motion perceptual-loss. And the
choreography level selects the order of the performed movements and drives the
system to follow the global structure of a dance genre. Our results demonstrate
the effectiveness of our music-driven framework to generate natural and
consistent movements on various dance types, having control over the content of
the synthesized motions, and respecting the overall structure of the dance.

Long Short Term Memory LSTM-based structures have demonstrated their
efficiency for daily living recognition activities in smart homes by capturing
the order of sensor activations and their temporal dependencies. Nevertheless,
they still fail in dealing with the semantics and the context of the sensors.
More than isolated id and their ordered activation values, sensors also carry
meaning. Indeed, their nature and type of activation can translate various
activities. Their logs are correlated with each other, creating a global
context. We propose to use and compare two Natural Language Processing
embedding methods to enhance LSTM-based structures in activity-sequences
classification tasks: Word2Vec, a static semantic embedding, and ELMo, a
contextualized embedding. Results, on real smart homes datasets, indicate that
this approach provides useful information, such as a sensor organization map,
and makes less confusion between daily activity classes. It helps to better
perform on datasets with competing activities of other residents or pets. Our
tests show also that the embeddings can be pretrained on different datasets
than the target one, enabling transfer learning. We thus demonstrate that
taking into account the context of the sensors and their semantics increases
the classification performances and enables transfer learning.

In many contexts it is useful to predict the number of individuals in some
population who will initiate a particular activity during a given period. For
example, the number of users who will install a software update, the number of
customers who will use a new feature on a website or who will participate in an
A/B test. In practical settings, there is heterogeneity amongst individuals
with regard to the distribution of time until they will initiate. For these
reasons it is inappropriate to assume that the number of new individuals
observed on successive days will be identically distributed. Given observations
on the number of unique users participating in an initial period, we present a
simple but novel Bayesian method for predicting the number of additional
individuals who will participate during a subsequent period. We illustrate the
performance of the method in predicting sample size in online experimentation.

Effective early detection of potato late blight (PLB) is an essential aspect
of potato cultivation. However, it is a challenge to detect late blight at an
early stage in fields with conventional imaging approaches because of the lack
of visual cues displayed at the canopy level. Hyperspectral imaging can,
capture spectral signals from a wide range of wavelengths also outside the
visual wavelengths. In this context, we propose a deep learning classification
architecture for hyperspectral images by combining 2D convolutional neural
network (2D-CNN) and 3D-CNN with deep cooperative attention networks
(PLB-2D-3D-A). First, 2D-CNN and 3D-CNN are used to extract rich spectral space
features, and then the attention mechanism AttentionBlock and SE-ResNet are
used to emphasize the salient features in the feature maps and increase the
generalization ability of the model. The dataset is built with 15,360 images
(64x64x204), cropped from 240 raw images captured in an experimental field with
over 20 potato genotypes. The accuracy in the test dataset of 2000 images
reached 0.739 in the full band and 0.790 in the specific bands (492nm, 519nm,
560nm, 592nm, 717nm and 765nm). This study shows an encouraging result for
early detection of PLB with deep learning and proximal hyperspectral imaging.

Brain computer interfaces systems are controlled by users through
neurophysiological input for a variety of applications including communication,
environmental control, motor rehabilitation, and cognitive training. Although
individuals with severe speech and physical impairment are the primary users of
this technology, BCIs have emerged as a potential tool for broader populations,
especially with regards to delivering cognitive training or interventions with
neurofeedback. The goal of this study was to investigate the feasibility of
using a BCI system with neurofeedback as an intervention for people with mild
Alzheimer's disease. The study focused on visual attention and language since
ad is often associated with functional impairments in language and reading. The
study enrolled five adults with mild ad in a nine to thirteen week BCI EEG
based neurofeedback intervention to improve attention and reading skills. Two
participants completed intervention entirely. The remaining three participants
could not complete the intervention phase because of restrictions related to
covid. Pre and post assessment measures were used to assess reliability of
outcome measures and generalization of treatment to functional reading,
processing speed, attention, and working memory skills. Participants
demonstrated steady improvement in most cognitive measures across experimental
phases, although there was not a significant effect of NFB on most measures of
attention. One subject demonstrated significantly significant improvement in
letter cancellation during NFB. All participants with mild AD learned to
operate a BCI system with training. Results have broad implications for the
design and use of bci systems for participants with cognitive impairment.
Preliminary evidence justifies implementing NFB-based cognitive measures in AD.

The best arm identification problem in the multi-armed bandit setting is an
excellent model of many real-world decision-making problems, yet it fails to
capture the fact that in the real-world, safety constraints often must be met
while learning. In this work we study the question of best-arm identification
in safety-critical settings, where the goal of the agent is to find the best
safe option out of many, while exploring in a way that guarantees certain,
initially unknown safety constraints are met. We first analyze this problem in
the setting where the reward and safety constraint takes a linear structure,
and show nearly matching upper and lower bounds. We then analyze a much more
general version of the problem where we only assume the reward and safety
constraint can be modeled by monotonic functions, and propose an algorithm in
this setting which is guaranteed to learn safely. We conclude with experimental
results demonstrating the effectiveness of our approaches in scenarios such as
safely identifying the best drug out of many in order to treat an illness.

With privacy as a motivation, Federated Learning (FL) is an increasingly used
paradigm where learning takes place collectively on edge devices, each with a
cache of user-generated training examples that remain resident on the local
device. These on-device training examples are gathered in situ during the
course of users' interactions with their devices, and thus are highly
reflective of at least part of the inference data distribution. Yet a
distribution shift may still exist; the on-device training examples may lack
for some data inputs expected to be encountered at inference time. This paper
proposes a way to mitigate this shift: selective usage of datacenter data,
mixed in with FL. By mixing decentralized (federated) and centralized
(datacenter) data, we can form an effective training data distribution that
better matches the inference data distribution, resulting in more useful models
while still meeting the private training data access constraints imposed by FL.

Categorizing individual cells into one of many known cell type categories,
also known as cell type annotation, is a critical step in the analysis of
single-cell genomics data. The current process of annotation is time-intensive
and subjective, which has led to different studies describing cell types with
labels of varying degrees of resolution. While supervised learning approaches
have provided automated solutions to annotation, there remains a significant
challenge in fitting a unified model for multiple datasets with inconsistent
labels. In this article, we propose a new multinomial logistic regression
estimator which can be used to model cell type probabilities by integrating
multiple datasets with labels of varying resolution. To compute our estimator,
we solve a nonconvex optimization problem using a blockwise proximal gradient
descent algorithm. We show through simulation studies that our approach
estimates cell type probabilities more accurately than competitors in a wide
variety of scenarios. We apply our method to ten single-cell RNA-seq datasets
and demonstrate its utility in predicting fine resolution cell type labels on
unlabeled data as well as refining cell type labels on data with existing
coarse resolution annotations. An R package implementing the method is
available at https://github.com/keshav-motwani/IBMR and the collection of
datasets we analyze is available at
https://github.com/keshav-motwani/AnnotatedPBMC.

General full-wave electromagnetic solvers, such as those utilizing the
finite-difference time-domain (FDTD) method, are computationally demanding for
simulating practical GPR problems. We explore the performance of a
near-real-time, forward modeling approach for GPR that is based on a machine
learning (ML) architecture. To ease the process, we have developed a framework
that is capable of generating these ML-based forward solvers automatically. The
framework uses an innovative training method that combines a predictive
dimensionality reduction technique and a large data set of modeled GPR
responses from our FDTD simulation software, gprMax. The forward solver is
parameterized for a specific GPR application, but the framework can be extended
in a straightforward manner to different electromagnetic problems.

Integrating knowledge across different domains is an essential feature of
human learning. Learning paradigms such as transfer learning, meta learning,
and multi-task learning reflect the human learning process by exploiting the
prior knowledge for new tasks, encouraging faster learning and good
generalization for new tasks. This article gives a detailed view of these
learning paradigms and their comparative analysis. The weakness of one learning
algorithm turns out to be a strength of another, and thus merging them is a
prevalent trait in the literature. There are numerous research papers that
focus on each of these learning paradigms separately and provide a
comprehensive overview of them. However, this article provides a review of
research studies that combine (two of) these learning algorithms. This survey
describes how these techniques are combined to solve problems in many different
fields of study, including computer vision, natural language processing,
hyperspectral imaging, and many more, in supervised setting only. As a result,
the global generic learning network an amalgamation of meta learning, transfer
learning, and multi-task learning is introduced here, along with some open
research questions and future research directions in the multi-task setting.

Deep neural networks are notorious for defying theoretical treatment.
However, when the number of parameters in each layer tends to infinity the
network function is a Gaussian process (GP) and quantitatively predictive
description is possible. Gaussian approximation allows to formulate criteria
for selecting hyperparameters, such as variances of weights and biases, as well
as the learning rate. These criteria rely on the notion of criticality defined
for deep neural networks. In this work we describe a new practical way to
diagnose criticality. We introduce \emph{partial Jacobians} of a network,
defined as derivatives of preactivations in layer $l$ with respect to
preactivations in layer $l_0\leq l$. We derive recurrence relations for the
norms of partial Jacobians and utilize these relations to analyze criticality
of deep fully connected neural networks with LayerNorm and/or residual
connections. We derive and implement a simple and cheap numerical test that
allows to select optimal initialization for a broad class of deep neural
networks. Using these tools we show quantitatively that proper stacking of the
LayerNorm (applied to preactivations) and residual connections leads to an
architecture that is critical for any initialization. Finally, we apply our
methods to analyze the MLP-Mixer architecture and show that it is everywhere
critical.

Analyzing the behaviour of a population in response to disease and
interventions is critical to unearth variability in healthcare as well as
understand sub-populations that require specialized attention, but also to
assist in designing future interventions. Two aspects become very essential in
such analysis namely: i) Discovery of differentiating patterns exhibited by
sub-populations, and ii) Characterization of the identified subpopulations. For
the discovery phase, an array of approaches in the anomalous pattern detection
literature have been employed to reveal differentiating patterns, especially to
identify anomalous subgroups. However, these techniques are limited to
describing the anomalous subgroups and offer little in form of insightful
characterization, thereby limiting interpretability and understanding of these
data-driven techniques in clinical practices. In this work, we propose an
analysis of differentiated output (rather than discovery) and quantify
anomalousness similarly to the counter-factual setting. To this end we design
an approach to perform post-discovery analysis of anomalous subsets, in which
we initially identify the most important features on the anomalousness of the
subsets, then by perturbation, the approach seeks to identify the least number
of changes necessary to lose anomalousness. Our approach is presented and the
evaluation results on the 2019 MarketScan Commercial Claims and Medicare data,
show that extra insights can be obtained by extrapolated examination of the
identified subgroups.

The Zwicky Transient Facility (ZTF), a state-of-the-art optical robotic sky
survey, registers on the order of a million transient events - such as
supernova explosions, changes in brightness of variable sources, or moving
object detections - every clear night, and generates associated real-time
alerts. We present Alert-Classifying Artificial Intelligence (ACAI), an
open-source deep-learning framework for the phenomenological classification of
ZTF alerts. ACAI uses a set of five binary classifiers to characterize objects
which, in combination with the auxiliary/contextual event information available
from alert brokers, provides a powerful tool for alert stream filtering
tailored to different science cases, including early identification of
supernova-like and anomalous transient events. We report on the performance of
ACAI during the first months of deployment in a production setting.

The amount of data for machine learning (ML) applications is constantly
growing. Not only the number of observations, especially the number of measured
variables (features) increases with ongoing digitization. Selecting the most
appropriate features for predictive modeling is an important lever for the
success of ML applications in business and research. Feature selection methods
(FSM) that are independent of a certain ML algorithm - so-called filter methods
- have been numerously suggested, but little guidance for researchers and
quantitative modelers exists to choose appropriate approaches for typical ML
problems. This review synthesizes the substantial literature on feature
selection benchmarking and evaluates the performance of 58 methods in the
widely used R environment. For concrete guidance, we consider four typical
dataset scenarios that are challenging for ML models (noisy, redundant,
imbalanced data and cases with more features than observations). Drawing on the
experience of earlier benchmarks, which have considered much fewer FSMs, we
compare the performance of the methods according to four criteria (predictive
performance, number of relevant features selected, stability of the feature
sets and runtime). We found methods relying on the random forest approach, the
double input symmetrical relevance filter (DISR) and the joint impurity filter
(JIM) were well-performing candidate methods for the given dataset scenarios.

We introduce ChebLieNet, a group-equivariant method on (anisotropic)
manifolds. Surfing on the success of graph- and group-based neural networks, we
take advantage of the recent developments in the geometric deep learning field
to derive a new approach to exploit any anisotropies in data. Via discrete
approximations of Lie groups, we develop a graph neural network made of
anisotropic convolutional layers (Chebyshev convolutions), spatial pooling and
unpooling layers, and global pooling layers. Group equivariance is achieved via
equivariant and invariant operators on graphs with anisotropic left-invariant
Riemannian distance-based affinities encoded on the edges. Thanks to its simple
form, the Riemannian metric can model any anisotropies, both in the spatial and
orientation domains. This control on anisotropies of the Riemannian metrics
allows to balance equivariance (anisotropic metric) against invariance
(isotropic metric) of the graph convolution layers. Hence we open the doors to
a better understanding of anisotropic properties. Furthermore, we empirically
prove the existence of (data-dependent) sweet spots for anisotropic parameters
on CIFAR10. This crucial result is evidence of the benefice we could get by
exploiting anisotropic properties in data. We also evaluate the scalability of
this approach on STL10 (image data) and ClimateNet (spherical data), showing
its remarkable adaptability to diverse tasks.

Data-driven simulators promise high data-efficiency for driving policy
learning. When used for modelling interactions, this data-efficiency becomes a
bottleneck: Small underlying datasets often lack interesting and challenging
edge cases for learning interactive driving. We address this challenge by
proposing a simulation method that uses in-painted ado vehicles for learning
robust driving policies. Thus, our approach can be used to learn policies that
involve multi-agent interactions and allows for training via state-of-the-art
policy learning methods. We evaluate the approach for learning standard
interaction scenarios in driving. In extensive experiments, our work
demonstrates that the resulting policies can be directly transferred to a
full-scale autonomous vehicle without making use of any traditional sim-to-real
transfer techniques such as domain randomization.

In this paper we propose a novel optimization framework to systematically
solve robust PCA problem with rigorous theoretical guarantee, based on which we
investigate very computationally economic updating algorithms.

While Graph Neural Networks (GNNs) have recently become the de facto standard
for modeling relational data, they impose a strong assumption on the
availability of the node or edge features of the graph. In many real-world
applications, however, features are only partially available; for example, in
social networks, age and gender are available only for a small subset of users.
We present a general approach for handling missing features in graph machine
learning applications that is based on minimization of the Dirichlet energy and
leads to a diffusion-type differential equation on the graph. The
discretization of this equation produces a simple, fast and scalable algorithm
which we call Feature Propagation. We experimentally show that the proposed
approach outperforms previous methods on seven common node-classification
benchmarks and can withstand surprisingly high rates of missing features: on
average we observe only around 4% relative accuracy drop when 99% of the
features are missing. Moreover, it takes only 10 seconds to run on a graph with
$\sim$2.5M nodes and $\sim$123M edges on a single GPU.

The ability to learn universal audio representations that can solve diverse
speech, music, and environment tasks can spur many applications that require
general sound content understanding. In this work, we introduce a holistic
audio representation evaluation suite (HARES) spanning 12 downstream tasks
across audio domains and provide a thorough empirical study of recent sound
representation learning systems on that benchmark. We discover that previous
sound event classification or speech models do not generalize outside of their
domains. We observe that more robust audio representations can be learned with
the SimCLR objective; however, the model's transferability depends heavily on
the model architecture. We find the Slowfast architecture is good at learning
rich representations required by different domains, but its performance is
affected by the normalization scheme. Based on these findings, we propose a
novel normalizer-free Slowfast NFNet and achieve state-of-the-art performance
across all domains.

Deformable registration consists of finding the best dense correspondence
between two different images. Many algorithms have been published, but the
clinical application was made difficult by the high calculation time needed to
solve the optimisation problem. Deep learning overtook this limitation by
taking advantage of GPU calculation and the learning process. However, many
deep learning methods do not take into account desirable properties respected
by classical algorithms.
  In this paper, we present MICS, a novel deep learning algorithm for medical
imaging registration. As registration is an ill-posed problem, we focused our
algorithm on the respect of different properties: inverse consistency, symmetry
and orientation conservation. We also combined our algorithm with a multi-step
strategy to refine and improve the deformation grid. While many approaches
applied registration to brain MRI, we explored a more challenging body
localisation: abdominal CT. Finally, we evaluated our method on a dataset used
during the Learn2Reg challenge, allowing a fair comparison with published
methods.

Vehicle classification is a hot computer vision topic, with studies ranging
from ground-view up to top-view imagery. In remote sensing, the usage of
top-view images allows for understanding city patterns, vehicle concentration,
traffic management, and others. However, there are some difficulties when
aiming for pixel-wise classification: (a) most vehicle classification studies
use object detection methods, and most publicly available datasets are designed
for this task, (b) creating instance segmentation datasets is laborious, and
(c) traditional instance segmentation methods underperform on this task since
the objects are small. Thus, the present research objectives are: (1) propose a
novel semi-supervised iterative learning approach using GIS software, (2)
propose a box-free instance segmentation approach, and (3) provide a city-scale
vehicle dataset. The iterative learning procedure considered: (1) label a small
number of vehicles, (2) train on those samples, (3) use the model to classify
the entire image, (4) convert the image prediction into a polygon shapefile,
(5) correct some areas with errors and include them in the training data, and
(6) repeat until results are satisfactory. To separate instances, we considered
vehicle interior and vehicle borders, and the DL model was the U-net with the
Efficient-net-B7 backbone. When removing the borders, the vehicle interior
becomes isolated, allowing for unique object identification. To recover the
deleted 1-pixel borders, we proposed a simple method to expand each prediction.
The results show better pixel-wise metrics when compared to the Mask-RCNN (82%
against 67% in IoU). On per-object analysis, the overall accuracy, precision,
and recall were greater than 90%. This pipeline applies to any remote sensing
target, being very efficient for segmentation and generating datasets.

This paper discusses model-agnostic searches for new physics at the Large
Hadron Collider (LHC) using anomaly-detection techniques for the identification
of event signatures that deviate from the Standard Model (SM). We investigate
anomaly detection in the context of machine-learning approaches using
autoencoders, and illustrate expected shapes of invariant masses in the outlier
region using Monte Carlo simulations. Challenges and conceptual limitations of
this approach are discussed.

Photometric redshift estimation algorithms are often based on representative
data from observational campaigns. Data-driven methods of this type are subject
to a number of potential deficiencies, such as sample bias and incompleteness.
Motivated by these considerations, we propose using physically motivated
synthetic spectral energy distributions in redshift estimation. In addition,
the synthetic data would have to span a domain in colour-redshift space
concordant with that of the targeted observational surveys. With a matched
distribution and realistically modelled synthetic data in hand, a suitable
regression algorithm can be appropriately trained; we use a mixture density
network for this purpose. We also perform a zero-point re-calibration to reduce
the systematic differences between noise-free synthetic data and the
(unavoidably) noisy observational data sets. This new redshift estimation
framework, SYTH-Z, demonstrates superior accuracy over a wide range of
redshifts compared to baseline models trained on observational data alone.
Approaches using realistic synthetic data sets can therefore greatly mitigate
the reliance on expensive spectroscopic follow-up for the next generation of
photometric surveys.

Advances in biomedicine are largely fueled by exploring uncharted territories
of human biology. Machine learning can both enable and accelerate discovery,
but faces a fundamental hurdle when applied to unseen data with distributions
that differ from previously observed ones -- a common dilemma in scientific
inquiry. We have developed a new deep learning framework, called
{\textit{Portal Learning}}, to explore dark chemical and biological space.
Three key, novel components of our approach include: (i) end-to-end, step-wise
transfer learning, in recognition of biology's sequence-structure-function
paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection.
Portal Learning provides a practical solution to the out-of-distribution (OOD)
problem in statistical machine learning. Here, we have implemented Portal
Learning to predict chemical-protein interactions on a genome-wide scale.
Systematic studies demonstrate that Portal Learning can effectively assign
ligands to unexplored gene families (unknown functions), versus existing
state-of-the-art methods, thereby allowing us to target previously
"undruggable" proteins and design novel polypharmacological agents for
disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning
is general-purpose and can be further applied to other areas of scientific
inquiry.

Data taken from observations of the natural world or laboratory measurements
often depend on parameters which can vary in unexpected ways. In this paper we
demonstrate how machine learning can be leveraged to detect changes in global
parameters from variations in an identified model using only observational
data. This capability, when paired with first principles analysis, can
effectively distinguish the effects of these changing parameters from the
intrinsic complexity of the system. Here we illustrate this by identifying a
set of governing equations for an experiment generating a weakly turbulent
fluid flow, then analyzing variation in the coefficients of these equations to
unravel the drift in its physical parameters.

The first Global e-Competition on Astronomy and Astrophysics was held on-line
in September-October 2020 as a replacement for the International Olympiad on
Astronomy and Astrophysics, which was postponed due to the ongoing pandemic
caused by COVID-19. Despite the short time available for organisation, the
competition was run successfully with 325 students from over 42 countries
participating with no major issues. The feedback from the participants was
positive and reflects the ways in which such events can boost interest in
astronomy and astronomy education. With on-line activities set to be more
prevalent in the future, we present an overview of the competition process and
some of the lessons learned in hindsight as a guide for other event organisers.

Transformer-based supervised pre-training achieves great performance in
person re-identification (ReID). However, due to the domain gap between
ImageNet and ReID datasets, it usually needs a larger pre-training dataset
(e.g. ImageNet-21K) to boost the performance because of the strong data fitting
ability of the transformer. To address this challenge, this work targets to
mitigate the gap between the pre-training and ReID datasets from the
perspective of data and model structure, respectively. We first investigate
self-supervised learning (SSL) methods with Vision Transformer (ViT) pretrained
on unlabelled person images (the LUPerson dataset), and empirically find it
significantly surpasses ImageNet supervised pre-training models on ReID tasks.
To further reduce the domain gap and accelerate the pre-training, the
Catastrophic Forgetting Score (CFS) is proposed to evaluate the gap between
pre-training and fine-tuning data. Based on CFS, a subset is selected via
sampling relevant data close to the down-stream ReID data and filtering
irrelevant data from the pre-training dataset. For the model structure, a
ReID-specific module named IBN-based convolution stem (ICS) is proposed to
bridge the domain gap by learning more invariant features. Extensive
experiments have been conducted to fine-tune the pre-training models under
supervised learning, unsupervised domain adaptation (UDA), and unsupervised
learning (USL) settings. We successfully downscale the LUPerson dataset to 50%
with no performance degradation. Finally, we achieve state-of-the-art
performance on Market-1501 and MSMT17. For example, our ViT-S/16 achieves
91.3%/89.9%/89.6% mAP accuracy on Market1501 for supervised/UDA/USL ReID. Codes
and models will be released to https://github.com/michuanhaohao/TransReID-SSL.

Simulation has the potential to transform the development of robust
algorithms for mobile agents deployed in safety-critical scenarios. However,
the poor photorealism and lack of diverse sensor modalities of existing
simulation engines remain key hurdles towards realizing this potential. Here,
we present VISTA, an open source, data-driven simulator that integrates
multiple types of sensors for autonomous vehicles. Using high fidelity,
real-world datasets, VISTA represents and simulates RGB cameras, 3D LiDAR, and
event-based cameras, enabling the rapid generation of novel viewpoints in
simulation and thereby enriching the data available for policy learning with
corner cases that are difficult to capture in the physical world. Using VISTA,
we demonstrate the ability to train and test perception-to-control policies
across each of the sensor types and showcase the power of this approach via
deployment on a full scale autonomous vehicle. The policies learned in VISTA
exhibit sim-to-real transfer without modification and greater robustness than
those trained exclusively on real-world data.

Remote photoplethysmography (rPPG), which aims at measuring heart activities
and physiological signals from facial video without any contact, has great
potential in many applications (e.g., remote healthcare and affective
computing). Recent deep learning approaches focus on mining subtle rPPG clues
using convolutional neural networks with limited spatio-temporal receptive
fields, which neglect the long-range spatio-temporal perception and interaction
for rPPG modeling. In this paper, we propose the PhysFormer, an end-to-end
video transformer based architecture, to adaptively aggregate both local and
global spatio-temporal features for rPPG representation enhancement. As key
modules in PhysFormer, the temporal difference transformers first enhance the
quasi-periodic rPPG features with temporal difference guided global attention,
and then refine the local spatio-temporal representation against interference.
Furthermore, we also propose the label distribution learning and a curriculum
learning inspired dynamic constraint in frequency domain, which provide
elaborate supervisions for PhysFormer and alleviate overfitting. Comprehensive
experiments are performed on four benchmark datasets to show our superior
performance on both intra- and cross-dataset testings. One highlight is that,
unlike most transformer networks needed pretraining from large-scale datasets,
the proposed PhysFormer can be easily trained from scratch on rPPG datasets,
which makes it promising as a novel transformer baseline for the rPPG
community. The codes will be released at
https://github.com/ZitongYu/PhysFormer.

Calibration is still an important issue for user experience in Brain-Computer
Interfaces (BCI). Common experimental designs often involve a lengthy training
period that raises the cognitive fatigue, before even starting to use the BCI.
Reducing or suppressing this subject-dependent calibration is possible by
relying on advanced machine learning techniques, such as transfer learning.
Building on Riemannian BCI, we present a simple and effective scheme to train a
classifier on data recorded from different subjects, to reduce the calibration
while preserving good performances. The main novelty of this paper is to
propose a unique approach that could be applied on very different paradigms. To
demonstrate the robustness of this approach, we conducted a meta-analysis on
multiple datasets for three BCI paradigms: event-related potentials (P300),
motor imagery and SSVEP. Relying on the MOABB open source framework to ensure
the reproducibility of the experiments and the statistical analysis, the
results clearly show that the proposed approach could be applied on any kind of
BCI paradigm and in most of the cases to significantly improve the classifier
reliability. We point out some key features to further improve transfer

This paper presents a data-driven modeling approach for developing
control-oriented thermal models of buildings. These models are developed with
the objective of reducing energy consumption costs while controlling the indoor
temperature of the building within required comfort limits. To combine the
interpretability of white/gray box physics models and the expressive power of
neural networks, we propose a physics informed neural network approach for this
modeling task. Along with measured data and building parameters, we encode the
neural networks with the underlying physics that governs the thermal behavior
of these buildings. Thus, realizing a model that is guided by physics, aids in
modeling the temporal evolution of room temperature and power consumption as
well as the hidden state, i.e., the temperature of building thermal mass for
subsequent time steps. The main research contributions of this work are: (1) we
propose two variants of physics informed neural network architectures for the
task of control-oriented thermal modeling of buildings, (2) we show that
training these architectures is data-efficient, requiring less training data
compared to conventional, non-physics informed neural networks, and (3) we show
that these architectures achieve more accurate predictions than conventional
neural networks for longer prediction horizons. We test the prediction
performance of the proposed architectures using simulated and real-word data to
demonstrate (2) and (3) and show that the proposed physics informed neural
network architectures can be used for this control-oriented modeling problem.

Collaborative deep reinforcement learning (CDRL) algorithms in which multiple
agents can coordinate over a wireless network is a promising approach to enable
future intelligent and autonomous systems that rely on real-time
decision-making in complex dynamic environments. Nonetheless, in practical
scenarios, CDRL faces many challenges due to the heterogeneity of agents and
their learning tasks, different environments, time constraints of the learning,
and resource limitations of wireless networks. To address these challenges, in
this paper, a novel semantic-aware CDRL method is proposed to enable a group of
heterogeneous untrained agents with semantically-linked DRL tasks to
collaborate efficiently across a resource-constrained wireless cellular
network. To this end, a new heterogeneous federated DRL (HFDRL) algorithm is
proposed to select the best subset of semantically relevant DRL agents for
collaboration. The proposed approach then jointly optimizes the training loss
and wireless bandwidth allocation for the cooperating selected agents in order
to train each agent within the time limit of its real-time task. Simulation
results show the superior performance of the proposed algorithm compared to

Self-supervised learning algorithms, including BERT and SimCLR, have enabled
significant strides in fields like natural language processing, computer
vision, and speech processing. However, these algorithms are domain-specific,
meaning that new self-supervised learning algorithms must be developed for each
new setting, including myriad healthcare, scientific, and multimodal domains.
To catalyze progress toward domain-agnostic methods, we introduce DABS: a
Domain-Agnostic Benchmark for Self-supervised learning. To perform well on
DABS, an algorithm is evaluated on seven diverse domains: natural images,
multichannel sensor data, English text, speech recordings, multilingual text,
chest x-rays, and images with text descriptions. Each domain contains an
unlabeled dataset for pretraining; the model is then is scored based on its
downstream performance on a set of labeled tasks in the domain. We also present
e-Mix and ShED: two baseline domain-agnostic algorithms; their relatively
modest performance demonstrates that significant progress is needed before
self-supervised learning is an out-of-the-box solution for arbitrary domains.
Code for benchmark datasets and baseline algorithms is available at
https://github.com/alextamkin/dabs.

One proposed mechanism of language change concerns the role played by
second-language (L2) learners in situations of language contact. If
sufficiently many L2 speakers are present in a speech community in relation to
the number of first-language (L1) speakers, then those features which present a
difficulty in L2 acquisition may be prone to disappearing from the language.
This paper presents a mathematical account of such contact situations based on
a stochastic model of learning and nonlinear population dynamics. The
equilibria of a deterministic reduction of the model, describing a mixed
population of L1 and L2 speakers, are fully characterized. Whether or not the
language changes in response to the introduction of L2 learners turns out to
depend on three factors: the overall proportion of L2 learners in the
population, the strength of the difficulty speakers face in acquiring the
language as an L2, and the language-internal utilities of the competing
linguistic variants. These factors are related by a mathematical formula
describing a phase transition from retention of the L2-difficult feature to its
loss from both speaker populations. This supplies predictions that can be
tested against empirical data. Here, the model is evaluated with the help of
two case studies, morphological levelling in Afrikaans and the erosion of null
subjects in Afro-Peruvian Spanish; the model is found to be broadly in
agreement with the historical development in both cases.

Variational particle-based Bayesian learning methods have the advantage of
not being limited by the bias affecting more conventional parametric
techniques. This paper proposes to leverage the flexibility of non-parametric
Bayesian approximate inference to develop a novel Bayesian federated unlearning
method, referred to as Forget-Stein Variational Gradient Descent (Forget-SVGD).
Forget-SVGD builds on SVGD - a particle-based approximate Bayesian inference
scheme using gradient-based deterministic updates - and on its distributed
(federated) extension known as Distributed SVGD (DSVGD). Upon the completion of
federated learning, as one or more participating agents request for their data
to be "forgotten", Forget-SVGD carries out local SVGD updates at the agents
whose data need to be "unlearned", which are interleaved with communication
rounds with a parameter server. The proposed method is validated via
performance comparisons with non-parametric schemes that train from scratch by
excluding data to be forgotten, as well as with existing parametric Bayesian
unlearning methods.

GPU compilers are complex software programs with many optimizations specific
to target hardware. These optimizations are often controlled by heuristics
hand-designed by compiler experts using time- and resource-intensive processes.
In this paper, we developed a GPU compiler autotuning framework that uses
off-policy deep reinforcement learning to generate heuristics that improve the
frame rates of graphics applications. Furthermore, we demonstrate the
resilience of these learned heuristics to frequent compiler updates by
analyzing their stability across a year of code check-ins without retraining.
We show that our machine learning-based compiler autotuning framework matches
or surpasses the frame rates for 98% of graphics benchmarks with an average
uplift of 1.6% up to 15.8%.

Pairwise learning refers to learning tasks where the loss function depends on
a pair of instances. It instantiates many important machine learning tasks such
as bipartite ranking and metric learning. A popular approach to handle
streaming data in pairwise learning is an online gradient descent (OGD)
algorithm, where one needs to pair the current instance with a buffering set of
previous instances with a sufficiently large size and therefore suffers from a
scalability issue. In this paper, we propose simple stochastic and online
gradient descent methods for pairwise learning. A notable difference from the
existing studies is that we only pair the current instance with the previous
one in building a gradient direction, which is efficient in both the storage
and computational complexity. We develop novel stability results, optimization,
and generalization error bounds for both convex and nonconvex as well as both
smooth and nonsmooth problems. We introduce novel techniques to decouple the
dependency of models and the previous instance in both the optimization and
generalization analysis. Our study resolves an open question on developing
meaningful generalization bounds for OGD using a buffering set with a very
small fixed size. We also extend our algorithms and stability analysis to
develop differentially private SGD algorithms for pairwise learning which
significantly improves the existing results.

We introduce a generic strategy for provably efficient multi-goal
exploration. It relies on AdaGoal, a novel goal selection scheme that leverages
a measure of uncertainty in reaching states to adaptively target goals that are
neither too difficult nor too easy. We show how AdaGoal can be used to tackle
the objective of learning an $\epsilon$-optimal goal-conditioned policy for the
(initially unknown) set of goal states that are reachable within $L$ steps in
expectation from a reference state $s_0$ in a reward-free Markov decision
process. In the tabular case with $S$ states and $A$ actions, our algorithm
requires $\tilde{O}(L^3 S A \epsilon^{-2})$ exploration steps, which is nearly
minimax optimal. We also readily instantiate AdaGoal in linear mixture Markov
decision processes, yielding the first goal-oriented PAC guarantee with linear
function approximation. Beyond its strong theoretical guarantees, we anchor
AdaGoal in goal-conditioned deep reinforcement learning, both conceptually and
empirically, by connecting its idea of selecting "uncertain" goals to
maximizing value ensemble disagreement.

Symbolic reasoning, rule-based symbol manipulation, is a hallmark of human
intelligence. However, rule-based systems have had limited success competing
with learning-based systems outside formalized domains such as automated
theorem proving. We hypothesize that this is due to the manual construction of
rules in past attempts. In this work, we ask how we can build a rule-based
system that can reason with natural language input but without the manual
construction of rules. We propose MetaQNL, a "Quasi-Natural" language that can
express both formal logic and natural language sentences, and MetaInduce, a
learning algorithm that induces MetaQNL rules from training data consisting of
questions and answers, with or without intermediate reasoning steps. Our
approach achieves state-of-the-art accuracy on multiple reasoning benchmarks;
it learns compact models with much less data and produces not only answers but
also checkable proofs. Further, experiments on a real-world morphological
analysis benchmark show that it is possible for our method to handle noise and
ambiguity. Code will be released at https://github.com/princeton-vl/MetaQNL.

Artificial intelligence (AI) is paving the way towards the fourth industrial
revolution with the fire domain (Fire 4.0). As a matter of fact, the next few
years will be elemental to how this technology will shape our academia,
practice, and entrepreneurship. Despite the growing interest between fire
research groups, AI remains absent of our curriculum, and we continue to lack a
methodical framework to adopt, apply and create AI solutions suitable for our
problems. The above is also true for parallel engineering domains (i.e.,
civil/mechanical engineering), and in order to negate the notion of history
repeats itself (e.g., look at the continued debate with regard to modernizing
standardized fire testing, etc.), it is the motivation behind this letter to
the Editor to demystify some of the big ideas behind AI to jump-start prolific
and strategic discussions on the front of AI & Fire. In addition, this letter
intends to explain some of the most fundamental concepts and clear common
misconceptions specific to the adoption of AI in fire engineering. This short
letter is a companion to the Smart Systems in Fire Engineering special issue
sponsored by Fire Technology. An in-depth review of AI algorithms [1] and
success stories to the proper implementations of such algorithms can be found
in the aforenoted special issue and collection of papers. This letter comprises
two sections. The first section outlines big ideas pertaining to AI, and
answers some of the burning questions with regard to the merit of adopting AI
in our domain. The second section presents a set of rules or technical
recommendations an AI user may deem helpful to practice whenever AI is used as
an investigation methodology. The presented set of rules are complementary to
the big ideas.

Let $P$ be a linear differential operator over $\mathcal{D} \subset
\mathbb{R}^d$ and $U = (U_x)_{x \in \mathcal{D}}$ a second order stochastic
process. In the first part of this article, we prove a new necessary and
sufficient condition for all the trajectories of $U$ to verify the partial
differential equation (PDE) $T(U) = 0$. This condition is formulated in terms
of the covariance kernel of $U$. When compared to previous similar results, the
novelty lies in that the equality $T(U) = 0$ is understood in the \textit{sense
of distributions}, which is a relevant framework for PDEs. This theorem
provides precious insights during the second part of this article, devoted to
performing "physically informed" machine learning for the homogeneous 3
dimensional free space wave equation. We perform Gaussian process regression
(GPR) on pointwise observations of a solution of this PDE. To do so, we
propagate Gaussian processes (GP) priors over its initial conditions through
the wave equation. We obtain explicit formulas for the covariance kernel of the
propagated GP, which can then be used for GPR. We then explore the particular
cases of radial symmetry and point source. For the former, we derive
convolution-free GPR formulas; for the latter, we show a direct link between
GPR and the classical triangulation method for point source localization used
in GPS systems. Additionally, this Bayesian framework provides a new answer for
the ill-posed inverse problem of reconstructing initial conditions for the wave
equation with a limited number of sensors, and simultaneously enables the
inference of physical parameters from these data. Finally, we illustrate this
physically informed GPR on a number of practical examples.

Adversarial examples are inputs to a machine learning system that result in
an incorrect output from that system. Attacks launched through this type of
input can cause severe consequences: for example, in the field of image
recognition, a stop signal can be misclassified as a speed limit
indication.However, adversarial examples also represent the fuel for a flurry
of research directions in different domains and applications. Here, we give an
overview of how they can be profitably exploited as powerful tools to build
stronger learning models, capable of better-withstanding attacks, for two
crucial tasks: fake news and social bot detection.

Accurate load forecasting is critical for electricity market operations and
other real-time decision-making tasks in power systems. This paper considers
the short-term load forecasting (STLF) problem for residential customers within
a community. Existing STLF work mainly focuses on forecasting the aggregated
load for either a feeder system or a single customer, but few efforts have been
made on forecasting the load at individual appliance level. In this work, we
present an STLF algorithm for efficiently predicting the power consumption of
individual electrical appliances. The proposed method builds upon a powerful
recurrent neural network (RNN) architecture in deep learning, termed as long
short-term memory (LSTM). As each appliance has uniquely repetitive consumption
patterns, the patterns of prediction error will be tracked such that past
prediction errors can be used for improving the final prediction performance.
Numerical tests on real-world load datasets demonstrate the improvement of the
proposed method over existing LSTM-based method and other benchmark approaches.

Multimodal classification is a core task in human-centric machine learning.
We observe that information is highly complementary across modalities, thus
unimodal information can be drastically sparsified prior to multimodal fusion
without loss of accuracy. To this end, we present Sparse Fusion Transformers
(SFT), a novel multimodal fusion method for transformers that performs
comparably to existing state-of-the-art methods while having greatly reduced
memory footprint and computation cost. Key to our idea is a sparse-pooling
block that reduces unimodal token sets prior to cross-modality modeling.
Evaluations are conducted on multiple multimodal benchmark datasets for a wide
range of classification tasks. State-of-the-art performance is obtained on
multiple benchmarks under similar experiment conditions, while reporting up to
six-fold reduction in computational cost and memory requirements. Extensive
ablation studies showcase our benefits of combining sparsification and
multimodal learning over naive approaches. This paves the way for enabling
multimodal learning on low-resource devices.

This paper develops a reinforcement learning (RL)approach to solve a
cooperative, multi-agent Volt-Var Control (VVC) problem for high solar
penetration distribution systems. The ingenuity of our RL method lies in a
novel two-stage progressive training strategy that can effectively improve
training speed and convergence of the machine learning algorithm. In Stage
1(individual training), while holding all the other agents inactive, we
separately train each agent to obtain its own optimal VVC actions in the action
space: {consume, generate, do-nothing}. In Stage 2 (cooperative training), all
agents are trained again coordinatively to share VVC responsibility. Rewards
and costs in our RL scheme include (i) a system-level reward (for taking an
action), (ii) an agent-level reward (for doing-nothing), and(iii) an
agent-level action cost function. This new framework allows rewards to be
dynamically allocated to each agent based on their contribution while
accounting for the trade-off between control effectiveness and action cost. The
proposed methodology is tested and validated in a modified IEEE 123-bus system
using realistic PV and load profiles. Simulation results confirm that the
proposed approach is robust and computationally efficient; and it achieves
desirable volt-var control performance under a wide range of operation

With the recent demand of deploying neural network models on mobile and edge
devices, it is desired to improve the model's generalizability on unseen
testing data, as well as enhance the model's robustness under fixed-point
quantization for efficient deployment. Minimizing the training loss, however,
provides few guarantees on the generalization and quantization performance. In
this work, we fulfill the need of improving generalization and quantization
performance simultaneously by theoretically unifying them under the framework
of improving the model's robustness against bounded weight perturbation and
minimizing the eigenvalues of the Hessian matrix with respect to model weights.
We therefore propose HERO, a Hessian-enhanced robust optimization method, to
minimize the Hessian eigenvalues through a gradient-based training process,
simultaneously improving the generalization and quantization performance. HERO
enables up to a 3.8% gain on test accuracy, up to 30% higher accuracy under 80%
training label perturbation, and the best post-training quantization accuracy
across a wide range of precision, including a >10% accuracy improvement over
SGD-trained models for common model architectures on various datasets.

With the growing popularity and ease of access to the internet, the problem
of online rumors is escalating. People are relying on social media to gain
information readily but fall prey to false information. There is a lack of
credibility assessment techniques for online posts to identify rumors as soon
as they arrive. Existing studies have formulated several mechanisms to combat
online rumors by developing machine learning and deep learning algorithms. The
literature so far provides supervised frameworks for rumor classification that
rely on huge training datasets. However, in the online scenario where
supervised learning is exigent, dynamic rumor identification becomes difficult.
Early detection of online rumors is a challenging task, and studies relating to
them are relatively few. It is the need of the hour to identify rumors as soon
as they appear online. This work proposes a novel framework for unsupervised
rumor detection that relies on an online post's content and social features
using state-of-the-art clustering techniques. The proposed architecture
outperforms several existing baselines and performs better than several
supervised techniques. The proposed method, being lightweight, simple, and
robust, offers the suitability of being adopted as a tool for online rumor

Graph Neural Networks (GNNs) are a popular technique for modelling
graph-structured data and computing node-level representations via aggregation
of information from the neighborhood of each node. However, this aggregation
implies an increased risk of revealing sensitive information, as a node can
participate in the inference for multiple nodes. This implies that standard
privacy-preserving machine learning techniques, such as differentially private
stochastic gradient descent (DP-SGD) - which are designed for situations where
each data point participates in the inference for one point only - either do
not apply, or lead to inaccurate models. In this work, we formally define the
problem of learning GNN parameters with node-level privacy, and provide an
algorithmic solution with a strong differential privacy guarantee. We employ a
careful sensitivity analysis and provide a non-trivial extension of the
privacy-by-amplification technique to the GNN setting. An empirical evaluation
on standard benchmark datasets demonstrates that our method is indeed able to
learn accurate privacy-preserving GNNs which outperform both private and
non-private methods that completely ignore graph information.

Effective and timely responses to unexpected contingencies are crucial for
enhancing the resilience of power grids. Given the fast, complex process of
cascading propagation, corrective actions such as optimal load shedding (OLS)
are difficult to attain in large-scale networks due to the computation
complexity and communication latency issues. This work puts forth an innovative
learning-for-OLS approach by constructing the optimal decision rules of load
shedding under a variety of potential contingency scenarios through offline
neural network (NN) training. Notably, the proposed NN-based OLS decisions are
fully decentralized, enabling individual load centers to quickly react to the
specific contingency using readily available local measurements. Numerical
studies on the IEEE 14-bus system have demonstrated the effectiveness of our
scalable OLS design for real-time responses to severe grid emergency events.

Unpaired 3D object completion aims to predict a complete 3D shape from an
incomplete input without knowing the correspondence between the complete and
incomplete shapes. In this paper, we propose the novel KTNet to solve this task
from the new perspective of knowledge transfer. KTNet elaborates a
teacher-assistant-student network to establish multiple knowledge transfer
processes. Specifically, the teacher network takes complete shape as input and
learns the knowledge of complete shape. The student network takes the
incomplete one as input and restores the corresponding complete shape. And the
assistant modules not only help to transfer the knowledge of complete shape
from the teacher to the student, but also judge the learning effect of the
student network. As a result, KTNet makes use of a more comprehensive
understanding to establish the geometric correspondence between complete and
incomplete shapes in a perspective of knowledge transfer, which enables more
detailed geometric inference for generating high-quality complete shapes. We
conduct comprehensive experiments on several datasets, and the results show
that our method outperforms previous methods of unpaired point cloud completion
by a large margin.

We study the problem of estimating the density $f(\boldsymbol x)$ of a random
vector ${\boldsymbol X}$ in $\mathbb R^d$. For a spanning tree $T$ defined on
the vertex set $\{1,\dots ,d\}$, the tree density $f_{T}$ is a product of
bivariate conditional densities. An optimal spanning tree minimizes the
Kullback-Leibler divergence between $f$ and $f_{T}$. From i.i.d. data we
identify an optimal tree $T^*$ and efficiently construct a tree density
estimate $f_n$ such that, without any regularity conditions on the density $f$,
one has $\lim_{n\to \infty} \int |f_n(\boldsymbol x)-f_{T^*}(\boldsymbol
x)|d\boldsymbol x=0$ a.s. For Lipschitz $f$ with bounded support, $\mathbb E
\left\{ \int |f_n(\boldsymbol x)-f_{T^*}(\boldsymbol x)|d\boldsymbol
x\right\}=O\big(n^{-1/4}\big)$, a dimension-free rate.

Fake news and misinformation are a matter of concern for people around the
globe. Users of the internet and social media sites encounter content with
false information much frequently. Fake news detection is one of the most
analyzed and prominent areas of research. These detection techniques apply
popular machine learning and deep learning algorithms. Previous work in this
domain covers fake news detection vastly among text circulating online.
Platforms that have extensively been observed and analyzed include news
websites and Twitter. Facebook, Reddit, WhatsApp, YouTube, and other social
applications are gradually gaining attention in this emerging field.
Researchers are analyzing online data based on multiple modalities composed of
text, image, video, speech, and other contributing factors. The combination of
various modalities has resulted in efficient fake news detection. At present,
there is an abundance of surveys consolidating textual fake news detection
algorithms. This review primarily deals with multi-modal fake news detection
techniques that include images, videos, and their combinations with text. We
provide a comprehensive literature survey of eighty articles presenting
state-of-the-art detection techniques, thereby identifying research gaps and
building a pathway for researchers to further advance this domain.

Lifting the 2D human pose to the 3D pose is an important yet challenging
task. Existing 3D pose estimation suffers from 1) the inherent ambiguity
between the 2D and 3D data, and 2) the lack of well labeled 2D-3D pose pairs in
the wild. Human beings are able to imagine the human 3D pose from a 2D image or
a set of 2D body key-points with the least ambiguity, which should be
attributed to the prior knowledge of the human body that we have acquired in
our mind. Inspired by this, we propose a new framework that leverages the
labeled 3D human poses to learn a 3D concept of the human body to reduce the
ambiguity. To have consensus on the body concept from 2D pose, our key insight
is to treat the 2D human pose and the 3D human pose as two different domains.
By adapting the two domains, the body knowledge learned from 3D poses is
applied to 2D poses and guides the 2D pose encoder to generate informative 3D
"imagination" as embedding in pose lifting. Benefiting from the domain
adaptation perspective, the proposed framework unifies the supervised and
semi-supervised 3D pose estimation in a principled framework. Extensive
experiments demonstrate that the proposed approach can achieve state-of-the-art
performance on standard benchmarks. More importantly, it is validated that the
explicitly learned 3D body concept effectively alleviates the 2D-3D ambiguity
in 2D pose lifting, improves the generalization, and enables the network to
exploit the abundant unlabeled 2D data.

Humans' continual learning (CL) ability is closely related to Stability
Versus Plasticity Dilemma that describes how humans achieve ongoing learning
capacity and preservation for learned information. The notion of CL has always
been present in artificial intelligence (AI) since its births. This paper
proposes a comprehensive review of CL. Different from previous reviews that
mainly focus on the catastrophic forgetting phenomenon in CL, this paper
surveys CL from a more macroscopic perspective based on the Stability Versus
Plasticity mechanism. Analogous to biological counterpart, "smart" AI agents
are supposed to i) remember previously learned information (information
retrospection); ii) infer on new information continuously (information
prospection:); iii) transfer useful information (information transfer), to
achieve high-level CL. According to the taxonomy, evaluation metrics,
algorithms, applications as well as some open issues are then introduced. Our
main contributions concern i) rechecking CL from the level of artificial
general intelligence; ii) providing a detailed and extensive overview on CL
topics; iii) presenting some novel ideas on the potential development of CL.

We consider the problem of identifying the units of measurement in a data
column that contains both numeric values and unit symbols in each row, e.g.,
"5.2 l", "7 pints". In this case we seek to identify the dimension of the
column (e.g. volume) and relate the unit symbols to valid units (e.g. litre,
pint) obtained from a knowledge graph. Below we present PUC, a Probabilistic
Unit Canonicalizer that can accurately identify the units of measurement,
extract semantic descriptions of quantitative data columns and canonicalize
their entries. We present the first messy real-world tabular datasets annotated
for units of measurement, which can enable and accelerate the research in this
area. Our experiments on these datasets show that PUC achieves better results
than existing solutions.

Type inference is the task of identifying the type of values in a data column
and has been studied extensively in the literature. Most existing type
inference methods support data types such as Boolean, date, float, integer and
string. However, these methods do not consider non-Boolean categorical
variables, where there are more than two possible values encoded by integers or
strings. Therefore, such columns are annotated either as integer or string
rather than categorical, and need to be transformed into categorical manually
by the user. In this paper, we propose a probabilistic type inference method
that can identify the general categorical data type (including non-Boolean
variables). Additionally, we identify the possible values of each categorical
variable by adapting the existing type inference method ptype. Combining these
methods, we present ptype-cat which achieves better results than existing
applicable solutions.

Inference in deep Bayesian neural networks is only fully understood in the
infinite-width limit, where the posterior flexibility afforded by increased
depth washes out and the posterior predictive collapses to a shallow Gaussian
process. Here, we interpret finite deep linear Bayesian neural networks as
data-dependent scale mixtures of Gaussian process predictors across output
channels. We leverage this observation to study representation learning in
these networks, allowing us to connect limiting results obtained in previous
studies within a unified framework. In total, these results advance our
analytical understanding of how depth affects inference in a simple class of
Bayesian neural networks.

Shapley value has recently become a popular way to explain the predictions of
complex and simple machine learning models. This paper is discusses the factors
that influence Shapley value. In particular, we explore the relationship
between the distribution of a feature and its Shapley value. We extend our
analysis by discussing the difference that arises in Shapley explanation for
different predicted outcomes from the same model. Our assessment is that
Shapley value for particular feature not only depends on its expected mean but
on other moments as well such as variance and there are disagreements for
baseline prediction, disagreements for signs and most important feature for
different outcomes such as probability, log odds, and binary decision generated
using same linear probability model (logit/probit). These disagreements not
only stay for local explainability but also affect the global feature
importance. We conclude that there is no unique Shapley explanation for a given
model. It varies with model outcome (Probability/Log-odds/binary decision such
as accept vs reject) and hence model application.

Pose variation is one of the key challenges in face recognition. Conventional
techniques mainly focus on face frontalization or face augmentation in image
space. However, transforming face images in image space is not guaranteed to
preserve the lossless identity features of the original image. Moreover, these
methods suffer from more computational costs and memory requirements due to the
additional models. We argue that it is more desirable to perform feature
transformation in hierarchical feature space rather than image space, which can
take advantage of different feature levels and benefit from joint learning with
representation learning. To this end, we propose a lightweight and
easy-to-implement attention block, named Pose Attention Module (PAM), for
pose-invariant face recognition. Specifically, PAM performs frontal-profile
feature transformation in hierarchical feature space by learning residuals
between pose variations with a soft gate mechanism. We validated the
effectiveness of PAM block design through extensive ablation studies and
verified the performance on several popular benchmarks, including LFW, CFP-FP,
AgeDB-30, CPLFW, and CALFW. Experimental results show that our method not only
outperforms state-of-the-art methods but also effectively reduces memory
requirements by more than 75 times. It is noteworthy that our method is not
limited to face recognition with large pose variations. By adjusting the soft
gate mechanism of PAM to a specific coefficient, such semantic attention block
can easily extend to address other intra-class imbalance problems in face
recognition, including large variations in age, illumination, expression, etc.

Recent 2D-to-3D human pose estimation works tend to utilize the graph
structure formed by the topology of the human skeleton. However, we argue that
this skeletal topology is too sparse to reflect the body structure and suffer
from serious 2D-to-3D ambiguity problem. To overcome these weaknesses, we
propose a novel graph convolution network architecture, Hierarchical Graph
Networks (HGN). It is based on denser graph topology generated by our
multi-scale graph structure building strategy, thus providing more delicate
geometric information. The proposed architecture contains three sparse-to-fine
representation subnetworks organized in parallel, in which multi-scale
graph-structured features are processed and exchange information through a
novel feature fusion strategy, leading to rich hierarchical representations. We
also introduce a 3D coarse mesh constraint to further boost detail-related
feature learning. Extensive experiments demonstrate that our HGN achieves the
state-of-the art performance with reduced network parameters

Deep image prior (DIP) was recently introduced as an effective unsupervised
approach for image restoration tasks. DIP represents the image to be recovered
as the output of a deep convolutional neural network, and learns the network's
parameters such that the output matches the corrupted observation. Despite its
impressive reconstructive properties, the approach is slow when compared to
supervisedly learned, or traditional reconstruction techniques. To address the
computational challenge, we bestow DIP with a two-stage learning paradigm: (i)
perform a supervised pretraining of the network on a simulated dataset; (ii)
fine-tune the network's parameters to adapt to the target reconstruction task.
We provide a thorough empirical analysis to shed insights into the impacts of
pretraining in the context of image reconstruction. We showcase that
pretraining considerably speeds up and stabilizes the subsequent reconstruction
task from real-measured 2D and 3D micro computed tomography data of biological
specimens. The code and additional experimental materials are available at
https://educateddip.github.io/docs.educated_deep_image_prior/.

We propose an over-the-air digital predistortion optimization algorithm using
reinforcement learning. Based on a symbol-based criterion, the algorithm
minimizes the errors between downsampled messages at the receiver side. The
algorithm does not require any knowledge about the underlying hardware or
channel. For a generalized memory polynomial power amplifier and additive white
Gaussian noise channel, we show that the proposed algorithm achieves
performance improvements in terms of symbol error rate compared with an
indirect learning architecture even when the latter is coupled with a full
sampling rate ADC in the feedback path. Furthermore, it maintains a
satisfactory adjacent channel power ratio.

Over the last few years, the DRL paradigm has been widely adopted for 5G and
beyond network optimization because of its extreme adaptability to many
different scenarios. However, collecting and processing learning data entail a
significant cost in terms of communication and computational resources, which
is often disregarded in the networking literature. In this work, we analyze the
cost of learning in a resource-constrained system, defining an optimization
problem in which training a DRL agent makes it possible to improve the resource
allocation strategy but also reduces the number of available resources. Our
simulation results show that the cost of learning can be critical when
evaluating DRL schemes on the network edge and that assuming a cost-free
learning model can lead to significantly overestimating performance.

Program merging is standard practice when developers integrate their
individual changes to a common code base. When the merge algorithm fails, this
is called a merge conflict. The conflict either manifests in textual merge
conflicts where the merge fails to produce code, or semantic merge conflicts
where the merged code results in compiler or test breaks. Resolving these
conflicts for large code projects is expensive because it requires developers
to manually identify the sources of conflict and correct them.
  In this paper, we explore the feasibility of automatically repairing merge
conflicts (both textual and semantic) using k-shot learning with large neural
language models (LM) such as GPT-3. One of the challenges in leveraging such
language models is to fit the examples and the queries within a small prompt
(2048 tokens). We evaluate LMs and k-shot learning for two broad applications:
(a) textual and semantic merge conflicts for a divergent fork Microsoft Edge,
and (b) textual merge conflicts for a large number of JavaScript projects in
GitHub. Our results are mixed: one one-hand, LMs provide the state-of-the-art
(SOTA) performance on semantic merge conflict resolution for Edge compared to
earlier symbolic approaches; on the other hand, LMs do not yet obviate the
benefits of fine-tuning neural models (when sufficient data is available) or
the design of special purpose domain-specific languages (DSL) for restricted
patterns for program synthesis.

Devising optimal operating strategies for a compressor station relies on the
knowledge of compressor characteristics. As the compressor characteristics
change with time and use, it is necessary to provide accurate models of the
characteristics that can be used in optimization of the operating strategy.
This paper proposes a new algorithm for online learning of the characteristics
of the compressors using Gaussian Processes. The performance of the new
approximation is shown in a case study with three compressors. The case study
shows that Gaussian Processes accurately capture the characteristics of
compressors even if no knowledge about the characteristics is initially
available. The results show that the flexible nature of Gaussian Processes
allows them to adapt to the data online making them amenable for use in
real-time optimization problems.

The dynamics of temporal networks lie in the continuous interactions between
nodes, which exhibit the dynamic node preferences with time elapsing. The
challenges of mining temporal networks are thus two-fold: the dynamic structure
of networks and the dynamic node preferences. In this paper, we investigate the
dynamic graph sampling problem, aiming to capture the preference structure of
nodes dynamically in cooperation with GNNs. Our proposed Dynamic Preference
Structure (DPS) framework consists of two stages: structure sampling and graph
fusion. In the first stage, two parameterized samplers are designed to learn
the preference structure adaptively with network reconstruction tasks. In the
second stage, an additional attention layer is designed to fuse two sampled
temporal subgraphs of a node, generating temporal node embeddings for
downstream tasks. Experimental results on many real-life temporal networks show
that our DPS outperforms several state-of-the-art methods substantially owing
to learning an adaptive preference structure. The code will be released soon at
https://github.com/doujiang-zheng/Dynamic-Preference-Structure.

In addition to formal learning at universities, like in lecture halls and
seminar rooms, students are regularly confronted with self-study activities.
Instead of being left to their own devices, students might benefit from a
proper design of such activities, including pedagogical interventions. Such
designs can increase the degree of activity and the contribution of self-study
activities to the achievement of learning outcomes.
  Especially in times of a global pandemic, self-study activities are
increasingly executed at home, where students already use technology-enhanced
materials, processes, and digital platforms. Thus we pick up these building
blocks and introduce TecCoBot within this paper. TecCoBot is not only a
chatbot, supporting students in reading texts by offering writing assignments
and providing automated feedback on these, but also implements a design for
self-study activities, typically only offered to a few students as face-to-face
mentoring.

The detection of clouds in satellite images is an essential preprocessing
task for big data in remote sensing. Convolutional neural networks (CNNs) have
greatly advanced the state-of-the-art in the detection of clouds in satellite
images, but existing CNN-based methods are costly as they require large amounts
of training images with expensive pixel-level cloud labels. To alleviate this
cost, we propose Fixed-Point GAN for Cloud Detection (FCD), a weakly-supervised
approach. Training with only image-level labels, we learn fixed-point
translation between clear and cloudy images, so only clouds are affected during
translation. Doing so enables our approach to predict pixel-level cloud labels
by translating satellite images to clear ones and setting a threshold to the
difference between the two images. Moreover, we propose FCD+, where we exploit
the label-noise robustness of CNNs to refine the prediction of FCD, leading to
further improvements. We demonstrate the effectiveness of our approach on the
Landsat-8 Biome cloud detection dataset, where we obtain performance close to
existing fully-supervised methods that train with expensive pixel-level labels.
By fine-tuning our FCD+ with just 1% of the available pixel-level labels, we
match the performance of fully-supervised methods.

Security risk assessment and prediction are critical for organisations
deploying Internet of Things (IoT) devices. An absolute minimum requirement for
enterprises is to verify the security risk of IoT devices for the reported
vulnerabilities in the National Vulnerability Database (NVD). This paper
proposes a novel risk prediction for IoT devices based on publicly available
information about them. Our solution provides an easy and cost-efficient
solution for enterprises of all sizes to predict the security risk of deploying
new IoT devices. After an extensive analysis of the NVD records over the past
eight years, we have created a unique, systematic, and balanced dataset for
vulnerable IoT devices, including key technical features complemented with
functional and descriptive features available from public resources. We then
use machine learning classification models such as Gradient Boosting Decision
Trees (GBDT) over this dataset and achieve 71% prediction accuracy in
classifying the severity of device vulnerability score.

Recent methods for knowledge grounded dialogs generate responses by
incorporating information from an external textual document. These methods do
not require the exact document to be known during training and rely on the use
of a retrieval system to fetch relevant documents from a large index. The
documents used to generate the responses are modeled as latent variables whose
prior probabilities need to be estimated. Models such as RAG and REALM,
marginalize the document probabilities over the documents retrieved from the
index to define the log likelihood loss function which is optimized end-to-end.
  In this paper, we develop a variational approach to the above technique
wherein, we instead maximize the Evidence Lower bound (ELBO). Using a
collection of three publicly available open-conversation datasets, we
demonstrate how the posterior distribution, that has information from the
ground-truth response, allows for a better approximation of the objective
function during training. To overcome the challenges associated with sampling
over a large knowledge collection, we develop an efficient approach to
approximate the ELBO. To the best of our knowledge we are the first to apply
variational training for open-scale unsupervised knowledge grounded dialog
systems.

Human beings keep exploring the physical space using information means. Only
recently, with the rapid development of information technologies and the
increasing accumulation of data, human beings can learn more about the unknown
world with data-driven methods. Given data timeliness, there is a growing
awareness of the importance of real-time data. There are two categories of
technologies accounting for data processing: batching big data and streaming
processing, which have not been integrated well. Thus, we propose an innovative
incremental processing technology named after Stream Cube to process both big
data and stream data. Also, we implement a real-time intelligent data
processing system, which is based on real-time acquisition, real-time
processing, real-time analysis, and real-time decision-making. The real-time
intelligent data processing technology system is equipped with a batching big
data platform, data analysis tools, and machine learning models. Based on our
applications and analysis, the real-time intelligent data processing system is
a crucial solution to the problems of the national society and economy.

In most optimization problems, users have a clear understanding of the
function to optimize (e.g., minimize the makespan for scheduling problems).
However, the constraints may be difficult to state and their modelling often
requires expertise in Constraint Programming. Active constraint acquisition has
been successfully used to support non-experienced users in learning constraint
networks through the generation of a sequence of queries. In this paper, we
propose Learn&Optimize, a method to solve optimization problems with known
objective function and unknown constraint network. It uses an active constraint
acquisition algorithm which learns the unknown constraints and computes
boundaries for the optimal solution during the learning process. As a result,
our method allows users to solve optimization problems without learning the
overall constraint network.

Asteroid exploration has been attracting more attention in recent years.
Nevertheless, we have just visited tens of asteroids while we have discovered
more than one million bodies. As our current observation and knowledge should
be biased, it is essential to explore multiple asteroids directly to better
understand the remains of planetary building materials. One of the mission
design solutions is utilizing asteroid flyby cycler trajectories with multiple
Earth gravity assists. An asteroid flyby cycler trajectory design problem is a
subclass of global trajectory optimization problems with multiple flybys,
involving a trajectory optimization problem for a given flyby sequence and a
combinatorial optimization problem to decide the sequence of the flybys. As the
number of flyby bodies grows, the computation time of this optimization problem
expands maliciously. This paper presents a new method to design asteroid flyby
cycler trajectories utilizing a surrogate model constructed by deep neural
networks approximating trajectory optimization results. Since one of the
bottlenecks of machine learning approaches is the computation time to generate
massive trajectory databases, we propose an efficient database generation
strategy by introducing pseudo-asteroids satisfying the Karush-Kuhn-Tucker
conditions. The numerical result applied to JAXA's DESTINY+ mission shows that
the proposed method is practically applicable to space mission design and can
significantly reduce the computational time for searching asteroid flyby

In this work we develop a new method, named Sub-graph Permutation Equivariant
Networks (SPEN), which provides a framework for building graph neural networks
that operate on sub-graphs, while using a base update function that is
permutation equivariant, that are equivariant to a novel choice of automorphism
group. Message passing neural networks have been shown to be limited in their
expressive power and recent approaches to over come this either lack
scalability or require structural information to be encoded into the feature
space. The general framework presented here overcomes the scalability issues
associated with global permutation equivariance by operating more locally on
sub-graphs. In addition, through operating on sub-graphs the expressive power
of higher-dimensional global permutation equivariant networks is improved; this
is due to fact that two non-distinguishable graphs often contain
distinguishable sub-graphs. Furthermore, the proposed framework only requires a
choice of $k$-hops for creating ego-network sub-graphs and a choice of
representation space to be used for each layer, which makes the method easily
applicable across a range of graph based domains. We experimentally validate
the method on a range of graph benchmark classification tasks, demonstrating
statistically indistinguishable results from the state-of-the-art on six out of
seven benchmarks. Further, we demonstrate that the use of local update
functions offers a significant improvement in GPU memory over global methods.

Deep learning (DL) methods have been recently proposed for user equipment
(UE) localization in wireless communication networks, based on the channel
state information (CSI) between a UE and each base station (BS) in the uplink.
With the CSI from the available BSs, UE localization can be performed in
different ways. One the one hand, a single neural network (NN) can be trained
for the UE localization by considering the CSI from all the available BSs as
one overall fingerprint of the user's location. On the other hand, the CSI at
each BS can be used to obtain an estimate of the UE's position with a separate
NN at each BS, and then the position estimates of all BSs are combined to
obtain an overall estimate of the UE position. In this work, we show that UE
localization with the latter approach can achieve a higher positioning
accuracy. We propose to consider the uncertainty in the UE localization at each
BS, such that overall UE's position is determined by combining the position
estimates of the different BSs based on the uncertainty at each BS. With this
approach, a more reliable position estimate can be obtained in case of
variations in the channel.

As spiking-based deep learning inference applications are increasing in
embedded systems, these systems tend to integrate neuromorphic accelerators
such as $\mu$Brain to improve energy efficiency. We propose a $\mu$Brain-based
scalable many-core neuromorphic hardware design to accelerate the computations
of spiking deep convolutional neural networks (SDCNNs). To increase energy
efficiency, cores are designed to be heterogeneous in terms of their neuron and
synapse capacity (big cores have higher capacity than the little ones), and
they are interconnected using a parallel segmented bus interconnect, which
leads to lower latency and energy compared to a traditional mesh-based
Network-on-Chip (NoC). We propose a system software framework called SentryOS
to map SDCNN inference applications to the proposed design. SentryOS consists
of a compiler and a run-time manager. The compiler compiles an SDCNN
application into subnetworks by exploiting the internal architecture of big and
little $\mu$Brain cores. The run-time manager schedules these sub-networks onto
cores and pipeline their execution to improve throughput. We evaluate the
proposed big little many-core neuromorphic design and the system software
framework with five commonlyused SDCNN inference applications and show that the
proposed solution reduces energy (between 37% and 98%), reduces latency
(between 9% and 25%), and increases application throughput (between 20% and
36%). We also show that SentryOS can be easily extended for other spiking
neuromorphic accelerators.

Variance reduction (VR) techniques have contributed significantly to
accelerating learning with massive datasets in the smooth and strongly convex
setting (Schmidt et al., 2017; Johnson & Zhang, 2013; Roux et al., 2012).
However, such techniques have not yet met the same success in the realm of
large-scale deep learning due to various factors such as the use of data
augmentation or regularization methods like dropout (Defazio & Bottou, 2019).
This challenge has recently motivated the design of novel variance reduction
techniques tailored explicitly for deep learning (Arnold et al., 2019; Ma &
Yarats, 2018). This work is an additional step in this direction. In
particular, we exploit the ubiquitous clustering structure of rich datasets
used in deep learning to design a family of scalable variance reduced
optimization procedures by combining existing optimizers (e.g., SGD+Momentum,
Quasi Hyperbolic Momentum, Implicit Gradient Transport) with a multi-momentum
strategy (Yuan et al., 2019). Our proposal leads to faster convergence than
vanilla methods on standard benchmark datasets (e.g., CIFAR and ImageNet). It
is robust to label noise and amenable to distributed optimization. We provide a
parallel implementation in JAX.

Salient object detection is subjective in nature, which implies that multiple
estimations should be related to the same input image. Most existing salient
object detection models are deterministic following a point to point estimation
learning pipeline, making them incapable of estimating the predictive
distribution. Although latent variable model based stochastic prediction
networks exist to model the prediction variants, the latent space based on the
single clean saliency annotation is less reliable in exploring the subjective
nature of saliency, leading to less effective saliency divergence modeling.
Given multiple saliency annotations, we introduce a general divergence modeling
strategy via random sampling, and apply our strategy to an ensemble based
framework and three latent variable model based solutions to explore the
subjective nature of saliency. Experimental results prove the superior
performance of our general divergence modeling strategy.

Existing deep clustering methods rely on either contrastive or
non-contrastive representation learning for downstream clustering task.
Contrastive-based methods thanks to negative pairs learn uniform
representations for clustering, in which negative pairs, however, may
inevitably lead to the class collision issue and consequently compromise the
clustering performance. Non-contrastive-based methods, on the other hand, avoid
class collision issue, but the resulting non-uniform representations may cause
the collapse of clustering. To enjoy the strengths of both worlds, this paper
presents a novel end-to-end deep clustering method with prototype scattering
and positive sampling, termed ProPos. Specifically, we first maximize the
distance between prototypical representations, named prototype scattering loss,
which improves the uniformity of representations. Second, we align one
augmented view of instance with the sampled neighbors of another view --
assumed to be truly positive pair in the embedding space -- to improve the
within-cluster compactness, termed positive sampling alignment. The strengths
of ProPos are avoidable class collision issue, uniform representations,
well-separated clusters, and within-cluster compactness. By optimizing ProPos
in an end-to-end expectation-maximization framework, extensive experimental
results demonstrate that ProPos achieves competing performance on
moderate-scale clustering benchmark datasets and establishes new
state-of-the-art performance on large-scale datasets. Source code is available
at \url{https://github.com/Hzzone/ProPos}.

We experimentally compare simple physics-based vs. data-driven
neural-network-based models for offline training of programmable photonic chips
using Mach-Zehnder interferometer meshes. The neural-network model outperforms
physics-based models for a chip with thermal crosstalk, yielding increased
testing accuracy.

Developing Named Entity Recognition (NER) systems for Indian languages has
been a long-standing challenge, mainly owing to the requirement of a large
amount of annotated clean training instances. This paper proposes an end-to-end
framework for NER for Indian languages in a low-resource setting by exploiting
parallel corpora of English and Indian languages and an English NER dataset.
The proposed framework includes an annotation projection method that combines
word alignment score and NER tag prediction confidence score on source language
(English) data to generate weakly labeled data in a target Indian language. We
employ a variant of the Teacher-Student model and optimize it jointly on the
pseudo labels of the Teacher model and predictions on the generated weakly
labeled data. We also present manually annotated test sets for three Indian
languages: Hindi, Bengali, and Gujarati. We evaluate the performance of the
proposed framework on the test sets of the three Indian languages. Empirical
results show a minimum 10% performance improvement compared to the zero-shot
transfer learning model on all languages. This indicates that weakly labeled
data generated using the proposed annotation projection method in target Indian
languages can complement well-annotated source language data to enhance
https://github.com/aksh555/CL-NERIL

Vision Transformers (ViTs) have achieved impressive performance over various
computer vision tasks. However, modelling global correlations with multi-head
self-attention (MSA) layers leads to two widely recognized issues: the massive
computational resource consumption and the lack of intrinsic inductive bias for
modelling local visual patterns. To solve both issues, we devise a simple yet
effective method named Single-Path Vision Transformer pruning (SPViT), to
efficiently and automatically compress the pre-trained ViTs into compact models
with proper locality added. Specifically, we first propose a novel
weight-sharing scheme between MSA and convolutional operations, delivering a
single-path space to encode all candidate operations. In this way, we cast the
operation search problem as finding which subset of parameters to use in each
MSA layer, which significantly reduces the computational cost and optimization
difficulty, and the convolution kernels can be well initialized using
pre-trained MSA parameters. Relying on the single-path space, we further
introduce learnable binary gates to encode the operation choices, which are
jointly optimized with network parameters to automatically determine the
configuration of each layer. We conduct extensive experiments on two
representative ViTs showing that our SPViT achieves a new SOTA for pruning on
ImageNet-1k. For example, our SPViT can trim 52.0% FLOPs for DeiT-B and get an
impressive 0.6% top-1 accuracy gain simultaneously. The source code is
available at https://github.com/ziplab/SPViT.

We introduce a compositional physics-aware FInite volume Neural Network
(FINN) for learning spatiotemporal advection-diffusion processes. FINN
implements a new way of combining the learning abilities of artificial neural
networks with physical and structural knowledge from numerical simulation by
modeling the constituents of partial differential equations (PDEs) in a
compositional manner. Results on both one- and two-dimensional PDEs (Burgers',
diffusion-sorption, diffusion-reaction, Allen--Cahn) demonstrate FINN's
superior modeling accuracy and excellent out-of-distribution generalization
ability beyond initial and boundary conditions. With only one tenth of the
number of parameters on average, FINN outperforms pure machine learning and
other state-of-the-art physics-aware models in all cases -- often even by
multiple orders of magnitude. Moreover, FINN outperforms a calibrated physical
model when approximating sparse real-world data in a diffusion-sorption
scenario, confirming its generalization abilities and showing explanatory
potential by revealing the unknown retardation factor of the observed process.

An ensuing challenge in Artificial Intelligence (AI) is the perceived
difficulty in interpreting sophisticated machine learning models, whose
ever-increasing complexity makes it hard for such models to be understood,
trusted and thus accepted by human beings. The lack, if not complete absence,
of interpretability for these so-called black-box models can lead to serious
economic and ethical consequences, thereby hindering the development and
deployment of AI in wider fields, particularly in those involving critical and
regulatory applications. Yet, the building services industry is a
highly-regulated domain requiring transparency and decision-making processes
that can be understood and trusted by humans. To this end, the design and
implementation of autonomous Heating, Ventilation and Air Conditioning systems
for the automatic but concurrently interpretable optimisation of energy
efficiency and room thermal comfort is of topical interest. This work therefore
presents an interpretable machine learning model aimed at predicting room
temperature in non-domestic buildings, for the purpose of optimising the use of
the installed HVAC system. We demonstrate experimentally that the proposed
model can accurately forecast room temperatures eight hours ahead in real-time
by taking into account historical RT information, as well as additional
environmental and time-series features. In this paper, an enhanced feature
engineering process is conducted based on the Exploratory Data Analysis
results. Furthermore, beyond the commonly used Interpretable Machine Learning
techniques, we propose a Permutation Feature-based Frequency Response Analysis
(PF-FRA) method for quantifying the contributions of the different predictors
in the frequency domain. Based on the generated reason codes, we find that the
historical RT feature is the dominant factor that has most impact on the model

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide,
with 2% of the population affected. It is associated with an increased risk of
strokes, heart failure and other heart-related complications. Monitoring
at-risk individuals and detecting asymptomatic AF could result in considerable
public health benefits, as individuals with asymptomatic AF could take
preventive measures with lifestyle changes. With increasing affordability to
wearables, personalized health care is becoming more accessible. These
personalized healthcare solutions require accurate classification of
bio-signals while being computationally inexpensive. By making inferences
on-device, we avoid issues inherent to cloud-based systems such as latency and
network connection dependency. We propose an efficient pipeline for real-time
Atrial Fibrillation Detection with high accuracy that can be deployed in
ultra-edge devices. The feature engineering employed in this research catered
to optimizing the resource-efficient classifier used in the proposed pipeline,
which was able to outperform the best performing standard ML model by
$10^5\times$ in terms of memory footprint with a mere trade-off of 2%
classification accuracy. We also obtain higher accuracy of approximately 6%
while consuming 403$\times$ lesser memory and being 5.2$\times$ faster compared
to the previous state-of-the-art (SoA) embedded implementation.

Generating user activity is a key capability for both evaluating security
monitoring tools as well as improving the credibility of attacker analysis
platforms (e.g., honeynets). In this paper, to generate this activity, we
instrument each machine by means of an external agent. This agent combines both
deterministic and deep learning based methods to adapt to different environment
(e.g., multiple OS, software versions, etc.), while maintaining high
performances. We also propose conditional text generation models to facilitate
the creation of conversations and documents to accelerate the definition of
coherent, system-wide, life scenarios.

In this paper, we present a self-training-based framework for glaucoma
grading using OCT B-scans under the presence of domain shift. Particularly, the
proposed two-step learning methodology resorts to pseudo-labels generated
during the first step to augment the training dataset on the target domain,
which is then used to train the final target model. This allows transferring
knowledge-domain from the unlabeled data. Additionally, we propose a novel
glaucoma-specific backbone which introduces residual and attention modules via
skip-connections to refine the embedding features of the latent space. By doing
this, our model is capable of improving state-of-the-art from a quantitative
and interpretability perspective. The reported results demonstrate that the
proposed learning strategy can boost the performance of the model on the target
dataset without incurring in additional annotation steps, by using only labels
from the source examples. Our model consistently outperforms the baseline by
1-3% across different metrics and bridges the gap with respect to training the
model on the labeled target data.

Learning the value function of a given policy from data samples is an
important problem in Reinforcement Learning. TD($\lambda$) is a popular class
of algorithms to solve this problem. However, the weights assigned to different
$n$-step returns in TD($\lambda$), controlled by the parameter $\lambda$,
decrease exponentially with increasing $n$. In this paper, we present a
$\lambda$-schedule procedure that generalizes the TD($\lambda$) algorithm to
the case when the parameter $\lambda$ could vary with time-step. This allows
flexibility in weight assignment, i.e., the user can specify the weights
assigned to different $n$-step returns by choosing a sequence $\{\lambda_t\}_{t
\geq 1}$. Based on this procedure, we propose an on-policy algorithm -
TD($\lambda$)-schedule, and two off-policy algorithms - GTD($\lambda$)-schedule
and TDC($\lambda$)-schedule, respectively. We provide proofs of almost sure
convergence for all three algorithms under a general Markov noise framework.

Hyperparameter optimization (HPO) is a necessary step to ensure the best
possible performance of Machine Learning (ML) algorithms. Several methods have
been developed to perform HPO; most of these are focused on optimizing one
performance measure (usually an error-based measure), and the literature on
such single-objective HPO problems is vast. Recently, though, algorithms have
appeared that focus on optimizing multiple conflicting objectives
simultaneously. This article presents a systematic survey of the literature
published between 2014 and 2020 on multi-objective HPO algorithms,
distinguishing between metaheuristic-based algorithms, metamodel-based
algorithms, and approaches using a mixture of both. We also discuss the quality
metrics used to compare multi-objective HPO procedures and present future

Although neural networks are powerful function approximators, the underlying
modelling assumptions ultimately define the likelihood and thus the hypothesis
class they are parameterizing. In classification, these assumptions are minimal
as the commonly employed softmax is capable of representing any categorical
distribution. In regression, however, restrictive assumptions on the type of
continuous distribution to be realized are typically placed, like the dominant
choice of training via mean-squared error and its underlying Gaussianity
assumption. Recently, modelling advances allow to be agnostic to the type of
continuous distribution to be modelled, granting regression the flexibility of
classification models. While past studies stress the benefit of such flexible
regression models in terms of performance, here we study the effect of the
model choice on uncertainty estimation. We highlight that under model
misspecification, aleatoric uncertainty is not properly captured, and that a
Bayesian treatment of a misspecified model leads to unreliable epistemic
uncertainty estimates. Overall, our study provides an overview on how modelling
choices in regression may influence uncertainty estimation and thus any
downstream decision making process.

Selection functionality is as fundamental to vector graphics as it is for
raster data. But vector selection is quite different: instead of pixel-level
labeling, we make a binary decision to include or exclude each vector
primitive. In the absence of intelligible metadata, this becomes a perceptual
grouping problem. These have previously relied on heuristics derived from
empirical principles like Gestalt Theory, but since these are ill-defined and
subjective, they often result in ambiguity. Here we take a data-centric
approach to the problem. By exploiting the recursive nature of perceptual
grouping, we interpret the task as constructing a hierarchy over the primitives
of a vector graphic, which is amenable to learning with recursive neural
networks with few human annotations. We verify this by building a dataset of
these hierarchies on which we train a hierarchical grouping network. We then
demonstrate how this can underpin a prototype selection tool.

In this work, we study the interplay between the data distribution and
Q-learning-based algorithms with function approximation. We provide a unified
theoretical and empirical analysis as to why different properties of the data
distribution can contribute to regulate sources of algorithmic instability.
First, we study theoretical bounds on the performance of approximate dynamic
programming algorithms. Second, we provide a novel four-state MDP that
highlights the impact of the data distribution in the performance of a
Q-learning algorithm with function approximation, both in online and offline
settings. Finally, we experimentally assess the impact of the data distribution
properties in the performance of offline Q-learning-based algorithms. Our
results emphasize the key role played by data distribution properties while
regulating algorithmic stability. We provide a systematic and comprehensive
study of the topic by connecting different lines of research, as well as
validating and extending previous results, both theoretically and empirically.

We propose Guided-TTS, a high-quality text-to-speech (TTS) model that does
not require any transcript of target speaker using classifier guidance.
Guided-TTS combines an unconditional diffusion probabilistic model with a
separately trained phoneme classifier for classifier guidance. Our
unconditional diffusion model learns to generate speech without any context
from untranscribed speech data. For TTS synthesis, we guide the generative
process of the diffusion model with a phoneme classifier trained on a
large-scale speech recognition dataset. We present a norm-based scaling method
that reduces the pronunciation errors of classifier guidance in Guided-TTS. We
show that Guided-TTS achieves a performance comparable to that of the
state-of-the-art TTS model, Grad-TTS, without any transcript for LJSpeech. We
further demonstrate that Guided-TTS performs well on diverse datasets including
a long-form untranscribed dataset.

Contrastive learning has been studied for improving the performance of
learning sentence embeddings. The current state-of-the-art method is the
SimCSE, which takes dropout as the data augmentation method and feeds a
pre-trained transformer encoder the same input sentence twice. The
corresponding outputs, two sentence embeddings derived from the same sentence
with different dropout masks, can be used to build a positive pair. A network
being applied with a dropout mask can be regarded as a sub-network of itsef,
whose expected scale is determined by the dropout rate. In this paper, we push
sub-networks with different expected scales learn similar embedding for the
same sentence. SimCSE failed to do so because they fixed the dropout rate to a
tuned hyperparameter. We achieve this by sampling dropout rate from a
distribution eatch forward process. As this method may make optimization
harder, we also propose a simple sentence-wise mask strategy to sample more
sub-networks. We evaluated the proposed S-SimCSE on several popular semantic
text similarity datasets. Experimental results show that S-SimCSE outperforms
the state-of-the-art SimCSE more than $1\%$ on BERT$_{base}$

Knowledge distillation is an effective and stable method for model
compression via knowledge transfer. Conventional knowledge distillation (KD) is
to transfer knowledge from a large and well pre-trained teacher network to a
small student network, which is a one-way process. Recently, deep mutual
learning (DML) has been proposed to help student networks learn collaboratively
and simultaneously. However, to the best of our knowledge, KD and DML have
never been jointly explored in a unified framework to solve the knowledge
distillation problem. In this paper, we investigate that the teacher model
supports more trustworthy supervision signals in KD, while the student captures
more similar behaviors from the teacher in DML. Based on these observations, we
first propose to combine KD with DML in a unified framework. Furthermore, we
propose a Semi-Online Knowledge Distillation (SOKD) method that effectively
improves the performance of the student and the teacher. In this method, we
introduce the peer-teaching training fashion in DML in order to alleviate the
student's imitation difficulty, and also leverage the supervision signals
provided by the well-trained teacher in KD. Besides, we also show our framework
can be easily extended to feature-based distillation methods. Extensive
experiments on CIFAR-100 and ImageNet datasets demonstrate the proposed method

It has been a common practice to adopt the ResBlock, which learns the
difference between blurry and sharp image pairs, in end-to-end image deblurring
architectures. Reconstructing a sharp image from its blurry counterpart
requires changes regarding both low- and high-frequency information. Although
conventional ResBlock may have good abilities in capturing the high-frequency
components of images, it tends to overlook the low-frequency information.
Moreover, ResBlock usually fails to felicitously model the long-distance
information which is non-trivial in reconstructing a sharp image from its
blurry counterpart. In this paper, we present a Residual Fast Fourier Transform
with Convolution Block (Res FFT-Conv Block), capable of capturing both
long-term and short-term interactions, while integrating both low- and
high-frequency residual information. Res FFT-Conv Block is a conceptually
simple yet computationally efficient, and plug-and-play block, leading to
remarkable performance gains in different architectures. With Res FFT-Conv
Block, we further propose a Deep Residual Fourier Transformation (DeepRFT)
framework, based upon MIMO-UNet, achieving state-of-the-art image deblurring
performance on GoPro, HIDE, RealBlur and DPDD datasets. Experiments show our
DeepRFT can boost image deblurring performance significantly (e.g., with 1.09
dB improvement in PSNR on GoPro dataset compared with MIMO-UNet), and DeepRFT+
even reaches 33.23 dB in PSNR on GoPro dataset.

Reinforcement learning (RL) has recently achieved tremendous successes in
many artificial intelligence applications. Many of the forefront applications
of RL involve multiple agents, e.g., playing chess and Go games, autonomous
driving, and robotics. Unfortunately, the framework upon which classical RL
builds is inappropriate for multi-agent learning, as it assumes an agent's
environment is stationary and does not take into account the adaptivity of
other agents. In this review paper, we present the model of stochastic games
for multi-agent learning in dynamic environments. We focus on the development
of simple and independent learning dynamics for stochastic games: each agent is
myopic and chooses best-response type actions to other agents' strategy without
any coordination with her opponent. There has been limited progress on
developing convergent best-response type independent learning dynamics for
stochastic games. We present our recently proposed simple and independent
learning dynamics that guarantee convergence in zero-sum stochastic games,
together with a review of other contemporaneous algorithms for dynamic
multi-agent learning in this setting. Along the way, we also reexamine some
classical results from both the game theory and RL literature, to situate both
the conceptual contributions of our independent learning dynamics, and the
mathematical novelties of our analysis. We hope this review paper serves as an
impetus for the resurgence of studying independent and natural learning
dynamics in game theory, for the more challenging settings with a dynamic

Recent years have witnessed the increasing application of place recognition
in various environments, such as city roads, large buildings, and a mix of
indoor and outdoor places. This task, however, still remains challenging due to
the limitations of different sensors and the changing appearance of
environments. Current works only consider the use of individual sensors, or
simply combine different sensors, ignoring the fact that the importance of
different sensors varies as the environment changes. In this paper, an adaptive
weighting visual-LiDAR fusion method, named AdaFusion, is proposed to learn the
weights for both images and point cloud features. Features of these two
modalities are thus contributed differently according to the current
environmental situation. The learning of weights is achieved by the attention
branch of the network, which is then fused with the multi-modality feature
extraction branch. Furthermore, to better utilize the potential relationship
between images and point clouds, we design a twostage fusion approach to
combine the 2D and 3D attention. Our work is tested on two public datasets, and
experiments show that the adaptive weights help improve recognition accuracy
and system robustness to varying environments.

The state-of-the-art methods for drum transcription in the presence of
melodic instruments (DTM) are machine learning models trained in a supervised
manner, which means that they rely on labeled datasets. The problem is that the
available public datasets are limited either in size or in realism, and are
thus suboptimal for training purposes. Indeed, the best results are currently
obtained via a rather convoluted multi-step training process that involves both
real and synthetic datasets. To address this issue, starting from the
observation that the communities of rhythm games players provide a large amount
of annotated data, we curated a new dataset of crowdsourced drum
transcriptions. This dataset contains real-world music, is manually annotated,
and is about two orders of magnitude larger than any other non-synthetic
dataset, making it a prime candidate for training purposes. However, due to
crowdsourcing, the initial annotations contain mistakes. We discuss how the
quality of the dataset can be improved by automatically correcting different
types of mistakes. When used to train a popular DTM model, the dataset yields a
performance that matches that of the state-of-the-art for DTM, thus
demonstrating the quality of the annotations.

Camera gimbal systems are important in various air or water borne systems for
applications such as navigation, target tracking, security and surveillance. A
higher steering rate (rotation angle per second) of gimbal is preferable for
real-time applications since a given field-of-view (FOV) can be revisited
within a short period of time. However, due to relative motion between the
gimbal and scene during the exposure time, the captured video frames can suffer
from motion blur. Since most of the post-capture applications require blurfree
images, motion deblurring in real-time is an important need. Even though there
exist blind deblurring methods which aim to retrieve latent images from blurry
inputs, they are constrained by very high-dimensional optimization thus
incurring large execution times. On the other hand, deep learning methods for
motion deblurring, though fast, do not generalize satisfactorily to different
domains (e.g., air, water, etc). In this work, we address the problem of
real-time motion deblurring in infrared (IR) images captured by a gimbal-based
system. We reveal how a priori knowledge of the blur-kernel can be used in
conjunction with non-blind deblurring methods to achieve real-time performance.
Importantly, our mathematical model can be leveraged to create large-scale
datasets with realistic gimbal motion blur. Such datasets which are a rarity
can be a valuable asset for contemporary deep learning methods. We show that,
in comparison to the state-of-the-art techniques in deblurring, our method is
better suited for practical gimbal-based imaging systems.

As an emerging biological identification technology, vision-based gait
identification is an important research content in biometrics. Most existing
gait identification methods extract features from gait videos and identify a
probe sample by a query in the gallery. However, video data contains redundant
information and can be easily influenced by bagging (BG) and clothing (CL).
Since human body skeletons convey essential information about human gaits, a
skeleton-based gait identification network is proposed in our project. First,
extract skeleton sequences from the video and map them into a gait graph. Then
a feature extraction network based on Spatio-Temporal Graph Convolutional
Network (ST-GCN) is constructed to learn gait representations. Finally, the
probe sample is identified by matching with the most similar piece in the
gallery. We tested our method on the CASIA-B dataset. The result shows that our
approach is highly adaptive and gets the advanced result in BG, CL conditions,
and average.

Estimation of riverbed profiles, also known as bathymetry, plays a vital role
in many applications, such as safe and efficient inland navigation, prediction
of bank erosion, land subsidence, and flood risk management. The high cost and
complex logistics of direct bathymetry surveys, i.e., depth imaging, have
encouraged the use of indirect measurements such as surface flow velocities.
However, estimating high-resolution bathymetry from indirect measurements is an
inverse problem that can be computationally challenging. Here, we propose a
reduced-order model (ROM) based approach that utilizes a variational
autoencoder (VAE), a type of deep neural network with a narrow layer in the
middle, to compress bathymetry and flow velocity information and accelerate
bathymetry inverse problems from flow velocity measurements. In our
application, the shallow-water equations (SWE) with appropriate boundary
conditions (BCs), e.g., the discharge and/or the free surface elevation,
constitute the forward problem, to predict flow velocity. Then, ROMs of the
SWEs are constructed on a nonlinear manifold of low dimensionality through a
variational encoder. Estimation with uncertainty quantification (UQ) is
performed on the low-dimensional latent space in a Bayesian setting. We have
tested our inversion approach on a one-mile reach of the Savannah River, GA,
USA. Once the neural network is trained (offline stage), the proposed technique
can perform the inversion operation orders of magnitude faster than traditional
inversion methods that are commonly based on linear projections, such as
principal component analysis (PCA), or the principal component geostatistical
approach (PCGA). Furthermore, tests show that the algorithm can estimate the
bathymetry with good accuracy even with sparse flow velocity measurements.

Imitation learning (IL) is a framework that learns to imitate expert behavior
from demonstrations. Recently, IL shows promising results on high dimensional
and control tasks. However, IL typically suffers from sample inefficiency in
terms of environment interaction, which severely limits their application to
simulated domains. In industrial applications, learner usually have a high
interaction cost, the more interactions with environment, the more damage it
causes to the environment and the learner itself. In this article, we make an
effort to improve sample efficiency by introducing a novel scheme of inverse
reinforcement learning. Our method, which we call \textit{Model Reward Function
Based Imitation Learning} (MRFIL), uses an ensemble dynamic model as a reward
function, what is trained with expert demonstrations. The key idea is to
provide the agent with an incentive to match the demonstrations over a long
horizon, by providing a positive reward upon encountering states in line with
the expert demonstration distribution. In addition, we demonstrate the
convergence guarantee for new objective function. Experimental results show
that our algorithm reaches the competitive performance and significantly
reducing the environment interactions compared to IL methods.

The increasing amounts of semantic resources offer valuable storage of human
knowledge; however, the probability of wrong entries increases with the
increased size. The development of approaches that identify potentially
spurious parts of a given knowledge base is thus becoming an increasingly
important area of interest. In this work, we present a systematic evaluation of
whether structure-only link analysis methods can already offer a scalable means
to detecting possible anomalies, as well as potentially interesting novel
relation candidates. Evaluating thirteen methods on eight different semantic
resources, including Gene Ontology, Food Ontology, Marine Ontology and similar,
we demonstrated that structure-only link analysis could offer scalable anomaly
detection for a subset of the data sets. Further, we demonstrated that by
considering symbolic node embedding, explanations of the predictions (links)
could be obtained, making this branch of methods potentially more valuable than
the black-box only ones. To our knowledge, this is currently one of the most
extensive systematic studies of the applicability of different types of link
analysis methods across semantic resources from different domains.

As solar capacity installed worldwide continues to grow, there is an
increasing awareness that advanced inspection systems are becoming of utmost
importance to schedule smart interventions and minimize downtime likelihood. In
this work we propose a novel automatic multi-stage model to detect panel
defects on aerial images captured by unmanned aerial vehicle by using the
YOLOv3 network and Computer Vision techniques. The model combines detections of
panels and defects to refine its accuracy and exhibits an average inference
time per image of 0.98 s. The main novelties are represented by its versatility
to process either thermographic or visible images and detect a large variety of
defects, to prescript recommended actions to O&M crew to give a more efficient
data-driven maintenance strategy and its portability to both rooftop and
ground-mounted PV systems and different panel types. The proposed model has
been validated on two big PV plants in the south of Italy with an outstanding
AP@0.5 exceeding 98% for panel detection, a remarkable AP@0.4 (AP@0.5) of
roughly 88.3% (66.9%) for hotspots by means of infrared thermography and a
mAP@0.5 of almost 70% in the visible spectrum for detection of anomalies
including panel shading induced by soiling and bird dropping, delamination,
presence of puddles and raised rooftop panels. The model predicts also the
severity of hotspot areas based on the estimated temperature gradients, as well
as it computes the soiling coverage based on visual images. Finally an analysis
of the influence of the different YOLOv3's output scales on the detection is

Syntax knowledge contributes its powerful strength in Neural machine
translation (NMT) tasks. Early NMT works supposed that syntax details can be
automatically learned from numerous texts via attention networks. However,
succeeding researches pointed out that limited by the uncontrolled nature of
attention computation, the NMT model requires an external syntax to capture the
deep syntactic awareness. Although existing syntax-aware NMT methods have born
great fruits in combining syntax, the additional workloads they introduced
render the model heavy and slow. Particularly, these efforts scarcely involve
the Transformer-based NMT and modify its core self-attention network (SAN). To
this end, we propose a parameter-free, Dependency-scaled Self-Attention Network
(Deps-SAN) for syntax-aware Transformer-based NMT. A quantified matrix of
dependency closeness between tokens is constructed to impose explicit syntactic
constraints into the SAN for learning syntactic details and dispelling the
dispersion of attention distributions. Two knowledge sparsing techniques are
further integrated to avoid the model overfitting the dependency noises
introduced by the external parser. Experiments and analyses on IWSLT14
German-to-English and WMT16 German-to-English benchmark NMT tasks verify the

Some generative models for sequences such as music and text allow us to edit
only subsequences, given surrounding context sequences, which plays an
important part in steering generation interactively. However, editing
subsequences mainly involves randomly resampling subsequences from a possible
generation space. We propose a contextual latent space model (CLSM) in order
for users to be able to explore subsequence generation with a sense of
direction in the generation space, e.g., interpolation, as well as exploring
variations -- semantically similar possible subsequences. A context-informed
prior and decoder constitute the generative model of CLSM, and a context
position-informed encoder is the inference model. In experiments, we use a
monophonic symbolic music dataset, demonstrating that our contextual latent
space is smoother in interpolation than baselines, and the quality of generated
samples is superior to baseline models. The generation examples are available
online.

Fast and reliable prediction of river flow velocities is important in many
applications, including flood risk management. The shallow water equations
(SWEs) are commonly used for this purpose. However, traditional numerical
solvers of the SWEs are computationally expensive and require high-resolution
riverbed profile measurement (bathymetry). In this work, we propose a two-stage
process in which, first, using the principal component geostatistical approach
(PCGA) we estimate the probability density function of the bathymetry from flow
velocity measurements, and then use machine learning (ML) algorithms to obtain
a fast solver for the SWEs. The fast solver uses realizations from the
posterior bathymetry distribution and takes as input the prescribed range of
BCs. The first stage allows us to predict flow velocities without direct
measurement of the bathymetry. Furthermore, we augment the bathymetry posterior
distribution to a more general class of distributions before providing them as
inputs to ML algorithm in the second stage. This allows the solver to
incorporate future direct bathymetry measurements into the flow velocity
prediction for improved accuracy, even if the bathymetry changes over time
compared to its original indirect estimation. We propose and benchmark three
different solvers, referred to as PCA-DNN (principal component analysis-deep
neural network), SE (supervised encoder), and SVE (supervised variational
encoder), and validate them on the Savannah river, Augusta, GA. Our results
show that the fast solvers are capable of predicting flow velocities for
different bathymetry and BCs with good accuracy, at a computational cost that
is significantly lower than the cost of solving the full boundary value problem
with traditional methods.

Unsupervised cross domain adaptation for OCT 7 layer segmentation and other
medical applications where labeled training data is only available in a source
domain and unavailable in the target domain. Our proposed method helps
generalize of deep learning to many areas in the medical field where labeled
training data are expensive and time consuming to acquire or where target
domains are too novel to have had labelling.

MARS is a popular method for nonparametric regression introduced by Friedman
in 1991. MARS fits simple nonlinear and non-additive functions to regression
data. We propose and study a natural LASSO variant of the MARS method. Our
method is based on least squares estimation over a convex class of functions
obtained by considering infinite-dimensional linear combinations of functions
in the MARS basis and imposing a variation based complexity constraint. We show
that our estimator can be computed via finite-dimensional convex optimization
and that it is naturally connected to nonparametric function estimation
techniques based on smoothness constraints. Under a simple design assumption,
we prove that our estimator achieves a rate of convergence that depends only
logarithmically on dimension and thus avoids the usual curse of dimensionality
to some extent. We implement our method with a cross-validation scheme for the
selection of the involved tuning parameter and show that it has favorable
performance compared to the usual MARS method in simulation and real data
settings.

Individual rationality, which involves maximizing expected individual
returns, does not always lead to high-utility individual or group outcomes in
multi-agent problems. For instance, in multi-agent social dilemmas,
Reinforcement Learning (RL) agents trained to maximize individual rewards
converge to a low-utility mutually harmful equilibrium. In contrast, humans
evolve useful strategies in such social dilemmas. Inspired by ideas from human
psychology that attribute this behavior to the status-quo bias, we present a
status-quo loss (SQLoss) and the corresponding policy gradient algorithm that
incorporates this bias in an RL agent. We demonstrate that agents trained with
SQLoss learn high-utility policies in several social dilemma matrix games
(Prisoner's Dilemma, Stag Hunt matrix variant, Chicken Game). We show how
SQLoss outperforms existing state-of-the-art methods to obtain high-utility
policies in visual input non-matrix games (Coin Game and Stag Hunt visual input
variant) using pre-trained cooperation and defection oracles. Finally, we show
that SQLoss extends to a 4-agent setting by demonstrating the emergence of
cooperative behavior in the popular Braess' paradox.

Matrix factorization (MF) can extract the low-rank features and integrate the
information of the data manifold distribution from high-dimensional data, which
can consider the nonlinear neighbourhood information. Thus, MF has drawn wide
attention for low-rank analysis of sparse big data, e.g., Collaborative
Filtering (CF) Recommender Systems, Social Networks, and Quality of Service.
However, the following two problems exist: 1) huge computational overhead for
the construction of the Graph Similarity Matrix (GSM), and 2) huge memory
overhead for the intermediate GSM. Therefore, GSM-based MF, e.g., kernel MF,
graph regularized MF, etc., cannot be directly applied to the low-rank analysis
of sparse big data on cloud and edge platforms. To solve this intractable
problem for sparse big data analysis, we propose Locality Sensitive Hashing
(LSH) aggregated MF (LSH-MF), which can solve the following problems: 1) The
proposed probabilistic projection strategy of LSH-MF can avoid the construction
of the GSM. Furthermore, LSH-MF can satisfy the requirement for the accurate
projection of sparse big data. 2) To run LSH-MF for fine-grained
parallelization and online learning on GPUs, we also propose CULSH-MF, which
works on CUDA parallelization. Experimental results show that CULSH-MF can not
only reduce the computational time and memory overhead but also obtain higher
accuracy. Compared with deep learning models, CULSH-MF can not only save
training time but also achieve the same accuracy performance.

This paper introduces rotation-equivariance as a self-supervisor to train
inertial odometry models. We demonstrate that the self-supervised scheme
provides a powerful supervisory signal at training phase as well as at
inference stage. It reduces the reliance on massive amounts of labeled data for
training a robust model and makes it possible to update the model using various
unlabeled data. Further, we propose adaptive Test-Time Training (TTT) based on
uncertainty estimations in order to enhance the generalizability of the
inertial odometry to various unseen data. We show in experiments that the
Rotation-equivariance-supervised Inertial Odometry (RIO) trained with 30% data
achieves on par performance with a model trained with the whole database.
Adaptive TTT improves models performance in all cases and makes more than 25%
improvements under several scenarios.

Two current methods used to train autonomous cars are reinforcement learning
and imitation learning. This research develops a new learning methodology and
systematic approach in both a simulated and a smaller real world environment by
integrating supervised imitation learning into reinforcement learning to make
the RL training data collection process more effective and efficient. By
combining the two methods, the proposed research successfully leverages the
advantages of both RL and IL methods. First, a real mini-scale robot car was
assembled and trained on a 6 feet by 9 feet real world track using imitation
learning. During the process, a handle controller was used to control the
mini-scale robot car to drive on the track by imitating a human expert driver
and manually recorded the actions using Microsoft AirSim's API. 331 accurate
human-like reward training samples were able to be generated and collected.
Then, an agent was trained in the Microsoft AirSim simulator using
reinforcement learning for 6 hours with the initial 331 reward data inputted
from imitation learning training. After a 6-hour training period, the
mini-scale robot car was able to successfully drive full laps around the 6 feet
by 9 feet track autonomously while the mini-scale robot car was unable to
complete one full lap round the track even after 30 hour training pure RL
training. With 80% less training time, the new methodology produced
significantly more average rewards per hour. Thus, the new methodology was able
to save a significant amount of training time and can be used to accelerate the
adoption of RL in autonomous driving, which would help produce more efficient
and better results in the long run when applied to real life scenarios.
  Key Words: Reinforcement Learning (RL), Imitation Learning (IL), Autonomous
Driving, Human Driving Data, CNN

Producing diverse and realistic images with generative models such as GANs
typically requires large scale training with vast amount of images. GANs
trained with limited data can easily memorize few training samples and display
undesirable properties like "stairlike" latent space where interpolation in the
latent space yields discontinuous transitions in the output space. In this
work, we consider a challenging task of pretraining-free few-shot image
synthesis, and seek to train existing generative models with minimal
overfitting and mode collapse. We propose mixup-based distance regularization
on the feature space of both a generator and the counterpart discriminator that
encourages the two players to reason not only about the scarce observed data
points but the relative distances in the feature space they reside. Qualitative
and quantitative evaluation on diverse datasets demonstrates that our method is
generally applicable to existing models to enhance both fidelity and diversity
under few-shot setting. Code is available.

Despite the routine use of electronic health record (EHR) data by
radiologists to contextualize clinical history and inform image interpretation,
the majority of deep learning architectures for medical imaging are unimodal,
i.e., they only learn features from pixel-level information. Recent research
revealing how race can be recovered from pixel data alone highlights the
potential for serious biases in models which fail to account for demographics
and other key patient attributes. Yet the lack of imaging datasets which
capture clinical context, inclusive of demographics and longitudinal medical
history, has left multimodal medical imaging underexplored. To better assess
these challenges, we present RadFusion, a multimodal, benchmark dataset of 1794
patients with corresponding EHR data and high-resolution computed tomography
(CT) scans labeled for pulmonary embolism. We evaluate several representative
multimodal fusion models and benchmark their fairness properties across
protected subgroups, e.g., gender, race/ethnicity, age. Our results suggest
that integrating imaging and EHR data can improve classification performance
and robustness without introducing large disparities in the true positive rate
between population groups.

Changes in cardiovascular hemodynamics are closely related to the development
of aortic regurgitation, a type of valvular heart disease. Metrics derived from
blood flows are used to indicate aortic regurgitation onset and evaluate its
severity. These metrics can be non-invasively obtained using four-dimensional
(4D) flow magnetic resonance imaging (MRI), where accuracy is primarily
dependent on spatial resolution. However, insufficient resolution often results
from limitations in 4D flow MRI and complex aortic regurgitation hemodynamics.
To address this, computational fluid dynamics simulations were transformed into
synthetic 4D flow MRI data and used to train a variety of neural networks.
These networks generated super resolution, full-field phase images with an
upsample factor of 4. Results showed decreased velocity error, high structural
similarity scores, and improved learning capabilities from previous work.
Further validation was performed on two sets of in-vivo 4D flow MRI data and
demonstrated success in de-noising flow images. This approach presents an
opportunity to comprehensively analyse aortic regurgitation hemodynamics in a
non-invasive manner.

Object detection has achieved substantial progress in the last decade.
However, detecting novel classes with only few samples remains challenging,
since deep learning under low data regime usually leads to a degraded feature
space. Existing works employ a holistic fine-tuning paradigm to tackle this
problem, where the model is first pre-trained on all base classes with abundant
samples, and then it is used to carve the novel class feature space.
Nonetheless, this paradigm is still imperfect. Durning fine-tuning, a novel
class may implicitly leverage the knowledge of multiple base classes to
construct its feature space, which induces a scattered feature space, hence
violating the inter-class separability. To overcome these obstacles, we propose
a two-step fine-tuning framework, Few-shot object detection via Association and
DIscrimination (FADI), which builds up a discriminative feature space for each
novel class with two integral steps. 1) In the association step, in contrast to
implicitly leveraging multiple base classes, we construct a compact novel class
feature space via explicitly imitating a specific base class feature space.
Specifically, we associate each novel class with a base class according to
their semantic similarity. After that, the feature space of a novel class can
readily imitate the well-trained feature space of the associated base class. 2)
In the discrimination step, to ensure the separability between the novel
classes and associated base classes, we disentangle the classification branches
for base and novel classes. To further enlarge the inter-class separability
between all classes, a set-specialized margin loss is imposed. Extensive
experiments on Pascal VOC and MS-COCO datasets demonstrate FADI achieves new
SOTA performance, significantly improving the baseline in any shot/split by
+18.7. Notably, the advantage is most announced on extremely few-shot

In this study, we develop a method for multi-task manifold learning. The
method aims to improve the performance of manifold learning for multiple tasks,
particularly when each task has a small number of samples. Furthermore, the
method also aims to generate new samples for new tasks, in addition to new
samples for existing tasks. In the proposed method, we use two different types
of information transfer: instance transfer and model transfer. For instance
transfer, datasets are merged among similar tasks, whereas for model transfer,
the manifold models are averaged among similar tasks. For this purpose, the
proposed method consists of a set of generative manifold models corresponding
to the tasks, which are integrated into a general model of a fiber bundle. We
applied the proposed method to artificial datasets and face image sets, and the
results showed that the method was able to estimate the manifolds, even for a
tiny number of samples.

In this work we use variational inference to quantify the degree of epistemic
uncertainty in model predictions of radio galaxy classification and show that
the level of model posterior variance for individual test samples is correlated
with human uncertainty when labelling radio galaxies. We explore the model
performance and uncertainty calibration for a variety of different weight
priors and suggest that a sparse prior produces more well-calibrated
uncertainty estimates. Using the posterior distributions for individual
weights, we show that signal-to-noise ratio (SNR) ranking allows pruning of the
fully-connected layers to the level of 30% without significant loss of
performance, and that this pruning increases the predictive uncertainty in the
model. Finally we show that, like other work in this field, we experience a
cold posterior effect. We examine whether adapting the cost function in our
model to accommodate model misspecification can compensate for this effect, but
find that it does not make a significant difference. We also examine the effect
of principled data augmentation and find that it improves upon the baseline but
does not compensate for the observed effect fully. We interpret this as the
cold posterior effect being due to the overly effective curation of our
training sample leading to likelihood misspecification, and raise this as a
potential issue for Bayesian deep learning approaches to radio galaxy
classification in future.

Event analysis in untrimmed videos has attracted increasing attention due to
the application of cutting-edge techniques such as CNN. As a well studied
property for CNN-based models, the receptive field is a measurement for
measuring the spatial range covered by a single feature response, which is
crucial in improving the image categorization accuracy. In video domain, video
event semantics are actually described by complex interaction among different
concepts, while their behaviors vary drastically from one video to another,
leading to the difficulty in concept-based analytics for accurate event
categorization. To model the concept behavior, we study temporal concept
receptive field of concept-based event representation, which encodes the
temporal occurrence pattern of different mid-level concepts. Accordingly, we
introduce temporal dynamic convolution (TDC) to give stronger flexibility to
concept-based event analytics. TDC can adjust the temporal concept receptive
field size dynamically according to different inputs. Notably, a set of
coefficients are learned to fuse the results of multiple convolutions with
different kernel widths that provide various temporal concept receptive field
sizes. Different coefficients can generate appropriate and accurate temporal
concept receptive field size according to input videos and highlight crucial
concepts. Based on TDC, we propose the temporal dynamic concept modeling
network (TDCMN) to learn an accurate and complete concept representation for
efficient untrimmed video analysis. Experiment results on FCVID and ActivityNet
show that TDCMN demonstrates adaptive event recognition ability conditioned on
different inputs, and improve the event recognition performance of
Concept-based methods by a large margin. Code is available at
https://github.com/qzhb/TDCMN.

Labels are costly and sometimes unreliable. Noisy label learning,
semi-supervised learning, and contrastive learning are three different
strategies for designing learning processes requiring less annotation cost.
Semi-supervised learning and contrastive learning have been recently
demonstrated to improve learning strategies that address datasets with noisy
labels. Still, the inner connections between these fields as well as the
potential to combine their strengths together have only started to emerge. In
this paper, we explore further ways and advantages to fuse them. Specifically,
we propose CSSL, a unified Contrastive Semi-Supervised Learning algorithm, and
CoDiM (Contrastive DivideMix), a novel algorithm for learning with noisy
labels. CSSL leverages the power of classical semi-supervised learning and
contrastive learning technologies and is further adapted to CoDiM, which learns
robustly from multiple types and levels of label noise. We show that CoDiM
brings consistent improvements and achieves state-of-the-art results on
multiple benchmarks.

We report a bio-inspired framework for training a neural network through
reinforcement learning to induce high level functions within the network. Based
on the interpretation that animals have gained their cognitive functions such
as object recognition - without ever being specifically trained for - as a
result of maximizing their fitness to the environment, we place our agent in an
environment where developing certain functions may facilitate decision making.
The experimental results show that high level functions, such as image
classification and hidden variable estimation, can be naturally and
simultaneously induced without any pre-training or specifying them.

Motivation: In recent years, image-based biological assays have steadily
become high-throughput, sparking a need for fast automated methods to extract
biologically-meaningful information from hundreds of thousands of images.
Taking inspiration from the success of ImageNet, we curate CytoImageNet, a
large-scale dataset of openly-sourced and weakly-labeled microscopy images
(890K images, 894 classes). Pretraining on CytoImageNet yields features that
are competitive to ImageNet features on downstream microscopy classification
tasks. We show evidence that CytoImageNet features capture information not
available in ImageNet-trained features. The dataset is made available at
https://www.kaggle.com/stanleyhua/cytoimagenet.

The isolation forest algorithm for outlier detection exploits a simple yet
effective observation: if taking some multivariate data and making uniformly
random cuts across the feature space recursively, it will take fewer such
random cuts for an outlier to be left alone in a given subspace as compared to
regular observations. The original idea proposed an outlier score based on the
tree depth (number of random cuts) required for isolation, but experiments here
show that using information about the size of the feature space taken and the
number of points assigned to it can result in improved results in many
situations without any modification to the tree structure, especially in the
presence of categorical features.

Graph Neural Networks (GNNs) have shown success in learning from graph
structured data containing node/edge feature information, with application to
social networks, recommendation, fraud detection and knowledge graph reasoning.
In this regard, various strategies have been proposed in the past to improve
the expressiveness of GNNs. For example, one straightforward option is to
simply increase the parameter size by either expanding the hid-den dimension or
increasing the number of GNN layers. However, wider hidden layers can easily
lead to overfitting, and incrementally adding more GNN layers can potentially
result in over-smoothing.In this paper, we present a model-agnostic
methodology, namely Network In Graph Neural Network (NGNN ), that allows
arbitrary GNN models to increase their model capacity by making the model
deeper. However, instead of adding or widening GNN layers, NGNN deepens a GNN
model by inserting non-linear feedforward neural network layer(s) within each
GNN layer. An analysis of NGNN as applied to a GraphSage base GNN on
ogbn-products data demonstrate that it can keep the model stable against either
node feature or graph structure perturbations. Furthermore, wide-ranging
evaluation results on both node classification and link prediction tasks show
that NGNN works reliably across diverse GNN architectures.For instance, it
improves the test accuracy of GraphSage on the ogbn-products by 1.6% and
improves the hits@100 score of SEAL on ogbl-ppa by 7.08% and the hits@20 score
of GraphSage+Edge-Attr on ogbl-ppi by 6.22%. And at the time of this
submission, it achieved two first places on the OGB link prediction

The constant population growth brings the needing to make up for food also
grows at the same rate. The livestock provides one-third of humans protein base
as meat and milk. To improve cattles health and welfare the pastoral farming
employs Precision Livestock farming (PLF). This technique implementation brings
a challenge to minimize energy consumption due to farmers not having enough
energy or devices to transmit large volumes of information at the size are
received from their farms monitors. Therefore, in this project, we will design
an algorithm to compress and decompress images reducing energy consumption with
the less information lost. Initially, the related problems have been read and
analyzed to learn about the techniques used in the past and to be updated with
the current works. We implemented Seam Carving and LZW algorithms. The
compression of all images, around 1000 takes a time of 5 hours 10 min. We got a
compression rate of 1.82:1 with 13.75s average time for each file and a
decompression rate of 1.64:1 and 7.5 s average time for each file. The memory
consumption we obtained was between 146MB and 504 MB and time consumption was
between 30,5s for 90MB to 12192s for 24410 MB, it was all files.

Music classification is a music information retrieval (MIR) task to classify
music items to labels such as genre, mood, and instruments. It is also closely
related to other concepts such as music similarity and musical preference. In
this tutorial, we put our focus on two directions - the recent training schemes
beyond supervised learning and the successful application of music
  The target audience for this web book is researchers and practitioners who
are interested in state-of-the-art music classification research and building
real-world applications. We assume the audience is familiar with the basic
machine learning concepts.
  In this book, we present three lectures as follows: 1. Music classification
overview: Task definition, applications, existing approaches, datasets, 2.
Beyond supervised learning: Semi- and self-supervised learning for music
classification, 3. Towards real-world applications: Less-discussed, yet
important research issues in practice.

Despite extensive progress on image generation, common deep generative model
architectures are not easily applied to lossless compression. For example, VAEs
suffer from a compression cost overhead due to their latent variables. This
overhead can only be partially eliminated with elaborate schemes such as
bits-back coding, often resulting in poor single-sample compression rates. To
overcome such problems, we establish a new class of tractable lossless
compression models that permit efficient encoding and decoding: Probabilistic
Circuits (PCs). These are a class of neural networks involving $|p|$
computational units that support efficient marginalization over arbitrary
subsets of the $D$ feature dimensions, enabling efficient arithmetic coding. We
derive efficient encoding and decoding schemes that both have time complexity
$\mathcal{O} (\log(D) \cdot |p|)$, where a naive scheme would have linear costs
in $D$ and $|p|$, making the approach highly scalable. Empirically, our
PC-based (de)compression algorithm runs 5-40 times faster than neural
compression algorithms that achieve similar bitrates. By scaling up the
traditional PC structure learning pipeline, we achieve state-of-the-art results
on image datasets such as MNIST. Furthermore, PCs can be naturally integrated
with existing neural compression algorithms to improve the performance of these
base models on natural image datasets. Our results highlight the potential
impact that non-standard learning architectures may have on neural data

Future activity anticipation is a challenging problem in egocentric vision.
As a standard future activity anticipation paradigm, recursive sequence
prediction suffers from the accumulation of errors. To address this problem, we
propose a simple and effective Self-Regulated Learning framework, which aims to
regulate the intermediate representation consecutively to produce
representation that (a) emphasizes the novel information in the frame of the
current time-stamp in contrast to previously observed content, and (b) reflects
its correlation with previously observed frames. The former is achieved by
minimizing a contrastive loss, and the latter can be achieved by a dynamic
reweighing mechanism to attend to informative frames in the observed content
with a similarity comparison between feature of the current frame and observed
frames. The learned final video representation can be further enhanced by
multi-task learning which performs joint feature learning on the target
activity labels and the automatically detected action and object class tokens.
SRL sharply outperforms existing state-of-the-art in most cases on two
egocentric video datasets and two third-person video datasets. Its
effectiveness is also verified by the experimental fact that the action and
object concepts that support the activity semantics can be accurately

A natural notion of rationality/consistency for aggregating models is that,
for all (possibly aggregated) models $A$ and $B$, if the output of model $A$ is
$f(A)$ and if the output model $B$ is $f(B)$, then the output of the model
obtained by aggregating $A$ and $B$ must be a weighted average of $f(A)$ and
$f(B)$. Similarly, a natural notion of rationality for aggregating preferences
of ensembles of experts is that, for all (possibly aggregated) experts $A$ and
$B$, and all possible choices $x$ and $y$, if both $A$ and $B$ prefer $x$ over
$y$, then the expert obtained by aggregating $A$ and $B$ must also prefer $x$
over $y$. Rational aggregation is an important element of uncertainty
quantification, and it lies behind many seemingly different results in economic
theory: spanning social choice, belief formation, and individual decision
making. Three examples of rational aggregation rules are as follows. (1) Give
each individual model (expert) a weight (a score) and use weighted averaging to
aggregate individual or finite ensembles of models (experts). (2) Order/rank
individual model (expert) and let the aggregation of a finite ensemble of
individual models (experts) be the highest-ranked individual model (expert) in
that ensemble. (3) Give each individual model (expert) a weight, introduce a
weak order/ranking over the set of models/experts, aggregate $A$ and $B$ as the
weighted average of the highest-ranked models (experts) in $A$ or $B$. Note
that (1) and (2) are particular cases of (3). In this paper, we show that all
rational aggregation rules are of the form (3). This result unifies aggregation
procedures across different economic environments. Following the main
representation, we show applications and extensions of our representation in
various separated economics topics such as belief formation, choice theory, and
social welfare economics.

Semi-supervised learning has made significant strides in the medical domain
since it alleviates the heavy burden of collecting abundant pixel-wise
annotated data for semantic segmentation tasks. Existing semi-supervised
approaches enhance the ability to extract features from unlabeled data with
prior knowledge obtained from limited labeled data. However, due to the
scarcity of labeled data, the features extracted by the models are limited in
supervised learning, and the quality of predictions for unlabeled data also
cannot be guaranteed. Both will impede consistency training. To this end, we
proposed a novel uncertainty-aware scheme to make models learn regions
purposefully. Specifically, we employ Monte Carlo Sampling as an estimation
method to attain an uncertainty map, which can serve as a weight for losses to
force the models to focus on the valuable region according to the
characteristics of supervised learning and unsupervised learning.
Simultaneously, in the backward process, we joint unsupervised and supervised
losses to accelerate the convergence of the network via enhancing the gradient
flow between different tasks. Quantitatively, we conduct extensive experiments
on three challenging medical datasets. Experimental results show desirable
improvements to state-of-the-art counterparts.

Recently, template-based trackers have become the leading tracking algorithms
with promising performance in terms of efficiency and accuracy. However, the
correlation operation between query feature and the given template only
exploits accurate target localization, leading to state estimation error
especially when the target suffers from severe deformable variations. To
address this issue, segmentation-based trackers have been proposed that employ
per-pixel matching to improve the tracking performance of deformable objects
effectively. However, most of existing trackers only refer to the target
features in the initial frame, thereby lacking the discriminative capacity to
handle challenging factors, e.g., similar distractors, background clutter,
appearance change, etc. To this end, we propose a dynamic compact memory
embedding to enhance the discrimination of the segmentation-based deformable
visual tracking method. Specifically, we initialize a memory embedding with the
target features in the first frame. During the tracking process, the current
target features that have high correlation with existing memory are updated to
the memory embedding online. To further improve the segmentation accuracy for
deformable objects, we employ a point-to-global matching strategy to measure
the correlation between the pixel-wise query features and the whole template,
so as to capture more detailed deformation information. Extensive evaluations
on six challenging tracking benchmarks including VOT2016, VOT2018, VOT2019,
GOT-10K, TrackingNet, and LaSOT demonstrate the superiority of our method over
recent remarkable trackers. Besides, our method outperforms the excellent
segmentation-based trackers, i.e., D3S and SiamMask on DAVIS2017 benchmark.

The structure of many complex networks includes edge directionality and
weights on top of their topology. Network analysis that can seamlessly consider
combination of these properties are desirable. In this paper, we study two
important such network analysis techniques, namely, centrality and clustering.
An information-flow based model is adopted for clustering, which itself builds
upon an information theoretic measure for computing centrality. Our principal
contributions include a generalized model of Markov entropic centrality with
the flexibility to tune the importance of node degrees, edge weights and
directions, with a closed-form asymptotic analysis. It leads to a novel
two-stage graph clustering algorithm. The centrality analysis helps reason
about the suitability of our approach to cluster a given graph, and determine
`query' nodes, around which to explore local community structures, leading to
an agglomerative clustering mechanism. The entropic centrality computations are
amortized by our clustering algorithm, making it computationally efficient:
compared to prior approaches using Markov entropic centrality for clustering,
our experiments demonstrate multiple orders of magnitude of speed-up. Our
clustering algorithm naturally inherits the flexibility to accommodate edge
directionality, as well as different interpretations and interplay between edge
weights and node degrees. Overall, this paper thus not only makes significant
theoretical and conceptual contributions, but also translates the findings into
artifacts of practical relevance, yielding new, effective and scalable
centrality computations and graph clustering algorithms, whose efficacy has
been validated through extensive benchmarking experiments.

While novel computer vision architectures are gaining traction, the impact of
model architectures is often related to changes or exploring in training
methods. Identity mapping-based architectures ResNets and DenseNets have
promised path-breaking results in the image classification task and are go-to
methods for even now if the data given is fairly limited. Considering the ease
of training with limited resources this work revisits the ResNets and improves
the ResNet50 \cite{resnets} by using mixup data-augmentation as regularization
and tuning the hyper-parameters.

Deep learning based speech enhancement has made rapid development towards
improving quality, while models are becoming more compact and usable for
real-time on-the-edge inference. However, the speech quality scales directly
with the model size, and small models are often still unable to achieve
sufficient quality. Furthermore, the introduced speech distortion and artifacts
greatly harm speech quality and intelligibility, and often significantly
degrade automatic speech recognition (ASR) rates. In this work, we shed light
on the success of the spectral complex compressed mean squared error (MSE)
loss, and how its magnitude and phase-aware terms are related to the speech
distortion vs. noise reduction trade off. We further investigate integrating
pre-trained reference-less predictors for mean opinion score (MOS) and word
error rate (WER), and pre-trained embeddings on ASR and sound event detection.
Our analyses reveal that none of the pre-trained networks added significant
performance over the strong spectral loss.

With many practical applications in human life, including manufacturing
surveillance cameras, analyzing and processing customer behavior, many
researchers are noticing face detection and head pose estimation on digital
images. A large number of proposed deep learning models have state-of-the-art
accuracy such as YOLO, SSD, MTCNN, solving the problem of face detection or
HopeNet, FSA-Net, RankPose model used for head pose estimation problem.
According to many state-of-the-art methods, the pipeline of this task consists
of two parts, from face detection to head pose estimation. These two steps are
completely independent and do not share information. This makes the model clear
in setup but does not leverage most of the featured resources extracted in each
model. In this paper, we proposed the Multitask-Net model with the motivation
to leverage the features extracted from the face detection model, sharing them
with the head pose estimation branch to improve accuracy. Also, with the
variety of data, the Euler angle domain representing the face is large, our
model can predict with results in the 360 Euler angle domain. Applying the
multitask learning method, the Multitask-Net model can simultaneously predict
the position and direction of the human head. To increase the ability to
predict the head direction of the model, we change there presentation of the
human face from the Euler angle to vectors of the Rotation matrix.

We propose techniques to incorporate coarse taxonomic labels to train image
classifiers in fine-grained domains. Such labels can often be obtained with a
smaller effort for fine-grained domains such as the natural world where
categories are organized according to a biological taxonomy. On the Semi-iNat
dataset consisting of 810 species across three Kingdoms, incorporating Phylum
labels improves the Species level classification accuracy by 6% in a transfer
learning setting using ImageNet pre-trained models. Incorporating the
hierarchical label structure with a state-of-the-art semi-supervised learning
algorithm called FixMatch improves the performance further by 1.3%. The
relative gains are larger when detailed labels such as Class or Order are
provided, or when models are trained from scratch. However, we find that most
methods are not robust to the presence of out-of-domain data from novel
classes. We propose a technique to select relevant data from a large collection
of unlabeled images guided by the hierarchy which improves the robustness.
Overall, our experiments show that semi-supervised learning with coarse
taxonomic labels are practical for training classifiers in fine-grained
domains.

AutoML (automated machine learning) has been extensively developed in the
past few years for the model-centric approach. As for the data-centric
approach, the processes to improve the dataset, such as fixing incorrect
labels, adding examples that represent edge cases, and applying data
augmentation, are still very artisanal and expensive. Here we develop an
automated data-centric tool (AutoDC), similar to the purpose of AutoML, aims to
speed up the dataset improvement processes. In our preliminary tests on 3 open
source image classification datasets, AutoDC is estimated to reduce roughly 80%
of the manual time for data improvement tasks, at the same time, improve the
model accuracy by 10-15% with the fixed ML code.

In this work, we introduce a novel workflow that couples robotics to
machine-learning for efficient optimization of a non-aqueous battery
electrolyte. A custom-built automated experiment named "Clio" is coupled to
Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously
optimizes electrolyte conductivity over a single-salt, ternary solvent design
space. Using this workflow, we identify 6 fast-charging electrolytes in 2
work-days and 42 experiments (compared with 60 days using exhaustive search of
the 1000 possible candidates, or 6 days assuming only 10% of candidates are
evaluated). Our method finds the highest reported conductivity electrolyte in a
design space heavily explored by previous literature, converging on a
high-conductivity mixture that demonstrates subtle electrolyte chemical
physics.

Estimating porosity models via seismic data is challenging due to the signal
noise and insufficient resolution of seismic data. Although impedance inversion
is often used by combining with well logs, several hurdles remain to retrieve
sub-seismic scale porosity. As an alternative, we propose a machine
learning-based workflow to convert seismic data to porosity models. A ResUNet++
based workflow is designed to take three seismic data in different frequencies
(i.e., decomposed seismic data) and estimate their corresponding porosity
model. The workflow is successfully demonstrated in the 3D channelized
reservoir to estimate the porosity model with more than 0.9 in R2 score for
training and validating data. Moreover, the application is extended for a
stress test by adding signal noise to the seismic data, and the workflow
results show a robust estimation even with 5\% of noise. Another two ResUNet++
are trained to take either the lowest or highest resolution seismic data only
to estimate the porosity model, but they show under- and over-fitting results,
supporting the importance of using decomposed seismic data in porosity

Weight pruning is an effective model compression technique to tackle the
challenges of achieving real-time deep neural network (DNN) inference on mobile
devices. However, prior pruning schemes have limited application scenarios due
to accuracy degradation, difficulty in leveraging hardware acceleration, and/or
restriction on certain types of DNN layers. In this paper, we propose a
general, fine-grained structured pruning scheme and corresponding compiler
optimizations that are applicable to any type of DNN layer while achieving high
accuracy and hardware inference performance. With the flexibility of applying
different pruning schemes to different layers enabled by our compiler
optimizations, we further probe into the new problem of determining the
best-suited pruning scheme considering the different acceleration and accuracy
performance of various pruning schemes. Two pruning scheme mapping methods, one
is search-based and the other is rule-based, are proposed to automatically
derive the best-suited pruning regularity and block size for each layer of any
given DNN. Experimental results demonstrate that our pruning scheme mapping
methods, together with the general fine-grained structured pruning scheme,
outperform the state-of-the-art DNN optimization framework with up to
2.48$\times$ and 1.73$\times$ DNN inference acceleration on CIFAR-10 and
ImageNet dataset without accuracy loss.

Space exploration has always been a source of inspiration for humankind, and
thanks to modern telescopes, it is now possible to observe celestial bodies far
away from us. With a growing number of real and imaginary images of space
available on the web and exploiting modern deep Learning architectures such as
Generative Adversarial Networks, it is now possible to generate new
representations of space. In this research, using a Lightweight GAN, a dataset
of images obtained from the web, and the Galaxy Zoo Dataset, we have generated
thousands of new images of celestial bodies, galaxies, and finally, by
combining them, a wide view of the universe. The code for reproducing our
results is publicly available at
https://github.com/davide-coccomini/GAN-Universe, and the generated images can
be explored at https://davide-coccomini.github.io/GAN-Universe/.

Most popular goal-oriented dialogue agents are capable of understanding the
conversational context. However, with the surge of virtual assistants with
screen, the next generation of agents are required to also understand screen
context in order to provide a proper interactive experience, and better
understand users' goals. In this paper, we propose a novel multimodal
conversational framework, where the dialogue agent's next action and their
arguments are derived jointly conditioned both on the conversational and the
visual context. Specifically, we propose a new model, that can reason over the
visual context within a conversation and populate API arguments with visual
entities given the user query. Our model can recognize visual features such as
color and shape as well as the metadata based features such as price or star
rating associated with a visual entity. In order to train our model, due to a
lack of suitable multimodal conversational datasets, we also propose a novel
multimodal dialog simulator to generate synthetic data and also collect
realistic user data from MTurk to improve model robustness. The proposed model
achieves a reasonable 85% model accuracy, without high inference latency. We
also demonstrate the proposed approach in a prototypical furniture shopping
experience for a multimodal virtual assistant.

Federated Learning (FL) is an increasingly popular machine learning paradigm
in which multiple nodes try to collaboratively learn under privacy,
communication and multiple heterogeneity constraints. A persistent problem in
federated learning is that it is not clear what the optimization objective
should be: the standard average risk minimization of supervised learning is
inadequate in handling several major constraints specific to federated
learning, such as communication adaptivity and personalization control. We
identify several key desiderata in frameworks for federated learning and
introduce a new framework, FLIX, that takes into account the unique challenges
brought by federated learning. FLIX has a standard finite-sum form, which
enables practitioners to tap into the immense wealth of existing (potentially
non-local) methods for distributed optimization. Through a smart initialization
that does not require any communication, FLIX does not require the use of local
steps but is still provably capable of performing dissimilarity regularization
on par with local methods. We give several algorithms for solving the FLIX
formulation efficiently under communication constraints. Finally, we
corroborate our theoretical results with extensive experimentation.

We report the automatic detection of 11 transients (7 possible supernovae and
4 active galactic nuclei candidates) within the Zwicky Transient Facility
fourth data release (ZTF DR4), all of them observed in 2018 and absent from
public catalogs. Among these, three were not part of the ZTF alert stream. Our
transient mining strategy employs 41 physically motivated features extracted
from both real light curves and four simulated light curve models (SN Ia, SN
II, TDE, SLSN-I). These features are input to a k-D tree algorithm, from which
we calculate the 15 nearest neighbors. After pre-processing and selection cuts,
our dataset contained approximately a million objects among which we visually
inspected the 105 closest neighbors from seven of our brightest, most
well-sampled simulations, comprising 89 unique ZTF DR4 sources. Our result
illustrates the potential of coherently incorporating domain knowledge and
automatic learning algorithms, which is one of the guiding principles directing
the SNAD team. It also demonstrates that the ZTF DR is a suitable testing
ground for data mining algorithms aiming to prepare for the next generation of
astronomical data.

Operating systems include many heuristic algorithms designed to improve
overall storage performance and throughput. Because such heuristics cannot work
well for all conditions and workloads, system designers resorted to exposing
numerous tunable parameters to users -- thus burdening users with continually
optimizing their own storage systems and applications. Storage systems are
usually responsible for most latency in I/O-heavy applications, so even a small
latency improvement can be significant. Machine learning (ML) techniques
promise to learn patterns, generalize from them, and enable optimal solutions
that adapt to changing workloads. We propose that ML solutions become a
first-class component in OSs and replace manual heuristics to optimize storage
systems dynamically. In this paper, we describe our proposed ML architecture,
called KML. We developed a prototype KML architecture and applied it to two
case studies: optimizing readahead and NFS read-size values. Our experiments
show that KML consumes less than 4KB of dynamic kernel memory, has a CPU
overhead smaller than 0.2%, and yet can learn patterns and improve I/O
throughput by as much as 2.3x and 15x for two case studies -- even for complex,
never-seen-before, concurrently running mixed workloads on different storage
devices.

We consider the framework of non-stationary Online Convex Optimization where
a learner seeks to control its dynamic regret against an arbitrary sequence of
comparators. When the loss functions are strongly convex or exp-concave, we
demonstrate that Strongly Adaptive (SA) algorithms can be viewed as a
principled way of controlling dynamic regret in terms of path variation $V_T$
of the comparator sequence. Specifically, we show that SA algorithms enjoy
$\tilde O(\sqrt{TV_T} \vee \log T)$ and $\tilde O(\sqrt{dTV_T} \vee d\log T)$
dynamic regret for strongly convex and exp-concave losses respectively without
apriori knowledge of $V_T$. The versatility of the principled approach is
further demonstrated by the novel results in the setting of learning against
bounded linear predictors and online regression with Gaussian kernels.
  Under a related setting, the second component of the paper addresses an open
question posed by Zhdanov and Kalnishkan (2010) that concerns online kernel
regression with squared error losses. We derive a new lower bound on a certain
penalized regret which establishes the near minimax optimality of online Kernel
Ridge Regression (KRR). Our lower bound can be viewed as an RKHS extension to
the lower bound derived in Vovk (2001) for online linear regression in finite

The need for remote tools for healthcare monitoring has never been more
apparent. Camera measurement of vital signs leverages imaging devices to
compute physiological changes by analyzing images of the human body. Building
on advances in optics, machine learning, computer vision and medicine these
techniques have progressed significantly since the invention of digital
cameras. This paper presents a comprehensive survey of camera measurement of
physiological vital signs, describing they vital signs that can be measured and
the computational techniques for doing so. I cover both clinical and
non-clinical applications and the challenges that need to be overcome for these
applications to advance from proofs-of-concept. Finally, I describe the current
resources (datasets and code) available to the research community and provide a
comprehensive webpage (https://cameravitals.github.io/) with links to these
resource and a categorized list of all the papers referenced in this article.

Lesion detection in digital breast tomosynthesis (DBT) is an important and a
challenging problem characterized by a low prevalence of images containing
tumors. Due to the label scarcity problem, large deep learning models and
computationally intensive algorithms are likely to fail when applied to this
task. In this paper, we present a practical yet lightweight backbone to improve
the accuracy of tumor detection. Specifically, we propose a novel modification
of visual transformer (ViT) on image feature patches to connect the feature
patches of a tumor with healthy backgrounds of breast images and form a more
robust backbone for tumor detection. To the best of our knowledge, our model is
the first work of Transformer backbone object detection for medical imaging.
Our experiments show that this model can considerably improve the accuracy of
lesion detection and reduce the amount of labeled data required in typical ViT.
We further show that with additional augmented tumor data, our model
significantly outperforms the Faster R-CNN model and state-of-the-art SWIN
transformer model.

In gradient descent, changing how we parametrize the model can lead to
drastically different optimization trajectories, giving rise to a surprising
range of meaningful inductive biases: identifying sparse classifiers or
reconstructing low-rank matrices without explicit regularization. This implicit
regularization has been hypothesised to be a contributing factor to good
generalization in deep learning. However, natural gradient descent is
approximately invariant to reparameterization, it always follows the same
trajectory and finds the same optimum. The question naturally arises: What
happens if we eliminate the role of parameterization, which solution will be
found, what new properties occur? We characterize the behaviour of natural
gradient flow in deep linear networks for separable classification under
logistic loss and deep matrix factorization. Some of our findings extend to
nonlinear neural networks with sufficient but finite over-parametrization. We
demonstrate that there exist learning problems where natural gradient descent
fails to generalize, while gradient descent with the right architecture
performs well.

Risk to human astronauts and interplanetary distance causing slow and limited
communication drives scientists to pursue an autonomous approach to exploring
distant planets, such as Mars. A portion of exploration of Mars has been
conducted through the autonomous collection and analysis of Martian data by
spacecraft such as the Mars rovers and the Mars Express Orbiter. The autonomy
used on these Mars exploration spacecraft and on Earth to analyze data
collected by these vehicles mainly consist of machine learning, a field of
artificial intelligence where algorithms collect data and self-improve with the
data. Additional applications of machine learning techniques for Mars
exploration have potential to resolve communication limitations and human risks
of interplanetary exploration. In addition, analyzing Mars data with machine
learning has the potential to provide a greater understanding of Mars in
numerous domains such as its climate, atmosphere, and potential future
habitation. To explore further utilizations of machine learning techniques for
Mars exploration, this paper will first summarize the general features and
phenomena of Mars to provide a general overview of the planet, elaborate upon
uncertainties of Mars that would be beneficial to explore and understand,
summarize every current or previous usage of machine learning techniques in the
exploration of Mars, explore implementations of machine learning that will be
utilized in future Mars exploration missions, and explore machine learning
techniques used in Earthly domains to provide solutions to the previously
described uncertainties of Mars.

Identifying players in video is a foundational step in computer vision-based
sports analytics. Obtaining player identities is essential for analyzing the
game and is used in downstream tasks such as game event recognition.
Transformers are the existing standard in Natural Language Processing (NLP) and
are swiftly gaining traction in computer vision. Motivated by the increasing
success of transformers in computer vision, in this paper, we introduce a
transformer network for recognizing players through their jersey numbers in
broadcast National Hockey League (NHL) videos. The transformer takes temporal
sequences of player frames (also called player tracklets) as input and outputs
the probabilities of jersey numbers present in the frames. The proposed network
performs better than the previous benchmark on the dataset used. We implement a
weakly-supervised training approach by generating approximate frame-level
labels for jersey number presence and use the frame-level labels for faster
training. We also utilize player shifts available in the NHL play-by-play data
by reading the game time using optical character recognition (OCR) to get the
players on the ice rink at a certain game time. Using player shifts improved
the player identification accuracy by 6%.

Disruptions of university campuses caused by COVID-19 have motivated
strategies to prevent the spread of infectious diseases while maintaining some
level of in person learning. In response, the proposed approach recursively
applied a quantum annealing algorithm for Max-Cut optimization on D-Wave
Systems, which grouped students into cohorts such that the number of possible
infection events via shared classrooms was minimized. To test this approach,
available coursework data was used to generate highly clustered course
enrollment networks representing students and the classes they share. The
algorithm was then recursively called on these networks to group students, and
a disease model was applied to forecast disease spread. Simulation results
showed that under some assumptions on disease statistics and methods of spread,
the quantum grouping method reduced both the total and peak percentage of
infected students when compared against random groupings of students. Scaling
to larger networks, it is possible that this quantum annealer-assisted grouping
approach may provide practical advantage over classical approaches. This paper,
however, is strictly a proof-of-concept demonstration of the approach and is
not intended to argue for a quantum speedup.

Integrative analysis of multi-level pharmacogenomic data for modeling
dependencies across various biological domains is crucial for developing
genomic-testing based treatments. Chain graphs characterize conditional
dependence structures of such multi-level data where variables are naturally
partitioned into multiple ordered layers, consisting of both directed and
undirected edges. Existing literature mostly focus on Gaussian chain graphs,
which are ill-suited for non-normal distributions with heavy-tailed marginals,
potentially leading to inaccurate inferences. We propose a Bayesian robust
chain graph model (RCGM) based on random transformations of marginals using
Gaussian scale mixtures to account for node-level non-normality in continuous
multivariate data. This flexible modeling strategy facilitates identification
of conditional sign dependencies among non-normal nodes while still being able
to infer conditional dependencies among normal nodes. In simulations, we
demonstrate that RCGM outperforms existing Gaussian chain graph inference
methods in data generated from various non-normal mechanisms. We apply our
method to genomic, transcriptomic and proteomic data to understand underlying
biological processes holistically for drug response and resistance in lung
cancer cell lines. Our analysis reveals inter- and intra- platform dependencies
of key signaling pathways to monotherapies of icotinib, erlotinib and
osimertinib among other drugs, along with shared patterns of molecular
mechanisms behind drug actions.

In this work we investigate a specific transfer learning approach for deep
reinforcement learning in the context where the internal dynamics between two
tasks are the same but the visual representations differ. We learn a
low-dimensional encoding of the environment, meant to capture summarizing
abstractions, from which the internal dynamics and value functions are learned.
Transfer is then obtained by freezing the learned internal dynamics and value
functions, thus reusing the shared low-dimensional embedding space. When
retraining the encoder for transfer, we make several observations: (i) in some
cases, there are local minima that have small losses but a mismatching
embedding space, resulting in poor task performance and (ii) in the absence of
local minima, the output of the encoder converges in our experiments to the
same embedding space, which leads to a fast and efficient transfer as compared
to learning from scratch. The local minima are caused by the reduced degree of
freedom of the optimization process caused by the frozen models. We also find
that the transfer performance is heavily reliant on the base model; some base
models often result in a successful transfer, whereas other base models often
result in a failing transfer.

With the great success of deep learning in various domains, graph neural
networks (GNNs) also become a dominant approach to graph classification. By the
help of a global readout operation that simply aggregates all node (or
node-cluster) representations, existing GNN classifiers obtain a graph-level
representation of an input graph and predict its class label using the
representation. However, such global aggregation does not consider the
structural information of each node, which results in information loss on the
global structure. Particularly, it limits the discrimination power by enforcing
the same weight parameters of the classifier for all the node representations;
in practice, each of them contributes to target classes differently depending
on its structural semantic. In this work, we propose structural semantic
readout (SSRead) to summarize the node representations at the position-level,
which allows to model the position-specific weight parameters for
classification as well as to effectively capture the graph semantic relevant to
the global structure. Given an input graph, SSRead aims to identify
structurally-meaningful positions by using the semantic alignment between its
nodes and structural prototypes, which encode the prototypical features of each
position. The structural prototypes are optimized to minimize the alignment
cost for all training graphs, while the other GNN parameters are trained to
predict the class labels. Our experimental results demonstrate that SSRead
significantly improves the classification performance and interpretability of
GNN classifiers while being compatible with a variety of aggregation functions,
GNN architectures, and learning frameworks.

Open book question answering is a subset of question answering tasks where
the system aims to find answers in a given set of documents (open-book) and
common knowledge about a topic. This article proposes a solution for answering
natural language questions from a corpus of Amazon Web Services (AWS) technical
documents with no domain-specific labeled data (zero-shot). These questions can
have yes-no-none answers, short answers, long answers, or any combination of
the above. This solution comprises a two-step architecture in which a retriever
finds the right document and an extractor finds the answers in the retrieved
document. We are introducing a new test dataset for open-book QA based on real
customer questions on AWS technical documentation. After experimenting with
several information retrieval systems and extractor models based on extractive
language models, the solution attempts to find the yes-no-none answers and text
answers in the same pass. The model is trained on the The Stanford Question
Answering Dataset - SQuAD (Rajpurkaret al., 2016) and Natural Questions
(Kwiatkowski et al., 2019) datasets. We were able to achieve 49% F1 and 39%
exact match score (EM) end-to-end with no domain-specific training.

Microbiology culture reports contain critical information for important
clinical and public health applications. However, microbiology reports often
have complex, semi-structured, free-text data that present a barrier for
secondary use. Here we present the development and validation of an open-source
package designed to ingest free-text microbiology reports, determine whether
the culture is positive, and return a list of SNOMED-CT mapped bacteria. Our
rule-based natural language processing algorithm was developed using
microbiology reports from two different electronic health record systems in a
large healthcare organization, and then externally validated on the reports of
two other institutions with manually-extracted results as a benchmark. Our
algorithm achieved F-1 scores >0.95 on all classification tasks across both
validation sets. Our concept extraction Python package, MicrobEx, is designed
to be reused and adapted to individual institutions as an upstream process for
other clinical applications, such as machine learning studies, clinical
decision support, and disease surveillance systems.

In neuromorphic computing, artificial synapses provide a multi-weight
conductance state that is set based on inputs from neurons, analogous to the
brain. Additional properties of the synapse beyond multiple weights can be
needed, and can depend on the application, requiring the need for generating
different synapse behaviors from the same materials. Here, we measure
artificial synapses based on magnetic materials that use a magnetic tunnel
junction and a magnetic domain wall. By fabricating lithographic notches in a
domain wall track underneath a single magnetic tunnel junction, we achieve 4-5
stable resistance states that can be repeatably controlled electrically using
spin orbit torque. We analyze the effect of geometry on the synapse behavior,
showing that a trapezoidal device has asymmetric weight updates with high
controllability, while a straight device has higher stochasticity, but with
stable resistance levels. The device data is input into neuromorphic computing
simulators to show the usefulness of application-specific synaptic functions.
Implementing an artificial neural network applied on streamed Fashion-MNIST
data, we show that the trapezoidal magnetic synapse can be used as a
metaplastic function for efficient online learning. Implementing a
convolutional neural network for CIFAR-100 image recognition, we show that the
straight magnetic synapse achieves near-ideal inference accuracy, due to the
stability of its resistance levels. This work shows multi-weight magnetic
synapses are a feasible technology for neuromorphic computing and provides
design guidelines for emerging artificial synapse technologies.

The community lacks theory-informed guidelines for building good data sets.
We analyse theoretical directions relating to what aspects of the data matter
and conclude that the intuitions derived from the existing literature are
incorrect and misleading. Using empirical counter-examples, we show that 1)
data dimension should not necessarily be minimised and 2) when manipulating
data, preserving the distribution is inessential. This calls for a more
data-aware theoretical understanding. Although not explored in this work, we
propose the study of the impact of data modification on learned representations
as a promising research direction.

Normalizing flows are flexible, parameterized distributions that can be used
to approximate expectations from intractable distributions via importance
sampling. However, current flow-based approaches are limited on challenging
targets where they either suffer from mode seeking behaviour or high variance
in the training loss, or rely on samples from the target distribution, which
may not be available. To address these challenges, we combine flows with
annealed importance sampling (AIS), while using the $\alpha$-divergence as our
objective, in a novel training procedure, FAB (Flow AIS Bootstrap). Thereby,
the flow and AIS improve each other in a bootstrapping manner. We demonstrate
that FAB can be used to produce accurate approximations to complex target
distributions, including Boltzmann distributions, in problems where previous
flow-based methods fail.

Recommender systems have been widely used in different application domains
including energy-preservation, e-commerce, healthcare, social media, etc. Such
applications require the analysis and mining of massive amounts of various
types of user data, including demographics, preferences, social interactions,
etc. in order to develop accurate and precise recommender systems. Such
datasets often include sensitive information, yet most recommender systems are
focusing on the models' accuracy and ignore issues related to security and the
users' privacy. Despite the efforts to overcome these problems using different
risk reduction techniques, none of them has been completely successful in
ensuring cryptographic security and protection of the users' private
information. To bridge this gap, the blockchain technology is presented as a
promising strategy to promote security and privacy preservation in recommender
systems, not only because of its security and privacy salient features, but
also due to its resilience, adaptability, fault tolerance and trust
characteristics. This paper presents a holistic review of blockchain-based
recommender systems covering challenges, open issues and solutions.
Accordingly, a well-designed taxonomy is introduced to describe the security
and privacy challenges, overview existing frameworks and discuss their
applications and benefits when using blockchain before indicating opportunities
for future research.

Approximate Bayesian Computation (ABC) enables statistical inference in
simulator-based models whose likelihoods are difficult to calculate but easy to
simulate from. ABC constructs a kernel-type approximation to the posterior
distribution through an accept/reject mechanism which compares summary
statistics of real and simulated data. To obviate the need for summary
statistics, we directly compare empirical distributions with a Kullback-Leibler
(KL) divergence estimator obtained via contrastive learning. In particular, we
blend flexible machine learning classifiers within ABC to automate fake/real
data comparisons. We consider the traditional accept/reject kernel as well as
an exponential weighting scheme which does not require the ABC acceptance
threshold. Our theoretical results show that the rate at which our ABC
posterior distributions concentrate around the true parameter depends on the
estimation error of the classifier. We derive limiting posterior shape results
and find that, with a properly scaled exponential kernel, asymptotic normality
holds. We demonstrate the usefulness of our approach on simulated examples as
well as real data in the context of stock volatility estimation.

The nudging data assimilation algorithm is a powerful tool used to forecast
phenomena of interest given incomplete and noisy observations. Machine learning
is becoming increasingly popular in data assimilation given its ease of
computation and forecasting ability. This work proposes a new approach to data
assimilation via machine learning where Deep Neural Networks (DNNs) are being
taught the nudging algorithm. The accuracy of the proposed DNN based algorithm
is comparable to the nudging algorithm and it is confirmed by the Lorenz 63 and
Lorenz 96 numerical examples. The key advantage of the proposed approach is the
fact that, once trained, DNNs are cheap to evaluate in comparison to nudging
where typically differential equations are needed to be solved. Standard
exponential type approximation results are established for the Lorenz 63 model
for both the continuous and discrete in time models. These results can be
directly coupled with estimates for DNNs (whenever available), to derive the
overall approximation error estimates of the proposed algorithm.

We consider the problem of generating adversarial malware by a cyber-attacker
where the attacker's task is to strategically modify certain bytes within
existing binary malware files, so that the modified files are able to evade a
malware detector such as machine learning-based malware classifier. We have
evaluated three recent adversarial malware generation techniques using binary
malware samples drawn from a single, publicly available malware data set and
compared their performances for evading a machine-learning based malware
classifier called MalConv. Our results show that among the compared techniques,
the most effective technique is the one that strategically modifies bytes in a
binary's header. We conclude by discussing the lessons learned and future
research directions on the topic of adversarial malware generation.

Representation learning lies at the heart of the empirical success of deep
learning for dealing with the curse of dimensionality. However, the power of
representation learning has not been fully exploited yet in reinforcement
learning (RL), due to i), the trade-off between expressiveness and
tractability; and ii), the coupling between exploration and representation
learning. In this paper, we first reveal the fact that under some noise
assumption in the stochastic control model, we can obtain the linear spectral
feature of its corresponding Markov transition operator in closed-form for
free. Based on this observation, we propose Spectral Dynamics Embedding
(SPEDE), which breaks the trade-off and completes optimistic exploration for
representation learning by exploiting the structure of the noise. We provide
rigorous theoretical analysis of SPEDE, and demonstrate the practical superior
performance over the existing state-of-the-art empirical algorithms on several

We focus on graph classification using a graph neural network (GNN) model
that precomputes the node features using a bank of neighborhood aggregation
graph operators arranged in parallel. These GNN models have a natural advantage
of reduced training and inference time due to the precomputations but are also
fundamentally different from popular GNN variants that update node features
through a sequential neighborhood aggregation procedure during training. We
provide theoretical conditions under which a generic GNN model with parallel
neighborhood aggregations (PA-GNNs, in short) are provably as powerful as the
well-known Weisfeiler-Lehman (WL) graph isomorphism test in discriminating
non-isomorphic graphs. Although PA-GNN models do not have an apparent
relationship with the WL test, we show that the graph embeddings obtained from
these two methods are injectively related. We then propose a specialized PA-GNN
model, called SPIN, which obeys the developed conditions. We demonstrate via
numerical experiments that the developed model achieves state-of-the-art
performance on many diverse real-world datasets while maintaining the
discriminative power of the WL test and the computational advantage of
preprocessing graphs before the training process.

COVID-19 pandemic is an ongoing global pandemic which has caused
unprecedented disruptions in the public health sector and global economy. The
virus, SARS-CoV-2 is responsible for the rapid transmission of coronavirus
disease. Due to its contagious nature, the virus can easily infect an
unprotected and exposed individual from mild to severe symptoms. The study of
the virus effects on pregnant mothers and neonatal is now a concerning issue
globally among civilians and public health workers considering how the virus
will affect the mother and the neonates health. This paper aims to develop a
predictive model to estimate the possibility of death for a COVID-diagnosed
mother based on documented symptoms: dyspnea, cough, rhinorrhea, arthralgia,
and the diagnosis of pneumonia. The machine learning models that have been used
in our study are support vector machine, decision tree, random forest, gradient
boosting, and artificial neural network. The models have provided impressive
results and can accurately predict the mortality of pregnant mothers with a
given input.The precision rate for 3 models(ANN, Gradient Boost, Random Forest)
is 100% The highest accuracy score(Gradient Boosting,ANN) is 95%,highest
recall(Support Vector Machine) is 92.75% and highest f1 score(Gradient
Boosting,ANN) is 94.66%. Due to the accuracy of the model, pregnant mother can
expect immediate medical treatment based on their possibility of death due to
the virus. The model can be utilized by health workers globally to list down
emergency patients, which can ultimately reduce the death rate of COVID-19
diagnosed pregnant mothers.

Identifying string theory vacua with desired physical properties at low
energies requires searching through high-dimensional solution spaces -
collectively referred to as the string landscape. We highlight that this search
problem is amenable to reinforcement learning and genetic algorithms. In the
context of flux vacua, we are able to reveal novel features (suggesting
previously unidentified symmetries) in the string theory solutions required for
properties such as the string coupling. In order to identify these features
robustly, we combine results from both search methods, which we argue is
imperative for reducing sampling bias.

The adoption of Uncrewed Aerial Vehicles (UAVs) for last-mile deliveries will
affect the energy productivity of package delivery and require new methods to
understand the associated energy consumption and greenhouse gas (GHG)
emissions. Here we combine empirical testing of 187 quadcopter flights with
first principles analysis to develop a usable energy model for drone package
delivery. We develop a machine-learning algorithm to assess energy use across
three different flight regimes: takeoff, cruise, and landing. Our model shows
that, in the US, a small electric quadcopter drone with a payload of 1 kg would
consume approximately 0.05 MJ/km and result in 41 g of CO$_{2}$e per package.
The energy per package delivered by drones (0.19 MJ/package) can be up to 96\%
lower than conventional transportation modes. Our open model and generalizable
coefficients can assist stakeholders in understanding and improving the energy
use of drone package delivery.

Hypomimia is a condition in the early stages of the progression of Parkinsons
disease that limits the movement of facial muscles, restricting the accurate
depiction of facial expressions. Also known as facial masking, this condition
is an early symptom of Parkinson's disease, a neurodegenerative disorder that
affects movement. To date, no specific test exists to diagnose the disease.
Instead, doctors rely on the patient medical history and symptoms to confirm
the onslaught of the disease, delaying treatment. This study aims to develop a
diagnostic tool for Parkinsons disease utilizing the Facial Action Coding
System, a comprehensive system describing all facially discernible movement. In
addition, this project generates image datasets by simulating faces or action
unit sets for both Parkinsons patients and non-affected individuals through
coding. Accordingly, the model is trained using supervised learning and neural
networks. The model efficiency is compared using Convolutional Neural Network
and Vision Transformer models and later tested with a test program by passing a
Parkinsons disease-affected image as input. The future goal is to develop a
self-determining mobile application (PDiagnosis) by utilizing the model to
determine the probability of Parkinsons progression.

Different mechanisms for quenching star formation in galaxies are commonly
invoked in the literature, but the relative impact of each one at different
cosmic epochs is still unknown. In particular, the relation between these
processes and morphological transformation remains poorly understood. In this
work, we measure the effectiveness of changes in star formation rates by
analysing a new parameter, the Star Formation Acceleration (SFA), as a function
of galaxy morphology. This methodology is capable of identifying both bursting
and quenching episodes that occurred in the preceding 300 Myrs. We use
morphological classification catalogs based on Deep learning techniques. Our
final sample has $\sim$14,200 spirals and $\sim$2,500 ellipticals. We find that
elliptical galaxies in the transition region have median shorter quenching
timescales ( $\tau$ < 1 Gyr) than spirals ($\tau \geq 1$ Gyr). This result
conforms to the scenario in which major mergers and other violent processes
play a fundamental role in galaxy evolution for most ellipticals, not only
quenching star formation more rapidly but also playing a role in morphological
transformation. We also find that $\sim$two thirds of galaxies bursting in the
green valley in our sample are massive spirals ($M_\star \geq
10^{11.0}M_\odot$) with signs of disturbance. This is in accordance with the
scenario where low mass galaxies are losing their gas in a interaction with a
massive galaxy: while the former is quenching, the last is being refueled and
going through a burst, showing signs of recent interaction.

For human action understanding, a popular research direction is to analyze
short video clips with unambiguous semantic content, such as jumping and
drinking. However, methods for understanding short semantic actions cannot be
directly translated to long human dynamics such as dancing, where it becomes
challenging even to label the human movements semantically. Meanwhile, the
natural language processing (NLP) community has made progress in solving a
similar challenge of annotation scarcity by large-scale pre-training, which
improves several downstream tasks with one model. In this work, we study how to
segment and cluster videos into recurring temporal patterns in a
self-supervised way, namely acton discovery, the main roadblock towards video
tokenization. We propose a two-stage framework that first obtains a frame-wise
representation by contrasting two augmented views of video frames conditioned
on their temporal context. The frame-wise representations across a collection
of videos are then clustered by K-means. Actons are then automatically
extracted by forming a continuous motion sequence from frames within the same
cluster. We evaluate the frame-wise representation learning step by Kendall's
Tau and the lexicon building step by normalized mutual information and language
entropy. We also study three applications of this tokenization: genre
classification, action segmentation, and action composition. On the AIST++ and
PKU-MMD datasets, actons bring significant performance improvements compared to
several baselines.

Automated visual understanding of our diverse and open world demands computer
vision models to generalize well with minimal customization for specific tasks,
similar to human vision. Computer vision foundation models, which are trained
on diverse, large-scale dataset and can be adapted to a wide range of
downstream tasks, are critical for this mission to solve real-world computer
vision applications. While existing vision foundation models such as CLIP,
ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual
representations to a cross-modal shared representation, we introduce a new
computer vision foundation model, Florence, to expand the representations from
coarse (scene) to fine (object), from static (images) to dynamic (videos), and
from RGB to multiple modalities (caption, depth). By incorporating universal
visual-language representations from Web-scale image-text data, our Florence
model can be easily adapted for various computer vision tasks, such as
classification, retrieval, object detection, VQA, image caption, video
retrieval and action recognition. Moreover, Florence demonstrates outstanding
performance in many types of transfer learning: fully sampled fine-tuning,
linear probing, few-shot transfer and zero-shot transfer for novel images and
objects. All of these properties are critical for our vision foundation model
to serve general purpose vision tasks. Florence achieves new state-of-the-art
results in majority of 44 representative benchmarks, e.g., ImageNet-1K
zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of
97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600.

Large datasets of paired images and text have become increasingly popular for
learning generic representations for vision and vision-and-language tasks. Such
datasets have been built by querying search engines or collecting HTML alt-text
-- since web data is noisy, they require complex filtering pipelines to
maintain quality. We explore alternate data sources to collect high quality
data with minimal filtering. We introduce RedCaps -- a large-scale dataset of
12M image-text pairs collected from Reddit. Images and captions from Reddit
depict and describe a wide variety of objects and scenes. We collect data from
a manually curated set of subreddits, which give coarse image labels and allow
us to steer the dataset composition without labeling individual instances. We
show that captioning models trained on RedCaps produce rich and varied captions
preferred by humans, and learn visual representations that transfer to many
downstream tasks.

What constitutes an object? This has been a long-standing question in
computer vision. Towards this goal, numerous learning-free and learning-based
approaches have been developed to score objectness. However, they generally do
not scale well across new domains and novel objects. In this paper, we advocate
that existing methods lack a top-down supervision signal governed by
human-understandable semantics. For the first time in literature, we
demonstrate that Multi-modal Vision Transformers (MViT) trained with aligned
image-text pairs can effectively bridge this gap. Our extensive experiments
across various domains and novel objects show the state-of-the-art performance
of MViTs to localize generic objects in images. Based on the observation that
existing MViTs do not include multi-scale feature processing and usually
require longer training schedules, we develop an efficient MViT architecture
using multi-scale deformable attention and late vision-language fusion. We show
the significance of MViT proposals in a diverse range of applications including
open-world object detection, salient and camouflage object detection,
supervised and self-supervised detection tasks. Further, MViTs can adaptively
generate proposals given a specific language query and thus offer enhanced
interactability. Code: \url{https://git.io/J1HPY}.

Object detection is a central downstream task used to test if pre-trained
network parameters confer benefits, such as improved accuracy or training
speed. The complexity of object detection methods can make this benchmarking
non-trivial when new architectures, such as Vision Transformer (ViT) models,
arrive. These difficulties (e.g., architectural incompatibility, slow training,
high memory consumption, unknown training formulae, etc.) have prevented recent
studies from benchmarking detection transfer learning with standard ViT models.
In this paper, we present training techniques that overcome these challenges,
enabling the use of standard ViT models as the backbone of Mask R-CNN. These
tools facilitate the primary goal of our study: we compare five ViT
initializations, including recent state-of-the-art self-supervised learning
methods, supervised initialization, and a strong random initialization
baseline. Our results show that recent masking-based unsupervised learning
methods may, for the first time, provide convincing transfer learning
improvements on COCO, increasing box AP up to 4% (absolute) over supervised and
prior self-supervised pre-training methods. Moreover, these masking-based
initializations scale better, with the improvement growing as model size

Recent advances in machine learning have created increasing interest in
solving visual computing problems using a class of coordinate-based neural
networks that parametrize physical properties of scenes or objects across space
and time. These methods, which we call neural fields, have seen successful
application in the synthesis of 3D shapes and image, animation of human bodies,
3D reconstruction, and pose estimation. However, due to rapid progress in a
short time, many papers exist but a comprehensive review and formulation of the
problem has not yet emerged. In this report, we address this limitation by
providing context, mathematical grounding, and an extensive review of
literature on neural fields. This report covers research along two dimensions.
In Part I, we focus on techniques in neural fields by identifying common
components of neural field methods, including different representations,
architectures, forward mapping, and generalization methods. In Part II, we
focus on applications of neural fields to different problems in visual
computing, and beyond (e.g., robotics, audio). Our review shows the breadth of
topics already covered in visual computing, both historically and in current
incarnations, demonstrating the improved quality, flexibility, and capability
brought by neural fields methods. Finally, we present a companion website that
contributes a living version of this review that can be continually updated by
the community.

Transformers have shown great potential in computer vision tasks. A common
belief is their attention-based token mixer module contributes most to their
competence. However, recent works show the attention-based module in
Transformers can be replaced by spatial MLPs and the resulted models still
perform quite well. Based on this observation, we hypothesize that the general
architecture of the Transformers, instead of the specific token mixer module,
is more essential to the model's performance. To verify this, we deliberately
replace the attention module in Transformers with an embarrassingly simple
spatial pooling operator to conduct only basic token mixing. Surprisingly, we
observe that the derived model, termed as PoolFormer, achieves competitive
performance on multiple computer vision tasks. For example, on ImageNet-1K,
PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned Vision
Transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with
35%/52% fewer parameters and 50%/62% fewer MACs. The effectiveness of
PoolFormer verifies our hypothesis and urges us to initiate the concept of
"MetaFormer", a general architecture abstracted from Transformers without
specifying the token mixer. Based on the extensive experiments, we argue that
MetaFormer is the key player in achieving superior results for recent
Transformer and MLP-like models on vision tasks. This work calls for more
future research dedicated to improving MetaFormer instead of focusing on the
token mixer modules. Additionally, our proposed PoolFormer could serve as a
starting baseline for future MetaFormer architecture design. Code is available
at https://github.com/sail-sg/poolformer.

Reliable modeling of conditional densities is important for quantitative
scientific fields such as particle physics. In domains outside physics,
implicit quantile neural networks (IQN) have been shown to provide accurate
models of conditional densities. We present a successful application of IQNs to
jet simulation and correction using the tools and simulated data from the
Compact Muon Solenoid (CMS) Open Data portal.

A critical aspect of reliable communication involves the design of codes that
allow transmissions to be robustly and computationally efficiently decoded
under noisy conditions. Advances in the design of reliable codes have been
driven by coding theory and have been sporadic. Recently, it is shown that
channel codes that are comparable to modern codes can be learned solely via
deep learning. In particular, Turbo Autoencoder (TURBOAE), introduced by Jiang
et al., is shown to achieve the reliability of Turbo codes for Additive White
Gaussian Noise channels. In this paper, we focus on applying the idea of
TURBOAE to various practical channels, such as fading channels and chirp noise
channels. We introduce TURBOAE-TI, a novel neural architecture that combines
TURBOAE with a trainable interleaver design. We develop a carefully-designed
training procedure and a novel interleaver penalty function that are crucial in
learning the interleaver and TURBOAE jointly. We demonstrate that TURBOAE-TI
outperforms TURBOAE and LTE Turbo codes for several channels of interest. We
also provide interpretation analysis to better understand TURBOAE-TI.

In general, Graph Neural Networks(GNN) have been using a message passing
method to aggregate and summarize information about neighbors to express their
information. Nonetheless, previous studies have shown that the performance of
graph neural networks becomes vulnerable when there are abnormal nodes in the
neighborhood due to this message passing method. In this paper, inspired by the
Neural Architecture Search method, we present an algorithm that recognizes
abnormal nodes and automatically excludes them from information aggregation.
Experiments on various real worlds datasets show that our proposed Neural
Architecture Search-based Anomaly Resistance Graph Neural Network (NASAR-GNN)
is actually effective.

Designing recommendation systems with limited or no available training data
remains a challenge. To that end, a new combinatorial optimization problem is
formulated to generate optimized item selection for experimentation with the
goal to shorten the time for collecting randomized training data. We first
present an overview of the optimized item selection problem and a multi-level
optimization framework to solve it. The approach integrates techniques from
discrete optimization, unsupervised clustering, and latent text embeddings. We
then discuss how to incorporate optimized item selection with active learning
as part of randomized exploration in an ongoing fashion.

Robot-assisted feeding in household environments is challenging because it
requires robots to generate trajectories that effectively bring food items of
varying shapes and sizes into the mouth while making sure the user is
comfortable. Our key insight is that in order to solve this challenge, robots
must balance the efficiency of feeding a food item with the comfort of each
individual bite. We formalize comfort and efficiency as heuristics to
incorporate in motion planning. We present an approach based on
heuristics-guided bi-directional Rapidly-exploring Random Trees (h-BiRRT) that
selects bite transfer trajectories of arbitrary food item geometries and shapes
using our developed bite efficiency and comfort heuristics and a learned
constraint model. Real-robot evaluations show that optimizing both comfort and
efficiency significantly outperforms a fixed-pose based method, and users
preferred our method significantly more than that of a method that maximizes
only user comfort. Videos and Appendices are found on our website:
https://sites.google.com/view/comfortbitetransfer-icra22/home.

Self-supervised learning is a powerful paradigm for representation learning
on unlabelled images. A wealth of effective new methods based on instance
matching rely on data-augmentation to drive learning, and these have reached a
rough agreement on an augmentation scheme that optimises popular recognition
benchmarks. However, there is strong reason to suspect that different tasks in
computer vision require features to encode different (in)variances, and
therefore likely require different augmentation strategies. In this paper, we
measure the invariances learned by contrastive methods and confirm that they do
learn invariance to the augmentations used and further show that this
invariance largely transfers to related real-world changes in pose and
lighting. We show that learned invariances